diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md deleted file mode 100644 index 0f4d0257..00000000 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ /dev/null @@ -1,34 +0,0 @@ ---- -name: Bug report -about: Create a report to help us improve -title: '' -labels: bug -assignees: '' - ---- - -**Describe the bug** -A clear and concise description of what the bug is. - -**To Reproduce** -Steps to reproduce the behavior: -1. -2. - -**Expected behavior** -A clear and concise description of what you expected to happen. - -**Screenshots** -If applicable, add screenshots to help explain your problem. - -**Your package version (please complete the following information):** - - dabest: [e.g. 2023.3.29] - - pandas: - - numpy: - - matplotlib: - - seaborn: - - scipy: - - -**Additional context** -Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md deleted file mode 100644 index b260e30f..00000000 --- a/.github/ISSUE_TEMPLATE/feature_request.md +++ /dev/null @@ -1,23 +0,0 @@ ---- -name: Feature request -about: Suggest an idea for this project -title: '' -labels: enhancement -assignees: '' - ---- - -**Is your feature request related to a problem? Please describe.** -A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] - -**Describe the solution you'd like** -A clear and concise description of what you want to happen. - -**Describe alternatives you've considered** -A clear and concise description of any alternative solutions or features you've considered. - -**Is a dataset available for testing out the functionality** -If yes, please leave a Google Drive link - -**Additional context** -Add any other context or screenshots about the feature request here. diff --git a/.github/workflows/deploy.yaml b/.github/workflows/deploy.yaml deleted file mode 100644 index 41e29a16..00000000 --- a/.github/workflows/deploy.yaml +++ /dev/null @@ -1,14 +0,0 @@ -name: Deploy to GitHub Pages - -permissions: - contents: write - pages: write - -on: - push: - branches: [ "main", "master" ] - workflow_dispatch: -jobs: - deploy: - runs-on: ubuntu-latest - steps: [uses: fastai/workflows/quarto-ghp3@master] diff --git a/.github/workflows/test-nbdev.yaml b/.github/workflows/test-nbdev.yaml deleted file mode 100644 index fef02636..00000000 --- a/.github/workflows/test-nbdev.yaml +++ /dev/null @@ -1,7 +0,0 @@ -name: nbdev_prepare -on: [workflow_dispatch, pull_request, push] - -jobs: - test-nbdev: - runs-on: ubuntu-latest - steps: [uses: fastai/workflows/nbdev3-ci@master] diff --git a/.github/workflows/test-pytest.yaml b/.github/workflows/test-pytest.yaml deleted file mode 100644 index 514c85d5..00000000 --- a/.github/workflows/test-pytest.yaml +++ /dev/null @@ -1,18 +0,0 @@ -name: Python pytest -on: [workflow_dispatch, pull_request, push] - -jobs: - test-pytest: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v3 - - uses: actions/setup-python@v4 - with: - python-version: 3.11 - cache: "pip" - cache-dependency-path: settings.ini - - name: Run pytest - run: | - python -m pip install --upgrade pip - pip install -e '.[dev]' - pytest nbs/tests/ --mpl --mpl-baseline-path=nbs/tests/mpl_image_tests/baseline_images diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 99237c7e..00000000 --- a/.gitignore +++ /dev/null @@ -1,152 +0,0 @@ -_docs/ -_proc/ - -*.bak -.gitattributes -.last_checked -.gitconfig -.cursorignore -*.bak -*.log -*~ -~* -_tmp* -tmp* -tags -*.pkg - -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -env/ -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -*.egg-info/ -.installed.cfg -*.egg - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -.hypothesis/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# pyenv -.python-version - -# celery beat schedule file -celerybeat-schedule - -# SageMath parsed files -*.sage.py - -# dotenv -.env - -# virtualenv -.venv -venv/ -ENV/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ - -.vscode -*.swp - -# osx generated files -.DS_Store -.DS_Store? -.Trashes -ehthumbs.db -Thumbs.db -.idea - -# pytest -.pytest_cache - -# tools/trust-doc-nbs -docs_src/.last_checked - -# symlinks to fastai -docs_src/fastai -tools/fastai - -# link checker -checklink/cookies.txt - -# .gitconfig is now autogenerated -.gitconfig - -# Quarto installer -.deb -.pkg - -# Quarto -.quarto diff --git a/dabest/_stats_tools/__init__.py b/.nojekyll similarity index 100% rename from dabest/_stats_tools/__init__.py rename to .nojekyll diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml deleted file mode 100644 index 44be211a..00000000 --- a/.pre-commit-config.yaml +++ /dev/null @@ -1,6 +0,0 @@ -repos: -- repo: https://github.com/fastai/nbdev - rev: 2.2.10 - hooks: - - id: nbdev_clean - - id: nbdev_export \ No newline at end of file diff --git a/01-getting_started.html b/01-getting_started.html new file mode 100644 index 00000000..756b47a8 --- /dev/null +++ b/01-getting_started.html @@ -0,0 +1,857 @@ + + + + + + + + + + +Getting Started – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Getting Started

+
+ +
+
+ Requirements and installation of dabest. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

Introduction

+

DABEST is a package for Data Analysis with Bootstrapped ESTimation

+

Estimation statistics is a simple framework that avoids the pitfalls of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one’s experiment/intervention, as opposed to a false dichotomy engendered by P values.

+

An estimation plot has two key features.

+
    +
  1. It presents all datapoints as a swarmplot, which orders each point to display the underlying distribution.
    2. +
  2. It presents the effect size as a bootstrap 95% confidence interval on a separate but aligned axes.
    3. +
+

DABEST powers estimationstats.com, allowing everyone access to high-quality estimation plots.

+
+
+

Requirements

+

Python 3.11 is recommended. DABEST has also been tested with Python 3.10 and onwards.

+

In addition, the following packages are also required (listed with their minimal versions):

+ +

To obtain these package dependencies easily, it is highly recommended to download the Anaconda distribution of Python.

+
+
+

Installation

+
    +
  1. Using pip
    2. +
+

At the command line, run

+
$ pip install dabest
+
    +
  1. Using Github
    2. +
+

Clone the DABEST-python repo locally (see instructions here).

+

Then, navigate to the cloned repo in the command line and run

+
$ pip install .
+
+
+

Testing

+

To test DABEST, you will need to install pytest and nbdev.

+

Run nbdev_export && nbdev_test in the root directory of the source distribution. This runs the value assertion tests in dabest/tests folder

+

Run pytest in the root directory of the source distribution. This runs the image-based tests in dabest/tests/mpl_image_tests sub folder.

+

The test suite will ensure that the bootstrapping functions and the plotting functions perform as expected.

+
+
+

Bugs

+

Please report any bugs on the Github issue tracker for DABEST-python.

+
+
+

Contributing

+

All contributions are welcome. Please fork the Github repo and open a pull request.

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/02-about.html b/02-about.html new file mode 100644 index 00000000..2435c38e --- /dev/null +++ b/02-about.html @@ -0,0 +1,835 @@ + + + + + + + + + +About – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

About

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

Authors

+

DABEST is written in Python by Joses W. Ho, with design and input from Adam Claridge-Chang and other lab members.

+

Features in v2025.10.20 were added by Jonathan Anns, Zinan Lu, Yishan Mai, and Sangyu Xu.

+

Features in v2025.03.27 were added by Jonathan Anns, Zinan Lu, Kah Seng Lian, Yishan Mai, Sangyu Xu, and Lucas Wang Zhuoyu.

+

Features in v2024.03.29 were added by Zinan Lu, Kah Seng Lian, Ana Rosa Castillo.

+

Features in v2023.02.14 were added by Yixuan Li, Zinan Lu and Rou Zhang.

+

To find out more about the authors’ research, please visit the Claridge-Chang lab webpage.

+
+
+

Contributors

+
    +

  • Statistics supervision by Hyungwon Choi

    • +

  • Alpha testers from the Claridge-Chang lab: Sangyu Xu, Xianyuan Zhang, Farhan Mohammad, Jurga Mituzaitė, Stanislav Ott, Tayfun Tumkaya, Jonathan Anns, Nicole Lee and Yishan Mai.

    • +

  • DizietAsahi (DizietAsahi) with PR #86: Fix bugs in slopegraph and reference line keyword parsing.

    • +

  • Adam Li (@adam2392) with PR #85: Implement Lq-Likelihood-Ratio-Type Test in statistical output.

    • +

  • Mason Malone (@MasonM) with PR #30: Fix plot error when effect size is 0.

    • +

  • Matthew Edwards (@mje-nz) with PR #71: Specify dependencies correctly in setup.py.

    • +

  • Adam Nekimken (@anekimken) with PR #73: Implement inset axes so estimation plots can be plotted on a pre-determined :py:mod:matplotlib :py:class:Axes object.

    • +

  • Marin Manuel (@MarinManuel) with PR #109: Fixed bug preventing non-string columns from being used.

    • +

  • Mike Lotinga (@mlotinga): Helped with addition of jitter and the adjusted p-value calculation, both of which are included in the v2025.03.27 release.

    • +
+
+
+

Typography

+

This documentation uses Spectral for the body text, Merriweather Sans for the side bar and titles, and IBM Plex Mono for the monospace code font.

+
+
+

License

+

The DABEST package in Python is licenced under the BSD 3-clause Clear License.

+

Copyright (c) 2016-2023, Joses W. Ho All rights reserved.

+

Redistribution and use in source and binary forms, with or without modification, are permitted (subject to the limitations in the disclaimer below) provided that the following conditions are met:

+
 * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+
+ * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+
+ * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
+
+

NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY’S PATENT RIGHTS ARE GRANTED BY THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/03-citation.html b/03-citation.html new file mode 100644 index 00000000..d85487eb --- /dev/null +++ b/03-citation.html @@ -0,0 +1,790 @@ + + + + + + + + + +Citing DABEST – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Citing DABEST

+
+ + + +
+ + + + +
+ + + +
+ + + +

If your publication features a graphic generated with this software library, please cite the following publication.

+

Moving beyond P values: Everyday data analysis with estimation plots Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang

+

Nature Methods 2019, 1548-7105. doi:10.1038/s41592-019-0470-3

+

Free-to-view PDF

+

Paywalled publisher site

+ + + +
+ +
+ + + + + \ No newline at end of file diff --git a/API/bootstrap.html b/API/bootstrap.html new file mode 100644 index 00000000..9b1dc515 --- /dev/null +++ b/API/bootstrap.html @@ -0,0 +1,865 @@ + + + + + + + + + +Bootstrap – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Bootstrap

+
+ + + +
+ + + + +
+ + + +
+ + + +
+
+

bootstrap

+

+def bootstrap(
+    x1:array, # The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded.
+    x2:array=None, # The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded.
+    paired:bool=False, # Whether or not x1 and x2 are paired samples. If 'paired' is None then the data will not be treated as paired data in the subsequent calculations. If 'paired' is 'baseline', then in each tuple of x, other groups will be paired up with the first group (as control). If 'paired' is 'sequential', then in each tuple of x, each group will be paired up with the previous group (as control).
+    stat_function:callable=mean, # The summary statistic called on data.
+    smoothboot:bool=False, # Taken from seaborn.algorithms.bootstrap. If True, performs a smoothed bootstrap (draws samples from a kernel destiny estimate).
+    alpha_level:float=0.05, # Denotes the likelihood that the confidence interval produced does not include the true summary statistic. When alpha = 0.05, a 95% confidence interval is produced.
+    reps:int=5000, # Number of bootstrap iterations to perform.
+):
+
+

Computes the summary statistic and a bootstrapped confidence interval.

+
+
+
+

bca

+

+def bca(
+    data, alphas, stat_array, stat_function, ostat, reps
+):
+
+

Subroutine called to calculate the BCa statistics. Borrowed heavily from scikits.bootstrap code.

+
+
+
+

jackknife_indexes

+

+def jackknife_indexes(
+    data
+):
+
+

From the scikits.bootstrap package. Given an array, returns a list of arrays where each array is a set of jackknife indexes.

+

For a given set of data Y, the jackknife sample J[i] is defined as the data set Y with the ith data point deleted.

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/API/confint_1group.html b/API/confint_1group.html new file mode 100644 index 00000000..12d79596 --- /dev/null +++ b/API/confint_1group.html @@ -0,0 +1,919 @@ + + + + + + + + + + +confint_1group – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

confint_1group

+
+ +
+
+ A range of functions to compute bootstraps for a single sample. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

summary_ci_1group

+

+def summary_ci_1group(
+    x:np.array, # An numerical iterable.
+    func, # The function to be applied to x.
+    resamples:int=5000, # The number of bootstrap resamples to be taken of func(x).
+    alpha:float=0.05, # Denotes the likelihood that the confidence interval produced _does not_ include the true summary statistic. When alpha = 0.05, a 95% confidence interval is produced.
+    random_seed:int=12345, # `random_seed` is used to seed the random number generator during bootstrap resampling. This ensures that the confidence intervals reported are replicable.
+    sort_bootstraps:bool=True, args:VAR_POSITIONAL, kwargs:VAR_KEYWORD
+): # `summary`: float.
+    The outcome of func(x).
+`func`: function.
+    The function applied to x.
+`bca_ci_low`: float
+`bca_ci_high`: float.
+    The bias-corrected and accelerated confidence interval, for the
+    given alpha.
+`bootstraps`: array.
+    The bootstraps used to generate the confidence interval.
+    These will be sorted in ascending order if `sort_bootstraps`
+    was True.
+
+

Given an array-like x, returns func(x), and a bootstrap confidence interval of func(x).

+
+

source

+
+
+

compute_1group_bias_correction

+

+def compute_1group_bias_correction(
+    x, bootstraps, func, args:VAR_POSITIONAL, kwargs:VAR_KEYWORD
+):
+
+

Call self as a function.

+
+

source

+
+
+

compute_1group_bootstraps

+

+def compute_1group_bootstraps(
+    x, func, resamples:int=5000, random_seed:int=12345, args:VAR_POSITIONAL, kwargs:VAR_KEYWORD
+):
+
+

Bootstraps func(x), with the number of specified resamples.

+
+

source

+
+
+

compute_1group_acceleration

+

+def compute_1group_acceleration(
+    jack_dist
+):
+
+

Returns the accaleration value based on the jackknife distribution.

+
+

source

+
+
+

compute_1group_jackknife

+

+def compute_1group_jackknife(
+    x, func, args:VAR_POSITIONAL, kwargs:VAR_KEYWORD
+):
+
+

Returns the jackknife bootstraps for func(x).

+
+

source

+
+
+

create_bootstrap_indexes

+

+def create_bootstrap_indexes(
+    array, resamples:int=5000, random_seed:int=12345
+):
+
+

Given an array-like, returns a generator of bootstrap indexes to be used for resampling.

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/API/confint_2group_diff.html b/API/confint_2group_diff.html new file mode 100644 index 00000000..3215eb85 --- /dev/null +++ b/API/confint_2group_diff.html @@ -0,0 +1,986 @@ + + + + + + + + + + +confint_2group_diff – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

confint_2group_diff

+
+ +
+
+ A range of functions to compute bootstraps for the mean difference +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

calculate_weighted_delta

+

+def calculate_weighted_delta(
+    bootstrap_dist_var, differences
+):
+
+

Compute the weighted deltas.

+
+

source

+
+
+

calculate_bootstraps_var

+

+def calculate_bootstraps_var(
+    bootstraps
+):
+
+

Call self as a function.

+
+

source

+
+
+

calculate_group_var

+

+def calculate_group_var(
+    control_var, control_N, test_var, test_N
+):
+
+

Call self as a function.

+
+

source

+
+
+

compute_interval_limits

+

+def compute_interval_limits(
+    bias, acceleration, n_boots, ci:int=95
+):
+
+

Returns the indexes of the interval limits for a given bootstrap.

+

Supply the bias, acceleration factor, and number of bootstraps.

+
+

source

+
+
+

compute_meandiff_bias_correction

+

+def compute_meandiff_bias_correction(
+    bootstraps, # An numerical iterable, comprising bootstrap resamples of the effect size.
+    effsize, # The effect size for the original sample.
+): # The bias correction value for the given bootstraps
+and effect size.
+
+

Computes the bias correction required for the BCa method of confidence interval construction.

+
+

source

+
+
+

compute_delta2_bootstrapped_diff

+

+def compute_delta2_bootstrapped_diff(
+    x1:np.ndarray, # Control group 1
+    x2:np.ndarray, # Test group 1
+    x3:np.ndarray, # Control group 2
+    x4:np.ndarray, # Test group 2
+    is_paired:str=None, resamples:int=5000, random_seed:int=12345, proportional:bool=False
+)->tuple:
+
+

Bootstraps the effect size deltas’ g or proportional delta-delta

+
+

source

+
+
+

delta2_bootstrap_loop

+

+def delta2_bootstrap_loop(
+    x1, x2, x3, x4, resamples, pooled_sd, rng_seed, is_paired, proportional:bool=False
+):
+
+

Compute bootstrapped differences for delta-delta, handling both regular and proportional data

+
+

source

+
+
+

compute_bootstrapped_diff

+

+def compute_bootstrapped_diff(
+    x0, x1, is_paired, effect_size, resamples:int=5000, random_seed:int=12345
+):
+
+

Bootstraps the effect_size for 2 groups.

+
+

source

+
+
+

bootstrap_indices

+

+def bootstrap_indices(
+    is_paired, x0_len, x1_len, resamples,
+    random_seed, # parallelization must be turned off for random number generation
+):
+
+

Call self as a function.

+
+

source

+
+
+

compute_meandiff_jackknife

+

+def compute_meandiff_jackknife(
+    x0, x1, is_paired, effect_size
+):
+
+

Given two arrays, returns the jackknife for their effect size.

+
+

source

+
+
+

create_repeated_indexes

+

+def create_repeated_indexes(
+    data
+):
+
+

Convenience function. Given an array-like with length N, returns a generator that yields N indexes [0, 1, …, N].

+
/opt/hostedtoolcache/Python/3.12.13/x64/lib/python3.12/site-packages/fastcore/docscrape.py:259: UserWarning: Unknown section Keywords
+  else: warn(msg)
+
+

source

+
+
+

create_jackknife_indexes

+

+def create_jackknife_indexes(
+    data
+):
+
+

Given an array-like, creates a jackknife bootstrap.

+

For a given set of data Y, the jackknife bootstrap sample J[i] is defined as the data set Y with the ith data point deleted.

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/API/dabest_object.html b/API/dabest_object.html new file mode 100644 index 00000000..cf361251 --- /dev/null +++ b/API/dabest_object.html @@ -0,0 +1,1174 @@ + + + + + + + + + + +Dabest object – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Dabest object

+
+ +
+
+ Main class for estimating statistics and generating plots. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+
+

Dabest

+

+def Dabest(
+    data, idx, x, y, paired, id_col, ci, resamples, random_seed, proportional, delta2, experiment, experiment_label,
+    x1_level, mini_meta, ps_adjust
+):
+
+

Class for estimation statistics and plots.

+
+

Example: mean_diff

+
+
control = norm.rvs(loc=0, size=30, random_state=12345)
+test    = norm.rvs(loc=0.5, size=30, random_state=12345)
+my_df   = pd.DataFrame({"control": control,
+                            "test": test})
+my_dabest_object = dabest.load(my_df, idx=("control", "test"))
+my_dabest_object.mean_diff
+
+
DABEST v2025.03.27
+==================
+                  
+Good morning!
+The current time is Tue Mar 25 10:08:38 2025.
+
+The unpaired mean difference between control and test is 0.5 [95%CI 0.00172, 1.04].
+The p-value of the two-sided permutation t-test is 0.0758, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`
+
+
+

This is simply the mean of the control group subtracted from the mean of the test group.

+

\[\text{Mean difference} = \overline{x}_{Test} - \overline{x}_{Control}\]

+

where \(\overline{x}\) is the mean for the group \(x\).

+
+
+

Example: median_diff

+
+
control = norm.rvs(loc=0, size=30, random_state=12345)
+test    = norm.rvs(loc=0.5, size=30, random_state=12345)
+my_df   = pd.DataFrame({"control": control,
+                            "test": test})
+my_dabest_object = dabest.load(my_df, idx=("control", "test"))
+my_dabest_object.median_diff
+
+
/Users/jonathananns/GitHub/DABEST-python/dabest/_stats_tools/effsize.py:82: UserWarning: Using median as the statistic in bootstrapping may result in a biased estimate and cause problems with BCa confidence intervals. Consider using a different statistic, such as the mean.
+When plotting, please consider using percetile confidence intervals by specifying `ci_type='pct'`. For detailed information, refer to https://github.com/ACCLAB/DABEST-python/issues/129 
+
+  warnings.warn(message=mes1+mes2, category=UserWarning)
+
+
+
DABEST v2025.03.27
+==================
+                  
+Good morning!
+The current time is Tue Mar 25 10:08:39 2025.
+
+The unpaired median difference between control and test is 0.5 [95%CI -0.0401, 1.04].
+The p-value of the two-sided permutation t-test is 0.103, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.median_diff.statistical_tests`
+
+
+

This is the median difference between the control group and the test group.

+

If the comparison(s) are unpaired, median_diff is computed with the following equation:

+

\[\text{Median difference} = \widetilde{x}_{Test} - \widetilde{x}_{Control}\]

+

where \(\widetilde{x}\) is the median for the group \(x\).

+

If the comparison(s) are paired, median_diff is computed with the following equation:

+

\[\text{Median difference} = \widetilde{x}_{Test - Control}\]

+
+
Things to note
+

Using median difference as the statistic in bootstrapping may result in a biased estimate and cause problems with BCa confidence intervals. Consider using mean difference instead.

+

When plotting, consider using percentile confidence intervals instead of BCa confidence intervals by specifying ci_type = 'percentile' in .plot().

+

For detailed information, please refer to Issue 129.

+
+
+
+

Example: cohens_d

+
+
control = norm.rvs(loc=0, size=30, random_state=12345)
+test    = norm.rvs(loc=0.5, size=30, random_state=12345)
+my_df   = pd.DataFrame({"control": control,
+                            "test": test})
+my_dabest_object = dabest.load(my_df, idx=("control", "test"))
+my_dabest_object.cohens_d
+
+
DABEST v2025.03.27
+==================
+                  
+Good morning!
+The current time is Tue Mar 25 10:08:39 2025.
+
+The unpaired Cohen's d between control and test is 0.471 [95%CI -0.0405, 0.973].
+The p-value of the two-sided permutation t-test is 0.0758, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.cohens_d.statistical_tests`
+
+
+

Cohen’s d is simply the mean of the control group subtracted from the mean of the test group.

+

If paired is None, then the comparison(s) are unpaired; otherwise the comparison(s) are paired.

+

If the comparison(s) are unpaired, Cohen’s d is computed with the following equation:

+

\[d = \frac{\overline{x}_{Test} - \overline{x}_{Control}} {\text{pooled standard deviation}}\]

+

For paired comparisons, Cohen’s d is given by

+

\[d = \frac{\overline{x}_{Test} - \overline{x}_{Control}} {\text{average standard deviation}}\]

+

where \(\overline{x}\) is the mean of the respective group of observations, \({Var}_{x}\) denotes the variance of that group,

+

\[\text{pooled standard deviation} = \sqrt{ \frac{(n_{control} - 1) * {Var}_{control} + (n_{test} - 1) * {Var}_{test} } {n_{control} + n_{test} - 2} }\]

+

and

+

\[\text{average standard deviation} = \sqrt{ \frac{{Var}_{control} + {Var}_{test}} {2}}\]

+

The sample variance (and standard deviation) uses N-1 degrees of freedoms. This is an application of Bessel’s correction, and yields the unbiased sample variance.

+

References:

+

https://en.wikipedia.org/wiki/Effect_size#Cohen's_d

+

https://en.wikipedia.org/wiki/Bessel%27s_correction

+

https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation

+
+
+

Example: cohens_h

+
+
control = randint.rvs(0, 2, size=30, random_state=12345)
+test    = randint.rvs(0, 2, size=30, random_state=12345)
+my_df   = pd.DataFrame({"control": control,
+                            "test": test})
+my_dabest_object = dabest.load(my_df, idx=("control", "test"))
+my_dabest_object.cohens_h
+
+
DABEST v2025.03.27
+==================
+                  
+Good morning!
+The current time is Tue Mar 25 10:08:41 2025.
+
+The unpaired Cohen's h between control and test is 0.0 [95%CI -0.563, 0.474].
+The p-value of the two-sided permutation t-test is 0.799, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.cohens_h.statistical_tests`
+
+
+

Cohen’s h uses the information of proportion in the control and test groups to calculate the distance between two proportions.

+

It can be used to describe the difference between two proportions as “small”, “medium”, or “large”.

+

It can be used to determine if the difference between two proportions is “meaningful”.

+

A directional Cohen’s h is computed with the following equation:

+

\[h = 2 * \arcsin{\sqrt{proportion_{Test}}} - 2 * \arcsin{\sqrt{proportion_{Control}}}\]

+

For a non-directional Cohen’s h, the equation is:

+

\[h = |2 * \arcsin{\sqrt{proportion_{Test}}} - 2 * \arcsin{\sqrt{proportion_{Control}}}|\]

+

References:

+

https://en.wikipedia.org/wiki/Cohen%27s_h

+
+
+

Example: hedges_g

+
+
control = norm.rvs(loc=0, size=30, random_state=12345)
+test    = norm.rvs(loc=0.5, size=30, random_state=12345)
+my_df   = pd.DataFrame({"control": control,
+                            "test": test})
+my_dabest_object = dabest.load(my_df, idx=("control", "test"))
+my_dabest_object.hedges_g
+
+
DABEST v2025.03.27
+==================
+                  
+Good morning!
+The current time is Tue Mar 25 10:08:41 2025.
+
+The unpaired Hedges' g between control and test is 0.465 [95%CI -0.04, 0.96].
+The p-value of the two-sided permutation t-test is 0.0758, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.hedges_g.statistical_tests`
+
+
+

Hedges’ g is cohens_d corrected for bias via multiplication with the following correction factor:

+

\[\frac{ \Gamma( \frac{a} {2} )} {\sqrt{ \frac{a} {2} } \times \Gamma( \frac{a - 1} {2} )}\]

+

where

+

\[a = {n}_{control} + {n}_{test} - 2\]

+

and \(\Gamma(x)\) is the Gamma function.

+

References:

+

https://en.wikipedia.org/wiki/Effect_size#Hedges'_g

+

https://journals.sagepub.com/doi/10.3102/10769986006002107

+
+
+

Example: cliffs_delta

+
+
control = norm.rvs(loc=0, size=30, random_state=12345)
+test    = norm.rvs(loc=0.5, size=30, random_state=12345)
+my_df   = pd.DataFrame({"control": control,
+                            "test": test})
+my_dabest_object = dabest.load(my_df, idx=("control", "test"))
+my_dabest_object.cliffs_delta
+
+
DABEST v2025.03.27
+==================
+                  
+Good morning!
+The current time is Tue Mar 25 10:08:41 2025.
+
+The unpaired Cliff's delta between control and test is 0.28 [95%CI -0.0111, 0.544].
+The p-value of the two-sided permutation t-test is 0.061, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.cliffs_delta.statistical_tests`
+
+
+

Cliff’s delta is a measure of ordinal dominance, ie. how often the values from the test sample are larger than values from the control sample.

+

\[\text{Cliff's delta} = \frac{\#({x}_{test} > {x}_{control}) - \#({x}_{test} < {x}_{control})} {{n}_{Test} \times {n}_{Control}}\]

+

where \(\#\) denotes the number of times a value from the test sample exceeds (or is lesser than) values in the control sample.

+

Cliff’s delta ranges from -1 to 1; it can also be thought of as a measure of the degree of overlap between the two samples. An attractive aspect of this effect size is that it does not make an assumptions about the underlying distributions that the samples were drawn from.

+

References:

+

https://en.wikipedia.org/wiki/Effect_size#Effect_size_for_ordinal_data

+

https://psycnet.apa.org/record/1994-08169-001

+
+
+

Example: delta_g via hedges_g

+
+
random.seed(12345) # Fix the seed so the results are replicable.
+N=20
+y = norm.rvs(loc=3, scale=0.4, size=N*4)
+y[N:2*N] = y[N:2*N]+1
+y[2*N:3*N] = y[2*N:3*N]-0.5
+t1 = repeat('Placebo', N*2).tolist()
+t2 = repeat('Drug', N*2).tolist()
+treatment = t1 + t2
+rep = []
+for i in range(N*2):
+    rep.append('Rep1')
+    rep.append('Rep2')
+wt = repeat('W', N).tolist()
+mt = repeat('M', N).tolist()
+wt2 = repeat('W', N).tolist()
+mt2 = repeat('M', N).tolist()
+genotype = wt + mt + wt2 + mt2
+id = list(range(0, N*2))
+id_col = id + id
+df_delta2 = pd.DataFrame({'ID'        : id_col,
+                          'Rep'      : rep,
+                          'Genotype'  : genotype,
+                          'Treatment': treatment,
+                          'Y'         : y})
+unpaired_delta2 = dabest.load(data = df_delta2, x = ["Genotype", "Genotype"], y = "Y", delta2 = True, experiment = "Treatment")
+unpaired_delta2.hedges_g
+
+
DABEST v2025.03.27
+==================
+                  
+Good morning!
+The current time is Tue Mar 25 10:08:42 2025.
+
+The unpaired Hedges' g between W Placebo and M Placebo is 1.74 [95%CI 1.09, 2.33].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+The unpaired Hedges' g between W Drug and M Drug is 1.33 [95%CI 0.632, 1.98].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+The delta g between Placebo and Drug is -0.651 [95%CI -1.53, 0.21].
+The p-value of the two-sided permutation t-test is 0.0694, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing the effect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.hedges_g.statistical_tests`
+
+
+

Delta g is an effect size that only applied on experiments with a 2-by-2 arrangement where two independent variables, A and B, each have two categorical values, 1 and 2, which calculates hedges_g for delta-delta statistics.

+

\[\Delta_{1} = \overline{X}_{A_{2}, B_{1}} - \overline{X}_{A_{1}, B_{1}}\]

+

\[\Delta_{2} = \overline{X}_{A_{2}, B_{2}} - \overline{X}_{A_{1}, B_{2}}\]

+

where \(\overline{X}_{A_{i}, B_{j}}\) is the mean of the sample with A = i and B = j, \(\Delta\) is the mean difference between two samples.

+

A delta-delta value is then calculated as the mean difference between the two primary deltas:

+

\[\Delta_{\Delta} = \Delta_{2} - \Delta_{1}\]

+

and the standard deviation of the delta-delta value is calculated from a pooled variance of the 4 samples:

+

\[s_{\Delta_{\Delta}} = \sqrt{\frac{(n_{A_{2}, B_{1}}-1)s_{A_{2}, B_{1}}^2+(n_{A_{1}, B_{1}}-1)s_{A_{1}, B_{1}}^2+(n_{A_{2}, B_{2}}-1)s_{A_{2}, B_{2}}^2+(n_{A_{1}, B_{2}}-1)s_{A_{1}, B_{2}}^2}{(n_{A_{2}, B_{1}} - 1) + (n_{A_{1}, B_{1}} - 1) + (n_{A_{2}, B_{2}} - 1) + (n_{A_{1}, B_{2}} - 1)}}\]

+

where \(s\) is the standard deviation and \(n\) is the sample size.

+

A delta g value is then calculated as delta-delta value divided by pooled standard deviation \(s_{\Delta_{\Delta}}\):

+

\(\Delta_{g} = \frac{\Delta_{\Delta}}{s_{\Delta_{\Delta}}}\)

+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/API/delta_objects.html b/API/delta_objects.html new file mode 100644 index 00000000..3b0cac57 --- /dev/null +++ b/API/delta_objects.html @@ -0,0 +1,1001 @@ + + + + + + + + + + +Delta objects – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Delta objects

+
+ +
+
+ Auxiliary delta classes for estimating statistics and generating plots. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+
+

DeltaDelta

+

+def DeltaDelta(
+    effectsizedataframe, permutation_count, bootstraps_delta_delta, ci:int=95
+):
+
+

A class to compute and store the delta-delta statistics for experiments with a 2-by-2 arrangement where two independent variables, A and B, each have two categorical values, 1 and 2. The data is divided into two pairs of two groups, and a primary delta is first calculated as the mean difference between each of the pairs:

+

\[\Delta_{1} = \overline{X}_{A_{2}, B_{1}} - \overline{X}_{A_{1}, B_{1}}\]

+

\[\Delta_{2} = \overline{X}_{A_{2}, B_{2}} - \overline{X}_{A_{1}, B_{2}}\]

+

where \(\overline{X}_{A_{i}, B_{j}}\) is the mean of the sample with A = i and B = j, \(\Delta\) is the mean difference between two samples.

+

A delta-delta value is then calculated as the mean difference between the two primary deltas:

+

\[\Delta_{\Delta} = \Delta_{2} - \Delta_{1}\]

+

and a delta g value is calculated as the mean difference between the two primary deltas divided by the standard deviation of the delta-delta value, which is calculated from a pooled variance of the 4 samples:

+

\[\Delta_{g} = \frac{\Delta_{\Delta}}{s_{\Delta_{\Delta}}}\]

+

\[s_{\Delta_{\Delta}} = \sqrt{\frac{(n_{A_{2}, B_{1}}-1)s_{A_{2}, B_{1}}^2+(n_{A_{1}, B_{1}}-1)s_{A_{1}, B_{1}}^2+(n_{A_{2}, B_{2}}-1)s_{A_{2}, B_{2}}^2+(n_{A_{1}, B_{2}}-1)s_{A_{1}, B_{2}}^2}{(n_{A_{2}, B_{1}} - 1) + (n_{A_{1}, B_{1}} - 1) + (n_{A_{2}, B_{2}} - 1) + (n_{A_{1}, B_{2}} - 1)}}\]

+

where \(s\) is the standard deviation and \(n\) is the sample size.

+

and the standard deviation of the delta-delta value is calculated from a pooled variance of the 4 samples:

+

\[s_{\Delta_{\Delta}} = \sqrt{\frac{(n_{A_{2}, B_{1}}-1)s_{A_{2}, B_{1}}^2+(n_{A_{1}, B_{1}}-1)s_{A_{1}, B_{1}}^2+(n_{A_{2}, B_{2}}-1)s_{A_{2}, B_{2}}^2+(n_{A_{1}, B_{2}}-1)s_{A_{1}, B_{2}}^2}{(n_{A_{2}, B_{1}} - 1) + (n_{A_{1}, B_{1}} - 1) + (n_{A_{2}, B_{2}} - 1) + (n_{A_{1}, B_{2}} - 1)}}\]

+

where \(s\) is the standard deviation and \(n\) is the sample size.

+
+

Example: delta-delta

+
+
np.random.seed(9999) # Fix the seed so the results are replicable.
+N = 20
+# Create samples
+y = norm.rvs(loc=3, scale=0.4, size=N*4)
+y[N:2*N] = y[N:2*N]+1
+y[2*N:3*N] = y[2*N:3*N]-0.5
+# Add a `Treatment` column
+t1 = np.repeat('Placebo', N*2).tolist()
+t2 = np.repeat('Drug', N*2).tolist()
+treatment = t1 + t2 
+# Add a `Rep` column as the first variable for the 2 replicates of experiments done
+rep = []
+for i in range(N*2):
+    rep.append('Rep1')
+    rep.append('Rep2')
+# Add a `Genotype` column as the second variable
+wt = np.repeat('W', N).tolist()
+mt = np.repeat('M', N).tolist()
+wt2 = np.repeat('W', N).tolist()
+mt2 = np.repeat('M', N).tolist()
+genotype = wt + mt + wt2 + mt2
+# Add an `id` column for paired data plotting.
+id = list(range(0, N*2))
+id_col = id + id 
+# Combine all columns into a DataFrame.
+df_delta2 = pd.DataFrame({'ID'        : id_col,
+                  'Rep'      : rep,
+                   'Genotype'  : genotype, 
+                   'Treatment': treatment,
+                   'Y'         : y
+                })
+unpaired_delta2 = dabest.load(data = df_delta2, x = ["Genotype", "Genotype"], y = "Y", delta2 = True, experiment = "Treatment")
+unpaired_delta2.mean_diff.plot();
+
+
C:\Users\maiyi\anaconda3\Lib\site-packages\dabest\plot_tools.py:2537: UserWarning: 5.0% of the points cannot be placed. You might want to decrease the size of the markers.
+  warnings.warn(err)
+C:\Users\maiyi\anaconda3\Lib\site-packages\dabest\plot_tools.py:2537: UserWarning: 5.0% of the points cannot be placed. You might want to decrease the size of the markers.
+  warnings.warn(err)
+C:\Users\maiyi\anaconda3\Lib\site-packages\dabest\plot_tools.py:2537: UserWarning: 20.0% of the points cannot be placed. You might want to decrease the size of the markers.
+  warnings.warn(err)
+
+
+
+
+

+
+
+
+
+
+
+
+
+

MiniMetaDelta

+

+def MiniMetaDelta(
+    effectsizedataframe, permutation_count, ci:int=95
+):
+
+

A class to compute and store the weighted delta. A weighted delta is calculated if the argument mini_meta=True is passed during dabest.load().

+

The weighted delta is calcuated as follows:

+

\[\theta_{\text{weighted}} = \frac{\Sigma\hat{\theta_{i}}w_{i}}{{\Sigma}w_{i}}\]

+

where:

+

\[\hat{\theta_{i}} = \text{Mean difference for replicate }i\]

+

\[w_{i} = \text{Weight for replicate }i = \frac{1}{s_{i}^2} \]

+

\[s_{i}^2 = \text{Pooled variance for replicate }i = \frac{(n_{test}-1)s_{test}^2+(n_{control}-1)s_{control}^2}{n_{test}+n_{control}-2}\]

+

\[n = \text{sample size and }s^2 = \text{variance for control/test.}\]

+
+

Example: mini-meta-delta

+
+
Ns = 20
+c1 = norm.rvs(loc=3, scale=0.4, size=Ns)
+c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
+c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
+t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)
+t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)
+t3 = norm.rvs(loc=3, scale=0.75, size=Ns)
+my_df   = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,
+                   'Control 2' : c2,     'Test 2' : t2,
+                   'Control 3' : c3,     'Test 3' : t3})
+my_dabest_object = dabest.load(my_df, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), mini_meta=True)
+my_dabest_object.mean_diff.mini_meta
+
+
DABEST v2025.03.27
+==================
+                  
+Good afternoon!
+The current time is Mon Sep  1 16:03:47 2025.
+
+The weighted-average unpaired mean differences is 0.0336 [95%CI -0.136, 0.236].
+The p-value of the two-sided permutation t-test is 0.736, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+
+

As of version 2023.02.14, weighted delta can only be calculated for mean difference, and not for standardized measures such as Cohen’s d.

+

Details about the calculated weighted delta are accessed as attributes of the mini_meta class. See the minimetadelta for details on usage.

+

Refer to Chapter 10 of the Cochrane handbook for further information on meta-analysis: https://training.cochrane.org/handbook/current/chapter-10

+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/API/delta_objects_files/figure-html/cell-3-output-2.png b/API/delta_objects_files/figure-html/cell-3-output-2.png new file mode 100644 index 00000000..7e9e82a6 Binary files /dev/null and b/API/delta_objects_files/figure-html/cell-3-output-2.png differ diff --git a/API/effsize.html b/API/effsize.html new file mode 100644 index 00000000..6b8f0c66 --- /dev/null +++ b/API/effsize.html @@ -0,0 +1,1013 @@ + + + + + + + + + + +effsize – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

effsize

+
+ +
+
+ A range of functions to compute various effect sizes. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

two_group_difference

+

+def two_group_difference(
+    control:list | tuple | np.ndarray, # Accepts lists, tuples, or numpy ndarrays of numeric types.
+    test:list | tuple | np.ndarray, # Accepts lists, tuples, or numpy ndarrays of numeric types.
+    is_paired:NoneType=None, # If not None, returns the paired Cohen's d
+    effect_size:str='mean_diff', # Any one of the following effect sizes: ["mean_diff", "median_diff", "cohens_d", "hedges_g", "cliffs_delta"]
+)->float: # The desired effect size.
+
+

Computes the following metrics for control and test:

+
- Unstandardized mean difference
+- Standardized mean differences (paired or unpaired)
+    * Cohen's d
+    * Hedges' g
+- Median difference
+- Cliff's Delta
+- Cohen's h (distance between two proportions)
+

See the Wikipedia entry here

+

effect_size:

+
mean_diff:      This is simply the mean of `control` subtracted from
+                the mean of `test`.
+
+cohens_d:       This is the mean of control subtracted from the
+                mean of test, divided by the pooled standard deviation
+                of control and test. The pooled SD is the square as:
+
+                       (n1 - 1) * var(control) + (n2 - 1) * var(test)
+                sqrt (   -------------------------------------------  )
+                                         (n1 + n2 - 2)
+
+                where n1 and n2 are the sizes of control and test
+                respectively.
+
+hedges_g:       This is Cohen's d corrected for bias via multiplication
+                 with the following correction factor:
+
+                                gamma(n/2)
+                J(n) = ------------------------------
+                       sqrt(n/2) * gamma((n - 1) / 2)
+
+                where n = (n1 + n2 - 2).
+
+median_diff:    This is the median of `control` subtracted from the
+                median of `test`.
+
+

source

+
+
+

func_difference

+

+def func_difference(
+    control:list | tuple | np.ndarray, # NaNs are automatically discarded.
+    test:list | tuple | np.ndarray, # NaNs are automatically discarded.
+    func, # Summary function to apply.
+    is_paired:str, # If not None, computes func(test - control). If None, computes func(test) - func(control).
+)->float:
+
+

Applies func to control and test, and then returns the difference.

+
+

source

+
+
+

cohens_d

+

+def cohens_d(
+    control:list | tuple | np.ndarray, test:list | tuple | np.ndarray,
+    is_paired:str=None, # If not None, the paired Cohen's d is returned.
+)->float:
+
+

Computes Cohen’s d for test v.s. control. See here

+

If is_paired is None, returns:

+

\[ +\frac{\bar{X}_2 - \bar{X}_1}{s_{pooled}} +\]

+

where

+

\[ +s_{pooled} = \sqrt{\frac{(n_1 - 1) s_1^2 + (n_2 - 1) s_2^2}{n_1 + n_2 - 2}} +\]

+

If is_paired is not None, returns:

+

\[ +\frac{\bar{X}_2 - \bar{X}_1}{s_{avg}} +\]

+

where

+

\[ +s_{avg} = \sqrt{\frac{s_1^2 + s_2^2}{2}} +\]

+

Notes:

+
    +
  • The sample variance (and standard deviation) uses N-1 degrees of freedoms. This is an application of Bessel’s correction, and yields the unbiased sample variance.
    • +
+

References:

+
- https://en.wikipedia.org/wiki/Bessel%27s_correction
+- https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation
+
+

source

+
+
+

cohens_h

+

+def cohens_h(
+    control:list | tuple | np.ndarray, test:list | tuple | np.ndarray
+)->float:
+
+

Computes Cohen’s h for test v.s. control. See here for reference.

+

Notes:

+
    +
  • Assuming the input data type is binary, i.e. a series of 0s and 1s, and a dict for mapping the 0s and 1s to the actual labels, e.g.{1: “Smoker”, 0: “Non-smoker”}
    • +
+
+

source

+
+
+

hedges_g

+

+def hedges_g(
+    control:list | tuple | np.ndarray, test:list | tuple | np.ndarray, is_paired:str=None
+)->float:
+
+

Computes Hedges’ g for for test v.s. control. It first computes Cohen’s d, then calulates a correction factor based on the total degress of freedom using the gamma function.

+

See here

+
+

source

+
+
+

cliffs_delta

+

+def cliffs_delta(
+    control:list | tuple | np.ndarray, test:list | tuple | np.ndarray
+)->float:
+
+

Computes Cliff’s delta for 2 samples. See here

+
+

source

+
+
+

weighted_delta

+

+def weighted_delta(
+    difference, bootstrap_dist_var
+):
+
+

Compute the weighted deltas where the weight is the inverse of the pooled group difference.

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/API/effsize_objects.html b/API/effsize_objects.html new file mode 100644 index 00000000..8863e2d6 --- /dev/null +++ b/API/effsize_objects.html @@ -0,0 +1,1156 @@ + + + + + + + + + + +Effectsize objects – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Effectsize objects

+
+ +
+
+ The auxiliary classes involved in the computations of bootstrapped effect sizes. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+
+

TwoGroupsEffectSize

+

+def TwoGroupsEffectSize(
+    control, test, effect_size, proportional:bool=False, is_paired:NoneType=None, ci:int=95, resamples:int=5000,
+    permutation_count:int=5000, random_seed:int=12345, ps_adjust:bool=False
+):
+
+

A class to compute and store the results of bootstrapped mean differences between two groups.

+

Compute the effect size between two groups.

+
+

Example

+
+
random.seed(12345)
+control = norm.rvs(loc=0, size=30)
+test = norm.rvs(loc=0.5, size=30)
+effsize = dabest.TwoGroupsEffectSize(control, test, "mean_diff")
+effsize
+
+
The unpaired mean difference is -0.253 [95%CI -0.782, 0.241].
+The p-value of the two-sided permutation t-test is 0.348, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+
+
+
effsize.to_dict()
+
+
{'alpha': 0.05,
+ 'bca_high': 0.2413346581369784,
+ 'bca_interval_idx': (109, 4858),
+ 'bca_low': -0.7818088458343655,
+ 'bec_bca_high': 0.5352403905584314,
+ 'bec_bca_interval_idx': (130, 4880),
+ 'bec_bca_low': -0.4982839949134528,
+ 'bec_bootstraps': array([-0.48953946, -0.18565285, -0.23896785, ..., -0.55130928,
+         0.16037238, -0.07364879]),
+ 'bec_difference': 0.0,
+ 'bec_pct_high': 0.5280564736117328,
+ 'bec_pct_interval_idx': (125, 4875),
+ 'bec_pct_low': -0.5041777340626885,
+ 'bootstraps': array([-0.23923425, -0.66013733, -0.42672232, ..., -0.33191074,
+        -0.16543251, -0.34179536]),
+ 'ci': 95,
+ 'difference': -0.25315417702752846,
+ 'effect_size': 'mean difference',
+ 'is_paired': None,
+ 'is_proportional': False,
+ 'pct_high': 0.25135646125431527,
+ 'pct_interval_idx': (125, 4875),
+ 'pct_low': -0.763588353717278,
+ 'permutation_count': 5000,
+ 'permutations': array([ 0.17221029,  0.03112419, -0.13911387, ..., -0.38007941,
+         0.30261507, -0.09073054]),
+ 'permutations_var': array([0.07201642, 0.07251104, 0.07219407, ..., 0.07003705, 0.07094885,
+        0.07238581]),
+ 'proportional_difference': nan,
+ 'pvalue_brunner_munzel': nan,
+ 'pvalue_kruskal': nan,
+ 'pvalue_mann_whitney': 0.5201446121616038,
+ 'pvalue_mcnemar': nan,
+ 'pvalue_paired_students_t': nan,
+ 'pvalue_permutation': 0.3484,
+ 'pvalue_students_t': 0.34743913903372836,
+ 'pvalue_welch': 0.3474493875548964,
+ 'pvalue_wilcoxon': nan,
+ 'random_seed': 12345,
+ 'resamples': 5000,
+ 'statistic_brunner_munzel': nan,
+ 'statistic_kruskal': nan,
+ 'statistic_mann_whitney': 494.0,
+ 'statistic_mcnemar': nan,
+ 'statistic_paired_students_t': nan,
+ 'statistic_students_t': 0.9472545159069105,
+ 'statistic_welch': 0.9472545159069105,
+ 'statistic_wilcoxon': nan}
+
+
+
+
+
+
+

EffectSizeDataFrame

+

+def EffectSizeDataFrame(
+    dabest, effect_size, is_paired, ci:int=95, proportional:bool=False, resamples:int=5000,
+    permutation_count:int=5000, random_seed:int=12345, x1_level:NoneType=None, x2:NoneType=None, delta2:bool=False,
+    experiment_label:NoneType=None, mini_meta:bool=False, ps_adjust:bool=False
+):
+
+

A class that generates and stores the results of bootstrapped effect sizes for several comparisons.

+
+

Example: plot

+

Create a Gardner-Altman estimation plot for the mean difference.

+
+
random.seed(9999) # Fix the seed so the results are replicable.
+# pop_size = 10000 # Size of each population.
+Ns = 20 # The number of samples taken from each population
+
+# Create samples
+c1 = norm.rvs(loc=3, scale=0.4, size=Ns)
+c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
+c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
+
+t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)
+t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)
+t3 = norm.rvs(loc=3, scale=0.75, size=Ns)
+t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
+t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
+t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
+
+
+# Add a `gender` column for coloring the data.
+females = repeat('Female', Ns/2).tolist()
+males = repeat('Male', Ns/2).tolist()
+gender = females + males
+
+# Add an `id` column for paired data plotting.
+id_col = pd.Series(range(1, Ns+1))
+
+# Combine samples and gender into a DataFrame.
+df = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,
+                 'Control 2' : c2,     'Test 2' : t2,
+                 'Control 3' : c3,     'Test 3' : t3,
+                 'Test 4'    : t4,     'Test 5' : t5, 'Test 6' : t6,
+                 'Gender'    : gender, 'ID'  : id_col
+                })
+my_data = dabest.load(df, idx=("Control 1", "Test 1"))
+
+
+
fig1 = my_data.mean_diff.plot();
+
+
+
+

+
+
+
+
+

Create a Gardner-Altman plot for the Hedges’ g effect size.

+
+
fig2 = my_data.hedges_g.plot();
+
+
+
+

+
+
+
+
+

Create a Cumming estimation plot for the mean difference.

+
+
fig3 = my_data.mean_diff.plot(float_contrast=True);
+
+
+
+

+
+
+
+
+

Create a paired Gardner-Altman plot.

+
+
my_data_paired = dabest.load(df, idx=("Control 1", "Test 1"),
+                       id_col = "ID", paired='baseline')
+fig4 = my_data_paired.mean_diff.plot();
+
+
+
+

+
+
+
+
+

Create a multi-group Cumming plot.

+
+
my_multi_groups = dabest.load(df, id_col = "ID", 
+                             idx=(("Control 1", "Test 1"),
+                                 ("Control 2", "Test 2")))
+fig5 = my_multi_groups.mean_diff.plot();
+
+
/Users/jonathananns/GitHub/DABEST-python/dabest/plot_tools.py:2778: UserWarning: 5.0% of the points cannot be placed. You might want to decrease the size of the markers.
+  warnings.warn(err)
+/Users/jonathananns/GitHub/DABEST-python/dabest/plot_tools.py:2778: UserWarning: 10.0% of the points cannot be placed. You might want to decrease the size of the markers.
+  warnings.warn(err)
+
+
+
+
+

+
+
+
+
+

Create a shared control Cumming plot.

+
+
my_shared_control = dabest.load(df, id_col = "ID",
+                                 idx=("Control 1", "Test 1",
+                                          "Test 2", "Test 3"))
+fig6 = my_shared_control.mean_diff.plot();
+
+
/Users/jonathananns/GitHub/DABEST-python/dabest/plot_tools.py:2778: UserWarning: 10.0% of the points cannot be placed. You might want to decrease the size of the markers.
+  warnings.warn(err)
+
+
+
+
+

+
+
+
+
+

Create a repeated meausures (against baseline) Slopeplot.

+
+
my_rm_baseline = dabest.load(df, id_col = "ID", paired = "baseline",
+                                 idx=("Control 1", "Test 1",
+                                          "Test 2", "Test 3"))
+fig7 = my_rm_baseline.mean_diff.plot();
+
+
+
+

+
+
+
+
+

Create a repeated meausures (sequential) Slopeplot.

+
+
my_rm_sequential = dabest.load(df, id_col = "ID", paired = "sequential",
+                                 idx=("Control 1", "Test 1",
+                                          "Test 2", "Test 3"))
+fig8 = my_rm_sequential.mean_diff.plot();
+
+
+
+

+
+
+
+
+
+
+
+
+

PermutationTest

+

+def PermutationTest(
+    control:array, test:array, # These should be numerical iterables.
+    effect_size:str, # Any one of the following are accepted inputs: 'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta'
+    is_paired:str=None, permutation_count:int=5000, # The number of permutations (reshuffles) to perform.
+    random_seed:int=12345, # `random_seed` is used to seed the random number generator during bootstrap resampling. This ensures that the generated permutations are replicable.
+    ps_adjust:bool=False, kwargs:VAR_KEYWORD
+):
+
+

A class to compute and report permutation tests.

+

Notes:

+

The basic concept of permutation tests is the same as that behind bootstrapping. In an “exact” permutation test, all possible resuffles of the control and test labels are performed, and the proportion of effect sizes that equal or exceed the observed effect size is computed. This is the probability, under the null hypothesis of zero difference between test and control groups, of observing the effect size: the p-value of the Student’s t-test.

+

Exact permutation tests are impractical: computing the effect sizes for all reshuffles quickly exceeds trivial computational loads. A control group and a test group both with 10 observations each would have a total of \(20!\) or \(2.43 \times {10}^{18}\) reshuffles. Therefore, in practice, “approximate” permutation tests are performed, where a sufficient number of reshuffles are performed (5,000 or 10,000), from which the p-value is computed.

+

More information can be found here.

+
+

Example: permutation test

+
+
control = norm.rvs(loc=0, size=30, random_state=12345)
+test = norm.rvs(loc=0.5, size=30, random_state=12345)
+perm_test = dabest.PermutationTest(control, test, 
+                                   effect_size="mean_diff", 
+                                   is_paired=None)
+perm_test
+
+
5000 permutations were taken. The p-value is 0.0758.
+
+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/API/effsize_objects_files/figure-html/cell-10-output-1.png b/API/effsize_objects_files/figure-html/cell-10-output-1.png new file mode 100644 index 00000000..7067a355 Binary files /dev/null and b/API/effsize_objects_files/figure-html/cell-10-output-1.png differ diff --git a/API/effsize_objects_files/figure-html/cell-11-output-2.png b/API/effsize_objects_files/figure-html/cell-11-output-2.png new file mode 100644 index 00000000..cc0fcb44 Binary files /dev/null and b/API/effsize_objects_files/figure-html/cell-11-output-2.png differ diff --git a/API/effsize_objects_files/figure-html/cell-12-output-2.png b/API/effsize_objects_files/figure-html/cell-12-output-2.png new file mode 100644 index 00000000..8a310ade Binary files /dev/null and b/API/effsize_objects_files/figure-html/cell-12-output-2.png differ diff --git a/API/effsize_objects_files/figure-html/cell-13-output-1.png b/API/effsize_objects_files/figure-html/cell-13-output-1.png new file mode 100644 index 00000000..33b50d90 Binary files /dev/null and b/API/effsize_objects_files/figure-html/cell-13-output-1.png differ diff --git a/API/effsize_objects_files/figure-html/cell-14-output-1.png b/API/effsize_objects_files/figure-html/cell-14-output-1.png new file mode 100644 index 00000000..15a1a7b5 Binary files /dev/null and b/API/effsize_objects_files/figure-html/cell-14-output-1.png differ diff --git a/API/effsize_objects_files/figure-html/cell-7-output-1.png b/API/effsize_objects_files/figure-html/cell-7-output-1.png new file mode 100644 index 00000000..b9760195 Binary files /dev/null and b/API/effsize_objects_files/figure-html/cell-7-output-1.png differ diff --git a/API/effsize_objects_files/figure-html/cell-8-output-1.png b/API/effsize_objects_files/figure-html/cell-8-output-1.png new file mode 100644 index 00000000..682b9386 Binary files /dev/null and b/API/effsize_objects_files/figure-html/cell-8-output-1.png differ diff --git a/API/effsize_objects_files/figure-html/cell-9-output-1.png b/API/effsize_objects_files/figure-html/cell-9-output-1.png new file mode 100644 index 00000000..b9760195 Binary files /dev/null and b/API/effsize_objects_files/figure-html/cell-9-output-1.png differ diff --git a/API/forest_plot.html b/API/forest_plot.html new file mode 100644 index 00000000..f02b19a7 --- /dev/null +++ b/API/forest_plot.html @@ -0,0 +1,926 @@ + + + + + + + + + + +Forest plot – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Forest plot

+
+ +
+
+ Creating forest plots from contrast objects. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

forest_plot

+

+def forest_plot(
+    data:list, # List of contrast objects.
+    idx:Optional[list[int]]=None, # List of indices to select from the contrast objects if delta-delta experiment. 
+If None, only the delta-delta objects are plotted.
+    ax:Optional[plt.Axes]=None, # Matplotlib Axes object for the plot; creates new if None.
+additional_plotting_kwargs : Optional[dict], default=None
+Further customization arguments for the plot.
+    fig_size:tuple[int, int]=None, # Figure size for the plot.
+    effect_size:str='mean_diff', # Type of effect size to plot (e.g., 'mean_diff', [`hedges_g`](https://acclab.github.io/DABEST-python/API/effsize.html#hedges_g) or 'delta_g').
+    ci_type:str='bca', # Type of confidence interval to plot (bca' or 'pct')
+    horizontal:bool=False, # If True, the plot will be horizontal.
+    marker_size:int=10, # Marker size for plotting effect size dots.
+    custom_palette:Optional[Union[dict, list, str]]=None, # Custom color palette for the plot.
+    contrast_alpha:float=0.8, # Transparency level for violin plots.
+    contrast_desat:float=1, # Saturation level for violin plots.
+    labels:list[str]=None, # Labels for each contrast. If None, defaults to 'Contrast 1', 'Contrast 2', etc.
+    labels_rotation:int=None, # Rotation angle for contrast labels.
+    labels_fontsize:int=10, # Font size for contrast labels.
+    title:str=None, # Plot title, summarizing the visualized data.
+    title_fontsize:int=16, # Font size for the plot title.
+    ylabel:str=None, # Label for the y-axis, describing the plotted data or effect size.
+    ylabel_fontsize:int=12, # Font size for the y-axis label.
+    ylim:Optional[list[float, float]]=None, # Limits for the y-axis.
+    yticks:Optional[list[float]]=None, # Custom y-ticks for the plot.
+    yticklabels:Optional[list[str]]=None, # Custom y-tick labels for the plot.
+    remove_spines:bool=True, # If True, removes plot spines (except the relevant dependent variable spine).
+    delta_text:bool=True, # If True, it adds text next to each curve representing the effect size value.
+    delta_text_kwargs:dict=None, # Additional keyword arguments for the delta_text.
+    contrast_bars:bool=True, # If True, it adds bars from the zeroline to the effect size curve.
+    contrast_bars_kwargs:dict=None, # Additional keyword arguments for the contrast_bars.
+    reference_band:list | tuple=None,
+    reference_band_kwargs:dict=None, # Additional keyword arguments for the reference_band.
+    violin_kwargs:Optional[dict]=None, # Additional arguments for violin plot customization.
+    zeroline_kwargs:Optional[dict]=None, # Additional arguments for the zero line customization.
+    marker_kwargs:Optional[dict]=None, # Additional arguments for the effect size marker customization.
+    errorbar_kwargs:Optional[dict]=None, # Additional arguments for the effect size error bar customization.
+)->plt.Figure: # The matplotlib figure object with the generated forest plot.
+
+

Custom function that generates a forest plot from given contrast objects, suitable for a range of data analysis types, including those from packages like DABEST-python.

+
+

source

+
+
+

color_palette

+

+def color_palette(
+    custom_palette, labels, number_of_curves_to_plot, contrast_desat
+):
+
+

Call self as a function.

+
+

source

+
+
+

get_kwargs

+

+def get_kwargs(
+    violin_kwargs, zeroline_kwargs, horizontal, marker_kwargs, errorbar_kwargs, delta_text_kwargs,
+    contrast_bars_kwargs, reference_band_kwargs, marker_size
+):
+
+

Call self as a function.

+
+

source

+
+
+

check_for_errors

+

+def check_for_errors(
+    kwargs:VAR_KEYWORD
+):
+
+

Call self as a function.

+
+

source

+
+
+

load_plot_data

+

+def load_plot_data(
+    data:List, effect_size:str='mean_diff', contrast_type:str=None, ci_type:str='bca', idx:Optional[List[int]]=None
+)->List:
+
+

Loads plot data based on specified effect size and contrast type.

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/API/index.html b/API/index.html new file mode 100644 index 00000000..e7cdf6fe --- /dev/null +++ b/API/index.html @@ -0,0 +1,1006 @@ + + + + + + + + + +API – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

API

+
+ + + +
+ + + + +
+ + + +
+ + +

This section contains API details for each of dabest’s python submodules. This reference documentation is mainly useful for people looking to customise or build on top of dabest, or wanting detailed information about how dabest works.

+ + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+Title + +Description +
+Loading Data + +Loading data and relevant groups +
+Dabest object + +Main class for estimating statistics and generating plots. +
+Bootstrap + +Computes the summary statistic and a bootstrapped confidence interval. +
+Forest plot + +Creating forest plots from contrast objects. +
+Plot + +Creating estimation plots. +
+plot_tools + +A set of convenience functions used for producing plots in dabest. +
+effsize + +A range of functions to compute various effect sizes. +
+confint_1group + +A range of functions to compute bootstraps for a single sample. +
+confint_2group_diff + +A range of functions to compute bootstraps for the mean difference +
+Delta objects + +Auxiliary delta classes for estimating statistics and generating plots. +
+misc_tools + +Convenience functions that don’t directly deal with plotting or bootstrap computations are placed here. +
+Effectsize objects + +The auxiliary classes involved in the computations of bootstrapped effect sizes. +
+precompile + +A tool to pre-compile Numba functions for speeding up DABEST bootstrapping +
+multi + +The MultiContrast class enables visualization of multiple contrast objects in grid-based layouts. +
+
No matching items
+
+ +
+ + + + + \ No newline at end of file diff --git a/API/load.html b/API/load.html new file mode 100644 index 00000000..f5a746c3 --- /dev/null +++ b/API/load.html @@ -0,0 +1,936 @@ + + + + + + + + + + +Loading Data – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Loading Data

+
+ +
+
+ Loading data and relevant groups +
+
+ + +
+ + + + +
+ + + +
+ + + +
+
+

load

+

+def load(
+    data,
+    idx:NoneType=None, # List of column names (if 'x' is not supplied) or of category names
+(if 'x' is supplied). This can be expressed as a tuple of tuples,
+with each individual tuple producing its own contrast plot
+    x:NoneType=None, # Column name(s) of the independent variable. This can be expressed as
+a list of 2 elements if and only if 'delta2' is True; otherwise it
+can only be a string.
+    y:NoneType=None, # Column names for data to be plotted on the x-axis and y-axis.
+    paired:NoneType=None, # The type of the experiment under which the data are obtained. If 'paired'
+is None then the data will not be treated as paired data in the subsequent
+calculations. If 'paired' is 'baseline', then in each tuple of x, other
+groups will be paired up with the first group (as control). If 'paired' is
+'sequential', then in each tuple of x, each group will be paired up with
+its previous group (as control).
+    id_col:NoneType=None, # Required if `paired` is True.
+    ci:int=95, # The confidence interval width. The default of 95 produces 95%
+confidence intervals.
+    resamples:int=5000, # The number of resamples taken to generate the bootstraps which are used
+to generate the confidence intervals.
+    random_seed:int=12345, # This integer is used to seed the random number generator during
+bootstrap resampling, ensuring that the confidence intervals
+reported are replicable.
+    proportional:bool=False, # An indicator of whether the data is binary or not. When set to True, it
+specifies that the data consists of binary data, where the values are
+limited to 0 and 1. The code is not suitable for analyzing proportion
+data that contains non-numeric values, such as strings like 'yes' and 'no'.
+When False or not provided, the algorithm assumes that
+the data is continuous and uses a non-proportional representation.
+    delta2:bool=False, # Indicator of delta-delta experiment
+    experiment:NoneType=None, # The name of the column of the dataframe which contains the label of
+experiments
+    experiment_label:NoneType=None,
+    x1_level:NoneType=None, # A list of String to specify the order of subplots for delta-delta plots.
+This can be expressed as a list of 2 elements if and only if 'delta2'
+is True; otherwise it can only be a string.
+    mini_meta:bool=False, # Indicator of weighted delta calculation.
+    ps_adjust:bool=False, # Indicator of whether to adjust calculated p-value according to Phipson & Smyth (2010)
+# https://doi.org/10.2202/1544-6115.1585
+):
+
+

Loads data in preparation for estimation statistics.

+

This is designed to work with pandas DataFrames.

+
+
+
+

prop_dataset

+

+def prop_dataset(
+    group:Union[list, tuple, np.ndarray, dict],
+    group_names:Optional[list]=None, # Accepts lists, tuples, or numpy ndarrays of numeric types.
+):
+
+

Convenient function to generate a dataframe of binary data.

+
+
+

Example

+
+
import numpy as np
+import pandas as pd
+import scipy as sp
+import dabest
+
+

Create dummy data for demonstration.

+
+
np.random.seed(88888)
+N = 10
+c1 = sp.stats.norm.rvs(loc=100, scale=5, size=N)
+t1 = sp.stats.norm.rvs(loc=115, scale=5, size=N)
+df = pd.DataFrame({"Control 1": c1, "Test 1": t1})
+
+

Load the data.

+
+
my_data = dabest.load(df, idx=("Control 1", "Test 1"))
+my_data
+
+
DABEST v2024.03.29
+==================
+                  
+Good afternoon!
+The current time is Tue Mar 19 15:34:58 2024.
+
+Effect size(s) with 95% confidence intervals will be computed for:
+1. Test 1 minus Control 1
+
+5000 resamples will be used to generate the effect size bootstraps.
+
+
+

For proportion plot.

+
+
np.random.seed(88888)
+N = 10
+c1 = np.random.binomial(1, 0.2, size=N)
+t1 = np.random.binomial(1, 0.5, size=N)
+df = pd.DataFrame({"Control 1": c1, "Test 1": t1})
+my_data = dabest.load(df, idx=("Control 1", "Test 1"), proportional=True)
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/API/misc_tools.html b/API/misc_tools.html new file mode 100644 index 00000000..b0dd1fb2 --- /dev/null +++ b/API/misc_tools.html @@ -0,0 +1,1189 @@ + + + + + + + + + + +misc_tools – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

misc_tools

+
+ +
+
+ Convenience functions that don’t directly deal with plotting or bootstrap computations are placed here. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

prepare_bars_for_plot

+

+def prepare_bars_for_plot(
+    bar_type, bar_kwargs, horizontal, plot_palette_raw, color_col, show_pairs, bootstraps_color_by_group,
+    plot_data:NoneType=None, xvar:NoneType=None, yvar:NoneType=None, # Raw data
+    results:NoneType=None, ticks_to_plot:NoneType=None, extra_delta:NoneType=None, # Contrast data
+    reference_band:NoneType=None, summary_axes:NoneType=None, ci_type:NoneType=None, # Summary data
+):
+
+

Call self as a function.

+
+

source

+
+
+

color_picker

+

+def color_picker(
+    color_type:str, kwargs:dict, elements:list, color_col:str, show_pairs:bool, color_palette:dict,
+    bootstraps_color_by_group:bool
+)->list:
+
+

Call self as a function.

+
+

source

+
+
+

extract_group_summaries

+

+def extract_group_summaries(
+    proportional:bool, # A boolean flag to determine if the plot is for proportional data.
+    rawdata_axes:axes.Axes, # The raw data axes.
+    asymmetric_side:str, # The side of the asymmetric error bars.
+    horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.
+    bootstraps_color_by_group:bool, # A boolean flag to determine if the bootstraps are colored by group.
+    plot_palette_raw:list, # A list of the plot palette colors.
+    all_plot_groups:list, # A list of all the plot groups.
+    n_groups:int, # The number of groups.
+    color_col, # The name of the color column.
+    ytick_color, # The color of the y-ticks.
+    group_summaries_kwargs:dict, # Kwargs passed to the group summaries.
+):
+
+

Extract the group summaries for the plotter function.

+
+

source

+
+
+

redraw_dependent_spines

+

+def redraw_dependent_spines(
+    rawdata_axes:axes.Axes, # The raw data axes.
+    contrast_axes:axes.Axes, # The contrast axes.
+    redraw_axes_kwargs:dict, # Kwargs passed to the redraw axes.
+    float_contrast:bool, # A boolean flag to determine if the plot is GA or Cum
+    horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.
+    show_delta2:bool, # A boolean flag to determine if the plot will have a delta-delta effect size.
+    delta2_axes:axes.Axes, # The delta2 axes.
+):
+
+

Draw the dependent axis spine lines.

+
+

source

+
+
+

redraw_independent_spines

+

+def redraw_independent_spines(
+    rawdata_axes:axes.Axes, # The raw data axes.
+    contrast_axes:axes.Axes, # The contrast axes.
+    horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.
+    two_col_sankey:bool, # A boolean flag to determine if the plot is for two-col sankey.
+    ticks_to_start_twocol_sankey:list, # A list of ticks to start for sankey plot.
+    idx:list, # A list of indices.
+    is_paired:str, # A boolean flag to determine if the data is paired.
+    show_pairs:bool, # A boolean flag to determine if pairs should be shown.
+    proportional:bool, # A boolean flag to determine if the plot is proportional/binary.
+    ticks_to_skip:list, # A list of ticks to be skipped in the raw data axes.
+    temp_idx:list, # A temporary list of indices to be used for skipping ticks in the raw data axes.
+    ticks_to_skip_contrast:list, # A list of ticks to be skipped in the contrast axes.
+    redraw_axes_kwargs:dict, # Kwargs passed to the redraw axes.
+):
+
+

Draw the independent axis spine lines.

+
+

source

+
+
+

draw_zeroline

+

+def draw_zeroline(
+    ax:axes.Axes, # The contrast data axes.
+    horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.
+    reflines_kwargs:dict, # Additional keyword arguments to be passed to the zeroline.
+    extra_delta:bool, # A boolean flag to determine if the plot includes an extra delta (delta-delta or mini-meta).
+):
+
+

Draw the independent axis spine lines.

+
+

source

+
+
+

gardner_altman_adjustments

+

+def gardner_altman_adjustments(
+    effect_size_type:str, # The type of effect size.
+    plot_data:pd.DataFrame, # A dataframe of plot data.
+    xvar:str, # The name of the x-axis variable.
+    yvar:str, # The name of the y-axis variable.
+    current_control:str, # The name of the current control group.
+    current_group:str, # The name of the current test group.
+    rawdata_axes:axes.Axes, # The raw data axes.
+    contrast_axes:axes.Axes, # The contrast axes.
+    results:pd.DataFrame, # A dataframe of the results.
+    current_effsize:float, # The current effect size.
+    is_paired:bool, # A boolean flag to determine if the plot is for paired data.
+    one_sankey:bool, # A boolean flag to determine if the plot is for a single sankey diagram.
+    reflines_kwargs:dict, # Kwargs passed to the reference lines.
+    redraw_axes_kwargs:dict, # Kwargs passed to the redraw axes.
+):
+
+

Aesthetic adjustments specific to Gardner-Altman plots (float_contrast=True).

+
+

source

+
+
+

show_legend

+

+def show_legend(
+    legend_labels:list, # A list of legend labels.
+    legend_handles:list, # A list of legend handles.
+    rawdata_axes:axes.Axes, # The raw data axes.
+    contrast_axes:axes.Axes, # The contrast axes.
+    table_axes:axes.Axes, # The table axes.
+    float_contrast:bool, # A boolean flag to determine if the plot is GA or Cumming format.
+    show_pairs:bool, # A boolean flag to determine if the plot will show the paired data.
+    horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.
+    legend_kwargs:dict, # Kwargs passed to the legend function.
+    table_kwargs:dict
+):
+
+

Show the legend for the plotter function.

+
+

source

+
+
+

set_xaxis_ticks_and_lims

+

+def set_xaxis_ticks_and_lims(
+    show_delta2:bool, # A boolean flag to determine if the plot will have a delta-delta effect size.
+    show_mini_meta:bool, # A boolean flag to determine if the plot will have a mini-meta effect size.
+    rawdata_axes:axes.Axes, # The raw data axes.
+    contrast_axes:axes.Axes, # The contrast axes.
+    show_pairs:bool, # A boolean flag to determine if the plot will show the paired data.
+    float_contrast:bool, # A boolean flag to determine if the plot is a GA or Cumming design.
+    ticks_to_skip:list, # A list of ticks to skip.
+    contrast_xtick_labels:list, # A list of contrast xtick labels.
+    plot_kwargs:dict, # Kwargs passed to the plot function.
+    proportional:bool, horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.
+):
+
+

Set the x-axis/yaxis ticks and limits for the plotter function.

+
+

source

+
+
+

extract_contrast_plotting_ticks

+

+def extract_contrast_plotting_ticks(
+    is_paired:bool, # A boolean flag to determine if the plot is for paired data.
+    show_pairs:bool, # A boolean flag to determine if the plot will show the paired data.
+    two_col_sankey:bool, # A boolean flag to determine if the plot will show a two-column sankey diagram.
+    plot_groups:list, # A list of the plot groups.
+    idx:list, # A list of tuples containing the group names.
+    sankey_control_group:list, # A list of the control group names.
+):
+
+

Extract the contrast plotting ticks from the idx object for use in the plotter function.

+
+

source

+
+
+

add_counts_to_ticks

+

+def add_counts_to_ticks(
+    plot_data:pd.DataFrame, # A dataframe of plot data.
+    xvar:str, # The name of the x-axis variable.
+    yvar:str, # The name of the y-axis variable.
+    rawdata_axes:axes.Axes, # The raw data axes.
+    plot_kwargs:dict, # Kwargs passed to the plot function.
+    flow:bool, # Whether sankey flow is enabled or not.
+    horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.
+):
+
+

Add the counts to the raw data axes labels.

+
+

source

+
+
+

get_plot_groups

+

+def get_plot_groups(
+    is_paired:bool, # A boolean flag to determine if the plot is for paired data.
+    idx:list, # A list of tuples containing the group names.
+    proportional:bool, # A boolean flag to determine if the plot is for proportional data.
+    all_plot_groups:list, # A list of all the group names.
+):
+
+

Extract the plot groups from the idx object for use in the plotter function.

+
+

source

+
+
+

initialize_fig

+

+def initialize_fig(
+    plot_kwargs:dict, # Kwargs passed to the plot function.
+    dabest_obj:object, # A `dabest` EffectSizeDataFrame object.
+    show_delta2:bool, # A boolean flag to determine if the plot will have a delta-delta effect size.
+    show_mini_meta:bool, # A boolean flag to determine if the plot will have a mini-meta effect size.
+    is_paired:bool, # A boolean flag to determine if the plot is for paired data.
+    show_pairs:bool, # A boolean flag to determine if the plot will show the paired data.
+    proportional:bool, # A boolean flag to determine if the plot is for proportional data.
+    float_contrast:bool, # A boolean flag to determine if the plot is for floating contrast data.
+    effect_size_type:str, # The type of effect size to be plotted.
+    yvar:str, # The name of the y-axis variable.
+    horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.
+    show_table:bool, # A boolean flag to determine if the table will be shown in horizontal plot.
+    color_col:str, # The column name for coloring the data points.
+):
+
+

Initialize the figure and axes for the plotter function.

+
+

source

+
+
+

get_color_palette

+

+def get_color_palette(
+    plot_kwargs:dict, # Kwargs passed to the plot function.
+    plot_data:pd.DataFrame, # A dataframe of plot data.
+    xvar:str, # The name of the x-axis variable.
+    show_pairs:bool, # A boolean flag to determine if the plot is for paired data.
+    idx:list, # A list of tuples containing the group names.
+    all_plot_groups:list, # A list of all the group names.
+    delta2:bool, # A boolean flag to determine if the plot will have a delta-delta effect size.
+    proportional:bool, # A boolean flag to determine if the plot is for a proportional plot.
+):
+
+

Create the color palette to be used in the plotter function.

+
+

source

+
+
+

get_kwargs

+

+def get_kwargs(
+    plot_kwargs:dict, # Kwargs passed to the plot function.
+    ytick_color, # Color of the yticks.
+    is_paired:bool=False, # A boolean flag to determine if the plot is for paired data. Default is False.
+):
+
+

Extracts the kwargs from the plot_kwargs object for use in the plotter function.

+
+

source

+
+
+

get_params

+

+def get_params(
+    effectsize_df:object, # A `dabest` EffectSizeDataFrame object.
+    plot_kwargs:dict, # Kwargs passed to the plot function.
+    sankey_kwargs:dict, barplot_kwargs:dict, # Kwargs relating to the barplot
+):
+
+

Extracts parameters from the effectsize_df and plot_kwargs objects for use in the plotter function.

+
+

source

+
+
+

get_unique_categories

+

+def get_unique_categories(
+    names
+):
+
+

Extract unique categories from various input types.

+
+

source

+
+
+

get_varname

+

+def get_varname(
+    obj
+):
+
+

Call self as a function.

+
+

source

+
+ +
+

unpack_and_add

+

+def unpack_and_add(
+    l, c
+):
+
+

Convenience function to allow me to add to an existing list without altering that list.

+
+

source

+
+
+

merge_two_dicts

+

+def merge_two_dicts(
+    x:dict, y:dict
+)->dict: # A dictionary containing a union of all keys in both original dicts.
+
+

Given two dicts, merge them into a new dict as a shallow copy. Any overlapping keys in y will override the values in x.

+

Taken from here

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/API/multi.html b/API/multi.html new file mode 100644 index 00000000..a7f1574e --- /dev/null +++ b/API/multi.html @@ -0,0 +1,923 @@ + + + + + + + + + +multi – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

multi

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

MultiContrast Class

+

The MultiContrast class enables visualization of multiple contrast objects in grid-based layouts.

+
+

source

+
+

MultiContrast

+

+def MultiContrast(
+    dabest_objs:Union, # Raw dabest objects. Can be:
+- 1D: [dabest_obj1, dabest_obj2, ...]  
+- 2D: [[dabest_obj1, dabest_obj2], [dabest_obj3, dabest_obj4]]
+    labels:Optional=None, # Labels matching the contrast array structure. If None, defaults will be generated.
+    row_labels:Optional=None, effect_size:str='mean_diff', # Effect size to extract from dabest objects
+    ci_type:str='bca', # Confidence interval type
+):
+
+

Unified multiple contrast object for forest plots and whorlmaps.

+

Takes raw dabest objects and provides validated, processed data for downstream visualizations.

+
+
+
+

Loading Function

+
+

source

+
+

combine

+

+def combine(
+    dabest_objs:Union, # Raw dabest objects in 1D or 2D structure
+    labels:Optional=None, # Labels for dabest_objs
+    row_labels:Optional=None, effect_size:str='mean_diff', # Effect size to extract
+    ci_type:str='bca', # Confidence interval type
+    allow_mixed_types:bool=False, # If True, allows different contrast types in different rows (whorlmap only)
+If False, enforces homogeneous types (forest_plot compatible)
+)->MultiContrast: # Validated multi-contrast object ready for visualization
+
+

Create a MultiContrast object from raw dabest objects.

+

This is the main entry point that users should use to create multi-contrast visualizations.

+
+
+
+

Whorlmap Visualization

+

The whorlmap creates spiral heatmaps showing the distribution of bootstrap samples for each contrast.

+
+

source

+
+

whorlmap

+

+def whorlmap(
+    multi_contrast, # Object containing multiple dabest objects
+    n:int=21, # Size of each spiral (n x n grid per contrast)
+    sort_by:NoneType=None, # Order to sort contrasts by
+    cmap:str='vlag', vmax:NoneType=None, vmin:NoneType=None,
+    reverse_neg:bool=True, # Whether to reverse negative values
+    abs_rank:bool=False, # Whether to rank by absolute value
+    chop_tail:int=0, # Percentage of extreme values to exclude
+    ax:NoneType=None, # Existing axes to plot on
+    fig_size:NoneType=None, # Figure size (width, height) in inches
+    title:NoneType=None, # Plot title
+    heatmap_kwargs:NoneType=None, # Additional keyword arguments passed to sns.heatmap().
+Common options include:
+- 'cmap': colormap (overrides direct cmap parameter)
+- 'vmin', 'vmax': color scale limits (override direct parameters)
+- 'center': center value for colormap
+- 'annot': whether to annotate cells with values
+- 'fmt': format string for annotations
+- 'linewidths': width of lines between cells
+- 'linecolor': color of lines between cells
+- 'cbar': whether to show colorbar
+- 'cbar_kws': colorbar customization dict
+- 'square': whether to make cells square
+- 'xticklabels', 'yticklabels': tick label control
+- 'mask': boolean array to mask cells
+    plot_kwargs:NoneType=None, # Additional keyword arguments for plot styling and layout.
+Available options (WIP):
+- 'title': plot title
+- 'xlabel', 'ylabel': axis labels
+- 'xticklabels', 'yticklabels': tick labels
+- 'xticklabels_rotation', 'yticklabels_rotation': tick label rotation angles
+- 'xticklabels_ha', 'yticklabels_ha': horizontal alignment 
+):
+
+

Create a whorlmap visualization of multiple contrasts.

+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/API/plot_tools.html b/API/plot_tools.html new file mode 100644 index 00000000..77221a7f --- /dev/null +++ b/API/plot_tools.html @@ -0,0 +1,1255 @@ + + + + + + + + + + +plot_tools – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

plot_tools

+
+ +
+
+ A set of convenience functions used for producing plots in dabest. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

add_counts_to_prop_plots

+

+def add_counts_to_prop_plots(
+    plot_data:pd.DataFrame, # Dataframe of the plot data.
+    xvar:str, # Column name of the x variable.
+    yvar:str, # Column name of the y variable.
+    rawdata_axes:axes.Axes, # Matplotlib axis object to plot on.
+    horizontal:bool, # If the plot is horizontal.
+    is_paired:bool, # Whether the data is paired.
+    prop_sample_counts_kwargs:dict, # Keyword arguments for the sample counts.
+):
+
+

Add counts to the proportion plots.

+
+

source

+
+
+

table_for_horizontal_plots

+

+def table_for_horizontal_plots(
+    effectsize_df:object, # Effect size DABEST object.
+    ax:axes.Axes, # Matplotlib axis object to plot the table axes.
+    contrast_axes:axes.Axes, # Matplotlib axis object to plot the contrast axes.
+    ticks_to_plot:list, # List of indices of the contrast objects.
+    show_mini_meta:bool, # Whether to show the mini meta-analysis.
+    show_delta2:bool, # Whether to show the delta-delta.
+    table_kwargs:dict, # Keyword arguments for the table.
+    ticks_to_skip:list
+):
+
+

Add table axes for showing the deltas for horizontal plots.

+
+

source

+
+
+

barplotter

+

+def barplotter(
+    xvar:str, # Column name of the x variable.
+    yvar:str, # Column name of the y variable.
+    all_plot_groups:list, # List of all plot groups.
+    rawdata_axes:axes.Axes, # Matplotlib axis object to plot on.
+    plot_data:pd.DataFrame, # Dataframe of the plot data.
+    raw_colors:str, # Color of the bar.
+    plot_palette_raw:dict, # Dictionary of colors used in the bar plot.
+    color_col:str, # Column name of the color column.
+    barplot_kwargs:dict, # Keyword arguments for the barplot.
+    horizontal:bool, # If the plot is horizontal.
+):
+
+

Add bars to the raw data plot.

+
+

source

+
+
+

gridkey_plotter

+

+def gridkey_plotter(
+    is_paired:bool, # Whether the data is paired.
+    idx:list, # List of indices of the contrast objects.
+    all_plot_groups:list, # List of all plot groups.
+    gridkey:list, # List of gridkey rows.
+    rawdata_axes:axes.Axes, # Matplotlib axis object for the raw data.
+    contrast_axes:axes.Axes, # Matplotlib axis object for the contrast data.
+    plot_data:pd.DataFrame, # Dataframe of the plot data.
+    xvar:str, # Column name of the x variable.
+    yvar:str, # Column name of the y variable.
+    results:pd.DataFrame, # Dataframe of contrast object comparisons.
+    show_delta2:bool, # Whether to show the delta-delta.
+    show_mini_meta:bool, # Whether to show the mini meta-analysis.
+    x1_level:list, # List of x1 levels.
+    experiment_label:list, # List of experiment labels.
+    float_contrast:bool, # Whether the DABEST plot uses Gardner-Altman or Cummings
+    horizontal:bool, # If the plot is horizontal.
+    delta_delta:object, # delta-delta object.
+    mini_meta:object, # Mini meta-analysis object.
+    effect_size:str, # Type of effect size to plot
+    gridkey_kwargs:dict, # Keyword arguments for the gridkey.
+):
+
+

Add gridkey to the contrast plot.

+
+

source

+
+
+

effect_size_curve_plotter

+

+def effect_size_curve_plotter(
+    ticks_to_plot:list, # List of indices of the contrast objects.
+    ticks_for_baseline_ec:list, # List of indices of the baseline effect curve objects.
+    results:pd.DataFrame, # Dataframe of contrast object comparisons.
+    ci_type:str, # Type of confidence interval to plot.
+    contrast_axes:axes.Axes, # Matplotlib axis object to plot on.
+    contrast_kwargs:dict, # Keyword arguments for the violinplot.
+    bootstraps_color_by_group:bool, # Whether to color the bootstraps by group.
+    plot_palette_contrast:dict, # Dictionary of colors used in the contrast plot.
+    horizontal:bool, # If the plot is horizontal.
+    contrast_marker_kwargs:dict, contrast_errorbar_kwargs:dict, idx:list, # List of indices of the raw groups.
+    is_paired:bool, # Whether the data is paired.
+    contrast_paired_lines:bool, # Whether to add lines for repeated measures data.
+    contrast_paired_lines_kwargs:dict, # Keyword arguments for the repeated measures lines.
+    show_baseline_ec:bool=False, # Whether to show the baseline effect curve.
+):
+
+

Add effect size curves to the contrast plot.

+
+

source

+
+
+

plot_minimeta_or_deltadelta_violins

+

+def plot_minimeta_or_deltadelta_violins(
+    dabest_obj:object, # DABEST Effectsize object delta-delta or mini_meta
+    type:str, ci_type:str, # Type of confidence interval to plot.
+    rawdata_axes:axes.Axes, # Matplotlib axis object to plot on.
+    contrast_axes:axes.Axes, # Matplotlib axis object to plot on.
+    contrast_kwargs:dict, # Keyword arguments for the violinplot.
+    contrast_xtick_labels:list, # List of xtick labels for the contrast plot.
+    effect_size:str, # Type of effect size to plot.
+    plot_kwargs:dict, # Keyword arguments for the plot.
+    horizontal:bool, # If the plot is horizontal.
+    show_pairs:bool, # Whether the data is paired and shown in pairs.
+    contrast_marker_kwargs:dict, contrast_errorbar_kwargs:dict
+):
+
+

Add mini meta-analysis or delta-delta violin plots to the contrast plot.

+
+

source

+
+
+

slopegraph_plotter

+

+def slopegraph_plotter(
+    dabest_obj:object, # DABEST object.
+    plot_data:pd.DataFrame, # Dataframe of the plot data.
+    xvar:str, # Column name of the x variable.
+    yvar:str, # Column name of the y variable.
+    color_col:str, # Column name of the color column.
+    plot_palette_raw:dict, # Dictionary of colors used in the plot.
+    slopegraph_kwargs:dict, # Keyword arguments for the slopegraph.
+    rawdata_axes:axes.Axes, # Matplotlib axis object to plot on.
+    ytick_color:str, # Color of the yticks.
+    temp_idx:list, # List of indices of the contrast objects.
+    horizontal:bool, # If the plotting will be in horizontal format.
+    temp_all_plot_groups:list, # List of all plot groups.
+    plot_kwargs:dict, # Keyword arguments for the plot.
+    group_summaries_kwargs:dict, # Keyword arguments for group summaries, if applicable.
+):
+
+

Add slopegraph to the rawdata axes.

+
+

source

+
+
+

delta_dots_plotter

+

+def delta_dots_plotter(
+    plot_data:pd.DataFrame, # Dataframe of the plot data.
+    contrast_axes:axes.Axes, # Matplotlib axis object to plot on.
+    delta_id_col:str, # Column name of the delta id column.
+    idx:list, # List of indices of the contrast objects.
+    xvar:str, # Column name of the x variable.
+    yvar:str, # Column name of the y variable.
+    is_paired:bool, # Whether the data is paired.
+    color_col:str, # Column name of the color column.
+    float_contrast:bool, # Whether the DABEST plot uses Gardner-Altman or Cummings
+    plot_palette_raw:dict, # Dictionary of colors used in the plot.
+    delta_dot_kwargs:dict, # Keyword arguments for the delta dots.
+    horizontal:bool, # If the rawplot is horizontal.
+):
+
+
+

source

+
+
+

delta_text_plotter

+

+def delta_text_plotter(
+    results:pd.DataFrame, # Dataframe of contrast object comparisons.
+    ax_to_plot:object, # Matplotlib axis object to plot on.
+    ticks_to_plot:list, # List of indices of the contrast objects.
+    delta_text_kwargs:dict, # Keyword arguments for the delta text.
+    color_col:str, # Column name of the color column.
+    plot_palette_raw:dict, # Dictionary of colors used in the plot.
+    show_pairs:bool, # Whether the data is paired and show pairs.
+    float_contrast:bool, # Whether the DABEST plot uses Gardner-Altman or Cummings.
+    extra_delta:float, # The extra mini-meta or delta-delta value if applicable.
+    bootstraps_color_by_group:bool=False, # Whether to color the bootstraps by group. Default is False.
+):
+
+

Add delta text to the contrast plot.

+
+

source

+
+
+

add_bars_to_plot

+

+def add_bars_to_plot(
+    bar_dict:dict, # Dictionary of bar values.
+    ax:axes.Axes, # Matplotlib axis object to plot on.
+    bar_kwargs:dict, # Keyword arguments for the bars.
+):
+
+

Add bars to the relevant axes.

+
+

source

+
+
+

sankeydiag

+

+def sankeydiag(
+    data:pd.DataFrame, xvar:str, # x column to be plotted.
+    yvar:str, # y column to be plotted.
+    temp_all_plot_groups:list, idx:list, temp_idx:list,
+    left_labels:list=None, # labels for the left side of the diagram. The diagram will be sorted by these labels.
+    right_labels:list=None, # labels for the right side of the diagram. The diagram will be sorted by these labels.
+    palette:str | dict=None, ax:NoneType=None, # matplotlib axes to be drawn on
+    flow:bool=True, # if True, draw the sankey in a flow, else draw 1 vs 1 Sankey diagram for each group comparison
+    sankey:bool=True, # if True, draw the sankey diagram, else draw barplot
+    one_sankey:bool=False, # determined by the driver function on plotter.py, if True, draw the sankey diagram across the whole raw data axes
+    width:float=0.4, # the width of each sankey diagram
+    right_color:bool=False, # if True, each strip of the diagram will be colored according to the corresponding left labels
+    align:str='center', # the alignment of each sankey diagram, can be 'center' or 'left'
+    alpha:float=0.65, # the transparency of each strip
+    horizontal:bool=False, # if True, the horizontal format for the sankey diagram will be used
+    kwargs:VAR_KEYWORD
+):
+
+

Read in melted pd.DataFrame, and draw multiple sankey diagram on a single axes using the value in column yvar according to the value in column xvar left_idx in the column xvar is on the left side of each sankey diagram right_idx in the column xvar is on the right side of each sankey diagram

+
+

source

+
+
+

single_sankey

+

+def single_sankey(
+    left:np.array, # data on the left of the diagram
+    right:np.array, # data on the right of the diagram, len(left) == len(right)
+    xpos:float=0, # the starting point on the x-axis
+    left_weight:np.array=None, # weights for the left labels, if None, all weights are 1
+    right_weight:np.array=None, # weights for the right labels, if None, all weights are corresponding left_weight
+    colorDict:dict=None, # input format: {'label': 'color'}
+    left_labels:list=None, # labels for the left side of the diagram. The diagram will be sorted by these labels.
+    right_labels:list=None, # labels for the right side of the diagram. The diagram will be sorted by these labels.
+    ax:NoneType=None, # matplotlib axes to be drawn on
+    flow:bool=True, # if True, draw the sankey in a flow, else draw 1 vs 1 Sankey diagram for each group comparison
+    sankey:bool=True, # if True, draw the sankey diagram, else draw barplot
+    width:float=0.5, alpha:float=0.65, bar_width:float=0.2,
+    error_bar_on:bool=True, # if True, draw error bar for each group comparison
+    strip_on:bool=True, # if True, draw strip for each group comparison
+    one_sankey:bool=False, # if True, only draw one sankey diagram
+    right_color:bool=False, # if True, each strip of the diagram will be colored according to the corresponding left labels
+    align:str='center', # if 'center', the diagram will be centered on each xtick,  if 'edge', the diagram will be aligned with the left edge of each xtick
+    horizontal:bool=False, # if True, the horizontal format for the sankey diagram will be used
+):
+
+

Make a single Sankey diagram showing proportion flow from left to right

+

Original code from: https://github.com/anazalea/pySankey

+

Changes are added to normalize each diagram’s height to be 1

+
+

source

+
+
+

width_determine

+

+def width_determine(
+    labels, data, pos:str='left'
+):
+
+

Calculates normalized width positions for a set of labels based on their associated data.

+

This function is designed to determine width positions for plotting or graphical representation. It takes into account the cumulative weight of each label in the data and adjusts their positions accordingly. The function allows for adjusting the position of labels to either the ‘left’ or ‘right’.

+

Parameters: labels (list): A list of labels whose width positions are to be calculated. data (DataFrame): A pandas DataFrame containing the data used for calculating width positions. The DataFrame should have columns corresponding to the ‘pos’ and ‘posWeight’. pos (str, optional): The position of labels. It can be either ‘left’ or ‘right’. Defaults to ‘left’.

+

Returns: defaultdict: A dictionary where each key is a label and the value is another dictionary with keys ‘bottom’, ‘top’, and ‘pos’, representing the calculated width positions.

+

Note: The function assumes that the data DataFrame contains columns named after the value of ‘pos’ and an additional column named ‘posWeight’ which represents the weight of each label.

+
+

source

+
+
+

normalize_dict

+

+def normalize_dict(
+    nested_dict, target
+):
+
+

Normalizes the values in a nested dictionary based on a target dictionary.

+

This function iterates through a nested dictionary, calculates the sum of values for each key across all sub-dictionaries, and then normalizes these values according to a target dictionary. The normalization is performed such that the values in each sub-dictionary are proportionally scaled to match the corresponding ‘right’ values in the target dictionary.

+

Parameters: nested_dict (dict of dict): A nested dictionary where each key maps to another dictionary. The values in these inner dictionaries are subject to normalization. target (dict): A dictionary with the target values for normalization. Each key in nested_dict should have a corresponding key in target, and each target[key] should be a dictionary with a ‘right’ key containing the target normalization value.

+

Returns: dict: The normalized nested dictionary. The original nested_dict is modified in place.

+

Note: - If the sum of values for a particular key in nested_dict is zero, the normalized value is set to 0. - If a key in a sub-dictionary of nested_dict does not exist in the target dictionary, the corresponding ‘right’ value from the target dictionary is directly assigned. - The function modifies the input nested_dict in place and also returns it.

+
+

source

+
+
+

check_data_matches_labels

+

+def check_data_matches_labels(
+    labels, # list of input labels
+    data, # Pandas Series of input data
+    side:str, # 'left' or 'right' on the sankey diagram
+):
+
+

Function to check that the labels and data match in the sankey diagram. And enforce labels and data to be lists. Raises an exception if the labels and data do not match.

+
+

source

+
+
+

error_bar

+

+def error_bar(
+    data:pd.DataFrame, # This DataFrame should be in 'long' format.
+    x:str, # x column to be plotted.
+    y:str, # y column to be plotted.
+    type:str='mean_sd', # Choose from ['mean_sd', 'median_quartiles']. Plots the summary statistics for each group. If 'mean_sd', then the mean and standard deviation of each group is plotted as a gapped line. If 'median_quantiles', then the median and 25th and 75th percentiles of each group is plotted instead.
+    offset:float=0.2, # Give a single float (that will be used as the x-offset of all gapped lines), or an iterable containing the list of x-offsets.
+    ax:NoneType=None, # If a matplotlib Axes object is specified, the gapped lines will be plotted in order on this axes. If None, the current axes (plt.gca()) is used.
+    line_color:str='black', # The color of the gapped lines.
+    gap_width_percent:int=1, # The width of the gap in the gapped lines, as a percentage of the y-axis span.
+    pos:list=[0, 1],
+    method:str='gapped_lines', # The method to use for drawing the error bars. Options are: 'gapped_lines', 'proportional_error_bar', and 'sankey_error_bar'.
+    horizontal:bool=False, # If True, the error bars will be horizontal. If False, the error bars will be vertical.
+    kwargs:dict
+):
+
+

Function to plot the standard deviations as vertical errorbars. The mean is a gap defined by negative space.

+

This function combines the functionality of gapped_lines(), proportional_error_bar(), and sankey_error_bar().

+
+

source

+
+
+

get_swarm_spans

+

+def get_swarm_spans(
+    coll
+):
+
+

Given a matplotlib Collection, will obtain the x and y spans for the collection. Will return None if this fails.

+
+

source

+
+
+

halfviolin

+

+def halfviolin(
+    v, half:str='right', fill_color:str='k', alpha:int=1, line_color:str='k', line_width:int=0
+):
+
+

Call self as a function.

+
+

source

+
+
+

SwarmPlot

+

+def SwarmPlot(
+    data:pd.DataFrame, # The input data as a pandas DataFrame.
+    x:str, # The column in the DataFrame to be used as the x-axis.
+    y:str, # The column in the DataFrame to be used as the y-axis.
+    ax:axes.Axes, # Matplotlib axes.Axes object for which the plot would be drawn on.
+    order:List=None, # The order in which x-axis categories should be displayed. Default is None.
+    hue:str=None, # The column in the DataFrame that determines the grouping for color.
+If None (by default), it assumes that it is being grouped by x.
+    palette:Union[Iterable, str]='black', # The color palette to be used for plotting. Default is "black".
+    zorder:float=1, # The z-order for drawing the swarm plot wrt other matplotlib drawings. Default is 1.
+    size:float=5,
+    side:str='center', # The side on which points are swarmed ("center", "left", or "right"). Default is "center".
+    jitter:float=1, # Determines the distance between points. Default is 1.
+    horizontal:bool=False, # If True, the swarm plot is drawn horizontally. Default is False.
+):
+
+

Initialize a SwarmPlot instance.

+
+

source

+
+
+

swarmplot

+

+def swarmplot(
+    data:pd.DataFrame, # The input data as a pandas DataFrame.
+    x:str, # The column in the DataFrame to be used as the x-axis.
+    y:str, # The column in the DataFrame to be used as the y-axis.
+    ax:axes.Axes, # Matplotlib axes.Axes object for which the plot would be drawn on. Default is None.
+    order:List=None, # The order in which x-axis categories should be displayed. Default is None.
+    hue:str=None, # The column in the DataFrame that determines the grouping for color.
+If None (by default), it assumes that it is being grouped by x.
+    palette:Union[Iterable, str]='black', # The color palette to be used for plotting. Default is "black".
+    zorder:float=1, # The z-order for drawing the swarm plot wrt other matplotlib drawings. Default is 1.
+    size:float=5,
+    side:str='center', # The side on which points are swarmed ("center", "left", or "right"). Default is "center".
+    jitter:float=1, # Determines the distance between points. Default is 1.
+    filled:Union[bool, List, Tuple]=True, # Determines whether the dots in the swarmplot are filled or not. If set to False,
+dots are not filled. If provided as a List or Tuple, it should contain boolean values,
+each corresponding to a swarm group in order, indicating whether the dot should be
+filled or not.
+    is_drop_gutter:bool=True, # If True, drop points that hit the gutters; otherwise, readjust them.
+    gutter_limit:float=0.5, # The limit for points hitting the gutters.
+    horizontal:bool=False, # If True, the swarm plot is drawn horizontally. Default is False.
+    kwargs:VAR_KEYWORD
+): # Matplotlib axes.Axes object for which the swarm plot has been drawn on.
+
+

API to plot a swarm plot.

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/API/plotter.html b/API/plotter.html new file mode 100644 index 00000000..b39f7e04 --- /dev/null +++ b/API/plotter.html @@ -0,0 +1,895 @@ + + + + + + + + + + +Plot – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Plot

+
+ +
+
+ Creating estimation plots. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

effectsize_df_plotter

+

+def effectsize_df_plotter(
+    effectsize_df:object, plot_kwargs:VAR_KEYWORD
+)->matplotlib.figure.Figure:
+
+

Custom function that creates an estimation plot from an EffectSizeDataFrame. Keywords ——– Parameters ———- effectsize_df A dabest EffectSizeDataFrame object. plot_kwargs color_col=None raw_marker_size=6, contrast_marker_kwargs=9, raw_label=None, contrast_label=None, delta2_label=None, raw_ylim=None, contrast_ylim=None, delta2_ylim=None, custom_palette=None, swarm_side=None, empty_circle=False, face_color=None, raw_desat=0.5, contrast_desat=1, raw_alpha=None, contrast_alpha=0.8, bar_width = 0.5, ci_type=‘bca’, float_contrast=True, show_pairs=True, show_sample_size=True, show_delta2=True, show_mini_meta=True, group_summaries=“mean_sd”, fig_size=None, dpi=100, ax=None, swarmplot_kwargs=None, slopegraph_kwargs=None, barplot_kwargs=None, sankey_kwargs=None, contrast_kwargs=None, reflines_kwargs=None, group_summaries_kwargs=None, legend_kwargs=None, title=None, fontsize_title=16, fontsize_rawxlabel=12, fontsize_rawylabel=12, fontsize_contrastxlabel=12, fontsize_contrastylabel=12, fontsize_delta2label=12,

+

raw_bars=True, raw_bars_kwargs=None, contrast_bars=True, contrast_bars_kwargs=None, reference_band=None, reference_band_kwargs=None, delta_text=True, delta_text_kwargs=None, delta_dot=True, delta_dot_kwargs=None,

+

horizontal=False, horizontal_table_kwargs=None, gridkey=None, gridkey_merge_pairs=False, gridkey_show_Ns=True, gridkey_show_es=True, gridkey_delimiters=[‘;’, ‘>’, ’_’], gridkey_kwargs=None, contrast_marker_kwargs=None, contrast_errorbar_kwargs=None, prop_sample_counts=False, prop_sample_counts_kwargs=None, contrast_paired_lines=True, contrast_paired_lines show_baseline_ec=False,

+

For details on how to control the aesthetic of the generated estimation plot by modifying the plot_kwargs, please refer to Controlling Plot Aesthetics

+
    +
  • effectsize_df: A dabest EffectSizeDataFrame object.
    • +
  • plot_kwargs: +
      +

    • color_col=None

      • +

    • raw_marker_size=6, contrast_marker_size=9,

      • +

    • raw_label=None, contrast_label=None, delta2_label=None,

      • +

    • raw_ylim=None, contrast_ylim=None, delta2_ylim=None,

      • +

    • custom_palette=None, swarm_side=None, empty_circle=False,

      • +

    • face_color = None,

      • +

    • raw_desat=0.5, contrast_desat=1,

      • +

    • raw_alpha=None, contrast_alpha=0.8,

      • +

    • bar_width=0.5,

      • +

    • ci_type=‘bca’,

      • +

    • float_contrast=True,

      • +

    • show_pairs=True,

      • +

    • show_sample_size=True

      • +

    • show_delta2=True, show_mini_meta=True,

      • +

    • group_summaries=“mean_sd”,

      • +

    • fig_size=None, dpi=100,

      • +

    • ax=None,

      • +

    • swarmplot_kwargs=None,

      • +

    • slopegraph_kwargs=None,

      • +

    • barplot_kwargs=None,

      • +

    • sankey_kwargs=None,

      • +

    • contrast_kwargs=None,

      • +

    • reflines_kwargs=None,

      • +

    • group_summaries_kwargs=None,

      • +

    • legend_kwargs=None,

      • +

    • title=None, fontsize_title=16,

      • +

    • fontsize_rawxlabel=12, fontsize_rawylabel=12,

      • +

    • fontsize_contrastxlabel=12, fontsize_contrastylabel=12,

      • +

    • fontsize_delta2label=12,

      • +

    • raw_bars=True, raw_bars_kwargs=None,

      • +

    • contrast_bars=True, contrast_bars_kwargs=None,

      • +

    • reference_band=None, reference_band_kwargs=None,

      • +

    • delta_text=True, delta_text_kwargs=None,

      • +

    • delta_dot=True, delta_dot_kwargs=None,

      • +

    • horizontal=False, horizontal_table_kwargs=None,

      • +

    • gridkey=None, gridkey_merge_pairs=False,

      • +

    • gridkey_show_Ns=True, gridkey_show_es=True,

      • +

    • gridkey_delimiters=[‘;’, ‘>’, ’_’],

      • +

    • gridkey_kwargs=None,

      • +

    • contrast_marker_kwargs=None, contrast_errorbar_kwargs=None

      • +

    • prop_sample_counts=False, prop_sample_counts_kwargs=None

      • +

    • contrast_paired_lines=True, contrast_paired_lines_kwargs=None,

      • +

    • show_baseline_ec=False

      • +
    • +
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/API/precompile.html b/API/precompile.html new file mode 100644 index 00000000..2b6a1cc0 --- /dev/null +++ b/API/precompile.html @@ -0,0 +1,843 @@ + + + + + + + + + + +precompile – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

precompile

+
+ +
+
+ A tool to pre-compile Numba functions for speeding up DABEST bootstrapping +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

precompile_all

+

+def precompile_all(
+    
+):
+
+

Pre-compile all numba functions with dummy data

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/CHANGELOG.md b/CHANGELOG.md deleted file mode 100644 index f5c9d60b..00000000 --- a/CHANGELOG.md +++ /dev/null @@ -1,94 +0,0 @@ -# Release notes - - - -## v2025.10.20 - -### New Features -1. **Whorlmap Visualization**: Introducing a new way to visualize effect sizes from multiple comparisons in a grid of whorled square cells. This design condenses information from full bootstrap distributions of an array of contrast objects into a compact visual representation. It optimizes visual real estate by presenting a clear impression of the whole dataset at a glance while retaining nuanced distributional information for further scrutiny. Whorlmaps are a space-efficient alternative to stacked forest plots when working with multi-dimensional DABEST objects from large scale experiments. - -2. **Slopegraphs have a new look**: Slopegraphs for paired continuous data now show summary statistics for each group. By default, a thick trend line connects group means, with vertical bars indicating standard deviation. Users can choose the summary type via the `group_summaries` argument in .plot() — options include `'mean_sd'`, `'median_quartiles'`, or `None`. Appearance can be customized using `group_summaries_kwargs`. See the group summaries section in the Plot Aesthetics tutorial for more details. - -3. **Fixed Mini-meta Weighted Delta Calculation**: The weighted delta in mini-meta plots has been updated to ensure accurate calculation and reporting of the weighted delta. - -4. **Expanded custom_palette functionality**: - - **Barplots (unpaired, proportional)**: The custom_palette dict can now take 0 and 1 as keys to color the filled and unfilled portions of the plots. See the custom palette section in the Plot Aesthetics tutorial for more details. - - - **Slopegraphs (paired, non proportional)**: The custom_palette can now be used to color the contrast bars and effect-size curves. See the custom palette section in the Plot Aesthetics tutorial for more details. - - - -## v2025.03.27 - -### New Features - -1. **Python 3.13 Support**: DABEST now supports Python 3.10—3.13. - -2. **Horizontal Plots**: Users can now create horizontal layout plots, providing compact data visualization. This can be achieved by setting `horizontal=True` in the `.plot()` method. - -3. **Forest Plots**: Forest plots provide a simple and intuitive way to visualize many delta-delta (or delta *g*), mini-meta, or regular delta effect sizes at once from multiple different dabest objects without presenting the raw data. - -4. **Gridkey**: Users can now represent experimental labels in a ‘gridkey’ table. This can be accessed with the `gridkey` parameter in the `.plot()` method. - -5. **Other Visualization Improvements**: - - **Comparing means and effect sizes**: The estimation plots now include three types of customizable visual features to enhance contextualization and comparison of means and effect sizes: - - **Bars for the mean of the observed values (`raw_bars`)**: Colored rectangles that extend from the zero line to the mean of each group's raw data. These bars visually highlight the central tendency of the raw data. - - **Bars for effect size/s (`contrast_bars`)**: Similar to raw bars, these highlight the effect-size difference between two groups (typically test and control) in the contrast axis. They provide a visual representation of the differences between groups. - - **Summary bands (`reference_band`)**: An optional band or ribbon that can be added to emphasize a specific effect size’s confidence interval that is used as a reference range across the entire contrast axis. Unlike raw and contrast bars, these span horizontally (or vertically if `horizontal=True`) and are not displayed by default. - - Raw and contrast bars are shown by default. Users can customize these bars and add summary bands as needed. - - - **Tighter spacing in delta-delta and mini-meta plots**: We have adjusted the spacing of delta-delta and mini-meta plots to reduce whitespace. The new format brings the overall effect size closer to the two-groups effect sizes. In addition, delta-delta plots now have a gap in the zero line to separate the delta-delta from the ∆ effect sizes. - - - **Delta-delta effect sizes for proportion plots**: In addition to continuous data, delta-delta plots now support binary data (proportions). This means that 2-way designs for binary outcomes can be analyzed with DABEST. - - - **Proportion plots sample sizes**: The sample size of each binary option for each group can now be displayed. These can be toggled on/off via the `prop_sample_counts` parameter. - - - **Effect size lines for paired plots**: Along with lines connecting paired observed values, the paired plots now also display lines linking the effect sizes within a group in the contrast axes. These lines can be toggled on/off via the `contrast_paired_lines` parameter. - - - **Baseline error curves**: To represent the baseline/control group in the contrast axes, it is now possible to plot the baseline dot and the baseline error curve. The dot is shown by default, while the curve can be toggled on/off via the `show_baseline_ec` parameter. This dot helps make it clear where the baseline comes from i.e. the control minus itself. The baseline error curve can be used to show that the baseline itself is an estimate inferred from the observed values of the control data. - - - **Delta text**: Effect-size deltas (e.g. mean differences) are now displayed as numerals next to their respective effect size. This can be toggled on/off via the `delta_text` parameter. - - - **Empty circle color palette**: A new swarmplot color palette modification is available for unpaired plots via the `empty_circle` parameter in the `.plot()` method. This option modifies the two-group swarmplots to have empty circles for the control group and filled circles for the experimental group. - -6. **Miscellaneous Improvements & Adjustments** - - **Numba for speed improvements**: We have added [Numba](https://numba.pydata.org/) to speed up the various calculations in DABEST. Precalculations will be performed during import, which will help speed up the subsequent loading and plotting of data. - - - **Terminology/naming updates**: During the refactoring of the code, we have made several updates to the documentation and terminology to improve clarity and consistency. For example: - - Plot arguments have been adjusted to bring more clarity and consistency in naming. Arguments relating to the rawdata plot axis will now be typically referred to with `raw` while arguments relating to the contrast axis will be referred to with `contrast`. For example, `raw_label` replaces `swarm_label` and `bar_label`. The various kwargs relating to each different type of plot (e.g., `swarmplot_kwargs`) remain unchanged. - - - The method to utilise the Delta *g* effect size is now via the .hedges_g.plot() method rather than creating a whole new Delta_g object as before. The functionality remains the same, it plots hedges_g effect sizes and then the Delta *g* effect size alongside these (if a delta-delta experiment was loaded correctly). - - - **Updated tutorial pages**: We have updated the tutorial pages to reflect the new features and changes. The tutorial pages are now more comprehensive and (hopefully!) more intuitive! - - - **Results dataframe for delta-delta and mini-meta plots**: A results dataframe can now be extracted for both the delta-delta and mini-meta effect size data (similar to the results dataframe for the regular effect sizes). These can be found via the `.results` attribute of the `.delta_delta` or `.mini_meta` object. - - - -## v2024.03.29 - -### New Features - -- **Standardized Delta-delta Effect Size**: We added a new metric akin to a Hedges’ g for delta-delta effect size, which allows comparisons between delta-delta effects generated from metrics with different units. - -- **New Paired Proportion Plot**: This feature builds upon the existing proportional analysis capabilities by introducing advanced aesthetics and clearer visualization of changes in proportions between different groups, inspired by the informative nature of Sankey Diagrams. It's particularly useful for studies that require detailed examination of how proportions shift in paired observations. - -- **Customizable Swarm Plot**: Enhancements allow for tailored swarm plot aesthetics, notably the adjustment of swarm sides to produce asymmetric swarm plots. This customization enhances data representation, making visual distinctions more pronounced and interpretations clearer. - -### Enhancement - -- **Miscellaneous Improvements**: This version also encompasses a broad range of miscellaneous enhancements, including bug fixes, Bootstrapping speed improvements, new templates for raising issues, and updated unit tests. These improvements are designed to streamline the user experience, increase the software's stability, and expand its versatility. By addressing user feedback and identified issues, DABEST continues to refine its functionality and reliability. - - - -## v2023.03.29 - -### New Features -- **Repeated measures**: Augments the prior function for plotting (independent) multiple test groups versus a shared control; it can now do the same for repeated-measures experimental designs. Thus, together, these two methods can be used to replace both flavors of the 1-way ANOVA with an estimation analysis. - -- **Proportional data**: Generates proportional bar plots, proportional differences, and calculates Cohen’s h. Also enables plotting Sankey diagrams for paired binary data. This is the estimation equivalent to a bar chart with Fischer’s exact test. - -- **The ∆∆ plot**: Calculates the delta-delta (∆∆) for 2 × 2 experimental designs and plots the four groups with their relevant effect sizes. This design can be used as a replacement for the 2 × 2 ANOVA. - -- **Mini-meta**: Calculates and plots a weighted delta (∆) for meta-analysis of experimental replicates. Useful for summarizing data from multiple replicated experiments, for example by different scientists in the same lab, or the same scientist at different times. When the observed values are known (and share a common metric), this makes meta-analysis available as a routinely accessible tool. \ No newline at end of file diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md deleted file mode 100644 index 191d1ab4..00000000 --- a/CODE_OF_CONDUCT.md +++ /dev/null @@ -1,76 +0,0 @@ -# Contributor Covenant Code of Conduct - -## Our Pledge - -In the interest of fostering an open and welcoming environment, we as -contributors and maintainers pledge to making participation in our project and -our community a harassment-free experience for everyone, regardless of age, body -size, disability, ethnicity, sex characteristics, gender identity and expression, -level of experience, education, socio-economic status, nationality, personal -appearance, race, religion, or sexual identity and orientation. - -## Our Standards - -Examples of behavior that contributes to creating a positive environment -include: - -* Using welcoming and inclusive language -* Being respectful of differing viewpoints and experiences -* Gracefully accepting constructive criticism -* Focusing on what is best for the community -* Showing empathy towards other community members - -Examples of unacceptable behavior by participants include: - -* The use of sexualized language or imagery and unwelcome sexual attention or - advances -* Trolling, insulting/derogatory comments, and personal or political attacks -* Public or private harassment -* Publishing others' private information, such as a physical or electronic - address, without explicit permission -* Other conduct which could reasonably be considered inappropriate in a - professional setting - -## Our Responsibilities - -Project maintainers are responsible for clarifying the standards of acceptable -behavior and are expected to take appropriate and fair corrective action in -response to any instances of unacceptable behavior. - -Project maintainers have the right and responsibility to remove, edit, or -reject comments, commits, code, wiki edits, issues, and other contributions -that are not aligned to this Code of Conduct, or to ban temporarily or -permanently any contributor for other behaviors that they deem inappropriate, -threatening, offensive, or harmful. - -## Scope - -This Code of Conduct applies both within project spaces and in public spaces -when an individual is representing the project or its community. Examples of -representing a project or community include using an official project e-mail -address, posting via an official social media account, or acting as an appointed -representative at an online or offline event. Representation of a project may be -further defined and clarified by project maintainers. - -## Enforcement - -Instances of abusive, harassing, or otherwise unacceptable behavior may be -reported by contacting the project team at joseshowh@gmail.com. All -complaints will be reviewed and investigated and will result in a response that -is deemed necessary and appropriate to the circumstances. The project team is -obligated to maintain confidentiality with regard to the reporter of an incident. -Further details of specific enforcement policies may be posted separately. - -Project maintainers who do not follow or enforce the Code of Conduct in good -faith may face temporary or permanent repercussions as determined by other -members of the project's leadership. - -## Attribution - -This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, -available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html - -[homepage]: https://www.contributor-covenant.org - -For answers to common questions about this code of conduct, see -https://www.contributor-covenant.org/faq diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md deleted file mode 100644 index 52b583c1..00000000 --- a/CONTRIBUTING.md +++ /dev/null @@ -1,23 +0,0 @@ -# Contributing to DABEST-Python - - -## Did you find a bug? -- Ensure the bug was not already reported by searching in [Issues](https://github.com/ACCLAB/DABEST-python/issues). Check that the bug hasn't been addressed in a closed issue. - -- If the bug isn't being addressed, open a new issue using the Bug report template. Be sure to fill in the necessary information, and a [minimally reproducible code sample](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) demonstrating the expected behavior that is not occurring. - - -## Did you write a patch that fixes a bug? -- Open a new GitHub [pull request](https://help.github.com/en/articles/about-pull-requests) (PR for short) with the patch. - -- Create the PR into the development branch, which is indicated by `v{latest version number}-dev`. - -- Clearly state the problem and solution in the PR description. Include the relevant [issue number](https://guides.github.com/features/issues/) if applicable. - - -## Do you intend to add a new feature or change an existing one? -- Suggest your change by opening an issue using the Feature request template. -- If the maintainers and the community are in favour, create a fork and start writing code. - - -DABEST is a community tool for estimation statistics and analysis. We look forward to more robust and more elegant data visualizations from you all! diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 3b106e87..00000000 --- a/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright 2022, fastai - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. diff --git a/MANIFEST.in b/MANIFEST.in deleted file mode 100644 index 5c0e7ced..00000000 --- a/MANIFEST.in +++ /dev/null @@ -1,5 +0,0 @@ -include settings.ini -include LICENSE -include CONTRIBUTING.md -include README.md -recursive-exclude * __pycache__ diff --git a/README.md b/README.md deleted file mode 100644 index 10c80543..00000000 --- a/README.md +++ /dev/null @@ -1,250 +0,0 @@ -# DABEST-Python - - - - -[![minimal Python -version](https://img.shields.io/badge/Python%3E%3D-3.10-6666ff.svg)](https://www.anaconda.com/distribution/) -[![PyPI -version](https://badge.fury.io/py/dabest.svg)](https://badge.fury.io/py/dabest) -[![Downloads](https://img.shields.io/pepy/dt/dabest.svg)](https://pepy.tech/project/dabest) -[![Free-to-view -citation](https://zenodo.org/badge/DOI/10.1038/s41592-019-0470-3.svg)](https://rdcu.be/bHhJ4) -[![License](https://img.shields.io/badge/License-BSD%203--Clause--Clear-orange.svg)](https://spdx.org/licenses/BSD-3-Clause-Clear.html) - -## Recent Version Update - -**✨ DABEST “Bingka” v2025.10.20 for Python is now released! ✨** - -Dear DABEST users, The latest version of the DABEST Python library -brings new visualizations, refined plots, and improved accuracy. - -1. **Whorlmap 🌀: Compact visualization for multi-dimensional effects** - - Introducing **Whorlmap**, a new way to visualize effect sizes from - multiple comparisons in a compact, grid-based format. - - Whorlmaps condense information from the full bootstrap distributions - of many contrast objects into a **2D heatmap-style grid of “whorled” - cells**. This provides an overview of the entire dataset while - preserving the underlying distributional detail. - - They are especially useful for large-scale or multi-condition - experiments, serving as a **space-efficient alternative to stacked - forest plots**. - - You can generate a Whorlmap directly from multi-dimensional DABEST - objects using the `.whorlmap()` method. See the [Whorlmap - tutorial](https://acclab.github.io/DABEST-python/tutorials/10-whorlmap.html) - for more details. - -2. **Slopegraphs 📈: Enhanced summaries for paired data** - - Slopegraphs for paired continuous data now display **group summary - statistics**. - - - By default, a thick trend line connects group means, with vertical - bars showing standard deviation. - - - Choose the summary type via the group_summaries argument in - `.plot()` — options include `'mean_sd'`, `'median_quartiles'`, or - `None`. - - - Customize appearance with `group_summaries_kwargs`. - - See the Group Summaries section in the [Plot Aesthetics - tutorial](https://acclab.github.io/DABEST-python/tutorials/08-plot_aesthetics.html) - for more details. - -3. **Mini-meta Weighted Delta Fix 🧮** - - The weighted delta calculation in mini-meta plots has been updated - for **greater accuracy and consistency**. - -4. **Expanded custom_palette functionality 🎨** - - - **Barplots (unpaired, proportional):** `custom_palette` can now - take `1` and `0` as dictionary keys to color the filled and - unfilled portions of the plot. - - - **Slopegraphs (paired, non-proportional):** `custom_palette` can - now color contrast bars and effect-size curves. - -See the Custom Palette section in the [Plot Aesthetics -tutorial](https://acclab.github.io/DABEST-python/tutorials/08-plot_aesthetics.html) -for examples. - -Thank you for your continued support! - -*The DABEST Development Team* - -## Contents - - - -- [About](#about) -- [Installation](#installation) -- [Usage](#usage) -- [How to cite](#how-to-cite) -- [Bugs](#bugs) -- [Contributing](#contributing) -- [Acknowledgements](#acknowledgements) -- [Testing](#testing) -- [DABEST in other languages](#dabest-in-other-languages) - - - -## About - -DABEST is a package for **D**ata **A**nalysis using -**B**ootstrap-Coupled **EST**imation. - -[Estimation -statistics](https://en.wikipedia.org/wiki/Estimation_statistics) are a -[simple framework](https://thenewstatistics.com/itns/) that avoids the -[pitfalls](https://www.nature.com/articles/nmeth.3288) of significance -testing. It employs familiar statistical concepts such as means, mean -differences, and error bars. More importantly, it focuses on the effect -size of one’s experiment or intervention, rather than succumbing to a -false dichotomy engendered by *P* values. - -An estimation plot comprises two key features. - -1. It presents all data points as a swarm plot, ordering each point to - display the underlying distribution. - -2. It illustrates the effect size as a **bootstrap 95% confidence - interval** on a **separate but aligned axis**. - -![The five kinds of estimation -plots](showpiece.png "The five kinds of estimation plots.") - -DABEST powers [estimationstats.com](https://www.estimationstats.com/), -allowing everyone access to high-quality estimation plots. - -## Installation - -This package is tested on Python 3.11 and onwards. It is highly -recommended to download the [Anaconda -distribution](https://www.continuum.io/downloads) of Python in order to -obtain the dependencies easily. - -You can install this package via `pip`. - -To install, at the command line run - -``` shell -pip install dabest -``` - -You can also -[clone](https://help.github.com/articles/cloning-a-repository) this repo -locally. - -Then, navigate to the cloned repo in the command line and run - -``` shell -pip install . -``` - -## Usage - -``` python3 -import pandas as pd -import dabest - -# Load the iris dataset. This step requires internet access. -iris = pd.read_csv("https://github.com/mwaskom/seaborn-data/raw/master/iris.csv") - -# Load the above data into `dabest`. -iris_dabest = dabest.load(data=iris, x="species", y="petal_width", - idx=("setosa", "versicolor", "virginica")) - -# Produce a Cumming estimation plot. -iris_dabest.mean_diff.plot(); -``` - -![A Cumming estimation plot of petal width from the iris -dataset](iris.png) - -Please refer to the official -[tutorial](https://acclab.github.io/DABEST-python/) for more useful code -snippets. - -## How to cite - -**Getting over ANOVA: Estimation graphics for multi-group comparisons** - -*Zinan Lu, Jonathan Anns, Yishan Mai, Rou Zhang, Kahseng Lian, Nicole -MynYi Lee, Shan Hashir, Lucas Wang Zhuoyu, A. Rosa Castillo Gonzalez, -Joses Ho, Hyungwon Choi, Sangyu Xu, Adam Claridge-Chang* - -bioRxiv preprint 2026. -[10.64898/2026.01.26.701654](http://dx.doi.org/10.64898/2026.01.26.701654) - -[PDF](https://www.biorxiv.org/content/10.64898/2026.01.26.701654v1.full.pdf) - -**Moving beyond P values: Everyday data analysis with estimation plots** - -*Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam -Claridge-Chang* - -Nature Methods 2019, 1548-7105. -[10.1038/s41592-019-0470-3](http://dx.doi.org/10.1038/s41592-019-0470-3) - -[Paywalled publisher -site](https://www.nature.com/articles/s41592-019-0470-3); [Free-to-view -PDF](https://rdcu.be/bHhJ4) - -## Bugs - -Please report any bugs on the [issue -page](https://github.com/ACCLAB/DABEST-python/issues/new). - -## Contributing - -All contributions are welcome; please read the [Guidelines for -contributing](../CONTRIBUTING.md) first. - -We also have a [Code of Conduct](../CODE_OF_CONDUCT.md) to foster an -inclusive and productive space. - -### A wish list for new features - -If you have any specific comments and ideas for new features that you -would like to share with us, please read the [Guidelines for -contributing](../CONTRIBUTING.md), create a new issue using Feature -request template or create a new post in [our Google -Group](https://groups.google.com/g/estimationstats). - -## Acknowledgements - -We would like to thank alpha testers from the [Claridge-Chang -lab](https://www.claridgechang.net/): [Sangyu -Xu](https://github.com/sangyu), [Xianyuan -Zhang](https://github.com/XYZfar), [Farhan -Mohammad](https://github.com/farhan8igib), Jurga Mituzaitė, and -Stanislav Ott. - -## Testing - -To test DABEST, you need to install -[pytest](https://docs.pytest.org/en/latest) and -[nbdev](https://nbdev.fast.ai/). - -- Run `pytest` in the root directory of the source distribution. This - runs the test suite in the folder `dabest/tests/mpl_image_tests`. -- Run `nbdev_test` in the root directory of the source distribution. - This runs the value assertion tests in the folder `dabest/tests` - -The test suite ensures that the bootstrapping functions and the plotting -functions perform as expected. - -For detailed information, please refer to the [test -folder](../nbs/tests/README.md) - -## DABEST in other languages - -DABEST is also available in R -([dabestr](https://github.com/ACCLAB/dabestr)) and Matlab -([DABEST-Matlab](https://github.com/ACCLAB/DABEST-Matlab)). diff --git a/blog/index.html b/blog/index.html new file mode 100644 index 00000000..1806f8cb --- /dev/null +++ b/blog/index.html @@ -0,0 +1,907 @@ + + + + + + + + + +DABEST Blog – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

DABEST Blog

+

News, tips, and commentary about all things dabest

+
+ + + +
+ + + + +
+ + + +
+ + + + + + +
+ +
+ + + + + \ No newline at end of file diff --git a/blog/index.xml b/blog/index.xml new file mode 100644 index 00000000..1ab8dbea --- /dev/null +++ b/blog/index.xml @@ -0,0 +1,235 @@ + + + +dabest +https://acclab.github.io/DABEST-python/blog/ + +Data Analysis and Visualization using Bootstrap-Coupled Estimation. +quarto-1.9.36 +Thu, 26 Mar 2026 04:08:50 GMT + + Preprint: Getting over ANOVA + https://acclab.github.io/DABEST-python/blog/posts/a-dabest2-preprint/a-dabest2-preprint.html + +

Here’s a dirty secret about ANOVA: it tests a null hypothesis that nobody cares about. When you run a one-way ANOVA, you’re testing whether “all group means are equal.” But even if you reject this hypothesis, you learn nothing about which groups differ, in which direction, or by how much.

+

So you embark on a second analytical step: multiple two-group comparisons. A modest six-group experiment suddenly requires testing 15 hypotheses. To manage this multiplicity, you apply corrections like Bonferroni, which undermine your statistical power. What you posed as a focused research question has sprawled into a complex web of subsidiary tests, forced by the ANOVA ritual.

+

Our new preprint, “Getting over ANOVA: Estimation graphics for multi-group comparisons,” makes the case for a better approach. Estimation statistics encourages you to compare each test group to a single control, focusing on the effect sizes that actually matter. A six-group experiment focuses attention on just five effect sizes with confidence intervals, showing magnitude and precision directly.

+

The preprint introduces estimation methods for a range of multi-group designs: repeated-measures experiments, 2×2 factorial designs, binary outcome data, and mini-meta analysis for internal replicates. Each can replace data-analysis practices used in thousands of studies every year.

+

Read our new preprint here: https://doi.org/10.64898/2026.01.26.701654

+

Also posted on LinkedIn #Statistics #OpenScience #DataVisualization #Research

+

+ + + + ]]> + https://acclab.github.io/DABEST-python/blog/posts/a-dabest2-preprint/a-dabest2-preprint.html + Thu, 26 Mar 2026 04:08:50 GMT + + + Bootstrap Confidence Intervals + https://acclab.github.io/DABEST-python/blog/posts/bootstraps/bootstraps.html + +
+

Sampling from populations

+

In a typical scientific experiment, we are interested in two populations (Control and Test), and whether there is a difference between their means .

+

+

We go about this by collecting observations from the control population and from the test population.

+

+

We can easily compute the mean difference in our observed samples. This is our estimate of the population effect size that we are interested in.

+

But how do we obtain a measure of the precision and confidence about our estimate? Can we get a sense of how it relates to the population mean difference?

+
+
+

The bootstrap confidence interval

+

We want to obtain a 95% confidence interval (95% CI) around our estimate of the mean difference. The 95% indicates that any such confidence interval will capture the population mean difference 95% of the time.

+

In other words, if we were to repeat our experiment 100 times, gathering 100 independent sets of observations and computing a 95% confidence interval for the mean difference each time, 95 of these intervals would capture the population mean difference. That is to say, we can be 95% confident the interval contains the true mean of the population.

+

We can calculate the 95% CI of the mean difference with bootstrap resampling.

+
+

The bootstrap in action

+

The bootstrap[1] is a simple but powerful technique. It was first described by Bradley Efron.

+

It creates multiple resamples (with replacement) from a single set of observations, and computes the effect size of interest on each of these resamples. The bootstrap resamples of the effect size can then be used to determine the 95% CI.

+

With computers, we can perform 5000 resamples very easily.

+

+

The resampling distribution of the difference in means approaches a normal distribution. This is due to the Central Limit Theorem: a large number of independent random samples will approach a normal distribution even if the underlying population is not normally distributed.

+

Bootstrap resampling gives us two important benefits:

+
    +

  1. Non-parametric statistical analysis. There is no need to assume that our observations, or the underlying populations, are normally distributed. Thanks to the Central Limit Theorem, the resampling distribution of the effect size will approach a normality.

    2. +

  2. Easy construction of the 95% CI from the resampling distribution. In the context of bootstrap resampling or other non-parametric methods, the 2.5th and 97.5th percentiles are often used to define the lower and upper limits, respectively. The use of these percentiles ensures that the resulting interval contains the central 95% of the resampled distribution. Such an interval construction is known as a percentile interval.

    3. +
+
+
+
+

Adjusting for asymmetrical resampling distributions

+

While resampling distributions of the difference in means often have a normal distribution, it is not uncommon to encounter a skewed distribution. Thus, Efron developed the bias-corrected and accelerated bootstrap (BCa bootstrap) to account for the skew, and still obtain the central 95% of the distribution.

+

DABEST applies the BCa correction to the resampling bootstrap distributions of the effect size.

+

+
+
+

Estimation plots incorporate bootstrap resampling

+

The estimation plot produced by DABEST presents the raw data and the bootstrap confidence interval of the effect size (the difference in means) side-by-side as a single integrated plot.

+

+

Thus, it tightly couples a visual presentation of the raw data with an indication of the population mean difference plus its confidence interval.

+

[1]: The name is derived from the saying “pull oneself by one’s bootstraps”, often used as an exhortation to achieve success without external help.

+ + +
+ + ]]> + https://acclab.github.io/DABEST-python/blog/posts/bootstraps/bootstraps.html + Thu, 26 Mar 2026 04:08:50 GMT + + + Robust and Beautiful Statistical Visualization + https://acclab.github.io/DABEST-python/blog/posts/robust-beautiful/robust-beautiful.html + +
+

Current plots do not work

+

What is data visualization? Battle-Baptiste and Rusert (2018) give a cogent and compelling definition:

+

Data visualization[1] is the rendering of information in a visual format to help communicate data while also generating new patterns and knowledge through the act of visualization itself.

+

Sadly, too many figures and visualizations in modern academic publications seemingly fail to “generate new patterns and knowledge through the act of visualization itself”. Here, we propose a solution: the estimation plot.

+
+

The barplot conceals the underlying shape

+

By only displaying the mean and standard deviation, barplots do not accurately represent the underlying distribution of the data.

+

+

In the above figure, four different samples with wildly different distributions–as seen in the swarmplot on the left panel–look exactly the same when visualized with a barplot on the right panel. (You can download the dataset to see for yourself.)

+

We’re not the first ones (see these articles: article 1, article 2, or article 3) to point out the barplot’s fatal flaws. Indeed, it is both sobering and fascinating to realise that the barplot is a 17th century invention initially used to compare single values, not to compare summarized and aggregated data.

+
+
+

The boxplot does not convey sample size

+

Boxplots are another widely used visualization tool. They arguably do include more information for each sample (medians, quartiles, maxima, minima, and outliers), but they do not convey to the viewer the size of each sample.

+

+

The figure above visualizes the same four samples as a swarmplot (left panel) and as a boxplot. If we did not label the x-axis with the sample size, it would be impossible to definitively distinguish the sample with 5 observations from the sample with 50.

+

Even if the world gets rid of barplots and boxplots, the problems plaguing statistical practices will remain unsolved. Null-hypothesis significance testing–the dominant statistical paradigm in basic research–does not indicate the effect size, or its confidence interval.

+
+
+
+

Introducing the Estimation Plot

+

+

This is a Gardner-Altman estimation plot. The plot draws its name from Martin J. Gardner and Douglas Altman, who are credited with creating the design in 1986.

+

This plot has two key features:

+
    +

  1. It presents all data points as a swarmplot, ordering each point to display the underlying distribution.

    2. +

  2. It presents the effect size as a bootstrap 95% confidence interval (95% CI) on a separate but aligned axis. The effect size is displayed to the right of the raw data, and the mean of the test group is aligned with the effect size.”

    3. +
+
+Thus, estimation plots are robust, beautiful, and convey important statistical information elegantly and efficiently. +
+

An estimation plot obtains and displays the 95% CI through nonparametric bootstrap resampling. This enables visualization of the confidence interval as a graded sampling distribution.

+

This is one important difference between estimation plots created by DABEST, and the original Gardner-Altman design. Here, the 95% CI is computed through parametric methods, and displayed as a vertical error bar.

+

Read more about this technique at bootstraps.

+
+

Introducing Estimation Statistics

+

Estimation plots emerge from estimation statistics, a simple framework that avoids the pitfalls of significance testing. It focuses on the effect sizes of one’s experiment/interventions, and uses familiar statistical concepts: means, mean differences, and error bars.

+

Significance testing calculates the probability (the P value) that the experimental data would be observed, if the intervention did not produce a change in the metric measured (i.e. the null hypothesis). This leads analysts to apply a false dichotomy on the experimental intervention.

+

Estimation statistics, on the other hand, focuses on the magnitude of the effect (the effect size) and its precision. This encourages analysts to gain a deeper understanding of the metrics used, and how they relate to the natural processes being studied.

+
+
+
+

An Estimation Plot For Every Experimental Design

+

For each of the most routine significance tests, there is an estimation replacement:

+
+

Unpaired Student’s t-test –> Two-group estimation plot

+

+
+
+

Paired Student’s t-test –> Paired estimation plot

+

The Gardner-Altman estimation plot can also display effect sizes for repeated measures (aka a paired experimental design) using a Tufte slopegraph instead of a swarmplot.

+

+
+
+

One-way ANOVA + multiple comparisons –> Multi two-group estimation plot

+

For comparisons between three or more groups that typically employ analysis of variance (ANOVA) methods, one can use the Cumming estimation plot, named after Geoff Cumming, and draws its design heavily from his 2012 textbook “Understanding the New Statistics”. This estimation plot design can be considered a variant of the Gardner-Altman plot.

+

+

The effect size and 95% CIs are still plotted on a separate axis, but unlike the Gardner-Altman plot, this axis is positioned beneath the raw data.

+

Such a design frees up visual space in the upper panel, allowing the display of summary measurements (mean ± standard deviation) for each group. These are shown as gapped lines to the right of each group. The mean of each group is indicated as a gap in the line, adhering to Edward Tufte’s dictum to keep the data-ink ratio low.

+
+
+

Repeated measures ANOVA –> Multi paired estimation plot

+

+
+
+

Ordered groups ANOVA –> Shared-control estimation plot

+

+
+
+

Estimation Plots: The Way Forward

+

In summary, estimation plots offer five key benefits relative to conventional plots:

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
BarplotBoxplotEstimation Plot
Displays all observed valuesNONOYes
Avoids false dichotomyNONOYes
Focusses on effect sizeNONOYes
Visualizes effect size precisionNONOYes
Shows mean difference distributionNONOYes
+

You can create estimation plots using the DABEST (Data Analysis with Bootstrap Estimation) packages, which are available in Matlab, Python, and R.

+

[1]:W. E. B. Du Bois’s Data Portraits: Visualizing Black America. Edited by Whitney Battle-Baptiste and Britt Rusert, Princeton Architectural Press, 2018

+ + +
+
+ + ]]> + https://acclab.github.io/DABEST-python/blog/posts/robust-beautiful/robust-beautiful.html + Thu, 26 Mar 2026 04:08:50 GMT + + + diff --git a/blog/posts/a-dabest2-preprint/a-dabest2-preprint.html b/blog/posts/a-dabest2-preprint/a-dabest2-preprint.html new file mode 100644 index 00000000..334aeba4 --- /dev/null +++ b/blog/posts/a-dabest2-preprint/a-dabest2-preprint.html @@ -0,0 +1,798 @@ + + + + + + + + + +Preprint: Getting over ANOVA – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Preprint: Getting over ANOVA

+
+ + + +
+ + + + +
+ + + +
+ + + +

Here’s a dirty secret about ANOVA: it tests a null hypothesis that nobody cares about. When you run a one-way ANOVA, you’re testing whether “all group means are equal.” But even if you reject this hypothesis, you learn nothing about which groups differ, in which direction, or by how much.

+

So you embark on a second analytical step: multiple two-group comparisons. A modest six-group experiment suddenly requires testing 15 hypotheses. To manage this multiplicity, you apply corrections like Bonferroni, which undermine your statistical power. What you posed as a focused research question has sprawled into a complex web of subsidiary tests, forced by the ANOVA ritual.

+

Our new preprint, “Getting over ANOVA: Estimation graphics for multi-group comparisons,” makes the case for a better approach. Estimation statistics encourages you to compare each test group to a single control, focusing on the effect sizes that actually matter. A six-group experiment focuses attention on just five effect sizes with confidence intervals, showing magnitude and precision directly.

+

The preprint introduces estimation methods for a range of multi-group designs: repeated-measures experiments, 2×2 factorial designs, binary outcome data, and mini-meta analysis for internal replicates. Each can replace data-analysis practices used in thousands of studies every year.

+

Read our new preprint here: https://doi.org/10.64898/2026.01.26.701654

+

Also posted on LinkedIn #Statistics #OpenScience #DataVisualization #Research

+

+ + + +
+ +
+ + + + + \ No newline at end of file diff --git a/nbs/blog/posts/a-dabest2-preprint/preprint_fig.png b/blog/posts/a-dabest2-preprint/preprint_fig.png similarity index 100% rename from nbs/blog/posts/a-dabest2-preprint/preprint_fig.png rename to blog/posts/a-dabest2-preprint/preprint_fig.png diff --git a/nbs/blog/posts/bootstraps/bootstrap-1.png b/blog/posts/bootstraps/bootstrap-1.png similarity index 100% rename from nbs/blog/posts/bootstraps/bootstrap-1.png rename to blog/posts/bootstraps/bootstrap-1.png diff --git a/nbs/blog/posts/bootstraps/bootstrap-2.png b/blog/posts/bootstraps/bootstrap-2.png similarity index 100% rename from nbs/blog/posts/bootstraps/bootstrap-2.png rename to blog/posts/bootstraps/bootstrap-2.png diff --git a/nbs/blog/posts/bootstraps/bootstrap-3.png b/blog/posts/bootstraps/bootstrap-3.png similarity index 100% rename from nbs/blog/posts/bootstraps/bootstrap-3.png rename to blog/posts/bootstraps/bootstrap-3.png diff --git a/nbs/blog/posts/bootstraps/bootstrap-4.png b/blog/posts/bootstraps/bootstrap-4.png similarity index 100% rename from nbs/blog/posts/bootstraps/bootstrap-4.png rename to blog/posts/bootstraps/bootstrap-4.png diff --git a/nbs/blog/posts/bootstraps/bootstrap-5.png b/blog/posts/bootstraps/bootstrap-5.png similarity index 100% rename from nbs/blog/posts/bootstraps/bootstrap-5.png rename to blog/posts/bootstraps/bootstrap-5.png diff --git a/blog/posts/bootstraps/bootstraps.html b/blog/posts/bootstraps/bootstraps.html new file mode 100644 index 00000000..fd26dd0f --- /dev/null +++ b/blog/posts/bootstraps/bootstraps.html @@ -0,0 +1,879 @@ + + + + + + + + + + +Bootstrap Confidence Intervals – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Bootstrap Confidence Intervals

+
+ +
+
+ Explanation of the bootstrap method and its application in hypothesis testing using DABEST. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

Sampling from populations

+

In a typical scientific experiment, we are interested in two populations (Control and Test), and whether there is a difference between their means \((\mu_{Test}-\mu_{Control})\).

+

+

We go about this by collecting observations from the control population and from the test population.

+

+

We can easily compute the mean difference in our observed samples. This is our estimate of the population effect size that we are interested in.

+

But how do we obtain a measure of the precision and confidence about our estimate? Can we get a sense of how it relates to the population mean difference?

+
+
+

The bootstrap confidence interval

+

We want to obtain a 95% confidence interval (95% CI) around our estimate of the mean difference. The 95% indicates that any such confidence interval will capture the population mean difference 95% of the time.

+

In other words, if we were to repeat our experiment 100 times, gathering 100 independent sets of observations and computing a 95% confidence interval for the mean difference each time, 95 of these intervals would capture the population mean difference. That is to say, we can be 95% confident the interval contains the true mean of the population.

+

We can calculate the 95% CI of the mean difference with bootstrap resampling.

+
+

The bootstrap in action

+

The bootstrap[1] is a simple but powerful technique. It was first described by Bradley Efron.

+

It creates multiple resamples (with replacement) from a single set of observations, and computes the effect size of interest on each of these resamples. The bootstrap resamples of the effect size can then be used to determine the 95% CI.

+

With computers, we can perform 5000 resamples very easily.

+

+

The resampling distribution of the difference in means approaches a normal distribution. This is due to the Central Limit Theorem: a large number of independent random samples will approach a normal distribution even if the underlying population is not normally distributed.

+

Bootstrap resampling gives us two important benefits:

+
    +

  1. Non-parametric statistical analysis. There is no need to assume that our observations, or the underlying populations, are normally distributed. Thanks to the Central Limit Theorem, the resampling distribution of the effect size will approach a normality.

    2. +

  2. Easy construction of the 95% CI from the resampling distribution. In the context of bootstrap resampling or other non-parametric methods, the 2.5th and 97.5th percentiles are often used to define the lower and upper limits, respectively. The use of these percentiles ensures that the resulting interval contains the central 95% of the resampled distribution. Such an interval construction is known as a percentile interval.

    3. +
+
+
+
+

Adjusting for asymmetrical resampling distributions

+

While resampling distributions of the difference in means often have a normal distribution, it is not uncommon to encounter a skewed distribution. Thus, Efron developed the bias-corrected and accelerated bootstrap (BCa bootstrap) to account for the skew, and still obtain the central 95% of the distribution.

+

DABEST applies the BCa correction to the resampling bootstrap distributions of the effect size.

+

+
+
+

Estimation plots incorporate bootstrap resampling

+

The estimation plot produced by DABEST presents the raw data and the bootstrap confidence interval of the effect size (the difference in means) side-by-side as a single integrated plot.

+

+

Thus, it tightly couples a visual presentation of the raw data with an indication of the population mean difference plus its confidence interval.

+

[1]: The name is derived from the saying “pull oneself by one’s bootstraps”, often used as an exhortation to achieve success without external help.

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/nbs/_static/four_samples.csv b/blog/posts/robust-beautiful/four_samples.csv similarity index 100% rename from nbs/_static/four_samples.csv rename to blog/posts/robust-beautiful/four_samples.csv diff --git a/blog/posts/robust-beautiful/robust-beautiful.html b/blog/posts/robust-beautiful/robust-beautiful.html new file mode 100644 index 00000000..ddaa4a91 --- /dev/null +++ b/blog/posts/robust-beautiful/robust-beautiful.html @@ -0,0 +1,938 @@ + + + + + + + + + +Robust and Beautiful Statistical Visualization – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Robust and Beautiful Statistical Visualization

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

Current plots do not work

+

What is data visualization? Battle-Baptiste and Rusert (2018) give a cogent and compelling definition:

+

Data visualization[1] is the rendering of information in a visual format to help communicate data while also generating new patterns and knowledge through the act of visualization itself.

+

Sadly, too many figures and visualizations in modern academic publications seemingly fail to “generate new patterns and knowledge through the act of visualization itself”. Here, we propose a solution: the estimation plot.

+
+

The barplot conceals the underlying shape

+

By only displaying the mean and standard deviation, barplots do not accurately represent the underlying distribution of the data.

+

+

In the above figure, four different samples with wildly different distributions–as seen in the swarmplot on the left panel–look exactly the same when visualized with a barplot on the right panel. (You can download the dataset to see for yourself.)

+

We’re not the first ones (see these articles: article 1, article 2, or article 3) to point out the barplot’s fatal flaws. Indeed, it is both sobering and fascinating to realise that the barplot is a 17th century invention initially used to compare single values, not to compare summarized and aggregated data.

+
+
+

The boxplot does not convey sample size

+

Boxplots are another widely used visualization tool. They arguably do include more information for each sample (medians, quartiles, maxima, minima, and outliers), but they do not convey to the viewer the size of each sample.

+

+

The figure above visualizes the same four samples as a swarmplot (left panel) and as a boxplot. If we did not label the x-axis with the sample size, it would be impossible to definitively distinguish the sample with 5 observations from the sample with 50.

+

Even if the world gets rid of barplots and boxplots, the problems plaguing statistical practices will remain unsolved. Null-hypothesis significance testing–the dominant statistical paradigm in basic research–does not indicate the effect size, or its confidence interval.

+
+
+
+

Introducing the Estimation Plot

+

+

This is a Gardner-Altman estimation plot. The plot draws its name from Martin J. Gardner and Douglas Altman, who are credited with creating the design in 1986.

+

This plot has two key features:

+
    +

  1. It presents all data points as a swarmplot, ordering each point to display the underlying distribution.

    2. +

  2. It presents the effect size as a bootstrap 95% confidence interval (95% CI) on a separate but aligned axis. The effect size is displayed to the right of the raw data, and the mean of the test group is aligned with the effect size.”

    3. +
+
+Thus, estimation plots are robust, beautiful, and convey important statistical information elegantly and efficiently. +
+

An estimation plot obtains and displays the 95% CI through nonparametric bootstrap resampling. This enables visualization of the confidence interval as a graded sampling distribution.

+

This is one important difference between estimation plots created by DABEST, and the original Gardner-Altman design. Here, the 95% CI is computed through parametric methods, and displayed as a vertical error bar.

+

Read more about this technique at bootstraps.

+
+

Introducing Estimation Statistics

+

Estimation plots emerge from estimation statistics, a simple framework that avoids the pitfalls of significance testing. It focuses on the effect sizes of one’s experiment/interventions, and uses familiar statistical concepts: means, mean differences, and error bars.

+

Significance testing calculates the probability (the P value) that the experimental data would be observed, if the intervention did not produce a change in the metric measured (i.e. the null hypothesis). This leads analysts to apply a false dichotomy on the experimental intervention.

+

Estimation statistics, on the other hand, focuses on the magnitude of the effect (the effect size) and its precision. This encourages analysts to gain a deeper understanding of the metrics used, and how they relate to the natural processes being studied.

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An Estimation Plot For Every Experimental Design

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For each of the most routine significance tests, there is an estimation replacement:

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Unpaired Student’s t-test –> Two-group estimation plot

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Paired Student’s t-test –> Paired estimation plot

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The Gardner-Altman estimation plot can also display effect sizes for repeated measures (aka a paired experimental design) using a Tufte slopegraph instead of a swarmplot.

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One-way ANOVA + multiple comparisons –> Multi two-group estimation plot

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For comparisons between three or more groups that typically employ analysis of variance (ANOVA) methods, one can use the Cumming estimation plot, named after Geoff Cumming, and draws its design heavily from his 2012 textbook “Understanding the New Statistics”. This estimation plot design can be considered a variant of the Gardner-Altman plot.

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The effect size and 95% CIs are still plotted on a separate axis, but unlike the Gardner-Altman plot, this axis is positioned beneath the raw data.

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Such a design frees up visual space in the upper panel, allowing the display of summary measurements (mean ± standard deviation) for each group. These are shown as gapped lines to the right of each group. The mean of each group is indicated as a gap in the line, adhering to Edward Tufte’s dictum to keep the data-ink ratio low.

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Repeated measures ANOVA –> Multi paired estimation plot

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Ordered groups ANOVA –> Shared-control estimation plot

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Estimation Plots: The Way Forward

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In summary, estimation plots offer five key benefits relative to conventional plots:

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BarplotBoxplotEstimation Plot
Displays all observed valuesNONOYes
Avoids false dichotomyNONOYes
Focusses on effect sizeNONOYes
Visualizes effect size precisionNONOYes
Shows mean difference distributionNONOYes
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You can create estimation plots using the DABEST (Data Analysis with Bootstrap Estimation) packages, which are available in Matlab, Python, and R.

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[1]:W. E. B. Du Bois’s Data Portraits: Visualizing Black America. Edited by Whitney Battle-Baptiste and Britt Rusert, Princeton Architectural Press, 2018

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DO NOT EDIT! File to edit: ../nbs/API/load.ipynb. - -# %% auto #0 -__all__ = ['load', 'prop_dataset'] - -# %% ../nbs/API/load.ipynb #218e4f14 -def load( - data, - idx=None, - x=None, - y=None, - paired=None, - id_col=None, - ci=95, - resamples=5000, - random_seed=12345, - proportional=False, - delta2=False, - experiment=None, - experiment_label=None, - x1_level=None, - mini_meta=False, - ps_adjust=False, -): - """ - Loads data in preparation for estimation statistics. - - This is designed to work with pandas DataFrames. - - Parameters - ---------- - data : pandas DataFrame - idx : tuple - List of column names (if 'x' is not supplied) or of category names - (if 'x' is supplied). This can be expressed as a tuple of tuples, - with each individual tuple producing its own contrast plot - x : string or list, default None - Column name(s) of the independent variable. This can be expressed as - a list of 2 elements if and only if 'delta2' is True; otherwise it - can only be a string. - y : string, default None - Column names for data to be plotted on the x-axis and y-axis. - paired : string, default None - The type of the experiment under which the data are obtained. If 'paired' - is None then the data will not be treated as paired data in the subsequent - calculations. If 'paired' is 'baseline', then in each tuple of x, other - groups will be paired up with the first group (as control). If 'paired' is - 'sequential', then in each tuple of x, each group will be paired up with - its previous group (as control). - id_col : default None. - Required if `paired` is True. - ci : integer, default 95 - The confidence interval width. The default of 95 produces 95% - confidence intervals. - resamples : integer, default 5000. - The number of resamples taken to generate the bootstraps which are used - to generate the confidence intervals. - random_seed : int, default 12345 - This integer is used to seed the random number generator during - bootstrap resampling, ensuring that the confidence intervals - reported are replicable. - proportional : boolean, default False. - An indicator of whether the data is binary or not. When set to True, it - specifies that the data consists of binary data, where the values are - limited to 0 and 1. The code is not suitable for analyzing proportion - data that contains non-numeric values, such as strings like 'yes' and 'no'. - When False or not provided, the algorithm assumes that - the data is continuous and uses a non-proportional representation. - delta2 : boolean, default False - Indicator of delta-delta experiment - experiment : String, default None - The name of the column of the dataframe which contains the label of - experiments - experiment_lab : list, default None - A list of String to specify the order of subplots for delta-delta plots. - This can be expressed as a list of 2 elements if and only if 'delta2' - is True; otherwise it can only be a string. - x1_level : list, default None - A list of String to specify the order of subplots for delta-delta plots. - This can be expressed as a list of 2 elements if and only if 'delta2' - is True; otherwise it can only be a string. - mini_meta : boolean, default False - Indicator of weighted delta calculation. - ps_adjust : boolean, default False - Indicator of whether to adjust calculated p-value according to Phipson & Smyth (2010) - # https://doi.org/10.2202/1544-6115.1585 - - Returns - ------- - A `Dabest` object. - """ - from dabest import Dabest - - return Dabest( - data, - idx, - x, - y, - paired, - id_col, - ci, - resamples, - random_seed, - proportional, - delta2, - experiment, - experiment_label, - x1_level, - mini_meta, - ps_adjust, - ) - -# %% ../nbs/API/load.ipynb #570ff65a -import numpy as np -from typing import Union, Optional -import pandas as pd - - -def prop_dataset( - group: Union[ - list, tuple, np.ndarray, dict - ], # Accepts lists, tuples, or numpy ndarrays of numeric types. - group_names: Optional[list] = None, -): - """ - Convenient function to generate a dataframe of binary data. - """ - - if isinstance(group, dict): - # If group_names is not provided, use the keys of the dict as group_names - if group_names is None: - group_names = list(group.keys()) - elif not set(group_names) == set(group.keys()): - # Check if the group_names provided is the same as the keys of the dict - raise ValueError("group_names must be the same as the keys of the dict.") - - # Check if the values in the dict are numeric - if not all( - [isinstance(group[name], (list, tuple, np.ndarray)) for name in group_names] - ): - raise ValueError( - "group must be a dict of lists, tuples, or numpy ndarrays of numeric types." - ) - - # Check if the values in the dict only have two elements under each parent key - if not all([len(group[name]) == 2 for name in group_names]): - raise ValueError("Each parent key should have only two elements.") - group_val = group - - else: - if group_names is None: - raise ValueError("group_names must be provided if group is not a dict.") - - # Check if the length of group is two times of the length of group_names - if not len(group) == 2 * len(group_names): - raise ValueError( - "The length of group must be two times of the length of group_names." - ) - group_val = { - group_names[i]: [group[i * 2], group[i * 2 + 1]] - for i in range(len(group_names)) - } - - # Check if the sum of values in group_val under each key are the same - if not all( - [ - sum(group_val[name]) == sum(group_val[group_names[0]]) - for name in group_val.keys() - ] - ): - raise ValueError("The sum of values under each key must be the same.") - - id_col = pd.Series(range(1, sum(group_val[group_names[0]]) + 1)) - - final_df = pd.DataFrame() - - for name in group_val.keys(): - col = ( - np.repeat(0, group_val[name][0]).tolist() - + np.repeat(1, group_val[name][1]).tolist() - ) - df = pd.DataFrame({name: col}) - final_df = pd.concat([final_df, df], axis=1) - - final_df["ID"] = id_col - - return final_df diff --git a/dabest/_bootstrap_tools.py b/dabest/_bootstrap_tools.py deleted file mode 100644 index b4333635..00000000 --- a/dabest/_bootstrap_tools.py +++ /dev/null @@ -1,282 +0,0 @@ -# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/bootstrap.ipynb. - -# %% auto #0 -__all__ = ['bootstrap', 'jackknife_indexes', 'bca'] - -# %% ../nbs/API/bootstrap.ipynb #4231300f -import numpy as np -import pandas as pd -import seaborn as sns -from scipy.stats import norm -from scipy.stats import ttest_1samp, ttest_ind, ttest_rel -from scipy.stats import mannwhitneyu, wilcoxon, norm -import warnings - -# %% ../nbs/API/bootstrap.ipynb #e86b4b8d -class bootstrap: - """ - Computes the summary statistic and a bootstrapped confidence interval. - - Returns - ------- - An `bootstrap` object reporting the summary statistics, percentile CIs, bias-corrected and accelerated (BCa) CIs, and the settings used: - `summary`: float. - The summary statistic. - `is_difference`: boolean. - Whether or not the summary is the difference between two groups. If False, only x1 was supplied. - `is_paired`: string, default None - The type of the experiment under which the data are obtained - `statistic`: callable - The function used to compute the summary. - `reps`: int - The number of bootstrap iterations performed. - `stat_array`:array - A sorted array of values obtained by bootstrapping the input arrays. - `ci`:float - The size of the confidence interval reported (in percentage). - `pct_ci_low,pct_ci_high`:floats - The upper and lower bounds of the confidence interval as computed by taking the percentage bounds. - `pct_low_high_indices`:array - An array with the indices in `stat_array` corresponding to the percentage confidence interval bounds. - `bca_ci_low, bca_ci_high`: floats - The upper and lower bounds of the bias-corrected and accelerated(BCa) confidence interval. See Efron 1977. - `bca_low_high_indices`: array - An array with the indices in `stat_array` corresponding to the BCa confidence interval bounds. - `pvalue_1samp_ttest`: float - P-value obtained from scipy.stats.ttest_1samp. If 2 arrays were passed (x1 and x2), returns 'NIL'. See - `pvalue_2samp_ind_ttest`: float - P-value obtained from scipy.stats.ttest_ind. If a single array was given (x1 only), or if `paired` is not None, returns 'NIL'. See - `pvalue_2samp_related_ttest`: float - P-value obtained from scipy.stats.ttest_rel. If a single array was given (x1 only), or if `paired` is None, returns 'NIL'. See - `pvalue_wilcoxon`: float - P-value obtained from scipy.stats.wilcoxon. If a single array was given (x1 only), or if `paired` is None, returns 'NIL'. The Wilcoxons signed-rank test is a nonparametric paired test of the null hypothesis that the related samples x1 and x2 are from the same distribution. See - `pvalue_mann_whitney`: float - Two-sided p-value obtained from scipy.stats.mannwhitneyu. If a single array was given (x1 only), returns 'NIL'. The Mann-Whitney U-test is a nonparametric unpaired test of the null hypothesis that x1 and x2 are from the same distribution. See - - """ - - def __init__( - self, - x1: np.array, # The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded. - x2: np.array = None, # The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded. - paired: bool = False, # Whether or not x1 and x2 are paired samples. If 'paired' is None then the data will not be treated as paired data in the subsequent calculations. If 'paired' is 'baseline', then in each tuple of x, other groups will be paired up with the first group (as control). If 'paired' is 'sequential', then in each tuple of x, each group will be paired up with the previous group (as control). - stat_function: callable = np.mean, # The summary statistic called on data. - smoothboot: bool = False, # Taken from seaborn.algorithms.bootstrap. If True, performs a smoothed bootstrap (draws samples from a kernel destiny estimate). - alpha_level: float = 0.05, # Denotes the likelihood that the confidence interval produced does not include the true summary statistic. When alpha = 0.05, a 95% confidence interval is produced. - reps: int = 5000, # Number of bootstrap iterations to perform. - ): - # Turn to pandas series. - # x1 = pd.Series(x1).dropna() - x1 = x1[~np.isnan(x1)] - - diff = False - - # Initialise stat_function - if stat_function is None: - stat_function = np.mean - - # Compute two-sided alphas. - if alpha_level > 1.0 or alpha_level < 0.0: - raise ValueError("alpha_level must be between 0 and 1.") - alphas = np.array([alpha_level / 2.0, 1 - alpha_level / 2.0]) - - sns_bootstrap_kwargs = { - "func": stat_function, - "n_boot": reps, - "smooth": smoothboot, - } - - if paired: - # check x2 is not None: - if x2 is None: - raise ValueError("Please specify x2.") - - # x2 = pd.Series(x2).dropna() - x2 = x1[~np.isnan(x2)] - - if len(x1) != len(x2): - raise ValueError("x1 and x2 are not the same length.") - - if (x2 is None) or (paired is not None): - if x2 is None: - tx = x1 - paired = False - ttest_single = ttest_1samp(x1, 0)[1] - ttest_2_ind = "NIL" - ttest_2_paired = "NIL" - wilcoxonresult = "NIL" - - else: # only two options to enter here - diff = True - tx = x2 - x1 - ttest_single = "NIL" - ttest_2_ind = "NIL" - ttest_2_paired = ttest_rel(x1, x2)[1] - - try: - wilcoxonresult = wilcoxon(x1, x2)[1] - except ValueError as e: - warnings.warn("Wilcoxon test could not be performed. This might be due " - "to no variability in the difference of the paired groups. \n" - "Error: {}\n" - "For detailed information, please refer to https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wilcoxon.html " - .format(e)) - mannwhitneyresult = "NIL" - - # Turns data into array, then tuple. - tdata = (tx,) - - # The value of the statistic function applied - # just to the actual data. - summ_stat = stat_function(*tdata) - statarray = sns.algorithms.bootstrap(tx, **sns_bootstrap_kwargs) - statarray.sort() - - # Get Percentile indices - pct_low_high = np.round((reps - 1) * alphas) - pct_low_high = np.nan_to_num(pct_low_high).astype("int") - - elif x2 is not None and paired is None: - diff = True - # x2 = pd.Series(x2).dropna() - x2 = x2[~np.isnan(x2)] - # Generate statarrays for both arrays. - ref_statarray = sns.algorithms.bootstrap(x1, **sns_bootstrap_kwargs) - exp_statarray = sns.algorithms.bootstrap(x2, **sns_bootstrap_kwargs) - - tdata = exp_statarray - ref_statarray - statarray = tdata.copy() - statarray.sort() - tdata = (tdata,) # Note tuple form. - - # The difference as one would calculate it. - summ_stat = stat_function(x2) - stat_function(x1) - - # Get Percentile indices - pct_low_high = np.round((reps - 1) * alphas) - pct_low_high = np.nan_to_num(pct_low_high).astype("int") - - # Statistical tests. - ttest_single = "NIL" - ttest_2_ind = ttest_ind(x1, x2)[1] - ttest_2_paired = "NIL" - mannwhitneyresult = mannwhitneyu(x1, x2, alternative="two-sided")[1] - wilcoxonresult = "NIL" - - # Get Bias-Corrected Accelerated indices convenience function invoked. - bca_low_high = bca(tdata, alphas, statarray, stat_function, summ_stat, reps) - - # Warnings for unstable or extreme indices. - for ind in [pct_low_high, bca_low_high]: - if np.any(ind == 0) or np.any(ind == reps - 1): - warnings.warn( - "Some values used extremal samples;" - " results are probably unstable." - ) - elif np.any(ind < 10) or np.any(ind >= reps - 10): - warnings.warn( - "Some values used top 10 low/high samples;" - " results may be unstable." - ) - - self.summary = summ_stat - self.is_paired = paired - self.is_difference = diff - self.statistic = str(stat_function) - self.n_reps = reps - - self.ci = (1 - alpha_level) * 100 - self.stat_array = np.array(statarray) - - self.pct_ci_low = statarray[pct_low_high[0]] - self.pct_ci_high = statarray[pct_low_high[1]] - self.pct_low_high_indices = pct_low_high - - self.bca_ci_low = statarray[bca_low_high[0]] - self.bca_ci_high = statarray[bca_low_high[1]] - self.bca_low_high_indices = bca_low_high - - self.pvalue_1samp_ttest = ttest_single - self.pvalue_2samp_ind_ttest = ttest_2_ind - self.pvalue_2samp_paired_ttest = ttest_2_paired - self.pvalue_wilcoxon = wilcoxonresult - self.pvalue_mann_whitney = mannwhitneyresult - - self.results = { - "stat_summary": self.summary, - "is_difference": diff, - "is_paired": paired, - "bca_ci_low": self.bca_ci_low, - "bca_ci_high": self.bca_ci_high, - "ci": self.ci, - } - - def __repr__(self): - if "mean" in self.statistic: - stat = "mean" - elif "median" in self.statistic: - stat = "median" - else: - stat = self.statistic - - diff_types = {"sequential": "paired", "baseline": "paired", None: "unpaired"} - if self.is_difference: - a = "The {} {} difference is {}.".format( - diff_types[self.is_paired], stat, self.summary - ) - else: - a = "The {} is {}.".format(stat, self.summary) - - b = "[{} CI: {}, {}]".format(self.ci, self.bca_ci_low, self.bca_ci_high) - return "\n".join([a, b]) - -# %% ../nbs/API/bootstrap.ipynb #00c814b9 -def jackknife_indexes(data): - # Taken without modification from scikits.bootstrap package. - """ - From the scikits.bootstrap package. - Given an array, returns a list of arrays where each array is a set of - jackknife indexes. - - For a given set of data Y, the jackknife sample J[i] is defined as the - data set Y with the ith data point deleted. - """ - - base = np.arange(0, len(data)) - return (np.delete(base, i) for i in base) - - -def bca(data, alphas, stat_array, stat_function, ostat, reps): - """ - Subroutine called to calculate the BCa statistics. - Borrowed heavily from scikits.bootstrap code. - """ - - # The bias correction value. - z0 = norm.ppf((1.0 * np.sum(stat_array < ostat, axis=0)) / reps) - - # Statistics of the jackknife distribution - jack_indexes = jackknife_indexes(data[0]) - jstat = [stat_function(*(x[indexes] for x in data)) for indexes in jack_indexes] - jmean = np.mean(jstat, axis=0) - - # Acceleration value - a = np.divide( - np.sum((jmean - jstat) ** 3, axis=0), - (6.0 * np.sum((jmean - jstat) ** 2, axis=0) ** 1.5), - ) - if np.any(np.isnan(a)): - nanind = np.nonzero(np.isnan(a)) - warnings.warn( - "Some acceleration values were undefined." - "This is almost certainly because all values" - "for the statistic were equal. Affected" - "confidence intervals will have zero width and" - "may be inaccurate (indexes: {})".format(nanind) - ) - zs = z0 + norm.ppf(alphas).reshape(alphas.shape + (1,) * z0.ndim) - avals = norm.cdf(z0 + zs / (1 - a * zs)) - nvals = np.round((reps - 1) * avals) - nvals = np.nan_to_num(nvals).astype("int") - - return nvals diff --git a/dabest/_dabest_object.py b/dabest/_dabest_object.py deleted file mode 100644 index 53b36360..00000000 --- a/dabest/_dabest_object.py +++ /dev/null @@ -1,722 +0,0 @@ -"""Main class for estimating statistics and generating plots.""" - -# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/dabest_object.ipynb. - -# %% auto #0 -__all__ = ['Dabest'] - -# %% ../nbs/API/dabest_object.ipynb #d3c6f47a -# Import standard data science libraries -import warnings -from numpy import array, repeat, random, issubdtype, number -import numpy as np -import pandas as pd -from scipy.stats import norm -from scipy.stats import randint - -# %% ../nbs/API/dabest_object.ipynb #350b12c1 -class Dabest(object): - - """ - Class for estimation statistics and plots. - """ - - def __init__( - self, - data, - idx, - x, - y, - paired, - id_col, - ci, - resamples, - random_seed, - proportional, - delta2, - experiment, - experiment_label, - x1_level, - mini_meta, - ps_adjust, - ): - """ - Parses and stores pandas DataFrames in preparation for estimation - statistics. You should not be calling this class directly; instead, - use `dabest.load()` to parse your DataFrame prior to analysis. - """ - - self.__delta2 = delta2 - self.__experiment = experiment - self.__ci = ci - self.__input_data = data - self.__output_data = data.copy() - self.__id_col = id_col - self.__is_paired = paired - self.__resamples = resamples - self.__random_seed = random_seed - self.__is_proportional = proportional - self.__is_mini_meta = mini_meta - self.__ps_adjust = ps_adjust - - # after this call the attributes self.__experiment_label and self.__x1_level are updated - self._check_errors(x, y, idx, experiment, experiment_label, x1_level) - - # create new x & idx and record the second variable if this is a valid 2x2 ANOVA case - idx, x, all_plot_groups = self._prep_idx(idx, x, y, experiment) - - self.__plot_data = self._get_plot_data(x, y, all_plot_groups) - self.__all_plot_groups = all_plot_groups - - self._compute_effectsize_dfs() - - def __repr__(self): - from .__init__ import __version__ - from .misc_tools import print_greeting - - greeting_header = print_greeting() - - RM_STATUS = { - "baseline": "for repeated measures against baseline \n", - "sequential": "for the sequential design of repeated-measures experiment \n", - "None": "", - } - - PAIRED_STATUS = {"baseline": "Paired e", "sequential": "Paired e", "None": "E"} - - first_line = { - "rm_status": RM_STATUS[str(self.__is_paired)], - "paired_status": PAIRED_STATUS[str(self.__is_paired)], - } - - s1 = "{paired_status}ffect size(s) {rm_status}".format(**first_line) - s2 = "with {}% confidence intervals will be computed for:".format(self.__ci) - desc_line = s1 + s2 - - out = [greeting_header + "\n\n" + desc_line] - - comparisons = [] - - if self.__is_paired == "sequential": - for j, current_tuple in enumerate(self.__idx): - for ix, test_name in enumerate(current_tuple[1:]): - control_name = current_tuple[ix] - comparisons.append("{} minus {}".format(test_name, control_name)) - else: - for j, current_tuple in enumerate(self.__idx): - control_name = current_tuple[0] - - for ix, test_name in enumerate(current_tuple[1:]): - comparisons.append("{} minus {}".format(test_name, control_name)) - - if self.__delta2: - comparisons.append( - "{} minus {} (only for mean difference)".format( - self.__experiment_label[1], self.__experiment_label[0] - ) - ) - - if self.__is_mini_meta: - comparisons.append("weighted delta (only for mean difference)") - - for j, g in enumerate(comparisons): - out.append("{}. {}".format(j + 1, g)) - - resamples_line1 = "\n{} resamples ".format(self.__resamples) - resamples_line2 = "will be used to generate the effect size bootstraps." - out.append(resamples_line1 + resamples_line2) - - return "\n".join(out) - - - def _prep_idx(self, idx, x, y, experiment): - """ - Function to prepare the idx. - """ - if idx is None and x is not None and y is not None: - # Add a length check for unique values in the first element in list x, - # if the length is greater than 2, force delta2 to be False - # Should be removed if delta2 for situations other than 2x2 is supported - if len(self.__output_data[x[0]].unique()) > 2: - self.__delta2 = False - - # add a new column which is a combination of experiment and the first variable - new_col_name = experiment + x[0] - while new_col_name in self.__output_data.columns: - new_col_name += "_" - - self.__output_data[new_col_name] = ( - self.__output_data[x[0]].astype(str) - + " " - + self.__output_data[experiment].astype(str) - ) - - # create idx and record the first and second x variable - idx = [] - for i in list(map(lambda x: str(x), self.__experiment_label)): - temp = [] - for j in list(map(lambda x: str(x), self.__x1_level)): - temp.append(j + " " + i) - idx.append(temp) - - self.__idx = idx - self.__x1 = x[0] - self.__x2 = x[1] - x = new_col_name - else: - self.__idx = idx - self.__x1 = None - self.__x2 = None - - # Determine the kind of estimation plot we need to produce. - if all([isinstance(i, (str, int, float)) for i in self.__idx]): - # flatten out idx. - all_plot_groups = pd.Series([t for t in self.__idx]).unique().tolist() - if len(self.__idx) > len(all_plot_groups): - err0 = "`idx` contains duplicated groups. Please remove any duplicates and try again." - raise ValueError(err0) - - # We need to re-wrap this idx inside another tuple so as to - # easily loop thru each pairwise group later on. - self.__idx = (idx,) - - elif all([isinstance(i, (tuple, list)) for i in self.__idx]): - all_plot_groups = pd.Series([tt for t in self.__idx for tt in t]).unique().tolist() - actual_groups_given = sum([len(i) for i in self.__idx]) - - if actual_groups_given > len(all_plot_groups): - err0 = "Groups are repeated across tuples," - err1 = " or a tuple has repeated groups in it." - err2 = " Please remove any duplicates and try again." - raise ValueError(err0 + err1 + err2) - - else: # mix of string and tuple? - err = "There seems to be a problem with the idx you " "entered--{}.".format(self.__idx) - raise ValueError(err) - - return idx, x, all_plot_groups - - @property - def mean_diff(self): - """ - Returns an :py:class:`EffectSizeDataFrame` for the mean difference, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()` - - """ - return self.__mean_diff - - @property - def median_diff(self): - """ - Returns an :py:class:`EffectSizeDataFrame` for the median difference, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`. - - """ - return self.__median_diff - - @property - def cohens_d(self): - """ - Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Cohen's `d`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`. - - """ - return self.__cohens_d - - @property - def cohens_h(self): - """ - Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Cohen's `h`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `directional` argument in `dabest.load()`. - - """ - return self.__cohens_h - - @property - def hedges_g(self): - """ - Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Hedges' `g`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`. - - """ - return self.__hedges_g - - @property - def cliffs_delta(self): - """ - Returns an :py:class:`EffectSizeDataFrame` for Cliff's delta, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`. - - """ - return self.__cliffs_delta - - - @property - def delta_g(self): - """ - Returns an :py:class:`EffectSizeDataFrame` for delta g, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`. - """ - raise DeprecationWarning("delta_g has been depreciated - Please use hedges_g (with delta2=True) for delta g experiments") - - - @property - def input_data(self): - """ - Returns the pandas DataFrame that was passed to `dabest.load()`. - When `delta2` is True, a new column is added to support the - function. The name of this new column is indicated by `x`. - """ - return self.__input_data - - @property - def idx(self): - """ - Returns the order of categories that was passed to `dabest.load()`. - """ - return self.__idx - - @property - def x1(self): - """ - Returns the first variable declared in x when it is a delta-delta - case; returns None otherwise. - """ - return self.__x1 - - @property - def x1_level(self): - """ - Returns the levels of first variable declared in x when it is a - delta-delta case; returns None otherwise. - """ - return self.__x1_level - - @property - def x2(self): - """ - Returns the second variable declared in x when it is a delta-delta - case; returns None otherwise. - """ - return self.__x2 - - @property - def experiment(self): - """ - Returns the column name of experiment labels that was passed to - `dabest.load()` when it is a delta-delta case; returns None otherwise. - """ - return self.__experiment - - @property - def experiment_label(self): - """ - Returns the experiment labels in order that was passed to `dabest.load()` - when it is a delta-delta case; returns None otherwise. - """ - return self.__experiment_label - - @property - def delta2(self): - """ - Returns the boolean parameter indicating if this is a delta-delta - situation. - """ - return self.__delta2 - - @property - def is_delta_delta(self): - """ - Returns the boolean parameter indicating if this is a delta-delta - situation. - """ - return self.__delta2 - - @property - def is_paired(self): - """ - Returns the type of repeated-measures experiment. - """ - return self.__is_paired - - @property - def id_col(self): - """ - Returns the id column declared to `dabest.load()`. - """ - return self.__id_col - - @property - def ci(self): - """ - The width of the desired confidence interval. - """ - return self.__ci - - @property - def resamples(self): - """ - The number of resamples used to generate the bootstrap. - """ - return self.__resamples - - @property - def random_seed(self): - """ - The number used to initialise the numpy random seed generator, ie. - `seed_value` from `numpy.random.seed(seed_value)` is returned. - """ - return self.__random_seed - - @property - def x(self): - """ - Returns the x column that was passed to `dabest.load()`, if any. - When `delta2` is True, `x` returns the name of the new column created - for the delta-delta situation. To retrieve the 2 variables passed into - `x` when `delta2` is True, please call `x1` and `x2` instead. - """ - return self.__x - - @property - def y(self): - """ - Returns the y column that was passed to `dabest.load()`, if any. - """ - return self.__y - - @property - def _xvar(self): - """ - Returns the xvar in dabest.plot_data. - """ - return self.__xvar - - @property - def _yvar(self): - """ - Returns the yvar in dabest.plot_data. - """ - return self.__yvar - - @property - def _plot_data(self): - """ - Returns the pandas DataFrame used to produce the estimation stats/plots. - """ - return self.__plot_data - - @property - def is_proportional(self): - """ - Returns the proportional parameter class. - """ - return self.__is_proportional - - @property - def is_mini_meta(self): - """ - Returns the mini_meta boolean parameter. - """ - return self.__is_mini_meta - - @property - def _all_plot_groups(self): - """ - Returns the all plot groups, as indicated via the `idx` keyword. - """ - return self.__all_plot_groups - - def _check_errors(self, x, y, idx, experiment, experiment_label, x1_level): - ''' - Function to check some input parameters and combinations between them. - At the end of this function these two class attributes are updated - self.__experiment_label and self.__x1_level - ''' - - # Check if idx is present (if not a 2x2 Anova case) - if idx is None: - if not self.__delta2: - err0 = "Please specify `idx`." - raise ValueError(err0) - - # Check if it is a valid mini_meta case - if self.__is_mini_meta: - # Only mini_meta calculation but not proportional and delta-delta function - if self.__is_proportional: - err0 = "`proportional` and `mini_meta` cannot be True at the same time." - raise ValueError(err0) - if self.__delta2: - err0 = "`delta2` and `mini_meta` cannot be True at the same time." - raise ValueError(err0) - - # Check if the columns stated are valid - # Initialize a flag to track if any element in idx is neither str nor (tuple, list) - valid_types = True - - # Initialize variables to track the conditions for str and (tuple, list) - is_str_condition_met, is_tuple_list_condition_met = False, False - - # Single traversal for optimization - for item in idx: - if isinstance(item, str): - is_str_condition_met = True - elif isinstance(item, (tuple, list)) and len(item) == 2: - is_tuple_list_condition_met = True - else: - valid_types = False - break # Exit the loop if an invalid type is found - - # Check if all types are valid - if not valid_types: - err0 = "`mini_meta` is True, but `idx` ({})".format(idx) - err1 = "does not contain exactly 2 unique columns." - raise ValueError(err0 + err1) - - # Handling str type condition - if is_str_condition_met: - if len(np.unique(idx).tolist()) != 2: - err0 = "`mini_meta` is True, but `idx` ({})".format(idx) - err1 = "does not contain exactly 2 unique columns." - raise ValueError(err0 + err1) - - # Handling (tuple, list) type condition - if is_tuple_list_condition_met: - all_idx_lengths = [len(t) for t in idx] - if (array(all_idx_lengths) != 2).any(): - err1 = "`mini_meta` is True, but some elements in idx " - err2 = "in {} do not consist only of two groups.".format(idx) - raise ValueError(err1 + err2) - - - # Check if this is a 2x2 ANOVA case and x & y are valid columns - # Create experiment_label and x1_level - elif self.__delta2: - if x is None: - error_msg = "If `delta2` is True. `x` parameter cannot be None. String or list expected" - raise ValueError(error_msg) - - # idx should not be specified - if idx: - err0 = "`idx` should not be specified when `delta2` is True.".format( - len(x) - ) - raise ValueError(err0) - - # Check if x is valid - if len(x) != 2: - err0 = "`delta2` is True but the number of variables indicated by `x` is {}.".format( - len(x) - ) - raise ValueError(err0) - - for i in x: - if i not in self.__output_data.columns: - err = "{0} is not a column in `data`. Please check.".format(i) - raise IndexError(err) - - # Check if y is valid - if not y: - err0 = "`delta2` is True but `y` is not indicated." - raise ValueError(err0) - - if y not in self.__output_data.columns: - err = "{0} is not a column in `data`. Please check.".format(y) - raise IndexError(err) - - # Check if experiment is valid - if experiment not in self.__output_data.columns: - err = "{0} is not a column in `data`. Please check.".format(experiment) - raise IndexError(err) - - # Check if experiment_label is valid and create experiment when needed - if experiment_label: - if len(experiment_label) != 2: - err0 = "`experiment_label` does not have a length of 2." - raise ValueError(err0) - - for i in experiment_label: - if i not in self.__output_data[experiment].unique(): - err = "{0} is not an element in the column `{1}` of `data`. Please check.".format( - i, experiment - ) - raise IndexError(err) - else: - experiment_label = self.__output_data[experiment].unique() - - # Check if x1_level is valid - if x1_level: - if len(x1_level) != 2: - err0 = "`x1_level` does not have a length of 2." - raise ValueError(err0) - - for i in x1_level: - if i not in self.__output_data[x[0]].unique(): - err = "{0} is not an element in the column `{1}` of `data`. Please check.".format( - i, experiment - ) - raise IndexError(err) - else: - x1_level = self.__output_data[x[0]].unique() - - elif experiment: - experiment_label = self.__output_data[experiment].unique() - x1_level = self.__output_data[x[0]].unique() - self.__experiment_label = experiment_label - self.__x1_level = x1_level - - if self.__is_paired and self.__output_data.isnull().values.any(): - warn1 = f"NaN values detected under paired setting," - warn2 = f" please check your data." - warnings.warn(warn1 + warn2) - if x is not None and y is not None: - rmname = self.__output_data[self.__output_data[y].isnull()][self.__id_col].tolist() - self.__output_data = self.__output_data[~self.__output_data[self.__id_col].isin(rmname)] - - # Check if there is a typo on paired - if self.__is_paired and self.__is_paired not in ("baseline", "sequential"): - err = "'{}' assigned for `paired` is not valid. Please use either 'baseline' or 'sequential'.".format(self.__is_paired) - raise ValueError(err) - - # Check if `id_col` is valid - if self.__is_paired: - if self.__id_col is None: - err = "`id_col` must be specified if `paired` is assigned with a not NoneType value." - raise IndexError(err) - - if self.__id_col not in self.__output_data.columns: - err = "`id_col` was given as '{}'; however, '{}' is not a column in `data`.".format(self.__id_col, self.__id_col) - raise IndexError(err) - - # Check if x and y are supplied (relevant to long format data) - if x is None and y is not None: - err = "You have only specified `y`. Please also specify `x` (for long format data)." - raise ValueError(err) - - if x is not None and y is None: - err = "You have only specified `x`. Please also specify `y` (for long format data)." - raise ValueError(err) - - if x is not None and y is not None: - # Assume we have a long dataset. - # check both x and y are column names in data. - if not self.__delta2: - if x not in self.__output_data.columns: - err = "'{0}' is not a column in `data`. Please check.".format(x) - raise IndexError(err) - if y not in self.__output_data.columns: - err = "'{0}' is not a column in `data`. Please check.".format(y) - raise IndexError(err) - # Check that the `y` column is numeric. - if not issubdtype(self.__output_data[y].dtype, number): - err = "The `y` column in `data` is not numeric. Please check." - raise ValueError(err) - - - def _get_plot_data(self, x, y, all_plot_groups): - # def _get_plot_data(self, x, y): - """ - Function to prepare some attributes for plotting - """ - # all_plot_groups = self.__all_plot_groups - # Identify the type of data that was passed in. - if x is not None and y is not None: - # Assume we have a long dataset. - # check all the idx can be found in self.__output_data[x] - for g in all_plot_groups: - if g not in self.__output_data[x].unique(): - err0 = '"{0}" is not a group in the column `{1}`.'.format(g, x) - err1 = " Please check `idx` and try again." - raise IndexError(err0 + err1) - - # Select only rows where the value in the `x` column - # is found in `idx`. - plot_data = self.__output_data[ - self.__output_data.loc[:, x].isin(all_plot_groups) - ].copy() - - # Assign attributes - self.__x = x - self.__y = y - self.__xvar = x - self.__yvar = y - - elif x is None and y is None: - # Assume we have a wide dataset. - # Assign attributes appropriately. - self.__x = None - self.__y = None - self.__xvar = "group" - self.__yvar = "Value" - - # First, check we have all columns in the dataset. - for g in all_plot_groups: - if g not in self.__output_data.columns: - err0 = '"{0}" is not a column in `data`.'.format(g) - err1 = " Please check `idx` and try again." - raise IndexError(err0 + err1) - - set_all_columns = set(self.__output_data.columns.tolist()) - set_all_plot_groups = set(all_plot_groups) - id_vars = set_all_columns.difference(set_all_plot_groups) - - plot_data = pd.melt( - self.__output_data, - id_vars=id_vars, - value_vars=all_plot_groups, - value_name=self.__yvar, - var_name=self.__xvar, - ) - - # Added in v0.2.7. - plot_data.dropna(axis=0, how="any", subset=[self.__yvar], inplace=True) - - - if isinstance(plot_data[self.__xvar].dtype, pd.CategoricalDtype): - plot_data[self.__xvar].cat.remove_unused_categories() - plot_data[self.__xvar].cat.reorder_categories( - all_plot_groups, ordered=True - ) - else: - plot_data[self.__xvar] = pd.Categorical( - plot_data[self.__xvar], categories=all_plot_groups, ordered=True - ) - - return plot_data - - def _compute_effectsize_dfs(self): - ''' - Function to compute all attributes based on EffectSizeDataFrame. - It returns nothing. - ''' - from ._effsize_objects import EffectSizeDataFrame - - effectsize_df_kwargs = dict( - ci=self.__ci, - is_paired=self.__is_paired, - random_seed=self.__random_seed, - resamples=self.__resamples, - proportional=self.__is_proportional, - delta2=self.__delta2, - experiment_label=self.__experiment_label, - x1_level=self.__x1_level, - x2=self.__x2, - mini_meta=self.__is_mini_meta, - ps_adjust=self.__ps_adjust, - ) - - self.__mean_diff = EffectSizeDataFrame( - self, "mean_diff", **effectsize_df_kwargs - ) - - self.__median_diff = EffectSizeDataFrame( - self, "median_diff", **effectsize_df_kwargs - ) - - self.__cohens_d = EffectSizeDataFrame(self, "cohens_d", **effectsize_df_kwargs) - - self.__cohens_h = EffectSizeDataFrame(self, "cohens_h", **effectsize_df_kwargs) - - self.__hedges_g = EffectSizeDataFrame(self, "hedges_g", **effectsize_df_kwargs) - - if not self.__is_paired: - self.__cliffs_delta = EffectSizeDataFrame( - self, "cliffs_delta", **effectsize_df_kwargs - ) - else: - self.__cliffs_delta = ( - "The data is paired; Cliff's delta is therefore undefined." - ) diff --git a/dabest/_delta_objects.py b/dabest/_delta_objects.py deleted file mode 100644 index b843ce06..00000000 --- a/dabest/_delta_objects.py +++ /dev/null @@ -1,871 +0,0 @@ -"""Auxiliary delta classes for estimating statistics and generating plots.""" - -# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/delta_objects.ipynb. - -# %% auto #0 -__all__ = ['DeltaDelta', 'MiniMetaDelta'] - -# %% ../nbs/API/delta_objects.ipynb #1814896d -from scipy.stats import norm -import pandas as pd -import numpy as np -from numpy import sort as npsort -from numpy import isnan -from string import Template -import warnings -import datetime as dt - -# %% ../nbs/API/delta_objects.ipynb #1bb53e06 -class DeltaDelta(object): - r""" - A class to compute and store the delta-delta statistics for experiments with a 2-by-2 arrangement where two independent variables, A and B, each have two categorical values, 1 and 2. The data is divided into two pairs of two groups, and a primary delta is first calculated as the mean difference between each of the pairs: - - - $$\Delta_{1} = \overline{X}_{A_{2}, B_{1}} - \overline{X}_{A_{1}, B_{1}}$$ - - $$\Delta_{2} = \overline{X}_{A_{2}, B_{2}} - \overline{X}_{A_{1}, B_{2}}$$ - - - where $\overline{X}_{A_{i}, B_{j}}$ is the mean of the sample with A = i and B = j, $\Delta$ is the mean difference between two samples. - - A delta-delta value is then calculated as the mean difference between the two primary deltas: - - - $$\Delta_{\Delta} = \Delta_{2} - \Delta_{1}$$ - - and a delta g value is calculated as the mean difference between the two primary deltas divided by - the standard deviation of the delta-delta value, which is calculated from a pooled variance of the 4 samples: - - $$\Delta_{g} = \frac{\Delta_{\Delta}}{s_{\Delta_{\Delta}}}$$ - - $$s_{\Delta_{\Delta}} = \sqrt{\frac{(n_{A_{2}, B_{1}}-1)s_{A_{2}, B_{1}}^2+(n_{A_{1}, B_{1}}-1)s_{A_{1}, B_{1}}^2+(n_{A_{2}, B_{2}}-1)s_{A_{2}, B_{2}}^2+(n_{A_{1}, B_{2}}-1)s_{A_{1}, B_{2}}^2}{(n_{A_{2}, B_{1}} - 1) + (n_{A_{1}, B_{1}} - 1) + (n_{A_{2}, B_{2}} - 1) + (n_{A_{1}, B_{2}} - 1)}}$$ - - where $s$ is the standard deviation and $n$ is the sample size. - - - """ - - def __init__( - self, effectsizedataframe, permutation_count, bootstraps_delta_delta, ci=95 - ): - from ._stats_tools import effsize as es - from ._stats_tools import confint_1group as ci1g - from ._stats_tools import confint_2group_diff as ci2g - - self.__effsizedf = effectsizedataframe.results - self.__dabest_obj = effectsizedataframe.dabest_obj - self.__ci = ci - self.__resamples = effectsizedataframe.resamples - self.__effect_size = effectsizedataframe.effect_size - self.__alpha = ci2g._compute_alpha_from_ci(ci) - self.__permutation_count = permutation_count - self.__bootstraps = np.array(self.__effsizedf["bootstraps"]) - self.__control = self.__dabest_obj.experiment_label[0] - self.__test = self.__dabest_obj.experiment_label[1] - - # Compute the bootstrap delta-delta or delta g and the true dela-delta based on the raw data - if self.__effect_size == "mean_diff": - self.__bootstraps_delta_delta = bootstraps_delta_delta[2] - self.__difference = ( - self.__effsizedf["difference"][1] - self.__effsizedf["difference"][0] - ) - else: - self.__bootstraps_delta_delta = bootstraps_delta_delta[0] - self.__difference = bootstraps_delta_delta[1] - - sorted_delta_delta = npsort(self.__bootstraps_delta_delta) - - self.__bias_correction = ci2g.compute_meandiff_bias_correction( - self.__bootstraps_delta_delta, self.__difference - ) - - self.__jackknives = np.array( - ci1g.compute_1group_jackknife(self.__bootstraps_delta_delta, np.mean) - ) - - self.__acceleration_value = ci2g._calc_accel(self.__jackknives) - - # Compute BCa intervals. - bca_idx_low, bca_idx_high = ci2g.compute_interval_limits( - self.__bias_correction, self.__acceleration_value, self.__resamples, ci - ) - - self.__bca_interval_idx = (bca_idx_low, bca_idx_high) - - if ~isnan(bca_idx_low) and ~isnan(bca_idx_high): - self.__bca_low = sorted_delta_delta[bca_idx_low] - self.__bca_high = sorted_delta_delta[bca_idx_high] - - err1 = "The $lim_type limit of the interval" - err2 = "was in the $loc 10 values." - err3 = "The result should be considered unstable." - err_temp = Template(" ".join([err1, err2, err3])) - - if bca_idx_low <= 10: - warnings.warn( - err_temp.substitute(lim_type="lower", loc="bottom"), stacklevel=1 - ) - - if bca_idx_high >= self.__resamples - 9: - warnings.warn( - err_temp.substitute(lim_type="upper", loc="top"), stacklevel=1 - ) - - else: - err1 = "The $lim_type limit of the BCa interval cannot be computed." - err2 = "It is set to the effect size itself." - err3 = "All bootstrap values were likely all the same." - err_temp = Template(" ".join([err1, err2, err3])) - - if isnan(bca_idx_low): - self.__bca_low = self.__difference - warnings.warn(err_temp.substitute(lim_type="lower"), stacklevel=0) - - if isnan(bca_idx_high): - self.__bca_high = self.__difference - warnings.warn(err_temp.substitute(lim_type="upper"), stacklevel=0) - - # Compute percentile intervals. - pct_idx_low = int((self.__alpha / 2) * self.__resamples) - pct_idx_high = int((1 - (self.__alpha / 2)) * self.__resamples) - - self.__pct_interval_idx = (pct_idx_low, pct_idx_high) - self.__pct_low = sorted_delta_delta[pct_idx_low] - self.__pct_high = sorted_delta_delta[pct_idx_high] - - def __permutation_test(self): - """ - Perform a permutation test and obtain the permutation p-value - based on the permutation data. - """ - self.__permutations = np.array(self.__effsizedf["permutations"]) - - THRESHOLD = np.abs(self.__difference) - - self.__permutations_delta_delta = np.array( - self.__permutations[1] - self.__permutations[0] - ) - - count = sum(np.abs(self.__permutations_delta_delta) > THRESHOLD) - self.__pvalue_permutation = count / self.__permutation_count - - def __repr__(self, header=True, sigfig=3): - from .misc_tools import print_greeting - - first_line = {"control": self.__control, "test": self.__test} - - if self.__effect_size == "mean_diff": - out1 = "The delta-delta between {control} and {test} ".format(**first_line) - else: - out1 = "The delta g between {control} and {test} ".format(**first_line) - - base_string_fmt = "{:." + str(sigfig) + "}" - if "." in str(self.__ci): - ci_width = base_string_fmt.format(self.__ci) - else: - ci_width = str(self.__ci) - - ci_out = { - "es": base_string_fmt.format(self.__difference), - "ci": ci_width, - "bca_low": base_string_fmt.format(self.__bca_low), - "bca_high": base_string_fmt.format(self.__bca_high), - } - - out2 = "is {es} [{ci}%CI {bca_low}, {bca_high}].".format(**ci_out) - out = out1 + out2 - - if header is True: - out = print_greeting() + "\n" + "\n" + out - - pval_rounded = base_string_fmt.format(self.pvalue_permutation) - - p1 = "The p-value of the two-sided permutation t-test is {}, ".format( - pval_rounded - ) - p2 = "calculated for legacy purposes only. " - pvalue = p1 + p2 - - bs1 = "{} bootstrap samples were taken; ".format(self.__resamples) - bs2 = "the confidence interval is bias-corrected and accelerated." - bs = bs1 + bs2 - - pval_def1 = ( - "Any p-value reported is the probability of observing the " - + "effect size (or greater),\nassuming the null hypothesis of " - + "zero difference is true." - ) - pval_def2 = ( - "\nFor each p-value, 5000 reshuffles of the " - + "control and test labels were performed." - ) - pval_def = pval_def1 + pval_def2 - - return "{}\n{}\n\n{}\n{}".format(out, pvalue, bs, pval_def) - - def to_dict(self): - """ - Returns the attributes of the `DeltaDelta` object as a - dictionary. - """ - # Only get public (user-facing) attributes. - attrs = [a for a in dir(self) if not a.startswith(("_", "to_dict", "results"))] - out = {} - for a in attrs: - out[a] = getattr(self, a) - return out - - def __compute_results(self): - # With some inspiration from @jungyangliao - delta_delta_results_df = pd.Series(self.to_dict()).to_frame().T - - column_index = ['control', 'test', 'difference', 'ci', 'bca_low', 'bca_high', 'bca_interval_idx', - 'pct_low', 'pct_high', 'pct_interval_idx', 'bootstraps_control', 'bootstraps_test', - 'bootstraps_delta_delta', 'permutations_control', 'permutations_test', 'permutations_delta_delta', - 'pvalue_permutation', 'permutation_count', 'bias_correction', 'jackknives' - ] - delta_delta_results_df['bootstraps_control'] = [delta_delta_results_df['bootstraps'][0][0]] - delta_delta_results_df['bootstraps_test'] = [delta_delta_results_df['bootstraps'][0][1]] - delta_delta_results_df['permutations_control'] = [delta_delta_results_df['permutations'][0][0]] - delta_delta_results_df['permutations_test'] = [delta_delta_results_df['permutations'][0][1]] - delta_delta_results_df = delta_delta_results_df.reindex(columns=column_index) - - self.__results = delta_delta_results_df - return self.__results - - @property - def ci(self): - """ - Returns the width of the confidence interval, in percent. - """ - return self.__ci - - @property - def alpha(self): - """ - Returns the significance level of the statistical test as a float - between 0 and 1. - """ - return self.__alpha - - @property - def bias_correction(self): - return self.__bias_correction - - @property - def bootstraps(self): - """ - Return the bootstrapped deltas from all the experiment groups. - """ - return self.__bootstraps - - @property - def jackknives(self): - return self.__jackknives - - @property - def acceleration_value(self): - return self.__acceleration_value - - @property - def bca_low(self): - """ - The bias-corrected and accelerated confidence interval lower limit. - """ - return self.__bca_low - - @property - def bca_high(self): - """ - The bias-corrected and accelerated confidence interval upper limit. - """ - return self.__bca_high - - @property - def bca_interval_idx(self): - return self.__bca_interval_idx - - @property - def control(self): - """ - Return the name of the control experiment group. - """ - return self.__control - - @property - def test(self): - """ - Return the name of the test experiment group. - """ - return self.__test - - @property - def bootstraps_delta_delta(self): - """ - Return the delta-delta values calculated from the bootstrapped - deltas. - """ - return self.__bootstraps_delta_delta - - @property - def difference(self): - """ - Return the delta-delta value calculated based on the raw data. - """ - return self.__difference - - @property - def pct_interval_idx(self): - return self.__pct_interval_idx - - @property - def pct_low(self): - """ - The percentile confidence interval lower limit. - """ - return self.__pct_low - - @property - def pct_high(self): - """ - The percentile confidence interval lower limit. - """ - return self.__pct_high - - @property - def pvalue_permutation(self): - try: - return self.__pvalue_permutation - except AttributeError: - self.__permutation_test() - return self.__pvalue_permutation - - @property - def permutation_count(self): - """ - The number of permuations taken. - """ - return self.__permutation_count - - @property - def permutations(self): - """ - Return the mean differences of permutations obtained during - the permutation test for each experiment group. - """ - try: - return self.__permutations - except AttributeError: - self.__permutation_test() - return self.__permutations - - @property - def permutations_delta_delta(self): - """ - Return the delta-delta values of permutations obtained - during the permutation test. - """ - try: - return self.__permutations_delta_delta - except AttributeError: - self.__permutation_test() - return self.__permutations_delta_delta - - @property - def results(self): - """ - Return the results of the delta-delta analysis. - """ - try: - return self.__results - except AttributeError: - self.__compute_results() - return self.__results - -# %% ../nbs/API/delta_objects.ipynb #18462ec5 -class MiniMetaDelta(object): - """ - A class to compute and store the weighted delta. - A weighted delta is calculated if the argument ``mini_meta=True`` is passed during ``dabest.load()``. - - """ - - def __init__(self, effectsizedataframe, permutation_count, - ci=95): - from ._stats_tools import effsize as es - from ._stats_tools import confint_1group as ci1g - from ._stats_tools import confint_2group_diff as ci2g - - self.__effsizedf = effectsizedataframe.results - self.__dabest_obj = effectsizedataframe.dabest_obj - self.__ci = ci - self.__resamples = effectsizedataframe.resamples - self.__alpha = ci2g._compute_alpha_from_ci(ci) - self.__permutation_count = permutation_count - self.__bootstraps = np.array(self.__effsizedf["bootstraps"]) - self.__control = np.array(self.__effsizedf["control"]) - self.__test = np.array(self.__effsizedf["test"]) - self.__control_N = np.array(self.__effsizedf["control_N"]) - self.__test_N = np.array(self.__effsizedf["test_N"]) - - - idx = self.__dabest_obj.idx - dat = self.__dabest_obj._plot_data - xvar = self.__dabest_obj._xvar - yvar = self.__dabest_obj._yvar - - # compute the variances of each control group and each test group - control_var=[] - test_var=[] - grouped_data = {name: group[yvar].copy() for name, group in dat.groupby(xvar, observed=False)} - for j, current_tuple in enumerate(idx): - cname = current_tuple[0] - control = grouped_data[cname] - control_var.append(np.var(control, ddof=1)) - - tname = current_tuple[1] - test = grouped_data[tname] - test_var.append(np.var(test, ddof=1)) - self.__control_var = np.array(control_var) - self.__test_var = np.array(test_var) - - # Compute pooled group variances for each pair of experiment groups - # based on the raw data - self.__group_var = ci2g.calculate_group_var(self.__control_var, - self.__control_N, - self.__test_var, - self.__test_N) - - self.__bootstraps_variance = ci2g.calculate_bootstraps_var(self.__bootstraps) - - # Compute the weighted average mean differences of the bootstrap data - # using the pooled group variances of the raw data as the inverse of - # weights - self.__bootstraps_weighted_delta = ci2g.calculate_weighted_delta( - self.__bootstraps_variance, - self.__bootstraps) - - # Compute the weighted average mean difference based on the raw data - self.__difference = es.weighted_delta(np.array(self.__effsizedf["difference"]), - self.__bootstraps_variance) - - sorted_weighted_deltas = npsort(self.__bootstraps_weighted_delta) - - - self.__bias_correction = ci2g.compute_meandiff_bias_correction( - self.__bootstraps_weighted_delta, self.__difference) - - self.__jackknives = np.array(ci1g.compute_1group_jackknife( - self.__bootstraps_weighted_delta, - np.mean)) - - self.__acceleration_value = ci2g._calc_accel(self.__jackknives) - - # Compute BCa intervals. - bca_idx_low, bca_idx_high = ci2g.compute_interval_limits( - self.__bias_correction, self.__acceleration_value, - self.__resamples, ci) - - self.__bca_interval_idx = (bca_idx_low, bca_idx_high) - - if ~isnan(bca_idx_low) and ~isnan(bca_idx_high): - self.__bca_low = sorted_weighted_deltas[bca_idx_low] - self.__bca_high = sorted_weighted_deltas[bca_idx_high] - - err1 = "The $lim_type limit of the interval" - err2 = "was in the $loc 10 values." - err3 = "The result should be considered unstable." - err_temp = Template(" ".join([err1, err2, err3])) - - if bca_idx_low <= 10: - warnings.warn(err_temp.substitute(lim_type="lower", - loc="bottom"), - stacklevel=1) - - if bca_idx_high >= self.__resamples-9: - warnings.warn(err_temp.substitute(lim_type="upper", - loc="top"), - stacklevel=1) - - else: - err1 = "The $lim_type limit of the BCa interval cannot be computed." - err2 = "It is set to the effect size itself." - err3 = "All bootstrap values were likely all the same." - err_temp = Template(" ".join([err1, err2, err3])) - - if isnan(bca_idx_low): - self.__bca_low = self.__difference - warnings.warn(err_temp.substitute(lim_type="lower"), - stacklevel=0) - - if isnan(bca_idx_high): - self.__bca_high = self.__difference - warnings.warn(err_temp.substitute(lim_type="upper"), - stacklevel=0) - - # Compute percentile intervals. - pct_idx_low = int((self.__alpha/2) * self.__resamples) - pct_idx_high = int((1-(self.__alpha/2)) * self.__resamples) - - self.__pct_interval_idx = (pct_idx_low, pct_idx_high) - self.__pct_low = sorted_weighted_deltas[pct_idx_low] - self.__pct_high = sorted_weighted_deltas[pct_idx_high] - - - - def __permutation_test(self): - """ - Perform a permutation test and obtain the permutation p-value - based on the permutation data. - """ - self.__permutations = np.array(self.__effsizedf["permutations"]) - self.__permutations_var = np.array(self.__effsizedf["permutations_var"]) - - THRESHOLD = np.abs(self.__difference) - - all_num = [] - all_denom = [] - - groups = len(self.__permutations) - for i in range(0, len(self.__permutations[0])): - weight = [1/self.__permutations_var[j][i] for j in range(0, groups)] - all_num.append(np.sum([weight[j]*self.__permutations[j][i] for j in range(0, groups)])) - all_denom.append(np.sum(weight)) - - output=[] - for i in range(0, len(all_num)): - output.append(all_num[i]/all_denom[i]) - - self.__permutations_weighted_delta = np.array(output) - - count = sum(np.abs(self.__permutations_weighted_delta)>THRESHOLD) - self.__pvalue_permutation = count/self.__permutation_count - - - - def __repr__(self, header=True, sigfig=3): - from .misc_tools import print_greeting - - is_paired = self.__dabest_obj.is_paired - - PAIRED_STATUS = {'baseline' : 'paired', - 'sequential' : 'paired', - 'None' : 'unpaired' - } - - first_line = {"paired_status": PAIRED_STATUS[str(is_paired)]} - - - out1 = "The weighted-average {paired_status} mean differences ".format(**first_line) - - base_string_fmt = "{:." + str(sigfig) + "}" - if "." in str(self.__ci): - ci_width = base_string_fmt.format(self.__ci) - else: - ci_width = str(self.__ci) - - ci_out = {"es" : base_string_fmt.format(self.__difference), - "ci" : ci_width, - "bca_low" : base_string_fmt.format(self.__bca_low), - "bca_high" : base_string_fmt.format(self.__bca_high)} - - out2 = "is {es} [{ci}%CI {bca_low}, {bca_high}].".format(**ci_out) - out = out1 + out2 - - if header is True: - out = print_greeting() + "\n" + "\n" + out - - - pval_rounded = base_string_fmt.format(self.pvalue_permutation) - - - p1 = "The p-value of the two-sided permutation t-test is {}, ".format(pval_rounded) - p2 = "calculated for legacy purposes only. " - pvalue = p1 + p2 - - - bs1 = "{} bootstrap samples were taken; ".format(self.__resamples) - bs2 = "the confidence interval is bias-corrected and accelerated." - bs = bs1 + bs2 - - pval_def1 = "Any p-value reported is the probability of observing the" + \ - "effect size (or greater),\nassuming the null hypothesis of " + \ - "zero difference is true." - pval_def2 = "\nFor each p-value, 5000 reshuffles of the " + \ - "control and test labels were performed." - pval_def = pval_def1 + pval_def2 - - - return "{}\n{}\n\n{}\n{}".format(out, pvalue, bs, pval_def) - - - def to_dict(self): - """ - Returns all attributes of the `dabest.MiniMetaDelta` object as a - dictionary. - """ - # Only get public (user-facing) attributes. - attrs = [a for a in dir(self) - if not a.startswith(("_", "to_dict", "results"))] - out = {} - for a in attrs: - out[a] = getattr(self, a) - return out - - - def __compute_results(self): - # With some inspiration from @jungyangliao - """ - Returns all attributes of the `dabest.MiniMetaDelta` object as a - DataFrame. - """ - mini_meta_delta_results_df = pd.Series(self.to_dict()).to_frame().T - column_index = ['control', 'test', 'control_N', 'test_N', 'control_var', 'test_var', 'group_var', - 'difference', 'ci', 'bca_low', 'bca_high', 'bca_interval_idx', - 'pct_low', 'pct_high', 'pct_interval_idx', 'bootstraps', 'bootstraps_weighted_delta', - 'permutations', 'permutations_var', 'permutations_weighted_delta', 'pvalue_permutation', - 'permutation_count', 'bias_correction', 'jackknives'] - mini_meta_delta_results_df = mini_meta_delta_results_df.reindex(columns=column_index) - mini_meta_delta_results_df.rename(columns={'bootstraps': 'bootstraps_deltas'}, inplace=True) - - self.__results = mini_meta_delta_results_df - return self.__results - - - @property - def ci(self): - """ - Returns the width of the confidence interval, in percent. - """ - return self.__ci - - - @property - def alpha(self): - """ - Returns the significance level of the statistical test as a float - between 0 and 1. - """ - return self.__alpha - - - @property - def bias_correction(self): - return self.__bias_correction - - - @property - def bootstraps(self): - ''' - Return the bootstrapped differences from all the experiment groups. - ''' - return self.__bootstraps - - - @property - def jackknives(self): - return self.__jackknives - - - @property - def acceleration_value(self): - return self.__acceleration_value - - - @property - def bca_low(self): - """ - The bias-corrected and accelerated confidence interval lower limit. - """ - return self.__bca_low - - - @property - def bca_high(self): - """ - The bias-corrected and accelerated confidence interval upper limit. - """ - return self.__bca_high - - - @property - def bca_interval_idx(self): - return self.__bca_interval_idx - - - @property - def control(self): - ''' - Return the names of the control groups from all the experiment - groups in order. - ''' - return self.__control - - - @property - def test(self): - ''' - Return the names of the test groups from all the experiment - groups in order. - ''' - return self.__test - - @property - def control_N(self): - ''' - Return the sizes of the control groups from all the experiment - groups in order. - ''' - return self.__control_N - - - @property - def test_N(self): - ''' - Return the sizes of the test groups from all the experiment - groups in order. - ''' - return self.__test_N - - - @property - def control_var(self): - ''' - Return the estimated population variances of the control groups - from all the experiment groups in order. Here the population - variance is estimated from the sample variance. - ''' - return self.__control_var - - - @property - def test_var(self): - ''' - Return the estimated population variances of the control groups - from all the experiment groups in order. Here the population - variance is estimated from the sample variance. - ''' - return self.__test_var - - - @property - def group_var(self): - ''' - Return the pooled group variances of all the experiment groups - in order. - ''' - return self.__group_var - - @property - def bootstraps_var(self): - ''' - Return the variances of each bootstrapped mean difference distribution - in order. - ''' - return self.__bootstraps_variance - - - @property - def bootstraps_weighted_delta(self): - ''' - Return the weighted-average mean differences calculated from the bootstrapped - deltas and weights across the experiment groups, where the weights are - the inverse of the pooled group variances. - ''' - return self.__bootstraps_weighted_delta - - - @property - def difference(self): - ''' - Return the weighted-average delta calculated from the raw data. - ''' - return self.__difference - - - @property - def pct_interval_idx (self): - return self.__pct_interval_idx - - - @property - def pct_low(self): - """ - The percentile confidence interval lower limit. - """ - return self.__pct_low - - - @property - def pct_high(self): - """ - The percentile confidence interval lower limit. - """ - return self.__pct_high - - - @property - def pvalue_permutation(self): - try: - return self.__pvalue_permutation - except AttributeError: - self.__permutation_test() - return self.__pvalue_permutation - - - @property - def permutation_count(self): - """ - The number of permuations taken. - """ - return self.__permutation_count - - - @property - def permutations(self): - ''' - Return the mean differences of permutations obtained during - the permutation test for each experiment group. - ''' - try: - return self.__permutations - except AttributeError: - self.__permutation_test() - return self.__permutations - - - @property - def permutations_var(self): - ''' - Return the pooled group variances of permutations obtained during - the permutation test for each experiment group. - ''' - try: - return self.__permutations_var - except AttributeError: - self.__permutation_test() - return self.__permutations_var - - - @property - def permutations_weighted_delta(self): - ''' - Return the weighted-average deltas of permutations obtained - during the permutation test. - ''' - try: - return self.__permutations_weighted_delta - except AttributeError: - self.__permutation_test() - return self.__permutations_weighted_delta - - @property - def results(self): - """ - Return the results of the mini-meta analysis. - """ - try: - return self.__results - except AttributeError: - self.__compute_results() - return self.__results diff --git a/dabest/_effsize_objects.py b/dabest/_effsize_objects.py deleted file mode 100644 index fce3e822..00000000 --- a/dabest/_effsize_objects.py +++ /dev/null @@ -1,1832 +0,0 @@ -"""The auxiliary classes involved in the computations of bootstrapped effect sizes.""" - -# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/effsize_objects.ipynb. - -# %% auto #0 -__all__ = ['TwoGroupsEffectSize', 'EffectSizeDataFrame', 'PermutationTest'] - -# %% ../nbs/API/effsize_objects.ipynb #430d4ea8 -import pandas as pd -import lqrt -from scipy.stats import norm -import numpy as np -from scipy.special import binom as binomcoeff # devMJBL -from scipy.stats import binom # devMJBL -from scipy.integrate import fixed_quad # devMJBL -from numpy import arange, mean # devMJBL -from numpy import array, isnan, isinf, repeat, random, isin, abs, var -from numpy import sort as npsort -from numpy import nan as npnan -from numpy.random import PCG64, RandomState -from statsmodels.stats.contingency_tables import mcnemar -import warnings -from string import Template -import scipy.stats as spstats - -# %% ../nbs/API/effsize_objects.ipynb #9d988bdd -class TwoGroupsEffectSize(object): - - """ - A class to compute and store the results of bootstrapped - mean differences between two groups. - - Compute the effect size between two groups. - - Parameters - ---------- - control : array-like - test : array-like - These should be numerical iterables. - effect_size : string. - Any one of the following are accepted inputs: - 'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta' - is_paired : string, default None - resamples : int, default 5000 - The number of bootstrap resamples to be taken for the calculation - of the confidence interval limits. - permutation_count : int, default 5000 - The number of permutations (reshuffles) to perform for the - computation of the permutation p-value - ci : float, default 95 - The confidence interval width. The default of 95 produces 95% - confidence intervals. - random_seed : int, default 12345 - `random_seed` is used to seed the random number generator during - bootstrap resampling. This ensures that the confidence intervals - reported are replicable. - ps_adjust : boolean, default False. - If True, adjust calculated p-value according to Phipson & Smyth (2010) - # https://doi.org/10.2202/1544-6115.1585 - - - Returns - ------- - A :py:class:`TwoGroupEffectSize` object: - `difference` : float - The effect size of the difference between the control and the test. - `effect_size` : string - The type of effect size reported. - `is_paired` : string - The type of repeated-measures experiment. - `ci` : float - Returns the width of the confidence interval, in percent. - `alpha` : float - Returns the significance level of the statistical test as a float between 0 and 1. - `resamples` : int - The number of resamples performed during the bootstrap procedure. - `bootstraps` : numpy ndarray - The generated bootstraps of the effect size. - `random_seed` : int - The number used to initialise the numpy random seed generator, ie.`seed_value` from `numpy.random.seed(seed_value)` is returned. - `bca_low, bca_high` : float - The bias-corrected and accelerated confidence interval lower limit and upper limits, respectively. - `pct_low, pct_high` : float - The percentile confidence interval lower limit and upper limits, respectively. - """ - - def __init__( - self, - control, - test, - effect_size, - proportional=False, - is_paired=None, - ci=95, - resamples=5000, - permutation_count=5000, - random_seed=12345, - ps_adjust=False, - ): - from ._stats_tools import confint_2group_diff as ci2g - from ._stats_tools import effsize as es - - self.__EFFECT_SIZE_DICT = { - "mean_diff": "mean difference", - "median_diff": "median difference", - "cohens_d": "Cohen's d", - "cohens_h": "Cohen's h", - "hedges_g": "Hedges' g", - "cliffs_delta": "Cliff's delta", - } - - self.__is_paired = is_paired - self.__resamples = resamples - self.__effect_size = effect_size - self.__random_seed = random_seed - self.__ci = ci - self.__is_proportional = proportional - self.__ps_adjust = ps_adjust - self._check_errors(control, test) - - # Convert to numpy arrays for speed. - # NaNs are automatically dropped. - control = array(control) - test = array(test) - self.__control = control[~isnan(control)] - self.__test = test[~isnan(test)] - self.__permutation_count = permutation_count - - self.__alpha = ci2g._compute_alpha_from_ci(self.__ci) - - self.__difference = es.two_group_difference( - self.__control, self.__test, self.__is_paired, self.__effect_size - ) - - self.__jackknives = ci2g.compute_meandiff_jackknife( - self.__control, self.__test, self.__is_paired, self.__effect_size - ) - - self.__acceleration_value = ci2g._calc_accel(self.__jackknives) - - bootstraps = ci2g.compute_bootstrapped_diff( - self.__control, - self.__test, - self.__is_paired, - self.__effect_size, - self.__resamples, - self.__random_seed, - ) - self.__bootstraps = bootstraps - - sorted_bootstraps = npsort(self.__bootstraps) - # Added in v0.2.6. - # Raises a UserWarning if there are any infiinities in the bootstraps. - num_infinities = len(self.__bootstraps[isinf(self.__bootstraps)]) - - if num_infinities > 0: - warn_msg = ( - "There are {} bootstrap(s) that are not defined. " - "This is likely due to smaple sample sizes. " - "The values in a bootstrap for a group will be more likely " - "to be all equal, with a resulting variance of zero. " - "The computation of Cohen's d and Hedges' g thus " - "involved a division by zero. " - ) - warnings.warn(warn_msg.format(num_infinities), category=UserWarning) - - self.__bias_correction = ci2g.compute_meandiff_bias_correction( - self.__bootstraps, self.__difference - ) - - self._compute_bca_intervals(sorted_bootstraps) - - # Compute percentile intervals. - pct_idx_low = int((self.__alpha / 2) * self.__resamples) - pct_idx_high = int((1 - (self.__alpha / 2)) * self.__resamples) - - self.__pct_interval_idx = (pct_idx_low, pct_idx_high) - self.__pct_low = sorted_bootstraps[pct_idx_low] - self.__pct_high = sorted_bootstraps[pct_idx_high] - - self._get_bootstrap_baseline_ec() - - self._perform_statistical_test() - - def __repr__(self, show_resample_count=True, define_pval=True, sigfig=3): - RM_STATUS = { - "baseline": "for repeated measures against baseline \n", - "sequential": "for the sequential design of repeated-measures experiment \n", - "None": "", - } - - PAIRED_STATUS = { - "baseline": "paired", - "sequential": "paired", - "None": "unpaired", - } - - first_line = { - "rm_status": RM_STATUS[str(self.__is_paired)], - "es": self.__EFFECT_SIZE_DICT[self.__effect_size], - "paired_status": PAIRED_STATUS[str(self.__is_paired)], - } - - out1 = "The {paired_status} {es} {rm_status}".format(**first_line) - - base_string_fmt = "{:." + str(sigfig) + "}" - if "." in str(self.__ci): - ci_width = base_string_fmt.format(self.__ci) - else: - ci_width = str(self.__ci) - - ci_out = { - "es": base_string_fmt.format(self.__difference), - "ci": ci_width, - "bca_low": base_string_fmt.format(self.__bca_low), - "bca_high": base_string_fmt.format(self.__bca_high), - } - - out2 = "is {es} [{ci}%CI {bca_low}, {bca_high}].".format(**ci_out) - out = out1 + out2 - - pval_rounded = base_string_fmt.format(self.pvalue_permutation) - - p1 = "The p-value of the two-sided permutation t-test is {}, ".format( - pval_rounded - ) - p2 = "calculated for legacy purposes only. " - pvalue = p1 + p2 - - bs1 = "{} bootstrap samples were taken; ".format(self.__resamples) - bs2 = "the confidence interval is bias-corrected and accelerated." - bs = bs1 + bs2 - - pval_def1 = ( - "Any p-value reported is the probability of observing the" - + "effect size (or greater),\nassuming the null hypothesis of " - + "zero difference is true." - ) - pval_def2 = ( - "\nFor each p-value, 5000 reshuffles of the " - + "control and test labels were performed." - ) - pval_def = pval_def1 + pval_def2 - - if show_resample_count and define_pval: - return "{}\n{}\n\n{}\n{}".format(out, pvalue, bs, pval_def) - elif not show_resample_count and define_pval: - return "{}\n{}\n\n{}".format(out, pvalue, pval_def) - elif show_resample_count and not define_pval: - return "{}\n{}\n\n{}".format(out, pvalue, bs) - else: - return "{}\n{}".format(out, pvalue) - - def _check_errors(self, control, test): - ''' - Function to check configuration errors for the given control and test data. - ''' - kosher_es = [a for a in self.__EFFECT_SIZE_DICT.keys()] - if self.__effect_size not in kosher_es: - err1 = "The effect size '{}'".format(self.__effect_size) - err2 = "is not one of {}".format(kosher_es) - raise ValueError(" ".join([err1, err2])) - - if self.__effect_size == "cliffs_delta" and self.__is_paired: - err1 = "`paired` is not None; therefore Cliff's delta is not defined." - raise ValueError(err1) - - if self.__is_proportional and self.__effect_size not in ["mean_diff", "cohens_h"]: - err1 = "`is_proportional` is True; therefore effect size other than mean_diff and cohens_h is not defined." - raise ValueError(err1) - - if self.__is_proportional and ( - isin(control, [0, 1]).all() == False or isin(test, [0, 1]).all() == False - ): - err1 = ( - "`is_proportional` is True; Only accept binary data consisting of 0 and 1." - ) - raise ValueError(err1) - - def _compute_bca_intervals(self, sorted_bootstraps): - ''' - Function to compute the bca intervals given the sorted bootstraps. - ''' - from ._stats_tools import confint_2group_diff as ci2g - - # Compute BCa intervals. - bca_idx_low, bca_idx_high = ci2g.compute_interval_limits( - self.__bias_correction, - self.__acceleration_value, - self.__resamples, - self.__ci, - ) - - self.__bca_interval_idx = (bca_idx_low, bca_idx_high) - - if ~isnan(bca_idx_low) and ~isnan(bca_idx_high): - self.__bca_low = sorted_bootstraps[bca_idx_low] - self.__bca_high = sorted_bootstraps[bca_idx_high] - - err1 = "The $lim_type limit of the interval" - err2 = "was in the $loc 10 values." - err3 = "The result should be considered unstable." - err_temp = Template(" ".join([err1, err2, err3])) - - if bca_idx_low <= 10: - warnings.warn( - err_temp.substitute(lim_type="lower", loc="bottom"), stacklevel=1 - ) - - if bca_idx_high >= self.__resamples - 9: - warnings.warn( - err_temp.substitute(lim_type="upper", loc="top"), stacklevel=1 - ) - - else: - err1 = "The $lim_type limit of the BCa interval cannot be computed." - err2 = "It is set to the effect size itself." - err3 = "All bootstrap values were likely all the same." - err_temp = Template(" ".join([err1, err2, err3])) - - if isnan(bca_idx_low): - self.__bca_low = self.__difference - warnings.warn(err_temp.substitute(lim_type="lower"), stacklevel=0) - - if isnan(bca_idx_high): - self.__bca_high = self.__difference - warnings.warn(err_temp.substitute(lim_type="upper"), stacklevel=0) - - def _perform_statistical_test(self): - ''' - Function to complete the statistical tests - ''' - from ._stats_tools import effsize as es - - # Perform statistical tests. - self.__PermutationTest_result = PermutationTest( - self.__control, - self.__test, - self.__effect_size, - self.__is_paired, - self.__permutation_count, - ps_adjust = self.__ps_adjust, - ) - - if self.__is_paired and not self.__is_proportional: - # Wilcoxon, a non-parametric version of the paired T-test. - try: - wilcoxon = spstats.wilcoxon(self.__control, self.__test) - self.__pvalue_wilcoxon = wilcoxon.pvalue - self.__statistic_wilcoxon = wilcoxon.statistic - except ValueError as e: - warnings.warn("Wilcoxon test could not be performed. This might be due " - "to no variability in the difference of the paired groups. \n" - "Error: {}\n" - "For detailed information, please refer to https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wilcoxon.html " - .format(e)) - - if self.__effect_size != "median_diff": - # Paired Student's t-test. - paired_t = spstats.ttest_rel( - self.__control, self.__test, nan_policy="omit" - ) - self.__pvalue_paired_students_t = paired_t.pvalue - self.__statistic_paired_students_t = paired_t.statistic - - elif self.__is_paired and self.__is_proportional: - # for binary paired data, use McNemar's test - # References: - # https://en.wikipedia.org/wiki/McNemar%27s_test - - x1 = np.sum((self.__control == 0) & (self.__test == 0)) - x2 = np.sum((self.__control == 0) & (self.__test == 1)) - x3 = np.sum((self.__control == 1) & (self.__test == 0)) - x4 = np.sum((self.__control == 1) & (self.__test == 1)) - table = np.array([[x1, x2], [x3, x4]]) - _mcnemar = mcnemar(table, exact=True, correction=True) - self.__pvalue_mcnemar = _mcnemar.pvalue - self.__statistic_mcnemar = _mcnemar.statistic - - elif self.__is_proportional: - # The Cohen's h calculation is for binary categorical data - try: - self.__proportional_difference = es.cohens_h( - self.__control, self.__test - ) - except ValueError as e: - warnings.warn(f"Calculation of Cohen's h failed. This method is applicable " - f"only for binary data (0's and 1's). Details: {e}") - - elif self.__effect_size == "cliffs_delta": - # Let's go with Brunner-Munzel! - brunner_munzel = spstats.brunnermunzel( - self.__control, self.__test, nan_policy="omit" - ) - self.__pvalue_brunner_munzel = brunner_munzel.pvalue - self.__statistic_brunner_munzel = brunner_munzel.statistic - - elif self.__effect_size == "median_diff": - # According to scipy's documentation of the function, - # "The Kruskal-Wallis H-test tests the null hypothesis - # that the population median of all of the groups are equal." - kruskal = spstats.kruskal(self.__control, self.__test, nan_policy="omit") - self.__pvalue_kruskal = kruskal.pvalue - self.__statistic_kruskal = kruskal.statistic - - else: # for mean difference, Cohen's d, and Hedges' g. - # Welch's t-test, assumes normality of distributions, - # but does not assume equal variances. - welch = spstats.ttest_ind( - self.__control, self.__test, equal_var=False, nan_policy="omit" - ) - self.__pvalue_welch = welch.pvalue - self.__statistic_welch = welch.statistic - - # Student's t-test, assumes normality of distributions, - # as well as assumption of equal variances. - students_t = spstats.ttest_ind( - self.__control, self.__test, equal_var=True, nan_policy="omit" - ) - self.__pvalue_students_t = students_t.pvalue - self.__statistic_students_t = students_t.statistic - - # Mann-Whitney test: Non parametric, - # does not assume normality of distributions - try: - mann_whitney = spstats.mannwhitneyu( - self.__control, self.__test, alternative="two-sided" - ) - self.__pvalue_mann_whitney = mann_whitney.pvalue - self.__statistic_mann_whitney = mann_whitney.statistic - except ValueError as e: - warnings.warn("Mann-Whitney test could not be performed. This might be due " - "to identical rank values in both control and test groups. " - "Details: {}".format(e)) - - standardized_es = es.cohens_d(self.__control, self.__test, is_paired=None) - - - def to_dict(self): - """ - Returns the attributes of the `dabest.TwoGroupEffectSize` object as a - dictionary. - """ - # Only get public (user-facing) attributes. - attrs = [a for a in dir(self) if not a.startswith(("_", "to_dict"))] - out = {} - for a in attrs: - out[a] = getattr(self, a) - return out - - def _get_bootstrap_baseline_ec(self): - from ._stats_tools import confint_2group_diff as ci2g - from ._stats_tools import effsize as es - - # Cannot use self.__is_paired because it's for baseline curve - is_paired = None - - difference = es.two_group_difference( - self.__control, self.__control, is_paired, self.__effect_size - ) - self.__bec_difference = difference - - jackknives = ci2g.compute_meandiff_jackknife( - self.__control, self.__control, is_paired, self.__effect_size - ) - - acceleration_value = ci2g._calc_accel(jackknives) - - bootstraps = ci2g.compute_bootstrapped_diff( - self.__control, - self.__control, - is_paired, - self.__effect_size, - self.__resamples, - self.__random_seed, - ) - self.__bootstraps_baseline_ec = bootstraps - - sorted_bootstraps = npsort(self.__bootstraps_baseline_ec) - # We don't have to consider infinities in bootstrap_baseline_ec - - bias_correction = ci2g.compute_meandiff_bias_correction( - self.__bootstraps_baseline_ec, difference - ) - - # Compute BCa intervals. - bca_idx_low, bca_idx_high = ci2g.compute_interval_limits( - bias_correction, - acceleration_value, - self.__resamples, - self.__ci, - ) - - self.__bec_bca_interval_idx = (bca_idx_low, bca_idx_high) - - if ~isnan(bca_idx_low) and ~isnan(bca_idx_high): - self.__bec_bca_low = sorted_bootstraps[bca_idx_low] - self.__bec_bca_high = sorted_bootstraps[bca_idx_high] - - err1 = "The $lim_type limit of the interval" - err2 = "was in the $loc 10 values." - err3 = "The result for baseline curve should be considered unstable." - err_temp = Template(" ".join([err1, err2, err3])) - - if bca_idx_low <= 10: - warnings.warn( - err_temp.substitute(lim_type="lower", loc="bottom"), stacklevel=1 - ) - - if bca_idx_high >= self.__resamples - 9: - warnings.warn( - err_temp.substitute(lim_type="upper", loc="top"), stacklevel=1 - ) - - else: - err1 = "The $lim_type limit of the BCa interval of baseline curve cannot be computed." - err2 = "It is set to the effect size itself." - err3 = "All bootstrap values were likely all the same." - err_temp = Template(" ".join([err1, err2, err3])) - - if isnan(bca_idx_low): - self.__bec_bca_low = difference - warnings.warn(err_temp.substitute(lim_type="lower"), stacklevel=0) - - if isnan(bca_idx_high): - self.__bec_bca_high = difference - warnings.warn(err_temp.substitute(lim_type="upper"), stacklevel=0) - - # Compute percentile intervals. - pct_idx_low = int((self.__alpha / 2) * self.__resamples) - pct_idx_high = int((1 - (self.__alpha / 2)) * self.__resamples) - - self.__bec_pct_interval_idx = (pct_idx_low, pct_idx_high) - self.__bec_pct_low = sorted_bootstraps[pct_idx_low] - self.__bec_pct_high = sorted_bootstraps[pct_idx_high] - - @property - def difference(self): - """ - Returns the difference between the control and the test. - """ - return self.__difference - - @property - def effect_size(self): - """ - Returns the type of effect size reported. - """ - return self.__EFFECT_SIZE_DICT[self.__effect_size] - - @property - def is_paired(self): - return self.__is_paired - - @property - def is_proportional(self): - return self.__is_proportional - - @property - def ci(self): - """ - Returns the width of the confidence interval, in percent. - """ - return self.__ci - - @property - def alpha(self): - """ - Returns the significance level of the statistical test as a float - between 0 and 1. - """ - return self.__alpha - - @property - def resamples(self): - """ - The number of resamples performed during the bootstrap procedure. - """ - return self.__resamples - - @property - def bootstraps(self): - """ - The generated bootstraps of the effect size. - """ - return self.__bootstraps - - @property - def random_seed(self): - """ - The number used to initialise the numpy random seed generator, ie. - `seed_value` from `numpy.random.seed(seed_value)` is returned. - """ - return self.__random_seed - - @property - def bca_interval_idx(self): - return self.__bca_interval_idx - - @property - def bca_low(self): - """ - The bias-corrected and accelerated confidence interval lower limit. - """ - return self.__bca_low - - @property - def bca_high(self): - """ - The bias-corrected and accelerated confidence interval upper limit. - """ - return self.__bca_high - - @property - def pct_interval_idx(self): - return self.__pct_interval_idx - - @property - def pct_low(self): - """ - The percentile confidence interval lower limit. - """ - return self.__pct_low - - @property - def pct_high(self): - """ - The percentile confidence interval lower limit. - """ - return self.__pct_high - - @property - def pvalue_brunner_munzel(self): - try: - return self.__pvalue_brunner_munzel - except AttributeError: - return npnan - - @property - def statistic_brunner_munzel(self): - try: - return self.__statistic_brunner_munzel - except AttributeError: - return npnan - - @property - def pvalue_wilcoxon(self): - try: - return self.__pvalue_wilcoxon - except AttributeError: - return npnan - - @property - def statistic_wilcoxon(self): - try: - return self.__statistic_wilcoxon - except AttributeError: - return npnan - - @property - def pvalue_mcnemar(self): - try: - return self.__pvalue_mcnemar - except AttributeError: - return npnan - - @property - def statistic_mcnemar(self): - try: - return self.__statistic_mcnemar - except AttributeError: - return npnan - - @property - def pvalue_paired_students_t(self): - try: - return self.__pvalue_paired_students_t - except AttributeError: - return npnan - - @property - def statistic_paired_students_t(self): - try: - return self.__statistic_paired_students_t - except AttributeError: - return npnan - - @property - def pvalue_kruskal(self): - try: - return self.__pvalue_kruskal - except AttributeError: - return npnan - - @property - def statistic_kruskal(self): - try: - return self.__statistic_kruskal - except AttributeError: - return npnan - - @property - def pvalue_welch(self): - try: - return self.__pvalue_welch - except AttributeError: - return npnan - - @property - def statistic_welch(self): - try: - return self.__statistic_welch - except AttributeError: - return npnan - - @property - def pvalue_students_t(self): - try: - return self.__pvalue_students_t - except AttributeError: - return npnan - - @property - def statistic_students_t(self): - try: - return self.__statistic_students_t - except AttributeError: - return npnan - - @property - def pvalue_mann_whitney(self): - try: - return self.__pvalue_mann_whitney - except AttributeError: - return npnan - - @property - def statistic_mann_whitney(self): - try: - return self.__statistic_mann_whitney - except AttributeError: - return npnan - - @property - def pvalue_permutation(self): - """ - p value of permutation test - """ - return self.__PermutationTest_result.pvalue - - @property - def permutation_count(self): - """ - The number of permutations taken. - """ - return self.__PermutationTest_result.permutation_count - - @property - def permutations(self): - return self.__PermutationTest_result.permutations - - @property - def permutations_var(self): - return self.__PermutationTest_result.permutations_var - - @property - def proportional_difference(self): - try: - return self.__proportional_difference - except AttributeError: - return npnan - - @property - def bec_difference(self): - return self.__bec_difference - - @property - def bec_bootstraps(self): - """ - The generated baseline error bootstraps. - """ - return self.__bootstraps_baseline_ec - - @property - def bec_bca_interval_idx(self): - return self.__bec_bca_interval_idx - - @property - def bec_bca_low(self): - """ - The bias-corrected and accelerated confidence interval lower limit for baseline error. - """ - return self.__bec_bca_low - - @property - def bec_bca_high(self): - """ - The bias-corrected and accelerated confidence interval upper limit for baseline error. - """ - return self.__bec_bca_high - - @property - def bec_pct_interval_idx(self): - return self.__bec_pct_interval_idx - - @property - def bec_pct_low(self): - """ - The percentile confidence interval lower limit for baseline error. - """ - return self.__bec_pct_low - - @property - def bec_pct_high(self): - """ - The percentile confidence interval lower limit for baseline error. - """ - return self.__bec_pct_high - - -# %% ../nbs/API/effsize_objects.ipynb #024b1d00 -class EffectSizeDataFrame(object): - """A class that generates and stores the results of bootstrapped effect - sizes for several comparisons.""" - - def __init__( - self, - dabest, - effect_size, - is_paired, - ci=95, - proportional=False, - resamples=5000, - permutation_count=5000, - random_seed=12345, - x1_level=None, - x2=None, - delta2=False, - experiment_label=None, - mini_meta=False, - ps_adjust=False, - ): - """ - Parses the data from a Dabest object, enabling plotting and printing - capability for the effect size of interest. - """ - - self.__dabest_obj = dabest - self.__effect_size = effect_size - self.__is_paired = is_paired - self.__ci = ci - self.__resamples = resamples - self.__permutation_count = permutation_count - self.__random_seed = random_seed - self.__is_proportional = proportional - self.__x1_level = x1_level - self.__experiment_label = experiment_label - self.__x2 = x2 - self.__delta2 = delta2 - self.__is_mini_meta = mini_meta - self.__ps_adjust = ps_adjust - - def __pre_calc(self): - from .misc_tools import print_greeting, get_varname - from ._stats_tools import confint_2group_diff as ci2g - from ._delta_objects import MiniMetaDelta, DeltaDelta - - idx = self.__dabest_obj.idx - dat = self.__dabest_obj._plot_data - xvar = self.__dabest_obj._xvar - yvar = self.__dabest_obj._yvar - - out = [] - reprs = [] - - grouped_data = {name: group[yvar].copy() for name, group in dat.groupby(xvar, observed=False)} - if self.__delta2: - mixed_data = [] - for j, current_tuple in enumerate(idx): - if self.__is_paired != "sequential": - cname = current_tuple[0] - control = grouped_data[cname] - - for ix, tname in enumerate(current_tuple[1:]): - if self.__is_paired == "sequential": - cname = current_tuple[ix] - control = grouped_data[cname] - test = grouped_data[tname] - mixed_data.append(control) - mixed_data.append(test) - bootstraps_delta_delta = ci2g.compute_delta2_bootstrapped_diff( - mixed_data[0], - mixed_data[1], - mixed_data[2], - mixed_data[3], - self.__is_paired, - self.__resamples, - self.__random_seed, - self.__is_proportional, - ) - - for j, current_tuple in enumerate(idx): - if self.__is_paired != "sequential": - cname = current_tuple[0] - control = grouped_data[cname] - - for ix, tname in enumerate(current_tuple[1:]): - if self.__is_paired == "sequential": - cname = current_tuple[ix] - control = grouped_data[cname] - test = grouped_data[tname] - result = TwoGroupsEffectSize( - control, - test, - self.__effect_size, - self.__is_proportional, - self.__is_paired, - self.__ci, - self.__resamples, - self.__permutation_count, - self.__random_seed, - self.__ps_adjust - ) - r_dict = result.to_dict() - r_dict["control"] = cname - r_dict["test"] = tname - r_dict["control_N"] = int(len(control)) - r_dict["test_N"] = int(len(test)) - out.append(r_dict) - if j == len(idx) - 1 and ix == len(current_tuple) - 2: - if self.__delta2 and self.__effect_size in ["mean_diff", "hedges_g"]: - resamp_count = False - def_pval = False - elif self.__is_mini_meta and self.__effect_size == "mean_diff": - resamp_count = False - def_pval = False - else: - resamp_count = True - def_pval = True - else: - resamp_count = False - def_pval = False - - text_repr = result.__repr__( - show_resample_count=resamp_count, define_pval=def_pval - ) - - to_replace = "between {} and {} is".format(cname, tname) - text_repr = text_repr.replace("is", to_replace, 1) - - reprs.append(text_repr) - - self.__for_print = "\n\n".join(reprs) - - out_ = pd.DataFrame(out) - - columns_in_order = [ - "control", - "test", - "control_N", - "test_N", - "effect_size", - "is_paired", - "difference", - "ci", - "bca_low", - "bca_high", - "bca_interval_idx", - "pct_low", - "pct_high", - "pct_interval_idx", - "bootstraps", - "resamples", - "random_seed", - "permutations", - "pvalue_permutation", - "permutation_count", - "permutations_var", - "pvalue_welch", - "statistic_welch", - "pvalue_students_t", - "statistic_students_t", - "pvalue_mann_whitney", - "statistic_mann_whitney", - "pvalue_brunner_munzel", - "statistic_brunner_munzel", - "pvalue_wilcoxon", - "statistic_wilcoxon", - "pvalue_mcnemar", - "statistic_mcnemar", - "pvalue_paired_students_t", - "statistic_paired_students_t", - "pvalue_kruskal", - "statistic_kruskal", - "proportional_difference", - "bec_difference", - "bec_bootstraps", - "bec_bca_interval_idx", - "bec_bca_low", - "bec_bca_high", - "bec_pct_interval_idx", - "bec_pct_low", - "bec_pct_high", - ] - self.__results = out_.reindex(columns=columns_in_order) - self.__results.dropna(axis="columns", how="all", inplace=True) - - # Add the is_paired column back when is_paired is None - if self.is_paired is None: - self.__results.insert( - 5, "is_paired", self.__results.apply(lambda _: None, axis=1) - ) - - # Create and compute the delta-delta statistics - if self.__delta2 and self.__effect_size not in ["mean_diff", "hedges_g"]: - self.__delta_delta = "Delta-delta is not supported for {}.".format( - self.__effect_size - ) - elif self.__delta2: - self.__delta_delta = DeltaDelta( - self, self.__permutation_count, bootstraps_delta_delta, self.__ci - ) - reprs.append(self.__delta_delta.__repr__(header=False)) - - else: - self.__delta_delta = ( - "`delta2` is False; delta-delta is therefore not calculated." - ) - - # Create and compute the weighted average statistics - if self.__is_mini_meta and self.__effect_size == "mean_diff": - self.__mini_meta = MiniMetaDelta( - self, self.__permutation_count, self.__ci - ) - reprs.append(self.__mini_meta.__repr__(header=False)) - elif self.__is_mini_meta and self.__effect_size != "mean_diff": - self.__mini_meta = "Weighted delta is not supported for {}.".format( - self.__effect_size - ) - else: - self.__mini_meta = ( - "`mini_meta` is False; weighted delta is therefore not calculated." - ) - - varname = get_varname(self.__dabest_obj) - lastline = ( - "To get the results of all valid statistical tests, " - + "use `{}.{}.statistical_tests`".format(varname, self.__effect_size) - ) - reprs.append(lastline) - - reprs.insert(0, print_greeting()) - - self.__for_print = "\n\n".join(reprs) - - def __repr__(self): - try: - return self.__for_print - except AttributeError: - self.__pre_calc() - return self.__for_print - - def __calc_lqrt(self): - rnd_seed = self.__random_seed - db_obj = self.__dabest_obj - dat = db_obj._plot_data - xvar = db_obj._xvar - yvar = db_obj._yvar - delta2 = self.__delta2 - - out = [] - - grouped_data = {name:group[yvar].copy() for name, group in dat.groupby(xvar)} - - for j, current_tuple in enumerate(db_obj.idx): - if self.__is_paired != "sequential": - cname = current_tuple[0] - control = grouped_data[cname] - - for ix, tname in enumerate(current_tuple[1:]): - if self.__is_paired == "sequential": - cname = current_tuple[ix] - control = grouped_data[cname] - test = grouped_data[tname] - - if self.__is_paired: - # Refactored here in v0.3.0 for performance issues. - lqrt_result = lqrt.lqrtest_rel(control, test, random_state=rnd_seed) - - out.append( - { - "control": cname, - "test": tname, - "control_N": int(len(control)), - "test_N": int(len(test)), - "pvalue_paired_lqrt": lqrt_result.pvalue, - "statistic_paired_lqrt": lqrt_result.statistic, - } - ) - - else: - # Likelihood Q-Ratio test: - lqrt_equal_var_result = lqrt.lqrtest_ind( - control, test, random_state=rnd_seed, equal_var=True - ) - - lqrt_unequal_var_result = lqrt.lqrtest_ind( - control, test, random_state=rnd_seed, equal_var=False - ) - - out.append( - { - "control": cname, - "test": tname, - "control_N": int(len(control)), - "test_N": int(len(test)), - "pvalue_lqrt_equal_var": lqrt_equal_var_result.pvalue, - "statistic_lqrt_equal_var": lqrt_equal_var_result.statistic, - "pvalue_lqrt_unequal_var": lqrt_unequal_var_result.pvalue, - "statistic_lqrt_unequal_var": lqrt_unequal_var_result.statistic, - } - ) - self.__lqrt_results = pd.DataFrame(out) - - def plot( - self, - color_col=None, - raw_marker_size=6, - contrast_marker_size=9, # es_marker_size=9, OLD - - raw_label=None, # swarm_label=None, OLD # bar_label=None, OLD - contrast_label=None, - delta2_label=None, - - raw_ylim=None, # swarm_ylim=None, OLD # bar_ylim=None, OLD - contrast_ylim=None, - delta2_ylim=None, - - custom_palette=None, - swarm_side=None, - empty_circle=False, # Not very intuitive name - - face_color=None, - - raw_desat=0.5, # swarm_desat=0.5, OLD # bar_desat=0.5, OLD - contrast_desat=1.0, # halfviolin_desat=1, OLD - - raw_alpha=None, # NEW - contrast_alpha=0.8, # halfviolin_alpha=0.8, OLD - - bar_width=0.5, - # ci=None, # Seems to be unused - ci_type="bca", - - float_contrast=True, - show_pairs=True, - show_sample_size=True, - show_delta2=True, # Would pref switch to delta_delta instead of delta2 - show_mini_meta=True, - - group_summaries="mean_sd", - # err_color=None, # Not intuitive name and doesnt fit with group_summaries argument - fig_size=None, - dpi=100, - ax=None, - - swarmplot_kwargs=None, - slopegraph_kwargs=None, - barplot_kwargs=None, - sankey_kwargs=None, - contrast_kwargs=None, # violinplot_kwargs=None, OLD - reflines_kwargs=None, - group_summaries_kwargs=None, - legend_kwargs=None, - - title=None, - fontsize_title=16, - fontsize_rawxlabel=12, - fontsize_rawylabel=12, - fontsize_contrastxlabel=12, - fontsize_contrastylabel=12, - fontsize_delta2label=12, - - # Raw bars, Contrast bars, delta text, and delta dots - raw_bars=True, # swarm_bars=True, OLD - raw_bars_kwargs=None, # swarm_bars_kwargs=None, OLD - contrast_bars=True, - contrast_bars_kwargs=None, - reference_band=None, - reference_band_kwargs=None, - delta_text=True, - delta_text_kwargs=None, - delta_dot=True, - delta_dot_kwargs=None, - - # Horizontal Plots - horizontal=False, - horizontal_table_kwargs=None, - - # Gridkey - gridkey=None, # gridkey_rows=None, OLD - gridkey_merge_pairs=False, - gridkey_show_Ns=True, - gridkey_show_es=True, - gridkey_delimiters=[';', '>', '_'], - gridkey_kwargs=None, - - contrast_marker_kwargs=None, # es_marker_kwargs=None, OLD - contrast_errorbar_kwargs=None, # es_errorbar_kwargs=None, OLD - - prop_sample_counts=False, - prop_sample_counts_kwargs=None, - - contrast_paired_lines=True, # es_paired_lines=True, OLD - contrast_paired_lines_kwargs=None, # es_paired_lines_kwargs=None, OLD - - # Baseline Effect Size Curve - show_baseline_ec=False, - ): - """ - Creates an estimation plot for the effect size of interest. - - - Parameters - ---------- - color_col : string, default None - Column to be used for colors. - raw_marker_size : float, default 6 - The diameter (in points) of the marker dots plotted in the - swarmplot. - contrast_marker_size : float, default 9 - The size (in points) of the effect size points on the difference - axes. - raw_label, contrast_label, delta2_label : strings, default None - Set labels for the y-axis of the raw plot and the contrast plot, - respectively. If `raw_label` is not specified, it defaults to - "Value" for non binary data (and "Proportion of Success" for binary data), - unless a column name was passed to `y`. If `contrast_label` is not specified, - it defaults to the effect size being plotted. If `delta2_label` is not specifed, - it defaults to "delta - delta". - raw_ylim, contrast_ylim, delta2_ylim : tuples, default None - The desired y-limits of the raw data axes, the - difference axes and the delta-delta axes respectively, as a tuple. - These will be autoscaled to sensible values if they are not - specified. The delta2 axes and contrast axes should have the same - limits for y. When `show_delta2` is True, if both of the `contrast_ylim` - and `delta2_ylim` are not None, then they must be specified with the - same values; when `show_delta2` is True and only one of them is specified, - then the other will automatically be assigned with the same value. - Specifying `delta2_ylim` does not have any effect when `show_delta2` is - False. - custom_palette : dict, list, or matplotlib color palette, default None - This keyword accepts a dictionary with {'group':'color'} pairings, - a list of RGB colors, or a specified matplotlib palette. This - palette will be used to color the swarmplot. If `color_col` is not - specified, then each group will be colored in sequence according - to the default palette currently used by matplotlib. - Please take a look at the seaborn commands `color_palette` - and `cubehelix_palette` to generate a custom palette. Both - these functions generate a list of RGB colors. - See: - https://seaborn.pydata.org/generated/seaborn.color_palette.html - https://seaborn.pydata.org/generated/seaborn.cubehelix_palette.html - The named colors of matplotlib can be found here: - https://matplotlib.org/examples/color/named_colors.html - swarm_side: string, default None - The side on which points are swarmed for swarmplots ("center", "left", or "right"). - empty_circle: boolean, default False - Boolean value determining if empty circles will be used for plotting of - swarmplot for control groups. Color of each individual swarm is also now - dependent on the comparison group. - face_color: string, default None - The face color of the plot. Defaults to "white". - raw_desat : float, default 1 - Decreases the saturation of the colors in the rawplot by the - desired proportion. Uses `seaborn.desaturate()` to acheive this. - contrast_desat : float, default 0.5 - Decreases the saturation of the colors of the half-violin bootstrap - curves by the desired proportion. Uses `seaborn.desaturate()` to - acheive this. - raw_alpha : float, default None - The alpha (transparency) level of the raw plot elements. This defaults - to 1.0 for all plots except sankey and slopegraphs, whereby it defaults to 0.4 - and 0.5, respectively. - contrast_alpha : float, default 0.8 - The alpha (transparency) level of the half-violin bootstrap curves. - bar_width : float, default 0.5 - The width of the bars in the barplot (binary, non-paired data). - ci_type : string, default - The confidence interval of the contrast plot to display. Defaults - to "bca". Otherwise, the user can choose "pct" for percentile. - float_contrast : boolean, default True - Whether or not to display the halfviolin bootstrapped difference - distribution alongside the raw data. - show_pairs : boolean, default True - If the data is paired, whether or not to show the raw data as a - swarmplot, or as slopegraph, with a line joining each pair of - observations. - show_sample_size : boolean, default True - Whether or not to display the sample size of each group in the axis label. - show_delta2, show_mini_meta : boolean, default True - If delta-delta or mini-meta delta is calculated, whether or not to - show the delta-delta plot or mini-meta plot. - group_summaries : ['mean_sd', 'median_quartiles', 'None'], default "mean_sd". - Plots the summary statistics for each group. If 'mean_sd', then - the mean and standard deviation of each group is plotted as a - notched line beside each group. If 'median_quantiles', then the - median and 25th and 75th percentiles of each group is plotted - instead. If 'None', the summaries are not shown. - fig_size : tuple, default None - The desired dimensions of the figure as a (length, width) tuple. - dpi : int, default 100 - The dots per inch of the resulting figure. - ax : matplotlib.Axes, default None - Provide an existing Axes for the plots to be created. If no Axes is - specified, a new matplotlib Figure will be created. - swarmplot_kwargs : dict, default None - Pass any keyword arguments accepted by the seaborn `swarmplot` - command here, as a dict. If None, the following keywords are - passed to sns.swarmplot : {'size':`raw_marker_size`}. - slopegraph_kwargs : dict, default None - This will change the appearance of the lines used to join each pair - of observations when `show_pairs=True`. Pass any keyword arguments - accepted by matplotlib `plot()` function here, as a dict. - If None, the following keywords are - passed to plot() : {'linewidth':1, 'alpha':0.5, 'jitter':0, 'jitter_seed':9876543210}. - barplot_kwargs : dict, default None - By default, the keyword arguments passed are: - {"estimator": np.mean, "errorbar": plot_kwargs["ci"], "err_kws" : {'color':'black'}} - sankey_kwargs: dict, default None - Whis will change the appearance of the sankey diagram used to depict - paired proportional data when `show_pairs=True` and `is_proportional=True`. - Pass any keyword arguments accepted by plot_tools.sankeydiag() function - here, as a dict. If None, the following keywords are passed to sankey diagram: - {"width": 0.5, "align": "center", "alpha": 0.4, "bar_width": 0.1, "rightColor": False} - contrast_kwargs : dict, default None - Pass any keyword arguments accepted by the matplotlib ` - pyplot.violinplot` command here, as a dict. If None, the following - keywords are passed to violinplot : {'widths':0.5, 'vert':True, - 'showextrema':False, 'showmedians':False}. - reflines_kwargs : dict, default None - This will change the appearance of the zero reference lines. Pass - any keyword arguments accepted by the matplotlib Axes `hlines` - command here, as a dict. If None, the following keywords are - passed to Axes.hlines : {'linestyle':'solid', 'linewidth':0.75, - 'zorder':2, 'color' : default y-tick color}. - group_summaries_kwargs : dict, default None - Pass any keyword arguments accepted by the matplotlib.lines.Line2D - command here, as a dict. This will change the appearance of the - vertical summary lines for each group, if `group_summaries` is not - 'None'. If None, the following keywords are passed to - matplotlib.lines.Line2D : {'lw':2, 'alpha':1, 'zorder':3, - 'gap_width_percent':1.5, 'offset':0.1, 'color':None}. - legend_kwargs : dict, default None - Pass any keyword arguments accepted by the matplotlib Axes - `legend` command here, as a dict. If None, the following keywords - are passed to matplotlib.Axes.legend : {'frameon':False}. - title : string, default None - Title for the plot. If None, no title will be displayed. Pass any - keyword arguments accepted by the matplotlib.pyplot.suptitle `t` command here, - as a string. - fontsize_title : float or {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'}, default 16 - Font size for the plot title. If a float, the fontsize in points. The - string values denote sizes relative to the default font size. Pass any keyword arguments accepted - by the matplotlib.pyplot.suptitle `fontsize` command here, as a string. - fontsize_rawxlabel : float, default 12 - Font size for the raw axes xlabel. - fontsize_rawylabel : float, default 12 - Font size for the raw axes ylabel. - fontsize_contrastxlabel : float, default 12 - Font size for the contrast axes xlabel. - fontsize_contrastylabel : float, default 12 - Font size for the contrast axes ylabel. - fontsize_delta2label : float, default 12 - Font size for the delta-delta axes ylabel. - - raw_bars : boolean, default True - Whether or not to display the raw bars. - raw_bars_kwargs : dict, default None - Pass relevant keyword arguments to the raw bars. Pass any keyword arguments accepted by - matplotlib.patches.Rectangle here, as a string. If None, the following keywords are passed: - {"color": None, "zorder":-3} - contrast_bars : boolean, default True - Whether or not to display the contrast bars. - contrast_bars_kwargs : dict, default None - Pass relevant keyword arguments to the contrast bars. Pass any keyword arguments accepted by - matplotlib.patches.Rectangle here, as a string. If None, the following keywords are passed: - {"color": None, "zorder":-3} - reference_band : list, default None - Pass a list of indices of the contrast objects to have reference bands displayed on the plot. - For example, [0,1] will show reference bands for the first two contrast objects. - reference_band_kwargs: dict, default None - If None, the following keywords are passed: {"span_ax": False, "color": None, "alpha": 0.15, "zorder":-3} - delta_text : boolean, default True - Whether or not to display the text deltas. - delta_text_kwargs : dict, default None - Pass relevant keyword arguments to the delta text. Pass any keyword arguments accepted by - matplotlib.text.Text here, as a string. If None, the following keywords are passed: - {"color": None, "alpha": 1, "fontsize": 10, "ha": 'center', "va": 'center', "rotation": 0, - "x_location": 'right', "x_coordinates": None, "y_coordinates": None, "offset": 0} - Use "x_coordinates" and "y_coordinates" if you would like to specify the text locations manually. - Use "x_adjust" to adjust the x location of all the texts (positive moves right, negative left). - delta_dot : boolean, default True - Whether or not to display the delta dots on paired or repeated measure plots. - delta_dot_kwargs : dict, default None - Pass relevant keyword arguments. If None, the following keywords are passed: - {"color": 'k', "marker": "^", "alpha": 0.5, "zorder": 2, "size": 3, "side": "right"} - horizontal : boolean, default False - Whether to display plots in the horizontal format. Default is False. - horizontal_table_kwargs : dict, default None - {'show: True, 'color' : 'yellow', 'alpha' :0.2, 'fontsize' : 12, 'text_color' : 'black', - 'text_units' : None, 'control_marker' : '-', 'fontsize_label': 12, 'label': 'Δ'} - - gridkey : list, default None - Provide either a list of grid keys or 'auto' for automatic grid selection. - gridkey_merge_pairs : boolean, default False - Merges the paired grid key groups together. - gridkey_show_Ns : boolean, default True - Whether to display the sample size row. - gridkey_show_es : boolean, default True - Whether to show the effect size row. - gridkey_delimiters : list, default [';', '>', '_'] - The delimiter used to split gridkey groups if required. - gridkey_kwargs : dict, default None - Pass relevant keyword arguments to the gridkey. If None, the following keywords are passed: - { 'show_es' : True, # If True, the gridkey will show the effect size of each comparison. - 'show_Ns' :True, # If True, the gridkey will show the number of observations in eachgroup. - 'merge_pairs' : False, # If True, the gridkey will merge the pairs of groups into a single cell. This is useful for when the groups are paired. - 'delimiters': [';', '>', '_'], # Delimiters to split the group names. - 'marker': "\u25CF", # Marker for the gridkey dots. - } - - contrast_marker_kwargs: dict, default None - Pass relevant keyword arguments to the effectsize marker plotting. If none, the following keywords are passed: - {'marker': 'o', 'size': plot_kwargs['contrast_marker_size'], 'color': 'black', 'alpha': 1, 'zorder': 1} - contrast_errorbar_kwargs: dict, default None - Pass relevant keyword arguments to the effectsize errorbar plotting. If none, the following keywords are passed: - {'color': 'black', 'lw': 2, 'linestyle': '-', 'alpha': 1,'zorder': 1,} - - prop_sample_counts: bool, default False - Show the sample counts for each group in proportional plots - prop_sample_counts_kwargs: dict, default None - Pass relevant keyword arguments. If None, the following keywords are passed: - {'color': 'k', 'zorder': 5, 'ha': 'center', 'va': 'center'}, - - contrast_paired_lines: bool, default True - Whether or not to add lines to connect the effect size curves in paired plots. - contrast_paired_lines_kwargs: dict, default None - Pass relevant plot keyword arguments. If None, the following keywords are passed: - {"linestyle": "-", "linewidth": 2, "zorder": -2, "color": 'dimgray', "alpha": 1} - - show_baseline_ec : boolean, default False - Whether or not to display the baseline error curve. The baseline error curve - represents the distribution of the effect size when comparing the control - group to itself, providing a reference for the inherent variability or noise - in the data. When True, this curve is plotted alongside the main effect size - distribution, allowing for a visual comparison of the observed effect against - the baseline variability. - - Returns - ------- - A :class:`matplotlib.figure.Figure` with 2 Axes, if ``ax = None``. - - The first axes (accessible with ``FigName.axes[0]``) contains the rawdata swarmplot; the second axes (accessible with ``FigName.axes[1]``) has the bootstrap distributions and effect sizes (with confidence intervals) plotted on it. - - If ``ax`` is specified, the rawdata swarmplot is accessed at ``ax`` - itself, while the effect size axes is accessed at ``ax.contrast_axes``. - See the last example below. - - - - """ - - from .plotter import effectsize_df_plotter - - if hasattr(self, "results") is False: - self.__pre_calc() - - if raw_alpha is None: - raw_alpha = (0.4 if self.is_proportional and self.is_paired - else 0.5 if self.is_paired and (color_col is not None or self.__delta2) - else 0.2 if self.is_paired and color_col is None - else 1.0 - ) - - if self.__delta2 and not empty_circle: - color_col = self.__x2 - - all_kwargs = locals() - del all_kwargs["self"] - - out = effectsize_df_plotter(self, **all_kwargs) - return out - - @property - def is_proportional(self): - """ - Returns the proportional parameter - class. - """ - return self.__is_proportional - - @property - def results(self): - """Prints all pairwise comparisons nicely.""" - try: - return self.__results - except AttributeError: - self.__pre_calc() - return self.__results - - @property - def statistical_tests(self): - results_df = self.results - - # Select only the statistics and p-values. - stats_columns = [ - c - for c in results_df.columns - if c.startswith("statistic") or c.startswith("pvalue") - ] - - default_cols = [ - "control", - "test", - "control_N", - "test_N", - "effect_size", - "is_paired", - "difference", - "ci", - "bca_low", - "bca_high", - ] - - cols_of_interest = default_cols + stats_columns - - return results_df[cols_of_interest] - - @property - def _for_print(self): - return self.__for_print - - @property - def _plot_data(self): - return self.__dabest_obj._plot_data - - @property - def idx(self): - return self.__dabest_obj.idx - - @property - def xvar(self): - return self.__dabest_obj._xvar - - @property - def yvar(self): - return self.__dabest_obj._yvar - - @property - def is_paired(self): - return self.__is_paired - - @property - def ci(self): - """ - The width of the confidence interval being produced, in percent. - """ - return self.__ci - - @property - def x1_level(self): - return self.__x1_level - - @property - def x2(self): - return self.__x2 - - @property - def experiment_label(self): - return self.__experiment_label - - @property - def resamples(self): - """ - The number of resamples (with replacement) during bootstrap resampling." - """ - return self.__resamples - - @property - def random_seed(self): - """ - The seed used by `numpy.seed()` for bootstrap resampling. - """ - return self.__random_seed - - @property - def effect_size(self): - """The type of effect size being computed.""" - return self.__effect_size - - @property - def dabest_obj(self): - """ - Returns the `dabest` object that invoked the current EffectSizeDataFrame - class. - """ - return self.__dabest_obj - - - @property - def lqrt(self): - """Returns all pairwise Lq-Likelihood Ratio Type test results - as a pandas DataFrame. - - For more information on LqRT tests, see https://arxiv.org/abs/1911.11922 - """ - try: - return self.__lqrt_results - except AttributeError: - self.__calc_lqrt() - return self.__lqrt_results - - @property - def is_mini_meta(self): - """ - Returns the mini_meta boolean parameter. - """ - return self.__is_mini_meta - - @property - def mini_meta(self): - """ - Returns the mini_meta results. - """ - try: - return self.__mini_meta - except AttributeError: - self.__pre_calc() - return self.__mini_meta - - @property - def delta_delta(self): - """ - Returns the delta_delta results. - """ - try: - return self.__delta_delta - except AttributeError: - self.__pre_calc() - return self.__delta_delta - - @property - def delta2(self): - return self.__delta2 - - @property - def is_delta_delta(self): - return self.__delta2 - -# %% ../nbs/API/effsize_objects.ipynb #5d49f77f -class PermutationTest: - """ - A class to compute and report permutation tests. - - Parameters - ---------- - control : array-like - test : array-like - These should be numerical iterables. - effect_size : string. - Any one of the following are accepted inputs: - 'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta' - is_paired : string, default None - permutation_count : int, default 10000 - The number of permutations (reshuffles) to perform. - random_seed : int, default 12345 - `random_seed` is used to seed the random number generator during - bootstrap resampling. This ensures that the generated permutations - are replicable. - ps_adjust : bool, default False - If True, the p-value is adjusted according to Phipson & Smyth (2010). - # https://doi.org/10.2202/1544-6115.1585 - - - Returns - ------- - A :py:class:`PermutationTest` object: - `difference`:float - The effect size of the difference between the control and the test. - `effect_size`:string - The type of effect size reported. - - - """ - - def __init__(self, control: array, - test: array, # These should be numerical iterables. - effect_size:str, # Any one of the following are accepted inputs: 'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta' - is_paired:str=None, - permutation_count:int=5000, # The number of permutations (reshuffles) to perform. - random_seed:int=12345,#`random_seed` is used to seed the random number generator during bootstrap resampling. This ensures that the generated permutations are replicable. - ps_adjust:bool=False, - **kwargs): - from ._stats_tools.effsize import two_group_difference - from ._stats_tools.confint_2group_diff import calculate_group_var - - - self.__permutation_count = permutation_count - - # Run Sanity Check. - if is_paired and len(control) != len(test): - raise ValueError("The two arrays do not have the same length.") - - # Initialise random number generator. - # rng = random.default_rng(seed=random_seed) - rng = RandomState(PCG64(random_seed)) - - # Set required constants and variables - control = array(control) - test = array(test) - - control_sample = control.copy() - test_sample = test.copy() - - BAG = array([*control, *test]) - CONTROL_LEN = int(len(control)) - TEST_LEN = int(len(test)) # devMJBL - EXTREME_COUNT = 0. - THRESHOLD = abs(two_group_difference(control, test, - is_paired, effect_size)) - self.__permutations = [] - self.__permutations_var = [] - - for i in range(int(self.__permutation_count)): - if is_paired: - # Select which control-test pairs to swap. - random_idx = rng.choice(CONTROL_LEN, - rng.randint(0, CONTROL_LEN+1), - replace=False) - - # Perform swap. - for i in random_idx: - _placeholder = control_sample[i] - control_sample[i] = test_sample[i] - test_sample[i] = _placeholder - - else: - # Shuffle the bag and assign to control and test groups. - # NB. rng.shuffle didn't produce replicable results... - shuffled = rng.permutation(BAG) - control_sample = shuffled[:CONTROL_LEN] - test_sample = shuffled[CONTROL_LEN:] - - - es = two_group_difference(control_sample, test_sample, - False, effect_size) - - group_var = calculate_group_var(var(control_sample, ddof=1), - CONTROL_LEN, - var(test_sample, ddof=1), - len(test_sample)) - self.__permutations.append(es) - self.__permutations_var.append(group_var) - - if abs(es) > THRESHOLD: - EXTREME_COUNT += 1. - - if ps_adjust: - # devMJBL - # adjust calculated p-value according to Phipson & Smyth (2010) - # https://doi.org/10.2202/1544-6115.1585 - # as per R code in statmod::permp - # https://rdrr.io/cran/statmod/src/R/permp.R - # (assumes two-sided test) - - if CONTROL_LEN == TEST_LEN: - totalPermutations = binomcoeff(CONTROL_LEN + TEST_LEN, TEST_LEN)/2 - else: - totalPermutations = binomcoeff(CONTROL_LEN + TEST_LEN, TEST_LEN) - - if totalPermutations <= 10e3: - # use exact calculation - p = arange(1, totalPermutations + 1)/totalPermutations - x2 = repeat(EXTREME_COUNT, repeats=totalPermutations) - Y = binom.cdf(k=x2, n=permutation_count, p=p) - self.pvalue = mean(Y) - else: - # use integral approximation - def binomcdf(p, k, n): - return binom.cdf(k, n, p) - - integrationVal, _ = fixed_quad(binomcdf, - a=0, b=0.5/totalPermutations, - args=(EXTREME_COUNT, permutation_count), - n=128) - - self.pvalue = (EXTREME_COUNT + 1)/(permutation_count + 1) - integrationVal - else: - self.pvalue = EXTREME_COUNT / self.__permutation_count - - self.__permutations = array(self.__permutations) - self.__permutations_var = array(self.__permutations_var) - - def __repr__(self): - return("{} permutations were taken. The p-value is {}.".format(self.__permutation_count, - self.pvalue)) - - - @property - def permutation_count(self): - """ - The number of permuations taken. - """ - return self.__permutation_count - - - @property - def permutations(self): - """ - The effect sizes of all the permutations in a list. - """ - return self.__permutations - - - @property - def permutations_var(self): - """ - The experiment group variance of all the permutations in a list. - """ - return self.__permutations_var - diff --git a/dabest/_modidx.py b/dabest/_modidx.py deleted file mode 100644 index 96231c81..00000000 --- a/dabest/_modidx.py +++ /dev/null @@ -1,182 +0,0 @@ -# Autogenerated by nbdev - -d = { 'settings': { 'branch': 'master', - 'doc_baseurl': '/DABEST-python', - 'doc_host': 'https://acclab.github.io', - 'git_url': 'https://github.com/acclab/DABEST-python', - 'lib_path': 'dabest'}, - 'syms': { 'dabest._stats_tools.confint_1group': { 'dabest._stats_tools.confint_1group.compute_1group_acceleration': ( 'API/confint_1group.html#compute_1group_acceleration', - 'dabest/_stats_tools/confint_1group.py'), - 'dabest._stats_tools.confint_1group.compute_1group_bias_correction': ( 'API/confint_1group.html#compute_1group_bias_correction', - 'dabest/_stats_tools/confint_1group.py'), - 'dabest._stats_tools.confint_1group.compute_1group_bootstraps': ( 'API/confint_1group.html#compute_1group_bootstraps', - 'dabest/_stats_tools/confint_1group.py'), - 'dabest._stats_tools.confint_1group.compute_1group_jackknife': ( 'API/confint_1group.html#compute_1group_jackknife', - 'dabest/_stats_tools/confint_1group.py'), - 'dabest._stats_tools.confint_1group.create_bootstrap_indexes': ( 'API/confint_1group.html#create_bootstrap_indexes', - 'dabest/_stats_tools/confint_1group.py'), - 'dabest._stats_tools.confint_1group.summary_ci_1group': ( 'API/confint_1group.html#summary_ci_1group', - 'dabest/_stats_tools/confint_1group.py')}, - 'dabest._stats_tools.confint_2group_diff': { 'dabest._stats_tools.confint_2group_diff._calc_accel': ( 'API/confint_2group_diff.html#_calc_accel', - 'dabest/_stats_tools/confint_2group_diff.py'), - 'dabest._stats_tools.confint_2group_diff._compute_alpha_from_ci': ( 'API/confint_2group_diff.html#_compute_alpha_from_ci', - 'dabest/_stats_tools/confint_2group_diff.py'), - 'dabest._stats_tools.confint_2group_diff._compute_quantile': ( 'API/confint_2group_diff.html#_compute_quantile', - 'dabest/_stats_tools/confint_2group_diff.py'), - 'dabest._stats_tools.confint_2group_diff._create_two_group_jackknife_indexes': ( 'API/confint_2group_diff.html#_create_two_group_jackknife_indexes', - 'dabest/_stats_tools/confint_2group_diff.py'), - 'dabest._stats_tools.confint_2group_diff.bootstrap_indices': ( 'API/confint_2group_diff.html#bootstrap_indices', - 'dabest/_stats_tools/confint_2group_diff.py'), - 'dabest._stats_tools.confint_2group_diff.calculate_bootstraps_var': ( 'API/confint_2group_diff.html#calculate_bootstraps_var', - 'dabest/_stats_tools/confint_2group_diff.py'), - 'dabest._stats_tools.confint_2group_diff.calculate_group_var': ( 'API/confint_2group_diff.html#calculate_group_var', - 'dabest/_stats_tools/confint_2group_diff.py'), - 'dabest._stats_tools.confint_2group_diff.calculate_weighted_delta': ( 'API/confint_2group_diff.html#calculate_weighted_delta', - 'dabest/_stats_tools/confint_2group_diff.py'), - 'dabest._stats_tools.confint_2group_diff.compute_bootstrapped_diff': ( 'API/confint_2group_diff.html#compute_bootstrapped_diff', - 'dabest/_stats_tools/confint_2group_diff.py'), - 'dabest._stats_tools.confint_2group_diff.compute_delta2_bootstrapped_diff': ( 'API/confint_2group_diff.html#compute_delta2_bootstrapped_diff', - 'dabest/_stats_tools/confint_2group_diff.py'), - 'dabest._stats_tools.confint_2group_diff.compute_interval_limits': ( 'API/confint_2group_diff.html#compute_interval_limits', - 'dabest/_stats_tools/confint_2group_diff.py'), - 'dabest._stats_tools.confint_2group_diff.compute_meandiff_bias_correction': ( 'API/confint_2group_diff.html#compute_meandiff_bias_correction', - 'dabest/_stats_tools/confint_2group_diff.py'), - 'dabest._stats_tools.confint_2group_diff.compute_meandiff_jackknife': ( 'API/confint_2group_diff.html#compute_meandiff_jackknife', - 'dabest/_stats_tools/confint_2group_diff.py'), - 'dabest._stats_tools.confint_2group_diff.create_jackknife_indexes': ( 'API/confint_2group_diff.html#create_jackknife_indexes', - 'dabest/_stats_tools/confint_2group_diff.py'), - 'dabest._stats_tools.confint_2group_diff.create_repeated_indexes': ( 'API/confint_2group_diff.html#create_repeated_indexes', - 'dabest/_stats_tools/confint_2group_diff.py'), - 'dabest._stats_tools.confint_2group_diff.delta2_bootstrap_loop': ( 'API/confint_2group_diff.html#delta2_bootstrap_loop', - 'dabest/_stats_tools/confint_2group_diff.py')}, - 'dabest._stats_tools.effsize': { 'dabest._stats_tools.effsize._cliffs_delta_core': ( 'API/effsize.html#_cliffs_delta_core', - 'dabest/_stats_tools/effsize.py'), - 'dabest._stats_tools.effsize._compute_hedges_correction_factor': ( 'API/effsize.html#_compute_hedges_correction_factor', - 'dabest/_stats_tools/effsize.py'), - 'dabest._stats_tools.effsize._compute_standardizers': ( 'API/effsize.html#_compute_standardizers', - 'dabest/_stats_tools/effsize.py'), - 'dabest._stats_tools.effsize._mann_whitney_u': ( 'API/effsize.html#_mann_whitney_u', - 'dabest/_stats_tools/effsize.py'), - 'dabest._stats_tools.effsize.cliffs_delta': ( 'API/effsize.html#cliffs_delta', - 'dabest/_stats_tools/effsize.py'), - 'dabest._stats_tools.effsize.cohens_d': ( 'API/effsize.html#cohens_d', - 'dabest/_stats_tools/effsize.py'), - 'dabest._stats_tools.effsize.cohens_h': ( 'API/effsize.html#cohens_h', - 'dabest/_stats_tools/effsize.py'), - 'dabest._stats_tools.effsize.func_difference': ( 'API/effsize.html#func_difference', - 'dabest/_stats_tools/effsize.py'), - 'dabest._stats_tools.effsize.hedges_g': ( 'API/effsize.html#hedges_g', - 'dabest/_stats_tools/effsize.py'), - 'dabest._stats_tools.effsize.two_group_difference': ( 'API/effsize.html#two_group_difference', - 'dabest/_stats_tools/effsize.py'), - 'dabest._stats_tools.effsize.weighted_delta': ( 'API/effsize.html#weighted_delta', - 'dabest/_stats_tools/effsize.py')}, - 'dabest._stats_tools.precompile': { 'dabest._stats_tools.precompile.precompile_all': ( 'API/precompile.html#precompile_all', - 'dabest/_stats_tools/precompile.py')}, - 'dabest.forest_plot': { 'dabest.forest_plot.check_for_errors': ( 'API/forest_plot.html#check_for_errors', - 'dabest/forest_plot.py'), - 'dabest.forest_plot.color_palette': ('API/forest_plot.html#color_palette', 'dabest/forest_plot.py'), - 'dabest.forest_plot.forest_plot': ('API/forest_plot.html#forest_plot', 'dabest/forest_plot.py'), - 'dabest.forest_plot.get_kwargs': ('API/forest_plot.html#get_kwargs', 'dabest/forest_plot.py'), - 'dabest.forest_plot.load_plot_data': ('API/forest_plot.html#load_plot_data', 'dabest/forest_plot.py')}, - 'dabest.misc_tools': { 'dabest.misc_tools.add_counts_to_ticks': ( 'API/misc_tools.html#add_counts_to_ticks', - 'dabest/misc_tools.py'), - 'dabest.misc_tools.color_picker': ('API/misc_tools.html#color_picker', 'dabest/misc_tools.py'), - 'dabest.misc_tools.draw_zeroline': ('API/misc_tools.html#draw_zeroline', 'dabest/misc_tools.py'), - 'dabest.misc_tools.extract_contrast_plotting_ticks': ( 'API/misc_tools.html#extract_contrast_plotting_ticks', - 'dabest/misc_tools.py'), - 'dabest.misc_tools.extract_group_summaries': ( 'API/misc_tools.html#extract_group_summaries', - 'dabest/misc_tools.py'), - 'dabest.misc_tools.gardner_altman_adjustments': ( 'API/misc_tools.html#gardner_altman_adjustments', - 'dabest/misc_tools.py'), - 'dabest.misc_tools.get_color_palette': ('API/misc_tools.html#get_color_palette', 'dabest/misc_tools.py'), - 'dabest.misc_tools.get_kwargs': ('API/misc_tools.html#get_kwargs', 'dabest/misc_tools.py'), - 'dabest.misc_tools.get_params': ('API/misc_tools.html#get_params', 'dabest/misc_tools.py'), - 'dabest.misc_tools.get_plot_groups': ('API/misc_tools.html#get_plot_groups', 'dabest/misc_tools.py'), - 'dabest.misc_tools.get_unique_categories': ( 'API/misc_tools.html#get_unique_categories', - 'dabest/misc_tools.py'), - 'dabest.misc_tools.get_varname': ('API/misc_tools.html#get_varname', 'dabest/misc_tools.py'), - 'dabest.misc_tools.initialize_fig': ('API/misc_tools.html#initialize_fig', 'dabest/misc_tools.py'), - 'dabest.misc_tools.merge_two_dicts': ('API/misc_tools.html#merge_two_dicts', 'dabest/misc_tools.py'), - 'dabest.misc_tools.prepare_bars_for_plot': ( 'API/misc_tools.html#prepare_bars_for_plot', - 'dabest/misc_tools.py'), - 'dabest.misc_tools.print_greeting': ('API/misc_tools.html#print_greeting', 'dabest/misc_tools.py'), - 'dabest.misc_tools.redraw_dependent_spines': ( 'API/misc_tools.html#redraw_dependent_spines', - 'dabest/misc_tools.py'), - 'dabest.misc_tools.redraw_independent_spines': ( 'API/misc_tools.html#redraw_independent_spines', - 'dabest/misc_tools.py'), - 'dabest.misc_tools.set_xaxis_ticks_and_lims': ( 'API/misc_tools.html#set_xaxis_ticks_and_lims', - 'dabest/misc_tools.py'), - 'dabest.misc_tools.show_legend': ('API/misc_tools.html#show_legend', 'dabest/misc_tools.py'), - 'dabest.misc_tools.unpack_and_add': ('API/misc_tools.html#unpack_and_add', 'dabest/misc_tools.py')}, - 'dabest.multi': { 'dabest.multi.MultiContrast': ('API/multi.html#multicontrast', 'dabest/multi.py'), - 'dabest.multi.MultiContrast.__init__': ('API/multi.html#multicontrast.__init__', 'dabest/multi.py'), - 'dabest.multi.MultiContrast.__repr__': ('API/multi.html#multicontrast.__repr__', 'dabest/multi.py'), - 'dabest.multi.MultiContrast._extract_data': ('API/multi.html#multicontrast._extract_data', 'dabest/multi.py'), - 'dabest.multi.MultiContrast._extract_single_contrast': ( 'API/multi.html#multicontrast._extract_single_contrast', - 'dabest/multi.py'), - 'dabest.multi.MultiContrast._validate_and_parse_structure': ( 'API/multi.html#multicontrast._validate_and_parse_structure', - 'dabest/multi.py'), - 'dabest.multi.MultiContrast._validate_ci_type': ( 'API/multi.html#multicontrast._validate_ci_type', - 'dabest/multi.py'), - 'dabest.multi.MultiContrast._validate_contrast_consistency': ( 'API/multi.html#multicontrast._validate_contrast_consistency', - 'dabest/multi.py'), - 'dabest.multi.MultiContrast._validate_effect_size': ( 'API/multi.html#multicontrast._validate_effect_size', - 'dabest/multi.py'), - 'dabest.multi.MultiContrast._validate_effect_size_compatibility': ( 'API/multi.html#multicontrast._validate_effect_size_compatibility', - 'dabest/multi.py'), - 'dabest.multi.MultiContrast._validate_individual_dabest_obj': ( 'API/multi.html#multicontrast._validate_individual_dabest_obj', - 'dabest/multi.py'), - 'dabest.multi.MultiContrast.bootstraps': ('API/multi.html#multicontrast.bootstraps', 'dabest/multi.py'), - 'dabest.multi.MultiContrast.confidence_intervals': ( 'API/multi.html#multicontrast.confidence_intervals', - 'dabest/multi.py'), - 'dabest.multi.MultiContrast.effect_sizes': ('API/multi.html#multicontrast.effect_sizes', 'dabest/multi.py'), - 'dabest.multi.MultiContrast.forest_plot': ('API/multi.html#multicontrast.forest_plot', 'dabest/multi.py'), - 'dabest.multi.MultiContrast.get_bootstrap_by_position': ( 'API/multi.html#multicontrast.get_bootstrap_by_position', - 'dabest/multi.py'), - 'dabest.multi.MultiContrast.whorlmap': ('API/multi.html#multicontrast.whorlmap', 'dabest/multi.py'), - 'dabest.multi._sample_bootstrap': ('API/multi.html#_sample_bootstrap', 'dabest/multi.py'), - 'dabest.multi._spiralize': ('API/multi.html#_spiralize', 'dabest/multi.py'), - 'dabest.multi.combine': ('API/multi.html#combine', 'dabest/multi.py'), - 'dabest.multi.whorlmap': ('API/multi.html#whorlmap', 'dabest/multi.py')}, - 'dabest.plot_tools': { 'dabest.plot_tools.SwarmPlot': ('API/plot_tools.html#swarmplot', 'dabest/plot_tools.py'), - 'dabest.plot_tools.SwarmPlot.__init__': ( 'API/plot_tools.html#swarmplot.__init__', - 'dabest/plot_tools.py'), - 'dabest.plot_tools.SwarmPlot._adjust_gutter_points': ( 'API/plot_tools.html#swarmplot._adjust_gutter_points', - 'dabest/plot_tools.py'), - 'dabest.plot_tools.SwarmPlot._check_errors': ( 'API/plot_tools.html#swarmplot._check_errors', - 'dabest/plot_tools.py'), - 'dabest.plot_tools.SwarmPlot._format_palette': ( 'API/plot_tools.html#swarmplot._format_palette', - 'dabest/plot_tools.py'), - 'dabest.plot_tools.SwarmPlot._generate_order': ( 'API/plot_tools.html#swarmplot._generate_order', - 'dabest/plot_tools.py'), - 'dabest.plot_tools.SwarmPlot._swarm': ('API/plot_tools.html#swarmplot._swarm', 'dabest/plot_tools.py'), - 'dabest.plot_tools.SwarmPlot.plot': ('API/plot_tools.html#swarmplot.plot', 'dabest/plot_tools.py'), - 'dabest.plot_tools.add_bars_to_plot': ('API/plot_tools.html#add_bars_to_plot', 'dabest/plot_tools.py'), - 'dabest.plot_tools.add_counts_to_prop_plots': ( 'API/plot_tools.html#add_counts_to_prop_plots', - 'dabest/plot_tools.py'), - 'dabest.plot_tools.barplotter': ('API/plot_tools.html#barplotter', 'dabest/plot_tools.py'), - 'dabest.plot_tools.check_data_matches_labels': ( 'API/plot_tools.html#check_data_matches_labels', - 'dabest/plot_tools.py'), - 'dabest.plot_tools.delta_dots_plotter': ( 'API/plot_tools.html#delta_dots_plotter', - 'dabest/plot_tools.py'), - 'dabest.plot_tools.delta_text_plotter': ( 'API/plot_tools.html#delta_text_plotter', - 'dabest/plot_tools.py'), - 'dabest.plot_tools.effect_size_curve_plotter': ( 'API/plot_tools.html#effect_size_curve_plotter', - 'dabest/plot_tools.py'), - 'dabest.plot_tools.error_bar': ('API/plot_tools.html#error_bar', 'dabest/plot_tools.py'), - 'dabest.plot_tools.get_swarm_spans': ('API/plot_tools.html#get_swarm_spans', 'dabest/plot_tools.py'), - 'dabest.plot_tools.gridkey_plotter': ('API/plot_tools.html#gridkey_plotter', 'dabest/plot_tools.py'), - 'dabest.plot_tools.halfviolin': ('API/plot_tools.html#halfviolin', 'dabest/plot_tools.py'), - 'dabest.plot_tools.normalize_dict': ('API/plot_tools.html#normalize_dict', 'dabest/plot_tools.py'), - 'dabest.plot_tools.plot_minimeta_or_deltadelta_violins': ( 'API/plot_tools.html#plot_minimeta_or_deltadelta_violins', - 'dabest/plot_tools.py'), - 'dabest.plot_tools.sankeydiag': ('API/plot_tools.html#sankeydiag', 'dabest/plot_tools.py'), - 'dabest.plot_tools.single_sankey': ('API/plot_tools.html#single_sankey', 'dabest/plot_tools.py'), - 'dabest.plot_tools.slopegraph_plotter': ( 'API/plot_tools.html#slopegraph_plotter', - 'dabest/plot_tools.py'), - 'dabest.plot_tools.swarmplot': ('API/plot_tools.html#swarmplot', 'dabest/plot_tools.py'), - 'dabest.plot_tools.table_for_horizontal_plots': ( 'API/plot_tools.html#table_for_horizontal_plots', - 'dabest/plot_tools.py'), - 'dabest.plot_tools.width_determine': ('API/plot_tools.html#width_determine', 'dabest/plot_tools.py')}, - 'dabest.plotter': {'dabest.plotter.effectsize_df_plotter': ('API/plotter.html#effectsize_df_plotter', 'dabest/plotter.py')}}} diff --git a/dabest/_stats_tools/confint_1group.py b/dabest/_stats_tools/confint_1group.py deleted file mode 100644 index cc71e389..00000000 --- a/dabest/_stats_tools/confint_1group.py +++ /dev/null @@ -1,140 +0,0 @@ -"""A range of functions to compute bootstraps for a single sample.""" - -# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/API/confint_1group.ipynb. - -# %% auto #0 -__all__ = ['create_bootstrap_indexes', 'compute_1group_jackknife', 'compute_1group_acceleration', 'compute_1group_bootstraps', - 'compute_1group_bias_correction', 'summary_ci_1group'] - -# %% ../../nbs/API/confint_1group.ipynb #9181f236 -import numpy as np -from numpy.random import PCG64, RandomState -from scipy.stats import norm -from numpy import sort as npsort - -# %% ../../nbs/API/confint_1group.ipynb #bac09924 -def create_bootstrap_indexes(array, resamples=5000, random_seed=12345): - """Given an array-like, returns a generator of bootstrap indexes - to be used for resampling. - """ - - rng = RandomState(PCG64(random_seed)) - - indexes = range(0, len(array)) - - out = (rng.choice(indexes, len(indexes), replace=True) for i in range(0, resamples)) - - return out - - -def compute_1group_jackknife(x, func, *args, **kwargs): - """ - Returns the jackknife bootstraps for func(x). - """ - from . import confint_2group_diff as ci_2g - - jackknives = [i for i in ci_2g.create_jackknife_indexes(x)] - out = [func(x[j], *args, **kwargs) for j in jackknives] - del jackknives # memory management. - return out - - -def compute_1group_acceleration(jack_dist): - """ - Returns the accaleration value based on the jackknife distribution. - """ - from . import confint_2group_diff as ci_2g - - return ci_2g._calc_accel(jack_dist) - - -def compute_1group_bootstraps( - x, func, resamples=5000, random_seed=12345, *args, **kwargs -): - """Bootstraps func(x), with the number of specified resamples.""" - - # Create bootstrap indexes. - boot_indexes = create_bootstrap_indexes( - x, resamples=resamples, random_seed=random_seed - ) - - out = [func(x[b], *args, **kwargs) for b in boot_indexes] - - del boot_indexes - - return out - - -def compute_1group_bias_correction(x, bootstraps, func, *args, **kwargs): - metric = func(x, *args, **kwargs) - prop_boots_less_than_metric = sum(bootstraps < metric) / len(bootstraps) - - return norm.ppf(prop_boots_less_than_metric) - - -def summary_ci_1group( - x: np.array, # An numerical iterable. - func, # The function to be applied to x. - resamples: int = 5000, # The number of bootstrap resamples to be taken of func(x). - alpha: float = 0.05, # Denotes the likelihood that the confidence interval produced _does not_ include the true summary statistic. When alpha = 0.05, a 95% confidence interval is produced. - random_seed: int = 12345, # `random_seed` is used to seed the random number generator during bootstrap resampling. This ensures that the confidence intervals reported are replicable. - sort_bootstraps: bool = True, - *args, - **kwargs -): - """ - Given an array-like x, returns func(x), and a bootstrap confidence - interval of func(x). - - Returns - ------- - A dictionary with the following five keys: - `summary`: float. - The outcome of func(x). - `func`: function. - The function applied to x. - `bca_ci_low`: float - `bca_ci_high`: float. - The bias-corrected and accelerated confidence interval, for the - given alpha. - `bootstraps`: array. - The bootstraps used to generate the confidence interval. - These will be sorted in ascending order if `sort_bootstraps` - was True. - - """ - from . import confint_2group_diff as ci2g - - boots = compute_1group_bootstraps( - x, func, resamples=resamples, random_seed=random_seed, *args, **kwargs - ) - bias = compute_1group_bias_correction(x, boots, func) - - jk = compute_1group_jackknife(x, func, *args, **kwargs) - accel = compute_1group_acceleration(jk) - del jk - - ci_idx = ci2g.compute_interval_limits(bias, accel, resamples, alpha) - - boots_sorted = npsort(boots) - - low = boots_sorted[ci_idx[0]] - high = boots_sorted[ci_idx[1]] - - if sort_bootstraps: - B = boots_sorted - else: - B = boots - del boots - del boots_sorted - - out = { - "summary": func(x), - "func": func, - "bca_ci_low": low, - "bca_ci_high": high, - "bootstraps": B, - } - - del B - return out diff --git a/dabest/_stats_tools/confint_2group_diff.py b/dabest/_stats_tools/confint_2group_diff.py deleted file mode 100644 index 03d7df11..00000000 --- a/dabest/_stats_tools/confint_2group_diff.py +++ /dev/null @@ -1,341 +0,0 @@ -"""A range of functions to compute bootstraps for the mean difference""" - -# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/API/confint_2group_diff.ipynb. - -# %% auto #0 -__all__ = ['create_jackknife_indexes', 'create_repeated_indexes', 'compute_meandiff_jackknife', 'bootstrap_indices', - 'compute_bootstrapped_diff', 'delta2_bootstrap_loop', 'compute_delta2_bootstrapped_diff', - 'compute_meandiff_bias_correction', 'compute_interval_limits', 'calculate_group_var', - 'calculate_bootstraps_var', 'calculate_weighted_delta'] - -# %% ../../nbs/API/confint_2group_diff.ipynb #fa733643 -import numpy as np -from numpy import arange, delete, errstate -from numpy import mean as npmean -from numpy import sum as npsum -from numpy.random import PCG64, RandomState -from numba import njit, prange -from scipy.stats import norm -from numpy import isnan - -# %% ../../nbs/API/confint_2group_diff.ipynb #8cf9b1fc -@njit(cache=True, parallel=True) -def create_jackknife_indexes(data): - """ - Given an array-like, creates a jackknife bootstrap. - - For a given set of data Y, the jackknife bootstrap sample J[i] - is defined as the data set Y with the ith data point deleted. - - Keywords - -------- - data: array-like - - Returns - ------- - Generator that yields all jackknife bootstrap samples. - """ - - n = len(data) - indexes = np.empty((n, n - 1), dtype=np.int64) - for i in prange(n): - indexes[i] = np.concatenate((np.arange(i), np.arange(i + 1, n))) - return indexes - - -@njit(cache=True, parallel=True) -def create_repeated_indexes(data): - """ - Convenience function. Given an array-like with length N, - returns a generator that yields N indexes [0, 1, ..., N]. - """ - - n = len(data) - indexes = np.empty((n, n), dtype=np.int64) # Pre-allocate the output array - for i in prange(n): - indexes[i, :] = np.arange(n) # Fill each row with the full index range - return indexes - - -def _create_two_group_jackknife_indexes(x0, x1, is_paired): - """Creates the jackknife bootstrap for 2 groups.""" - - if is_paired and len(x0) == len(x1): - out = list( - zip( - [j for j in create_jackknife_indexes(x0)], - [i for i in create_jackknife_indexes(x1)], - ) - ) - else: - jackknife_c = list( - zip( - [j for j in create_jackknife_indexes(x0)], - [i for i in create_repeated_indexes(x1)], - ) - ) - - jackknife_t = list( - zip( - [i for i in create_repeated_indexes(x0)], - [j for j in create_jackknife_indexes(x1)], - ) - ) - out = jackknife_c + jackknife_t - del jackknife_c - del jackknife_t - - return out - - -def compute_meandiff_jackknife(x0, x1, is_paired, effect_size): - """ - Given two arrays, returns the jackknife for their effect size. - """ - from . import effsize as __es - - jackknives = _create_two_group_jackknife_indexes(x0, x1, is_paired) - - out = [] - - for j in jackknives: - x0_shuffled = x0[j[0]] - x1_shuffled = x1[j[1]] - - es = __es.two_group_difference(x0_shuffled, x1_shuffled, is_paired, effect_size) - out.append(es) - - return out - - -def _calc_accel(jack_dist): - """ - Given the Jackknife distribution, calculates the acceleration factor. - """ - jack_mean = npmean(jack_dist) - - numer = npsum((jack_mean - jack_dist) ** 3) - denom = 6.0 * (npsum((jack_mean - jack_dist) ** 2) ** 1.5) - - with errstate(invalid="ignore"): - # does not raise warning if invalid division encountered. - return numer / denom - - -@njit(cache=True) # parallelization must be turned off for random number generation -def bootstrap_indices(is_paired, x0_len, x1_len, resamples, random_seed): - np.random.seed(random_seed) - indices = np.empty((resamples, x0_len if is_paired else x0_len + x1_len), dtype=np.int64) - - for i in range(resamples): - if is_paired: - indices[i, :x0_len] = np.random.choice(x0_len, x0_len) - else: - indices[i, :x0_len] = np.random.choice(x0_len, x0_len) - indices[i, x0_len:x0_len+x1_len] = np.random.choice(x1_len, x1_len) - return indices - - -def compute_bootstrapped_diff( - x0, x1, is_paired, effect_size, resamples=5000, random_seed=12345 -): - """Bootstraps the effect_size for 2 groups.""" - - from . import effsize as __es - - x0_len, x1_len = len(x0), len(x1) - indices = bootstrap_indices(is_paired, x0_len, x1_len, resamples, random_seed) - out = np.empty(resamples, dtype=np.float64) - - for i in range(resamples): - if is_paired: - x0_sample = x0[indices[i, :x0_len]] - x1_sample = x1[indices[i, :x0_len]] - else: - x0_sample = x0[indices[i, :x0_len]] - x1_sample = x1[indices[i, x0_len:x0_len+x1_len]] - - out[i] = __es.two_group_difference(x0_sample, x1_sample, is_paired, effect_size) - - return out - - -@njit(cache=True) -def delta2_bootstrap_loop(x1, x2, x3, x4, resamples, pooled_sd, rng_seed, is_paired, proportional=False): - """ - Compute bootstrapped differences for delta-delta, handling both regular and proportional data - """ - np.random.seed(rng_seed) - deltadelta = np.empty(resamples) - out_delta_g = np.empty(resamples) - - n1, n2, n3, n4 = len(x1), len(x2), len(x3), len(x4) - if is_paired and (n1 != n2 or n3 != n4): - raise ValueError("Each control group must have the same length as its corresponding test group in paired analysis.") - - # Bootstrapping - for i in range(resamples): - # Paired or unpaired resampling - if is_paired: - indices_1 = np.random.choice(len(x1), len(x1)) - indices_2 = np.random.choice(len(x3), len(x3)) - x1_sample, x2_sample = x1[indices_1], x2[indices_1] - x3_sample, x4_sample = x3[indices_2], x4[indices_2] - else: - indices_1 = np.random.randint(0, len(x1), len(x1)) - indices_2 = np.random.randint(0, len(x2), len(x2)) - indices_3 = np.random.randint(0, len(x3), len(x3)) - indices_4 = np.random.randint(0, len(x4), len(x4)) - x1_sample, x2_sample = x1[indices_1], x2[indices_2] - x3_sample, x4_sample = x3[indices_3], x4[indices_4] - - # Calculate deltas - delta_1 = np.mean(x2_sample) - np.mean(x1_sample) - delta_2 = np.mean(x4_sample) - np.mean(x3_sample) - delta_delta = delta_2 - delta_1 - - deltadelta[i] = delta_delta - - out_delta_g[i] = delta_delta if proportional else delta_delta/pooled_sd - - return out_delta_g, deltadelta - - -def compute_delta2_bootstrapped_diff( - x1: np.ndarray, # Control group 1 - x2: np.ndarray, # Test group 1 - x3: np.ndarray, # Control group 2 - x4: np.ndarray, # Test group 2 - is_paired: str = None, - resamples: int = 5000, - random_seed: int = 12345, - proportional: bool = False -) -> tuple: - """ - Bootstraps the effect size deltas' g or proportional delta-delta - """ - x1, x2, x3, x4 = map(np.asarray, [x1, x2, x3, x4]) - - if proportional: - # For proportional data, pass 1.0 as dummy pooled_sd (won't be used) - out_delta_g, deltadelta = delta2_bootstrap_loop( - x1, x2, x3, x4, resamples, 1.0, random_seed, is_paired, proportional=True - ) - # For proportional data, delta_g is the empirical delta-delta - delta_g = ((np.mean(x4) - np.mean(x3)) - (np.mean(x2) - np.mean(x1))) - else: - # Calculate pooled sample standard deviation for non-proportional data - stds = [np.std(x) for x in [x1, x2, x3, x4]] - ns = [len(x) for x in [x1, x2, x3, x4]] - - sd_numerator = sum((n - 1) * s**2 for n, s in zip(ns, stds)) - sd_denominator = sum(n - 1 for n in ns) - - if sd_denominator == 0: - raise ValueError("Insufficient data to compute pooled standard deviation.") - - pooled_sample_sd = np.sqrt(sd_numerator / sd_denominator) - - if np.isnan(pooled_sample_sd) or pooled_sample_sd == 0: - raise ValueError("Pooled sample standard deviation is NaN or zero.") - - out_delta_g, deltadelta = delta2_bootstrap_loop( - x1, x2, x3, x4, resamples, pooled_sample_sd, random_seed, is_paired, proportional=False - ) - delta_g = ((np.mean(x4) - np.mean(x3)) - (np.mean(x2) - np.mean(x1))) / pooled_sample_sd - - return out_delta_g, delta_g, deltadelta - - -def compute_meandiff_bias_correction( - bootstraps, # An numerical iterable, comprising bootstrap resamples of the effect size. - effsize, # The effect size for the original sample. -): # The bias correction value for the given bootstraps and effect size. - """ - Computes the bias correction required for the BCa method - of confidence interval construction. - - Returns - ------- - bias: numeric - The bias correction value for the given bootstraps - and effect size. - - """ - - B = np.array(bootstraps) - prop_less_than_es = sum(B < effsize) / len(B) - - return norm.ppf(prop_less_than_es) - - -def _compute_alpha_from_ci(ci): - if ci < 0 or ci > 100: - raise ValueError("`ci` must be a number between 0 and 100.") - - return (100.0 - ci) / 100.0 - - -@njit(cache=True) -def _compute_quantile(z, bias, acceleration): - numer = bias + z - denom = 1 - (acceleration * numer) - - return bias + (numer / denom) - - -def compute_interval_limits(bias, acceleration, n_boots, ci=95): - """ - Returns the indexes of the interval limits for a given bootstrap. - - Supply the bias, acceleration factor, and number of bootstraps. - """ - - alpha = _compute_alpha_from_ci(ci) - - alpha_low = alpha / 2 - alpha_high = 1 - (alpha / 2) - - z_low = norm.ppf(alpha_low) - z_high = norm.ppf(alpha_high) - - kws = {"bias": bias, "acceleration": acceleration} - low = _compute_quantile(z_low, **kws) - high = _compute_quantile(z_high, **kws) - - if isnan(low) or isnan(high): - return low, high - - - low = int(norm.cdf(low) * n_boots) - high = int(norm.cdf(high) * n_boots) - return low, high - - -@njit(cache=True) -def calculate_group_var(control_var, control_N, test_var, test_N): - - pooled_var = ((control_N - 1) * control_var + (test_N - 1) * test_var) / (control_N + test_N - 2) - - return pooled_var - -def calculate_bootstraps_var(bootstraps): - - bootstraps_var_list = [np.var(x, ddof=1) for x in bootstraps] - bootstraps_var_array = np.array(bootstraps_var_list) - - return bootstraps_var_array - - - -def calculate_weighted_delta(bootstrap_dist_var, differences): - """ - Compute the weighted deltas. - """ - - weight = np.true_divide(1, bootstrap_dist_var) - denom = np.sum(weight) - num = 0.0 - for i in range(len(weight)): - num += weight[i] * differences[i] - return np.true_divide(num, denom) diff --git a/dabest/_stats_tools/effsize.py b/dabest/_stats_tools/effsize.py deleted file mode 100644 index 216a513f..00000000 --- a/dabest/_stats_tools/effsize.py +++ /dev/null @@ -1,402 +0,0 @@ -"""A range of functions to compute various effect sizes.""" - -# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/API/effsize.ipynb. - -# %% ../../nbs/API/effsize.ipynb #9d92d449 -from __future__ import annotations -import numpy as np -from numba import njit -import warnings -from scipy.special import gamma -from scipy.stats import mannwhitneyu - - -# %% auto #0 -__all__ = ['two_group_difference', 'func_difference', 'cohens_d', 'cohens_h', 'hedges_g', 'cliffs_delta', 'weighted_delta'] - -# %% ../../nbs/API/effsize.ipynb #0547f8a7 -def two_group_difference(control:list|tuple|np.ndarray, #Accepts lists, tuples, or numpy ndarrays of numeric types. - test:list|tuple|np.ndarray, #Accepts lists, tuples, or numpy ndarrays of numeric types. - is_paired=None, #If not None, returns the paired Cohen's d - effect_size:str="mean_diff" # Any one of the following effect sizes: ["mean_diff", "median_diff", "cohens_d", "hedges_g", "cliffs_delta"] - )->float: # The desired effect size. - """ - Computes the following metrics for control and test: - - - Unstandardized mean difference - - Standardized mean differences (paired or unpaired) - * Cohen's d - * Hedges' g - - Median difference - - Cliff's Delta - - Cohen's h (distance between two proportions) - - See the Wikipedia entry [here](https://bit.ly/2LzWokf) - - `effect_size`: - - mean_diff: This is simply the mean of `control` subtracted from - the mean of `test`. - - cohens_d: This is the mean of control subtracted from the - mean of test, divided by the pooled standard deviation - of control and test. The pooled SD is the square as: - - - (n1 - 1) * var(control) + (n2 - 1) * var(test) - sqrt ( ------------------------------------------- ) - (n1 + n2 - 2) - - where n1 and n2 are the sizes of control and test - respectively. - - hedges_g: This is Cohen's d corrected for bias via multiplication - with the following correction factor: - - gamma(n/2) - J(n) = ------------------------------ - sqrt(n/2) * gamma((n - 1) / 2) - - where n = (n1 + n2 - 2). - - median_diff: This is the median of `control` subtracted from the - median of `test`. - - """ - - if not isinstance(control, np.ndarray): - control = np.array(control) - if not isinstance(test, np.ndarray): - test = np.array(test) - - if effect_size == "mean_diff": - return func_difference(control, test, np.mean, is_paired) - - if effect_size == "median_diff": - mes1 = "Using median as the statistic in bootstrapping may " + \ - "result in a biased estimate and cause problems with " + \ - "BCa confidence intervals. Consider using a different statistic, such as the mean.\n" - mes2 = "When plotting, please consider using percetile confidence intervals " + \ - "by specifying `ci_type='pct'`. For detailed information, " + \ - "refer to https://github.com/ACCLAB/DABEST-python/issues/129 \n" - warnings.warn(message=mes1+mes2, category=UserWarning) - return func_difference(control, test, np.median, is_paired) - - if effect_size == "cohens_d": - return cohens_d(control, test, is_paired) - - if effect_size == "cohens_h": - return cohens_h(control, test) - - if effect_size == "hedges_g": - return hedges_g(control, test, is_paired) - - if effect_size == "cliffs_delta": - if is_paired: - err1 = "`is_paired` is not None; therefore Cliff's delta is not defined." - raise ValueError(err1) - - return cliffs_delta(control, test) - - -# %% ../../nbs/API/effsize.ipynb #c93f36d4 -def func_difference(control:list|tuple|np.ndarray, # NaNs are automatically discarded. - test:list|tuple|np.ndarray, # NaNs are automatically discarded. - func, # Summary function to apply. - is_paired:str # If not None, computes func(test - control). If None, computes func(test) - func(control). - )->float: - """ - - Applies func to `control` and `test`, and then returns the difference. - - """ - - # Convert to numpy arrays for speed. - # NaNs are automatically dropped. - if not isinstance(control, np.ndarray): - control = np.array(control) - if not isinstance(test, np.ndarray): - test = np.array(test) - - if is_paired: - if len(control) != len(test): - err = "The two arrays supplied do not have the same length." - raise ValueError(err) - - non_nan_mask = ~np.isnan(control) & ~np.isnan(test) - control_non_nan = control[non_nan_mask] - test_non_nan = test[non_nan_mask] - - return func(test_non_nan - control_non_nan) - - - control = control[~np.isnan(control)] - test = test[~np.isnan(test)] - return func(test) - func(control) - - -# %% ../../nbs/API/effsize.ipynb #c6dd20e4 -@njit(cache=True) -def cohens_d(control:list|tuple|np.ndarray, - test:list|tuple|np.ndarray, - is_paired:str=None # If not None, the paired Cohen's d is returned. - )->float: - """ - Computes Cohen's d for test v.s. control. - See [here](https://en.wikipedia.org/wiki/Effect_size#Cohen's_d) - - If `is_paired` is None, returns: - - $$ - \\frac{\\bar{X}_2 - \\bar{X}_1}{s_{pooled}} - $$ - - where - - $$ - s_{pooled} = \\sqrt{\\frac{(n_1 - 1) s_1^2 + (n_2 - 1) s_2^2}{n_1 + n_2 - 2}} - $$ - - If `is_paired` is not None, returns: - - $$ - \\frac{\\bar{X}_2 - \\bar{X}_1}{s_{avg}} - $$ - - where - - $$ - s_{avg} = \\sqrt{\\frac{s_1^2 + s_2^2}{2}} - $$ - - `Notes`: - - - The sample variance (and standard deviation) uses N-1 degrees of freedoms. - This is an application of Bessel's correction, and yields the unbiased - sample variance. - - `References`: - - - https://en.wikipedia.org/wiki/Bessel%27s_correction - - https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation - """ - - control = control[~np.isnan(control)] - test = test[~np.isnan(test)] - - pooled_sd, average_sd = _compute_standardizers(control, test) - # pooled SD is used for Cohen's d of two independant groups. - # average SD is used for Cohen's d of two paired groups - # (aka repeated measures). - # NOT IMPLEMENTED YET: Correlation adjusted SD is used for Cohen's d of - # two paired groups but accounting for the correlation between - # the two groups. - - if is_paired: - # Check control and test are same length. - if len(control) != len(test): - raise ValueError("`control` and `test` are not the same length.") - # assume the two arrays are ordered already. - delta = test - control - M = np.mean(delta) - divisor = average_sd - - else: - M = np.mean(test) - np.mean(control) - divisor = pooled_sd - - if divisor == 0: - raise ValueError("The divisor is zero, indicating no variability in the data.") - - return M / divisor - -# %% ../../nbs/API/effsize.ipynb #93688770 -# @njit(cache=True) # It uses np.seterr which is not supported by Numba -def cohens_h(control:list|tuple|np.ndarray, - test:list|tuple|np.ndarray - )->float: - ''' - Computes Cohen's h for test v.s. control. - See [here](https://en.wikipedia.org/wiki/Cohen%27s_h) for reference. - - `Notes`: - - - Assuming the input data type is binary, i.e. a series of 0s and 1s, - and a dict for mapping the 0s and 1s to the actual labels, e.g.{1: "Smoker", 0: "Non-smoker"} - ''' - - np.seterr(divide='ignore', invalid='ignore') - - # Check whether dataframe contains only 0s and 1s. - if np.isin(control, [0, 1]).all() == False or np.isin(test, [0, 1]).all() == False: - raise ValueError("Input data must be binary.") - - # Convert to numpy arrays for speed. - # NaNs are automatically dropped. - # Aligned with cohens_d calculation. - control = control[~np.isnan(control)] - test = test[~np.isnan(test)] - - prop_control = sum(control)/len(control) - prop_test = sum(test)/len(test) - - # Arcsine transformation - phi_control = 2 * np.arcsin(np.sqrt(prop_control)) - phi_test = 2 * np.arcsin(np.sqrt(prop_test)) - - return phi_test - phi_control - - -# %% ../../nbs/API/effsize.ipynb #bcd77c32 -def hedges_g(control:list|tuple|np.ndarray, - test:list|tuple|np.ndarray, - is_paired:str=None)->float: - """ - Computes Hedges' g for for test v.s. control. - It first computes Cohen's d, then calulates a correction factor based on - the total degress of freedom using the gamma function. - - See [here](https://en.wikipedia.org/wiki/Effect_size#Hedges'_g) - - """ - - # Convert to numpy arrays for speed. - # NaNs are automatically dropped. - control = control[~np.isnan(control)] - test = test[~np.isnan(test)] - - d = cohens_d(control, test, is_paired) - len_c = len(control) - len_t = len(test) - correction_factor = _compute_hedges_correction_factor(len_c, len_t) - return correction_factor * d - -# %% ../../nbs/API/effsize.ipynb #8fafb111 -@njit(cache=True) -def _mann_whitney_u(x, y): - """Numba-optimized Mann-Whitney U calculation""" - n1, n2 = len(x), len(y) - combined = np.concatenate((x, y)) - - # Use numpy broadcasting for comparison - less_than = (combined.reshape(-1, 1) > combined).sum(axis=1) - equal_to = (combined.reshape(-1, 1) == combined).sum(axis=1) - - # Calculate ranks directly - ranks = less_than + (equal_to + 1) / 2 - - R1 = np.sum(ranks[:n1]) - U1 = R1 - (n1 * (n1 + 1)) / 2 - return U1 - -@njit(cache=True) -def _cliffs_delta_core(control, test): - """Numba-optimized Cliff's delta calculation""" - U = _mann_whitney_u(test, control) - return ((2 * U) / (len(control) * len(test))) - 1 - -def cliffs_delta(control:list|tuple|np.ndarray, - test:list|tuple|np.ndarray - )->float: - """ - Computes Cliff's delta for 2 samples. - See [here](https://en.wikipedia.org/wiki/Effect_size#Effect_size_for_ordinal_data) - """ - c = control[~np.isnan(control)] - t = test[~np.isnan(test)] - return _cliffs_delta_core(c, t) - - -# %% ../../nbs/API/effsize.ipynb #7a772510 -@njit(cache=True) -def _compute_standardizers(control, test): - """ - Computes the pooled and average standard deviations for two datasets. - - This function is useful in the context of statistical analysis, particularly - when calculating standardized mean differences between two groups. It supports - both unpaired and paired data scenarios. - - Parameters: - control (array-like): A numeric array representing the control group data. - test (array-like): A numeric array representing the test group data. - - Returns: - tuple: A tuple containing two elements: - - pooled (float): The pooled standard deviation, calculated for unpaired two-group - scenarios. It is computed using the sample variances of the - control and test groups, weighted by their sample sizes. - - average (float): The average standard deviation, calculated for paired data - scenarios. It is the average of the sample standard deviations - of the control and test groups. - - Note: - The function assumes that the input arrays are independent samples and calculates - the sample variances using N-1 degrees of freedom. - - For calculation of correlation; not currently used. - - """ - # from scipy.stats import pearsonr - - control_n = len(control) - test_n = len(test) - - # ddof parameter is not supported by numba. - control_var = np.var(control)*control_n/(control_n-1) # use N-1 to compute the variance. - test_var = np.var(test)*test_n/(test_n-1) - - # For unpaired 2-groups standardized mean difference. - pooled = np.sqrt(((control_n - 1) * control_var + (test_n - 1) * test_var) / - (control_n + test_n - 2) - ) - - # For paired standardized mean difference. - average = np.sqrt((control_var + test_var) / 2) - - return pooled, average - -# %% ../../nbs/API/effsize.ipynb #4529e82c -def _compute_hedges_correction_factor(n1, - n2 - )->float: - """ - Computes the bias correction factor for Hedges' g. - - See [here](https://en.wikipedia.org/wiki/Effect_size#Hedges'_g) - - `References`: - - - Larry V. Hedges & Ingram Olkin (1985). - Statistical Methods for Meta-Analysis. Orlando: Academic Press. - ISBN 0-12-336380-2. - """ - - df = n1 + n2 - 2 - # gamma function is not supported by numba. - numer = gamma(df / 2) - denom0 = gamma((df - 1) / 2) - denom = np.sqrt(df / 2) * denom0 - - if np.isinf(numer) or np.isinf(denom): - # occurs when df is too large. - # Apply Hedges and Olkin's approximation. - df_sum = n1 + n2 - denom = (4 * df_sum) - 9 - out = 1 - (3 / denom) - - else: - out = numer / denom - - return out - -# %% ../../nbs/API/effsize.ipynb #249e5375 -@njit(cache=True) -def weighted_delta(difference, bootstrap_dist_var): - ''' - Compute the weighted deltas where the weight is the inverse of the - pooled group difference. - ''' - - weight = np.true_divide(1, bootstrap_dist_var) - return np.sum(difference*weight)/np.sum(weight) diff --git a/dabest/_stats_tools/precompile.py b/dabest/_stats_tools/precompile.py deleted file mode 100644 index e6205c0f..00000000 --- a/dabest/_stats_tools/precompile.py +++ /dev/null @@ -1,53 +0,0 @@ -"""A tool to pre-compile Numba functions for speeding up DABEST bootstrapping""" - -# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/API/precompile.ipynb. - -# %% auto #0 -__all__ = ['precompile_all'] - -# %% ../../nbs/API/precompile.ipynb #35aa9337 -import numpy as np -from tqdm import tqdm -from . import effsize -from . import confint_2group_diff - -# %% ../../nbs/API/precompile.ipynb #472d8104 -_NUMBA_COMPILED = False - -def precompile_all(): - """Pre-compile all numba functions with dummy data""" - global _NUMBA_COMPILED - - if _NUMBA_COMPILED: - return - - print("Pre-compiling numba functions for DABEST...") - - # Create dummy data - dummy_control = np.array([1.0, 2.0, 3.0]) - dummy_test = np.array([4.0, 5.0, 6.0]) - - funcs = [ - # effsize.py functions - (effsize.cohens_d, (dummy_control, dummy_test)), - (effsize._mann_whitney_u, (dummy_control, dummy_test)), - (effsize._cliffs_delta_core, (dummy_control, dummy_test)), - (effsize._compute_standardizers, (dummy_control, dummy_test)), - (effsize.weighted_delta, (np.array([1.0, 2.0]), np.array([0.1, 0.2]))), - - # confint_2group_diff.py functions - (confint_2group_diff.create_jackknife_indexes, (dummy_control,)), - (confint_2group_diff.create_repeated_indexes, (dummy_control,)), - (confint_2group_diff.bootstrap_indices, (True, 3, 3, 10, 12345)), - (confint_2group_diff.delta2_bootstrap_loop, - (dummy_control, dummy_test, dummy_control, dummy_test, 10, 1.0, 12345, False)), - (confint_2group_diff._compute_quantile, (0.5, 0.1, 0.1)), - (confint_2group_diff.calculate_group_var, (1.0, 3, 1.0, 3)) - ] - - for func, args in tqdm(funcs, desc="Compiling numba functions"): - func(*args) - - _NUMBA_COMPILED = True - - print("Numba compilation complete!") diff --git a/dabest/forest_plot.py b/dabest/forest_plot.py deleted file mode 100644 index b404e963..00000000 --- a/dabest/forest_plot.py +++ /dev/null @@ -1,769 +0,0 @@ -"""Creating forest plots from contrast objects.""" - -# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/forest_plot.ipynb. - -# %% auto #0 -__all__ = ['load_plot_data', 'check_for_errors', 'get_kwargs', 'color_palette', 'forest_plot'] - -# %% ../nbs/API/forest_plot.ipynb #a1cba2aa -import matplotlib.pyplot as plt -# %matplotlib inline -import seaborn as sns -from typing import List, Optional, Union -import numpy as np -import matplotlib.axes as axes -import matplotlib.patches as mpatches - -# %% ../nbs/API/forest_plot.ipynb #b4634b4b -def load_plot_data( - data: List, - effect_size: str = "mean_diff", - contrast_type: str = None, - ci_type: str = "bca", - idx: Optional[List[int]] = None -) -> List: - """ - Loads plot data based on specified effect size and contrast type. - - Parameters - ---------- - contrasts : List - List of contrast objects. - effect_size: str - Type of effect size ('mean_diff', 'median_diff', etc.). - contrast_type: str - Type of dabest object to plot ('delta2' or 'mini-meta' or 'delta'). - ci_type: str - Type of confidence interval to plot ('bca' or 'pct') - idx: Optional[List[int]], default=None - List of indices to select from the contrast objects if delta-delta experiment. - If None, only the delta-delta objects are plotted. - - Returns - ------- - List: Contrast plot data based on specified parameters. - """ - # Effect size and contrast types - effect_attr = "hedges_g" if effect_size == 'delta_g' else effect_size - contrast_attr = {"delta2": "delta_delta", "mini_meta": "mini_meta"}.get(contrast_type) - - # Testing - if idx is not None: - bootstraps, differences, bcalows, bcahighs = [], [], [], [] - for current_idx, index_group in enumerate(idx): - current_contrast = data[current_idx] - if len(index_group)>0: - for index in index_group: - current_plot_data = getattr(current_contrast, effect_attr) - if contrast_type == 'delta2': - if index == 2: - current_plot_data = getattr(current_plot_data, contrast_attr) - bootstrap_name, index_val = "bootstraps_delta_delta", 0 - elif index == 0 or index == 1: - bootstrap_name, index_val = "bootstraps", index - else: - raise ValueError("The selected indices must be 0, 1, or 2.") - elif contrast_type == "mini_meta": - num_of_groups = len(getattr(current_contrast, effect_attr).results) - if index == num_of_groups: - current_plot_data = getattr(getattr(current_contrast, effect_attr), contrast_attr) - bootstrap_name, index_val = "bootstraps_weighted_delta", 0 - elif index < num_of_groups: - bootstrap_name, index_val = "bootstraps", index - else: - msg1 = "There are only {} groups (starting from zero) in this dabest object. ".format(num_of_groups) - msg2 = "The idx given is {}.".format(index) - raise ValueError(msg1+msg2) - else: # contrast_type == 'delta' - bootstrap_name, index_val = "bootstraps", index - - bootstraps.append(getattr(current_plot_data.results, bootstrap_name)[index_val]) - differences.append(current_plot_data.results.difference[index_val]) - bcalows.append(current_plot_data.results.get(ci_type+'_low')[index_val]) - bcahighs.append(current_plot_data.results.get(ci_type+'_high')[index_val]) - else: - if contrast_type == 'delta': - contrast_plot_data = [getattr(contrast, effect_attr) for contrast in data] - bootstraps_nested = [result.results.bootstraps.to_list() for result in contrast_plot_data] - differences_nested = [result.results.difference.to_list() for result in contrast_plot_data] - bcalows_nested = [result.results.get(ci_type+'_low').to_list() for result in contrast_plot_data] - bcahighs_nested = [result.results.get(ci_type+'_high').to_list() for result in contrast_plot_data] - - bootstraps = [element for innerList in bootstraps_nested for element in innerList] - differences = [element for innerList in differences_nested for element in innerList] - bcalows = [element for innerList in bcalows_nested for element in innerList] - bcahighs = [element for innerList in bcahighs_nested for element in innerList] - - else: # contrast_type == 'delta2' or 'mini_meta' - contrast_plot_data = [getattr(getattr(contrast, effect_attr), contrast_attr) for contrast in data] - attribute_suffix = "weighted_delta" if contrast_type == "mini_meta" else "delta_delta" - - bootstraps = [getattr(result, f"bootstraps_{attribute_suffix}") for result in contrast_plot_data] - differences = [result.difference for result in contrast_plot_data] - bcalows = [result.results.get(ci_type+'_low')[0] for result in contrast_plot_data] - bcahighs = [result.results.get(ci_type+'_high')[0] for result in contrast_plot_data] - - return bootstraps, differences, bcalows, bcahighs - -def check_for_errors(**kwargs): - data = kwargs.get('data') - # Contrasts - if not isinstance(data, list) or not data: - raise ValueError("The `data` argument must be a non-empty list of dabest objects.") - - ## Check if all contrasts are delta-delta or all are mini-meta - contrast_type = ("delta2" if data[0].delta2 - else "mini_meta" if data[0].is_mini_meta - else "delta" - ) - - # contrast_type = "delta2" if data[0].delta2 else "mini_meta" - for contrast in data: - check_contrast_type = ("delta2" if contrast.delta2 - else "mini_meta" if contrast.is_mini_meta - else "delta" - ) - if check_contrast_type != contrast_type: - raise ValueError("Each dabest object supplied must be the same experimental type (mini-meta or delta-delta or neither.)") - - # Idx - idx = kwargs.get('idx') - effect_size = kwargs.get('effect_size') - if idx is not None: - if not isinstance(idx, (tuple, list)): - raise TypeError("`idx` must be a tuple or list of integers.") - - msg1 = "The `idx` argument must have the same length as the number of dabest objects. " - msg2 = "E.g., If two dabest objects are supplied, there should be two lists within `idx`. " - msg3 = "E.g., `idx` = [[1,2],[0,1]]." - _total = 0 - for _group in idx: - if isinstance(_group, int | float): - raise ValueError(msg1+msg2+msg3) - else: - _total += 1 - if _total != len(data): - raise ValueError(msg1+msg2+msg3) - - if idx is not None: - number_of_curves_to_plot = sum([len(i) for i in idx]) - else: - if contrast_type == 'delta': - number_of_curves_to_plot = sum(len(getattr(i, effect_size).results) for i in data) - else: - number_of_curves_to_plot = len(data) - - # Axes - ax = kwargs.get('ax') - fig_size = kwargs.get('fig_size') - if ax is not None and not isinstance(ax, plt.Axes): - raise TypeError("The `ax` must be a `matplotlib.axes.Axes` instance or `None`.") - - # Figure size - if fig_size is not None and not isinstance(fig_size, (tuple, list)): - raise TypeError("`fig_size` must be a tuple or list of two positive integers.") - - # Effect size - effect_size_options = ['mean_diff', 'median_diff', 'cohens_d', 'cohens_h', 'cliffs_delta', 'hedges_g', 'delta_g'] - if not isinstance(effect_size, str) or effect_size not in effect_size_options: - raise TypeError("The `effect_size` argument must be a string and please choose from the following effect sizes: 'mean_diff', 'median_diff', 'cohens_d', 'cohens_h', 'cliffs_delta', 'hedges_g', 'delta_g'.") - if data[0].is_mini_meta and effect_size != 'mean_diff': - raise ValueError("The `effect_size` argument must be `mean_diff` for mini-meta analyses.") - if data[0].delta2 and effect_size not in ['mean_diff', 'hedges_g', 'delta_g']: - raise ValueError("The `effect_size` argument must be `mean_diff`, `hedges_g`, or `delta_g` for delta-delta analyses.") - - # CI type - ci_type = kwargs.get('ci_type') - if ci_type not in ('bca', 'pct'): - raise TypeError("`ci_type` must be either 'bca' or 'pct'.") - - # Horizontal - horizontal = kwargs.get('horizontal') - if not isinstance(horizontal, bool): - raise TypeError("`horizontal` must be a boolean value.") - - # Marker size - marker_size = kwargs.get('marker_size') - if not isinstance(marker_size, (int, float)) or marker_size <= 0: - raise TypeError("`marker_size` must be a positive integer or float.") - - # Custom palette - custom_palette = kwargs.get('custom_palette') - labels = kwargs.get('labels') - if custom_palette is not None and not isinstance(custom_palette, (dict, list, tuple, str, type(None))): - raise TypeError("The `custom_palette` must be either a dictionary, list, string, or `None`.") - if isinstance(custom_palette, dict) and labels is None: - raise ValueError("The `labels` argument must be provided if `custom_palette` is a dictionary.") - if isinstance(custom_palette, (list, tuple)) and len(custom_palette) < number_of_curves_to_plot: - raise ValueError("The `custom_palette` list/tuple must have the same length as the number of `data` provided.") - - # Contrast alpha and desat - contrast_alpha = kwargs.get('contrast_alpha') - contrast_desat = kwargs.get('contrast_desat') - if not isinstance(contrast_alpha, float) or not 0 <= contrast_alpha <= 1: - raise TypeError("`contrast_alpha` must be a float between 0 and 1.") - - if not isinstance(contrast_desat, (float, int)) or not 0 <= contrast_desat <= 1: - raise TypeError("`contrast_desat` must be a float between 0 and 1 or an int (1).") - - # Contrast labels - labels_fontsize = kwargs.get('labels_fontsize') - labels_rotation = kwargs.get('labels_rotation') - if labels is not None and not all(isinstance(label, str) for label in labels): - raise TypeError("The `labels` must be a list of strings or `None`.") - - if labels is not None and len(labels) != number_of_curves_to_plot: - raise ValueError("`labels` must match the number of `data` provided.") - - if not isinstance(labels_fontsize, (int, float)): - raise TypeError("`labels_fontsize` must be an integer or float.") - - if labels_rotation is not None and (not isinstance(labels_rotation, (int, float)) or not 0 <= labels_rotation <= 360): - raise TypeError("`labels_rotation` must be an integer or float between 0 and 360.") - - # Title - title = kwargs.get('title') - title_fontsize = kwargs.get('title_fontsize') - if title is not None and not isinstance(title, str): - raise TypeError("The `title` argument must be a string.") - - if not isinstance(title_fontsize, (int, float)): - raise TypeError("`title_fontsize` must be an integer or float.") - - # Y-label - ylabel = kwargs.get('ylabel') - ylabel_fontsize = kwargs.get('ylabel_fontsize') - if ylabel is not None and not isinstance(ylabel, str): - raise TypeError("The `ylabel` argument must be a string.") - - if not isinstance(ylabel_fontsize, (int, float)): - raise TypeError("`ylabel_fontsize` must be an integer or float.") - - # Y-lim - ylim = kwargs.get('ylim') - if ylim is not None and not isinstance(ylim, (tuple, list)): - raise TypeError("`ylim` must be a tuple or list of two floats.") - if ylim is not None and len(ylim) != 2: - raise ValueError("`ylim` must be a tuple or list of two floats.") - - # Y-ticks - yticks = kwargs.get('yticks') - if yticks is not None and not isinstance(yticks, (tuple, list)): - raise TypeError("`yticks` must be a tuple or list of floats.") - - # Y-ticklabels - yticklabels = kwargs.get('yticklabels') - if yticklabels is not None and not isinstance(yticklabels, (tuple, list)): - raise TypeError("`yticklabels` must be a tuple or list of strings.") - - if yticklabels is not None and not all(isinstance(label, str) for label in yticklabels): - raise TypeError("`yticklabels` must be a list of strings.") - - # Remove spines - remove_spines = kwargs.get('remove_spines') - if not isinstance(remove_spines, bool): - raise TypeError("`remove_spines` must be a boolean value.") - - # Reference band - reference_band = kwargs.get('reference_band') - if reference_band is not None: - if not isinstance(reference_band, list | tuple): - raise TypeError("`reference_band` must be a list/tuple of indices (ints).") - if not all(isinstance(i, int) for i in reference_band): - raise TypeError("`reference_band` must be a list/tuple of indices (ints).") - if any(i >= number_of_curves_to_plot for i in reference_band): - raise ValueError("Index {} chosen is out of range for the contrast objects.".format([i for i in reference_band if i >= number_of_curves_to_plot])) - - # Delta text - delta_text = kwargs.get('delta_text') - if delta_text is not None: - if not isinstance(delta_text, bool): - raise TypeError("`delta_text` must be a boolean value.") - - # Contrast bars - contrast_bars = kwargs.get('contrast_bars') - if contrast_bars is not None: - if not isinstance(contrast_bars, bool): - raise TypeError("`contrast_bars` must be a boolean value.") - - return contrast_type - -def get_kwargs( - violin_kwargs, - zeroline_kwargs, - horizontal, - marker_kwargs, - errorbar_kwargs, - delta_text_kwargs, - contrast_bars_kwargs, - reference_band_kwargs, - marker_size - ): - from .misc_tools import merge_two_dicts - - # Violin kwargs - default_violin_kwargs = { - "widths": 0.5, - "showextrema": False, - "showmedians": False, - "orientation": 'horizontal' if horizontal else 'vertical', - } - if violin_kwargs is None: - violin_kwargs = default_violin_kwargs - else: - violin_kwargs = merge_two_dicts(default_violin_kwargs, violin_kwargs) - - # zeroline kwargs - default_zeroline_kwargs = { - "linewidth": 1, - "color": "black" - } - if zeroline_kwargs is None: - zeroline_kwargs = default_zeroline_kwargs - else: - zeroline_kwargs = merge_two_dicts(default_zeroline_kwargs, zeroline_kwargs) - - # Effect size marker kwargs - default_marker_kwargs = { - 'marker': 'o', - 'markersize': marker_size, - 'color': 'black', - 'alpha': 1, - 'zorder': 2, - } - if marker_kwargs is None: - marker_kwargs = default_marker_kwargs - else: - marker_kwargs = merge_two_dicts(default_marker_kwargs, marker_kwargs) - - # Effect size error bar kwargs - default_errorbar_kwargs = { - 'color': 'black', - 'lw': 2.5, - 'linestyle': '-', - 'alpha': 1, - 'zorder': 1, - } - if errorbar_kwargs is None: - errorbar_kwargs = default_errorbar_kwargs - else: - errorbar_kwargs = merge_two_dicts(default_errorbar_kwargs, errorbar_kwargs) - - # Delta text kwargs - default_delta_text_kwargs = { - "color": None, - "alpha": 1, - "fontsize": 10, - "ha": 'center', - "va": 'center', - "rotation": 0, - "x_coordinates": None, - "y_coordinates": None, - "offset": 0 - } - if delta_text_kwargs is None: - delta_text_kwargs = default_delta_text_kwargs - else: - delta_text_kwargs = merge_two_dicts(default_delta_text_kwargs, delta_text_kwargs) - - # Contrast bars kwargs. - default_contrast_bars_kwargs = { - "color": None, - "zorder":-3, - 'alpha': 0.15 - } - if contrast_bars_kwargs is None: - contrast_bars_kwargs = default_contrast_bars_kwargs - else: - contrast_bars_kwargs = merge_two_dicts(default_contrast_bars_kwargs, contrast_bars_kwargs) - - # reference band kwargs. - default_reference_band_kwargs = { - "span_ax": False, - "color": None, - "alpha": 0.15, - "zorder":-3 - } - if reference_band_kwargs is None: - reference_band_kwargs = default_reference_band_kwargs - else: - reference_band_kwargs = merge_two_dicts(default_reference_band_kwargs, reference_band_kwargs) - - return (violin_kwargs, zeroline_kwargs, marker_kwargs, errorbar_kwargs, - delta_text_kwargs, contrast_bars_kwargs, reference_band_kwargs) - -def color_palette( - custom_palette, - labels, - number_of_curves_to_plot, - contrast_desat - ): - if custom_palette is not None: - if isinstance(custom_palette, dict): - violin_colors = [ - custom_palette.get(c, sns.color_palette()[0]) for c in labels - ] - elif isinstance(custom_palette, list): - violin_colors = custom_palette[: number_of_curves_to_plot] - elif isinstance(custom_palette, str): - if custom_palette in plt.colormaps(): - violin_colors = sns.color_palette(custom_palette, number_of_curves_to_plot) - else: - raise ValueError( - f"The specified `custom_palette` {custom_palette} is not a recognized Matplotlib palette." - ) - else: - violin_colors = sns.color_palette(n_colors=number_of_curves_to_plot) - violin_colors = [sns.desaturate(color, contrast_desat) for color in violin_colors] - return violin_colors - -def forest_plot( - data: list, - idx: Optional[list[int]] = None, - ax: Optional[plt.Axes] = None, - fig_size: tuple[int, int] = None, - effect_size: str = "mean_diff", - ci_type='bca', - horizontal: bool = False, - - marker_size: int = 10, - custom_palette: Optional[Union[dict, list, str]] = None, - contrast_alpha: float = 0.8, - contrast_desat: float = 1, - - labels: list[str] = None, - labels_rotation: int = None, - labels_fontsize: int = 10, - title: str = None, - title_fontsize: int = 16, - ylabel: str = None, - ylabel_fontsize: int = 12, - ylim: Optional[list[float, float]] = None, - yticks: Optional[list[float]] = None, - yticklabels: Optional[list[str]] = None, - remove_spines: bool = True, - - delta_text: bool = True, - delta_text_kwargs: dict = None, - - contrast_bars: bool = True, - contrast_bars_kwargs: dict = None, - reference_band: list|tuple = None, - reference_band_kwargs: dict = None, - - violin_kwargs: Optional[dict] = None, - zeroline_kwargs: Optional[dict] = None, - marker_kwargs: Optional[dict] = None, - errorbar_kwargs: Optional[dict] = None, -)-> plt.Figure: - """ - Custom function that generates a forest plot from given contrast objects, suitable for a range of data analysis types, including those from packages like DABEST-python. - - Parameters - ---------- - data : List - List of contrast objects. - idx : Optional[List[int]], default=None - List of indices to select from the contrast objects if delta-delta experiment. - If None, only the delta-delta objects are plotted. - ax : Optional[plt.Axes], default=None - Matplotlib Axes object for the plot; creates new if None. - additional_plotting_kwargs : Optional[dict], default=None - Further customization arguments for the plot. - fig_size : Tuple[int, int], default=None - Figure size for the plot. - effect_size : str - Type of effect size to plot (e.g., 'mean_diff', `hedges_g` or 'delta_g'). - ci_type : str - Type of confidence interval to plot (bca' or 'pct') - horizontal : bool, default=False - If True, the plot will be horizontal. - marker_size : int, default=12 - Marker size for plotting effect size dots. - custom_palette : Optional[Union[dict, list, str]], default=None - Custom color palette for the plot. - contrast_alpha : float, default=0.8 - Transparency level for violin plots. - contrast_desat : float, default=1 - Saturation level for violin plots. - labels : List[str] - Labels for each contrast. If None, defaults to 'Contrast 1', 'Contrast 2', etc. - labels_rotation : int, default=45 for vertical, 0 for horizontal - Rotation angle for contrast labels. - labels_fontsize : int, default=10 - Font size for contrast labels. - title : str - Plot title, summarizing the visualized data. - title_fontsize : int, default=16 - Font size for the plot title. - ylabel : str - Label for the y-axis, describing the plotted data or effect size. - ylabel_fontsize : int, default=12 - Font size for the y-axis label. - ylim : Optional[Tuple[float, float]] - Limits for the y-axis. - yticks : Optional[List[float]] - Custom y-ticks for the plot. - yticklabels : Optional[List[str]] - Custom y-tick labels for the plot. - remove_spines : bool, default=True - If True, removes plot spines (except the relevant dependent variable spine). - delta_text : bool, default=True - If True, it adds text next to each curve representing the effect size value. - delta_text_kwargs : dict, default=None - Additional keyword arguments for the delta_text. - contrast_bars : bool, default=True - If True, it adds bars from the zeroline to the effect size curve. - contrast_bars_kwargs : dict, default=None - Additional keyword arguments for the contrast_bars. - reference_band: list | tuple, default=None, - It adds reference bands to the relevant effect size curves. - reference_band_kwargs : dict, default=None, - Additional keyword arguments for the reference_band. - violin_kwargs : Optional[dict], default=None - Additional arguments for violin plot customization. - zeroline_kwargs : Optional[dict], default=None - Additional arguments for the zero line customization. - marker_kwargs : Optional[dict], default=None - Additional arguments for the effect size marker customization. - errorbar_kwargs : Optional[dict], default=None - Additional arguments for the effect size error bar customization. - - Returns - ------- - plt.Figure - The matplotlib figure object with the generated forest plot. - """ - from .plot_tools import halfviolin - - # Check for errors in the input arguments - all_kwargs = locals() - contrast_type = check_for_errors(**all_kwargs) - - # Load plot data and extract info - bootstraps, differences, bcalows, bcahighs = load_plot_data( - data = data, - effect_size = effect_size, - contrast_type = contrast_type, - ci_type = ci_type, - idx = idx - ) - # Adjust figure size based on orientation - number_of_curves_to_plot = len(bootstraps) - if ax is not None: - fig = ax.figure - else: - if fig_size is None: - fig_size = (4, 1.3 * number_of_curves_to_plot) if horizontal else (1.3 * number_of_curves_to_plot, 4) - fig, ax = plt.subplots(figsize=fig_size) - - # Get Kwargs - (violin_kwargs, zeroline_kwargs, marker_kwargs, errorbar_kwargs, - delta_text_kwargs, contrast_bars_kwargs, reference_band_kwargs) = get_kwargs( - violin_kwargs = violin_kwargs, - zeroline_kwargs = zeroline_kwargs, - horizontal = horizontal, - marker_kwargs = marker_kwargs, - errorbar_kwargs = errorbar_kwargs, - delta_text_kwargs = delta_text_kwargs, - contrast_bars_kwargs = contrast_bars_kwargs, - reference_band_kwargs = reference_band_kwargs, - marker_size = marker_size - ) - - # Plot the violins and make adjustments - v = ax.violinplot( - bootstraps, - **violin_kwargs - ) - halfviolin( - v, - alpha = contrast_alpha, - half = "bottom" if horizontal else "right", - ) - - ## Plotting the effect sizes and confidence intervals - for k in range(1, number_of_curves_to_plot + 1): - if horizontal: - ax.plot(differences[k - 1], k, **marker_kwargs) - ax.plot([bcalows[k - 1], bcahighs[k - 1]], [k, k], **errorbar_kwargs) - else: - ax.plot(k, differences[k - 1], **marker_kwargs) - ax.plot([k, k], [bcalows[k - 1], bcahighs[k - 1]], **errorbar_kwargs) - - # Aesthetic Adjustments - ## Handle the custom color palette - violin_colors = color_palette( - custom_palette = custom_palette, - labels = labels, - number_of_curves_to_plot = number_of_curves_to_plot, - contrast_desat = contrast_desat - ) - - for patch, color in zip(v["bodies"], violin_colors): - patch.set_facecolor(color) - - ## Add a zero line to the plot - if horizontal: - ax.plot([0, 0], [0, number_of_curves_to_plot+1], **zeroline_kwargs) - else: - ax.plot([0, number_of_curves_to_plot+1], [0, 0], **zeroline_kwargs) - - ## lims - ### Indepedent variable - if horizontal: - ax.set_ylim([0.7, number_of_curves_to_plot + 0.2]) - else: - ax.set_xlim([0.7, number_of_curves_to_plot + 0.5]) - - ## Depedent variable - if ylim is not None: - lim_key = ax.set_xlim if horizontal else ax.set_ylim - lim_key(ylim) - - ## Ticks - ### Indepedent variable - lim_key = ax.set_yticks if horizontal else ax.set_xticks - lim_key(range(1, number_of_curves_to_plot + 1)) - - if labels_rotation == None: - labels_rotation = 0 if horizontal else 45 - if labels is None: - labels = [f"Contrast {i}" for i in range(1, number_of_curves_to_plot + 1)] - lim_key = ax.set_yticklabels if horizontal else ax.set_xticklabels - lim_key(labels, rotation=labels_rotation, fontsize=labels_fontsize, ha="right") - - ### Depedent variable - if yticks is not None: - lim_key = ax.set_xticks if horizontal else ax.set_yticks - lim_key(yticks) - - if yticklabels is not None: - lim_key = ax.set_xticklabels if horizontal else ax.set_yticklabels - lim_key(yticklabels) - - ## y-label - if ylabel is None: - effect_attr_map = { - "mean_diff": "Mean difference", - "median_diff": "Median difference", - "cohens_d": "Cohen's d", - "cohens_h": "Cohen's h", - "cliffs_delta": "Cliff's delta", - "hedges_g": "Hedges' g", - "delta_g": "Delta g" - } - if contrast_type=='delta2' and idx is None and effect_size == "hedges_g": - ylabel = "Delta g" - elif contrast_type=='delta2' and idx is not None and (effect_size == "delta_g" or effect_size == "hedges_g"): - ylabel = "Hedges' g with Delta g" - else: - ylabel = effect_attr_map[effect_size] - lim_key = ax.set_xlabel if horizontal else ax.set_ylabel - lim_key(ylabel, fontsize=ylabel_fontsize) - - ## Setting the title - if title is not None: - ax.set_title(title, fontsize=title_fontsize) - - ## Adjust Spines - if remove_spines: - spines = ["top", "right", "left"] if horizontal else ["top", "right", "bottom"] - ax.spines[spines].set_visible(False) - - # Delta Text - if delta_text: - if delta_text_kwargs.get('color') is not None: - delta_text_colors = [delta_text_kwargs.pop('color')] * number_of_curves_to_plot - else: - delta_text_colors = violin_colors - delta_text_kwargs.pop('color') - - # Collect the X-coordinates for the delta text - delta_text_x_coordinates = delta_text_kwargs.pop('x_coordinates') - delta_text_x_adjustment = delta_text_kwargs.pop('offset') - - if delta_text_x_coordinates is not None: - if not isinstance(delta_text_x_coordinates, (list, tuple)) or not all(isinstance(x, (int, float)) for x in delta_text_x_coordinates): - raise TypeError("delta_text_kwargs['x_coordinates'] must be a list of x-coordinates.") - if len(delta_text_x_coordinates) != number_of_curves_to_plot: - raise ValueError("delta_text_kwargs['x_coordinates'] must have the same length as the number of ticks to plot.") - else: - delta_text_x_coordinates = (np.arange(1, number_of_curves_to_plot + 1) - + (0.5 if not horizontal else -0.4) - + delta_text_x_adjustment - ) - - # Collect the Y-coordinates for the delta text - delta_text_y_coordinates = delta_text_kwargs.pop('y_coordinates') - - if delta_text_y_coordinates is not None: - if not isinstance(delta_text_y_coordinates, (list, tuple)) or not all(isinstance(y, (int, float)) for y in delta_text_y_coordinates): - raise TypeError("delta_text_kwargs['y_coordinates'] must be a list of y-coordinates.") - if len(delta_text_y_coordinates) != number_of_curves_to_plot: - raise ValueError("delta_text_kwargs['y_coordinates'] must have the same length as the number of ticks to plot.") - else: - delta_text_y_coordinates = differences - - if horizontal: - delta_text_x_coordinates, delta_text_y_coordinates = delta_text_y_coordinates, delta_text_x_coordinates - - for idx, x, y, delta in zip(np.arange(0, number_of_curves_to_plot, 1), delta_text_x_coordinates, - delta_text_y_coordinates, differences): - delta_text = np.format_float_positional(delta, precision=2, sign=True, trim="k", min_digits=2) - ax.text(x, y, delta_text, color=delta_text_colors[idx], zorder=5, **delta_text_kwargs) - - # Contrast bars - if contrast_bars: - _bar_color = contrast_bars_kwargs.pop('color') - if _bar_color is not None: - bar_colors = [_bar_color] * number_of_curves_to_plot - else: - bar_colors = violin_colors - for x, y in zip(np.arange(1, number_of_curves_to_plot + 1), differences): - if horizontal: - ax.add_patch(mpatches.Rectangle((0, x-0.25), y, 0.25, color=bar_colors[x-1], **contrast_bars_kwargs)) - else: - ax.add_patch(mpatches.Rectangle((x, 0), 0.25, y, color=bar_colors[x-1], **contrast_bars_kwargs)) - - # Reference band - if reference_band: - _bar_color = reference_band_kwargs.pop('color') - if _bar_color is not None: - bar_colors = [_bar_color] * number_of_curves_to_plot - else: - bar_colors = violin_colors - - span_ax = reference_band_kwargs.pop("span_ax") - summary_xmin, summary_xmax = ax.get_xlim() - summary_ymin, summary_ymax = ax.get_ylim() - - for summary_index in reference_band: - if span_ax == True: - starting_location = summary_ymin if horizontal else summary_xmin - else: - starting_location = summary_index+1 - - summary_color = bar_colors[summary_index] - summary_ci_low, summary_ci_high = bcalows[summary_index], bcahighs[summary_index] - - if horizontal: - ax.add_patch(mpatches.Rectangle( - (summary_ci_low, starting_location), - summary_ci_high-summary_ci_low, summary_ymax+1, - color=summary_color, - **reference_band_kwargs) - ) - else: - ax.add_patch(mpatches.Rectangle( - (starting_location, summary_ci_low), - summary_xmax+1, summary_ci_high-summary_ci_low, - color=summary_color, - **reference_band_kwargs) - ) - - ## Invert Y-axis if horizontal - if horizontal: - ax.invert_yaxis() - - return fig diff --git a/dabest/misc_tools.py b/dabest/misc_tools.py deleted file mode 100644 index d0a56422..00000000 --- a/dabest/misc_tools.py +++ /dev/null @@ -1,1985 +0,0 @@ -"""Convenience functions that don't directly deal with plotting or bootstrap computations are placed here.""" - -# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/misc_tools.ipynb. - -# %% auto #0 -__all__ = ['merge_two_dicts', 'unpack_and_add', 'print_greeting', 'get_varname', 'get_unique_categories', 'get_params', - 'get_kwargs', 'get_color_palette', 'initialize_fig', 'get_plot_groups', 'add_counts_to_ticks', - 'extract_contrast_plotting_ticks', 'set_xaxis_ticks_and_lims', 'show_legend', 'gardner_altman_adjustments', - 'draw_zeroline', 'redraw_independent_spines', 'redraw_dependent_spines', 'extract_group_summaries', - 'color_picker', 'prepare_bars_for_plot'] - -# %% ../nbs/API/misc_tools.ipynb #3c9a6ef1 -import datetime as dt -import numpy as np -from numpy import repeat -import pandas as pd -import seaborn as sns -import matplotlib.pyplot as plt -import matplotlib -import matplotlib.axes as axes - -# %% ../nbs/API/misc_tools.ipynb #5f54be1c -def merge_two_dicts( - x: dict, y: dict -) -> dict: # A dictionary containing a union of all keys in both original dicts. - """ - Given two dicts, merge them into a new dict as a shallow copy. - Any overlapping keys in `y` will override the values in `x`. - - Taken from [here](https://stackoverflow.com/questions/38987/ - how-to-merge-two-python-dictionaries-in-a-single-expression) - - """ - z = x.copy() - z.update(y) - return z - - -def unpack_and_add(l, c): - """Convenience function to allow me to add to an existing list - without altering that list.""" - t = [a for a in l] - t.append(c) - return t - - -def print_greeting(): - """ - Generates a greeting message based on the current time, along with the version information of DABEST. - - This function dynamically generates a greeting ('Good morning', 'Good afternoon', 'Good evening') - based on the current system time. It also retrieves and displays the version of DABEST (Data Analysis - using Bootstrap-Coupled ESTimation). The message includes a header with the DABEST version and the - current time formatted in a user-friendly manner. - - Returns: - str: A formatted string containing the greeting message, DABEST version, and current time. - """ - from .__init__ import __version__ - - line1 = "DABEST v{}".format(__version__) - header = "".join(repeat("=", len(line1))) - spacer = "".join(repeat(" ", len(line1))) - - now = dt.datetime.now() - if 0 < now.hour < 12: - greeting = "Good morning!" - elif 12 < now.hour < 18: - greeting = "Good afternoon!" - else: - greeting = "Good evening!" - - current_time = "The current time is {}.".format(now.ctime()) - - return "\n".join([line1, header, spacer, greeting, current_time]) - - -def get_varname(obj): - matching_vars = [k for k, v in globals().items() if v is obj] - if len(matching_vars) > 0: - return matching_vars[0] - return "" - - -def get_unique_categories(names): - """ - Extract unique categories from various input types. - """ - if isinstance(names, list): - return names - if isinstance(names, np.ndarray): - return names # numpy.unique() returns a sorted array - elif isinstance(names, (pd.Categorical, pd.Series)): - return names.cat.categories if hasattr(names, 'cat') else names.unique() - else: - # For dict_keys and other iterables - return np.unique(list(names)) - -def get_params( - effectsize_df: object, - plot_kwargs: dict, - sankey_kwargs: dict, - barplot_kwargs: dict - ): - """ - Extracts parameters from the `effectsize_df` and `plot_kwargs` objects for use in the plotter function. - - Parameters - ---------- - effectsize_df : object - A `dabest` EffectSizeDataFrame object. - plot_kwargs : dict - Kwargs passed to the plot function. - sankey kwargs : dict - Kwargs relating to the sankey diagram plots - barplot_kwargs : dict - Kwargs relating to the barplot - """ - plot_data = effectsize_df._plot_data - xvar = effectsize_df.xvar - yvar = effectsize_df.yvar - is_paired = effectsize_df.is_paired - delta2 = effectsize_df.delta2 - is_mini_meta = effectsize_df.is_mini_meta - effect_size = effectsize_df.effect_size - proportional = effectsize_df.is_proportional - results = effectsize_df.results - dabest_obj = effectsize_df.dabest_obj - all_plot_groups = dabest_obj._all_plot_groups - idx = dabest_obj.idx - x1_level = dabest_obj.x1_level - experiment_label = dabest_obj.experiment_label - - if effect_size not in ["mean_diff", "hedges_g"] or not delta2: - show_delta2 = False - else: - show_delta2 = plot_kwargs["show_delta2"] - - if effect_size != "mean_diff" or not is_mini_meta: - show_mini_meta = False - else: - show_mini_meta = plot_kwargs["show_mini_meta"] - - if show_delta2 and show_mini_meta: raise ValueError("`show_delta2` and `show_mini_meta` cannot be True at the same time.") - - # Horizontal - horizontal = plot_kwargs["horizontal"] - - # Disable Gardner-Altman plotting if any of the idxs comprise of more than - # two groups or if it is a delta-delta plot. - float_contrast = plot_kwargs["float_contrast"] - if len(idx) > 1 or len(idx[0]) > 2: - float_contrast = False - - if effect_size in ["cliffs_delta"]: - float_contrast = False - - if show_delta2 or show_mini_meta or horizontal: - float_contrast = False - - if not is_paired: - show_pairs = False - else: - show_pairs = plot_kwargs["show_pairs"] - - # Group summaries - group_summaries = plot_kwargs["group_summaries"] - group_summaries = None if barplot_kwargs['errorbar'] is not None else group_summaries - - # Contrast Axes kwargs - ci_type = plot_kwargs["ci_type"] - if ci_type not in ["bca", "pct"]: - raise ValueError("Invalid `ci_type`. Must be either 'bca' or 'pct'.") - - # Boolean for showing Baseline Curve - show_baseline_ec = plot_kwargs["show_baseline_ec"] - - # Sankey details - # We need to extract the `sankey` and `flow` from the kwargs - # to use for varying different kinds of paired proportional plots - # We also don't want to pop the parameter from the kwargs - one_sankey = ( - False if is_paired is not None else None - ) # Flag to indicate if only one sankey is plotted. - two_col_sankey = ( - True if proportional and not one_sankey and sankey_kwargs["sankey"] and not sankey_kwargs["flow"] else False - ) - - # Asymmetric side for swarmplots - asymmetric_side = ( - plot_kwargs["swarm_side"] # Default asymmetric side is right - if plot_kwargs["swarm_side"] is not None - else "right" if not horizontal - else "left" - ) - # Whether to show sample sizes with ticklabels - show_sample_size = plot_kwargs["show_sample_size"] - - return (dabest_obj, plot_data, xvar, yvar, is_paired, effect_size, proportional, all_plot_groups, - idx, show_delta2, show_mini_meta, float_contrast, show_pairs, group_summaries, - horizontal, results, ci_type, x1_level, experiment_label, show_baseline_ec, - one_sankey, two_col_sankey, asymmetric_side, show_sample_size) - -def get_kwargs( - plot_kwargs: dict, - ytick_color, - is_paired: bool = False - ): - """ - Extracts the kwargs from the `plot_kwargs` object for use in the plotter function. - - Parameters - ---------- - plot_kwargs : dict - Kwargs passed to the plot function. - ytick_color : str or color list - Color of the yticks. - is_paired : bool, optional - A boolean flag to determine if the plot is for paired data. Default is False. - """ - from .misc_tools import merge_two_dicts - - # Swarmplot kwargs - default_swarmplot_kwargs = { - "size": plot_kwargs["raw_marker_size"], - "alpha": plot_kwargs["raw_alpha"], - "fontsize": plot_kwargs.get("fontsize_rawxlabel"), - } - if plot_kwargs["swarmplot_kwargs"] is None: - swarmplot_kwargs = default_swarmplot_kwargs - else: - swarmplot_kwargs = merge_two_dicts( - default_swarmplot_kwargs, plot_kwargs["swarmplot_kwargs"] - ) - - # Barplot kwargs - default_barplot_kwargs = { - "estimator": np.mean, - "errorbar": None, - "width": plot_kwargs["bar_width"], - "alpha": plot_kwargs["raw_alpha"], - "err_kws": {'color': 'black'}, - "fontsize": plot_kwargs["fontsize_rawxlabel"] - } - if plot_kwargs["barplot_kwargs"] is None: - barplot_kwargs = default_barplot_kwargs - else: - barplot_kwargs = merge_two_dicts( - default_barplot_kwargs, plot_kwargs["barplot_kwargs"] - ) - - # Sankey Diagram kwargs - default_sankey_kwargs = { - "width": 0.4, - "align": "center", - "sankey": True, - "flow": True, - "alpha": plot_kwargs['raw_alpha'], - "rightColor": False, - "bar_width": 0.2, - "fontsize": plot_kwargs.get("fontsize_rawxlabel") - } - if plot_kwargs["sankey_kwargs"] is None: - sankey_kwargs = default_sankey_kwargs - else: - sankey_kwargs = merge_two_dicts( - default_sankey_kwargs, plot_kwargs["sankey_kwargs"] - ) - - # Violinplot kwargs. - default_contrast_kwargs = { - "widths": 0.5, - "orientation": 'vertical', - "showextrema": False, - "showmedians": False, - "alpha": plot_kwargs["contrast_alpha"], - - } - if plot_kwargs["contrast_kwargs"] is None: - contrast_kwargs = default_contrast_kwargs - else: - contrast_kwargs = merge_two_dicts( - default_contrast_kwargs, plot_kwargs["contrast_kwargs"] - ) - - # Slopegraph kwargs. - default_slopegraph_kwargs = { - "linewidth": 1, - "alpha": plot_kwargs["raw_alpha"], - 'jitter': 0, - 'jitter_seed': 9876543210, - } - if plot_kwargs["slopegraph_kwargs"] is None: - slopegraph_kwargs = default_slopegraph_kwargs - else: - slopegraph_kwargs = merge_two_dicts( - default_slopegraph_kwargs, plot_kwargs["slopegraph_kwargs"] - ) - - # Zero reference-line kwargs. - default_reflines_kwargs = { - "linestyle": "solid", - "linewidth": 0.75, - "zorder": 2, - "color": ytick_color, - } - if plot_kwargs["reflines_kwargs"] is None: - reflines_kwargs = default_reflines_kwargs - else: - reflines_kwargs = merge_two_dicts( - default_reflines_kwargs, plot_kwargs["reflines_kwargs"] - ) - - # Legend kwargs. - default_legend_kwargs = { - "loc": "upper left", - "frameon": False, - } - if plot_kwargs["legend_kwargs"] is None: - legend_kwargs = default_legend_kwargs - else: - legend_kwargs = merge_two_dicts( - default_legend_kwargs, plot_kwargs["legend_kwargs"] - ) - - # Group summaries kwargs. - gs_default = { - "mean_sd", - "median_quartiles", - None - } - if plot_kwargs["group_summaries"] not in gs_default: - raise ValueError( - "group_summaries must be one of" " these: {}.".format(gs_default) - ) - - default_group_summaries_kwargs = { - "zorder": 3, - "lw": 2, - "alpha": 1 if not is_paired else 0.6, - 'gap_width_percent': 1.5, - 'offset': 0.1, - 'color': None - } - if plot_kwargs["group_summaries_kwargs"] is None: - group_summaries_kwargs = default_group_summaries_kwargs - else: - group_summaries_kwargs = merge_two_dicts( - default_group_summaries_kwargs, plot_kwargs["group_summaries_kwargs"] - ) - - # Redraw axes kwargs. - redraw_axes_kwargs = { - "colors": ytick_color, - "facecolors": ytick_color, - "lw": 1, - "zorder": 10, - "clip_on": False, - } - - # Delta dots kwargs. - default_delta_dot_kwargs = { - "color": 'k', - "marker": "^", - "alpha": 0.5, - "zorder": 2, - "size": 3, - "side": "right" - } - if plot_kwargs["delta_dot_kwargs"] is None: - delta_dot_kwargs = default_delta_dot_kwargs - else: - delta_dot_kwargs = merge_two_dicts(default_delta_dot_kwargs, plot_kwargs["delta_dot_kwargs"]) - - # Delta text kwargs. - default_delta_text_kwargs = { - "alpha": 1, - "fontsize": 10, - "ha": 'center', - "va": 'center', - "rotation": 0, - "x_coordinates": None, - "y_coordinates": None, - "offset": 0 - } - if plot_kwargs["delta_text_kwargs"] is None: - delta_text_kwargs = default_delta_text_kwargs - else: - delta_text_kwargs = merge_two_dicts(default_delta_text_kwargs, plot_kwargs["delta_text_kwargs"]) - - # Reference band kwargs. - default_reference_band_kwargs = { - "span_ax": False, - "alpha": 0.15, - "zorder":-3 - } - if plot_kwargs["reference_band_kwargs"] is None: - reference_band_kwargs = default_reference_band_kwargs - else: - reference_band_kwargs = merge_two_dicts(default_reference_band_kwargs, plot_kwargs["reference_band_kwargs"]) - - # Swarm bars kwargs. - default_raw_bars_kwargs = { - "zorder":-3, - "alpha": 0.2 - } - if plot_kwargs["raw_bars_kwargs"] is None: - raw_bars_kwargs = default_raw_bars_kwargs - else: - raw_bars_kwargs = merge_two_dicts(default_raw_bars_kwargs, plot_kwargs["raw_bars_kwargs"]) - - # Contrast bars kwargs. - default_contrast_bars_kwargs = { - "zorder":-3, - "alpha": 0.2 - } - if plot_kwargs["contrast_bars_kwargs"] is None: - contrast_bars_kwargs = default_contrast_bars_kwargs - else: - contrast_bars_kwargs = merge_two_dicts(default_contrast_bars_kwargs, plot_kwargs["contrast_bars_kwargs"]) - - # Table axes for horizontal plot kwargs. - default_table_kwargs = { - 'show': True, - 'color' : 'yellow', - 'alpha' : 0.2, - 'fontsize' : 12, - 'text_color' : 'black', - 'text_units' : '', - 'control_marker' : '-', - 'fontsize_label': 12, - 'label': 'Δ' - } - if plot_kwargs["horizontal_table_kwargs"] is None: - table_kwargs = default_table_kwargs - else: - table_kwargs = merge_two_dicts(default_table_kwargs, plot_kwargs["horizontal_table_kwargs"]) - - # Gridkey kwargs. - default_gridkey_kwargs = { - 'show_es' : plot_kwargs['gridkey_show_es'], # If True, the gridkey will show the effect size of each comparison. - 'show_Ns' : plot_kwargs['gridkey_show_Ns'], # If True, the gridkey will show the number of observations in eachgroup. - 'merge_pairs' : plot_kwargs['gridkey_merge_pairs'], # If True, the gridkey will merge the pairs of groups into a single cell. This is useful for when the groups are paired. - 'delimiters': plot_kwargs['gridkey_delimiters'], # Delimiters to split the group names. - 'marker': "\u25CF", # Marker for the gridkey dots. - } - if plot_kwargs["gridkey_kwargs"] is None: - gridkey_kwargs = default_gridkey_kwargs - else: - gridkey_kwargs = merge_two_dicts(default_gridkey_kwargs, plot_kwargs["gridkey_kwargs"]) - - # Effect size marker kwargs - default_contrast_marker_kwargs = { - 'marker': 'o', - 'markersize': plot_kwargs['contrast_marker_size'], - 'color': ytick_color, - 'alpha': 1, - 'zorder': 2, - } - if plot_kwargs['contrast_marker_kwargs'] is None: - contrast_marker_kwargs = default_contrast_marker_kwargs - else: - contrast_marker_kwargs = merge_two_dicts(default_contrast_marker_kwargs, plot_kwargs['contrast_marker_kwargs']) - - # Effect size error bar kwargs - default_contrast_errorbar_kwargs = { - 'color': ytick_color, - 'lw': 2, - 'linestyle': '-', - 'alpha': 1, - 'zorder': 1, - } - if plot_kwargs['contrast_errorbar_kwargs'] is None: - contrast_errorbar_kwargs = default_contrast_errorbar_kwargs - else: - contrast_errorbar_kwargs = merge_two_dicts(default_contrast_errorbar_kwargs, plot_kwargs['contrast_errorbar_kwargs']) - - # Prop sample counts kwargs - default_prop_sample_counts_kwargs = { - 'color': 'k', - 'zorder': 5, - 'ha': 'center', - 'va': 'center' - } - if plot_kwargs['prop_sample_counts_kwargs'] is None: - prop_sample_counts_kwargs = default_prop_sample_counts_kwargs - else: - prop_sample_counts_kwargs = merge_two_dicts(default_prop_sample_counts_kwargs, plot_kwargs['prop_sample_counts_kwargs']) - - - # RM Lines kwargs - default_contrast_paired_lines_kwargs = { - "linestyle": "-", - "linewidth": 2, - "zorder": -2, - "color": 'dimgray', - "alpha": 1 - } - if plot_kwargs["contrast_paired_lines_kwargs"] is None: - contrast_paired_lines_kwargs = default_contrast_paired_lines_kwargs - else: - contrast_paired_lines_kwargs = merge_two_dicts(default_contrast_paired_lines_kwargs, plot_kwargs["contrast_paired_lines_kwargs"]) - - # Return the kwargs. - return (swarmplot_kwargs, barplot_kwargs, sankey_kwargs, contrast_kwargs, slopegraph_kwargs, - reflines_kwargs, legend_kwargs, group_summaries_kwargs, redraw_axes_kwargs, delta_dot_kwargs, - delta_text_kwargs, reference_band_kwargs, raw_bars_kwargs, contrast_bars_kwargs, table_kwargs, gridkey_kwargs, - contrast_marker_kwargs, contrast_errorbar_kwargs, prop_sample_counts_kwargs, contrast_paired_lines_kwargs) - - -def get_color_palette( - plot_kwargs: dict, - plot_data: pd.DataFrame, - xvar: str, - show_pairs: bool, - idx: list, - all_plot_groups: list, - delta2: bool, - proportional: bool - ): - """ - Create the color palette to be used in the plotter function. - - Parameters - ---------- - plot_kwargs : dict - Kwargs passed to the plot function. - plot_data : object (Dataframe) - A dataframe of plot data. - xvar : str - The name of the x-axis variable. - show_pairs : bool - A boolean flag to determine if the plot is for paired data. - idx : list - A list of tuples containing the group names. - all_plot_groups : list - A list of all the group names. - delta2 : bool - A boolean flag to determine if the plot will have a delta-delta effect size. - proportional : bool - A boolean flag to determine if the plot is for a proportional plot. - """ - sankey = True if proportional and show_pairs else False - - # Create color palette that will be shared across subplots. - color_col = plot_kwargs["color_col"] - if color_col is None: - color_groups = pd.unique(plot_data[xvar]) - bootstraps_color_by_group = True - else: - if color_col not in plot_data.columns: - raise KeyError("``{}`` is not a column in the data.".format(color_col)) - color_groups = pd.unique(plot_data[color_col]) - bootstraps_color_by_group = False - if show_pairs: - if plot_kwargs["custom_palette"] is not None: - if delta2 or sankey: - bootstraps_color_by_group = False - else: - bootstraps_color_by_group = True - else: - bootstraps_color_by_group = False - - # Handle the color palette. - filled = True - empty_circle = plot_kwargs["empty_circle"] - color_by_subgroups = ( - True if empty_circle else False - ) # boolean flag to determine if colour is being grouped by subgroup or the default - if empty_circle: - # Handling color_by_subgroups - # For now, color_by_subgroups can only be True for multi-2-group and 2-group comparison - if isinstance(idx[0], str): - if len(idx) > 2: - color_by_subgroups = False - else: - for group_i in idx: - if len(group_i) > 2: - color_by_subgroups = False - - # filled is now a list, which determines the which group in idx has their dots filled for the swarmplot - filled = [] - for i in range(len(idx)): - filled.append(False) - filled.extend([True] * (len(idx[i]) - 1)) - - if color_col is not None: - if sankey: - names = [1, 0] - else: - names = color_groups if not color_by_subgroups else idx - else: - if sankey: - names = [1, 0] - else: - names = all_plot_groups if not color_by_subgroups else idx - - n_groups = len(color_groups) - custom_pal = plot_kwargs["custom_palette"] - raw_desat = plot_kwargs["raw_desat"] - contrast_desat = plot_kwargs["contrast_desat"] - - if custom_pal is None: - unsat_colors = sns.color_palette(n_colors=n_groups) - if empty_circle and not color_by_subgroups: - unsat_colors = [sns.color_palette("gray")[3]] + unsat_colors - else: - if isinstance(custom_pal, dict): - if delta2: - groups_in_palette = { - k: custom_pal[k] for k in color_groups - } - elif proportional and not sankey: # barplots (unpaired proportional data) - keys = list(custom_pal.keys()) - if all(k in keys for k in [1, 0]) and len(keys) == 2: - groups_in_palette = { - k: custom_pal[k] for k in [1, 0] - } - bootstraps_color_by_group = False - else: - groups_in_palette = { - k: custom_pal[k] for k in all_plot_groups if k in color_groups - } - elif sankey: - groups_in_palette = { - k: custom_pal[k] for k in [1, 0] - } - elif color_col is None: - groups_in_palette = { - k: custom_pal[k] for k in all_plot_groups if k in color_groups - } - else: - raise ValueError("The `custom_palette` dictionary is not supported when `color_col` is not None.") - - names = groups_in_palette.keys() - unsat_colors = groups_in_palette.values() - - elif isinstance(custom_pal, list): - if sankey: - if len(custom_pal) != 2: - raise ValueError("To specify a custom palette for a Sankey diagram, you must provide exactly two colors.") - else: - groups_in_palette = { - k: custom_pal[k] for k in [1, 0] - } - names = groups_in_palette.keys() - unsat_colors = groups_in_palette.values() - elif len(custom_pal) < n_groups: - err1 = "The specified `custom_palette` has fewer colors than the number of groups." - err2 = " Please specify a custom palette with at least {} colors.".format(n_groups) - raise ValueError(err1 + err2) - else: - unsat_colors = custom_pal[0:n_groups] - - elif isinstance(custom_pal, str): - # check it is in the list of matplotlib palettes. - if custom_pal in plt.colormaps(): - unsat_colors = sns.color_palette(custom_pal, n_groups) - else: - err1 = "The specified `custom_palette` {}".format(custom_pal) - err2 = " is not a matplotlib palette. Please check." - raise ValueError(err1 + err2) - - if custom_pal is None and color_col is None: - categories = get_unique_categories(names) - raw_colors = [sns.desaturate(c, raw_desat) for c in unsat_colors] - contrast_colors = [sns.desaturate(c, contrast_desat) for c in unsat_colors] - if color_by_subgroups: - plot_palette_raw = dict() - plot_palette_contrast = dict() - for i in range(len(idx)): - for names_i in idx[i]: - plot_palette_raw[names_i] = raw_colors[i] - plot_palette_contrast[names_i] = contrast_colors[i] - else: - plot_palette_raw = dict(zip(categories, raw_colors)) - plot_palette_contrast = dict(zip(categories, contrast_colors)) - else: - raw_colors = [sns.desaturate(c, raw_desat) for c in unsat_colors] - contrast_colors = [sns.desaturate(c, contrast_desat) for c in unsat_colors] - if color_by_subgroups: - plot_palette_raw = dict() - plot_palette_contrast = dict() - for i in range(len(idx)): - for names_i in idx[i]: - plot_palette_raw[names_i] = raw_colors[i] - plot_palette_contrast[names_i] = contrast_colors[i] - else: - plot_palette_raw = dict(zip(names, raw_colors)) - plot_palette_contrast = dict(zip(names, contrast_colors)) - plot_palette_sankey = dict(zip(names, unsat_colors)) - - # For Sankey Diagram plot, each bar will have the same two colors if custom_pal is None - # default color palette will be set to "hls" - if custom_pal is None: - plot_palette_sankey = None - - return (color_col, bootstraps_color_by_group, n_groups, filled, raw_colors, - plot_palette_raw, plot_palette_contrast, plot_palette_sankey) - -def initialize_fig( - plot_kwargs: dict, - dabest_obj: object, - show_delta2: bool, - show_mini_meta: bool, - is_paired: bool, - show_pairs: bool, - proportional: bool, - float_contrast: bool, - effect_size_type: str, - yvar: str, - horizontal: bool, - show_table: bool, - color_col: str, - ): - """ - Initialize the figure and axes for the plotter function. - - Parameters - ---------- - plot_kwargs : dict - Kwargs passed to the plot function. - dabest_obj : object (EffectSizeDataFrame) - A `dabest` EffectSizeDataFrame object. - show_delta2 : bool - A boolean flag to determine if the plot will have a delta-delta effect size. - show_mini_meta : bool - A boolean flag to determine if the plot will have a mini-meta effect size. - is_paired : bool - A boolean flag to determine if the plot is for paired data. - show_pairs : bool - A boolean flag to determine if the plot will show the paired data. - proportional : bool - A boolean flag to determine if the plot is for proportional data. - float_contrast : bool - A boolean flag to determine if the plot is for floating contrast data. - effect_size_type : str - The type of effect size to be plotted. - yvar : str - The name of the y-axis variable. - horizontal : bool - A boolean flag to determine if the plot is for horizontal plotting. - show_table : dict - A boolean flag to determine if the table will be shown in horizontal plot. - color_col : str - The column name for coloring the data points. - """ - # Params - fig_size = plot_kwargs["fig_size"] - face_color = plot_kwargs["face_color"] - if plot_kwargs["face_color"] is None: - face_color = "white" - - # Create Figure and Axes - if fig_size is None: - all_groups_count = np.sum([len(i) for i in dabest_obj.idx]) - # Increase the width (vertical layout) or height (horizontal layout) for delta-delta or mini-meta graph - if show_delta2 or show_mini_meta: - all_groups_count += 1 - - if horizontal: - frac = 0.3 if is_paired or show_mini_meta else 0.5 - fig_size = (7, 1 + (frac * all_groups_count)) - else: - if is_paired and show_pairs and proportional is False: - if color_col is not None and float_contrast: - frac = 0.9 - else: - frac = 0.8 - else: - frac = 1 - if float_contrast: - height_inches = 4 - each_group_width_inches = 2.5 * frac - else: - height_inches = 6 - each_group_width_inches = 1.5 * frac - - width_inches = each_group_width_inches * all_groups_count - fig_size = (width_inches, height_inches) - - init_fig_kwargs = dict(figsize=fig_size, dpi=plot_kwargs["dpi"], tight_layout=True) - - width_ratios_ga = [2.5, 1] - h_space_cummings = (0.1 if plot_kwargs["gridkey"] is not None - else 0.3) - - if plot_kwargs["ax"] is not None: - # New in v0.2.6. - # Use inset axes to create the estimation plot inside a single axes. - # Author: Adam L Nekimken. (PR #73) - rawdata_axes = plot_kwargs["ax"] - ax_position = rawdata_axes.get_position() # [[x0, y0], [x1, y1]] - - fig = rawdata_axes.get_figure() - fig.patch.set_facecolor(face_color) - - if horizontal: - plot_width_ratios = [1, 0.7, 0.3] - contrast_wspace = 0.05 - contrast_axes = rawdata_axes.inset_axes( - [1+contrast_wspace, 0, (plot_width_ratios[1]/plot_width_ratios[0]), 1] - ) - if show_table: - table_axes = rawdata_axes.inset_axes( - [1+contrast_wspace+(plot_width_ratios[1]/plot_width_ratios[0]), 0, (plot_width_ratios[2]/plot_width_ratios[0]), 1] - ) - else: - table_axes = None - - rawdata_axes.set_position( - [ax_position.x0, - ax_position.y0, - (ax_position.x1 - ax_position.x0) * (plot_width_ratios[0] / sum(plot_width_ratios)), - (ax_position.y1 - ax_position.y0)] - ) - rawdata_axes.contrast_axes = contrast_axes - rawdata_axes.table_axes = table_axes - - else: - if float_contrast: - axins = rawdata_axes.inset_axes( - [1, 0, width_ratios_ga[1] / width_ratios_ga[0], 1] - ) - rawdata_axes.set_position( # [l, b, w, h] - [ - ax_position.x0, - ax_position.y0, - (ax_position.x1 - ax_position.x0) - * (width_ratios_ga[0] / sum(width_ratios_ga)), - (ax_position.y1 - ax_position.y0), - ] - ) - - contrast_axes = axins - else: - axins = rawdata_axes.inset_axes([0, -1 - h_space_cummings, 1, 1]) - plot_height = (ax_position.y1 - ax_position.y0) / (2 + h_space_cummings) - rawdata_axes.set_position( - [ - ax_position.x0, - ax_position.y0 + (1 + h_space_cummings) * plot_height, - (ax_position.x1 - ax_position.x0), - plot_height, - ] - ) - - # Set axes - contrast_axes = axins - rawdata_axes.contrast_axes = axins - table_axes = None - - else: - # Here, we hardcode some figure parameters. - if horizontal: - if show_table: - fig, axx = plt.subplots( - ncols=3, gridspec_kw={'width_ratios' : [1,0.7,0.3], 'wspace' : 0.05}, **init_fig_kwargs - ) - else: - fig, axx = plt.subplots( - ncols=2, gridspec_kw={'width_ratios' : [1,0.7], 'wspace' : 0.05}, **init_fig_kwargs - ) - else: - if float_contrast: - fig, axx = plt.subplots( - ncols=2, - gridspec_kw={"width_ratios": width_ratios_ga, "wspace": 0}, - **init_fig_kwargs - ) - else: - fig, axx = plt.subplots( - nrows=2, gridspec_kw={"hspace": h_space_cummings}, **init_fig_kwargs - ) - fig.patch.set_facecolor(face_color) - - # Set axes - rawdata_axes = axx[0] - contrast_axes = axx[1] - table_axes = axx[2] if horizontal and show_table else None - - - # Title - title, fontsize_title = plot_kwargs["title"], plot_kwargs["fontsize_title"] - if title is not None: - if plot_kwargs["ax"] is not None: - rawdata_axes.set_title(title, fontsize=fontsize_title) - else: - fig.suptitle(title, fontsize=fontsize_title) - - rawdata_axes.set_frame_on(False) - contrast_axes.set_frame_on(False) - if horizontal and show_table: - table_axes.set_frame_on(False) - - # Swarmplot ylim (Vertical) or xlim (Horizontal) - raw_ylim = plot_kwargs["raw_ylim"] - if raw_ylim is not None: - if not isinstance(raw_ylim, list) and not isinstance(raw_ylim, tuple) or len(raw_ylim) != 2: - raise ValueError("`raw_ylim` must be a tuple/list of the lower and upper bound.") - if horizontal: - rawdata_axes.set_xlim(raw_ylim) - else: - rawdata_axes.set_ylim(raw_ylim) - - # Contrastplot ylim (Vertical) or xlim (Horizontal) - if horizontal or not float_contrast: - contrast_ylim, delta2_ylim = plot_kwargs["contrast_ylim"], plot_kwargs["delta2_ylim"] - if contrast_ylim is not None or (delta2_ylim is not None and show_delta2): - if contrast_ylim is not None: - if delta2_ylim is not None and show_delta2: - if contrast_ylim != delta2_ylim: - raise ValueError("Please check if `contrast_ylim` and `delta2_ylim` are assigned with same values.") - else: - contrast_ylim = delta2_ylim - - if not isinstance(contrast_ylim, list) and not isinstance(contrast_ylim, tuple) or len(contrast_ylim) != 2: - raise ValueError("`contrast_ylim` must be a tuple/list of the lower and upper bound.") - - if effect_size_type == "cliffs_delta": - # Ensure the ylims for a cliffs_delta plot never exceed [-1, 1]. - l = contrast_ylim[0] - h = contrast_ylim[1] - low = -1 if l < -1 else l - high = 1 if h > 1 else h - if horizontal: - contrast_axes.set_xlim(low, high) - else: - contrast_axes.set_ylim(low, high) - else: - if horizontal: - contrast_axes.set_xlim(contrast_ylim) - else: - contrast_axes.set_ylim(contrast_ylim) - - # Set raw axes y-label. - raw_label = plot_kwargs["raw_label"] - if raw_label is None: - if proportional: - raw_label = "Proportion of success" if effect_size_type != "cohens_h" else "Value" - else: - raw_label = yvar - - fontsize_rawylabel = plot_kwargs["fontsize_rawylabel"] - if horizontal: - rawdata_axes.set_xlabel(raw_label, fontsize=fontsize_rawylabel) - rawdata_axes.set_ylabel("") - else: - rawdata_axes.set_ylabel(raw_label, fontsize=fontsize_rawylabel) - rawdata_axes.set_xlabel("") - - # Set contrast axes y-label. - contrast_label_dict = { - "mean_diff": "mean difference", - "median_diff": "median difference", - "cohens_d": "Cohen's d", - "hedges_g": "Hedges' g", - "cliffs_delta": "Cliff's delta", - "cohens_h": "Cohen's h", - } - - if proportional and effect_size_type != "cohens_h": - default_contrast_label = "proportion difference" - else: - default_contrast_label = contrast_label_dict[effect_size_type] - - if plot_kwargs["contrast_label"] is None: - if is_paired: - contrast_label = "Paired\n{}".format(default_contrast_label) - else: - contrast_label = default_contrast_label.capitalize() - else: - contrast_label = plot_kwargs["contrast_label"] - - fontsize_contrastylabel = plot_kwargs["fontsize_contrastylabel"] - - if horizontal: - contrast_axes.set_xlabel(contrast_label, fontsize=fontsize_contrastylabel) - else: - contrast_axes.set_ylabel(contrast_label, fontsize=fontsize_contrastylabel) - if float_contrast: - contrast_axes.yaxis.set_label_position("right") - - return fig, rawdata_axes, contrast_axes, table_axes - -def get_plot_groups( - is_paired: bool, - idx: list, - proportional: bool, - all_plot_groups: list - ): - """ - Extract the plot groups from the `idx` object for use in the plotter function. - - Parameters - ---------- - is_paired : bool - A boolean flag to determine if the plot is for paired data. - idx : list - A list of tuples containing the group names. - proportional : bool - A boolean flag to determine if the plot is for proportional data. - all_plot_groups : list - A list of all the group names. - """ - - if is_paired == "baseline": - idx_pairs = [ - (control, test) - for i in idx - for control, test in zip([i[0]] * (len(i) - 1), i[1:]) - ] - temp_idx = idx if not proportional else idx_pairs - else: - idx_pairs = [ - (control, test) for i in idx for control, test in zip(i[:-1], i[1:]) - ] - temp_idx = idx if not proportional else idx_pairs - - # Determine temp_all_plot_groups based on proportional condition - plot_groups = [item for i in temp_idx for item in i] - temp_all_plot_groups = all_plot_groups if not proportional else plot_groups - - return temp_idx, temp_all_plot_groups - - -def add_counts_to_ticks( - plot_data: pd.DataFrame, - xvar: str, - yvar: str, - rawdata_axes: axes.Axes, - plot_kwargs: dict, - flow: bool, - horizontal: bool - ): - """ - - Add the counts to the raw data axes labels. - - Parameters - ---------- - plot_data : object (Dataframe) - A dataframe of plot data. - xvar : str - The name of the x-axis variable. - yvar : str - The name of the y-axis variable. - rawdata_axes : object (Axes) - The raw data axes. - plot_kwargs : dict - Kwargs passed to the plot function. - flow : bool - Whether sankey flow is enabled or not. - horizontal : bool - A boolean flag to determine if the plot is for horizontal plotting. - """ - - # Add the counts to the rawdata axes xticks. - counts = plot_data.groupby(xvar, observed=False).count()[yvar] - - def lookup_value(text): - try: - return str(counts.loc[text]) - except KeyError: - try: - numeric_key = pd.to_numeric(text, errors='coerce') - if pd.notnull(numeric_key): - return str(counts.loc[numeric_key]) - except (ValueError, KeyError): - pass - print(f"Key '{text}' not found in counts.") - return "N/A" - - ticks_with_counts = [] - if horizontal: - get_label, get_ticks = rawdata_axes.get_yticklabels, rawdata_axes.get_yticks - set_label, set_major_loc_method= rawdata_axes.set_yticklabels, rawdata_axes.yaxis.set_major_locator - else: - get_label, get_ticks = rawdata_axes.get_xticklabels, rawdata_axes.get_xticks - set_label, set_major_loc_method = rawdata_axes.set_xticklabels, rawdata_axes.xaxis.set_major_locator - - for ticklab in get_label(): - t = ticklab.get_text() - - if horizontal and not flow: - te = t.split('v.s. ')[-1] # Get the last line of the label - else: - te = t.split('\n')[-1] # Get the last line of the label - - value = lookup_value(te) - if horizontal: - ticks_with_counts.append(f"{t} (N={value})") - else: - ticks_with_counts.append(f"{t}\n(N={value})") - - set_major_loc_method(plt.FixedLocator(get_ticks())) - - # label = ticks_with_counts if plot_kwargs['show_sample_size'] else get_label() - # set_label(label, fontsize=plot_kwargs.get("fontsize_rawxlabel")) - - set_label(ticks_with_counts, fontsize=plot_kwargs.get("fontsize_rawxlabel")) - - # Ensure ticks are at the correct locations - set_major_loc_method(plt.FixedLocator(get_ticks())) - -def extract_contrast_plotting_ticks( - is_paired: bool, - show_pairs: bool, - two_col_sankey: bool, - plot_groups: list, - idx: list, - sankey_control_group: list - ): - """ - Extract the contrast plotting ticks from the `idx` object for use in the plotter function. - - Parameters - ---------- - is_paired : bool - A boolean flag to determine if the plot is for paired data. - show_pairs : bool - A boolean flag to determine if the plot will show the paired data. - two_col_sankey : bool - A boolean flag to determine if the plot will show a two-column sankey diagram. - plot_groups : list - A list of the plot groups. - idx : list - A list of tuples containing the group names. - sankey_control_group : list - A list of the control group names. - """ - # Take note of where the `control` groups are. - ticks_to_skip_contrast = None - ticks_to_start_twocol_sankey = None - if is_paired == "baseline" and show_pairs: - if two_col_sankey: - ticks_to_skip = [] - ticks_to_plot = np.arange(0, len(plot_groups) / 2).tolist() - ticks_to_start_twocol_sankey = np.cumsum([len(i) - 1 for i in idx]).tolist() - ticks_to_start_twocol_sankey.pop() - ticks_to_start_twocol_sankey.insert(0, 0) - else: - ticks_to_skip = np.cumsum([len(t) for t in idx])[:-1].tolist() - ticks_to_skip.insert(0, 0) - # Then obtain the ticks where we have to plot the effect sizes. - ticks_to_plot = [ - t for t in range(0, len(plot_groups)) if t not in ticks_to_skip - ] - ticks_to_skip_contrast = np.cumsum([(len(t)) for t in idx])[:-1].tolist() - ticks_to_skip_contrast.insert(0, 0) - else: - if two_col_sankey: - ticks_to_skip = [len(sankey_control_group)] - # Then obtain the ticks where we have to plot the effect sizes. - ticks_to_plot = [ - t for t in range(0, len(plot_groups)) if t not in ticks_to_skip - ] - ticks_to_skip = [] - ticks_to_start_twocol_sankey = np.cumsum([len(i) - 1 for i in idx]).tolist() - ticks_to_start_twocol_sankey.pop() - ticks_to_start_twocol_sankey.insert(0, 0) - else: - ticks_to_skip = np.cumsum([len(t) for t in idx])[:-1].tolist() - ticks_to_skip.insert(0, 0) - # Then obtain the ticks where we have to plot the effect sizes. - ticks_to_plot = [ - t for t in range(0, len(plot_groups)) if t not in ticks_to_skip - ] - - ticks_for_baseline_ec = ticks_to_skip - - return ticks_to_skip, ticks_to_plot, ticks_for_baseline_ec, ticks_to_skip_contrast, ticks_to_start_twocol_sankey - -def set_xaxis_ticks_and_lims( - show_delta2: bool, - show_mini_meta: bool, - rawdata_axes: axes.Axes, - contrast_axes: axes.Axes, - show_pairs: bool, - float_contrast: bool, - ticks_to_skip: list, - contrast_xtick_labels: list, - plot_kwargs: dict, - proportional: bool, - horizontal: bool - ): - """ - Set the x-axis/yaxis ticks and limits for the plotter function. - - Parameters - ---------- - show_delta2 : bool - A boolean flag to determine if the plot will have a delta-delta effect size. - show_mini_meta : bool - A boolean flag to determine if the plot will have a mini-meta effect size. - rawdata_axes : object (Axes) - The raw data axes. - contrast_axes : object (Axes) - The contrast axes. - show_pairs : bool - A boolean flag to determine if the plot will show the paired data. - float_contrast : bool - A boolean flag to determine if the plot is a GA or Cumming design. - ticks_to_skip : list - A list of ticks to skip. - contrast_xtick_labels : list - A list of contrast xtick labels. - plot_kwargs : dict - Kwargs passed to the plot function. - proportional: bool - A boolean flag to determine if the plot is a proportional plot. - horizontal : bool - A boolean flag to determine if the plot is for horizontal plotting. - """ - - if horizontal: - # Ticks - if show_delta2 is False and show_mini_meta is False: - contrast_axes.set_yticks(rawdata_axes.get_yticks()) - else: - temp = rawdata_axes.get_yticks() - temp = np.append(temp, [max(temp) + 0, max(temp) + 1]) - contrast_axes.set_yticks(temp) - - # Lims - if show_pairs: - max_x = contrast_axes.get_ylim()[1] - rawdata_axes.set_ylim(-0.375, max_x) - - if proportional: - rawdata_axes.set_ylim(-0.375, max_x+0.1) - - if show_delta2 or show_mini_meta: - # Increase the ylim of raw data by 2 - temp = rawdata_axes.get_ylim() - if show_pairs: - rawdata_axes.set_ylim(temp[0], temp[1] + 0.00) - else: - rawdata_axes.set_ylim(temp[0], temp[1] + 1) - contrast_axes.set_ylim(rawdata_axes.get_ylim()) - else: - contrast_axes.set_ylim(rawdata_axes.get_ylim()) - # Vertical - else: - # Ticks - if show_delta2 is False and show_mini_meta is False: - contrast_axes.set_xticks(rawdata_axes.get_xticks()) - else: - temp = rawdata_axes.get_xticks() - temp = np.append(temp, [max(temp) + 1]) - contrast_axes.set_xticks(temp) - - # Lims - if show_pairs: - max_x = contrast_axes.get_xlim()[1] - rawdata_axes.set_xlim(-0.375, max_x) - - if float_contrast: - contrast_axes.set_xlim(0.5, 1.5) - - elif show_delta2: - if show_pairs: - rawdata_axes.set_xlim(-0.375, 4.75) - else: - rawdata_axes.set_xlim(-0.5, 4.75) - contrast_axes.set_xlim(rawdata_axes.get_xlim()) - - elif show_mini_meta: - # Increase the xlim of raw data by 2 - temp = rawdata_axes.get_xlim() - if show_pairs: - rawdata_axes.set_xlim(temp[0], temp[1] + 0.5) - else: - rawdata_axes.set_xlim(temp[0], temp[1] + 1) - contrast_axes.set_xlim(rawdata_axes.get_xlim()) - else: - contrast_axes.set_xlim(rawdata_axes.get_xlim()) - - # Properly label the contrast ticks. - for t in ticks_to_skip: - contrast_xtick_labels.insert(t, "") - - contrast_axes.set_xticklabels( - contrast_xtick_labels, fontsize=plot_kwargs["fontsize_contrastxlabel"] - ) - - -def show_legend( - legend_labels: list, - legend_handles: list, - rawdata_axes: axes.Axes, - contrast_axes: axes.Axes, - table_axes: axes.Axes, - float_contrast: bool, - show_pairs: bool, - horizontal: bool, - legend_kwargs: dict, - table_kwargs: dict - ): - """ - Show the legend for the plotter function. - - Parameters - ---------- - legend_labels : list - A list of legend labels. - legend_handles : list - A list of legend handles. - rawdata_axes : object (Axes) - The raw data axes. - contrast_axes : object (Axes) - The contrast axes. - table_axes : object (Axes) - The table axes. - float_contrast : bool - A boolean flag to determine if the plot is GA or Cumming format. - show_pairs : bool - A boolean flag to determine if the plot will show the paired data. - horizontal : bool - A boolean flag to determine if the plot is for horizontal plotting. - legend_kwargs : dict - Kwargs passed to the legend function. - """ - - legend_labels_unique = np.unique(legend_labels) - unique_idx = np.unique(legend_labels, return_index=True)[1] - legend_handles_unique = ( - pd.Series(legend_handles, dtype="object").loc[unique_idx] - ).tolist() - - # Location of the legend - if "bbox_to_anchor" not in legend_kwargs.keys(): - if horizontal: - bta = (1,1) - else: - if float_contrast: - bta = (2.00, 1.02) if show_pairs else (1.5, 1.02) - else: - bta = (1.02, 1.0) if show_pairs else (1.0, 1.0) - legend_kwargs.update({'bbox_to_anchor': bta}) - - # Pick the ax to plot - if horizontal: - if table_kwargs['show']: - axes_with_legend = table_axes - else: - axes_with_legend = contrast_axes - elif float_contrast: - axes_with_legend = contrast_axes - else: - axes_with_legend = rawdata_axes - - # Plot the legend - if len(legend_handles_unique) > 0: - leg = axes_with_legend.legend( - legend_handles_unique, - legend_labels_unique, - handlelength=0.5, - **legend_kwargs - ) - if show_pairs: - for line in leg.get_lines(): - line.set_linewidth(3.0) - -def gardner_altman_adjustments( - effect_size_type: str, - plot_data: pd.DataFrame, - xvar: str, - yvar: str, - current_control: str, - current_group: str, - rawdata_axes: axes.Axes, - contrast_axes: axes.Axes, - results: pd.DataFrame, - current_effsize: float, - is_paired: bool, - one_sankey: bool, - reflines_kwargs: dict, - redraw_axes_kwargs: dict - ): - """ - Aesthetic adjustments specific to Gardner-Altman plots (float_contrast=True). - - Parameters - ---------- - effect_size_type : str - The type of effect size. - plot_data : object (Dataframe) - A dataframe of plot data. - xvar : str - The name of the x-axis variable. - yvar : str - The name of the y-axis variable. - current_control : str - The name of the current control group. - current_group : str - The name of the current test group. - rawdata_axes : object (Axes) - The raw data axes. - contrast_axes : object (Axes) - The contrast axes. - results : object (DataFrame) - A dataframe of the results. - current_effsize : float - The current effect size. - is_paired : bool - A boolean flag to determine if the plot is for paired data. - one_sankey : bool - A boolean flag to determine if the plot is for a single sankey diagram. - reflines_kwargs : dict - Kwargs passed to the reference lines. - redraw_axes_kwargs : dict - Kwargs passed to the redraw axes. - """ - from ._stats_tools.effsize import ( - _compute_standardizers, - _compute_hedges_correction_factor, - ) - - og_xlim_raw, og_ylim_raw = rawdata_axes.get_xlim(), rawdata_axes.get_ylim() - - # Normalize ylims and despine the floating contrast axes. - # Check that the effect size is within the swarm ylims. - if effect_size_type in ["mean_diff", "cohens_d", "hedges_g", "cohens_h"]: - control_group_summary = ( - plot_data.groupby(xvar, observed=False) - .mean(numeric_only=True) - .loc[current_control, yvar] - ) - test_group_summary = ( - plot_data.groupby(xvar, observed=False).mean(numeric_only=True).loc[current_group, yvar] - ) - elif effect_size_type == "median_diff": - control_group_summary = ( - plot_data.groupby(xvar, observed=False).median(numeric_only=True).loc[current_control, yvar] - ) - test_group_summary = ( - plot_data.groupby(xvar, observed=False).median(numeric_only=True).loc[current_group, yvar] - ) - - _, contrast_xlim_max = contrast_axes.get_xlim() - - difference = float(results.difference[0]) - - if effect_size_type in ["mean_diff", "median_diff"]: - # Align 0 of contrast_axes to reference group mean of rawdata_axes. - # If the effect size is positive, shift the contrast axis up. - rawdata_ylims = np.array(rawdata_axes.get_ylim()) - if current_effsize > 0: - rightmin, rightmax = rawdata_ylims - current_effsize - # If the effect size is negative, shift the contrast axis down. - elif current_effsize < 0: - rightmin, rightmax = rawdata_ylims + current_effsize - else: - rightmin, rightmax = rawdata_ylims - - contrast_axes.set_ylim(rightmin, rightmax) - - og_ylim_contrast = rawdata_axes.get_ylim() - np.array(control_group_summary) - - contrast_axes.set_ylim(og_ylim_contrast) - contrast_axes.set_xlim(contrast_xlim_max - 1, contrast_xlim_max) - - elif effect_size_type in ["cohens_d", "hedges_g", "cohens_h"]: - - which_std = 1 if is_paired else 0 ############################ Unused line of code - temp_control = np.array(plot_data[plot_data[xvar] == current_control][yvar]) - temp_test = np.array(plot_data[plot_data[xvar] == current_group][yvar]) - - stds = _compute_standardizers(temp_control, temp_test) - if is_paired: - pooled_sd = stds[1] - else: - pooled_sd = stds[0] - - if effect_size_type == "hedges_g": - gby_count = plot_data.groupby(xvar, observed=False).count() - len_control = gby_count.loc[current_control, yvar] - len_test = gby_count.loc[current_group, yvar] - - hg_correction_factor = _compute_hedges_correction_factor( - len_control, len_test - ) - - ylim_scale_factor = pooled_sd / hg_correction_factor - - elif effect_size_type == "cohens_h": - ylim_scale_factor = ( - np.mean(temp_test) - np.mean(temp_control) - ) / difference - - else: - ylim_scale_factor = pooled_sd - - scaled_ylim = ( - (rawdata_axes.get_ylim() - control_group_summary) / ylim_scale_factor - ).tolist() - - contrast_axes.set_ylim(scaled_ylim) - og_ylim_contrast = scaled_ylim - - contrast_axes.set_xlim(contrast_xlim_max - 1, contrast_xlim_max) - - if one_sankey is None: - # Draw summary lines for control and test groups.. - for jj, axx in enumerate([rawdata_axes, contrast_axes]): - # Draw effect size line. - if jj == 0: - ref = control_group_summary - diff = test_group_summary - effsize_line_start = 1 - - elif jj == 1: - ref = 0 - diff = ref + difference - effsize_line_start = contrast_xlim_max - 1.1 - - xlimlow, xlimhigh = axx.get_xlim() - - # Draw reference line. - axx.hlines( - ref, # y-coordinates - 0, - xlimhigh, # x-coordinates, start and end. - **reflines_kwargs - ) - - # Draw effect size line. - axx.hlines(diff, effsize_line_start, xlimhigh, **reflines_kwargs) - else: - ref = 0 - diff = ref + difference - effsize_line_start = contrast_xlim_max - 0.9 - xlimlow, xlimhigh = contrast_axes.get_xlim() - # Draw reference line. - contrast_axes.hlines( - ref, # y-coordinates - effsize_line_start, - xlimhigh, # x-coordinates, start and end. - **reflines_kwargs - ) - - # Draw effect size line. - contrast_axes.hlines(diff, effsize_line_start, xlimhigh, **reflines_kwargs) - rawdata_axes.set_xlim(og_xlim_raw) # to align the axis - # Despine appropriately. - sns.despine(ax=rawdata_axes, bottom=True) - sns.despine(ax=contrast_axes, left=True, right=False) - - # Insert break between the rawdata axes and the contrast axes - # by re-drawing the x-spine. - rawdata_axes.hlines( - og_ylim_raw[0], # yindex - rawdata_axes.get_xlim()[0], - 1.3, # xmin, xmax - **redraw_axes_kwargs - ) - rawdata_axes.set_ylim(og_ylim_raw) - - contrast_axes.hlines( - contrast_axes.get_ylim()[0], - contrast_xlim_max - 0.8, - contrast_xlim_max, - **redraw_axes_kwargs - ) - -def draw_zeroline( - ax : axes.Axes, - horizontal : bool, - reflines_kwargs : dict, - extra_delta : bool, - ): - """ - Draw the independent axis spine lines. - - Parameters - ---------- - ax : object (Axes) - The contrast data axes. - horizontal : bool - A boolean flag to determine if the plot is for horizontal plotting. - reflines_kwargs : dict - Additional keyword arguments to be passed to the zeroline. - extra_delta : bool - A boolean flag to determine if the plot includes an extra delta (delta-delta or mini-meta). - """ - # If 0 lies within the ylim of the contrast axes, draw a zero reference line. - if extra_delta and not horizontal: - contrast_xlim = [-0.5, 3.4] - delta2_xlim = [3.6, 4.75] - - if ax.get_ylim()[0] < ax.get_ylim()[1]: - contrast_lim_low, contrast_lim_high = ax.get_ylim() - else: - contrast_lim_high, contrast_lim_low = ax.get_ylim() - - if contrast_lim_low < 0 < contrast_lim_high: - ax.hlines(y=0, xmin=contrast_xlim[0], xmax=contrast_xlim[1], **reflines_kwargs) - ax.hlines(y=0, xmin=delta2_xlim[0], xmax=delta2_xlim[1], **reflines_kwargs) - else: - ax_lim = ax.get_xlim() if horizontal else ax.get_ylim() - method = ax.axvline if horizontal else ax.axhline - - if ax_lim[0] < ax_lim[1]: - contrast_lim_low, contrast_lim_high = ax_lim - else: - contrast_lim_high, contrast_lim_low = ax_lim - - if contrast_lim_low < 0 < contrast_lim_high: - method(0, **reflines_kwargs) - -def redraw_independent_spines( - rawdata_axes : axes.Axes, - contrast_axes : axes.Axes, - horizontal : bool, - two_col_sankey : bool, - ticks_to_start_twocol_sankey : list, - idx : list, - is_paired : str, - show_pairs : bool, - proportional : bool, - ticks_to_skip : list, - temp_idx : list, - ticks_to_skip_contrast : list, - redraw_axes_kwargs : dict - ): - """ - Draw the independent axis spine lines. - - Parameters - ---------- - rawdata_axes : object (Axes) - The raw data axes. - contrast_axes : object (Axes) - The contrast axes. - horizontal : bool - A boolean flag to determine if the plot is for horizontal plotting. - two_col_sankey : bool - A boolean flag to determine if the plot is for two-col sankey. - ticks_to_start_twocol_sankey : list - A list of ticks to start for sankey plot. - idx : list - A list of indices. - is_paired : bool - A boolean flag to determine if the data is paired. - show_pairs : bool - A boolean flag to determine if pairs should be shown. - proportional : bool - A boolean flag to determine if the plot is proportional/binary. - ticks_to_skip : list, - A list of ticks to be skipped in the raw data axes. - temp_idx : list, - A temporary list of indices to be used for skipping ticks in the raw data axes. - ticks_to_skip_contrast : list, - A list of ticks to be skipped in the contrast axes. - redraw_axes_kwargs : dict - Kwargs passed to the redraw axes. - """ - # Extract the ticks - if two_col_sankey: - rightend_ticks_raw = rightend_ticks_contrast = np.array([len(i) - 2 for i in idx]) + np.array(ticks_to_start_twocol_sankey) - starting_ticks_raw = starting_ticks_contrast = ticks_to_start_twocol_sankey - else: - if is_paired == "baseline" and show_pairs: - if proportional and is_paired is not None: - rightend_ticks_raw = rightend_ticks_contrast = np.array([len(i) - 1 for i in idx]) + np.array(ticks_to_skip) - else: - rightend_ticks_raw = np.array([len(i) - 1 for i in temp_idx]) + np.array(ticks_to_skip) - temp_length = [(len(i) - 1) * 2 - 1 for i in idx] if proportional else [(len(i) - 1) for i in idx] - rightend_ticks_contrast = np.array(temp_length) + np.array(ticks_to_skip_contrast) - starting_ticks_raw, starting_ticks_contrast = ticks_to_skip, ticks_to_skip_contrast - else: - rightend_ticks_raw = rightend_ticks_contrast = np.array([len(i) - 1 for i in idx]) + np.array(ticks_to_skip) - starting_ticks_raw = starting_ticks_contrast = ticks_to_skip - - # Plot the spines - if horizontal: - sns.despine(ax=rawdata_axes, left=True) - xlim, ylim = rawdata_axes.get_xlim(), rawdata_axes.get_ylim() - redraw_axes_kwargs["x"] = xlim[0] - for k, start_tick in enumerate(starting_ticks_raw): - end_tick = rightend_ticks_raw[k] - rawdata_axes.vlines( - ymin = start_tick, - ymax = end_tick, - **redraw_axes_kwargs - ) - rawdata_axes.set_xlim(xlim) - rawdata_axes.set_ylim(ylim) - del redraw_axes_kwargs["x"] - - # Remove y ticks and labels from the contrast axes. - sns.despine(ax=contrast_axes, left=True) - contrast_axes.set_yticks([]) - contrast_axes.set_yticklabels([]) - - else: - for ax, starting_ticks_current, rightend_ticks_current in zip( - [rawdata_axes, contrast_axes], - [starting_ticks_raw, starting_ticks_contrast], - [rightend_ticks_raw, rightend_ticks_contrast], - ): - sns.despine(ax=ax, bottom=True) - xlim, ylim = ax.get_xlim(), ax.get_ylim() - redraw_axes_kwargs["y"] = ylim[0] - for k, start_tick in enumerate(starting_ticks_current): - end_tick = rightend_ticks_current[k] - ax.hlines( - xmin=start_tick, - xmax=end_tick, - **redraw_axes_kwargs - ) - ax.set_xlim(xlim) - ax.set_ylim(ylim) - del redraw_axes_kwargs["y"] - -def redraw_dependent_spines( - rawdata_axes: axes.Axes, - contrast_axes: axes.Axes, - redraw_axes_kwargs: dict, - float_contrast: bool, - horizontal: bool, - show_delta2: bool, - delta2_axes: axes.Axes - ): - """ - Draw the dependent axis spine lines. - - Parameters - ---------- - rawdata_axes : object (Axes) - The raw data axes. - contrast_axes : object (Axes) - The contrast axes. - redraw_axes_kwargs : dict - Kwargs passed to the redraw axes. - float_contrast : bool - A boolean flag to determine if the plot is GA or Cum - horizontal : bool - A boolean flag to determine if the plot is for horizontal plotting. - show_delta2 : bool - A boolean flag to determine if the plot will have a delta-delta effect size. - delta2_axes : object (Axes) - The delta2 axes. - """ - - # Because we turned the axes frame off, we also need to draw back the x-spine for both axes. - og_xlim_raw, og_ylim_raw = rawdata_axes.get_xlim(), rawdata_axes.get_ylim() - og_xlim_contrast, og_ylim_contrast = contrast_axes.get_xlim(), contrast_axes.get_ylim() - if horizontal: - for current_ax, current_ylim, current_xlim in zip((rawdata_axes, contrast_axes), (og_ylim_raw, og_ylim_contrast), - (og_xlim_raw, og_xlim_contrast)): - current_ax.hlines( - current_ylim[0], - current_xlim[0], - current_xlim[1], - **redraw_axes_kwargs - ) - else: - for current_ax, current_ylim, current_xlim in zip((rawdata_axes, contrast_axes), (og_ylim_raw, og_ylim_contrast), - (og_xlim_raw[0], og_xlim_contrast[1] if float_contrast else og_xlim_contrast[0])): - current_ax.vlines( - current_xlim, - current_ylim[0], - current_ylim[1], - **redraw_axes_kwargs - ) - - if show_delta2: - og_xlim_delta, og_ylim_delta = contrast_axes.get_xlim(), contrast_axes.get_ylim() - delta2_axes.set_ylim(og_ylim_delta) - - delta2_axes.vlines( - og_xlim_delta[1], - og_ylim_delta[0], - og_ylim_delta[1], - **redraw_axes_kwargs - ) - - for current_ax, xlim, ylim in zip([rawdata_axes, contrast_axes], [og_xlim_raw, og_xlim_contrast], [og_ylim_raw, og_ylim_contrast]): - current_ax.set_xlim(xlim) - current_ax.set_ylim(ylim) - -def extract_group_summaries( - proportional: bool, - rawdata_axes: axes.Axes, - asymmetric_side: str, - horizontal: bool, - bootstraps_color_by_group: bool, - plot_palette_raw: list, - all_plot_groups: list, - n_groups: int, - color_col, - ytick_color, - group_summaries_kwargs: dict - ): - """ - Extract the group summaries for the plotter function. - - Parameters - ---------- - proportional : bool - A boolean flag to determine if the plot is for proportional data. - rawdata_axes : object (Axes) - The raw data axes. - asymmetric_side : str - The side of the asymmetric error bars. - horizontal : bool - A boolean flag to determine if the plot is for horizontal plotting. - bootstraps_color_by_group : bool - A boolean flag to determine if the bootstraps are colored by group. - plot_palette_raw : list - A list of the plot palette colors. - all_plot_groups : list - A list of all the plot groups. - n_groups : int - The number of groups. - color_col : str - The name of the color column. - ytick_color : str - The color of the y-ticks. - group_summaries_kwargs : dict - Kwargs passed to the group summaries. - """ - - from .plot_tools import get_swarm_spans - - if proportional: - group_summaries_method = "proportional_error_bar" - group_summaries_offset = 0 - group_summaries_line_color = "black" - else: - # Create list to gather xspans. - xspans = [] - line_colors = [] - for jj, c in enumerate(rawdata_axes.collections): - try: - if asymmetric_side == "right": - # currently offset is hardcoded with value of -0.2 - x_max_span = -0.2 - else: - if horizontal: - x_max_span = 0.1 # currently offset is hardcoded with value of 0.1 - else: - _, x_max, _, _ = get_swarm_spans(c) - x_max_span = x_max - jj - xspans.append(x_max_span) - except TypeError: - # we have got a None, so skip and move on. - pass - - if bootstraps_color_by_group: - line_colors.append(plot_palette_raw[all_plot_groups[jj]]) - - # Break the loop since hue in Seaborn adds collections to axes and it will result in index out of range - if jj >= n_groups - 1 and color_col is None: - break - - if len(line_colors) != len(all_plot_groups): - line_colors = ytick_color - - # hue in swarmplot would add collections to axes which will result in len(xspans) = len(all_plot_groups) + len(unique groups in hue) - if len(xspans) > len(all_plot_groups): - xspans = xspans[:len(all_plot_groups)] - - group_summaries_method = "gapped_lines" - group_summaries_offset = xspans + np.array(group_summaries_kwargs["offset"]) - group_summaries_line_color = line_colors - - if group_summaries_kwargs['color'] is not None: - group_summaries_line_color = group_summaries_kwargs.pop("color") - group_summaries_kwargs.pop("offset") - - return group_summaries_method, group_summaries_offset, group_summaries_line_color - -def color_picker(color_type: str, - kwargs: dict, - elements: list, - color_col: str, - show_pairs: bool, - color_palette: dict, - bootstraps_color_by_group: bool) -> list: - num_of_elements = len(elements) - colors = ( - [kwargs.pop('color')] * num_of_elements - if kwargs.get('color', None) is not None - else ['black'] * num_of_elements - # if color_col is not None or show_pairs - if color_col is not None or not bootstraps_color_by_group - else list(color_palette.values()) - ) - if color_type in ['contrast', 'summary', 'delta_text']: - if len(colors) == num_of_elements: - final_colors = colors - else: - final_colors = [] - for tick in elements: - final_colors.append(colors[int(tick)]) - else: - final_colors = colors - return final_colors - - -def prepare_bars_for_plot(bar_type, bar_kwargs, horizontal, plot_palette_raw, color_col, show_pairs, bootstraps_color_by_group, - plot_data = None, xvar = None, yvar = None, # Raw data - results = None, ticks_to_plot = None, extra_delta = None, # Contrast data - reference_band = None, summary_axes = None, ci_type = None # Summary data - ): - from .misc_tools import color_picker - bar_dict = {} - if bar_type in ['raw', 'contrast']: - if bar_type == 'raw': - if isinstance(plot_data[xvar].dtype, pd.CategoricalDtype): - order = pd.unique(plot_data[xvar]).categories - else: - order = pd.unique(plot_data[xvar]) - means = plot_data.groupby(xvar, observed=False)[yvar].mean().reindex(index=order).values - ticks = list(range(len(order))) - elif bar_type == 'contrast': - means = results.difference.to_list() - ticks = ticks_to_plot.copy() - if extra_delta is not None: - ticks.append(ticks[-1]+1) # Add an extra tick - means.append(extra_delta) - - num_of_bars = len(means) - y_start_values, y_distances = [0]*num_of_bars, means - x_start_values, x_distances = [num - (0.5 if horizontal else 0.25) for num in ticks], [0.5,]*num_of_bars - - elif bar_type == 'summary': - # Begin checks - if not isinstance(reference_band, list): - raise TypeError("reference_band must be a list of indices (ints).") - if not all(isinstance(i, int) for i in reference_band): - raise TypeError("reference_band must be a list of indices (ints).") - if any(i >= len(results) for i in reference_band): - raise ValueError("Index {} chosen is out of range for the contrast objects.".format([i for i in reference_band if i >= len(results)])) - - ticks = [ticks_to_plot[tick] for tick in reference_band] - summary_xmin, summary_xmax = summary_axes.get_xlim() - summary_ymin, summary_ymax = summary_axes.get_ylim() - span_ax = bar_kwargs.pop("span_ax") - - x_start_values, y_start_values, x_distances, y_distances = [], [], [], [] - for summary_index in reference_band: - summary_ci_low = results.get(ci_type+'_low')[summary_index] - summary_ci_high = results.get(ci_type+'_high')[summary_index] - - if span_ax == True: - starting_location = summary_ymax if horizontal else summary_xmin - else: - starting_location = ticks_to_plot[summary_index] - x_distance = summary_ymin if horizontal else summary_xmax - - x_start_values.append(starting_location) - y_start_values.append(summary_ci_low) - x_distances.append(x_distance + 1) - y_distances.append(summary_ci_high - summary_ci_low) - else: - raise ValueError("Invalid bar_type. Must be 'raw' or 'contrast'.") - - if horizontal: - x_start_values, y_start_values = y_start_values, x_start_values - x_distances, y_distances = y_distances, x_distances - - for name, values in zip(['x_start_values', 'x_distances', 'y_start_values', 'y_distance'], - [x_start_values, x_distances, y_start_values, y_distances] - ): - bar_dict[name] = values - - # Colors - colors = color_picker( - color_type = bar_type, - kwargs = bar_kwargs, - elements = ticks_to_plot if bar_type=='contrast' else ticks, - color_col = color_col, - show_pairs = show_pairs, - color_palette = plot_palette_raw, - bootstraps_color_by_group = bootstraps_color_by_group - ) - if bar_type == 'contrast' and extra_delta is not None: - colors.append('black') - bar_dict['colors'] = colors - - return bar_dict, bar_kwargs diff --git a/dabest/multi.py b/dabest/multi.py deleted file mode 100644 index 50b38721..00000000 --- a/dabest/multi.py +++ /dev/null @@ -1,801 +0,0 @@ -# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/multi.ipynb. - -# %% auto #0 -__all__ = ['MultiContrast', 'combine', 'whorlmap'] - -# %% ../nbs/API/multi.ipynb #80099a4b -import pandas as pd -import numpy as np -import matplotlib.pyplot as plt -import seaborn as sns -import warnings -from typing import List, Optional, Union, Tuple, Dict, Any - - -# %% ../nbs/API/multi.ipynb #e4b58920 -class MultiContrast: - """ - Unified multiple contrast object for forest plots and whorlmaps. - - Takes raw dabest objects and provides validated, processed data - for downstream visualizations. - """ - - def __init__(self, - dabest_objs: Union[List, List[List]], - labels: Optional[List[str]] = None, - row_labels: Optional[List[str]] = None, - effect_size: str = "mean_diff", - ci_type: str = "bca"): - """ - Initialize MultiContrast object with checking. - - Parameters - ---------- - dabest_objs : Union[List, List[List]] - Raw dabest objects. Can be: - - 1D: [dabest_obj1, dabest_obj2, ...] - - 2D: [[dabest_obj1, dabest_obj2], [dabest_obj3, dabest_obj4]] - labels : Optional[Union[List[str], List[List[str]]]], default=None - Labels matching the contrast array structure. If None, defaults will be generated. - effect_size : str, default="mean_diff" - Effect size to extract from dabest objects - ci_type : str, default="bca" - Confidence interval type - """ - # Store raw inputs for validation - self._raw_dabest_objs = dabest_objs - self._raw_labels = labels - self._raw_row_labels = row_labels - - # Validate and process inputs - self.effect_size = self._validate_effect_size(effect_size) - self.ci_type = self._validate_ci_type(ci_type) - - # Process structure (adapts forest_plot logic to handle 2D) - self.structure = self._validate_and_parse_structure(dabest_objs, labels) - - # Validate all dabest objects consistency - self.contrast_type = self._validate_contrast_consistency() - - # Extract data (adapts forest_plot's load_plot_data logic) - self._bootstrap_data = None - self._effect_size_data = None - self._ci_data = None - - def _validate_effect_size(self, effect_size: str) -> str: - """Validate effect size parameter (from forest_plot).""" - possible_effect_sizes = [ - 'mean_diff', 'median_diff', 'cohens_d', - 'cohens_h', 'cliffs_delta', 'hedges_g', 'delta_g' - ] - - if not isinstance(effect_size, str) or effect_size not in possible_effect_sizes: - raise TypeError( - f"effect_size must be one of: {possible_effect_sizes}" - ) - return effect_size - - def _validate_ci_type(self, ci_type: str) -> str: - """Validate CI type parameter (from forest_plot).""" - if ci_type not in ('bca', 'pct'): - raise TypeError("ci_type must be either 'bca' or 'pct'") - return ci_type - - def _validate_and_parse_structure(self, dabest_objs, labels): - """ - Validate and parse contrast structure, combining forest_plot - validation with whorlmap's 2D handling. - """ - # Basic validation (from forest_plot) - if not isinstance(dabest_objs, (list, tuple)) or len(dabest_objs) == 0: - raise ValueError("dabest_objs must be a non-empty list") - - # Determine if 1D or 2D structure - if isinstance(dabest_objs[0], (list, tuple)): - # 2D structure (can be used to plot whorlmap or a stack of forest plots) - structure_type = "2D" - dabest_objs_2d = dabest_objs - n_rows = len(dabest_objs) - n_cols = len(dabest_objs[0]) - - # Validate rectangular structure - for i, row in enumerate(dabest_objs): - if not isinstance(row, (list, tuple)): - raise TypeError(f"Row {i} must be a list/tuple in 2D structure") - if len(row) != n_cols: - raise ValueError("All rows must have the same number of dabest_objs") - - # Handle 2D labels - if labels: - if not isinstance(labels, (list, tuple)): - raise TypeError("labels must be a list for 2D dabest_objs") - if len(labels) != n_cols: - raise ValueError("Number of labels must match number of columns of dabest_objs") - col_labels = labels - else: - col_labels = [f"Contrast {i+1}" for i in range(n_cols)] - # Handle row_labels - use self._raw_row_labels if available - if hasattr(self, '_raw_row_labels') and self._raw_row_labels: - if not isinstance(self._raw_row_labels, (list, tuple)): - raise TypeError("row_labels must be a list for 2D dabest_objs") - if len(self._raw_row_labels) != n_rows: - raise ValueError("Number of row_labels must match number of rows of dabest_objs") - row_labels = self._raw_row_labels - else: - row_labels = [f"Row {i+1}" for i in range(n_rows)] - else: - # 1D structure (like forest_plot) - structure_type = "1D" - dabest_objs_2d = [dabest_objs] # Wrap in single row for unified processing - n_rows = 1 - n_cols = len(dabest_objs) - - # Handle 1D labels - if labels: - if not isinstance(labels, (list, tuple)): - raise TypeError("labels must be a list for 1D dabest_objs") - if len(labels) != n_cols: - raise ValueError("Number of labels must match number of dabest_objs") - col_labels = labels - else: - col_labels = [f"Contrast {i+1}" for i in range(n_cols)] - row_labels = [""] # Single empty row label - - return { - 'type': structure_type, - 'dabest_objs_2d': dabest_objs_2d, - 'n_rows': n_rows, - 'n_cols': n_cols, - 'col_labels': col_labels, - 'row_labels': row_labels, - 'total_dabest_objs': n_rows * n_cols - } - - def _validate_contrast_consistency(self) -> Union[str, Dict]: - """ - Validate contrast consistency with support for mixed types in whorlmap. - - Returns either: - - str: Single contrast type for homogeneous data (forest_plot compatible) - - dict: Row-wise contrast types for mixed data (whorlmap only) - """ - all_dabest_objs = [] - for row in self.structure['dabest_objs_2d']: - all_dabest_objs.extend(row) - - if not all_dabest_objs: - raise ValueError("No valid dabest_objs found") - - # First, validate EACH contrast individually - for i, dabest_obj in enumerate(all_dabest_objs): - self._validate_individual_dabest_obj(dabest_obj, i) - - # Analyze contrast type structure - contrast_types_by_row = [] - for row_idx, row in enumerate(self.structure['dabest_objs_2d']): - row_types = [] - for contrast in row: - contrast_type = ("delta2" if contrast.delta2 - else "mini_meta" if contrast.is_mini_meta - else "delta") - row_types.append(contrast_type) - contrast_types_by_row.append(row_types) - - # Check if all dabest_objs are the same type (forest_plot requirement) - all_types_flat = [t for row_types in contrast_types_by_row for t in row_types] - unique_types = set(all_types_flat) - - if len(unique_types) == 1: - # Homogeneous: all same type (forest_plot compatible) - contrast_type = list(unique_types)[0] - self._validate_effect_size_compatibility(contrast_type) - return contrast_type - - else: - # Heterogeneous: mixed types (whorlmap only) - if self.structure['type'] == '1D': - raise ValueError( - "Mixed contrast types are only supported for 2D structures (whorlmaps). " - f"Found types: {unique_types}. For forest plots, all dabest_objs must be the same type." - ) - - # Validate within-row consistency for whorlmap - for row_idx, row_types in enumerate(contrast_types_by_row): - unique_row_types = set(row_types) - if len(unique_row_types) > 1: - raise ValueError( - f"Within each row, all dabest_objs must be the same type. " - f"Row {row_idx} has mixed types: {unique_row_types}" - ) - - # Validate effect size compatibility for each row type - for row_types in contrast_types_by_row: - row_type = row_types[0] # All same within row - self._validate_effect_size_compatibility(row_type) - - # Return row-wise type information - return { - 'mixed': True, - 'by_row': [row_types[0] for row_types in contrast_types_by_row], - 'unique_types': list(unique_types) - } - - def _validate_effect_size_compatibility(self, contrast_type: str): - """Validate effect size compatibility with a specific contrast type.""" - if contrast_type == "mini_meta" and self.effect_size != 'mean_diff': - raise ValueError("effect_size must be 'mean_diff' for mini-meta analyses") - - if contrast_type == "delta2" and self.effect_size not in ['mean_diff', 'hedges_g', 'delta_g']: - raise ValueError( - "effect_size must be 'mean_diff', 'hedges_g', or 'delta_g' for delta-delta analyses" - ) - - def _validate_individual_dabest_obj(self, dabest_obj, position: int): - """ - Validate individual dabest object. - - Parameters - ---------- - dabest_obj : object - Individual dabest object to validate - position : int - Position in the contrast list for error reporting - """ - # Basic existence check - if dabest_obj is None: - raise ValueError(f"Dabest object at position {position} is None") - - # Required attributes for dabest objects - required_attrs = ['delta2', 'is_mini_meta'] - for attr in required_attrs: - if not hasattr(dabest_obj, attr): - raise TypeError( - f"Object at position {position} is not a valid dabest object. " - f"Missing required attribute: '{attr}'" - ) - - # Validate effect size attribute exists - effect_attr = "hedges_g" if self.effect_size == 'delta_g' else self.effect_size - if not hasattr(dabest_obj, effect_attr): - raise AttributeError( - f"Dabest Object at position {position} does not have effect size '{self.effect_size}'. " - f"Expected attribute: '{effect_attr}'" - ) - - # Test that we can actually access the effect size data - try: - effect_obj = getattr(dabest_obj, effect_attr) - - # For delta2/mini_meta, check the nested attributes exist - if dabest_obj.delta2: - if not hasattr(effect_obj, 'delta_delta'): - raise AttributeError(f"Delta-delta contrast at position {position} missing 'delta_delta' attribute") - elif dabest_obj.is_mini_meta: - if not hasattr(effect_obj, 'mini_meta'): - raise AttributeError(f"Mini-meta contrast at position {position} missing 'mini_meta' attribute") - else: - # Standard contrast - check results structure - if not hasattr(effect_obj, 'results'): - raise AttributeError(f"Standard contrast at position {position} missing 'results' attribute") - except Exception as e: - raise ValueError( - f"Failed to access effect size data for dabest object at position {position}: {str(e)}" - ) - - def _extract_data(self) -> Tuple[List, List, List, List]: - """ - Extract bootstrap, effect sizes, CI low bounds and CI high bounds. - Handles mixed contrast types for whorlmap. - """ - if self._bootstrap_data is not None: - return self._bootstrap_data, self._effect_data, self._ci_lows, self._ci_highs - - # Process effect size attribute name - effect_attr = "hedges_g" if self.effect_size == 'delta_g' else self.effect_size - - bootstraps = [] - differences = [] - ci_lows = [] - ci_highs = [] - - if isinstance(self.contrast_type, dict) and self.contrast_type.get('mixed'): - # Mixed types: process row by row - for row_idx, row in enumerate(self.structure['dabest_objs_2d']): - row_contrast_type = self.contrast_type['by_row'][row_idx] - contrast_attr = {"delta2": "delta_delta", "mini_meta": "mini_meta"}.get(row_contrast_type) - - for contrast in row: - bootstrap, diff, ci_low, ci_high = self._extract_single_contrast( - contrast, effect_attr, row_contrast_type, contrast_attr - ) - bootstraps.extend(bootstrap if isinstance(bootstrap, list) else [bootstrap]) - differences.extend(diff if isinstance(diff, list) else [diff]) - ci_lows.extend(ci_low if isinstance(ci_low, list) else [ci_low]) - ci_highs.extend(ci_high if isinstance(ci_high, list) else [ci_high]) - - else: - # Homogeneous types: process all together (original logic) - contrast_attr = {"delta2": "delta_delta", "mini_meta": "mini_meta"}.get(self.contrast_type) - - all_dabest_objs = [] - for row in self.structure['dabest_objs_2d']: - all_dabest_objs.extend(row) - - for contrast in all_dabest_objs: - bootstrap, diff, ci_low, ci_high = self._extract_single_contrast( - contrast, effect_attr, self.contrast_type, contrast_attr - ) - bootstraps.extend(bootstrap if isinstance(bootstrap, list) else [bootstrap]) - differences.extend(diff if isinstance(diff, list) else [diff]) - ci_lows.extend(ci_low if isinstance(ci_low, list) else [ci_low]) - ci_highs.extend(ci_high if isinstance(ci_high, list) else [ci_high]) - - # Cache results - self._bootstrap_data = bootstraps - self._effect_data = differences - self._ci_lows = ci_lows - self._ci_highs = ci_highs - - return bootstraps, differences, ci_lows, ci_highs - - def _extract_single_contrast(self, contrast, effect_attr, contrast_type, contrast_attr): - """Extract data from a single contrast object.""" - if contrast_type == 'delta': - # Standard dabest_objs - may have multiple comparisons - effect_obj = getattr(contrast, effect_attr) - boot_list = effect_obj.results.bootstraps.to_list() - diff_list = effect_obj.results.difference.to_list() - low_list = effect_obj.results.get(f'{self.ci_type}_low').to_list() - high_list = effect_obj.results.get(f'{self.ci_type}_high').to_list() - return boot_list, diff_list, low_list, high_list - - else: - # Delta-delta or mini-meta - single value per contrast - effect_obj = getattr(contrast, effect_attr) - processed_obj = getattr(effect_obj, contrast_attr) - - if contrast_type == "delta2": - bootstrap = processed_obj.bootstraps_delta_delta - difference = processed_obj.difference - else: # mini_meta - bootstrap = processed_obj.bootstraps_weighted_delta - difference = processed_obj.difference - - ci_low = processed_obj.results.get(f'{self.ci_type}_low')[0] - ci_high = processed_obj.results.get(f'{self.ci_type}_high')[0] - - return bootstrap, difference, ci_low, ci_high - @property - def bootstraps(self) -> List: - """Get bootstrap samples for all dabest_objs.""" - bootstraps, _, _, _ = self._extract_data() - return bootstraps - - @property - def effect_sizes(self) -> List: - """Get effect sizes for all dabest_objs.""" - _, effects, _, _ = self._extract_data() - return effects - - @property - def confidence_intervals(self) -> Tuple[List, List]: - """Get confidence interval bounds.""" - _, _, ci_lows, ci_highs = self._extract_data() - return ci_lows, ci_highs - - def forest_plot(self, forest_plot_title = None, forest_plot_kwargs = {}): - """ - Create forest plot using validated data. - - This is a convenience method that calls the existing forest_plot function - with validated dabest objects. # TODO: decide whether to - migrate forest_plot to use MultiContrast data directly. - """ - # Check compatibility with forest plot (mixed contrast types not supported) - if isinstance(self.contrast_type, dict) and self.contrast_type.get('mixed'): - raise ValueError( - "Forest plots require all dabest_objs to be the same type. " - f"This MultiContrast has mixed types: {self.contrast_type['unique_types']}. " - "Consider creating separate MultiContrast objects for each type, " - "or use whorlmap() which supports mixed types." - ) - - # Import forest_plot function - from .forest_plot import forest_plot - - # # Get flattened contrast list for existing forest_plot function - # all_dabest_objs = [] - # for row in self.structure['dabest_objs_2d']: - # all_dabest_objs.extend(row) - - # Call existing forest_plot with validated dabest objects - - f_forest, axes = plt.subplots(self.structure['n_rows'], 1, - figsize=(8, 2 * self.structure['n_rows']), squeeze=False) - for i, row in enumerate(self.structure['dabest_objs_2d']): - # Set default parameters, allow kwargs to override - forest_kwargs = { - 'effect_size': self.effect_size, - 'ci_type': self.ci_type, - 'ax': axes[i, 0], - 'labels': self.structure['col_labels'], - 'title': self.structure['row_labels'][i] if self.structure['n_rows'] > 1 else None,} - forest_kwargs.update(forest_plot_kwargs) - forest_plot(data = row, **forest_kwargs) - if i == self.structure['n_rows'] - 1: - axes[i, 0].set_xticks(axes[i, 0].get_xticks()) - else: - axes[i, 0].set_xticks([]) - self.f_forest = f_forest - if forest_plot_title: - f_forest.suptitle(forest_plot_title) - return f_forest, axes - - def whorlmap(self, **heatmap_kwargs): - """ - Create whorlmap using validated data. - - This uses the whorlmap that can handle both homogeneous - and mixed contrast types. - """ - from .multi import whorlmap - f_whorlmap = whorlmap(multi_contrast=self, **heatmap_kwargs) - self.f_whorlmap = f_whorlmap - # Call whorlmap with self as the multi_contrast object - return f_whorlmap - def get_bootstrap_by_position(self, row: int, col: int): - """ - Get bootstrap data for a specific position in the grid. - Useful for mixed-type whorlmaps. - """ - if row >= self.structure['n_rows'] or col >= self.structure['n_cols']: - raise IndexError(f"Position ({row}, {col}) out of bounds for {self.structure['n_rows']}×{self.structure['n_cols']} grid") - - contrast = self.structure['dabest_objs_2d'][row][col] - effect_attr = "hedges_g" if self.effect_size == 'delta_g' else self.effect_size - - # Determine contrast type for this position - if isinstance(self.contrast_type, dict) and self.contrast_type.get('mixed'): - position_type = self.contrast_type['by_row'][row] - else: - position_type = self.contrast_type - - contrast_attr = {"delta2": "delta_delta", "mini_meta": "mini_meta"}.get(position_type) - - # Extract bootstrap for this specific contrast - bootstrap, _, _, _ = self._extract_single_contrast(contrast, effect_attr, position_type, contrast_attr) - - # For standard dabest_objs, return first bootstrap (they may have multiple) - if isinstance(bootstrap, list) and len(bootstrap) > 0: - return bootstrap[0] - return bootstrap - - def __repr__(self): - if isinstance(self.contrast_type, dict) and self.contrast_type.get('mixed'): - types_info = f"mixed({', '.join(self.contrast_type['unique_types'])})" - else: - types_info = self.contrast_type - - return (f"MultiContrast({self.structure['type']}: " - f"{self.structure['n_rows']}x{self.structure['n_cols']}, " - f"effect_size='{self.effect_size}', " - f"contrast_type='{types_info}')") - -# %% ../nbs/API/multi.ipynb #b6952d49 -def combine(dabest_objs: Union[List, List[List]], - labels: Optional[List[str]] = None, - row_labels: Optional[List[str]] = None, - effect_size: str = "mean_diff", - ci_type: str = "bca", - allow_mixed_types: bool = False) -> MultiContrast: - """ - Create a MultiContrast object from raw dabest objects. - - This is the main entry point that users should use to create - multi-contrast visualizations. - - Parameters - ---------- - dabest_objs : Union[List, List[List]] - Raw dabest objects in 1D or 2D structure - labels : Optional[Union[List[str], List[List[str]]]], default=None - Labels for dabest_objs - effect_size : str, default="mean_diff" - Effect size to extract - ci_type : str, default="bca" - Confidence interval type - allow_mixed_types : bool, default=False - If True, allows different contrast types in different rows (whorlmap only) - If False, enforces homogeneous types (forest_plot compatible) - - Returns - ------- - MultiContrast - Validated multi-contrast object ready for visualization - - Examples - -------- - # Homogeneous 1D structure (forest_plot and whorlmap compatible) - mc = combine([dabest1, dabest2, dabest3], - labels=['Treatment A', 'Treatment B', 'Treatment C']) - mc.forest_plot() - mc.whorlmap() # Will arrange in single row - - # Homogeneous 2D structure (forest_plot flattens, whorlmap uses grid) - mc = combine([[dabest1, dabest2], [dabest3, dabest4]], - labels=[['Dose Low', 'Dose High'], ['Time 1', 'Time 2']]) - mc.whorlmap() # 2x2 grid - mc.forest_plot() # Flattened to 1D - - # Mixed types 2D structure (whorlmap only!) - mc = combine([[standard_dabest1, standard_dabest2], - [delta2_dabest1, delta2_dabest2]], - labels=[['Standard A', 'Standard B'], - ['Delta2 A', 'Delta2 B']], - allow_mixed_types=True) - mc.whorlmap() # Works: mixed spiral types per row - # mc.forest_plot() # Raises error: incompatible with mixed types - - # Mini-meta + Delta2 mixed example - mc = combine([[mini_meta1, mini_meta2], - [delta2_obj1, delta2_obj2]], - allow_mixed_types=True) - mc.whorlmap() # Top row: mini-meta spirals, bottom row: delta2 spirals - """ - mc = MultiContrast(dabest_objs, labels, row_labels, effect_size, ci_type) - - # Check mixed types policy - if isinstance(mc.contrast_type, dict) and mc.contrast_type.get('mixed'): - if not allow_mixed_types: - raise ValueError( - f"Mixed contrast types detected: {mc.contrast_type['unique_types']}. " - "Set allow_mixed_types=True to enable mixed-type whorlmaps, " - "or ensure all dabest_objs are the same type for forest_plot compatibility." - ) - - return mc - -# %% ../nbs/API/multi.ipynb #7814cc58 -def _sample_bootstrap(bootstrap, m, n, reverse_neg, abs_rank, chop_tail): - """Sample bootstrap values and prepare for spiral visualization.""" - bootstrap_sorted = sorted(bootstrap) - chop_tail_int = int(np.ceil(len(bootstrap_sorted) * chop_tail / 100)) - bootstrap_sorted = bootstrap_sorted[chop_tail_int : len(bootstrap_sorted) - chop_tail_int] - - ranks_to_look = np.linspace(0, len(bootstrap_sorted), m * n, dtype=int) - ranks_to_look[0] = 1 - - if np.sum(np.array(bootstrap_sorted) > 0) < len(bootstrap_sorted) / 2: - if reverse_neg: - bootstrap_sorted = bootstrap_sorted[::-1] - - if abs_rank: - bootstrap_sorted = sorted(bootstrap_sorted, key=abs) - - long_ranks = [bootstrap_sorted[r - 1] for r in ranks_to_look] - return long_ranks - -# %% ../nbs/API/multi.ipynb #725c96b5 -def _spiralize(fill, m, n): - """Convert linear array into spiral pattern.""" - i = 0 - j = 0 - k = 0 - array = np.zeros((m, n)) - - while m > 0 and k < len(fill): - jj = j - ii = i - - # Right - for j in range(j, n): - if k >= len(fill): - break - array[i, j] = fill[k] - k += 1 - - # Down - for i in range(ii + 1, m): - if k >= len(fill): - break - array[i, j] = fill[k] - k += 1 - - # Left - for j in range(n - 2, jj - 1, -1): - if k >= len(fill): - break - array[i, j] = fill[k] - k += 1 - - # Up - for i in range(m - 2, ii, -1): - if k >= len(fill): - break - array[i, j] = fill[k] - k += 1 - - m -= 1 - n -= 1 - j += 1 - - return array - -# %% ../nbs/API/multi.ipynb #20809f1d -def whorlmap(multi_contrast, n=21, sort_by=None, cmap = 'vlag', vmax = None, vmin = None, reverse_neg=True, - abs_rank=False, chop_tail=0, ax=None, fig_size=None, title = None, heatmap_kwargs=None, plot_kwargs=None): - """ - Create a whorlmap visualization of multiple contrasts. - - Parameters - ---------- - multi_contrast : MultiContrast - Object containing multiple dabest objects - n : int, default 21 - Size of each spiral (n x n grid per contrast) - sort_by : list, optional - Order to sort contrasts by - vmax, vmin : float, default None, None - Color scale limits - reverse_neg : bool, default True - Whether to reverse negative values - abs_rank : bool, default False - Whether to rank by absolute value - chop_tail : float, default 0 - Percentage of extreme values to exclude - ax : matplotlib.Axes, optional - Existing axes to plot on - fig_size : tuple, optional - Figure size (width, height) in inches - title : str, optional - Plot title - heatmap_kwargs : dict, optional - Additional keyword arguments passed to sns.heatmap(). - Common options include: - - 'cmap': colormap (overrides direct cmap parameter) - - 'vmin', 'vmax': color scale limits (override direct parameters) - - 'center': center value for colormap - - 'annot': whether to annotate cells with values - - 'fmt': format string for annotations - - 'linewidths': width of lines between cells - - 'linecolor': color of lines between cells - - 'cbar': whether to show colorbar - - 'cbar_kws': colorbar customization dict - - 'square': whether to make cells square - - 'xticklabels', 'yticklabels': tick label control - - 'mask': boolean array to mask cells - plot_kwargs : dict, optional - Additional keyword arguments for plot styling and layout. - Available options (WIP): - - 'title': plot title - - 'xlabel', 'ylabel': axis labels - - 'xticklabels', 'yticklabels': tick labels - - 'xticklabels_rotation', 'yticklabels_rotation': tick label rotation angles - - 'xticklabels_ha', 'yticklabels_ha': horizontal alignment - Returns - ------- - tuple - (figure, axes, mean_delta_dataframe) if ax is None, - else (axes, mean_delta_dataframe) - """ - from .misc_tools import merge_two_dicts - - structure = multi_contrast.structure - n_rows = structure['n_rows'] - n_cols = structure['n_cols'] - col_labels = structure['col_labels'] - row_labels = structure['row_labels'] - was_1d = (structure['type'] == '1D') - - # Initialize spirals and mean_delta DataFrames - spirals = pd.DataFrame(np.zeros((n_rows * n, n_cols * n))) - - mean_delta = pd.DataFrame(np.zeros((n_rows, n_cols)), - columns=col_labels, - index=row_labels) - - # Get all bootstrap data from MultiContrast - all_bootstraps = multi_contrast.bootstraps - bootstrap_idx = 0 - - for i in range(n_rows): - for j in range(n_cols): - contrast_idx = sort_by[j] if sort_by is not None else j - - # For mixed types, get bootstrap for specific position - if isinstance(multi_contrast.contrast_type, dict) and multi_contrast.contrast_type.get('mixed'): - bootstrap = multi_contrast.get_bootstrap_by_position(i, contrast_idx) - else: - # For homogeneous types, use the flattened bootstrap list - flat_idx = i * n_cols + contrast_idx - if flat_idx < len(all_bootstraps): - bootstrap = all_bootstraps[flat_idx] - else: - # Handle case where we have fewer bootstraps than expected - bootstrap = all_bootstraps[bootstrap_idx] - bootstrap_idx += 1 - - long_ranks = _sample_bootstrap(bootstrap, n, n, reverse_neg, abs_rank, chop_tail) - spiral = _spiralize(long_ranks, n, n) - spirals.iloc[i*n:i*n+n, j*n:j*n+n] = spiral - mean_delta.iloc[i, j] = np.mean(long_ranks) - - if ax is None: - f, a = plt.subplots(1, 1) - else: - a = ax - if was_1d: - cbar_orientation, cbar_location = 'horizontal', 'top' - else: - cbar_orientation, cbar_location = 'vertical', 'right' - - # heatmap kwargs - default_heatmap_kwargs = { - "cmap": cmap, - "vmax": np.max(spirals.values) if vmax is None else vmax, - "vmin": np.min(spirals.values) if vmin is None else vmin, - "center": 0, - "cbar_kws": {"shrink": 1, "pad": .05, "orientation": cbar_orientation, "location": cbar_location}, - } - if heatmap_kwargs is None: - heatmap_kwargs = default_heatmap_kwargs - else: - heatmap_kwargs = merge_two_dicts( - default_heatmap_kwargs, heatmap_kwargs - ) - - # Create heatmap - sns.heatmap(spirals, ax=a, **heatmap_kwargs) - - - # Plot kwargs - default_plot_kwargs = { - "title": title, - "xticklabels": col_labels, - "xticklabels_rotation": 45, - "xticklabels_ha":'right', - "yticklabels": row_labels if not was_1d else [], - "yticklabels_rotation": 0, - "yticklabels_ha": 'right', - } - if plot_kwargs is None: - plot_kwargs = default_plot_kwargs - else: - plot_kwargs = merge_two_dicts( - default_plot_kwargs, plot_kwargs - ) - - # Set title - if plot_kwargs.get('title') is not None: - if ax is None: - f.suptitle(plot_kwargs.get('title')) - else: - a.set_title(plot_kwargs.get('title')) - - # Set labels - if plot_kwargs.get('xlabel') is not None: - a.set_xlabel(plot_kwargs.get('xlabel')) - if plot_kwargs.get('ylabel') is not None: - a.set_ylabel(plot_kwargs.get('ylabel')) - - # Set labels - a.set_xticks(np.linspace(n/2, n_cols*n-n/2, n_cols)) - a.set_xticklabels(plot_kwargs.get("xticklabels"), rotation=plot_kwargs.get("xticklabels_rotation"), ha=plot_kwargs.get("xticklabels_ha")) - - a.set_yticks([] if was_1d else np.linspace(n/2, n_rows*n-n/2, n_rows)) - a.set_yticklabels(plot_kwargs.get("yticklabels"), rotation=plot_kwargs.get("yticklabels_rotation"), ha=plot_kwargs.get("yticklabels_ha")) - - if ax is None: - f.gca().set_aspect('equal') - if fig_size is None: - f.set_size_inches(n_cols/3, n_rows/3) - else: - f.set_size_inches(fig_size) - return f, a, mean_delta - else: - return a, mean_delta - -# %% ../nbs/API/multi.ipynb #4f23adcf -__all__ = ['MultiContrast', 'combine', 'whorlmap'] - diff --git a/dabest/plot_tools.py b/dabest/plot_tools.py deleted file mode 100644 index 45cb2134..00000000 --- a/dabest/plot_tools.py +++ /dev/null @@ -1,2753 +0,0 @@ -"""A set of convenience functions used for producing plots in `dabest`.""" - -# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/plot_tools.ipynb. - -# %% ../nbs/API/plot_tools.ipynb #dd831e92 -from __future__ import annotations - -# %% auto #0 -__all__ = ['halfviolin', 'get_swarm_spans', 'error_bar', 'check_data_matches_labels', 'normalize_dict', 'width_determine', - 'single_sankey', 'sankeydiag', 'add_bars_to_plot', 'delta_text_plotter', 'delta_dots_plotter', - 'slopegraph_plotter', 'plot_minimeta_or_deltadelta_violins', 'effect_size_curve_plotter', 'gridkey_plotter', - 'barplotter', 'table_for_horizontal_plots', 'add_counts_to_prop_plots', 'swarmplot', 'SwarmPlot'] - -# %% ../nbs/API/plot_tools.ipynb #b070950d -import math -import warnings -import itertools -import numpy as np -import pandas as pd -import seaborn as sns -import matplotlib.pyplot as plt -import matplotlib.lines as mlines -import matplotlib.axes as axes -import matplotlib.patches as mpatches -from collections import defaultdict -from typing import List, Tuple, Dict, Iterable, Union -from pandas.api.types import CategoricalDtype -from matplotlib.colors import ListedColormap - -# %% ../nbs/API/plot_tools.ipynb #98550688 -def halfviolin(v, half="right", fill_color="k", alpha=1, line_color="k", line_width=0): - for b in v["bodies"]: - V = b.get_paths()[0].vertices - - mean_vertical = np.mean(V[:, 0]) - mean_horizontal = np.mean(V[:, 1]) - - if half == "right": - V[:, 0] = np.clip(V[:, 0], mean_vertical, np.inf) - elif half == "left": - V[:, 0] = np.clip(V[:, 0], -np.inf, mean_vertical) - elif half == "bottom": - V[:, 1] = np.clip(V[:, 1], -np.inf, mean_horizontal) - elif half == "top": - V[:, 1] = np.clip(V[:, 1], mean_horizontal, np.inf) - - b.set_color(fill_color) - b.set_alpha(alpha) - b.set_edgecolor(line_color) - b.set_linewidth(line_width) - - -def get_swarm_spans(coll): - """ - Given a matplotlib Collection, will obtain the x and y spans - for the collection. Will return None if this fails. - """ - if coll is None: - raise ValueError("The collection `coll` parameter cannot be None") - - x, y = np.array(coll.get_offsets()).T - try: - return x.min(), x.max(), y.min(), y.max() - except ValueError as e: - warnings.warn(f"Failed to calculate spans for the collection. Details: {e}") - return None - - -def error_bar( - data: pd.DataFrame, # This DataFrame should be in 'long' format. - x: str, # x column to be plotted. - y: str, # y column to be plotted. - type: str = "mean_sd", # Choose from ['mean_sd', 'median_quartiles']. Plots the summary statistics for each group. If 'mean_sd', then the mean and standard deviation of each group is plotted as a gapped line. If 'median_quantiles', then the median and 25th and 75th percentiles of each group is plotted instead. - offset: float = 0.2, # Give a single float (that will be used as the x-offset of all gapped lines), or an iterable containing the list of x-offsets. - ax=None, # If a matplotlib Axes object is specified, the gapped lines will be plotted in order on this axes. If None, the current axes (plt.gca()) is used. - line_color="black", # The color of the gapped lines. - gap_width_percent=1, # The width of the gap in the gapped lines, as a percentage of the y-axis span. - pos: list = [ - 0, - 1, - ], # The positions of the error bars for the sankey_error_bar method. - method: str = "gapped_lines", # The method to use for drawing the error bars. Options are: 'gapped_lines', 'proportional_error_bar', and 'sankey_error_bar'. - horizontal: bool = False, # If True, the error bars will be horizontal. If False, the error bars will be vertical. - **kwargs: dict, -): - """ - Function to plot the standard deviations as vertical errorbars. - The mean is a gap defined by negative space. - - This function combines the functionality of gapped_lines(), - proportional_error_bar(), and sankey_error_bar(). - - """ - - if gap_width_percent < 0 or gap_width_percent > 100: - raise ValueError("`gap_width_percent` must be between 0 and 100.") - if method not in ["gapped_lines", "proportional_error_bar", "sankey_error_bar"]: - raise ValueError( - "Invalid `method`. Must be one of 'gapped_lines', \ - 'proportional_error_bar', or 'sankey_error_bar'." - ) - - if ax is None: - ax = plt.gca() - - if horizontal: - ax_ylims = ax.get_xlim() - else: - ax_ylims = ax.get_ylim() - ax_yspan = np.abs(ax_ylims[1] - ax_ylims[0]) - gap_width = ax_yspan * gap_width_percent / 100 - - keys = kwargs.keys() - if "clip_on" not in keys: - kwargs["clip_on"] = False - - if "zorder" not in keys: - kwargs["zorder"] = 5 - - if "lw" not in keys: - kwargs["lw"] = 2.0 - - if isinstance(data[x].dtype, pd.CategoricalDtype): - group_order = pd.unique(data[x]).categories - else: - group_order = pd.unique(data[x]) - - means = data.groupby(x, observed=False)[y].mean().reindex(index=group_order) - - if method in ["proportional_error_bar", "sankey_error_bar"]: - g = lambda x: np.sqrt( - (np.sum(x) * (len(x) - np.sum(x))) / (len(x) * len(x) * len(x)) - ) - sd = data.groupby(x, observed=False)[y].apply(g) - else: - sd = data.groupby(x, observed=False)[y].std().reindex(index=group_order) - - lower_sd = means - sd - upper_sd = means + sd - - if (lower_sd < ax_ylims[0]).any() or (upper_sd > ax_ylims[1]).any(): - kwargs["clip_on"] = True - - medians = data.groupby(x, observed=False)[y].median().reindex(index=group_order) - quantiles = ( - data.groupby(x, observed=False)[y].quantile([0.25, 0.75]).unstack().reindex(index=group_order) - ) - lower_quartiles = quantiles[0.25] - upper_quartiles = quantiles[0.75] - - if type == "mean_sd": - central_measures = means - lows = lower_sd - highs = upper_sd - elif type == "median_quartiles": - central_measures = medians - lows = lower_quartiles - highs = upper_quartiles - else: - raise ValueError("Only accepted values for type are ['mean_sd', 'median_quartiles']") - - n_groups = len(central_measures) - - if isinstance(line_color, str): - custom_palette = np.repeat(line_color, n_groups) - else: - if len(line_color) != n_groups: - err1 = "{} groups are being plotted, but ".format(n_groups) - err2 = "{} colors(s) were supplied in `line_color`.".format(len(line_color)) - raise ValueError(err1 + err2) - custom_palette = line_color - - try: - len_offset = len(offset) - except TypeError: - offset = np.repeat(offset, n_groups) - len_offset = len(offset) - - if len_offset != n_groups: - err1 = "{} groups are being plotted, but ".format(n_groups) - err2 = "{} offset(s) were supplied in `offset`.".format(len_offset) - raise ValueError(err1 + err2) - - kwargs["zorder"] = kwargs["zorder"] - - for xpos, val in enumerate(central_measures.index): - central_measure = central_measures[val] - kwargs["color"] = custom_palette[xpos] - - if method == "sankey_error_bar": - _xpos = pos[xpos] + offset[xpos] - else: - _xpos = xpos + offset[xpos] - - # Fix for the non-string x-axis issue #108 - if central_measures.index.dtype.name == "category": - low = lows[xpos] - high = highs[xpos] - else: - low = lows[val] - high = highs[val] - - if low == high == central_measure: - if horizontal: - low2mean_x, low2mean_y = [low, central_measure], [_xpos, _xpos] - mean2high_x, mean2high_y = [central_measure, high], [_xpos, _xpos] - else: - low2mean_x, low2mean_y = [_xpos, _xpos], [low, central_measure] - mean2high_x, mean2high_y = [_xpos, _xpos], [central_measure, high] - else: - if horizontal: - low2mean_x, low2mean_y = [low, central_measure - gap_width], [_xpos, _xpos] - mean2high_x, mean2high_y = [central_measure + gap_width, high], [_xpos, _xpos] - else: - low2mean_x, low2mean_y = [_xpos, _xpos], [low, central_measure - gap_width] - mean2high_x, mean2high_y = [_xpos, _xpos], [central_measure + gap_width, high] - # Add lines - ax.add_line(mlines.Line2D( - low2mean_x, low2mean_y, **kwargs - )) - ax.add_line(mlines.Line2D( - mean2high_x, mean2high_y, **kwargs - )) - -def check_data_matches_labels( - labels, # list of input labels - data, # Pandas Series of input data - side: str, # 'left' or 'right' on the sankey diagram -): - """ - Function to check that the labels and data match in the sankey diagram. - And enforce labels and data to be lists. - Raises an exception if the labels and data do not match. - """ - if len(labels) > 0: - if isinstance(data, list): - data = set(data) - if isinstance(data, pd.Series): - data = set(data.unique()) - if isinstance(labels, list): - labels = set(labels) - if labels != data: - msg = "\n" - if len(labels) <= 20: - msg = "Labels: " + ",".join(labels) + "\n" - if len(data) < 20: - msg += "Data: " + ",".join(data) - raise Exception(f"{side} labels and data do not match.{msg}") - - -def normalize_dict(nested_dict, target): - """ - Normalizes the values in a nested dictionary based on a target dictionary. - - This function iterates through a nested dictionary, calculates the sum of values for each key - across all sub-dictionaries, and then normalizes these values according to a target dictionary. - The normalization is performed such that the values in each sub-dictionary are proportionally - scaled to match the corresponding 'right' values in the target dictionary. - - Parameters: - nested_dict (dict of dict): A nested dictionary where each key maps to another dictionary. - The values in these inner dictionaries are subject to normalization. - target (dict): A dictionary with the target values for normalization. Each key in nested_dict - should have a corresponding key in target, and each target[key] should be a - dictionary with a 'right' key containing the target normalization value. - - Returns: - dict: The normalized nested dictionary. The original nested_dict is modified in place. - - Note: - - If the sum of values for a particular key in nested_dict is zero, the normalized value is set to 0. - - If a key in a sub-dictionary of nested_dict does not exist in the target dictionary, the - corresponding 'right' value from the target dictionary is directly assigned. - - The function modifies the input nested_dict in place and also returns it. - """ - val = {} - for key in nested_dict.keys(): - val[key] = np.sum( - [ - nested_dict[sub_key][key] - for sub_key in nested_dict.keys() - if key in nested_dict[sub_key] - ] - ) - - for key, value in nested_dict.items(): - if isinstance(value, dict): - for subkey in value.keys(): - if subkey in val.keys(): - if val[subkey] != 0: - # Address the problem when one of the labels has zero value - value[subkey] = ( - value[subkey] * target[subkey]["right"] / val[subkey] - ) - else: - value[subkey] = 0 - else: - value[subkey] = target[subkey]["right"] - return nested_dict - - -def width_determine(labels, data, pos="left"): - """ - Calculates normalized width positions for a set of labels based on their associated data. - - This function is designed to determine width positions for plotting or graphical representation. - It takes into account the cumulative weight of each label in the data and adjusts their positions - accordingly. The function allows for adjusting the position of labels to either the 'left' or 'right'. - - Parameters: - labels (list): A list of labels whose width positions are to be calculated. - data (DataFrame): A pandas DataFrame containing the data used for calculating width positions. - The DataFrame should have columns corresponding to the 'pos' and 'posWeight'. - pos (str, optional): The position of labels. It can be either 'left' or 'right'. Defaults to 'left'. - - Returns: - defaultdict: A dictionary where each key is a label and the value is another dictionary with keys - 'bottom', 'top', and 'pos', representing the calculated width positions. - - Note: - The function assumes that the data DataFrame contains columns named after the value of 'pos' and - an additional column named 'posWeight' which represents the weight of each label. - """ - if labels is None: - raise ValueError("The `labels` parameter cannot be None") - - if data is None: - raise ValueError("The `data` parameter cannot be None") - - widths_norm = defaultdict() - for i, label in enumerate(labels): - myD = {} - myD[pos] = data[data[pos] == label][pos + "Weight"].sum() - if len(labels) != 1: - if i == 0: - myD["bottom"] = 0 - myD[pos] -= 0.01 - myD["top"] = myD[pos] - elif i == len(labels) - 1: - myD[pos] -= 0.01 - myD["bottom"] = 1 - myD[pos] - myD["top"] = 1 - else: - myD[pos] -= 0.02 - myD["bottom"] = widths_norm[labels[i - 1]]["top"] + 0.02 - myD["top"] = myD["bottom"] + myD[pos] - else: - myD["bottom"] = 0 - myD["top"] = 1 - widths_norm[label] = myD - return widths_norm - - -def single_sankey( - left: np.array, # data on the left of the diagram - right: np.array, # data on the right of the diagram, len(left) == len(right) - xpos: float = 0, # the starting point on the x-axis - left_weight: np.array = None, # weights for the left labels, if None, all weights are 1 - right_weight: np.array = None, # weights for the right labels, if None, all weights are corresponding left_weight - colorDict: dict = None, # input format: {'label': 'color'} - left_labels: list = None, # labels for the left side of the diagram. The diagram will be sorted by these labels. - right_labels: list = None, # labels for the right side of the diagram. The diagram will be sorted by these labels. - ax=None, # matplotlib axes to be drawn on - flow: bool = True, # if True, draw the sankey in a flow, else draw 1 vs 1 Sankey diagram for each group comparison - sankey: bool = True, # if True, draw the sankey diagram, else draw barplot - width=0.5, - alpha=0.65, - bar_width=0.2, - error_bar_on: bool = True, # if True, draw error bar for each group comparison - strip_on: bool = True, # if True, draw strip for each group comparison - one_sankey: bool = False, # if True, only draw one sankey diagram - right_color: bool = False, # if True, each strip of the diagram will be colored according to the corresponding left labels - align: str = "center", # if 'center', the diagram will be centered on each xtick, if 'edge', the diagram will be aligned with the left edge of each xtick - horizontal: bool = False, # if True, the horizontal format for the sankey diagram will be used -): - """ - Make a single Sankey diagram showing proportion flow from left to right - - Original code from: https://github.com/anazalea/pySankey - - Changes are added to normalize each diagram's height to be 1 - - """ - - # Initiating values - if ax is None: - ax = plt.gca() - - if left_weight is None: - left_weight = [] - if right_weight is None: - right_weight = [] - if left_labels is None: - left_labels = [] - if right_labels is None: - right_labels = [] - # Check weights - if len(left_weight) == 0: - left_weight = np.ones(len(left)) - if len(right_weight) == 0: - right_weight = np.ones(len(right)) - - # Create Dataframe - if isinstance(left, pd.Series): - left.reset_index(drop=True, inplace=True) - if isinstance(right, pd.Series): - right.reset_index(drop=True, inplace=True) - dataFrame = pd.DataFrame( - { - "left": left, - "right": right, - "left_weight": left_weight, - "right_weight": right_weight, - }, - index=range(len(left)), - ) - - if dataFrame[["left", "right"]].isnull().any(axis=None): - raise Exception("Sankey graph does not support null values.") - - # Identify all labels that appear 'left' or 'right' - allLabels = pd.Series( - np.sort(np.r_[dataFrame.left.unique(), dataFrame.right.unique()])[::-1] - ).unique() - - # Identify left labels - if len(left_labels) == 0: - left_labels = pd.Series(np.sort(dataFrame.left.unique())[::-1]).unique() - else: - check_data_matches_labels(left_labels, dataFrame["left"], "left") - - # Identify right labels - if len(right_labels) == 0: - right_labels = pd.Series(np.sort(dataFrame.right.unique())[::-1]).unique() - else: - check_data_matches_labels(left_labels, dataFrame["right"], "right") - - # If no colorDict given, make one - if colorDict is None: - colorDict = {} - palette = "hls" - colorPalette = sns.color_palette(palette, len(allLabels)) - for i, label in enumerate(allLabels): - colorDict[label] = colorPalette[i] - fail_color = {0: "grey"} - colorDict.update(fail_color) - else: - missing = [label for label in allLabels if label not in colorDict.keys()] - if missing: - msg = "The palette parameter is missing values for the following labels : " - msg += "{}".format(", ".join(missing)) - raise ValueError(msg) - - if align not in ("center", "edge"): - err = "{} assigned for `align` is not valid.".format(align) - raise ValueError(err) - - if align == "center": - try: - leftpos = xpos - width / 2 - except TypeError as e: - raise TypeError( - f"the dtypes of parameters x ({xpos.dtype}) " - f"and width ({width.dtype}) " - f"are incompatible" - ) from e - else: - leftpos = xpos - - # Combine left and right arrays to have a pandas.DataFrame in the 'long' format - left_series = pd.Series(left, name="values").to_frame().assign(groups="left") - right_series = pd.Series(right, name="values").to_frame().assign(groups="right") - concatenated_df = pd.concat([left_series, right_series], ignore_index=True) - - # Determine positions of left label patches and total widths - # We also want the height of the graph to be 1 - leftWidths_norm = defaultdict() - for i, left_label in enumerate(left_labels): - myD = {} - myD["left"] = ( - dataFrame[dataFrame.left == left_label].left_weight.sum() - / dataFrame.left_weight.sum() - ) - if len(left_labels) != 1: - if i == 0: - myD["bottom"] = 0 - myD["left"] -= 0.01 - myD["top"] = myD["left"] - elif i == len(left_labels) - 1: - myD["left"] -= 0.01 - myD["bottom"] = 1 - myD["left"] - myD["top"] = 1 - else: - myD["left"] -= 0.02 - myD["bottom"] = leftWidths_norm[left_labels[i - 1]]["top"] + 0.02 - myD["top"] = myD["bottom"] + myD["left"] - topEdge = myD["top"] - else: - myD["bottom"] = 0 - myD["top"] = 1 - myD["left"] = 1 - leftWidths_norm[left_label] = myD - - # Determine positions of right label patches and total widths - rightWidths_norm = defaultdict() - for i, right_label in enumerate(right_labels): - myD = {} - myD["right"] = ( - dataFrame[dataFrame.right == right_label].right_weight.sum() - / dataFrame.right_weight.sum() - ) - if len(right_labels) != 1: - if i == 0: - myD["bottom"] = 0 - myD["right"] -= 0.01 - myD["top"] = myD["right"] - elif i == len(right_labels) - 1: - myD["right"] -= 0.01 - myD["bottom"] = 1 - myD["right"] - myD["top"] = 1 - else: - myD["right"] -= 0.02 - myD["bottom"] = rightWidths_norm[right_labels[i - 1]]["top"] + 0.02 - myD["top"] = myD["bottom"] + myD["right"] - topEdge = myD["top"] - else: - myD["bottom"] = 0 - myD["top"] = 1 - myD["right"] = 1 - rightWidths_norm[right_label] = myD - - # Total width of the graph - xMax = width - - # Plot vertical bars for each label - for left_label in left_labels: - if horizontal: - fill_method = ax.fill_betweenx - else: - fill_method = ax.fill_between - fill_method( - [leftpos + (-(bar_width) * xMax * 0.5), leftpos + (bar_width * xMax * 0.5)], - 2 * [leftWidths_norm[left_label]["bottom"]], - 2 * [leftWidths_norm[left_label]["top"]], - color=colorDict[left_label], - alpha=0.99, - ) - if (not flow and sankey) or one_sankey: - for right_label in right_labels: - if horizontal: - fill_method = ax.fill_betweenx - else: - fill_method = ax.fill_between - fill_method( - [ - xMax + leftpos + (-bar_width * xMax * 0.5), - leftpos + xMax + (bar_width * xMax * 0.5), - ], - 2 * [rightWidths_norm[right_label]["bottom"]], - 2 * [rightWidths_norm[right_label]["top"]], - color=colorDict[right_label], - alpha=0.99, - ) - - # Plot error bars - if error_bar_on and strip_on: - if horizontal: - error_bar( - concatenated_df, - x="groups", - y="values", - ax=ax, - offset=0, - gap_width_percent=2, - method="sankey_error_bar", - pos=[leftpos, leftpos + xMax], - horizontal=True, - ) - else: - error_bar( - concatenated_df, - x="groups", - y="values", - ax=ax, - offset=0, - gap_width_percent=2, - method="sankey_error_bar", - pos=[leftpos, leftpos + xMax], - ) - - # Determine widths of individual strips, all widths are normalized to 1 - ns_l = defaultdict() - ns_r = defaultdict() - ns_l_norm = defaultdict() - ns_r_norm = defaultdict() - for left_label in left_labels: - leftDict = {} - rightDict = {} - for right_label in right_labels: - leftDict[right_label] = dataFrame[ - (dataFrame.left == left_label) & (dataFrame.right == right_label) - ].left_weight.sum() - - rightDict[right_label] = dataFrame[ - (dataFrame.left == left_label) & (dataFrame.right == right_label) - ].right_weight.sum() - factorleft = leftWidths_norm[left_label]["left"] / sum(leftDict.values()) - leftDict_norm = {k: v * factorleft for k, v in leftDict.items()} - ns_l_norm[left_label] = leftDict_norm - ns_r[left_label] = rightDict - - # ns_r should be using a different way of normalization to fit the right side - # It is normalized using the value with the same key in each sub-dictionary - ns_r_norm = normalize_dict(ns_r, rightWidths_norm) - - # Plot strips - if sankey and strip_on: - for left_label, right_label in itertools.product(left_labels, right_labels): - labelColor = left_label - - if right_color: - labelColor = right_label - - if len(dataFrame[(dataFrame.left == left_label) & - (dataFrame.right == right_label)]) > 0: - # Create array of y values for each strip, half at left value, - # half at right, convolve - ys_d = np.array( - 50 * [leftWidths_norm[left_label]["bottom"]] - + 50 * [rightWidths_norm[right_label]["bottom"]] - ) - ys_d = np.convolve(ys_d, 0.05 * np.ones(20), mode="valid") - ys_d = np.convolve(ys_d, 0.05 * np.ones(20), mode="valid") - # to remove the array wrapping behaviour of black - # fmt: off - ys_u = np.array(50 * [leftWidths_norm[left_label]['bottom'] + ns_l_norm[left_label][right_label]] + \ - 50 * [rightWidths_norm[right_label]['bottom'] + ns_r_norm[left_label][right_label]]) - # fmt: on - ys_u = np.convolve(ys_u, 0.05 * np.ones(20), mode="valid") - ys_u = np.convolve(ys_u, 0.05 * np.ones(20), mode="valid") - - # Update bottom edges at each label so next strip starts at the right place - leftWidths_norm[left_label]["bottom"] += ns_l_norm[left_label][right_label] - rightWidths_norm[right_label]["bottom"] += ns_r_norm[left_label][ - right_label - ] - if horizontal: - fill_method = ax.fill_betweenx - else: - fill_method = ax.fill_between - fill_method( - np.linspace( - leftpos + (bar_width * xMax * 0.5), - leftpos + xMax - (bar_width * xMax * 0.5), - len(ys_d), - ), - ys_d, - ys_u, - alpha=alpha, - color=colorDict[labelColor], - edgecolor="none", - ) - -def sankeydiag( - data: pd.DataFrame, - xvar: str, # x column to be plotted. - yvar: str, # y column to be plotted. - temp_all_plot_groups: list, - idx: list, - temp_idx: list, - left_labels: list = None, # labels for the left side of the diagram. The diagram will be sorted by these labels. - right_labels: list = None, # labels for the right side of the diagram. The diagram will be sorted by these labels. - palette: str | dict = None, - ax=None, # matplotlib axes to be drawn on - flow: bool = True, # if True, draw the sankey in a flow, else draw 1 vs 1 Sankey diagram for each group comparison - sankey: bool = True, # if True, draw the sankey diagram, else draw barplot - one_sankey: bool = False, # determined by the driver function on plotter.py, if True, draw the sankey diagram across the whole raw data axes - width: float = 0.4, # the width of each sankey diagram - right_color: bool = False, # if True, each strip of the diagram will be colored according to the corresponding left labels - align: str = "center", # the alignment of each sankey diagram, can be 'center' or 'left' - alpha: float = 0.65, # the transparency of each strip - horizontal: bool = False, # if True, the horizontal format for the sankey diagram will be used - **kwargs, -): - """ - Read in melted pd.DataFrame, and draw multiple sankey diagram on a single axes - using the value in column yvar according to the value in column xvar - left_idx in the column xvar is on the left side of each sankey diagram - right_idx in the column xvar is on the right side of each sankey diagram - - """ - if "width" in kwargs: - width = kwargs["width"] - - if "align" in kwargs: - align = kwargs["align"] - - if "alpha" in kwargs: - alpha = kwargs["alpha"] - - if "right_color" in kwargs: - right_color = kwargs["right_color"] - - if "bar_width" in kwargs: - bar_width = kwargs["bar_width"] - - if "sankey" in kwargs: - sankey = kwargs["sankey"] - - if "flow" in kwargs: - flow = kwargs["flow"] - - fontsize = kwargs.pop("fontsize") - - if ax is None: - ax = plt.gca() - - left_idx = [] - right_idx = [] - # Design for Sankey Flow Diagram - sankey_idx = ( - [ - (control, test) - for i in idx - for control, test in zip( - i[:], - (tuple(i[1:]) + (i[0],)) if isinstance(i, tuple) else (list(i[1:]) + [i[0]]) - ) - ] - if flow - else temp_idx - ) - - for i in sankey_idx: - left_idx.append(i[0]) - right_idx.append(i[1]) - - if len(temp_all_plot_groups) == 2: - one_sankey = True - left_idx.pop() - right_idx.pop() # Remove the last element from two lists - - # two_col_sankey = True if proportional == True and one_sankey == False and sankey == True and flow == False else False - - allLabels = pd.Series(np.sort(data[yvar].unique())[::-1]).unique() - - # Check if all the elements in left_idx and right_idx are in xvar column - unique_xvar = data[xvar].unique() - if not all(elem in unique_xvar for elem in left_idx): - raise ValueError(f"{left_idx} not found in {xvar} column") - if not all(elem in unique_xvar for elem in right_idx): - raise ValueError(f"{right_idx} not found in {xvar} column") - - xpos = 0 - - # For baseline comparison, broadcast left_idx to the same length as right_idx - # so that the left of sankey diagram will be the same - # For sequential comparison, left_idx and right_idx can have anything different - # but should have the same length - if len(left_idx) == 1: - broadcasted_left = np.broadcast_to(left_idx, len(right_idx)) - elif len(left_idx) != len(right_idx): - raise ValueError(f"left_idx and right_idx should have the same length") - else: - broadcasted_left = left_idx - - if isinstance(palette, dict): - if not all(key in allLabels for key in palette.keys()): - raise ValueError(f"keys in palette should be in {yvar} column") - plot_palette = palette - elif isinstance(palette, str): - plot_palette = {} - colorPalette = sns.color_palette(palette, len(allLabels)) - for i, label in enumerate(allLabels): - plot_palette[label] = colorPalette[i] - else: - plot_palette = None - - # Create a strip_on list to determine whether to draw the strip during repeated measures - strip_on = [ - int(right not in broadcasted_left[:i]) for i, right in enumerate(right_idx) - ] - - draw_idx = list(zip(broadcasted_left, right_idx)) - for i, (left, right) in enumerate(draw_idx): - if not one_sankey: - if flow: - width = 1 - align = "edge" - sankey = ( - False if i == len(draw_idx) - 1 else sankey - ) # Remove last strip in flow - error_bar_on = ( - False if i == len(draw_idx) - 1 and flow else True - ) # Remove last error_bar in flow - bar_width = 0.4 if sankey == False and flow == False else bar_width - single_sankey( - data[data[xvar] == left][yvar], - data[data[xvar] == right][yvar], - xpos=xpos, - ax=ax, - colorDict=plot_palette, - width=width, - left_labels=left_labels, - right_labels=right_labels, - strip_on=strip_on[i], - right_color=right_color, - bar_width=bar_width, - sankey=sankey, - error_bar_on=error_bar_on, - flow=flow, - align=align, - alpha=alpha, - horizontal=horizontal, - ) - xpos += 1 - else: - xpos = 0 - width = 1 - if not sankey: - bar_width = 0.5 - single_sankey( - data[data[xvar] == left][yvar], - data[data[xvar] == right][yvar], - xpos=xpos, - ax=ax, - colorDict=plot_palette, - width=width, - left_labels=left_labels, - right_labels=right_labels, - right_color=right_color, - bar_width=bar_width, - sankey=sankey, - one_sankey=one_sankey, - flow=False, - align="edge", - alpha=alpha, - horizontal=horizontal, - ) - - # Now only draw vs xticks for two-column sankey diagram - - if not one_sankey or (sankey and not flow): - sankey_tick_vals = ( - [f"{left}" for left in broadcasted_left] - if flow - else [f"{left} v.s. {right}" if horizontal - else f"{left}\n v.s.\n{right}" - for left, right in zip(broadcasted_left, right_idx) - ] - ) - sankey_tick_locs = np.arange(len(right_idx)) - else: - sankey_tick_vals, sankey_tick_locs = [broadcasted_left[0], right_idx[0]], [0, 1] - - if horizontal: - ax.set_yticks(sankey_tick_locs) - ax.set_yticklabels(sankey_tick_vals, fontsize = fontsize) - else: - ax.set_xticks(sankey_tick_locs) - ax.set_xticklabels(sankey_tick_vals, fontsize = fontsize) - - return (left_idx, right_idx) - -def add_bars_to_plot(bar_dict: dict, ax: axes.Axes, bar_kwargs: dict): - """ - Add bars to the relevant axes. - - Parameters - ---------- - bar_dict : dict - Dictionary of bar values. - ax : axes.Axes - Matplotlib axis object to plot on. - bar_kwargs : dict - Keyword arguments for the bars. - """ - og_xlim, og_ylim = ax.get_xlim(), ax.get_ylim() - - x_start_values, x_distances, y_start_values, y_distances, colors = bar_dict.values() - - for start_x, start_y, distance_x, distance_y, current_color in zip( - x_start_values, - y_start_values, - x_distances, - y_distances, - colors - ): - ax.add_patch(mpatches.Rectangle((start_x, start_y), - distance_x, distance_y, - color=current_color, **bar_kwargs - ) - ) - ax.set_xlim(og_xlim) - ax.set_ylim(og_ylim) - -def delta_text_plotter( - results: pd.DataFrame, - ax_to_plot: object, - ticks_to_plot: list, - delta_text_kwargs: dict, - color_col: str, - plot_palette_raw: dict, - show_pairs: bool, - float_contrast: bool, - extra_delta: float, - bootstraps_color_by_group: bool = False - ): - """ - Add delta text to the contrast plot. - - Parameters - ---------- - results : object (Dataframe) - Dataframe of contrast object comparisons. - ax_to_plot : axes.Axes - Matplotlib axis object to plot on. - ticks_to_plot : list - List of indices of the contrast objects. - delta_text_kwargs : dict - Keyword arguments for the delta text. - color_col : str - Column name of the color column. - plot_palette_raw : dict - Dictionary of colors used in the plot. - show_pairs : bool - Whether the data is paired and show pairs. - float_contrast : bool - Whether the DABEST plot uses Gardner-Altman or Cummings. - extra_delta : float or None - The extra mini-meta or delta-delta value if applicable. - bootstraps_color_by_group : bool, optional - Whether to color the bootstraps by group. Default is False. - """ - # Colors - from .misc_tools import color_picker - delta_text_colors = color_picker(color_type = 'delta_text', - kwargs = delta_text_kwargs, - elements = ticks_to_plot, - color_col = color_col, - show_pairs = show_pairs, - color_palette = plot_palette_raw, - bootstraps_color_by_group = bootstraps_color_by_group - ) - - num_of_elements = len(ticks_to_plot) + 1 if extra_delta is not None else len(ticks_to_plot) - - # Collect the means for the delta text - delta_values = [] - for j, tick in enumerate(ticks_to_plot): - delta_values.append(results.difference[int(j)]) - if extra_delta is not None: - delta_values.append(extra_delta) - delta_text_colors.append('black') - - # Collect the X-coordinates for the delta text - delta_text_x_coordinates = delta_text_kwargs.pop('x_coordinates') - delta_text_x_offset = delta_text_kwargs.pop('offset') - - if delta_text_x_coordinates is not None: - if not isinstance(delta_text_x_coordinates, (list, tuple)) or not all(isinstance(x, (int, float)) for x in delta_text_x_coordinates): - raise TypeError("delta_text_kwargs['x_coordinates'] must be a list of x-coordinates.") - if len(delta_text_x_coordinates) != num_of_elements: - raise ValueError("delta_text_kwargs['x_coordinates'] must have the same length as the number of ticks to plot.") - else: - x_adjust = (-0.4 if float_contrast else 0.48) + delta_text_x_offset - delta_text_x_coordinates = [x+x_adjust for x in ticks_to_plot] - if extra_delta is not None: delta_text_x_coordinates.append(max(ticks_to_plot)+1+x_adjust) - - # Collect the Y-coordinates for the delta text - delta_text_y_coordinates = delta_text_kwargs.pop('y_coordinates') - if float_contrast: delta_text_kwargs["va"] = 'bottom' if results.difference[0] >= 0 else 'top' - - if delta_text_y_coordinates is not None: - if not isinstance(delta_text_y_coordinates, (list, tuple)) or not all(isinstance(y, (int, float)) for y in delta_text_y_coordinates): - raise TypeError("delta_text_kwargs['y_coordinates'] must be a list of y-coordinates.") - if len(delta_text_y_coordinates) != num_of_elements: - raise ValueError("delta_text_kwargs['y_coordinates'] must have the same length as the number of ticks to plot.") - else: - delta_text_y_coordinates = delta_values - - # Plot the delta text - for x, y, text, color in zip(delta_text_x_coordinates, delta_text_y_coordinates, delta_values, delta_text_colors): - delta_text = np.format_float_positional(text, precision=2, sign=True, trim="k", min_digits=2) - ax_to_plot.text(x, y, delta_text, color=color, zorder=5, **delta_text_kwargs) - -def delta_dots_plotter( - plot_data: pd.DataFrame, - contrast_axes: axes.Axes, - delta_id_col: str, - idx: list, - xvar: str, - yvar: str, - is_paired: bool, - color_col: str, - float_contrast: bool, - plot_palette_raw: dict, - delta_dot_kwargs: dict, - horizontal: bool - ): - """ - Parameters - ---------- - plot_data : object (Dataframe) - Dataframe of the plot data. - contrast_axes : axes.Axes - Matplotlib axis object to plot on. - delta_id_col : str - Column name of the delta id column. - idx : list - List of indices of the contrast objects. - xvar : str - Column name of the x variable. - yvar : str - Column name of the y variable. - is_paired : bool - Whether the data is paired. - color_col : str - Column name of the color column. - float_contrast : bool - Whether the DABEST plot uses Gardner-Altman or Cummings - plot_palette_raw : dict - Dictionary of colors used in the plot. - delta_dot_kwargs : dict - Keyword arguments for the delta dots. - horizontal : bool - If the rawplot is horizontal. - """ - - # Checks and initializations - # from .plot_tools import swarmplot - delta_dot_color = delta_dot_kwargs.pop('color') - if color_col is not None: - plot_palette_deltapts = plot_palette_raw - delta_plot_data = plot_data[[xvar, yvar, delta_id_col, color_col]] - else: - plot_palette_deltapts = delta_dot_color - delta_plot_data = plot_data[[xvar, yvar, delta_id_col]] - - # TODO: to make jitter value more accurate and not just a hardcoded eyeball value - jitter = 0.6 if float_contrast else 1 - - # Create dataframe of delta values - final_deltas = pd.DataFrame() - for i in idx: - for j in i: - if i.index(j) != 0: - temp_df_exp = delta_plot_data[ - delta_plot_data[xvar].str.contains(j) - ].reset_index(drop=True) - if is_paired == "baseline": - temp_df_cont = delta_plot_data[ - delta_plot_data[xvar].str.contains(i[0]) - ].reset_index(drop=True) - elif is_paired == "sequential": - temp_df_cont = delta_plot_data[ - delta_plot_data[xvar].str.contains( - i[i.index(j) - 1] - ) - ].reset_index(drop=True) - delta_df = temp_df_exp.copy() - delta_df[yvar] = temp_df_exp[yvar] - temp_df_cont[yvar] - final_deltas = pd.concat([final_deltas, delta_df]) - - if horizontal: - delta_dot_kwargs.update({'side': 'left'}) - # Plot the delta dots - swarmplot( - data=final_deltas, - x=xvar, - y=yvar, - ax=contrast_axes, - order=None, - hue=color_col, - palette=plot_palette_deltapts, - jitter=jitter, - is_drop_gutter=True, - gutter_limit=1, - horizontal=horizontal, - **delta_dot_kwargs - ) - contrast_axes.legend().set_visible(False) - - -def slopegraph_plotter( - dabest_obj: object, - plot_data: pd.DataFrame, - xvar: str, - yvar: str, - color_col: str, - plot_palette_raw: dict, - slopegraph_kwargs: dict, - rawdata_axes: axes.Axes, - ytick_color: str, - temp_idx: list, - horizontal: bool, - temp_all_plot_groups: list, - plot_kwargs: dict, - group_summaries_kwargs: dict - ): - """ - Add slopegraph to the rawdata axes. - - Parameters - ---------- - dabest_obj : object - DABEST object. - plot_data : object (Dataframe) - Dataframe of the plot data. - xvar : str - Column name of the x variable. - yvar : str - Column name of the y variable. - color_col : str - Column name of the color column. - plot_palette_raw : dict - Dictionary of colors used in the plot. - slopegraph_kwargs : dict - Keyword arguments for the slopegraph. - rawdata_axes : axes.Axes - Matplotlib axis object to plot on. - ytick_color : str - Color of the yticks. - temp_idx : list - List of indices of the contrast objects. - horizontal : bool - If the plotting will be in horizontal format. - temp_all_plot_groups : list - List of all plot groups. - plot_kwargs : dict - Keyword arguments for the plot. - group_summaries_kwargs : dict, optional - Keyword arguments for group summaries, if applicable. - - """ - # Jitter Kwargs - # With help from GitHub user: devMJBL - jitter = slopegraph_kwargs.pop("jitter") - if jitter > 1: - err0 = "Jitter value is too high. Defaulting to 1." - warnings.warn(err0) - jitter = 1 - rng = np.random.default_rng(slopegraph_kwargs.pop("jitter_seed")) - - # Pivot the long (melted) data. - if color_col is None: - pivot_values = [yvar] - else: - pivot_values = [yvar, color_col] - pivoted_plot_data = pd.pivot( - data=plot_data, - index=dabest_obj.id_col, - columns=xvar, - values=pivot_values, - ) - - x_start = 0 - for ii, current_tuple in enumerate(temp_idx): - current_pair = pivoted_plot_data.loc[ - :, pd.MultiIndex.from_product([pivot_values, current_tuple]) - ].dropna() - - # Check for correct pairing - if len(current_pair) == 0: - raise ValueError('There are no pairs to plot... check original dataframe for correct ID pairing') - - current_pair = pivoted_plot_data.loc[ - :, pd.MultiIndex.from_product([pivot_values, current_tuple]) - ] - grp_count = len(current_tuple) - - # Iterate through the data for the current tuple. - for ID, observation in current_pair.iterrows(): - x_points = [t + 0.15*jitter*rng.standard_t(df=6, size=None) for t in range(x_start, x_start + grp_count)] # devMJBL - y_points = observation[yvar].tolist() - - if color_col is None: - slopegraph_kwargs["color"] = ytick_color - else: - color_key = observation[color_col].iloc[0] - if isinstance(color_key, (str, np.int64, np.float64)): - slopegraph_kwargs["color"] = plot_palette_raw[color_key] - slopegraph_kwargs["label"] = color_key - - x_points, y_points = (y_points, x_points) if horizontal else (x_points, y_points) - rawdata_axes.plot(x_points, y_points, **slopegraph_kwargs) - - # Add the group summaries if applicable. - group_summaries = plot_kwargs.get("group_summaries", None) - if group_summaries is not None: - for key in ['gap_width_percent', 'offset']: - group_summaries_kwargs.pop(key, None) - group_summaries_kwargs['color'] = 'black' if group_summaries_kwargs.get('color') is None else group_summaries_kwargs['color'] - group_summaries_kwargs['capsize'] = 0 if group_summaries_kwargs.get('capsize') is None else group_summaries_kwargs['capsize'] - - index_points = [t for t in range(x_start, x_start + grp_count)] - av_points, err_points, lo_points, hi_points = [], [], [], [] - for group in range(len(index_points)): - if group_summaries == "mean_sd": - av_points.append(current_pair.iloc[:, int(group)].mean()) - err_points.append(current_pair.iloc[:, int(group)].std()) - elif group_summaries == "median_quartiles": - median = current_pair.iloc[:, int(group)].median() - av_points.append(median) - lo_points.append(median - current_pair.iloc[:, int(group)].quantile(0.25)) - hi_points.append(current_pair.iloc[:, int(group)].quantile(0.75) - median) - - if group_summaries == "median_quartiles": - err_points = [lo_points, hi_points] - - # Plot the lines - if horizontal: - rawdata_axes.errorbar( - av_points, - index_points, - xerr=err_points, - **group_summaries_kwargs - ) - else: - rawdata_axes.errorbar( - index_points, - av_points, - yerr=err_points, - **group_summaries_kwargs - ) - - x_start = x_start + grp_count - - # Set the tick labels, because the slopegraph plotting doesn't. - if horizontal: - rawdata_axes.set_yticks(np.arange(0, len(temp_all_plot_groups))) - rawdata_axes.set_yticklabels(temp_all_plot_groups, fontsize = plot_kwargs.get("fontsize_rawxlabel")) - else: - rawdata_axes.set_xticks(np.arange(0, len(temp_all_plot_groups))) - rawdata_axes.set_xticklabels(temp_all_plot_groups, fontsize = plot_kwargs.get("fontsize_rawxlabel")) - - -def plot_minimeta_or_deltadelta_violins( - dabest_obj: object, - type: str, - ci_type: str, - rawdata_axes: axes.Axes, - contrast_axes: axes.Axes, - contrast_kwargs: dict, - contrast_xtick_labels: list, - effect_size: str, - plot_kwargs: dict, - horizontal: bool, - show_pairs: bool, - contrast_marker_kwargs: dict, - contrast_errorbar_kwargs: dict, - ): - """ - Add mini meta-analysis or delta-delta violin plots to the contrast plot. - - Parameters - ---------- - dabest_obj : object - DABEST Effectsize object delta-delta or mini_meta - type: str - mini_meta or delta_delta - ci_type : str - Type of confidence interval to plot. - rawdata_axes : axes.Axes - Matplotlib axis object to plot on. - contrast_axes : axes.Axes - Matplotlib axis object to plot on. - contrast_kwargs : dict - Keyword arguments for the violinplot. - contrast_xtick_labels : list - List of xtick labels for the contrast plot. - effect_size : str - Type of effect size to plot. - plot_kwargs : dict - Keyword arguments for the plot. - horizontal : bool - If the plot is horizontal. - show_pairs : bool - Whether the data is paired and shown in pairs. - contrast_marker_kwargs: dict - Keyword arguments for the effectsize marker. - contrast_errorbar_kwargs: dict - Keyword arguments for the effectsize errorbar. - """ - - # Plot the curve - def extract_curve_data(dabest_object): - try: - data = dabest_object.bootstraps_weighted_delta - except AttributeError: - data = dabest_object.bootstraps_delta_delta - - ci_low, ci_high = dabest_object.results.get(ci_type+'_low')[0], dabest_object.results.get(ci_type+'_high')[0] - return data, dabest_object.difference, ci_low, ci_high - - data, difference, ci_low, ci_high = extract_curve_data(dabest_obj) - - if contrast_kwargs.get('alpha') is not None: - contrast_alpha = contrast_kwargs.pop('alpha') - - if horizontal: - contrast_kwargs.update({'orientation': 'horizontal', 'widths': 1}) - position = max(rawdata_axes.get_yticks()) + 1 - half = "bottom" - effsize_x, effsize_y = difference, [position] - ci_x, ci_y = [ci_low, ci_high], [position, position] - else: - position = max(rawdata_axes.get_xticks()) + 1 - half = "right" - effsize_x, effsize_y = [position], difference - ci_x, ci_y = [position, position], [ci_low, ci_high] - - v = contrast_axes.violinplot( - data[~np.isinf(data)], positions=[position], **contrast_kwargs - ) - - halfviolin(v, fill_color="grey", alpha=contrast_alpha, half=half) - - # Plot the effect size. - contrast_axes.plot( - effsize_x, - effsize_y, - **contrast_marker_kwargs - ) - # Plot the confidence interval. - contrast_axes.plot( - ci_x, - ci_y, - **contrast_errorbar_kwargs - ) - - # Add labels and ticks - if horizontal: - current_ylabels = rawdata_axes.get_yticklabels() - if type == 'mini_meta': - current_ylabels.extend(["Weighted delta"]) - elif effect_size == "hedges_g": - current_ylabels.extend(["Delta g"]) - else: - current_ylabels.extend(["Delta-delta"]) - - rawdata_axes.set_yticks(np.append(rawdata_axes.get_yticks(), position)) - rawdata_axes.set_yticklabels(current_ylabels) - else: - if type == 'mini_meta': - if show_pairs: - contrast_xtick_labels.extend(["Weighted\n delta"]) - else: - contrast_xtick_labels.extend(["Weighted delta"]) - elif effect_size == "hedges_g": - contrast_xtick_labels.extend(["Delta g"]) - else: - contrast_xtick_labels.extend(["Delta-delta"]) - - # Create the delta-delta axes. - if type == 'delta_delta' and not horizontal: - if plot_kwargs["delta2_label"] is not None: - delta2_label = plot_kwargs["delta2_label"] - elif effect_size == "mean_diff": - delta2_label = "Delta-delta" - else: - delta2_label = "Delta g" - fontsize_delta2label = plot_kwargs["fontsize_delta2label"] - delta2_axes = contrast_axes.twinx() - delta2_axes.set_frame_on(False) - delta2_axes.set_ylabel(delta2_label, fontsize=fontsize_delta2label) - og_xlim_delta, og_ylim_delta = contrast_axes.get_xlim(), contrast_axes.get_ylim() - delta2_axes.set_ylim(og_ylim_delta) - else: - delta2_axes = None - - return delta2_axes, contrast_xtick_labels - - -def effect_size_curve_plotter( - ticks_to_plot: list, - ticks_for_baseline_ec: list, - results: pd.DataFrame, - ci_type: str, - contrast_axes: axes.Axes, - contrast_kwargs: dict, - bootstraps_color_by_group: bool, - plot_palette_contrast: dict, - horizontal: bool, - contrast_marker_kwargs: dict, - contrast_errorbar_kwargs: dict, - idx: list, - is_paired: bool, - contrast_paired_lines: bool, - contrast_paired_lines_kwargs: dict, - show_baseline_ec: bool = False - ): - """ - Add effect size curves to the contrast plot. - - Parameters - ---------- - ticks_to_plot : list - List of indices of the contrast objects. - ticks_for_baseline_ec : list - List of indices of the baseline effect curve objects. - results : object (Dataframe) - Dataframe of contrast object comparisons. - ci_type : str - Type of confidence interval to plot. - contrast_axes : axes.Axes - Matplotlib axis object to plot on. - contrast_kwargs : dict - Keyword arguments for the violinplot. - bootstraps_color_by_group : bool - Whether to color the bootstraps by group. - plot_palette_contrast : dict - Dictionary of colors used in the contrast plot. - horizontal : bool - If the plot is horizontal. - contrast_marker_kwargs: dict - Keyword arguments for the effectsize marker. - contrast_errorbar_kwargs: dict - Keyword arguments for the effectsize errorbar. - idx : list - List of indices of the raw groups. - is_paired : bool - Whether the data is paired. - contrast_paired_lines : bool - Whether to add lines for repeated measures data. - contrast_paired_lines_kwargs : dict - Keyword arguments for the repeated measures lines. - show_baseline_ec : bool - Whether to show the baseline effect curve. - """ - - def plot_effect_size(tick, group, control, bootstrap, effsize, ci_low, ci_high): - # Create the violinplot - if horizontal: - contrast_kwargs.update({'orientation': 'horizontal', 'widths': 1}) - - v = contrast_axes.violinplot( - bootstrap[~np.isinf(bootstrap)], - positions=[tick], - **contrast_kwargs - ) - - # Color the violin plot - fc = plot_palette_contrast[group] if bootstraps_color_by_group else "grey" - half = "bottom" if horizontal else "right" - halfviolin(v, fill_color=fc, alpha=contrast_alpha, half=half) - - # Plot the confidence interval - if horizontal: - ci_x, ci_y = [ci_low, ci_high], [tick, tick] - else: - ci_x, ci_y = [tick, tick], [ci_low, ci_high] - - contrast_axes.plot(ci_x, ci_y, **contrast_errorbar_kwargs) - - return "{}\nminus\n{}".format(group, control) - - if contrast_kwargs.get('alpha') is not None: - contrast_alpha = contrast_kwargs.pop('alpha') - - # Plot the curves - contrast_xtick_labels = [] - for j, tick in enumerate(ticks_to_plot): - current_group = results.test[int(j)] - current_control = results.control[int(j)] - current_bootstrap = results.bootstraps[int(j)] - current_effsize = results.difference[int(j)] - current_ci_low = results.get(ci_type+'_low')[int(j)] - current_ci_high = results.get(ci_type+'_high')[int(j)] - - # Plot the effect size marker - if horizontal: - effsize_x, effsize_y = current_effsize, [tick] - else: - effsize_x, effsize_y = [tick], current_effsize - - contrast_axes.plot( - effsize_x, - effsize_y, - **contrast_marker_kwargs - ) - - label = plot_effect_size(tick, current_group, current_control, current_bootstrap, - current_effsize, current_ci_low, current_ci_high) - contrast_xtick_labels.append(label) - - # Add baseline effect curve plotting - bec_results = results.drop_duplicates(subset='control', keep='first').reset_index(drop=True) - for j, tick in enumerate(ticks_for_baseline_ec): - bec_group = bec_results.control[j] - bec_control = bec_results.control[j] - bec_bootstrap = bec_results.bec_bootstraps[j] - bec_effsize = bec_results.bec_difference[j] - bec_ci_low = bec_results.get('bec_'+ci_type+'_low')[j] - bec_ci_high = bec_results.get('bec_'+ci_type+'_high')[j] - - # Plot the effect size marker regardless of show_baseline_ec - if horizontal: - effsize_x, effsize_y = bec_effsize, [tick] - else: - effsize_x, effsize_y = [tick], bec_effsize - - contrast_axes.plot(effsize_x, effsize_y, **contrast_marker_kwargs) - - if show_baseline_ec: - _ = plot_effect_size(tick, bec_group, bec_control, bec_bootstrap, - bec_effsize, bec_ci_low, bec_ci_high) - # Baseline Curve doesn't need tick text - - # Add lines for repeated measures data - if is_paired and contrast_paired_lines: - temp_num = 0 - lines_to_plot_list = [] - - for group in idx: - new_group = [] - if len(group) >= 2: - new_group.append(temp_num) - for i in range(1, len(group)): - new_group.append(temp_num+i) - temp_num += len(group) - lines_to_plot_list.append(new_group) - - for group in lines_to_plot_list: - if len(group) > 0: - mean_diffs_for_lines = [] - for ticks in group: - if ticks in ticks_to_plot: - mean_diffs_for_lines.append(results.loc[ticks_to_plot.index(ticks)]["difference"]) - else: - mean_diffs_for_lines.append(int(0)) - - x_data = mean_diffs_for_lines if horizontal else group - y_data = group if horizontal else mean_diffs_for_lines - - contrast_axes.plot( - x_data, - y_data, - **contrast_paired_lines_kwargs - ) - - contrast_kwargs['alpha'] = contrast_alpha - return current_group, current_control, current_effsize, contrast_xtick_labels - -def gridkey_plotter( - is_paired: bool, - idx: list, - all_plot_groups: list, - gridkey: list, - rawdata_axes: axes.Axes, - contrast_axes: axes.Axes, - plot_data: pd.DataFrame, - xvar: str, - yvar: str, - results: pd.DataFrame, - show_delta2: bool, - show_mini_meta: bool, - x1_level: list, - experiment_label: list, - float_contrast: bool, - horizontal: bool, - delta_delta: object, - mini_meta: object, - effect_size: str, - gridkey_kwargs: dict, - ): - """ - Add gridkey to the contrast plot. - - Parameters - ---------- - is_paired : bool - Whether the data is paired. - idx : list - List of indices of the contrast objects. - all_plot_groups : list - List of all plot groups. - gridkey : list - List of gridkey rows. - rawdata_axes : axes.Axes - Matplotlib axis object for the raw data. - contrast_axes : axes.Axes - Matplotlib axis object for the contrast data. - plot_data : object (Dataframe) - Dataframe of the plot data. - xvar : str - Column name of the x variable. - yvar : str - Column name of the y variable. - results : object (Dataframe) - Dataframe of contrast object comparisons. - show_delta2 : bool - Whether to show the delta-delta. - show_mini_meta : bool - Whether to show the mini meta-analysis. - x1_level : list - List of x1 levels. - experiment_label : list - List of experiment labels. - float_contrast : bool - Whether the DABEST plot uses Gardner-Altman or Cummings - horizontal : bool - If the plot is horizontal. - delta_delta : object - delta-delta object. - mini_meta : object - Mini meta-analysis object. - effect_size : str - Type of effect size to plot - gridkey_kwargs : dict - Keyword arguments for the gridkey. - """ - # Extract relevant kwargs - gridkey_show_Ns = gridkey_kwargs["show_Ns"] - gridkey_show_es = gridkey_kwargs["show_es"] - gridkey_merge_pairs = gridkey_kwargs["merge_pairs"] - gridkey_marker = gridkey_kwargs["marker"] - gridkey_delimiters = gridkey_kwargs["delimiters"] - labels_fontsize = gridkey_kwargs.get('labels_fontsize') - fontsize = gridkey_kwargs.get('fontsize') - - # Auto parser for gridkey - implemented by SangyuXu - if gridkey == "auto" or gridkey == True: - if experiment_label is not None: - gridkey = list(np.concatenate([experiment_label, x1_level])) - else: - temp_groups = ";".join(all_plot_groups) - for delimiter in gridkey_delimiters: - temp_groups = temp_groups.replace(delimiter, ";") - temp_groups = [i.strip() for i in temp_groups.split(';')] - unique_groups = list(set(temp_groups)) - rank = [sum([temp_groups.index(i) for i in temp_groups if(j in i)]) for j in unique_groups] - gridkey = [x for _,x in sorted(zip(rank,unique_groups))] - - # Raise error if there are more than 2 items in any idx and gridkey_merge_pairs is True and is_paired is not None - if gridkey_merge_pairs and is_paired is not None: - for i in idx: - if len(i) > 2: - warnings.warn( - "gridkey_merge_pairs=True only works if all idx in tuples have only two items. gridkey_merge_pairs has automatically been set to False" - ) - gridkey_merge_pairs = False - break - elif gridkey_merge_pairs and is_paired is None: - warnings.warn( - "gridkey_merge_pairs=True is only applicable for paired data." - ) - gridkey_merge_pairs = False - - # Checks for gridkey_merge_pairs and is_paired; if both are true, "merges" the gridkey per pair - if gridkey_merge_pairs and is_paired is not None: - groups_for_gridkey = [] - for i in idx: - groups_for_gridkey.append(i[1]) - else: - groups_for_gridkey = all_plot_groups - - # raise errors if gridkey is not a list, or if the list is empty - if isinstance(gridkey, list) is False: - raise TypeError("gridkey must be a list (or a string 'auto').") - if any(isinstance(i, str) is False for i in gridkey): - raise TypeError("gridkey must contain only strings.") - if len(gridkey) == 0: - warnings.warn("gridkey is an empty list.") - - # raise Warning if an item in gridkey is not contained in any idx - for i in gridkey: - in_idx = 0 - for j in groups_for_gridkey: - if i in j: - in_idx += 1 - if in_idx == 0: - if is_paired is not None: - warnings.warn( - i - + " is not in any idx. Please check. Alternatively, merging gridkey pairs may not be suitable for your data; try passing gridkey_merge_pairs=False." - ) - else: - warnings.warn(i + " is not in any idx. Please check.") - - # Populate table: checks if idx for each column contains rowlabel name - # IF so, marks that element as present w black dot (default "\u25CF"), or space if not present - table_cellcols = [] - for i in gridkey: - thisrow = [] - for q in groups_for_gridkey: - if str(i) in q: - thisrow.append(gridkey_marker) - else: - thisrow.append("") - table_cellcols.append(thisrow) - - # Adds a row for Ns with the Ns values - if gridkey_show_Ns: - gridkey.append("Ns") - list_of_Ns = [] - for i in groups_for_gridkey: - list_of_Ns.append(str(plot_data.groupby(xvar, observed=False).count()[yvar].loc[i])) - table_cellcols.append(list_of_Ns) - - # Adds a row for effectsizes with effectsize values - if gridkey_show_es and not horizontal: - gridkey.append("\u0394") - effsize_list = [] - results_list = results.test.to_list() - - # get the effect size, append + or -, 2 dec places - for i in enumerate(groups_for_gridkey): - if i[1] in results_list: - curr_esval = results.loc[results["test"] == i[1]]["difference"].iloc[0] - curr_esval_str = np.format_float_positional( - curr_esval, - precision=2, - sign=True, - trim="k", - min_digits=2, - ) - effsize_list.append(curr_esval_str) - else: - effsize_list.append("-") - - table_cellcols.append(effsize_list) - - # Set the axes to plot on - if float_contrast or horizontal: - ax_to_plot = rawdata_axes - else: - ax_to_plot = contrast_axes - - # Add delta-delta or mini_meta details to the table - if show_mini_meta or show_delta2: - if show_delta2: - added_group_name = ["Deltas' g"] if effect_size == "hedges_g" else ["Delta-Delta"] - else: - added_group_name = ["Weighted Delta"] - gridkey = added_group_name + gridkey - table_cellcols = [[""]*len(table_cellcols[0])] + table_cellcols - - if not horizontal and show_delta2: - extra_table_cellcols = [[] for i in range(len(table_cellcols))] - - for group_idx, group_vals in enumerate(extra_table_cellcols): - if group_idx == 0: - added_group = [gridkey_marker] - elif gridkey_show_es and (group_idx == len(extra_table_cellcols)-1) and not horizontal: - added_delta_effectsize = delta_delta.difference - added_delta_effectsize_str = np.format_float_positional( - added_delta_effectsize, - precision=2, - sign=True, - trim="k", - min_digits=2, - ) - added_group = [added_delta_effectsize_str] - else: - added_group = [''] - for n in added_group: - group_vals.append(n) - - elif horizontal or show_mini_meta: - for group_idx, group_vals in enumerate(table_cellcols): - if group_idx == 0: - added_group = [gridkey_marker] - elif gridkey_show_es and (group_idx == len(table_cellcols)-1) and not horizontal: - added_delta_effectsize = delta_delta.difference if show_delta2 else mini_meta.difference - added_delta_effectsize_str = np.format_float_positional( - added_delta_effectsize, - precision=2, - sign=True, - trim="k", - min_digits=2, - ) - added_group = [added_delta_effectsize_str] - else: - added_group = [''] - for n in added_group: - group_vals.append(n) - - # Create the table object - def add_table(celltext, bbox, rowlabels=None): - gridkey_to_plot = ax_to_plot.table( - cellText=celltext, - rowLabels=rowlabels, - cellLoc="center", - bbox=bbox, - ) - return gridkey_to_plot - - if horizontal: - # Convert the cells format for horizontal table plotting - converted_list = [] - for j in range(0, len(table_cellcols[0])): - temp_list = [] - for i in table_cellcols: - temp_list.append(i[j]) - converted_list.append(temp_list) - - gridkey_to_plot = add_table(celltext = converted_list, bbox = [-len(gridkey) * 0.2, 0, len(gridkey) * 0.2, 1]) - - # Add the column labels as text below the table - text_locs = np.arange((-len(gridkey)*0.2) +0.1, 0, 0.2) - for loc, txt in zip(text_locs, gridkey): - ax_to_plot.text( - loc+0.04, - -0.01, - txt, - transform=ax_to_plot.transAxes, - ha='right', - rotation=45, - fontsize=labels_fontsize if labels_fontsize is not None else 10, - va='top', - ) - else: - # Plot the table for vertical format - if show_mini_meta: - gridkey_to_plot = add_table(celltext = table_cellcols, rowlabels=gridkey, bbox = [0, -len(gridkey) * 0.1 - 0.05, 1, len(gridkey) * 0.1]) - elif show_delta2: - gridkey_to_plot = add_table(celltext = table_cellcols, rowlabels=gridkey, bbox = [0, -len(gridkey) * 0.1 - 0.05, 0.75, len(gridkey) * 0.1]) - extra_gridkey = add_table(celltext = extra_table_cellcols, bbox = [0.78, -len(gridkey) * 0.1 - 0.05, 0.15, len(gridkey) * 0.1]) - else: - gridkey_to_plot = add_table(celltext = table_cellcols, rowlabels=gridkey, bbox = [0, -len(gridkey) * 0.1 - 0.05, 1, len(gridkey) * 0.1]) - - # modifies row label cells - for cell in gridkey_to_plot._cells: - if cell[1] == -1: - gridkey_to_plot._cells[cell].visible_edges = "open" - gridkey_to_plot._cells[cell].set_text_props(**{"ha": "right"}) - - if fontsize is not None: - gridkey_to_plot.auto_set_font_size(False) - gridkey_to_plot.set_fontsize(fontsize) - if show_delta2 and not horizontal: - extra_gridkey.auto_set_font_size(False) - extra_gridkey.set_fontsize(fontsize) - - if labels_fontsize is not None and not horizontal: - gridkey_to_plot.auto_set_font_size(False) - for cell in gridkey_to_plot._cells: - if cell[1] == -1: - gridkey_to_plot._cells[cell].set_text_props(**{"fontsize": labels_fontsize}) - - # turns off both x axes - if horizontal: - rawdata_axes.get_yaxis().set_visible(False) - contrast_axes.get_yaxis().set_visible(False) - else: - rawdata_axes.get_xaxis().set_visible(False) - contrast_axes.get_xaxis().set_visible(False) - -def barplotter( - xvar: str, - yvar: str, - all_plot_groups: list, - rawdata_axes: axes.Axes, - plot_data: pd.DataFrame, - raw_colors: str, - plot_palette_raw: dict, - color_col: str, - barplot_kwargs: dict, - horizontal: bool - ): - """ - Add bars to the raw data plot. - - Parameters - ---------- - xvar : str - Column name of the x variable. - yvar : str - Column name of the y variable. - all_plot_groups : list - List of all plot groups. - rawdata_axes : object - Matplotlib axis object to plot on. - plot_data : object (Dataframe) - Dataframe of the plot data. - raw_colors : str - Color of the bar. - plot_palette_raw : dict - Dictionary of colors used in the bar plot. - color_col : str - Column name of the color column. - barplot_kwargs : dict - Keyword arguments for the barplot. - horizontal : bool - If the plot is horizontal. - """ - # Check if the custom_palette is a dictionary with two keys 0 and 1 (for filled bar coloring) - filled_bars = True if len(plot_palette_raw.keys())==2 and all(k in plot_palette_raw for k in [1, 0]) else False - - bar_width = barplot_kwargs.get('width', 0.5) - fontsize = barplot_kwargs.pop('fontsize') - - x_label, y_label = rawdata_axes.get_xlabel(), rawdata_axes.get_ylabel() - if horizontal: - x_var, y_var, orient = np.ones(len(all_plot_groups)), all_plot_groups, "h" - else: - x_var, y_var, orient = all_plot_groups, np.ones(len(all_plot_groups)), "v" - - # Create bar1_df with basic columns - bar1_df = pd.DataFrame({ - xvar: x_var, - "proportion": y_var - }) - - # Handle colors - if color_col: - # Get first color value for each group - color_mapping = plot_data.groupby(xvar, observed=False)[color_col].first() - bar1_df[color_col] = [color_mapping.get(group) for group in all_plot_groups] - - # Map colors, defaulting to bar_color if no match - edge_colors = [ - plot_palette_raw.get(hue_val, raw_colors) - for hue_val in bar1_df[color_col] - ] - else: - edge_colors = len(all_plot_groups)*['black',] if filled_bars else raw_colors - - bar1 = sns.barplot( - data=bar1_df, - x=xvar, - y="proportion", - ax=rawdata_axes, - order=all_plot_groups, - linewidth=1 if filled_bars else 2, - facecolor=plot_palette_raw[0] if filled_bars else (1, 1, 1, 0), - edgecolor=edge_colors, - zorder=1, - orient=orient, - ) - - if filled_bars: - barplot_kwargs['facecolor'] = plot_palette_raw[1] - barplot_kwargs['edgecolor'] = 'black' - barplot_kwargs['linewidth'] = 1 - else: - barplot_kwargs['palette'] = plot_palette_raw - - bar2 = sns.barplot( - data=plot_data, - x=yvar if horizontal else xvar, - y=xvar if horizontal else yvar, - hue=xvar if color_col is None else color_col, - ax=rawdata_axes, - order=all_plot_groups, - dodge=False, - zorder=1, - orient=orient, - **barplot_kwargs - ) - - # adjust the width of bars - if horizontal: - for bar in bar1.patches: - y = bar.get_y() - height = bar.get_height() - centre = y + height / 2.0 - bar.set_y(centre - bar_width / 2.0) - bar.set_height(bar_width) - else: - for bar in bar1.patches: - x = bar.get_x() - width = bar.get_width() - centre = x + width / 2.0 - bar.set_x(centre - bar_width / 2.0) - bar.set_width(bar_width) - - # reset the x and y labels - rawdata_axes.set_xlabel(x_label) - rawdata_axes.set_ylabel(y_label) - - if horizontal: - rawdata_axes.set_yticks(rawdata_axes.get_yticks()) - rawdata_axes.set_yticklabels(rawdata_axes.get_yticklabels(), fontsize = fontsize) - else: - rawdata_axes.set_xticks(rawdata_axes.get_xticks()) - rawdata_axes.set_xticklabels(rawdata_axes.get_xticklabels(), fontsize = fontsize) - -def table_for_horizontal_plots( - effectsize_df: object, - ax: axes.Axes, - contrast_axes: axes.Axes, - ticks_to_plot: list, - show_mini_meta: bool, - show_delta2: bool, - table_kwargs: dict, - ticks_to_skip: list - ): - """ - Add table axes for showing the deltas for horizontal plots. - - Parameters - ---------- - effectsize_df : object - Effect size DABEST object. - ax : object - Matplotlib axis object to plot the table axes. - contrast_axes : object - Matplotlib axis object to plot the contrast axes. - ticks_to_plot : list - List of indices of the contrast objects. - show_mini_meta : bool - Whether to show the mini meta-analysis. - show_delta2 : bool - Whether to show the delta-delta. - table_kwargs : dict - Keyword arguments for the table. - ticks_to_skip: list - List of ticks to skip in the table. - """ - - table_color = table_kwargs['color'] - table_alpha = table_kwargs['alpha'] - table_font_size = table_kwargs['fontsize'] - table_text_color = table_kwargs['text_color'] - text_units = table_kwargs['text_units'] - table_font_size -= 2 if text_units != '' else 0 - control_marker = table_kwargs['control_marker'] - fontsize_label = table_kwargs['fontsize_label'] - label = table_kwargs['label'] - - ### Create a table of deltas - cols=['Δ','N'] - lst = [] - for n in np.arange(0, len(effectsize_df.results.difference), 1): - lst.append([effectsize_df.results.difference[n], 0]) - if show_mini_meta: - lst.append([effectsize_df.mini_meta.difference, 0]) - elif show_delta2: - lst.append([effectsize_df.delta_delta.difference, 0]) - tab = pd.DataFrame(lst, columns=cols) - - ### Plot the text - if show_mini_meta or show_delta2: - new_ticks = ticks_to_plot + [max(ticks_to_plot)+1] - else: - new_ticks = ticks_to_plot.copy() - for i,loc in zip(tab.index, new_ticks): - ax.text(0.5, loc, "{:+.2f}".format(tab.iloc[i,0])+text_units, ha="center", va="center", color=table_text_color, size=table_font_size) - - # Plot the dashes - if control_marker is not None: - for loc in ticks_to_skip: - ax.text(0.5, loc, control_marker, ha="center", va="center", color=table_text_color, size=table_font_size) - - ### Parameters for table - ax.axvspan(0, 1, facecolor=table_color, alpha=table_alpha) #### Plot the background color - ax.set_xticks([0.5]) - ax.set_xticklabels([]) - ax.set_ylim(contrast_axes.get_ylim()) - ax.set_yticks([]) - ax.set_yticklabels([]) - ax.tick_params(left=False, bottom=False) - ax.set_xlabel(label, fontsize=fontsize_label) # Set the x-axis label - hardcoded for now - sns.despine(ax=ax, left=True, bottom=True) - - -def add_counts_to_prop_plots( - plot_data: pd.DataFrame, - xvar: str, - yvar: str, - rawdata_axes: axes.Axes, - horizontal: bool, - is_paired: bool, - prop_sample_counts_kwargs: dict - ): - """ - Add counts to the proportion plots. - - Parameters - ---------- - plot_data : object (Dataframe) - Dataframe of the plot data. - xvar : str - Column name of the x variable. - yvar : str - Column name of the y variable. - rawdata_axes : axes.Axes - Matplotlib axis object to plot on. - horizontal : bool - If the plot is horizontal. - is_paired : bool - Whether the data is paired. - prop_sample_counts_kwargs : dict - Keyword arguments for the sample counts. - """ - - # Group orders - if isinstance(plot_data[xvar].dtype, pd.CategoricalDtype): - sample_size_text_order = pd.unique(plot_data[xvar]).categories - else: - sample_size_text_order = pd.unique(plot_data[xvar]) - - # Get the sample size values - ones, zeros = plot_data[plot_data[yvar] == 1], plot_data[plot_data[yvar] == 0] - - sample_size_val1 = ones.groupby(xvar, observed=False)[yvar].count().reindex(index=sample_size_text_order) - sample_size_val0 = zeros.groupby(xvar, observed=False)[yvar].count().reindex(index=sample_size_text_order) - - if "fontsize" not in prop_sample_counts_kwargs.keys(): - fontsize = 8 if horizontal else 10 - fontsize -= 2 if is_paired else 0 - prop_sample_counts_kwargs.update({'fontsize': fontsize}) - - for sample_text_x, sample_text_y0, sample_text_y1 in zip( - np.arange(0, len(sample_size_text_order) + 1, 1), - sample_size_val0, - sample_size_val1, - ): - if horizontal: - rawdata_axes.text(0.05, sample_text_x, sample_text_y1, **prop_sample_counts_kwargs) - rawdata_axes.text(0.95, sample_text_x, sample_text_y0, **prop_sample_counts_kwargs) - else: - rawdata_axes.text(sample_text_x, 0.05, sample_text_y1, **prop_sample_counts_kwargs) - rawdata_axes.text(sample_text_x, 0.95, sample_text_y0, **prop_sample_counts_kwargs) - -# %% ../nbs/API/plot_tools.ipynb #24823471 -def swarmplot( - data: pd.DataFrame, - x: str, - y: str, - ax: axes.Axes, - order: List = None, - hue: str = None, - palette: Union[Iterable, str] = "black", - zorder: float = 1, - size: float = 5, - side: str = "center", - jitter: float = 1, - filled: Union[bool, List, Tuple] = True, - is_drop_gutter: bool = True, - gutter_limit: float = 0.5, - horizontal: bool = False, - **kwargs, -): - """ - API to plot a swarm plot. - - Parameters - ---------- - data : pd.DataFrame - The input data as a pandas DataFrame. - x : str - The column in the DataFrame to be used as the x-axis. - y : str - The column in the DataFrame to be used as the y-axis. - ax : axes.Axes - Matplotlib axes.Axes object for which the plot would be drawn on. Default is None. - order : List - The order in which x-axis categories should be displayed. Default is None. - hue : str - The column in the DataFrame that determines the grouping for color. - If None (by default), it assumes that it is being grouped by x. - palette : Union[Iterable, str] - The color palette to be used for plotting. Default is "black". - zorder : int | float - The z-order for drawing the swarm plot wrt other matplotlib drawings. Default is 1. - dot_size : int | float - The size of the markers in the swarm plot. Default is 20. - side : str - The side on which points are swarmed ("center", "left", or "right"). Default is "center". - jitter : int | float - Determines the distance between points. Default is 1. - filled : bool | List | Tuple - Determines whether the dots in the swarmplot are filled or not. If set to False, - dots are not filled. If provided as a List or Tuple, it should contain boolean values, - each corresponding to a swarm group in order, indicating whether the dot should be - filled or not. - is_drop_gutter : bool - If True, drop points that hit the gutters; otherwise, readjust them. - gutter_limit : int | float - The limit for points hitting the gutters. - horizontal : bool - If True, the swarm plot is drawn horizontally. Default is False. - **kwargs: - Additional keyword arguments to be passed to the swarm plot. - - Returns - ------- - axes.Axes - Matplotlib axes.Axes object for which the swarm plot has been drawn on. - """ - s = SwarmPlot(data, x, y, ax, order, hue, palette, zorder, size, side, jitter, horizontal) - ax = s.plot(is_drop_gutter, gutter_limit, ax, filled, horizontal, **kwargs) - return ax - - -class SwarmPlot: - def __init__( - self, - data: pd.DataFrame, - x: str, - y: str, - ax: axes.Axes, - order: List = None, - hue: str = None, - palette: Union[Iterable, str] = "black", - zorder: float = 1, - size: float = 5, - side: str = "center", - jitter: float = 1, - horizontal: bool = False, - ): - """ - Initialize a SwarmPlot instance. - - Parameters - ---------- - data : pd.DataFrame - The input data as a pandas DataFrame. - x : str - The column in the DataFrame to be used as the x-axis. - y : str - The column in the DataFrame to be used as the y-axis. - ax : axes.Axes - Matplotlib axes.Axes object for which the plot would be drawn on. - order : List - The order in which x-axis categories should be displayed. Default is None. - hue : str - The column in the DataFrame that determines the grouping for color. - If None (by default), it assumes that it is being grouped by x. - palette : Union[Iterable, str] - The color palette to be used for plotting. Default is "black". - zorder : int | float - The z-order for drawing the swarm plot wrt other matplotlib drawings. Default is 1. - dot_size : int | float - The size of the markers in the swarm plot. Default is 20. - side : str - The side on which points are swarmed ("center", "left", or "right"). Default is "center". - jitter : int | float - Determines the distance between points. Default is 1. - horizontal : bool - If True, the swarm plot is drawn horizontally. Default is False. - - Returns - ------- - None - """ - self.__x = x - self.__y = y - self.__order = order - self.__hue = hue - self.__zorder = zorder - self.__palette = palette - self.__jitter = jitter - - # Input validation - self._check_errors(data, ax, size, side) - - self.__size = size * 4 - self.__side = side.lower() - self.__data = data - self.__color_col = self.__x if self.__hue is None else self.__hue - - # Generate default values - if order is None: - self.__order = self._generate_order() - - # Reformatting - if not isinstance(self.__palette, dict): - self.__palette = self._format_palette(self.__palette) - data_copy = data.copy(deep=True) - if not isinstance(self.__data[self.__x].dtype, pd.CategoricalDtype): - # make x column into CategoricalDType to sort by - data_copy[self.__x] = data_copy[self.__x].astype( - CategoricalDtype(categories=self.__order, ordered=True) - ) - data_copy.sort_values(by=[self.__x, self.__y], inplace=True) - self.__data_copy = data_copy - - x_vals = range(len(self.__order)) - y_vals = self.__data_copy[self.__y] - - x_min = min(x_vals) - x_max = max(x_vals) - y_range = max(y_vals) - min(y_vals) - y_min = min(y_vals) - 0.05 * y_range - y_max = max(y_vals) + 0.05 * y_range - - if horizontal: - ax.set_ylim(bottom=x_min - 0.5, top=x_max + 0.5) - # ylim is set manually to override Axes.autoscale if it hasn't already been scaled at least once - if ax.get_autoscalex_on(): - ax.set_xlim(left=y_min, right=y_max) - else: - ax.set_xlim(left=x_min - 0.5, right=x_max + 0.5) - # ylim is set manually to override Axes.autoscale if it hasn't already been scaled at least once - if ax.get_autoscaley_on(): - ax.set_ylim(bottom=y_min, top=y_max) - - figw, figh = ax.get_figure().get_size_inches() - w = (ax.get_position().xmax - ax.get_position().xmin) * figw - h = (ax.get_position().ymax - ax.get_position().ymin) * figh - ax_xspan = ax.get_xlim()[1] - ax.get_xlim()[0] - ax_yspan = ax.get_ylim()[1] - ax.get_ylim()[0] - if horizontal: - ax_xspan, ax_yspan = ax_yspan, ax_xspan - - # increases jitter distance based on number of swarms that is going to be drawn - jitter = jitter * (1 + 0.05 * (math.log(ax_xspan))) - - gsize = ( - math.sqrt(self.__size) * 1.0 / (70 / jitter) * ax_xspan * 1.0 / (w * 0.8) - ) - dsize = ( - math.sqrt(self.__size) * 1.0 / (70 / jitter) * ax_yspan * 1.0 / (h * 0.8) - ) - self.__gsize = gsize - self.__dsize = dsize - - def _check_errors( - self, data: pd.DataFrame, ax: axes.Axes, size: float, side: str - ) -> None: - """ - Check the validity of input parameters. Raises exceptions if detected. - - Parameters - ---------- - data : pd.Dataframe - Input data used for generation of the swarmplot. - ax : axes.Axes - Matplotlib axes.Axes object for which the plot would be drawn on. - size : int | float - scalar value determining size of dots of the swarmplot. - side: str - The side on which points are swarmed ("center", "left", or "right"). Default is "center". - - Returns - ------- - None - """ - # Type enforcement - if not isinstance(data, pd.DataFrame): - raise ValueError("`data` must be a Pandas Dataframe.") - if not isinstance(ax, axes.Axes): - raise ValueError( - f"`ax` must be a Matplotlib axes.Axes. The current `ax` is a {type(ax)}" - ) - if not isinstance(size, (int, float)): - raise ValueError("`size` must be a scalar or float.") - if not isinstance(side, str): - raise ValueError( - "Invalid `side`. Must be one of 'center', 'right', or 'left'." - ) - if not isinstance(self.__x, str): - raise ValueError("`x` must be a string.") - if not isinstance(self.__y, str): - raise ValueError("`y` must be a string.") - if not isinstance(self.__zorder, (int, float)): - raise ValueError("`zorder` must be a scalar or float.") - if not isinstance(self.__jitter, (int, float)): - raise ValueError("`jitter` must be a scalar or float.") - if not isinstance(self.__palette, (str, Iterable)): - raise ValueError( - "`palette` must be either a string indicating a color name or an Iterable." - ) - if self.__hue is not None and not isinstance(self.__hue, str): - raise ValueError("`hue` must be either a string or None.") - if self.__order is not None and not isinstance(self.__order, Iterable): - raise ValueError("`order` must be either an Iterable or None.") - - # More thorough input validation checks - if self.__x not in data.columns: - err = "{0} is not a column in `data`.".format(self.__x) - raise IndexError(err) - if self.__y not in data.columns: - err = "{0} is not a column in `data`.".format(self.__y) - raise IndexError(err) - if self.__hue is not None and self.__hue not in data.columns: - err = "{0} is not a column in `data`.".format(self.__hue) - raise IndexError(err) - - color_col = self.__x if self.__hue is None else self.__hue - if self.__order is not None: - for group_i in self.__order: - if group_i not in pd.unique(data[self.__x]): - err = "{0} in `order` is not in the '{1}' column of `data`.".format( - group_i, self.__x - ) - raise IndexError(err) - - if isinstance(self.__palette, str) and self.__palette.strip() == "": - err = "`palette` cannot be an empty string. It must be either a string indicating a color name or an Iterable." - raise ValueError(err) - if isinstance(self.__palette, dict): - for group_i, color_i in self.__palette.items(): - if group_i not in pd.unique(data[color_col]): - err = ( - "{0} in `palette` is not in the '{1}' column of `data`.".format( - group_i, color_col - ) - ) - raise IndexError(err) - if isinstance(color_i, str) and color_i.strip() == "": - err = "The color mapping for {0} in `palette` is an empty string. It must contain a color name.".format( - group_i - ) - raise ValueError(err) - - if side.lower() not in ["center", "right", "left"]: - raise ValueError( - "Invalid `side`. Must be one of 'center', 'right', or 'left'." - ) - - return None - - def _generate_order(self) -> List: - """ - Generates order value that determines the order in which x-axis categories should be displayed. - - Parameters - ---------- - None - - Returns - ------- - List: - contains the order in which the x-axis categories should be displayed. - """ - if isinstance(self.__data[self.__x].dtype, pd.CategoricalDtype): - order = pd.unique(self.__data[self.__x]).categories.tolist() - else: - order = pd.unique(self.__data[self.__x]).tolist() - - return order - - def _format_palette(self, palette: Union[str, List, Tuple]) -> Dict: - """ - Reformats palette into appropriate Dictionary form for swarm plot - - Parameters - ---------- - palette: str | List | Tuple - The color palette used for the swarm plot. Conventions are based on Matplotlib color - specifications. - - Could be a singular string value - in which case, would be a singular color name. - In the case of a List or Tuple - it could be a Sequence of color names or RGB(A) values. - - Returns - ------- - Dict: - Dictionary mapping unique groupings in the color column (of the data used for the swarm plot) - to a color name (str) or a RGB(A) value (Tuple[float, float, float] | List[float, float, float]). - """ - reformatted_palette = dict() - groups = pd.unique(self.__data[self.__color_col]).tolist() - - if isinstance(palette, str): - for group_i in groups: - reformatted_palette[group_i] = palette - if isinstance(palette, (list, tuple)): - if len(groups) != len(palette): - err = ( - "unique values in '{0}' column in `data` " - "and `palette` do not have the same length. Number of unique values is {1} " - "while length of palette is {2}. The assignment of the colors in the " - "palette will be cycled." - ).format(self.__color_col, len(groups), len(palette)) - warnings.warn(err) - for i, group_i in enumerate(groups): - reformatted_palette[group_i] = palette[i % len(palette)] - - return reformatted_palette - - def _swarm( - self, values: Iterable[float], gsize: float, dsize: float, side: str - ) -> pd.Series: - """ - Perform the swarm algorithm to position points without overlap. - - Parameters - ---------- - values : Iterable[int | float] - The values to be plotted. - gsize : int | float - The size of the gap between points. - dsize : int | float - The size of the markers. - side : str - The side on which points are swarmed ("center", "left", or "right"). - - Returns - ------- - pd.Series: - The x-offset values for the swarm plot. - """ - # Input validation - if not isinstance(values, Iterable): - raise ValueError("`values` must be an Iterable") - if not isinstance(gsize, (int, float)): - raise ValueError("`gsize` must be a scalar or float.") - if not isinstance(dsize, (int, float)): - raise ValueError("`dsize` must be a scalar or float.") - - # Sorting algorithm based off of: https://github.com/mgymrek/pybeeswarm - points_data = pd.DataFrame({ - "y": [yval * 1.0 / dsize for yval in values], - "x": np.zeros(len(values), dtype=float) # Initialize with float zeros - }) - for i in range(1, points_data.shape[0]): - y_i = points_data["y"].values[i] - points_placed = points_data[0:i] - is_points_overlap = ( - abs(y_i - points_placed["y"]) < 1 - ) # Checks if y_i is overlapping with any points already placed - if any(is_points_overlap): - points_placed = points_placed[is_points_overlap] - x_offsets = points_placed["y"].apply( - lambda y_j: math.sqrt(1 - (y_i - y_j) ** 2) - ) - if side == "center": - potential_x_offsets = pd.Series( - [0] - + (points_placed["x"] + x_offsets).tolist() - + (points_placed["x"] - x_offsets).tolist() - ) - if side == "right": - potential_x_offsets = pd.Series( - [0] + (points_placed["x"] + x_offsets).tolist() - ) - if side == "left": - potential_x_offsets = pd.Series( - [0] + (points_placed["x"] - x_offsets).tolist() - ) - bad_x_offsets = [] - for x_i in potential_x_offsets: - dists = (y_i - points_placed["y"]) ** 2 + ( - x_i - points_placed["x"] - ) ** 2 - if any([item < 0.999 for item in dists]): - bad_x_offsets.append(True) - else: - bad_x_offsets.append(False) - potential_x_offsets[bad_x_offsets] = np.inf - abs_potential_x_offsets = [abs(_) for _ in potential_x_offsets] - valid_x_offset = potential_x_offsets[ - abs_potential_x_offsets.index(min(abs_potential_x_offsets)) - ] - points_data.loc[i, "x"] = valid_x_offset - else: - points_data.loc[i, "x"] = 0 - - points_data.loc[np.isnan(points_data["y"]), "x"] = np.nan - - return points_data["x"] * gsize - - def _adjust_gutter_points( - self, - points_data: pd.DataFrame, - x_position: float, - is_drop_gutter: bool, - gutter_limit: float, - value_column: str, - ) -> pd.DataFrame: - """ - Adjust points that hit the gutters or drop them based on the provided conditions. - - Parameters - ---------- - points_data: pd.DataFrame - Data containing coordinates of points for the swarm plot. - x_position: int | float - X-coordinate of the center of a singular swarm group of the swarm plot - is_drop_gutter : bool - If True, drop points that hit the gutters; otherwise, readjust them. - gutter_limit : int | float - The limit for points hitting the gutters. - value_column : str - column in points_data that contains the coordinates for the points in the axis against the gutter - - Returns - ------- - pd.DataFrame: - DataFrame with adjusted points based on the gutter limit. - """ - if self.__side == "center": - gutter_limit = gutter_limit / 2 - - hit_gutter = abs(points_data[value_column] - x_position) >= gutter_limit - total_num_of_points = points_data.shape[0] - num_of_points_hit_gutter = points_data[hit_gutter].shape[0] - if any(hit_gutter): - if is_drop_gutter: - # Drop points that hit gutter - points_data.drop(points_data[hit_gutter].index.to_list(), inplace=True) - err = ( - "{0:.1%} of the points cannot be placed. " - "You might want to decrease the size of the markers." - ).format(num_of_points_hit_gutter / total_num_of_points) - warnings.warn(err) - else: - for i in points_data[hit_gutter].index: - points_data.loc[i, value_column] = np.sign( - points_data.loc[i, value_column] - ) * (x_position + gutter_limit) - - return points_data - - def plot( - self, - is_drop_gutter: bool, - gutter_limit: float, - ax: axes.Subplot, - filled: Union[bool, List, Tuple], - horizontal: bool, - **kwargs, - ) -> axes.Axes: - """ - Generate a swarm plot. - - Parameters - ---------- - is_drop_gutter : bool - If True, drop points that hit the gutters; otherwise, readjust them. - gutter_limit : int | float - The limit for points hitting the gutters. - ax : axes.Axes - The matplotlib figure object to which the swarm plot will be added. - filled : bool | List | Tuple - Determines whether the dots in the swarmplot are filled or not. If set to False, - dots are not filled. If provided as a List or Tuple, it should contain boolean values, - each corresponding to a swarm group in order, indicating whether the dot should be - filled or not. - **kwargs: - Additional keyword arguments to be passed to the scatter plot. - - Returns - ------- - axes.Axes: - The matplotlib axes containing the swarm plot. - """ - # Input validation - if not isinstance(is_drop_gutter, bool): - raise ValueError("`is_drop_gutter` must be a boolean.") - if not isinstance(gutter_limit, (int, float)): - raise ValueError("`gutter_limit` must be a scalar or float.") - if not isinstance(filled, (bool, list, tuple)): - raise ValueError("`filled` must be a boolean, list or tuple.") - - fontsize = kwargs.pop('fontsize', 12) - - # More thorough input validation checks - if isinstance(filled, (list, tuple)): - if len(filled) != len(self.__order): - err = ( - "There are {0} unique values in `x` column in `data` " - "but `filled` has a length of {1}. If `filled` is a list " - "or a tuple, it must have the same length as the number of " - "unique values/groups in the `x` column of data." - ).format(len(self.__order), len(filled)) - raise ValueError(err) - if not all(isinstance(_, bool) for _ in filled): - raise ValueError("All values in `filled` must be a boolean.") - - # Assumptions are that self.__data_copy is already sorted according to self.__order - x_position = ( - 0 # x-coordinate of center of each individual swarm of the swarm plot - ) - x_tick_tabels = [] - for group_i, values_i in self.__data_copy.groupby(self.__x, observed=False): - x_new = [] - values_i_y = values_i[self.__y] - x_offset = self._swarm( - values=values_i_y, - gsize=self.__gsize, - dsize=self.__dsize, - side=self.__side, - ) - x_new = [ - x_position + offset for offset in x_offset - ] # apply x-offsets based on _swarm algo - values_i["x_new"] = x_new - values_i = self._adjust_gutter_points( - values_i, x_position, is_drop_gutter, gutter_limit, "x_new" - ) - x_tick_tabels.extend([group_i]) - x_position = x_position + 1 - - if values_i.empty: - ax.scatter( - values_i["x_new"] if not horizontal else values_i[self.__y], - values_i[self.__y] if not horizontal else values_i["x_new"], - s=self.__size, - zorder=self.__zorder, - **kwargs, - ) - continue - - if self.__hue is not None: - # color swarms based on `hue` column - cmap_values, index = np.unique( - values_i[self.__hue], return_inverse=True - ) - cmap = [] - for cmap_group_i in cmap_values: - cmap.append(self.__palette[cmap_group_i]) - - cmap = ListedColormap(cmap) - ax.scatter( - values_i["x_new"] if not horizontal else values_i[self.__y], - values_i[self.__y] if not horizontal else values_i["x_new"], - s=self.__size, - c=index, - cmap=cmap, - zorder=self.__zorder, - edgecolor="face", - **kwargs, - ) - - else: - # color swarms based on `x` column - if not isinstance(filled, bool): - facecolor = ( - "none" - if not filled[x_position - 1] - else self.__palette[group_i] - ) - else: - facecolor = "none" if not filled else self.__palette[group_i] - - ax.scatter( - values_i["x_new"] if not horizontal else values_i[self.__y], - values_i[self.__y] if not horizontal else values_i["x_new"], - s=self.__size, - zorder=self.__zorder, - facecolor=facecolor, - edgecolor=self.__palette[group_i], - label=group_i, - **kwargs, - ) - - # Handling of legends - # This is currently a workaround because c and cmap is unable to map the labels when calling scatter() - # labels has to be used to designate legend labels and handles in scatter() due to the potential calling of ax.get_legend_handles_labels() - if self.__hue is not None: - for cmap_group_i in self.__palette: - ax.scatter( - [], - [], - color=self.__palette[cmap_group_i], - label=cmap_group_i, - ) - - if horizontal: - ax.get_yaxis().set_ticks(np.arange(x_position)) - ax.get_yaxis().set_ticklabels(x_tick_tabels, fontsize = fontsize) - else: - ax.get_xaxis().set_ticks(np.arange(x_position)) - ax.get_xaxis().set_ticklabels(x_tick_tabels, fontsize = fontsize) - - return ax diff --git a/dabest/plotter.py b/dabest/plotter.py deleted file mode 100644 index 23302e39..00000000 --- a/dabest/plotter.py +++ /dev/null @@ -1,636 +0,0 @@ -"""Creating estimation plots.""" - -# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/plotter.ipynb. - -# %% auto #0 -__all__ = ['effectsize_df_plotter'] - -# %% ../nbs/API/plotter.ipynb #7562c1a1 -import numpy as np -import seaborn as sns -import matplotlib -import matplotlib.pyplot as plt -import matplotlib.patches as mpatches -from matplotlib.lines import Line2D -import pandas as pd -import warnings -import logging - -# %% ../nbs/API/plotter.ipynb #36a42b1c -# TODO refactor function name -def effectsize_df_plotter(effectsize_df: object, **plot_kwargs) -> matplotlib.figure.Figure: - """ - Custom function that creates an estimation plot from an EffectSizeDataFrame. - Keywords - -------- - Parameters - ---------- - effectsize_df - A `dabest` EffectSizeDataFrame object. - plot_kwargs - color_col=None - raw_marker_size=6, contrast_marker_kwargs=9, - raw_label=None, contrast_label=None, delta2_label=None, - raw_ylim=None, contrast_ylim=None, delta2_ylim=None, - custom_palette=None, - swarm_side=None, - empty_circle=False, - face_color=None, - raw_desat=0.5, contrast_desat=1, - raw_alpha=None, contrast_alpha=0.8, - bar_width = 0.5, - ci_type='bca', - float_contrast=True, - show_pairs=True, - show_sample_size=True, - show_delta2=True, show_mini_meta=True, - group_summaries="mean_sd", - fig_size=None, - dpi=100, - ax=None, - swarmplot_kwargs=None, - slopegraph_kwargs=None, - barplot_kwargs=None, - sankey_kwargs=None, - contrast_kwargs=None, - reflines_kwargs=None, - group_summaries_kwargs=None, - legend_kwargs=None, - title=None, fontsize_title=16, - fontsize_rawxlabel=12, fontsize_rawylabel=12, - fontsize_contrastxlabel=12, fontsize_contrastylabel=12, - fontsize_delta2label=12, - - raw_bars=True, raw_bars_kwargs=None, - contrast_bars=True, contrast_bars_kwargs=None, - reference_band=None, reference_band_kwargs=None, - delta_text=True, delta_text_kwargs=None, - delta_dot=True, delta_dot_kwargs=None, - - horizontal=False, horizontal_table_kwargs=None, - gridkey=None, - gridkey_merge_pairs=False, - gridkey_show_Ns=True, - gridkey_show_es=True, - gridkey_delimiters=[';', '>', '_'], - gridkey_kwargs=None, - contrast_marker_kwargs=None, contrast_errorbar_kwargs=None, - prop_sample_counts=False, prop_sample_counts_kwargs=None, - contrast_paired_lines=True, contrast_paired_lines - show_baseline_ec=False, - - """ - from .misc_tools import ( - get_params, - get_kwargs, - get_color_palette, - initialize_fig, - get_plot_groups, - add_counts_to_ticks, - extract_contrast_plotting_ticks, - set_xaxis_ticks_and_lims, - show_legend, - gardner_altman_adjustments, - extract_group_summaries, - draw_zeroline, - redraw_dependent_spines, - redraw_independent_spines, - prepare_bars_for_plot - ) - from .plot_tools import ( - error_bar, - sankeydiag, - swarmplot, - delta_text_plotter, - delta_dots_plotter, - slopegraph_plotter, - plot_minimeta_or_deltadelta_violins, - effect_size_curve_plotter, - gridkey_plotter, - barplotter, - table_for_horizontal_plots, - add_counts_to_prop_plots, - add_bars_to_plot - ) - - warnings.filterwarnings( - "ignore", "This figure includes Axes that are not compatible with tight_layout" - ) - - # Have to disable logging of warning when get_legend_handles_labels() - # tries to get from slopegraph. - logging.disable(logging.WARNING) - - # Save rcParams that I will alter, so I can reset back. - original_rcParams = {} - _changed_rcParams = ["axes.grid"] - for parameter in _changed_rcParams: - original_rcParams[parameter] = plt.rcParams[parameter] - - plt.rcParams["axes.grid"] = False - ytick_color = plt.rcParams["ytick.color"] - - # Extract parameters and set kwargs - (swarmplot_kwargs, barplot_kwargs, sankey_kwargs, contrast_kwargs, - slopegraph_kwargs, reflines_kwargs, legend_kwargs, group_summaries_kwargs, - redraw_axes_kwargs, delta_dot_kwargs, delta_text_kwargs, reference_band_kwargs, - raw_bars_kwargs, contrast_bars_kwargs, table_kwargs, gridkey_kwargs, contrast_marker_kwargs, - contrast_errorbar_kwargs, prop_sample_counts_kwargs, contrast_paired_lines_kwargs) = get_kwargs( - plot_kwargs = plot_kwargs, - ytick_color = ytick_color, - is_paired = effectsize_df.is_paired - ) - - (dabest_obj, plot_data, xvar, yvar, is_paired, effect_size, proportional, - all_plot_groups, idx, show_delta2, show_mini_meta, float_contrast, - show_pairs, group_summaries, horizontal, results, ci_type, x1_level, experiment_label, - show_baseline_ec, one_sankey, two_col_sankey, asymmetric_side, show_sample_size) = get_params( - effectsize_df = effectsize_df, - plot_kwargs = plot_kwargs, - sankey_kwargs = sankey_kwargs, - barplot_kwargs = barplot_kwargs - ) - - # Extract Color palette - (color_col, bootstraps_color_by_group, n_groups, filled, raw_colors, - plot_palette_raw, plot_palette_contrast, plot_palette_sankey) = get_color_palette( - plot_kwargs = plot_kwargs, - plot_data = plot_data, - xvar = xvar, - show_pairs = show_pairs, - idx = idx, - all_plot_groups = all_plot_groups, - delta2 = effectsize_df.delta2, - proportional = proportional - ) - - # Initialise the figure. - fig, rawdata_axes, contrast_axes, table_axes = initialize_fig( - plot_kwargs = plot_kwargs, - dabest_obj = dabest_obj, - show_delta2 = show_delta2, - show_mini_meta = show_mini_meta, - is_paired = is_paired, - show_pairs = show_pairs, - proportional = proportional, - float_contrast = float_contrast, - effect_size_type = effect_size, - yvar = yvar, - horizontal = horizontal, - show_table = table_kwargs['show'], - color_col = color_col - ) - - # Plotting the rawdata. - if show_pairs: ## Paired plots! - temp_idx, temp_all_plot_groups = get_plot_groups( - is_paired = is_paired, - idx = idx, - proportional = proportional, - all_plot_groups = all_plot_groups - ) - if proportional: ## Plot the raw data as a set of Sankey Diagrams aligned like barplot. - if sankey_kwargs["flow"] == False and len(temp_all_plot_groups) == 2: - sankey_kwargs["flow"], two_col_sankey = True, False - warnings.warn("Sankey flow must be true for singular two-group sankey plots") - sankey_control_test_groups = sankeydiag( - plot_data, - xvar = xvar, - yvar = yvar, - temp_all_plot_groups = temp_all_plot_groups, - idx = idx, - temp_idx = temp_idx, - palette = plot_palette_sankey, - ax = rawdata_axes, - horizontal = horizontal, - **sankey_kwargs - ) - else: ## Plot the raw data as a slopegraph. - slopegraph_plotter( - dabest_obj = dabest_obj, - plot_data = plot_data, - xvar = xvar, - yvar = yvar, - color_col = color_col, - plot_palette_raw = plot_palette_raw, - slopegraph_kwargs = slopegraph_kwargs, - rawdata_axes = rawdata_axes, - ytick_color = ytick_color, - temp_idx = temp_idx, - horizontal = horizontal, - temp_all_plot_groups = temp_all_plot_groups, - plot_kwargs = plot_kwargs, - group_summaries_kwargs = group_summaries_kwargs - ) - - ## Add delta dots to the contrast axes for paired plots. - show_delta_dots = plot_kwargs["delta_dot"] - unavailable_effect_sizes = ["hedges_g", "delta_g", "cohens_d"] - if show_delta_dots and is_paired and not any([es in effect_size for es in unavailable_effect_sizes]): - delta_dots_plotter( - plot_data = plot_data, - contrast_axes = contrast_axes, - delta_id_col = dabest_obj.id_col, - idx = idx, - xvar = xvar, - yvar = yvar, - is_paired = is_paired, - color_col = color_col, - float_contrast = float_contrast, - plot_palette_raw = plot_palette_raw, - delta_dot_kwargs = delta_dot_kwargs, - horizontal = horizontal, - ) - - else: ## Unpaired plots! - if proportional: # Plot the raw data as a barplot. - barplotter( - xvar = xvar, - yvar = yvar, - all_plot_groups = all_plot_groups, - rawdata_axes = rawdata_axes, - plot_data = plot_data, - raw_colors = raw_colors, - plot_palette_raw = plot_palette_raw, - color_col = color_col, - barplot_kwargs = barplot_kwargs, - horizontal = horizontal, - ) - else: ## Plot the raw data as a swarmplot. - ## swarmplot() plots swarms based on current size of ax - ## Therefore, since the ax size for show_mini_meta and show_delta changes later on, there has to be increased jitter - rawdata_plot = swarmplot( - data = plot_data, - x = xvar, - y = yvar, - ax = rawdata_axes, - order = all_plot_groups, - hue = color_col, - palette = plot_palette_raw, - zorder = 1, - side = asymmetric_side, - jitter = 1.25 if show_mini_meta else 1.4 if show_delta2 else 1, # TODO: to make jitter value more accurate and not just a hardcoded eyeball value - filled = filled, - is_drop_gutter = True, - gutter_limit = 0.45, - horizontal = horizontal, - **swarmplot_kwargs - ) - if color_col is None: - rawdata_plot.legend().set_visible(False) - - ## Plot the error bars on unpaired plots. - if group_summaries is not None: - (group_summaries_method, - group_summaries_offset, group_summaries_line_color) = extract_group_summaries( - proportional = proportional, - rawdata_axes = rawdata_axes, - asymmetric_side = asymmetric_side if not proportional else None, - horizontal = horizontal, - bootstraps_color_by_group = bootstraps_color_by_group, - plot_palette_raw = plot_palette_raw, - all_plot_groups = all_plot_groups, - n_groups = n_groups, - color_col = color_col, - ytick_color = ytick_color, - group_summaries_kwargs = group_summaries_kwargs - ) - ## Plot the error bar - error_bar( - plot_data, - x = xvar, - y = yvar, - offset = group_summaries_offset, - line_color = group_summaries_line_color, - type = group_summaries, - ax = rawdata_axes, - method = group_summaries_method, - horizontal = horizontal, - **group_summaries_kwargs - ) - - # Add the counts to the rawdata axes xticks. - if show_sample_size: - add_counts_to_ticks( - plot_data = plot_data, - xvar = xvar, - yvar = yvar, - rawdata_axes = rawdata_axes, - plot_kwargs = plot_kwargs, - flow = sankey_kwargs["flow"], - horizontal = horizontal, - ) - - # Add counts to prop plots (embedded in the plot bars) - if proportional and plot_kwargs['prop_sample_counts'] and sankey_kwargs["flow"]: - add_counts_to_prop_plots( - plot_data = plot_data, - xvar = xvar, - yvar = yvar, - rawdata_axes = rawdata_axes, - horizontal = horizontal, - is_paired = is_paired, - prop_sample_counts_kwargs = prop_sample_counts_kwargs, - ) - - ## Swarm bars - raw_bars = plot_kwargs["raw_bars"] - if raw_bars and not proportional and not is_paired and not horizontal: #Currently not supporting swarm bars for horizontal plots (looks weird) - raw_bars_dict, raw_bars_kwargs = prepare_bars_for_plot( - bar_type = 'raw', - bar_kwargs = raw_bars_kwargs, - horizontal = horizontal, - plot_palette_raw = plot_palette_raw, - color_col = color_col, - show_pairs = show_pairs, - plot_data = plot_data, - xvar = xvar, - yvar = yvar, - bootstraps_color_by_group = bootstraps_color_by_group, - ) - add_bars_to_plot(bar_dict = raw_bars_dict, - ax = rawdata_axes, - bar_kwargs = raw_bars_kwargs - ) - - # Plot the contrast axes - effect sizes and bootstraps! - plot_groups = (temp_all_plot_groups if (is_paired == "baseline" and show_pairs and two_col_sankey) - else temp_idx if two_col_sankey - else all_plot_groups - ) - - ## Extract ticks for contrast plot - (ticks_to_skip, ticks_to_plot, ticks_for_baseline_ec, - ticks_to_skip_contrast, ticks_to_start_twocol_sankey) = extract_contrast_plotting_ticks( - is_paired = is_paired, - show_pairs = show_pairs, - two_col_sankey = two_col_sankey, - plot_groups = plot_groups, - idx = idx, - sankey_control_group = sankey_control_test_groups[0] if two_col_sankey else None, - ) - - ## Adjust contrast tick locations to account for different plotting styles in horizontal plots - table_axes_ticks_to_plot = ticks_to_plot - if (horizontal and proportional and not show_pairs) or (horizontal and plot_kwargs["swarm_side"] == "right"): - ticks_to_plot = [x+0.25 for x in ticks_to_plot] - - ## Plot the bootstraps, then the effect sizes and CIs. - contrast_paired_lines = False if float_contrast or not sankey_kwargs["flow"] else plot_kwargs["contrast_paired_lines"] - (current_group, current_control, - current_effsize, contrast_xtick_labels) = effect_size_curve_plotter( - ticks_to_plot = ticks_to_plot, - ticks_for_baseline_ec = ticks_for_baseline_ec, - results = results, - ci_type = ci_type, - contrast_axes = contrast_axes, - contrast_kwargs = contrast_kwargs, - bootstraps_color_by_group = bootstraps_color_by_group, - plot_palette_contrast = plot_palette_contrast, - horizontal = horizontal, - contrast_marker_kwargs = contrast_marker_kwargs, - contrast_errorbar_kwargs = contrast_errorbar_kwargs, - idx = idx, - is_paired = is_paired, - contrast_paired_lines = contrast_paired_lines, - contrast_paired_lines_kwargs = contrast_paired_lines_kwargs, - show_baseline_ec = show_baseline_ec, - ) - - ## Plot mini-meta or delta-delta violin - delta2_axes = None - if show_mini_meta or show_delta2: - delta2_axes, contrast_xtick_labels = plot_minimeta_or_deltadelta_violins( - dabest_obj = effectsize_df.mini_meta if show_mini_meta else effectsize_df.delta_delta, - type = 'mini_meta' if show_mini_meta else 'delta_delta', - ci_type = ci_type, - rawdata_axes = rawdata_axes, - contrast_axes = contrast_axes, - contrast_kwargs = contrast_kwargs, - contrast_xtick_labels = contrast_xtick_labels, - effect_size = effect_size, - plot_kwargs = plot_kwargs, - horizontal = horizontal, - show_pairs = show_pairs, - contrast_marker_kwargs = contrast_marker_kwargs, - contrast_errorbar_kwargs = contrast_errorbar_kwargs, - ) - ## Contrast bars - contrast_bars = plot_kwargs["contrast_bars"] - if contrast_bars: - contrast_bars_dict, contrast_bars_kwargs = prepare_bars_for_plot( - bar_type = 'contrast', - bar_kwargs = contrast_bars_kwargs, - horizontal = horizontal, - plot_palette_raw = plot_palette_raw, - color_col = color_col, - show_pairs = show_pairs, - results = results, - ticks_to_plot = ticks_to_plot, - bootstraps_color_by_group = bootstraps_color_by_group, - extra_delta = (effectsize_df.mini_meta.difference if show_mini_meta - else effectsize_df.delta_delta.difference if show_delta2 - else None) - ) - add_bars_to_plot(bar_dict = contrast_bars_dict, - ax = contrast_axes, - bar_kwargs = contrast_bars_kwargs - ) - - ## Delta text - delta_text = plot_kwargs["delta_text"] - if delta_text and not horizontal: - delta_text_plotter( - results = results, - ax_to_plot = contrast_axes, - ticks_to_plot = ticks_to_plot, - delta_text_kwargs = delta_text_kwargs, - color_col = color_col, - plot_palette_raw = plot_palette_raw, - show_pairs = show_pairs, - float_contrast = float_contrast, - bootstraps_color_by_group = bootstraps_color_by_group, - extra_delta = (effectsize_df.mini_meta.difference if show_mini_meta - else effectsize_df.delta_delta.difference if show_delta2 - else None), - ) - - ## Make sure the contrast_axes x-lims match the rawdata_axes xlims, - ## and add an extra violinplot tick for delta-delta plot. - ## Name is xaxis but it is actually y-axis for horizontal plots - set_xaxis_ticks_and_lims( - show_delta2 = show_delta2, - show_mini_meta = show_mini_meta, - rawdata_axes = rawdata_axes, - contrast_axes = contrast_axes, - show_pairs = show_pairs, - float_contrast = float_contrast, - ticks_to_skip = ticks_to_skip, - contrast_xtick_labels = contrast_xtick_labels, - plot_kwargs = plot_kwargs, - proportional = proportional, - horizontal = horizontal, - ) - # Plot aesthetic adjustments. - if float_contrast: # For Gardner-Altman (float contrast) plots only. - gardner_altman_adjustments( - effect_size_type = effect_size, - plot_data = plot_data, - xvar = xvar, - yvar = yvar, - current_control = current_control, - current_group = current_group, - rawdata_axes = rawdata_axes, - contrast_axes = contrast_axes, - results = results, - current_effsize = current_effsize, - is_paired = is_paired, - one_sankey = one_sankey, - reflines_kwargs = reflines_kwargs, - redraw_axes_kwargs = redraw_axes_kwargs, - ) - else: # For Cumming plots only. - ## Add Zero line if lies within the ylim of contrast axes - draw_zeroline( - ax = contrast_axes, - horizontal = horizontal, - reflines_kwargs = reflines_kwargs, - extra_delta = True if show_delta2 else False, - ) - ## Axes independent spine lines - is_gridkey = True if plot_kwargs["gridkey"] is not None else False - if not is_gridkey: - redraw_independent_spines( - rawdata_axes = rawdata_axes, - contrast_axes = contrast_axes, - horizontal = horizontal, - two_col_sankey = two_col_sankey, - ticks_to_start_twocol_sankey = ticks_to_start_twocol_sankey, - idx = idx, - is_paired = is_paired, - show_pairs = show_pairs, - proportional = proportional, - ticks_to_skip = ticks_to_skip, - temp_idx = temp_idx if is_paired == "baseline" and show_pairs else None, - ticks_to_skip_contrast = ticks_to_skip_contrast, - redraw_axes_kwargs = redraw_axes_kwargs - ) - - # Modify ylims of axes to flip the plot for horizontal format - if horizontal: - if not proportional or (proportional and show_pairs): - raw_ylim, contrast_ylim = rawdata_axes.get_ylim(), contrast_axes.get_ylim() - rawdata_axes.set_ylim(raw_ylim[1], raw_ylim[0]) - contrast_axes.set_ylim(contrast_ylim[1], contrast_ylim[0]) - - ## Modify the ylim to reduce whitespace in specific plots. - if show_delta2 or show_mini_meta or (proportional and show_pairs): - raw_ylim, contrast_ylim = rawdata_axes.get_ylim(), contrast_axes.get_ylim() - rawdata_axes.set_ylim(raw_ylim[0]-0.5, raw_ylim[1]) - contrast_axes.set_ylim(contrast_ylim[0]-0.5, contrast_ylim[1]) - - # Add the dependent axes spines back in. - redraw_dependent_spines( - rawdata_axes = rawdata_axes, - contrast_axes = contrast_axes, - redraw_axes_kwargs = redraw_axes_kwargs, - float_contrast = float_contrast, - horizontal = horizontal, - show_delta2 = show_delta2, - delta2_axes = delta2_axes - ) - - # Table Axes for horizontal plots - if horizontal and table_kwargs['show']: - table_for_horizontal_plots( - effectsize_df = effectsize_df, - ax = table_axes, - contrast_axes = contrast_axes, - ticks_to_plot = table_axes_ticks_to_plot, - show_mini_meta = show_mini_meta, - show_delta2 = show_delta2, - table_kwargs = table_kwargs, - ticks_to_skip = ticks_to_skip - ) - - # Gridkey - gridkey = plot_kwargs["gridkey"] - if gridkey is not None: - gridkey_plotter( - is_paired = is_paired, - idx = idx, - all_plot_groups = all_plot_groups, - gridkey = gridkey, - rawdata_axes = rawdata_axes, - contrast_axes = contrast_axes, - plot_data = plot_data, - xvar = xvar, - yvar = yvar, - results = results, - show_delta2 = show_delta2, - show_mini_meta = show_mini_meta, - x1_level = x1_level, - experiment_label = experiment_label, - float_contrast = float_contrast, - horizontal = horizontal, - delta_delta = effectsize_df.delta_delta if show_delta2 else None, - mini_meta = effectsize_df.mini_meta if show_mini_meta else None, - effect_size = effect_size, - gridkey_kwargs = gridkey_kwargs, - ) - - # Reference band - reference_band = plot_kwargs["reference_band"] - if reference_band is not None and not float_contrast: - reference_band_dict, reference_band_kwargs = prepare_bars_for_plot(bar_type = 'summary', - bar_kwargs = reference_band_kwargs, - horizontal = horizontal, - plot_palette_raw = plot_palette_raw, - color_col = color_col, - show_pairs = show_pairs, - results = results, - ticks_to_plot = ticks_to_plot, - reference_band = reference_band, - summary_axes = contrast_axes, - ci_type = ci_type, - bootstraps_color_by_group = bootstraps_color_by_group, - ) - - add_bars_to_plot(bar_dict = reference_band_dict, - ax = contrast_axes, - bar_kwargs = reference_band_kwargs - ) - - # Legend - handles, labels = rawdata_axes.get_legend_handles_labels() - legend_labels = [l for l in labels] - legend_handles = [h for h in handles] - - if bootstraps_color_by_group is False and color_col is not None: - rawdata_axes.legend().set_visible(False) - show_legend( - legend_labels = legend_labels, - legend_handles = legend_handles, - rawdata_axes = rawdata_axes, - contrast_axes = contrast_axes, - table_axes = table_axes, - float_contrast = float_contrast, - show_pairs = show_pairs, - horizontal = horizontal, - legend_kwargs = legend_kwargs, - table_kwargs = table_kwargs - ) - - # Make sure no stray ticks appear! - rawdata_axes.xaxis.set_ticks_position("bottom") - rawdata_axes.yaxis.set_ticks_position("left") - contrast_axes.xaxis.set_ticks_position("bottom") - if float_contrast is False: - contrast_axes.yaxis.set_ticks_position("left") - - # Reset rcParams. - for parameter in _changed_rcParams: - plt.rcParams[parameter] = original_rcParams[parameter] - - # Return the figure. - return fig diff --git a/nbs/images/Favicon-3-outline.svg b/images/Favicon-3-outline.svg similarity index 100% rename from nbs/images/Favicon-3-outline.svg rename to images/Favicon-3-outline.svg diff --git a/nbs/images/customizable.svg b/images/customizable.svg similarity index 100% rename from nbs/images/customizable.svg rename to images/customizable.svg diff --git a/nbs/images/estimations.svg b/images/estimations.svg similarity index 100% rename from nbs/images/estimations.svg rename to images/estimations.svg diff --git a/nbs/images/gaussian.svg b/images/gaussian.svg similarity index 100% rename from nbs/images/gaussian.svg rename to images/gaussian.svg diff --git a/nbs/images/git.svg b/images/git.svg similarity index 100% rename from nbs/images/git.svg rename to images/git.svg diff --git a/nbs/images/jupyter.svg b/images/jupyter.svg similarity index 100% rename from nbs/images/jupyter.svg rename to images/jupyter.svg diff --git a/nbs/images/python.svg b/images/python.svg similarity index 100% rename from nbs/images/python.svg rename to images/python.svg diff --git a/nbs/images/splash-propplot.png b/images/splash-propplot.png similarity index 100% rename from nbs/images/splash-propplot.png rename to images/splash-propplot.png diff --git a/nbs/index.css b/index.css similarity index 100% rename from nbs/index.css rename to index.css diff --git a/index.html b/index.html new file mode 100644 index 00000000..941e126b --- /dev/null +++ b/index.html @@ -0,0 +1,620 @@ + + + + + + + + + +dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Data Analysis using
Bootstrap-Coupled ESTimation

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Analyze your data with effect sizes and beautiful estimation plots.

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Get started

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Robust and Beautiful
Statistical Visualization

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Estimation statistics is a simple framework that avoids the pitfalls of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one’s experiment/intervention, as opposed to a false dichotomy engendered by P values.

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Shift from p-values to effect size and confidence intervals for richer data insights

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Robust and elegant statistical visualizations for efficient information conveyance

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Seamless integration with the scientific Python libraries for comprehensive data analysis

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Flexible plot customization to meet diverse presentation needs

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Promotes reproducibility with easy sharing of data and analysis code

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Ongoing support and development through an engaged user community

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Get started in seconds

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Install dabest

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+ + + + + \ No newline at end of file diff --git a/listings.json b/listings.json new file mode 100644 index 00000000..da157835 --- /dev/null +++ b/listings.json @@ -0,0 +1,44 @@ +[ + { + "listing": "/tutorials/index.html", + "items": [ + "/tutorials/01-basics.html", + "/tutorials/02-two_group.html", + "/tutorials/03-shared_control_and_repeated_measures.html", + "/tutorials/04-proportion_plot.html", + "/tutorials/05-mini_meta.html", + "/tutorials/06-delta_delta.html", + "/tutorials/07-horizontal_plot.html", + "/tutorials/08-plot_aesthetics.html", + "/tutorials/09-forest_plot.html", + "/tutorials/10-whorlmap.html" + ] + }, + { + "listing": "/blog/index.html", + "items": [ + "/blog/posts/a-dabest2-preprint/a-dabest2-preprint.html", + "/blog/posts/bootstraps/bootstraps.html", + "/blog/posts/robust-beautiful/robust-beautiful.html" + ] + }, + { + "listing": "/API/index.html", + "items": [ + "/API/load.html", + "/API/dabest_object.html", + "/API/bootstrap.html", + "/API/forest_plot.html", + "/API/plotter.html", + "/API/plot_tools.html", + "/API/effsize.html", + "/API/confint_1group.html", + "/API/confint_2group_diff.html", + "/API/delta_objects.html", + "/API/misc_tools.html", + "/API/effsize_objects.html", + "/API/precompile.html", + "/API/multi.html" + ] + } +] \ No newline at end of file diff --git a/nbs/01-getting_started.ipynb b/nbs/01-getting_started.ipynb deleted file mode 100644 index 299e40f4..00000000 --- a/nbs/01-getting_started.ipynb +++ /dev/null @@ -1,186 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "68f51e09", - "metadata": {}, - "source": [ - "# Getting Started\n", - "\n", - "> Requirements and installation of dabest.\n", - "\n", - "- order: 1" - ] - }, - { - "cell_type": "markdown", - "id": "5b3dcdd6", - "metadata": {}, - "source": [ - "## Introduction" - ] - }, - { - "cell_type": "markdown", - "id": "2aebebc2", - "metadata": {}, - "source": [ - "DABEST is a package for **D**ata **A**nalysis with **B**ootstrapped **EST**imation\n", - "\n", - "[Estimation statistics](https://en.wikipedia.org/wiki/Estimation_statistics) is a simple framework that avoids the [pitfalls](https://www.nature.com/articles/nmeth.3288) of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one’s experiment/intervention, as opposed to a false dichotomy engendered by *P* values." - ] - }, - { - "cell_type": "markdown", - "id": "0fc075f5", - "metadata": {}, - "source": [ - "An estimation plot has two key features.\n", - "\n", - "1. It **presents all datapoints** as a swarmplot, which orders each point to display the underlying distribution.\n", - "2. It presents the **effect size** as a **bootstrap 95% confidence interval** on a **separate but aligned axes**." - ] - }, - { - "cell_type": "markdown", - "id": "e4c2e459", - "metadata": {}, - "source": [ - "DABEST powers [estimationstats.com](https://www.estimationstats.com/#/), allowing everyone access to high-quality estimation plots." - ] - }, - { - "cell_type": "markdown", - "id": "d1d5cb1a", - "metadata": {}, - "source": [ - "## Requirements" - ] - }, - { - "cell_type": "markdown", - "id": "d2aca73b", - "metadata": {}, - "source": [ - "\n", - "\n", - "Python 3.11 is recommended. DABEST has also been tested with Python 3.10 and onwards.\n", - "\n", - "In addition, the following packages are also required (listed with their minimal versions):\n", - "\n", - "* [numpy 2.1.3](https://www.numpy.org)\n", - "* [scipy 1.15.2](https://www.scipy.org)\n", - "* [matplotlib 3.10.0](https://www.matplotlib.org)\n", - "* [pandas 2.2.3](https://pandas.pydata.org)\n", - "* [seaborn 0.13.2](https://seaborn.pydata.org)\n", - "* [numba 0.61.0](https://numba.pydata.org)\n", - "* [lqrt 0.3.3](https://github.com/alyakin314/lqrt)\n", - "\n", - "To obtain these package dependencies easily, it is highly recommended to download the [Anaconda](https://www.continuum.io/downloads) distribution of Python.\n" - ] - }, - { - "cell_type": "markdown", - "id": "c0de00b2", - "metadata": {}, - "source": [ - "## Installation" - ] - }, - { - "cell_type": "markdown", - "id": "304a3639", - "metadata": {}, - "source": [ - "1. Using ``pip``\n", - "\n", - "At the command line, run\n", - "\n", - "``` shell\n", - "$ pip install dabest\n", - "```\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "89d3cfb0", - "metadata": {}, - "source": [ - "2. Using Github\n", - "\n", - "Clone the [DABEST-python repo](https://github.com/ACCLAB/DABEST-python) locally (see instructions [here](https://help.github.com/articles/cloning-a-repository/)).\n", - "\n", - "Then, navigate to the cloned repo in the command line and run\n", - "``` shell\n", - "$ pip install .\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "2e7dc4c0", - "metadata": {}, - "source": [ - "## Testing" - ] - }, - { - "cell_type": "markdown", - "id": "a9f8cb3e", - "metadata": {}, - "source": [ - "To test DABEST, you will need to install [pytest](https://docs.pytest.org/en/latest/) and [nbdev](https://nbdev.fast.ai/). \n", - "\n", - "Run ``nbdev_export && nbdev_test`` in the root directory of the source distribution. This runs the value assertion tests in ``dabest/tests`` folder\n", - "\n", - "Run ``pytest`` in the root directory of the source distribution. This runs the image-based tests in ``dabest/tests/mpl_image_tests`` sub folder.\n", - "\n", - "The test suite will ensure that the bootstrapping functions and the plotting functions perform as expected.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "31d110fe", - "metadata": {}, - "source": [ - "## Bugs" - ] - }, - { - "cell_type": "markdown", - "id": "c4d6be79", - "metadata": {}, - "source": [ - "Please report any bugs on the [Github issue tracker](https://github.com/ACCLAB/DABEST-python/issues/new) for DABEST-python.\n" - ] - }, - { - "cell_type": "markdown", - "id": "ced1616b", - "metadata": {}, - "source": [ - "## Contributing" - ] - }, - { - "cell_type": "markdown", - "id": "99c17867", - "metadata": {}, - "source": [ - "All contributions are welcome. Please fork the [Github repo](https://github.com/ACCLAB/DABEST-python/) and open a pull request.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/02-about.ipynb b/nbs/02-about.ipynb deleted file mode 100644 index 812fd714..00000000 --- a/nbs/02-about.ipynb +++ /dev/null @@ -1,109 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c67c8b91", - "metadata": {}, - "source": [ - "# About" - ] - }, - { - "cell_type": "markdown", - "id": "2dbd3752", - "metadata": {}, - "source": [ - "## Authors\n", - "\n", - "DABEST is written in Python by [Joses W. Ho](https://twitter.com/jacuzzijo), with design and input from [Adam Claridge-Chang](https://twitter.com/adamcchang) and other [lab members](https://www.claridgechang.net/people.html).\n", - "\n", - "Features in v2025.10.20 were added by [Jonathan Anns](https://github.com/JAnns98), [Zinan Lu](https://github.com/Jacobluke-), [Yishan Mai](https://github.com/maiyishan), and [Sangyu Xu](https://github.com/sangyu).\n", - "\n", - "Features in v2025.03.27 were added by [Jonathan Anns](https://github.com/JAnns98), [Zinan Lu](https://github.com/Jacobluke-), [Kah Seng Lian](https://github.com/sunroofgod), [Yishan Mai](https://github.com/maiyishan), [Sangyu Xu](https://github.com/sangyu), and [Lucas Wang Zhuoyu](https://github.com/Lucas1213WZY).\n", - "\n", - "Features in v2024.03.29 were added by [Zinan Lu](https://github.com/Jacobluke-), [Kah Seng Lian](https://github.com/sunroofgod), [Ana Rosa Castillo](https://github.com/cyberosa).\n", - "\n", - "Features in v2023.02.14 were added by [Yixuan Li](https://github.com/LI-Yixuan), [Zinan Lu](https://github.com/Jacobluke-) and [Rou Zhang](https://github.com/ZHANGROU-99).\n", - "\n", - "To find out more about the authors' research, please visit the [Claridge-Chang lab webpage](http://www.claridgechang.net/).\n", - "\n", - "## Contributors\n", - "\n", - "\n", - "- Statistics supervision by Hyungwon Choi\n", - "\n", - "- Alpha testers from the Claridge-Chang lab: [Sangyu Xu](https://github.com/sangyu), [Xianyuan Zhang](https://github.com/XYZfar), [Farhan Mohammad](https://github.com/farhan8igib), Jurga Mituzaitė, Stanislav Ott, Tayfun Tumkaya, Jonathan Anns, Nicole Lee and Yishan Mai.\n", - "\n", - "- DizietAsahi ([DizietAsahi](https://github.com/DizietAsahi)) with [PR #86](https://github.com/ACCLAB/DABEST-python/pull/86): Fix bugs in slopegraph and reference line keyword parsing.\n", - "\n", - "- Adam Li ([@adam2392](https://github.com/adam2392)) with [PR #85](https://github.com/ACCLAB/DABEST-python/pull/85): Implement [Lq-Likelihood-Ratio-Type Test](https://github.com/alyakin314/lqrt) in statistical output.\n", - "\n", - "- Mason Malone ([@MasonM](https://github.com/MasonM)) with [PR #30](https://github.com/ACCLAB/DABEST-python/pull/30): Fix plot error when effect size is 0.\n", - "\n", - "- Matthew Edwards ([@mje-nz](https://github.com/mje-nz)) with [PR #71](https://github.com/ACCLAB/DABEST-python/pull/30): Specify dependencies correctly in ``setup.py``. \n", - "\n", - "- Adam Nekimken ([@anekimken](https://github.com/anekimken)) with [PR #73](https://github.com/ACCLAB/DABEST-python/pull/73): Implement inset axes so estimation plots can be plotted on a pre-determined :py:mod:`matplotlib` :py:class:`Axes` object.\n", - "\n", - "- Marin Manuel ([@MarinManuel](https://github.com/MarinManuel)) with [PR #109](https://github.com/ACCLAB/DABEST-python/pull/109): Fixed bug preventing non-string columns from being used.\n", - "\n", - "- Mike Lotinga ([@mlotinga](https://github.com/mlotinga)): Helped with addition of jitter and the adjusted p-value calculation, both of which are included in the *v2025.03.27* release.\n", - "\n", - "\n", - "\n", - "## Typography\n", - "\n", - "\n", - "This documentation uses [Spectral](https://spectral.prototypo.io/) for the body text, [Merriweather Sans](https://ebensorkin.wordpress.com/) for the side bar and titles, and [IBM Plex Mono](https://github.com/IBM/plex) for the monospace code font.\n", - "\n", - "\n", - "## License\n", - "\n", - "\n", - "The DABEST package in Python is licenced under the [BSD 3-clause Clear License](https://choosealicense.com/licenses/bsd-3-clause-clear/).\n", - "\n", - "Copyright (c) 2016-2023, Joses W. Ho\n", - "All rights reserved.\n", - "\n", - "Redistribution and use in source and binary forms, with or without\n", - "modification, are permitted (subject to the limitations in the disclaimer\n", - "below) provided that the following conditions are met:\n", - "\n", - " * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.\n", - "\n", - " * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.\n", - "\n", - " * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.\n", - "\n", - "
\n", - "NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY\n", - "THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND\n", - "CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n", - "LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A\n", - "PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR\n", - "CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\n", - "EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n", - "PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR\n", - "BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER\n", - "IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n", - "ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n", - "POSSIBILITY OF SUCH DAMAGE.\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "e1dcfc63", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/03-citation.ipynb b/nbs/03-citation.ipynb deleted file mode 100644 index 27cdb437..00000000 --- a/nbs/03-citation.ipynb +++ /dev/null @@ -1,48 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "95d72bf3", - "metadata": {}, - "source": [ - "# Citing DABEST" - ] - }, - { - "cell_type": "markdown", - "id": "b0833edd", - "metadata": {}, - "source": [ - "\n", - "\n", - "If your publication features a graphic generated with this software library, please cite the following publication.\n", - "\n", - "**Moving beyond P values: Everyday data analysis with estimation plots**\n", - "Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang\n", - "\n", - "`Nature Methods` 2019, 1548-7105. [doi:10.1038/s41592-019-0470-3](https://doi.org/10.1038/s41592-019-0470-3)\n", - "\n", - "[Free-to-view PDF](https://rdcu.be/bHhJ4)\n", - "\n", - "[Paywalled publisher site](https://www.nature.com/articles/s41592-019-0470-3)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "808f8708", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/API/bootstrap.ipynb b/nbs/API/bootstrap.ipynb deleted file mode 100644 index 79de8c0e..00000000 --- a/nbs/API/bootstrap.ipynb +++ /dev/null @@ -1,351 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b391aa10", - "metadata": {}, - "source": [ - "# Bootstrap\n", - "\n", - "\n", - "- order: 3" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9c45d5de", - "metadata": {}, - "outputs": [], - "source": [ - "#| default_exp _bootstrap_tools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fc72d776", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from __future__ import division\n", - "from nbdev.showdoc import *\n", - "import nbdev\n", - "\n", - "nbdev.nbdev_export()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4231300f", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "from scipy.stats import norm\n", - "from scipy.stats import ttest_1samp, ttest_ind, ttest_rel\n", - "from scipy.stats import mannwhitneyu, wilcoxon, norm\n", - "import warnings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e86b4b8d", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "class bootstrap:\n", - " \"\"\"\n", - " Computes the summary statistic and a bootstrapped confidence interval.\n", - "\n", - " Returns\n", - " -------\n", - " An `bootstrap` object reporting the summary statistics, percentile CIs, bias-corrected and accelerated (BCa) CIs, and the settings used:\n", - " `summary`: float.\n", - " The summary statistic.\n", - " `is_difference`: boolean.\n", - " Whether or not the summary is the difference between two groups. If False, only x1 was supplied.\n", - " `is_paired`: string, default None\n", - " The type of the experiment under which the data are obtained\n", - " `statistic`: callable\n", - " The function used to compute the summary.\n", - " `reps`: int\n", - " The number of bootstrap iterations performed.\n", - " `stat_array`:array\n", - " A sorted array of values obtained by bootstrapping the input arrays.\n", - " `ci`:float\n", - " The size of the confidence interval reported (in percentage).\n", - " `pct_ci_low,pct_ci_high`:floats\n", - " The upper and lower bounds of the confidence interval as computed by taking the percentage bounds.\n", - " `pct_low_high_indices`:array\n", - " An array with the indices in `stat_array` corresponding to the percentage confidence interval bounds.\n", - " `bca_ci_low, bca_ci_high`: floats\n", - " The upper and lower bounds of the bias-corrected and accelerated(BCa) confidence interval. See Efron 1977.\n", - " `bca_low_high_indices`: array\n", - " An array with the indices in `stat_array` corresponding to the BCa confidence interval bounds.\n", - " `pvalue_1samp_ttest`: float\n", - " P-value obtained from scipy.stats.ttest_1samp. If 2 arrays were passed (x1 and x2), returns 'NIL'. See \n", - " `pvalue_2samp_ind_ttest`: float\n", - " P-value obtained from scipy.stats.ttest_ind. If a single array was given (x1 only), or if `paired` is not None, returns 'NIL'. See \n", - " `pvalue_2samp_related_ttest`: float\n", - " P-value obtained from scipy.stats.ttest_rel. If a single array was given (x1 only), or if `paired` is None, returns 'NIL'. See \n", - " `pvalue_wilcoxon`: float\n", - " P-value obtained from scipy.stats.wilcoxon. If a single array was given (x1 only), or if `paired` is None, returns 'NIL'. The Wilcoxons signed-rank test is a nonparametric paired test of the null hypothesis that the related samples x1 and x2 are from the same distribution. See \n", - " `pvalue_mann_whitney`: float\n", - " Two-sided p-value obtained from scipy.stats.mannwhitneyu. If a single array was given (x1 only), returns 'NIL'. The Mann-Whitney U-test is a nonparametric unpaired test of the null hypothesis that x1 and x2 are from the same distribution. See \n", - "\n", - " \"\"\"\n", - "\n", - " def __init__(\n", - " self,\n", - " x1: np.array, # The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded.\n", - " x2: np.array = None, # The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded.\n", - " paired: bool = False, # Whether or not x1 and x2 are paired samples. If 'paired' is None then the data will not be treated as paired data in the subsequent calculations. If 'paired' is 'baseline', then in each tuple of x, other groups will be paired up with the first group (as control). If 'paired' is 'sequential', then in each tuple of x, each group will be paired up with the previous group (as control).\n", - " stat_function: callable = np.mean, # The summary statistic called on data.\n", - " smoothboot: bool = False, # Taken from seaborn.algorithms.bootstrap. If True, performs a smoothed bootstrap (draws samples from a kernel destiny estimate).\n", - " alpha_level: float = 0.05, # Denotes the likelihood that the confidence interval produced does not include the true summary statistic. When alpha = 0.05, a 95% confidence interval is produced.\n", - " reps: int = 5000, # Number of bootstrap iterations to perform.\n", - " ):\n", - " # Turn to pandas series.\n", - " # x1 = pd.Series(x1).dropna()\n", - " x1 = x1[~np.isnan(x1)]\n", - "\n", - " diff = False\n", - "\n", - " # Initialise stat_function\n", - " if stat_function is None:\n", - " stat_function = np.mean\n", - "\n", - " # Compute two-sided alphas.\n", - " if alpha_level > 1.0 or alpha_level < 0.0:\n", - " raise ValueError(\"alpha_level must be between 0 and 1.\")\n", - " alphas = np.array([alpha_level / 2.0, 1 - alpha_level / 2.0])\n", - "\n", - " sns_bootstrap_kwargs = {\n", - " \"func\": stat_function,\n", - " \"n_boot\": reps,\n", - " \"smooth\": smoothboot,\n", - " }\n", - "\n", - " if paired:\n", - " # check x2 is not None:\n", - " if x2 is None:\n", - " raise ValueError(\"Please specify x2.\")\n", - " \n", - " # x2 = pd.Series(x2).dropna()\n", - " x2 = x1[~np.isnan(x2)]\n", - "\n", - " if len(x1) != len(x2):\n", - " raise ValueError(\"x1 and x2 are not the same length.\")\n", - "\n", - " if (x2 is None) or (paired is not None):\n", - " if x2 is None:\n", - " tx = x1\n", - " paired = False\n", - " ttest_single = ttest_1samp(x1, 0)[1]\n", - " ttest_2_ind = \"NIL\"\n", - " ttest_2_paired = \"NIL\"\n", - " wilcoxonresult = \"NIL\"\n", - "\n", - " else: # only two options to enter here\n", - " diff = True\n", - " tx = x2 - x1\n", - " ttest_single = \"NIL\"\n", - " ttest_2_ind = \"NIL\"\n", - " ttest_2_paired = ttest_rel(x1, x2)[1]\n", - "\n", - " try:\n", - " wilcoxonresult = wilcoxon(x1, x2)[1]\n", - " except ValueError as e:\n", - " warnings.warn(\"Wilcoxon test could not be performed. This might be due \"\n", - " \"to no variability in the difference of the paired groups. \\n\"\n", - " \"Error: {}\\n\"\n", - " \"For detailed information, please refer to https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wilcoxon.html \"\n", - " .format(e))\n", - " mannwhitneyresult = \"NIL\"\n", - "\n", - " # Turns data into array, then tuple.\n", - " tdata = (tx,)\n", - "\n", - " # The value of the statistic function applied\n", - " # just to the actual data.\n", - " summ_stat = stat_function(*tdata)\n", - " statarray = sns.algorithms.bootstrap(tx, **sns_bootstrap_kwargs)\n", - " statarray.sort()\n", - "\n", - " # Get Percentile indices\n", - " pct_low_high = np.round((reps - 1) * alphas)\n", - " pct_low_high = np.nan_to_num(pct_low_high).astype(\"int\")\n", - "\n", - " elif x2 is not None and paired is None:\n", - " diff = True\n", - " # x2 = pd.Series(x2).dropna()\n", - " x2 = x2[~np.isnan(x2)]\n", - " # Generate statarrays for both arrays.\n", - " ref_statarray = sns.algorithms.bootstrap(x1, **sns_bootstrap_kwargs)\n", - " exp_statarray = sns.algorithms.bootstrap(x2, **sns_bootstrap_kwargs)\n", - "\n", - " tdata = exp_statarray - ref_statarray\n", - " statarray = tdata.copy()\n", - " statarray.sort()\n", - " tdata = (tdata,) # Note tuple form.\n", - "\n", - " # The difference as one would calculate it.\n", - " summ_stat = stat_function(x2) - stat_function(x1)\n", - "\n", - " # Get Percentile indices\n", - " pct_low_high = np.round((reps - 1) * alphas)\n", - " pct_low_high = np.nan_to_num(pct_low_high).astype(\"int\")\n", - "\n", - " # Statistical tests.\n", - " ttest_single = \"NIL\"\n", - " ttest_2_ind = ttest_ind(x1, x2)[1]\n", - " ttest_2_paired = \"NIL\"\n", - " mannwhitneyresult = mannwhitneyu(x1, x2, alternative=\"two-sided\")[1]\n", - " wilcoxonresult = \"NIL\"\n", - "\n", - " # Get Bias-Corrected Accelerated indices convenience function invoked.\n", - " bca_low_high = bca(tdata, alphas, statarray, stat_function, summ_stat, reps)\n", - "\n", - " # Warnings for unstable or extreme indices.\n", - " for ind in [pct_low_high, bca_low_high]:\n", - " if np.any(ind == 0) or np.any(ind == reps - 1):\n", - " warnings.warn(\n", - " \"Some values used extremal samples;\"\n", - " \" results are probably unstable.\"\n", - " )\n", - " elif np.any(ind < 10) or np.any(ind >= reps - 10):\n", - " warnings.warn(\n", - " \"Some values used top 10 low/high samples;\"\n", - " \" results may be unstable.\"\n", - " )\n", - "\n", - " self.summary = summ_stat\n", - " self.is_paired = paired\n", - " self.is_difference = diff\n", - " self.statistic = str(stat_function)\n", - " self.n_reps = reps\n", - "\n", - " self.ci = (1 - alpha_level) * 100\n", - " self.stat_array = np.array(statarray)\n", - "\n", - " self.pct_ci_low = statarray[pct_low_high[0]]\n", - " self.pct_ci_high = statarray[pct_low_high[1]]\n", - " self.pct_low_high_indices = pct_low_high\n", - "\n", - " self.bca_ci_low = statarray[bca_low_high[0]]\n", - " self.bca_ci_high = statarray[bca_low_high[1]]\n", - " self.bca_low_high_indices = bca_low_high\n", - "\n", - " self.pvalue_1samp_ttest = ttest_single\n", - " self.pvalue_2samp_ind_ttest = ttest_2_ind\n", - " self.pvalue_2samp_paired_ttest = ttest_2_paired\n", - " self.pvalue_wilcoxon = wilcoxonresult\n", - " self.pvalue_mann_whitney = mannwhitneyresult\n", - "\n", - " self.results = {\n", - " \"stat_summary\": self.summary,\n", - " \"is_difference\": diff,\n", - " \"is_paired\": paired,\n", - " \"bca_ci_low\": self.bca_ci_low,\n", - " \"bca_ci_high\": self.bca_ci_high,\n", - " \"ci\": self.ci,\n", - " }\n", - "\n", - " def __repr__(self):\n", - " if \"mean\" in self.statistic:\n", - " stat = \"mean\"\n", - " elif \"median\" in self.statistic:\n", - " stat = \"median\"\n", - " else:\n", - " stat = self.statistic\n", - "\n", - " diff_types = {\"sequential\": \"paired\", \"baseline\": \"paired\", None: \"unpaired\"}\n", - " if self.is_difference:\n", - " a = \"The {} {} difference is {}.\".format(\n", - " diff_types[self.is_paired], stat, self.summary\n", - " )\n", - " else:\n", - " a = \"The {} is {}.\".format(stat, self.summary)\n", - "\n", - " b = \"[{} CI: {}, {}]\".format(self.ci, self.bca_ci_low, self.bca_ci_high)\n", - " return \"\\n\".join([a, b])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "00c814b9", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "def jackknife_indexes(data):\n", - " # Taken without modification from scikits.bootstrap package.\n", - " \"\"\"\n", - " From the scikits.bootstrap package.\n", - " Given an array, returns a list of arrays where each array is a set of\n", - " jackknife indexes.\n", - "\n", - " For a given set of data Y, the jackknife sample J[i] is defined as the\n", - " data set Y with the ith data point deleted.\n", - " \"\"\"\n", - "\n", - " base = np.arange(0, len(data))\n", - " return (np.delete(base, i) for i in base)\n", - "\n", - "\n", - "def bca(data, alphas, stat_array, stat_function, ostat, reps):\n", - " \"\"\"\n", - " Subroutine called to calculate the BCa statistics.\n", - " Borrowed heavily from scikits.bootstrap code.\n", - " \"\"\"\n", - "\n", - " # The bias correction value.\n", - " z0 = norm.ppf((1.0 * np.sum(stat_array < ostat, axis=0)) / reps)\n", - "\n", - " # Statistics of the jackknife distribution\n", - " jack_indexes = jackknife_indexes(data[0])\n", - " jstat = [stat_function(*(x[indexes] for x in data)) for indexes in jack_indexes]\n", - " jmean = np.mean(jstat, axis=0)\n", - "\n", - " # Acceleration value\n", - " a = np.divide(\n", - " np.sum((jmean - jstat) ** 3, axis=0),\n", - " (6.0 * np.sum((jmean - jstat) ** 2, axis=0) ** 1.5),\n", - " )\n", - " if np.any(np.isnan(a)):\n", - " nanind = np.nonzero(np.isnan(a))\n", - " warnings.warn(\n", - " \"Some acceleration values were undefined.\"\n", - " \"This is almost certainly because all values\"\n", - " \"for the statistic were equal. Affected\"\n", - " \"confidence intervals will have zero width and\"\n", - " \"may be inaccurate (indexes: {})\".format(nanind)\n", - " )\n", - " zs = z0 + norm.ppf(alphas).reshape(alphas.shape + (1,) * z0.ndim)\n", - " avals = norm.cdf(z0 + zs / (1 - a * zs))\n", - " nvals = np.round((reps - 1) * avals)\n", - " nvals = np.nan_to_num(nvals).astype(\"int\")\n", - "\n", - " return nvals" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/API/confint_1group.ipynb b/nbs/API/confint_1group.ipynb deleted file mode 100644 index d15c1499..00000000 --- a/nbs/API/confint_1group.ipynb +++ /dev/null @@ -1,208 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "6e9be445", - "metadata": {}, - "source": [ - "# confint_1group\n", - "\n", - "> A range of functions to compute bootstraps for a single sample.\n", - "\n", - "- order: 7" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "80646fa2", - "metadata": {}, - "outputs": [], - "source": [ - "#| default_exp _stats_tools/confint_1group" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "65a5b76c", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from __future__ import annotations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ef2b7e1", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from nbdev.showdoc import *\n", - "import nbdev\n", - "nbdev.nbdev_export()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9181f236", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "import numpy as np\n", - "from numpy.random import PCG64, RandomState\n", - "from scipy.stats import norm\n", - "from numpy import sort as npsort" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bac09924", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "def create_bootstrap_indexes(array, resamples=5000, random_seed=12345):\n", - " \"\"\"Given an array-like, returns a generator of bootstrap indexes\n", - " to be used for resampling.\n", - " \"\"\"\n", - "\n", - " rng = RandomState(PCG64(random_seed))\n", - "\n", - " indexes = range(0, len(array))\n", - "\n", - " out = (rng.choice(indexes, len(indexes), replace=True) for i in range(0, resamples))\n", - "\n", - " return out\n", - "\n", - "\n", - "def compute_1group_jackknife(x, func, *args, **kwargs):\n", - " \"\"\"\n", - " Returns the jackknife bootstraps for func(x).\n", - " \"\"\"\n", - " from . import confint_2group_diff as ci_2g\n", - "\n", - " jackknives = [i for i in ci_2g.create_jackknife_indexes(x)]\n", - " out = [func(x[j], *args, **kwargs) for j in jackknives]\n", - " del jackknives # memory management.\n", - " return out\n", - "\n", - "\n", - "def compute_1group_acceleration(jack_dist):\n", - " \"\"\"\n", - " Returns the accaleration value based on the jackknife distribution.\n", - " \"\"\"\n", - " from . import confint_2group_diff as ci_2g\n", - "\n", - " return ci_2g._calc_accel(jack_dist)\n", - "\n", - "\n", - "def compute_1group_bootstraps(\n", - " x, func, resamples=5000, random_seed=12345, *args, **kwargs\n", - "):\n", - " \"\"\"Bootstraps func(x), with the number of specified resamples.\"\"\"\n", - "\n", - " # Create bootstrap indexes.\n", - " boot_indexes = create_bootstrap_indexes(\n", - " x, resamples=resamples, random_seed=random_seed\n", - " )\n", - "\n", - " out = [func(x[b], *args, **kwargs) for b in boot_indexes]\n", - "\n", - " del boot_indexes\n", - "\n", - " return out\n", - "\n", - "\n", - "def compute_1group_bias_correction(x, bootstraps, func, *args, **kwargs):\n", - " metric = func(x, *args, **kwargs)\n", - " prop_boots_less_than_metric = sum(bootstraps < metric) / len(bootstraps)\n", - "\n", - " return norm.ppf(prop_boots_less_than_metric)\n", - "\n", - "\n", - "def summary_ci_1group(\n", - " x: np.array, # An numerical iterable.\n", - " func, # The function to be applied to x.\n", - " resamples: int = 5000, # The number of bootstrap resamples to be taken of func(x).\n", - " alpha: float = 0.05, # Denotes the likelihood that the confidence interval produced _does not_ include the true summary statistic. When alpha = 0.05, a 95% confidence interval is produced.\n", - " random_seed: int = 12345, # `random_seed` is used to seed the random number generator during bootstrap resampling. This ensures that the confidence intervals reported are replicable.\n", - " sort_bootstraps: bool = True,\n", - " *args,\n", - " **kwargs\n", - "):\n", - " \"\"\"\n", - " Given an array-like x, returns func(x), and a bootstrap confidence\n", - " interval of func(x).\n", - "\n", - " Returns\n", - " -------\n", - " A dictionary with the following five keys:\n", - " `summary`: float.\n", - " The outcome of func(x).\n", - " `func`: function.\n", - " The function applied to x.\n", - " `bca_ci_low`: float\n", - " `bca_ci_high`: float.\n", - " The bias-corrected and accelerated confidence interval, for the\n", - " given alpha.\n", - " `bootstraps`: array.\n", - " The bootstraps used to generate the confidence interval.\n", - " These will be sorted in ascending order if `sort_bootstraps`\n", - " was True.\n", - "\n", - " \"\"\"\n", - " from . import confint_2group_diff as ci2g\n", - "\n", - " boots = compute_1group_bootstraps(\n", - " x, func, resamples=resamples, random_seed=random_seed, *args, **kwargs\n", - " )\n", - " bias = compute_1group_bias_correction(x, boots, func)\n", - "\n", - " jk = compute_1group_jackknife(x, func, *args, **kwargs)\n", - " accel = compute_1group_acceleration(jk)\n", - " del jk\n", - "\n", - " ci_idx = ci2g.compute_interval_limits(bias, accel, resamples, alpha)\n", - "\n", - " boots_sorted = npsort(boots)\n", - "\n", - " low = boots_sorted[ci_idx[0]]\n", - " high = boots_sorted[ci_idx[1]]\n", - "\n", - " if sort_bootstraps:\n", - " B = boots_sorted\n", - " else:\n", - " B = boots\n", - " del boots\n", - " del boots_sorted\n", - "\n", - " out = {\n", - " \"summary\": func(x),\n", - " \"func\": func,\n", - " \"bca_ci_low\": low,\n", - " \"bca_ci_high\": high,\n", - " \"bootstraps\": B,\n", - " }\n", - "\n", - " del B\n", - " return out" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/API/confint_2group_diff.ipynb b/nbs/API/confint_2group_diff.ipynb deleted file mode 100644 index bdc009b3..00000000 --- a/nbs/API/confint_2group_diff.ipynb +++ /dev/null @@ -1,408 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ebadf154", - "metadata": {}, - "source": [ - "# confint_2group_diff\n", - "\n", - "> A range of functions to compute bootstraps for the mean difference \n", - "between two groups.\n", - "\n", - "- order: 8" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "77004140", - "metadata": {}, - "outputs": [], - "source": [ - "#| default_exp _stats_tools/confint_2group_diff" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "97f96815", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from __future__ import annotations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2fd5572a", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from nbdev.showdoc import *\n", - "import nbdev\n", - "nbdev.nbdev_export()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fa733643", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "import numpy as np\n", - "from numpy import arange, delete, errstate\n", - "from numpy import mean as npmean\n", - "from numpy import sum as npsum\n", - "from numpy.random import PCG64, RandomState\n", - "from numba import njit, prange\n", - "from scipy.stats import norm\n", - "from numpy import isnan" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8cf9b1fc", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "@njit(cache=True, parallel=True)\n", - "def create_jackknife_indexes(data):\n", - " \"\"\"\n", - " Given an array-like, creates a jackknife bootstrap.\n", - "\n", - " For a given set of data Y, the jackknife bootstrap sample J[i]\n", - " is defined as the data set Y with the ith data point deleted.\n", - "\n", - " Keywords\n", - " --------\n", - " data: array-like\n", - "\n", - " Returns\n", - " -------\n", - " Generator that yields all jackknife bootstrap samples.\n", - " \"\"\"\n", - "\n", - " n = len(data)\n", - " indexes = np.empty((n, n - 1), dtype=np.int64)\n", - " for i in prange(n):\n", - " indexes[i] = np.concatenate((np.arange(i), np.arange(i + 1, n)))\n", - " return indexes\n", - "\n", - "\n", - "@njit(cache=True, parallel=True)\n", - "def create_repeated_indexes(data):\n", - " \"\"\"\n", - " Convenience function. Given an array-like with length N,\n", - " returns a generator that yields N indexes [0, 1, ..., N].\n", - " \"\"\"\n", - "\n", - " n = len(data)\n", - " indexes = np.empty((n, n), dtype=np.int64) # Pre-allocate the output array\n", - " for i in prange(n):\n", - " indexes[i, :] = np.arange(n) # Fill each row with the full index range\n", - " return indexes\n", - "\n", - "\n", - "def _create_two_group_jackknife_indexes(x0, x1, is_paired):\n", - " \"\"\"Creates the jackknife bootstrap for 2 groups.\"\"\"\n", - "\n", - " if is_paired and len(x0) == len(x1):\n", - " out = list(\n", - " zip(\n", - " [j for j in create_jackknife_indexes(x0)],\n", - " [i for i in create_jackknife_indexes(x1)],\n", - " )\n", - " )\n", - " else:\n", - " jackknife_c = list(\n", - " zip(\n", - " [j for j in create_jackknife_indexes(x0)],\n", - " [i for i in create_repeated_indexes(x1)],\n", - " )\n", - " )\n", - "\n", - " jackknife_t = list(\n", - " zip(\n", - " [i for i in create_repeated_indexes(x0)],\n", - " [j for j in create_jackknife_indexes(x1)],\n", - " )\n", - " )\n", - " out = jackknife_c + jackknife_t\n", - " del jackknife_c\n", - " del jackknife_t\n", - "\n", - " return out\n", - "\n", - "\n", - "def compute_meandiff_jackknife(x0, x1, is_paired, effect_size):\n", - " \"\"\"\n", - " Given two arrays, returns the jackknife for their effect size.\n", - " \"\"\"\n", - " from . import effsize as __es\n", - "\n", - " jackknives = _create_two_group_jackknife_indexes(x0, x1, is_paired)\n", - "\n", - " out = []\n", - "\n", - " for j in jackknives:\n", - " x0_shuffled = x0[j[0]]\n", - " x1_shuffled = x1[j[1]]\n", - "\n", - " es = __es.two_group_difference(x0_shuffled, x1_shuffled, is_paired, effect_size)\n", - " out.append(es)\n", - "\n", - " return out\n", - "\n", - "\n", - "def _calc_accel(jack_dist):\n", - " \"\"\"\n", - " Given the Jackknife distribution, calculates the acceleration factor.\n", - " \"\"\"\n", - " jack_mean = npmean(jack_dist)\n", - "\n", - " numer = npsum((jack_mean - jack_dist) ** 3)\n", - " denom = 6.0 * (npsum((jack_mean - jack_dist) ** 2) ** 1.5)\n", - "\n", - " with errstate(invalid=\"ignore\"):\n", - " # does not raise warning if invalid division encountered.\n", - " return numer / denom\n", - "\n", - "\n", - "@njit(cache=True) # parallelization must be turned off for random number generation\n", - "def bootstrap_indices(is_paired, x0_len, x1_len, resamples, random_seed):\n", - " np.random.seed(random_seed)\n", - " indices = np.empty((resamples, x0_len if is_paired else x0_len + x1_len), dtype=np.int64)\n", - " \n", - " for i in range(resamples):\n", - " if is_paired:\n", - " indices[i, :x0_len] = np.random.choice(x0_len, x0_len)\n", - " else: \n", - " indices[i, :x0_len] = np.random.choice(x0_len, x0_len)\n", - " indices[i, x0_len:x0_len+x1_len] = np.random.choice(x1_len, x1_len)\n", - " return indices\n", - "\n", - "\n", - "def compute_bootstrapped_diff(\n", - " x0, x1, is_paired, effect_size, resamples=5000, random_seed=12345\n", - "):\n", - " \"\"\"Bootstraps the effect_size for 2 groups.\"\"\"\n", - "\n", - " from . import effsize as __es\n", - "\n", - " x0_len, x1_len = len(x0), len(x1)\n", - " indices = bootstrap_indices(is_paired, x0_len, x1_len, resamples, random_seed)\n", - " out = np.empty(resamples, dtype=np.float64)\n", - "\n", - " for i in range(resamples):\n", - " if is_paired:\n", - " x0_sample = x0[indices[i, :x0_len]]\n", - " x1_sample = x1[indices[i, :x0_len]]\n", - " else:\n", - " x0_sample = x0[indices[i, :x0_len]]\n", - " x1_sample = x1[indices[i, x0_len:x0_len+x1_len]]\n", - "\n", - " out[i] = __es.two_group_difference(x0_sample, x1_sample, is_paired, effect_size)\n", - "\n", - " return out\n", - "\n", - "\n", - "@njit(cache=True)\n", - "def delta2_bootstrap_loop(x1, x2, x3, x4, resamples, pooled_sd, rng_seed, is_paired, proportional=False):\n", - " \"\"\"\n", - " Compute bootstrapped differences for delta-delta, handling both regular and proportional data\n", - " \"\"\"\n", - " np.random.seed(rng_seed)\n", - " deltadelta = np.empty(resamples)\n", - " out_delta_g = np.empty(resamples)\n", - " \n", - " n1, n2, n3, n4 = len(x1), len(x2), len(x3), len(x4)\n", - " if is_paired and (n1 != n2 or n3 != n4):\n", - " raise ValueError(\"Each control group must have the same length as its corresponding test group in paired analysis.\")\n", - "\n", - " # Bootstrapping\n", - " for i in range(resamples):\n", - " # Paired or unpaired resampling\n", - " if is_paired:\n", - " indices_1 = np.random.choice(len(x1), len(x1))\n", - " indices_2 = np.random.choice(len(x3), len(x3))\n", - " x1_sample, x2_sample = x1[indices_1], x2[indices_1]\n", - " x3_sample, x4_sample = x3[indices_2], x4[indices_2]\n", - " else:\n", - " indices_1 = np.random.randint(0, len(x1), len(x1))\n", - " indices_2 = np.random.randint(0, len(x2), len(x2))\n", - " indices_3 = np.random.randint(0, len(x3), len(x3))\n", - " indices_4 = np.random.randint(0, len(x4), len(x4))\n", - " x1_sample, x2_sample = x1[indices_1], x2[indices_2]\n", - " x3_sample, x4_sample = x3[indices_3], x4[indices_4]\n", - "\n", - " # Calculate deltas\n", - " delta_1 = np.mean(x2_sample) - np.mean(x1_sample)\n", - " delta_2 = np.mean(x4_sample) - np.mean(x3_sample)\n", - " delta_delta = delta_2 - delta_1\n", - " \n", - " deltadelta[i] = delta_delta\n", - "\n", - " out_delta_g[i] = delta_delta if proportional else delta_delta/pooled_sd\n", - "\n", - " return out_delta_g, deltadelta\n", - "\n", - "\n", - "def compute_delta2_bootstrapped_diff(\n", - " x1: np.ndarray, # Control group 1\n", - " x2: np.ndarray, # Test group 1\n", - " x3: np.ndarray, # Control group 2\n", - " x4: np.ndarray, # Test group 2\n", - " is_paired: str = None,\n", - " resamples: int = 5000,\n", - " random_seed: int = 12345,\n", - " proportional: bool = False\n", - ") -> tuple:\n", - " \"\"\"\n", - " Bootstraps the effect size deltas' g or proportional delta-delta\n", - " \"\"\"\n", - " x1, x2, x3, x4 = map(np.asarray, [x1, x2, x3, x4])\n", - " \n", - " if proportional:\n", - " # For proportional data, pass 1.0 as dummy pooled_sd (won't be used)\n", - " out_delta_g, deltadelta = delta2_bootstrap_loop(\n", - " x1, x2, x3, x4, resamples, 1.0, random_seed, is_paired, proportional=True\n", - " )\n", - " # For proportional data, delta_g is the empirical delta-delta\n", - " delta_g = ((np.mean(x4) - np.mean(x3)) - (np.mean(x2) - np.mean(x1)))\n", - " else:\n", - " # Calculate pooled sample standard deviation for non-proportional data\n", - " stds = [np.std(x) for x in [x1, x2, x3, x4]]\n", - " ns = [len(x) for x in [x1, x2, x3, x4]]\n", - " \n", - " sd_numerator = sum((n - 1) * s**2 for n, s in zip(ns, stds))\n", - " sd_denominator = sum(n - 1 for n in ns)\n", - " \n", - " if sd_denominator == 0:\n", - " raise ValueError(\"Insufficient data to compute pooled standard deviation.\")\n", - " \n", - " pooled_sample_sd = np.sqrt(sd_numerator / sd_denominator)\n", - " \n", - " if np.isnan(pooled_sample_sd) or pooled_sample_sd == 0:\n", - " raise ValueError(\"Pooled sample standard deviation is NaN or zero.\")\n", - " \n", - " out_delta_g, deltadelta = delta2_bootstrap_loop(\n", - " x1, x2, x3, x4, resamples, pooled_sample_sd, random_seed, is_paired, proportional=False\n", - " )\n", - " delta_g = ((np.mean(x4) - np.mean(x3)) - (np.mean(x2) - np.mean(x1))) / pooled_sample_sd\n", - "\n", - " return out_delta_g, delta_g, deltadelta\n", - "\n", - "\n", - "def compute_meandiff_bias_correction(\n", - " bootstraps, # An numerical iterable, comprising bootstrap resamples of the effect size.\n", - " effsize, # The effect size for the original sample.\n", - "): # The bias correction value for the given bootstraps and effect size.\n", - " \"\"\"\n", - " Computes the bias correction required for the BCa method\n", - " of confidence interval construction.\n", - "\n", - " Returns\n", - " -------\n", - " bias: numeric\n", - " The bias correction value for the given bootstraps\n", - " and effect size.\n", - "\n", - " \"\"\"\n", - "\n", - " B = np.array(bootstraps)\n", - " prop_less_than_es = sum(B < effsize) / len(B)\n", - "\n", - " return norm.ppf(prop_less_than_es)\n", - "\n", - "\n", - "def _compute_alpha_from_ci(ci):\n", - " if ci < 0 or ci > 100:\n", - " raise ValueError(\"`ci` must be a number between 0 and 100.\")\n", - "\n", - " return (100.0 - ci) / 100.0\n", - "\n", - "\n", - "@njit(cache=True)\n", - "def _compute_quantile(z, bias, acceleration):\n", - " numer = bias + z\n", - " denom = 1 - (acceleration * numer)\n", - "\n", - " return bias + (numer / denom)\n", - "\n", - "\n", - "def compute_interval_limits(bias, acceleration, n_boots, ci=95):\n", - " \"\"\"\n", - " Returns the indexes of the interval limits for a given bootstrap.\n", - "\n", - " Supply the bias, acceleration factor, and number of bootstraps.\n", - " \"\"\"\n", - "\n", - " alpha = _compute_alpha_from_ci(ci)\n", - "\n", - " alpha_low = alpha / 2\n", - " alpha_high = 1 - (alpha / 2)\n", - "\n", - " z_low = norm.ppf(alpha_low)\n", - " z_high = norm.ppf(alpha_high)\n", - "\n", - " kws = {\"bias\": bias, \"acceleration\": acceleration}\n", - " low = _compute_quantile(z_low, **kws)\n", - " high = _compute_quantile(z_high, **kws)\n", - "\n", - " if isnan(low) or isnan(high):\n", - " return low, high\n", - "\n", - " \n", - " low = int(norm.cdf(low) * n_boots)\n", - " high = int(norm.cdf(high) * n_boots)\n", - " return low, high\n", - "\n", - "\n", - "@njit(cache=True)\n", - "def calculate_group_var(control_var, control_N, test_var, test_N):\n", - " \n", - " pooled_var = ((control_N - 1) * control_var + (test_N - 1) * test_var) / (control_N + test_N - 2) \n", - " \n", - " return pooled_var\n", - "\n", - "def calculate_bootstraps_var(bootstraps):\n", - "\n", - " bootstraps_var_list = [np.var(x, ddof=1) for x in bootstraps]\n", - " bootstraps_var_array = np.array(bootstraps_var_list)\n", - " \n", - " return bootstraps_var_array\n", - " \n", - "\n", - "\n", - "def calculate_weighted_delta(bootstrap_dist_var, differences):\n", - " \"\"\"\n", - " Compute the weighted deltas.\n", - " \"\"\"\n", - "\n", - " weight = np.true_divide(1, bootstrap_dist_var)\n", - " denom = np.sum(weight)\n", - " num = 0.0\n", - " for i in range(len(weight)):\n", - " num += weight[i] * differences[i]\n", - " return np.true_divide(num, denom)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/API/dabest_object.ipynb b/nbs/API/dabest_object.ipynb deleted file mode 100644 index f082c62b..00000000 --- a/nbs/API/dabest_object.ipynb +++ /dev/null @@ -1,1420 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "ed122c74", - "metadata": {}, - "source": [ - "# Dabest object\n", - "\n", - "> Main class for estimating statistics and generating plots.\n", - "\n", - "- order: 2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2654032b", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fb97d9b1", - "metadata": {}, - "outputs": [], - "source": [ - "#| default_exp _dabest_object" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1d5d586f", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from __future__ import annotations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dcd32470", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from nbdev.showdoc import *\n", - "import nbdev\n", - "nbdev.nbdev_export()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d3c6f47a", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "# Import standard data science libraries\n", - "import warnings\n", - "from numpy import array, repeat, random, issubdtype, number\n", - "import numpy as np\n", - "import pandas as pd\n", - "from scipy.stats import norm\n", - "from scipy.stats import randint" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "204a64b4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pre-compiling numba functions for DABEST...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Compiling numba functions: 100%|██████████| 11/11 [00:04<00:00, 2.20it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Numba compilation complete!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "#| hide\n", - "import dabest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "350b12c1", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "class Dabest(object):\n", - "\n", - " \"\"\"\n", - " Class for estimation statistics and plots.\n", - " \"\"\"\n", - "\n", - " def __init__(\n", - " self,\n", - " data,\n", - " idx,\n", - " x,\n", - " y,\n", - " paired,\n", - " id_col,\n", - " ci,\n", - " resamples,\n", - " random_seed,\n", - " proportional,\n", - " delta2,\n", - " experiment,\n", - " experiment_label,\n", - " x1_level,\n", - " mini_meta,\n", - " ps_adjust,\n", - " ):\n", - " \"\"\"\n", - " Parses and stores pandas DataFrames in preparation for estimation\n", - " statistics. You should not be calling this class directly; instead,\n", - " use `dabest.load()` to parse your DataFrame prior to analysis.\n", - " \"\"\"\n", - "\n", - " self.__delta2 = delta2\n", - " self.__experiment = experiment\n", - " self.__ci = ci\n", - " self.__input_data = data\n", - " self.__output_data = data.copy()\n", - " self.__id_col = id_col\n", - " self.__is_paired = paired\n", - " self.__resamples = resamples\n", - " self.__random_seed = random_seed\n", - " self.__is_proportional = proportional\n", - " self.__is_mini_meta = mini_meta\n", - " self.__ps_adjust = ps_adjust\n", - "\n", - " # after this call the attributes self.__experiment_label and self.__x1_level are updated\n", - " self._check_errors(x, y, idx, experiment, experiment_label, x1_level)\n", - "\n", - " # create new x & idx and record the second variable if this is a valid 2x2 ANOVA case\n", - " idx, x, all_plot_groups = self._prep_idx(idx, x, y, experiment)\n", - "\n", - " self.__plot_data = self._get_plot_data(x, y, all_plot_groups)\n", - " self.__all_plot_groups = all_plot_groups\n", - "\n", - " self._compute_effectsize_dfs()\n", - "\n", - " def __repr__(self):\n", - " from .__init__ import __version__\n", - " from .misc_tools import print_greeting\n", - "\n", - " greeting_header = print_greeting()\n", - "\n", - " RM_STATUS = {\n", - " \"baseline\": \"for repeated measures against baseline \\n\",\n", - " \"sequential\": \"for the sequential design of repeated-measures experiment \\n\",\n", - " \"None\": \"\",\n", - " }\n", - "\n", - " PAIRED_STATUS = {\"baseline\": \"Paired e\", \"sequential\": \"Paired e\", \"None\": \"E\"}\n", - "\n", - " first_line = {\n", - " \"rm_status\": RM_STATUS[str(self.__is_paired)],\n", - " \"paired_status\": PAIRED_STATUS[str(self.__is_paired)],\n", - " }\n", - "\n", - " s1 = \"{paired_status}ffect size(s) {rm_status}\".format(**first_line)\n", - " s2 = \"with {}% confidence intervals will be computed for:\".format(self.__ci)\n", - " desc_line = s1 + s2\n", - "\n", - " out = [greeting_header + \"\\n\\n\" + desc_line]\n", - "\n", - " comparisons = []\n", - "\n", - " if self.__is_paired == \"sequential\":\n", - " for j, current_tuple in enumerate(self.__idx):\n", - " for ix, test_name in enumerate(current_tuple[1:]):\n", - " control_name = current_tuple[ix]\n", - " comparisons.append(\"{} minus {}\".format(test_name, control_name))\n", - " else:\n", - " for j, current_tuple in enumerate(self.__idx):\n", - " control_name = current_tuple[0]\n", - "\n", - " for ix, test_name in enumerate(current_tuple[1:]):\n", - " comparisons.append(\"{} minus {}\".format(test_name, control_name))\n", - "\n", - " if self.__delta2:\n", - " comparisons.append(\n", - " \"{} minus {} (only for mean difference)\".format(\n", - " self.__experiment_label[1], self.__experiment_label[0]\n", - " )\n", - " )\n", - "\n", - " if self.__is_mini_meta:\n", - " comparisons.append(\"weighted delta (only for mean difference)\")\n", - "\n", - " for j, g in enumerate(comparisons):\n", - " out.append(\"{}. {}\".format(j + 1, g))\n", - "\n", - " resamples_line1 = \"\\n{} resamples \".format(self.__resamples)\n", - " resamples_line2 = \"will be used to generate the effect size bootstraps.\"\n", - " out.append(resamples_line1 + resamples_line2)\n", - "\n", - " return \"\\n\".join(out)\n", - "\n", - "\n", - " def _prep_idx(self, idx, x, y, experiment):\n", - " \"\"\"\n", - " Function to prepare the idx.\n", - " \"\"\"\n", - " if idx is None and x is not None and y is not None:\n", - " # Add a length check for unique values in the first element in list x,\n", - " # if the length is greater than 2, force delta2 to be False\n", - " # Should be removed if delta2 for situations other than 2x2 is supported\n", - " if len(self.__output_data[x[0]].unique()) > 2:\n", - " self.__delta2 = False\n", - "\n", - " # add a new column which is a combination of experiment and the first variable\n", - " new_col_name = experiment + x[0]\n", - " while new_col_name in self.__output_data.columns:\n", - " new_col_name += \"_\"\n", - "\n", - " self.__output_data[new_col_name] = (\n", - " self.__output_data[x[0]].astype(str)\n", - " + \" \"\n", - " + self.__output_data[experiment].astype(str)\n", - " )\n", - "\n", - " # create idx and record the first and second x variable\n", - " idx = []\n", - " for i in list(map(lambda x: str(x), self.__experiment_label)):\n", - " temp = []\n", - " for j in list(map(lambda x: str(x), self.__x1_level)):\n", - " temp.append(j + \" \" + i)\n", - " idx.append(temp)\n", - "\n", - " self.__idx = idx\n", - " self.__x1 = x[0]\n", - " self.__x2 = x[1]\n", - " x = new_col_name\n", - " else:\n", - " self.__idx = idx\n", - " self.__x1 = None\n", - " self.__x2 = None\n", - "\n", - " # Determine the kind of estimation plot we need to produce.\n", - " if all([isinstance(i, (str, int, float)) for i in self.__idx]):\n", - " # flatten out idx.\n", - " all_plot_groups = pd.Series([t for t in self.__idx]).unique().tolist()\n", - " if len(self.__idx) > len(all_plot_groups):\n", - " err0 = \"`idx` contains duplicated groups. Please remove any duplicates and try again.\"\n", - " raise ValueError(err0)\n", - "\n", - " # We need to re-wrap this idx inside another tuple so as to\n", - " # easily loop thru each pairwise group later on.\n", - " self.__idx = (idx,)\n", - "\n", - " elif all([isinstance(i, (tuple, list)) for i in self.__idx]):\n", - " all_plot_groups = pd.Series([tt for t in self.__idx for tt in t]).unique().tolist()\n", - " actual_groups_given = sum([len(i) for i in self.__idx])\n", - "\n", - " if actual_groups_given > len(all_plot_groups):\n", - " err0 = \"Groups are repeated across tuples,\"\n", - " err1 = \" or a tuple has repeated groups in it.\"\n", - " err2 = \" Please remove any duplicates and try again.\"\n", - " raise ValueError(err0 + err1 + err2)\n", - "\n", - " else: # mix of string and tuple?\n", - " err = \"There seems to be a problem with the idx you \" \"entered--{}.\".format(self.__idx)\n", - " raise ValueError(err)\n", - " \n", - " return idx, x, all_plot_groups\n", - "\n", - " @property\n", - " def mean_diff(self):\n", - " \"\"\"\n", - " Returns an :py:class:`EffectSizeDataFrame` for the mean difference, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`\n", - "\n", - " \"\"\"\n", - " return self.__mean_diff\n", - "\n", - " @property\n", - " def median_diff(self):\n", - " \"\"\"\n", - " Returns an :py:class:`EffectSizeDataFrame` for the median difference, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.\n", - "\n", - " \"\"\"\n", - " return self.__median_diff\n", - "\n", - " @property\n", - " def cohens_d(self):\n", - " \"\"\"\n", - " Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Cohen's `d`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.\n", - "\n", - " \"\"\"\n", - " return self.__cohens_d\n", - "\n", - " @property\n", - " def cohens_h(self):\n", - " \"\"\"\n", - " Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Cohen's `h`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `directional` argument in `dabest.load()`.\n", - "\n", - " \"\"\"\n", - " return self.__cohens_h\n", - "\n", - " @property\n", - " def hedges_g(self):\n", - " \"\"\"\n", - " Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Hedges' `g`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.\n", - "\n", - " \"\"\"\n", - " return self.__hedges_g\n", - "\n", - " @property\n", - " def cliffs_delta(self):\n", - " \"\"\"\n", - " Returns an :py:class:`EffectSizeDataFrame` for Cliff's delta, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.\n", - "\n", - " \"\"\"\n", - " return self.__cliffs_delta\n", - "\n", - "\n", - " @property\n", - " def delta_g(self):\n", - " \"\"\"\n", - " Returns an :py:class:`EffectSizeDataFrame` for delta g, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.\n", - " \"\"\"\n", - " raise DeprecationWarning(\"delta_g has been depreciated - Please use hedges_g (with delta2=True) for delta g experiments\")\n", - "\n", - "\n", - " @property\n", - " def input_data(self):\n", - " \"\"\"\n", - " Returns the pandas DataFrame that was passed to `dabest.load()`.\n", - " When `delta2` is True, a new column is added to support the\n", - " function. The name of this new column is indicated by `x`.\n", - " \"\"\"\n", - " return self.__input_data\n", - "\n", - " @property\n", - " def idx(self):\n", - " \"\"\"\n", - " Returns the order of categories that was passed to `dabest.load()`.\n", - " \"\"\"\n", - " return self.__idx\n", - "\n", - " @property\n", - " def x1(self):\n", - " \"\"\"\n", - " Returns the first variable declared in x when it is a delta-delta\n", - " case; returns None otherwise.\n", - " \"\"\"\n", - " return self.__x1\n", - "\n", - " @property\n", - " def x1_level(self):\n", - " \"\"\"\n", - " Returns the levels of first variable declared in x when it is a\n", - " delta-delta case; returns None otherwise.\n", - " \"\"\"\n", - " return self.__x1_level\n", - "\n", - " @property\n", - " def x2(self):\n", - " \"\"\"\n", - " Returns the second variable declared in x when it is a delta-delta\n", - " case; returns None otherwise.\n", - " \"\"\"\n", - " return self.__x2\n", - "\n", - " @property\n", - " def experiment(self):\n", - " \"\"\"\n", - " Returns the column name of experiment labels that was passed to\n", - " `dabest.load()` when it is a delta-delta case; returns None otherwise.\n", - " \"\"\"\n", - " return self.__experiment\n", - "\n", - " @property\n", - " def experiment_label(self):\n", - " \"\"\"\n", - " Returns the experiment labels in order that was passed to `dabest.load()`\n", - " when it is a delta-delta case; returns None otherwise.\n", - " \"\"\"\n", - " return self.__experiment_label\n", - "\n", - " @property\n", - " def delta2(self):\n", - " \"\"\"\n", - " Returns the boolean parameter indicating if this is a delta-delta\n", - " situation.\n", - " \"\"\"\n", - " return self.__delta2\n", - " \n", - " @property\n", - " def is_delta_delta(self):\n", - " \"\"\"\n", - " Returns the boolean parameter indicating if this is a delta-delta\n", - " situation.\n", - " \"\"\"\n", - " return self.__delta2\n", - "\n", - " @property\n", - " def is_paired(self):\n", - " \"\"\"\n", - " Returns the type of repeated-measures experiment.\n", - " \"\"\"\n", - " return self.__is_paired\n", - "\n", - " @property\n", - " def id_col(self):\n", - " \"\"\"\n", - " Returns the id column declared to `dabest.load()`.\n", - " \"\"\"\n", - " return self.__id_col\n", - "\n", - " @property\n", - " def ci(self):\n", - " \"\"\"\n", - " The width of the desired confidence interval.\n", - " \"\"\"\n", - " return self.__ci\n", - "\n", - " @property\n", - " def resamples(self):\n", - " \"\"\"\n", - " The number of resamples used to generate the bootstrap.\n", - " \"\"\"\n", - " return self.__resamples\n", - "\n", - " @property\n", - " def random_seed(self):\n", - " \"\"\"\n", - " The number used to initialise the numpy random seed generator, ie.\n", - " `seed_value` from `numpy.random.seed(seed_value)` is returned.\n", - " \"\"\"\n", - " return self.__random_seed\n", - "\n", - " @property\n", - " def x(self):\n", - " \"\"\"\n", - " Returns the x column that was passed to `dabest.load()`, if any.\n", - " When `delta2` is True, `x` returns the name of the new column created\n", - " for the delta-delta situation. To retrieve the 2 variables passed into\n", - " `x` when `delta2` is True, please call `x1` and `x2` instead.\n", - " \"\"\"\n", - " return self.__x\n", - "\n", - " @property\n", - " def y(self):\n", - " \"\"\"\n", - " Returns the y column that was passed to `dabest.load()`, if any.\n", - " \"\"\"\n", - " return self.__y\n", - "\n", - " @property\n", - " def _xvar(self):\n", - " \"\"\"\n", - " Returns the xvar in dabest.plot_data.\n", - " \"\"\"\n", - " return self.__xvar\n", - "\n", - " @property\n", - " def _yvar(self):\n", - " \"\"\"\n", - " Returns the yvar in dabest.plot_data.\n", - " \"\"\"\n", - " return self.__yvar\n", - "\n", - " @property\n", - " def _plot_data(self):\n", - " \"\"\"\n", - " Returns the pandas DataFrame used to produce the estimation stats/plots.\n", - " \"\"\"\n", - " return self.__plot_data\n", - "\n", - " @property\n", - " def is_proportional(self):\n", - " \"\"\"\n", - " Returns the proportional parameter class.\n", - " \"\"\"\n", - " return self.__is_proportional\n", - "\n", - " @property\n", - " def is_mini_meta(self):\n", - " \"\"\"\n", - " Returns the mini_meta boolean parameter.\n", - " \"\"\"\n", - " return self.__is_mini_meta\n", - "\n", - " @property\n", - " def _all_plot_groups(self):\n", - " \"\"\"\n", - " Returns the all plot groups, as indicated via the `idx` keyword.\n", - " \"\"\"\n", - " return self.__all_plot_groups\n", - "\n", - " def _check_errors(self, x, y, idx, experiment, experiment_label, x1_level):\n", - " '''\n", - " Function to check some input parameters and combinations between them.\n", - " At the end of this function these two class attributes are updated\n", - " self.__experiment_label and self.__x1_level\n", - " '''\n", - "\n", - " # Check if idx is present (if not a 2x2 Anova case)\n", - " if idx is None:\n", - " if not self.__delta2:\n", - " err0 = \"Please specify `idx`.\"\n", - " raise ValueError(err0)\n", - "\n", - " # Check if it is a valid mini_meta case\n", - " if self.__is_mini_meta:\n", - " # Only mini_meta calculation but not proportional and delta-delta function\n", - " if self.__is_proportional:\n", - " err0 = \"`proportional` and `mini_meta` cannot be True at the same time.\"\n", - " raise ValueError(err0)\n", - " if self.__delta2:\n", - " err0 = \"`delta2` and `mini_meta` cannot be True at the same time.\"\n", - " raise ValueError(err0)\n", - "\n", - " # Check if the columns stated are valid\n", - " # Initialize a flag to track if any element in idx is neither str nor (tuple, list)\n", - " valid_types = True\n", - "\n", - " # Initialize variables to track the conditions for str and (tuple, list)\n", - " is_str_condition_met, is_tuple_list_condition_met = False, False\n", - "\n", - " # Single traversal for optimization\n", - " for item in idx:\n", - " if isinstance(item, str):\n", - " is_str_condition_met = True\n", - " elif isinstance(item, (tuple, list)) and len(item) == 2:\n", - " is_tuple_list_condition_met = True\n", - " else:\n", - " valid_types = False\n", - " break # Exit the loop if an invalid type is found\n", - "\n", - " # Check if all types are valid\n", - " if not valid_types:\n", - " err0 = \"`mini_meta` is True, but `idx` ({})\".format(idx)\n", - " err1 = \"does not contain exactly 2 unique columns.\"\n", - " raise ValueError(err0 + err1)\n", - "\n", - " # Handling str type condition\n", - " if is_str_condition_met:\n", - " if len(np.unique(idx).tolist()) != 2:\n", - " err0 = \"`mini_meta` is True, but `idx` ({})\".format(idx)\n", - " err1 = \"does not contain exactly 2 unique columns.\"\n", - " raise ValueError(err0 + err1)\n", - "\n", - " # Handling (tuple, list) type condition\n", - " if is_tuple_list_condition_met:\n", - " all_idx_lengths = [len(t) for t in idx]\n", - " if (array(all_idx_lengths) != 2).any():\n", - " err1 = \"`mini_meta` is True, but some elements in idx \"\n", - " err2 = \"in {} do not consist only of two groups.\".format(idx)\n", - " raise ValueError(err1 + err2)\n", - "\n", - "\n", - " # Check if this is a 2x2 ANOVA case and x & y are valid columns\n", - " # Create experiment_label and x1_level\n", - " elif self.__delta2:\n", - " if x is None:\n", - " error_msg = \"If `delta2` is True. `x` parameter cannot be None. String or list expected\"\n", - " raise ValueError(error_msg)\n", - "\n", - " # idx should not be specified\n", - " if idx:\n", - " err0 = \"`idx` should not be specified when `delta2` is True.\".format(\n", - " len(x)\n", - " )\n", - " raise ValueError(err0)\n", - "\n", - " # Check if x is valid\n", - " if len(x) != 2:\n", - " err0 = \"`delta2` is True but the number of variables indicated by `x` is {}.\".format(\n", - " len(x)\n", - " )\n", - " raise ValueError(err0)\n", - "\n", - " for i in x:\n", - " if i not in self.__output_data.columns:\n", - " err = \"{0} is not a column in `data`. Please check.\".format(i)\n", - " raise IndexError(err)\n", - "\n", - " # Check if y is valid\n", - " if not y:\n", - " err0 = \"`delta2` is True but `y` is not indicated.\"\n", - " raise ValueError(err0)\n", - "\n", - " if y not in self.__output_data.columns:\n", - " err = \"{0} is not a column in `data`. Please check.\".format(y)\n", - " raise IndexError(err)\n", - "\n", - " # Check if experiment is valid\n", - " if experiment not in self.__output_data.columns:\n", - " err = \"{0} is not a column in `data`. Please check.\".format(experiment)\n", - " raise IndexError(err)\n", - "\n", - " # Check if experiment_label is valid and create experiment when needed\n", - " if experiment_label:\n", - " if len(experiment_label) != 2:\n", - " err0 = \"`experiment_label` does not have a length of 2.\"\n", - " raise ValueError(err0)\n", - "\n", - " for i in experiment_label:\n", - " if i not in self.__output_data[experiment].unique():\n", - " err = \"{0} is not an element in the column `{1}` of `data`. Please check.\".format(\n", - " i, experiment\n", - " )\n", - " raise IndexError(err)\n", - " else:\n", - " experiment_label = self.__output_data[experiment].unique()\n", - "\n", - " # Check if x1_level is valid\n", - " if x1_level:\n", - " if len(x1_level) != 2:\n", - " err0 = \"`x1_level` does not have a length of 2.\"\n", - " raise ValueError(err0)\n", - "\n", - " for i in x1_level:\n", - " if i not in self.__output_data[x[0]].unique():\n", - " err = \"{0} is not an element in the column `{1}` of `data`. Please check.\".format(\n", - " i, experiment\n", - " )\n", - " raise IndexError(err)\n", - " else:\n", - " x1_level = self.__output_data[x[0]].unique()\n", - "\n", - " elif experiment:\n", - " experiment_label = self.__output_data[experiment].unique()\n", - " x1_level = self.__output_data[x[0]].unique()\n", - " self.__experiment_label = experiment_label\n", - " self.__x1_level = x1_level\n", - "\n", - " if self.__is_paired and self.__output_data.isnull().values.any():\n", - " warn1 = f\"NaN values detected under paired setting,\"\n", - " warn2 = f\" please check your data.\"\n", - " warnings.warn(warn1 + warn2)\n", - " if x is not None and y is not None:\n", - " rmname = self.__output_data[self.__output_data[y].isnull()][self.__id_col].tolist()\n", - " self.__output_data = self.__output_data[~self.__output_data[self.__id_col].isin(rmname)]\n", - "\n", - " # Check if there is a typo on paired\n", - " if self.__is_paired and self.__is_paired not in (\"baseline\", \"sequential\"):\n", - " err = \"'{}' assigned for `paired` is not valid. Please use either 'baseline' or 'sequential'.\".format(self.__is_paired)\n", - " raise ValueError(err)\n", - " \n", - " # Check if `id_col` is valid\n", - " if self.__is_paired:\n", - " if self.__id_col is None:\n", - " err = \"`id_col` must be specified if `paired` is assigned with a not NoneType value.\"\n", - " raise IndexError(err)\n", - "\n", - " if self.__id_col not in self.__output_data.columns:\n", - " err = \"`id_col` was given as '{}'; however, '{}' is not a column in `data`.\".format(self.__id_col, self.__id_col)\n", - " raise IndexError(err)\n", - " \n", - " # Check if x and y are supplied (relevant to long format data)\n", - " if x is None and y is not None:\n", - " err = \"You have only specified `y`. Please also specify `x` (for long format data).\"\n", - " raise ValueError(err)\n", - "\n", - " if x is not None and y is None:\n", - " err = \"You have only specified `x`. Please also specify `y` (for long format data).\"\n", - " raise ValueError(err)\n", - " \n", - " if x is not None and y is not None:\n", - " # Assume we have a long dataset.\n", - " # check both x and y are column names in data.\n", - " if not self.__delta2:\n", - " if x not in self.__output_data.columns:\n", - " err = \"'{0}' is not a column in `data`. Please check.\".format(x)\n", - " raise IndexError(err)\n", - " if y not in self.__output_data.columns:\n", - " err = \"'{0}' is not a column in `data`. Please check.\".format(y)\n", - " raise IndexError(err)\n", - " # Check that the `y` column is numeric.\n", - " if not issubdtype(self.__output_data[y].dtype, number):\n", - " err = \"The `y` column in `data` is not numeric. Please check.\"\n", - " raise ValueError(err)\n", - "\n", - "\n", - " def _get_plot_data(self, x, y, all_plot_groups):\n", - " # def _get_plot_data(self, x, y):\n", - " \"\"\"\n", - " Function to prepare some attributes for plotting\n", - " \"\"\"\n", - " # all_plot_groups = self.__all_plot_groups\n", - " # Identify the type of data that was passed in.\n", - " if x is not None and y is not None:\n", - " # Assume we have a long dataset.\n", - " # check all the idx can be found in self.__output_data[x]\n", - " for g in all_plot_groups:\n", - " if g not in self.__output_data[x].unique():\n", - " err0 = '\"{0}\" is not a group in the column `{1}`.'.format(g, x)\n", - " err1 = \" Please check `idx` and try again.\"\n", - " raise IndexError(err0 + err1)\n", - "\n", - " # Select only rows where the value in the `x` column\n", - " # is found in `idx`.\n", - " plot_data = self.__output_data[\n", - " self.__output_data.loc[:, x].isin(all_plot_groups)\n", - " ].copy()\n", - "\n", - " # Assign attributes\n", - " self.__x = x\n", - " self.__y = y\n", - " self.__xvar = x\n", - " self.__yvar = y\n", - "\n", - " elif x is None and y is None:\n", - " # Assume we have a wide dataset.\n", - " # Assign attributes appropriately.\n", - " self.__x = None\n", - " self.__y = None\n", - " self.__xvar = \"group\"\n", - " self.__yvar = \"Value\"\n", - "\n", - " # First, check we have all columns in the dataset.\n", - " for g in all_plot_groups:\n", - " if g not in self.__output_data.columns:\n", - " err0 = '\"{0}\" is not a column in `data`.'.format(g)\n", - " err1 = \" Please check `idx` and try again.\"\n", - " raise IndexError(err0 + err1)\n", - "\n", - " set_all_columns = set(self.__output_data.columns.tolist())\n", - " set_all_plot_groups = set(all_plot_groups)\n", - " id_vars = set_all_columns.difference(set_all_plot_groups)\n", - "\n", - " plot_data = pd.melt(\n", - " self.__output_data,\n", - " id_vars=id_vars,\n", - " value_vars=all_plot_groups,\n", - " value_name=self.__yvar,\n", - " var_name=self.__xvar,\n", - " )\n", - "\n", - " # Added in v0.2.7.\n", - " plot_data.dropna(axis=0, how=\"any\", subset=[self.__yvar], inplace=True)\n", - "\n", - "\n", - " if isinstance(plot_data[self.__xvar].dtype, pd.CategoricalDtype):\n", - " plot_data[self.__xvar].cat.remove_unused_categories()\n", - " plot_data[self.__xvar].cat.reorder_categories(\n", - " all_plot_groups, ordered=True\n", - " )\n", - " else:\n", - " plot_data[self.__xvar] = pd.Categorical(\n", - " plot_data[self.__xvar], categories=all_plot_groups, ordered=True\n", - " )\n", - "\n", - " return plot_data\n", - "\n", - " def _compute_effectsize_dfs(self):\n", - " '''\n", - " Function to compute all attributes based on EffectSizeDataFrame.\n", - " It returns nothing.\n", - " '''\n", - " from ._effsize_objects import EffectSizeDataFrame\n", - "\n", - " effectsize_df_kwargs = dict(\n", - " ci=self.__ci,\n", - " is_paired=self.__is_paired,\n", - " random_seed=self.__random_seed,\n", - " resamples=self.__resamples,\n", - " proportional=self.__is_proportional,\n", - " delta2=self.__delta2,\n", - " experiment_label=self.__experiment_label,\n", - " x1_level=self.__x1_level,\n", - " x2=self.__x2,\n", - " mini_meta=self.__is_mini_meta,\n", - " ps_adjust=self.__ps_adjust,\n", - " )\n", - "\n", - " self.__mean_diff = EffectSizeDataFrame(\n", - " self, \"mean_diff\", **effectsize_df_kwargs\n", - " )\n", - "\n", - " self.__median_diff = EffectSizeDataFrame(\n", - " self, \"median_diff\", **effectsize_df_kwargs\n", - " )\n", - "\n", - " self.__cohens_d = EffectSizeDataFrame(self, \"cohens_d\", **effectsize_df_kwargs)\n", - "\n", - " self.__cohens_h = EffectSizeDataFrame(self, \"cohens_h\", **effectsize_df_kwargs)\n", - "\n", - " self.__hedges_g = EffectSizeDataFrame(self, \"hedges_g\", **effectsize_df_kwargs)\n", - "\n", - " if not self.__is_paired:\n", - " self.__cliffs_delta = EffectSizeDataFrame(\n", - " self, \"cliffs_delta\", **effectsize_df_kwargs\n", - " )\n", - " else:\n", - " self.__cliffs_delta = (\n", - " \"The data is paired; Cliff's delta is therefore undefined.\"\n", - " )" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "c86c0487", - "metadata": {}, - "source": [ - "#### Example: mean_diff" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6d07d58b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.03.27\n", - "==================\n", - " \n", - "Good morning!\n", - "The current time is Tue Mar 25 10:08:38 2025.\n", - "\n", - "The unpaired mean difference between control and test is 0.5 [95%CI 0.00172, 1.04].\n", - "The p-value of the two-sided permutation t-test is 0.0758, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "control = norm.rvs(loc=0, size=30, random_state=12345)\n", - "test = norm.rvs(loc=0.5, size=30, random_state=12345)\n", - "my_df = pd.DataFrame({\"control\": control,\n", - " \"test\": test})\n", - "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", - "my_dabest_object.mean_diff" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "cf5ca0a0", - "metadata": {}, - "source": [ - "This is simply the mean of the control group subtracted from\n", - "the mean of the test group.\n", - "\n", - "$$\\text{Mean difference} = \\overline{x}_{Test} - \\overline{x}_{Control}$$\n", - "\n", - "where $\\overline{x}$ is the mean for the group $x$." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "8b3b146c", - "metadata": {}, - "source": [ - "#### Example: median_diff" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8e9b8635", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathananns/GitHub/DABEST-python/dabest/_stats_tools/effsize.py:82: UserWarning: Using median as the statistic in bootstrapping may result in a biased estimate and cause problems with BCa confidence intervals. Consider using a different statistic, such as the mean.\n", - "When plotting, please consider using percetile confidence intervals by specifying `ci_type='pct'`. For detailed information, refer to https://github.com/ACCLAB/DABEST-python/issues/129 \n", - "\n", - " warnings.warn(message=mes1+mes2, category=UserWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "DABEST v2025.03.27\n", - "==================\n", - " \n", - "Good morning!\n", - "The current time is Tue Mar 25 10:08:39 2025.\n", - "\n", - "The unpaired median difference between control and test is 0.5 [95%CI -0.0401, 1.04].\n", - "The p-value of the two-sided permutation t-test is 0.103, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.median_diff.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "control = norm.rvs(loc=0, size=30, random_state=12345)\n", - "test = norm.rvs(loc=0.5, size=30, random_state=12345)\n", - "my_df = pd.DataFrame({\"control\": control,\n", - " \"test\": test})\n", - "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", - "my_dabest_object.median_diff" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "838b2978", - "metadata": {}, - "source": [ - "\n", - "This is the median difference between the control group and the test group.\n", - "\n", - "If the comparison(s) are unpaired, median_diff is computed with the following equation:\n", - "\n", - "\n", - "$$\\text{Median difference} = \\widetilde{x}_{Test} - \\widetilde{x}_{Control}$$\n", - "\n", - "where $\\widetilde{x}$ is the median for the group $x$.\n", - "\n", - "If the comparison(s) are paired, median_diff is computed with the following equation:\n", - "\n", - "$$\\text{Median difference} = \\widetilde{x}_{Test - Control}$$\n", - " \n", - "\n", - "##### Things to note\n", - "\n", - "Using median difference as the statistic in bootstrapping may result in a biased estimate and cause problems with BCa confidence intervals. Consider using mean difference instead. \n", - "\n", - "When plotting, consider using percentile confidence intervals instead of BCa confidence intervals by specifying `ci_type = 'percentile'` in .plot(). \n", - "\n", - "For detailed information, please refer to [Issue 129](https://github.com/ACCLAB/DABEST-python/issues/129). \n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "a5324d21", - "metadata": {}, - "source": [ - "#### Example: cohens_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "748b5c60", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.03.27\n", - "==================\n", - " \n", - "Good morning!\n", - "The current time is Tue Mar 25 10:08:39 2025.\n", - "\n", - "The unpaired Cohen's d between control and test is 0.471 [95%CI -0.0405, 0.973].\n", - "The p-value of the two-sided permutation t-test is 0.0758, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.cohens_d.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "control = norm.rvs(loc=0, size=30, random_state=12345)\n", - "test = norm.rvs(loc=0.5, size=30, random_state=12345)\n", - "my_df = pd.DataFrame({\"control\": control,\n", - " \"test\": test})\n", - "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", - "my_dabest_object.cohens_d" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6f66579c", - "metadata": {}, - "source": [ - "\n", - "Cohen's `d` is simply the mean of the control group subtracted from\n", - "the mean of the test group.\n", - "\n", - "If `paired` is None, then the comparison(s) are unpaired; \n", - "otherwise the comparison(s) are paired.\n", - "\n", - "If the comparison(s) are unpaired, Cohen's `d` is computed with the following equation:\n", - "\n", - "\n", - "$$d = \\frac{\\overline{x}_{Test} - \\overline{x}_{Control}} {\\text{pooled standard deviation}}$$\n", - "\n", - "\n", - "For paired comparisons, Cohen's d is given by\n", - "\n", - "$$d = \\frac{\\overline{x}_{Test} - \\overline{x}_{Control}} {\\text{average standard deviation}}$$\n", - "\n", - "where $\\overline{x}$ is the mean of the respective group of observations, ${Var}_{x}$ denotes the variance of that group,\n", - "\n", - "\n", - "$$\\text{pooled standard deviation} = \\sqrt{ \\frac{(n_{control} - 1) * {Var}_{control} + (n_{test} - 1) * {Var}_{test} } {n_{control} + n_{test} - 2} }$$\n", - "\n", - "and\n", - "\n", - "\n", - "$$\\text{average standard deviation} = \\sqrt{ \\frac{{Var}_{control} + {Var}_{test}} {2}}$$\n", - "\n", - "The sample variance (and standard deviation) uses N-1 degrees of freedoms.\n", - "This is an application of [Bessel's correction](https://en.wikipedia.org/wiki/Bessel%27s_correction), and yields the unbiased sample variance.\n", - "\n", - "References:\n", - "\n", - "\n", - " \n", - "\n", - " \n", - "" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "40f4eff9", - "metadata": {}, - "source": [ - "#### Example: cohens_h" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f713781c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.03.27\n", - "==================\n", - " \n", - "Good morning!\n", - "The current time is Tue Mar 25 10:08:41 2025.\n", - "\n", - "The unpaired Cohen's h between control and test is 0.0 [95%CI -0.563, 0.474].\n", - "The p-value of the two-sided permutation t-test is 0.799, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.cohens_h.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "control = randint.rvs(0, 2, size=30, random_state=12345)\n", - "test = randint.rvs(0, 2, size=30, random_state=12345)\n", - "my_df = pd.DataFrame({\"control\": control,\n", - " \"test\": test})\n", - "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", - "my_dabest_object.cohens_h" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "9e3e57bd", - "metadata": {}, - "source": [ - "Cohen's *h* uses the information of proportion in the control and test groups to calculate the distance between two proportions.\n", - "\n", - "It can be used to describe the difference between two proportions as \"small\", \"medium\", or \"large\".\n", - "\n", - "It can be used to determine if the difference between two proportions is \"meaningful\".\n", - "\n", - "A directional Cohen's *h* is computed with the following equation:\n", - "\n", - "\n", - "$$h = 2 * \\arcsin{\\sqrt{proportion_{Test}}} - 2 * \\arcsin{\\sqrt{proportion_{Control}}}$$\n", - "\n", - "For a non-directional Cohen's *h*, the equation is:\n", - "\n", - "$$h = |2 * \\arcsin{\\sqrt{proportion_{Test}}} - 2 * \\arcsin{\\sqrt{proportion_{Control}}}|$$\n", - "\n", - "References:\n", - "\n", - "" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "970fb3b2", - "metadata": {}, - "source": [ - "#### Example: hedges_g" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "26960f9e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.03.27\n", - "==================\n", - " \n", - "Good morning!\n", - "The current time is Tue Mar 25 10:08:41 2025.\n", - "\n", - "The unpaired Hedges' g between control and test is 0.465 [95%CI -0.04, 0.96].\n", - "The p-value of the two-sided permutation t-test is 0.0758, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.hedges_g.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "control = norm.rvs(loc=0, size=30, random_state=12345)\n", - "test = norm.rvs(loc=0.5, size=30, random_state=12345)\n", - "my_df = pd.DataFrame({\"control\": control,\n", - " \"test\": test})\n", - "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", - "my_dabest_object.hedges_g" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "66c8a83a", - "metadata": {}, - "source": [ - "Hedges' `g` is `cohens_d` corrected for bias via multiplication with the following correction factor:\n", - " \n", - "$$\\frac{ \\Gamma( \\frac{a} {2} )} {\\sqrt{ \\frac{a} {2} } \\times \\Gamma( \\frac{a - 1} {2} )}$$\n", - "\n", - "where\n", - "\n", - "$$a = {n}_{control} + {n}_{test} - 2$$\n", - "\n", - "and $\\Gamma(x)$ is the [Gamma function](https://en.wikipedia.org/wiki/Gamma_function).\n", - "\n", - "\n", - "\n", - "References:\n", - "\n", - "\n", - " \n", - "" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "b1cf0080", - "metadata": {}, - "source": [ - "#### Example: cliffs_delta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dce86c76", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.03.27\n", - "==================\n", - " \n", - "Good morning!\n", - "The current time is Tue Mar 25 10:08:41 2025.\n", - "\n", - "The unpaired Cliff's delta between control and test is 0.28 [95%CI -0.0111, 0.544].\n", - "The p-value of the two-sided permutation t-test is 0.061, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.cliffs_delta.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "control = norm.rvs(loc=0, size=30, random_state=12345)\n", - "test = norm.rvs(loc=0.5, size=30, random_state=12345)\n", - "my_df = pd.DataFrame({\"control\": control,\n", - " \"test\": test})\n", - "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", - "my_dabest_object.cliffs_delta" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "9661ab37", - "metadata": {}, - "source": [ - "Cliff's delta is a measure of ordinal dominance, ie. how often the values from the test sample are larger than values from the control sample.\n", - "\n", - "$$\\text{Cliff's delta} = \\frac{\\#({x}_{test} > {x}_{control}) - \\#({x}_{test} < {x}_{control})} {{n}_{Test} \\times {n}_{Control}}$$\n", - " \n", - " \n", - "where $\\#$ denotes the number of times a value from the test sample exceeds (or is lesser than) values in the control sample. \n", - " \n", - "Cliff's delta ranges from -1 to 1; it can also be thought of as a measure of the degree of overlap between the two samples. An attractive aspect of this effect size is that it does not make an assumptions about the underlying distributions that the samples were drawn from. \n", - "\n", - "References:\n", - "\n", - "\n", - " \n", - "" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "bd341f7c", - "metadata": {}, - "source": [ - "#### Example: delta_g via hedges_g" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9abb53c1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.03.27\n", - "==================\n", - " \n", - "Good morning!\n", - "The current time is Tue Mar 25 10:08:42 2025.\n", - "\n", - "The unpaired Hedges' g between W Placebo and M Placebo is 1.74 [95%CI 1.09, 2.33].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "The unpaired Hedges' g between W Drug and M Drug is 1.33 [95%CI 0.632, 1.98].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "The delta g between Placebo and Drug is -0.651 [95%CI -1.53, 0.21].\n", - "The p-value of the two-sided permutation t-test is 0.0694, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing the effect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.hedges_g.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "random.seed(12345) # Fix the seed so the results are replicable.\n", - "N=20\n", - "y = norm.rvs(loc=3, scale=0.4, size=N*4)\n", - "y[N:2*N] = y[N:2*N]+1\n", - "y[2*N:3*N] = y[2*N:3*N]-0.5\n", - "t1 = repeat('Placebo', N*2).tolist()\n", - "t2 = repeat('Drug', N*2).tolist()\n", - "treatment = t1 + t2\n", - "rep = []\n", - "for i in range(N*2):\n", - " rep.append('Rep1')\n", - " rep.append('Rep2')\n", - "wt = repeat('W', N).tolist()\n", - "mt = repeat('M', N).tolist()\n", - "wt2 = repeat('W', N).tolist()\n", - "mt2 = repeat('M', N).tolist()\n", - "genotype = wt + mt + wt2 + mt2\n", - "id = list(range(0, N*2))\n", - "id_col = id + id\n", - "df_delta2 = pd.DataFrame({'ID' : id_col,\n", - " 'Rep' : rep,\n", - " 'Genotype' : genotype,\n", - " 'Treatment': treatment,\n", - " 'Y' : y})\n", - "unpaired_delta2 = dabest.load(data = df_delta2, x = [\"Genotype\", \"Genotype\"], y = \"Y\", delta2 = True, experiment = \"Treatment\")\n", - "unpaired_delta2.hedges_g" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "8d41dad3", - "metadata": {}, - "source": [ - "Delta g is an effect size that only applied on experiments with a 2-by-2 arrangement where two independent variables, A and B, each have two categorical values, 1 and 2, which calculates `hedges_g` for delta-delta statistics.\n", - "\n", - "\n", - " $$\\Delta_{1} = \\overline{X}_{A_{2}, B_{1}} - \\overline{X}_{A_{1}, B_{1}}$$\n", - "\n", - " $$\\Delta_{2} = \\overline{X}_{A_{2}, B_{2}} - \\overline{X}_{A_{1}, B_{2}}$$\n", - "\n", - "\n", - "where $\\overline{X}_{A_{i}, B_{j}}$ is the mean of the sample with A = i and B = j, $\\Delta$ is the mean difference between two samples.\n", - "\n", - "A delta-delta value is then calculated as the mean difference between the two primary deltas:\n", - "\n", - "$$\\Delta_{\\Delta} = \\Delta_{2} - \\Delta_{1}$$\n", - "\n", - "and the standard deviation of the delta-delta value is calculated from a pooled variance of the 4 samples:\n", - "\n", - "\n", - "$$s_{\\Delta_{\\Delta}} = \\sqrt{\\frac{(n_{A_{2}, B_{1}}-1)s_{A_{2}, B_{1}}^2+(n_{A_{1}, B_{1}}-1)s_{A_{1}, B_{1}}^2+(n_{A_{2}, B_{2}}-1)s_{A_{2}, B_{2}}^2+(n_{A_{1}, B_{2}}-1)s_{A_{1}, B_{2}}^2}{(n_{A_{2}, B_{1}} - 1) + (n_{A_{1}, B_{1}} - 1) + (n_{A_{2}, B_{2}} - 1) + (n_{A_{1}, B_{2}} - 1)}}$$\n", - "\n", - "where $s$ is the standard deviation and $n$ is the sample size.\n", - "\n", - "A delta g value is then calculated as delta-delta value divided by pooled standard deviation $s_{\\Delta_{\\Delta}}$:\n", - "\n", - "\n", - "$\\Delta_{g} = \\frac{\\Delta_{\\Delta}}{s_{\\Delta_{\\Delta}}}$" - ] - }, - { - "cell_type": "markdown", - "id": "33b5fc3c", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/API/delta_objects.ipynb b/nbs/API/delta_objects.ipynb deleted file mode 100644 index 91656b16..00000000 --- a/nbs/API/delta_objects.ipynb +++ /dev/null @@ -1,1177 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "3bce2f06", - "metadata": {}, - "source": [ - "# Delta objects\n", - "\n", - "> Auxiliary delta classes for estimating statistics and generating plots.\n", - "\n", - "- order: 9" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "639a4774", - "metadata": {}, - "outputs": [], - "source": [ - "#| default_exp _delta_objects" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e883312c", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from __future__ import annotations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aa0262b3", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from nbdev.showdoc import *\n", - "import nbdev\n", - "nbdev.nbdev_export()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fae983d8", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maiyi\\anaconda3\\Lib\\site-packages\\pandas\\core\\arrays\\masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n", - " from pandas.core import (\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pre-compiling numba functions for DABEST...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Compiling numba functions: 100%|███████████████████████████████████████████████████████| 11/11 [01:01<00:00, 5.55s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Numba compilation complete!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "#| hide\n", - "import dabest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1814896d", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "from scipy.stats import norm\n", - "import pandas as pd\n", - "import numpy as np\n", - "from numpy import sort as npsort\n", - "from numpy import isnan\n", - "from string import Template\n", - "import warnings\n", - "import datetime as dt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1bb53e06", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "class DeltaDelta(object):\n", - " r\"\"\"\n", - " A class to compute and store the delta-delta statistics for experiments with a 2-by-2 arrangement where two independent variables, A and B, each have two categorical values, 1 and 2. The data is divided into two pairs of two groups, and a primary delta is first calculated as the mean difference between each of the pairs:\n", - "\n", - "\n", - " $$\\Delta_{1} = \\overline{X}_{A_{2}, B_{1}} - \\overline{X}_{A_{1}, B_{1}}$$\n", - "\n", - " $$\\Delta_{2} = \\overline{X}_{A_{2}, B_{2}} - \\overline{X}_{A_{1}, B_{2}}$$\n", - "\n", - "\n", - " where $\\overline{X}_{A_{i}, B_{j}}$ is the mean of the sample with A = i and B = j, $\\Delta$ is the mean difference between two samples.\n", - "\n", - " A delta-delta value is then calculated as the mean difference between the two primary deltas:\n", - "\n", - "\n", - " $$\\Delta_{\\Delta} = \\Delta_{2} - \\Delta_{1}$$\n", - "\n", - " and a delta g value is calculated as the mean difference between the two primary deltas divided by\n", - " the standard deviation of the delta-delta value, which is calculated from a pooled variance of the 4 samples:\n", - "\n", - " $$\\Delta_{g} = \\frac{\\Delta_{\\Delta}}{s_{\\Delta_{\\Delta}}}$$\n", - "\n", - " $$s_{\\Delta_{\\Delta}} = \\sqrt{\\frac{(n_{A_{2}, B_{1}}-1)s_{A_{2}, B_{1}}^2+(n_{A_{1}, B_{1}}-1)s_{A_{1}, B_{1}}^2+(n_{A_{2}, B_{2}}-1)s_{A_{2}, B_{2}}^2+(n_{A_{1}, B_{2}}-1)s_{A_{1}, B_{2}}^2}{(n_{A_{2}, B_{1}} - 1) + (n_{A_{1}, B_{1}} - 1) + (n_{A_{2}, B_{2}} - 1) + (n_{A_{1}, B_{2}} - 1)}}$$\n", - "\n", - " where $s$ is the standard deviation and $n$ is the sample size.\n", - "\n", - "\n", - " \"\"\"\n", - "\n", - " def __init__(\n", - " self, effectsizedataframe, permutation_count, bootstraps_delta_delta, ci=95\n", - " ):\n", - " from ._stats_tools import effsize as es\n", - " from ._stats_tools import confint_1group as ci1g\n", - " from ._stats_tools import confint_2group_diff as ci2g\n", - "\n", - " self.__effsizedf = effectsizedataframe.results\n", - " self.__dabest_obj = effectsizedataframe.dabest_obj\n", - " self.__ci = ci\n", - " self.__resamples = effectsizedataframe.resamples\n", - " self.__effect_size = effectsizedataframe.effect_size\n", - " self.__alpha = ci2g._compute_alpha_from_ci(ci)\n", - " self.__permutation_count = permutation_count\n", - " self.__bootstraps = np.array(self.__effsizedf[\"bootstraps\"])\n", - " self.__control = self.__dabest_obj.experiment_label[0]\n", - " self.__test = self.__dabest_obj.experiment_label[1]\n", - "\n", - " # Compute the bootstrap delta-delta or delta g and the true dela-delta based on the raw data\n", - " if self.__effect_size == \"mean_diff\":\n", - " self.__bootstraps_delta_delta = bootstraps_delta_delta[2]\n", - " self.__difference = (\n", - " self.__effsizedf[\"difference\"][1] - self.__effsizedf[\"difference\"][0]\n", - " )\n", - " else:\n", - " self.__bootstraps_delta_delta = bootstraps_delta_delta[0]\n", - " self.__difference = bootstraps_delta_delta[1]\n", - "\n", - " sorted_delta_delta = npsort(self.__bootstraps_delta_delta)\n", - "\n", - " self.__bias_correction = ci2g.compute_meandiff_bias_correction(\n", - " self.__bootstraps_delta_delta, self.__difference\n", - " )\n", - "\n", - " self.__jackknives = np.array(\n", - " ci1g.compute_1group_jackknife(self.__bootstraps_delta_delta, np.mean)\n", - " )\n", - "\n", - " self.__acceleration_value = ci2g._calc_accel(self.__jackknives)\n", - "\n", - " # Compute BCa intervals.\n", - " bca_idx_low, bca_idx_high = ci2g.compute_interval_limits(\n", - " self.__bias_correction, self.__acceleration_value, self.__resamples, ci\n", - " )\n", - "\n", - " self.__bca_interval_idx = (bca_idx_low, bca_idx_high)\n", - "\n", - " if ~isnan(bca_idx_low) and ~isnan(bca_idx_high):\n", - " self.__bca_low = sorted_delta_delta[bca_idx_low]\n", - " self.__bca_high = sorted_delta_delta[bca_idx_high]\n", - "\n", - " err1 = \"The $lim_type limit of the interval\"\n", - " err2 = \"was in the $loc 10 values.\"\n", - " err3 = \"The result should be considered unstable.\"\n", - " err_temp = Template(\" \".join([err1, err2, err3]))\n", - "\n", - " if bca_idx_low <= 10:\n", - " warnings.warn(\n", - " err_temp.substitute(lim_type=\"lower\", loc=\"bottom\"), stacklevel=1\n", - " )\n", - "\n", - " if bca_idx_high >= self.__resamples - 9:\n", - " warnings.warn(\n", - " err_temp.substitute(lim_type=\"upper\", loc=\"top\"), stacklevel=1\n", - " )\n", - "\n", - " else:\n", - " err1 = \"The $lim_type limit of the BCa interval cannot be computed.\"\n", - " err2 = \"It is set to the effect size itself.\"\n", - " err3 = \"All bootstrap values were likely all the same.\"\n", - " err_temp = Template(\" \".join([err1, err2, err3]))\n", - "\n", - " if isnan(bca_idx_low):\n", - " self.__bca_low = self.__difference\n", - " warnings.warn(err_temp.substitute(lim_type=\"lower\"), stacklevel=0)\n", - "\n", - " if isnan(bca_idx_high):\n", - " self.__bca_high = self.__difference\n", - " warnings.warn(err_temp.substitute(lim_type=\"upper\"), stacklevel=0)\n", - "\n", - " # Compute percentile intervals.\n", - " pct_idx_low = int((self.__alpha / 2) * self.__resamples)\n", - " pct_idx_high = int((1 - (self.__alpha / 2)) * self.__resamples)\n", - "\n", - " self.__pct_interval_idx = (pct_idx_low, pct_idx_high)\n", - " self.__pct_low = sorted_delta_delta[pct_idx_low]\n", - " self.__pct_high = sorted_delta_delta[pct_idx_high]\n", - "\n", - " def __permutation_test(self):\n", - " \"\"\"\n", - " Perform a permutation test and obtain the permutation p-value\n", - " based on the permutation data.\n", - " \"\"\"\n", - " self.__permutations = np.array(self.__effsizedf[\"permutations\"])\n", - "\n", - " THRESHOLD = np.abs(self.__difference)\n", - "\n", - " self.__permutations_delta_delta = np.array(\n", - " self.__permutations[1] - self.__permutations[0]\n", - " )\n", - "\n", - " count = sum(np.abs(self.__permutations_delta_delta) > THRESHOLD)\n", - " self.__pvalue_permutation = count / self.__permutation_count\n", - "\n", - " def __repr__(self, header=True, sigfig=3):\n", - " from .misc_tools import print_greeting\n", - "\n", - " first_line = {\"control\": self.__control, \"test\": self.__test}\n", - "\n", - " if self.__effect_size == \"mean_diff\":\n", - " out1 = \"The delta-delta between {control} and {test} \".format(**first_line)\n", - " else:\n", - " out1 = \"The delta g between {control} and {test} \".format(**first_line)\n", - "\n", - " base_string_fmt = \"{:.\" + str(sigfig) + \"}\"\n", - " if \".\" in str(self.__ci):\n", - " ci_width = base_string_fmt.format(self.__ci)\n", - " else:\n", - " ci_width = str(self.__ci)\n", - "\n", - " ci_out = {\n", - " \"es\": base_string_fmt.format(self.__difference),\n", - " \"ci\": ci_width,\n", - " \"bca_low\": base_string_fmt.format(self.__bca_low),\n", - " \"bca_high\": base_string_fmt.format(self.__bca_high),\n", - " }\n", - "\n", - " out2 = \"is {es} [{ci}%CI {bca_low}, {bca_high}].\".format(**ci_out)\n", - " out = out1 + out2\n", - "\n", - " if header is True:\n", - " out = print_greeting() + \"\\n\" + \"\\n\" + out\n", - "\n", - " pval_rounded = base_string_fmt.format(self.pvalue_permutation)\n", - "\n", - " p1 = \"The p-value of the two-sided permutation t-test is {}, \".format(\n", - " pval_rounded\n", - " )\n", - " p2 = \"calculated for legacy purposes only. \"\n", - " pvalue = p1 + p2\n", - "\n", - " bs1 = \"{} bootstrap samples were taken; \".format(self.__resamples)\n", - " bs2 = \"the confidence interval is bias-corrected and accelerated.\"\n", - " bs = bs1 + bs2\n", - "\n", - " pval_def1 = (\n", - " \"Any p-value reported is the probability of observing the \"\n", - " + \"effect size (or greater),\\nassuming the null hypothesis of \"\n", - " + \"zero difference is true.\"\n", - " )\n", - " pval_def2 = (\n", - " \"\\nFor each p-value, 5000 reshuffles of the \"\n", - " + \"control and test labels were performed.\"\n", - " )\n", - " pval_def = pval_def1 + pval_def2\n", - "\n", - " return \"{}\\n{}\\n\\n{}\\n{}\".format(out, pvalue, bs, pval_def)\n", - "\n", - " def to_dict(self):\n", - " \"\"\"\n", - " Returns the attributes of the `DeltaDelta` object as a\n", - " dictionary.\n", - " \"\"\"\n", - " # Only get public (user-facing) attributes.\n", - " attrs = [a for a in dir(self) if not a.startswith((\"_\", \"to_dict\", \"results\"))]\n", - " out = {}\n", - " for a in attrs:\n", - " out[a] = getattr(self, a)\n", - " return out\n", - " \n", - " def __compute_results(self):\n", - " # With some inspiration from @jungyangliao\n", - " delta_delta_results_df = pd.Series(self.to_dict()).to_frame().T\n", - "\n", - " column_index = ['control', 'test', 'difference', 'ci', 'bca_low', 'bca_high', 'bca_interval_idx', \n", - " 'pct_low', 'pct_high', 'pct_interval_idx', 'bootstraps_control', 'bootstraps_test', \n", - " 'bootstraps_delta_delta', 'permutations_control', 'permutations_test', 'permutations_delta_delta',\n", - " 'pvalue_permutation', 'permutation_count', 'bias_correction', 'jackknives'\n", - " ]\n", - " delta_delta_results_df['bootstraps_control'] = [delta_delta_results_df['bootstraps'][0][0]]\n", - " delta_delta_results_df['bootstraps_test'] = [delta_delta_results_df['bootstraps'][0][1]]\n", - " delta_delta_results_df['permutations_control'] = [delta_delta_results_df['permutations'][0][0]]\n", - " delta_delta_results_df['permutations_test'] = [delta_delta_results_df['permutations'][0][1]]\n", - " delta_delta_results_df = delta_delta_results_df.reindex(columns=column_index)\n", - "\n", - " self.__results = delta_delta_results_df\n", - " return self.__results\n", - "\n", - " @property\n", - " def ci(self):\n", - " \"\"\"\n", - " Returns the width of the confidence interval, in percent.\n", - " \"\"\"\n", - " return self.__ci\n", - "\n", - " @property\n", - " def alpha(self):\n", - " \"\"\"\n", - " Returns the significance level of the statistical test as a float\n", - " between 0 and 1.\n", - " \"\"\"\n", - " return self.__alpha\n", - "\n", - " @property\n", - " def bias_correction(self):\n", - " return self.__bias_correction\n", - "\n", - " @property\n", - " def bootstraps(self):\n", - " \"\"\"\n", - " Return the bootstrapped deltas from all the experiment groups.\n", - " \"\"\"\n", - " return self.__bootstraps\n", - "\n", - " @property\n", - " def jackknives(self):\n", - " return self.__jackknives\n", - "\n", - " @property\n", - " def acceleration_value(self):\n", - " return self.__acceleration_value\n", - "\n", - " @property\n", - " def bca_low(self):\n", - " \"\"\"\n", - " The bias-corrected and accelerated confidence interval lower limit.\n", - " \"\"\"\n", - " return self.__bca_low\n", - "\n", - " @property\n", - " def bca_high(self):\n", - " \"\"\"\n", - " The bias-corrected and accelerated confidence interval upper limit.\n", - " \"\"\"\n", - " return self.__bca_high\n", - "\n", - " @property\n", - " def bca_interval_idx(self):\n", - " return self.__bca_interval_idx\n", - "\n", - " @property\n", - " def control(self):\n", - " \"\"\"\n", - " Return the name of the control experiment group.\n", - " \"\"\"\n", - " return self.__control\n", - "\n", - " @property\n", - " def test(self):\n", - " \"\"\"\n", - " Return the name of the test experiment group.\n", - " \"\"\"\n", - " return self.__test\n", - "\n", - " @property\n", - " def bootstraps_delta_delta(self):\n", - " \"\"\"\n", - " Return the delta-delta values calculated from the bootstrapped\n", - " deltas.\n", - " \"\"\"\n", - " return self.__bootstraps_delta_delta\n", - "\n", - " @property\n", - " def difference(self):\n", - " \"\"\"\n", - " Return the delta-delta value calculated based on the raw data.\n", - " \"\"\"\n", - " return self.__difference\n", - "\n", - " @property\n", - " def pct_interval_idx(self):\n", - " return self.__pct_interval_idx\n", - "\n", - " @property\n", - " def pct_low(self):\n", - " \"\"\"\n", - " The percentile confidence interval lower limit.\n", - " \"\"\"\n", - " return self.__pct_low\n", - "\n", - " @property\n", - " def pct_high(self):\n", - " \"\"\"\n", - " The percentile confidence interval lower limit.\n", - " \"\"\"\n", - " return self.__pct_high\n", - "\n", - " @property\n", - " def pvalue_permutation(self):\n", - " try:\n", - " return self.__pvalue_permutation\n", - " except AttributeError:\n", - " self.__permutation_test()\n", - " return self.__pvalue_permutation\n", - "\n", - " @property\n", - " def permutation_count(self):\n", - " \"\"\"\n", - " The number of permuations taken.\n", - " \"\"\"\n", - " return self.__permutation_count\n", - "\n", - " @property\n", - " def permutations(self):\n", - " \"\"\"\n", - " Return the mean differences of permutations obtained during\n", - " the permutation test for each experiment group.\n", - " \"\"\"\n", - " try:\n", - " return self.__permutations\n", - " except AttributeError:\n", - " self.__permutation_test()\n", - " return self.__permutations\n", - "\n", - " @property\n", - " def permutations_delta_delta(self):\n", - " \"\"\"\n", - " Return the delta-delta values of permutations obtained\n", - " during the permutation test.\n", - " \"\"\"\n", - " try:\n", - " return self.__permutations_delta_delta\n", - " except AttributeError:\n", - " self.__permutation_test()\n", - " return self.__permutations_delta_delta\n", - " \n", - " @property\n", - " def results(self):\n", - " \"\"\"\n", - " Return the results of the delta-delta analysis.\n", - " \"\"\"\n", - " try:\n", - " return self.__results\n", - " except AttributeError:\n", - " self.__compute_results()\n", - " return self.__results" - ] - }, - { - "cell_type": "markdown", - "id": "4dc056b1", - "metadata": {}, - "source": [ - "\n", - "\n", - "and the standard deviation of the delta-delta value is calculated from a pooled variance of the 4 samples:\n", - "\n", - "\n", - "$$s_{\\Delta_{\\Delta}} = \\sqrt{\\frac{(n_{A_{2}, B_{1}}-1)s_{A_{2}, B_{1}}^2+(n_{A_{1}, B_{1}}-1)s_{A_{1}, B_{1}}^2+(n_{A_{2}, B_{2}}-1)s_{A_{2}, B_{2}}^2+(n_{A_{1}, B_{2}}-1)s_{A_{1}, B_{2}}^2}{(n_{A_{2}, B_{1}} - 1) + (n_{A_{1}, B_{1}} - 1) + (n_{A_{2}, B_{2}} - 1) + (n_{A_{1}, B_{2}} - 1)}}$$\n", - "\n", - "where $s$ is the standard deviation and $n$ is the sample size." - ] - }, - { - "cell_type": "markdown", - "id": "81decd9b", - "metadata": {}, - "source": [ - "#### Example: delta-delta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "37dacdfc", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maiyi\\anaconda3\\Lib\\site-packages\\dabest\\plot_tools.py:2537: UserWarning: 5.0% of the points cannot be placed. You might want to decrease the size of the markers.\n", - " warnings.warn(err)\n", - "C:\\Users\\maiyi\\anaconda3\\Lib\\site-packages\\dabest\\plot_tools.py:2537: UserWarning: 5.0% of the points cannot be placed. You might want to decrease the size of the markers.\n", - " warnings.warn(err)\n", - "C:\\Users\\maiyi\\anaconda3\\Lib\\site-packages\\dabest\\plot_tools.py:2537: UserWarning: 20.0% of the points cannot be placed. You might want to decrease the size of the markers.\n", - " warnings.warn(err)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "np.random.seed(9999) # Fix the seed so the results are replicable.\n", - "N = 20\n", - "# Create samples\n", - "y = norm.rvs(loc=3, scale=0.4, size=N*4)\n", - "y[N:2*N] = y[N:2*N]+1\n", - "y[2*N:3*N] = y[2*N:3*N]-0.5\n", - "# Add a `Treatment` column\n", - "t1 = np.repeat('Placebo', N*2).tolist()\n", - "t2 = np.repeat('Drug', N*2).tolist()\n", - "treatment = t1 + t2 \n", - "# Add a `Rep` column as the first variable for the 2 replicates of experiments done\n", - "rep = []\n", - "for i in range(N*2):\n", - " rep.append('Rep1')\n", - " rep.append('Rep2')\n", - "# Add a `Genotype` column as the second variable\n", - "wt = np.repeat('W', N).tolist()\n", - "mt = np.repeat('M', N).tolist()\n", - "wt2 = np.repeat('W', N).tolist()\n", - "mt2 = np.repeat('M', N).tolist()\n", - "genotype = wt + mt + wt2 + mt2\n", - "# Add an `id` column for paired data plotting.\n", - "id = list(range(0, N*2))\n", - "id_col = id + id \n", - "# Combine all columns into a DataFrame.\n", - "df_delta2 = pd.DataFrame({'ID' : id_col,\n", - " 'Rep' : rep,\n", - " 'Genotype' : genotype, \n", - " 'Treatment': treatment,\n", - " 'Y' : y\n", - " })\n", - "unpaired_delta2 = dabest.load(data = df_delta2, x = [\"Genotype\", \"Genotype\"], y = \"Y\", delta2 = True, experiment = \"Treatment\")\n", - "unpaired_delta2.mean_diff.plot();" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "18462ec5", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "class MiniMetaDelta(object):\n", - " \"\"\"\n", - " A class to compute and store the weighted delta.\n", - " A weighted delta is calculated if the argument ``mini_meta=True`` is passed during ``dabest.load()``.\n", - " \n", - " \"\"\"\n", - "\n", - " def __init__(self, effectsizedataframe, permutation_count,\n", - " ci=95):\n", - " from ._stats_tools import effsize as es\n", - " from ._stats_tools import confint_1group as ci1g\n", - " from ._stats_tools import confint_2group_diff as ci2g\n", - " \n", - " self.__effsizedf = effectsizedataframe.results\n", - " self.__dabest_obj = effectsizedataframe.dabest_obj\n", - " self.__ci = ci\n", - " self.__resamples = effectsizedataframe.resamples\n", - " self.__alpha = ci2g._compute_alpha_from_ci(ci)\n", - " self.__permutation_count = permutation_count\n", - " self.__bootstraps = np.array(self.__effsizedf[\"bootstraps\"])\n", - " self.__control = np.array(self.__effsizedf[\"control\"])\n", - " self.__test = np.array(self.__effsizedf[\"test\"])\n", - " self.__control_N = np.array(self.__effsizedf[\"control_N\"])\n", - " self.__test_N = np.array(self.__effsizedf[\"test_N\"])\n", - "\n", - "\n", - " idx = self.__dabest_obj.idx\n", - " dat = self.__dabest_obj._plot_data\n", - " xvar = self.__dabest_obj._xvar\n", - " yvar = self.__dabest_obj._yvar\n", - "\n", - " # compute the variances of each control group and each test group\n", - " control_var=[]\n", - " test_var=[]\n", - " grouped_data = {name: group[yvar].copy() for name, group in dat.groupby(xvar, observed=False)}\n", - " for j, current_tuple in enumerate(idx):\n", - " cname = current_tuple[0]\n", - " control = grouped_data[cname]\n", - " control_var.append(np.var(control, ddof=1))\n", - "\n", - " tname = current_tuple[1]\n", - " test = grouped_data[tname]\n", - " test_var.append(np.var(test, ddof=1))\n", - " self.__control_var = np.array(control_var)\n", - " self.__test_var = np.array(test_var)\n", - "\n", - " # Compute pooled group variances for each pair of experiment groups\n", - " # based on the raw data\n", - " self.__group_var = ci2g.calculate_group_var(self.__control_var, \n", - " self.__control_N,\n", - " self.__test_var, \n", - " self.__test_N)\n", - " \n", - " self.__bootstraps_variance = ci2g.calculate_bootstraps_var(self.__bootstraps)\n", - "\n", - " # Compute the weighted average mean differences of the bootstrap data\n", - " # using the pooled group variances of the raw data as the inverse of \n", - " # weights\n", - " self.__bootstraps_weighted_delta = ci2g.calculate_weighted_delta(\n", - " self.__bootstraps_variance, \n", - " self.__bootstraps)\n", - "\n", - " # Compute the weighted average mean difference based on the raw data\n", - " self.__difference = es.weighted_delta(np.array(self.__effsizedf[\"difference\"]),\n", - " self.__bootstraps_variance)\n", - "\n", - " sorted_weighted_deltas = npsort(self.__bootstraps_weighted_delta)\n", - "\n", - "\n", - " self.__bias_correction = ci2g.compute_meandiff_bias_correction(\n", - " self.__bootstraps_weighted_delta, self.__difference)\n", - " \n", - " self.__jackknives = np.array(ci1g.compute_1group_jackknife(\n", - " self.__bootstraps_weighted_delta, \n", - " np.mean))\n", - "\n", - " self.__acceleration_value = ci2g._calc_accel(self.__jackknives)\n", - "\n", - " # Compute BCa intervals.\n", - " bca_idx_low, bca_idx_high = ci2g.compute_interval_limits(\n", - " self.__bias_correction, self.__acceleration_value,\n", - " self.__resamples, ci)\n", - " \n", - " self.__bca_interval_idx = (bca_idx_low, bca_idx_high)\n", - "\n", - " if ~isnan(bca_idx_low) and ~isnan(bca_idx_high):\n", - " self.__bca_low = sorted_weighted_deltas[bca_idx_low]\n", - " self.__bca_high = sorted_weighted_deltas[bca_idx_high]\n", - "\n", - " err1 = \"The $lim_type limit of the interval\"\n", - " err2 = \"was in the $loc 10 values.\"\n", - " err3 = \"The result should be considered unstable.\"\n", - " err_temp = Template(\" \".join([err1, err2, err3]))\n", - "\n", - " if bca_idx_low <= 10:\n", - " warnings.warn(err_temp.substitute(lim_type=\"lower\",\n", - " loc=\"bottom\"),\n", - " stacklevel=1)\n", - "\n", - " if bca_idx_high >= self.__resamples-9:\n", - " warnings.warn(err_temp.substitute(lim_type=\"upper\",\n", - " loc=\"top\"),\n", - " stacklevel=1)\n", - "\n", - " else:\n", - " err1 = \"The $lim_type limit of the BCa interval cannot be computed.\"\n", - " err2 = \"It is set to the effect size itself.\"\n", - " err3 = \"All bootstrap values were likely all the same.\"\n", - " err_temp = Template(\" \".join([err1, err2, err3]))\n", - "\n", - " if isnan(bca_idx_low):\n", - " self.__bca_low = self.__difference\n", - " warnings.warn(err_temp.substitute(lim_type=\"lower\"),\n", - " stacklevel=0)\n", - "\n", - " if isnan(bca_idx_high):\n", - " self.__bca_high = self.__difference\n", - " warnings.warn(err_temp.substitute(lim_type=\"upper\"),\n", - " stacklevel=0)\n", - "\n", - " # Compute percentile intervals.\n", - " pct_idx_low = int((self.__alpha/2) * self.__resamples)\n", - " pct_idx_high = int((1-(self.__alpha/2)) * self.__resamples)\n", - "\n", - " self.__pct_interval_idx = (pct_idx_low, pct_idx_high)\n", - " self.__pct_low = sorted_weighted_deltas[pct_idx_low]\n", - " self.__pct_high = sorted_weighted_deltas[pct_idx_high]\n", - " \n", - " \n", - "\n", - " def __permutation_test(self):\n", - " \"\"\"\n", - " Perform a permutation test and obtain the permutation p-value\n", - " based on the permutation data.\n", - " \"\"\"\n", - " self.__permutations = np.array(self.__effsizedf[\"permutations\"])\n", - " self.__permutations_var = np.array(self.__effsizedf[\"permutations_var\"])\n", - "\n", - " THRESHOLD = np.abs(self.__difference)\n", - "\n", - " all_num = []\n", - " all_denom = []\n", - "\n", - " groups = len(self.__permutations)\n", - " for i in range(0, len(self.__permutations[0])):\n", - " weight = [1/self.__permutations_var[j][i] for j in range(0, groups)]\n", - " all_num.append(np.sum([weight[j]*self.__permutations[j][i] for j in range(0, groups)]))\n", - " all_denom.append(np.sum(weight))\n", - " \n", - " output=[]\n", - " for i in range(0, len(all_num)):\n", - " output.append(all_num[i]/all_denom[i])\n", - " \n", - " self.__permutations_weighted_delta = np.array(output)\n", - "\n", - " count = sum(np.abs(self.__permutations_weighted_delta)>THRESHOLD)\n", - " self.__pvalue_permutation = count/self.__permutation_count\n", - "\n", - "\n", - "\n", - " def __repr__(self, header=True, sigfig=3):\n", - " from .misc_tools import print_greeting\n", - " \n", - " is_paired = self.__dabest_obj.is_paired\n", - "\n", - " PAIRED_STATUS = {'baseline' : 'paired', \n", - " 'sequential' : 'paired',\n", - " 'None' : 'unpaired'\n", - " }\n", - "\n", - " first_line = {\"paired_status\": PAIRED_STATUS[str(is_paired)]}\n", - " \n", - "\n", - " out1 = \"The weighted-average {paired_status} mean differences \".format(**first_line)\n", - " \n", - " base_string_fmt = \"{:.\" + str(sigfig) + \"}\"\n", - " if \".\" in str(self.__ci):\n", - " ci_width = base_string_fmt.format(self.__ci)\n", - " else:\n", - " ci_width = str(self.__ci)\n", - " \n", - " ci_out = {\"es\" : base_string_fmt.format(self.__difference),\n", - " \"ci\" : ci_width,\n", - " \"bca_low\" : base_string_fmt.format(self.__bca_low),\n", - " \"bca_high\" : base_string_fmt.format(self.__bca_high)}\n", - " \n", - " out2 = \"is {es} [{ci}%CI {bca_low}, {bca_high}].\".format(**ci_out)\n", - " out = out1 + out2\n", - "\n", - " if header is True:\n", - " out = print_greeting() + \"\\n\" + \"\\n\" + out\n", - "\n", - "\n", - " pval_rounded = base_string_fmt.format(self.pvalue_permutation)\n", - "\n", - " \n", - " p1 = \"The p-value of the two-sided permutation t-test is {}, \".format(pval_rounded)\n", - " p2 = \"calculated for legacy purposes only. \"\n", - " pvalue = p1 + p2\n", - "\n", - "\n", - " bs1 = \"{} bootstrap samples were taken; \".format(self.__resamples)\n", - " bs2 = \"the confidence interval is bias-corrected and accelerated.\"\n", - " bs = bs1 + bs2\n", - "\n", - " pval_def1 = \"Any p-value reported is the probability of observing the\" + \\\n", - " \"effect size (or greater),\\nassuming the null hypothesis of \" + \\\n", - " \"zero difference is true.\"\n", - " pval_def2 = \"\\nFor each p-value, 5000 reshuffles of the \" + \\\n", - " \"control and test labels were performed.\"\n", - " pval_def = pval_def1 + pval_def2\n", - "\n", - "\n", - " return \"{}\\n{}\\n\\n{}\\n{}\".format(out, pvalue, bs, pval_def)\n", - "\n", - "\n", - " def to_dict(self):\n", - " \"\"\"\n", - " Returns all attributes of the `dabest.MiniMetaDelta` object as a\n", - " dictionary.\n", - " \"\"\"\n", - " # Only get public (user-facing) attributes.\n", - " attrs = [a for a in dir(self)\n", - " if not a.startswith((\"_\", \"to_dict\", \"results\"))]\n", - " out = {}\n", - " for a in attrs:\n", - " out[a] = getattr(self, a)\n", - " return out\n", - " \n", - "\n", - " def __compute_results(self):\n", - " # With some inspiration from @jungyangliao\n", - " \"\"\"\n", - " Returns all attributes of the `dabest.MiniMetaDelta` object as a\n", - " DataFrame.\n", - " \"\"\"\n", - " mini_meta_delta_results_df = pd.Series(self.to_dict()).to_frame().T\n", - " column_index = ['control', 'test', 'control_N', 'test_N', 'control_var', 'test_var', 'group_var',\n", - " 'difference', 'ci', 'bca_low', 'bca_high', 'bca_interval_idx', \n", - " 'pct_low', 'pct_high', 'pct_interval_idx', 'bootstraps', 'bootstraps_weighted_delta', \n", - " 'permutations', 'permutations_var', 'permutations_weighted_delta', 'pvalue_permutation', \n", - " 'permutation_count', 'bias_correction', 'jackknives']\n", - " mini_meta_delta_results_df = mini_meta_delta_results_df.reindex(columns=column_index)\n", - " mini_meta_delta_results_df.rename(columns={'bootstraps': 'bootstraps_deltas'}, inplace=True)\n", - "\n", - " self.__results = mini_meta_delta_results_df\n", - " return self.__results\n", - "\n", - "\n", - " @property\n", - " def ci(self):\n", - " \"\"\"\n", - " Returns the width of the confidence interval, in percent.\n", - " \"\"\"\n", - " return self.__ci\n", - "\n", - "\n", - " @property\n", - " def alpha(self):\n", - " \"\"\"\n", - " Returns the significance level of the statistical test as a float\n", - " between 0 and 1.\n", - " \"\"\"\n", - " return self.__alpha\n", - "\n", - "\n", - " @property\n", - " def bias_correction(self):\n", - " return self.__bias_correction\n", - "\n", - "\n", - " @property\n", - " def bootstraps(self):\n", - " '''\n", - " Return the bootstrapped differences from all the experiment groups.\n", - " '''\n", - " return self.__bootstraps\n", - "\n", - "\n", - " @property\n", - " def jackknives(self):\n", - " return self.__jackknives\n", - "\n", - "\n", - " @property\n", - " def acceleration_value(self):\n", - " return self.__acceleration_value\n", - "\n", - "\n", - " @property\n", - " def bca_low(self):\n", - " \"\"\"\n", - " The bias-corrected and accelerated confidence interval lower limit.\n", - " \"\"\"\n", - " return self.__bca_low\n", - "\n", - "\n", - " @property\n", - " def bca_high(self):\n", - " \"\"\"\n", - " The bias-corrected and accelerated confidence interval upper limit.\n", - " \"\"\"\n", - " return self.__bca_high\n", - "\n", - "\n", - " @property\n", - " def bca_interval_idx(self):\n", - " return self.__bca_interval_idx\n", - "\n", - "\n", - " @property\n", - " def control(self):\n", - " '''\n", - " Return the names of the control groups from all the experiment \n", - " groups in order.\n", - " '''\n", - " return self.__control\n", - "\n", - "\n", - " @property\n", - " def test(self):\n", - " '''\n", - " Return the names of the test groups from all the experiment \n", - " groups in order.\n", - " '''\n", - " return self.__test\n", - " \n", - " @property\n", - " def control_N(self):\n", - " '''\n", - " Return the sizes of the control groups from all the experiment \n", - " groups in order.\n", - " '''\n", - " return self.__control_N\n", - "\n", - "\n", - " @property\n", - " def test_N(self):\n", - " '''\n", - " Return the sizes of the test groups from all the experiment \n", - " groups in order.\n", - " '''\n", - " return self.__test_N\n", - "\n", - "\n", - " @property\n", - " def control_var(self):\n", - " '''\n", - " Return the estimated population variances of the control groups \n", - " from all the experiment groups in order. Here the population \n", - " variance is estimated from the sample variance. \n", - " '''\n", - " return self.__control_var\n", - "\n", - "\n", - " @property\n", - " def test_var(self):\n", - " '''\n", - " Return the estimated population variances of the control groups \n", - " from all the experiment groups in order. Here the population \n", - " variance is estimated from the sample variance. \n", - " '''\n", - " return self.__test_var\n", - "\n", - " \n", - " @property\n", - " def group_var(self):\n", - " '''\n", - " Return the pooled group variances of all the experiment groups \n", - " in order. \n", - " '''\n", - " return self.__group_var\n", - " \n", - " @property\n", - " def bootstraps_var(self):\n", - " '''\n", - " Return the variances of each bootstrapped mean difference distribution\n", - " in order. \n", - " '''\n", - " return self.__bootstraps_variance\n", - "\n", - "\n", - " @property\n", - " def bootstraps_weighted_delta(self):\n", - " '''\n", - " Return the weighted-average mean differences calculated from the bootstrapped \n", - " deltas and weights across the experiment groups, where the weights are \n", - " the inverse of the pooled group variances.\n", - " '''\n", - " return self.__bootstraps_weighted_delta\n", - "\n", - "\n", - " @property\n", - " def difference(self):\n", - " '''\n", - " Return the weighted-average delta calculated from the raw data.\n", - " '''\n", - " return self.__difference\n", - "\n", - "\n", - " @property\n", - " def pct_interval_idx (self):\n", - " return self.__pct_interval_idx \n", - "\n", - "\n", - " @property\n", - " def pct_low(self):\n", - " \"\"\"\n", - " The percentile confidence interval lower limit.\n", - " \"\"\"\n", - " return self.__pct_low\n", - "\n", - "\n", - " @property\n", - " def pct_high(self):\n", - " \"\"\"\n", - " The percentile confidence interval lower limit.\n", - " \"\"\"\n", - " return self.__pct_high\n", - "\n", - "\n", - " @property\n", - " def pvalue_permutation(self):\n", - " try:\n", - " return self.__pvalue_permutation\n", - " except AttributeError:\n", - " self.__permutation_test()\n", - " return self.__pvalue_permutation\n", - " \n", - "\n", - " @property\n", - " def permutation_count(self):\n", - " \"\"\"\n", - " The number of permuations taken.\n", - " \"\"\"\n", - " return self.__permutation_count\n", - "\n", - " \n", - " @property\n", - " def permutations(self):\n", - " '''\n", - " Return the mean differences of permutations obtained during\n", - " the permutation test for each experiment group.\n", - " '''\n", - " try:\n", - " return self.__permutations\n", - " except AttributeError:\n", - " self.__permutation_test()\n", - " return self.__permutations\n", - "\n", - "\n", - " @property\n", - " def permutations_var(self):\n", - " '''\n", - " Return the pooled group variances of permutations obtained during\n", - " the permutation test for each experiment group.\n", - " '''\n", - " try:\n", - " return self.__permutations_var\n", - " except AttributeError:\n", - " self.__permutation_test()\n", - " return self.__permutations_var\n", - "\n", - " \n", - " @property\n", - " def permutations_weighted_delta(self):\n", - " '''\n", - " Return the weighted-average deltas of permutations obtained \n", - " during the permutation test.\n", - " '''\n", - " try:\n", - " return self.__permutations_weighted_delta\n", - " except AttributeError:\n", - " self.__permutation_test()\n", - " return self.__permutations_weighted_delta\n", - "\n", - " @property\n", - " def results(self):\n", - " \"\"\"\n", - " Return the results of the mini-meta analysis.\n", - " \"\"\"\n", - " try:\n", - " return self.__results\n", - " except AttributeError:\n", - " self.__compute_results()\n", - " return self.__results" - ] - }, - { - "cell_type": "markdown", - "id": "02f5a7a2", - "metadata": {}, - "source": [ - "The weighted delta is calcuated as follows:\n", - "\n", - "$$\\theta_{\\text{weighted}} = \\frac{\\Sigma\\hat{\\theta_{i}}w_{i}}{{\\Sigma}w_{i}}$$\n", - "\n", - "where:\n", - "\n", - "$$\\hat{\\theta_{i}} = \\text{Mean difference for replicate }i$$\n", - "\n", - "\n", - "$$w_{i} = \\text{Weight for replicate }i = \\frac{1}{s_{i}^2} $$\n", - "\n", - "$$s_{i}^2 = \\text{Pooled variance for replicate }i = \\frac{(n_{test}-1)s_{test}^2+(n_{control}-1)s_{control}^2}{n_{test}+n_{control}-2}$$\n", - "\n", - "$$n = \\text{sample size and }s^2 = \\text{variance for control/test.}$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "33de49d5", - "metadata": {}, - "source": [ - "#### Example: mini-meta-delta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a7f99dff", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.03.27\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Mon Sep 1 16:03:47 2025.\n", - "\n", - "The weighted-average unpaired mean differences is 0.0336 [95%CI -0.136, 0.236].\n", - "The p-value of the two-sided permutation t-test is 0.736, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Ns = 20\n", - "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n", - "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", - "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", - "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n", - "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n", - "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n", - "my_df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n", - " 'Control 2' : c2, 'Test 2' : t2,\n", - " 'Control 3' : c3, 'Test 3' : t3})\n", - "my_dabest_object = dabest.load(my_df, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), mini_meta=True)\n", - "my_dabest_object.mean_diff.mini_meta" - ] - }, - { - "cell_type": "markdown", - "id": "419b00fb", - "metadata": {}, - "source": [ - "As of version 2023.02.14, weighted delta can only be calculated for mean difference, and not for standardized measures such as Cohen's *d*.\n", - "\n", - "Details about the calculated weighted delta are accessed as attributes of the ``mini_meta`` class. See the `minimetadelta` for details on usage.\n", - "\n", - "Refer to Chapter 10 of the Cochrane handbook for further information on meta-analysis: \n", - "https://training.cochrane.org/handbook/current/chapter-10\n", - "\t\t" - ] - }, - { - "cell_type": "markdown", - "id": "2dd1d796", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/API/effsize.ipynb b/nbs/API/effsize.ipynb deleted file mode 100644 index 475b38a9..00000000 --- a/nbs/API/effsize.ipynb +++ /dev/null @@ -1,530 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "8a807e60", - "metadata": {}, - "source": [ - "# effsize\n", - "\n", - "> A range of functions to compute various effect sizes.\n", - "\n", - "- order: 6" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "27e1432b", - "metadata": {}, - "outputs": [], - "source": [ - "#| default_exp _stats_tools/effsize" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a6673b99", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from __future__ import annotations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aa0a0c20", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from nbdev.showdoc import *\n", - "import nbdev\n", - "nbdev.nbdev_export()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9d92d449", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "from __future__ import annotations\n", - "import numpy as np\n", - "from numba import njit\n", - "import warnings\n", - "from scipy.special import gamma\n", - "from scipy.stats import mannwhitneyu\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0547f8a7", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "def two_group_difference(control:list|tuple|np.ndarray, #Accepts lists, tuples, or numpy ndarrays of numeric types.\n", - " test:list|tuple|np.ndarray, #Accepts lists, tuples, or numpy ndarrays of numeric types.\n", - " is_paired=None, #If not None, returns the paired Cohen's d\n", - " effect_size:str=\"mean_diff\" # Any one of the following effect sizes: [\"mean_diff\", \"median_diff\", \"cohens_d\", \"hedges_g\", \"cliffs_delta\"]\n", - " )->float: # The desired effect size.\n", - " \"\"\"\n", - " Computes the following metrics for control and test:\n", - " \n", - " - Unstandardized mean difference\n", - " - Standardized mean differences (paired or unpaired)\n", - " * Cohen's d\n", - " * Hedges' g\n", - " - Median difference\n", - " - Cliff's Delta\n", - " - Cohen's h (distance between two proportions)\n", - "\n", - " See the Wikipedia entry [here](https://bit.ly/2LzWokf)\n", - " \n", - " `effect_size`:\n", - " \n", - " mean_diff: This is simply the mean of `control` subtracted from\n", - " the mean of `test`.\n", - "\n", - " cohens_d: This is the mean of control subtracted from the\n", - " mean of test, divided by the pooled standard deviation\n", - " of control and test. The pooled SD is the square as:\n", - "\n", - "\n", - " (n1 - 1) * var(control) + (n2 - 1) * var(test)\n", - " sqrt ( ------------------------------------------- )\n", - " (n1 + n2 - 2)\n", - "\n", - " where n1 and n2 are the sizes of control and test\n", - " respectively.\n", - "\n", - " hedges_g: This is Cohen's d corrected for bias via multiplication\n", - " with the following correction factor:\n", - "\n", - " gamma(n/2)\n", - " J(n) = ------------------------------\n", - " sqrt(n/2) * gamma((n - 1) / 2)\n", - "\n", - " where n = (n1 + n2 - 2).\n", - "\n", - " median_diff: This is the median of `control` subtracted from the\n", - " median of `test`.\n", - "\n", - " \"\"\"\n", - "\n", - " if not isinstance(control, np.ndarray):\n", - " control = np.array(control)\n", - " if not isinstance(test, np.ndarray):\n", - " test = np.array(test)\n", - "\n", - " if effect_size == \"mean_diff\":\n", - " return func_difference(control, test, np.mean, is_paired)\n", - "\n", - " if effect_size == \"median_diff\":\n", - " mes1 = \"Using median as the statistic in bootstrapping may \" + \\\n", - " \"result in a biased estimate and cause problems with \" + \\\n", - " \"BCa confidence intervals. Consider using a different statistic, such as the mean.\\n\"\n", - " mes2 = \"When plotting, please consider using percetile confidence intervals \" + \\\n", - " \"by specifying `ci_type='pct'`. For detailed information, \" + \\\n", - " \"refer to https://github.com/ACCLAB/DABEST-python/issues/129 \\n\"\n", - " warnings.warn(message=mes1+mes2, category=UserWarning)\n", - " return func_difference(control, test, np.median, is_paired)\n", - "\n", - " if effect_size == \"cohens_d\":\n", - " return cohens_d(control, test, is_paired)\n", - "\n", - " if effect_size == \"cohens_h\":\n", - " return cohens_h(control, test)\n", - "\n", - " if effect_size == \"hedges_g\":\n", - " return hedges_g(control, test, is_paired)\n", - "\n", - " if effect_size == \"cliffs_delta\":\n", - " if is_paired:\n", - " err1 = \"`is_paired` is not None; therefore Cliff's delta is not defined.\"\n", - " raise ValueError(err1)\n", - " \n", - " return cliffs_delta(control, test)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c93f36d4", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "def func_difference(control:list|tuple|np.ndarray, # NaNs are automatically discarded.\n", - " test:list|tuple|np.ndarray, # NaNs are automatically discarded.\n", - " func, # Summary function to apply.\n", - " is_paired:str # If not None, computes func(test - control). If None, computes func(test) - func(control).\n", - " )->float:\n", - " \"\"\"\n", - " \n", - " Applies func to `control` and `test`, and then returns the difference.\n", - " \n", - " \"\"\"\n", - "\n", - " # Convert to numpy arrays for speed.\n", - " # NaNs are automatically dropped.\n", - " if not isinstance(control, np.ndarray):\n", - " control = np.array(control)\n", - " if not isinstance(test, np.ndarray):\n", - " test = np.array(test)\n", - "\n", - " if is_paired:\n", - " if len(control) != len(test):\n", - " err = \"The two arrays supplied do not have the same length.\"\n", - " raise ValueError(err)\n", - "\n", - " non_nan_mask = ~np.isnan(control) & ~np.isnan(test)\n", - " control_non_nan = control[non_nan_mask]\n", - " test_non_nan = test[non_nan_mask]\n", - "\n", - " return func(test_non_nan - control_non_nan)\n", - "\n", - " \n", - " control = control[~np.isnan(control)]\n", - " test = test[~np.isnan(test)]\n", - " return func(test) - func(control)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c6dd20e4", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "@njit(cache=True)\n", - "def cohens_d(control:list|tuple|np.ndarray,\n", - " test:list|tuple|np.ndarray,\n", - " is_paired:str=None # If not None, the paired Cohen's d is returned.\n", - " )->float:\n", - " \"\"\"\n", - " Computes Cohen's d for test v.s. control.\n", - " See [here](https://en.wikipedia.org/wiki/Effect_size#Cohen's_d)\n", - "\n", - " If `is_paired` is None, returns:\n", - " \n", - " $$\n", - " \\\\frac{\\\\bar{X}_2 - \\\\bar{X}_1}{s_{pooled}}\n", - " $$\n", - " \n", - " where\n", - " \n", - " $$\n", - " s_{pooled} = \\\\sqrt{\\\\frac{(n_1 - 1) s_1^2 + (n_2 - 1) s_2^2}{n_1 + n_2 - 2}}\n", - " $$\n", - " \n", - " If `is_paired` is not None, returns:\n", - " \n", - " $$\n", - " \\\\frac{\\\\bar{X}_2 - \\\\bar{X}_1}{s_{avg}}\n", - " $$\n", - " \n", - " where\n", - " \n", - " $$\n", - " s_{avg} = \\\\sqrt{\\\\frac{s_1^2 + s_2^2}{2}}\n", - " $$\n", - " \n", - " `Notes`:\n", - "\n", - " - The sample variance (and standard deviation) uses N-1 degrees of freedoms.\n", - " This is an application of Bessel's correction, and yields the unbiased\n", - " sample variance.\n", - "\n", - " `References`:\n", - " \n", - " - https://en.wikipedia.org/wiki/Bessel%27s_correction\n", - " - https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation\n", - " \"\"\"\n", - "\n", - " control = control[~np.isnan(control)]\n", - " test = test[~np.isnan(test)]\n", - "\n", - " pooled_sd, average_sd = _compute_standardizers(control, test)\n", - " # pooled SD is used for Cohen's d of two independant groups.\n", - " # average SD is used for Cohen's d of two paired groups\n", - " # (aka repeated measures).\n", - " # NOT IMPLEMENTED YET: Correlation adjusted SD is used for Cohen's d of\n", - " # two paired groups but accounting for the correlation between\n", - " # the two groups.\n", - "\n", - " if is_paired:\n", - " # Check control and test are same length.\n", - " if len(control) != len(test):\n", - " raise ValueError(\"`control` and `test` are not the same length.\")\n", - " # assume the two arrays are ordered already.\n", - " delta = test - control\n", - " M = np.mean(delta)\n", - " divisor = average_sd\n", - "\n", - " else:\n", - " M = np.mean(test) - np.mean(control)\n", - " divisor = pooled_sd\n", - " \n", - " if divisor == 0:\n", - " raise ValueError(\"The divisor is zero, indicating no variability in the data.\")\n", - "\n", - " return M / divisor" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "93688770", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "# @njit(cache=True) # It uses np.seterr which is not supported by Numba\n", - "def cohens_h(control:list|tuple|np.ndarray, \n", - " test:list|tuple|np.ndarray\n", - " )->float:\n", - " '''\n", - " Computes Cohen's h for test v.s. control.\n", - " See [here](https://en.wikipedia.org/wiki/Cohen%27s_h) for reference.\n", - " \n", - " `Notes`:\n", - " \n", - " - Assuming the input data type is binary, i.e. a series of 0s and 1s,\n", - " and a dict for mapping the 0s and 1s to the actual labels, e.g.{1: \"Smoker\", 0: \"Non-smoker\"}\n", - " '''\n", - "\n", - " np.seterr(divide='ignore', invalid='ignore')\n", - "\n", - " # Check whether dataframe contains only 0s and 1s.\n", - " if np.isin(control, [0, 1]).all() == False or np.isin(test, [0, 1]).all() == False:\n", - " raise ValueError(\"Input data must be binary.\")\n", - "\n", - " # Convert to numpy arrays for speed.\n", - " # NaNs are automatically dropped.\n", - " # Aligned with cohens_d calculation.\n", - " control = control[~np.isnan(control)]\n", - " test = test[~np.isnan(test)]\n", - "\n", - " prop_control = sum(control)/len(control)\n", - " prop_test = sum(test)/len(test)\n", - "\n", - " # Arcsine transformation\n", - " phi_control = 2 * np.arcsin(np.sqrt(prop_control))\n", - " phi_test = 2 * np.arcsin(np.sqrt(prop_test))\n", - "\n", - " return phi_test - phi_control\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bcd77c32", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "def hedges_g(control:list|tuple|np.ndarray, \n", - " test:list|tuple|np.ndarray, \n", - " is_paired:str=None)->float:\n", - " \"\"\"\n", - " Computes Hedges' g for for test v.s. control.\n", - " It first computes Cohen's d, then calulates a correction factor based on\n", - " the total degress of freedom using the gamma function.\n", - "\n", - " See [here](https://en.wikipedia.org/wiki/Effect_size#Hedges'_g)\n", - "\n", - " \"\"\"\n", - "\n", - " # Convert to numpy arrays for speed.\n", - " # NaNs are automatically dropped.\n", - " control = control[~np.isnan(control)]\n", - " test = test[~np.isnan(test)]\n", - "\n", - " d = cohens_d(control, test, is_paired)\n", - " len_c = len(control)\n", - " len_t = len(test)\n", - " correction_factor = _compute_hedges_correction_factor(len_c, len_t)\n", - " return correction_factor * d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8fafb111", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "@njit(cache=True)\n", - "def _mann_whitney_u(x, y):\n", - " \"\"\"Numba-optimized Mann-Whitney U calculation\"\"\"\n", - " n1, n2 = len(x), len(y)\n", - " combined = np.concatenate((x, y))\n", - " \n", - " # Use numpy broadcasting for comparison\n", - " less_than = (combined.reshape(-1, 1) > combined).sum(axis=1)\n", - " equal_to = (combined.reshape(-1, 1) == combined).sum(axis=1)\n", - " \n", - " # Calculate ranks directly\n", - " ranks = less_than + (equal_to + 1) / 2\n", - " \n", - " R1 = np.sum(ranks[:n1])\n", - " U1 = R1 - (n1 * (n1 + 1)) / 2\n", - " return U1\n", - "\n", - "@njit(cache=True)\n", - "def _cliffs_delta_core(control, test):\n", - " \"\"\"Numba-optimized Cliff's delta calculation\"\"\"\n", - " U = _mann_whitney_u(test, control)\n", - " return ((2 * U) / (len(control) * len(test))) - 1\n", - "\n", - "def cliffs_delta(control:list|tuple|np.ndarray, \n", - " test:list|tuple|np.ndarray\n", - " )->float:\n", - " \"\"\"\n", - " Computes Cliff's delta for 2 samples.\n", - " See [here](https://en.wikipedia.org/wiki/Effect_size#Effect_size_for_ordinal_data)\n", - " \"\"\"\n", - " c = control[~np.isnan(control)]\n", - " t = test[~np.isnan(test)]\n", - " return _cliffs_delta_core(c, t)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7a772510", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "@njit(cache=True)\n", - "def _compute_standardizers(control, test):\n", - " \"\"\"\n", - " Computes the pooled and average standard deviations for two datasets.\n", - "\n", - " This function is useful in the context of statistical analysis, particularly\n", - " when calculating standardized mean differences between two groups. It supports\n", - " both unpaired and paired data scenarios.\n", - "\n", - " Parameters:\n", - " control (array-like): A numeric array representing the control group data.\n", - " test (array-like): A numeric array representing the test group data.\n", - "\n", - " Returns:\n", - " tuple: A tuple containing two elements:\n", - " - pooled (float): The pooled standard deviation, calculated for unpaired two-group \n", - " scenarios. It is computed using the sample variances of the \n", - " control and test groups, weighted by their sample sizes.\n", - " - average (float): The average standard deviation, calculated for paired data \n", - " scenarios. It is the average of the sample standard deviations \n", - " of the control and test groups.\n", - "\n", - " Note:\n", - " The function assumes that the input arrays are independent samples and calculates\n", - " the sample variances using N-1 degrees of freedom.\n", - "\n", - " For calculation of correlation; not currently used.\n", - "\n", - " \"\"\"\n", - " # from scipy.stats import pearsonr\n", - "\n", - " control_n = len(control)\n", - " test_n = len(test)\n", - "\n", - " # ddof parameter is not supported by numba.\n", - " control_var = np.var(control)*control_n/(control_n-1) # use N-1 to compute the variance.\n", - " test_var = np.var(test)*test_n/(test_n-1)\n", - "\n", - " # For unpaired 2-groups standardized mean difference.\n", - " pooled = np.sqrt(((control_n - 1) * control_var + (test_n - 1) * test_var) /\n", - " (control_n + test_n - 2)\n", - " )\n", - "\n", - " # For paired standardized mean difference.\n", - " average = np.sqrt((control_var + test_var) / 2)\n", - "\n", - " return pooled, average " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4529e82c", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "def _compute_hedges_correction_factor(n1, \n", - " n2\n", - " )->float:\n", - " \"\"\"\n", - " Computes the bias correction factor for Hedges' g.\n", - "\n", - " See [here](https://en.wikipedia.org/wiki/Effect_size#Hedges'_g)\n", - "\n", - " `References`:\n", - " \n", - " - Larry V. Hedges & Ingram Olkin (1985).\n", - " Statistical Methods for Meta-Analysis. Orlando: Academic Press.\n", - " ISBN 0-12-336380-2.\n", - " \"\"\"\n", - "\n", - " df = n1 + n2 - 2\n", - " # gamma function is not supported by numba.\n", - " numer = gamma(df / 2)\n", - " denom0 = gamma((df - 1) / 2)\n", - " denom = np.sqrt(df / 2) * denom0\n", - "\n", - " if np.isinf(numer) or np.isinf(denom):\n", - " # occurs when df is too large.\n", - " # Apply Hedges and Olkin's approximation.\n", - " df_sum = n1 + n2\n", - " denom = (4 * df_sum) - 9\n", - " out = 1 - (3 / denom)\n", - "\n", - " else:\n", - " out = numer / denom\n", - "\n", - " return out" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "249e5375", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "@njit(cache=True)\n", - "def weighted_delta(difference, bootstrap_dist_var):\n", - " '''\n", - " Compute the weighted deltas where the weight is the inverse of the\n", - " pooled group difference.\n", - " '''\n", - "\n", - " weight = np.true_divide(1, bootstrap_dist_var)\n", - " return np.sum(difference*weight)/np.sum(weight)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/API/effsize_objects.ipynb b/nbs/API/effsize_objects.ipynb deleted file mode 100644 index 1d200a2e..00000000 --- a/nbs/API/effsize_objects.ipynb +++ /dev/null @@ -1,2441 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a8f6e14c", - "metadata": {}, - "source": [ - "# Effectsize objects\n", - "\n", - "> The auxiliary classes involved in the computations of bootstrapped effect sizes.\n", - "\n", - "- order: 10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "becd3bdf", - "metadata": {}, - "outputs": [], - "source": [ - "#| default_exp _effsize_objects" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "956b12a4", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from __future__ import annotations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9a991fb5", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from nbdev.showdoc import *\n", - "import nbdev\n", - "nbdev.nbdev_export()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ab97c8f0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pre-compiling numba functions for DABEST...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Compiling numba functions: 100%|██████████| 11/11 [00:02<00:00, 3.82it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Numba compilation complete!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "#| hide\n", - "import dabest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "430d4ea8", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "import pandas as pd\n", - "import lqrt\n", - "from scipy.stats import norm\n", - "import numpy as np\n", - "from scipy.special import binom as binomcoeff # devMJBL\n", - "from scipy.stats import binom # devMJBL\n", - "from scipy.integrate import fixed_quad # devMJBL\n", - "from numpy import arange, mean # devMJBL\n", - "from numpy import array, isnan, isinf, repeat, random, isin, abs, var\n", - "from numpy import sort as npsort\n", - "from numpy import nan as npnan\n", - "from numpy.random import PCG64, RandomState\n", - "from statsmodels.stats.contingency_tables import mcnemar\n", - "import warnings\n", - "from string import Template\n", - "import scipy.stats as spstats" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9d988bdd", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "class TwoGroupsEffectSize(object):\n", - "\n", - " \"\"\"\n", - " A class to compute and store the results of bootstrapped\n", - " mean differences between two groups.\n", - "\n", - " Compute the effect size between two groups.\n", - "\n", - " Parameters\n", - " ----------\n", - " control : array-like\n", - " test : array-like\n", - " These should be numerical iterables.\n", - " effect_size : string.\n", - " Any one of the following are accepted inputs:\n", - " 'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta'\n", - " is_paired : string, default None\n", - " resamples : int, default 5000\n", - " The number of bootstrap resamples to be taken for the calculation\n", - " of the confidence interval limits.\n", - " permutation_count : int, default 5000\n", - " The number of permutations (reshuffles) to perform for the\n", - " computation of the permutation p-value\n", - " ci : float, default 95\n", - " The confidence interval width. The default of 95 produces 95%\n", - " confidence intervals.\n", - " random_seed : int, default 12345\n", - " `random_seed` is used to seed the random number generator during\n", - " bootstrap resampling. This ensures that the confidence intervals\n", - " reported are replicable.\n", - " ps_adjust : boolean, default False.\n", - " If True, adjust calculated p-value according to Phipson & Smyth (2010)\n", - " # https://doi.org/10.2202/1544-6115.1585\n", - " \n", - "\n", - " Returns\n", - " -------\n", - " A :py:class:`TwoGroupEffectSize` object:\n", - " `difference` : float\n", - " The effect size of the difference between the control and the test.\n", - " `effect_size` : string\n", - " The type of effect size reported.\n", - " `is_paired` : string\n", - " The type of repeated-measures experiment.\n", - " `ci` : float\n", - " Returns the width of the confidence interval, in percent.\n", - " `alpha` : float\n", - " Returns the significance level of the statistical test as a float between 0 and 1.\n", - " `resamples` : int\n", - " The number of resamples performed during the bootstrap procedure.\n", - " `bootstraps` : numpy ndarray\n", - " The generated bootstraps of the effect size.\n", - " `random_seed` : int\n", - " The number used to initialise the numpy random seed generator, ie.`seed_value` from `numpy.random.seed(seed_value)` is returned.\n", - " `bca_low, bca_high` : float\n", - " The bias-corrected and accelerated confidence interval lower limit and upper limits, respectively.\n", - " `pct_low, pct_high` : float\n", - " The percentile confidence interval lower limit and upper limits, respectively.\n", - " \"\"\"\n", - "\n", - " def __init__(\n", - " self,\n", - " control,\n", - " test,\n", - " effect_size,\n", - " proportional=False,\n", - " is_paired=None,\n", - " ci=95,\n", - " resamples=5000,\n", - " permutation_count=5000,\n", - " random_seed=12345,\n", - " ps_adjust=False,\n", - " ):\n", - " from ._stats_tools import confint_2group_diff as ci2g\n", - " from ._stats_tools import effsize as es\n", - "\n", - " self.__EFFECT_SIZE_DICT = {\n", - " \"mean_diff\": \"mean difference\",\n", - " \"median_diff\": \"median difference\",\n", - " \"cohens_d\": \"Cohen's d\",\n", - " \"cohens_h\": \"Cohen's h\",\n", - " \"hedges_g\": \"Hedges' g\",\n", - " \"cliffs_delta\": \"Cliff's delta\",\n", - " }\n", - " \n", - " self.__is_paired = is_paired\n", - " self.__resamples = resamples\n", - " self.__effect_size = effect_size\n", - " self.__random_seed = random_seed\n", - " self.__ci = ci\n", - " self.__is_proportional = proportional\n", - " self.__ps_adjust = ps_adjust\n", - " self._check_errors(control, test)\n", - "\n", - " # Convert to numpy arrays for speed.\n", - " # NaNs are automatically dropped.\n", - " control = array(control)\n", - " test = array(test)\n", - " self.__control = control[~isnan(control)]\n", - " self.__test = test[~isnan(test)]\n", - " self.__permutation_count = permutation_count\n", - "\n", - " self.__alpha = ci2g._compute_alpha_from_ci(self.__ci)\n", - "\n", - " self.__difference = es.two_group_difference(\n", - " self.__control, self.__test, self.__is_paired, self.__effect_size\n", - " )\n", - "\n", - " self.__jackknives = ci2g.compute_meandiff_jackknife(\n", - " self.__control, self.__test, self.__is_paired, self.__effect_size\n", - " )\n", - "\n", - " self.__acceleration_value = ci2g._calc_accel(self.__jackknives)\n", - "\n", - " bootstraps = ci2g.compute_bootstrapped_diff(\n", - " self.__control,\n", - " self.__test,\n", - " self.__is_paired,\n", - " self.__effect_size,\n", - " self.__resamples,\n", - " self.__random_seed,\n", - " )\n", - " self.__bootstraps = bootstraps\n", - "\n", - " sorted_bootstraps = npsort(self.__bootstraps)\n", - " # Added in v0.2.6.\n", - " # Raises a UserWarning if there are any infiinities in the bootstraps.\n", - " num_infinities = len(self.__bootstraps[isinf(self.__bootstraps)])\n", - "\n", - " if num_infinities > 0:\n", - " warn_msg = (\n", - " \"There are {} bootstrap(s) that are not defined. \"\n", - " \"This is likely due to smaple sample sizes. \"\n", - " \"The values in a bootstrap for a group will be more likely \"\n", - " \"to be all equal, with a resulting variance of zero. \"\n", - " \"The computation of Cohen's d and Hedges' g thus \"\n", - " \"involved a division by zero. \"\n", - " )\n", - " warnings.warn(warn_msg.format(num_infinities), category=UserWarning)\n", - "\n", - " self.__bias_correction = ci2g.compute_meandiff_bias_correction(\n", - " self.__bootstraps, self.__difference\n", - " )\n", - "\n", - " self._compute_bca_intervals(sorted_bootstraps)\n", - "\n", - " # Compute percentile intervals.\n", - " pct_idx_low = int((self.__alpha / 2) * self.__resamples)\n", - " pct_idx_high = int((1 - (self.__alpha / 2)) * self.__resamples)\n", - "\n", - " self.__pct_interval_idx = (pct_idx_low, pct_idx_high)\n", - " self.__pct_low = sorted_bootstraps[pct_idx_low]\n", - " self.__pct_high = sorted_bootstraps[pct_idx_high]\n", - " \n", - " self._get_bootstrap_baseline_ec()\n", - "\n", - " self._perform_statistical_test()\n", - "\n", - " def __repr__(self, show_resample_count=True, define_pval=True, sigfig=3):\n", - " RM_STATUS = {\n", - " \"baseline\": \"for repeated measures against baseline \\n\",\n", - " \"sequential\": \"for the sequential design of repeated-measures experiment \\n\",\n", - " \"None\": \"\",\n", - " }\n", - "\n", - " PAIRED_STATUS = {\n", - " \"baseline\": \"paired\",\n", - " \"sequential\": \"paired\",\n", - " \"None\": \"unpaired\",\n", - " }\n", - "\n", - " first_line = {\n", - " \"rm_status\": RM_STATUS[str(self.__is_paired)],\n", - " \"es\": self.__EFFECT_SIZE_DICT[self.__effect_size],\n", - " \"paired_status\": PAIRED_STATUS[str(self.__is_paired)],\n", - " }\n", - "\n", - " out1 = \"The {paired_status} {es} {rm_status}\".format(**first_line)\n", - "\n", - " base_string_fmt = \"{:.\" + str(sigfig) + \"}\"\n", - " if \".\" in str(self.__ci):\n", - " ci_width = base_string_fmt.format(self.__ci)\n", - " else:\n", - " ci_width = str(self.__ci)\n", - "\n", - " ci_out = {\n", - " \"es\": base_string_fmt.format(self.__difference),\n", - " \"ci\": ci_width,\n", - " \"bca_low\": base_string_fmt.format(self.__bca_low),\n", - " \"bca_high\": base_string_fmt.format(self.__bca_high),\n", - " }\n", - "\n", - " out2 = \"is {es} [{ci}%CI {bca_low}, {bca_high}].\".format(**ci_out)\n", - " out = out1 + out2\n", - "\n", - " pval_rounded = base_string_fmt.format(self.pvalue_permutation)\n", - "\n", - " p1 = \"The p-value of the two-sided permutation t-test is {}, \".format(\n", - " pval_rounded\n", - " )\n", - " p2 = \"calculated for legacy purposes only. \"\n", - " pvalue = p1 + p2\n", - "\n", - " bs1 = \"{} bootstrap samples were taken; \".format(self.__resamples)\n", - " bs2 = \"the confidence interval is bias-corrected and accelerated.\"\n", - " bs = bs1 + bs2\n", - "\n", - " pval_def1 = (\n", - " \"Any p-value reported is the probability of observing the\"\n", - " + \"effect size (or greater),\\nassuming the null hypothesis of \"\n", - " + \"zero difference is true.\"\n", - " )\n", - " pval_def2 = (\n", - " \"\\nFor each p-value, 5000 reshuffles of the \"\n", - " + \"control and test labels were performed.\"\n", - " )\n", - " pval_def = pval_def1 + pval_def2\n", - "\n", - " if show_resample_count and define_pval:\n", - " return \"{}\\n{}\\n\\n{}\\n{}\".format(out, pvalue, bs, pval_def)\n", - " elif not show_resample_count and define_pval:\n", - " return \"{}\\n{}\\n\\n{}\".format(out, pvalue, pval_def)\n", - " elif show_resample_count and not define_pval:\n", - " return \"{}\\n{}\\n\\n{}\".format(out, pvalue, bs)\n", - " else:\n", - " return \"{}\\n{}\".format(out, pvalue)\n", - "\n", - " def _check_errors(self, control, test):\n", - " '''\n", - " Function to check configuration errors for the given control and test data.\n", - " '''\n", - " kosher_es = [a for a in self.__EFFECT_SIZE_DICT.keys()]\n", - " if self.__effect_size not in kosher_es:\n", - " err1 = \"The effect size '{}'\".format(self.__effect_size)\n", - " err2 = \"is not one of {}\".format(kosher_es)\n", - " raise ValueError(\" \".join([err1, err2]))\n", - "\n", - " if self.__effect_size == \"cliffs_delta\" and self.__is_paired:\n", - " err1 = \"`paired` is not None; therefore Cliff's delta is not defined.\"\n", - " raise ValueError(err1)\n", - "\n", - " if self.__is_proportional and self.__effect_size not in [\"mean_diff\", \"cohens_h\"]:\n", - " err1 = \"`is_proportional` is True; therefore effect size other than mean_diff and cohens_h is not defined.\"\n", - " raise ValueError(err1)\n", - "\n", - " if self.__is_proportional and (\n", - " isin(control, [0, 1]).all() == False or isin(test, [0, 1]).all() == False\n", - " ):\n", - " err1 = (\n", - " \"`is_proportional` is True; Only accept binary data consisting of 0 and 1.\"\n", - " )\n", - " raise ValueError(err1)\n", - "\n", - " def _compute_bca_intervals(self, sorted_bootstraps):\n", - " '''\n", - " Function to compute the bca intervals given the sorted bootstraps.\n", - " '''\n", - " from ._stats_tools import confint_2group_diff as ci2g\n", - "\n", - " # Compute BCa intervals.\n", - " bca_idx_low, bca_idx_high = ci2g.compute_interval_limits(\n", - " self.__bias_correction,\n", - " self.__acceleration_value,\n", - " self.__resamples,\n", - " self.__ci,\n", - " )\n", - "\n", - " self.__bca_interval_idx = (bca_idx_low, bca_idx_high)\n", - "\n", - " if ~isnan(bca_idx_low) and ~isnan(bca_idx_high):\n", - " self.__bca_low = sorted_bootstraps[bca_idx_low]\n", - " self.__bca_high = sorted_bootstraps[bca_idx_high]\n", - "\n", - " err1 = \"The $lim_type limit of the interval\"\n", - " err2 = \"was in the $loc 10 values.\"\n", - " err3 = \"The result should be considered unstable.\"\n", - " err_temp = Template(\" \".join([err1, err2, err3]))\n", - "\n", - " if bca_idx_low <= 10:\n", - " warnings.warn(\n", - " err_temp.substitute(lim_type=\"lower\", loc=\"bottom\"), stacklevel=1\n", - " )\n", - "\n", - " if bca_idx_high >= self.__resamples - 9:\n", - " warnings.warn(\n", - " err_temp.substitute(lim_type=\"upper\", loc=\"top\"), stacklevel=1\n", - " )\n", - "\n", - " else:\n", - " err1 = \"The $lim_type limit of the BCa interval cannot be computed.\"\n", - " err2 = \"It is set to the effect size itself.\"\n", - " err3 = \"All bootstrap values were likely all the same.\"\n", - " err_temp = Template(\" \".join([err1, err2, err3]))\n", - "\n", - " if isnan(bca_idx_low):\n", - " self.__bca_low = self.__difference\n", - " warnings.warn(err_temp.substitute(lim_type=\"lower\"), stacklevel=0)\n", - "\n", - " if isnan(bca_idx_high):\n", - " self.__bca_high = self.__difference\n", - " warnings.warn(err_temp.substitute(lim_type=\"upper\"), stacklevel=0)\n", - "\n", - " def _perform_statistical_test(self):\n", - " '''\n", - " Function to complete the statistical tests\n", - " '''\n", - " from ._stats_tools import effsize as es\n", - "\n", - " # Perform statistical tests.\n", - " self.__PermutationTest_result = PermutationTest(\n", - " self.__control,\n", - " self.__test,\n", - " self.__effect_size,\n", - " self.__is_paired,\n", - " self.__permutation_count,\n", - " ps_adjust = self.__ps_adjust,\n", - " )\n", - "\n", - " if self.__is_paired and not self.__is_proportional:\n", - " # Wilcoxon, a non-parametric version of the paired T-test.\n", - " try:\n", - " wilcoxon = spstats.wilcoxon(self.__control, self.__test)\n", - " self.__pvalue_wilcoxon = wilcoxon.pvalue\n", - " self.__statistic_wilcoxon = wilcoxon.statistic\n", - " except ValueError as e:\n", - " warnings.warn(\"Wilcoxon test could not be performed. This might be due \"\n", - " \"to no variability in the difference of the paired groups. \\n\"\n", - " \"Error: {}\\n\"\n", - " \"For detailed information, please refer to https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wilcoxon.html \"\n", - " .format(e))\n", - "\n", - " if self.__effect_size != \"median_diff\":\n", - " # Paired Student's t-test.\n", - " paired_t = spstats.ttest_rel(\n", - " self.__control, self.__test, nan_policy=\"omit\"\n", - " )\n", - " self.__pvalue_paired_students_t = paired_t.pvalue\n", - " self.__statistic_paired_students_t = paired_t.statistic\n", - "\n", - " elif self.__is_paired and self.__is_proportional:\n", - " # for binary paired data, use McNemar's test\n", - " # References:\n", - " # https://en.wikipedia.org/wiki/McNemar%27s_test\n", - "\n", - " x1 = np.sum((self.__control == 0) & (self.__test == 0))\n", - " x2 = np.sum((self.__control == 0) & (self.__test == 1))\n", - " x3 = np.sum((self.__control == 1) & (self.__test == 0))\n", - " x4 = np.sum((self.__control == 1) & (self.__test == 1))\n", - " table = np.array([[x1, x2], [x3, x4]])\n", - " _mcnemar = mcnemar(table, exact=True, correction=True)\n", - " self.__pvalue_mcnemar = _mcnemar.pvalue\n", - " self.__statistic_mcnemar = _mcnemar.statistic\n", - "\n", - " elif self.__is_proportional:\n", - " # The Cohen's h calculation is for binary categorical data\n", - " try:\n", - " self.__proportional_difference = es.cohens_h(\n", - " self.__control, self.__test\n", - " )\n", - " except ValueError as e:\n", - " warnings.warn(f\"Calculation of Cohen's h failed. This method is applicable \"\n", - " f\"only for binary data (0's and 1's). Details: {e}\")\n", - "\n", - " elif self.__effect_size == \"cliffs_delta\":\n", - " # Let's go with Brunner-Munzel!\n", - " brunner_munzel = spstats.brunnermunzel(\n", - " self.__control, self.__test, nan_policy=\"omit\"\n", - " )\n", - " self.__pvalue_brunner_munzel = brunner_munzel.pvalue\n", - " self.__statistic_brunner_munzel = brunner_munzel.statistic\n", - "\n", - " elif self.__effect_size == \"median_diff\":\n", - " # According to scipy's documentation of the function,\n", - " # \"The Kruskal-Wallis H-test tests the null hypothesis\n", - " # that the population median of all of the groups are equal.\"\n", - " kruskal = spstats.kruskal(self.__control, self.__test, nan_policy=\"omit\")\n", - " self.__pvalue_kruskal = kruskal.pvalue\n", - " self.__statistic_kruskal = kruskal.statistic\n", - "\n", - " else: # for mean difference, Cohen's d, and Hedges' g.\n", - " # Welch's t-test, assumes normality of distributions,\n", - " # but does not assume equal variances.\n", - " welch = spstats.ttest_ind(\n", - " self.__control, self.__test, equal_var=False, nan_policy=\"omit\"\n", - " )\n", - " self.__pvalue_welch = welch.pvalue\n", - " self.__statistic_welch = welch.statistic\n", - "\n", - " # Student's t-test, assumes normality of distributions,\n", - " # as well as assumption of equal variances.\n", - " students_t = spstats.ttest_ind(\n", - " self.__control, self.__test, equal_var=True, nan_policy=\"omit\"\n", - " )\n", - " self.__pvalue_students_t = students_t.pvalue\n", - " self.__statistic_students_t = students_t.statistic\n", - "\n", - " # Mann-Whitney test: Non parametric,\n", - " # does not assume normality of distributions\n", - " try:\n", - " mann_whitney = spstats.mannwhitneyu(\n", - " self.__control, self.__test, alternative=\"two-sided\"\n", - " )\n", - " self.__pvalue_mann_whitney = mann_whitney.pvalue\n", - " self.__statistic_mann_whitney = mann_whitney.statistic\n", - " except ValueError as e:\n", - " warnings.warn(\"Mann-Whitney test could not be performed. This might be due \"\n", - " \"to identical rank values in both control and test groups. \"\n", - " \"Details: {}\".format(e))\n", - "\n", - " standardized_es = es.cohens_d(self.__control, self.__test, is_paired=None)\n", - "\n", - "\n", - " def to_dict(self):\n", - " \"\"\"\n", - " Returns the attributes of the `dabest.TwoGroupEffectSize` object as a\n", - " dictionary.\n", - " \"\"\"\n", - " # Only get public (user-facing) attributes.\n", - " attrs = [a for a in dir(self) if not a.startswith((\"_\", \"to_dict\"))]\n", - " out = {}\n", - " for a in attrs:\n", - " out[a] = getattr(self, a)\n", - " return out\n", - " \n", - " def _get_bootstrap_baseline_ec(self):\n", - " from ._stats_tools import confint_2group_diff as ci2g\n", - " from ._stats_tools import effsize as es\n", - " \n", - " # Cannot use self.__is_paired because it's for baseline curve\n", - " is_paired = None\n", - " \n", - " difference = es.two_group_difference(\n", - " self.__control, self.__control, is_paired, self.__effect_size\n", - " )\n", - " self.__bec_difference = difference\n", - "\n", - " jackknives = ci2g.compute_meandiff_jackknife(\n", - " self.__control, self.__control, is_paired, self.__effect_size\n", - " )\n", - "\n", - " acceleration_value = ci2g._calc_accel(jackknives)\n", - "\n", - " bootstraps = ci2g.compute_bootstrapped_diff(\n", - " self.__control,\n", - " self.__control,\n", - " is_paired,\n", - " self.__effect_size,\n", - " self.__resamples,\n", - " self.__random_seed,\n", - " )\n", - " self.__bootstraps_baseline_ec = bootstraps\n", - "\n", - " sorted_bootstraps = npsort(self.__bootstraps_baseline_ec)\n", - " # We don't have to consider infinities in bootstrap_baseline_ec\n", - "\n", - " bias_correction = ci2g.compute_meandiff_bias_correction(\n", - " self.__bootstraps_baseline_ec, difference\n", - " )\n", - "\n", - " # Compute BCa intervals.\n", - " bca_idx_low, bca_idx_high = ci2g.compute_interval_limits(\n", - " bias_correction,\n", - " acceleration_value,\n", - " self.__resamples,\n", - " self.__ci,\n", - " )\n", - "\n", - " self.__bec_bca_interval_idx = (bca_idx_low, bca_idx_high)\n", - "\n", - " if ~isnan(bca_idx_low) and ~isnan(bca_idx_high):\n", - " self.__bec_bca_low = sorted_bootstraps[bca_idx_low]\n", - " self.__bec_bca_high = sorted_bootstraps[bca_idx_high]\n", - "\n", - " err1 = \"The $lim_type limit of the interval\"\n", - " err2 = \"was in the $loc 10 values.\"\n", - " err3 = \"The result for baseline curve should be considered unstable.\"\n", - " err_temp = Template(\" \".join([err1, err2, err3]))\n", - "\n", - " if bca_idx_low <= 10:\n", - " warnings.warn(\n", - " err_temp.substitute(lim_type=\"lower\", loc=\"bottom\"), stacklevel=1\n", - " )\n", - "\n", - " if bca_idx_high >= self.__resamples - 9:\n", - " warnings.warn(\n", - " err_temp.substitute(lim_type=\"upper\", loc=\"top\"), stacklevel=1\n", - " )\n", - "\n", - " else:\n", - " err1 = \"The $lim_type limit of the BCa interval of baseline curve cannot be computed.\"\n", - " err2 = \"It is set to the effect size itself.\"\n", - " err3 = \"All bootstrap values were likely all the same.\"\n", - " err_temp = Template(\" \".join([err1, err2, err3]))\n", - "\n", - " if isnan(bca_idx_low):\n", - " self.__bec_bca_low = difference\n", - " warnings.warn(err_temp.substitute(lim_type=\"lower\"), stacklevel=0)\n", - "\n", - " if isnan(bca_idx_high):\n", - " self.__bec_bca_high = difference\n", - " warnings.warn(err_temp.substitute(lim_type=\"upper\"), stacklevel=0)\n", - "\n", - " # Compute percentile intervals.\n", - " pct_idx_low = int((self.__alpha / 2) * self.__resamples)\n", - " pct_idx_high = int((1 - (self.__alpha / 2)) * self.__resamples)\n", - "\n", - " self.__bec_pct_interval_idx = (pct_idx_low, pct_idx_high)\n", - " self.__bec_pct_low = sorted_bootstraps[pct_idx_low]\n", - " self.__bec_pct_high = sorted_bootstraps[pct_idx_high]\n", - "\n", - " @property\n", - " def difference(self):\n", - " \"\"\"\n", - " Returns the difference between the control and the test.\n", - " \"\"\"\n", - " return self.__difference\n", - "\n", - " @property\n", - " def effect_size(self):\n", - " \"\"\"\n", - " Returns the type of effect size reported.\n", - " \"\"\"\n", - " return self.__EFFECT_SIZE_DICT[self.__effect_size]\n", - "\n", - " @property\n", - " def is_paired(self):\n", - " return self.__is_paired\n", - "\n", - " @property\n", - " def is_proportional(self):\n", - " return self.__is_proportional\n", - "\n", - " @property\n", - " def ci(self):\n", - " \"\"\"\n", - " Returns the width of the confidence interval, in percent.\n", - " \"\"\"\n", - " return self.__ci\n", - "\n", - " @property\n", - " def alpha(self):\n", - " \"\"\"\n", - " Returns the significance level of the statistical test as a float\n", - " between 0 and 1.\n", - " \"\"\"\n", - " return self.__alpha\n", - "\n", - " @property\n", - " def resamples(self):\n", - " \"\"\"\n", - " The number of resamples performed during the bootstrap procedure.\n", - " \"\"\"\n", - " return self.__resamples\n", - "\n", - " @property\n", - " def bootstraps(self):\n", - " \"\"\"\n", - " The generated bootstraps of the effect size.\n", - " \"\"\"\n", - " return self.__bootstraps\n", - "\n", - " @property\n", - " def random_seed(self):\n", - " \"\"\"\n", - " The number used to initialise the numpy random seed generator, ie.\n", - " `seed_value` from `numpy.random.seed(seed_value)` is returned.\n", - " \"\"\"\n", - " return self.__random_seed\n", - "\n", - " @property\n", - " def bca_interval_idx(self):\n", - " return self.__bca_interval_idx\n", - "\n", - " @property\n", - " def bca_low(self):\n", - " \"\"\"\n", - " The bias-corrected and accelerated confidence interval lower limit.\n", - " \"\"\"\n", - " return self.__bca_low\n", - "\n", - " @property\n", - " def bca_high(self):\n", - " \"\"\"\n", - " The bias-corrected and accelerated confidence interval upper limit.\n", - " \"\"\"\n", - " return self.__bca_high\n", - "\n", - " @property\n", - " def pct_interval_idx(self):\n", - " return self.__pct_interval_idx\n", - "\n", - " @property\n", - " def pct_low(self):\n", - " \"\"\"\n", - " The percentile confidence interval lower limit.\n", - " \"\"\"\n", - " return self.__pct_low\n", - "\n", - " @property\n", - " def pct_high(self):\n", - " \"\"\"\n", - " The percentile confidence interval lower limit.\n", - " \"\"\"\n", - " return self.__pct_high\n", - "\n", - " @property\n", - " def pvalue_brunner_munzel(self):\n", - " try:\n", - " return self.__pvalue_brunner_munzel\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def statistic_brunner_munzel(self):\n", - " try:\n", - " return self.__statistic_brunner_munzel\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def pvalue_wilcoxon(self):\n", - " try:\n", - " return self.__pvalue_wilcoxon\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def statistic_wilcoxon(self):\n", - " try:\n", - " return self.__statistic_wilcoxon\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def pvalue_mcnemar(self):\n", - " try:\n", - " return self.__pvalue_mcnemar\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def statistic_mcnemar(self):\n", - " try:\n", - " return self.__statistic_mcnemar\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def pvalue_paired_students_t(self):\n", - " try:\n", - " return self.__pvalue_paired_students_t\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def statistic_paired_students_t(self):\n", - " try:\n", - " return self.__statistic_paired_students_t\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def pvalue_kruskal(self):\n", - " try:\n", - " return self.__pvalue_kruskal\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def statistic_kruskal(self):\n", - " try:\n", - " return self.__statistic_kruskal\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def pvalue_welch(self):\n", - " try:\n", - " return self.__pvalue_welch\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def statistic_welch(self):\n", - " try:\n", - " return self.__statistic_welch\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def pvalue_students_t(self):\n", - " try:\n", - " return self.__pvalue_students_t\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def statistic_students_t(self):\n", - " try:\n", - " return self.__statistic_students_t\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def pvalue_mann_whitney(self):\n", - " try:\n", - " return self.__pvalue_mann_whitney\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def statistic_mann_whitney(self):\n", - " try:\n", - " return self.__statistic_mann_whitney\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def pvalue_permutation(self):\n", - " \"\"\"\n", - " p value of permutation test\n", - " \"\"\"\n", - " return self.__PermutationTest_result.pvalue\n", - "\n", - " @property\n", - " def permutation_count(self):\n", - " \"\"\"\n", - " The number of permutations taken.\n", - " \"\"\"\n", - " return self.__PermutationTest_result.permutation_count\n", - "\n", - " @property\n", - " def permutations(self):\n", - " return self.__PermutationTest_result.permutations\n", - "\n", - " @property\n", - " def permutations_var(self):\n", - " return self.__PermutationTest_result.permutations_var\n", - "\n", - " @property\n", - " def proportional_difference(self):\n", - " try:\n", - " return self.__proportional_difference\n", - " except AttributeError:\n", - " return npnan\n", - " \n", - " @property\n", - " def bec_difference(self):\n", - " return self.__bec_difference \n", - " \n", - " @property\n", - " def bec_bootstraps(self):\n", - " \"\"\"\n", - " The generated baseline error bootstraps.\n", - " \"\"\"\n", - " return self.__bootstraps_baseline_ec\n", - "\n", - " @property\n", - " def bec_bca_interval_idx(self):\n", - " return self.__bec_bca_interval_idx\n", - "\n", - " @property\n", - " def bec_bca_low(self):\n", - " \"\"\"\n", - " The bias-corrected and accelerated confidence interval lower limit for baseline error.\n", - " \"\"\"\n", - " return self.__bec_bca_low\n", - "\n", - " @property\n", - " def bec_bca_high(self):\n", - " \"\"\"\n", - " The bias-corrected and accelerated confidence interval upper limit for baseline error.\n", - " \"\"\"\n", - " return self.__bec_bca_high\n", - "\n", - " @property\n", - " def bec_pct_interval_idx(self):\n", - " return self.__bec_pct_interval_idx\n", - "\n", - " @property\n", - " def bec_pct_low(self):\n", - " \"\"\"\n", - " The percentile confidence interval lower limit for baseline error.\n", - " \"\"\"\n", - " return self.__bec_pct_low\n", - "\n", - " @property\n", - " def bec_pct_high(self):\n", - " \"\"\"\n", - " The percentile confidence interval lower limit for baseline error.\n", - " \"\"\"\n", - " return self.__bec_pct_high\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "955a36d1", - "metadata": {}, - "source": [ - "#### Example" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c1f1647d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "The unpaired mean difference is -0.253 [95%CI -0.782, 0.241].\n", - "The p-value of the two-sided permutation t-test is 0.348, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "random.seed(12345)\n", - "control = norm.rvs(loc=0, size=30)\n", - "test = norm.rvs(loc=0.5, size=30)\n", - "effsize = dabest.TwoGroupsEffectSize(control, test, \"mean_diff\")\n", - "effsize" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0142a8ab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'alpha': 0.05,\n", - " 'bca_high': 0.2413346581369784,\n", - " 'bca_interval_idx': (109, 4858),\n", - " 'bca_low': -0.7818088458343655,\n", - " 'bec_bca_high': 0.5352403905584314,\n", - " 'bec_bca_interval_idx': (130, 4880),\n", - " 'bec_bca_low': -0.4982839949134528,\n", - " 'bec_bootstraps': array([-0.48953946, -0.18565285, -0.23896785, ..., -0.55130928,\n", - " 0.16037238, -0.07364879]),\n", - " 'bec_difference': 0.0,\n", - " 'bec_pct_high': 0.5280564736117328,\n", - " 'bec_pct_interval_idx': (125, 4875),\n", - " 'bec_pct_low': -0.5041777340626885,\n", - " 'bootstraps': array([-0.23923425, -0.66013733, -0.42672232, ..., -0.33191074,\n", - " -0.16543251, -0.34179536]),\n", - " 'ci': 95,\n", - " 'difference': -0.25315417702752846,\n", - " 'effect_size': 'mean difference',\n", - " 'is_paired': None,\n", - " 'is_proportional': False,\n", - " 'pct_high': 0.25135646125431527,\n", - " 'pct_interval_idx': (125, 4875),\n", - " 'pct_low': -0.763588353717278,\n", - " 'permutation_count': 5000,\n", - " 'permutations': array([ 0.17221029, 0.03112419, -0.13911387, ..., -0.38007941,\n", - " 0.30261507, -0.09073054]),\n", - " 'permutations_var': array([0.07201642, 0.07251104, 0.07219407, ..., 0.07003705, 0.07094885,\n", - " 0.07238581]),\n", - " 'proportional_difference': nan,\n", - " 'pvalue_brunner_munzel': nan,\n", - " 'pvalue_kruskal': nan,\n", - " 'pvalue_mann_whitney': 0.5201446121616038,\n", - " 'pvalue_mcnemar': nan,\n", - " 'pvalue_paired_students_t': nan,\n", - " 'pvalue_permutation': 0.3484,\n", - " 'pvalue_students_t': 0.34743913903372836,\n", - " 'pvalue_welch': 0.3474493875548964,\n", - " 'pvalue_wilcoxon': nan,\n", - " 'random_seed': 12345,\n", - " 'resamples': 5000,\n", - " 'statistic_brunner_munzel': nan,\n", - " 'statistic_kruskal': nan,\n", - " 'statistic_mann_whitney': 494.0,\n", - " 'statistic_mcnemar': nan,\n", - " 'statistic_paired_students_t': nan,\n", - " 'statistic_students_t': 0.9472545159069105,\n", - " 'statistic_welch': 0.9472545159069105,\n", - " 'statistic_wilcoxon': nan}" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "effsize.to_dict() " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "024b1d00", - "metadata": {}, - "outputs": [], - "source": [ - "# | export\n", - "class EffectSizeDataFrame(object):\n", - " \"\"\"A class that generates and stores the results of bootstrapped effect\n", - " sizes for several comparisons.\"\"\"\n", - "\n", - " def __init__(\n", - " self,\n", - " dabest,\n", - " effect_size,\n", - " is_paired,\n", - " ci=95,\n", - " proportional=False,\n", - " resamples=5000,\n", - " permutation_count=5000,\n", - " random_seed=12345,\n", - " x1_level=None,\n", - " x2=None,\n", - " delta2=False,\n", - " experiment_label=None,\n", - " mini_meta=False,\n", - " ps_adjust=False,\n", - " ):\n", - " \"\"\"\n", - " Parses the data from a Dabest object, enabling plotting and printing\n", - " capability for the effect size of interest.\n", - " \"\"\"\n", - "\n", - " self.__dabest_obj = dabest\n", - " self.__effect_size = effect_size\n", - " self.__is_paired = is_paired\n", - " self.__ci = ci\n", - " self.__resamples = resamples\n", - " self.__permutation_count = permutation_count\n", - " self.__random_seed = random_seed\n", - " self.__is_proportional = proportional\n", - " self.__x1_level = x1_level\n", - " self.__experiment_label = experiment_label\n", - " self.__x2 = x2\n", - " self.__delta2 = delta2\n", - " self.__is_mini_meta = mini_meta\n", - " self.__ps_adjust = ps_adjust\n", - "\n", - " def __pre_calc(self):\n", - " from .misc_tools import print_greeting, get_varname\n", - " from ._stats_tools import confint_2group_diff as ci2g\n", - " from ._delta_objects import MiniMetaDelta, DeltaDelta\n", - "\n", - " idx = self.__dabest_obj.idx\n", - " dat = self.__dabest_obj._plot_data\n", - " xvar = self.__dabest_obj._xvar\n", - " yvar = self.__dabest_obj._yvar\n", - "\n", - " out = []\n", - " reprs = []\n", - "\n", - " grouped_data = {name: group[yvar].copy() for name, group in dat.groupby(xvar, observed=False)}\n", - " if self.__delta2:\n", - " mixed_data = []\n", - " for j, current_tuple in enumerate(idx):\n", - " if self.__is_paired != \"sequential\":\n", - " cname = current_tuple[0]\n", - " control = grouped_data[cname]\n", - "\n", - " for ix, tname in enumerate(current_tuple[1:]):\n", - " if self.__is_paired == \"sequential\":\n", - " cname = current_tuple[ix]\n", - " control = grouped_data[cname]\n", - " test = grouped_data[tname]\n", - " mixed_data.append(control)\n", - " mixed_data.append(test)\n", - " bootstraps_delta_delta = ci2g.compute_delta2_bootstrapped_diff(\n", - " mixed_data[0],\n", - " mixed_data[1],\n", - " mixed_data[2],\n", - " mixed_data[3],\n", - " self.__is_paired,\n", - " self.__resamples,\n", - " self.__random_seed,\n", - " self.__is_proportional,\n", - " )\n", - "\n", - " for j, current_tuple in enumerate(idx):\n", - " if self.__is_paired != \"sequential\":\n", - " cname = current_tuple[0]\n", - " control = grouped_data[cname]\n", - "\n", - " for ix, tname in enumerate(current_tuple[1:]):\n", - " if self.__is_paired == \"sequential\":\n", - " cname = current_tuple[ix]\n", - " control = grouped_data[cname]\n", - " test = grouped_data[tname]\n", - " result = TwoGroupsEffectSize(\n", - " control,\n", - " test,\n", - " self.__effect_size,\n", - " self.__is_proportional,\n", - " self.__is_paired,\n", - " self.__ci,\n", - " self.__resamples,\n", - " self.__permutation_count,\n", - " self.__random_seed,\n", - " self.__ps_adjust\n", - " )\n", - " r_dict = result.to_dict()\n", - " r_dict[\"control\"] = cname\n", - " r_dict[\"test\"] = tname\n", - " r_dict[\"control_N\"] = int(len(control))\n", - " r_dict[\"test_N\"] = int(len(test))\n", - " out.append(r_dict)\n", - " if j == len(idx) - 1 and ix == len(current_tuple) - 2:\n", - " if self.__delta2 and self.__effect_size in [\"mean_diff\", \"hedges_g\"]:\n", - " resamp_count = False\n", - " def_pval = False\n", - " elif self.__is_mini_meta and self.__effect_size == \"mean_diff\":\n", - " resamp_count = False\n", - " def_pval = False\n", - " else:\n", - " resamp_count = True\n", - " def_pval = True\n", - " else:\n", - " resamp_count = False\n", - " def_pval = False\n", - "\n", - " text_repr = result.__repr__(\n", - " show_resample_count=resamp_count, define_pval=def_pval\n", - " )\n", - "\n", - " to_replace = \"between {} and {} is\".format(cname, tname)\n", - " text_repr = text_repr.replace(\"is\", to_replace, 1)\n", - "\n", - " reprs.append(text_repr)\n", - "\n", - " self.__for_print = \"\\n\\n\".join(reprs)\n", - "\n", - " out_ = pd.DataFrame(out)\n", - "\n", - " columns_in_order = [\n", - " \"control\",\n", - " \"test\",\n", - " \"control_N\",\n", - " \"test_N\",\n", - " \"effect_size\",\n", - " \"is_paired\",\n", - " \"difference\",\n", - " \"ci\",\n", - " \"bca_low\",\n", - " \"bca_high\",\n", - " \"bca_interval_idx\",\n", - " \"pct_low\",\n", - " \"pct_high\",\n", - " \"pct_interval_idx\",\n", - " \"bootstraps\",\n", - " \"resamples\",\n", - " \"random_seed\",\n", - " \"permutations\",\n", - " \"pvalue_permutation\",\n", - " \"permutation_count\",\n", - " \"permutations_var\",\n", - " \"pvalue_welch\",\n", - " \"statistic_welch\",\n", - " \"pvalue_students_t\",\n", - " \"statistic_students_t\",\n", - " \"pvalue_mann_whitney\",\n", - " \"statistic_mann_whitney\",\n", - " \"pvalue_brunner_munzel\",\n", - " \"statistic_brunner_munzel\",\n", - " \"pvalue_wilcoxon\",\n", - " \"statistic_wilcoxon\",\n", - " \"pvalue_mcnemar\",\n", - " \"statistic_mcnemar\",\n", - " \"pvalue_paired_students_t\",\n", - " \"statistic_paired_students_t\",\n", - " \"pvalue_kruskal\",\n", - " \"statistic_kruskal\",\n", - " \"proportional_difference\",\n", - " \"bec_difference\",\n", - " \"bec_bootstraps\",\n", - " \"bec_bca_interval_idx\",\n", - " \"bec_bca_low\",\n", - " \"bec_bca_high\",\n", - " \"bec_pct_interval_idx\",\n", - " \"bec_pct_low\",\n", - " \"bec_pct_high\",\n", - " ]\n", - " self.__results = out_.reindex(columns=columns_in_order)\n", - " self.__results.dropna(axis=\"columns\", how=\"all\", inplace=True)\n", - "\n", - " # Add the is_paired column back when is_paired is None\n", - " if self.is_paired is None:\n", - " self.__results.insert(\n", - " 5, \"is_paired\", self.__results.apply(lambda _: None, axis=1)\n", - " )\n", - "\n", - " # Create and compute the delta-delta statistics\n", - " if self.__delta2 and self.__effect_size not in [\"mean_diff\", \"hedges_g\"]:\n", - " self.__delta_delta = \"Delta-delta is not supported for {}.\".format(\n", - " self.__effect_size\n", - " )\n", - " elif self.__delta2:\n", - " self.__delta_delta = DeltaDelta(\n", - " self, self.__permutation_count, bootstraps_delta_delta, self.__ci\n", - " )\n", - " reprs.append(self.__delta_delta.__repr__(header=False))\n", - "\n", - " else:\n", - " self.__delta_delta = (\n", - " \"`delta2` is False; delta-delta is therefore not calculated.\"\n", - " )\n", - "\n", - " # Create and compute the weighted average statistics\n", - " if self.__is_mini_meta and self.__effect_size == \"mean_diff\":\n", - " self.__mini_meta = MiniMetaDelta(\n", - " self, self.__permutation_count, self.__ci\n", - " )\n", - " reprs.append(self.__mini_meta.__repr__(header=False))\n", - " elif self.__is_mini_meta and self.__effect_size != \"mean_diff\":\n", - " self.__mini_meta = \"Weighted delta is not supported for {}.\".format(\n", - " self.__effect_size\n", - " )\n", - " else:\n", - " self.__mini_meta = (\n", - " \"`mini_meta` is False; weighted delta is therefore not calculated.\"\n", - " )\n", - "\n", - " varname = get_varname(self.__dabest_obj)\n", - " lastline = (\n", - " \"To get the results of all valid statistical tests, \"\n", - " + \"use `{}.{}.statistical_tests`\".format(varname, self.__effect_size)\n", - " )\n", - " reprs.append(lastline)\n", - "\n", - " reprs.insert(0, print_greeting())\n", - "\n", - " self.__for_print = \"\\n\\n\".join(reprs)\n", - "\n", - " def __repr__(self):\n", - " try:\n", - " return self.__for_print\n", - " except AttributeError:\n", - " self.__pre_calc()\n", - " return self.__for_print\n", - "\n", - " def __calc_lqrt(self):\n", - " rnd_seed = self.__random_seed\n", - " db_obj = self.__dabest_obj\n", - " dat = db_obj._plot_data\n", - " xvar = db_obj._xvar\n", - " yvar = db_obj._yvar\n", - " delta2 = self.__delta2\n", - "\n", - " out = []\n", - "\n", - " grouped_data = {name:group[yvar].copy() for name, group in dat.groupby(xvar)}\n", - "\n", - " for j, current_tuple in enumerate(db_obj.idx):\n", - " if self.__is_paired != \"sequential\":\n", - " cname = current_tuple[0]\n", - " control = grouped_data[cname]\n", - "\n", - " for ix, tname in enumerate(current_tuple[1:]):\n", - " if self.__is_paired == \"sequential\":\n", - " cname = current_tuple[ix]\n", - " control = grouped_data[cname]\n", - " test = grouped_data[tname]\n", - "\n", - " if self.__is_paired:\n", - " # Refactored here in v0.3.0 for performance issues.\n", - " lqrt_result = lqrt.lqrtest_rel(control, test, random_state=rnd_seed)\n", - "\n", - " out.append(\n", - " {\n", - " \"control\": cname,\n", - " \"test\": tname,\n", - " \"control_N\": int(len(control)),\n", - " \"test_N\": int(len(test)),\n", - " \"pvalue_paired_lqrt\": lqrt_result.pvalue,\n", - " \"statistic_paired_lqrt\": lqrt_result.statistic,\n", - " }\n", - " )\n", - "\n", - " else:\n", - " # Likelihood Q-Ratio test:\n", - " lqrt_equal_var_result = lqrt.lqrtest_ind(\n", - " control, test, random_state=rnd_seed, equal_var=True\n", - " )\n", - "\n", - " lqrt_unequal_var_result = lqrt.lqrtest_ind(\n", - " control, test, random_state=rnd_seed, equal_var=False\n", - " )\n", - "\n", - " out.append(\n", - " {\n", - " \"control\": cname,\n", - " \"test\": tname,\n", - " \"control_N\": int(len(control)),\n", - " \"test_N\": int(len(test)),\n", - " \"pvalue_lqrt_equal_var\": lqrt_equal_var_result.pvalue,\n", - " \"statistic_lqrt_equal_var\": lqrt_equal_var_result.statistic,\n", - " \"pvalue_lqrt_unequal_var\": lqrt_unequal_var_result.pvalue,\n", - " \"statistic_lqrt_unequal_var\": lqrt_unequal_var_result.statistic,\n", - " }\n", - " )\n", - " self.__lqrt_results = pd.DataFrame(out)\n", - "\n", - " def plot(\n", - " self,\n", - " color_col=None,\n", - " raw_marker_size=6,\n", - " contrast_marker_size=9, # es_marker_size=9, OLD\n", - "\n", - " raw_label=None, # swarm_label=None, OLD # bar_label=None, OLD\n", - " contrast_label=None,\n", - " delta2_label=None,\n", - "\n", - " raw_ylim=None, # swarm_ylim=None, OLD # bar_ylim=None, OLD\n", - " contrast_ylim=None,\n", - " delta2_ylim=None,\n", - "\n", - " custom_palette=None,\n", - " swarm_side=None,\n", - " empty_circle=False, # Not very intuitive name\n", - "\n", - " face_color=None,\n", - "\n", - " raw_desat=0.5, # swarm_desat=0.5, OLD # bar_desat=0.5, OLD\n", - " contrast_desat=1.0, # halfviolin_desat=1, OLD\n", - "\n", - " raw_alpha=None, # NEW\n", - " contrast_alpha=0.8, # halfviolin_alpha=0.8, OLD\n", - "\n", - " bar_width=0.5,\n", - " # ci=None, # Seems to be unused\n", - " ci_type=\"bca\",\n", - "\n", - " float_contrast=True,\n", - " show_pairs=True,\n", - " show_sample_size=True,\n", - " show_delta2=True, # Would pref switch to delta_delta instead of delta2\n", - " show_mini_meta=True,\n", - "\n", - " group_summaries=\"mean_sd\",\n", - " # err_color=None, # Not intuitive name and doesnt fit with group_summaries argument \n", - " fig_size=None,\n", - " dpi=100,\n", - " ax=None,\n", - "\n", - " swarmplot_kwargs=None,\n", - " slopegraph_kwargs=None,\n", - " barplot_kwargs=None,\n", - " sankey_kwargs=None,\n", - " contrast_kwargs=None, # violinplot_kwargs=None, OLD\n", - " reflines_kwargs=None,\n", - " group_summaries_kwargs=None,\n", - " legend_kwargs=None,\n", - "\n", - " title=None,\n", - " fontsize_title=16,\n", - " fontsize_rawxlabel=12,\n", - " fontsize_rawylabel=12,\n", - " fontsize_contrastxlabel=12,\n", - " fontsize_contrastylabel=12,\n", - " fontsize_delta2label=12,\n", - "\n", - " # Raw bars, Contrast bars, delta text, and delta dots\n", - " raw_bars=True, # swarm_bars=True, OLD \n", - " raw_bars_kwargs=None, # swarm_bars_kwargs=None, OLD\n", - " contrast_bars=True,\n", - " contrast_bars_kwargs=None,\n", - " reference_band=None,\n", - " reference_band_kwargs=None,\n", - " delta_text=True,\n", - " delta_text_kwargs=None,\n", - " delta_dot=True,\n", - " delta_dot_kwargs=None,\n", - "\n", - " # Horizontal Plots\n", - " horizontal=False,\n", - " horizontal_table_kwargs=None,\n", - "\n", - " # Gridkey\n", - " gridkey=None, # gridkey_rows=None, OLD\n", - " gridkey_merge_pairs=False,\n", - " gridkey_show_Ns=True,\n", - " gridkey_show_es=True,\n", - " gridkey_delimiters=[';', '>', '_'],\n", - " gridkey_kwargs=None,\n", - "\n", - " contrast_marker_kwargs=None, # es_marker_kwargs=None, OLD\n", - " contrast_errorbar_kwargs=None, # es_errorbar_kwargs=None, OLD\n", - "\n", - " prop_sample_counts=False,\n", - " prop_sample_counts_kwargs=None,\n", - "\n", - " contrast_paired_lines=True, # es_paired_lines=True, OLD\n", - " contrast_paired_lines_kwargs=None, # es_paired_lines_kwargs=None, OLD\n", - " \n", - "\t\t# Baseline Effect Size Curve\n", - "\t\tshow_baseline_ec=False,\n", - " ):\n", - " \"\"\"\n", - " Creates an estimation plot for the effect size of interest.\n", - "\n", - "\n", - " Parameters\n", - " ----------\n", - " color_col : string, default None\n", - " Column to be used for colors.\n", - " raw_marker_size : float, default 6\n", - " The diameter (in points) of the marker dots plotted in the\n", - " swarmplot.\n", - " contrast_marker_size : float, default 9\n", - " The size (in points) of the effect size points on the difference\n", - " axes.\n", - " raw_label, contrast_label, delta2_label : strings, default None\n", - " Set labels for the y-axis of the raw plot and the contrast plot,\n", - " respectively. If `raw_label` is not specified, it defaults to\n", - " \"Value\" for non binary data (and \"Proportion of Success\" for binary data), \n", - " unless a column name was passed to `y`. If `contrast_label` is not specified, \n", - " it defaults to the effect size being plotted. If `delta2_label` is not specifed, \n", - " it defaults to \"delta - delta\".\n", - " raw_ylim, contrast_ylim, delta2_ylim : tuples, default None\n", - " The desired y-limits of the raw data axes, the\n", - " difference axes and the delta-delta axes respectively, as a tuple.\n", - " These will be autoscaled to sensible values if they are not\n", - " specified. The delta2 axes and contrast axes should have the same\n", - " limits for y. When `show_delta2` is True, if both of the `contrast_ylim`\n", - " and `delta2_ylim` are not None, then they must be specified with the\n", - " same values; when `show_delta2` is True and only one of them is specified,\n", - " then the other will automatically be assigned with the same value.\n", - " Specifying `delta2_ylim` does not have any effect when `show_delta2` is\n", - " False.\n", - " custom_palette : dict, list, or matplotlib color palette, default None\n", - " This keyword accepts a dictionary with {'group':'color'} pairings,\n", - " a list of RGB colors, or a specified matplotlib palette. This\n", - " palette will be used to color the swarmplot. If `color_col` is not\n", - " specified, then each group will be colored in sequence according\n", - " to the default palette currently used by matplotlib.\n", - " Please take a look at the seaborn commands `color_palette`\n", - " and `cubehelix_palette` to generate a custom palette. Both\n", - " these functions generate a list of RGB colors.\n", - " See:\n", - " https://seaborn.pydata.org/generated/seaborn.color_palette.html\n", - " https://seaborn.pydata.org/generated/seaborn.cubehelix_palette.html\n", - " The named colors of matplotlib can be found here:\n", - " https://matplotlib.org/examples/color/named_colors.html\n", - " swarm_side: string, default None\n", - " The side on which points are swarmed for swarmplots (\"center\", \"left\", or \"right\").\n", - " empty_circle: boolean, default False\n", - " Boolean value determining if empty circles will be used for plotting of\n", - " swarmplot for control groups. Color of each individual swarm is also now\n", - " dependent on the comparison group.\n", - " face_color: string, default None\n", - " The face color of the plot. Defaults to \"white\".\n", - " raw_desat : float, default 1\n", - " Decreases the saturation of the colors in the rawplot by the\n", - " desired proportion. Uses `seaborn.desaturate()` to acheive this.\n", - " contrast_desat : float, default 0.5\n", - " Decreases the saturation of the colors of the half-violin bootstrap\n", - " curves by the desired proportion. Uses `seaborn.desaturate()` to\n", - " acheive this.\n", - " raw_alpha : float, default None\n", - " The alpha (transparency) level of the raw plot elements. This defaults\n", - " to 1.0 for all plots except sankey and slopegraphs, whereby it defaults to 0.4\n", - " and 0.5, respectively.\n", - " contrast_alpha : float, default 0.8\n", - " The alpha (transparency) level of the half-violin bootstrap curves.\n", - " bar_width : float, default 0.5\n", - " The width of the bars in the barplot (binary, non-paired data).\n", - " ci_type : string, default\n", - " The confidence interval of the contrast plot to display. Defaults\n", - " to \"bca\". Otherwise, the user can choose \"pct\" for percentile. \n", - " float_contrast : boolean, default True\n", - " Whether or not to display the halfviolin bootstrapped difference\n", - " distribution alongside the raw data.\n", - " show_pairs : boolean, default True\n", - " If the data is paired, whether or not to show the raw data as a\n", - " swarmplot, or as slopegraph, with a line joining each pair of\n", - " observations.\n", - " show_sample_size : boolean, default True\n", - " Whether or not to display the sample size of each group in the axis label.\n", - " show_delta2, show_mini_meta : boolean, default True\n", - " If delta-delta or mini-meta delta is calculated, whether or not to\n", - " show the delta-delta plot or mini-meta plot.\n", - " group_summaries : ['mean_sd', 'median_quartiles', 'None'], default \"mean_sd\".\n", - " Plots the summary statistics for each group. If 'mean_sd', then\n", - " the mean and standard deviation of each group is plotted as a\n", - " notched line beside each group. If 'median_quantiles', then the\n", - " median and 25th and 75th percentiles of each group is plotted\n", - " instead. If 'None', the summaries are not shown.\n", - " fig_size : tuple, default None\n", - " The desired dimensions of the figure as a (length, width) tuple.\n", - " dpi : int, default 100\n", - " The dots per inch of the resulting figure.\n", - " ax : matplotlib.Axes, default None\n", - " Provide an existing Axes for the plots to be created. If no Axes is\n", - " specified, a new matplotlib Figure will be created.\n", - " swarmplot_kwargs : dict, default None\n", - " Pass any keyword arguments accepted by the seaborn `swarmplot`\n", - " command here, as a dict. If None, the following keywords are\n", - " passed to sns.swarmplot : {'size':`raw_marker_size`}.\n", - " slopegraph_kwargs : dict, default None\n", - " This will change the appearance of the lines used to join each pair\n", - " of observations when `show_pairs=True`. Pass any keyword arguments\n", - " accepted by matplotlib `plot()` function here, as a dict.\n", - " If None, the following keywords are\n", - " passed to plot() : {'linewidth':1, 'alpha':0.5, 'jitter':0, 'jitter_seed':9876543210}.\n", - " barplot_kwargs : dict, default None\n", - " By default, the keyword arguments passed are:\n", - " {\"estimator\": np.mean, \"errorbar\": plot_kwargs[\"ci\"], \"err_kws\" : {'color':'black'}}\n", - " sankey_kwargs: dict, default None\n", - " Whis will change the appearance of the sankey diagram used to depict\n", - " paired proportional data when `show_pairs=True` and `is_proportional=True`.\n", - " Pass any keyword arguments accepted by plot_tools.sankeydiag() function\n", - " here, as a dict. If None, the following keywords are passed to sankey diagram:\n", - " {\"width\": 0.5, \"align\": \"center\", \"alpha\": 0.4, \"bar_width\": 0.1, \"rightColor\": False}\n", - " contrast_kwargs : dict, default None\n", - " Pass any keyword arguments accepted by the matplotlib `\n", - " pyplot.violinplot` command here, as a dict. If None, the following\n", - " keywords are passed to violinplot : {'widths':0.5, 'vert':True,\n", - " 'showextrema':False, 'showmedians':False}.\n", - " reflines_kwargs : dict, default None\n", - " This will change the appearance of the zero reference lines. Pass\n", - " any keyword arguments accepted by the matplotlib Axes `hlines`\n", - " command here, as a dict. If None, the following keywords are\n", - " passed to Axes.hlines : {'linestyle':'solid', 'linewidth':0.75,\n", - " 'zorder':2, 'color' : default y-tick color}.\n", - " group_summaries_kwargs : dict, default None\n", - " Pass any keyword arguments accepted by the matplotlib.lines.Line2D\n", - " command here, as a dict. This will change the appearance of the\n", - " vertical summary lines for each group, if `group_summaries` is not\n", - " 'None'. If None, the following keywords are passed to\n", - " matplotlib.lines.Line2D : {'lw':2, 'alpha':1, 'zorder':3, \n", - " 'gap_width_percent':1.5, 'offset':0.1, 'color':None}.\n", - " legend_kwargs : dict, default None\n", - " Pass any keyword arguments accepted by the matplotlib Axes\n", - " `legend` command here, as a dict. If None, the following keywords\n", - " are passed to matplotlib.Axes.legend : {'frameon':False}.\n", - " title : string, default None\n", - " Title for the plot. If None, no title will be displayed. Pass any\n", - " keyword arguments accepted by the matplotlib.pyplot.suptitle `t` command here,\n", - " as a string.\n", - " fontsize_title : float or {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'}, default 16\n", - " Font size for the plot title. If a float, the fontsize in points. The\n", - " string values denote sizes relative to the default font size. Pass any keyword arguments accepted\n", - " by the matplotlib.pyplot.suptitle `fontsize` command here, as a string.\n", - " fontsize_rawxlabel : float, default 12\n", - " Font size for the raw axes xlabel.\n", - " fontsize_rawylabel : float, default 12\n", - " Font size for the raw axes ylabel.\n", - " fontsize_contrastxlabel : float, default 12\n", - " Font size for the contrast axes xlabel.\n", - " fontsize_contrastylabel : float, default 12\n", - " Font size for the contrast axes ylabel.\n", - " fontsize_delta2label : float, default 12\n", - " Font size for the delta-delta axes ylabel.\n", - " \n", - " raw_bars : boolean, default True\n", - " Whether or not to display the raw bars.\n", - " raw_bars_kwargs : dict, default None\n", - " Pass relevant keyword arguments to the raw bars. Pass any keyword arguments accepted by \n", - " matplotlib.patches.Rectangle here, as a string. If None, the following keywords are passed:\n", - " {\"color\": None, \"zorder\":-3}\n", - " contrast_bars : boolean, default True\n", - " Whether or not to display the contrast bars.\n", - " contrast_bars_kwargs : dict, default None\n", - " Pass relevant keyword arguments to the contrast bars. Pass any keyword arguments accepted by \n", - " matplotlib.patches.Rectangle here, as a string. If None, the following keywords are passed:\n", - " {\"color\": None, \"zorder\":-3}\n", - " reference_band : list, default None\n", - " Pass a list of indices of the contrast objects to have reference bands displayed on the plot.\n", - " For example, [0,1] will show reference bands for the first two contrast objects.\n", - " reference_band_kwargs: dict, default None\n", - " If None, the following keywords are passed: {\"span_ax\": False, \"color\": None, \"alpha\": 0.15, \"zorder\":-3}\n", - " delta_text : boolean, default True\n", - " Whether or not to display the text deltas.\n", - " delta_text_kwargs : dict, default None\n", - " Pass relevant keyword arguments to the delta text. Pass any keyword arguments accepted by\n", - " matplotlib.text.Text here, as a string. If None, the following keywords are passed:\n", - " {\"color\": None, \"alpha\": 1, \"fontsize\": 10, \"ha\": 'center', \"va\": 'center', \"rotation\": 0, \n", - " \"x_location\": 'right', \"x_coordinates\": None, \"y_coordinates\": None, \"offset\": 0}\n", - " Use \"x_coordinates\" and \"y_coordinates\" if you would like to specify the text locations manually. \n", - " Use \"x_adjust\" to adjust the x location of all the texts (positive moves right, negative left).\n", - " delta_dot : boolean, default True\n", - " Whether or not to display the delta dots on paired or repeated measure plots.\n", - " delta_dot_kwargs : dict, default None\n", - " Pass relevant keyword arguments. If None, the following keywords are passed:\n", - " {\"color\": 'k', \"marker\": \"^\", \"alpha\": 0.5, \"zorder\": 2, \"size\": 3, \"side\": \"right\"}\n", - " horizontal : boolean, default False\n", - " Whether to display plots in the horizontal format. Default is False. \n", - " horizontal_table_kwargs : dict, default None\n", - " {'show: True, 'color' : 'yellow', 'alpha' :0.2, 'fontsize' : 12, 'text_color' : 'black', \n", - " 'text_units' : None, 'control_marker' : '-', 'fontsize_label': 12, 'label': 'Δ'}\n", - " \n", - " gridkey : list, default None\n", - " Provide either a list of grid keys or 'auto' for automatic grid selection.\n", - " gridkey_merge_pairs : boolean, default False\n", - " Merges the paired grid key groups together.\n", - " gridkey_show_Ns : boolean, default True\n", - " Whether to display the sample size row.\n", - " gridkey_show_es : boolean, default True\n", - " Whether to show the effect size row. \n", - " gridkey_delimiters : list, default [';', '>', '_']\n", - " The delimiter used to split gridkey groups if required.\n", - " gridkey_kwargs : dict, default None\n", - " Pass relevant keyword arguments to the gridkey. If None, the following keywords are passed:\n", - " { 'show_es' : True, # If True, the gridkey will show the effect size of each comparison.\n", - " 'show_Ns' :True, # If True, the gridkey will show the number of observations in eachgroup.\n", - " 'merge_pairs' : False, # If True, the gridkey will merge the pairs of groups into a single cell. This is useful for when the groups are paired.\n", - " 'delimiters': [';', '>', '_'], # Delimiters to split the group names.\n", - " 'marker': \"\\u25CF\", # Marker for the gridkey dots.\n", - " }\n", - "\n", - " contrast_marker_kwargs: dict, default None\n", - " Pass relevant keyword arguments to the effectsize marker plotting. If none, the following keywords are passed:\n", - " {'marker': 'o', 'size': plot_kwargs['contrast_marker_size'], 'color': 'black', 'alpha': 1, 'zorder': 1}\n", - " contrast_errorbar_kwargs: dict, default None\n", - " Pass relevant keyword arguments to the effectsize errorbar plotting. If none, the following keywords are passed:\n", - " {'color': 'black', 'lw': 2, 'linestyle': '-', 'alpha': 1,'zorder': 1,}\n", - "\n", - " prop_sample_counts: bool, default False\n", - " Show the sample counts for each group in proportional plots\n", - " prop_sample_counts_kwargs: dict, default None\n", - " Pass relevant keyword arguments. If None, the following keywords are passed:\n", - " {'color': 'k', 'zorder': 5, 'ha': 'center', 'va': 'center'},\n", - "\n", - " contrast_paired_lines: bool, default True\n", - " Whether or not to add lines to connect the effect size curves in paired plots.\n", - " contrast_paired_lines_kwargs: dict, default None\n", - " Pass relevant plot keyword arguments. If None, the following keywords are passed:\n", - " {\"linestyle\": \"-\", \"linewidth\": 2, \"zorder\": -2, \"color\": 'dimgray', \"alpha\": 1}\n", - " \n", - "\t\tshow_baseline_ec : boolean, default False\n", - " Whether or not to display the baseline error curve. The baseline error curve\n", - " represents the distribution of the effect size when comparing the control\n", - " group to itself, providing a reference for the inherent variability or noise\n", - " in the data. When True, this curve is plotted alongside the main effect size\n", - " distribution, allowing for a visual comparison of the observed effect against\n", - " the baseline variability.\n", - "\n", - " Returns\n", - " -------\n", - " A :class:`matplotlib.figure.Figure` with 2 Axes, if ``ax = None``.\n", - "\n", - " The first axes (accessible with ``FigName.axes[0]``) contains the rawdata swarmplot; the second axes (accessible with ``FigName.axes[1]``) has the bootstrap distributions and effect sizes (with confidence intervals) plotted on it.\n", - "\n", - " If ``ax`` is specified, the rawdata swarmplot is accessed at ``ax``\n", - " itself, while the effect size axes is accessed at ``ax.contrast_axes``.\n", - " See the last example below.\n", - "\n", - "\n", - "\n", - " \"\"\"\n", - "\n", - " from .plotter import effectsize_df_plotter\n", - "\n", - " if hasattr(self, \"results\") is False:\n", - " self.__pre_calc()\n", - "\n", - " if raw_alpha is None:\n", - " raw_alpha = (0.4 if self.is_proportional and self.is_paired \n", - " else 0.5 if self.is_paired and (color_col is not None or self.__delta2)\n", - " else 0.2 if self.is_paired and color_col is None\n", - " else 1.0\n", - " )\n", - "\n", - " if self.__delta2 and not empty_circle:\n", - " color_col = self.__x2\n", - "\n", - " all_kwargs = locals()\n", - " del all_kwargs[\"self\"]\n", - "\n", - " out = effectsize_df_plotter(self, **all_kwargs)\n", - " return out\n", - "\n", - " @property\n", - " def is_proportional(self):\n", - " \"\"\"\n", - " Returns the proportional parameter\n", - " class.\n", - " \"\"\"\n", - " return self.__is_proportional\n", - "\n", - " @property\n", - " def results(self):\n", - " \"\"\"Prints all pairwise comparisons nicely.\"\"\"\n", - " try:\n", - " return self.__results\n", - " except AttributeError:\n", - " self.__pre_calc()\n", - " return self.__results\n", - "\n", - " @property\n", - " def statistical_tests(self):\n", - " results_df = self.results\n", - "\n", - " # Select only the statistics and p-values.\n", - " stats_columns = [\n", - " c\n", - " for c in results_df.columns\n", - " if c.startswith(\"statistic\") or c.startswith(\"pvalue\")\n", - " ]\n", - "\n", - " default_cols = [\n", - " \"control\",\n", - " \"test\",\n", - " \"control_N\",\n", - " \"test_N\",\n", - " \"effect_size\",\n", - " \"is_paired\",\n", - " \"difference\",\n", - " \"ci\",\n", - " \"bca_low\",\n", - " \"bca_high\",\n", - " ]\n", - "\n", - " cols_of_interest = default_cols + stats_columns\n", - "\n", - " return results_df[cols_of_interest]\n", - "\n", - " @property\n", - " def _for_print(self):\n", - " return self.__for_print\n", - "\n", - " @property\n", - " def _plot_data(self):\n", - " return self.__dabest_obj._plot_data\n", - "\n", - " @property\n", - " def idx(self):\n", - " return self.__dabest_obj.idx\n", - "\n", - " @property\n", - " def xvar(self):\n", - " return self.__dabest_obj._xvar\n", - "\n", - " @property\n", - " def yvar(self):\n", - " return self.__dabest_obj._yvar\n", - "\n", - " @property\n", - " def is_paired(self):\n", - " return self.__is_paired\n", - "\n", - " @property\n", - " def ci(self):\n", - " \"\"\"\n", - " The width of the confidence interval being produced, in percent.\n", - " \"\"\"\n", - " return self.__ci\n", - "\n", - " @property\n", - " def x1_level(self):\n", - " return self.__x1_level\n", - "\n", - " @property\n", - " def x2(self):\n", - " return self.__x2\n", - "\n", - " @property\n", - " def experiment_label(self):\n", - " return self.__experiment_label\n", - "\n", - " @property\n", - " def resamples(self):\n", - " \"\"\"\n", - " The number of resamples (with replacement) during bootstrap resampling.\"\n", - " \"\"\"\n", - " return self.__resamples\n", - "\n", - " @property\n", - " def random_seed(self):\n", - " \"\"\"\n", - " The seed used by `numpy.seed()` for bootstrap resampling.\n", - " \"\"\"\n", - " return self.__random_seed\n", - "\n", - " @property\n", - " def effect_size(self):\n", - " \"\"\"The type of effect size being computed.\"\"\"\n", - " return self.__effect_size\n", - "\n", - " @property\n", - " def dabest_obj(self):\n", - " \"\"\"\n", - " Returns the `dabest` object that invoked the current EffectSizeDataFrame\n", - " class.\n", - " \"\"\"\n", - " return self.__dabest_obj\n", - "\n", - "\n", - " @property\n", - " def lqrt(self):\n", - " \"\"\"Returns all pairwise Lq-Likelihood Ratio Type test results\n", - " as a pandas DataFrame.\n", - "\n", - " For more information on LqRT tests, see https://arxiv.org/abs/1911.11922\n", - " \"\"\"\n", - " try:\n", - " return self.__lqrt_results\n", - " except AttributeError:\n", - " self.__calc_lqrt()\n", - " return self.__lqrt_results\n", - "\n", - " @property\n", - " def is_mini_meta(self):\n", - " \"\"\"\n", - " Returns the mini_meta boolean parameter.\n", - " \"\"\"\n", - " return self.__is_mini_meta\n", - "\n", - " @property\n", - " def mini_meta(self):\n", - " \"\"\"\n", - " Returns the mini_meta results.\n", - " \"\"\"\n", - " try:\n", - " return self.__mini_meta\n", - " except AttributeError:\n", - " self.__pre_calc()\n", - " return self.__mini_meta\n", - "\n", - " @property\n", - " def delta_delta(self):\n", - " \"\"\"\n", - " Returns the delta_delta results.\n", - " \"\"\"\n", - " try:\n", - " return self.__delta_delta\n", - " except AttributeError:\n", - " self.__pre_calc()\n", - " return self.__delta_delta\n", - " \n", - " @property\n", - " def delta2(self):\n", - " return self.__delta2\n", - " \n", - " @property\n", - " def is_delta_delta(self):\n", - " return self.__delta2" - ] - }, - { - "cell_type": "markdown", - "id": "0c622e1a", - "metadata": {}, - "source": [ - "#### Example: plot\n", - "\n", - "Create a Gardner-Altman estimation plot for the mean difference." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a9a1388f", - "metadata": {}, - "outputs": [], - "source": [ - "random.seed(9999) # Fix the seed so the results are replicable.\n", - "# pop_size = 10000 # Size of each population.\n", - "Ns = 20 # The number of samples taken from each population\n", - "\n", - "# Create samples\n", - "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n", - "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", - "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", - "\n", - "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n", - "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n", - "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n", - "t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", - "t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", - "t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", - "\n", - "\n", - "# Add a `gender` column for coloring the data.\n", - "females = repeat('Female', Ns/2).tolist()\n", - "males = repeat('Male', Ns/2).tolist()\n", - "gender = females + males\n", - "\n", - "# Add an `id` column for paired data plotting.\n", - "id_col = pd.Series(range(1, Ns+1))\n", - "\n", - "# Combine samples and gender into a DataFrame.\n", - "df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n", - " 'Control 2' : c2, 'Test 2' : t2,\n", - " 'Control 3' : c3, 'Test 3' : t3,\n", - " 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6,\n", - " 'Gender' : gender, 'ID' : id_col\n", - " })\n", - "my_data = dabest.load(df, idx=(\"Control 1\", \"Test 1\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e7f5f4bc", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig1 = my_data.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "f7fb5efe", - "metadata": {}, - "source": [ - " Create a Gardner-Altman plot for the Hedges' g effect size." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e457ae1e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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9AQBpaUXLzDZo0AAGgwG3b9+GJEkYOHAgtm3bBmfnii9+xF7fdir5TiYUkmT2mEKSkHTbtiZLILJb12KtHYFN+/HHH6FWq7FkyRLcvn3b+Lp16xYWL14MJycn/Pbbb0hLS8PChQuxe/duLFy4sFLXYKK2Uz5ebjCU0jvSIAR867nXckREVCP+PAnkplk7Cpv1/PPPY8iQIVi0aJGxNg0ULQKyePFiDBo0CM8//zzc3d2xZMkSPPbYY9i2bVulrsFEbaf6dw6BSmn+9quUCvTrFFLLERFRjRAGIOGgtaOwWYcOHUK7du1KPd6uXTscOHDAuP3QQw8hJaVy49uZqO2Up6sLFk0aCgeVEpIkQalQQJIkOKiUWDRpKDxdK/78hIhk7nrdXpRCzjw8PPDTTz+Venz37t1wd/+7hTI7O7tSc5YD7Exm17q1CsKmRU/h52MXkHQ7A7713NGvUwiTNJGt+eMYoMsHVOWvOEWVM23aNLz++usYPXo0nn32WTRv3hwAcOXKFaxatQo7duwweSa9a9cuk1XAKoKJ2s55urqwdzeRrdPlA3+cAPwrPzSIyrZ48WLk5ubigw8+wPbt202OKZVKzJ49G4sXLwYA5OXlYdKkSWjbtm2lrsFETURkD+L3MVHXAEmSsGLFCsyZMwe//PILEhISAAD+/v7o27cvvL29jWU1Gg0mTpxY6WswURMR2YP4WODBWWz+riHe3t4YN25cjZybncmIiOxBQQ5wLcbaUVAVMFETEdmLM18DNri6lK1joiYisjGdOnWC3/PfotObJ0wP3LwI/HnC/JtItpioiYhsTHJyMv64k4vkzIKSB0/+u/YDomphoiYisid/nACSTlk7CqoEJmoiIntzdB2fVdchHJ5FRGRvkk4BiQcB/+7WjqRO6tOnT4l9e/furbHrMVETEdmjg/8HNO4EqBytHUmd4+/vX6vXY6ImIrJHGb8DpzYDHSdZO5I6Z/369bV6vSo/o9br9diyZQumT5+OUaNG4fTp0wCAjIwMfPPNN5VexouIiGrZiY3A7avWjoLKUaVEnZ6ejh49euDxxx/Hl19+ie+//x43b94EAGi1WsycORMrV660aKBERGRhBh3w63JAZ2YYF1VIXFwcvvzyS5N90dHR6NmzJ7p27WqRXFilRP3KK6/g7NmziI6OxrVr1yDu6T2oVCoxevRo7Nq1q9rBERFRDbt9FTi61tpR1Flz587FV199ZdyOj4/HqFGjEB8fDwCYPXs21qxZU61rVClRf/vtt3jhhRfQv39/SJJU4vgDDzyA69evVyswIiKqJf/7D5B42NpR1EmnTp3Cgw8+aNz+4osvoFQqcfLkSRw+fBijR4/Gp59+Wq1rVClRZ2RkIDAwsNTjhYWF0Ol0VQ6KiIhq2a/Lgeyb1o6izsnIyEC9evWM27t27UL//v1Rv359AED//v1x5cqVal2jSom6WbNmOHGi9Plif/rpJ4SGhlY5KCIiqmV5GcDeZYBBb+1I6hRfX1+cP38eAJCUlITjx49jwIABxuPZ2dlQKKo3t1iVhmdNnToV8+bNQ69evdC3b18ARYtn5+fn4/XXX8fu3bur3SZPRES1LOkUcOILoNNka0dSZ4wYMQIfffQR8vLycPjwYajVaowaNcp4/NSpUwgKCqrWNaqUqF988UWcPXsW48aNg4eHBwDg8ccfx+3bt6HT6TB9+nRMmTKlWoEREQFAQXYaUk/vRX56CtQeDeHdpg8ctZ7WDst2nfgC8OsE+LSxdiR1whtvvIGbN29i48aN8PDwQFRUFBo2bAgAyMzMxLZt2zBjxoxqXaNKiVqSJKxduxYTJ07Etm3bcPnyZRgMBjRr1gyPPvooevbsWaVgVq1ahVWrVhk7orVq1QqLFi3C4MGDzZaPiorC5Mmmf/mp1Wrk5eVV6fpEJC+3Lx3Gha8jIQw6SJICQhiQELMRIaMXwKtFF2uHZ5uEAdi7HBj9OeDobO1oZE+r1WLTpk2lHvv999/h7Fy9f8dqzUz24IMPmvR2qy4/Pz+89dZbaNGiBYQQ2LBhA0aMGIGTJ0+iVatWZt/j5uaGixcvGrfN9UInInkqq7ZckJ1WlKT1hQAAIYqenQp9Ic5vexOdX1jPmnVNyUoCDn0C9HzJ2pHUORkZGdBqtVAqlVAoFHB3d6/2OWW1etY//vEPDBkyBC1atMADDzyA5cuXQ6vV4tChQ6W+R5Ik+Pj4GF/FTQ5EVHkF2Wn4/eDXuPrjJ/j94NcoyE6rsWvdvnQYRz+ajOt71yP55G5c37seRz+ajDuXjwAAUk/vhTCYHz0iDDqknq65RRAIwPkfOGSrgo4dO4ZBgwbB2dkZ9erVQ2xsLADg1q1bGDFiBGJiYqp1/irVqAMDA8utuUqShKtXqz41nV6vx9atW5GTk4Pw8PBSy2VnZ8Pf3x8GgwEdOnTAm2++WWrtGwDy8/ORn59v8n4iqt1m5orUlvPTU/6Ko2QvZElSID+d0xTXuNgVwOh1gBNbLkpz4MAB9OnTB40bN8YTTzyBzz77zHisfv36yMjIwOrVq9GrV68qX6NKiToiIqJEotbr9UhISMBvv/2G1q1bo3379lUK6PTp0wgPD0deXh60Wi22b99e6lCv4OBgfP7552jbti0yMjLw7rvvonv37jh79iz8/PzMvicyMhJLly6tUmxEtqqmmplLa9quSG1Z7dEQQhjMlxEGqD3Yelbj7t4GYt4CBkYC1RxiZKsWLFiAkJAQHDp0CFlZWSaJGgB69+6NDRs2VOsaVUrUUVFRpR47deoUBg4ciPHjx1cpoODgYMTFxSEjIwPbtm3DxIkTERsbazZZh4eHm9S2u3fvjpCQEKxevRrLli0ze/758+dj9uzZxu24uDhERERUKVYiW1GRxOkX/kiJY2U9Yy6rhl6R2nKTh8YhIWaj8Y8HkzIKFbzb9q3mb00VkngIiNsEdHjS2pHI0tGjRxEZGQm1Wm22hbZx48ZITk6u1jUsvsxlu3btMH36dMybNw/Hjx+v9PsdHR3RvHlzAEDHjh1x9OhRrFy5EqtXry73vQ4ODmjfvn2Zs8Co1Wqo1WrjtlarrXSMRLamKs3MZSVirW+LMmvofuGPlFtbdtR6ImT0Apzf9qbJNSSFCiGjF8DRxcNy/wBm3Ni/BXeuHEVOcjwkpQrhL/+n3PcIIZAY+28kx0VDn5cDV78QNB8yA05ejY1lzn21FDkp8SjISYfKSQuPgDAE9J0MtWu9Ms5sZcfWAfUfAJp2tXYksuPg4ACDwfxnGQD++OOPaueZGmnLaNiwIc6dO2eRcxkMBpNnymXR6/U4ffo0fH19LXJtIntR2WZmk6ZyISAM+qKffyXipGM7yqyhAxIkhfl6wr21Za8WXdD5hfUI6DMZPu0HIaDPZHSeGWWxZ+b/++IVpJzaY/aYQa9D/ZAH4dNxSIXP98fBbfjz6A9oPngG2k1+H0pHDc5sXgjDPatTuQe0RfDDr6Djs2sQ8sgC5KUn4cLXb1b7d6lRQhTNWpbxh7UjkZ1u3bph27ZtZo/l5ORg/fr11W61tXiivn37NtatW1fqM+KyzJ8/H/v27cP169dx+vRpzJ8/HzExMcZm9AkTJmD+/PnG8q+//jp++uknXLt2DSdOnMATTzyBhIQETJ061WK/D5E98G7Tp0KJs1h5TeXp8XGQJPNfL5KkgC43EyGjF0BSOgCSBEmhLPqpdChRW3bUesIv/BE0G/wc/MIfqfGadDH/iCfQuOsouHj7V6i8EAJ/HPkOTR4ci3rB4XBpGIgHhs9BQdYd3L540FiucddRcPNrCY2HN9yahMKv+xhk/X4RBr3M10fIzwL2LOKSmPdZunQpjh07hqFDh+LHH38EUPQI+LPPPkPHjh1x8+ZNLFy4sFrXqFLTd58+fczuT09Px4ULF1BQUICNGzdW+rypqamYMGECkpKS4O7ujrZt2yI6Ohr9+/cHACQmJprMmZqWloZp06YhOTkZnp6e6NixIw4cOMB5xokqqbLNzOU1lQMot4ZeXFs2ecbdtm+tJWJLy09PRmF2GjwCw4z7VBoXuDYORubvF9CgVclaVWFuFm6eiYGbXwgUSos/ibS821eAI6uB7i9YOxLZ6Nq1K3bt2oVnn30WEyZMAADMmTMHQNG6GLt27ULbtm2rdY0qfTIMBkOJXt+SJCEwMBD9+vXDU089hZYtW1b6vOvWrSvz+P1j0T744AN88MEHlb4OEf3t3g5hfuGjAQnQ3c00Jk4Igd8Pfm3SYay8pnKPwPbITr5abkew4tqyLSgec+7oYto73tHFA4U5puPR43/5HEnHdsBQmA/Xxi0ROnZxrcVZbWe+BoJ6cYrRe/Tp0wcXL15EXFycyUydHTt2tMgkXFVK1NUdvE1E8mCuQ1hxLdqrRZdSO4y1GDYTkkJVaiL27TwMro0fsFpHsNLc2P8Vbvz2d6cwg64AWX9cwNXdf68X3OGZVdC4e9doHH7hj8AnbCDyMlJxY99mXPr+PYSOXVI3ZlYUAjjwETDyUw7Zuk9YWBjCwsIsft460NZCRDWhvLHTYU99WOrxyzv+heZDZ+LKzn+Vmojl2LTt03EI6oc+ZNy++O07qN+yB+q17G7cV9Xe18apT3PS4OjqZdxfkJMOl4amqyc5OLvDwdkdTvUaw7l+Exz910Rk/XEBbn4hVbr2vRITE5GTkwMAyMnXI/FOHpp6aap9XhM3LwIJ+4HAqq3rYEv27dtX5nFJkqDRaODn51fljs4VStRffPFFlU5e3F5PRPJTXoew679GlXm8MCet3EQst6ZtBydXODi5GrcVKjUcXNzh5NWo2udWe/jAQeuJ9OunoPVpBgDQ5d9F1h8X4VtGz/HiRwgGXcnWico4cuQIli1bhp07d0IIAQBIz9Uj4NUjGNbGCwuH+KNzgGs5Z6mEM18zUQPo1atXhVtCWrRogaVLl2Ls2LGVukaFEvWkSZMqdVKg6K8IJmoi+Sp37HRGarljq+WWiC0pLyMVutws5GfcBIQB2clFUyI7eTWC0tEJAHB81XT4956I+i27Q5IkNO4yAjf2b4GTVyNoPHyQELMRjq5eqBdcNDFT1h8XkPXnZbg1CYVK44q8tCQkxG6ExtO3WrXpb775BmPHjoUQwpikiwkB7DpzBz+eScNX00LwcPv6Vb6OiT/jgKwUwNW+Z4jbvXs35s2bh/z8fEybNs04D8jly5fx2WefwcnJCa+99hoSEhKwevVqPP7441AqlRg9enSFr1GhRB0fH1+134CIZKvcsdPu3rh7K7H04zY+hWdi7L+R+r9fjNtxn80EALR+IhIeAUW9eHNv/w59fo6xTOPw0dAX5OHKzo+gy8uBW5NQtB63DAqVIwBA4aDB7QsHkLhvE/QFeXDUesGzWUc0eXAsFCqHKsV55MgRjB07Fnq9vkSSLqY3ABIExq49jwNzwyxXs074DWj9sGXOVUft3r0bGo0Ghw8fhqOjo8mx5557Dr169cKhQ4ewYsUKPPPMM+jUqRNWrFhh+UTt71+xcYREZH3Fz0rLm5vbu02fMqfoDOgzGenxcTY9hWfbCW+VeuyB4bPxwPDZpR4HgAdf22myLUkS/Hs9Cf9e5qfbdPEOQJsnIysfaBneeOMNszXp+wkAAgJv7ErAd8+1tszFk+Jkmaj37duHd955B8ePH0dSUhK2b9+OkSNH1si1Nm3ahNdee61EkgYAjUaD8ePHY/ny5VixYgU0Gg2eeOKJUqe4Lg277BHZmLApK9HlxS8QNmVlmeWKx06XNumIi7d/hSclIetITEzEjh07oNeXfDxhjt4A/HD6DhLv5FkmgLQEy5zHwnJyctCuXTv83//9X61cKyWl9JXckpKSTOYA9/DwgFKprNQ1qtzrOzk5GevWrcOJEyeQkZFRYq5TSZLwyy+/lPJuIrqXQVdglZmp3Jq2Rvvpn+DW2X24eycZDm710SC0Jxxc3JGbnQEn32C0fmolbp37L/IzbkLt3sDkuCUplCpjE7Ets+S93rXzh3Jr0vcTAog+dwcTu1lgCFrmTUiF1esEVx6drvL/VoMHD8bgwYNrIJqS+vTpgw8//BDdunXDsGHDTI798MMPWLlyJfr2/bv1KS4uDgEBAZW6RpUS9f/+9z/06tULubm5CA4OxunTpxEaGor09HT88ccfaNasGZo0aVKVUxPZHYOuAOnXT0GXd9dqMWg8ffHRl7vx7qrqLcdHdcPT/76Cp/9d+uJFlTLN9v+4KsvHH3+M3r17Y8SIEWjcuDGaNSvq8X/16lX88ccf8Pf3x0cffQQAyMvLQ2JiYqWnua5Son7llVeg1WoRFxcHZ2dneHt7Y+XKlejTpw+2bt2KZ599Fps2barKqYnsjkGvgy7vLhRKFSQr1ihfnvkM5jw/3SrXFn/VMr1adIJK7WyVGGqDLv8u7lw+ZrF7vWX7Dvzztcov6PHpY/6Y0KV6q3UJIaDzaAaHRz+r0Xt28uRJdO3aFdnZ2cjMzDTuv38lRGtp2rQpTp8+jU8//RTR0dFISCh6HBASEoJZs2Zh+vTpcHFxAVD0zHrXrl2VvkaVEvVvv/2GuXPnomnTprhz5w4AGJu+x4wZg/379+Pll19GbGxsVU5PZJcklSOUDtb74lFWrdOxRegLVdDn34WDgwNUDlYMpIZJBgc4OKigVDtb5F737dUTkhRZqeZvSQL6t3SHg6pyz0lLEAK6+s1r/J6pVEVp6v4VqBYvXowlS5bU2HUrw9nZGbNnz8bs2WV3PqyqKs/13bBh0dCM4gfjxQkbANq0aVPuvN1EVPfocrOQdu0kCnPS4ODiCc+g9lA5WXASDaqUJo19MbBPT+yJ2V+hDmVKBTAk1ANNvSzzB2FBww6orfaP2NhYk+k55VCbri1VStSBgYHGsdUKhQKBgYH4+eef8eijjwIADhw4AA8PD4sFSUQVd2XXx9DlZUGlcUXzIc9b7LyZv5/HjX2bIAyGomqZEEg99ROa9BxvkakvqWpefuFp/By7H5IklVmzlgBIkDB/YNWmsbyfrl5LGFxqdk70e2m1Wri5udXa9UpT2uqRZalu5+oKD89KS/t79ZcBAwZg69atxu1nn30Wn332Gfr164e+fftiw4YNePzxx6scFBFVnS4vC7q7mdDlZVnunLlZfyVpPQABCAMAAWHQ48a+TdDlWu5aVDkd2rXG5x+9DaVSUeqwH6UCUCokfDm5GTr7ay1y3fygARY5T03Izs5GXFwc4uLiABRN2hUXF4fERPMT+FSGwWAwjlsvfiUmJiImJgYnT55ERkYGMjIyEBcXh5iYGNy4caPSPfPvV+EatY+PD4YMGYLx48djzpw5GDduHAoLC+Hg4IBZs2YhJycHX3/9NZRKJRYuXIgFCxZUKzCyjrSsHOw5eh7JdzLh4+WG/p1D4OnqYu2wyMrSrp0sqkmbIQwGpMefRP1QzvtsLf8Y1A/R277AOx+tQfTefSaJQZKKmrvnD/S1WJLW13sAunrBQEGuRc5naceOHUPv3r2N28XPjidOnIioqKhqnfv+1SP379+P4cOHY+3atZg4caLxmbpOp8P69esxb968al+zwol69OjR+P777/H999/D1dUVDz/8MMaPH48+ffpAkiS89tpreO2116oVDFXfc+9tRlrWXXi6OuOTOZVr1Th45hqWbdgJnd4AhSTBIASifjyIRZOGoluroPJPQDarMCfN2NxdgiQZ12Im6+nQrjW+/OxfuPFHEnoOGYP0zCx4OClxfF4riz2TLiIhP/TRos+DTPXq1avatdiKeumllzB58mRMmTLFZL9KpcK0adNw4cIFzJ49G4cPH67yNSrc9L1p0yakpqbi3//+Nx566CFs2rQJAwYMQOPGjTFnzhycOHGiykGQ5aRl3cWtjGykZVVuTG5aVg6WbdiJQl3RfMH6v5p3CnV6vB61E2lZOeWfhGyGLjcLN8/uw59HvsPNs/ugdHQ2n6QBQIhypyul2tOksS+cnYsWDXFxVFg4SQOFAb1g8Aiw6Dnrsv/9738ICiq9IhMYGIjTp09X6xqVmkLUyckJ48aNww8//IDk5GR88sknaNGiBT788EN07twZLVu2xBtvvIFr165VKyiqfXuOnodOb75pU6c34OdjF2o5IrKWzN/P4+L2FUg5uRt3Lh9BysnduHk2ptQalKRQwCOoQ+0GSVYhnOsjP8Q2V0urqkaNGuGrr74yO4OaTqfDV199hUaNqreMapXn+vb09MT06dMRGxuLxMREvPXWW3B2dsaiRYvQokULdO/evfyTkGwk38mEopQvYoUkIem2ZaeLJHkqrdMY/lplS1IoAUiApABQNP93k57jodJY5tknyZikQG6H6YBKY+1IZGXu3LnYv38/unXrhs8++wwxMTGIiYnB2rVr0bVrVxw4cAAvv/xyta5R5bm+79W4cWO8/PLLGDRoEBYtWoTvvvuuWu3xVPt8vNxgKKVp0yAEfOu513JEZA1ldRqDEKjfuheUDmoUZKfBUesJj6AOTNJ2Ir/VWBg8A60dhuw8/fTTUCqVePXVV/H0009D+qvCI4RAgwYN8Omnn2LatGnVuka1E3ViYiI2b96ML7/8EmfOnIEQAt27d8f48eOre2qqRf07hyDqx4Mo1JWcNEGlVKBfJ46TtQfldRrT599Fw3b9az8wsqrCJj1QGFD58cP2YsqUKZg4cSKOHj1qHALm7++PTp06GXuBV0eVznDr1i385z//webNm3Hw4EEIIdCyZUu8/vrrGD9+fKVXBiHruXc4Vu8Owfj1+AXoDMLY61ulVGDRpKHwdLXd+Zfpbw4unuw0Rib09R5AftsnZd3LWw5UKhXCw8MRHh5u+XNXtGBOTg62b9+OzZs345dffkFhYSF8fX0xa9YsjB8/Hh06sDNJXWNuOJZSIWFA5xA4OqjgW88d/TqFMEnbEc+g9kg99dNfz6hNsdOY/TFofZDb6TlAYZGnpDYrMzMTn3zyCX799VekpqZi9erV6NKlC+7cuYOoqCgMHz4czZs3r/L5K/yv7+3tjby8PGi1Wjz++OPGMdQKRZX7o5EV3TscCwD0f9WidHqBvScuYtOipzjRiZ24f/7uxt0exh+HvjGZKlRSKNhpzM4ItRtyu84CHHnPy/L7778jIiICN27cQIsWLXDhwgVkZ2cDALy8vLB69WokJCRg5cqVVb5GhRN1v379MH78eAwfPhwaDXv91XUVGY41pnfHWo6Kapu5+bslhQKNuj0MfV42O43ZK5UGuV1nQTjXt3Yksvfyyy8jKysLcXFx8Pb2hre36fznI0eOxI4dO6p1jQon6u+++65aFyJ5KR6OpTfzPJLDseyD6VAsGJ9NC4Mefx76BsGj5nFlLHskKZHb6TkY3JtaO5I64aeffsI///lPhIaG4vbt2yWOBwUF4caNG9W6Btut7RSHY1FF5u8m+5MXNgn6BqHWDqPOyM3NRYMGDUo9npVV/QVrmKjtVP/OIVApzd9+DseyD8ahWOZw/m67VNBiKHR+lu+1bMtCQ0Oxb9++Uo9/++23aN++fbWuwURtpzxdXbBo0lA4qJSQJAlKhQKSJMFBpeRwLDvBoVh0L713GxQEj7B2GHXOrFmzsGXLFqxYsQIZGUWPDA0GA65cuYInn3wSBw8exD//+c9qXYN97u1Yt1ZB2LToKfx87AKSbmdwOJad4VAsKiY0HsgLe+qvqWGpMp544gkkJCTgtddew6uvvgoAGDRoEIQQUCgUePPNNzFy5MhqXYOJ2s55urqwd7edUjm5oknP8WZ7fXMoln3JazcRQs2Og1X16quv4sknn8TXX3+NK1euwGAwoFmzZnj44YfLXFmropioieyYm18IgkfNQ3r8SQ7FslOFTXpA793G2mHUeU2bNq12E3dpmKiJ7JzKyRX1Q3taOwyyAqHxQH6rsdYOg8rBRE1EZKfy2k0EHNgnpbLatm1bqfKSJOHUqVNVvh4TNRGRHSr078Um7yry8vIyLmcJAIWFhThw4ADatm0LT0/Lj5ZgoiYisjMGrQ/yQ8dYO4w6KyYmxmT71q1b8Pb2xvvvv48+fSy/HCj74hMR2RNJgbz2UwCV2tqR2AyphpcAZaImIrIjBc0Hw+ARaO0wqBKYqImI7IRwro+CFsOsHQZVEp9RE1G13L+etWdQe666JVP5LR8GlA7WDoMqiYmaiMpUViI2t5516qmf0KTneLj5cWEXOTG4NoKuUSdrh2ETTpw4YbJdPMf35cuX4eHhYfY9HTpUfUpeJmqymLSsHOw5eh7JdzLh4+WG/p1D4OnqYu2wqBrKSsTO9fxKXc/6xr5NXM9aZgqCBnAubwvp1KmT2Q5kzz33XIl9QghIkgS9vuSc+hXFRE0WcfDMNSzbsBM6vQEKSYJBCET9eBCLJg1Ft1bVn+uWak5pNWZdblaZibh+aES561lzxjOZUGmga9TZ2lHYjPXr19fq9ZioqdrSsnKwbMNOFOqKvtD1f32hF+r0eD1qJzYteoo1a5kqq8acn3GzzESck3zF+J4SuJ61rBT6duRwLAuaOHFirV6P7SBUYWlZOfjP3mP417a9+M/eY0jLygEA7Dl6Hjq9+S90nd6An49dqM0wqYJMa8wCEAYAwlhjzs9IKUrE5kgSBMD1rOsI1qbrNtaoqULKatpOvpMJhSQZa9L3UkgSkm5nWCFiKk/atZNl1pj1+TllJmKtb3Pk3fmD61nLnUoDff2W1o6CqoE1airXvU3bQgjoDQYIIYxN2+4uGhhK+UI3CAHfeu61HDFVRGFOWpk1ZqXaBZLC/FeEpFCgXnA4mvQcD0mhBCD91VFJgqRQcj1rGdE1CAUUrJPVZbx7VK7ymrYBCSqlwviM+l4qpQL9OnGYjhw5uHiWWWPWeDSEW8/xJZ5hSwqFMRFzPWv50zdobe0QqJqYqMmotOFV5TVtZ+TkYtGkoXg9yrRpXKVUYNGkofB05TJ6cuQZ1B6pp34qs+lapdGWm4i5nrW86RqEWjsEqiZZJepVq1Zh1apVuH79OgCgVatWWLRoEQYPHlzqe7Zu3YqFCxfi+vXraNGiBVasWIEhQ4bUUsS2o6xn0D5ebuU2bXdrFYRNi57Cz8cuIOl2BnzruaNfpxAmaRlTObmiSTk15uJyTMR1i3eD+pDyM9HQzRHCub61w6FqklWi9vPzw1tvvYUWLVpACIENGzZgxIgROHnyJFq1alWi/IEDBzBu3DhERkZi2LBh2Lx5M0aOHIkTJ06gdWs291RUecOr/m/2Y4j68WC5Tdueri4Y07tj7QVO1cama9v06/dfwuWX+dB7tUCetYOhapNVZ7J//OMfGDJkCFq0aIEHHngAy5cvh1arxaFDh8yWX7lyJQYNGoSXX34ZISEhWLZsGTp06ICPP/64liOv28p7Bn3sQiIWTRoKB5USkiRBqVBAkiQ4qJRs2rYBxTXmRl1GoH5oTyZpG6L3ambtEMgCZFWjvpder8fWrVuRk5OD8PBws2UOHjyI2bNnm+wbOHAgvv3221qI0HZUZHjVmN4d2bRNVMfo3ZtaOwSyANkl6tOnTyM8PBx5eXnQarXYvn07QkPNd4ZITk5Gw4YNTfY1bNgQycnJpZ4/Pz8f+fn5xu3s7GzLBF6HVeQZNMCmbaK6REgSDNpG1g6DLEBWTd8AEBwcjLi4OBw+fBjPPvssJk6ciHPnzlns/JGRkXB3dze+IiIiLHbuuqp/5xColOY/ChxeZft0uVm4eXYf/jzyHW6e3Qddbpa1QyILEBoPThtqI2SXqB0dHdG8eXN07NgRkZGRaNeuHVauXGm2rI+PD1JSUkz2paSkwMfHp9Tzz58/HxkZGcZXbGysReOvizxdXfgM2k5l/n4eF7evQMrJ3bhz+QhSTu7Gxe0rkPn7eWuHRtUknLysHQJZiOyavu9nMBhMmqrvFR4ejl9++QWzZs0y7tuzZ0+pz7QBQK1WQ63++69Mrda+O87cO3b60T4dIaFoXDSfQdu+8lbH4jKVdZtBzRkBbYWsEvX8+fMxePBgNG3aFFlZWdi8eTNiYmIQHR0NAJgwYQIaN26MyMhIAMCLL76IiIgIvPfeexg6dCi2bNmCY8eOYc2aNdb8NeoMc2Oniycp4dKUtq+8ub65TGXdJhztuxJiS2TV9J2amooJEyYgODgYffv2xdGjRxEdHY3+/fsDABITE5GUlGQs3717d2zevBlr1qxBu3btsG3bNnz77bccQ10B5c3fXbwyFtmu8ub65jKVdZwDW8Nshaxq1OvWrSvzeExMTIl9Y8aMwZgxY2ooIttVkaUp2cPbtpU31zeXqazbhFJj7RDIQmRVo6baUzx22hwuTWkfPIPal7k6FpeprOOUjtaOgCyEidpOVXTsNNmu4rm+uUylbRJM1DaDidpOcew0AX/P9e3TYRC8WnSBT4dBCH74Fbj58f7XeXaeqP/v//4PAQEB0Gg06Nq1K44cOWLtkKqMidrGeLo6o767ttxhVRw7TcU417dtEkoHa4dgNV999RVmz56NxYsX48SJE2jXrh0GDhyI1NRUa4dWJbLqTEbV98mcxytclktTEtkwO65Rv//++5g2bRomT54MAPj000+xc+dOfP7553jllVesHF3lMVHLQEGhrtQe2DVN7eCAoeFtoNfrYTAYAAhkWGFolkqpgKODfX4cdYWFKCzUwaDQwWCJRi7x98/CwsLqn68W6At10BfqUFhYCKGoGzFXRfG91hvuQirU1ei1FPmFMChr7v9loSuAQV/z90ynK/p3ys7ORmZmpnH//ZNXFSsoKMDx48cxf/584z6FQoF+/frh4MGDNRZnTbLPb0YZKSjUIe7yDeTkmZ99rbZErf4YX6z5P6vGQJbx9Uu94e3uhD+TUxD+QCdrh0NkEfevy7B48WIsWbKkRLlbt25Br9ebXbDpwoULNRlijWGitjKd3oCcvHw4qJRwUCmtFsf051/E08+9gOzcPMRd/gPp2XfhoXVGWIvG0DpVfTzmhcQUfPXLcRgMBkgKCcIgoFAoMLZvR7RsWvQ/UqFOj0KdHl1Cg+Cssb/mOl3+Xdy5fAxKtTOUDtX//a9+/y50uZlo5NMQqZeOWSDCmqcvLIA+/y68WnSCSm3bj14Mf9VEa1xuGuBUs2PhFUoVFKqa/X/25MmT6Nq1K2JjYxEWFmbcb642bauYqGXCQaWExtGanT8ccDb+T2yMPgy9wQBJkiCEQMypK5gwqBtCA3wrfcasu3nYFhsHIUmQlEV/hEjKopbZbbFxeHXCYLg6a5BXUAiRVwAHBwc4ONhfBxjJ4AAHBxWUDiooLfH7S3//rCv/ngoYoDCo4ODgAFUdibnKauv3U0mAxq12rlWDVKqiNKXVauHmVv7vU79+fSiVykov2CRn7PVNAIqS6sbow9DpDRACMBgEhCiq8X+x+xCy7uaV+/5fT1zEN7En8euJi8i6m4djFxKgL2Uuab3BgOMXE2viV7F7Ko0rVM5uUGm4oIZdk+zz693R0REdO3bEL7/8YtxnMBjwyy+/lLlgk5yxRm1jPvzPL8i6mw9XZzVmPdq3wu+rSFLt1f4Bs8fN1cSjj5xDcz9v4/b9JEnCnUzOJ14Tmg953tohkByUNo+7HZg9ezYmTpyITp06oUuXLvjwww+Rk5Nj7AVe1zBR25isu/nIyMmt9PvSsu5WKaneWxMHYHy/Tm/ApcQUs+crLufl5lLpOImoouw3UY8dOxY3b97EokWLkJycjLCwMOzevbtEB7O6gomaABRNlFKVpFpWTdwgBBSlJH+lQoGOwU2rHjARlc2Oa9QA8Pzzz+P5522jdck+H2JQCZ1a+kNZygINZSXV4pq4OQqFhAeaeEOlVECSirYlqWjM9IRB3eDqzNV9iGqOfSdqW8IaNQEAXJ01mDCoG77YfcjkWbNSUZRUAeDXExeRlnUXnq7O6NTSH67OmnJr4s39vDG2byccv5iIO5k58HJzQcfgpkzSdYguNwtp106iMCcNDi6e8AxqD5UTO6rJnp3XqG0JE7WdK+6dXZyAXxzTBxcTU0ySamLKHSz/4scSncUmDOqGTi39EX3knNmZ1Ypr4q7OmlI7opG8Zf5+Hjf2bYIwGIq++IVA6qmf0KTneC7cIXcKGx/mZkeYqO1Yab21JwzqZkysZXUW+2L3Ibw6YXCZNXHWnOWvtBqzLjfrryStLyr4170XBj1u7NuE4FHzWLOWs1IeZVHdw0RtpyqSgF2dNRUetvXqhMFs3q6Dyqox52fcLNpvhjAYkB5/EvVDe9ZyxET2h4naTlU0AVd02Babt+ue8mrM7gHtjMm7BElCQXZaLUZLZL/YNmKnyuqtfW8CruqwLZK/tGsny6wx6/NzzCdpABACjtqanUeaiIowUdupiibgqg7bIvkrzEkrvWewJEGpdoFUyr2XFAp4BHWoweiIqBgTtZ2qaAIuHrbFsdC2x8HFs8was8ajIZr0HA9JoQQg/TV3tARJoUSTnuOh0mhrM1wiu8Vn1HaqvHHTrs4ak6FbvcIegCQBOXkF7CxmIzyD2iP11E9/P6O+R3GNWaXRInjUPKTHn0RBdhoctZ7G/URUO5io7VhogG+pvbXNDd0qTuJVWfKS5Efl5IomPceX6PUtKRQmNWaVkyt7dxNZERO1nTPXW7uiQ7eo7nPzC2GNmUjmmKiphOoseUl1D2vMRPLGRG3n7p9CtFNL/yoveUlERJbHRG3HSptCtH2LJhw7TUQkExyeZafufQ4tBGAwCAhR9Bz6xKVEKCSOnSYikgMmajtV1nNogxDo8EATjp0mIpIBNn3bqfKeQzuolFxog4hIBpio7VRFphDlQhtERNbHpm87xTm8iYjqBiZqO8U5vKmYLjcLN8/uw59HvsPNs/ugy82ydkhEdA82fduxsqYQJfuQ+fv5ElOIpp76CU16joebX4i1wyMiMFHbPT6Htl+63Ky/kvRfi3L81WdBGPS4sW8TgkfNg8rJ1YoREhHApm8iu5V27WRRTdoMYTAgPf5kLUdEROYwURPZqcKctKLmbnMkCQXZabUbEBGZxURNZKccXDyNzd0lCAFHrWftBkREZjFRE9kpz6D2kEoZoicpFPAI6lDLERGROUzURHZK5eSKJj3HQ1IoAUiApAAgQVIo0aTneK5JTSQT7PVNZMfc/EIQPGoe0uNPoiA7DY5aT3gEdWCSJpIRJmoiO6dyckX90J7WDoOISsGmbyIiIhljoiYiIpIxNn3buay7eTh2IQFpWXfh6eqMTi39OYUoEZGMMFHbsbPxf2Jj9GHoDQbj2tTRR85hwqBuCA3wtXZ4REQENn3bray7edgYfRg6vQFCAAaDgBCATm/AF7sPIetunrVDJCIiMFHbrWMXEqAvZZ5nvcGA4xcTazkiIiIyh4naTqVl3YVUyjzPkiThTmZOLUdERETmyCpRR0ZGonPnznB1dYW3tzdGjhyJixcvlvmeqKgoSJJk8tJo2BmqPJ6uzhClzPMshICXm0stR0RERObIKlHHxsZixowZOHToEPbs2YPCwkIMGDAAOTll1+7c3NyQlJRkfCUkJNRSxHVXp5b+UJYyz7NSoUDH4Ka1HBEREZkjq17fu3fvNtmOioqCt7c3jh8/jp49S585SZIk+Pj41HR4NsXVWYMJg7rhi92HTHp9KxUKTBjUjUO0iIhkQlaJ+n4ZGRkAAC8vrzLLZWdnw9/fHwaDAR06dMCbb76JVq1amS2bn5+P/Px8k/faq9AAX7w6YTCOX0zEncwceLm5oGNwUyZpIiIZkW2iNhgMmDVrFnr06IHWrVuXWi44OBiff/452rZti4yMDLz77rvo3r07zp49Cz8/vxLlIyMjsXTp0poMvU5xddagV/sHrB0GERGVQlbPqO81Y8YMnDlzBlu2bCmzXHh4OCZMmICwsDBERETgm2++QYMGDbB69Wqz5efPn4+MjAzjKzY2tibCJyIisghZ1qiff/557NixA/v27TNbKy6Lg4MD2rdvjytXrpg9rlaroVarjdtaLZfzKw2nFyUisj5ZJWohBF544QVs374dMTExCAwMrPQ59Ho9Tp8+jSFDhtRAhPaD04sSEcmDrJq+Z8yYgX//+9/YvHkzXF1dkZycjOTkZOTm5hrLTJgwAfPnzzduv/766/jpp59w7do1nDhxAk888QQSEhIwdepUa/wKVufqrIa7ixNcndXlFy4FpxclInuwfPlydO/eHc7OzvDw8LB2OKWSVY161apVAIBevXqZ7F+/fj0mTZoEAEhMTITinvG/aWlpmDZtGpKTk+Hp6YmOHTviwIEDCA0Nra2wZWXWo30rVd5c83ZFphdlBzQiqusKCgowZswYhIeHY926ddYOp1SyStSlzZR1r5iYGJPtDz74AB988EENRWTbSmvebu7nbdy+H6cXJSJbUTwCKCoqyrqBlENWiZpqz73N28DffyTp9AZcSkzh9KJERDLBRG2nymreNggBRSk1ak4vSkTWkJ2djczMTOP2/SN4bJmsOpNR7Slr9SyFQsIDTbyhUiogSUXbkgSolJxelIisIyIiAu7u7sZXZGSk2XKvvPJKiYWa7n9duHChlqOvHtao7VR5q2c19/PG2L6dOL0oEclCbGwswsLCjNul1abnzJlj7HxcmqCgIAtGVvOYqO1Up5b+iD5yzviM+l7FzducXpSI5EKr1cLNza3ccg0aNECDBg1qIaLaw6ZvO1W8ehabt4nIXiUmJiIuLg6JiYnQ6/WIi4tDXFyc7BZrYo3ajnH1LCKyZ4sWLcKGDRuM2+3btwcA/PrrryXm87AmJmo7x+ZtIrJXUVFRsh9DDbDpm4iISNZYo6ZScfUsIiLrY6Ims7h6FhGRPLDpm0rg6llERPLBRE0lVGT1LCIiqh1s+rZz5p5DF08vytWziIisj4najpX2HLp9iyZcPYuISCbY9G2nynoOfeJSIhSS+Y8GV88iIqpdTNR2qrxlLjs80ITTixIRyQCbvu1Uec+hHVRKTi9KRCQDTNR2qrxlLr3cXDi9KBGRDLDp2051aukPpYLPoYmI5I6J2k5xmUsiorqBTd92jMtcEhHJHxO1neNzaCIieWPTNxERkYwxURMREckYEzUREZGMMVETERHJGBM1ERGRjDFRExERyRgTNRERkYxxHPVfzp8/b5Xr5uYX4tK136FRO0DtYJ+3I79Qh7z8QjjmZ8BJ7WDtcGqdriAXmYmXoHDUQKlytHY4VqHXFcBQkAe3bBVUjk4mx3x9feHr62ulyKonKSkJSUlJ1g6jTrPWd7Oc2GdmuIevry8iIiLwxBNPWDsUIjJj8eLFWLJkibXDqJLVq1dj6dKl1g6jzouIiKizf6xZgiRKW0LJjtj7X73Z2dmIiIhAbGwstFqttcMhK5DzZ4A16sqT8/2sirr8GbAEJmpCZmYm3N3dkZGRATc3N2uHQ1bAz4Bt4f20LexMRkREJGNM1ERERDLGRE1Qq9VYvHgx1Gq1tUMhK+FnwLbwftoWPqMmIiKSMdaoiYiIZIyJmoiISMaYqMmirl+/DkmSEBUVZe1QiIhsAhO1FV29ehXTp09HUFAQNBoN3Nzc0KNHD6xcuRK5ubk1dt1z585hyZIluH79eo1doyKWL1+O4cOHo2HDhpAkqc7OPlUbJEmq0CsmJqba17p79y6WLFlSqXPxXlYO7ydVht1PIWotO3fuxJgxY6BWqzFhwgS0bt0aBQUF2L9/P15++WWcPXsWa9asqZFrnzt3DkuXLkWvXr0QEBBQI9eoiNdeew0+Pj5o3749oqOjrRZHXbBx40aT7S+++AJ79uwpsT8kJKTa17p7965x2stevXpV6D28l5XD+0mVwURtBfHx8Xjsscfg7++PvXv3mkyNN2PGDFy5cgU7d+60YoR/E0IgLy8PTk5O5ReupPj4eAQEBODWrVto0KCBxc9vS+6fi/7QoUPYs2ePbOao572sHN5Pqgw2fVvB22+/jezsbKxbt87s/LXNmzfHiy++aNzW6XRYtmwZmjVrBrVajYCAACxYsAD5+fkm7wsICMCwYcOwf/9+dOnSBRqNBkFBQfjiiy+MZaKiojBmzBgAQO/evUs0sRWfIzo6Gp06dYKTkxNWr14NALh27RrGjBkDLy8vODs7o1u3btX6g8KatXlbZDAY8OGHH6JVq1bQaDRo2LAhpk+fjrS0NJNyx44dw8CBA1G/fn04OTkhMDAQTz31FICiPgbFX8xLly41fj7Ka/rkvbQ83k8qxhq1Ffzwww8ICgpC9+7dK1R+6tSp2LBhA0aPHo05c+bg8OHDiIyMxPnz57F9+3aTsleuXMHo0aMxZcoUTJw4EZ9//jkmTZqEjh07olWrVujZsydmzpyJf/3rX1iwYIGxae3eJraLFy9i3LhxmD59OqZNm4bg4GCkpKSge/fuuHv3LmbOnIl69ephw4YNGD58OLZt24ZRo0ZZ7h+IqmT69OmIiorC5MmTMXPmTMTHx+Pjjz/GyZMn8dtvv8HBwQGpqakYMGAAGjRogFdeeQUeHh64fv06vvnmGwBAgwYNsGrVKjz77LMYNWoUHn74YQBA27Ztrfmr2SXeTzISVKsyMjIEADFixIgKlY+LixMAxNSpU032v/TSSwKA2Lt3r3Gfv7+/ACD27dtn3JeamirUarWYM2eOcd/WrVsFAPHrr7+WuF7xOXbv3m2yf9asWQKA+O9//2vcl5WVJQIDA0VAQIDQ6/VCCCHi4+MFALF+/foK/X5CCHHz5k0BQCxevLjC77F3M2bMEPf+7/vf//5XABCbNm0yKbd7926T/du3bxcAxNGjR0s9d3XuB+9l1fB+UlnY9F3LMjMzAQCurq4VKr9r1y4AwOzZs032z5kzBwBKND2HhobioYceMm43aNAAwcHBuHbtWoVjDAwMxMCBA0vE0aVLFzz44IPGfVqtFk8//TSuX7+Oc+fOVfj8ZHlbt26Fu7s7+vfvj1u3bhlfHTt2hFarxa+//goA8PDwAADs2LEDhYWFVoyYysL7Sfdioq5lxUvOZWVlVah8QkICFAoFmjdvbrLfx8cHHh4eSEhIMNnftGnTEufw9PQs8VyrLIGBgWbjCA4OLrG/uMn8/jiodl2+fBkZGRnw9vZGgwYNTF7Z2dlITU0FAEREROCRRx7B0qVLUb9+fYwYMQLr168v0d+BrIv3k+7FZ9S1zM3NDY0aNcKZM2cq9T5JkipUTqlUmt0vKjGle0308KaaZTAY4O3tjU2bNpk9XtyhSJIkbNu2DYcOHcIPP/yA6OhoPPXUU3jvvfdw6NAhaLXa2gybSsH7SfdioraCYcOGYc2aNTh48CDCw8PLLOvv7w+DwYDLly+bdPhKSUlBeno6/P39K339iib9++O4ePFiif0XLlwwHifradasGX7++Wf06NGjQn9odevWDd26dcPy5cuxefNmjB8/Hlu2bMHUqVOr9Pkgy+L9pHux6dsK5s6dCxcXF0ydOhUpKSkljl+9ehUrV64EAAwZMgQA8OGHH5qUef/99wEAQ4cOrfT1XVxcAADp6ekVfs+QIUNw5MgRHDx40LgvJycHa9asQUBAAEJDQysdB1nOo48+Cr1ej2XLlpU4ptPpjPc6LS2tROtKWFgYABibS52dnQFU7vNBlsX7SfdijdoKmjVrhs2bN2Ps2LEICQkxmZnswIED2Lp1KyZNmgQAaNeuHSZOnIg1a9YgPT0dEREROHLkCDZs2ICRI0eid+/elb5+WFgYlEolVqxYgYyMDKjVavTp0wfe3t6lvueVV17Bl19+icGDB2PmzJnw8vLChg0bEB8fj6+//hoKReX/5tu4cSMSEhJw9+5dAMC+ffvwxhtvAACefPJJ1tIrISIiAtOnT0dkZCTi4uIwYMAAODg44PLly9i6dStWrlyJ0aNHY8OGDfjkk08watQoNGvWDFlZWVi7di3c3NyMfxQ6OTkhNDQUX331FR544AF4eXmhdevWaN26danX5720LN5PMmHlXud27dKlS2LatGkiICBAODo6CldXV9GjRw/x0Ucfiby8PGO5wsJCsXTpUhEYGCgcHBxEkyZNxPz5803KCFE0tGro0KElrhMRESEiIiJM9q1du1YEBQUJpVJpMlSrtHMIIcTVq1fF6NGjhYeHh9BoNKJLly5ix44dJmUqMzwrIiJCADD7Mjd0jP52/3CeYmvWrBEdO3YUTk5OwtXVVbRp00bMnTtX/Pnnn0IIIU6cOCHGjRsnmjZtKtRqtfD29hbDhg0Tx44dMznPgQMHRMeOHYWjo2OFhufwXlYP7yeVRRKiEr2MiIiIqFbxGTUREZGMMVETERHJGBM1ERGRjDFRExERyRgTNRERkYwxURMREckYE7WMvf3222jZsiUMBoO1Q6m2V155BV27drV2GHUOPwNkzvXr1yFJEqKioqwdCtUCJmqZyszMxIoVKzBv3jzjrF+SJEGSJLz33nslykdFRUGSJBw7dqza1/7mm28wduxYBAUFwdnZGcHBwZgzZ06pUxB+//336NChAzQaDZo2bYrFixdDp9OZlJk1axZOnTqF77//vtrx2Qt+BogIAGcmk6sPPvhAuLm5idzcXOM+/DUzUMOGDUVOTo5J+fXr15e7gHxF1atXT7Rp00YsXLhQrF27VsycOVM4OjqKli1birt375qU3bVrl5AkSfTu3VusWbNGvPDCC0KhUIhnnnmmxHkfffRR8dBDD1U7PnvBzwCVxmAwiNzcXKHT6awdCtUCJmqZatu2rXjiiSdM9gEQYWFhAoB47733TI5Z8kva3BSBGzZsEADE2rVrTfaHhoaKdu3aicLCQuO+V199VUiSJM6fP29Sdtu2bUKSJHH16tVqx2gP+BkgIiGEYNO3DMXHx+N///sf+vXrV+JYjx490KdPH7z99tvIzc2tkev36tWrxL5Ro0YBAM6fP2/cd+7cOZw7dw5PP/00VKq/13d57rnnIITAtm3bTM5R/Pt89913NRC1beFnwPYtWbIEkiTh0qVLeOKJJ+Du7o4GDRpg4cKFEELgxo0bGDFiBNzc3ODj42PyuMPcM+pJkyZBq9Xijz/+wMiRI6HVatGgQQO89NJL0Ov1xnIxMTGQJAkxMTEm8Zg7Z3JyMiZPngw/Pz+o1Wr4+vpixIgRuH79eg39q5A5TNQydODAAQBAhw4dzB5fsmQJUlJSsGrVqjLPk5+fj1u3blXoVZ7k5GQAQP369Y37Tp48CQDo1KmTSdlGjRrBz8/PeLyYu7s7mjVrht9++63c69k7fgbsx9ixY2EwGPDWW2+ha9eueOONN/Dhhx+if//+aNy4MVasWIHmzZvjpZdewr59+8o8l16vx8CBA1GvXj28++67iIiIwHvvvYc1a9ZUKbZHHnkE27dvx+TJk/HJJ59g5syZyMrKQmJiYpXOR1XDZS5l6MKFCwCAwMBAs8cfeugh9O7dG++88w6effbZUheW//LLLzF58uQKXVOUszbLihUroFQqMXr0aOO+pKQkAICvr2+J8r6+vvjzzz9L7A8KCsK5c+cqFJM942fAfnTp0gWrV68GADz99NMICAjAnDlzEBkZiXnz5gEAxo0bh0aNGuHzzz9Hz549Sz1XXl4exo4di4ULFwIAnnnmGXTo0AHr1q3Ds88+W6m40tPTceDAAbzzzjt46aWXjPvnz59f2V+RqomJWoZu374NlUoFrVZbapklS5YgIiICn376Kf75z3+aLTNw4EDs2bOn2vFs3rwZ69atw9y5c9GiRQvj/uJmV7VaXeI9Go0GmZmZJfZ7enqWqGVRSfwM2I+pU6ca/1upVKJTp074/fffMWXKFON+Dw8PBAcH49q1a+We75lnnjHZfuihh7Bx48ZKx+Xk5ARHR0fExMRgypQp8PT0rPQ5yDKYqOuonj17onfv3nj77bdL/I9ZzNfX12xNpzL++9//YsqUKRg4cCCWL19ucqy4Fpefn1/ifXl5eWZreUIISJJUrZioCD8DtqFp06Ym2+7u7tBoNCaPGIr33759u8xzaTQaNGjQwGSfp6cn0tLSKh2XWq3GihUrMGfOHDRs2BDdunXDsGHDMGHCBPj4+FT6fFR1fEYtQ/Xq1YNOp0NWVlaZ5RYvXozk5GRjs9n9cnNzkZycXKGXOadOncLw4cPRunVrbNu2zaSzEPB3c2dx8+e9kpKS0KhRoxL709LSSnwBUUn8DNgPpVJZoX1A+Y8nSnvfvUr7I+neDmfFZs2ahUuXLiEyMhIajQYLFy5ESEgIW0RqGRO1DLVs2RJAUc/fskRERKBXr15YsWKF2d6/X331lbFGVd7rflevXsWgQYPg7e2NXbt2mW2CDQsLA4ASE2z8+eef+P33343H7xUfH4+QkJAyfy/iZ4BqTnET9v2T1yQkJJgt36xZM8yZMwc//fQTzpw5g4KCArMT7lDNYdO3DIWHhwMo+vJr27ZtmWWXLFmCXr16me3VWdXnk8nJyRgwYAAUCgWio6NLNKUVa9WqFVq2bIk1a9Zg+vTpxr/mV61aBUmSTDodAUBGRgauXr1a6U4t9oifAaop/v7+UCqV2LdvH0aOHGnc/8knn5iUu3v3LhQKBTQajXFfs2bN4OrqavZRB9UcJmoZCgoKQuvWrfHzzz/jqaeeKrNsREQEIiIiEBsbW+JYVZ9PDho0CNeuXcPcuXOxf/9+7N+/33isYcOG6N+/v3H7nXfewfDhwzFgwAA89thjOHPmDD7++GNMnTq1RK3p559/hhACI0aMqHRM9oafAaop7u7uGDNmDD766CNIkoRmzZphx44dSE1NNSl36dIl9O3bF48++ihCQ0OhUqmwfft2pKSk4LHHHrNS9HbKWjOtUNnef/99odVqTaZrBCBmzJhRouyvv/5qnFrSErNSFZ/L3CsiIqJE+e3bt4uwsDChVquFn5+feO2110RBQUGJcmPHjhUPPvhgteOzF/wM2LbFixcLAOLmzZsm+ydOnChcXFxKlI+IiBCtWrUSQggRHx8vAIj169eX+77i69zr5s2b4pFHHhHOzs7C09NTTJ8+XZw5c8bknLdu3RIzZswQLVu2FC4uLsLd3V107dpV/Oc//6nmb06VJQlRTu8EsoqMjAwEBQXh7bffNhmmUVclJycjMDAQW7ZsYW2qgvgZICKAnclky93dHXPnzsU777xjE0scfvjhh2jTpg2/oCuBnwEiAgDWqImIiGSMNWoiIiIZY6ImIiKSMSZqIiIiGWOiJiIikjEmaiIiKtX169chSRKioqKsHYrdYqImIrKQq1evYvr06QgKCoJGo4Gbmxt69OiBlStXmp2L3VLOnTuHJUuW4Pr16zV2jYpYvnw5hg8fjoYNG0KSJCxZssSq8dgKTiFKRGQBO3fuxJgxY6BWqzFhwgS0bt0aBQUF2L9/P15++WWcPXvW7HzslnDu3DksXboUvXr1QkBAQI1coyJee+01+Pj4oH379oiOjrZaHLaGiZqIqJri4+Px2GOPwd/fH3v37jWZX33GjBm4cuUKdu7cacUI/yaEKHWt8OqKj49HQEAAbt26VepCLlR5bPomIqqmt99+G9nZ2Vi3bp3ZRVCaN2+OF1980bit0+mwbNkyNGvWDGq1GgEBAViwYEGJVakCAgIwbNgw7N+/H126dIFGo0FQUBC++OILY5moqCiMGTMGANC7d29IkgRJkhATE2NyjujoaHTq1AlOTk7G9cuvXbuGMWPGwMvLC87OzujWrVu1/qCwZm3eljFRExFV0w8//ICgoCB07969QuWnTp2KRYsWoUOHDvjggw8QERGByMhIs6tSXblyBaNHj0b//v3x3nvvwdPTE5MmTcLZs2cBAD179sTMmTMBAAsWLMDGjRuxceNGk5XLLl68iHHjxqF///5YuXIlwsLCkJKSgu7duyM6OhrPPfccli9fjry8PAwfPhzbt2+3wL8KWYxVlwQhIqrjMjIyBAAxYsSICpWPi4sTAMTUqVNN9r/00ksCgNi7d69xn7+/vwAg9u3bZ9yXmpoq1Gq1mDNnjnHf1q1bBQDx66+/lrhe8Tl2795tsn/WrFkCgPjvf/9r3JeVlSUCAwNFQECA0Ov1QgjzK3WV5+bNmwKAWLx4cYXfQ6VjjZqIqBoyMzMBAK6urhUqv2vXLgDA7NmzTfbPmTMHAEo0PYeGhuKhhx4ybjdo0ADBwcG4du1ahWMMDAzEwIEDS8TRpUsXPPjgg8Z9Wq0WTz/9NK5fv45z585V+PxUs5ioiYiqwc3NDQCQlZVVofIJCQlQKBRo3ry5yX4fHx94eHggISHBZH/Tpk1LnMPT0xNpaWkVjjEwMNBsHMHBwSX2FzeZ3x8HWQ8TNRFRNbi5uaFRo0Y4c+ZMpd4nSVKFyimVSrP7RSUWPqyJHt5Ue5ioiYiqadiwYbh69SoOHjxYbll/f38YDAZcvnzZZH9KSgrS09Ph7+9f6etXNOnfH8fFixdL7L9w4YLxOMkDEzURUTXNnTsXLi4umDp1KlJSUkocv3r1KlauXAkAGDJkCADgww8/NCnz/vvvAwCGDh1a6eu7uLgAANLT0yv8niFDhuDIkSMmf1zk5ORgzZo1CAgIQGhoaKXjoJrBCU+IiKqpWbNm2Lx5M8aOHYuQkBCTmckOHDiArVu3YtKkSQCAdu3aYeLEiVizZg3S09MRERGBI0eOYMOGDRg5ciR69+5d6euHhYVBqVRixYoVyMjIgFqtRp8+feDt7V3qe1555RV8+eWXGDx4MGbOnAkvLy9s2LAB8fHx+Prrr6FQVL4et3HjRiQkJODu3bsAgH379uGNN94AADz55JOspVeVtbudExHZikuXLolp06aJgIAA4ejoKFxdXUWPHj3ERx99JPLy8ozlCgsLxdKlS0VgYKBwcHAQTZo0EfPnzzcpI0TR0KqhQ4eWuE5ERISIiIgw2bd27VoRFBQklEqlyVCt0s4hhBBXr14Vo0ePFh4eHkKj0YguXbqIHTt2mJSpzPCsiIgIAcDsy9zQMaoYSYhK9EggIiKiWsVn1ERERDLGRE1ERCRjTNREREQyxkRNREQkY0zUREREMsZETUREJGNM1ERERDLGRE1ERCRjTNREREQyxkRNREQkY0zUREREMsZETUREJGNM1ERERDL2/7mGilLfZOTeAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig2 = my_data.hedges_g.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "012f7a06", - "metadata": {}, - "source": [ - "Create a Cumming estimation plot for the mean difference." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0dd903b1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig3 = my_data.mean_diff.plot(float_contrast=True);" - ] - }, - { - "cell_type": "markdown", - "id": "0838d251", - "metadata": {}, - "source": [ - " Create a paired Gardner-Altman plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c38cb10f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "my_data_paired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"),\n", - " id_col = \"ID\", paired='baseline')\n", - "fig4 = my_data_paired.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "86a5516a", - "metadata": {}, - "source": [ - "Create a multi-group Cumming plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ea5da412", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathananns/GitHub/DABEST-python/dabest/plot_tools.py:2778: UserWarning: 5.0% of the points cannot be placed. You might want to decrease the size of the markers.\n", - " warnings.warn(err)\n", - "/Users/jonathananns/GitHub/DABEST-python/dabest/plot_tools.py:2778: UserWarning: 10.0% of the points cannot be placed. You might want to decrease the size of the markers.\n", - " warnings.warn(err)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "my_multi_groups = dabest.load(df, id_col = \"ID\", \n", - " idx=((\"Control 1\", \"Test 1\"),\n", - " (\"Control 2\", \"Test 2\")))\n", - "fig5 = my_multi_groups.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "10675566", - "metadata": {}, - "source": [ - "Create a shared control Cumming plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "97e2eb40", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathananns/GitHub/DABEST-python/dabest/plot_tools.py:2778: UserWarning: 10.0% of the points cannot be placed. You might want to decrease the size of the markers.\n", - " warnings.warn(err)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "my_shared_control = dabest.load(df, id_col = \"ID\",\n", - " idx=(\"Control 1\", \"Test 1\",\n", - " \"Test 2\", \"Test 3\"))\n", - "fig6 = my_shared_control.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "58dc1b2d", - "metadata": {}, - "source": [ - "Create a repeated meausures (against baseline) Slopeplot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4f101c31", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "my_rm_baseline = dabest.load(df, id_col = \"ID\", paired = \"baseline\",\n", - " idx=(\"Control 1\", \"Test 1\",\n", - " \"Test 2\", \"Test 3\"))\n", - "fig7 = my_rm_baseline.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "45dda7bb", - "metadata": {}, - "source": [ - "Create a repeated meausures (sequential) Slopeplot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "83847a02", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "my_rm_sequential = dabest.load(df, id_col = \"ID\", paired = \"sequential\",\n", - " idx=(\"Control 1\", \"Test 1\",\n", - " \"Test 2\", \"Test 3\"))\n", - "fig8 = my_rm_sequential.mean_diff.plot();" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "68bcc034", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5d49f77f", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "class PermutationTest:\n", - " \"\"\"\n", - " A class to compute and report permutation tests.\n", - " \n", - " Parameters\n", - " ----------\n", - " control : array-like\n", - " test : array-like\n", - " These should be numerical iterables.\n", - " effect_size : string.\n", - " Any one of the following are accepted inputs:\n", - " 'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta'\n", - " is_paired : string, default None\n", - " permutation_count : int, default 10000\n", - " The number of permutations (reshuffles) to perform.\n", - " random_seed : int, default 12345\n", - " `random_seed` is used to seed the random number generator during\n", - " bootstrap resampling. This ensures that the generated permutations\n", - " are replicable.\n", - " ps_adjust : bool, default False\n", - " If True, the p-value is adjusted according to Phipson & Smyth (2010).\n", - " # https://doi.org/10.2202/1544-6115.1585\n", - "\n", - " \n", - " Returns\n", - " -------\n", - " A :py:class:`PermutationTest` object:\n", - " `difference`:float\n", - " The effect size of the difference between the control and the test.\n", - " `effect_size`:string\n", - " The type of effect size reported.\n", - " \n", - " \n", - " \"\"\"\n", - " \n", - " def __init__(self, control: array,\n", - " test: array, # These should be numerical iterables.\n", - " effect_size:str, # Any one of the following are accepted inputs: 'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta'\n", - " is_paired:str=None,\n", - " permutation_count:int=5000, # The number of permutations (reshuffles) to perform.\n", - " random_seed:int=12345,#`random_seed` is used to seed the random number generator during bootstrap resampling. This ensures that the generated permutations are replicable.\n", - " ps_adjust:bool=False,\n", - " **kwargs):\n", - " from ._stats_tools.effsize import two_group_difference\n", - " from ._stats_tools.confint_2group_diff import calculate_group_var\n", - " \n", - "\n", - " self.__permutation_count = permutation_count\n", - "\n", - " # Run Sanity Check.\n", - " if is_paired and len(control) != len(test):\n", - " raise ValueError(\"The two arrays do not have the same length.\")\n", - "\n", - " # Initialise random number generator.\n", - " # rng = random.default_rng(seed=random_seed)\n", - " rng = RandomState(PCG64(random_seed))\n", - "\n", - " # Set required constants and variables\n", - " control = array(control)\n", - " test = array(test)\n", - "\n", - " control_sample = control.copy()\n", - " test_sample = test.copy()\n", - "\n", - " BAG = array([*control, *test])\n", - " CONTROL_LEN = int(len(control))\n", - " TEST_LEN = int(len(test)) # devMJBL\n", - " EXTREME_COUNT = 0.\n", - " THRESHOLD = abs(two_group_difference(control, test, \n", - " is_paired, effect_size))\n", - " self.__permutations = []\n", - " self.__permutations_var = []\n", - "\n", - " for i in range(int(self.__permutation_count)):\n", - " if is_paired:\n", - " # Select which control-test pairs to swap.\n", - " random_idx = rng.choice(CONTROL_LEN,\n", - " rng.randint(0, CONTROL_LEN+1),\n", - " replace=False)\n", - "\n", - " # Perform swap.\n", - " for i in random_idx:\n", - " _placeholder = control_sample[i]\n", - " control_sample[i] = test_sample[i]\n", - " test_sample[i] = _placeholder\n", - " \n", - " else:\n", - " # Shuffle the bag and assign to control and test groups.\n", - " # NB. rng.shuffle didn't produce replicable results...\n", - " shuffled = rng.permutation(BAG) \n", - " control_sample = shuffled[:CONTROL_LEN]\n", - " test_sample = shuffled[CONTROL_LEN:]\n", - "\n", - "\n", - " es = two_group_difference(control_sample, test_sample, \n", - " False, effect_size)\n", - " \n", - " group_var = calculate_group_var(var(control_sample, ddof=1), \n", - " CONTROL_LEN, \n", - " var(test_sample, ddof=1), \n", - " len(test_sample))\n", - " self.__permutations.append(es)\n", - " self.__permutations_var.append(group_var)\n", - "\n", - " if abs(es) > THRESHOLD:\n", - " EXTREME_COUNT += 1.\n", - " \n", - " if ps_adjust:\n", - " # devMJBL\n", - " # adjust calculated p-value according to Phipson & Smyth (2010)\n", - " # https://doi.org/10.2202/1544-6115.1585\n", - " # as per R code in statmod::permp\n", - " # https://rdrr.io/cran/statmod/src/R/permp.R\n", - " # (assumes two-sided test)\n", - "\n", - " if CONTROL_LEN == TEST_LEN:\n", - " totalPermutations = binomcoeff(CONTROL_LEN + TEST_LEN, TEST_LEN)/2\n", - " else:\n", - " totalPermutations = binomcoeff(CONTROL_LEN + TEST_LEN, TEST_LEN)\n", - "\n", - " if totalPermutations <= 10e3:\n", - " # use exact calculation\n", - " p = arange(1, totalPermutations + 1)/totalPermutations\n", - " x2 = repeat(EXTREME_COUNT, repeats=totalPermutations)\n", - " Y = binom.cdf(k=x2, n=permutation_count, p=p)\n", - " self.pvalue = mean(Y)\n", - " else:\n", - " # use integral approximation\n", - " def binomcdf(p, k, n):\n", - " return binom.cdf(k, n, p)\n", - "\n", - " integrationVal, _ = fixed_quad(binomcdf,\n", - " a=0, b=0.5/totalPermutations,\n", - " args=(EXTREME_COUNT, permutation_count),\n", - " n=128)\n", - "\n", - " self.pvalue = (EXTREME_COUNT + 1)/(permutation_count + 1) - integrationVal\n", - " else:\n", - " self.pvalue = EXTREME_COUNT / self.__permutation_count\n", - " \n", - " self.__permutations = array(self.__permutations)\n", - " self.__permutations_var = array(self.__permutations_var)\n", - "\n", - " def __repr__(self):\n", - " return(\"{} permutations were taken. The p-value is {}.\".format(self.__permutation_count, \n", - " self.pvalue))\n", - "\n", - "\n", - " @property\n", - " def permutation_count(self):\n", - " \"\"\"\n", - " The number of permuations taken.\n", - " \"\"\"\n", - " return self.__permutation_count\n", - "\n", - "\n", - " @property\n", - " def permutations(self):\n", - " \"\"\"\n", - " The effect sizes of all the permutations in a list.\n", - " \"\"\"\n", - " return self.__permutations\n", - "\n", - " \n", - " @property\n", - " def permutations_var(self):\n", - " \"\"\"\n", - " The experiment group variance of all the permutations in a list.\n", - " \"\"\"\n", - " return self.__permutations_var\n" - ] - }, - { - "cell_type": "markdown", - "id": "b82bc6e5", - "metadata": {}, - "source": [ - "**Notes**:\n", - " \n", - "The basic concept of permutation tests is the same as that behind bootstrapping.\n", - "In an \"exact\" permutation test, all possible resuffles of the control and test \n", - "labels are performed, and the proportion of effect sizes that equal or exceed \n", - "the observed effect size is computed. This is the probability, under the null \n", - "hypothesis of zero difference between test and control groups, of observing the\n", - "effect size: the p-value of the Student's t-test.\n", - "\n", - "Exact permutation tests are impractical: computing the effect sizes for all reshuffles quickly exceeds trivial computational loads. A control group and a test group both with 10 observations each would have a total of $20!$ or $2.43 \\times {10}^{18}$ reshuffles.\n", - "Therefore, in practice, \"approximate\" permutation tests are performed, where a sufficient number of reshuffles are performed (5,000 or 10,000), from which the p-value is computed.\n", - "\n", - "More information can be found [here](https://en.wikipedia.org/wiki/Resampling_(statistics)#Permutation_tests).\n" - ] - }, - { - "cell_type": "markdown", - "id": "4456d8db", - "metadata": {}, - "source": [ - "#### Example: permutation test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "284f1c75", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5000 permutations were taken. The p-value is 0.0758." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "control = norm.rvs(loc=0, size=30, random_state=12345)\n", - "test = norm.rvs(loc=0.5, size=30, random_state=12345)\n", - "perm_test = dabest.PermutationTest(control, test, \n", - " effect_size=\"mean_diff\", \n", - " is_paired=None)\n", - "perm_test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7f5be104", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/API/forest_plot.ipynb b/nbs/API/forest_plot.ipynb deleted file mode 100644 index 6fd10835..00000000 --- a/nbs/API/forest_plot.ipynb +++ /dev/null @@ -1,849 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9bbabcc9", - "metadata": {}, - "source": [ - "# Forest plot\n", - "\n", - "> Creating forest plots from contrast objects.\n", - "\n", - "- order: 4" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a726b52e", - "metadata": {}, - "outputs": [], - "source": [ - "#| default_exp forest_plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fe67d602", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from __future__ import annotations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "efe6d6bf", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from nbdev.showdoc import *\n", - "import nbdev\n", - "nbdev.nbdev_export()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5e714e51", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "import dabest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a1cba2aa", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "import matplotlib.pyplot as plt\n", - "# %matplotlib inline\n", - "import seaborn as sns\n", - "from typing import List, Optional, Union\n", - "import numpy as np\n", - "import matplotlib.axes as axes\n", - "import matplotlib.patches as mpatches" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b4634b4b", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "def load_plot_data(\n", - " data: List, \n", - " effect_size: str = \"mean_diff\", \n", - " contrast_type: str = None,\n", - " ci_type: str = \"bca\",\n", - " idx: Optional[List[int]] = None\n", - ") -> List:\n", - " \"\"\"\n", - " Loads plot data based on specified effect size and contrast type.\n", - "\n", - " Parameters\n", - " ----------\n", - " contrasts : List\n", - " List of contrast objects.\n", - " effect_size: str\n", - " Type of effect size ('mean_diff', 'median_diff', etc.).\n", - " contrast_type: str\n", - " Type of dabest object to plot ('delta2' or 'mini-meta' or 'delta').\n", - " ci_type: str\n", - " Type of confidence interval to plot ('bca' or 'pct')\n", - " idx: Optional[List[int]], default=None\n", - " List of indices to select from the contrast objects if delta-delta experiment. \n", - " If None, only the delta-delta objects are plotted.\n", - "\n", - " Returns\n", - " -------\n", - " List: Contrast plot data based on specified parameters.\n", - " \"\"\"\n", - " # Effect size and contrast types\n", - " effect_attr = \"hedges_g\" if effect_size == 'delta_g' else effect_size\n", - " contrast_attr = {\"delta2\": \"delta_delta\", \"mini_meta\": \"mini_meta\"}.get(contrast_type)\n", - "\n", - " # Testing\n", - " if idx is not None:\n", - " bootstraps, differences, bcalows, bcahighs = [], [], [], []\n", - " for current_idx, index_group in enumerate(idx):\n", - " current_contrast = data[current_idx]\n", - " if len(index_group)>0:\n", - " for index in index_group:\n", - " current_plot_data = getattr(current_contrast, effect_attr)\n", - " if contrast_type == 'delta2':\n", - " if index == 2:\n", - " current_plot_data = getattr(current_plot_data, contrast_attr)\n", - " bootstrap_name, index_val = \"bootstraps_delta_delta\", 0\n", - " elif index == 0 or index == 1:\n", - " bootstrap_name, index_val = \"bootstraps\", index\n", - " else:\n", - " raise ValueError(\"The selected indices must be 0, 1, or 2.\")\n", - " elif contrast_type == \"mini_meta\":\n", - " num_of_groups = len(getattr(current_contrast, effect_attr).results)\n", - " if index == num_of_groups:\n", - " current_plot_data = getattr(getattr(current_contrast, effect_attr), contrast_attr)\n", - " bootstrap_name, index_val = \"bootstraps_weighted_delta\", 0\n", - " elif index < num_of_groups:\n", - " bootstrap_name, index_val = \"bootstraps\", index\n", - " else:\n", - " msg1 = \"There are only {} groups (starting from zero) in this dabest object. \".format(num_of_groups)\n", - " msg2 = \"The idx given is {}.\".format(index)\n", - " raise ValueError(msg1+msg2) \n", - " else: # contrast_type == 'delta'\n", - " bootstrap_name, index_val = \"bootstraps\", index \n", - "\n", - " bootstraps.append(getattr(current_plot_data.results, bootstrap_name)[index_val])\n", - " differences.append(current_plot_data.results.difference[index_val])\n", - " bcalows.append(current_plot_data.results.get(ci_type+'_low')[index_val])\n", - " bcahighs.append(current_plot_data.results.get(ci_type+'_high')[index_val]) \n", - " else:\n", - " if contrast_type == 'delta':\n", - " contrast_plot_data = [getattr(contrast, effect_attr) for contrast in data]\n", - " bootstraps_nested = [result.results.bootstraps.to_list() for result in contrast_plot_data]\n", - " differences_nested = [result.results.difference.to_list() for result in contrast_plot_data]\n", - " bcalows_nested = [result.results.get(ci_type+'_low').to_list() for result in contrast_plot_data]\n", - " bcahighs_nested = [result.results.get(ci_type+'_high').to_list() for result in contrast_plot_data]\n", - " \n", - " bootstraps = [element for innerList in bootstraps_nested for element in innerList]\n", - " differences = [element for innerList in differences_nested for element in innerList]\n", - " bcalows = [element for innerList in bcalows_nested for element in innerList]\n", - " bcahighs = [element for innerList in bcahighs_nested for element in innerList]\n", - "\n", - " else: # contrast_type == 'delta2' or 'mini_meta'\n", - " contrast_plot_data = [getattr(getattr(contrast, effect_attr), contrast_attr) for contrast in data]\n", - " attribute_suffix = \"weighted_delta\" if contrast_type == \"mini_meta\" else \"delta_delta\"\n", - "\n", - " bootstraps = [getattr(result, f\"bootstraps_{attribute_suffix}\") for result in contrast_plot_data]\n", - " differences = [result.difference for result in contrast_plot_data]\n", - " bcalows = [result.results.get(ci_type+'_low')[0] for result in contrast_plot_data]\n", - " bcahighs = [result.results.get(ci_type+'_high')[0] for result in contrast_plot_data]\n", - "\n", - " return bootstraps, differences, bcalows, bcahighs\n", - "\n", - "def check_for_errors(**kwargs):\n", - " data = kwargs.get('data')\n", - " # Contrasts\n", - " if not isinstance(data, list) or not data:\n", - " raise ValueError(\"The `data` argument must be a non-empty list of dabest objects.\")\n", - " \n", - " ## Check if all contrasts are delta-delta or all are mini-meta\n", - " contrast_type = (\"delta2\" if data[0].delta2 \n", - " else \"mini_meta\" if data[0].is_mini_meta\n", - " else \"delta\"\n", - " )\n", - "\n", - " # contrast_type = \"delta2\" if data[0].delta2 else \"mini_meta\"\n", - " for contrast in data:\n", - " check_contrast_type = (\"delta2\" if contrast.delta2 \n", - " else \"mini_meta\" if contrast.is_mini_meta\n", - " else \"delta\"\n", - " )\n", - " if check_contrast_type != contrast_type:\n", - " raise ValueError(\"Each dabest object supplied must be the same experimental type (mini-meta or delta-delta or neither.)\")\n", - "\n", - " # Idx\n", - " idx = kwargs.get('idx')\n", - " effect_size = kwargs.get('effect_size')\n", - " if idx is not None:\n", - " if not isinstance(idx, (tuple, list)):\n", - " raise TypeError(\"`idx` must be a tuple or list of integers.\")\n", - "\n", - " msg1 = \"The `idx` argument must have the same length as the number of dabest objects. \"\n", - " msg2 = \"E.g., If two dabest objects are supplied, there should be two lists within `idx`. \"\n", - " msg3 = \"E.g., `idx` = [[1,2],[0,1]].\"\n", - " _total = 0\n", - " for _group in idx:\n", - " if isinstance(_group, int | float):\n", - " raise ValueError(msg1+msg2+msg3)\n", - " else:\n", - " _total += 1\n", - " if _total != len(data):\n", - " raise ValueError(msg1+msg2+msg3)\n", - " \n", - " if idx is not None:\n", - " number_of_curves_to_plot = sum([len(i) for i in idx])\n", - " else:\n", - " if contrast_type == 'delta':\n", - " number_of_curves_to_plot = sum(len(getattr(i, effect_size).results) for i in data)\n", - " else:\n", - " number_of_curves_to_plot = len(data)\n", - "\n", - " # Axes\n", - " ax = kwargs.get('ax')\n", - " fig_size = kwargs.get('fig_size')\n", - " if ax is not None and not isinstance(ax, plt.Axes):\n", - " raise TypeError(\"The `ax` must be a `matplotlib.axes.Axes` instance or `None`.\")\n", - " \n", - " # Figure size\n", - " if fig_size is not None and not isinstance(fig_size, (tuple, list)):\n", - " raise TypeError(\"`fig_size` must be a tuple or list of two positive integers.\")\n", - "\n", - " # Effect size\n", - " effect_size_options = ['mean_diff', 'median_diff', 'cohens_d', 'cohens_h', 'cliffs_delta', 'hedges_g', 'delta_g']\n", - " if not isinstance(effect_size, str) or effect_size not in effect_size_options:\n", - " raise TypeError(\"The `effect_size` argument must be a string and please choose from the following effect sizes: 'mean_diff', 'median_diff', 'cohens_d', 'cohens_h', 'cliffs_delta', 'hedges_g', 'delta_g'.\")\n", - " if data[0].is_mini_meta and effect_size != 'mean_diff':\n", - " raise ValueError(\"The `effect_size` argument must be `mean_diff` for mini-meta analyses.\")\n", - " if data[0].delta2 and effect_size not in ['mean_diff', 'hedges_g', 'delta_g']:\n", - " raise ValueError(\"The `effect_size` argument must be `mean_diff`, `hedges_g`, or `delta_g` for delta-delta analyses.\")\n", - " \n", - " # CI type\n", - " ci_type = kwargs.get('ci_type')\n", - " if ci_type not in ('bca', 'pct'):\n", - " raise TypeError(\"`ci_type` must be either 'bca' or 'pct'.\")\n", - "\n", - " # Horizontal\n", - " horizontal = kwargs.get('horizontal')\n", - " if not isinstance(horizontal, bool):\n", - " raise TypeError(\"`horizontal` must be a boolean value.\")\n", - "\n", - " # Marker size\n", - " marker_size = kwargs.get('marker_size')\n", - " if not isinstance(marker_size, (int, float)) or marker_size <= 0:\n", - " raise TypeError(\"`marker_size` must be a positive integer or float.\")\n", - "\n", - " # Custom palette\n", - " custom_palette = kwargs.get('custom_palette')\n", - " labels = kwargs.get('labels')\n", - " if custom_palette is not None and not isinstance(custom_palette, (dict, list, tuple, str, type(None))):\n", - " raise TypeError(\"The `custom_palette` must be either a dictionary, list, string, or `None`.\")\n", - " if isinstance(custom_palette, dict) and labels is None:\n", - " raise ValueError(\"The `labels` argument must be provided if `custom_palette` is a dictionary.\")\n", - " if isinstance(custom_palette, (list, tuple)) and len(custom_palette) < number_of_curves_to_plot:\n", - " raise ValueError(\"The `custom_palette` list/tuple must have the same length as the number of `data` provided.\")\n", - "\n", - " # Contrast alpha and desat\n", - " contrast_alpha = kwargs.get('contrast_alpha')\n", - " contrast_desat = kwargs.get('contrast_desat')\n", - " if not isinstance(contrast_alpha, float) or not 0 <= contrast_alpha <= 1:\n", - " raise TypeError(\"`contrast_alpha` must be a float between 0 and 1.\")\n", - " \n", - " if not isinstance(contrast_desat, (float, int)) or not 0 <= contrast_desat <= 1:\n", - " raise TypeError(\"`contrast_desat` must be a float between 0 and 1 or an int (1).\")\n", - " \n", - " # Contrast labels\n", - " labels_fontsize = kwargs.get('labels_fontsize')\n", - " labels_rotation = kwargs.get('labels_rotation')\n", - " if labels is not None and not all(isinstance(label, str) for label in labels):\n", - " raise TypeError(\"The `labels` must be a list of strings or `None`.\")\n", - " \n", - " if labels is not None and len(labels) != number_of_curves_to_plot:\n", - " raise ValueError(\"`labels` must match the number of `data` provided.\")\n", - " \n", - " if not isinstance(labels_fontsize, (int, float)):\n", - " raise TypeError(\"`labels_fontsize` must be an integer or float.\")\n", - " \n", - " if labels_rotation is not None and (not isinstance(labels_rotation, (int, float)) or not 0 <= labels_rotation <= 360):\n", - " raise TypeError(\"`labels_rotation` must be an integer or float between 0 and 360.\") \n", - "\n", - " # Title\n", - " title = kwargs.get('title')\n", - " title_fontsize = kwargs.get('title_fontsize')\n", - " if title is not None and not isinstance(title, str):\n", - " raise TypeError(\"The `title` argument must be a string.\")\n", - " \n", - " if not isinstance(title_fontsize, (int, float)):\n", - " raise TypeError(\"`title_fontsize` must be an integer or float.\")\n", - " \n", - " # Y-label\n", - " ylabel = kwargs.get('ylabel')\n", - " ylabel_fontsize = kwargs.get('ylabel_fontsize')\n", - " if ylabel is not None and not isinstance(ylabel, str):\n", - " raise TypeError(\"The `ylabel` argument must be a string.\")\n", - "\n", - " if not isinstance(ylabel_fontsize, (int, float)):\n", - " raise TypeError(\"`ylabel_fontsize` must be an integer or float.\")\n", - " \n", - " # Y-lim\n", - " ylim = kwargs.get('ylim')\n", - " if ylim is not None and not isinstance(ylim, (tuple, list)):\n", - " raise TypeError(\"`ylim` must be a tuple or list of two floats.\")\n", - " if ylim is not None and len(ylim) != 2:\n", - " raise ValueError(\"`ylim` must be a tuple or list of two floats.\")\n", - "\n", - " # Y-ticks\n", - " yticks = kwargs.get('yticks')\n", - " if yticks is not None and not isinstance(yticks, (tuple, list)):\n", - " raise TypeError(\"`yticks` must be a tuple or list of floats.\")\n", - " \n", - " # Y-ticklabels\n", - " yticklabels = kwargs.get('yticklabels')\n", - " if yticklabels is not None and not isinstance(yticklabels, (tuple, list)):\n", - " raise TypeError(\"`yticklabels` must be a tuple or list of strings.\")\n", - " \n", - " if yticklabels is not None and not all(isinstance(label, str) for label in yticklabels):\n", - " raise TypeError(\"`yticklabels` must be a list of strings.\")\n", - " \n", - " # Remove spines\n", - " remove_spines = kwargs.get('remove_spines')\n", - " if not isinstance(remove_spines, bool):\n", - " raise TypeError(\"`remove_spines` must be a boolean value.\")\n", - " \n", - " # Reference band\n", - " reference_band = kwargs.get('reference_band')\n", - " if reference_band is not None:\n", - " if not isinstance(reference_band, list | tuple):\n", - " raise TypeError(\"`reference_band` must be a list/tuple of indices (ints).\")\n", - " if not all(isinstance(i, int) for i in reference_band):\n", - " raise TypeError(\"`reference_band` must be a list/tuple of indices (ints).\")\n", - " if any(i >= number_of_curves_to_plot for i in reference_band):\n", - " raise ValueError(\"Index {} chosen is out of range for the contrast objects.\".format([i for i in reference_band if i >= number_of_curves_to_plot]))\n", - " \n", - " # Delta text\n", - " delta_text = kwargs.get('delta_text')\n", - " if delta_text is not None:\n", - " if not isinstance(delta_text, bool):\n", - " raise TypeError(\"`delta_text` must be a boolean value.\")\n", - "\n", - " # Contrast bars\n", - " contrast_bars = kwargs.get('contrast_bars')\n", - " if contrast_bars is not None:\n", - " if not isinstance(contrast_bars, bool):\n", - " raise TypeError(\"`contrast_bars` must be a boolean value.\")\n", - "\n", - " return contrast_type \n", - "\n", - "def get_kwargs(\n", - " violin_kwargs,\n", - " zeroline_kwargs,\n", - " horizontal,\n", - " marker_kwargs,\n", - " errorbar_kwargs,\n", - " delta_text_kwargs,\n", - " contrast_bars_kwargs,\n", - " reference_band_kwargs,\n", - " marker_size\n", - " ):\n", - " from .misc_tools import merge_two_dicts\n", - "\n", - " # Violin kwargs\n", - " default_violin_kwargs = {\n", - " \"widths\": 0.5,\n", - " \"showextrema\": False,\n", - " \"showmedians\": False,\n", - " \"orientation\": 'horizontal' if horizontal else 'vertical',\n", - " }\n", - " if violin_kwargs is None:\n", - " violin_kwargs = default_violin_kwargs\n", - " else:\n", - " violin_kwargs = merge_two_dicts(default_violin_kwargs, violin_kwargs)\n", - "\n", - " # zeroline kwargs\n", - " default_zeroline_kwargs = {\n", - " \"linewidth\": 1,\n", - " \"color\": \"black\"\n", - " }\n", - " if zeroline_kwargs is None:\n", - " zeroline_kwargs = default_zeroline_kwargs\n", - " else:\n", - " zeroline_kwargs = merge_two_dicts(default_zeroline_kwargs, zeroline_kwargs)\n", - "\n", - " # Effect size marker kwargs\n", - " default_marker_kwargs = {\n", - " 'marker': 'o',\n", - " 'markersize': marker_size,\n", - " 'color': 'black',\n", - " 'alpha': 1,\n", - " 'zorder': 2,\n", - " }\n", - " if marker_kwargs is None:\n", - " marker_kwargs = default_marker_kwargs\n", - " else:\n", - " marker_kwargs = merge_two_dicts(default_marker_kwargs, marker_kwargs)\n", - "\n", - " # Effect size error bar kwargs\n", - " default_errorbar_kwargs = {\n", - " 'color': 'black',\n", - " 'lw': 2.5,\n", - " 'linestyle': '-',\n", - " 'alpha': 1,\n", - " 'zorder': 1,\n", - " }\n", - " if errorbar_kwargs is None:\n", - " errorbar_kwargs = default_errorbar_kwargs\n", - " else:\n", - " errorbar_kwargs = merge_two_dicts(default_errorbar_kwargs, errorbar_kwargs)\n", - "\n", - " # Delta text kwargs\n", - " default_delta_text_kwargs = {\n", - " \"color\": None, \n", - " \"alpha\": 1,\n", - " \"fontsize\": 10, \n", - " \"ha\": 'center', \n", - " \"va\": 'center', \n", - " \"rotation\": 0, \n", - " \"x_coordinates\": None, \n", - " \"y_coordinates\": None,\n", - " \"offset\": 0\n", - " }\n", - " if delta_text_kwargs is None:\n", - " delta_text_kwargs = default_delta_text_kwargs\n", - " else:\n", - " delta_text_kwargs = merge_two_dicts(default_delta_text_kwargs, delta_text_kwargs)\n", - "\n", - " # Contrast bars kwargs.\n", - " default_contrast_bars_kwargs = {\n", - " \"color\": None, \n", - " \"zorder\":-3,\n", - " 'alpha': 0.15\n", - " }\n", - " if contrast_bars_kwargs is None:\n", - " contrast_bars_kwargs = default_contrast_bars_kwargs\n", - " else:\n", - " contrast_bars_kwargs = merge_two_dicts(default_contrast_bars_kwargs, contrast_bars_kwargs)\n", - "\n", - " # reference band kwargs.\n", - " default_reference_band_kwargs = {\n", - " \"span_ax\": False,\n", - " \"color\": None, \n", - " \"alpha\": 0.15,\n", - " \"zorder\":-3\n", - " }\n", - " if reference_band_kwargs is None:\n", - " reference_band_kwargs = default_reference_band_kwargs\n", - " else:\n", - " reference_band_kwargs = merge_two_dicts(default_reference_band_kwargs, reference_band_kwargs)\n", - "\n", - " return (violin_kwargs, zeroline_kwargs, marker_kwargs, errorbar_kwargs, \n", - " delta_text_kwargs, contrast_bars_kwargs, reference_band_kwargs)\n", - "\n", - "def color_palette(\n", - " custom_palette, \n", - " labels, \n", - " number_of_curves_to_plot,\n", - " contrast_desat\n", - " ):\n", - " if custom_palette is not None:\n", - " if isinstance(custom_palette, dict):\n", - " violin_colors = [\n", - " custom_palette.get(c, sns.color_palette()[0]) for c in labels\n", - " ]\n", - " elif isinstance(custom_palette, list):\n", - " violin_colors = custom_palette[: number_of_curves_to_plot]\n", - " elif isinstance(custom_palette, str):\n", - " if custom_palette in plt.colormaps():\n", - " violin_colors = sns.color_palette(custom_palette, number_of_curves_to_plot)\n", - " else:\n", - " raise ValueError(\n", - " f\"The specified `custom_palette` {custom_palette} is not a recognized Matplotlib palette.\"\n", - " )\n", - " else:\n", - " violin_colors = sns.color_palette(n_colors=number_of_curves_to_plot)\n", - " violin_colors = [sns.desaturate(color, contrast_desat) for color in violin_colors]\n", - " return violin_colors\n", - "\n", - "def forest_plot(\n", - " data: list,\n", - " idx: Optional[list[int]] = None,\n", - " ax: Optional[plt.Axes] = None,\n", - " fig_size: tuple[int, int] = None,\n", - " effect_size: str = \"mean_diff\",\n", - " ci_type='bca',\n", - " horizontal: bool = False, \n", - "\n", - " marker_size: int = 10,\n", - " custom_palette: Optional[Union[dict, list, str]] = None,\n", - " contrast_alpha: float = 0.8,\n", - " contrast_desat: float = 1,\n", - "\n", - " labels: list[str] = None,\n", - " labels_rotation: int = None,\n", - " labels_fontsize: int = 10,\n", - " title: str = None,\n", - " title_fontsize: int = 16,\n", - " ylabel: str = None,\n", - " ylabel_fontsize: int = 12,\n", - " ylim: Optional[list[float, float]] = None,\n", - " yticks: Optional[list[float]] = None,\n", - " yticklabels: Optional[list[str]] = None,\n", - " remove_spines: bool = True,\n", - "\n", - " delta_text: bool = True,\n", - " delta_text_kwargs: dict = None,\n", - "\n", - " contrast_bars: bool = True,\n", - " contrast_bars_kwargs: dict = None,\n", - " reference_band: list|tuple = None,\n", - " reference_band_kwargs: dict = None,\n", - "\n", - " violin_kwargs: Optional[dict] = None,\n", - " zeroline_kwargs: Optional[dict] = None,\n", - " marker_kwargs: Optional[dict] = None,\n", - " errorbar_kwargs: Optional[dict] = None,\n", - ")-> plt.Figure:\n", - " \"\"\" \n", - " Custom function that generates a forest plot from given contrast objects, suitable for a range of data analysis types, including those from packages like DABEST-python.\n", - "\n", - " Parameters\n", - " ----------\n", - " data : List\n", - " List of contrast objects.\n", - " idx : Optional[List[int]], default=None\n", - " List of indices to select from the contrast objects if delta-delta experiment. \n", - " If None, only the delta-delta objects are plotted.\n", - " ax : Optional[plt.Axes], default=None\n", - " Matplotlib Axes object for the plot; creates new if None.\n", - " additional_plotting_kwargs : Optional[dict], default=None\n", - " Further customization arguments for the plot.\n", - " fig_size : Tuple[int, int], default=None\n", - " Figure size for the plot.\n", - " effect_size : str\n", - " Type of effect size to plot (e.g., 'mean_diff', `hedges_g` or 'delta_g').\n", - " ci_type : str\n", - " Type of confidence interval to plot (bca' or 'pct')\n", - " horizontal : bool, default=False\n", - " If True, the plot will be horizontal.\n", - " marker_size : int, default=12\n", - " Marker size for plotting effect size dots.\n", - " custom_palette : Optional[Union[dict, list, str]], default=None\n", - " Custom color palette for the plot.\n", - " contrast_alpha : float, default=0.8\n", - " Transparency level for violin plots.\n", - " contrast_desat : float, default=1\n", - " Saturation level for violin plots.\n", - " labels : List[str]\n", - " Labels for each contrast. If None, defaults to 'Contrast 1', 'Contrast 2', etc.\n", - " labels_rotation : int, default=45 for vertical, 0 for horizontal\n", - " Rotation angle for contrast labels.\n", - " labels_fontsize : int, default=10\n", - " Font size for contrast labels.\n", - " title : str\n", - " Plot title, summarizing the visualized data.\n", - " title_fontsize : int, default=16\n", - " Font size for the plot title.\n", - " ylabel : str\n", - " Label for the y-axis, describing the plotted data or effect size.\n", - " ylabel_fontsize : int, default=12\n", - " Font size for the y-axis label.\n", - " ylim : Optional[Tuple[float, float]]\n", - " Limits for the y-axis.\n", - " yticks : Optional[List[float]]\n", - " Custom y-ticks for the plot.\n", - " yticklabels : Optional[List[str]]\n", - " Custom y-tick labels for the plot.\n", - " remove_spines : bool, default=True\n", - " If True, removes plot spines (except the relevant dependent variable spine).\n", - " delta_text : bool, default=True\n", - " If True, it adds text next to each curve representing the effect size value.\n", - " delta_text_kwargs : dict, default=None\n", - " Additional keyword arguments for the delta_text.\n", - " contrast_bars : bool, default=True\n", - " If True, it adds bars from the zeroline to the effect size curve.\n", - " contrast_bars_kwargs : dict, default=None\n", - " Additional keyword arguments for the contrast_bars.\n", - " reference_band: list | tuple, default=None,\n", - " It adds reference bands to the relevant effect size curves.\n", - " reference_band_kwargs : dict, default=None,\n", - " Additional keyword arguments for the reference_band.\n", - " violin_kwargs : Optional[dict], default=None\n", - " Additional arguments for violin plot customization.\n", - " zeroline_kwargs : Optional[dict], default=None\n", - " Additional arguments for the zero line customization.\n", - " marker_kwargs : Optional[dict], default=None\n", - " Additional arguments for the effect size marker customization.\n", - " errorbar_kwargs : Optional[dict], default=None\n", - " Additional arguments for the effect size error bar customization.\n", - "\n", - " Returns\n", - " -------\n", - " plt.Figure\n", - " The matplotlib figure object with the generated forest plot.\n", - " \"\"\"\n", - " from .plot_tools import halfviolin\n", - "\n", - " # Check for errors in the input arguments\n", - " all_kwargs = locals()\n", - " contrast_type = check_for_errors(**all_kwargs)\n", - "\n", - " # Load plot data and extract info\n", - " bootstraps, differences, bcalows, bcahighs = load_plot_data(\n", - " data = data, \n", - " effect_size = effect_size, \n", - " contrast_type = contrast_type,\n", - " ci_type = ci_type,\n", - " idx = idx\n", - " )\n", - " # Adjust figure size based on orientation\n", - " number_of_curves_to_plot = len(bootstraps)\n", - " if ax is not None:\n", - " fig = ax.figure\n", - " else:\n", - " if fig_size is None:\n", - " fig_size = (4, 1.3 * number_of_curves_to_plot) if horizontal else (1.3 * number_of_curves_to_plot, 4)\n", - " fig, ax = plt.subplots(figsize=fig_size)\n", - "\n", - " # Get Kwargs\n", - " (violin_kwargs, zeroline_kwargs, marker_kwargs, errorbar_kwargs, \n", - " delta_text_kwargs, contrast_bars_kwargs, reference_band_kwargs) = get_kwargs(\n", - " violin_kwargs = violin_kwargs,\n", - " zeroline_kwargs = zeroline_kwargs,\n", - " horizontal = horizontal,\n", - " marker_kwargs = marker_kwargs,\n", - " errorbar_kwargs = errorbar_kwargs,\n", - " delta_text_kwargs = delta_text_kwargs,\n", - " contrast_bars_kwargs = contrast_bars_kwargs,\n", - " reference_band_kwargs = reference_band_kwargs,\n", - " marker_size = marker_size\n", - " )\n", - " \n", - " # Plot the violins and make adjustments\n", - " v = ax.violinplot(\n", - " bootstraps, \n", - " **violin_kwargs\n", - " )\n", - " halfviolin(\n", - " v, \n", - " alpha = contrast_alpha, \n", - " half = \"bottom\" if horizontal else \"right\",\n", - " )\n", - " \n", - " ## Plotting the effect sizes and confidence intervals\n", - " for k in range(1, number_of_curves_to_plot + 1):\n", - " if horizontal:\n", - " ax.plot(differences[k - 1], k, **marker_kwargs) \n", - " ax.plot([bcalows[k - 1], bcahighs[k - 1]], [k, k], **errorbar_kwargs) \n", - " else:\n", - " ax.plot(k, differences[k - 1], **marker_kwargs)\n", - " ax.plot([k, k], [bcalows[k - 1], bcahighs[k - 1]], **errorbar_kwargs)\n", - " \n", - " # Aesthetic Adjustments\n", - " ## Handle the custom color palette\n", - " violin_colors = color_palette(\n", - " custom_palette = custom_palette, \n", - " labels = labels, \n", - " number_of_curves_to_plot = number_of_curves_to_plot,\n", - " contrast_desat = contrast_desat\n", - " )\n", - " \n", - " for patch, color in zip(v[\"bodies\"], violin_colors):\n", - " patch.set_facecolor(color)\n", - "\n", - " ## Add a zero line to the plot\n", - " if horizontal:\n", - " ax.plot([0, 0], [0, number_of_curves_to_plot+1], **zeroline_kwargs) \n", - " else:\n", - " ax.plot([0, number_of_curves_to_plot+1], [0, 0], **zeroline_kwargs)\n", - "\n", - " ## lims\n", - " ### Indepedent variable\n", - " if horizontal:\n", - " ax.set_ylim([0.7, number_of_curves_to_plot + 0.2])\n", - " else:\n", - " ax.set_xlim([0.7, number_of_curves_to_plot + 0.5])\n", - "\n", - " ## Depedent variable\n", - " if ylim is not None:\n", - " lim_key = ax.set_xlim if horizontal else ax.set_ylim\n", - " lim_key(ylim)\n", - "\n", - " ## Ticks\n", - " ### Indepedent variable\n", - " lim_key = ax.set_yticks if horizontal else ax.set_xticks\n", - " lim_key(range(1, number_of_curves_to_plot + 1))\n", - "\n", - " if labels_rotation == None:\n", - " labels_rotation = 0 if horizontal else 45\n", - " if labels is None:\n", - " labels = [f\"Contrast {i}\" for i in range(1, number_of_curves_to_plot + 1)]\n", - " lim_key = ax.set_yticklabels if horizontal else ax.set_xticklabels\n", - " lim_key(labels, rotation=labels_rotation, fontsize=labels_fontsize, ha=\"right\")\n", - "\n", - " ### Depedent variable\n", - " if yticks is not None:\n", - " lim_key = ax.set_xticks if horizontal else ax.set_yticks\n", - " lim_key(yticks)\n", - "\n", - " if yticklabels is not None:\n", - " lim_key = ax.set_xticklabels if horizontal else ax.set_yticklabels\n", - " lim_key(yticklabels)\n", - "\n", - " ## y-label \n", - " if ylabel is None:\n", - " effect_attr_map = {\n", - " \"mean_diff\": \"Mean difference\",\n", - " \"median_diff\": \"Median difference\", \n", - " \"cohens_d\": \"Cohen's d\",\n", - " \"cohens_h\": \"Cohen's h\",\n", - " \"cliffs_delta\": \"Cliff's delta\",\n", - " \"hedges_g\": \"Hedges' g\",\n", - " \"delta_g\": \"Delta g\"\n", - " }\n", - " if contrast_type=='delta2' and idx is None and effect_size == \"hedges_g\":\n", - " ylabel = \"Delta g\"\n", - " elif contrast_type=='delta2' and idx is not None and (effect_size == \"delta_g\" or effect_size == \"hedges_g\"):\n", - " ylabel = \"Hedges' g with Delta g\"\n", - " else:\n", - " ylabel = effect_attr_map[effect_size]\n", - " lim_key = ax.set_xlabel if horizontal else ax.set_ylabel\n", - " lim_key(ylabel, fontsize=ylabel_fontsize)\n", - "\n", - " ## Setting the title\n", - " if title is not None:\n", - " ax.set_title(title, fontsize=title_fontsize)\n", - "\n", - " ## Adjust Spines\n", - " if remove_spines:\n", - " spines = [\"top\", \"right\", \"left\"] if horizontal else [\"top\", \"right\", \"bottom\"]\n", - " ax.spines[spines].set_visible(False)\n", - "\n", - " # Delta Text\n", - " if delta_text:\n", - " if delta_text_kwargs.get('color') is not None:\n", - " delta_text_colors = [delta_text_kwargs.pop('color')] * number_of_curves_to_plot\n", - " else:\n", - " delta_text_colors = violin_colors\n", - " delta_text_kwargs.pop('color')\n", - "\n", - " # Collect the X-coordinates for the delta text\n", - " delta_text_x_coordinates = delta_text_kwargs.pop('x_coordinates')\n", - " delta_text_x_adjustment = delta_text_kwargs.pop('offset')\n", - "\n", - " if delta_text_x_coordinates is not None:\n", - " if not isinstance(delta_text_x_coordinates, (list, tuple)) or not all(isinstance(x, (int, float)) for x in delta_text_x_coordinates):\n", - " raise TypeError(\"delta_text_kwargs['x_coordinates'] must be a list of x-coordinates.\")\n", - " if len(delta_text_x_coordinates) != number_of_curves_to_plot:\n", - " raise ValueError(\"delta_text_kwargs['x_coordinates'] must have the same length as the number of ticks to plot.\")\n", - " else:\n", - " delta_text_x_coordinates = (np.arange(1, number_of_curves_to_plot + 1) \n", - " + (0.5 if not horizontal else -0.4)\n", - " + delta_text_x_adjustment\n", - " )\n", - "\n", - " # Collect the Y-coordinates for the delta text\n", - " delta_text_y_coordinates = delta_text_kwargs.pop('y_coordinates')\n", - "\n", - " if delta_text_y_coordinates is not None:\n", - " if not isinstance(delta_text_y_coordinates, (list, tuple)) or not all(isinstance(y, (int, float)) for y in delta_text_y_coordinates):\n", - " raise TypeError(\"delta_text_kwargs['y_coordinates'] must be a list of y-coordinates.\")\n", - " if len(delta_text_y_coordinates) != number_of_curves_to_plot:\n", - " raise ValueError(\"delta_text_kwargs['y_coordinates'] must have the same length as the number of ticks to plot.\")\n", - " else:\n", - " delta_text_y_coordinates = differences\n", - "\n", - " if horizontal:\n", - " delta_text_x_coordinates, delta_text_y_coordinates = delta_text_y_coordinates, delta_text_x_coordinates\n", - "\n", - " for idx, x, y, delta in zip(np.arange(0, number_of_curves_to_plot, 1), delta_text_x_coordinates, \n", - " delta_text_y_coordinates, differences):\n", - " delta_text = np.format_float_positional(delta, precision=2, sign=True, trim=\"k\", min_digits=2)\n", - " ax.text(x, y, delta_text, color=delta_text_colors[idx], zorder=5, **delta_text_kwargs)\n", - "\n", - " # Contrast bars\n", - " if contrast_bars:\n", - " _bar_color = contrast_bars_kwargs.pop('color')\n", - " if _bar_color is not None:\n", - " bar_colors = [_bar_color] * number_of_curves_to_plot\n", - " else:\n", - " bar_colors = violin_colors\n", - " for x, y in zip(np.arange(1, number_of_curves_to_plot + 1), differences):\n", - " if horizontal:\n", - " ax.add_patch(mpatches.Rectangle((0, x-0.25), y, 0.25, color=bar_colors[x-1], **contrast_bars_kwargs))\n", - " else:\n", - " ax.add_patch(mpatches.Rectangle((x, 0), 0.25, y, color=bar_colors[x-1], **contrast_bars_kwargs))\n", - "\n", - " # Reference band\n", - " if reference_band:\n", - " _bar_color = reference_band_kwargs.pop('color')\n", - " if _bar_color is not None:\n", - " bar_colors = [_bar_color] * number_of_curves_to_plot\n", - " else:\n", - " bar_colors = violin_colors\n", - "\n", - " span_ax = reference_band_kwargs.pop(\"span_ax\")\n", - " summary_xmin, summary_xmax = ax.get_xlim()\n", - " summary_ymin, summary_ymax = ax.get_ylim()\n", - "\n", - " for summary_index in reference_band:\n", - " if span_ax == True:\n", - " starting_location = summary_ymin if horizontal else summary_xmin\n", - " else:\n", - " starting_location = summary_index+1 \n", - "\n", - " summary_color = bar_colors[summary_index]\n", - " summary_ci_low, summary_ci_high = bcalows[summary_index], bcahighs[summary_index]\n", - "\n", - " if horizontal:\n", - " ax.add_patch(mpatches.Rectangle(\n", - " (summary_ci_low, starting_location),\n", - " summary_ci_high-summary_ci_low, summary_ymax+1, \n", - " color=summary_color, \n", - " **reference_band_kwargs)\n", - " )\n", - " else:\n", - " ax.add_patch(mpatches.Rectangle(\n", - " (starting_location, summary_ci_low),\n", - " summary_xmax+1, summary_ci_high-summary_ci_low, \n", - " color=summary_color, \n", - " **reference_band_kwargs)\n", - " )\n", - "\n", - " ## Invert Y-axis if horizontal \n", - " if horizontal:\n", - " ax.invert_yaxis()\n", - "\n", - " return fig" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/API/index.qmd b/nbs/API/index.qmd deleted file mode 100644 index ab16229a..00000000 --- a/nbs/API/index.qmd +++ /dev/null @@ -1,11 +0,0 @@ ---- -order: 9 -title: API -listing: - fields: [title, description] - type: table - sort-ui: false - filter-ui: false ---- - -This section contains API details for each of dabest's python submodules. This reference documentation is mainly useful for people looking to customise or build on top of dabest, or wanting detailed information about how dabest works. diff --git a/nbs/API/load.ipynb b/nbs/API/load.ipynb deleted file mode 100644 index 86c7782c..00000000 --- a/nbs/API/load.ipynb +++ /dev/null @@ -1,364 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "bba9fe58", - "metadata": {}, - "source": [ - "# Loading Data\n", - "\n", - "> Loading data and relevant groups\n", - "\n", - "- order: 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "292ee915", - "metadata": {}, - "outputs": [], - "source": [ - "#| default_exp _api" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8b51d167", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from __future__ import annotations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bc61493e", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from nbdev.showdoc import *\n", - "import nbdev\n", - "\n", - "nbdev.nbdev_export()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "218e4f14", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "def load(\n", - " data,\n", - " idx=None,\n", - " x=None,\n", - " y=None,\n", - " paired=None,\n", - " id_col=None,\n", - " ci=95,\n", - " resamples=5000,\n", - " random_seed=12345,\n", - " proportional=False,\n", - " delta2=False,\n", - " experiment=None,\n", - " experiment_label=None,\n", - " x1_level=None,\n", - " mini_meta=False,\n", - " ps_adjust=False,\n", - "):\n", - " \"\"\"\n", - " Loads data in preparation for estimation statistics.\n", - "\n", - " This is designed to work with pandas DataFrames.\n", - "\n", - " Parameters\n", - " ----------\n", - " data : pandas DataFrame\n", - " idx : tuple\n", - " List of column names (if 'x' is not supplied) or of category names\n", - " (if 'x' is supplied). This can be expressed as a tuple of tuples,\n", - " with each individual tuple producing its own contrast plot\n", - " x : string or list, default None\n", - " Column name(s) of the independent variable. This can be expressed as\n", - " a list of 2 elements if and only if 'delta2' is True; otherwise it\n", - " can only be a string.\n", - " y : string, default None\n", - " Column names for data to be plotted on the x-axis and y-axis.\n", - " paired : string, default None\n", - " The type of the experiment under which the data are obtained. If 'paired'\n", - " is None then the data will not be treated as paired data in the subsequent\n", - " calculations. If 'paired' is 'baseline', then in each tuple of x, other\n", - " groups will be paired up with the first group (as control). If 'paired' is\n", - " 'sequential', then in each tuple of x, each group will be paired up with\n", - " its previous group (as control).\n", - " id_col : default None.\n", - " Required if `paired` is True.\n", - " ci : integer, default 95\n", - " The confidence interval width. The default of 95 produces 95%\n", - " confidence intervals.\n", - " resamples : integer, default 5000.\n", - " The number of resamples taken to generate the bootstraps which are used\n", - " to generate the confidence intervals.\n", - " random_seed : int, default 12345\n", - " This integer is used to seed the random number generator during\n", - " bootstrap resampling, ensuring that the confidence intervals\n", - " reported are replicable.\n", - " proportional : boolean, default False.\n", - " An indicator of whether the data is binary or not. When set to True, it\n", - " specifies that the data consists of binary data, where the values are\n", - " limited to 0 and 1. The code is not suitable for analyzing proportion\n", - " data that contains non-numeric values, such as strings like 'yes' and 'no'.\n", - " When False or not provided, the algorithm assumes that\n", - " the data is continuous and uses a non-proportional representation.\n", - " delta2 : boolean, default False\n", - " Indicator of delta-delta experiment\n", - " experiment : String, default None\n", - " The name of the column of the dataframe which contains the label of\n", - " experiments\n", - " experiment_lab : list, default None\n", - " A list of String to specify the order of subplots for delta-delta plots.\n", - " This can be expressed as a list of 2 elements if and only if 'delta2'\n", - " is True; otherwise it can only be a string.\n", - " x1_level : list, default None\n", - " A list of String to specify the order of subplots for delta-delta plots.\n", - " This can be expressed as a list of 2 elements if and only if 'delta2'\n", - " is True; otherwise it can only be a string.\n", - " mini_meta : boolean, default False\n", - " Indicator of weighted delta calculation.\n", - " ps_adjust : boolean, default False\n", - " Indicator of whether to adjust calculated p-value according to Phipson & Smyth (2010)\n", - " # https://doi.org/10.2202/1544-6115.1585\n", - "\n", - " Returns\n", - " -------\n", - " A `Dabest` object.\n", - " \"\"\"\n", - " from dabest import Dabest\n", - "\n", - " return Dabest(\n", - " data,\n", - " idx,\n", - " x,\n", - " y,\n", - " paired,\n", - " id_col,\n", - " ci,\n", - " resamples,\n", - " random_seed,\n", - " proportional,\n", - " delta2,\n", - " experiment,\n", - " experiment_label,\n", - " x1_level,\n", - " mini_meta,\n", - " ps_adjust,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "570ff65a", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "import numpy as np\n", - "from typing import Union, Optional\n", - "import pandas as pd\n", - "\n", - "\n", - "def prop_dataset(\n", - " group: Union[\n", - " list, tuple, np.ndarray, dict\n", - " ], # Accepts lists, tuples, or numpy ndarrays of numeric types.\n", - " group_names: Optional[list] = None,\n", - "):\n", - " \"\"\"\n", - " Convenient function to generate a dataframe of binary data.\n", - " \"\"\"\n", - "\n", - " if isinstance(group, dict):\n", - " # If group_names is not provided, use the keys of the dict as group_names\n", - " if group_names is None:\n", - " group_names = list(group.keys())\n", - " elif not set(group_names) == set(group.keys()):\n", - " # Check if the group_names provided is the same as the keys of the dict\n", - " raise ValueError(\"group_names must be the same as the keys of the dict.\")\n", - " \n", - " # Check if the values in the dict are numeric\n", - " if not all(\n", - " [isinstance(group[name], (list, tuple, np.ndarray)) for name in group_names]\n", - " ):\n", - " raise ValueError(\n", - " \"group must be a dict of lists, tuples, or numpy ndarrays of numeric types.\"\n", - " )\n", - " \n", - " # Check if the values in the dict only have two elements under each parent key\n", - " if not all([len(group[name]) == 2 for name in group_names]):\n", - " raise ValueError(\"Each parent key should have only two elements.\")\n", - " group_val = group\n", - "\n", - " else:\n", - " if group_names is None:\n", - " raise ValueError(\"group_names must be provided if group is not a dict.\")\n", - " \n", - " # Check if the length of group is two times of the length of group_names\n", - " if not len(group) == 2 * len(group_names):\n", - " raise ValueError(\n", - " \"The length of group must be two times of the length of group_names.\"\n", - " )\n", - " group_val = {\n", - " group_names[i]: [group[i * 2], group[i * 2 + 1]]\n", - " for i in range(len(group_names))\n", - " }\n", - "\n", - " # Check if the sum of values in group_val under each key are the same\n", - " if not all(\n", - " [\n", - " sum(group_val[name]) == sum(group_val[group_names[0]])\n", - " for name in group_val.keys()\n", - " ]\n", - " ):\n", - " raise ValueError(\"The sum of values under each key must be the same.\")\n", - "\n", - " id_col = pd.Series(range(1, sum(group_val[group_names[0]]) + 1))\n", - "\n", - " final_df = pd.DataFrame()\n", - "\n", - " for name in group_val.keys():\n", - " col = (\n", - " np.repeat(0, group_val[name][0]).tolist()\n", - " + np.repeat(1, group_val[name][1]).tolist()\n", - " )\n", - " df = pd.DataFrame({name: col})\n", - " final_df = pd.concat([final_df, df], axis=1)\n", - "\n", - " final_df[\"ID\"] = id_col\n", - "\n", - " return final_df" - ] - }, - { - "cell_type": "markdown", - "id": "db38d192", - "metadata": {}, - "source": [ - "## Example" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f8762853", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import scipy as sp\n", - "import dabest" - ] - }, - { - "cell_type": "markdown", - "id": "5ccb211a", - "metadata": {}, - "source": [ - "Create dummy data for demonstration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "30187b91", - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(88888)\n", - "N = 10\n", - "c1 = sp.stats.norm.rvs(loc=100, scale=5, size=N)\n", - "t1 = sp.stats.norm.rvs(loc=115, scale=5, size=N)\n", - "df = pd.DataFrame({\"Control 1\": c1, \"Test 1\": t1})" - ] - }, - { - "cell_type": "markdown", - "id": "7cc8867d", - "metadata": {}, - "source": [ - "Load the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "171a15fc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2024.03.29\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Tue Mar 19 15:34:58 2024.\n", - "\n", - "Effect size(s) with 95% confidence intervals will be computed for:\n", - "1. Test 1 minus Control 1\n", - "\n", - "5000 resamples will be used to generate the effect size bootstraps." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "my_data = dabest.load(df, idx=(\"Control 1\", \"Test 1\"))\n", - "my_data" - ] - }, - { - "cell_type": "markdown", - "id": "b480536a", - "metadata": {}, - "source": [ - "For proportion plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f74b6325", - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(88888)\n", - "N = 10\n", - "c1 = np.random.binomial(1, 0.2, size=N)\n", - "t1 = np.random.binomial(1, 0.5, size=N)\n", - "df = pd.DataFrame({\"Control 1\": c1, \"Test 1\": t1})\n", - "my_data = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), proportional=True)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/API/misc_tools.ipynb b/nbs/API/misc_tools.ipynb deleted file mode 100644 index 3e28644e..00000000 --- a/nbs/API/misc_tools.ipynb +++ /dev/null @@ -1,2059 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f570f144", - "metadata": {}, - "source": [ - "# misc_tools\n", - "\n", - "> Convenience functions that don't directly deal with plotting or bootstrap computations are placed here.\n", - "\n", - "- order: 9" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7ddd606b", - "metadata": {}, - "outputs": [], - "source": [ - "#| default_exp misc_tools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f82b1fe9", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from __future__ import annotations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "094b4e53", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from nbdev.showdoc import *\n", - "import nbdev\n", - "nbdev.nbdev_export()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3c9a6ef1", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "\n", - "import datetime as dt\n", - "import numpy as np\n", - "from numpy import repeat\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib\n", - "import matplotlib.axes as axes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5f54be1c", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "def merge_two_dicts(\n", - " x: dict, y: dict\n", - ") -> dict: # A dictionary containing a union of all keys in both original dicts.\n", - " \"\"\"\n", - " Given two dicts, merge them into a new dict as a shallow copy.\n", - " Any overlapping keys in `y` will override the values in `x`.\n", - "\n", - " Taken from [here](https://stackoverflow.com/questions/38987/\n", - " how-to-merge-two-python-dictionaries-in-a-single-expression)\n", - "\n", - " \"\"\"\n", - " z = x.copy()\n", - " z.update(y)\n", - " return z\n", - "\n", - "\n", - "def unpack_and_add(l, c):\n", - " \"\"\"Convenience function to allow me to add to an existing list\n", - " without altering that list.\"\"\"\n", - " t = [a for a in l]\n", - " t.append(c)\n", - " return t\n", - "\n", - "\n", - "def print_greeting():\n", - " \"\"\"\n", - " Generates a greeting message based on the current time, along with the version information of DABEST.\n", - "\n", - " This function dynamically generates a greeting ('Good morning', 'Good afternoon', 'Good evening')\n", - " based on the current system time. It also retrieves and displays the version of DABEST (Data Analysis\n", - " using Bootstrap-Coupled ESTimation). The message includes a header with the DABEST version and the\n", - " current time formatted in a user-friendly manner.\n", - "\n", - " Returns:\n", - " str: A formatted string containing the greeting message, DABEST version, and current time.\n", - " \"\"\"\n", - " from .__init__ import __version__\n", - "\n", - " line1 = \"DABEST v{}\".format(__version__)\n", - " header = \"\".join(repeat(\"=\", len(line1)))\n", - " spacer = \"\".join(repeat(\" \", len(line1)))\n", - "\n", - " now = dt.datetime.now()\n", - " if 0 < now.hour < 12:\n", - " greeting = \"Good morning!\"\n", - " elif 12 < now.hour < 18:\n", - " greeting = \"Good afternoon!\"\n", - " else:\n", - " greeting = \"Good evening!\"\n", - "\n", - " current_time = \"The current time is {}.\".format(now.ctime())\n", - "\n", - " return \"\\n\".join([line1, header, spacer, greeting, current_time])\n", - "\n", - "\n", - "def get_varname(obj):\n", - " matching_vars = [k for k, v in globals().items() if v is obj]\n", - " if len(matching_vars) > 0:\n", - " return matching_vars[0]\n", - " return \"\"\n", - "\t\n", - "\n", - "def get_unique_categories(names):\n", - " \"\"\"\n", - " Extract unique categories from various input types.\n", - " \"\"\"\n", - " if isinstance(names, list):\n", - " return names\n", - " if isinstance(names, np.ndarray):\n", - " return names # numpy.unique() returns a sorted array\n", - " elif isinstance(names, (pd.Categorical, pd.Series)):\n", - " return names.cat.categories if hasattr(names, 'cat') else names.unique()\n", - " else:\n", - " # For dict_keys and other iterables\n", - " return np.unique(list(names))\n", - "\n", - "def get_params(\n", - " effectsize_df: object, \n", - " plot_kwargs: dict,\n", - " sankey_kwargs: dict,\n", - " barplot_kwargs: dict\n", - " ):\n", - " \"\"\"\n", - " Extracts parameters from the `effectsize_df` and `plot_kwargs` objects for use in the plotter function.\n", - " \n", - " Parameters\n", - " ----------\n", - " effectsize_df : object\n", - " A `dabest` EffectSizeDataFrame object.\n", - " plot_kwargs : dict\n", - " Kwargs passed to the plot function.\n", - " sankey kwargs : dict\n", - " Kwargs relating to the sankey diagram plots\n", - " barplot_kwargs : dict\n", - " Kwargs relating to the barplot\n", - " \"\"\"\n", - " plot_data = effectsize_df._plot_data\n", - " xvar = effectsize_df.xvar\n", - " yvar = effectsize_df.yvar\n", - " is_paired = effectsize_df.is_paired\n", - " delta2 = effectsize_df.delta2\n", - " is_mini_meta = effectsize_df.is_mini_meta\n", - " effect_size = effectsize_df.effect_size\n", - " proportional = effectsize_df.is_proportional\n", - " results = effectsize_df.results\n", - " dabest_obj = effectsize_df.dabest_obj\n", - " all_plot_groups = dabest_obj._all_plot_groups\n", - " idx = dabest_obj.idx\n", - " x1_level = dabest_obj.x1_level\n", - " experiment_label = dabest_obj.experiment_label\n", - " \n", - " if effect_size not in [\"mean_diff\", \"hedges_g\"] or not delta2:\n", - " show_delta2 = False\n", - " else:\n", - " show_delta2 = plot_kwargs[\"show_delta2\"]\n", - "\n", - " if effect_size != \"mean_diff\" or not is_mini_meta:\n", - " show_mini_meta = False\n", - " else:\n", - " show_mini_meta = plot_kwargs[\"show_mini_meta\"]\n", - "\n", - " if show_delta2 and show_mini_meta: raise ValueError(\"`show_delta2` and `show_mini_meta` cannot be True at the same time.\")\n", - "\n", - " # Horizontal\n", - " horizontal = plot_kwargs[\"horizontal\"]\n", - "\n", - " # Disable Gardner-Altman plotting if any of the idxs comprise of more than\n", - " # two groups or if it is a delta-delta plot.\n", - " float_contrast = plot_kwargs[\"float_contrast\"]\n", - " if len(idx) > 1 or len(idx[0]) > 2:\n", - " float_contrast = False\n", - "\n", - " if effect_size in [\"cliffs_delta\"]:\n", - " float_contrast = False\n", - "\n", - " if show_delta2 or show_mini_meta or horizontal:\n", - " float_contrast = False\n", - "\n", - " if not is_paired:\n", - " show_pairs = False\n", - " else:\n", - " show_pairs = plot_kwargs[\"show_pairs\"]\n", - "\n", - " # Group summaries\n", - " group_summaries = plot_kwargs[\"group_summaries\"]\n", - " group_summaries = None if barplot_kwargs['errorbar'] is not None else group_summaries\n", - "\n", - " # Contrast Axes kwargs\n", - " ci_type = plot_kwargs[\"ci_type\"]\n", - " if ci_type not in [\"bca\", \"pct\"]:\n", - " raise ValueError(\"Invalid `ci_type`. Must be either 'bca' or 'pct'.\")\n", - " \n", - " # Boolean for showing Baseline Curve\n", - " show_baseline_ec = plot_kwargs[\"show_baseline_ec\"]\n", - "\n", - " # Sankey details\n", - " # We need to extract the `sankey` and `flow` from the kwargs\n", - " # to use for varying different kinds of paired proportional plots\n", - " # We also don't want to pop the parameter from the kwargs\n", - " one_sankey = (\n", - " False if is_paired is not None else None\n", - " ) # Flag to indicate if only one sankey is plotted.\n", - " two_col_sankey = (\n", - " True if proportional and not one_sankey and sankey_kwargs[\"sankey\"] and not sankey_kwargs[\"flow\"] else False\n", - " )\n", - "\n", - " # Asymmetric side for swarmplots\n", - " asymmetric_side = (\n", - " plot_kwargs[\"swarm_side\"] # Default asymmetric side is right\n", - " if plot_kwargs[\"swarm_side\"] is not None\n", - " else \"right\" if not horizontal\n", - " else \"left\"\n", - " ) \n", - " # Whether to show sample sizes with ticklabels\n", - " show_sample_size = plot_kwargs[\"show_sample_size\"]\n", - " \n", - " return (dabest_obj, plot_data, xvar, yvar, is_paired, effect_size, proportional, all_plot_groups, \n", - " idx, show_delta2, show_mini_meta, float_contrast, show_pairs, group_summaries, \n", - " horizontal, results, ci_type, x1_level, experiment_label, show_baseline_ec, \n", - " one_sankey, two_col_sankey, asymmetric_side, show_sample_size)\n", - "\n", - "def get_kwargs(\n", - " plot_kwargs: dict, \n", - " ytick_color,\n", - " is_paired: bool = False\n", - " ):\n", - " \"\"\"\n", - " Extracts the kwargs from the `plot_kwargs` object for use in the plotter function.\n", - "\n", - " Parameters\n", - " ----------\n", - " plot_kwargs : dict\n", - " Kwargs passed to the plot function.\n", - " ytick_color : str or color list\n", - " Color of the yticks.\n", - " is_paired : bool, optional\n", - " A boolean flag to determine if the plot is for paired data. Default is False.\n", - " \"\"\"\n", - " from .misc_tools import merge_two_dicts\n", - "\n", - " # Swarmplot kwargs\n", - " default_swarmplot_kwargs = {\n", - " \"size\": plot_kwargs[\"raw_marker_size\"],\n", - " \"alpha\": plot_kwargs[\"raw_alpha\"],\n", - " \"fontsize\": plot_kwargs.get(\"fontsize_rawxlabel\"),\n", - " }\n", - " if plot_kwargs[\"swarmplot_kwargs\"] is None:\n", - " swarmplot_kwargs = default_swarmplot_kwargs\n", - " else:\n", - " swarmplot_kwargs = merge_two_dicts(\n", - " default_swarmplot_kwargs, plot_kwargs[\"swarmplot_kwargs\"]\n", - " )\n", - "\n", - " # Barplot kwargs\n", - " default_barplot_kwargs = {\n", - " \"estimator\": np.mean, \n", - " \"errorbar\": None,\n", - " \"width\": plot_kwargs[\"bar_width\"],\n", - " \"alpha\": plot_kwargs[\"raw_alpha\"],\n", - " \"err_kws\": {'color': 'black'},\n", - " \"fontsize\": plot_kwargs[\"fontsize_rawxlabel\"]\n", - " }\n", - " if plot_kwargs[\"barplot_kwargs\"] is None:\n", - " barplot_kwargs = default_barplot_kwargs\n", - " else:\n", - " barplot_kwargs = merge_two_dicts(\n", - " default_barplot_kwargs, plot_kwargs[\"barplot_kwargs\"]\n", - " )\n", - "\n", - " # Sankey Diagram kwargs\n", - " default_sankey_kwargs = {\n", - " \"width\": 0.4,\n", - " \"align\": \"center\",\n", - " \"sankey\": True,\n", - " \"flow\": True,\n", - " \"alpha\": plot_kwargs['raw_alpha'],\n", - " \"rightColor\": False,\n", - " \"bar_width\": 0.2,\n", - " \"fontsize\": plot_kwargs.get(\"fontsize_rawxlabel\")\n", - " }\n", - " if plot_kwargs[\"sankey_kwargs\"] is None:\n", - " sankey_kwargs = default_sankey_kwargs\n", - " else:\n", - " sankey_kwargs = merge_two_dicts(\n", - " default_sankey_kwargs, plot_kwargs[\"sankey_kwargs\"]\n", - " )\n", - "\n", - " # Violinplot kwargs.\n", - " default_contrast_kwargs = {\n", - " \"widths\": 0.5,\n", - " \"orientation\": 'vertical',\n", - " \"showextrema\": False,\n", - " \"showmedians\": False,\n", - " \"alpha\": plot_kwargs[\"contrast_alpha\"],\n", - " \n", - " }\n", - " if plot_kwargs[\"contrast_kwargs\"] is None:\n", - " contrast_kwargs = default_contrast_kwargs\n", - " else:\n", - " contrast_kwargs = merge_two_dicts(\n", - " default_contrast_kwargs, plot_kwargs[\"contrast_kwargs\"]\n", - " )\n", - "\n", - " # Slopegraph kwargs.\n", - " default_slopegraph_kwargs = {\n", - " \"linewidth\": 1, \n", - " \"alpha\": plot_kwargs[\"raw_alpha\"],\n", - " 'jitter': 0, \n", - " 'jitter_seed': 9876543210,\n", - " }\n", - " if plot_kwargs[\"slopegraph_kwargs\"] is None:\n", - " slopegraph_kwargs = default_slopegraph_kwargs\n", - " else:\n", - " slopegraph_kwargs = merge_two_dicts(\n", - " default_slopegraph_kwargs, plot_kwargs[\"slopegraph_kwargs\"]\n", - " )\n", - "\n", - " # Zero reference-line kwargs.\n", - " default_reflines_kwargs = {\n", - " \"linestyle\": \"solid\",\n", - " \"linewidth\": 0.75,\n", - " \"zorder\": 2,\n", - " \"color\": ytick_color,\n", - " }\n", - " if plot_kwargs[\"reflines_kwargs\"] is None:\n", - " reflines_kwargs = default_reflines_kwargs\n", - " else:\n", - " reflines_kwargs = merge_two_dicts(\n", - " default_reflines_kwargs, plot_kwargs[\"reflines_kwargs\"]\n", - " )\n", - "\n", - " # Legend kwargs.\n", - " default_legend_kwargs = {\n", - " \"loc\": \"upper left\", \n", - " \"frameon\": False,\n", - " }\n", - " if plot_kwargs[\"legend_kwargs\"] is None:\n", - " legend_kwargs = default_legend_kwargs\n", - " else:\n", - " legend_kwargs = merge_two_dicts(\n", - " default_legend_kwargs, plot_kwargs[\"legend_kwargs\"]\n", - " )\n", - "\n", - " # Group summaries kwargs.\n", - " gs_default = {\n", - " \"mean_sd\", \n", - " \"median_quartiles\", \n", - " None\n", - " }\n", - " if plot_kwargs[\"group_summaries\"] not in gs_default:\n", - " raise ValueError(\n", - " \"group_summaries must be one of\" \" these: {}.\".format(gs_default)\n", - " )\n", - "\n", - " default_group_summaries_kwargs = {\n", - " \"zorder\": 3, \n", - " \"lw\": 2, \n", - " \"alpha\": 1 if not is_paired else 0.6,\n", - " 'gap_width_percent': 1.5,\n", - " 'offset': 0.1,\n", - " 'color': None\n", - " }\n", - " if plot_kwargs[\"group_summaries_kwargs\"] is None:\n", - " group_summaries_kwargs = default_group_summaries_kwargs\n", - " else:\n", - " group_summaries_kwargs = merge_two_dicts(\n", - " default_group_summaries_kwargs, plot_kwargs[\"group_summaries_kwargs\"]\n", - " )\n", - "\n", - " # Redraw axes kwargs.\n", - " redraw_axes_kwargs = {\n", - " \"colors\": ytick_color,\n", - " \"facecolors\": ytick_color,\n", - " \"lw\": 1,\n", - " \"zorder\": 10,\n", - " \"clip_on\": False,\n", - " }\n", - " \n", - " # Delta dots kwargs.\n", - " default_delta_dot_kwargs = {\n", - " \"color\": 'k',\n", - " \"marker\": \"^\", \n", - " \"alpha\": 0.5, \n", - " \"zorder\": 2, \n", - " \"size\": 3, \n", - " \"side\": \"right\"\n", - " }\n", - " if plot_kwargs[\"delta_dot_kwargs\"] is None:\n", - " delta_dot_kwargs = default_delta_dot_kwargs\n", - " else:\n", - " delta_dot_kwargs = merge_two_dicts(default_delta_dot_kwargs, plot_kwargs[\"delta_dot_kwargs\"])\n", - "\n", - " # Delta text kwargs.\n", - " default_delta_text_kwargs = {\n", - " \"alpha\": 1,\n", - " \"fontsize\": 10, \n", - " \"ha\": 'center', \n", - " \"va\": 'center', \n", - " \"rotation\": 0, \n", - " \"x_coordinates\": None, \n", - " \"y_coordinates\": None,\n", - " \"offset\": 0\n", - " }\n", - " if plot_kwargs[\"delta_text_kwargs\"] is None:\n", - " delta_text_kwargs = default_delta_text_kwargs\n", - " else:\n", - " delta_text_kwargs = merge_two_dicts(default_delta_text_kwargs, plot_kwargs[\"delta_text_kwargs\"])\n", - "\n", - " # Reference band kwargs.\n", - " default_reference_band_kwargs = {\n", - " \"span_ax\": False,\n", - " \"alpha\": 0.15,\n", - " \"zorder\":-3\n", - " }\n", - " if plot_kwargs[\"reference_band_kwargs\"] is None:\n", - " reference_band_kwargs = default_reference_band_kwargs\n", - " else:\n", - " reference_band_kwargs = merge_two_dicts(default_reference_band_kwargs, plot_kwargs[\"reference_band_kwargs\"])\n", - "\n", - " # Swarm bars kwargs.\n", - " default_raw_bars_kwargs = {\n", - " \"zorder\":-3,\n", - " \"alpha\": 0.2\n", - " }\n", - " if plot_kwargs[\"raw_bars_kwargs\"] is None:\n", - " raw_bars_kwargs = default_raw_bars_kwargs\n", - " else:\n", - " raw_bars_kwargs = merge_two_dicts(default_raw_bars_kwargs, plot_kwargs[\"raw_bars_kwargs\"])\n", - "\n", - " # Contrast bars kwargs.\n", - " default_contrast_bars_kwargs = {\n", - " \"zorder\":-3,\n", - " \"alpha\": 0.2\n", - " }\n", - " if plot_kwargs[\"contrast_bars_kwargs\"] is None:\n", - " contrast_bars_kwargs = default_contrast_bars_kwargs\n", - " else:\n", - " contrast_bars_kwargs = merge_two_dicts(default_contrast_bars_kwargs, plot_kwargs[\"contrast_bars_kwargs\"])\n", - "\n", - " # Table axes for horizontal plot kwargs.\n", - " default_table_kwargs = {\n", - " 'show': True,\n", - " 'color' : 'yellow',\n", - " 'alpha' : 0.2,\n", - " 'fontsize' : 12,\n", - " 'text_color' : 'black', \n", - " 'text_units' : '',\n", - " 'control_marker' : '-',\n", - " 'fontsize_label': 12,\n", - " 'label': 'Δ'\n", - " }\n", - " if plot_kwargs[\"horizontal_table_kwargs\"] is None:\n", - " table_kwargs = default_table_kwargs\n", - " else:\n", - " table_kwargs = merge_two_dicts(default_table_kwargs, plot_kwargs[\"horizontal_table_kwargs\"])\n", - "\n", - " # Gridkey kwargs.\n", - " default_gridkey_kwargs = {\n", - " 'show_es' : plot_kwargs['gridkey_show_es'], # If True, the gridkey will show the effect size of each comparison.\n", - " 'show_Ns' : plot_kwargs['gridkey_show_Ns'], # If True, the gridkey will show the number of observations in eachgroup.\n", - " 'merge_pairs' : plot_kwargs['gridkey_merge_pairs'], # If True, the gridkey will merge the pairs of groups into a single cell. This is useful for when the groups are paired.\n", - " 'delimiters': plot_kwargs['gridkey_delimiters'], # Delimiters to split the group names.\n", - " 'marker': \"\\u25CF\", # Marker for the gridkey dots.\n", - " }\n", - " if plot_kwargs[\"gridkey_kwargs\"] is None:\n", - " gridkey_kwargs = default_gridkey_kwargs\n", - " else:\n", - " gridkey_kwargs = merge_two_dicts(default_gridkey_kwargs, plot_kwargs[\"gridkey_kwargs\"])\n", - "\n", - " # Effect size marker kwargs\n", - " default_contrast_marker_kwargs = {\n", - " 'marker': 'o',\n", - " 'markersize': plot_kwargs['contrast_marker_size'],\n", - " 'color': ytick_color,\n", - " 'alpha': 1,\n", - " 'zorder': 2,\n", - " }\n", - " if plot_kwargs['contrast_marker_kwargs'] is None:\n", - " contrast_marker_kwargs = default_contrast_marker_kwargs\n", - " else:\n", - " contrast_marker_kwargs = merge_two_dicts(default_contrast_marker_kwargs, plot_kwargs['contrast_marker_kwargs'])\n", - "\n", - " # Effect size error bar kwargs\n", - " default_contrast_errorbar_kwargs = {\n", - " 'color': ytick_color,\n", - " 'lw': 2,\n", - " 'linestyle': '-',\n", - " 'alpha': 1,\n", - " 'zorder': 1,\n", - " }\n", - " if plot_kwargs['contrast_errorbar_kwargs'] is None:\n", - " contrast_errorbar_kwargs = default_contrast_errorbar_kwargs\n", - " else:\n", - " contrast_errorbar_kwargs = merge_two_dicts(default_contrast_errorbar_kwargs, plot_kwargs['contrast_errorbar_kwargs'])\n", - "\n", - " # Prop sample counts kwargs\n", - " default_prop_sample_counts_kwargs = {\n", - " 'color': 'k', \n", - " 'zorder': 5, \n", - " 'ha': 'center', \n", - " 'va': 'center'\n", - " }\n", - " if plot_kwargs['prop_sample_counts_kwargs'] is None:\n", - " prop_sample_counts_kwargs = default_prop_sample_counts_kwargs\n", - " else:\n", - " prop_sample_counts_kwargs = merge_two_dicts(default_prop_sample_counts_kwargs, plot_kwargs['prop_sample_counts_kwargs'])\n", - "\n", - "\n", - " # RM Lines kwargs\n", - " default_contrast_paired_lines_kwargs = {\n", - " \"linestyle\": \"-\",\n", - " \"linewidth\": 2,\n", - " \"zorder\": -2,\n", - " \"color\": 'dimgray',\n", - " \"alpha\": 1\n", - " }\n", - " if plot_kwargs[\"contrast_paired_lines_kwargs\"] is None:\n", - " contrast_paired_lines_kwargs = default_contrast_paired_lines_kwargs\n", - " else:\n", - " contrast_paired_lines_kwargs = merge_two_dicts(default_contrast_paired_lines_kwargs, plot_kwargs[\"contrast_paired_lines_kwargs\"])\n", - "\n", - " # Return the kwargs.\n", - " return (swarmplot_kwargs, barplot_kwargs, sankey_kwargs, contrast_kwargs, slopegraph_kwargs, \n", - " reflines_kwargs, legend_kwargs, group_summaries_kwargs, redraw_axes_kwargs, delta_dot_kwargs,\n", - " delta_text_kwargs, reference_band_kwargs, raw_bars_kwargs, contrast_bars_kwargs, table_kwargs, gridkey_kwargs,\n", - " contrast_marker_kwargs, contrast_errorbar_kwargs, prop_sample_counts_kwargs, contrast_paired_lines_kwargs)\n", - "\n", - "\n", - "def get_color_palette(\n", - " plot_kwargs: dict, \n", - " plot_data: pd.DataFrame, \n", - " xvar: str, \n", - " show_pairs: bool, \n", - " idx: list, \n", - " all_plot_groups: list,\n", - " delta2: bool,\n", - " proportional: bool\n", - " ):\n", - " \"\"\"\n", - " Create the color palette to be used in the plotter function.\n", - "\n", - " Parameters\n", - " ----------\n", - " plot_kwargs : dict\n", - " Kwargs passed to the plot function.\n", - " plot_data : object (Dataframe)\n", - " A dataframe of plot data.\n", - " xvar : str\n", - " The name of the x-axis variable.\n", - " show_pairs : bool\n", - " A boolean flag to determine if the plot is for paired data.\n", - " idx : list\n", - " A list of tuples containing the group names.\n", - " all_plot_groups : list\n", - " A list of all the group names.\n", - " delta2 : bool\n", - " A boolean flag to determine if the plot will have a delta-delta effect size.\n", - " proportional : bool\n", - " A boolean flag to determine if the plot is for a proportional plot.\n", - " \"\"\"\n", - " sankey = True if proportional and show_pairs else False\n", - "\n", - " # Create color palette that will be shared across subplots.\n", - " color_col = plot_kwargs[\"color_col\"]\n", - " if color_col is None:\n", - " color_groups = pd.unique(plot_data[xvar])\n", - " bootstraps_color_by_group = True\n", - " else:\n", - " if color_col not in plot_data.columns:\n", - " raise KeyError(\"``{}`` is not a column in the data.\".format(color_col))\n", - " color_groups = pd.unique(plot_data[color_col])\n", - " bootstraps_color_by_group = False\n", - " if show_pairs:\n", - " if plot_kwargs[\"custom_palette\"] is not None:\n", - " if delta2 or sankey:\n", - " bootstraps_color_by_group = False\n", - " else:\n", - " bootstraps_color_by_group = True\n", - " else:\n", - " bootstraps_color_by_group = False\n", - "\n", - " # Handle the color palette.\n", - " filled = True\n", - " empty_circle = plot_kwargs[\"empty_circle\"]\n", - " color_by_subgroups = (\n", - " True if empty_circle else False\n", - " ) # boolean flag to determine if colour is being grouped by subgroup or the default\n", - " if empty_circle:\n", - " # Handling color_by_subgroups\n", - " # For now, color_by_subgroups can only be True for multi-2-group and 2-group comparison\n", - " if isinstance(idx[0], str):\n", - " if len(idx) > 2:\n", - " color_by_subgroups = False\n", - " else:\n", - " for group_i in idx:\n", - " if len(group_i) > 2:\n", - " color_by_subgroups = False\n", - "\n", - " # filled is now a list, which determines the which group in idx has their dots filled for the swarmplot\n", - " filled = []\n", - " for i in range(len(idx)):\n", - " filled.append(False)\n", - " filled.extend([True] * (len(idx[i]) - 1))\n", - "\n", - " if color_col is not None:\n", - " if sankey:\n", - " names = [1, 0]\n", - " else:\n", - " names = color_groups if not color_by_subgroups else idx\n", - " else:\n", - " if sankey:\n", - " names = [1, 0]\n", - " else:\n", - " names = all_plot_groups if not color_by_subgroups else idx\n", - "\n", - " n_groups = len(color_groups)\n", - " custom_pal = plot_kwargs[\"custom_palette\"]\n", - " raw_desat = plot_kwargs[\"raw_desat\"]\n", - " contrast_desat = plot_kwargs[\"contrast_desat\"]\n", - "\n", - " if custom_pal is None:\n", - " unsat_colors = sns.color_palette(n_colors=n_groups)\n", - " if empty_circle and not color_by_subgroups:\n", - " unsat_colors = [sns.color_palette(\"gray\")[3]] + unsat_colors\n", - " else:\n", - " if isinstance(custom_pal, dict):\n", - " if delta2:\n", - " groups_in_palette = {\n", - " k: custom_pal[k] for k in color_groups\n", - " }\n", - " elif proportional and not sankey: # barplots (unpaired proportional data)\n", - " keys = list(custom_pal.keys())\n", - " if all(k in keys for k in [1, 0]) and len(keys) == 2:\n", - " groups_in_palette = {\n", - " k: custom_pal[k] for k in [1, 0]\n", - " }\n", - " bootstraps_color_by_group = False\n", - " else:\n", - " groups_in_palette = {\n", - " k: custom_pal[k] for k in all_plot_groups if k in color_groups\n", - " }\n", - " elif sankey:\n", - " groups_in_palette = {\n", - " k: custom_pal[k] for k in [1, 0]\n", - " }\n", - " elif color_col is None:\n", - " groups_in_palette = {\n", - " k: custom_pal[k] for k in all_plot_groups if k in color_groups\n", - " }\n", - " else:\n", - " raise ValueError(\"The `custom_palette` dictionary is not supported when `color_col` is not None.\")\n", - "\n", - " names = groups_in_palette.keys()\n", - " unsat_colors = groups_in_palette.values()\n", - "\n", - " elif isinstance(custom_pal, list):\n", - " if sankey:\n", - " if len(custom_pal) != 2:\n", - " raise ValueError(\"To specify a custom palette for a Sankey diagram, you must provide exactly two colors.\")\n", - " else:\n", - " groups_in_palette = {\n", - " k: custom_pal[k] for k in [1, 0]\n", - " }\n", - " names = groups_in_palette.keys()\n", - " unsat_colors = groups_in_palette.values()\n", - " elif len(custom_pal) < n_groups:\n", - " err1 = \"The specified `custom_palette` has fewer colors than the number of groups.\"\n", - " err2 = \" Please specify a custom palette with at least {} colors.\".format(n_groups)\n", - " raise ValueError(err1 + err2)\n", - " else:\n", - " unsat_colors = custom_pal[0:n_groups]\n", - "\n", - " elif isinstance(custom_pal, str):\n", - " # check it is in the list of matplotlib palettes.\n", - " if custom_pal in plt.colormaps():\n", - " unsat_colors = sns.color_palette(custom_pal, n_groups)\n", - " else:\n", - " err1 = \"The specified `custom_palette` {}\".format(custom_pal)\n", - " err2 = \" is not a matplotlib palette. Please check.\"\n", - " raise ValueError(err1 + err2)\n", - "\n", - " if custom_pal is None and color_col is None:\n", - " categories = get_unique_categories(names)\n", - " raw_colors = [sns.desaturate(c, raw_desat) for c in unsat_colors]\n", - " contrast_colors = [sns.desaturate(c, contrast_desat) for c in unsat_colors]\n", - " if color_by_subgroups:\n", - " plot_palette_raw = dict()\n", - " plot_palette_contrast = dict()\n", - " for i in range(len(idx)):\n", - " for names_i in idx[i]:\n", - " plot_palette_raw[names_i] = raw_colors[i]\n", - " plot_palette_contrast[names_i] = contrast_colors[i]\n", - " else:\n", - " plot_palette_raw = dict(zip(categories, raw_colors))\n", - " plot_palette_contrast = dict(zip(categories, contrast_colors))\n", - " else:\n", - " raw_colors = [sns.desaturate(c, raw_desat) for c in unsat_colors]\n", - " contrast_colors = [sns.desaturate(c, contrast_desat) for c in unsat_colors]\n", - " if color_by_subgroups:\n", - " plot_palette_raw = dict()\n", - " plot_palette_contrast = dict()\n", - " for i in range(len(idx)):\n", - " for names_i in idx[i]:\n", - " plot_palette_raw[names_i] = raw_colors[i]\n", - " plot_palette_contrast[names_i] = contrast_colors[i]\n", - " else:\n", - " plot_palette_raw = dict(zip(names, raw_colors))\n", - " plot_palette_contrast = dict(zip(names, contrast_colors))\n", - " plot_palette_sankey = dict(zip(names, unsat_colors))\n", - "\n", - " # For Sankey Diagram plot, each bar will have the same two colors if custom_pal is None\n", - " # default color palette will be set to \"hls\"\n", - " if custom_pal is None:\n", - " plot_palette_sankey = None\n", - "\n", - " return (color_col, bootstraps_color_by_group, n_groups, filled, raw_colors,\n", - " plot_palette_raw, plot_palette_contrast, plot_palette_sankey)\n", - "\n", - "def initialize_fig(\n", - " plot_kwargs: dict, \n", - " dabest_obj: object, \n", - " show_delta2: bool, \n", - " show_mini_meta: bool, \n", - " is_paired: bool, \n", - " show_pairs: bool, \n", - " proportional: bool,\n", - " float_contrast: bool,\n", - " effect_size_type: str, \n", - " yvar: str, \n", - " horizontal: bool, \n", - " show_table: bool,\n", - " color_col: str,\n", - " ):\n", - " \"\"\"\n", - " Initialize the figure and axes for the plotter function.\n", - "\n", - " Parameters\n", - " ----------\n", - " plot_kwargs : dict\n", - " Kwargs passed to the plot function.\n", - " dabest_obj : object (EffectSizeDataFrame)\n", - " A `dabest` EffectSizeDataFrame object.\n", - " show_delta2 : bool\n", - " A boolean flag to determine if the plot will have a delta-delta effect size.\n", - " show_mini_meta : bool\n", - " A boolean flag to determine if the plot will have a mini-meta effect size.\n", - " is_paired : bool\n", - " A boolean flag to determine if the plot is for paired data.\n", - " show_pairs : bool\n", - " A boolean flag to determine if the plot will show the paired data.\n", - " proportional : bool\n", - " A boolean flag to determine if the plot is for proportional data.\n", - " float_contrast : bool\n", - " A boolean flag to determine if the plot is for floating contrast data.\n", - " effect_size_type : str\n", - " The type of effect size to be plotted.\n", - " yvar : str\n", - " The name of the y-axis variable.\n", - " horizontal : bool\n", - " A boolean flag to determine if the plot is for horizontal plotting.\n", - " show_table : dict\n", - " A boolean flag to determine if the table will be shown in horizontal plot.\n", - " color_col : str\n", - " The column name for coloring the data points.\n", - " \"\"\"\n", - " # Params\n", - " fig_size = plot_kwargs[\"fig_size\"]\n", - " face_color = plot_kwargs[\"face_color\"]\n", - " if plot_kwargs[\"face_color\"] is None:\n", - " face_color = \"white\"\n", - "\n", - " # Create Figure and Axes\n", - " if fig_size is None:\n", - " all_groups_count = np.sum([len(i) for i in dabest_obj.idx])\n", - " # Increase the width (vertical layout) or height (horizontal layout) for delta-delta or mini-meta graph\n", - " if show_delta2 or show_mini_meta:\n", - " all_groups_count += 1\n", - " \n", - " if horizontal:\n", - " frac = 0.3 if is_paired or show_mini_meta else 0.5\n", - " fig_size = (7, 1 + (frac * all_groups_count))\n", - " else:\n", - " if is_paired and show_pairs and proportional is False:\n", - " if color_col is not None and float_contrast:\n", - " frac = 0.9\n", - " else:\n", - " frac = 0.8\n", - " else:\n", - " frac = 1\n", - " if float_contrast:\n", - " height_inches = 4\n", - " each_group_width_inches = 2.5 * frac\n", - " else:\n", - " height_inches = 6\n", - " each_group_width_inches = 1.5 * frac\n", - "\n", - " width_inches = each_group_width_inches * all_groups_count\n", - " fig_size = (width_inches, height_inches) \n", - "\n", - " init_fig_kwargs = dict(figsize=fig_size, dpi=plot_kwargs[\"dpi\"], tight_layout=True)\n", - "\n", - " width_ratios_ga = [2.5, 1]\n", - " h_space_cummings = (0.1 if plot_kwargs[\"gridkey\"] is not None\n", - " else 0.3)\n", - "\n", - " if plot_kwargs[\"ax\"] is not None:\n", - " # New in v0.2.6.\n", - " # Use inset axes to create the estimation plot inside a single axes.\n", - " # Author: Adam L Nekimken. (PR #73)\n", - " rawdata_axes = plot_kwargs[\"ax\"]\n", - " ax_position = rawdata_axes.get_position() # [[x0, y0], [x1, y1]]\n", - "\n", - " fig = rawdata_axes.get_figure()\n", - " fig.patch.set_facecolor(face_color)\n", - "\n", - " if horizontal:\n", - " plot_width_ratios = [1, 0.7, 0.3]\n", - " contrast_wspace = 0.05\n", - " contrast_axes = rawdata_axes.inset_axes(\n", - " [1+contrast_wspace, 0, (plot_width_ratios[1]/plot_width_ratios[0]), 1]\n", - " )\n", - " if show_table:\n", - " table_axes = rawdata_axes.inset_axes(\n", - " [1+contrast_wspace+(plot_width_ratios[1]/plot_width_ratios[0]), 0, (plot_width_ratios[2]/plot_width_ratios[0]), 1]\n", - " )\n", - " else:\n", - " table_axes = None\n", - "\n", - " rawdata_axes.set_position(\n", - " [ax_position.x0,\n", - " ax_position.y0,\n", - " (ax_position.x1 - ax_position.x0) * (plot_width_ratios[0] / sum(plot_width_ratios)),\n", - " (ax_position.y1 - ax_position.y0)]\n", - " )\n", - " rawdata_axes.contrast_axes = contrast_axes\n", - " rawdata_axes.table_axes = table_axes\n", - " \n", - " else:\n", - " if float_contrast:\n", - " axins = rawdata_axes.inset_axes(\n", - " [1, 0, width_ratios_ga[1] / width_ratios_ga[0], 1]\n", - " )\n", - " rawdata_axes.set_position( # [l, b, w, h]\n", - " [\n", - " ax_position.x0,\n", - " ax_position.y0,\n", - " (ax_position.x1 - ax_position.x0)\n", - " * (width_ratios_ga[0] / sum(width_ratios_ga)),\n", - " (ax_position.y1 - ax_position.y0),\n", - " ]\n", - " )\n", - "\n", - " contrast_axes = axins\n", - " else:\n", - " axins = rawdata_axes.inset_axes([0, -1 - h_space_cummings, 1, 1])\n", - " plot_height = (ax_position.y1 - ax_position.y0) / (2 + h_space_cummings)\n", - " rawdata_axes.set_position(\n", - " [\n", - " ax_position.x0,\n", - " ax_position.y0 + (1 + h_space_cummings) * plot_height,\n", - " (ax_position.x1 - ax_position.x0),\n", - " plot_height,\n", - " ]\n", - " )\n", - "\n", - " # Set axes\n", - " contrast_axes = axins\n", - " rawdata_axes.contrast_axes = axins\n", - " table_axes = None\n", - "\n", - " else:\n", - " # Here, we hardcode some figure parameters.\n", - " if horizontal:\n", - " if show_table:\n", - " fig, axx = plt.subplots(\n", - " ncols=3, gridspec_kw={'width_ratios' : [1,0.7,0.3], 'wspace' : 0.05}, **init_fig_kwargs\n", - " )\n", - " else:\n", - " fig, axx = plt.subplots(\n", - " ncols=2, gridspec_kw={'width_ratios' : [1,0.7], 'wspace' : 0.05}, **init_fig_kwargs\n", - " )\n", - " else:\n", - " if float_contrast:\n", - " fig, axx = plt.subplots(\n", - " ncols=2,\n", - " gridspec_kw={\"width_ratios\": width_ratios_ga, \"wspace\": 0},\n", - " **init_fig_kwargs\n", - " )\n", - " else:\n", - " fig, axx = plt.subplots(\n", - " nrows=2, gridspec_kw={\"hspace\": h_space_cummings}, **init_fig_kwargs\n", - " )\n", - " fig.patch.set_facecolor(face_color)\n", - "\n", - " # Set axes \n", - " rawdata_axes = axx[0]\n", - " contrast_axes = axx[1]\n", - " table_axes = axx[2] if horizontal and show_table else None\n", - "\n", - "\n", - " # Title\n", - " title, fontsize_title = plot_kwargs[\"title\"], plot_kwargs[\"fontsize_title\"]\n", - " if title is not None:\n", - " if plot_kwargs[\"ax\"] is not None:\n", - " rawdata_axes.set_title(title, fontsize=fontsize_title)\n", - " else:\n", - " fig.suptitle(title, fontsize=fontsize_title)\n", - "\n", - " rawdata_axes.set_frame_on(False)\n", - " contrast_axes.set_frame_on(False)\n", - " if horizontal and show_table:\n", - " table_axes.set_frame_on(False)\n", - " \n", - " # Swarmplot ylim (Vertical) or xlim (Horizontal)\n", - " raw_ylim = plot_kwargs[\"raw_ylim\"]\n", - " if raw_ylim is not None:\n", - " if not isinstance(raw_ylim, list) and not isinstance(raw_ylim, tuple) or len(raw_ylim) != 2:\n", - " raise ValueError(\"`raw_ylim` must be a tuple/list of the lower and upper bound.\")\n", - " if horizontal:\n", - " rawdata_axes.set_xlim(raw_ylim)\n", - " else:\n", - " rawdata_axes.set_ylim(raw_ylim)\n", - "\n", - " # Contrastplot ylim (Vertical) or xlim (Horizontal)\n", - " if horizontal or not float_contrast:\n", - " contrast_ylim, delta2_ylim = plot_kwargs[\"contrast_ylim\"], plot_kwargs[\"delta2_ylim\"]\n", - " if contrast_ylim is not None or (delta2_ylim is not None and show_delta2):\n", - " if contrast_ylim is not None:\n", - " if delta2_ylim is not None and show_delta2:\n", - " if contrast_ylim != delta2_ylim:\n", - " raise ValueError(\"Please check if `contrast_ylim` and `delta2_ylim` are assigned with same values.\")\n", - " else:\n", - " contrast_ylim = delta2_ylim\n", - "\n", - " if not isinstance(contrast_ylim, list) and not isinstance(contrast_ylim, tuple) or len(contrast_ylim) != 2:\n", - " raise ValueError(\"`contrast_ylim` must be a tuple/list of the lower and upper bound.\")\n", - "\n", - " if effect_size_type == \"cliffs_delta\":\n", - " # Ensure the ylims for a cliffs_delta plot never exceed [-1, 1].\n", - " l = contrast_ylim[0]\n", - " h = contrast_ylim[1]\n", - " low = -1 if l < -1 else l\n", - " high = 1 if h > 1 else h\n", - " if horizontal:\n", - " contrast_axes.set_xlim(low, high)\n", - " else:\n", - " contrast_axes.set_ylim(low, high)\n", - " else:\n", - " if horizontal:\n", - " contrast_axes.set_xlim(contrast_ylim)\n", - " else:\n", - " contrast_axes.set_ylim(contrast_ylim)\n", - "\n", - " # Set raw axes y-label.\n", - " raw_label = plot_kwargs[\"raw_label\"]\n", - " if raw_label is None:\n", - " if proportional:\n", - " raw_label = \"Proportion of success\" if effect_size_type != \"cohens_h\" else \"Value\"\n", - " else:\n", - " raw_label = yvar \n", - "\n", - " fontsize_rawylabel = plot_kwargs[\"fontsize_rawylabel\"]\n", - " if horizontal:\n", - " rawdata_axes.set_xlabel(raw_label, fontsize=fontsize_rawylabel)\n", - " rawdata_axes.set_ylabel(\"\")\n", - " else:\n", - " rawdata_axes.set_ylabel(raw_label, fontsize=fontsize_rawylabel)\n", - " rawdata_axes.set_xlabel(\"\")\n", - "\n", - " # Set contrast axes y-label.\n", - " contrast_label_dict = {\n", - " \"mean_diff\": \"mean difference\",\n", - " \"median_diff\": \"median difference\",\n", - " \"cohens_d\": \"Cohen's d\",\n", - " \"hedges_g\": \"Hedges' g\",\n", - " \"cliffs_delta\": \"Cliff's delta\",\n", - " \"cohens_h\": \"Cohen's h\",\n", - " }\n", - "\n", - " if proportional and effect_size_type != \"cohens_h\":\n", - " default_contrast_label = \"proportion difference\"\n", - " else:\n", - " default_contrast_label = contrast_label_dict[effect_size_type]\n", - "\n", - " if plot_kwargs[\"contrast_label\"] is None:\n", - " if is_paired:\n", - " contrast_label = \"Paired\\n{}\".format(default_contrast_label)\n", - " else:\n", - " contrast_label = default_contrast_label.capitalize()\n", - " else:\n", - " contrast_label = plot_kwargs[\"contrast_label\"]\n", - "\n", - " fontsize_contrastylabel = plot_kwargs[\"fontsize_contrastylabel\"]\n", - "\n", - " if horizontal:\n", - " contrast_axes.set_xlabel(contrast_label, fontsize=fontsize_contrastylabel)\n", - " else:\n", - " contrast_axes.set_ylabel(contrast_label, fontsize=fontsize_contrastylabel)\n", - " if float_contrast:\n", - " contrast_axes.yaxis.set_label_position(\"right\")\n", - "\n", - " return fig, rawdata_axes, contrast_axes, table_axes\n", - "\n", - "def get_plot_groups(\n", - " is_paired: bool, \n", - " idx: list, \n", - " proportional: bool, \n", - " all_plot_groups: list\n", - " ):\n", - " \"\"\"\n", - " Extract the plot groups from the `idx` object for use in the plotter function.\n", - "\n", - " Parameters\n", - " ----------\n", - " is_paired : bool\n", - " A boolean flag to determine if the plot is for paired data.\n", - " idx : list\n", - " A list of tuples containing the group names.\n", - " proportional : bool\n", - " A boolean flag to determine if the plot is for proportional data.\n", - " all_plot_groups : list\n", - " A list of all the group names.\n", - " \"\"\"\n", - "\n", - " if is_paired == \"baseline\":\n", - " idx_pairs = [\n", - " (control, test)\n", - " for i in idx\n", - " for control, test in zip([i[0]] * (len(i) - 1), i[1:])\n", - " ]\n", - " temp_idx = idx if not proportional else idx_pairs\n", - " else:\n", - " idx_pairs = [\n", - " (control, test) for i in idx for control, test in zip(i[:-1], i[1:])\n", - " ]\n", - " temp_idx = idx if not proportional else idx_pairs\n", - "\n", - " # Determine temp_all_plot_groups based on proportional condition\n", - " plot_groups = [item for i in temp_idx for item in i]\n", - " temp_all_plot_groups = all_plot_groups if not proportional else plot_groups\n", - " \n", - " return temp_idx, temp_all_plot_groups\n", - "\n", - "\n", - "def add_counts_to_ticks(\n", - " plot_data: pd.DataFrame, \n", - " xvar: str, \n", - " yvar: str, \n", - " rawdata_axes: axes.Axes, \n", - " plot_kwargs: dict, \n", - " flow: bool, \n", - " horizontal: bool\n", - " ):\n", - " \"\"\"\n", - "\n", - " Add the counts to the raw data axes labels.\n", - "\n", - " Parameters\n", - " ----------\n", - " plot_data : object (Dataframe)\n", - " A dataframe of plot data.\n", - " xvar : str\n", - " The name of the x-axis variable.\n", - " yvar : str\n", - " The name of the y-axis variable.\n", - " rawdata_axes : object (Axes)\n", - " The raw data axes.\n", - " plot_kwargs : dict\n", - " Kwargs passed to the plot function.\n", - " flow : bool\n", - " Whether sankey flow is enabled or not.\n", - " horizontal : bool\n", - " A boolean flag to determine if the plot is for horizontal plotting.\n", - " \"\"\"\n", - "\n", - " # Add the counts to the rawdata axes xticks.\n", - " counts = plot_data.groupby(xvar, observed=False).count()[yvar]\n", - " \n", - " def lookup_value(text):\n", - " try:\n", - " return str(counts.loc[text])\n", - " except KeyError:\n", - " try:\n", - " numeric_key = pd.to_numeric(text, errors='coerce')\n", - " if pd.notnull(numeric_key):\n", - " return str(counts.loc[numeric_key])\n", - " except (ValueError, KeyError):\n", - " pass\n", - " print(f\"Key '{text}' not found in counts.\")\n", - " return \"N/A\"\n", - " \n", - " ticks_with_counts = []\n", - " if horizontal:\n", - " get_label, get_ticks = rawdata_axes.get_yticklabels, rawdata_axes.get_yticks\n", - " set_label, set_major_loc_method= rawdata_axes.set_yticklabels, rawdata_axes.yaxis.set_major_locator\n", - " else:\n", - " get_label, get_ticks = rawdata_axes.get_xticklabels, rawdata_axes.get_xticks\n", - " set_label, set_major_loc_method = rawdata_axes.set_xticklabels, rawdata_axes.xaxis.set_major_locator\n", - " \n", - " for ticklab in get_label():\n", - " t = ticklab.get_text()\n", - "\n", - " if horizontal and not flow:\n", - " te = t.split('v.s. ')[-1] # Get the last line of the label\n", - " else:\n", - " te = t.split('\\n')[-1] # Get the last line of the label\n", - "\n", - " value = lookup_value(te)\n", - " if horizontal:\n", - " ticks_with_counts.append(f\"{t} (N={value})\")\n", - " else:\n", - " ticks_with_counts.append(f\"{t}\\n(N={value})\")\n", - "\n", - " set_major_loc_method(plt.FixedLocator(get_ticks()))\n", - "\n", - " # label = ticks_with_counts if plot_kwargs['show_sample_size'] else get_label()\n", - " # set_label(label, fontsize=plot_kwargs.get(\"fontsize_rawxlabel\"))\n", - "\n", - " set_label(ticks_with_counts, fontsize=plot_kwargs.get(\"fontsize_rawxlabel\"))\n", - "\n", - " # Ensure ticks are at the correct locations\n", - " set_major_loc_method(plt.FixedLocator(get_ticks()))\n", - "\n", - "def extract_contrast_plotting_ticks(\n", - " is_paired: bool, \n", - " show_pairs: bool, \n", - " two_col_sankey: bool, \n", - " plot_groups: list, \n", - " idx: list, \n", - " sankey_control_group: list\n", - " ):\n", - " \"\"\"\n", - " Extract the contrast plotting ticks from the `idx` object for use in the plotter function.\n", - "\n", - " Parameters\n", - " ----------\n", - " is_paired : bool\n", - " A boolean flag to determine if the plot is for paired data.\n", - " show_pairs : bool\n", - " A boolean flag to determine if the plot will show the paired data.\n", - " two_col_sankey : bool\n", - " A boolean flag to determine if the plot will show a two-column sankey diagram.\n", - " plot_groups : list\n", - " A list of the plot groups.\n", - " idx : list\n", - " A list of tuples containing the group names.\n", - " sankey_control_group : list\n", - " A list of the control group names.\n", - " \"\"\"\n", - " # Take note of where the `control` groups are.\n", - " ticks_to_skip_contrast = None\n", - " ticks_to_start_twocol_sankey = None\n", - " if is_paired == \"baseline\" and show_pairs:\n", - " if two_col_sankey:\n", - " ticks_to_skip = []\n", - " ticks_to_plot = np.arange(0, len(plot_groups) / 2).tolist()\n", - " ticks_to_start_twocol_sankey = np.cumsum([len(i) - 1 for i in idx]).tolist()\n", - " ticks_to_start_twocol_sankey.pop()\n", - " ticks_to_start_twocol_sankey.insert(0, 0)\n", - " else:\n", - " ticks_to_skip = np.cumsum([len(t) for t in idx])[:-1].tolist()\n", - " ticks_to_skip.insert(0, 0)\n", - " # Then obtain the ticks where we have to plot the effect sizes.\n", - " ticks_to_plot = [\n", - " t for t in range(0, len(plot_groups)) if t not in ticks_to_skip\n", - " ]\n", - " ticks_to_skip_contrast = np.cumsum([(len(t)) for t in idx])[:-1].tolist()\n", - " ticks_to_skip_contrast.insert(0, 0)\n", - " else:\n", - " if two_col_sankey:\n", - " ticks_to_skip = [len(sankey_control_group)]\n", - " # Then obtain the ticks where we have to plot the effect sizes.\n", - " ticks_to_plot = [\n", - " t for t in range(0, len(plot_groups)) if t not in ticks_to_skip\n", - " ]\n", - " ticks_to_skip = []\n", - " ticks_to_start_twocol_sankey = np.cumsum([len(i) - 1 for i in idx]).tolist()\n", - " ticks_to_start_twocol_sankey.pop()\n", - " ticks_to_start_twocol_sankey.insert(0, 0)\n", - " else:\n", - " ticks_to_skip = np.cumsum([len(t) for t in idx])[:-1].tolist()\n", - " ticks_to_skip.insert(0, 0)\n", - " # Then obtain the ticks where we have to plot the effect sizes.\n", - " ticks_to_plot = [\n", - " t for t in range(0, len(plot_groups)) if t not in ticks_to_skip\n", - " ]\n", - " \n", - " ticks_for_baseline_ec = ticks_to_skip\n", - " \n", - " return ticks_to_skip, ticks_to_plot, ticks_for_baseline_ec, ticks_to_skip_contrast, ticks_to_start_twocol_sankey\n", - "\n", - "def set_xaxis_ticks_and_lims(\n", - " show_delta2: bool, \n", - " show_mini_meta: bool, \n", - " rawdata_axes: axes.Axes, \n", - " contrast_axes: axes.Axes, \n", - " show_pairs: bool, \n", - " float_contrast: bool,\n", - " ticks_to_skip: list, \n", - " contrast_xtick_labels: list, \n", - " plot_kwargs: dict, \n", - " proportional: bool, \n", - " horizontal: bool\n", - " ):\n", - " \"\"\"\n", - " Set the x-axis/yaxis ticks and limits for the plotter function.\n", - "\n", - " Parameters\n", - " ----------\n", - " show_delta2 : bool\n", - " A boolean flag to determine if the plot will have a delta-delta effect size.\n", - " show_mini_meta : bool\n", - " A boolean flag to determine if the plot will have a mini-meta effect size.\n", - " rawdata_axes : object (Axes)\n", - " The raw data axes.\n", - " contrast_axes : object (Axes)\n", - " The contrast axes.\n", - " show_pairs : bool\n", - " A boolean flag to determine if the plot will show the paired data.\n", - " float_contrast : bool\n", - " A boolean flag to determine if the plot is a GA or Cumming design.\n", - " ticks_to_skip : list\n", - " A list of ticks to skip.\n", - " contrast_xtick_labels : list\n", - " A list of contrast xtick labels.\n", - " plot_kwargs : dict\n", - " Kwargs passed to the plot function.\n", - " proportional: bool\n", - " A boolean flag to determine if the plot is a proportional plot.\n", - " horizontal : bool\n", - " A boolean flag to determine if the plot is for horizontal plotting.\n", - " \"\"\"\n", - "\n", - " if horizontal:\n", - " # Ticks\n", - " if show_delta2 is False and show_mini_meta is False:\n", - " contrast_axes.set_yticks(rawdata_axes.get_yticks())\n", - " else:\n", - " temp = rawdata_axes.get_yticks()\n", - " temp = np.append(temp, [max(temp) + 0, max(temp) + 1])\n", - " contrast_axes.set_yticks(temp) \n", - "\n", - " # Lims\n", - " if show_pairs:\n", - " max_x = contrast_axes.get_ylim()[1]\n", - " rawdata_axes.set_ylim(-0.375, max_x)\n", - "\n", - " if proportional:\n", - " rawdata_axes.set_ylim(-0.375, max_x+0.1)\n", - "\n", - " if show_delta2 or show_mini_meta:\n", - " # Increase the ylim of raw data by 2\n", - " temp = rawdata_axes.get_ylim()\n", - " if show_pairs:\n", - " rawdata_axes.set_ylim(temp[0], temp[1] + 0.00)\n", - " else:\n", - " rawdata_axes.set_ylim(temp[0], temp[1] + 1)\n", - " contrast_axes.set_ylim(rawdata_axes.get_ylim())\n", - " else:\n", - " contrast_axes.set_ylim(rawdata_axes.get_ylim())\n", - " # Vertical\n", - " else:\n", - " # Ticks\n", - " if show_delta2 is False and show_mini_meta is False:\n", - " contrast_axes.set_xticks(rawdata_axes.get_xticks())\n", - " else:\n", - " temp = rawdata_axes.get_xticks()\n", - " temp = np.append(temp, [max(temp) + 1])\n", - " contrast_axes.set_xticks(temp)\n", - "\n", - " # Lims\n", - " if show_pairs:\n", - " max_x = contrast_axes.get_xlim()[1]\n", - " rawdata_axes.set_xlim(-0.375, max_x)\n", - "\n", - " if float_contrast:\n", - " contrast_axes.set_xlim(0.5, 1.5)\n", - "\n", - " elif show_delta2:\n", - " if show_pairs:\n", - " rawdata_axes.set_xlim(-0.375, 4.75)\n", - " else:\n", - " rawdata_axes.set_xlim(-0.5, 4.75)\n", - " contrast_axes.set_xlim(rawdata_axes.get_xlim())\n", - "\n", - " elif show_mini_meta:\n", - " # Increase the xlim of raw data by 2\n", - " temp = rawdata_axes.get_xlim()\n", - " if show_pairs:\n", - " rawdata_axes.set_xlim(temp[0], temp[1] + 0.5)\n", - " else:\n", - " rawdata_axes.set_xlim(temp[0], temp[1] + 1)\n", - " contrast_axes.set_xlim(rawdata_axes.get_xlim())\n", - " else:\n", - " contrast_axes.set_xlim(rawdata_axes.get_xlim())\n", - "\n", - " # Properly label the contrast ticks.\n", - " for t in ticks_to_skip:\n", - " contrast_xtick_labels.insert(t, \"\")\n", - "\n", - " contrast_axes.set_xticklabels(\n", - " contrast_xtick_labels, fontsize=plot_kwargs[\"fontsize_contrastxlabel\"]\n", - " )\n", - "\n", - "\n", - "def show_legend(\n", - " legend_labels: list, \n", - " legend_handles: list, \n", - " rawdata_axes: axes.Axes, \n", - " contrast_axes: axes.Axes, \n", - " table_axes: axes.Axes, \n", - " float_contrast: bool, \n", - " show_pairs: bool, \n", - " horizontal: bool, \n", - " legend_kwargs: dict, \n", - " table_kwargs: dict\n", - " ):\n", - " \"\"\"\n", - " Show the legend for the plotter function.\n", - "\n", - " Parameters\n", - " ----------\n", - " legend_labels : list\n", - " A list of legend labels.\n", - " legend_handles : list\n", - " A list of legend handles.\n", - " rawdata_axes : object (Axes)\n", - " The raw data axes.\n", - " contrast_axes : object (Axes)\n", - " The contrast axes.\n", - " table_axes : object (Axes)\n", - " The table axes.\n", - " float_contrast : bool\n", - " A boolean flag to determine if the plot is GA or Cumming format.\n", - " show_pairs : bool\n", - " A boolean flag to determine if the plot will show the paired data.\n", - " horizontal : bool\n", - " A boolean flag to determine if the plot is for horizontal plotting.\n", - " legend_kwargs : dict\n", - " Kwargs passed to the legend function.\n", - " \"\"\"\n", - "\n", - " legend_labels_unique = np.unique(legend_labels)\n", - " unique_idx = np.unique(legend_labels, return_index=True)[1]\n", - " legend_handles_unique = (\n", - " pd.Series(legend_handles, dtype=\"object\").loc[unique_idx]\n", - " ).tolist()\n", - "\n", - " # Location of the legend\n", - " if \"bbox_to_anchor\" not in legend_kwargs.keys():\n", - " if horizontal:\n", - " bta = (1,1)\n", - " else:\n", - " if float_contrast:\n", - " bta = (2.00, 1.02) if show_pairs else (1.5, 1.02)\n", - " else:\n", - " bta = (1.02, 1.0) if show_pairs else (1.0, 1.0)\n", - " legend_kwargs.update({'bbox_to_anchor': bta})\n", - "\n", - " # Pick the ax to plot\n", - " if horizontal:\n", - " if table_kwargs['show']:\n", - " axes_with_legend = table_axes\n", - " else:\n", - " axes_with_legend = contrast_axes\n", - " elif float_contrast:\n", - " axes_with_legend = contrast_axes\n", - " else:\n", - " axes_with_legend = rawdata_axes\n", - "\n", - " # Plot the legend\n", - " if len(legend_handles_unique) > 0:\n", - " leg = axes_with_legend.legend(\n", - " legend_handles_unique,\n", - " legend_labels_unique,\n", - " handlelength=0.5,\n", - " **legend_kwargs\n", - " )\n", - " if show_pairs:\n", - " for line in leg.get_lines():\n", - " line.set_linewidth(3.0)\n", - " \n", - "def gardner_altman_adjustments(\n", - " effect_size_type: str, \n", - " plot_data: pd.DataFrame, \n", - " xvar: str, \n", - " yvar: str, \n", - " current_control: str, \n", - " current_group: str,\n", - " rawdata_axes: axes.Axes, \n", - " contrast_axes: axes.Axes, \n", - " results: pd.DataFrame, \n", - " current_effsize: float, \n", - " is_paired: bool, \n", - " one_sankey: bool,\n", - " reflines_kwargs: dict, \n", - " redraw_axes_kwargs: dict\n", - " ):\n", - " \"\"\"\n", - " Aesthetic adjustments specific to Gardner-Altman plots (float_contrast=True).\n", - " \n", - " Parameters\n", - " ----------\n", - " effect_size_type : str\n", - " The type of effect size.\n", - " plot_data : object (Dataframe)\n", - " A dataframe of plot data.\n", - " xvar : str\n", - " The name of the x-axis variable.\n", - " yvar : str\n", - " The name of the y-axis variable.\n", - " current_control : str\n", - " The name of the current control group.\n", - " current_group : str\n", - " The name of the current test group.\n", - " rawdata_axes : object (Axes)\n", - " The raw data axes.\n", - " contrast_axes : object (Axes)\n", - " The contrast axes.\n", - " results : object (DataFrame)\n", - " A dataframe of the results.\n", - " current_effsize : float\n", - " The current effect size.\n", - " is_paired : bool\n", - " A boolean flag to determine if the plot is for paired data.\n", - " one_sankey : bool\n", - " A boolean flag to determine if the plot is for a single sankey diagram.\n", - " reflines_kwargs : dict\n", - " Kwargs passed to the reference lines.\n", - " redraw_axes_kwargs : dict\n", - " Kwargs passed to the redraw axes.\n", - " \"\"\"\n", - " from ._stats_tools.effsize import (\n", - " _compute_standardizers,\n", - " _compute_hedges_correction_factor,\n", - " )\n", - "\n", - " og_xlim_raw, og_ylim_raw = rawdata_axes.get_xlim(), rawdata_axes.get_ylim()\n", - " \n", - " # Normalize ylims and despine the floating contrast axes.\n", - " # Check that the effect size is within the swarm ylims.\n", - " if effect_size_type in [\"mean_diff\", \"cohens_d\", \"hedges_g\", \"cohens_h\"]:\n", - " control_group_summary = (\n", - " plot_data.groupby(xvar, observed=False)\n", - " .mean(numeric_only=True)\n", - " .loc[current_control, yvar]\n", - " )\n", - " test_group_summary = (\n", - " plot_data.groupby(xvar, observed=False).mean(numeric_only=True).loc[current_group, yvar]\n", - " )\n", - " elif effect_size_type == \"median_diff\":\n", - " control_group_summary = (\n", - " plot_data.groupby(xvar, observed=False).median(numeric_only=True).loc[current_control, yvar]\n", - " )\n", - " test_group_summary = (\n", - " plot_data.groupby(xvar, observed=False).median(numeric_only=True).loc[current_group, yvar]\n", - " )\n", - "\n", - " _, contrast_xlim_max = contrast_axes.get_xlim()\n", - "\n", - " difference = float(results.difference[0])\n", - "\n", - " if effect_size_type in [\"mean_diff\", \"median_diff\"]:\n", - " # Align 0 of contrast_axes to reference group mean of rawdata_axes.\n", - " # If the effect size is positive, shift the contrast axis up.\n", - " rawdata_ylims = np.array(rawdata_axes.get_ylim())\n", - " if current_effsize > 0:\n", - " rightmin, rightmax = rawdata_ylims - current_effsize\n", - " # If the effect size is negative, shift the contrast axis down.\n", - " elif current_effsize < 0:\n", - " rightmin, rightmax = rawdata_ylims + current_effsize\n", - " else:\n", - " rightmin, rightmax = rawdata_ylims\n", - "\n", - " contrast_axes.set_ylim(rightmin, rightmax)\n", - "\n", - " og_ylim_contrast = rawdata_axes.get_ylim() - np.array(control_group_summary)\n", - "\n", - " contrast_axes.set_ylim(og_ylim_contrast)\n", - " contrast_axes.set_xlim(contrast_xlim_max - 1, contrast_xlim_max)\n", - "\n", - " elif effect_size_type in [\"cohens_d\", \"hedges_g\", \"cohens_h\"]:\n", - "\n", - " which_std = 1 if is_paired else 0 ############################ Unused line of code\n", - " temp_control = np.array(plot_data[plot_data[xvar] == current_control][yvar])\n", - " temp_test = np.array(plot_data[plot_data[xvar] == current_group][yvar])\n", - "\n", - " stds = _compute_standardizers(temp_control, temp_test)\n", - " if is_paired:\n", - " pooled_sd = stds[1]\n", - " else:\n", - " pooled_sd = stds[0]\n", - "\n", - " if effect_size_type == \"hedges_g\":\n", - " gby_count = plot_data.groupby(xvar, observed=False).count()\n", - " len_control = gby_count.loc[current_control, yvar]\n", - " len_test = gby_count.loc[current_group, yvar]\n", - "\n", - " hg_correction_factor = _compute_hedges_correction_factor(\n", - " len_control, len_test\n", - " )\n", - "\n", - " ylim_scale_factor = pooled_sd / hg_correction_factor\n", - "\n", - " elif effect_size_type == \"cohens_h\":\n", - " ylim_scale_factor = (\n", - " np.mean(temp_test) - np.mean(temp_control)\n", - " ) / difference\n", - "\n", - " else:\n", - " ylim_scale_factor = pooled_sd\n", - "\n", - " scaled_ylim = (\n", - " (rawdata_axes.get_ylim() - control_group_summary) / ylim_scale_factor\n", - " ).tolist()\n", - "\n", - " contrast_axes.set_ylim(scaled_ylim)\n", - " og_ylim_contrast = scaled_ylim\n", - "\n", - " contrast_axes.set_xlim(contrast_xlim_max - 1, contrast_xlim_max)\n", - "\n", - " if one_sankey is None:\n", - " # Draw summary lines for control and test groups..\n", - " for jj, axx in enumerate([rawdata_axes, contrast_axes]):\n", - " # Draw effect size line.\n", - " if jj == 0:\n", - " ref = control_group_summary\n", - " diff = test_group_summary\n", - " effsize_line_start = 1\n", - "\n", - " elif jj == 1:\n", - " ref = 0\n", - " diff = ref + difference\n", - " effsize_line_start = contrast_xlim_max - 1.1\n", - "\n", - " xlimlow, xlimhigh = axx.get_xlim()\n", - "\n", - " # Draw reference line.\n", - " axx.hlines(\n", - " ref, # y-coordinates\n", - " 0,\n", - " xlimhigh, # x-coordinates, start and end.\n", - " **reflines_kwargs\n", - " )\n", - "\n", - " # Draw effect size line.\n", - " axx.hlines(diff, effsize_line_start, xlimhigh, **reflines_kwargs)\n", - " else:\n", - " ref = 0\n", - " diff = ref + difference\n", - " effsize_line_start = contrast_xlim_max - 0.9\n", - " xlimlow, xlimhigh = contrast_axes.get_xlim()\n", - " # Draw reference line.\n", - " contrast_axes.hlines(\n", - " ref, # y-coordinates\n", - " effsize_line_start,\n", - " xlimhigh, # x-coordinates, start and end.\n", - " **reflines_kwargs\n", - " )\n", - "\n", - " # Draw effect size line.\n", - " contrast_axes.hlines(diff, effsize_line_start, xlimhigh, **reflines_kwargs)\n", - " rawdata_axes.set_xlim(og_xlim_raw) # to align the axis\n", - " # Despine appropriately.\n", - " sns.despine(ax=rawdata_axes, bottom=True)\n", - " sns.despine(ax=contrast_axes, left=True, right=False)\n", - "\n", - " # Insert break between the rawdata axes and the contrast axes\n", - " # by re-drawing the x-spine.\n", - " rawdata_axes.hlines(\n", - " og_ylim_raw[0], # yindex\n", - " rawdata_axes.get_xlim()[0],\n", - " 1.3, # xmin, xmax\n", - " **redraw_axes_kwargs\n", - " )\n", - " rawdata_axes.set_ylim(og_ylim_raw)\n", - "\n", - " contrast_axes.hlines(\n", - " contrast_axes.get_ylim()[0],\n", - " contrast_xlim_max - 0.8,\n", - " contrast_xlim_max,\n", - " **redraw_axes_kwargs\n", - " )\n", - "\n", - "def draw_zeroline(\n", - " ax : axes.Axes,\n", - " horizontal : bool,\n", - " reflines_kwargs : dict,\n", - " extra_delta : bool,\n", - " ):\n", - " \"\"\"\n", - " Draw the independent axis spine lines.\n", - "\n", - " Parameters\n", - " ----------\n", - " ax : object (Axes)\n", - " The contrast data axes.\n", - " horizontal : bool\n", - " A boolean flag to determine if the plot is for horizontal plotting.\n", - " reflines_kwargs : dict\n", - " Additional keyword arguments to be passed to the zeroline.\n", - " extra_delta : bool\n", - " A boolean flag to determine if the plot includes an extra delta (delta-delta or mini-meta).\n", - " \"\"\"\n", - " # If 0 lies within the ylim of the contrast axes, draw a zero reference line.\n", - " if extra_delta and not horizontal:\n", - " contrast_xlim = [-0.5, 3.4]\n", - " delta2_xlim = [3.6, 4.75]\n", - " \n", - " if ax.get_ylim()[0] < ax.get_ylim()[1]:\n", - " contrast_lim_low, contrast_lim_high = ax.get_ylim()\n", - " else:\n", - " contrast_lim_high, contrast_lim_low = ax.get_ylim()\n", - "\n", - " if contrast_lim_low < 0 < contrast_lim_high:\n", - " ax.hlines(y=0, xmin=contrast_xlim[0], xmax=contrast_xlim[1], **reflines_kwargs)\n", - " ax.hlines(y=0, xmin=delta2_xlim[0], xmax=delta2_xlim[1], **reflines_kwargs)\n", - " else:\n", - " ax_lim = ax.get_xlim() if horizontal else ax.get_ylim()\n", - " method = ax.axvline if horizontal else ax.axhline\n", - "\n", - " if ax_lim[0] < ax_lim[1]:\n", - " contrast_lim_low, contrast_lim_high = ax_lim\n", - " else:\n", - " contrast_lim_high, contrast_lim_low = ax_lim\n", - "\n", - " if contrast_lim_low < 0 < contrast_lim_high:\n", - " method(0, **reflines_kwargs)\n", - "\n", - "def redraw_independent_spines(\n", - " rawdata_axes : axes.Axes,\n", - " contrast_axes : axes.Axes,\n", - " horizontal : bool,\n", - " two_col_sankey : bool,\n", - " ticks_to_start_twocol_sankey : list,\n", - " idx : list,\n", - " is_paired : str,\n", - " show_pairs : bool,\n", - " proportional : bool,\n", - " ticks_to_skip : list,\n", - " temp_idx : list,\n", - " ticks_to_skip_contrast : list,\n", - " redraw_axes_kwargs : dict\n", - " ):\n", - " \"\"\"\n", - " Draw the independent axis spine lines.\n", - "\n", - " Parameters\n", - " ----------\n", - " rawdata_axes : object (Axes)\n", - " The raw data axes.\n", - " contrast_axes : object (Axes)\n", - " The contrast axes.\n", - " horizontal : bool\n", - " A boolean flag to determine if the plot is for horizontal plotting.\n", - " two_col_sankey : bool\n", - " A boolean flag to determine if the plot is for two-col sankey.\n", - " ticks_to_start_twocol_sankey : list\n", - " A list of ticks to start for sankey plot.\n", - " idx : list\n", - " A list of indices.\n", - " is_paired : bool\n", - " A boolean flag to determine if the data is paired.\n", - " show_pairs : bool\n", - " A boolean flag to determine if pairs should be shown.\n", - " proportional : bool\n", - " A boolean flag to determine if the plot is proportional/binary.\n", - " ticks_to_skip : list,\n", - " A list of ticks to be skipped in the raw data axes.\n", - " temp_idx : list,\n", - " A temporary list of indices to be used for skipping ticks in the raw data axes.\n", - " ticks_to_skip_contrast : list,\n", - " A list of ticks to be skipped in the contrast axes.\n", - " redraw_axes_kwargs : dict\n", - " Kwargs passed to the redraw axes.\n", - " \"\"\"\n", - " # Extract the ticks\n", - " if two_col_sankey:\n", - " rightend_ticks_raw = rightend_ticks_contrast = np.array([len(i) - 2 for i in idx]) + np.array(ticks_to_start_twocol_sankey)\n", - " starting_ticks_raw = starting_ticks_contrast = ticks_to_start_twocol_sankey\n", - " else:\n", - " if is_paired == \"baseline\" and show_pairs:\n", - " if proportional and is_paired is not None:\n", - " rightend_ticks_raw = rightend_ticks_contrast = np.array([len(i) - 1 for i in idx]) + np.array(ticks_to_skip)\n", - " else:\n", - " rightend_ticks_raw = np.array([len(i) - 1 for i in temp_idx]) + np.array(ticks_to_skip)\n", - " temp_length = [(len(i) - 1) * 2 - 1 for i in idx] if proportional else [(len(i) - 1) for i in idx]\n", - " rightend_ticks_contrast = np.array(temp_length) + np.array(ticks_to_skip_contrast)\n", - " starting_ticks_raw, starting_ticks_contrast = ticks_to_skip, ticks_to_skip_contrast\n", - " else:\n", - " rightend_ticks_raw = rightend_ticks_contrast = np.array([len(i) - 1 for i in idx]) + np.array(ticks_to_skip)\n", - " starting_ticks_raw = starting_ticks_contrast = ticks_to_skip\n", - "\n", - " # Plot the spines\n", - " if horizontal:\n", - " sns.despine(ax=rawdata_axes, left=True)\n", - " xlim, ylim = rawdata_axes.get_xlim(), rawdata_axes.get_ylim()\n", - " redraw_axes_kwargs[\"x\"] = xlim[0]\n", - " for k, start_tick in enumerate(starting_ticks_raw):\n", - " end_tick = rightend_ticks_raw[k]\n", - " rawdata_axes.vlines(\n", - " ymin = start_tick, \n", - " ymax = end_tick, \n", - " **redraw_axes_kwargs\n", - " )\n", - " rawdata_axes.set_xlim(xlim)\n", - " rawdata_axes.set_ylim(ylim)\n", - " del redraw_axes_kwargs[\"x\"] \n", - "\n", - " # Remove y ticks and labels from the contrast axes.\n", - " sns.despine(ax=contrast_axes, left=True)\n", - " contrast_axes.set_yticks([])\n", - " contrast_axes.set_yticklabels([])\n", - " \n", - " else:\n", - " for ax, starting_ticks_current, rightend_ticks_current in zip(\n", - " [rawdata_axes, contrast_axes],\n", - " [starting_ticks_raw, starting_ticks_contrast],\n", - " [rightend_ticks_raw, rightend_ticks_contrast],\n", - " ):\n", - " sns.despine(ax=ax, bottom=True)\n", - " xlim, ylim = ax.get_xlim(), ax.get_ylim()\n", - " redraw_axes_kwargs[\"y\"] = ylim[0]\n", - " for k, start_tick in enumerate(starting_ticks_current):\n", - " end_tick = rightend_ticks_current[k]\n", - " ax.hlines(\n", - " xmin=start_tick, \n", - " xmax=end_tick, \n", - " **redraw_axes_kwargs\n", - " )\n", - " ax.set_xlim(xlim)\n", - " ax.set_ylim(ylim)\n", - " del redraw_axes_kwargs[\"y\"]\n", - " \n", - "def redraw_dependent_spines(\n", - " rawdata_axes: axes.Axes, \n", - " contrast_axes: axes.Axes, \n", - " redraw_axes_kwargs: dict, \n", - " float_contrast: bool, \n", - " horizontal: bool,\n", - " show_delta2: bool, \n", - " delta2_axes: axes.Axes\n", - " ):\n", - " \"\"\"\n", - " Draw the dependent axis spine lines.\n", - "\n", - " Parameters\n", - " ----------\n", - " rawdata_axes : object (Axes)\n", - " The raw data axes.\n", - " contrast_axes : object (Axes)\n", - " The contrast axes.\n", - " redraw_axes_kwargs : dict\n", - " Kwargs passed to the redraw axes.\n", - " float_contrast : bool\n", - " A boolean flag to determine if the plot is GA or Cum\n", - " horizontal : bool\n", - " A boolean flag to determine if the plot is for horizontal plotting.\n", - " show_delta2 : bool\n", - " A boolean flag to determine if the plot will have a delta-delta effect size.\n", - " delta2_axes : object (Axes)\n", - " The delta2 axes.\n", - " \"\"\"\n", - "\n", - " # Because we turned the axes frame off, we also need to draw back the x-spine for both axes.\n", - " og_xlim_raw, og_ylim_raw = rawdata_axes.get_xlim(), rawdata_axes.get_ylim()\n", - " og_xlim_contrast, og_ylim_contrast = contrast_axes.get_xlim(), contrast_axes.get_ylim()\n", - " if horizontal:\n", - " for current_ax, current_ylim, current_xlim in zip((rawdata_axes, contrast_axes), (og_ylim_raw, og_ylim_contrast), \n", - " (og_xlim_raw, og_xlim_contrast)):\n", - " current_ax.hlines(\n", - " current_ylim[0], \n", - " current_xlim[0], \n", - " current_xlim[1], \n", - " **redraw_axes_kwargs\n", - " ) \n", - " else:\n", - " for current_ax, current_ylim, current_xlim in zip((rawdata_axes, contrast_axes), (og_ylim_raw, og_ylim_contrast), \n", - " (og_xlim_raw[0], og_xlim_contrast[1] if float_contrast else og_xlim_contrast[0])):\n", - " current_ax.vlines(\n", - " current_xlim, \n", - " current_ylim[0], \n", - " current_ylim[1], \n", - " **redraw_axes_kwargs\n", - " )\n", - "\n", - " if show_delta2:\n", - " og_xlim_delta, og_ylim_delta = contrast_axes.get_xlim(), contrast_axes.get_ylim()\n", - " delta2_axes.set_ylim(og_ylim_delta)\n", - " \n", - " delta2_axes.vlines(\n", - " og_xlim_delta[1], \n", - " og_ylim_delta[0], \n", - " og_ylim_delta[1], \n", - " **redraw_axes_kwargs\n", - " )\n", - "\n", - " for current_ax, xlim, ylim in zip([rawdata_axes, contrast_axes], [og_xlim_raw, og_xlim_contrast], [og_ylim_raw, og_ylim_contrast]):\n", - " current_ax.set_xlim(xlim)\n", - " current_ax.set_ylim(ylim)\n", - "\n", - "def extract_group_summaries(\n", - " proportional: bool, \n", - " rawdata_axes: axes.Axes, \n", - " asymmetric_side: str, \n", - " horizontal: bool, \n", - " bootstraps_color_by_group: bool, \n", - " plot_palette_raw: list, \n", - " all_plot_groups: list,\n", - " n_groups: int, \n", - " color_col, \n", - " ytick_color, \n", - " group_summaries_kwargs: dict\n", - " ):\n", - " \"\"\"\n", - " Extract the group summaries for the plotter function.\n", - "\n", - " Parameters\n", - " ----------\n", - " proportional : bool\n", - " A boolean flag to determine if the plot is for proportional data.\n", - " rawdata_axes : object (Axes)\n", - " The raw data axes.\n", - " asymmetric_side : str\n", - " The side of the asymmetric error bars.\n", - " horizontal : bool\n", - " A boolean flag to determine if the plot is for horizontal plotting.\n", - " bootstraps_color_by_group : bool\n", - " A boolean flag to determine if the bootstraps are colored by group.\n", - " plot_palette_raw : list\n", - " A list of the plot palette colors.\n", - " all_plot_groups : list\n", - " A list of all the plot groups.\n", - " n_groups : int\n", - " The number of groups.\n", - " color_col : str\n", - " The name of the color column.\n", - " ytick_color : str\n", - " The color of the y-ticks.\n", - " group_summaries_kwargs : dict\n", - " Kwargs passed to the group summaries.\n", - " \"\"\"\n", - " \n", - " from .plot_tools import get_swarm_spans\n", - "\n", - " if proportional:\n", - " group_summaries_method = \"proportional_error_bar\"\n", - " group_summaries_offset = 0\n", - " group_summaries_line_color = \"black\"\n", - " else:\n", - " # Create list to gather xspans.\n", - " xspans = []\n", - " line_colors = []\n", - " for jj, c in enumerate(rawdata_axes.collections):\n", - " try:\n", - " if asymmetric_side == \"right\":\n", - " # currently offset is hardcoded with value of -0.2\n", - " x_max_span = -0.2\n", - " else:\n", - " if horizontal:\n", - " x_max_span = 0.1 # currently offset is hardcoded with value of 0.1\n", - " else:\n", - " _, x_max, _, _ = get_swarm_spans(c)\n", - " x_max_span = x_max - jj\n", - " xspans.append(x_max_span)\n", - " except TypeError:\n", - " # we have got a None, so skip and move on.\n", - " pass\n", - "\n", - " if bootstraps_color_by_group:\n", - " line_colors.append(plot_palette_raw[all_plot_groups[jj]])\n", - "\n", - " # Break the loop since hue in Seaborn adds collections to axes and it will result in index out of range\n", - " if jj >= n_groups - 1 and color_col is None:\n", - " break\n", - "\n", - " if len(line_colors) != len(all_plot_groups):\n", - " line_colors = ytick_color\n", - " \n", - " # hue in swarmplot would add collections to axes which will result in len(xspans) = len(all_plot_groups) + len(unique groups in hue)\n", - " if len(xspans) > len(all_plot_groups):\n", - " xspans = xspans[:len(all_plot_groups)]\n", - "\n", - " group_summaries_method = \"gapped_lines\"\n", - " group_summaries_offset = xspans + np.array(group_summaries_kwargs[\"offset\"])\n", - " group_summaries_line_color = line_colors\n", - "\n", - " if group_summaries_kwargs['color'] is not None:\n", - " group_summaries_line_color = group_summaries_kwargs.pop(\"color\")\n", - " group_summaries_kwargs.pop(\"offset\")\n", - "\n", - " return group_summaries_method, group_summaries_offset, group_summaries_line_color\n", - "\n", - "def color_picker(color_type: str,\n", - " kwargs: dict, \n", - " elements: list, \n", - " color_col: str, \n", - " show_pairs: bool, \n", - " color_palette: dict,\n", - " bootstraps_color_by_group: bool) -> list:\n", - " num_of_elements = len(elements)\n", - " colors = (\n", - " [kwargs.pop('color')] * num_of_elements\n", - " if kwargs.get('color', None) is not None\n", - " else ['black'] * num_of_elements\n", - " # if color_col is not None or show_pairs\n", - " if color_col is not None or not bootstraps_color_by_group\n", - " else list(color_palette.values())\n", - " )\n", - " if color_type in ['contrast', 'summary', 'delta_text']:\n", - " if len(colors) == num_of_elements:\n", - " final_colors = colors\n", - " else:\n", - " final_colors = []\n", - " for tick in elements:\n", - " final_colors.append(colors[int(tick)])\n", - " else:\n", - " final_colors = colors\n", - " return final_colors\n", - "\n", - "\n", - "def prepare_bars_for_plot(bar_type, bar_kwargs, horizontal, plot_palette_raw, color_col, show_pairs, bootstraps_color_by_group,\n", - " plot_data = None, xvar = None, yvar = None, # Raw data\n", - " results = None, ticks_to_plot = None, extra_delta = None, # Contrast data\n", - " reference_band = None, summary_axes = None, ci_type = None # Summary data\n", - " ):\n", - " from .misc_tools import color_picker\n", - " bar_dict = {}\n", - " if bar_type in ['raw', 'contrast']:\n", - " if bar_type == 'raw':\n", - " if isinstance(plot_data[xvar].dtype, pd.CategoricalDtype):\n", - " order = pd.unique(plot_data[xvar]).categories\n", - " else:\n", - " order = pd.unique(plot_data[xvar])\n", - " means = plot_data.groupby(xvar, observed=False)[yvar].mean().reindex(index=order).values\n", - " ticks = list(range(len(order)))\n", - " elif bar_type == 'contrast':\n", - " means = results.difference.to_list()\n", - " ticks = ticks_to_plot.copy()\n", - " if extra_delta is not None:\n", - " ticks.append(ticks[-1]+1) # Add an extra tick\n", - " means.append(extra_delta)\n", - "\n", - " num_of_bars = len(means)\n", - " y_start_values, y_distances = [0]*num_of_bars, means\n", - " x_start_values, x_distances = [num - (0.5 if horizontal else 0.25) for num in ticks], [0.5,]*num_of_bars\n", - "\n", - " elif bar_type == 'summary':\n", - " # Begin checks \n", - " if not isinstance(reference_band, list):\n", - " raise TypeError(\"reference_band must be a list of indices (ints).\")\n", - " if not all(isinstance(i, int) for i in reference_band):\n", - " raise TypeError(\"reference_band must be a list of indices (ints).\")\n", - " if any(i >= len(results) for i in reference_band):\n", - " raise ValueError(\"Index {} chosen is out of range for the contrast objects.\".format([i for i in reference_band if i >= len(results)]))\n", - "\n", - " ticks = [ticks_to_plot[tick] for tick in reference_band]\n", - " summary_xmin, summary_xmax = summary_axes.get_xlim()\n", - " summary_ymin, summary_ymax = summary_axes.get_ylim()\n", - " span_ax = bar_kwargs.pop(\"span_ax\")\n", - "\n", - " x_start_values, y_start_values, x_distances, y_distances = [], [], [], []\n", - " for summary_index in reference_band:\n", - " summary_ci_low = results.get(ci_type+'_low')[summary_index]\n", - " summary_ci_high = results.get(ci_type+'_high')[summary_index] \n", - "\n", - " if span_ax == True:\n", - " starting_location = summary_ymax if horizontal else summary_xmin\n", - " else:\n", - " starting_location = ticks_to_plot[summary_index] \n", - " x_distance = summary_ymin if horizontal else summary_xmax \n", - "\n", - " x_start_values.append(starting_location)\n", - " y_start_values.append(summary_ci_low)\n", - " x_distances.append(x_distance + 1)\n", - " y_distances.append(summary_ci_high - summary_ci_low)\n", - " else:\n", - " raise ValueError(\"Invalid bar_type. Must be 'raw' or 'contrast'.\")\n", - " \n", - " if horizontal:\n", - " x_start_values, y_start_values = y_start_values, x_start_values\n", - " x_distances, y_distances = y_distances, x_distances\n", - "\n", - " for name, values in zip(['x_start_values', 'x_distances', 'y_start_values', 'y_distance'],\n", - " [x_start_values, x_distances, y_start_values, y_distances]\n", - " ):\n", - " bar_dict[name] = values\n", - "\n", - " # Colors\n", - " colors = color_picker(\n", - " color_type = bar_type,\n", - " kwargs = bar_kwargs, \n", - " elements = ticks_to_plot if bar_type=='contrast' else ticks, \n", - " color_col = color_col, \n", - " show_pairs = show_pairs, \n", - " color_palette = plot_palette_raw,\n", - " bootstraps_color_by_group = bootstraps_color_by_group\n", - " )\n", - " if bar_type == 'contrast' and extra_delta is not None:\n", - " colors.append('black')\n", - " bar_dict['colors'] = colors\n", - "\n", - " return bar_dict, bar_kwargs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4c5fe4f1", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/API/multi.ipynb b/nbs/API/multi.ipynb deleted file mode 100644 index 6647f189..00000000 --- a/nbs/API/multi.ipynb +++ /dev/null @@ -1,937 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "11740caf", - "metadata": {}, - "source": [ - "# multi\n", - "\n", - "In nbs/API/multi.ipynb\n", - "\n", - "This module provides functionality for visualizing multiple DABEST contrast objects simultaneously using advanced visualization techniques like whorlmaps and forest plots.\n", - "- order: 11" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "518492d2", - "metadata": {}, - "outputs": [], - "source": [ - "#| default_exp multi" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fbcc3115", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from nbdev.showdoc import *\n", - "import nbdev\n", - "nbdev.nbdev_export()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "80099a4b", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import warnings\n", - "from typing import List, Optional, Union, Tuple, Dict, Any\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "316ebd45", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "985e0e49", - "metadata": {}, - "source": [ - "## MultiContrast Class\n", - "\n", - "The `MultiContrast` class enables visualization of multiple contrast objects in grid-based layouts.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4b58920", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "class MultiContrast:\n", - " \"\"\"\n", - " Unified multiple contrast object for forest plots and whorlmaps.\n", - " \n", - " Takes raw dabest objects and provides validated, processed data\n", - " for downstream visualizations.\n", - " \"\"\"\n", - " \n", - " def __init__(self, \n", - " dabest_objs: Union[List, List[List]], \n", - " labels: Optional[List[str]] = None,\n", - " row_labels: Optional[List[str]] = None,\n", - " effect_size: str = \"mean_diff\",\n", - " ci_type: str = \"bca\"):\n", - " \"\"\"\n", - " Initialize MultiContrast object with checking.\n", - " \n", - " Parameters\n", - " ----------\n", - " dabest_objs : Union[List, List[List]]\n", - " Raw dabest objects. Can be:\n", - " - 1D: [dabest_obj1, dabest_obj2, ...] \n", - " - 2D: [[dabest_obj1, dabest_obj2], [dabest_obj3, dabest_obj4]]\n", - " labels : Optional[Union[List[str], List[List[str]]]], default=None\n", - " Labels matching the contrast array structure. If None, defaults will be generated.\n", - " effect_size : str, default=\"mean_diff\"\n", - " Effect size to extract from dabest objects\n", - " ci_type : str, default=\"bca\"\n", - " Confidence interval type\n", - " \"\"\"\n", - " # Store raw inputs for validation\n", - " self._raw_dabest_objs = dabest_objs\n", - " self._raw_labels = labels\n", - " self._raw_row_labels = row_labels \n", - "\n", - " # Validate and process inputs\n", - " self.effect_size = self._validate_effect_size(effect_size)\n", - " self.ci_type = self._validate_ci_type(ci_type)\n", - "\n", - " # Process structure (adapts forest_plot logic to handle 2D)\n", - " self.structure = self._validate_and_parse_structure(dabest_objs, labels)\n", - "\n", - " # Validate all dabest objects consistency\n", - " self.contrast_type = self._validate_contrast_consistency()\n", - "\n", - " # Extract data (adapts forest_plot's load_plot_data logic)\n", - " self._bootstrap_data = None\n", - " self._effect_size_data = None\n", - " self._ci_data = None\n", - " \n", - " def _validate_effect_size(self, effect_size: str) -> str:\n", - " \"\"\"Validate effect size parameter (from forest_plot).\"\"\"\n", - " possible_effect_sizes = [\n", - " 'mean_diff', 'median_diff', 'cohens_d', \n", - " 'cohens_h', 'cliffs_delta', 'hedges_g', 'delta_g'\n", - " ]\n", - " \n", - " if not isinstance(effect_size, str) or effect_size not in possible_effect_sizes:\n", - " raise TypeError(\n", - " f\"effect_size must be one of: {possible_effect_sizes}\"\n", - " )\n", - " return effect_size\n", - "\n", - " def _validate_ci_type(self, ci_type: str) -> str:\n", - " \"\"\"Validate CI type parameter (from forest_plot).\"\"\"\n", - " if ci_type not in ('bca', 'pct'):\n", - " raise TypeError(\"ci_type must be either 'bca' or 'pct'\")\n", - " return ci_type\n", - " \n", - " def _validate_and_parse_structure(self, dabest_objs, labels):\n", - " \"\"\"\n", - " Validate and parse contrast structure, combining forest_plot \n", - " validation with whorlmap's 2D handling.\n", - " \"\"\"\n", - " # Basic validation (from forest_plot)\n", - " if not isinstance(dabest_objs, (list, tuple)) or len(dabest_objs) == 0:\n", - " raise ValueError(\"dabest_objs must be a non-empty list\")\n", - " \n", - " # Determine if 1D or 2D structure\n", - " if isinstance(dabest_objs[0], (list, tuple)):\n", - " # 2D structure (can be used to plot whorlmap or a stack of forest plots)\n", - " structure_type = \"2D\"\n", - " dabest_objs_2d = dabest_objs\n", - " n_rows = len(dabest_objs)\n", - " n_cols = len(dabest_objs[0])\n", - " \n", - " # Validate rectangular structure\n", - " for i, row in enumerate(dabest_objs):\n", - " if not isinstance(row, (list, tuple)):\n", - " raise TypeError(f\"Row {i} must be a list/tuple in 2D structure\")\n", - " if len(row) != n_cols:\n", - " raise ValueError(\"All rows must have the same number of dabest_objs\")\n", - " \n", - " # Handle 2D labels\n", - " if labels:\n", - " if not isinstance(labels, (list, tuple)):\n", - " raise TypeError(\"labels must be a list for 2D dabest_objs\")\n", - " if len(labels) != n_cols:\n", - " raise ValueError(\"Number of labels must match number of columns of dabest_objs\")\n", - " col_labels = labels\n", - " else:\n", - " col_labels = [f\"Contrast {i+1}\" for i in range(n_cols)]\n", - " # Handle row_labels - use self._raw_row_labels if available\n", - " if hasattr(self, '_raw_row_labels') and self._raw_row_labels:\n", - " if not isinstance(self._raw_row_labels, (list, tuple)):\n", - " raise TypeError(\"row_labels must be a list for 2D dabest_objs\")\n", - " if len(self._raw_row_labels) != n_rows:\n", - " raise ValueError(\"Number of row_labels must match number of rows of dabest_objs\")\n", - " row_labels = self._raw_row_labels\n", - " else:\n", - " row_labels = [f\"Row {i+1}\" for i in range(n_rows)]\n", - " else:\n", - " # 1D structure (like forest_plot)\n", - " structure_type = \"1D\"\n", - " dabest_objs_2d = [dabest_objs] # Wrap in single row for unified processing\n", - " n_rows = 1\n", - " n_cols = len(dabest_objs)\n", - " \n", - " # Handle 1D labels\n", - " if labels:\n", - " if not isinstance(labels, (list, tuple)):\n", - " raise TypeError(\"labels must be a list for 1D dabest_objs\")\n", - " if len(labels) != n_cols:\n", - " raise ValueError(\"Number of labels must match number of dabest_objs\")\n", - " col_labels = labels\n", - " else:\n", - " col_labels = [f\"Contrast {i+1}\" for i in range(n_cols)]\n", - " row_labels = [\"\"] # Single empty row label\n", - " \n", - " return {\n", - " 'type': structure_type,\n", - " 'dabest_objs_2d': dabest_objs_2d,\n", - " 'n_rows': n_rows,\n", - " 'n_cols': n_cols,\n", - " 'col_labels': col_labels,\n", - " 'row_labels': row_labels,\n", - " 'total_dabest_objs': n_rows * n_cols\n", - " }\n", - " \n", - " def _validate_contrast_consistency(self) -> Union[str, Dict]:\n", - " \"\"\"\n", - " Validate contrast consistency with support for mixed types in whorlmap.\n", - " \n", - " Returns either:\n", - " - str: Single contrast type for homogeneous data (forest_plot compatible)\n", - " - dict: Row-wise contrast types for mixed data (whorlmap only)\n", - " \"\"\"\n", - " all_dabest_objs = []\n", - " for row in self.structure['dabest_objs_2d']:\n", - " all_dabest_objs.extend(row)\n", - " \n", - " if not all_dabest_objs:\n", - " raise ValueError(\"No valid dabest_objs found\")\n", - " \n", - " # First, validate EACH contrast individually\n", - " for i, dabest_obj in enumerate(all_dabest_objs):\n", - " self._validate_individual_dabest_obj(dabest_obj, i)\n", - " \n", - " # Analyze contrast type structure\n", - " contrast_types_by_row = []\n", - " for row_idx, row in enumerate(self.structure['dabest_objs_2d']):\n", - " row_types = []\n", - " for contrast in row:\n", - " contrast_type = (\"delta2\" if contrast.delta2 \n", - " else \"mini_meta\" if contrast.is_mini_meta\n", - " else \"delta\")\n", - " row_types.append(contrast_type)\n", - " contrast_types_by_row.append(row_types)\n", - " \n", - " # Check if all dabest_objs are the same type (forest_plot requirement)\n", - " all_types_flat = [t for row_types in contrast_types_by_row for t in row_types]\n", - " unique_types = set(all_types_flat)\n", - " \n", - " if len(unique_types) == 1:\n", - " # Homogeneous: all same type (forest_plot compatible)\n", - " contrast_type = list(unique_types)[0]\n", - " self._validate_effect_size_compatibility(contrast_type)\n", - " return contrast_type\n", - " \n", - " else:\n", - " # Heterogeneous: mixed types (whorlmap only)\n", - " if self.structure['type'] == '1D':\n", - " raise ValueError(\n", - " \"Mixed contrast types are only supported for 2D structures (whorlmaps). \"\n", - " f\"Found types: {unique_types}. For forest plots, all dabest_objs must be the same type.\"\n", - " )\n", - " \n", - " # Validate within-row consistency for whorlmap\n", - " for row_idx, row_types in enumerate(contrast_types_by_row):\n", - " unique_row_types = set(row_types)\n", - " if len(unique_row_types) > 1:\n", - " raise ValueError(\n", - " f\"Within each row, all dabest_objs must be the same type. \"\n", - " f\"Row {row_idx} has mixed types: {unique_row_types}\"\n", - " )\n", - " \n", - " # Validate effect size compatibility for each row type\n", - " for row_types in contrast_types_by_row:\n", - " row_type = row_types[0] # All same within row\n", - " self._validate_effect_size_compatibility(row_type)\n", - " \n", - " # Return row-wise type information\n", - " return {\n", - " 'mixed': True,\n", - " 'by_row': [row_types[0] for row_types in contrast_types_by_row],\n", - " 'unique_types': list(unique_types)\n", - " }\n", - " \n", - " def _validate_effect_size_compatibility(self, contrast_type: str):\n", - " \"\"\"Validate effect size compatibility with a specific contrast type.\"\"\"\n", - " if contrast_type == \"mini_meta\" and self.effect_size != 'mean_diff':\n", - " raise ValueError(\"effect_size must be 'mean_diff' for mini-meta analyses\")\n", - " \n", - " if contrast_type == \"delta2\" and self.effect_size not in ['mean_diff', 'hedges_g', 'delta_g']:\n", - " raise ValueError(\n", - " \"effect_size must be 'mean_diff', 'hedges_g', or 'delta_g' for delta-delta analyses\"\n", - " ) \n", - " \n", - " def _validate_individual_dabest_obj(self, dabest_obj, position: int):\n", - " \"\"\"\n", - " Validate individual dabest object.\n", - " \n", - " Parameters\n", - " ----------\n", - " dabest_obj : object\n", - " Individual dabest object to validate\n", - " position : int\n", - " Position in the contrast list for error reporting\n", - " \"\"\"\n", - " # Basic existence check\n", - " if dabest_obj is None:\n", - " raise ValueError(f\"Dabest object at position {position} is None\")\n", - " \n", - " # Required attributes for dabest objects\n", - " required_attrs = ['delta2', 'is_mini_meta']\n", - " for attr in required_attrs:\n", - " if not hasattr(dabest_obj, attr):\n", - " raise TypeError(\n", - " f\"Object at position {position} is not a valid dabest object. \"\n", - " f\"Missing required attribute: '{attr}'\"\n", - " )\n", - " \n", - " # Validate effect size attribute exists\n", - " effect_attr = \"hedges_g\" if self.effect_size == 'delta_g' else self.effect_size\n", - " if not hasattr(dabest_obj, effect_attr):\n", - " raise AttributeError(\n", - " f\"Dabest Object at position {position} does not have effect size '{self.effect_size}'. \"\n", - " f\"Expected attribute: '{effect_attr}'\"\n", - " )\n", - " \n", - " # Test that we can actually access the effect size data\n", - " try:\n", - " effect_obj = getattr(dabest_obj, effect_attr)\n", - "\n", - " # For delta2/mini_meta, check the nested attributes exist\n", - " if dabest_obj.delta2:\n", - " if not hasattr(effect_obj, 'delta_delta'):\n", - " raise AttributeError(f\"Delta-delta contrast at position {position} missing 'delta_delta' attribute\")\n", - " elif dabest_obj.is_mini_meta:\n", - " if not hasattr(effect_obj, 'mini_meta'):\n", - " raise AttributeError(f\"Mini-meta contrast at position {position} missing 'mini_meta' attribute\")\n", - " else:\n", - " # Standard contrast - check results structure\n", - " if not hasattr(effect_obj, 'results'):\n", - " raise AttributeError(f\"Standard contrast at position {position} missing 'results' attribute\") \n", - " except Exception as e:\n", - " raise ValueError(\n", - " f\"Failed to access effect size data for dabest object at position {position}: {str(e)}\"\n", - " )\n", - " \n", - " def _extract_data(self) -> Tuple[List, List, List, List]:\n", - " \"\"\"\n", - " Extract bootstrap, effect sizes, CI low bounds and CI high bounds.\n", - " Handles mixed contrast types for whorlmap.\n", - " \"\"\"\n", - " if self._bootstrap_data is not None:\n", - " return self._bootstrap_data, self._effect_data, self._ci_lows, self._ci_highs\n", - " \n", - " # Process effect size attribute name\n", - " effect_attr = \"hedges_g\" if self.effect_size == 'delta_g' else self.effect_size\n", - " \n", - " bootstraps = []\n", - " differences = []\n", - " ci_lows = []\n", - " ci_highs = []\n", - " \n", - " if isinstance(self.contrast_type, dict) and self.contrast_type.get('mixed'):\n", - " # Mixed types: process row by row\n", - " for row_idx, row in enumerate(self.structure['dabest_objs_2d']):\n", - " row_contrast_type = self.contrast_type['by_row'][row_idx]\n", - " contrast_attr = {\"delta2\": \"delta_delta\", \"mini_meta\": \"mini_meta\"}.get(row_contrast_type)\n", - " \n", - " for contrast in row:\n", - " bootstrap, diff, ci_low, ci_high = self._extract_single_contrast(\n", - " contrast, effect_attr, row_contrast_type, contrast_attr\n", - " )\n", - " bootstraps.extend(bootstrap if isinstance(bootstrap, list) else [bootstrap])\n", - " differences.extend(diff if isinstance(diff, list) else [diff])\n", - " ci_lows.extend(ci_low if isinstance(ci_low, list) else [ci_low])\n", - " ci_highs.extend(ci_high if isinstance(ci_high, list) else [ci_high])\n", - " \n", - " else:\n", - " # Homogeneous types: process all together (original logic)\n", - " contrast_attr = {\"delta2\": \"delta_delta\", \"mini_meta\": \"mini_meta\"}.get(self.contrast_type)\n", - " \n", - " all_dabest_objs = []\n", - " for row in self.structure['dabest_objs_2d']:\n", - " all_dabest_objs.extend(row)\n", - " \n", - " for contrast in all_dabest_objs:\n", - " bootstrap, diff, ci_low, ci_high = self._extract_single_contrast(\n", - " contrast, effect_attr, self.contrast_type, contrast_attr\n", - " )\n", - " bootstraps.extend(bootstrap if isinstance(bootstrap, list) else [bootstrap])\n", - " differences.extend(diff if isinstance(diff, list) else [diff])\n", - " ci_lows.extend(ci_low if isinstance(ci_low, list) else [ci_low])\n", - " ci_highs.extend(ci_high if isinstance(ci_high, list) else [ci_high])\n", - " \n", - " # Cache results\n", - " self._bootstrap_data = bootstraps\n", - " self._effect_data = differences\n", - " self._ci_lows = ci_lows\n", - " self._ci_highs = ci_highs\n", - " \n", - " return bootstraps, differences, ci_lows, ci_highs\n", - " \n", - " def _extract_single_contrast(self, contrast, effect_attr, contrast_type, contrast_attr):\n", - " \"\"\"Extract data from a single contrast object.\"\"\"\n", - " if contrast_type == 'delta':\n", - " # Standard dabest_objs - may have multiple comparisons\n", - " effect_obj = getattr(contrast, effect_attr)\n", - " boot_list = effect_obj.results.bootstraps.to_list()\n", - " diff_list = effect_obj.results.difference.to_list()\n", - " low_list = effect_obj.results.get(f'{self.ci_type}_low').to_list()\n", - " high_list = effect_obj.results.get(f'{self.ci_type}_high').to_list()\n", - " return boot_list, diff_list, low_list, high_list\n", - " \n", - " else:\n", - " # Delta-delta or mini-meta - single value per contrast\n", - " effect_obj = getattr(contrast, effect_attr)\n", - " processed_obj = getattr(effect_obj, contrast_attr)\n", - " \n", - " if contrast_type == \"delta2\":\n", - " bootstrap = processed_obj.bootstraps_delta_delta\n", - " difference = processed_obj.difference\n", - " else: # mini_meta\n", - " bootstrap = processed_obj.bootstraps_weighted_delta\n", - " difference = processed_obj.difference\n", - " \n", - " ci_low = processed_obj.results.get(f'{self.ci_type}_low')[0]\n", - " ci_high = processed_obj.results.get(f'{self.ci_type}_high')[0]\n", - " \n", - " return bootstrap, difference, ci_low, ci_high\n", - " @property \n", - " def bootstraps(self) -> List:\n", - " \"\"\"Get bootstrap samples for all dabest_objs.\"\"\"\n", - " bootstraps, _, _, _ = self._extract_data()\n", - " return bootstraps\n", - " \n", - " @property\n", - " def effect_sizes(self) -> List:\n", - " \"\"\"Get effect sizes for all dabest_objs.\"\"\"\n", - " _, effects, _, _ = self._extract_data()\n", - " return effects\n", - " \n", - " @property \n", - " def confidence_intervals(self) -> Tuple[List, List]:\n", - " \"\"\"Get confidence interval bounds.\"\"\"\n", - " _, _, ci_lows, ci_highs = self._extract_data()\n", - " return ci_lows, ci_highs\n", - " \n", - " def forest_plot(self, forest_plot_title = None, forest_plot_kwargs = {}):\n", - " \"\"\"\n", - " Create forest plot using validated data.\n", - " \n", - " This is a convenience method that calls the existing forest_plot function\n", - " with validated dabest objects. # TODO: decide whether to\n", - " migrate forest_plot to use MultiContrast data directly.\n", - " \"\"\"\n", - " # Check compatibility with forest plot (mixed contrast types not supported)\n", - " if isinstance(self.contrast_type, dict) and self.contrast_type.get('mixed'):\n", - " raise ValueError(\n", - " \"Forest plots require all dabest_objs to be the same type. \"\n", - " f\"This MultiContrast has mixed types: {self.contrast_type['unique_types']}. \"\n", - " \"Consider creating separate MultiContrast objects for each type, \"\n", - " \"or use whorlmap() which supports mixed types.\"\n", - " )\n", - " \n", - " # Import forest_plot function\n", - " from .forest_plot import forest_plot\n", - " \n", - " # # Get flattened contrast list for existing forest_plot function\n", - " # all_dabest_objs = []\n", - " # for row in self.structure['dabest_objs_2d']:\n", - " # all_dabest_objs.extend(row)\n", - " \n", - " # Call existing forest_plot with validated dabest objects\n", - "\n", - " f_forest, axes = plt.subplots(self.structure['n_rows'], 1, \n", - " figsize=(8, 2 * self.structure['n_rows']), squeeze=False)\n", - " for i, row in enumerate(self.structure['dabest_objs_2d']):\n", - " # Set default parameters, allow kwargs to override\n", - " forest_kwargs = {\n", - " 'effect_size': self.effect_size,\n", - " 'ci_type': self.ci_type,\n", - " 'ax': axes[i, 0],\n", - " 'labels': self.structure['col_labels'],\n", - " 'title': self.structure['row_labels'][i] if self.structure['n_rows'] > 1 else None,}\n", - " forest_kwargs.update(forest_plot_kwargs)\n", - " forest_plot(data = row, **forest_kwargs)\n", - " if i == self.structure['n_rows'] - 1:\n", - " axes[i, 0].set_xticks(axes[i, 0].get_xticks())\n", - " else:\n", - " axes[i, 0].set_xticks([])\n", - " self.f_forest = f_forest\n", - " if forest_plot_title:\n", - " f_forest.suptitle(forest_plot_title)\n", - " return f_forest, axes\n", - "\n", - " def whorlmap(self, **heatmap_kwargs):\n", - " \"\"\"\n", - " Create whorlmap using validated data.\n", - " \n", - " This uses the whorlmap that can handle both homogeneous\n", - " and mixed contrast types.\n", - " \"\"\"\n", - " from .multi import whorlmap\n", - " f_whorlmap = whorlmap(multi_contrast=self, **heatmap_kwargs)\n", - " self.f_whorlmap = f_whorlmap\n", - " # Call whorlmap with self as the multi_contrast object\n", - " return f_whorlmap\n", - " def get_bootstrap_by_position(self, row: int, col: int):\n", - " \"\"\"\n", - " Get bootstrap data for a specific position in the grid.\n", - " Useful for mixed-type whorlmaps.\n", - " \"\"\"\n", - " if row >= self.structure['n_rows'] or col >= self.structure['n_cols']:\n", - " raise IndexError(f\"Position ({row}, {col}) out of bounds for {self.structure['n_rows']}×{self.structure['n_cols']} grid\")\n", - " \n", - " contrast = self.structure['dabest_objs_2d'][row][col]\n", - " effect_attr = \"hedges_g\" if self.effect_size == 'delta_g' else self.effect_size\n", - " \n", - " # Determine contrast type for this position\n", - " if isinstance(self.contrast_type, dict) and self.contrast_type.get('mixed'):\n", - " position_type = self.contrast_type['by_row'][row]\n", - " else:\n", - " position_type = self.contrast_type\n", - " \n", - " contrast_attr = {\"delta2\": \"delta_delta\", \"mini_meta\": \"mini_meta\"}.get(position_type)\n", - " \n", - " # Extract bootstrap for this specific contrast\n", - " bootstrap, _, _, _ = self._extract_single_contrast(contrast, effect_attr, position_type, contrast_attr)\n", - " \n", - " # For standard dabest_objs, return first bootstrap (they may have multiple)\n", - " if isinstance(bootstrap, list) and len(bootstrap) > 0:\n", - " return bootstrap[0]\n", - " return bootstrap\n", - " \n", - " def __repr__(self):\n", - " if isinstance(self.contrast_type, dict) and self.contrast_type.get('mixed'):\n", - " types_info = f\"mixed({', '.join(self.contrast_type['unique_types'])})\"\n", - " else:\n", - " types_info = self.contrast_type\n", - " \n", - " return (f\"MultiContrast({self.structure['type']}: \"\n", - " f\"{self.structure['n_rows']}x{self.structure['n_cols']}, \"\n", - " f\"effect_size='{self.effect_size}', \"\n", - " f\"contrast_type='{types_info}')\") " - ] - }, - { - "cell_type": "markdown", - "id": "75517120", - "metadata": {}, - "source": [ - "## Loading Function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b6952d49", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "def combine(dabest_objs: Union[List, List[List]], \n", - " labels: Optional[List[str]] = None,\n", - " row_labels: Optional[List[str]] = None,\n", - " effect_size: str = \"mean_diff\",\n", - " ci_type: str = \"bca\",\n", - " allow_mixed_types: bool = False) -> MultiContrast:\n", - " \"\"\"\n", - " Create a MultiContrast object from raw dabest objects.\n", - " \n", - " This is the main entry point that users should use to create\n", - " multi-contrast visualizations.\n", - " \n", - " Parameters\n", - " ----------\n", - " dabest_objs : Union[List, List[List]]\n", - " Raw dabest objects in 1D or 2D structure\n", - " labels : Optional[Union[List[str], List[List[str]]]], default=None\n", - " Labels for dabest_objs\n", - " effect_size : str, default=\"mean_diff\" \n", - " Effect size to extract\n", - " ci_type : str, default=\"bca\"\n", - " Confidence interval type\n", - " allow_mixed_types : bool, default=False\n", - " If True, allows different contrast types in different rows (whorlmap only)\n", - " If False, enforces homogeneous types (forest_plot compatible)\n", - " \n", - " Returns\n", - " -------\n", - " MultiContrast\n", - " Validated multi-contrast object ready for visualization\n", - " \n", - " Examples\n", - " --------\n", - " # Homogeneous 1D structure (forest_plot and whorlmap compatible)\n", - " mc = combine([dabest1, dabest2, dabest3], \n", - " labels=['Treatment A', 'Treatment B', 'Treatment C'])\n", - " mc.forest_plot()\n", - " mc.whorlmap() # Will arrange in single row\n", - " \n", - " # Homogeneous 2D structure (forest_plot flattens, whorlmap uses grid)\n", - " mc = combine([[dabest1, dabest2], [dabest3, dabest4]], \n", - " labels=[['Dose Low', 'Dose High'], ['Time 1', 'Time 2']])\n", - " mc.whorlmap() # 2x2 grid\n", - " mc.forest_plot() # Flattened to 1D\n", - " \n", - " # Mixed types 2D structure (whorlmap only!)\n", - " mc = combine([[standard_dabest1, standard_dabest2], \n", - " [delta2_dabest1, delta2_dabest2]],\n", - " labels=[['Standard A', 'Standard B'], \n", - " ['Delta2 A', 'Delta2 B']],\n", - " allow_mixed_types=True)\n", - " mc.whorlmap() # Works: mixed spiral types per row\n", - " # mc.forest_plot() # Raises error: incompatible with mixed types\n", - " \n", - " # Mini-meta + Delta2 mixed example\n", - " mc = combine([[mini_meta1, mini_meta2], \n", - " [delta2_obj1, delta2_obj2]],\n", - " allow_mixed_types=True)\n", - " mc.whorlmap() # Top row: mini-meta spirals, bottom row: delta2 spirals\n", - " \"\"\"\n", - " mc = MultiContrast(dabest_objs, labels, row_labels, effect_size, ci_type)\n", - " \n", - " # Check mixed types policy\n", - " if isinstance(mc.contrast_type, dict) and mc.contrast_type.get('mixed'):\n", - " if not allow_mixed_types:\n", - " raise ValueError(\n", - " f\"Mixed contrast types detected: {mc.contrast_type['unique_types']}. \"\n", - " \"Set allow_mixed_types=True to enable mixed-type whorlmaps, \"\n", - " \"or ensure all dabest_objs are the same type for forest_plot compatibility.\"\n", - " )\n", - " \n", - " return mc" - ] - }, - { - "cell_type": "markdown", - "id": "3a1a62e8", - "metadata": {}, - "source": [ - "## Whorlmap Visualization\n", - "\n", - "The whorlmap creates spiral heatmaps showing the distribution of bootstrap samples for each contrast." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7814cc58", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "def _sample_bootstrap(bootstrap, m, n, reverse_neg, abs_rank, chop_tail):\n", - " \"\"\"Sample bootstrap values and prepare for spiral visualization.\"\"\"\n", - " bootstrap_sorted = sorted(bootstrap)\n", - " chop_tail_int = int(np.ceil(len(bootstrap_sorted) * chop_tail / 100))\n", - " bootstrap_sorted = bootstrap_sorted[chop_tail_int : len(bootstrap_sorted) - chop_tail_int]\n", - " \n", - " ranks_to_look = np.linspace(0, len(bootstrap_sorted), m * n, dtype=int) \n", - " ranks_to_look[0] = 1\n", - " \n", - " if np.sum(np.array(bootstrap_sorted) > 0) < len(bootstrap_sorted) / 2:\n", - " if reverse_neg:\n", - " bootstrap_sorted = bootstrap_sorted[::-1]\n", - " \n", - " if abs_rank:\n", - " bootstrap_sorted = sorted(bootstrap_sorted, key=abs)\n", - " \n", - " long_ranks = [bootstrap_sorted[r - 1] for r in ranks_to_look]\n", - " return long_ranks" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "725c96b5", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "def _spiralize(fill, m, n):\n", - " \"\"\"Convert linear array into spiral pattern.\"\"\"\n", - " i = 0\n", - " j = 0\n", - " k = 0\n", - " array = np.zeros((m, n))\n", - " \n", - " while m > 0 and k < len(fill):\n", - " jj = j\n", - " ii = i\n", - " \n", - " # Right\n", - " for j in range(j, n):\n", - " if k >= len(fill):\n", - " break\n", - " array[i, j] = fill[k]\n", - " k += 1\n", - " \n", - " # Down\n", - " for i in range(ii + 1, m):\n", - " if k >= len(fill):\n", - " break\n", - " array[i, j] = fill[k]\n", - " k += 1\n", - " \n", - " # Left\n", - " for j in range(n - 2, jj - 1, -1):\n", - " if k >= len(fill):\n", - " break\n", - " array[i, j] = fill[k]\n", - " k += 1\n", - " \n", - " # Up\n", - " for i in range(m - 2, ii, -1):\n", - " if k >= len(fill):\n", - " break\n", - " array[i, j] = fill[k]\n", - " k += 1\n", - " \n", - " m -= 1\n", - " n -= 1\n", - " j += 1\n", - " \n", - " return array" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "20809f1d", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "def whorlmap(multi_contrast, n=21, sort_by=None, cmap = 'vlag', vmax = None, vmin = None, reverse_neg=True, \n", - " abs_rank=False, chop_tail=0, ax=None, fig_size=None, title = None, heatmap_kwargs=None, plot_kwargs=None):\n", - " \"\"\"\n", - " Create a whorlmap visualization of multiple contrasts.\n", - " \n", - " Parameters\n", - " ----------\n", - " multi_contrast : MultiContrast\n", - " Object containing multiple dabest objects\n", - " n : int, default 21\n", - " Size of each spiral (n x n grid per contrast)\n", - " sort_by : list, optional\n", - " Order to sort contrasts by\n", - " vmax, vmin : float, default None, None\n", - " Color scale limits\n", - " reverse_neg : bool, default True\n", - " Whether to reverse negative values\n", - " abs_rank : bool, default False\n", - " Whether to rank by absolute value\n", - " chop_tail : float, default 0\n", - " Percentage of extreme values to exclude\n", - " ax : matplotlib.Axes, optional\n", - " Existing axes to plot on\n", - " fig_size : tuple, optional\n", - " Figure size (width, height) in inches\n", - " title : str, optional\n", - " Plot title\n", - " heatmap_kwargs : dict, optional\n", - " Additional keyword arguments passed to sns.heatmap().\n", - " Common options include:\n", - " - 'cmap': colormap (overrides direct cmap parameter)\n", - " - 'vmin', 'vmax': color scale limits (override direct parameters)\n", - " - 'center': center value for colormap\n", - " - 'annot': whether to annotate cells with values\n", - " - 'fmt': format string for annotations\n", - " - 'linewidths': width of lines between cells\n", - " - 'linecolor': color of lines between cells\n", - " - 'cbar': whether to show colorbar\n", - " - 'cbar_kws': colorbar customization dict\n", - " - 'square': whether to make cells square\n", - " - 'xticklabels', 'yticklabels': tick label control\n", - " - 'mask': boolean array to mask cells\n", - " plot_kwargs : dict, optional\n", - " Additional keyword arguments for plot styling and layout.\n", - " Available options (WIP):\n", - " - 'title': plot title\n", - " - 'xlabel', 'ylabel': axis labels\n", - " - 'xticklabels', 'yticklabels': tick labels\n", - " - 'xticklabels_rotation', 'yticklabels_rotation': tick label rotation angles\n", - " - 'xticklabels_ha', 'yticklabels_ha': horizontal alignment \n", - " Returns\n", - " -------\n", - " tuple\n", - " (figure, axes, mean_delta_dataframe) if ax is None, \n", - " else (axes, mean_delta_dataframe)\n", - " \"\"\"\n", - " from .misc_tools import merge_two_dicts\n", - "\n", - " structure = multi_contrast.structure\n", - " n_rows = structure['n_rows']\n", - " n_cols = structure['n_cols']\n", - " col_labels = structure['col_labels'] \n", - " row_labels = structure['row_labels']\n", - " was_1d = (structure['type'] == '1D')\n", - "\n", - " # Initialize spirals and mean_delta DataFrames\n", - " spirals = pd.DataFrame(np.zeros((n_rows * n, n_cols * n)))\n", - " \n", - " mean_delta = pd.DataFrame(np.zeros((n_rows, n_cols)), \n", - " columns=col_labels, \n", - " index=row_labels)\n", - " \n", - " # Get all bootstrap data from MultiContrast\n", - " all_bootstraps = multi_contrast.bootstraps\n", - " bootstrap_idx = 0\n", - "\n", - " for i in range(n_rows):\n", - " for j in range(n_cols):\n", - " contrast_idx = sort_by[j] if sort_by is not None else j\n", - " \n", - " # For mixed types, get bootstrap for specific position\n", - " if isinstance(multi_contrast.contrast_type, dict) and multi_contrast.contrast_type.get('mixed'):\n", - " bootstrap = multi_contrast.get_bootstrap_by_position(i, contrast_idx)\n", - " else:\n", - " # For homogeneous types, use the flattened bootstrap list\n", - " flat_idx = i * n_cols + contrast_idx\n", - " if flat_idx < len(all_bootstraps):\n", - " bootstrap = all_bootstraps[flat_idx]\n", - " else:\n", - " # Handle case where we have fewer bootstraps than expected\n", - " bootstrap = all_bootstraps[bootstrap_idx]\n", - " bootstrap_idx += 1\n", - " \n", - " long_ranks = _sample_bootstrap(bootstrap, n, n, reverse_neg, abs_rank, chop_tail)\n", - " spiral = _spiralize(long_ranks, n, n)\n", - " spirals.iloc[i*n:i*n+n, j*n:j*n+n] = spiral\n", - " mean_delta.iloc[i, j] = np.mean(long_ranks)\n", - " \n", - " if ax is None:\n", - " f, a = plt.subplots(1, 1)\n", - " else:\n", - " a = ax\n", - " if was_1d:\n", - " cbar_orientation, cbar_location = 'horizontal', 'top'\n", - " else:\n", - " cbar_orientation, cbar_location = 'vertical', 'right'\n", - "\n", - " # heatmap kwargs\n", - " default_heatmap_kwargs = {\n", - " \"cmap\": cmap,\n", - " \"vmax\": np.max(spirals.values) if vmax is None else vmax,\n", - " \"vmin\": np.min(spirals.values) if vmin is None else vmin,\n", - " \"center\": 0,\n", - " \"cbar_kws\": {\"shrink\": 1, \"pad\": .05, \"orientation\": cbar_orientation, \"location\": cbar_location},\n", - " }\n", - " if heatmap_kwargs is None:\n", - " heatmap_kwargs = default_heatmap_kwargs\n", - " else:\n", - " heatmap_kwargs = merge_two_dicts(\n", - " default_heatmap_kwargs, heatmap_kwargs\n", - " )\n", - "\n", - " # Create heatmap\n", - " sns.heatmap(spirals, ax=a, **heatmap_kwargs)\n", - "\n", - "\n", - " # Plot kwargs\n", - " default_plot_kwargs = {\n", - " \"title\": title,\n", - " \"xticklabels\": col_labels,\n", - " \"xticklabels_rotation\": 45,\n", - " \"xticklabels_ha\":'right',\n", - " \"yticklabels\": row_labels if not was_1d else [],\n", - " \"yticklabels_rotation\": 0,\n", - " \"yticklabels_ha\": 'right',\n", - " }\n", - " if plot_kwargs is None:\n", - " plot_kwargs = default_plot_kwargs\n", - " else:\n", - " plot_kwargs = merge_two_dicts(\n", - " default_plot_kwargs, plot_kwargs\n", - " )\n", - "\n", - " # Set title\n", - " if plot_kwargs.get('title') is not None:\n", - " if ax is None:\n", - " f.suptitle(plot_kwargs.get('title'))\n", - " else:\n", - " a.set_title(plot_kwargs.get('title'))\n", - "\n", - " # Set labels\n", - " if plot_kwargs.get('xlabel') is not None:\n", - " a.set_xlabel(plot_kwargs.get('xlabel'))\n", - " if plot_kwargs.get('ylabel') is not None:\n", - " a.set_ylabel(plot_kwargs.get('ylabel'))\n", - "\n", - " # Set labels\n", - " a.set_xticks(np.linspace(n/2, n_cols*n-n/2, n_cols))\n", - " a.set_xticklabels(plot_kwargs.get(\"xticklabels\"), rotation=plot_kwargs.get(\"xticklabels_rotation\"), ha=plot_kwargs.get(\"xticklabels_ha\"))\n", - "\n", - " a.set_yticks([] if was_1d else np.linspace(n/2, n_rows*n-n/2, n_rows))\n", - " a.set_yticklabels(plot_kwargs.get(\"yticklabels\"), rotation=plot_kwargs.get(\"yticklabels_rotation\"), ha=plot_kwargs.get(\"yticklabels_ha\"))\n", - "\n", - " if ax is None:\n", - " f.gca().set_aspect('equal')\n", - " if fig_size is None:\n", - " f.set_size_inches(n_cols/3, n_rows/3)\n", - " else:\n", - " f.set_size_inches(fig_size)\n", - " return f, a, mean_delta\n", - " else:\n", - " return a, mean_delta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4f23adcf", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "__all__ = ['MultiContrast', 'combine', 'whorlmap']\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/API/plot_tools.ipynb b/nbs/API/plot_tools.ipynb deleted file mode 100644 index 1ab8cddf..00000000 --- a/nbs/API/plot_tools.ipynb +++ /dev/null @@ -1,2825 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "5d5d1b29", - "metadata": {}, - "source": [ - "# plot_tools\n", - "\n", - "> A set of convenience functions used for producing plots in `dabest`.\n", - "\n", - "- order: 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3eaf534c", - "metadata": {}, - "outputs": [], - "source": [ - "#| default_exp plot_tools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dd831e92", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "from __future__ import annotations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "27f1b07c", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from nbdev.showdoc import *\n", - "import nbdev\n", - "nbdev.nbdev_export()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b070950d", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "import math\n", - "import warnings\n", - "import itertools\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.lines as mlines\n", - "import matplotlib.axes as axes\n", - "import matplotlib.patches as mpatches\n", - "from collections import defaultdict\n", - "from typing import List, Tuple, Dict, Iterable, Union\n", - "from pandas.api.types import CategoricalDtype\n", - "from matplotlib.colors import ListedColormap" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "98550688", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "def halfviolin(v, half=\"right\", fill_color=\"k\", alpha=1, line_color=\"k\", line_width=0):\n", - " for b in v[\"bodies\"]:\n", - " V = b.get_paths()[0].vertices\n", - "\n", - " mean_vertical = np.mean(V[:, 0])\n", - " mean_horizontal = np.mean(V[:, 1])\n", - "\n", - " if half == \"right\":\n", - " V[:, 0] = np.clip(V[:, 0], mean_vertical, np.inf)\n", - " elif half == \"left\":\n", - " V[:, 0] = np.clip(V[:, 0], -np.inf, mean_vertical)\n", - " elif half == \"bottom\":\n", - " V[:, 1] = np.clip(V[:, 1], -np.inf, mean_horizontal)\n", - " elif half == \"top\":\n", - " V[:, 1] = np.clip(V[:, 1], mean_horizontal, np.inf)\n", - "\n", - " b.set_color(fill_color)\n", - " b.set_alpha(alpha)\n", - " b.set_edgecolor(line_color)\n", - " b.set_linewidth(line_width)\n", - "\n", - "\n", - "def get_swarm_spans(coll):\n", - " \"\"\"\n", - " Given a matplotlib Collection, will obtain the x and y spans\n", - " for the collection. Will return None if this fails.\n", - " \"\"\"\n", - " if coll is None:\n", - " raise ValueError(\"The collection `coll` parameter cannot be None\")\n", - "\n", - " x, y = np.array(coll.get_offsets()).T\n", - " try:\n", - " return x.min(), x.max(), y.min(), y.max()\n", - " except ValueError as e:\n", - " warnings.warn(f\"Failed to calculate spans for the collection. Details: {e}\")\n", - " return None\n", - "\n", - "\n", - "def error_bar(\n", - " data: pd.DataFrame, # This DataFrame should be in 'long' format.\n", - " x: str, # x column to be plotted.\n", - " y: str, # y column to be plotted.\n", - " type: str = \"mean_sd\", # Choose from ['mean_sd', 'median_quartiles']. Plots the summary statistics for each group. If 'mean_sd', then the mean and standard deviation of each group is plotted as a gapped line. If 'median_quantiles', then the median and 25th and 75th percentiles of each group is plotted instead.\n", - " offset: float = 0.2, # Give a single float (that will be used as the x-offset of all gapped lines), or an iterable containing the list of x-offsets.\n", - " ax=None, # If a matplotlib Axes object is specified, the gapped lines will be plotted in order on this axes. If None, the current axes (plt.gca()) is used.\n", - " line_color=\"black\", # The color of the gapped lines.\n", - " gap_width_percent=1, # The width of the gap in the gapped lines, as a percentage of the y-axis span.\n", - " pos: list = [\n", - " 0,\n", - " 1,\n", - " ], # The positions of the error bars for the sankey_error_bar method.\n", - " method: str = \"gapped_lines\", # The method to use for drawing the error bars. Options are: 'gapped_lines', 'proportional_error_bar', and 'sankey_error_bar'.\n", - " horizontal: bool = False, # If True, the error bars will be horizontal. If False, the error bars will be vertical.\n", - " **kwargs: dict,\n", - "):\n", - " \"\"\"\n", - " Function to plot the standard deviations as vertical errorbars.\n", - " The mean is a gap defined by negative space.\n", - "\n", - " This function combines the functionality of gapped_lines(),\n", - " proportional_error_bar(), and sankey_error_bar().\n", - "\n", - " \"\"\"\n", - "\n", - " if gap_width_percent < 0 or gap_width_percent > 100:\n", - " raise ValueError(\"`gap_width_percent` must be between 0 and 100.\")\n", - " if method not in [\"gapped_lines\", \"proportional_error_bar\", \"sankey_error_bar\"]:\n", - " raise ValueError(\n", - " \"Invalid `method`. Must be one of 'gapped_lines', \\\n", - " 'proportional_error_bar', or 'sankey_error_bar'.\"\n", - " )\n", - "\n", - " if ax is None:\n", - " ax = plt.gca()\n", - "\n", - " if horizontal:\n", - " ax_ylims = ax.get_xlim()\n", - " else:\n", - " ax_ylims = ax.get_ylim()\n", - " ax_yspan = np.abs(ax_ylims[1] - ax_ylims[0])\n", - " gap_width = ax_yspan * gap_width_percent / 100\n", - "\n", - " keys = kwargs.keys()\n", - " if \"clip_on\" not in keys:\n", - " kwargs[\"clip_on\"] = False\n", - "\n", - " if \"zorder\" not in keys:\n", - " kwargs[\"zorder\"] = 5\n", - "\n", - " if \"lw\" not in keys:\n", - " kwargs[\"lw\"] = 2.0\n", - "\n", - " if isinstance(data[x].dtype, pd.CategoricalDtype):\n", - " group_order = pd.unique(data[x]).categories\n", - " else:\n", - " group_order = pd.unique(data[x])\n", - "\n", - " means = data.groupby(x, observed=False)[y].mean().reindex(index=group_order)\n", - "\n", - " if method in [\"proportional_error_bar\", \"sankey_error_bar\"]:\n", - " g = lambda x: np.sqrt(\n", - " (np.sum(x) * (len(x) - np.sum(x))) / (len(x) * len(x) * len(x))\n", - " )\n", - " sd = data.groupby(x, observed=False)[y].apply(g)\n", - " else:\n", - " sd = data.groupby(x, observed=False)[y].std().reindex(index=group_order)\n", - "\n", - " lower_sd = means - sd\n", - " upper_sd = means + sd\n", - "\n", - " if (lower_sd < ax_ylims[0]).any() or (upper_sd > ax_ylims[1]).any():\n", - " kwargs[\"clip_on\"] = True\n", - "\n", - " medians = data.groupby(x, observed=False)[y].median().reindex(index=group_order)\n", - " quantiles = (\n", - " data.groupby(x, observed=False)[y].quantile([0.25, 0.75]).unstack().reindex(index=group_order)\n", - " )\n", - " lower_quartiles = quantiles[0.25]\n", - " upper_quartiles = quantiles[0.75]\n", - "\n", - " if type == \"mean_sd\":\n", - " central_measures = means\n", - " lows = lower_sd\n", - " highs = upper_sd\n", - " elif type == \"median_quartiles\":\n", - " central_measures = medians\n", - " lows = lower_quartiles\n", - " highs = upper_quartiles\n", - " else:\n", - " raise ValueError(\"Only accepted values for type are ['mean_sd', 'median_quartiles']\")\n", - "\n", - " n_groups = len(central_measures)\n", - "\n", - " if isinstance(line_color, str):\n", - " custom_palette = np.repeat(line_color, n_groups)\n", - " else:\n", - " if len(line_color) != n_groups:\n", - " err1 = \"{} groups are being plotted, but \".format(n_groups)\n", - " err2 = \"{} colors(s) were supplied in `line_color`.\".format(len(line_color))\n", - " raise ValueError(err1 + err2)\n", - " custom_palette = line_color\n", - "\n", - " try:\n", - " len_offset = len(offset)\n", - " except TypeError:\n", - " offset = np.repeat(offset, n_groups)\n", - " len_offset = len(offset)\n", - "\n", - " if len_offset != n_groups:\n", - " err1 = \"{} groups are being plotted, but \".format(n_groups)\n", - " err2 = \"{} offset(s) were supplied in `offset`.\".format(len_offset)\n", - " raise ValueError(err1 + err2)\n", - "\n", - " kwargs[\"zorder\"] = kwargs[\"zorder\"]\n", - "\n", - " for xpos, val in enumerate(central_measures.index):\n", - " central_measure = central_measures[val]\n", - " kwargs[\"color\"] = custom_palette[xpos]\n", - "\n", - " if method == \"sankey_error_bar\":\n", - " _xpos = pos[xpos] + offset[xpos]\n", - " else:\n", - " _xpos = xpos + offset[xpos]\n", - "\n", - " # Fix for the non-string x-axis issue #108\n", - " if central_measures.index.dtype.name == \"category\":\n", - " low = lows[xpos]\n", - " high = highs[xpos]\n", - " else: \n", - " low = lows[val]\n", - " high = highs[val]\n", - "\n", - " if low == high == central_measure:\n", - " if horizontal:\n", - " low2mean_x, low2mean_y = [low, central_measure], [_xpos, _xpos]\n", - " mean2high_x, mean2high_y = [central_measure, high], [_xpos, _xpos]\n", - " else:\n", - " low2mean_x, low2mean_y = [_xpos, _xpos], [low, central_measure]\n", - " mean2high_x, mean2high_y = [_xpos, _xpos], [central_measure, high]\n", - " else:\n", - " if horizontal:\n", - " low2mean_x, low2mean_y = [low, central_measure - gap_width], [_xpos, _xpos]\n", - " mean2high_x, mean2high_y = [central_measure + gap_width, high], [_xpos, _xpos]\n", - " else:\n", - " low2mean_x, low2mean_y = [_xpos, _xpos], [low, central_measure - gap_width]\n", - " mean2high_x, mean2high_y = [_xpos, _xpos], [central_measure + gap_width, high]\n", - " # Add lines\n", - " ax.add_line(mlines.Line2D(\n", - " low2mean_x, low2mean_y, **kwargs\n", - " ))\n", - " ax.add_line(mlines.Line2D(\n", - " mean2high_x, mean2high_y, **kwargs\n", - " ))\n", - " \n", - "def check_data_matches_labels(\n", - " labels, # list of input labels\n", - " data, # Pandas Series of input data\n", - " side: str, # 'left' or 'right' on the sankey diagram\n", - "):\n", - " \"\"\"\n", - " Function to check that the labels and data match in the sankey diagram.\n", - " And enforce labels and data to be lists.\n", - " Raises an exception if the labels and data do not match.\n", - " \"\"\"\n", - " if len(labels) > 0:\n", - " if isinstance(data, list):\n", - " data = set(data)\n", - " if isinstance(data, pd.Series):\n", - " data = set(data.unique())\n", - " if isinstance(labels, list):\n", - " labels = set(labels)\n", - " if labels != data:\n", - " msg = \"\\n\"\n", - " if len(labels) <= 20:\n", - " msg = \"Labels: \" + \",\".join(labels) + \"\\n\"\n", - " if len(data) < 20:\n", - " msg += \"Data: \" + \",\".join(data)\n", - " raise Exception(f\"{side} labels and data do not match.{msg}\")\n", - "\n", - "\n", - "def normalize_dict(nested_dict, target):\n", - " \"\"\"\n", - " Normalizes the values in a nested dictionary based on a target dictionary.\n", - "\n", - " This function iterates through a nested dictionary, calculates the sum of values for each key\n", - " across all sub-dictionaries, and then normalizes these values according to a target dictionary.\n", - " The normalization is performed such that the values in each sub-dictionary are proportionally\n", - " scaled to match the corresponding 'right' values in the target dictionary.\n", - "\n", - " Parameters:\n", - " nested_dict (dict of dict): A nested dictionary where each key maps to another dictionary.\n", - " The values in these inner dictionaries are subject to normalization.\n", - " target (dict): A dictionary with the target values for normalization. Each key in nested_dict\n", - " should have a corresponding key in target, and each target[key] should be a\n", - " dictionary with a 'right' key containing the target normalization value.\n", - "\n", - " Returns:\n", - " dict: The normalized nested dictionary. The original nested_dict is modified in place.\n", - "\n", - " Note:\n", - " - If the sum of values for a particular key in nested_dict is zero, the normalized value is set to 0.\n", - " - If a key in a sub-dictionary of nested_dict does not exist in the target dictionary, the\n", - " corresponding 'right' value from the target dictionary is directly assigned.\n", - " - The function modifies the input nested_dict in place and also returns it.\n", - " \"\"\"\n", - " val = {}\n", - " for key in nested_dict.keys():\n", - " val[key] = np.sum(\n", - " [\n", - " nested_dict[sub_key][key]\n", - " for sub_key in nested_dict.keys()\n", - " if key in nested_dict[sub_key]\n", - " ]\n", - " )\n", - "\n", - " for key, value in nested_dict.items():\n", - " if isinstance(value, dict):\n", - " for subkey in value.keys():\n", - " if subkey in val.keys():\n", - " if val[subkey] != 0:\n", - " # Address the problem when one of the labels has zero value\n", - " value[subkey] = (\n", - " value[subkey] * target[subkey][\"right\"] / val[subkey]\n", - " )\n", - " else:\n", - " value[subkey] = 0\n", - " else:\n", - " value[subkey] = target[subkey][\"right\"]\n", - " return nested_dict\n", - "\n", - "\n", - "def width_determine(labels, data, pos=\"left\"):\n", - " \"\"\"\n", - " Calculates normalized width positions for a set of labels based on their associated data.\n", - "\n", - " This function is designed to determine width positions for plotting or graphical representation.\n", - " It takes into account the cumulative weight of each label in the data and adjusts their positions\n", - " accordingly. The function allows for adjusting the position of labels to either the 'left' or 'right'.\n", - "\n", - " Parameters:\n", - " labels (list): A list of labels whose width positions are to be calculated.\n", - " data (DataFrame): A pandas DataFrame containing the data used for calculating width positions.\n", - " The DataFrame should have columns corresponding to the 'pos' and 'posWeight'.\n", - " pos (str, optional): The position of labels. It can be either 'left' or 'right'. Defaults to 'left'.\n", - "\n", - " Returns:\n", - " defaultdict: A dictionary where each key is a label and the value is another dictionary with keys\n", - " 'bottom', 'top', and 'pos', representing the calculated width positions.\n", - "\n", - " Note:\n", - " The function assumes that the data DataFrame contains columns named after the value of 'pos' and\n", - " an additional column named 'posWeight' which represents the weight of each label.\n", - " \"\"\"\n", - " if labels is None:\n", - " raise ValueError(\"The `labels` parameter cannot be None\")\n", - "\n", - " if data is None:\n", - " raise ValueError(\"The `data` parameter cannot be None\")\n", - " \n", - " widths_norm = defaultdict()\n", - " for i, label in enumerate(labels):\n", - " myD = {}\n", - " myD[pos] = data[data[pos] == label][pos + \"Weight\"].sum()\n", - " if len(labels) != 1:\n", - " if i == 0:\n", - " myD[\"bottom\"] = 0\n", - " myD[pos] -= 0.01\n", - " myD[\"top\"] = myD[pos]\n", - " elif i == len(labels) - 1:\n", - " myD[pos] -= 0.01\n", - " myD[\"bottom\"] = 1 - myD[pos]\n", - " myD[\"top\"] = 1\n", - " else:\n", - " myD[pos] -= 0.02\n", - " myD[\"bottom\"] = widths_norm[labels[i - 1]][\"top\"] + 0.02\n", - " myD[\"top\"] = myD[\"bottom\"] + myD[pos]\n", - " else:\n", - " myD[\"bottom\"] = 0\n", - " myD[\"top\"] = 1\n", - " widths_norm[label] = myD\n", - " return widths_norm\n", - "\n", - "\n", - "def single_sankey(\n", - " left: np.array, # data on the left of the diagram\n", - " right: np.array, # data on the right of the diagram, len(left) == len(right)\n", - " xpos: float = 0, # the starting point on the x-axis\n", - " left_weight: np.array = None, # weights for the left labels, if None, all weights are 1\n", - " right_weight: np.array = None, # weights for the right labels, if None, all weights are corresponding left_weight\n", - " colorDict: dict = None, # input format: {'label': 'color'}\n", - " left_labels: list = None, # labels for the left side of the diagram. The diagram will be sorted by these labels.\n", - " right_labels: list = None, # labels for the right side of the diagram. The diagram will be sorted by these labels.\n", - " ax=None, # matplotlib axes to be drawn on\n", - " flow: bool = True, # if True, draw the sankey in a flow, else draw 1 vs 1 Sankey diagram for each group comparison\n", - " sankey: bool = True, # if True, draw the sankey diagram, else draw barplot\n", - " width=0.5,\n", - " alpha=0.65,\n", - " bar_width=0.2,\n", - " error_bar_on: bool = True, # if True, draw error bar for each group comparison\n", - " strip_on: bool = True, # if True, draw strip for each group comparison\n", - " one_sankey: bool = False, # if True, only draw one sankey diagram\n", - " right_color: bool = False, # if True, each strip of the diagram will be colored according to the corresponding left labels\n", - " align: str = \"center\", # if 'center', the diagram will be centered on each xtick, if 'edge', the diagram will be aligned with the left edge of each xtick\n", - " horizontal: bool = False, # if True, the horizontal format for the sankey diagram will be used\n", - "):\n", - " \"\"\"\n", - " Make a single Sankey diagram showing proportion flow from left to right\n", - "\n", - " Original code from: https://github.com/anazalea/pySankey\n", - " \n", - " Changes are added to normalize each diagram's height to be 1\n", - "\n", - " \"\"\"\n", - "\n", - " # Initiating values\n", - " if ax is None:\n", - " ax = plt.gca()\n", - "\n", - " if left_weight is None:\n", - " left_weight = []\n", - " if right_weight is None:\n", - " right_weight = []\n", - " if left_labels is None:\n", - " left_labels = []\n", - " if right_labels is None:\n", - " right_labels = []\n", - " # Check weights\n", - " if len(left_weight) == 0:\n", - " left_weight = np.ones(len(left))\n", - " if len(right_weight) == 0:\n", - " right_weight = np.ones(len(right))\n", - "\n", - " # Create Dataframe\n", - " if isinstance(left, pd.Series):\n", - " left.reset_index(drop=True, inplace=True)\n", - " if isinstance(right, pd.Series):\n", - " right.reset_index(drop=True, inplace=True)\n", - " dataFrame = pd.DataFrame(\n", - " {\n", - " \"left\": left,\n", - " \"right\": right,\n", - " \"left_weight\": left_weight,\n", - " \"right_weight\": right_weight,\n", - " },\n", - " index=range(len(left)),\n", - " )\n", - "\n", - " if dataFrame[[\"left\", \"right\"]].isnull().any(axis=None):\n", - " raise Exception(\"Sankey graph does not support null values.\")\n", - "\n", - " # Identify all labels that appear 'left' or 'right'\n", - " allLabels = pd.Series(\n", - " np.sort(np.r_[dataFrame.left.unique(), dataFrame.right.unique()])[::-1]\n", - " ).unique()\n", - "\n", - " # Identify left labels\n", - " if len(left_labels) == 0:\n", - " left_labels = pd.Series(np.sort(dataFrame.left.unique())[::-1]).unique()\n", - " else:\n", - " check_data_matches_labels(left_labels, dataFrame[\"left\"], \"left\")\n", - "\n", - " # Identify right labels\n", - " if len(right_labels) == 0:\n", - " right_labels = pd.Series(np.sort(dataFrame.right.unique())[::-1]).unique()\n", - " else:\n", - " check_data_matches_labels(left_labels, dataFrame[\"right\"], \"right\")\n", - "\n", - " # If no colorDict given, make one\n", - " if colorDict is None:\n", - " colorDict = {}\n", - " palette = \"hls\"\n", - " colorPalette = sns.color_palette(palette, len(allLabels))\n", - " for i, label in enumerate(allLabels):\n", - " colorDict[label] = colorPalette[i]\n", - " fail_color = {0: \"grey\"}\n", - " colorDict.update(fail_color)\n", - " else:\n", - " missing = [label for label in allLabels if label not in colorDict.keys()]\n", - " if missing:\n", - " msg = \"The palette parameter is missing values for the following labels : \"\n", - " msg += \"{}\".format(\", \".join(missing))\n", - " raise ValueError(msg)\n", - "\n", - " if align not in (\"center\", \"edge\"):\n", - " err = \"{} assigned for `align` is not valid.\".format(align)\n", - " raise ValueError(err)\n", - " \n", - " if align == \"center\":\n", - " try:\n", - " leftpos = xpos - width / 2\n", - " except TypeError as e:\n", - " raise TypeError(\n", - " f\"the dtypes of parameters x ({xpos.dtype}) \"\n", - " f\"and width ({width.dtype}) \"\n", - " f\"are incompatible\"\n", - " ) from e\n", - " else:\n", - " leftpos = xpos\n", - "\n", - " # Combine left and right arrays to have a pandas.DataFrame in the 'long' format\n", - " left_series = pd.Series(left, name=\"values\").to_frame().assign(groups=\"left\")\n", - " right_series = pd.Series(right, name=\"values\").to_frame().assign(groups=\"right\")\n", - " concatenated_df = pd.concat([left_series, right_series], ignore_index=True)\n", - "\n", - " # Determine positions of left label patches and total widths\n", - " # We also want the height of the graph to be 1\n", - " leftWidths_norm = defaultdict()\n", - " for i, left_label in enumerate(left_labels):\n", - " myD = {}\n", - " myD[\"left\"] = (\n", - " dataFrame[dataFrame.left == left_label].left_weight.sum()\n", - " / dataFrame.left_weight.sum()\n", - " )\n", - " if len(left_labels) != 1:\n", - " if i == 0:\n", - " myD[\"bottom\"] = 0\n", - " myD[\"left\"] -= 0.01\n", - " myD[\"top\"] = myD[\"left\"]\n", - " elif i == len(left_labels) - 1:\n", - " myD[\"left\"] -= 0.01\n", - " myD[\"bottom\"] = 1 - myD[\"left\"]\n", - " myD[\"top\"] = 1\n", - " else:\n", - " myD[\"left\"] -= 0.02\n", - " myD[\"bottom\"] = leftWidths_norm[left_labels[i - 1]][\"top\"] + 0.02\n", - " myD[\"top\"] = myD[\"bottom\"] + myD[\"left\"]\n", - " topEdge = myD[\"top\"]\n", - " else:\n", - " myD[\"bottom\"] = 0\n", - " myD[\"top\"] = 1\n", - " myD[\"left\"] = 1\n", - " leftWidths_norm[left_label] = myD\n", - "\n", - " # Determine positions of right label patches and total widths\n", - " rightWidths_norm = defaultdict()\n", - " for i, right_label in enumerate(right_labels):\n", - " myD = {}\n", - " myD[\"right\"] = (\n", - " dataFrame[dataFrame.right == right_label].right_weight.sum()\n", - " / dataFrame.right_weight.sum()\n", - " )\n", - " if len(right_labels) != 1:\n", - " if i == 0:\n", - " myD[\"bottom\"] = 0\n", - " myD[\"right\"] -= 0.01\n", - " myD[\"top\"] = myD[\"right\"]\n", - " elif i == len(right_labels) - 1:\n", - " myD[\"right\"] -= 0.01\n", - " myD[\"bottom\"] = 1 - myD[\"right\"]\n", - " myD[\"top\"] = 1\n", - " else:\n", - " myD[\"right\"] -= 0.02\n", - " myD[\"bottom\"] = rightWidths_norm[right_labels[i - 1]][\"top\"] + 0.02\n", - " myD[\"top\"] = myD[\"bottom\"] + myD[\"right\"]\n", - " topEdge = myD[\"top\"]\n", - " else:\n", - " myD[\"bottom\"] = 0\n", - " myD[\"top\"] = 1\n", - " myD[\"right\"] = 1\n", - " rightWidths_norm[right_label] = myD\n", - "\n", - " # Total width of the graph\n", - " xMax = width\n", - "\n", - " # Plot vertical bars for each label\n", - " for left_label in left_labels:\n", - " if horizontal:\n", - " fill_method = ax.fill_betweenx\n", - " else:\n", - " fill_method = ax.fill_between\n", - " fill_method(\n", - " [leftpos + (-(bar_width) * xMax * 0.5), leftpos + (bar_width * xMax * 0.5)],\n", - " 2 * [leftWidths_norm[left_label][\"bottom\"]],\n", - " 2 * [leftWidths_norm[left_label][\"top\"]],\n", - " color=colorDict[left_label],\n", - " alpha=0.99,\n", - " )\n", - " if (not flow and sankey) or one_sankey:\n", - " for right_label in right_labels:\n", - " if horizontal:\n", - " fill_method = ax.fill_betweenx\n", - " else:\n", - " fill_method = ax.fill_between\n", - " fill_method(\n", - " [\n", - " xMax + leftpos + (-bar_width * xMax * 0.5),\n", - " leftpos + xMax + (bar_width * xMax * 0.5),\n", - " ],\n", - " 2 * [rightWidths_norm[right_label][\"bottom\"]],\n", - " 2 * [rightWidths_norm[right_label][\"top\"]],\n", - " color=colorDict[right_label],\n", - " alpha=0.99,\n", - " )\n", - "\n", - " # Plot error bars\n", - " if error_bar_on and strip_on:\n", - " if horizontal:\n", - " error_bar(\n", - " concatenated_df,\n", - " x=\"groups\",\n", - " y=\"values\",\n", - " ax=ax,\n", - " offset=0,\n", - " gap_width_percent=2,\n", - " method=\"sankey_error_bar\",\n", - " pos=[leftpos, leftpos + xMax],\n", - " horizontal=True,\n", - " )\n", - " else:\n", - " error_bar(\n", - " concatenated_df,\n", - " x=\"groups\",\n", - " y=\"values\",\n", - " ax=ax,\n", - " offset=0,\n", - " gap_width_percent=2,\n", - " method=\"sankey_error_bar\",\n", - " pos=[leftpos, leftpos + xMax],\n", - " )\n", - "\n", - " # Determine widths of individual strips, all widths are normalized to 1\n", - " ns_l = defaultdict()\n", - " ns_r = defaultdict()\n", - " ns_l_norm = defaultdict()\n", - " ns_r_norm = defaultdict()\n", - " for left_label in left_labels:\n", - " leftDict = {}\n", - " rightDict = {}\n", - " for right_label in right_labels:\n", - " leftDict[right_label] = dataFrame[\n", - " (dataFrame.left == left_label) & (dataFrame.right == right_label)\n", - " ].left_weight.sum()\n", - "\n", - " rightDict[right_label] = dataFrame[\n", - " (dataFrame.left == left_label) & (dataFrame.right == right_label)\n", - " ].right_weight.sum()\n", - " factorleft = leftWidths_norm[left_label][\"left\"] / sum(leftDict.values())\n", - " leftDict_norm = {k: v * factorleft for k, v in leftDict.items()}\n", - " ns_l_norm[left_label] = leftDict_norm\n", - " ns_r[left_label] = rightDict\n", - "\n", - " # ns_r should be using a different way of normalization to fit the right side\n", - " # It is normalized using the value with the same key in each sub-dictionary\n", - " ns_r_norm = normalize_dict(ns_r, rightWidths_norm)\n", - "\n", - " # Plot strips\n", - " if sankey and strip_on:\n", - " for left_label, right_label in itertools.product(left_labels, right_labels):\n", - " labelColor = left_label\n", - " \n", - " if right_color:\n", - " labelColor = right_label\n", - " \n", - " if len(dataFrame[(dataFrame.left == left_label) & \n", - " (dataFrame.right == right_label)]) > 0:\n", - " # Create array of y values for each strip, half at left value,\n", - " # half at right, convolve\n", - " ys_d = np.array(\n", - " 50 * [leftWidths_norm[left_label][\"bottom\"]]\n", - " + 50 * [rightWidths_norm[right_label][\"bottom\"]]\n", - " )\n", - " ys_d = np.convolve(ys_d, 0.05 * np.ones(20), mode=\"valid\")\n", - " ys_d = np.convolve(ys_d, 0.05 * np.ones(20), mode=\"valid\")\n", - " # to remove the array wrapping behaviour of black\n", - " # fmt: off\n", - " ys_u = np.array(50 * [leftWidths_norm[left_label]['bottom'] + ns_l_norm[left_label][right_label]] + \\\n", - " 50 * [rightWidths_norm[right_label]['bottom'] + ns_r_norm[left_label][right_label]])\n", - " # fmt: on\n", - " ys_u = np.convolve(ys_u, 0.05 * np.ones(20), mode=\"valid\")\n", - " ys_u = np.convolve(ys_u, 0.05 * np.ones(20), mode=\"valid\")\n", - "\n", - " # Update bottom edges at each label so next strip starts at the right place\n", - " leftWidths_norm[left_label][\"bottom\"] += ns_l_norm[left_label][right_label]\n", - " rightWidths_norm[right_label][\"bottom\"] += ns_r_norm[left_label][\n", - " right_label\n", - " ]\n", - " if horizontal:\n", - " fill_method = ax.fill_betweenx\n", - " else:\n", - " fill_method = ax.fill_between\n", - " fill_method(\n", - " np.linspace(\n", - " leftpos + (bar_width * xMax * 0.5),\n", - " leftpos + xMax - (bar_width * xMax * 0.5),\n", - " len(ys_d),\n", - " ),\n", - " ys_d,\n", - " ys_u,\n", - " alpha=alpha,\n", - " color=colorDict[labelColor],\n", - " edgecolor=\"none\",\n", - " )\n", - "\n", - "def sankeydiag(\n", - " data: pd.DataFrame,\n", - " xvar: str, # x column to be plotted.\n", - " yvar: str, # y column to be plotted.\n", - " temp_all_plot_groups: list,\n", - " idx: list,\n", - " temp_idx: list,\n", - " left_labels: list = None, # labels for the left side of the diagram. The diagram will be sorted by these labels.\n", - " right_labels: list = None, # labels for the right side of the diagram. The diagram will be sorted by these labels.\n", - " palette: str | dict = None,\n", - " ax=None, # matplotlib axes to be drawn on\n", - " flow: bool = True, # if True, draw the sankey in a flow, else draw 1 vs 1 Sankey diagram for each group comparison\n", - " sankey: bool = True, # if True, draw the sankey diagram, else draw barplot\n", - " one_sankey: bool = False, # determined by the driver function on plotter.py, if True, draw the sankey diagram across the whole raw data axes\n", - " width: float = 0.4, # the width of each sankey diagram\n", - " right_color: bool = False, # if True, each strip of the diagram will be colored according to the corresponding left labels\n", - " align: str = \"center\", # the alignment of each sankey diagram, can be 'center' or 'left'\n", - " alpha: float = 0.65, # the transparency of each strip\n", - " horizontal: bool = False, # if True, the horizontal format for the sankey diagram will be used\n", - " **kwargs,\n", - "):\n", - " \"\"\"\n", - " Read in melted pd.DataFrame, and draw multiple sankey diagram on a single axes\n", - " using the value in column yvar according to the value in column xvar\n", - " left_idx in the column xvar is on the left side of each sankey diagram\n", - " right_idx in the column xvar is on the right side of each sankey diagram\n", - "\n", - " \"\"\"\n", - " if \"width\" in kwargs:\n", - " width = kwargs[\"width\"]\n", - "\n", - " if \"align\" in kwargs:\n", - " align = kwargs[\"align\"]\n", - "\n", - " if \"alpha\" in kwargs:\n", - " alpha = kwargs[\"alpha\"]\n", - "\n", - " if \"right_color\" in kwargs:\n", - " right_color = kwargs[\"right_color\"]\n", - "\n", - " if \"bar_width\" in kwargs:\n", - " bar_width = kwargs[\"bar_width\"]\n", - "\n", - " if \"sankey\" in kwargs:\n", - " sankey = kwargs[\"sankey\"]\n", - "\n", - " if \"flow\" in kwargs:\n", - " flow = kwargs[\"flow\"]\n", - "\n", - " fontsize = kwargs.pop(\"fontsize\")\n", - "\n", - " if ax is None:\n", - " ax = plt.gca()\n", - "\n", - " left_idx = []\n", - " right_idx = []\n", - " # Design for Sankey Flow Diagram\n", - " sankey_idx = (\n", - " [\n", - " (control, test)\n", - " for i in idx\n", - " for control, test in zip(\n", - " i[:],\n", - " (tuple(i[1:]) + (i[0],)) if isinstance(i, tuple) else (list(i[1:]) + [i[0]])\n", - " )\n", - " ]\n", - " if flow\n", - " else temp_idx \n", - " )\n", - "\n", - " for i in sankey_idx:\n", - " left_idx.append(i[0])\n", - " right_idx.append(i[1])\n", - "\n", - " if len(temp_all_plot_groups) == 2:\n", - " one_sankey = True\n", - " left_idx.pop()\n", - " right_idx.pop() # Remove the last element from two lists\n", - "\n", - " # two_col_sankey = True if proportional == True and one_sankey == False and sankey == True and flow == False else False\n", - "\n", - " allLabels = pd.Series(np.sort(data[yvar].unique())[::-1]).unique()\n", - "\n", - " # Check if all the elements in left_idx and right_idx are in xvar column\n", - " unique_xvar = data[xvar].unique()\n", - " if not all(elem in unique_xvar for elem in left_idx):\n", - " raise ValueError(f\"{left_idx} not found in {xvar} column\")\n", - " if not all(elem in unique_xvar for elem in right_idx):\n", - " raise ValueError(f\"{right_idx} not found in {xvar} column\")\n", - "\n", - " xpos = 0\n", - "\n", - " # For baseline comparison, broadcast left_idx to the same length as right_idx\n", - " # so that the left of sankey diagram will be the same\n", - " # For sequential comparison, left_idx and right_idx can have anything different\n", - " # but should have the same length\n", - " if len(left_idx) == 1:\n", - " broadcasted_left = np.broadcast_to(left_idx, len(right_idx))\n", - " elif len(left_idx) != len(right_idx):\n", - " raise ValueError(f\"left_idx and right_idx should have the same length\")\n", - " else:\n", - " broadcasted_left = left_idx\n", - "\n", - " if isinstance(palette, dict):\n", - " if not all(key in allLabels for key in palette.keys()):\n", - " raise ValueError(f\"keys in palette should be in {yvar} column\")\n", - " plot_palette = palette\n", - " elif isinstance(palette, str):\n", - " plot_palette = {}\n", - " colorPalette = sns.color_palette(palette, len(allLabels))\n", - " for i, label in enumerate(allLabels):\n", - " plot_palette[label] = colorPalette[i]\n", - " else:\n", - " plot_palette = None\n", - "\n", - " # Create a strip_on list to determine whether to draw the strip during repeated measures\n", - " strip_on = [\n", - " int(right not in broadcasted_left[:i]) for i, right in enumerate(right_idx)\n", - " ]\n", - "\n", - " draw_idx = list(zip(broadcasted_left, right_idx))\n", - " for i, (left, right) in enumerate(draw_idx):\n", - " if not one_sankey:\n", - " if flow:\n", - " width = 1\n", - " align = \"edge\"\n", - " sankey = (\n", - " False if i == len(draw_idx) - 1 else sankey\n", - " ) # Remove last strip in flow\n", - " error_bar_on = (\n", - " False if i == len(draw_idx) - 1 and flow else True\n", - " ) # Remove last error_bar in flow\n", - " bar_width = 0.4 if sankey == False and flow == False else bar_width\n", - " single_sankey(\n", - " data[data[xvar] == left][yvar],\n", - " data[data[xvar] == right][yvar],\n", - " xpos=xpos,\n", - " ax=ax,\n", - " colorDict=plot_palette,\n", - " width=width,\n", - " left_labels=left_labels,\n", - " right_labels=right_labels,\n", - " strip_on=strip_on[i],\n", - " right_color=right_color,\n", - " bar_width=bar_width,\n", - " sankey=sankey,\n", - " error_bar_on=error_bar_on,\n", - " flow=flow,\n", - " align=align,\n", - " alpha=alpha,\n", - " horizontal=horizontal,\n", - " )\n", - " xpos += 1\n", - " else:\n", - " xpos = 0\n", - " width = 1\n", - " if not sankey:\n", - " bar_width = 0.5\n", - " single_sankey(\n", - " data[data[xvar] == left][yvar],\n", - " data[data[xvar] == right][yvar],\n", - " xpos=xpos,\n", - " ax=ax,\n", - " colorDict=plot_palette,\n", - " width=width,\n", - " left_labels=left_labels,\n", - " right_labels=right_labels,\n", - " right_color=right_color,\n", - " bar_width=bar_width,\n", - " sankey=sankey,\n", - " one_sankey=one_sankey,\n", - " flow=False,\n", - " align=\"edge\",\n", - " alpha=alpha,\n", - " horizontal=horizontal,\n", - " )\n", - "\n", - " # Now only draw vs xticks for two-column sankey diagram\n", - "\n", - " if not one_sankey or (sankey and not flow):\n", - " sankey_tick_vals = (\n", - " [f\"{left}\" for left in broadcasted_left]\n", - " if flow\n", - " else [f\"{left} v.s. {right}\" if horizontal\n", - " else f\"{left}\\n v.s.\\n{right}\"\n", - " for left, right in zip(broadcasted_left, right_idx)\n", - " ]\n", - " )\n", - " sankey_tick_locs = np.arange(len(right_idx))\n", - " else:\n", - " sankey_tick_vals, sankey_tick_locs = [broadcasted_left[0], right_idx[0]], [0, 1]\n", - "\n", - " if horizontal:\n", - " ax.set_yticks(sankey_tick_locs)\n", - " ax.set_yticklabels(sankey_tick_vals, fontsize = fontsize)\n", - " else:\n", - " ax.set_xticks(sankey_tick_locs)\n", - " ax.set_xticklabels(sankey_tick_vals, fontsize = fontsize)\n", - "\n", - " return (left_idx, right_idx)\n", - "\n", - "def add_bars_to_plot(bar_dict: dict, ax: axes.Axes, bar_kwargs: dict):\n", - " \"\"\"\n", - " Add bars to the relevant axes.\n", - "\n", - " Parameters\n", - " ----------\n", - " bar_dict : dict\n", - " Dictionary of bar values.\n", - " ax : axes.Axes\n", - " Matplotlib axis object to plot on.\n", - " bar_kwargs : dict\n", - " Keyword arguments for the bars.\n", - " \"\"\"\n", - " og_xlim, og_ylim = ax.get_xlim(), ax.get_ylim()\n", - "\n", - " x_start_values, x_distances, y_start_values, y_distances, colors = bar_dict.values()\n", - "\n", - " for start_x, start_y, distance_x, distance_y, current_color in zip(\n", - " x_start_values, \n", - " y_start_values, \n", - " x_distances, \n", - " y_distances, \n", - " colors\n", - " ):\n", - " ax.add_patch(mpatches.Rectangle((start_x, start_y), \n", - " distance_x, distance_y, \n", - " color=current_color, **bar_kwargs\n", - " )\n", - " )\n", - " ax.set_xlim(og_xlim)\n", - " ax.set_ylim(og_ylim) \n", - "\n", - "def delta_text_plotter(\n", - " results: pd.DataFrame, \n", - " ax_to_plot: object, \n", - " ticks_to_plot: list, \n", - " delta_text_kwargs: dict, \n", - " color_col: str, \n", - " plot_palette_raw: dict, \n", - " show_pairs: bool,\n", - " float_contrast: bool,\n", - " extra_delta: float,\n", - " bootstraps_color_by_group: bool = False\n", - " ):\n", - " \"\"\"\n", - " Add delta text to the contrast plot.\n", - "\n", - " Parameters\n", - " ----------\n", - " results : object (Dataframe)\n", - " Dataframe of contrast object comparisons.\n", - " ax_to_plot : axes.Axes\n", - " Matplotlib axis object to plot on.\n", - " ticks_to_plot : list\n", - " List of indices of the contrast objects.\n", - " delta_text_kwargs : dict\n", - " Keyword arguments for the delta text.\n", - " color_col : str\n", - " Column name of the color column.\n", - " plot_palette_raw : dict\n", - " Dictionary of colors used in the plot.\n", - " show_pairs : bool\n", - " Whether the data is paired and show pairs.\n", - " float_contrast : bool\n", - " Whether the DABEST plot uses Gardner-Altman or Cummings.\n", - " extra_delta : float or None\n", - " The extra mini-meta or delta-delta value if applicable.\n", - " bootstraps_color_by_group : bool, optional\n", - " Whether to color the bootstraps by group. Default is False.\n", - " \"\"\"\n", - " # Colors\n", - " from .misc_tools import color_picker\n", - " delta_text_colors = color_picker(color_type = 'delta_text',\n", - " kwargs = delta_text_kwargs, \n", - " elements = ticks_to_plot, \n", - " color_col = color_col, \n", - " show_pairs = show_pairs, \n", - " color_palette = plot_palette_raw,\n", - " bootstraps_color_by_group = bootstraps_color_by_group\n", - " )\n", - "\n", - " num_of_elements = len(ticks_to_plot) + 1 if extra_delta is not None else len(ticks_to_plot)\n", - "\n", - " # Collect the means for the delta text\n", - " delta_values = []\n", - " for j, tick in enumerate(ticks_to_plot):\n", - " delta_values.append(results.difference[int(j)])\n", - " if extra_delta is not None: \n", - " delta_values.append(extra_delta)\n", - " delta_text_colors.append('black')\n", - "\n", - " # Collect the X-coordinates for the delta text\n", - " delta_text_x_coordinates = delta_text_kwargs.pop('x_coordinates')\n", - " delta_text_x_offset = delta_text_kwargs.pop('offset')\n", - "\n", - " if delta_text_x_coordinates is not None:\n", - " if not isinstance(delta_text_x_coordinates, (list, tuple)) or not all(isinstance(x, (int, float)) for x in delta_text_x_coordinates):\n", - " raise TypeError(\"delta_text_kwargs['x_coordinates'] must be a list of x-coordinates.\")\n", - " if len(delta_text_x_coordinates) != num_of_elements:\n", - " raise ValueError(\"delta_text_kwargs['x_coordinates'] must have the same length as the number of ticks to plot.\")\n", - " else:\n", - " x_adjust = (-0.4 if float_contrast else 0.48) + delta_text_x_offset\n", - " delta_text_x_coordinates = [x+x_adjust for x in ticks_to_plot]\n", - " if extra_delta is not None: delta_text_x_coordinates.append(max(ticks_to_plot)+1+x_adjust)\n", - "\n", - " # Collect the Y-coordinates for the delta text\n", - " delta_text_y_coordinates = delta_text_kwargs.pop('y_coordinates')\n", - " if float_contrast: delta_text_kwargs[\"va\"] = 'bottom' if results.difference[0] >= 0 else 'top'\n", - "\n", - " if delta_text_y_coordinates is not None:\n", - " if not isinstance(delta_text_y_coordinates, (list, tuple)) or not all(isinstance(y, (int, float)) for y in delta_text_y_coordinates):\n", - " raise TypeError(\"delta_text_kwargs['y_coordinates'] must be a list of y-coordinates.\")\n", - " if len(delta_text_y_coordinates) != num_of_elements:\n", - " raise ValueError(\"delta_text_kwargs['y_coordinates'] must have the same length as the number of ticks to plot.\")\n", - " else:\n", - " delta_text_y_coordinates = delta_values\n", - "\n", - " # Plot the delta text\n", - " for x, y, text, color in zip(delta_text_x_coordinates, delta_text_y_coordinates, delta_values, delta_text_colors):\n", - " delta_text = np.format_float_positional(text, precision=2, sign=True, trim=\"k\", min_digits=2)\n", - " ax_to_plot.text(x, y, delta_text, color=color, zorder=5, **delta_text_kwargs)\n", - "\n", - "def delta_dots_plotter(\n", - " plot_data: pd.DataFrame, \n", - " contrast_axes: axes.Axes, \n", - " delta_id_col: str, \n", - " idx: list, \n", - " xvar: str, \n", - " yvar: str, \n", - " is_paired: bool, \n", - " color_col: str, \n", - " float_contrast: bool, \n", - " plot_palette_raw: dict, \n", - " delta_dot_kwargs: dict, \n", - " horizontal: bool\n", - " ):\n", - " \"\"\"\n", - " Parameters\n", - " ----------\n", - " plot_data : object (Dataframe)\n", - " Dataframe of the plot data.\n", - " contrast_axes : axes.Axes\n", - " Matplotlib axis object to plot on.\n", - " delta_id_col : str\n", - " Column name of the delta id column.\n", - " idx : list\n", - " List of indices of the contrast objects.\n", - " xvar : str\n", - " Column name of the x variable.\n", - " yvar : str\n", - " Column name of the y variable.\n", - " is_paired : bool\n", - " Whether the data is paired.\n", - " color_col : str\n", - " Column name of the color column.\n", - " float_contrast : bool\n", - " Whether the DABEST plot uses Gardner-Altman or Cummings\n", - " plot_palette_raw : dict\n", - " Dictionary of colors used in the plot.\n", - " delta_dot_kwargs : dict\n", - " Keyword arguments for the delta dots.\n", - " horizontal : bool\n", - " If the rawplot is horizontal.\n", - " \"\"\"\n", - "\n", - " # Checks and initializations\n", - " # from .plot_tools import swarmplot\n", - " delta_dot_color = delta_dot_kwargs.pop('color')\n", - " if color_col is not None:\n", - " plot_palette_deltapts = plot_palette_raw\n", - " delta_plot_data = plot_data[[xvar, yvar, delta_id_col, color_col]]\n", - " else:\n", - " plot_palette_deltapts = delta_dot_color\n", - " delta_plot_data = plot_data[[xvar, yvar, delta_id_col]]\n", - "\n", - " # TODO: to make jitter value more accurate and not just a hardcoded eyeball value\n", - " jitter = 0.6 if float_contrast else 1 \n", - "\n", - " # Create dataframe of delta values\n", - " final_deltas = pd.DataFrame()\n", - " for i in idx:\n", - " for j in i:\n", - " if i.index(j) != 0:\n", - " temp_df_exp = delta_plot_data[\n", - " delta_plot_data[xvar].str.contains(j)\n", - " ].reset_index(drop=True)\n", - " if is_paired == \"baseline\":\n", - " temp_df_cont = delta_plot_data[\n", - " delta_plot_data[xvar].str.contains(i[0])\n", - " ].reset_index(drop=True)\n", - " elif is_paired == \"sequential\":\n", - " temp_df_cont = delta_plot_data[\n", - " delta_plot_data[xvar].str.contains(\n", - " i[i.index(j) - 1]\n", - " )\n", - " ].reset_index(drop=True)\n", - " delta_df = temp_df_exp.copy()\n", - " delta_df[yvar] = temp_df_exp[yvar] - temp_df_cont[yvar]\n", - " final_deltas = pd.concat([final_deltas, delta_df])\n", - "\n", - " if horizontal:\n", - " delta_dot_kwargs.update({'side': 'left'})\n", - " # Plot the delta dots\n", - " swarmplot(\n", - " data=final_deltas,\n", - " x=xvar,\n", - " y=yvar,\n", - " ax=contrast_axes,\n", - " order=None,\n", - " hue=color_col,\n", - " palette=plot_palette_deltapts,\n", - " jitter=jitter,\n", - " is_drop_gutter=True,\n", - " gutter_limit=1,\n", - " horizontal=horizontal,\n", - " **delta_dot_kwargs\n", - " )\n", - " contrast_axes.legend().set_visible(False)\n", - "\n", - "\n", - "def slopegraph_plotter(\n", - " dabest_obj: object, \n", - " plot_data: pd.DataFrame, \n", - " xvar: str, \n", - " yvar: str, \n", - " color_col: str, \n", - " plot_palette_raw: dict, \n", - " slopegraph_kwargs: dict, \n", - " rawdata_axes: axes.Axes, \n", - " ytick_color: str, \n", - " temp_idx: list, \n", - " horizontal: bool,\n", - " temp_all_plot_groups: list,\n", - " plot_kwargs: dict,\n", - " group_summaries_kwargs: dict\n", - " ):\n", - " \"\"\"\n", - " Add slopegraph to the rawdata axes.\n", - "\n", - " Parameters\n", - " ----------\n", - " dabest_obj : object\n", - " DABEST object.\n", - " plot_data : object (Dataframe)\n", - " Dataframe of the plot data.\n", - " xvar : str\n", - " Column name of the x variable.\n", - " yvar : str\n", - " Column name of the y variable.\n", - " color_col : str\n", - " Column name of the color column.\n", - " plot_palette_raw : dict\n", - " Dictionary of colors used in the plot.\n", - " slopegraph_kwargs : dict\n", - " Keyword arguments for the slopegraph.\n", - " rawdata_axes : axes.Axes\n", - " Matplotlib axis object to plot on.\n", - " ytick_color : str\n", - " Color of the yticks.\n", - " temp_idx : list\n", - " List of indices of the contrast objects.\n", - " horizontal : bool\n", - " If the plotting will be in horizontal format.\n", - " temp_all_plot_groups : list\n", - " List of all plot groups.\n", - " plot_kwargs : dict\n", - " Keyword arguments for the plot.\n", - " group_summaries_kwargs : dict, optional\n", - " Keyword arguments for group summaries, if applicable.\n", - "\n", - " \"\"\"\n", - " # Jitter Kwargs \n", - " # With help from GitHub user: devMJBL\n", - " jitter = slopegraph_kwargs.pop(\"jitter\")\n", - " if jitter > 1:\n", - " err0 = \"Jitter value is too high. Defaulting to 1.\"\n", - " warnings.warn(err0)\n", - " jitter = 1\n", - " rng = np.random.default_rng(slopegraph_kwargs.pop(\"jitter_seed\"))\n", - "\n", - " # Pivot the long (melted) data.\n", - " if color_col is None:\n", - " pivot_values = [yvar]\n", - " else:\n", - " pivot_values = [yvar, color_col]\n", - " pivoted_plot_data = pd.pivot(\n", - " data=plot_data,\n", - " index=dabest_obj.id_col,\n", - " columns=xvar,\n", - " values=pivot_values,\n", - " )\n", - "\n", - " x_start = 0\n", - " for ii, current_tuple in enumerate(temp_idx):\n", - " current_pair = pivoted_plot_data.loc[\n", - " :, pd.MultiIndex.from_product([pivot_values, current_tuple])\n", - " ].dropna()\n", - "\n", - " # Check for correct pairing\n", - " if len(current_pair) == 0:\n", - " raise ValueError('There are no pairs to plot... check original dataframe for correct ID pairing')\n", - "\n", - " current_pair = pivoted_plot_data.loc[\n", - " :, pd.MultiIndex.from_product([pivot_values, current_tuple])\n", - " ]\n", - " grp_count = len(current_tuple)\n", - "\n", - " # Iterate through the data for the current tuple.\n", - " for ID, observation in current_pair.iterrows():\n", - " x_points = [t + 0.15*jitter*rng.standard_t(df=6, size=None) for t in range(x_start, x_start + grp_count)] # devMJBL\n", - " y_points = observation[yvar].tolist()\n", - "\n", - " if color_col is None:\n", - " slopegraph_kwargs[\"color\"] = ytick_color\n", - " else:\n", - " color_key = observation[color_col].iloc[0]\n", - " if isinstance(color_key, (str, np.int64, np.float64)):\n", - " slopegraph_kwargs[\"color\"] = plot_palette_raw[color_key]\n", - " slopegraph_kwargs[\"label\"] = color_key\n", - "\n", - " x_points, y_points = (y_points, x_points) if horizontal else (x_points, y_points)\n", - " rawdata_axes.plot(x_points, y_points, **slopegraph_kwargs)\n", - "\n", - " # Add the group summaries if applicable.\n", - " group_summaries = plot_kwargs.get(\"group_summaries\", None)\n", - " if group_summaries is not None:\n", - " for key in ['gap_width_percent', 'offset']:\n", - " group_summaries_kwargs.pop(key, None)\n", - " group_summaries_kwargs['color'] = 'black' if group_summaries_kwargs.get('color') is None else group_summaries_kwargs['color']\n", - " group_summaries_kwargs['capsize'] = 0 if group_summaries_kwargs.get('capsize') is None else group_summaries_kwargs['capsize']\n", - "\n", - " index_points = [t for t in range(x_start, x_start + grp_count)]\n", - " av_points, err_points, lo_points, hi_points = [], [], [], []\n", - " for group in range(len(index_points)):\n", - " if group_summaries == \"mean_sd\":\n", - " av_points.append(current_pair.iloc[:, int(group)].mean())\n", - " err_points.append(current_pair.iloc[:, int(group)].std())\n", - " elif group_summaries == \"median_quartiles\":\n", - " median = current_pair.iloc[:, int(group)].median()\n", - " av_points.append(median)\n", - " lo_points.append(median - current_pair.iloc[:, int(group)].quantile(0.25))\n", - " hi_points.append(current_pair.iloc[:, int(group)].quantile(0.75) - median)\n", - "\n", - " if group_summaries == \"median_quartiles\":\n", - " err_points = [lo_points, hi_points] \n", - "\n", - " # Plot the lines\n", - " if horizontal:\n", - " rawdata_axes.errorbar(\n", - " av_points,\n", - " index_points, \n", - " xerr=err_points, \n", - " **group_summaries_kwargs\n", - " )\n", - " else:\n", - " rawdata_axes.errorbar(\n", - " index_points, \n", - " av_points, \n", - " yerr=err_points, \n", - " **group_summaries_kwargs\n", - " )\n", - "\n", - " x_start = x_start + grp_count\n", - "\n", - " # Set the tick labels, because the slopegraph plotting doesn't.\n", - " if horizontal:\n", - " rawdata_axes.set_yticks(np.arange(0, len(temp_all_plot_groups)))\n", - " rawdata_axes.set_yticklabels(temp_all_plot_groups, fontsize = plot_kwargs.get(\"fontsize_rawxlabel\"))\n", - " else:\n", - " rawdata_axes.set_xticks(np.arange(0, len(temp_all_plot_groups)))\n", - " rawdata_axes.set_xticklabels(temp_all_plot_groups, fontsize = plot_kwargs.get(\"fontsize_rawxlabel\"))\n", - " \n", - "\n", - "def plot_minimeta_or_deltadelta_violins(\n", - " dabest_obj: object,\n", - " type: str,\n", - " ci_type: str, \n", - " rawdata_axes: axes.Axes,\n", - " contrast_axes: axes.Axes, \n", - " contrast_kwargs: dict, \n", - " contrast_xtick_labels: list, \n", - " effect_size: str, \n", - " plot_kwargs: dict, \n", - " horizontal: bool, \n", - " show_pairs: bool,\n", - " contrast_marker_kwargs: dict, \n", - " contrast_errorbar_kwargs: dict,\n", - " ):\n", - " \"\"\"\n", - " Add mini meta-analysis or delta-delta violin plots to the contrast plot.\n", - "\n", - " Parameters\n", - " ----------\n", - " dabest_obj : object\n", - " DABEST Effectsize object delta-delta or mini_meta\n", - " type: str\n", - " mini_meta or delta_delta\n", - " ci_type : str\n", - " Type of confidence interval to plot.\n", - " rawdata_axes : axes.Axes\n", - " Matplotlib axis object to plot on.\n", - " contrast_axes : axes.Axes\n", - " Matplotlib axis object to plot on.\n", - " contrast_kwargs : dict\n", - " Keyword arguments for the violinplot.\n", - " contrast_xtick_labels : list\n", - " List of xtick labels for the contrast plot.\n", - " effect_size : str\n", - " Type of effect size to plot.\n", - " plot_kwargs : dict\n", - " Keyword arguments for the plot.\n", - " horizontal : bool\n", - " If the plot is horizontal.\n", - " show_pairs : bool\n", - " Whether the data is paired and shown in pairs.\n", - " contrast_marker_kwargs: dict\n", - " Keyword arguments for the effectsize marker.\n", - " contrast_errorbar_kwargs: dict\n", - " Keyword arguments for the effectsize errorbar.\n", - " \"\"\"\n", - "\n", - " # Plot the curve\n", - " def extract_curve_data(dabest_object):\n", - " try:\n", - " data = dabest_object.bootstraps_weighted_delta\n", - " except AttributeError:\n", - " data = dabest_object.bootstraps_delta_delta\n", - "\n", - " ci_low, ci_high = dabest_object.results.get(ci_type+'_low')[0], dabest_object.results.get(ci_type+'_high')[0]\n", - " return data, dabest_object.difference, ci_low, ci_high\n", - "\n", - " data, difference, ci_low, ci_high = extract_curve_data(dabest_obj)\n", - "\n", - " if contrast_kwargs.get('alpha') is not None:\n", - " contrast_alpha = contrast_kwargs.pop('alpha')\n", - "\n", - " if horizontal: \n", - " contrast_kwargs.update({'orientation': 'horizontal', 'widths': 1})\n", - " position = max(rawdata_axes.get_yticks()) + 1\n", - " half = \"bottom\"\n", - " effsize_x, effsize_y = difference, [position]\n", - " ci_x, ci_y = [ci_low, ci_high], [position, position]\n", - " else:\n", - " position = max(rawdata_axes.get_xticks()) + 1\n", - " half = \"right\"\n", - " effsize_x, effsize_y = [position], difference\n", - " ci_x, ci_y = [position, position], [ci_low, ci_high]\n", - "\n", - " v = contrast_axes.violinplot(\n", - " data[~np.isinf(data)], positions=[position], **contrast_kwargs\n", - " )\n", - "\n", - " halfviolin(v, fill_color=\"grey\", alpha=contrast_alpha, half=half)\n", - "\n", - " # Plot the effect size.\n", - " contrast_axes.plot(\n", - " effsize_x,\n", - " effsize_y,\n", - " **contrast_marker_kwargs\n", - " )\n", - " # Plot the confidence interval.\n", - " contrast_axes.plot(\n", - " ci_x,\n", - " ci_y,\n", - " **contrast_errorbar_kwargs\n", - " )\n", - "\n", - " # Add labels and ticks\n", - " if horizontal:\n", - " current_ylabels = rawdata_axes.get_yticklabels()\n", - " if type == 'mini_meta':\n", - " current_ylabels.extend([\"Weighted delta\"])\n", - " elif effect_size == \"hedges_g\":\n", - " current_ylabels.extend([\"Delta g\"])\n", - " else:\n", - " current_ylabels.extend([\"Delta-delta\"])\n", - "\n", - " rawdata_axes.set_yticks(np.append(rawdata_axes.get_yticks(), position))\n", - " rawdata_axes.set_yticklabels(current_ylabels)\n", - " else:\n", - " if type == 'mini_meta':\n", - " if show_pairs:\n", - " contrast_xtick_labels.extend([\"Weighted\\n delta\"])\n", - " else:\n", - " contrast_xtick_labels.extend([\"Weighted delta\"])\n", - " elif effect_size == \"hedges_g\":\n", - " contrast_xtick_labels.extend([\"Delta g\"])\n", - " else:\n", - " contrast_xtick_labels.extend([\"Delta-delta\"])\n", - "\n", - " # Create the delta-delta axes.\n", - " if type == 'delta_delta' and not horizontal:\n", - " if plot_kwargs[\"delta2_label\"] is not None:\n", - " delta2_label = plot_kwargs[\"delta2_label\"]\n", - " elif effect_size == \"mean_diff\":\n", - " delta2_label = \"Delta-delta\"\n", - " else:\n", - " delta2_label = \"Delta g\"\n", - " fontsize_delta2label = plot_kwargs[\"fontsize_delta2label\"]\n", - " delta2_axes = contrast_axes.twinx()\n", - " delta2_axes.set_frame_on(False)\n", - " delta2_axes.set_ylabel(delta2_label, fontsize=fontsize_delta2label)\n", - " og_xlim_delta, og_ylim_delta = contrast_axes.get_xlim(), contrast_axes.get_ylim()\n", - " delta2_axes.set_ylim(og_ylim_delta)\n", - " else:\n", - " delta2_axes = None\n", - "\n", - " return delta2_axes, contrast_xtick_labels\n", - "\n", - "\n", - "def effect_size_curve_plotter(\n", - " ticks_to_plot: list, \n", - " ticks_for_baseline_ec: list, \n", - " results: pd.DataFrame, \n", - " ci_type: str, \n", - " contrast_axes: axes.Axes, \n", - " contrast_kwargs: dict, \n", - " bootstraps_color_by_group: bool, \n", - " plot_palette_contrast: dict,\n", - " horizontal: bool, \n", - " contrast_marker_kwargs: dict, \n", - " contrast_errorbar_kwargs: dict,\n", - " idx: list, \n", - " is_paired: bool, \n", - " contrast_paired_lines: bool, \n", - " contrast_paired_lines_kwargs: dict,\n", - " show_baseline_ec: bool = False\n", - " ):\n", - " \"\"\"\n", - " Add effect size curves to the contrast plot.\n", - "\n", - " Parameters\n", - " ----------\n", - " ticks_to_plot : list\n", - " List of indices of the contrast objects.\n", - " ticks_for_baseline_ec : list\n", - " List of indices of the baseline effect curve objects.\n", - " results : object (Dataframe)\n", - " Dataframe of contrast object comparisons.\n", - " ci_type : str\n", - " Type of confidence interval to plot.\n", - " contrast_axes : axes.Axes\n", - " Matplotlib axis object to plot on.\n", - " contrast_kwargs : dict\n", - " Keyword arguments for the violinplot.\n", - " bootstraps_color_by_group : bool\n", - " Whether to color the bootstraps by group.\n", - " plot_palette_contrast : dict\n", - " Dictionary of colors used in the contrast plot.\n", - " horizontal : bool\n", - " If the plot is horizontal.\n", - " contrast_marker_kwargs: dict\n", - " Keyword arguments for the effectsize marker.\n", - " contrast_errorbar_kwargs: dict\n", - " Keyword arguments for the effectsize errorbar.\n", - " idx : list\n", - " List of indices of the raw groups.\n", - " is_paired : bool\n", - " Whether the data is paired.\n", - " contrast_paired_lines : bool\n", - " Whether to add lines for repeated measures data.\n", - " contrast_paired_lines_kwargs : dict\n", - " Keyword arguments for the repeated measures lines.\n", - " show_baseline_ec : bool\n", - " Whether to show the baseline effect curve.\n", - " \"\"\"\n", - "\n", - " def plot_effect_size(tick, group, control, bootstrap, effsize, ci_low, ci_high):\n", - " # Create the violinplot\n", - " if horizontal: \n", - " contrast_kwargs.update({'orientation': 'horizontal', 'widths': 1})\n", - " \n", - " v = contrast_axes.violinplot(\n", - " bootstrap[~np.isinf(bootstrap)],\n", - " positions=[tick],\n", - " **contrast_kwargs\n", - " )\n", - " \n", - " # Color the violin plot\n", - " fc = plot_palette_contrast[group] if bootstraps_color_by_group else \"grey\"\n", - " half = \"bottom\" if horizontal else \"right\"\n", - " halfviolin(v, fill_color=fc, alpha=contrast_alpha, half=half)\n", - "\n", - " # Plot the confidence interval\n", - " if horizontal:\n", - " ci_x, ci_y = [ci_low, ci_high], [tick, tick]\n", - " else:\n", - " ci_x, ci_y = [tick, tick], [ci_low, ci_high]\n", - " \n", - " contrast_axes.plot(ci_x, ci_y, **contrast_errorbar_kwargs)\n", - " \n", - " return \"{}\\nminus\\n{}\".format(group, control)\n", - " \n", - " if contrast_kwargs.get('alpha') is not None:\n", - " contrast_alpha = contrast_kwargs.pop('alpha')\n", - "\n", - " # Plot the curves\n", - " contrast_xtick_labels = []\n", - " for j, tick in enumerate(ticks_to_plot):\n", - " current_group = results.test[int(j)]\n", - " current_control = results.control[int(j)]\n", - " current_bootstrap = results.bootstraps[int(j)]\n", - " current_effsize = results.difference[int(j)]\n", - " current_ci_low = results.get(ci_type+'_low')[int(j)]\n", - " current_ci_high = results.get(ci_type+'_high')[int(j)]\n", - "\n", - " # Plot the effect size marker\n", - " if horizontal:\n", - " effsize_x, effsize_y = current_effsize, [tick]\n", - " else:\n", - " effsize_x, effsize_y = [tick], current_effsize\n", - "\n", - " contrast_axes.plot(\n", - " effsize_x,\n", - " effsize_y,\n", - " **contrast_marker_kwargs\n", - " )\n", - "\n", - " label = plot_effect_size(tick, current_group, current_control, current_bootstrap,\n", - " current_effsize, current_ci_low, current_ci_high)\n", - " contrast_xtick_labels.append(label)\n", - "\n", - " # Add baseline effect curve plotting\n", - " bec_results = results.drop_duplicates(subset='control', keep='first').reset_index(drop=True)\n", - " for j, tick in enumerate(ticks_for_baseline_ec):\n", - " bec_group = bec_results.control[j]\n", - " bec_control = bec_results.control[j]\n", - " bec_bootstrap = bec_results.bec_bootstraps[j]\n", - " bec_effsize = bec_results.bec_difference[j]\n", - " bec_ci_low = bec_results.get('bec_'+ci_type+'_low')[j]\n", - " bec_ci_high = bec_results.get('bec_'+ci_type+'_high')[j]\n", - " \n", - " # Plot the effect size marker regardless of show_baseline_ec\n", - " if horizontal:\n", - " effsize_x, effsize_y = bec_effsize, [tick]\n", - " else:\n", - " effsize_x, effsize_y = [tick], bec_effsize\n", - " \n", - " contrast_axes.plot(effsize_x, effsize_y, **contrast_marker_kwargs)\n", - " \n", - " if show_baseline_ec:\n", - " _ = plot_effect_size(tick, bec_group, bec_control, bec_bootstrap, \n", - " bec_effsize, bec_ci_low, bec_ci_high)\n", - " # Baseline Curve doesn't need tick text\n", - "\n", - " # Add lines for repeated measures data\n", - " if is_paired and contrast_paired_lines:\n", - " temp_num = 0\n", - " lines_to_plot_list = []\n", - "\n", - " for group in idx:\n", - " new_group = []\n", - " if len(group) >= 2:\n", - " new_group.append(temp_num)\n", - " for i in range(1, len(group)):\n", - " new_group.append(temp_num+i)\n", - " temp_num += len(group)\n", - " lines_to_plot_list.append(new_group)\n", - "\n", - " for group in lines_to_plot_list:\n", - " if len(group) > 0:\n", - " mean_diffs_for_lines = []\n", - " for ticks in group:\n", - " if ticks in ticks_to_plot:\n", - " mean_diffs_for_lines.append(results.loc[ticks_to_plot.index(ticks)][\"difference\"])\n", - " else:\n", - " mean_diffs_for_lines.append(int(0))\n", - "\n", - " x_data = mean_diffs_for_lines if horizontal else group\n", - " y_data = group if horizontal else mean_diffs_for_lines\n", - "\n", - " contrast_axes.plot(\n", - " x_data, \n", - " y_data,\n", - " **contrast_paired_lines_kwargs\n", - " )\n", - "\n", - " contrast_kwargs['alpha'] = contrast_alpha\n", - " return current_group, current_control, current_effsize, contrast_xtick_labels\n", - "\n", - "def gridkey_plotter(\n", - " is_paired: bool, \n", - " idx: list,\n", - " all_plot_groups: list, \n", - " gridkey: list, \n", - " rawdata_axes: axes.Axes, \n", - " contrast_axes: axes.Axes, \n", - " plot_data: pd.DataFrame, \n", - " xvar: str, \n", - " yvar: str, \n", - " results: pd.DataFrame, \n", - " show_delta2: bool, \n", - " show_mini_meta: bool, \n", - " x1_level: list, \n", - " experiment_label: list, \n", - " float_contrast: bool, \n", - " horizontal: bool, \n", - " delta_delta: object, \n", - " mini_meta: object, \n", - " effect_size: str, \n", - " gridkey_kwargs: dict,\n", - " ):\n", - " \"\"\"\n", - " Add gridkey to the contrast plot.\n", - "\n", - " Parameters\n", - " ----------\n", - " is_paired : bool\n", - " Whether the data is paired.\n", - " idx : list\n", - " List of indices of the contrast objects.\n", - " all_plot_groups : list\n", - " List of all plot groups.\n", - " gridkey : list\n", - " List of gridkey rows.\n", - " rawdata_axes : axes.Axes\n", - " Matplotlib axis object for the raw data.\n", - " contrast_axes : axes.Axes\n", - " Matplotlib axis object for the contrast data.\n", - " plot_data : object (Dataframe)\n", - " Dataframe of the plot data.\n", - " xvar : str\n", - " Column name of the x variable.\n", - " yvar : str\n", - " Column name of the y variable.\n", - " results : object (Dataframe)\n", - " Dataframe of contrast object comparisons.\n", - " show_delta2 : bool\n", - " Whether to show the delta-delta.\n", - " show_mini_meta : bool\n", - " Whether to show the mini meta-analysis.\n", - " x1_level : list\n", - " List of x1 levels.\n", - " experiment_label : list\n", - " List of experiment labels.\n", - " float_contrast : bool\n", - " Whether the DABEST plot uses Gardner-Altman or Cummings\n", - " horizontal : bool\n", - " If the plot is horizontal.\n", - " delta_delta : object\n", - " delta-delta object.\n", - " mini_meta : object\n", - " Mini meta-analysis object.\n", - " effect_size : str\n", - " Type of effect size to plot\n", - " gridkey_kwargs : dict\n", - " Keyword arguments for the gridkey.\n", - " \"\"\"\n", - " # Extract relevant kwargs\n", - " gridkey_show_Ns = gridkey_kwargs[\"show_Ns\"]\n", - " gridkey_show_es = gridkey_kwargs[\"show_es\"]\n", - " gridkey_merge_pairs = gridkey_kwargs[\"merge_pairs\"]\n", - " gridkey_marker = gridkey_kwargs[\"marker\"]\n", - " gridkey_delimiters = gridkey_kwargs[\"delimiters\"] \n", - " labels_fontsize = gridkey_kwargs.get('labels_fontsize')\n", - " fontsize = gridkey_kwargs.get('fontsize')\n", - "\n", - " # Auto parser for gridkey - implemented by SangyuXu\n", - " if gridkey == \"auto\" or gridkey == True:\n", - " if experiment_label is not None:\n", - " gridkey = list(np.concatenate([experiment_label, x1_level]))\n", - " else:\n", - " temp_groups = \";\".join(all_plot_groups)\n", - " for delimiter in gridkey_delimiters:\n", - " temp_groups = temp_groups.replace(delimiter, \";\")\n", - " temp_groups = [i.strip() for i in temp_groups.split(';')]\n", - " unique_groups = list(set(temp_groups))\n", - " rank = [sum([temp_groups.index(i) for i in temp_groups if(j in i)]) for j in unique_groups]\n", - " gridkey = [x for _,x in sorted(zip(rank,unique_groups))]\n", - " \n", - " # Raise error if there are more than 2 items in any idx and gridkey_merge_pairs is True and is_paired is not None\n", - " if gridkey_merge_pairs and is_paired is not None:\n", - " for i in idx:\n", - " if len(i) > 2:\n", - " warnings.warn(\n", - " \"gridkey_merge_pairs=True only works if all idx in tuples have only two items. gridkey_merge_pairs has automatically been set to False\"\n", - " )\n", - " gridkey_merge_pairs = False\n", - " break\n", - " elif gridkey_merge_pairs and is_paired is None:\n", - " warnings.warn(\n", - " \"gridkey_merge_pairs=True is only applicable for paired data.\"\n", - " )\n", - " gridkey_merge_pairs = False\n", - "\n", - " # Checks for gridkey_merge_pairs and is_paired; if both are true, \"merges\" the gridkey per pair\n", - " if gridkey_merge_pairs and is_paired is not None:\n", - " groups_for_gridkey = []\n", - " for i in idx:\n", - " groups_for_gridkey.append(i[1])\n", - " else:\n", - " groups_for_gridkey = all_plot_groups\n", - "\n", - " # raise errors if gridkey is not a list, or if the list is empty\n", - " if isinstance(gridkey, list) is False:\n", - " raise TypeError(\"gridkey must be a list (or a string 'auto').\")\n", - " if any(isinstance(i, str) is False for i in gridkey):\n", - " raise TypeError(\"gridkey must contain only strings.\")\n", - " if len(gridkey) == 0:\n", - " warnings.warn(\"gridkey is an empty list.\")\n", - "\n", - " # raise Warning if an item in gridkey is not contained in any idx\n", - " for i in gridkey:\n", - " in_idx = 0\n", - " for j in groups_for_gridkey:\n", - " if i in j:\n", - " in_idx += 1\n", - " if in_idx == 0:\n", - " if is_paired is not None:\n", - " warnings.warn(\n", - " i\n", - " + \" is not in any idx. Please check. Alternatively, merging gridkey pairs may not be suitable for your data; try passing gridkey_merge_pairs=False.\"\n", - " )\n", - " else:\n", - " warnings.warn(i + \" is not in any idx. Please check.\")\n", - "\n", - " # Populate table: checks if idx for each column contains rowlabel name\n", - " # IF so, marks that element as present w black dot (default \"\\u25CF\"), or space if not present\n", - " table_cellcols = []\n", - " for i in gridkey:\n", - " thisrow = []\n", - " for q in groups_for_gridkey:\n", - " if str(i) in q:\n", - " thisrow.append(gridkey_marker)\n", - " else:\n", - " thisrow.append(\"\")\n", - " table_cellcols.append(thisrow)\n", - "\n", - " # Adds a row for Ns with the Ns values\n", - " if gridkey_show_Ns:\n", - " gridkey.append(\"Ns\")\n", - " list_of_Ns = []\n", - " for i in groups_for_gridkey:\n", - " list_of_Ns.append(str(plot_data.groupby(xvar, observed=False).count()[yvar].loc[i]))\n", - " table_cellcols.append(list_of_Ns)\n", - "\n", - " # Adds a row for effectsizes with effectsize values\n", - " if gridkey_show_es and not horizontal:\n", - " gridkey.append(\"\\u0394\")\n", - " effsize_list = []\n", - " results_list = results.test.to_list()\n", - "\n", - " # get the effect size, append + or -, 2 dec places\n", - " for i in enumerate(groups_for_gridkey):\n", - " if i[1] in results_list:\n", - " curr_esval = results.loc[results[\"test\"] == i[1]][\"difference\"].iloc[0]\n", - " curr_esval_str = np.format_float_positional(\n", - " curr_esval,\n", - " precision=2,\n", - " sign=True,\n", - " trim=\"k\",\n", - " min_digits=2,\n", - " )\n", - " effsize_list.append(curr_esval_str)\n", - " else:\n", - " effsize_list.append(\"-\")\n", - "\n", - " table_cellcols.append(effsize_list)\n", - "\n", - " # Set the axes to plot on\n", - " if float_contrast or horizontal:\n", - " ax_to_plot = rawdata_axes\n", - " else:\n", - " ax_to_plot = contrast_axes\n", - "\n", - " # Add delta-delta or mini_meta details to the table\n", - " if show_mini_meta or show_delta2:\n", - " if show_delta2:\n", - " added_group_name = [\"Deltas' g\"] if effect_size == \"hedges_g\" else [\"Delta-Delta\"]\n", - " else:\n", - " added_group_name = [\"Weighted Delta\"]\n", - " gridkey = added_group_name + gridkey\n", - " table_cellcols = [[\"\"]*len(table_cellcols[0])] + table_cellcols\n", - "\n", - " if not horizontal and show_delta2:\n", - " extra_table_cellcols = [[] for i in range(len(table_cellcols))]\n", - "\n", - " for group_idx, group_vals in enumerate(extra_table_cellcols):\n", - " if group_idx == 0:\n", - " added_group = [gridkey_marker]\n", - " elif gridkey_show_es and (group_idx == len(extra_table_cellcols)-1) and not horizontal:\n", - " added_delta_effectsize = delta_delta.difference\n", - " added_delta_effectsize_str = np.format_float_positional(\n", - " added_delta_effectsize,\n", - " precision=2,\n", - " sign=True,\n", - " trim=\"k\",\n", - " min_digits=2,\n", - " )\n", - " added_group = [added_delta_effectsize_str]\n", - " else:\n", - " added_group = ['']\n", - " for n in added_group:\n", - " group_vals.append(n)\n", - "\n", - " elif horizontal or show_mini_meta:\n", - " for group_idx, group_vals in enumerate(table_cellcols):\n", - " if group_idx == 0:\n", - " added_group = [gridkey_marker]\n", - " elif gridkey_show_es and (group_idx == len(table_cellcols)-1) and not horizontal:\n", - " added_delta_effectsize = delta_delta.difference if show_delta2 else mini_meta.difference\n", - " added_delta_effectsize_str = np.format_float_positional(\n", - " added_delta_effectsize,\n", - " precision=2,\n", - " sign=True,\n", - " trim=\"k\",\n", - " min_digits=2,\n", - " )\n", - " added_group = [added_delta_effectsize_str]\n", - " else:\n", - " added_group = ['']\n", - " for n in added_group:\n", - " group_vals.append(n)\n", - "\n", - " # Create the table object\n", - " def add_table(celltext, bbox, rowlabels=None):\n", - " gridkey_to_plot = ax_to_plot.table(\n", - " cellText=celltext,\n", - " rowLabels=rowlabels,\n", - " cellLoc=\"center\",\n", - " bbox=bbox,\n", - " )\n", - " return gridkey_to_plot\n", - "\n", - " if horizontal:\n", - " # Convert the cells format for horizontal table plotting\n", - " converted_list = []\n", - " for j in range(0, len(table_cellcols[0])):\n", - " temp_list = []\n", - " for i in table_cellcols:\n", - " temp_list.append(i[j])\n", - " converted_list.append(temp_list)\n", - "\n", - " gridkey_to_plot = add_table(celltext = converted_list, bbox = [-len(gridkey) * 0.2, 0, len(gridkey) * 0.2, 1])\n", - "\n", - " # Add the column labels as text below the table\n", - " text_locs = np.arange((-len(gridkey)*0.2) +0.1, 0, 0.2)\n", - " for loc, txt in zip(text_locs, gridkey):\n", - " ax_to_plot.text(\n", - " loc+0.04, \n", - " -0.01, \n", - " txt, \n", - " transform=ax_to_plot.transAxes, \n", - " ha='right',\n", - " rotation=45,\n", - " fontsize=labels_fontsize if labels_fontsize is not None else 10,\n", - " va='top',\n", - " )\n", - " else:\n", - " # Plot the table for vertical format\n", - " if show_mini_meta:\n", - " gridkey_to_plot = add_table(celltext = table_cellcols, rowlabels=gridkey, bbox = [0, -len(gridkey) * 0.1 - 0.05, 1, len(gridkey) * 0.1])\n", - " elif show_delta2:\n", - " gridkey_to_plot = add_table(celltext = table_cellcols, rowlabels=gridkey, bbox = [0, -len(gridkey) * 0.1 - 0.05, 0.75, len(gridkey) * 0.1])\n", - " extra_gridkey = add_table(celltext = extra_table_cellcols, bbox = [0.78, -len(gridkey) * 0.1 - 0.05, 0.15, len(gridkey) * 0.1])\n", - " else:\n", - " gridkey_to_plot = add_table(celltext = table_cellcols, rowlabels=gridkey, bbox = [0, -len(gridkey) * 0.1 - 0.05, 1, len(gridkey) * 0.1]) \n", - "\n", - " # modifies row label cells\n", - " for cell in gridkey_to_plot._cells:\n", - " if cell[1] == -1:\n", - " gridkey_to_plot._cells[cell].visible_edges = \"open\"\n", - " gridkey_to_plot._cells[cell].set_text_props(**{\"ha\": \"right\"})\n", - "\n", - " if fontsize is not None:\n", - " gridkey_to_plot.auto_set_font_size(False)\n", - " gridkey_to_plot.set_fontsize(fontsize)\n", - " if show_delta2 and not horizontal:\n", - " extra_gridkey.auto_set_font_size(False)\n", - " extra_gridkey.set_fontsize(fontsize)\n", - "\n", - " if labels_fontsize is not None and not horizontal:\n", - " gridkey_to_plot.auto_set_font_size(False)\n", - " for cell in gridkey_to_plot._cells:\n", - " if cell[1] == -1:\n", - " gridkey_to_plot._cells[cell].set_text_props(**{\"fontsize\": labels_fontsize})\n", - "\n", - " # turns off both x axes\n", - " if horizontal:\n", - " rawdata_axes.get_yaxis().set_visible(False)\n", - " contrast_axes.get_yaxis().set_visible(False)\n", - " else:\n", - " rawdata_axes.get_xaxis().set_visible(False)\n", - " contrast_axes.get_xaxis().set_visible(False)\n", - "\n", - "def barplotter(\n", - " xvar: str, \n", - " yvar: str, \n", - " all_plot_groups: list, \n", - " rawdata_axes: axes.Axes, \n", - " plot_data: pd.DataFrame, \n", - " raw_colors: str, \n", - " plot_palette_raw: dict, \n", - " color_col: str,\n", - " barplot_kwargs: dict, \n", - " horizontal: bool\n", - " ):\n", - " \"\"\"\n", - " Add bars to the raw data plot.\n", - "\n", - " Parameters\n", - " ----------\n", - " xvar : str\n", - " Column name of the x variable.\n", - " yvar : str\n", - " Column name of the y variable.\n", - " all_plot_groups : list\n", - " List of all plot groups.\n", - " rawdata_axes : object\n", - " Matplotlib axis object to plot on.\n", - " plot_data : object (Dataframe)\n", - " Dataframe of the plot data.\n", - " raw_colors : str\n", - " Color of the bar.\n", - " plot_palette_raw : dict\n", - " Dictionary of colors used in the bar plot.\n", - " color_col : str\n", - " Column name of the color column.\n", - " barplot_kwargs : dict\n", - " Keyword arguments for the barplot.\n", - " horizontal : bool\n", - " If the plot is horizontal.\n", - " \"\"\"\n", - " # Check if the custom_palette is a dictionary with two keys 0 and 1 (for filled bar coloring)\n", - " filled_bars = True if len(plot_palette_raw.keys())==2 and all(k in plot_palette_raw for k in [1, 0]) else False\n", - "\n", - " bar_width = barplot_kwargs.get('width', 0.5)\n", - " fontsize = barplot_kwargs.pop('fontsize')\n", - "\n", - " x_label, y_label = rawdata_axes.get_xlabel(), rawdata_axes.get_ylabel()\n", - " if horizontal:\n", - " x_var, y_var, orient = np.ones(len(all_plot_groups)), all_plot_groups, \"h\"\n", - " else:\n", - " x_var, y_var, orient = all_plot_groups, np.ones(len(all_plot_groups)), \"v\"\n", - "\n", - " # Create bar1_df with basic columns\n", - " bar1_df = pd.DataFrame({\n", - " xvar: x_var, \n", - " \"proportion\": y_var\n", - " })\n", - "\n", - " # Handle colors\n", - " if color_col:\n", - " # Get first color value for each group\n", - " color_mapping = plot_data.groupby(xvar, observed=False)[color_col].first()\n", - " bar1_df[color_col] = [color_mapping.get(group) for group in all_plot_groups]\n", - " \n", - " # Map colors, defaulting to bar_color if no match\n", - " edge_colors = [\n", - " plot_palette_raw.get(hue_val, raw_colors) \n", - " for hue_val in bar1_df[color_col]\n", - " ]\n", - " else:\n", - " edge_colors = len(all_plot_groups)*['black',] if filled_bars else raw_colors\n", - "\n", - " bar1 = sns.barplot(\n", - " data=bar1_df,\n", - " x=xvar,\n", - " y=\"proportion\",\n", - " ax=rawdata_axes,\n", - " order=all_plot_groups,\n", - " linewidth=1 if filled_bars else 2,\n", - " facecolor=plot_palette_raw[0] if filled_bars else (1, 1, 1, 0),\n", - " edgecolor=edge_colors,\n", - " zorder=1,\n", - " orient=orient,\n", - " )\n", - "\n", - " if filled_bars:\n", - " barplot_kwargs['facecolor'] = plot_palette_raw[1]\n", - " barplot_kwargs['edgecolor'] = 'black'\n", - " barplot_kwargs['linewidth'] = 1\n", - " else:\n", - " barplot_kwargs['palette'] = plot_palette_raw\n", - "\n", - " bar2 = sns.barplot(\n", - " data=plot_data,\n", - " x=yvar if horizontal else xvar,\n", - " y=xvar if horizontal else yvar,\n", - " hue=xvar if color_col is None else color_col,\n", - " ax=rawdata_axes,\n", - " order=all_plot_groups,\n", - " dodge=False,\n", - " zorder=1,\n", - " orient=orient,\n", - " **barplot_kwargs\n", - " )\n", - "\n", - " # adjust the width of bars\n", - " if horizontal:\n", - " for bar in bar1.patches:\n", - " y = bar.get_y()\n", - " height = bar.get_height()\n", - " centre = y + height / 2.0\n", - " bar.set_y(centre - bar_width / 2.0)\n", - " bar.set_height(bar_width)\n", - " else:\n", - " for bar in bar1.patches:\n", - " x = bar.get_x()\n", - " width = bar.get_width()\n", - " centre = x + width / 2.0\n", - " bar.set_x(centre - bar_width / 2.0)\n", - " bar.set_width(bar_width)\n", - "\n", - " # reset the x and y labels\n", - " rawdata_axes.set_xlabel(x_label)\n", - " rawdata_axes.set_ylabel(y_label)\n", - "\n", - " if horizontal:\n", - " rawdata_axes.set_yticks(rawdata_axes.get_yticks())\n", - " rawdata_axes.set_yticklabels(rawdata_axes.get_yticklabels(), fontsize = fontsize)\n", - " else:\n", - " rawdata_axes.set_xticks(rawdata_axes.get_xticks())\n", - " rawdata_axes.set_xticklabels(rawdata_axes.get_xticklabels(), fontsize = fontsize)\n", - "\n", - "def table_for_horizontal_plots(\n", - " effectsize_df: object, \n", - " ax: axes.Axes, \n", - " contrast_axes: axes.Axes, \n", - " ticks_to_plot: list, \n", - " show_mini_meta: bool, \n", - " show_delta2: bool, \n", - " table_kwargs: dict,\n", - " ticks_to_skip: list\n", - " ):\n", - " \"\"\"\n", - " Add table axes for showing the deltas for horizontal plots.\n", - "\n", - " Parameters\n", - " ----------\n", - " effectsize_df : object\n", - " Effect size DABEST object.\n", - " ax : object\n", - " Matplotlib axis object to plot the table axes.\n", - " contrast_axes : object\n", - " Matplotlib axis object to plot the contrast axes.\n", - " ticks_to_plot : list\n", - " List of indices of the contrast objects.\n", - " show_mini_meta : bool\n", - " Whether to show the mini meta-analysis.\n", - " show_delta2 : bool\n", - " Whether to show the delta-delta.\n", - " table_kwargs : dict\n", - " Keyword arguments for the table.\n", - " ticks_to_skip: list\n", - " List of ticks to skip in the table.\n", - " \"\"\"\n", - "\n", - " table_color = table_kwargs['color']\n", - " table_alpha = table_kwargs['alpha']\n", - " table_font_size = table_kwargs['fontsize']\n", - " table_text_color = table_kwargs['text_color']\n", - " text_units = table_kwargs['text_units']\n", - " table_font_size -= 2 if text_units != '' else 0\n", - " control_marker = table_kwargs['control_marker'] \n", - " fontsize_label = table_kwargs['fontsize_label']\n", - " label = table_kwargs['label']\n", - "\n", - " ### Create a table of deltas\n", - " cols=['Δ','N']\n", - " lst = []\n", - " for n in np.arange(0, len(effectsize_df.results.difference), 1):\n", - " lst.append([effectsize_df.results.difference[n], 0])\n", - " if show_mini_meta:\n", - " lst.append([effectsize_df.mini_meta.difference, 0])\n", - " elif show_delta2:\n", - " lst.append([effectsize_df.delta_delta.difference, 0])\n", - " tab = pd.DataFrame(lst, columns=cols)\n", - "\n", - " ### Plot the text\n", - " if show_mini_meta or show_delta2:\n", - " new_ticks = ticks_to_plot + [max(ticks_to_plot)+1]\n", - " else:\n", - " new_ticks = ticks_to_plot.copy()\n", - " for i,loc in zip(tab.index, new_ticks):\n", - " ax.text(0.5, loc, \"{:+.2f}\".format(tab.iloc[i,0])+text_units, ha=\"center\", va=\"center\", color=table_text_color, size=table_font_size)\n", - "\n", - " # Plot the dashes\n", - " if control_marker is not None:\n", - " for loc in ticks_to_skip:\n", - " ax.text(0.5, loc, control_marker, ha=\"center\", va=\"center\", color=table_text_color, size=table_font_size)\n", - "\n", - " ### Parameters for table\n", - " ax.axvspan(0, 1, facecolor=table_color, alpha=table_alpha) #### Plot the background color\n", - " ax.set_xticks([0.5])\n", - " ax.set_xticklabels([])\n", - " ax.set_ylim(contrast_axes.get_ylim())\n", - " ax.set_yticks([])\n", - " ax.set_yticklabels([])\n", - " ax.tick_params(left=False, bottom=False)\n", - " ax.set_xlabel(label, fontsize=fontsize_label) # Set the x-axis label - hardcoded for now\n", - " sns.despine(ax=ax, left=True, bottom=True)\n", - "\n", - "\n", - "def add_counts_to_prop_plots(\n", - " plot_data: pd.DataFrame, \n", - " xvar: str, \n", - " yvar: str, \n", - " rawdata_axes: axes.Axes, \n", - " horizontal: bool, \n", - " is_paired: bool, \n", - " prop_sample_counts_kwargs: dict\n", - " ):\n", - " \"\"\"\n", - " Add counts to the proportion plots.\n", - "\n", - " Parameters\n", - " ----------\n", - " plot_data : object (Dataframe)\n", - " Dataframe of the plot data.\n", - " xvar : str\n", - " Column name of the x variable.\n", - " yvar : str\n", - " Column name of the y variable.\n", - " rawdata_axes : axes.Axes\n", - " Matplotlib axis object to plot on.\n", - " horizontal : bool\n", - " If the plot is horizontal.\n", - " is_paired : bool\n", - " Whether the data is paired.\n", - " prop_sample_counts_kwargs : dict\n", - " Keyword arguments for the sample counts.\n", - " \"\"\"\n", - "\n", - " # Group orders\n", - " if isinstance(plot_data[xvar].dtype, pd.CategoricalDtype):\n", - " sample_size_text_order = pd.unique(plot_data[xvar]).categories\n", - " else:\n", - " sample_size_text_order = pd.unique(plot_data[xvar])\n", - "\n", - " # Get the sample size values\n", - " ones, zeros = plot_data[plot_data[yvar] == 1], plot_data[plot_data[yvar] == 0]\n", - "\n", - " sample_size_val1 = ones.groupby(xvar, observed=False)[yvar].count().reindex(index=sample_size_text_order)\n", - " sample_size_val0 = zeros.groupby(xvar, observed=False)[yvar].count().reindex(index=sample_size_text_order)\n", - "\n", - " if \"fontsize\" not in prop_sample_counts_kwargs.keys():\n", - " fontsize = 8 if horizontal else 10\n", - " fontsize -= 2 if is_paired else 0\n", - " prop_sample_counts_kwargs.update({'fontsize': fontsize})\n", - "\n", - " for sample_text_x, sample_text_y0, sample_text_y1 in zip(\n", - " np.arange(0, len(sample_size_text_order) + 1, 1), \n", - " sample_size_val0,\n", - " sample_size_val1,\n", - " ):\n", - " if horizontal:\n", - " rawdata_axes.text(0.05, sample_text_x, sample_text_y1, **prop_sample_counts_kwargs)\n", - " rawdata_axes.text(0.95, sample_text_x, sample_text_y0, **prop_sample_counts_kwargs)\n", - " else:\n", - " rawdata_axes.text(sample_text_x, 0.05, sample_text_y1, **prop_sample_counts_kwargs)\n", - " rawdata_axes.text(sample_text_x, 0.95, sample_text_y0, **prop_sample_counts_kwargs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "24823471", - "metadata": {}, - "outputs": [], - "source": [ - "# | export\n", - "def swarmplot(\n", - " data: pd.DataFrame,\n", - " x: str,\n", - " y: str,\n", - " ax: axes.Axes,\n", - " order: List = None,\n", - " hue: str = None,\n", - " palette: Union[Iterable, str] = \"black\",\n", - " zorder: float = 1,\n", - " size: float = 5,\n", - " side: str = \"center\",\n", - " jitter: float = 1,\n", - " filled: Union[bool, List, Tuple] = True,\n", - " is_drop_gutter: bool = True,\n", - " gutter_limit: float = 0.5,\n", - " horizontal: bool = False,\n", - " **kwargs,\n", - "):\n", - " \"\"\"\n", - " API to plot a swarm plot.\n", - "\n", - " Parameters\n", - " ----------\n", - " data : pd.DataFrame\n", - " The input data as a pandas DataFrame.\n", - " x : str\n", - " The column in the DataFrame to be used as the x-axis.\n", - " y : str\n", - " The column in the DataFrame to be used as the y-axis.\n", - " ax : axes.Axes\n", - " Matplotlib axes.Axes object for which the plot would be drawn on. Default is None.\n", - " order : List\n", - " The order in which x-axis categories should be displayed. Default is None.\n", - " hue : str\n", - " The column in the DataFrame that determines the grouping for color.\n", - " If None (by default), it assumes that it is being grouped by x.\n", - " palette : Union[Iterable, str]\n", - " The color palette to be used for plotting. Default is \"black\".\n", - " zorder : int | float\n", - " The z-order for drawing the swarm plot wrt other matplotlib drawings. Default is 1.\n", - " dot_size : int | float\n", - " The size of the markers in the swarm plot. Default is 20.\n", - " side : str\n", - " The side on which points are swarmed (\"center\", \"left\", or \"right\"). Default is \"center\".\n", - " jitter : int | float\n", - " Determines the distance between points. Default is 1.\n", - " filled : bool | List | Tuple\n", - " Determines whether the dots in the swarmplot are filled or not. If set to False,\n", - " dots are not filled. If provided as a List or Tuple, it should contain boolean values,\n", - " each corresponding to a swarm group in order, indicating whether the dot should be\n", - " filled or not.\n", - " is_drop_gutter : bool\n", - " If True, drop points that hit the gutters; otherwise, readjust them.\n", - " gutter_limit : int | float\n", - " The limit for points hitting the gutters.\n", - " horizontal : bool\n", - " If True, the swarm plot is drawn horizontally. Default is False.\n", - " **kwargs:\n", - " Additional keyword arguments to be passed to the swarm plot.\n", - "\n", - " Returns\n", - " -------\n", - " axes.Axes\n", - " Matplotlib axes.Axes object for which the swarm plot has been drawn on.\n", - " \"\"\"\n", - " s = SwarmPlot(data, x, y, ax, order, hue, palette, zorder, size, side, jitter, horizontal)\n", - " ax = s.plot(is_drop_gutter, gutter_limit, ax, filled, horizontal, **kwargs)\n", - " return ax\n", - "\n", - "\n", - "class SwarmPlot:\n", - " def __init__(\n", - " self,\n", - " data: pd.DataFrame,\n", - " x: str,\n", - " y: str,\n", - " ax: axes.Axes,\n", - " order: List = None,\n", - " hue: str = None,\n", - " palette: Union[Iterable, str] = \"black\",\n", - " zorder: float = 1,\n", - " size: float = 5,\n", - " side: str = \"center\",\n", - " jitter: float = 1,\n", - " horizontal: bool = False,\n", - " ):\n", - " \"\"\"\n", - " Initialize a SwarmPlot instance.\n", - "\n", - " Parameters\n", - " ----------\n", - " data : pd.DataFrame\n", - " The input data as a pandas DataFrame.\n", - " x : str\n", - " The column in the DataFrame to be used as the x-axis.\n", - " y : str\n", - " The column in the DataFrame to be used as the y-axis.\n", - " ax : axes.Axes\n", - " Matplotlib axes.Axes object for which the plot would be drawn on.\n", - " order : List\n", - " The order in which x-axis categories should be displayed. Default is None.\n", - " hue : str\n", - " The column in the DataFrame that determines the grouping for color.\n", - " If None (by default), it assumes that it is being grouped by x.\n", - " palette : Union[Iterable, str]\n", - " The color palette to be used for plotting. Default is \"black\".\n", - " zorder : int | float\n", - " The z-order for drawing the swarm plot wrt other matplotlib drawings. Default is 1.\n", - " dot_size : int | float\n", - " The size of the markers in the swarm plot. Default is 20.\n", - " side : str\n", - " The side on which points are swarmed (\"center\", \"left\", or \"right\"). Default is \"center\".\n", - " jitter : int | float\n", - " Determines the distance between points. Default is 1.\n", - " horizontal : bool\n", - " If True, the swarm plot is drawn horizontally. Default is False.\n", - "\n", - " Returns\n", - " -------\n", - " None\n", - " \"\"\"\n", - " self.__x = x\n", - " self.__y = y\n", - " self.__order = order\n", - " self.__hue = hue\n", - " self.__zorder = zorder\n", - " self.__palette = palette\n", - " self.__jitter = jitter\n", - "\n", - " # Input validation\n", - " self._check_errors(data, ax, size, side)\n", - "\n", - " self.__size = size * 4\n", - " self.__side = side.lower()\n", - " self.__data = data\n", - " self.__color_col = self.__x if self.__hue is None else self.__hue\n", - "\n", - " # Generate default values\n", - " if order is None:\n", - " self.__order = self._generate_order()\n", - "\n", - " # Reformatting\n", - " if not isinstance(self.__palette, dict):\n", - " self.__palette = self._format_palette(self.__palette)\n", - " data_copy = data.copy(deep=True)\n", - " if not isinstance(self.__data[self.__x].dtype, pd.CategoricalDtype):\n", - " # make x column into CategoricalDType to sort by\n", - " data_copy[self.__x] = data_copy[self.__x].astype(\n", - " CategoricalDtype(categories=self.__order, ordered=True)\n", - " )\n", - " data_copy.sort_values(by=[self.__x, self.__y], inplace=True)\n", - " self.__data_copy = data_copy\n", - "\n", - " x_vals = range(len(self.__order))\n", - " y_vals = self.__data_copy[self.__y]\n", - "\n", - " x_min = min(x_vals)\n", - " x_max = max(x_vals)\n", - " y_range = max(y_vals) - min(y_vals)\n", - " y_min = min(y_vals) - 0.05 * y_range\n", - " y_max = max(y_vals) + 0.05 * y_range\n", - "\n", - " if horizontal:\n", - " ax.set_ylim(bottom=x_min - 0.5, top=x_max + 0.5)\n", - " # ylim is set manually to override Axes.autoscale if it hasn't already been scaled at least once\n", - " if ax.get_autoscalex_on():\n", - " ax.set_xlim(left=y_min, right=y_max)\n", - " else:\n", - " ax.set_xlim(left=x_min - 0.5, right=x_max + 0.5)\n", - " # ylim is set manually to override Axes.autoscale if it hasn't already been scaled at least once\n", - " if ax.get_autoscaley_on():\n", - " ax.set_ylim(bottom=y_min, top=y_max)\n", - "\n", - " figw, figh = ax.get_figure().get_size_inches()\n", - " w = (ax.get_position().xmax - ax.get_position().xmin) * figw\n", - " h = (ax.get_position().ymax - ax.get_position().ymin) * figh\n", - " ax_xspan = ax.get_xlim()[1] - ax.get_xlim()[0]\n", - " ax_yspan = ax.get_ylim()[1] - ax.get_ylim()[0]\n", - " if horizontal:\n", - " ax_xspan, ax_yspan = ax_yspan, ax_xspan\n", - "\n", - " # increases jitter distance based on number of swarms that is going to be drawn\n", - " jitter = jitter * (1 + 0.05 * (math.log(ax_xspan)))\n", - "\n", - " gsize = (\n", - " math.sqrt(self.__size) * 1.0 / (70 / jitter) * ax_xspan * 1.0 / (w * 0.8)\n", - " )\n", - " dsize = (\n", - " math.sqrt(self.__size) * 1.0 / (70 / jitter) * ax_yspan * 1.0 / (h * 0.8)\n", - " )\n", - " self.__gsize = gsize\n", - " self.__dsize = dsize\n", - "\n", - " def _check_errors(\n", - " self, data: pd.DataFrame, ax: axes.Axes, size: float, side: str\n", - " ) -> None:\n", - " \"\"\"\n", - " Check the validity of input parameters. Raises exceptions if detected.\n", - "\n", - " Parameters\n", - " ----------\n", - " data : pd.Dataframe\n", - " Input data used for generation of the swarmplot.\n", - " ax : axes.Axes\n", - " Matplotlib axes.Axes object for which the plot would be drawn on.\n", - " size : int | float\n", - " scalar value determining size of dots of the swarmplot.\n", - " side: str\n", - " The side on which points are swarmed (\"center\", \"left\", or \"right\"). Default is \"center\".\n", - "\n", - " Returns\n", - " -------\n", - " None\n", - " \"\"\"\n", - " # Type enforcement\n", - " if not isinstance(data, pd.DataFrame):\n", - " raise ValueError(\"`data` must be a Pandas Dataframe.\")\n", - " if not isinstance(ax, axes.Axes):\n", - " raise ValueError(\n", - " f\"`ax` must be a Matplotlib axes.Axes. The current `ax` is a {type(ax)}\"\n", - " )\n", - " if not isinstance(size, (int, float)):\n", - " raise ValueError(\"`size` must be a scalar or float.\")\n", - " if not isinstance(side, str):\n", - " raise ValueError(\n", - " \"Invalid `side`. Must be one of 'center', 'right', or 'left'.\"\n", - " )\n", - " if not isinstance(self.__x, str):\n", - " raise ValueError(\"`x` must be a string.\")\n", - " if not isinstance(self.__y, str):\n", - " raise ValueError(\"`y` must be a string.\")\n", - " if not isinstance(self.__zorder, (int, float)):\n", - " raise ValueError(\"`zorder` must be a scalar or float.\")\n", - " if not isinstance(self.__jitter, (int, float)):\n", - " raise ValueError(\"`jitter` must be a scalar or float.\")\n", - " if not isinstance(self.__palette, (str, Iterable)):\n", - " raise ValueError(\n", - " \"`palette` must be either a string indicating a color name or an Iterable.\"\n", - " )\n", - " if self.__hue is not None and not isinstance(self.__hue, str):\n", - " raise ValueError(\"`hue` must be either a string or None.\")\n", - " if self.__order is not None and not isinstance(self.__order, Iterable):\n", - " raise ValueError(\"`order` must be either an Iterable or None.\")\n", - "\n", - " # More thorough input validation checks\n", - " if self.__x not in data.columns:\n", - " err = \"{0} is not a column in `data`.\".format(self.__x)\n", - " raise IndexError(err)\n", - " if self.__y not in data.columns:\n", - " err = \"{0} is not a column in `data`.\".format(self.__y)\n", - " raise IndexError(err)\n", - " if self.__hue is not None and self.__hue not in data.columns:\n", - " err = \"{0} is not a column in `data`.\".format(self.__hue)\n", - " raise IndexError(err)\n", - "\n", - " color_col = self.__x if self.__hue is None else self.__hue\n", - " if self.__order is not None:\n", - " for group_i in self.__order:\n", - " if group_i not in pd.unique(data[self.__x]):\n", - " err = \"{0} in `order` is not in the '{1}' column of `data`.\".format(\n", - " group_i, self.__x\n", - " )\n", - " raise IndexError(err)\n", - "\n", - " if isinstance(self.__palette, str) and self.__palette.strip() == \"\":\n", - " err = \"`palette` cannot be an empty string. It must be either a string indicating a color name or an Iterable.\"\n", - " raise ValueError(err)\n", - " if isinstance(self.__palette, dict):\n", - " for group_i, color_i in self.__palette.items():\n", - " if group_i not in pd.unique(data[color_col]):\n", - " err = (\n", - " \"{0} in `palette` is not in the '{1}' column of `data`.\".format(\n", - " group_i, color_col\n", - " )\n", - " )\n", - " raise IndexError(err)\n", - " if isinstance(color_i, str) and color_i.strip() == \"\":\n", - " err = \"The color mapping for {0} in `palette` is an empty string. It must contain a color name.\".format(\n", - " group_i\n", - " )\n", - " raise ValueError(err)\n", - "\n", - " if side.lower() not in [\"center\", \"right\", \"left\"]:\n", - " raise ValueError(\n", - " \"Invalid `side`. Must be one of 'center', 'right', or 'left'.\"\n", - " )\n", - "\n", - " return None\n", - "\n", - " def _generate_order(self) -> List:\n", - " \"\"\"\n", - " Generates order value that determines the order in which x-axis categories should be displayed.\n", - "\n", - " Parameters\n", - " ----------\n", - " None\n", - "\n", - " Returns\n", - " -------\n", - " List:\n", - " contains the order in which the x-axis categories should be displayed.\n", - " \"\"\"\n", - " if isinstance(self.__data[self.__x].dtype, pd.CategoricalDtype):\n", - " order = pd.unique(self.__data[self.__x]).categories.tolist()\n", - " else:\n", - " order = pd.unique(self.__data[self.__x]).tolist()\n", - "\n", - " return order\n", - "\n", - " def _format_palette(self, palette: Union[str, List, Tuple]) -> Dict:\n", - " \"\"\"\n", - " Reformats palette into appropriate Dictionary form for swarm plot\n", - "\n", - " Parameters\n", - " ----------\n", - " palette: str | List | Tuple\n", - " The color palette used for the swarm plot. Conventions are based on Matplotlib color\n", - " specifications.\n", - "\n", - " Could be a singular string value - in which case, would be a singular color name.\n", - " In the case of a List or Tuple - it could be a Sequence of color names or RGB(A) values.\n", - "\n", - " Returns\n", - " -------\n", - " Dict:\n", - " Dictionary mapping unique groupings in the color column (of the data used for the swarm plot)\n", - " to a color name (str) or a RGB(A) value (Tuple[float, float, float] | List[float, float, float]).\n", - " \"\"\"\n", - " reformatted_palette = dict()\n", - " groups = pd.unique(self.__data[self.__color_col]).tolist()\n", - "\n", - " if isinstance(palette, str):\n", - " for group_i in groups:\n", - " reformatted_palette[group_i] = palette\n", - " if isinstance(palette, (list, tuple)):\n", - " if len(groups) != len(palette):\n", - " err = (\n", - " \"unique values in '{0}' column in `data` \"\n", - " \"and `palette` do not have the same length. Number of unique values is {1} \"\n", - " \"while length of palette is {2}. The assignment of the colors in the \"\n", - " \"palette will be cycled.\"\n", - " ).format(self.__color_col, len(groups), len(palette))\n", - " warnings.warn(err)\n", - " for i, group_i in enumerate(groups):\n", - " reformatted_palette[group_i] = palette[i % len(palette)]\n", - "\n", - " return reformatted_palette\n", - "\n", - " def _swarm(\n", - " self, values: Iterable[float], gsize: float, dsize: float, side: str\n", - " ) -> pd.Series:\n", - " \"\"\"\n", - " Perform the swarm algorithm to position points without overlap.\n", - "\n", - " Parameters\n", - " ----------\n", - " values : Iterable[int | float]\n", - " The values to be plotted.\n", - " gsize : int | float\n", - " The size of the gap between points.\n", - " dsize : int | float\n", - " The size of the markers.\n", - " side : str\n", - " The side on which points are swarmed (\"center\", \"left\", or \"right\").\n", - "\n", - " Returns\n", - " -------\n", - " pd.Series:\n", - " The x-offset values for the swarm plot.\n", - " \"\"\"\n", - " # Input validation\n", - " if not isinstance(values, Iterable):\n", - " raise ValueError(\"`values` must be an Iterable\")\n", - " if not isinstance(gsize, (int, float)):\n", - " raise ValueError(\"`gsize` must be a scalar or float.\")\n", - " if not isinstance(dsize, (int, float)):\n", - " raise ValueError(\"`dsize` must be a scalar or float.\")\n", - "\n", - " # Sorting algorithm based off of: https://github.com/mgymrek/pybeeswarm\n", - " points_data = pd.DataFrame({\n", - " \"y\": [yval * 1.0 / dsize for yval in values],\n", - " \"x\": np.zeros(len(values), dtype=float) # Initialize with float zeros\n", - " })\n", - " for i in range(1, points_data.shape[0]):\n", - " y_i = points_data[\"y\"].values[i]\n", - " points_placed = points_data[0:i]\n", - " is_points_overlap = (\n", - " abs(y_i - points_placed[\"y\"]) < 1\n", - " ) # Checks if y_i is overlapping with any points already placed\n", - " if any(is_points_overlap):\n", - " points_placed = points_placed[is_points_overlap]\n", - " x_offsets = points_placed[\"y\"].apply(\n", - " lambda y_j: math.sqrt(1 - (y_i - y_j) ** 2)\n", - " )\n", - " if side == \"center\":\n", - " potential_x_offsets = pd.Series(\n", - " [0]\n", - " + (points_placed[\"x\"] + x_offsets).tolist()\n", - " + (points_placed[\"x\"] - x_offsets).tolist()\n", - " )\n", - " if side == \"right\":\n", - " potential_x_offsets = pd.Series(\n", - " [0] + (points_placed[\"x\"] + x_offsets).tolist()\n", - " )\n", - " if side == \"left\":\n", - " potential_x_offsets = pd.Series(\n", - " [0] + (points_placed[\"x\"] - x_offsets).tolist()\n", - " )\n", - " bad_x_offsets = []\n", - " for x_i in potential_x_offsets:\n", - " dists = (y_i - points_placed[\"y\"]) ** 2 + (\n", - " x_i - points_placed[\"x\"]\n", - " ) ** 2\n", - " if any([item < 0.999 for item in dists]):\n", - " bad_x_offsets.append(True)\n", - " else:\n", - " bad_x_offsets.append(False)\n", - " potential_x_offsets[bad_x_offsets] = np.inf\n", - " abs_potential_x_offsets = [abs(_) for _ in potential_x_offsets]\n", - " valid_x_offset = potential_x_offsets[\n", - " abs_potential_x_offsets.index(min(abs_potential_x_offsets))\n", - " ]\n", - " points_data.loc[i, \"x\"] = valid_x_offset\n", - " else:\n", - " points_data.loc[i, \"x\"] = 0\n", - "\n", - " points_data.loc[np.isnan(points_data[\"y\"]), \"x\"] = np.nan\n", - "\n", - " return points_data[\"x\"] * gsize\n", - "\n", - " def _adjust_gutter_points(\n", - " self,\n", - " points_data: pd.DataFrame,\n", - " x_position: float,\n", - " is_drop_gutter: bool,\n", - " gutter_limit: float,\n", - " value_column: str,\n", - " ) -> pd.DataFrame:\n", - " \"\"\"\n", - " Adjust points that hit the gutters or drop them based on the provided conditions.\n", - "\n", - " Parameters\n", - " ----------\n", - " points_data: pd.DataFrame\n", - " Data containing coordinates of points for the swarm plot.\n", - " x_position: int | float\n", - " X-coordinate of the center of a singular swarm group of the swarm plot\n", - " is_drop_gutter : bool\n", - " If True, drop points that hit the gutters; otherwise, readjust them.\n", - " gutter_limit : int | float\n", - " The limit for points hitting the gutters.\n", - " value_column : str\n", - " column in points_data that contains the coordinates for the points in the axis against the gutter\n", - "\n", - " Returns\n", - " -------\n", - " pd.DataFrame:\n", - " DataFrame with adjusted points based on the gutter limit.\n", - " \"\"\"\n", - " if self.__side == \"center\":\n", - " gutter_limit = gutter_limit / 2\n", - "\n", - " hit_gutter = abs(points_data[value_column] - x_position) >= gutter_limit\n", - " total_num_of_points = points_data.shape[0]\n", - " num_of_points_hit_gutter = points_data[hit_gutter].shape[0]\n", - " if any(hit_gutter):\n", - " if is_drop_gutter:\n", - " # Drop points that hit gutter\n", - " points_data.drop(points_data[hit_gutter].index.to_list(), inplace=True)\n", - " err = (\n", - " \"{0:.1%} of the points cannot be placed. \"\n", - " \"You might want to decrease the size of the markers.\"\n", - " ).format(num_of_points_hit_gutter / total_num_of_points)\n", - " warnings.warn(err)\n", - " else:\n", - " for i in points_data[hit_gutter].index:\n", - " points_data.loc[i, value_column] = np.sign(\n", - " points_data.loc[i, value_column]\n", - " ) * (x_position + gutter_limit)\n", - "\n", - " return points_data\n", - "\n", - " def plot(\n", - " self,\n", - " is_drop_gutter: bool,\n", - " gutter_limit: float,\n", - " ax: axes.Subplot,\n", - " filled: Union[bool, List, Tuple],\n", - " horizontal: bool,\n", - " **kwargs,\n", - " ) -> axes.Axes:\n", - " \"\"\"\n", - " Generate a swarm plot.\n", - "\n", - " Parameters\n", - " ----------\n", - " is_drop_gutter : bool\n", - " If True, drop points that hit the gutters; otherwise, readjust them.\n", - " gutter_limit : int | float\n", - " The limit for points hitting the gutters.\n", - " ax : axes.Axes\n", - " The matplotlib figure object to which the swarm plot will be added.\n", - " filled : bool | List | Tuple\n", - " Determines whether the dots in the swarmplot are filled or not. If set to False,\n", - " dots are not filled. If provided as a List or Tuple, it should contain boolean values,\n", - " each corresponding to a swarm group in order, indicating whether the dot should be\n", - " filled or not.\n", - " **kwargs:\n", - " Additional keyword arguments to be passed to the scatter plot.\n", - "\n", - " Returns\n", - " -------\n", - " axes.Axes:\n", - " The matplotlib axes containing the swarm plot.\n", - " \"\"\"\n", - " # Input validation\n", - " if not isinstance(is_drop_gutter, bool):\n", - " raise ValueError(\"`is_drop_gutter` must be a boolean.\")\n", - " if not isinstance(gutter_limit, (int, float)):\n", - " raise ValueError(\"`gutter_limit` must be a scalar or float.\")\n", - " if not isinstance(filled, (bool, list, tuple)):\n", - " raise ValueError(\"`filled` must be a boolean, list or tuple.\")\n", - " \n", - " fontsize = kwargs.pop('fontsize', 12)\n", - "\n", - " # More thorough input validation checks\n", - " if isinstance(filled, (list, tuple)):\n", - " if len(filled) != len(self.__order):\n", - " err = (\n", - " \"There are {0} unique values in `x` column in `data` \"\n", - " \"but `filled` has a length of {1}. If `filled` is a list \"\n", - " \"or a tuple, it must have the same length as the number of \"\n", - " \"unique values/groups in the `x` column of data.\"\n", - " ).format(len(self.__order), len(filled))\n", - " raise ValueError(err)\n", - " if not all(isinstance(_, bool) for _ in filled):\n", - " raise ValueError(\"All values in `filled` must be a boolean.\")\n", - "\n", - " # Assumptions are that self.__data_copy is already sorted according to self.__order\n", - " x_position = (\n", - " 0 # x-coordinate of center of each individual swarm of the swarm plot\n", - " )\n", - " x_tick_tabels = []\n", - " for group_i, values_i in self.__data_copy.groupby(self.__x, observed=False):\n", - " x_new = []\n", - " values_i_y = values_i[self.__y]\n", - " x_offset = self._swarm(\n", - " values=values_i_y,\n", - " gsize=self.__gsize,\n", - " dsize=self.__dsize,\n", - " side=self.__side,\n", - " )\n", - " x_new = [\n", - " x_position + offset for offset in x_offset\n", - " ] # apply x-offsets based on _swarm algo\n", - " values_i[\"x_new\"] = x_new\n", - " values_i = self._adjust_gutter_points(\n", - " values_i, x_position, is_drop_gutter, gutter_limit, \"x_new\"\n", - " )\n", - " x_tick_tabels.extend([group_i])\n", - " x_position = x_position + 1\n", - "\n", - " if values_i.empty:\n", - " ax.scatter(\n", - " values_i[\"x_new\"] if not horizontal else values_i[self.__y],\n", - " values_i[self.__y] if not horizontal else values_i[\"x_new\"],\n", - " s=self.__size,\n", - " zorder=self.__zorder,\n", - " **kwargs,\n", - " )\n", - " continue\n", - "\n", - " if self.__hue is not None:\n", - " # color swarms based on `hue` column\n", - " cmap_values, index = np.unique(\n", - " values_i[self.__hue], return_inverse=True\n", - " )\n", - " cmap = []\n", - " for cmap_group_i in cmap_values:\n", - " cmap.append(self.__palette[cmap_group_i])\n", - "\n", - " cmap = ListedColormap(cmap)\n", - " ax.scatter(\n", - " values_i[\"x_new\"] if not horizontal else values_i[self.__y],\n", - " values_i[self.__y] if not horizontal else values_i[\"x_new\"],\n", - " s=self.__size,\n", - " c=index,\n", - " cmap=cmap,\n", - " zorder=self.__zorder,\n", - " edgecolor=\"face\",\n", - " **kwargs,\n", - " )\n", - "\n", - " else:\n", - " # color swarms based on `x` column\n", - " if not isinstance(filled, bool):\n", - " facecolor = (\n", - " \"none\"\n", - " if not filled[x_position - 1]\n", - " else self.__palette[group_i]\n", - " )\n", - " else:\n", - " facecolor = \"none\" if not filled else self.__palette[group_i]\n", - "\n", - " ax.scatter(\n", - " values_i[\"x_new\"] if not horizontal else values_i[self.__y],\n", - " values_i[self.__y] if not horizontal else values_i[\"x_new\"],\n", - " s=self.__size,\n", - " zorder=self.__zorder,\n", - " facecolor=facecolor,\n", - " edgecolor=self.__palette[group_i],\n", - " label=group_i,\n", - " **kwargs,\n", - " )\n", - "\n", - " # Handling of legends\n", - " # This is currently a workaround because c and cmap is unable to map the labels when calling scatter()\n", - " # labels has to be used to designate legend labels and handles in scatter() due to the potential calling of ax.get_legend_handles_labels()\n", - " if self.__hue is not None:\n", - " for cmap_group_i in self.__palette:\n", - " ax.scatter(\n", - " [],\n", - " [],\n", - " color=self.__palette[cmap_group_i],\n", - " label=cmap_group_i,\n", - " )\n", - "\n", - " if horizontal:\n", - " ax.get_yaxis().set_ticks(np.arange(x_position))\n", - " ax.get_yaxis().set_ticklabels(x_tick_tabels, fontsize = fontsize)\n", - " else:\n", - " ax.get_xaxis().set_ticks(np.arange(x_position))\n", - " ax.get_xaxis().set_ticklabels(x_tick_tabels, fontsize = fontsize)\n", - " \n", - " return ax" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/API/plotter.ipynb b/nbs/API/plotter.ipynb deleted file mode 100644 index 9d4a0986..00000000 --- a/nbs/API/plotter.ipynb +++ /dev/null @@ -1,776 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "984371b3", - "metadata": {}, - "source": [ - "# Plot\n", - "\n", - "> Creating estimation plots.\n", - "\n", - "- order: 4" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "112c011a", - "metadata": {}, - "outputs": [], - "source": [ - "#| default_exp plotter" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "90c48b71", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from __future__ import annotations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "68fce47a", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from nbdev.showdoc import *\n", - "import nbdev\n", - "nbdev.nbdev_export()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7562c1a1", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "import numpy as np\n", - "import seaborn as sns\n", - "import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.patches as mpatches\n", - "from matplotlib.lines import Line2D\n", - "import pandas as pd\n", - "import warnings\n", - "import logging" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "36a42b1c", - "metadata": {}, - "outputs": [], - "source": [ - "# | export\n", - "# TODO refactor function name\n", - "def effectsize_df_plotter(effectsize_df: object, **plot_kwargs) -> matplotlib.figure.Figure:\n", - " \"\"\"\n", - " Custom function that creates an estimation plot from an EffectSizeDataFrame.\n", - " Keywords\n", - " --------\n", - " Parameters\n", - " ----------\n", - " effectsize_df\n", - " A `dabest` EffectSizeDataFrame object.\n", - " plot_kwargs\n", - " color_col=None\n", - " raw_marker_size=6, contrast_marker_kwargs=9,\n", - " raw_label=None, contrast_label=None, delta2_label=None,\n", - " raw_ylim=None, contrast_ylim=None, delta2_ylim=None,\n", - " custom_palette=None, \n", - " swarm_side=None, \n", - " empty_circle=False,\n", - " face_color=None,\n", - " raw_desat=0.5, contrast_desat=1,\n", - " raw_alpha=None, contrast_alpha=0.8,\n", - " bar_width = 0.5,\n", - " ci_type='bca',\n", - " float_contrast=True,\n", - " show_pairs=True,\n", - " show_sample_size=True,\n", - " show_delta2=True, show_mini_meta=True,\n", - " group_summaries=\"mean_sd\",\n", - " fig_size=None,\n", - " dpi=100,\n", - " ax=None,\n", - " swarmplot_kwargs=None,\n", - " slopegraph_kwargs=None,\n", - " barplot_kwargs=None,\n", - " sankey_kwargs=None,\n", - " contrast_kwargs=None,\n", - " reflines_kwargs=None,\n", - " group_summaries_kwargs=None,\n", - " legend_kwargs=None,\n", - " title=None, fontsize_title=16,\n", - " fontsize_rawxlabel=12, fontsize_rawylabel=12,\n", - " fontsize_contrastxlabel=12, fontsize_contrastylabel=12,\n", - " fontsize_delta2label=12,\n", - "\n", - " raw_bars=True, raw_bars_kwargs=None,\n", - " contrast_bars=True, contrast_bars_kwargs=None,\n", - " reference_band=None, reference_band_kwargs=None,\n", - " delta_text=True, delta_text_kwargs=None,\n", - " delta_dot=True, delta_dot_kwargs=None,\n", - "\n", - " horizontal=False, horizontal_table_kwargs=None,\n", - " gridkey=None, \n", - " gridkey_merge_pairs=False,\n", - " gridkey_show_Ns=True,\n", - " gridkey_show_es=True,\n", - " gridkey_delimiters=[';', '>', '_'],\n", - " gridkey_kwargs=None,\n", - " contrast_marker_kwargs=None, contrast_errorbar_kwargs=None,\n", - " prop_sample_counts=False, prop_sample_counts_kwargs=None, \n", - " contrast_paired_lines=True, contrast_paired_lines\n", - "\t\tshow_baseline_ec=False,\n", - "\n", - " \"\"\"\n", - " from .misc_tools import (\n", - " get_params,\n", - " get_kwargs,\n", - " get_color_palette,\n", - " initialize_fig,\n", - " get_plot_groups,\n", - " add_counts_to_ticks,\n", - " extract_contrast_plotting_ticks,\n", - " set_xaxis_ticks_and_lims,\n", - " show_legend,\n", - " gardner_altman_adjustments,\n", - " extract_group_summaries,\n", - " draw_zeroline,\n", - " redraw_dependent_spines,\n", - " redraw_independent_spines,\n", - " prepare_bars_for_plot\n", - " )\n", - " from .plot_tools import (\n", - " error_bar,\n", - " sankeydiag,\n", - " swarmplot,\n", - " delta_text_plotter,\n", - " delta_dots_plotter,\n", - " slopegraph_plotter,\n", - " plot_minimeta_or_deltadelta_violins,\n", - " effect_size_curve_plotter,\n", - " gridkey_plotter,\n", - " barplotter,\n", - " table_for_horizontal_plots,\n", - " add_counts_to_prop_plots,\n", - " add_bars_to_plot\n", - " )\n", - "\n", - " warnings.filterwarnings(\n", - " \"ignore\", \"This figure includes Axes that are not compatible with tight_layout\"\n", - " )\n", - "\n", - " # Have to disable logging of warning when get_legend_handles_labels()\n", - " # tries to get from slopegraph.\n", - " logging.disable(logging.WARNING)\n", - "\n", - " # Save rcParams that I will alter, so I can reset back.\n", - " original_rcParams = {}\n", - " _changed_rcParams = [\"axes.grid\"]\n", - " for parameter in _changed_rcParams:\n", - " original_rcParams[parameter] = plt.rcParams[parameter]\n", - "\n", - " plt.rcParams[\"axes.grid\"] = False\n", - " ytick_color = plt.rcParams[\"ytick.color\"]\n", - "\n", - " # Extract parameters and set kwargs\n", - " (swarmplot_kwargs, barplot_kwargs, sankey_kwargs, contrast_kwargs, \n", - " slopegraph_kwargs, reflines_kwargs, legend_kwargs, group_summaries_kwargs, \n", - " redraw_axes_kwargs, delta_dot_kwargs, delta_text_kwargs, reference_band_kwargs, \n", - " raw_bars_kwargs, contrast_bars_kwargs, table_kwargs, gridkey_kwargs, contrast_marker_kwargs, \n", - " contrast_errorbar_kwargs, prop_sample_counts_kwargs, contrast_paired_lines_kwargs) = get_kwargs(\n", - " plot_kwargs = plot_kwargs, \n", - " ytick_color = ytick_color,\n", - " is_paired = effectsize_df.is_paired\n", - " )\n", - "\n", - " (dabest_obj, plot_data, xvar, yvar, is_paired, effect_size, proportional, \n", - " all_plot_groups, idx, show_delta2, show_mini_meta, float_contrast, \n", - " show_pairs, group_summaries, horizontal, results, ci_type, x1_level, experiment_label, \n", - " show_baseline_ec, one_sankey, two_col_sankey, asymmetric_side, show_sample_size) = get_params(\n", - " effectsize_df = effectsize_df, \n", - " plot_kwargs = plot_kwargs,\n", - " sankey_kwargs = sankey_kwargs,\n", - " barplot_kwargs = barplot_kwargs\n", - " )\n", - "\n", - " # Extract Color palette\n", - " (color_col, bootstraps_color_by_group, n_groups, filled, raw_colors,\n", - " plot_palette_raw, plot_palette_contrast, plot_palette_sankey) = get_color_palette(\n", - " plot_kwargs = plot_kwargs, \n", - " plot_data = plot_data, \n", - " xvar = xvar, \n", - " show_pairs = show_pairs,\n", - " idx = idx,\n", - " all_plot_groups = all_plot_groups,\n", - " delta2 = effectsize_df.delta2,\n", - " proportional = proportional\n", - " )\n", - "\n", - " # Initialise the figure.\n", - " fig, rawdata_axes, contrast_axes, table_axes = initialize_fig(\n", - " plot_kwargs = plot_kwargs, \n", - " dabest_obj = dabest_obj, \n", - " show_delta2 = show_delta2, \n", - " show_mini_meta = show_mini_meta, \n", - " is_paired = is_paired, \n", - " show_pairs = show_pairs, \n", - " proportional = proportional, \n", - " float_contrast = float_contrast,\n", - " effect_size_type = effect_size,\n", - " yvar = yvar,\n", - " horizontal = horizontal,\n", - " show_table = table_kwargs['show'],\n", - " color_col = color_col\n", - " )\n", - " \n", - " # Plotting the rawdata.\n", - " if show_pairs: ## Paired plots!\n", - " temp_idx, temp_all_plot_groups = get_plot_groups(\n", - " is_paired = is_paired, \n", - " idx = idx, \n", - " proportional = proportional, \n", - " all_plot_groups = all_plot_groups\n", - " )\n", - " if proportional: ## Plot the raw data as a set of Sankey Diagrams aligned like barplot.\n", - " if sankey_kwargs[\"flow\"] == False and len(temp_all_plot_groups) == 2: \n", - " sankey_kwargs[\"flow\"], two_col_sankey = True, False\n", - " warnings.warn(\"Sankey flow must be true for singular two-group sankey plots\")\n", - " sankey_control_test_groups = sankeydiag(\n", - " plot_data,\n", - " xvar = xvar,\n", - " yvar = yvar,\n", - " temp_all_plot_groups = temp_all_plot_groups,\n", - " idx = idx,\n", - " temp_idx = temp_idx,\n", - " palette = plot_palette_sankey,\n", - " ax = rawdata_axes,\n", - " horizontal = horizontal,\n", - " **sankey_kwargs\n", - " )\n", - " else: ## Plot the raw data as a slopegraph.\n", - " slopegraph_plotter(\n", - " dabest_obj = dabest_obj, \n", - " plot_data = plot_data, \n", - " xvar = xvar, \n", - " yvar = yvar, \n", - " color_col = color_col, \n", - " plot_palette_raw = plot_palette_raw, \n", - " slopegraph_kwargs = slopegraph_kwargs, \n", - " rawdata_axes = rawdata_axes, \n", - " ytick_color = ytick_color, \n", - " temp_idx = temp_idx,\n", - " horizontal = horizontal,\n", - " temp_all_plot_groups = temp_all_plot_groups, \n", - " plot_kwargs = plot_kwargs,\n", - " group_summaries_kwargs = group_summaries_kwargs\n", - " )\n", - " \n", - " ## Add delta dots to the contrast axes for paired plots.\n", - " show_delta_dots = plot_kwargs[\"delta_dot\"]\n", - " unavailable_effect_sizes = [\"hedges_g\", \"delta_g\", \"cohens_d\"]\n", - " if show_delta_dots and is_paired and not any([es in effect_size for es in unavailable_effect_sizes]):\n", - " delta_dots_plotter(\n", - " plot_data = plot_data, \n", - " contrast_axes = contrast_axes, \n", - " delta_id_col = dabest_obj.id_col, \n", - " idx = idx, \n", - " xvar = xvar, \n", - " yvar = yvar, \n", - " is_paired = is_paired, \n", - " color_col = color_col, \n", - " float_contrast = float_contrast, \n", - " plot_palette_raw = plot_palette_raw, \n", - " delta_dot_kwargs = delta_dot_kwargs,\n", - " horizontal = horizontal,\n", - " )\n", - " \n", - " else: ## Unpaired plots!\n", - " if proportional: # Plot the raw data as a barplot.\n", - " barplotter(\n", - " xvar = xvar, \n", - " yvar = yvar, \n", - " all_plot_groups = all_plot_groups, \n", - " rawdata_axes = rawdata_axes, \n", - " plot_data = plot_data, \n", - " raw_colors = raw_colors, \n", - " plot_palette_raw = plot_palette_raw, \n", - " color_col = color_col,\n", - " barplot_kwargs = barplot_kwargs,\n", - " horizontal = horizontal,\n", - " )\n", - " else: ## Plot the raw data as a swarmplot.\n", - " ## swarmplot() plots swarms based on current size of ax\n", - " ## Therefore, since the ax size for show_mini_meta and show_delta changes later on, there has to be increased jitter\n", - " rawdata_plot = swarmplot(\n", - " data = plot_data,\n", - " x = xvar,\n", - " y = yvar,\n", - " ax = rawdata_axes,\n", - " order = all_plot_groups,\n", - " hue = color_col,\n", - " palette = plot_palette_raw,\n", - " zorder = 1,\n", - " side = asymmetric_side,\n", - " jitter = 1.25 if show_mini_meta else 1.4 if show_delta2 else 1, # TODO: to make jitter value more accurate and not just a hardcoded eyeball value\n", - " filled = filled,\n", - " is_drop_gutter = True,\n", - " gutter_limit = 0.45,\n", - " horizontal = horizontal,\n", - " **swarmplot_kwargs\n", - " )\n", - " if color_col is None:\n", - " rawdata_plot.legend().set_visible(False)\n", - " \n", - " ## Plot the error bars on unpaired plots.\n", - " if group_summaries is not None:\n", - " (group_summaries_method, \n", - " group_summaries_offset, group_summaries_line_color) = extract_group_summaries(\n", - " proportional = proportional, \n", - " rawdata_axes = rawdata_axes, \n", - " asymmetric_side = asymmetric_side if not proportional else None, \n", - " horizontal = horizontal, \n", - " bootstraps_color_by_group = bootstraps_color_by_group, \n", - " plot_palette_raw = plot_palette_raw, \n", - " all_plot_groups = all_plot_groups,\n", - " n_groups = n_groups, \n", - " color_col = color_col, \n", - " ytick_color = ytick_color, \n", - " group_summaries_kwargs = group_summaries_kwargs\n", - " )\n", - " ## Plot the error bar\n", - " error_bar(\n", - " plot_data,\n", - " x = xvar,\n", - " y = yvar,\n", - " offset = group_summaries_offset,\n", - " line_color = group_summaries_line_color,\n", - " type = group_summaries,\n", - " ax = rawdata_axes,\n", - " method = group_summaries_method,\n", - " horizontal = horizontal,\n", - " **group_summaries_kwargs\n", - " )\n", - "\n", - " # Add the counts to the rawdata axes xticks.\n", - " if show_sample_size:\n", - " add_counts_to_ticks(\n", - " plot_data = plot_data, \n", - " xvar = xvar, \n", - " yvar = yvar, \n", - " rawdata_axes = rawdata_axes, \n", - " plot_kwargs = plot_kwargs,\n", - " flow = sankey_kwargs[\"flow\"],\n", - " horizontal = horizontal,\n", - " )\n", - "\n", - " # Add counts to prop plots (embedded in the plot bars)\n", - " if proportional and plot_kwargs['prop_sample_counts'] and sankey_kwargs[\"flow\"]:\n", - " add_counts_to_prop_plots(\n", - " plot_data = plot_data, \n", - " xvar = xvar, \n", - " yvar = yvar, \n", - " rawdata_axes = rawdata_axes, \n", - " horizontal = horizontal,\n", - " is_paired = is_paired,\n", - " prop_sample_counts_kwargs = prop_sample_counts_kwargs,\n", - " )\n", - "\n", - " ## Swarm bars\n", - " raw_bars = plot_kwargs[\"raw_bars\"]\n", - " if raw_bars and not proportional and not is_paired and not horizontal: #Currently not supporting swarm bars for horizontal plots (looks weird)\n", - " raw_bars_dict, raw_bars_kwargs = prepare_bars_for_plot(\n", - " bar_type = 'raw', \n", - " bar_kwargs = raw_bars_kwargs, \n", - " horizontal = horizontal,\n", - " plot_palette_raw = plot_palette_raw,\n", - " color_col = color_col, \n", - " show_pairs = show_pairs, \n", - " plot_data = plot_data,\n", - " xvar = xvar, \n", - " yvar = yvar, \n", - " bootstraps_color_by_group = bootstraps_color_by_group, \n", - " )\n", - " add_bars_to_plot(bar_dict = raw_bars_dict, \n", - " ax = rawdata_axes, \n", - " bar_kwargs = raw_bars_kwargs\n", - " )\n", - "\n", - " # Plot the contrast axes - effect sizes and bootstraps!\n", - " plot_groups = (temp_all_plot_groups if (is_paired == \"baseline\" and show_pairs and two_col_sankey) \n", - " else temp_idx if two_col_sankey \n", - " else all_plot_groups\n", - " )\n", - "\n", - " ## Extract ticks for contrast plot\n", - " (ticks_to_skip, ticks_to_plot, ticks_for_baseline_ec,\n", - " ticks_to_skip_contrast, ticks_to_start_twocol_sankey) = extract_contrast_plotting_ticks(\n", - " is_paired = is_paired, \n", - " show_pairs = show_pairs, \n", - " two_col_sankey = two_col_sankey, \n", - " plot_groups = plot_groups,\n", - " idx = idx,\n", - " sankey_control_group = sankey_control_test_groups[0] if two_col_sankey else None,\n", - " ) \n", - "\n", - " ## Adjust contrast tick locations to account for different plotting styles in horizontal plots\n", - " table_axes_ticks_to_plot = ticks_to_plot\n", - " if (horizontal and proportional and not show_pairs) or (horizontal and plot_kwargs[\"swarm_side\"] == \"right\"):\n", - " ticks_to_plot = [x+0.25 for x in ticks_to_plot]\n", - "\n", - " ## Plot the bootstraps, then the effect sizes and CIs.\n", - " contrast_paired_lines = False if float_contrast or not sankey_kwargs[\"flow\"] else plot_kwargs[\"contrast_paired_lines\"]\n", - " (current_group, current_control,\n", - " current_effsize, contrast_xtick_labels) = effect_size_curve_plotter(\n", - " ticks_to_plot = ticks_to_plot, \n", - " ticks_for_baseline_ec = ticks_for_baseline_ec,\n", - " results = results, \n", - " ci_type = ci_type, \n", - " contrast_axes = contrast_axes, \n", - " contrast_kwargs = contrast_kwargs, \n", - " bootstraps_color_by_group = bootstraps_color_by_group,\n", - " plot_palette_contrast = plot_palette_contrast,\n", - " horizontal = horizontal,\n", - " contrast_marker_kwargs = contrast_marker_kwargs,\n", - " contrast_errorbar_kwargs = contrast_errorbar_kwargs,\n", - " idx = idx,\n", - " is_paired = is_paired,\n", - " contrast_paired_lines = contrast_paired_lines,\n", - "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcontrast_paired_lines_kwargs = contrast_paired_lines_kwargs,\n", - "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tshow_baseline_ec = show_baseline_ec,\n", - " )\n", - "\n", - " ## Plot mini-meta or delta-delta violin\n", - " delta2_axes = None\n", - " if show_mini_meta or show_delta2:\n", - " delta2_axes, contrast_xtick_labels = plot_minimeta_or_deltadelta_violins(\n", - " dabest_obj = effectsize_df.mini_meta if show_mini_meta else effectsize_df.delta_delta,\n", - " type = 'mini_meta' if show_mini_meta else 'delta_delta',\n", - " ci_type = ci_type, \n", - " rawdata_axes = rawdata_axes,\n", - " contrast_axes = contrast_axes, \n", - " contrast_kwargs = contrast_kwargs, \n", - " contrast_xtick_labels = contrast_xtick_labels, \n", - " effect_size = effect_size,\n", - " plot_kwargs = plot_kwargs, \n", - " horizontal = horizontal,\n", - " show_pairs = show_pairs,\n", - " contrast_marker_kwargs = contrast_marker_kwargs,\n", - " contrast_errorbar_kwargs = contrast_errorbar_kwargs,\n", - " )\n", - " ## Contrast bars\n", - " contrast_bars = plot_kwargs[\"contrast_bars\"]\n", - " if contrast_bars:\n", - " contrast_bars_dict, contrast_bars_kwargs = prepare_bars_for_plot(\n", - " bar_type = 'contrast', \n", - " bar_kwargs = contrast_bars_kwargs, \n", - " horizontal = horizontal,\n", - " plot_palette_raw = plot_palette_raw,\n", - " color_col = color_col, \n", - " show_pairs = show_pairs, \n", - " results = results, \n", - " ticks_to_plot = ticks_to_plot, \n", - " bootstraps_color_by_group = bootstraps_color_by_group, \n", - " extra_delta = (effectsize_df.mini_meta.difference if show_mini_meta \n", - " else effectsize_df.delta_delta.difference if show_delta2\n", - " else None)\n", - " )\n", - " add_bars_to_plot(bar_dict = contrast_bars_dict, \n", - " ax = contrast_axes, \n", - " bar_kwargs = contrast_bars_kwargs\n", - " )\n", - " \n", - " ## Delta text\n", - " delta_text = plot_kwargs[\"delta_text\"]\n", - " if delta_text and not horizontal: \n", - " delta_text_plotter(\n", - " results = results, \n", - " ax_to_plot = contrast_axes, \n", - " ticks_to_plot = ticks_to_plot, \n", - " delta_text_kwargs = delta_text_kwargs, \n", - " color_col = color_col, \n", - " plot_palette_raw = plot_palette_raw, \n", - " show_pairs = show_pairs,\n", - " float_contrast = float_contrast, \n", - " bootstraps_color_by_group = bootstraps_color_by_group,\n", - " extra_delta = (effectsize_df.mini_meta.difference if show_mini_meta \n", - " else effectsize_df.delta_delta.difference if show_delta2\n", - " else None),\n", - " )\n", - "\n", - " ## Make sure the contrast_axes x-lims match the rawdata_axes xlims,\n", - " ## and add an extra violinplot tick for delta-delta plot.\n", - " ## Name is xaxis but it is actually y-axis for horizontal plots\n", - " set_xaxis_ticks_and_lims(\n", - " show_delta2 = show_delta2, \n", - " show_mini_meta = show_mini_meta, \n", - " rawdata_axes = rawdata_axes, \n", - " contrast_axes = contrast_axes, \n", - " show_pairs = show_pairs, \n", - " float_contrast = float_contrast,\n", - " ticks_to_skip = ticks_to_skip, \n", - " contrast_xtick_labels = contrast_xtick_labels, \n", - " plot_kwargs = plot_kwargs,\n", - " proportional = proportional,\n", - " horizontal = horizontal,\n", - " )\n", - " # Plot aesthetic adjustments.\n", - " if float_contrast: # For Gardner-Altman (float contrast) plots only.\n", - " gardner_altman_adjustments(\n", - " effect_size_type = effect_size, \n", - " plot_data = plot_data, \n", - " xvar = xvar, \n", - " yvar = yvar, \n", - " current_control = current_control, \n", - " current_group = current_group,\n", - " rawdata_axes = rawdata_axes, \n", - " contrast_axes = contrast_axes, \n", - " results = results, \n", - " current_effsize = current_effsize, \n", - " is_paired = is_paired, \n", - " one_sankey = one_sankey,\n", - " reflines_kwargs = reflines_kwargs, \n", - " redraw_axes_kwargs = redraw_axes_kwargs, \n", - " )\n", - " else: # For Cumming plots only.\n", - " ## Add Zero line if lies within the ylim of contrast axes\n", - " draw_zeroline(\n", - " ax = contrast_axes,\n", - " horizontal = horizontal,\n", - " reflines_kwargs = reflines_kwargs,\n", - " extra_delta = True if show_delta2 else False,\n", - " )\n", - " ## Axes independent spine lines\n", - " is_gridkey = True if plot_kwargs[\"gridkey\"] is not None else False\n", - " if not is_gridkey:\n", - " redraw_independent_spines(\n", - " rawdata_axes = rawdata_axes,\n", - " contrast_axes = contrast_axes,\n", - " horizontal = horizontal,\n", - " two_col_sankey = two_col_sankey,\n", - " ticks_to_start_twocol_sankey = ticks_to_start_twocol_sankey,\n", - " idx = idx,\n", - " is_paired = is_paired,\n", - " show_pairs = show_pairs,\n", - " proportional = proportional,\n", - " ticks_to_skip = ticks_to_skip,\n", - " temp_idx = temp_idx if is_paired == \"baseline\" and show_pairs else None,\n", - " ticks_to_skip_contrast = ticks_to_skip_contrast,\n", - " redraw_axes_kwargs = redraw_axes_kwargs\n", - " )\n", - "\n", - " # Modify ylims of axes to flip the plot for horizontal format\n", - " if horizontal:\n", - " if not proportional or (proportional and show_pairs):\n", - " raw_ylim, contrast_ylim = rawdata_axes.get_ylim(), contrast_axes.get_ylim()\n", - " rawdata_axes.set_ylim(raw_ylim[1], raw_ylim[0])\n", - " contrast_axes.set_ylim(contrast_ylim[1], contrast_ylim[0])\n", - "\n", - " ## Modify the ylim to reduce whitespace in specific plots.\n", - " if show_delta2 or show_mini_meta or (proportional and show_pairs):\n", - " raw_ylim, contrast_ylim = rawdata_axes.get_ylim(), contrast_axes.get_ylim()\n", - " rawdata_axes.set_ylim(raw_ylim[0]-0.5, raw_ylim[1])\n", - " contrast_axes.set_ylim(contrast_ylim[0]-0.5, contrast_ylim[1])\n", - "\n", - " # Add the dependent axes spines back in.\n", - " redraw_dependent_spines(\n", - " rawdata_axes = rawdata_axes, \n", - " contrast_axes = contrast_axes, \n", - " redraw_axes_kwargs = redraw_axes_kwargs, \n", - " float_contrast = float_contrast, \n", - " horizontal = horizontal,\n", - " show_delta2 = show_delta2, \n", - " delta2_axes = delta2_axes\n", - " )\n", - "\n", - " # Table Axes for horizontal plots\n", - " if horizontal and table_kwargs['show']:\n", - " table_for_horizontal_plots(\n", - " effectsize_df = effectsize_df,\n", - " ax = table_axes,\n", - " contrast_axes = contrast_axes,\n", - " ticks_to_plot = table_axes_ticks_to_plot, \n", - " show_mini_meta = show_mini_meta,\n", - " show_delta2 = show_delta2,\n", - " table_kwargs = table_kwargs,\n", - " ticks_to_skip = ticks_to_skip\n", - " )\n", - "\n", - " # Gridkey\n", - " gridkey = plot_kwargs[\"gridkey\"]\n", - " if gridkey is not None:\n", - " gridkey_plotter(\n", - " is_paired = is_paired, \n", - " idx = idx, \n", - " all_plot_groups = all_plot_groups, \n", - " gridkey = gridkey, \n", - " rawdata_axes = rawdata_axes,\n", - " contrast_axes = contrast_axes,\n", - " plot_data = plot_data, \n", - " xvar = xvar, \n", - " yvar = yvar, \n", - " results = results, \n", - " show_delta2 = show_delta2, \n", - " show_mini_meta = show_mini_meta, \n", - " x1_level = x1_level,\n", - " experiment_label = experiment_label,\n", - " float_contrast = float_contrast,\n", - " horizontal = horizontal,\n", - " delta_delta = effectsize_df.delta_delta if show_delta2 else None,\n", - " mini_meta = effectsize_df.mini_meta if show_mini_meta else None,\n", - " effect_size = effect_size,\n", - " gridkey_kwargs = gridkey_kwargs,\n", - " )\n", - " \n", - " # Reference band\n", - " reference_band = plot_kwargs[\"reference_band\"]\n", - " if reference_band is not None and not float_contrast:\n", - " reference_band_dict, reference_band_kwargs = prepare_bars_for_plot(bar_type = 'summary', \n", - " bar_kwargs = reference_band_kwargs, \n", - " horizontal = horizontal, \n", - " plot_palette_raw = plot_palette_raw, \n", - " color_col = color_col, \n", - " show_pairs = show_pairs,\n", - " results = results, \n", - " ticks_to_plot = ticks_to_plot, \n", - " reference_band = reference_band, \n", - " summary_axes = contrast_axes, \n", - " ci_type = ci_type,\n", - " bootstraps_color_by_group = bootstraps_color_by_group,\n", - " )\n", - " \n", - " add_bars_to_plot(bar_dict = reference_band_dict,\n", - " ax = contrast_axes,\n", - " bar_kwargs = reference_band_kwargs\n", - " )\n", - "\n", - " # Legend\n", - " handles, labels = rawdata_axes.get_legend_handles_labels()\n", - " legend_labels = [l for l in labels]\n", - " legend_handles = [h for h in handles]\n", - "\n", - " if bootstraps_color_by_group is False and color_col is not None:\n", - " rawdata_axes.legend().set_visible(False)\n", - " show_legend(\n", - " legend_labels = legend_labels, \n", - " legend_handles = legend_handles, \n", - " rawdata_axes = rawdata_axes, \n", - " contrast_axes = contrast_axes, \n", - " table_axes = table_axes,\n", - " float_contrast = float_contrast, \n", - " show_pairs = show_pairs, \n", - " horizontal = horizontal,\n", - " legend_kwargs = legend_kwargs,\n", - " table_kwargs = table_kwargs\n", - " )\n", - "\n", - " # Make sure no stray ticks appear!\n", - " rawdata_axes.xaxis.set_ticks_position(\"bottom\")\n", - " rawdata_axes.yaxis.set_ticks_position(\"left\")\n", - " contrast_axes.xaxis.set_ticks_position(\"bottom\")\n", - " if float_contrast is False:\n", - " contrast_axes.yaxis.set_ticks_position(\"left\")\n", - "\n", - " # Reset rcParams.\n", - " for parameter in _changed_rcParams:\n", - " plt.rcParams[parameter] = original_rcParams[parameter]\n", - "\n", - " # Return the figure.\n", - " return fig" - ] - }, - { - "cell_type": "markdown", - "id": "7355251f", - "metadata": {}, - "source": [ - "For details on how to control the aesthetic of the generated estimation plot by modifying the **plot_kwargs**, please refer to [Controlling Plot Aesthetics](../tutorials/08-plot_aesthetics.ipynb)\n", - "\n", - "- **effectsize_df**: A `dabest` `EffectSizeDataFrame` object.\n", - "- **plot_kwargs**:\n", - " - color_col=None\n", - " - raw_marker_size=6, contrast_marker_size=9,\n", - " - raw_label=None, contrast_label=None, delta2_label=None,\n", - " - raw_ylim=None, contrast_ylim=None, delta2_ylim=None,\n", - " - custom_palette=None, swarm_side=None, empty_circle=False,\n", - " - face_color = None,\n", - " - raw_desat=0.5, contrast_desat=1,\n", - " - raw_alpha=None, contrast_alpha=0.8,\n", - " - bar_width=0.5, \n", - " - ci_type='bca',\n", - " - float_contrast=True,\n", - " - show_pairs=True,\n", - " - show_sample_size=True\n", - " - show_delta2=True, show_mini_meta=True,\n", - " - group_summaries=\"mean_sd\",\n", - " - fig_size=None, dpi=100,\n", - " - ax=None,\n", - " - swarmplot_kwargs=None,\n", - " - slopegraph_kwargs=None,\n", - " - barplot_kwargs=None,\n", - " - sankey_kwargs=None,\n", - " - contrast_kwargs=None,\n", - " - reflines_kwargs=None,\n", - " - group_summaries_kwargs=None,\n", - " - legend_kwargs=None,\n", - " \n", - " - title=None, fontsize_title=16,\n", - " - fontsize_rawxlabel=12, fontsize_rawylabel=12,\n", - " - fontsize_contrastxlabel=12, fontsize_contrastylabel=12,\n", - " - fontsize_delta2label=12,\n", - " - raw_bars=True, raw_bars_kwargs=None,\n", - " - contrast_bars=True, contrast_bars_kwargs=None,\n", - " - reference_band=None, reference_band_kwargs=None,\n", - " - delta_text=True, delta_text_kwargs=None,\n", - " - delta_dot=True, delta_dot_kwargs=None,\n", - " \n", - " - horizontal=False, horizontal_table_kwargs=None,\n", - " - gridkey=None, gridkey_merge_pairs=False,\n", - " - gridkey_show_Ns=True, gridkey_show_es=True,\n", - " - gridkey_delimiters=[';', '>', '_'],\n", - " - gridkey_kwargs=None,\n", - " - contrast_marker_kwargs=None, contrast_errorbar_kwargs=None\n", - " - prop_sample_counts=False, prop_sample_counts_kwargs=None\n", - " - contrast_paired_lines=True, contrast_paired_lines_kwargs=None,\n", - " - show_baseline_ec=False" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7d23e292", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "f1cc27d4", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/API/precompile.ipynb b/nbs/API/precompile.ipynb deleted file mode 100644 index 95b8c834..00000000 --- a/nbs/API/precompile.ipynb +++ /dev/null @@ -1,117 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "60e69f4a", - "metadata": {}, - "source": [ - "# precompile\n", - "\n", - "> A tool to pre-compile Numba functions for speeding up DABEST bootstrapping\n", - "\n", - "- order: 10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3bcca32a", - "metadata": {}, - "outputs": [], - "source": [ - "#| default_exp _stats_tools/precompile" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5d3e285a", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from __future__ import annotations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e6eb02d0", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from nbdev.showdoc import *\n", - "import nbdev\n", - "nbdev.nbdev_export()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "35aa9337", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "import numpy as np\n", - "from tqdm import tqdm\n", - "from dabest._stats_tools import effsize\n", - "from dabest._stats_tools import confint_2group_diff" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "472d8104", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "\n", - "_NUMBA_COMPILED = False\n", - "\n", - "def precompile_all():\n", - " \"\"\"Pre-compile all numba functions with dummy data\"\"\"\n", - " global _NUMBA_COMPILED\n", - " \n", - " if _NUMBA_COMPILED:\n", - " return\n", - " \n", - " print(\"Pre-compiling numba functions for DABEST...\")\n", - " \n", - " # Create dummy data\n", - " dummy_control = np.array([1.0, 2.0, 3.0])\n", - " dummy_test = np.array([4.0, 5.0, 6.0])\n", - " \n", - " funcs = [\n", - " # effsize.py functions\n", - " (effsize.cohens_d, (dummy_control, dummy_test)),\n", - " (effsize._mann_whitney_u, (dummy_control, dummy_test)),\n", - " (effsize._cliffs_delta_core, (dummy_control, dummy_test)),\n", - " (effsize._compute_standardizers, (dummy_control, dummy_test)),\n", - " (effsize.weighted_delta, (np.array([1.0, 2.0]), np.array([0.1, 0.2]))),\n", - " \n", - " # confint_2group_diff.py functions\n", - " (confint_2group_diff.create_jackknife_indexes, (dummy_control,)),\n", - " (confint_2group_diff.create_repeated_indexes, (dummy_control,)),\n", - " (confint_2group_diff.bootstrap_indices, (True, 3, 3, 10, 12345)),\n", - " (confint_2group_diff.delta2_bootstrap_loop, \n", - " (dummy_control, dummy_test, dummy_control, dummy_test, 10, 1.0, 12345, False)),\n", - " (confint_2group_diff._compute_quantile, (0.5, 0.1, 0.1)),\n", - " (confint_2group_diff.calculate_group_var, (1.0, 3, 1.0, 3))\n", - " ]\n", - " \n", - " for func, args in tqdm(funcs, desc=\"Compiling numba functions\"):\n", - " func(*args)\n", - " \n", - " _NUMBA_COMPILED = True\n", - " \n", - " print(\"Numba compilation complete!\")" - ] - } - ], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/_quarto.yml b/nbs/_quarto.yml deleted file mode 100644 index ef7598c9..00000000 --- a/nbs/_quarto.yml +++ /dev/null @@ -1,50 +0,0 @@ -project: - type: website - render: - - "**/*.ipynb" - - "**/*.qmd" - - "!tests/**" - -format: - html: - theme: cosmo - css: styles.css - toc: true - toc-depth: 4 - -website: - twitter-card: true - open-graph: true - repo-actions: [issue] - sidebar: - style: floating - contents: - - auto: "/0*.ipynb" - - auto: "tutorials/[012]*.ipynb" # Autogenerate a section of tutorial notebooks - - section: API - contents: API/* - favicon: images/Favicon-3-outline.svg - navbar: - background: primary - search: true - collapse-below: lg - left: - - text: "Get Started" - href: 01-getting_started.ipynb - - text: "Tutorial" - href: tutorials/index.qmd - - text: "Blog" - href: blog/index.qmd - - text: "Help" - menu: - - text: "Report an Issue" - icon: bug - href: https://github.com/ACCLAB/DABEST-python/issues - right: - - icon: github - href: "https://github.com/ACCLAB/DABEST-python" - - icon: twitter - href: https://twitter.com/EstimationStats - aria-label: ACCLAB Twitter - -metadata-files: [nbdev.yml, sidebar.yml] \ No newline at end of file diff --git a/nbs/_static/css/custom.css b/nbs/_static/css/custom.css deleted file mode 100644 index 1c3d6d16..00000000 --- a/nbs/_static/css/custom.css +++ /dev/null @@ -1,681 +0,0 @@ - -@import url("basic.css"); -@import url('https://fonts.googleapis.com/css?family=IBM+Plex+Mono|Spectral:500,500i|Merriweather+Sans:400,400i'); - -/* -- page layout ----------------------------------------------------------- */ - -body { - font-family: 'Spectral', 'Garamond'; - font-size: 19px; - background-color: #fff; - color: #000; - margin: 0; - padding: 0; -} - - -div.document { - width: 940px; - margin: 30px auto 0 auto; -} - -div.documentwrapper { - float: left; - width: 100%; -} - -div.bodywrapper { - margin: 0 0 0 220px; -} - -div.sphinxsidebar { - font-family: 'Merriweather Sans', sans-serif; - width: 220px; - font-size: 12px; - line-height: 1.5; -} - -hr { - border: 1px solid #B1B4B6; -} - -div.body { - background-color: #fff; - color: #3E4349; - padding: 0 30px 0 30px; -} - -div.body > .section { - text-align: left; -} - -div.footer { - width: 940px; - margin: 20px auto 30px auto; - font-size: 14px; - color: #888; - text-align: right; -} - -div.footer a { - color: #888; -} - -p.caption { - font-family: inherit; - font-size: inherit; -} - - -div.relations { - display: none; -} - - -div.sphinxsidebar a { - color: #444; - text-decoration: none; - border-bottom: 1px dotted #999; -} - -div.sphinxsidebar a:hover { - 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-table.footnote td { - padding: 0.3em 0.5em; -} - -dl { - margin: 0; - padding: 0; -} - -dl dd { - margin-left: 30px; -} - -blockquote { - margin: 0 0 0 30px; - padding: 0; -} - -ul, ol { - /* Matches the 30px from the narrow-screen "li > ul" selector below */ - margin: 10px 0 10px 30px; - padding: 0; -} - -pre { - background: #EEE; - padding: 7px 30px; - margin: 15px 0px; - line-height: 1.3em; -} - -div.viewcode-block:target { - background: #ffd; -} - -dl pre, blockquote pre, li pre { - margin-left: 0; - padding-left: 30px; -} - -tt, code { - background-color: #ecf0f3; - color: #222; - /* padding: 1px 2px; */ -} - -tt.xref, code.xref, a tt { - background-color: #FBFBFB; - border-bottom: 1px solid #fff; -} - -a.reference { - text-decoration: none; - border-bottom: 1px dotted #004B6B; -} - -/* Don't put an underline on images */ -a.image-reference, a.image-reference:hover { - border-bottom: none; -} - -a.reference:hover { - border-bottom: 1px solid #6D4100; -} - -a.footnote-reference { - text-decoration: none; - font-size: 0.7em; - vertical-align: top; - border-bottom: 1px dotted #004B6B; -} - -a.footnote-reference:hover { - border-bottom: 1px solid #6D4100; -} - -a:hover tt, a:hover code { - background: #EEE; -} - - -@media screen and (max-width: 870px) { - - div.sphinxsidebar { - display: none; - } - - div.document { - width: 100%; - } - - div.documentwrapper { - margin-left: 0; - margin-top: 0; - margin-right: 0; - margin-bottom: 0; - } - - div.bodywrapper { - margin-top: 0; - margin-right: 0; - margin-bottom: 0; - margin-left: 0; - } - - ul { - margin-left: 0; - } - - li > ul { - /* Matches the 30px from the "ul, ol" selector above */ - margin-left: 30px; - } - - .document { - width: auto; - } - - .footer { - width: auto; - } - - .bodywrapper { - margin: 0; - } - - .footer { - width: auto; - } - - .github { - display: none; - } - - - -} - - - -@media screen and (max-width: 875px) { - - body { - margin: 0; - padding: 20px 30px; - } - - div.documentwrapper { - float: none; - background: #fff; - } - - div.sphinxsidebar { - display: block; - float: none; - width: 102.5%; - margin: 50px -30px -20px -30px; - padding: 10px 20px; - background: #333; - color: #FFF; - } - - div.sphinxsidebar h3, div.sphinxsidebar h4, div.sphinxsidebar p, - div.sphinxsidebar h3 a { - color: #fff; - } - - div.sphinxsidebar a { - color: #AAA; - } - - div.sphinxsidebar p.logo { - display: none; - } - - div.document { - width: 100%; - margin: 0; - } - - div.footer { - display: none; - } - - div.bodywrapper { - margin: 0; - } - - div.body { - min-height: 0; - padding: 0; - } - - .rtd_doc_footer { - display: none; - } - - .document { - width: auto; - } - - .footer { - width: auto; - } - - .footer { - width: auto; - } - - .github { - display: none; - } -} - - -/* misc. */ - -.revsys-inline { - display: none!important; -} - -/* Make nested-list/multi-paragraph items look better in Releases changelog - * pages. Without this, docutils' magical list fuckery causes inconsistent - * formatting between different release sub-lists. - */ -div#changelog > div.section > ul > li > p:only-child { - margin-bottom: 0; -} - -/* Hide fugly table cell borders in ..bibliography:: directive output */ -table.docutils.citation, table.docutils.citation td, table.docutils.citation th { - border: none; - /* Below needed in some edge cases; if not applied, bottom shadows appear */ - -moz-box-shadow: none; - -webkit-box-shadow: none; - box-shadow: none; -} diff --git a/nbs/blog/index.qmd b/nbs/blog/index.qmd deleted file mode 100644 index 90f738e4..00000000 --- a/nbs/blog/index.qmd +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: DABEST Blog -subtitle: News, tips, and commentary about all things dabest -listing: - sort: "date desc" - contents: "posts" - sort-ui: false - filter-ui: false - categories: true - feed: true -page-layout: full ---- \ No newline at end of file diff --git a/nbs/blog/posts/a-dabest2-preprint/a-dabest2-preprint.ipynb b/nbs/blog/posts/a-dabest2-preprint/a-dabest2-preprint.ipynb deleted file mode 100644 index 3d13476b..00000000 --- a/nbs/blog/posts/a-dabest2-preprint/a-dabest2-preprint.ipynb +++ /dev/null @@ -1,49 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "3aaa3ea8", - "metadata": {}, - "source": [ - "# Preprint: Getting over ANOVA\n", - "\n", - "- order: 3" - ] - }, - { - "cell_type": "markdown", - "id": "1637313a", - "metadata": {}, - "source": [ - "Here's a dirty secret about ANOVA: it tests a null hypothesis that nobody cares about. When you run a one-way ANOVA, you're testing whether \"all group means are equal.\" But even if you reject this hypothesis, you learn nothing about which groups differ, in which direction, or by how much.\n", - "\n", - "So you embark on a second analytical step: multiple two-group comparisons. A modest six-group experiment suddenly requires testing 15 hypotheses. To manage this multiplicity, you apply corrections like Bonferroni, which undermine your statistical power. What you posed as a focused research question has sprawled into a complex web of subsidiary tests, forced by the ANOVA ritual.\n", - "\n", - "Our new preprint, \"Getting over ANOVA: Estimation graphics for multi-group comparisons,\" makes the case for a better approach. Estimation statistics encourages you to compare each test group to a single control, focusing on the effect sizes that actually matter. A six-group experiment focuses attention on just five effect sizes with confidence intervals, showing magnitude and precision directly.\n", - "\n", - "The preprint introduces estimation methods for a range of multi-group designs: repeated-measures experiments, 2×2 factorial designs, binary outcome data, and mini-meta analysis for internal replicates. Each can replace data-analysis practices used in thousands of studies every year.\n", - "\n", - "Read our new preprint here: https://doi.org/10.64898/2026.01.26.701654" - ] - }, - { - "cell_type": "markdown", - "id": "a189c68d", - "metadata": {}, - "source": [ - "Also posted on [LinkedIn](https://www.linkedin.com/posts/adam-claridge-chang-9a00819_statistics-openscience-datavisualization-activity-7422207336259190785-w1LT) [#Statistics](https://www.linkedin.com/search/results/all/?keywords=%23statistics&origin=HASH_TAG_FROM_FEED) [#OpenScience](https://www.linkedin.com/search/results/all/?keywords=%23openscience&origin=HASH_TAG_FROM_FEED) [#DataVisualization](https://www.linkedin.com/search/results/all/?keywords=%23datavisualization&origin=HASH_TAG_FROM_FEED) [#Research](https://www.linkedin.com/search/results/all/?keywords=%23research&origin=HASH_TAG_FROM_FEED)" - ] - }, - { - "cell_type": "markdown", - "id": "efdc1c76", - "metadata": {}, - "source": [ - "![](preprint_fig.png)" - ] - } - ], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/blog/posts/bootstraps/bootstraps.ipynb b/nbs/blog/posts/bootstraps/bootstraps.ipynb deleted file mode 100644 index 8a5f73c9..00000000 --- a/nbs/blog/posts/bootstraps/bootstraps.ipynb +++ /dev/null @@ -1,223 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "1a3ec507", - "metadata": {}, - "source": [ - "# Bootstrap Confidence Intervals\n", - "\n", - "> Explanation of the bootstrap method and its application in hypothesis testing using **DABEST**.\n", - "\n", - "- order: 3" - ] - }, - { - "cell_type": "markdown", - "id": "6321ea6f", - "metadata": {}, - "source": [ - "## Sampling from populations" - ] - }, - { - "cell_type": "markdown", - "id": "49954f18", - "metadata": {}, - "source": [ - "In a typical scientific experiment, we are interested in two populations\n", - "(Control and Test), and whether there is a difference between their means\n", - "$(\\mu_{Test}-\\mu_{Control})$.\n" - ] - }, - { - "cell_type": "markdown", - "id": "e12b893f", - "metadata": {}, - "source": [ - "![](bootstrap-1.png)" - ] - }, - { - "cell_type": "markdown", - "id": "5573045c", - "metadata": {}, - "source": [ - "We go about this by collecting observations from the control population and from the test population." - ] - }, - { - "cell_type": "markdown", - "id": "359a36fb", - "metadata": {}, - "source": [ - "![](bootstrap-2.png)" - ] - }, - { - "cell_type": "markdown", - "id": "786aa6c8", - "metadata": {}, - "source": [ - "We can easily compute the mean difference in our observed samples. This is our\n", - "estimate of the population effect size that we are interested in.\n", - "\n", - "**But how do we obtain a measure of the precision and confidence about our estimate?\n", - "Can we get a sense of how it relates to the population mean difference?**\n" - ] - }, - { - "cell_type": "markdown", - "id": "bfadcada", - "metadata": {}, - "source": [ - "## The bootstrap confidence interval" - ] - }, - { - "cell_type": "markdown", - "id": "fe977cc6", - "metadata": {}, - "source": [ - "We want to obtain a 95% confidence interval (95% CI) around our estimate of the mean difference. The 95% indicates that any such confidence interval will capture the population mean difference 95% of the time.\n", - "\n", - "In other words, if we were to repeat our experiment 100 times, gathering 100 independent sets of observations and computing a 95% confidence interval for the mean difference each time, 95 of these intervals would capture the population mean difference. That is to say, we can be 95% confident the interval contains the true mean of the population.\n", - "\n", - "We can calculate the 95% CI of the mean difference with [bootstrap resampling](https://en.wikipedia.org/wiki/Bootstrapping_(statistics)).\n" - ] - }, - { - "cell_type": "markdown", - "id": "b9d76d7f", - "metadata": {}, - "source": [ - "### The bootstrap in action" - ] - }, - { - "cell_type": "markdown", - "id": "0685adaf", - "metadata": {}, - "source": [ - "The [`bootstrap`](#1)[1] is a simple but powerful technique. It was [first described](https://projecteuclid.org/euclid.aos/1176344552) by [Bradley Efron](https://statistics.stanford.edu/people/bradley-efron).\n", - "\n", - "It creates multiple *resamples* (with replacement) from a single set of\n", - "observations, and computes the effect size of interest on each of these\n", - "resamples. The bootstrap resamples of the effect size can then be used to\n", - "determine the 95% CI.\n", - "\n", - "With computers, we can perform 5000 resamples very easily." - ] - }, - { - "cell_type": "markdown", - "id": "87e785c4", - "metadata": {}, - "source": [ - "![](bootstrap-3.png)" - ] - }, - { - "cell_type": "markdown", - "id": "0b68ae9c", - "metadata": {}, - "source": [ - "The resampling distribution of the difference in means approaches a normal\n", - "distribution. This is due to the [Central Limit Theorem](https://en.wikipedia.org/wiki/Central_limit_theorem): a large number of\n", - "independent random samples will approach a normal distribution even if the\n", - "underlying population is not normally distributed.\n", - "\n", - "Bootstrap resampling gives us two important benefits:\n", - "\n", - "1. *Non-parametric statistical analysis.* There is no need to assume that our\n", - "observations, or the underlying populations, are normally distributed. Thanks to\n", - "the Central Limit Theorem, the resampling distribution of the effect size will\n", - "approach a normality.\n", - "\n", - "2. *Easy construction of the 95% CI from the resampling distribution.* In the context of bootstrap resampling or other non-parametric methods, the 2.5th and 97.5th percentiles are often used to define the lower and upper limits, respectively. The use of these percentiles ensures that the resulting interval contains the central 95% of the resampled distribution. Such an interval construction is known as a *percentile interval*." - ] - }, - { - "cell_type": "markdown", - "id": "cc5b9fa3", - "metadata": {}, - "source": [ - "## Adjusting for asymmetrical resampling distributions" - ] - }, - { - "cell_type": "markdown", - "id": "e83634e9", - "metadata": {}, - "source": [ - "While resampling distributions of the difference in means often have a normal\n", - "distribution, it is not uncommon to encounter a skewed distribution. Thus, Efron\n", - "developed the [bias-corrected and accelerated bootstrap](https://en.wikipedia.org/wiki/Bootstrapping_(statistics)#History) (BCa bootstrap) to account for the skew, and still obtain the central 95% of the\n", - "distribution.\n", - "\n", - "**DABEST** applies the BCa correction to the resampling bootstrap distributions of\n", - "the effect size." - ] - }, - { - "cell_type": "markdown", - "id": "88ab2684", - "metadata": {}, - "source": [ - "![](bootstrap-4.png)" - ] - }, - { - "cell_type": "markdown", - "id": "c1f02904", - "metadata": {}, - "source": [ - "## Estimation plots incorporate bootstrap resampling" - ] - }, - { - "cell_type": "markdown", - "id": "fb1a8fa6", - "metadata": {}, - "source": [ - "The estimation plot produced by DABEST presents the raw data and the bootstrap\n", - "confidence interval of the effect size (the difference in means) side-by-side as\n", - "a single integrated plot." - ] - }, - { - "cell_type": "markdown", - "id": "418f5fb4", - "metadata": {}, - "source": [ - "![](bootstrap-5.png)" - ] - }, - { - "cell_type": "markdown", - "id": "eaad7dd5", - "metadata": {}, - "source": [ - "Thus, it tightly couples a visual presentation of the raw data with an indication of the population mean difference plus its confidence interval." - ] - }, - { - "cell_type": "markdown", - "id": "c540cd14", - "metadata": {}, - "source": [ - "\n", - "`[1]`: The name is derived from the saying \"[pull oneself by one's bootstraps](https://en.wiktionary.org/wiki/pull_oneself_up_by_one%27s_bootstraps)\", often used as an exhortation to achieve success without external help.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/blog/posts/robust-beautiful/four_samples.csv b/nbs/blog/posts/robust-beautiful/four_samples.csv deleted file mode 100644 index 547296b5..00000000 --- a/nbs/blog/posts/robust-beautiful/four_samples.csv +++ /dev/null @@ -1,16 +0,0 @@ -A,B,C,D -8.109188439895592,9.33184689521894,9.354787823122058,12.672612419124242 -10.131749781263766,9.33184689521894,9.455474658800501,7.66146433397944 -7.178065881431648,14.342995181076896,9.577728760474645,7.66146410459298 -7.070698283373329,6.826272752289961,9.580692407448097,7.66146410459298 -14.530513949101707,11.837421038147918,8.976055745893488,7.66146410459298 -12.837160139759924,9.33184689521894,13.280120639175362,12.672612419124242 -8.98967523846707,9.33184689521894,5.284223374586355,7.66146410459298 -9.548347716611921,14.342995181076896,9.580597428100837,12.672612419124242 -10.98994063849879,9.33184689521894,9.547969576803277,7.66146410459298 -12.402350479094743,6.826272752289961,9.435510391485826,7.66146410459298 -11.694550072150143,6.826272752289961,9.277034488877351,12.672612419124242 -4.8799780463809785,9.33184689521894,9.389691597155812,12.672612419124242 -9.528364669906528,9.33184689521894,9.586309213728654,12.672612419124242 -9.392042031837274,9.33184689521894,17.540147276068026,7.66146410459298 -12.717374632226587,14.342995181076896,10.13365661827971,12.672612419124242 diff --git a/nbs/blog/posts/robust-beautiful/robust-beautiful.ipynb b/nbs/blog/posts/robust-beautiful/robust-beautiful.ipynb deleted file mode 100644 index 6672337e..00000000 --- a/nbs/blog/posts/robust-beautiful/robust-beautiful.ipynb +++ /dev/null @@ -1,419 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "5b2f1637", - "metadata": {}, - "source": [ - "# Robust and Beautiful Statistical Visualization\n", - "\n", - "- order: 3" - ] - }, - { - "cell_type": "markdown", - "id": "f086f2b7", - "metadata": {}, - "source": [ - "## Current plots do not work" - ] - }, - { - "cell_type": "markdown", - "id": "3146c6cf", - "metadata": {}, - "source": [ - "What is data visualization? Battle-Baptiste and Rusert (2018) give a\n", - "cogent and compelling definition:\n", - "\n", - " [`Data visualization`](#1)[1] is the rendering of information in a visual\n", - " format to help communicate data while also generating new patterns\n", - " and knowledge through the act of visualization itself. \n", - "\n", - "Sadly, too many figures and visualizations in modern academic\n", - "publications seemingly fail to \"generate new patterns and knowledge\n", - "through the act of visualization itself\". Here, we propose a solution:\n", - "*the estimation plot*." - ] - }, - { - "cell_type": "markdown", - "id": "d30acb26", - "metadata": {}, - "source": [ - "### The barplot conceals the underlying shape" - ] - }, - { - "cell_type": "markdown", - "id": "c5fee24b", - "metadata": {}, - "source": [ - "By only displaying the mean and standard deviation, barplots do not\n", - "accurately represent the underlying distribution of the data.\n" - ] - }, - { - "cell_type": "markdown", - "id": "9e1ffa32", - "metadata": {}, - "source": [ - "![](robust_5_1.png)" - ] - }, - { - "cell_type": "markdown", - "id": "21f7b0cf", - "metadata": {}, - "source": [ - "In the above figure, four different samples with wildly different\n", - "distributions--as seen in the swarmplot on the left panel--look exactly\n", - "the same when visualized with a barplot on the right panel. (You can\n", - "download the [dataset](four_samples.csv) to see for yourself.)\n", - "\n", - "We're not the first ones (see these articles:\n", - "[article 1](https://www.nature.com/articles/nmeth.2837),\n", - "[article 2](http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002128),\n", - "or\n", - "[article 3](https://onlinelibrary.wiley.com/doi/full/10.1111/ejn.13400))\n", - "to point out the barplot's fatal flaws. Indeed, it is both sobering and\n", - "fascinating to realise that the barplot is a [17th century\n", - "invention](https://en.wikipedia.org/wiki/Bar_chart#History) initially\n", - "used to compare single values, not to compare summarized and aggregated\n", - "data. " - ] - }, - { - "cell_type": "markdown", - "id": "a9be5a36", - "metadata": {}, - "source": [ - "### The boxplot does not convey sample size" - ] - }, - { - "cell_type": "markdown", - "id": "0efd5b25", - "metadata": {}, - "source": [ - "Boxplots are another widely used visualization tool. They arguably do\n", - "include more information for each sample (medians, quartiles, maxima,\n", - "minima, and outliers), but they do not convey to the viewer the size of\n", - "each sample." - ] - }, - { - "cell_type": "markdown", - "id": "9bff917e", - "metadata": {}, - "source": [ - "![](robust_7_1.png)" - ] - }, - { - "cell_type": "markdown", - "id": "7cd5dc07", - "metadata": {}, - "source": [ - "The figure above visualizes the same four samples as a swarmplot (left\n", - "panel) and as a boxplot. If we did not label the x-axis with the sample\n", - "size, it would be impossible to definitively distinguish the sample with\n", - "5 observations from the sample with 50.\n", - "\n", - "Even if the world gets rid of barplots and boxplots, the problems\n", - "plaguing statistical practices will remain unsolved. Null-hypothesis\n", - "significance testing--the dominant statistical paradigm in basic\n", - "research--does not indicate the **effect size**, or its **confidence\n", - "interval**." - ] - }, - { - "cell_type": "markdown", - "id": "3d2c600c", - "metadata": {}, - "source": [ - "## Introducing the Estimation Plot" - ] - }, - { - "cell_type": "markdown", - "id": "41652f5d", - "metadata": {}, - "source": [ - "![](robust_9_0.png)" - ] - }, - { - "cell_type": "markdown", - "id": "a7e3b1ad", - "metadata": {}, - "source": [ - "This is a *Gardner-Altman* estimation plot. The plot draws its name from\n", - "[Martin J. Gardner](https://www.independent.co.uk/news/people/obituary-professor-martin-gardner-1470261.html)\n", - "and [Douglas Altman](https://www.bmj.com/content/361/bmj.k2588), who are\n", - "credited with [creating the design](https://www.bmj.com/content/bmj/292/6522/746.full.pdf) in 1986.\n", - "\n", - "This plot has two key features:\n", - "\n", - " 1. It presents all data points as a swarmplot, ordering each point to display the underlying distribution.\n", - "\n", - " 2. It presents the effect size as a *bootstrap 95% confidence interval* (95% CI)\n", - " on a separate but aligned axis. The effect size is displayed to the right of the raw data, and the mean of the test group is aligned with the effect size.\"\n", - "\n", - "
Thus, estimation plots are robust, beautiful, and convey important statistical\n", - "information elegantly and efficiently.
\n", - "\n", - "\n", - "An estimation plot obtains and displays the 95% CI through nonparametric\n", - "bootstrap resampling. This enables visualization of the confidence interval as\n", - "a graded sampling distribution.\n", - "\n", - "This is one important difference between estimation plots created by DABEST, and\n", - "the original Gardner-Altman design. Here, the 95% CI is computed through\n", - "parametric methods, and displayed as a vertical error bar.\n", - "\n", - "Read more about this technique at [bootstraps](../bootstraps/bootstraps.ipynb). \n" - ] - }, - { - "cell_type": "markdown", - "id": "fe2e12b5", - "metadata": {}, - "source": [ - "### Introducing Estimation Statistics" - ] - }, - { - "cell_type": "markdown", - "id": "e21d785b", - "metadata": {}, - "source": [ - "Estimation plots emerge from *estimation statistics*, a simple\n", - "[framework](https://thenewstatistics.com/itns/) that avoids the\n", - "[pitfalls of significance\n", - "testing](https://www.nature.com/articles/nmeth.3288). It focuses on\n", - "the effect sizes of one's experiment/interventions, and uses familiar\n", - "statistical concepts: means, mean differences, and error bars.\n", - "\n", - "Significance testing calculates the probability (the *P* value) that the\n", - "experimental data would be observed, if the intervention did not produce\n", - "a change in the metric measured (i.e. the null hypothesis). This leads\n", - "analysts to apply a false dichotomy on the experimental intervention.\n", - "\n", - "Estimation statistics, on the other hand, focuses on the magnitude of\n", - "the effect (the effect size) and its precision. This encourages analysts\n", - "to gain a deeper understanding of the metrics used, and how they relate\n", - "to the natural processes being studied." - ] - }, - { - "cell_type": "markdown", - "id": "36e0fe1e", - "metadata": {}, - "source": [ - "## An Estimation Plot For Every Experimental Design" - ] - }, - { - "cell_type": "markdown", - "id": "d2668b9c", - "metadata": {}, - "source": [ - "For each of the most routine significance tests, there is an estimation\n", - "replacement:" - ] - }, - { - "cell_type": "markdown", - "id": "ab4458b8", - "metadata": {}, - "source": [ - "### Unpaired Student's t-test --> Two-group estimation plot" - ] - }, - { - "cell_type": "markdown", - "id": "af5b3ccb", - "metadata": {}, - "source": [ - "![](robust_12_0.png)" - ] - }, - { - "cell_type": "markdown", - "id": "e408503b", - "metadata": {}, - "source": [ - "### Paired Student's t-test --> Paired estimation plot" - ] - }, - { - "cell_type": "markdown", - "id": "6998d765", - "metadata": {}, - "source": [ - "\n", - "The Gardner-Altman estimation plot can also display effect sizes for\n", - "repeated measures (aka a paired experimental design) using a [Tufte\n", - "slopegraph](http://charliepark.org/slopegraphs/) instead of a\n", - "swarmplot." - ] - }, - { - "cell_type": "markdown", - "id": "fdb34170", - "metadata": {}, - "source": [ - "![](robust_14_0.png)" - ] - }, - { - "cell_type": "markdown", - "id": "6e6fd77d", - "metadata": {}, - "source": [ - "### One-way ANOVA + multiple comparisons --> Multi two-group estimation plot" - ] - }, - { - "cell_type": "markdown", - "id": "b7b643f8", - "metadata": {}, - "source": [ - "For comparisons between three or more groups that typically employ analysis\n", - "of variance (ANOVA) methods, one can use the [Cumming estimation\n", - "plot](https://en.wikipedia.org/wiki/Estimation_statistics#Cumming_plot),\n", - "named after [Geoff Cumming](https://www.youtube.com/watch?v=nDN-hcKR7j8), and draws its\n", - "design heavily from his 2012 textbook [\"Understanding the New Statistics\"](https://www.routledge.com/Understanding-The-New-Statistics-Effect-Sizes-Confidence-Intervals-and/Cumming/p/book/9780415879682).\n", - "This estimation plot design can be considered a variant of the\n", - "Gardner-Altman plot.\n" - ] - }, - { - "cell_type": "markdown", - "id": "3f38c1fe", - "metadata": {}, - "source": [ - "![](robust_16_0.png)" - ] - }, - { - "cell_type": "markdown", - "id": "b443b0a8", - "metadata": {}, - "source": [ - "The effect size and 95% CIs are still plotted on a separate axis, but\n", - "unlike the Gardner-Altman plot, this axis is positioned beneath the raw\n", - "data.\n", - "\n", - "Such a design frees up visual space in the upper panel, allowing the\n", - "display of summary measurements (mean ± standard deviation) for each\n", - "group. These are shown as gapped lines to the right of each group. The\n", - "mean of each group is indicated as a gap in the line, adhering to Edward\n", - "Tufte's dictum to [keep the data-ink ratio\n", - "low](https://medium.com/@plotlygraphs/maximizing-the-data-ink-ratio-in-dashboards-and-slide-deck-7887f7c1fab).\n" - ] - }, - { - "cell_type": "markdown", - "id": "da932b0e", - "metadata": {}, - "source": [ - "### Repeated measures ANOVA --> Multi paired estimation plot" - ] - }, - { - "cell_type": "markdown", - "id": "f0442420", - "metadata": {}, - "source": [ - "![](robust_19_0.png)" - ] - }, - { - "cell_type": "markdown", - "id": "2769a16c", - "metadata": {}, - "source": [ - "### Ordered groups ANOVA --> Shared-control estimation plot" - ] - }, - { - "cell_type": "markdown", - "id": "6eb943a9", - "metadata": {}, - "source": [ - "![](robust_21_0.png)" - ] - }, - { - "cell_type": "markdown", - "id": "7bde79f4", - "metadata": {}, - "source": [ - "### Estimation Plots: The Way Forward" - ] - }, - { - "cell_type": "markdown", - "id": "2e221c93", - "metadata": {}, - "source": [ - "In summary, estimation plots offer five key benefits relative to\n", - "conventional plots:" - ] - }, - { - "cell_type": "markdown", - "id": "fddf7595", - "metadata": {}, - "source": [ - "| | Barplot | Boxplot | Estimation Plot |\n", - "|---------------------|---------|---------|----------------|\n", - "| Displays all observed values | NO | NO | Yes |\n", - "| Avoids false dichotomy | NO | NO | Yes |\n", - "| Focusses on effect size | NO | NO | Yes |\n", - "| Visualizes effect size precision | NO | NO | Yes |\n", - "| Shows mean difference distribution | NO | NO | Yes |" - ] - }, - { - "cell_type": "markdown", - "id": "714f26b5", - "metadata": {}, - "source": [ - "You can create estimation plots using the DABEST (Data Analysis with\n", - "Bootstrap Estimation) packages, which are available in\n", - "[Matlab](https://github.com/ACCLAB/DABEST-Matlab),\n", - "[Python](https://github.com/ACCLAB/DABEST-python), and\n", - "[R](https://github.com/ACCLAB/dabestr)." - ] - }, - { - "cell_type": "markdown", - "id": "c55bb639", - "metadata": {}, - "source": [ - "\n", - "`[1]`:[W. E. B. Du Bois's Data Portraits: Visualizing Black America](https://www.papress.com/html/product.details.dna?isbn=9781616897062). Edited by Whitney Battle-Baptiste and Britt Rusert, Princeton Architectural Press, 2018\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a43239a3", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/images/DABEST-square-outline.svg b/nbs/images/DABEST-square-outline.svg deleted file mode 100644 index 4290401c..00000000 --- a/nbs/images/DABEST-square-outline.svg +++ /dev/null @@ -1,45 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - diff --git a/nbs/images/amd.svg b/nbs/images/amd.svg deleted file mode 100644 index ecfeab70..00000000 --- a/nbs/images/amd.svg +++ /dev/null @@ -1,15 +0,0 @@ - - - - - - - - - - - - - - - diff --git a/nbs/images/bootstrap-1.png b/nbs/images/bootstrap-1.png deleted file mode 100644 index 32fdc5d0..00000000 Binary files a/nbs/images/bootstrap-1.png and /dev/null differ diff --git a/nbs/images/bootstrap-2.png b/nbs/images/bootstrap-2.png deleted file mode 100644 index 4a21076e..00000000 Binary files a/nbs/images/bootstrap-2.png and /dev/null differ diff --git a/nbs/images/bootstrap-3.png b/nbs/images/bootstrap-3.png deleted file mode 100644 index 0d640299..00000000 Binary files a/nbs/images/bootstrap-3.png and /dev/null differ diff --git a/nbs/images/bootstrap-4.png b/nbs/images/bootstrap-4.png deleted file mode 100644 index ab0287c2..00000000 Binary files a/nbs/images/bootstrap-4.png and /dev/null differ diff --git a/nbs/images/bootstrap-5.png b/nbs/images/bootstrap-5.png deleted file mode 100644 index 70a57246..00000000 Binary files a/nbs/images/bootstrap-5.png and /dev/null differ diff --git a/nbs/images/dataframe_out.png b/nbs/images/dataframe_out.png deleted file mode 100644 index ad836350..00000000 Binary files a/nbs/images/dataframe_out.png and /dev/null differ diff --git a/nbs/images/docs.svg b/nbs/images/docs.svg deleted file mode 100644 index a9e2d3f1..00000000 --- a/nbs/images/docs.svg +++ /dev/null @@ -1,8 +0,0 @@ - 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- - - - - - - diff --git a/nbs/images/transform.svg b/nbs/images/transform.svg deleted file mode 100644 index 3d27613d..00000000 --- a/nbs/images/transform.svg +++ /dev/null @@ -1,20 +0,0 @@ - - - - - - - - - - - - - - - - - - - - diff --git a/nbs/images/vscode.svg b/nbs/images/vscode.svg deleted file mode 100644 index 40766f05..00000000 --- a/nbs/images/vscode.svg +++ /dev/null @@ -1,7 +0,0 @@ - - - - - - - diff --git a/nbs/index.qmd.py b/nbs/index.qmd.py deleted file mode 100644 index f6dbe449..00000000 --- a/nbs/index.qmd.py +++ /dev/null @@ -1,67 +0,0 @@ -"""--- -title: Home -pagetitle: dabest -page-layout: custom -section-divs: false -css: index.css -toc: false ----""" - -from fastcore.foundation import L -from nbdev import qmd - -def img(fname, classes=None, **kwargs): return qmd.img(f"images/{fname}", classes=classes, **kwargs) -def btn(txt, link): return qmd.btn(txt, link=link, classes=['btn-action-primary', 'btn-action', 'btn', 'btn-success', 'btn-lg']) -def banner(txt, classes=None, style=None): return qmd.div(txt, L('hero-banner')+classes, style=style) - -features = L( - ('estimations', 'Shift from p-values to effect size and confidence intervals for richer data insights'), - # ('User-Friendly Interface', 'Accessible to both novices and experts, ensuring ease of use'), - ('gaussian', 'Robust and elegant statistical visualizations for efficient information conveyance'), - ('python', 'Seamless integration with the scientific Python libraries for comprehensive data analysis'), - ('customizable', 'Flexible plot customization to meet diverse presentation needs'), - ('jupyter', 'Promotes reproducibility with easy sharing of data and analysis code'), - # ('Educational Resources', 'Extensive documentation and tutorials to enhance statistical literacy'), - ('git', 'Ongoing support and development through an engaged user community') -) - -def industry(im, **kwargs): return qmd.div(img(im, **kwargs), ["g-col-12", "g-col-sm-6", "g-col-md-3"]) - -def testm(im, nm, detl, txt): - return qmd.div(f"""{img(im, link=True)} - -# {nm} - -## {detl} - -### {txt}""", ["testimonial", "g-col-12", "g-col-md-6"]) - - -def feature(im, desc): return qmd.div(f"{img(im+'.svg')}\n\n{desc}\n", ['feature', 'g-col-12', 'g-col-sm-6', 'g-col-md-4']) - -feature_d = qmd.div('\n'.join(features.starmap(feature)), ['grid', 'gap-4'], style={"padding-bottom": "60px"}) - -def b(*args, **kwargs): print(banner (*args, **kwargs)) -def d(*args, **kwargs): print(qmd.div(*args, **kwargs)) - -### -# Output section -### - -b(f"""# Data Analysis using
Bootstrap\-Coupled ESTimation - -### Analyze your data with effect sizes and beautiful estimation plots. - -{btn('Get started', '/01-getting_started.ipynb')} - -{img('splash-propplot.png', style={"margin-top": "100px", "margin-bottom": "20px"}, link=True)}""", "content-block") - -feature_h = banner(f"""## Robust and Beautiful
Statistical Visualization - -### Estimation statistics is a simple framework that avoids the pitfalls of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to a false dichotomy engendered by P values.""") - -d(feature_h+feature_d, "content-block") - -b(f"""## Get started in seconds - -{btn('Install dabest', '/01-getting_started.ipynb')}""", 'content-block', style={"margin-top": "40px"}) \ No newline at end of file diff --git a/nbs/iris.png b/nbs/iris.png deleted file mode 100644 index 92d58275..00000000 Binary files a/nbs/iris.png and /dev/null differ diff --git a/nbs/nbdev.yml b/nbs/nbdev.yml deleted file mode 100644 index 34cfc3c6..00000000 --- a/nbs/nbdev.yml +++ /dev/null @@ -1,9 +0,0 @@ -project: - output-dir: _docs - -website: - title: "dabest" - site-url: "https://acclab.github.io/DABEST-python" - description: "Data Analysis and Visualization using Bootstrap-Coupled Estimation." - repo-branch: master - repo-url: "https://github.com/acclab/DABEST-python" diff --git a/nbs/pytest.ini b/nbs/pytest.ini deleted file mode 100644 index 5856c886..00000000 --- a/nbs/pytest.ini +++ /dev/null @@ -1,9 +0,0 @@ -[pytest] -filterwarnings = - ignore::UserWarning - ignore::DeprecationWarning - -addopts = --mpl --mpl-baseline-path=nbs/tests/mpl_image_tests/baseline_images - -markers = - mpl_image_compare: mark a test as implementing mpl image comparison. \ No newline at end of file diff --git a/nbs/read_me.ipynb b/nbs/read_me.ipynb deleted file mode 100644 index de60a0a6..00000000 --- a/nbs/read_me.ipynb +++ /dev/null @@ -1,263 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "205a828a", - "metadata": {}, - "source": [ - "# DABEST-Python" - ] - }, - { - "cell_type": "markdown", - "id": "5164f940", - "metadata": {}, - "source": [ - "[![minimal Python version](https://img.shields.io/badge/Python%3E%3D-3.10-6666ff.svg)](https://www.anaconda.com/distribution/)\n", - "[![PyPI version](https://badge.fury.io/py/dabest.svg)](https://badge.fury.io/py/dabest)\n", - "[![Downloads](https://img.shields.io/pepy/dt/dabest.svg\n", - ")](https://pepy.tech/project/dabest)\n", - "[![Free-to-view citation](https://zenodo.org/badge/DOI/10.1038/s41592-019-0470-3.svg)](https://rdcu.be/bHhJ4)\n", - "[![License](https://img.shields.io/badge/License-BSD%203--Clause--Clear-orange.svg)](https://spdx.org/licenses/BSD-3-Clause-Clear.html)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "8fcb9b6e", - "metadata": {}, - "source": [ - "## Recent Version Update\n", - "\n", - "\n", - "**✨ DABEST “Bingka” v2025.10.20 for Python is now released! ✨**\n", - "\n", - "Dear DABEST users,\n", - "The latest version of the DABEST Python library brings new visualizations, refined plots, and improved accuracy.\n", - "\n", - "1. **Whorlmap 🌀: Compact visualization for multi-dimensional effects**\n", - " \n", - " Introducing **Whorlmap**, a new way to visualize effect sizes from multiple comparisons in a compact, grid-based format.\n", - "\n", - " Whorlmaps condense information from the full bootstrap distributions of many contrast objects into a **2D heatmap-style grid of “whorled” cells**. This provides an overview of the entire dataset while preserving the underlying distributional detail.\n", - "\n", - " They are especially useful for large-scale or multi-condition experiments, serving as a **space-efficient alternative to stacked forest plots**.\n", - "\n", - " You can generate a Whorlmap directly from multi-dimensional DABEST objects using the `.whorlmap()` method. See the [Whorlmap tutorial](https://acclab.github.io/DABEST-python/tutorials/10-whorlmap.html) for more details.\n", - "\n", - "2. **Slopegraphs 📈: Enhanced summaries for paired data**\n", - " \n", - " Slopegraphs for paired continuous data now display **group summary statistics**.\n", - "\n", - " - By default, a thick trend line connects group means, with vertical bars showing standard deviation.\n", - " \n", - " - Choose the summary type via the group_summaries argument in `.plot()` — options include `'mean_sd'`, `'median_quartiles'`, or `None`.\n", - " \n", - " - Customize appearance with `group_summaries_kwargs`.\n", - "\n", - " See the Group Summaries section in the [Plot Aesthetics tutorial](https://acclab.github.io/DABEST-python/tutorials/08-plot_aesthetics.html) for more details.\n", - "\n", - "3. **Mini-meta Weighted Delta Fix 🧮**\n", - " \n", - " The weighted delta calculation in mini-meta plots has been updated for **greater accuracy and consistency**.\n", - "\n", - "4. **Expanded custom_palette functionality 🎨**\n", - " \n", - " - **Barplots (unpaired, proportional):**\n", - " `custom_palette` can now take `1` and `0` as dictionary keys to color the filled and unfilled portions of the plot.\n", - "\n", - " - **Slopegraphs (paired, non-proportional):**\n", - " `custom_palette` can now color contrast bars and effect-size curves.\n", - "\n", - " See the Custom Palette section in the [Plot Aesthetics tutorial](https://acclab.github.io/DABEST-python/tutorials/08-plot_aesthetics.html) for examples.\n", - "\n", - "Thank you for your continued support! \n", - "\n", - "*The DABEST Development Team*" - ] - }, - { - "cell_type": "markdown", - "id": "ac5ef3e3", - "metadata": {}, - "source": [ - "## Contents\n", - "\n", - "- [About](#about)\n", - "- [Installation](#installation)\n", - "- [Usage](#usage)\n", - "- [How to cite](#how-to-cite)\n", - "- [Bugs](#bugs)\n", - "- [Contributing](#contributing)\n", - "- [Acknowledgements](#acknowledgements)\n", - "- [Testing](#testing)\n", - "- [DABEST in other languages](#dabest-in-other-languages)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "be09ee89", - "metadata": {}, - "source": [ - "## About\n", - "\n", - "DABEST is a package for **D**ata **A**nalysis using **B**ootstrap-Coupled **EST**imation.\n", - "\n", - "[Estimation statistics](https://en.wikipedia.org/wiki/Estimation_statistics) are a [simple framework](https://thenewstatistics.com/itns/) that avoids the [pitfalls](https://www.nature.com/articles/nmeth.3288) of significance testing. It employs familiar statistical concepts such as means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment or intervention, rather than succumbing to a false dichotomy engendered by *P* values.\n", - "\n", - "An estimation plot comprises two key features.\n", - "\n", - "1. It presents all data points as a swarm plot, ordering each point to display the underlying distribution.\n", - "\n", - "2. It illustrates the effect size as a **bootstrap 95% confidence interval** on a **separate but aligned axis**.\n", - "\n", - "![The five kinds of estimation plots](showpiece.png \"The five kinds of estimation plots.\")\n", - "\n", - "DABEST powers [estimationstats.com](https://www.estimationstats.com/), allowing everyone access to high-quality estimation plots.\n" - ] - }, - { - "cell_type": "markdown", - "id": "1044e2c0", - "metadata": {}, - "source": [ - "## Installation\n", - "\n", - "This package is tested on Python 3.11 and onwards.\n", - "It is highly recommended to download the [Anaconda distribution](https://www.continuum.io/downloads) of Python in order to obtain the dependencies easily.\n", - "\n", - "You can install this package via `pip`.\n", - "\n", - "To install, at the command line run\n", - "\n", - "```shell\n", - "pip install dabest\n", - "```\n", - "You can also [clone](https://help.github.com/articles/cloning-a-repository) this repo locally.\n", - "\n", - "Then, navigate to the cloned repo in the command line and run\n", - "\n", - "```shell\n", - "pip install .\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "bf4d69d6", - "metadata": {}, - "source": [ - "## Usage\n", - "\n", - "```python3\n", - "import pandas as pd\n", - "import dabest\n", - "\n", - "# Load the iris dataset. This step requires internet access.\n", - "iris = pd.read_csv(\"https://github.com/mwaskom/seaborn-data/raw/master/iris.csv\")\n", - "\n", - "# Load the above data into `dabest`.\n", - "iris_dabest = dabest.load(data=iris, x=\"species\", y=\"petal_width\",\n", - " idx=(\"setosa\", \"versicolor\", \"virginica\"))\n", - "\n", - "# Produce a Cumming estimation plot.\n", - "iris_dabest.mean_diff.plot();\n", - "```\n", - "![A Cumming estimation plot of petal width from the iris dataset](iris.png)\n", - "\n", - "Please refer to the official [tutorial](https://acclab.github.io/DABEST-python/) for more useful code snippets.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "af18a2c0", - "metadata": {}, - "source": [ - "## How to cite\n", - "**Getting over ANOVA: Estimation graphics for multi-group comparisons**\n", - "\n", - "*Zinan Lu, Jonathan Anns, Yishan Mai, Rou Zhang, Kahseng Lian, Nicole MynYi Lee, Shan Hashir, Lucas Wang Zhuoyu, A. Rosa Castillo Gonzalez, Joses Ho, Hyungwon Choi, Sangyu Xu, Adam Claridge-Chang*\n", - "\n", - "bioRxiv preprint 2026. [10.64898/2026.01.26.701654](http://dx.doi.org/10.64898/2026.01.26.701654)\n", - "\n", - "[PDF](https://www.biorxiv.org/content/10.64898/2026.01.26.701654v1.full.pdf)\n", - "\n", - "\n", - "\n", - "\n", - "**Moving beyond P values: Everyday data analysis with estimation plots**\n", - "\n", - "*Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang*\n", - "\n", - "Nature Methods 2019, 1548-7105. [10.1038/s41592-019-0470-3](http://dx.doi.org/10.1038/s41592-019-0470-3)\n", - "\n", - "[Paywalled publisher site](https://www.nature.com/articles/s41592-019-0470-3); [Free-to-view PDF](https://rdcu.be/bHhJ4)\n", - "\n", - "\n", - "## Bugs\n", - "\n", - "Please report any bugs on the [issue page](https://github.com/ACCLAB/DABEST-python/issues/new).\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "24b19856", - "metadata": {}, - "source": [ - "## Contributing\n", - "\n", - "All contributions are welcome; please read the [Guidelines for contributing](../CONTRIBUTING.md) first.\n", - "\n", - "We also have a [Code of Conduct](../CODE_OF_CONDUCT.md) to foster an inclusive and productive space.\n" - ] - }, - { - "cell_type": "markdown", - "id": "b1e2578a", - "metadata": {}, - "source": [ - "### A wish list for new features\n", - "If you have any specific comments and ideas for new features that you would like to share with us, please read the [Guidelines for contributing](../CONTRIBUTING.md), create a new issue using Feature request template or create a new post in [our Google Group](https://groups.google.com/g/estimationstats)." - ] - }, - { - "cell_type": "markdown", - "id": "a528d95d", - "metadata": {}, - "source": [ - "## Acknowledgements\n", - "\n", - "We would like to thank alpha testers from the [Claridge-Chang lab](https://www.claridgechang.net/): [Sangyu Xu](https://github.com/sangyu), [Xianyuan Zhang](https://github.com/XYZfar), [Farhan Mohammad](https://github.com/farhan8igib), Jurga Mituzaitė, and Stanislav Ott.\n", - "\n", - "\n", - "## Testing\n", - "\n", - "To test DABEST, you need to install [pytest](https://docs.pytest.org/en/latest) and [nbdev](https://nbdev.fast.ai/).\n", - "\n", - "- Run `pytest` in the root directory of the source distribution. This runs the test suite in the folder `dabest/tests/mpl_image_tests`. \n", - "- Run `nbdev_test` in the root directory of the source distribution. This runs the value assertion tests in the folder `dabest/tests`\n", - "\n", - "The test suite ensures that the bootstrapping functions and the plotting functions perform as expected.\n", - "\n", - "For detailed information, please refer to the [test folder](../nbs/tests/README.md)\n", - "\n", - "## DABEST in other languages\n", - "\n", - "DABEST is also available in R ([dabestr](https://github.com/ACCLAB/dabestr)) and Matlab ([DABEST-Matlab](https://github.com/ACCLAB/DABEST-Matlab)).\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/showpiece.png b/nbs/showpiece.png deleted file mode 100644 index 6e46cd50..00000000 Binary files a/nbs/showpiece.png and /dev/null differ diff --git a/nbs/tests/README.md b/nbs/tests/README.md deleted file mode 100644 index 8372b68c..00000000 --- a/nbs/tests/README.md +++ /dev/null @@ -1,14 +0,0 @@ -# Testing - -We use [pytest](https://docs.pytest.org/en/latest) to execute the tests. For testing of plot generation, we use the [mpl plugin](https://github.com/matplotlib/pytest-mpl) for pytest. A range of different plots are created, and compared against the baseline images in the `baseline_images` subfolder. - -If you have developed a new feature for the package and it is related to modifying original plots or generating new plots, you will need to generate new baseline images. To do so, run -```shell -pip install -e '.[dev]' -pytest --mpl-generate-path=nbs/tests/mpl_image_tests/baseline_images -``` - -To run the tests, go to the root of this repo directory and run -```shell -pytest dabest -``` \ No newline at end of file diff --git a/nbs/tests/data/iris.csv b/nbs/tests/data/iris.csv deleted file mode 100644 index 45d1b3b3..00000000 --- a/nbs/tests/data/iris.csv +++ /dev/null @@ -1,151 +0,0 @@ -,sepal_length,sepal_width,petal_length,petal_width,species -0,5.1,3.5,1.4,0.2,setosa -1,4.9,3.0,1.4,0.2,setosa -2,4.7,3.2,1.3,0.2,setosa -3,4.6,3.1,1.5,0.2,setosa -4,5.0,3.6,1.4,0.2,setosa -5,5.4,3.9,1.7,0.4,setosa -6,4.6,3.4,1.4,0.3,setosa -7,5.0,3.4,1.5,0.2,setosa -8,4.4,2.9,1.4,0.2,setosa -9,4.9,3.1,1.5,0.1,setosa -10,5.4,3.7,1.5,0.2,setosa -11,4.8,3.4,1.6,0.2,setosa -12,4.8,3.0,1.4,0.1,setosa -13,4.3,3.0,1.1,0.1,setosa -14,5.8,4.0,1.2,0.2,setosa -15,5.7,4.4,1.5,0.4,setosa -16,5.4,3.9,1.3,0.4,setosa -17,5.1,3.5,1.4,0.3,setosa -18,5.7,3.8,1.7,0.3,setosa 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-119,6.0,2.2,5.0,1.5,virginica -120,6.9,3.2,5.7,2.3,virginica -121,5.6,2.8,4.9,2.0,virginica -122,7.7,2.8,6.7,2.0,virginica -123,6.3,2.7,4.9,1.8,virginica -124,6.7,3.3,5.7,2.1,virginica -125,7.2,3.2,6.0,1.8,virginica -126,6.2,2.8,4.8,1.8,virginica -127,6.1,3.0,4.9,1.8,virginica -128,6.4,2.8,5.6,2.1,virginica -129,7.2,3.0,5.8,1.6,virginica -130,7.4,2.8,6.1,1.9,virginica -131,7.9,3.8,6.4,2.0,virginica -132,6.4,2.8,5.6,2.2,virginica -133,6.3,2.8,5.1,1.5,virginica -134,6.1,2.6,5.6,1.4,virginica -135,7.7,3.0,6.1,2.3,virginica -136,6.3,3.4,5.6,2.4,virginica -137,6.4,3.1,5.5,1.8,virginica -138,6.0,3.0,4.8,1.8,virginica -139,6.9,3.1,5.4,2.1,virginica -140,6.7,3.1,5.6,2.4,virginica -141,6.9,3.1,5.1,2.3,virginica -142,5.8,2.7,5.1,1.9,virginica -143,6.8,3.2,5.9,2.3,virginica -144,6.7,3.3,5.7,2.5,virginica -145,6.7,3.0,5.2,2.3,virginica -146,6.3,2.5,5.0,1.9,virginica -147,6.5,3.0,5.2,2.0,virginica -148,6.2,3.4,5.4,2.3,virginica -149,5.9,3.0,5.1,1.8,virginica diff --git a/nbs/tests/data/mocked_data_test_01.py b/nbs/tests/data/mocked_data_test_01.py deleted file mode 100644 index c6bd49ab..00000000 --- a/nbs/tests/data/mocked_data_test_01.py +++ /dev/null @@ -1,71 +0,0 @@ -import pandas as pd -import numpy as np - -# Data for tests. -# See Cumming, G. Understanding the New Statistics: -# Effect Sizes, Confidence Intervals, and Meta-Analysis. Routledge, 2012, -# from Cumming 2012 Table 11.1 Pg 287. -wb = { - "control": [34, 54, 33, 44, 45, 53, 37, 26, 38, 58], - "expt": [66, 38, 35, 55, 48, 39, 65, 32, 57, 41], -} -wellbeing = pd.DataFrame(wb) - - -# from Cumming 2012 Table 11.2 Page 291 -paired_wb = { - "pre": [43, 28, 54, 36, 31, 48, 50, 69, 29, 40], - "post": [51, 33, 58, 42, 39, 45, 54, 68, 35, 44], - "ID": np.arange(10), -} -paired_wellbeing = pd.DataFrame(paired_wb) - - -# Data for testing Cohen's calculation. -# Only work with binary data. -# See Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer. -# Make two groups of `smoke` by choosing `low` as a standard, and the data is trimed from the back. - -# to remove the array wrapping behaviour of black -# fmt: off -sk = { "low": [0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, - 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0], - "high": [1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, - 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1]} -# fmt: on -smoke = pd.DataFrame(sk) - - -# Data from Hogarty and Kromrey (1999) -# Kromrey, Jeffrey D., and Kristine Y. Hogarty. 1998. -# "Analysis Options for Testing Group Differences on Ordered Categorical -# Variables: An Empirical Investigation of Type I Error Control -# Statistical Power." -# Multiple Linear Regression Viewpoints 25 (1): 70 - 82. -likert_control = [1, 1, 2, 2, 2, 3, 3, 3, 4, 5] -likert_treatment = [1, 2, 3, 4, 4, 5] - - -# Data from Cliff (1993) -# Cliff, Norman. 1993. "Dominance Statistics: Ordinal Analyses to Answer -# Ordinal Questions." -# Psychological Bulletin 114 (3): 494-509. -a_scores = [6, 7, 9, 10] -b_scores = [1, 3, 4, 7, 8] - - -# kwargs for Dabest class init. -dabest_default_kwargs = dict( - x=None, - y=None, - ci=95, - resamples=5000, - random_seed=12345, - proportional=False, - delta2=False, - experiment=None, - experiment_label=None, - x1_level=None, - mini_meta=False, - ps_adjust=False, -) diff --git a/nbs/tests/data/mocked_data_test_04.py b/nbs/tests/data/mocked_data_test_04.py deleted file mode 100644 index b1f74e17..00000000 --- a/nbs/tests/data/mocked_data_test_04.py +++ /dev/null @@ -1,34 +0,0 @@ -import pandas as pd -import numpy as np - -# Data for tests -# See Der, G., & Everitt, B. S. (2009). A handbook -# of statistical analyses using SAS, from Display 11.1 - -# to remove the array wrapping behaviour of black -# fmt: off -group = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, - 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2] -first = [20, 14, 7, 6, 9, 9, 7, 18, 6, 10, 5, 11, 10, 17, 16, 7, 5, 16, 2, 7, 9, 2, 7, 19, - 7, 9, 6, 13, 9, 6, 11, 7, 8, 3, 4, 11, 1, 6, 0, 18, 15, 10, 6, 9, 4, 4, 10] -second = [15, 12, 5, 10, 7, 9, 3, 17, 9, 15, 9, 11, 2, 12, 15, 10, 0, 7, 1, 11, 16, - 5, 3, 13, 5, 12, 7, 18, 10, 7, 11, 10, 18, 3, 10, 10, 3, 7, 3, 18, 15, 14, 6, 9, 3, 13, 11] -third = [14, 12, 5, 9, 9, 9, 7, 16, 9, 12, 7, 8, 9, 14, 12, 4, 5, 7, 1, 7, 14, 6, 5, 14, 8, 16, 10, - 14, 12, 8, 12, 11, 19, 3, 11, 10, 2, 7, 3, 19, 15, 16, 7, 13, 4, 13, 13] -fourth = [13, 10, 6, 8, 5, 11, 6, 14, 9, 12, 3, 8, 3, 10, 7, 7, 0, 6, 2, 5, 10, 7, 5, 12, 8, 17, 15, - 21, 14, 9, 14, 12, 19, 7, 17, 15, 4, 9, 4, 22, 18, 17, 9, 16, 7, 16, 17] -fifth = [13, 10, 5, 7, 4, 8, 5, 12, 9, 11, 5, 9, 5, 9, 9, 5, 0, 4, 2, 8, 6, 6, 5, 10, 6, 18, 16, 21, - 15, 12, 16, 14, 22, 8, 18, 16, 5, 10, 6, 22, 19, 19, 10, 20, 9, 19, 21] -# fmt: on - -df = pd.DataFrame( - { - "Group": group, - "First": first, - "Second": second, - "Third": third, - "Fourth": fourth, - "Fifth": fifth, - "ID": np.arange(0, 47), - } -) diff --git a/nbs/tests/data/mocked_data_test_06.py b/nbs/tests/data/mocked_data_test_06.py deleted file mode 100644 index fec52abb..00000000 --- a/nbs/tests/data/mocked_data_test_06.py +++ /dev/null @@ -1,66 +0,0 @@ -import pandas as pd -import numpy as np - - -# Data for tests. -# See: Asheber Abebe. Introduction to Design and Analysis of Experiments -# with the SAS, from Example: Two-way RM Design Pg 137. -# to remove the array wrapping behaviour of black -# fmt: off -hr = [72, 78, 71, 72, 66, 74, 62, 69, 69, 66, 84, 80, 72, 65, 75, 71, - 86, 83, 82, 83, 79, 83, 73, 75, 73, 62, 90, 81, 72, 62, 69, 70] -# fmt: on - -# Add experiment column -e1 = np.repeat("Treatment1", 8).tolist() -e2 = np.repeat("Control", 8).tolist() -experiment = e1 + e2 + e1 + e2 - -# Add a `Drug` column as the first variable -d1 = np.repeat("AX23", 8).tolist() -d2 = np.repeat("CONTROL", 8).tolist() -drug = d1 + d2 + d1 + d2 - -# Add a `Time` column as the second variable -t1 = np.repeat("T1", 16).tolist() -t2 = np.repeat("T2", 16).tolist() -time = t1 + t2 - -# Add an `id` column for paired data plotting. -id_col = [] -for i in range(1, 9): - id_col.append(str(i) + "a") -for i in range(1, 9): - id_col.append(str(i) + "c") -id_col.extend(id_col) - -# Combine samples and gender into a DataFrame. -df_test = pd.DataFrame( - { - "ID": id_col, - "Drug": drug, - "Time": time, - "Experiment": experiment, - "Heart Rate": hr, - } -) - - -df_test_control = df_test[df_test["Experiment"] == "Control"] -df_test_control = df_test_control.pivot(index="ID", columns="Time", values="Heart Rate") - - -df_test_treatment1 = df_test[df_test["Experiment"] == "Treatment1"] -df_test_treatment1 = df_test_treatment1.pivot( - index="ID", columns="Time", values="Heart Rate" -) - -dabest_default_kwargs = dict( - ci=95, - resamples=5000, - random_seed=12345, - idx=None, - proportional=False, - mini_meta=False, - ps_adjust=False, -) diff --git a/nbs/tests/data/mocked_data_test_08.py b/nbs/tests/data/mocked_data_test_08.py deleted file mode 100644 index b87724d0..00000000 --- a/nbs/tests/data/mocked_data_test_08.py +++ /dev/null @@ -1,32 +0,0 @@ -import pandas as pd - -# Data for tests. -# See Oehlert, G. W. (2000). A First Course in Design -# and Analysis of Experiments (1st ed.). W. H. Freeman. -# from Problem 16.3 Pg 444. - -rep1_yes = [53.4, 54.3, 55.9, 53.8, 56.3, 58.6] -rep1_no = [58.2, 60.4, 62.4, 59.5, 64.5, 64.5] -rep2_yes = [46.5, 57.2, 57.4, 51.1, 56.9, 60.2] -rep2_no = [49.2, 61.6, 57.2, 51.3, 66.8, 62.7] -df_mini_meta = pd.DataFrame( - {"Rep1_Yes": rep1_yes, "Rep1_No": rep1_no, "Rep2_Yes": rep2_yes, "Rep2_No": rep2_no} -) -N = 6 # Size of each group - -# kwargs for Dabest class init. -dabest_default_kwargs = dict( - x=None, - y=None, - ci=95, - resamples=5000, - random_seed=12345, - proportional=False, - delta2=False, - experiment=None, - experiment_label=None, - x1_level=None, - paired=None, - id_col=None, - ps_adjust=False, -) diff --git a/nbs/tests/data/mocked_data_test_forestplot.py b/nbs/tests/data/mocked_data_test_forestplot.py deleted file mode 100644 index 9c766d87..00000000 --- a/nbs/tests/data/mocked_data_test_forestplot.py +++ /dev/null @@ -1,54 +0,0 @@ -import pandas as pd -import scipy as sp -import numpy as np -import matplotlib.pyplot as plt -from numpy import random -from scipy.stats import norm -import dabest - -np.random.seed(9999) # Set the seed for reproducibility -N=20 -# Create samples -y = norm.rvs(loc=3, scale=0.4, size=N*4) -y[N:2*N] += 1 -y[2*N:3*N] -= 0.5 - -# Treatment, Rep, Genotype, and ID columns -treatment = np.repeat(['Placebo', 'Drug'], N*2).tolist() -rep = ['Rep1', 'Rep2'] * (N*2) -genotype = np.repeat(['W', 'M', 'W', 'M'], N).tolist() -id_col = list(range(0, N*2)) * 2 - - # Combine all columns into a DataFrame -dummy_df = pd.DataFrame({ - 'ID': id_col, - 'Rep': rep, - 'Genotype': genotype, - 'Treatment': treatment, - 'Y': y -}) - -unpaired_delta_01 = dabest.load(data = dummy_df, - x = ["Genotype", "Genotype"], - y = "Y", delta2 = True, - experiment = "Treatment") - -dummy_contrasts = [unpaired_delta_01] - -# Default forestplot params for unit testing -default_forestplot_kwargs = { - "data": dummy_contrasts, # Ensure this is a list of contrast objects. - "idx": None, # Valid as None or a list of lists of integers. - "effect_size": "mean_diff", # Ensure it's a string. - "labels": ["Drug1"], # This should be a list of strings. - "ylabel": "Effect Size", # Ensure it's a string. - "title": "ΔΔ Forest Plot", # Ensure it's a string. - "custom_palette": None, # Valid as None, a dictionary, list, or string. - "violin_kwargs": None, # No specific checks needed based on your tests. - "marker_size": 20, # Ensure it's a positive integer or float. - "remove_spines": True, # Ensure it's a boolean. - "labels_rotation": 45, # Ensure it's an integer or float between 0 and 360. - "contrast_alpha": 0.8, # Ensure it's a float between 0 and 1. - "horizontal": False, # Ensure it's a boolean. -} - diff --git a/nbs/tests/data/mocked_data_test_load_errors.py b/nbs/tests/data/mocked_data_test_load_errors.py deleted file mode 100644 index d83d08fa..00000000 --- a/nbs/tests/data/mocked_data_test_load_errors.py +++ /dev/null @@ -1,17 +0,0 @@ -import pandas as pd -import scipy as sp -from numpy import random - -random.seed(88888) -N = 10 -c1 = sp.stats.norm.rvs(loc=100, scale=5, size=N) -c2 = sp.stats.norm.rvs(loc=115, scale=5, size=N) -c3 = sp.stats.norm.rvs(loc=3.25, scale=0.4, size=N) - -t1 = sp.stats.norm.rvs(loc=3.5, scale=0.5, size=N) -t2 = sp.stats.norm.rvs(loc=2.5, scale=0.6, size=N) -id_col = pd.Series(range(1, N+1)) -dummy_df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1, - 'Control 2' : c2, 'Test 2' : t2, - 'Control 3' : c3, 'ID' : id_col - }) diff --git a/nbs/tests/data/mocked_data_test_multi.py b/nbs/tests/data/mocked_data_test_multi.py deleted file mode 100644 index f2a5b136..00000000 --- a/nbs/tests/data/mocked_data_test_multi.py +++ /dev/null @@ -1,139 +0,0 @@ -""" -Mocked data for testing multi.py module. - -This module provides test fixtures and default parameters for testing -the MultiContrast class and its associated functions (combine, whorlmap). -""" - -import pandas as pd -import numpy as np -import dabest - -# Set seed for reproducibility -np.random.seed(9999) - - -def create_two_group_contrast(): - """Create a standard dabest contrast object.""" - N = 20 - y = np.random.normal(loc=3, scale=0.4, size=N*2) - y[N:] += 1 # Treatment effect - - df = pd.DataFrame({ - 'ID': list(range(N*2)), - 'Group': ['Control'] * N + ['Treatment'] * N, - 'Y': y - }) - - return dabest.load(data=df, x='Group', y='Y', idx=('Control', 'Treatment')) - - -def create_delta2_contrast(): - """Create a delta-delta (delta2) dabest contrast object.""" - N = 20 - y = np.random.normal(loc=3, scale=0.4, size=N*4) - y[N:2*N] += 1 - y[2*N:3*N] -= 0.5 - - treatment = np.repeat(['Placebo', 'Drug'], N*2).tolist() - genotype = np.repeat(['W', 'M', 'W', 'M'], N).tolist() - id_col = list(range(0, N*2)) * 2 - - df = pd.DataFrame({ - 'ID': id_col, - 'Genotype': genotype, - 'Treatment': treatment, - 'Y': y - }) - - return dabest.load(data=df, x=["Genotype", "Genotype"], y="Y", - delta2=True, experiment="Treatment") - - -def create_minimeta_contrast(): - """Create a mini-meta analysis dabest contrast object.""" - N = 20 - y = np.random.normal(loc=3, scale=0.4, size=N*4) - y[N:2*N] += 0.8 - y[2*N:3*N] += 1.2 - - experiment = ['Exp1'] * (N*2) + ['Exp2'] * (N*2) - group = (['Control'] * N + ['Treatment'] * N) * 2 - - df = pd.DataFrame({ - 'Experiment': experiment, - 'Group': group, - 'Y': y - }) - - return dabest.load(data=df, x='Group', y='Y', idx=('Control', 'Treatment'), - mini_meta=True, experiment='Experiment') - - - -# Single contrast objects -two_group_contrast_1 = create_two_group_contrast() -two_group_contrast_2 = create_two_group_contrast() -two_group_contrast_3 = create_two_group_contrast() - -delta2_contrast_1 = create_delta2_contrast() -delta2_contrast_2 = create_delta2_contrast() - -minimeta_contrast_1 = create_minimeta_contrast() -minimeta_contrast_2 = create_minimeta_contrast() - - -default_combine_kwargs = { - "dabest_objs": [two_group_contrast_1, two_group_contrast_2], - "labels": ["Contrast 1", "Contrast 2"], - "row_labels": None, - "effect_size": "mean_diff", - "ci_type": "bca", - "allow_mixed_types": False -} - - -# Create a valid MultiContrast object for whorlmap testing -from dabest.multi import MultiContrast - -valid_multi_contrast_1d = MultiContrast( - dabest_objs=[two_group_contrast_1, two_group_contrast_2], - labels=["Treatment A", "Treatment B"], - effect_size="mean_diff", - ci_type="bca" -) - -valid_multi_contrast_2d = MultiContrast( - dabest_objs=[[two_group_contrast_1, two_group_contrast_2], - [two_group_contrast_3, two_group_contrast_1]], - labels=["Col1", "Col2"], - row_labels=["Row1", "Row2"], - effect_size="mean_diff", - ci_type="bca" -) - -valid_multi_contrast_mixed = MultiContrast( - dabest_objs=[[two_group_contrast_1, two_group_contrast_2], - [delta2_contrast_1, delta2_contrast_2]], - labels=["Col1", "Col2"], - row_labels=["Standard", "Delta2"], - effect_size="mean_diff", - ci_type="bca" -) - - -default_whorlmap_kwargs = { - "multi_contrast": valid_multi_contrast_1d, - "n": 21, - "sort_by": None, - "cmap": "vlag", - "vmax": None, - "vmin": None, - "reverse_neg": True, - "abs_rank": False, - "chop_tail": 0, - "ax": None, - "fig_size": None, - "title": None, - "heatmap_kwargs": None -} \ No newline at end of file diff --git a/nbs/tests/data/mocked_data_test_swarmplot.py b/nbs/tests/data/mocked_data_test_swarmplot.py deleted file mode 100644 index 26bd7338..00000000 --- a/nbs/tests/data/mocked_data_test_swarmplot.py +++ /dev/null @@ -1,32 +0,0 @@ -import pandas as pd -import scipy as sp -import numpy as np -import matplotlib.pyplot as plt -from numpy import random - -# Dummy Pandas DataFrame used for swarmplots unit testing -random.seed(88888) -N = 10 -c1 = sp.stats.norm.rvs(loc=100, scale=5, size=N) -t1 = sp.stats.norm.rvs(loc=115, scale=5, size=N) - -females = np.repeat("Female", N / 2).tolist() -males = np.repeat("Male", N / 2).tolist() -gender = females + males - -dummy_df = pd.DataFrame({"Control 1": c1, "Test 1": t1, "gender": gender}) -dummy_df = pd.melt( - dummy_df, - 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a/nbs/tests/mpl_image_tests/test_03_plotting.py b/nbs/tests/mpl_image_tests/test_03_plotting.py deleted file mode 100644 index f8c588db..00000000 --- a/nbs/tests/mpl_image_tests/test_03_plotting.py +++ /dev/null @@ -1,488 +0,0 @@ -import pytest -import numpy as np -from scipy.stats import norm -import pandas as pd -import matplotlib as mpl -import os -from pathlib import Path - -mpl.use("Agg") -import matplotlib.ticker as Ticker -import matplotlib.pyplot as plt - -from dabest._api import load - - -def create_demo_dataset(seed=9999, N=20): - import numpy as np - import pandas as pd - from scipy.stats import norm # Used in generation of populations. - - np.random.seed(9999) # Fix the seed so the results are replicable. - # pop_size = 10000 # Size of each population. - - # Create samples - c1 = norm.rvs(loc=3, scale=0.4, size=N) - c2 = norm.rvs(loc=3.5, scale=0.75, size=N) - c3 = norm.rvs(loc=3.25, scale=0.4, size=N) - - t1 = norm.rvs(loc=3.5, scale=0.5, size=N) - t2 = norm.rvs(loc=2.5, scale=0.6, size=N) - t3 = norm.rvs(loc=3, scale=0.75, size=N) - t4 = norm.rvs(loc=3.5, scale=0.75, size=N) - t5 = norm.rvs(loc=3.25, scale=0.4, size=N) - t6 = norm.rvs(loc=3.25, scale=0.4, size=N) - - # Add a `gender` column for coloring the data. - females = np.repeat("Female", N / 2).tolist() - males = np.repeat("Male", N / 2).tolist() - gender = females + males - - # Add an `id` column for paired data plotting. - id_col = pd.Series(range(1, N + 1)) - - # Combine samples and gender into a DataFrame. - df = pd.DataFrame( - { - "Control 1": c1, - "Test 1": t1, - "Control 2": c2, - "Test 2": t2, - "Control 3": c3, - "Test 3": t3, - "Test 4": t4, - "Test 5": t5, - "Test 6": t6, - "Gender": gender, - "ID": id_col, - } - ) - - return df - - -df = create_demo_dataset() - -two_groups_unpaired = load(df, idx=("Control 1", "Test 1")) - -two_groups_paired = load( - df, idx=("Control 1", "Test 1"), paired="baseline", id_col="ID" -) - -multi_2group = load( - df, - idx=( - ( - "Control 1", - "Test 1", - ), - ("Control 2", "Test 2"), - ), -) - -multi_2group_paired = load( - df, - idx=(("Control 1", "Test 1"), ("Control 2", "Test 2")), - paired="baseline", - id_col="ID", -) - -shared_control = load( - df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6") -) - -multi_groups = load( - df, - idx=( - ( - "Control 1", - "Test 1", - ), - ("Control 2", "Test 2", "Test 3"), - ("Control 3", "Test 4", "Test 5", "Test 6"), - ), -) - -multi_groups_baseline = load( - df, - idx=( - ( - "Control 1", - "Test 1", - ), - ("Control 2", "Test 2", "Test 3"), - ("Control 3", "Test 4", "Test 5", "Test 6"), - ), - paired="baseline", - id_col="ID", -) - -multi_groups_sequential = load( - df, - idx=( - ( - "Control 1", - "Test 1", - ), - ("Control 2", "Test 2", "Test 3"), - ("Control 3", "Test 4", "Test 5", "Test 6"), - ), - paired="sequential", - id_col="ID", -) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_01_gardner_altman_unpaired_meandiff(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_02_gardner_altman_unpaired_mediandiff(): - plt.rcdefaults() - return two_groups_unpaired.median_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_03_gardner_altman_unpaired_hedges_g(): - plt.rcdefaults() - return two_groups_unpaired.hedges_g.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_04_gardner_altman_paired_meandiff(): - plt.rcdefaults() - return two_groups_paired.mean_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_04_gardner_altman_paired_hedges_g(): - plt.rcdefaults() - return two_groups_paired.hedges_g.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_05_cummings_two_group_unpaired_meandiff(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(fig_size=(4, 6), float_contrast=False) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_06_cummings_two_group_paired_meandiff(): - plt.rcdefaults() - return two_groups_paired.mean_diff.plot(fig_size=(6, 6), float_contrast=False) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_07_cummings_multi_group_unpaired(): - plt.rcdefaults() - return multi_2group.mean_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_08_cummings_multi_group_paired(): - plt.rcdefaults() - return multi_2group_paired.mean_diff.plot(fig_size=(6, 6)) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_09_cummings_shared_control(): - plt.rcdefaults() - return shared_control.mean_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_10_cummings_multi_groups(): - plt.rcdefaults() - return multi_groups.mean_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_11_inset_plots(): - plt.rcdefaults() - # Load the iris dataset. - try: - # parent directory of the current working directory - parent = Path(__file__).parent.parent.absolute() - # print(f"parent={parent}") - iris_path = os.path.join(str(parent), "data", "iris.csv") - # print(f"iris_path={iris_path}") - iris = pd.read_csv(iris_path) - print(iris.head()) - except Exception as e: - print(f"Error while loading the iris dataset. Reason {e}") - - iris_melt = pd.melt( - iris.reset_index(), id_vars=["species", "index"], var_name="metric" - ) - - # Load the above data into `dabest`. - iris_dabest1 = load( - data=iris, - x="species", - y="petal_width", - idx=("setosa", "versicolor", "virginica"), - ) - - iris_dabest2 = load( - data=iris, x="species", y="sepal_width", idx=("setosa", "versicolor") - ) - - iris_dabest3 = load( - data=iris_melt[iris_melt.species == "setosa"], - x="metric", - y="value", - idx=("sepal_length", "sepal_width"), - paired="baseline", - id_col="index", - ) - - # Create Figure. - fig, ax = plt.subplots( - nrows=2, ncols=2, figsize=(15, 15), gridspec_kw={"wspace": 0.5} - ) - - iris_dabest1.mean_diff.plot(ax=ax.flat[0]) - - iris_dabest2.mean_diff.plot(ax=ax.flat[1]) - - iris_dabest3.mean_diff.plot(ax=ax.flat[2]) - - iris_dabest3.mean_diff.plot(ax=ax.flat[3], float_contrast=False) - - return fig - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_12_gardner_altman_ylabel(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot( - raw_label="This is my\nrawdata", contrast_label="The bootstrap\ndistribtions!" - ) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_13_multi_2group_color(): - plt.rcdefaults() - return multi_2group.mean_diff.plot(color_col="Gender") - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_14_gardner_altman_paired_color(): - plt.rcdefaults() - return two_groups_paired.mean_diff.plot(fig_size=(6, 6), color_col="Gender") - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_15_change_palette_a(): - plt.rcdefaults() - return multi_2group.mean_diff.plot( - fig_size=(8, 6), color_col="Gender", custom_palette="Dark2" - ) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_16_change_palette_b(): - plt.rcdefaults() - return multi_2group.mean_diff.plot(custom_palette="Paired") - - -my_color_palette = { - "Control 1": "blue", - "Test 1": "purple", - "Control 2": "#cb4b16", # This is a hex string. - "Test 2": (0.0, 0.7, 0.2), # This is a RGB tuple. -} - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_17_change_palette_c(): - plt.rcdefaults() - return multi_2group.mean_diff.plot(custom_palette=my_color_palette) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_18_desat(): - plt.rcdefaults() - return multi_2group.mean_diff.plot( - custom_palette=my_color_palette, raw_desat=0.75, contrast_desat=0.25 - ) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_19_dot_sizes(): - plt.rcdefaults() - return multi_2group.mean_diff.plot(raw_marker_size=3, contrast_marker_size=12) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_20_change_ylims(): - plt.rcdefaults() - return multi_2group.mean_diff.plot(raw_ylim=(0, 5), contrast_ylim=(-2, 2)) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_21_invert_ylim(): - plt.rcdefaults() - return multi_2group.mean_diff.plot( - contrast_ylim=(2, -2), contrast_label="More negative is better!" - ) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_22_ticker_gardner_altman(): - plt.rcdefaults() - f = two_groups_unpaired.mean_diff.plot() - - rawswarm_axes = f.axes[0] - contrast_axes = f.axes[1] - - rawswarm_axes.yaxis.set_major_locator(Ticker.MultipleLocator(1)) - rawswarm_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.5)) - - contrast_axes.yaxis.set_major_locator(Ticker.MultipleLocator(0.5)) - contrast_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.25)) - - return f - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_23_ticker_cumming(): - plt.rcdefaults() - f = multi_2group.mean_diff.plot(raw_ylim=(0, 6), contrast_ylim=(-3, 1)) - - rawswarm_axes = f.axes[0] - contrast_axes = f.axes[1] - - rawswarm_axes.yaxis.set_major_locator(Ticker.MultipleLocator(2)) - rawswarm_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(1)) - - contrast_axes.yaxis.set_major_locator(Ticker.MultipleLocator(0.5)) - contrast_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.25)) - - return f - - -np.random.seed(9999) -Ns = [20, 10, 21, 20] -c1 = pd.DataFrame({"Control": norm.rvs(loc=3, scale=0.4, size=Ns[0])}) -t1 = pd.DataFrame({"Test 1": norm.rvs(loc=3.5, scale=0.5, size=Ns[1])}) -t2 = pd.DataFrame({"Test 2": norm.rvs(loc=2.5, scale=0.6, size=Ns[2])}) -t3 = pd.DataFrame({"Test 3": norm.rvs(loc=3, scale=0.75, size=Ns[3])}) -wide_df = pd.concat([c1, t1, t2, t3], axis=1) - - -long_df = pd.melt( - wide_df, - value_vars=["Control", "Test 1", "Test 2", "Test 3"], - value_name="value", - var_name="group", -) -long_df["dummy"] = np.repeat(np.nan, len(long_df)) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_24_wide_df_nan(): - plt.rcdefaults() - wide_df_dabest = load(wide_df, idx=("Control", "Test 1", "Test 2", "Test 3")) - - return wide_df_dabest.mean_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_25_long_df_nan(): - plt.rcdefaults() - long_df_dabest = load( - long_df, x="group", y="value", idx=("Control", "Test 1", "Test 2", "Test 3") - ) - - return long_df_dabest.mean_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_26_slopegraph_kwargs(): - plt.rcdefaults() - return two_groups_paired.mean_diff.plot(slopegraph_kwargs=dict(linestyle="dotted")) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_27_gardner_altman_reflines_kwargs(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(reflines_kwargs=dict(linestyle="dotted")) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_28_unpaired_cumming_reflines_kwargs(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot( - fig_size=(12, 10), - float_contrast=False, - reflines_kwargs=dict(linestyle="dotted", linewidth=2), - contrast_ylim=(-1, 1), - ) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_29_paired_cumming_slopegraph_reflines_kwargs(): - plt.rcdefaults() - return two_groups_paired.mean_diff.plot( - float_contrast=False, - color_col="Gender", - slopegraph_kwargs=dict(linestyle="dotted"), - reflines_kwargs=dict(linestyle="dashed", linewidth=2), - contrast_ylim=(-1, 1), - ) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_30_sequential_cumming_slopegraph(): - plt.rcdefaults() - return multi_groups_sequential.mean_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_31_baseline_cumming_slopegraph(): - plt.rcdefaults() - return multi_groups_baseline.mean_diff.plot() - -# color palette change for paired plots -@pytest.mark.mpl_image_compare(tolerance=8) -def test_32_multigroups_baseline_change_palette(): - plt.rcdefaults() - return multi_groups_baseline.mean_diff.plot(custom_palette="Dark2", delta_text=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_33_multi_paired_different_sizes(): - # Test for GitHub issue #216: multi-group paired data with different sample sizes - plt.rcdefaults() - np.random.seed(9999) - - # Create three test pairs with different sample sizes (20, 10, 40) - c1DF = pd.DataFrame({'Test 1_pre': norm.rvs(loc=3, scale=0.4, size=20)}) - t1DF = pd.DataFrame({'Test 1_post': norm.rvs(loc=3.5, scale=0.5, size=20)}) - t2DF = pd.DataFrame({'Test 2_pre': norm.rvs(loc=2.5, scale=0.6, size=10)}) - t3DF = pd.DataFrame({'Test 2_post': norm.rvs(loc=3, scale=0.75, size=10)}) - t4DF = pd.DataFrame({'Test 3_pre': norm.rvs(loc=3.5, scale=0.75, size=40)}) - t5DF = pd.DataFrame({'Test 3_post': norm.rvs(loc=3.25, scale=0.4, size=40)}) - - df = pd.concat([c1DF, t1DF, t2DF, t3DF, t4DF, t5DF], axis=1) - df["ID"] = pd.Series(range(1, len(df)+1)) - - multi_paired_diff_sizes = load( - df, - idx=(("Test 1_pre", "Test 1_post"), - ("Test 2_pre", "Test 2_post"), - ("Test 3_pre", "Test 3_post")), - paired="baseline", - id_col="ID" - ) - - return multi_paired_diff_sizes.mean_diff.plot() - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_99_style_sheets(): - # Perform this test last so we don't have to reset the plot style. - plt.rcdefaults() - plt.style.use("dark_background") - - return multi_2group.mean_diff.plot(face_color="black") diff --git a/nbs/tests/mpl_image_tests/test_05_forest_plot.py b/nbs/tests/mpl_image_tests/test_05_forest_plot.py deleted file mode 100644 index 578258bf..00000000 --- a/nbs/tests/mpl_image_tests/test_05_forest_plot.py +++ /dev/null @@ -1,456 +0,0 @@ -import pytest -import numpy as np -from scipy.stats import norm -import pandas as pd -import matplotlib as mpl -import os -from pathlib import Path - -mpl.use("Agg") -import matplotlib.ticker as Ticker -import matplotlib.pyplot as plt - -from dabest._api import load - -import numpy as np -import pandas as pd -from scipy.stats import norm - -def create_delta_dataset(N=20, - seed=9999, - second_quarter_adjustment=3, - third_quarter_adjustment=-0.1): - np.random.seed(seed) # Set the seed for reproducibility - - # Create samples - y = norm.rvs(loc=3, scale=0.4, size=N*4) - y[N:2*N] += second_quarter_adjustment - y[2*N:3*N] += third_quarter_adjustment - - # Treatment, Rep, Genotype, and ID columns - treatment = np.repeat(['Placebo', 'Drug'], N*2).tolist() - rep = ['Rep1', 'Rep2'] * (N*2) - genotype = np.repeat(['W', 'M', 'W', 'M'], N).tolist() - id_col = list(range(0, N*2)) * 2 - - # Combine all columns into a DataFrame - df = pd.DataFrame({ - 'ID': id_col, - 'Rep': rep, - 'Genotype': genotype, - 'Treatment': treatment, - 'Y': y - }) - - return df - -def create_mini_meta_dataset(N=20, seed=9999, control_locs=[3, 3.5, 3.25], control_scales=[0.4, 0.75, 0.4], - test_locs=[3.5, 2.5, 3], test_scales=[0.5, 0.6, 0.75]): - np.random.seed(seed) # Set the seed for reproducibility - - # Create samples for controls and tests - controls_tests = [] - for loc, scale in zip(control_locs + test_locs, control_scales + test_scales): - controls_tests.append(norm.rvs(loc=loc, scale=scale, size=N)) - - # Add a `Gender` column for coloring the data - gender = ['Female'] * (N // 2) + ['Male'] * (N // 2) - - # Add an `ID` column for paired data plotting - id_col = list(range(1, N + 1)) - - # Combine samples and gender into a DataFrame - df_columns = {f'Control {i+1}': controls_tests[i] for i in range(len(control_locs))} - df_columns.update({f'Test {i+1}': controls_tests[i + len(control_locs)] for i in range(len(test_locs))}) - df_columns['Gender'] = gender - df_columns['ID'] = id_col - - df = pd.DataFrame(df_columns) - - return df - -# Generate the first dataset with a different seed and adjustments -df_delta2_drug1 = create_delta_dataset(seed=9999, - second_quarter_adjustment=1, - third_quarter_adjustment=-0.5) - -# Generate the second dataset with a different seed and adjustments -df_delta2_drug2 = create_delta_dataset(seed=9999, - second_quarter_adjustment=0.1, - third_quarter_adjustment=-1) - -# Generate the third dataset with the same seed as the first but different adjustments -df_delta2_drug3 = create_delta_dataset(seed=9999, - second_quarter_adjustment=3, - third_quarter_adjustment=-0.1) - - -unpaired_delta_01 = load(data = df_delta2_drug1, - x = ["Genotype", "Genotype"], - y = "Y", delta2 = True, - experiment = "Treatment") - -unpaired_delta_02 = load(data = df_delta2_drug2, - x = ["Genotype", "Genotype"], - y = "Y", delta2 = True, - experiment = "Treatment") - -unpaired_delta_03 = load(data = df_delta2_drug3, - x = ["Genotype", "Genotype"], - y = "Y", - delta2 = True, - experiment = "Treatment") - -paired_delta_01 = load(data = df_delta2_drug1, - paired = "baseline", id_col="ID", - x = ["Treatment", "Rep"], y = "Y", - delta2 = True, experiment = "Genotype") - -paired_delta_02 = load(data = df_delta2_drug2, - paired = "baseline", id_col="ID", - x = ["Treatment", "Rep"], y = "Y", - delta2 = True, experiment = "Genotype") -paired_delta_03 = load(data = df_delta2_drug3, - paired = "baseline", id_col="ID", - x = ["Treatment", "Rep"], y = "Y", - delta2 = True, experiment = "Genotype") - -contrasts = [unpaired_delta_01, unpaired_delta_02, unpaired_delta_03] - -paired_contrasts = [paired_delta_01, paired_delta_02, paired_delta_03] - -# Customizable dataset creation with different arguments -df_mini_meta01 = create_mini_meta_dataset(seed=9999, - control_locs=[3, 3.5, 3.25], - control_scales=[0.4, 0.75, 0.4], - test_locs=[3.5, 2.5, 3], - test_scales=[0.5, 0.6, 0.75]) - -df_mini_meta02 = create_mini_meta_dataset(seed=9999, - control_locs=[4, 2, 3.25], - control_scales=[0.3, 0.75, 0.45], - test_locs=[2, 1.5, 2.75], - test_scales=[0.5, 0.6, 0.4]) - -df_mini_meta03 = create_mini_meta_dataset(seed=9999, - control_locs=[6, 5.5, 4.25], - control_scales=[0.4, 0.75, 0.45], - test_locs=[4.5, 3.5, 3], - test_scales=[0.5, 0.6, 0.9]) - -contrast_mini_meta01 = load(data = df_mini_meta01, - idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), - mini_meta=True) - -contrast_mini_meta02 = load(data = df_mini_meta02, - idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), - mini_meta=True) - -contrast_mini_meta03 = load(data = df_mini_meta03, - idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), - mini_meta=True) - -contrasts_mini_meta = [contrast_mini_meta01, contrast_mini_meta02, contrast_mini_meta03] - - -delta1 = load(data = df_mini_meta01, - idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3"))) -delta2 = load(data = df_mini_meta02, - idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3"))) -delta3 = load(data = df_mini_meta03, - idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3"))) -contrasts_deltas = [delta1, delta2, delta3] - -# Import your forest_plot function here -from dabest.forest_plot import forest_plot - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_500_deltadelta_forest(): - plt.rcdefaults() - return forest_plot( - contrasts, - labels=['Drug1', 'Drug2', 'Drug3'] - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_501_deltadelta_with_deltas_idx_forest(): - plt.rcdefaults() - return forest_plot( - contrasts, - labels=['Drug1 Delta', 'Drug1 Delta-Delta', - 'Drug2 Delta', 'Drug2 Delta-Delta', - 'Drug3 Delta', 'Drug3 Delta-Delta' - ], - idx = [(0, 2), (0, 2), (0, 2)] - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_502_minimeta_forest(): - plt.rcdefaults() - return forest_plot( - contrasts_mini_meta, - labels=['mini_meta1', 'mini_meta2', 'mini_meta3'] - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_503_deltadelta_custompalette_forest(): - plt.rcdefaults() - return forest_plot( - contrasts, - labels=['Drug1', 'Drug2', 'Drug3'], - custom_palette=['gray', 'blue', 'green'] - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_504_deltadelta_horizontal_forest(): - plt.rcdefaults() - return forest_plot( - contrasts, - labels=['Drug1', 'Drug2', 'Drug3'], - horizontal=True - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_505_deltadelta_insert_ax_forest(): - plt.rcdefaults() - f_forest_drug_profiles, axes = plt.subplots(2, 2, figsize=[15, 14]) - f_forest_drug_profiles.subplots_adjust(hspace=0.3, wspace=0.3) - - for ax, contrast in zip(axes.flatten(), [unpaired_delta_01, unpaired_delta_02, unpaired_delta_03]): - contrast.mean_diff.plot( - contrast_label='Mean Diff', - raw_marker_size = 1, - contrast_marker_size = 5, - color_col='Genotype', - ax = ax - ) - forest_plot( - data = contrasts, - labels = ['Drug1', 'Drug2', 'Drug3'], - ax = axes[1,1], - ) - - for ax, title in zip(axes.flatten(), ['Drug 1', 'Drug 2', 'Drug 3', 'Forest plot']): - ax.set_title(title, fontsize = 12) - - return f_forest_drug_profiles - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_506a_deltadelta_delta_g_using_hedges_g_forest(): - plt.rcdefaults() - return forest_plot( - contrasts, - labels=['Drug1', 'Drug2', 'Drug3'], - effect_size='hedges_g' - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_506b_deltadelta_delta_g_using_delta_g_forest(): - plt.rcdefaults() - return forest_plot( - contrasts, - labels=['Drug1', 'Drug2', 'Drug3'], - effect_size='delta_g' - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_507_deltadelta_fig_size_forest(): - plt.rcdefaults() - return forest_plot( - contrasts, - labels=['Drug1', 'Drug2', 'Drug3'], - fig_size=[6, 6] - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_508_deltadelta_fig_size_forest(): - plt.rcdefaults() - return forest_plot( - contrasts, - labels=['Drug1', 'Drug2', 'Drug3'], - fig_size=[6, 6] - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_509_deltadelta_halfviolin_aesthetics_forest(): - plt.rcdefaults() - return forest_plot( - contrasts, - labels=['Drug1', 'Drug2', 'Drug3'], - contrast_alpha=0.2, - contrast_desat=0.2 - ) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_510_deltadelta_labels_and_title_aesthetics_forest(): - plt.rcdefaults() - return forest_plot( - contrasts, - labels=['Drug1', 'Drug2', 'Drug3'], - labels_fontsize=12, - labels_rotation=0, - ylabel='Effect Size', - ylabel_fontsize=14, - title='Drug Efficacy', - title_fontsize=20 - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_511_deltadelta_lims_and_ticks_forest(): - plt.rcdefaults() - return forest_plot( - contrasts, - labels=['Drug1', 'Drug2', 'Drug3'], - ylim=[-1, 1], - yticks=[-1, 0, 1], - yticklabels=['Negative', 'Zero', 'Positive'] - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_512_deltadelta_spines_forest(): - plt.rcdefaults() - return forest_plot( - contrasts, - labels=['Drug1', 'Drug2', 'Drug3'], - remove_spines=False - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_513_deltadelta_violinkwargs_forest(): - plt.rcdefaults() - return forest_plot( - contrasts, - labels=['Drug1', 'Drug2', 'Drug3'], - violin_kwargs={ - "widths": 0.8, "showextrema": True, - "showmedians": True, "orientation": 'vertical' - } - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_514_deltadelta_zerolinekwargs_forest(): - plt.rcdefaults() - return forest_plot( - contrasts, - labels=['Drug1', 'Drug2', 'Drug3'], - zeroline_kwargs={"linewidth": 2, "color": "red"} - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_515_deltadelta_esmarkerkwargs_forest(): - plt.rcdefaults() - return forest_plot( - contrasts, - labels=['Drug1', 'Drug2', 'Drug3'], - marker_kwargs={ - 'marker': '^', 'markersize': 15,'color': 'blue', - 'alpha': 0.5, - } - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_516_deltadelta_eserrorbarkwargs_forest(): - plt.rcdefaults() - return forest_plot( - contrasts, - labels=['Drug1', 'Drug2', 'Drug3'], - errorbar_kwargs={ - 'color': 'red', 'lw': 4, 'linestyle': '--', 'alpha': 0.6, - } - ) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_517_regular_delta_no_idx(): - plt.rcdefaults() - return forest_plot( - contrasts_deltas, - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_518_regular_delta_idx(): - plt.rcdefaults() - return forest_plot( - contrasts_deltas, - idx = [(0,), (0,), (0,)], - labels=['Drug1 \nTest 1 - Control 1', 'Drug2 \nTest 2 - Control 2', 'Drug3 \nTest 3 - Control 3'] - ) - - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_519_minimeta_with_deltas_forest(): - plt.rcdefaults() - return forest_plot( - contrasts_mini_meta, - idx=[(0, 3),(0, 3),(0, 3)], - labels=['Contrast A1', 'Mini_Meta A', 'Contrast B1', 'Mini_Meta B', 'Contrast C1', 'Mini_Meta C'] - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_520_minimeta_with_deltas_and_delta_text_kwargs_forest(): - plt.rcdefaults() - return forest_plot( - contrasts_mini_meta, - idx=[(0, 3),(0, 3),(0, 3)], - labels=['Contrast A1', 'Mini_Meta A', 'Contrast B1', 'Mini_Meta B', 'Contrast C1', 'Mini_Meta C'], - delta_text_kwargs={'color': 'black','fontsize': 8, 'rotation': 45, 'va': 'bottom', - 'x_coordinates': [1.4, 2.4, 3.4, 4.4, 5.4, 6.4], - 'y_coordinates': [0.6, 0.1, -2, -1.5, -1.5, -1.5]} - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_521_minimeta_with_deltas_with_contrast_bars_kwargs_forest(): - plt.rcdefaults() - return forest_plot( - contrasts_mini_meta, - idx=[(0, 3),(0, 3),(0, 3)], - labels=['Contrast A1', 'Mini_Meta A', 'Contrast B1', 'Mini_Meta B', 'Contrast C1', 'Mini_Meta C'], - contrast_bars_kwargs={'color': 'red', 'alpha': 0.4} - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_522a_minimeta_with_deltas_with_summary_bars(): - plt.rcdefaults() - return forest_plot( - contrasts_mini_meta, - idx=[(0, 3),(0, 3),(0, 3)], - labels=['Contrast A1', 'Mini_Meta A', 'Contrast B1', 'Mini_Meta B', 'Contrast C1', 'Mini_Meta C'], - reference_band=[0, 2], - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_522b_minimeta_with_deltas_with_summary_bars_horizontal(): - plt.rcdefaults() - return forest_plot( - contrasts_mini_meta, - idx=[(0, 3),(0, 3),(0, 3)], - labels=['Contrast A1', 'Mini_Meta A', 'Contrast B1', 'Mini_Meta B', 'Contrast C1', 'Mini_Meta C'], - reference_band=[0, 2], - horizontal=True - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_522c_minimeta_with_deltas_with_summary_bars_kwargs(): - plt.rcdefaults() - return forest_plot( - contrasts_mini_meta, - idx=[(0, 3),(0, 3),(0, 3)], - labels=['Contrast A1', 'Mini_Meta A', 'Contrast B1', 'Mini_Meta B', 'Contrast C1', 'Mini_Meta C'], - reference_band=[0, 2], - reference_band_kwargs={'span_ax': True, 'color': 'grey', 'alpha': 0.1} - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_522d_minimeta_with_deltas_with_summary_bars_kwargs_horizontal(): - plt.rcdefaults() - return forest_plot( - contrasts_mini_meta, - idx=[(0, 3),(0, 3),(0, 3)], - labels=['Contrast A1', 'Mini_Meta A', 'Contrast B1', 'Mini_Meta B', 'Contrast C1', 'Mini_Meta C'], - reference_band=[0, 2], - horizontal=True, - reference_band_kwargs={'span_ax': True, 'color': 'grey', 'alpha': 0.1} - ) \ No newline at end of file diff --git a/nbs/tests/mpl_image_tests/test_07_delta-delta_plots.py b/nbs/tests/mpl_image_tests/test_07_delta-delta_plots.py deleted file mode 100644 index e3d3eac1..00000000 --- a/nbs/tests/mpl_image_tests/test_07_delta-delta_plots.py +++ /dev/null @@ -1,235 +0,0 @@ -import pytest -import numpy as np -import pandas as pd -from scipy.stats import norm - - -import matplotlib as mpl -mpl.use('Agg') -import matplotlib.pyplot as plt -import matplotlib.ticker as Ticker - -from dabest._api import load - -def create_demo_dataset_delta(seed=9999, N=20): - - import numpy as np - import pandas as pd - from scipy.stats import norm # Used in generation of populations. - - np.random.seed(seed) # Fix the seed so the results are replicable. - # pop_size = 10000 # Size of each population. - - from scipy.stats import norm # Used in generation of populations. - - # Create samples - y = norm.rvs(loc=3, scale=0.4, size=N*4) - y[N:2*N] = y[N:2*N]+1 - y[2*N:3*N] = y[2*N:3*N]-0.5 - ind = np.random.binomial(1, 0.5, size=N*4) - ind[N:2*N] = np.random.binomial(1, 0.2, size=N) - ind[2*N:3*N] = np.random.binomial(1, 0.7, size=N) - - # Add drug column - t1 = np.repeat('Placebo', N*2).tolist() - t2 = np.repeat('Drug', N*2).tolist() - treatment = t1 + t2 - - # Add a `rep` column as the first variable for the 2 replicates of experiments done - rep = [] - for i in range(N*2): - rep.append('Rep1') - rep.append('Rep2') - - # Add a `genotype` column as the second variable - wt = np.repeat('W', N).tolist() - mt = np.repeat('M', N).tolist() - wt2 = np.repeat('W', N).tolist() - mt2 = np.repeat('M', N).tolist() - - - genotype = wt + mt + wt2 + mt2 - - # Add an `id` column for paired data plotting. - id = list(range(0, N*2)) - id_col = id + id - - - # Combine all columns into a DataFrame. - df = pd.DataFrame({'ID' : id_col, - 'Rep' : rep, - 'Genotype' : genotype, - 'Treatment' : treatment, - 'Y' : y, - 'Cat' :ind - }) - return df - - -df = create_demo_dataset_delta() - - -unpaired = load(data = df, x = ["Genotype", "Genotype"], y = "Y", delta2 = True, - experiment = "Treatment") - -unpaired_specified = load(data = df, x = ["Genotype", "Genotype"], y = "Y", - delta2 = True, experiment = "Treatment", - experiment_label = ["Drug", "Placebo"], - x1_level = ["M", "W"]) - -baseline = load(data = df, x = ["Treatment", "Rep"], y = "Y", delta2 = True, - experiment = "Genotype", - paired="baseline", id_col="ID") - -sequential = load(data = df, x = ["Treatment", "Rep"], y = "Y", delta2 = True, - experiment = "Genotype", - paired="sequential", id_col="ID") - -unpaired_prop = load(data = df, proportional=True, - # id_col="index", paired='baseline', - x = ["Genotype", "Genotype"], - y = "Cat", delta2=True, - experiment="Treatment",) - -unpaired_specified_prop = load(data = df, proportional=True, - # id_col="index", paired='baseline', - x = ["Genotype", "Genotype"], - y = "Cat", delta2=True, - experiment="Treatment", - experiment_label = ["Drug", "Placebo"], - x1_level = ["M", "W"]) - -paired_prop = load(data = df, proportional=True, - id_col="ID", paired='baseline', - x = ["Genotype", "Genotype"], - y = "Cat", delta2=True, - experiment="Treatment",) - -paired_specified_prop = load(data = df, proportional=True, - id_col="ID", paired='baseline', - x = ["Genotype", "Genotype"], - y = "Cat", delta2=True, - experiment="Treatment", - experiment_label = ["Drug", "Placebo"], - x1_level = ["M", "W"]) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_47_cummings_unpaired_delta_delta_meandiff(): - plt.rcdefaults() - return unpaired.mean_diff.plot(); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_48_cummings_sequential_delta_delta_meandiff(): - plt.rcdefaults() - return sequential.mean_diff.plot(); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_49_cummings_baseline_delta_delta_meandiff(): - plt.rcdefaults() - return baseline.mean_diff.plot(); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_50_delta_plot_ylabel(): - plt.rcdefaults() - return baseline.mean_diff.plot(raw_label="This is my\nrawdata", - contrast_label="The bootstrap\ndistribtions!", - delta2_label="This is delta!"); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_51_delta_plot_change_palette_a(): - plt.rcdefaults() - return sequential.mean_diff.plot(custom_palette="Dark2"); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_52_delta_specified(): - plt.rcdefaults() - return unpaired_specified.mean_diff.plot(); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_53_delta_change_ylims(): - plt.rcdefaults() - return sequential.mean_diff.plot(raw_ylim=(0, 9), - contrast_ylim=(-2, 2), - fig_size=(15,6)); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_54_delta_invert_ylim(): - plt.rcdefaults() - return sequential.mean_diff.plot(contrast_ylim=(2, -2), - contrast_label="More negative is better!"); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_55_delta_median_diff(): - plt.rcdefaults() - return sequential.median_diff.plot(); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_56_delta_cohens_d(): - plt.rcdefaults() - return unpaired.cohens_d.plot(); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_57_delta_show_delta2(): - plt.rcdefaults() - return unpaired.mean_diff.plot(show_delta2=False); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_58_delta_axes_invert_ylim(): - plt.rcdefaults() - return unpaired.mean_diff.plot(delta2_ylim=(2, -2), - delta2_label="More negative is better!"); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_59_delta_axes_invert_ylim_not_showing_delta2(): - plt.rcdefaults() - return unpaired.mean_diff.plot(delta2_ylim=(2, -2), - delta2_label="More negative is better!", - show_delta2=False); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_71_unpaired_delta_g(): - plt.rcdefaults() - return unpaired.hedges_g.plot(); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_72_sequential_delta_g(): - plt.rcdefaults() - return sequential.hedges_g.plot(); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_73_baseline_delta_g(): - plt.rcdefaults() - return baseline.hedges_g.plot(); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_74_unpaired_prop_delta2(): - plt.rcdefaults() - return unpaired_prop.mean_diff.plot() - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_75_unpaired_specified_prop_delta2(): - plt.rcdefaults() - return unpaired_specified_prop.mean_diff.plot() - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_76_paired_prop_delta2(): - plt.rcdefaults() - return paired_prop.mean_diff.plot() - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_77_paired_specified_prop_delta2(): - plt.rcdefaults() - return paired_specified_prop.mean_diff.plot() \ No newline at end of file diff --git a/nbs/tests/mpl_image_tests/test_09_mini_meta_plots.py b/nbs/tests/mpl_image_tests/test_09_mini_meta_plots.py deleted file mode 100644 index 4e510ac2..00000000 --- a/nbs/tests/mpl_image_tests/test_09_mini_meta_plots.py +++ /dev/null @@ -1,142 +0,0 @@ -import pytest -import numpy as np -import pandas as pd -from scipy.stats import norm - - -import matplotlib as mpl -mpl.use('Agg') -import matplotlib.pyplot as plt -import matplotlib.ticker as Ticker - -from dabest._api import load - -def create_demo_dataset(seed=9999, N=20): - - import numpy as np - import pandas as pd - from scipy.stats import norm # Used in generation of populations. - - np.random.seed(9999) # Fix the seed so the results are replicable. - # pop_size = 10000 # Size of each population. - - # Create samples - c1 = norm.rvs(loc=3, scale=0.4, size=N) - c2 = norm.rvs(loc=3.5, scale=0.75, size=N) - c3 = norm.rvs(loc=3.25, scale=0.4, size=N) - - t1 = norm.rvs(loc=3.5, scale=0.5, size=N) - t2 = norm.rvs(loc=2.5, scale=0.6, size=N) - t3 = norm.rvs(loc=3, scale=0.75, size=N) - t4 = norm.rvs(loc=3.5, scale=0.75, size=N) - t5 = norm.rvs(loc=3.25, scale=0.4, size=N) - t6 = norm.rvs(loc=3.25, scale=0.4, size=N) - - - # Add a `gender` column for coloring the data. - females = np.repeat('Female', N/2).tolist() - males = np.repeat('Male', N/2).tolist() - gender = females + males - - # Add an `id` column for paired data plotting. - id_col = pd.Series(range(1, N+1)) - - # Combine samples and gender into a DataFrame. - df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1, - 'Control 2' : c2, 'Test 2' : t2, - 'Control 3' : c3, 'Test 3' : t3, - 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6, - 'Gender' : gender, 'ID' : id_col - }) - - return df - - -df = create_demo_dataset() - - -unpaired = load(df, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), - mini_meta=True) - - -baseline = load(df, id_col = "ID", - idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), - paired = "baseline", mini_meta=True) - - -sequential = load(df, id_col = "ID", - idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), - paired = "sequential", mini_meta=True) - - - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_60_cummings_unpaired_mini_meta_meandiff(): - plt.rcdefaults() - return unpaired.mean_diff.plot(); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_61_cummings_sequential_mini_meta_meandiff(): - plt.rcdefaults() - return sequential.mean_diff.plot(); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_62_cummings_baseline_mini_meta_meandiff(): - plt.rcdefaults() - return baseline.mean_diff.plot(); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_63_mini_meta_plot_ylabel(): - plt.rcdefaults() - return baseline.mean_diff.plot(raw_label="This is my\nrawdata", - contrast_label="The bootstrap\ndistribtions!"); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_64_mini_meta_plot_change_palette_a(): - plt.rcdefaults() - return unpaired.mean_diff.plot(custom_palette="Dark2"); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_65_mini_meta_dot_sizes(): - plt.rcdefaults() - return sequential.mean_diff.plot(show_pairs=False,raw_marker_size=3, - contrast_marker_size=12); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_66_mini_meta_change_ylims(): - plt.rcdefaults() - return sequential.mean_diff.plot(raw_ylim=(0, 5), - contrast_ylim=(-2, 2), - fig_size=(15,6)); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_67_mini_meta_invert_ylim(): - plt.rcdefaults() - return sequential.mean_diff.plot(contrast_ylim=(2, -2), - contrast_label="More negative is better!"); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_68_mini_meta_median_diff(): - plt.rcdefaults() - return sequential.median_diff.plot(); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_69_mini_meta_cohens_d(): - plt.rcdefaults() - return unpaired.cohens_d.plot(); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_70_mini_meta_not_show(): - plt.rcdefaults() - return unpaired.mean_diff.plot(show_mini_meta=False); diff --git a/nbs/tests/mpl_image_tests/test_10_proportion_plot.py b/nbs/tests/mpl_image_tests/test_10_proportion_plot.py deleted file mode 100644 index 03989aaa..00000000 --- a/nbs/tests/mpl_image_tests/test_10_proportion_plot.py +++ /dev/null @@ -1,472 +0,0 @@ -import pytest -import numpy as np -import pandas as pd -import matplotlib as mpl - -mpl.use("Agg") -import matplotlib.ticker as Ticker -import matplotlib.pyplot as plt -from dabest._api import load - - -def create_demo_prop_dataset(seed=9999, N=40): - np.random.seed(9999) # Fix the seed so the results are replicable. - # Create samples - n = 1 - c1 = np.random.binomial(n, 0.2, size=N) - c2 = np.random.binomial(n, 0.2, size=N) - c3 = np.random.binomial(n, 0.8, size=N) - - t1 = np.random.binomial(n, 0.5, size=N) - t2 = np.random.binomial(n, 0.2, size=N) - t3 = np.random.binomial(n, 0.3, size=N) - t4 = np.random.binomial(n, 0.4, size=N) - t5 = np.random.binomial(n, 0.5, size=N) - t6 = np.random.binomial(n, 0.6, size=N) - t7 = np.zeros(N) - t8 = np.ones(N) - t9 = np.zeros(N) - - # Add a `gender` column for coloring the data. - females = np.repeat("Female", N / 2).tolist() - males = np.repeat("Male", N / 2).tolist() - gender = females + males - - # Add an `id` column for paired data plotting. - id_col = pd.Series(range(1, N + 1)) - - # Combine samples and gender into a DataFrame. - df = pd.DataFrame( - { - "Control 1": c1, - "Test 1": t1, - "Control 2": c2, - "Test 2": t2, - "Control 3": c3, - "Test 3": t3, - "Test 4": t4, - "Test 5": t5, - "Test 6": t6, - "Test 7": t7, - "Test 8": t8, - "Test 9": t9, - "Gender": gender, - "ID": id_col, - } - ) - - return df - - -df = create_demo_prop_dataset() - -two_groups_unpaired = load(df, idx=("Control 1", "Test 1"), proportional=True) - -multi_2group = load( - df, - idx=( - ( - "Control 1", - "Test 1", - ), - ("Control 2", "Test 2"), - ), - proportional=True, -) - -shared_control = load( - df, - idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6"), - proportional=True, -) - -multi_groups = load( - df, - idx=( - ( - "Control 1", - "Test 1", - ), - ("Control 2", "Test 2", "Test 3"), - ("Control 3", "Test 4", "Test 5", "Test 6"), - ), - proportional=True, -) - -two_groups_paired = load( - df, idx=("Control 1", "Test 1"), paired="baseline", id_col="ID", proportional=True -) - -multi_2group_paired = load( - df, - idx=(("Control 1", "Test 1"), ("Control 2", "Test 2")), - paired="baseline", - id_col="ID", - proportional=True, -) - -multi_groups_paired = load( - df, - idx=( - ( - "Control 1", - "Test 1", - ), - ("Control 2", "Test 2", "Test 3"), - ("Control 3", "Test 4", "Test 5", "Test 6"), - ), - paired="baseline", - id_col="ID", - proportional=True, -) - -two_groups_sequential = load( - df, idx=("Control 1", "Test 1"), paired="sequential", id_col="ID", proportional=True -) - -multi_2group_sequential = load( - df, - idx=(("Control 1", "Test 1"), ("Control 2", "Test 2")), - paired="sequential", - id_col="ID", - proportional=True, -) - -multi_groups_sequential = load( - df, - idx=( - ( - "Control 1", - "Test 1", - ), - ("Control 2", "Test 2", "Test 3"), - ("Control 3", "Test 4", "Test 5", "Test 6"), - ), - paired="sequential", - id_col="ID", - proportional=True, -) -shared_control_paired = load( - df, - idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6"), - paired="sequential", - id_col="ID", - proportional=True, -) - -zero_to_zero = load( - df, idx=("Test 7", "Test 9"), proportional=True, paired="sequential", id_col="ID" -) -zero_to_one = load( - df, idx=("Test 7", "Test 8"), proportional=True, paired="sequential", id_col="ID" -) -one_to_zero = load( - df, idx=("Test 8", "Test 7"), proportional=True, paired="sequential", id_col="ID" -) - -one_in_separate_control = load( - df, - idx=( - (("Control 1", "Test 1"), ("Test 2", "Test 3"), ("Test 4", "Test 8", "Test 6")) - ), - proportional=True, - paired="sequential", - id_col="ID", -) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_101_gardner_altman_unpaired_propdiff(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_103_cummings_two_group_unpaired_propdiff(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(fig_size=(4, 6), float_contrast=False) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_105_cummings_multi_group_unpaired_propdiff(): - plt.rcdefaults() - return multi_2group.mean_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_106_cummings_shared_control_propdiff(): - plt.rcdefaults() - return shared_control.mean_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_107_cummings_multi_groups_propdiff(): - plt.rcdefaults() - return multi_groups.mean_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_109_gardner_altman_ylabel(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot( - raw_label="This is my\nrawdata", contrast_label="The bootstrap\ndistribtions!" - ) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_110_change_fig_size(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(fig_size=(6, 6), custom_palette="Dark2") - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_111_change_palette_b(): - plt.rcdefaults() - return multi_2group.mean_diff.plot(custom_palette="Paired") - - -my_color_palette = { - "Control 1": "blue", - "Test 1": "purple", - "Control 2": "#cb4b16", # This is a hex string. - "Test 2": (0.0, 0.7, 0.2), # This is a RGB tuple. -} - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_112_change_palette_c(): - plt.rcdefaults() - return multi_2group.mean_diff.plot(custom_palette=my_color_palette) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_113_desat(): - plt.rcdefaults() - return multi_2group.mean_diff.plot( - custom_palette=my_color_palette, raw_desat=0.1, contrast_desat=0.25 - ) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_114_change_ylims(): - plt.rcdefaults() - return multi_2group.mean_diff.plot(contrast_ylim=(-2, 2)) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_115_invert_ylim(): - plt.rcdefaults() - return multi_2group.mean_diff.plot( - contrast_ylim=(2, -2), contrast_label="More negative is better!" - ) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_116_ticker_gardner_altman(): - plt.rcdefaults() - fig = two_groups_unpaired.mean_diff.plot() - - rawswarm_axes = fig.axes[0] - contrast_axes = fig.axes[1] - - rawswarm_axes.yaxis.set_major_locator(Ticker.MultipleLocator(1)) - rawswarm_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.5)) - - contrast_axes.yaxis.set_major_locator(Ticker.MultipleLocator(0.5)) - contrast_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.25)) - return fig - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_117_err_color(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(barplot_kwargs={"err_kws": {"color" : "purple"}}) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_118_cummings_two_group_unpaired_meandiff_bar_width(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(bar_width=0.4, float_contrast=False) - - -np.random.seed(9999) -Ns = [20, 10, 21, 20] -n = 1 -c1 = pd.DataFrame({"Control": np.random.binomial(n, 0.2, size=Ns[0])}) -t1 = pd.DataFrame({"Test 1": np.random.binomial(n, 0.5, size=Ns[1])}) -t2 = pd.DataFrame({"Test 2": np.random.binomial(n, 0.4, size=Ns[2])}) -t3 = pd.DataFrame({"Test 3": np.random.binomial(n, 0.7, size=Ns[3])}) -wide_df = pd.concat([c1, t1, t2, t3], axis=1) - - -long_df = pd.melt( - wide_df, - value_vars=["Control", "Test 1", "Test 2", "Test 3"], - value_name="value", - var_name="group", -) -long_df["dummy"] = np.repeat(np.nan, len(long_df)) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_119_wide_df_nan(): - plt.rcdefaults() - wide_df_dabest = load( - wide_df, idx=("Control", "Test 1", "Test 2", "Test 3"), proportional=True - ) - - return wide_df_dabest.mean_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_120_long_df_nan(): - plt.rcdefaults() - long_df_dabest = load( - long_df, - x="group", - y="value", - idx=("Control", "Test 1", "Test 2", "Test 3"), - proportional=True, - ) - - return long_df_dabest.mean_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_121_cohens_h_gardner_altman(): - plt.rcdefaults() - return two_groups_unpaired.cohens_h.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_122_cohens_h_cummings(): - plt.rcdefaults() - return two_groups_unpaired.cohens_h.plot(float_contrast=False) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_123_sankey_gardner_altman(): - plt.rcdefaults() - return two_groups_paired.mean_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_124_sankey_cummings(): - plt.rcdefaults() - return two_groups_paired.mean_diff.plot(float_contrast=False) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_125_sankey_2paired_groups(): - plt.rcdefaults() - return multi_2group_paired.mean_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_126_sankey_2sequential_groups(): - plt.rcdefaults() - return multi_2group_sequential.mean_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_127_sankey_multi_group_paired(): - plt.rcdefaults() - return multi_groups_paired.mean_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_128_sankey_transparency(): - plt.rcdefaults() - return two_groups_paired.mean_diff.plot(sankey_kwargs={"alpha": 0.2}) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_129_zero_to_zero(): - plt.rcdefaults() - return zero_to_zero.mean_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_130_zero_to_one(): - plt.rcdefaults() - return zero_to_one.mean_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_131_one_to_zero(): - plt.rcdefaults() - return one_to_zero.mean_diff.plot() - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_132_shared_control_sankey_off(): - plt.rcdefaults() - return shared_control_paired.mean_diff.plot(sankey_kwargs={"sankey": False}) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_133_shared_control_flow_off(): - plt.rcdefaults() - return shared_control_paired.mean_diff.plot(sankey_kwargs={"flow": False}) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_134_separate_control_sankey_off(): - plt.rcdefaults() - return multi_groups_sequential.mean_diff.plot(sankey_kwargs={"sankey": False}) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_135_separate_control_flow_off(): - plt.rcdefaults() - return multi_groups_sequential.mean_diff.plot(sankey_kwargs={"flow": False}) - -# Show sample counts -@pytest.mark.mpl_image_compare(tolerance=8) -def test_137_multi_2group_show_sample_counts(): - plt.rcdefaults() - return multi_2group.mean_diff.plot(prop_sample_counts=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_138_multi_groups_paired_show_sample_counts(): - plt.rcdefaults() - return multi_groups_paired.mean_diff.plot(prop_sample_counts=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_139_multi_2group_show_sample_counts_and_kwargs(): - plt.rcdefaults() - return multi_2group.mean_diff.plot(prop_sample_counts=True, prop_sample_counts_kwargs={ - "color": "red", "fontsize": 12, "fontweight": "bold"}) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_140_multi_groups_paired_show_sample_counts_with_sankey_off(): - plt.rcdefaults() - return multi_groups_paired.mean_diff.plot(prop_sample_counts=True, sankey_kwargs={"sankey": False}) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_141_sankey_change_palette_a(): - plt.rcdefaults() - return multi_groups_paired.mean_diff.plot(custom_palette="Dark2") - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_142_sankey_change_palette_b(): - plt.rcdefaults() - return multi_groups_paired.mean_diff.plot(custom_palette={1: 'red', 0: 'blue'}) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_143_sankey_change_palette_c(): - plt.rcdefaults() - return multi_groups_paired.mean_diff.plot(custom_palette=['red', 'blue']) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_144_change_palette_d(): - plt.rcdefaults() - return multi_2group.mean_diff.plot(custom_palette={0:'blue', 1: 'red'}) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_136_style_sheets(): - # Perform this test last so we don't have to reset the plot style. - plt.rcdefaults() - plt.style.use("dark_background") - return multi_2group.mean_diff.plot(face_color="black") diff --git a/nbs/tests/mpl_image_tests/test_11_whorlmap.py b/nbs/tests/mpl_image_tests/test_11_whorlmap.py deleted file mode 100644 index 04ad0242..00000000 --- a/nbs/tests/mpl_image_tests/test_11_whorlmap.py +++ /dev/null @@ -1,124 +0,0 @@ -import pytest -import pandas as pd -import numpy as np -import matplotlib.pyplot as plt -from scipy.stats import norm -import dabest -from dabest.multi import combine, whorlmap - -def create_delta_dataset(N=50, - seed=9999, - second_quarter_adjustment=3, - third_quarter_adjustment= -0.5, - fourth_quarter_adjustment= -3, - scale4=1, initial_loc = 10): - """Create a sample dataset for delta-delta analysis.""" - np.random.seed(seed) - - # Create samples - y = norm.rvs(loc=initial_loc, scale=0.4, size=N*4) - y[N:2*N] = norm.rvs(loc=initial_loc + second_quarter_adjustment, scale= 1, size=N) - y[2*N:3*N] = norm.rvs(loc=initial_loc + third_quarter_adjustment, scale=0.4, size=N) - y[3*N:4*N] = norm.rvs(loc=initial_loc + fourth_quarter_adjustment, scale=scale4, size=N) - - # Treatment, Rep, Genotype, and ID columns - treatment = np.repeat(['Placebo', 'Drug'], N*2).tolist() - genotype = np.repeat(['W', 'M', 'W', 'M'], N).tolist() - id_col = list(range(0, N*2)) * 2 - - # Combine all columns into a DataFrame - df = pd.DataFrame({ - 'ID': id_col, - 'Genotype': genotype, - 'Treatment': treatment, - 'Transcript Level': y - }) - return df - -dabest_objects_2d = [[None for _ in range(2)] for _ in range(2)] -labels_2d = ["Transcript 1", "Transcript 2"] -row_labels_2d = ["Drug A", "Drug B"] -drug_effect_2d = [[.9, 2], - [0.1, -.3], - ] -drug_effect_scale_2d = [[5, 10], - [7, .2], - ] -seeds = [1, 1000] - -for i in range(len(row_labels_2d)): - for j in range(len(labels_2d)): - df = create_delta_dataset(seed=seeds[i], - fourth_quarter_adjustment=drug_effect_2d[i][j], - scale4=drug_effect_scale_2d[i][j], - initial_loc = 20) - dabest_objects_2d[i][j] = dabest.load(data=df, - x=["Genotype", "Genotype"], - y="Transcript Level", - delta2=True, - experiment="Treatment") - -multi_2d_mean_diff = combine(dabest_objects_2d, labels_2d, row_labels=row_labels_2d, effect_size="mean_diff") -multi_2d_delta_g = combine(dabest_objects_2d, labels_2d, row_labels=row_labels_2d, effect_size="delta_g") -multi_1d = combine(dabest_objects_2d[0], labels_2d, row_labels="Drug A", effect_size="mean_diff") - -dabest_objects_2d_two_group_delta = [[None for _ in range(2)] for _ in range(2)] -for i in range(len(row_labels_2d)): - for j in range(len(labels_2d)): - df = create_delta_dataset(seed=seeds[i], - fourth_quarter_adjustment=drug_effect_2d[i][j], - scale4=drug_effect_scale_2d[i][j], - initial_loc = 20) - dabest_objects_2d_two_group_delta[i][j] = dabest.load(data=df, - x="Treatment", - y="Transcript Level", - idx = ("Placebo", "Drug")) -multi_2d_two_group_delta_mean_diff = combine(dabest_objects_2d_two_group_delta, labels_2d, row_labels=row_labels_2d, effect_size="mean_diff") - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_550_forest_plot_2d_mean_diff(): - plt.rcdefaults() - f, a = multi_2d_mean_diff.forest_plot( - forest_plot_title="Forest Plot", - forest_plot_kwargs={'marker_size': 6} - ) - return f - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_551_whorlmap_2d_mean_diff(): - plt.rcdefaults() - f, a, m = multi_2d_mean_diff.whorlmap( - title="Whorlmap", - chop_tail=2.5, # Remove 5% extreme values - fig_size=(2, 2) - ) - return f - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_552_whorlmap_2d_delta_g(): - plt.rcdefaults() - f, a, m = multi_2d_delta_g.whorlmap( - title="Delta g Whorlmap", - chop_tail=2.5, # Remove 5% extreme values - fig_size=(2, 2) - ) - return f - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_553_whorlmap_1d(): - plt.rcdefaults() - f, a, m = multi_1d.whorlmap( - chop_tail=2.5, # Remove 5% extreme values - fig_size=(2, 1) - - ) - return f - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_554_whorlmap_2d_two_group_delta_mean_diff(): - plt.rcdefaults() - f, a, m = multi_2d_two_group_delta_mean_diff.whorlmap( - chop_tail=2.5, # Remove 5% extreme values - fig_size=(2, 2) - ) - return f \ No newline at end of file diff --git a/nbs/tests/mpl_image_tests/test_Gridkey.py b/nbs/tests/mpl_image_tests/test_Gridkey.py deleted file mode 100644 index 3a36374e..00000000 --- a/nbs/tests/mpl_image_tests/test_Gridkey.py +++ /dev/null @@ -1,353 +0,0 @@ -import pytest -import numpy as np -import pandas as pd -from scipy.stats import norm - - -import matplotlib as mpl - -mpl.use("Agg") -import matplotlib.pyplot as plt -import matplotlib.ticker as Ticker -import seaborn as sns - -from dabest._api import load - -def create_demo_dataset(seed=9999, N=20): - import numpy as np - import pandas as pd - from scipy.stats import norm # Used in generation of populations. - - np.random.seed(9999) # Fix the seed so the results are replicable. - # pop_size = 10000 # Size of each population. - - # Create samples - c1 = norm.rvs(loc=3, scale=0.4, size=N) - c2 = norm.rvs(loc=3.5, scale=0.75, size=N) - c3 = norm.rvs(loc=3.25, scale=0.4, size=N) - - t1 = norm.rvs(loc=3.5, scale=0.5, size=N) - t2 = norm.rvs(loc=2.5, scale=0.6, size=N) - t3 = norm.rvs(loc=3, scale=0.75, size=N) - t4 = norm.rvs(loc=3.5, scale=0.75, size=N) - t5 = norm.rvs(loc=3.25, scale=0.4, size=N) - t6 = norm.rvs(loc=3.25, scale=0.4, size=N) - - # Add a `gender` column for coloring the data. - females = np.repeat("Female", N / 2).tolist() - males = np.repeat("Male", N / 2).tolist() - gender = females + males - - # Add an `id` column for paired data plotting. - id_col = pd.Series(range(1, N + 1)) - - # Combine samples and gender into a DataFrame. - df = pd.DataFrame( - { - "Control 1": c1, - "Test 1": t1, - "Control 2": c2, - "Test 2": t2, - "Control 3": c3, - "Test 3": t3, - "Test 4": t4, - "Test 5": t5, - "Test 6": t6, - "Gender": gender, - "ID": id_col, - } - ) - - return df - -def create_demo_dataset_delta(seed=9999, N=20): - - import numpy as np - import pandas as pd - from scipy.stats import norm # Used in generation of populations. - - np.random.seed(seed) # Fix the seed so the results are replicable. - # pop_size = 10000 # Size of each population. - - from scipy.stats import norm # Used in generation of populations. - - # Create samples - y = norm.rvs(loc=3, scale=0.4, size=N * 4) - y[N : 2 * N] = y[N : 2 * N] + 1 - y[2 * N : 3 * N] = y[2 * N : 3 * N] - 0.5 - - # Add drug column - t1 = np.repeat("Placebo", N * 2).tolist() - t2 = np.repeat("Drug", N * 2).tolist() - treatment = t1 + t2 - - # Add a `rep` column as the first variable for the 2 replicates of experiments done - rep = [] - for i in range(N * 2): - rep.append("Rep1") - rep.append("Rep2") - - # Add a `genotype` column as the second variable - wt = np.repeat("W", N).tolist() - mt = np.repeat("M", N).tolist() - wt2 = np.repeat("W", N).tolist() - mt2 = np.repeat("M", N).tolist() - - genotype = wt + mt + wt2 + mt2 - - # Add an `id` column for paired data plotting. - id = list(range(0, N * 2)) - id_col = id + id - - # Combine all columns into a DataFrame. - df = pd.DataFrame( - {"ID": id_col, "Rep": rep, "Genotype": genotype, "Treatment": treatment, "Y": y} - ) - return df - - -def create_demo_prop_dataset(seed=9999, N=40): - np.random.seed(9999) # Fix the seed so the results are replicable. - # Create samples - n = 1 - c1 = np.random.binomial(n, 0.2, size=N) - c2 = np.random.binomial(n, 0.2, size=N) - c3 = np.random.binomial(n, 0.8, size=N) - - t1 = np.random.binomial(n, 0.5, size=N) - t2 = np.random.binomial(n, 0.2, size=N) - t3 = np.random.binomial(n, 0.3, size=N) - t4 = np.random.binomial(n, 0.4, size=N) - t5 = np.random.binomial(n, 0.5, size=N) - t6 = np.random.binomial(n, 0.6, size=N) - t7 = np.zeros(N) - t8 = np.ones(N) - t9 = np.zeros(N) - - # Add a `gender` column for coloring the data. - females = np.repeat("Female", N / 2).tolist() - males = np.repeat("Male", N / 2).tolist() - gender = females + males - - # Add an `id` column for paired data plotting. - id_col = pd.Series(range(1, N + 1)) - - # Combine samples and gender into a DataFrame. - df = pd.DataFrame( - { - "Control 1": c1, - "Test 1": t1, - "Control 2": c2, - "Test 2": t2, - "Control 3": c3, - "Test 3": t3, - "Test 4": t4, - "Test 5": t5, - "Test 6": t6, - "Test 7": t7, - "Test 8": t8, - "Test 9": t9, - "Gender": gender, - "ID": id_col, - } - ) - - return df - -df = create_demo_dataset() -df_delta = create_demo_dataset_delta() -df_prop = create_demo_prop_dataset() - -# Two group -two_groups_unpaired = load(df, idx=("Control 1", "Test 1")) -two_groups_paired = load(df, idx=("Control 1", "Test 1"), paired='baseline', id_col='ID') - -# Multi two group -multi_2group_unpaired = load(df, idx=(("Control 1","Test 1",),("Control 2", "Test 2"),),) -multi_2group_paired = load(df, idx=(("Control 1","Test 1",),("Control 2", "Test 2"),), paired='baseline', id_col='ID') - -# Multi-group -shared_control = load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6")) -repeated_measures = load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6"), paired='baseline', id_col='ID') - -# Mixed multi group and two group -multi_groups_unpaired = load(df,idx=(("Control 1","Test 1",),("Control 2", "Test 2", "Test 3"),("Control 3", "Test 4", "Test 5", "Test 6"),),) -multi_groups_paired = load(df,idx=(("Control 1","Test 1",),("Control 2", "Test 2", "Test 3"),("Control 3", "Test 4", "Test 5", "Test 6"),), - paired='baseline', id_col='ID') - - -# Proportions -multi_groups_unpaired_prop = load(df_prop, idx=(("Control 1","Test 1",),("Control 2", "Test 2", "Test 3"),("Control 3", "Test 4", "Test 5", "Test 6"),), - proportional=True,) - -multi_groups_paired_baseline_prop = load(df_prop, idx=(("Control 1","Test 1",),("Control 2", "Test 2", "Test 3"),("Control 3", "Test 4", "Test 5", "Test 6"),), - paired="baseline", id_col="ID", proportional=True,) - -# delta-delta -delta_delta_unpaired = load(data=df_delta, x=["Genotype", "Genotype"], y="Y", delta2=True, experiment="Treatment") -delta_delta_paired = load(data = df_delta, x = ["Treatment", "Rep"], y = "Y", delta2 = True, experiment = "Genotype", paired="baseline", id_col="ID") - -# mini_meta -mini_meta_unpaired = load(df, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")),mini_meta=True) -mini_meta_paired = load(df, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")),mini_meta=True, paired='baseline', id_col='ID') - - -# Two Group -@pytest.mark.mpl_image_compare(tolerance=8) -def test_250_2group_unpaired_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(gridkey='auto'); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_251_2group_unpaired_meandiff_gridkey_userdefinedrows(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(gridkey=['Control', 'Test']); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_252_2group_paired_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return two_groups_paired.mean_diff.plot(gridkey='auto'); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_253_2group_paired_meandiff_gridkey_userdefinedrows(): - plt.rcdefaults() - return two_groups_paired.mean_diff.plot(gridkey=['Control', 'Test']); - -# Multi 2 Group -@pytest.mark.mpl_image_compare(tolerance=8) -def test_254_multi_2group_unpaired_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return multi_groups_unpaired.mean_diff.plot(gridkey='auto'); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_255_multi_2group_unpaired_meandiff_gridkey_userdefinedrows(): - plt.rcdefaults() - return multi_groups_unpaired.mean_diff.plot(gridkey=['Control', 'Test']); - -# Shared Control and Repeated Measures -@pytest.mark.mpl_image_compare(tolerance=8) -def test_256_shared_control_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return shared_control.mean_diff.plot(gridkey='auto'); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_257_shared_control_meandiff_gridkey_userdefinedrows(): - plt.rcdefaults() - return shared_control.mean_diff.plot(gridkey=['Control', 'Test']); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_258_repeated_measures_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return repeated_measures.mean_diff.plot(gridkey='auto'); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_259_repeated_measures_meandiff_gridkey_userdefinedrows(): - plt.rcdefaults() - return repeated_measures.mean_diff.plot(gridkey=['Control', 'Test']); - - -# Multi groups -@pytest.mark.mpl_image_compare(tolerance=8) -def test_260_multigroups_unpaired_meandiff_gridkey_userdefinedrows(): - plt.rcdefaults() - return multi_groups_unpaired.mean_diff.plot(gridkey=['Control', 'Test']); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_261_multigroups_unpaired_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return multi_groups_unpaired.mean_diff.plot(gridkey='auto'); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_262_multigroups_paired_meandiff_gridkey_userdefinedrows(): - plt.rcdefaults() - return multi_groups_paired.mean_diff.plot(gridkey=['Control', 'Test']); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_263_multigroups_paired_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return multi_groups_paired.mean_diff.plot(gridkey='auto'); - -# Proportions -@pytest.mark.mpl_image_compare(tolerance=8) -def test_264_multigroups_prop_unpaired_meandiff_gridkey_userdefinedrows(): - plt.rcdefaults() - return multi_groups_unpaired_prop.mean_diff.plot(gridkey=['Control', 'Test']); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_265_multigroups_prop_unpaired_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return multi_groups_unpaired_prop.mean_diff.plot(gridkey='auto'); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_266_multigroups_prop_paired_meandiff_gridkey_userdefinedrows(): - plt.rcdefaults() - return multi_groups_paired_baseline_prop.mean_diff.plot(gridkey=['Control', 'Test']); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_267_multigroups_prop_paired_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return multi_groups_paired_baseline_prop.mean_diff.plot(gridkey='auto'); - - -# delta-delta -@pytest.mark.mpl_image_compare(tolerance=8) -def test_268_delta_delta_unpaired_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return delta_delta_unpaired.mean_diff.plot(gridkey='auto'); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_269_delta_delta_paired_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return delta_delta_paired.mean_diff.plot(gridkey='auto'); - - -# mini-meta -@pytest.mark.mpl_image_compare(tolerance=8) -def test_270_mini_meta_unpaired_meandiff_gridkey_userdefinedrows(): - plt.rcdefaults() - return mini_meta_unpaired.mean_diff.plot(gridkey=['Control', 'Test']); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_271_mini_meta_unpaired_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return mini_meta_unpaired.mean_diff.plot(gridkey='auto'); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_272_mini_meta_paired_meandiff_gridkey_userdefinedrows(): - plt.rcdefaults() - return mini_meta_paired.mean_diff.plot(gridkey=['Control', 'Test']); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_273_mini_meta_paired_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return mini_meta_paired.mean_diff.plot(gridkey='auto'); - - -# Gridkey kwargs -multi_2group_paired_test = load(df, idx=(("Control 1","Control 2",),("Test 1", "Test 2"),), paired='baseline', id_col='ID') -@pytest.mark.mpl_image_compare(tolerance=8) -def test_274_gridkey_merge_pairs_and_autoparser(): - plt.rcdefaults() - return multi_2group_paired_test.mean_diff.plot(gridkey=['Control', 'Test'], gridkey_kwargs={'merge_pairs': True}); - -gridkey_kwargs = {'show_es': False, 'show_Ns': False, 'marker': '√'} -@pytest.mark.mpl_image_compare(tolerance=8) -def test_275_gridkey_kwargs_and_autoparser(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(gridkey='auto', gridkey_kwargs=gridkey_kwargs); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_276_gridkey_fontsize_and_autoparser(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(gridkey='auto', gridkey_kwargs={'fontsize': 15}); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_277_gridkey_labels_fontsize_and_autoparser(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(gridkey='auto', gridkey_kwargs={'labels_fontsize': 15}); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_278_gridkey_labels_fontsize_and_fontsize_and_autoparser(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(gridkey='auto', - gridkey_kwargs={'fontsize': 8, 'labels_fontsize': 15}); \ No newline at end of file diff --git a/nbs/tests/mpl_image_tests/test_Horizontal_Plots.py b/nbs/tests/mpl_image_tests/test_Horizontal_Plots.py deleted file mode 100644 index 0595ed77..00000000 --- a/nbs/tests/mpl_image_tests/test_Horizontal_Plots.py +++ /dev/null @@ -1,1045 +0,0 @@ -import pytest -import numpy as np -import pandas as pd -from scipy.stats import norm - - -import matplotlib as mpl - -mpl.use("Agg") -import matplotlib.pyplot as plt -import matplotlib.ticker as Ticker -import seaborn as sns - -from dabest._api import load - -def create_demo_dataset(seed=9999, N=20): - import numpy as np - import pandas as pd - from scipy.stats import norm # Used in generation of populations. - - np.random.seed(9999) # Fix the seed so the results are replicable. - # pop_size = 10000 # Size of each population. - - # Create samples - c1 = norm.rvs(loc=3, scale=0.4, size=N) - c2 = norm.rvs(loc=3.5, scale=0.75, size=N) - c3 = norm.rvs(loc=3.25, scale=0.4, size=N) - - t1 = norm.rvs(loc=3.5, scale=0.5, size=N) - t2 = norm.rvs(loc=2.5, scale=0.6, size=N) - t3 = norm.rvs(loc=3, scale=0.75, size=N) - t4 = norm.rvs(loc=3.5, scale=0.75, size=N) - t5 = norm.rvs(loc=3.25, scale=0.4, size=N) - t6 = norm.rvs(loc=3.25, scale=0.4, size=N) - - # Add a `gender` column for coloring the data. - females = np.repeat("Female", N / 2).tolist() - males = np.repeat("Male", N / 2).tolist() - gender = females + males - - # Add an `id` column for paired data plotting. - id_col = pd.Series(range(1, N + 1)) - - # Combine samples and gender into a DataFrame. - df = pd.DataFrame( - { - "Control 1": c1, - "Test 1": t1, - "Control 2": c2, - "Test 2": t2, - "Control 3": c3, - "Test 3": t3, - "Test 4": t4, - "Test 5": t5, - "Test 6": t6, - "Gender": gender, - "ID": id_col, - } - ) - - return df - -def create_demo_dataset_delta(seed=9999, N=20): - - import numpy as np - import pandas as pd - from scipy.stats import norm # Used in generation of populations. - - np.random.seed(seed) # Fix the seed so the results are replicable. - # pop_size = 10000 # Size of each population. - - from scipy.stats import norm # Used in generation of populations. - - # Create samples - y = norm.rvs(loc=3, scale=0.4, size=N * 4) - y[N : 2 * N] = y[N : 2 * N] + 1 - y[2 * N : 3 * N] = y[2 * N : 3 * N] - 0.5 - - # Add drug column - t1 = np.repeat("Placebo", N * 2).tolist() - t2 = np.repeat("Drug", N * 2).tolist() - treatment = t1 + t2 - - # Add a `rep` column as the first variable for the 2 replicates of experiments done - rep = [] - for i in range(N * 2): - rep.append("Rep1") - rep.append("Rep2") - - # Add a `genotype` column as the second variable - wt = np.repeat("W", N).tolist() - mt = np.repeat("M", N).tolist() - wt2 = np.repeat("W", N).tolist() - mt2 = np.repeat("M", N).tolist() - - genotype = wt + mt + wt2 + mt2 - - # Add an `id` column for paired data plotting. - id = list(range(0, N * 2)) - id_col = id + id - - # Combine all columns into a DataFrame. - df = pd.DataFrame( - {"ID": id_col, "Rep": rep, "Genotype": genotype, "Treatment": treatment, "Y": y} - ) - return df - -def create_demo_prop_dataset(seed=9999, N=40): - np.random.seed(9999) # Fix the seed so the results are replicable. - # Create samples - n = 1 - c1 = np.random.binomial(n, 0.2, size=N) - c2 = np.random.binomial(n, 0.2, size=N) - c3 = np.random.binomial(n, 0.8, size=N) - - t1 = np.random.binomial(n, 0.5, size=N) - t2 = np.random.binomial(n, 0.2, size=N) - t3 = np.random.binomial(n, 0.3, size=N) - t4 = np.random.binomial(n, 0.4, size=N) - t5 = np.random.binomial(n, 0.5, size=N) - t6 = np.random.binomial(n, 0.6, size=N) - t7 = np.zeros(N) - t8 = np.ones(N) - t9 = np.zeros(N) - - # Add a `gender` column for coloring the data. - females = np.repeat("Female", N / 2).tolist() - males = np.repeat("Male", N / 2).tolist() - gender = females + males - - # Add an `id` column for paired data plotting. - id_col = pd.Series(range(1, N + 1)) - - # Combine samples and gender into a DataFrame. - df = pd.DataFrame( - { - "Control 1": c1, - "Test 1": t1, - "Control 2": c2, - "Test 2": t2, - "Control 3": c3, - "Test 3": t3, - "Test 4": t4, - "Test 5": t5, - "Test 6": t6, - "Test 7": t7, - "Test 8": t8, - "Test 9": t9, - "Gender": gender, - "ID": id_col, - } - ) - - return df - -df = create_demo_dataset() -df_delta = create_demo_dataset_delta() -df_prop = create_demo_prop_dataset() - -# Two group -two_groups_unpaired = load(df, idx=("Control 1", "Test 1")) -two_groups_paired = load(df, idx=("Control 1", "Test 1"), paired='baseline', id_col='ID') - -# Multi two group -multi_2group_unpaired = load(df, idx=(("Control 1","Test 1",),("Control 2", "Test 2"),),) -multi_2group_paired = load(df, idx=(("Control 1","Test 1",),("Control 2", "Test 2"),), paired='baseline', id_col='ID') - -# Multi-group -shared_control = load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6")) -repeated_measures = load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6"), paired='baseline', id_col='ID') - - -# Mixed multi group and two group -multi_groups_unpaired = load(df,idx=(("Control 1","Test 1",),("Control 2", "Test 2", "Test 3"),("Control 3", "Test 4", "Test 5", "Test 6"),),) -multi_groups_paired_baseline = load(df,idx=(("Control 1","Test 1",),("Control 2", "Test 2", "Test 3"),("Control 3", "Test 4", "Test 5", "Test 6"),), - paired='baseline', id_col='ID') -multi_groups_paired_sequential = load(df,idx=(("Control 1","Test 1",),("Control 2", "Test 2", "Test 3"),("Control 3", "Test 4", "Test 5", "Test 6"),), - paired='sequential', id_col='ID') - -# Proportion plots -df_prop = create_demo_prop_dataset() - -two_groups_unpaired_prop = load(df_prop, idx=("Control 1", "Test 1"), proportional=True) - -two_groups_paired_baseline_prop = load(df_prop, idx=("Control 1", "Test 1"), paired="baseline", id_col="ID", proportional=True) - -two_groups_paired_sequential_prop = load(df_prop, idx=("Control 1", "Test 1"), paired="sequential", id_col="ID", proportional=True) - -multi_2group_unpaired_prop = load(df_prop, idx=(("Control 1","Test 1",),("Control 2", "Test 2"),), proportional=True,) - -multi_2group_paired_baseline_prop = load(df_prop, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2")), paired="baseline", id_col="ID", proportional=True,) - -multi_2group_paired_sequential_prop = load(df_prop, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2")), paired="sequential", id_col="ID", proportional=True,) - -shared_control_prop = load(df_prop, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6"), proportional=True,) - -repeated_measures_baseline_prop = load(df_prop, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6"), - paired="baseline", id_col="ID", proportional=True,) - -repeated_measures_sequential_prop = load(df_prop, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6"), - paired="sequential", id_col="ID", proportional=True,) - -multi_groups_unpaired_prop = load(df_prop, idx=(("Control 1","Test 1",),("Control 2", "Test 2", "Test 3"),("Control 3", "Test 4", "Test 5", "Test 6"),), - proportional=True,) - -multi_groups_paired_baseline_prop = load(df_prop, idx=(("Control 1","Test 1",),("Control 2", "Test 2", "Test 3"),("Control 3", "Test 4", "Test 5", "Test 6"),), - paired="baseline", id_col="ID", proportional=True,) - -multi_groups_paired_sequential_prop = load(df_prop, idx=(("Control 1","Test 1",),("Control 2", "Test 2", "Test 3"), - ("Control 3", "Test 4", "Test 5", "Test 6"),), paired="sequential", - id_col="ID", proportional=True,) - -zero_to_zero_prop = load(df_prop, idx=("Test 7", "Test 9"), proportional=True, paired="sequential", id_col="ID") -zero_to_one_prop = load(df_prop, idx=("Test 7", "Test 8"), proportional=True, paired="sequential", id_col="ID") -one_to_zero_prop = load(df_prop, idx=("Test 8", "Test 7"), proportional=True, paired="sequential", id_col="ID") -one_in_separate_control_prop = load(df_prop,idx=((("Control 1", "Test 1"), ("Test 2", "Test 3"), ("Test 4", "Test 8", "Test 6"))), - proportional=True, paired="sequential", id_col="ID",) - - - -# delta-delta -delta_delta_unpaired = load(data = df_delta, x = ["Genotype", "Genotype"], y = "Y", delta2 = True, experiment = "Treatment") -delta_delta_unpaired_specified = load(data = df_delta, x = ["Genotype", "Genotype"], y = "Y", - delta2 = True, experiment = "Treatment", - experiment_label = ["Drug", "Placebo"], - x1_level = ["M", "W"]) -delta_delta_paired_baseline = load(data = df_delta, x = ["Treatment", "Rep"], y = "Y", delta2 = True, experiment = "Genotype", paired="baseline", id_col="ID") -delta_delta_paired_sequential = load(data = df_delta, x = ["Treatment", "Rep"], y = "Y", delta2 = True, experiment = "Genotype", paired="sequential", id_col="ID") - -# mini_meta -mini_meta_unpaired = load(df, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), mini_meta=True) -mini_meta_paired_baseline = load(df, id_col = "ID", idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), - paired = "baseline", mini_meta=True) -mini_meta_paired_sequential = load(df, id_col = "ID", idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), - paired = "sequential", mini_meta=True) - - -# Tests -# Two Group - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_300_2group_unpaired_meandiff(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_301_2group_unpaired_mediandiff(): - plt.rcdefaults() - return two_groups_unpaired.median_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_302_2group_unpaired_hedges_g(): - plt.rcdefaults() - return two_groups_unpaired.hedges_g.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_303_2group_paired_meandiff(): - plt.rcdefaults() - return two_groups_paired.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_304_2group_paired_hedges_g(): - plt.rcdefaults() - return two_groups_paired.hedges_g.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_305_2group_cummings_unpaired_meandiff(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(horizontal=True, fig_size=(6, 4), float_contrast=False) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_306_2group_cummings_paired_meandiff(): - plt.rcdefaults() - return two_groups_paired.mean_diff.plot(horizontal=True, float_contrast=False) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_307_multi2group_unpaired(): - plt.rcdefaults() - return multi_2group_unpaired.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_308_multi2group_paired(): - plt.rcdefaults() - return multi_2group_paired.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_309_sharedcontrol(): - plt.rcdefaults() - return shared_control.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_310_repeatedmeasure(): - plt.rcdefaults() - return repeated_measures.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_311_multigroups_unpaired(): - plt.rcdefaults() - return multi_groups_unpaired.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_312_multigroups_paired_baseline(): - plt.rcdefaults() - return multi_groups_paired_baseline.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_313_multigroups_paired_sequential(): - plt.rcdefaults() - return multi_groups_paired_sequential.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_314_2group_unpaired_ylabel(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(horizontal=True, - raw_label="This is my\nrawdata", contrast_label="The bootstrap\ndistribtions!" - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_315_multi2group_color(): - plt.rcdefaults() - return multi_2group_unpaired.mean_diff.plot(horizontal=True, color_col="Gender") - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_316_2group_paired_color(): - plt.rcdefaults() - return two_groups_paired.mean_diff.plot(horizontal=True, color_col="Gender") - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_317_multi2group_unpaired_change_palette_a(): - plt.rcdefaults() - return multi_2group_unpaired.mean_diff.plot(horizontal=True, color_col="Gender", custom_palette="Dark2") - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_318_multi2group_unpaired_change_palette_b(): - plt.rcdefaults() - return multi_2group_unpaired.mean_diff.plot(horizontal=True, custom_palette="Paired") - -my_color_palette = { - "Control 1": "blue", - "Test 1": "purple", - "Control 2": "#cb4b16", # This is a hex string. - "Test 2": (0.0, 0.7, 0.2), # This is a RGB tuple. -} - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_319_multi2group_unpaired_change_palette_c(): - plt.rcdefaults() - return multi_2group_unpaired.mean_diff.plot(horizontal=True, custom_palette=my_color_palette) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_320_multi2group_unpaired_desat(): - plt.rcdefaults() - return multi_2group_unpaired.mean_diff.plot(horizontal=True, - custom_palette=my_color_palette, raw_desat=0.75, contrast_desat=0.25 - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_321_multi2group_unpaired_dot_sizes(): - plt.rcdefaults() - return multi_2group_unpaired.mean_diff.plot(horizontal=True, raw_marker_size=3, contrast_marker_size=12) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_322_multi2group_unpaired_change_ylims(): - plt.rcdefaults() - return multi_2group_unpaired.mean_diff.plot(horizontal=True, raw_ylim=(0, 5), contrast_ylim=(-2, 2)) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_323_2group_unpaired_ticker(): - plt.rcdefaults() - f = two_groups_unpaired.mean_diff.plot(horizontal=True) - - rawswarm_axes = f.axes[0] - contrast_axes = f.axes[1] - - rawswarm_axes.xaxis.set_major_locator(Ticker.MultipleLocator(1)) - rawswarm_axes.xaxis.set_minor_locator(Ticker.MultipleLocator(0.5)) - - contrast_axes.xaxis.set_major_locator(Ticker.MultipleLocator(0.5)) - contrast_axes.xaxis.set_minor_locator(Ticker.MultipleLocator(0.25)) - - return f - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_324_multi2group_unpaired_ticker(): - plt.rcdefaults() - f = multi_2group_unpaired.mean_diff.plot(horizontal=True, raw_ylim=(0, 6), contrast_ylim=(-3, 1)) - - rawswarm_axes = f.axes[0] - contrast_axes = f.axes[1] - - rawswarm_axes.xaxis.set_major_locator(Ticker.MultipleLocator(2)) - rawswarm_axes.xaxis.set_minor_locator(Ticker.MultipleLocator(1)) - - contrast_axes.xaxis.set_major_locator(Ticker.MultipleLocator(0.5)) - contrast_axes.xaxis.set_minor_locator(Ticker.MultipleLocator(0.25)) - - return f - -np.random.seed(9999) -Ns = [20, 10, 21, 20] -c1 = pd.DataFrame({"Control": norm.rvs(loc=3, scale=0.4, size=Ns[0])}) -t1 = pd.DataFrame({"Test 1": norm.rvs(loc=3.5, scale=0.5, size=Ns[1])}) -t2 = pd.DataFrame({"Test 2": norm.rvs(loc=2.5, scale=0.6, size=Ns[2])}) -t3 = pd.DataFrame({"Test 3": norm.rvs(loc=3, scale=0.75, size=Ns[3])}) -wide_df = pd.concat([c1, t1, t2, t3], axis=1) - -long_df = pd.melt( - wide_df, - value_vars=["Control", "Test 1", "Test 2", "Test 3"], - value_name="value", - var_name="group", -) -long_df["dummy"] = np.repeat(np.nan, len(long_df)) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_325_wide_df_nan(): - plt.rcdefaults() - wide_df_dabest = load(wide_df, idx=("Control", "Test 1", "Test 2", "Test 3")) - - return wide_df_dabest.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_326_long_df_nan(): - plt.rcdefaults() - long_df_dabest = load( - long_df, x="group", y="value", idx=("Control", "Test 1", "Test 2", "Test 3") - ) - - return long_df_dabest.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_327_2group_paired_slopegraph_kwargs(): - plt.rcdefaults() - return two_groups_paired.mean_diff.plot(horizontal=True, slopegraph_kwargs=dict(linestyle="dotted")) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_328_2group_unpaired_reflines_kwargs(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(horizontal=True, reflines_kwargs=dict(linestyle="dotted")) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_329_2group_unpaired_cumming_reflines_kwargs(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(horizontal=True, - fig_size=(12, 10), - float_contrast=False, - reflines_kwargs=dict(linestyle="dotted", linewidth=2), - contrast_ylim=(-1, 1), - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_330_2group_paired_cumming_slopegraph_reflines_kwargs(): - plt.rcdefaults() - return two_groups_paired.mean_diff.plot(horizontal=True, - float_contrast=False, - color_col="Gender", - slopegraph_kwargs=dict(linestyle="dotted"), - reflines_kwargs=dict(linestyle="dashed", linewidth=2), - contrast_ylim=(-1, 1), - ) - - -# Proportion plots -@pytest.mark.mpl_image_compare(tolerance=8) -def test_331_2group_unpaired_propdiff(): - plt.rcdefaults() - return two_groups_unpaired_prop.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_332_2group_unpaired_cummings_propdiff(): - plt.rcdefaults() - return two_groups_unpaired_prop.mean_diff.plot(horizontal=True, fig_size=(6,4), float_contrast=False) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_333_multi2group_unpaired_propdiff(): - plt.rcdefaults() - return multi_2group_unpaired_prop.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_334_shared_control_propdiff(): - plt.rcdefaults() - return shared_control_prop.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_335_repeated_measures_baseline_propdiff(): - plt.rcdefaults() - return repeated_measures_baseline_prop.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_336_repeated_measures_sequential_propdiff(): - plt.rcdefaults() - return repeated_measures_sequential_prop.mean_diff.plot(horizontal=True) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_337_multi_groups_unpaired_propdiff(): - plt.rcdefaults() - return multi_groups_unpaired_prop.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_338_multi_groups_paired_baseline_propdiff(): - plt.rcdefaults() - return multi_groups_paired_baseline_prop.mean_diff.plot(horizontal=True) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_339_multi_groups_paired_sequential_propdiff(): - plt.rcdefaults() - return multi_groups_paired_sequential_prop.mean_diff.plot(horizontal=True) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_340_2group_unpaired_prop_change_fig_size_and_palette_a(): - plt.rcdefaults() - return two_groups_unpaired_prop.mean_diff.plot(horizontal=True, fig_size=(6, 6), custom_palette="Dark2") - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_341_multi2group_unpaired_prop_change_palette_b(): - plt.rcdefaults() - return multi_2group_unpaired_prop.mean_diff.plot(horizontal=True, custom_palette="Paired") - -my_color_palette = { - "Control 1": "blue", - "Test 1": "purple", - "Control 2": "#cb4b16", # This is a hex string. - "Test 2": (0.0, 0.7, 0.2), # This is a RGB tuple. -} - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_342_multi2group_unpaired_prop_change_palette_c(): - plt.rcdefaults() - return multi_2group_unpaired_prop.mean_diff.plot(horizontal=True, custom_palette=my_color_palette) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_343_multi2group_unpaired_prop_desat(): - plt.rcdefaults() - return multi_2group_unpaired_prop.mean_diff.plot(horizontal=True, - custom_palette=my_color_palette, raw_desat=0.1, contrast_desat=0.25 - ) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_344_2group_unpaired_prop_err_color(): - plt.rcdefaults() - return two_groups_unpaired_prop.mean_diff.plot(horizontal=True, barplot_kwargs={"err_kws": {"color": "purple"}}) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_345_2group_unpaired_cummings_meandiff_bar_width(): - plt.rcdefaults() - return two_groups_unpaired_prop.mean_diff.plot(horizontal=True, bar_width=0.4, float_contrast=False) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_346_2group_unpaired_prop_cohens_h(): - plt.rcdefaults() - return two_groups_unpaired_prop.cohens_h.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_347_2group_unpaired_prop_cummings_cohens_h(): - plt.rcdefaults() - return two_groups_unpaired_prop.cohens_h.plot(horizontal=True, float_contrast=False) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_348_2group_sankey(): - plt.rcdefaults() - return two_groups_paired_baseline_prop.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_349_2group_sankey_cummings(): - plt.rcdefaults() - return two_groups_paired_baseline_prop.mean_diff.plot(horizontal=True, float_contrast=False) - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_350_multi2group_sankey_baseline(): - plt.rcdefaults() - return multi_2group_paired_baseline_prop.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_351_multi2group_sankey_sequential(): - plt.rcdefaults() - return multi_2group_paired_sequential_prop.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_352_multigroups_sankey_baseline(): - plt.rcdefaults() - return multi_groups_paired_baseline_prop.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_353_multigroups_sankey_sequential(): - plt.rcdefaults() - return multi_groups_paired_sequential_prop.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_354_2group_sankey_transparency(): - plt.rcdefaults() - return two_groups_paired_baseline_prop.mean_diff.plot(horizontal=True, sankey_kwargs={"alpha": 0.2}) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_355_zero_to_zero(): - plt.rcdefaults() - return zero_to_zero_prop.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_356_zero_to_one_prop(): - plt.rcdefaults() - return zero_to_one_prop.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_357_one_to_zero(): - plt.rcdefaults() - return one_to_zero_prop.mean_diff.plot(horizontal=True) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_358_repeated_measures_baseline_sankey_off(): - plt.rcdefaults() - return repeated_measures_baseline_prop.mean_diff.plot(horizontal=True, sankey_kwargs={"sankey": False}) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_359_repeated_measures_baseline_flow_off(): - plt.rcdefaults() - return repeated_measures_baseline_prop.mean_diff.plot(horizontal=True, sankey_kwargs={"flow": False}) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_360_multigroups_paired_sequential_sankey_off(): - plt.rcdefaults() - return multi_groups_paired_sequential_prop.mean_diff.plot(horizontal=True, sankey_kwargs={"sankey": False}) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_361_multigroups_paired_sequential_flow_off(): - plt.rcdefaults() - return multi_groups_paired_sequential_prop.mean_diff.plot(horizontal=True, sankey_kwargs={"flow": False}) - - - -# delta-delta -@pytest.mark.mpl_image_compare(tolerance=8) -def test_362_cummings_unpaired_delta_delta_meandiff(): - plt.rcdefaults() - return delta_delta_unpaired.mean_diff.plot(horizontal=True); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_363_cummings_sequential_delta_delta_meandiff(): - plt.rcdefaults() - return delta_delta_paired_sequential.mean_diff.plot(horizontal=True); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_364_cummings_baseline_delta_delta_meandiff(): - plt.rcdefaults() - return delta_delta_paired_baseline.mean_diff.plot(horizontal=True); - - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_365_delta_plot_ylabel(): - plt.rcdefaults() - return delta_delta_paired_baseline.mean_diff.plot(horizontal=True, - raw_label="This is my\nrawdata", - contrast_label="The bootstrap\ndistribtions!", - delta2_label="This is delta!"); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_366_delta_plot_change_palette_a(): - plt.rcdefaults() - return delta_delta_paired_sequential.mean_diff.plot(horizontal=True, custom_palette="Dark2"); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_367_delta_specified(): - plt.rcdefaults() - return delta_delta_unpaired_specified.mean_diff.plot(horizontal=True); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_368_delta_change_ylims(): - plt.rcdefaults() - return delta_delta_paired_sequential.mean_diff.plot(horizontal=True, raw_ylim=(0, 9), - contrast_ylim=(-2, 2), - fig_size=(15,6)); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_369_delta_invert_ylim(): - plt.rcdefaults() - return delta_delta_paired_sequential.mean_diff.plot(horizontal=True, - contrast_ylim=(2, -2), - contrast_label="More negative is better!"); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_370_delta_median_diff(): - plt.rcdefaults() - return delta_delta_paired_sequential.median_diff.plot(horizontal=True); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_371_delta_cohens_d(): - plt.rcdefaults() - return delta_delta_unpaired.cohens_d.plot(horizontal=True); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_372_delta_show_delta2(): - plt.rcdefaults() - return delta_delta_unpaired.mean_diff.plot(horizontal=True, show_delta2=False); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_373_delta_axes_invert_ylim(): - plt.rcdefaults() - return delta_delta_unpaired.mean_diff.plot(horizontal=True, delta2_ylim=(2, -2), - delta2_label="More negative is better!"); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_374_delta_axes_invert_ylim_not_showing_delta2(): - plt.rcdefaults() - return delta_delta_unpaired.mean_diff.plot(horizontal=True, delta2_ylim=(2, -2), - delta2_label="More negative is better!", - show_delta2=False); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_375_unpaired_delta_g(): - plt.rcdefaults() - return delta_delta_unpaired.hedges_g.plot(horizontal=True); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_376_sequential_delta_g(): - plt.rcdefaults() - return delta_delta_paired_sequential.hedges_g.plot(horizontal=True); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_377_baseline_delta_g(): - plt.rcdefaults() - return delta_delta_paired_baseline.hedges_g.plot(horizontal=True); - - -# mini_meta -@pytest.mark.mpl_image_compare(tolerance=8) -def test_378_cummings_unpaired_mini_meta_meandiff(): - plt.rcdefaults() - return mini_meta_unpaired.mean_diff.plot(horizontal=True); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_379_cummings_sequential_mini_meta_meandiff(): - plt.rcdefaults() - return mini_meta_paired_sequential.mean_diff.plot(horizontal=True); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_380_cummings_baseline_mini_meta_meandiff(): - plt.rcdefaults() - return mini_meta_paired_baseline.mean_diff.plot(horizontal=True); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_381_mini_meta_plot_ylabel(): - plt.rcdefaults() - return mini_meta_paired_baseline.mean_diff.plot(horizontal=True, raw_label="This is my\nrawdata", - contrast_label="The bootstrap\ndistribtions!"); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_382_mini_meta_plot_change_palette_a(): - plt.rcdefaults() - return mini_meta_unpaired.mean_diff.plot(horizontal=True, custom_palette="Dark2"); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_383_mini_meta_dot_sizes(): - plt.rcdefaults() - return mini_meta_paired_sequential.mean_diff.plot(horizontal=True, show_pairs=False,raw_marker_size=3, - contrast_marker_size=12); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_384_mini_meta_change_ylims(): - plt.rcdefaults() - return mini_meta_paired_sequential.mean_diff.plot(horizontal=True, raw_ylim=(0, 5), - contrast_ylim=(-2, 2), - fig_size=(15,6)); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_385_mini_meta_invert_ylim(): - plt.rcdefaults() - return mini_meta_paired_sequential.mean_diff.plot(horizontal=True, contrast_ylim=(2, -2), - contrast_label="More negative is better!"); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_386_mini_meta_median_diff(): - plt.rcdefaults() - return mini_meta_paired_sequential.median_diff.plot(horizontal=True); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_387_mini_meta_cohens_d(): - plt.rcdefaults() - return mini_meta_unpaired.cohens_d.plot(horizontal=True); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_388_mini_meta_not_show(): - plt.rcdefaults() - return mini_meta_unpaired.mean_diff.plot(horizontal=True, show_mini_meta=False); - - -# Aesthetic kwargs -# Swarm_Side -@pytest.mark.mpl_image_compare(tolerance=8) -def test_389_Swarm_Side_Center(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(horizontal=True, swarm_side='center'); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_390_Swarm_Side_Right(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(horizontal=True, swarm_side='right'); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_391_Swarm_Side_Left(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(horizontal=True, swarm_side='left'); - -# Empty Circle -@pytest.mark.mpl_image_compare(tolerance=8) -def test_392_Empty_Circle(): - plt.rcdefaults() - return multi_2group_unpaired.mean_diff.plot(horizontal=True, empty_circle=True); - -# Table kwargs -@pytest.mark.mpl_image_compare(tolerance=8) -def test_393_Horizontal_Table_Kwargs(): - plt.rcdefaults() - return multi_2group_unpaired.mean_diff.plot(horizontal=True, horizontal_table_kwargs={'color': 'red', 'alpha': 0.5, 'text_color': 'white', - 'text_units':'mm', 'label': 'delta mm', 'control_marker': 'o',}); - -# Gridkey -# Two Group -@pytest.mark.mpl_image_compare(tolerance=8) -def test_394_2group_unpaired_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(horizontal=True, gridkey='auto'); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_395_2group_paired_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return two_groups_paired.mean_diff.plot(horizontal=True, gridkey='auto'); - -# Multi 2 Group -@pytest.mark.mpl_image_compare(tolerance=8) -def test_396_multi_2group_unpaired_meandiff_gridkey_userdefinedrows(): - plt.rcdefaults() - return multi_groups_unpaired.mean_diff.plot(horizontal=True, gridkey=['Control', 'Test']); - -# Shared Control and Repeated Measures -@pytest.mark.mpl_image_compare(tolerance=8) -def test_397_shared_control_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return shared_control.mean_diff.plot(horizontal=True, gridkey='auto'); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_398_repeated_measures_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return repeated_measures.mean_diff.plot(horizontal=True, gridkey='auto'); - - -# Multi groups -@pytest.mark.mpl_image_compare(tolerance=8) -def test_399_multigroups_unpaired_meandiff_gridkey_userdefinedrows(): - plt.rcdefaults() - return multi_groups_unpaired.mean_diff.plot(horizontal=True, gridkey=['Control', 'Test']); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_400_multigroups_unpaired_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return multi_groups_unpaired.mean_diff.plot(horizontal=True, gridkey='auto'); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_401_multigroups_paired_meandiff_gridkey_userdefinedrows(): - plt.rcdefaults() - return multi_groups_paired_baseline.mean_diff.plot(horizontal=True, gridkey=['Control', 'Test']); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_402_multigroups_paired_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return multi_groups_paired_baseline.mean_diff.plot(horizontal=True, gridkey='auto'); - -# Proportions -@pytest.mark.mpl_image_compare(tolerance=8) -def test_403_multigroups_prop_unpaired_meandiff_gridkey_userdefinedrows(): - plt.rcdefaults() - return multi_groups_unpaired_prop.mean_diff.plot(horizontal=True, gridkey=['Control', 'Test']); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_404_multigroups_prop_unpaired_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return multi_groups_unpaired_prop.mean_diff.plot(horizontal=True, gridkey='auto'); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_405_multigroups_prop_paired_meandiff_gridkey_userdefinedrows(): - plt.rcdefaults() - return multi_groups_paired_baseline_prop.mean_diff.plot(horizontal=True, gridkey=['Control', 'Test']); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_406_multigroups_prop_paired_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return multi_groups_paired_baseline_prop.mean_diff.plot(horizontal=True, gridkey='auto'); - - -# delta-delta -@pytest.mark.mpl_image_compare(tolerance=8) -def test_407_delta_delta_unpaired_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return delta_delta_unpaired.mean_diff.plot(horizontal=True, gridkey='auto'); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_408_delta_delta_paired_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return delta_delta_paired_baseline.mean_diff.plot(horizontal=True, gridkey='auto'); - - -# mini-meta -@pytest.mark.mpl_image_compare(tolerance=8) -def test_409_mini_meta_unpaired_meandiff_gridkey_userdefinedrows(): - plt.rcdefaults() - return mini_meta_unpaired.mean_diff.plot(horizontal=True, gridkey=['Control', 'Test']); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_410_mini_meta_unpaired_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return mini_meta_unpaired.mean_diff.plot(horizontal=True, gridkey='auto'); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_411_mini_meta_paired_meandiff_gridkey_userdefinedrows(): - plt.rcdefaults() - return mini_meta_paired_baseline.mean_diff.plot(horizontal=True, gridkey=['Control', 'Test']); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_412_mini_meta_paired_meandiff_gridkey_autoparser(): - plt.rcdefaults() - return mini_meta_paired_baseline.mean_diff.plot(horizontal=True, gridkey='auto'); - -# Gridkey kwargs -multi_2group_paired_test = load(df, idx=(("Control 1","Control 2",),("Test 1", "Test 2"),), paired='baseline', id_col='ID') -@pytest.mark.mpl_image_compare(tolerance=8) -def test_413_gridkey_merge_pairs_and_autoparser(): - plt.rcdefaults() - return multi_2group_paired_test.mean_diff.plot(horizontal=True, gridkey=['Control', 'Test'], gridkey_kwargs={'merge_pairs': True}); - -gridkey_kwargs = {'show_es': False, 'show_Ns': False, 'marker': '√'} -@pytest.mark.mpl_image_compare(tolerance=8) -def test_414_gridkey_kwargs_and_autoparser(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(horizontal=True, gridkey='auto', gridkey_kwargs=gridkey_kwargs); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_429_gridkey_fontsize_and_autoparser(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(horizontal=True, gridkey='auto', gridkey_kwargs={'fontsize': 15}); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_430_gridkey_labels_fontsize_and_autoparser(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(horizontal=True, gridkey='auto', - gridkey_kwargs={'labels_fontsize': 15}); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_431_gridkey_labels_fontsize_and_fontsize_and_autoparser(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(horizontal=True, gridkey='auto', - gridkey_kwargs={'fontsize': 8, 'labels_fontsize': 15}); - -# Table hide -@pytest.mark.mpl_image_compare(tolerance=8) -def test_415_Horizontal_Table_hide(): - plt.rcdefaults() - return multi_2group_unpaired.mean_diff.plot(horizontal=True, horizontal_table_kwargs={'show': False}); - -# Delta-dots -@pytest.mark.mpl_image_compare(tolerance=8) -def test_416_delta_dot_hide(): - plt.rcdefaults() - return multi_2group_paired.mean_diff.plot(horizontal=True, delta_dot=False); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_417_delta_dot_kwargs(): - plt.rcdefaults() - return multi_2group_paired.mean_diff.plot(horizontal=True, delta_dot_kwargs={"color":'red', "alpha":0.1, 'zorder': 2, 'size': 5, 'side': 'left'}); - -# Contrast bars -@pytest.mark.mpl_image_compare(tolerance=8) -def test_418_shared_control_meandiff_showcontrastbars(): - plt.rcdefaults() - return shared_control.mean_diff.plot(horizontal=True, contrast_bars=True, raw_bars=False); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_419_shared_control_meandiff_hidecontrastbars(): - plt.rcdefaults() - return shared_control.mean_diff.plot(horizontal=True, contrast_bars=False, raw_bars=False); - -contrast_kwargs = {'color': "red", 'alpha': 0.2} -@pytest.mark.mpl_image_compare(tolerance=8) -def test_420_shared_control_meandiff_contrastbars_kwargs(): - plt.rcdefaults() - return shared_control.mean_diff.plot(horizontal=True, contrast_bars=True, contrast_bars_kwargs = contrast_kwargs, raw_bars=False); - -# reference_band -reference_band=[0, 1] -@pytest.mark.mpl_image_compare(tolerance=8) -def test_421_shared_control_meandiff_summarybars(): - plt.rcdefaults() - return shared_control.mean_diff.plot(horizontal=True, reference_band=[0, 1], raw_bars=False, contrast_bars=False,); - -reference_band_kwargs = {'color': "black", 'alpha': 0.2, 'span_ax': True} -@pytest.mark.mpl_image_compare(tolerance=8) -def test_422_shared_control_meandiff_summarybars_kwargs(): - plt.rcdefaults() - return shared_control.mean_diff.plot(horizontal=True, reference_band=[0, 1], reference_band_kwargs = reference_band_kwargs, - contrast_bars=False, raw_bars=False); - -# Add counts to prop plots -@pytest.mark.mpl_image_compare(tolerance=8) -def test_423_shared_control_propdiff_show_counts(): - plt.rcdefaults() - return shared_control_prop.mean_diff.plot(horizontal=True, prop_sample_counts=True,) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_424_repeated_measures_baseline_propdiff_show_counts(): - plt.rcdefaults() - return repeated_measures_baseline_prop.mean_diff.plot(horizontal=True, prop_sample_counts=True,) - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_425_repeated_measures_baseline_propdiff_show_counts_and_kwargs(): - plt.rcdefaults() - return repeated_measures_baseline_prop.mean_diff.plot(horizontal=True, - prop_sample_counts=True, prop_sample_counts_kwargs={"color": "red", "fontsize": 12, "fontweight": "bold"}) - -# Effect size paired lines -@pytest.mark.mpl_image_compare(tolerance=8) -def test_426_repeatedmeasures_meandiff_show_es_paired_lines(): - plt.rcdefaults() - return repeated_measures.mean_diff.plot(horizontal=True, contrast_paired_lines=True); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_427_repeatedmeasures_meandiff_hide_es_paired_lines(): - plt.rcdefaults() - return repeated_measures.mean_diff.plot(horizontal=True, contrast_paired_lines=False); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_428_multigroups_paired_meandiff_es_paired_lines_kwargs(): - plt.rcdefaults() - return multi_groups_paired_baseline.mean_diff.plot(horizontal=True, contrast_paired_lines=True, contrast_paired_lines_kwargs={'color':'red', 'linestyle': '--', 'linewidth': 2, 'alpha': 0.5}); - -# color palette change for paired plots -@pytest.mark.mpl_image_compare(tolerance=8) -def test_429_multigroups_paired_baseline_change_palette(): - plt.rcdefaults() - return multi_groups_paired_baseline.mean_diff.plot(horizontal=True, custom_palette="Dark2", - delta_text=True) \ No newline at end of file diff --git a/nbs/tests/mpl_image_tests/test_plot_aesthetics.py b/nbs/tests/mpl_image_tests/test_plot_aesthetics.py deleted file mode 100644 index b85d85e7..00000000 --- a/nbs/tests/mpl_image_tests/test_plot_aesthetics.py +++ /dev/null @@ -1,347 +0,0 @@ -import pytest -import numpy as np -import pandas as pd -from scipy.stats import norm - - -import matplotlib as mpl - -mpl.use("Agg") -import matplotlib.pyplot as plt -import matplotlib.ticker as Ticker -import seaborn as sns - -from dabest._api import load - - -def create_demo_dataset(seed=9999, N=20): - import numpy as np - import pandas as pd - from scipy.stats import norm # Used in generation of populations. - - np.random.seed(9999) # Fix the seed so the results are replicable. - # pop_size = 10000 # Size of each population. - - # Create samples - c1 = norm.rvs(loc=3, scale=0.4, size=N) - c2 = norm.rvs(loc=3.5, scale=0.75, size=N) - c3 = norm.rvs(loc=3.25, scale=0.4, size=N) - - t1 = norm.rvs(loc=3.5, scale=0.5, size=N) - t2 = norm.rvs(loc=2.5, scale=0.6, size=N) - t3 = norm.rvs(loc=3, scale=0.75, size=N) - t4 = norm.rvs(loc=3.5, scale=0.75, size=N) - t5 = norm.rvs(loc=3.25, scale=0.4, size=N) - t6 = norm.rvs(loc=3.25, scale=0.4, size=N) - - # Add a `gender` column for coloring the data. - females = np.repeat("Female", N / 2).tolist() - males = np.repeat("Male", N / 2).tolist() - gender = females + males - - # Add an `id` column for paired data plotting. - id_col = pd.Series(range(1, N + 1)) - - # Combine samples and gender into a DataFrame. - df = pd.DataFrame( - { - "Control 1": c1, - "Test 1": t1, - "Control 2": c2, - "Test 2": t2, - "Control 3": c3, - "Test 3": t3, - "Test 4": t4, - "Test 5": t5, - "Test 6": t6, - "Gender": gender, - "ID": id_col, - } - ) - - return df - - -def create_demo_dataset_delta(seed=9999, N=20): - - import numpy as np - import pandas as pd - from scipy.stats import norm # Used in generation of populations. - - np.random.seed(seed) # Fix the seed so the results are replicable. - # pop_size = 10000 # Size of each population. - - from scipy.stats import norm # Used in generation of populations. - - # Create samples - y = norm.rvs(loc=3, scale=0.4, size=N * 4) - y[N : 2 * N] = y[N : 2 * N] + 1 - y[2 * N : 3 * N] = y[2 * N : 3 * N] - 0.5 - - # Add drug column - t1 = np.repeat("Placebo", N * 2).tolist() - t2 = np.repeat("Drug", N * 2).tolist() - treatment = t1 + t2 - - # Add a `rep` column as the first variable for the 2 replicates of experiments done - rep = [] - for i in range(N * 2): - rep.append("Rep1") - rep.append("Rep2") - - # Add a `genotype` column as the second variable - wt = np.repeat("W", N).tolist() - mt = np.repeat("M", N).tolist() - wt2 = np.repeat("W", N).tolist() - mt2 = np.repeat("M", N).tolist() - - genotype = wt + mt + wt2 + mt2 - - # Add an `id` column for paired data plotting. - id = list(range(0, N * 2)) - id_col = id + id - - # Combine all columns into a DataFrame. - df = pd.DataFrame( - {"ID": id_col, "Rep": rep, "Genotype": genotype, "Treatment": treatment, "Y": y} - ) - return df - - -df = create_demo_dataset() -df_delta = create_demo_dataset_delta() - -two_groups_unpaired = load(df, idx=("Control 1", "Test 1")) - -multi_2group = load(df, idx=(("Control 1", "Test 1",), ("Control 2", "Test 2"),),) - -multi_2group_paired = load(df, idx=(("Control 1", "Test 1"), - ("Control 2", "Test 2")),paired='baseline', id_col='ID') - -shared_control = load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6")) - -repeated_measures = load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6"), paired='baseline', id_col='ID') - -multi_groups_paired_baseline = load(df,idx=(("Control 1","Test 1", "Test 2"),("Control 2", "Test 3"),("Control 3", "Test 4", "Test 5", "Test 6"),), - paired='baseline', id_col='ID') - -multi_groups = load(df, idx=(("Control 1", "Test 1",), ("Control 2", "Test 2", "Test 3"), - ("Control 3", "Test 4", "Test 5", "Test 6"),),) - -unpaired_delta_delta = load(data=df_delta, x=["Genotype", "Genotype"], y="Y", delta2=True, experiment="Treatment") - -unpaired_mini_meta = load(df, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), mini_meta=True) - -multi_groups_change_idx_original = load(df, - idx=( - ("Control 1", "Test 1", "Test 2"), - ("Control 2", "Test 3", "Test 4"), - ("Control 3", "Test 5", "Test 6"), - ), -) -multi_groups_change_idx_new = load( - df, - idx=( - ("Control 1", "Control 2", "Control 3"), - ("Test 1", "Test 3", "Test 5"), - ("Test 2", "Test 4", "Test 6"), - ), -) -palette = {"Control 1": sns.color_palette("magma")[5], - "Test 1": sns.color_palette("magma")[3], - "Test 2": sns.color_palette("magma")[1], - "Control 2": sns.color_palette("magma")[5], - "Test 3": sns.color_palette("magma")[3], - "Test 4": sns.color_palette("magma")[1], - "Control 3": sns.color_palette("magma")[5], - "Test 5": sns.color_palette("magma")[3], - "Test 6": sns.color_palette("magma")[1]} - -# Jitter tests -np.random.seed(9999) # Fix the seed to ensure reproducibility of results. -Ns = 20 # The number of samples taken from each population -# Create samples -c1 = [0.5]*Ns + [1.5]*Ns -c2 = [2]*Ns + [1]*Ns -t1 = [1]*Ns + [2]*Ns -t2 = [1.5]*Ns + [2.5]*Ns -t3 = [2]*Ns + [1]*Ns -t4 = [1]*Ns + [2]*Ns -t5 = [1.5]*Ns + [2.5]*Ns -id_col = pd.Series(range(1, 2*Ns+1)) -df_jittertest= pd.DataFrame({'Control 1' : c1, 'Test 1' : t1, - 'Control 2' : c2, 'Test 2' : t2, 'Test 3' : t3, - 'Test 4' : t4, 'Test 5' : t5, 'ID' : id_col}) -multi_2group_jitter = load(df, idx=(("Control 1","Test 1",), ("Control 2", "Test 2"), ), paired='baseline', id_col = 'ID') - - -# Tests - -# Empty circle -@pytest.mark.mpl_image_compare(tolerance=8) -def test_207_gardner_altman_meandiff_empty_circle(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(empty_circle=True); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_208_cummings_two_group_unpaired_meandiff_empty_circle(): - plt.rcdefaults() - return two_groups_unpaired.mean_diff.plot(empty_circle=True, float_contrast=False); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_209_cummings_shared_control_meandiff_empty_circle(): - plt.rcdefaults() - return shared_control.mean_diff.plot(empty_circle=True); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_210_cummings_multi_groups_meandiff_empty_circle(): - plt.rcdefaults() - return multi_groups.mean_diff.plot(empty_circle=True); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_211_cummings_multi_2_group_meandiff_empty_circle(): - plt.rcdefaults() - return multi_2group.mean_diff.plot(empty_circle=True); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_212_cummings_unpaired_delta_delta_meandiff_empty_circle(): - plt.rcdefaults() - return unpaired_delta_delta.mean_diff.plot(empty_circle=True); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_213_cummings_unpaired_mini_meta_meandiff_empty_circle(): - plt.rcdefaults() - return unpaired_mini_meta.mean_diff.plot(empty_circle=True); - - -# Change palette -@pytest.mark.mpl_image_compare(tolerance=8) -def test_214_change_idx_order_custom_palette_original(): - plt.rcdefaults() - return multi_groups_change_idx_original.mean_diff.plot(custom_palette=palette); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_215_change_idx_order_custom_palette_new(): - plt.rcdefaults() - return multi_groups_change_idx_new.mean_diff.plot(custom_palette=palette); - -# Swarm bars -@pytest.mark.mpl_image_compare(tolerance=8) -def test_216_cummings_shared_control_meandiff_showswarmbars(): - plt.rcdefaults() - return shared_control.mean_diff.plot(raw_bars=True, contrast_bars=False); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_217_cummings_shared_control_meandiff_hideswarmbars(): - plt.rcdefaults() - return shared_control.mean_diff.plot(raw_bars=False, contrast_bars=False); - -raw_kwargs = {'color': "red", 'alpha': 0.2} -@pytest.mark.mpl_image_compare(tolerance=8) -def test_218_cummings_shared_control_meandiff_swarmbars_kwargs(): - plt.rcdefaults() - return shared_control.mean_diff.plot(raw_bars=True, raw_bars_kwargs = raw_kwargs, contrast_bars=False); - - -# Contrast bars -@pytest.mark.mpl_image_compare(tolerance=8) -def test_219_cummings_shared_control_meandiff_showcontrastbars(): - plt.rcdefaults() - return shared_control.mean_diff.plot(contrast_bars=True,raw_bars=False); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_220_cummings_shared_control_meandiff_hidecontrastbars(): - plt.rcdefaults() - return shared_control.mean_diff.plot(contrast_bars=False, raw_bars=False); - -contrast_kwargs = {'color': "red", 'alpha': 0.2} -@pytest.mark.mpl_image_compare(tolerance=8) -def test_221_cummings_shared_control_meandiff_contrastbars_kwargs(): - plt.rcdefaults() - return shared_control.mean_diff.plot(contrast_bars=True, contrast_bars_kwargs = contrast_kwargs, raw_bars=False); - - -# reference_band -reference_band=[0, 1] -@pytest.mark.mpl_image_compare(tolerance=8) -def test_222_cummings_shared_control_meandiff_summarybars(): - plt.rcdefaults() - return shared_control.mean_diff.plot(reference_band=[0, 1], raw_bars=False, contrast_bars=False,); - -reference_band_kwargs = {'color': "black", 'alpha': 0.2, 'span_ax': True} -@pytest.mark.mpl_image_compare(tolerance=8) -def test_223_cummings_shared_control_meandiff_summarybars_kwargs(): - plt.rcdefaults() - return shared_control.mean_diff.plot(reference_band=[0, 1], reference_band_kwargs = reference_band_kwargs, - contrast_bars=False, raw_bars=False); - - -# Delta text -@pytest.mark.mpl_image_compare(tolerance=8) -def test_224_multi_2group_meandiff_showdeltatext(): - plt.rcdefaults() - return multi_2group.mean_diff.plot(delta_text=True); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_225_multi_2group_meandiff_hidedeltatext(): - plt.rcdefaults() - return multi_2group.mean_diff.plot(delta_text=False); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_226_multi_2group_meandiff_deltatext_kwargs(): - plt.rcdefaults() - return multi_2group.mean_diff.plot(delta_text=True, delta_text_kwargs={"color":"red", "rotation":45, "va":"bottom", "alpha":0.7}); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_227_multi_2group_meandiff_deltatext_kwargs_specificy_coordinates(): - plt.rcdefaults() - return multi_2group.mean_diff.plot(delta_text=True, delta_text_kwargs={"x_coordinates":(0.5, 2.75), "y_coordinates":(0.5, -1.7)}); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_228_multi_2group_meandiff_deltatext_kwargs_x_adjust(): - plt.rcdefaults() - return multi_2group.mean_diff.plot(delta_text=True, delta_text_kwargs={"offset":0.1}); - -# Jitter -@pytest.mark.mpl_image_compare(tolerance=8) -def test_229_samevalues_jitter(): - plt.rcdefaults() - return multi_2group_jitter.mean_diff.plot(slopegraph_kwargs={'jitter': 1}); - -# Delta-dots -@pytest.mark.mpl_image_compare(tolerance=8) -def test_230_delta_dot_hide(): - plt.rcdefaults() - return multi_2group_paired.mean_diff.plot(delta_dot=False); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_231_delta_dot_kwargs(): - plt.rcdefaults() - return multi_2group_paired.mean_diff.plot(delta_dot_kwargs={"color":'red', "alpha":0.1, 'zorder': 2, 'size': 5, 'side': 'left'}); - -# Effect size paired lines -@pytest.mark.mpl_image_compare(tolerance=8) -def test_232_repeatedmeasures_meandiff_show_es_paired_lines(): - plt.rcdefaults() - return repeated_measures.mean_diff.plot(contrast_paired_lines=True); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_233_repeatedmeasures_meandiff_hide_es_paired_lines(): - plt.rcdefaults() - return repeated_measures.mean_diff.plot(contrast_paired_lines=False); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_234_multigroups_paired_meandiff_es_paired_lines_kwargs(): - plt.rcdefaults() - return multi_groups_paired_baseline.mean_diff.plot(contrast_paired_lines=True, contrast_paired_lines_kwargs={'color':'red', 'linestyle': '--', 'linewidth': 2, 'alpha': 0.5}); - -# Baseline Error Curve -@pytest.mark.mpl_image_compare(tolerance=8) -def test_235_cummings_multi_groups_meandiff_show_baseline_ec(): - plt.rcdefaults() - return multi_groups.mean_diff.plot(show_baseline_ec=True); - -@pytest.mark.mpl_image_compare(tolerance=8) -def test_236_cummings_multi_2_group_meandiff_show_baseline_ec(): - plt.rcdefaults() - return multi_2group.mean_diff.plot(show_baseline_ec=True); \ No newline at end of file diff --git a/nbs/tests/test_01_effsizes_pvals.ipynb b/nbs/tests/test_01_effsizes_pvals.ipynb deleted file mode 100644 index d499f31b..00000000 --- a/nbs/tests/test_01_effsizes_pvals.ipynb +++ /dev/null @@ -1,455 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "6e2c4075", - "metadata": {}, - "outputs": [], - "source": [ - "import pytest\n", - "import lqrt\n", - "import numpy as np\n", - "import scipy as sp\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2f9abde6", - "metadata": {}, - "outputs": [], - "source": [ - "from dabest._stats_tools import effsize\n", - "from dabest import Dabest, TwoGroupsEffectSize, PermutationTest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "30de06ac", - "metadata": {}, - "outputs": [], - "source": [ - "from data.mocked_data_test_01 import wellbeing, paired_wellbeing, smoke, likert_control, likert_treatment, a_scores, b_scores, dabest_default_kwargs" - ] - }, - { - "cell_type": "markdown", - "id": "8443abc1", - "metadata": {}, - "source": [ - "test_mean_diff_unpaired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0f61f756", - "metadata": {}, - "outputs": [], - "source": [ - "mean_diff = effsize.func_difference(wellbeing.control, wellbeing.expt,\n", - " np.mean, is_paired=False)\n", - "assert mean_diff == pytest.approx(5.4)" - ] - }, - { - "cell_type": "markdown", - "id": "ed34f114", - "metadata": {}, - "source": [ - "test_median_diff_unpaired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "767dd8cf", - "metadata": {}, - "outputs": [], - "source": [ - "median_diff = effsize.func_difference(wellbeing.control, wellbeing.expt,\n", - " np.median, is_paired=False)\n", - "assert median_diff == pytest.approx(3.5)" - ] - }, - { - "cell_type": "markdown", - "id": "3b5a8f2f", - "metadata": {}, - "source": [ - "test_mean_diff_paired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0392c89c", - "metadata": {}, - "outputs": [], - "source": [ - "mean_diff = effsize.func_difference(paired_wellbeing.pre,\n", - " paired_wellbeing.post,\n", - " np.mean, is_paired=\"baseline\")\n", - "assert mean_diff == pytest.approx(4.10)" - ] - }, - { - "cell_type": "markdown", - "id": "2e8c9402", - "metadata": {}, - "source": [ - "test_median_diff_paired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0dff5a08", - "metadata": {}, - "outputs": [], - "source": [ - "median_diff = effsize.func_difference(paired_wellbeing.pre,\n", - " paired_wellbeing.post,\n", - " np.median, is_paired=\"baseline\")\n", - "assert median_diff == pytest.approx(4.5)" - ] - }, - { - "cell_type": "markdown", - "id": "246036ea", - "metadata": {}, - "source": [ - "test_cohens_d_unpaired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8c4f2889", - "metadata": {}, - "outputs": [], - "source": [ - "cohens_d = effsize.cohens_d(np.array(wellbeing.control), np.array(wellbeing.expt),\n", - " is_paired=False)\n", - "assert np.round(cohens_d, 2) == pytest.approx(0.47)" - ] - }, - { - "cell_type": "markdown", - "id": "dfc37dbc", - "metadata": {}, - "source": [ - "test_hedges_g_unpaired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "47d33c67", - "metadata": {}, - "outputs": [], - "source": [ - "hedges_g = effsize.hedges_g(np.array(wellbeing.control), np.array(wellbeing.expt),\n", - " is_paired=False)\n", - "assert np.round(hedges_g, 2) == pytest.approx(0.45)" - ] - }, - { - "cell_type": "markdown", - "id": "4fd38d44", - "metadata": {}, - "source": [ - "test_cohens_d_paired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ec76bc0f", - "metadata": {}, - "outputs": [], - "source": [ - "cohens_d = effsize.cohens_d(np.array(paired_wellbeing.pre), np.array(paired_wellbeing.post),\n", - " is_paired=\"baseline\")\n", - "assert np.round(cohens_d, 2) == pytest.approx(0.34)\n" - ] - }, - { - "cell_type": "markdown", - "id": "2a772b30", - "metadata": {}, - "source": [ - "test_hedges_g_paired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fe28bdc1", - "metadata": {}, - "outputs": [], - "source": [ - "hedges_g = effsize.hedges_g(np.array(paired_wellbeing.pre), np.array(paired_wellbeing.post),\n", - " is_paired=\"baseline\")\n", - "assert np.round(hedges_g, 2) == pytest.approx(0.33)" - ] - }, - { - "cell_type": "markdown", - "id": "c70d5415", - "metadata": {}, - "source": [ - "test_cohens_h" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "36ddc28c", - "metadata": {}, - "outputs": [], - "source": [ - "cohens_h = effsize.cohens_h(np.array(smoke.low), np.array(smoke.high))\n", - "assert np.round(cohens_h, 2) == pytest.approx(0.17)" - ] - }, - { - "cell_type": "markdown", - "id": "85935481", - "metadata": {}, - "source": [ - "test_cliffs_delta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "33bd09cc", - "metadata": {}, - "outputs": [], - "source": [ - "likert_delta = effsize.cliffs_delta(np.array(likert_treatment), np.array(likert_control))\n", - "assert likert_delta == pytest.approx(-0.25)\n", - "\n", - "scores_delta = effsize.cliffs_delta(np.array(b_scores), np.array(a_scores))\n", - "assert scores_delta == pytest.approx(0.65)" - ] - }, - { - "cell_type": "markdown", - "id": "ca1a50c5", - "metadata": {}, - "source": [ - "test_unpaired_stats" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "16884a64", - "metadata": {}, - "outputs": [], - "source": [ - "c = wellbeing.control\n", - "t = wellbeing.expt\n", - "\n", - "unpaired_es = TwoGroupsEffectSize(c, t, \"mean_diff\", is_paired=False, proportional=False)\n", - "\n", - "p1 = sp.stats.mannwhitneyu(c, t, alternative=\"two-sided\").pvalue\n", - "assert unpaired_es.pvalue_mann_whitney == pytest.approx(p1)\n", - "\n", - "p2 = sp.stats.ttest_ind(c, t, nan_policy='omit').pvalue\n", - "assert unpaired_es.pvalue_students_t == pytest.approx(p2)\n", - "\n", - "p3 = sp.stats.ttest_ind(c, t, equal_var=False, nan_policy='omit').pvalue\n", - "assert unpaired_es.pvalue_welch == pytest.approx(p3)" - ] - }, - { - "cell_type": "markdown", - "id": "0ced5798", - "metadata": {}, - "source": [ - "test_paired_stats" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5be74408", - "metadata": {}, - "outputs": [], - "source": [ - "before = paired_wellbeing.pre\n", - "after = paired_wellbeing.post\n", - "\n", - "paired_es = TwoGroupsEffectSize(before, after, \"mean_diff\", is_paired=\"baseline\", proportional=False)\n", - "\n", - "p1 = sp.stats.ttest_rel(before, after, nan_policy='omit').pvalue\n", - "assert paired_es.pvalue_paired_students_t == pytest.approx(p1)\n", - "\n", - "p2 = sp.stats.wilcoxon(before, after).pvalue\n", - "assert paired_es.pvalue_wilcoxon == pytest.approx(p2)" - ] - }, - { - "cell_type": "markdown", - "id": "10b9fb80", - "metadata": {}, - "source": [ - "test_median_diff_stats" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "371d7182", - "metadata": {}, - "outputs": [], - "source": [ - "c = wellbeing.control\n", - "t = wellbeing.expt\n", - "\n", - "es = TwoGroupsEffectSize(c, t, \"median_diff\", is_paired=False, proportional=False)\n", - "\n", - "p1 = sp.stats.kruskal(c, t, nan_policy='omit').pvalue\n", - "assert es.pvalue_kruskal == pytest.approx(p1)" - ] - }, - { - "cell_type": "markdown", - "id": "fbf5962b", - "metadata": {}, - "source": [ - "test_ordinal_dominance" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "65a0d9c6", - "metadata": {}, - "outputs": [], - "source": [ - "es = TwoGroupsEffectSize(likert_control, likert_treatment, \n", - " \"cliffs_delta\", is_paired=False, proportional=False)\n", - " \n", - "p1 = sp.stats.brunnermunzel(likert_control, likert_treatment).pvalue\n", - "assert es.pvalue_brunner_munzel == pytest.approx(p1)" - ] - }, - { - "cell_type": "markdown", - "id": "34885930", - "metadata": {}, - "source": [ - "test_unpaired_permutation_test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "44d1b0d3", - "metadata": {}, - "outputs": [], - "source": [ - "perm_test = PermutationTest(wellbeing.control, wellbeing.expt, \n", - " effect_size=\"mean_diff\", \n", - " is_paired=False)\n", - "assert perm_test.pvalue == pytest.approx(0.2976) " - ] - }, - { - "cell_type": "markdown", - "id": "c36603ed", - "metadata": {}, - "source": [ - "test_paired_permutation_test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "45477ae1", - "metadata": {}, - "outputs": [], - "source": [ - "perm_test = PermutationTest(paired_wellbeing.pre, \n", - " paired_wellbeing.post, \n", - " effect_size=\"mean_diff\", \n", - " is_paired=\"baseline\")\n", - "assert perm_test.pvalue == pytest.approx(0.0124)" - ] - }, - { - "cell_type": "markdown", - "id": "3279e7c7", - "metadata": {}, - "source": [ - "test_lqrt_unpaired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "78a98593", - "metadata": {}, - "outputs": [], - "source": [ - "unpaired_dabest = Dabest(wellbeing, idx=(\"control\", \"expt\"), \n", - " paired=None, id_col=None, \n", - " **dabest_default_kwargs)\n", - "lqrt_result = unpaired_dabest.mean_diff.lqrt\n", - "\n", - "p1 = lqrt.lqrtest_ind(wellbeing.control, wellbeing.expt,\n", - " equal_var=True,\n", - " random_state=12345)\n", - "\n", - "p2 = lqrt.lqrtest_ind(wellbeing.control, wellbeing.expt,\n", - " equal_var=False,\n", - " random_state=12345)\n", - "\n", - "assert lqrt_result.pvalue_lqrt_equal_var[0] == pytest.approx(p1.pvalue)\n", - "assert lqrt_result.pvalue_lqrt_unequal_var[0] == pytest.approx(p2.pvalue)" - ] - }, - { - "cell_type": "markdown", - "id": "fd63ddff", - "metadata": {}, - "source": [ - "test_lqrt_paired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "680aa3f0", - "metadata": {}, - "outputs": [], - "source": [ - "paired_dabest = Dabest(paired_wellbeing, idx=(\"pre\", \"post\"),\n", - " paired=\"baseline\", id_col=\"ID\",\n", - " **dabest_default_kwargs)\n", - "lqrt_result = paired_dabest.mean_diff.lqrt\n", - "\n", - "p1 = lqrt.lqrtest_rel(paired_wellbeing.pre, paired_wellbeing.post, \n", - " random_state=12345)\n", - "\n", - "assert lqrt_result.pvalue_paired_lqrt[0] == pytest.approx(p1.pvalue)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/tests/test_02_edge_cases.ipynb b/nbs/tests/test_02_edge_cases.ipynb deleted file mode 100644 index 42fb6377..00000000 --- a/nbs/tests/test_02_edge_cases.ipynb +++ /dev/null @@ -1,81 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "a25d3bb6", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from numpy.random import PCG64, RandomState\n", - "import scipy as sp\n", - "import pytest\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "359558a0", - "metadata": {}, - "outputs": [], - "source": [ - "from dabest._api import load" - ] - }, - { - "cell_type": "markdown", - "id": "f538f98b", - "metadata": {}, - "source": [ - "### test_unrelated_columns\n", - "\n", - " Test to see if 'unrelated' columns jam up the analysis.\n", - " See Github Issue 43.\n", - " https://github.com/ACCLAB/DABEST-python/issues/44.\n", - " \n", - " Added in v0.2.5.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "417bd33a", - "metadata": {}, - "outputs": [], - "source": [ - "N=60\n", - "random_seed=12345\n", - "\n", - "# rng = RandomState(MT19937(random_seed))\n", - "rng = RandomState(PCG64(random_seed))\n", - "# rng = np.random.default_rng(seed=random_seed)\n", - "\n", - "df = pd.DataFrame(\n", - " {'groups': rng.choice(['Group 1', 'Group 2', 'Group 3'], size=(N,)),\n", - " 'color' : rng.choice(['green', 'red', 'purple'], size=(N,)),\n", - " 'value': rng.random(size=(N,))})\n", - "\n", - "df['unrelated'] = np.nan\n", - "\n", - "test = load(data=df, x='groups', y='value', \n", - " idx=['Group 1', 'Group 2'])\n", - "\n", - "md = test.mean_diff.results\n", - "assert md.difference[0] == pytest.approx(-0.0322, abs=1e-4)\n", - "assert md.bca_low[0] == pytest.approx(-0.2268, abs=1e-4)\n", - "assert md.bca_high[0] == pytest.approx(0.1524, abs=1e-4)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/tests/test_04_repeated_measures_effsizes_pvals.ipynb b/nbs/tests/test_04_repeated_measures_effsizes_pvals.ipynb deleted file mode 100644 index ac53339e..00000000 --- a/nbs/tests/test_04_repeated_measures_effsizes_pvals.ipynb +++ /dev/null @@ -1,331 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "e7a36df7", - "metadata": {}, - "outputs": [], - "source": [ - "import pytest\n", - "import lqrt\n", - "import numpy as np\n", - "import scipy as sp\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d7094f95", - "metadata": {}, - "outputs": [], - "source": [ - "from dabest import Dabest\n", - "from data.mocked_data_test_01 import dabest_default_kwargs\n", - "from data.mocked_data_test_04 import df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d83b66c0", - "metadata": {}, - "outputs": [], - "source": [ - "# example of sequential repeated measures\n", - "sequential = Dabest(df, id_col = \"ID\",\n", - " idx=(\"First\", \"Second\", \"Third\", \"Fourth\", \"Fifth\"),\n", - " paired = \"sequential\",\n", - " **dabest_default_kwargs)\n", - "\n", - "# example of baseline repeated measures\n", - "baseline = Dabest(df, id_col = \"ID\",\n", - " idx=(\"First\", \"Second\", \"Third\", \"Fourth\", \"Fifth\"),\n", - " paired = \"baseline\",\n", - " **dabest_default_kwargs)" - ] - }, - { - "cell_type": "markdown", - "id": "476b71b7", - "metadata": {}, - "source": [ - "test_mean_diff_sequential" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c771e929", - "metadata": {}, - "outputs": [], - "source": [ - "mean_diff = sequential.mean_diff.results['difference'].to_list()\n", - "np_result = [np.mean(df.iloc[:,i+1]-df.iloc[:,i]) for i in range(1,5)]\n", - "assert mean_diff == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "4140c277", - "metadata": {}, - "source": [ - "test_median_diff_sequential" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "65cf8502", - "metadata": {}, - "outputs": [], - "source": [ - "median_diff = sequential.median_diff.results['difference'].to_list()\n", - "np_result = [np.median(df.iloc[:,i+1]-df.iloc[:,i]) for i in range(1,5)]\n", - "assert median_diff == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "97a15450", - "metadata": {}, - "source": [ - "test_mean_diff_baseline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0325ff0b", - "metadata": {}, - "outputs": [], - "source": [ - "mean_diff = baseline.mean_diff.results['difference'].to_list()\n", - "np_result = [np.mean(df.iloc[:,i]-df.iloc[:,1]) for i in range(2,6)]\n", - "assert mean_diff == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "483c03ec", - "metadata": {}, - "source": [ - "test_median_diff_baseline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c568cd42", - "metadata": {}, - "outputs": [], - "source": [ - "median_diff = baseline.median_diff.results['difference'].to_list()\n", - "np_result = [np.median(df.iloc[:,i]-df.iloc[:,1]) for i in range(2,6)]\n", - "assert median_diff == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "3095a98c", - "metadata": {}, - "source": [ - "test_cohens_d_sequential" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2485a2df", - "metadata": {}, - "outputs": [], - "source": [ - "cohens_d = sequential.cohens_d.results['difference'].to_list()\n", - "np_result = [np.mean(df.iloc[:,i+1]-df.iloc[:,i])\n", - " /np.sqrt((np.var(df.iloc[:,i+1], ddof=1)+np.var(df.iloc[:,i], ddof=1))/2) \n", - " for i in range(1,5)]\n", - "assert cohens_d == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "ba3546ce", - "metadata": {}, - "source": [ - "test_hedges_g_sequential" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "29b0cc7c", - "metadata": {}, - "outputs": [], - "source": [ - "from math import gamma\n", - "hedges_g = sequential.hedges_g.results['difference'].to_list()\n", - "a = 47*2-2\n", - "fac = gamma(a/2)/(np.sqrt(a/2)*gamma((a-1)/2))\n", - "np_result = [np.mean(df.iloc[:,i+1]-df.iloc[:,i])*fac\n", - " /np.sqrt((np.var(df.iloc[:,i+1], ddof=1)+np.var(df.iloc[:,i], ddof=1))/2) \n", - " for i in range(1,5)] \n", - "assert hedges_g == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "c4a96519", - "metadata": {}, - "source": [ - "test_cohens_d_baseline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "93d9b97f", - "metadata": {}, - "outputs": [], - "source": [ - "cohens_d = baseline.cohens_d.results['difference'].to_list()\n", - "np_result = [np.mean(df.iloc[:,i]-df.iloc[:,1])\n", - " /np.sqrt((np.var(df.iloc[:,i], ddof=1)+np.var(df.iloc[:,1], ddof=1))/2) \n", - " for i in range(2,6)]\n", - "assert cohens_d == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "c2c71cac", - "metadata": {}, - "source": [ - "test_hedges_g_baseline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2b60e360", - "metadata": {}, - "outputs": [], - "source": [ - "from math import gamma\n", - "hedges_g = baseline.hedges_g.results['difference'].to_list()\n", - "a = 47*2-2\n", - "fac = gamma(a/2)/(np.sqrt(a/2)*gamma((a-1)/2))\n", - "np_result = [np.mean(df.iloc[:,i]-df.iloc[:,1])*fac\n", - " /np.sqrt((np.var(df.iloc[:,i], ddof=1)+np.var(df.iloc[:,1], ddof=1))/2) \n", - " for i in range(2,6)]\n", - "assert hedges_g == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "eb5d853c", - "metadata": {}, - "source": [ - "test_paired_stats_sequential" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9452641e", - "metadata": {}, - "outputs": [], - "source": [ - "np_result = sequential.mean_diff.results\n", - " \n", - "p1 = [sp.stats.ttest_rel(df.iloc[:,i], df.iloc[:,i+1], nan_policy='omit').pvalue\n", - " for i in range(1,5)] \n", - "assert np_result[\"pvalue_paired_students_t\"].to_list() == pytest.approx(p1)\n", - "\n", - "p2 = [sp.stats.wilcoxon(df.iloc[:,i], df.iloc[:,i+1]).pvalue\n", - " for i in range(1,5)] \n", - "assert np_result[\"pvalue_wilcoxon\"].to_list() == pytest.approx(p2)" - ] - }, - { - "cell_type": "markdown", - "id": "ca3cf91e", - "metadata": {}, - "source": [ - "test_paired_stats_baseline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "412aa272", - "metadata": {}, - "outputs": [], - "source": [ - "np_result = baseline.mean_diff.results\n", - " \n", - "p1 = [sp.stats.ttest_rel(df.iloc[:,1], df.iloc[:,i], nan_policy='omit').pvalue\n", - " for i in range(2,6)] \n", - "assert np_result[\"pvalue_paired_students_t\"].to_list() == pytest.approx(p1)\n", - "\n", - "p2 = [sp.stats.wilcoxon(df.iloc[:,1], df.iloc[:,i]).pvalue\n", - " for i in range(2,6)] \n", - "assert np_result[\"pvalue_wilcoxon\"].to_list() == pytest.approx(p2)" - ] - }, - { - "cell_type": "markdown", - "id": "b7ba349a", - "metadata": {}, - "source": [ - "test_lqrt_paired_sequential" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9e20ecd3", - "metadata": {}, - "outputs": [], - "source": [ - "lqrt_result = sequential.mean_diff.lqrt[\"pvalue_paired_lqrt\"].to_list()\n", - " \n", - "p1 = [lqrt.lqrtest_rel(df.iloc[:,i], df.iloc[:,i+1], random_state=12345).pvalue\n", - " for i in range(1,5)] \n", - "\n", - "assert lqrt_result == pytest.approx(p1)" - ] - }, - { - "cell_type": "markdown", - "id": "f43ac68d", - "metadata": {}, - "source": [ - "test_lqrt_paired_baseline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5aee61a0", - "metadata": {}, - "outputs": [], - "source": [ - "lqrt_result = baseline.mean_diff.lqrt[\"pvalue_paired_lqrt\"].to_list()\n", - " \n", - "p1 = [lqrt.lqrtest_rel(df.iloc[:,1], df.iloc[:,i], random_state=12345).pvalue\n", - " for i in range(2,6)] \n", - "\n", - "assert lqrt_result == pytest.approx(p1)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/tests/test_06_delta-delta_effsize_pvals.ipynb b/nbs/tests/test_06_delta-delta_effsize_pvals.ipynb deleted file mode 100644 index 43a0295f..00000000 --- a/nbs/tests/test_06_delta-delta_effsize_pvals.ipynb +++ /dev/null @@ -1,556 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "4cfc6826", - "metadata": {}, - "outputs": [], - "source": [ - "import pytest\n", - "import numpy as np\n", - "from math import gamma" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "381bae02", - "metadata": {}, - "outputs": [], - "source": [ - "from dabest import Dabest, PermutationTest\n", - "from data.mocked_data_test_06 import df_test, df_test_control, df_test_treatment1, dabest_default_kwargs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "77080b66", - "metadata": {}, - "outputs": [], - "source": [ - "# example of unpaired delta-delta calculation\n", - "unpaired = Dabest(data = df_test, x = [\"Time\", \"Drug\"], y = \"Heart Rate\", \n", - " delta2 = True, experiment = \"Experiment\",\n", - " experiment_label=None, x1_level=None, paired=None, id_col=None,\n", - " **dabest_default_kwargs)\n", - "\n", - "\n", - "# example of paired delta-delta calculation\n", - "paired = Dabest(data = df_test, x = [\"Time\", \"Drug\"], y = \"Heart Rate\", \n", - " delta2 = True, experiment = \"Experiment\", paired=\"sequential\", id_col=\"ID\",\n", - " experiment_label=None, x1_level=None,\n", - " **dabest_default_kwargs)\n", - "\n", - "\n", - "# example of paired data with specified experiment/x1 level\n", - "paired_specified_level = Dabest(data = df_test, x = [\"Time\", \"Drug\"], y = \"Heart Rate\", \n", - " delta2 = True, experiment = \"Experiment\", paired=\"sequential\", id_col=\"ID\",\n", - " experiment_label=[\"Control\", \"Treatment1\"], x1_level=[\"T2\", \"T1\"],\n", - " **dabest_default_kwargs)" - ] - }, - { - "cell_type": "markdown", - "id": "a7ee54b4", - "metadata": {}, - "source": [ - "test_mean_diff_delta_unpaired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c496dd6f", - "metadata": {}, - "outputs": [], - "source": [ - "mean_diff_results = unpaired.mean_diff.results\n", - "all_mean_diff = mean_diff_results['difference'].to_list()\n", - "diff1 = np.mean(df_test_treatment1[\"T2\"])-np.mean(df_test_treatment1[\"T1\"])\n", - "diff2 = np.mean(df_test_control[\"T2\"])-np.mean(df_test_control[\"T1\"])\n", - "np_result = [diff1, diff2]\n", - "assert all_mean_diff == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "fb9d5333", - "metadata": {}, - "source": [ - "test_mean_diff_delta_paired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aff642eb", - "metadata": {}, - "outputs": [], - "source": [ - "mean_diff_results = paired.mean_diff.results\n", - "all_mean_diff = mean_diff_results['difference'].to_list()\n", - "diff1 = np.mean(df_test_treatment1[\"T2\"]-df_test_treatment1[\"T1\"])\n", - "diff2 = np.mean(df_test_control[\"T2\"]-df_test_control[\"T1\"])\n", - "np_result = [diff1, diff2]\n", - "assert all_mean_diff == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "b2e7102f", - "metadata": {}, - "source": [ - "test_mean_diff_delta_paired_specified_level" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d64e6a3e", - "metadata": {}, - "outputs": [], - "source": [ - "mean_diff_results = paired_specified_level.mean_diff.results\n", - "all_mean_diff = mean_diff_results['difference'].to_list()\n", - "diff1 = np.mean(df_test_control[\"T1\"]-df_test_control[\"T2\"])\n", - "diff2 = np.mean(df_test_treatment1[\"T1\"]-df_test_treatment1[\"T2\"])\n", - "np_result = [diff1, diff2]\n", - "assert all_mean_diff == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "9b846033", - "metadata": {}, - "source": [ - "test_median_diff_unpaired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1b7878eb", - "metadata": {}, - "outputs": [], - "source": [ - "all_median_diff = unpaired.median_diff.results\n", - "median_diff = all_median_diff['difference'].to_list()\n", - "diff1 = np.median(df_test_treatment1[\"T2\"])-np.median(df_test_treatment1[\"T1\"])\n", - "diff2 = np.median(df_test_control[\"T2\"])-np.median(df_test_control[\"T1\"])\n", - "np_result = [diff1, diff2]\n", - "assert median_diff == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "2fb4a865", - "metadata": {}, - "source": [ - "test_median_diff_paired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "37c52bfc", - "metadata": {}, - "outputs": [], - "source": [ - "all_median_diff = paired.median_diff.results\n", - "median_diff = all_median_diff['difference'].to_list()\n", - "diff1 = np.median(df_test_treatment1[\"T2\"]-df_test_treatment1[\"T1\"])\n", - "diff2 = np.median(df_test_control[\"T2\"]-df_test_control[\"T1\"])\n", - "np_result = [diff1, diff2]\n", - "assert median_diff == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "b79f3e7f", - "metadata": {}, - "source": [ - "test_median_diff_paired_specified_level" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "66abcc7f", - "metadata": {}, - "outputs": [], - "source": [ - "all_median_diff = paired_specified_level.median_diff.results\n", - "median_diff = all_median_diff['difference'].to_list()\n", - "diff1 = np.median(df_test_control[\"T1\"]-df_test_control[\"T2\"])\n", - "diff2 = np.median(df_test_treatment1[\"T1\"]-df_test_treatment1[\"T2\"])\n", - "np_result = [diff1, diff2]\n", - "assert median_diff == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "5e83804b", - "metadata": {}, - "source": [ - "test_cohens_d_unpaired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e202acf4", - "metadata": {}, - "outputs": [], - "source": [ - "all_cohens_d = unpaired.cohens_d.results\n", - "cohens_d = all_cohens_d['difference'].to_list()\n", - "diff1 = np.mean(df_test_treatment1[\"T2\"])-np.mean(df_test_treatment1[\"T1\"])\n", - "diff1 = diff1/np.sqrt((np.var(df_test_treatment1[\"T2\"], ddof=1)+np.var(df_test_treatment1[\"T1\"], ddof=1))/2) \n", - "diff2 = np.mean(df_test_control[\"T2\"])-np.mean(df_test_control[\"T1\"])\n", - "diff2 = diff2/np.sqrt((np.var(df_test_control[\"T2\"], ddof=1)+np.var(df_test_control[\"T1\"], ddof=1))/2) \n", - "np_result = [diff1, diff2]\t \n", - "assert cohens_d == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "ddeca599", - "metadata": {}, - "source": [ - "test_cohens_d_paired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "71f570b4", - "metadata": {}, - "outputs": [], - "source": [ - "all_cohens_d = paired.cohens_d.results\n", - "cohens_d = all_cohens_d['difference'].to_list()\n", - "diff1 = np.mean(df_test_treatment1[\"T2\"]-df_test_treatment1[\"T1\"])\n", - "diff1 = diff1/np.sqrt((np.var(df_test_treatment1[\"T2\"], ddof=1)+np.var(df_test_treatment1[\"T1\"], ddof=1))/2) \n", - "diff2 = np.mean(df_test_control[\"T2\"]-df_test_control[\"T1\"])\n", - "diff2 = diff2/np.sqrt((np.var(df_test_control[\"T2\"], ddof=1)+np.var(df_test_control[\"T1\"], ddof=1))/2) \n", - "np_result = [diff1, diff2]\t \n", - "assert cohens_d == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "1e4daeef", - "metadata": {}, - "source": [ - "test_cohens_d_paired_specified_level" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d32dd490", - "metadata": {}, - "outputs": [], - "source": [ - "all_cohens_d = paired_specified_level.cohens_d.results\n", - "cohens_d = all_cohens_d['difference'].to_list()\n", - "diff1 = np.mean(df_test_control[\"T1\"])-np.mean(df_test_control[\"T2\"])\n", - "diff1 = diff1/np.sqrt((np.var(df_test_control[\"T2\"], ddof=1)+np.var(df_test_control[\"T1\"], ddof=1))/2)\n", - "diff2 = np.mean(df_test_treatment1[\"T1\"])-np.mean(df_test_treatment1[\"T2\"])\n", - "diff2 = diff2/np.sqrt((np.var(df_test_treatment1[\"T2\"], ddof=1)+np.var(df_test_treatment1[\"T1\"], ddof=1))/2) \n", - "np_result = [diff1, diff2] \n", - "assert cohens_d == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "5b1f2d2a", - "metadata": {}, - "source": [ - "test_hedges_g_unpaired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b6b944ab", - "metadata": {}, - "outputs": [], - "source": [ - "hedges_g = unpaired.hedges_g.results['difference'].to_list()\n", - "a = 8*2-2\n", - "fac = gamma(a/2)/(np.sqrt(a/2)*gamma((a-1)/2))\n", - "diff1 = (np.mean(df_test_treatment1[\"T2\"])-np.mean(df_test_treatment1[\"T1\"]))*fac\n", - "diff1 = diff1/np.sqrt((np.var(df_test_treatment1[\"T2\"], ddof=1)+np.var(df_test_treatment1[\"T1\"], ddof=1))/2) \n", - "diff2 = (np.mean(df_test_control[\"T2\"])-np.mean(df_test_control[\"T1\"]))*fac\n", - "diff2 = diff2/np.sqrt((np.var(df_test_control[\"T2\"], ddof=1)+np.var(df_test_control[\"T1\"], ddof=1))/2) \n", - "np_result=[diff1, diff2]\n", - "assert hedges_g == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "e25ad33a", - "metadata": {}, - "source": [ - "test_hedges_g_paired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7b2a18ea", - "metadata": {}, - "outputs": [], - "source": [ - "hedges_g = paired.hedges_g.results['difference'].to_list()\n", - "a = 8*2-2\n", - "fac = gamma(a/2)/(np.sqrt(a/2)*gamma((a-1)/2))\n", - "diff1 = (np.mean(df_test_treatment1[\"T2\"]-df_test_treatment1[\"T1\"]))*fac\n", - "diff1 = diff1/np.sqrt((np.var(df_test_treatment1[\"T2\"], ddof=1)+np.var(df_test_treatment1[\"T1\"], ddof=1))/2) \n", - "diff2 = (np.mean(df_test_control[\"T2\"]-df_test_control[\"T1\"]))*fac\n", - "diff2 = diff2/np.sqrt((np.var(df_test_control[\"T2\"], ddof=1)+np.var(df_test_control[\"T1\"], ddof=1))/2) \n", - "np_result=[diff1, diff2]\n", - "assert hedges_g == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "2928958a", - "metadata": {}, - "source": [ - "test_hedges_g_paired_specified_level" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "86167882", - "metadata": {}, - "outputs": [], - "source": [ - "hedges_g = paired_specified_level.hedges_g.results['difference'].to_list()\n", - "a = 8*2-2\n", - "fac = gamma(a/2)/(np.sqrt(a/2)*gamma((a-1)/2))\n", - "diff1 = (np.mean(df_test_control[\"T1\"]-df_test_control[\"T2\"]))*fac\n", - "diff1 = diff1/np.sqrt((np.var(df_test_control[\"T2\"], ddof=1)+np.var(df_test_control[\"T1\"], ddof=1))/2) \n", - "diff2 = (np.mean(df_test_treatment1[\"T1\"]-df_test_treatment1[\"T2\"]))*fac\n", - "diff2 = diff2/np.sqrt((np.var(df_test_treatment1[\"T2\"], ddof=1)+np.var(df_test_treatment1[\"T1\"], ddof=1))/2) \n", - "np_result=[diff1, diff2]\n", - "assert hedges_g == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "42d1366d", - "metadata": {}, - "source": [ - "test_unpaired_delta_delta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "da1dac3d", - "metadata": {}, - "outputs": [], - "source": [ - "delta_delta = unpaired.mean_diff.delta_delta.difference\n", - "\n", - "diff1 = np.mean(df_test_treatment1[\"T2\"])-np.mean(df_test_treatment1[\"T1\"])\n", - "diff2 = np.mean(df_test_control[\"T2\"])-np.mean(df_test_control[\"T1\"])\n", - "np_result = diff2-diff1\n", - "\n", - "assert delta_delta == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "64edc5b7", - "metadata": {}, - "source": [ - "test_paired_delta_delta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7677ed29", - "metadata": {}, - "outputs": [], - "source": [ - "delta_delta = paired.mean_diff.delta_delta.difference\n", - "\n", - "diff1 = np.mean(df_test_treatment1[\"T2\"] - df_test_treatment1[\"T1\"])\n", - "diff2 = np.mean(df_test_control[\"T2\"] - df_test_control[\"T1\"])\n", - "np_result = diff2-diff1\n", - "\n", - "assert delta_delta == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "33ee19f2", - "metadata": {}, - "source": [ - "test_paired_specified_level_delta_delta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5a413527", - "metadata": {}, - "outputs": [], - "source": [ - "delta_delta = paired_specified_level.mean_diff.delta_delta.difference\n", - "\n", - "diff1 = np.mean(df_test_control[\"T1\"] - df_test_control[\"T2\"])\n", - "diff2 = np.mean(df_test_treatment1[\"T1\"] - df_test_treatment1[\"T2\"])\n", - "np_result = diff2-diff1\n", - "\n", - "assert delta_delta == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "94a0571f", - "metadata": {}, - "source": [ - "test_unpaired_permutation_test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4f039f9a", - "metadata": {}, - "outputs": [], - "source": [ - "delta_delta = unpaired.mean_diff.delta_delta\n", - "pvalue = delta_delta.pvalue_permutation\n", - "permutations_delta_delta = delta_delta.permutations_delta_delta\n", - "\n", - "perm_test_1 = PermutationTest(df_test_treatment1[\"T1\"], \n", - " df_test_treatment1[\"T2\"], \n", - " effect_size=\"mean_diff\", \n", - " is_paired=False)\n", - "perm_test_2 = PermutationTest(df_test_control[\"T1\"], \n", - " df_test_control[\"T2\"], \n", - " effect_size=\"mean_diff\", \n", - " is_paired=False)\n", - "permutations_1 = perm_test_1.permutations\n", - "permutations_2 = perm_test_2.permutations\n", - "\n", - "delta_deltas = permutations_2-permutations_1\n", - "assert permutations_delta_delta == pytest.approx(delta_deltas)\n", - "\n", - "diff1 = np.mean(df_test_treatment1[\"T2\"])-np.mean(df_test_treatment1[\"T1\"])\n", - "diff2 = np.mean(df_test_control[\"T2\"])-np.mean(df_test_control[\"T1\"])\n", - "np_diff = diff2-diff1\n", - "\n", - "np_pvalues = len(list(filter(lambda x: np.abs(x)>np.abs(np_diff), \n", - " delta_deltas)))/len(delta_deltas)\n", - "\n", - "assert pvalue == pytest.approx(np_pvalues)" - ] - }, - { - "cell_type": "markdown", - "id": "39082d96", - "metadata": {}, - "source": [ - "test_paired_permutation_test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "39f3a041", - "metadata": {}, - "outputs": [], - "source": [ - "delta_delta = paired.mean_diff.delta_delta\n", - "pvalue = delta_delta.pvalue_permutation\n", - "permutations_delta_delta = delta_delta.permutations_delta_delta\n", - "\n", - "perm_test_1 = PermutationTest(df_test_treatment1[\"T1\"], \n", - " df_test_treatment1[\"T2\"], \n", - " effect_size=\"mean_diff\", \n", - " is_paired=\"sequential\")\n", - "perm_test_2 = PermutationTest(df_test_control[\"T1\"], \n", - " df_test_control[\"T2\"], \n", - " effect_size=\"mean_diff\", \n", - " is_paired=\"sequential\")\n", - "permutations_1 = perm_test_1.permutations\n", - "permutations_2 = perm_test_2.permutations\n", - "\n", - "delta_deltas = permutations_2-permutations_1\n", - "assert permutations_delta_delta == pytest.approx(delta_deltas)\n", - "\n", - "diff1 = np.mean(df_test_treatment1[\"T2\"]-df_test_treatment1[\"T1\"])\n", - "diff2 = np.mean(df_test_control[\"T2\"]-df_test_control[\"T1\"])\n", - "np_diff = diff2-diff1\n", - "\n", - "np_pvalues = len(list(filter(lambda x: np.abs(x)>np.abs(np_diff), \n", - " delta_deltas)))/len(delta_deltas)\n", - "\n", - "assert pvalue == pytest.approx(np_pvalues)" - ] - }, - { - "cell_type": "markdown", - "id": "b033f55a", - "metadata": {}, - "source": [ - "test_paired_specified_level_permutation_test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f6310737", - "metadata": {}, - "outputs": [], - "source": [ - "delta_delta = paired_specified_level.mean_diff.delta_delta\n", - "pvalue = delta_delta.pvalue_permutation\n", - "permutations_delta_delta = delta_delta.permutations_delta_delta\n", - "\n", - "perm_test_1 = PermutationTest(df_test_control[\"T2\"], \n", - " df_test_control[\"T1\"], \n", - " effect_size=\"mean_diff\", \n", - " is_paired=\"sequential\")\n", - "perm_test_2 = PermutationTest(df_test_treatment1[\"T2\"], \n", - " df_test_treatment1[\"T1\"], \n", - " effect_size=\"mean_diff\", \n", - " is_paired=\"sequential\")\n", - "permutations_1 = perm_test_1.permutations\n", - "permutations_2 = perm_test_2.permutations\n", - "\n", - "delta_deltas = permutations_2-permutations_1\n", - "assert permutations_delta_delta == pytest.approx(delta_deltas)\n", - "\n", - "diff1 = np.mean(df_test_control[\"T1\"]-df_test_control[\"T2\"])\n", - "diff2 = np.mean(df_test_treatment1[\"T1\"]-df_test_treatment1[\"T2\"])\n", - "np_diff = diff2-diff1\n", - "\n", - "np_pvalues = len(list(filter(lambda x: np.abs(x)>np.abs(np_diff), \n", - " delta_deltas)))/len(delta_deltas)\n", - "\n", - "assert pvalue == pytest.approx(np_pvalues)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/tests/test_08_mini_meta_pvals.ipynb b/nbs/tests/test_08_mini_meta_pvals.ipynb deleted file mode 100644 index 89e24bcc..00000000 --- a/nbs/tests/test_08_mini_meta_pvals.ipynb +++ /dev/null @@ -1,272 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "0fc777d3", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pytest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "90ea3a40", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pre-compiling numba functions for DABEST...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 41.81it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Numba compilation complete!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "from dabest._stats_tools import effsize\n", - "from dabest._stats_tools import confint_2group_diff as ci2g\n", - "from dabest import Dabest, PermutationTest\n", - "from data.mocked_data_test_08 import df_mini_meta, rep1_yes, rep1_no, rep2_yes, rep2_no, N, dabest_default_kwargs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2538a232", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 1.51539707, 10.22387374])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unpaired = Dabest(data = df_mini_meta, idx =((\"Rep1_No\", \"Rep1_Yes\"), \n", - " (\"Rep2_No\", \"Rep2_Yes\")), \n", - " mini_meta=True,\n", - " **dabest_default_kwargs)\n", - "\n", - "unpaired.mean_diff.mini_meta.bootstraps_var" - ] - }, - { - "cell_type": "markdown", - "id": "86994f88", - "metadata": {}, - "source": [ - "test_mean_diff" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0368caad", - "metadata": {}, - "outputs": [], - "source": [ - "mean_diff = unpaired.mean_diff.results['difference'].to_list()\n", - "np_result = [np.mean(rep1_yes)-np.mean(rep1_no), \n", - " np.mean(rep2_yes)-np.mean(rep2_no)]\n", - "assert mean_diff == pytest.approx(np_result)" - ] - }, - { - "cell_type": "markdown", - "id": "7cf4d56d", - "metadata": {}, - "source": [ - "test_pooled_variances" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "973b227a", - "metadata": {}, - "outputs": [], - "source": [ - "mini_meta_delta = unpaired.mean_diff.mini_meta\n", - "\n", - "control_var = mini_meta_delta.control_var\n", - "np_control_var = [np.var(rep1_no, ddof=1),\n", - " np.var(rep2_no, ddof=1)]\n", - "assert control_var == pytest.approx(np_control_var)\n", - "\n", - "test_var = mini_meta_delta.test_var\n", - "np_test_var = [np.var(rep1_yes, ddof=1),\n", - " np.var(rep2_yes, ddof=1)]\n", - "assert test_var == pytest.approx(np_test_var)\n", - "\n", - "group_var = mini_meta_delta.group_var\n", - "np_group_var = [ci2g.calculate_group_var(control_var[i], mini_meta_delta.control_N[i],\n", - " test_var[i], mini_meta_delta.test_N[i])\n", - " for i in range(0, 2)]\n", - "assert group_var == pytest.approx(np_group_var)" - ] - }, - { - "cell_type": "markdown", - "id": "e06ceb8e-4f54-4ba5-9703-42089e1b6b86", - "metadata": {}, - "source": [ - "test_bootstrap_distribution_variances" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "88931a0f-a9cb-4e16-8cc3-c7c5be282171", - "metadata": {}, - "outputs": [], - "source": [ - "bootstrap_distributions = unpaired.mean_diff.mini_meta.bootstraps\n", - "bootstrap_distribution_variances = unpaired.mean_diff.mini_meta.bootstraps_var\n", - "\n", - "np_bootstrap_distribution_variances = np.array([np.var(x, ddof=1) for x in bootstrap_distributions])\n", - "\n", - "assert bootstrap_distribution_variances == pytest.approx(np_bootstrap_distribution_variances)" - ] - }, - { - "cell_type": "markdown", - "id": "a2c934e5", - "metadata": {}, - "source": [ - "test_weighted_mean_delta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "258f4b73", - "metadata": {}, - "outputs": [], - "source": [ - "difference = unpaired.mean_diff.mini_meta.difference\n", - "\n", - "np_means = np.array([np.mean(rep1_yes)-np.mean(rep1_no), \n", - " np.mean(rep2_yes)-np.mean(rep2_no)])\n", - "\n", - "np_var = np_bootstrap_distribution_variances\n", - "\n", - "np_difference = effsize.weighted_delta(np_means, np_var)\n", - "\n", - "weight = np.true_divide(1, np_var)\n", - "np_difference_calc = np.sum(np_means*weight)/np.sum(weight)\n", - "\n", - "assert difference == pytest.approx(np_difference) == pytest.approx(np_difference_calc)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4b9e81da-01f9-4880-acde-0dd9dd6caf12", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-1.32919358, 1.17274469, 0.51495794, ..., 0.20620551,\n", - " -2.86746452, 2.19964192])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mini_meta_delta.permutations_weighted_delta" - ] - }, - { - "cell_type": "markdown", - "id": "d3a468f5", - "metadata": {}, - "source": [ - "test_unpaired_permutation_test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d674181c-82c1-4116-804a-69e9def7d5c8", - "metadata": {}, - "outputs": [], - "source": [ - "mini_meta_delta = unpaired.mean_diff.mini_meta\n", - "pvalue = mini_meta_delta.pvalue_permutation\n", - "permutations_delta = mini_meta_delta.permutations_weighted_delta\n", - "\n", - "perm_test_1 = PermutationTest(rep1_no, rep1_yes, \n", - " effect_size=\"mean_diff\", \n", - " is_paired=False)\n", - "perm_test_2 = PermutationTest(rep2_no, rep2_yes, \n", - " effect_size=\"mean_diff\", \n", - " is_paired=False)\n", - "permutations_1 = perm_test_1.permutations\n", - "permutations_2 = perm_test_2.permutations\n", - "permutations_1_var = perm_test_1.permutations_var\n", - "permutations_2_var = perm_test_2.permutations_var\n", - "\n", - "weight_1 = np.true_divide(1,permutations_1_var)\n", - "weight_2 = np.true_divide(1,permutations_2_var)\n", - "\n", - "weighted_deltas = (weight_1*permutations_1 + weight_2*permutations_2)/(weight_1+weight_2)\n", - "assert permutations_delta == pytest.approx(weighted_deltas)\n", - "\n", - "\n", - "np_means = [np.mean(rep1_yes)-np.mean(rep1_no), \n", - " np.mean(rep2_yes)-np.mean(rep2_no)]\n", - "\n", - "np_weight = np.true_divide(1, np.array([np.var(x, ddof=1) for x in mini_meta_delta.bootstraps]))\n", - "\n", - "np_difference = np.sum(np_means*np_weight)/np.sum(np_weight)\n", - "\n", - "np_pvalues = len(list(filter(lambda x: np.abs(x)>np.abs(np_difference), \n", - " weighted_deltas)))/len(weighted_deltas)\n", - "\n", - "assert pvalue == pytest.approx(np_pvalues)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/tests/test_99_confidence_intervals.ipynb b/nbs/tests/test_99_confidence_intervals.ipynb deleted file mode 100644 index 2926a5c7..00000000 --- a/nbs/tests/test_99_confidence_intervals.ipynb +++ /dev/null @@ -1,213 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "a3d966b3", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from scipy.stats import norm\n", - "from scipy.stats import skewnorm\n", - "import pandas as pd\n", - "import pytest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9920ab6c", - "metadata": {}, - "outputs": [], - "source": [ - "from dabest._api import load" - ] - }, - { - "cell_type": "markdown", - "id": "fa5cc9c1", - "metadata": {}, - "source": [ - "test_paired_mean_diff_ci" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c39d5daa", - "metadata": {}, - "outputs": [], - "source": [ - "# See Altman et al., Statistics with Confidence: \n", - "# Confidence Intervals and Statistical Guidelines (Second Edition). Wiley, 2000.\n", - "# Pg 31.\n", - "# Added in v0.2.5.\n", - "blood_pressure = {\"before\": [148, 142, 136, 134, 138, 140, 132, 144,\n", - " 128, 170, 162, 150, 138, 154, 126, 116],\n", - " \"after\" : [152, 152, 134, 148, 144, 136, 144, 150, \n", - " 146, 174, 162, 162, 146, 156, 132, 126],\n", - " \"subject_id\" : np.arange(1, 17)}\n", - "exercise_bp = pd.DataFrame(blood_pressure)\n", - "\n", - "\n", - "ex_bp = load(data=exercise_bp, idx=(\"before\", \"after\"), \n", - " paired=\"baseline\", id_col=\"subject_id\")\n", - "paired_mean_diff = ex_bp.mean_diff.results\n", - "\n", - "\n", - "assert pytest.approx(3.625) == paired_mean_diff.bca_low[0]\n", - "assert pytest.approx(9.125) == paired_mean_diff.bca_high[0]" - ] - }, - { - "cell_type": "markdown", - "id": "de5c07cc", - "metadata": {}, - "source": [ - "test_unpaired_ci" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "11e82b97", - "metadata": {}, - "outputs": [], - "source": [ - "# Dropped to 30 reps to save time. v0.2.5.\n", - "reps=30\n", - "ci=95\n", - "POPULATION_N = 10000\n", - "SAMPLE_N = 10\n", - "\n", - "# Create data for hedges g and cohens d.\n", - "CONTROL_MEAN = np.random.randint(1, 1000)\n", - "POP_SD = np.random.randint(1, 15)\n", - "POP_D = np.round(np.random.uniform(-2, 2, 1)[0], 2)\n", - "\n", - "TRUE_STD_DIFFERENCE = CONTROL_MEAN + (POP_D * POP_SD)\n", - "norm_sample_kwargs = dict(scale=POP_SD, size=SAMPLE_N)\n", - "c1 = norm.rvs(loc=CONTROL_MEAN, **norm_sample_kwargs)\n", - "t1 = norm.rvs(loc=CONTROL_MEAN+TRUE_STD_DIFFERENCE, **norm_sample_kwargs)\n", - "\n", - "std_diff_df = pd.DataFrame({'Control' : c1, 'Test': t1})\n", - "\n", - "\n", - "\n", - "# Create mean_diff data\n", - "CONTROL_MEAN = np.random.randint(1, 1000)\n", - "POP_SD = np.random.randint(1, 15)\n", - "TRUE_DIFFERENCE = np.random.randint(-POP_SD*5, POP_SD*5)\n", - "\n", - "c1 = norm.rvs(loc=CONTROL_MEAN, **norm_sample_kwargs)\n", - "t1 = norm.rvs(loc=CONTROL_MEAN+TRUE_DIFFERENCE, **norm_sample_kwargs)\n", - "\n", - "mean_df = pd.DataFrame({'Control' : c1, 'Test': t1})\n", - "\n", - "\n", - "\n", - "# Create median_diff data\n", - "MEDIAN_DIFFERENCE = np.random.randint(-5, 5)\n", - "A = np.random.randint(-7, 7)\n", - "\n", - "skew_kwargs = dict(a=A, scale=5, size=POPULATION_N)\n", - "skewpop1 = skewnorm.rvs(**skew_kwargs, loc=100)\n", - "skewpop2 = skewnorm.rvs(**skew_kwargs, loc=100+MEDIAN_DIFFERENCE)\n", - "\n", - "sample_kwargs = dict(replace=False, size=SAMPLE_N)\n", - "skewsample1 = np.random.choice(skewpop1, **sample_kwargs)\n", - "skewsample2 = np.random.choice(skewpop2, **sample_kwargs)\n", - "\n", - "median_df = pd.DataFrame({'Control' : skewsample1, 'Test': skewsample2})\n", - "\n", - "\n", - "\n", - "# Create two populations with a 50% overlap.\n", - "CD_DIFFERENCE = np.random.randint(1, 10)\n", - "SD = np.abs(CD_DIFFERENCE)\n", - "\n", - "pop_kwargs = dict(scale=SD, size=POPULATION_N)\n", - "pop1 = norm.rvs(loc=100, **pop_kwargs)\n", - "pop2 = norm.rvs(loc=100+CD_DIFFERENCE, **pop_kwargs)\n", - "\n", - "sample_kwargs = dict(replace=False, size=SAMPLE_N)\n", - "sample1 = np.random.choice(pop1, **sample_kwargs)\n", - "sample2 = np.random.choice(pop2, **sample_kwargs)\n", - "\n", - "cd_df = pd.DataFrame({'Control' : sample1, 'Test': sample2})\n", - "\n", - "\n", - "\n", - "# Create several CIs and see if the true population difference lies within.\n", - "error_count_cohens_d = 0\n", - "error_count_hedges_g = 0\n", - "error_count_mean_diff = 0\n", - "error_count_median_diff = 0\n", - "error_count_cliffs_delta = 0\n", - "\n", - "for i in range(0, reps):\n", - " print(i) # for debug.\n", - " # pick a random seed\n", - " rnd_sd = np.random.randint(0, 999999)\n", - " load_kwargs = dict(ci=ci, random_seed=rnd_sd)\n", - "\n", - " std_diff_data = load(data=std_diff_df, idx=(\"Control\", \"Test\"), **load_kwargs)\n", - " cd = std_diff_data.cohens_d.results\n", - " # print(\"cohen's d\") # for debug.\n", - " cd_low, cd_high = float(cd.bca_low), float(cd.bca_high)\n", - " if cd_low < POP_D < cd_high is False:\n", - " error_count_cohens_d += 1\n", - "\n", - " hg = std_diff_data.hedges_g.results\n", - " # print(\"hedges' g\") # for debug.\n", - " hg_low, hg_high = float(hg.bca_low), float(hg.bca_high)\n", - " if hg_low < POP_D < hg_high is False:\n", - " error_count_hedges_g += 1\n", - "\n", - "\n", - " mean_diff_data = load(data=mean_df, idx=(\"Control\", \"Test\"), **load_kwargs)\n", - " mean_d = mean_diff_data.mean_diff.results\n", - " # print(\"mean diff\") # for debug.\n", - " mean_d_low, mean_d_high = float(mean_d.bca_low), float(mean_d.bca_high)\n", - " if mean_d_low < TRUE_DIFFERENCE < mean_d_high is False:\n", - " error_count_mean_diff += 1\n", - "\n", - "\n", - " median_diff_data = load(data=median_df, idx=(\"Control\", \"Test\"),\n", - " **load_kwargs)\n", - " median_d = median_diff_data.median_diff.results\n", - " # print(\"median diff\") # for debug.\n", - " median_d_low, median_d_high = float(median_d.bca_low), float(median_d.bca_high)\n", - " if median_d_low < MEDIAN_DIFFERENCE < median_d_high is False:\n", - " error_count_median_diff += 1\n", - "\n", - "\n", - " cd_data = load(data=cd_df, idx=(\"Control\", \"Test\"), **load_kwargs)\n", - " cliffs = cd_data.cliffs_delta.results\n", - " # print(\"cliff's delta\") # for debug.\n", - " low, high = float(cliffs.bca_low), float(cliffs.bca_high)\n", - " if low < 0.5 < high is False:\n", - " error_count_cliffs_delta += 1\n", - "\n", - "\n", - "max_errors = int(np.ceil(reps * (100 - ci) / 100))\n", - "\n", - "assert error_count_cohens_d <= max_errors\n", - "assert error_count_hedges_g <= max_errors\n", - "assert error_count_mean_diff <= max_errors\n", - "assert error_count_median_diff <= max_errors\n", - "assert error_count_cliffs_delta <= max_errors\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/tests/test_forest_plot.py b/nbs/tests/test_forest_plot.py deleted file mode 100644 index d8d28642..00000000 --- a/nbs/tests/test_forest_plot.py +++ /dev/null @@ -1,66 +0,0 @@ -import pytest -import pandas as pd -import numpy as np -import matplotlib.pyplot as plt -from dabest.forest_plot import forest_plot -from data.mocked_data_test_forestplot import default_forestplot_kwargs - -def test_forest_plot_no_input_parameters(): - error_msg = "The `data` argument must be a non-empty list of dabest objects." - with pytest.raises(ValueError) as excinfo: - forest_plot(data = None) - - assert error_msg in str(excinfo.value) - -idx_msg1 = "The `idx` argument must have the same length as the number of dabest objects. " -idx_msg2 = "E.g., If two dabest objects are supplied, there should be two lists within `idx`. " -idx_msg3 = "E.g., `idx` = [[1,2],[0,1]]." - -@pytest.mark.parametrize("param_name, param_value, error_msg, error_type", [ - ("data", [], "The `data` argument must be a non-empty list of dabest objects.", ValueError), - ("idx", 123, "`idx` must be a tuple or list of integers.", TypeError), - ("idx", ((0,1),(0,1),(0,1),(0,1),(0,1)), idx_msg1+idx_msg2+idx_msg3, ValueError), - ("ax", "axes", "The `ax` must be a `matplotlib.axes.Axes` instance or `None`.", TypeError), - ("fig_size", "huge", "`fig_size` must be a tuple or list of two positive integers.", TypeError), - ("effect_size", 456, "The `effect_size` argument must be a string and please choose from the following effect sizes: 'mean_diff', 'median_diff', 'cohens_d', 'cohens_h', 'cliffs_delta', 'hedges_g', 'delta_g'.", TypeError), - ("ci_type", 'linear', "`ci_type` must be either 'bca' or 'pct'.", TypeError), - ("horizontal", "sideways", "`horizontal` must be a boolean value.", TypeError), - ("marker_size", "large", "`marker_size` must be a positive integer or float.", TypeError), - ("custom_palette", 123, "The `custom_palette` must be either a dictionary, list, string, or `None`.", TypeError), - ("custom_palette", "test_palette", "The specified `custom_palette` test_palette is not a recognized Matplotlib palette.", ValueError), - ("contrast_alpha", "opaque", "`contrast_alpha` must be a float between 0 and 1.", TypeError), - ("contrast_desat", "yes", "`contrast_desat` must be a float between 0 and 1 or an int (1).", TypeError), - ("labels", ["valid", 123], "The `labels` must be a list of strings or `None`.", TypeError), - ("labels", ['valid', 'valid'], "`labels` must match the number of `data` provided.", ValueError), - ("labels_fontsize", "big", "`labels_fontsize` must be an integer or float.", TypeError), - ("labels_rotation", "right", "`labels_rotation` must be an integer or float between 0 and 360.", TypeError), - ("title", 123, "The `title` argument must be a string.", TypeError), - ("title_fontsize", "big", "`title_fontsize` must be an integer or float.", TypeError), - ("ylabel", 789, "The `ylabel` argument must be a string.", TypeError), - ("ylabel_fontsize", "big", "`ylabel_fontsize` must be an integer or float.", TypeError), - ("ylim", "auto", "`ylim` must be a tuple or list of two floats.", TypeError), - ("ylim", [0, 1, 2], "`ylim` must be a tuple or list of two floats.", ValueError), - ("yticks", "auto", "`yticks` must be a tuple or list of floats.", TypeError), - ("yticklabels", "auto", "`yticklabels` must be a tuple or list of strings.", TypeError), - ("yticklabels", [532, 123], "`yticklabels` must be a list of strings.", TypeError), - ("remove_spines", "yes", "`remove_spines` must be a boolean value.", TypeError), - ("reference_band", "yes", "`reference_band` must be a list/tuple of indices (ints).", TypeError), - ("reference_band", [0.1, 0.5], "`reference_band` must be a list/tuple of indices (ints).", TypeError), - ("reference_band", [10,], "Index [10] chosen is out of range for the contrast objects.", ValueError), - ("delta_text", "auto", "`delta_text` must be a boolean value.", TypeError), - ("contrast_bars", "auto", "`contrast_bars` must be a boolean value.", TypeError), -]) - -def test_forest_plot_input_error_handling(param_name, param_value, error_msg, error_type): - # Setup: Define a base set of valid inputs to forest_plot - valid_inputs = default_forestplot_kwargs.copy() - - # Replace the tested parameter with the invalid value - valid_inputs[param_name] = param_value - - # Perform the test - with pytest.raises(error_type) as excinfo: - forest_plot(**valid_inputs) - - # Check the error message - assert error_msg in str(excinfo.value) diff --git a/nbs/tests/test_load_errors.py b/nbs/tests/test_load_errors.py deleted file mode 100644 index 9084b6bb..00000000 --- a/nbs/tests/test_load_errors.py +++ /dev/null @@ -1,204 +0,0 @@ -import pytest -from dabest._api import load -from data.mocked_data_test_load_errors import dummy_df, N - - -def test_wrong_params_combinations(): - error_msg = "`proportional` and `mini_meta` cannot be True at the same time." - with pytest.raises(ValueError) as excinfo: - my_data = load( - dummy_df, idx=("Control 1", "Test 1"), proportional=True, mini_meta=True - ) - - assert error_msg in str(excinfo.value) - - error_msg = ( - "If `delta2` is True. `x` parameter cannot be None. String or list expected" - ) - with pytest.raises(ValueError) as excinfo: - my_data = load( - dummy_df, - idx=("Control 1", "Test 1"), - delta2=True - ) - assert error_msg in str(excinfo.value) - - error_msg = "`delta2` and `mini_meta` cannot be True at the same time." - with pytest.raises(ValueError) as excinfo: - my_data = load( - dummy_df, - x=["Control 1", "Control 1"], - y="Test 1", - delta2=True, - mini_meta=True - ) - - assert error_msg in str(excinfo.value) - - error_msg = "`idx` should not be specified when `delta2` is True.".format(N) - with pytest.raises(ValueError) as excinfo: - my_data = load( - dummy_df, - x=["Control 1", "Control 1"], - idx=("Control 1", "Test 1"), - delta2=True - ) - - assert error_msg in str(excinfo.value) - - error_msg = "`id_col` must be specified if `paired` is assigned with a not NoneType value." - with pytest.raises(IndexError) as excinfo: - my_data = load( - dummy_df, idx=("Control 1", "Test 1"), paired="baseline" - ) - - assert error_msg in str(excinfo.value) - - error_msg = "`delta2` is True but `y` is not indicated." - with pytest.raises(ValueError) as excinfo: - my_data = load( - dummy_df, - x=["Control 1", "Control 1"], - delta2=True - ) - - -def test_param_validations(): - error_msg = "`idx` contains duplicated groups. Please remove any duplicates and try again.".format(N) - with pytest.raises(ValueError) as excinfo: - my_data = load( - dummy_df, idx=("Control 1", "Control 1") - ) - - assert error_msg in str(excinfo.value) - - err0 = "Groups are repeated across tuples," - err1 = " or a tuple has repeated groups in it." - err2 = " Please remove any duplicates and try again." - error_msg = err0 + err1 + err2 - with pytest.raises(ValueError) as excinfo: - my_data = load( - dummy_df, idx=(("Control 1", "Control 1", "Test 1"), ("Control 2", "Test 2")) - ) - - assert error_msg in str(excinfo.value) - - wrong_idx = ("Control 1", ("Control 1", "Test 1")) - error_msg = "There seems to be a problem with the idx you " "entered--{}.".format(wrong_idx) - with pytest.raises(ValueError) as excinfo: - my_data = load( - dummy_df, idx=wrong_idx - ) - - assert error_msg in str(excinfo.value) - - wrong_paired = 'not_valid' - error_msg = "'{}' assigned for `paired` is not valid. Please use either 'baseline' or 'sequential'.".format(wrong_paired) - with pytest.raises(ValueError) as excinfo: - my_data = load( - dummy_df, idx=("Control 1", "Test 1"), paired=wrong_paired, id_col="ID" - ) - - assert error_msg in str(excinfo.value) - - - wrong_id_col = 'not_valid' - error_msg = "`id_col` was given as '{}'; however, '{}' is not a column in `data`.".format(wrong_id_col, wrong_id_col) - with pytest.raises(IndexError) as excinfo: - my_data = load( - dummy_df, idx=("Control 1", "Test 1"), paired="baseline", id_col=wrong_id_col - ) - - assert error_msg in str(excinfo.value) - - wrong_idx_mmeta = ("Control 1", "Test 1", "Test 2") - err0 = "`mini_meta` is True, but `idx` ({})".format(wrong_idx_mmeta) - err1 = "does not contain exactly 2 unique columns." - error_msg = err0 + err1 - with pytest.raises(ValueError) as excinfo: - my_data = load( - dummy_df, idx=wrong_idx_mmeta, mini_meta=True - ) - - assert error_msg in str(excinfo.value) - - wrong_idx_mmeta = (("Control 1", "Test 1", "Test 2"), ("Control 1", "Control 2", "Test 3")) - err0 = "`mini_meta` is True, but `idx` ({})".format(wrong_idx_mmeta) - err1 = "does not contain exactly 2 unique columns." - error_msg = err0 + err1 - with pytest.raises(ValueError) as excinfo: - my_data = load( - dummy_df, idx=wrong_idx_mmeta, mini_meta=True - ) - - assert error_msg in str(excinfo.value) - - wrong_x = ["Control 1", "Control 1", "Control 2"] - error_msg = "`delta2` is True but the number of variables indicated by `x` is {}.".format(len(wrong_x)) - with pytest.raises(ValueError) as excinfo: - my_data = load( - dummy_df, x=wrong_x, y="Test 1", delta2=True - ) - - assert error_msg in str(excinfo.value) - - wrong_x = ["Control 4", "Control 5"] - error_msg = "is not a column in `data`. Please check." - with pytest.raises(IndexError) as excinfo: - my_data = load( - dummy_df, x=wrong_x, y="Test 1", delta2=True - ) - - assert error_msg in str(excinfo.value) - - wrong_y = "Test 3" - error_msg = "is not a column in `data`. Please check." - with pytest.raises(IndexError) as excinfo: - my_data = load( - dummy_df, x=["Control 1", "Control 2"], y=wrong_y, delta2=True - ) - - assert error_msg in str(excinfo.value) - - wrong_experiment = "not_valid" - error_msg = "is not a column in `data`. Please check." - with pytest.raises(IndexError) as excinfo: - my_data = load( - dummy_df, - x=["Control 1", "Control 1"], - y="Test 1", - delta2=True, - experiment=wrong_experiment - ) - - assert error_msg in str(excinfo.value) - - #TODO experiment and experiment_label are different - - wrong_experiment_label = ["A", "B", "C"] - error_msg = "`experiment_label` does not have a length of 2." - with pytest.raises(ValueError) as excinfo: - my_data = load( - dummy_df, - x=["Control 1", "Control 1"], - y="Test 1", - delta2=True, - experiment="Control 1", - experiment_label=wrong_experiment_label - ) - - assert error_msg in str(excinfo.value) - - wrong_x1_level = "not_valid" - error_msg = "`x1_level` does not have a length of 2." - with pytest.raises(ValueError) as excinfo: - my_data = load( - dummy_df, - x=["Control 1", "Control 1"], - y="Test 1", - delta2=True, - experiment="Control 1", - x1_level=wrong_x1_level - ) - - assert error_msg in str(excinfo.value) \ No newline at end of file diff --git a/nbs/tests/test_multi_whorlmap.py b/nbs/tests/test_multi_whorlmap.py deleted file mode 100644 index 87e21268..00000000 --- a/nbs/tests/test_multi_whorlmap.py +++ /dev/null @@ -1,72 +0,0 @@ -""" -Unit tests for whorlmap() function in multi.py. - -Tests input validation, visualization parameters, and return types -for the whorlmap spiral heatmap visualization. -""" - -import pytest -import numpy as np -import matplotlib.pyplot as plt -import matplotlib.axes -from dabest.multi import whorlmap, combine -from data.mocked_data_test_multi import ( - default_whorlmap_kwargs, - valid_multi_contrast_1d, - valid_multi_contrast_2d, - valid_multi_contrast_mixed, - two_group_contrast_1, - two_group_contrast_2, - delta2_contrast_1, - delta2_contrast_2 -) - - - -def test_whorlmap_no_multi_contrast(): - """Test that whorlmap raises error when multi_contrast is None.""" - with pytest.raises(AttributeError): - whorlmap(multi_contrast=None) - - -def test_whorlmap_invalid_multi_contrast_type(): - """Test that whorlmap raises error with invalid multi_contrast type.""" - with pytest.raises(AttributeError): - whorlmap(multi_contrast="not_a_multicontrast") - - -@pytest.mark.parametrize("param_name, param_value, error_msg, error_type", [ - # n parameter validation - ("n", "large", "'str' object cannot be interpreted as an integer", TypeError), - ("n", -5, "negative dimensions are not allowed", ValueError), - - # sort_by validation - ("sort_by", 123, "'int' object is not subscriptable", TypeError), - - # cmap validation - ("cmap", 123, "'int' object is not callable", TypeError), - ("cmap", ["vlag"], "Invalid RGBA argument: 'vlag'", ValueError), - - # vmax/vmin validation - ("vmax", "high", "unsupported operand type(s) for -: 'str' and 'int'", TypeError), - ("vmin", "low", "unsupported operand type(s) for -: 'int' and 'str'", TypeError), - - # chop_tail validation - ("chop_tail", ['str'], "unsupported operand type(s) for /: 'list' and 'int'", TypeError), - - # ax validation - ("ax", "axes", "'str' object has no attribute 'spines'", AttributeError), - ("ax", 123, "'int' object has no attribute 'spines'", AttributeError), - -]) - -def test_whorlmap_input_validation(param_name, param_value, error_msg, error_type): - """Test input validation for whorlmap() parameters.""" - valid_inputs = default_whorlmap_kwargs.copy() - valid_inputs[param_name] = param_value - - with pytest.raises(error_type) as excinfo: - whorlmap(**valid_inputs) - - assert error_msg in str(excinfo.value) - diff --git a/nbs/tests/test_multicontrast_class.py b/nbs/tests/test_multicontrast_class.py deleted file mode 100644 index 4c290385..00000000 --- a/nbs/tests/test_multicontrast_class.py +++ /dev/null @@ -1,222 +0,0 @@ -import pytest -import numpy as np -import matplotlib.pyplot as plt -from dabest.multi import MultiContrast, combine -from data.mocked_data_test_multi import ( - two_group_contrast_1, - two_group_contrast_2, - two_group_contrast_3, - delta2_contrast_1, - delta2_contrast_2, - minimeta_contrast_1, - minimeta_contrast_2 -) - - -def test_multicontrast_init_basic(): - """Test basic MultiContrast initialization.""" - mc = MultiContrast( - dabest_objs=[two_group_contrast_1, two_group_contrast_2] - ) - - assert mc is not None - assert isinstance(mc.structure, dict) - assert mc.effect_size == "mean_diff" - assert mc.ci_type == "bca" - - - -def test_multicontrast_bootstraps_property(): - """Test that bootstraps property returns data correctly.""" - mc = MultiContrast( - dabest_objs=[two_group_contrast_1, two_group_contrast_2] - ) - - bootstraps = mc.bootstraps - - assert isinstance(bootstraps, list) - assert len(bootstraps) > 0 - # Each bootstrap should be an array-like - for bs in bootstraps: - assert hasattr(bs, '__len__') - - -def test_multicontrast_effect_sizes_property(): - """Test that effect_sizes property returns data correctly.""" - mc = MultiContrast( - dabest_objs=[two_group_contrast_1, two_group_contrast_2] - ) - - effect_sizes = mc.effect_sizes - - assert isinstance(effect_sizes, list) - assert len(effect_sizes) == 2 # Two contrasts - - -def test_multicontrast_ci_lows_property(): - """Test that ci_lows property returns data correctly.""" - mc = MultiContrast( - dabest_objs=[two_group_contrast_1, two_group_contrast_2] - ) - - ci_lows, ci_highs = mc.confidence_intervals - - assert isinstance(ci_lows, list) - assert len(ci_lows) == 2 - - -def test_multicontrast_ci_highs_property(): - """Test that ci_highs property returns data correctly.""" - mc = MultiContrast( - dabest_objs=[two_group_contrast_1, two_group_contrast_2] - ) - - # Use the correct property name - ci_lows, ci_highs = mc.confidence_intervals - - assert isinstance(ci_highs, list) - assert len(ci_highs) == 2 - - -def test_multicontrast_get_bootstrap_by_position_1d(): - """Test getting bootstrap data by position for 1D structure.""" - mc = MultiContrast( - dabest_objs=[two_group_contrast_1, two_group_contrast_2] - ) - - # In 1D, all contrasts are in row 0 - bootstrap = mc.get_bootstrap_by_position(0, 0) - - assert bootstrap is not None - assert hasattr(bootstrap, '__len__') - - -def test_multicontrast_get_bootstrap_by_position_2d(): - """Test getting bootstrap data by position for 2D structure.""" - mc = MultiContrast( - dabest_objs=[[two_group_contrast_1, two_group_contrast_2], - [two_group_contrast_3, two_group_contrast_1]] - ) - - bootstrap = mc.get_bootstrap_by_position(1, 1) - - assert bootstrap is not None - assert hasattr(bootstrap, '__len__') - - -def test_multicontrast_get_bootstrap_by_position_out_of_bounds(): - """Test that out-of-bounds position raises IndexError.""" - mc = MultiContrast( - dabest_objs=[two_group_contrast_1, two_group_contrast_2] - ) - - error_msg = "out of bounds" - with pytest.raises(IndexError) as excinfo: - mc.get_bootstrap_by_position(10, 10) - - assert error_msg in str(excinfo.value) - - - -def test_validate_individual_dabest_obj_missing_attribute(): - """Test that validation catches missing required attributes.""" - # Create a mock object without required attributes - class FakeDabest: - pass - - fake_obj = FakeDabest() - - with pytest.raises(TypeError): - mc = MultiContrast(dabest_objs=[fake_obj], delta2 = False) - - -def test_validate_effect_size_compatibility_delta2(): - """Test effect size validation for delta2 contrasts.""" - error_msg = "effect_size must be 'mean_diff', 'hedges_g', or 'delta_g' for delta-delta analyses" - - with pytest.raises(ValueError) as excinfo: - MultiContrast( - dabest_objs=[delta2_contrast_1], - effect_size="cohens_d" - ) - - assert error_msg in str(excinfo.value) - - -def test_validate_effect_size_compatibility_minimeta(): - """Test effect size validation for mini-meta contrasts.""" - error_msg = "effect_size must be 'mean_diff' for mini-meta analyses" - - with pytest.raises(ValueError) as excinfo: - MultiContrast( - dabest_objs=[minimeta_contrast_1], - effect_size="hedges_g" - ) - - assert error_msg in str(excinfo.value) - -def test_multicontrast_structure_1d(): - """Test structure property for 1D dabest_objs.""" - mc = MultiContrast( - dabest_objs=[two_group_contrast_1, two_group_contrast_2, two_group_contrast_3] - ) - - assert mc.structure['type'] == '1D' - assert mc.structure['n_rows'] == 1 - assert mc.structure['n_cols'] == 3 - assert mc.structure['total_dabest_objs'] == 3 - - -def test_multicontrast_structure_2d(): - """Test structure property for 2D dabest_objs.""" - mc = MultiContrast( - dabest_objs=[[two_group_contrast_1, two_group_contrast_2], - [two_group_contrast_3, two_group_contrast_1]] - ) - - assert mc.structure['type'] == '2D' - assert mc.structure['n_rows'] == 2 - assert mc.structure['n_cols'] == 2 - assert mc.structure['total_dabest_objs'] == 4 - - -def test_multicontrast_structure_1d_to_2d(): - """Test that structure normalizes 1D to 2D internally.""" - mc = MultiContrast( - dabest_objs=[two_group_contrast_1, two_group_contrast_2] - ) - - # Even for 1D input, internally stored as 2D - assert len(mc.structure['dabest_objs_2d']) == 1 - assert len(mc.structure['dabest_objs_2d'][0]) == 2 - - - - - -def test_multicontrast_contrast_type_mixed(): - """Test contrast_type for mixed contrasts.""" - mc = combine( - dabest_objs=[[two_group_contrast_1, two_group_contrast_2], - [delta2_contrast_1, delta2_contrast_2]], - allow_mixed_types=True - ) - - assert isinstance(mc.contrast_type, dict) - assert mc.contrast_type['mixed'] == True - assert 'delta' in mc.contrast_type['unique_types'] - assert 'delta2' in mc.contrast_type['unique_types'] - - -def test_multicontrast_contrast_type_by_row(): - """Test that mixed contrast types track row-wise types.""" - mc = combine( - dabest_objs=[[two_group_contrast_1, two_group_contrast_2], - [delta2_contrast_1, delta2_contrast_2]], - allow_mixed_types=True - ) - - assert 'by_row' in mc.contrast_type - assert len(mc.contrast_type['by_row']) == 2 - assert mc.contrast_type['by_row'][0] == 'delta' - assert mc.contrast_type['by_row'][1] == 'delta2' \ No newline at end of file diff --git a/nbs/tests/test_plot_tools.py b/nbs/tests/test_plot_tools.py deleted file mode 100644 index 70f73640..00000000 --- a/nbs/tests/test_plot_tools.py +++ /dev/null @@ -1,193 +0,0 @@ -import pytest -import pandas as pd -import numpy as np -import matplotlib.pyplot as plt -from dabest.plot_tools import get_swarm_spans, width_determine, error_bar, check_data_matches_labels, swarmplot -from data.mocked_data_test_swarmplot import dummy_df, default_swarmplot_kwargs - - -def test_get_swarm_spans_wrong_parameters(): - error_msg = "The collection `coll` parameter cannot be None" - with pytest.raises(ValueError) as excinfo: - get_swarm_spans(None) - - assert error_msg in str(excinfo.value) - - -def test_width_determine(): - error_msg = "The `labels` parameter cannot be None" - with pytest.raises(ValueError) as excinfo: - width_determine(None, []) - - assert error_msg in str(excinfo.value) - - error_msg = "The `data` parameter cannot be None" - with pytest.raises(ValueError) as excinfo: - width_determine("some_labels", None) - - assert error_msg in str(excinfo.value) - - -def test_error_bar(): - data = pd.DataFrame({ - 'group': ['A', 'A', 'B', 'B'], - 'value': [1, 2, 3, 4] - }) - error_msg = "`gap_width_percent` must be between 0 and 100." - with pytest.raises(ValueError) as excinfo: - error_bar( - data=data, - x='group', - y='value', - type='mean_sd', - gap_width_percent=-10 # Invalid as it's less than 0 - ) - - assert error_msg in str(excinfo.value) - - error_msg = "Invalid `method`. Must be one of 'gapped_lines', \ - 'proportional_error_bar', or 'sankey_error_bar'." - with pytest.raises(ValueError) as excinfo: - error_bar( - data=data, - x='group', - y='value', - type='mean_sd', - method='invalid_method' # Invalid as it's not one of the accepted values - ) - - assert error_msg in str(excinfo.value) - - error_msg = "Only accepted values for type are ['mean_sd', 'median_quartiles']" - with pytest.raises(ValueError) as excinfo: - error_bar( - data=data, - x='group', - y='value', - type='invalid_type' - ) - assert error_msg in str(excinfo.value) - -def test_check_data_matches_labels(): - wrong_labels = ['A', 'B', 'C'] - wrong_data = pd.Series(['A', 'B', 'D']) - error_msg = "labels and data do not match." - with pytest.raises(Exception) as excinfo: - check_data_matches_labels(wrong_labels, wrong_data, side='left') - - assert error_msg in str(excinfo.value) - -# swarmplot() UNIT TESTS -# fmt: off -@pytest.mark.parametrize("param_name, param_value, error_msg, error_type", [ - # Basic input validation checks - ("data", None, "`data` must be a Pandas Dataframe.", ValueError), - ("x", None, "`x` must be a string.", ValueError), - ("y", None, "`y` must be a string.", ValueError), - ("ax", None, "`ax` must be a Matplotlib axes.Axes. The current `ax` is a ", ValueError), - ("order", 5, "`order` must be either an Iterable or None.", ValueError), - ("hue", 5, "`hue` must be either a string or None.", ValueError), - ("palette", None, "`palette` must be either a string indicating a color name or an Iterable.", ValueError), - ("zorder", None, "`zorder` must be a scalar or float.", ValueError), - ("size", None, "`size` must be a scalar or float.", ValueError), - ("side", None, "Invalid `side`. Must be one of 'center', 'right', or 'left'.", ValueError), - ("jitter", None, "`jitter` must be a scalar or float.", ValueError), - ("is_drop_gutter", None, "`is_drop_gutter` must be a boolean.", ValueError), - ("gutter_limit", None, "`gutter_limit` must be a scalar or float.", ValueError), - ("filled", 1, "`filled` must be a boolean, list or tuple.", ValueError), - - # More thorough input validation checks - ("x", "a", "a is not a column in `data`.", IndexError), - ("y", "b", "b is not a column in `data`.", IndexError), - ("hue", "c", "c is not a column in `data`.", IndexError), - ("order", ["Control 1", "Test 2"], "Test 2 in `order` is not in the 'group' column of `data`.", IndexError), - ("palette", " ", "`palette` cannot be an empty string. It must be either a string indicating a color name or an Iterable.", ValueError), - ("palette", {"Control 1": " "}, "The color mapping for Control 1 in `palette` is an empty string. It must contain a color name.", ValueError), - ("palette", {"Control 3": "black"}, "Control 3 in `palette` is not in the 'group' column of `data`.", IndexError), - # TODO: to add palette validation testing for when color_col is hue - ("side", "top", "Invalid `side`. Must be one of 'center', 'right', or 'left'.", ValueError), - ("filled", [True, "a"], "All values in `filled` must be a boolean.", ValueError), - ("filled", [True], "There are 2 unique values in `x` column in `data` but `filled` has a length of 1.", ValueError), -]) -def test_swarmplot_input_error_handling(param_name, param_value, error_msg, error_type): - with pytest.raises(error_type) as excinfo: - my_data = swarmplot( - data=dummy_df if param_name != "data" else param_value, - x="group" if param_name != "x" else param_value, - y="value" if param_name != "y" else param_value, - ax=plt.gca() if param_name != "ax" else param_value, - order=["Control 1", "Test 1"] if param_name != "order" else param_value, - hue=None if param_name != "hue" else param_value, - palette="black" if param_name != "palette" else param_value, - zorder=1 if param_name != "zorder" else param_value, - size=5 if param_name != "size" else param_value, - side="center" if param_name != "side" else param_value, - jitter=1 if param_name != "jitter" else param_value, - filled=True if param_name != "filled" else param_value, - is_drop_gutter=True if param_name != "is_drop_gutter" else param_value, - gutter_limit=0.5 if param_name != "gutter_limit" else param_value, - ) - - assert error_msg in str(excinfo.value) - -def test_swarmplot_warnings(): - warning_msg = ( - "{0:.1f}% of the points cannot be placed. " - "You might want to decrease the size of the markers." - ) - with pytest.warns(UserWarning) as warn_rec: - my_data = swarmplot(size=100, **default_swarmplot_kwargs) - - assert warning_msg.format(10) in str(warn_rec[0].message) - assert warning_msg.format(20) in str(warn_rec[1].message) - - warning_msg = ( - "unique values in '{0}' column in `data` " - "and `palette` do not have the same length. Number of unique values is {1} " - "while length of palette is {2}. The assignment of the colors in the " - "palette will be cycled." - ) - with pytest.warns(UserWarning) as warn_rec: - my_data = swarmplot(palette=["black"], **default_swarmplot_kwargs) - - assert warning_msg.format("group", 2, 1) in str(warn_rec[0].message) - - -def test_swarmplot_order_params(): - # `order` should be able to handle customised order -> swapping of params in `order` list - swarmplot(order=["Control 1", "Test 1"], **default_swarmplot_kwargs) - swarmplot(order=["Test 1", "Control 1"], **default_swarmplot_kwargs) - - # `order` should be able to handle None, where it will then be autogenerated - swarmplot(order=None, **default_swarmplot_kwargs) - - -def test_swarmplot_hue_params(): - swarmplot(hue="gender", **default_swarmplot_kwargs) - - -@pytest.mark.parametrize("hue, palette", [ - # `palette` can be a string, list, tuple or a dict - # Testing `palette` when color of swarms is based on `x` value - (None, "black"), - (None, ("black", "red")), - (None, {"Control 1": "black", "Test 1": "red"}), - - # Testing `palette` when color of swarms is based on `hue` value - ("gender", "black"), - ("gender", ["black", "red"]), - ("gender", ("black", "red")), - ("gender", {"Female": "black", "Male": "red"}), - - # Testing auto assignment of `palette` when `palette` is: - # (list | tuple) and len(palette) != len(unique_color_groups) - (None, ["black"]), -]) -def test_swarmplot_palette_params(hue, palette): - swarmplot(hue=hue, palette=palette, **default_swarmplot_kwargs) - - -def test_swarmplot_side_params(): - swarmplot(side="center", **default_swarmplot_kwargs) - swarmplot(side="right", **default_swarmplot_kwargs) - swarmplot(side="left", **default_swarmplot_kwargs) diff --git a/nbs/tutorials/01-basics.ipynb b/nbs/tutorials/01-basics.ipynb deleted file mode 100644 index 8122498b..00000000 --- a/nbs/tutorials/01-basics.ipynb +++ /dev/null @@ -1,1358 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "e18874ef", - "metadata": {}, - "source": [ - "# Basics\n", - "\n", - "> An end-to-end tutorial on how to use the dabest library.\n", - "\n", - "- order: 1" - ] - }, - { - "cell_type": "markdown", - "id": "ae22b5d6", - "metadata": {}, - "source": [ - "## Load libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "111cdeea", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pre-compiling numba functions for DABEST...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 57.10it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Numba compilation complete!\n", - "We're using DABEST v2025.10.20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import dabest\n", - "\n", - "print(\"We're using DABEST v{}\".format(dabest.__version__))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2cf8be3b", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)" - ] - }, - { - "cell_type": "markdown", - "id": "edc6c807", - "metadata": {}, - "source": [ - "## Create dataset for demo" - ] - }, - { - "cell_type": "markdown", - "id": "c447ed0f", - "metadata": {}, - "source": [ - "Here, we create a dataset to illustrate how ``dabest`` works. In\n", - "this dataset, each column corresponds to a group of observations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5460d95", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Control 1Test 1Control 2Test 2Control 3Test 3Test 4Test 5Test 6GenderID
02.7939843.4208753.3246611.7074673.8169401.7965814.4400502.9372843.486127Female1
13.2367593.4679723.6851861.1218463.7503583.9445663.7234942.8370622.338094Female2
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" - ], - "text/plain": [ - " Control 1 Test 1 Control 2 Test 2 Control 3 Test 3 Test 4 \\\n", - "0 2.793984 3.420875 3.324661 1.707467 3.816940 1.796581 4.440050 \n", - "1 3.236759 3.467972 3.685186 1.121846 3.750358 3.944566 3.723494 \n", - "2 3.019149 4.377179 5.616891 3.301381 2.945397 2.832188 3.214014 \n", - "3 2.804638 4.564780 2.773152 2.534018 3.575179 3.048267 4.968278 \n", - "4 2.858019 3.220058 2.550361 2.796365 3.692138 3.276575 2.662104 \n", - "\n", - " Test 5 Test 6 Gender ID \n", - "0 2.937284 3.486127 Female 1 \n", - "1 2.837062 2.338094 Female 2 \n", - "2 3.111950 3.270897 Female 3 \n", - "3 3.743378 3.151188 Female 4 \n", - "4 2.977341 2.328601 Female 5 " - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from scipy.stats import norm # Used in generation of populations.\n", - "\n", - "np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n", - "\n", - "Ns = 20 # The number of samples taken from each population\n", - "\n", - "# Create samples\n", - "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n", - "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", - "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", - "\n", - "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n", - "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n", - "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n", - "t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", - "t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", - "t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", - "\n", - "# Add a `gender` column for coloring the data.\n", - "females = np.repeat('Female', Ns/2).tolist()\n", - "males = np.repeat('Male', Ns/2).tolist()\n", - "gender = females + males\n", - "\n", - "# Add an `id` column for paired data plotting.\n", - "id_col = pd.Series(range(1, Ns+1))\n", - "\n", - "# Combine samples and gender into a DataFrame.\n", - "df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n", - " 'Control 2' : c2, 'Test 2' : t2,\n", - " 'Control 3' : c3, 'Test 3' : t3,\n", - " 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6,\n", - " 'Gender' : gender, 'ID' : id_col\n", - " })\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "id": "86d8a014", - "metadata": {}, - "source": [ - "Note that we have 9 groups (3 Control samples and 6 Test samples). Our\n", - "dataset has also a non\\-numerical column indicating gender, and another\n", - "column indicating the identity of each observation." - ] - }, - { - "cell_type": "markdown", - "id": "cd100b68", - "metadata": {}, - "source": [ - "This is known as a *wide* dataset. See this \n", - "[writeup](https://sejdemyr.github.io/r-tutorials/basics/wide-and-long/) \n", - "for more details." - ] - }, - { - "cell_type": "markdown", - "id": "a443fa57", - "metadata": {}, - "source": [ - "## Loading data" - ] - }, - { - "cell_type": "markdown", - "id": "5cad58c8", - "metadata": {}, - "source": [ - "Before creating estimation plots and obtaining confidence intervals for our effect sizes, we need to load the data and specify the relevant groups.\n", - "\n", - "We can achieve this by supplying the dataframe to ``dabest.load()``. Additionally, we must provide the two groups to be compared in the ``idx`` argument as a tuple or list." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1199b169", - "metadata": {}, - "outputs": [], - "source": [ - "two_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), resamples=5000)" - ] - }, - { - "cell_type": "markdown", - "id": "035952a1", - "metadata": {}, - "source": [ - "Calling this ``Dabest`` object gives you a gentle greeting, as well as\n", - "the comparisons that can be computed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "99952ec9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:03 2025.\n", - "\n", - "Effect size(s) with 95% confidence intervals will be computed for:\n", - "1. Test 1 minus Control 1\n", - "\n", - "5000 resamples will be used to generate the effect size bootstraps." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "two_groups_unpaired" - ] - }, - { - "cell_type": "markdown", - "id": "0287ca98", - "metadata": {}, - "source": [ - "### Changing statistical parameters" - ] - }, - { - "cell_type": "markdown", - "id": "be5b3ece", - "metadata": {}, - "source": [ - "You can change the width of the confidence interval by manipulating the ``ci`` argument." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1056c239", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:03 2025.\n", - "\n", - "Effect size(s) with 90% confidence intervals will be computed for:\n", - "1. Test 1 minus Control 1\n", - "\n", - "5000 resamples will be used to generate the effect size bootstraps." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "two_groups_unpaired_ci90 = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), ci=90)\n", - "two_groups_unpaired_ci90" - ] - }, - { - "cell_type": "markdown", - "id": "a0d70d5e", - "metadata": {}, - "source": [ - "## Effect sizes" - ] - }, - { - "cell_type": "markdown", - "id": "47ad9810", - "metadata": {}, - "source": [ - "The **dabest** library now features a range of effect sizes:\n", - "\n", - " - Mean difference (`mean＿diff`)\n", - " - Median difference (`median＿diff`)\n", - " - [Cohen's d](https://en.wikipedia.org/wiki/Effect_size#Cohen's_d) (`cohens＿d`)\n", - " - [Hedges' g](https://en.wikipedia.org/wiki/Effect_size#Hedges'_g) (`hedges＿g`)\n", - " - [Cohen's h](https://en.wikipedia.org/wiki/Cohen's_h) (`cohens＿h`)\n", - " - [Cliff's delta](https://en.wikipedia.org/wiki/Effect_size#Effect_size_for_ordinal_data) (`cliffs＿delta`)\n", - " \n", - "[comment]: <> (Please copy this underline for the above ＿)\n", - " \n", - "Each of these are attributes of the ``Dabest`` object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aa21edeb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:03 2025.\n", - "\n", - "The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].\n", - "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "two_groups_unpaired.mean_diff" - ] - }, - { - "cell_type": "markdown", - "id": "600ca0d1", - "metadata": {}, - "source": [ - "For each comparison, the type of effect size is reported (here, it's the\n", - "\"unpaired mean difference\"). The confidence interval is reported as:\n", - "[*confidenceIntervalWidth* *LowerBound*, *UpperBound*]\n", - "\n", - "This confidence interval is generated through bootstrap resampling. See [`bootstraps`](/blog/posts/bootstraps/bootstraps.ipynb) for more details.\n", - "\n", - "Since v0.3.0, DABEST will report the p-value of the [non-parametric two-sided approximate permutation t-test](https://en.wikipedia.org/wiki/Resampling_(statistics)#Permutation_tests). This is also known as *the Monte Carlo permutation test*.\n", - "\n", - "For unpaired comparisons, the p-values and test statistics of [Welch's t test](https://en.wikipedia.org/wiki/Welch%27s_t-test), \n", - "[Student's t test](https://en.wikipedia.org/wiki/Student%27s_t-test), \n", - "and [Mann-Whitney U test](https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test) can be found. For paired comparisons, the p-values and test statistics of the \n", - "[paired Student's t](https://en.wikipedia.org/wiki/Student%27s_t-test#Paired_samples)\n", - "and [Wilcoxon](https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test) tests are presented.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e3dae0a5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " control test control_N test_N effect_size is_paired \\\n", - "0 Control 1 Test 1 20 20 mean difference None \n", - "\n", - " difference ci bca_low bca_high pvalue_permutation pvalue_welch \\\n", - "0 0.48029 95 0.205161 0.773647 0.001 0.002094 \n", - "\n", - " statistic_welch pvalue_students_t statistic_students_t \\\n", - "0 -3.308806 0.002057 -3.308806 \n", - "\n", - " pvalue_mann_whitney statistic_mann_whitney \n", - "0 0.001625 83.0 " - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "two_groups_unpaired.mean_diff.statistical_tests" - ] - }, - { - "cell_type": "markdown", - "id": "416979fe", - "metadata": {}, - "source": [ - "**Note:**\n", - "A research paper [Phipson & Smyth (2010)](https://doi.org/10.2202/1544-6115.1585) suggested that permutation p-values should never be zero, and provided a slightly adjusted formula to compute permutation p-values. \n", - "\n", - "Since **v2025.03.27**, DABEST provides a `ps_adjust` parameter in the `.load()` function. This parameter allows you to adjust the permutation p-values using the formula suggested by Phipson & Smyth (2010). By default, DABEST uses the unadjusted p-values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "530c46db", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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controltestcontrol_Ntest_Neffect_sizeis_paireddifferencecibca_lowbca_highpvalue_permutationpvalue_welchstatistic_welchpvalue_students_tstatistic_students_tpvalue_mann_whitneystatistic_mann_whitney
0Control 1Test 12020mean differenceNone0.48029950.2051610.7736470.00120.002094-3.3088060.002057-3.3088060.00162583.0
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" - ], - "text/plain": [ - " control test control_N test_N effect_size is_paired \\\n", - "0 Control 1 Test 1 20 20 mean difference None \n", - "\n", - " difference ci bca_low bca_high pvalue_permutation pvalue_welch \\\n", - "0 0.48029 95 0.205161 0.773647 0.0012 0.002094 \n", - "\n", - " statistic_welch pvalue_students_t statistic_students_t \\\n", - "0 -3.308806 0.002057 -3.308806 \n", - "\n", - " pvalue_mann_whitney statistic_mann_whitney \n", - "0 0.001625 83.0 " - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "two_groups_unpaired_adjusted = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), resamples=5000, ps_adjust=True)\n", - "two_groups_unpaired_adjusted.mean_diff.statistical_tests" - ] - }, - { - "cell_type": "markdown", - "id": "d3f49f35", - "metadata": {}, - "source": [ - "Let's compute the *Hedges'g* for our comparison." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5831bdc1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:04 2025.\n", - "\n", - "The unpaired Hedges' g between Control 1 and Test 1 is 1.03 [95%CI 0.317, 1.62].\n", - "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.hedges_g.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "two_groups_unpaired.hedges_g" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "64e6eb76", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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controltestcontrol_Ntest_Neffect_sizeis_paireddifferencecibca_lowbca_highbca_interval_idxpct_lowpct_highpct_interval_idxbootstrapsresamplesrandom_seedpermutationspvalue_permutationpermutation_countpermutations_varpvalue_welchstatistic_welchpvalue_students_tstatistic_students_tpvalue_mann_whitneystatistic_mann_whitneybec_differencebec_bootstrapsbec_bca_interval_idxbec_bca_lowbec_bca_highbec_pct_interval_idxbec_pct_lowbec_pct_high
0Control 1Test 12020Hedges' gNone1.025525950.3165061.616235(42, 4725)0.444861.745146(125, 4875)[1.469217954462509, 1.5972518056777079, 0.6051...500012345[-0.329508986559053, 0.07158401210924781, -0.2...0.0015000[0.26356588154404337, 0.2710249543904699, 0.26...0.002094-3.3088060.002057-3.3088060.00162583.00.0[-0.2669450878059954, 0.21187593591106418, -0....(127, 4877)-0.6423870.629464(125, 4875)-0.6436040.627968
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" - ], - "text/plain": [ - " control test control_N test_N effect_size is_paired difference ci \\\n", - "0 Control 1 Test 1 20 20 Hedges' g None 1.025525 95 \n", - "\n", - " bca_low bca_high bca_interval_idx pct_low pct_high pct_interval_idx \\\n", - "0 0.316506 1.616235 (42, 4725) 0.44486 1.745146 (125, 4875) \n", - "\n", - " bootstraps resamples random_seed \\\n", - "0 [1.469217954462509, 1.5972518056777079, 0.6051... 5000 12345 \n", - "\n", - " permutations pvalue_permutation \\\n", - "0 [-0.329508986559053, 0.07158401210924781, -0.2... 0.001 \n", - "\n", - " permutation_count permutations_var \\\n", - "0 5000 [0.26356588154404337, 0.2710249543904699, 0.26... \n", - "\n", - " pvalue_welch statistic_welch pvalue_students_t statistic_students_t \\\n", - "0 0.002094 -3.308806 0.002057 -3.308806 \n", - "\n", - " pvalue_mann_whitney statistic_mann_whitney bec_difference \\\n", - "0 0.001625 83.0 0.0 \n", - "\n", - " bec_bootstraps bec_bca_interval_idx \\\n", - "0 [-0.2669450878059954, 0.21187593591106418, -0.... (127, 4877) \n", - "\n", - " bec_bca_low bec_bca_high bec_pct_interval_idx bec_pct_low bec_pct_high \n", - "0 -0.642387 0.629464 (125, 4875) -0.643604 0.627968 " - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "two_groups_unpaired.hedges_g.results" - ] - }, - { - "cell_type": "markdown", - "id": "cc0f5147", - "metadata": {}, - "source": [ - "## Producing estimation plots" - ] - }, - { - "cell_type": "markdown", - "id": "0c80d617", - "metadata": {}, - "source": [ - "To generate a **Gardner-Altman estimation plot**, simply use the\n", - "``.plot()`` method. You can learn more about its genesis and design\n", - "inspiration at [`robust-beautiful`](/blog/posts/robust-beautiful/robust-beautiful.ipynb).\n", - "\n", - "Each instance of an effect size has access to the ``.plot()`` method. This allows you to quickly create plots for different effect sizes with ease." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "62cdc948", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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bAJCVVbrNbJMmTaDX63Hz5k1IkoRBgwZhx44dcHWt/uZHHPVtp9Jv5UAhSSavKSQJaTdta7EEIrt1Nd7aEdi0b7/9FiqVCkuXLsXNmzcNrxs3bmDJkiVwcXHBjz/+iKysLCxatAh79+7FokWLalQHE7Wd8vfxgL6C0ZF6IdC0kaeFIyKievHnKaAgy9pR2KznnnsOQ4YMweLFiw2taaB0E5AlS5bgwQcfxHPPPQdPT08sXboUjz32GHbs2FGjOpio7dSALmFwUJr++B2UCvTvHGbhiIioXgg9kHLE2lHYrKNHj6JDhw4VXu/QoQMSEhIMx/fffz8yMmo2v52J2k55u7th8aShcHRQQpIkKBUKSJIERwclFk8aCm/36j8/ISKZS27Ym1LImZeXF7777rsKr+/duxeenn/3UObl5dVozXKAg8nsWve2Idiy+El8f/wC0m5q0LSRJ/p3DmOSJrI1fxwHtEWAQ9U7TlHNTJs2Da+88gpGjRqFZ555Bq1atQIAXL58GWvWrMGuXbuMnknv2bPHaBew6mCitnPe7m4c3U1k67RFwB8ngcCaTw2iyi1ZsgQFBQVYtWoVdu7caXRNqVRi9uzZWLJkCQCgsLAQkyZNQkRERI3qYKImIrIH1w4xUdcDSZKwcuVKzJkzBwcOHEBKSgoAIDAwEP369YOvr6+hrLOzMyZOnFjjOpioiYjswbV4oNcsdn/XE19fX4wdO7Ze7s3BZERE9qA4H7gaZ+0oqBaYqImI7MWZzwEb3F3K1jFRExHZmM6dOyPguS/R+bWTxheuXwT+PGn6TSRbTNRERDYmPT0df9wqQHpOcfmLp/5j+YCoTpioiYjsyR8ngbSfrR0F1QATNRGRvUncwGfVDQinZxER2Zu0n4HUI0BgD2tH0iD17du33LmDBw/WW31M1ERE9ujI/wHNOwMOTtaOpMEJDAy0aH1M1ERE9kjzO/DzVqDTJGtH0uBs3LjRovXV+hm1TqfDtm3bMH36dDz88MM4ffo0AECj0eCLL76o8TZeRERkYSc3AzevWDsKqkKtEnV2djZ69uyJxx9/HJ9++im+/vprXL9+HQCgVqsxc+ZMrF692qyBEhGRmem1wA8rAK2JaVxULUlJSfj000+Nzu3btw8PPPAAunXrZpZcWKtEPX/+fJw9exb79u3D1atXIe4YPahUKjFq1Cjs2bOnzsEREVE9u3kFSFxv7SgarLlz5+Kzzz4zHF+7dg0PP/wwrl27BgCYPXs21q1bV6c6apWov/zySzz//PMYMGAAJEkqd/3ee+9FcnJynQIjIiIL+eW/QOpP1o6iQfr555/Rq1cvw/Enn3wCpVKJU6dO4aeffsKoUaPw4Ycf1qmOWiVqjUaD4ODgCq+XlJRAq9XWOigiIrKwH1YAedetHUWDo9Fo0KhRI8Pxnj17MGDAADRu3BgAMGDAAFy+fLlOddQqUbds2RInT1a8Xux3332H8PDwWgdFREQWVqgBDi4H9DprR9KgNG3aFOfPnwcApKWl4cSJExg4cKDhel5eHhSKuq0tVqvpWVOnTsW8efPQu3dv9OvXD0Dp5tlFRUV45ZVXsHfv3jr3yRMRkYWl/Qyc/AToPNnakTQYI0aMwHvvvYfCwkL89NNPUKlUePjhhw3Xf/75Z4SEhNSpjlol6hdeeAFnz57F2LFj4eXlBQB4/PHHcfPmTWi1WkyfPh1TpkypU2BERABQnJeFzNMHUZSdAZWXH3zb94WT2tvaYdmuk58AAZ0B//bWjqRBePXVV3H9+nVs3rwZXl5eiI2NhZ+fHwAgJycHO3bswIwZM+pUR60StSRJWL9+PSZOnIgdO3bg0qVL0Ov1aNmyJR599FE88MADtQpmzZo1WLNmjWEgWtu2bbF48WIMHjzYZPnY2FhMnmz8l59KpUJhYWGt6iciebn560+48HkMhF4LSVJACD1S4jYjbNRC+LTuau3wbJPQAwdXAKM+BpxcrR2N7KnVamzZsqXCa7///jtcXev237FOK5P16tXLaLRbXQUEBOD1119H69atIYTApk2bMGLECJw6dQpt27Y1+R4PDw9cvHjRcGxqFDoRyVNlreXivKzSJK0rAQAIUfrsVOhKcH7Ha+jy/Ea2rOtLbhpw9APggRetHUmDo9FooFaroVQqoVAo4OnpWed7ymr3rIceeghDhgxB69atce+992LFihVQq9U4evRohe+RJAn+/v6GV1mXAxHVXHFeFn4/8jmufPsBfj/yOYrzsuqtrpu//oTE9yYj+eBGpJ/ai+SDG5H43mTcunQMAJB5+iCE3vTsEaHXIvN0/W2CQADOf8MpW9V0/PhxPPjgg3B1dUWjRo0QHx8PALhx4wZGjBiBuLi4Ot2/Vi3q4ODgKluukiThypXaL02n0+mwfft25OfnIyoqqsJyeXl5CAwMhF6vR8eOHfHaa69V2PoGgKKiIhQVFRm9n4gs281cndZyUXbGX3GUH4UsSQoUZXOZ4noXvxIYtQFwYc9FRRISEtC3b180b94c48ePx0cffWS41rhxY2g0Gqxduxa9e/eudR21StTR0dHlErVOp0NKSgp+/PFHtGvXDvfdd1+tAjp9+jSioqJQWFgItVqNnTt3VjjVKzQ0FB9//DEiIiKg0Wjw73//Gz169MDZs2cREBBg8j0xMTFYtmxZrWIjslX11c1cUdd2dVrLKi8/CKE3XUboofJi71m9u30TiHsdGBQD1HGKka1auHAhwsLCcPToUeTm5holagDo06cPNm3aVKc6apWoY2NjK7z2888/Y9CgQRg3blytAgoNDUVSUhI0Gg127NiBiRMnIj4+3mSyjoqKMmpt9+jRA2FhYVi7di2WL19u8v4LFizA7NmzDcdJSUmIjo6uVaxEtqI6iTMg6pFy1yp7xlxZC706reV77h+LlLjNhj8ejMooHOAb0a+OPzVVS+pRIGkL0PEJa0ciS4mJiYiJiYFKpTLZQ9u8eXOkp6fXqQ6zb3PZoUMHTJ8+HfPmzcOJEydq/H4nJye0atUKANCpUyckJiZi9erVWLt2bZXvdXR0xH333VfpKjAqlQoqlcpwrFaraxwjka2pTTdzZYlY3bR1pS30gKhHqmwtO6m9ETZqIc7veM2oDknhgLBRC+Hk5mW+/wB3+O3wNty6nIj89GuQlA6Ieum/Vb5HCIHU+P8gPWkfdIX5cA8IQ6shM+Di09xQ5txny5CfcQ3F+dlwcFHDKygSQf0mQ+XeqJI7y8TxDUDje4EW3awdiew4OjpCrzf9XQaAP/74o855pl76Mvz8/HDu3Dmz3Euv1xs9U66MTqfD6dOn0bRpU7PUTWQvatrNbNRVLgSEXlf671+JOO34rkpb6IAESWG6nXBna9mndVd0eX4jgvpOhv99DyKo72R0mRlb52fmv3wyHxk/7zd5Ta/TonFYL/h3GlLt+/1xZAf+TPwGrQbPQIfJb0Pp5IwzWxdBf8euVJ5BEQj9x3x0emYdwh5ZiMLsNFz4/LU6/RwWI0TpqmWaP6wdiex0794dO3bsMHktPz8fGzdurHOvrdkT9c2bN7Fhw4YKnxFXZsGCBTh06BCSk5Nx+vRpLFiwAHFxcYZu9AkTJmDBggWG8q+88gq+++47XL16FSdPnsT48eORkpKCqVOnmu3nIbIHvu37Vitxlqmqqzz7WhIkyfSvF0lSQFuQg7BRCyEpHQFJgqRQlv6rdCzXWnZSeyMg6hG0HPwsAqIeqbeWdJnA6PFo3u1huPkGVqu8EAJ/HPsK9/Qag0ahUXDzC8a9w+egOPcWbl48YijXvNvD8AhoA2cvX3jcE46AHqOR+/tF6HUNZF+Eolxg/2JuiXmXZcuW4fjx4xg6dCi+/fZbAKWPgD/66CN06tQJ169fx6JFi+pUR626vvv27WvyfHZ2Ni5cuIDi4mJs3ry5xvfNzMzEhAkTkJaWBk9PT0RERGDfvn0YMGAAACA1NdVozdSsrCxMmzYN6enp8Pb2RqdOnZCQkMB1xolqqKbdzFV1lQOosoVe1lo2esYd0a/eE7G5FWWnoyQvC17BkYZzDs5ucG8eipzfL6BJ2/KtqZKCXFw/EwePgDAolGZ/All/bl4Gjq0Fejxv7Uhko1u3btizZw+eeeYZTJgwAQAwZ84cAKX7YuzZswcRERF1qqNW3xC9Xl9u1LckSQgODkb//v3x5JNPok2bNjW+74YNGyq9fvdctFWrVmHVqlU1roeI/nbngLCAqFGABGhv5xgSJ4TA70c+NxowVlVXuVfwfchLv1LlQLCy1nJDVjbX3MnNeFS8k5sXSvKN56FfO/Ax0o7vgr6kCO7N2yB8zBKLxWk2Zz4HQnpzidE79O3bFxcvXkRSUpLRSp2dOnUyyyJctUrUdZ28TUTyYGpAWFkr2qd11woHjLUeNhOSwqHCRNy0yzC4N7/X4gPBKvLb4c/w249/DwrTa4uR+8cFXNn79z7BHZ9eA2dP33qNIyDqEfhHDkKhJhO/HdqKX79+C+FjljasFRWFABLeA0Z+yClbd4mMjERkZKTZ79uA+lyIyJyqmjsd+eQ7FV6/tOtdtBo6E5d3v1thIpZT17Z/pyFoHH6/4fjil2+icZueaNSmh+FcbUdfG5Y8zc+Ck7uP4Xxxfjbc/Ix3TXJ09YSjqydcGjWHa+N7kPjuROT+cQEeAWG1qtuU1NRU5OfnAwDyi3RIvVWIFj7OZrs/AOD6RSDlMBBcu30dbMmhQ4cqvS5JEpydnREQEFDrgc7VStSffPJJrW5e1l9PRPJT1YCw5B9iK71ekp9VZSKWS9e2o4s7HF3cDccKBxUc3Tzh4tOszvdWefnDUe2N7OSfofZvCQDQFt1G7h8X0bSSkeNljw702vK9ErVx7NgxLF++HLt374YQAgCQXaBD0L+OYVh7HywaEoguQe5V3KUGznzORA2gd+/e1e4Rad26NZYtW4YxY8bUqI5qJepJkybV6KZA6V8RTNRE8lXl3GlNZpVzq+WSiM2pUJMJbUEuijTXAaFHXnrpUsguPs2gdHIBAJxYMx2BfSaicZsekCQJzbuOwG+Ht8HFpxmcvfyRErcZTu4+aBRauiBT7h8XkPvnJXjcEw4HZ3cUZqUhJX4znL2bmqU1/cUXX2DMmDEQQhiSdBkhgD1nbuHbM1n4bFoY/nFf4zrXBwD4MwnIzQDc7XuFuL1792LevHkoKirCtGnTDOuAXLp0CR999BFcXFzw8ssvIyUlBWvXrsXjjz8OpVKJUaNGVbuOaiXqa9eu1e4nICLZqnLutKcvbt9Irfi6jS7hmRr/H2T+csBwnPTRTABAu/Ex8AoqHb1bcPN36IryDWWaR42CrrgQl3e/B21hPjzuCUe7scuhcHACACgcnXHzQgJSD22BrrgQTmofeLfshHt6jYHCwbFO8R47dgxjxoyBTqcrl6TL6PSABIEx688jYW6k+VrWKT8C7f5hnns1UHv37oWzszN++uknODk5GV179tln0bt3bxw9ehQrV67E008/jc6dO2PlypXmT9SBgdWbT0hE1lf2zLSqtbl92/etdInOoL6TkX0tySaX8IyY8HqF1+4dPhv3Dp9d4XUA6PXybqNjSZIQ2PsJBPY2vcymm28Q2j8RU/NAq+HVV1812ZK+mwAgIPDqnhR89Ww781SeliTLRH3o0CG8+eabOHHiBNLS0rBz506MHDmyXurasmULXn755XJJGgCcnZ0xbtw4rFixAitXroSzszPGjx9f4RLXFeGQPSIbEzllNbq+8Akip6yutFzZ3OmKFh1x8w2s9qIkZB2pqanYtWsXdLryjydM0emBb07fQuqtQvMEkJVinvuYWX5+Pjp06ID/+7//s0hdGRkV7+SWlpZmtAa4l5cXlEpljeqo9ajv9PR0bNiwASdPnoRGoym31qkkSThw4EAF7yaiO+l1JaXLcFqYZ1AEOj7zIW6ciUf+rXQ4eTRGk7bRcHTzROHtXLg2D0P7qe/hxtlDKNRch7NnE6Pr5iQplFAo69YN3BCY87P+ds/uKlvSdxMC2HcuCxO7m2EqWs51SCXmGQxXEa225iu3DR48GIMHD66HaMrr27cv3nnnHXTv3h3Dhg0zuvbNN99g9erV6Nfv796npKQkBAUF1aiOWiXqX375Bb1790ZBQQFCQ0Nx+vRphIeHIzs7G3/88QdatmyJe+65pza3JrI7el0Jcv/4FbriAqvF4ObfEu//9wDeeG+91WIgy3nqP5fw1H8umedm08p3+dqT999/H3369MGIESPQvHlztGxZOvL/ypUr+OOPPxAYGIj33nsPAFBYWIjU1NQaL3Ndq0Q9f/58qNVqJCUlwdXVFb6+vli9ejX69u2L7du345lnnsGWLVtqc2siuyP0OuiKC6BwcLBqi3L+7Ocwd9azVqlbryuBXquDZ2BbKB3NPOdXRnQlhdCknIXCwTy9B1t3fIWZ85bW+H1rxwZhQre6jf4WQkDr3RqOo9fV62d26tQpdOvWDXl5ecjJyTGcv3snRGtp0aIFTp8+jQ8//BD79u1DSkrp44CwsDDMmjUL06dPh5ubG4DSZ9Z79uypcR21StQ//vgj5s6dixYtWuDWrVsAYOj6Hj16NA4fPoyXXnoJ8fHxtbk9kV1SKB0No4StUr8Vlz/Sa4uhLSqAo6MjlI622/2tgA6Ojg5wULmY5bPu36d0Dm9Nur8lCRjQxguONXxOWp6A3je03j8zB4fSL+bdO1AtWbIES5curbd6a8LV1RWzZ8/G7NmVD0KsrVqv9e3nVzo1o+zBeFnCBoD27dtXuW43ETU8JQW5yLqUiOK8W6VTjFp3MVpIhCzrnoBmeLB/H3x3ML5aA8qUCmBIWy+08DFPS7SkaRdYqv8jPj7eaHlOObSmLaVWiTo4ONgwt1qhUCA4OBjff/89Hn30UQBAQkICvLy8zBYkEVXfxa/egvZ2DhxcPRA6Yo7Z7qtJPYPkA7GlA6EUEqAXSDuxB0H9JsOzRVuz1UM1M++fz2L/D4eqbFlLACRIWDiouVnq1TUJh15tubn0arUaHh4eFquvIhXtHlmZug6urvb0rKysv3eBGThwILZv3244fuaZZ/DRRx+hf//+6NevHzZt2oTHH3+81kERUe1pb+eg5LYG2ts5VReuppKC3L+StBaAAPR6AKJ0qdEDG1FSYN4R4FR9nSIjEPvhKiiVygqn/SgVgFIhYduTrdAlUG2WeotbV7w8qrXl5eUhKSkJSUlJAEoX7UpKSkJqqukFfGpCr9cb5q2XvVJTUxEXF4dTp05Bo9FAo9EgKSkJcXFx+O2332o8Mv9u1W5R+/v7Y8iQIRg3bhzmzJmDsWPHoqSkBI6Ojpg1axby8/Px+eefQ6lUYtGiRVi4cGGdAiPryMrNx/7E80i/lQN/Hw8M6BIGb3c3a4dFVpZ1KbHCKUVCr0PW5UT4tq95S4PMY8SQQfj+621YueoD7P3+B6PEIEml3d0LBzU3W5LWN24DfeMwoNhM87HN7Pjx4+jTp4/huOzZ8cSJExEbG1une9+9e+Thw4cxfPhwrF+/HhMnTjQ8U9dqtdi4cSPmzZtX5zqrnahHjRqFr7/+Gl9//TXc3d3xj3/8A+PGjUPfvn0hSRJefvllvPzyy3UKhuru2be2Iiv3NrzdXfHBnJr1ahw5cxXLN+2GVqeHQpKgFwKx3x7B4klD0b1tSNU3IJtVnHfL0N1djkJCce6t8ufJojpFRuC/mz7Eb7//iR4DhiNbkwMvFyVOzm9vtmfSAABJQnHE46V/AchU796969yKra4XX3wRkydPxpQpU4zOOzg4YNq0abhw4QJmz56Nn376qdZ1VLvre8uWLcjMzMR//vMf3H///diyZQsGDhyI5s2bY86cOTh58mStgyDzycq9jRuaPGTl3q7h+/KxfNNulGhL1wvW/dW9U6LV4ZXY3cjKza/6JmQzSgpykfnLQfyesAOZvxyEUuVmOkkDgF4Ybe9I1nVPQDO4upZuHuKmUpg3SQPQhvSF8A426z0bsl9++QUhIRU3ZIKDg3H69Ok61VGjJURdXFwwduxYfPPNN0hPT8cHH3yA1q1b45133kGXLl3Qpk0bvPrqq7h69WqdgiLL2594Hlqd6Q0atDo9vj9+wcIRkbVoUs/g3LZl+DPxG9y4mIA/E79Bxs/fl7aoTZAUSni36mLhKMkahFtjlLR9zNphyEqzZs3w2WefmVxBTavV4rPPPkOzZnXbTrXWa317e3tj+vTpiI+PR2pqKl5//XW4urpi8eLFaN26NXr06FH1TUg20m/lQFFBV5ZCkpB2U2PhiMgaKho0BqEDRGlSBiRAoQAglW7e0W8yp2jZA4UCxV2fA2x4QZramDt3Lg4fPozu3bvjo48+QlxcHOLi4rB+/Xp069YNCQkJeOmll+pUh1mWOGjevDleeuklPPjgg1i8eDG++uqrOvXHk+X5+3hAX8EzHb0QaNrI08IRkTVUNmgMQsA3sj+Ujs4ozr0FJ3cfeLfiPGp7UdJ+HPQ+La0dhuw89dRTUCqV+Ne//oWnnnoK0l8NHiEEmjRpgg8//BDTpk2rUx11TtSpqanYunUrPv30U5w5cwZCCPTo0QPjxo2r663JggZ0CUPst0dQoi3/S9pBqUD/znXf3J7kr6pBY7rC22jaUb7Tcqh+aIMegLblAGuHIVtTpkzBxIkTkZiYaJgCFhgYiM6dOxtGgddFre5w48YN/Pe//8XWrVtx5MgRCCHQpk0bvPLKKxg3blyNdwYh67lzOlafjqH44cQFaPXCMOrbQanA4klD4e3uau1QyQKc1D4cNEZG9I3boCRysqxHecuBg4MDoqKiEBUVZf57V7dgfn4+du7cia1bt+LAgQMoKSlB06ZNMWvWLIwbNw4dO3Y0e3BUv0xNx1IqJAzsEgYnRwc0beSJ/p3DmKTtiHfrLkg7seevZ9TGOGjM/gh3fxR1fwFQWnEh+AYgJycHH3zwAX744QdkZmZi7dq16Nq1K27duoXY2FgMHz4crVq1qvX9q/1f39fXF4WFhVCr1Xj88ccNc6gVilqPRyMrunM6FgDo/no+rdUJHDx5EVsWP8mFTuzE3et333P/Y/jtf9uMlgqVFEoOGrMzwtkDRT3nAirzLJJiq37//XdER0fjt99+Q+vWrXHhwgXk5eUBAHx8fLB27VqkpKRg9erVta6j2om6f//+GDduHIYPHw5nZ476a+iqMx1rdJ9OFo6KLM3U+t2SQol7eo2BtjCXg8bslYMzinq+BOHWxNqRyN5LL72E3NxcJCUlwdfXF76+vkbXR44ciV27dtWpjmon6q+++qpOFZG8lE3H0pkY6c3pWPbBeCoWDM+mhV6L3w5/hvDHljA52yOFEkVRL0B4BVk7kgbhu+++wz//+U+Eh4fj5s2b5a6HhITgt99+q1Md7Le2U5yORdVZv5vsT3GnqdD7trN2GA1GQUEBmjSpuOchN7fuG9YwUdupAV3C4KA0/fFzOpZ9MEzFMoXrd9ulkjYjoGvRy9phNCjh4eE4dOhQhde//PJL3HfffXWqg4naTnm7u2HxpKFwdFBCkiQoFQpIkgRHByWnY9kJTsWiO+n8O0Ab/g9rh9HgzJo1C9u2bcPKlSuh0ZQ+MtTr9bh8+TKeeOIJHDlyBP/85z/rVAfH3Nux7m1DsGXxk/j++AWk3dRwOpad4VQsKiNcvFHceTogse1WU+PHj0dKSgpefvll/Otf/wIAPPjggxBCQKFQ4LXXXsPIkSPrVAcTtZ3zdnfj6G475ejijqB+k5F8YCOnYtm54o5TABU/79r617/+hSeeeAKff/45Ll++DL1ej5YtW+If//hHpTtrVRcTNZEd82zRFuGPLUHW5UROxbJT2qAHoPfvYO0wGrwWLVrUuYu7IkzURHbO0cUdvu37WjsMsgLh4o2S9o9bOwyqAhM1EZGdKu44BXDiCoQ1FRERUaPykiTh559/rnV9TNRERHZIG9KPXd615OPjY9jOEgBKSkqQkJCAiIgIeHt7m70+JmoiIjsj3P1R0n6stcNosOLi4oyOb9y4AV9fX7z99tvo29f8j5E4Fp+IyJ4oFCjq8gzgoLJ2JDZDquctQJmoiYjsSMm9wyC86z5liCyHiZqIyE4It8bQthlp7TCohviMmojq5O79rL1bcx62XJW0HQMoHa0dBtUQEzURVaqyRGxqP+u0E3sQ1G8yPFu0tXLkdCe9R3PoArpaOwybcPLkSaPjsjW+L126BC8vL5Pv6dixY63rY6Ims8nKzcf+xPNIv5UDfx8PDOgSBm93ztFsyCpLxK5NWlS4n3XygY3cz1pmtK0Hcy1vM+ncubPJAWTPPvtsuXNCCEiSBJ3O9Jay1cFETWZx5MxVLN+0G1qdHgpJgl4IxH57BIsnDUX3thy4ImcVtZhLCnIrTcS+Ef2q3M+aK57JhKMLdAHdrR2Fzdi4caNF62OipjrLys3H8k27UaIt/aWtE6W/0Eu0OrwSuxtbFj/JlrVMVdZiLsrOqDQR5/550fCecriftaxom3fhdCwzmjhxokXrYz8IVVtWbj7+e/A43t1xEP89eBxZufkAgP2J56HV6U2+R6vT4/vjFywZJlWTcYtZAHo9AGFoMRdkp5cmYlPKznM/6wZB17ybtUOgOmCLmqqlsq7t9Fs5UEiSoSV9J4UkIe2mxgoRU1WyLiVW2mLWFeZVmojdm9+Lghu/cz9ruXN0gb5JuLWjoDpgi5qqdGfXthACOr0eQghD17anmzP0JpI0AOiFQNNGnhaOmKqjOO9WpS1mB2c1JIXS5GVJoUTjsPsR1G8yJIUDAAlQKABIkBQO3M9aRnS+bQEl22QNGT89qlJVXduABAelwvCM+k4OSgX6dw6r5wipNpzUPpW2mJ29/RHUbzKSD2w0eoYtKZSGRMz9rOVP51eznZ5IfpioyaCi6VVVdW1r8guweNJQvBJr3DXuoFRg8aSh8HZ3tcJPQ1Xxbt0FaSf2VNp17ejiXmUi5n7W8qb3bW/tEKiOZJWo16xZgzVr1iA5ORkA0LZtWyxevBiDBw+u8D3bt2/HokWLkJycjNatW2PlypUYMmSIhSK2HZU9g/b38aiya7t72xBsWfwkvj9+AWk3NWjayBP9O4cxScuYo4t7lS3msnJMxA2LX5MmkAo18PNQQbg1tnY4VEeyStQBAQF4/fXX0bp1awghsGnTJowYMQKnTp1C27blVzlKSEjA2LFjERMTg2HDhmHr1q0YOXIkTp48iXbt2lnhJ2iYqppe9X+zH0Pst0eq7Nr2dnfD6D6dLBc41Rm7rm3Tob1fwHnfHOgb3YtiawdDdSarwWQPPfQQhgwZgtatW+Pee+/FihUroFarcfToUZPlV69ejQcffBAvvfQSwsLCsHz5cnTs2BHvv/++hSNv2Kp6Bn38QioWTxoKRwclJEmCUqGAJElwdFCya9sGlLWYA3qMgm/7vkzSNkTn09raIZAZyKpFfSedToft27cjPz8fUVFRJsscOXIEs2fPNjo3aNAgfPnllxaI0HZUZ3rV6D6d2LVN1MDovYOsHQKZgewS9enTpxEVFYXCwkKo1Wrs3LkT4eGm5wCmp6fDz8/P6Jyfnx/S09MrvH9RURGKiooMx3l5eeYJvAGrzjNogF3bRA2LBOHezNpBkBnIqusbAEJDQ5GUlISffvoJzzzzDCZOnIhz586Z7f4xMTHw9PQ0vKKjo81274ZqQJcwOChNfxU4vcr2lRTkIvOXg/g9YQcyfzmIkoJca4dEZiCcvQEHZ2uHQWYgu0Tt5OSEVq1aoVOnToiJiUGHDh2wevVqk2X9/f2RkZFhdC4jIwP+/v4V3n/BggXQaDSGV3x8vFnjb4i83d34DNpOaVLP4Ny2Zfgz8RvcuJiAPxO/wblty6BJPWvt0KiOhGsja4dAZiK7ru+76fV6o67qO0VFReHAgQOYNWuW4dz+/fsrfKYNACqVCirV34vTq9Vqs8XaEN05d/rRvp0goXReNJ9B276qdsfiNpUNm3DmioC2QlaJesGCBRg8eDBatGiB3NxcbN26FXFxcdi3bx8AYMKECWjevDliYmIAAC+88AKio6Px1ltvYejQodi2bRuOHz+OdevWWfPHaDBMzZ0uW6SEW1PavqrW+uY2lQ2bcOIfWbZCVl3fmZmZmDBhAkJDQ9GvXz8kJiZi3759GDBgAAAgNTUVaWlphvI9evTA1q1bsW7dOnTo0AE7duzAl19+yTnU1VDV+t1lO2OR7apqrW9uU9mwCUf2htkKWbWoN2zYUOn1uLi4cudGjx6N0aNH11NEtqs6W1NyhLdtq2qtb25T2cBxIJnNkFWLmiynbO60Kdya0j54t+5S6e5Y3KaygXNwsnYEZCZM1HaqunOnyXaVrfXNbSptk1Cqqi5EDQITtZ3i3GkC/l7ru1nXh9A4tAeadX0I4Y8tgWeL8mvrUwOjtO8W9f/93/8hKCgIzs7O6NatG44dO2btkGqNidrGeLu7orGnusppVZw7TWW41rdtEnacqD/77DPMnj0bS5YswcmTJ9GhQwcMGjQImZmZ1g6tVmQ1mIzq7oM5j1e7LLemJLJhSkdrR2A1b7/9NqZNm4bJkycDAD788EPs3r0bH3/8MebPn2/l6GqOiVoGSrQ66PSmR2DXNxeVEx7qGQGdTgf9XzHk5t+2eBxKhQKODqYHNtk6XUkJSkq0EIoSKEQF06VqQNzxb0lJSZ3vZwl6bQm0JVqUlJRAD9v9HpR91jpxGwpl/X42UrEOoqD+plnqdSXQa3X1/plptaUL8uTl5SEnJ8dw/u7Fq8oUFxfjxIkTWLBggeGcQqFA//79ceTIkXqLsz4xUVtZiVaHi6npuF1k3V+o699/Bx99YHqpVmpYPn+xD3w9XfBnWjqiAvmsmWzD3fsyLFmyBEuXLi1X7saNG9DpdCY3bLpw4UJ9hlhvmKitTKfX43ZRCRyV1m1RPjdrNp6dOQt5twtx/GIqbuXkw8fDDZ1DW0DtWvv5mGeT07B571Ho9HpIkgQhBJQKBSY82B3hQU0BlP6xotXp0b5lczg72V93na6kEJqUs3BQOUNhhik1F3e8Cu1tDZo19cetlIaxZrdeWwxtUSE8A9tC6Wjb83/1upIKV4Qzq4IswMW7XquQFEoo6rmL/dSpU+jWrRvi4+MRGRlpOG+qNW2rmKhlwtFBCSdHK34cjg44feUPfLTrsFFS/e74BUx9qBfahzSv8S1z8guwZX8i9JAM83UlCdAD+M/+RCyfOhwebi4oLtGW/rHi6AhHR/tL1Aro4OjoAAdHRygc6v7zS3f821D+e+olAUmvhaOjI5QNJOZas9TPpxCAc8MfGOjgUPp7Ua1Ww8PDo8ryjRs3hlKprPGGTXLGUd8EoDSpfrTrMLQ6PYQA9HoBIUpXKfvom8PIyS+o8v37E8/jswPHsT/xPHLyC/DTueQKn73r9HocO59cDz8JObh6wNHVEw6uVf9SIxsm2eevdycnJ3Tq1AkHDhwwnNPr9Thw4EClGzbJGVvUNmblln3IyS+Ah5sL5o0bVO33VSepVjS32lRLfFfCLwht4Wc4vptCknBTw/XE60PoiDnWDoHkoIKVB+3B7NmzMXHiRHTu3Bldu3bFO++8g/z8fMMo8IaGidrG5OQXIDuv8tavKbdy8muVVO9siQMwvF+r0+N8cjoEKl79rJGnW43jJKLqst9EPWbMGFy/fh2LFy9Geno6IiMjsXfv3nIDzBoKJmoCAPh4uJlM0kDlSbWylrheCCgkyWSyVioU6BYeXPuAiahydtyiBoDnnnsOzz33nLXDMAv7fIhB5XQLD4JSYfrrUFlSLWuJm36fhLAgfzgoFZCk0mNJKl2idOpDveBeh9HkRFQV+07UtoQtagIAeLi5YOpDvfDRN6XPmhWSBP1fU6mmPtQLQgjsTzxvmLbVLTwIHm4uVbbE773HD+MHdsOx88m4qclHI083dAsPZpJuQEoKcpF1KRHFebfgpPaBd+suXGa0IbDzFrUtYaK2c2Wjs8sS8NzHB+J8SrpRUk1Ou4lFH31dbrDY1Id6oVt4EHYl/GJyb+uylri7qzM3+WigNKlnkHwgtnTer0IC9AJpJ/YgqN9kbtwhdwobn+ZmR5io7Zjp0dqlLeiyxFrZYLGPvjmM5VOHV9oSZ8tZ/ipqMZcU5P6VpEuXcIS+9LMXei2SD2xE+GNL2LKWswoeZVHDw0Rtp6qTgD3cXKo9bWv51OHs3m6AKmsxF2VnVLiCltDrkHU5Eb7t+1o4YiL7w0Rtp6qbgKs7bcvDzYXd2w1MVS1mr5YdDcm7HIWE4txbFoyWyH6xb8ROVTZa+84EXNtpWyR/WZcSK20x6wrzTCdpANALOLn71GN0RFSGidpOVTcB13baFslfcd6t0hazKQoJDs5qwxrtd5MUSni36lKP0RFRGSZqO1XdBFw2bYtzoW2Pk9qn0hazs7c/gvpNhqRwACD9NThJgqRwQFC/yRxIRmQhfEZtp6qaN+3u6mw0dat/5zBIEpBfUMzBYjbCu3UXpJ3Y8/cz6juUtZgdXdwR/tgSZF1ORHHuLTi5+xjOE5FlMFHbsfYhzSscrW1q6lZZEq/NlpckP44u7gjqNxnJBzYajfqWFEqjFrOjiztHdxNZERO1nTM1Wru6U7eo4fNs0ZYtZiKZY6Kmcuqy5SU1PGwxE8kbE7Wdu3sJ0W7hQbXe8pKIiMyPidqOmV5C9Bd0bhPIudNERDLB6Vl26s7n0EIAer2AEKXPoRPPJ0PBudNERLLARG2nKnsOrRcCXdoEcu40EZEMsOvbTlX1HNrJwYEbbRARyQATtZ2qzhKi3GiDiMj62PVtp7iGNxFRw8BEbae4hjeVKSnIReYvB/F7wg5k/nIQJQW51g6JiO7Arm87VtkSomQfNKln/tqT+u8lRNNO7EFQv8nwbNHW2uEREZio7R6fQ9uvkoLcv5L0X5ty/LWTltBrkXxgI8IfW8KlRIlkgF3fRHYq61JiaUvaBKHXIetyooUjIiJTmKiJ7FRx3q3S7m5TFBKKc29ZNiAiMomJmshOOal9DN3d5egFnNx9LBsQEZnERE1kp7xbd4GkUJq8JimU8G7VxcIREZEpTNREdsrRxR1B/SZDUjgAkACFAoAESeGAoH6TOZCMSCY46pvIjnm2aIvwx5Yg63IiinNvwcndB96tujBJE8kIEzWRnXN0cYdv+77WDoOIKsCubyIiIhljoiYiIpIxdn3buZz8Avx0Lhm3cvLh4+GGbuFB8HBzsXZYRET0FyZqO3b6yh/4aNdh6PR6w97UuxJ+wdSHeqF9SHNrh0dERGDXt93KyS/AR7sOQ6vTQwhArxcQAtDq9Pjom8PIyS+wdohERAQmarv107lk6PR6k9d0ej2OnU+2bEBERGQSE7WdupWTD0kyvc6zQpJwU5Nv4YiIiMgUWSXqmJgYdOnSBe7u7vD19cXIkSNx8eLFSt8TGxsLSZKMXs7O3E+5Kj4ebhDC9DrPeiHQyNPNwhEREZEpskrU8fHxmDFjBo4ePYr9+/ejpKQEAwcORH5+5a07Dw8PpKWlGV4pKSkWirjh6hYeBKXC9MevVCjQLTzYwhEREZEpshr1vXfvXqPj2NhY+Pr64sSJE3jggQcqfJ8kSfD396/v8GyKh5sLpj7UCx99UzrqWyFJ0AsBpUKBqQ/1grsreyWIiORAVon6bhqNBgDg41P5dnt5eXkIDAyEXq9Hx44d8dprr6Ft27YmyxYVFaGoqMjovfaqfUhzLJ86HMfOJ+OmJh+NPN3QLTyYSZqISEZkm6j1ej1mzZqFnj17ol27dhWWCw0Nxccff4yIiAhoNBr8+9//Ro8ePXD27FkEBASUKx8TE4Nly5bVZ+gNioebC/p3DrN2GEREVAFZPaO+04wZM3DmzBls27at0nJRUVGYMGECIiMjER0djS+++AJNmjTB2rVrTZZfsGABNBqN4RUfH18f4RMREZmFLFvUzz33HHbt2oVDhw6ZbBVXxtHREffddx8uX75s8rpKpYJKpTIcq9XqOsVqy7i8KBGR9ckqUQsh8Pzzz2Pnzp2Ii4tDcHDNRx7rdDqcPn0aQ4YMqYcI7QeXFyUikgdZdX3PmDED//nPf7B161a4u7sjPT0d6enpKCj4eznLCRMmYMGCBYbjV155Bd999x2uXr2KkydPYvz48UhJScHUqVOt8SNYnYebC7zULnVq+XJ5USKyBytWrECPHj3g6uoKLy8va4dTIVm1qNesWQMA6N27t9H5jRs3YtKkSQCA1NRUKO6Y/5uVlYVp06YhPT0d3t7e6NSpExISEhAeHm6psGVl3rhBNSpvqnu7OsuLcgAaETV0xcXFGD16NKKiorBhwwZrh1MhWSXqilbKulNcXJzR8apVq7Bq1ap6isi2VdS9HdrCz3B8Ny4vSkS2omwGUGxsrHUDqYKsEjVZzp3d28DffyRpdXqcT06HAJcXJSKSAyZqO1VZ97ZeCCgkyWSy5vKiRGQNeXl5yMnJMRzfPYPHlslqMBlZTmW7ZykVEsKC/OGgVECSSo8lCXBQcnlRIrKO6OhoeHp6Gl4xMTEmy82fP7/cRk13vy5cuGDh6OuGLWo7VdXuWffe44fxA7txeVEikoX4+HhERkYajitqTc+ZM8cw+LgiISEhZoys/jFR26lu4UHYlfCL4Rn1ncq6t91dnTm6m4hkQa1Ww8PDo8pyTZo0QZMmTSwQkeWw69tOle2exe5tIrJXqampSEpKQmpqKnQ6HZKSkpCUlCS7zZrYorZj3D2LiOzZ4sWLsWnTJsPxfffdBwD44Ycfyq3nYU1M1HaOu2cRkb2KjY2V/RxqgF3fREREssYWNVWIu2cREVkfEzWZxN2ziIjkgV3fVA53zyIikg8maiqnOrtnERGRZbDr286Zeg5dtrwod88iIrI+Jmo7VtFz6M5tAitdXpS7ZxERWQ67vu1UZc+hE88nQ6Ew/dXg7llERJbFRG2nqtrmskubQC4vSkQkA+z6tlNVPYd2cnDg8qJERDLARG2nqtrmspGnG5cXJSKSAXZ926lu4UFQ8jk0EZHsMVHbKW5zSUTUMLDr245xm0siIvljorZzfA5NRCRv7PomIiKSMSZqIiIiGWOiJiIikjEmaiIiIhljoiYiIpIxJmoiIiIZY6ImIiKSMc6j/sv58+etUm9RiRaXfs+Es6MDHB2UVonB2kq0OhSWaKHLyYTK0f6+kjptEfLSrkDpqIJC6WjtcKxCryuBrqQI6lslUDqojK41bdoUTZs2tVJkdZOWloa0tDRrh9GgWet3s5zY32/FuzRt2hTR0dEYP368tUMhIhOWLFmCpUuXWjuMWlm7di2WLVtm7TAavOjo6Ab7x5o5SKKiLZTsiL3/1ZuXl4fo6GjEx8dDrVZbOxyyAjl/B9iirjk5f5610ZC/A+bARE3IycmBp6cnNBoNPDw8rB0OWQG/A7aFn6dt4WAyIiIiGWOiJiIikjEmaoJKpcKSJUugUqmqLkw2id8B28LP07bwGTUREZGMsUVNREQkY0zUREREMsZETWaVnJwMSZIQGxtr7VCIiGwCE7UVXblyBdOnT0dISAicnZ3h4eGBnj17YvXq1SgoKKi3es+dO4elS5ciOTm53uqojhUrVmD48OHw8/ODJEkNdvUpS5AkqVqvuLi4Otd1+/ZtLF26tEb34mdZM/w8qSbsfglRa9m9ezdGjx4NlUqFCRMmoF27diguLsbhw4fx0ksv4ezZs1i3bl291H3u3DksW7YMvXv3RlBQUL3UUR0vv/wy/P39cd9992Hfvn1Wi6Mh2Lx5s9HxJ598gv3795c7HxYWVue6bt++bVj2snfv3tV6Dz/LmuHnSTXBRG0F165dw2OPPYbAwEAcPHjQaGm8GTNm4PLly9i9e7cVI/ybEAKFhYVwcXEx+72vXbuGoKAg3LhxA02aNDH7/W3J3WvRHz16FPv375fNGvX8LGuGnyfVBLu+reCNN95AXl4eNmzYYHL92latWuGFF14wHGu1WixfvhwtW7aESqVCUFAQFi5ciKKiIqP3BQUFYdiwYTh8+DC6du0KZ2dnhISE4JNPPjGUiY2NxejRowEAffr0KdfFVnaPffv2oXPnznBxccHatWsBAFevXsXo0aPh4+MDV1dXdO/evU5/UFizNW+L9Ho93nnnHbRt2xbOzs7w8/PD9OnTkZWVZVTu+PHjGDRoEBo3bgwXFxcEBwfjySefBFA6xqDsF/OyZcsM34+quj75WZofP08qwxa1FXzzzTcICQlBjx49qlV+6tSp2LRpE0aNGoU5c+bgp59+QkxMDM6fP4+dO3calb18+TJGjRqFKVOmYOLEifj4448xadIkdOrUCW3btsUDDzyAmTNn4t1338XChQsNXWt3drFdvHgRY8eOxfTp0zFt2jSEhoYiIyMDPXr0wO3btzFz5kw0atQImzZtwvDhw7Fjxw48/PDD5vsPRLUyffp0xMbGYvLkyZg5cyauXbuG999/H6dOncKPP/4IR0dHZGZmYuDAgWjSpAnmz58PLy8vJCcn44svvgAANGnSBGvWrMEzzzyDhx9+GP/4xz8AABEREdb80ewSP08yEGRRGo1GABAjRoyoVvmkpCQBQEydOtXo/IsvvigAiIMHDxrOBQYGCgDi0KFDhnOZmZlCpVKJOXPmGM5t375dABA//PBDufrK7rF3716j87NmzRIAxP/+9z/DudzcXBEcHCyCgoKETqcTQghx7do1AUBs3LixWj+fEEJcv35dABBLliyp9nvs3YwZM8Sd//f93//+JwCILVu2GJXbu3ev0fmdO3cKACIxMbHCe9fl8+BnWTv8PKky7Pq2sJycHACAu7t7tcrv2bMHADB79myj83PmzAGAcl3P4eHhuP/++w3HTZo0QWhoKK5evVrtGIODgzFo0KBycXTt2hW9evUynFOr1XjqqaeQnJyMc+fOVfv+ZH7bt2+Hp6cnBgwYgBs3bhhenTp1glqtxg8//AAA8PLyAgDs2rULJSUlVoyYKsPPk+7ERG1hZVvO5ebmVqt8SkoKFAoFWrVqZXTe398fXl5eSElJMTrfokWLcvfw9vYu91yrMsHBwSbjCA0NLXe+rMv87jjIsi5dugSNRgNfX180adLE6JWXl4fMzEwAQHR0NB555BEsW7YMjRs3xogRI7Bx48Zy4x3Iuvh50p34jNrCPDw80KxZM5w5c6ZG75MkqVrllEqlyfOiBku618cIb6pfer0evr6+2LJli8nrZQOKJEnCjh07cPToUXzzzTfYt28fnnzySbz11ls4evQo1Gq1JcOmCvDzpDsxUVvBsGHDsG7dOhw5cgRRUVGVlg0MDIRer8elS5eMBnxlZGQgOzsbgYGBNa6/ukn/7jguXrxY7vyFCxcM18l6WrZsie+//x49e/as1h9a3bt3R/fu3bFixQps3boV48aNw7Zt2zB16tRafT/IvPh50p3Y9W0Fc+fOhZubG6ZOnYqMjIxy169cuYLVq1cDAIYMGQIAeOedd4zKvP322wCAoUOH1rh+Nzc3AEB2dna13zNkyBAcO3YMR44cMZzLz8/HunXrEBQUhPDw8BrHQebz6KOPQqfTYfny5eWuabVaw2edlZVVrnclMjISAAzdpa6urgBq9v0g8+LnSXdii9oKWrZsia1bt2LMmDEICwszWpksISEB27dvx6RJkwAAHTp0wMSJE7Fu3TpkZ2cjOjoax44dw6ZNmzBy5Ej06dOnxvVHRkZCqVRi5cqV0Gg0UKlU6Nu3L3x9fSt8z/z58/Hpp59i8ODBmDlzJnx8fLBp0yZcu3YNn3/+ORSKmv/Nt3nzZqSkpOD27dsAgEOHDuHVV18FADzxxBNspddAdHQ0pk+fjpiYGCQlJWHgwIFwdHTEpUuXsH37dqxevRqjRo3Cpk2b8MEHH+Dhhx9Gy5YtkZubi/Xr18PDw8PwR6GLiwvCw8Px2Wef4d5774WPjw/atWuHdu3aVVg/P0vz4udJRqw86tyu/frrr2LatGkiKChIODk5CXd3d9GzZ0/x3nvvicLCQkO5kpISsWzZMhEcHCwcHR3FPffcIxYsWGBURojSqVVDhw4tV090dLSIjo42Ord+/XoREhIilEql0VStiu4hhBBXrlwRo0aNEl5eXsLZ2Vl07dpV7Nq1y6hMTaZnRUdHCwAmX6amjtHf7p7OU2bdunWiU6dOwsXFRbi7u4v27duLuXPnij///FMIIcTJkyfF2LFjRYsWLYRKpRK+vr5i2LBh4vjx40b3SUhIEJ06dRJOTk7Vmp7Dz7Ju+HlSZSQhajDKiIiIiCyKz6iJiIhkjImaiIhIxpioiYiIZIyJmoiISMaYqImIiGSMiZqIiEjGmKhl7I033kCbNm2g1+utHUqdzZ8/H926dbN2GA0OvwNkSnJyMiRJQmxsrLVDIQtgopapnJwcrFy5EvPmzTOs+iVJEiRJwltvvVWufGxsLCRJwvHjx+tc9xdffIExY8YgJCQErq6uCA0NxZw5cypcgvDrr79Gx44d4ezsjBYtWmDJkiXQarVGZWbNmoWff/4ZX3/9dZ3jsxf8DhARAK5MJlerVq0SHh4eoqCgwHAOf60M5OfnJ/Lz843Kb9y4scoN5KurUaNGon379mLRokVi/fr1YubMmcLJyUm0adNG3L5926jsnj17hCRJok+fPmLdunXi+eefFwqFQjz99NPl7vvoo4+K+++/v87x2Qt+B6gier1eFBQUCK1Wa+1QyAKYqGUqIiJCjB8/3ugcABEZGSkAiLfeesvomjl/SZtaInDTpk0CgFi/fr3R+fDwcNGhQwdRUlJiOPevf/1LSJIkzp8/b1R2x44dQpIkceXKlTrHaA/4HSAiIYRg17cMXbt2Db/88gv69+9f7lrPnj3Rt29fvPHGGygoKKiX+nv37l3u3MMPPwwAOH/+vOHcuXPncO7cOTz11FNwcPh7f5dnn30WQgjs2LHD6B5lP89XX31VD1HbFn4HbN/SpUshSRJ+/fVXjB8/Hp6enmjSpAkWLVoEIQR+++03jBgxAh4eHvD39zd63GHqGfWkSZOgVqvxxx9/YOTIkVCr1WjSpAlefPFF6HQ6Q7m4uDhIkoS4uDijeEzdMz09HZMnT0ZAQABUKhWaNm2KESNGIDk5uZ7+q5ApTNQylJCQAADo2LGjyetLly5FRkYG1qxZU+l9ioqKcOPGjWq9qpKeng4AaNy4seHcqVOnAACdO3c2KtusWTMEBAQYrpfx9PREy5Yt8eOPP1ZZn73jd8B+jBkzBnq9Hq+//jq6deuGV199Fe+88w4GDBiA5s2bY+XKlWjVqhVefPFFHDp0qNJ76XQ6DBo0CI0aNcK///1vREdH46233sK6detqFdsjjzyCnTt3YvLkyfjggw8wc+ZM5ObmIjU1tVb3o9rhNpcydOHCBQBAcHCwyev3338/+vTpgzfffBPPPPNMhRvLf/rpp5g8eXK16hRV7M2ycuVKKJVKjBo1ynAuLS0NANC0adNy5Zs2bYo///yz3PmQkBCcO3euWjHZM34H7EfXrl2xdu1aAMBTTz2FoKAgzJkzBzExMZg3bx4AYOzYsWjWrBk+/vhjPPDAAxXeq7CwEGPGjMGiRYsAAE8//TQ6duyIDRs24JlnnqlRXNnZ2UhISMCbb76JF1980XB+wYIFNf0RqY6YqGXo5s2bcHBwgFqtrrDM0qVLER0djQ8//BD//Oc/TZYZNGgQ9u/fX+d4tm7dig0bNmDu3Llo3bq14XxZt6tKpSr3HmdnZ+Tk5JQ77+3tXa6VReXxO2A/pk6davjfSqUSnTt3xu+//44pU6YYznt5eSE0NBRXr16t8n5PP/200fH999+PzZs31zguFxcXODk5IS4uDlOmTIG3t3eN70HmwUTdQD3wwAPo06cP3njjjXL/xyzTtGlTky2dmvjf//6HKVOmYNCgQVixYoXRtbJWXFFRUbn3FRYWmmzlCSEgSVKdYqJS/A7YhhYtWhgde3p6wtnZ2egRQ9n5mzdvVnovZ2dnNGnSxOict7c3srKyahyXSqXCypUrMWfOHPj5+aF79+4YNmwYJkyYAH9//xrfj2qPz6hlqFGjRtBqtcjNza203JIlS5Cenm7oNrtbQUEB0tPTq/Uy5eeff8bw4cPRrl077Nixw2iwEPB3d2dZ9+ed0tLS0KxZs3Lns7Kyyv0CovL4HbAfSqWyWueAqh9PVPS+O1X0R9KdA87KzJo1C7/++itiYmLg7OyMRYsWISwsjD0iFsZELUNt2rQBUDrytzLR0dHo3bs3Vq5caXL072effWZoUVX1utuVK1fw4IMPwtfXF3v27DHZBRsZGQkA5RbY+PPPP/H7778brt/p2rVrCAsLq/TnIn4HqP6UdWHfvXhNSkqKyfItW7bEnDlz8N133+HMmTMoLi42ueAO1R92fctQVFQUgNJffhEREZWWXbp0KXr37m1yVGdtn0+mp6dj4MCBUCgU2LdvX7mutDJt27ZFmzZtsG7dOkyfPt3w1/yaNWsgSZLRoCMA0Gg0uHLlSo0HtdgjfgeovgQGBkKpVOLQoUMYOXKk4fwHH3xgVO727dtQKBRwdnY2nGvZsiXc3d1NPuqg+sNELUMhISFo164dvv/+ezz55JOVlo2OjkZ0dDTi4+PLXavt88kHH3wQV69exdy5c3H48GEcPnzYcM3Pzw8DBgwwHL/55psYPnw4Bg4ciMceewxnzpzB+++/j6lTp5ZrNX3//fcQQmDEiBE1jsne8DtA9cXT0xOjR4/Ge++9B0mS0LJlS+zatQuZmZlG5X799Vf069cPjz76KMLDw+Hg4ICdO3ciIyMDjz32mJWit1PWWmmFKvf2228LtVpttFwjADFjxoxyZX/44QfD0pLmWJWq7F6mXtHR0eXK79y5U0RGRgqVSiUCAgLEyy+/LIqLi8uVGzNmjOjVq1ed47MX/A7YtiVLlggA4vr160bnJ06cKNzc3MqVj46OFm3bthVCCHHt2jUBQGzcuLHK95XVc6fr16+LRx55RLi6ugpvb28xffp0cebMGaN73rhxQ8yYMUO0adNGuLm5CU9PT9GtWzfx3//+t44/OdWUJEQVoxPIKjQaDUJCQvDGG28YTdNoqNLT0xEcHIxt27axNVVN/A4QEcDBZLLl6emJuXPn4s0337SJLQ7feecdtG/fnr+ga4DfASICALaoiYiIZIwtaiIiIhljoiYiIpIxJmoiIiIZY6ImIiKSMSZqIiKqUHJyMiRJQmxsrLVDsVtM1EREZnLlyhVMnz4dISEhcHZ2hoeHB3r27InVq1ebXIvdXM6dO4elS5ciOTm53uqojhUrVmD48OHw8/ODJElYunSpVeOxFVxClIjIDHbv3o3Ro0dDpVJhwoQJaNeuHYqLi3H48GG89NJLOHv2rMn12M3h3LlzWLZsGXr37o2goKB6qaM6Xn75Zfj7++O+++7Dvn37rBaHrWGiJiKqo2vXruGxxx5DYGAgDh48aLS++owZM3D58mXs3r3bihH+TQhR4V7hdXXt2jUEBQXhxo0bFW7kQjXHrm8iojp64403kJeXhw0bNpjcBKVVq1Z44YUXDMdarRbLly9Hy5YtoVKpEBQUhIULF5bblSooKAjDhg3D4cOH0bVrVzg7OyMkJASffPKJoUxsbCxGjx4NAOjTpw8kSYIkSYiLizO6x759+9C5c2e4uLgY9i+/evUqRo8eDR8fH7i6uqJ79+51+oPCmq15W8ZETURUR9988w1CQkLQo0ePapWfOnUqFi9ejI4dO2LVqlWIjo5GTEyMyV2pLl++jFGjRmHAgAF466234O3tjUmTJuHs2bMAgAceeAAzZ84EACxcuBCbN2/G5s2bjXYuu3jxIsaOHYsBAwZg9erViIyMREZGBnr06IF9+/bh2WefxYoVK1BYWIjhw4dj586dZvivQmZj1S1BiIgaOI1GIwCIESNGVKt8UlKSACCmTp1qdP7FF18UAMTBgwcN5wIDAwUAcejQIcO5zMxMoVKpxJw5cwzntm/fLgCIH374oVx9ZffYu3ev0flZs2YJAOJ///uf4Vxubq4IDg4WQUFBQqfTCSFM79RVlevXrwsAYsmSJdV+D1WMLWoiojrIyckBALi7u1er/J49ewAAs2fPNjo/Z84cACjX9RweHo7777/fcNykSROEhobi6tWr1Y4xODgYgwYNKhdH165d0atXL8M5tVqNp556CsnJyTh37ly170/1i4maiKgOPDw8AAC5ubnVKp+SkgKFQoFWrVoZnff394eXlxdSUlKMzrdo0aLcPby9vZGVlVXtGIODg03GERoaWu58WZf53XGQ9TBRExHVgYeHB5o1a4YzZ87U6H2SJFWrnFKpNHle1GDjw/oY4U2Ww0RNRFRHw4YNw5UrV3DkyJEqywYGBkKv1+PSpUtG5zMyMpCdnY3AwMAa11/dpH93HBcvXix3/sKFC4brJA9M1EREdTR37ly4ublh6tSpyMjIKHf9ypUrWL16NQBgyJAhAIB33nnHqMzbb78NABg6dGiN63dzcwMAZGdnV/s9Q4YMwbFjx4z+uMjPz8e6desQFBSE8PDwGsdB9YMLnhAR1VHLli2xdetWjBkzBmFhYUYrkyUkJGD79u2YNGkSAKBDhw6YOHEi1q1bh+zsbERHR+PYsWPYtGkTRo4ciT59+tS4/sjISCiVSqxcuRIajQYqlQp9+/aFr69vhe+ZP38+Pv30UwwePBgzZ86Ej48PNm3ahGvXruHzzz+HQlHzdtzmzZuRkpKC27dvAwAOHTqEV199FQDwxBNPsJVeW9Yedk5EZCt+/fVXMW3aNBEUFCScnJyEu7u76Nmzp3jvvfdEYWGhoVxJSYlYtmyZCA4OFo6OjuKee+4RCxYsMCojROnUqqFDh5arJzo6WkRHRxudW79+vQgJCRFKpdJoqlZF9xBCiCtXrohRo0YJLy8v4ezsLLp27Sp27dplVKYm07Oio6MFAJMvU1PHqHokIWowIoGIiIgsis+oiYiIZIyJmoiISMaYqImIiGSMiZqIiEjGmKiJiIhkjImaiIhIxpioiYiIZIyJmoiISMaYqImIiGSMiZqIiEjGmKiJiIhkjImaiIhIxpioiYiIZOz/AdILi0M0uBynAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired.hedges_g.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "70030bd6", - "metadata": {}, - "source": [ - "Instead of a Gardner-Altman plot, you can generate a **Cumming estimation\n", - "plot** by setting ``float_contrast=False`` in the ``.plot()`` method.\n", - "This will plot the bootstrap effect sizes below the raw data, and also\n", - "displays the the mean (gap) and ± standard deviation of each group\n", - "(vertical ends) as gapped lines. This design was inspired by Edward\n", - "Tufte's dictum to maximise the data-ink ratio." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dfaa2a76", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired.hedges_g.plot(float_contrast=False);" - ] - }, - { - "cell_type": "markdown", - "id": "9ca13ba8", - "metadata": {}, - "source": [ - "The confidence interval shown on the contrast axis is a BCa confidence interval by default.\n", - "This can be modified using the `ci_type` parameter in the `.plot()` method, whereby you can \n", - "select between `bca` and `pct` (percentile)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f04a831f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired.mean_diff.plot(ci_type='pct');" - ] - }, - { - "cell_type": "markdown", - "id": "b0e04dec", - "metadata": {}, - "source": [ - "### Using long (aka 'melted') data frames" - ] - }, - { - "cell_type": "markdown", - "id": "122503d9", - "metadata": {}, - "source": [ - "``dabest`` can also handle 'melted' or 'long' data. This term is used because each row now corresponds to a single data point, with one column carrying the value and other columns containing 'metadata'\n", - "describing that data point.\n", - "\n", - "For more details on wide vs long or 'melted' data, refer to this\n", - "[Wikipedia article](https://en.wikipedia.org/wiki/Wide_and_narrow_data). The\n", - "[pandas documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html)\n", - "provides recipes for melting dataframes.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c339b076", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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GenderIDgroupmetric
0Female1Control 12.793984
1Female2Control 13.236759
2Female3Control 13.019149
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" - ], - "text/plain": [ - " Gender ID group metric\n", - "0 Female 1 Control 1 2.793984\n", - "1 Female 2 Control 1 3.236759\n", - "2 Female 3 Control 1 3.019149\n", - "3 Female 4 Control 1 2.804638\n", - "4 Female 5 Control 1 2.858019" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x='group'\n", - "y='metric'\n", - "\n", - "value_cols = df.columns[:-2] # select all but the \"Gender\" and \"ID\" columns.\n", - "\n", - "df_melted = pd.melt(df.reset_index(),\n", - " id_vars=[\"Gender\", \"ID\"],\n", - " value_vars=value_cols,\n", - " value_name=y,\n", - " var_name=x)\n", - "\n", - "df_melted.head() # Gives the first five rows of `df_melted`." - ] - }, - { - "cell_type": "markdown", - "id": "5899e787", - "metadata": {}, - "source": [ - "When your data is in this format, you need to specify the ``x`` and\n", - "``y`` columns in ``dabest.load()``.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c786fe1b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:04 2025.\n", - "\n", - "Effect size(s) with 95% confidence intervals will be computed for:\n", - "1. Test 1 minus Control 1\n", - "\n", - "5000 resamples will be used to generate the effect size bootstraps." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "analysis_of_long_df = dabest.load(df_melted, idx=(\"Control 1\", \"Test 1\"),\n", - " x=\"group\", y=\"metric\")\n", - "\n", - "analysis_of_long_df" - ] - }, - { - "cell_type": "markdown", - "id": "e38e0455", - "metadata": {}, - "source": [ - "## Dabest estimation plot designs" - ] - }, - { - "cell_type": "markdown", - "id": "0ac037ad", - "metadata": {}, - "source": [ - "The ``dabest`` package implements a range of estimation plot\n", - "designs aimed at depicting common experimental designs:\n", - "\n", - "1. [Two-Group](02-two_group.html)\n", - " \n", - "2. [Shared Control (Unpaired) and Repeated Measures (Paired)](03-shared_control_and_repeated_measures.html)\n", - " \n", - "3. [Proportion Plots](04-proportion_plot.html)\n", - " \n", - "4. [Mini-Meta](05-mini_meta.html)\n", - " \n", - "5. [Delta-Delta](06-delta_delta.html)\n", - " \n", - "6. [Forest Plots](09-forest_plot.html)\n", - " \n", - "In addition, as of Dabest **v2025.03.27**, we introduce a new plotting orientation: **[Horizontal Plots](07-horizontal_plot.html)**. \n", - "\n", - "Lastly, we have a whole tutorial page for making [aesthetic changes to dabest plots](08-plot_aesthetics.html).\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/tutorials/02-two_group.ipynb b/nbs/tutorials/02-two_group.ipynb deleted file mode 100644 index 89660520..00000000 --- a/nbs/tutorials/02-two_group.ipynb +++ /dev/null @@ -1,683 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a1cdbcc7", - "metadata": {}, - "source": [ - "# Two-Group Experiments\n", - "\n", - "> Explanation of how to use dabest for two-group and multi two-group analysis.\n", - "\n", - "- order: 2" - ] - }, - { - "cell_type": "markdown", - "id": "792fd7f1", - "metadata": {}, - "source": [ - "## Load libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bf339009", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pre-compiling numba functions for DABEST...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 41.28it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Numba compilation complete!\n", - "We're using DABEST v2025.10.20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import dabest\n", - "\n", - "print(\"We're using DABEST v{}\".format(dabest.__version__))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4add02e", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)" - ] - }, - { - "cell_type": "markdown", - "id": "0328684f", - "metadata": {}, - "source": [ - "## Creating a demo dataset" - ] - }, - { - "cell_type": "markdown", - "id": "ee9ab56d", - "metadata": {}, - "source": [ - "Here, we create a dataset to illustrate how to perform Two-Group analyses using dabest." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3093c1ea", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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02.7939843.4208753.3246611.7074673.8169401.7965814.4400502.9372843.486127Female1
13.2367593.4679723.6851861.1218463.7503583.9445663.7234942.8370622.338094Female2
23.0191494.3771795.6168913.3013812.9453972.8321883.2140143.1119503.270897Female3
32.8046384.5647802.7731522.5340183.5751793.0482674.9682783.7433783.151188Female4
42.8580193.2200582.5503612.7963653.6921383.2765752.6621042.9773412.328601Female5
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" - ], - "text/plain": [ - " Control 1 Test 1 Control 2 Test 2 Control 3 Test 3 Test 4 \\\n", - "0 2.793984 3.420875 3.324661 1.707467 3.816940 1.796581 4.440050 \n", - "1 3.236759 3.467972 3.685186 1.121846 3.750358 3.944566 3.723494 \n", - "2 3.019149 4.377179 5.616891 3.301381 2.945397 2.832188 3.214014 \n", - "3 2.804638 4.564780 2.773152 2.534018 3.575179 3.048267 4.968278 \n", - "4 2.858019 3.220058 2.550361 2.796365 3.692138 3.276575 2.662104 \n", - "\n", - " Test 5 Test 6 Gender ID \n", - "0 2.937284 3.486127 Female 1 \n", - "1 2.837062 2.338094 Female 2 \n", - "2 3.111950 3.270897 Female 3 \n", - "3 3.743378 3.151188 Female 4 \n", - "4 2.977341 2.328601 Female 5 " - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from scipy.stats import norm # Used in generation of populations.\n", - "\n", - "np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n", - "\n", - "Ns = 20 # The number of samples taken from each population\n", - "\n", - "# Create samples\n", - "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n", - "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", - "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", - "\n", - "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n", - "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n", - "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n", - "t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", - "t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", - "t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", - "\n", - "\n", - "# Add a `gender` column for coloring the data.\n", - "females = np.repeat('Female', Ns/2).tolist()\n", - "males = np.repeat('Male', Ns/2).tolist()\n", - "gender = females + males\n", - "\n", - "# Add an `id` column for paired data plotting.\n", - "id_col = pd.Series(range(1, Ns+1))\n", - "\n", - "# Combine samples and gender into a DataFrame.\n", - "df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n", - " 'Control 2' : c2, 'Test 2' : t2,\n", - " 'Control 3' : c3, 'Test 3' : t3,\n", - " 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6,\n", - " 'Gender' : gender, 'ID' : id_col\n", - " })\n", - "df.head(5)" - ] - }, - { - "cell_type": "markdown", - "id": "ab098bed", - "metadata": {}, - "source": [ - "## Loading data" - ] - }, - { - "cell_type": "markdown", - "id": "80318f9c", - "metadata": {}, - "source": [ - "First, we need to load the data and specify the relevant groups. \n", - "\n", - "We can achieve this by supplying the dataframe to `dabest.load()`. Additionally, we must provide the groups to be compared in the `idx` argument as a tuple or list.\n", - "\n", - "For this tutorial, we will create two separate analyses: \n", - "\n", - "- A singular two-group comparison between Control 1 and Test 1.\n", - " \n", - "- A multi two-group comparison between Control 1 and Test 1, and between Control 2 and Test 2. \n", - " \n", - "The **multi two-group estimation plot** tiles two or more Cumming plots\n", - "horizontally, and is created by passing a *nested tuple* to ``idx`` when\n", - "``dabest.load()`` is first invoked." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "de97031d", - "metadata": {}, - "outputs": [], - "source": [ - "two_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"))\n", - "multi_two_groups_unpaired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\")))" - ] - }, - { - "cell_type": "markdown", - "id": "b6252cfc", - "metadata": {}, - "source": [ - "In addition, we can specify the `paired` argument to indicate paired data.\n", - "\n", - " `paired` can be set as `'baseline'` or `'sequential'` or left as `None` (unpaired). \n", - " \n", - " **Note: For two-group, both `'baseline'` and `'sequential'` are equivalent.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b84cb874", - "metadata": {}, - "outputs": [], - "source": [ - "two_groups_paired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), paired='baseline', id_col='ID')\n", - "multi_two_groups_paired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\")), \n", - " paired='baseline', id_col='ID')" - ] - }, - { - "cell_type": "markdown", - "id": "3fa1ced9", - "metadata": {}, - "source": [ - "The **dabest** library features a range of effect sizes. In this case, we shall proceed with the default effect size, which is the mean difference.\n", - "\n", - "Here we will show the two-group unpaired analysis as an example." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bba3d764", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 15:59:53 2025.\n", - "\n", - "The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].\n", - "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "two_groups_unpaired.mean_diff" - ] - }, - { - "cell_type": "markdown", - "id": "ede08ee8", - "metadata": {}, - "source": [ - "A dataframe of the mean_diff results can be extracted by calling the `results` attribute of the `dabest.mean_diff` object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "262835eb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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controltestcontrol_Ntest_Neffect_sizeis_paireddifferencecibca_lowbca_high...pvalue_mann_whitneystatistic_mann_whitneybec_differencebec_bootstrapsbec_bca_interval_idxbec_bca_lowbec_bca_highbec_pct_interval_idxbec_pct_lowbec_pct_high
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" - ], - "text/plain": [ - " control test control_N test_N effect_size is_paired \\\n", - "0 Control 1 Test 1 20 20 mean difference None \n", - "\n", - " difference ci bca_low bca_high ... pvalue_mann_whitney \\\n", - "0 0.48029 95 0.205161 0.773647 ... 0.001625 \n", - "\n", - " statistic_mann_whitney bec_difference \\\n", - "0 83.0 0.0 \n", - "\n", - " bec_bootstraps bec_bca_interval_idx \\\n", - "0 [-0.09732932551566487, 0.08087009665445155, -0... (127, 4877) \n", - "\n", - " bec_bca_low bec_bca_high bec_pct_interval_idx bec_pct_low bec_pct_high \n", - "0 -0.256862 0.259558 (125, 4875) -0.25826 0.25759 \n", - "\n", - "[1 rows x 35 columns]" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "two_groups_unpaired.mean_diff.results" - ] - }, - { - "cell_type": "markdown", - "id": "0ab07696", - "metadata": {}, - "source": [ - "## Producing estimation plots\n", - "\n", - "We can now call the `.plot()` method to generate the estimation plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "90a3b185", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "3714b336", - "metadata": {}, - "source": [ - "For singular two-group comparisons, the plot will display the effect size curve by default to the right of the raw data.\n", - "We term this a **Gardner-Altman plot**.\n", - "\n", - "This can be changed by setting the `float_contrast` argument to `False`. Here, the effect size curve will be displayed below the raw data - a **Cumming estimation plot**.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "59ca18fb", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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XpjEnPUUolBjT0tI0HQchjV51r3Jdb4Io1MvLmEI9wY016SlCocTo7u6u6TgIMXhiUUXVmEAVdQhbJfn/ogr5E6HwBebwHvExbh/4osZjsPeIj8E3EwJAvWVqu74iOB4fPL6pyucbAr3qfCHEUIlFFSh8eAei8lKN34tnKkTrobORl/4nKopyYWppB1uPjuCZCpGfcU2hMpXPi5CXloqK4jyYWtjC1tMfJkLFxvTyzcxh1ayNUSdHlRNjVlYWtm7dipSUFOTn50Mslm7T4DgOiYmJDQ6QEEPAxCKIykvBMzHRSsIwEZjDpWOwSmUK/r6BjFP/V1W75TiAMTy+ehLufd+BtVvdIzrEogqIykurzqXEKO2vv/5C3759UVpaCm9vb1y9ehU+Pj7Iy8vDw4cP4eXlBTc3N3XHSjQkt7AYxy7dRNazArjYW2Ng13aws7LQdVgGicc3Bc/ETKVzbx9cg8qSApg0sYb38I/UHFmVitLC/yXF/w3n+d9idkxciYxT/wefsZEwNbeq8xriRrA0skqJcd68ebC0tERqaiqaNGkCJycnrFu3Dv3798eePXswbdo07Ny5U92xEg1IuvYAy+IOo1IkBo/jIGYMsb8mYfHEIejRvqWuw2tUKksKUFGSr9F75N69VGs7KBOLkHvvEpw6yH/ltzFRaRKJ33//HREREWjRooVkwHX1o/SoUaMwfvx4fPLJJ+qLksj1nzXxCF3yHf6zJl6l83MLi7Es7jAqKkVgjEEkFoMxhopKEZbGHkZuYbGaIybaUFFaiJy/TuCf83uR89cJVJQWSo6VFz0DeLVM5szjUF74TEtR6jeV35V2dq4aL2Vraws+n49nz178gXbo0KHewdqk4XILS/AkX/X5JI9duolKkfzxbpUiMY5fvoVR/QJUvj7RvvzMa0hPjK2qFfI4QMzwKPkIPAZMgk2L9jCztAfETP7JYgYzK3vtBqynVKoxenp6SsY28ng8eHp64vjx45Lj58+fl6w3TfRX1rMC8GpZCoLHcXj0VLOPdUS9KkoL/5cUKwEwQCwGwMDElUhP3I6K0kLYte4KjseXez7H48OuVVetxqyvFE6MubkvZvcdNGgQ9uzZI/k8bdo0fPfddwgODsaAAQMQFxeHcePGqTdSorLcwmL8eOIyvt57Aj+euCx5RHaxt4aYya89iBmDa1MbbYZJGkiR9kNTcyt4DJgEjmcCgAN4PFS/O+0xYFK9HS+NhcKP0i4uLhg8eDDGjx+Pjz76CKGhoaioqICpqSlmz56N4uJi/PTTT+Dz+Vi0aBEWLFigybiJgurqXBnYtR1if01CRWXNv0wmfB6Cu7TTQcREERWlhci9ewnlRc9gZmkPu9ZdX7QfyntUfqn90KZFe/iMjUTuvUsoL3wGMyt72LXqSknxJQonxpEjR+Lnn3/Gzz//DCsrK7z11lsYP348+vfvD47jsHDhQixcuFCTsRIlvdy5AgCi/9UOqztXdi5+D4snDsHSWOnEacLnYfHEIbCzaqLL8EktamtHtPXqrHD7oam5FfU+10HhxLhz506UlpbiwIEDiI+Px86dOxEXFwdnZ2eEhoZi/Pjx6Ny5syZjJUpStHNl5+L3cPzyLTx6mg/XpjYI7tKOkqKekm5HhCQRMnElcu8lg+Px5D5OU/uhcpTqfDE3N0doaCh++eUXZGVlYcOGDWjdujXWrl2Lrl27om3btli+fDkePHigqXiJEhTtXLGzssCofgGYObI/RvULoKSox+pqRwQTw84rgNoP1UDlxbDs7OwQERGB06dPIzMzEytXrkSTJk2wePFitG7dGj179lRnnEQF1LlifOobh8gzMYPP2Ei80m0oHLx74pVuQ+EzNhI2LeSvy07kU8sqgc2aNcMnn3yCuLg4DB8+HIwx/PHHH+q4NGmAgV3bwYQv/yemzhXDpMg4xOr2w+Y9R8KpQ3+qKaqgwYmxurbYsWNH+Pv74+DBg+jZsyfWr1+vjvhIA9hZWWDxxCEwNeGD4zjweTxwHAdTEz51rhgoGoeoHSq9+fLkyRP8+OOPiI+PR1JSEhhjaNu2LZYuXYrx48fDw8NDzWESVfVo35I6V4xI9TjE9MTtUr3SHI9P7YhqpHBiLC4uxv79+xEfH4/ExERUVFTA1dUVs2fPph5pPVfduUKMA41D1DyFE6OTkxOeP38OS0tLjBs3TjKG8eVV+wgh2kHjEDVL4cQYHByM8ePHY9iwYRAKhZqMiRBCdErhxHjw4EFNxkEI0QPyXjVsjI/otOZLI0AzdBOg/qRX35RljQklRiNHM3Q3Dg1NenW9apieuF2hJQ+MCfWcGDF1z9Bd2/RlRLfyM6/hxq4o/HvpFzy5fR7/XvoFN3ZFIT/zOgDF5mlUZMqyxoRqjEZMnTN0U81TPylS01Mk6Sk6ZVljQTVGI6auGbppbRj9pVTSk+d/SY+WPJBGidGIKTuJRG2PyorUPIluqCvp0auG0vQqMW7cuBF+fn6wtraGtbU1AgMD8euvv9Z5zp49e9C2bVsIhUJ06NABR44c0VK0+k+ZSSSSrj3A+KXb8N2h33Ek6Rq+O/Q7xi/dhgvXH9DaMHpMXUmPljyQpleJsXnz5li5ciWSk5Nx+fJl9O/fH8OHD8f169fllj9//jxCQ0MxefJkXLlyBSNGjMCIESNw7do1LUeunxSdRKK+R2UbCyFNX6an1Jn0ql81pCnL9KzzZejQoVKfV6xYgY0bN+LChQto377mj7Nu3Tq89tprkjWsly1bhmPHjmH9+vXYtGmTVmLWd4pMIlHfozLAwYTPo7Vh9JCik0oo+n41vWpYRa8S48tEIhH27NmD4uJiBAYGyi2TlJSEOXPmSO0LCQnBgQMHar1uWVkZysrKJJ+LilRfl9lQ1DeJRPWjskhOrZDHccgvLqW1YfSM7LjFNsM/QuHDm5T01ETvEuPVq1cRGBgombBi//798PHxkVs2KysLzs7OUvucnZ2RlZVV6/Wjo6MRFRWl1pgNRW1vwCjSSUPTl+kPeYO1uf8N1qbEpx56lxi9vb2RmpqK/Px87N27FxMmTMDp06drTY7Kmj9/vlQtMzU1FUFBQWq5tj5TxzKqNH2Z7tEbKtqhV50vAGBmZoZWrVohICAA0dHR6NixI9atWye3rIuLC7Kzs6X2ZWdnw8XFpdbrCwQCSa+3tbU1LC0t1Rq/PqqvcwUAzfRtIOgNFe3QuxqjLLFYLNUm+LLAwEAkJiZi9uzZkn3Hjh2rtU2ysaJlVI0HvaGiHXqVGOfPn4/XX38dLVq0QGFhIeLj43Hq1CkkJCQAAMLCwtCsWTNER0cDAGbNmoWgoCCsWbMGQ4YMwa5du3D58mVs2bJFl19D79TXuSK7jCrRX/SGinbo1aN0Tk4OwsLC4O3tjQEDBuDSpUtISEjAwIEDAVQtvPXo0SNJ+Z49eyI+Ph5btmxBx44dsXfvXhw4cAC+vr66+gp6iZZRNR70hop26FWNcevWrXUeP3XqVI19o0aNwqhRozQUkXFQtHOF6D9aDEs79CoxEuVUt//V1w5Y/QaMLsch0mS56kOLYWkeJUYdqagUQSSW3yGiqC9nvKgpPy+vqLOsf2s3bJ0XhpMpt5GdWwBnO2v0D/CGrWUTPC+vQG5hCU6m3EJ2biGc7azQr3NbtSRMPo+Hy7cyaMoyNaPB2ppFiVEHKipFuJ2ZhZKyupOZuhSVPMeVu38jt7AEdlZN0Km1GyybCJGZnYvM7FzczHiEH45dgpiJwXEcGGP4/ugfGDewG9q61z70SREikQjL4o5IHuOrO4CqhwrtXPwe1RyJ3qHEqAMisRglZRUw5fNgaiK/IV1drqX9i7hfkyASv0h6xy/fxMTXA9He8xUUljzHruOXJLVX9r/EJWJi/HD8IhZPHAKrJqqtCllRKcLZP++qbbJcQrSFEqMOmZrwYWaq+k+wamcCCopLYW1hjrnjQ2ocLyguRdyvSZLEVJ30KkVixP6ahGXhw5By5+9aH+lFYjGu3P27QZ0zuYUlCg0VIoBJE2up/yW6Q4nRgBUUlyKvqLTW43/cSK8z6V28mY5nBcWSmqQsHsfhab7is3MXFJfijxtV17S3tkDnNm6ws2pCQ4UU5D38I7Vej5ZCVR0lRiOmSNKzt7aQexyoSlxNbara/2STXncfD1hbmEvKXr3/EN8dOif1yH7o/F8Y8ao/TVmmAbQUqmZRYjRiiiS9bu08cOj8X3LbAfk8Hrr7eNaa9MKH9kaHls1QUFyK7w6dk/vIvv9MKmaO6o/1P52kKcvUhJZC1Ty9evOFqFd3Hw/wefJ/4uqkZ21hjvChvWHC54HjAD6PA8dV1ebCh/YGY0yS9BgDxGIGxqqS3ne/nJPUJGt7ZBczMfIKS7Bz8XuYMrQ3Bgf6YsrQ3ti5eDIN1VEBLYWqHVRjNGLVSe+7X6pqe9W1NT6vKulV9zZ3aNkMy8KH4eLNdDzNL0ZTGwt09/GEVRMhjl262aB2So7jkJ1bQO9hqwkthaodlBiNXF1JT1Z1XmPsxeNwQ9spGWNwtqNeVnVRJOnRRBMNR4mxEbC2MK+zk6OuNsSGtlPyOB76B3ir7bs0dgqtCtiqKx4lH3nRxvgSmmhCMdTG2Mi93HEirw3Rx8OlQe2U4wZ2g60ldbCoCy2Fqh1UY2wE6hpqU99Yx5sZWSq3U3Zq7QY+X7Nv9jQ26l4VkMhHidHI1TfURpE2xOAu7RRqp5R9ZC+vqNTa++CNCS2FqnmUGI1YXeMLv/vlHJaFD1N4gHd97ZREuyjpaRa1MRoxRV4JVGSsIyGNDSVGI1b9mCxP9WNyfQO8VZ1ZhxBDRo/SRkzRx2RlxjoS/UATRGgWJUYj1t2n/vegq1EbouGgCSI0jx6ljRg9JhsfRd6VJg1HNUYjR4/JxkWRd6Wpt7rhKDE2AvSYbDxoggjtoEdpQgwITRChHZQYCTEgirwrTRqOEiMhBoQmiNAOamNsBOpbr4UYFpogQvMoMRq5+iaRIIaJ3pXWLHqUNmL1zbVYUFz70quENGZ6lRijo6PRtWtXWFlZwcnJCSNGjMDt27frPCc2NhYcx0ltQiGN0QMUm0SCEFKTXiXG06dPY/r06bhw4QKOHTuGiooKDBo0CMXFdS/6bm1tjUePHkm2jIwMLUWs3xSZRIIQUpNetTEePXpU6nNsbCycnJyQnJyMPn361Hoex3FwcXHRdHgGR9FJJAgh0vSqxigrPz8fAGBvX/eg1aKiIri7u8PNzQ3Dhw/H9evXtRGe3qO5FglRjd4mRrFYjNmzZ6NXr17w9fWttZy3tze2bduGgwcP4v/+7/8gFovRs2dP/PPPP3LLl5WVoaCgQLIVFRVp6ivoHE0iQYhq9OpR+mXTp0/HtWvXcO7cuTrLBQYGIjAwUPK5Z8+eaNeuHTZv3oxly5bVKB8dHY2oqCi1x6uvlJ1EgsY8EqKnifGDDz7AoUOHcObMGTRv3lypc01NTdGpUyfcu3dP7vH58+djzpw5ks+pqakICgpqULz6TtFJJGjMIyFV9OpRmjGGDz74APv378eJEyfg6al8G5hIJMLVq1fh6uoq97hAIIC1tbVks7S0bGjYOmNtYQ5bS/N6a3QFxaU4dukmdidexrFLN+WOX6Qxj4S8oFc1xunTpyM+Ph4HDx6ElZUVsrKyAAA2NjYwN6/6yx8WFoZmzZohOjoaALB06VL06NEDrVq1Ql5eHlavXo2MjAyEh4fr7Htoy9zxIfWWUbQWqMiYR5q6jDQWelVj3LhxI/Lz89G3b1+4urpKtt27d0vKZGZm4tGjR5LPubm5mDJlCtq1a4fBgwejoKAA58+fh4+Pjy6+gl5RphZIYx4JeUGvaoy1jbl72alTp6Q+f/XVV/jqq680FJFhU6YWSGMeCXlBr2qMRL2UqQXSmEdCXqDEaMSUqQXSmEdCXtCrR2miXsosn1pQXIqspwXo4u2OwtIyWJkL4OpgQwtnkUaJEqMRq64FfvdLVa80j+MgZgx8nnQtUF7PNZ/HQ3gbqimSxokSo5Gr782Xl3uugRcdYNU918vCh9GbL6TRoTbGRqK6qZEx6d5/mrORkJqoxmjk6hvgXd1zLa+ThsYvksaKaoxGTJEB3jR+kZCaKDEaMUUek2n8IiE1UWI0YooM8Kbxi4TURG2MRkzRx2Rl52wkxNhRYjRiygzwVnTORkIaA3qUNmL0mEyIaqjGaOToMZkQ5VFibAToMZkQ5dCjNCGEyKDESAghMigxEkKIDGpjbERysrPxOCdba/erqBTheUUlRAU5EJga939qosoyFD26D76pADy+qdbu6+LsCBdnJ63dr7Ew7v9aFeDq6orIyMhal1vVBKGZKbq189Da/QCgrKwMU98dg9OnT2v1vkSzgoKCkJCQAIFAoOtQjArHFFmBihi8goIC2NjY4PTp0wa9ljZ5oaioCEFBQcjPz4e1tbWuwzEqjb7G2Nj4+/vTXyIjUVBQoOsQjBZ1vhBCiAxKjIQQIoMSYyMhEAgQGRlJjfRGhH5TzaHOF0IIkUE1RkIIkUGJkRBCZFBiJIQQGZQYiUrS09PBcRxiY2N1HQohakeJUQvu37+PiIgItGzZEkKhENbW1ujVqxfWrVuH0tJSjd33xo0bWLJkCdLT0zV2D0WsWLECw4YNg7OzMziOw5IlS3Qaj7ZwHKfQdurUqQbfq6SkBEuWLFHqWo31d1EEvfmiYYcPH8aoUaMgEAgQFhYGX19flJeX49y5c/jkk09w/fp1bNmyRSP3vnHjBqKiotC3b194eHho5B6KWLhwIVxcXNCpUyckJCToLA5t27Fjh9Tn77//HseOHauxv127hk8iXFJSgqioKABA3759FTqnsf4uiqDEqEFpaWkYO3Ys3N3dceLECamJKqZPn4579+7h8OHDOozwBcYYnj9/DnNzc7VfOy0tDR4eHnjy5AkcHR3Vfn199c4770h9vnDhAo4dO1Zjv6401t9FEfQorUGff/45ioqKsHXrVrmz97Rq1QqzZs2SfK6srMSyZcvg5eUFgUAADw8PLFiwAGVlZVLneXh44I033sC5c+fQrVs3CIVCtGzZEt9//72kTGxsLEaNGgUA6NevX43HtuprJCQkoEuXLjA3N8fmzZsBAA8ePMCoUaNgb2+PJk2aoEePHg1K4Lqsreo7sViMtWvXon379hAKhXB2dkZERARyc3Olyl2+fBkhISFwcHCAubk5PD098d577wGoau+tTmxRUVGS37q+R2P6XWpHNUYN+uWXX9CyZUv07NlTofLh4eGIi4vDyJEj8dFHH+GPP/5AdHQ0bt68if3790uVvXfvHkaOHInJkydjwoQJ2LZtGyZOnIiAgAC0b98effr0wcyZM/H1119jwYIFkse1lx/bbt++jdDQUERERGDKlCnw9vZGdnY2evbsiZKSEsycORNNmzZFXFwchg0bhr179+LNN99U3x8QQUREBGJjYzFp0iTMnDkTaWlpWL9+Pa5cuYLff/8dpqamyMnJwaBBg+Do6Ih58+bB1tYW6enp2LdvHwDA0dERGzduxLRp0/Dmm2/irbfeAgD4+fnp8qsZNkY0Ij8/nwFgw4cPV6h8amoqA8DCw8Ol9n/88ccMADtx4oRkn7u7OwPAzpw5I9mXk5PDBAIB++ijjyT79uzZwwCwkydP1rhf9TWOHj0qtX/27NkMADt79qxkX2FhIfP09GQeHh5MJBIxxhhLS0tjANj27dsV+n6MMfb48WMGgEVGRip8jjGZPn06e/mv3NmzZxkAtnPnTqlyR48eldq/f/9+BoBdunSp1ms35M+2sf8u8tCjtIZUTwllZWWlUPkjR44AAObMmSO1/6OPPgKAGo+yPj4+ePXVVyWfHR0d4e3tjQcPHigco6enJ0JCQmrE0a1bN/Tu3Vuyz9LSElOnTkV6ejpu3Lih8PVJ3fbs2QMbGxsMHDgQT548kWwBAQGwtLTEyZMnAQC2trYAgEOHDqGiokKHETcelBg1pHrOw8LCQoXKZ2RkgMfjoVWrVlL7XVxcYGtri4yMDKn9LVq0qHENOzu7Gm1TdfH09JQbh7e3d4391Y/gsnEQ1d29exf5+flwcnKCo6Oj1FZUVIScnBwAVbN0v/3224iKioKDgwOGDx+O7du312h7JupDbYwaYm1tjVdeeQXXrl1T6jyO4xQqx+fz5e5nSswJookeaKI4sVgMJycn7Ny5U+7x6g4VjuOwd+9eXLhwAb/88gsSEhLw3nvvYc2aNbhw4QLNyK4BlBg16I033sCWLVuQlJSEwMDAOsu6u7tDLBbj7t27Uh0k2dnZyMvLg7u7u9L3VzTJysZx+/btGvtv3bolOU7Uw8vLC8ePH0evXr0U+keqR48e6NGjB1asWIH4+HiMHz8eu3btQnh4uEq/NakdPUpr0KeffgoLCwuEh4cjO7vm6nz379/HunXrAACDBw8GAKxdu1aqzJdffgkAGDJkiNL3t7CwAADk5eUpfM7gwYNx8eJFJCUlSfYVFxdjy5Yt8PDwgI+Pj9JxEPlGjx4NkUiEZcuW1ThWWVkp+d1yc3NrPAn4+/sDgORxukmTJgCU+61J7ajGqEFeXl6Ij4/HmDFj0K5dO6k3X86fP489e/Zg4sSJAICOHTtiwoQJ2LJlC/Ly8hAUFISLFy8iLi4OI0aMQL9+/ZS+v7+/P/h8PlatWoX8/HwIBAL0798fTk61L7c5b948/PDDD3j99dcxc+ZM2NvbIy4uDmlpafjpp5/A4yn/b+mOHTuQkZGBkpISAMCZM2ewfPlyAMC7777baGuhQUFBiIiIQHR0NFJTUzFo0CCYmpri7t272LNnD9atW4eRI0ciLi4OGzZswJtvvgkvLy8UFhYiJiYG1tbWkn9Qzc3N4ePjg927d6NNmzawt7eHr68vfH19a70//S510HW3eGNw584dNmXKFObh4cHMzMyYlZUV69WrF/vmm2/Y8+fPJeUqKipYVFQU8/T0ZKampszNzY3Nnz9fqgxjVUNthgwZUuM+QUFBLCgoSGpfTEwMa9myJePz+VJDd2q7BmOM3b9/n40cOZLZ2toyoVDIunXrxg4dOiRVRpnhOkFBQQyA3E3eUCJjJTtcp9qWLVtYQEAAMzc3Z1ZWVqxDhw7s008/Zf/++y9jjLGUlBQWGhrKWrRowQQCAXNycmJvvPEGu3z5stR1zp8/zwICApiZmZlCw2/od6kdzeBNCCEyqI2REEJkUGIkhBAZlBgJIUQGJUZCCJFBiZEQQmRQYtQDn3/+Odq2bQuxWKzrUBps3rx56N69u67D0Cn6PY2ArscLNXb5+fnM3t6ebdu2TbIP/xtL9sUXX9Qov3379nqnoFLUTz/9xEaPHs08PT2Zubk5a9OmDZszZw7Lzc2VW/7gwYOsU6dOTCAQMDc3N7Z48WJWUVEhVebRo0dMIBCwgwcPNjg+Q0S/p3GgxKhjX331FbO2tmalpaWSfdV/kZydnVlxcbFUeXX+RWratCnr0KEDW7RoEYuJiWEzZ85kZmZmrG3btqykpESq7JEjRxjHcaxfv35sy5YtbMaMGYzH47H333+/xnVHjx7NXn311QbHZ4jo9zQOlBh1zM/Pj73zzjtS+wAwf39/BoCtWbNG6pg6/yLJe7shLi6OAWAxMTFS+318fFjHjh2lahT//e9/Gcdx7ObNm1Jl9+7dyziOY/fv329wjIaGfk/jQG2MOpSWloa//voLwcHBNY716tUL/fv3x+eff66xJVblrSZXvXTBzZs3Jftu3LiBGzduYOrUqTAxefF6/X/+8x8wxrB3716pa1R/n4MHD2ogav1Fv6fxoMSoQ+fPnwcAdO7cWe7xJUuWIDs7Gxs3bqzzOmVlZVIzQNe11ScrKwsA4ODgINl35coVAECXLl2kyr7yyito3ry55Hg1GxsbeHl54ffff6/3fsaEfk/jQbPr6FD1HIfyZtIGgFdffRX9+vXD6tWrMW3atFrn7Pvhhx8wadIkhe7J6nk1ftWqVeDz+Rg5cqRk36NHjwBA7kqHrq6u+Pfff2vsb9myZaNbBoF+T+NBiVGHnj59ChMTkzpnYF6yZAmCgoKwadMmfPjhh3LLhISE4NixYw2OJz4+Hlu3bsWnn36K1q1bS/ZXP/oJBIIa5wiFQsn6Ni+zs7OrUfMwdvR7Gg9KjHquT58+6NevHz7//HO8//77csu4urrK/ddfGWfPnsXkyZMREhKCFStWSB2rrtnIW2Pk+fPncms+jDGaVVoO+j0NAyVGHWratCkqKytRWFhY52qCkZGR6Nu3LzZv3ixZMe5lpaWlyM/PV+ieLi4uNfb9+eefGDZsGHx9fbF3716pBnngxSPXo0eP4ObmJnXs0aNH6NatW41r5ubmSrVrNQb0exoP6nzRobZt2wKo6s2sS1BQEPr27YtVq1bJ7dHcvXu3pJZR3ybr/v37eO211+Dk5IQjR47IfQysnkb/8uXLUvv//fdf/PPPP5LjL0tLS5Nau6YxoN/TeFCNUYeqF8i6fPky/Pz86iy7ZMkS9O3bF1u2bKlxTNU2qaysLAwaNAg8Hg8JCQmSVelktW/fHm3btsWWLVsQEREhWaFw48aN4DhOqmEfAPLz83H//n1MmzZN6ZgMGf2exoMSow61bNkSvr6+OH78ON577706ywYFBSEoKAinT5+ucUzVNqnXXnsNDx48wKeffopz587h3LlzkmPOzs4YOHCg5PPq1asxbNgwDBo0CGPHjsW1a9ewfv16hIeH16hJHD9+HIwxDB8+XOmYDBn9nkZEd2PLCWOMffnll8zS0lLqlS0AbPr06TXKnjx5UvJ6mTrelADkr/cBoMbaMYwxtn//fubv788EAgFr3rw5W7hwISsvL69RbsyYMax3794Njs8Q0e9pHCgx6lheXh6zt7dn3333na5DUYtHjx4xoVDIDhw4oOtQdIJ+T+NAnS86ZmNjg08//RSrV682immq1q5diw4dOjSux66X0O9pHGiVQEIIkUE1RkIIkUGJkRBCZFBiJIQQGZQYCSFEBiVGQgiRQYmREEJkUGIkhBAZlBgJIUQGJUZCCJFBiZEQQmRQYiSEEBl6lRjPnDmDoUOH4pVXXgHHcThw4EC955w6dQqdO3eGQCBAq1atEBsbq/E4CSHGTa8SY3FxMTp27Ihvv/1WofJpaWkYMmQI+vXrh9TUVMyePRvh4eFISEjQcKSEEGOmt7PrcByH/fv3Y8SIEbWWmTt3Lg4fPoxr165J9o0dOxZ5eXk4evSoQvd59OgRNm/ejIiIiAavzEYIMQ56VWNUVlJSEoKDg6X2hYSEICkpqdZzysrKUFBQINnu3r2LqKgoySLkhBBi0IkxKysLzs7OUvucnZ1RUFAgd/U1AIiOjoaNjY1kCwoK0kaohBADYtCJURXz589Hfn6+ZJO3GBEhpHEz6FUCXVxckJ2dLbUvOzsb1tbWMDc3l3uOQCCAQCCQfJa37i4hpHEz6BpjYGAgEhMTpfYdO3ZMsr4vIYSoQq8SY1FREVJTU5GamgqgajhOamoqMjMzAVQ9BoeFhUnKv//++5J1dG/duoUNGzbgxx9/xIcffqiL8AkhRkKvEuPly5fRqVMndOrUCQAwZ84cdOrUCYsXLwZQNbSmOkkCgKenJw4fPoxjx46hY8eOWLNmDb777juEhIToJH5CiHHQ23GM2pKSkoKAgAAkJyejc+fOug6HEKIH9KrGSAhRUlGOriMwSpQYCTFkxY91HYFRosRIiCETVeg6AqNEiZEQQ8ZEuo7AKFFiJMSQiSkxagIlRkIMmbhS1xEYJUqMhBgySowaQYmREENGnS8aQYmREENGbYwaQYmREEMmKtd1BEaJEiMhhkxUpusIjBIlRkIMWSUlRk2gxEiIIauQv4QHaRhKjIQYsooSXUdglCgxEmLIyop0HYFRosRIiCErK9B1BEaJEiMhhqw0V9cRGCVKjIQYsrJC6pnWAEqMhBg6mqxW7SgxEmLoKDGqHSVGQgxdYbauIzA6lBgJMXRFWbqOwOhQYiTE0BVSYlQ3SoyEGLqCf3UdgdHRu8T47bffwsPDA0KhEN27d8fFixfrLL927Vp4e3vD3Nwcbm5u+PDDD/H8+XMtRUuIHih8pOsIjI5eJcbdu3djzpw5iIyMREpKCjp27IiQkBDk5MhfVDw+Ph7z5s1DZGQkbt68ia1bt2L37t1YsGCBliMnRIeKcmgso5o1KDGWlZUhKSkJBw8exJMnTxoczJdffokpU6Zg0qRJ8PHxwaZNm9CkSRNs27ZNbvnz58+jV69eGDduHDw8PDBo0CCEhobWW8skxOjk/6PrCIyKyonx66+/hqurK3r37o233noLf/31FwDgyZMncHBwqDWZ1aa8vBzJyckIDg5+ERyPh+DgYCQlJck9p2fPnkhOTpYkwgcPHuDIkSMYPHiwit+KEAOVm67rCIyKSolx+/btmD17Nl577TVs3boVjDHJMQcHB/Tv3x+7du1S6ppPnjyBSCSCs7Oz1H5nZ2dkZcnvdRs3bhyWLl2K3r17w9TUFF5eXujbt2+dj9JlZWUoKCiQbEVFNDsJMQLPHug6AqOiUmJcs2YNhg8fjvj4eAwdOrTG8YCAAFy/fr3BwdXn1KlT+Oyzz7BhwwakpKRg3759OHz4MJYtW1brOdHR0bCxsZFsQUFBGo+TEI17lqbrCIyKSonx3r17eP3112s9bm9vj6dPnyp1TQcHB/D5fGRnS4/iz87OhouLi9xzFi1ahHfffRfh4eHo0KED3nzzTXz22WeIjo6GWCyWe878+fORn58v2U6fPq1UnITopSd3dB2BUVEpMdra2tbZ2XLjxo1ak1ltzMzMEBAQgMTERMk+sViMxMREBAYGyj2npKQEPJ70V+Dz+QAg9Xj/MoFAAGtra8lmaWmpVJyE6KXix0CxcpURUjuVEuPgwYOxZcsW5OXl1Th2/fp1xMTEYNiwYUpfd86cOYiJiUFcXBxu3ryJadOmobi4GJMmTQIAhIWFYf78+ZLyQ4cOxcaNG7Fr1y6kpaXh2LFjWLRoEYYOHSpJkIQYqy5duqD59P3o8llK1Y7sa7oNyIiYqHLS8uXL0b17d/j6+mLo0KHgOA5xcXHYtm0bfvrpJ7i6umLx4sVKX3fMmDF4/PgxFi9ejKysLPj7++Po0aOSDpnMzEypGuLChQvBcRwWLlyIhw8fwtHREUOHDsWKFStU+VqEGJSsrCw8fFYKiM3+t+Mq0JLazNWBY7U9c9YjJycHCxYswL59+yQ1RysrK7z99ttYuXIlnJyc1BmnxqSkpCAgIADJycno3LmzrsMhRGHNmzfHw4cP0czWDP+s7AHYewKjYnUdllFQqcYIAE5OTvjuu+/w3Xff4fHjxxCLxXB0dKzR5kcI0ZJnaUDRY8DSUdeRGDy1ZDFHR0c4OztTUiRE1zLO6ToCo6BSJlu4cCH8/f1rPd6pUydERUWpGhMhRFUPTuk6AqOgUmLcu3dvneMYBw8ejN27d6scFCFERY/+rJpUgjSISokxMzMTXl5etR739PRERkaGykERQlTEGHAnQddRGDyVEqOlpWWdiS8tLQ1CoVDloAghDXDrEFDLm19EMSolxr59+2Lz5s14+PBhjWN///03tmzZgn79+jU4OEKICgqzgIzfdR2FQVNpuM6yZcvQrVs3tG/fHpMnT0b79u0BANeuXcO2bdvAGKtzIgdCiIb9uQvw6A1wnK4jMUgqJUZvb2+cPXsWM2bMwFdffSV1rE+fPvj666/Rrl07tQRICFFB9jXgYQrQPEDXkRgklQd4+/n54fTp03jy5AkePKiaC65ly5ZwcHBQW3CEkAa4FAM060y1RhWonBirOTg4UDIkRB/l3ATuJQKtg+svS6SonBhFIhESEhLw4MED5Obm1pjmi+M4LFq0qMEBEkIa4I9NgHtPwKyJriMxKColxsuXL+Ptt9/GP//8U+u8h5QYCdEDxY+By1uBnjN0HYlBUWm4zn/+8x+UlpbiwIEDePbsGcRicY1NJBKpO1ZCiCqu7QNybuk6CoOiUmL866+/MHfuXAwdOhS2trZqDokQolZMDJz5HBBV6joSg6FSYmzevHmtj9CEED309D5wdY+uozAYKiXGuXPnIiYmBgUFBeqOhxCiKSlxVfM1knqp1PlSWFgIS0tLtGrVCmPHjoWbm1uNNVY4jsOHH36oliAJIWpQUVo1trFf7euukyoqLW2gyIS0HMcZRAcMLW1ADFWNpQ0UwXHAyG2AfUvNBmfgVKoxpqXR4t6EGCTGgJQdQHCkriPRayolRnd3d3XHQQjRlgengPxwwKaZriPRWw1apOXhw4f44YcfsG7dOvzzzz8Aqt6IefbsmUE8RhPSKDEx9VDXQ6XEyBjDnDlz4OnpifHjx2POnDm4c+cOAKCoqAgeHh745ptv1BooIUSNbv8KPM/XdRR6S6XEuHr1aqxbtw4ff/wxjh07JjWm0cbGBm+99RZ++ukntQVJCFGzyufA1b26jkJvqZQYY2JiEBYWhs8++0zuaoF+fn6SGiQhRE9d2wc8p7HI8qiUGP/++2/07Nmz1uMWFhYqD/7+9ttv4eHhAaFQiO7du+PixYt1ls/Ly8P06dPh6uoKgUCANm3a4MiRIyrdm5BGpbwISPle11HoJZUSo5OTE/7+++9ajycnJ6NFixZKX3f37t2YM2cOIiMjkZKSgo4dOyIkJAQ5OfKXgywvL8fAgQORnp6OvXv34vbt24iJiUGzZtTbRohCru8HctN1HYXeUSkxvvXWW9i0aZNk5m6gakA3APz222+IjY3FqFGjlL7ul19+iSlTpmDSpEnw8fHBpk2b0KRJE2zbtk1u+W3btuHZs2c4cOAAevXqBQ8PDwQFBaFjx46qfC1CGh9xJXBmNa0qKEOlxBgVFQVXV1f4+/sjLCwMHMdh1apV6N27N15//XX4+flhwQLlXjsqLy9HcnIygoNfzDbM4/EQHByMpKQkuef8/PPPCAwMxPTp0+Hs7AxfX1989tlndQ4VKisrQ0FBgWQrKipSKk5CjE7WNeD6Pl1HoVdUSow2Nja4cOECPv30Uzx8+BBCoRCnT59GXl4eIiMjcfbsWTRpotyMwU+ePIFIJIKzs7PUfmdnZ2RlZck958GDB9i7dy9EIhGOHDmCRYsWYc2aNVi+fHmt94mOjoaNjY1kCwoKUipOQozSH5uBZw/qL9dIKJ0Ynz9/jq+//hqXLl3CwoULkZqaiuLiYpSWluLatWtYvHgxzM3NNRFrDWKxGE5OTtiyZQsCAgIwZswY/Pe//8WmTZtqPWf+/PnIz8+XbKdPn9ZKrIToNVE5kLgUqCzTdSR6QenEKBQKMXfuXNy+fVutgTg4OIDP5yM7O1tqf3Z2NlxcXOSe4+rqijZt2kjN7NOuXTtkZWWhvLxc7jkCgQDW1taSzdLSUn1fghBD9iwNOP+1rqPQCyo9Svv6+iI9PV2tgZiZmSEgIACJiYmSfWKxGImJiQgMDJR7Tq9evXDv3j2IX2o4vnPnDlxdXWFmZqbW+AhpFG4eAu4e03UUOqdSYlyxYgU2b96M48ePqzWYOXPmICYmBnFxcbh58yamTZuG4uJiTJo0CQAQFhaG+fPnS8pPmzYNz549w6xZs3Dnzh0cPnwYn332GaZPn67WuAhpVM58UVV7bMRUml1n/fr1sLe3R0hICDw9PeHp6VmjXZHjOBw8eFCp644ZMwaPHz/G4sWLkZWVBX9/fxw9elTSIZOZmSk1F6SbmxsSEhLw4Ycfws/PD82aNcOsWbMwd+5cVb4WIQSoel3w+BLgzU2AqXb6C/SNShPVenh4SMYt1nphjpMa56ivaKJaYqhUmqhWGd6Dgb6Ns5KhUo1R3e2LhBA9dPsI0LwL0GqAriPRugbNx0gIMXJnv2yUC2ipnBhFIhF27dqFiIgIvPnmm7h69SoAID8/H/v27asx7IYQYoDKi4BzX1UtidCIqJQY8/Ly0KtXL4wbNw4//PADfv75Zzx+XPWviqWlJWbOnIl169apNVBCiI5k/A6kn9N1FFqlUmKcN28erl+/joSEBDx48EBqolo+n4+RI0fS1F+EGJM/NgGiSl1HoTUqJcYDBw5gxowZGDhwoNze6TZt2lAHDSHGJP8f4H5i/eWMhEqJMT8/H56enrUer6ioQGVl4/nXhZBG4ereRtPWqFJi9PLyQkpKSq3Hf/vtN/j4+KgcFCFEDz2502gmtVUpMYaHh2Pbtm3YvXu3pH2R4ziUlZXhv//9L44ePYqIiAi1BkoIeSEzMxPFxcUAgOIyETKfPdfOjTPOa+c+OqbSAO9Zs2bh+vXrCA0Nha2tLQBg3LhxePr0KSorKxEREYHJkyerM05CCICLFy9i2bJlOHz4sKRSklcqgsd/L+KNDvZYNNgdXT2sNBdA9jW1XaqitBAPjm7Cs7t/ABwPDm17omVIBPhm9b+GyBjDjV2RyL2fjHajFqKp94uJZgr/vYP0E7EoenQP4ACrV7zhMWASLJ1bKhybSjVGjuMQExODM2fOICwsDK+//jr8/f0xdepUnDp1Chs3blTlsoSQOuzbtw+9evXCr7/+Ctk3eRkDjlx7hp6fp2LflSeaC+LJXaWK//X9PGT/KX+2njsHVqPkSQZ8xy+Hz5hI5Gdex73Diq1H/+/FAwBqdvyKyktx/YfFEFg7ouN7X8JvwmrwzcxxPX4RxEr0qiuUGN966y2cPXtW8vnMmTN4/PgxevfujbVr1+Lw4cP49ddfsX79evTp00fhmxNCFHPx4kWMGTMGIpGo1qU7RGJAJGYYE3MTl9ILNRNI8WOgUv5cp8ooeZKJ3PvJaDVkFqyatYVNi/bwei0Cj6+fQVnh0zrPLcq6j4cX9qP10FlyrvsPKksL4R70Dpo0bQ4LR3e06DMOFcV5KMuXv6iePAolxoMHDyIzM1PyuV+/fjh2jOZsI0Rbli9fDsZYjZqiLAaAgWH5kQzNBVP6rMGXKPjnFvhCC1i90lqyz9azE8BxKHxY+yTYoornuH1gNbxemwYzS/sax82bNoOJuTWyUn+DWFQBUUUZslN/g7mDG4S2znKuKJ9CibFZs2a4cuWK5DNjrN7ZdQgh6pGZmYlDhw7Vucjby0Ri4JerzzTXIVNe3OBLVBTlwqyJrdQ+jseHqbkVKopzaz0v7bcYWDdvJ9Wm+DITQRN0eDcaj6+dxPmVbyHp85HIvZ+M9mOXguPx5Z4j9zqKFBo7diy++OIL/Pjjj5LOlnnz5iE6OrrWcziOw59//qlwIIQYOrGoAkysWPJSxrHfjtZbU5TFGJB4Kw8TAxWvJSl87fKSWmtUf5/bjb9//1HyWVxZjsKHt3D/6It1mDq/r1ofxNM7F5CX/hc6Tal9+QVRRRnuHloH6+Y+8H7zUzCxGA8v7MON3UvQ8b2vwDcVKHQvhRJjdHQ0WrVqhZMnTyInJwccx8HCwgJNmzZV7BsRYuTEogoUPrwDUXmp2q+dk3EHPB5PagmP+vA4IK+oVCPxlD7OhIVTW/D4pjWOuQQMhoPPq5LPtw+shkPbXmjatqdkn8CqKUwt7VBekid1LhOLUFFaCFMLO7n3zU//C89zHyFp9Wip/Tf3fgZrt/bwC1uJx9dOoSw/Bx0nrQHHVaVvyzc/wYUvxuDZnQtwbK/YqqAKJUY+n4+pU6di6tSpAKrWe164cCHGjRun0E0IMXZMLIKovBQ8ExO5CaMhbGxtlUqKACBmgI25CTieemcWZIxBJBZX1YzlfE9TcyuYmr8YLsQzEcDUwgbm9q9IlbNu3hai58UoenQXlq5V7Yx5aX8CjMGqmbfcezfvORLO/oOk9l3ZMh0tB06BfetuAABxZRnAcXi5x7oqQXJK1boV+lPr3Lkzjh49Kvm8fft2dOrUSeGbENJY8Pim4JmYqXXr1+dVpdv0OQ7o18YGVQlCfRvHcWBmDR8n2cShBey8AnD38DcofHgbBX/fwP2EjXBs3wcCq6on0bKCJ0jeGCHpjDGztIeFk4fUBgACG0cI7apWErX17ITK0iLcP7oBJU8yUfw4A3d+/gocjw9bdz+F41MoMf7111948uTF2Kj33ntPqjOGEKI5bs1fwWvB/aSWCa4Lnwe84WuLFvaKtacpg5k2ATO1UMu12oz4BE2aNse1nf/F9V2RsHZrj1ZDZry4l1iE0qf/QFSh+FrXTRzc4DMmEiXZ6fhz+8e4GvcpyoueoX3oUphZ1ezFro1Cj9Lu7u44fvw4QkNDwefzqVeaEC2b++F/cOzkmaoaWx2PhP+r12FBSDONxCG2bv6/R1XF+IWtrPWYqbkVvN/8tNbjQltn9F54uM7ryztu17IT7Fo27IlWoRrj+++/j++//x5CoRDW1tbgOA6TJ0+WWrhedrOxsWlQYISQFwL8/RC76Svw+fxaa458HsDncdj1Xit0dbfUSBwih3Yaua6+UajG+Mknn6Bjx444efIksrOzERcXh65du6JlS8XfPSSENMzwwSE4/vMurPpqA44ePylVc+Q4YHB7WywIaaaxpAgAImfF2+kMmUrLp/J4PPzf//2fUfRK0/KpRB1EFc+Rn3ENJgJz8EzMNH6/v//5Fz0HDkNefgFszflImddBI22KL2OWLijpvxyV5c9h4+4LvqlQo/fTJZVm11F26AAhRL3cmr+CJk3MkZdfAAsBT+NJEQAq3Xsr1b5oyBRKjNXvSbdo0ULqc32qyxNCDByPj0oPxQZHGwOFOl88PDzg6emJ8vJyqc/1bar69ttv4eHhAaFQiO7du+PixYsKnbdr1y5wHIcRI0aofG9CSE2iV7oCQltdh6E1CtUYt23bBo7jYGpqKvVZE3bv3o05c+Zg06ZN6N69O9auXYuQkBDcvn0bTk5OtZ6Xnp6Ojz/+GK+++mqtZQghqqn0GqDrELRKocQ4ceLEOj+r05dffokpU6Zg0qRJAIBNmzbh8OHD2LZtG+bNmyf3HJFIhPHjxyMqKgpnz55FXl6exuIjpLERWzeHuKn81/SMlXpfpGyg8vJyJCcnIzg4WLKPx+MhODgYSUlJtZ63dOlSODk5KbScQllZGQoKCiRbUVGRWmInxFhVthzQaDpdqilUY1y6dKnSF+Y4DosWLVLqnCdPnkAkEsHZWXqqJGdnZ9y6dUvuOefOncPWrVuRmpqq0D2io6MRFRWlVFyENFomQoha9NJ1FFqnUGJcsmRJjX3VbYyywyCrX1lSJTEqq7CwEO+++y5iYmLg4OCg0Dnz58/HnDlzJJ9TU1MRFNR4etsIUUZli56Aaf2LUxkbhRKj7LjFhw8fYsiQIfD19cXs2bPh7V3V/nDr1i2sXbsWN27cwOHDdb/jKI+DgwP4fD6ys7Ol9mdnZ8PFxaVG+fv37yM9PR1Dhw6tEauJiQlu374NLy8vqXMEAgEEghdjviwtNfeWACGGrtJrUP2FjJBKbYzTp09H69at8X//93/o0qULrKysYGVlha5du2Lnzp3w8vLC9OnTlb6umZkZAgICkJiYKNknFouRmJiIwMCaU5m3bdsWV69eRWpqqmQbNmwY+vXrh9TUVLi5uany9QghAEQuHcGsNTMZhb5T6c2XEydOYNWqVbUeHzBgAObOnatSQHPmzMGECRPQpUsXdOvWDWvXrkVxcbGklzosLAzNmjVDdHQ0hEIhfH19pc6vXnpBdj8hRDmVbYfrOgSdUSkxCoVCJCUlYdq0aXKPnz9/HkKhau9RjhkzBo8fP8bixYuRlZUFf39/HD16VNIhk5mZCZ6aZyUmhEgTOflC3LR1/QWNlEqJcfz48fj6669ha2uLGTNmSNrx7t+/j6+//hrx8fGYOXOmykF98MEH+OCDD+QeO3XqVJ3nxsbGqnxfQggAjkNFh7G6jkKnVEqMq1atwpMnT7B+/Xp8++23khqcWCwGYwyhoaF1PmoTQvRXpfurYLbuug5Dp1RKjGZmZtixYwc++eQTHDlyBBkZVYt7u7u74/XXX0fHjh3VGiQhRDuYmQUqfBt3bRFQMTFW8/Pzg59f45i4kpDGoKLDWEDQ8MWuDB31YhBCAABip/YQudPLDgAlRkIIAJgIUd55cqN7J7o2lBgJISjvEApm4ajrMPQGJUZCGjmRiz9Env10HYZeocRISCPGBFYoDwinR2gZlBgJacTKu0QAQloDXpbKw3USEhKwdetWPHjwALm5uXKnH7t//36DAySEaEZl6xCIXWjMsTwqJcbVq1dj3rx5cHZ2Rrdu3dChQwd1x0UI0SCxfUtUtKeB3LVRKTGuW7cO/fv3x5EjRyQLZBFCDAMzs0B5txkAv0Hvdxg1ldoYc3NzMXLkSEqKhBig8i7vg1koNuN9Y6VSYuzWrRtu376t7lgIIRpW6T0EYld/XYeh91RKjBs2bMC+ffsQHx+v7ngIIRoibtoKFT4jdR2GQVCpkWHMmDGorKzEu+++i2nTpqF58+bg8/lSZTiOw59//qmWIAkhDWQiRHnX6QCP2hUVodKfkr29PZo2bYrWrRvvDL+EGJLyju9Qu6ISVEqM9c2iTQjRH1Wz5vTRdRgGhd58IcSY8UxQ7j+RXvlTUoMaHCoqKnDr1i3k5+fXWHsaAPr0oX+lCNGlypYDwKxqrslO6qZSYhSLxZg/fz42bNiAkpKSWsuJRCKVAyOENJCJABXeQ3UdhUFS6VH6s88+w+rVq/HOO+/g+++/B2MMK1euxKZNm+Dn54eOHTsiISFB3bESQpRQ6dGXJohQkUqJMTY2FqNHj8bGjRvx2muvAQACAgIwZcoU/PHHH+A4DidOnFBroIQQJXAcKluF6DoKg6VSYvznn3/Qv39/AIBAIAAAPH/+HEDVCoLvvPMOduzYoaYQCSHKEjl3oBm5G0ClxNi0aVMUFRUBACwtLWFtbY0HDx5IlcnNzW14dIQQlYjcX9V1CAZNpcTYqVMnXLp0SfK5X79+WLt2LX7//XecPXsWX3/9dYPWlv7222/h4eEBoVCI7t274+LFi7WWjYmJwauvvgo7OzvY2dkhODi4zvKEGD2+KUQu/rqOwqCplBinTp2KsrIylJWVAQBWrFiBvLw89OnTB0FBQSgoKMCaNWtUCmj37t2YM2cOIiMjkZKSgo4dOyIkJAQ5OTlyy586dQqhoaE4efIkkpKS4ObmhkGDBuHhw4cq3Z8QQydy9gNMhLoOw6BxTHbqbRXl5+fj1KlT4PP56NmzJ+zt7VW6Tvfu3dG1a1esX78eQNXQIDc3N8yYMQPz5s2r93yRSAQ7OzusX78eYWFh9ZZPSUlBQEAAkpOT0blzZ5ViJkRU8Rz5GddgIjAHz8RMK/f0DngV/z7KRjNbU2Qse/HfbnmnSRC17K+Re4ory1FZVgobd1/wTY03+artjXIbGxsMHz68QdcoLy9HcnIy5s+fL9nH4/EQHByMpKQkha5RUlKCioqKWhPzyzVdAJK2UkKMhdiZZtRvKJVfCRSJRNi1axciIiLw5ptv4urVqwCqao779u1Ddna20td88uQJRCIRnJ2dpfY7OzsjKytLoWvMnTsXr7zyCoKDg+Uej46Oho2NjWQLCgpSOk5C9IGzoyOa2QnhYvWihsosHKk3Wg1USox5eXno1asXxo0bhx9++AE///wzHj9+DKCql3rmzJlYt26dWgNVxMqVK7Fr1y7s378fQqH8av78+fORn58v2U6fPq3lKAlRjzNH9yH9yxD88amvZJ/Iqb0OIzIeKiXGefPm4fr160hISMCDBw+kVgjk8/kYOXIkjhw5ovR1HRwcwOfza9Q2s7Oz4eJS9/ueX3zxBVauXInffvsNfn5+tZYTCASwtraWbJaWlkrHSYi+Ejv66DoEo6BSYjxw4ABmzJiBgQMHgpMza0ebNm2Qnp6u9HXNzMwQEBCAxMREyT6xWIzExEQEBgbWet7nn3+OZcuW4ejRo+jSpYvS9yXEWFCNUT1U6nzJz8+Hp6dnrccrKipQWVmpUkBz5szBhAkT0KVLF3Tr1g1r165FcXExJk2aBAAICwtDs2bNEB0dDQBYtWoVFi9ejPj4eHh4eEjaIi0tLak2SBoVsa07ILDWdRhGQaXE6OXlhZSUlFqP//bbb/DxUa1KP2bMGDx+/BiLFy9GVlYW/P39cfToUUmHTGZmJni8FxXdjRs3ory8HCNHSq9lERkZiSVLlqgUAyGGSOSi+ksVRJpKiTE8PBxz585F3759MWDAAABVa7yUlZVh6dKlOHr0KLZs2aJyUB988AE++OADucdkZw9X5ZGdEGMkdqJhOuqiUmKcNWsWrl+/jtDQUNja2gIAxo0bh6dPn6KyshIRERGYPHmyOuMkhNTFRAhx01a6jsJoqJQYOY5DTEwMJkyYgL179+Lu3bsQi8Xw8vLC6NGjaeZuQrRM5ORDKwCqUYP+JHv37o3evXurKxZCiIrEju10HYJRocWwCDECoqbeug7BqChcYxw2bJhSF+Y4DgcPHlQ6IEKIkvimYDZuuo7CqCicGA8dOgShUAgXFxcoMiGPvIHfhBD1E1s3p/ZFNVP4T7NZs2Z4+PAhHBwcMG7cOIwdO7be1/QIIZonptqi2incxvj333/j5MmT6NSpE5YtWwY3NzcEBwdj+/btKCws1GSMhJA6MCtXXYdgdJTqfAkKCsLmzZuRlZWFvXv3omnTpvjggw/g5OSEt956C3v37pWa65AQonnMwrn+QkQpKvVKm5qaYvjw4di9ezeys7MlyXLMmDH4/PPP1R0jIaQO4iY0/6K6NWi4TllZGRISEnDw4EFcuXIFQqEQHh4eagqNEKIIZm6n6xCMjtKJUSwWIyEhARMnToSzszNCQ0NRWlqKmJgY5OTk4N1339VEnIQQORiPDwisdB2G0VG4V/r8+fOIj4/Hnj178PTpU/To0QOfffYZRo8eDQcHB03GSAipjcAK4Og9DXVTODH27t0b5ubmGDx4MEJDQyWPzJmZmcjMzJR7Dq26R4hmMTOac1QTlBoVWlpaip9++gn79u2rsxxjDBzHQSQSNSg4QkjdmKmFrkMwSgonxu3bt2syDkKIKkzNdR2BUVI4MU6YMEGTcRBCVMBMKDFqArXaEmLITMzqL0OURomREEPGF+g6AqNEiZEQA8b4VGPUBEqMhBgyqjFqBCVGQgwY45vqOgSjRLNbNgKZmZlITExEYWEhrKysMGDAALRo0ULXYRF1oEdpjaDEaMQuXryIZcuW4fDhw2CMgcfjQSwWg+M4vPHGG1i0aBG6du2q6zBJQ/CoxqgJ9ChtpPbt24devXrh119/lSxFIRaLAVS9mXTkyBH07Nmz3reYiH5jNFxHI/QyMX777bfw8PCAUChE9+7dcfHixTrL79mzB23btoVQKESHDh1w5MgRLUWqny5evIgxY8ZAJBLV+lpm9bExY8bg0qVLWo6QqA2t9aIRepcYd+/ejTlz5iAyMhIpKSno2LEjQkJCkJOTI7f8+fPnERoaismTJ+PKlSsYMWIERowYgWvXrmk5cv2xfPlyMMbqXbSsuszy5cu1FBlRO3qU1giOKbLknxZ1794dXbt2xfr16wFUPf65ublhxowZmDdvXo3yY8aMQXFxMQ4dOiTZ16NHD/j7+2PTpk313i8lJQUBAQFITk42itmAMjMz4eHhodBKjtU4jkN6ejp1yDSAqOI58jOuwURgDp4WH2+54sdgFtqbwVtcWY7KslLYuPuCbyrU2n21Ta/q4eXl5UhOTsb8+fMl+3g8HoKDg5GUlCT3nKSkJMyZM0dqX0hICA4cOCC3fFlZmdS6NEVFRQCAyspKVFRUNPAb6F5CQoJSSRGoqjn+9ttv9D58A4gqKlBRUQkRKwGPr73/jriyMjBesdbuJxZVQFwpQkVFBcTga+2+pqbarRnrVWJ88uQJRCIRnJ2lF/dxdnbGrVu35J6TlZUlt3xWVpbc8tHR0YiKiqqxv3v37ipGbRymTJmCKVOm6DoMQuTS9oOtXiVGbZg/f75UDTM1NRVBQUH4448/0KlTJx1Gph6xsbGYOnWq0ufFxMRQjbGBxKIKMLGW5yAtzQPMbbV6S47HB8/IB5brVWJ0cHAAn89Hdna21P7s7Gy4uLjIPcfFxUWp8gKBAALBi9eoLC2rZkA2MTHRenVdE0JCQsBxnNJtjIMGDTKK769Tuvjz4zFASGu+qJte9UqbmZkhICAAiYmJkn1isRiJiYkIDAyUe05gYKBUeQA4duxYreWNXYsWLfDGG2+Az1es/YfP52Po0KHU8ULIy5ie2bVrFxMIBCw2NpbduHGDTZ06ldna2rKsrCzGGGPvvvsumzdvnqT877//zkxMTNgXX3zBbt68ySIjI5mpqSm7evWqQvdLTk5mAFhycrJGvo8uXLx4kZmYmDCO4xiAWjeO45iJiQm7ePGirkMmqnpeqOsIjJLeJUbGGPvmm29YixYtmJmZGevWrRu7cOGC5FhQUBCbMGGCVPkff/yRtWnThpmZmbH27duzw4cPK3wvY0yMjDH2008/MRMTE8bn8+UmRT6fz0xMTNi+fft0HSppiLIiXUdglPRuHKO2Gds4xpddunQJy5Ytw6FDh2q8Kz106FAsXLiQ3pU2dOUlgFkTXUdhdPSq84WoV9euXfHzzz8jMzMTJ06cQEFBAaytrdG/f39qUySkDpQYG4EWLVpg4sSJug6DEIOhV73ShBCiDygxEmLIOE7XERglSoyEGDIT453IQZcoMRJiyKjGqBGUGAkhRAYlRkIIkUGJkRBCZFBiJIQQGZQYCSFEBiVGQgiRQa8ENiKPHj3Co0ePdB0GUSNXV1e4urrqOgyj0+gTo6urKyIjI43+P66ysjKEhobi9OnTug6FqFFQUBASEhKkZqUnDdfopx1rLAoKCmBjY4PTp09LlnMghq2oqAhBQUHIz8+HtbW1rsMxKo2+xtjY+Pv7018iI1FQUKDrEIwWdb4QQogMSoyEECKDEmMjIRAIEBkZSY30RoR+U82hzhdCCJFBNUZCCJFBiZEQQmRQYiSEEBmUGAkhRAYlRkI0hOM4hbZTp041+F4lJSVYsmSJUtdasWIFhg0bBmdnZ3AchyVLljQ4DmNBb74QoiE7duyQ+vz999/j2LFjNfa3a9euwfcqKSlBVFQUAKBv374KnbNw4UK4uLigU6dOSEhIaHAMxoQSIyEa8s4770h9vnDhAo4dO1Zjv66kpaXBw8MDT548gaOjo67D0Sv0KE2IDonFYqxduxbt27eHUCiEs7MzIiIikJubK1Xu8uXLCAkJgYODA8zNzeHp6Yn33nsPAJCeni5JbFFRUZJH9PoejT08PDTxlYwC1RgJ0aGIiAjExsZi0qRJmDlzJtLS0rB+/XpcuXIFv//+O0xNTZGTk4NBgwbB0dER8+bNg62tLdLT07Fv3z4AgKOjIzZu3Ihp06bhzTffxFtvvQUA8PPz0+VXM2yMEKIV06dPZy//lTt79iwDwHbu3ClV7ujRo1L79+/fzwCwS5cu1Xrtx48fMwAsMjJS6bgacq6xokdpQnRkz549sLGxwcCBA/HkyRPJFhAQAEtLS5w8eRIAYGtrCwA4dOgQKioqdBhx40GJkRAduXv3LvLz8+Hk5ARHR0epraioCDk5OQCqZul+++23ERUVBQcHBwwfPhzbt29HWVmZjr+B8aI2RkJ0RCwWw8nJCTt37pR7vLpDheM47N27FxcuXMAvv/yChIQEvPfee1izZg0uXLhAM7JrACVGQnTEy8sLx48fR69evWBubl5v+R49eqBHjx5YsWIF4uPjMX78eOzatQvh4eHgOE4LETce9ChNiI6MHj0aIpEIy5Ytq3GssrISeXl5AIDc3FwwmdkB/f39AUDyON2kSRMAkJxDGoZqjIToSFBQECIiIhAdHY3U1FQMGjQIpqamuHv3Lvbs2YN169Zh5MiRiIuLw4YNG/Dmm2/Cy8sLhYWFiImJgbW1NQYPHgwAMDc3h4+PD3bv3o02bdrA3t4evr6+8PX1rfX+O3bsQEZGBkpKSgAAZ86cwfLlywEA7777Ltzd3TX/h6CvdN0tTkhjITtcp9qWLVtYQEAAMzc3Z1ZWVqxDhw7s008/Zf/++y9jjLGUlBQWGhrKWrRowQQCAXNycmJvvPEGu3z5stR1zp8/zwICApiZmZlCw2+CgoIYALnbyZMn1fW1DRLN4E0IITKojZEQQmRQYiSEEBmUGAkhRAYlRkIIkUGJkRBCZFBiJIQQGZQYCdFT6enp4DgOsbGxug6l0aHESAghMmiANyF6ijGGsrIymJqags/n6zqcRoUSIyGEyKBHaUI0aMmSJeA4Dnfu3ME777wDGxsbODo6YtGiRWCM4e+//8bw4cNhbW0NFxcXrFmzRnKuvDbGiRMnwtLSEg8fPsSIESNgaWkJR0dHfPzxxxCJRJJyp06dkrtmtbxrZmVlYdKkSWjevDkEAgFcXV0xfPhwpKena+hPRf9RYiREC8aMGQOxWIyVK1eie/fuWL58OdauXYuBAweiWbNmWLVqFVq1aoWPP/4YZ86cqfNaIpEIISEhaNq0Kb744gsEBQVhzZo12LJli0qxvf3229i/fz8mTZqEDRs2YObMmSgsLERmZqZK1zMKupu/ghDjFxkZyQCwqVOnSvZVVlay5s2bM47j2MqVKyX7c3Nzmbm5OZswYQJjjLG0tDQGgG3fvl1SZsKECQwAW7p0qdR9OnXqxAICAiSfT548KXeWHNlr5ubmMgBs9erV6vnCRoJqjIRoQXh4uOT/8/l8dOnSBYwxTJ48WbLf1tYW3t7eePDgQb3Xe//996U+v/rqqwqdJ8vc3BxmZmY4depUjbWsGzNKjIRoQYsWLaQ+29jYQCgUwsHBocb++hKUUCiUrAdTzc7OTqXEJhAIsGrVKvz6669wdnZGnz598PnnnyMrK0vpaxkTSoyEaIG84Ta1DcFh9QwUUWToTm1rwLzcQVNt9uzZuHPnDqKjoyEUCrFo0SK0a9cOV65cqfc+xooSIyFGyM7ODkDNNWAyMjLklvfy8sJHH32E3377DdeuXUN5eblUD3ljQ4mRECPk7u4OPp9fo4d7w4YNUp9LSkrw/PlzqX1eXl6wsrJq1OtW02JYhBghGxsbjBo1Ct988w04joOXlxcOHTqEnJwcqXJ37tzBgAEDMHr0aPj4+MDExAT79+9HdnY2xo4dq6PodY8SIyFG6ptvvkFFRQU2bdoEgUCA0aNHY/Xq1VIrB7q5uSE0NBSJiYnYsWMHTExM0LZtW/z44494++23dRi9btErgYQQIoPaGAkhRAYlRkIIkUGJkRBCZFBiJIQQGZQYCSFEBiVGQgitLyODEiMhSrp//z4iIiLQsmVLCIVCWFtbo1evXli3bh1KS0s1dt8bN25gyZIlOp9AdsWKFRg2bBicnZ3BcRyWLFmi03g0gQZ4E6KEw4cPY9SoURAIBAgLC4Ovry/Ky8tx7tw5fPLJJ7h+/brKE8bW58aNG4iKikLfvn3h4eGhkXsoYuHChXBxcUGnTp2QkJCgszg0iRIjIQpKS0vD2LFj4e7ujhMnTsDV1VVybPr06bh37x4OHz6swwhfYIzh+fPnMDc3V/u109LS4OHhgSdPntSY/sxY0KM0IQr6/PPPUVRUhK1bt0olxWqtWrXCrFmzJJ8rKyuxbNkyeHl5QSAQwMPDAwsWLKgxOYOHhwfeeOMNnDt3Dt26dYNQKETLli3x/fffS8rExsZi1KhRAIB+/fqB4zipNV2qr5GQkIAuXbrA3NwcmzdvBgA8ePAAo0aNgr29PZo0aYIePXo0KIHrsraqLZQYCVHQL7/8gpYtW6Jnz54KlQ8PD8fixYvRuXNnfPXVVwgKCkJ0dLTcyRnu3buHkSNHYuDAgVizZg3s7OwwceJEXL9+HQDQp08fzJw5EwCwYMEC7NixAzt27EC7du0k17h9+zZCQ0MxcOBArFu3Dv7+/sjOzkbPnj2RkJCA//znP1ixYgWeP3+OYcOGYf/+/Wr4UzFSOl1YgRADkZ+fzwCw4cOHK1Q+NTWVAWDh4eFS+z/++GMGgJ04cUKyz93dnQFgZ86ckezLyclhAoGAffTRR5J9e/bskbuOy8vXOHr0qNT+2bNnMwDs7Nmzkn2FhYXM09OTeXh4MJFIxBiTv75MfR4/fswAsMjISIXPMRRUYyREAQUFBQAAKysrhcofOXIEADBnzhyp/R999BEA1HiU9fHxwauvvir57OjoqPD6L9U8PT0REhJSI45u3bqhd+/ekn2WlpaYOnUq0tPTcePGDYWv35hQYiREAdbW1gCAwsJChcpnZGSAx+OhVatWUvtdXFxga2tbYyZt2TVhAOXXcfH09JQbh7e3d4391Y/gtc3o3dhRYiREAdbW1njllVdw7do1pc6rbe0VWaqu//IyTfRAN1aUGAlR0BtvvIH79+8jKSmp3rLu7u4Qi8W4e/eu1P7s7Gzk5eXB3d1d6fsrmmRl47h9+3aN/bdu3ZIcJzVRYiREQZ9++iksLCwQHh6O7OzsGsfv37+PdevWAQAGDx4MAFi7dq1UmS+//BIAMGTIEKXvb2FhAaDmAld1GTx4MC5evCiVzIuLi7FlyxZ4eHjAx8dH6TgaAxrgTYiCvLy8EB8fjzFjxqBdu3ZSb76cP38ee/bswcSJEwEAHTt2xIQJE7Blyxbk5eUhKCgIFy9eRFxcHEaMGIF+/fopfX9/f3/w+XysWrUK+fn5EAgE6N+/P5ycnGo9Z968efjhhx/w+uuvY+bMmbC3t0dcXBzS0tLw008/gcdTvm60Y8cOZGRkoKSkBABw5swZLF++HADw7rvvGkctVNfd4oQYmjt37rApU6YwDw8PZmZmxqysrFivXr3YN998w54/fy4pV1FRwaKiopinpyczNTVlbm5ubP78+VJlGKsaajNkyJAa9wkKCmJBQUFS+2JiYljLli0Zn8+XGrpT2zUYY+z+/fts5MiRzNbWlgmFQtatWzd26NAhqTLKDNcJCgpiAORu8oYSGSJa84UQQmRQGyMhhMigxEgIITIoMRJCiAxKjIQQIoMSIyGEyKDESAghMigxEkKIDEqMhBAigxIjIYTIoMRICCEyKDESQogMSoyEECKDEiMhhMj4f7oJsT8zkQM+AAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired.mean_diff.plot(float_contrast=False);" - ] - }, - { - "cell_type": "markdown", - "id": "a47f367a", - "metadata": {}, - "source": [ - "For multi two-group comparisons, the effect size curves will always be displayed below the raw data.\n", - "\n", - "The lower axes in the Cumming plot is effectively a [forest\n", - "plot](https://en.wikipedia.org/wiki/Forest_plot), commonly used in\n", - "meta-analyses to aggregate and to compare data from different experiments.\n", - "\n", - "**Note: If you're interested in just plotting the contrast ax (the violin plots), you may be interested in the new [forest plot](09-forest_plot.html) feature added in v2025.03.27!**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "09c70e90", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_two_groups_unpaired.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "027cce0f", - "metadata": {}, - "source": [ - "For paired data, we use\n", - "[slopegraphs](https://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0003nk>)\n", - "(another innovation from Edward Tufte) to connect paired observations.\n", - "Both Gardner-Altman and Cumming plots support this.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1d60fb95", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_two_groups_paired.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "08937736", - "metadata": {}, - "source": [ - "For further aesthetic changes, the [Plot Aesthetics Tutorial](08-plot_aesthetics.html) provides detailed examples of how to customize the plot.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/tutorials/03-shared_control_and_repeated_measures.ipynb b/nbs/tutorials/03-shared_control_and_repeated_measures.ipynb deleted file mode 100644 index de380b59..00000000 --- a/nbs/tutorials/03-shared_control_and_repeated_measures.ipynb +++ /dev/null @@ -1,659 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "50ee557d", - "metadata": {}, - "source": [ - "# Shared Control & Repeated Measures\n", - "\n", - "> Explanation of how to use dabest for shared control and repeated measures analyses.\n", - "\n", - "- order: 3" - ] - }, - { - "cell_type": "markdown", - "id": "3e3615d9", - "metadata": {}, - "source": [ - "The **shared control plot** and **repeated measures plot** display common experimental\n", - "paradigms, where several test samples are compared against a common\n", - "reference sample. The shared control plot is for unpaired data, while the\n", - "repeated measures plot is for paired data.\n", - "\n", - "These types of Cumming plots are automatically generated if the tuple passed\n", - "to ``idx`` has more than two data columns." - ] - }, - { - "cell_type": "markdown", - "id": "234c2c3d", - "metadata": {}, - "source": [ - "## Load libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d091cc8f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pre-compiling numba functions for DABEST...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 62.10it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Numba compilation complete!\n", - "We're using DABEST v2025.10.20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import dabest\n", - "\n", - "print(\"We're using DABEST v{}\".format(dabest.__version__))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8db598c8", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning) # to suppress warnings related to points not being able to be plotted due to dot size" - ] - }, - { - "cell_type": "markdown", - "id": "9aad420a", - "metadata": {}, - "source": [ - "## Creating a demo dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7057855f", - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.stats import norm # Used in generation of populations.\n", - "\n", - "np.random.seed(9999) # Fix the seed so the results are reproducible.\n", - "Ns = 20 # The number of samples taken from each population\n", - "\n", - "# Create samples\n", - "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n", - "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", - "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", - "\n", - "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n", - "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n", - "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n", - "t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", - "t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", - "t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", - "\n", - "\n", - "# Add a `gender` column for coloring the data.\n", - "females = np.repeat('Female', Ns/2).tolist()\n", - "males = np.repeat('Male', Ns/2).tolist()\n", - "gender = females + males\n", - "\n", - "# Add an `id` column for paired data plotting.\n", - "id_col = pd.Series(range(1, Ns+1))\n", - "\n", - "# Combine samples and gender into a DataFrame.\n", - "df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n", - " 'Control 2' : c2, 'Test 2' : t2,\n", - " 'Control 3' : c3, 'Test 3' : t3,\n", - " 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6,\n", - " 'Gender' : gender, 'ID' : id_col\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "83066710", - "metadata": {}, - "source": [ - "## Shared control plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "575eacac", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:11 2025.\n", - "\n", - "Effect size(s) with 95% confidence intervals will be computed for:\n", - "1. Test 1 minus Control 1\n", - "2. Test 2 minus Control 1\n", - "3. Test 3 minus Control 1\n", - "4. Test 4 minus Control 1\n", - "5. Test 5 minus Control 1\n", - "6. Test 6 minus Control 1\n", - "\n", - "5000 resamples will be used to generate the effect size bootstraps." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "shared_control = dabest.load(df, idx=(\"Control 1\", \"Test 1\",\n", - " \"Test 2\", \"Test 3\",\n", - " \"Test 4\", \"Test 5\", \"Test 6\")\n", - " )\n", - "shared_control" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b3833f45", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:12 2025.\n", - "\n", - "The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].\n", - "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n", - "\n", - "The unpaired mean difference between Control 1 and Test 2 is -0.542 [95%CI -0.915, -0.206].\n", - "The p-value of the two-sided permutation t-test is 0.0042, calculated for legacy purposes only. \n", - "\n", - "The unpaired mean difference between Control 1 and Test 3 is 0.174 [95%CI -0.273, 0.647].\n", - "The p-value of the two-sided permutation t-test is 0.479, calculated for legacy purposes only. \n", - "\n", - "The unpaired mean difference between Control 1 and Test 4 is 0.79 [95%CI 0.325, 1.33].\n", - "The p-value of the two-sided permutation t-test is 0.0042, calculated for legacy purposes only. \n", - "\n", - "The unpaired mean difference between Control 1 and Test 5 is 0.265 [95%CI 0.0115, 0.497].\n", - "The p-value of the two-sided permutation t-test is 0.0404, calculated for legacy purposes only. \n", - "\n", - "The unpaired mean difference between Control 1 and Test 6 is 0.288 [95%CI 0.00913, 0.524].\n", - "The p-value of the two-sided permutation t-test is 0.0324, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "shared_control.mean_diff" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b7d077b1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "shared_control.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "b5cb76fb", - "metadata": {}, - "source": [ - "``dabest`` allows for combining both two-group and shared control experiments into the same plot. This empowers you to perform robust analyses and present complex visualizations of your statistics elegantly." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5d4dd491", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:12 2025.\n", - "\n", - "Effect size(s) with 95% confidence intervals will be computed for:\n", - "1. Test 1 minus Control 1\n", - "2. Test 2 minus Control 2\n", - "3. Test 3 minus Control 2\n", - "4. Test 4 minus Control 3\n", - "5. Test 5 minus Control 3\n", - "6. Test 6 minus Control 3\n", - "\n", - "5000 resamples will be used to generate the effect size bootstraps." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "multi_groups = dabest.load(df, idx=((\"Control 1\", \"Test 1\",),\n", - " (\"Control 2\", \"Test 2\",\"Test 3\"),\n", - " (\"Control 3\", \"Test 4\",\"Test 5\", \"Test 6\")\n", - " ))\n", - "multi_groups" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "26f2a0ff", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:13 2025.\n", - "\n", - "The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].\n", - "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n", - "\n", - "The unpaired mean difference between Control 2 and Test 2 is -1.38 [95%CI -1.93, -0.905].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "The unpaired mean difference between Control 2 and Test 3 is -0.666 [95%CI -1.29, -0.0788].\n", - "The p-value of the two-sided permutation t-test is 0.0352, calculated for legacy purposes only. \n", - "\n", - "The unpaired mean difference between Control 3 and Test 4 is 0.362 [95%CI -0.111, 0.901].\n", - "The p-value of the two-sided permutation t-test is 0.161, calculated for legacy purposes only. \n", - "\n", - "The unpaired mean difference between Control 3 and Test 5 is -0.164 [95%CI -0.398, 0.0747].\n", - "The p-value of the two-sided permutation t-test is 0.208, calculated for legacy purposes only. \n", - "\n", - "The unpaired mean difference between Control 3 and Test 6 is -0.14 [95%CI -0.4, 0.0937].\n", - "The p-value of the two-sided permutation t-test is 0.282, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "multi_groups.mean_diff" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d88959b3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_groups.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "d2831872", - "metadata": {}, - "source": [ - "## Repeated measures plot" - ] - }, - { - "cell_type": "markdown", - "id": "3f46c2db", - "metadata": {}, - "source": [ - "DABEST **v2023.02.14** expands the repertoire of plots for experiments with repeated-measures designs. DABEST now allows the visualization of paired experiments with one control and multiple test \n", - "groups, as well as repeated measurements of the same group. This is an improved version of paired data plotting in previous versions, which only supported computations involving one test group and one control group.\n", - "\n", - "The repeated-measures function supports the calculation of effect sizes for\n", - "paired data, either based on sequential comparisons (group i vs group i + 1) \n", - "or baseline comparisons (control vs group i). To use these features, \n", - "you can simply declare the argument ``paired = \"sequential\"`` or ``paired = \"baseline\"`` \n", - "correspondingly while running ``dabest.load()``. As in the previous version, you must also pass a column in the dataset that indicates the identity of each observation, using the \n", - "``id_col`` keyword. \n", - "\n", - "
\n", - " **(Please note that** ``paired = True`` **and** ``paired = False`` **are no longer valid since v2023.02.14)**\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a33de166", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:13 2025.\n", - "\n", - "Paired effect size(s) for repeated measures against baseline \n", - "with 95% confidence intervals will be computed for:\n", - "1. Test 1 minus Control 1\n", - "2. Test 2 minus Control 1\n", - "3. Test 3 minus Control 1\n", - "\n", - "5000 resamples will be used to generate the effect size bootstraps." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "baseline_repeated_measures = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\"),\n", - " paired=\"baseline\", id_col=\"ID\")\n", - "baseline_repeated_measures" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5c5a31f3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:14 2025.\n", - "\n", - "The paired mean difference for repeated measures against baseline \n", - "between Control 1 and Test 1 is 0.48 [95%CI 0.241, 0.749].\n", - "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n", - "\n", - "The paired mean difference for repeated measures against baseline \n", - "between Control 1 and Test 2 is -0.542 [95%CI -0.977, -0.179].\n", - "The p-value of the two-sided permutation t-test is 0.014, calculated for legacy purposes only. \n", - "\n", - "The paired mean difference for repeated measures against baseline \n", - "between Control 1 and Test 3 is 0.174 [95%CI -0.303, 0.702].\n", - "The p-value of the two-sided permutation t-test is 0.505, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "baseline_repeated_measures.mean_diff" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0105af59", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "baseline_repeated_measures.mean_diff.plot();" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7a73ccfc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:14 2025.\n", - "\n", - "Paired effect size(s) for the sequential design of repeated-measures experiment \n", - "with 95% confidence intervals will be computed for:\n", - "1. Test 1 minus Control 1\n", - "2. Test 2 minus Test 1\n", - "3. Test 3 minus Test 2\n", - "\n", - "5000 resamples will be used to generate the effect size bootstraps." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sequential_repeated_measures = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\"),\n", - " paired=\"sequential\", id_col=\"ID\")\n", - "sequential_repeated_measures" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "901b65f4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:15 2025.\n", - "\n", - "The paired mean difference for the sequential design of repeated-measures experiment \n", - "between Control 1 and Test 1 is 0.48 [95%CI 0.241, 0.749].\n", - "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n", - "\n", - "The paired mean difference for the sequential design of repeated-measures experiment \n", - "between Test 1 and Test 2 is -1.02 [95%CI -1.35, -0.709].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "The paired mean difference for the sequential design of repeated-measures experiment \n", - "between Test 2 and Test 3 is 0.716 [95%CI 0.153, 1.2].\n", - "The p-value of the two-sided permutation t-test is 0.022, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sequential_repeated_measures.mean_diff" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "968787fd", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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82wx2j0MWqrTbbUilUrRaLXi93om9x5VKBbFYjI4D3ojBYIBsNou1tTUIBAI4HA622/8WwYryGMlkEr/3e7+HH/3oRzveE/qpT30Kv//7v0//fenSJTz22GO36hJZdki9Xkc4HIZYLIbdbkc8HketVqNpur2kcmu1GjKZDJrNJhQKBRYWFnadhuZyuXA4HJBIJEilUmi323A6nTdt20kE3+/301Q2qSWLRCK43W40Gg2kUikEAgGo1WpYrVZ2H+49Btl33Ol0oNFoUCwWYbPZJg5h1WoVkUgEarX6hoI8Go1QKBSQy+UAABaLZVelkF6vt/dv5g0KK8pjXLhwAYVCAQ888AD9v+FwiBdeeAH/43/8D3S73U03T5FINHEC3a+tQSx7p1wuIxqNQqFQQKfTIRAIgMPhwOPx7MkusNFoIJPJoF6vQyaT7cvIlMlkgkQiQSQSwerqKjwez8TraC8IhUJ4vV4EAgEEg0H4fL6J16tcLsfCwgLW19fZZrB7kHFBNpvNyGQyMBgME2sYyWFVpVLB6XRumaVhGAbr6+vIZDLo9/swGAyYmZnZ1eukVqthfX2d7cDeJexv4hhvfetbcfXq1Yn/+w//4T9gYWEBn/zkJ2/pEgKW/WFtbQ2JRAIqlQp8Ph/RaJRGBLsVnlarhUwmg2q1Sn2x99MDWKlUYmFhgTaAuVyumzb9F4vFm4R5Y1Sj1WqhVqvpmki2GezuZ1yQ7XY7nUcerxU3Gg2EQqGpDV/j1Go1msXRaDSwWq27PjAOh0PEYjGIRCJWlHcJK8pjKBQKHDlyZOL/ZDIZdDrdpv+/G2AYBul0GlKpFDKZ7KYjsTsd4tIll8vR6XTQ7/dht9t37UzU6XSQyWRQLpchEoluelxqO8RiMRYWFhCNRhEMBvfFZ5scIILBIEKhEDwezyax5XK5MJlMdE1kJpPB2toarFbrLfteWW4Ng8GAThe43W4kEgnweLwJ4W02mwiFQpDJZHC73VOFst1uI5VKoVar0azKXqcEEokERqMRlEol6vX6TX1/bzRYUb6H6fV6qFQqyOfzAK43BBGBlslkkEql98RCA4ZhkEwmUSgUwOfz0Wg0IJPJNjW33Ihxe0ChUAi73Q6dTnfLT/o8Hg9utxuZTIZGKONe3HtBJpNRYY5EIlveiEkzmMFgQDqdRjQapc5gbCnmzmdckL1eL003Lyws0MwQaXgkh7WNB7SNtphutxtqtXrP11Qul7G+vr4vvRJvRFhRvgHPPffcQV/CnhGJRDhy5AgGgwGazSZarRaazSZNWwLX65BEoMmfd9MvEsMwtKsYuB4BWiwWmM3mHYtav99HLpfD2toaeDwe9f3dj1TuTs0TOBwOrFYrJBLJvhmNyOVyuN1uhEIhRKPRbWuI5GZcr9eRSqXg9/uhVqsxOzt7z2dY7lYGgwECgQD6/T58Ph9KpRKq1Sq8Xi9t4Gu32wgGgxCJRJsE+VbYYvb7fSQSCWg0GjbjskdYUX4DwOfzoVKpJuqhvV4PzWaTinU2m8VoNAJw/QY9Hk1LpdI7si5EXLqSySQEAgG0Wi2cTueOU27En7pQKIDD4ey6s3Qn7NY8Yb+NRshKykgkgng8DofDse3jFQoFFhcXaTPY8vLynpp8WG4tGwW51WrRRRKkL6Hb7SIYDEIgEMDr9dLDNsMw9GA+Go1gMplgMpn25TAej8fB4XBgs9lu+nO9UWF/y96gCIVCCIVCuiuVYRh0u10q1M1mE+vr62AYBhwOh4ozEeuDHqXp9/tYXV1FNBqFRqOBzWbD3NzcjgR1OByiUCggn8+DYZh9vSntB8RoJBwOIxAI3PQKPbVaDYfDgWg0SiOiG0Gawcg4TKlUwszMDIxG4x15QHsj0e/3EQgEMBwOMT8/j8FggHg8Dr1eTxdJ9Ho9BAIB8Hg8+Hw+eqCqVCpIpVJb2mLeDMViEdVqFR6Phz3A3QTsM8cC4Hr6VCwWQywWU1MAhmFoyrvVaqFer2NtbQ3A9TroeMpbJpPdtvVs3W4XFy9eRCqVgs1mw/z8/I5qYNP8qe/UCHC/jUa0Wi1GoxHi8Th4PN6OLBW5XC7MZjP0ej0ymQzS6TStN7OL7w+GcUEmnfXhcBgymYwetoggczgcKsjNZhOpVAqNRgNKpXLHhjc7pdfrIZVKQa/X7+uEwhuRO+9uxHLHwOFwaGRMGA6HVKhJNE2MBQQCwaZGsv0WvGaziRdffBGVSgVHjhyBz+e74Umf+FNnMhkMBoOp/tR3IvttNKLX6zEajZBMJsHlcne8fIDP58Nms8FoNCKdTiMSiUAmk7HNYLeZjRGyQCDA6uoquFwubeQjPu8Mw2B+fp5uRiuXy5BKpfB6vTc9djeNWCwGHo9Hd5Oz7B1WlO9xSqXSvqabibH9uHlGv9+fEOp8Po/hcAjgerPZxkayvdZs8/k8XnzxRQDAmTNnbigqDMOgXC4jk8mg2+1Cq9XCYrHcdY1LG41G3G73nn+eRqMRw+EQmUwGPB5vV3tzpzWD7XWOlWV3kOiXiC3pO+h2u7TTmnRiD4dDuN1u5PP522KLWSgUUK/XN5nVsOwNVpTvYbrdLmKxGIDrYiqTySCXy2kku1+/QAKBYFMj2Xh9utVqoVKp0EYyiUQyEU1LJJJt07IMw8Dv9+PChQvQarU4c+bMDZufxv2p1Wr1vqfrbjfjRiOrq6s3ZTQyMzMzETHvdo6bNIOR7APbDHZrGRdkn88HkUiETCaDSqVCX9fEPKTb7UKj0SAYDALYvS3mbul0Okin0zAajayf+j7B/gbdw4hEItx///0TzVvjUex4l7VMJruhOO72a4tEItqgxDAMOp3OxLWUSiUwDAMul7upkYxEXt1uF6+//jpCoRDcbjcefvjhbW8w4/7USqUSdrt9X9Yk3gnsp9GI1WrFcDhEPB4Hl8vdUyOZTqeDRqOhayLZZrD9Z2OELBQKsb6+jmw2C6vVCrVajeFwiEAggEKhAKlUivX19dtySCLjiEKhcFdrH1m2hxXlexwejwelUjkRVW0UR9JlTcRxPJrer85MDocDiUQCiURCI7PRaIRWq0VT39VqFYVCgV53r9dDJBJBq9XCiRMncPTo0S1v9rfCn/pOZJrRiM1m21MkZLPZMBqNEIvFwOVy92QYQZrBiDNYOp2mzmBsM9jN0e12acMWEeRms4l4PA6dTgez2YzRaIRLly4hFovRn8PtKifkcjm0223Mz8+z9qz7CCvKb0A2dlkTcRyPYEnzFjEXGU8379cvIJfLhVwun2gWGgwGqNVqCAQCuHr1KtrtNjweD/r9PpaWljY1knW73VvqT30nsp9GI3a7nTYDeTyePafEBQIBbQZLpVK0GWxubu6eyVTcTsYFmez87vf7CIfDkEgksNlsaDabePnll1EoFLC4uIj5+fnb9lwTbwOz2cz+fPcZVpRZporjuLlIs9lEJpPBaDSiEe94NL2fp/JWq4VkMolSqQSPx4MjR45ApVJNHBpyuRxarRbW1tbQ7XbpxhsiVG8U9sNohMPhwOl0IhwOIxwOw+v13lRHtVgshsfjoc1gq6urbDPYLtkYIQsEAoxGI4RCIXA4HMzNzSEej+Py5cvo9/s4c+bMxOKJW81oNEI0GoVEItlxBz/LzmFFmWUq08xF2u02FcbxVDOfz5+IpvfSRDYajZBOp5HNZlGtVmEwGOD1eunXFwqFUKvV1Ke31WpBq9VCqVRCKBSi0WhgdXWVHhrGo2mxWHzP1jj3w2iEw+HA5XIhFAohFArB5/NBKpXe1HWNN4MRZzCj0YiZmRm2Q3cbOp3OhOkHyX7EYjE0m02o1Wr4/X5ks1koFAqcOHHitmeGyDTD4uLiPft7dZCwosyyI8Zdvch+VuKpPa2JbFwYyUjWVr/A7XYb0WgUjUYDvV4PBoMBbrd709hVNptFsVgEj8eDx+OBwWCgn3PjoWHc6ITUysev506fUd4N+2E0wuVy4fF4JlY+7kfWgTSD5fP5CWew8Z/dzcIwDEajEYbD4aY/ORwOlErlXSEeWwlyJpNBJBKBSCQCl8tFr9eD0WiEz+e77YLcaDSQz+cxOzv7hspK3U5YUWbZM9M8tcebyBqNBorFIoDrN/2N0bRAIEA+n0c6nQaXy6VNaR6Ph0ZqO/WnnnZoIEYnJPVdLpcnNmaNR9MymeyuHufZD6MRLpe7aRfzfsy3E6MS4gwWj8eRTqcxMzMDpVK5paDu5E/yth06ne6Gnt8HDRFkPp8/YYsZj8dx7tw5qNVqzM3Nod/vo1arweVy3XZBJjuS5XL5rubbWXbH3XsXYrkjmdZEtnEMKpfLod/vo1gsYjQa0V9w0jUtEon2xZ96mtHJxui+UChgMBgAuLVNbbeLmzUa4fF48Hq98Pv9CAaD8Hq94PP5OxLHnQppr9ejKW2JREKvmcDlcukhbeOfAoFg6v9v9TG1Wg2xWAwikeiOrX+2220EAgGa8SC2mMFgEMvLyzCbzXjTm96EtbU11Go1OByOA+lsT6VS6Pf78Hq9d0Xm4W6FFWWWWwqXy90kjLlcDoFAgM4yR6NR2r0bDofR6XTQaDQgEolgtVphNpv3LYqdFt13u92JRjLS1Ab8NA1Poun9nOXeb0gaVywWw+FwIBgM4sKFC5ibm4NcLt9VFNrtdhGNRnHt2jU4HI4tn38Oh7OlSAqFwi3fd+TIETQaDeRyOXS7XSgUCpoS3c/nV6fT0T6E8bn5O4WNgjwcDpFIJOgWJ5/PhwcffBCZTAZra2t0x/ftplqtolgswm63sw17txhWlFluG+SGs76+jrm5OahUKsTjcTzwwAMwm81Ip9OIxWJoNBqQSqXQ6/VYX19Ht9udiF73u1GIHA7Gm9pIGp6INTE6IWnyjY1ke2E3KdqdPJZhmInPT1b0RaNRGI1G6HS6LUVSIBBAJBJN/P/MzAyi0ShGoxFcLteEyJLH3IyAqlQqWCwWuiZydXX1ljSDzczMUHc7oVB4x/h1t1otBINBCIVCOJ1OZLNZutN7NBrB6XTi0KFDyOVydC3jbt3X9gOyhUqlUh3I13+jwYoyy22hXq8jFothOBzC6XSCYRjEYjEolUqoVCokk0na0WmxWMDj8SZq02SzEzDZRCaXy/d9jeS40QlhNBpNNJLVarUJo5PxaJrUy+v1OiqVCiqVCgaDAZaWliaE9kZslcLl8XgQCoU3TN3yeDwcO3aM1uR1Ot2ujUb0ej38fj9yudwt8TbmcDi3pRnMbrej1+shHA5jYWHhwKO9VqtFI2SlUonV1VUA120xW60WhsMhPB4PSqUSde86qDpuIpHAaDSC3W7f9cdOOyyybA8ryiy3FIZhkMlkkMvlIJfL4XQ6aWQkEAjQ6XRQrVan+lOPp5mn2XSSJjIiiuNv+920Nd6oRhgOh5ui6Vwuh2aziUqlQg0WeDwe+Hw+NBrNluI57c/9wm63Q6FQ7MloRCwWw+fzwe/3IxQKwev13pI6+8ZmsGQySddE7sVpbCNk7IvUyskSh4OACHK73QZwfdEKscUslUool8twOp1oNpu0Ic5sNh/ItZbLZXo9O33NtFotVKtVVKtVrK2t3XV9GQcNK8ost4x2u41YLIZ2uw2r1QqTyYR0Oo1wOAwAkMvlUCqVcDqdNzS9mGbTOb5GstFo0Doc8NPtVMTk5FbUgjdamNbrdUSjUVSrVWSzWfR6PbTbbTAMA6VSiV6vB71efyD2nzdjNCKRSGhXdigUgsfjuWU3WoFAALvdTp3BwuEw5HI5Zmdnb9o5is/nw+PxYHV1FeFwGD6f77b3BzSbTbz++uuoVCowGo1QKpXUWKVarSKVSsFsNmM4HCKZTMJsNsNisdzWayT0+30kEgloNJpta/Gj0Qi1Wo0Kcb/fp78bKpWKHj5YdgYryiy3hEKhgFQqBZFIhIWFBUgkEiwvL8Pv90OlUmFubg4Wi+WmBGpad/X4dioyBjXu673XWWWGYTAYDOiIFUllt9ttFItFpNNprK+vo9Fo0AOEVqsFn89HpVJBuVxGKpVCqVSCWCyGXq+HTqfbN2/xnXAzRiMymQwejwehUAiRSITu771VkINArVajzmBarRZWq/WmZsxFIhHcbjcCgQDi8fhtHZUqFot46aWXMBgMsLi4OLEspd1uIxKJQK1WQywWIxaLwWg0Huiih3g8Dg6HA5vNtul93W6XinC9XgfDMBCLxdBqtVCpVJDL5fT3oNvtHsDV372wosyyr/T7fcRiMdRqNXpTabVaeOaZZ5DP5+FyuXDo0KFbNmO5cTvVeC240WjQWWXStEVGuAQCAfh8Pu3EJsLb6XTon4PBYKI+RvZIE5MKjUYDt9sNm81GI7uLFy9iNBqh2+0il8vBbDaj0+lgfX0dUqkUarUaer0eKpXqtkRtN2M0olAo4Ha7EQqFEI1G4XQ6b/k1K5XKiTWRS0tLMJlMMJvNe65vy+VyOBwORKPR2zIq1e/3EQgEcPnyZSgUCrz5zW+e6KAeDAYIh8MQiURQq9WIxWLQ6/W31TpzI8ViEdVqFR6PB3w+HwzDoNFoUCHudDrgcDi0a16lUh14nf5egRVlln2jUqnQ07XX64VQKEQoFMLVq1fBMAweffTRPTWL7JTBYIDBYIB+vz/1791uF91ulzZg1et1Ktb9fh+j0Yg2kUkkEqjValrXJsYkEokEo9EIlUoF3W4XvV6PRgkGgwFmsxnNZhOxWAyJRALpdBqDwQBSqRSdTgfFYpGKQK1Ww/r6OpLJJDQaDQwGA/R6/S2/ud2M0YhSqYTL5UIkEkEikbilP08Ch8OBXq+HVqulncjFYhEWiwV6vX5PBwOtVkuXmdyqUanhcIhcLodYLIZUKgWXy4WTJ09OPM8MwyAcDmM4HMJoNNINULfjed2KbreLZDJJ10JGIhHUajUMh0O6O91qtUKhULCWqbcAVpRZbhpS/yqVSlCr1ZiZmUGhUEAul0M2m4Ver8eJEyd2XRMcDodbCuw08d34RiLb0WhEI2PSdKVUKmEymajQcrlcMAyDXq+Hfr9Po1/SRCaXy6kYkzrxaDSintwKhQL5fB5XrlxBsViko1ykxkZq2tVqFb1eD2azGVwuFxwOB41GA+vr67QbnewkJo1ht4q9Go2o1WoaaXK53NsW0XG5XFgsFhgMBqTTaSQSCRQKBbpXeLfcqlEphmFQLBbpKtFms4ljx45NXXGYSCTQbDZhNpuRSqWgVqsPVJBbrRYuXryI9fV1mM1mVCoVSKVSGI1GqFQqdiPUbYAVZZabotFoIBaLod/vw2q1otfrYXV1FcPhkHb6ErvG4XC4Y4HdmCoejUbo9/v0/4jQMgxDxy7IuJBYLIZKpYJEIoFIJKLLNcbfblTLJZ3VxNN7ZWUFtVptYqZXLBZDKpUim81idXUVhUIBtVoNzWYTYrEYXC4X9Xod7XabbrV68MEHweVyMRqNYLFYMBwOaUqwWq0iEonA7/dDJBLBaDTC4/HAZrPd9IKIrVAqlVhYWEA4HMbq6ipcLteO1jdqtVo6d87lcm9r7VMgEMDhcMBkMtFmMJJG3e3ztN+jUpVKBel0Gp1Oh772TCYT3G73JkEuFAooFovQ6XTI5/O06fF2Np+NRiPU63X6+iOz0kePHoXFYoFKpbqtfQ8srCiz7BGGYZBOp5FKpcDlciESibCyskIdpSqVCr0JBYNBDAaDqbO5fD5/YjSFPIbH403M9DIMA6FQSG9Y04R2/O1m02rEGKNer2M4HMLhcKDb7SKbzSKXyyGRSCCZTGJtbQ3NZhMymQxKpZLWqMdvwOTvL774Il555RW85z3vwZEjR9DpdOiuXFLLrlQqtEkuGo3i6tWrtO7p9XrpeJNIJNq3m7dYLMbCwgKi0SiCwSBmZ2dhMplu+HEGgwGj0QipVAo8Hu+2j+2QZjDStbyysrLrZjAOhwO3243V1dWbGpVqNptIpVJoNBpQKpV0tIv0GWwU5FqthmQyCblcjnK5DLlcDpfLdVsEudfrTTRpjUYjesgUi8X7ugqSWNiy7BxWlFkoDMPsKIJtNpuIx+NoNBq0CQS4bmkokUiQyWQgl8vhdrshEonoY4i4jottv99Hr9ebEGxi0SiVSjeJrUgkgkAguKU3r3q9jmw2i3q9DoFAgFarhcuXL9NouFKp0IUWWq0WOp2OPke1Wg0ajQYOhwMLCwtoNpuIRCJotVrQaDQol8v44he/iMXFRbz97W8HwzA0k0C6V10uF4DrN/psNotwOIxoNIqnn34aAoEABoMBNpsNZrOZpt/H0/B7gcfjwe12I5PJ0DrzToxGTCYTXbvJ5XIPxOBCpVJBqVTSNZFk3GinzWBk69heRqW63S7S6TTK5TI9JABAKBSi9feNz2Gn00EkEqGvLZlMNlW49wuGYei61Wq1ina7DQ6HA7lcTqNhkUiE1dVV6HS6fct6EGvOO9WW9k6FFeV7nK1qstP+ThyzxiHpWhLRtlot6mRFapImkwlGoxHlchnRaBQWiwVms5mKVK/Xm/icfD6fiqxEIpkQXKFQeGCmDo1GA5lMBvl8HolEAuFwGEtLS0in0xNbpWZmZiYiVbJGz+fzwel00v3OzWYT3/72t2Gz2SCXy+HxeBAOh8EwDJaXl6m38ZkzZ/DYY49tWoVHxpA8Hg8GgwHW1tYQDocRiUSwvLyMYDAIg8EAg8FAa8EkpT7emLbT55PD4cBqtUIikezKaGRmZob2FXC53AOxYiTNYMQZbLwZTKPR3PA5EIlE8Hg88Pv9iMVicDqd2z5+MBjQVC9Jp2u1WtRqNYTDYSiVyqljY8PhEKFQCMPhEMPh8JYJ8nhZpFarYTAYUN93sp1r/MCSyWTQbrexsLCwL9fSarUQiUT23W3vjcBdLcrD4RBf/epX8eyzz6JQKOCzn/0sjh49imq1imeeeQaPPPLIjtJw9yrdbhdf+MIX0O12IRKJ8PDDD4PL5VKBJbVRYgs5/v/k71wul47+BINBRCIR9Ho9yOVy8Hg8SKVSNBoNxONxFAoF2kU8GAxo88zGuu6d5vBD5kdff/11JBIJZDIZtFot9Pt9iMViGI1GuhOax+PBZDJREfb5fFObjMj/qdVqJJNJcDgcfOITn8DS0hK+9rWv4cqVK1hfX0coFEIoFMJLL72EX/3VX91SDPh8PmZmZjAzM4PTp08jlUohFotRMxalUgmz2QyFQoFut4tKpUKzDyTrQCJqkoHYir0YjczOzmI0GiEej4PH4x3IFiPgetRLurIzmQwSiQQSiQQkEgnkcjkUCgXkcvnUg4ZMJoPT6aS7i6eZdoxGI9rECGBilWi1WkU4HIZKpZqaimYYhmZNSKTq9Xr3rYO50+mgUqmgWq2i2WyCYRi6ypRMEEyLWpvNJnK5HGZmZvald6Hf7yMUCkEsFkOhUKBWq93053wjcdeKcqVSwTve8Q68+uqrkMvlaDab+J3f+R0A1+cQf/d3fxcf+chH8F/+y3854Cs9OIRCIQKBAGq1GrRaLX7rt35rkyCSjmPy1mq1Jv5N6k+BQACVSgUajYbO4SoUCgiFQqytrUEikeCRRx6Bw+G449NVnU4HoVAIFy5cwLlz5xCJRGiWgHReSyQSmM1myOVyzM3NTYjwtBnrfr8/MddM/l4ulwFcv/GFw2EcPXoUR48exauvvor/9b/+F1KpFAwGAwKBAP7oj/4Ib3/72/HzP//z24oaj8eD3W6nTUrxeByxWAzhcBixWAwmkwl2ux0ajQYMw9CZ67W1NVrj4/P5EyItlUonov9xoxG/308jwe2w2WwYjUa0K/t27/sdRygUwuFwwGKxoF6vo9FooFarYW1tDcD1jIJcLqdCTQ4pGo0GVqsV6XQaIpFoYp6YzEr3+31qi0ki8EqlgkgksqUgA6DmMQzDQKVS3bQgj0YjOjtcqVTQ6/XoVjabzQalUnnD2vpoNEIsFqOv95ul3+/j9ddfR7Vahdlspgs2WHbOXSvK/+k//ScsLy/jBz/4AY4fPz5Ry+LxePiFX/gFfO9733tDizKHw6H118FggFKpRIWWzNj2+/2JjyGpZZFIBIVCgWg0img0CpVKhUceeQQul4umWRmGQTweR6/Xw/z8/B27+LzdbiMUCiEQCODatWtYWVlBpVJBp9MBwzAQCAS05i2VSjE/P48TJ05gYWEBPp9vohu53+/TjupxESaizuVyaX2YzDl3Oh0AoNHxyZMncerUKTz44IP4/Oc/jx/+8Id0d/P3v/99vPrqq3jb296Gd7zjHTcc0xEKhfB6vfB6vWg2m4hGo4jH4zh37hxEIhHMZjPsdjusViukUim1/iQGKcRMhVz7eH1aKpXC4/HQprOdGI3Y7XYMh0OEw2F4PJ4ddXLfSoRCIXQ6HRXXfr+PRqNBhZr4p5OsjkKhgFqtRrfbRTweh1AopE2NpC+A2GISiCCr1eotu6fX1taQTqfR7Xah1+vpnurd0u/3J9LSZCyPzNMrFIpdZaLINR06dGhPh+nhcEifz3q9jkAggEajAa/XC7lcztps7oG7VpS/8Y1v4Hd+53fw5JNPolQqbXq/z+fDF7/4xdt/YXcQ3W4XhUIB9Xodg8EAyWSSppDFYjE9SU9LLWezWbz22muo1+s4dOgQjhw5MpHCHI1G1FTA6XTeUXtqW60WFeFAIIBoNEqXWTQaDRpRkG5upVIJj8eDRx99FG9605ugUqkwGAyo6CYSiQlXLwDUDUwikUCpVFJf7vEO8dFoROt4MpkMPp8PV69eRaFQwIkTJ2AymfDRj34UR48exdNPP41EIgGGYVAqlfC9730PP/nJT/Dkk0/iiSee2FFtTiaT4ciRIzh06BDK5TJisRht3JLL5XQtokajmXCxIh7iJKImYkUa+Mg42/LyMtbW1rC4uLjl6BBZ/BAKhRAOh+nN+U5BIBBAo9HQTMRgMECj0aDCsr6+DuD64TSXy2FpaQlzc3OYmZnB/Pz8pu+F9FFsJ8jEE73RaMBsNsPn8+1qzGi8SavVagG4ng2cmZmho397oV6v06UfO639DodDeqCp1+v0eoRCIer1OuRyOU6fPk0P6KzN5u65a0W5Wq1u24xBmpfeyJATNI/Hg1arxfHjx294Gq7X67h06RI1on/b294Gg8Ew8RjSrNJqte6IaIjUu4kIJ5NJ9Pt9mg3odDrodDro9/vgcrmw2+2w2WwwmUzweDxwu91QKBTo9XrI5/OIxWKbxJccYiQSCcRi8aaRJFJ3L5fLVOC63S5KpRIdq1KpVDhx4gSWl5fxyiuvwGazwWg04vjx4xAIBLTB7LXXXkO1WgXDMPj2t7+N5557Du985zvx2GOP7Si64nK5NDo8cuQIisUi4vE48vk8kskkFAoF9Ho9bYySyWSbPMRHoxE6nQ79Xng8HrrdLq5du4ZAIACXywWNRjO1Tk3GjEi93Ofz3bI565uFz+dDrVbTHoDhcIhKpYJQKIRms4m1tTX0+31qDtNsNiGXyyGVSlGpVBCNRmm3/bTfrW63C7/fj2KxiNnZ2R0J8nA4nFjwMBgMwOPxoFKpYDKZoFQqb7oZcjgcIhaLQaFQbNt3Qw4tRIjHRVihUMBgMEChUKBer6PX68Hj8dyxGbO7hbtWlN1uNy5evLjl+3/4wx/i0KFDt/GK7jzGDeFvtJC+1WohGo1idXUVDMPgwQcfxPz8/KaP6ff7CAaD6Pf78Pl8B+Lw02w2J0Q4lUrRcS6Smifdrd1ul3odk+5oUjeWyWTQaDSo1Wqo1+sQiUSQSCQwGAw08t0ovmSFJBFfkgomIk6a30hjDckgCAQCusDCYDAgHo8jEAigWCxCLBZDJpNBrVZDq9XixIkTeOmll3D+/Hkamf3Lv/wLnn76afzsz/4sTp06teMUpUgkgtVqhcVioTXVbDZL3+RyORUljUZDFwmQBR7jYjo/P49qtYpr165hfX0dPB4PjUZjyzq1xWJBMplEMBiEz+fbc0R3uxgOh7RzWywW47HHHoNcLselS5fQbrchl8uRyWSo4Ua5XIbVat2y23w4HGJ1dRXpdBo2mw3z8/Nb1njJCtNqtYpGowGGYehGNJJp2c9ejVQqhcFggPn5+Yn/HxdhUqYBfirCRqNxogYPXD/IJxIJGAwGVpD3gbtWlP/jf/yP+OQnP4k3v/nNeOtb3wrgugh1u1189rOfxb//+7/j7//+7w/4Ku98Op0OMpkMotEoyuUyZmZmcN99901NOXY6HQSDQQDXb9C3a9yh2WxSAQ4EAkin09TNi3ha93o9DIdDcLlcqNVq2i2t1+tpmq/dbqNcLkOr1dJGKBL5isXiqeMrREhJxNjpdDZ1NRuNRipGG2+6pFSgUqlw6NAhmiq2WCxYWlqiN71cLodyuYzhcAiz2YwzZ87g4Ycfxo9//GMkk0mIRCKsr6/jS1/6En74wx/ife97H+6///4d36g5HA6tO9rtdqyvr6NYLKJcLlNnKVJLJiKtVConPj+Hw4FarcbDDz9MV1SSkaOt6tSj0Qj5fB65XA6HDx+mz/md1IE/bos5Go02zTjfd999CAQCEIlEmJ+fRyqVQrFYhEajgUAgQCAQoN3U5E0mk9EU/uzs7KaUP8MwE05a3W6XllLm5uagUqluahvWdpD5YbvdDi6Xi3K5TIWYvB5FIhHkcjlMJtMmER6n0+lQR7WDXKBxL3HXivLv/d7vYXl5GR/60Ido6unDH/4wSqUSBoMBfvM3fxO/9mu/drAXeQdD3Kny+TxKpRJEIhEefPBB2Gy2qd2SzWYToVAIAoEAHo/nlt0wgOsn7/FIOJ1O0/eRiJg4YA2HQ+h0OrjdbiiVSgyHQ/R6PUgkEmpRSdLXVqsVLpdr6ggTST+PR7+kFja+y5kYpEil0j11lY6vmzQajQiFQhAKhXjggQeQy+Xwyiuv4Nq1a4jFYpDL5XjggQfg9XoRiUSoQUc2m8Xf/u3fwuFw4AMf+AAWFhZ2dQ0CgQAmkwkmk4nWj0nkT7ya19bWJlK7SqWSCulWRiPj3dbjdWq1Wo2VlRW89NJLsNvtEAgENzVPvZ+M22LqdDpYLJZNr22ZTAaHw4FIJIJms4lut4uFhQXY7XYwDEPr8PV6fSITEQ6HMTc3B5vNBoFAgH6/j1qtRpehkAUPpCFwt01ae6HT6eDq1at0tCsejwMAbey8kQiPMxgM6Ov3drmRvRG4a0WZw+HgH/7hH/DLv/zL+Nd//VcEg0GMRiO43W784i/+Is6cOXPQl3hH0u/3kc1mUSwW0el00Ov1YLFYaOQ4DWKIIJVK4Xa79/3mSUTY7/cjEAggk8nQ95FomNQ3u90uNUBwuVywWq30BjMajWA2m3HkyBE4nU4UCgWsra1BLpdjfn6eppJJ5/RO0s9SqXRqFL0fKBQKuqO4Uqng8OHDOHz4MF577TUkEgk0Gg0kEgmIRCK4XC44nU5cunQJuVwOAoEAwWAQ//2//3csLi7iAx/4wJ52A5PIbm5uDuVyGcViEc1mkz4f5XIZpVKJjjhpNBqoVCrqd72V0cj44YOMaK2srNDXGzkE3cw89c2w0RZzfKpgGhqNBmKxGK+//jqOHDlCl0ZwOBx6rSR1m0wmcfXqVboz+/Lly5tS0mSW+lan9Me7zev1Ou0FWVhYgEwmo2N/u32eR6MR3W7l8/mmHlDJAXrcw57lxty1okx49NFH8eijjx70ZdzxEI/itbU1mvollo0Oh2PL5pPx7UXTLAP3Qq1Wm0hHZ7NZar85vrSCRLgqlQpmsxkOhwNHjx6l4y3lchnlchkcDgf33XcfFd61tTWsrKxgMBjQRqZGo4FCoUA3PAGgNeTt0s+3mnFhDofDcLvdOH78OD0INBoNRKNR9Ho9yGQyfPjDH0Y2m8X3v/995PN5cDgcnDt3DufPn8cDDzyAX/iFX9jTliEej0ebv9rtNt10NRgMIBAIwOPxaPqfy+VCqVRSgZ6fn7+h0YhQKMTCwgL8fj/K5TJ8Ph+1XyVZj/HFHVvVqUm5Ya+HpGm2mDtpVCSHWJfLRWvK441xhEqlghdeeAEMw9BDChFishVsOBzS5SXEzGQvwjgNMrJHhJiM44nFYgwGAygUionu6L0Sj8fRarWodzsAOhNPDgCNRoMe6Fh2zl0vyizbMxqNqCHI2toalEoljYTsdvu2v5yFQgHJZJLud93rjZCYjwQCAfj9fqTTaSq+438SYSZLBex2OxYXF2Gz2ejsaKlUonU4Pp8Pt9sNu90OmUyGTCaDK1euoNFo0FEl4nA0nn4mN/f9NDUgKXVS3+52u7Re3+12wTAMotHoVOc0kv6LRCJUmN1uNwKBAGZnZ2Gz2XDlyhUUCgX4/X4sLi7ir//6r/Haa6/h61//OgqFAnq9Hl566SW8/PLLOH78ON75znfCarVSa9Dd3BglEgnm5uYwOzuLSqWCYrGIWq1Go18+n49er4doNErroEajEcVicVujEZFIBJ/PB7/fT5u/yFavjf0J4+UEElFvnKfeKNbbfY/jtph8Pp9e405e02tra0gkEjAajZidnUUwGKRbpcRiMV3RWSgU8PTTT6Pf7+PMmTOwWq1QqVS0eY7Q6/UmxrCIoQmp45J56Z1srCIiTERwXIQVCgVmZmbo4WF5eRlOp/OmBTmbzWJ9fZ1Ov+RyOfr1R6MRuFwuZDIZtYYldXqWnXHXivJOVpxxOByEw+Edf87Pf/7z+PznP49YLAYAOHz4MP70T/8U73znO2/mUg+MTqeDYrGIVqsFo9EIg8GAQqEAsVgMr9e7beosnU4jl8vBZDJhdnZ2V1+3UqkgEAhgaWkJKysryGazNAom4guAWn5aLBb65nA4YDAYaB2z2WzSaJ2MN5EImMxZr6ysIJPJ0Kja6XROjOvsR/p5MBhQsR0XXvLneIqOLO0olUqoVCpgGAZqtZo6ppHu74202216wydLKZaXl2nnrkQiQT6fx9LSEorFItxuNz796U/j8uXL+M53voNKpYLBYIDV1VWsrKzg2LFjOH36NGQyGaRSKRVo0gR3IzgcDp3p7fV6KBaLdMSLeJ6T9ZSk+a5er+P111+n9fyNzzt57QUCAYRCIXi93qliKhAIaGMagdSpSURNnuPxeeqNdWoul7ulLeZOIAdTo9FIG5lcLhcuXryIF198kVrKjkYjXL16FTKZDO95z3sm5sA3IhQKodVq6cFl49gR8V0QCAQTkbREIqGCToSY9D0QEbZYLFNtRIPB4E3vvmYYhm7jksvlSCQStLmSzE0PBgNcvXoVr7zyCtLpNOLxOPh8Po4dO7bnr/tG464V5ccee2xqt2w8HsdLL72EI0eO4Pjx47v6nLOzs/jLv/xLeL1eMAyDL33pS3jf+96H119/HYcPH97Py78tCIVC9Ho9uo0mnU7DarXCarVuKVIMwyCRSNC5yht5hzMMg3w+j6tXr8Lv99NNStPEVyAQQCaT0Tlhs9kMjUZD3ZRIOrTT6aBUKiGZTNL5YhIFkMUDIpGImqP0+33YbDZ4PJ49m5iMRqMJkd0ovOMiyuPxqOuZUqmkm6sajQat15MRGplMhkKhgEajgbm5OTpiRGrlG5eDGAwGhEIhJJNJzMzMQCAQ4MqVK7BareDz+RiNRlS8ycyxQqHAz//8z8Pv9+PFF1+kN0qy9eiRRx7BqVOnJqIyHo8HmUxGr1Emk22bORAKhbBYLJiZmUG9XkexWKSLSdRqNRwOB0ajETUt+clPfgKTyYQjR45Ar9dPiIRUKqXCTJy/dnJoGq9Tj//cxuep2+32RJ2ay+WCYRhax91NPwQRZJPJBLPZTLM0xEmLpPLvu+8+XL58GWKxGI8//vi2gjyNabPSJJIulUqIRqO0n4Kk83U6HfR6PaxW65Ze3oS1tTXUarVdu4iRJjZyACgUCtQohcwnk8zJpUuX8N3vfherq6vU/rNWq6HVakEkEqFer+/qOXkjw2HuwSr85cuX8fa3vx3/9E//hCeeeOKmPpdWq8V/+2//bced3BcvXsSDDz6ICxcu4IEHHripr32z9Ho9/Pqv/zrS6TSkUin+8A//kNZYSeQ03kVMfIur1SrsdvuE7y+p/XU6HWSzWayurtKbPnFBIuLL4/HA4/GoELvdbjidTpjNZhoxkhESjUYDtVpNDw3ZbBaNRgPAT0fcer0eFAoFHA4HzGYzBAIBKpUKyuUyBAIBrFbrDcWYeHxvJbzjRjMcDocu0RCJRBN/37jFimzDicfjNE1uNBphs9mg0Wjw2c9+FolEAq1WC7/xG7+BxcXFGxr/k4YcsmM3Eomg0WjA4/Gg3+9jdXUV+XwefD4fcrkcDMOgVqvRvdZXr17Fq6++OvE9SSQSnDlzBidOnACAiZluDodDRZpEpyqV6oYZBmLdSuqtQqGQztXm83lcuXIFvV6P/nxI1E1qkOT7JP0K+9VMN16n/tGPfkRd1XZzL8jn8wiFQjTb0mw2AWDiOWIYhjrGtVot/MzP/Mye6vnj9Hq9iXR0t9uduoecy+WCx+NtGsPa+BwSwxedTgebzbbt194owo1Ggx7whEIhstkstFotHcULBoM4e/YsLly4gHa7PRHFDwYDOJ1O5PN56HQ6mM1m/NVf/dVNPTdvFO7aSHk77rvvPvzmb/4mPvnJT+LChQt7+hxkA1Wz2cTp06e3fBy5uROIoNwJkF9mYq949OhRNJtNtFotZLNZ+n5S0yM1O5fLBR6Ph1wuh3a7jWw2i2AwiFgshng8TuuLRIDVajV4PB69UTidTvh8PjgcDmg0GppqJO5YarUaQqGQOnHlcjnqGqXVamE0Gmm62Gq10nQ0icrT6TRdM2g0GumNaKOv97jwblwfSUSWzBCPC++NHJd6vR4SiQSi0SgKhQK4XC7MZjMOHToEk8k0IbhkPpg0k0WjUVQqFdpBPq0parz5KxKJwOFwIBAIIJFIYGFhASdPnkQoFEImk6HPt1arRT6fR7lcxpNPPol3v/vdOHfuHF566SW6v/rFF1/ElStX8Nhjj+G+++6j+6zJz4fUTkk6nqRPFQoFlEollEolxGLxRD1cpVJBr9fTUkkul0Mmk4FSqcRDDz2EQqGAcrmMTqeDdDqNVCoFqVRKBdrlctFFGvu1zGTche3ixYuoVCpQq9U3FGXSwEWcyJRKJaRSKV3NqFQqN702Wq0WlpeXcebMmT0J8rgIE1cs4PohioxJyeXyiYMg2Y9MBJD8LpNaLvkYqVSKWCwGgUCwZQmq1WpNpMOJCMtkMjoeJRaL4ff7YTabodVq8d3vfhcvvfQScrkc+v0++v0+9YBXKBR48skn8bM/+7Pw+Xz41Kc+hUqlsuvn5Y3MPSnKwPVdv9euXdv1x129ehWnT59Gp9OBXC7H17/+9W2dwT73uc/hM5/5zM1c6i2DmGc0Gg3w+fwJz18SXa2vryOfz+PcuXPUWOPKlSsolUrUYKLT6UAkElERHo+g+Xw+FWGfzweLxYJms0lnMZPJJF0RyePxUK1WkUqlUK1W0ev16Io80pCyvr6OarUKpVIJi8VCU3rr6+uIx+Not9tQqVRQq9V02xMR3vGkj0AgoCJL1keOr5Dc7c2/3+8jl8shGo0ik8lgMBhAp9PhxIkTsNvtN6zRSiQSHDt2DPF4nKbeV1dXoVQqMTMzs8msZVyYyX5fEpW53W7avZxIJBAMBuHxeOByudDtduns+f3334/7778fr776Kl5//XWaMn/mmWewtLSE973vfXjggQdoRzBJo5PNYLVajbqdra2t0Rs26Sge74TmcDhUrJvNJl1UIhAI6Oid0+mkG92y2SzS6TT9PJlMhlqg3k7I91qtVmmKtlKpwOVyYX5+HgqFYsvXCjmoPvzww5DL5SiVShO/G9PodrsT3dFEhKVSKdRq9VQR3si4UYnZbJ6IcMmUQSaTofX/o0ePUl/qjYeA4XBIPx8R4fGIm2EYLC0t4dVXX6U7xsnrhPy+cTgcHD58GGfOnMHP/MzP3FFe53cj96Qol0olfOELX9h1gxJw3anq0qVLqFar+Nd//Vf88i//Mp5//vkthflTn/oUfv/3f5/++9KlS3jsscf2fO37DZ/Pp3aT+Xx+YrsRqaOurKygWCxiOBzi/PnzKJfLdGsSSYGSJelisRgej4fO1LpcLvT7fdpME4/H0e12qUEE2btMmp86nQ4EAgG8Xi+MRiM0Gg01MgmFQjRaJnuaY7EYkskkvakYjUZ0u10Ui0UqsiTyHhfe/RjD6Ha79DBAlsArFAosLi7C5XJtGokhKeHxaL1SqdBMisFgAMMwSCaTsFgsEIvFyGaz8Pv9mzplgUlhTqfTcDgcCIfDyGQysFqt8Hg8EAgECIfD1B7VZrPBZrPBYrFgbW2NLr84cuQIXnnlFfj9fgDX07N///d/D7vdjve///1YXFycOFhsLAf0ej0anTWbTdRqNbq0gzzvpJOcCAypiZKFDJcvX4bFYoHH4wGfz0en08H6+joVlCtXrsDlcuHQoUM0Kh3f670fkCiTCHG73aaiRGrWx44du2FdOBaL4cKFC3A4HDh9+jSSySTdKjX+MyQiTARzXISJralCobipSYDxWWnSA7K+vk5XrabTaaysrKDVatG+DmKJScpZG5/f0WiEixcv4utf/zouXbpED+TkUCaVSjE3N4eFhQUcO3YMTqfzQFd13kvctaL8lre8Zer/VyoVrK6uotfr4f/8n/+z688rFArh8XgAAA8++CBee+01/M3f/A3+7u/+burjyQ2JcCedEkkjVLVaRb/fRyaTgVgsRqvVQiaTQTgcxuuvv452uw2lUklvDONzmzweD7Ozs5ibm5uwVKxUKjh//jyef/55GvGSOVe73U5P+yQ12uv1Jk7jHA4HlUoFV69exfr6Ou30VSqVWF9fp7aTvV4PKpUKR44cgVarpeJ7q3a0kvEbEm3U63WIxWJYLBa6Q5qMoYyvwtwYqZNmMLJcgMfjoVwuw2AwYDQaIZ1OY3Z2FocOHUKlUkE2m0UgEKBdrORnMC7MwPXuYfJz1Ol0cDgcEAqFWF1dxdLSEobDIZxOJ/h8PmZmZmAymVAqlZDP5/HYY4/h2LFjOHfuHJLJJIDr86Z/8zd/A5/Phw984AO063sjJMMwnmkhP1si1uTAJxAIIJfLMTs7S+1Y19fX4ff7aTPgwsICTCYTdboql8uIRCK4cOECAoEA9VgmS0BIuWQ8db7d38ejW9IcF41G6WGCpN7Jc022qTmdzhsKciaTwfnz52EymfDQQw+By+XCZrOh1+thZWWFmqPU63W6GpWIMImEb9Xrt91uo1arUcOSubk5OoNOnhMyhri2toZGo0GjbpFIhGg0ihdeeAGvvvoq1tbWaGaKzFFbrVb4fD7Mzc1Bo9HAYrFAp9NNzSYQ97JpkwYsW3PXivJoNNr0QuBwOHA6nXjiiSfwq7/6q7u2H9zq69ytq8dI1NJut9HtdvH6668jEAigXC7Tvazk5kROyqQxy+v1wuPxwGQyod/v0/GTXC6HSqVCa8Qk3UW6kHk8HvL5PMLhMI2KSUMZcfjJZrMolUq0ROD1emEwGCAWi8Hlcmm3qM1mw+zs7FRbzP2CRE4kqiwUClhfX6e7lUmXKZfLpcsKyPNEhIqYjoynx8lNV6fTYTQagcfjIRKJQCwWU7FMpVLgcrl0BKxardL6vUwmo57dG4VZo9EgHo/TRRYWiwUCgQBLS0vUQtHtdtPFEgaDAXq9ns76vuMd70AqlcKrr76KYrEIDoeDQCCAv/qrv8J9992H97///bBYLNs+bxwOh3ZtE8jrhLwRL2liU+pwOGCxWOgSkX6/D61WC71eD4fDgZMnT9LMAcl2kIYtmUwGoVAIgUCA4XCIdrtN06jTGqF4PB4EAgEtwygUCnQ6HRgMBrrggZDJZJDNZmG1WmE2m7f9vguFAl577TUolUo8/PDDdKsU6ZaOx+OIRqM4fPgwtFrtLRfhTqczkY4eDAbUle2+++6D0WicGgmTtH02m8WlS5fw2muv0T3jpG+Aw+HQ1+qJEydo9zaHw4HZbJ46WjYajVAqlVAoFOj44j3YS3xLuWtF+bnnntv3z/mpT30K73znO2Gz2VCv1/GVr3wFzz33HH7wgx/s+9e61QwGA3zpS1/C+fPnUavVIBKJaHqJpNQEAgF0Oh2tR87NzcFgMKDX61E/33w+j0ajgX6/Dz6fD5FIhIWFBWo7SFK1+Xwe8XicjgMR1yeSWiYHqG63Cy6XC7fbTSNvUtMkokcij/HT/c0wvkGKXG+pVKKdw2Qv7HA4hEgkot2iOp2OrmncuHd6N9dF/I0XFhaQzWYRjUYhFoshFAoRj8fpqkXS1Vur1Wg6XyqVYmZmBmq1mgqzTCaDRCKhBhZCoRAGgwEPPPAALl++jCtXrlD7Q3Kd4zPHjUYDarUas7OzCAQCeO211+ghi3z8ww8/jJ/92Z/dcgPSdt8nOUSRWicR6Vqthk6nA61Wi06ng2QySQ97JPLX6/Xw+XzIZrOYm5uDRCKhnfbkNatWq2EymagpBymzjI+Wkb/z+Xy6YnBxcXHTNZN5/J0IcrFYxLlz59BqtWC32xEKhdDv92n6mJiLpFIpOn+/325W00SYHJAMBgN9Pqal4BmGmdgbffnyZVy9ehXRaJR2WBuNRnQ6HTpC9fjjj8Nut1OBNZvNmJmZ2VTz7na7WFtbo2UwtVpNf4fZRq/dcdeK8q2gUCjgIx/5CLLZLFQqFY4dO4Yf/OAHePLJJw/60nYNn89HNBrdFOUPBgOoVCo8+OCDuP/++6HT6egcLsMw9IRLurRHoxGtk5FVhgzDYH19fcLMgWEYWCwWHD9+nDYuEbOMYrGIaDSKYrFIrT05HA69qRD7RoZhNm3o2Qmj0WgijTzecd3r9egNutls0k04ZH6Vx+PRLmCTyTSROt5vZDIZPB4PLR9Uq1Xa2Hb8+HFaxyWdzqSzNhwOQyKRYGZmBm63G+FwmP4cwuEw5ufnqTf1gw8+iNdffx1XrlzBYDDA4uLiJmGQy+XweDw0cvR6vbh69SrOnz+PXq8HHo+Hc+fO4bXXXsOZM2fwrne9a0/PyXitk+zkJj8Hq9WKYDCIbDZLO8Cz2Sw9zPH5fEQiEXi9XszNzWFubg6NRoMK9PjCDJIWnmZTSUaXpr2eiCBvN49PRDCRSOD1119Hs9mkTl6kM33jjLdUKkUgEEAsFtuyHLBTyNcnYjp+CNDr9TQS53K5GI1GWFlZgUKhoAcMYntJGvbS6TSWl5cRDAaphep4Jmp2dhYzMzM4duwYDAYDIpEIUqkUHXdbX19Hv9+nKW+SBq9UKuDz+TAYDDAYDLfdrvZe4q4R5f/9v//3nj7uIx/5yI4f+4UvfGFPX+NOxeVy0Q5YjUaDI0eOoN/v0xsZiT5ImpDUBBmGoaNLBoMBGo2GRos8Ho/+onc6HajVarhcLuh0uk3jPaPRCOvr66jX65idncWJEycgFotp9JTNZpFMJtHtdunJejQaoVKpTLhOjbtpjYsteRufxwV+Wv8kYkRGNoRCIW3+I4vjSYSh0WhuiUfvk08+iXa7PeGeJpVKqThns1lcu3YNTz/9NB588MGJeV1y0yemJCT9rdVqUSqVaM0+Ho9Ty0OZTIaTJ0/i0qVLWFpawmAwwNGjR6d+b2KxGHa7nbqpHT16FK+++urEnvJnn30WL7/8Mt761rfiySef3Ha+eieQcolKpcLs7CwtdZCsDeknIOsFybz/4uIirVPPzs7SFZFkiQYZzdu40WorUqkU8vk85ubmJiwgxzc+kUNjvV5HJpOBUCjEQw89hCNHjmz7+WUyGZxOJ8LhMDXs2SnjjWEbRVin002I8EbS6TSazSYsFgtisRitnzcaDQSDQZqeJnV3Eu2SdZwPP/ww7biXSqVYX1/H3NwcTp8+DbFYTFP01WoV0WiUbuTTaDSw2+2Ym5vbZCnKsnvuGvOQvdwwSVr0dnInmYdkMhn86q/+KprNJrhcLh555BFaw+NyubTW3O/36XYkjUYDrVY7MZZBmnqIQw+Xy4VCoYBWq4VKpZrozORyueh0OrSJRCKRwGKxQKvV0scQT+hms0mFp9/vU5/qZrOJfr9Pa7FknphE6htTyeP1XIZhUK1W6UgWAFpTI+JMxrpux5aendBqtfDqq68inU7D5XLB6/VObZ4hB5lqtYrBYIBWq0UbC+fm5ibSr/1+H5cvX6Ye0/fff/8Nsw/D4RDFYhHxeBw/+clPcOnSJQgEAjqbK5VK8Y53vAOPP/74vkZCtVoNkUiE9jMQd7RkMokLFy4gnU7TLIbRaIRer6f1bIlEgk6nQwW60+lMbLT63Oc+h2q1CrVaTc0rkskkCoUC5ubmoFAoJrqjx9PBCoUCg8EAmUwG5XIZdrsdCwsLO74X5fN5pFIp2O32LcsA00QYwMS88XY1aXJoyGazuHr1KrXvFAgEiMfjWF5eRjQa3fRxAoEADzzwAE6dOoWFhQVwOBxcu3YNkUiEGr3Mzs5OZEjGU9T9fp8udAEw4Xs9bmjyZ3/2Z5uef5btuWsi5WkvLJbtIX7XxPT/F3/xFyGTyehOWBIpKpVKKq6j0Yi+kZEW0mWsVCrpLyqPx6OPI5Fss9lEPp9HrVYDn8+HVqsFn89HJpOhrlakq5q4EYlEIjrnSsYtyGGA1MdIzXd8zIrcqHg8Hvr9Pk0Dk5q1Wq2G0Wik3c/9fp/uxb1VUfFekUqleOyxx+gNdDAYQK1Ww2w2T9TVx9PfZM43FotBIpFMZDeAn950hUIhlpeX0e/3cfLkyW2FmcfjwWQywWg00vr1008/jatXr9J0+de+9jX8+Mc/xrvf/W488sgj+9LApFQqsbCwQEe7XC4XjaYPHz6MlZUVhEIhcDgc5PN5FAoFuv+XjOfI5XJYrVbweDya5o5EIigUCrRkA/x0tlilUtFMzXhNlqSjuVwu3WZWr9dhsVi29OneCpPJhG63S9dvKhSKbUekdtIYNhqNaH2eHJKHwyHS6TTtWL9y5Qpef/31qQ2qXq8Xp0+fxoMPPkgzUf1+HxcvXkQoFILT6cT8/PzEvPX4/Dbp5DYajRMHM3JwJ1mGfD6PTCaDQqHARs675K4R5dttKnAvwOFwaFOSXC7HaDRCrVaDXC6nxhwbIx4icKVSiY63eDwe6HS6qVEliXCTySRKpRIdKZNIJLSWSzpU19fXIRKJcP/990Ov10+k0cjIC3D9xkNcqMhbt9ulde5isYhAIEBvSkSQSJRA3MkajQaNmkj0kM1mkc/nJyL7jW+7fd9WNx1ScydR/I1+VocOHYJIJKLr7hKJBLLZLBVnIghkr7XFYkEwGMTVq1eRzWZRq9XoAgrgenbp6NGjEAqFuHjxInq9Ht70pjfd0P+Yw+FAq9XioYcewsLCAlZXV/Hd734XKysrEIvFYBgGX/7yl/GjH/0I733ve3HixImbvvGKxWIsLCwgGo0iGAzSOi+Hw8Hi4iIEAgHq9TqMRiMdyev1ejQDQtLewPXyBfH15vP51ADnq1/9Krrd7kRX+7gIj1Ov1xEOh1Gv12kz5I2c3qZhMpmQy+Xwk5/8ZMJ9bjcjUiRLNb6Jic/n0+1cpH5NJiM2otfrcfr0aZw6dWoiYifNlaurq8jlcrjvvvtw6NAhWp9eX1+n604lEgnsdju0Wu3Ug8l4N77JZALDMOh0OrTrnWXn3DWizLJ7ms0mrbcJhULMzc1BrVZvurmQOm6pVEKtVgOHw4FaraYGF2TUhcwNkzdS9yNdsSaTCQaDgYqQQCCgrmEzMzM4efIkzGbznqJU0jlaqVRQqVRgtVppepusEszlcjSqJ/VHk8lEnafIashxsSdWk9MOAtNGbaZBonxi0kLMWYhokOefiACZv5VKpfRAAoB2pZO5WqfTiVqthmQyiVwut0mciUvYzMwMzp49i2vXrqFcLuORRx6hzzOHw6Ed2q+88gqee+45PPbYYzsWGGKXeeTIEVy5cgXf/OY3EQwGIRaLkcvl8I//+I/4wQ9+gPe///04fPjwTYkzj8eD2+1GJpNBKpVCu92GzWajz0swGESxWITP54Pdbqfd891uF2KxGEajka5SJAYhZGqg0+kgn8/j8OHDOHXq1LZli0ajgVAoRJeKOByOqXao09jKNnM4HKJcLuP48ePQaDTbijCJpokQDwYDWjKyWCxQKpVgGAYXLlzAj3/8Y1y+fJnaYRLEYjFOnjyJU6dO0fE4AsMwKBaLtNmw2+3iTW96ExwOB3q9HgqFwkQXNUnz7wYyBieVSjdZ3LJsz11TU55GLpfDF77wBVy8eBHVanXTTZTD4eCZZ565rdd0J9WUAeB3f/d30W63odVqN9V0yJzi2toabYQidTpiuzgOmc0lN5h2uw2ZTEY3Po1HYOVyGel0Gt1ul27o2W2kQSJ7UmseDAbUwUutVkMmk1ELyGq1Sk/r5IbbbDZp4xq5SZDT/E5XOo4L9EbRHo9gyBo90q1OauBCoXBijKXdbtPGNFJWIMtBSIc7h8NBKpXCcDjE/Pw8BAIB3WksEomo5/f44aZer+PSpUtYXV2FVquFx+OB2WyGwWCgj0ulUnjppZegVCrxlre8ZUf7ejfS6/Xw6quv4mtf+xq1DCVLTbxeLz7wgQ/A7Xbv+vNuhKzrJFkBMp8cCATQ6/WoKQlwvSZdLBZplKhWq6HX6yGXy/GJT3wC2WwWAoEA/9//9//RfgVSo954QGy1WggEAvS1b7Vat53ZnrZAArh+YCIHMJKlWl1dhVAohM/nm/i6pC5MXktkSYhUKqWd+DKZDAzD4Nq1azh79iwuXbqEXq9Hl7IolUqabTl9+jTuv//+qb9v47+X5PdHo9HAaDTSLmqSoiYH7Jvhk5/8JPUeZ2vKO+OujZSvXLmCN7/5zWi325ifn8fVq1epO1I6nYbb7b6p3aH3AiSSJJFuIpGYWLtH7DCJlzRx7dlohEHeyHKKZrMJtVqNxcXFTYviG40GUqkUms0mVCoV3G73rpqphsMhbdQiBy3i4a3RaCCVStHv91EsFumOZalUCpvNRpvJNj4HpNubdI2T9YVcLndiz7BUKt10EyIpajLrTDpQNzYFmc1mWuveLj1MVg0Sb+nxG3q1WgVw/fDD5XKRy+WQz+fhcrloGYJs6OLz+bT+SzZXGQwGOi5E3Mji8Tj1Fp+dncXjjz9OZ++feOKJXXdTC4VCPProozh16hReeukl/Nu//RtSqRREIhGuXbuGYDCIo0eP4v3vf/+ebG4JWq0WIpEI4XAYKysrcLvdkMlkdOVjIBDA/Pw8XZ+pVCoxGAyoX3swGIRQKKSz0Xq9Hi6XC6PRCLlcDrlcjnYXkzp8u91GIBAA8NO57o2CTJy6Nu4z3m6BBMHj8cDv9yMSicBoNFIhbrVaAECXoxAxJ6/lVCqF73//+3j11VdRq9Xo5yPLb7xeLx599FE89NBDWxrtjP9eKpVK2O12RCIR6kkQDAYhkUhgs9mg0+nuqJ6LNxp3baT8rne9C0tLS3jxxRfp4P7TTz+Nt7zlLfjqV7+Kj370o/je976Hhx566LZe150UKZPVjbVaDWKxGL/0S79Eo029Xg+TyQSdTkdTzVtBGosqlQpEIhFmZmY2iTHZAlSpVCCVSqkl5U4g7mJkRIthGMhkMhoRk9QziYhIVEw6yXeaWiSQWi8RamIDClwf2SFRNICJevb4Jp7xDtP9uIGRgxPZC0zmv+PxOBiGgc/no41NwHUji7W1NTAMQ+1JSef51atXUa/XqVlGpVIBh8OB0Wik5hCvvvoqBAIB3vzmN0Oj0UzU93fjNz0cDvHcc8/hX//1X5HP5+myCpI+fe9730tnlPf6vITDYbRaLTgcDmi1WvT7ffj9fjAMg/n5+an1elK6+fSnPw2GYWAymSYitW63i2QySZefGAwGauQyHA4hFAqxsLCA4XC4pQiPR8LbHcRIE1S9XkcqlYLf74dWq4XVap3YwDX+O1iv1/Hqq6/i7Nmz1BJ1HB6Ph7m5ObznPe/BkSNHtsz4dDodugCG/F4KBAKcO3cOhUIBNpuN+mDvNkW9E/7wD/8Q9XqdjZR3wV0bKb/00kv4oz/6I9hsNrrPl6SvP/jBD+LFF1/EJz7xCTz//PMHeZkHjkAgoA1fPp8POp0OarV6RzfcjWJMborjN4DBYEBT4AKBgK4RvBHdbpfWh8mJX6FQUFtNcqMltpzFYhG9Xg8SiQRzc3NTo+KdQhYPjN+E2u02tdnMZrMol8sYDofg8XhQqVTQ6XTUrnKjWcR+sNENC7j+vbtcLtrINV6e4fP5sNlsEweL2dlZLCws4MiRI/jJT36CZrOJRx55BHw+H9lslvp5q9VqnDp1CmfPnsV3vvMdPPjgg1NvyGQ/9o3e3vSmN+HRRx/Fj3/8Y3z9619HsVhEq9XCCy+8QA1I3v3ud+9pYYFAIIDP56OrMtvtNiwWC3w+H/x+Px352nioJNkPo9E4tflJJBLB4/GgWq0iHA7j4sWL1O600WhgZmYGKysrtElJIpFAqVTS182NmuW63e7Eli2yYUutVuO+++5DtVqFxWKZaLwaDAa4cuUKzp49i6WlpU3lOB6Ph2PHjtHGOq1WS+fTN0K87sniFqfTCYFAgEKhgKWlJbTbbTzwwAOw2+03naLeClKbZruvd8ddK8qj0Yi68JB9vkScAeDo0aP3nBnIbiHCQcY9vF7vjj5uJ2I8Go3oZijg+qKEaV6445CuWVKP5nA4UCqVcDgcdCSLUKvVJmrFGo0GBoNh11HxVoyv0NtYC1xYWKCp/OFwSIWvVquhXC4DwEQtmNSx9zvlR9zPHn30Ufj9fnC5XLqekUTTpLu9UCggHA7T7T2HDx/G+fPnceHCBTz55JPUwzyfz9P0/Tvf+U4sLS0hn8/TMRhiUzntrd/v05r4NE9jo9GIX//1X8crr7yCZ599FrlcDlwuF//2b/+GH/7wh3j88cfxtre9jTp2jTe5bQeXy4XD4YBEIqENYGRd6Lgw30gop8Hn89FsNgEAy8vLaDQaOHbsGDXqsFgsOxLhwWBABbhWq6HX69HSxrSViIlEAolEgvpzEwc1ksoeh2yiOnnyJGQyGYLBIG2C28hwOEQul6N7vsmYGNmNXqvVIJPJ8Oijj95wzeRe6fV6iMViqNfrtD+FZefctaLsdDrp7DKXy4XT6cTTTz+NX/zFXwQAvPzyy7d0kcHdADHJIM0bN6LdbiOTyWwrxsD11ZiZTAb9fh8Gg2GqFy6BdG2TFYYk8iRjKeNC1u/3JzpqJRIJZmdnodPpbioyJR7M4/Vg0shDaoEkFT0tjU82IwGgFqREqIk96Hi3KRHqnTSS7QSS5fD7/YhGo5ifn594bRM702q1ikQigUAggOFwCIFAgNXVVaytreGJJ56ASqWiNqZk3ndmZgaBQADPPPMMHnvssV31YZBtQxuFe2ZmBk8++SSefvppPPPMM6jVaqhWq/jnf/5nfP/738eb3vQmnDx5ktbJdxKNk33gIpEIsVgMq6urdHFKIBBAKBSC1+u94euENFUR/2eyylKv10MoFEKpVNLXAenmngaZ4yfRcLvdBnD99URcxbYbdZLL5fjhD3+Iv/3bv8VwONz0+0OyGadOnZrwsCbLWjZ+rwzDYG1tjWZUSKCSz+fp3Ds50Fqt1lsmyKVSCclkEjweDz6fD0qlkvW+3iV3lSiXy2V6g3zb296Gr371q/iLv/gLAMBHP/pR/MEf/AEikQgYhsFzzz2HP/iDPzjIy71r2KkYk5pYq9WCRqOB1WrdlPpiGAb1ep2mpslIChmtIF2iGz8v6fwkUbHD4djzGkxisDAuwmRbkUwmg06nu6FT0laIxWK6PIF8v+NLF0gtE/hpIxn5GPK224UWwPV0KxGgYDA4cVMmjXhqtRp2ux39fh/JZBKpVIr6IX/ve9/Dgw8+SLcnEdvGZrMJj8eDy5cv4xvf+AaefPLJHW9XI+YtW6U/5+fn8ZGPfATf+c538IMf/IBmJF588UWsrq7ine98J06cOAEAE8Le7XapwE9reen3+4hGo/D7/XA6nVSoybgUaXwb3yVeq9WwvLxM09F8Ph9ra2uwWq10DGt+fh6zs7OoVqtIJpO4du0ajEYj7dLeOC/MMAztfDabzRM1/2mQTW1nz56F3++no4gMw9DeiePHj+P06dPUz3zjx6dSKRgMhgmnrfX1dWQyGToaRrKG413U/X4fgUAAer3+hos39kK/30cikUClUoFOp6MrI1l2z10lymazGe9617vw1FNP4Q/+4A/woQ99CP1+HwKBAB//+MfRbDbxb//2b+DxePjP//k/44//+I8P+pLvaHYqxu12G+l0GtVqFTKZDPPz8xOCSUaXyuUyqtUqbZTRarX0hL7xcw4GA5RKJaytrdGbyezsLHUB2w3E35e8EUMR4hpGFmRMu46bZdrSBTIuNd64RdZBko8h1qHkjfx7u+9dIpFsigynpcwFAgFcLhdsNhvy+TxUKhUuXLiAaDSKxx9/nFp0lstl6gHudrtx/vx5fPGLX8Tp06fx1re+dV92gysUCnzoQx/C29/+dnzrW9/Cs88+S53dvvzlL+P555/HBz/4QZw8eXLq90Ic4za+WSwWRKNRJBIJ6PV66HQ6RCIRrK2tYW5ujv6c19bWqL87eS1IJBJEo1GYTCbY7XZEo1GoVCrqUU06oOPxOAKBAC5fvjxxiCO9D0qlcstImsAwDAKBAM6ePYuLFy9OuGyR8o1KpcKJEyfwvve9b8tueIZhEI1GIRAIaFc7OSSTAwKZxxaLxRNd1N1uF+FwGHK5fGrK+2Ypl8tIJBIAALfb/YbPUN4sd5Uo/8Iv/AK+9a1v4Vvf+hYUCgV+7ud+Dk899RTe8pa3gMPh4E/+5E/wJ3/yJwd9mXc8ZLSpXC5vK8Ybm0VcLhfNVAwGAzq6VKvVMBqNIJFIYDQaoVart7y5kJEsUpvdS1RMZkOJCJPoRygUQi6X00j4oHytx/1/xyFLNTqdDn1bX1+fMFfg8/kTIj0u2uQAQIQ5HA7D7XZvWcvm8/mwWq20pvn888+j3+/jzJkzsNvtEAgENPXdarVgsVjw4x//GD/60Y+wtLSE48ePw+Fw0J+nTCbbk6sVcH3E6Vd+5Vfw9re/Hd/85jfpCsRIJIK//uu/hsvlwoc+9CEcO3Zs4nVIVgpO67Cem5tDJpNBLpeDTqeDz+dDOByms/PD4RAajYYuu7Db7XTWud/vw+12IxaL0QbFjfPCvV4PMpkMw+EQnU4HKpUKPp9vR2Nk+Xwe586dw7lz5yZ6XQhGo5GmpyUSCfx+P7LZ7JYz3vl8Hs1mE/Pz8zRiLpVK6Ha79PkhDl/jUfRwOEQoFAKfz59YdrIfDIdDJBIJrK+v0yzNXur6LJPcVc/gl7/8ZbTbbXzjG9/AV77yFXz5y1/Gl770JZhMJnzoQx/CU089deBjSHcyG8XYbrdPXXxAZjnJbmOyZ3l8Tdv46BKx7NwqjUmiYmJ3KBaLaV3rRr/ExK6PCPC4aT8ZSyGR8J2+Lo7cPDd2OpOxq3GxJk1xpEmGdNATkdZoNEin09RgZLubLZ/PxyOPPAKZTIaXXnoJ586dg9vtpvVl0vVtsViwuLiIV155BWfPnkUsFkOr1YJUKqU/X5L6JiItlUp3JdQzMzP4rd/6LbzjHe/AN77xDVy5coUadvzZn/0ZFhcX8dRTT+0ohc7hcGC1WiGRSBCLxdDpdGCz2ZBIJJDL5Whqm9R7R6MRQqEQut0uvF4vkskkKpUKZmZm4Pf7J+rCxAJToVBQH+xEIoHV1VWa0t6Ynm21Wjh//jzOnj2LSCSy6XolEglOnDiB06dPbxJIslUqlUptmu8mGS2tVotisYhkMkmXvRCjlGlGH2S152AwwMLCwr4KZq1WQywWw2g0gsPh2LJG3ev1ppYgWLbmrp1TBq6nTf7lX/4FX/nKV/Diiy8CuD5I/0u/9Ev48Ic/fNO7TPfCnTSnDFx31CkWi+Dz+fiN3/gNiEQimM3mqWLMMAxt4hoMBjCZTNBoNNRVq9lsgsPhQKFQ0Bv5djfkRqOBtbW1iaiY7IDdCpL6HU9HD4dDGiWOzwe/EU7lZLNVp9OZEO5ut0sNIcZNWjZG2RvtFS9evIhAIDBh12gwGGAymeihZjQaYWlpCcvLyzT6Gg6H9BBE5ryJM9nNCPXq6iq+/vWvU1tL4mZ17Ngx/NIv/dKOf4ebzSbC4TCA61F5Pp+HwWDA//yf/xOVSgUqlQq/9mu/hlKpBJPJhEQigUwmg7m5OSrAZORpq2tnGAb5fB7ZbBY8Ho+O7y0vL+Ps2bO4fPnypjWiHA4Hhw8fxunTp3Hfffdt+7wUCgUkk8mJrVIMw9AOeQ6HQ+eqzWYz9RnYKlMSj8dRKpXg8/n2pRQBXH9tpFIprK2tUROSaYdhsrnqL/7iL8DhcDbNibNszV0tyuOk02l85Stfwf/9v/8Xly5dAofDwcMPP4yXX375tl7HnSTK/X4fv/3bv421tTWo1Wr81//6X6eKMXDdcjOdTlPzealUSg0syOJ5IsTbNXAQVyVi3SkSiWAwGLaMiofD4YQAN5tNMAyzaQXctKUBb2RIBqFQKMDv99Ol951OZ2IEhQj0eM16aWkJhUIBhw4dgkqlQqFQwGg0ok1AZAXm6uoqlpaWYDKZYLPZaEpXo9HQSHFj7XwvQs0wDC5duoRvfvOb9DXY6XTAMAxOnDiBD3/4wztaSDNuNKJQKFCr1fCP//iPdCvaBz7wAVitVurKNT8/D4fDccO68EZ6vR5ee+01vPjii/D7/bSDfByr1YrTp0/j4Ycfnkgn34hEIoFisQiPxwO5XI7z58/jtddeg0KhgNFohNPpxMzMzA0/Zy6XQzqd3jaK3S2NRgOxWAz9fh+zs7NTTWGIGNfrdUilUvzt3/4t3bvOivLOuGdCDavVik984hN4xzvegT/90z/FN7/5TbzyyisHfVkHCnEnUigU0Ov1U3e6tlotul+W1IXb7TZ6vR5Nae5kaXyj0aC1YtJNarPZNkXF5IZIRJikDAUCAV0iQerBrOnA1pARLLvdDoVCgWg0CqlUioWFBQwGg4lUOLH1JA5gXC4X1WoVzz77LBYXF+HxeOiu5kKhQMfcFhYWwOPxsLS0BIZhcOzYMToHe+3aNajVaszMzEwdzyJCTcZ0gJ8KNRFpItQcDgfHjx/Hfffdh3PnzuHb3/42SqUSOp0OLly4gAsXLuD06dP44Ac/uG2j0rjRSKlUojPI+XweUqmUmqSk02l4vd5d2/DWajXqspVKpei60uFwCLFYDJPJhFOnTuH06dOYnZ3d0+t3bm4OvV4PL774Il0/6XK5cOrUqanTDtMgVsMzMzP7IsgMw9DaPbE63XgdG8WYNHyJxWJ2S9QuuSdEOZFI0CiZ3EDe9KY34amnnjroSztQxueUN94gut0u/H4/EokEXRqh0WhoNKxQKG54UxkOh7RW3G63qQWnTqejUdHGejBpaiJ7kU0mE92rzLI3tFotGIZBLBYDl8ulB5uNKUuGYWgKXK/X49KlS4hEItTBisvlolQqIRaLUW/t2dlZuFwuhEIhvP766zh69CgOHz6M9fV1ZLNZrKys0LlzmUw2MZ5F2CjUZMcxsFmoT548iYceeggvvPACvve976FWq6Hb7eKVV17BK6+8gkcffRQ/93M/B6vVOvX1udFohIzB2Ww22Gw2rKys0L3gO6Hf71OXreXl5QmXLaFQCIlEAofDAbvdDp/PR7ue98JoNEIikcDS0hJWVlbQbDbx2GOP4fHHH99xqabVaiEajU717d4L7XYb0WgUnU6HNg2OP+/1eh2ZTAaNRgNSqRQej2dPzm0sP+WuFeVisUjryWfPngXDMFhYWMBnP/tZPPXUU3A4HAd9iXccZLsTWfTO4XBgsVioNeZO3bKazSatFZOomHhdk/GfjUsbSFMKcTbaaxcvy3R0Oh29qXO53Kk3ZA6HQ2vNxOAiEAjQfdtWq5UKaCaTQTqdRiQSocsRlpaWEIvFsLi4CLvdjpmZGbTbbZTLZayurkKpVNKmu3G2EurxtPdGobbZbPjYxz6G1157DT/5yU8gFovR6/Xw8ssv4+zZs/iZn/kZvPe974XFYpmaxSErO8neYYFAgFAoRMfFtjtwkvGjs2fP4vz581NdtpxOJ06fPo0TJ05AJpOh1+shlUrReWmbzbbj7v9er4dEIoFr166hVqvBaDTiscceQzKZvKFL3sbPEwqF6EHhZiD180wmA7FYjMXFxYnvhxXjW8ddJcrNZhNf//rX8ZWvfAXPPPMMdQ/6+Mc/znZeb8Fb3vIWFItF9Pt9PPvss1hbW6Nm+16vd8cNIMPhkNaK2+02hELhxP7aXC6HcDg8sbTBYDDQejBrJHDrMRgMtBGHy+Xe0CSCiGo6ncb6+jr4fD7cbjc1fxiNRlhbW0MymUSz2QSfz0ehUMDq6ioqlQoMBgM4HA6tb+fzeQQCAWi1WrroYKuOeCLU425pG4W6VqvB7XbDYrHg1VdfxcWLF+lh7vnnn8fZs2dx5swZvP3tb4fFYtkUTSqVSuj1ejo/3+/3aUp+GqVSCa+88grOnTuHfD6/6f0ajYaOMW18boVCIVwuF91/TYxHLBbLll+v0WggmUwiFAqhXq/DaDTi4YcfhkQiQTAYxKlTp7C+vo5oNHrDgwQZfeJwONuOye2ETqeDWCyGZrMJs9kMi8VCv3atVkM2m2XF+BZyV4ky2XAjl8vx4Q9/mM4osw1A0yFpSpFIhHq9jtFohAcffJCa0+8E4lC1vr5O3blIvTefz1PTArlcDovFArlcDqlUytaDDwiTyYTRaIR0Og0ulwuj0bjt4x0OB10OQuZp3W43OBwOuFwuTCYTDAYDisUi9bKuVCoYjUb0Z046xLvdLgqFAtLpNMLhMJ1b1+l0U41SNorVdkLtdDpx5swZ/PCHP8TFixcxGo3Q7/fx7W9/G8888wzOnDmDJ598EjabbVMppNVq0TndjU1d3W4XFy9epC5bGxEKhXjggQeoy9aNXtdKpRKHDh2iyz/W19epVSxwPUVdLpeRyWSQSCTQarWg1+vx0EMPwWg0YjQa0a53u90OtVqNUCiEdDq9ZcqdRPZkz/TNZKHIz08gEGBhYYFmz8bFWCaTsWJ8C7mrRPmJJ57AU089hfe+97277ph8I8LlctHv9yGRSGC1WjE7O7ujlBqJiolxSL/fp8vsGYahN2QSCbM/izuLmZkZDIdDJJNJcLncqQ1+BC6XC7fbjZWVFWoIQ0xJiAARcScNYJcvX0Y0GsX6+joOHz6Mo0ePUtFxuVzUhzkej6NSqSCXy0GlUkEgENAZc+B6mnqaUcq4Dem4UFutVpw4cQKJRAJf+9rXcP78eQwGA/R6PXznO9/BM888g5MnT+LkyZNwOBzQ6/U06rZarbRjmWEY+P1+nD17Fq+//vqEyxZhfn4ep0+fxgMPPLDrfgcyAqTVamlKO5PJQCqVolar0V3mWq0WDzzwAEwmEw0skskkRqMR7TZXqVSYm5tDMpmkkwwbSaVSqNVq8Hg8ezbMGV8iYTAYMDs7S+ezWTG+vdxVovzNb37zoC/hroLP50On08FkMt1whILMKMfjcWQyGTSbTWpSQXx9ye5Yth585zM7O4vRaER3BG+3TlMoFMLtdiMQCEAoFKJWq20SZuC62BgMBrz1rW/F8vIyLl68iFdeeQX5fB6nTp2iEe747ubxm7pAIKBmH+Od4c1m84Y2pOSNx+PBZrPh4x//OBKJBL7xjW9geXkZw+EQ/X4fFy5cwOrqKo4cOQKn04lisUg3euXzeZw9exbnzp2js/PjGI1GOsa0H13LZMtXvV7HysoKyuUyZDIZHA4HDh06tGmRS6VSQalUgsPhmEj7G41Guv9ZJBJN/C6vra3Rvci7Gb0aZ3yJhNfrhVKpRK1Wo/cB0nG918/PsjvuKlFm2R1cLnfLdY1kaUOtVkMqlaIr8QQCAWZmZnDkyBFotdo9LW1guTOw2Wy0K5ss+tgKuVwOu92OWCwGtVo9NWImcDgcHDlyBDqdDpcvX0Ymk8H3v/99HDlyBB6PZ8KGUqlUQqlU0pGZWCwGiUSCmZmZTc1ovV5vQqy73e6WNqTk7SMf+QiSySS++93vIhqN0qwNmcUmtpkkBSwQCOgYFgBIpVLqsuV0Ovel7EJS1IVCAa1WC71ejx5sh8MhRCIRbUIj9Pt9xONxqNXqqQeC2dlZdLtdRCIRzM/PQyKRUJcxksXYLYPBgGYzSB9Bs9nE6uoqK8YHCCvKbxA2Lm0olUqoVCpoNBoQiUSwWCyYm5ubah/Icvdis9kwGo0QjUbB5XK3TT3qdDq0223k83mYTCa6o3maMAOgrxWNRoN8Po/V1VWUSiX6Ohrv5ieZlkajgWw2i0gkArFYjJmZGWg0GmojSjycxxmNRpsczTYu+XjyyScRi8XwwgsvoFQqgcfjoVgsIh6Po9Pp0GUNrVYLXC4XCwsLdEcx8ce+Wfr9PtbW1rC2tobBYAA+nw+GYSAUCqHT6WC1WiEUCpFKpRCPx2mXtlQqpQsdtjJJ4XA4cLlc8Pv9CIVCsNvtiEQiUKlUOx7vGqdSqSAejwMAbQwLBoOsGN8BsKJ8D0MWSpClDcTCktT1DAYDjh8/TnfJstx7cDgcOBwOjEYjhMNheL3ebW1OrVYr2u02isUirWVuJ8xGoxEcDgc8Hg/tdpu+5qrV6sT8MkEul8Pr9VKzkmg0imw2C7PZPHUpCvDTFZjTFkGM25AajUYcOXIE58+fxzPPPEN3eJOxPKPRiMXFRSpCnU4HL7zwAk2XjzvIicVicLlccLlc2vRG/tz491arhVKphGq1St3vgOuNllKpFHNzcxPjYE6nE3q9HslkEisrK/TAMD8/v+08MpfLhcfjwZUrV/Dss8/C4/HseskE6TUolUrUkyCXy7FifAfBivI9DJfLpaMsAoEA3W4XcrkcSqUSBoMBKpWK7ZJ+A0CirFAoRFc+bjUKRx67urqKQqEAp9OJaDS6rTCT0ah4PI5erwc+nw+hUIhut7vl/DJpGmq1WjStTcR5KyvYaZB09PhBY35+Hh/84Afx7LPP4s///M/B4/Hgcrnw6U9/mjYqjkYjaqZCyjiNRoN2lo+nyYnzGMMw9OMZhkG1WkW5XEan04FAIIBKpUKv10MkEgGfz4derwePx6Oe3NNEvlar4dKlS5DL5eBwONDr9VPFn/wJXLfELRaLMJvNqNVq4PF4234M+bharYZ4PI7hcAitVotut4tYLEYPSqwY3xmwonwPMxgMwOVyaZONxWKhI1IsbyzI/CoR5u1WEPJ4PHg8HqysrKBYLMLtdiMcDm8rzHq9HlwuF9FolG690ul0mJmZQS6Xg9/vh0KhoGNzBGLJSHYsx+NxKs5EoPaCUCjE29/+dvz4xz9GpVKBWq2+4XgYsLUXO8Mw1HmOuNSR70er1dLrB4BDhw5NLJQYPwRs/Pva2hq8Xi9kMhmy2SzW19ep8cm0j0kkEqjX65idnUUwGKQLNrZjNBqhUCigWq2Cw+FAIBBgNBpBJpPRj83n81hbW7thVmC3779HVivcVlhRvochUYTL5YJarWaj4jc4ZPwpGAwiEAjQhqFpkP3ZxCHK4/EgFAptK8wk/RyNRsHhcOhs+/z8PO3CJuI8MzMzEd1KpVK4XC60223kcjkkEokJcb5dXgQ8Hg8qlYrW3klDZKFQQDweRy6Xm3CxGwwGCIfDEAgEmJubg9ls3rElZqFQgEKhoJ7cjUYDiUSCLoWxWq0Tn4us6nS73VAqlcjn80gkEjCZTNDr9ZsEn2EY1Ot1xGIxDIdDeqgg8+MymWzTxwyHw4mPn3ag2A1k/SvLzmFF+R6G3IRZWAhk7CUQCFBh3mrOnHhEJ5PJHQszadoiKdxmswm/3w+Px4NDhw6hUqkgm80iEAhALpdv2ngkkUjoJqRcLodUKoVcLkdNTG7nDZ5hGFQqFRQKBTSbTRiNRhw+fBhSqRS5XA6hUAiVSgUymQxGo5HuGCd16e2axzqdDtLpNIxGIz2cyOVyLC4uYm1tDZlMBpVKhWa31tfXkcvl6LpI4Hqj3WAwQD6fh0KhmGjiI0skiLe5Xq+HTqeDxWLZtqdgp8/LRvGfdiAYjUZQqVR06QzLzmBFmYXlDcY0Yd6qpGE0GtFutxGPx+Hz+XYkzGq1Gh6Ph0aQ/X4fq6ur8Hq9tLmoWq0ik8kgGAxOFWexWAyHw0HFOZ1OT4jzrZwQIF3UxDhHqVRS04xGo4F0Oo1GowGPxwOr1UpLRI1GA9VqFYVCgX4P481j5DkmY2pCoRBWq3Xia5OGNI1Gg3Q6jUQigXg8jm63i9nZ2U2p6vFRqYWFBbrl7fLly0in05DL5XC73bBarTctxuPXuNPnXyKRTDVnYdkaVpRZWN6A8Pl8eL1e+P1+KsxbdeDbbDZ0Oh1649+JMBMhC4VC9PP6/X643W4a1alUKlSrVWSzWQSDQchkMszMzExEfCKRiC6/yOVyyGQyyOfz1JxkP8WZpKnL5TI4HA50Oh31d+90OgiHw6hUKpBKpZsao8RiMa0j93q9ibp0sVgE8NP1pK1WC9VqFffff/+Wkb9AIIDD4YBcLscLL7wA4HpnPBm1IoyPSgWDQXC5XPj9foxGI8zPz8PpdO6bGLPcHlhRZmF5g0L2D48L87SUK2kSW1lZQTgcxvz8PBVcsu93mjArFAp4vV4Eg0Hq0BUMBuFwOKjDGBFnUnMOhUKQSqWb9jQLhULYbDaYzWbk83lks9kJcd5pHXcjDMNQo49mswmRSES9qnk8HjX1KJVKEAgEdKPadgiFQmi1Wvq48eaxQqGAa9euQavV0iwBeZPJZBPP42AwQC6Xg8/ng16vRy6XQ6VSgdVqnWiC43K5UCgU+OY3v4lGo4FTp07h+PHjbDf1XQoryiwsb2CEQiEV5mAwCJ/PN1Xg+Hw+PB4PVldXEYvF4HK5aFf2dsIsl8vh8/moMKtUKkSjUfT7/YlU7LjzVyaTQTgc3lKcSUNVPp9HPp9HoVCAwWCAyWTasTj3+30Ui0Wsra1NpKiVSiU4HA6GwyGNyjkcDqxW655r2qR5TKFQoFqt4r777oPNZkOz2aRmKuPb1YhAZzIZDIdDLCwsQCQSQa/X05Q2MR7p9/tYXl5GLBajrmH7mapmuf2woszC8gZHJBJtEuZpaWHShBUOh5HNZjEzM7MjYZbJZFSYgetzzalUCr1eD7OzsxMfo1AoMD8/T205ybYp4vxFEAgEmJ2dpeJcKBQmxHmrJqvtUtTA9ci5UChQoTQajTCbzfuSJs9kMuh0OnQ38fiCjHa7jXq9jkajgbW1NVy4cAG1Wg3z8/O0S5tYoer1eiwtLeFb3/oWarUaXWxBnrdQKIRUKoW5ubmbvmaW2w8ryiwsLBCLxZuEeVpUqFarYbFYkMlk6MKSnQizVCqFz+dDIBAAwzCYmZlBNptFv9+Hw+HY9LWm2XIScR4f7+Pz+bBardQWtFAoYG1tDXq9nu48JqJHPJ1FIhFNAY+LbblcRjqdpjPWFotl35zuGo0G8vk8XcgxDofDoY5lJpOJOmwtLCyAz+fTDnDg+qpJUpNutVr0OSGGKyqVCjabDYlEAiKRaEez2Sx3Fqwos7CwALgeCZMacCgUgsfjmSrMMzMzaLfbiMViNCW9E2GWSCSYn59HIBBAuVyGzWZDKpVCMBiE2+2emnomblPbeWYD18XZYrFQcSZmGGRPtEwmA4/Hg9vt3uRkV6/XkU6n0Ww26fey1xWI0xiNRtQ560ZGH+Rg4HA4JhZ25PN5hMNhrK2toVQq0Sh+ZmYG3W4XV65cgVarhc/ng8FgmNgqxa5avLtgRZmFhYVC7C+DwSDC4TA8Hs9UgXU4HPD7/QiHw1hcXNyxMIvFYirM+XweDocDiUQCfr8fXq93y8h0p57ZPB4PMzMzMBqNdDEEqcdu3JjWbreRTqdRrVZpJH8rarGpVAr9fh9er3dbA59ms4lYLAatVksFuVwuI5vNot1u08jX4XDAarVCIBDQlHepVEI0GsXq6iosFgtcLheEQiHC4TAWFha2dG9jufNgRZmFhWUCuVxOhXkrgSXGNKurqwiHw/D5fDsWZpFIRIU5lUrB6XQikUhgdXV10+rHjWzlmT0zM7NJnIkbWKVSmYjCe70estksisUidS7bbq3lzVCr1bC2tgabzbatvW2v16PNbXa7fUKMZTIZHcsyGo2w2Wy0Zr7ReSwejyMSieDll1+GRqNBvV5HKpXCfffdB41GA7lczjps3eGwP50xPve5z+HkyZNQKBQwGo14//vfD7/ff9CXxcJy21EoFHC73ahWq4jFYlM9jIVCIdxuN5rNJl09SIS5Wq0iEols6X1Mur45HA5isRjsdjsEAgH8fj9qtdoNr494ZpOmqVgshuXlZRSLxS2/5nA4RDqdxvLyMiqVCubm5nD48OFbJsjD4RCxWIwugNnucaFQCBwOB1qtFqurq4hEItSvvt/v09q72+2e2sRGxqKOHDmCd73rXTh9+jRkMhm0Wi36/T4uXrwIv9+PS5cuYWVlBalUCpVKBYPB4JZ874Qnn3wS73nPe/Dkk0/e0q9zL8FGymM8//zz+NjHPoaTJ09iMBjgj//4j/G2t70N165dm1g/x8LyRkClUsHpdCISiYDL5U7d9SuTyWC32xGLxain8k4jZqFQSCPmaDQKt9tNZ5Xtdjt0Ot0Nr5GIc7vdRjab3bTQArje6NVsNrG0tITRaASTyQSTyXTL94YnEgmMRiM4HI4tH8MwDK0VKxQKJBIJKJVKzM3NoVKpIJPJQKFQwOFw7LjpjM/nw263w2Aw0IavSqVCRb7ZbKJcLiOfzwOYdB5TKBT7usb1iSee2LfP9UaBFeUx/v3f/33i31/84hdhNBpx4cIFnDlz5oCuioXl4NBoNHR9I5fLnTpmo9Pp0G63kUwmIRaLoVQqdyzMAoGACjNZKykQCBCLxdDv92kH9Y2QSCR0oUU2m0UikUAul0Oj0UC5XIZUKqWd49t5Uu8X5XIZ6+vrcDqdW349hmFw9epV+P1+6oFNasmxWAy9Xg9zc3N77qCWSqVYWFhAsViE3+/HtWvXMBqNcPz4cXA4nAnnsXq9Tp3HhELhhKnJfja9sdwYVpS3oVqtAsC2Dj7dbnfC27XRaNzy62JhuZ1otVqMRiPE43FwudxNfs3AdQvIcStOsVi8Y2Hm8/l0jjkQCMDn80EoFCKdTlNh2umGMyLOnU4H2WwWjUaD7jaeFunfCvr9PhKJBDQazdR7B3ERW1paQiKRgM/no4suMpkMcrkcZDIZFhcXt1wWshv0ej3UajWkUimWl5fRaDRw/PhxKBSKCeexwWAwYQ9aLpfBMAz4fD5kMhmdlZZKpezGuVsIh2EXXk5lNBrhve99LyqVCl588cUtH/fpT38an/nMZzb9/4ULF/DAAw/cyktkYbmtFAoFJJNJWCwWzMzMbHr/cDjE6uoqAGBhYYGmh6vVKsLhMFQq1ZbCTD4+GAyi0+nA6/Wi3W7TdK7L5dpTg9If/dEfoVqtQq1W46/+6q92/fF7IRQKodVq4dChQxMNZkSMSZNZuVzGoUOH4PP50G63EY1G0el0MDMzA7PZfEuELxgM4sqVK9Dr9bDZbJidnd0ykifNY6TDu9lsbnIeI29s89j+wYryFnz0ox/F97//fbz44ouYnZ3d8nEbI+VLly7hscceY0WZ5Z6EbGyatrEIuP77sLKyQrukibDsRpiJqHk8HoxGI2oc4vF4du1x/clPfhKVSuW2iXKxWEQ8HqdbpYBJMe50OhCJRKjVajCZTHA6nSgUCtSMxeFw3NLxJYZhEIlEkEgkIJFIIBKJYLFYYDQab3gIYBgGrVZrIpoeDAbU/GRcpPfqRc7Cpq+n8tu//dv4zne+gxdeeGFbQQauj3eMjzrI5fJbfXksLAeG2WzGaDRCKpUCl8vd1FVMRoxCoRAVbwA7TmWTtZKhUAihUAhutxvz8/MIBoN0/eN2o0UHSa/XQzKZhE6ng0ql2iTGKpUKVqsVyWQSGo0GFosFwWAQjUYDZrMZFovllqeFORwOnE4ner0eOp0OlEolUqkU9dLebk6bw+FAJpNBJpPRA1m73Z5Id29sHlMoFFAqlaxI7wI25zAGwzD47d/+bXz961/Hj3/8YzidzoO+JBaWOw7inJVIJFAqlTa9X6lUYnZ2Fvl8njYPATsfl+JyufB4PFAoFAiFQuj3+1hYWAAAapV5JxKLxcDn8zE7O4tSqYTl5WVEo1GIRCIsLi7C5XIhm82CYRioVCqsrq6i3+9jfn4eVqv1ttVpyfPL5/PR7XYxPz8PPp+PQCCASCSCXq+3488lkUhgMBjgdDpx9OhRHD16FE6nE3K5HI1GA9FoFO12+xZ+N/cerCiP8bGPfQz/9E//hK985StQKBTI5XLI5XLsi4qFZQOzs7MwGAyIxWJYX1/f9H6j0Qi9Xo9EIjHR/LgbYSaWmOFwGO12m25LCgQCtAnzTiGfz6NWq0GhUNBNWhKJBIuLi9QQJRqNol6vAwCy2Sx0Oh0OHTp0INk1gUAAj8eDXq9H10M6HA40Gg0sLy8jl8tt+bPZDrK20m634/Dhw7jvvvvY7OEuYUV5jM9//vOoVqt485vfjJmZGfr2//7f/zvoS2NhueOw2WzQ6XSIxWKoVCpT3y+TyRAOhyeir50KM4fDgcvlglqtRiQSQb1eh8/ng1KpRDgcnojCD5J2u00NSUqlEhVjt9tN68PpdBrxeJz2n3i9XthstgNtkCKd6rVajabdDx8+DL1ej0wmg2vXru3IyGU7+Hw+26m9S1hRHoNhmKlvv/Irv3LQl8bCckdit9upaG68gXM4HLjdbnC5XIRCIYxGI/q+3Qiz0+mERqNBJBJBuVyGy+WCXq9HPB5HJpO5pd/fdjAMg2KxiB/96EcoFouw2+2bxBi43hx3/vx5DAYDzM7O4vDhw3Rt40GjVCphs9mwtraGQqEAHo+Hubk5LC4ugs/nUw/03aS0WW4OVpRZWFj2DBFNEr2S9CyBz+fD4/Gg2+0iFotNvG83wuxwOKDX6xGLxVAqlWCz2WC1WqmL1+0cImEYhtaML1y4AIZh8Pjjj8Pr9W7qnE4mk3juuecgEolw8uRJOJ3OW+4ktlv0ej1MJhOSySTNeJCNXk6nE81m86ZS2iy7gxVlFhaWm4KkmeVyOUKh0KZGLIlEAqfTSTuRx9mNMBPryHg8jrW1NZjNZjgcDpRKJYTD4YlI/FZAIuPl5WV6wJDL5Th58uSmLvThcAi/34+f/OQn0Ov1eMtb3nLLPLb3g9nZWajVakSjUbRaLfr/Wq0Whw8fhsFgQCaTwfLy8k2ntFm2hxVlFhaWm4Y0ZkmlUgSDwYkbOwBqcZnJZFAulyfeNy7M0Wh022jMZrPRzu98Pg+dTgePx4NGowG/349+v7/v39u4GMfjcUgkEiwsLNAFEhuNVOr1Oq5cuYKrV6/Cbrfj8ccf31c/6VuF0+mERCJBKBSaSFfzeDzMzs5icXERQqEQgUAAfr8fjUYDnU6HGoxUq1Wsr6+jWCyiUCggm80inU5P+Diw3Bh2eIyFhWVfIKM2wWAQwWAQPp9vwjd5ZmYGnU4HsVgMIpFoItU7PsccjUbhdDq3bBCanZ0Fh8NBKpUCwzAwm83w+XwIhULw+/3weDz7Yk9J0tTZbBa9Xg8ajQZutxsSiQSpVArdbheLi4v0OkejEdLpNB0FczgcOHr06IGlqxmGwWg0wmg0wnA43NHf+Xw+otEo0uk0XaSx8bG1Wg2hUAjnz5+HXq+HVqvd1LDG5XLB4/HA5XKhUqnu2NnyOxFWlFlYWPYNHo8Hj8eDQCCAYDCI+fn5iRuy3W5Hp9NBOBzGwsLChMUjcfuKRCI3FGar1Qoul4t0Oo3RaASLxYKFhQUEg0EqzHvd7DZNjD0eDz1g1Ot15PN5zM7O0v9rNpt0iQRwfSRs4/e+HRuFb6ciut37d5LO53A4VDzJn1arlW7bcrlc4PP54HK59DHEQ7xQKKBUKoHD4WBubg4ajYZ+Dpa9w4oyCwvLvkIWTPj9fgQCAczPz9P0LUlzk53BZKcyQa1W71iYZ2ZmwOFwkE6nwTAMrFYr5ufnEQ6HEQgEdm3+cyMxBq4vbQiFQhAKhVAqlWg0GshkMtQmUyAQYH19na5eLJVKOxLUG8HhcCaEcfxPPp9P/z7+/9Meu/HvWz23DocDoVAIXC53S1dDnU6HTqeDRCKBZDKJWq2Gubk5Niq+SVhRZmFh2XemCTOJioVCIdxuN/x+P+Lx+KZ9w7sRZrPZDC6Xi2QyidFohLm5OXi9XsRiMYTDYbRaLQyHQ/T7fTQajanR5WAwQLFYRD6fR6fTgUKhgF6vR7fbRSQSmXh8JpNBtVqFy+XCxYsXkc1m0e12odfrMRwOkc1mYTQa0e/3US6XN4mgQCDYViS3EtTbPetLRqXi8ThEItFUn3Pgup2mz+dDuVxGMpnE8vIyzGYz/bmw7B5WlFlYWG4JAoFgkzATD2SZTAa73U6drzbe9HcjzGSZQiKRAMMwsNlsdI9xrVZDvV5Hv9+H3++f+DgOh0Obk0ajEdRqNZxOJ2Qy2VRhJP7OCwsLYBgG+XweLpeLXlsoFKL/vhcgB5NUKgWRSAS1Wr3lYzUaDVQqFbLZLHK5HM0WkKUcLDuHFWUWFpZbhlAonBBmn89HhVmn06HdbiOVSkEikWwy1NiNMBsMBnC5XMRiMYxGI9jtdszNzUGn04HH40GtVuPw4cM06lxfX0c+n4dSqYTD4cDMzMy2zWGDwQD5fB5msxnD4RCNRgMOhwMWiwX9fh+rq6tQKBSbov67HavVim63i2g0ivn5+W03WJF6tE6nQzKZRCgUgkqlYlPau4TNL7CwsNxSRCIRvF4v+v0+QqEQhsMhfZ/VaoVKpUIkEkGn09n0sUSYK5XKDceldDodnE4n1tfXEYvFwDAMBAIBfROJRKhUKlhZWUEymYRcLsfhw4fhdDpv2K2dSCRQLBbRbDbpEonZ2VkwDINQKAQejwe3231PWko6HI6po1JbIRaL4fV64Xa70W63J37eLDeG3ae8z1y8eBEPPvggu0+ZhWUDrVYLgUAAEokEXq+X1hyHwyFWV1fBMAwWFxenjhBVKhVEIhGaYt5O/MrlMqLRKBX7VqtFhbTX69HZ4p2OTeXzebz88stQqVTweDyYnZ0Fl8ulgtxsNrGwsLAvY1h3KiQbwOPxMD8/v+MxL4Zh7smDyq2EjZRZWFhuC1KpFF6vF61Wa8KBi4xRDQaDLV29dhMxk3niarVK68tkneBOI2NCPp+nNpknTpyYWCKRTCZRr9fhdrvvaUEGrvcHeL1e9Hq9bZ3XNsIK8u5hRZmFheW2IZPJqAPX+M1dJBLB7XajXq8jlUpN/djdCDOJatvtNhQKxa7FmBwQzp49C4VCgbe85S0TNe9CoYC1tTXYbDYoFIpdPAN3L2KxGC6XC/V6Hclk8qAv556FFWUWFpbbikKhgNvtRq1WmxBXhUKBubk5FAqFLdcy7kaYlUol7r///l2JMQBUq1Vcu3YNiUQCKpUKjzzyyMTHV6tVJJNJmM1m6PX6HX/ee4HxrVL5fP6gL+eehBVlFhaW245SqaTiGo/H6f8bDAYYDAYkEgk0Go2pH7sbYd4Nw+EQsVgMoVAIfD4fUqkULpdrYqyn1WrR2rbVat2Xr3u3of//tXffcVFc6//AP0Nbeu9IRykCFhRBVMResPcSe4lJNIlJvJob29cYY9rVFOs1aowmdokViWJFRAQsVEWKdAm9LrDz+8O782NlwaXuAM/79dpXsmdmZ88egWfPmXOeY2gIU1NTpKWlSd1HmzQPBWVCiFyIJ239888/SE1N5cotLS2hqanZ4D6+LR2Yi4uLERMTg/z8fFhZWUFRURFqamoS2azEs8dVVVU7zFrkprKwsICenh6SkpLq7ApGmoeCMiFEbvT09GBjY4NXr15x9ynFW0EqKCjg+fPn9aahbInALBKJ8PLlSyQkJEAgEKB79+4QiUTcOmTxLGORSITnz5+DYRg4ODhQtir8/6VSDX15Io1HP1mEELkyMDCAlZUVcnJykJGRAeB1mk4HBwcucUV9mhOYS0tLERsbi9zcXFhaWqJbt26oqalBeno6TExMuAlcLMsiKSkJFRUVcHBwkNhEozMT7wom/vJE65FbBgVlQojcGRkZoUuXLlyaRgBQU1ODra0tCgoKuGAtTWMDM8uyyMjIQHx8PBQUFODs7AxjY2OwLMttK2lubs6dn56ejoKCAtjZ2UlsTkH+/5enxi6VIvWjoEwI4QUTExOYm5sjPT0dOTk5AMBNqMrMzER+fn69r5U1MJeXlyMuLg5ZWVkwMzOTSPqRmZmJ8vJy2NracsPT4o0qKI9z/VRVVbnlbLXnBpCmoaBMCOENMzMzmJqa4uXLl9yyKFNTU+jr6yM5ORllZWX1vrahwCzeQCI2NhYikQhOTk7c1o/A66FscaAW53cuKipCamoqjIyMYGxs3Iqfuv3T0tKCtbU19yWGNB0FZUIIr1hYWMDY2BgpKSnIy8sDAFhbW0NVVRWJiYmoqqqq97XSAnNlZSUSEhKQlpYGY2NjODs7S2ysIBKJuN2qTE1NAQAVFRV48eIFt3aavJ2BgQG3VKqhUQ3SMArKhBDesbS0hKGhIZKSkrh9iR0cHMCyLBITExu8d1k7MCckJCAmJkZiE4k3Z06np6ejsrKSy6ldXV2N58+fQ0VFBXZ2dpQqshHES6WSk5NpqVQTUVAmhPCSlZUV9PX1kZSUhMLCQigrK8Pe3h5lZWUSCUekEQfm0tJS6Ovrw9nZGZqamnXOKy4uRk5ODiwsLKCqqgqRSITExETU1NTAwcFB5o0XyP9nY2MDdXV1mXeVIpIoKBNCeIlhGNjY2EBHRweJiYkoKiqChoYGbGxs8M8//7z13qWuri569eoFa2trqcFVnMFLS0sLJiYmAICUlBSUlZXBwcEBKioqrfK5OjoFBQXY29tDUVERz549o6VSjURBmRDCW+JEIlpaWkhMTERJSQn09fW5e5eFhYVvfX190tLSUF1dDRsbGwCvZ1/n5eXBxsYGGhoaLfkxOh3xUqmampoGJ+eRuigoE0J4jWEY2NvbQ0NDg9u/2NzcHDo6OlxSj8YqLCzkkoaoqKggLy8PGRkZ3D1R0nyqqqpwdXXtNLtotRQKyoQQ3hNP9FJVVcWzZ89QUVEBW1tbqKioNDqbVHV1NVJSUqCjowNDQ0OUlJQgOTmZmz1MWg6lI208ajFCSLugoKCArl27QiAQICEhAVVVVbC3t0dNTU2jskmlpqaCZVlYW1ujsrISiYmJ0NTUhLW1dSt/AkLejoIyIaTdUFRURNeuXaGsrIyEhAQAgJ2dHYqLi5GWlvbW1+fl5XE7QYlzNisqKtLSJ8IbFJQJIe2KkpISunbtCkVFRW53J0tLS+Tk5HBZwKSpqqpCamoq9PX1oauri8TERFRXV6Nr165QUlJqw09ASP0oKBNC2h1lZWV07doVAJCQkABdXV0YGRkhNTUVJSUlUl+TnJwMBQUFWFpacufZ2dlBIBC0ZdUJaRAFZUJIu6SiooJu3bpBJBLh2bNnMDMzg6amptT9fV+9eoWioiJujXNubi6sra1pZjDhHQrKhJB2SyAQoFu3bqiqqsLz58+5RCHPnz+HSCQCAFRWViItLQ1GRkYQiURIS0uDqakpDAwM5Fx7QuqioEwIaddUVVXRrVs3CIVCJCUlwcbGBpWVldyGFMnJyVBWVuZSdurp6cHCwkLe1SZEKgrKhJB2T01NDV27dkVFRQUyMjJgY2ODgoICxMXFoaSkBObm5njx4gXU1NS4DF6E8BEFZUJIh6Curg4HBweUlpYiNzcX5ubmKCsrg5GREbKzs7nMYJTQgvAZ/XQSQjoMTU1NODg4oLi4GGVlZejWrRsqKytRWVkJBwcHKCsry7uKhDSIFucRQjoULS0t2NvbIzExEeXl5aisrETXrl2hpqYm76oR8lbUU37DrVu3MG7cOJibm4NhGJw7d07eVSKENJKOjg5sbW0hFAphZWUFbW1teVeJEJlQUH5DaWkpevTogV9++UXeVSGENIOenh569eoFIyMjeVeFEJnR8PUbRo8ejdGjR8u7GoSQFkD5rEl7Q0G5mcSTSMTqS/FHCCGEvA0NXzfTtm3boKOjwz18fX3lXSVCCCHtFAXlZlq3bh0KCwu5x82bN+VdJUIIIe0UDV83k0AgkNhlRlNTU461IYQQ0p5RT5kQQgjhCeopv6GkpATPnz/nniclJSEqKgr6+vqwsrKSY80IIYR0dBSU3xAeHg4/Pz/u+erVqwEA8+fPx6FDh+RUq+bJzMxEZmamvKtBCOmEzMzMYGZmJu9qtBsMy7KsvCvRkWRmZmLv3r1Yvnw5L34QKysrMXLkSJqARgiRC19fXwQGBkrMvSH1o6DcwRUVFUFHRwc3b96kSWhyUFJSAl9fX2p/OaH2ly9x+xcWFlKqUxnR8HUn0bNnT/qlkIOioiIA1P7yQu0vX+L2J7Kj2deEEEIIT1BQJoQQQniCgnIHJxAIsHHjRppkISfU/vJF7S9f1P6NRxO9CCGEEJ6gnjIhhBDCExSUCSGEEJ6goEwIIYTwBAVlIrPk5GQwDNNu040SQgjfUVBuJYmJiVi+fDns7OygqqoKbW1t+Pj4YOfOnSgvL2+1942JicGmTZuQnJzcau8hi61bt2L8+PEwMTEBwzDYtGmTXOtTH4ZhZHrcuHGj2e9VVlaGTZs2Nepa7aUdm4rP7R8XF4c1a9agZ8+e0NLSgpmZGcaOHYvw8PBm14Uv+Nz+GRkZmDt3LhwdHaGlpQVdXV14enri8OHD6MjzkymjVyu4ePEipk2bBoFAgHnz5sHV1RVCoRB37tzBZ599hujoaOzbt69V3jsmJgabN2/G4MGDYWNj0yrvIYsvvvgCpqam6NWrFwIDA+VWj7c5cuSIxPPffvsNQUFBdcqdnZ2b/V5lZWXYvHkzAGDw4MEyvaa9tGNT8bn9//vf/+LAgQOYMmUK3nvvPRQWFmLv3r3w8vLClStXMGzYsGbXSd743P65ublIS0vD1KlTYWVlhaqqKgQFBWHBggWIj4/HV1991ew68RJLWtSLFy9YTU1N1snJic3IyKhz/NmzZ+yOHTta7f1PnjzJAmCDg4Pfeq5IJGLLyspkvnZSUhILgD148KBM57Isy7569YoFwG7cuFHm95Gn999/n22tX4umtEV7bcem4lP7h4eHs8XFxRJlubm5rJGREevj49MKNZQ/PrV/ffz9/VkNDQ22urq6ZSrGMzR83cK++eYblJSU4MCBA1J3iXJwcMCHH37IPa+ursaWLVtgb28PgUAAGxsbfP7556isrJR4nY2NDfz9/XHnzh14enpCVVUVdnZ2+O2337hzDh06hGnTpgEA/Pz86gw9ia8RGBiIPn36QE1NDXv37gUAvHjxAtOmTYO+vj7U1dXh5eWFixcvNrkd5NlLb2kikQg7duxA9+7doaqqChMTEyxfvhz5+fkS54WHh2PkyJEwNDSEmpoabG1tsWjRIgCv78cbGRkBADZv3sz927xtOLojtWNTyav9PTw86mxiYWBggIEDByI2NrZlPySPyfPnXxobGxuUlZVBKBQ2+7PxEQ1ft7Dz58/Dzs4O/fv3l+n8JUuW4PDhw5g6dSo++eQT3L9/H9u2bUNsbCzOnj0rce7z588xdepULF68GPPnz8evv/6KBQsWwMPDA927d8egQYOwatUq/Pjjj/j888+5IafaQ0/x8fGYNWsWli9fjqVLl8LR0RHZ2dno378/ysrKsGrVKhgYGODw4cMYP348Tp06hUmTJrVcA7VDy5cvx6FDh7Bw4UKsWrUKSUlJ+PnnnxEZGYm7d+9CWVkZOTk5GDFiBIyMjLB27Vro6uoiOTkZZ86cAQAYGRlh9+7dWLFiBSZNmoTJkycDANzd3eX50doFvrV/VlYWDA0NW/Qz8pm827+8vBylpaUoKSnBzZs3cfDgQXh7e0NNTa1VP7fcyLur3pEUFhayANgJEybIdH5UVBQLgF2yZIlE+aeffsoCYK9fv86VWVtbswDYW7ducWU5OTmsQCBgP/nkE66soeFr8TWuXLkiUf7RRx+xANjbt29zZcXFxaytrS1rY2PD1tTUsCzbuOFrsfY27Prm8N3t27dZAOzRo0clzrty5YpE+dmzZ1kA7IMHD+q9dnPaor21Y1Pxtf3Fbt26xTIMw65fv77J1+AzPrb/tm3bWADcY+jQoWxqamqjrtGe0PB1CxJvU6alpSXT+ZcuXQIArF69WqL8k08+AYA6w8cuLi4YOHAg99zIyAiOjo548eKFzHW0tbXFyJEj69TD09MTAwYM4Mo0NTWxbNkyJCcnIyYmRubrdzQnT56Ejo4Ohg8fjtzcXO4hHtoMDg4GAOjq6gIALly4gKqqKjnWuGPhU/vn5ORg9uzZsLW1xZo1a1rlPfiGD+0/a9YsBAUF4dixY5g9ezYAtOoKFnmjoNyCxPu1FhcXy3R+SkoKFBQU4ODgIFFuamoKXV1dpKSkSJRbWVnVuYaenl6dezsNsbW1lVoPR0fHOuXiYe8369GZPHv2DIWFhTA2NoaRkZHEo6SkBDk5OQAAX19fTJkyBZs3b4ahoSEmTJiAgwcP1pkbQBqHL+1fWloKf39/FBcXIyAgoM695o6KD+1vbW2NYcOGYdasWTh69Cjs7OwwbNiwDhuY6Z5yC9LW1oa5uTmePn3aqNcxDCPTeYqKilLL2Uas2euw92FaiUgkgrGxMY4ePSr1uHjyCsMwOHXqFEJDQ3H+/HkEBgZi0aJF+P777xEaGtpp/oi3ND60v1AoxOTJk/H48WMEBgbC1dW1yddqb/jQ/m+aOnUq9u/fj1u3btUZ9esIKCi3MH9/f+zbtw/37t2Dt7d3g+daW1tDJBLh2bNnEpOxsrOzUVBQAGtr60a/v6wB/s16xMfH1ymPi4vjjndW9vb2+Pvvv+Hj4yPTFxovLy94eXlh69atOHbsGObMmYM///wTS5YsadK/TWcn7/YXiUSYN28erl27hhMnTsDX17cpH6Pdknf7SyPuIRcWFrbI9fiGhq9b2Jo1a6ChoYElS5YgOzu7zvHExETs3LkTADBmzBgAwI4dOyTO+eGHHwAAY8eObfT7a2hoAAAKCgpkfs2YMWMQFhaGe/fucWWlpaXYt28fbGxs4OLi0uh6dBTTp09HTU0NtmzZUudYdXU11875+fl1Rix69uwJANwQnrq6OoDG/dt0dvJu/5UrV+L48ePYtWsXN2O4M5Fn+7969Upq+YEDB8AwDHr37i3Tddob6im3MHt7exw7dgwzZsyAs7OzREavkJAQnDx5EgsWLAAA9OjRA/Pnz8e+fftQUFAAX19fhIWF4fDhw5g4cSL8/Pwa/f49e/aEoqIitm/fjsLCQggEAgwZMgTGxsb1vmbt2rX4448/MHr0aKxatQr6+vo4fPgwkpKScPr0aSgoNP6725EjR5CSkoKysjIAwK1bt/Dll18CAN5555120/v29fXF8uXLsW3bNkRFRWHEiBFQVlbGs2fPcPLkSezcuRNTp07F4cOHsWvXLkyaNAn29vYoLi7G/v37oa2tzX35UlNTg4uLC44fP45u3bpBX18frq6uDQ6HdpR2bCp5tv+OHTuwa9cueHt7Q11dHb///rvE8UmTJnFfgjsqebb/1q1bcffuXYwaNQpWVlbIy8vD6dOn8eDBA6xcubLOXJwOQ76TvzuuhIQEdunSpayNjQ2roqLCamlpsT4+PuxPP/3EVlRUcOdVVVWxmzdvZm1tbVllZWXW0tKSXbduncQ5LPt6OdPYsWPrvI+vry/r6+srUbZ//37Wzs6OVVRUlFgeVd81WJZlExMT2alTp7K6urqsqqoq6+npyV64cEHinMYsifL19ZVYxlD7IUu2MXmpL6PRvn37WA8PD1ZNTY3V0tJi3dzc2DVr1nBZ2yIiIthZs2axVlZWrEAgYI2NjVl/f382PDxc4johISGsh4cHq6KiItPykPbajk3Fp/afP39+vW0PgMu21pHwqf2vXr3K+vv7s+bm5qyysjL3N/TgwYOsSCRq0c/NJwzLduDM3oQQQkg7QveUCSGEEJ6goEwIIYTwBAVlQgghhCcoKBNCCCE8QUGZEEII4QkKynLyzTffwMnJCSKRSN5Vaba1a9eiX79+8q5Go1D7yxe1v3xR+/OYvNdkdUaFhYWsvr4+++uvv3Jl+N/ax++++67O+QcPHnzrtmiyOn36NDt9+nTW1taWVVNTY7t168auXr2azc/Pl3p+QEAA26tXL1YgELCWlpbshg0b2KqqKolzMjMzWYFAwAYEBDS7fm2B2l++qP3li9qf3ygoy8F//vMfVltbmy0vL+fKxL8UJiYmbGlpqcT5LflLYWBgwLq5ubHr169n9+/fz65atYpVUVFhnZyc2LKyMolzL126xDIMw/r5+bH79u1jV65cySooKLDvvvtunetOnz6dHThwYLPr1xao/eWL2l++qP35jYKyHLi7u7Nz586VKAPA9uzZkwXAfv/99xLHWvKXQloWqMOHD7MA2P3790uUu7i4sD169JD4Zvrvf/+bZRiGjY2NlTj31KlTLMMwbGJiYrPr2Nqo/eWL2l++qP35je4pt7GkpCQ8fvwYw4YNq3PMx8cHQ4YMwTfffNNqe4UOHjy4TtmkSZMAALGxsVxZTEwMYmJisGzZMigp/f8U6e+99x5YlsWpU6ckriH+PAEBAa1Q65ZD7S9f1P7yRe3PfxSU21hISAgA1LvDyaZNm5CdnY3du3c3eJ3Kykrk5ubK9HibrKwsAIChoSFXFhkZCQDo06ePxLnm5ubo0qULd1xMR0cH9vb2uHv37lvfT56o/eWL2l++qP35j3aJamPiPYptbW2lHh84cCD8/Pzw7bffYsWKFfXuYfrHH39g4cKFMr0n+5b05tu3b4eioiKmTp3KlWVmZgIAzMzM6pxvZmaGjIyMOuV2dnaIiYmRqU7yQu0vX9T+8kXtz38UlNvYP//8AyUlJWhqatZ7zqZNm+Dr64s9e/bg448/lnrOyJEjERQU1Oz6HDt2DAcOHMCaNWvQtWtXrlw8fCUQCOq8RlVVFUVFRXXK9fT06nyD5Rtqf/mi9pcvan/+o6DMQ4MGDYKfnx+++eYbvPvuu1LPMTMzk/otsjFu376NxYsXY+TIkdi6davEMfE3ZPEG5bVVVFRI/QbNsiwYhmlWnfiA2l++qP3li9pfvigotzEDAwNUV1ejuLgYWlpa9Z63ceNGDB48GHv37oWurm6d4+Xl5SgsLJTpPU1NTeuUPXr0COPHj4erqytOnTolMZkC+P/DRpmZmbC0tJQ4lpmZCU9PzzrXzM/Pl7gvxEfU/vJF7S9f1P78RxO92piTkxOA17MgG+Lr64vBgwdj+/btUmdCHj9+nPu2+rbHmxITEzFq1CgYGxvj0qVLUoeyevbsCQAIDw+XKM/IyEBaWhp3vLakpCQ4Ozs3+Lnkjdpfvqj95Yvan/+op9zGvL29Abz+YXN3d2/w3E2bNmHw4MHYt29fnWNNvaeTlZWFESNGQEFBAYGBgTAyMpJ6Xvfu3eHk5IR9+/Zh+fLlUFRUBADs3r0bDMNITMoAgMLCQiQmJmLFihWNrlNbovaXL2p/+aL2bwfkszy6c3N1dWVnzZolUQaAff/99+uc6+vry2XbaYnF+z169GABsGvWrGGPHDki8bh69arEuefPn2cZhmGHDBnC7tu3j121ahWroKDALl26tM51T506xQJgnz9/3uw6tjZqf/mi9pcvan9+o6AsBz/88AOrqakpkVauvl+K4ODgFv2lEF9L2sPX17fO+WfPnmV79uzJCgQCtkuXLuwXX3zBCoXCOufNmDGDHTBgQLPr1xao/eWL2l++qP35jYKyHBQUFLD6+vrsf//7X3lXpUVkZmayqqqq7Llz5+RdFZlQ+8sXtb98UfvzG030kgMdHR2sWbMG3377bYfYOm3Hjh1wc3PDhAkT5F0VmVD7yxe1v3xR+/Mbw7JvSbdCCCGEkDZBPWVCCCGEJygoE0IIITxBQZkQQgjhCQrKhBBCCE9QUCaEEEJ4goIyIYQQwhMUlAkhhBCeoKBMCCGE8AQFZUIIIYQnKCgTQgghPEFBmRBCCOEJCsq17N69G+7u7tDW1oa2tja8vb1x+fJleVeLEEJIJ0EbUtRy/vx5KCoqomvXrmBZFocPH8a3336LyMhIdO/eXd7VI4QQ0sFRUH4LfX19fPvtt1i8eLFM52dmZmLv3r1Yvnw5zMzMWrl2hBBCOhIavq5HTU0N/vzzT5SWlsLb21vm12VmZmLz5s3IzMxsxdoRQgjpiJTkXQG+efLkCby9vVFRUQFNTU2cPXsWLi4u9Z5fWVmJyspK7nlJSUlbVJMQQkgHRD3lNzg6OiIqKgr379/HihUrMH/+fMTExNR7/rZt26Cjo8M9fH1927C2hBBCOhK6p/wWw4YNg729Pfbu3Sv1+Js95aioKPj6+uLhw4fo3bt3W1WTEEJIB0DD128hEokkgu6bBAIBBAIB91xTU7MtqkUIIaQDoqBcy7p16zB69GhYWVmhuLgYx44dw40bNxAYGCjvqhFCSLsjFAoRHh6OPn36QEVFRd7VaRcoKNeSk5ODefPmITMzEzo6OnB3d0dgYCCGDx8u76oRQki7ExoaiiNHjqCmpgYDBw6Ud3XaBQrKtRw4cEDeVSCEkA6hsrISgYGBSEpKwpUrV+Dp6Slxq49IR7OvCSGEtLj79+8jISEB7u7uSEhIQFhYmLyr1C5QUCaEENKixL1kFRUVaGtrQ0VFBVeuXGlw0ix5jYIyIYSQFhUZGYnExESUlpYiOjoapaWlSExMRGRkpLyrxnt0T5kQQkiLsrS0xJw5c6SWk4ZRUCaEENKiLCwsYGFhIe9qtEs0fE0IIYTwBAVlQgghhCcoKBNCCCE8QUGZEEII4QkKyoQQQghPUFAmhBBCeIKCMiGEEMITFJQJIYQQnqCgTAghhPAEBWVCCCFtSigUIiQkBEKhUN5V4R0KyoQQQtpUaGgoDh48iPv378u7KrxDQZkQQkirkNYjFm/rmJSURNs5SkFBmRBCSKuQ1iO+f/8+EhIS4O7ujoSEBISFhcmxhvxDQZkQQkiLk9YjFpepqKhAW1sbKioq1Ft+AwVlQgghLU5ajzgyMhKJiYkoLS1FdHQ0SktLkZiYiMjISHlXlzdoP2VCCCEtqr4e8eLFizFnzpw651taWsqhlvxEQZkQQkiLEveIKyoqEB0djaqqKiQmJiInJwfjxo2Td/V4jYIyIYSQFmVpaUk94iaioEwIIaRFWVhYwMLCQt7VaJdoohchhBDCE7wMypmZmXj06BFKS0vb9H23bduGvn37QktLC8bGxpg4cSLi4+PbtA6EEEI6L14F5YCAADg5OaFLly7o3bs3t+A8NzcXvXr1wrlz51r1/W/evIn3338foaGhCAoKQlVVFUaMGNHmXw4IIaSjorzXDeNNUD5//jwmT54MQ0NDbNy4ESzLcscMDQ1hYWGBgwcPtmodrly5ggULFqB79+7o0aMHDh06hNTUVDx8+LBV35cQQjoLynvdMN4E5f/7v//DoEGDcOfOHbz//vt1jnt7e7f5AvPCwkIAgL6+fr3nVFZWoqioiHuUlJS0VfUIIaRdobzXb8eboPz06VNMnz693uMmJibIyclps/qIRCJ89NFH8PHxgaura73nbdu2DTo6OtzD19e3zepICCF89uZQNeW9fjveBGV1dfUG792+ePECBgYGbVaf999/H0+fPsWff/7Z4Hnr1q1DYWEh97h582Yb1ZAQQvit9lA15b2WDW+Csp+fHw4fPozq6uo6x7KysrB//36MGDGiTerywQcf4MKFCwgODkaXLl0aPFcgEEBbW5t7aGpqtkkdCSGEz94cqr5//z7lvZYBb5KHbN26FV5eXujbty+mTZsGhmEQGBiI69evY+/evWBZFhs3bmzVOrAsi5UrV+Ls2bO4ceMGbG1tW/X9CCGko3pzqNrDw4OyfMmAN0HZ0dERd+7cwYcffoj169eDZVl8++23AIDBgwfjl19+gY2NTavW4f3338exY8cQEBAALS0tZGVlAQB0dHSgpqbWqu9NCCEdhbSh6ocPH2LDhg0QCATyrh6v8SYoA0D37t3x999/Iz8/H8+fP4dIJIKdnR2MjIza5P13794N4PWXgNoOHjyIBQsWtEkdSMciFAoRHh6OPn36QEVFRd7VIaRN1LchRWRkJLy8vORdPV7jVVAW09PTQ9++fdv8fWuvjSakJYSGhuLIkSOoqanBwIED5V0dQtoEbUjRdLwJyj/++CMuXryIwMBAqcdHjx6N8ePHY8WKFW1cM0Ka5s2JLp6enjR0RzoF2pCi6Xgz+/rAgQNwcXGp97iLiwv27dvXhjUipHloTSYhpLF4E5QTExPh7Oxc73EnJyckJia2YY0IaTpak0kIaQreBGUVFRVutrM0mZmZUFDgTXUJaZB4ogutySSENAZv7il7eXnh0KFD+Pjjj6GlpSVxrLCwEAcPHqRZe6TdoIkuhJCm4E1Q3rhxI3x9fdGzZ0989NFH6N69O4DXObF37NiBzMxMHDt2TM61JEQ2NNGFENIUvAnK/fr1w/nz57F8+XJ8+OGHYBgGwOtlSra2tvjrr7/g7e0t51oSQgghrYc3QRkAhg8fjufPn3P34wDA3t4evXv35oI0IYSQ9oNlWfr73Qi8CsoAoKCgAA8PD3h4eMi7KoQQQpqpvLwc6urq8q5Gu8G7oBwTE4MXL14gPz9faoatefPmyaFWhBBCmkLazn+kfrwJyomJiZg7dy7CwsLqTXfJMAwFZUIIaUdobX7j8CYoL1++HE+ePMGOHTswcOBA6OnpybtKhBBCmqm0tLTNNhXqCHgTlO/evYvPP/8cK1eulHdVCCGEtJCioiJ5V6Fd4U2KLENDQ+jo6Mi7GoS0KKFQiJCQEAiFQnlXhRC5KCgokHcV2hXeBOV3330Xv//+O2pqauRdFUJaTGhoKA4ePIj79+/LuyqEtDmhUIi7d+/Sl9JG4M3wdbdu3VBTU4MePXpg0aJFsLS0hKKiYp3zJk+eLIfaEdJ4tHUj6exCQ0MRGBgIDw8PjBgxQt7VaRd4E5RnzJjB/f+nn34q9RyGYagnTdoNaVs3Dhw4UN7VIqRNiL+Uvnr1CqdPn4avry99KZUBb4JycHCwvKtASIupb+tG6i2TzkL8pdTS0hKPHz/G/fv3MWjQIHlXi/d4E5R9fX3lXQVCWow4VWxFRQWio6NRVVXFbd1Iu52Rjq72l1I1NTXk5eXhzz//RL9+/ehL6VvwJiiLVVZWIiIiAjk5OfDx8YGhoaG8q0RIo9HWjaQzq/2lND09HVVVVYiIiEBoaCh1wN6CV0H5xx9/xKZNm1BYWAgACAoKwpAhQ5CbmwsnJyd88803WLRokZxrScjb0daNpDMTfyn9+OOPkZWVBTU1NUyfPh1JSUkYMGCA1Em85DXeLIk6ePAgPvroI4waNQoHDhyQSLVpaGiIIUOG4M8//5RjDQkhhMjCwsIC48aNQ0VFBUpLS1FdXY0ePXpAKBTi6tWrNGG3AbwJyt9//z0mTJiAY8eOYdy4cXWOe3h4IDo6Wg41I4QQ0lJevnyJS5cuUU7sevAmKD9//hyjR4+u97i+vj7++eefNqwRIfJBWcBIR5eZmYmAgACZsn11tt8H3gRlXV1d5Obm1ns8JiYGpqamrV6PW7duYdy4cTA3NwfDMDh37lyrvychtVEWMNIZFBQU4OzZs3j+/HmDgbez/T7wJiiPGTMG+/btk/rNKTo6Gvv378f48eNbvR6lpaXo0aMHfvnll1Z/L9J51fdH6M0sYDTERzqyqqoqXL9+Hbt27cKBAwfqBN7O+PvAm6D85ZdfoqamBq6urvjiiy/AMAwOHz6MuXPnok+fPjA2NsaGDRtavR6jR4/Gl19+iUmTJrX6e5GOr77gW9+3f2lZwAjpyKqqqnD16lWEhYXh7NmzEoG3M/4+8CYom5ub4+HDhxg1ahSOHz8OlmVx5MgRnD9/HrNmzUJoaCgv1yxXVlaiqKiIe5SUlMi7SoRHpAXf+r7915cFrDP0DkjnUF1djcTERFRXV3NlSUlJyM7OhomJCW7fvo3z588D6Ly/D7wIypWVlfjrr7+QlZWF//73v8jLy0N2djYyMzORn5+PX3/9FcbGxvKuplTbtm2Djo4O96CF8USsvuBb37d/ccKF0tJSREdHo7S0lMsCRkhH8OLFC4SEhCApKQnA615yTEwMFBUVoaamBoZh8OuvvyIyMrLT/j7wInmIiooKpk2bhp07d8Ld3R0AYGRkJOdayWbdunVYvXo19zwqKooCMwHw/4Ovm5sbF3w9PT3rzYlNWcBIRyYOwLm5uYiOjoaNjQ1evnyJV69eoaqqChkZGaipqcGrV69w/PhxeHp6YubMmXUSjXT03wdeBGWGYdC1a9cGZ1/zlUAgkMjlqqmpKcfaEL6oPfSmpaXFBV+WZRvMiU1ZwEhHJR6mtrCwQHZ2NpKTk2FoaAhPT8865+rp6SE3NxcmJiYYMmQINDQ05FBj+eBFUAaAzz//HKtXr8a0adPg6Ogo7+oQ0iy1c/8+efKEC8YFBQXUGyadQmpqKkpLSwEAFRUVePjwITdMXVRUhOjoaIwdOxY9evSo9xrZ2dk4ffo0/Pz8Os3vCG+CcmhoKAwMDODq6orBgwfDxsYGampqEucwDIOdO3e2aj1KSkrw/Plz7nlSUhKioqKgr68PKyurVn1v0nHUHoouLi6GlpYWAKB3797UGyYdWlhYGLZs2YKLFy9y6ZIrKipw48YNaGtro6ioCKqqqnj16hVevnwJOzu7Bq9XUVGBy5cvw83NDZ6enh0+bzbD1k4yLUcKCm+fc8YwTKvnTL1x4wb8/PzqlM+fPx+HDh166+sjIiLg4eGBhw8fonfv3q1QQ9LexMfHw9HREUKhEOHh4ejTpw9UVFTkXS1CWtyZM2cwY8YMsCwr9W81wzBgGAbDhw+Hvb09rKysoKenJ/P1jY2NMXz48GYPZ+fl5WHlypU4f/48FBQUMGXKFOzcubPe24/JycmwtbWVeuzEiROYNm0aHj16hK+//hp37txBbm4ubGxs8O677+LDDz9sVN0a1VO2tbUFwzCNegOGYZCYmPjW80QiUaOu21oGDx4MnnxPIR3E8+fP4ejoiNDQUBw5cgQ1NTUYOHCgvKtFSIsKCwvDjBkzUFNTU+/fUJZlwbIsgoKC0Lt370YFZADIycnBuXPnMHr0aOjr6zd47uDBg7FgwQIsWLCgzrE5c+YgMzMTQUFBqKqqwsKFC7Fs2TIcO3ZM6rUsLS2RmZkpUbZv3z58++23XHrohw8fwtjYGL///jssLS0REhKCZcuWQVFRER988IHMn7FRQdnX17dOUA4PD0d0dDRcXFy4e8Hx8fGIiYmBq6srPDw8GvMWhHQ4//zzD9LS0iSWR3l6eja42Tv1qkl78+WXX3JBVxaXLl3Ce++91+j3KS0txYULF+Dv7//WwCxNbGwsrly5ggcPHqBPnz4AgJ9++gljxozBd999B3Nz8zqvUVRUrJPm+ezZs5g+fTrXu35zW2E7Ozvcu3cPZ86cab2g/Obw7blz53Du3DkEBQVh6NChEseCgoIwffp0bNmypTFvgdDQUAQHByMnJwfvvfceunbtirKyMsTFxaFbt240u5m0K3369MGLFy+gpqYGb29vibXJDfWWqVdN2pPU1FRcuHBB5oAsEonw+PFj5OXlNSmwiu8zT5w4sdFD2ffu3YOuri4XkAFg2LBhUFBQwP3792XK5vjw4UNERUW9NR1zYWFhoz9fs5KHbNiwAStXrqwTkAFg+PDh+OCDD/DFF1/IdC2hUIjJkyfDx8cH//73v/Hjjz/i5cuXryupoIARI0a0+iQvQlpaVlYW8vPz8c8//+Cff/6BhobGWzMTdcZ8v0R+hEIhysrKmvW4dOlSo2/7sSyL2NhYiESiJj2Ki4sRHBzc6N2jsrKy6iSjUlJSgr6+PrKysmS6xoEDB+Ds7Iz+/fvXe05ISAiOHz+OZcuWNap+zZp9/ezZMxgYGNR73MDAQKb7yQCwfv16XLhwAbt374afn5/EsihVVVVMmzYNAQEB+Pe//92cKhMiFyKRCPn5+QgMDISOjg5qamq4tclvkpbxi3rLpDUIhUKEhYU1Oz3wo0ePwDBMowIzwzAoLCxs1nvHxcVBW1sbXl5e+O677/DVV19xx8rLyxEaGioxdBwTE9Pk96p93WPHjmH9+vX1nvP06VNMmDABGzduxIgRIxp1/WYFZXt7exw8eBCLFy+uM6xcXFyMX3/99a3T3cX++OMPrFixAsuWLZO6b7KzszNOnjzZnOoS0ubEf6SUlZXh4+PDlevq6kJBQQGVlZV4+PAhd++4vny/b7sHTUhTVFdXo6SkBCoqKs36+dLT02tST1lVVVWmlTf1UVZWRmlpKaqrq/Huu+9i+vTp3LE5c+ZgypQpmDx5Mldmbm4OU1NT5OTkSFynuroaeXl5Mm0PfOrUKZSVlWHevHlSj8fExGDo0KFYtmyZzCPFtTUrKH/55ZeYOnUqnJycsGDBAjg4OAB43YM+fPgwsrOzZQ6kOTk5cHNzq/e4oqIiysrKmlNdQtpUamoqioqKAAA1NTWwtLSUuL8UFRWFixcv4vHjx1i6dClGjx4tkXREWsYvQlqDQCCAqqpqk17LsizMzMwa/TqGYeDo6NjkoKygoAAHBwdu8rG+vr7E75eamhqMjY25uCTm7e2NgoICPHz4kJuIfP36dYhEIvTr1++t73vgwAGMHz9eairo6OhoDBkyBPPnz8fWrVub9rma9Kr/mThxIi5dugQjIyN89dVXWLRoERYtWoRt27bB2NgYFy5cwMSJE2W6lqWlJeLi4uo9fvfu3TqNSwgfhYWFYdy4cbCxseGG5iorK/H555/jl19+QXJyMoDXuYAfPHiAuLg4/Pzzzzh69Chyc3Mxbtw4LFq0CPPmzcPixYsxZ86cFs9mlJeXhzlz5kBbWxu6urpYvHixzMOILMti9OjRYBgG586dkzj24MEDDB06FLq6utDT08PIkSPx6NGjFq074Y+ioiJcvnwZsbGxsLa2lnnJrIKCAlxcXBq9JKq6uhpJSUlgGAZubm7Q1tZudJ2dnZ0xatQoLF26FGFhYbh79y4++OADzJw5k5t5nZ6eDicnpzpbRT5//hy3bt3CkiVL6lz36dOn8PPzw4gRI7B69WpkZWUhKysLr169alT9mr1L1IgRIxAZGYmMjAzcu3cP9+7dQ0ZGBiIiIjBy5EiZrzN79mzs3bsX9+7d48rE/8D79+/HiRMn6h0uIIQvzpw5Ax8fH1y+fLnOcB7Lsnj69Cm2b9+OiIiIOrmAHz16hIyMDJSWlqKqqgoODg4YPXo0xo0b16QsYIMHD6434c2cOXMQHR2NoKAgXLhwAbdu3ZJ5QsqOHTuk/vEtKSnBqFGjYGVlhfv37+POnTvQ0tLCyJEjUVVV1ej6E/4SiUR49OgRzpw5w63f7dOnD5ccRBb13WsVB97a2zuKpaSkICIiAsrKyk0KyGJHjx6Fk5MThg4dijFjxmDAgAHYt28fd7yqqgrx8fF1Rmd//fVXdOnSRWrdT506hVevXuH333+HmZkZ9+jbt2+j6sabjF5CoRDjxo3D9evX4ezsjOjoaLi5uSEvLw9paWkYM2YMAgICeJ9ijTJ6dV5hYWHw8fFpMHmCmIKCAvr164fq6mqYmJggOzsbhoaGGDt2LJSVlbnzBAIBnJ2d4eLi0ujlgPUlT4iNjYWLi4vEOs0rV65gzJgxSEtLk7pOUywqKgr+/v4IDw+HmZkZzp49y42GhYeHo2/fvkhNTeV69k+ePIG7uzuePXtGI108VFZWhlu3bkFLS0vm4evc3Fzcvn0beXl5XJmmpiZ8fHyQkJCAdevWAYDUjF7ioeq5c+dCV1cXlpaWUFKSvIuamJjIrdG3t7fnyqurqxEcHIxXr17B1dUVS5cu5WZhDxo0COrq6o3+/HzU7J5yamoq3n33XTg6OkJfXx+3bt0C8PofbtWqVTLvfSme0HLw4EHY2dnByckJlZWVcHd3x6FDh3D+/HneB2TSuTUmeQLLsoiJiYFQKERGRgaEQiGXC7i2yspKREVF4Y8//sDff/9dZ4JKU7xtnWZ9ysrKMHv2bPzyyy9SJ8Q4OjrCwMAABw4cgFAoRHl5Obd0xMbGptn1JvJVVVWF+/fv46+//uICMsMw6N69OyZPnowuXbpgyJAh+PXXX+Hj41Onx8wwDJydnfHhhx9CW1sbYWFhSElJkTinuroacXFx+OeffxAXFyfRW3758iXKysrQtWtXpKamIjo6uvU/tBw0a6JXTEwMBg4cyN0gf/78OdeIhoaGuHPnDkpLS3HgwIE6r129ejXeeecd9OrVC8Dr4G5kZIS5c+di7ty5zakWIW2usckTWJZFYWEh3N3duc0qANR7j41lWbx48QIvXryAqakp3NzcYG1t3aRJMk1dp/nxxx+jf//+mDBhgtTjWlpauHHjBiZOnMglDeratSsCAwPr9IZI+5Keno67d++iuLiYK9PX18eAAQPqTHjq3r07fvjhB2RlZWHatGkoLy+HiooK1q5dCz09PVRXVyMwMJALvNbW1tzPR0pKCnJycmBubo6cnBykpKTA3t4e1dXVyMnJgUAggIaGBpSUlBASEiLRk+4omvWbsmbNGujq6iI0NBQMw9T5RR87diyOHz8u9bU7duxAnz59uKBsa2uLI0eOYPbs2c2pEiGNJhQKpd6/agjLsigoKEBmZiYyMzNx+vTpJuVMF09Yqe1teeAzMjKQkZEBTU1N9OrVC127doWKigq++uqrVlun+ddff+H69esNjnyVl5dj8eLF8PHxwR9//IGamhp89913GDt2LB48eFBn1zfCfxUVFbh//77EznmKioro1asX3NzcGvxSqK+vL9FbFn/5bCjwxsXFQUlJCaqqqlBSUkJcXBxsbW2hqKiIf/75B0KhEC9evEB1dTXS0tKQkJAAa2vr1msAOWhWUL516xY2bNgAIyMjqWuLrayskJ6eLvW1JiYmePHiBfecJ7e2SScjS/KEqqoqFBUVoaioCMXFxdz/1w7kz58/b3TyBOD1LOimJk8oKSnB1atXkZeXh379+rXqOs3r168jMTERurq6EuVTpkzBwIEDcePGDRw7dgzJycm4d+8e98f62LFj0NPTQ0BAAGbOnNmkz0nannhkJjQ0FBUVFVy5qakpBgwYAB0dnbde4+nTp9x9ZZFIhJSUFFhbW0sNvNbW1khLS0Nubi6qq6uRlZWFmpoaFBQUQFVVtd4dmkxMTFrmA/NIs4KySCRq8Ob6q1ev6l2QPnbsWPzf//0frl69yv2if//99/jzzz/rvR7DMAgICGhOlQmRUDt5grKyMoqKipCfn4+CggLuIcv6eBUVlSZ9sSwrK2vyOk0DAwOYmJhwyRNac53m2rVr6ywDcXNzw3/+8x+MGzdO4rPU7h2Jn/NlFzjydsXFxQgJCUFaWhpXpqKiAk9PT3Tr1k2m2dVCoVBiJQ0Absnrm4E3NzcXaWlp0NXV5X4eGYaBoaEhjIyMYGtrC2Nj4zojscDrnnztIfWOoFlBuXfv3rh48aLUnT6qq6vx559/1pvwYOfOnTA2NkZwcDCio6PBMAxevnwpMaPvTY3dNpIQaViWRX5+PjIyMpCcnIxHjx6htLQURUVFMgcPDQ0N6OnpQV9fH3p6evDx8cGNGzcaHZhLS0tRU1MjMeP6bVRVVeHg4AA9PT3uj5JQKERUVJRMu0rVXqe5Z88eVFVVSV2nOXToUPz222/w9PSEqamp1F60lZUV14sZPnw4PvvsM7z//vtYuXIlRCIRvv76aygpKUndo5zwi0gkQkxMDB4+fCgxCmRjYwNvb+9GzW6Oj49HWlqaxO9Dbm4uysvLpe4cqKurK/FwcHDotLc7mhWU161bB39/f6xYsYIbmsrOzsbff/+Nr776CrGxsfj555+lvlZDQ0Pi/peCggJ27NhB95RJiyovL0dGRgbS09O5e7Hp6ekoLy+X6fXKysoSwVf8X2kjQAMHDsTdu3elLgWRRkNDA0VFRYiJiYGjoyMEAkGDXzyVlJRgZmYGS0vLOisRwsLCcPLkSZl3lTp69Cg++OADDB06lNvk/ccff+SO17dOsyFOTk44f/48Nm/eDG9vbygoKKBXr164cuVKkzI+kbaTn5+PBw8eSCS6UFdXh7e3d5NmzpuYmGD06NEICwuDUCiEiooKPDw80KVLlzq3QMRUVFRgZ2cHQ0PDTt0Ba1ZQHj16NA4dOoQPP/yQW3g9d+5csCwLbW1t/Pbbbxg0aJDU106ePBkff/wx9wckODgYLi4uzakO6cRqamqQnZ3NBV1xEG5o5KU2hmGgo6NTJ/hqamrK/Adi8eLFuHv3rkz3lhmGgZeXF/T19aGlpYWSkhKUl5dzu0iJqampQUdHB3p6etDT05O6LFAoFOLOnTt19mq+ceNGve+vr69f74buwOve0ds+g7Tjw4cPx/Dhwxt8HeGPqqoqxMXF4dmzZxL/nk5OTujbt2+T9/IWDzeLv7wqKyvD1dW1wfPt7OwaNWLUUTV7ncI777yDyZMnIygoCM+ePYNIJIK9vT1GjhwpsdTjTQEBAZgyZQr3fMiQITT7mryVeNZz7d5veno6srOzZZ5BraurC3Nzc1hYWMDQ0BAZGRkwMzNr9L6sb+revTu2bduGdevWgWVZqUPh4vvH8+fPR/fu3VFWVsZtzVhTU4P8/HwoKSlBWVkZgwcPrlOnqqoqxMTEwMXFhfsDFh8fj2fPntGuUqRRnj17ht9//12id6yjo4MBAwbItDFDS2AYBg4ODm32fu1Bk4NyWVkZLC0tsXbtWnz22Wcy57gWs7CwQGRkJObMmQPg9R/bzjxkQeqqqKioE3wzMjJkHlIVCARc8K3939qBrqysDKWlpS2WmEacPOE///kPoqKi6hy3sbHBhAkTYG5uzmW+0tHRQX5+Pl69egWRSISMjAwkJCQgOzsb06ZNk/hy++TJE1y6dAkikQi9evWCUCjEw4cPaVcpIrPy8nKcPXsWd+7c4coUFBTQo0cP9OjRo82SNDU1/3VH1+SgrK6uDiUlpSb3LmbOnInvvvsOJ06c4O4xrF27Ftu2bav3NQzDUHL7DqimpgY5OTl1gq+0ZXbSKCgowMTEBObm5hLBV19fv1nbwjVV9+7d8dVXX+HevXv47rvvuOQJkyZNgpOTE3R1dZGYmIjHjx/DxsaG2yg9JSUF9+7dw8uXL1FUVITQ0FCIRCK4u7ujR48eYBgG9+7dQ3p6OkJCQtC9e3ckJCQgMzMT2tratKsUeauoqCgcP34chYWFXJmenh769+/fpr1VhmEoINejWcPXU6ZMwalTp7BixYpG93K3bdsGBwcHBAcHIycnBwzDQENDAwYGBs2pEuExcRarN4NvVlaWzEPPOjo6dXq+pqamvLsXZWxsjAkTJmDPnj0oLy+HqqoqFyTV1dWRl5eH8vJyPHnyBF5eXlBRUYGNjQ1yc3Nx69YtGBsbc8lJGIZBQkIC1NTUkJKSIpFm0MTEBH5+fnB1dZXoGbf0rlKkfSsoKMCJEyckRm8EAgFGjx6NmpqaZm3u0BROTk4UkOvRrKA8c+ZMvPfee/Dz88PSpUthY2MjdRq7tI0ZFBUVsWzZMm5nGgUFBXzxxRd0T7mDqKioQGZmpsSkK/EOSLIQDz2/2ftt7KYM8qaurg5lZWUIBAKoqKjAwcEBycnJyM7Olgiu4qHo+/fvQ19fHxYWFnjy5AnS0tJgYmLCbRxQXl4OAwMDLs3gO++8Ay8vrw6VkJ+0HJFIhJCQEJw9e1ZixYGrqytmzpwJVVVVbr+CtmJrawtDQ8M2fc/2pFlBefDgwdz/3759u85x8X1iWZaIJCUlSd00mjRdamoqrl27huLiYmhpaWHo0KGwsrJq0feoqanBq1evJHq/6enpMg89i9OzWlhYSPSA5TX03JKEQiGGDBmCu3fvws7ODt27d4eKigpCQ0O5Wz/i4Nq9e3dubadQKERKSgrU1dWhoKAAgUCAtLQ0FBYWoqamBg8fPoSysjLKyso6ZJrBliQUCrkdh5o6k7i9ys7OxtGjRyVSZGpqamL69Onw8PAAwzCNWvLWEgwMDJq0DWln0qygfPDgwZaqB/1haUFhYWHYsmULLl68CJZloaCgAJFIBIZh4O/vj/Xr1zd6j0+WZVFUVFSn55uZmdmooefaPV8LCwteDj23lKdPnyI1NRUWFhYoKyvDs2fPoKSkxAXe2jl84+PjubWdb3JyckJeXh7U1NSQn58vcSwpKYlu+TQgNDQUR44ckXn9dkdQXV2NoKAgXL58WeJ308vLC5MnT5bbaJOSkhKsra3x+PFjidUDRFKzgvL8+fOb/FoFBQUoKCigrKwMKioqddLzScMwTKM3DmiKX375Bd9++y2ysrLQo0cP/PTTT/D09Gz1920JZ86cwYwZMyS2EBQvzWFZFpcuXcLly5dx/PhxiZzItYmHnt9c89uYoWczMzOu5ysOwu1t6Lk5xGkGlZSUoK6ujrKyMoSEhGDixIlSA6+JiUm9qQSB1/eoHR0d8fLlSzx48AAFBQUAgKKiIgQHB0MoFGL8+PGdqo3fprKyEoGBgXXWb3dkSUlJOHr0KDIyMrgyQ0NDzJ49G05OTm1eHwMDAwiFQmhqaqJLly6Ij4+XWD1A6pLbfmobNmwAwzDcll3i5/J2/PhxrF69Gnv27EG/fv2wY8cOjBw5EvHx8fX+weSLsLAwzJgxAzU1NfUmfqipqQHDMJgxYwbu3LkDGxubOr3f3NxcmdJF1h56rh18DQwM2v3Qc3PVHoouKSnhesh5eXn1JtR5G4ZhYGVlhS5duiAhIQEREREoLy8Hy7K4c+cOHj58iJEjR8LPz496IQDu37+PhISETrF+u6KiAufPn5dI9cowDIYOHQp/f3+5Dd0fOXIE9+7dg0gkgoGBAf766y+J1QOd7ZaCLBoVlBctWgSGYbBv3z4oKipi0aJFb30NwzBS91PetGlTg8/l5YcffsDSpUuxcOFCAMCePXtw8eJF/Prrr1i7dq2ca9ewL7/8UqKHXB+WZVFTU4NZs2Zh1KhRMl1bW1tbYthZPOuZfqmkqz0UXVpayi0dbIldbRQUFODk5AQ7OztERkYiNjYWNTU1KC8vx7lz53Dr1i2MHz8effr06bRfjsS95M6wfjs6Ohp//PGHRPY6S0tLzJkzp8XnkDSVnp4e4uLikJqaWmeCI5HUqKB8/fp17v6koqIirl+/LtOQc3shTsSwbt06rkxBQQHDhg2rs+MJ36SmpuLChQsyb4jAsiySkpK4SWBiKioqUmc9N5SdjdRVeyi6vLy8VZLrq6iooEePHjAzM0NhYSEePHgAlmWRl5eHQ4cO4fr165g8eTK6devW4u/Nd5GRkUhMTERFRUWHXb9dXFyMU6dO4cGDB1yZsrIy/P39MWTIkDZLAiILLS0tBAcHS53gSF/sJTUqKCcnJzf4vDF+++23Jr1u3rx5TX7Pt8nNzUVNTU2d3oyJiQm37dibKisruTSJALi9caurq1FVVdVqdX1TYGBgk7YOVFBQwKhRo2BmZgZzc3MYGhpK7V215WdpS1VVVaiurua2P2wtTd0z+W0qKyuhrKyMSZMmYfDgwQgICEBMTAyA1/cXv//+e7i5uWH8+PG83RRCKBS2eNvr6OhIzTKoo6MjkTijJSgpKbVpYGFZFg8ePMDp06cl5nk4Ojpi5syZMDIygkgkkmnHs9b++a+qqkJCQgIqKiqQnJyMyspKxMfHo7q6GsnJyYiMjET37t2bfP3Kykrub21r/Y1q81tBrJwwDFPnoaCgwCooKNRbrqCg0Kp1Sk9PZwGwISEhEuWfffYZ6+npKfU1GzduZAHQgx70oAc9OuCjrcltoldSUpLE84KCAsyfPx86OjpYuXIlHB0dAbzeGPunn35CcXExDh8+3Kp1MjQ0hKKiIrKzsyXKs7Oz601Bt27dOqxevZp7HhUVBV9fX9y/f79N75ccOnSIS8TSGPv372/WLPqOoDV6amJ3797F8ePHMXPmTC6dZkuT1lMTiUQICwvDhQsXuJnawOuZ8cOHD8eQIUN4MWxYVlaG27dvQ0VFpV3e662srIRQKMTAgQNbNXlLTU0Nbt68iQsXLkAoFHLlHh4emDJlSrMycrXWz39lZSW2b9+OoKAgDB8+HP/6179a5d+4rUcqWluzg/Lly5fxww8/ICIiAoWFhVKHUKUlD3lzXfKmTZtgZGSEq1evStyHdnNzw5QpUzBixAj85z//adG10W8S7/l57do1buhLJBLh2rVr+OCDD6S+RiAQSPygiZekiHf6aSsjR46UacvA2hiGwYgRIzr9TN3W+vyVlZW4e/cu0tPTcefOHQwZMqRNA8/AgQPRr18/XLt2DVevXuWG+i5fvoyQkBCMHz8e/fr1k+tkMGVlZe4+o6qqaotfX9quWg2VN5aSkhJEIhGUlZVb7efo5cuXOHr0KFJTUwG8zoaoq6uLWbNmwc3NrdnXb61637p1C6mpqXByckJqairi4+M77Oz3ltSs38bTp0/D398f2dnZmDlzJkQiEWbNmoWZM2dCTU0N7u7u2LBhg0zXOnfuHCZNmiR1YpiCggImT56MgICA5lRXJqtXr8b+/ftx+PBhxMbGYsWKFSgtLeVmY/OVlZUV/P39ZZ7coaioiHHjxvFmdmZHJG1JTltTUVHB6NGjsXnzZgwcOJALwIWFhThy5Ai2bdvG3YPuiJ48eYLz58/j6dOnMpXziVAoxLlz57B9+3YuIDMMg8GDB2PDhg0tEpBbS+3Z73p6etzs99rzb4h0zeopb9u2DZ6enrhz5w7y8/Oxe/duLFq0CEOGDEFycjK8vLxga2sr07VYlq13MhUAxMTENGkiU2PNmDEDr169woYNG5CVlYWePXviypUrLbKUpbWtX78ely9ffmuPmWEYMAyDL774og1r17nwbUmOtrY2Zs2aBT8/P5w7dw6PHz8GAKSnp+Pnn3+Gs7MzJk2ahC5durR53VqLOIHLm+ti6yvnk/j4eBw7dkxir2MzMzPMmTMHdnZ2cqyZbGrPfs/NzYWamlqHm/3eWpoVlGNiYrBt2zYoKipySUDEM+BsbGzw3nvvYfv27TLNmJ44cSJ2794NGxsbvPvuu9z9mbKyMuzevRt79+7l9l5ubR988EG9w9V81rdvXxw/fpzL6CXttoGioiIYhsGJEycanWqTyI6vS3JMTU3x7rvvIiEhAWfOnOF6YLGxsYiLi4OXlxfGjRvHbafanonTnL65Lra+cj4oKyvDmTNnEBISwpUpKSlh1KhRGDFiBPd3lu/E66QB4NWrV9y+BrR72ds1619YXV2d+4apq6sLgUCAzMxM7riJiUmdCV312blzJ5KSkvDpp59i3bp13PKNzMxMVFVVwcfHBzt27GhOdTuFyZMnIyQkBFu2bOHWLdfOfT127Fh88cUXFJBbWe0/Sm+W80G3bt2wZs0aPHz4EAEBAcjLywPLsrh37x7Cw8MxbNgwDB8+vFXu87aF2mlOa6+L7dq1q9RyefeWWZZFZGQkTpw4gaKiIq7c3t4es2fP5u1ytvqIkwwBryfKtoeRRr5g2GaMCfv4+KB379746aefAADe3t5QV1dHYGAgqqurMXLkSGRnZzc4LP2mgIAAXL58GSkpKQBeTwgbM2YMxo0b1y4SkURERMDDwwMPHz6UumVlW0pNTcX169dRVFQEbW1tDBkyhO4hkzqqqqpw48YNXLlyRWJ7Py0tLYwdOxY+Pj6tlohCvCWllpZWi34BePLkCY4dOwahUAglJSVUV1dDRUUF/fr1w/379+uUz549+633aKVNDquoqEBxcXGzts7Mz8/H8ePHuVsKAKCqqoqJEydiwIAB7T4rW0FBQYcYeWkrzQrK33//PX788UckJCRAIBDgwoULmDBhAtTU1MAwDEpLS/Hrr79iwYIFLVhlfuNTUCakMUpKSnDlyhXcvHlT4taHqakpJk2aBFdX1xb/YtxaQTknJ0dqZ8DQ0BC5ubl1yp2cnN6a2z4iIgKXLl3C2LFjueHu5gRlkUiE27dvIyAgABUVFVy5u7s7Zs6c2WECWWtltOuomjR8XVFRgYCAAFRVVeGLL75AXl4ezMzM4O/vjxs3buDMmTNQVFTE2LFj4efn19J1JoS0Ak1NTUydOhWDBg1CQEAAIiMjAQBZWVnYvXs3unbtiilTprSL0ZaGdtxqipaeHJaZmYmjR4/ixYsXXJm2tjZmzJiBnj17totRQVlRQG6cRgflnJwc9O/fH0lJSWBZFgzDQE1NDefOncOwYcMwcOBAWotGSDtmbGyMpUuXIjExEWfOnOHmhTx79gxff/01+vbti/Hjx7frfZwbWqcs7VhLTQ6rqqrC1atXuVt8Yj4+Ppg0aVKrJiAh7UOjb1Zs2bIFycnJ+Pjjj3HhwgX85z//gZqaGpYvX94a9SOkQxIKhQgJCZHIziTLsbZkb2+PTz/9FEuXLuVmzwLAgwcPsHnzZpw9exZlZWVyrGHTNbRO+c1j9U0aa+y/T2JiIr7++mtcvHiRC8jGxsb46KOPMGfOHArIBEATespXr17FvHnz8N1333FlJiYmmD17NuLj47n0mISQ13/Qw8PD0adPH4nhztDQUBw5cgQ1NTV1RpYaOtbWGIZBr1694Obmhtu3b+PSpUvc5gVBQUEICQnBmDFjMHDgQF4u15HW621oKFrasdp7Y7948QLV1dVIS0tDfHw8unbt+tY6lJeX46+//sKtW7e4/AEKCgoYPnw4Ro8ezbs10kS+Gv1blJqain/9618SZQMGDADLssjOzqagTEgt0gKsOLFIUlJSnYQiDR2TJyUlJfj5+aFfv34IDAxEcHAwt7vQyZMncePGDUycOJF390OfPHmCS5cuQSQSccPNDQ1FSztmYWHB7Y1dmyzLfJ48eYI//vhDIv+4lZUV5s6d26EStdSnvi+l9ZWTJgTlysrKOrMkxc9bc+s7Qtqb+gKstPSb4oDd0DE+UFdXx6RJkzBo0CD89ddf3F6+r169wv79+2FnZ4cpU6bInMmvNUnr9QKod51yfceWLl2KQYMGSX2P2rOmaysqKsKJEycQERHBlamoqGDcuHHw8/Nr98ucZFXfqA+fRoP4pknjTcnJyRI/bOL9SZ89eyZ1Gr+sS4NiY2Nx8OBBvHjxAvn5+XVSRTIMg2vXrjWlyoS0OWkB1tPTs970mwB4lZqzIQYGBli4cCGGDBmCM2fO4NmzZwCAFy9e4Ntvv0Xv3r0xYcIEiXvRbU1ar1dJSaneoWgA9R6TNc80y7IIDQ3F6dOnJe63u7i4YObMmTA0NGyVz8pH9X0p5etoEF80KSivX78e69evr1P+3nvvSTwXz86Wlu7xTUeOHMHChQuhrKwMR0dH6Onp1TmnLXJfE9IS6st9zbJsvek3AfAyNWdDrK2t8dFHH+HJkyc4e/Yst+1pREQEHj16BF9fX4waNYrbPa2t1Dc5a+LEiQ0ORTd1mBp4vTLljz/+4AI8AGhoaGDq1Knw9PTk1bB+W6hv1Ifvo0Hy1uig3FpbJ27atAm9evXC5cuXO9W3SdIx1Zf7uqCgoMH0m3xOzVkfhmHg7u6O7t274+7du7h48SKKi4tRU1OD69ev4969exg1ahQGDx7cZtuE1jc5Ky8vr96haABNWtssEokQHByMq1evcrn/AcDT0xNTpkyBlpZWkz5De1bfl9IePXq0m9EgeWlWRq+WpKamhh9++AErVqyQd1WahTJ6EeD17ku1b/GI9e7dm8sJ3FFVVFQgKCgIf//9t0SQ0tfXx4QJE+Dh4cHdU22LjF5CoZCbTCRL5q7GyMjIQEhICHcLD3j9OWfPng0XF5cWe5/2JjQ0FDt27EBFRQWUlZVRVVUFVVVVDBs2DH///Xed8o8++oi3o0FtjTdrGNzd3ZGRkSHvahDSImon5O9sVFVVMW7cOAwcOBDnz59HaGgoWJZFXl4eDh48iGvXrmHy5Mno1q1bq9WhdkYvce53sYYSh8iquroaERERePr0KXdbjWEY+Pn5wd/fv91u5NFS6tuQxczMTOrtAL6PBrUl3vSU7969i2nTpuHUqVPo37+/vKvTZNRTJkRSWloazp49i9jYWIlyd3d3jBo1CnFxcS3eU64tIyMD5ubm3HNpOawbIz09HXfv3kVxcTFXZmZmhnfeeQc2NjYtUWXSifGmp7x9+3bo6Ohg4MCBcHFxgZWVVZ2daRiGQUBAgJxqSAhpii5dumDlypWIiYnBmTNnuBGxx48f4+nTp7CysoKHh0erBWXx1oGKiorNymFdUVGBsLAwbqY58DoJiKOjIxYsWNAp7x2TlseboPz48WMwDAMrKyuUlJQgJiamzjmdbfYiIR2Ji4sLnJycEBoaivPnz6OwsBAikQjJyclIS0tDjx494Orq2uKZwSoqKhAfHw8nJ6cm5bBmWRYvXrxAaGioxLpkU1NT9O3bFwzDtNrWlqTz4U1QTk5OlncVCCGtTEFBAf3794eHhweuX7+Oq1evorKyEtXV1Xj48CFiY2PRp08f2Nvbt0iCjaqqKrx48QLm5uZcTnFpiUPq6y2XlJTg7t27SEtL48pUVFTg6emJbt26obKyUmIYm5Dm4k1QJoR0HgKBAKNHj0bv3r1x6NAhpKamgmVZbjb206dP4enp2ezJcjNnzkRGRgZ0dXUxe/ZsxMTEQFVV9a3JQUQiEWJjYxEeHi6RqdDGxgbe3t60eQRpNbwMysXFxdzQ1pvaw16uhBDZaGlpoUePHnBzc8Pjx4+RmpoKAMjLy8OVK1fQpUsX9O3bF/r6+o2+tlAoxKtXr1BVVYXCwkJoaWnBzc0NqqqqsLS05HrHb84GzsvLw507d/Dq1SuuTF1dHd7e3jSRi7Q6XgXl3bt344cffpDY+PtNsmQHI4S0Lzo6Ohg+fDgyMzMRFhaG3NxcAK9nbqenp6Nr167o3bs3NDQ0ZL7m06dPub8XIpEIBQUFcHV1BfB6g41u3bpJ7AldXV2NR48e4dGjRxLZA52cnNC3b1/aOIG0Cd5kRd+zZw/ef/99ODg44MsvvwTLsvjoo4+wdu1amJqaokePHjhw4IC8q0kIaUVmZmYYP348fH19uQDMsiwSEhJw6tQpRERESCQkqY94lnVtcXFx3FB0dXU1YmJiEB8fj4qKCmRlZeHcuXOIioriArKOjg7Gjh0LHx8fCsikzfAmKP/0008YOXIkLl++jGXLlgEAxo4di61btyImJgbFxcX4559/5FxLQkhrYxgGDg4OmDp1Kvr27csl+KiurkZkZCROnjyJuLg4qbe3xMRpNmv3eHNzcyUmbAGvl0sFBwfjxo0bKC0tBfB6MlrPnj0xceJEmJqaAng9YezRo0cyfSEgpDl4E5QTExMxbtw4AJDYjBx4/Y11yZIl2LVrl9zqRwhpW0pKSnB3d8f06dPh4uLCLYksLy/H3bt3cfbsWbx8+VLqRjUmJibw8vLiliqJ1xPX3sWusrIS+fn5qKiogEAggLa2NoyNjdG3b19069ZNYpnTkydPcP78eTx9+rR1PzTp9HgTlHV0dLihJW1tbairq+Ply5fccS0tLWRlZcmreoQQOVFVVYW3tzemTJkiMdGqoKAAV69exeXLl7l70MDre8nbtm3Dl19+ifLycgCvv+CfOHECp06dQmJiIp4+fYqCggKut80wDDQ0NKChoYGIiAiEh4cjIiIC6enpKCsrk0g4Iu4sNMaZM2cwYsQIGBgYgGEYREVFyfS6kydPwsnJCaqqqnBzc8OlS5e4Y1VVVfjXv/4FNzc3aGhowNzcHPPmzaN0xe0cb4Kyq6srHj16xD338vLC7t27kZ6ejpcvX2Lv3r2tmisXALZu3Yr+/ftDXV1d6r7QhBD50dHRwdChQ+Hv7y+xT3NmZiYCAgJw8+ZNXLp0CYsXL0ZISEidHjTLsoiNjcUvv/yCa9euIT09HcDrdce6urpQU1NDamoqwsLCkJKSgrKyMrx48QKnTp1CbGws7OzsuIQjjVVaWooBAwZg+/btMr8mJCQEs2bNwuLFixEZGYmJEydi4sSJXG+9rKwMERERWL9+PSIiInDmzBnEx8dj/Pjxja4f4Q/ezL6eO3cu9uzZg8rKSggEAmzevBnDhg3jlkApKyvj9OnTrVoHoVCIadOmwdvbmyaVEcJTJiYmGDduHJKTk/HgwQMuecfdu3dx7ty5Bu81i48lJydDW1sbjo6O3ISy6upqxMXF4Z9//kFcXBysra0BADExMRAKhSgoKEB5eTlu3brVqPScAPDOO+9w7yurnTt3YtSoUfjss88AAFu2bEFQUBB+/vln7NmzBzo6OggKCpJ4zc8//wxPT0+kpqbS8tF2ijdBeeHChVi4cCH33MfHB9HR0Th//jwUFRUxYsSIVu8pb968GQBw6NChVn0fQkjzMAwDW1tbWFlZITY2FlFRUXj48KHU+8v1SUpKQlZWFuzt7QEAKSkpyMnJgbm5OXJycpCSkgJFRUXk5uaiuroaWVlZqKmpwZMnT3D58mUMHjy4VfcAvnfvHlavXi1RNnLkSJw7d67e1xQWFoJhGBrpa8d4E5SlsbOzw4cffijvajSosrISlZWV3POSkhI51oaQzkVRURGurq7Q0tLCzp07GxWUi4uLERUVxfWI4+LioKSkBFVVVSgpKSEuLg5eXl7w8PCo81oFBQU8fvwYurq6UrcibAlZWVl1rm1iYlLv3JqKigr861//wqxZsyS2qiTtC++CcmhoKIKDg5GTk4P33nsPXbt2RVlZGeLi4tCtWzdoamrKu4oStm3bxvWwCSFNU/uLbVOEh4c3KiCLpaSkcBNKc3NzUVVVhYyMDIhEIuTm5qKoqAguLi5SXysSiVBUVMTt2yx29OhRLF++nHt++fJlDBw4sNF1a4yqqipMnz4dLMti9+7drfpepHXxJigLhULMnDkTAQEBYFkWDMNg3Lhx6Nq1KxQUFDBixAh8/PHH+Pe//92o665du/atkytiY2Ph5OTUpHqvW7dOYogpKioKvr6+TboWIZ2NkpISNDU1UVJS0qRZzWL5+flgGKbRgdnc3JzrVUrbLUpbW7vBe9QmJibQ1taW2Nlq/Pjx6NevH/e8qfm7TU1NkZ2dLVGWnZ3NrZ0WEwfklJQUXL9+nXrJ7RxvgvL69etx4cIF7N69G35+fnB0dOSOqaqqYtq0aQgICGh0UP7kk0+wYMGCBs+xs7NrSpUBvE6sX/u+Et968oTwmXjHpdqbPjRFcnJyk3rKjo6OXNBsSvAcPHgw9PT0JCZ9aWlptcjeyt7e3rh27Ro++ugjriwoKAje3t7cc3FAfvbsGYKDgyXShvKBUChEeHg4+vTpQ1nRZMSboPzHH39gxYoVWLZsmdTMXc7Ozjh58mSjr2tkZCSxfIIQwi8qKirN/oM9ZsyYRveUGYaBs7Nzk7eIFP9tkeX1eXl5SE1N5dYQx8fHA3jdGxb3fOfNmwcLCwts27YNAPDhhx/C19cX33//PcaOHYs///wT4eHh2LdvH4DXAXnq1KmIiIjAhQsXUFNTw91v1tfX50UQDA0NxZEjR1BTU9PqQ/gdBW/WKefk5NTZPq02RUVFlJWVtWodUlNTERUVhdTUVNTU1CAqKgpRUVE0eYsQnrOysoK/v79EFq6GKCgowN3dvUm7T6mqqqJv374YN26czAH9r7/+Qq9evTB27FgAr7eU7NWrF/bs2cOdk5qaiszMTO55//79cezYMezbtw89evTAqVOncO7cOW5TjfT0dPz1119IS0tDz549YWZmxj1CQkIa/blaWmVlJQIDA5GUlIQrV640e95AZ8GbnrKlpSXi4uLqPX737l04ODi0ah02bNiAw4cPc8/F95iCg4MxePDgVn1vQkjzrF+/HpcvX5a5xzxmzBiZr80wDCwsLNC1a1fY2dnJHPzFFixY8NbbaDdu3KhTNm3aNEybNk3q+TY2Nk0asm8r9+/fR0JCAtzd3ZGQkICwsDDqLcuANz3l2bNnY+/evRI7u4hz3e7fvx8nTpzAvHnzWrUOhw4dAsuydR4UkAnhv759++L48eNQVFSsN2gqKChAQUEBy5Yte+veyAzDwMzMDAMGDMDcuXMxZswYdO3atdEBuTMS95JVVFSgra0NFRUV6i3LiDc95X//+98IDQ3FoEGD4OzsDIZh8PHHHyMvLw9paWkYM2YMPv74Y3lXkxDCY5MnT0ZISAi2bNmCCxcuSPQkGYaBm5sbxowZ02BAVldXh7OzMxwdHWniZhNFRkYiMTERFRUViI6ORlVVFRITExEZGQkvLy95V4/XGJZH4x8sy+Lo0aM4deoUnj17BpFIBHt7e0yfPh3vvPMO13Pms4iICHh4eODhw4fo3bu3vKtDSKeVmpqKHj16oKCgAGpqatiwYUOD95AFAgF69eoFFxcXiSVOpPHS09MRERFRp7x3795NXiLWWfDqJ49hGMydOxdz586Vd1UIIe2clZUVNDQ0UFBQAIFAUG9AVlBQgLOzMzw8PKCqqtrGteyYLCwsKPg2Ea+CMiGEtBUlJSU4OTnB3d2dhqkJb/AqKN+5cwe//vorXrx4gfz8/DozCxmGkdjekRBCGktJSQlubm5wc3OjnjHhHd4E5R9++AGfffYZVFVV4ejo2KT1g4QQ0hADAwMMHz6cUlES3uJNUP7222/h4+OD8+fPQ0dHR97VIYR0AKampigrK4OWlhZ0dHQwduxY6h0TXuNNUC4rK8OcOXMoIBNCWkx4eDh+++03VFVVYfjw4RSQCe/xJnmIn58fnjx5Iu9qEEI6oJ49e9ItMdIu8CYo//TTT7h27Rq+++475OXlybs6hJAOQllZGe7u7vKuBiEy4U1QtrS0xPLly7F27VoYGRlBQ0MD2traEg8a2iaENJaVlRWUlZXlXQ1CZMKbe8obNmzA1q1bYWFhgT59+lAAJoS0CDMzM+7/aX9fwne8Ccp79uzB2LFjce7cuSbvb0oIIW/S1dXl/p/29yV8x5voJxQKMXbsWArIhJAWpaamBoD29yXtA28ioL+/P27fvi3vahBCOhjxVovS9vclhG94E5Q3btyImJgYvPfee3j48CFevXqFvLy8Og9CCJGVUCjE/fv3UVxcTPv7knaBN/eUHR0dAQBRUVHYu3dvvefV1NS0VZUIIe2c+B5ydHQ07e9L2gXeBOUNGza0i/2SCSHtQ+17yGpqapg+fXqdpVGWlpZyqh0h0vEmKG/atEneVSCEdCC17yG/fPkSRkZGNOOa8B5v7ikTQkhLEfeS6R4yaW8oKBNCOpzIyEgkJiaitLQU0dHRKC0t5e4hE8JnvBm+JoSQlmJpaYk5c+ZILW8qygZG2gIFZUJIh2NhYQELC4sWvSZlAyNtgYavCSGkFqFQiJCQEAiFQq6MsoGRtkJB+X+Sk5OxePFi2NraQk1NDfb29ti4caPELyYhpOMLDQ3FwYMHcf/+fa6MsoGRtkJB+X/i4uIgEomwd+9eREdH4z//+Q/27NmDzz//XN5VI4S0EWk9YprJTdoS3VP+n1GjRmHUqFHcczs7O8THx2P37t347rvv5FgzQkhT1Tc5q75yaT1iZWVlygZG2gwF5QYUFhZCX1+/wXPE36TFSkpKWrtahBAZ1Tc5S1p5fT3ixYsXt/hMbkLqQ0G5Hs+fP8dPP/301l7ytm3bsHnz5jaqFSFEVm8ORXt6ekIgENRbLl7b/GaPOCcnB+PGjZP3xyGdRIe/p7x27VowDNPgIy4uTuI16enpGDVqFKZNm4alS5c2eP1169ahsLCQe9y8ebM1Pw4hREb1Tc6qr1y8tnnx4sWYN28e10OmHjFpSx2+p/zJJ59gwYIFDZ5jZ2fH/X9GRgb8/PzQv39/7Nu3763XFwgEEAgE3HNNTc0m15UQ0jLqG4ru0aOH1HJPT89WWdtMSGN1+KBsZGQEIyMjmc5NT0+Hn58fPDw8cPDgQSgodPiBBEI6pPqGok+cOEGTtgivdfigLKv09HQMHjwY1tbW+O677/Dq1SvumKmpqRxrRghprPrSbJqZmcHExETq+YTwAQXl/wkKCsLz58/x/PlzdOnSReIYy7JyqhUhpCloKJq0VzQ++z8LFiwAy7JSH4QQQhpPWspS0jAKyoQQQlqFtJSlpGEUlAkhhLQ42sSjaSgoE0IIaXG0iUfTUFAmhHRYdE9TPmgTj6ajoEwI6bDonqZ8iNeJl5aWIjo6GqWlpdx6cNIwWhJFCOmQ6stxTVpffevEaT3421FQJoR0SNLuadbeKYq0Hlon3nQ0fE0I6XDoniZprygoE0I6HLqnSdorGr4mhHQ4dE+TtFcUlAkhHQ7d0yTtFQ1fE0IIITxBQZkQQgjhCQrKhBBCCE/QPeVOIDMzE5mZmfKuBiGkEzIzM4OZmZm8q9FuUFBuYWZmZti4cSNvfggrKysxa9Ys3Lx5U95VIYR0Qr6+vggMDKRsajJiWJZl5V0J0nqKioqgo6ODmzdvQlNTU97V6XRKSkrg6+tL7S8n1P7yJW7/wsJCaGtry7s67QL1lDuJnj170i+FHBQVFQGg9pcXan/5Erc/kR1N9CKEEEJ4goIyIYQQwhMUlDs4gUCAjRs30iQLOaH2ly9qf/mi9m88muhFCCGE8AT1lAkhhBCeoKBMCCGE8AQFZUIIIYQnKCgTQgghPEFBmXRqDMPI9Lhx40az36usrAybNm1q1LW2bt2K8ePHw8TEBAzDYNOmTc2uB5/wuf3j4uKwZs0a9OzZE1paWjAzM8PYsWMRHh7e7LrwBZ/bPyMjA3PnzoWjoyO0tLSgq6sLT09PHD58GB15fjJl9CKd2pEjRySe//bbbwgKCqpT7uzs3Oz3Kisrw+bNmwEAgwcPluk1X3zxBUxNTdGrVy8EBgY2uw58w+f2/+9//4sDBw5gypQpeO+991BYWIi9e/fCy8sLV65cwbBhw5pdJ3njc/vn5uYiLS0NU6dOhZWVFaqqqhAUFIQFCxYgPj4eX331VbPrxEssIYTz/vvvs631a/Hq1SsWALtx40aZX5OUlNTk17ZHfGr/8PBwtri4WKIsNzeXNTIyYn18fFqhhvLHp/avj7+/P6uhocFWV1e3TMV4hoavCXkLkUiEHTt2oHv37lBVVYWJiQmWL1+O/Px8ifPCw8MxcuRIGBoaQk1NDba2tli0aBEAIDk5GUZGRgCAzZs3c8OCbxuOtrGxaY2P1K7Iq/09PDzqbGJhYGCAgQMHIjY2tmU/JI/J8+dfGhsbG5SVlUEoFDb7s/ERDV8T8hbLly/HoUOHsHDhQqxatQpJSUn4+eefERkZibt370JZWRk5OTkYMWIEjIyMsHbtWujq6iI5ORlnzpwBABgZGWH37t1YsWIFJk2ahMmTJwMA3N3d5fnR2gW+tX9WVhYMDQ1b9DPymbzbv7y8HKWlpSgpKcHNmzdx8OBBeHt7Q01NrVU/t9zIu6tOCJ+8OXx3+/ZtFgB79OhRifOuXLkiUX727FkWAPvgwYN6r92c4bvOOnzNl/YXu3XrFsswDLt+/fomX4PP+Nj+27ZtYwFwj6FDh7KpqamNukZ7QsPXhDTg5MmT0NHRwfDhw5Gbm8s9xEObwcHBAABdXV0AwIULF1BVVSXHGncsfGr/nJwczJ49G7a2tlizZk2rvAff8KH9Z82ahaCgIBw7dgyzZ88G8Lr33FFRUCakAc+ePUNhYSGMjY1hZGQk8SgpKUFOTg4AwNfXF1OmTMHmzZthaGiICRMm4ODBg6isrJTzJ2jf+NL+paWl8Pf3R3FxMQICAurca+6o+ND+1tbWGDZsGGbNmoWjR4/Czs4Ow4YN67CBme4pE9IAkUgEY2NjHD16VOpx8eQVhmFw6tQphIaG4vz58wgMDMSiRYvw/fffIzQ0tNP8EW9pfGh/oVCIyZMn4/HjxwgMDISrq2uTr9Xe8KH93zR16lTs378ft27dwsiRI1vsunxBQZmQBtjb2+Pvv/+Gj4+PTBNLvLy84OXlha1bt+LYsWOYM2cO/vzzTyxZsgQMw7RBjTsWebe/SCTCvHnzcO3aNZw4cQK+vr5N+RjtlrzbXxpxD7mwsLBFrsc3NHxNSAOmT5+OmpoabNmypc6x6upqFBQUAADy8/PrZBnq2bMnAHBDeOrq6gDAvYa8nbzbf+XKlTh+/Dh27drFzRjuTOTZ/q9evZJafuDAATAMg969e8t0nfaGesqENMDX1xfLly/Htm3bEBUVhREjRkBZWRnPnj3DyZMnsXPnTkydOhWHDx/Grl27MGnSJNjb26O4uBj79++HtrY2xowZAwBQU1ODi4sLjh8/jm7dukFfXx+urq4NDoceOXIEKSkpKCsrAwDcunULX375JQDgnXfegbW1des3ghzJs/137NiBXbt2wdvbG+rq6vj9998ljk+aNAkaGhqt3gbyJM/237p1K+7evYtRo0bBysoKeXl5OH36NB48eICVK1fCwcGhLZui7ch59jchvFJfRqN9+/axHh4erJqaGqulpcW6ubmxa9asYTMyMliWZdmIiAh21qxZrJWVFSsQCFhjY2PW39+fDQ8Pl7hOSEgI6+HhwaqoqMi0PMTX11diOUjtR3BwcEt9bN7gU/vPnz+/3rYHwGVb60j41P5Xr15l/f39WXNzc1ZZWZnV0tJifXx82IMHD7IikahFPzefMCzbgTN7E0IIIe0I3VMmhBBCeIKCMiGEEMITFJQJIYQQnqCgTAghhPAEBWVCCCGEJygoE0IIITxBQZmQZkhOTgbDMDh06JC8q9IpUfvLF7V/y6OgTAghhPAEJQ8hpBlYlkVlZSWUlZWhqKgo7+p0OtT+8kXt3/IoKBNCCCE8QcPXpNPbtGkTGIZBQkIC5s6dCx0dHRgZGWH9+vVgWRYvX77EhAkToK2tDVNTU3z//ffca6XdU1uwYAE0NTWRnp6OiRMnQlNTE0ZGRvj0009RU1PDnXfjxg0wDIMbN25I1EfaNbOysrBw4UJ06dIFAoEAZmZmmDBhApKTk1upVdoOtb98UfvzCwVlQv5nxowZEIlE+Prrr9GvXz98+eWX2LFjB4YPHw4LCwts374dDg4O+PTTT3Hr1q0Gr1VTU4ORI0fCwMAA3333HXx9ffH9999j3759TarblClTcPbsWSxcuBC7du3CqlWrUFxcjNTU1CZdj4+o/eWL2p8n5LMPBiH8sXHjRhYAu2zZMq6surqa7dKlC8swDPv1119z5fn5+ayamho7f/58lmVZNikpiQXAHjx4kDtHvLvQ//3f/0m8T69evVgPDw/ueXBwsNTdnt68Zn5+PguA/fbbb1vmA/MMtb98UfvzC/WUCfmfJUuWcP+vqKiIPn36gGVZLF68mCvX1dWFo6MjXrx48dbrvfvuuxLPBw4cKNPr3qSmpgYVFRXcuHED+fn5jX59e0HtL1/U/vxAQZmQ/7GyspJ4rqOjA1VVVRgaGtYpf9sfB1VVVRgZGUmU6enpNemPikAgwPbt23H58mWYmJhg0KBB+Oabb5CVldXoa/EZtb98UfvzAwVlQv5H2pKO+pZ5sG9ZtCDL8hCGYaSW154MI/bRRx8hISEB27Ztg6qqKtavXw9nZ2dERka+9X3aC2p/+aL25wcKyoTIiZ6eHgCgoKBAojwlJUXq+fb29vjkk09w9epVPH36FEKhUGImLGkcan/5ovaXjoIyIXJibW0NRUXFOjNZd+3aJfG8rKwMFRUVEmX29vbQ0tJCZWVlq9ezo6L2ly9qf+mU5F0BQjorHR0dTJs2DT/99BMYhoG9vT0uXLiAnJwcifMSEhIwdOhQTJ8+HS4uLlBSUsLZs2eRnZ2NmTNnyqn27R+1v3xR+0tHQZkQOfrpp59QVVWFPXv2QCAQYPr06fj222/h6urKnWNpaYlZs2bh2rVrOHLkCJSUlODk5IQTJ05gypQpcqx9+0ftL1/U/nVRmk1CCCGEJ+ieMiGEEMITFJQJIYQQnqCgTAghhPAEBWVCCCGEJygoE0IIITxBQZmQdkLaPrOEkI6FgjLpkBITE7F8+XLY2dlBVVUV2tra8PHxwc6dO1FeXt5q7xsTE4NNmzbJffP1rVu3Yvz48TAxMQHDMNi0aZNc61MfhmFkety4caPZ71VWVoZNmzY16lrtpR2bis/tHxcXhzVr1qBnz57Q0tKCmZkZxo4di/Dw8GbXhc8oeQjpcC5evIhp06ZBIBBg3rx5cHV1hVAoxJ07d/DZZ58hOjq6yZutv01MTAw2b96MwYMHw8bGplXeQxZffPEFTE1N0atXLwQGBsqtHm9z5MgRiee//fYbgoKC6pQ7Ozs3+73KysqwefNmAMDgwYNlek17acem4nP7//e//8WBAwcwZcoUvPfeeygsLMTevXvh5eWFK1euYNiwYc2uEx9RUCYdSlJSEmbOnAlra2tcv34dZmZm3LH3338fz58/x8WLF+VYw/+PZVlUVFRATU2txa+dlJQEGxsb5Obm1tlCj0/mzp0r8Tw0NBRBQUF1yuWlvbRjU/G5/WfNmoVNmzZBU1OTK1u0aBGcnZ2xadOmDhuUafiadCjffPMNSkpKcODAAYmALObg4IAPP/yQe15dXY0tW7bA3t4eAoEANjY2+Pzzz+skurexsYG/vz/u3LkDT09PqKqqws7ODr/99ht3zqFDhzBt2jQAgJ+fX52hP/E1AgMD0adPH6ipqWHv3r0AgBcvXmDatGnQ19eHuro6vLy8mvXlQZ699JYmEomwY8cOdO/eHaqqqjAxMcHy5cvr7M0bHh6OkSNHwtDQEGpqarC1tcWiRYsAvL4fLw6qmzdv5v5t3jYc3ZHasank1f4eHh4SARkADAwMMHDgQMTGxrbsh+QR6imTDuX8+fOws7ND//79ZTp/yZIlOHz4MKZOnYpPPvkE9+/fx7Zt2xAbG4uzZ89KnPv8+XNMnToVixcvxvz58/Hrr79iwYIF8PDwQPfu3TFo0CCsWrUKP/74Iz7//HNuyK/20F98fDxmzZqF5cuXY+nSpXB0dER2djb69++PsrIyrFq1CgYGBjh8+DDGjx+PU6dOYdKkSS3XQO3Q8uXLcejQISxcuBCrVq1CUlISfv75Z0RGRuLu3btQVlZGTk4ORowYASMjI6xduxa6urpITk7GmTNnAABGRkbYvXs3VqxYgUmTJmHy5MkAAHd3d3l+tHaBb+2flZUFQ0PDFv2MvMIS0kEUFhayANgJEybIdH5UVBQLgF2yZIlE+aeffsoCYK9fv86VWVtbswDYW7ducWU5OTmsQCBgP/nkE67s5MmTLAA2ODi4zvuJr3HlyhWJ8o8++ogFwN6+fZsrKy4uZm1tbVkbGxu2pqaGZVmWTUpKYgGwBw8elOnzsSzLvnr1igXAbty4UebXyNP777/P1v6zdPv2bRYAe/ToUYnzrly5IlF+9uxZFgD74MGDeq/dnLZob+3YVHxtf7Fbt26xDMOw69evb/I1+I6Gr0mHUVRUBADQ0tKS6fxLly4BAFavXi1R/sknnwBAneFjFxcXDBw4kHtuZGQER0dHvHjxQuY62traYuTIkXXq4enpiQEDBnBlmpqaWLZsGZKTkxETEyPz9TuakydPQkdHB8OHD0dubi73EA9tBgcHAwB0dXUBABcuXEBVVZUca9yx8Kn9c3JyMHv2bNja2mLNmjWt8h58QEGZdBja2toAgOLiYpnOT0lJgYKCAhwcHCTKTU1Noauri5SUFIlyKyurOtfQ09Orc2+tIba2tlLr4ejoWKdcPOz9Zj06k2fPnqGwsBDGxsYwMjKSeJSUlHB77/r6+mLKlCnYvHkzDA0NMWHCBBw8eLDO3ADSOHxp/9LSUvj7+6O4uBgBAQF17jV3JHRPmXQY2traMDc3x9OnTxv1OoZhZDpPUVFRajnbiN1PW2OmdUcmEolgbGyMo0ePSj0unjzEMAxOnTqF0NBQnD9/HoGBgVi0aBG+//57hIaGdug/4q2JD+0vFAoxefJkPH78GIGBgRJ7LXdEFJRJh+Lv7499+/bh3r178Pb2bvBca2triEQiPHv2TGIyVnZ2NgoKCmBtbd3o95c1wL9Zj/j4+DrlcXFx3PHOyt7eHn///Td8fHxk+kLj5eUFLy8vbN26FceOHcOcOXPw559/YsmSJU36t+ns5N3+IpEI8+bNw7Vr13DixAn4+vo25WO0KzR8TTqUNWvWQENDA0uWLEF2dnad44mJidi5cycAYMyYMQCAHTt2SJzzww8/AADGjh3b6PfX0NAAABQUFMj8mjFjxiAsLAz37t3jykpLS7Fv3z7Y2NjAxcWl0fXoKKZPn46amhps2bKlzrHq6mqunfPz8+uMWPTs2RMAuCFUdXV1AI37t+ns5N3+K1euxPHjx7Fr1y5uxnZHRz1l0qHY29vj2LFjmDFjBpydnSUyeoWEhODkyZNYsGABAKBHjx6YP38+9u3bh4KCAvj6+iIsLAyHDx/GxIkT4efn1+j379mzJxQVFbF9+3YUFhZCIBBgyJAhMI0D070AAAIkSURBVDY2rvc1a9euxR9//IHRo0dj1apV0NfXx+HDh5GUlITTp09DQaHx352PHDmClJQUlJWVAQBu3bqFL7/8EgDwzjvvtJvet6+vL5YvX45t27YhKioKI0aMgLKyMp49e4aTJ09i586dmDp1Kg4fPoxdu3Zh0qRJsLe3R3FxMfbv3w9tbW3uy5eamhpcXFxw/PhxdOvWDfr6+nB1dW1wOLSjtGNTybP9d+zYgV27dsHb2xvq6ur4/fffJY5PmjSJ+xLcoch59jchrSIhIYFdunQpa2Njw6qoqLBaWlqsj48P+9NPP7EVFRXceVVVVezmzZtZW1tbVllZmbW0tGTXrVsncQ7Lvl7ONHbs2Drv4+vry/r6+kqU7d+/n7Wzs2MVFRUllkfVdw2WZdnExER26tSprK6uLquqqsp6enqyFy5ckDinMUuifH19WQBSH9KWa/HFm0tyxPbt28d6eHiwampqrJaWFuvm5sauWbOGzcjIYFmWZSMiIthZs2axVlZWrEAgYI2NjVl/f382PDxc4johISGsh4cHq6KiItPynPbajk3Fp/afP39+vW0PgE1KSmrJj84bDMs2YpYKIYQQQloN3VMmhBBCeIKCMiGEEMITFJQJIYQQnqCgTAghhPAEBWVCCCGEJygoE0IIITxBQZkQQgjhCQrKhBBCCE9QUCaEEEJ4goIyIYQQwhMUlAkhhBCeoKBMCCGE8AQFZUIIIYQn/h8Ms+k4PzFy9wAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sequential_repeated_measures.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "5bafd7ea", - "metadata": {}, - "source": [ - "Similar to unpaired data, DABEST empowers you to perform complex \n", - "visualizations and statistics for paired data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f307d789", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_baseline_repeated_measures = dabest.load(df, idx=((\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\"),\n", - " (\"Control 2\", \"Test 4\", \"Test 5\", \"Test 6\")),\n", - " paired=\"baseline\", id_col=\"ID\")\n", - "multi_baseline_repeated_measures.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "9a08d862", - "metadata": {}, - "source": [ - "For further aesthetic changes, the [Plot Aesthetics Tutorial](08-plot_aesthetics.html) provides detailed examples of how to customize the plot.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/tutorials/04-proportion_plot.ipynb b/nbs/tutorials/04-proportion_plot.ipynb deleted file mode 100644 index 69a9afbf..00000000 --- a/nbs/tutorials/04-proportion_plot.ipynb +++ /dev/null @@ -1,2005 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9d86b658", - "metadata": {}, - "source": [ - "# Proportion Plots\n", - "\n", - "> A guide to plot proportion plots with binary data.\n", - "\n", - "- order: 4" - ] - }, - { - "cell_type": "markdown", - "id": "55afb331", - "metadata": {}, - "source": [ - "
As of v2023.02.14, DABEST can be used to generate Cohen's *h* and the corresponding proportion plot for binary data. It's important to note that the code we provide only supports numerical proportion data, \n", - "where the values are limited to 0 (failure) and 1 (success). This means that the code is not suitable for \n", - "analyzing proportion data that contains non-numeric values, such as strings like 'yes' and 'no'.
\n" - ] - }, - { - "cell_type": "markdown", - "id": "af2a0920", - "metadata": {}, - "source": [ - "## Load libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7ba2369c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pre-compiling numba functions for DABEST...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 61.07it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Numba compilation complete!\n", - "We're using DABEST v2025.10.20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import dabest\n", - "\n", - "print(\"We're using DABEST v{}\".format(dabest.__version__))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0fefb0a2", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning) # to suppress warnings related to points not being able to be plotted due to dot size\n", - "warnings.filterwarnings(\"ignore\", category=FutureWarning) # to suppress warnings related to points not being able to be plotted due to dot size" - ] - }, - { - "cell_type": "markdown", - "id": "bbff8ce9", - "metadata": {}, - "source": [ - "## Creating a demo dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "13d6539a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Control 1Test 1Control 2Test 2Control 3Test 3Test 4Test 5Test 6Test 7Test 8Test 9GenderID
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" - ], - "text/plain": [ - " Control 1 Test 1 Control 2 Test 2 Control 3 Test 3 Test 4 Test 5 \\\n", - "0 1 0 0 0 1 0 0 1 \n", - "1 0 1 0 1 1 1 0 0 \n", - "2 0 1 0 0 1 0 1 1 \n", - "3 0 1 0 0 1 0 0 1 \n", - "4 0 0 0 0 1 0 0 0 \n", - "\n", - " Test 6 Test 7 Test 8 Test 9 Gender ID \n", - "0 0 1.0 0.0 0.0 Female 1 \n", - "1 0 1.0 0.0 0.0 Female 2 \n", - "2 0 1.0 0.0 0.0 Female 3 \n", - "3 0 1.0 0.0 0.0 Female 4 \n", - "4 1 1.0 0.0 0.0 Female 5 " - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def create_demo_prop_dataset(seed=9999, N=40):\n", - " import numpy as np\n", - " import pandas as pd\n", - "\n", - " np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n", - " # Create samples\n", - " n = 1\n", - " c1 = np.random.binomial(n, 0.2, size=N)\n", - " c2 = np.random.binomial(n, 0.2, size=N)\n", - " c3 = np.random.binomial(n, 0.8, size=N)\n", - "\n", - " t1 = np.random.binomial(n, 0.6, size=N)\n", - " t2 = np.random.binomial(n, 0.2, size=N)\n", - " t3 = np.random.binomial(n, 0.3, size=N)\n", - " t4 = np.random.binomial(n, 0.4, size=N)\n", - " t5 = np.random.binomial(n, 0.5, size=N)\n", - " t6 = np.random.binomial(n, 0.6, size=N)\n", - " t7 = np.ones(N)\n", - " t8 = np.zeros(N)\n", - " t9 = np.zeros(N)\n", - "\n", - " # Add a `gender` column for coloring the data.\n", - " females = np.repeat('Female', N / 2).tolist()\n", - " males = np.repeat('Male', N / 2).tolist()\n", - " gender = females + males\n", - "\n", - " # Add an `id` column for paired data plotting.\n", - " id_col = pd.Series(range(1, N + 1))\n", - "\n", - " # Combine samples and gender into a DataFrame.\n", - " df = pd.DataFrame({'Control 1': c1, 'Test 1': t1,\n", - " 'Control 2': c2, 'Test 2': t2,\n", - " 'Control 3': c3, 'Test 3': t3,\n", - " 'Test 4': t4, 'Test 5': t5, 'Test 6': t6,\n", - " 'Test 7': t7, 'Test 8': t8, 'Test 9': t9,\n", - " 'Gender': gender, 'ID': id_col\n", - " })\n", - "\n", - " return df\n", - "df = create_demo_prop_dataset()\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "id": "e6353a8b", - "metadata": {}, - "source": [ - "### Helper function to create a binary table - `dabest.prop_dataset` " - ] - }, - { - "cell_type": "markdown", - "id": "f92c4f73", - "metadata": {}, - "source": [ - "In DABEST **v2024.3.29**, we incorporated feedback from biologists who may not have tables of 0’s and 1’s readily available. As a result, a convenient function - `dabest.prop_dataset` - to generate a binary dataset based on the specified sample sizes is provided. Users can generate a pandas.DataFrame containing the sample sizes for each element in the groups and the group names (optional if the sample sizes are provided in a dict)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a9bf4e00", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " a b ID\n", - "0 0 0 1\n", - "1 0 0 2\n", - "2 0 1 3\n", - "3 1 1 4\n", - "4 1 1 5" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sample_size_1 = {'a':[3, 4], 'b':[2, 5]}\n", - "sample_size_2 = [3, 4, 2, 5]\n", - "names = ['a', 'b']\n", - "sample_df_1 = dabest.prop_dataset(sample_size_1)\n", - "sample_df_2 = dabest.prop_dataset(sample_size_2, names)\n", - "print(all(sample_df_1 == sample_df_2))\n", - "sample_df_1.head()" - ] - }, - { - "cell_type": "markdown", - "id": "d1bd3244", - "metadata": {}, - "source": [ - "## Loading data" - ] - }, - { - "cell_type": "markdown", - "id": "5bfaa1dc", - "metadata": {}, - "source": [ - "When loading data, you need to set the parameter ``proportional=True``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e55047ff", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:22 2025.\n", - "\n", - "Effect size(s) with 95% confidence intervals will be computed for:\n", - "1. Test 1 minus Control 1\n", - "\n", - "5000 resamples will be used to generate the effect size bootstraps." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "two_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), proportional=True)\n", - "two_groups_unpaired" - ] - }, - { - "cell_type": "markdown", - "id": "10f6baae", - "metadata": {}, - "source": [ - "## Effect sizes" - ] - }, - { - "cell_type": "markdown", - "id": "065fa56e", - "metadata": {}, - "source": [ - "To generate a proportion plot, the **dabest** library features two effect sizes:\n", - "\n", - " - Mean difference (``mean_diff``)\n", - " - [Cohen's h](https://en.wikipedia.org/wiki/Cohen's_h) (`cohens＿h`)\n", - "\n", - "These are attributes of the ``Dabest`` object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "07c58d18", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:23 2025.\n", - "\n", - "The unpaired mean difference between Control 1 and Test 1 is 0.575 [95%CI 0.35, 0.725].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "two_groups_unpaired.mean_diff" - ] - }, - { - "cell_type": "markdown", - "id": "93cd38c0", - "metadata": {}, - "source": [ - "Let's compute the *Cohen's h* for our comparison." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "23784a52", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:23 2025.\n", - "\n", - "The unpaired Cohen's h between Control 1 and Test 1 is 1.24 [95%CI 0.784, 1.66].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.cohens_h.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "two_groups_unpaired.cohens_h" - ] - }, - { - "cell_type": "markdown", - "id": "7e87cf4c", - "metadata": {}, - "source": [ - "## Generating proportion plots" - ] - }, - { - "cell_type": "markdown", - "id": "78792b19", - "metadata": {}, - "source": [ - "To generate an **estimation plot**, simply use the\n", - "``.plot()`` method. \n", - "\n", - "Each effect size instance has access to the ``.plot()`` method, allowing you to quickly create plots for different effect sizes with ease." - ] - }, - { - "cell_type": "markdown", - "id": "4928f5a7", - "metadata": {}, - "source": [ - "### Unpaired proportion plots" - ] - }, - { - "cell_type": "markdown", - "id": "885f928d", - "metadata": {}, - "source": [ - "Unpaired proportion plots utilise the common bar plot. The bar plot displays the proportion of observations in the dataset that belong to the category of interest: \n", - "\n", - "- The white portion represents the proportion of observations that do not belong to the category (proportion of 0s in the data). \n", - "- The colored portion represents the proportion of observations belonging to the category (proportion of 1s in the data)." - ] - }, - { - "cell_type": "markdown", - "id": "b4cf37fd", - "metadata": {}, - "source": [ - "#### Two-Group" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a7dccfe5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired.cohens_h.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "243a1a0b", - "metadata": {}, - "source": [ - "Instead of a Gardner-Altman plot, you can generate a **Cumming estimation plot** by setting ``float_contrast=False`` in the ``.plot()`` method. This will plot the bootstrap effect sizes below the raw data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8f79c196", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired.mean_diff.plot(float_contrast=False);" - ] - }, - { - "cell_type": "markdown", - "id": "99528f73", - "metadata": {}, - "source": [ - "#### Multi Two-Group, Shared-Control, and Multi Groups\n", - "As with regular (non-binary) unpaired data, multi two-group, shared-control, and multi group plots can be generated for binary data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7d189c13", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:24 2025.\n", - "\n", - "Effect size(s) with 95% confidence intervals will be computed for:\n", - "1. Test 1 minus Control 1\n", - "2. Test 2 minus Control 2\n", - "3. Test 3 minus Control 3\n", - "\n", - "5000 resamples will be used to generate the effect size bootstraps." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "multi_two_groups_unpaired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\")),\n", - " proportional=True)\n", - "multi_two_groups_unpaired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "314910b1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:25 2025.\n", - "\n", - "The unpaired mean difference between Control 1 and Test 1 is 0.575 [95%CI 0.35, 0.725].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "The unpaired mean difference between Control 2 and Test 2 is 0.025 [95%CI -0.15, 0.15].\n", - "The p-value of the two-sided permutation t-test is 0.535, calculated for legacy purposes only. \n", - "\n", - "The unpaired mean difference between Control 3 and Test 3 is -0.6 [95%CI -0.75, -0.425].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "multi_two_groups_unpaired.mean_diff" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ff871f09", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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kdwAiIiIyKLJf8+Hl5YV33nkHc+bMkTsKERERERHpkaR3uyqOnZ0d4uLi5I5BZLTGrdqDxxnZcLG3wbJ3Q+WOQ0RERCZK9uLj4sWL+Oyzz3jaFZEePc7IxsO0bLljEBERkYmTpPjw9fUt8m5XKSkpSE1Nha2tLX755RcpohARERERkUwkKT6Cg4MLFR+CIMDFxQXVq1dH3759UaFCBSmiEBERERGRTCQpPjZu3CjFboiIiIiIyIDJerervLw8ZGZmyhmBiIiIiIgkIknxsXXrVowfP16jbfbs2bC3t4ezszO6d++OjIwMKaIQEREREZFMJCk+Fi9erHGE4/jx45g9ezZCQ0Mxfvx4REREYN68eVJEISIiIiIimUhyzcf169cxcOBA9fstW7bA09MT27dvh7m5OVQqFX766ScsWLBAijhERERERCQDSY585ObmwtraWv1+79696NChA8zNn9Q+derUwe3bt6WIQkREREREMpGk+PD19cUff/wBAIiOjsa1a9fQvn179fKkpCTY29tLEYWIiIiIiGQiyWlXI0aMwNixY3Hp0iXcvn0bL730Ejp37qxefuzYMbzyyitSRCEiIiIiIplIUnyMHj0a1tbW2L17NwIDA/Hhhx/CxsYGAPDo0SMkJibinXfekSIKkUlysbfR+JWIiIhIDpIUHwAwbNgwDBs2rFB7hQoVEB0dLVUMIpO07N1QuSMQERERyfuQQSIiIiIiMh0sPoiIiIiISBIsPoiIiIiISBIsPoiIiIiISBJ6KT527tyJu3fv6qPrQlauXAkfHx9YW1ujSZMmOHXq1HPXT0lJwXvvvQcvLy9YWVmhZs2a2L17tyRZiYiIiIhMmV6Kj+7du+PgwYPq99WqVcPOnTt1vp9t27YhPDwcM2fOxNmzZ1GvXj2Ehobi/v37Ra6fl5eH119/HfHx8fjxxx8RGxuLdevWoXLlyjrPRkREREREmvRyq10HBwekpKSo38fHxyMjI0Pn+1myZAmGDRuGwYMHAwBWr16NXbt24auvvsKkSZMKrf/VV1/h0aNHOH78OCwsLAAAPj4+Os9FRERERESF6aX4aNy4MebNm4ekpCQ4OTkBAHbv3o3ExMRitxEEAePHjy/xPvLy8nDmzBlMnjxZ3aZQKBASEoKoqKgit9m5cyeCgoLw3nvvYceOHXBzc0P//v3x4YcfwszMrMhtcnNzkZubq36vjyKKiIiIiMgU6KX4WLVqFcLCwjBnzhwATwqLLVu2YMuWLcVuU9ri48GDB1AqlfDw8NBo9/DwwJUrV4rc5saNG9i/fz/efPNN7N69G9euXcO7776L/Px8zJw5s8htFixYgNmzZ5c4FxERERERFU0vxUeNGjVw/Phx5OTk4P79+/Dx8cGyZcvQtWtXfeyuxFQqFdzd3bF27VqYmZkhMDAQd+7cwSeffFJs8TF58mSEh4er38fExCA4OFiqyERERERERkMvxcdT1tbWqFq1KmbOnIm2bdvC29tbZ327urrCzMwMSUlJGu1JSUnw9PQschsvLy9YWFhonGJVu3ZtJCYmIi8vD5aWloW2sbKygpWVlfq9vb29jj4BEREREZFpkeQ5HzNnzoS/vz+AJ9dMXL58GZcvXy7T9ROWlpYIDAxEZGSkuk2lUiEyMhJBQUFFbtO8eXNcu3YNKpVK3fb333/Dy8uryMKDiIiIiIh0R7KHDJ4+fRpt2rSBi4sL/P394e/vDxcXF7Rt2xbR0dFa9RkeHo5169Zh06ZNuHz5MkaOHInMzEz13a/CwsI0LkgfOXIkHj16hLFjx+Lvv//Grl27MH/+fLz33ns6+YxERERERFQ8vZ529dTJkyfRunVrWFpaYujQoahduzYA4PLly/juu+/QqlUrHDx4EI0bNy5Vv3369EFycjJmzJiBxMREBAQEICIiQn0RekJCAhSKf+urKlWqYM+ePRg/fjzq1q2LypUrY+zYsfjwww9192GJiIiIiKhIkhQfU6dOReXKlXH06NFC12PMmjULzZs3x9SpU7Fv375S9z1q1CiMGjWqyGX/fdDhU0FBQThx4kSp90NERERERGUjyWlXJ0+exIgRI4q8ENzDwwPDhw9nQUBEREREZOQkKT4UCgUKCgqKXa5UKjVOjyIiIiIiIuMjyU/8zZo1w8qVK3Hz5s1CyxISErBq1So0b95ciihERERERCQTSa75mD9/Plq1aoVatWqhe/fuqFmzJgAgNjYWO3bsgLm5ORYsWCBFFCIiIiIikokkxUf9+vVx8uRJTJ06FTt37kRWVhYAwNbWFu3bt8fcuXNRp04dKaIQEREREZFMJCk+AKBOnTrYvn07VCoVkpOTAQBubm681oOIiIiIyERIVnw8pVAo1M/hICIiIiIi08HDDkREREREJAkWH0REREREJAkWH0REREREJAkWH0REREREJAkWH0REREREJAlJ73Z16dIl3LhxA48fP4YoioWWh4WFSRmHiIiIiIgkJEnxcf36dQwYMACnTp0qsugAAEEQWHwQERERERkxSYqPESNG4M8//8SyZcvQsmVLuLi4SLFbIiIiIiIyIJIUH8eOHcOUKVMwevRoKXZHREREREQGSJILzl1dXeHk5CTFroiIiIiIyEBJUny88847+Oabb6BUKqXYHRERERERGSBJTruqWbMmlEol6tWrhyFDhqBKlSowMzMrtF6PHj2kiENERERERDKQpPjo06eP+vfvv/9+kesIgsAjI0RERERERkyS4uPAgQNS7IaIiIiIiAyYJMVHcHCwFLshIiIiIiIDJukTzoEnTzm/efMmAMDb2xt16tSROgIREREREclAsuJjx44dCA8PR3x8vEa7r68vlixZgjfeeEOqKEREREREJANJbrW7e/du9OzZEwAwf/58bN++Hdu3b8f8+fMhiiJ69OiBiIgIKaIQEREREZFMJDnyMWfOHNStWxdHjhyBnZ2duv2NN97AqFGj0KJFC8yePRvt27eXIg4REREREclAkiMfFy5cwMCBAzUKj6fs7OwwaNAgXLhwQYooREREREQkE0mKD2trazx69KjY5Y8ePYK1tbVWfa9cuRI+Pj6wtrZGkyZNcOrUqRJtt3XrVgiCgG7dumm1XyIiIiIiKh1Jio+2bdti+fLliIqKKrTs5MmT+OyzzxASElLqfrdt24bw8HDMnDkTZ8+eRb169RAaGor79+8/d7v4+Hi8//77aNmyZan3SURERERE2pGk+Fi0aBGsra3RokULBAUFYdCgQRg0aBCCgoLQrFkzWFtbY+HChaXud8mSJRg2bBgGDx6MOnXqYPXq1bC1tcVXX31V7DZKpRJvvvkmZs+ejWrVqpXlYxERERERUSlIUnz4+vriwoULGDNmDB4/foxt27Zh27ZtePz4McaOHYvz58/Dx8enVH3m5eXhzJkzGkdMFAoFQkJCijzC8tRHH30Ed3d3vP322yXaT25uLtLS0tSvjIyMUuUkIiIiIqInJHvOh7u7O5YuXYqlS5fqpL8HDx5AqVTCw8NDo93DwwNXrlwpcpujR49i/fr1iImJKfF+FixYgNmzZ5clKhERERERQaIjH4YgPT0db731FtatWwdXV9cSbzd58mSkpqaqX4cOHdJjSiIiIiIi46WXIx9DhgyBIAhYu3YtzMzMMGTIkBduIwgC1q9fX+J9uLq6wszMDElJSRrtSUlJ8PT0LLT+9evXER8fjy5duqjbVCoVAMDc3ByxsbGoXr16oe2srKxgZWWlfm9vb1/ijERERERE9C+9FB/79++HQqGASqWCmZkZ9u/fD0EQnrvNi5Y/y9LSEoGBgYiMjFTfLlelUiEyMhKjRo0qtH6tWrXw559/arRNmzYN6enpWL58OapUqVKq/RMRERERUenopfiIj49/7ntdCQ8Px8CBA9GwYUM0btwYy5YtQ2ZmJgYPHgwACAsLQ+XKlbFgwQJYW1vD399fY3tnZ2cAKNRORERERES6J8kF5wkJCXBzc4ONjU2Ry7Ozs5GcnIyqVauWqt8+ffogOTkZM2bMQGJiIgICAhAREaG+CD0hIQEKhclc1kJEREREZNAkKT58fX2xefNm9O/fv8jlO3fuRP/+/aFUKkvd96hRo4o8zQoADh48+NxtN27cWOr9ERERERGRdiQ5LCCK4nOX5+fn8wgFEREREZGR09uRj7S0NKSkpKjfP3z4EAkJCYXWS0lJwdatW+Hl5aWvKEREREREZAD0VnwsXboUH330EYAnd7IaN24cxo0bV+S6oihi7ty5+opCRERERKSVhg0bIjExEZ6enoiOjpY7Trmnt+KjXbt2sLe3hyiKmDhxIvr164cGDRporCMIAuzs7BAYGIiGDRvqKwoRERERkVYSExNx584duWMYDb0VH0FBQQgKCgIAZGZmomfPnrylLRERERGRCdP73a6ysrLw2WefwdbWlsUHEREREZEJ0/stpmxtbWFubg47Ozt974qIiIiIiAyYJPe37dmzJ3788ccX3nKXiIiIiIiMlyQPGezbty/effddtGnTBsOGDYOPj0+RTzt/9oJ0IiIiIiIyHpIUH61bt1b//siRI4WWi6IIQRC0esI5ERERERGVD5IUHxs2bJBiN0REREREZMAkKT4GDhwoxW6IiIiIiMiASVJ8/FdGRgZu3boFAKhSpQrs7e2ljkBERERERDKQ5G5XAHD69Gm0adMGLi4u8Pf3h7+/P1xcXNC2bVs+qp6IiIiIyARIcuTj5MmTaN26NSwtLTF06FDUrl0bAHD58mV89913aNWqFQ4ePIjGjRtLEYeIiIiIiGQgSfExdepUVK5cGUePHoWnp6fGslmzZqF58+aYOnUq9u3bJ0UcIiIiIqISefqz67M/w5J2JDnt6uTJkxgxYkSRg+bh4YHhw4fjxIkTUkQhIiIiIiqx6Oho3L59m5cJ6IgkRz4UCgUKCgqKXa5UKqFQSHb5CRERERGVUwUKczxKTcfkoYPkjqJXkt8VSiKS/MTfrFkzrFy5Ejdv3iy0LCEhAatWrULz5s2liEJERERERDKRpKiaP38+WrVqhVq1aqF79+6oWbMmACA2NhY7duyAubk5FixYIEUUIiIiIqIS27jjN2RmZcPO1gaDunaWO065J0nxUb9+fZw8eRJTp07Fzp07kZWVBQCwtbVF+/btMXfuXNSpU0eKKEREREREJZaZlY30f352pbKT7HSyOnXqYPv27VCpVEhOTgYAuLm58VoPIiIiIiITIfm1LIIgQBAE9e+JiIiIiMg0SHbY4dKlS+jVqxccHR3h5eUFLy8vODo6olevXrh48aJUMYiIiIiISCaSHPk4cuQIOnToAJVKha5du2pccL5z5078/vvviIiIQMuWLaWIQ0REZFAaNmyIxMREeHp68lkCRGTUJCk+xo8fD3d3dxw6dAhVqlTRWHbr1i20atUK4eHhOH36tBRxiIiIDEpiYiLu3LkjdwwiIr2T5LSrv/76C++++26hwgMAqlSpgpEjR+Kvv/6SIgoREREREclEkuLD29sbubm5xS7Py8srsjApiZUrV8LHxwfW1tZo0qQJTp06Vey669atQ8uWLeHi4gIXFxeEhIQ8d30iIiIiItIdSYqPGTNm4LPPPkNMTEyhZefOncPnn3+OWbNmlbrfbdu2ITw8HDNnzsTZs2dRr149hIaG4v79+0Wuf/DgQfTr1w8HDhxAVFQUqlSpgnbt2vFQNxERERGRBCS55uPEiRPw8PBAYGAgmjVrhho1agAArl69iqioKPj7+yMqKgpRUVHqbQRBwPLly5/b75IlSzBs2DAMHjwYALB69Wrs2rULX331FSZNmlRo/W+//Vbj/ZdffomffvoJkZGRCAsLK+vHNCq8+JHIsHGOEhFReSRJ8bFixQr1748dO4Zjx45pLP/zzz/x559/arS9qPjIy8vDmTNnMHnyZHWbQqFASEiIRhHzPFlZWcjPz0eFChWKXSc3N1fjlLGMjIwS9V3e8eJHIsPGOUpEROWRJMWHSqXSeZ8PHjyAUqmEh4eHRruHhweuXLlSoj4+/PBDVKpUCSEhIcWus2DBAsyePbtMWYmIiIiofLKztdH4lcpG8iecG4qPP/4YW7duxcGDB2FtbV3sepMnT0Z4eLj6fUxMDIKDg6WISEREREQyG9S1s9wRjIqkxUdcXBx+//133Lx5E8CTu2B16NABvr6+pe7L1dUVZmZmSEpK0mhPSkqCp6fnc7f99NNP8fHHH+OPP/5A3bp1n7uulZUVrKys1O/t7e1LnZWIiIiIiCQsPiZMmIDly5cXOgVLoVBg3Lhx+PTTT0vVn6WlJQIDAxEZGYlu3boBeHJ6V2RkJEaNGlXsdosWLcK8efOwZ88eNGzYsNSfg4iISNeefmn2oi/PiIjKO0lutbt48WIsXboUPXr0QFRUFFJSUpCSkoKoqCj06tULS5cuxdKlS0vdb3h4ONatW4dNmzbh8uXLGDlyJDIzM9V3vwoLC9O4IH3hwoWYPn06vvrqK/j4+CAxMRGJiYkmcxE5EREZpujoaNy+fZt3LiMioyfJkY9169bhjTfewPfff6/R3qRJE2zduhU5OTlYs2YNxo8fX6p++/Tpg+TkZMyYMQOJiYkICAhARESE+iL0hIQEKBT/1ldffPEF8vLy0KtXL41+Zs6cqdVzRoiIyDhl2tkhMzcXGwcOlDuKXtnJHYCITI4kxUd8fDzGjh1b7PLQ0FBERERo1feoUaOKPc3q4MGDhXIQEREREZE8JCk+3N3dcf78+WKXnz9/Hm5ublJEISIiMjizd+1CanY2nGxsMLNTJ7njEBHpjSTFR+/evbF8+XL4+Phg9OjRsLN7cqA3MzMTK1aswJdffolx48ZJEYVKiBc/Ehk2zlHjkpqdjcdZWXLHICLSO0mKjzlz5iAmJgZTpkzBjBkzUKlSJQDA3bt3UVBQgDZt2uCjjz6SIgqVEC96JDJsnKNERFQeSVJ82NraIjIyEjt27NB4zkf79u3RsWNHdOnSBYIgSBGl3LIVs5CVkoWu4+bLHUW/BFu5ExCVmlWuFTJyM9B9ene5o+if1YtXISIiKo7ei4+srCwMGDAAPXv2xJtvvomuXbvqe5dERERERGSA9F582Nra4o8//kCHDh30vSvSoUPfrURuVgasbO0R3O89ueMQ0TMOrj+InIwcWNtbo/XbreWOQ0REVCKSPGSwRYsWiIqKkmJXpCO5WRnIyUhDbhYfwEhkiHIycpCTnoOcjBy5oxAREZWYJMXHihUrcOTIEUybNg23b9+WYpdERERERGRgJCk+6tWrh9u3b2PBggXw9vaGlZUVHB0dNV5OTk5SRCEiIiIiIplIcrernj178m5WREREREQmTpLiY+PGjVLshoiIiIiIDJhei4+cnBzs2LEDcXFxcHV1RadOneDl5aXPXRIREZU7TjY2Gr8SERkrvRUf9+/fR7NmzRAXFwdRFAE8ue3uL7/8gpCQEH3tloiIqNyZ2amT3BGIiCShtwvO58yZg/j4eIwfPx6//fYbli1bBhsbG4wYMUJfuyQiIiIiIgOmtyMfe/fuRVhYGD799FN1m4eHB/r374/Y2Fj4+fnpa9dERERERGSA9HbkIyEhAS1atNBoa9GiBURRRFJSkr52SzpiZWsPa3tHWNnayx2FiIpgbW8NawdrWNtbyx2FiIioxPR25CM3NxfW1pr/KT59X1BQoK/dko4E93tP7ghE9Byt324tdwQiIqJS0+vdruLj43H27Fn1+9TUVADA1atX4ezsXGj9Bg0a6DMOERERERHJSK/Fx/Tp0zF9+vRC7e+++67Ge1EUIQgClEqlPuMQEREREZGM9FZ8bNiwQV9dExERERFROaS34mPgwIH66pqIiIiIiMohvd3tioiIiIiI6L9YfBARERERkSRYfBARERERkSRYfBARERERkSRYfBARERERkSRYfBARERERkSTKffGxcuVK+Pj4wNraGk2aNMGpU6eeu/4PP/yAWrVqwdraGq+++ip2794tUVIiIiIiItNWrouPbdu2ITw8HDNnzsTZs2dRr149hIaG4v79+0Wuf/z4cfTr1w9vv/02zp07h27duqFbt264ePGixMmJiIiIiExPuS4+lixZgmHDhmHw4MGoU6cOVq9eDVtbW3z11VdFrr98+XK0b98eH3zwAWrXro05c+agQYMGWLFihcTJiYiIiIhMT7ktPvLy8nDmzBmEhISo2xQKBUJCQhAVFVXkNlFRURrrA0BoaGix6xMRERERke6Yyx1AWw8ePIBSqYSHh4dGu4eHB65cuVLkNomJiUWun5iYWOx+cnNzkZubq36fkZFRhtTlS05mGnIy06Xdp2ANRZYtzp49K+l+pXTl1kM8zlZKvt8KDjao4GAj+X5Jf3LSc5CTkSPpPvMs8wBbGPUcvfbgAVLy8iTfr7ONDZxtbSXfL+lHanoK0tJTpN1pvgVs0qyMen7eTrqPtIwsyfdrb2sDe85PnSi3xYdUFixYgNmzZ2u0BQcHw8vLS7IM380aKtm+nsrNzUVoaCgOHzok+b4BYO/aubLs15gFBwdjz56tsLKykjuKUdk8drMs+306Rw/JNEd/X/y7LPs1Zk/m6B7OUR0b/MEbku9T7vk5+zNZdmvUOD91p9wWH66urjAzM0NSUpJGe1JSEjw9PYvcxtPTs1TrA8DkyZMRHh6u0WZlZWX0f/lyc3Nx6NAhHDp0CPb29nLHoTLKyMhAcHAwcnNzjf7vrqngHDUunKPGhfPTuHB+6la5LT4sLS0RGBiIyMhIdOvWDQCgUqkQGRmJUaNGFblNUFAQIiMjMW7cOHXbvn37EBQUVOx+TKHQeJ6AgAA4OjrKHYPKKC0tTe4IpCeco8aBc9Q4cX4aB85P3Sq3xQcAhIeHY+DAgWjYsCEaN26MZcuWITMzE4MHDwYAhIWFoXLlyliwYAEAYOzYsQgODsbixYvRqVMnbN26FdHR0Vi7dq2cH4OIiIiIyCSU6+KjT58+SE5OxowZM5CYmIiAgABERESoLypPSEiAQvHvDb2aNWuGLVu2YNq0aZgyZQpefvll/PLLL/D395frIxARERERmYxyXXwAwKhRo4o9zergwYOF2nr37o3evXvrOVX5Z2VlhZkzZ5r0KWfGhONpfDimxoXjaVw4nsaF46lbgiiKotwhiIiIiIjI+JXbhwwSEREREVH5wuKDiIiIiIgkweKDiIiIiIgkweKD9C4+Ph6CIGDjxo1yRyGiInCOEhkuzk8yNiw+DMz169cxYsQIVKtWDdbW1nB0dETz5s2xfPlyZGdn622/ly5dwqxZsxAfH6+3fZTEvHnz8MYbb8DDwwOCIGDWrFmy5pGSIAglehV1F7fSysrKwqxZs0rVlymPzX+Z8hy9cuUKJk6ciICAADg4OMDLywudOnVCdHS0bJmkYsjz05TH5VmmPD/v3r2LAQMGwM/PDw4ODnB2dkbjxo2xadMmmMK9hQx5jpr62Dyr3N9q15js2rULvXv3hpWVFcLCwuDv74+8vDwcPXoUH3zwAf766y+9PRDx0qVLmD17Nlq3bg0fHx+97KMkpk2bBk9PT9SvXx979uyRLYccNm/erPH+66+/xr59+wq1165du8z7ysrKwuzZswEArVu3LtE2pjw2T5n6HP3yyy+xfv169OzZE++++y5SU1OxZs0aNG3aFBEREQgJCZEllxQMeX6a8rj8l6nPzwcPHuD27dvo1asXqlativz8fOzbtw+DBg1CbGws5s+fL0suqRjyHDX1sSlEJINw48YN0d7eXqxVq5Z49+7dQsuvXr0qLlu2TG/7/+GHH0QA4oEDB164rkqlErOyskrcd1xcnAhA3LBhQ4nWFUVRTE5OFgGIM2fOLPF+jM17770n6muKavPna+pjwzkqitHR0WJ6erpG24MHD0Q3NzexefPmJd6fMTCk+clx4fx8ns6dO4t2dnZiQUGBVtuXV4Y0R4tjqmPD064MxKJFi5CRkYH169fDy8ur0PIaNWpg7Nix6vcFBQWYM2cOqlevDisrK/j4+GDKlCnIzc3V2M7HxwedO3fG0aNH0bhxY1hbW6NatWr4+uuv1ets3LhR/eDFNm3aFDo0+bSPPXv2oGHDhrCxscGaNWsAADdu3EDv3r1RoUIF2NraomnTpti1a5fWfw5yHnUpD1QqFZYtW4ZXXnkF1tbW8PDwwIgRI/D48WON9aKjoxEaGgpXV1fY2NjA19cXQ4YMAfDk/GE3NzcAwOzZs9Xj/aLTqEx9bDhHgcDAQNjb22u0VaxYES1btsTly5e16tOYyDU/OS6cn8/j4+ODrKws5OXl6bTf8kjO/0OLYrJjI3f1Q09UrlxZrFatWonXHzhwoAhA7NWrl7hy5UoxLCxMBCB269ZNYz1vb2/Rz89P9PDwEKdMmSKuWLFCbNCggSgIgnjx4kVRFEXx+vXr4pgxY0QA4pQpU8TNmzeLmzdvFhMTE9V91KhRQ3RxcREnTZokrl69Wjxw4ICYmJgoenh4iA4ODuLUqVPFJUuWiPXq1RMVCoX4888/qzNo862NqX67/l9FfWszdOhQ0dzcXBw2bJi4evVq8cMPPxTt7OzERo0aiXl5eaIoimJSUpLo4uIi1qxZU/zkk0/EdevWiVOnThVr164tiqIoZmRkiF988YUIQOzevbt6vM+fP1+iXKY6NpyjxWvWrJlYs2ZNrbYtrwx1fv6XKY0L5+e/srKyxOTkZDEuLk7cuHGjaGdnJzZr1qzEfzbGwhDnKMfmCRYfBiA1NVUEIHbt2rVE68fExIgAxKFDh2q0v//++yIAcf/+/eo2b29vEYB4+PBhddv9+/dFKysrccKECeq25x0yftpHRESERvu4ceNEAOKRI0fUbenp6aKvr6/o4+MjKpVKURRZfGjr2X84jxw5IgIQv/32W431IiIiNNq3b98uAhBPnz5dbN9l+fM1xbHhHC3e4cOHRUEQxOnTp5d62/LMUOfnU6Y0LpyfmhYsWCACUL9ee+01MSEhoUTbGhNDnKMcmyd42pUBSEtLAwA4ODiUaP3du3cDAMLDwzXaJ0yYAACFDtnWqVMHLVu2VL93c3ODn58fbty4UeKMvr6+CA0NLZSjcePGaNGihbrN3t4ew4cPR3x8PC5dulTi/unFfvjhBzg5OeH111/HgwcP1K+np1wcOHAAAODs7AwA+O2335Cfny9jYuPBOVq0+/fvo3///vD19cXEiRPL1Fd5Z0jz09TGhfNTU79+/bBv3z5s2bIF/fv3BwC93umrvDCEOcqxeYLFhwFwdHQEAKSnp5do/Zs3b0KhUKBGjRoa7Z6ennB2dsbNmzc12qtWrVqoDxcXl0LnOD6Pr69vkTn8/PwKtT+9k8SzOahsrl69itTUVLi7u8PNzU3jlZGRgfv37wMAgoOD0bNnT8yePRuurq7o2rUrNmzYUOhcZio5ztHCMjMz0blzZ6Snp2PHjh2FrjkwNYYyP01xXDg/NXl7eyMkJAT9+vXDt99+i2rVqiEkJMQkf8j9L0OYoxybJ3irXQPg6OiISpUq4eLFi6XaThCEEq1nZmZWZLtYintL29jYlHhd0g+VSgV3d3d8++23RS5/egGcIAj48ccfceLECfz666/Ys2cPhgwZgsWLF+PEiRMm8cOIrnGOasrLy0OPHj1w4cIF7NmzB/7+/pLt21AZwvw01XHh/Hy+Xr16Yd26dTh8+HChoy+mxBDm6LNMdWxYfBiIzp07Y+3atYiKikJQUNBz1/X29oZKpcLVq1c17ledlJSElJQUeHt7l3r/Jf1H+NkcsbGxhdqvXLmiXk66U716dfzxxx9o3rx5if4ja9q0KZo2bYp58+Zhy5YtePPNN7F161YMHTpUq/E2dZyjT6hUKoSFhSEyMhLff/89goODS92HMZJ7fpr6uHB+Fu/pt+qpqak66a+8knuOFsVUx4anXRmIiRMnws7ODkOHDkVSUlKh5devX8fy5csBAB07dgQALFu2TGOdJUuWAAA6depU6v3b2dkBAFJSUkq8TceOHXHq1ClERUWp2zIzM7F27Vr4+PigTp06pc5Bxfvf//4HpVKJOXPmFFpWUFCgHrvHjx8X+kYuICAAANSHjW1tbQGUbrxNHefoE6NHj8a2bduwatUq9OjRo9TbGyu556epjwvnJ5CcnFxk+/r16yEIAho0aFCq/oyNnHOUY6OJRz4MRPXq1bFlyxb06dMHtWvX1ng66/Hjx/HDDz9g0KBBAIB69eph4MCBWLt2LVJSUhAcHIxTp05h06ZN6NatG9q0aVPq/QcEBMDMzAwLFy5EamoqrKys0LZtW7i7uxe7zaRJk/Ddd9+hQ4cOGDNmDCpUqIBNmzYhLi4OP/30ExSK0te2mzdvxs2bN5GVlQUAOHz4MObOnQsAeOutt0z6aEpwcDBGjBiBBQsWICYmBu3atYOFhQWuXr2KH374AcuXL0evXr2wadMmrFq1Ct27d0f16tWRnp6OdevWwdHRUf2fro2NDerUqYNt27ahZs2aqFChAvz9/Z97moapjw3n6JMf1latWoWgoCDY2trim2++0VjevXt39Q9hpkbO+clx4fwEgHnz5uHYsWNo3749qlatikePHuGnn37C6dOnMXr06ELXuJgaOecox+YZct5qiwr7+++/xWHDhok+Pj6ipaWl6ODgIDZv3lz8/PPPxZycHPV6+fn54uzZs0VfX1/RwsJCrFKlijh58mSNdUTxyS3+OnXqVGg/wcHBYnBwsEbbunXrxGrVqolmZmYatwwsrg9RfHJ/8169eonOzs6itbW12LhxY/G3337TWKc0twkMDg7WuA3df18leXKsMSnu6axr164VAwMDRRsbG9HBwUF89dVXxYkTJ6qf6nv27FmxX79+YtWqVUUrKyvR3d1d7Ny5sxgdHa3Rz/Hjx8XAwEDR0tKyRLcM5Ng8Ycpz9OmzEYp7xcXFPXd7Y2JI85Pj8i9Tnp979+4VO3fuLFaqVEm0sLBQf/YNGzaIKpXqudsaI0OaoxwbTYIoluKKKSIiIiIiIi3xmg8iIiIiIpIEiw8iIiIiIpIEiw8iIiIiIpIEiw8iIiIiIpIEiw8iIiIiIpIEi49yZtGiRahVqxZUKpXcUcqsb9+++N///id3DNlxTI0Lx9O4cDyNC8fT+HBMyyG57/VLJZeamipWqFBB/Oqrr9Rt+Oc+7p9++mmh9Tds2CACEE+fPq3zLCEhISIA8b333ity+ZdffinWqlVLtLKyEmvUqCF+9tlnhdY5e/asqFAoxJiYGJ3nKy84psaF42lcOJ7GheNpfDim5ROPfJQjX331FQoKCtCvX79Cyz755BP1k6f17eeff0ZUVFSxy9esWYOhQ4filVdeweeff46goCCMGTMGCxcu1Fivfv36aNiwIRYvXqzvyAaLY2pcOJ7GheNpXDiexodjWk7JXf1QydWtW1ccMGCARhsAMSAgQAQgLl68WGOZPir87Oxs0cfHR/zoo4+KrPCzsrLEihUrFnqa65tvvina2dmJjx490mj/9NNPRTs7OzE9PV1nGcsTjqlx4XgaF46nceF4Gh+OafnEIx/lRFxcHC5cuICQkJBCy5o3b462bdti0aJFyM7O1muORYsWQaVS4f333y9y+YEDB/Dw4UO8++67Gu3vvfceMjMzsWvXLo32119/HZmZmdi3b5/eMhsqjqlx4XgaF46nceF4Gh+OafnF4qOcOH78OACgQYMGRS6fNWsWkpKS8MUXXzy3n9zcXDx48KBEr2clJCTg448/xsKFC2FjY1Nk/+fOnQMANGzYUKM9MDAQCoVCvfypOnXqwMbGBseOHXtubmPEMTUuHE/jwvE0LhxP48MxLb/M5Q5AJXPlyhUAgK+vb5HLW7ZsiTZt2uCTTz7ByJEji50E3333HQYPHlyifYqiqPF+woQJqF+/Pvr27VvsNvfu3YOZmRnc3d012i0tLVGxYkXcvXtXo93c3BxVqlTBpUuXSpTJmHBMjQvH07hwPI0Lx9P4cEzLLxYf5cTDhw9hbm4Oe3v7YteZNWsWgoODsXr1aowfP77IdUJDQ7U6lHfgwAH89NNPOHny5HPXy87OhqWlZZHLrK2tizz86eLiUuQ3CsaOY2pcOJ7GheNpXDiexodjWn6x+DAirVq1Qps2bbBo0SK88847Ra7j5eUFLy+vUvVbUFCAMWPG4K233kKjRo2eu66NjQ3y8vKKXJaTk1PkNw+iKEIQhFJlMhUcU+PC8TQuHE/jwvE0PhxTw8Tio5yoWLEiCgoKkJ6eDgcHh2LXmzlzJlq3bo01a9bA2dm50PLs7GykpqaWaJ+enp4AgK+//hqxsbFYs2YN4uPjNdZJT09HfHw83N3dYWtrCy8vLyiVSty/f1/jEGNeXh4ePnyISpUqFdrP48eP8fLLL5cokzHhmBoXjqdx4XgaF46n8eGYll+84LycqFWrFoAnd3d4nuDgYLRu3RoLFy4s8lDetm3b1FX+i15PJSQkID8/H82bN4evr6/6BTyZgL6+vti7dy8AICAgAAAQHR2tsd/o6GioVCr18qcKCgpw69Yt1K5du1R/HsaAY2pcOJ7GheNpXDiexodjWn7xyEc5ERQUBODJX9a6des+d91Zs2ahdevWWLt2baFl2pzb2Ldv30KTAwC6d++Ojh07YtiwYWjSpAkAoG3btqhQoQK++OILdOzYUb3uF198AVtbW3Tq1Emjj0uXLiEnJwfNmjUrVSZjwDE1LhxP48LxNC4cT+PDMS3H5Hm8CGnD399f7Nevn0YbinigjSiKYnBwsAhA5w/TKcm+V65cKQIQe/XqJa5bt04MCwsTAYjz5s0rtO6nn34q2traimlpaXrJaOg4psaF42lcOJ7GheNpfDim5ROLj3JkyZIlor29vZiVlaVuK+4v+oEDB2SbZKIoimvXrhX9/PxES0tLsXr16uLSpUtFlUpVaL0mTZoUejqpKeGYGheOp3HheBoXjqfx4ZiWTyw+ypGUlBSxQoUK4pdffil3FJ04d+6cKAiCeO7cObmjyIZjalw4nsaF42lcOJ7Gh2NaPgmi+MwTU8igLVy4EBs2bMClS5egUJTv+wX07dsXKpUK33//vdxRZMUxNS4cT+PC8TQuHE/jwzEtf1h8EBERERGRJMp3iUhEREREROUGiw8iIiIiIpIEiw8iIiIiIpIEiw8iIiIiIpIEiw8iIiIiIpIEiw8iIiIiIpIEiw8iIiIiIpIEiw8iIiIiIpIEiw8iIiIiIpIEiw8iIiIiIpIEiw8iIiIiIpIEiw8iIiIiIpIEiw8iIiIiIpIEi49SunfvHmbNmoV79+7JHYWIiIiIqFxh8VFK9+7dw+zZs1l8EBERERGVEosPIiIiIiKSBIsPIiIiIiKSBIsPIiIiIiKSBIsPIiIiIiKSBIsPIiIiIiKSBIsPIiIiIiKSBIsPIiIiIiKSBIsPIlOR9UjuBERERGTiWHwQmYr0RKAgT+4UREREZMJYfBCZDBFIvyt3CCIiIjJhLD6ITMnD63InICIiIhPG4oPIlCT+KXcCIiIiMmEsPohMyZ1ouRMQERGRCdO6+EhLS8PHH3+M0NBQ1K9fH6dOnQIAPHr0CEuWLMG1a9d0FpKIdCTlFvAoTu4UREREZKK0Kj5u376N+vXrY8aMGbh9+zYuXLiAjIwMAECFChWwZs0afP755zoNSkQ6cv47uRMQERGRidKq+Pjggw+Qnp6OmJgYHDp0CKIoaizv1q0b/vjjD50EJCId+3sPcHWf3CmIiIjIBGlVfOzduxdjxoxBnTp1IAhCoeXVqlXDrVu3yhyOiPTkwPwnRQgRERGRhLQqPrKzs+Hm5lbs8vT0dK0DEZEERNWTAuTcN8AzRy6JiIiI9EWr4qNOnTo4fPhwsct/+eUX1K9fX+tQRCSRU+uAqJWASiV3EiIiIjIBWhUf48aNw9atW7Fw4UKkpqYCAFQqFa5du4a33noLUVFRGD9+vE6DEpH2GjZsiJfqv4aG888WXvjnD0DkbCAvS/pgREREZFLMtdlowIABuHnzJqZNm4apU6cCANq3bw9RFKFQKDB//nx069ZNlzmJqAwSExNx514S4GxZ9Ao3DgIPrwHBHwJedSXNRkRERKZDq+IDAKZOnYq33noLP/30E65duwaVSoXq1aujR48eqFatmi4zEpEUUm8DO0cDL7cDGg0FHDzkTkRERERGRuviAwCqVq3K06uIjM3VvU+OhPj3AALeBKwd5U5ERERERkKraz7Onj2LVatWFbt81apViImJ0TYTEclNmQec3wps7Q9c+AFQ5sudiIiIiIyAVsXH1KlTn/sQwf3792PatGlahyIiA5GbDkStAL4fCFyL5F2xiIiIqEy0Kj7OnDmDli1bFru8ZcuWiI6O1joUERmYtDtA5EfAz0OBuMMsQoiIiEgrWhUf6enpMDcv/nIRhUKhvgUvERmRh9eBvdOBn94Grv3BIoSIiIhKRavi4+WXX8bevXuLXR4REcE7XhEZs0c3gMg5wA9hwI1DfEo6ERERlYhWxcfbb7+NXbt2ITw8HCkpKer2lJQUjB8/HhEREXj77bd1lZGIyiAhIQGZmZkAgMxcJRIe5eiu85RbwL4ZwO8TgaxHuuuXiIiIjJIgiqX/ylIURQwZMgSbNm2CQqFApUqVAAB3796FSqXCW2+9hY0bN0IQBJ0HltvZs2cRGBiIM2fOoEGDBnLHISrWqVOnMGfOHOzatQv/neaCAHR+tQKmd/RGIx8H3e3QzhUIXQC41dRdn0RERGRUtCo+njpw4AB++ukn3LhxAwBQvXp19OzZE61bt9ZVPoPD4oPKg59//hl9+vSBKIpQKpWFlpspAAECtg2rjR71XXW3Y3NrIGQW4B2kuz4B5Gen40bEajy6ehIQFHCt1QzVQkfAzNKm2G0ufD0JaQl/arR5NuiAGh1HAQCSzu/D1V+XFblt4/HfwtLOWVfxiYiI6B9lKj5MEYsPMnSnTp1C8+bNoVQq8bzpLQAwUwg4PjFAt0dABAXQMhyo3aVUm134ehI86r0Gj3qvF1r213czkJfxCDU6joJKqcTVX5fBodLL8Os+8bn92VSsBO/gAeo2hYU1zK1sAQDK/FwoczM1tvl751KoCvJRN+zjUmUnIiKiktHqmg8iMlxz586FKIrPLTwAQAQgQsTc3Td1G0BUAYc/ffKQQh3IepCAx9fPoEansXCoXAtOVV9B9fYjkPzXYeSmP3zutmYW1rC0r6B+PS08niyz0lgmCGZIjb8Az4B2OslNREREhWlVfIiiiDVr1qBx48ZwdXWFmZlZodfzbsVLRPqRkJCA3377rchTrYqiVAG//vlItxehP3XiC+DehTJ3k3b7Csys7eBQ6WV1m7NvfUAQkH4n9rnb3r94ACcW98PZNe8ifv9GKPOL/5xJf0ZCYWGFirWblzkzERERFU2rCmHixIlYsmQJAgICMGDAALi4uOg6F5FRUynzIapKViCUxr69ES884vEsUQQir6RgUJCHzvPg0g4IXnXL1EV+xmNY2jprtAkKM1jYOCA/83Gx27n7B8PKyR2WDhWRmRSH+P0bkP3wNmr3nlbk+kkxe+HmHwwzC6sy5SUiIqLiaVV8bNq0CT179sT333+v6zxERk+lzEf6nb+hzMvWed/3b/4NhUIBVSke/qcQgJSMbL3kUd05D3NlPhRmFoWW3Tq6DbeO/ftviKogD+l3ruB6xGp1W4N3vtB6354NOqh/b+fuA0v7Crj47RRkP7oHmwpeGuum3b6M7Ae34Nd1gtb7IyIiohfTqvjIzs5GSEiIrrMQmQRRpYQyLxsKc/MifygvCydn51IVHgCgEgEnG3MICt1eAiaKIkSV6skRniI+p2dgR7jWaal+H/vLJ3Ct1RwVazVTt1k5VISFvQvyslI0+1YpkZ+dDgu7kh91dajsBwDIeXy3UPGRdG4P7Dyqwd7r5aI2JSIiIh3Rqvh47bXXcPr0aQwfPlzXeYhMhsLMAgpzS5322aZVSwiCUKpTrwQBaFPTCU/uf6U7ggCoHCoXu9zCxgEWNv/eZUthbgULOyfYVKiksZ7jS7WgzMlExr2r6uIgJe48IIrqgqIkMpOe3BLc0r6CRrsyLxsPLh+Fd5uBJe6LiIiItKPVV52rVq3CiRMnMH/+fDx8+Py7zRCRdKq8VAntQ9rAzMysROubKYDO/s6oWkH31zmICnPk+r5W5n5sXavCpXogru76HOl3YpF26xKu7/kCbq+0gpVDRQBAbtoDnPlihPoC9OxH95Bw5Dtk3LuKnJQkPPz7BP7esRiOVf1h5+Gr0X/yX4chqpRwf7VNmbMSERHR82lVfPj5+eHGjRuYPn063N3dYWdnB0dHR42Xk5OTrrMWaeXKlfDx8YG1tTWaNGmCU6dOFbvu06eu//dlbW0tSU4iqXw4/l313+/nEfDkQYNTQos/OqEt0dIOOU3HQeX4kk76q9ntA9hWfAkXv52Kv7bOhGOVV1Cj0+h/96dSIvvhbSjzcwEACjNzpMTF4OKW6TjzxQjE7VuPirWbo06fmYX6Tjq/FxX9msHc2l4nWYmIiKh4Wp121bNnzxf+YCOFbdu2ITw8HKtXr0aTJk2wbNkyhIaGIjY2Fu7u7kVu4+joiNjYf2/PaQifg0iXAgPqYuPqpRj0zvgXPuF865AaaOSt2x+6lZUaIj/gLags7IHckl/E/rwH+1nYODz3gYLWzh5oMW2X+r2Vkxvqhi0s0X7rDVpc4oxERERUNloVHxs3btRxDO0sWbIEw4YNw+DBgwEAq1evxq5du/DVV19h0qRJRW4jCAI8PT2ljEkkua4dQ/HHzq1YuHQVIv44oHENiCAAHV9xxpTQyjotPJRe9VHg9wZUFWs8aSjI01nfREREZBzK7ZMA8/LycObMGUyePFndplAoEBISgqioqGK3y8jIgLe3N1QqFRo0aID58+fjlVdeKXb93Nxc5ObmamxPVB4EBtTF95tW49btu2j2+htISU2Ds40Zzk56VXfXeAgClFWaI9+vE0QdnWJFRERExkvre2smJCTgnXfegZ+fH1xcXHD48GEAwIMHDzBmzBicO3dOZyGL8uDBAyiVSnh4aD4YzcPDA4mJiUVu4+fnh6+++go7duzAN998A5VKhWbNmuH27dvF7mfBggVwcnJSv4KDg3X6OYj0rcpLlWBrawMAsLNS6KzwUHoGIOf1hchrNIKFBxEREZWIVsXHpUuXUL9+fWzbtg2+vr5IS0tDQUEBAMDV1RVHjx7FihUrdBpUF4KCghAWFoaAgAAEBwfj559/hpubG9asWVPsNpMnT0Zqaqr6dejQIQkTExkgCxvkNRqJvGbhEB28Xrw+ERER0T+0Ou1q4sSJcHZ2xokTJyAIQqGLuzt16oRt27bpJGBxXF1dYWZmhqSkJI32pKSkEl/TYWFhgfr16+PatWvFrmNlZQUrq3+/Kba35x1xyHSpXGshr+FwiHZuckchIiKickirIx+HDx/GyJEj4ebmVuTdoqpWrYo7d+6UOdzzWFpaIjAwEJGRkeo2lUqFyMhIBAUFlagPpVKJP//8E15e/PaW6HlE24rIazgCua0ms/AgIiIirWl15EOlUsHW1rbY5cnJyRpHC/QlPDwcAwcORMOGDdG4cWMsW7YMmZmZ6rtfhYWFoXLlyliwYAEA4KOPPkLTpk1Ro0YNpKSk4JNPPsHNmzcxdOhQvWclKo9EO1fk1+wCpXcrwKzc3p+CiIiIDIRWP000aNAAu3btwrvvvltoWUFBAbZu3YqmTZuWOdyL9OnTB8nJyZgxYwYSExMREBCAiIgI9UXoCQkJUCj+Pbjz+PFjDBs2DImJiXBxcUFgYCCOHz+OOnXq6D0rUXki2rggv3Z3KL1bAgoWHURERKQbWv1UMXnyZHTu3BkjR45E3759ATy51uKPP/7A/PnzcfnyZckuOB81ahRGjRpV5LKDBw9qvF+6dCmWLl0qQSqicsrMAvl+XVDwckfAXP9HL4mIiMi0aFV8dOjQARs3bsTYsWOxdu1aAMCAAQMgiiIcHR3x9ddfo1WrVjoNSkT6papYA3mBIyA68CGcREREpB9an0/x1ltvoUePHti3bx+uXr0KlUqF6tWrIzQ0FA4ODrrMSER6VlCzE/Jf6Q0ozOSOQkREREas1MVHVlYWqlSpgkmTJuGDDz5At27d9BCLiCQhCMhrMBRKHx6pJCIiIv0rdfFha2sLc3Nz2NnZ6SMPEUlFEJDX+D0oX2oidxIiIiIyEVo956Nnz5748ccfIYqirvMQkUTyX+3PwoOIiIgkpdU1H3379sW7776LNm3aYNiwYfDx8YGNjU2h9Ro0aFDmgESke0qv+iioESp3DCIiIjIxWhUfrVu3Vv/+yJEjhZaLoghBEKBUKrUORkR6ojBDfr23AEGQOwkRERGZGK2Kjw0bNug6BxFJRPlSU4h2bnLHICIiIhOkVfExcOBAXecgIokUeLeQOwIRFeHpWQNERMZM6+d8PHXv3j3cv38fNWrU4B2wiAyUh5sboCyAp3UuVK615I5DREXJzwcsLeVOQUSkV1rd7QoAduzYgVq1auGll15CgwYNcPLkSQDAgwcPUL9+fWzfvl1nIYmobA5H/IyrkZtxfGkYoCjzdw5EpA886kFEJkCr4uPXX39Fjx494OrqipkzZ2rcctfV1RWVK1fGxo0bdZWRiHRE5eondwQiKg5vX09EJkCr4uOjjz5Cq1atcPToUbz33nuFlgcFBeHcuXNlDkdEuqVy9pU7AhEVQ5WXL3cEIiK906r4uHjxIv73v/8Vu9zDwwP379/XOhQR6YfK3lPuCERUDDE3R+4IRER6p1XxYWtri8zMzGKX37hxAxUrVtQ6FBHpibm13AmIqBhibq7cEYiI9E6r4qNNmzbYtGkTCgoKCi1LTEzEunXr0K5duzKHIyIdEwvPWSIyDGJentwRiIj0TqviY968ebh9+zYaNWqENWvWQBAE7NmzB9OmTcOrr74KURQxc+ZMXWclorJSKeVOQETFUGVnyx2BiEjvtCo+/Pz8cPToUVSsWBHTp0+HKIr45JNPMH/+fLz66qs4cuQIfHx8dByViMrMjM8QIDJUypQUuSMQEeldiW74f+HCBXh7e8PJyUnd9sorr+CPP/7A48ePce3aNahUKlSrVg1ubm56C0tE2hMVZrzmg8iA5d9LlDsCEZHelejIR/369bFr1y71+7Zt2yIyMhIA4OLigkaNGqFJkyYsPIgMmOj4Eh9iRmTA8uLjIapUcscgItKrEhUfNjY2yMrKUr8/ePAgkpKS9BaKiPTAzELuBET0HKrMTORevSp3DCIivSrRaVf16tXDkiVLYGZmpj716vTp07C2fv4pHD169Ch7QiIiIhOR9vvvsPbzkzsGEZHelKj4WLZsGXr37o23334bACAIApYvX47ly5cXu40gCFAqeWcdIiKikso8chTZbdvCpm5duaMQEelFiYqPRo0a4dq1a7h+/TqSkpLQunVrTJkyBa+//rq+8xEREZmU5GXLUGnxYpi7uMgdhYhI50pUfOzcuRMNGzaEn58f/Pz8MHDgQHTp0gVNmjTRdz4iIiKTokxNw+PN38BtzGi5oxAR6VyJLjjv3r07Dh48qH5/6NAhXnBORESkJxlHjiD//n25YxAR6VyJig8HBwek/OfhR/Hx8cjIyNBXJiIiItOmUuHh2nUQee0kERmZEp121bhxY8ybNw9JSUnqu13t3r0biYnFPxBJEASMHz9eNymJiIhMTPa5c0iavwCuo97j9R9EZDQEURTFF6107do1hIWF4cSJE082EgS8aDNjvdvV2bNnERgYiDNnzqBBgwZyx6FySJmfg9SbF2FuZQOFuaXccfRGVZCHgtxsOHn7w8yCT1Ynep6GDRvi7tWrqKhQYGfb1zSWKWxs4NSrJ5w6doRgabz/ZhCRaSjRkY8aNWrg+PHjyMnJwf379+Hj44Nly5aha9eu+s5HRERk9BITE3EvLQ2itU2hZarsbDze/A3SI/bA5c3+sGveHIKiRGdNExEZnBIVH09ZW1ujatWqmDlzJtq2bQtvb2995SIiIqL/KEhORvKy5Uj99Ve49O0Hm/oBEARB7lhERKVSquLjqZkzZ+o6BxEREZVA3vUbSJo3D1Y1asCpRw/YNmrIIyFEVG6UqPgYMmQIBEHA2rVrYWZmhiFDhrxwG0EQsH79+jIHJCIiosJyr13D/UWLYFG5Mpze6AK7Vq2g4DUhRGTgSlR87N+/HwqFAiqVCmZmZti/f/8LD/XyUDAREZH+5d+5gwdfrMajb76FQ9u2cAhtBwsPD7ljEREVqUTFR3x8/HPfExERkbxU6elI3bEDqTt3wrZJY7j07QvLKlXkjkVEpIEniRIREckoISEBmZmZAICsggLcycoqW4eiiKwTJ3EnfAIefvkllBmZOkhJRKQbWl1wTkRERGVz6tQpzJkzB7t27VI/OyutIB+tIn5HW08vjKpVC/UqVNB+ByoV0n6PQObxKDj36wuH117T24XpeenpOLd+Pe6eOQNBEFC5aVPUHzwY5jaFbx38lDIvD+c3bcKtY8egLCiAZ716aDBsGKydnQEAKfHxuLJ9Ox5cuYLc9HTYubmhert2eLlTJ718BiKSRomKD4VCodU1HMb4kEFTkJCQgMjISKSnp8PBwQGvvfYaqlatKncsIvoH52j59/PPP6NPnz4QRbHQQ3tFAAeTEnEoKRGfNW6C9pUrl2lfytRUPFy9BukRe1BhyGDYvPKKVv0cnDEDPm3awKdNm0LLTi5fjuyUFLSaPh2iUonTK1cies0aNB03rtj+YjZuxL2zZxE0YQIsbG1xdv16HP/kE7SdNw8A8Pj6dVg5OaHJmDGwcXXFw9hYnFm9GoJCgRodOmj1GaTA+Un0fCUqPmbMmFGo+Ni+fTv++usvhIaGws/PDwBw5coV7N27F/7+/ujWrZvOw5J+Pfst3NObDAiCgM6dO2P69Olo1KiR3DGJTBbnqHE4deoU+vTpA6VSWajweEopihAAjDl1Ej8Ety7bEZB/5MXHI3HGTNg2bYIKAwbAwsurzH0CQNrt20iMicFrH3+MCjVqAADqv/02jsyfj3phYbApInt+Zibi9u9H07Fj4f7qqwCARu+9hz1jx+Lh33+jYs2a8H1N80nv9h4eeBgbi9snTxpk8cH5SVQyJSo+Zs2apfF+7dq1uH//Pi5evKguPJ66fPky2rZti0qVKuksJOlfUd/CqVQqAIAoiti9ezd+//13bNu2DT169JAzKpFJ4hw1HnPnzi3yiMezxH9eK2OvYG1QM53tP+vESWSfOQvX0aNg37x5mft7GBsLCzs7deEBAO5160IQBDy6ehWVmzQptM3jGzcgFhTAvW5ddZtj5cqw/ecIR8WaNYvcV35WFizt7cucWdc4P4lKTquTPz/55BOMGjWqUOEBALVr18aoUaOwaNGiMocriZUrV8LHxwfW1tZo0qQJTp069dz1f/jhB9SqVQvW1tZ49dVXsXv3bklyGrL/fgtX3KlyT5f16dMHp0+fljghkWnjHDUeCQkJ+O2330p8WrJSFBF5717ZL0J/hpifj9QdO3TSV05KCqycnDTaFGZmsLS3R05KSrHbKMzNYWlnp9Fu5exc7DYPrlzBrePHUS0kRBexdYbzk6h0tLrg/Pbt27CwsCh2uYWFBW7fvq11qJLatm0bwsPDsXr1ajRp0gTLli1DaGgoYmNj4e7uXmj948ePo1+/fliwYAE6d+6MLVu2oFu3bjh79iz8/f31ntdQlfhbuH/WmTt3Lnbo6D8tInoxzlHpqfLz1d9c69LeiIgXjuOzRADH799HL29vnWYRbGyhys+Hopj/zy//9BMub9+ufq/My8PDq1dx9j8PEG6/dKlOMxUnNSEBxxYtQp3eveEZECDJPkuK85OodLQqPvz9/bFq1Sr0798flZ+5EO727dtYtWoVXv3nHE59WrJkCYYNG4bBgwcDAFavXo1du3bhq6++wqRJkwqtv3z5crRv3x4ffPABAGDOnDnYt28fVqxYgdWrV+s9ryF6+i1cSf8zVCqV+PXXX5GQkMAL6IgkwDkqPVV+Ph5eu4aCnByd933v2jX1tQAlpQCQlpWFguxsnWbJunsHD69dQ8UaNYosQKq3a4cqzf493evk8uWo3LQpXvrPaVTWFSrA2tkZuampGtuqlErkZWSo71z1LGtnZ6gKCpCXmalx9CM3JaXQNmm3buHQ7NmoFhKCOr16afFJ9Yfzk6j0tCo+li5ditDQUNSsWRPdu3dHjX/O87x69Sp++eUXiKKIb775RqdBn5WXl4czZ85g8uTJ6jaFQoGQkBBERUUVuU1UVBTCw8M12kJDQ/HLL78Uu5/c3Fzk5uaq32dkZAAACgoKkJ+fX4ZPYBj27NlT+m/hRBF79+7FwIED9ZTKuCnz85GfXwClmAWFWfn/O1QclTIfqgIl8vPzoYKZ3HHKLc5R6RXk5iI7PR0CAMFMt393bW1sSn1ERQXAxtwc+aX8e/BCd+4i6/592FepUuQPA4K1Naysrf99b2EBc3t7WLm6qtuUKhWcqldHfmYmkmNj4VytGgDg/oULEEURDj4+Rf5faV+1KgQzM9w9d059TUj63bvIevDgSX//bJN26xaOzZ2LKq1aoVbv3gb3/y7npzwKCgqgMoE7qirMzGBuLu1TMZ53ZpPOiFr6888/xW7duom2traiIAiiIAiira2t2L17d/HChQvadltid+7cEQGIx48f12j/4IMPxMaNGxe5jYWFhbhlyxaNtpUrV4ru7u7F7mfmzJlPr/njiy+++OKLL5N9zWzVSgz29i5y2eTmzcWP27YVq7u4iH4VK4rL2rUTxzRqpF7uYm0tLm3XTqzu4qJuG1q/vriyfXvxFTc30dfZWZzTurU4p3Vr9fIqjo7iuk6dxFENG4pOVlbql4Olpex/FnzxZawvKWhdTvn7+2P79u1QqVRITk4GALi5uUGhpwcYyWXy5MkaR0tiYmIQHByMkydPon79+jIm042NGzdi+PDhpd5u3bp1/NamDFTKfIgq4//WRlCYQWEmwbcoRoxzVB76uuYDAHr973+I2LOnRP0rAAR7eOKLpk11G0IQ4Ni5E1z69IGZlVWJNjny0UcYEBwM7+DgQsvyMjJwYcMG1PTyAgQBlRo3xvBBg/DpP0dOMpOTsW/MGBw+cABudeoAeHINycVvvkEld3eo/rnzVb0hQ/DhP6ddXf7xR8T+9BNaeXuj1X+ud7FxdUXo55+X8Q9ANzg/pZeXm4v42CswszCHmcRHBaSkLCiAMr8APn61YFnCOVpelHnUFAoFPDw8dJGlVFxdXWFmZoakpCSN9qSkJHh6eha5jaenZ6nWBwArKytY/WfQ7f+5xZ+5ubk0h6b0LDQ0FIIglOqwsSAIaNeunVF8ftnwz45KiHNUJnr8s5s1ezb27tv3wouUBQAKQcCY2rVhqcPTv2wCG6DCW2/BskqVUm3Xds6cYpdZuLgg6JnTmv/LuVIl9P7xR81tLCzQcMQINBwxosht6vbrh7r9+pUqo9Q4P6UnqlSwsLCApY01LCws5Y6jN/n5echDDiwsLIzu70q5PUxhaWmJwMBAREZGqttUKhUiIyMRFBRU5DZBQUEa6wPAvn37il3fFFStWhWdO3eGWQn/YzMzM0OXLl14oRyRRDhHjU+jRo2wbds2mJmZFTuuZoIAM0HA542b6OQBgwBgU78+vObPg+eUKaUuPKhonJ9EpVduiw8ACA8Px7p167Bp0yZcvnwZI0eORGZmpvruV2FhYRoXpI8dOxYRERFYvHgxrly5glmzZiE6OhqjRo2S6yMYhOnTp0MQhEJPsX/W03WmTZsmUTIiAjhHjVGPHj1w/PhxdOzYsdC4CgDaeHrih+DWCH3mjpKlJVhYwOH111F52VJ4TpsK6yKez0Vlw/lJVDrl+mS5Pn36IDk5GTNmzEBiYiICAgIQERGhPg0sISFB4xqUZs2aYcuWLZg2bRqmTJmCl19+Gb/88otJP+MD+PdbuKdPZy3qIUlmZmYQBAHff/89GjVqJENKItPFOWqcGjVqhJ07dyIhIQH16tVDSkoKHM0tsCskBJVtbcvUt8LeHo7tQ+HYsSPMnnkAIOkW5ydR6QhiaU5UJJw9exaBgYE4c+YMGjRoIHccnTp9+jTmzJmjvmf503vRC4KALl26YNq0afxHk0hGnKPG66WXXsKdO3fgaW2D4x07at2PuYcHHDt1hEPbtlDY2OgwIb0I56c0nl5wbhLXfGTn8IJzMm7//RZu//79SEtLg6OjI9q2bcvzU4kMAOcoFce6Th04vdEFNoGBEIzsrpPlBecnUcmUqfi4dOkSbty4gcePHxd5p4ewsLCydE8yqVq1KgYNGiR3DCIqBucoAQAEAbaNGsGpezdY16wpdxr6B+cn0fNpVXxcv34dAwYMwKlTp4q9vZwgCCw+iIiIdEwwN4d9mzZw6tIZFmW8IJ2ISGpaFR8jRozAn3/+iWXLlqFly5ZwcXHRdS4iIiJ6hn3bNnDp2xfmFSvKHYWISCtaFR/Hjh3DlClTMHr0aF3nISIiomco7O3hHj4eNvXqyR2FiKhMtCo+XF1d4cRb9xEREemdwsYGnrNmwsrXV+4oRERlptUtMd555x188803Rd7LmoiIiHREoYBbeDgLDyIyGlod+ahZsyaUSiXq1auHIUOGoEqVKjAzMyu0Xo8ePcockIiIyCQpFHAbMxq2DerLnYSISGe0Kj769Omj/v37779f5DqCIPDICBERkTYEAW5jxsC+ZQu5kxAR6ZRWxceBAwd0nYOIiIj+4dKvHwsPIjJKWhUfwcHBus5BRERksjw9PaFKT0dFhQJWfn5w6t5N7khERHpRpiecA0+ecn7z5k0AgLe3N+rUqVPmUERERKYkOjoaiXPmIjsmBhUGhkFQaHU/GCIig6d18bFjxw6Eh4cjPj5eo93X1xdLlizBG2+8UdZsREREJsWqdi1Y+/nJHYOISG+0+mpl9+7d6NmzJwBg/vz52L59O7Zv34758+dDFEX06NEDEREROg1KRERk7Bxff13uCEREeqXVkY85c+agbt26OHLkCOzs7NTtb7zxBkaNGoUWLVpg9uzZaN++vc6CEhERGTWFAjaBDeVOQUSkV1od+bhw4QIGDhyoUXg8ZWdnh0GDBuHChQtlDkdERGQqLKtWhZl94f9XiYiMiVbFh7W1NR49elTs8kePHsHa2lrrUERERKbGospLckcgItI7rYqPtm3bYvny5YiKiiq07OTJk/jss88QEhJS5nBERESmwtzFRe4IRER6p9U1H4sWLUJQUBBatGiBxo0bw++fO3PExsbi1KlTcHd3x8KFC3UalIiIyJgJNjZyRyAi0jutjnz4+vriwoULGDNmDB4/foxt27Zh27ZtePz4McaOHYvz58/Dx8dHx1GJiIiMl3nFinJHIKJiqJRKuSMYDa2f8+Hu7o6lS5di6dKlusxDRERkklh8EBkuZX4eFGY8OqkLfIQqEREREdFzqJQquSMYjRId+RgyZAgEQcDatWthZmaGIUOGvHAbQRCwfv36MgckIiIiIpKTSsXTrnSlRMXH/v37oVAooFKpYGZmhv3790MQhOdu86LlRERERETlAa/50J0SFR/x8fHPfU9EREREZKxUBQVyRzAaWl3zkZCQgOzs7GKXZ2dnIyEhQetQRERERESGQpmfB5WK133ogta32t2+fXuxy3fu3AlfX1+tQxERERERGZKC3Fy5IxgFrYoPURSfuzw/Px8KBW+kRURERETGIT83R+4IRqHEz/lIS0tDSkqK+v3Dhw+LPLUqJSUFW7duhZeXl04CEhERERHJrYDFh06UuPhYunQpPvroIwBP7mQ1btw4jBs3rsh1RVHE3LlzdRKQiIiIiEhu+c+53plKrsTFR7t27WBvbw9RFDFx4kT069cPDRo00FhHEATY2dkhMDAQDRs21HlYIiIiIiI5ZGekyR3BKJS4+AgKCkJQUBAAIDMzEz179oS/v7/eghERERERGYr05PtyRzAKJS4+nsrKysJnn30GW1tbFh9EREREZBIe3IyDKIp8kHYZlfqWVLa2tjA3N4ednZ0+8hARERERGZzstFQkx1+XO0a5p9X9cHv27Ikff/zxhbfcJSIiIiIyFn8fPQQln3ZeJqU+7QoA+vbti3fffRdt2rTBsGHD4OPjAxsbm0LrPXtBOhERERFReZX5+CGuHI7EK21D5Y5SbmlVfLRu3Vr9+yNHjhRa/vR8OKVSqXUwIiIiIiK5dXzjDSQlJsFaAGa82RO3L56HfYWK8A7gnV21oVXxsWHDBl3nICIiIiIyOPeTHyApORku9v9e7xx75ADsnCvA1aeajMnKJ62Kj4EDB+o6BxERERFRuSCKIi7s/Q1BfQfCxtFJ7jjlilYXnP9XRkYGLl++jMuXLyMjI0MXmYiIiIiIDFp+Tg5idv2CgrxcuaOUK1oXH6dPn0abNm3g4uICf39/+Pv7w8XFBW3btkV0dLQuMxIRERERGZy05CRE//I9crMy5Y5SbmhVfJw8eRKtWrXC2bNnMXToUCxduhRLly7F0KFDcfbsWbRq1QqnTp3SdVYNjx49wptvvglHR0c4Ozvj7bfffuGRl9atW0MQBI3XO++8o9ecRERERGS8UhPv4cTWr/Hw1k25o5QLWl3zMXXqVFSuXBlHjx6Fp6enxrJZs2ahefPmmDp1Kvbt26eTkEV58803ce/ePezbtw/5+fkYPHgwhg8fji1btjx3u2HDhuGjjz5Sv7e1tdVbRiIiIiIyfjkZ6Yjevg0vvVIXNZsHw8K68CMo6Amtj3yMGDGiUOEBAB4eHhg+fDhOnDhR5nDFuXz5MiIiIvDll1+iSZMmaNGiBT7//HNs3boVd+/efe62tra28PT0VL8cHR31lpOIiIiITMftvy7g6OYvcetiDESVSu44Bkmr4kOhUKDgOU93VCqVUCjKfC17saKiouDs7IyGDf+9v3JISAgUCgVOnjz53G2//fZbuLq6wt/fH5MnT0ZWVtZz18/NzUVaWpr6xYvqiYiIiKg4ednZuLR/L6K+24QHN+PkjmNwtKoQmjVrhpUrV+LmzcLntiUkJGDVqlVo3rx5mcMVJzExEe7u7hpt5ubmqFChAhITE4vdrn///vjmm29w4MABTJ48GZs3b8aAAQOeu68FCxbAyclJ/QoODtbJZyAiIiIi45X+MBlndvyAszt/RObjR3LHMRhaXfMxf/58tGrVCrVq1UL37t1Rs2ZNAEBsbCx27NgBc3NzLFiwoNT9Tpo0CQsXLnzuOpcvX9YmMgBg+PDh6t+/+uqr8PLywmuvvYbr16+jevXqRW4zefJkhIeHq9/HxMSwACEiIiKiEkmOv4GHCTfhXT8Q1RoFwdzSSu5IstKq+Khfvz5OnjyJqVOnYufOnepTl2xtbdG+fXvMnTsXderUKXW/EyZMwKBBg567TrVq1eDp6Yn79+9rtBcUFODRo0dFXodSnCZNmgAArl27VmzxYWVlBSurf/+S2Nvbl7h/IiIiIiKVSom4M6dw9/JfeLl5MCr51YGgx0sUDJlWxQcA1KlTB9u3b4dKpUJycjIAwM3NrUzXeri5ucHNze2F6wUFBSElJQVnzpxBYGAgAGD//v1QqVTqgqIkYmJiAABeXl5a5SUiIiIi43Xnzh31l+y5+fl4mJaOio4OWveXm5WJi/t24/bF86jVqi2cPEzvZ1Cti4+nnj4v4+nvpVC7dm20b98ew4YNw+rVq5Gfn49Ro0ahb9++qFSpEoAnf1lee+01fP3112jcuDGuX7+OLVu2oGPHjqhYsSIuXLiA8ePHo1WrVqhbt64kuYmIiIjI8J2LOY/ln3+OyAMHIIoiACArNw8frv8Wdat5o0uTQPh6ur+gl+Kl3LuDk99/Ay+/Oni5WStY22tf0BRFFEXs/+UnRB8+gJysLFStURNvhA1GRY/nnyGU9vgR9vywFVf/vID8vFxUcPdAjyHDUdm3Wpn6/S+tD1NcunQJvXr1gqOjI7y8vODl5QVHR0f06tULFy9e1LbbEvv2229Rq1YtvPbaa+jYsSNatGiBtWvXqpfn5+cjNjZWXa1aWlrijz/+QLt27VCrVi1MmDABPXv2xK+//qr3rERERERUPuyOiED33r1x4NAhdeHxlAjgz7gEzN+2HWeu3ijTfkRRxN0rf+HIpnW4evwwCvJyy9Tffx35/Tec+GMv3ggbghHTZsPSygqbFi9Efn5esdtkZ2Zi3fyPYGZmhrDxH2DM3IXo0OdN2NjZlanfZwnis3+qJflAR46gQ4cOUKlU6Nq1q8YF5zt37oQgCIiIiEDLli1L27XBO3v2LAIDA3HmzBk0aNBA7jhEREREJiMvNxfxsVdgaWMNCwtLnfd/LuY8uvfuDaVSWajweJZCIWBKn+5lOgLyX5a2dng5qCUq1/ZHgbIAedk58PGrBUur0l2gLooiFoWPQvPQjmjRvhMAICcrCwvHvYfubw9H3SZBRW6394etSLj2N4ZOnqHTfp+l1WlX48ePh7u7Ow4dOoQqVapoLLt16xZatWqF8PBwnD59WpvuiYiIiIgk99mKFRBF8YWFBwBABH47eQaju3bQyb7zsjLxV2QE7lz6E7XbtoOZhXZ3xXqcnIyM1FRUr+OvbrO2tcVL1arj1vWrxRYJV2LOooZ/XWxd9RniY6/AwcUFTdqEoGFwmzL1+yytTrv666+/8O677xYqPACgSpUqGDlyJP766y9tuiYiIiIiktydO3fwx/79UCqVJVpfJYo4f+MmHqal6zRHyr07iP5pK3K1fLB1RloKAMDe0VGj3c7RERmpqcVu9zg5GacPRKKihwfCwieicevXsGvL1zh37HCZ+n2WVkc+vL29kZtb/HlpeXl5RRYmRERERERlpSwo0Hmfh44cLtkRj/8QAVxOuIPmr/jpNEteViZuxpyGX2DDF657PuoYdn79lfr9gHHva7VPUVShkk81vN6zDwCgkrcP7t+5jdMH96N+81Za9VkUrYqPGTNmYPz48ejUqRMCAgI0lp07dw6ff/45li1bpoN4RERERERPKMzMYGVtjdycHCjzdVuApD5OhUKhgEqlKvE2ggBk5+ZCLMU2JZX16CEUZmYvXK9WQAO8VO3f59UV/FOYZaSlwcHZRd2emZYGz6pVi+3H3tkZ7v/cNfYpt0qV8NeZJ5dR2Ds6a9Xvs7QqPk6cOAEPDw8EBgaiWbNmqFGjBgDg6tWriIqKgr+/P6KiohAVFaXeRhAELF++XJvdERERERHB3NwclatVh6qEp0aVhnf16qUqPABAFAEHOztYWdvoPI97VW+Ym7/4R3UrGxtY2fy7f1EUYe/khBuX/oJXVW8AQE52Fm7fuI5GbV4rtp+qNWriQeI9jbYHiYlwrugKAHBxc9Oq32dpVXysWLFC/ftjx47h2LFjGsv//PNP/PnnnxptLD6IiIiIqKzMzc2BEvxQXlqh7dtDEIRSnXolAKjjUwWCQvfPuvOpp91dVQVBQNDr7XHwt19QwcMDLm7uiNz+IxycnVG7QaB6vQ2fzEftBg3R9LV2AIBm7dpj3fyPcOi3HfBv1AS3424g+tABdB04pFT9vohWI1faqpCIiIiIyJBVrVoVnTt3xu7du0t00blCEBBQ3ReuTo4vXLe0HCq6osor2j8Eu2WHzsjPzcXOTV89eRjgyzURFj5R4/bEj+7fR1b6vxfLv+RbHf3fG4e9P23DwZ2/wNnNDR37DUC9oOal6vdFtHrOhynjcz6IiIiIjNPp06fRrFmzEj3nw0yhwIy3eqOaV8mf7l0SgiAgZOh78KhWQ6f9GooyHbOKi4vD77//jps3bwJ4chesDh06wNfXVyfhiIiIiIik0qhRI2zbtg19+vSBKIpFHgFRCAIEQcCorh10XngAApp072O0hQdQhuJjwoQJWL58eaFTsBQKBcaNG4dPP/20zOGIiIiIiKTUo0cPHD9+HHPmzMFvv/2mcQREABBQ3RddmzfSeeFh6+iEoN794VVDt7ftNTRaFR+LFy/G0qVL0atXL0yYMAG1a9cGAFy+fBlLly7F0qVLUblyZYwfP16nYYmIiIiI9K1Ro0bYuXMnEhISUK9ePaSkpMDW2grzBvfX+TUegiDAL6gl6r3eERbW1jrt2xBpdc1HrVq1UKtWLfzyyy9FLu/WrRuuXLmCK1eulDWfweE1H0RERESm46WXXsKdO3fg4mCPz957W6d9V671Cuq37wJnD12fvmW4tDryER8fj7Fjxxa7PDQ0FBEREVqHIiIiIiIyVpVq1kbdkPZwreItdxTJaVV8uLu74/z588UuP3/+PNzc3LQORURERERkbCrXegWvtm1nkkXHU1oVH71798by5cvh4+OD0aNHw87ODgCQmZmJFStW4Msvv8S4ceN0mZOIiIiIqNwRBAFVXw3AK8EhqFCpstxxZKdV8TFnzhzExMRgypQpmDFjBipVqgQAuHv3LgoKCtCmTRt89NFHOg1KRERERFReKBRmqN6wCeq0aguHiq5yxzEYWhUftra2iIyMxI4dOzSe89G+fXt07NgRXbp0gSDo/jHzRERERESGTFAoUKNhU/i3eR12zi5yxzE4pS4+srKyMGDAAPTs2RNvvvkmunbtqo9cRERERETlim/9hqgX0gH2FSrKHcVglbr4sLW1xR9//IEOHTroIw8RERERUbniWsUbjd7oiYovVZU7isFTaLNRixYtEBUVpessRERERETlhsLcHIGduiH0nbEsPEpIq+JjxYoVOHLkCKZNm4bbt2/rOhMRERERkUHw9PSEl6cnnO1sNdrtnF3QfuR41G7RGoJCqx+pTZJWTzh3cHBAQUEB8vLyAADm5uawsrLS7FgQkJqaqpuUBoRPOCciIiIyLck347Bn9XL1ezuXCggdMQa2Ts7yhSqntLrbVc+ePXk3KyIiIiIyOYJCgeABQ1h4aEmr4mPjxo06jkFEREREZPj8glqiQqWX5I5RbpWq+MjJycGOHTsQFxcHV1dXdOrUCV5eXvrKRkRERERkMCysrOHf5nW5Y5RrJS4+7t+/j2bNmiEuLg5PLxOxtbXFL7/8gpCQEL0FJCIiIiIyBK8EvwZrO3u5Y5RrJb40f86cOYiPj8f48ePx22+/YdmyZbCxscGIESP0mY+IiIiISHYWVtbwa9ZS7hjlXomPfOzduxdhYWH49NNP1W0eHh7o378/YmNj4efnp5eARERERERyq/JKXVhYWcsdo9wr8ZGPhIQEtGjRQqOtRYsWEEURSUlJOg9GRERERGQoPGu8LHcEo1Di4iM3NxfW1prV3tP3BQUFuk1FRERERGRAnD0qyR3BKJTqblfx8fE4e/as+v3ThwhevXoVzs7OhdbnQ/iIiIiIyBg4VKwodwSjUOInnCsUiiIfLCiKYqH2p21KpVI3KQ0In3BOREREZFoe37sDF6/KcscwCiU+8rFhwwZ95iAiIiIiMkiWNrZyRzAaJS4+Bg4cqM8cREREREQGSWFeqisV6DlKfME5EREREZEpUijM5I5gNFh8EBERERE9h0LBH5l1hX+SRERERETPU/ieS6QlFh9ERERERM9hbmkldwSjweKDiIiIiOg5inrcBGmHxQcREREREUmi3BYf8+bNQ7NmzWBra1vk09WLIooiZsyYAS8vL9jY2CAkJARXr17Vb1AiIiIiIgJQjouPvLw89O7dGyNHjizxNosWLcJnn32G1atX4+TJk7Czs0NoaChycnL0mJSIiIiIiIBSPGTQ0MyePRsAsHHjxhKtL4oili1bhmnTpqFr164AgK+//hoeHh745Zdf0LdvX31FJSIiIiIilOMjH6UVFxeHxMREhISEqNucnJzQpEkTREVFFbtdbm4u0tLS1K+MjAwp4hIRERERGR2TKT4SExMBAB4eHhrtHh4e6mVFWbBgAZycnNSv4OBgveYkIiIiIjJWBnXa1aRJk7Bw4cLnrnP58mXUqlVLokTA5MmTER4ern4fExNjMgXIvXv3cO/ePbljkI54eXnBy8tL7hikQ5yjxoVz1LhwfhoXzk/dMajiY8KECRg0aNBz16lWrZpWfXt6egIAkpKSNP7yJCUlISAgoNjtrKysYGX174NlXn75ZcycOdPo/wLm5uaiX79+OHTokNxRSEeCg4OxZ88ejb/PVH5xjhofzlHjwflpfDg/dcegig83Nze4ubnppW9fX194enoiMjJSXWykpaXh5MmTpbpjlpeXF2bNmqWXjIYkNzcXhw4dwqFDh2Bvby93HCqjjIwMBAcHIzc3l/9wGgnOUePCOWpcOD+NC+enbhlU8VEaCQkJePToERISEqBUKhETEwMAqFGjhnqi16pVCwsWLED37t0hCALGjRuHuXPn4uWXX4avry+mT5+OSpUqoVu3bvJ9EAMXEBAAR0dHuWNQGaWlpckdgfSEc9Q4cI4aJ85P48D5qVvltviYMWMGNm3apH5fv359AMCBAwfQunVrAEBsbCxSU1PV60ycOBGZmZkYPnw4UlJS0KJFC0RERMDa2lrS7EREREREpkgQRVGUOwQZnrS0NDg5OSE1NZXf2hgBjqfx4ZgaF46nceF4GheOp26ZzK12qXSsrKwwc+ZMnttoJDiexodjalw4nsaF42lcOJ66xSMfREREREQkCR75ICIiIiIiSbD4ICIiIiIiSbD4ICIiIiIiSbD4ICIiIiIiSbD4IDIQgiCU6HXw4MEy7ysrKwuzZs0qVV/z5s3DG2+8AQ8PDwiCgFmzZpU5B1F5Ycjz88qVK5g4cSICAgLg4OAALy8vdOrUCdHR0WXOQlReGPIcvXv3LgYMGAA/Pz84ODjA2dkZjRs3xqZNm2CK930qtw8ZJDI2mzdv1nj/9ddfY9++fYXaa9euXeZ9ZWVlYfbs2QCgfijni0ybNg2enp6oX78+9uzZU+YMROWJIc/PL7/8EuvXr0fPnj3x7rvvIjU1FWvWrEHTpk0RERGBkJCQMmciMnSGPEcfPHiA27dvo1evXqhatSry8/Oxb98+DBo0CLGxsZg/f36ZM5UnLD6IDMSAAQM03p84cQL79u0r1C6XuLg4+Pj44MGDB3Bzc5M7DpGkDHl+9uvXD7NmzYK9vb26bciQIahduzZmzZrF4oNMgiHP0bp16xY6SjJq1Ch06dIFn332GebMmQMzMzN5wsmAp10RlSMqlQrLli3DK6+8Amtra3h4eGDEiBF4/PixxnrR0dEIDQ2Fq6srbGxs4OvriyFDhgAA4uPj1cXD7Nmz1YeiX3QalY+Pjz4+EpHRkGt+BgYGahQeAFCxYkW0bNkSly9f1u2HJCrH5Pw/tCg+Pj7IyspCXl5emT9becIjH0TlyIgRI7Bx40YMHjwYY8aMQVxcHFasWIFz587h2LFjsLCwwP3799GuXTu4ublh0qRJcHZ2Rnx8PH7++WcAgJubG7744guMHDkS3bt3R48ePQA8+WaGiLRnaPMzMTERrq6uOv2MROWZ3HM0OzsbmZmZyMjIwKFDh7BhwwYEBQXBxsZGr5/b4IhEZJDee+898b9T9MiRIyIA8dtvv9VYLyIiQqN9+/btIgDx9OnTxfadnJwsAhBnzpxZ6lxl2ZbIWBjq/Hzq8OHDoiAI4vTp07Xug6g8M8Q5umDBAhGA+vXaa6+JCQkJperDGPC0K6Jy4ocffoCTkxNef/11PHjwQP16esrFgQMHAADOzs4AgN9++w35+fkyJiYyHYY0P+/fv4/+/fvD19cXEydO1Ms+iMobQ5ij/fr1w759+7Blyxb0798fwJOjIaaGxQdROXH16lWkpqbC3d0dbm5uGq+MjAzcv38fABAcHIyePXti9uzZcHV1RdeuXbFhwwbk5ubK/AmIjJehzM/MzEx07twZ6enp2LFjR6FrQYhMlSHMUW9vb4SEhKBfv3749ttvUa1aNYSEhJhcAcJrPojKCZVKBXd3d3z77bdFLn96AZwgCPjxxx9x4sQJ/Prrr9izZw+GDBmCxYsX48SJE/xhhEgPDGF+5uXloUePHrhw4QL27NkDf39/rfsiMjaGMEef1atXL6xbtw6HDx9GaGiozvo1dCw+iMqJ6tWr448//kDz5s1LdHFa06ZN0bRpU8ybNw9btmzBm2++ia1bt2Lo0KEQBEGCxESmQ+75qVKpEBYWhsjISHz//fcIDg7W5mMQGS2552hRnh7xSE1N1Ul/5QVPuyIqJ/73v/9BqVRizpw5hZYVFBQgJSUFAPD48eNCT0wNCAgAAPVhY1tbWwBQb0NEZSP3/Bw9ejS2bduGVatWqe++Q0T/knOOJicnF9m+fv16CIKABg0alKgfY8EjH0TlRHBwMEaMGIEFCxYgJiYG7dq1g4WFBa5evYoffvgBy5cvR69evbBp0yasWrUK3bt3R/Xq1ZGeno5169bB0dERHTt2BADY2NigTp062LZtG2rWrIkKFSrA39//uadpbN68GTdv3kRWVhYA4PDhw5g7dy4A4K233oK3t7f+/xCIDJSc83PZsmVYtWoVgoKCYGtri2+++UZjeffu3WFnZ6f3PwMiQybnHJ03bx6OHTuG9u3bo2rVqnj06BF++uknnD59GqNHj0aNGjWk/KOQn8x32yKiYjx7m8Cn1q5dKwYGBoo2Njaig4OD+Oqrr4oTJ04U7969K4qiKJ49e1bs16+fWLVqVdHKykp0d3cXO3fuLEZHR2v0c/z4cTEwMFC0tLQs0S0Dg4ODNW4R+N/XgQMHdPWxicoFQ5qfAwcOLHZuAhDj4uJ0+dGJygVDmqN79+4VO3fuLFaqVEm0sLAQHRwcxObNm4sbNmwQVSqVTj93eSCI4jPHloiIiIiIiPSA13wQEREREZEkWHwQEREREZEkWHwQEREREZEkWHwQEREREZEkWHwQEREREZEkWHwQEREREZEkWHwQGYH4+HgIgoCNGzfKHYWIisA5SmS4OD+lxeKDiIiIiIgkwYcMEhkBURSRm5sLCwsLmJmZyR2HiJ7BOUpkuDg/pcXig4iIiIiIJMHTrogMxKxZsyAIAv7++28MGDAATk5OcHNzw/Tp0yGKIm7duoWuXbvC0dERnp6eWLx4sXrbos5XHTRoEOzt7XHnzh1069YN9vb2cHNzw/vvvw+lUqle7+DBgxAEAQcPHtTIU1SfiYmJGDx4MF566SVYWVnBy8sLXbt2RXx8vJ7+VIgMB+cokeHi/Cw/WHwQGZg+ffpApVLh448/RpMmTTB37lwsW7YMr7/+OipXroyFCxeiRo0aeP/993H48OHn9qVUKhEaGoqKFSvi008/RXBwMBYvXoy1a9dqla1nz57Yvn07Bg8ejFWrVmHMmDFIT09HQkKCVv0RlUeco0SGi/OzHBCJyCDMnDlTBCAOHz5c3VZQUCC+9NJLoiAI4scff6xuf/z4sWhjYyMOHDhQFEVRjIuLEwGIGzZsUK8zcOBAEYD40Ucfaeynfv36YmBgoPr9gQMHRADigQMHNNZ7ts/Hjx+LAMRPPvlENx+YqJzhHCUyXJyf5QePfBAZmKFDh6p/b2ZmhoYNG0IURbz99tvqdmdnZ/j5+eHGjRsv7O+dd97ReN+yZcsSbfcsGxsbWFpa4uDBg3j8+HGptycyFpyjRIaL89PwsfggMjBVq1bVeO/k5ARra2u4uroWan/RP2DW1tZwc3PTaHNxcdHqHz4rKyssXLgQv//+Ozw8PNCqVSssWrQIiYmJpe6LqDzjHCUyXJyfho/FB5GBKeo2f8Xd+k98wc3qSnLLQEEQimz/7wV1T40bNw5///03FixYAGtra0yfPh21a9fGuXPnXrgfImPBOUpkuDg/DR+LDyIT5+LiAgBISUnRaL9582aR61evXh0TJkzA3r17cfHiReTl5WncNYSIdItzlMhwcX6WHosPIhPn7e0NMzOzQnf9WLVqlcb7rKws5OTkaLRVr14dDg4OyM3N1XtOIlPFOUpkuDg/S89c7gBEJC8nJyf07t0bn3/+OQRBQPXq1fHbb7/h/v37Guv9/fffeO211/C///0PderUgbm5ObZv346kpCT07dtXpvRExo9zlMhwcX6WHosPIsLnn3+O/Px8rF69GlZWVvjf//6HTz75BP7+/up1qlSpgn79+iEyMhKbN2+Gubk5atWqhe+//x49e/aUMT2R8eMcJTJcnJ+lI4gvutqGiIiIiIhIB3jNBxERERERSYLFBxERERERSYLFBxERERERSYLFBxERERERSYLFBxERERERSYLFBxGVSnx8PARBwMaNG+WOQkRF4BwlMlycnyw+iPTq+vXrGDFiBKpVqwZra2s4OjqiefPmWL58ObKzs/W230uXLmHWrFmIj4/X2z5KYt68eXjjjTfg4eEBQRAwa9YsWfMQPcuU5+iVK1cwceJEBAQEwMHBAV5eXujUqROio6Nly0T0X6Y8P+/evYsBAwbAz88PDg4OcHZ2RuPGjbFp0yaU96dk8CGDRHqya9cu9O7dG1ZWVggLC4O/vz/y8vJw9OhRfPDBB/jrr7+wdu1avez70qVLmD17Nlq3bg0fHx+97KMkpk2bBk9PT9SvXx979uyRLQdRUUx9jn755ZdYv349evbsiXfffRepqalYs2YNmjZtioiICISEhMiSiwjg/Hzw4AFu376NXr16oWrVqsjPz8e+ffswaNAgxMbGYv78+bLk0gUWH0R6EBcXh759+8Lb2xv79++Hl5eXetl7772Ha9euYdeuXTIm/JcoisjJyYGNjY3O+46Li4OPjw8ePHgANzc3nfdPpC3OUaBfv36YNWsW7O3t1W1DhgxB7dq1MWvWLBYfJBvOT6Bu3bo4ePCgRtuoUaPQpUsXfPbZZ5gzZw7MzMx0uk+p8LQrIj1YtGgRMjIysH79eo1/NJ+qUaMGxo4dq35fUFCAOXPmoHr16rCysoKPjw+mTJmC3Nxcje18fHzQuXNnHD16FI0bN4a1tTWqVauGr7/+Wr3Oxo0b0bt3bwBAmzZtIAgCBEFQ/yP2tI89e/agYcOGsLGxwZo1awAAN27cQO/evVGhQgXY2tqiadOmZfoHXs6jLkTPwzkKBAYGahQeAFCxYkW0bNkSly9f1qpPIl3g/Cyej48PsrKykJeXp9N+JSUSkc5VrlxZrFatWonXHzhwoAhA7NWrl7hy5UoxLCxMBCB269ZNYz1vb2/Rz89P9PDwEKdMmSKuWLFCbNCggSgIgnjx4kVRFEXx+vXr4pgxY0QA4pQpU8TNmzeLmzdvFhMTE9V91KhRQ3RxcREnTZokrl69Wjxw4ICYmJgoenh4iA4ODuLUqVPFJUuWiPXq1RMVCoX4888/qzPExcWJAMQNGzaU+PMlJyeLAMSZM2eWeBsifeIcLV6zZs3EmjVrarUtkS5wfv4rKytLTE5OFuPi4sSNGzeKdnZ2YrNmzUr8Z2OIWHwQ6VhqaqoIQOzatWuJ1o+JiREBiEOHDtVof//990UA4v79+9Vt3t7eIgDx8OHD6rb79++LVlZW4oQJE9RtP/zwgwhAPHDgQKH9Pe0jIiJCo33cuHEiAPHIkSPqtvT0dNHX11f08fERlUqlKIosPqj84xwt3uHDh0VBEMTp06eXelsiXeD81LRgwQIRgPr12muviQkJCSXa1lDxtCsiHUtLSwMAODg4lGj93bt3AwDCw8M12idMmAAAhQ7Z1qlTBy1btlS/d3Nzg5+fH27cuFHijL6+vggNDS2Uo3HjxmjRooW6zd7eHsOHD0d8fDwuXbpU4v6JDBnnaNHu37+P/v37w9fXFxMnTixTX0Ta4vzU1K9fP+zbtw9btmxB//79AUCvd/qSAosPIh1zdHQEAKSnp5do/Zs3b0KhUKBGjRoa7Z6ennB2dsbNmzc12qtWrVqoDxcXFzx+/LjEGX19fYvM4efnV6i9du3a6uVExoBztLDMzEx07twZ6enp2LFjR6FrQYikwvmpydvbGyEhIejXrx++/fZbVKtWDSEhIeW6AGHxQaRjjo6OqFSpEi5evFiq7QRBKNF6xd3dQizFfb/1cWcrovKCc1RTXl4eevTogQsXLmDHjh3w9/eXbN9Ez+L8fL5evXrh1q1bOHz4sGwZyorFB5EedO7cGdevX0dUVNQL1/X29oZKpcLVq1c12pOSkpCSkgJvb+9S77+k/wg/myM2NrZQ+5UrV9TLiYwF5+gTKpUKYWFhiIyMxJYtWxAcHFzqPoh0jfOzeE+PeKSmpuqkPzmw+CDSg4kTJ8LOzg5Dhw5FUlJSoeXXr1/H8uXLAQAdO3YEACxbtkxjnSVLlgAAOnXqVOr929nZAQBSUlJKvE3Hjh1x6tQpjX/sMzMzsXbtWvj4+KBOnTqlzkFkqDhHnxg9ejS2bduGVatWoUePHqXenkgfOD+B5OTkItvXr18PQRDQoEGDUvVnSPiQQSI9qF69OrZs2YI+ffqgdu3aGk9nPX78OH744QcMGjQIAFCvXj0MHDgQa//f3h27nBbHcRz/3kc8UkoGgyL8BSYlw4mJGCir0cjMqBjMyvAYJMpkMxmVhf/AIv+AWKSU7x10dfVcT7l1f8+993m/6rec8/ud0xl+9fuc0+973t5kv9+LZVmyXC5lMBhIPp+XZDL59P2j0ajYbDZpt9tyOBzk9fVVUqmU+Hy+h2NqtZqMx2PJZDJSrVbF6/XKYDCQzWYjk8lEXl6ef1cxHA5lu93K8XgUEZH5fC7NZlNEREqlEl9T8GmYo9fFWrfblXg8Li6XS0aj0d35QqFwW4QBJjE/RVqtliwWC0mn0xIMBmW328lkMpHVaiWVSuXdHpd/yidX2wL+a+v1WsvlsoZCIXU4HOp2uzWRSGin09HT6XTrdz6ftdFoaDgcVrvdroFAQOv1+l0f1WuJv2w2++4+lmWpZVl3x3q9nkYiEbXZbHclAx9dQ/Va37xYLKrH41Gn06mxWEyn0+ldn2fKBFqWdVci8Of2qxKGgGlfeY7++DfCo7bZbD4cD/xpX3l+zmYzzeVy6vf71W6335693+/r5XL5cOzf7pvqEztsAAAAAOA3secDAAAAgBGEDwAAAABGED4AAAAAGEH4AAAAAGAE4QMAAACAEYQPAAAAAEYQPgAAAAAYQfgAAAAAYAThAwAAAIARhA8AAAAARhA+AAAAABhB+AAAAABgBOEDAAAAgBHfAVLmJdDuGjwTAAAAAElFTkSuQmCC", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_two_groups_unpaired.mean_diff.plot();" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9cbe7f9b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:25 2025.\n", - "\n", - "Effect size(s) with 95% confidence intervals will be computed for:\n", - "1. Test 1 minus Control 1\n", - "2. Test 2 minus Control 1\n", - "3. Test 3 minus Control 1\n", - "4. Test 4 minus Control 1\n", - "\n", - "5000 resamples will be used to generate the effect size bootstraps." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "shared_control = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\", \"Test 4\"),\n", - " proportional=True)\n", - "shared_control" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d4dbcf1b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:26 2025.\n", - "\n", - "The unpaired mean difference between Control 1 and Test 1 is 0.575 [95%CI 0.35, 0.725].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "The unpaired mean difference between Control 1 and Test 2 is 0.025 [95%CI -0.15, 0.15].\n", - "The p-value of the two-sided permutation t-test is 0.539, calculated for legacy purposes only. \n", - "\n", - "The unpaired mean difference between Control 1 and Test 3 is 0.125 [95%CI -0.025, 0.325].\n", - "The p-value of the two-sided permutation t-test is 0.0936, calculated for legacy purposes only. \n", - "\n", - "The unpaired mean difference between Control 1 and Test 4 is 0.15 [95%CI -0.05, 0.3].\n", - "The p-value of the two-sided permutation t-test is 0.0604, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "shared_control.mean_diff" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "55fa5ca2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "shared_control.mean_diff.plot();" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "36620e79", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:26 2025.\n", - "\n", - "Effect size(s) with 95% confidence intervals will be computed for:\n", - "1. Test 1 minus Control 1\n", - "2. Test 2 minus Control 2\n", - "3. Test 3 minus Control 2\n", - "4. Test 4 minus Control 2\n", - "\n", - "5000 resamples will be used to generate the effect size bootstraps." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "multi_groups_unpaired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\", \"Test 3\", \"Test 4\")),\n", - " proportional=True)\n", - "multi_groups_unpaired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a6817f58", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:26 2025.\n", - "\n", - "The unpaired mean difference between Control 1 and Test 1 is 0.575 [95%CI 0.35, 0.725].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "The unpaired mean difference between Control 2 and Test 2 is 0.025 [95%CI -0.15, 0.15].\n", - "The p-value of the two-sided permutation t-test is 0.535, calculated for legacy purposes only. \n", - "\n", - "The unpaired mean difference between Control 2 and Test 3 is 0.125 [95%CI -0.05, 0.325].\n", - "The p-value of the two-sided permutation t-test is 0.099, calculated for legacy purposes only. \n", - "\n", - "The unpaired mean difference between Control 2 and Test 4 is 0.15 [95%CI -0.05, 0.3].\n", - "The p-value of the two-sided permutation t-test is 0.0604, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "multi_groups_unpaired.mean_diff" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "40d8ee2b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_groups_unpaired.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "d015dc02", - "metadata": {}, - "source": [ - "### Paired proportion plots" - ] - }, - { - "cell_type": "markdown", - "id": "487b18ed", - "metadata": {}, - "source": [ - "For the paired version of the proportion plot, we adopt the style of a Sankey Diagram. The width of each bar in each xtick represents the proportion of the corresponding label in the group, and the strip denotes the paired relationship for each observation.\n", - "\n", - "Starting from **v2024.3.29**, the paired version of the proportion plot receives a major upgrade. We introduce the ``sankey`` and ``flow`` parameters to control the plot. By default, both ``sankey`` and ``flow`` are set to True to cater the needs of repeated measures. When ``sankey`` is set to False, DABEST will generate a bar plot with a similar aesthetic to the paired proportion plot. When ``flow`` is set to False, each group of comparsion forms a Sankey diagram that does not connect to other groups of comparison.\n", - "\n", - "Similar to the unpaired version, the ``.plot()`` method is used to produce an **estimation plot**.\n" - ] - }, - { - "cell_type": "markdown", - "id": "b662e9e6", - "metadata": {}, - "source": [ - "#### Two-Group" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6bc7c8f1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:27 2025.\n", - "\n", - "Paired effect size(s) for repeated measures against baseline \n", - "with 95% confidence intervals will be computed for:\n", - "1. Test 1 minus Control 1\n", - "\n", - "5000 resamples will be used to generate the effect size bootstraps." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "two_groups_paired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), \n", - " proportional=True, paired=\"baseline\", id_col=\"ID\")\n", - "two_groups_paired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4f4b7bc3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:27 2025.\n", - "\n", - "The paired mean difference for repeated measures against baseline \n", - "between Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "two_groups_paired.mean_diff" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ca073736", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_paired.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "17832542", - "metadata": {}, - "source": [ - "The Sankey plots for paired proportions also supports the ``float_contrast`` parameter, which can be set to ``False`` to produce a **Cumming estimation plot**.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "24f34bc2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_paired.mean_diff.plot(float_contrast=False);" - ] - }, - { - "cell_type": "markdown", - "id": "0c13d47c", - "metadata": {}, - "source": [ - "#### Multi Two-Group, Repeated Measures, and Multi Groups\n", - "As with regular (non-binary) unpaired data, multi two-group, repeated-measures, and multi group plots can be generated for binary data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "897bbfd5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:27 2025.\n", - "\n", - "Paired effect size(s) for repeated measures against baseline \n", - "with 95% confidence intervals will be computed for:\n", - "1. Test 1 minus Control 1\n", - "2. Test 2 minus Control 2\n", - "3. Test 3 minus Control 3\n", - "\n", - "5000 resamples will be used to generate the effect size bootstraps." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "multi_two_groups_paired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\")),\n", - " proportional=True, paired=\"baseline\", id_col=\"ID\")\n", - "multi_two_groups_paired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9f3903b6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:28 2025.\n", - "\n", - "The paired mean difference for repeated measures against baseline \n", - "between Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "The paired mean difference for repeated measures against baseline \n", - "between Control 2 and Test 2 is 0.025 [95%CI -0.15, 0.175].\n", - "The p-value of the two-sided permutation t-test is 0.571, calculated for legacy purposes only. \n", - "\n", - "The paired mean difference for repeated measures against baseline \n", - "between Control 3 and Test 3 is -0.6 [95%CI -0.775, -0.425].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "multi_two_groups_paired.mean_diff" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bb3e2aa2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_two_groups_paired.mean_diff.plot();" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "126405f4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:28 2025.\n", - "\n", - "Paired effect size(s) for repeated measures against baseline \n", - "with 95% confidence intervals will be computed for:\n", - "1. Test 1 minus Control 1\n", - "2. Test 2 minus Control 1\n", - "3. Test 3 minus Control 1\n", - "4. Test 4 minus Control 1\n", - "\n", - "5000 resamples will be used to generate the effect size bootstraps." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "repeated_measures_baseline = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\", \"Test 4\"),\n", - " proportional=True, paired=\"baseline\", id_col=\"ID\")\n", - "repeated_measures_baseline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e852ebf7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:29 2025.\n", - "\n", - "The paired mean difference for repeated measures against baseline \n", - "between Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "The paired mean difference for repeated measures against baseline \n", - "between Control 1 and Test 2 is 0.025 [95%CI -0.15, 0.175].\n", - "The p-value of the two-sided permutation t-test is 0.555, calculated for legacy purposes only. \n", - "\n", - "The paired mean difference for repeated measures against baseline \n", - "between Control 1 and Test 3 is 0.125 [95%CI -0.075, 0.275].\n", - "The p-value of the two-sided permutation t-test is 0.277, calculated for legacy purposes only. \n", - "\n", - "The paired mean difference for repeated measures against baseline \n", - "between Control 1 and Test 4 is 0.15 [95%CI -0.05, 0.325].\n", - "The p-value of the two-sided permutation t-test is 0.075, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "repeated_measures_baseline.mean_diff" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "47edfc40", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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88ccxbtw43H777Zg1axbat28PADh69ChefPFF7NmzBytXrpQ5SgpUJpMJp0+fhkajQUxMDKKjo6u9ap/9bBpEs8nLEQYOo9GIY8eO4bLLLkNsbKxscYg2G0SzCS6zCaLZDNFihmixQLJaIXGQJRFRwPHLBPnee+/FqVOnMG/ePHz11VfuWSVEUXTPKDF+/HiZo6RAZ7fbcf78eVy4cMGjJKCyXmXRZoMtLbVhg6xAdepzVSqVe6lpuet5JUnC2bNnYbVa0bRp0xr32tf0XqLJBJexCK7iYohGI1wmIySnw2v3JCIi3+OXCTIAzJ49G/feey/WrVuH06dPAyhZKOTWW29Fq1atZI6OGhNJklBQUICCggKoVCqEh4cjIiICISEhHsmc7cypas9aISiUUEXHQBURCUVQUJ2TQnthEYSfd1c72Z0xY0at7tOmTRtERkbW6lygpAbZbDajsLAQxcXFHvuys7MhiiKSkpJqff2LiVYrXMVFcBUVlryKiyGJta+hJiKiwOC3CTJQkhA/88wzcodB5OZ0OpGTk4OcnBwolUr30tYGhQKOjPRLX0BQQNMkEbqklhDU6nqLKyYkFI899liVg1vrSqPRICoqqs7XCQkJQVxcHGw2Gy5cuIC8vDz3vtzcXCgUiiqneayIJIolJRJGI0STES6jES5jMSSH9/5/EBGR//LrBJnIl7lcLnfPsv3CeSjMRhg0GujVaujVKujUaqjKLDqj0OpguKIzlHWYuaEq9ZG8NiStVosWLVogKioKqampcDhKyhyys7Oh0+nK1SRLTidEqxWizQrRYnHXCYtmM0SrBRwYSURE1eWXCbJCoajWI+e6TDdFVF9E27+P8QEUWqwoLDPjgEqpgEaphD4kFCEtLofVZoMqPx8qlQoKhaLa/9YvRaFQAHm5kGzeme1AEkVAEKCOja/jhUqnPRMBUYIkuqB3udA6JhqnU1NhMpogiS6czswAYmMQpFSWzBxht3PRFSIiqjd+mSDPmjWrXNLgcrmQmpqK9evXo23btrjppptkio7IkzM3t/J9LhGiRgcxIgrmnByvxRBks0K1/G2vXb80sY0ceTuUYWFeuUWiJOGfonwYbSVlEaeLC3F5TLRHLzwREVF98MsEec6cOZXuS09Px1VXXYU2bdo0XEBElRDtdrguGmxWlqDRQpN4GQRvz9dbw9rjTh9/DrvogkahxF9jK1+18mKS03u9uApBQPPICKTm5cNks8PpEnGuoBBJkRFeuycRETVOAdf1kpCQgIcffhjz5s2TOxQiOPNyUWntq1IJbUMkxxcThEu+7KILogTYRVe1jm8oCkFA84hwaP+dd7rYakOBxdJg9yciosYh4BJkAAgKCsKZM2fkDoMaOcnphKuosNL9mvgECBpNA0YUGJQKBZpHhkP5b2nFhcJiOEUu1kFERPUn4BLkw4cP480332SJBcnOWVjwv0FnF1FGREIZHNKwAQUQrUqFxPCS2T5cooiMosrLWIiIiGrKL2uQW7RoUeHI/oKCAhQWFsJgMGD9+vUNHxjRvyRJgqsgv8J9gkYDdXRMA0cUeMJ0OkQGGZBnMiPfYkFUkAH6epw7moiIGi+/TJAHDhxYLkEWBAERERFo1aoV7rrrrjqt5kVUV67iokoHrKnjEiBw5oV6kRAaAqPNBrvThQuFRWgV7V9zPRMRkW/yywR51apVcodAVCVXQUGF25WhYVAaDA0bTABTCAISw0JxJjcfZrsDhVYrwnQ6ucMiIiI/F1DdWHa7HSaTSe4wqJETbTaIFnP5HQoF1DGx5bdTnQRrtYgw6AEAmcVGSJXUfRMREVWXXybIa9aswZQpUzy2zZ07F8HBwQgPD8fIkSNhNBplio4aO2dhQYXb1ZHREFR++dDG58WHBEOhEGBzOFFg8c5qgURE1Hj4ZYL8+uuve/QU7969G3PnzsWwYcMwZcoUbNmyBS+99JKMEVJjJUkSXEVF5bYLKjWUrIv3GpVSidjgYABAlpG9yEREVDd+2Z116tQpjB8/3v1+9erViI+Px7p166BSqSCKIr788kskJyfLGCU1RqKxGHCVH5ynioqqcOYVqj/RQQbkms2wO10otFoRrtfLHRIREfkpv+xBttls0JUZiPP999/j+uuvh+rfx9cdOnTAuXPn5AqPGjFnYfmFQQS1Gsqw8IYPpg40CiUUQsmf/kIQBMT924ucbeRYBCIiqj2/TJBbtGiBH374AQCwf/9+nDx5EsOHD3fvz8zMRPC/vyiJGorkdEI0l0/MVJHRftd7/Ne4O5By3xj8Ne4OuUOpkXC9Djq1ClaHE0VWm9zhEBGRn/LLBHnSpEn47LPP0LlzZwwdOhSXXXYZbrrpJvf+X375BVdccYWMEVJj5CoqKrdynqBSQRkWJlNEjY8gCO5a5BzOaENERLXklzXIjz/+OHQ6HTZt2oQePXrgueeeg/7fesO8vDxkZGTg4YcfljlKamxcxeXLK1QRkX7Xe+zvwvQ6aI0qmGx2WBwOrq5HREQ15pcJMgBMnDgREydOLLc9MjIS+/fvlyEiasxEux2i9aLpxRQKv6s9DhSxwUE4m1+IHJMZTcPZg09ERDXjlyUW3vbOO+8gKSkJOp0OvXv3xr59+6o8vqCgAI899hgSEhKg1WrRpk0bbNq0qYGiJV/gKi4/tZsyLByC0n8GuQWSMJ0OGpUShRYrnKIodzhERORnmCBfZO3atZg6dSpmz56NgwcPokuXLhg2bBiysrIqPN5ut+O6665DamoqvvjiC6SkpGD58uVITExs4MhJTmK5uY8FqMMjZImFSmqRo4IMkCQJ+WaL3OEQEZGf8dsSC29ZuHAhJk6ciAkTJgAAlixZgm+//RYrV67EtGnTyh2/cuVK5OXlYffu3VD/W+uYlJTUkCGTzESbDaLdc8YERXAwBI1GpogIACL0emQWG5FrNiM6yMBacCIiqjb2IJdht9tx4MABDBkyxL1NoVBgyJAh2LNnT4XnbNiwAX369MFjjz2GuLg4dOzYEQsWLIDL5ar0PjabDUVFRe4Xl8X2by5j+fIKFWuPZadUKBBpMMDhdMFos8sdDhER+RG/SJA3bNiACxcueP0+OTk5cLlciIuL89geFxeHjIyMCs85ffo0vvjiC7hcLmzatAkvvPACXn/9dcyfP7/S+yQnJyMsLMz9GjhwYL1+HdSwxOJij/eCWg1FUJBM0VBZUUEGQADyWGZBREQ14BcJ8siRI7Fjxw73+5YtW2LDhg3yBVSGKIqIjY3FsmXL0KNHD9x5552YMWMGlixZUuk506dPR2Fhofu1c+fOBoyY6pNot0O0eZZXKMPC+TjfR2iUSoRotSi2cbAeERFVn1/UIIeEhKCgoMD9PjU11StlCdHR0VAqlcjMzPTYnpmZifj4+ArPSUhIgFqthrLMbAXt27dHRkYG7HY7NBXUoWq1Wmi1Wvd7rvrnvy7uPYYgsLzCx0QFGVBstSHfbEFMMHv2iYjo0vwiQe7VqxdeeuklZGZmIuzfVck2bdpUadkDUDKKfcqUKTW6j0ajQY8ePbBt2zbceuutAEp6iLdt24bJkydXeE6/fv2wevVqiKIIhaKkQ/7vv/9GQkJChckxBZaL648VhiAIKr/4tmo0QrRaaFRK5FuYIBMRUfX4xW/yd999F+PGjcO8efMAlCS/q1evxurVqys9pzYJMgBMnToV48ePR8+ePdGrVy8sWrQIJpPJPavFuHHjkJiYiOTkZADAI488grfffhtPPvkkHn/8cZw4cQILFizAE088UYuvlPyJ5HSWWxxExWWlfVKEQY/MIiNX1iMiomrxiwS5devW2L17N6xWK7KyspCUlIRFixbhlltuqfd73XnnncjOzsasWbOQkZGBrl27YsuWLe6Be2lpae6eYgBo2rQpvvvuO0yZMgWdO3dGYmIinnzySTz33HP1Hhv5FtfFZT5KJRTBIfIEQ1UqnfIt32yBPowJMhERVc0vEuRSOp0OzZo1w+zZs3HNNdegefPmXrnP5MmTKy2pKDtYsFSfPn2wd+9er8RCvstl9Kw/VoaEcnCej1L/O1iv0GpFQmgI24mIiKrkVwlyqdmzZ7v/bjQacfbsWQAlvbkc8EYNQRJFiGaTxzZlSKhM0VB1RBj0KM6zwWizI0SnvfQJRETUaPnFNG8V+e233zB48GBERESgY8eO6NixIyIiInDNNddg//79codHAU40mQBJcr8X1GooDQYZI6JLCdVqoVQoUHBR3TgREdHF/LIH+ddff8WgQYOg0Wjw4IMPon379gCAY8eO4dNPP8WAAQOwY8cO9OrVS+ZIKVC5TJ71x+w99n2CICBcr0O+xQJRCoWCZRZERFQJv0yQZ8yYgcTERPz888/l5ieeM2cO+vXrhxkzZmDr1q0yRUiBTjRdXF7BwXn+IFyvQ67JjGKbDWE6ndzhEBGRj/LLEotff/0VkyZNqnDxjri4ODz00EMcNEdeI9pskJwO93tBo4FCp5cxIqoug0YDjUqJQgvLLIiIqHJ+mSArFAo4nc5K97tcLo+p2Ijqk3jR9G7KYJZX+JMIvR7FVhvEMjXkREREZfllFtm3b1+88847+Oeff8rtS0tLw7vvvot+/frJEBk1BuXrjzlzij8J1esgShKKbTa5QyEiIh/llzXICxYswIABA9CuXTuMHDkSbdq0AQCkpKTg66+/hkqlcq90R1SfJJcLotXifi+o1Syv8DM6lQo6tQpFFivrkImIqEJ+mSB369YNv/76K2bMmIENGzbAbDYDAAwGA4YPH4758+ejQ4cOMkdJgUg0mz2md2N5hX8K0+uQbTRBkiQuGkJEROX4ZYIMAB06dMC6desgiiKys7MBADExMaw9Jq9yWTxnr1CwvMIvhet0yCwyctEQojIUCkWdfocKglAv16iLhvzAKzmdkFwuQBTrdB1NPcVD9ctvE+RSCoUCcXFxcodBjYTH9G5KFcsr/JRGpYJerUaR1coEmehfoihCrGOy53K5IAgCNBoN9Ho9goKCEBISAoPB4NdPaySHA878XDjz8+EqLoZoNkESXfVybe1F41rIN/h9gkzUUCS7HZLd7n6vDA726x/4jV2oTotcsxmJcgdCFGAkSYLNZoPNZkNBQQEAQKVSISIiAlFRUQgKCpI3wGqSJAnOnGzY0y/AmZ8HSHX78ED+hQkyUTW5/q11L6UMYnmFPwvV65BZbITZbodBw4ecRN7kdDqRnZ2N7OxsGAwGxMXFISIiwic7GSRRhCP9Amxn//EYlE2NCxNkomoSzWXKKxQKKPykF4QqplOpoFWrUGS1MUEmakBmsxlnzpzBhQsXkJiYiIiICLlDcnNkZ8F66iREq/nSB1NAY4JMVE1le5AVhiAIHBDq98J0WhRZbYgHlwonamg2mw2nT59GcHAwmjVrBr1evjEdotUKy9/H4czLkS0G8i38DU9UDaLNBrj+t3qjkr3HASFEq4PV4YS9ipU5ici7jEYjjh07hgsXLkCSYYVLe0Y6jL/tZXJMHvy6B/no0aM4ffo08vPzK/ymGjdunAxRUSDymL0CrD8OFAaNGmqlAsU2O6JUfv3jkMivSZKE9PR0FBYWomXLltBqvT+7jCSKsP59HPaMC16/F/kfv/yNcOrUKdx7773Yt29fpZ82BUFggkz1xmUpU16h0UJQq2WMhupTiE6HYpsNUUEGuUMhavTMZjOOHTuGpKQkhIeHe+0+os0G8+E/4Cou8to9yL/5ZYI8adIk/PXXX1i0aBH69+/vUwX+FHgkSfIYoKcIZu9xIAnVapGWXwBRkqDwwRH1RI2Ny+XCqVOn0KRJEyQkJNT/9Y1GmP88BNFurfdrU+DwywT5l19+wfPPP4/HH39c7lCoERCtVo+Vkjh7RWAJ1moAATDa7AjloiFEPuPChQuwWq1ISkqqt+ngnAUFMP91CJKL4w6oan45SC86OhphYWFyh0GNRLnp3fR8FB9IBEFAsEYDo80mdyhEdJG8vDycPHmyziv8AYAzPw/mP39nckzV4pcJ8sMPP4z/+7//g8tVP8s8ElWl7AA9hSHIJye2p7oJ+Xe6NyLyPUVFRfj777/r9Du/JDk+VG/LQ1Pg88sSizZt2sDlcqFLly64//770bRpUyiVynLH3XbbbTJER4FEcrk8VlJSciBXQArRanG+oAgWhwN6DsAk8jkmkwknTpzA5ZdfXuHv+6qUlFX8AYlLRVMN+GWCfOedd7r//swzz1R4jCAI7GGmOhMtZqDMTCkKAwfoBSK1UgmdWoViq40JMpGPqk2S7DIaS2qO2XNMNeSXCfL27dvlDoEaCVeZ+mNBrYaCSxIHrBBtSZlFbAg/BBH5KpPJhFOnTqF169ZQXGI1U9FmLSmrYM0x1YJfJsgDBw706vXfeecdvPbaa8jIyECXLl3w1ltvoVevXpc8b82aNRgzZgxuueUWrF+/3qsxUsMQjZ71xxS4QnRaZBtNcLhcUNfwES4RNZzi4mKkpqaiZcuWlR4juVww//kHp3KjWvPLQXplHT16FJs3b8bmzZtx9OjROl9v7dq1mDp1KmbPno2DBw+iS5cuGDZsGLKysqo8LzU1Fc888wz69+9f5xjIN4h2OySH3f2ey0sHNoNaDZVSwcF6RH4gPz8fZ8+erXS/5dhhuEzFDRgRBRq/TZC//vprtGrVCp06dcJNN92Em266CZ06dULr1q2xYcOGWl934cKFmDhxIiZMmIAOHTpgyZIlMBgMWLlyZaXnuFwu3HPPPZg7d26Vn2jJv4hGo8d7Tu8W2ARBQIhWi0Ire5yI/EFWVhays7PLbbemnoYjp/x2oprwywR506ZNGDVqFABgwYIFWLduHdatW4cFCxZAkiTcdttt2LJlS42va7fbceDAAQwZMsS9TaFQYMiQIdizZ0+l57344ouIjY3FAw88UK372Gw2FBUVuV/GixIx8g0u8//aRaHVQlD5ZUUS1UCITguT3Q5nPcy5SkTed/bsWRQV/W+5aEduDmypp2WMiAKFX/7GnzdvHjp37oxdu3YhqMxj75tvvhmTJ0/G1Vdfjblz52L48OE1um5OTg5cLhfi4uI8tsfFxeH48eMVnvPzzz9jxYoVOHToULXvk5ycjLlz59YoNmpYkihCNJvd7wXWHzcKIVotFBBQZLUi0sAnBkS+TpIknDlzBu3atYNakmA5dkTukChA+GUP8p9//onx48d7JMelgoKCcN999+HPP//0ehzFxcUYO3Ysli9fjujo6GqfN336dBQWFrpfO3fu9GKUVBui2eQxvZuSyVKjoBAEBGs1KLSwDpnIXzidTpw6eRLGv/6A5HTIHQ4FCL/sQdbpdMjLy6t0f15eHnQ6XY2vGx0dDaVSiczMTI/tmZmZiI+PL3f8qVOnkJqaihEjRri3lS6HqVKpkJKSglatWpU7T6vVQqvVut8HB3NaKV/j8ih7EVh/3IiE6nQ4X1gIpyhCdYlppIjINxSdTUOq2YTmkeFyh0IBwi9/+l9zzTVYvHhxhXXBv/76K958802POuLq0mg06NGjB7Zt2+beJooitm3bhj59+pQ7vl27dvjrr79w6NAh9+vmm2/G4MGDcejQITRt2rTGMZBvKJsgK/R6CJz2q9EI1ZV8eC3iYD0iv+Aym+HMy0OR1YqMIs5cQfXDL3uQX331VfTp0wdXX301evXqhbZt2wIAUlJSsG/fPsTGxuKVV16p1bWnTp2K8ePHo2fPnujVqxcWLVoEk8mECRMmAADGjRuHxMREJCcnQ6fToWPHjh7nh4eHA0C57eQ/XBYLUGZieQWXl25UlAoFgjQaFFpYh0zk6ySnE470CwBKSuKyjSZolEpEBvDP7aVLl8JoNCI4OBiTJk2SO5yA5ZcJcosWLfDnn38iOTkZmzdvxtq1awEAzZs3x5NPPolp06YhNja2Vte+8847kZ2djVmzZiEjIwNdu3bFli1b3AP30tLSLrl6D/k30ejZA6HQc4BeYxOm1+F8YRGcLhdUfHpA5LPsGRfK1R2fLyqCUqlAWC1KLf2B0Wj0mLmDvMMvE2QAiI2NxRtvvIE33nij3q89efJkTJ48ucJ9O3bsqPLcVatW1Xs81LA85j9WKKDQ6+ULhmRRUodchEKrDVEB3BNF5M8cuTkQTabyOyTgbH4hlJECgsuM9yGqCb9NkIm8QbTbIdr/N4OB0hAEQRBkjIjkoFIoEKzRIN9iYYLsx8p+71b29+q8v9T2SokiJJfLY0ac2uAv6vJcRiOcOTmV7pckCf/kFSApMgJBWk0DRkaBwi++7+6//34IgoBly5ZBqVTi/vvvv+Q5giBgxYoVDRAdBRKx+KLyCi4v3WiF6XU4X1AEm9MJLReJqTdKpRIKhQIKhQIqlQpqtRpqtdo9u49er4fq3//fVSWk1U1qG4IkinAVFsBZWAjRWASX2QzJYoEk1c+CM3oTF5MqS7TZYE8/j9K640qPkySk5uUzSaZa8Yuf+j/++CMUCgVEUYRSqcSPP/54yR+G7PWj2nAZPeu6lFwgpNEK1elwQShCgcWCuJAQucMJGC6XCy6XC0DJqqIVUavVMBgMMBgMCA4ORnBwsM+N/ZBEEc6cLDiysuDMy4UkuuQOyW3+/PlwOp1QqVSYOXOm3OHUK8nlgv38OaCaq12WJsnNIsIRomO5BVWfXyTIqampVb4nqg+S3Q6xzNRegkYLQcNeh8ZKpVAgWKtFvsXKBLmBORwO90JKQEmHR1BQEMLCwhAWFga9jOMCRKsV9nNnKxwc5iucTickSYLT6bz0wX5EkiTYz5+D5LDX6DxRkvBPfj4Sw8IQYeCYEqoev0iQL5aWloaYmJhKf0haLBZkZ2ejWbNmDRwZ+TPXRbNXKIO4gEtjF67X4Wx+IYw2Gwf7yEiSJBiNRhiNRpw/fx5arRYRERGIjIxssGRZtFhgTT0NR1YmUE+lE1R9kiTBceE8RIu5lucD5woK4XC5EBvCn+10ab71zKqaWrRogXXr1lW6f8OGDWjRokUDRkSBwFXsWV6hCGZ5RWMXqtNBoRCQb7bIHQqVYbPZkJGRgaNHj+Lo0aPIyspyl23UN8nphPXkCRTv2wNHZjqTY5k4MjPKdWLURmaxEWn5BRDrOHCSAp9f9iBLl/iH7XA4fK5ejXzbxeUVJdO7cfaCxk4hCAjT6VBoscIlilDy54rPsVgsOHv2LM6fP4/IyEjExsbWW6+yIysT1pN/e8xsQw3PnpUBV2FBvV2v0GKF1eFEs4hw6NR+mQZRA/CbfxlFRUUoKChwv8/NzUVaWlq54woKCrBmzRokJCQ0YHTk75zF5QfncaAnASVlFvlmCwosVk755sNEUUROTg5ycnIQGhqK+Ph4hNSydly022E9kQJHdmY9R0k1Zc/MgKsgv96va3M6cTInF/GhwYjmbEVUAb9JkN944w28+OKLAEoGbDz11FN46qmnKjxWkiTMnz+/AaMjfyeWK69gjRqVCNZqoVYpkWc2M0H2E0VFRSgqKkJwcDCaNGlSo0TZmZ8Hy7Ej7DWWmSRJcGSkw1VU6NV7pBcWo9BiRWJ4GHSczpHK8Jt/DUOHDkVwcDAkScKzzz6LMWPGoHv37h7HlI507tGjB3r27ClTpORvRJsVosd0UwIH6JGHSL0emcVGmOx2BHFmE79hNBrx999/IyQkBImJiQi6RE+h7Z8zsJ45jUvNr0veJblcsKefr3iVPC8w2x04mZ2DSIMBsSHBUFWzlEpQayCo1RCEupVeKZz/+/d2qRJSAAgKCoIkSe4/yTv8JkHu06cP+vTpAwAwmUwYNWoUOnbsKHNUFAichZ49FAq9HgJ7EqiMCIMemUYj8kxmJsh+qLi4GMePH0d4eDiaNGlSrkZZcrlgOX6UJRU+QLLbYb9w7qJOiwa4rwTkmszIt1gQHRSEqCCDR6IsqDVQRURCFR4BZWgoFHoDBKWyXu5ty82FsHtPtZPdSZMm1fgegiBAw59dNeJ3WYDZbMabb74Jg8HABJnqTJIkuIouqj9meQVdRK1UIlijRaHVigSXC6p6+sVIDaugoAAFBQUIDw9HfHw8goKCINrtMP91qNwsNtTwXEYj7BkXAC/NSFIdoighq9iIHKMJYQYDYptehoikllBGRHptXEpUVBQee+wx2O01m9+5JjQaDaKiorx2/UDkdwmywWCASqW65KMyouoQTSbA5TmZviKYi0JQeZFBehhtNuSazVw4xM+VJsoGtRrBOVkIESQOypWRJElwZGfBlZ8ndyglFAoowiJgiYxEmqRAxvkLCDeZERISguDgYPdS6PUpxGGHZLde+sBa4j/vmvO7BBkARo0ahS+++AKPPPIIf6hRnVw8dZBCq4WCj6GoAqFaLdRKBXJNFsQEB0PBnz1+TbTZkHvyBHJdTqiVCkQY9IjQ66Hx8fKq6jyGV6lU7qWmfb1G1WU2w5GZAcknBkUKUIaFQR0d41FmZ7fbkZWVhaysLAAlS6HrdDpoNBoolco65yFiTjbsi1+t0zWqo1nyQmjiOcNXdfn2T4JK3HXXXXj00UcxePBgTJw4EUlJSRXOe3nxID6isiSnEy6T0WObgj2DVAlBEBBpMCCz2IgCswWRnNHCb4lWC2znzrmfHjlcIrKKTcgqNiFIo0G4XodQva7ag7XcBAWUhiAoggxQ6PQlg7iUSkBRtwQqpLAQwu9/VjvZnTFjRq3u06xZM0RERNTqXKAkeRdFEQ6HAzabDRaLBVZrxb2iksMBR05WuRI3uSi0WqjiEqCsxhzaDocDDkf9LTOuyM5EQ/zmkWze66EORH6ZIA8aNMj99127dpXbL0klj8u8tbISBQZXYWHJyIwylMGhMkVD/iDCoEeW0YhskwkRBj2fYPkh0WaF7dzZSutcTXY7THY7zhcVIUitQYhOixCtttIFJZTBoVBFR0MVHgllaCgELywmEx/fxG9rVF0uF4xGI4qKipCfnw+7yQRHfl7J0zuf6N0WoIqMhCo6xje+n6sRw8gN3yHbYkGMXo91Nw+79DV94v+z//HLBPmDDz6QOwQKAM6LyisEjRYKrVaeYMgvqJVKhOl0KLBYUWi1IryeVmyjhiHabLCdrTw59iD9L1nOQDFUSgWCNJqSV1AwQpo2ha5JYoOtuOmvA6yUSiVCg4NhsFkQCREFRfnIs1lQBEn+yfSUKmgSEvxuWs9siwWZZovcYQQ8v0yQx48fL3cI5OdcRiMkh2dvjJKD86gaooODUGCxIttoYoLsRySHA/ZzZ8sNyq0up0tEsSjBbAhGXnAoFIXF0FrPQK/XQ6vVQqvVQq1WQ61WQ6VSQalUQqFQ1FuvpD0j3auPyAWtrl7qUyWnE6LNBtFqgau4CK7CQrgK8iFJIgAgWKtBsFYDu8uFbKMJ+WazLB2cCq0OmsTLIKjVDX9z8gt+mSCXZTQacfbsWQBA06ZNEcwpuqganAXlR0srQ1leQZemV6sRpNXAZLOj0GJFmF4nd0h0CZLLBdu5s5CctasbFdQaqGNioAz5388ISZJgtVorrbF1n1sPCbKiIA/BH6+o83WqJEmIvO1OKMPC6ngdsVqHaZRKJIaFIjqopK6/0NJw9bGKoGBomiR6pRyGAoff/uv47bffMHjwYERERKBjx47o2LEjIiIicM0112D//v1yh0c+TLTby63QpNCyvIKqL+bfaSYzi40+P0tAYyeJIuznz9VulgSFAuqYWGhbtPRIjmt0f0mq+6uGtcedPv4cbT9cg04ff16zWB32kgS3Lq8a0qpUaBYRjqSoCGhU3p9fXBkWXtJzzOSYLsEve5B//fVXDBo0CBqNBg8++CDat28PADh27Bg+/fRTDBgwADt27ECvXr1kjpR8UYW9x7X85UeNU4hOC71aDYvDgQKLFREGllr4IkmSSlZls5hrfK4iKAiauATfewRfjR5pu+iCKJX8Wa0JcH3gQ16IVovLY6KRXlSMPFPN26s6lBER0MTGe+XaFHj8MkGeMWMGEhMT8fPPPyM+3vMf+5w5c9CvXz/MmDEDW7dulSlC8lWS01kye8VFmCBTTcUEByEtvwCZxUaE6XWcF9nHSJIEx4Xz5Z4WXdK/vcaq8NpPd0a1oxAEJIaFIkSrwbmCIrjEmvdIV0YZEQlNbFy9XY8Cn18myL/++itmzZpVLjkGgLi4ODz00EOYN2+eDJGRr3MW5AMX/dBV6A0QuDgI1VCYXgetUQWbw4kcowmxIf4z/mH+/PnuhSRmzpwpdzj1TpIkONIvwGUsrtF5gkYLTZNEllvJLFSnQ+toFf7JL4DVUbtBlWUpwyOYHFON+WURjkKhgNNZ+TeNy+WCgvVFdBHJ5SpJkC+iDK3joBRqtOL+HRScbTTB7kfzrjudTkiSVOXPUX9V2nPsKq7ZAhTKkFBomzVncuwjNCoVWkVHIVRXt0GwytBQaOJYVkE155dZZN++ffHOO+/gn3/+KbcvLS0N7777Lvr16ydDZOTLXAUF5ec/FQQouXoe1VKYXge9Rg1RkpBe6BsrgjVmkssF+7mzNe45VkVHl8xqoPT+IDGqPoUgoFlEGKJquWqlwhAEdXyTeo6KGgu/LLFYsGABBgwYgHbt2mHkyJFo06YNACAlJQVff/01VCoVkpOTZY6SfInkcsGRn1tuuzIkhL8UqU7iQ4JxJjcfRVYbp32TkeR0wn7+LMRLTLvmQRCgjkuAqq5Tm5HXCIKAJmGhUCkVyCwyVvs8xb/lMj6xOh75Jb/sQe7WrRt+/fVXDB8+HBs2bMCLL76IF198Ed988w2GDx+OvXv3okuXLrW+/jvvvIOkpCTodDr07t0b+/btq/TY5cuXo3///oiIiEBERASGDBlS5fEkD2dBXoWrZ7G8guoqWKtFqK7ksfyFoiI463FgEVWPaLPClpZas+RYoYAm8TImx34iNjgYTcKqOZhaqSqZyi1AOz9i9HrEGfSI4UJFXuWXPcgA0KFDB6xbtw6iKCI7OxsAEBMTU+fa47Vr12Lq1KlYsmQJevfujUWLFmHYsGFISUlBbGxsueN37NiBMWPGoG/fvtDpdHjllVcwdOhQHDlyBImJiXWKheqH5HTCmVd+ajdBrfa7JUbJN8WHhqDYZofTJeJcQSGSIjkDQkNxFRfCnpFRbvBtlRQKaBKbQmlomGWiqX6UllpcqKqcSRCgadIkoAder7t5mNwhNAp+2YNcliAIHq+6WrhwISZOnIgJEyagQ4cOWLJkCQwGA1auXFnh8Z988gkeffRRdO3aFe3atcP7778PURSxbdu2Osfiy5YuXYrXX38dS5culTuUS3LkZFf4y1MVxiSG6odWpUL0v7+8i6025NR0ajGqMUmSYM/KgP3ChZonx5c1Y3Lsp6KCDEgIrXzciDo6FkpDUANGRIHKbxPko0ePYvTo0QgNDUVCQgISEhIQGhqK0aNH4/Dhw7W6pt1ux4EDBzBkyBD3NoVCgSFDhmDPnj3VuobZbIbD4UBkZGSlx9hsNhQVFblfRmP166p8hdFo9IvYRaulwnmPIQh1X1KVqIzYkGBoVSUP5TKKimG01WLlNqoW0WaD/Z9UuPLLz0pTpdLkmI+m/Vp0cBBiQ8onwcrgEKiq+N1LVBN+WWKxa9cuXH/99RBFEbfccovHIL0NGzZg8+bN2LJlC/r371+j6+bk5MDlciEuznO+xLi4OBw/frxa13juuefQpEkTjyT7YsnJyZg7d26NYqOakyQJjswMAOVXiVKGhEBQ+eU/f/JRCkFAYngoTufmQZKAtPxCtIqOdCfNVHeSJMGVlwtHbk7NV38rLatoBMmxRqGEXXRBowjMGlwAiAsJgdMlIs9sAQAIag3U8QkyR0WBxC9/ck+ZMgWxsbHYuXMnmjZt6rHv7NmzGDBgAKZOnYrffvutQeN6+eWXsWbNGuzYsQO6KuZunD59OqZOnep+f+jQIQwcOLAhQmxUXPl5lQ7aUXKVLPKCII0GMUFByDaa4BJFnMnNQ8uoSGh8LElWqVTuhUL8hctkhCMrC5K9Fj3zggBNQmKjKav4a9wdcofQIJqEhcIhiii22aFJaBKwg/JIHv7z07GMI0eOYN68eeWSYwBo2rQpHnnkEcyZM6fG142OjoZSqURmZqbH9szMzApX7Svrv//9L15++WX88MMP6Ny5c5XHarVaaMtMRh8czIFi9U202Up6mSqg0Omg1DeOX5TU8OJCgmG022GxO+CQgDOFxWgRHwe9TlfyC1yhgKBQAIICEISSVx2pygxakqrRszpjxowaHV8qLCysyvKx6pAkCaIowuVywel0wuFwwFXFIiuixQJHbnbNl4wuQx0XDyV/zgYcQRDQNDwMZ1VaOBvBkwFqWH6ZIDdv3hy2Kur77HZ7hcnzpWg0GvTo0QPbtm3DrbfeCgDuAXeTJ0+u9LxXX30VL730Er777jv07Nmzxvel+lWyzOz5SgfuKCNYo0Z1IwgKCHo9FFodFDodBK0OCq0WgkYDhUaDKyQg5dQp95Rv55VKtGjRAmFeqnsPyc2F8P3WGiW7NSUIApKSkhAVFVXv1xZFEXa7HTabDVarFRaLBcasLBSnX4DLVLdxDurYOKjCwusnUPI52pg4tGvTDsePHw/IlSFJPn6ZIM+aNQtTpkzBjTfeiK5du3rs+/333/HWW29h0aJFtbr21KlTMX78ePTs2RO9evXCokWLYDKZMGHCBADAuHHjkJiY6F6I5JVXXsGsWbOwevVqJCUlISMjA0BJrzB7huXhyMyAWMkHKEGthjKkmnNpUqMnqDVQBgVBERQMpSEICoMBCr0BglZb5aw5SgCt27RBSkpKSd2sy4WTJ08iOjoaiYmJ9V7aEBUVhcceewx2u71er1uWRqPxSnIMlAyG1ul0ULtc0FrMMGSkI8JqhhgaBIteA7PdAaPNDrPdDrEGHwLU0bFQ8QNxwFLo9DC0vwKCSoWWLVvixIkTXv2QSI2LXybIe/fuRVxcHHr06IG+ffuidevWAIATJ05gz5496NixI/bs2eMx84QgCFi8ePElr33nnXciOzsbs2bNQkZGBrp27YotW7a4B+6lpaV5zLX83nvvwW63Y/To0R7XmT17dq3KPKhunHm5cBUWVLpfFRnJlZWoQgqtDsqQ0H9fIVAEBUNRphSqpoKCgtCqVSucOnXK/Us7JycH+fn5iImJQVRUVJVjFWoqxGGHZK/BQhk1VN/fNpIoQrRY4DIWw1VUCGduLkSr2eMYhSAgSKMpqe0ODoIoSTDb7Siy2lBktcFRRWmGOjoWKi8l9CQ/QaGEoWMX92DrkJAQNGvWDP/884/MkVGg8MsE+e2333b//ZdffsEvv/zisf+vv/7CX3/95bGtugkyAEyePLnSkoodO3Z4vE9NTa3WNf1JdT6BBwUFQZIk95++wFlYAEd2VuUHKFVQhoY3WDzkuxQaHRTBwVCGhLiT4rokw5UJCwtDy5Ytcfr0aff3icvlQkZGBjIyMqBWq6HT6er8oU1RkA/p3TfqI+SK/Rt7zISHoIqowwBXqWThHslhL3nKI9Vs1UGFICBYq0WwVosmYYDJbkehxYoCixWuMiVV6tg49hwHOH27DuXqyqOjo2Gz2dxPconqwi8TZJFLudY7jUYDQRCqnexOmjSpVvdp06ZNnQb5SJLkfmRdOsjH6XTCfOE8ivNzAaWy0l4ldWRkyeAoaiQEKLRaKP4tjVAagqAICoIyKBiCWt1gUYSHh6NNmzY4depUuRpJh8MBh8NR53uo8/NQk2GnnT7+3D0N2F9jb6/2ec6c7Bontd5U2rucEBqCIqsNeRYrbOGRXD46wOlatII6Nq7CfYmJibDb7cirYPVUoprwywSZ6p+/1jBKogjryb8RZCxETFRJ4i1KEqwOJ6xOByx2B8wOB2wSB+cFFEEBhVoNQa0pGRin1ULQav83aE6nL/nTRz4QBQcHo0OHDvjnn39QWNHCNfWpGr3RdtEFUSr5s1q1Ez7ylKgygiAgPDgYTa7qC5chCFlZWcjJyWFnSgDSxDeBtnmLKo9JSkqCy+Xy/vcaBTS/TpDPnDmDzZs3u2uOmjdvjuuvvx4tWlT9zUMV89YAHG9x5uXCcvJviGbP6Z8UggCDRg2DRo3SbjVNy8thDwt3r15orWR+ZKobQa2CoNbU4QKC+09BofjflGgKJQSVEoJSBUFV8iqZIk1Rcsq/U6ZJklgy97XNBlexoqR0wT2tmgAole4p1gSF4n/vS4/xIrVajdatW6OwsBDp6ekwcTnqeqPQGWDo1AXKoCCoUDLdZ5MmTZCVlYWsrCzObhAgVJHR0LVpd8njBEFAy5YtcfLkSRQXFzdAZBSI/DZBfvrpp7F48eJyPQQKhQJPPfUU/vvf/8oUmf+yZ6RDsnkncZRcLkChgDq26vmkL3kdmxWu4mI4MtOrPf2TwhAE3WVNoVco3NNsWa1WFBYWIj8/n4lKPZIcJfWl9XKterlKDZQmzYoyybNQNklXeCbbpUl86d8FBaAQPBN3lL4vuT4A6ASgRUgQbFo1jCYLrHZbnev4BQCNta9UFRkFQ/uO5cpmlEolEhISEBcXh6ysLGRmZjJR9mOqsAgYruhU7Q+yCoUCrVu3ZpJMteaXCfLrr7+ON954A6NHj8bTTz+N9u3bAwCOHTuGN954A2+88QYSExMxZcoUmSP1H/aMdKRNn3rpA2vr3wQgcuTtUDZwfaCu1eXlfqjqdDrodDrExcXBZrMhLy8Pubm5Vc6vTQFOEiG5RMDVcMl50L+vunKajcivh+v4FUEBXVKLSz5uVygUiI+PR0xMDLKzs5ko+yFlaDgMnbrUeKW80iT51KlTKCoquvQJRGX4ZYK8fPly3Hzzzfjss888tvfu3Rtr1qyB1WrF0qVLmSDXQE17jkd+8x2yLVbE6HVYN2JY9e/TwL+YVJHRUEdFV3mMVqtFQkICEhISUFRUhJycHBQUFPjM7BxE5ElhCIK+XQeoQqv/YVupVCI+Ph6xsbHIzc1FZmYmPxD7AVVYOAydurqnc6up0iT5zJkzyM9vdB8jqQ78MkFOTU3Fk08+Wen+YcOGYcuWLQ0YUYCpxqCdbIsVmWZLtY+XY5CPoFRBX416tbJCQ0MRGhoKu92O7OxsZGdnV7kMLhE1IEEBbdNm0Ca1rHXNuEKhQExMDGJiYlBYWIisrCz2LvooVWQUDFd0rnHP8cUEQUCLFi2gVquRlVXFVKBEZfhlghwbG4s//vij0v1//PEHYmJiGjAi8kXaFq2gqOVCDBqNBomJiYiPj+djWSIfoIqMgq5VGyiD6qMopURYWBjCwsJgt9uRm5vLMisfoolvAl2bdvU2eFYQBDRt2hQ6nQ5nz57lE0K6JL9MkG+//XYsXrwYSUlJePzxxxH07w9Mk8mEt99+G++//z6eeuopeYMkWanCI6FJvKzO1yn7WLY0Ua6PeWuJqHpUYeHQJrX06sIfGo3GXWZlMpmQn5+PgoICJsuyEKBr2RraZs29cvWYmBjo9XqcPn2aP8upSn6ZIM+bNw+HDh3C888/j1mzZqFJkyYAgAsXLsDpdGLw4MF48cUXZY6S5CKoNdB36FivS0orFArExcV51C9yqjjyVxqF0r1QiE8SFFBHRUFzWXOowsMb9NZBQUEICgrCZZddBovF4p4a0mg0cl5lLxPUGhjaXwFVpHenHC2dlzw1NZVzJVOl/DJBNhgM2LZtG77++muPeZCHDx+OG264ASNGjKjX5Ij8iKCAoUNHKDR1mIu3qssLAqKjoxEdHY3i4mJkZWWhsLCQj+vIr/w17g65Q6iQMiQM6thYqGPjvbL0d03p9Xro9XrExcVBkiSYzWaYTCaYTCZYLBZYrVZ+79cTVXgk9O07QKGtXVlcje+nUqF169bIzs7GuXPn+OGHyvG7BNlsNuPee+/FqFGjcM899+CWW26ROyTyIfq27bz6KLaskJAQhISEwOFwuKeJs1gsDXJvIn8nqDVQGgxQBIdAFRYGZXik1z7YlpIk6X8Dhi/6u1R2e7k/S/6uVwjQBQcj6t+yPkmSYLNZYbPbYbc73EuHO10uiC4XRFGEWA8JtIDA7fARlCroWraul5K42oiJiUFYWBjOnj2LgoICWWIg3+R3CbLBYMAPP/yA66+/Xu5QyMfoWreFJr5Jg99XrVYjLi4OcXFxsFgsyM/PR35+PkswKPAoFRCU6ksfV+U1lCWrISoUkFwiXIWFcBUUAP/8A48ZqEvzU1ycsP77n4sSWamqxLdMkusNSgD6f1/e4DAVosBL15aPAE18QslgapmfFmg0GrRq1QrFxcU4f/48F48iAH6YIAPA1VdfjT179mDixIlyh9Joxej1Hn/KSlBAf3lbaJokyh2J+5FskyZNYLPZUFhY6K5f5HRx5PdcIiRXHQc2uRyQ6mexRfJLAtSxsdA2bwFlULDcwXgICQlBu3btUFRUhMzMTE7/18j5ZYL89ttvY9iwYZg5cyYefvhhXHaZPI9mGrN1N1d/cRBvUmh10HfoCFVYuNyhlKPVahEbG4vY2FhIkgSLxQKj0Qiz2Qyz2cz6RSJqNBQ6PdRx8dAkJNZ6+s2GUjofvtVqRW5uLvLy8mC381NdY+OXCXKXLl3gdDqRnJyM5ORkqFQqaC96RCMIAkenBjBBqYLmsqbQNkuq8yTyDUEQhJLe5dJfDJIESRRhs9lgtdlgt1phdzhgt9vhdDr/V8P473F15fv/h4gokCg0Wnd9uSoyCsqQULlDqjGdTofExEQkJibCYrGguLgYRqMRFosFNpuNHRwBzi8T5FGjRnGWCn+lUkJQ17aGUYCgUUMZFAxleAQEhQKO7Mz/lRaW/WElSSW1ixL+V3/479+l0vfi//6UpH+TUFF0J68ltYwiJFG66O9iybnSv+dJKNlWeu3ShLaa9Y8KALp/X94iWq3I9eL1iSgAKJVAHWrMBQGAouRnvKBQQHI44MjJgSMnpwZXqUbSWd28tMoEtubJreHfVymHywWXKNY5UXbZLDDW6QrkDX6ZIK9atUruEKi2nC5IdZicXXLYIZpMcGRl1mNQREQElwuoQ415SZrogGRvHAOUFf++6srh5IIlvsivEmSr1Yqvv/4aZ86cQXR0NG688UYkJCTIHRYRERERBRC/SZCzsrLQt29fnDlzxv04w2AwYP369RgyZIjM0RERERFRoKiPpwMNYt68eUhNTcWUKVOwceNGLFq0CHq9HpMmTZI7NCIiIiIKIH7Tg/z9999j3Lhx+O9//+veFhcXh7vvvhspKSlo27atjNERERERUaDwmx7ktLQ0XH311R7brr76akiShMxMDtgiIiIiovrhNwmyzWaD7qLJxUvfO51OOUIiIiIiogDkNyUWAJCamoqDBw+635cuBHLixAmEh4eXO7579+4NFRoRERERBQi/6UEGgBdeeAFXXnml+1U6e8Wjjz7qsb1nz5648sora32fd955B0lJSdDpdOjduzf27dtX5fGff/452rVrB51Oh06dOmHTpk21vjcRERERyctvepA/+OCDBrnP2rVrMXXqVCxZsgS9e/fGokWLMGzYMKSkpCA2Nrbc8bt378aYMWOQnJyMm266CatXr8att96KgwcPomPHjg0SMxERERHVH79JkMePH98g91m4cCEmTpyICRMmAACWLFmCb7/9FitXrsS0adPKHb948WIMHz4c//nPfwCUTEe3detWvP3221iyZEmDxExERERE9cevSiy8zW6348CBAx4LjygUCgwZMgR79uyp8Jw9e/aUW6hk2LBhlR4PlAw4LCoqcr+MRh9bhV2S6v9FvoPtG9jYvoGN7RvY2L4+w296kBtCTk4OXC4X4uLiPLbHxcXh+PHjFZ6TkZFR4fEZGRmV3ic5ORlz586te8D1SNDqLn1QPdC2vhzq6JgGuRf9j+vfAa3exvaVhzM3t0Huw/aVhzIiskHuw/aVR0O1b0P9ng8UTJBlMH36dEydOtX9/tChQxg4cKCMEQGa+AQ0S14IyWb12j0ErQ6a+ASvXZ+qkNiU7RvAtGzfgMb2DWxsX9/EBLmM6OhoKJXKcguPZGZmIj4+vsJz4uPja3Q8AGi1Wmi1Wvf74ODgOkRdf/jNE9jYvoGN7RvY2L6Bje3re1iDXIZGo0GPHj2wbds29zZRFLFt2zb06dOnwnP69OnjcTwAbN26tdLjiYiIiMi3sQf5IlOnTsX48ePRs2dP9OrVC4sWLYLJZHLPajFu3DgkJiYiOTkZAPDkk09i4MCBeP3113HjjTdizZo12L9/P5YtWybnl0FEREREtcQE+SJ33nknsrOzMWvWLGRkZKBr167YsmWLeyBeWloaFIr/dbz37dsXq1evxsyZM/H888/j8ssvx/r16zkHMhEREZGfEiSJc4DI7eDBg+jRowcOHDjA5bGJiIiIZMYaZCIiIiKiMpggExERERGVwRpkqrX09HSkp6fLHQYREVGjkpCQgIQETg3nTUyQfUBCQgJmz57tV//YbTYbxowZg507d8odChERUaMycOBAfPfddx5rKlD94iA9qpWioiKEhYVh586dPrPQCdUfo9GIgQMHsn0DFNs3sLF9A1tp+xYWFiI0NFTucAIWE2SqldIEmd+ggYntG9jYvoGN7RvY2L4Ng4P0iIiIiIjKYIJMRERERFQGE2SqFa1Wi9mzZ3OAQIBi+wY2tm9gY/sGNrZvw2ANMhERERFRGexBJiIiIiIqgwkyEREREVEZTJCJiIiIiMpggkyyS01NhSAIWLVqldyhEBERETFB9jenTp3CpEmT0LJlS+h0OoSGhqJfv35YvHgxLBaL1+579OhRzJkzB6mpqV67R3W89NJLuPnmmxEXFwdBEDBnzhxZ45GLIAjVeu3YsaPO9zKbzZgzZ06NrsV2qjtfbuPjx4/j2WefRdeuXRESEoKEhATceOON2L9/f51jaSx8uX0vXLiAe++9F23btkVISAjCw8PRq1cvfPjhh+C4/urx5fa92CeffAJBELjq4kVUcgdA1fftt9/i9ttvh1arxbhx49CxY0fY7Xb8/PPP+M9//oMjR45g2bJlXrn30aNHMXfuXAwaNAhJSUleuUd1zJw5E/Hx8ejWrRu+++472eKQ28cff+zx/qOPPsLWrVvLbW/fvn2d72U2mzF37lwAwKBBg6p1Dtup7ny5jd9//32sWLECo0aNwqOPPorCwkIsXboUV111FbZs2YIhQ4bUOaZA58vtm5OTg3PnzmH06NFo1qwZHA4Htm7divvuuw8pKSlYsGBBnWMKdL7cvmUZjUY8++yzCAoKqnMcgYYJsp84c+YM7rrrLjRv3hw//vgjEhIS3Psee+wxnDx5Et9++62MEf6PJEmwWq3Q6/X1fu0zZ84gKSkJOTk5iImJqffr+4t7773X4/3evXuxdevWctvlwnaqO19u4zFjxmDOnDkePU73338/2rdvjzlz5jBBrgZfbt/OnTuX642cPHkyRowYgTfffBPz5s2DUqmUJzg/4cvtW9b8+fMREhKCwYMHY/369XKH41NYYuEnXn31VRiNRqxYscIjOS7VunVrPPnkk+73TqcT8+bNQ6tWraDVapGUlITnn38eNpvN47ykpCTcdNNN+Pnnn9GrVy/odDq0bNkSH330kfuYVatW4fbbbwcADB48uNyjodJrfPfdd+jZsyf0ej2WLl0KADh9+jRuv/12REZGwmAw4KqrrqpTIi9n77W/EUURixYtwhVXXAGdToe4uDhMmjQJ+fn5Hsft378fw4YNQ3R0NPR6PVq0aIH7778fQEl9eGmCO3fuXHfbX6pkgu3UMORq4x49epR7HBsVFYX+/fvj2LFj9ftFNmJyfg9XJCkpCWazGXa7vc5fG8nfvidOnMAbb7yBhQsXQqVif+nF+H/ET3zzzTdo2bIl+vbtW63jH3zwQXz44YcYPXo0nn76afz6669ITk7GsWPHsG7dOo9jT548idGjR+OBBx7A+PHjsXLlStx3333o0aMHrrjiCgwYMABPPPEE3nzzTTz//PPuR0JlHw2lpKRgzJgxmDRpEiZOnIi2bdsiMzMTffv2hdlsxhNPPIGoqCh8+OGHuPnmm/HFF19g5MiR9fc/iMqZNGkSVq1ahQkTJuCJJ57AmTNn8Pbbb+P333/HL7/8ArVajaysLAwdOhQxMTGYNm0awsPDkZqaiq+++goAEBMTg/feew+PPPIIRo4cidtuuw1ASQ8Tyc/X2jgjIwPR0dH1+jU2ZnK3r8VigclkgtFoxM6dO/HBBx+gT58+Xnk62BjJ3b5PPfUUBg8ejBtuuAGfffaZV79WvySRzyssLJQASLfccku1jj906JAEQHrwwQc9tj/zzDMSAOnHH390b2vevLkEQPrpp5/c27KysiStVis9/fTT7m2ff/65BEDavn17ufuVXmPLli0e25966ikJgLRr1y73tuLiYqlFixZSUlKS5HK5JEmSpDNnzkgApA8++KBaX58kSVJ2drYEQJo9e3a1zwlkjz32mFT223nXrl0SAOmTTz7xOG7Lli0e29etWycBkH777bdKr12X/9dsp/rjq21c6qeffpIEQZBeeOGFWl+jMfPF9k1OTpYAuF/XXnutlJaWVqNrUAlfa9+NGzdKKpVKOnLkiCRJkjR+/HgpKCioBl9R4GOJhR8oKioCAISEhFTr+E2bNgEApk6d6rH96aefBoByJQ4dOnRA//793e9jYmLQtm1bnD59utoxtmjRAsOGDSsXR69evXD11Ve7twUHB+Ohhx5Camoqjh49Wu3rU818/vnnCAsLw3XXXYecnBz3q/TR+Pbt2wEA4eHhAICNGzfC4XDIGDHVlC+1cVZWFu6++260aNECzz77rFfu0dj4QvuOGTMGW7duxerVq3H33XcDgFdnS2pM5Gxfu92OKVOm4OGHH0aHDh3q5ZqBiAmyHwgNDQUAFBcXV+v4f/75BwqFAq1bt/bYHh8fj/DwcPzzzz8e25s1a1buGhEREeXqoKrSokWLCuNo27Ztue2lpRkXx0H158SJEygsLERsbCxiYmI8XkajEVlZWQCAgQMHYtSoUZg7dy6io6Nxyy234IMPPihXq06+x1fa2GQy4aabbkJxcTG+/vprThVVT3yhfZs3b44hQ4ZgzJgx+OSTT9CyZUsMGTKESXI9kLN933jjDeTk5LhnvqCKsQbZD4SGhqJJkyY4fPhwjc4TBKFax1U2GlmqwXyXrEnzLaIoIjY2Fp988kmF+0sHdQiCgC+++AJ79+7FN998g++++w73338/Xn/9dezdu5fJjg/zhTa22+247bbb8Oeff+K7775Dx44da30t8uQL7Xux0aNHY/ny5fjpp5/KPTGkmpGrfQsLCzF//nw8+uijKCoqcj+hNhqNkCQJqampMBgMiI2NrdsXGACYIPuJm266CcuWLcOePXvQp0+fKo9t3rw5RFHEiRMnPAbSZWZmoqCgAM2bN6/x/aubbF8cR0pKSrntx48fd+8n72jVqhV++OEH9OvXr1ofXq666ipcddVVeOmll7B69Wrcc889WLNmDR588MFatT15n9xtLIoixo0bh23btuGzzz7DwIEDa/NlUCXkbt+KlPYcFxYW1sv1GjO52jc/Px9GoxGvvvoqXn311XL7W7RogVtuuYVTvoElFn6jdCLvBx98EJmZmeX2nzp1CosXLwYA3HDDDQCARYsWeRyzcOFCAMCNN95Y4/uXTiJeUFBQ7XNuuOEG7Nu3D3v27HFvM5lMWLZsGZKSklj75EV33HEHXC4X5s2bV26f0+l0t2N+fn65JwVdu3YFAPcjPIPBAKBmbU/eJ3cbP/7441i7di3effdd98h5qj9ytm92dnaF21esWAFBENC9e/dqXYcqJ1f7xsbGYt26deVegwcPhk6nw7p16zB9+vTaf2EBhD3IfqJVq1ZYvXo17rzzTrRv395jJb3du3fj888/x3333QcA6NKlC8aPH49ly5ahoKAAAwcOxL59+/Dhhx/i1ltvxeDBg2t8/65du0KpVOKVV15BYWEhtFotrrnmmiofw0ybNg2ffvoprr/+ejzxxBOIjIzEhx9+iDNnzuDLL7+EQlHzz2cff/wx/vnnH5jNZgDATz/9hPnz5wMAxo4dy17pfw0cOBCTJk1CcnIyDh06hKFDh0KtVuPEiRP4/PPPsXjxYowePRoffvgh3n33XYwcORKtWrVCcXExli9fjtDQUPcHLb1ejw4dOmDt2rVo06YNIiMj0bFjxyofp7OdvE/ONl60aBHeffdd9OnTBwaDAf/3f//nsX/kyJFcmauO5Gzfl156Cb/88guGDx+OZs2aIS8vD19++SV+++03PP744+XGt1DNydW+BoMBt956a7nt69evx759+yrc12jJOYUG1dzff/8tTZw4UUpKSpI0Go0UEhIi9evXT3rrrbckq9XqPs7hcEhz586VWrRoIanVaqlp06bS9OnTPY6RpJIp2m688cZy9xk4cKA0cOBAj23Lly+XWrZsKSmVSo8p3yq7hiRJ0qlTp6TRo0dL4eHhkk6nk3r16iVt3LjR45iaTPM2cOBAj2mHyr4qmoKusbh4CqFSy5Ytk3r06CHp9XopJCRE6tSpk/Tss89KFy5ckCRJkg4ePCiNGTNGatasmaTVaqXY2Fjppptukvbv3+9xnd27d0s9evSQNBpNtaYTYjvVP19q4/Hjx1favgCkM2fO1OeX3ij4Uvt+//330k033SQ1adJEUqvV7t8zH3zwgSSKYr1+3Y2FL7VvRTjNW3mCJNVgJBYRERERUYBjDTIRERERURlMkImIiIiIymCCTERERERUBhNkIiIiIqIymCATEREREZXBBDnAvPrqq2jXrh1EUZQ7lDq76667cMcdd8gdhk9h+wY2tm9gY/sGPrZxAJF7njmqP4WFhVJkZKS0cuVK9zb8Oy/pf//733LHf/DBBxIA6bfffqv3WIYMGSIBkB577LEK97///vtSu3btJK1WK7Vu3Vp68803yx1z8OBBSaFQSIcOHar3+PwR2zewsX0DG9s38LGNAwt7kAPIypUr4XQ6MWbMmHL7XnvtNfeqZt721VdfeSwvfbGlS5fiwQcfxBVXXIG33noLffr0wRNPPIFXXnnF47hu3bqhZ8+eeP31170dsl9g+wY2tm9gY/sGPrZxgJE7Q6f607lzZ+nee+/12AZA6tq1qwRAev311z32eePTq8VikZKSkqQXX3yxwk+vZrNZioqKKrfy3j333CMFBQVJeXl5Htv/+9//SkFBQVJxcXG9xeiv2L6Bje0b2Ni+gY9tHFjYgxwgzpw5gz///BNDhgwpt69fv3645ppr8Oqrr8JisXg1jldffRWiKOKZZ56pcP/27duRm5uLRx991GP7Y489BpPJhG+//dZj+3XXXQeTyYStW7d6LWZ/wPYNbGzfwMb2DXxs48DDBDlA7N69GwDQvXv3CvfPmTMHmZmZeO+996q8js1mQ05OTrVeF0tLS8PLL7+MV155BXq9vsLr//777wCAnj17emzv0aMHFAqFe3+pDh06QK/X45dffqky7kDH9g1sbN/AxvYNfGzjwKOSOwCqH8ePHwcAtGjRosL9/fv3x+DBg/Haa6/hkUceqfSb59NPP8WECROqdU9JkjzeP/300+jWrRvuuuuuSs9JT0+HUqlEbGysx3aNRoOoqChcuHDBY7tKpULTpk1x9OjRasUUqNi+gY3tG9jYvoGPbRx4mCAHiNzcXKhUKgQHB1d6zJw5czBw4EAsWbIEU6ZMqfCYYcOG1epRyvbt2/Hll1/i119/rfI4i8UCjUZT4T6dTlfh46eIiIgKPy03JmzfwMb2DWxs38DHNg48TJAbkQEDBmDw4MF49dVX8fDDD1d4TEJCAhISEmp0XafTiSeeeAJjx47FlVdeWeWxer0edru9wn1Wq7XCT9WSJEEQhBrF1BixfQMb2zewsX0DH9vYvzBBDhBRUVFwOp0oLi5GSEhIpcfNnj0bgwYNwtKlSxEeHl5uv8ViQWFhYbXuGR8fDwD46KOPkJKSgqVLlyI1NdXjmOLiYqSmpiI2NhYGgwEJCQlwuVzIysryeMRjt9uRm5uLJk2alLtPfn4+Lr/88mrFFKjYvoGN7RvY2L6Bj20ceDhIL0C0a9cOQMlI2qoMHDgQgwYNwiuvvFLho5S1a9e6P8Fe6lUqLS0NDocD/fr1Q4sWLdwvoOQbt0WLFvj+++8BAF27dgUA7N+/3+O++/fvhyiK7v2lnE4nzp49i/bt29fo/0egYfsGNrZvYGP7Bj62ceBhD3KA6NOnD4CSf+SdO3eu8tg5c+Zg0KBBWLZsWbl9tal/uuuuu8p9UwHAyJEjccMNN2DixIno3bs3AOCaa65BZGQk3nvvPdxwww3uY9977z0YDAbceOONHtc4evQorFYr+vbtW6OYAg3bN7CxfQMb2zfwsY0DkFwTMFP969ixozRmzBiPbahkqcmBAwe6l8D0xjKXVd37nXfekQBIo0ePlpYvXy6NGzdOAiC99NJL5Y7973//KxkMBqmoqMgrMfoTtm9gY/sGNrZv4GMbBxYmyAFk4cKFUnBwsGQ2m93bKvsG2b59u2zfnJIkScuWLZPatm0raTQaqVWrVtIbb7whiaJY7rjevXuXW5mosWL7Bja2b2Bj+wY+tnFgYYIcQAoKCqTIyEjp/ffflzuUevH7779LgiBIv//+u9yh+AS2b2Bj+wY2tm/gYxsHFkGSLpppmvzaK6+8gg8++ABHjx6FQuHfYzDvuusuiKKIzz77TO5QfAbbN7CxfQMb2zfwsY0DBxNkIiIiIqIy/PvjDRERERFRPWOCTERERERUBhNkIiIiIqIymCATEREREZXBBJmIiIiIqAwmyEREREREZTBBJiIiIiIqgwkyEREREVEZTJCJiIiIiMpggkxEREREVAYTZCIiIiKiMpggExERERGVwQSZiIiIiKgMJsg+ID09HXPmzEF6errcoRARERE1ekyQfUB6ejrmzp3LBJmIiIjIBzBBJiIiIiIqgwkyEREREVEZTJCJiIiIiMpggkxEREREVAYTZCIiIiKiMpggExERERGVwQSZiIiIiKgMJshEVCGn0yl3CERERLJggkxEFWKCTEREjRUTZCKqkMvlkjsEIiIiWTBBJqIKMUEmIqLGigkyEVWIJRZERNRYqeQO4GLnz5/HTz/9hKysLIwaNQqXXXYZXC4XCgsLERYWBqVSKXeIRI0CE2QiImqsfKYHWZIkTJ06FS1atMA999yDqVOn4u+//wYAGI1GJCUl4a233pI5SqLGw+FwyB0CERGRLHwmQX7ttdewePFiPPPMM9i6dSskSXLvCwsLw2233YYvv/xSxgiJGhe73S53CERERLLwmQR5+fLlGDduHBYsWICuXbuW29+5c2d3jzIReZ/VapU7BCIiIln4TIJ89uxZ9O3bt9L9QUFBKCoqasCIiBo3JshERNRY+UyCHBsbi7Nnz1a6/8CBA2jWrFkDRkTUuJlMJrlDICIikoXPJMi33XYblixZgtOnT7u3CYIAAPj++++xatUq3H777Q0SyzvvvIOkpCTodDr07t0b+/btq/L4RYsWoW3bttDr9WjatCmmTJnC3jfye0ajkXMhExFRo+QzCfLcuXORkJCArl27Yty4cRAEAa+88gquvvpqXH/99ejcuTOef/55r8exdu1aTJ06FbNnz8bBgwfRpUsXDBs2DFlZWRUev3r1akybNg2zZ8/GsWPHsGLFCqxdu7ZBYiXyJkmSkJeXJ3cYREREDc5nEuSwsDDs3bsXzz77LM6fPw+dToedO3eioKAAs2fPxq5du2AwGLwex8KFCzFx4kRMmDABHTp0wJIlS2AwGLBy5coKj9+9ezf69euHu+++G0lJSRg6dCjGjBlzyV5nIn+QmZkpdwhEREQNzmcSZADQ6/WYOXMmDh06BJPJBIvFgsOHD2PWrFnQ6/Vev7/dbseBAwcwZMgQ9zaFQoEhQ4Zgz549FZ7Tt29fHDhwwJ0Qnz59Gps2bcINN9xQ6X1sNhuKiorcL6PRWL9fCFE9OXfunNwhEBERNTifWUnP6XTCbDYjNDS0wv1FRUUwGAxQqbwXck5ODlwuF+Li4jy2x8XF4fjx4xWec/fddyMnJwdXX301JEmC0+nEww8/XGWJRXJyMubOnVuvsRN5w/nz5+FwOKBWq+UOhYiIqMH4TA/yE088UeU0b/369cPTTz/dgBFVz44dO7BgwQK8++67OHjwIL766it8++23mDdvXqXnTJ8+HYWFhe7Xzp07GzBioupzuVxITU2VOwwiIqIG5TMJ8pYtWzB69OhK948ePRqbNm3yagzR0dFQKpXl6i4zMzMRHx9f4TkvvPACxo4diwcffBCdOnXCyJEjsWDBAiQnJ0MUxQrP0Wq1CA0Ndb+Cg4Pr/Wshqi9coIeIiBobn0mQL1y4gMTExEr3N2nSBOfPn/dqDBqNBj169MC2bdvc20RRxLZt29CnT58KzzGbzVAoPP83KpVKAPBYLpvIX50/fx75+flyh0FERNRgfCZBjoqKQkpKSqX7jx07Vml9cn2aOnUqli9fjg8//BDHjh3DI488ApPJhAkTJgAAxo0bh+nTp7uPHzFiBN577z2sWbMGZ86cwdatW/HCCy9gxIgR7kSZyN/9+eefcodARETUYHxmkN7w4cOxdOlS3HPPPejWrZvHvoMHD2LZsmUNslDInXfeiezsbMyaNQsZGRno2rUrtmzZ4h64l5aW5tFjPHPmTAiCgJkzZ+L8+fOIiYnBiBEj8NJLL3k9VqKG8vfff6NLly4IDw+XOxQiIiKvEyQfqQO4cOECrrzySmRlZeHmm2/GFVdcAQA4fPgwvvnmG8TGxuLXX3/FZZddJnOk9e/gwYPo0aMHDhw4gO7du8sdDhEAYPPmzR7LvycmJuKGG25wr3BJREQUqHymB7lJkybYv38/pk2bhq+//hrr1q0DAISGhuKee+7BggUL0KRJE5mjJGq8zp8/j7/++gudO3eWOxQiIiKv8pkEGQASEhLw4YcfQpIkZGdnAwBiYmLYY0XkI3799VeEhoYiKSlJ7lCIiIi8xmcG6ZUlCAJiY2MRGxvL5JjIh0iShG3btnFuZCIiCmg+1YOcn5+PTz/9FKdPn0Z+fn65adIEQcCKFStkio6IgJLFQ7Zu3Yqrr74a7du3lzscIiKieuczCfJ3332H0aNHw2QyITQ0FBEREeWOYW8yUcPo2bMnUlNTERQUhBkzZpTbL0kSdu3ahYKCAvTu3bvcXOBERET+zGcS5Keffhrx8fH46quv0KlTJ7nDIWrUMjIykJubC5fLVeVxf/31F3JzczFkyBDodLoGio6IiMi7fKbb5+TJk3jiiSeYHBP5mQsXLmDdunXIycmROxQiIqJ64TMJ8uWXX47i4mK5wyCiWiguLsbXX3+NkydPyh0KERFRnflMgjx//ny8++67HB1P5KdcLhd+/PFH7Nu3r9wAWyIiIn/iMzXI27ZtQ0xMDNq3b4/rrrsOTZs2hVKp9DhGEAQsXrxYpgiJqDoOHTqEoqIiDB48uNz3MBERkT/wmQT57bffdv9948aNFR7DBJnIP5w+fRpWqxXDhg2DWq2WOxwiIqIa8ZkSC1EUL/m61Ih6IvIdFy5cwKZNm2C32+UOhYiIqEZ8JkEmosCTmZmJzZs3M0kmIiK/4nMJ8t69e5GcnIwpU6bgxIkTAACz2YyDBw/CaDTKHB0R1VRmZia+++47PgEiIiK/4TMJst1ux2233YZ+/fphxowZePPNN3H27FkAgEKhwNChQ1l/TOSn0tPTsW3bNs5uQUREfsFnEuQXXngBGzduxHvvvYeUlBSPX6Q6nQ633347vv76axkjJKK6SE1NxZ49e+QOg4iI6JJ8JkH+9NNP8cgjj+Chhx5CZGRkuf3t27fH6dOnZYiMiOrL4cOHcfjwYbnDICIiqpLPJMhZWVlVLjOtVCphNpsbMCIi8oY9e/YgLS1N7jCIiIgq5TMJctOmTXH8+PFK9//yyy9o3bp1A0ZERN4gSRK2bduGnJwcuUMhIiKqkM8kyHfffTeWLl3qUaMoCAIAYPny5fjss88wbty4BonlnXfeQVJSEnQ6HXr37o19+/ZVeXxBQQEee+wxJCQkQKvVok2bNti0aVODxErkjxwOBzZv3oyCggK5QyEiIirHZ1bSmzFjBvbu3YsBAwagffv2EAQBU6ZMQV5eHs6dO4cbbrgBU6ZM8Xoca9euxdSpU7FkyRL07t0bixYtwrBhw5CSkoLY2Nhyx9vtdlx33XWIjY3FF198gcTERPzzzz8IDw/3eqxE3pCWlgaTyQQAsNlsyMvLq3BcQF1ZLBZ8++23uOmmmxAWFlbv1yciIqotQfKheZckScInn3yCL774AidOnIAoimjVqhXuuOMOjB071t2j7E29e/fGlVde6V76WhRFNG3aFI8//jimTZtW7vglS5bgtddew/Hjx2u9pO7BgwfRo0cPHDhwAN27d69T/ES1tW/fPsybNw/ffvutxywygiCgU6dOuPHGG5GUlFTv9w0KCmKSTEREPsUnSiwsFgumTp2KjRs34t5778X69etx5MgRHDt2DBs3bsS4ceMaJDm22+04cOAAhgwZ4t6mUCgwZMiQSqen2rBhA/r06YPHHnsMcXFx6NixIxYsWMBFEcivfPXVV+jXrx82b95cbq5iSZJw+PBhvPLKKzh48GC939tkMuHbb7/1ykJAeXl5uOeeexAaGorw8HA88MADl7zPoEGDIAiCx+vhhx9271+1alW5/aWvrKysev8aiIio4flEgqzX67F06VJkZmbKGkdOTg5cLhfi4uI8tsfFxSEjI6PCc06fPo0vvvgCLpcLmzZtwgsvvIDXX38d8+fPr/Q+NpsNRUVF7hdXCCQ57du3D3feeSdcLlelH+xEUYQoili+fDlSU1PrPQaj0YgtW7bA6XTW+NxBgwZh1apVFe675557cOTIEWzduhUbN27ETz/9hIceeuiS15w4cSLS09Pdr1dffdW978477/TYl56ejmHDhmHgwIEVlmEREZH/8YkEGQB69Ojhl/OjiqKI2NhYLFu2DD169MCdd96JGTNmYMmSJZWek5ycjLCwMPdr4MCBDRgxkaf58+dDkqRqr3LnrQGoeXl5OHDgQL1d79ixY9iyZQvef/999O7dG1dffTXeeustrFmzBhcuXKjyXIPBgPj4ePcrNDTUvU+v13vsUyqV+PHHH/HAAw/UW+xERCQvn0mQFy1ahDVr1uD999+vVS9SfYiOjoZSqSzXk52ZmYn4+PgKz0lISECbNm2gVCrd29q3b4+MjAzY7fYKz5k+fToKCwvdr507d9bfF0FUA2lpadi4cWO1S4JEUcSff/6JvLw8r8Rz9OjReitP2rNnD8LDw9GzZ0/3tiFDhkChUODXX3+t8txPPvkE0dHR6NixI6ZPn17lHOwfffQRDAYDRo8eXS9xExGR/HxmFov77rsPCoUCkyZNwhNPPIHExETo9XqPYwRBwB9//OG1GDQaDXr06IFt27bh1ltvBVCSEGzbtg2TJ0+u8Jx+/fph9erVEEURCkXJ542///4bCQkJ0Gg0FZ6j1Wqh1Wrd74ODg+v3C6GAYrfbvfahcdOmTdXuOS4lSRKOHTuGPn361Hs8NpsNFy5cQNOmTet8rYyMjHIlDyqVCpGRkZWWTAElU042b94cTZo0wZ9//onnnnsOKSkp+Oqrryo8fsWKFbj77rvL/bwiIiL/5TMJcmRkJKKiotC2bVtZ45g6dSrGjx+Pnj17olevXli0aBFMJhMmTJgAABg3bhwSExORnJwMAHjkkUfw9ttv48knn8Tjjz+OEydOYMGCBXjiiSfk/DIoQNjtduzbt89rdep//PEHBEGoUZIsCAIKCwu9FtOhQ4cQFxdX6QfMBQsWYMGCBe73FosFe/fu9fgQe/To0Vrfv2yNcqdOnZCQkIBrr70Wp06dQqtWrTyO3bNnD44dO4aPP/641vcjIiLf4zMJ8o4dO+QOAUDJAJzs7GzMmjULGRkZ6Nq1K7Zs2eIeuJeWlubuKQZKVgD87rvvMGXKFHTu3BmJiYl48skn8dxzz8n1JVAAcTqdMBqN0Gg0Hk8d6ktEREStepB1Op3H90F9kSTJ3WNeWYL88MMP44477nC/v+eeezBq1Cjcdttt7m1NmjRBfHx8uVklnE4n8vLyKi2Zqkjv3r0BACdPniyXIL///vvo2rUrevToUe3rERGR7/OZBNmXTJ48udKSiooS+T59+mDv3r1ejooaM61WC51OV+/X7dOnT616kNu2beuVBBnAJUsVIiMjPRYu0ev1iI2NLbcUfZ8+fVBQUIADBw64E9gff/wRoii6k97qOHToEICS8QZlGY1GfPbZZ+6nSUREFDh8ZpAeABQVFeHll1/GsGHD0K1bN/cSz3l5eVi4cCFOnjwpc4REgcNkMuHQoUNo1qxZtecZFwQBbdq08dpKkVFRUfWWeLdv3x7Dhw/HxIkTsW/fPvzyyy+YPHky7rrrLjRp0gQAcP78ebRr1879s+bUqVOYN28eDhw4gNTUVGzYsAHjxo3DgAED0LlzZ4/rr127Fk6nE/fee2+9xEtERL7DZ3qQz507h4EDB+Ls2bO4/PLLcfz4cXeNY2RkJJYuXYp//vkHixcvljlSIv+XlpaGXbt2wWq1omfPnkhLS6vWeYIgoG/fvsjPz0dISEitV4+siEqlwmWXXQabzVZv1/zkk08wefJkXHvttVAoFBg1ahTefPNN936Hw4GUlBT3LBUajQY//PCDe+xB06ZNMWrUKMycObPctVesWIHbbruNy8oTEQUgn0mQ//Of/6C4uBiHDh1CbGxsudHnt956KzZu3ChTdESBweVy4bfffsORI0fc25KSkjBt2jT3YhgVTbNW2qt72223oUmTJhBFEYWFhdDr9TAYDHVe6VIQBLRr1w4ajabGCXJV4xciIyOxevXqSvcnJSV5lJc0bdq02tMu7t69u9oxEhGRf/GZBPn777/HlClT0KFDB+Tm5pbb37JlS5w9e1aGyIgCQ0FBAbZv3+4xh3GzZs3Qv39/6HQ6tGvXDitWrMCuXbs8kkZBENC+fXsMHToUiYmJMBqNcDgcAEpmkLDb7QgJCYFKVbsfJ6XJcUREBKxWa92+SCIionrgMwmyxWJBTExMpfuLi4sbMBqiwCFJEk6cOIE9e/a451NWKpXo1asX2rdv7+79veKKK7Bw4UJkZGRgzJgxKC4uhl6vx3/+8x9ERES4rxcaGgqr1QqTyQSgpMe5oKAAQUFBNZ4LWKFQoH379h6D7oiIiOTmM4P0OnTogJ9++qnS/evXr0e3bt0aMCIi/2e327Fjxw7s2rXLnRyHhYVhxIgR6NChQ4WlEfHx8e5EV6PReCTHQEmPr16vR3h4uMcKkiaTCYWFhdVeCU+lUqFTp05MjomIyOf4TA/yU089hfHjx6Nz5864/fbbAZSsYnfy5EnMnTsXe/bswZdffilzlET+Izs7G9u3b/d4+tKmTRtcddVV9TK4TqVSITw8HGazGRaLBUDJoLeCggIEBwdXOW+zVqtFx44dYTAY6hwHERFRffOZBPnee+/FP//8g5kzZ2LGjBkAgOHDh0OSJCgUCixYsMC9/DMRVU6SJPz111/Yv3+/u5ZYo9GgX79+aNmyZb3eSxAEBAUFQaPRoLi4GKIoQpIkFBcXw263IygoqNy0bQaDAR07dvTKwidERET1wWcSZACYMWMGxo4diy+//BInT56EKIpo1aoVbrvttnr/xU4UiMxmM3bu3IkLFy64t8XExGDw4MEICQnx2n3VajXCw8NhMpncs1DYbDY4HA4EBwe7V8ULCwtD+/bt63V6OCIiovomW4LcvXt3LFiwAMOHDwcAfPTRRxgwYACSkpIwZcoUucIi8ltnz57FTz/95DETRJcuXdC9e3evrXpXlkKhQEhICDQaDYxGIyRJgiiKKCoqgk6nQ/PmzdGuXbsGiYWIiKguZEuQ//zzT+Tk5LjfT5gwAR9//DGSkpLkConIL7lcLuzfvx+HDx92bzMYDBg4cKB7xbiGpNVqoVarUVxc7J4OLj8/HyaTCbGxsYiOjm7wmIiIiGpCtq6c5s2b44cffnCPeJckqc6LDRA1NoWFhfjmm288kuOmTZvi1ltvlSU5LqVQKBAWFoagoCBYLBZYLBYUFhZiw4YN+P333yGKomyxERERXYpsCfLDDz+Mjz76CDqdDqGhoRAEAQ888ABCQ0MrfYWFhckVLpHPOXHiBNavX+9eWEehUKB379647rrrajwfsbd06dIFN9xwA6KiogCUfBA+ePAgvv32WxQVFckcHRERUcVkK7H4z3/+gy5dumD79u3IzMzEqlWrcOWVV3IwHtEl2O127N69G6dOnXJvCwsLw6BBg3yqfKFVq1aIi4sDAIwYMQK///47/vzzT0iShKysLKxbtw69e/dG27Zt+fSIiKgGTCYTgoKC5A4joMk6i8XQoUMxdOhQAMCqVaswadIk3H333XKGROTTKprb+PLLL0efPn18amaIhIQEjxIPpVKJnj17omnTpti5cyeKi4vhdDrxyy+/4OzZs7j66qt9ptebiMjXlY7vIO+RrcQiMjISX3zxhfv97Nmz0blzZ7nCIfJppXMbf/PNN+7kWK1WY9CgQRgwYIBPJcehoaGVPgmKi4vDyJEj0aZNG/e2tLQ0fPXVV/jnn38aKkQiIr/GBNn7ZOtBNhqNMJvN7vcvvvgiLr/8cnTs2FGukIh8ktVqxW+//Yb09HT3tpiYGAwaNAihoaEyRlaeTqdD+/btq5zKTa1Wo3///mjWrBl+/vlnWK1WWK1W/PDDD2jVqhXatm3bgBETBSYOfA9sTqcTLpcLSqVS7lAClmwJcqtWrfDFF1+gf//+CA0NhSRJMJlMyMvLq/K8yMjIBoqQSH4pKSnYsWMH7Ha7e1vnzp3Ro0cPn5tPWKPRoGPHju5FQS6lefPmiI2Nxc8//4y0tDQAwKlTp5Ceno6WLVuiQ4cO3gyXKKBZLBYu5R7grFYr65C9SJBK16KthhYtWtT4E6kgCB6DiUp9/PHHmDBhAmpwewBwTwsXSA4ePIgePXrgwIED6N69u9zhkA9wOp3YsGEDfvjhB/c2vV6PgQMHIjEx0ev3v+GGG5CVlYWwsDDMmTPnksdrtVp06tSpVnXEkiTh77//xt69e+F0OgGU/NwYNmwYbrjhBqhUPrXgJ5Ff4CCuwJaeng6lUonY2Fi5QwlYNfrNM3DgwHIJ8v79+3HkyBF06NDB/Wg0JSUFR48eRceOHdGjR48KrzV27Fj06tULO3bsQGZmJubMmYORI0eyDpkavezsbKxcudKjJrdJkyYYNGiQTw5k0+v16NSpE7Raba3OFwQBbdu2RUJCArZv346cnBxIkoQtW7bgyJEjuO+++5CQkFDPURMFNs41HviMRiMTZC+qUYK8atUqj/fr16/H+vXrsXXrVlx77bUe+7Zu3Yo77rgD8+bNq/R6bdu2dSfVH3zwAcaPH4+bb765JiF5xTvvvIPXXnsNGRkZ6NKlC9566y306tXrkuetWbMGY8aMwS233IL169d7P1AKOPv27cOaNWvcy0UrlUq0a9cOXbp08cnkWKfT1Sk5Lis0NBTXXXcdfv/9d/z9998QRRFnz57Fyy+/jFtvvRUDBw70ubISIl/FBDnwlZ3NiOpfnX7bzJo1C48//ni55BgArrvuOkyePBkzZ86s1rXOnDnjE8nx2rVrMXXqVMyePRsHDx5Ely5dMGzYMGRlZVV5XmpqKp555hn079+/gSKlQGK1WvHhhx9i1apV7uQ4JiYGjz/+OFq1auWTg21UKhU6duxYL8lxKYVCgTZt2uCJJ55wz6HscDjw+eef4+2330ZBQUG93YsokDFBDnxMkL2rTsV9J06ccK+QVZGoqKgK648B4KeffgIADBgwwOP9pZQe7y0LFy7ExIkTMWHCBADAkiVL8O2332LlypWYNm1ahee4XC7cc889mDt3Lnbt2sVf4lQjaWlpWLlypceHsKuuugp33HEHRFHE6dOnZYyucm3btvVar/Zll12G6dOnY/369dixYwcA4Pjx45g/fz7GjBlTaekWEZUoreenwMXVSL2rTglyq1at8MEHH+CBBx5AcHCwx77i4mKsXLmy0vlQBw0aBEEQYLFYoNFo3O8rUzpljTcH6dntdhw4cADTp093b1MoFBgyZAj27NlT6XkvvvgiYmNj8cADD2DXrl2XvI/NZoPNZnO/NxqNdQuc/JIoiti+fTvWr1/v/net1WoxZswYd0lP2akQfUlcXJzXZ5TRaDS444470LFjR3z88ccoLCyE2WzGihUr8Oeff+LOO+/kKH2iSpSd+YYCEzvjvKtOCfL8+fMxevRotGvXDvfddx9at24NoKRn+cMPP0RmZiY+//zzCs/dvn07ALinhCp9L6ecnBy4XC73o91ScXFxOH78eIXn/Pzzz1ixYgUOHTpU7fskJydj7ty5dQmV/FxxcTE++ugjHDlyxL2tWbNmuP/++31+0IVSqURSUlKD3a9Dhw6YOXMmPv30Uxw8eBAA8Ntvv+HEiRMYP348500mqkBpqRYFLqPRCKvVCp1OJ3coAalOCfKtt96KTZs24bnnnsOCBQs89nXt2hUrVqzAsGHDKjx34MCBVb73B8XFxRg7diyWL1+O6Ojoap83ffp0TJ061f3+0KFDfvn1U+0cP34cq1at8ng8NmTIENx8881+MaVZkyZNqj3XcX0JCgrCAw88gM6dO2Pt2rWwWCwoKCjA4sWLce211+Lmm2/2qdUEieRmt9u5kEQjkJGR0aAdFo1JnX8bDx06FEOHDkVGRoZ7WqrmzZsjPj6+zsE1tOjoaCiVSmRmZnpsz8zMrPDrOXXqFFJTUzFixAj3ttKBESqVCikpKWjVqlW587RarcfApovLUygwuVwufPPNN9i6dat7/u+QkBCMHz/ebxbFUCqVDTIPc0UEQUCvXr3QunVrfPTRR/j7778BANu2bcPRo0cxYcIEXHbZZbLERuSLjEYjwsLC5A6DvOjs2bNMkL2k3rqr4uPja5QU33///TW+hyAIWLFiRY3Pqy6NRoMePXpg27ZtuPXWWwGUJLzbtm3D5MmTyx3frl07/PXXXx7bZs6cieLiYixevBhNmzb1WqzkX3JycrBy5Uqkpqa6t7Vv3x7jx4/3ueWiqxIfHy97T21kZCSeeOIJbN++HV9//TWcTifS09PxyiuvYMSIERgyZAingyNCyVNOJsiBLS0tjcuKe0mdE+S0tDQsWLAA27dvR3Z2NtavX48BAwYgJycHL774IiZMmIBu3bqVO+/HH38s16BmsxnZ2dkAgIiICABAfn4+gJIprxpiVaCpU6di/Pjx6NmzJ3r16oVFixbBZDK5Z7UYN24cEhMTkZycDJ1Oh44dO3qcHx4eDgDltlPjtX//fqxevdpdE6hQKHDLLbfg2muv9atETqFQ+EwPrUKhwLXXXot27dph1apVOH/+PFwuF9avX4/Dhw9j/PjxVc6wQ9QYFBQU+Mz3LHmHyWRCTk4OYmJi5A4l4NQpQT569Cj69+8PURTRu3dvnDx50j21THR0NH7++WeYTKYKe33L9qSVXmvo0KF4/vnn8dRTT7lrenNycvDGG2/go48+wrfffluXcKvlzjvvRHZ2NmbNmoWMjAx07doVW7ZscQ/cS0tL86ukhuRjs9nw2WefecyAEhMTgwkTJvjlI7GEhIQGrz2+lMTERDz77LPYuHEjfvjhB0iShJMnT+Kll17CHXfcgd69e7NnhRqt0g4mCmxnzpxhguwFdUqQn332WYSHh2Pv3r0QBKHc6Psbb7wRa9eurda1Hn/8cVx//fWYP3++x/bo6Gi89NJLyMrKwuOPP44ffvihLiFXy+TJkyssqQDgnpO1MhevNkiN09mzZ7Fy5UqPevYrr7wSd911l0+uiHcpSqXSZ3ui1Go1Ro4ciY4dO+LDDz9EXl4erFYrPvroI/z555+4++67WedPjVJeXp7cIVADOHnyJK688kp2BtSzOnWF/vTTT3jkkUcQExNTYcM0a9YM58+fr9a19u7di+7du1e6v1u3bti7d2+tYyVqCJIkYfv27XjttdfcybFWq8X48eMxYcIEv0yOAXlmrqipyy+/HDNmzMBVV13l3nbo0CHMnz/fYzo9osYiLy/PPSCYApfRaHRPkkD1p04JsiiKVU7Un52dXe1laCMjI7F58+ZK92/atMld30vki4qLi/Hee+/h888/d5caNW3aFNOnT0fv3r1ljq72FAoFmjRpIncY1aLX6zFu3DhMnDjRPWahqKgI77zzDj799FOPBXqIAp3D4fDZxYaofv3+++/8MFTP6pQgd+/evdK6YKfTiTVr1nj05lRl0qRJ2LhxI2655Rb88MMPSE1NRWpqKrZu3Yqbb74ZmzdvxsMPP1yXcIm8JiUlBQsWLMDhw4fd26699lo888wzPr/wx6XExMT4fO/xxbp164aZM2fiiiuucG/btWsXkpOTy41/IApkrENuHLKzs3HixAm5wwgodapBnj59Om666SY88sgjuOuuuwCUzBn8ww8/YMGCBTh27Bjefvvtal1r5syZsNlseO2117Bx40bPIFUqTJs2DTNnzqxLuET1zuVyYePGjfj+++/dn96Dg4Mxfvx4j+TMn/njnOYAEBYWhkcffRS7du3Cl19+CYfDgaysLPz3v//F9ddfj+HDh3MRBQp42dnZPjt+gGqnZ8+euHDhAtRqNWbMmOHevnfvXjRt2tRvS/l8TZ0S5Ouvvx6rVq3Ck08+iWXLlgEA7r33XkiShNDQUHz00UcYMGBAta83b948PPnkk/jhhx88Fh0ZMmRIjVaqI2oIubm5WLlyJc6cOePe1q5dO4wfPz5g5h4VBAEhISFyh1FrgiBgwIABaNu2LVatWoV//vkHoiji22+/xZEjRzB+/PhyS8sTBZJz585VONUq+a+MjAykp6eXKzu1Wq34+eefMWTIEA7Yqwd1ngd57NixuO2227B161acOHECoiiiVatWGDZsWK1+sUZHR7t7o4l81YEDB7B69WpYLBYAJXW6N998c8AsUhEVFQWHw4GIiIiA+EEbFxeHZ555Bps3b8aWLVsgiiJSU1ORnJyM2267Df379w+Ir5PoYunp6SgsLAyYD+1UtTNnziAlJQXt2rWTOxS/V+sE2Ww2o2nTppg2bRr+85//uFeeIwpkNpsNX3zxBX755Rf3tqioKNx///1o0aKFjJHVr48//hiHDx/2+/rpspRKJW666SZcccUV+PDDD5GVlQW73Y41a9bgr7/+wr333sskggLSwYMHMXjwYLnDoAaye/duxMbGIjIyUu5Q/Fqtu7oMBgNUKlWDrG5H5AvOnTuHl19+2SM57tmzJ55//vmASo7LCsT5g1u0aIHp06ejf//+7m1HjhzB/PnzcejQIfkCI/KSkydPuleppcDndDrx3XffuZ9wUu3U6VnwqFGj8MUXX3BqEQpokiRhx44dePXVV91zG2s0GowdO9av5za+FIVCAZ1OJ3cYXqHVajFmzBg88sgjCA0NBVCyZOuyZcvw8ccfN4pfLJzyrvGQJAm7d+/m7+pGpLi4GJs3b4bdbpc7FL9VpwT5rrvuQlZWFgYPHoxPPvkEv/zyCw4ePFjuReSvjEYjli5dis8++8w9t/Fll12G6dOno0+fPgFdt6rRaAKinroqnTp1wowZM9ClSxf3tj179mDBggU4efKkjJF5X9lVHinwZWZm4tSpU3KHQQ0oJycHGzdubBQf+L2hToP0Bg0a5P77rl27yu2XJAmCIMDlctXlNkSy+Pvvv7Fq1SoUFBS4tw0ePBi33nor1Gq1fIE1kOou8uPvQkJC8NBDD2Hv3r34/PPPYbVakZubizfeeAPXXXcdbrrpJqhUdR7P7HPYg9z4lE4D1li+t6kkSf76668xfPhwLrZWQ3X6qf/BBx/UVxxEPsPlcmHTpk3YsmWLx9zGY8eORadOnWSOruE0hg8BpQRBQJ8+fXD55Zfjww8/xKlTpyBJEr7//nscPXoU9913n9+sJlhdTJADV8+ePXHu3DlotVqPeXLNZjN27dqFa6+9NqCffpGnoqIirF+/Htdeey2aNm0qdzh+o04J8vjx4+srDkiShGXLlmHFihU4ffp0hav/CILgfsxN5A25ublYtWqVx6PINm3a4L777mt0n74b4yIa0dHRmDJlCn744Qd88803cLlc7sGZt9xyCwYPHhwwZSdWq9X9lI8CS0ZGBjIzMyv8mXX69GmEhobiyiuvZNs3Ina7HVu2bMFVV12Fjh07su2rwWeeGz777LNYuHAhunbtinvvvRcRERFyh0SNzO+//47/+7//85jb+KabbsLQoUMDJimqicb4NQMlX/fQoUPRvn17rFq1Cunp6XA6nfjyyy9x+PBhjB07NiCmT3I4HHA4HH63jDjV3aFDh2C329G3b99G+33eGEmShD179iAvLw/9+/dn219CjRLk+++/H4IgYNmyZVAqlbj//vsveY4gCFixYsUlj/vwww8xatQofPbZZzUJiajO7HY7vvjiC/z888/ubZGRkbj//vvRsmVLGSOTV2P/4Vk6z/vXX3+NH3/8EQCQkpKCl156CXfddReuvPJKmSOsu+LiYkRFRckdBsng6NGjyM/Px7XXXguDwSB3ONSAUlJSYDQacd111/EDchVqlCD/+OOPUCgUEEURSqUSP/744yW76avbjW+xWDBkyJCahENUZ+fPn8fKlSuRnp7u3ta9e3fcfffd/KVBUKvVGD16NDp27IiPPvoIBQUFsFgs+OCDD/DXX3/hrrvu8ut/J0yQG7f09HR89dVXGDx4MBITE+UOhxrQ+fPnsWHDBlx//fVcz6ISNUqQU1NTq3xfF9deey1+++03PPTQQ/V2TaLKSJKEXbt24csvv4TD4QBQkgzdcccd6Nu3L+uzUP0Pt41Bu3btMHPmTKxduxa//fYbAGD//v04efIkxo0b57fLuhYWFsodAsnMbDZj06ZN6N69O7p3787v+0YkLy8PX3/9NW688UauIloBn3mG+u6772Lv3r1YsGABcnNz5Q6HAljpghBr1qxxJ8eJiYmYNm0a+vXrx18Q/+L/B08GgwETJkzA/fff714cpqCgAG+++SY+//xzv5yQv+wUhtR4SZKEAwcOYMuWLZzdpJExGo3YsGEDcnJy5A7F5/hMgty2bVucPn0aL7zwAmJjYxEUFITQ0FCPFz/hUF2dOHECL730Ev744w/3tkGDBuHZZ59FQkKCjJH5nsZeg1yZnj17YubMmWjbtq172/bt2/Hyyy8jLS1Nxshqjp0RVNbZs2exfv36CmeRosBlsVjwzTff4MKFC3KH4lPqPIvF5s2bsXDhQhw8eBCFhYUVLmVZnYVCRo0a5TM9Vu+88w5ee+01ZGRkoEuXLnjrrbfQq1evCo9dvnw5PvroIxw+fBgA0KNHDyxYsKDS40keLpcLmzdvxubNm93/RoOCgjB27Fh07txZ5uh8k698P/qiiIgIPP7449i5cyfWr18Ph8OBjIwMvPrqq34180lubi5sNhsXjiC3wsJCfP311xgyZAguu+wyucOhBuJwOLB582YMHjy4UQ9OL6tOCfKXX36JO+64A1dccQXuuusuvPfee7j77rshSRK+/vprXH755bj11lurda1Vq1bVJZR6s3btWkydOhVLlixB7969sWjRIgwbNgwpKSmIjY0td/yOHTswZswY9O3bFzqdDq+88gqGDh2KI0eOcNCDj8jLy8OqVas8lg6+/PLLMWHChEY3t3FNNMZ5kGtCoVBg8ODBaNeuHVatWoWzZ89CFEVs2LABR44cwfjx4xEdHS13mFWSJAmpqakeveFEpXPm9u/fn/82GhGXy4Vt27bBYrHgiiuukDsc2dWpiyM5ORm9evXC77//jrlz5wIomQruk08+weHDh5Geno4WLVrUS6ANZeHChZg4cSImTJiADh06YMmSJTAYDFi5cmWFx3/yySd49NFH0bVrV7Rr1w7vv/8+RFHEtm3bGjhyqsihQ4ewYMECd3KsUCgwYsQIPPnkk0yOL4E9yNWTkJCA//znPxg2bJj7/9mpU6fw0ksvYffu3RU+VfMlR48e9fkYqeGJooidO3fi999/57+PRkSSJPzyyy/Yv39/o2/3OiXIR48exV133QWlUgmVqqQzunTQU1JSEh599FG88sor1b5eUVER5s6di169eiEuLg5xcXHo1asXXnzxRRQVFdUl1Gqx2+04cOCAx3RzCoUCQ4YMwZ49e6p1DbPZDIfDUeVCAjabDUVFRe6X0Wisc+zkyW6349NPP8WyZctgNpsBlMxtPGXKFFx//fV+8fhbbvx/VH0qlQq33HILpk6d6p42zWaz4f/+7/+wdOlSFBcXyxxh5bKzs3H27Fm5wyAf9dtvv2HXrl0QRVHuUKgBHTx40D1jT2NVp9+ABoPBPcl0eHg4tFqtx3yycXFxOHPmTLWudeHCBXTr1g1z586F0WhEv3790K9fP5hMJsyZMwfdu3f3uLY35OTkwOVyIS4uzmN7XFwcMjIyqnWN5557Dk2aNKlyTufk5GSEhYW5XwMHDqxT3OTpwoULePXVV7Fr1y73tm7duuH5559Hq1atZIzMvzBBrrlWrVphxowZ6Nu3r3vbn3/+ifnz/7+9O4+Lqmz/B/45DMwwsqPAuAKKIki5YKiRgopiSLmgmUqK+5KZWe77415W+phmaCoumPkNXNBAUhHNBdEscbcESgU09n2GuX9/8OM8jCyyDWc4XO/X67x0zpw5c42XM3PNfe5lDW7duiVgZJW7evUqFUCkQvfu3cPPP/9MM1w0Mjdv3sTt27eFDkMwtfoGdHR0xJ07d/jbXbp0wf79+6FSqZCfn4/g4GC0adOmSudasGABkpKSEBYWhjt37iAkJAQhISG4ffs2Tp48iaSkJCxcuLA24Wrdhg0b8MMPPyA0NBSGhoYVHrdo0SJkZGTw2/nz5+sxSvFijOHixYvYuHEjPxrXwMAAY8aMweTJkxv0gg5CoC4WNWNoaAh/f39MnToVxsbGAIoX5Pj2228RHByM/Px8gSMsKy0tTeOznJCXPXnyBKGhoUhNTRU6lEYtMTEROTk5AIqvUmk7H1euXGm0Oa9VgTx8+HAcP36c/1W5ZMkSREVFwdzcHFZWVrhw4UKVi9rw8HDMmTMHPj4+Ze57++23MXv2bJw6dao24b5Ss2bNIJFIkJycrLE/OTkZCoWi0sdu2rQJGzZswOnTp185K4JMJtOYvq7kS5TUXG5uLnbt2oXg4GC+m0+LFi2wYMECvPXWW1TskXrXpUsXLF26FC4uLvy+ixcvYv369VW+slafrl27Rt29SKUyMzNx7Ngx/Pnnn0KH0ujExMTgnXfegZ2dHT9/eV5eHhYvXoxt27bV6cJtpRUVFeHKlStaOXdqairGjh0LU1NTmJubY9KkSa/8DAoMDISnpydMTU3BcVy5c7nb2dmB4ziNbcOGDdWOr0YFcn5+Pg4fPgylUomlS5fyvy58fX0RFRWFKVOmYNq0aThz5gwCAgKqdM6cnJwyXRtKUygU/K8mbZFKpXB1ddUYYFcy4K5Xr14VPu7zzz/H6tWrER4eju7du2s1RlLWn3/+iXXr1uG3337j9/Xp0wfz589HixYtBIyMNHampqaYMWMGxowZw3dHe/78OTZt2oQTJ05UaQpMbejevTuGDBmCtWvX8vuUSiWioqIa/cAcUjmlUokzZ87g0qVLgv3/bWxCQkLg7u6uMU1pCcYY4uLisHHjRty4cUMrz//PP/8gJSWlRo/19PSscJaysWPH4vbt24iMjERYWBiio6NfuZpybm4uBg0ahMWLF1d63H/+8x88e/aM3z766KNqx17tad5SUlLw5ptv4vHjx2CMgeM4yOVyHD16FF5eXujduzd69+5d7UCcnZ1x6NAhTJ8+nf8iKaFUKnHo0CE4OztX+7zVNXfuXIwfPx7du3eHm5sbNm/ejJycHEyYMAEAMG7cOLRs2RLr168HAGzcuBHLly9HcHAw7Ozs+L7KxsbG1DKsZWq1GuHh4Th58iT/odGkSRP4+/ujS5cuwgYnAtTqXjc4jsNbb72FDh06ICgoiP/s/Pnnn3H79m1MmDCh0sYBbUhKSsLz58/LzOTy9OlTXL9+nX7ok1eKi4vDixcvMGDAAH5lSVL3YmJiMGrUKBQVFVX447Vk/MDOnTuxYMEC2NnZ1Xkcv/32G7y9vevsfHfv3kV4eDiuXbvGf95s3boVPj4+2LRpU4WNW3PmzAFQPMVuZUxMTF555f9Vqt2CvHr1asTHx+OTTz5BWFgYvv76a8jlckybNq1WgSxYsABXr16Fm5sbAgMDERUVhaioKHz33Xdwc3NDTExMvfRBHjVqFDZt2oTly5ejS5cuuHnzJsLDw/kvsMTERI3Bgt9++y0KCwsxYsQING/enN82bdqk9Vgbs7S0NGzZsgVhYWH8h4aDgwMWL15MxXEdoUF6dcva2hpz586Fr68v/2+bmJiIdevW4fz58zrTcnvjxg26hN6A1Wcf1aSkJBw7dkynZ2lp6NasWQPGWJU/H7TVFTUhIaFM99PauHz5MszNzTV+jHt5eUFPTw9Xr16t9fk3bNiApk2bomvXrvjiiy+gUqmqfY5qtyCfPn0a48aN0ygAbWxsMGbMGNy/f7/Gk4qPHDkSOTk5WLhwIaZPn863XjHGYG1tjd27d2PEiBE1Ond1zZo1C7NmzSr3vpd/tWir3w+p2O+//44DBw7wXwIcx8HHx4embyM6TyKRwMfHB87OzggKCkJycjKUSiUOHz6MW7duwd/fXyfm546KioJUKkXr1q2FDoVUUUxMDFavXq1xRa2kj+prr72GwYMHa6VlMTMzEydOnMC7775b51dNU1NT8dFHH+HEiRPQ09ODn58ftmzZUunzBAYGIjg4GDdu3EBWVhbS0tI03lPx8fFYvXo1zp49i6SkJLRo0QL+/v5YsmRJmavXQigqKoJSqYRKpcLjx481GoFeRa1W448//kBqamqlU83W1NWrV/HOO+/UydXFpKSkMouv6evrw9LSssqzhlVk9uzZ6NatGywtLXHp0iUsWrQIz549w1dffVWt81S7QE5MTMSCBQs09r311ltgjCE5OblWq+4EBATA398fsbGxSEhIAADY2tqie/fu/DzLpPFSKpUICQnRmPXDwsICEyZMgIODg4CREVI9dnZ2WLRoEUJDQ/n/z3fu3MHatWsxevRodOvWTdD4ioqKcPr0aXh7e9Nyww1ASEgIRo0aVW5LY0kf1bi4OEyZMkUr/7eys7Nx+vRpDB06tNqNFJ6enggICCh3vNLYsWPx7NkzREZGQqlUYsKECZg6dSqCg4MrPF9JH9VBgwZh0aJFZe6/d+8e1Go1vvvuOzg4OPD/Ljk5Odi0aRMYY1CpVFAqlRpb6X2l/56Xl4eCggKoVCp+/8t/L/24io4p+Xvp6Rbv3btX7StLjDHcvXu30nFTNfX06VMkJCRU+kNr3bp1WLduHX87Ly8PV65c0Wh01PaMOXPnzuX//vrrr0MqlWLatGlYv349ZDJZlc9T7aqzoKCgzBRmJbdr0oRdJiB9ffTs2RM9e/as9bmIeDx79gy7d+/GkydP+H2dO3eGv78/jIyMBIyMkJqRSqUYNWoUXFxccODAAWRkZCAnJwe7du2Cm5sbRo0aJWjfzqKiIkRERGDAgAFVnq6T1D9d6aP64sUL3L9/H05OTrU6D2MMarUat27dQnh4OH755RfY29ujsLAQS5cu5YtkS0vLcgvW9u3bQ6lU4vfffwcAHDp0CDKZTKPYdXJyQmxsLC5fvgylUglnZ2fs2rULhYWFdVLH1JXCwkJwHFetIpnjOGRkZGhtRpro6Gi0aNGiwtb26dOn47333uNvjx07Fn5+fhg+fDi/r0WLFlAoFGUG/qlUKqSmpta67/DLevToAZVKhfj4+Go14taoWTY+Pl5jtGRGRgYA4OHDh+VeHizvF2t0dDSA4tkGSt9+lZLjSePAGMOlS5fw448/8tO3GRgYwM/PD71796aBZKTB69SpE5YsWYJDhw7xM7HExMTg0aNHGDduHDp06CBYbCUtyX379qVFdnRUTfqozpw585XHlZyzOtuFCxcQHx9frRbY+Ph4HDlyBLdv3+b3lbSCSqVS/PTTT/jpp58A/K/Q37BhA9q2bVtp/CWNKbGxsa9sNczMzIRUKq3X4lgikVS66enpISUlpUYtyIaGhlrpbsgYQ1ZWFlQqVYUFsqWlpUb3DrlcDmtr6zJXeXv16oX09HRcv34drq6uAICzZ89CrVajR48edRr3zZs3oaenV6ZLx6vUqEBetmwZli1bVmb/y2+6klkuypsKxtPTExzHIS8vD1KplL9dkcrORcQpNzeX70tWonnz5pg0aRJN30ZExdjYGJMnT0ZMTAwOHz6M/Px8pKamYsuWLejfvz/eeecdGBgYCBJbyVSXWVlZ6Ny5M/0o1SGJiYnV7qP6+++/4/79+zAzM6uw0K2p58+f4+bNm9V6TEk3hZencc3NzS1zBUVPTw8ymQy5ubk1jrGEvr4+DAwMkJ2djdu3b2PgwIFo1aoVDAwM+PtKttK39fX1IZVKoa+vD8YYHj9+DCMjIxgaGmoUuPr6+uUWvvr6+vzcvK/StWtXhISEVLsF2dHRUSsFslqtrrPurk5OThg0aBCmTJmCHTt2QKlUYtasWXj//ff57/cnT56gf//+2LdvH9zc3AAU911OSkrCo0ePAAC3bt2CiYkJ2rRpA0tLS1y+fBlXr15F3759YWJigsuXL+OTTz6Bv78/LCwsqhVjtV/pnj17qvuQcp07dw4A+F8hJbcJAYC//voLu3fv1hiB/dZbb2HEiBE6MZCCkLrGcRx69OgBBwcH7Nu3Dw8fPgRjDL/88gvu3LmDgIAAQfsDx8TE4Pnz5+jTp0+1+vE1dnV12T4vLw+pqalIS0tDamoqUlNTcerUqRoVtA8ePEDnzp1rHdPLqlKUXb9+nW/04DgOKpUKycnJuHjxIl80zp8/H1ZWVoiPj0e3bt00CtSDBw+iU6dOePfddystYq9fv47jx49j3rx5sLKy0ri/pIX2yZMn8PDwgL+/P3bt2lXt15ubmwvGGExMTCpdPbemFAoFevfujV9//bVKjYN6enpwcnKqdiFYHaampnV2roMHD2LWrFno378/Pwjzv//9L3+/UqnE/fv3NX4Q7dixA6tWreJvl/Qq2LNnDwICAiCTyfDDDz9g5cqVKCgogL29PT755BONfslVxTFdmVuoEbtx4wZcXV1x/fp1wQfnCE2tVuP06dMICwvjL6fJ5XL4+/uja9euAkdX/3JzcxEdHa21D2Bdk5+fj6ysLPTp06dRLw1e0mp74sQJvrjS19fHO++8w3+Z1EarVq3w5MkTmJubY+PGjdV6rJGREfr06UMzXFRBYWEhYmJiqtQfVKVSITc3t9wtLy+P72JW2h9//IFLly5Vu4VxwIABeOONN/jbdfmnvb09XFxcIJfLyy1es7KykJmZCYlEAqD8Pqp2dnbYt28fPv30U6SlpWn8GxkaGuLIkSMYNmxYpa8zKioKffv2LTOLRYmnT5/C09MTPXv2xN69e2v0nqqPz+fbt29j4sSJUKvVr8yznp4ePv74Y62NGZDJZLCzs4OHh0ej+HzWmakh+vXrhyVLlqB///7l3n/u3Dl+ahYiTunp6di7dy8ePHjA72vXrh0CAgLQtGlTASMjpH7p6elhwIABcHJywt69e/H06VOoVCqEhoYiLi4O48aNE+w9kZOTg59//hkODg7o2bNno/iirCmVSoXs7GxIpVJIJBLk5OQgOzu73D8LCgqqfX6pVFqjPqoWFhZamQaMMQapVIp27dpV+P/C2NgYzZs3528L0Uf1yZMn6Nu3L1xdXbFnzx6dnh60U6dOWL9+PT8jR3ktySXxjx8/XmvFsZ6eHhwcHBpVN1edKZCjoqIwefLkCu9PSUnRmN6LiMutW7ewb98+jbmNBw0aBB8fH76lgZDGplWrVliwYAFOnDiBM2fOgDGGhw8fYu3atRg1ahTc3NwE6xP86NEjJCQkwNXVFS4uLjpdZNQHpVKJ1NRU/Pvvv/yWkpKChIQE5OXlIT8/v0bn5TiOX5nVxMRE48++fftWe3lwbfdRfbkvcU1pq4/qkydP4OnpCVtbW2zatAnPnz/nn7OuZ0+oK/369cPu3bvx/fff48KFCxr55jgOTk5OGDhwoFZnm3F0dESTJk0a1aIwOlMgA5Uvbfvo0SOYmJjUYzSkPiiVSoSGhmoswGJubo6AgABBR+8ToisMDAwwfPhwuLi4YN++fUhNTUV+fj6CgoLwxx9/YPTo0YIta69UKnHlyhXcv38fnp6esLKyEiSO+lBUVIS0tDS8ePFCowgu2Upmc6oujuPQpEkTmJiYlCmAjY2NYWRkVGkxq2t9VMvrClJT2uijGhkZiUePHuHRo0dl+vTrco/TTp064auvvkJSUhJGjx6NrKwsyOVyzJs3T6v5BIqv5DZr1qzGP/IaKkEL5KCgIAQFBfG316xZg507d5Y5Lj09HX/88Qd8fHzqMzyiZcnJyfj+++/xzz//8Ptef/11+Pv7C/aFT4iu6tChA5YsWYLDhw8jJiYGAPDbb7/hr7/+wgcffABnZ2fBYktLS8PRo0fh7u4uaBy1UVRUhPT09HKL33///Rfp6ek1LqDkcjlMTU3LbQU2MjKq1VWySZMm4ddff63yfLkDBw6s8XNVRXUHUb+8Om1plpaWlS4KYmdnV+Y1r1y5EitXrqzwMRUtStJQKBQKyOVyZGVlQSqVar04btWqVaOdNUrQAjk3N1fj8kZWVlaZX8ocx8HIyAjTp0/H8uXL6ztEogWMMVy5cgWHDx9GYWEhgOIBSH5+fujTpw9NI0VIBeRyOQICAvD666/j0KFDyMnJQUZGBr755ht4eHhg2LBhgs3ywhjDxYsXkZ+fr5ODjdVqNTIyMiosgNPS0jRWMasOU1NTWFpaolmzZhp/GhkZIS4uDubm5lobxKUrfVSB4qsd2i7YSP2xsrLSyqIyDYWgBfKMGTMwY8YMAMUjX7ds2YJ3331XyJCIluXl5eHQoUOIjY3l9ykUCkycOJGWtCWkirp164a2bdviwIED/LKt58+fx7179xAQEABbW1vBYouNjYVEItHKNGKVYYwhMzOz0gK4ptOtGRsbw9LSEk2bNuW3kiK4adOmFf4oyc3NrZcxFLrQR1VPTw/t27fX2vlJ/TI1NUWHDh0adYOVTvRBzsvLw9ChQxt1IhqDx48fY/fu3fj333/5fe7u7hgxYgTNq0pINZmbm+PDDz9EdHQ0QkJCoFQqkZycjC+++AI+Pj7w9vYWbIDr1atXIZFI4OLiUmfnZIwhOzu7wgI4NTW1xv1f5XK5RsH7chHcEKZYFLKPqkQigbOzMwwNDRvVIC6xMjQ0hJOTU6MfeKsTBbJcLkdgYCA6deokdChEC9RqNSIjI3HixAmNuY3HjBnDT99DCKk+juPg4eEBR0dH7N27F4mJiVCr1QgLC8Pt27cxfvz4MsurJiYm8jMNFBQUIDU1VStTfl26dAkSiQROTk5VOp4xhtzc3EoL4JpMhQYUz9/6cqtv6U1MU9XVdx9VQ0NDODs7w8jIqNEN4hIjAwMDuLi40IJc0JECGQBcXV0RFxcndBikjmVkZCAoKAj37t3j99nb22PixIk0tzEhdUShUGDevHk4deoUwsPD+SVw161bhxEjRsDd3R3Xrl3D6tWrcfLkSf4SfF5eHhYvXozXXnsNgwcPrvP+hhcuXAAAvkjOy8ursAD+999/q11g5efn4+LFi4iPj4dEIkHXrl0xbdo0tGjRQqMgNjIy4q9Q5ufn49NPP8UPP/yAgoICeHt7Y/v27bCxsQEA/Pvvvxg7diz++OMP/Pvvv7C2tsaQIUOwbt26Ol1FTAyaNm2K9u3bC7YMOqlbJVd9Xl7iu7HSmQJ58+bN8PHxgYuLCwICAupsvW8inNu3byMoKIhfRYrjOHh7e2Pw4ME0tzEhdUwikeCdd96Bi4sL9u7di+fPn6OwsBDBwcHYt28f9uzZA8ZYmVH/jDHExcUhLi4OU6ZMqdUAO8YYVCqVxhYaGoqQkBCkpaVpTMdVVceOHYOzszPc3d3LDIL77LPPYGhoiOjoaKhUKkyYMAERERGVznzwySef4OTJkzhy5AjMzMwwa9YsDB8+HL/++iuA4r60Q4YMwZo1a2BlZYVHjx7hww8/RGpqaqXnbWzs7e3RsmVL6hopEiXFMc0g9T86U4UGBARAT08P06ZNw+zZs9GyZcsyv2I4jsPvv/8uUISkqpRKJY4dO6ax6qGZmRkCAgLg6OgoYGSEiJ+9vT0WLVqEkJAQXLx4EcnJyQgNDa10CrCSrk87d+7EggULKmxJLimAi4qKyhTCKpWq0lkgSmasKY9EIoGlpWW5M0GUzPU8ceJEjcfcvXsX58+fx7Vr19C9e3cAwNatW+Hj44NNmzaVOzVVRkYGvv/+ewQHB6Nfv34AiufHdXJywpUrV9CzZ09YWFjwg8cBwNbWFjNnzsQXX3xRYfyNib6+PpycnMpdvpk0TBKJBJ06daIrJC/RmQK5pF8YFVC6LzExEWfOnEFWVhZMTEzQv39/fnR0cnIydu/ejb///ps/3sXFBePGjaNfpoTUE0NDQ4wZMwavvfYahg0bVq3HhoWFYfLkyRqFb0lBXJtlZi0sLGBubq7R9aHkT3Nz8woHBOnr65d73+XLl2Fubs4XxwDg5eUFPT09XL16tdzXff36dSiVSnh5efH7OnbsiDZt2uDy5cvo2bNnmcc8ffoUISEh8PDwqMnLFhW5XI5OnTrRJXgRKSmOzczMhA5F5+hMgVzZZOFEN8TExGj0YdTT04NarQbHcfD19cXQoUNx69YtfiCNvr4+hg0bBk9PT7oMR4gAzMzM8Ndff1V5gQu1Wo1bt27h4cOH1f7ClEgk0NfX19hK7+M4DuPGjauzGWuSkpLKDEDU19eHpaUlkpKSKnyMVCot0/ppY2NT5jGjR4/GsWPHkJeXh3feeQe7du2qk7gbKnNzc3Ts2JH6G4uIvr4+tRxXQmcKZKLbQkJCMGrUKI0+jCWXUxljOHnyJMLCwjBw4EC0bdsWNjY2mDhxIlq3bi1k2ITovMLCwhrPz/sqp06dqtHqb/Hx8WXmMZZIJHzBW96flf0ILvmsSEhIgJ2dXaUj5NetW4d169bxt/Py8nDlyhXMmjWL31cy97M2ff3111ixYgUePHiARYsWYe7cudi+fbvWn1cXtWjRAvb29o1+2i8xkclk6NSpE4yMjIQORWfpVIFcVFSEAwcO4OTJk0hISABQ3P/L19cXY8eOrbeBXdu2bcMXX3yBpKQkdO7cGVu3boWbm1uFxx85cgTLli1DfHw82rdvj40bN4pqWeyYmBiMGjUKRUVFFX7ZlnwBnj59GitWrMDChQtpbmNCXqGwsBAxMTH8QNa69vvvv1d5CeISHMdBrVZDLpdDT0+P314ugEsPyKuqCxcuICUlBW5ubhUWydOnT8d7773H3x47diz8/PwwfPhwfl+LFi2gUCiQkpKi8ViVSoXU1FQoFIpyz61QKFBYWIj09HSNVuTk5OQyj1EoFFAoFOjYsSMsLS3Ru3dvLFu2DM2bN6/y623opFIpHBwcaMYhkTExMYGTkxN9R7+CzhTIGRkZ8Pb2xrVr12BiYoK2bdsCACIjI/HTTz/h22+/RUREhNYvBRw+fBhz587Fjh070KNHD2zevBne3t64f/9+mct5QPFcn6NHj8b69evh6+uL4OBgDB06FDdu3KjTSfKFtGbNmnJHv5dHT08PN27coDceIVWgUqmQnZ0NqVSqlfeMhYVFtVuQGWMwMTHRSj/T7OxsZGVlQaVSVVgglwzWKyGXy2FtbQ0HBweN43r16oX09HRcv36dn0/97NmzUKvV6NGjR7nndnV1hYGBAc6cOQM/Pz8AwP3795GYmIhevXpVGHdJA0BN52FuiJo3bw5bW1vqUiEyCoUC7dq1o6sBVaAz/0JLlizB9evXsXXrVjx//hw3btzAjRs3kJKSgm+++QaxsbFYsmSJ1uP46quvMGXKFEyYMAHOzs7YsWMHmjRpgt27d5d7/JYtWzBo0CDMmzcPTk5OWL16Nbp164ZvvvlG67HWh8TERISFhVV5cE5RURFOnDiBxMRELUdGiHjIZDIYGhrW+ebu7l7t/v8cx8HR0VGj9biutqKiIn6RktpycnLCoEGDMGXKFMTExODXX3/FrFmz8P777/MzWDx58gQdO3ZETEwMgOI+2ZMmTcLcuXNx7tw5XL9+HRMmTECvXr34AXqnTp3Cnj17EBcXh/j4eJw8eRLTp0+Hu7t7nc8TrYtMTU3RtWtXODg4UHEsIiVLgbdv356K4yrSmRbk0NBQzJw5EzNnztTYb2BggBkzZuDu3bv4v//7P2zdulVrMRQWFuL69etYtGgRv09PTw9eXl64fPlyuY+5fPky5s6dq7HP29sbR48erfB5CgoKNFoiSi6vqlSqGi+Vqi0RERE1aoE6ffo0xo8fr6WoGg+lUgmVSoWcnByt9VPVJQUFBfz7QNfeC9qg7fwaGxujZ8+euHLlSpXexxzHwcnJCcbGxlr592eMISsrq1r5ZYyhqKio3OP37t2Ljz/+GP3794eenh6GDRuGr7/+mj82NzcX9+/fR2ZmJr/v888/BwD4+fmhoKAAAwYMwNatW/n7DQwMEBgYiE8++QQFBQVo1aoVhg4divnz51f730So92/p8SFVjVkikaB169awsrICgBp1+6H3b/2obn5lMhkcHBxgZGRUq+5c9ZlfnfhxxnSETCZj27Ztq/D+bdu2MZlMptUYnjx5wgCwS5cuaeyfN28ec3NzK/cxBgYGLDg4WGPftm3bmLW1dYXPs2LFCgaANtpoo4022mijjbaXNl2gMy3IDg4OOH78eJkW5BLHjx9Hu3bt6jkq7SgZEV3i5s2b8PDwwNWrV9G1a1cBIytr7969mDp1arUft3PnTmpBriPanOVAF+nr61c6y4HY1Ed+jx8/jgkTJoAxVu5iHiXdMCZNmoQuXbpoJQY9PT306NEDjo6OlF8tc3JywrNnz2BmZoa1a9dWeqyZmRkGDhxYZ33g6f2rfVXNr52dHXr16lWnExw0pvzqTIE8c+ZMzJo1Cz4+PpgzZw46dOgAoHgAxX//+19ERkZqvV9vs2bNIJFIkJycrLG/vBHOJRQKRbWOB4ovd5T+MCpZQENfX183LiuU4u3tXaNR8AMHDtS519JQ0b+juNVHfj/44AN07NgRq1evRlhYmMb7meM4vP766/Dx8dFaH1sjIyN4eXnBxsZGK+fXZUK8f0v6mHIcV+nzS6VSvPvuuzQPbi3oan47dOgADw8PWoOgFnSqQE5JScGGDRsQERGhcZ+BgQGWL1+usfynNkilUri6uuLMmTMYOnQogOK+PmfOnNGYg7O0Xr164cyZM5gzZw6/LzIystIR0Q1JmzZt4Ovri1OnTlVpoJ5EIsHgwYP5lfUIIbrhjTfewPHjx5GYmIjOnTsjPT0dcrkcy5cv15g1oq61bt0affv2haGhodaeg9RMr169qDgWIVtbWyqO64DOFMgAsHLlSsyaNQuRkZH8LAi2trbw8vJCs2bN6iWGuXPnYvz48ejevTvc3NywefNm5OTkYMKECQCAcePGoWXLlli/fj0A4OOPP4aHhwe+/PJLDB48GD/88ANiY2MRGBhYL/HWh2XLluHnn39+ZUsyx3HgOA5Lly6tx+gIIdXRpk0bGBkZIT09HTKZTKvFcbdu3eDq6kpf1Dqobdu2/JVaIh4WFhbo168fvefqgE4VyEBxN4fRo0cL9vyjRo3C8+fPsXz5ciQlJaFLly4IDw/nLw0mJiZqTJHy5ptvIjg4GEuXLsXixYvRvn17HD16VDRzIAPFLU+HDx/mV9IrryW5ZCWtH3/8EW+88YYAURJCdIVEIoGnp6doxo2IjYWFBbUwipBEIoGXlxd1y6sjOlcgh4WF4dSpU4iPjwdQ3Mncx8cHvr6+9RbDrFmzKuxSERUVVWbfyJEjMXLkSC1HJazhw4fj0qVLGn0Y9fT0oFarwXEcBg8ejKVLl1JxTEgjJ5VK4e3t3ahWnGtI5HI5Bg0aREWUCHXr1g0WFhZChyEaOlMgp6enY9iwYYiOjoZEIuE/XH/55Rd899136N27N44ePaqxPCipX6X7MJ49exaZmZkwNTVFv379qM8xIQTGxsbw9vampYl1lEQiwcCBA2FiYiJ0KKSOmZmZ4fXXXxc6DFHRmQL5448/xoULF7Bx40bMmDEDRkZGAICcnBxs374dixYtwscff4ygoCCBIyVt2rRBQECA0GEQQnRI69at4enpqZUlqkndePPNNxvlTCKNQY8ePep0OjeiQwXy0aNHMXPmTHz22Wca+42MjDBv3jwkJiZi3759AkVHCCGkPFKpFD179oSjoyP1adVhdnZ26Nixo9BhkDqgUCigVqv5bjLNmjWDra2twFGJj84UyAYGBnB0dKzw/o4dO1KfKUII0SF2dnZwd3fnr/gR3WRgYAB3d3f6ASMSsbGxePbsGU6cOAEA6Ny5M+VWC/RefUj98PPzw5EjR8qdIUGlUuHHH38U/UA4QghpCGQyGfr164cBAwZQcdwAdOrUifIkUnK5HPb29kKHIUo604Ls7++PWbNm4c0338TUqVPh4OAAAHj48CECAwNRWFiIsWPH4saNGxqP69atmxDhEkJIo9S6dWt4eHigSZMmQodCKqBQKKBSqSCTycBxHDp16iR0SERL7O3tNaaeJXVHZwpkDw8P/u/Xrl3jLxeUXpii9DGMMXAcV6XV3QghhNSOvr4+evXqhY4dO9LlXB0XGxuLu3fv4sKFC1AoFNR6LGKtW7cWOgTR0pkCec+ePUKHQAghpBxmZmYYMGCAVlfdI9pBU3CKm0KhEDoE0dKZAnn8+PFCh0AIIeQlLVu2hJeXF2QymdChkBpo0aKF0CEQLTEyMqL3pRbpTIFcWnZ2Nv7++28AxZcPjI2NBY6IEEIanw4dOqBPnz7Ux7GBkkqlaNasmdBhEC0xMzMTOgRR06lPvWvXrqFv376wsLCAi4sLXFxcYGFhgX79+iE2Nlbo8AghpNF47bXX4OHhQcVxA6ZQKKi/uIhR46F26UwL8tWrV+Hp6QmpVIrJkyfDyckJAHD37l0cOnQIffr0QVRUFNzc3ASOlBBCxM3JyQk9e/ak4qqBo1XzxI0GX2qXzhTIS5YsQcuWLXHx4sUync5XrlwJd3d3LFmyBJGRkQJFSAgh4teqVStaVEIkqHuFuNFUi9qlM9fOrl69imnTppU7ItPGxgZTp07FlStXBIiMEEIaB2NjY/Tr14+6VYiEhYWF0CEQLaICWbt05lNQT08PKpWqwvuLioroQ5sQQrTI09MThoaGQodB6oCenh5dghc5KpC1S2cqzjfffBPbtm1DQkJCmfsSExOxfft2uLu7CxAZIYSIn6OjI00JJiKGhobUTUbkqEDWLp3pg7xu3Tr07t0bHTt2xLBhw9ChQwcAwP3793Hs2DHo6+tj/fr1AkdJCCHiI5PJ0KNHD6HDIHWI5scVPyqQtUtnCuSuXbsiJiYGS5YswfHjx5Gbmwug+D/AoEGDsGbNGjg7OwscJSGENHwKhQKFhYWQy+UAAFdXV+paITL6+jrz9U60wMDAABKJROgwRE0n3kEFBQWIiIiAnZ0dQkNDoVar8fz5cwCAlZUV9T0mhJA6FBsbi8uXL+PWrVswMTHhp9Uk4kHFk7jRFQLt04nKUyqVYuTIkbh06RKA4sEFNjY2sLGxqdfiODU1FWPHjoWpqSnMzc0xadIkZGdnV3r8Rx99BEdHR8jlcrRp0wazZ89GRkZGvcVMCCG14eLiQsWUCFHDkrgZGBgIHYLo6cQ7iOM4tG/fHi9evBA0jrFjx+L27duIjIxEWFgYoqOjMXXq1AqPf/r0KZ4+fYpNmzYhLi4Oe/fuRXh4OCZNmlSPURNCSM1wHAcHBwehwyBaQAWyuNGPWu3jGGNM6CAAIDg4GHPnzsX58+fh6OhY789/9+5dODs749q1a+jevTsAIDw8HD4+Pvjnn3+qPLr7yJEj8Pf3R05OTpX7gN24cQOurq64fv06unXrVuPXQAghVXX58mU8ffoUfn5+QodCtCA3N5cGcYmYWq2mH0FaphN9kAHgypUraNq0KVxcXODp6Qk7Ozt+AEkJjuOwZcsWrTz/5cuXYW5uzhfHAODl5QU9PT1cvXoVw4YNq9J5MjIyYGpqWmlxXFBQgIKCAv52Zd04CCFEW6ysrIQOgWgJtTCKG03hp306UyB/8803/N/PnDlT7jHaLJCTkpJgbW2tsU9fXx+WlpZISkqq0jlevHiB1atXV9otAwDWr1+PVatW1ThWQgipC5aWlkKHQLSEWhcJqR2deQep1epXbkVFRdU+78KFC8FxXKXbvXv3ah1/ZmYmBg8eDGdnZ6xcubLSYxctWoSMjAx+O3/+fK2fnxBCqosKZPGiFkZxo/xqn860IGvLp59+ioCAgEqPadu2LRQKBVJSUjT2q1QqpKamQqFQVPr4rKwsDBo0CCYmJggNDX3l6FKZTKYxRYuxsXHlL4IQQrSgadOmQodAtIQKKHFjjFGOtUznCuS4uDicOnUK8fHxAAA7Ozu8/fbbeO2112p0Pisrqyr1s+vVqxfS09Nx/fp1uLq6AgDOnj0LtVpd6QpTmZmZ8Pb2hkwmw/Hjx2myfUJIg2BkZERzqYqYjoy/J6TB0pkCuaCgANOmTcP+/fvBGOP7T6nVaixatAhjx47Frl27IJVKtfL8Tk5OGDRoEKZMmYIdO3ZAqVRi1qxZeP/99/kZLJ48eYL+/ftj3759cHNzQ2ZmJgYOHIjc3FwcOHAAmZmZyMzMBFBcmNMgCUKIrjI1NRU6BKJF1LpISO3oTB/kBQsWYN++fZgxYwbu3r2L/Px8FBQU4O7du5g+fToOHDiA+fPnazWGgwcPomPHjujfvz98fHzw1ltvITAwkL9fqVTi/v37/DLYN27cwNWrV3Hr1i04ODigefPm/Pb3339rNVZCCKkNmgJM3KiBRtzoCoH26cw8yM2aNcPgwYMRFBRU7v0ffPABfv75Z8EXE9EGmgeZEFLf0tPTYW5uLnQYhJAaKCoqoh9BWqYzLchKpRI9e/as8P4333wTKpWqHiMihBDxoi9XQgipmM4UyN7e3oiIiKjw/vDwcAwcOLAeIyKEEPGiPqqENFz0/tU+nRmkt3r1arz33nsYPnw4PvzwQzg4OAAAHj58iG3btiEhIQGHDx9GamqqxuNoHk9CCKk++oIlpOGihWC0T2cKZCcnJwDArVu3cOzYMY37SrpJOzs7l3lcTRYPIYSQxo4KZEIIqZjOFMjLly+nD2xCCCGEECI4nSmQX7U8MyGEkLpDDRKEEFIx6sRCCCGNEBXIhBBSMSqQCSGkEaICmRBCKkYFMiGENEJUIBNCSMWoQCaEkEbIwMBA6BAIIURnUYFMCCGNELUgE0JIxahAJoQQQgghpBQqkAkhhBBCCCmFCmRCCCGEEEJKoQKZEEIIIYSQUqhAJoQQQgghpBQqkAkhhBBCCClFX+gASMP17NkzPHv2TOgwCCGEkEalefPmaN68udBhiBoVyDqgefPmWLFiRYP6z15QUIDRo0fj/PnzQodCCCGENCoeHh6IiIiATCYTOhTR4hhjTOggSMOTmZkJMzMznD9/HsbGxkKHQ+pYdnY2PDw8KL8iRfkVN8qvuJXkNyMjA6ampkKHI1pUIJMaKSmQ6Q0qTpRfcaP8ihvlV9wov/WDBukRQgghhBBSChXIhBBCCCGElEIFMqkRmUyGFStW0AABkaL8ihvlV9wov+JG+a0f1AeZEEIIIYSQUqgFmRBCCCGEkFKoQCaEEEIIIaQUKpAJIYQQQggphQpkQgghhBBCSqECmZAGiOO4Km1RUVG1fq7c3FysXLmyWudau3Yt3n33XdjY2IDjOKxcubLWcTQ2upzje/fuYf78+ejSpQtMTEzQvHlzDB48GLGxsbWOpbHQ5fw+ffoU/v7+cHR0hImJCczNzeHm5oagoCDQuP6q0eX8vuzgwYPgOI5WXXyJvtABEEKqb//+/Rq39+3bh8jIyDL7nZycav1cubm5WLVqFQDA09OzSo9ZunQpFAoFunbtioiIiFrH0Bjpco537dqF77//Hn5+fpg5cyYyMjLw3XffoWfPnggPD4eXl1etYxI7Xc7vixcv8M8//2DEiBFo06YNlEolIiMjERAQgPv372PdunW1jknsdDm/pWVnZ2P+/PkwMjKqdRyiwwghDd6HH37ItPV2fv78OQPAVqxYUeXHPH78uMaPJeXTpRzHxsayrKwsjX0vXrxgVlZWzN3dXQsRip8u5bcivr6+zMjIiKlUqroJrBHR1fwuWLCAOTo6srFjxzIjI6O6D64Boy4WhIiUWq3G5s2b0alTJxgaGsLGxgbTpk1DWlqaxnGxsbHw9vZGs2bNIJfLYW9vj4kTJwIA4uPjYWVlBQBYtWoVf1nwVV0m7OzstPGSyEuEyrGrq2uZy7FNmzZF7969cffu3bp9kY2YkO/h8tjZ2SE3NxeFhYW1fm1E+Pw+fPgQX3/9Nb766ivo61OHgpfRvwghIjVt2jTs3bsXEyZMwOzZs/H48WN88803+O233/Drr7/CwMAAKSkpGDhwIKysrLBw4UKYm5sjPj4eISEhAAArKyt8++23mDFjBoYNG4bhw4cDAF5//XUhXxr5/3Qtx0lJSWjWrFmdvsbGTOj85uXlIScnB9nZ2Th//jz27NmDXr16QS6Xa/V1NxZC53fOnDno27cvfHx88OOPP2r1tTZIQjdhE0Jq7+XLdxcuXGAA2MGDBzWOCw8P19gfGhrKALBr165VeO7aXL6jLhZ1R1dzXCI6OppxHMeWLVtW43M0ZrqY3/Xr1zMA/Na/f3+WmJhYrXOQYrqW37CwMKavr89u377NGGNs/Pjx1MXiJdTFghAROnLkCMzMzDBgwAC8ePGC30oujZ87dw4AYG5uDgAICwuDUqkUMGJSXbqU45SUFIwZMwb29vaYP3++Vp6jsdGF/I4ePRqRkZEIDg7GmDFjABS3KpPaEzK/hYWF+OSTTzB9+nQ4OzvXyTnFiApkQkTo4cOHyMjIgLW1NaysrDS27OxspKSkAAA8PDzg5+eHVatWoVmzZhgyZAj27NmDgoICgV8BeRVdyXFOTg58fX2RlZWFY8eO0VRRdUQX8mtrawsvLy+MHj0aBw8eRNu2beHl5UVFch0QMr9ff/01Xrx4wc98QcpHfZAJESG1Wg1ra2scPHiw3PtLBnVwHIf/+7//w5UrV3DixAlERERg4sSJ+PLLL3HlyhUqdnSYLuS4sLAQw4cPxx9//IGIiAi4uLjU+FxEky7k92UjRozAzp07ER0dDW9v7zo7b2MkVH4zMjKwZs0azJw5E5mZmcjMzARQPN0bYwzx8fFo0qQJrK2ta/cCxUDoPh6EkNp7uX/bzJkzmUQiYbm5udU+18GDBxkAtnPnTsZY8fRdoD7IgtO1HBcVFbFRo0YxiUTCfvrpp2rHQDTpWn7Lc/ToUQaAHT58uFbnaYx0Jb+PHz/W6Fde3jZkyJBqxyRG1MWCEBF67733UFRUhNWrV5e5T6VSIT09HQCQlpZWZmWsLl26AAB/Ca9JkyYAwD+G6Aahc/zRRx/h8OHD2L59Oz9yntQdIfP7/Pnzcvd///334DgO3bp1q9J5SMWEyq+1tTVCQ0PLbH379oWhoSFCQ0OxaNGimr8wEaEuFoSIkIeHB6ZNm4b169fj5s2bGDhwIAwMDPDw4UMcOXIEW7ZswYgRIxAUFITt27dj2LBhaNeuHbKysrBz506YmprCx8cHACCXy+Hs7IzDhw+jQ4cOsLS0hIuLS6WX0/fv34+EhATk5uYCAKKjo7FmzRoAwAcffABbW1vt/yOInJA53rx5M7Zv345evXqhSZMmOHDggMb9w4YNo5W5aknI/K5duxa//vorBg0ahDZt2iA1NRU//fQTrl27ho8++ggODg71+U8hSkLlt0mTJhg6dGiZ/UePHkVMTEy59zVawjZgE0LqQkWrNAUGBjJXV1cml8uZiYkJe+2119j8+fPZ06dPGWOM3bhxg40ePZq1adOGyWQyZm1tzXx9fVlsbKzGeS5dusRcXV2ZVCqt0qU8Dw+PCi/fnTt3rq5edqOiSzkeP358pZdoS1ZSJFWnS/k9ffo08/X1ZS1atGAGBgbMxMSEubu7sz179jC1Wl2nr7ux0KX8loemeSuLY+yltntCCCGEEEIaMeqDTAghhBBCSClUIBNCCCGEEFIKFciEEEIIIYSUQgUyIYQQQgghpVCBTAghhBBCSClUIBNCCCGEEFIKFciENELx8fHgOA579+4VOhSiBZRfcaP8ihvlVzdQgUwIIYQQQkgptFAIIY0QYwwFBQUwMDCARCIROhxSxyi/4kb5FTfKr26gApkQQgghhJBSqIsFIQ3UypUrwXEcHjx4AH9/f5iZmcHKygrLli0DYwx///03hgwZAlNTUygUCnz55Zf8Y8vr4xYQEABjY2M8efIEQ4cOhbGxMaysrPDZZ5+hqKiIPy4qKgocxyEqKkojnvLOmZSUhAkTJqBVq1aQyWRo3rw5hgwZgvj4eC39q4gH5VfcKL/iRvlt+KhAJqSBGzVqFNRqNTZs2IAePXpgzZo12Lx5MwYMGICWLVti48aNcHBwwGeffYbo6OhKz1VUVARvb280bdoUmzZtgoeHB7788ksEBgbWKDY/Pz+EhoZiwoQJ2L59O2bPno2srCwkJibW6HyNEeVX3Ci/4kb5bcAYIaRBWrFiBQPApk6dyu9TqVSsVatWjOM4tmHDBn5/Wloak8vlbPz48Ywxxh4/fswAsD179vDHjB8/ngFg//nPfzSep2vXrszV1ZW/fe7cOQaAnTt3TuO4l8+ZlpbGALAvvviibl5wI0P5FTfKr7hRfhs+akEmpIGbPHky/3eJRILu3buDMYZJkybx+83NzeHo6Ii//vrrleebPn26xu3evXtX6XEvk8vlkEqliIqKQlpaWrUfT4pRfsWN8itulN+GiwpkQhq4Nm3aaNw2MzODoaEhmjVrVmb/qz4IDQ0NYWVlpbHPwsKiRh+gMpkMGzduxM8//wwbGxv06dMHn3/+OZKSkqp9rsaM8itulF9xo/w2XFQgE9LAlTcNUEVTA7FXTFpTlSmFOI4rd3/pgSIl5syZgwcPHmD9+vUwNDTEsmXL4OTkhN9+++2Vz0OKUX7FjfIrbpTfhosKZEJItVhYWAAA0tPTNfYnJCSUe3y7du3w6aef4vTp04iLi0NhYaHGiG2iWyi/4kb5FTfKb92hApkQUi22traQSCRlRlxv375d43Zubi7y8/M19rVr1w4mJiYoKCjQepykZii/4kb5FTfKb93RFzoAQkjDYmZmhpEjR2Lr1q3gOA7t2rVDWFgYUlJSNI578OAB+vfvj/feew/Ozs7Q19dHaGgokpOT8f777wsUPXkVyq+4UX7FjfJbd6hAJoRU29atW6FUKrFjxw7IZDK89957+OKLL+Di4sIf07p1a4wePRpnzpzB/v37oa+vj44dO+LHH3+En5+fgNGTV6H8ihvlV9wov3WDlpomhBBCCCGkFOqDTAghhBBCSClUIBNCCCGEEFIKFciEEEIIIYSUQgUyIYQQQgghpVCBTAghhBBCSClUIBNCtCo+Ph4cx2Hv3r1Ch0K0gPIrbpRfcaP8VowKZEJ0yJ9//olp06ahbdu2MDQ0hKmpKdzd3bFlyxbk5eVp7Xnv3LmDlStXIj4+XmvPURVr167Fu+++CxsbG3Ach5UrVwoaT12j/FJ+tYHyWz8ov+LO78tooRBCdMTJkycxcuRIyGQyjBs3Di4uLigsLMTFixcxb9483L59G4GBgVp57jt37mDVqlXw9PSEnZ2dVp6jKpYuXQqFQoGuXbsiIiJCsDi0gfJL+aX8NlyUX3HntzxUIBOiAx4/foz3338ftra2OHv2LJo3b87f9+GHH+LRo0c4efKkgBH+D2MM+fn5kMvldX7ux48fw87ODi9evICVlVWdn18olN9ilF/hUX6rj/JbTKz5rQh1sSBEB3z++efIzs7G999/r/HhW8LBwQEff/wxf1ulUmH16tVo164dZDIZ7OzssHjxYhQUFGg8zs7ODr6+vrh48SLc3NxgaGiItm3bYt++ffwxe/fuxciRIwEAffv2Bcdx4DgOUVFRGueIiIhA9+7dIZfL8d133wEA/vrrL4wcORKWlpZo0qQJevbsWasvCiFbR7SJ8vu/eMWI8vu/eMWI8vu/eBsVRggRXMuWLVnbtm2rfPz48eMZADZixAi2bds2Nm7cOAaADR06VOM4W1tb5ujoyGxsbNjixYvZN998w7p168Y4jmNxcXGMMcb+/PNPNnv2bAaALV68mO3fv5/t37+fJSUl8edwcHBgFhYWbOHChWzHjh3s3LlzLCkpidnY2DATExO2ZMkS9tVXX7HOnTszPT09FhISwsfw+PFjBoDt2bOnyq/v+fPnDABbsWJFlR+jyyi/mii/lN+GhPKrSWz5rQgVyIQILCMjgwFgQ4YMqdLxN2/eZADY5MmTNfZ/9tlnDAA7e/Ysv8/W1pYBYNHR0fy+lJQUJpPJ2KeffsrvO3LkCAPAzp07V+b5Ss4RHh6usX/OnDkMALtw4QK/Lysri9nb2zM7OztWVFTEGKMPYMpvWZRfym9DQfktS0z5rQx1sSBEYJmZmQAAExOTKh1/6tQpAMDcuXM19n/66acAUOYSmrOzM3r37s3ftrKygqOjI/76668qx2hvbw9vb+8ycbi5ueGtt97i9xkbG2Pq1KmIj4/HnTt3qnx+MaP8ihvlV9wov40XFciECMzU1BQAkJWVVaXjExISoKenBwcHB439CoUC5ubmSEhI0Njfpk2bMuewsLBAWlpalWO0t7cvNw5HR8cy+52cnPj7CeVX7Ci/4kb5bbyoQCZEYKampmjRogXi4uKq9TiO46p0nEQiKXc/Y6zKz6WNEdGNBeVX3Ci/4kb5bbyoQCZEB/j6+uLPP//E5cuXX3msra0t1Go1Hj58qLE/OTkZ6enpsLW1rfbzV/XD/OU47t+/X2b/vXv3+PtJMcqvuFF+xY3y2zhRgUyIDpg/fz6MjIwwefJkJCcnl7n/zz//xJYtWwAAPj4+AIDNmzdrHPPVV18BAAYPHlzt5zcyMgIApKenV/kxPj4+iImJ0fjSyMnJQWBgIOzs7ODs7FztOMSK8itulF9xo/w2TrRQCCE6oF27dggODsaoUaPg5OSksVLTpUuXcOTIEQQEBAAAOnfujPHjxyMwMBDp6enw8PBATEwMgoKCMHToUPTt27faz9+lSxdIJBJs3LgRGRkZkMlk6NevH6ytrSt8zMKFC3Ho0CG8/fbbmD17NiwtLREUFITHjx/jp59+gp5e9X9/79+/HwkJCcjNzQUAREdHY82aNQCADz74oMG2elB+i1F+Kb8NEeW3mFjzWyFhJ9EghJT24MEDNmXKFGZnZ8ekUikzMTFh7u7ubOvWrSw/P58/TqlUslWrVjF7e3tmYGDAWrduzRYtWqRxDGPFUwANHjy4zPN4eHgwDw8PjX07d+5kbdu2ZRKJRGNKoYrOwVjxHJ0jRoxg5ubmzNDQkLm5ubGwsDCNY6ozjZCHhwcDUO5W3hRHDQ3ll/LLGOW3oaL8iju/L+MYq0ZPcEIIIYQQQkSO+iATQgghhBBSChXIhBBCCCGElEIFMiGEEEIIIaVQgUwIIYQQQkgpVCATQgghhBBSChXIhBBCCCGElEIFMiGEEEIIIaVQgUwIIYQQQkgpVCATQgghhBBSChXIhBBCCCGElEIFMiGEEEIIIaVQgUwIIYQQQkgpVCATQgghhBBSyv8DZwOv3tw20uQAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "repeated_measures_baseline.mean_diff.plot();" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e74c1dfe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:29 2025.\n", - "\n", - "Paired effect size(s) for the sequential design of repeated-measures experiment \n", - "with 95% confidence intervals will be computed for:\n", - "1. Test 1 minus Control 1\n", - "2. Test 2 minus Test 1\n", - "3. Test 3 minus Test 2\n", - "4. Test 4 minus Test 3\n", - "\n", - "5000 resamples will be used to generate the effect size bootstraps." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "repeated_measures_sequential = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\", \"Test 4\"),\n", - " proportional=True, paired=\"sequential\", id_col=\"ID\")\n", - "repeated_measures_sequential" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6fd66091", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:30 2025.\n", - "\n", - "The paired mean difference for the sequential design of repeated-measures experiment \n", - "between Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "The paired mean difference for the sequential design of repeated-measures experiment \n", - "between Test 1 and Test 2 is -0.55 [95%CI -0.725, -0.4].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "The paired mean difference for the sequential design of repeated-measures experiment \n", - "between Test 2 and Test 3 is 0.1 [95%CI -0.075, 0.225].\n", - "The p-value of the two-sided permutation t-test is 0.342, calculated for legacy purposes only. \n", - "\n", - "The paired mean difference for the sequential design of repeated-measures experiment \n", - "between Test 3 and Test 4 is 0.025 [95%CI -0.2, 0.2].\n", - "The p-value of the two-sided permutation t-test is 0.624, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "repeated_measures_sequential.mean_diff" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3dfcf13c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "repeated_measures_sequential.mean_diff.plot();" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "73e4aaf1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:30 2025.\n", - "\n", - "Paired effect size(s) for repeated measures against baseline \n", - "with 95% confidence intervals will be computed for:\n", - "1. Test 1 minus Control 1\n", - "2. Test 2 minus Control 2\n", - "3. Test 3 minus Control 2\n", - "4. Test 4 minus Control 2\n", - "\n", - "5000 resamples will be used to generate the effect size bootstraps." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "multi_groups_baseline = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\", \"Test 3\", \"Test 4\")),\n", - " proportional=True, paired=\"baseline\", id_col=\"ID\")\n", - "multi_groups_baseline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "664ad256", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:31 2025.\n", - "\n", - "The paired mean difference for repeated measures against baseline \n", - "between Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "The paired mean difference for repeated measures against baseline \n", - "between Control 2 and Test 2 is 0.025 [95%CI -0.15, 0.175].\n", - "The p-value of the two-sided permutation t-test is 0.571, calculated for legacy purposes only. \n", - "\n", - "The paired mean difference for repeated measures against baseline \n", - "between Control 2 and Test 3 is 0.125 [95%CI -0.075, 0.3].\n", - "The p-value of the two-sided permutation t-test is 0.309, calculated for legacy purposes only. \n", - "\n", - "The paired mean difference for repeated measures against baseline \n", - "between Control 2 and Test 4 is 0.15 [95%CI -0.025, 0.3].\n", - "The p-value of the two-sided permutation t-test is 0.0362, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "multi_groups_baseline.mean_diff" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "716f3bae", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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IiIiIGjZFdkW79957cfLkScyZMwfffPONawYrURRdM5SNHj1a5ijJX0mShNzcXOTm5sJgMCAyMhLh4eHQarWVnlN64lg9RlixK62rFBAQAEmSXP8lIiIiUhJFJjYAMHPmTNx7771YuXIlTp06BaBsgc4hQ4agRYsWMkdHDUVpaSnOnz+P8+fPIzg42LXop0bzv1vLnpUJR0F+9QoUVNBGREATHglVQACEOk47bSssgiAI1UpUxo0bV+PyBUGATqerTWhEREREHqXYxAYoS2T+85//yB0GEQCgsLAQhYWFOHPmDAIDAxEcHIygoCCIp05U63xtdCwMzVtCZTB4LKaooGA89thjtR4XdCU6nQ4RERFeKZuIiIioJhSd2BD5KrPZDLPZDEdBAZyZaTBptTDpdDBqNTBotdBdNruaoNbA2KYdtFHRXomFiQcRERE1BIpMbFQqFQRBuOJxnlx/hKimJEmCIzcbkijBbLXBbP1fq4lKJUCv0cCgNyCobXuUSoAmNxcajQZqtbrav+NXolarIeVkQ7KW1rmsigh6A3SxcV4pm4iIiKgmFJnYzJgxo9yXPqfTidTUVKxatQqtW7fG4MGDZYqOqIxoLoRUSRcwUZRQKgJSZDRKCwqAggKvxBAOCc63XvVK2Zc0SVnA5IaIiIhkp8jEZtasWZXuS0tLw7XXXotWrVrVX0BEFXDk5Fa+U6WCrnECBG8PvK/BujlDv9uArJJSRBkNWHnrwGqf563WICIiIqKaUOQ6NlWJi4vD+PHjMWfOHLlDoQbMWWyGWMUXfl1cPFR6fT1GBEAQqnxllZQiw1KCrJLSKx4LD3STIyIiIvIkv0tsgLL1OE6fPi13GNSAOfIqb63RhEdAHRhYj9EQERER+T+/S2wOHDiAN954g13RSDai1QqxuLjCfSqDEZrIqHqOiIiIiMj/KXKMTbNmzSqcMSo/Px8FBQUwmUxYtWpV/QdGBMBRkFfxDkGANjbOI7OdEREREZE7RSY2ffr0KfflUBAEhIWFoUWLFrjrrrsQHh4uU3TUkElOJ5yVzHCmCY+o/3E1RERERA2EIhObpUuXyh0CUYWcRUWAKJbbLmi10EREyhARERERUcPgV2NsbDYbiisZ20BUH5yVdEPTRkWzCxoRERGRFykysfniiy8wceJEt22zZ89GYGAgQkNDMXToUJjNZpmio4ZKtFohlpaf4lllNEEdFCxDREREREQNhyITm1dffdWtZea3337D7NmzMXDgQEycOBHr16/HCy+8IGOE1BBVNmmAJoqzoBERERF5myLH2Jw8eRKjR492vV+2bBliY2OxcuVKaDQaiKKIr7/+GikpKTJGSQ2JJElwFhaV264KCIDaaJIhIiIiIqKGRZEtNlarFQaDwfX+hx9+wE033QSNpixPa9euHc6fPy9XeNQAiWYz4HSU266NUE5rTZTRiBiTEVFGo9yhEBEREdWYIltsmjVrhh9//BEPPfQQdu/ejRMnTrh1PcvIyEAgV3aneuQsLD/FsyogACoFJQkrbxsodwhEREREtabIxGbcuHF48skncejQIZw/fx6NGzfG4MGDXfu3bduG9u3byxghNSSS0wlncfnJKjThETJEQ0RERNQwKTKxefzxx2EwGLBu3TokJSXh2WefhfHvJ+O5ublIT0/H+PHjZY6SGgpnUREgSW7bVAYj1KYAmSIiIiIiangUmdgAwNixYzF27Nhy28PDw7F7924ZIqKGyllUWG6bJjxchkiIiIiIGi5FTh7gbW+//TYSExNhMBjQrVs37Nq1q8rj8/Pz8dhjjyEuLg56vR6tWrXCunXr6ilakpPkcEC0WNy2CRotVIFBMkVERERE1DAptsXGW5YvX45JkyZh0aJF6NatGxYuXIiBAwfi6NGjiI6OLne8zWbDjTfeiOjoaKxYsQLx8fE4c+YMQkND6z94qndOcxEA925omtBQCIIgT0BEREREDRQTm39YsGABxo4dizFjxgAAFi1ahLVr1+LDDz/E5MmTyx3/4YcfIjc3F7/99hu0Wi0AIDExsT5DJhmV64YmCFCHhMoSCxEREVFDxq5ol7HZbNizZw/69+/v2qZSqdC/f39s3769wnNWr16N7t2747HHHkNMTAw6dOiAefPmwel0Vnodq9WKwsJC18tsLj+jFvm+sm5oJW7b1EFBEDR8XkBERERU3xSR2KxevRoXL170+nWys7PhdDoRExPjtj0mJgbp6ekVnnPq1CmsWLECTqcT69atw3PPPYdXX30Vc+fOrfQ6KSkpCAkJcb369Onj0Z+D6ofTbMY/u6GxtYaIiIhIHopIbIYOHYrNmze73jdv3hyrV6+WL6DLiKKI6OhoLF68GElJSRgxYgSmTZuGRYsWVXrOlClTUFBQ4Hpt2bKlHiMmTykbX/M/glbHKZ6JiIiIZKKIPjNBQUHIz893vU9NTfVK963IyEio1WpkZGS4bc/IyEBsbGyF58TFxUGr1UKtVru2tW3bFunp6bDZbNDpdOXO0ev10Ov1rveBgYEe+gmovkhOJ0RLsds2DVtriIiIiGSjiMQmOTkZL7zwAjIyMhASEgIAWLduXaXdwwBAEARMnDixRtfR6XRISkrCpk2bMGTIEABlLTKbNm3ChAkTKjynZ8+eWLZsGURRhEpV1gB27NgxxMXFVZjUkH8QLeZ/LMopQP337yYRERER1T9FJDbvvPMORo0ahTlz5gAoS1qWLVuGZcuWVXpObRIbAJg0aRJGjx6Nrl27Ijk5GQsXLkRxcbFrlrRRo0YhPj4eKSkpAIBHHnkEb731Fp588kk8/vjjOH78OObNm4cnnniiFj8pKYXzHy2GqgATJw0gIiIikpEivom1bNkSv/32G0pLS5GZmYnExEQsXLgQt99+u8evNWLECGRlZWHGjBlIT09H586dsX79eteEAmfPnnW1zABAQkICNmzYgIkTJ6Jjx46Ij4/Hk08+iWeffdbjsZFvkCQJzmL3bmjqILbWEBEREclJEYnNJQaDAU2aNMHMmTNx/fXXo2nTpl65zoQJEyrtenb5JAaXdO/eHTt27PBKLOR7xJIS4PLpvFUqqIOC5AuIiIiIiJSV2Fwyc+ZM17/NZjPOnTsHoKz1hAPxydvEf3RDUwcGQVApYoJBIiIiIr+l2G9jv//+O/r164ewsDB06NABHTp0QFhYGK6//nrs3r1b7vDIjzmL/5HYBAXLFAkRERERXaLIFpudO3eib9++0Ol0eOihh9C2bVsAwOHDh/H555+jd+/e2Lx5M5KTk2WOlPyNZLNBsln/t0GthiqAa9cQERERyU2Ric20adMQHx+PX3/9tdz6MrNmzULPnj0xbdo0bNy4UaYIyV85/7F2jTowCIIgyBQNEREREV2iyK5oO3fuxLhx4ypcNDMmJgYPP/wwB/OTV5SfDY2TBhARERH5AkUmNiqVCg6Ho9L9TqfTbUpmIk+QJAni5S02ajVUJnZDIyIiIvIFivz236NHD7z99ts4c+ZMuX1nz57FO++8g549e8oQGfkzscQCiKLrvTogkN3QiIiIiHyEIsfYzJs3D71790abNm0wdOhQtGrVCgBw9OhRfPvtt9BoNEhJSZE5SvI34j+7oQWyGxoRERGRr1BkYtOlSxfs3LkT06ZNw+rVq2GxWAAAJpMJgwYNwty5c9GuXTuZoyR/45bYqFScDY2IiIjIhygysQGAdu3aYeXKlRBFEVlZWQCAqKgojq0hr5AcDojWUtd7tSmAi3ISEZFPUalUdfoeJAgC1Gp1rbtZ17V7dr1175YkSHY7JNEJSHUrih3SfYtiE5tLVCoVYmJi5A6D/JxY4t4NTRUYKFMkREREFRNFEeJlY0Frw+l0QqPRwGg0wmQyISgoCEFBQYp+cOwsNsORmwNnQT6cRUVuDyrrKj8wxGNlUd0pPrEhqg/OYstl7wSoA5jYEBGRf3I4HCgqKkJRUREyMjIgCAJCQkIQERGBkJAQRUycI9pssKddhC0jzX1GU/JrTGyIquHyD0WV0QhBw1uHiIgaBkmSkJ+fj/z8fGi1WkRHRyMqKgpqtVru0MoRrVZYz6bCfvECJKlurVekPPx2RnQFos0GyW53vWdrDRERNVR2ux0XLlxARkYGYmNjER0d7RMtOJLTCevZVNjOnS0bO0MNEhMboisQLRa39xxfQ0REDZ3D4cD58+eRlZWFpk2bIihIviUQHHm5KDl6BGKp5coHk19jYkN0BZdPHCBotVDp9TJGQ0RE5DusViuOHTuGyMhIJCQk1OskA5IownrqJKznyy/YTg2TohObQ4cO4dSpU8jLy4MklZ+vb9SoUTJERf7m8hYblYlr1xAREf1TdnY2ioqK0Lx5c5hMJq9fT7SWwnJgP5xFBV6/FimHIhObkydP4t5778WuXbsqTGiAsrnQmdhQXYlWKySHw/We42uIiIgqZrVaceTIETRp0gSRkZFeu46jsACW/X9Cstu8dg1SJkUmNuPGjcP+/fuxcOFCXHfddQgLC5M7JPJTYsll/XUFAap6eApFRESkVJIk4cyZMygpKUHjxo09PrGAPSsTJYcOcMYzqpAiE5tt27Zh6tSpePzxx+UOhfycWPyPaZ59cGpLIiIiX5OZmQmr1YrmzZt7bNyN7eIFlBw7AqDi3jpEilxGNjIyEiEhXOmVvEuSJDgvW7+G3dCIiIiqr6CgAMeOHYPTWffpl20XzqPk2GEwqaGqKDKxGT9+PP7v//7PIzcKUWXE0lJA/F9TN7uhERER1UxxcTGOHTsGx2XjVWvKduE8So4f8WBU5K8U2RWtVatWcDqd6NSpEx544AEkJCRUuPrtHXfcIUN05C/Ey1proNZAZTDKFwwREZFCWSwWHDt2DK1atYJGU7OvnraMdCY1VG2KTGxGjBjh+vd//vOfCo8RBIEtOlQnl4+vUQewtYaIiKi2SkpKcPz4cbRq1arCh9EVceTmoOTIIS9HRv5EkYnNzz//LHcI5OckUYRYWuJ6z/VriIiI6sZiseDkyZNo2bLlFScUcJqLYDm4H+DsZ1QDikxs+vTp49Xy3377bbz88stIT09Hp06d8OabbyI5OfmK533xxRcYOXIkbr/9dqxatcqrMZJ3iZZi4LI1ktRMbIiIiOqsqKgIqampaN68eaXHiFYrLH/9CclZ+3E51DApcvKAyx06dAjff/89vv/+exw6VPfmyuXLl2PSpEmYOXMm9u7di06dOmHgwIHIzMys8rzU1FT85z//wXXXXVfnGEh+zsu6oQlaHQStVsZoiIiI/EdeXh7Onz9f4T5JFGE5uB+irbSeoyJ/oNjE5ttvv0WLFi1w9dVXY/DgwRg8eDCuvvpqtGzZEqtXr651uQsWLMDYsWMxZswYtGvXDosWLYLJZMKHH35Y6TlOpxP33HMPZs+eXeUTCFIOsdjs+jdnQyMiIvKsjIwMZGdnl9teevwonIX59R8Q+QVFJjbr1q3DsGHDAADz5s3DypUrsXLlSsybNw+SJOGOO+7A+vXra1yuzWbDnj170L9/f9c2lUqF/v37Y/v27ZWe9/zzzyM6OhoPPvhgta5jtVpRWFjoepnN5iufRPVGtFoh2e2u95w4gIiIyPPOnj3r9h3Iln4RtrQLMkZESqfIMTZz5sxBx44dsXXrVgQE/G/sw2233YYJEyagV69emD17NgYNGlSjcrOzs+F0OhETE+O2PSYmBkeOVDzV4K+//oolS5Zg37591b5OSkoKZs+eXaPYqP64TfMMQGXk+BoiIiJPkyQJp06dQtu2baGyWVF67KjcIZHCKbLF5q+//sLo0aPdkppLAgICcP/99+Ovv/7yehxFRUW477778P777yMyMrLa502ZMgUFBQWu15YtW7wYJdWU8/JuaDo9hBrOuU9ERETVY7fbcfLECVgO7IckcpkOqhtFfmMzGAzIzc2tdH9ubi4MBkONy42MjIRarUZGRobb9oyMDMTGxpY7/uTJk0hNTcWtt97q2ib+vVK9RqPB0aNH0aJFi3Ln6fV66PV61/vAwMAax0reITmdEC0W13uB3dCIiIi8Ku/0KThtpWgcGiJ3KKRwimyxuf766/H6669XOO5l586deOONN9zGyVSXTqdDUlISNm3a5NomiiI2bdqE7t27lzu+TZs22L9/P/bt2+d63XbbbejXrx/27duHhISEGsdA8hItFvdpntkNjYiIyGucZjOc+XnIs5Qg21x85ROIqqDIFpuXXnoJ3bt3R69evZCcnIzWrVsDAI4ePYpdu3YhOjoa8+fPr1XZkyZNwujRo9G1a1ckJydj4cKFKC4uxpgxYwAAo0aNQnx8PFJSUmAwGNChQwe380NDQwGg3HZSBqe56LJ3AmdEIyIi8hLJ4YAtPc31Pq2oCFqNGiG16HWjBO+99x7MZjMCAwMxbtw4ucPxS4pMbJo1a4a//voLKSkp+P7777F8+XIAQNOmTfHkk09i8uTJiI6OrlXZI0aMQFZWFmbMmIH09HR07twZ69evd00ocPbs2SuulkvKJEmS2/o1KqMRglotY0RERET+SZIk2NIuApcvwikB5/MKoIlQIUCnky84LzGbzSgsLJQ7DL+myMQGAKKjo/Haa6/htdde83jZEyZMwIQJEyrct3nz5irPXbp0qcfjofohlpa4fcCytYaIiMg7HLnZ5WYhBQBRknAmNx/NI8Jg4OLYVEOKTWyIPE0scl9PSGXi+BoiIjkJglDp+8r+XdNtVRLFspm6ROnKx1aBX7bcOc1mOCpYnNO1XxRxOjcPzSLCYeDMpFQDivhteeCBByAIAhYvXgy1Wo0HHnjgiucIgoAlS5bUQ3TkL9zG16hUUBmN8gVDROTD1Go1VCoVVCoVNBoNtFottFot9Ho9DAYDDAYDNH9/Ia0qmagqcalvTrMZjoI8OAsLIZrNEEtLIF3eTaoOjMVciPsS0WaDLf3iFY9zOEWczslF84hw6JncUDUp4jflp59+gkqlgiiKUKvV+Omnn6744SfnhyMpj2i1QrLbXO/VpgD+DhERVcLpdMLpLFtzxGq1VniMVqtFQEAAAgICEBgYiIAA3/tcdRQWwJ6RDkdWFkRbqdzhuMydOxcOhwMajQbTp0+XOxyPkZxO2C6cB5zVW6/G4RRxKicXzcLZLY2qRxGJTWpqapXvierKaXYfzKcK4NpCRER1YbfbkZ+fj/z8fACASqVCUFAQgoODERoaCp1Mg8MlUYQ9PQ22C+fcFmT2JQ6HA5IkweHwTIuRL5AkCbaL5yHZKk6EK1OW3OShaXioX04oQJ6liMTmn86ePYuoqCgYK+kqVFJSgqysLDRp0qSeIyOlEouK3N6rAzi+hojIk0RRREFBAQoKCnDu3DmYTCaEh4cjLCysXpIcSRRhS7sA25lUiDX8ck11Z09Pc1sAuyacoojTOXlICA1BiNE/p4Imz1DkvMXNmjXDypUrK92/evVqNGvWrB4jIiUTrVaIl3WlUOn0ENjkTUTkVRaLBefPn8f+/ftx/Phx5OXlQZLqNki/MvbsLJh37UDp8aNMamRgy0yHs7CgTmVIkoSzefnILPLNVjbyDYpssbnSB5/dbudaM1Rt7IZGRCSvwsJCFBYWQqvVIioqClFRUa7JB+pCtJai5NhROHKyPBAl1YY9OwvOvDyPlZdRZIbFbkfj0BBo+F2P/kExiU1hYaGrny4A5OTk4OzZs+WOy8/PxxdffIG4uLh6jI6UzFno3g1NFchuaEREcrDb7bh48SLS09MRERGB2NjYWndTs2Wko/TYEY/NbEY1Z8/OgiOn8mmda6uo1IoTWdloHBqCQL3e4+WTcikmsXnttdfw/PPPAyib8eypp57CU089VeGxkiRh7ty59RgdKZVotboPZFSpoDJyYU4iIjmJooisrCxkZ2cjMjKyRgmO5HSi5NgR2DPSvBwlVcWemQlHXo73yneWjbsJNxkRGxwENVtvCApKbAYMGIDAwEBIkoRnnnkGI0eOxDXXXON2jCAICAgIQFJSErp27SpTpKQk/+zzqw4M9LnpSImIGipJklwJTnR0NOLi4qBWqys9XiyxwHLgL5+d7awhkCQJ9vS0Oo+pqa5cSwkKrVbEBAUizGis1t9wQaWGoNNBUNf9a/DlY3KvNFQiICAAkiS5/kuep5jEpnv37ujevTsAoLi4GMOGDUOHDh1kjoqUTJIkOAs5voaoMoIg1CnRv3wBR51OB61WC6PRCIPBAP0Vuo944gGDNx5SSJIEsdgMZ1ERnJZiiKWlkKxWSA57tdfmqEq+gS3GFZEkCRkZGcjOzkZMTAyio6PLJTiOvFxYDvzFrmcykpxO2C6er/XsZ7XlcIq4kF+IbLMF0UEBCDEY3O5/dUAQNOHhUIeEQh0UBJXeczOrWXNyIAhCtRKVcePG1bh8QRBkmxpdiRST2FxisVjwxhtvwGQyMbGhOhFLLGVfRi4RBKiZ2BC5SJJUp6eKlxZxtNvtKCkpcdun0WgQGBiI4OBghISE+PQfbtFmgyM7C/acbDjz89y+OAt/vzxFxae4VXI6nbh48SIyMzMRHR3tmmTAln4RJUePAJIod4gNllhaAtvFC5Ds9isf7CVWhwPn8gqQrjEjMiQE0c1bIKBxgle7mEdEROCxxx6DzWa78sG1oNPpEBER4ZWy/ZHiEhuTyQSNRoMArjNCdeQscG8mVxmNEKro4kBEnuNwONwWb7y0pkl4eDi0PjDduiSKcORkw5Z2AY7cXABMOHyJw+FwTTIQ7HAgqCAHeg/Moka148jNhT07E/CBxFzQ6oCICOQFhyC/0IzAs+cQEhKC4OBgGKvZVa2mguw2SLZSj5cLAOwdXzOK/BQYNmwYVqxYgUceeYTjIahWJKcTTvM/FuUMDJIpGiKyWCywWCy4cOECQkNDER0djcDA+m9BvdSVxnb+HESrd76okOdYMzKQlpuNNAEI1OkRHmBEsF7v098NqtMKqtFo4HA4oNFofHoshmSzwZZR+4U3PUqthjYiEurQMLf6N5vNMJvNuHDhAgRBcHWF1Wg0VY7Xqi5TaQlyX5hR53Kq0iRlAXSxnO23OhSZ2Nx111149NFH0a9fP4wdOxaJiYkwGo3ljvvn5AJElziLigDRvcsCExsi+UmShLy8POTl5SEgIABxcXEICQnx/nVFEbYL52A9ewaS3TtdSsiz7FmZcOT+PeuWBJitVpitVmjUKoQYDAg1GmCqRRdHlcEEdWAABIOxbMFmjQZQ1S1RCioogPDHX9VOUqZNm1ar6zRp0gRhYWG1Ohcom43O4XDAZrOhpKQEJSUlEMXy3fskpxOO3Bw48nJ9opVGHRwMbVRMWV1VQZIk18/lKdGi98d0SXzIUm2KTGz69u3r+vfWrVvL7ZckCYIgwOmBgZzkn5wF7ouFqQwGt5lNiEh+xcXFOHHiBAICAtC4cWOvteDYszJRevI4xFLPfdkh73JLav7B4RSRU2xBTrEFWrUaQQY9gvV6BOh1UFXQkqPS6aGJjIImLBya0DCv/C2IjW3k1XEYgHfGYkiSBIvF4lpLsLioCM78fNjzcgFfmKRBpYIuNhbqIO8//KiWK7QUDl29AVklJYgyGrHytoFXLs8HkkalUWRi89FHH8kdAimYaC2FWOr+9EMdGCxTNER0JcXFxTh69ChCQ0ORkJDgsYkGuCq9MjlycipNav7J7nQit9iC3GILBEGASatFgF4Lo06PkEbxMDVOKNd1yVuUOAD80jIaersNoYVAUVE+ckrMyJOckHuaBpXeAF2jeAg+PPHIP2WVlCDDwgco3qTIxGb06NFyh0AK5vh7sPLlVEHshkbk6/Lz81FYWIi4uDjExMTU6cuoLf0iSo8f49TACuPIzy8bpF4LkiSh2OGANTAImtBwZNoc0J47D2N2jmvcxaVpyS+Nv1Cr1R5LemzpaV7tUiToDXUehyGJIiS7DWJJCZxmM5yFBXDk5bq6Z+rVKjQKCUZMUCByii3ILrbAWUFXNW9TBwVDGxsHgYty0j8oMrG5nNlsxrlz5wAACQkJsgw2JeWQnM5yi4apDAaoFPTEh6ghE0URFy5cQF5eXqXjK6siORwoOXoY9qwML0VI3uI0m2HPSK/dyYIATWg4NOHhbuMw7HY77HY7Cv+xpln50+uW3KjycxH46ZI6lVGlv7sshd8xAuq6jEmr5nTZapUK0UGBCA8wIbPIjFyLpd56TWnCI6CNiq6fi5HiKDbV/f3339GvXz+EhYWhQ4cO6NChA8LCwnD99ddj9+7dcodHPspZUFB+0oAgdkMjUhqLxYLDhw8jI6P6CYrTXATz7l1MahRILCmBLe0CajPttiogAIbE5tBGR19xcHllLq3pVOtXDcfWXP3pV2j98Re4+tOvahan3VaWnNT2VUMaVVkLTovICBh13h+nqo2OYVJDVVJki83OnTvRt29f6HQ6PPTQQ2jbti0A4PDhw/j888/Ru3dvbN68GcnJyTJHSr5EkiQ4/jFpAMDZ0IiUSpIknD9/HkVFRUhMTISmii+ttox0lB49DEnkpDJKI1qtsF44X+6h1BWp1dBGxUBTD7Pq1Ug1Wn9sohOiVPbfai1kIvMgc6NWixYR4cgyFyPTbPZKONqYOGhCQz1fMPkVRSY206ZNQ3x8PH799VfExsa67Zs1axZ69uyJadOmYePGjTJFSL5INJvLPTVTGYyKGnhIROUVFBTg8OHDaNGiBUym8iuMl54+CeuZ0zJERnUl2WywnT9X4xm4VCYTdHHxtW6hoZoTBAHRQYEI0OtwLq8Adg/OTMukhqpLkV3Rdu7ciXHjxpVLagAgJiYGDz/8MHbs2CFDZOTLKppFRx3CbmhE/sBms+HIkSPIyfnffS6JIiyHD/ptUjN37lzMmjULc+fOlTsUr5DsdljPn4XksNfgLAGayEjoGjdhUiOTAJ0OLaMiEOChh4ba6BgmNVRtikxsVCoVHI7Kn944nU6oOFMGXcZpsZRfo0IQOM0zkR+RJAmpqam4cOECJKcTlv1/wp6RJndYXuNwOMq62Fbx91CpJJsN1nNnINlrkNSoVNDFN4Y2Iqpepm+mymlUKjSLCEOYqWaTe5QrJzwSmrBwD0VFDYEiv/336NEDb7/9Ns6cOVNu39mzZ/HOO++gZ8+eMkRGvsqRm11umyogkE/0iPxQ2oULOPjjD7ByfRpFEq3WGic1glYHfZNEqDkzqs8QBAGNQ0MQE1S7OlEHh0AbFeXhqMjfKfJb3bx589C7d2+0adMGQ4cORatWrQAAR48exbfffguNRoOUlBSZoyRf4bRYIBYXl9uuCfaxAaVEVGeS0wnb+XMoLS2BRaNG07BQGLywkjx5h9Nige3ieaAG4zNUBgN08Ql8UOWjooMCoVapcLGg6im1L6cymqCt45o81DApssWmS5cu2LlzJwYNGoTVq1fj+eefx/PPP4/vvvsOgwYNwo4dO9CpU6dal//2228jMTERBoMB3bp1w65duyo99v3338d1112HsLAwhIWFoX///lUeT/WvwlXF1Rqo+GSPyK9IDgds58+6up3aHE6czM5FTrFF5sioOhyFBbCdP1uzpMZk4ngaBYgIMCE+tHpdvwWtFrpG8X7ZnTDKaESMyYioGq6/RdWn2E+Cdu3aYeXKlRBFEVlZZV9co6Ki6jy2Zvny5Zg0aRIWLVqEbt26YeHChRg4cCCOHj2K6Ojyc6dv3rwZI0eORI8ePWAwGDB//nwMGDAABw8eRHx8fJ1iobpzFpshWsp/qdEEh/jlhyZRQyU57LCdOwfRZnXbLkoSLhYUoshqReOQYGjUapkipMpIkgRHdlaFE7xURRUQUNZSw89yRQj/e8bCC/lVtNz8PU7KXxPVlbcNlDsEv6f43xxBEFwfap74cFuwYAHGjh2LMWPGAAAWLVqEtWvX4sMPP8TkyZPLHf/ZZ5+5vf/ggw/w9ddfY9OmTRg1alSd4/FF7733HsxmMwIDAzFu3Di5w6mUJElwZGZWuK9OKzMTKYxS7tnaEq1W2C6cq3JMRlGpFcdsOWgUEoRQPi31GZLDAVv6xQq7C1dFFRBY9gWYSY2ihJtMECUJaQVFFe7XxsRCpTfUc1TkTxTZFQ0ADh06hOHDhyM4OBhxcXGIi4tDcHAwhg8fjgMHDtSqTJvNhj179qB///6ubSqVCv3798f27durVYbFYoHdbkd4eOWzeFitVhQWFrpeZrO5VvHKxWw2KyJuZ0F+uae3QFnfXZVeL0NERPJQyj1bG05LMaxnU6s10NwpijiXV4DTObmw+uFMYkrjNJthPXO65kmNKYBJjYJFBgQgOiig3HZ1SCjHvlKdKbLFZuvWrbjpppsgiiJuv/12t8kDVq9eje+//x7r16/HddddV6Nys7Oz4XQ6ERMT47Y9JiYGR44cqVYZzz77LBo1auSWHP1TSkoKZs+eXaPYqGYkhwP27IpnRFJzPnwiv+DIz4U9M7PGq66brTYcz8pGREAAogIDoOHyAPVKcjphz8qEsyC/xueqjKYGkdToVGrYRCd0Kv/sOhkTFAS7U0SepWw8nEqvhzam/NqERDWlyMRm4sSJiI6OxpYtW5CQkOC279y5c+jduzcmTZqE33//vV7jevHFF/HFF19g8+bNMBgqb0qdMmUKJk2a5Hq/b98+9OnTpz5CbDDsWRkVDkAVNBqog7h2DZGSSaIIe0Y6nIUFtS9DArLNxcizlCAywISIABPUCktwNBoNHA4HNAoaj+AoKIAjOxNSLVrMBJ2+LKlRWD3Vxv5R/5Y7BK+LDwmG3emE2e6ANs4/Jwug+qecT8PLHDx4EHPmzCmX1ABAQkICHnnkEcyaNavG5UZGRkKtViMjI8Nte0ZGBmJjq36S8Morr+DFF1/Ejz/+iI4dO1Z5rF6vh/6yrlCBnJ3Lo5xFRXAWVjw4UR0ayg9PIgUTrVbYL16osJtpbThFERlFZmQXWxARYES4yQRtNScYEAQVBKMRKp0OgkYLeGBiApVTdP1bukJL1LRp06p97OVCQkKq7C5dHZIkQRRFOJ1OOBwO2O12OKuYzcxZbIYjOwtiaWmtridotNA3ToDAyR/8hiAIaBIWinNaAxzsHk4eosjEpmnTprBaK/+jZrPZKkx6rkSn0yEpKQmbNm3CkCFDAACiKGLTpk2YMGFCpee99NJLeOGFF7BhwwZ07dq1xtclz5HsdtgqW2lcEKAJCavfgIjIYxy5ubBn17zrWXU4RRGZRcXIMhcj2GBAmMmIQJ3O7UGIOjAYmtAwqENCoA4MhGAwevxBSUBODgRBqFGiUhOCICAxMREREREeL1sURdhsNlitVpSWlsJisaAoIx3mtDSIJXWYclutga5xAgSuR+R3DDFxaN3iKhw5cqTKxJiouhSZ2MyYMQMTJ07ELbfcgs6dO7vt++OPP/Dmm29i4cKFtSp70qRJGD16NLp27Yrk5GQsXLgQxcXFrlnSRo0ahfj4eNcCoPPnz8eMGTOwbNkyJCYmIj09HUBZKwxbYuqXJEmwpV2sdA0EdXCI304hSeTPRKsV9oz0un05riZJAgpKSlFQUgqNWoWwyChEJDRBaJOm0FTRxdhTIiIi8Nhjj8Fms3mlfJ1O55WkBiibbMdgMEDrcEBfbIYpPQ3htlKIoUGwmPQottlRbLXBYrdVPzdVqaBv3JgTvvghlcEEY+u2EDQaJCYm4uTJk3KHRH5Akd/yduzYgZiYGCQlJaFHjx5o2bIlAOD48ePYvn07OnTogO3bt7vNZCYIAl5//fUrlj1ixAhkZWVhxowZSE9PR+fOnbF+/XrXhAJnz551Wyvn3Xffhc1mw/Dhw93KmTlzZq26w1Ht2dOreiooQBNWt64XRFS/JKcTjtwcOPJyvdJKUym1BpqQEGhCQmHW6WAuMuPsoUMICAhAYGAgTCYTjEYjdDpdnddOq0iQ3QbJVrsuW1fi6Z64kihCLCmB01wEZ0E+HLm5EEvdP4dVgoBAvR6Bej0QVNY6ZrbaUFhaikKrFaJYSd2qVNA3bgKVgdNz+xtBrYHp6o6uh42hoaGIj4/HhQsXZI6MlE6Ric1bb73l+ve2bduwbds2t/379+/H/v373bZVN7EBgAkTJlTa9Wzz5s1u71NTU6tVplJUp/tDQEAAJEly/dcX2LOyqhxIrA4M5BM/8ktKvWerIkkSnPl5sOfkAM76m5ZZ0GihCQ+HOiS03AB1SZJgNpvLTZmt1Wqh0WigVqs9kuQE2W0wvzy3zuVUSpIQNeZhaMLq0C1XKpt5UrLbIFqtgCRe+ZzLqFUqhBgNCDEaIEkSiqw25JeUoLDU+r/fT7WmrKWGSY1fMrZuC3WAe6+W2NhYlJaWIienZgu1El1OkYmNKNbsQ5Sqpvu7H3l1v/DUdoG/Vq1a1WnAqiRJZV94nE63AavmM6dRbC4A1Co4nBX/bmi81PWCSA5KuWdFUYQoiq571WazobS0FCUlJbBXsO6M5HSWPfXPy4PkuPK6NB6jVkMbHgF1WHiNx8zY7fYKf5ba0tXg5776069cUwLvv+/Oap/nyM6qcTLiLYIgINigR7BBD4cooqCkFHk2O8SYOD6M8lP6xBbQRsdUuK9p06aw2+0orGQCIKIrUWRiQ57l7T7dgHf6dUsOB0qOHEKgrRSILCvbKYootTtQ4rCjxGZHsd0O0WDiUz/yK0q9Zy9nt9thNptRVFSEvPQ0WLKy4CwqBOr5wZU6JBTayCjfHH93hSTLJjohSmX/rVYfMx9vrdOoVIiOjkZix84wW23IzMxEQUHtp/Qm36ONjoUhsVml+wVBQIsWLXDs2DEU13DhViJA4YnN6dOn8f333+PMmTMAyjL9m266Cc2aVX7TUMW8+QXGG+xZmSg9cQyi1b0fulqlQoBehwC9Dvh7YWNdx2tgkSQUFhaisLAQDq44Tn5Aaffs5SSnE4K5CIacbGiysxFSakGpTo2CACPyS0phc3h/diRBp4c2NhZqo8nr16Lq0YRHwNTuaggaDYL1BgQHB6OkpATp6enIzc2VOzyqI01YBIxt2l3xOJVKhZYtW+LYsWMoKSmph8jInyg2sXn66afx+uuvl+uWplKp8NRTT+GVV16RKTJlsqWnQbJ6Z7CqJIqAIEAbXbdVhSVrKZyFBbBlpEO0VO9JjjYmDsbwcBhR9kVQkiQUFxcjLy8PeXl5Hu1CQlSflHDPQhTLxmLYrBBLSyGWWMrWmSo2l+sKZdBqYdBqERMUBLPVilxLCQpLS73SyKAJi4AmKoprWvkQfZNE6Ju1KFcnRqMRzZo1Q1xcHNLS0pjgKJQ6KASm9ldXe3FVjUaDq666CseOHUNpLdc+ooZJkYnNq6++itdeew3Dhw/H008/jbZt2wIADh8+jNdeew2vvfYa4uPjMXHiRJkjVQZbehrOTpnkvQv8/c0kfOidUIeEeO86/yCo1DA0b+m+TRBcU3E3btwYZrMZOTk5yMvL49gtUgx/vWcvuTSDls3pRLa5GHmWEoieyHDUGugaNYLaFFD3ssgjBI0WxtZtoY2KrvI4g8GAZs2aITY2FmlpacjLy6unCKmu1AFBCOjYucbdPbVaLVq1aoXjx4+z5YaqTZGJzfvvv4/bbrsNX375pdv2bt264YsvvkBpaSnee+89JjbVVNOnvkO/24CsklJEGQ1YeevA6l+nnruA6ZokVjn4VBAEBAUFISgoCAkJCcjJyUFWVhafDpHP89d79p90ajUahQQjOjAAmeZi5FpKaj2rm8pogq5RvG+OpWmgNKFhMLZpD1UN1gcyGo1o3rw5SktLkZGRgZycHEXM9NdQqQODYerYudaLq15Kbk6cOMExN1Qtnp+Avx6kpqZi4MDK/zgPHDjQ76ZhrjeCcMVXVkkpMiwlyCoprdbxclCZAqBv0rTax6vVakRHR6N9+/a46qqrEBwc7MXoiDzIT+7Zqmj+TnBaRUUgxFjzRTLVYWHQJTRhUuMjBLUGxqvaIKBzUo2SmssZDAY0bdoUHTt2RKNGjaCt5Rdn8h51UAgCOnWBSqerUzkajQatWrVCiAytx6Q8ivyUj46Oxp9//lnp/j///BNRUVH1GBH5GmPrttXuy/tPwcHBCA4OhsViQVpaGvLz8z0bHBHVik6jQZOwUJhNVlwsKIL1Si1KggBtdAw0oXVYs4U8ShsTB0Pzlh6bylmj0SAuLg6xsbEoLCxEdnY2CgoK2IojM014ZNmYGrXaI+WpVCq0aNEC58+fR2ZmpkfKJP+kyMTmzjvvxOuvv47ExEQ8/vjjCAgo6y9dXFyMt956Cx988AGeeuopeYMk2egbN4UmJLTO5ZhMJrRo0YIJDpGPCdTr0TJKhyyzGVnm4oonGFCpoIuLhzowsIKdVN804ZEwNGsOdZB3WsMFQUBISAhCQkLgcDiQn5+PvLw8FBUVMcmpZ7r4BBhatvL45ByCICAhIQFGoxFnz55lvVKFFJnYzJkzB/v27cPUqVMxY8YMNGrUCABw8eJFOBwO9OvXD88//7zMUZIc1EHB0Ddv4dEyLyU4JSUlyMjIQG5uLj9QiWSmEgTEBAUhSG/A+fwC99YbtQa6+MZQG/17/SqdSu1aoNMXCYIKmqho6BOaeC2hqYhGo0FkZCQiIyPhdDpRWFiIgoICFBUVeXXtp4ZOUKlhaNUauthGXr1OZGQkTCYTTp8+zTGxVI4iExuTyYRNmzbh22+/dVvHZtCgQbj55ptx6623chrPBkjQ6mBq16HWXdCuxGg0IjExEfHx8cjOzkZWVhaniyaSmUmnRcuoCKQVFCLXUgJBo4GucZMGsWr9/lH/ljuECgjQhIRAExUDbXRMncdX1JVarUZYWBjCwsq6I9psNpjNZhQXF8NisaCkpAROp/fXTfJ36oBAGNt2qLcWUpPJhLZt2+L8+fPIysqql2uSMigusbFYLLj33nsxbNgw3HPPPbj99tvlDol8gCCoYGrfEap6WGxPq9WyTzeRD1EJAuJDQxAYFILsoCBIasX9aVMsQauD2mSCKjAImpBQqEPDvJ7MSJJUNiX5pdel7ZdP2S9JACRAwmXHSNAACDEYEGIwAOHhAMqSHavNCpvVBpvdDrvdDofTCafTCdHp/LuIun2+C/DTh62CCvqEJtAnNvfaQ8XKqFQqNGnSBOHh4Th79iynhCYACkxsTCYTfvzxR9x0001yh0K+QlDB2P5qaEJD6/eyl/XpdjqdrkU/2aebqP6pg4LR+OrOiBZFnDhxAlarVe6QfJdaBUFdx1nE1CoIGi0ElQqSU4SzoADO/HzgTCrKsom/SZf+c+kf0mW7L0tM/v6v67PzUpLyd/IiuY7xzmerAED/98sb7MUFyPdS2XLRhIbD0LKV7OPYAgMD0bZtW2RnZyMtLY09KRo4xSU2ANCrVy9s374dY8eOlTuUBinq737rUT7Qf11Qa2Bs1wHaiEhZ41Cr1eX6dF96sU83yc2X7llv0EZGw9i2PQS1GgYAbdu2RWpqKif8qIxThOSs45c/JyDZmDw2RJqQUOibNoMmPELuUFwEQUBUVBQiIiKQnZ2NzMxMPtxooBSZ2Lz11lsYOHAgpk+fjvHjx6Nx48Zyh9SgrLyt+gv8eZPaFAhj+6uhDvCtVcT/2afbarWiuLjY9SopKYF4eZcJIi/zlXvWG/RNm8HQzH3CELVajRYtWiAjIwMXLlxgCypRHQlqDbRR0dA1agy1D6/zplKpEB0djaioKBQWFiInJwcFBQX8m9uAKDKx6dSpExwOB1JSUpCSkgKNRgP9PwaKCoKAgoICmSIkbxK0OugTmkLXOKHe+/TWhk6ng06rRVhoqKtbhdVqLXuVlsJmt8Nms8Fut8PpdMIpihBF8X/9yOvAT3t1E5W11rZtD21k5WuWxcTEICgoiLMnEdWQoNZAFRAITXAI1GFh0ISFK+Lv7SWXdxUXRRFmsxlFRUUoLi5GaWkpu6v5MUUmNsOGDeOsZ0qkUUOo9erQAgSdFurAIKhDQiGoVLBnpv+vu/XlCYAk/e8J7aU+2WLZf8veipcNPAWky95L4j//LZad67bv76RDlP4uy70M1/nV6A/uzT7dl3C+GPI36uBQmNq2h6oaXesuzZ508eJFZGRk1EN0RDJSq4E6jJ8SVCoIGjUEjRaQJDgK8uEoyIc19XQ1S6jGw7jqPq+r8sFezR76qQCE/P0CAFEU4RBFOD3QkqNRq8FOb75DkYnN0qVL5Q6BasPhhFSHpySS3QaxuBj2jHQPBkVEiiGooG/aDPqmiTV6uKVSqdC4cWOEhYXhzJkznD2J/JfTCdRh/JTkBKQG0pih/vtVV4LXH09STSgqsSktLcW3336L06dPIzIyErfccgvi4uLkDouIiLxMHRQMY+u2UAcG1bqMgIAAtG3bFllZWbh48SLXLyEi8jOKSWwyMzPRo0cPnD592tXNyGQyYdWqVejfv7/M0RERkTcIWh0Mic2hbRTvkS7IgiAgOjoa4eHhSEtLQ1ZWFicXICLyE4oZCTZnzhykpqZi4sSJWLNmDRYuXAij0Yhx48bJHRoREXmYoFJD3yQRQd16QBff2OPjKjUaDRISEtChQwdER0dDpaCB0UREVDHFtNj88MMPGDVqFF555RXXtpiYGNx99904evQoWrduLWN0RETkCYJaA118Y+gaN/H6CvZA2ayFCQkJiIuLQ1ZWFrKysjhjEhGRQinmEdXZs2fRq1cvt229evWCJEmc6YaISOHUgcEwXtUGQd17wdC8Zb0kNZfTaDSIi4vD1VdfjRYtWiAsLIyzbxIRKYxiWmysVisMBoPbtkvvHQ6HHCEREVEdqAODoYmIgDYqBurAQLnDAVA2Bic0NBShoaFwOp0oLCxEQUEBCgsL2ZJDROTjFJPYAEBqair27t3ren9pAc7jx48jNDS03PHXXHNNfYVGRESVEVRQ6Q1QmYxla1EFBUMdElrvrTI1pVarERYWhrCwMABlD9gsFgssFgtKS0thtVphs9k4uxoRkY9QVGLz3HPP4bnnniu3/dFHH3V7L0kSBEGo9R+bt99+Gy+//DLS09PRqVMnvPnmm0hOTq70+K+++grPPfccUlNTcdVVV2H+/Pm4+eaba3VtIiKfoVIB6jr8mRAECGoVhL/LEEtLIZaWwp7997KxNZmNrEYTl1VycG1nP7vsNA2A4L9f/ytWgvPvRXnrOsOaWiUgu04lEBE1XIpJbD766KN6uc7y5csxadIkLFq0CN26dcPChQsxcOBAHD16FNHR0eWO/+233zBy5EikpKRg8ODBWLZsGYYMGYK9e/eiQ4cO9RIzEZFXiCLgrFtXX8kBSA1gXW6PDVgVFPNnmYjI5yjmE3T06NH1cp0FCxZg7NixGDNmDABg0aJFWLt2LT788ENMnjy53PGvv/46Bg0ahP/+978Ayqal3rhxI9566y0sWrSoXmImIiIiImroFDMrWn2w2WzYs2eP24KfKpUK/fv3x/bt2ys8Z/v27eUWCB04cGClxwNl/bQLCwtdL7PZ7JkfwBMkyfMv8g2sV//Ee9Z/sV79E+9Z/8V6lZ1iWmzqQ3Z2NpxOJ2JiYty2x8TE4MiRIxWek56eXuHx6enplV4nJSUFs2fPrnvAHiLoDVc+yAP0La+CNjKqXq5FZcTiYq9fo75+f+h/eM/6L9Fi8fo1WK/1Tx0WXi/XYd3WP7GkxOvX4N/Z6mNiI4MpU6Zg0qRJrvf79u1Dnz59ZItHFxuHJikLIFlLvXYNQW+ALjbOa+VT5bxZt6xXefCe9W+8Z/2PPj6B96wf4z3rO5jYXCYyMhJqtbrcgp8ZGRmIjY2t8JzY2NgaHQ8Aer0eer3e9T7QB9Zv4E3jv1i3/on16r9Yt/6J9eq/WLe+g2NsLqPT6ZCUlIRNmza5tomiiE2bNqF79+4VntO9e3e34wFg48aNlR5PRERERESexxabf5g0aRJGjx6Nrl27Ijk5GQsXLkRxcbFrlrRRo0YhPj4eKSkpAIAnn3wSffr0wauvvopbbrkFX3zxBXbv3o3FixfL+WMQERERETUoTGz+YcSIEcjKysKMGTOQnp6Ozp07Y/369a4JAs6ePQuV6n8NXT169MCyZcswffp0TJ06FVdddRVWrVrFNWyIiIiIiOqRINV1mWSqs7179yIpKQl79uzBNddcI3c4RERERESKwzE2RERERESkeExsiIiIiIhI8TjGhmolLS0NaWlpcodBHhYXF4e4OE5b6Y94zxIRyY9/Z72LiY0PiIuLw8yZMxXzi261WjFy5Ehs2bJF7lDIw/r06YMNGza4rbNEysd7lojIN/DvrHdx8gCqscLCQoSEhGDLli0+sbgoeYbZbEafPn1QUFCA4OBgucMhD+I9658u3bOsV//DuvVP/DvrfWyxoVrr3Lkzb0w/UlhYKHcI5GW8Z/3LpXuW9ep/WLf+iX9nvY+TBxARERERkeIxsSEiIiIiIsVjYkM1ptfrMXPmTA588zOsV//FuvVPrFf/xbr1T6xX7+PkAUREREREpHhssSEiIiIiIsVjYkNERERERIrHxIaIiIiIiBSPiQ3JKjU1FYIgYOnSpXKHQkTVwHuWiIh8FRMbBTl58iTGjRuH5s2bw2AwIDg4GD179sTrr7+OkpISr1330KFDmDVrFlJTU712jep44YUXcNtttyEmJgaCIGDWrFmyxiMHQRCq9dq8eXOdr2WxWDBr1qwalcU6cteQ79kjR47gmWeeQefOnREUFIS4uDjccsst2L17t2wxycGX71nWUd34ct1evHgR9957L1q3bo2goCCEhoYiOTkZH3/8MThnVNV8uV7/6bPPPoMgCAgMDKxzLP5CI3cAVD1r167FnXfeCb1ej1GjRqFDhw6w2Wz49ddf8d///hcHDx7E4sWLvXLtQ4cOYfbs2ejbty8SExO9co3qmD59OmJjY9GlSxds2LBBtjjk9Omnn7q9/+STT7Bx48Zy29u2bVvna1ksFsyePRsA0Ldv32qdwzr6n4Z+z37wwQdYsmQJhg0bhkcffRQFBQV47733cO2112L9+vXo37+/LHHVN1++Z1lHdePLdZudnY3z589j+PDhaNKkCex2OzZu3Ij7778fR48exbx58+ock7/y5Xq9nNlsxjPPPIOAgIA6x+FXJPJ5p06dkgIDA6U2bdpIFy9eLLf/+PHj0sKFC712/a+++koCIP38889XPFYURclisVS77NOnT0sApI8++qhax0qSJGVlZUkApJkzZ1b7Ov7qsccek7x1G9fm/zPrqAzvWUnavXu3VFRU5LYtOztbioqKknr27Fnt6/kbX7pnWUee5Ut1W5nBgwdLAQEBksPh8ExgDYCv1uuzzz4rtW7dWrrnnnukgIAAzwenUOyKpgAvvfQSzGYzlixZgri4uHL7W7ZsiSeffNL13uFwYM6cOWjRogX0ej0SExMxdepUWK1Wt/MSExMxePBg/Prrr0hOTobBYEDz5s3xySefuI5ZunQp7rzzTgBAv379yjXBXipjw4YN6Nq1K4xGI9577z0AwKlTp3DnnXciPDwcJpMJ1157LdauXVvr/w9ythYpiSiKWLhwIdq3bw+DwYCYmBiMGzcOeXl5bsft3r0bAwcORGRkJIxGI5o1a4YHHngAQNk4iqioKADA7NmzXfV+pa5lrKMyvGeBpKSkct0jIiIicN111+Hw4cO1KtNfyXXPso68T87P44okJibCYrHAZrPV+WdryOSu1+PHj+O1117DggULoNGw89Xl+H9DAb777js0b94cPXr0qNbxDz30ED7++GMMHz4cTz/9NHbu3ImUlBQcPnwYK1eudDv2xIkTGD58OB588EGMHj0aH374Ie6//34kJSWhffv26N27N5544gm88cYbmDp1qqvp9fIm2KNHj2LkyJEYN24cxo4di9atWyMjIwM9evSAxWLBE088gYiICHz88ce47bbbsGLFCgwdOtRz/4PIzbhx47B06VKMGTMGTzzxBE6fPo233noLf/zxB7Zt2watVovMzEwMGDAAUVFRmDx5MkJDQ5GamopvvvkGABAVFYV3330XjzzyCIYOHYo77rgDANCxY0c5fzTF4D1bufT0dERGRnqkLH/ha/cs68hz5K7bkpISFBcXw2w2Y8uWLfjoo4/QvXt3GI1Gr/7c/k7uen3qqafQr18/3Hzzzfjyyy+9+rMqjtxNRlS1goICCYB0++23V+v4ffv2SQCkhx56yG37f/7zHwmA9NNPP7m2NW3aVAIg/fLLL65tmZmZkl6vl55++mnXtqq6tVwqY/369W7bn3rqKQmAtHXrVte2oqIiqVmzZlJiYqLkdDolSapZV7RLGno3p8v9s4l869atEgDps88+cztu/fr1bttXrlwpAZB+//33Ssuuy//nhlxHvGcr98svv0iCIEjPPfdcjc/1F756z17COqo9X6zblJQUCYDrdcMNN0hnz56tURkNna/V65o1aySNRiMdPHhQkiRJGj16NLuiXYZd0XxcYWEhACAoKKhax69btw4AMGnSJLftTz/9NACU61bSrl07XHfdda73UVFRaN26NU6dOlXtGJs1a4aBAweWiyM5ORm9evVybQsMDMTDDz+M1NRUHDp0qNrlU/V99dVXCAkJwY033ojs7GzX61KXk59//hkAEBoaCgBYs2YN7Ha7jBH7H96zFcvMzMTdd9+NZs2a4ZlnnqlTWf7El+5Z1pFn+ULdjhw5Ehs3bsSyZctw9913A4BXZ2RsCOSsV5vNhokTJ2L8+PFo166dR8r0N0xsfFxwcDAAoKioqFrHnzlzBiqVCi1btnTbHhsbi9DQUJw5c8Zte5MmTcqVERYWVq6faFWaNWtWYRytW7cut/1Sd5h/xkGecfz4cRQUFCA6OhpRUVFuL7PZjMzMTABAnz59MGzYMMyePRuRkZG4/fbb8dFHH5Ub00E1x3u2vOLiYgwePBhFRUX49ttvOTXpZXzlnmUdeZ4v1G3Tpk3Rv39/jBw5Ep999hmaN2+O/v37M7mpAznr9bXXXkN2drZrJjUqj2NsfFxwcDAaNWqEAwcO1Og8QRCqdZxara5wu1SDee7ZV9d3iKKI6OhofPbZZxXuvzRQURAErFixAjt27MB3332HDRs24IEHHsCrr76KHTt28EtNHfCedWez2XDHHXfgr7/+woYNG9ChQ4d6u7YS+MI9yzryDl+o238aPnw43n//ffzyyy/lWm2peuSq14KCAsydOxePPvooCgsLXb0DzGYzJElCamoqTCYToqOj6/YDKhwTGwUYPHgwFi9ejO3bt6N79+5VHtu0aVOIoojjx4+7DRbOyMhAfn4+mjZtWuPrV/cL1z/jOHr0aLntR44cce0nz2vRogV+/PFH9OzZs1pfXq+99lpce+21eOGFF7Bs2TLcc889+OKLL/DQQw/Vqt6pDO/ZMqIoYtSoUdi0aRO+/PJL9OnTp8Zl+Du571nWkffIXbcVudRSU1BQ4JHyGiK56jUvLw9msxkvvfQSXnrppXL7mzVrhttvvx2rVq2qyY/jd9gVTQEuLcD00EMPISMjo9z+kydP4vXXXwcA3HzzzQCAhQsXuh2zYMECAMAtt9xS4+tfWvwpPz+/2ufcfPPN2LVrF7Zv3+7aVlxcjMWLFyMxMZF9Q73k3//+N5xOJ+bMmVNun8PhcNVhXl5euSf8nTt3BgBXM7nJZAJQs3qnMrxnyzz++ONYvnw53nnnHdeMP+RO7nuWdeQ9ctZtVlZWhduXLFkCQRBwzTXXVKscKk+ueo2OjsbKlSvLvfr16weDwYCVK1diypQptf/B/ARbbBSgRYsWWLZsGUaMGIG2bdu6rWL+22+/4auvvsL9998PAOjUqRNGjx6NxYsXIz8/H3369MGuXbvw8ccfY8iQIejXr1+Nr9+5c2eo1WrMnz8fBQUF0Ov1uP7666ts7pw8eTI+//xz3HTTTXjiiScQHh6Ojz/+GKdPn8bXX38NlarmOfWnn36KM2fOwGKxAAB++eUXzJ07FwBw3333sRUIZX16x40bh5SUFOzbtw8DBgyAVqvF8ePH8dVXX+H111/H8OHD8fHHH+Odd97B0KFD0aJFCxQVFeH9999HcHCw64u20WhEu3btsHz5crRq1Qrh4eHo0KFDld1UWEdleM+WJWrvvPMOunfvDpPJhP/7v/9z2z906FCumA1571nWkXfJWbcvvPACtm3bhkGDBqFJkybIzc3F119/jd9//x2PP/54uTF9VH1y1avJZMKQIUPKbV+1ahV27dpV4b4GScYZ2aiGjh07Jo0dO1ZKTEyUdDqdFBQUJPXs2VN68803pdLSUtdxdrtdmj17ttSsWTNJq9VKCQkJ0pQpU9yOkaSyaV9vueWWctfp06eP1KdPH7dt77//vtS8eXNJrVa7TSNbWRmSJEknT56Uhg8fLoWGhkoGg0FKTk6W1qxZ43ZMTaaO7dOnj9u0lZe/qrPCuj+qbEXkxYsXS0lJSZLRaJSCgoKkq6++WnrmmWekixcvSpIkSXv37pVGjhwpNWnSRNLr9VJ0dLQ0ePBgaffu3W7l/Pbbb1JSUpKk0+mqNSUl68hdQ75nR48eXenvAgDp9OnTVZ7vr3zpnmUdeZYv1e0PP/wgDR48WGrUqJGk1Wpdnz0fffSRJIqiR39uf+dL9VoRTvfsTpCkGow4JSIiIiIi8kEcY0NERERERIrHxIaIiIiIiBSPiQ0RERERESkeExsiIiIiIlI8JjZERERERKR4TGz8yEsvvYQ2bdpAFEW5Q6mzu+66C//+97/lDsNnsG79E+vVf7Fu/RPr1X+xbv2E3PNNk2cUFBRI4eHh0ocffujahr/XInjllVfKHf/RRx9JAKTff//d47H0799fAiA99thjFe7/4IMPpDZt2kh6vV5q2bKl9MYbb5Q7Zu/evZJKpZL27dvn8fiUhnXrn1iv/ot1659Yr/6Ldes/2GLjJz788EM4HA6MHDmy3L6XX37ZtRK8t33zzTfYvn17pfvfe+89PPTQQ2jfvj3efPNNdO/eHU888QTmz5/vdlyXLl3QtWtXvPrqq94O2eexbv0T69V/sW79E+vVf7Fu/YjcmRV5RseOHaV7773XbRsAqXPnzhIA6dVXX3Xb542nDSUlJVJiYqL0/PPPV/i0wWKxSBEREeVWPb/nnnukgIAAKTc31237K6+8IgUEBEhFRUUei1GJWLf+ifXqv1i3/on16r9Yt/6DLTZ+4PTp0/jrr7/Qv3//cvt69uyJ66+/Hi+99BJKSkq8GsdLL70EURTxn//8p8L9P//8M3JycvDoo4+6bX/sscdQXFyMtWvXum2/8cYbUVxcjI0bN3otZl/HuvVPrFf/xbr1T6xX/8W69S9MbPzAb7/9BgC45pprKtw/a9YsZGRk4N13362yHKvViuzs7Gq9/uns2bN48cUXMX/+fBiNxgrL/+OPPwAAXbt2dduelJQElUrl2n9Ju3btYDQasW3btirj9mesW//EevVfrFv/xHr1X6xb/6KROwCquyNHjgAAmjVrVuH+6667Dv369cPLL7+MRx55pNKb5vPPP8eYMWOqdU1JktzeP/300+jSpQvuuuuuSs9JS0uDWq1GdHS023adToeIiAhcvHjRbbtGo0FCQgIOHTpUrZj8EevWP7Fe/Rfr1j+xXv0X69a/MLHxAzk5OdBoNAgMDKz0mFmzZqFPnz5YtGgRJk6cWOExAwcOrFWT5c8//4yvv/4aO3furPK4kpIS6HS6CvcZDIYKm3nDwsIqfLrRULBu/RPr1X+xbv0T69V/sW79CxObBqJ3797o168fXnrpJYwfP77CY+Li4hAXF1ejch0OB5544gncd999+Ne//lXlsUajETabrcJ9paWlFT4FkSQJgiDUKKaGhnXrn1iv/ot1659Yr/6LdascTGz8QEREBBwOB4qKihAUFFTpcTNnzkTfvn3x3nvvITQ0tNz+kpISFBQUVOuasbGxAIBPPvkER48exXvvvYfU1FS3Y4qKipCamoro6GiYTCbExcXB6XQiMzPTrSnVZrMhJycHjRo1KnedvLw8XHXVVdWKyR+xbv0T69V/sW79E+vVf7Fu/QsnD/ADbdq0AVA2s0dV+vTpg759+2L+/PkVNlkuX77c9cThSq9Lzp49C7vdjp49e6JZs2auF1B2wzZr1gw//PADAKBz584AgN27d7tdd/fu3RBF0bX/EofDgXPnzqFt27Y1+v/hT1i3/on16r9Yt/6J9eq/WLf+hS02fqB79+4Ayn65O3bsWOWxs2bNQt++fbF48eJy+2rTP/Suu+4qdzMBwNChQ3HzzTdj7Nix6NatGwDg+uuvR3h4ON59913cfPPNrmPfffddmEwm3HLLLW5lHDp0CKWlpejRo0eNYvInrFv/xHr1X6xb/8R69V+sWz8jz/I55GkdOnSQRo4c6bYNFSzwJEmS1KdPHwmAxxeXqs613377bQmANHz4cOn999+XRo0aJQGQXnjhhXLHvvLKK5LJZJIKCwu9EqNSsG79E+vVf7Fu/RPr1X+xbv0HExs/sWDBAikwMFCyWCyubZXdGD///LNsN6UkSdLixYul1q1bSzqdTmrRooX02muvSaIoljuuW7du5VYCbohYt/6J9eq/WLf+ifXqv1i3/oOJjZ/Iz8+XwsPDpQ8++EDuUDzijz/+kARBkP744w+5Q5Ed69Y/sV79F+vWP7Fe/Rfr1n8IkvSPVYJIsebPn4+PPvoIhw4dgkql7Hkh7rrrLoiiiC+//FLuUHwC69Y/sV79F+vWP7Fe/Rfr1j8wsSEiIiIiIsVTdkpKREREREQEJjZEREREROQHmNgQEREREZHiMbEhIiIiIiLFY2JDRERERESKx8SGiIiIiIgUj4kNEREREREpHhMbIiIiIiJSPCY2RERERESkeExsiIiIiIhI8ZjYEBERERGR4jGxISIiIiIixWNiQ0REREREisfExgekpaVh1qxZSEtLkzsUIiIiIiJFYmLjA9LS0jB79mwmNkREREREtcTEhoiIiIiIFI+JDRERERERKR4TGyIiIiIiUjwmNkREREREpHhMbIiIiIiISPGY2BARERERkeIxsSEiIiIiIsVjYkNELqIowul0yh0GERERUY0xsSEiF0mSmNgQERGRIjGxISI3oijKHQIRERFRjTGxISI3bLEhIiIiJWJiQ0RuHA6H3CEQERER1ZhG7gD+6cKFC/jll1+QmZmJYcOGoXHjxnA6nSgoKEBISAjUarXcIRL5NSY2REREpEQ+02IjSRImTZqEZs2a4Z577sGkSZNw7NgxAIDZbEZiYiLefPNNmaMk8n82m03uEIiIiIhqzGcSm5dffhmvv/46/vOf/2Djxo2QJMm1LyQkBHfccQe+/vprGSMkahiY2BAREZES+Uxi8/7772PUqFGYN28eOnfuXG5/x44dXS04ROQ9VqtV7hCIiIiIasxnEptz586hR48ele4PCAhAYWFhPUZE1DCVlpbKHQIRERFRjflMYhMdHY1z585Vun/Pnj1o0qRJPUZE1DAVFxfLHQIRERFRjflMYnPHHXdg0aJFOHXqlGubIAgAgB9++AFLly7FnXfeWS+xvP3220hMTITBYEC3bt2wa9euKo9fuHAhWrduDaPRiISEBEycOJFPvUmx2DJKRERESuQzic3s2bMRFxeHzp07Y9SoURAEAfPnz0evXr1w0003oWPHjpg6darX41i+fDkmTZqEmTNnYu/evejUqRMGDhyIzMzMCo9ftmwZJk+ejJkzZ+Lw4cNYsmQJli9fXi+xEnlDbm6u3CEQERER1ZjPJDYhISHYsWMHnnnmGVy4cAEGgwFbtmxBfn4+Zs6cia1bt8JkMnk9jgULFmDs2LEYM2YM2rVrh0WLFsFkMuHDDz+s8PjffvsNPXv2xN13343ExEQMGDAAI0eOvGIrD5GvKioqYnc0IiIiUhyfSWwAwGg0Yvr06di3bx+Ki4tRUlKCAwcOYMaMGTAajV6/vs1mw549e9C/f3/XNpVKhf79+2P79u0VntOjRw/s2bPHlcicOnUK69atw80331zpdaxWKwoLC10vs9ns2R+EqI4uXLggdwhERERENaKRO4BLHA4HLBYLgoODK9xfWFgIk8kEjcZ7IWdnZ8PpdCImJsZte0xMDI4cOVLhOXfffTeys7PRq1cvSJIEh8OB8ePHV9kVLSUlBbNnz/Zo7ESedObMGbRq1UruMIiIiIiqzWdabJ544okqp3vu2bMnnn766XqMqHo2b96MefPm4Z133sHevXvxzTffYO3atZgzZ06l50yZMgUFBQWu15YtW+oxYqIrO3fuHOx2u9xhEBEREVWbzyQ269evx/DhwyvdP3z4cKxbt86rMURGRkKtViMjI8Nte0ZGBmJjYys857nnnsN9992Hhx56CFdffTWGDh2KefPmISUlBaIoVniOXq9HcHCw6xUYGOjxn4WoLhwOB06ePCl3GERERETV5jOJzcWLFxEfH1/p/kaNGnm9379Op0NSUhI2bdrk2iaKIjZt2oTu3btXeI7FYoFK5f6/Ua1WAwAkSfJesERedujQIf4OExERkWL4zBibiIgIHD16tNL9hw8frnT8jSdNmjQJo0ePRteuXZGcnIyFCxeiuLgYY8aMAQCMGjUK8fHxSElJAQDceuutWLBgAbp06YJu3brhxIkTeO6553Drrbe6EhwiJcrOzkZaWhoaNWokdyhEREREV+Qzic2gQYPw3nvv4Z577kGXLl3c9u3duxeLFy+ulwU6R4wYgaysLMyYMQPp6eno3Lkz1q9f75pQ4OzZs24tNNOnT4cgCJg+fTouXLiAqKgo3HrrrXjhhRe8HiuRt/3++++47bbbXIvlEhEREfkqQfKRviYXL17Ev/71L2RmZuK2225D+/btAQAHDhzAd999h+joaOzcuRONGzeWOVLP27t3L5KSkrBnzx5cc801codDDZjT6cSSJUvctvXr1w9XXXWVTBERERERVY/PtNg0atQIu3fvxuTJk/Htt99i5cqVAIDg4GDcc889mDdvHrvEEMlg27ZtiI2NRVBQkNyhEBEREVXKZxIbAIiLi8PHH38MSZKQlZUFAIiKimI3GCIZ2Ww2rF+/Hrfddhv0er3c4RARERFVyGdmRbucIAiIjo5GdHQ0kxoiH5CXl4d169bBarXKHQoRERFRhXyqxSYvLw+ff/45Tp06hby8vHJTzQqCUK7/PxHVj6ysLKxevRqDBg1itzQiIiLyOT6T2GzYsAHDhw9HcXExgoODERYWVu4Ytt4QeU/Xrl2Rnp4OtVqNadOmVXhMXl4eVq1ahQEDBrhmCiQiIiLyBT6T2Dz99NOIjY3FN998g6uvvlrucIganPT0dFy4cAGhoaFVHldSUoI1a9agV69eaN26df0ER0RERHQFPjPG5sSJE3jiiSeY1BApgNPpxJYtW7Bt2zaIoih3OERERES+k9hcddVVKCoqkjsMIqqBgwcPYu3atSgtLZU7FCIiImrgfCaxmTt3Lt555x2kpqbKHQoR1UBaWhpWrVqF/Px8uUMhIiKiBsxnxths2rQJUVFRaNu2LW688UYkJCRArVa7HSMIAl5//XWZIiSiyhQWFuLbb7/FwIEDERsbK3c4RERE1AD5TGLz1ltvuf69Zs2aCo9hYkPku6xWK9atW+d6MEFERERUn3ymK5ooild8OZ1OucMkoio4HA788MMPOHPmjNyhEBERUQPjM4kNEfkHp9OJH3/8EefOnZM7FCIiImpAfC6x2bFjB1JSUjBx4kQcP34cAGCxWLB3716YzWaZoyOi6nA6ndi4cSPS09PlDoWIiIgaCJ9JbGw2G+644w707NkT06ZNwxtvvOF64qtSqTBgwACOryFSEIfDgQ0bNnC2NCIiIqoXPpPYPPfcc1izZg3effddHD16FJIkufYZDAbceeed+Pbbb2WMkIhqymq14vvvv0dJSYncoRAREZGf85nE5vPPP8cjjzyChx9+GOHh4eX2t23bFqdOnZIhMiKqi6KiIvzwww+c/IOIiIi8ymcSm8zMTFx99dWV7ler1bBYLPUYERF5SkZGBn755Re3llgiIiIiT/KZxCYhIQFHjhypdP+2bdvQsmXLeoyIiDzp+PHj2Lt3r9xhEBERkZ/ymcTm7rvvxnvvvYft27e7tgmCAAB4//338eWXX2LUqFH1Esvbb7+NxMREGAwGdOvWDbt27ary+Pz8fDz22GOIi4uDXq9Hq1atsG7dunqJlUhJ9uzZg0OHDskdBhEREfkhjdwBXDJt2jTs2LEDvXv3Rtu2bSEIAiZOnIjc3FycP38eN998MyZOnOj1OJYvX45JkyZh0aJF6NatGxYuXIiBAwfi6NGjiI6OLne8zWbDjTfeiOjoaKxYsQLx8fE4c+YMQkNDvR4rkaecPXsWxcXFAMoG/Ofm5lY41s0Ttm3bBo1Gg1atWnmlfCIiImqYBMmHOr1LkoTPPvsMK1aswPHjxyGKIlq0aIF///vfuO+++1wtON7UrVs3/Otf/8Jbb70FABBFEQkJCXj88ccxefLkcscvWrQIL7/8Mo4cOQKtVlura+7duxdJSUnYs2cPrrnmmjrFT1QTu3btwpw5c7B27Vq38S+CIODqq6/GLbfcgsTERI9fVxAE9O3bF1dddZXHyyYiIqKGySe6opWUlGDSpElYs2YN7r33XqxatQoHDx7E4cOHsWbNGowaNapekhqbzYY9e/agf//+rm0qlQr9+/d36yJ3udWrV6N79+547LHHEBMTgw4dOmDevHmcAYp83jfffIOePXvi+++/LzeoX5IkHDhwAPPnz/fKuBhJkrB582akpqZ6vOzc3Fzcc889CA4ORmhoKB588MErLu7bt29fCILg9ho/frxr/9KlS8vtv/TKzMz0+M9ARERENecTiY3RaMR7772HjIwMWePIzs6G0+lETEyM2/aYmJhKV1A/deoUVqxYAafTiXXr1uG5557Dq6++irlz51Z6HavVisLCQtfrSl+6iDxt165dGDFiBJxOZ6VJuCiKEEUR77//vlcSEEmSsGnTJuTk5NT43L59+2Lp0qUV7rvnnntw8OBBbNy4EWvWrMEvv/yChx9++Ipljh07Fmlpaa7XSy+95No3YsQIt31paWkYOHAg+vTpU2EXVSIiIqp/PpHYAEBSUhIOHDggdxg1JooioqOjsXjxYiQlJWHEiBGYNm0aFi1aVOk5KSkpCAkJcb369OlTjxETAXPnzoUkSdWeftlbk2E4nU5s2bLFY9NAHz58GOvXr8cHH3yAbt26oVevXnjzzTfxxRdf4OLFi1WeazKZEBsb63oFBwe79hmNRrd9arUaP/30Ex588EGPxE1ERER15zOJzcKFC/HFF1/ggw8+gMPhkCWGyMhIqNXqci1HGRkZiI2NrfCcuLg4tGrVCmq12rWtbdu2SE9Ph81mq/CcKVOmoKCgwPXasmWL534Iois4e/Ys1qxZU+3ukqIo4q+//kJubq5X4snOzkZaWppHytq+fTtCQ0PRtWtX17b+/ftDpVJh586dVZ772WefITIyEh06dMCUKVOqXDfrk08+gclkwvDhwz0SNxEREdWdz8yKdv/990OlUmHcuHF44oknEB8fD6PR6HaMIAj4888/vRaDTqdDUlISNm3ahCFDhgAo+1K3adMmTJgwocJzevbsiWXLlkEURahUZXnisWPHEBcXB51OV+E5er0eer3e9T4wMNCzPwj5BZvN5pUkf926dTVuIZEkCYcPH0b37t09Hg8AnDt3Do0aNapzOenp6eW6hmk0GoSHh1fanRQom26+adOmaNSoEf766y88++yzOHr0KL755psKj1+yZAnuvvvucp9RREREJB+fSWzCw8MRERGB1q1byxrHpEmTMHr0aHTt2hXJyclYuHAhiouLMWbMGADAqFGjEB8fj5SUFADAI488grfeegtPPvkkHn/8cRw/fhzz5s3DE088IeePQQpns9mwa9cur4y/+vPPPyEIQo2SG0EQUFBQ4LXxYCdOnECXLl0qfRgwb948zJs3z/W+pKQEO3bscHvgUJf1cS4fg3P11VcjLi4ON9xwA06ePIkWLVq4Hbt9+3YcPnwYn376aa2vR0RERJ7nM4nN5s2b5Q4BQNkg4aysLMyYMQPp6eno3Lkz1q9f75pQ4OzZs66WGQBISEjAhg0bMHHiRHTs2BHx8fF48skn8eyzz8r1I5AfcDgcMJvN0Ol0bq17nhAWFlarFhuDweD2u+8pkiTB6XTC4XBUmtiMHz8e//73v13v77nnHgwbNgx33HGHa1ujRo0QGxtbbpYyh8OB3NzcSruTVqRbt24AyhKufyY2H3zwATp37oykpKRql0dERETe5zOJjS+ZMGFCpV3PKkrAunfvjh07dng5KmqI9Ho9DAaDR8vs2bNnrVpsWrdu7ZXERhRFmEymKo8JDw93WzDUaDQiOjoaLVu2dDuue/fuyM/Px549e1yJx08//QRRFF3JSnXs27cPQNkYusuZzWZ8+eWXrhZbIiIi8h0+M3kAABQWFuLFF1/EwIED0aVLF+zatQtA2boUCxYswIkTJ2SOkEj51Go1mjdvXu21oVQqFdq1a4ewsDCvxKPX6xEQEOCRstq2bYtBgwZh7Nix2LVrF7Zt24YJEybgrrvuco3huXDhAtq0aeP6fDl58iTmzJmDPXv2IDU1FatXr8aoUaPQu3dvdOzY0a385cuXw+Fw4N577/VIvEREROQ5PtNic/78efTp0wfnzp3DVVddhSNHjrj684eHh+O9997DmTNn8Prrr8scKZEyXVp0c/fu3ejcuTNOnTpV7XMHDBjgtbiaNm3q0QV4P/vsM0yYMAE33HADVCoVhg0bhjfeeMO132634+jRo65Zz3Q6HX788UfXeLqEhAQMGzYM06dPL1f2kiVLcMcddyA0NNRj8RIREZFn+Exi89///hdFRUXYt28foqOjy81sNGTIEKxZs0am6IiUrbS0FL/88gvOnTsHAIiOjsbw4cNds35VNPXzpW5no0ePRpMmTbwSV+PGjREWFoaioqIanVfVmLzw8HAsW7as0v2JiYlu3fASEhKqPeX6b7/9Vu0YiYiIqH75TGLzww8/YOLEiWjXrl2FK5E3b97c9aWMiKovLS0NmzdvdluXpWPHjkhKSsLgwYOxZMkSbN261e3LviAIaNu2LQYMGOC1pCYuLg6JiYmwWq1eKZ+IiIgaFp9JbEpKShAVFVXp/po+0SVq6ERRxJ9//ok//vjDlbQYDAb06dMHjRs3BgC0b98eCxYsQHp6OkaOHImioiIYjUb897//9dqYGgCIj49Hs2bNPNoFjYiIiBo2n5k8oF27dvjll18q3b9q1Sp06dKlHiMiUi6LxYL169dj7969rqQmLi4OQ4YMcSU1l4uNjXUtNqnT6bya1DRt2pRJDREREXmcz7TYPPXUUxg9ejQ6duyIO++8E0DZE+cTJ05g9uzZ2L59O77++muZoyTyfefPn8eWLVtQWloKoKxbWZcuXdCpUyevTNdcEy1btiw3hTIRERGRJ/hMYnPvvffizJkzmD59OqZNmwYAGDRoECRJgkqlwrx58zBkyBB5gyTyYaIoYvfu3di/f79rm8lkQt++fWVPJgRBQJs2bRAZGSlrHEREROS/fCaxAYBp06bhvvvuw9dff40TJ05AFEW0aNECd9xxB5o3by53eEQ+q6ioCD///DOysrJc2xISEtC7d2+PL/BZU2q1Gu3ateMUyURERORVsiU211xzDebNm4dBgwYBAD755BP07t0biYmJmDhxolxhESlOamoqtm7dCpvNBqBsmuZ//etfaN++vezjWHQ6Hdq3b4/AwEBZ4yAiIiL/J1ti89dffyE7O9v1fsyYMfj000+RmJgoV0hEiuJwOLBr1y4cPnzYtS0oKAj9+vWrcobB+mIymdChQwfo9Xq5QyEiIqIGQLaRxE2bNsWPP/7oWhhQkiTZny4TKUV+fj6+++47t6SmWbNmGDJkiE8kNcHBwejYsSOTGiIiIqo3siU248ePxyeffAKDwYDg4GAIgoAHH3wQwcHBlb5CQkLkCpfIZxw/fhzffvstcnNzAZSNYenZsyf69esHnU4nc3RASEgIOnToAK1WK3coRERE1IDI1hXtv//9Lzp16oSff/4ZGRkZWLp0Kf71r39xkgCiStjtdvz22284ceKEa1toaCj69euH8PBwGSP7n4CAALRr1w5qtVruUIiIFM1iscBkMskdBpGiyDor2oABAzBgwAAAwNKlSzFu3DjcfffdcoZE5JNycnLw888/o6CgwLWtVatWuPbaa32mZUSj0aBdu3bQaHxqskUiIkW61FWfiKpPtq5o4eHhWLFihev9zJkz0bFjR7nCIfJJkiTh0KFD+O6771xJjVarRd++fXHdddf5TFIDAG3atJF9amkiIn8hiqLcIRApjmyPVs1mMywWi+v9888/j6uuugodOnSQKyQin2K327F161acO3fOtS0iIgL9+vXzufFmzZo1Q1hYmNxhEBH5DSY2/omTZXmXbIlNixYtsGLFClx33XUIDg6GJEkoLi52DYiujK+MJSDypjNnzmDLli1uyX+7du2QnJzsc+NXGjdujMaNG8sdBhGRX2Fi459KSko4dsqLapTYNGvWrMZZpiAIOHnyZLntU6dOxZgxY7B27VrXcePHj8f48eOrLI99TsmfiaKITZs24dtvv3X9UdPpdOjduzeaNm0qc3TlNW7cmGtPERF5gSRJcodAXsB69a4aJTZ9+vQpl9js3r0bBw8eRLt27dC6dWsAwNGjR3Ho0CF06NABSUlJFZZ13333ITk5GZs3b0ZGRgZmzZqFoUOHcpwNNVhFRUX45JNPcPDgQde2qKgoXH/99QgMDJQxsoo1adIETZo0YZM6EZEX8EGufxJFkd3RvKhGic3SpUvd3q9atQqrVq3Cxo0bccMNN7jt27hxI/79739jzpw5lZbXunVrVzL00UcfYfTo0bjttttqEpJXvP3223j55ZeRnp6OTp064c0330RycvIVz/viiy8wcuRI3H777Vi1apX3AyW/cfToUSxdutQ1QYAgCGjZsiW6du3qk03WCQkJPtmCRETkL9gVzT9JkgSn08kZRL2kTrOizZgxA48//ni5pAYAbrzxRkyYMAHTp0+vVlmnT5/2iaRm+fLlmDRpEmbOnIm9e/eiU6dOGDhwIDIzM6s8LzU1Ff/5z39w3XXX1VOk5A9EUcSaNWvwxhtvuJKa4OBgjB07Fm3btoVKJdvEhZWKiYlhUkNE5GUOh0PuEMhLbDab3CH4rTqli8ePH0dERESl+yMiIiocXwMAv/zyCwCgd+/ebu+v5NLx3rJgwQKMHTsWY8aMAQAsWrQIa9euxYcffojJkydXeI7T6cQ999yD2bNnY+vWrcjPz/dqjOQf8vPz8eGHH7otuNmmTRuMHj0aWq0W6enpMkZXsYCAALRs2ZJN6EREXma32+UOgbyEi696T50SmxYtWuCjjz7Cgw8+WG4MQFFRET788EM0b968wnP79u0LQRBQUlICnU7nel+ZS/0Rvdnn1GazYc+ePZgyZYprm0qlQv/+/bF9+/ZKz3v++ecRHR2NBx98EFu3br3idaxWK6xWq+u92WyuW+CkOPv378cnn3yC4uJiAGW/Z4MHD8aAAQOgUqncZkPzJVdddZVPtiIREfmb0tJSuUMgL/HVv/H+oE6Jzdy5czF8+HC0adMG999/P1q2bAmgrCXn448/RkZGBr766qsKz/35558BlM34dPl7OWVnZ8PpdCImJsZte0xMDI4cOVLhOb/++iuWLFmCffv2Vfs6KSkpmD17dl1CJYVyOBz49ttvsWnTJte2sLAwPPDAA2jRooWMkV1ZdHQ0goKC5A6DiKhBuPTgi/wP69Z76pTYDBkyBOvWrcOzzz6LefPmue3r3LkzlixZgoEDB1Z4bp8+fap8rwRFRUW477778P777yMyMrLa502ZMgWTJk1yvd+3b58if36qmezsbCxZsgRnzpxxbevYsSPuu+8+BAQEyBhZ9TRp0kTuEIiIGgyLxQK73Q6tVit3KORhbLHxnjpPyTBgwAAMGDAA6enpri9sTZs2RWxsbJ2Dq2+RkZFQq9XIyMhw256RkVHhz3Py5Emkpqbi1ltvdW27NIuJRqPB0aNHK3wKr9frodfrXe99cSpf8qw9e/bgs88+c3Ut0Gg0GDp06BW7YPqK6OhoGI1GucMgImpQ8vLyEB0dLXcY5GFMbLzHY3PNxcbG1iiZeeCBB2p8DUEQsGTJkhqfV106nQ5JSUnYtGkThgwZAuB/CyZOmDCh3PFt2rTB/v373bZNnz4dRUVFeP3115GQkOC1WEkZbDYbVqxYgV9//dW1LSoqCg8++KCiWkAaN24sdwhERA0OExv/xMTGe+qc2Jw9exbz5s3Dzz//jKysLKxatQq9e/dGdnY2nn/+eYwZMwZdunQpd95PP/1U7km1xWJBVlYWgLJxB0DZTQ2UfRmsj+46kyZNwujRo9G1a1ckJydj4cKFKC4uds2SNmrUKMTHxyMlJQUGgwEdOnRwOz80NBQAym2nhictLQ1LlizBxYsXXdv+9a9/YeTIkTAYDDJGVjORkZGK6CpHRORvOMuqfyopKZE7BL9Vp8Tm0KFDuO666yCKIrp164YTJ0645l2PjIzEr7/+iuLi4gpbWVJTU8uVNWDAAEydOhVPPfWUa8xKdnY2XnvtNXzyySdYu3ZtXcKtlhEjRiArKwszZsxAeno6OnfujPXr17smFDh79ixnhaIqSZKEHTt2YPny5a656rVaLUaMGIHu3bsrouvZ5ZTUskRE5E+Y2PgnTh7gPXVKbJ555hmEhoZix44dEAShXHPpLbfcguXLl1errMcffxw33XQT5s6d67Y9MjISL7zwAjIzM/H444/jxx9/rEvI1TJhwoQKu54BwObNm6s8d+nSpZ4PiBSjtLQUn3/+OX7//XfXtkaNGuHBBx9EXFycjJHVDltriIjkc2nhZvIvxcXFsNlsrpmByXPq1PTwyy+/4JFHHkFUVFSFT6GbNGmCCxcuVKusHTt24Jprrql0f5cuXbBjx45ax0rkbefOncOLL77oltT06tULzzzzjCKTGoCtNUREciooKOBCnX4qLS1N7hD8Up0SG1EUq1w5NSsry232r6qEh4fj+++/r3T/unXrXONXiHyJJEn4+eef8fLLLyMzMxMAYDAY8MADD+Duu+9W7BOZ8PBwttYQEclIkqRyM7WSfzh27JjcIfilOiU211xzTaXjXhwOB7744gtce+211Spr3LhxWLNmDW6//Xb8+OOPSE1NRWpqKjZu3IjbbrsN33//PcaPH1+XcIk8rri4GIsXL8ZXX33lGl/WpEkTTJ06FV27dpU5urqJj4+XOwQiogbvn2OSyT+cPn0a2dnZcofhd+o0xmbKlCkYPHgwHnnkEdx1110AytZ8+fHHHzFv3jwcPnwYb731VrXKmj59OqxWK15++WWsWbPGPUiNBpMnT8b06dPrEi6RR508eRIfffQRcnNzXduuv/56DBkyBBqNx2ZSl4XBYEBISIjcYRARNXgnT57Etddeq/i/K1Te1q1bMWTIEMVNKuTL6nSX3HTTTVi6dCmefPJJLF68GABw7733QpIkBAcH45NPPkHv3r2rXd6cOXPw5JNP4scff3Rb7LN///6uWdKI5CaKIjZu3IjvvvvOtSBrQEAARo0ahauvvlrm6DwjMjKSH7RERD7AarXi6NGjaN++vdyhkIdlZWXhwIEDfvPdwRfUOf2/7777cMcdd2Djxo04fvw4RFFEixYtMHDgQAQFBdW4vMjISFfrD5GvKSwsxNKlS3HkyBHXtpYtW2LMmDGutZeUKiIiAkDZQrVK/1mIiPzJvn370Lp1a7ba+KHdu3ejefPmHNPqIbW+QywWCxISEjB58mT897//xZAhQzwYFpHvOXLkCJYuXYrCwkIAgCAIGDRoEG6++Wao1WqZo6u7Tz/9FKIoYtu2bbV6KEFERN5RXFyMP//8E0lJSXKHQh5mt9uxY8cO3HDDDXKH4hdqndiYTCZoNBpmmOT3nE4n1q5diw0bNkCSJABAcHAwxowZg9atW8scneeZTCa/SNSIiJSqa9euOHPmDEwmE6ZNmwagrNWmRYsWnCFWwbp27Yq0tDRoNBpXvQJl46hatWqFhIQEGaPzD3WaFW3YsGFYsWKF68sekb/Jzc3FwoULsX79etfvebt27TBt2jS/TGoAwGg0yh0CEVWT0+l0jfUj/5Geno7s7GxXDwGgrK63bNnC71wKlp6ejosXL7rV6yW//PILSktLZYjKv9Sps+Zdd92FRx99FP369cPYsWORmJhY4ZeiqhbeJPJVf/31Fz799FMUFxcDAFQqFW677Tb0798fKlWdngn4NIPBIHcIRFRNdrsdTqeTvScaiIyMDOzdu5dd0vxQcXExfv75ZwwaNIiT99RBnRKbvn37uv69devWcvslSYIgCHA6nXW5DFG9stvtWLVqFX7++WfXtvDwcDz44INo1qyZjJHVj+ouqktEvuHSGlrUMOzduxdxcXFo1KiR3KGQh507dw7bt29H9+7dmdzUUp0Sm48++shTcRD5hMzMTCxZsgTnzp1zbevcuTPuvfdemEwmGSOrP5x1h0hZrFar3CFQPZIkCZs2bcLQoUMRGBgodzjkYQcOHIDBYGBvp1qq0zeY0aNHeyoOSJKExYsXY8mSJTh16hTy8vLKHSMIAp9Mkdf8/vvv+Pzzz119XDUaDYYNG4bevXs3qCcnTGyIlKWkpETuEKielZSU4IcffsCtt94KrVYrdzjkYbt374Zer+faRbXgM99gnnnmGSxYsMD1dJzraFB9sdls+PLLL/Hbb7+5tkVHR+PBBx9skDOU+PP4ISJ/VFRUJHcIJIPs7Gz89NNPuPHGG/m57Ye2bdsGrVaLVq1ayR2KotQosXnggQcgCAIWL14MtVqNBx544IrnCIKAJUuWXPG4jz/+GMOGDcOXX35Zk5CI6uTixYtYsmQJ0tLSXNuSk5Nx1113NdhB9A2pdYrIH1Q0wxI1DGfOnMGWLVvQt29ffnb7oUuz4PnrLKzeUKPE5qeffoJKpYIoilCr1fjpp5+ueCNV90YrKSlB//79axIOUa1JkoRt27bhq6++gt1uBwDodDrcdddduPbaa2WOTl7840ikLAUFBXKHQDI6fvw4AKBPnz5sufEzkiRhy5YtsFgs6Ny5M/8+V0ONEpvU1NQq39fFDTfcgN9//x0PP/ywx8okqkhJSQmWLVuGPXv2uLbFx8fjwQcfRGxsrIyR+QZ+cBIpi9lsljsEktnx48dhtVpxww03cMyNH/r999+Rk5OD3r17Q6fTyR2OT/OZ1P6dd97Bjh07MG/ePOTk5MgdDvmpM2fOICUlxS2pue666/Df//6XSc3fmNgQKcultbaoYTt79ixWr17NRNdPnTp1Ct98841b13kqz2cSm9atW+PUqVN47rnnEB0djYCAAAQHB7u9QkJC5A6TFEqSJPz000945ZVXkJ2dDQAwGo0YO3YsRo4cyScgl2FiQ6QsNpvN1aWWGracnBysXLmSX379VGFhIdasWYNff/2V07xXos6zon3//fdYsGAB9u7di4KCAkiSVO6Y6izQOWzYMJ/5QvX222/j5ZdfRnp6Ojp16oQ333wTycnJFR77/vvv45NPPsGBAwcAAElJSZg3b16lx1P9M5vN+PTTT7F//37XtsTERDzwwAOIjIyUMTLfxD7aRMqTm5uLmJgYucMgH1BSUoK1a9eiZ8+eaNu2rdzhkIdJkoRDhw7h9OnT6NGjB5o3b+4z3599QZ0Sm6+//hr//ve/0b59e9x111149913cffdd0OSJHz77be46qqrMGTIkGqVtXTp0rqE4jHLly/HpEmTsGjRInTr1g0LFy7EwIEDcfToUURHR5c7fvPmzRg5ciR69OgBg8GA+fPnY8CAATh48CDi4+Nl+AnocidOnMCHH36I/Px817b+/fvj9ttvh1qtli8wH8bEhkh5Lly4wMSGXERRxNatW1FYWIjk5GR+8fVDJSUl2LRpE06cOIHevXvDaDTKHZJPqNM3mJSUFCQnJ+OPP/7A7NmzAZRNCf3ZZ5/hwIEDSEtLQ7NmzTwSaH1ZsGABxo4dizFjxqBdu3ZYtGgRTCYTPvzwwwqP/+yzz/Doo4+ic+fOaNOmDT744AOIoohNmzbVc+R0OVEU8f333+O1115zJTWBgYF47LHHcMcddzCpqQITGyLlOXbsWIU9Jqhh+/PPP7FlyxaIoih3KOQlZ86cwYoVK3DmzBm5Q/EJdfoGc+jQIdx1111Qq9Wu1cov9fNNTEzEo48+ivnz51e7vMLCQsyePRvJycmIiYlBTEwMkpOT8fzzz9fLPP02mw179uxxm3ZapVKhf//+2L59e7XKsFgssNvtCA8Pr/QYq9WKwsJC14sD/TyroKAAb775Jr777jvXH/qrrroKU6dO5Sq+1cDEhkh5CgsLceLECbnDIB907NgxbNq0qVrDAkiZSkpKsGHDBmzbtq3B13OdvsGYTCbXoOvQ0FDo9Xq3AWsxMTE4ffp0tcq6ePEiunTpgtmzZ8NsNqNnz57o2bMniouLMWvWLFxzzTVeHwyXnZ0Np9NZrjk/JiYG6enp1Srj2WefRaNGjapckyclJQUhISGuV58+feoUN/3PoUOHMG/ePBw9ehRA2UD4wYMH48knn0RoaKi8wSkEuywQKdOuXbs4iQBV6PTp09iwYQN/P/zcwYMHsWbNmgY9sUCdEpvWrVvj0KFDrvedO3fGp59+CofDgdLSUixbtgxNmjSpVlnPPvss0tPTsWbNGhw6dAjffPMNvvnmGxw8eBBr165Feno6Jk+eXJdwve7FF1/EF198gZUrV1a5av2UKVNQUFDgem3ZsqUeo/RPTqcTq1atwltvvYWioiIAQEhICJ588kncfPPNbIWoASY2RMpUXFyMHTt2yB0G+ajz589j3bp1KC0tlTuUBuns2bOuqdmtVityc3O9cp2MjAysX7++wbbc1Onb3h133IHVq1e7MsNp06Zh8+bNCA0NRVRUFLZu3VrtZGT9+vV46qmncPPNN5fbd9NNN+GJJ57AunXr6hLuFUVGRkKtViMjI8Nte0ZGxhXXOHnllVfw4osv4ocffkDHjh2rPFav17tNYx0YGFjn2BuynJwcLFiwAD/88INrW/v27TF16lS0atVKxsiUiYkNkXIdPny42j0lqOHJyMjAt99+Wy/d+6nMrl27cOuttyIxMdE15rekpARTp07F22+/7dHF7i/JyMjAzp07PV7uJbm5ubjnnnsQHByM0NBQPPjgg1ccVrF48WL07dsXwcHBEATBbVKnSxITEyEIgtvrxRdfrFFstUpsSktLsXz5ctjtdkyfPt2VdQ4ePBibN2/G2LFjMW7cOGzatAn3339/tcosLi6uckaX2NhYry9CptPpkJSU5Dbw/9JEAN27d6/0vJdeeglz5szB+vXr0bVrV6/GSO727duHefPmuf6Qq1QqDBs2DI888giCgoJkjo6IyHu6du2KFi1a4IUXXnDbvmXLFhQUFMgUFfm6goICfPvtt+Ue4pLnffPNN+jZsye+//77cpN7SJKEAwcOYP78+di7d6/Hr33gwAGcO3eu1uf37du30hmL77nnHhw8eBAbN27EmjVr8Msvv+Dhhx+usjyLxYJBgwZh6tSpVR73/PPPIy0tzfV6/PHHaxR3jad7zszMRI8ePXD69GlIkgRBEGA0GrFq1Sr0798f1113Ha677rqaFot27drh888/x/jx48stlmi32/H555+jXbt2NS63piZNmoTRo0eja9euSE5OxsKFC1FcXIwxY8YAAEaNGoX4+HikpKQAAObPn48ZM2Zg2bJlSExMdI3FCQwMZEuMF9ntdnzzzTdu3fgiIiLw4IMPIjExUb7AiIjqSXp6Oi5evFhu/KDNZsMPP/yAIUOGQKvVyhMc+bSSkhKsWbMGN9xwA/9mesmuXbswYsQIOJ3OSmcsvDRb3fvvv49nn33W43WxefNmDB8+3KNTQR8+fBjr16/H77//7nqY/+abb+Lmm2/GK6+8gkaNGlV43lNPPeWKqSpBQUFX7CVVlRq32MyZMwepqamYOHEi1qxZg9deew1GoxHjxo2rdRBA2RibnTt3Ijk5GYsXL8bmzZuxefNmvPfee0hOTsauXbvqZYzNiBEj8Morr2DGjBno3Lkz9u3bh/Xr17tak86ePes2icG7774Lm82G4cOHIy4uzvV65ZVXvB5rQ5WRkYGXX37ZLam55pprMHXqVH5A/397dx4XVdX/AfxzZ4AB2VE2lU1RRCkXTEUz3FGk3DXSUnM3s7I0zUx8NM0nM800c8ktTTPXxCVyz32ppzS3SqRHQ0RkkR3m/P7gN/M4ssjADHdm+Lxfr/uSOXPvne9wPMN87z1LJSkUCnZFI7IADx48wIkTJ+QOgyqgqsZiFBYWIi4uDjdu3DDK+au7OXPmQAhR7mnYjTHcIjs7G0ePHjXoVPCnTp2Ci4uLTg+lLl26QKFQGKT720cffYSaNWuiefPm+Pjjj1FQUKDX8Xrfsfnhhx/wyiuv6Hxx9/T0xEsvvYRr164hKChI31MCAAYMGIDMzExMnToVY8eO1X65EkLAw8MDX331Ffr371+hc+trwoQJmDBhQonPPZ5pGqNvJJXuzJkz2Lx5s3Zcl7W1Nfr3749nn32WX8iJiB5x/fp1eHh4VElvB6q8s2fPYvbs2YiNjdV+EdWMxXjqqafQs2dPg1+8E0LgyJEjUCgUqF+/vkHPnZKSgtdffx3ff/+9tpv44sWLy+zNsmLFCmzatAkXL15ERkYGHjx4oHNHMj4+HrNnz8ahQ4eQmJiI2rVrY8iQIZg+fXqx3j5VTa1Wo6CgAPn5+fjrr7+wZ8+ecicUarUav/76K1JSUspcLqQiEhIScPXqVQQHBxvkfImJicUWrLeysoKbm1u5ZxAuzcSJE9GiRQu4ubnh5MmTmDZtGv755x8sXLiw3OfQO7FJSEjAu+++q1P27LPPQgiBu3fvVjixAYBhw4ZhyJAhOH/+vHahIT8/P7Rs2VK7Tg5VT7m5udiyZYvOjD+enp4YOXIk6tSpI2NkRESm68SJE3BwcCj3DKUkj+3bt2PQoEElXuHXjMW4dOkSRo0ahRYtWhj0tYUQOHz4MBwcHMoc61ySDh06YNiwYSWOpx48eDD++ecfxMXFIT8/H8OHD8fo0aOxadOmUs+nGYfRvXt3TJs2rdjzV69ehVqtxpdffonAwEDt7yQzMxMLFiyAWq1Gfn4+8vPzUVBQgLy8PG2y8eimKcvJyUFOTo72cUn/Pnp8WY8fnYXs6tWret8lEULgypUrZY7prqiTJ0+idu3acHZ2LnWfuXPnYu7cudrH2dnZOH36tM6F/kdnQjaGSZMmaX9++umnYWNjgzFjxmDevHlQqVTlOofe2UJubm6xqYw1j/W9XVRiQFZWaNOmDdq0aVPpc5Fl+O9//4vVq1frDHQMCwvDwIEDy/0fnYioOhJCIC4uDt26dYOPj4/c4VAJTGEshmaipIEDB1b6QnJhYSF+++037N+/Hz/++CMCAgK0k01pkhs3N7cSk40GDRogPz8fv/zyCwDgm2++gY2Njc5+wcHBOHfuHE6ePKl9vHLlSuTk5Gh/T3LLy8uDJEl6JTeSJCEtLc1oi7b/+OOPeP7550u9szV27FgMHDhQ+3jw4MHo168f+vbtqy2rXbs2vLy8kJSUpHNsQUEBUlJSKjU2piStW7dGQUEB4uPjy33jpEL/e+Pj43VmcNDMvnLjxo0SF0Es6erCsWPHAADPPfeczuMn0exPlk8IgePHj+O7777TJs0qlQovvvgiWrduLXN0RETmobCwEAcOHED79u0r1auCjKMiYzHGjx//xP00X/I1537SlpGRgc2bN8PFxaXYnY3HExDNnZD4+Hh89913+P3337XPq9VqXLlyBTY2Nti2bRu2bdumE89HH32EevXqlRn77du3AQDnz59/4gXMjIwMqFSqKktqFAoFrKysoFAooFQqS9ySkpIqdMfG1tbWKOvuCSG0dVZaYuPm5qbTDc7Ozg4eHh4IDAzU2S8sLAypqam4cOECQkNDAQCHDh2CWq02+HezX375BQqFoljXt7JUKLGZMWMGZsyYUaz88YammTWtpEWCOnToAEmSkJ2dDRsbG+3j0pR1LrI8WVlZ2LhxI37++WdtmY+PD1599VW9b5UTEVV3arUaR48eRXJyMtq0aQOlUil3SISi7v36jsX4z3/+g2vXrsHZ2bnUJKWi7t27p9caN/n5+cjKyip2lyErK6vYTFwKhQIqlQpZWVkVjk/D2toa1tbWePjwIS5fvoyIiAj4+Phoy62srGBjYwMrKytt2aPPWVtbQwiBmzdvokaNGrCzsyuWnFhZWZWYtJRnPG/z5s2xfft2ve/YBAUFGW1BcUMtgREcHIzu3btj1KhRWL58OfLz8zFhwgS8+OKL2hnRbt++jc6dO2P9+vVo1aoVgKKxOYmJifjjjz8AAL/99hscHR3h6+sLNzc3nDp1CmfOnEHHjh3h6OiIU6dO4a233sKQIUPg6upa7vj0TmzWrFmj7yElOnz4MABoM0fNY6L4+HisXr0a9+/f15Z16NABffr04dSlRESVcPnyZdy7dw9du3aFvb293OGYBc2V7srKyspCSkqKzrZ3794KJSLXr19H06ZNKx3T457UDe3ChQvaHjuSJKGgoAB3797FTz/9pP3CP3nyZLi7uyM+Ph7NmzfXSSo2btyIJk2a4IUXXigx2dBsFy5cwO7duzFlyhTUqlVLZz8rKytIkoTbt28jPDwcQ4YMwapVq/R+r1lZWSgsLISjo2OxIRaV5eXlhfbt2+PEiRPluiCvUCgQHBys1xd4fbi6uhp0rPrGjRsxYcIEdO7cWTsxxGeffaZ9Pj8/H9euXdNJYpcvX45Zs2ZpH2t6YK1ZswbDhg2DSqXC5s2bERMTg9zcXAQEBOCtt97SGXdTHnq/y6FDh+p7SInCw8PLfEzVj1qtxqFDh7Bz507tLWU7Ozu8/PLLaNasmbzBERFZiKSkJOzcuRM9evQw+AxMliYvLw9nz54t17iHgoICZGVllbqVlBwlJCRUaCxGXl6ezuPS/i3rudL+ffrppxESEgI7O7tidzvS09Px8OFD7dIAJY3D8Pf3x/r163HixAmMGjVK5/fz0ksvoVu3bujevXuZ7/HOnTsAiiYJKmmIw507d9CxY0e0bdsWK1asKM+vrcqNGDECJ06cKHf9duvWzWix6NOVS6Os9Wbc3NzKnATC39+/2HuOiYlBTExMqce0aNFCZ4KoijKZqcY6deqE6dOno3PnziU+f/jwYe0Uf2R5MjIysH79ely+fFlbVq9ePQwfPhw1a9aUMTIiIsuTmZmJ77//HpGRkXB3d5c7HJNVUFCAhw8fars1abpdabbMzEztvzk5OXqf38bGpkJjMVxdXY2SlNrb28PJyQn+/v6oUaNGsecf784kxziM27dvo2PHjggNDcWaNWuM1nWrspo0aYJ58+ZpZ3cr6c6NJvahQ4cabebCGjVqwNHR0WiTEpgak0lsjhw5gpEjR5b6fFJSks6CjGQ5rl+/jjVr1mgnoQCAiIgIREVFsR84EZGR5ObmIjY2FhEREfD29pY7HNmp1Wqkpqbi/v37uH//PpKTk3H37l3Ex8cjOzsb2dnZFeo2JkkSHBwc4OjoqP1Xs3Xq1AlHjhwxmbEYpa0ary9jjcO4ffs2OnToAD8/PyxYsAD37t3TvqahZ+QyhE6dOuGrr77C6tWrcfz4cZ16liQJwcHB6Natm1GnY69du3a1WufPZBIbAGX+4v/44w+DDXwi06BWq7Fv3z6dPsaOjo4YOnQoF5QjIqoCeXl52Lt3L5599lmLnzFNCIGHDx8iOTlZm7hokpj79+8jJSWlwhMUaa6Ka7ZHExl7e/sykxBTGYvh6uoKV1dXg13ZN8Y4jLi4OPzxxx/4448/ULduXZ3Xq8ykCcbUpEkTLFy4EImJiYiOjkZGRgbs7OwwefJko42p0XBwcICXl5d2UfPqQNbEZt26dVi3bp328Zw5c7By5cpi+6WmpuLXX39FZGRkVYZHRpSamoo1a9bgxo0b2rKgoCAMGzaszAWkiIjIsAoLC3H06FHcuXMH7dq1k30F98rIzs7WSVge/TklJaXCX/BUKlWJiYvm58r0LjCFsRi2trZo2LCh3lMmV/U4jNIWAzUHXl5esLOzQ0ZGBmxsbKokqWnSpEm1ulsDyJzYZGVl6dxGzMjIKHZVQ5Ik2NvbY+zYsfjggw+qOkQygsuXL2PdunXaq0KSJCEqKgoREREm21eWiMjS3bhxA3fu3EHbtm3h7+9vkl+I8vLydO6yJCcnIyUlRZvAVHQqYVtbW9SsWVNnq1WrFuzt7XHlyhW4uroafOYsDbnHYlhbWyMkJAQ2NjYVGidEpkWSJPj4+MDHx6dafqeSNbEZN24cxo0bBwAICAjA4sWL8cILL8gZEhlRQUEBdu/ejR9//FFb5uLigldffbXYwEMiIqp6mZmZiIuLQ+3atREWFlblk7cUFhbiwYMHxbqJaR7rs8bKo6ysrEpMXDQ/29vbl5jIZWVl6fQsMBa5xmIolUrtDGhk/pycnBAYGFitp3I3iTE22dnZ6N27t0leHSLDSE5OxldffYX4+Hht2VNPPYWXX34ZDg4O8gVGRETF3LlzB9u3b0dwcDCeeeaZJ67+Xl5qtRrp6emlJi6pqakVWkFekiS4uroWS1g0Pzs5OZn81Ws5xmIEBQXxb7AFUCqVCAgIgJeXV7X/Lm0SiY2dnR1WrFiBJk2ayB0KGcHFixexceNGZGdnAyhqgH369EHHjh2rfQMkIqqIhIQEZGZmAiia3SwlJcXg0/8KIfD7778jPj4e4eHh8PHxKdcxmZmZxRKXR7uNVXSxSycnp1ITF1dXV4uZRbOqxmJ4enpyOQUL4OzsjIYNGxqtq6S5MYnEBgBCQ0Nx6dIlucMgA8rLy8O2bdtw/PhxbZm7uztGjBhh1KkNiYgs1dmzZzF79mzExsZquytlZ2fjvffew1NPPYWePXvC39/foK+ZlZWFffv2oXnz5mjZsiVyc3NLTVzu37+v9wD9nJwc/PTTT7h16xYUCgVCQ0Mxbtw41K1bV5u4uLm56UxqkJOTg7fffhubN29Gbm4uIiIisGzZMnh6egIA7t+/j8GDB+PXX3/F/fv34eHhgV69emHu3LlwcnIy6O/HHCkUCoP/P6Gq5+fnBx8fH14kfoTJJDaLFi1CZGQkQkJCMGzYMFhZmUxoVAGJiYlYvXo1bt++rS1r2bIloqOj2ZeXiKgCtm/fjkGDBkEIUWw2KSEELl26hEuXLmHUqFFo0aJFhV9HCIHCwkIUFBTobPv27cPOnTvx4MEDvc+5a9cuNGnSBO3bty921+Wtt96Cra0tjh07hvz8fAwfPhyxsbFlzqj11ltvITY2Flu3boWzszMmTJiAvn374sSJEwCKvrj36tULc+bMgbu7O/744w+89tprSElJKfO81YWHh4dZz35X3VlbW6NRo0ZwcXGROxSTYzLZw7Bhw6BQKDBmzBhMnDgRderUKfYFWJIk/Oc//5EpQiqv06dPY/PmzcjLywNQ1AAHDhyItm3b8qoCEVEFnD17FoMGDUJhYWGpUwJrxqasXLkS7777bplX5AsKCkpMXjTlpdHMVKrpBvcopVIJNze3YolLzZo18dtvvyE6OhrDhw/XOebKlSs4cuQIzp07h5YtWwIAlixZgsjISCxYsKDEBSPT0tKwevVqbNq0CZ06dQJQtM5JcHAwTp8+jTZt2sDV1VU7ORFQdGV7/Pjx+Pjjj0t9b9WFJEnF1oAh8+Hs7IygoCCDjXuzNCaT2Gg+DC19gTBzl5CQgIMHDyIjIwOOjo7o3LmztltZTk4ONm/ejLNnz2r39/b2xogRIwy2mjER6aesNkvmY86cOSXeqSlNbGwsRo4cqZOsPJq8VJRSqYSTkxOCg4O1YzQ0m4uLS6kD9JVKZYkXtk6dOgUXFxdtUgMAXbp0gUKhwJkzZ9CnT59ix1y4cAH5+fno0qWLtqxRo0bw9fXFqVOn0KZNm2LHaCZDCA8Pr8jbtig+Pj7sOWGGFAoF/Pz8UKdOHV4kLoPJJDZlLfJE8nu8X7dCoYBardauQTN69GicP38eSUlJ2mPatWuHAQMG8HY3kQye1GZnzJiBZ555Ru4wqQyFhYXIz8/HX3/9hT179pQ7qVGr1fj1119x/fp1vRc8VigUsLKyKnF7NDnp3Lkz/Pz89H5Pj0tMTISHh4dOmZWVFdzc3JCYmFjqMTY2NsW64Xh6ehY7Jjo6Grt27UJ2djaef/55rFq1qtIxmzNXV1de2DBDTk5OaNiwIRPScjCZxIZMV0n9ujVdHoQQiI2NRWxsLLp27Yp69erB1tYWL730ks4VOCKqOk9qs3v37sW+ffuwZcsW9O3bV85QTZoQAmq1Gnl5eSgoKEB+fn6x7dHy7Oxs5Obmau+KPLpPRR5r6uzq1avlTmoeFR8fj6ZNm+qUSZKkk6g8/nNZV4If/f+UmppaZmIzd+5czJ07V/s4Ozsbp0+fxoQJE7Rlv//+u97vSV+ffvopZs6cievXr2PatGmYNGkSli1bZvTXNUUuLi4IDg7m1X4zopnkoXbt2qy3cjKpxKawsBBff/01YmNjcevWLQBF/WKjoqIwePDgKpvKcenSpfj444+RmJiIpk2bYsmSJWjVqlWp+2/duhUzZsxAfHw8GjRogPnz5yMyMrJKYjU2ffp1//DDDxg3bhymTp1a7AocEVWN8rTZwsJCSJKEQYMG4eTJkyZ950YzkP3xZKK0ZKM8CUhpZSXtU5GEwtDy8vIgSZJesUiShMLCQtja2kKhUOhsj9MkUvrMZnblyhUEBweXekd+7NixGDhwoPbx4MGD0a9fP51Eunbt2vDy8tK506+JJyUlBV5eXiWe28vLC3l5eUhNTdW5a3P37t1ix3h5ecHLywuNGjWCm5sb2rdvjxkzZsDb27vc79USeHp6IjAw0OTX8qH/sbW1RXBwMNcZ0pPJJDZpaWmIiIjAuXPn4OjoiHr16gEA4uLisG3bNnzxxRc4cOCA0adp3LJlCyZNmoTly5ejdevWWLRoESIiInDt2rUSv6yfPHkS0dHRmDdvHqKiorBp0yb07t0bFy9eREhIiFFjrQr69OuWJAkJCQlMaohkVN42q9lnzpw52LVr1xP3ffyOQklJwuPJhr4JSGllppBcGJNSqSxzS0xM1Pt3IISAk5MTatSoYfB4hRDIyclBQUFBqYmNm5ubzro6dnZ28PDwQGBgoM5+YWFhSE1NxYULFxAaGgoAOHToENRqNVq3bl3iuUNDQ2FtbY2DBw+iX79+AIBr164hISEBYWFhpcatuQin73TU5kyhUKBevXpcuNHMODk5oXHjxrC2tpY7FLNjMonN9OnTceHCBSxZsgSjRo3SVmZ+fj5WrVqFiRMnYvr06ViyZIlR41i4cCFGjRqlnbll+fLliI2NxVdffYWpU6cW23/x4sXo3r07Jk+eDACYPXs24uLi8Pnnn2P58uVGjdXYEhIS9O7XHRsbi4SEBPbhJZKBvm22sLAQ33//PWbOnAkHB4cykw1LJkkSbGxsYGVlBWtra+326GMrK6sS99E8BwA3b96Evb09bG1ttV27SkpUHi1XKBRP/MIZGhqKnTt36n3HJigoyChX6DVjtQwhODgY3bt3x6hRo7B8+XLk5+djwoQJePHFF7WTzty+fRudO3fG+vXr0apVKzg7O2PEiBGYNGkS3Nzc4OTkhNdffx1hYWHaiQP27t2Lu3fv4plnnoGDgwMuX76MyZMno127dtVm/RZ7e3sEBQXB3t5e7lBIDy4uLmjcuLHFLDhb1UwmsdmxYwfGjx+P8ePH65RbW1tj3LhxuHLlCr777jujJjZ5eXm4cOECpk2bpi1TKBTo0qULTp06VeIxp06dwqRJk3TKIiIisHPnzlJfJzc3V+eK0cOHDwHA5L5AHDhwoEJXCX/44QcMHTrUSFFVD5ovlJmZmZWawcgcaMYkaL5IU8VVtM0ePXpU9hkpFQpFmUmEpkypVJaYWJRUVlai8uhWnuTiSbKysrR3MJ40DWthYWGZUyo/zsHBAW3atMHp06fLffdc04XFGG1KCAErKyu92uyjXQoft3btWrzxxhvo3LkzFAoF+vTpg08//VS7b1ZWFq5du4b09HRt2b///W8AQL9+/ZCbm4uuXbtiyZIl2uetra2xYsUKvPXWW8jNzUXdunXRu3dvTJkyRa/fiVyfxY+OidO3DiVJgqenJ+rWrQshhPY7RnlUp89jOer2SfXq4OAAX19fZGdnG/R15a7XKr3zJEyESqUSS5cuLfX5pUuXCpVKZdQYbt++LQCIkydP6pRPnjxZtGrVqsRjrK2txaZNm3TKli5dKjw8PEp9nZkzZwoA3Lhx48aNGzdu3LhZ9FaVTOaOTWBgIHbv3l3sjo3G7t27Ub9+/SqOyjg0M7No/PLLLwgPD8eZM2fQvHlzGSPTtXbtWowePVrv41auXMk7NgagGZtQHWiuxlPlVLTNfvHFFxg+fDgHFleSsdvs7t27MXz4cO1sbY/T3HUaMWIEmjVrZpQYAgMD0bJlS9ja2labNivHZ3FwcDD++ecfODs748MPPyzXMT4+PmjTpk2lF26sTp/HVV23pdWrlZUVevTooff07PqoLvVqMonN+PHjMWHCBERGRuLNN99Ew4YNARQNCPzss8+041aMqVatWlAqlbh7965OeUkzrWh4eXnptT8AqFQqnQ8ezYwXmm4SpiIiIqJCM/F069bNpN6HueLvkPRV0TYbGRnJVawNwNht9uWXX0ajRo0we/bsYmOpJEnC008/jcjISKOMIbG3t8dzzz0HHx8fg5/b1MnxWay5yCBJ0hNfX5IktGnTBiEhIZwgQE9VXbel1WvXrl05NtlATCqxSUpKwkcffYQDBw7oPGdtbY0PPvgA48aNM2oMNjY2CA0NxcGDB9G7d28ARf0hDx48qDP3/qPCwsJw8OBBvPnmm9qyuLi4MmdmMRe+vr6IiorC3r17y9UfXKlUomfPnmycRDJhm7V8zzzzDHbv3o2EhAQ0bdoUqampsLOzwwcffKAzC5khNWzYEG3btq0WV3vNjUqlQpcuXVCnTh25Q6EKatCgAQICAuQOw2KYTGIDADExMZgwYQLi4uKQkJAAoGgdmy5duqBWrVpVEsOkSZMwdOhQtGzZEq1atcKiRYuQmZmpnSXtlVdeQZ06dTBv3jwAwBtvvIHw8HB88skn6NmzJzZv3ozz589jxYoVVRKvsc2YMQP79u174lVgSZIgSRLef//9KoyOiB7HNls9+Pr6wt7eHqmpqVCpVEZJaqytrdG+fftiUzSTabCzs0NkZCRq1qwpdyhUQUqlssx1Ekl/JpXYAEXdwaKjo2V7/UGDBuHevXv44IMPkJiYiGbNmmH//v3w9PQEUDSd6qP90Nu2bYtNmzbh/fffx3vvvYcGDRpg586dFrGGDVB0dXDLli3aVcxLugqsVCohSRK+/fZbk17oj6g6YJslQ3B0dET37t3h6uoqdyhUApVKhaioKNaPmfHy8tLOKggA9erV43TcBiYJfTpjV4E9e/Zg7969iI+PBwD4+/sjMjISUVFR8gZmRBcvXkRoaCguXLiAFi1ayB1Oic6dO6fTr1uhUGjXMnj++efx/vvv8wsSkQlhm7V8devWxe3bt+Hi4oL58+cb7Lxubm7o2bMn7OzsDHZO0k9ZdatQKBAVFVXmWF4yXenp6di8eTMAoEePHtVy3Joxmcwdm9TUVPTp0wfHjh2DUqmEt7c3AODHH3/El19+ifbt22Pnzp1wcXGRN9Bq6tF+3YcOHUJ6ejqcnJzQqVMn9s8nMkFss1QRrq6uTGpMXKtWrZjUWABra2vtIrRkOCaT2Lzxxhs4fvw45s+fj3HjxmlvzWVmZmLZsmWYNm0a3njjDaxbt07mSKs3X19fDBs2TO4wiKic2GapvGrWrIkePXowqTFh7u7ueOqpp+QOgwzA19cXSqVS7jAsjskkNjt37sT48ePxzjvv6JTb29tj8uTJSEhIwPr162WKjoiIyHJ5e3ujW7dunPbbxIWFhXFKZwthjGnZyYQSG2trawQFBZX6fKNGjbiuBxERkYE1aNAAzz33HK8em7jatWuzC5qFUCgU7BJsJCazzHS/fv2wdevWEmfwKSgowLfffosBAwbIEBkREZHlUSgUCAsLQ4cOHZjUmIGmTZvKHQIZiKenJy/WG4nJ3LEZMmQIJkyYgLZt22L06NHaefNv3LiBFStWIC8vD4MHD8bFixd1jjPVWcSIiIhMVa1atRAeHs41UEyUl5cXcnNzUaNGDQCAs7Mz6tatK3NUZCgeHh5yh2CxTCaxCQ8P1/587tw5bR/SR2ejfnQfIQQkSSrX6tpERERU1O37mWeeQePGjXXWZCPTcv78eRw9ehTXrl0DADRu3JhjayyIMRbUpSImk9isWbNG7hCIiIgslo+PD9q3bw8HBwe5QyE9KJVKNGjQQO4wyIAcHR3lDsFimUxiM3ToULlDICIisjiSJKFNmzYICQnhVX8z5OPjA1tbW7nDIAPSdDEkwzOZxOZRDx8+xN9//w2gqEHz6hIREZH+lEolunbtyhmYzFj9+vXlDoEMjImq8ZhUB9tz586hY8eOcHV1RUhICEJCQuDq6opOnTrh/PnzcodHRERkNiRJQpcuXZjUmDFJkuDj4yN3GGRgnBHNeEzmjs2ZM2fQoUMH2NjYYOTIkQgODgYAXLlyBd988w2ee+45HDlyBK1atZI5UiIiItPXsmVL+Pn5yR0GVYKHhwdsbGzkDoMMyMrKil1CjchkEpvp06ejTp06+Omnn4otQBUTE4N27dph+vTpiIuLkylCIiIi8+Dt7Y1mzZrJHQZVEqcFtjxcM8q4TKYr2pkzZzBmzJgSV9X19PTE6NGjcfr0aRkiIyIiMh/W1tbo0KEDrwpbgFq1askdAhkYp1k3LpP57SoUChQUFJT6fGFhIf8zEBERPUGrVq04nayFcHFxkTsEMjB+lzUuk/nttm3bFkuXLsWtW7eKPZeQkIBly5ahXbt2MkRGRERkHtzd3dG4cWO5wyADcXJykjsEMjDeSTUukxljM3fuXLRv3x6NGjVCnz590LBhQwDAtWvXsGvXLlhZWWHevHkyR0lERGS62rZtyy9OFkKlUkGlUskdBpFZMZnEpnnz5jh79iymT5+O3bt3IysrC0DRIkbdu3fHnDlzeBWKiIjo/3l5eUEIASuroj/l9evXh6enp8xRkaFwDT8i/ZlEYpObm4sDBw7A398fO3bsgFqtxr179wAU3VZnf0QiIiJd58+fR05ODtavXw9JktCyZUu5QyIDsre3lzsEMgIhhNwhWDSTyBhsbGwwYMAAnDx5EkDRwCpPT094enpWaVKTkpKCwYMHw8nJCS4uLhgxYgQePnxY5v6vv/46goKCYGdnB19fX0ycOBFpaWlVFjMREZG/vz+cnZ3lDoMMyM7OTu4QiMyOSSQ2kiShQYMGSE5OljWOwYMH4/Lly4iLi8OePXtw7NgxjB49utT979y5gzt37mDBggW4dOkS1q5di/3792PEiBFVGDUREVV3gYGBcodABsbxNUT6M4muaADw3nvvYdKkSRgwYACCgoKq/PWvXLmC/fv349y5c9rb+UuWLEFkZCQWLFiA2rVrFzsmJCQE27Zt0z6uX78+PvzwQwwZMgQFBQXafs9ERETGVNLfKDJvNjY2codAZHZM5pv36dOnUbNmTYSEhKBDhw7w9/cvdhtWkiQsXrzYKK9/6tQpuLi46PRR7tKlCxQKBc6cOYM+ffqU6zxpaWlwcnIqM6nJzc1Fbm6u9nFZ3d2IiIjK4uTkxKv7FogXRy0TZy00LpNpNZ9//rn254MHD5a4jzETm8TERHh4eOiUWVlZwc3NDYmJieU6R3JyMmbPnl1m9zUAmDdvHmbNmlXhWImIiDQ4tsYyKZVKuUMgMjsmMcYGANRq9RO3wsJCvc87depUSJJU5nb16tVKx5+eno6ePXuicePGiImJKXPfadOmIS0tTbsdPXq00q9PRETVE2fPskycEZZIfyZzx8ZY3n77bQwbNqzMferVqwcvLy8kJSXplBcUFCAlJQVeXl5lHp+RkYHu3bvD0dERO3bsgLW1dZn7P77oFueqJyKiiuLsWZaJXZYsE6d7Ni6TS2wuXbqEvXv3Ij4+HkDRFJY9evTAU089VaHzubu7w93d/Yn7hYWFITU1FRcuXEBoaCgA4NChQ1Cr1WjdunWpx6WnpyMiIgIqlQq7d++Gra1theIkIiKqCP7dISIqYjKJTW5uLsaMGYMNGzZACKG9BatWqzFt2jQMHjwYq1atMtosIcHBwejevTtGjRqF5cuXIz8/HxMmTMCLL76onW3m9u3b6Ny5M9avX49WrVohPT0d3bp1Q1ZWFr7++mukp6cjPT0dQFFCxf6xRERkbJw4gMh88E6ccZlMB853330X69evx7hx43DlyhXk5OQgNzcXV65cwdixY/H1119jypQpRo1h48aNaNSoETp37ozIyEg8++yzWLFihfb5/Px8XLt2DVlZWQCAixcv4syZM/jtt98QGBgIb29v7fb3338bNVYiIiKAiY2lYpcly8SxU8YlCRNpObVq1ULPnj2xbt26Ep9/+eWXsW/fPtkX8TSGixcvIjQ0FBcuXECLFi3kDoeIiMxETk4OUlNTnzgWlMxPYWEhe34Q6clk0sb8/Hy0adOm1Ofbtm2LgoKCKoyIiIjI9HEhRyKiIiaT2ERERODAgQOlPr9//35069atCiMiIiIyfVzI0TJxLAaR/kzm03D27NkYOHAg+vbti9deew2BgYEAgBs3bmDp0qW4desWtmzZgpSUFJ3j3Nzc5AiXiIjIJPALsGXiWAwi/ZlMYhMcHAwA+O2337Br1y6d5zTDgBo3blzsuIos2klERGQpmNgQERUxmcTmgw8+4IczERGRnkxkDiAiItmZTGITExMjdwhERERmh12WiIiK8NOQiIjIjDGxISIqwk9DIiIiM8ZZ0YiIijCxISIiMmNMbIiIijCxISIiMlMqlYoT7xAR/T8mNkRERGaKSQ0R0f8wsSEiIiIiIrPHxIaIiIiIiMweExsiIiIiIjJ7TGyIiIiIiMjsMbEhIiIiIiKzx8SGiIiIiIjMHlf1ogr5559/8M8//8gdBhmYt7c3vL295Q6DjIBtlohIfvw7a1xMbEyAt7c3Zs6caTb/0XNzcxEdHY2jR4/KHQoZWHh4OA4cOACVSiV3KGRAbLNERKaBf2eNSxJCCLmDIPOSnp4OZ2dnHD16FA4ODnKHQwby8OFDhIeHIy0tDU5OTnKHQwbENmuZNG2W9Wp5WLeWiX9njY93bKjCmjVrxoZpQdLT0+UOgYyMbdayaNos69XysG4tE//OGh8nDyAiIiIiIrPHxIaIiIiIiMweExvSm0qlwsyZMznwzcKwXi0X69YysV4tF+vWMrFejY+TBxARERERkdnjHRsiIiIiIjJ7TGyIiIiIiMjsMbEhIiIiIiKzx8SGiIiIiIjMHhMbIjMiSVK5tiNHjlT6tbKyshATE6PXuT788EO88MIL8PT0hCRJiImJqXQcRObMlNvs1atXMWXKFDRr1gyOjo7w9vZGz549cf78+UrHUh2Yct3euXMHQ4YMQVBQEBwdHeHi4oJWrVph3bp14JxRZTPlen3cxo0bIUkSHBwcKh2LpbCSOwAiKr8NGzboPF6/fj3i4uKKlQcHB1f6tbKysjBr1iwAQIcOHcp1zPvvvw8vLy80b94cBw4cqHQMRObOlNvsqlWrsHr1avTr1w/jx49HWloavvzyS7Rp0wb79+9Hly5dKh2TJTPluk1OTsZ///tf9O/fH76+vsjPz0dcXByGDRuGa9euYe7cuZWOyVKZcr0+6uHDh5gyZQrs7e0rHYclYWJDZEaGDBmi8/j06dOIi4srVi6Xmzdvwt/fH8nJyXB3d5c7HCLZmXKbjY6ORkxMjM7V3ldffRXBwcGIiYlhYvMEply3Tz/9dLG7ABMmTMDzzz+Pzz77DLNnz4ZSqZQnOBNnyvX6qDlz5sDR0REdO3bEzp075Q7HZLArGpGFUavVWLRoEZo0aQJbW1t4enpizJgxePDggc5+58+fR0REBGrVqgU7OzsEBATg1VdfBQDEx8drE5NZs2Zpb70/qWuZv7+/Md4SkUWTq82GhoYW68JSs2ZNtG/fHleuXDHsm6ym5Pw8Lom/vz+ysrKQl5dX6fdWncldrzdu3MCnn36KhQsXwsqK9ygexd8GkYUZM2YM1q5di+HDh2PixIm4efMmPv/8c/z88884ceIErK2tkZSUhG7dusHd3R1Tp06Fi4sL4uPjsX37dgCAu7s7vvjiC4wbNw59+vRB3759ARRdBSQiwzK1NpuYmIhatWoZ9D1WV3LXbXZ2NjIzM/Hw4UMcPXoUa9asQVhYGOzs7Iz6vi2d3PX65ptvomPHjoiMjMS3335r1PdqdgQRma3XXntNPNqMjx8/LgCIjRs36uy3f/9+nfIdO3YIAOLcuXOlnvvevXsCgJg5c6becVXmWCJLZqptVuPYsWNCkiQxY8aMCp+jujLFup03b54AoN06d+4sEhIS9DpHdWdq9bpnzx5hZWUlLl++LIQQYujQocLe3l6Pd2TZ2BWNyIJs3boVzs7O6Nq1K5KTk7WbpsvJ4cOHAQAuLi4AgD179iA/P1/GiImqN1Nqs0lJSXjppZcQEBCAKVOmGOU1qhNTqNvo6GjExcVh06ZNeOmllwAU3cWhipOzXvPy8vDWW29h7NixaNy4sUHOaWmY2BBZkBs3biAtLQ0eHh5wd3fX2R4+fIikpCQAQHh4OPr164dZs2ahVq1a6NWrF9asWYPc3FyZ3wFR9WIqbTYzMxNRUVHIyMjArl27OH2sAZhC3fr5+aFLly6Ijo7Gxo0bUa9ePXTp0oXJTSXIWa+ffvopkpOTtTOpUXEcY0NkQdRqNTw8PLBx48YSn9cMVJQkCd999x1Onz6N77//HgcOHMCrr76KTz75BKdPn+aXGqIqYgptNi8vD3379sWvv/6KAwcOICQkpMLnov8xhbp9XP/+/bFy5UocO3YMERERBjtvdSJXvaalpWHOnDkYP3480tPTkZ6eDqBo2mchBOLj41GjRg14eHhU7g2aO7n7whFRxT3e93f8+PFCqVSKrKwsvc+1ceNGAUCsXLlSCCFEcnIyx9gQGZiptdnCwkIxaNAgoVQqxbZt2/SOgf7H1Oq2JDt37hQAxJYtWyp1nurEVOr15s2bOuOlStp69eqld0yWhl3RiCzIwIEDUVhYiNmzZxd7rqCgAKmpqQCABw8eFFt9ulmzZgCgvU1eo0YNANAeQ0SGJ3ebff3117FlyxYsW7ZMOysTGYacdXvv3r0Sy1evXg1JktCiRYtynYeKk6tePTw8sGPHjmJbx44dYWtrix07dmDatGkVf2MWgl3RiCxIeHg4xowZg3nz5uGXX35Bt27dYG1tjRs3bmDr1q1YvHgx+vfvj3Xr1mHZsmXo06cP6tevj4yMDKxcuRJOTk6IjIwEANjZ2aFx48bYsmULGjZsCDc3N4SEhJTZTWXDhg24desWsrKyAADHjh3DnDlzAAAvv/wy/Pz8jP9LIDIjcrbZRYsWYdmyZQgLC0ONGjXw9ddf6zzfp08frmpeCXLW7YcffogTJ06ge/fu8PX1RUpKCrZt24Zz587h9ddfR2BgYFX+KiyKXPVao0YN9O7du1j5zp07cfbs2RKfq5bkvWFERJXx+C1yjRUrVojQ0FBhZ2cnHB0dxVNPPSWmTJki7ty5I4QQ4uLFiyI6Olr4+voKlUolPDw8RFRUlDh//rzOeU6ePClCQ0OFjY1NuW6Xh4eHl3qL/PDhw4Z620Rmy5Ta7NChQ8vs1nLz5k1DvnWLZ0p1+8MPP4ioqChRu3ZtYW1tLRwdHUW7du3EmjVrhFqtNuj7tnSmVK8l4XTPuiQhHrtPRkREREREZGY4xoaIiIiIiMweExsiIiIiIjJ7TGyIiIiIiMjsMbEhIiIiIiKzx8SGiIiIiIjMHhMbIiIiIiIye0xsiKqR+Ph4SJKEtWvXyh0KEZUD26xlYr1aLtatvJjYEBERERGR2eMCnUTViBACubm5sLa2hlKplDscInoCtlnLxHq1XKxbeTGxISIiIiIis8euaERmJiYmBpIk4fr16xgyZAicnZ3h7u6OGTNmQAiBv//+G7169YKTkxO8vLzwySefaI8tqe/vsGHD4ODggNu3b6N3795wcHCAu7s73nnnHRQWFmr3O3LkCCRJwpEjR3TiKemciYmJGD58OOrWrQuVSgVvb2/06tUL8fHxRvqtEJkutlnLxHq1XKxb88XEhshMDRo0CGq1Gh999BFat26NOXPmYNGiRejatSvq1KmD+fPnIzAwEO+88w6OHTtW5rkKCwsRERGBmjVrYsGCBQgPD8cnn3yCFStWVCi2fv36YceOHRg+fDiWLVuGiRMnIiMjAwkJCRU6H5ElYJu1TKxXy8W6NUOCiMzKzJkzBQAxevRobVlBQYGoW7eukCRJfPTRR9ryBw8eCDs7OzF06FAhhBA3b94UAMSaNWu0+wwdOlQAEP/61790Xqd58+YiNDRU+/jw4cMCgDh8+LDOfo+f88GDBwKA+Pjjjw3zhonMHNusZWK9Wi7WrfniHRsiMzVy5Ejtz0qlEi1btoQQAiNGjNCWu7i4ICgoCH/99dcTzzd27Fidx+3bty/XcY+zs7ODjY0Njhw5ggcPHuh9PJGlYpu1TKxXy8W6NT9MbIjMlK+vr85jZ2dn2NraolatWsXKn/TBZ2trC3d3d50yV1fXCn1gqlQqzJ8/H/v27YOnpyeee+45/Pvf/0ZiYqLe5yKyJGyzlon1arlYt+aHiQ2RmSppGsnSppYUT5j8sDxTUkqSVGL5owMfNd58801cv34d8+bNg62tLWbMmIHg4GD8/PPPT3wdIkvFNmuZWK+Wi3VrfpjYEFG5uLq6AgBSU1N1ym/dulXi/vXr18fbb7+NH374AZcuXUJeXp7OzDFEZFxss5aJ9Wq5WLeVx8SGiMrFz88PSqWy2Mwvy5Yt03mclZWFnJwcnbL69evD0dERubm5Ro+TiIqwzVom1qvlYt1WnpXcARCReXB2dsaAAQOwZMkSSJKE+vXrY8+ePUhKStLZ7/r16+jcuTMGDhyIxo0bw8rKCjt27MDdu3fx4osvyhQ9UfXDNmuZWK+Wi3VbeUxsiKjclixZgvz8fCxfvhwqlQoDBw7Exx9/jJCQEO0+Pj4+iI6OxsGDB7FhwwZYWVmhUaNG+Pbbb9GvXz8ZoyeqfthmLRPr1XKxbitHEk8a7URERERERGTiOMaGiIiIiIjMHhMbIiIiIiIye0xsiIiIiIjI7DGxISIiIiIis8fEhoiIiIiIzB4TGyIyivj4eEiShLVr18odChGVA9usZWK9WibWa8mY2BCZgD///BNjxoxBvXr1YGtrCycnJ7Rr1w6LFy9Gdna20V73999/R0xMDOLj4432GuXx4Ycf4oUXXoCnpyckSUJMTIys8RA9SXVus1evXsWUKVPQrFkzODo6wtvbGz179sT58+dli8lQWK+sV0NivVY9LtBJJLPY2FgMGDAAKpUKr7zyCkJCQpCXl4effvoJkydPxuXLl7FixQqjvPbvv/+OWbNmoUOHDvD39zfKa5TH+++/Dy8vLzRv3hwHDhyQLQ6i8qjubXbVqlVYvXo1+vXrh/HjxyMtLQ1ffvkl2rRpg/3796NLly6yxFVZrFfWq6GxXqseExsiGd28eRMvvvgi/Pz8cOjQIXh7e2ufe+211/DHH38gNjZWxgj/RwiBnJwc2NnZGfzcN2/ehL+/P5KTk+Hu7m7w8xMZCtssEB0djZiYGDg4OGjLXn31VQQHByMmJsYsvyixXlmvcmO9GoggItmMHTtWABAnTpwo1/75+fniX//6l6hXr56wsbERfn5+Ytq0aSInJ0dnPz8/P9GzZ09x/Phx8cwzzwiVSiUCAgLEunXrtPusWbNGACi2HT58WOcc+/fvF6GhoUKlUolPP/1UCCHEn3/+Kfr37y9cXV2FnZ2daN26tdizZ49ODDdv3hQAxJo1a8r9+7h3754AIGbOnFnuY4iqEtts6fr27Svc3NwqdKzcWK+lY72yXs0JExsiGdWpU0fUq1ev3PsPHTpUABD9+/cXS5cuFa+88ooAIHr37q2zn5+fnwgKChKenp7ivffeE59//rlo0aKFkCRJXLp0SQhR9ME5ceJEAUC89957YsOGDWLDhg0iMTFRe47AwEDh6uoqpk6dKpYvXy4OHz4sEhMThaenp3B0dBTTp08XCxcuFE2bNhUKhUJs375dGwMTG7JEbLOla9u2rWjYsGGFjpUb67V0rFfWqzlhYkMkk7S0NAFA9OrVq1z7//LLLwKAGDlypE75O++8IwCIQ4cOacv8/PwEAHHs2DFtWVJSklCpVOLtt9/Wlm3dulXnCtKjNOfYv3+/Tvmbb74pAIjjx49ryzIyMkRAQIDw9/cXhYWFQggmNmR52GZLd+zYMSFJkpgxY4bex8qN9Vo61ivr1dxwVjQimaSnpwMAHB0dy7X/3r17AQCTJk3SKX/77bcBoFg/4caNG6N9+/bax+7u7ggKCsJff/1V7hgDAgIQERFRLI5WrVrh2Wef1ZY5ODhg9OjRiI+Px++//17u8xOZE7bZkiUlJeGll15CQEAApkyZUqlzyYH1WjLWaxHWq3lhYkMkEycnJwBARkZGufa/desWFAoFAgMDdcq9vLzg4uKCW7du6ZT7+voWO4erqysePHhQ7hgDAgJKjCMoKKhYeXBwsPZ5IkvENltcZmYmoqKikJGRgV27dukMUDYXrNfiWK//w3o1L5wVjUgmTk5OqF27Ni5duqTXcZIklWs/pVJZYrkQotyvZYwZ0IjMFdusrry8PPTt2xe//vorDhw4gJCQkCp7bUNivepivbJezRnv2BDJKCoqCn/++SdOnTr1xH39/PygVqtx48YNnfK7d+8iNTUVfn5+er9+eT/AH4/j2rVrxcqvXr2qfZ7IUrHNFlGr1XjllVdw8OBBbNq0CeHh4Xqfw5SwXouwXlmv5o6JDZGMpkyZAnt7e4wcORJ3794t9vyff/6JxYsXAwAiIyMBAIsWLdLZZ+HChQCAnj176v369vb2AIDU1NRyHxMZGYmzZ8/q/KHIzMzEihUr4O/vj8aNG+sdB5G5YJst8vrrr2PLli1YtmwZ+vbtq/fxpob1WoT1yno1d+yKRiSj+vXrY9OmTRg0aBCCg4N1VkU+efIktm7dimHDhgEAmjZtiqFDh2LFihVITU1FeHg4zp49i3Xr1qF3797o2LGj3q/frFkzKJVKzJ8/H2lpaVCpVOjUqRM8PDxKPWbq1Kn45ptv0KNHD0ycOBFubm5Yt24dbt68iW3btkGh0P96yYYNG3Dr1i1kZWUBAI4dO4Y5c+YAAF5++WXeBSKTwTZb9MVv2bJlCAsLQ40aNfD111/rPN+nTx/tFzpzwXplvbJeLYS8k7IRkRBCXL9+XYwaNUr4+/sLGxsb4ejoKNq1ayeWLFmiszBYfn6+mDVrlggICBDW1tbCx8enzMXDHhceHi7Cw8N1ylauXCnq1asnlEpliYuHlUSzeJiLi4uwtbUVrVq1qtTiYeHh4SUuZIZSpskkklt1brOatT5K227evFnm8aaM9cp6Zb2aN0kIPUY5ERERERERmSCOsSEiIiIiIrPHxIaIiIiIiMweExsiIiIiIjJ7TGyIiIiIiMjsMbEhIiIiIiKzx8SGiIiIiIjMHhMbIiIiIiIye0xsiIiIiIjI7DGxISIiIiIis8fEhoiIiIiIzB4TGyIiIiIiMntMbIiIiIiIyOwxsSEiIiIiIrP3f1oYTqzgt7SWAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_groups_baseline.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "fbf34d17", - "metadata": {}, - "source": [ - "## Aesthetic adjustments\n", - "\n", - "Here we demonstrate a few proportion plot specific aesthetic adjustments." - ] - }, - { - "cell_type": "markdown", - "id": "8dd438f2", - "metadata": {}, - "source": [ - "### Bar Width\n", - "\n", - "You can modify the width of the bar plot bars (unpaired data) by setting the parameter ``bar_width`` in the ``.plot()`` method. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a5cabdfa", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired.mean_diff.plot(bar_width=0.3);" - ] - }, - { - "cell_type": "markdown", - "id": "651cd0d4", - "metadata": {}, - "source": [ - "### Bar desaturation\n", - "\n", - "The ``raw_desat`` is used to control the amount of desaturation applied to the bar plot bar colors (specific to unpaired data). A value of 0.0 means full desaturation (i.e., grayscale), \n", - "while a value of 1.0 means no desaturation (i.e., full color saturation). The default one is 0.8.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9b41c4a5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired.mean_diff.plot(raw_desat=1.0);" - ] - }, - { - "cell_type": "markdown", - "id": "03639659", - "metadata": {}, - "source": [ - "### Raw Label and Contrast Label\n", - "The parameters ``raw_label`` and ``contrast_label`` can be used to set labels for the y-axis of the bar plot and the contrast plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2783f021", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired.mean_diff.plot(raw_label=\"success\",contrast_label=\"difference\");\n", - "two_groups_paired.mean_diff.plot(raw_label=\"success\",contrast_label=\"difference\");" - ] - }, - { - "cell_type": "markdown", - "id": "3763336c", - "metadata": {}, - "source": [ - "### Barplot kwargs\n", - "The parameters ``barplot_kwargs`` can be used to alter the aesthetics of the bar plot. This is a dictionary that can be used to pass additional arguments to the bar plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8b9225c1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired.mean_diff.plot(barplot_kwargs={\"alpha\":0.5, \"edgecolor\":\"red\", \"linewidth\":2, 'errorbar': ('sd', 0.1)});" - ] - }, - { - "cell_type": "markdown", - "id": "2e283cfc", - "metadata": {}, - "source": [ - "### Sankey and Flow\n", - "\n", - "By changing the ``sankey`` and ``flow`` parameters, you can generate different types of Sankey plots for paired proportions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "68515743", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "separate_control = dabest.load(df, idx=(((\"Control 1\", \"Test 1\"),\n", - " (\"Test 2\", \"Test 3\"),\n", - " (\"Test 4\", \"Test 7\", \"Test 6\"))),\n", - " proportional=True, paired=\"sequential\", id_col=\"ID\")\n", - "\n", - "separate_control.mean_diff.plot();\n", - "separate_control.mean_diff.plot(sankey_kwargs={'sankey':False});\n", - "separate_control.mean_diff.plot(sankey_kwargs={'flow':False});" - ] - }, - { - "cell_type": "markdown", - "id": "f47070a9", - "metadata": {}, - "source": [ - "### Sankey kwargs\n", - "Several exclusive parameters can be provided to the ``.plot()`` method to customize the Sankey plots for paired proportions.\n", - "By modifying the `sankey_kwargs` parameter, you can customize the Sankey plot. The following parameters are supported:\n", - "\n", - "- **align**: The alignment of each Sankey bar. Default is \"center\".\n", - "- **alpha**: The transparency of each Sankey bar. Default is 0.4.\n", - "- **bar_width**: The width of each bar on the side in the plot. Default is 0.1." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e482c557", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "repeated_measures_baseline.mean_diff.plot(sankey_kwargs = {\"alpha\": 0.2,\n", - " \"bar_width\": 0.4});" - ] - }, - { - "cell_type": "markdown", - "id": "32f41159", - "metadata": {}, - "source": [ - "### Custom Palette\n", - "\n", - "The `custom_palette` parameter functions in a similar way for proportion plots as for other plots - however, there are some differences!" - ] - }, - { - "cell_type": "markdown", - "id": "6626a173", - "metadata": {}, - "source": [ - "A `custom_palette` dict can be passed for sankey plots, whereby two keys used are 0 and 1. The color associated with these keys will be used to color the bars in the sankey plot.\n", - "\n", - "For bar plots, the `custom_palette` dict can be passed like a regular plot, with a color associated to each group. The chosen color will then be used to color the filled portion of the bar plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c244c015", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "repeated_measures_baseline.mean_diff.plot(custom_palette={0: \"red\", 1: \"blue\"});\n", - "shared_control.mean_diff.plot(custom_palette={'Control 1': \"red\", 'Test 1': \"blue\", 'Test 2': \"green\", 'Test 3': \"purple\", 'Test 4': \"orange\"});" - ] - }, - { - "cell_type": "markdown", - "id": "802b4eb6", - "metadata": {}, - "source": [ - "Similarly, premade matplotlib/seaborn color palette can be passed. For sankey plots, the first two colors in the palette will be used to color the bars in the sankey plot. For bar plots, the colors will be used to color the filled portion of the bar plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c2dbbb57", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "repeated_measures_baseline.mean_diff.plot(custom_palette='Set1');\n", - "shared_control.mean_diff.plot(custom_palette='Set1');" - ] - }, - { - "cell_type": "markdown", - "id": "d3da6311", - "metadata": {}, - "source": [ - "Passing a custom palette list functions differently for bar plots and sankey plots:\n", - "\n", - "- For bar plots, the list should contain the colors associated with each group. \n", - "- For sankey plots, the list should contain two colors, the first color will be used to color the binary '1's, and the second color will be used to color the '0's.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "32005eb2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "repeated_measures_baseline.mean_diff.plot(custom_palette=['red', 'blue']);\n", - "shared_control.mean_diff.plot(custom_palette=['red', 'blue', 'green', 'purple', 'orange']);" - ] - }, - { - "cell_type": "markdown", - "id": "ed09d7ed", - "metadata": {}, - "source": [ - "### Add counts to proportion plots\n", - "\n", - "By default, the sample counts for each bar in proportion plots are not shown.\n", - "\n", - "This feature can be turned on by setting `prop_sample_counts=True` in the `.plot()` method.\n", - "\n", - "**Note**: This feature is not compatible with `flow=False` in `sankey_kwargs`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f4c41e28", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), proportional=True)\n", - "two_groups_unpaired.mean_diff.plot(prop_sample_counts=True);" - ] - }, - { - "cell_type": "markdown", - "id": "96671380", - "metadata": {}, - "source": [ - "The sample counts kwargs can be utilised via `prop_sample_counts_kwargs` in the `.plot()` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "37ddc29b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired.mean_diff.plot(prop_sample_counts=True, prop_sample_counts_kwargs={\"color\":\"red\"});" - ] - }, - { - "cell_type": "markdown", - "id": "e7fb10a4", - "metadata": {}, - "source": [ - "For further aesthetic changes, the [Plot Aesthetics Tutorial](08-plot_aesthetics.html) provides detailed examples of how to customize the plot.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/tutorials/05-mini_meta.ipynb b/nbs/tutorials/05-mini_meta.ipynb deleted file mode 100644 index e89510f8..00000000 --- a/nbs/tutorials/05-mini_meta.ipynb +++ /dev/null @@ -1,910 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "e074b4cd", - "metadata": {}, - "source": [ - "# Mini-Meta\n", - "\n", - "> Explanation of how to compute the meta-analyzed weighted effect size using dabest.\n", - "\n", - "- order: 5" - ] - }, - { - "cell_type": "markdown", - "id": "d7602abf", - "metadata": {}, - "source": [ - "When scientists conduct replicates of the same experiment, the effect size of each replicate often varies, complicating the interpretation of the results. Starting from v2023.02.14, DABEST can now compute the meta-analyzed weighted effect size given multiple replicates of the same experiment. This can help resolve differences between replicates and simplify interpretation.\n", - "\n", - "For this function, the generic *inverse-variance* weighting method is used to calculate a weighted mean difference, as follows:\n", - "\n", - "$\\hat{\\Delta}_w = \\frac{\\sum\\hat{\\Delta}_i\\,w_i}{\\sum w_i},\\quad w_i =\\frac{1}{\\hat{\\sigma}_i^{2}},\\quad \\hat{\\sigma}_i^{2} =\\operatorname{var}\\!\\big(\\hat{\\Delta}_i\\big)$\n", - "\n", - "The variance used is calculated empirically as the sample variance of the bootstrapped values of the mean difference.\n", - "\n", - "\n", - "$\\hat{\\Delta}_w=\\frac{\\sum\\hat{\\Delta}_i\\,\\hat{w}_i}{\\sum\\hat{w}_i},\\quad \\hat{w}_i=\\frac{n_i-1}{\\sum_{r=1}^{n_i}\\bigl(\\hat{\\Delta}_i^{(r)}-\\bar{\\Delta}_i^{\\mathrm{b}}\\bigr)^2},\\quad \\bar{\\Delta}_i^{\\mathrm{b}}=\\frac{1}{n_i}\\sum_{r=1}^{n_i}\\hat{\\Delta}_i^{(r)}.$\n", - "\n", - "Where\n", - "$\\hat{\\Delta}_w$: estimated weighted delta;\n", - "\n", - "$w_i$: weight for replicate $i$;\n", - "\n", - "$\\hat{\\sigma}_i^2$: sampling variance of the mean-difference estimator for replicate $i$;\n", - "\n", - "$\\hat{\\Delta}_i$: estimated mean difference for replicate $i$;\n", - "\n", - "$\\hat{w}_i$: estimated weight for replicate $i$;\n", - "\n", - "$n_i$: number of bootstrap replicates used for replicate $i$;\n", - "\n", - "$\\hat{\\Delta}_i^{(r)}$: the $r$-th bootstrap estimate of the mean difference for replicate $i$;\n", - "\n", - "$\\bar{\\Delta}_i^{\\mathrm{b}}$: bootstrap mean of the mean differences for replicate $i$" - ] - }, - { - "cell_type": "markdown", - "id": "f60f347a", - "metadata": {}, - "source": [ - "Note that this utilizes the fixed-effects model of meta-analysis, in contrast to the random-effects model. In the fixed-effects model, all variation between the results of each replicate is assumed to be solely due to sampling error. Therefore, we recommend using this function exclusively for replications of the same experiment, where it can be safely assumed that each replicate estimates the same population mean $\\mu$.\n", - "\n", - "Additionally, be aware that as of **v2023.02.14**, DABEST can only compute weighted effect size *for mean difference only*, and not for standardized measures such as Cohen's *d*.\n", - "\n", - "For more information on meta-analysis, please refer to [Chapter 10 of the Cochrane handbook](https://training.cochrane.org/handbook/current/chapter-10)." - ] - }, - { - "cell_type": "markdown", - "id": "93359477", - "metadata": {}, - "source": [ - "## Load libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5f889eb3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pre-compiling numba functions for DABEST...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 62.75it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Numba compilation complete!\n", - "We're using DABEST v2025.10.20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import dabest\n", - "\n", - "print(\"We're using DABEST v{}\".format(dabest.__version__))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0d6f8ba8", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning) # to suppress warnings related to points not being able to be plotted due to dot size" - ] - }, - { - "cell_type": "markdown", - "id": "2a77eb0b", - "metadata": {}, - "source": [ - "## Creating a demo dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6bd57c4c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Control 1Test 1Control 2Test 2Control 3Test 3GenderID
02.7939843.4208753.3246611.7074673.8169401.796581Female1
13.2367593.4679723.6851861.1218463.7503583.944566Female2
23.0191494.3771795.6168913.3013812.9453972.832188Female3
32.8046384.5647802.7731522.5340183.5751793.048267Female4
42.8580193.2200582.5503612.7963653.6921383.276575Female5
\n", - "
" - ], - "text/plain": [ - " Control 1 Test 1 Control 2 Test 2 Control 3 Test 3 Gender ID\n", - "0 2.793984 3.420875 3.324661 1.707467 3.816940 1.796581 Female 1\n", - "1 3.236759 3.467972 3.685186 1.121846 3.750358 3.944566 Female 2\n", - "2 3.019149 4.377179 5.616891 3.301381 2.945397 2.832188 Female 3\n", - "3 2.804638 4.564780 2.773152 2.534018 3.575179 3.048267 Female 4\n", - "4 2.858019 3.220058 2.550361 2.796365 3.692138 3.276575 Female 5" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from scipy.stats import norm # Used in generation of populations.\n", - "\n", - "np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n", - "Ns = 20 # The number of samples taken from each population\n", - "\n", - "# Create samples\n", - "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n", - "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", - "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", - "\n", - "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n", - "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n", - "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n", - "\n", - "\n", - "# Add a `gender` column for coloring the data.\n", - "females = np.repeat('Female', Ns/2).tolist()\n", - "males = np.repeat('Male', Ns/2).tolist()\n", - "gender = females + males\n", - "\n", - "# Add an `id` column for paired data plotting.\n", - "id_col = pd.Series(range(1, Ns+1))\n", - "\n", - "# Combine samples and gender into a DataFrame.\n", - "df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n", - " 'Control 2' : c2, 'Test 2' : t2,\n", - " 'Control 3' : c3, 'Test 3' : t3,\n", - " 'Gender' : gender, 'ID' : id_col\n", - " })\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "id": "5acf961f", - "metadata": {}, - "source": [ - "We now have three *Control* and three *Test* groups, simulating three replicates of the same experiment. Our\n", - "dataset has also a non-numerical column indicating gender, and another\n", - "column indicating the identity of each observation." - ] - }, - { - "cell_type": "markdown", - "id": "f634f0b7", - "metadata": {}, - "source": [ - "This is known as a 'wide' dataset. See this\n", - "[writeup](https://sejdemyr.github.io/r-tutorials/basics/wide-and-long/)\n", - "for more details." - ] - }, - { - "cell_type": "markdown", - "id": "148c542e", - "metadata": {}, - "source": [ - "## Loading data" - ] - }, - { - "cell_type": "markdown", - "id": "fbdc1083", - "metadata": {}, - "source": [ - "Next, we load data as usual using ``dabest.load()``. However, this time, we also specify the argument ``mini_meta=True``. Since we are loading data from three experiments, ``idx`` is passed as a tuple of tuples, as shown below.\n", - "\n", - "When this `dabest` object is invoked, it should indicate that effect sizes will be calculated for each group, along with the weighted delta. It is important to note once again that the weighted delta will only be calculated for mean differences." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b456a848", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:46 2025.\n", - "\n", - "Effect size(s) with 95% confidence intervals will be computed for:\n", - "1. Test 1 minus Control 1\n", - "2. Test 2 minus Control 2\n", - "3. Test 3 minus Control 3\n", - "4. weighted delta (only for mean difference)\n", - "\n", - "5000 resamples will be used to generate the effect size bootstraps." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unpaired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), mini_meta=True)\n", - "unpaired" - ] - }, - { - "cell_type": "markdown", - "id": "adbf8252", - "metadata": {}, - "source": [ - "By calling the ``mean_diff`` attribute, you can view the mean differences for each group as well as the weighted delta.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b9fec10b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:47 2025.\n", - "\n", - "The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].\n", - "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n", - "\n", - "The unpaired mean difference between Control 2 and Test 2 is -1.38 [95%CI -1.93, -0.905].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "The unpaired mean difference between Control 3 and Test 3 is -0.255 [95%CI -0.696, 0.208].\n", - "The p-value of the two-sided permutation t-test is 0.293, calculated for legacy purposes only. \n", - "\n", - "The weighted-average unpaired mean differences is -0.00983 [95%CI -0.225, 0.213].\n", - "The p-value of the two-sided permutation t-test is 0.941, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unpaired.mean_diff" - ] - }, - { - "cell_type": "markdown", - "id": "be1075b3", - "metadata": {}, - "source": [ - "You can view the details of each experiment by accessing the property `mean_diff.results` as follows." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "49d8375e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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controltestcontrol_Ntest_Neffect_sizeis_paireddifferencecibca_lowbca_highbca_interval_idxpct_lowpct_highpct_interval_idxbootstrapsresamplesrandom_seedpermutationspvalue_permutationpermutation_countpermutations_varpvalue_welchstatistic_welchpvalue_students_tstatistic_students_tpvalue_mann_whitneystatistic_mann_whitneybec_differencebec_bootstrapsbec_bca_interval_idxbec_bca_lowbec_bca_highbec_pct_interval_idxbec_pct_lowbec_pct_high
0Control 1Test 12020mean differenceNone0.480290950.2051610.773647(145, 4893)0.1974270.758752(125, 4875)[0.6148498102262239, 0.6752095203445543, 0.300...500012345[-0.17259843762502491, 0.03802293852634886, -0...0.00105000[0.26356588154404337, 0.2710249543904699, 0.26...0.002094-3.3088060.002057-3.3088060.00162583.00.0[-0.09732932551566487, 0.08087009665445155, -0...(127, 4877)-0.2568620.259558(125, 4875)-0.2582600.257590
1Control 2Test 22020mean differenceNone-1.38108595-1.934192-0.905164(94, 4838)-1.901802-0.877098(125, 4875)[-1.7266697532252988, -1.7990605927248775, -1....500012345[0.015164519971271773, 0.017231919606192303, -...0.00005000[1.2241741427801065, 1.2241565174150129, 1.128...0.0000115.1388400.0000095.1388400.000026356.00.0[-0.7109511916465152, -0.3436697507223183, -0....(126, 4876)-0.5786210.598647(125, 4875)-0.5793060.598009
2Control 3Test 32020mean differenceNone-0.25483195-0.6963790.207659(123, 4873)-0.6947900.208585(125, 4875)[0.3059887140714319, -0.22727011648745288, 0.0...500012345[-0.05901068591042824, -0.13617667681797307, 0...0.29345000[0.5835889750166371, 0.5796253365278035, 0.581...0.2947661.0697980.2914591.0697980.285305240.00.0[0.07996849455952271, 0.24534680794041375, 0.0...(124, 4874)-0.2437540.240283(125, 4875)-0.2437130.240490
\n", - "
" - ], - "text/plain": [ - " control test control_N test_N effect_size is_paired \\\n", - "0 Control 1 Test 1 20 20 mean difference None \n", - "1 Control 2 Test 2 20 20 mean difference None \n", - "2 Control 3 Test 3 20 20 mean difference None \n", - "\n", - " difference ci bca_low bca_high bca_interval_idx pct_low pct_high \\\n", - "0 0.480290 95 0.205161 0.773647 (145, 4893) 0.197427 0.758752 \n", - "1 -1.381085 95 -1.934192 -0.905164 (94, 4838) -1.901802 -0.877098 \n", - "2 -0.254831 95 -0.696379 0.207659 (123, 4873) -0.694790 0.208585 \n", - "\n", - " pct_interval_idx bootstraps \\\n", - "0 (125, 4875) [0.6148498102262239, 0.6752095203445543, 0.300... \n", - "1 (125, 4875) [-1.7266697532252988, -1.7990605927248775, -1.... \n", - "2 (125, 4875) [0.3059887140714319, -0.22727011648745288, 0.0... \n", - "\n", - " resamples random_seed permutations \\\n", - "0 5000 12345 [-0.17259843762502491, 0.03802293852634886, -0... \n", - "1 5000 12345 [0.015164519971271773, 0.017231919606192303, -... \n", - "2 5000 12345 [-0.05901068591042824, -0.13617667681797307, 0... \n", - "\n", - " pvalue_permutation permutation_count \\\n", - "0 0.0010 5000 \n", - "1 0.0000 5000 \n", - "2 0.2934 5000 \n", - "\n", - " permutations_var pvalue_welch \\\n", - "0 [0.26356588154404337, 0.2710249543904699, 0.26... 0.002094 \n", - "1 [1.2241741427801065, 1.2241565174150129, 1.128... 0.000011 \n", - "2 [0.5835889750166371, 0.5796253365278035, 0.581... 0.294766 \n", - "\n", - " statistic_welch pvalue_students_t statistic_students_t \\\n", - "0 -3.308806 0.002057 -3.308806 \n", - "1 5.138840 0.000009 5.138840 \n", - "2 1.069798 0.291459 1.069798 \n", - "\n", - " pvalue_mann_whitney statistic_mann_whitney bec_difference \\\n", - "0 0.001625 83.0 0.0 \n", - "1 0.000026 356.0 0.0 \n", - "2 0.285305 240.0 0.0 \n", - "\n", - " bec_bootstraps bec_bca_interval_idx \\\n", - "0 [-0.09732932551566487, 0.08087009665445155, -0... (127, 4877) \n", - "1 [-0.7109511916465152, -0.3436697507223183, -0.... (126, 4876) \n", - "2 [0.07996849455952271, 0.24534680794041375, 0.0... (124, 4874) \n", - "\n", - " bec_bca_low bec_bca_high bec_pct_interval_idx bec_pct_low bec_pct_high \n", - "0 -0.256862 0.259558 (125, 4875) -0.258260 0.257590 \n", - "1 -0.578621 0.598647 (125, 4875) -0.579306 0.598009 \n", - "2 -0.243754 0.240283 (125, 4875) -0.243713 0.240490 " - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.options.display.max_columns = 50\n", - "unpaired.mean_diff.results" - ] - }, - { - "cell_type": "markdown", - "id": "fb25bb87", - "metadata": {}, - "source": [ - "Note, however, that this does not contain the relevant information for our weighted delta. The details of the weighted delta are stored as attributes of the ``mini_meta`` object, such as:\n", - "\n", - " - ``group_var``: the pooled group variances of each set of 2 experiment groups.\n", - " - ``difference``: the weighted mean difference calculated based on the raw data.\n", - " - ``bootstraps``: the deltas of each set of 2 experiment groups calculated based on the bootstraps.\n", - " - ``bootstraps_weighted_delta``: the weighted deltas calculated based on the bootstraps.\n", - " - ``permutations``: the deltas of each set of 2 experiment groups calculated based on the permutation data.\n", - " - ``permutations_var``: the pooled group variances of each set of 2 experiment groups calculated based on permutation data.\n", - " - ``permutations_weighted_delta``: the weighted deltas calculated based on the permutation data.\n", - "\n", - "A dataframe of this mini meta dabest object can also be called via the `mini_meta.results` attribute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8e428551", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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controltestcontrol_Ntest_Ncontrol_vartest_vargroup_vardifferencecibca_lowbca_highbca_interval_idxpct_lowpct_highpct_interval_idxbootstraps_deltasbootstraps_weighted_deltapermutationspermutations_varpermutations_weighted_deltapvalue_permutationpermutation_countbias_correctionjackknives
0[Control 1, Control 2, Control 3][Test 1, Test 2, Test 3][20, 20, 20][20, 20, 20][0.17628013404546258, 0.9584767911266554, 0.16...[0.24512071870152594, 0.48609989925165153, 0.9...[0.2107004263734943, 0.7222883451891535, 0.567...-0.0098395-0.2250730.213221(133, 4883)-0.2271990.210616(125, 4875)[[0.6148498102262239, 0.6752095203445543, 0.30...[0.1383993266160009, 0.040698566036827026, -0....[[-0.17259843762502491, 0.03802293852634886, -...[[0.26356588154404337, 0.2710249543904699, 0.2...[-0.11757207833491819, -0.01292867970093462, -...0.941250000.014539[-0.010633066723935882, -0.010613522663007862,...
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" - ], - "text/plain": [ - " control test control_N \\\n", - "0 [Control 1, Control 2, Control 3] [Test 1, Test 2, Test 3] [20, 20, 20] \n", - "\n", - " test_N control_var \\\n", - "0 [20, 20, 20] [0.17628013404546258, 0.9584767911266554, 0.16... \n", - "\n", - " test_var \\\n", - "0 [0.24512071870152594, 0.48609989925165153, 0.9... \n", - "\n", - " group_var difference ci bca_low \\\n", - "0 [0.2107004263734943, 0.7222883451891535, 0.567... -0.00983 95 -0.225073 \n", - "\n", - " bca_high bca_interval_idx pct_low pct_high pct_interval_idx \\\n", - "0 0.213221 (133, 4883) -0.227199 0.210616 (125, 4875) \n", - "\n", - " bootstraps_deltas \\\n", - "0 [[0.6148498102262239, 0.6752095203445543, 0.30... \n", - "\n", - " bootstraps_weighted_delta \\\n", - "0 [0.1383993266160009, 0.040698566036827026, -0.... \n", - "\n", - " permutations \\\n", - "0 [[-0.17259843762502491, 0.03802293852634886, -... \n", - "\n", - " permutations_var \\\n", - "0 [[0.26356588154404337, 0.2710249543904699, 0.2... \n", - "\n", - " permutations_weighted_delta pvalue_permutation \\\n", - "0 [-0.11757207833491819, -0.01292867970093462, -... 0.9412 \n", - "\n", - " permutation_count bias_correction \\\n", - "0 5000 0.014539 \n", - "\n", - " jackknives \n", - "0 [-0.010633066723935882, -0.010613522663007862,... " - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unpaired.mean_diff.mini_meta.results" - ] - }, - { - "cell_type": "markdown", - "id": "fdb67182", - "metadata": {}, - "source": [ - "## Generating mini meta plots" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "95ae87cd", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "unpaired.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "18855f31", - "metadata": {}, - "source": [ - "You can also hide the weighted delta by passing the argument ``show_mini_meta=False``. In this case, the resulting graph would be identical to a multiple two-groups plot.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "52b75aa6", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "unpaired.mean_diff.plot(show_mini_meta=False);" - ] - }, - { - "cell_type": "markdown", - "id": "07dd8ed5", - "metadata": {}, - "source": [ - "As with regular two-groups plots, you can also analyse paired mini meta experiments via the `paired=baseline` argument." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f8a30435", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "paired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), mini_meta=True, id_col=\"ID\", paired=\"baseline\")\n", - "paired.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "3fa36675", - "metadata": {}, - "source": [ - "For further aesthetic changes, the [Plot Aesthetics Tutorial](08-plot_aesthetics.html) provides detailed examples of how to customize the plot.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/tutorials/06-delta_delta.ipynb b/nbs/tutorials/06-delta_delta.ipynb deleted file mode 100644 index 20c76929..00000000 --- a/nbs/tutorials/06-delta_delta.ipynb +++ /dev/null @@ -1,1051 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "20ccf903", - "metadata": {}, - "source": [ - "# Delta-Delta\n", - "\n", - "> Explanation of how to calculate delta-delta using DABEST.\n", - "\n", - "- order: 6" - ] - }, - { - "cell_type": "markdown", - "id": "0f3b6b88", - "metadata": {}, - "source": [ - "**Since v2023.02.14, DABEST also supports the calculation of delta-delta, an experimental function that facilitates the comparison between two bootstrapped effect sizes computed from two independent categorical variables.** \n", - "\n", - "**Since v2025.03.27, DABEST also supports the calculation of delta-delta for binary data (proportion plots).**\n", - "\n", - "Many experimental designs investigate the effects of two interacting independent variables on a dependent variable. The delta-delta effect size enables us distill the net effect of the two variables. To illustrate this, let's explore the following problem. \n", - "\n", - "Consider an experiment where we test the efficacy of a drug named ``Drug`` on a disease-causing mutation ``M`` based on disease metric ``Y``. The greater the value ``Y`` has, the more severe the disease phenotype is. Phenotype ``Y`` has been shown to be caused by a gain-of-function mutation ``M``, so we expect a difference between wild type (``W``) subjects and mutant subjects (``M``). Now, we want to know whether this effect is ameliorated by the administration of ``Drug`` treatment. We also administer a placebo as a control. In theory, we only expect ``Drug`` to have an effect on the ``M`` group, although in practice, many drugs have non-specific effects on healthy populations too.\n", - "\n", - "Effectively, we have four groups of subjects for comparison." - ] - }, - { - "cell_type": "markdown", - "id": "4cab153a", - "metadata": {}, - "source": [ - "| | Wildtype | Mutant |\n", - "|-------|---------|----------|\n", - "| Drug | XD, W | XD, M |\n", - "| Placebo | XP, W | XP, M |" - ] - }, - { - "cell_type": "markdown", - "id": "cba57b53", - "metadata": {}, - "source": [ - "There are two ``Treatment`` conditions, ``Placebo`` (control group) and ``Drug`` (test group). There are two ``Genotype``\\s: ``W`` (wild type population) and ``M`` (mutant population). Additionally, each experiment was conducted twice (``Rep1`` and ``Rep2``). We will perform several analyses to visualise these differences in a simulated dataset. \n" - ] - }, - { - "cell_type": "markdown", - "id": "eba4dd06", - "metadata": {}, - "source": [ - "## Load libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f7051d73", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "We're using DABEST v2025.10.20\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import dabest\n", - "\n", - "print(\"We're using DABEST v{}\".format(dabest.__version__))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0c1bdbb7", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning) # to suppress warnings related to points not being able to be plotted due to dot size" - ] - }, - { - "cell_type": "markdown", - "id": "aeeafbcf", - "metadata": {}, - "source": [ - "## Creating a demo dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a5686f3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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IDRepGenotypeTreatmentY
00Rep1WPlacebo2.793984
11Rep2WPlacebo3.236759
22Rep1WPlacebo3.019149
33Rep2WPlacebo2.804638
44Rep1WPlacebo2.858019
\n", - "
" - ], - "text/plain": [ - " ID Rep Genotype Treatment Y\n", - "0 0 Rep1 W Placebo 2.793984\n", - "1 1 Rep2 W Placebo 3.236759\n", - "2 2 Rep1 W Placebo 3.019149\n", - "3 3 Rep2 W Placebo 2.804638\n", - "4 4 Rep1 W Placebo 2.858019" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from scipy.stats import norm # Used in generation of populations.\n", - "np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n", - "\n", - "# Create samples\n", - "N = 20\n", - "y = norm.rvs(loc=3, scale=0.4, size=N*4)\n", - "y[N:2*N] = y[N:2*N]+1\n", - "y[2*N:3*N] = y[2*N:3*N]-0.5\n", - "\n", - "# Add a `Treatment` column\n", - "t1 = np.repeat('Placebo', N*2).tolist()\n", - "t2 = np.repeat('Drug', N*2).tolist()\n", - "treatment = t1 + t2 \n", - "\n", - "# Add a `Rep` column as the first variable for the 2 replicates of experiments done\n", - "rep = []\n", - "for i in range(N*2):\n", - " rep.append('Rep1')\n", - " rep.append('Rep2')\n", - "\n", - "# Add a `Genotype` column as the second variable\n", - "wt = np.repeat('W', N).tolist()\n", - "mt = np.repeat('M', N).tolist()\n", - "wt2 = np.repeat('W', N).tolist()\n", - "mt2 = np.repeat('M', N).tolist()\n", - "\n", - "\n", - "genotype = wt + mt + wt2 + mt2\n", - "\n", - "# Add an `id` column for paired data plotting.\n", - "id = list(range(0, N*2))\n", - "id_col = id + id \n", - "\n", - "\n", - "# Combine all columns into a DataFrame.\n", - "df_delta2 = pd.DataFrame({'ID' : id_col,\n", - " 'Rep' : rep,\n", - " 'Genotype' : genotype, \n", - " 'Treatment': treatment,\n", - " 'Y' : y\n", - " })\n", - "df_delta2.head(5)" - ] - }, - { - "cell_type": "markdown", - "id": "f645a28b", - "metadata": {}, - "source": [ - "## Loading data" - ] - }, - { - "cell_type": "markdown", - "id": "accc2f5c", - "metadata": {}, - "source": [ - "To create a delta-delta plot, you simply need to set ``delta2=True`` in the \n", - "``dabest.load()`` method. However, in this case,``x`` needs to be declared as a list consisting of 2 elements, unlike most cases where it is a single element. The first element in ``x`` will represent the variable plotted along the horizontal axis, and the second one will determine the color of dots for scattered plots or the color of lines for slope graphs. We use the ``experiment`` input to specify the grouping of the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ab57196f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:50 2025.\n", - "\n", - "Effect size(s) with 95% confidence intervals will be computed for:\n", - "1. M Placebo minus W Placebo\n", - "2. M Drug minus W Drug\n", - "3. Drug minus Placebo (only for mean difference)\n", - "\n", - "5000 resamples will be used to generate the effect size bootstraps." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unpaired_delta2 = dabest.load(data = df_delta2, x = [\"Genotype\", \"Genotype\"], y = \"Y\", delta2 = True, experiment = \"Treatment\")\n", - "unpaired_delta2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d3d23b98", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:51 2025.\n", - "\n", - "The unpaired mean difference between W Placebo and M Placebo is 1.23 [95%CI 0.937, 1.51].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "The unpaired mean difference between W Drug and M Drug is 0.326 [95%CI 0.0956, 0.574].\n", - "The p-value of the two-sided permutation t-test is 0.0122, calculated for legacy purposes only. \n", - "\n", - "The delta-delta between Placebo and Drug is -0.903 [95%CI -1.27, -0.522].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing the effect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unpaired_delta2.mean_diff" - ] - }, - { - "cell_type": "markdown", - "id": "55eecede", - "metadata": {}, - "source": [ - "## Generating delta-delta plots" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9beac2bf", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "unpaired_delta2.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "9beeb024", - "metadata": {}, - "source": [ - "In the above plot, the horizontal axis represents the ``Genotype`` condition\n", - "and the dot colour is also specified by ``Genotype``. The left pair of \n", - "scattered plots is based on the ``Placebo`` group while the right pair is based\n", - "on the ``Drug`` group. The bottom left axis contains the two primary deltas: the ``Placebo`` delta \n", - "and the ``Drug`` delta. We can easily see that when only the placebo was \n", - "administered, the mutant phenotype is around 1.23 [95%CI 0.948, 1.52]. This difference was shrunken to around 0.326 [95%CI 0.0934, 0.584] when the drug was administered. This gives us some indication that the drug is effective in amiliorating the disease phenotype. Since the ``Drug`` did not completely eliminate the mutant phenotype, we have to calculate how much net effect the drug had. This is where ``delta-delta`` comes in. We use the ``Placebo`` delta as a reference for how much the mutant phenotype is supposed to be, and we subtract the ``Drug`` delta from it. The bootstrapped mean differences (delta-delta) between the ``Placebo`` \n", - "and ``Drug`` group are plotted at the right bottom with a separate y-axis from other bootstrap plots. \n", - "This effect size, at about -0.903 [95%CI -1.28, -0.513], is the net effect size of the drug treatment. That is to say that treatment with drug A reduced disease phenotype by 0.903.\n", - "\n", - "The mean difference between mutants and wild types given the placebo treatment is:\n", - "\n", - "$\\Delta_{1} = \\overline{X}_{P, M} - \\overline{X}_{P, W}$\n", - "\n", - "The mean difference between mutants and wild types given the drug treatment is:\n", - "\n", - "\n", - "$\\Delta_{2} = \\overline{X}_{D, M} - \\overline{X}_{D, W}$\n", - "\n", - "The net effect of the drug on mutants is:\n", - " \n", - "\n", - "\n", - "$\\Delta_{\\Delta} = \\Delta_{2} - \\Delta_{1}$\n", - " \n", - "\n", - "where $\\overline{X}$ is the sample mean, $\\Delta$ is the mean difference." - ] - }, - { - "cell_type": "markdown", - "id": "cbe874ee", - "metadata": {}, - "source": [ - "The configuration of comparison we performed above is reminiscent of a two-way ANOVA. In fact, the delta - delta is an effect size estimated for the interaction term between ``Treatment`` and ``Genotype``. Main effects of ``Treatment`` and ``Genotype``, on the other hand, can be determined by simpler, univariate contrast plots. " - ] - }, - { - "cell_type": "markdown", - "id": "5d743a46", - "metadata": {}, - "source": [ - "## Specifying grouping for comparisons" - ] - }, - { - "cell_type": "markdown", - "id": "5f603498", - "metadata": {}, - "source": [ - "In the example above, we used the convention of *test - control* but you can manipulate the orders of the experiment groups as well as the horizontal axis variable by setting the paremeters ``experiment_label`` and ``x1_level``.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7e29be58", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "unpaired_delta2_specified = dabest.load(data = df_delta2, \n", - " x = [\"Genotype\", \"Genotype\"], y = \"Y\", \n", - " delta2 = True, experiment = \"Treatment\",\n", - " experiment_label = [\"Drug\", \"Placebo\"],\n", - " x1_level = [\"M\", \"W\"])\n", - "\n", - "unpaired_delta2_specified.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "347872d1", - "metadata": {}, - "source": [ - "Utilising the `show_delta2` argument within the `.plot()` method allows for control of whether the delta-delta effect size is displayed on the plot. By default, this is set to `True`. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "21886e87", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "unpaired_delta2_specified.mean_diff.plot(show_delta2=False);" - ] - }, - { - "cell_type": "markdown", - "id": "69aa38f7", - "metadata": {}, - "source": [ - "The delta-delta function also supports paired data, providing a useful alternative visualization of the data. Assuming that the placebo and drug treatment were administered to the same subjects, our data is paired between the treatment conditions. We can specify this by using ``Treatment`` as ``x`` and ``Genotype`` as ``experiment``, and we further specify that ``id_col`` is ``ID``, linking data from the same subject with each other. Since we have conducted two replicates of the experiments, we can also colour the slope lines according to ``Rep``. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fc76ceea", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "paired_delta2 = dabest.load(data = df_delta2, \n", - " paired = \"baseline\", id_col=\"ID\",\n", - " x = [\"Treatment\", \"Rep\"], y = \"Y\", \n", - " delta2 = True, experiment = \"Genotype\")\n", - "paired_delta2.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "b89c0f82", - "metadata": {}, - "source": [ - "We see that the drug had a non-specific effect of -0.321 [95%CI -0.498, -0.131] on wild type subjects even when they were not sick, and it had a bigger effect of -1.22 [95%CI -1.52, -0.906] in mutant subjects. In this visualisation, we can see the delta-delta value of -0.903 [95%CI -1.21, -0.587] as the net effect of the drug accounting for non-specific actions in healthy individuals. \n" - ] - }, - { - "cell_type": "markdown", - "id": "c35731fc", - "metadata": {}, - "source": [ - "The mean difference between drug and placebo treatments in wild type subjects is:\n", - "\n", - "$$\\Delta_{1} = \\overline{X}_{D, W} - \\overline{X}_{P, W}$$\n", - "\n", - "The mean difference between drug and placebo treatments in mutant subjects is:\n", - "\n", - "$$\\Delta_{2} = \\overline{X}_{D, M} - \\overline{X}_{P, M}$$\n", - "\n", - "The net effect of the drug on mutants is:\n", - "\n", - "$$\\Delta_{\\Delta} = \\Delta_{2} - \\Delta_{1}$$\n", - "\n", - "where $\\overline{X}$ is the sample mean, $\\Delta$ is the mean difference." - ] - }, - { - "cell_type": "markdown", - "id": "bbddc245", - "metadata": {}, - "source": [ - "## Standardising delta-delta effect sizes with Delta g" - ] - }, - { - "cell_type": "markdown", - "id": "1677b7e1", - "metadata": {}, - "source": [ - "Standardized mean difference statistics like Cohen's d and Hedges' g quantify effect sizes in terms of the sample variance. We have introduced a metric, *Delta g*, to standardize delta-delta effects. This metric enables the comparison between measurements of different dimensions.\n", - "\n", - "The standard deviation of the delta-delta value is calculated from a pooled variance of the 4 samples:\n", - "\n", - "$$s_{\\Delta_{\\Delta}} = \\sqrt{\\frac{(n_{D, W}-1)s_{D, W}^2+(n_{P, W}-1)s_{P, W}^2+(n_{D, M}-1)s_{D, M}^2+(n_{P, M}-1)s_{P, M}^2}{(n_{D, W} - 1) + (n_{P, W} - 1) + (n_{D, M} - 1) + (n_{P, M} - 1)}}$$\n", - "\n", - "where $s$ is the standard deviation and $n$ is the sample size.\n", - "\n", - "A delta g value is then calculated as delta-delta value divided by pooled standard deviation $s_{\\Delta_{\\Delta}}$:\n", - "\n", - "\n", - "$\\Delta_{g} = \\frac{\\Delta_{\\Delta}}{s_{\\Delta_{\\Delta}}}$\n", - "\n", - "This metric can be accessed via the 'hedges_g' effect size when utilising `delta2=True` in `dabest.load()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1da01bb3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:53 2025.\n", - "\n", - "The unpaired Hedges' g between W Placebo and M Placebo is 2.54 [95%CI 1.71, 3.31].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "The unpaired Hedges' g between W Drug and M Drug is 0.793 [95%CI 0.17, 1.33].\n", - "The p-value of the two-sided permutation t-test is 0.0122, calculated for legacy purposes only. \n", - "\n", - "The delta g between Placebo and Drug is -2.11 [95%CI -2.97, -1.22].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing the effect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.hedges_g.statistical_tests`" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unpaired_delta2.hedges_g" - ] - }, - { - "cell_type": "markdown", - "id": "e19e2991", - "metadata": {}, - "source": [ - "We see the standardised delta-delta (delta *g*) value of -2.11 standard deviations [95%CI -2.98, -1.2] as the net effect of the drug accounting for non-specific actions in healthy individuals. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6d7e0170", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "unpaired_delta2.hedges_g.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "54c0438a", - "metadata": {}, - "source": [ - "## Delta-delta for binary data\n", - "Since **v2025.03.27**, the delta-delta function also supports binary data (proportion plots). In this case, the delta-delta value is calculated as the difference between the two proportions. This can be used for both unpaired and paired binary data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e8b8c2d7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def create_demo_dataset_delta(seed=9999, N=20):\n", - " \n", - " import numpy as np\n", - " import pandas as pd\n", - " from scipy.stats import norm # Used in generation of populations.\n", - "\n", - " np.random.seed(seed) # Fix the seed so the results are replicable.\n", - " # pop_size = 10000 # Size of each population.\n", - "\n", - " from scipy.stats import norm # Used in generation of populations.\n", - "\n", - " # Create samples\n", - " y = norm.rvs(loc=3, scale=0.4, size=N*4)\n", - " y[N:2*N] = y[N:2*N]+1\n", - " y[2*N:3*N] = y[2*N:3*N]-0.5\n", - " ind = np.random.binomial(1, 0.5, size=N*4)\n", - " ind[N:2*N] = np.random.binomial(1, 0.2, size=N)\n", - " ind[2*N:3*N] = np.random.binomial(1, 0.1, size=N)\n", - "\n", - " # Add drug column\n", - " t1 = np.repeat('Placebo', N*2).tolist()\n", - " t2 = np.repeat('Drug', N*2).tolist()\n", - " treatment = t1 + t2 \n", - "\n", - " # Add a `rep` column as the first variable for the 2 replicates of experiments done\n", - " rep = []\n", - " for i in range(N*2):\n", - " rep.append('Rep1')\n", - " rep.append('Rep2')\n", - "\n", - " # Add a `genotype` column as the second variable\n", - " wt = np.repeat('WT', N).tolist()\n", - " mt = np.repeat('Mut', N).tolist()\n", - " wt2 = np.repeat('WT', N).tolist()\n", - " mt2 = np.repeat('Mut', N).tolist()\n", - "\n", - "\n", - " genotype = wt + mt + wt2 + mt2\n", - "\n", - " # Add an `id` column for paired data plotting.\n", - " id = list(range(0, N*2))\n", - " id_col = id + id \n", - "\n", - "\n", - " # Combine all columns into a DataFrame.\n", - " df_prop = pd.DataFrame({'ID' : id_col,\n", - " 'Rep' : rep,\n", - " 'Genotype' : genotype, \n", - " 'Treatment' : treatment,\n", - " 'Y' : y,\n", - " 'Cat' :ind\n", - " })\n", - " return df_prop\n", - "\n", - "df_prop = create_demo_dataset_delta()\n", - "\n", - "unpaired_prop = dabest.load(data = df_prop, proportional=True,\n", - " # id_col=\"index\", paired='baseline', \n", - " x = [\"Treatment\", \"Treatment\"], \n", - " y = \"Cat\", delta2=True,\n", - " experiment=\"Genotype\",)\n", - "\n", - "unpaired_prop.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "bdbf61d8", - "metadata": {}, - "source": [ - "## Statistics" - ] - }, - { - "cell_type": "markdown", - "id": "38e23d1d", - "metadata": {}, - "source": [ - "You can find all outputs of the delta-delta calculation by assessing the attribute named ``delta_delta`` of the effect size object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d070ee64", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2025.10.20\n", - "==================\n", - " \n", - "Good afternoon!\n", - "The current time is Sun Oct 19 16:00:54 2025.\n", - "\n", - "The delta-delta between Placebo and Drug is -0.903 [95%CI -1.27, -0.522].\n", - "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "Any p-value reported is the probability of observing the effect size (or greater),\n", - "assuming the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unpaired_delta2.mean_diff.delta_delta" - ] - }, - { - "cell_type": "markdown", - "id": "8fc68867", - "metadata": {}, - "source": [ - "The ``delta_delta`` object has its own attributes, containing various information of delta-delta.\n", - "\n", - " - ``difference``: the mean bootstrapped differences between the 2 groups of bootstrapped mean differences \n", - " - ``bootstraps``: the 2 groups of bootstrapped mean differences \n", - " - ``bootstraps_delta_delta``: the bootstrapped differences between the 2 groups of bootstrapped mean differences \n", - " - ``permutations``: the mean difference between the two groups of bootstrapped mean differences calculated based on the permutation data\n", - " - ``permutations_var``: the pooled group variances of two groups of bootstrapped mean differences calculated based on permutation data\n", - " - ``permutations_delta_delta``: the delta-delta calculated based on the permutation data\n", - "\n", - "A dataframe of this delta delta dabest object can also be called via the `delta_delta.results` attribute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f3bec88c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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controltestdifferencecibca_lowbca_highbca_interval_idxpct_lowpct_highpct_interval_idxbootstraps_controlbootstraps_testbootstraps_delta_deltapermutations_controlpermutations_testpermutations_delta_deltapvalue_permutationpermutation_countbias_correctionjackknives
0PlaceboDrug-0.90317995-1.271483-0.521935(124, 4874)-1.270426-0.519652(125, 4875)[1.0890043559982234, 1.1472720447282119, 1.072...[0.6003430615628478, 0.6547912656551773, 0.294...[-0.43421309034304567, -0.7324573148122022, -1...[-0.15899787281865496, 0.23958268043726694, 0....[-0.036113268018566735, -0.05491466432013192, ...[0.12288460480008823, -0.29449734475739886, -0...0.05000-0.000501[-0.9006797310317582, -0.9006200702547091, -0....
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" - ], - "text/plain": [ - " control test difference ci bca_low bca_high bca_interval_idx \\\n", - "0 Placebo Drug -0.903179 95 -1.271483 -0.521935 (124, 4874) \n", - "\n", - " pct_low pct_high pct_interval_idx \\\n", - "0 -1.270426 -0.519652 (125, 4875) \n", - "\n", - " bootstraps_control \\\n", - "0 [1.0890043559982234, 1.1472720447282119, 1.072... \n", - "\n", - " bootstraps_test \\\n", - "0 [0.6003430615628478, 0.6547912656551773, 0.294... \n", - "\n", - " bootstraps_delta_delta \\\n", - "0 [-0.43421309034304567, -0.7324573148122022, -1... \n", - "\n", - " permutations_control \\\n", - "0 [-0.15899787281865496, 0.23958268043726694, 0.... \n", - "\n", - " permutations_test \\\n", - "0 [-0.036113268018566735, -0.05491466432013192, ... \n", - "\n", - " permutations_delta_delta pvalue_permutation \\\n", - "0 [0.12288460480008823, -0.29449734475739886, -0... 0.0 \n", - "\n", - " permutation_count bias_correction \\\n", - "0 5000 -0.000501 \n", - "\n", - " jackknives \n", - "0 [-0.9006797310317582, -0.9006200702547091, -0.... " - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unpaired_delta2.mean_diff.delta_delta.results" - ] - }, - { - "cell_type": "markdown", - "id": "c10aa935", - "metadata": {}, - "source": [ - "Similarly, for the standardised delta-delta effect size, the `hedges_g` object has its own delta delta (Delta *g*) results attribute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3b30812a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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controltestdifferencecibca_lowbca_highbca_interval_idxpct_lowpct_highpct_interval_idxbootstraps_controlbootstraps_testbootstraps_delta_deltapermutations_controlpermutations_testpermutations_delta_deltapvalue_permutationpermutation_countbias_correctionjackknives
0PlaceboDrug-2.10668195-2.965759-1.217424(124, 4874)-2.963294-1.212099(125, 4875)[2.3610871907095192, 2.7764672664031567, 2.350...[1.549355181508767, 1.7247260954921417, 0.6471...[-1.0128104949604284, -1.708470960574333, -2.7...[-0.1986457235842243, 0.3014021841951519, 0.31...[-0.08117530110708499, -0.12358349103957916, -...[0.11747042247713932, -0.4249856752347311, -0....0.05000-0.000501[-2.1008530246437576, -2.10071386471865, -2.10...
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" - ], - "text/plain": [ - " control test difference ci bca_low bca_high bca_interval_idx \\\n", - "0 Placebo Drug -2.106681 95 -2.965759 -1.217424 (124, 4874) \n", - "\n", - " pct_low pct_high pct_interval_idx \\\n", - "0 -2.963294 -1.212099 (125, 4875) \n", - "\n", - " bootstraps_control \\\n", - "0 [2.3610871907095192, 2.7764672664031567, 2.350... \n", - "\n", - " bootstraps_test \\\n", - "0 [1.549355181508767, 1.7247260954921417, 0.6471... \n", - "\n", - " bootstraps_delta_delta \\\n", - "0 [-1.0128104949604284, -1.708470960574333, -2.7... \n", - "\n", - " permutations_control \\\n", - "0 [-0.1986457235842243, 0.3014021841951519, 0.31... \n", - "\n", - " permutations_test \\\n", - "0 [-0.08117530110708499, -0.12358349103957916, -... \n", - "\n", - " permutations_delta_delta pvalue_permutation \\\n", - "0 [0.11747042247713932, -0.4249856752347311, -0.... 0.0 \n", - "\n", - " permutation_count bias_correction \\\n", - "0 5000 -0.000501 \n", - "\n", - " jackknives \n", - "0 [-2.1008530246437576, -2.10071386471865, -2.10... " - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unpaired_delta2.hedges_g.delta_delta.results" - ] - }, - { - "cell_type": "markdown", - "id": "991ca7ab", - "metadata": {}, - "source": [ - "For further aesthetic changes, the [Plot Aesthetics Tutorial](08-plot_aesthetics.html) provides detailed examples of how to customize the plot.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/tutorials/07-horizontal_plot.ipynb b/nbs/tutorials/07-horizontal_plot.ipynb deleted file mode 100644 index 206f6073..00000000 --- a/nbs/tutorials/07-horizontal_plot.ipynb +++ /dev/null @@ -1,709 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "717ffa0a", - "metadata": {}, - "source": [ - "# Horizontal Plots\n", - "\n", - "> A guide to plot data in a horizontal format.\n", - "\n", - "- order: 7" - ] - }, - { - "cell_type": "markdown", - "id": "2063d217", - "metadata": {}, - "source": [ - "In DABEST **v2025.03.27**, we introduce a new plotting orientation: **horizontal plots**. \n", - "\n", - "To access this, provide `horizontal=True` to the `.plot()` method." - ] - }, - { - "cell_type": "markdown", - "id": "83c78436", - "metadata": {}, - "source": [ - "## Load libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "90f630c9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pre-compiling numba functions for DABEST...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 42.06it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Numba compilation complete!\n", - "We're using DABEST v2025.10.20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import dabest\n", - "\n", - "print(\"We're using DABEST v{}\".format(dabest.__version__))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "afc3ff31", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\") # to suppress warnings related to points not being able to be plotted due to dot size" - ] - }, - { - "cell_type": "markdown", - "id": "c563c6f3", - "metadata": {}, - "source": [ - "## Creating a demo dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ab728900", - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.stats import norm # Used in generation of populations.\n", - "\n", - "np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n", - "\n", - "Ns = 20 # The number of samples taken from each population\n", - "\n", - "# Create samples\n", - "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n", - "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", - "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", - "\n", - "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n", - "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n", - "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n", - "t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", - "t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", - "t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", - "\n", - "\n", - "# Add a `gender` column for coloring the data.\n", - "females = np.repeat('Female', Ns/2).tolist()\n", - "males = np.repeat('Male', Ns/2).tolist()\n", - "gender = females + males\n", - "\n", - "# Add an `id` column for paired data plotting.\n", - "id_col = pd.Series(range(1, Ns+1))\n", - "\n", - "# Combine samples and gender into a DataFrame.\n", - "df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n", - " 'Control 2' : c2, 'Test 2' : t2,\n", - " 'Control 3' : c3, 'Test 3' : t3,\n", - " 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6,\n", - " 'Gender' : gender, 'ID' : id_col\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "c1395293", - "metadata": {}, - "source": [ - "## Generating two-group plots" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "228a940a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"))\n", - "two_groups_unpaired.mean_diff.plot(horizontal=True);\n", - "\n", - "two_groups_paired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), paired='baseline', id_col='ID')\n", - "two_groups_paired.mean_diff.plot(horizontal=True);" - ] - }, - { - "cell_type": "markdown", - "id": "f181ddf6", - "metadata": {}, - "source": [ - "## Generating shared-control and repeated-measures plots" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "42b671b5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "shared_control = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\", \"Test 4\", \"Test 5\"))\n", - "shared_control.mean_diff.plot(horizontal=True);\n", - "\n", - "repeated_measures_baseline = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\", \"Test 4\", \"Test 5\"), paired='baseline', id_col='ID') \n", - "repeated_measures_baseline.mean_diff.plot(horizontal=True);\n", - "\n", - "repeated_measures_sequential = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\", \"Test 4\", \"Test 5\"), paired='sequential', id_col='ID') \n", - "repeated_measures_sequential.mean_diff.plot(horizontal=True);" - ] - }, - { - "cell_type": "markdown", - "id": "1a95d54f", - "metadata": {}, - "source": [ - "## Generating multi-group plots" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a23d4b81", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\", \"Test 4\", \"Test 5\")))\n", - "multi_2group.mean_diff.plot(horizontal=True);" - ] - }, - { - "cell_type": "markdown", - "id": "39ca23c1", - "metadata": {}, - "source": [ - "## Generating proportion plots\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "818e7fe6", - "metadata": {}, - "outputs": [], - "source": [ - "def create_demo_prop_dataset(seed=9999, N=40):\n", - " import numpy as np\n", - " import pandas as pd\n", - "\n", - " np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n", - " # Create samples\n", - " n = 1\n", - " c1 = np.random.binomial(n, 0.2, size=N)\n", - " c2 = np.random.binomial(n, 0.2, size=N)\n", - " c3 = np.random.binomial(n, 0.8, size=N)\n", - "\n", - " t1 = np.random.binomial(n, 0.6, size=N)\n", - " t2 = np.random.binomial(n, 0.2, size=N)\n", - " t3 = np.random.binomial(n, 0.3, size=N)\n", - " t4 = np.random.binomial(n, 0.4, size=N)\n", - " t5 = np.random.binomial(n, 0.5, size=N)\n", - " t6 = np.random.binomial(n, 0.6, size=N)\n", - " t7 = np.ones(N)\n", - " t8 = np.zeros(N)\n", - " t9 = np.zeros(N)\n", - "\n", - " # Add a `gender` column for coloring the data.\n", - " females = np.repeat('Female', N / 2).tolist()\n", - " males = np.repeat('Male', N / 2).tolist()\n", - " gender = females + males\n", - "\n", - " # Add an `id` column for paired data plotting.\n", - " id_col = pd.Series(range(1, N + 1))\n", - "\n", - " # Combine samples and gender into a DataFrame.\n", - " df = pd.DataFrame({'Control 1': c1, 'Test 1': t1,\n", - " 'Control 2': c2, 'Test 2': t2,\n", - " 'Control 3': c3, 'Test 3': t3,\n", - " 'Test 4': t4, 'Test 5': t5, 'Test 6': t6,\n", - " 'Test 7': t7, 'Test 8': t8, 'Test 9': t9,\n", - " 'Gender': gender, 'ID': id_col\n", - " })\n", - "\n", - " return df\n", - "df_prop = create_demo_prop_dataset()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a15c416d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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fIRA1GU9yC/QdAhGR1pgkU4NQJDBFocBM32HIFebk6zsEIiIi0iMmydQgWJmbwcay7o6oJiL9s27ecN4IExFVh0kyNQirZ/ihV69e+g6DiIiICAAX7hERERERqWCSTERERESkhEkyEREREZESJslEREREREqYJBMRERERKWGSTERERESkhEkyEREREZESJslEREREREqYJBMRERERKWGSTERERESkhEkyEREREZESJslEREREREqYJBMRERERKWGSTERERESkhEkyEREREZESJslEREREREqYJBMRERERKWGSTERERESkhEkyEREREZESJslEREREREoM9R0AEQD898uPUXreVt9hEFEdMja3huvbm/QdRqNRVlIEA5TpO4xGTWAghIHQSN9hUCPFJJkahGxxGf7OL9V3GEQ6YSnL0XcI1IhJy0oAADn3foGREX9Na8Mg7wEMM2/BQJwJlBbBwEgEY4dOMLB7EWjdCzAy1XeI1IjwXx8RkY4ZN+enIs+S5GcDMqm+w2g0ZNLy2WMDQyEMTUR6jqZxEIifwPj6Lhg+viFvk8lkgEwGwf1LgEAAGJsDroFA97GAkOkPVY//lxAR6Vjv93fpO4QG5eqmNyHJy9J3GI2OgdAIBobG+g6jwTPIugPjSxsgKM4DIJC3CwT/S5QrSPKBq9uAu2cBr0VAixfqP1hqVLhwj4iIiBolQXYqTC6E/y9B1lDW78BXM4BruwEpa76pckySiYiIqPEpegqTSxuA0iLtr5WWAj9GA0fmALkPdR9bE/f0aR6mT18JO7shaNbsFXh7z8C1a7c0unby5BAIBO4qX506va4wLiQkSu24iq+LF1Pq4JUpYrmFnoSHh+OLL77AzZs3YWDQ+N+rjBs3DlKpFAcOHNB3KERE1NRJy2By5TMIxNm1u8/j/wKHg4ChK4DWrjoJramTSqXw9X0f16/fwYIFk9CihRUiIw/Cy2sGfvppNzp0cKz2HiYmxoiOXqLQZmlprvC9v/8gvPBCW5VrFy/+DPn5Ynh4dK3dC9FAg8zOBAKBRl/nzp2r9bMKCwsREhKi1b1WrlyJUaNGoVWrVhAIBAgJCdHqmbm5uVi7di2Cg4MVEuSK1xUREaFyTUxMDAQCAZKSkrR6liZ8fHwgEAjw7rvvqu3//PPP0blzZ5iamqJDhw7YvHmzypjg4GAcOnQI169f13l8REREzzL6734YZGo2c1mt4jzg24XAwxvVj30OeHlNx+TJIZX2Hzx4BomJNxATswzLlk3H7NljcO5cFIRCAyxbFqXRMwwNhZg4cbjC18iRAxXGdO/eQWWMp2cv/PVXBgICBsPYuO639muQM8m7d+9W+H7Xrl2Ij49Xae/cuXOtn1VYWIjQ0FAAgJeXl0bXLFmyBPb29ujZsydOnjyp9TO/+OILlJaWYvz48Wr7161bh5kzZ8LMzEzre2vr8OHDuHTpUqX9UVFReOedd/D6669j3rx5OH/+PObMmYPCwkIEBwfLx/Xs2RPu7u6IiIjArl1ctERERHVD+MdpGN75Vrc3LS0GTi4G/KIAyza6vXcTc/DgGbRqZQt//0HyNjs7a4wZMwR79nyL4mIJTEyqX3BaVlaGggIxLCzMqx1bYe/ek5DJZJgw4dUaxa6tBjmTPHHiRIWvjh07qm1v1aqVXuJLTU3Fw4cPsWfPnhpdv2PHDowaNQqmpqr7Nbq6uuLx48fYunVrbcOsVlFREebPn6+Q7D5LLBbjo48+gq+vLw4ePIigoCDs2rULEyZMQFhYGLKzFT/mGjNmDA4fPoz8/Pw6j52IiJ4/wj9Owzh5Z93cvDivPFGWFNbN/ZuI5OTb6NXrRZVS0d69u6KwsAi//ZZe7T0KC4tgYeEJS0sv2NgMwuzZa5GfX/2fe2zsd2jbthUGDuxV4/i10SCTZE1IpVJs3LgRXbt2hampKVq1aoUZM2aoJG5JSUkYNmwYWrRoAZFIBGdnZ0ydOhUAkJaWBjs7OwBAaGiovNyhuvIJJyenGsedmpqKGzduYMiQIWr7+/fvj0GDBiE8PBxisbjGz9FEeHg4pFIp/v3vf6vtT0hIQFZWFmbNmqXQPnv2bBQUFOD48eMK7T4+PigoKEB8fHydxUxERM8hmRRGN76suwS5QnYacG4VIOW+3pV5+DATDg4tVNor2h48+LvK6x0cWmDhwjexY8cy7N27EqNGDURkZBxeffU9lJZWfqjYL7/cxY0bdzB+/DAIBIJKx+lSgyy30MSMGTMQExODKVOmYM6cOUhNTcWnn36K5ORkXLx4EUZGRsjIyMDQoUNhZ2eHDz/8EFZWVkhLS8Phw4cBAHZ2dtiyZQtmzpwJPz8/+Pv7AwC6d+9eZ3EnJiYCAHr1qvxdUEhICAYOHIgtW7Zg3rx5lY4rLi5GXp5m2960aKH4P3R6ejrWrFmDL774AiKR+s3qk5OTAQDu7u4K7W5ubjAwMEBycjImTpwob+/SpQtEIhEuXrwIPz8/jeIiIiKqUpkExlc+hfBhcv08L/U8kPQ50Duofp6nRyUlpcjJyVdpKy6WIDPzqUK7jY0FDAwMIBYXqy2nMDU1AQCIxcVVPnP1asX1T+PGDUPHjo746KNIHDx4BuPGDVN7XWzsdwCACRP+r8r761KjTJIvXLiA6OhoxMbGIjAwUN7u7e2NV199FXFxcQgMDERiYiKys7Nx6tQphURvxYoVAIBmzZohICAAM2fORPfu3RUSvrpy61b5QgNnZ+dKxwwYMADe3t7y2uTKkti9e/diypQpGj1XYUN1APPnz0fPnj0xbty4Sq95+PAhhEIhWrZsqdBubGwMW1tbPHjwQKHd0NAQbdu2xc2bNyu9Z3FxMYqL//kHxNIMakg+iDyJ7PyafYJjbS7CxlmqP9zd3d3x6NGjGsdkb29fJwt2iRqFslIYX9oI4eOf6/e5yXuA5vZA55H1+9x6dvFiCry931FpT0y8gX37Tim0paYegZNTa4hEJigulqhcU1RU/rtdJDLROo65cwPx8cdbcfr0VbVJskwmw5dffodu3dqje/cOWt+/phplkhwXFwdLS0v4+PggMzNT3u7m5gZzc3MkJCQgMDAQVlZWAIBjx46hR48eMDKq+5WQ1cnKyoKhoSHMzasuVA8JCYGnpye2bt2KuXPnqh0zbNiwGpU2JCQk4NChQ7hy5UqV48RiMYyN1Rffm5qaqi0Hsba2Vvg7UbZ69Wr5QskK3ZzsYNOcR6+S/mXni5GVq9syp0ePHuH+/fs6vSfRc0Emg1HyF/WfIFc4/x/A1ApwHqCf59eDHj06Ij7+M4W2+fM3wt7eFgsWTFJot7e3BVBeLvHwoerv+Yq21q3ttI5DJDKFra0lnjzJVdt/8eJ13Lv3UGUWuq41yiT5zp07yMnJUZnhrJCRkQEA8PT0xOuvv47Q0FBs2LABXl5eGD16NAIDA2Fiov07nfo0cOBAeHt7Izw8HO+8o/ouDwAcHBzg4OCg1X1LS0sxZ84cTJo0CR4eHlWOFYlEkEhU3y0C5Yv+1M1wy2SyKmuFFi1apFJCcnZDEIwMhRpET1S3rM1r/matsmvt7e1rfE9dXE/UWBn+/h0M753XXwAyKXAmFPi/tUAbN/3FUYesrS0wZMjLSm3N4eDQQqW9gqtrR5w/nwKpVKqweO/KlV9gZmaKjh2r3ydZWV5eATIzn8LOzkptf2zstxAIBAgMrJ9dLSo0yiRZKpWiZcuWiI2NVdtfsRhPIBDg4MGDuHz5Mo4ePYqTJ09i6tSpiIiIwOXLl6udza0Ltra2KC0tRV5eHpo3b17l2GXLlsHLywtRUVHyWfFnicVi5OTkaPTcil+0u3btwu3btxEVFYW0tDSFMXl5eUhLS0PLli1hZmYGBwcHlJWVISMjQ+ENiUQiQVZWFlq3bq3ynOzsbHToUPlHISYmJipvUJggU0OhrlyitlgqQaQ9g4cpMPp5r77DAMpKgJNLgJEbAbsX9R1NgxAQMBgHD57B4cNnERBQvglBZuZTxMWdxsiRAxTqle/e/QsA0L79vwCUl2SUlJSiefNmCvcMC4uGTCbDq6/2U3leSUkp4uJO45VXXOHoWL+TBo0ySW7fvj1Onz6N/v37V1qv+6w+ffqgT58+WLlyJb788ktMmDAB+/btw7Rp0+pthWSFTp06ASjf5aK6BYKenp7w8vLC2rVrsXTpUpX+/fv3a12TnJ6ejpKSEvTv319lzK5du7Br1y589dVXGD16NFxdXQGU/5IfPny4fFxSUhKkUqm8v0JpaSn+/PNPjBo1SqOYiIiIlAmy/4DJ1U8BpbU0elNSWH7YyGufAZb/0nc0ehcQMBh9+ryEKVOW4+bN1P+duBeHsjIpQkNnKIwdPHgmACAt7SgA4NGjLPTsOQHjxw9Dp05OAICTJy/hxImLePXVfnjtNU+V5508eQlZWTn1tjfysxplkjxmzBhERkYiLCwMq1atUugrLS1Ffn4+rKyskJ2dDSsrK4VEuCKxq1g8VnFgx9OnT+sl9r59+wIoTzQ12UUjJCQEXl5e2LZtm0pfTWqSx40bp5LcAoCfnx+GDx+OoKAgvPxy+UcsgwYNgo2NDbZs2aKQJG/ZsgVmZmbw9fVVuMfNmzdRVFSEfv1U3wkSERFVR/D0HkwuhJcf7tGQiJ8Cx/8NjNoMmGtfc9uUCIVCnDixCQsWbMInn+yDWFwMD48uiIkJwYsvOlV5rZVVc4wY8Qri469g585jKCuT4oUX/oVVq2bj3/+epLL3MlBeamFkZIg33lC/dW5dapRJsqenJ2bMmIHVq1cjJSUFQ4cOhZGREe7cuYO4uDhs2rQJAQEB2LlzJyIjI+Hn54f27dsjLy8P27dvh4WFhTzpE4lE6NKlC/bv34+OHTvCxsYG3bp1Q7du3Sp9/u7du3Hv3j0UFpZvfP3DDz/Id8yYNGkS2rVrV+m1Li4u6NatG06fPi3fr7m61+rp6Ynvv/9epa8mNcmdOnWSz2Yrc3Z2xujRo+Xfi0QihIWFYfbs2XjjjTcwbNgwnD9/Hnv27MHKlSthY2OjcH18fDzMzMzg4+OjVUxEREQGWXdgnBgBgaSg1vdKf1KMs7/lIFdcCstmphjS2RqONqoHeGkl7yFwdA7gGwFYqJYbNhXnzqlOyimztrZAdPTHiI7+uMpxFTPIFaysmmP37jCt4tm7d1X1g+pIo0ySAWDr1q1wc3NDVFQUFi9eDENDQzg5OWHixInyUgJPT09cvXoV+/btw+PHj2FpaYnevXsjNjZWYQu26OhovPfee5g7dy4kEgmWLVtWZZL8+eefKyStCQkJSEhIAAC88sorVSbJADB16lQsXboUYrFYo3KRkJAQeHt7VzuuLsyaNQtGRkaIiIjAkSNH0LZtW2zYsAHvv/++yti4uDj4+/tXW2tNRET0LGHaDzBO3gFIKz9MQhNX0/Kx8uR9nPjlKWQywEAASGWAQACMeMkGHw9vBw+nWvyOyn0AfPUOMHgp8C/36sdToyaQKW+gS3UuJycHLi4uCA8Px9tvv63vcHQiJSUFvXr1wrVr19SWc1Tl+MrA6gcRNSK+H32p7xAalKub3oQkLwvGzW3R+/1d+g6nwSsqzIOomQUy7ybDRNSs+gsas5JCGF/fDeG9C7W+1VcpTzB+x++QQYYyNQfmCQ0AAQTYH9QZ/j1VT4zTWrfXAY9pgLFZ7e+lEz/pO4AGTvsdShrtsdSNmaWlJRYuXIh169ZB2kSOvlyzZg0CAgK0TpCJiOg5JJNB+NdlmMZ/qJME+WpaPsbv+B1lUvUJMgCUSYEyqQxjt/+KH9M0O622Sv89BBycCtxnctpUMUnWk+DgYNy6dUttkXpjtG/fPhw4cEDfYRARUUMmk0L4IAkmCctgfOUzCMTZOrntqpP3IYMM1X00LgMggwwrTtzTyXOR9xA4Ng+4sAHQQS01NSyNtiaZiIiIGoGyUhhk/w7hwxQI/7oEQeETnd4+/Ukxjv+vBlmjcKTA0Z+fIP1JUe0X81X45Wsg9QfAdQLQYShgaqGb+5JeMUkmItKxq5ve1HcIDYokXzezhc8baVkJpKXqTz1tkKRlEIifQFD4NwzyHmLAtFV4nJULyMqAaud4a66gWKr1lsoyGdAj7Cc0M6nZYVb2FsZIWtxLsbHwCZC4Gbi8BWjZGbDrBNi4AFaOgGWb8iOu6/lsBqodJsmkd5YyzU4NJGosJDood6Tnl8CgPHGTlpahtFis52gASEthUPg3UFoMQZmk/LhmgQHwvzhRVgJBSQEMinPL+wBIATx6KsaD7AYQfyWeisvwVFxWs4uNzYG+71Y/TlIAZPxa/mVkCpjZAqaWgElzwKLNP3+G1CAxSSa9M25uq+8QiKgeGJtb6zuERsFAaAQAsGzXFUZGRnqOpuYcHA9AYPKozp9TUFBYowPBrKys0KxZzXYPsbe3B7q/UaNrqfHgFnBEREQNSElJCYyNjSGRSBp1klxf0tPT4eTkBG3SGYFAgLS0NDg6OtZhZPWNu2xUjVvAERER0XPE0dERI0aMgFCoWemCUCjEyJEjm1iCTHWBSTIRERE1ah9//DEEAgEE1SyMqxizZMmSeoqMGjMmyURERNSoeXh4YP/+/RAKhZXOKFf0HThwAB4eHvUcITVGTJKJiIio0fP390diYiKGDx8un1GuOLBLIBDA19cXiYmJ8PPz02eY1Ihw4R4REVEDwoV7tZeeno6zZ88iNzcXFhYWGDRo0HNQg8yFe1XTfuEek2QiIqIGhEky1QyT5KpxdwsiIiIiolpjkkxEREREpIRJMhERERGREibJRERERERKmCQTERERESlhkkxEREREpIRJMhERERGREibJRERERERKmCQTERERESlhkkxEREREpIRJMhERERGREibJRERERERKmCQTERERESlhkkxEREREpMRQ3wEQTdo0Sd8hEFE9sDa3xidvf6LvMIiINMIkmfQuPzNf3yGoKDYp1ncIREREpEdMkonUsG1uq+8QiJqM7PxsSGVSfYdBRKQVJslEaux+f7e+QyBqMiZtmoSsvCx9h0FEpBUu3CMiIiIiUsIkmYiIiIhICZPk/0lLS4NAIEBMTEy9PC88PBydOnWCVNo06vTGjRuHMWPG6DsMIqJGLT09Xf57KCYmBunp6foNiOg5VqMk+e7du5gxYwZcXFxgamoKCwsL9O/fH5s2bYJYLNZ1jHI3b95ESEgI0tLS6uwZ1bl16xYWLlwIV1dXNG/eHA4ODvD19UVSUpLG98jNzcXatWsRHBwMA4N//goEAgEEAgEiIiJUromJiYFAINDqOZry8fGBQCDAu+++q7b/888/R+fOnWFqaooOHTpg8+bNKmOCg4Nx6NAhXL9+XefxERE1dVevXsXIkSPh5OSE6dOnAwCmT58OJycnjBo1Cj/++KOeIyR6/midJB8/fhwvvfQSDhw4gJEjR2Lz5s1YvXo1HB0dsWDBArz//vt1ESeA8iQ5NDRUr0lydHQ0tm/fDnd3d0RERGDevHm4ffs2+vTpg9OnT2t0jy+++AKlpaUYP3682v5169ahsLBQl2FX6vDhw7h06VKl/VFRUZg2bRq6du2KzZs3o2/fvpgzZw7Wrl2rMK5nz57yPxMiItLc4cOH0b9/f3z77beQyWQKfTKZDCdOnEC/fv1w+PBhPUVI9HzSKklOTU3FuHHj0K5dO9y8eRObNm1CUFAQZs+ejb179+LmzZvo2rVrXcWqFZlMViez2uPHj8eff/6J6OhoTJ8+HQsWLMCVK1dgY2ODkJAQje6xY8cOjBo1Cqampip9rq6uePz4MbZu3arjyFUVFRVh/vz5CA4OVtsvFovx0UcfwdfXFwcPHkRQUBB27dqFCRMmICwsDNnZ2Qrjx4wZg8OHDyM/v+Hte0xE1BBdvXoVY8eORVlZGcrKytSOqegbO3YsZ5SJ6pFWSXJ4eDjy8/Px+eefw8HBQaX/hRdeUJhJLi0tRVhYGNq3bw8TExM4OTlh8eLFKC5WPKjByckJI0aMwIULF9C7d2+YmprCxcUFu3btko+JiYnBG2+8AQDw9vaWlyacO3dO4R4nT56Eu7s7RCIRoqKiAAB//PEH3njjDdjY2MDMzAx9+vTB8ePHtXnpcm5ubjA3N1dos7W1xYABA/Drr79We31qaipu3LiBIUOGqO3v378/Bg0ahPDw8DotXQHK/z6lUin+/e9/q+1PSEhAVlYWZs2apdA+e/ZsFBQUqPwZ+vj4oKCgAPHx8XUWMxFRU7JixQrIZDKVGWRlFWNWrFhRT5ERkVZJ8tGjR+Hi4oJ+/fppNH7atGlYunQpevXqhQ0bNsDT0xOrV6/GuHHjVMb+/vvvCAgIgI+PDyIiImBtbY3Jkyfjl19+AQAMHDgQc+bMAQAsXrwYu3fvxu7du9G5c2f5PW7fvo3x48fDx8cHmzZtks/K9uvXDydPnsSsWbOwcuVKFBUVYdSoUfjqq6+0eflVevToEVq0aFHtuMTERABAr169Kh0TEhKCx48fY8uWLVXeq7i4GJmZmRp9KUtPT8eaNWuwdu1aiEQitfdPTk4GALi7uyu0u7m5wcDAQN5foUuXLhCJRLh48WKVcRMRUfnP4WPHjlU6g6ysrKwMR48e5WI+onqi8WEiubm5uH//Pl577TWNxl+/fh07d+7EtGnTsH37dgDArFmz0LJlS6xfvx4JCQnw9vaWj799+zZ++OEHDBgwAED5R/dt27bFjh07sH79eri4uGDAgAH45JNP4OPjAy8vL5Vn/v777/juu+8wbNgwedvcuXPx+PFjnD9/Hq+88goAICgoCN27d8e8efPw2muvKSyeq4nz58/j0qVLWLJkSbVjb926BQBwdnaudMyAAQPg7e2NdevWYebMmZUmsXv37sWUKVM0ilF5lmL+/Pno2bOn2jcsFR4+fAihUIiWLVsqtBsbG8PW1hYPHjxQaDc0NETbtm1x8+bNSu9ZXFys8klCWWkZhIZCjV6HNs59fg5F+UVaXWNqbgqvt70U2tzd3fHo0SOt7mNvb18niyyJqOk4c+ZMtTPIymQyGc6ePYvJkyfXTVBEJKdVkgwAzZs312j8iRMnAADz5s1TaJ8/fz7Wr1+P48ePKyTJXbp0kSfIAGBnZ4cXX3wRf/zxh6YhwtnZWSFBroijd+/e8gQZAMzNzTF9+nQsWrQIN2/eRLdu3TR+hrKMjAwEBgbC2dkZCxcurHZ8VlYWDA0NVUo2lIWEhMDT0xNbt27F3Llz1Y4ZNmxYjUobEhIScOjQIVy5cqXKcWKxGMbGxmr7TE1N1ZaDWFtbq525rrB69WqEhoYqtNk62sLDzwOmzVVrtGujKL8IRXnaJcnqPHr0CPfv39dBRERE/8jLy4OBgYFWW4EaGBjIfx8TNUQSSQmePMlRaLOzs4ZQqPvJsLqmcZJsYWEBoPwftSbu3bsHAwMDvPDCCwrt9vb2sLKywr179xTaHR0dVe5hbW2tsjisKupmZ+/du4eXX35Zpb2iTOPevXs1TpILCgowYsQI5OXl4cKFC9UmvtoYOHAgvL29ER4ejnfeeUftGAcHB7W14VUpLS3FnDlzMGnSJHh4eFQ5ViQSQSKRqO0rKipSO8Mtk8kgEAgqveeiRYsU3jilpKTA09MTRflFOk+STc21v5+6a+zt7bW+T02uIaLnS/PmzbXeK18qlcp/HxM1RImJ1+HtrZi3pKYegZNTaz1FVHNaJcmtW7fGf//7X60eUFXC9KzK3mFo81FUZWUJdUEikcDf3x83btzAyZMnNU60bW1tUVpairy8vGpn5ZctWwYvLy9ERUXByspKpV8sFiMnJ0f1QjUqkrZdu3bh9u3biIqKUtlKLy8vD2lpaWjZsiXMzMzg4OCAsrIyZGRkKJRcSCQSZGVloXVr1f/hs7Oz0aFDh0rjMDExgYmJifx7Xb6xUKZcNlFTLJsgorowePBgCAQCrX7PCQQCDBo0qA6jIqqdHj06Ij7+M4U2e3tbPUVTO1oV444YMQJ3796tcl/dCu3atYNUKsWdO3cU2h8/foynT5+iXbt22kUKzRNu5Thu376t0l5RG1yTOKRSKd58802cOXMGX375JTw9PTW+tlOnTgDKd7mojqenJ7y8vLB27Vq1pQ379++XzyZX91UhPT0dJSUl6N+/P5ydneVfQHkC7ezsjFOnTgEo344OUE0Sk5KSIJVK5f0VSktL8eeffyospiQiIvUcHR0xYsQIjT+GFgqFGDlypNpPXokaCmtrCwwZ8rLCl6mpSfUXNkAazyQDwMKFCxEbG4tp06bh7NmzaNWqlUL/3bt3cezYMbz//vsYPnw4Fi9ejI0bN8q3YgOA//znPwAAX19frYNt1qwZAODp06caXzN8+HBs3LgRly5dQt++fQGUl0ls27YNTk5O6NKli9ZxvPfee9i/fz+ioqLg7++v1bUVMSQlJaF79+7Vjg8JCYGXlxe2bdum0leTmuRx48apJLcA4Ofnh+HDhyMoKEhenjJo0CDY2Nhgy5YtGD58uHzsli1bYGZmpvJ3ePPmTRQVFWm8+wkR0fPu448/xrffflvtjHLFtqeaLBAnIt3QKklu3749vvzyS4wdOxadO3fGm2++iW7dukEikSAxMRFxcXHyFbc9evTAW2+9hW3btuHp06fw9PTE1atXsXPnTowePVph0Z6mXF1dIRQKsXbtWuTk5MDExASDBg1S2X3hWR9++CH27t2L//u//8OcOXNgY2ODnTt3IjU1FYcOHdJ6Z4uNGzciMjISffv2hZmZGfbs2aPQ7+fnJ0/m1XFxcUG3bt1w+vRpTJ06tdrneXp6wtPTE99//71KX01qkjt16iSfzVbm7OyM0aNHy78XiUQICwvD7Nmz8cYbb2DYsGE4f/489uzZg5UrV8LGxkbh+vj4eJiZmcHHx0ermIiInlceHh7Yv38/xo4dC5lMpnY7OKFQCIFAgAMHDlS7loSIdEerJBkARo0ahRs3bmDdunX45ptvsGXLFpiYmKB79+6IiIhAUFCQfGx0dDRcXFwQExODr776Cvb29li0aBGWLVtWo2Dt7e2xdetWrF69Gm+//TbKysqQkJBQZZLcqlUrJCYmIjg4GJs3b0ZRURG6d++Oo0eP1mg2OyUlBQBw6dIltWUnqampVSbJADB16lQsXboUYrFYozrqkJCQGr2p0IVZs2bByMgIEREROHLkCNq2bYsNGzaoPX48Li4O/v7+Gu+AQkREgL+/PxITExEWFoZjx44pzCgLBAL4+vpiyZIlTJCJ6plApu0mjVRrOTk5cHFxQXh4ON5++219h6MTKSkp6NWrF65du6a2nKMy165dg5ubG7ze9oKVg1Wdxaetr8J0d9AM0fNu0qZJyMrLgm1zW+x+f7e+w2nQ0tPTcerUKQQFBWH79u0YOnQoa5BJQz/pO4AGzk3rK2p3igbViKWlJRYuXIh169Zpvf1PQ7VmzRoEBARolSATEZEiR0dHvPXWWwCAt956iwkykR5pXW5BuhEcHIzg4GB9h6Ez+/bt03cIRERERDrDmWQiIiIiIiWcSSZSY9KmSfoOgajJyM7X/ORUIqKGgkkyNQgSYwmKTYr1HYZccV7DiYWIiIjqH5NkahAszSxh27xxHltJRJqxNrfWdwhERBpjkkwNworAFejVq5e+wyAiIiICwIV7REREREQqmCQTERERESlhkkxEREREpIRJMhERERGREibJRERERERKmCQTERERESlhkkxEREREpIRJMhERERGREibJRERERERKmCQTERERESlhkkxEREREpIRJMhERERGREibJRERERERKmCQTERERESlhkkxEREREpIRJMhERERGREibJRERERERKmCQTERERESlhkkxEREREpIRJMhERERGREkN9B0AEAD+sWIEHLVroOwwiqkOmVlYYEh6u7zAajdLiYgikUhgYGMDAyEjf4RA9d5gkU4PwVCJBZnGxvsPQSLOCAn2HQERNWFlREQDg4ZEjMG3XDkY2NrB94QUmykT1jEkykZZENjb6DoGoURE/fQpIpfoOo1GQFhTg0cqVAADxrt0oNTCAQCSCuKUdIJVCViyBqHt32E4PgtDcXM/REjVtTJKJtDRi2zZ9h0DUqBybPh3iJ0/0HUaDJy0uxqPVq1F86zYAQCAQQCAQQFZYiNLHGRAIBACAgosXIfkzHQ5hYUyUieoQF+4RERHpmUwqxd8bNqL411sajS9J/xOPV6yEVCyu48iInl9MkomIiPQse+9eFP74o1bXFN+5g8erVkPaSNZzUNPw9Gkepk9fCTu7IWjW7BV4e8/AtWuavbkDgF9/TcWrr74Hc/MBsLEZhEmTPsbff2crjLl1Kw0LF26Cq2sgmjcfCAeHYfD1fR9JSTd1/XKqxCRZT8LDw9GpUydIm0id3rhx4zBmzBh9h0FE1OgUXktGzuGvanRt0c2byFgbDplEouOoiFRJpVL4+r6PL7/8Du++Owbh4XOQkZENL68ZuHMnvdrr//rrMQYODMLvv/+JVatm49//nojjxy/Cx2c2JJIS+bjo6K+xffvXcHfvjIiIDzBv3gTcvn0PffpMwenTV+ryJSpokElyRR1WdV/nzp2r9bMKCwsREhKi8b1u3bqFhQsXwtXVFc2bN4eDgwN8fX2RlJSk8TNzc3Oxdu1aBAcHw8Dgn7+CitcVERGhck1MTAwEAoFWz9GUj48PBAIB3n33XbX9n3/+OTp37gxTU1N06NABmzdvVhkTHByMQ4cO4fr16zqPj4ioqSrNzkbmp6o/U7Uhvn4dGRH/YaJMteblNR2TJ4dU2n/w4BkkJt5ATMwyLFs2HbNnj8G5c1EQCg2wbFlUtfdftWoHCgrEOHt2K+bMGYfFi6fiwIHVuH79N8TEHJWPGz9+GP788ziioz/G9On+WLDgTVy5EgMbGwuEhNTfuqAGmSTv3r1b4cvHx0dte+fOnWv9rMLCQoSGhmqcJEdHR2P79u1wd3dHREQE5s2bh9u3b6NPnz44ffq0Rvf44osvUFpaivHjx6vtX7duHQoLCzV9CbVy+PBhXLp0qdL+qKgoTJs2DV27dsXmzZvRt29fzJkzB2vXrlUY17NnT/mfCRERVU9WVoa//7MBZTm5tb5XYVISHq9fD1lJSfWDiWro4MEzaNXKFv7+g+RtdnbWGDNmCL755nsUF1f9Ru3QobMYMWIAHB3t5W1DhryMjh0dceDAPzmUm1tnmJubKVxra2uFAQNc8euvabp5MRpokEnyxIkTFb46duyotr1Vq1b1Htv48ePx559/Ijo6GtOnT8eCBQtw5coV2NjYICQkRKN77NixA6NGjYKpqalKn6urKx4/foytW7fqOHJVRUVFmD9/PoKDg9X2i8VifPTRR/D19cXBgwcRFBSEXbt2YcKECQgLC0N2tmIN0ZgxY3D48GHk5+fXeexERI1d9p49KLqpuxpL8U/X8HdkJGQymc7uSfSs5OTb6NXrRYVPwQGgd++uKCwswm+/VV5ycf9+BjIynsDdXXWCs3fvrkhOvl3t8x89ykKLFpbaB15DDTJJ1oRUKsXGjRvRtWtXmJqaolWrVpgxY4ZK4paUlIRhw4ahRYsWEIlEcHZ2xtSpUwEAaWlpsLOzAwCEhobKyx2qSnbd3NxgrrTljq2tLQYMGIBff/212rhTU1Nx48YNDBkyRG1///79MWjQIISHh0Ncx6uWw8PDIZVK8e9//1ttf0JCArKysjBr1iyF9tmzZ6OgoADHjx9XaPfx8UFBQQHi4+PrLGYioqag4MpV5Bw5Wv1Abe/7w3nkneLPYKobDx9mwsFB9XTcirYHD/6u8tpnxypf/+RJTpUz0efPJ+PSpZ8xduxQbcOusUa7T/KMGTMQExODKVOmYM6cOUhNTcWnn36K5ORkXLx4EUZGRsjIyMDQoUNhZ2eHDz/8EFZWVkhLS8Phw4cBAHZ2dtiyZQtmzpwJPz8/+Pv7AwC6d++udTyPHj1CCw2OVU5MTAQA9OrVq9IxISEhGDhwILZs2YJ58+ZVOq64uBh5eXkaxaccW3p6OtasWYMvvvgCIpFI7TXJyckAAHd3d4V2Nzc3GBgYIDk5GRMnTpS3d+nSBSKRCBcvXoSfn59GcRERPW9KMzORGRlZZ/d/EhMDUbeuMGrTps6eQY1fSUkpcnLyVdqKiyXIzHyq0G5jYwEDAwOIxcUwMTFWuZepqQkAQCyufKeVir7qrlfXn5HxBIGBS+Ds3BoLF75Z9QvToUaZJF+4cAHR0dGIjY1FYGCgvN3b2xuvvvoq4uLiEBgYiMTERGRnZ+PUqVMKid6KFSsAAM2aNUNAQABmzpyJ7t27KyR82jh//jwuXbqEJUuWVDv21q3ybVKcnZ0rHTNgwAB4e3tj3bp1mDlzZqVJ7N69ezFlyhSNYlT++G3+/Pno2bMnxo0bV+k1Dx8+hFAoRMuWLRXajY2NYWtriwcPHii0Gxoaom3btrhZxceHxcXFKH5muyJ9lGaEHj+OHC1n6S1FIizz9VVpd3d3x6NHj7S6l729fZ0swCSihk9WVoa/N22CtA5/9skkEvz9ySdwWLECAh5lTZW4eDEF3t7vqLQnJt7Avn2nFNpSU4/Ayak1RCITtbO9RUXlv9dFIpNKn1fRp+31BQVijBjxAfLyCnDhQrRKrXJdapRJclxcHCwtLeHj44PMzEx5e0UpREJCAgIDA2FlZQUAOHbsGHr06AGjOvhhkZGRgcDAQDg7O2PhwoXVjs/KyoKhoaFKyYaykJAQeHp6YuvWrZg7d67aMcOGDatRaUNCQgIOHTqEK1eq3kZFLBbD2Fj1HR0AmJqaqi0Hsba2Vvg7UbZ69WqEhoYqtL3YsiWsKnkjUBdyxGJk62hh5KNHj3D//n2d3IuImr7svftQdLP60rzaKv79Lp7ExsJ28uQ6fxY1Tj16dER8/GcKbfPnb4S9vS0WLJik0G5vbwugvCyiomziWRVtrVvbVfq8ijKLyq63sbFUmUWWSErg778AN278jpMnN6Nbtxc0eGW60yiT5Dt37iAnJ0dlhrNCRkYGAMDT0xOvv/46QkNDsWHDBnh5eWH06NEIDAyEiUnl73Y0VVBQgBEjRiAvLw8XLlyoNvHVxsCBA+Ht7Y3w8HC8847qOz0AcHBwgIODg1b3LS0txZw5czBp0iR4eHhUOVYkEkFSyZZCRUVFame4ZTKZ/OhUdRYtWqRSQrJ/1iwYCYUaRK8bljVIyCu7xt7eXm17VWpyDRE1fgVXriLnq5rth1wTuUePwbRTJzTr06fenkmNh7W1BYYMeVmprTkcHFqotFdwde2I8+dTIJVKFRbvXbnyC8zMTNGxo2Olz2vTpiXs7KyRlKT6JvHq1V/g6tpRoU0qleLNN5fizJkfceDAanh6umnz8nSiUSbJUqkULVu2RGxsrNr+isV4AoEABw8exOXLl3H06FGcPHkSU6dORUREBC5fvlyrpFYikcDf3x83btzAyZMn0a1bN42us7W1RWlpKfLy8tC8efMqxy5btgxeXl6IioqSz4o/SywWIycnR6PnViRmu3btwu3btxEVFYW0tDSFMXl5eUhLS0PLli1hZmYGBwcHlJWVISMjQ+ENiUQiQVZWFlq3bq3ynOzsbHTo0KHSOExMTFTeoNRnggxAbdlETbFsgog0UfLoETLV7DFf1/7+9FMYtWkD47Zt6/3Z1PQEBAzGwYNncPjwWQQElG9AkJn5FHFxpzFy5ACFmeC7d/8CALRv/y952+uvD8LOncfw55+P0LZteV5y5sxV/PZbOubODcSz3ntvHfbvj0dU1GKFLefqU6NMktu3b4/Tp0+jf//+ldbrPqtPnz7o06cPVq5ciS+//BITJkzAvn37MG3atCpnPStT/u7mTZw5cwYHDhyAp6enxtd26tQJQPkuF9UtEPT09ISXlxfWrl2LpUuXqvTv379f65rk9PR0lJSUoH///ipjdu3ahV27duGrr77C6NGj4erqCqA8ERw+fLh8XFJSEqRSqby/QmlpKf7880+MGjVKo5iIiJ4HUokEGevWQ1rHOxapIxMX4fGatWi9ehWEFhb1/nxqWgICBqNPn5cwZcpy3LyZihYtrBAZGYeyMilCQ2cojB08eCYAIC3tn11cFi+egri40/D2fgfvvz8O+flirFu3Gy+99AKmTPknd9i48UtERsahb9/uMDMzxZ49JxTu7efnjWbN6r5Ms1EmyWPGjEFkZCTCwsKwatUqhb7S0lLk5+fDysoK2dnZsLKyUkiEKxK7isVjZmblBeBPnz7V+Pnvvfce9u/fj6ioKPmOGJrq27cvgPJEU5NdNEJCQuDl5YVt21RPmKlJTfK4ceNUklsA8PPzw/DhwxEUFISXXy7/mGXQoEGwsbHBli1bFJLkLVu2wMzMDL5KM7I3b95EUVER+vXrp1VMRERNlUwqReZnkZAofXJXn0ofPcLj1Wtgv2wpDNTsz0+kKaFQiBMnNmHBgk345JN9EIuL4eHRBTExIXjxRadqr2/b1h7ff78N8+ZtwIcffgpjYyP4+r6CiIgPFGahU1J+AwBcunQDly7dULlPauoRJsmV8fT0xIwZM7B69WqkpKRg6NChMDIywp07dxAXF4dNmzYhICAAO3fuRGRkJPz8/NC+fXvk5eVh+/btsLCwkCd9IpEIXbp0wf79+9GxY0fY2NigW7dulZZPbNy4EZGRkejbty/MzMywZ88ehX4/Pz80a9as0thdXFzQrVs3nD59Wr5fc3Wv1dPTE99//71KX01qkjt16iSfzVbm7OyM0aNHy78XiUQICwvD7Nmz8cYbb2DYsGE4f/489uzZg5UrV8LGxkbh+vj4eJiZmclPSCQiep7JSkuRuW0bCi5cqNH1D8Vi/PjkCfJLSmBhJkK/lq3QxqxmK/uLf/sNGevWo9WHwdzxgip17lz1Rz5bW1sgOvpjREd/XOW4Z2eQn9W1a3ucPPlpldfGxIQgJiak2ljqWqNMkgFg69atcHNzQ1RUFBYvXgxDQ0M4OTlh4sSJ8lICT09PXL16Ffv27cPjx49haWmJ3r17IzY2VmELtujoaLz33nuYO3cuJBIJli1bVmmSnJKSAgC4dOmS2uOcU1NTq0ySAWDq1KlYunQpxGKxRuUiISEh8Pb2rnZcXZg1axaMjIwQERGBI0eOoG3bttiwYQPef/99lbFxcXHw9/evttaaiKipK3nwAH9HRqL411taX3vjyRMAwJDvzwEoP/VLCkAAYJC9A97t1Ak9lCYpNCFOScHfmz+F3QfvQ2DQaM8SI6o3AhnPr6x3OTk5cHFxQXh4ON5++219h6MTKSkp6NWrF65du6a2nKMqMW+9VTdB1ZHJO3fqOwSiRuXY9OkQP3kCkY0NRqgpHWtKSjIykHP4K+SdPQuUlWl9/Xf37+O9K5dR2ZVCgQACAJ/0fhmv1vCwEPNB3mgxcyYT5SbnJ30H0MBpvzsG/4XogaWlJRYuXIh169ZBKpXqOxydWLNmDQICArROkImImgJpcTGefPkl7r83B3nx8TVKkK8/eYI5V69UmiADQJlMhjKZDHOuXsH1/804ayv/bAIer1qN0qysGl1P9LxotOUWjV1wcDCCg4P1HYbO7Nu3T98hEBHVO5lUioKLF5Ed+yVK//67Vvf69NYtaPLRrux/X5/dvoVtfWu2UFqcnIy/Zr8Ls5dfhplbL4i6d4dQzVajRM8zJslERERaKn3yBAUXLyL35EmUPtTuaHp17hcW4uyjhxolyUD5jPKZhw9xv7Cwxov5ZCUlKLhwoXxhoYEBRD16wHLkCJh2716j7VGJmhomyURaOjZ9ur5DIGpUxFpssdlQleXlofjO7yi6eRNFP99A8e93MersGWQWFevk/oWlpRonyBVkAHxPn4aZoea/yluYmuDIoMGqHVIpxMnJECcnw6htW5gPHAiRaw8YOzpCoMX9iZoS/p9PetesoEDfIWhF3MjiJaLqSYuLUZqRAVlpKWSSEsiKxCjLzUPZkyyUPHqMsmfqf4VW1jBzd0fW6Xg8Kqr/A0KelVtagtzSEo3HC4yNYObuXu244tu3UXz7NgRGhjBs2QqGdi0gtLaGgXlzGIhMITA2hsDQsDyBNjSEwMgIAqEQAqEQEAiAij0BjIw4K02NFpNk0jtRDbYyIqLGx7QB17wamJhofXRz60MHYfCo9qUWAFBQUKDVoVYVrKysqt129Fn29vZotehDrZ9D9DziFnBERER6lp6eDicnJ2jzK1kgECAtLQ2Ojo51GBk1HtwCrmrcAo6IiKjRcXR0xIgRIyAUCjUaLxQKMXLkSCbIRHWISTIREVED8PHHH2tUvysQCCAQCLBkyZJ6iIro+cUkmYiIqAHw8PDA/v37q5xNFgqFEAqFOHDgADw8POoxOqLnD5NkIiKiBsLf3x/nz59XaDP43/HRAoEAvr6+SExMhJ+fnz7CI3qucOEeERFRA1JSUgJjY2P8/vvvOH/+PHJzc2FhYYFBgwaxBpmqwIV7VdN+4R6TZCIiogakIkmWSCQwMjLSdzjUaDBJrhp3tyAiIiIiqjUmyURERERESpgkExEREREpYZJMRERERKSESTIRERERkRImyURERERESpgkExEREREpYZJMRERERKSESTIRERERkRImyURERERESpgkExEREREpYZJMRERERKSESTIRERERkRImyURERERESgz1HQDRjnVH9B0CEdUDM3NTjJ05VN9hEBFphEky6V1BUY6+Q2h4JMb6joCIiOi5xiSZqAFqZiHSdwhEOlOYVwSZTKbvMIiItMIkmagBmrJglL5DINKZHeuOoCBXrO8wiIi0woV7RERERERKmCQTERERESlhkkxERNRApKenIyYmBgAQExOD9PR0/QZE9Bxjkqwn4eHh6NSpE6RSqb5D0Ylx48ZhzJgx+g6DiKhRunr1KkaOHAknJydMnz4dADB9+nQ4OTlh1KhR+PHHH/UcIdHzp0EmyQKBQKOvc+fO1fpZhYWFCAkJ0fheDx48wMSJE/Hiiy+iefPmsLKyQu/evbFz506NV2/n5uZi7dq1CA4OhoHBP38FFa8rIiJC5ZqYmBgIBAIkJSVp9Axt+Pj4QCAQ4N1331Xb//nnn6Nz584wNTVFhw4dsHnzZpUxwcHBOHToEK5fv67z+IiImrLDhw+jf//++Pbbb1V+j8hkMpw4cQL9+vXD4cOH9RQh0fOpQe5usXv3boXvd+3ahfj4eJX2zp071/pZhYWFCA0NBQB4eXlVOz4zMxN//fUXAgIC4OjoiJKSEsTHx2Py5Mm4ffs2Vq1aVe09vvjiC5SWlmL8+PFq+9etW4eZM2fCzMxMq9dSE4cPH8alS5cq7Y+KisI777yD119/HfPmzcP58+cxZ84cFBYWIjg4WD6uZ8+ecHd3R0REBHbt2lXncRMRNQVXr17F2LFjUVZWVulES1lZGQQCAcaOHYvExER4eHjUc5REz6cGmSRPnDhR4fvLly8jPj5epV0funfvrjLr/O6772LkyJH45JNPEBYWBqFQWOU9duzYgVGjRsHU1FSlz9XVFSkpKdi6dSvmzZuny9BVFBUVYf78+QgODsbSpUtV+sViMT766CP4+vri4MGDAICgoCBIpVKEhYVh+vTpsLa2lo8fM2YMli1bhsjISJibm9dp7ERETcGKFSsgk8mq/SSyYsyKFSvwzTff1FN0RM+3BlluoQmpVIqNGzeia9euMDU1RatWrTBjxgxkZ2crjEtKSsKwYcPQokULiEQiODs7Y+rUqQCAtLQ02NnZAQBCQ0Pl5Q4hISFax+Pk5ITCwkJIJJIqx6WmpuLGjRsYMmSI2v7+/ftj0KBBCA8Ph1hct/uKhoeHQyqV4t///rfa/oSEBGRlZWHWrFkK7bNnz0ZBQQGOHz+u0O7j44OCggLEx8fXWcxERE1Feno6jh07hrKyMo3Gl5WV4ejRo1zMR1RPGuRMsiZmzJiBmJgYTJkyBXPmzEFqaio+/fRTJCcn4+LFizAyMkJGRgaGDh0KOzs7fPjhh7CyskJaWpq8rsvOzg5btmzBzJkz4efnB39/fwDls8XVEYvFKCgoQH5+Pr7//nvs2LEDffv2hUhU9UlpiYmJAIBevXpVOiYkJAQDBw7Eli1bqpxNLi4uRl5eXrWxAkCLFi0Uvk9PT8eaNWvwxRdfVBpzcnIyAMDd3V2h3c3NDQYGBkhOTlaY3e/SpQtEIhEuXrwIPz+/SmMuLi5WaCspLYGRoZFGr6MhC9+6FHn5NT9iu7m5JRa+s1yl3d3dHY8eParRPe3t7eukjp2Iau/MmTNan0Qok8lw9uxZTJ48uW6CIiK5RpkkX7hwAdHR0YiNjUVgYKC83dvbG6+++iri4uIQGBiIxMREZGdn49SpUwqJ3ooVKwAAzZo1Q0BAAGbOnInu3btrVc6xadMmLFq0SP794MGDsWPHjmqvu3XrFgDA2dm50jEDBgyAt7e3vDa5siR27969mDJlikbxKv8gnj9/Pnr27Ilx48ZVes3Dhw8hFArRsmVLhXZjY2PY2triwYMHCu2GhoZo27Ytbt68Wek9V69eLa8Br/BCuxcxecxsWDa30ui1NFR5+Tl4mptd/UAtPXr0CPfv39f5fYlIv/Ly8mBgYKDVLkcGBgbIzc2tw6iIakciKcGTJ4oTRnZ21tWWojZEjTJJjouLg6WlJXx8fJCZmSlvd3Nzg7m5ORISEhAYGAgrKysAwLFjx9CjRw8YGelutnL8+PFwd3fH33//jWPHjuHx48calUdkZWXB0NCw2prdkJAQeHp6YuvWrZg7d67aMcOGDatRaUNCQgIOHTqEK1euVDlOLBbD2NhYbZ+pqana12ttba3wd6Js0aJFCrPjKSkp8PT0RG7e00afJDc3t6yT6+3t7Wt8z9pcS0R1q3nz5lpvAyqVSmFhYVFHERHVXmLidXh7v6PQlpp6BE5OrfUUUc01yiT5zp07yMnJUZnhrJCRkQEA8PT0xOuvv47Q0FBs2LABXl5eGD16NAIDA2FiYlKrGNq1a4d27doBKE+Yp0+fjiFDhuD27dvVllxoYuDAgfD29kZ4eDjeeecdtWMcHBzg4OCg1X1LS0sxZ84cTJo0qdoV0iKRqNIa66KiIrWvUyaTQSAQVHpPExMThT/7prTAT12phC6wXIKoaRo8eDAEAoFWJRcCgQCDBg2qw6iIaqdHj46Ij/9Moc3e3lZP0dROo0ySpVIpWrZsidjYWLX9FYvxBAIBDh48iMuXL+Po0aM4efIkpk6dioiICFy+fFmnCVpAQAC2b9+OH374AcOGDat0nK2tLUpLS5GXl4fmzZtXec9ly5bBy8sLUVFR8lnxZ4nFYuTkaFYDWzGjuGvXLty+fRtRUVFIS0tTGJOXl4e0tDS0bNkSZmZmcHBwQFlZGTIyMhTekEgkEmRlZaF1a9V3hdnZ2ejQoYNGMRERPc8cHR0xYsQInDhxQqPFe0KhEL6+vnB0dKyH6IhqxtraAkOGvKzvMHSiUe5u0b59e2RlZaF///4YMmSIylePHj0Uxvfp0wcrV65EUlISYmNj8csvv2Dfvn0AUOWspzYqSg+qS1o7deoEoHyXi+p4enrCy8sLa9euVVvasH//fvlscnVfFdLT01FSUoL+/fvD2dlZ/gWUJ9DOzs44deoUgPLt6ADVmcykpCRIpVJ5f4XS0lL8+eefOtm/mojoefDxxx/Ld1aqSsWYJUuW1FNkRNQoZ5LHjBmDyMhIhIWFqRzeUVpaivz8fFhZWSE7OxtWVlYKP3wqEruKHRYqDux4+vSpRs/++++/5TPVz/r8888hEAiq3LUCAPr27QugPNHUZBeNkJAQeHl5Ydu2bSp9NalJHjdunEpyCwB+fn4YPnw4goKC8PLL5e8ABw0aBBsbG2zZsgXDhw+Xj92yZQvMzMzg6+urcI+bN2+iqKgI/fr10yomIqLnlYeHB/bv34+xY8dCJpOpnVEWCoUQCAQ4cOAADxIhqkeNMkn29PTEjBkzsHr1aqSkpGDo0KEwMjLCnTt3EBcXh02bNiEgIAA7d+5EZGQk/Pz80L59e+Tl5WH79u2wsLCQJ30ikQhdunTB/v370bFjR9jY2KBbt27o1q2b2mevXLkSFy9exKuvvgpHR0c8efIEhw4dwo8//oj33nsPL7zwQpWxu7i4oFu3bjh9+rR8v+bqXqunpye+//57lb6a1CR36tRJPputzNnZGaNHj5Z/LxKJEBYWhtmzZ+ONN97AsGHDcP78eezZswcrV66EjY2NwvXx8fEwMzODj4+PVjERET3P/P39kZiYiLCwMBw7dkyhRlkgEMDX1xdLlixhgkxUzxplkgwAW7duhZubG6KiorB48WIYGhrCyckJEydORP/+/QGUJ5hXr17Fvn378PjxY1haWqJ3796IjY1V2IItOjoa7733HubOnQuJRIJly5ZVmiT7+vri7t27+OKLL/D333/D1NQU3bt3x44dO/DWW29pFPvUqVOxdOlSiMVijRb5hYSEwNvbW6N769qsWbNgZGSEiIgIHDlyBG3btsWGDRvw/vvvq4yNi4uDv79/tbXWRESkyMPDA0eOHEF6ejpOnTqFoKAgbN++HUOHDmUNMpGeCGTa7mROtZaTkwMXFxeEh4fj7bff1nc4OpGSkoJevXrh2rVrass5KnPt2jW4ublh4TvL0ba1U53F19i8+/EkfYdApDM71h1BQa4YzSxEmLJglL7DafBKSkpgbGwMiUSi061Lqan7Sd8BNHBuWl/RKBfuNXaWlpZYuHAh1q1bp/UemQ3VmjVrEBAQoFWCTERERNRQNdpyi8YuODgYwcHB+g5DZyp2CyEiIiJqCpgkEzVAO9Yd0XcIRDpTmFek7xCIiLTGJJkahhIjQKL+COznUYGk+iPOiYiIqO4wSaYGQdTMBM0san+cNxE1XGbmpvoOgYhIY0ySqUEY9ZZntQexEBEREdUX7m5BRERERKSESTIRERERkRImyURERERESpgkExEREREpYZJMRERERKSESTIRERERkRImyURERERESpgkExEREREpYZJMRERERKSESTIRERERkRImyURERERESpgkExEREREpYZJMRERERKSESTIRERERkRImyURERERESpgkExEREREpYZJMRERERKSESTIRERERkRImyURERERESpgkExEREREpMdR3AEQAsPM/a3GmVUt9h0FEdcjcwgozl4XpO4xGQ1JcDJlUCgOhEIaG/HVNVN/4r44ahNz8QjwxzdN3GA2eobRU3yEQUR0rLS3/d37rpx9hZt4cZs2bo41LeybKRPWM/+KIGhELa2t9h0CktbynTyGTyfQdRqNQ8DQbl7+OAwAkHfwShoZCiCyt4Ny9J9p07AQ7JxeIzJvrOUqi5wOTZKJGZEHEZn2HQKS1dfPfQ252tr7DaPDu3/4VF/fvRkFubnmDoPw/hdlP8PuVRNz98RIAwNKuFVq/2BnOPT1g07qNnqIlavqYJBMREenZ7z9expWv9ms0457z92Pk/P0Yv144hzYvdoH7KH80t2lRD1ESPV+4uwUREZEe/fL9GVw+vK9GJSn3b9/E8U3rkP7f63UQGdHzjUkyERGRHshkMqScPI7k747W6j6lkmL8ELsD/02IZ+031bmnT/MwffpK2NkNQbNmr8DbewauXbul8fW//pqKV199D+bmA2BjMwiTJn2Mv/9WLMe6dSsNCxdugqtrIJo3HwgHh2Hw9X0fSUk3df1yqsQkWU9mzZoFHx8ffYehM3369MHChQv1HQYRUaMgk0qRdPQw/nsuXmf3TDl1HFe/joNUWqazexI9SyqVwtf3fXz55Xd4990xCA+fg4yMbHh5zcCdO+nVXv/XX48xcGAQfv/9T6xaNRv//vdEHD9+ET4+syGRlMjHRUd/je3bv4a7e2dERHyAefMm4Pbte+jTZwpOn75Sly9RQYNMkgUCgUZf586dq/WzCgsLERISUuN7xcbGQiAQwNzcXONrUlNTER0djcWLF8vb0tLS5K/r0KFDKteEhIRAIBAgMzOzRnFWpqSkBF26dIFAIMD69etV+qVSKcLDw+Hs7AxTU1N0794de/fuVRkXHByMzz77DI8ePdJpfERETY1UWoZLh/bi9qXzOr/3nauJ+H7X5yiRFOv83tT0eXlNx+TJIZX2Hzx4BomJNxATswzLlk3H7NljcO5cFIRCAyxbFlXt/Vet2oGCAjHOnt2KOXPGYfHiqThwYDWuX/8NMTH/fKIyfvww/PnncURHf4zp0/2xYMGbuHIlBjY2FggJ2aaLl6qRBrlwb/fu3Qrf79q1C/Hx8SrtnTt3rvWzCgsLERoaCgDw8vLS6tr8/HwsXLgQzZo10+q6TZs2wdnZGd7e3mr7ly9fDn9/fwgEAq3uWxObN29Genrl7/4++ugjrFmzBkFBQfDw8MA333yDwMBACAQCjBs3Tj7utddeg4WFBSIjI7F8+fI6j5uIqDEqLSnBhb078dev/62zZ9y/fRNnoiPhPXkGTMzM6uw59Pw5ePAMWrWyhb//IHmbnZ01xowZgj17vkVxsQQmJsaVXn/o0FmMGDEAjo728rYhQ15Gx46OOHDgNKZP9wcAuLmp5ne2tlYYMMAV585d0+ErqlqDnEmeOHGiwlfHjh3Vtrdq1Uqvca5YsQLNmzfH6NGjNb6mpKQEsbGxGDNmjNp+V1dX3LhxA1999ZWOoqxcRkYGli9fjuDgYLX99+/fR0REBGbPno1t27YhKCgIR48exYABA7BgwQKUlf3zkZ6BgQECAgKwa9cu1sQREalRlJ+P09Gf1WmCXCHzz3uI37YZRfn5df4sen4kJ99Gr14vwsBAMX3s3bsrCguL8NtvlU+63b+fgYyMJ3B3V02Ae/fuiuTk29U+/9GjLLRoYal94DXUIJNkTUilUmzcuBFdu3aFqakpWrVqhRkzZiBbaS/OpKQkDBs2DC1atIBIJIKzszOmTp0KoLzEwc7ODgAQGhoqL3cICQmp9vl37tzBhg0b8J///EerU5AuXLiAzMxMDBkyRG3/uHHj0LFjRyxfvrzOk80PP/wQL774IiZOnKi2/5tvvkFJSQlmzZolbxMIBJg5cyb++usvXLp0SWG8j48P7t27h5SUlLoMm4io0Xn6+BG+jfwPMtPT6vGZD3H680gUFxbW2zOpaXv4MBMODqrbDVa0PXjwd5XXPjtW+fonT3JQXCyp9Prz55Nx6dLPGDt2qLZh11iDLLfQxIwZMxATE4MpU6Zgzpw5SE1Nxaeffork5GRcvHgRRkZGyMjIwNChQ2FnZ4cPP/wQVlZWSEtLw+HDhwEAdnZ22LJlC2bOnAk/Pz/4+5dP83fv3r3a53/wwQfw9vbG8OHDceDAAY3jTkxMhEAgQM+ePdX2C4VCLFmyBG+++Sa++uoreUzqFBYWolCDH35CoRDWSie1Xb16FTt37sSFCxcqLetITk5Gs2bNVMpaevfuLe9/5ZVX5O1ubm4AgIsXL1b6+oqLi1Fc/E+tXP5zMMsR880xFBSKa3x9MzMRJr82QqXd3d29VjXg9vb2SEpKqvH1RKSZ+7du4vzenSjVQ53w00cPkLBzGwa/PRNGxib1/nxquEpKSpGTk6/SVlwsQWbmU4V2GxsLGBgYQCwuVltOYWpa/v+WWFz5/+MVfdVdr64/I+MJAgOXwNm5NRYufLPqF6ZDjTJJvnDhAqKjoxEbG4vAwEB5u7e3N1599VXExcUhMDAQiYmJyM7OxqlTp+Du7i4ft2LFCgBAs2bNEBAQgJkzZ6J79+6VzqgqO378OE6dOoXr17Xfl/LWrVuwsbGBhYVFpWMCAwMRFhaG5cuXw8/Pr9IkNjw8XF5PXZV27dohLS1N/r1MJsN7772HsWPHom/fvgp9z3r48CFatWql8nwHBwcAwIMHDxTa27RpA2NjY9y8WfkWLatXr1aJua19K5ibiap9HY1VQaEYeXUwk/Po0SPcv39f5/clIt25cyURV7+J02sZWmZ6Gs7HxsBz0tsQavHJJzVtFy+mwNv7HZX2xMQb2LfvlEJbauoRODm1hkhkona2t6ioPAEWiSp/I1bRp+31BQVijBjxAfLyCnDhQjTMzeuvzr5R/muJi4uDpaUlfHx8FHZ7cHNzg7m5ORISEhAYGAgrKysAwLFjx9CjRw8YGRnV+tkSiQRz587FO++8gy5dumh9fVZWlsqsrrKK2eS33noLX3/9Nfz8/NSOe/PNNxVmcisjEikmoDExMfj5559x8ODBKq8Ti8UwMVH9H9bU1FTer8za2rrKHTgWLVqEefPmKbSFvT8bhkJhlbE0Zs1q+Qagsuvt7e3VtmuqttcTUdV+PZ+An058o+8wAAAPfvsVFw/swSvjJsHAoOn+vCXN9ejREfHxnym0zZ+/Efb2tliwYJJCu729LYDysoiKsolnVbS1bm1X6fMqyiwqu97GxlJlFlkiKYG//wLcuPE7Tp7cjG7dXtDglelOo0yS79y5g5ycHLRs2VJtf0ZGBgDA09MTr7/+OkJDQ7FhwwZ4eXlh9OjRCAwMVJv8aWLDhg3IzMzUaAa3MprMKEyYMEE+m1zZwkAXFxe4uLho9ezc3FwsWrQICxYsQNu2bascKxKJFEojKhQVFcn7lclksip35TAxMVH5s2/KCTIAtaUSusBSCaKG69bF7xtMglwh/ecUXBQI0H/sRCbKBGtrCwwZ8rJSW3M4OLRQaa/g6toR58+nQCqVKizeu3LlF5iZmaJjR8dKn9emTUvY2VkjKelXlb6rV3+Bq2tHhTapVIo331yKM2d+xIEDq+Hp6abNy9OJRpkkS6VStGzZErGxsWr7KxbjCQQCHDx4EJcvX8bRo0dx8uRJTJ06FREREbh8+bJWexsDQE5ODlasWIFZs2YhNzcXubm5AMrramUyGdLS0mBmZlZp8g4Atra2KosL1amYTZ48eTK++Ub9D9r8/HyNanqFQqH8z2T9+vWQSCQYO3asvMzir7/+AgBkZ2cjLS0NrVu3hrGxMRwcHJCQkKCS+D58+BAA0Lp1a5VnPX36FC1aqBblExE9L37/8TKSjtX9DkU1ce9GMqRlZXhl3CQIDWv/6So9XwICBuPgwTM4fPgsAgLKNyDIzHyKuLjTGDlygMJM8N275blF+/b/kre9/vog7Nx5DH/++Qht25Z/mnnmzFX89ls65s4NxLPee28d9u+PR1TUYoUt5+pTo0yS27dvj9OnT6N///5qZzOV9enTB3369MHKlSvx5ZdfYsKECdi3bx+mTZum1V7E2dnZyM/PR3h4OMLDw1X6nZ2d8dprr+Hrr7+u9B6dOnVCbGwscnJyYGlZ9TYmEydOxIoVKxAaGopRo0ap9K9fv17rmuT09HRkZ2eja9euKuNWrVqFVatWITk5Ga6urnB1dUV0dDR+/fVXhdKSK1fKT7txdXVVuP7+/fuQSCQ62b+aiKgx+iP5R1w+vF/fYVTpz19u4MwXW+E58W3uo0xaCQgYjD59XsKUKctx82YqWrSwQmRkHMrKpAgNnaEwdvDgmQCAtLR/DglZvHgK4uJOw9v7Hbz//jjk54uxbt1uvPTSC5gy5Z88Z+PGLxEZGYe+fbvDzMwUe/acULi3n583mjWr+7VMjTJJHjNmDCIjIxEWFoZVq1Yp9JWWliI/Px9WVlbIzs6GlZWVQiJckdhVlBGY/e8HxNOnT6t9bsuWLdXuX/zJJ5/g0qVL2Lt3r3xRW2X69u0LmUyGn376CYMGVf3O6NnZZHVqUpM8Z84clfKNjIwMzJgxA5MnT8Zrr70GZ2dnAOUHhMydOxeRkZH49NNPAZSXU2zduhVt2rRBv379FO7z008/AYBKOxHR8+DuT1dx+dBeALpZpPckLx+/3X+IomIJzJs1Q1entmhhWfmib21kpN7Fqa2b4DU5CM1t+OkfaUYoFOLEiU1YsGATPvlkH8TiYnh4dEFMTAhefNGp2uvbtrXH999vw7x5G/Dhh5/C2NgIvr6vICLiA4VZ6JSU3wAAly7dwKVLN1Tuk5p6hElyZTw9PTFjxgysXr0aKSkpGDp0KIyMjHDnzh3ExcVh06ZNCAgIwM6dOxEZGQk/Pz+0b98eeXl52L59OywsLDB8+HAA5Qlkly5dsH//fnTs2BE2Njbo1q0bunXrpvJcMzMztfXBX3/9Na5evarRoSKvvPIKbG1tcfr06WqTZOCf2mR1ew/XpCa5V69e6NWrl0JbxSxz165dFV7Dv/71L3zwwQdYt24dSkpK4OHhga+//hrnz59HbGwshEq1xPHx8XB0dKx0+zcioqZIJpPh5g9nkfzd0eoHa+CPR48BAItj9gEoLx2UyWQQAHB9wRmj+/eGi0PtF97m/P0Y3332H/Qf+yZad+xU6/tR43fuXPVHPltbWyA6+mNER39c5bhnZ5Cf1bVre5w8+WmV18bEhCAmJqTaWOpao0ySAWDr1q1wc3NDVFQUFi9eDENDQzg5OWHixIno378/gPJk+urVq9i3bx8eP34MS0tL9O7dG7GxsfLZUgCIjo7Ge++9h7lz50IikWDZsmVqk2RdMDY2xoQJExAXF6cyC66OoaEhlixZgilTptRJPNVZs2YNrK2tERUVhZiYGHTo0AF79uxR2HoPKK8TP3ToEN5+++16OU6biKghkIgLceWrA7j3c4pO7vfj7d/x6deKHy1XLPaWAbh+Nw03/riH2a/9HzxerP1K/+LCQpzdEYXOr3ihx9DhMNTBLlBETYVAxjOE690ff/yBTp064dtvv8XgwYP1HY5OfP311wgMDMTdu3erLTlRtmja5LoJqglaHR2j7xCItLZu/nvIzc6GhbU1FkRs1nc4OiGTyXDv+jX8dOIbiPNydXLPuw8eIWxPHMqk0mrHCg0MsHTSGzqZUa7QzNoGPV8diXbdekBg0GgP5H2O/aTvABo47XfH4L8CPXBxccHbb7+NNWvW6DsUnVm7di3effddrRNkIqLGRCaVIv2/13Fi83pc2L9bZwkyAHyTeFXjQ0dkMhm+ufijzp4NAAXZT3Bh704c+2Qd/kj+EdKyMp3en6ixabTlFo3dli1b9B2CTl26dEnfIRAR1ZnczL+Rdv0a7iZdRsHT6rfx1FZmTi5Sfk/VeMmfVCZD8u9/IDMnV2eL+SrkPH6IxAOxSP7uGF5wfxnOPd1h0aLyrU2JmiqWW5DesdxCczaWzfUdApHW8p4+hUwmazTlFsWFhXj6+CFyHj/EaxPexN9ZWZBqUAJRq2eWlKCwSPXwpuqYmZrAREd1xFbNzLB88ni1fZZ2rdCqfQfYOTrBpk1bWLSwY0lGg8Nyi6ppX27BmWTSO0Npqb5DaDRyNTiIhoj+UVxYiLLSEkjLyiAtK0VZSQnKSkpQWlKCkuIiSMSFKMrPR2HOU+Q/yUJu5t8oys+TX5/x9994klf9oU36UlhUXKPkWh0BACMTU/XPyc1BanISUpPLT/oUGhnBwq4lmtu0gMjCEiZmZjAyFcHIxATGpiIYi0QwMjWFobEJDI2MYCA0hMBAcVG3gYGQiTY1aEySSe8srK31HQIR1QNzC6t6f2ZtD8tYd+w0RI8e6SiayhUUFGi0X78yKysrNGvWTCcx2NvbY2xI01krQ1RbLLcgIiLSs/T0dDg5OWm8cA8o3z85LS0Njo6OdRgZNR4st6gad7cgIiJqdBwdHTFixAiVQ5oqIxQKMXLkSCbIRHWISTIREVED8PHHH2t0GJNAIIBAIMCSJUvqISqi5xeTZCIiogbAw8MD+/fvr3I2WSgUQigU4sCBA/Dw8KjH6IieP0ySiYiIGgh/f3+cP39eoc3gfztACAQC+Pr6IjExEX5+fvoIj+i5woV7REREDUhJSQmMjY3x+++/4/z588jNzYWFhQUGDRrEGmSqAhfuVU37hXtMkomIiBqQiiRZIpHASEcHhdDzgEly1bi7BRERERFRrTFJJiIiIiJSwiSZiIiIiEgJk2QiIiIiIiVMkomIiIiIlDBJJiIiIiJSwiSZiIiIiEgJk2QiIiIiIiVMkomIiIiIlDBJJiIiIiJSwiSZiIiIiEgJk2QiIiIiIiVMkomIiIiIlDBJJiIiIiJSwiSZiIiIiEiJob4DILr2RYK+QyCiemDczATdxvbTdxhERBphkkx6l18q1ncIVEdMivlhFRERNU5Mkomozhg1M9F3CNQAlBQWAzJ9R0FEpB0myURUZ3pN9dZ3CNQAXPsiASUFxfoOg4hIK/wslIiIiIhICZNkIiIiIiIlTJL1ZNasWfDx8dF3GDrTp08fLFy4UN9hEBE1aunp6YiJiQEAxMTEID09Xb8BET3HGmSSLBAINPo6d+5crZ9VWFiIkJAQje+VlpZWaTz79u3T6B6pqamIjo7G4sWL1d730KFDKteEhIRAIBAgMzNTo2doqqSkBF26dIFAIMD69etV+qVSKcLDw+Hs7AxTU1N0794de/fuVRkXHByMzz77DI8ePdJpfEREz4OrV69i5MiRcHJywvTp0wEA06dPh5OTE0aNGoUff/xRzxESPX8a5MK93bt3K3y/a9cuxMfHq7R37ty51s8qLCxEaGgoAMDLy0vj68aPH4/hw4crtPXt21ejazdt2gRnZ2d4e6tf1LR8+XL4+/tDIBBoHE9Nbd68ucqZio8++ghr1qxBUFAQPDw88M033yAwMBACgQDjxo2Tj3vttddgYWGByMhILF++vM7jJiJqKg4fPoyxY8dCJpNBJlPcBkQmk+HEiRP49ttvsX//fvj7++spSqLnT4NMkidOnKjw/eXLlxEfH6/Srk+9evWqUTwlJSWIjY3FO++8o7bf1dUVKSkp+Oqrr+r8h2FGRgaWL1+O4OBgLF26VKX//v37iIiIwOzZs/Hpp58CAKZNmwZPT08sWLAAb7zxBoRCIQDAwMAAAQEB2LVrF0JDQ+slwSciauyuXr2KsWPHoqysTCVBrlBWVgaBQICxY8ciMTERHh4e9Rwl0fOpQZZbaEIqlWLjxo3o2rUrTE1N0apVK8yYMQPZ2dkK45KSkjBs2DC0aNECIpEIzs7OmDp1KoDyEgc7OzsAkCd2AoEAISEhGsVQUFAAiUSiVdwXLlxAZmYmhgwZorZ/3Lhx6NixI5YvX17pD0xd+fDDD/Hiiy9Wmux/8803KCkpwaxZs+RtAoEAM2fOxF9//YVLly4pjPfx8cG9e/eQkpJSl2ETETUZK1asUDuDrKxizIoVK+opMiJqtEnyjBkzsGDBAvTv3x+bNm3ClClTEBsbi2HDhqGkpARA+Uzp0KFDkZaWhg8//BCbN2/GhAkTcPnyZQCAnZ0dtmzZAgDw8/PD7t27sXv3bo1mcENDQ2Fubg5TU1N4eHjg1KlTGsWdmJgIgUCAnj17qu0XCoVYsmQJrl+/jq+++qrKexUWFiIzM7PaL+U3DkD57MXOnTuxcePGSmd9k5OT0axZM5Wylt69e8v7n+Xm5gYAuHjxYpVxExFR+SK9Y8eOoaysTKPxZWVlOHr0KBfzEdWTBlluUZ0LFy4gOjoasbGxCAwMlLd7e3vj1VdfRVxcHAIDA5GYmIjs7GycOnUK7u7u8nEV78SbNWuGgIAAzJw5E927d9eofMLAwABDhw6Fn58f2rRpgz/++AP/+c9/8H//9384cuQIfH19q7z+1q1bsLGxgYWFRaVjAgMDERYWhuXLl8PPz6/SJDY8PFxeT12Vdu3aIS0tTf69TCbDe++9h7Fjx6Jv374Kfc96+PAhWrVqpfJ8BwcHAMCDBw8U2tu0aQNjY2PcvHmz0liKi4tRXKx4qICkpATGRkbVvg6qW9NXvo8nuapvqLRlY2GNbR9tUml3d3ev9cJOe3t7JCUl1eoeRA3FmTNntP7EUCaT4ezZs5g8eXLdBEVUSxJJCZ48yVFos7OzlpdnNiaNMkmOi4uDpaUlfHx8FHZ7cHNzg7m5ORISEhAYGAgrKysAwLFjx9CjRw8Y6SARc3R0xMmTJxXaJk2ahC5dumD+/PnVJslZWVmwtrauckzFbPJbb72Fr7/+Gn5+fmrHvfnmm3jllVeqjVkkEil8HxMTg59//hkHDx6s8jqxWAwTE9VjhU1NTeX9yqytravcgWP16tUqiX2PDt2wLCgYtpY2VcZDdetJbjb+fppVZ/d/9OgR7t+/X2f3J2ps8vLyYGBgAKlUqvE1BgYGyM3NrcOoiGonMfE6vL0V112lph6Bk1NrPUVUc40ySb5z5w5ycnLQsmVLtf0ZGRkAAE9PT7z++usIDQ3Fhg0b4OXlhdGjRyMwMFBt8ldTNjY2mDJlCtasWYO//voL//rXv6ocr8nMwYQJE+SzyaNHj1Y7xsXFBS4uLlrFmpubi0WLFmHBggVo27ZtlWNFIpHKrC8AFBUVyfuVyWSyKhftLVq0CPPmzZN/n5KSAk9PT2TlPGGSrGc2FlW/eavtfezt7Wt9b13cg6ihaN68uVYJMlC+HqeqTyKJ9K1Hj46Ij/9Moc3e3lZP0dROo0ySpVIpWrZsidjYWLX9FYvxBAIBDh48iMuXL+Po0aM4efIkpk6dioiICFy+fBnm5uY6i6ki4Xzy5EmVSbKtra3aGmFlFbPJkydPxjfffKN2TH5+PvLz8zW6V8Wfyfr16yGRSDB27Fh5mcVff/0FAMjOzkZaWhpat24NY2NjODg4ICEhQSXxffjwIQCgdWvVd4VPnz5FixYtKo3FxMRE4Q2KLv8OqHbUlUjoEsskiBQNHjwYAoFAq5ILgUCAQYMG1WFURLVjbW2BIUNe1ncYOtEoF+61b98eWVlZ6N+/P4YMGaLy1aNHD4Xxffr0wcqVK5GUlITY2Fj88ssv8oM/dLVV2R9//AHgnwS9Mp06dUJ2djZycnKqHAeUb4X3wgsvIDQ0VO0P0fXr18PBwaHar2e3C0pPT0d2dja6du0KZ2dnODs7Y8CAAQCAVatWwdnZWV5T7OrqisLCQvz6668Kz71y5Yq8/1n379+HRCLRyf7VRERNnaOjI0aMGKFxraZQKMTIkSPh6OhYx5EREdBIZ5LHjBmDyMhIhIWFYdWqVQp9paWlyM/Ph5WVFbKzs2FlZaWQCFckdhVlBGZmZgDKZ0A18ffff6skwvfv38cXX3yB7t27yxe1VaZv376QyWT46aefqp0NeHY2WZ2a1CTPmTNHpXwjIyMDM2bMwOTJk/Haa6/B2dkZQPkBIXPnzkVkZKR8n2SZTIatW7eiTZs26Nevn8J9fvrpJwBQaSciIvU+/vhjfPvtt9XOKFdsUbpkyZJ6jI7o+dYok2RPT0/MmDEDq1evRkpKCoYOHQojIyPcuXMHcXFx2LRpEwICArBz505ERkbCz88P7du3R15eHrZv3w4LCwv5aXkikQhdunTB/v370bFjR9jY2KBbt27o1q2b2mcvXLgQd+/exeDBg9G6dWukpaUhKioKBQUF2LSp+o+rX3nlFdja2uL06dMafWRWUZusbu/hmtQk9+rVC7169VJoqyi76Nq1q0IC/a9//QsffPAB1q1bh5KSEnh4eODrr7/G+fPnERsbqzL7ER8fD0dHx0q3tyMiIkUeHh7Yv3+//MQ9ddvBCYVCCAQCHDhwgAeJENWjRlluAQBbt27Ftm3bkJGRgcWLF2PRokU4e/YsJk6ciP79+wMoT6bd3d2xb98+zJkzB+Hh4ejQoQPOnj0rny0FgOjoaLRp0wZz587F+PHjq9z1YejQoRAIBPjss88wa9YsbNu2DQMHDsSlS5c0Otba2NgYEyZMQFxcnEav09DQUK8zB2vWrMGqVatw8uRJzJ49G2lpadizZ4/C1ntAeZ34oUOH8Oabb/K0PSIiLfj7+yMxMRHDhw9X+fkpEAjg6+uLxMTESnc6IqK6IZDV9bFupOKPP/5Ap06d8O2332Lw4MH6Dkcnvv76awQGBuLu3bvVlpw869q1a3Bzc8P2jzaho+MLdRgh6cPA6cP1HQI1ANe+SEBJQTGMmpmg11RvfYfToKWnp+PUqVMICgrC9u3bMXToUNYgk4Z+0ncADZyb1lc02pnkxszFxQVvv/021qxZo+9QdGbt2rV49913tUqQiYhIkaOjI9566y0AwFtvvcUEmUiPGmVNclNQcRx2U3Hp0iV9h0BERESkM5xJJiIiIiJSwplkIqoz175I0HcI1ACUFKqe3ElE1NAxSaYGwUhiAJNifrDR1JSoOdaciIioMWCSTA2CocgIRs1Mqh9IRI2WMf+NE1EjwiSZGoTOr3moHHJCREREpC/8fJuIiIiISAmTZCIiIiIiJUySiYiIiIiUMEkmIiIiIlLCJJmIiIiISAmTZCIiIiIiJUySiYiIiIiUMEkmIiIiIlLCJJmIiIiISAmTZCIiIiIiJUySiYiIiIiUMEkmIiIiIlLCJJmIiIiISAmTZCIiIiIiJUySiYiIiIiUMEkmIiIiIlLCJJmIiIiISAmTZCIiIiIiJUySiYiIiIiUMEkmIiIiIlJiqO8AiABg9+7dSEhI0HcYRFSHzM3NMWPGDH2H0WhIJBLIZDKtrjEwMIChIX+1E+kC/yVRg1BYWIi8vDx9h0FEpHelpaUAgHv37sHIyKjScWVlZcjMzERmZiZyc3NRXFwMIyMjtG7dGu3atYOzszOMjY3rK2yiJodJMhE1as2bN9d3CFSN/Px8rWdEn2dSqRQAIBQKYWJiotJfUlKCu3fvIjU1FRKJRKFPLBajqKgIqampuHjxIl544QV06dIFLVq0qJfYiZoSJslE1KjNnz9f3yFQNSIiIvhJUQ0YGhqqlE5kZGTgp59+glgsBgAIBAJ5n/IbkZKSEvz666/49ddf0aJFC3To0AEvvPACzMzM6j54oiaASTIREVEDJ5PJcOfOHfzyyy81mpWvKMu4fPky2rVrh5deegmtW7eug0iJmg7ubkFERNSASaVSpKSk4L///W+ty1ZkMhnS0tJw9OhRnDhxAjk5OTqKkp4XT5/mYfr0lbCzG4JmzV6Bt/cMXLt2S6Nrr179L2bNWgM3t4kwMnoZAoF7leM///xrdO4cAFPTfujQwQ+bN+/TxUvQGJNkPZk1axZ8fHz0HYbO9OnTBwsXLtR3GERETUppaSmuXLmC1NRUnd/7zz//xMGDB/Hbb7/p/N7UNEmlUvj6vo8vv/wO7747BuHhc5CRkQ0vrxm4cye92utPnLiI6OivIRAI4OLSpsqxUVGHMG3aCnTt6oLNmxegb9+XMGfOeqxdG6OjV1O9BpkkCwQCjb7OnTtX62cVFhYiJCRE63vdvXsXgYGBaNmyJUQiETp06ICPPvpIo2tTU1MRHR2NxYsXy9vS0tLkr+vQoUMq14SEhEAgECAzM1OrOKtTUlKCLl26QCAQYP369Sr9UqkU4eHhcHZ2hqmpKbp37469e/eqjAsODsZnn32GR48e6TQ+IqLnVWFhIX744Qc8fPiwzp5RWlqKhIQEXLp0Sb5gkJ5fXl7TMXlySKX9Bw+eQWLiDcTELMOyZdMxe/YYnDsXBaHQAMuWRVV7/5kzA5CTcw5JSbvh4/NypePE4iJ89FEkfH1fwcGD4QgK8sOuXcsxYcL/ISzsc2Rn59bk5WmtQdYk7969W+H7Xbt2IT4+XqW9c+fOtX5WYWEhQkNDAQBeXl4aXZOSkgIvLy+0adMG8+fPh62tLdLT0/Hnn39qdP2mTZvg7OwMb29vtf3Lly+Hv7+/woKMurJ582akp1f+7u+jjz7CmjVrEBQUBA8PD3zzzTcIDAyEQCDAuHHj5ONee+01WFhYIDIyEsuXL6/zuImImroLFy6grKysXp5148YNFBYWwsvLC0KhsF6eSY3PwYNn0KqVLfz9B8nb7OysMWbMEOzZ8y2KiyUwMal828FWrWw1ek5CQhKysnIwa1aAQvvs2W8gNvZbHD9+ARMnDq/Zi9BCg0ySJ06cqPD95cuXER8fr9KuD1KpFJMmTUKnTp2QkJAAkUik1fUlJSWIjY3FO++8o7bf1dUVKSkp+Oqrr+Dv76+LkCuVkZGB5cuXIzg4GEuXLlXpv3//PiIiIjB79mx8+umnAIBp06bB09MTCxYswBtvvCH/YWpgYICAgADs2rULoaGh9ZLgExE1NTKZDD///DMAoLi4uF4PBvn9999RUlKCIUOG8EASUis5+TZ69XoRBgaKhQi9e3fFtm1f4bff0vHSSy/o5DkA4O7eRaHdza0zDAwMkJx8u16S5AZZbqEJqVSKjRs3omvXrjA1NUWrVq0wY8YMZGdnK4xLSkrCsGHD0KJFC4hEIjg7O2Pq1KkAyksc7OzsAECe2AkEAoSEhFT63FOnTuG///0vli1bBpFIhMLCQq3e6V+4cAGZmZkYMmSI2v5x48ahY8eOWL58eZ3vK/rhhx/ixRdfrPTNxzfffIOSkhLMmjVL3iYQCDBz5kz89ddfuHTpksJ4Hx8f3Lt3DykpKXUZNhFRkySRSBAfH4+ffvpJbzHcu3cP3333HUpKSvQWAzVcDx9mwsFBdc/tirYHD/7W2XOEQiFatrRRaDc2NoKtraXOnlOdRvtWccaMGYiJicGUKVMwZ84cpKam4tNPP0VycjIuXrwIIyMjZGRkYOjQobCzs8OHH34IKysrpKWl4fDhwwAAOzs7bNmyBTNnzoSfn5985rZ79+6VPvf06dMAABMTE7i7u+Onn36CsbEx/Pz8EBkZCRsbm0qvBYDExEQIBAL07NlTbb9QKMSSJUvw5ptvVjubXFhYiMLCwiqfV3FPa2trhbarV69i586duHDhQqWzvsnJyWjWrJlKWUvv3r3l/a+88oq83c3NDQBw8eLFSl8fERGpysvLw3fffYcnT57oOxTcv38fx48fx6uvvgpTU1N9h0N1pKSkFDk5+SptxcUSZGY+VWi3sbGAgYEBxOJiteUUpqblh96IxcU6iU0sLoaxsfoU1dTUWGfPqU6jTJIvXLiA6OhoxMbGIjAwUN7u7e2NV199FXFxcQgMDERiYiKys7Nx6tQpuLv/s83IihUrAADNmjVDQEAAZs6cie7du2tUznHnzh0AwJgxY/Dqq69i0aJFuH79OlavXo0///yzyqQTAG7dugUbGxtYWFhUOiYwMBBhYWFYvnw5/Pz8Kr1feHi4vJ66Ku3atUNaWpr8e5lMhvfeew9jx45F3759Ffqe9fDhQ7Rq1Url+Q4ODgCABw8eKLS3adMGxsbGuHnzZqWxFBcXo7j4n/+58/PzKx1LTc+2bdt08ndubm6O6dOnq7S7u7vXevGovb09kpKSanUPIm1kZmbi22+/1WjSo748fvwYR48exfDhw9GsWTN9h0N14OLFFHh7q5Z+JibewL59pxTaUlOPwMmpNUQiExQXS1SuKSoq/70uEqmeEFkTIpEJJJJStX1FRRKdPac6jTJJjouLg6WlJXx8fBR2e3Bzc4O5uTkSEhIQGBgIKysrAMCxY8fQo0cPGBkZ1frZFb/gPTw8sGfPHgDA66+/DjMzMyxatAhnzpyptJQCALKyslRmdZVVzCa/9dZb+Prrr+Hn56d23Jtvvqkwk1sZ5brpmJgY/Pzzzzh48GCV14nFYrVHolbMLFSc+PQsa2vrKnfgWL16tUpi365dO5ibm1cZCzUN+fn5dXry2qNHj3D//v06uz+Rrt2/fx8nT55skOUNT548wZEjR+Dr61vlxA41Tj16dER8/GcKbfPnb4S9vS0WLJik0G5vX77gzsGhBR4+VP0dX9HWurWdTmJzcGiBsrIyZGQ8USi5kEhKkJWVo7PnVKdRJsl37txBTk4OWrZsqbY/IyMDAODp6YnXX38doaGh2LBhA7y8vDB69GgEBgaqTf40UZFwjh8/XqE9MDAQixYtQmJiYpVJMqB6dKg6EyZMkM8mjx49Wu0YFxcXuLi4aBb4/+Tm5mLRokVYsGAB2rZtW+VYkUikMOtboaioSN6vTCaTVTmTvmjRIsybN0+hLTw8nItEnhO6ejNU2X3s7e1rfW9d3INIE3fv3kVCQkK97WBRE7m5ufJEuboJHmpcrK0tMGTIy0ptzeHg0EKlvYKra0ecP58CqVSqsHjvypVfYGZmio4dHXUSm6vriwCApKSbGD78n8nApKSbkEqlcHXtqJPnVKdRZiZSqRQtW7ZEbGys2v6KxXgCgQAHDx7E5cuXcfToUZw8eRJTp05FREQELl++XKNf2BXHeLZq1UqhvSJhV144qMzW1rbaMcA/s8mTJ0/GN998o3ZMfn6+Rh9dC4VC+Z/J+vXrIZFIMHbsWHmZxV9//SWPPS0tDa1bt4axsTEcHByQkJCgkvhW7Nmp7kjTp0+fokUL1aL+CiYmJipvUJggPz/UlUjoEsskqDGQyWS4ceMGrly5UucLtHWhoKAAR48exYgRI6pdd0NNW0DAYBw8eAaHD59FQED5hGBm5lPExZ3GyJEDFOqV794tzy3at/+X1s8ZNMgdNjaW2LLlkEKSvGXLQZiZmcLXt/pP0XWhUWYn7du3x+nTp9G/f3+NtmDr06cP+vTpg5UrV+LLL7/EhAkTsG/fPkybNk3rrcrc3Nywfft2lY90K+pzK5LRynTq1AmxsbHIycmBpaVllWMnTpyIFStWIDQ0FKNGjVLpX79+vdY1yenp6cjOzkbXrl1Vxq1atQqrVq1CcnIyXF1d4erqiujoaPz666/o0uWfbViuXLkCoHy7umfdv38fEolEJ/tXExE1RSUlJTh//rx8fUtjIRaL5Ymyra1me91S0xMQMBh9+ryEKVOW4+bNVLRoYYXIyDiUlUkRGjpDYezgwTMBAGlpR+Vt9+49xO7dxwEASUm/AgBWrIgGALRr54BJk3wBACKRKcLC3sHs2WvxxhvBGDasL86fT8aePd9i5cpZsLGpOn/SlUaZJI8ZMwaRkZEICwvDqlWrFPpKS0uRn58PKysrZGdnw8rKSiERrkjsKsoIzMzMAJTPgGritddew/vvv48dO3Zg8uTJ8o8boqPL/5KrO2q6b9++kMlk+OmnnzBo0KAqxz47m6xOTWqS58yZo1K+kZGRgRkzZmDy5Ml47bXX4OzsDKD8tc6dOxeRkZHyfZJlMhm2bt2KNm3aoF+/fgr3qdi2SLmdiIjK16ScOXNGo08TG6KioiIcO3YMw4cPr3ZCiJomoVCIEyc2YcGCTfjkk30Qi4vh4dEFMTEhePFFp2qvT029j48/3qrQVvG9p2cveZIMALNmvQEjI0NEROzBkSM/oG3bVtiwYR7ef1+x3LUuNcok2dPTEzNmzMDq1auRkpKCoUOHwsjICHfu3EFcXBw2bdqEgIAA7Ny5E5GRkfDz80P79u2Rl5eH7du3w8LCAsOHl29CLRKJ0KVLF+zfvx8dO3aEjY0NunXrhm7duql9tr29PT766CMsXboUr776KkaPHo3r169j+/btGD9+PDw8PKqM/ZVXXoGtrS1Onz5dbZIM/FObrG7v4ZrUJPfq1Qu9evVSaKuYZe7atatCAv2vf/0LH3zwAdatW4eSkhJ4eHjg66+/xvnz5xEbG6tyKlN8fDwcHR25/RsR0TPKyspw/fp1XLt2rc7qj588eYLbt2+jqKhIvnVnXZRGVCTKQ4cORZs2bXR+f9Kvc+e2VTvG2toC0dEfIzr64yrHPTuDXMHLyx0ymeZlcUFBfggKUr95QX1olEkyAGzduhVubm6IiorC4sWLYWhoCCcnJ0ycOBH9+/cHUJ5MX716Ffv27cPjx49haWmJ3r17IzY2Vj5bCpTPAr/33nuYO3cuJBIJli1bVmmSDABLliyBtbU1Nm/ejA8++EAhca6OsbExJkyYgLi4OJVZcHUMDQ2xZMkSTJkyRYM/Fd1bs2YNrK2tERUVhZiYGHTo0AF79uxR2HoPKK8TP3ToEN5++22etkdE9D8PHjzAhQsX6mz2OC0tDSdPnsQvv/wiXz9S8d+XXnoJw4cPh5OTk06fKZFIcOLECfTr1w9dunThz3xqsgSyxrBqoIn5448/0KlTJ3z77bcYPHiwvsPRia+//hqBgYG4e/eufB9lTVV1wiFRdfj/T8MXERGBvLw8NG/eHPPnz9d3OPUiNzcXV69exd27d7W+trS0FO+++y42bNhQ5cLmlJQU7NixA0D5RIWyinLAoKCgOvuEr127dujXrx+3iGsQ9HdSY+PgpvUVjfZY6sbMxcUFb7/9NtasWaPvUHRm7dq1ePfdd7VOkImImpKnT5/ihx9+wP79+2uUIGsqLS0NO3bsgFQqVZsgA5D3bd++vdJDo2rr3r172L9/P77//ntkZGQ0it06iDTVaMstGrstW7boOwSdunTpkr5DICLSC4lEgnv37uG3336Tb6lZ106ePKnV+BMnTmDWrFl1EotUKsWtW7dw69YtWFpaol27dmjbti3s7e25xSc1avy/l4iISAsSiQSZmZl4/Pgx7t+/j4cPH1Y6m1sXnjx5Iq9B1oRUKsXPP/+MJ0+e1Pk+xzk5Obhx4wZu3LgBQ0NDODg4oE2bNnBwcICtra3Kgm+ihow1yaR3rCml2mjevLm+Q6Bq5OfnQyaTNciaZKlUirKyMpSVlUEikUAikaCoqAhisRiFhYUoLCyEWCyWtxUUFEAsFgMo31s+NzdX5zHJZDLk5OTAwsJC7aI4iUQij0EbIpGoxqfNKrOwsMDixYu1ukYoFMLa2hqWlpZo3rw5mjVrBpFIBFNTU/lBU0ZGRjA0NIRQKOSCQK2xJrlq2tckcyaZiBq1vLw8fYdAjURpaSkkEok8Ka6sntfU1BSmpqbVHsMcGhqq8R77NaHrBFwsFtcouVZHJBLh9ddf18m9KhQXF8vPMADKT80VCAQwNDTkwkDSCybJpHecCSR6Ppibm+v1+YaGhjqtkW3durV8BwldkslkePDgAVq3bq12NrWgoKBGybmVlRWaNWumgwjLzwxo0aKFTu5F1FCx3IKIiKgBKSkpgbGxMSQSCYyMjFT609PT4eTkpNVOEgKBAGlpaXB0dNRlqNSgsNyiatwCjoiIqElzdHTEiBEjNF4EJxQKMXLkSCbIRFpikkxERNTIfPzxx/Ka3apUjFmyZEk9RUbUdDBJJiIiamQ8PDywf/9+CIXCSmeUK/oOHDgADw+Peo6QqPFjkkxERNQI+fv7IzExEcOHD5fPKFcsJBQIBPD19UViYiL8/Pz0GSZRo8WFe0RERA1IdQv31ElPT8fZs2eRm5sLCwsLDBo0iDXIzx0u3Kua9gv3mCQTERE1IDVJkomYJFeHu1sQEREREdUak2QiIiIiIiU8cY/07uHDh3j48KG+wyCiBsTBwQEODg5VjmmqPztKS0sBAMnJyTo9IZAaF03+DVDdYk0y6VVxcTGGDRuG77//Xt+hEFEDsmzZMoSEhFQ5JiQkBKGhofUTEFE90+TfANUtJsmkV7m5ubC0tMT3338Pc3NzfYdTY/n5+fD09OTraCD4OhqWmryO53kmuan8vdeF5+nPhjPJ+sckmfSqIknOycmBhYWFvsOpMb6OhoWvo2FpKq+jvvDPq3L8s6H6xIV7RERERERKmCQTERERESlhkkx6ZWJigmXLlsHExETfodQKX0fDwtfRsDSV11Ff+OdVOf7ZUH1iTfL/t3fvUVFVexzAv8PAzPB+GG8JcQQ0JB8oXHlIqDFCV8UXEl2DSq2Ay9XSsksGPlBTyrrmK7upS0luqagpCpJoms8Ui/ARIJqakloSiS+Y3/3DNWc5D5AZZQbx91lr/pg9+5zz++1hH/bsc2YPY4wxxhhjGngmmTHGGGOMMQ08SGaMMcYYY0wDD5IZY4wxxhjTwINkxhhjjDHGNPAgmbW6RYsWoVOnTpDJZAgJCcGhQ4earf/VV1+ha9eukMlkCAwMREFBgZEibZ4+eSxfvhwRERFwdHSEo6MjBg0adN+8jUXf90MlLy8PIpEIcXFxrRtgC+mbx7Vr15Camgp3d3dIpVL4+fm1ib8tffP46KOP4O/vD0tLS3h5eWHSpEm4efOmkaLV7dtvv8WQIUPg4eEBkUiEjRs33nebXbt2oXfv3pBKpejSpQtWrlzZ6nG2Je3lvNga9GmblStXQiQSqT1kMpkRo2XtGjHWivLy8kgikdDnn39O5eXlNH78eHJwcKCamhqd9b/77jsSi8U0b948On78OL377rtkYWFBZWVlRo5cnb55JCYm0qJFi6i0tJROnDhBycnJZG9vT+fPnzdy5Or0zUOlurqaPD09KSIigoYNG2acYJuhbx63bt2iPn36UGxsLO3du5eqq6tp165ddOzYMSNHrk7fPHJzc0kqlVJubi5VV1dTYWEhubu706RJk4wcubqCggLKyMigDRs2EADKz89vtv7p06fJysqK3njjDTp+/DgtXLiQxGIxbd++3TgBm1h7OS+2Bn3bZsWKFWRnZ0cXL14UHpcuXTJy1Ky94kEya1XBwcGUmpoqPG9sbCQPDw+aM2eOzvrx8fH03HPPqZWFhITQq6++2qpx3o++eWhqaGggW1tbWrVqVWuF2CKG5NHQ0EChoaH02WefUVJSUpsYJOubx5IlS6hz5850+/ZtY4XYIvrmkZqaSgMGDFAre+ONNygsLKxV49RHSwbJb731FgUEBKiVjRkzhhQKRStG1na0l/Nia9C3bVasWEH29vZGio49bvh2C9Zqbt++jSNHjmDQoEFCmZmZGQYNGoT9+/fr3Gb//v1q9QFAoVA0Wd8YDMlDU319Pe7cuQMnJ6fWCvO+DM1jxowZcHFxwSuvvGKMMO/LkDw2b96Mfv36ITU1Fa6urujevTtmz56NxsZGY4WtxZA8QkNDceTIEeHy8+nTp1FQUIDY2FijxPywtMV+bizt5bzYGgw9R/3111/w9vaGl5cXhg0bhvLycmOEyx4D5qYOgLVfV65cQWNjI1xdXdXKXV1dcfLkSZ3bXLp0SWf9S5cutVqc92NIHprefvtteHh4aP2jMyZD8ti7dy/++9//4tixY0aIsGUMyeP06dPYuXMnXnjhBRQUFKCyshIpKSm4c+cOMjMzjRG2FkPySExMxJUrVxAeHg4iQkNDA1577TX8+9//NkbID01T/fzPP//EjRs3YGlpaaLIWl97OS+2BkPaxt/fH59//jmefvpp1NbWIicnB6GhoSgvL0fHjh2NETZrx3gmmbFWNnfuXOTl5SE/P/+R+kJJXV0dxo4di+XLl+OJJ54wdTgPRKlUwsXFBZ9++imCgoIwZswYZGRkYOnSpaYOTS+7du3C7NmzsXjxYhw9ehQbNmzA1q1bMXPmTFOHxphJ9OvXDy+++CJ69uyJyMhIbNiwAc7Ozli2bJmpQ3usLV68GCKRCCEhIaYO5YHwTDJrNU888QTEYjFqamrUymtqauDm5qZzGzc3N73qG4Mheajk5ORg7ty5KC4uxtNPP92aYd6XvnlUVVXhzJkzGDJkiFCmVCoBAObm5jh16hTkcnnrBq2DIe+Hu7s7LCwsIBaLhbJu3brh0qVLuH37NiQSSavGrIsheUybNg1jx47FuHHjAACBgYG4fv06JkyYgIyMDJiZPRrzHk31czs7u3Y9iwy0n/Nia3iQc62KhYUFevXqhcrKytYIkbVQbm4uJBIJDh06hMrKSnTp0sXUIRnk0TijskeSRCJBUFAQvvnmG6FMqVTim2++Qb9+/XRu069fP7X6ALBjx44m6xuDIXkAwLx58zBz5kxs374dffr0MUaozdI3j65du6KsrAzHjh0THkOHDkVUVBSOHTsGLy8vY4YvMOT9CAsLQ2VlpTDIB4Cff/4Z7u7uJhkgA4blUV9frzUQVg38iaj1gn3I2mI/N5b2cl5sDYaea+/V2NiIsrIyuLu7t1aY7D6qq6uxb98+vPvuu7CwsEBubq6pQzKcqb85yNq3vLw8kkqltHLlSjp+/DhNmDCBHBwchCV6xo4dS1OnThXqf/fdd2Rubk45OTl04sQJyszMbBNLHembx9y5c0kikdC6devUliaqq6szVQpEpH8emtrK6hb65vHLL7+Qra0tpaWl0alTp2jLli3k4uJCs2bNMlUKRKR/HpmZmWRra0tr166l06dPU1FREcnlcoqPjzdVCkREVFdXR6WlpVRaWkoA6MMPP6TS0lI6e/YsERFNnTqVxo4dK9RXLQE3ZcoUOnHiBC1atOixWwKuPZwXW4O+bTN9+nQqLCykqqoqOnLkCCUkJJBMJqPy8nJTpfDYmzlzJonFYrp06RLFxsaSr6+vqUMyGA+SWatbuHAhPfnkkySRSCg4OJgOHDggvBYZGUlJSUlq9b/88kvy8/MjiURCAQEBtHXrViNHrJs+eXh7exMArUdmZqbxA9eg7/txr7YySCbSP499+/ZRSEgISaVS6ty5M2VnZ1NDQ4ORo9amTx537tyhrKwsksvlJJPJyMvLi1JSUuiPP/4wfuD3KCkp0fn3roo9KSmJIiMjtbbp2bMnSSQS6ty5M61YscLocZtSezkvtgZ92mbixIlCXVdXV4qNjaWjR4+aIGqm0rVrV4qKiiIiopUrVxIAOnTokImjMoyI6BG6RscYY4wxxtqkI0eOoE+fPli6dCleffVV1NbWwsXFBa+//jo++ugjU4enN74nmTHGGGOMPbDc3FyYm5tj5MiRAAB7e3sMHjwYeXl5Jl2X3lA8SGaMMcYYYw+ksbEReXl5GDBggNqyoWPGjEFNTY3Wl08fBTxIZowxxhhjD2Tnzp24ePEixowZo1Y+dOhQWFpaPpKrXPAgmTHGGGOMPZDc3FxYWFhg+PDhauU2NjaIjY1Ffn4+bty4YaLoDMODZMYYY4wxZrAbN25gw4YNePbZZ+Ho6Kj1enx8POrq6rB582YTRGc4/sU9xhhjjDFmsM2bN6Ourg4AMHfuXK3X6+vrAdydbda8HaMt4yXgGGOMMcaYwYYOHYqvv/76vvUsLCxw8eJFdOjQwQhRPTi+3YIx1qxOnTohOTnZ1GE0q6KiAtHR0bC3t4dIJMLGjRtNHRJjRtXW+mlWVhZEIpFama4Ym+q7hw8fRmhoKKytrSESiXDs2DHjBM4MsnnzZtDdH6hr9nH79u1HZoAM8CCZMb2sXLkSIpFIeMhkMvj5+SEtLQ01NTWmDs9g+/btQ1ZWFq5du2bqUAySlJSEsrIyZGdnY/Xq1ejTp4+pQ2ImxP300aGr7965cwejR4/G77//jgULFmD16tXw9vY2dajsMcT3JDNmgBkzZsDHxwc3b97E3r17sWTJEhQUFOCnn36ClZWVqcPT2759+zB9+nQkJyfDwcFB7bVTp07BzKztfp6+ceMG9u/fj4yMDKSlpZk6HNaGcD9tWzRjbKrvnjx5EmfPnsXy5csxbtw4U4TKGAAeJDNmkJiYGGG2cty4cejQoQM+/PBDbNq0Cc8//7zOba5fvw5ra2tjhnlfLYlJKpUaKRrDXL58GQC0Bg2McT9tWzRjbKrv/vbbbzrLH0RbfF9Z29e2P3Yy9ogYMGAAAKC6uhoAkJycDBsbG1RVVSE2Nha2trZ44YUXANw9Wb/55pvw8vKCVCqFv78/cnJyoPkdWpFIhLS0NOTm5sLf3x8ymQxBQUH49ttvtY5fWlqKmJgY2NnZwcbGBgMHDsSBAwfU6qguQe/evRspKSlwcXFBx44dkZWVhSlTpgAAfHx8hEvUZ86cAaD7PsLTp09j9OjRcHJygpWVFf72t79h69atanV27doFkUiEL7/8EtnZ2ejYsSNkMhkGDhyIysrKFrXr/fLKysoSLsNOmTIFIpEInTp1anafCxcuREBAAKysrODo6Ig+ffrgiy++EF5PTk7WuQ9d91gCwJo1axAcHCzsr3///igqKlKrs23bNkRGRsLW1hZ2dnbo27ev2jEB4ODBgxg8eDDs7e1hZWWFyMhIfPfdd2p16urqMHHiRHTq1AlSqRQuLi549tlncfToUaFORUUFRo4cCTc3N8hkMnTs2BEJCQmora1ttl0eB9xPW6ef7t27F3379oVMJoNcLseyZct01rs3xqb6bnJyMiIjIwEAo0ePhkgkwjPPPCPs4+TJkxg1ahScnJwgk8nQp08frWXFmmpDlW3btiEiIgLW1tawtbXFc889h/LycrV9qP42Lly4gLi4ONjY2MDZ2RmTJ0/W+nllpVKJjz/+GIGBgZDJZHB2dsbgwYPx/fffq9Vbs2YNgoKCYGlpCScnJyQkJODcuXMtamNmGjyTzNhDUFVVBQBqX0hoaGiAQqFAeHg4cnJyYGVlBSLC0KFDUVJSgldeeQU9e/ZEYWEhpkyZggsXLmDBggVq+929ezf+97//IT09HVKpFIsXL8bgwYNx6NAhdO/eHQBQXl6OiIgI2NnZ4a233oKFhQWWLVuGZ555Brt370ZISIjaPlNSUuDs7Iz33nsP169fR0xMDH7++WesXbsWCxYsEH5O1NnZWWeuNTU1CA0NRX19PdLT09GhQwesWrUKQ4cOxbp167QWkp87dy7MzMwwefJk1NbWYt68eXjhhRdw8ODBZtu0JXmNGDECDg4OmDRpEp5//nnExsbCxsamyX0uX74c6enpGDVqFP71r3/h5s2b+PHHH3Hw4EEkJiY2G48u06dPR1ZWFkJDQzFjxgxIJBIcPHgQO3fuRHR0NIC7/7BffvllBAQE4J133oGDgwNKS0uxfft24Zg7d+5ETEwMgoKCkJmZCTMzM6xYsQIDBgzAnj17EBwcDAB47bXXsG7dOqSlpeGpp57C1atXsXfvXpw4cQK9e/fG7du3oVAocOvWLfzzn/+Em5sbLly4gC1btuDatWuwt7fXO8f2hPvpw++nZWVliI6OhrOzM7KystDQ0IDMzEy4uro2u11TfdfV1RWenp6YPXs20tPT0bdvX2Ff5eXlCAsLg6enJ6ZOnQpra2t8+eWXiIuLw/r167Vy0mxDAFi9ejWSkpKgUCjw/vvvo76+HkuWLEF4eDhKS0vVPiA3NjZCoVAgJCQEOTk5KC4uxgcffAC5XI7XX39dqPfKK69g5cqViImJwbhx49DQ0IA9e/bgwIEDwpWM7OxsTJs2DfHx8Rg3bhwuX76MhQsXon///igtLeUrYW0VMcZabMWKFQSAiouL6fLly3Tu3DnKy8ujDh06kKWlJZ0/f56IiJKSkggATZ06VW37jRs3EgCaNWuWWvmoUaNIJBJRZWWlUAaAAND3338vlJ09e5ZkMhkNHz5cKIuLiyOJREJVVVVC2a+//kq2trbUv39/rdjDw8OpoaFB7fjz588nAFRdXa2Vs7e3NyUlJQnPJ06cSABoz549QlldXR35+PhQp06dqLGxkYiISkpKCAB169aNbt26JdT9+OOPCQCVlZVpN/A9WppXdXU1AaD58+c3uz8iomHDhlFAQECzdZKSksjb21urPDMzk+49ZVZUVJCZmRkNHz5cyFlFqVQSEdG1a9fI1taWQkJC6MaNGzrrKJVK8vX1JYVCIZQREdXX15OPjw89++yzQpm9vT2lpqY2GXtpaSkBoK+++qrZHNs77qfG7acymYzOnj0rlB0/fpzEYjFpDjE0Y2yq76pi0vw7HjhwIAUGBtLNmzeFMqVSSaGhoeTr6yuUNdWGdXV15ODgQOPHj1fb76VLl8je3l6tXPW3MWPGDLW6vXr1oqCgIOH5zp07CQClp6drtY2qP585c4bEYjFlZ2ervV5WVkbm5uZa5azt4NstGDPAoEGD4OzsDC8vLyQkJMDGxgb5+fnw9PRUq3fvbAMAFBQUQCwWIz09Xa38zTffBBFh27ZtauX9+vVDUFCQ8PzJJ5/EsGHDUFhYiMbGRjQ2NqKoqAhxcXHo3LmzUM/d3R2JiYnYu3cv/vzzT7V9jh8/HmKx2ODcCwoKEBwcjPDwcKHMxsYGEyZMwJkzZ3D8+HG1+i+99BIkEonwPCIiAsDdS8FNMSSvlnBwcMD58+dx+PBhvbfVtHHjRiiVSrz33ntaX5hS3ZaxY8cO1NXVYerUqZDJZDrrHDt2DBUVFUhMTMTVq1dx5coVXLlyBdevX8fAgQPx7bffQqlUCvEfPHgQv/76q86YVDPFhYWFwuL9jzPup63fTwsLCxEXF4cnn3xSKO/WrRsUCoXBsevy+++/Y+fOncIvt6n6ydWrV6FQKFBRUYELFy6obaPZhjt27MC1a9fw/PPPC9tfuXIFYrEYISEhKCkp0Trua6+9pvY8IiJCrU3Wr18PkUiEzMxMrW1VfXzDhg1QKpWIj49XO66bmxt8fX11Hpe1DXy7BWMGWLRoEfz8/GBubg5XV1f4+/trDZTMzc3V7oMDgLNnz8LDwwO2trZq5d26dRNev5evr6/Wsf38/FBfXy986aW+vh7+/v5a9bp16walUolz584hICBAKPfx8dEjU21nz57VujSsmYPqEjMAtX+eAISfLP3jjz+aPMbly5f1zqsl3n77bRQXFyM4OBhdunRBdHQ0EhMTERYWptd+gLuX7s3MzPDUU081WweAWntoqqioAHB3Kaym1NbWwtHREfPmzUNSUhK8vLwQFBSE2NhYvPjii8LAy8fHB2+88QY+/PBD5ObmIiIiAkOHDsU//vGPx/JWC+6nrd9Pb9y4oTN/f39/FBQUGBS7LpWVlSAiTJs2DdOmTdNZ57ffflP7AKTZhqq+pro3XZOdnZ3ac9X9xfdydHRUa5Oqqip4eHjAycmpydgrKipARDrbCbj7AxusbeJBMmMGCA4Ovu9avFKptE0uyWRpaWnU4zU1G0Ym+LHPbt264dSpU9iyZQu2b9+O9evXY/HixXjvvfcwffp0AND55TwAWl/WeVhUs8Tz589Hz549ddZR3WcdHx+PiIgI5Ofno6ioCPPnz8f777+PDRs2ICYmBgDwwQcfIDk5GZs2bUJRURHS09MxZ84cHDhwQGsw2N5xP225ttRPdVH1k8mTJzc5S92lSxe155ptqNrH6tWr4ebmprW9ubn6kOhBZvI1jysSibBt2zad+2zuexTMtHiQzJgReXt7o7i4GHV1dWqzVCdPnhRev5dq5uNeP//8M6ysrIQZDisrK5w6dUqr3smTJ2FmZgYvL6/7xtXUwLCpHJo6nur1B+Xs7PxQ8tLF2toaY8aMwZgxY3D79m2MGDEC2dnZeOeddyCTyeDo6Kjzxxo0Zw/lcjmUSiWOHz/e5OBWLpcDAH766Setf+Cadezs7DBo0KD7xu/u7o6UlBSkpKTgt99+Q+/evZGdnS0MkgEgMDAQgYGBePfdd7Fv3z6EhYVh6dKlmDVr1n33z7iftpSzszMsLS115q/r2A9CdbXEwsKiRf1EF1Vfc3FxMXgfuvZZWFiI33//vcnZZLlcDiKCj48P/Pz8HspxmXG0vY/PjLVjsbGxaGxsxCeffKJWvmDBAohEIrWBDgDs379fbXmvc+fOYdOmTYiOjoZYLIZYLEZ0dDQ2bdokLAUF3P1m+xdffIHw8HCtS4i6qNYPbckvecXGxuLQoUPYv3+/UHb9+nV8+umn6NSpU7O3H7TUw8pL09WrV9WeSyQSPPXUUyAi3LlzB8Ddf2i1tbX48ccfhXoXL15Efn6+2rZxcXEwMzPDjBkzhBkqFdXsW3R0NGxtbTFnzhzcvHlTZ52goCDI5XLk5OTgr7/+0opZdbm+sbFRaxk3FxcXeHh44NatWwCAP//8Ew0NDWp1AgMDYWZmJtRh98f9tGXEYjEUCgU2btyIX375RSg/ceIECgsLH3j/93JxccEzzzyDZcuW4eLFi1qvq/pJcxQKBezs7DB79myhv+u7D00jR44EEQlXou6l6uMjRoyAWCzG9OnTtWbmiUjrvMTaDp5JZsyIhgwZgqioKGRkZODMmTPo0aMHioqKsGnTJkycOFGY6VDp3r07FAqF2tJSANROyLNmzcKOHTsQHh6OlJQUmJubY9myZbh16xbmzZvXorhUXzrKyMhAQkICLCwsMGTIEJ2L70+dOhVr165FTEwM0tPT4eTkhFWrVqG6uhrr169/aJeuH0ZemqKjo+Hm5oawsDC4urrixIkT+OSTT/Dcc88JM4YJCQl4++23MXz4cKSnpwtLRPn5+akNhLp06YKMjAzMnDkTERERGDFiBKRSKQ4fPgwPDw/MmTMHdnZ2WLBgAcaNG4e+ffsiMTERjo6O+OGHH1BfX49Vq1bBzMwMn332GWJiYhAQEICXXnoJnp6euHDhAkpKSmBnZ4evv/4adXV16NixI0aNGoUePXrAxsYGxcXFOHz4MD744AMAd5eSS0tLw+jRo+Hn54eGhgasXr0aYrEYI0eOfPA35THB/bTlpk+fju3btyMiIgIpKSloaGgQ1iK/94Pmw7Bo0SKEh4cjMDAQ48ePR+fOnVFTU4P9+/fj/Pnz+OGHH5rd3s7ODkuWLMHYsWPRu3dvJCQkwNnZGb/88gu2bt2KsLAwrQ9G9xMVFYWxY8fiP//5DyoqKjB48GAolUrs2bMHUVFRSEtLg1wux6xZs/DOO+/gzJkziIuLg62tLaqrq5Gfn48JEyZg8uTJD9I0rLWYYkkNxh5VqqWFDh8+3Gy9pKQksra21vlaXV0dTZo0iTw8PMjCwoJ8fX1p/vz5ast/Ed1dWio1NZXWrFlDvr6+JJVKqVevXlRSUqK1z6NHj5JCoSAbGxuysrKiqKgo2rdvn16xz5w5kzw9PcnMzExtmSnNZZuIiKqqqmjUqFHk4OBAMpmMgoODacuWLWp1mlrGSbXs04oVK3TGoW9e+iwBt2zZMurfvz916NCBpFIpyeVymjJlCtXW1qrVKyoqou7du5NEIiF/f39as2aN1hJwKp9//jn16tWLpFIpOTo6UmRkJO3YsUOtzubNmyk0NJQsLS3Jzs6OgoODae3atWp1SktLacSIEUJs3t7eFB8fT9988w0REd26dYumTJlCPXr0IFtbW7K2tqYePXrQ4sWLhX2cPn2aXn75ZZLL5SSTycjJyYmioqKouLj4vm3TnnA/vctY/XT37t0UFBREEomEOnfuTEuXLtXZXx50CThVTi+++CK5ubmRhYUFeXp60t///ndat26dUOd+bVhSUkIKhYLs7e1JJpORXC6n5ORktWX8mvrb0JVXQ0MDzZ8/n7p27UoSiYScnZ0pJiaGjhw5olZv/fr1FB4eTtbW1mRtbU1du3al1NRUOnXqlM44memJiNrIXfmMMTUikQipqal6z2wwxoyH+ylj7Rffk8wYY4wxxpgGHiQzxhhjjDGmgQfJjDHGGGOMaeB7khljjDHGGNPAM8mMMcYYY4xp4EEyY4wxxhhjGniQzBhjjDHGmAYeJDPGGGOMMaaBB8mMMcYYY4xp4EEyY4wxxhhjGniQzBhjjDHGmAYeJDPGGGOMMaaBB8mMMcYYY4xp+D/1ynmw6x0dowAAAABJRU5ErkJggg==", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_two_groups_unpaired = dabest.load(df_prop, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Test 3\", \"Test 4\"), (\"Test 5\", \"Test 6\")), proportional=True)\n", - "multi_two_groups_unpaired.mean_diff.plot(horizontal=True);\n", - "\n", - "multi_group_baseline = dabest.load(df_prop, idx=(((\"Control 1\", \"Test 1\",\"Test 2\", \"Test 3\"),(\"Test 4\", \"Test 5\", \"Test 6\"))),proportional=True, paired=\"baseline\", id_col=\"ID\")\n", - "multi_group_baseline.mean_diff.plot(horizontal=True);" - ] - }, - { - "cell_type": "markdown", - "id": "ff282295", - "metadata": {}, - "source": [ - "## Generating delta-delta plots" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cfaa807c", - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.stats import norm # Used in generation of populations.\n", - "np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n", - "\n", - "# Create samples\n", - "N = 20\n", - "y = norm.rvs(loc=3, scale=0.4, size=N*4)\n", - "y[N:2*N] = y[N:2*N]+1\n", - "y[2*N:3*N] = y[2*N:3*N]-0.5\n", - "\n", - "# Add a `Treatment` column\n", - "t1 = np.repeat('Placebo', N*2).tolist()\n", - "t2 = np.repeat('Drug', N*2).tolist()\n", - "treatment = t1 + t2 \n", - "\n", - "# Add a `Rep` column as the first variable for the 2 replicates of experiments done\n", - "rep = []\n", - "for i in range(N*2):\n", - " rep.append('Rep1')\n", - " rep.append('Rep2')\n", - "\n", - "# Add a `Genotype` column as the second variable\n", - "wt = np.repeat('W', N).tolist()\n", - "mt = np.repeat('M', N).tolist()\n", - "wt2 = np.repeat('W', N).tolist()\n", - "mt2 = np.repeat('M', N).tolist()\n", - "\n", - "\n", - "genotype = wt + mt + wt2 + mt2\n", - "\n", - "# Add an `id` column for paired data plotting.\n", - "id = list(range(0, N*2))\n", - "id_col = id + id \n", - "\n", - "\n", - "# Combine all columns into a DataFrame.\n", - "df_delta2 = pd.DataFrame({'ID' : id_col,\n", - " 'Rep' : rep,\n", - " 'Genotype' : genotype, \n", - " 'Treatment': treatment,\n", - " 'Y' : y\n", - " })" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4fd9646", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "unpaired_delta2 = dabest.load(data = df_delta2, x = [\"Genotype\", \"Genotype\"], y = \"Y\", delta2 = True, experiment = \"Treatment\")\n", - "unpaired_delta2.mean_diff.plot(horizontal=True);" - ] - }, - { - "cell_type": "markdown", - "id": "1d95a2e1", - "metadata": {}, - "source": [ - "## Generating mini-meta plots" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f101777c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "unpaired = dabest.load(df, idx=((\"Control 1\", \"Test 2\"), (\"Control 2\", \"Test 3\"), (\"Test 4\", \"Test 5\")), mini_meta=True)\n", - "unpaired.mean_diff.plot(horizontal=True);" - ] - }, - { - "cell_type": "markdown", - "id": "42cc7f56", - "metadata": {}, - "source": [ - "## Controlling aesthetics\n", - "\n", - "As with the vertical plots, horizontal plots can be customized using the same options. Shown below are a few cases where the aesthetics are modified, added functionality, or just less intuitive." - ] - }, - { - "cell_type": "markdown", - "id": "78e4e3e1", - "metadata": {}, - "source": [ - "### Swarm side\n", - "\n", - "As with the vertical plots, you can specify the side of the swarms via `swarm_side` in the `.plot()` method. \n", - "\n", - "In this case, `swarm_side='left'` would plot the swarms upwards, and `swarm_side='right'` would plot the swarms downwards." - ] - }, - { - "cell_type": "markdown", - "id": "a1583cd9", - "metadata": {}, - "source": [ - "Default is `swarm_side='left'`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "27a1ac31", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\", 'Test 2'), resamples=5000)\n", - "two_groups_unpaired.mean_diff.plot(swarm_side='left', horizontal=True);" - ] - }, - { - "cell_type": "markdown", - "id": "5db94d12", - "metadata": {}, - "source": [ - "`swarm_side='center'`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3f37935e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired.mean_diff.plot(swarm_side='center', horizontal=True);" - ] - }, - { - "cell_type": "markdown", - "id": "752c6946", - "metadata": {}, - "source": [ - "`swarm_side='right'`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "30eb0a24", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired.mean_diff.plot(swarm_side='right', horizontal=True);" - ] - }, - { - "cell_type": "markdown", - "id": "aea8e8b5", - "metadata": {}, - "source": [ - "### Table kwargs\n", - "\n", - "The table axis can be customized using the `horizontal_table_kwargs` argument. A dict of keywords can be passed to customize the table. \n", - "\n", - "If None, the following keywords are passed:\n", - "\n", - "- `'show'` - Whether to show the table. Default is True.\n", - "- `'color'` - The color of the table. Default is 'yellow'.\n", - "- `'alpha'` - The transparency of the table. Default is 0.2.\n", - "- `'fontsize'` - The fontsize of the table. Default is 12.\n", - "- `'text_color'` - The color of the text in the table. Default is 'black'.\n", - "- `'text_units'` - The units of the text in the table. Default is None. \n", - "- `'control_marker'` - The marker for the control group. Default is '-'.\n", - "- `'fontsize_label'` - The fontsize of the table x-label. Default is 12.\n", - "- `'label'` - The table x-label." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "74b97613", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\"),(\"Test 4\", \"Test 5\")))\n", - "multi_2group.mean_diff.plot(horizontal=True, \n", - " horizontal_table_kwargs={'color': 'red', \n", - " 'alpha': 0.5, \n", - " 'text_color': \n", - " 'white',\n", - " 'text_units':'mm', \n", - " 'label': 'delta mm',\n", - " 'control_marker': 'o',\n", - " });" - ] - }, - { - "cell_type": "markdown", - "id": "f521d0fe", - "metadata": {}, - "source": [ - "The table axis can be hidden using the `'show':False` in the `horizontal_table_kwargs` dict." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "112b692a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\"),(\"Test 4\", \"Test 5\")))\n", - "multi_2group.mean_diff.plot(horizontal=True, horizontal_table_kwargs={'show': False});" - ] - }, - { - "cell_type": "markdown", - "id": "f26c5762", - "metadata": {}, - "source": [ - "### Gridkey \n", - "\n", - "As with the vertical plots, you can utilise a gridkey table for representing the groupings. This can be reached via `gridkey` in the `.plot()` method. \n", - "\n", - "You can either use `gridkey='auto'` to automatically generate the gridkey, or pass a list of indexes to represent the groupings (e.g., `gridkey=['Control', 'Test']`).\n", - "\n", - "See the examples in the [Plot Aesthetics Tutorial](08-plot_aesthetics.html) for more information with regards to kwargs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a0b1cc86", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\"),(\"Test 4\", \"Test 5\")))\n", - "multi_2group.mean_diff.plot(horizontal=True, gridkey=['Control', 'Test']);" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/tutorials/08-plot_aesthetics.ipynb b/nbs/tutorials/08-plot_aesthetics.ipynb deleted file mode 100644 index 450cdd55..00000000 --- a/nbs/tutorials/08-plot_aesthetics.ipynb +++ /dev/null @@ -1,2150 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f050658e", - "metadata": {}, - "source": [ - "# Controlling Plot Aesthetics\n", - "\n", - "> A guide to various plot aesthetic changes that can be done.\n", - "\n", - "- order: 8" - ] - }, - { - "cell_type": "markdown", - "id": "9608e377", - "metadata": {}, - "source": [ - " Since **v2024.03.29**, swarmplots are, by default, plotted asymmetrically to the right side. For detailed information, please refer to [Swarm Side](#changing-swarm-side).\n", - "\n", - " Since **v2025.03.27**, further aesthetic changes were added/updated which include:\n", - "\n", - " - [Raw bars](#raw-bars)\n", - " \n", - " - [Contrast bars](#contrast-bars)\n", - " \n", - " - [Reference Band](#reference-band)\n", - " \n", - " - [Delta text](#delta-text)\n", - " \n", - " - [Jitter](#adding-jitter-to-slopegraph-plots)\n", - " \n", - " - [Gridkey](#gridkey)\n", - " \n", - " - [Delta dots](#delta-dot)\n", - " \n", - " - [Effect size paired lines](#effect-size-paired-lines)\n", - " \n", - " - [Baseline error curve](#baseline-error-curve)" - ] - }, - { - "cell_type": "markdown", - "id": "7a82e9a2", - "metadata": {}, - "source": [ - "## Load libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2fbf7ec7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pre-compiling numba functions for DABEST...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 50.20it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Numba compilation complete!\n", - "We're using DABEST v2025.10.20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import dabest\n", - "import seaborn as sns\n", - "\n", - "print(\"We're using DABEST v{}\".format(dabest.__version__))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e74d9aaf", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\") # to suppress warnings related to points not being able to be plotted due to dot size" - ] - }, - { - "cell_type": "markdown", - "id": "e127428c", - "metadata": {}, - "source": [ - "## Creating a demo dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4a87b641", - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.stats import norm # Used in generation of populations.\n", - "\n", - "np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n", - "\n", - "Ns = 20 # The number of samples taken from each population\n", - "\n", - "# Create samples\n", - "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n", - "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", - "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", - "\n", - "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n", - "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n", - "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n", - "t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", - "t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", - "t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", - "\n", - "\n", - "# Add a `gender` column for coloring the data.\n", - "females = np.repeat('Female', Ns/2).tolist()\n", - "males = np.repeat('Male', Ns/2).tolist()\n", - "gender = females + males\n", - "\n", - "# Add an `id` column for paired data plotting.\n", - "id_col = pd.Series(range(1, Ns+1))\n", - "\n", - "# Combine samples and gender into a DataFrame.\n", - "df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n", - " 'Control 2' : c2, 'Test 2' : t2,\n", - " 'Control 3' : c3, 'Test 3' : t3,\n", - " 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6,\n", - " 'Gender' : gender, 'ID' : id_col\n", - " })\n", - "\n", - "np.random.seed(9999) # Fix the seed so the results are replicable.\n", - "\n", - "# Create samples\n", - "N = 20\n", - "y = norm.rvs(loc=3, scale=0.4, size=N*4)\n", - "y[N:2*N] = y[N:2*N]+1\n", - "y[2*N:3*N] = y[2*N:3*N]-0.5\n", - "\n", - "# Add a `Treatment` column\n", - "t1 = np.repeat('Placebo', N*2).tolist()\n", - "t2 = np.repeat('Drug', N*2).tolist()\n", - "treatment = t1 + t2 \n", - "\n", - "# Add a `Rep` column as the first variable for the 2 replicates of experiments done\n", - "rep = []\n", - "for i in range(N*2):\n", - " rep.append('Rep1')\n", - " rep.append('Rep2')\n", - "\n", - "# Add a `Genotype` column as the second variable\n", - "wt = np.repeat('W', N).tolist()\n", - "mt = np.repeat('M', N).tolist()\n", - "wt2 = np.repeat('W', N).tolist()\n", - "mt2 = np.repeat('M', N).tolist()\n", - "\n", - "genotype = wt + mt + wt2 + mt2\n", - "\n", - "# Add an `id` column for paired data plotting.\n", - "id = list(range(0, N*2))\n", - "id_col = id + id \n", - "\n", - "# Combine all columns into a DataFrame.\n", - "df_delta2 = pd.DataFrame({'ID' : id_col,\n", - " 'Rep' : rep,\n", - " 'Genotype' : genotype, \n", - " 'Treatment': treatment,\n", - " 'Y' : y\n", - " })\n", - "\n", - "def create_demo_prop_dataset(seed=9999, N=40):\n", - " import numpy as np\n", - " import pandas as pd\n", - "\n", - " np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n", - " # Create samples\n", - " n = 1\n", - " c1 = np.random.binomial(n, 0.2, size=N)\n", - " c2 = np.random.binomial(n, 0.2, size=N)\n", - " c3 = np.random.binomial(n, 0.8, size=N)\n", - "\n", - " t1 = np.random.binomial(n, 0.6, size=N)\n", - " t2 = np.random.binomial(n, 0.2, size=N)\n", - " t3 = np.random.binomial(n, 0.3, size=N)\n", - " t4 = np.random.binomial(n, 0.4, size=N)\n", - " t5 = np.random.binomial(n, 0.5, size=N)\n", - " t6 = np.random.binomial(n, 0.6, size=N)\n", - " t7 = np.ones(N)\n", - " t8 = np.zeros(N)\n", - " t9 = np.zeros(N)\n", - "\n", - " # Add a `gender` column for coloring the data.\n", - " females = np.repeat('Female', N / 2).tolist()\n", - " males = np.repeat('Male', N / 2).tolist()\n", - " gender = females + males\n", - "\n", - " # Add an `id` column for paired data plotting.\n", - " id_col = pd.Series(range(1, N + 1))\n", - "\n", - " # Combine samples and gender into a DataFrame.\n", - " df = pd.DataFrame({'Control 1': c1, 'Test 1': t1,\n", - " 'Control 2': c2, 'Test 2': t2,\n", - " 'Control 3': c3, 'Test 3': t3,\n", - " 'Test 4': t4, 'Test 5': t5, 'Test 6': t6,\n", - " 'Test 7': t7, 'Test 8': t8, 'Test 9': t9,\n", - " 'Gender': gender, 'ID': id_col\n", - " })\n", - "\n", - " return df\n", - "df_prop = create_demo_prop_dataset()\n", - "\n", - "\n", - "two_groups_prop_paired = dabest.load(df_prop, idx=(\"Control 1\", \"Test 1\"), proportional=True, paired=\"baseline\", id_col=\"ID\")\n", - "two_groups_prop = dabest.load(df_prop, idx=(\"Control 1\", \"Test 1\"), proportional=True)\n", - "two_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"))\n", - "multi_2group = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\")))\n", - "repeated_measures = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\"),paired=\"baseline\", id_col=\"ID\")\n", - "two_groups_paired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), paired=\"baseline\", id_col=\"ID\")\n", - "mini_meta_paired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), mini_meta=True, id_col=\"ID\", paired=\"baseline\")\n", - "paired_delta2 = dabest.load(data = df_delta2, \n", - " paired = \"baseline\", id_col=\"ID\",\n", - " x = [\"Treatment\", \"Rep\"], y = \"Y\", \n", - " delta2 = True, experiment = \"Genotype\")" - ] - }, - { - "cell_type": "markdown", - "id": "312bf080", - "metadata": {}, - "source": [ - "## Changing the graph colours\n", - "\n", - "### Color categories from another variable\n", - "Use the parameter `color_col` to specify which column in the dataframe will be used to create the different colours for your graph." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "54dd7271", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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fPmkKgiAci0iKwpRQ396NDCMOnmns7ME84BCtREEQTohIisKkJ8syta0mspJi0WrCjtjvDwQoq20jPTGGxJjIEEQoCMKpQiRFYdLr6u2n3+EccYBNXZsJu9PNnPxTe0VwQRAmnkiKwqRX02okSq8bthXo8/vZX99Odmo8MZFioeETsa+mhS3ltaEOQxBCSiRFYVJzuDy0mPooyEgc9l5hVXMXLo+X2XnpIYju1GFzuth6oJ5PK5votgyEOhxBCBmRFIVJra7dhEKhYFrqkQNsPF4fFY0dFKQnHXW1DOHY9te302O1MeBwsa+m5dgnCMIpSiRFYdIKBAYH2OSkxKEOO7JMb0VjB/6ATMm0tBBEd+qwOV3srW5Br9UQa9BxoKFDtBaF05ZIisKk1dFjwe5yDzvAxun2UNncRXFmMrpwdQiiO3Xsr2/HZO5Hr9MQrg7DYnOI1qJw2hKrZAiTVk2rkTiDnvgo/RH79te3o5QkZuSkhiCyU0tdmwlduAbLgBOAiHANjZ29eHw+1JN0dXRBmCjiN16YlGxOF+3dFhbPyD1i34DDRW2bidJp6WjU4lf4RF26bA5Ot2fINo1aJRKicFoSv/XCpFTb1o1KpSA7Je6IfeV1bWjCVBRliQWEx4MhIhxDhBioJAgg7ikKk5A/EKCuzURuagJhKuWQfeYBB40dPZTkph2xTxAE4USJpChMOq1GM063Z9gBNmW1rURoNeRnJIYgMkEQTnUiKQqTTk2rkcQYwxEVarotA7Sa+ijNS0epEL+6giCMP/HOIkwqVpuTrj4rBcO0BPfVtBKt15GTEh+CyARBOB2IpChMKjVtRjRhYWQlDR1g09ljpavPyuz8DBQKsTSUIAgTQyRFYdLw+f3Ut3eTl56AUvn5r6Ysy+ytaSE+OpKMxJgQRigIwqlOJEVh0mju6sPj9ZGfPnSATYuxj95+G3PyM8QCwoIgTCiRFIVJo6bVSEpc9JA5c4GAzL7aVlLiokmJiwphdIIgnA5EUhQmhb5+O92WAQoyh7YSGzq66bc7mVOQEaLIBEE4nYikKEwKta0mtBo16QnRwW1+f4DyujaykuKGrX8qCIIw3kRSFELO4/NR39FNfnrikPmH1a1GHC4PpfliAWFBEE4OkRSFkGvs6MXvD5Cf/vncRI/PR0VDO9PSEojW645ytiAIwvgRSVEIKVmWqWk1kpYYTYRWE9xe2dSFx+enNE+0EgVBOHlEUhRCqsdqwzxgH1Ln1OXxcqipg8KMpCGJUhAEYaKJpCiEVE2rEb1WQ2pcdHBbRUMHsgwzc9NCF5ggCKclkRSFkHF7fDR19lKQkRQs3WZ3ualu6WJ6dgpaTdgJXd9jt2Bu2Icc8I9HuIIgnAZEUhRCpr6jGxmYlpYQ3La/rh2VUsn0nJQTurYc8NNbtRVXX/sJRikIwulEJEUhJA4PsMlMjEWrUQPQb3dS125iZm4qapXqhK5vadyH1zFAXNEZSAqxGLEgCKMjkqIQEkZzP/12JwWZn0/DKKttQ6tRU5h55OLCx8PZ10F/WzXRObNR60UBcUEQRk8kRSEkalpMGCK0JMUYgMEyb01dPcyaloZKOfaWnd/jord6O9qYFCLTCscrXEEQThMiKQonndPtocXUR0FGUnDVi321rRh0WvLSjlxceLRkWaavZgfIMrGFi8WKGoIgHLdJmxR/9atfIUkSt91221GPe/XVVykqKiI8PJySkhLefvvtkxOgMGZ1bd1IwLTUwQE2JnM/7d1mSvPTT2gBYVtnHY7edmILFqHSiCo4giAcv0mZFHft2sXTTz/NrFmzjnrctm3buOqqq/jmN7/Jvn37uPTSS7n00kupqKg4SZEKxysQkKltM5KdEodGrfpsAeFWYiIjyE6OG/N1vQ4rloY9RKbmo4sXK2oIgjA2ky4p2mw2rrnmGp555hliYo4+SOKJJ57gggsu4Mc//jHFxcU8+OCDzJ07lz/84Q8nKVrheHX0WrA53cEKNu09FkzmfuYUjH0BYTngp6dyG0pNBNG5c8YzXEEQTjOTLinefPPNrFmzhpUrVx7z2O3btx9x3KpVq9i+fftEhSecoJpWIzGREcRH6ZFlmX01rSTGGEiLjx7zNa1N+/E6LMQXnYFCeWIT/gVBOL2d2GSwcfbyyy+zd+9edu3aNarju7q6SEoaOnw/KSmJrq6uEc9xu9243e7gY5vNNrZgheNmd7ppN1lYOD0HSZJo7OzBPGDngkUzxtxKdJm76G+rHJx+ERk7zhELgnC6mTRJsbW1lVtvvZUNGzYQHh4+Yc/z8MMP88ADD0zY9YWR1baZUKkU5KTG4Q8EKKttJT0hhsTPpmUcL7/XTW/1djRRiUSmF49ztIIgnI4mTffpnj17MJlMzJ07F5VKhUqlYvPmzfzud79DpVLh9x9ZvzI5ORmj0Thkm9FoJDk5ecTnWbduHVarNfi1efPmcX8twpH8gQC1bSZyUxJQq1TUt3cz4HAxO39sg2JkWaavdidywEdc4RIx/WIc2DprsTTtD3UYwnHo7OykvLwcu90e6lBOGZMmKZ577rkcOHCAsrKy4Nf8+fO55pprKCsrQznMhO4lS5awcePGIds2bNjAkiVLRnwejUaDwWAIfun1+nF/LcKR2rotON0e8jMS8fn9lNe1kZMST6whYkzXsxsbcHS3EJO/EFX42K4hfM7vcdFd8TE9hz7G67CGOhzhGF577TWKiopIT09n7ty5fPrppwD09PQwZ84c1q9fH9oAp7BJkxQjIyOZOXPmkK+IiAji4uKYOXMmAGvXrmXdunXBc2699Vbeffddfvvb31JVVcX999/P7t27ueWWW0L1MoQR1LQYSYiOJNYQQXWLEZfHS2ne2FqJXucA5rrd6JNziUjIGudIT08DbZW4LF147BYszaK1OJm98cYbXHbZZcTHx3Pfffchy3JwX3x8PGlpaTz//PMhjHBqmzRJcTRaWlro7OwMPl66dCkvvfQSf/rTnygtLeVf//oX69evDyZRYXLot7vo7LVQkJGEx+vjQEM7+emJGCKO/97x4OoX21Cow4mZNn8Coj39+D0uzPV7UYSFE6Y1YG0sF63FSeznP/85Z511Flu2bOHmm28+Yv+SJUvYt29fCCI7NUyagTbD2bRp01EfA1xxxRVcccUVJycgYUxqWo2ow1RkJcdS0dCB3x+gZNrYFhC2tlTgGeglafb5KFRi+sV4ONxKDI9LR5IUOHqasTTvJ6F4WahDE4ZRUVHBo48+OuL+pKQkTCbTSYzo1DKlWorC1OP3B6hv72ZaWgJeX4BDTZ0UZSUTEa457mu5rCb6Ww4SlVWCxhA/AdGenqyth0ACV18bzt4WJEnC1lZNwOcNdWjCMHQ63VEH1jQ0NBAXN/bqUKe7Sd1SFKa+ZmMvbq+XgvQkDjS0IUkwIyf1uK8T8HnordqG2hCPIXPGBER6+kqatQKf2zFkmyJMg6QUbw+T0TnnnMOLL744bF3orq4unnnmGS666KKTH9gpQrQUhQlV02oiOTYKpVKiptXEjJxUwtXH3+3ZV7eLgM9DfNFSJEn82o6n8JgU9MnTUKp1SAol+uRp6OLSp8Q0l48//piLL76Y1NRUJEka1ajLTZs2MXfuXDQaDXl5ebzwwgsTHud4+uUvf0lbWxsLFizg6aefRpIk3nvvPX72s59RUlKCLMvcd999oQ5zyhLvLsKEMQ84MJn7KchIoryuDbVKSXFWynFfx25qwm5sIjZvAapwMYVmIgT8Xkz7P8BY9v4RrcbJzG63U1payh//+MdRHd/Y2MiaNWs455xzKCsr47bbbuNb3/oW77333gRHOn4KCwvZsmULcXFx3HPPPciyzG9+8xseeughSkpK+OSTT8jOzg51mFOW6B8RJkxNqxGtRk1khIaG8h7mF2cRpjq+BYR9Lht9tTuJSMwmIilngiIVbB21OHrbkP1+BtoOTZmRvatXr2b16tWjPv6pp54iJyeH3/72twAUFxezZcsWHnvsMVatWjVRYY67GTNm8MEHH2A2m6mrqyMQCJCbm0tCQkKoQ5vyRFIUJoTX56eho5uirBQO1LcToVVTkJ507BO/QJYD9FRtQ6FSE5u/YIIiFQJ+L+b6PUgKJQqVBnP9XiLTp4d0TUqbzUZ/f3/wsUajQaM5/sFZXzbSIgLHWrd1soqJiWHBAvG3MZ5E92mIybLMvtpW+u2uUIcyrpo6e/H5AsRG6mgx9jErLx2l8vh+3fpbDuHp7yGucCkKlXqCIhUOtxI1hgQ0hnjc1m4G2g6FNKazzz6bqKio4NfDDz88LtcdaRGB/v5+nE7nuDzHRPvd73531Fbt6tWrefLJJ09iRKcWkRRDzOH2UN/ezetby9ld1YzH6wt1SOOiptVIWkI0Na1GovQ6clOOr1vH3d+DtXk/howZhEcnTlCUAoClqZyA181ARw3WlgME/F4sjeUE/KGbkrF58+YhNYq/WMnqdPfss88yffr0EfdPnz6dP/3pTycxolOLSIohFhGu4dJlpcyalkZNq5H/flxGVXMXgYB87JMnqR6rjd5+G9GROjp7rczOz0ChGP1IxoDfOzj9Qh9LVJaoTjTRYnLnkjxnFapwPcowLYmzVhBbsBhJcXz3f8eTXq8fUqN4PLpOYeRFBAwGA1qtdlyeY6LV19dTXDzyqjBFRUXU19efxIhOLeKe4iSgUiqZNS2dvLREympb2VXZRHWrkXmFmaTFR0+JofFfVNNqRBeuprPHQnyUnszEmOM631y3G7/HScLM5Sf0xvzBBx/gdDrRarWjWrT6dBWZVoi5fg8gI31WJSjqFJ0LumTJEt5+++0h2461iMBko1arj7pmbGdnJwqFaO+MlfjOTSK6cDVLS6Zx4ZISwtUqPtxTxQe7qzAPTJ0h8h6vj8bOXqIidPT225mTn3lcSd3R3YKtq4GYvPmE6ca2zuJhGzZs4M0332TDhg0ndJ1Tnd/jxFy/F6VaR5guCmtjOR67JdRhjYrNZguuqgODUy7KyspoaWkBBpeKW7t2bfD47373uzQ0NHDnnXdSVVXF//t//49//vOf3H777aEIf0wWL17MCy+8wMDAwBH7rFYrzz//PIsXLw5BZKcG0VKchOKiIjh/wXRaTWb2Vrfw5tb95GckUpqXgVYzuet91nd04/cHsNgcpMRFkRIfNepzfW4HfbWfokvIJCIpdwKjFL6ov/UQLqsRbWw6AM7eVqzN+0mYflaIIzu23bt3c8455wQf33HHHQBcd911vPDCC3R2dgYTJEBOTg5vvfUWt99+O0888QTp6en8+c9/nlLTMe677z7OPvtsZs+ezW233caMGYOt+oqKCh5//HE6Ozt56aWXQhzl1CWS4iQlSRKZSbGkJURT3WJkf10bjZ09lOSmUZyVctwjOU8GWZapaTWhCVPhdHs4Z07hcZ3bW7UNSaEiNn/hlOsynsr626qQFEpclk5kWUZSqrC11xBXsGTSF11fvnz5kKWTvmy4ajXLly+f0qtILFq0iDfeeIObbrqJW2+9Nfi3IssyOTk5vP7661OqO3iyEUlxklMqFEzPTiE3NZ799W3sq22lptXI3IIsspJjJ1XyMJkHMA/YQZbJToknPnr01WcG2ipxW00klqxAGTY+gyqE0UmefT4+92CB6UAggEKhQClqn05q5513HnV1dezbty84qGbatGnMnTt3Ur0nTEXit36KCFeHsbA4h8KMZPbUNPNxeQ0JzZEsKMo+ruQzkWpajbg9PrTqMGbnj34BYc9AH5amciLTiwmPSZ7ACIXhaKIS0DA4Zcbn86FSibeFqUChUDBv3jzmzZsX6lBOKeK3f4qJ0mtZMbeIzh4ru6ubeHvHAXJS4plbkEmENnQtLKfbS0NHDx6fn5m5qUTrR1cNJeD30VO1lTBdNNHZsyY4SuFYAoFAqEMQRunQoUM0NDRgNpuH7UL+4gAjYfREUpyiUuKjWBM7i/r2bsrqWln/SRnTc1KZkZOCOgSf9Ovbu+m12oiNiqA0L33U51ka9uJz2UmZuzqk8+KEQUe7PydMDvX19Vx77bXs3LlzxJ+XJEkiKY6RSIpTmEIhkZ+RSFZKLAcbOznU2EFdm4nZeRlMS0s4rgnzJ0KWZQ41deLx+ZmenYJeGz6q8xy9bQx01BKbv4CwiNGPUhUmjmgpTn433XQTBw4c4PHHH2fZsmXExBzfPGDh6ERSPAWoVSrm5GdQkJ7I3poWth+sp6qli/lFWaTETXyy6ey10tjRTawhgpLctFGd4/c46avegTYuDX1K/gRHKIyWSIqT39atW/npT3/KD37wg1CHckqafOP6hTGL0GpYVprP6sUzUSkVbNh1iA/3VtFvn9hCxwca2rG5PMwvykKrOXbhblmW6a3eDpJEXMFiMVpuEhHdp5NffHw8UVGiZ2WiiKR4CkqIjuSCRTNYVpqPZcDBa1vK2VXZhNsz/sXG7S43e6tbSYzRMzMndVTn2DqqcfZ1Ele4BKV6dF2twskhWoqT33e/+13+9re/4ff7Qx3KKUl0n56iJEkiJyWezMRYKps7OdDQTn1HN7OmpVOYmYRynGojlte2YR6ws3rxDNRhx/518tgtmBvKiEwrRBs7uiQqnDw+36mxSsuprKCgAL/fT2lpKTfeeCMZGRkolUcOUrvssstCEN3UJ5LiKU6pVDAzN41paQmU17Wxp6qZmhYjcwszyUiMOaGuy0BAZsuBehJjIpmZe+wEJwf89FZtJUyrJyZ3zpifV5g4Xm/olosSRufrX/968P8/+tGPhj1GkiTRkhwjkRQnAbfHh0Y9sT8KrUbN4hm5FGYmsbuqhU37qkmOjWJ+URaxhogxXfNQUwfGPitfX7EA1TCfVL/M0rgPr2OA5DmrxPSLScrj8YQ6BOEYPvroo1CHcEoTSTHEHC4Pr28pJyclntL8dMLVE1trMiYygpXzi2jvsbC7qpm3th1gWloCs/Mz0IUf3+r2H+6tJjYygnlFmcc81tnXQX9bNTHT5qHWiyHkk5XL5Qp1CMIxnH322aEO4ZQmkmKIadQqZuWlU17XSmNXD3PyM8hPT5rQOYaSJJGeEENKXBS1rSbK69po6uplxmeT/0fT6mvs6KG5q5f/OaP0mPcn/R4XvdXb0cakEJk2+iLhwsnndrsHi4KLEcGTntvtZu/evZhMJs444wzi4+NDHdIpQYw+DbHDBb8vXTabzMRYPj3UyFvbD2Ds6z8pz12UlcxXzppNQUYSBxraWf9JOQ0d3Ucdmi/LMht2H0Kv1bC05OhLPMmyTF/NDpBlYgvF9IvJTpZl0YU6Bfzud78jJSWFM888k8suu4z9+/cD0NPTQ3x8PM8991yII5y6RFKcJLSazxYYXlyCUiHx3s6DfFJei93lnvDnVoepmF+Uxf+cUUp8VARb9tfxzo4KTObhE3OryUx9ezeLp+cSdoyScrbOOhy97cQWLEKlGV09VCG03O6J/50Txu7555/ntttu44ILLuDZZ58d8gE2Pj6eFStW8PLLL4cwwqlNJMVJJj5az+rFM1k6cxqdvVZe+6ScA/Xt+P0TP3/MEBHO8jmFrFo4A1mGdz89yOayGgYcn99nkmWZD/dWoQ5TsXhmzlGv53VYsTTsITI1H1386FfNEEJL3Fec3H77299yySWX8NJLL3HxxRcfsX/evHkcPHgwBJGdGkRSnIQkSSIvPZFLl80mPyORsrpWXt9aTpvJfFKePynWwIVLZnJGSR4m8wCvbSlnT3UzHq+P5q4+Gjt6mDUtnZjIkUetygE/PZXbUGoiiBbTL6YUkRQnt7q6OlavXj3i/tjYWHp7e09iRKcWMdBmElOHqVhQlE1+eiK7Kpv4cG8VaQkxLCjKwhChndDnliSJaWkJZCbFcqipg4rGTuraujF9dq9zflHWUc+3Nu3H67CQPHsVCuXkXr1dGGQ3NeHra8Gpzgt1KMJRREdH09PTM+L+Q4cOkZws1iUdK9FSnAKi9TpWzi/m7DmFWG0OXt+6n73VLXh9Ez85N0ylpDQvg0uXlaJQSOxvaMfr86M6yohTl7mL/rZKorNLUUfGTniMwokL+Dx0H/gIb+s+bJaR33CF0Lvwwgv505/+hMViOWLfwYMHeeaZZ/if//mfkx/YKUIkxSlCkiSykmL5nzNLKclNpbK5k/WflB1zpOh4CQ8LIyDLpMVHk5Ucx0f7qvlgdyUWm2PIcX6vm97q7WiiEolML57wuITx0d9WhdPcScBlxdZWGepwhKP4xS9+gd/vZ+bMmfzsZz9DkiRefPFFrr32WubPn09iYiL33ntvqMOcskRSnGJUysGW2yXLSkmMjmTL/jre23mIXqt9Qp+3urWLzl4r6YnRXLVyAWfPKWTA4eKNrfv59GAjTrd3cPpF7U7kgI+4wiVi+sUUEfB5sNTvQVKFIal1eE01+JwDoQ5LGEFqaip79uzhggsu4JVXXkGWZf7617/yxhtvcNVVV7Fjxw4xZ/EEiHuKU5ReG87Zcwro7LGys6qJt7cfID8jkdn5GeNeFcfj83GgvgOlpCA7JR5DhBZDhJb0+GiqWro4UN9OQ2c3xTEBogeaSZixDFX42ErHCSff4VZieGwqkkfC392JpbmC+KIloQ5N+BK32817771HdnY2f/7zn/nzn/9Md3c3gUCAhIQEFONU6P90Jr6DU1xKfBQXLS1hXlEWjZ09rP+kjOqWLgKB8etSrWzqpN/uRBseRkFGUnC7UqlgRk4ql541m5x4HS37t1DRK9Hj14t1+aaQ/pYK5IAfV18HgYFu/H4/fY3lBPyiOPhko1arueKKK9i2bVtwW0JCAklJSSIhjhPRUjwFHK6Kk5MSz77aFnYeaqKm1cTC4mySYg0ndG2n28vBxk7C1WFow9WkJxxZt1SjUpAttxOfl0WLppjNZTUkxhiYX5hFfLT+hJ5fmHjxxWcSnTObG7/5TUxGI2q1mj+eex2SQrw9TDaSJJGfn3/U0afCiREfLU4hWk0YS2dOY/XimeNWFaeisR1/IICMTH564rA1Wa0tFXgGeskoPYcVC2eycn4xHq+Pt3cc4JP9dSelKo8wdrqETAwZ0/m0spXtB5vYU2ek26kQ94QnqZ/+9Kf84Q9/oLq6OtShnJLER8FT0OGqOPUd3eytbmH9J+XMyk1jenYKSuXoPwfZnW5qWoxE63VYbA7y0hOOOMZlNdHfcpCorBI0hsGb+6nx0Vy0NIq6dhNlta2sN/YxIzuFGTmphKnEklGT1Zl50fjSVXxQZaW1tVUUBp+kduzYQVxcHDNnzmT58uVkZ2ej1Q6dtyxJEk888USIIpzaRFI8RUmSRF5aIpmJsZTXt1FW10pdu4kFRdmkJ45u6aby+jZUSgU+v5+MxBgiwjVD9gd8HnqrtqE2xGPInDFkn0IhUZCRRHZKHBX1HRxs7KC2zcSc/AympSWIN9tJxm3tpjhVj9etoqbbw8DAAH19fcTFxYU6NOFL/vCHPwT/v3HjxmGPEUlx7ET36SnucFWci8+YhV6r4cO9VWzcU0W/3XnU86w2J/Vt3aQnxjLgcA0ZYHNYX90uAj4P8UVLkaThf5XUKhVzCzO5ZFkpSTEGtlXU89b2A3T1Wcfl9Qnjw9JUjl6jRK1SMDsrGmSZ+vr6UIclDCMQCBzzy++f+MIepyrRUjxNHK6K02Iys6eqide37qc4K5mSaWmoh1npoqyuFV24Gp/fj14bTkpc1JD9dlMTdmMT8UVLUYUfezCNXhvOWbPzKTIns7u6mfd3HiIjMZZ5hZkTXrJOODq3tRtrSwUWhw+z1UVekh61f4D6+noWLFggWvVjkJOTc9zfN0mSxAeRSWDMSdHv9/Pqq6/y0UcfYTKZ+PnPf05JSQlWq5WNGzdyxhlnkJR0ZOtCCJ3DVXHS4qM42NhJRUM7DR09zCvMJCclPvhH3Gu109zVy/zCLPbVtlKalzHkD9znstFXu5OIxGwiko6+UsaXJcZEsnrRDJo6e9lb08LrW/dTmJnErNx0NGrxGS0ULE3leGx9KBSgCVMSGa5E7zbR1x9JV1cXKSkpoQ5xyjn77LOPSIq7d+/m4MGDTJ8+ncLCwcW2q6urOXToEDNnzmTevHnH9Rw7duwIvv9+//vfJz8/H4fDQVVVFQUFBej1YuT3WIzpXchisXDBBRewc+dO9Ho9drudH/zgBwDo9Xr+93//l7Vr1/LQQw+Na7DC+BisipPOtLR49lS1sGV/HTWtg/cb46Ii2FfbgiFCiz8gIwPT0j4fYCPLAXqqtqFQqYnNXzCm55ckiZzUeDKSYqhs6uJAQzv17d2U5qVTkJGEUsy3Oqm8Divh0cn4A+APBOiyusmUfUgEqK6uFklxDF544YUhj9evX8/69evZsGED55577pB9GzZs4Gtf+xoPPvjgqK7t8Xi48soree2114KDoS6++GLy8/NRKBScf/753H777dx9993j9XJOK2N697nrrrs4ePAg7733Hg0NDUMmaiuVSr761a/y9ttvj1uQwsQ4XBXnvAXTB6dQbD/AOzsqaDH2Mjsvnfr2brKSYtFqPq+Q099yCE9/D3GFS1Go1Cf0/CqlkpJpaXzlrNlkJsWyu7KZN7bsp9XUJyb/n0Rpiy9j2gXf4/kt7Tz+1iGe/rCRnohCZElJfX09TufR7z8Lx3bvvffygx/84IiECHDeeedxyy238LOf/WxU17rnnnt48803efLJJ6murh7ytxIeHs4VV1zBa6+9Nm6xn27GlBTXr1/PD37wA84777xh+80LCgpoamo60diEkyQlLoo1n1XF2VnZRIvRTKvJjNXuGDLAxt3fg7V5P4aMGYRHJ47b82s1apbOnMaapSXowtV8tHew2Hhf/8TWcxUGSZJ05N/xZ4/9fj8VFRUhiOrUUltbe9SRvHFxcaO+n/iPf/yD733ve3znO98hNvbIVWiKi4tpaGgYc6ynuzElRavVSk7OyPeSvF4vPp9vzEEJJ59SoSBSpyE9IZr5RVls2ldDq8lC4LNPoQG/d3D6hT6WqKyZExJDrCGC8xYUs2JuEXaXh7e2HWBbRT1Ot2dCnk8YnYqKChwOx7EPFEY0bdo0nn/+eWw22xH7BgYGeO6558jNzR3VtUwmEyUlJSPuVyqV4ud1AsZ0T3HatGns3bt3xP3vv/8+06dPH3NQwsknyzJlta2kJ8ZwZkke7d0WVCoFG3YdIjs5nmmqLgIeJwkzlyMpJm4CviRJpCfGkBIfRU2rkfK6Npo6eynJTaM4OxmVUkz+P9m8Xi+7du3i7LPPDnUoU9YvfvELvvrVr1JUVMT1119PXt7gQs61tbW8+OKLGI1GXn311VFdKyMjg6qqqhH3b926NXh94fiNqaX4rW99i+eeey64bAkMvpm53W7uvvtu3n33XW666aZxDVSYWI2dPZgHHMwtyKS+oxu9Lpxrz1vE0pJp9LbXsW/Xdno1mSg0J2dEm1KhoDgrha8sm0N+RiJlda289kk5jR094n7jSSLLMmZngIAsU11dTWdnZ6hDmrIuvfRS3n77bRISEnjooYe48cYbufHGG3n44YdJTEzkzTff5NJLLx3Vta6++mqefvpptm/fHtx2uPv7mWee4Z///Cdr166diJdxWhhTS/HWW2/l4MGDXHXVVURHRwODP6je3l58Ph833XQT3/zmN8czTmEC+QMBymrbSE+MIc6g5+OyWnJS4tCow8iO16My9GNU53GgFxq3ljO/KIv0hJiTMn9Nox4sPlCYkcSe6hY+2V9LZUsX8wuzSIyJnPDnP52ZXQEaLT4yDCoSI5Rs2bKFyy67DKVorY/J+eefz/nnn09XVxfNzc0AZGVlkZycfFzXufvuu9mxYwdnnXUWxcXFSJLE7bffTl9fH21tbVx44YXcfvvtE/ESTgtjailKksQzzzzDxx9/zNq1a1m9ejWzZ8/mO9/5Dps2beLJJ58cUzBPPvkks2bNwmAwYDAYWLJkCe+8886Ix7/wwgvBQQKHv8LDw8f03KezujYTdqebOfmZdPRYsLvcFGQkIcsyvVXbCAtTM3/5Gi4+sxS9NpyP9laPqirOeDJEaDlnbiHnLZiO3x/g3U8r+LisFpvTddJiONXpEjJImDZ4r0qWZYz2ADaPTJfNjz8gYzabOXDgQIijnPqSk5NZtGgRixYtOu6ECIPLR7377rs8//zz5ObmUlRUhNvtZtasWbzwwgu88cYb4oPLCTih2dJnnnkmZ5555njFQnp6Or/61a/Iz89HlmVefPFFLrnkEvbt28eMGTOGPcdgMAypFi+qbxwfn9/P/vp2slPjiYnUsa+mhTiDnvgoPf2th3BbTSSWrEAZpiE6DFbOL6LVZGb3KKriTISUuCjWLCmhoaObfbWDXarFWSnMnJZ60mI4FdmcLiLT8lG5PfQ4LZhdAazuAAaNhN0r0+sMkBihZN++fRQWFh5RgFo4tpaWFh566CE++ugjuru7Wb9+PWeddRY9PT38/Oc/54YbbmDOnDlHnHfHHXfwjW98I7ivpaWFhIQErr32Wq699tqT/TJOeZNqlvTFF1/MhRdeSH5+PgUFBfzyl79Er9ezY8eOEc+RJInk5OTgl6iic3yqmrtwebzMzkvH5nTR3m2hICMJz0AflqZyItOLCY/5/NOsJElkJsXyP2eWUpKbRlWLkdc+Kaeho/uk3etTKCTy0hO5dNlsZuSkUtnSxfqPy6hpNQ5ZXNnnsuM0dyIHRB3IY9lf345KqycsXIchOQejPQAyqJUSCunz1qLX62Xfvn2hDnfKOXToEHPmzOGVV14hJycHq9UaHKEfHx/Pli1bhhT6/qLHH3+cysrK4OOcnBz++9//npS4T0dj+mg9mrp+J1rH73AZObvdzpIlS0Y8zmazkZWVRSAQYO7cuTz00EMjtioB3G43brd7yPmnK4/XR0VjBwXpSUTqwtlb04JKpSAzMYqe/e8TposmOnvWsOcOqYpTPVgVp7rFyMLiHOKiIk5K/GEqJbPzM8hPT2RvbSs7DjZQ3dLFvMIskgzhOPvaIRCAEYqVC4NsThd7q1vwe9y47f1EZRXR6xz8IGF1gyyDzROgzxUgQaeksrKSOXPmiNbicbjzzjuJjo5mx44dSJJEYuLQeb5r1qzhlVdeGfbcpKSkIfMOxUCziTWmpDhcXT+/309zczNbt25l5syZw3YDjMaBAwdYsmQJLpcLvV7Pf//73xGndxQWFvLcc88xa9YsrFYrjzzyCEuXLuXgwYOkp6cPe87DDz/MAw88MKbYTjUVjR34AzIl09LwBwLUt3eTm5qAvaUcv9tO8pzVx5x+odeGc/bsAjp7reyqbOLt7QfIS09kTkEG4eqwo547XiK0GpbNyqM4c7DY+Ae7DpHhbcTr9aLSGUSX+jHsr2/HZO7H73Xh97pRqMLQqSQSI4b+7LWqoRP6FywYW5m/09HHH3/MvffeS0JCAr29vUfsz8zMpL29fdhz16xZw89//nPef//94MDG3/72t7z88ssjPp8kSaKqzRiNKSl+ua7fF5WXl7Nq1SquueaaMQVUWFhIWVkZVquVf/3rX1x33XVs3rx52MS4ZMmSIa3IpUuXUlxczNNPPz1iHcF169Zxxx13BB+XlZWdlvOvnG4Plc1dFGclowtX09TZi9PtIUvvY6Cxltj8BYRFRB37Qp9JiYvioqWzqG7tory2jeauXmbnZ1CQkYRCcXKSUny0nlULp1NbvoPG/Z30DrgJeNuITRC1O4+mrs2ENlxNWHgEmoCE3+VAqZBI0StRjvCzO3ToELNnzyYs7OR88JnqAoEAOp1uxP3d3d1oNJph9z3xxBMkJiby0UcfcfDgQSRJorW1lb6+vhGvJz4Ijt249yuVlpZy00038ZOf/GRM56vVavLy8pg3bx4PP/wwpaWlo14sMywsjDlz5lBXVzfiMRqNJji61WAwnLaV5PfXt6OUJGZkpwJQ02okMVKDr60cbVwa+pT8476mQiFRnJXCJctmk5kcy67KJt7avv+krp3o6e8hvL+Rkpkl6DUKvOpIFJN8JF5fXx/XXHMNBoOB6OhovvnNb466W1+WZVavXo0kSaxfv37Ivl27dnHuuecSHR1NTEwMq1atory8/IhrXLpsDjdcuJTuii3Ub3+b9n0fUhgXNmJChMHbEF8c4CYc3dy5c3nrrbeG3efz+Xj55ZdZvHjxsPsjIiJ46KGH2L59O/X19ciyzOOPP05jY+OIX6LM29hNyM2WpKQkDh06NC7XCgQCQ+4BHo3f7+fAgQOiqv8xDDhc1LaZmJmbikatwmpz0tVrId3fCpJEXMHiE/qkqdWEsXTmNFYvmYlSqeT9nYf4uKwWu3N0P8ex8nvd9FRtQaUz4Hf1o9VHoTfETopPzcuXLx+xh+Waa67h4MGDbNiwgTfffJOPP/6Y73znO6O67uOPPz7s67PZbFxwwQVkZmby6aefsmXLFiIjI1m1ahVer3fIsYaIcJJjo/A6+nH1m/HYrISrjv09O3DgAIFAYFRxnu7WrVvHu+++y/e+971gLVmj0cgHH3zA+eefT2VlJXfdddew51522WV88sknwccfffQR55133kmJ+3Q07kmxt7eXZ599dsR7ekezbt06Pv74Y5qamjhw4ADr1q1j06ZNwa7YtWvXsm7duuDxh/vZGxoa2Lt3L9deey3Nzc1861vfGrfXcyoqr2tDE6aiKGtwVGlNmxG924Q+MEBc4RKU6vGZ6xkfpWf1ohmcUZKH0dzP+i3l7K9vw+8f/zdSWZbprdnBgM1Oc4+dQzWNdFpdWHo6J/Ubd2VlJe+++y5//vOfWbRoEWeeeSa///3vefnll+no6DjquWVlZfz2t7/lueeeO2JfVVUVfX19/PznP6ewsJAZM2Zw3333YTQagxPHT9TAwACNjY3jcq1T3erVq3nhhRd45ZVXWLFiBQDXXnst559/Pnv37uUvf/kLZ5111rDnvvbaa7S0tAQfr1ixgg0bNpyUuE9HY7qnePiH+mUWi4Wqqio8Hg9//etfj/u6JpOJtWvX0tnZSVRUFLNmzeK9994LfipqaWlB8YW19sxmM9/+9rfp6uoiJiaGefPmsW3bNlF39SjMAw4aO3pYOD0blVKJz++nsamRTLkLQ/p8tLGp4/p8kiQxLS2BjKQY9te1U17XRl1bNwuKx6cqTiAg09ndQ3P5J9gad+ORNGhd3UTEppGg8eFWqZkEDcURbd++nejoaObPnx/ctnLlShQKBZ9++ilf+cpXhj3P4XBw9dVX88c//nHYCeCFhYXExcXx7LPP8tOf/hS/38+zzz5LcXEx2dnZ4xb//v37yc3NnRSt8cnuG9/4BpdddhkbNmygtraWQCDAtGnTWLVqFZGRI1dnSktLY9++fcHGweE1FIWJMaakGAgEjvihSJJETk4OK1eu5MYbb6SoqOi4r/vss88edf+mTZuGPH7sscd47LHHjvt5Tmdlta1EaDXkpQ8OCW/q6CbMdJDEvHRicsc2Yng01CoV84uyyE9PZGdlEx/trSY1PpoFRdlE6Uc/tF+WZdw2K+1tTXS1NWE2dYCjB53TSER0IkkaFdEFi0mafR6v7+/Dau1HmsRTMrq6uo4Ynq9SqYiNjaWrq2vE826//XaWLl3KJZdcMuz+yMhINm3axKWXXhocdJafn897772HahyLHHR3d9PV1SVuWRyFw+EgIyODu+66ix//+MejrnF62JVXXskjjzzCP//5z+Do07vuuouHH354xHMkSRr2/rFwbGP66/hychKmhm7LAK2mPs6clRdc3b6hfCvRapn0WWdP6OoXh0XptV+oitPMG9uOXhVHDvhxD/TitBjpam+hp6uNfquVQCCAKiKaxORUIlwKomJnoo6MxdPfQ8r8NSjV2pAmw4ceeoiHHnoo+NjpdLJjxw5uueWW4Lax3nd//fXX+fDDD486id7pdPLNb36TM844g3/84x/4/X4eeeQR1qxZw65du8Z1juH+/ftFUjwKnU6HSqUiImJs83cffvhh8vLy+OijjzCZTEiSRERExFHXZxTGTtTFOk3Issy+mlai9Tqyk+MB6Gqpx22sJXvBCtT6mJMWy+GqOGnx0Rxs6uBAQwcNHT3MLcgkKz4Cz0APbmsPdksXvcZOrAN2rE4vXlUE4VGJpM6eR2b2NGKiouir2Y6z10NkehGWhn3EF5+BUh36SeXf/e53+drXvhZ8fM0113D55Zdz2WWXBbelpqaSnJyMyWQacq7P56Ovr2/Eupgffvgh9fX1wVbDYZdffjnLli1j06ZNvPTSSzQ1NbF9+/bgLYeXXnqJmJgYXnvtNa688soh57a0tGC3Dy7q7Ha76evrG3YB2+G0tLTQ39+PwWAY1fHj7Y9//CO/+c1v6OrqorS0lN///vcsXLhw2GNfeOEFbrjhhiHbNBoNLtfE1tC9/PLL+de//sX3vve94+76VCqVfOc73wkOvlIoFPzsZz/j6quvnohQT3ujSop/+ctfxnRxsXzJ5NHZa6Wrz8o5cwtRKCT8HhcNez9E0ieSO2NeSGJSKKAoSUeyQkdlVRUHPthCg8JHpC4cl6yiz6fBq4oiMrGI7MxMspLjMUR8nvBsXfXYjU3E5M2jv7kCXUImuoSs4H5rv5WOtuaQvFnHxsYOSSparZbExMQj1rlbsmQJFouFPXv2MG/e4M/hww8/JBAIsGjRomGvfddddx0xmKykpITHHnuMiy++GBjsslMoFEPegA8//uLAo507d/Lggw/y1ltvBSulOJ1OfvrTn1JSUsKaNWuOeQ9SlmUOHjx41MpTE+WVV17hjjvu4KmnnmLRokU8/vjjrFq1iurq6iO6pQ8LRb3kK6+8ku9///ucc845fPvb3yY7O3vY1vrcuXOPea3GxkYSEhImIkyBUSbF66+//rgvLEmSSIqTxOFWYkJ0JOkJMciyjKlqG71WG+kLLjppC/cGfF7c/T24+7vx9PfgHugh4PPi8weIClNj0Sey2+TEagknIy2VM+ZNIz89kQjtkZOavXYr5rpdRCTn4raYQJKIzVsQfIOrqq2lu7kGfAFkefKOPi0uLuaCCy7g29/+Nk899RRer5dbbrmFK6+8ktTUwUFP7e3tnHvuufzlL39h4cKFwTq/X5aZmUlOTg4A5513Hj/+8Y+5+eab+cEPfkAgEOBXv/oVKpWKc845B4D//Oc/fP3rX0eW5SNKh8myTEVFBRUVFXz7298+5pt1VVUVc+fOHXEC+kR59NFH+fa3vx1s/T311FO89dZbPPfccyNOcThcL/lkWr58efD/X5xecdjhwTN+/7Hr9GZlZR3zGGHsRpUUxbDrqa3F2Edvv43zF05HkiQGOmrpbKrFHpVPQU7mhDynLMv4XbZgEnT3d+O1W5FlGWWYGjk8mr6wRDo9CrpdSlAqSc6M4tL5Mbg9Xqqau6hq7iJcHXZEVZyA30dP5Scow/VoohLpq95BwvRlKNXhyLLM3gMHadv7PlqtltiYZJTKyX2X4O9//zu33HIL5557LgqFgssvv5zf/e53wf1er5fq6mocDseor1lUVMQbb7zBAw88wJIlS1AoFMyZM4d3332XlJQUdu7cyde//nX8fv+ItTQPtyifeeYZfvKTnxy1xej1eqmqqqK0tHTUMZ4oj8fDnj17hkzTUigUrFy5csgCvF92vPWSx8Pzzz8/5nMVCgUKhQKHw4FarT6iB2A4kiQFC44Lx2dU7xbik8nUFQjI7KttJSUu+rMJ2lbM9XswydEkZuYN2wobCzngxzPQ91kCHEyEfs/gfZownQFNVAJSTDZGl5JWs5s+kwOlQkFKfBRLCmLJSIhFo/7817EwM5my2lZ2VTZR22ZkQXE2ybGDZefM9XvwuWzEz1hOb+UnRCRmoUvIxO8PsG1PGX2HNpGekkxKThH9A5Oj4PvRBqfFxsby0ksvjbg/Ozv7mEWgh9t/3nnnjTjJ+xe/+MWwLcSRvP3223z/+98/6jEVFRWUlJQMmTY1Vjabjf7+/uBjjUZzRCu0p6cHv99/xMo4SUlJVFVVDXvdsdRLHg/XXXfdmM+99957kSQpOGr48GNhYkzuj9DCCWvo6Kbf7uTMWXnIAT89ldtwyUpMYSmsyBj7Mlt+j2tIAvQM9CEH/CiUStSRceiTp6GOjMch6Wjrs9Ns7MNq60elVJKWEM2M3DTSEqJHXANRqwljycxc8jMS2VXZxPs7D5GVHMeMOLB31hGbvxBbRzVICmLy5uP2+Nj06R7cDduYlplO8bL/4R8b9tLR0UFU1OhruJ4OWlpaePPNN0edEAOBAPv37z/m4Bu73U57ezsZGRknHOOX6xHfd9993H///Sd83bHUSw61L7/u8fg+CCMbc1Ls6uri2WefZe/evVg/GyL/RZIksXHjxhMOUBg7vz9AeV0bWUlxxEfpMTfsw+uwYNIWoJclUuOiR3UdWZbxOqx4+rtxWQfvB3qdAwCoNDo0hnh0ObPRRCUQpouid8BJvbGP5oPd2Jwu1CoV6YkxzMnPIDU+6rjuYcZH6blg0QwaOnrYd7CK7WU7Sc3OJxoJR08bCTOW4fTBh9s/RdG2i8KcTPKWrKGppZ1t27ZhNptPifq2fr9/VJV5PB7PMbvN3njjjeNefujwYJqR6nMeVltbe0L36w7HvnnzZmbPnh3cPty9yvj4eJRKJUajcch2o9E46hhGUy95LG688UYkSeJPf/oTSqWSG2+88ZjnSJJ0zLnawsQbU1Lcv38/y5cvx+l0UlhYyIEDB5g+fToWi4X29namTZs2Lp8WhRNT3WrE4fIwe34GLnMXA22V6NJLaK5zMGta+oirVwT83sGBMMH7gYMDYiRJIiwihvDYVKIM8WgMCajCIwgEZEyWfqra+mgxNuNwuQlXh5GRGEtmUg7JcYbgvMixkCSJ3JRYNJ39tLnjqLRF0P3hm2TnFeNQxbL540+J6CmnIC+b1Nnn8t/X3uSDDz4I3oOrq6vD4/GgVqvHHEOoPfjggyFf8uxvf/sbf/vb307Kc+n1+mOOGlar1cybN4+NGzcGJ8QHAgE2btw4ZD7o0Ryul3zhhReeaMhDfPjhhygUCgKBAEqlkg8//HBU9wGHI0b/n1xjSop33XUXer2esrIydDodiYmJPPHEE6xYsYJXX32V733ve/z9738f71iF4+Dx+ahoaGdaWgJ6jYKuiu1oohIxSnHIOMhLHxzSLcsyfrf98wRo7cFrNyPLMgpVGBpDAob06WgM8agNcSiUg0sF+QMBunr7aa6rp81kxuXxogvXkJkUQ2ZSLInRhnFdMsrSWEbA2c/ssy8ipWYvDU4NG1slBg6+wyx1B9MLcnBH5/PQr34zpOVgMBiYP3/+lE6IAPfccw933333UY9xOBx88sknqNXqo44Cffvtt/m///u/447hiiuuOOoairIso9VqWbt27VGXSTqaffv2jTgVZTh33HEH1113HfPnz2fhwoU8/vjj2O324GjUtWvXkpaWFqz+8vOf/5zFixeTl5eHxWLhN7/5zYTUS25qajrq4+Mx3Oj/wwn0yy3+LyZWkRTHZkxJcevWrdx5551kZmYG1/Q63LVzxRVXsGXLFn784x+zefPm8YtUOC6VTV14fH5mTUujr3YncsBHbMFitu+qITNSgbe7Adtno0L9bicAYbpINIYEIlPzUBsGu0K/+Efm8/tpM/bRbOyj3WTG4xucUzgtLYHMpFjio/QTMgDA0dtGf1sVsXnz8DmsYO8msWgJ1YfaSHfVYJf0vLy9hdpDb6L47PlVKhUFBQVER0efEt2nSqUS5TG6ncPCwoKVU8LDRy7qftZZZ/Gb3/zmuLpQJUmiuLj4qOsnBgIBNBoNYWFhY15n8XhL0H3961+nu7ube++9l66uLmbPns27774bHHxzKtRL/vLof4vFwnXXXUdUVBQ/+MEPKCwsBAanxfz+979nYGCAF198MRShnhLGXPv08C9ddHQ0SqVyyIKXJSUlom88hFweL4eaOijMSCLQ14i1uYKI5Fwa925EUVdNXGocFl8E6shYIhJz0EQloImMH3Z1DI/PR3u3hZauPtp6LPj9fqL1OoqykslMiiUmUjehI+F8Ljt91dvRxacTHpdB1563MPojqW3qZpa6Ayk9hRc3lFPT2IJSIREfpadkehE33HADjz/+OBaLZcJim6qSk5NZtmwZW7duHdW8OIVCQXFxMTExx656NNYW4om45ZZbRuwuPRXqJX959P/9999PQkIC77///pC/vZKSEi6//HLOP/98HnvssROaBnI6G1NSzMnJCX56USgU5OTk8MEHHwTLWm3btu2IElTCxJNlGZ+jn/379xPe20i8upHm3WWERUThd9nosvpQJBYxbckSNJGxI9Y6dXm8tHWbaenqo6N3cBBVnEHPrNw0MpNij6uA94m9ngA9VVuRlCpi8hfRU7mVJqOFFmUG02jiUEMrn1Z3gaQgOzkOs81JWtEcSpefS5hu5FUHBPjmN7/J1q1bkSRpVC3G888/f1TXPdpqD6e7d955h0cffTQ4OHG47/toPqSsX7+eX/7yl8N+GFUoFFx22WX87Gc/G5eYT0ejTopmszn4SfH888/n1Vdf5Ze//CUA3/ve9/jhD39IQ0MDsiyzadMmfvjDH05MxEJQwO/9fG6gtRvPQA8upwNTi5G0pBQCDjP61ALSFl2KT6Xjo837mDc9i/CoI0tEOd0eWoxmWoy9dPX1gwwJMZHMLcggMykWvXZ81lg8HtamA3j6e0gsXcmAsZlDB/fTo8kgznqIf23dR38gPJjY86blcv3116MIj2BXZRPvbK+gvduCRjF5q9mE0owZM3j44YdZt24dsiwPO7L1cLfjddddR2bmsYs86HS6k17RZqr497//zde+9jVmzJjBlVdeyZNPPsnVV1+NLMu89tpr5Ofnj3r1DFmWR5yHCYOF5o93dLHwuVEnxeTkZC688EKuueYafvjDH3LVVVfh9XoJCwvjtttuw2638+9//xulUsk999zDT3/604mM+7TkcztwW024+3vw9HfjsX1hQExkPPrUQlqMLuzpheTmqnG0V5E0+3zU+hiq69tRSBLTUj9PiDanixZjHy1GM93mAZAgOdbAwuJsMhJj0YWHbnCKy9xJf+tBorJn4ZaV7Pz4XeyyFtPBrby1vwZNTAqSYvA+20UXXcSqVauC99zWLJlFTauRV+1OHHYbMTHRIXsdk9mKFSt47rnn+PMzf2LL1m1D3kgP30M8//zzR5UQgVEXED8dPfzwwyxcuJAtW7ZgNpt58sknufHGG1mxYgVNTU0sXrw4WKLvWC699FKefPJJsrOz+e53vxvssnY4HDz55JM8/fTTwbUXheM36qT41a9+lddff53XX3+dyMhILrvsMq655hpWrFiBJEn87Gc/E032cSQH/HjsFjxfmCDvcw1OMQjTRqI2xBORnIfGEE9YRBSSpKDf7qS+upzZaRE42g8QlVWCxhBPICBT22YkOyUOt9dLTasxWPpNoVCQGhfFkpJc0hNiCFePbYDEePJ7nPRUbSM8OhlPZBafvvdPzKZudpdX0tfvRBuXiqRQkpGRwfXXX39EJRKFQqIoK5n8jEQ6jdIJTQc51c2YMYPHHn+Crq4urrrqKgYGBtBqtfz4xz8e1T3EL4qNjRWlxUZw6NAhHn74YZRKZXAwkdfrBQYrFn3/+9/n17/+9ahGjD7xxBM0Njbyox/9iHXr1gWX7ers7MTr9XLGGWfw+OOPT9hrOdWNOin+/e9/x+l0sn79el566SX+/ve/8+KLL5KUlMRVV13FNddcM6oK78KRPvjgA2wD/YTJHs6YU4y734RnoJeA34+kUKDWx6KLz0ITNTg3cKSlkcpq29CGSUQP1BJmiMeQOWOwq6Wlk7q2blxuL/Xt3SiVStLjo5mek3LUqjKhIMsyvVWDdStd8cVs+/B9ardvprKuBaVahzYuFaUqjDVr1rB69eqjjshUKZVE6k5+t+9UlJycjFarZWBgALVafdwJUavVBs8XjqTT6YLTgqKjo9FoNHR2dgb3JyUljbrGdFRUFJs3b+a1117jnXfeobm5GYALLriACy+8kIsvvliUgTsBx/VuqNVqueqqq7jqqqswm83885//5KWXXuLxxx/n8ccfJz8/n2uvvZarr76a3NzciYr5lOL3uPjvX5/CbO7DoI+gNOV6NIZ4orJmoTEkoD7KgJgv6uu309TVwxy9GfxepJRS9ta00mLsY399G4GATHpSLNnJccddVeZk6m89iMvShT2+hA0btlD+5nPY+i2oI2LQxqWSkZnF9ddfL4pDTDKi6/ToCgsLhywqPXv2bP76179y7bXX4vP5eOmll0bdTX3YJZdcwiWXXDLeoZ72xtxEiImJ4aabbuKmm26ivb2dl156iX/84x/ce++93HfffSxatIht27aNZ6ynJEWYBqUmgvDoMPQJyaQtvmxMn/L2Vregdpjot7VSr82lf18DmrAwEmL0xEdFsmJeIcVZk3t1dJfVhKVpP52BGP756ns07niLMG8/akMCEQnprLnoYlavXn3cc9mEiSdWgT+6yy67jN/97nc88sgjaDQa7r77bi655BKio6ORJAm73c5zzz0X6jAFxqkgeFpaGj/+8Y+54IILuPfee3nttdf49NNPx+PSpzxJktDGJOO2WFCq1MeVEA9Xldlf38aWPfuZpWqmLy6d1NxiFn9WVWZ/fRsxkTqmpU3uRUn9XjfdlVvY09jDSx9uRra0onJbCIuMY9rMBdz4zW8d92otLo8X84BDTA8aJVV4BOGRMcDxjdgNCwvDYDDgdrsnJrApzOVy8dprr+H1evnZz35GX18fKSkpXHTRRWzatIn//Oc/KJVK1qxZE1znUgitE06KLS0twVZiRUUFsiyzdOlSMfppgvj8fjp6rLQY+2gzmXF7vbSZzMzU9jI7N4e8M76CMmxwWLw/EKC2zcS01IRJdd/wy2RZpqviE/62/gM2VnQQr1Pht3Wh1kby1bXf5eJLLh1161CWZbp6+znY1EFdmwmv24l/FIW0T3eBgExMTgnadD+9VTuO69yEhARxD2sYJpOJpUuX0tjYGFxEWKvVsn79elauXMmyZctYtmxZqMMUvmRM75Q9PT3B+4nbt29HlmWKior4+c9/zjXXXHPUxUiF4xesKmPso73bgs/vJ+qzqjJhKiXhAy3MiNSRNXtFMCECtHVbcLo95GckhjD6YzuwfSOPPfYobQOQHBeNp7uOxLgYbvv578gvGt3ir/5AgOauXg42dmIesBMTGUF6YgyyVydGn45Cp8WONiYRhceLO+H41hVMTU2doKimtgcffJCmpiZuv/12VqxYQV1dHQ8++CA33XQT9fX1oQ5PGMGok6Ldbue///0vL730Ehs3bsTr9ZKSksJtt90mRp5OALfHR2t3Hy3GPjp7rPgDAWINEZR8oaqMLMu89dEW4n1dpBUtJzx6aPKraTGSEB1JrCEiRK/i6AKBAK/84yX+/vyT+JRaUpOS8fY0c0ZJLtf/8JdEpU475jU8Ph+1rSYqm7twuNykxkczv3A6yXEGtryuw2LxnIRXMrUFAjI1HWaQJAJ+H4bUafhlUI6i8ZeSkoJWe3IqHE0177//PmvXruWRRx4JbktKSuLqq6+muro6WLNUmFxGnRQTExNxuVzo9Xquvvrq4BzF8VhlWxjk8/upbhmcQ9jVZw1WlZkzQlWZxnYjvta9ZEzLJjqrZMi+fruTzl4LZ5TkncyXMGqdnZ08/vs/UL5jE0qlipSkZAwKO2sunM/c8644ZkK0u9xUNXdR02rEH5DJSYljenYKMZGT8wPAZNZpsdNptuF19OMasBGVkILFDXHHmM2i0WhEr9BRtLS08JOf/GTItjPPPBNZljEajSIpTlKjToorV67kmmuu4X/+53+OWoFfOD4en48eq41OkxlFn4OdlY2jqirjDwSo3Pkh0VolOXNXHDFto6bVhDpMRVby5BoqHwgE+OCDD3jxb/+gu70RjUIiISWNRYVJnFGURFTqNGLzRl6eyDxg52BjJ42dPaiUCgoykijKSiYiXJQXG6vaDjMOtw9dfDrqWBl8HrqdEKOBkVb/OlwkXIwEHpnb7T7ivfLw4xMtclBZWcnzzz9PQ0MDZrN52CWkxCLvYzPq3+jXXnttIuM4rXWbB1BIEqkJ0VxxzrxRVZWpqigjYG6lYPkawnRDF2P1+wPUt3czLS1hUs1HNBqNPP/88+wqO4C1rxu9wkd+8QwuOXMmafGRSJKChOJlwTUbDzs8eKaisYPOXgsR4RrmFWaSl544qQcQTRWpsXq0ahX79nnx+/0MtNUSM3saR+s9LSgoEMW/R6GpqYm9e/cGH1utVgBqa2uHHRU9mttQf/3rX7nhhhsICwujsLBw2EILovbp2Il3lBBTq1QUZibT328lOlI3qoTocgzQXP4x0ak5pOQeORCl2diL2+ulID1pIkI+boFAgA8//JD//ve/tBp7sdlsxCjcXHDuOaxcNB21Ohyf24EhvXDIfdHhBs8sm5VPZnLsqAbPuL0+LDanmJJxDAWpMZQ1mgj4PHhdLuSAn0QtjDSgNDMzk4SEyT3FZ7K45557uOeee47Y/v3vf3/I48OjU0ezSsb999/PnDlzeOedd4iPjx+3WIVBIilOAsezQr0syxza9h7eAJQsXTXsUPiaVhPJsVEnbYmnozGZTLzwwgvU1NbS2WMdHA0br+baiy4jKz2ZMJ0BORAgTKEkKns2cPTBM6MZ+j/gcHGgvp26NhNup4MMMSXjqAacHuqNFnwuO27bAPqEdBw+iBjm81lcXNxxV145XU3UeoYdHR386Ec/EglxgoikOMX0NVXQ2dZI4vSziY6KOmK/ecCOydzPWaUFIYjuc7IsB1uHDqeL9h4L/kCAS8+axarZ6YSHa1HrogiPTaG/5SCJpStxev1U1rZT2za2wTM2p4v99e3Ut3cTrg4jKdaAIqAVUzKOob7LQr/DgxwIgCShDNfR7QKdamhrUa1Wk5+fL+YkjtJ11103IdedNWsWHR0dE3JtQSTFKcUz0Edd+VZc+nRKZ80e9piaVhNajZqMpOMr6Dyeuru7efHFF6mtrcXl8dLRY8EQHcOPbryCJF8bcsCHxpBAzLR5GPdvhJgsdjf309jZQJhSSWFGMoVZSaMePGN3ujnQ0E5dezdqlZK5BZkUZiax+109FotlYl/sKaDP5sKgVSMpVSiVYfgcA7h8g3VtvnhHOjc3l7Cw0K+icrp79NFHueKKK1i9ejVLly4NdTinHJEUp4iA30dnxccYbQGy5i0adlSq1+enoaObwszkkLSODi8w/Z///AePx4Pd6aaz18q8RUu486ZrGKh4H4/dgiFzJvEzz6Zuxzt0mAZoG5CJ0PUf9+AZu8tNRX0Hte0mwpRKZudlUJiZRJhq8gwumgrOmp6Ozx/g+Yc30t3TjcEQxTfOmzdknqLBYBDddZPEr3/9a6Kioli2bBnTp08nMzPziNViJEkSgyPHSCTFKcLSsJeOrk5ccdMpmTb8PZ2mzl58vgAFIahg09PTw4svvkhNTQ0AVpsTh19i7Y3f4aqLz8W46zWcPS1E5c7DHlfCvg3v4zPWosxeyrKiolEPngFwuDwcaGints2ESqlgdl46hZnJIhmOkUqpQKVUEPB58HvcBLzuIybuZ2Zmim7TSWL//v1IkkRmZiY2m23I6huHiZ/V2ImkOAU4etvobamkVU6mqCAPjXr4H1tNq5G0hOgjJvlPJFmW+fjjj/n3v/+N2+0eXA+x305iVgE3X/l1zigtpPvgR5gb9uGNLeDQQAKurkrSHS1kzD2D3NIzRv0H7HR7qGjooKbViFKpYNa0NIqyksW0jAkWEREhRvBOIk1NTaEO4ZQm3k0mOb/HSV/1DoyecIhKH3H5px6Ljd5+GyvmFp202Hp7e/nLX/5CVVUVMJggHX4FC869iIvOPZsZOSkYa/dRv+1NrGEJmJX5ZMXHkBLehTYlh5RZS0aVEJ1uDxWNHdS0GFEqFMzMTaM4Kxl1mPj1PRnS0tKCP6f2Xhs2t4fC1MlVFEIQxot4V5nEZFmmt3o7Lq+PpkAq8/LSR+wirGk1EhGuITU++qTEtWXLFl599dXgckH+QIC49DwWz1vCOfOmExWhZcu2rTj3vEJAE0PioitYPi0Tr7EGq8VO3Mzzj7l4stPt5WBjB9WtRhQSzMhJZXp2ikiGJ5FKpQreS/T6/JQ3m3C6faRER2DQiSpCoTYwMIDVaiUwzLQjMXVmbMS7yyRm66jG2ddJmzILnV434mR8j9dHY1cvJbmpxzXncSz6+vr4y1/+QmVlZXCbPtJATukS4lLSmZGdSl1bN6aOZpI6NhIdE8vMS29HFxmNx2amp+UAhozpaAwjD9pwebwcauykqqULgOnZKUzPShmx21gYH8lRGiKlSHpcn2/TGGLpsjhJi9PT3DNA34CLgCxT12Vhbu7kKA5xOnryySd59NFHaWhoGPGY0RQCEI4k3mUmKY/NjLmhDDk6k5ZOJUtL0lEqhx+IUt/RTSAQIC9t4gbYyLLMtm3b+Oc//4nL9fm7Zumc+URmFeP0BlAqFJTVtRKn8VPsKkcTG0nued9GGxmNHPDTW72DMG0kUV8qXn6Y2+PjYFMHVc2DybAoK5np2SmjqvIjnBg54GN5URyyV8+ru40A+GXocChoazASqQ2jpqMPlVKBWqWk0WglLzlatBZD4KmnnuLmm29m1apV3Hjjjdx9993cfvvthIeH88ILL5CUlMT//u//hjrMKUskxUlIDvjprdpKmFZPhSuaKH2A3JThy2rJskxNq4nMoxQPP1Fms5m//vWvHDx4MLgtOjqacy64iEqTm16jldT4KOKi9BTEa/DWbMSJm7RFl6KNHbwH2t96CK/dTNKcVUd0m7o9PiqbO6ls7iQgQ1FmEtOzU9FqRDI8WQLWdlJjwvF5FExPH6xp6lPpsDi9BAKwq85I74CLuEgtSknCaLWL1mKI/P73v2fVqlW888479Pb2cvfdd7NmzRpWrFjBnXfeyfz58+nt7Q11mFOWSIqTkKVxH16nDSl7KZ0H2zl7TuGI3aIm8wBWm4OFRdnjHocsy2zfvp1//vOfOJ3O4PZ58xcQnT2TDyvb0Ws1LJ9TSMm0NHSyg66y93D3dxNXuDjYIvTY+rA2H8CQMQNNZFzwOh7vZ8mwqQu/LFOYkcSMHJEMTzY54CPQXYtKpcbl8bEoPxE54KNf0qCUFGjUCqo7+tCHh2G2Df4eKBUSHX02ZmbGoxZTYU6q+vp6br75ZoBgMQWPZ3Dd0KioKL71rW/x//7f/+OHP/xhyGKcykRSnGScfR30t1UTM20uW1qtxEfpyUwcuTpNTasRg05LcpxhxGPGwmKx8Le//Y0DBw4Et2nCdcxddi4mj5q62nZK89K55MzZ6HUa3P09mA5sxG3tRp+SR8L0s5Ak6bNu0+2ERUQRlTUTGKxtWtnURWVTJ/5AgILMJGbmpKLVTExLVzi6gLWdgL2XpStWYbM5cPa1UeWyYw9PJD5Sg0KSGHC6mZYUzbTk6OB5KpWCsBG69IWJExUVFVx6ymAwoNPpaG1tDe6PjIykq6srVOFNeSIpTiIBv4/e6u1oY1MwqxLpsdZw3vzpI05bcLq9NBv7mJOfMW6TdWVZ5tNPP+WVV17B4XAMTrNweUjOzid/zhK6XT6UCrj6vIWU5qUjSdJnCfFDfE47msg4EoqXoVQPzpW0tlTgtVtJnnMB3oBMdWM7B5s68PsD5GckMjMnbcK6fYXRCfQ2gt+L7DCjcDuRkLF5ZfxhMr0Dh1uGCiwON/EGLSqRCENq5syZlJeXBx8vXryYJ598kgsvvJBAIMDTTz9NQUFoax9PZSIpThKyLOPq6wBZJiZvEdv31JESF0VK/JFFvw+rb+9GAqaljc8yPlarlb/97W/s378fWZYZcLhwByTmnnkuJSWzsDnd6HQyZ87KIydlcPTo4YSIpEChCiMqu5TwmGRgsFZrf8tBItKmU9Pt5GBjA16/n/z0RGbmpp6UhYEDAbGu3LEoE/JRRGcA4O7pweTpJC8phfCkNPjCqopqlXLCRzcLx3bttdfy1FNP4Xa70Wg0PPDAA6xcuTI4BSMsLIx///vfIY5y6hJJcZLwOfpRBOzEFi6mpc+B1ebgjJnDj9KEwwNsjGQlx53w6ExZltm5cycvv/wyAzYbVpsTi81BZl4R11z+VabnZHCgoR2lUsHKOYUkxQ521R5OiKrwCPxuJ+ExycEuUjngx1S5lW4nbG/04A20kZeWQEluGhHaiU2GLo+X5q4+Gjt7MJvNxMRET+jzTXWKqLTg/33ucAa6PcwvmIFWG/qlx4Qj3XDDDdxwww3Bx2eccQYHDx7kjTfeQKlUcv7554uW4gkQSXES8HvduAd60CeloolJpfyTMjKTYomP1o94TmevFZvTxZmz8k7oufv7+/n73//Ort17sNgcWO1ONOE6rvnG9VyyeiWBgMyHe6tQKiRWL5oZXKPxcEIM00WBQgluJ3FFZyBJCnx+Pwd3bqK9phpz3Gxy0+IpmZY6oeXnvD4/raY+mjp7ae+xgAySz0W05Bh2YrNwJNljR/LaUavVhIefvFKBwonLzc3l1ltvDXUYpwSRFENMDvhx9rYjKVWERyVS22rC7vSwYt7Ry7VVtxiJidSRcJTEedTnlWX27NnDs8+/SLuxG5vDhUKhYOmSxfzgpm+TGB9Le7eFzWU1REVoWTGvMDgQJpgQI6IJj0nB2rSfxJLloNJwqKmTyqoq1B27ic2dzbKFZxKpm5g3WL8/QFuPhabOHtq6Lfj9fhKiI1lQlE2KHrb9dQC7WiW6/EZBlgP4WveiMfcQocsVBaWngB07dvDRRx9hMpn4/ve/T35+Pg6Hg6qqKgoKCtDrx/becLoTSTHE/F43kiQRHp1MAIkDDe3kpsUTrdeNeI7d5aat28zC4uwxvXn19/fz5J+e5eNt23G4PISplGSlJfO9b3+ThQsXAFDbamLHoQbS4qNZVpofLC/3xYQYlVlC98FN6FMLaR5QcqC8DJfbTY67ibTiYrIXrTxmKbfjFQjIdPVZaezsodVoxuPzERMZQem0NLJT4tBrw/E6rBjLNuCVlVjkcJIQb/DHIg+YCAx0oXTbiAp3hDoc4Sg8Hg9XXnklr732GrIsI0kSF198Mfn5+SgUCs4//3xuv/127r777lCHOiWJpBhiKo0OXWI2XquVvn47bq+P0rz0o55T12ZCqVCQk3p869v5AwHeeG8jL7z4V6z9/YSrw0iJi2LZ0iVce+01REZGIssyZXVtHKhvozAjmQXF2cGW1hcTYnzRGXSVbaDPrWBnGzjdTeSkxpOj6sXfG07yrLPGLSHKsky3ZYDGzl6au3pxebxE6sIpykomOyVuyAcIn8uO6cCH9Ng8dHm0IPuRZTHY5mhkOYC/uxZkP0hKItxdyH4fklK8PUxG99xzD2+++SZPPvkk55xzDoWFhcF94eHhXHHFFbz22msiKY6R+K0PMVkO4DJ34PFI9LokCjISj3rvLRCQqW3rJiclftRLJnm8PsqqGnn2hRepqawgIlxDemIM8THRXHPNNcyfPx8YTJrbKhpo7OhmbkEWM3JSgi3RLybE2OlncWjHB7Q3N9EXU0pmZjSzctMID9gxlu0kKrsUdUT0CX5fZMwDDho7e2jq7MXucqML1wwm3pR44gwRR7SS/V4XpgMf0mo0U6vMJSYqEq1SDsmCy1PJ4VaiFB5FwOckzDtAwNqGMjY71KEJw/jHP/7B9773Pb7zne8MW7mmuLiYV199NQSRnRpEUgwxv8eF3+PG3NmJMtxAcWr0UY9v6zbjcLkpyDh2eS27082h5k42fPQJWza+i1oKkJUchyZMxZw5c7j66qsxGAZHknq8Pjbtq8FkGWBZaX5wygWA29qNqeIjVLpo+qNnsvP9DUjGQ0QVLGXJnHlE63UE/D669m5HrY/FkFE85u9Hv91JY2cvjZ099NudaMLCyEqOJTsljqQYw4jdxQG/l679H1LX3EZbeCHzZxZS8XE0FotlzLGcLvw9deCxI8t+FB4XCslPoLsORXTGuHd/CyfOZDJRUjLyyHSlUonDIbrAx2pSJcUnn3ySJ598MriI5owZM7j33ntZvXr1iOe8+uqr3HPPPTQ1NZGfn8+vf/1rLrzwwpMU8YlTaXRo4rOwdQ2Qqgbz/nfxpRYSlTUTherISe01rUbio/TERUWMeM1eq51DTR1UN7Wx8+MP6G1vIiVai0qpJCIigquuuor58+cHE4zd6Wbjniocbg/nzS8OTrmAwYRoPPAhFq+KOk8MzraDZLiayV6wlMxZy4LHWZv243PZSZm7Gkk6vpaZ3eWmqbOXps5eevttqJRKMpJimV+URUpc1DFbenLAT0f5R1TV1NFrmMFZC2aTkSjW+xstKSIepXqwC9prtqDR65HCR76nLYRWRkZGcA3T4WzdupW8vBMblX46m1RJMT09nV/96lfk5+cjyzIvvvgil1xyCfv27WPGjBlHHL9t2zauuuoqHn74YS666CJeeuklLr30Uvbu3cvMmTND8ArGptsyAGE6ErJyiMqcSX/rQezGBqKyStCn5AU/rQ84XHT0WFg6c9oR15BlmY4eK4eaOujstdLT3kzFzs0oA37iowZHoZWWlnLttdcGW4cwmECHm3IB4LSYqN7+Jh0DAXr0RWQm6UgPayFCm0PyzKXB49zWbgbaq4jOmU1YxMjFBr7oi3MJu80DKBQSaQnRzMgpID0xGpVydC0UWQ7QXLaJqooKHAmzWLl08VE/MAhHUiV9PtLZI7WjSUlBIbqcJ62rr76aRx99lMsvvzw4H/HwB9xnnnmGf/7zn/zqV78KZYhT2qRKihdffPGQx7/85S958skn2bFjx7BJ8YknnuCCCy7gxz/+MQAPPvggGzZs4A9/+ANPPfXUSYn5RNmdbswDDmIidahUKqKySohInoa1qRxz/R4GOmqIzp2DNjaN2lYTapWK7JTPi2r7AwEaO3s41NiJxeYgIkxB16Fd1FVVECZJoJDQ6XRceeWVLFy4cEj340hTLgIBmfq6ahp2vocdNTFFZ3FRQTZy534cTh/xxWcGE3XA76O3ZjvqyDgi048+jcTj89FqNNPU1UtHjwWA5NgolpTkkpkYe9yLB8uyTN2eTdRU7IHUuaw6a9lJqZJzKlMqlSIhTnJ33303O3bs4KyzzqK4uBhJkrj99tvp6+ujra2NCy+8kNtvvz3UYU5ZkyopfpHf7+fVV1/FbrezZMmSYY/Zvn07d9xxx5Btq1atYv369SchwvGhC1eTmRSL1/35KhQqjY64wiVEphVibthHd8Vm1IZEGowqpmXloFIq8Xh91LQaqWzuwun2kJ4Qg1628c7r/6G/vz+Y/GbNmsU111xDdHT0kOcdbspFICDT3NXLgYMV0LYLQ2wS85ZdQnxsFDZjA71dDcQVLRmcsP8Za1MZPpeDlHlnD9ttGpxL2NFDW7cZfyBAYoyBBUXZZCXHndCKGBU7N9N0cBf67HmccdbyUQ88EkamHGULXQgdtVrNu+++y9///nf+9a9/4ff7cbvdzJo1i1/84hd84xvfEPNMT8Ckexc5cOAAS5YsweVyodfr+e9//8v06dOHPbarq4ukpKEDTpKSko5aId7tduN2u4OPbTbb+AQ+RpIkEakLx+JxHbFPrY8lsWQFrr4O6so+Rt3RQlScn50HXNR19ROQZXJT48lOjOL9t99k+/btwXO1Wi1f//rXWbx48ZA/kOGmXEgSNHX2Ul7fhq2vkzRXHemFReQuugCFMgyvw4q5dhf65Fz0SbnBa7ksJgbaB1uyX0yUgYBMZ6+Vpq4eWox9eH3+wbmEeRlkp8SecGUbWZbZtW0TxqpPSShcwMKlK8QE/XEikuLUIEkS1157Lddee22oQznlTLqkWFhYSFlZGVarlX/9619cd911bN68ecTEeLwefvhhHnjggXG51skgSRLauDSqFflY1TIc3I9SKqcgr5TiOUtobGjkid/+acgoy5kzZ/KNb3zjiNahPxBge0UDDZ9NuZienUyrqY/yujYsNgepETKFOhMxWYUkzFyOQhmGHPDTU7kVpUZHTN784LUCfu9gt6khnsi0ws/nEnb00mwcnEto0GkpzkohJyV+yL3KE+Hz+9myZTMDdbvInLGA0sXnjst1hUGi61Q43U26pKhWq4Mjp+bNm8euXbt44oknePrpp484Njk5GaPROGSb0WgkOTl5xOuvW7duSJdrWVkZZ5999jhFP75kWaa9x8Luqma2H2ykKCuDvBlnkug30dd8kGd+tY79jd2otJGDVXHCw/na177G0qVLj+g++eKUizNn5aFUKnlr+wHMAw5S4qKYl5lMoGUn6oSUYEIEMNfvwevoJ3nOquA2AEtjOX63E1XmIvbWtA6ZS5ibmkB2StywcwlPhMPlYfMnW/C37aGwZB75C1aM27WFQSIpTg1btmzhueeeo6GhAbPZfESBCkmShiwvJYzepEuKXxYIBIZ0d37RkiVL2LhxI7fddltw24YNG0a8Bwmg0WjQaD4fjDEZ6wP6/QEauz4fPNPvcJGfkcj1FywlLEzJoUM2Xni3HGNLM17XAEq7hdIFS/nmd28hJubIBYkPT7mwu9zMyE7lYGMn5gE7ybFRXLBoBgaFi+4DH6GOjB2SEB3dLQx01BKbvxC1/vPr9nQ003BgJyZ1Bn1ljcG5hDkp8STGRE7I/QzzgJ1NWz8l3LSPGTNnkz1/xaieR1SzOT4iKU5+jz76KD/+8Y8JDw+nsLCQ2Fgx/Wg8TaqkuG7dOlavXk1mZiYDAwO89NJLbNq0iffeew+AtWvXkpaWxsMPPwzArbfeytlnn81vf/tb1qxZw8svv8zu3bv505/+FMqXMWZ+f4CKhvbPB88kxjCvMJOPy2opzEzG7/fyyiv/4JNPPgEgPCaZSCmJ8xYWUpqbhK+9HK9m6P29vn47H+yuxOZ0YdBpOdDQRmKMgfMXTic5NgqX1TRsQvQ5B+it2YEuIRN9Sh52p5umrl4a27vw1X+MIkxLbG4Rs1PjRzWX8ES0d1vYsmsvsZYKCqfPIGPuiqPOhfR7XDh723B0t+Aw9x3RjSyMTAzQmPx+85vfcMYZZ/DGG28QFTW6KVDC6E2qpGgymVi7di2dnZ1ERUUxa9Ys3nvvPc477zwAWlpahnySXbp0KS+99BI/+9nP+OlPf0p+fj7r16+fUnMUYXCqQrd5gA6Lk7K6NnJT45menUK0XkddmwmfP0DA3scDDzxJX19f8LyioiLWrl1LbGwsju5mLI1ldO5+C31qPlFZJXSanbyxbT9Wm5OEaD16XThnzMojOXawMsznCTGOhJlnBxOiHPDTU7UNv6SiV5vNnp2HMJn7USoUpAc6iE2MIn/ZVwjXR0/496a6pYvdBypJc1STXzCNlNnnDltlxed24OxpxdHTgtvaPfg65MBn5eZEa3G0REtx8nM4HFxzzTUiIU6QSZUUn3322aPu37Rp0xHbrrjiCq644ooJimji2V1ualtNuBwu0lOTuPzsOcH5ggAVdS0c2vUxW9rqg9s0Gg1f/epXWbZsWfCTfURiNrr4DAbaq7E2H6CyfA87jArsmiTmFucwryCL5LjPy6SNlBA9Ph91ez/B2FBFZ0Qx/oF2UuKiOKMkjwS1G/OhOmLzlk54QgwEZPbUNFNV30Sev5HMzDSSZ52LQvX5fU2fcwBHTyuOnlbc/T1ICgXh0UnE5i9AG5dBxFvleC2W466wczoTLcXJ75xzzuHAgQOhDuOUNamS4unocHFuv1tLbIxhSELcsWsvzz71ewxqCf1nq9UXFhaydu1a4uOHWSFDUmDTpvJOdweWlg5ytU6KsyPJzjOg+2JCtJjorjicEJcTQKK1a7DMWldrHbru/WjSS5hXPIvMpMG5hAGfl849bxIenYQ+dWJX9fb6/GzZX0d7l4mZYR0kxsWQVHouijANXrsVR08Ljp5WPDYzkkKJNjaF+KKlaOPShi2NJ4yeSIqT3+9//3vOP/98HnnkEW688UZxT3GciaQ4CURFaLF4Px9M5Ha7+c9//sMr/3kdl8tDiiEOtVrN5Zdfztlnnz3sG1dXn5V9Na3sqW7G5fFx7tkXsjQ/EWtzOT2VW9G0VxMzbR5yIEB3xUeo9LF4k2az7WATrabBuYSxOiV5ii4S58wjfc7KIc9jbthLwOshrnTxhL5xOlwePtpbjXXAxjx9D3pVGFE5s7F11uLoacXr6EehCkMbm0ZU5kzCY1OGjIoVToxIipNfRkYGN910Ez/60Y/4yU9+Qnh4+BHzSyVJwmq1hijCqU0kxUmmpqaGF198EaPJxIDDRUykjsLCQq677rphW4fGvn7K6lrp6LbQbbURExnB6sUzyE1NACBx5nJc5k7MDfto2/5vBiw9OHTptDoNuLtqMURomZ6dQlZSLO6GbfjUMSSXLBvy5ujs68TWWUds/kJU4RM3Wtc8YOfDPdUE/D4WaFuRLW0EopLordqGMkyNNi6dmNy5hMcki9UbJshUvKf4xz/+kd/85jd0dXVRWlrK73//exYuXDji8VN9EYF7772XX/7yl6SlpTF//nxxb3GciaQ4Sfj9fiorK/ntb38LDBb/ViiV3HjDWlaff/4Rn+BN5n7K69ro7LUSEa5BkiQyEmNYMbdoyCoXsixjVxholZOwtO0i4LKBXkNOTjI5MxYSHxuNJElYmvbjtnaTOGslyrDPK84EfB76anYQHpOMPmXiKu+3GfvYtmsXkT4LmYFmnPY+ItMKiUzNRxefgSYqUSTCk0A1xUrlvfLKK9xxxx089dRTLFq0iMcff5xVq1ZRXV1NYmLiEcefCosIPPXUU6xZs4b169dPyQ8xk93U+gs4RfX19bF3714CgQApKSnIsowuOp4rb7yCC89dOuRYk3ngs2RoISZSx+z8DKqauzBEhLNiXlFwFXqrzUlTVw+NHb04zJ1EWyuJSS0kZ+4KNI5ObO2VuGs+xJY1C5VWT39LBVFZJYRHD30jMdfvIeD3ElewaNy71uSAH5e5i9rKcprqKknSKEnWS4CC1DO/TnT2bNGdJxzVo48+yre//W1uuOEGYDBhvPXWWzz33HPcddddRxx/Kiwi4PF4WLNmjUiIE0QkxRDyer2sX7+eXbt24Xa70Wg0hIWFsXzl+djUcSwq/by0XY/FNthN2mMhSq/jrNICVEoFH5fXBle5CARkKho6aOrqoa/fTphKSUYkxIQbicueReLM5SiUKiCRyNR8rE3l9FZvx9HTgiGtkMiMoaX0nH3t2LoaiCtYNG7dpgG/F2dfx+D0id522o09GO1+knNmkJugx9ndRFzBIvQp+ePyfKcKv99PIBA46jFerxefz4fdbsfn852kyD7ndrvx+Xx4vV68Xu+YrnE4bpvNRn9/f3D7l4tuwGBy2LNnD+vWrQtuUygUrFy5ckgd4C86FRYRuOiii/jkk0+46aabQh3KKUkkxRDy+Xzs2bMn+Dg6Opp77rmHGuMACvMAKXFR9Fht7K9ro63bjCFCy7LSfLKS4qhv7+aT/bUkROvJSIxlc1nt53MJE2IomZZOvMpNX+Vm1CkZJMw4+7OEOEil0RFbsBh3fw+uvna8Tjs9FZuJzp2DWh+D3+umt+ZTtLEpRCQfuX7j8Qj4PDh623D2tOLs60QO+FHqomhwRdKujmfe/Bmkqaz01e0mOme2SIjDePDBB6dUzd4T9eXSi/fddx/333//kG09PT34/f5hFwUYaRHesSwiMNncd999fP3rX+f73/8+3/zmN8nMzBy2kLsYlTo2IimGkFarZe3atbz//vvk5uYyY8YMomJiaSlvIjslno/2VdNmMmPQaVk2K5+s5DgkCXZXNbP1QB1atRqTbKPbYiM1PpozSvLISIpBrVLhshg/W3Iq4YiEeNhAWyVeRz+Zy9cCMpaGvXTtfYeI5FwCXjey30dswdhGmw5WlRmcQ+gydyHLMhpDPNHZsyAymU8OtWOVnZy9KJ8YrPRW7cGQXoQh48h1MwW45557uPvuu495nMfjCUkr8TCVSoVaPfZpMfv27WPRokVs3ryZ2bNnB7d/uZV4OissLAQG6zYPVxP6ML/ff7JCOqWIpBhi06dP56yzzsLpdCJJEvtqWmjo6MHl8RFr0HFGSR45KfEE5ABNXT28v7OSunYTybEGslJiyUlJICs5lnD159MSBhPiR0dNiO7+HiyNZURlTEcXlwaANiYFW2ctPYe2YOuqHyz7dhzz/garygzOITxcVUYTlUjMtHlo4zNQaXSYB+xs3FMNwAWLZqD1Wemu2IYuMZvo3Lnjeg/xvPPOw+l0otWOzwodoaRUKke1rFNY2NSennJ4oI9er8dgMBz12Pj4eJRK5XEtCjCWRQQmm3vvvVfca59AIimGmBzw47N2MtBvZ8Dcw8ebPiAmMoozC/PJSkuj2+ZmW0U9TZ091LaZkOXBZLJkZu6wq8yPJiH6vW56KregjowjKntWcLukUKJLzEHVWI4+tQD3QC+du94gKqeUiMScYf8Qvc6BwfuD3S24B3o/qyqTTGz+QrRx6SjVn49kbe+2sLmsBkNEOOfMLUTl7sd06GO0sakTMpBn5cqV43o9YXJRq9XMmzePjRs3cumllwKDCwhs3LiRW265ZdhzxrKIwGTz5W5kYXyJpBhiDqeLPpubHrOViHA1WYpucg1+esrfp36rE19ARqnRoXTDTK2O+TOLiI9XobD34PHpUGp0KFTqwVqmnyVEjSGR+BlnDZsQZVmmr+ZTAj4PSaUrj5jmYK7fDZJExplfRw74sTSW0Vu1HVt7NdG5c9FEJeJ1WHF0t+DsbcVjs6BQKgmPSSU+rXDEqjLVLV3sPNREWmI0y2blI7v6MVZ8hFofR1zxmWK6hTAmd9xxB9dddx3z589n4cKFPP7449jt9uBo1FN9EQFh/ImkGGLh4Vo8mlgi4nW4/BJVmll4wqKI1MmkZ4UTGQbVDU0YNB6K02NQO42Y65qGLImkUKoIBHw4TM1oDHHoUwtxmJpQaiJQaXQow3XBqi+2jhocPa0kzDjriBGljp5W7MYm4oqWoNIMTu1ImL4Mp8VI76FPaN70NySFRJguGpVWP1hVJquE8JiRq8ocrmFa2dRJcVYK8wqzCLhtGCs+QhWu/6zuqvg1FMbm61//Ot3d3dx77710dXUxe/Zs3n333eBgmlN1EQFh4oh3oxBze30EAjJ9/XZcsoKZ09I4qzSfhOhIOnqsbC6rISp5BivmFQbrospyAL/Hhd9lx+dx4Oxupa/uU8IiotHGpmM3NRLwuIYmzs8Kads669AlZOK1W7H5PKg0ESg1OlAo6KvdiS4ujYjEHGQ5gNvag7N38B6h3+siTGfA57YT8HuISMwiKns2yrCRB0B4fX4+2V9Lu8nCwuIcirKS8XucmA58hEKhIrHkHFGrVDhht9xyy4jdpafiIgLCxBJJMcS0mjD0Og0edziRSjX/c0Yp6jAVta0mdhxqIC0+mmWl+YSpPu9elCQFKo0OlUaHbO7C2ddGdPacIV2mcsCP3+PE53Lg9zjw2q30VH6CUhOBOiKGgfYq/F+ot+roacHvdqJPyce28Tm8zgGQZVS6SHQJWURllaJLyAAkBtqr6G89hKO7BUPmTCJTC47o/nS4PHy4t4p+u4tz5haSnhhDwOfBdOAjAgEfyaXnoVRP/QEwpxJLUzl+t4O4wqlzf00QxptIiiEmSRLJsQa6jN0kJkQTplJSVtvK/vo2CjOSWVCcjUIx/AAUl7mL7oOb0EQlEj996D1ESaFEFa5HFa5HlmV6+7ahMSSQPHc1YbrBUX0Bv29wMeHanQy0V6JU63CZO5CUysEWZFg4kioMt8WE22Kir2YHCnU4KrUOdWQcbosRY9n79NV8SnTuHCKS81BptJgHnHy49/MRprGGCAJ+H90Vm/G5bCTNPh+VNnLiv7nCqPlcNnqrt+P3uNGn5KMxDLMKiyCcBkRSnAQGnC68fj8xkVq2HqinoaObuQVZzMhJGXFE5hcTYsKMs486UMVubMBubCK++AzCdAYCPi9OcwfO7hbs3c30t1WiMSSQMGM5EYmZhEXEBJ834PPidzvwuR343fbgv363E5U2koDfi6Onlf62SlSaCHzaOJotPgw6PdPzc5G6q+nv19Lfegivw0rSrPMI04kCxpONteUg7v4eYLDFmDTr3BBHJAihIZLiJGDud6BWqTCaB2jq6mVZaT45KSN/UneZO+k+uHlUCdFrt2Ku24UuIRM54Ke7YhNOcxdywI9aH4skKTCkFZO+9PJhuzMVqjAUqijCIoZPZLIsE/B5sBsbqduzia72NiJjEijIy0aJD0dvK7b2atwDfUQkZtN96GMkhQLlZ92/g/9GfOFfLUpNRHBErTDxfC4blsZ9qDR6JFUY/S0VRGeXitaicFoSSTHE7C43VrsTl8eLxuPlvPnFQ1a5+DKXuRNTxWbCo0eREJ0DtO34D15bHwGfF0d3y2BVmZxSdHHpuG199BzaQvz0M8d8f0+SJCSlmuqBcCrVsygsnU4qJnwD3WjTilBpPficNlIWXoLGEI8/2OJ0DrZAXXbc1m78bseXRtQqUQaTpe4L/36eQA8PHhqJzzmAs6+DyLTCMb2204W15SBuq4nwmFSQFDh7W0VrUThtiaQYavJgYlGHKclJiT/hhOhz2XH0tOLsacXSuA+PzUxs0VIMaYVo49KDUy38Hifm2sEWpC4ha8zhDxlhOj2XoqxkAj4v/a2HMFVswt1vIrHkHCJT85EkBUTGDf9tODyi1u34rLvWHuy29Tr6cZo7hx1RezhhBqefaHQo1Vqcve3YOmtRanREJOUeM4GezmyddSjVWrx2MzBYF9dubCTg84rvm3DaEUkxxHThahJjIlEGwoeUavuyoyVEr6M/mAgPV5WRlGEo1Foyl68h6kv1RGVZpq92F0gSsXkLxtxNOWSE6bxC0hNigM+SVbiOMJ0BTVQCLrORrj1vE507D21syrDX+uKI2pEER9R+lji/mDw9A304e9vw2Mw4e9vwe1xoDHHoErORxDzIo0pdcDF+t2PINkWYRiRE4bQk3i1CTJIkkmIMWCyWEY/5PCEmkTDjLJAUeGzmzxJhCx67dbCqTGwa8elFhOmiMJZvICZ3Dob06Udcz9HdPDiBf/qyIWXYjkdfv/2IEaafX7+FvtpdRGXNJGbafDwDvVga9mE68CHa2BSic+eijog+7uf84ojaLwv4fVibD+D3HCQqqwRDxnQUYRpkn0fcmzyGMJ0hOCJZEE53IilOcs6+zwbVRCdiyJiOpWk/zp5WvM4BFKowtHFpRGWXflZVRoUc8GMsex9lWDix+f+/vXsPa/rK8wf+TgIkkJALkHARIQSUi6Ao1rsGtGprXbVbrXbbsVpR23HGcYtj1W1XHOu46tjW7TztFp9WrVs7s9r6a2tdnXa9X6l3kZty8waEW8IlBEhyfn8AqTEBgoAJ8nk9D8/TnJx8vyfnsXw453vO59jmEzU26FF1+xcIFaHwkoc8VpvulVXh5NVbzQcbD4uCl+DXDfj1lcUozz4DL3koZOHDweFwwBf7QTHk2eZnVflXUXLpEESBEZCExnXLXkVDVTEqbqXD1FAPaVg8xMHRlDaOEPJYKCi6MH3lfZRc+l9wOFw01VSh9NrP4Lnz4ekXDFnEcAik/ja//LUFV9FYp4V//BSb6S/GGKpupQMcLmQRwx+rTdlFJfgl69ccpg8nFWioLkd55kkIpAHwjbQ+corD4cDLLwSePv1Q8yAXuqIbqNMUQtw/Bt79oh4r1ZupyQBt/hXUluRDIFVAEZtEIx5CSJdQUHQxzGyCQVsKbeE1VGSfBdedD3G/aHgpQuHl1x98ibx5wYod9RX3UX0vG7LwBPDtLGip0xRAX3Ef8kHjwXPv3LSp2cxwKacIWUW/5jB9OKlAU50OZRnH4C6Uwi9mfJsjNQ6XB3FwNIT+Yai+kwFd4XXUFt+CNGwovOShDk11MsagLytCVd4lMLMJvgNHQhgQTtOkhJAuo6DoApjZDKOhFvUVdbh37pvmRSPaYgj9VQhMmAa+RNHhL3xjgx4VOWfh5dvP7haE5mnTixD6K+Hl17lp04dXmI6MCUNkiPXZc0ZDHTQ3joLn4dl8BqMDoz6euwCy8OEQBQ6EtuAKyrPOgH8vG9LwYRBIFG1/T0MdKm+lo77yAbzkIZCFJ7S7OIcQQjqDgqKTGRv0qHmQg/qaWghkPvAQ+8FoqIV8kBqKDvYhtmLMjPKs0+Bw3eDzyLRl8/vNx0VxuDzIwjs3bdrWCtNWpkYDNDeOAlwu5HET200Qbo+7lxjyQWoYtKWoyruM0qs/wUseAmlYPNwfSgXHmBk193OhK7wGrpsH5LFqePkGd+pehBDSEQqKTubG94JA6g+jwBcCoRcatBoI/VWQtzMF+Shd4Q00VpdDMeRZu9OidaX5qK98AHmsulNBq70VpkBzCriyjGPNZzPGT+nSiE0g9UfAsOdQpymAruAaii8ehHdQJMQhg2Bq0KMy9wIaayshCoyANGwobRcghPQICoouwEPkA3PdfejL70IgC+hUQDRUFaP67k1IlIPtTjsaG/SoyrsEoX9Yp0ZW7a0wBZqffZbdPIGm+hr4D3nWalT3uDgcDkT+Knj5haDmXjZ0RTdQnnUK4HAglCvhP2Qy+BJ5l+9DCCFtoaDoAoz1tTBUlsDLT96pgGhqrEd59lnwpf4QP7JBH2idNj0PDs8NsvAEh9vT3grT5uuaUZ51Bg3V5VDETYSHyMfhazuCy3MDXyIH190DpqYGcHnuzRlvjA1gjNGCGkJIj6Gg6GRGQy305XfB43vC0y/Y4YDIGENF9jkAgF/UGLuBoq4kD/WVxVDEJTo0bWo2M1zMKUR2UQmilYFIGBhqc2xVczacdNRX3IPfoAkQSNteFPM4TE0N0BZcQW1xHvgSOcKnLAVjZlTlX0ZZS0YfmSoBHt7dG4gJIQSgoOh0bgIRPP36wWQwtbnVwp7qu5kwaEugiEuyuwHeaKhDVf5liAJU8PTp1+H1mowmnLp2C/fL7K8wbaUruIra4jz4Ro3u1oUujDHUl99F5e1fwMwm+AwYAVFghCXYK+ImwlD1AFX5V1By5TCE/mGQKIfQylNCSLeioOgC3D3F4DRoHa5v0GmgK7wGcf8YCGS2uUQZY6jIPQ8uz92hadM6QwOOXc5pc4Vpq+q7mdDdzYQsPAEif5XD7e2IsUGPqlvp0Ffch5dfMGQRz9gEOw6HA0+ffhDIAlFbfBu6ohvQlxXBOzi6OaUbjxbeEEK6joJiL2NqakBF9hl4iP0gUQ62W6e2+DYMVc2jSK6bh906rR5eYfr8qEGQeQvt1qstyUNV/hVIQmIhDo7q2pdowRhDbfEtaAuugsPlQT5ofId7KDkcLryDBkKoUKL6zk1U381CbUkepMohEPqHdWq0TQghj6Kg2Iu0LpxhJiP8osbaDQDG+hpo8y9DFBgBT5+gdq93T1OFk9faXmHaSl9+F5W5F+AdNKDNQNxZTXU6VOSeR0N1OUSBEZCphnYYwB/GdfOAVDUUoqAB0BZcRUXOedTcz4FMNdTu6JkQQhxBQbEXqX2QA335Pchj1XAT2I7omqdNL4Dr7gGZali718oqKsbFrCIEK2QYNzjCZoVpK4O2FOVZZ+Dl1x+yiOFdXvnJzCbo7txE9d2bcBMI4T9kcpcW67gJRPCLHgfvflGoyruE0utH4enbDzLVULh7SbrUVkJI30NBsZdorKlEVf4ViIMj21zgUvsgFwZtKfwHT2xzc7sjK0wfvmfZzRMQSBTwjRrT5alJg06DylvpMOqrIe4/CJLQ2G47zYIv9oN//BToy+9AW3AVxRd/hChoACQhcY99PBYhpO+hoNgLmI1NKM86DQ+hFNKwoXbrGOtroC24Au+gAW1OH1pWmJa3v8IUaD64WJNxDO6eYvgNcnzvpP32N0JbcBU1D26BL/ZDwLDn4SGyv5inKzgcDoTyUHj5BqPmfg50dzKg1xQhaMRMyoBDCHEIBUUX17ov0NRkgDwuyW5wYoyhIuc8uO4CSFX2g2brCtMavQETh0Whn1za5j2NDXpobvwfuO58yOMSu7SyU9+6zcLYBJ+IBIiCBvb4YhgOlwdx/xgIA1QwaEspIBJCHEZB0cXVleShTlMIv+ixbaZSq32QA4NOA//Bk+wGsNYVphw05zBta4Up0HxGoebGUQDNewM7e8RUq9ZTOfTld+Hp2w8+Ec/YfQ7ak3juAgjloU/0noSQ3o2CogtrrNOiKu8iRIERECqUdus06auhLbgK734DIZDZToe2rjCVCD2RNCyyzRWmAGA2NaEs4wTMjQb4x09+rI3xjDHUldxGVf4VcLg8+EWPdficREIIcTYKii7KbDKiPOs03ASiNjfgM2Zu3qTv4QlpWLzN+46uMAWaV4WW3zyFpjotFEOefayVm016HSpz02HQaSAKUEGqGtbpo6QIIcSZKCi6qKq8izAZahEw9Pk2D+2tuZ+DBl0Z/IdMtpo2fXiFaYwyCMMGhrS5whRoCa7ZZ2HQlUIRmwS+t2+n2srMJlTfzYLuzg248YXwHzzJ7qiVEEJcHQVFF1SnKWzOLxo5Cu5C+yO2Jr0O2oJrEAdHWu3zs15hqkJkiH+792KMWZ79+cWM63Qwa6guR2XueTTpqyEOjoY4NK7NIE4IIa6Ofnu5mKb6GlTmXoBQoYSwjfyijJlRkXMebnwvSJTxlvI6QwOOXspBbX3HK0xb6Yquo+bBLfhGjuowxdrDzMYmaAuvofZBLjxEMgQMe67bj5AihJAnjYKiC2HMjPLM0+B5eMJnwIg2F6fU3MtGY00FFEOetYzKKnR1OHo5G1wOp8MVpq2q72VDV5QBmWooRAHhDrezvuI+Km+nw9zUAKlqKLz7RVLOUULIU4GCogsxaDVo0msRED+1zb11TXU6aAuvw7tfFASS5mnTu5pKnLp226EVpq3qSgtQlXcJ4v4xEPePcah9psZ6VOVdQp2mCJ4+gfAZMAJuApHjX5AQQlwcBUUXYTTUgtNUDZlqaJsH6DZPm56Dm0BoSczdusK0v78MY+PaX2Haqr7iPipyzkEUGG531artfRnqSvOhzb8MgAO/qDHwUihpmwUh5KlDQdEFmI1NaNBp4OUrhygoss161Xez0FhbCf/4KQCHhwuZBci549gK01YGrQZlmaeaN9S3M0XbqvUZp0FbCqF/GGThwx57Qz8hhLg6CopOxswm1FfcAzgcCGRBbQapxjotdEXX4R0cDY6XFMev5Di8wtRyjdpKlN08Dr7YF37R49p9DsjMppZnjjfA43tCETcRnj50JBMh5OlGQdHJTE0NYMwMgTQAXJ79qU9mNqEy5xzcPL3hHhCJIxcyO7XCFGge8WluHIObwBvyQYntJvhuqKlAZe4FNNVp4d0vChJlHJ1sTwjpE1xqyeCmTZvwzDPPwNvbGwqFArNmzUJOTk67n9m1axc4HI7Vj0DQe6b33PheEPqrwPPwbLNO87RpFbiBQ/C/F7LQ2GTEcyMHORwQmxN8HwXXzR2KuKS2j5UyNTWfSXjlCAAO/IdOhSx8GAVEQkif4VIjxRMnTmDZsmV45plnYDQasXbtWkyZMgWZmZkQCtveYiAWi62CZ29bANJeextrq6C7cwMG7/44lVkMqdATExMi4cl37JR6U1MDyjKOAWYzFPGT2zxbsL7yQfNpHI0GSMPi4R0cRdssiA3GGJjJSCePkKeWSwXFw4cPW73etWsXFAoFLl26hAkTJrT5OQ6Hg4CApy+tGDObUJFzDhX1DBm1PPQPkGDc4Ai4tTHN+iizyYiymydgatDDf8gUu9snTI0GVOVfQl1pIQSyACgGT2rzNA7StxSVVOCupgpj48Itf7hV3voFek0B+o1+iTIXkaeSS/+r1ul0AAAfn/YzpdTW1iI0NBRmsxnDhg3Dn//8ZwwaNMhu3YaGBjQ0NFh91lVpizJQUFiIAvcIRA/sh4TIEIdHwcxsQnnmKTTVVjUHukfSxTHGUKcpgDbvMgAG38hREPqret0om/QMo8mEk9duobhCB2WgL4LlsuaDrPMvobG2CrXFtyAOjnZ2Mwnpdi47P2Y2m7FixQqMHTsWsbGxbdaLjIzEF198ge+++w7//d//DbPZjDFjxuDevXt262/atAkSicTyo1are+ordEmtVoNrF07gvtkHw+OHYHiU48cvMcZQkXseBm0J/GImgC/2s3rfWF+DshvHUJF9DgJZIAKH/xNEAeEUEPu42/c0uJ7X/P/NrXsaFJVUokZvwKXsO2CMQXvnJhpqKsDhcFGVdxlmk9HJLSak+7nsSHHZsmXIyMjA6dOn2603evRojB492vJ6zJgxiI6OxmeffYYNGzbY1F+zZg3efvtty+urV6+6XGCs1etx5h//Dw1GHkYnTUGwv+M5RRljqMq7BL2mCL7RY622UTBmRs29bOiKroPrzociLhGePv164iuQXsbQ2ISjl7NR39CEfn5SXMwuAgdAgI8Y2XdKEH/fF8aCK3ATiODuKUF9+R0aLZKnkksGxd/97nc4ePAgTp48ieDg4E591t3dHUOHDsXt27ftvs/n88Hn/3rGn0jkWmnKKnR1OH30EPiGGoyaMg+KTgREAKi+k4Ga+znwGTDC6tT5xppKVOReQFNdFbz7DYREOYRWlRKLzMJiFFdUw8wY/pGeicLiCihkIni4u6FMW4ubv5xCaEMp3EUyGBvqYGpqQFXeZYgCB9CzRfJUcal/zYwx/P73v8eBAwdw/PhxhIWFdfoaJpMJN27cwLRp03qghT1j8uTJqK+vR42hCT+dPg8//T0MHp0ERUDnRnE1D3KhLbwOqXIIvIMGAGhebKMruoGae1lwF0rgHz/FZjqV9G2GxiZczC6EwMMN7m48XMgqgNjLE5qq5uftPC4XukoNIPexBEC+RAFmNsLcWA8uLcwiTxGXCorLli3D3r178d1338Hb2xslJSUAAIlEAk/P5n188+fPR79+/bBp0yYAwJ/+9CeMGjUKERER0Gq12Lp1K4qKipCcnOy079FZkyZNQlZRCS5lFaCfIRsRAwdAHj6kU9eo0xSi6vZFiIMjIQ5pXmRkqCpGxa10mBrqIVEOhjg4ut1N+6Rvah0l9ldIweVwUa6txeDwYCRE/XqUmBt3NIL8JI+kEuTQc2jy1HGpoPjpp58CABITE63Kd+7ciQULFgAA7ty5Ay731/VBVVVVWLx4MUpKSiCTyZCQkICzZ88iJsaxkx+czWxm+CW7EDl3ShDpVYNAoQDy6LGdCl71lQ9QkX0WXgolpKoEmI0N0OZfQW1JPgRSBRSxSXD3EvfgtyC92c38BwBjuKvRAgD47m4or65FP7kUHm4u9SuCkB7nUv/iGWMd1jl+/LjV6w8//BAffvhhD7Wo59U3NuJOaSWeUcog1BRAEjoYHiKZw59v0JWhPPMkBD5B8BkwAvqyIlTlXQIzm+A7cCSEtKqUdGBiQhRGDrJ+VMH3cIe7g/thCXmauFRQ7IuEAj5mjo1D+fV/gCuSQRzi+Ai3sU6LspvH4SHyhTRsKMozT6G+8gGE8hDIIoa3mzqOkFaOpgskpC9w2X2KfUndvZsw1tfAJ3K0w6nVjIba5nymHl7gSwNQeuUwmuq0kMeq4RczngIiIY+orKzEq6++CrFYDKlUikWLFnWYvCMxMdEmt/Kbb775hFpMnIFGik5mNNSh5l4WJMoh8BBKHfqMqbEemutHYW5qBJfrhuo7NyAKGgCpMp5yUhLShldffRXFxcX46aef0NTUhIULF2LJkiXYu3dvu59bvHgx/vSnP1lee3l59XRTiRNRUHQyN4EQ/vFT4OHt2H5Es7ERmutHoS+/Aze+EPAUwX/IZPAl8h5uKSG9V1ZWFg4fPoxffvkFw4cPBwB8/PHHmDZtGv7yl78gKCiozc96eXk9lbmViX00feoC+GI/h6ZNzSYjHqR/j6q8i+DxvSCLGI7AYc9TQCSkA+fOnYNUKrUERAB49tlnweVyceHChXY/+9VXX8HPzw+xsbFYs2YN9Hp9TzeXOBGNFHsJY2M97pz8CnXFeZBGDEfA4GdtknwT8rSora1FdXW15fWjmag6q6SkBAqFwqrMzc0NPj4+lv3Q9vzLv/wLQkNDERQUhOvXr+Odd95BTk4Ovv3228duC3FtFBRdHGMM+rI7uHf+GzRWVyBw+D/BN2o0bbMgT7VH8xGvW7cOqampNvVWr16NzZs3t3utrKysx27HkiVLLP8dFxeHwMBATJo0CXl5eQgPD3/s6xLXRUHRhRkb9KjMvYDKvItgxkaEJr5GCZhJn3DixAnEx8dbXrc1SkxJSbEk9miLSqVCQEAANBqNVbnRaERlZWWnnheOHDkSAHD79m0Kik8pCoouiDGG2ge50BZeQ0NNOdz4Qvg/80/w7hfl7KYR8kSIRCKIxR1nYZLL5ZDLO36mPnr0aGi1Wly6dAkJCQkAgKNHj8JsNlsCnSOuXr0KAAgMDGy/Ium1aKGNi2mq06H06j9QefsiuG4ecOOL4Bc9jgIiIV0QHR2N5557DosXL0Z6ejrOnDmD3/3ud5g3b55l5en9+/cRFRWF9PR0AEBeXh42bNiAS5cuobCwEN9//z3mz5+PCRMmYPDgwc78OqQHUVB0EcxsgrbwOoovH4LZ2Ahx/xiYGvQQ94+GJDTO2c0jpNf76quvEBUVhUmTJmHatGkYN24c0tLSLO83NTUhJyfHsrrUw8MDP//8M6ZMmYKoqCikpKTgpZdewg8//OCsr0CeAJo+dQEGnQaVt9Jh1FdD3H8Q+GI/lGWehJc8FLLw4bSohpBu4OPj0+5GfaVSaZV/uX///jhx4sSTaBpxIRQUncxoqIPm2s/w8PZFQMI0MJMRmuv/B4HUH76RoyggEkLIE0RB0cncBEIoBj8LvsQPxvoaaDKOw10ohV/MeDr7kBBCnjB6pugCBFIFTA3N+Ux5HgLIYxPB5VEOU0IIedIoKLoAU6MBmhtHAQ4H8riJ4Lk/fuYOQgghj4+CopOZjU0oyzgGs7ERisGT4ManDPyEEOIs9EzRyRgzgevGh2LASLh7eju7OYQQ0qdRUHQynrsAisETnd0MQgghoOlTQgghxIKCIiGEENKCgiIhhBDSgoIiIYQQ0oKCIiGEENKCgiIhhBDSgoIiIYQQ0oKCIiGEENKCgiIhhBDSgoIiIYQQ0oKCIiGEENKCcp+2yMrKcnYTSC8WGBiIwMBAp9y7uLgYxcXFTrl3d6P/D4mz9fmgGBgYCLVajddee83ZTSG92Lp165CamuqUe3/22WdYv369U+7dE9RqtdP+wCCEwxhjzm6Eszn7L+3a2lqo1WqcOHECIpHIae142jzJfn0aRoqu8u/QmX1JCAVFF1BdXQ2JRAKdTgexWOzs5jw1qF87h/qLEFpoQwghhFhQUCSEEEJaUFB0AXw+H+vWrQOfz3d2U54q1K+dQ/1FCD1TJIQQQixopEgIIYS0oKBICCGEtKCg+JQpLCwEh8PBrl27nN0UQgjpdfp0UMzLy8PSpUuhUqkgEAggFosxduxYbN++HfX19T1238zMTKSmpqKwsLDH7uGIjRs3YsaMGfD39weHw3FKRhYOh+PQz/Hjx7t8L71ej9TU1E5dyxX66GHUX4T0rD6b5u3HH3/EnDlzwOfzMX/+fMTGxqKxsRGnT5/GH//4R9y8eRNpaWk9cu/MzEysX78eiYmJUCqVPXIPR7z77rsICAjA0KFDceTIEae0Yc+ePVavv/zyS/z000825dHR0V2+l16vt6RDS0xMdOgzrtBHD6P+IqRn9cmgWFBQgHnz5iE0NBRHjx61Sim1bNky3L59Gz/++KMTW/grxhgMBgM8PT27/doFBQVQKpUoLy+HXC7v9us74tGcs+fPn8dPP/3kMrloXaGPHkb9RUjP6pPTp1u2bEFtbS0+//xzuzkWIyIi8Ic//MHy2mg0YsOGDQgPDwefz4dSqcTatWvR0NBg9TmlUonp06fj9OnTGDFiBAQCAVQqFb788ktLnV27dmHOnDkAgKSkJJvprtZrHDlyBMOHD4enpyc+++wzAEB+fj7mzJkDHx8feHl5YdSoUV0K3s4cpXaG2WzGRx99hEGDBkEgEMDf3x9Lly5FVVWVVb2LFy9i6tSp8PPzg6enJ8LCwvDGG28AaH7W2vpLev369ZZ+72h6r7f00cOovwh5fH1ypPjDDz9ApVJhzJgxDtVPTk7G7t27MXv2bKSkpODChQvYtGkTsrKycODAAau6t2/fxuzZs7Fo0SK8/vrr+OKLL7BgwQIkJCRg0KBBmDBhApYvX47//M//xNq1ay3TXA9Pd+Xk5OCVV17B0qVLsXjxYkRGRqK0tBRjxoyBXq/H8uXL4evri927d2PGjBnYv38/Xnzxxe7rIBezdOlS7Nq1CwsXLsTy5ctRUFCAv/71r7hy5QrOnDkDd3d3aDQaTJkyBXK5HKtXr4ZUKkVhYSG+/fZbAIBcLsenn36Kt956Cy+++CL++Z//GQAwePBgZ361HkH9RUgXsD5Gp9MxAGzmzJkO1b969SoDwJKTk63KV65cyQCwo0ePWspCQ0MZAHby5ElLmUajYXw+n6WkpFjK9u3bxwCwY8eO2dyv9RqHDx+2Kl+xYgUDwE6dOmUpq6mpYWFhYUypVDKTycQYY6ygoIABYDt37nTo+zHGWFlZGQPA1q1b5/BnesqyZcvYw/8sT506xQCwr776yqre4cOHrcoPHDjAALBffvmlzWt35Xu6Uh89jPqLkO7V56ZPq6urAQDe3t4O1T906BAA4O2337YqT0lJAQCb6cuYmBiMHz/e8loulyMyMhL5+fkOtzEsLAxTp061aceIESMwbtw4S5lIJMKSJUtQWFiIzMxMh6/fm+zbtw8SiQSTJ09GeXm55SchIQEikQjHjh0DAEilUgDAwYMH0dTU5MQWOxf1FyFd0+eCYuuRODU1NQ7VLyoqApfLRUREhFV5QEAApFIpioqKrMpDQkJsriGTyWye57QnLCzMbjsiIyNtylunXR9tx9Pi1q1b0Ol0UCgUkMvlVj+1tbXQaDQAmg+mfemll7B+/Xr4+flh5syZ2Llzp81z36cd9RchXdPnnimKxWIEBQUhIyOjU5/jcDgO1ePxeHbLWSdSzPbEStPeymw2Q6FQ4KuvvrL7futiEA6Hg/379+P8+fP44YcfcOTIEbzxxhvYtm0bzp8/32cOb6b+IqRr+lxQBIDp06cjLS0N586dw+jRo9utGxoaCrPZjFu3blkthiktLYVWq0VoaGin7+9ogH20HTk5OTbl2dnZlvefRuHh4fj5558xduxYh/5YGDVqFEaNGoWNGzdi7969ePXVV/G3v/0NycnJj9XvvQ31FyFd0+emTwFg1apVEAqFSE5ORmlpqc37eXl52L59OwBg2rRpAICPPvrIqs4HH3wAAHjhhRc6fX+hUAgA0Gq1Dn9m2rRpSE9Px7lz5yxldXV1SEtLg1KpRExMTKfb0Ru8/PLLMJlM2LBhg817RqPR0odVVVU2o/H4+HgAsEwJenl5Aehcv/c21F+EdE2fHCmGh4dj7969mDt3LqKjo60y2pw9exb79u3DggULAABDhgzB66+/jrS0NGi1WqjVaqSnp2P37t2YNWsWkpKSOn3/+Ph48Hg8bN68GTqdDnw+HxMnToRCoWjzM6tXr8bXX3+N559/HsuXL4ePjw92796NgoICfPPNN+ByO//3zZ49e1BUVAS9Xg8AOHnyJN5//30AwG9+8xuXGH2q1WosXboUmzZtwtWrVzFlyhS4u7vj1q1b2LdvH7Zv347Zs2dj9+7d+OSTT/Diiy8iPDwcNTU12LFjB8RiseUPG09PT8TExODvf/87Bg4cCB8fH8TGxiI2NrbN+/eGPnoY9RchXeTk1a9OlZubyxYvXsyUSiXz8PBg3t7ebOzYsezjjz9mBoPBUq+pqYmtX7+ehYWFMXd3d9a/f3+2Zs0aqzqMNW+neOGFF2zuo1armVqttirbsWMHU6lUjMfjWW3PaOsajDGWl5fHZs+ezaRSKRMIBGzEiBHs4MGDVnU6syVDrVYzAHZ/7G0XeRIe3WLQKi0tjSUkJDBPT0/m7e3N4uLi2KpVq9iDBw8YY4xdvnyZvfLKKywkJITx+XymUCjY9OnT2cWLF62uc/bsWZaQkMA8PDwc2jLgin30MOovQroXHTJMCCGEtOiTzxQJIYQQeygoEkIIIS0oKBJCCCEtKCgSQgghLSgoEkIIIS0oKBJCCCEtKCi2Y8uWLYiKioLZbHZ2U7ps9erVGDlypLObAYD61VkKCwvB4XCwa9cuZzeFEJdFQbEN1dXV2Lx5M9555x1LtpjW08e3bdtmU3/Xrl3gcDi4ePFil+/97bffYu7cuVCpVPDy8kJkZCRSUlLaTLf1/fffY9iwYRAIBAgJCcG6detgNBqt6qxYsQLXrl3D999/3+X2dQX1KyHEpTk7e4Cr+vDDD5lYLGb19fWWMrRk5vD392d1dXVW9Xfu3Nnhoa2O8vX1ZXFxcey9995jO3bsYMuXL2ceHh4sKiqK6fV6q7qHDh1iHA6HJSUlsbS0NPb73/+ecblc9uabb9pc9+WXX2bjx4/vcvu6gvrVecxmM6uvr2dGo9HZTSHEZVFQbMPgwYPZa6+9ZlUGgMXHxzMAbNu2bVbvdecvb3vpsHbv3s0AsB07dliVx8TEsCFDhrCmpiZL2b/9278xDofDsrKyrOru37+fcTgclpeX1+U2Pi7qV0KIK6PpUzsKCgpw/fp1PPvsszbvjR07FhMnTsSWLVtQX1/fI/dPTEy0KXvxxRcBAFlZWZayzMxMZGZmYsmSJXBz+zW3+29/+1swxrB//36ra7R+n++++64HWt0x6teuS01NBYfDQW5uLl577TVIJBLI5XK89957YIzh7t27mDlzJsRiMQICAqympO09U1ywYAFEIhHu37+PWbNmQSQSQS6XY+XKlTCZTJZ6x48fB4fDwfHjx63aY++aJSUlWLhwIYKDg8Hn8xEYGIiZM2eisLCwh3qFkO5DQdGOs2fPAgCGDRtm9/3U1FSUlpbi008/bfc6DQ0NKC8vd+inIyUlJQAAPz8/S9mVK1cAAMOHD7eqGxQUhODgYMv7rSQSCcLDw3HmzJkO79cTqF+7z9y5c2E2m/Ef//EfGDlyJN5//3189NFHmDx5Mvr164fNmzcjIiICK1euxMmTJ9u9lslkwtSpU+Hr64u//OUvUKvV2LZtG9LS0h6rbS+99BIOHDiAhQsX4pNPPsHy5ctRU1ODO3fuPNb1CHmS+uTRUR1pPbg3LCzM7vvjx49HUlIStm7dirfeeqvNw1y//vprLFy40KF7sg7ysm/evBk8Hg+zZ8+2lBUXFwMAAgMDbeoHBgbiwYMHNuUqlQqZmZkOtam7Ub92nxEjRuCzzz4DACxZsgRKpRIpKSnYtGkT3nnnHQDAK6+8gqCgIHzxxReYMGFCm9cyGAyYO3cu3nvvPQDAm2++iWHDhuHzzz/HW2+91al2abVanD17Flu3bsXKlSst5WvWrOnsVyTEKSgo2lFRUQE3NzeIRKI266SmpkKtVuO//uu/8K//+q9260ydOhU//fRTl9uzd+9efP7551i1ahUGDBhgKW+dZuTz+TafEQgEqK6utimXyWQ2I50nhfq1+yQnJ1v+m8fjYfjw4bh37x4WLVpkKZdKpYiMjER+fn6H13vzzTetXo8fPx579uzpdLs8PT3h4eGB48ePY9GiRZDJZJ2+BiHOREHxMU2YMAFJSUnYsmWLzS+UVoGBgXZHG51x6tQpLFq0CFOnTsXGjRut3msdSbWelP4wg8Fgd6TFGAOHw+lSm3oS9atjQkJCrF5LJBIIBAKraeDW8oqKinavJRAIIJfLrcpkMhmqqqo63S4+n4/NmzcjJSUF/v7+GDVqFKZPn4758+cjICCg09cj5EmjZ4p2+Pr6wmg0oqampt1669atQ0lJiWUa61H19fUoKSlx6Meea9euYcaMGYiNjcX+/futFn0Av07vtU73Pay4uBhBQUE25VVVVTa/OJ8U6tfuw+PxHCoDOp5CbutzD2sr4D+8GKfVihUrkJubi02bNkEgEOC9995DdHS002YoCOkMCop2REVFAWheLdketVqNxMREbN682e6Kyb///e+WUU1HP4/Ky8vDc889B4VCgUOHDtmdcoyPjwcAm43tDx48wL179yzvP6ygoADR0dHtfq+eQv3ae7VOgz6a6KCoqMhu/fDwcKSkpOAf//gHMjIy0NjYaDc5AyGuhqZP7Rg9ejSA5l+KgwcPbrduamoqEhMT7a7Ue9xnXyUlJZgyZQq4XC6OHDliM7XVatCgQYiKikJaWhqWLl1q+Yv/008/BYfDsVo8AgA6nQ55eXmdXjzRXahfe6/Q0FDweDycPHkSs2bNspR/8sknVvX0ej24XC4EAoGlLDw8HN7e3nanowlxNRQU7VCpVIiNjcXPP/+MN954o926arUaarUaJ06csHnvcZ99Pffcc8jPz8eqVatw+vRpnD592vKev78/Jk+ebHm9detWzJgxA1OmTMG8efOQkZGBv/71r0hOTrYZufz8889gjGHmzJmdblN3oH7tvSQSCebMmYOPP/4YHA4H4eHhOHjwIDQajVW93NxcTJo0CS+//DJiYmLg5uaGAwcOoLS0FPPmzXNS6wnpBGdlDXB1H3zwAROJRFbpvwCwZcuW2dQ9duyYJVVZd2Reab2WvR+1Wm1T/8CBAyw+Pp7x+XwWHBzM3n33XdbY2GhTb+7cuWzcuHFdbl9XUL92zbp16xgAVlZWZlX++uuvM6FQaFNfrVazQYMGMcYYKygoYADYzp07O/xc630eVlZWxl566SXm5eXFZDIZW7p0KcvIyLC6Znl5OVu2bBmLiopiQqGQSSQSNnLkSPY///M/XfzmhDwZHMY6eArfR+l0OqhUKmzZssVqmXtvVVJSgrCwMPztb39z6oiG+pUQ4spooU0bJBIJVq1aha1btz4VRxx99NFHiIuLc/ovbupXQogro5EiIYQQ0oJGioQQQkgLCoqEEEJICwqKhBBCSAsKioQQQkgLCoqEkG5n7/BhQnoDCoqEOFleXh6WLl0KlUoFgUAAsViMsWPHYvv27XZzv3aXzMxMpKamorCwsMfu4YiNGzdixowZ8Pf3B4fDQWpqqlPbQ/o2SvNGiBP9+OOPmDNnDvh8PubPn4/Y2Fg0Njbi9OnT+OMf/4ibN2/azf/aHTIzM7F+/XokJiZCqVT2yD0c8e677yIgIABDhw7FkSNHnNYOQgAKioQ4TUFBAebNm4fQ0FAcPXrUKp/rsmXLcPv2bfz4449ObOGvGGNtniXZVQUFBVAqlSgvL28zSTshTwpNnxLiJFu2bEFtbS0+//xzuwnOIyIi8Ic//MHy2mg0YsOGDQgPDwefz4dSqcTatWttTp9QKpWYPn06Tp8+jREjRkAgEEClUuHLL7+01Nm1axfmzJkDAEhKSgKHwwGHw8Hx48etrnHkyBEMHz4cnp6elvMt8/PzMWfOHPj4+MDLywujRo3qUvB25iiVkEdRUCTESX744QeoVCqMGTPGofrJycn493//dwwbNgwffvgh1Go1Nm3aZPf0idu3b2P27NmYPHkytm3bBplMhgULFuDmzZsAgAkTJmD58uUAgLVr12LPnj3Ys2eP1QkgOTk5eOWVVzB58mRs374d8fHxKC0txZgxY3DkyBH89re/xcaNG2EwGDBjxgwcOHCgG3qFECdzajpyQvoonU7HALCZM2c6VP/q1asMAEtOTrYqX7lyJQPAjh49aikLDQ1lANjJkyctZRqNhvH5fJaSkmIp27dvHwPAjh07ZnO/1mscPnzYqnzFihUMADt16pSlrKamhoWFhTGlUslMJhNjzP6JHB0pKytjANi6desc/gwh3Y1GioQ4QXV1NQDA29vbofqHDh0CALz99ttW5SkpKQBgM30ZExOD8ePHW17L5XJERkYiPz/f4TaGhYVh6tSpNu0YMWIExo0bZykTiURYsmQJCgsLkZmZ6fD1CXFFFBQJcQKxWAwAqKmpcah+UVERuFwuIiIirMoDAgIglUpRVFRkVR4SEmJzDZlMhqqqKofbGBYWZrcdkZGRNuWt066PtoOQ3oaCIiFOIBaLERQUhIyMjE59jsPhOFSPx+PZLWedOBSnJ1aaEuLqKCgS4iTTp09HXl4ezp0712Hd0NBQmM1m3Lp1y6q8tLQUWq0WoaGhnb6/owH20Xbk5OTYlGdnZ1veJ6Q3o6BIiJOsWrUKQqEQycnJKC0ttXk/Ly8P27dvBwBMmzYNQPOhxg/74IMPAAAvvPBCp+8vFAoBAFqt1uHPTJs2Denp6VaBvK6uDmlpaVAqlYiJiel0OwhxJbR5nxAnCQ8Px969ezF37lxER0dbZbQ5e/Ys9u3bhwULFgAAhgwZgtdffx1paWnQarVQq9VIT0/H7t27MWvWLCQlJXX6/vHx8eDxeNi8eTN0Oh34fD4mTpwIhULR5mdWr16Nr7/+Gs8//zyWL18OHx8f7N69GwUFBfjmm2/A5Xb+7+w9e/agqKgIer0eAHDy5Em8//77AIDf/OY3NPokT5azl78S0tfl5uayxYsXM6VSyTw8PJi3tzcbO3Ys+/jjj5nBYLDUa2pqYuvXr2dhYWHM3d2d9e/fn61Zs8aqDmPN2yleeOEFm/uo1WqmVqutynbs2MFUKhXj8XhW2zPaugZjjOXl5bHZs2czqVTKBAIBGzFiBDt48KBVnc5syVCr1QyA3R9720UI6Ukcxjrx5J0QQgh5itEzRUIIIaQFBUVCCCGkBQVFQgghpAUFRUIIIaQFBUVCCCGkBQVFQgghpAUFRUIIIaQFBUVCCCGkBQVFQgghpAUFRUIIIaQFBUVCCCGkBQVFQgghpAUFRUIIIaTF/wfr5Kg1z0d9QAAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(color_col=\"Gender\");\n", - "\n", - "two_groups_paired.mean_diff.plot(color_col=\"Gender\");" - ] - }, - { - "cell_type": "markdown", - "id": "e822f932", - "metadata": {}, - "source": [ - "### Custom palette\n", - "The colour palette for the graph can be changed using the parameter `custom_palette`. Multiple types of color palettes can be used:\n", - "\n", - "- A list of colors (named colors, hex, rgb, etc) e.g. `['red', 'blue', 'green']`\n", - " \n", - "- A seaborn color palette e.g. `'Set1'`\n", - " \n", - "- A matplotlib color map e.g. `'viridis'`\n", - " - `'paired'` is an interesting option for two-group (or multi two-group) comparisons\n", - " \n", - "- A dictionary with the keys as the column names and the values as the colors e.g. `{'Control 1': 'red', 'Test 1': 'blue', 'Test 2': 'green'}`\n", - " - Or, a dictionary with the keys as the binary options for proportion plots (barplots and sankey) and the values as the colors e.g. `{0: 'red', 1: 'blue'}`" - ] - }, - { - "cell_type": "markdown", - "id": "6b551273", - "metadata": {}, - "source": [ - "#### A list of colors" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "823b7192", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(custom_palette=['red', 'blue', 'green', 'purple', 'orange', 'brown']);" - ] - }, - { - "cell_type": "markdown", - "id": "3d78e17f", - "metadata": {}, - "source": [ - "#### Seaborn color palette" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1aa7bbea", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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dutjylm7Lw9cHUX174NLP2++s5yMDRB0EGUf2EFlbfnEe8oqUFe4P9gsxmmwAgCiKqOEfYtHzyPpyCwrwwU/fQ60ta5YrfU5qrRYf/PQ9Ph33FGtWzOS0U+hT1QJi66LB2OEI79wWNZo1RHjntmgwdjgXHSSys1axiZDJjLe5yGRytIoxvp6PueeR9e06eRyaCprlNDoddp06YeOIXIdNalRiY2ONzp9yN0EQcPbsWVuE41Y8fH04sofIwfh7B2B40qP4ds8K6HRafXOOTCbH8KRHK+wQa8p5+cV5OJJ5CLkF/yDYLwStYhPh7x1gcL3bhbk4mPE7hzlbQPbt25AJgr4m5W4yQUC2suIaNqqcTRKV5OTkcomKVqvF+fPnsWfPHjRv3hytW7e2RShE5AAKC3Jx5tgu5ClvICAoDA2aJ8PXL9jeYdlUwzpNMXngyziadQg5+f+ghn8IWsW0NZqk3Jt0PNl7Es5eO13heacvn8Cae5KZHX+lYXjSo2hYp3QqiLPX/sZHP719zzBnrtBsrvDAQOgqaJbTiSLCg4JsHJHrsEmismzZsgr3HTlyBH369MHo0aNtEQqRU7PVF7w173P+zEFs3fBfiDoNBEEGUdThwO7V6DV4CurVT7TIPZyFv3cAOlcx02xlSYexc/OL87Bmzwr9woZlfVq0Og2+3bMCkwe+DLVGjZ8OfmdkmHPpCs3vP76QNSsmSm7SDN/8vkffR+VuHjIZUppwrjBz2b2PSqtWrZCamoqXXnrJ3qEQObTzZw5i1YJnsX/nSpxK/wX7d67EqgXP4nzGIae5T2FBLrZu+C90WjVEUYROpy39U6vG1vUfoNDNFmXML87DnpM78dPBddhzcifyi/PK7S9LOkSI0Ik6iBD1Sce9xwPAkcxD0OnKf1kCgE6nxdGsQzh2/s8KhzmXrdBMpgn288OU/gPhKZdDEATIZTIIggBPuRxT+g9EkC870prLpqN+KhIREYETJ9jRiKgid3/BA4B45zdg8c4X/KhnF0iu8aistsQS96ns+meO7YKoM75chqjT4Myx3WjVYaCkz+HspDTPSEk67q1VyS34p9IhzDn5/0An6io8his0m69tXDw+HfcUdp06gWylEuFBQUhp0pRJSjXZPVG5desWlixZgqioKHuHQuSwLPUFX1WzS3XvU9X185Q37mwv/+UrCDLkuckXpJTmGX/vAElJx72kDGHWarUVHsMVmqsn2M8PgxLb2TsMl2KTRKVHjx5Gt+fm5uLUqVMoKSnBihUrbBEKkVOyxBe8lNqS6txHyvUDgsIgihWsQSXqEOAmX5BSa0rMmTelVWwidvyVpk+C7lY2hLlEU4Jdx7fq+6jcjSs0k6OxSR8VnU4HURQNXkDpsOXnnnsOx44dw8iRI20RCpFD8vELhl9ACHwqaFYx5Qu+sCAXR/7YiN+2fI4jf2zU9/uQUltSnURCyvUbNE+GIDP++5Eg80DD5skVXt+VlNWUGHN3TYmUeVPu7ecCAMOTHoVc5gEBAmSCDAIEyGUe+iHM/t7+GNB2KDzkHnf6U5T2q/CQe+pXaCZyFDapUdm5c6ctbkNm0hQWIfdUBtS38+EZ6I/gxvU5c60JtFp1hStVSzVw9Ov6v2uMrLYb16gTDuz+BqK2fCIgyOSIa9wRGrUKF84exvYfPoTuriGnB3Z/gx4PPA/lP9cqrS1R/nMVrTsNrvQ+lSUSUmpjfP2C0WvwFGxd/4FB85Ag80CvwVPg4+ceQzil1pRUNW/KpVsXKuznUtXQ57jIBnjrkY9w8OzvuKnMRmhQOJIapzBJqSau9WN5du+jQvaVl3kBl37ecWea/dLVlm/sO4yovj04g60EWq0a2VcyoC4ptvq9EjoORvrv66DT6e76wpIhoeNg5Ny8jGvFp7Hrp0/0TQq6OwmDTqvB9u/nIbZRx0oXtBNFHXJuXa7wPm27jICXt2+FnWWl1sbUq5+IUc8uwJlju5GnzEZAUDgaNk92myQFkNY8U6ai+VZEiJj3/VuV9nOpauhzoG8w+rZ50GKfy91xrR/rsEqi8uWXX5p13mOPPWbhSKgymsKi0iSlbOHCOz/oRK0Ol37ejgZjh7NmpQqiTgd1STHkHh6QWXG1XQCoE9sCoZGxuJSZjsL8XPj6ByMqLgGKO78ln8+ofGVduYcHZDKZ0b4RMpkM9Rq2hafC2+h9atdrAQ8PBc6fOYjtP3xktLNsg+bJOLB7NUQji93d26zj6xfsNqN7jDF1Zlpj863sObnT5BFBZD1c68d6rPKTdezYsSafIwgCExWJzq3+HpqCInj4+SBuROU/7Ctr1sk9lVFhk4Wo0yH3VAan35dIJveARzVW293xw0coLsqDt08Auj8wscLjPAJC0Kil8c7pxUW3Kx4hIhOgVqvQvvsj2L9jZemXo0yAqCv9cmzffTT87uqYee99NJoS5CtvYtemBdBpy36DL99Zls060kmZmbayafDNGRFE1iNlrR+OBjKPVRKVzMxMa1yW7tAUFEFTUFjlcVU166hv5+u3lyPISveTTRQX5aG48Lak4y6cPYzC/Bz4+tdAdHwbePuUfnH5+teouN+DToSffwhq1W2CPsNewsWzf6Ig/x/4+YcgOr4NFD7G15a525Xzxyr8Df7uocvu3qxjispmpq1qnhWupOxYuNaP9VglUalXr541LksmkNKs4xnobzxJAQBRV7qfHMbViyewf8cqgy+uk4e3on330ahVtwmi49vg5OGt0FXQ7yE6vg0AwNsnAA2adzP5/kUFuXc66FY+dNndm3UsQco8K6b0cyHr41o/1mP3KfTJOqQ06wQ3rg9BZvy/gCCTIbhxfWuGSCYoLsq7k6RoAIh3Oq2K0Ok02L9jpb7ZqH330ZDJPAAId56tAJnMA+27j5ZUa1IZH79g6DgHik1ImWelrJ9LZcOQyXaSmzSDRwU/T7nWT/XYbNTPtWvXsGTJEhw+fBhKpbJcpz9BELBt2zZbhePypDTrePj6IKpvD1z6efud5iEZIOogyGSI6tuDHWkdyIWzhyv94rp49k80aN6tWk07ValdrznOHN+t76NyN3eaA8UWpPY/MWUFZrKusrV+7h314yGTca2farJJonL06FGkpKSgqKgIjRo1wl9//YWmTZsiNzcXly9fRnx8POrW5VBYS5LarBMQWxcNxg7nPCoOrjA/p9KOsgV3dZw0t2mnKgpvf/R44Hls/+FDdpa1kIo6y5rS/0TKCsxkG1zrxzpskqhMnz4d/v7+SE9Ph6+vL8LDwzF//nz06NEDa9aswTPPPIOVK1faIhS3Edy4Pm7sO/xvH5W73Nus4+HrY7PRPZxczjxSOsqaorJOucaO8fYJRGRUY8Q2bM/OshZSWWdZ9j9xXlzrx/Jskqjs2bMH06ZNQ3R0NP75p/Q3v7Kmn2HDhuG3337D1KlTsWvXLluE4xbs0axTVRLCyeXMJ7WjLFB1ElJVp9yKjjl9ZBvkck/ENe7IzrLVJKWzrCnzrJDz4My1prNJoqLT6RAREQEACA4Ohlwu1ycsANCiRQssWbLEFqG4FUs261Q3CeHkctVT1lG2ojlQyvqgVJWEGHbK/fcLsqxTbp9hLwFABcdosf2H+Yis2xi+FaxJRNJIXZSQ/U9cC2euNY9NRv3Exsbq51aRyWSIjY3FL7/8ot+/d+9eBAcHm3TNTz/9FC1btkRgYCACAwPRqVMn/Pzzz5YM2yWUNevUSumE0DYtzEoG8jIv4Myyb5G99yByjp9G9t6DOLPsW+RlXgRwz1BoEYBOBMR/k5CyJKeqUUhUubKOss3b9kVMw/Zo3rYv7h82XV8LImVkkJROuVUdc+bYbit9QvchdVHCsv4n/dsOQefGKUxSnNjdM9eKogjtncV6y2auzS0osHeIDstqiUpOTo7+771798aaNWv075955hl8/vnn6NmzJ+677z4sX74co0aNMun6UVFRePvtt3Ho0CEcPHgQPXr0wKBBg3D8+HGLfQayXBKiH4VkDCeXM4kIUf9n2d8BaSODyjrlGlPWKbeyY2R3zZdC5uNkbc5l2qoVeOrzzzBt1QqzryFl5loyzmpNP5GRkejXrx9Gjx6NKVOmYOTIkVCr1fD09MTkyZNRUFCA7777DnK5HK+++ipefvllk67/wAMPGLyfM2cOPv30U+zbtw/NmjWz5EdxedWdZl/KUGhOLld9VTXrSBkZJKVTrgixwmN0nC/FIthZ1rnkFhbgn/zq/TLFmWvNZ7VEZejQofj+++/x/fffIyAgAEOGDMHo0aPRo0cPCIKAV155Ba+88opF7qXVarFmzRoUFBSgU6dOFR6nUqmgUqn07/Or+R/PFVhimn0pSYgpo5CoPCl9S6QkIXXjW1fZKVeEWOkxnC+l+kxdlJCcR0WdZTlzrfms1vSzcuVKZGdn46uvvkLXrl2xcuVK9O7dG3Xq1MGUKVNw+PDhat/jr7/+gr+/PxQKBZ5++mmsX78eTZtWPPvf3LlzERQUpH8lJ7v3D1wpzTpSk5CqZrgtG4UkyGWAAEBW+qcg5+RyUkhp1omObwOZTG70mLIkRMrstRUfI0ePB57nUGQLKZusrWdCP7SJ74CeCf3wn4H/h4Z1OIOpszp47iye+WIRvtrzK345dhRf7fkVz3yxCAfPneXMtdVg1c60Pj4+GDlyJH744Qdcu3YNCxYsQIMGDTBv3jy0a9cOjRs3xptvvolz586Zdf1GjRohPT0df/zxB5555hmMGTMGJ05U3M43Y8YMKJVK/csdhkNrCotw8/BfuLrzd9w8/Bc0hUX6fZaaZl9qElI2Cim8c1vUaNYQ4Z3bosHY4RyaLIGUviVSp9CvqlOusWOatumF5P7PITq+tQ0+rftgZ1nXUVVnWQCY0n8gPOVyCIIAuUwGQRDgKZdz5toq2GwK/Ro1aiA1NRWpqam4fPkyVq1aha+//hqvvfYaZs6ciQ4dOmDv3r0mXdPLywv165c2GSQmJuLAgQOYP38+Fi5caPR4hUIBhUKhf+/v75w/FDz8fAz+rIglmnWkzscidSh0VZPLcUI446RO+CZ1Cn0ps9fefYxGUwK1qtgCn4TINUnpLDsosR1nrjWDzRKVu9WpUwdTp07F/fffj9deew0bN27EH3/8Ue3r6nQ6gz4ojkrUaiHqKmhOkSBmSD/933Xq8v0IgKpXT44fPQQefr6VNut4+PlCp9bAL6oW4kcPgfLvc1Dn5cMzwB9BjeLg4eNjcH+ZpydCWvz7W7kgq2CUTwU4IVzFTJnwzVpT6BNRxaR2lrXUzLXuNHGczROVCxcu6GtTjh07BlEU0blzZ4wePdqk68yYMQN9+/ZFdHQ08vLysGrVKuzcuRNpaWlWitwyRK0WhddvQleitup9bmdkGe24WhqDDjcOHoFfVC19DUk5ggyegQEouHxNv8k7rCa8w2oCAFT/KKFCacHTqlQouHgV2qIiyH184Fe3FuQKBWRenvCNCIUgl1dZU8IJ4SondcI3KaRMn09EprFkZ9mqkhB3mzjOJonKzZs38e2332LVqlX4/fffIYoiGjdujNdffx2jR49GTEyMydfMzs7GY489hqtXryIoKAgtW7ZEWloaevXqZfkPYEGiToSuRA1BLivt02ElWpWqkmYdAdpiFTwD/BDWsTVu7PsTuKsWAzIZwjq2hmeAb5X3KbxyHTf+SDc4X3n6LMLat4J3WChEnYj8C1XXlEjpL2Or9YgclZRmHUtMn0/Oo6JFDcn2kps0wze/74FaW77TuymdZatKQu7uCwNAX4NT1hfm03FPuVzNitUSlYKCAqxfvx6rVq3Ctm3boFarUatWLUyePBmjR49GmzZtqr5IJZx9yn1BLoPMw7x//stbdkNbrILcW4E6vY1X8XsG+FXSrCPCK8AfMg8P+NetDZ+wmsjPugR1QSE8/XzhHxMFubfC+Ll30Rar/k1S7lwXAKDT4cb+I6jds4vkmhIp/WVcWVkyUVXNRmXNOpaYPp81K86jskUNOXLI9oL9/DCl/8BySYaHTGbQWbay2hIpSYjUvjCuxGqJSnh4OIqLi+Hv749Ro0bp51CRVTCChKTTFqugLaq8Y2NATF3k/HX63yTibjIZ/GOi9G/l3goENTZeXagtViEv6yI0BUXw8PNBQExdfRKTl3XR+PUBQKdDwaWrUN/Ol1RT4u4TwnV/YGK1zpeShEgZ4sy+Lc5ByqKGrFmxvbZx8ZV2lq2qtkRKEuKOE8dZLVHp2bMnRo8ejYEDB8Lb29tat6EKyL0ViEhqi+t7DpZr1olIagu5t6LSJAQACi9fw/W9hwzOz/nrNCKS2sK3dgQ0BUWVNy8VFkEtCJJqSjghXPWYMn1+ZTPXknOQuqgh2V5FnWWl1JZISULcceI4qyUqGzdutNalSSLf2hGIfqCn0WadqpIQbbHq3/2AQbPO9T0HEf1Az9Lh0ZU0L8l9feAZIK2mROowaDLOUtPnk3MoW9TQ6PO+a1FDchxSakukJCHJjZtapC+MM2E7jIsra9YJTWyBoMbx+pqUypKQspqWypp18rMuISCmbunkbsbIZPCLqo2ghnFVThhXhhPCVa24KA9/H9uF9H0b8PexXSguygMgbZ4VKTPXknPgoobOp6y2xJiy2hIps9eW9YVxp4nj7DKPCtmXlCSkqmYddUFhpc1LYR1bQ67wMrmmpKoJ4dxZZZ1lpcyzovDxt9gQZ6pcWf8Qa/UT4aKGjqs6a/1I7ZBbVV8YV8NExQ1JSUKqatbx9CsdulxR85LgIYeupPSHqNRZa6liUjrLSklCpM5cS9XzVJ/nLXo9Y8OQuaih46mss6zU4ctSkxBLTRznDJiouCEpSYh/TFS1Rg3pNIa/6bGmpHqkjtix1PT55DgqG4Y8eeDLOJp1CDn5/6CGfwhaxbRlkmInUjrLSqktAdwrCZGCiYqLMzayR8rQZSmjhsh2pI7YYRLiXKqasE3KMGSO7nEMXOvHepiouLDKRvZISUIqGzVEtsURO65HyoRtHIbsPGy91o9UrrAmEBMVFyVleLGUJKSyyeDIdkxZlJAcn9QJ2zgM2XnYY34Td1kTiMOTXZSUkT3Ghi6TYypblFAm8wAg3BnyLUAm8+CIHSckpaYE4DBkZyJlaLElHTx3Fs98sQhf7fkVvxw7iq/2/IpnvliEg+fOAjDsMyOKIrQ6HURR1PeZyS0osGg81sRExUXpR/YYc2dkj6Vpi1XIPZWBm4f+gvL0udKFEcliykbsNG/bFzEN26N52764f9h0LibohMpqSoy5u6akVWxipXPfcBiy47Dl/CZSkhApfWacBZt+XJTU4cVSmTPdfu6JM5D1kSOwfkw1PgndjZ1lXYPUmhJ/7wAOQ3YilpzfpLJmHXdbE4iJiosyZVHCqlRnuv3LW3bBt3YE50whuospE7Y1rNOUw5CdiJTOstXtW+JuawKx6cdFlQ0v1k9xX1bNbOLw4upOt1+2QjIR/auspkQu84AAATJBBgEC5DIPozUl/t4B6Nw4Bf3bDkHnxilMUpyYJfqWSFoTyMZ9ZqyJNSpOqCzJqCrZkDq8uLJmHYtMt39nhWQi+hdrSlxTZbUlUiaFk9KsI2WW2yBfadPxOwMmKk6oTm/pfRSqGl5cVbOORabbD+QPXiJjympKyDVU1WRjqb4l7rYmEBMVF1dZbYmUuVaqO93+vSskExE5m+A7X+zBlXzBS6ktsWTfEndaE4iJigurqrZESrNOdafbr9M7mR1piciu1FotdBX9rJPgjWEP6/+uUquNHrPt2F+V1pZsP/4XQvz8K01Cavr7o1ODhvj69z3QGGnWkctk6FS/oT4GHy8v3N8yQb9fVkGfFGfHRMVFSaktkdKsI3XNn3v7w8h9FPCtFQn/etJHFxERWZpaq0XGtasoriDBsJS/r12BAMBYGiIAOH31Cro0alJpjUp4UBAu5+RgSLsO+G7/Puh0uruGpMswpF0HXM75B5dzjM9I7O3pifqRteApNz73jrNiouKipNSWSJ1rRWqn3Lv7w+g0GuhKyg+9JCKyJZ1Oh2K1Gh4yGTys+AUeGhBQ6dw4oQGBCA0IwMhOSfj69z3lkpCRnZJQ0790QcoWdaMRGxaO9PNZyCnIRw0/fyTUi4G/t3eF99dotShWq0trjpiokDOQUltSo1lDyXOtcM0fInJmHnI5vDzM+8r7MG0T8oqKEeDjjUl9+hk9pl1c/Qqbf+QyGdrH14eXhwdaRNdDTFg4Dmdl6pOQxNi4cklIiL8/ejRrblKcFTU9OTsmKi5KSm2J1GYdIrKO/OI8HMk8hNyCfxDsF4JWsYnw9w6wd1h0j7yiYtwuqnzZkQAfHzzSpRu++m03tHfVlshlMjzSpZtBIhLg44NkJ5rHxN6YqLgoqTPTSm3WISLLOn35BNbcMzX+jr/SMDzpUTSswy8xZ9SkThSmDxxcZW0JmYaJiosypbaEzTpEtpVfnIc1e1bop9Av69ug1Wnw7Z4VmDzwZdasOKmqakvyiopwOOsccgoKUMPPD21i4hDgw5GRlWGi4sJYW0LkmI5kHoJOV374KQDodFoczTrEieBc0InLl7DynqahLUeP4JEu3dCkDkdIVsQ1B12TXlltSWhiCwQ1jmeSQuQAcgv+gVC2/tY9BEFATr7x4afkvPKKirDyt93Q6HQQUTpviojSDrBf/bYbeUVF9g7RYTFRISKysWC/kEqHstbwD7FxRGRth7POQVvBqBytTofDWZk2jsh5OG2iMnfuXLRr1w4BAQEIDw/Hgw8+iNOnT9s7LCKiKrWKTYRMZnyuC5lMjlYxbW0cEVlKXlERdp08jg0H92PXyeP6mpKcgoLKa9EKuHhrRZy2j8quXbswYcIEtGvXDhqNBi+//DJ69+6NEydOwM/PuRZcIiL34u8dgOFJj+Lbe0b9yGRyDE96lCsoO6nK+qDU8POrvBbNj8+8Ik6bqGzevNng/bJlyxAeHo5Dhw6hWzfpqwsTEdlDwzpNMXngyziadQg5+f+ghn8IWsW0ZZLipO7ugwL8O5KrrA/Kc737YovsSIUTwiXGxtk0XmfitInKvZRKJQAgJIRtu0TkHPy9Azi6x0VU1Qfl72tXJU8IR4ZcIlHR6XSYPHkykpKS0Lx5xVMOq1QqqFQq/fv8fLYJEhFR9ZX1QTHWvFPWByW5SVNOCGcGl0hUJkyYgGPHjuG3336r9Li5c+di9uzZNoqKiIjchdQ+KJw+33ROO+qnzHPPPYcff/wRO3bsQFRU5RPmzJgxA0qlUv/atWuXjaIkIiJX1iYmDnKZ8a9U9kGpHqdNVERRxHPPPYf169dj+/btiI2NrfIchUKBwMBA/cvfn53WiIio+soWJfSQySAAkAkCBAAe7INSbU7b9DNhwgSsWrUKGzduREBAAK5duwYACAoKgg/XTSAiIhvjooTW4bSJyqeffgoASElJMdi+dOlSjB071vYBERGR22MfFMtz2kSlok5LRERE5Dqcto8KERERuT6nrVEhIiJyZnlFRTicdQ45BQWo4eeHNjFxCGAfy3KYqBAREdlYZesCNalT+VQb7oZNP0RERDZ097pAIgCdKELEv+sCla24TKWYqBAREdlQVesCHc7KtHFEjo1NP0RERFZQUR8UKesC0b+YqBAREVlYZX1QpK4LRKXY9ENERGRBVfVBaRhZm+sCmYCJChERUSUCfLwR6OOLAB9pU+FX1Qfl72tXuS6QCdj0Q0REVIlJffqZdLyUPijJTZpyXSCJmKgQERFZkNQ+KFwXSBo2/RAREVlQm5g49kGxICYqREREFhTg48M+KBbEph8iIiILa1Inin1QLISJChERkYVwoUHLY6JCRERkAVxo0DrYR4WIiKiauNCg9TBRISIiqiYuNGg9TFSIiIiqqWySN2O40GD1MFEhIiKqJi40aD1MVIiIiKqJk7xZDxMVIiKiauIkb9bD4clEREQWwEnerIOJChERkYVwoUHLY6JCRERkQ5y91jRMVIiIiGyEs9eajp1piYiILCSvqAi7Th7HhoP7sevkcYMZaTl7rXlYo0JERGQBVdWWSJm9lv1bymONChERUTVJqS3h7LXmcepEZffu3XjggQdQu3ZtCIKADRs22DskIiJyQ1JqSzh7rXmcOlEpKChAq1at8Mknn9g7FCIicmNSaks4e615nLqPSt++fdG3b197h0FERG5OSm1J2ey1X93Tj0XO2Wsr5dSJiqlUKhVUKpX+fX4+2wOJiKj62sTEYcvRI9AYaf65u7aEs9eazq0Slblz52L27Nn2DoOIiFyMKbUlnL3WNG6VqMyYMQMvvPCC/n16ejqSk5PtGBEREbkK1pZYh1slKgqFAgqFQv/e3589rImIyHJYW2J5Tj3qh4iIiFybU9eo5OfnIyMjQ/8+MzMT6enpCAkJQXR0tB0jIyIiIktw6kTl4MGD6N69u/59Wf+TMWPGYNmyZXaKioiIiCzFqROVlJSUCsetk6Gr16/h2vXrNrufqNVCp9bCJ/sKZB5O/d+sUhpNCW5cPQcPLwU85Lb7nBER4YiICLfZ/ci6sq9nI/t6ts3up9FqoFIXozC7BF4eXja7rz2UaDQ4l30dCg8PeMjlNrtveEQEwiMibHY/VyaIbvxNf/XqVSxcuBCpqamoVauWvcOxGpVKhT59+mDXrl32DoUsJDk5GWlpaQadw8k5sXy6JpZRy3HrRMVd3L59G0FBQdi1axdHOrmA/Px8JCcnQ6lUIjAw0N7hUDWxfLoellHLct06eSonISGBhcYF3L59294hkBWwfLoOllHL4vBkIiIiclhMVIiIiMhhMVFxAwqFAjNnzmSnLhfB5+la+DxdD5+pZbEzLRERETks1qgQERGRw2KiQkRERA6LiQoRERE5LCYqZJKsrCwIgsC1lIgcFMsouRomKlZ09uxZpKamIi4uDt7e3ggMDERSUhLmz5+PoqIiq933xIkTmDVrFrKysqx2DynmzJmDgQMHIiIiAoIgYNasWXaNx5YEQZD02rlzZ7XvVVhYiFmzZpl0LXd+Nndz5zJ66tQpTJs2DQkJCQgICECtWrXQv39/HDx40G4x2Yojl093fi4V4cy0VvLTTz9h2LBhUCgUeOyxx9C8eXOUlJTgt99+w9SpU3H8+HEsWrTIKvc+ceIEZs+ejZSUFMTExFjlHlK88soriIyMROvWrZGWlma3OOxhxYoVBu+//PJLbN26tdz2Jk2aVPtehYWFmD17NoDShTqlcOdnU8bdy+jnn3+OJUuW4KGHHsKzzz4LpVKJhQsXomPHjti8eTN69uxpl7hswZHLpzs/l4owUbGCzMxMPPzww6hXrx62b99usODhhAkTkJGRgZ9++smOEf5LFEUUFxfDx8fH4tfOzMxETEwMbt68ibCwMItf35E98sgjBu/37duHrVu3lttuL+78bACWUQAYOXIkZs2aZbC+0Lhx49CkSRPMmjXLpb8QHbl8uvNzqQibfqzg3XffRX5+PpYsWWJ0Veb69evj+eef17/XaDR44403EB8fD4VCgZiYGLz88stQqVQG58XExGDAgAH47bff0L59e3h7eyMuLg5ffvml/phly5Zh2LBhAIDu3buXq8Isu0ZaWhratm0LHx8fLFy4EABw7tw5DBs2DCEhIfD19UXHjh2r9cPanrU5zkCn02HevHlo1qwZvL29ERERgdTUVOTk5Bgcd/DgQfTp0wehoaHw8fFBbGwsxo0bB6C0P0JZojF79mz9866qKcfdnw3LKJCYmFhuEcSaNWuia9euOHnypFnXdCX2Kp98LuWxRsUKfvjhB8TFxaFz586Sjh8/fjyWL1+OoUOHYsqUKfjjjz8wd+5cnDx5EuvXrzc4NiMjA0OHDsUTTzyBMWPG4IsvvsDYsWORmJiIZs2aoVu3bpg0aRI+/PBDvPzyy/qqy7urME+fPo2RI0ciNTUVTz75JBo1aoTr16+jc+fOKCwsxKRJk1CzZk0sX74cAwcOxNq1azF48GDL/QMRACA1NRXLli3D448/jkmTJiEzMxMff/wx/vzzT+zZsweenp7Izs5G7969ERYWhunTpyM4OBhZWVlYt24dACAsLAyffvopnnnmGQwePBhDhgwBALRs2dKeH83hsYxW7Nq1awgNDbXItZyZo5VPt34uIlmUUqkUAYiDBg2SdHx6eroIQBw/frzB9hdffFEEIG7fvl2/rV69eiIAcffu3fpt2dnZokKhEKdMmaLftmbNGhGAuGPHjnL3K7vG5s2bDbZPnjxZBCD++uuv+m15eXlibGysGBMTI2q1WlEURTEzM1MEIC5dulTS5xNFUbxx44YIQJw5c6bkc1zNhAkTxLuL26+//ioCEFeuXGlw3ObNmw22r1+/XgQgHjhwoMJrV+ff1x2fDctoxXbv3i0KgiC++uqrJp/rzBy1fJZx1+dShk0/Fla2vHdAQICk4zdt2gQAeOGFFwy2T5kyBQDKVes2bdoUXbt21b8PCwtDo0aNcO7cOckxxsbGok+fPuXiaN++Pbp06aLf5u/vj6eeegpZWVk4ceKE5OtT1dasWYOgoCD06tULN2/e1L/Kqn137NgBAAgODgYA/Pjjj1Cr1XaM2HWwjBqXnZ2NUaNGITY2FtOmTavWtZydI5VPPhf2UbG4wMBAAEBeXp6k48+fPw+ZTIb69esbbI+MjERwcDDOnz9vsD06OrrcNWrUqFGu3bQysbGxRuNo1KhRue1l1dH3xkHVc+bMGSiVSoSHhyMsLMzglZ+fj+zsbABAcnIyHnroIcyePRuhoaEYNGgQli5dWq5vBEnHMlpeQUEBBgwYgLy8PGzcuLFcHwl34yjlk8+lFPuoWFhgYCBq166NY8eOmXSeIAiSjpPL5Ua3iyasLWmNET5kGp1Oh/DwcKxcudLo/rIOeIIgYO3atdi3bx9++OEHpKWlYdy4cfjggw+wb98+t/3BVR0so4ZKSkowZMgQHD16FGlpaWjevLnN7u2oHKF88rn8i4mKFQwYMACLFi3C77//jk6dOlV6bL169aDT6XDmzBmDznTXr19Hbm4u6tWrZ/L9pf5AvTeO06dPl9t+6tQp/X6ynPj4ePzyyy9ISkqS9KXUsWNHdOzYEXPmzMGqVaswevRofPPNNxg/frxZz9vdsYyW0ul0eOyxx7Bt2zZ8++23SE5ONvkarsje5ZPPxRCbfqxg2rRp8PPzw/jx43H9+vVy+8+ePYv58+cDAPr16wcAmDdvnsEx//3vfwEA/fv3N/n+fn5+AIDc3FzJ5/Tr1w/79+/H77//rt9WUFCARYsWISYmBk2bNjU5DqrY8OHDodVq8cYbb5Tbp9Fo9M8uJyen3G/iCQkJAKCvXvb19QVg2vN2dyyjpSZOnIjVq1djwYIF+hEpZP/yyediiDUqVhAfH49Vq1ZhxIgRaNKkicGsl3v37sWaNWswduxYAECrVq0wZswYLFq0CLm5uUhOTsb+/fuxfPlyPPjgg+jevbvJ909ISIBcLsc777wDpVIJhUKBHj16IDw8vMJzpk+fjq+//hp9+/bFpEmTEBISguXLlyMzMxPfffcdZDLTc9oVK1bg/PnzKCwsBADs3r0bb775JgDg0UcfdetamuTkZKSmpmLu3LlIT09H79694enpiTNnzmDNmjWYP38+hg4diuXLl2PBggUYPHgw4uPjkZeXh8WLFyMwMFD/Berj44OmTZti9erVaNiwIUJCQtC8efNKq4rd/dmwjJYmXgsWLECnTp3g6+uLr776ymD/4MGD9QmVu7Fn+eRzMcK+g45c299//y0++eSTYkxMjOjl5SUGBASISUlJ4kcffSQWFxfrj1Or1eLs2bPF2NhY0dPTU6xbt644Y8YMg2NEsXTYYv/+/cvdJzk5WUxOTjbYtnjxYjEuLk6Uy+UGwyAruoYoiuLZs2fFoUOHisHBwaK3t7fYvn178ccffzQ4xpShj8nJySIAoy9jwzJd2b3DH8ssWrRITExMFH18fMSAgACxRYsW4rRp08QrV66IoiiKhw8fFkeOHClGR0eLCoVCDA8PFwcMGCAePHjQ4Dp79+4VExMTRS8vL0lDIflsSrlzGR0zZkyF/wcAiJmZmZWe70ocqXzyuZQniKIJPbyIiIiIbIh9VIiIiMhhMVEhIiIih8VEhYiIiBwWExUiIiJyWExUiIiIyGExUbGjd999F40bN4ZOp7N3KNU2ffp0dOjQwd5h2BWfp+vhM3UtfJ5Oyt7jo92VUqkUQ0JCxC+++EK/DXfGyb///vvljl+6dGmVy4lL9d1334nDhw8XY2NjRR8fH7Fhw4biCy+8IObk5Bg9fuPGjWLr1q1FhUIh1q1bV3zttddEtVptcMzVq1dFhUIhbty4sdrxOSM+T9fDZ+pa+DydFxMVO/nf//4nBgYGikVFRfptZYUmIiJCLCgoMDjekoWmZs2aYosWLcRXX31VXLx4sThp0iTRy8tLbNy4sVhYWGhw7KZNm0RBEMTu3buLixYtEidOnCjKZDLx6aefLnfd4cOHi127dq12fM6Iz9P18Jm6Fj5P58VExU5atmwpPvLIIwbbAIgJCQkiAPGDDz4w2GfJQmNs5tHly5eLAMTFixcbbG/atKnYqlUrg2z+//7v/0RBEMSTJ08aHLt27VpREATx7Nmz1Y7R2fB5uh4+U9fC5+m82EfFDjIzM3H06FH07Nmz3L6kpCT06NED7777LoqKiqxy/5SUlHLbBg8eDAA4efKkftuJEydw4sQJPPXUU/Dw+HdZqGeffRaiKGLt2rUG1yj7PBs3brRC1I6Lz9P18Jm6Fj5P58ZExQ727t0LAGjTpo3R/bNmzcL169fx6aefVnodlUqFmzdvSnpV5dq1awCA0NBQ/bY///wTANC2bVuDY2vXro2oqCj9/jJBQUGIj4/Hnj17qryfK+HzdD18pq6Fz9O5cfVkOzh16hQAIDY21uj+rl27onv37njvvffwzDPPwMfHx+hxX3/9NR5//HFJ9xSrWNLpnXfegVwux9ChQ/Xbrl69CgCoVatWueNr1aqFK1eulNseFxeHEydOSIrJVfB5uh4+U9fC5+ncmKjYwa1bt+Dh4QF/f/8Kj5k1axaSk5Px2Wef4T//+Y/RY/r06YOtW7dWO55Vq1ZhyZIlmDZtGho0aKDfXlYNqlAoyp3j7e2N27dvl9teo0aNclm/q+PzdD18pq6Fz9O5MVFxUN26dUP37t3x7rvv4umnnzZ6TK1atYxm3qb49ddf8cQTT6BPnz6YM2eOwb6y3ypUKlW584qLi43+1iGKIgRBqFZMrojP0/XwmboWPk/HxUTFDmrWrAmNRoO8vDwEBARUeNzMmTORkpKChQsXIjg4uNz+oqIiKJVKSfeMjIwst+3IkSMYOHAgmjdvjrVr1xp03gL+rX68evUq6tata7Dv6tWraN++fblr5uTkGLS5ugM+T9fDZ+pa+DydGzvT2kHjxo0BlPZEr0xycjJSUlLwzjvvGO2Nvnr1an2GX9XrXmfPnsX999+P8PBwbNq0yWiVaEJCAgDg4MGDBtuvXLmCS5cu6fffLTMzE02aNKn0c7kaPk/Xw2fqWvg8nRtrVOygU6dOAEr/M7Zs2bLSY2fNmoWUlBQsWrSo3D5z20uvXbuG3r17QyaTIS0tDWFhYUaPa9asGRo3boxFixYhNTUVcrkcAPDpp59CEASDTmAAoFQqcfbsWTzzzDMmx+TM+DxdD5+pa+HzdHL2mb6FmjdvLo4cOdJgGwBxwoQJ5Y5NTk7Wz6BoicmHWrVqJQIQp02bJq5YscLgtWXLFoNjf/jhB1EQBLFHjx7iokWLxEmTJokymUx88skny1137dq1IgAxIyOj2jE6Gz5P18Nn6lr4PJ0XExU7+e9//yv6+/sbTJ9cUaHZsWOHRQtN2bWMvZKTk8sdv379ejEhIUFUKBRiVFSU+Morr4glJSXljhsxYoTYpUuXasfnjPg8XQ+fqWvh83ReTFTsJDc3VwwJCRE///xze4diEVevXhW9vb3FDRs22DsUu+DzdD18pq6Fz9N5sTOtnQQFBWHatGl47733XGLJ8Xnz5qFFixYYNGiQvUOxCz5P18Nn6lr4PJ2XIIpVTJ9HREREZCesUSEiIiKHxUSFiIiIHBYTFSIiInJYTFSIiIjIYTFRISIiIofFRIWIiIgcFhMVIiIiclhMVIiIiMhhMVEhIiIih8VEhYiIiBwWExUiIiJyWExUiIiIyGExUSEiIiKH5daJytWrVzFr1ixcvXrV3qEQERGREW6fqMyePZuJChERkYNy6kRl9+7deOCBB1C7dm0IgoANGzbYOyQiIiKyIKdOVAoKCtCqVSt88skn9g6FiIiIrMDD3gFUR9++fdG3b197h0FERERW4tQ1KkREROTanLpGxVQqlQoqlUr/Pj8/347REBERUVXcqkZl7ty5CAoK0r+Sk5PtHRIRERFVwq0SlRkzZkCpVOpfu3btsndIREREVAm3avpRKBRQKBT69/7+/naMhsh8qoIbUPiF2TsMIiKrc+pEJT8/HxkZGfr3mZmZSE9PR0hICKKjo+0YGZF1FRdcZ6JCRG7BqROVgwcPonv37vr3L7zwAgBgzJgxWLZsmZ2iIrI+rboQoihCEAR7h0JEZFVOnaikpKRAFEV7h0Fkc6JOA626EB5efvYOhYjIqtyqMy2RK1EVZNs7BCIiq2OiQuSk8v85Y+8QiIisjokKkZP65/JBe4dARGR1TFSInNTtGydQzOYfInJxTFSInNj1s1vsHQIRkVUxUSFyYtnntqOk6B97h0FEZDVMVIicTNu2bdGi/YMYO+1n6LQlyPpzGYfpE5HLYqJC5GSuXbuGq9du4FZuEQAg5+phXDn9vZ2jIiKyDiYqRC7g0vE1uHxyHWtWiMjlMFEhchGXTqxDxh8fQlOSb+9QiIgsxqmn0CciQ/9cPoC8W38jusVI1KzbGYLA30WIyLnxpxiRi1EXK3H2wGc4vv1V5Fw5zOYgInJqrFEhclEFuefx9+//hV+NONRtNgxBES3sHRIRkclYo0Lk4gpyzuHUb+/g773/5ZwrROR0mKgQuYmcq4fx1y/TceviXjYHEZHTYKJC5EY0JYXI2L8AZ37/H4rzr9s7HCKiKrGPCpETuXDhAgoKCgAAxSoNrt0oQGSYn8nXybl6GLnX0xEa3RW1Gg6AT0AtS4dKRGQRrFEhcgL79+/HAw88gJiYGOTm5gIA8grUGPzsBrz49k6cyLhl8jVFnQ43snbhr63TcHrP+1Be/wuiqLNw5ERE1cMaFSIHt27dOowYMQKiKJbrWyKKwN7DV/D7n1fw5n+6oHvHaJOvL4oicq+lI/daOrz9IxER3wth9bpB7uljqY9goLgoD3u3foHzGYcgCAJiG3VA556Pw9Or6vuJoojNa97CxXPp6D1kKmIattfvy76agf07V+LmtXMABITXro8OKY+gZkSMVT4HEdkGa1SIHNj+/fsxYsQIaLVaaLVao8fodCJ0WhGv/O83s2pW7lacfw3nj6zAn5sm4sJfq1BSnGvWdX5YOROnj+4wum/HDx8i5+ZF9H/4Vdw/dDquXjyJ3ZsXSrruXwd+AiCU264uKcLPq+fAPzAUDz72FgY+8gY8vbyx6ds3odNqzPoMROQYmKgQObA333zTaE3KvUSU1q4sXfuXRe6r1RTj6t+bcCTtBVw+tRGizniSZKqcm5dw8Vw6uvV9BuG1GyCybhMk9RqHsyf2oiCv8qHTN69n4q8DPyC53zPl9uXeugJVcT7adh2B4Jp1EBJWF4lJw1BUoETe7RsWiZ2I7IOJCpGDunDhAn788ccKa1LupdOJ+O3QZVy7UWCxGHSaElw6vgan97wHnVZd7etdv/w3vBR+CKsVr99WJ6YlBEFA9pUzFZ6nUauw/fv5SOo1Hr7+NcrtDwqpDYVPAE4d2Q6tVg2NWoVTR7cjuGYdBASFVztuIrKfavVRUalUOHz4MLKzs5GUlITQ0FBLxUXkNLRaNUSd5Tuhbtmy2eT5TkQROPjXNfTvHmfRWHKv/4VrGWmo3WhAta5TVJALH79Ag20ymRwKH38UFeRWeN7ebcsQUacRYhq2M7rfS+GDB0bNwpbv3sWfe9cCAAJr1EK/Ea9AJpNXK2Yisi+zE5UPP/wQs2bNglKpBABs3boVPXr0wM2bN9G4cWO8++67GDdunMUCJXJEWq0a2VcyoC4ptvi1L1/IgEwmg86EJEgQgNt5hVCXFFk8nuvn9yGifh/I5Z7l9v25dx3+/H2d/r1WU4LsK2ewZ+sS/bbh4/9n1n2zzhzAlfPH8NDj71Z4jEatwq5NnyIiqjF6DJoMUafD0f3fY/OauRg8Zi48PBVm3ZuI7M+sRGXp0qWYPHkyHn74YfTu3dsgIQkNDUWPHj3wzTffMFEhlyfqdFCXFEPu4QGZ3LKD6AKDgkxKUoDSGhU/P08IMgu36ooiBLlPac2RkQqKJq17Ia5JJ/377d9/iNhGHRHb6N9ROb4BIfDxC0ZRwW2Dc3U6LVRF+fDxCzZ66yvnj+F2znUs+99Yg+1b17+PyKgmeGD0bGSc+A35yht48LE5+hWjewx8HsvnPY6sMwdRv2mSeZ+biOzOrJ+sH3zwAQYNGoRVq1bh1q3yowwSExPx4YcfVjs4Imchk3vAw8PLotdMSe4GQRBMav4RBCCxWQQEIyNjqkPm5YfAWhV/2Xv7BMDbJ0D/3sPDCz6+gQiqYTiRXESdhihRFeDGtbMIiyztp3Ll/DGIoojw2g2MXjuh44No3Oo+g21rl0xBp/vGIrp+IgBAoy4p/fB3fe6yhAWcG4bIqZn1a1dGRgb69u1b4f6QkBCjCQwRSRcVVRu9e3WHXC6tj4VMJiCpTS1EhvpaNA4vv3DUafkkPBRB1b5WjdAo1I1LwO6fFyL7yhlcu3QKe7YsQXzTzvALCAEAFOTdwupFz+s71/r610BIWLTBCwD8A0MRGBwBAIiKbYmS4gLs2fI5cm5ewj83LmLnT59AJpOjdr3m1Y6biOzHrBqV4OBg3Lx5s8L9J06cQGRkpNlBEVGpF/7zHLZt311lzYqA0gqFMYObWPT+AeEtEdHwIeggQK2yTD+c7g9Mwp6tS/DTN68DgoDYhh2R1Otx/X6dVgvlP1dKa0kkCq5ZB32GvoRDe9Zg44r/gyAIqBkRi77D/8/oKCEich6CaMYyquPGjcP27duRnp4OrVaLsLAw/PLLL+jRoweOHz+ODh06YNy4cQ7f/HP48GEkJibi0KFDaNOmjb3DISekUatw5cIJeCq8Ld70U+bHn9Lw1NPPQxRFo0OVZTIBggC8/nxHJLerY5F7CoIMofH9EVynMwRBgEZTArWqGLWjm7JjKhHZlFlNP2+++Sa0Wi2aN2+OV155BYIgYPny5XjkkUfQtm1bhIeH47XXXrN0rERuaUD/Pvjph2/R875kCIJh3xNBADq3jsRns7tbLEnx8gtH3dbPokZUUrn7ERHZmllNP7Vr18ahQ4fw8ssvY/Xq1RBFEStWrEBAQABGjhyJt99+m3OqEFlQ64SWWLF8ES5duoLuPQdAqbyNAD9PLH27l8X6pMg9fRAS3b20FkXGZcCIyDGY/dMoPDwcn3/+OT7//HPcuHEDOp0OYWFhkFl6WCQR6UVF1Yavrw+UytvwVnhYJEmRefqgRp0kBNdJstpChERE5rLIr01hYWGWuAwR2ZCHIgg1opIQVKsDZB7sd0JEjsms6o9XXnkFCQkJFe5v3bo1Zs+ebW5MRGRF3oF1Edn0YcR2mIYadbsxSSEih2ZWorJ27dpK51Hp168fVq9ebXZQRGRZgswDgZFtEN3mOUS3mYDA8AQIXAOHiJyAWU0/Fy5cQHx8fIX7Y2Njcf78ebODIiLLkHv6IrhOZwTV7ggPL397h0NEZDKzEhV/f/9KE5HMzEx4e3ubHRQRVY8g90RI3WTUiOrKph0icmpmNf2kpKRg4cKFuHz5crl9Fy9exKJFi9C9e/dqB0dEpvMOikZM2/+gZkxPJilE5PTMqlF544030L59ezRr1gxPPPEEmjVrBgA4duwYvvjiC4iiiDfeeMOigRJR1QLCWyGy8TDOg0JELsOsn2aNGjXCr7/+iokTJ+J///ufwb5u3brhww8/RJMmll1zhIgq5xfSEJFNRvy7ajARkQsw+9euli1bYteuXbh58ybOnTsHAIiLi+OMtERWFh4WBp1WjWC/f5fp8vINRWSTkUxSiMjlVLt+ODQ0lMkJkQ1tTduA29npuHbiGwCA3NMPtVuM5ayyROSSzE5UtFot0tLScO7cOeTk5JRbgl4QBLz66qvVDpCIKibI5KjdYgy8fPjLAhG5JrMSlYMHD+Khhx7CpUuXyiUoZZioEFlfzZhe8AmMtncYRERWY1ai8uyzz6KoqAgbNmxA165dERwcbOGwyFIuXLiAbdu2IS8vDwEBAbjvvvsQHc0vNlcg9wpAcFQXe4dB1cDySVQ1sxKVo0ePYs6cOXjggQcsHQ9ZyP79+/HGG2/gp59+giiKkMlk0Ol0EAQBAwYMwKuvvop27drZO0yqhsCIVpBxGLJTYvkkks6sIQJRUVEVNvmQ/a1btw5JSUn4+eef9c9Jp9MBAERRxKZNm9C5c2esW7fOnmFSNfkE17d3CGQGlk8i05iVqLz00ktYvHgxbt++bel4TPbJJ58gJiYG3t7e6NChA/bv32/vkOxq//79GDFiBLRaLbRardFjyvaNGDECBw4csHGEZCkK33B7h0AmYvkkMp1Z9cZ5eXnw9/dH/fr18fDDD6Nu3bqQyw1XYhUEAf/5z38sEmRFVq9ejRdeeAGfffYZOnTogHnz5qFPnz44ffo0wsPd84f4m2++CVEUq6zxKjvmzTffxMaNG20UHVmKABk8FEH2DoNMxPJJZDpBNKMNRyaruiJGEIQKf2OwlA4dOqBdu3b4+OOPAZRWn9atWxcTJ07E9OnTqzz/8OHDSExMxKFDh9CmTRurxmoLFy5cQExMjEnNcoIgICsrix34zKRRq3Dlwgl4Krzh4eFls/sW5p6Db3Ccze6n0ZRArSpG7eim8PDk+kHmYPkkMo9ZNSqZmZmWjsNkJSUlOHToEGbMmKHfJpPJ0LNnT/z+++9Gz1GpVFCpVPr3+fn5AACNRgO1Wm3dgG0gLS3N5L5Doihiy5YtGDNmjJWicm0atRpqtRo6UYRabrv/Q2qtHEVFBTa7n06rgfZOORHNazF2eyyf9qPWavX9gFyZTCaD5z2tG9bm6elp/ZuITury5csiAHHv3r0G26dOnSq2b9/e6DkzZ84UAfDFF1988cUXXxZ42UK1xjZevnwZu3fvRnZ2Nh566CFERUVBq9VCqVQiKCioXL8Ve5sxYwZeeOEF/fv09HQkJyfjjz/+QOvWre0YmWUsW7YMTz31lMnnLV68mL+xVYNWq4Zo49/WNCUF8PDys+k9BZkMcrkNfntyUSyf9qFSq3Hi8iV4yGTwcLDvJEvSaLXQ6HRoWicKClvUctiQWYmKKIqYMmUKPv74Y2g0GgiCgBYtWiAqKgr5+fmIiYnB66+/jsmTJ1s43H+FhoZCLpfj+vXrBtuvX7+OyMhIo+coFAooFP+2r/v7+wMAPDw8bFN9ZWV9+vSBIAgmt4H37t3bJT6/vdjj307rKefaPk6G5dM+dCgto96envDycN15h0o0GhSr1fD09HS5/y9mNTa/9957mD9/Pl588UVs3brVoOAFBQVhyJAh+O677ywWpDFeXl5ITEzEtm3b9Nt0Oh22bduGTp06WfXejio6OhoDBgyQXJMll8vxwAMPsKOeM+IqyU6H5ZPIPGb9tFu8eDEee+wxvPXWW0hISCi3v2XLlvj777+rG1uVXnjhBSxevBjLly/HyZMn8cwzz6CgoACPP/641e/tqF599VUIggBBECo9ruyYV155xUaRkSXJPTjyxhmxfBKZzqxE5eLFi+jcuXOF+/38/GwyGdyIESPw/vvv47XXXkNCQgLS09OxefNmREREWP3ejqpdu3ZYvXo15HJ5hb+5le379ttvOU03kQ2xfBKZzqxEJTw8HBcvXqxw/6FDh2xWXfncc8/h/PnzUKlU+OOPP9ChQweb3NeRDRkyBHv37kW/fv30v7mVzX0jCAL69++PvXv3YvDgwfYMk8gtsXwSmcasnkVDhgzBZ599hrFjxyIoKAgA9AVuy5YtWLZsGaZNm2a5KMlk7dq1w/fff48LFy5g+/btuH37NgIDA9GjRw+2eRPZGcsnkXRmzUyrVCrRrVs3ZGZmomvXrti8eTN69eqF/Px8/P7772jdujV2794NX19fa8RsMa42My0RERkqG57sLqN+XHF4sllNP0FBQdi3bx+mTZuGy5cvw9vbG7t27UJubi5mzpyJX3/91eGTFCIiInJ8JqeXxcXFWLRoERISEvDKK6+wVzoRERFZjck1Kt7e3njppZdw+vRpa8RDREREpGdW00/z5s2RlZVl4VCIiIiIDJmVqMyZMwcLFy7EL7/8Yul4iIiIiPTM6gL98ccfIyQkBH369EFsbCxiY2Ph42O47oggCNi4caNFgiQiInImGp0OHjIudWEJZiUqR48ehSAIiI6OhlarRUZGRrljqpoimoiIiKgqZiUq7J9CRERUMf6ubjmslyIiIrIwAcxULMXsREWr1eKbb75BamoqBg8ejL/++gtA6ay169atw/Xr1y0WJBEREbknsxKV3NxcJCUlYdSoUfj666/x/fff48aNGwAAf39/TJo0CfPnz7dooEREROR+zEpUpk+fjuPHjyMtLQ3nzp3D3csFyeVyDB06FJs2bbJYkEREROSezEpUNmzYgIkTJ6JXr15GR/c0bNiQHW6JiIio2sxKVJRKJWJjYyvcr1arodFozA6KiIjImenuammg6jErUYmPj8fhw4cr3L9lyxY0bdrU7KCIiIicGdMUyzErURk/fjy++OILrF69Wt8/RRAEqFQq/N///R82b96M1NRUiwZKRETkLETWqFiMWRO+Pf/88zh+/DhGjhyJ4OBgAMCoUaNw69YtaDQapKam4oknnrBknERERE6DaYrlmJWoCIKAxYsXY8yYMVi7di3OnDkDnU6H+Ph4DB8+HN26dbN0nERERE5DyxoVi5GUqAwZMgT/+c9/0LVrVwDA7t270aRJE3Tp0gVdunSxaoBERETORmSdisVI6qOyceNGXLhwQf++e/fu2Lp1q9WCIiIicmasUbEcSYlKnTp18Oeff+rfi6LI1ZGJiIgqwM60liOp6efhhx/G+++/j2+//VbfeXb69OmYO3duhecIgoAjR45YJEgiIiJnomOeYjGSEpW5c+eifv362LFjB7KzsyEIAvz8/FCzZk1rx0dEROR0tOyjYjGSEhW5XI6nnnoKTz31FABAJpPhlVdewahRo6waHBERkTPizLSWI6mPSps2bbB582b9+6VLl6J169ZWC4qIiMiZaXQ6e4fgMiQlKkePHsXNmzf178eNG2fQuZaIiIj+pdJp7R2Cy5CUqNSrVw+//PILtNrSf3iO+iEiIqpYARfmtRhJicrTTz+NL7/8Et7e3ggMDIQgCHjiiScQGBhY4SsoKMjasRMRETkkpbrE3iG4DEmdaadOnYpWrVphx44duH79OpYvX4527dohLi7O2vERERE5nX9UKnuH4DIkr/XTu3dv9O7dGwCwbNkypKamctQPERGRETdVRfYOwWWYtSihjr2ZiYiIKqRUl0Cl1UIhl9s7FKcnKVEpW+cnOjra4H1Vyo4nIiJyJyKAG6oiRPn62zsUpycpUYmJiYEgCCgqKoKXl5f+fVXKRgkRERG5mytFhUxULEBSovLFF19AEAR4enoavCciIiLjLhbko33NcHuH4fQkJSpjx46t9D0REREZOpefx3nHLEDSPCpERERkmtwSFW6oiu0dhtOTVKPy+uuvm3xhQRDw6quvmnweERGRqziW+w96RNaxdxhOTVKiMmvWrHLbyqqyxHtWiBQEQV/VxUSFiIjcWXrOLaRE1IaMzT9mk9T0o9PpDF4XL15EixYtMHLkSOzfvx9KpRJKpRJ//PEHHn74YbRq1QoXL160duxEREQO7ZaqGH/nKe0dhlMzq4/KhAkT0KBBA3z11Vdo27YtAgICEBAQgHbt2mHlypWIj4/HhAkTLB0rERGR09l27TJ097Q+kHRmJSrbt29Hjx49Ktx/3333Ydu2bWYHRURE5CquFBZg/61se4fhtMxKVLy9vfH7779XuH/v3r3w9vY2OygiIiJX8vOVi7heVGjvMJySWYnK6NGjsXLlSkyaNAlnzpzR9105c+YMJk6ciFWrVmH06NGWjpWIiMjhPdi7D5YNHY0tE1/Qb9PodFiVlYEijcaOkTknsxYlfOedd3Dz5k18/PHH+OSTTyCTleY7Op0Ooihi5MiReOeddywaKBERkTO4ceMGCm7ehA6G/VJuqorx7YWzeDS2IUcBmcCsRMXLywsrVqzA1KlTsWnTJpw/fx4AUK9ePfTt2xetWrWyaJDGzJkzBz/99BPS09Ph5eWF3Nxcq9+TiIioOv6+rUTa1YvoW5uL9kplVqJSpmXLlmjZsqWlYjFJSUkJhg0bhk6dOmHJkiV2iYGIiMhUv2VfQ7jCB4k1w+wdilOoVqJiT7NnzwYALFu2zL6BEBERmWjDpSzU8FIgLiDQ3qE4PLda60elUuH27dv6V35+vr1DIiIiN6QTRXyVdQZXORKoSm6VqMydOxdBQUH6V3Jysr1DIiIiN6XSavHF2VNMVqrgUInK9OnTIQhCpa9Tp06Zff0ZM2bop/tXKpXYtWuXBaMnIiIyTaFGgyUZJ5GVn2fvUByWQ/VRmTJlCsaOHVvpMXFxcWZfX6FQQKFQ6N/7+/ubfS0iIiJLKNJqsfTcaQypG4tWNWraOxyH41CJSlhYGMLC2AuaiIjci0anw7fnzyK7uAj3RdbhPCt3MTtRSUtLw5IlS3Du3Dnk5ORAvGfBJUEQcPbs2WoHWJELFy7gn3/+wYULF6DVapGeng4AqF+/PmtKiIjIKe28fgXXigoxrF48vOVye4fjEMxKVN577z1Mnz4dERERaN++PVq0aGHpuKr02muvYfny5fr3rVu3BgDs2LEDKSkpNo+HiIjIEk7dzsXCMyfwWGxD1Liru4K7MitRmT9/Pnr06IFNmzbB09PT0jFJsmzZMs6hQkRELim7uAifZZzA2LhGqOXja+9w7MqsUT85OTkYOnSo3ZIUIiIiV5evVmPJ2VO4XFhg71DsyqxEpX379jh9+rSlYyEiIqK7FGk0WHruNK678VwrZiUqCxYswLp167Bq1SpLx0NERER3KdJo8MW507ilKrZ3KHZhVh+VESNGQKPR4NFHH8UzzzyDqKgoyO/pnSwIAo4cOWKRIImIiNxZWTPQE/GNUVPhbe9wbMqsRCUkJAQ1a9ZEgwYNLB0PERERGaEsKcHnZ09hbFwjRHj72DscmzErUdm5c6eFwyAiIqKq3C4pweKMk3gstgGi/QLsHY5NONRaP0RERFS5Io0GX5w9jVO3c+0dik1Uawp9tVqNU6dOQalUQqfTldvfrVu36lyeiIiIjFDrdFiZeQbDouPQ0sXXBzIrUdHpdJgxYwYWLFiAwsKKh0xptVqzAyMiInI2Vy5dQtGd70VNcTEKsm/AL9w6a9jpRBHfXjgHuSCggX+gVe7hCMxKVN566y289957SE1NRZcuXfDoo4/inXfeQXBwMBYsWABBEPDuu+9aOlYiIiKHdOTwn/j4f//Dzl9+0a99p84vwI9jnkTt9m3RdNQI1Gxk+QEooihizYVzGB/XCIFy8xpJ9mX8jS1Hj+Bc9nXkFxfjvVGPITY8vMpz1u3/A9dyc6HVaVEruAYeSGyL5CbN9McUlZRg5Z7d2H82A/lFxQgPCkTfhDbo0zLBpPjM+lTLli3D8OHD8emnn+LWrVsAgMTERPTo0QNjxoxBp06dsH37dvTs2dOcyxMRETmNtJ9+wvOpT0MUxXIL9EIUcfXAIVw9eBidZ0xFVJdOFr+/WqdD2rVLGFYnxqzzVWo1mtSug84NG+GzX7ZIOsdf4Y2H2ndEnZAQeMjkOJR5Fp9s2YwgH18kxMQCAJbv3oljFy9gUp9+CA8MwpELWVi8/ReE+PmjXXx9yfGZ1Zn20qVL6NGjBwBAcWfBpOLi0olovLy88Mgjj2DFihXmXJqIiMhpHDn8J55PfRparbbC7g6iTgdRq8Xeue/h1ukzVonjbP5tFGjUZp2b3KQZhnXsjJZ160k+p3ndaHSo3wBRITURGRyM/q0TUS80DCevXNYfc/rqZSQ3bYbmdaMRHhSEXi1aISYsHBnXr5kUn1mJSs2aNZGfnw8A8Pf3R2BgIM6dO2dwTE5OjjmXJiIichqfzJtnvCbFGFHEia+/tVosBRqN1a5dGVEUcfTCeVzJ+QdN60TptzeqVQcHz2XgVn4eRFHEsYsXcCXnH7SKlp4QAWY2/bRu3RoHDhzQv+/evTvmzZuH1q1bQ6fT4cMPP0SrVq3MuTQREZFTuHLpEnZs3SotSUFpzcqVPw5YpYOtDAKCvbwses2qFKhUSP38M6i1WsgEAeN79ESrejH6/U+k9MBn27Yg9fOFkMtkEAQBT9/XG02j6pp0H7MSlaeeegrLli2DSqWCQqHAnDlz0K1bN3Tr1g2iKKJGjRr4+uuvzbk0ERGRxWmsMAp1965dkpMUPVHE9fQjiO19n0VjifULgJdMXuVxu0+dwKJtW/XvX37wIYNaEFP4eHnhvdGPobhEjb8unsfyXTsRERiE5nWjAQCbjvyJM9euYvrAwQgNCMTJyxfx+Y5fEOLvj5Ym1KqYlagMHDgQAwcO1L9v2rQpzp49i507d0Iul6Nz584ICQkx59JEREQWI5PJ4O3piWK1Ghoj831Vh1J5GzKZzOg8YhUSBJQUFkJnaoJThWaBwfD29IRMVnmPjnZx9dEgspb+fYi/v9n3lAkCagXXAADEhofj8j//YP2B/WheNxoqjRpf7/kVUx8YhMTYeABATFgYsm7cwPeHDlg/UTEmKCgIgwYNstTliIiIqs1TLkf9yFqmJRMS1a9b1/TriiL8AgLg42m5ZhqFXI6BDZvCx9MTnvLKa1V8vLzgY6UmIp0oQq0t7Sej1eqg0ekgQDA4RiYIJidpZicqWq0Wa9aswY4dO5CdnY3XX38dLVq0gFKpxLZt25CUlISIiAhzL09ERGQRnnI5UMUXuDnu790bgiCY1vwjCIhsnQBBEKo+VqJWoeEI9DZ/ReW84iLcvJ2HnILSQTJXcv4BAAT7+aGGnx8A4MO0Tajp54/RXUpnnF+3/w/ER0QgMjgYaq0WhzPPYfepE3iyR+m0JL4KBZrWicKK33bBy8MDYYGBOHHpEnadPIEx3VJMis+sRCU3Nxf3338/9u/fD39/fxQUFGDixIkASkcBTZo0CY899hjeeustcy5PRETk8KKjozFgwABs2rRJ0kzsgkyGOh3bwT+i8snUTNUuLLJa5x88exafbN2sf/+/n38EAAzr0AkjOiUBAG7evg3ZXbUjKo0ai3f8gn/y8uHl4YHaISGY1Kcfkho11h/zn34PYNWe3fhw8ybkFxcjNDAQI5O6oHdL0wbbCKLJPYGAp59+GitXrsS6devQunVrhIeH45dfftHPrTJ58mTs3LkT6enppl7apg4fPozExEQcOnQIbdq0sXc4RETkZA4cOIDOnTtDq9VWWbMiyOXo++EHCG3c0GL39/f0xP86d4eXFWqMHIVZ86hs2LABEydORK9evYxWXzVs2BBZWVnVjY2IiMihtWvXDqtXr4ZcLoe8gmRBkMkgyOVIfm2GRZMUAOhTN9alkxTAzERFqVQiNja2wv1qtRoaO008Q0REZEtDhgzB3r170a9fv/K/vAsC6nRsh74ffoDoLp0tet8wbx/cXzfGotd0RGb1UYmPj8fhw4cr3L9lyxY0bdrU7KCIiIicSbt27fD999/jwoULaNWqFXJzc+Hl74cBiz6xeJ8UABAAjG/S0uVrUwAza1TGjx+PL774AqtXr9a3yQmCAJVKhf/7v//D5s2bkZqaatFAiYiIHF10dDT87oyU8fD2tkqSAgAPxMSjcQ33mK/MrBqV559/HsePH8fIkSMRHBwMABg1ahRu3boFjUaD1NRUPPHEE5aMk4iIiAA0q1ETg2Mb2DsMmzErUREEAYsXL8aYMWOwdu1anDlzBjqdDvHx8Rg+fDi6detm6TiJiIjcXqSvH55t3hoyC87D4uiqNTNtly5d0KVLF0vFQkRERBUI9PLCCy3bwt/T096h2JRZfVSIiIjIdvw9PTEtoT0ifH3tHYrNSa5RuXsRQikEQcDGjRtNDoiIiIj+FeDphWkJ7VDXP8DeodiF5ETlxx9/hLe3NyIjIyWta2DJdQyIiIjcUbCXAtNat0MdP/dMUgATEpU6derg8uXLCA0NxahRo/Dwww8jMrJ66wsQERGRcWHePpjqps09d5PcR+XixYvYsWMHWrdujTfeeAN169ZFz549sXTpUuTl5VkzRiIiIrdS288fL7fp6PZJCmBiZ9rk5GQsXLgQ165dw9q1a1GzZk0899xzCA8Px5AhQ7B27VqoVCprxUpEROTy4gKD8H9tOiDE29veoTgEs0b9eHp6YtCgQVi9ejWuX7+uT15GjBiBd99919IxEhERuYXmIaF4KaE9/D297B2Kw6jWPCoqlQppaWnYuHEj/vzzT3h7eyMmJsZCoREREbmP9uGRSG3aCh4yzhxyN5MTFZ1Oh61bt+Lrr7/Ghg0bUFhYiJ49e2Lx4sUYPHiwfo0DIiIikqZrrToY17iFW804K5XkRGXv3r1YtWoV1qxZg1u3bqFjx4546623MHz4cISGhlozRiIiIpfVJZJJSmUkJypdunSBj48P+vXrh5EjR+qbeC5cuIALFy4YPadNmzYWCZKIiMgVtQ4Nx7gmzZmkVMKkpp+ioiJ89913WLduXaXHiaIIQRCg1WqrFRwREZGrigkIxNPNWkEusE9KZSQnKkuXLrVmHERERG4j2EuB51skwlterTEtbkHyv9CYMWOsGQcREZFb8JTJ8HzLNpwnRSLWNxEREdnQ442bIy4w2N5hOA0mKkRERDZyf3QskiLr2DsMp8JEhYiIyAYaBdfA8PiG9g7D6TBRISIisjIfDw+kNuUIH3PwX4yIiMjKRsQ3Qk1vH3uH4ZScMlHJysrCE088gdjYWPj4+CA+Ph4zZ85ESUmJvUMjIiIyEOUfgOTade0dhtNyygHcp06dgk6nw8KFC1G/fn0cO3YMTz75JAoKCvD+++/bOzwiIiK9QTHxnHm2GpwyUbn//vtx//3369/HxcXh9OnT+PTTT5moEBGRXUVGRkJZUgJFjSDUUHgjMSzC3iE5NadMVIxRKpUICQmp9BiVSgWVSqV/n5+fb+2wiIjIzRw8eBAv7duNa4UF6BBRix1oq8kl/vUyMjLw0UcfITU1tdLj5s6di6CgIP0rOTnZRhESEZE7SqgZZu8QnJ5DJSrTp0+HIAiVvk6dOmVwzuXLl3H//fdj2LBhePLJJyu9/owZM6BUKvWvXbt2WfPjEBGRG5MJAmegtQCHavqZMmUKxo4dW+kxcXFx+r9fuXIF3bt3R+fOnbFo0aIqr69QKKBQKPTv/f39zY6ViIioMpG+flDI5fYOw+k5VKISFhaGsDBp1WSXL19G9+7dkZiYiKVLl0Imc6jKISIicnO1ff3sHYJLcKhERarLly8jJSUF9erVw/vvv48bN27o90VGRtoxMiIiolK1fFlrbwlOmahs3boVGRkZyMjIQFRUlME+URTtFBUREdG/avuxRsUSnLK9ZOzYsRBF0eiLiIjIEdTxC7B3CC7BKRMVIiIiRyYAiPD1tXcYLoGJChERkYWFKLzhLXfK3hUOh4kKERGRhYX6cKVkS2GiQkREZGE1FN72DsFlMFEhIiKysGAvRdUHkSRMVIiIiCwswNPL3iG4DCYqREREFubPRMVimKgQERFZWMPgGvYOwWUwUSEiIrIwmSDYOwSXwUSFiIiIHBYTFSIiInJYTFSIiIjIYTFRISIiIofFRIWIiIgcFhMVIiIiclhc2tFNXL16FVevXrV3GGQhtWrVQq1atewdBlkIy6frYRm1HLdOVGrVqoWZM2e6/H8mlUqFkSNHYteuXfYOhSwkOTkZaWlpUCi4noizY/l0TSyjliOIoijaOwiyrtu3byMoKAi7du2Cv7+/vcOhasrPz0dycjKUSiUCAwPtHQ5VE8un62EZtSy3rlFxNwkJCSw0LuD27dv2DoGsgOXTdbCMWhY70xIREZHDYqJCREREDouJihtQKBSYOXMmO3W5CD5P18Ln6Xr4TC2LnWmJiIjIYbFGhYiIiBwWExUiIiJyWExUiIiIyGExUSEiIiKHxUSFyAoEQZD02rlzZ7XvVVhYiFmzZpl0rTlz5mDgwIGIiIiAIAiYNWtWteMgchaOXD5PnTqFadOmISEhAQEBAahVqxb69++PgwcPVjsWZ8WZaYmsYMWKFQbvv/zyS2zdurXc9iZNmlT7XoWFhZg9ezYAICUlRdI5r7zyCiIjI9G6dWukpaVVOwYiZ+LI5fPzzz/HkiVL8NBDD+HZZ5+FUqnEwoUL0bFjR2zevBk9e/asdkzOhokKkRU88sgjBu/37duHrVu3lttuL5mZmYiJicHNmzcRFhZm73CIbMqRy+fIkSMxa9Ysg3Wfxo0bhyZNmmDWrFlumaiw6YfITnQ6HebNm4dmzZrB29sbERERSE1NRU5OjsFxBw8eRJ8+fRAaGgofHx/ExsZi3LhxAICsrCx9ojF79mx9lXVVTTkxMTHW+EhELsNe5TMxMbHc4pQ1a9ZE165dcfLkSct+SCfBGhUiO0lNTcWyZcvw+OOPY9KkScjMzMTHH3+MP//8E3v27IGnpyeys7PRu3dvhIWFYfr06QgODkZWVhbWrVsHAAgLC8Onn36KZ555BoMHD8aQIUMAAC1btrTnRyNyeo5WPq9du4bQ0FCLfkanIRKR1U2YMEG8u7j9+uuvIgBx5cqVBsdt3rzZYPv69etFAOKBAwcqvPaNGzdEAOLMmTNNjqs65xK5Ckctn2V2794tCoIgvvrqq2Zfw5mx6YfIDtasWYOgoCD06tULN2/e1L/Kqn137NgBAAgODgYA/Pjjj1Cr1XaMmMh9OFL5zM7OxqhRoxAbG4tp06ZZ5R6OjokKkR2cOXMGSqUS4eHhCAsLM3jl5+cjOzsbAJCcnIyHHnoIs2fPRmhoKAYNGoSlS5dCpVLZ+RMQuS5HKZ8FBQUYMGAA8vLysHHjxnJ9V9wF+6gQ2YFOp0N4eDhWrlxpdH9ZBzxBELB27Vrs27cPP/zwA9LS0jBu3Dh88MEH2Ldvn9v+4CKyJkconyUlJRgyZAiOHj2KtLQ0NG/e3OxrOTsmKkR2EB8fj19++QVJSUnw8fGp8viOHTuiY8eOmDNnDlatWoXRo0fjm2++wfjx4yEIgg0iJnIf9i6fOp0Ojz32GLZt24Zvv/0WycnJ5nwMl8GmHyI7GD58OLRaLd54441y+zQaDXJzcwEAOTk5EEXRYH9CQgIA6KuXfX19AUB/DhFVj73L58SJE7F69WosWLBAP1LInbFGhcgOkpOTkZqairlz5yI9PR29e/eGp6cnzpw5gzVr1mD+/PkYOnQoli9fjgULFmDw4MGIj49HXl4eFi9ejMDAQPTr1w8A4OPjg6ZNm2L16tVo2LAhQkJC0Lx580qrilesWIHz58+jsLAQALB79268+eabAIBHH30U9erVs/4/ApGDsmf5nDdvHhYsWIBOnTrB19cXX331lcH+wYMHw8/Pz+r/Bg7F3sOOiNzBvcMfyyxatEhMTEwUfXx8xICAALFFixbitGnTxCtXroiiKIqHDx8WR44cKUZHR4sKhUIMDw8XBwwYIB48eNDgOnv37hUTExNFLy8vSUMhk5OTRQBGXzt27LDUxyZyCo5UPseMGVNh2QQgZmZmWvKjOwVBFO+ptyIiIiJyEOyjQkRERA6LiQoRERE5LCYqRERE5LCYqBAREZHDYqJCREREDouJChERETksJipEDiYrKwuCIGDZsmX2DoWIjGAZtS0mKkREROSwOOEbkYMRRREqlQqenp6Qy+X2DoeI7sEyaltMVIiIiMhhsemHyApmzZoFQRDw999/45FHHkFQUBDCwsLw6quvQhRFXLx4EYMGDUJgYCAiIyPxwQcf6M811v49duxY+Pv74/Lly3jwwQfh7++PsLAwvPjii9Bqtfrjdu7cCUEQsHPnToN4jF3z2rVrePzxxxEVFQWFQoFatWph0KBByMrKstK/CpHjYBl1HkxUiKxoxIgR0Ol0ePvtt9GhQwe8+eabmDdvHnr16oU6dergnXfeQf369fHiiy9i9+7dlV5Lq9WiT58+qFmzJt5//30kJyfjgw8+wKJFi8yK7aGHHsL69evx+OOPY8GCBZg0aRLy8vJw4cIFs65H5IxYRp2AvVZDJHJlM2fOFAGITz31lH6bRqMRo6KiREEQxLffflu/PScnR/Tx8RHHjBkjiqIoZmZmigDEpUuX6o8pW1H19ddfN7hP69atxcTERP37HTt2GF0B+d5r5uTkiADE9957zzIfmMjJsIw6D9aoEFnR+PHj9X+Xy+Vo27YtRFHEE088od8eHByMRo0a4dy5c1Ve7+mnnzZ437VrV0nn3cvHxwdeXl7YuXMncnJyTD6fyFWwjDo+JipEVhQdHW3wPigoCN7e3ggNDS23vaofRt7e3ggLCzPYVqNGDbN+iCkUCrzzzjv4+eefERERgW7duuHdd9/FtWvXTL4WkTNjGXV8TFSIrMjY0MWKhjOKVQzAkzIMUhAEo9vv7sxXZvLkyfj7778xd+5ceHt749VXX0WTJk3w559/VnkfIlfBMur4mKgQuZAaNWoAAHJzcw22nz9/3ujx8fHxmDJlCrZs2YJjx46hpKTEYHQDEVkWy6jpmKgQuZB69epBLpeXG52wYMECg/eFhYUoLi422BYfH4+AgACoVCqrx0nkrlhGTedh7wCIyHKCgoIwbNgwfPTRRxAEAfHx8fjxxx+RnZ1tcNzff/+N++67D8OHD0fTpk3h4eGB9evX4/r163j44YftFD2R62MZNR0TFSIX89FHH0GtVuOzzz6DQqHA8OHD8d5776F58+b6Y+rWrYuRI0di27ZtWLFiBTw8PNC4cWN8++23eOihh+wYPZHrYxk1DafQJyIiIofFPipERETksJioEBERkcNiokJEREQOi4kKEREROSwmKkREROSwmKgQubGsrCwIgoBly5bZOxQiMoJllIkKkWRnz55Famoq4uLi4O3tjcDAQCQlJWH+/PkoKiqy2n1PnDiBWbNmISsry2r3kGLOnDkYOHAgIiIiIAgCZs2aZdd4iO7lzmX01KlTmDZtGhISEhAQEIBatWqhf//+OHjwoN1ishRO+EYkwU8//YRhw4ZBoVDgscceQ/PmzVFSUoLffvsNU6dOxfHjx7Fo0SKr3PvEiROYPXs2UlJSEBMTY5V7SPHKK68gMjISrVu3Rlpamt3iIDLG3cvo559/jiVLluChhx7Cs88+C6VSiYULF6Jjx47YvHkzevbsaZe4LIGJClEVMjMz8fDDD6NevXrYvn07atWqpd83YcIEZGRk4KeffrJjhP8SRRHFxcXw8fGx+LUzMzMRExODmzdvllvKnsieWEaBkSNHYtasWfD399dvGzduHJo0aYJZs2Y5daLCph+iKrz77rvIz8/HkiVLDH4Alqlfvz6ef/55/XuNRoM33ngD8fHxUCgUiImJwcsvv1xuIbGYmBgMGDAAv/32G9q3bw9vb2/ExcXhyy+/1B+zbNkyDBs2DADQvXt3CIIAQRCwc+dOg2ukpaWhbdu28PHxwcKFCwEA586dw7BhwxASEgJfX1907NixWj+s7VmbQ1QZllEgMTHRIEkBgJo1a6Jr1644efKkWdd0FExUiKrwww8/IC4uDp07d5Z0/Pjx4/Haa6+hTZs2+N///ofk5GTMnTvX6EJiGRkZGDp0KHr16oUPPvgANWrUwNixY3H8+HEAQLdu3TBp0iQAwMsvv4wVK1ZgxYoVaNKkif4ap0+fxsiRI9GrVy/Mnz8fCQkJuH79Ojp37oy0tDQ8++yzmDNnDoqLizFw4ECsX7/eAv8qRI6DZbRi165dQ2hoqMWuZxciEVVIqVSKAMRBgwZJOj49PV0EII4fP95g+4svvigCELdv367fVq9ePRGAuHv3bv227OxsUaFQiFOmTNFvW7NmjQhA3LFjR7n7lV1j8+bNBtsnT54sAhB//fVX/ba8vDwxNjZWjImJEbVarSiKopiZmSkCEJcuXSrp84miKN64cUMEIM6cOVPyOUTWwjJasd27d4uCIIivvvqqyec6EtaoEFXi9u3bAICAgABJx2/atAkA8MILLxhsnzJlCgCUq9Zt2rQpunbtqn8fFhaGRo0a4dy5c5JjjI2NRZ8+fcrF0b59e3Tp0kW/zd/fH0899RSysrJw4sQJydcncmQso8ZlZ2dj1KhRiI2NxbRp06p1LXtjokJUicDAQABAXl6epOPPnz8PmUyG+vXrG2yPjIxEcHAwzp8/b7A9Ojq63DVq1KiBnJwcyTHGxsYajaNRo0bltpdVR98bB5GzYhktr6CgAAMGDEBeXh42btxYru+Ks+GoH6JKBAYGonbt2jh27JhJ5wmCIOk4uVxudLsoipLvZY0RPkTOgmXUUElJCYYMGYKjR48iLS0NzZs3t9m9rYU1KkRVGDBgAM6ePYvff/+9ymPr1asHnU6HM2fOGGy/fv06cnNzUa9ePZPvL/UH6r1xnD59utz2U6dO6fcTuQqW0VI6nQ6PPfYYtm3bhlWrViE5OdnkazgiJipEVZg2bRr8/Pwwfvx4XL9+vdz+s2fPYv78+QCAfv36AQDmzZtncMx///tfAED//v1Nvr+fnx8AIDc3V/I5/fr1w/79+w1+cBcUFGDRokWIiYlB06ZNTY6DyFGxjJaaOHEiVq9ejQULFmDIkCEmn++o2PRDVIX4+HisWrUKI0aMQJMmTQxmvdy7dy/WrFmDsWPHAgBatWqFMWPGYNGiRcjNzUVycjL279+P5cuX48EHH0T37t1Nvn9CQgLkcjneeecdKJVKKBQK9OjRA+Hh4RWeM336dHz99dfo27cvJk2ahJCQECxfvhyZmZn47rvvIJOZ/jvKihUrcP78eRQWFgIAdu/ejTfffBMA8Oijj7KWhuyGZbQ08VqwYAE6deoEX19ffPXVVwb7Bw8erE+onI69hx0ROYu///5bfPLJJ8WYmBjRy8tLDAgIEJOSksSPPvpILC4u1h+nVqvF2bNni7GxsaKnp6dYt25dccaMGQbHiGLpsMX+/fuXu09ycrKYnJxssG3x4sViXFycKJfLDYZBVnQNURTFs2fPikOHDhWDg4NFb29vsX379uKPP/5ocIwpQx+Tk5NFAEZfxoZlEtmaO5fRMWPGVFg+AYiZmZmVnu/IBFE0oUcQERERkQ2xjwoRERE5LCYqRERE5LCYqBAREZHDYqJCREREDouJChERETksJipERETksJioEBERkcNiokJEREQOi4kKEREROSwmKkREROSwmKgQERGRw2KiQkRERA6LiQoRERE5rP8H7tj0hrLvrAoAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(custom_palette=sns.color_palette(\"husl\", 6));" - ] - }, - { - "cell_type": "markdown", - "id": "45b3ceff", - "metadata": {}, - "source": [ - "#### Matplotlib color map/palette" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2d9a0e16", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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cV65VIyf/MtSaEqg8fREZHFPpvGpNKa7lXOAwZyvIyc+GQlAYalLupBAUyM7PkiEq12CXRCUlJaVSoqLT6XDhwgX8+uuvaNmyJdq2bWuPUIjIAeQVFmPL/hPIyruFOiGB6NchESEBfnKHZVehAVHo0LhPjckEUDnpSIrrjryi61Ued6MwGyfvSmYuXD+FxJhkhAbcXlMtr+g6DpzdzhWarSQyOKraNZqiguvYOSLXYZdEZfny5VXu+/PPPzFgwACMGzfOHqEQOTV7fcHb8jq/HTuH11ZshFanh0IQoBdFLN/8G14ZPwidW9Q8YsWVeHmoapxptrqkw9Sx5Vq1oTzw9xBmUdTjxOWD6NC4D3R6Hc5mHTE5zPng2R3o02YEa1bMlNqqD1btXGnoo3InD4UHUlv3kSEq1yB7H5U2bdogLS0NL730ktyhEDm0346dw7jXPsOnG3/Fpt+O4tONv2Lca59h77FzTnOdvMJivLZiIzTa25Oq6fR6iKIIjVaHV1dsRF5hsRU+gfMo16pxOTcDGdf+wuXcDJRr1ZX2V5d03F0eAHLyLxvK300U9cjJv4zrBVeqHJpcsUIzmSfEPwQzhr8MT6UnBEGAUqGEIAjwVHpixvCXEewXLHeITsshhn5ERUXh+PHjcodB5LDu/IIHAN3/vmQqvuBXzZ4oucajutoSa1ynuvNv2X8CWp3pL1GtTo+tB05iRGqypM/h7KQ0z0hJOu6uVVFrSqodwqzWlNweCVhVGa7QbLEOjTvh0ynLsfOv7cjOz0JUcB2ktu7DJKWWZE9Ubty4gaVLlyImhr3ViapirS/4mppdanudms6flXcLCkEwJEB3UggCrt0sqPEzuAIpzTNeHipJScfdpAxh1ot6rtBsIyH+IRja5UG5w3ApdklUevfubXJ7fn4+Tp48ifLycqxcudIeoRA5JWt8wUupLanNdaScv05IYLVrUEWHBtX4OVyB1JoSS+ZNiQyOwYXrp0yev2IIs06vw6Xc0yabf7hCMzkau/RR0f+vHfrOF3B72PIzzzyDo0ePYsyYMfYIhcghhQT4IjzIHyEBpifsMucLPq+wGF9vP4APvtmOr7cfMPT7kFJbUptEQsr5+3VIhIfS9K8dD6UCfdsnVnl+V1JRU2LKnTUlUuZNubufCwAkxiQbjqu4zp1DmL08VGhUpzUUFWX+9987V2gmchR2qVHZuXOnPS5DFlJrtLiSm48SdTl8VV6oFx7MmWvNoNHqoNPXbjryfz0zwvD/ZeWVRw30aNMYyzf/ZqituJOHUoEebRqhrFyDfccz8ebq9EpNLzPG3YMrufnV1pZcvp6H0X06VHud6hIJKbUxIQF+eGX8ILx6V/OQh1KBV8YPqjJRczVSa0pqmjelsDS/yn4uNQ19DvGPQK+WQ3Etj/OoWBPX+rE+fhu5uey8wkrT7J++nIO2jepzBlsJNFodTl3MQonatqvtAsDo3h2weuu+2/NeCAJEUYRCUGB07w64mJ2H45nX8PaXPxmSpjubXv658r/o2aYx9FUkVHpRD70o4lJOXpXXGT+gM/x9VFV2lpVaG9O5RTxWzZ6IrQdO4trNAkSHBqFv+0S3SVIAac0zFaqabwUA9p/ZVm0/l5qacFSePkiIbmmtj+X2uNaPbdgkUfn8888tOu6RRx6xciRUHbVGa0hSABhWW9aLIv44ewmpSU1Ys1IDnV6PErUGnh4KeCptt9ouALRrUh+NYyJw4OQF3CgsRliAH9o3a4gAX28AwN5j56qdcMpTqYBSqTDZPKNUKNC1RTx8VZ4mr5PUOAYqLy/8djTDZI3NK+MHoV+HRMm1MSEBfm4zuscUc2emNTXfyuXcDLNHBJHtcK0f27HJt9CECRPMPkYQBCYqEv1yNAPlGi28PD3QvWX1v4iqa9a5kptf7V/AV3LzOf2+RJ5KJbxqkdS9tSodt0pKEejrg5fGDaiyXFiQPwZ0amFyX0FxqaEG5G4KQUCZRotJg7vj0x9/gU7/d6KhVCgwaXB3hAX9PdLj7uuUa7TIySvEu2u2VNtZls060kmZmba6afAtGRFEtiNlrR+OBrKMTRKVzMxMW5yW/qdco0WZxvQDcaeamnVK1OWG7XcTBKBEXW6D6MmUWyWlyC8qrblccSl+P3EeN28VIzTQD50SYxHod7tPQWigXzWTeIkIC/RDq/h6eO2x+7HvxHncuFWMsEA/dGoeZ6iVqc4fZy5JGrrs7s065qhuZtqa5lnhSsqOhWv92I5NEpWGDRva4rRkBinNOr4qL5NJSkV5X5WXnaIlKf7KuIJPN96uDamoOflxzxFMGtwdreLroVNiLH7cc6TKpp1OzeMAAIF+PhaNrskrKpE0dNndm3WsQco8K+b0cyHb41o/tiP7FPpkG1KadeqFB0NRxarWCkFAvfBgG0ZI5rhVXIpPN/4CrU4PUQT0ehGieLsm49Mff8Gt4lIE+vlg0uDu8FAqIAiAUiFAEG73D5k0uLukWpPqhPj7cg4UO5Eyz0pFP5fqhiGT/aS26gMPhem//bnWT+3YradkVlYWli5dikOHDqGgoKDS6ANBELBt2zZ7hePypDTrqDw90LZR/UrNQwpBQNtG9dmR1oH8fuJ8lUOgdXo99p04j77tE2vVtFOTto3rY9uhkxYNXSbzSO1/Ys4KzGRbFWv9vLnOeNSPh8KDa/3Ukl2+iY4cOYJevXqhtLQUTZs2xV9//YXmzZsjPz8fV65cQUJCAurX57Li1iS1WScqJACpSU04j4qDu3mruNqOsjdu/b2Yn6VNOzXx9/XGjHH34M1Vm9lZ1kqq6ixrTv8TKSswk31wrR/bsMu30YwZM+Dv74/Dhw/D19cXkZGRWLBgAXr37o21a9fiqaeewqpVq+wRituoFx6M05dzTFbV392so/L0sNvoHk4uZxkpHWXNUV2nXFNlgvx80DKuLrq2SmBnWSuprrMs+584L671Y312+Yb49ddfMX36dDRo0AA3b94EAEPTz4gRI/DLL7/gxRdfxK5du+wRjluQo1mnpiSEk8tZTmpHWaDmJKSmTrlVldm09yhe9hiInm0as7NsLUnpLGvOPCvkPDhzrfnskqjo9XpERd1etjw4OBhKpdKQsABAq1atsHTpUnuE4las2axT2ySEk8vVTkVH2armQKnog1JTEnJnp1wAhlqaik65rz12PwCYLKMT9Xhz1Wa0iq+LkADzanDImNRFCdn/xLVw5lrL2GXUT1xcnGFuFYVCgbi4OGzdutWwf8+ePQgODjbrnB9//DFat26NwMBABAYGokuXLvjvf/9rzbBdQkWzTsvYuoiPDrcoGcjOK8SOw6dx8lI2Ll3Pw8lL2dhx+DSy8woB1DwUuiLJqWkUElWvoqPsA92T0K1VIzzQPQmvTxpiqAWRMjJISqfc6spUzJdCtSN1UcKK/icJ0a0QE57AJMWJ3TlzrSiK0Ol1EEXRMHNtXlGe3CE6LJslKnl5f//Q+/fvj7Vr1xreP/XUU/j000/Rt29f9OnTBytWrMDYsWPNOn9MTAzefPNNHDx4EAcOHEDv3r0xZMgQHDt2zGqfgayXhFSMQjKFk8uZR7zjv3f2W5GShFR0yjWlolNuTWUq5kshy3GyNufy/NKpeHTBw3h+6VSLzyFl5loyzWZ17XXq1MG9996LcePGYdq0aRgzZgw0Gg08PT3x3HPPobi4GN988w2USiVmz56Nl19+2azz33fffUbv33jjDXz88cfYu3cvWrQwPcU4mVbbafalDIXm5HK1V1OzjpSRQVI65d6dAN1dhvOl1B47yzqXvKI83Ci8UatzcOZay9ksURk+fDi+//57fP/99wgICMCwYcMwbtw49O7dG4IgYNasWZg1a5ZVrqXT6bB27VoUFxejS5cuVZZTq9VQq9WG90VFRVa5vjOzxjT7UpIQc0YhUWVS+pZISUI6SuiUW5EAmSrD+VKsw9xFCcl5VNVZljPXWs5mTT+rVq1CTk4OvvjiC/To0QOrVq1C//79Ua9ePUybNg2HDh2q9TX++usv+Pv7Q6VS4cknn8T69evRvHnzKsvPnz8fQUFBhldKSkqtY3BmUpp1pCYhNc1wWzEKqaJcRXFOLieNlGadTomxUCpMP9IVSYiU2WurKqNUKDBj3D0cimwlFZO1xUYmok5IA8RGJqJD4z4IDYiSOzSy0L7Tv2PShxOwYvsypP+xGSu2L8OkDydg/5nfOXNtLdi0M62Pjw/GjBmDH374AVlZWVi4cCEaN26M999/Hx06dECzZs3w+uuv49y5cxadv2nTpjh8+DB+//13PPXUUxg/fjyOHz9eZfmZM2eioKDA8HKH4dBqjRbnruXi6PmrOHctF+o7FjO01jT7UpOQilFIzepHoX5ECJrVj0JqUhMOTZZASt8SqVPo19Qp11SZ+7q0xvSxA9AxMdYeH9dtsLOs66ipsywAzBj+MjyVnhAEAUqFEoIgwFPpyZlra2C3P2NDQkKQlpaGtLQ0XLlyBatXr8aXX36JV155BXPmzEGnTp2wZ88es87p5eWFRo0aAQCSk5Oxf/9+LFiwAIsWLTJZXqVSQaX6+xeBv7+/yXKOzut/X/xeNdRCWKNZR+p8LFKHQtc0uRwnhDNN6oRvUqfQlzJ77Z1lyjValKg1VvgkRK5JSmfZoV0e5My1FpDlG6BevXp48cUXcc899+CVV17Bhg0b8Pvvv9f6vHq93qgPiqPS6/VVNqdI0SXx78m9dCb6EQA1N+v0bNUI3p6e1TbreHt6QqfTIzzQDz1bNcLVGwUoLdfAx8sTdcOCoPL0MLq+h0KBhpGhhvdVjfKpCieEq5o5E77Zagp9Iqqa1M6y1pq51p0mjrN7onLx4kVDbcrRo0chiiK6du2KcePGmXWemTNnYuDAgWjQoAEKCwuxevVq7Ny5E+np6TaK3Dr0ej3yi0tNfuFYU9bNW9U265y9eh1hgX7V1qj4qDxxo/DONWS8Eeh3+y/zojI1ispuJ4UarQ43bhVDrdFC5emBsEA/eHoo4aFUINjPBwqFosaaEk4IVz2pE75JIWX6fCIyjzU7y9aUhLjbxHF2+c2fm5uLr7/+GqtXr8Zvv/0GURTRrFkzvPrqqxg3bhxiY2PNPmdOTg4eeeQRXLt2DUFBQWjdujXS09PRr18/638AK6qYgEshCFAozKxyMINGp4MAmJypQfjffh+VJ5rUi8TpKzkQRRjKCwLQpF4kfFSeNV4nr7Ck0vFXbuSjUd0IBPn5QBSl1ZRI6S9jr/WIHJWUZh1rTJ9PzqOqRQ3J/lJb9cGqnSuh0VVuIjWns2xNScidfWEAGGpwKvrCfDplucvVrNgsUSkuLsb69euxevVqbNu2DRqNBtHR0Xjuuecwbtw4tGvXrlbnd/Yp9xUKocoRGjU5nHEZGq0Onh5KJCWYnm/B28uziumkbicTPl6eUCoUCA/yR6CfN67nF6GsXAtvLw9EBAfAy0NZYxzlWq0hSak4L3A7ETl79Tpax9WTXFMipb+MKwv09TH6b5XlqmnWscb0+axZcR7VLWrIkUP2F+IfghnDX8ab64yTDA+Fh1Fn2epqS6QkIVL7wrgSmyUqkZGRKCsrg7+/P8aOHWuYQ0Vh4Zcz/U2j1aFcW7kd9E6Rwf64mHOzyi/+iOC/+3x4eXhUOY9JuVaLnPwiqMu1UHl5IDLYH14et//Z5OQXVdvH5catYpSqNZJqStx9QriXxg2o1fFSkhApQ5zZt8U5SFnUkDUr9tehcadqO8vWVFsiJQlxx4njbJao9O3bF+PGjcP9998Pb2/p7edkHV4eHmhWPwonL2VXatZpVj8KXh7KapMQALhZWFzp+Is5N9GsfhRCA/ygLtdW27yk1mghCIKkmhJOCFc75kyfX93MteQcpC5qSPZXVWdZKbUlUpIQd5w4zmaJyoYNG2x1apIoNMAP7Zs0MNmsU1MSUq7VGvYDxs06Jy9lo32TBlB5eVTbvKTy9ICPV/UjiypqSqQOgybTrDV9PjmHikUNTa0XdOeihuQ4pNSWSElCerXqbZW+MM6E7TAurqJZJ6FuOOqFBxtqUqpLQipqWqpLMK7nFyEy2L/ahQbDAv1QNyyoxgnjKnBCuJrdKi7FlgMnsGb7AWw5cAK3iksBSJtnRcrMteQcuKih86moLTGlorZEyuy1FX1h3GniOP6Z6oakJCE1NeuUlWurbV5qUi8Snh5Ks2tKapoQzp1V11lWyjwrAb7eVhviTNWr6B9iq34iXNTQcdVmrR+pHXJr6gvjapiouCEpSUhNzTreXrf/6VTVvKRUCIYvTamz1lLVpHSWlZKESJ25lmonKb6HVc9nahgyFzV0PNV1lpU6fFlqEmKtieOcAb8p3JCUJCSilqOG7u7YyZqS2pE6Ysda0+eT46huGHKHxn04j4qDkNJZVkptCeBeSYgUTFRcnKmRPVKGLnt5KGscNUT2I3XEDpMQ51LThG1ShiFzdI9j4Fo/tsNExYVVN7JHShJS3aghsi+O2HE9UiZs4zBk52HvtX6kcoU1gZiouCgpw4ulJCHVTQZH9mPOooTk+KRO2MZhyM5DjvlN3GVNIA5PdlFSRvaYGrpMjqliUUIPpQKCACgVtyfS81ByxI4zklJTAnAYsjORMrTYmvad/h2TPpyAFduXIf2PzVixfRkmfTgB+8/8DsC4z4woitDpdRBF0dBnJq8oz6rx2BITFRdVMbLHlIqRPdZWrtXicm4+Mq7m4uqNAmhqmOafzFMxYueB7kno1qoRHuiehNcnDeFigk6ooqbElDtrSiKDYyBUMfcGhyE7FnvObyIlCZHSZ8ZZsOnHRUkdXiyVJdPtX7qeB4UgIDosyNKPQXdhZ1nXILWmxMtDxWHITsSa85tU16zjbmsCMVFxUeYsSliT2ky3/+e5KwgN9OOcKUR3MGfCttCAKA5DdiJSOsvWtm+Ju60JxKYfF1Uxa2zF7PUVlczmDi+u7XT7FSskE9HfKmpKKpp1KpqBqqop8fJQISY8AQnRrRATnsAkxYlZo2+JlCTE3n1mbIl/5johz/8lGZ41JBtShxdX16xjjen2K1ZIJqK/sabENVVXWyJlUjgpzTpSZrkN9guWPMGco2Oi4oSSEqR3oKtpeHFNzTrWmG6/YoVkIjJWUVNCrqGmJhtr9S1xtzWBmKi4uOpqS6TMtVLb6fbvXiGZiMjZVNSIVDdRmpTaEmv2LXGnNYGYqLiwmmpLpDTr1Ha6/Tbx9diRlohkpdFpoNdbPl3C/EfeNvy/WlNmssyWw+nV1pZsPZyO0ICwapOQsIAwdEvsXm2zTrfEboYYfFU+uLf9IMN+hcI158LiN4iLklJbIqVZp6JTrrnT7Xt5KhHi74tIM0YXERFZm0anwekrp1BWbjrBsJaTl09WO4vwicsnkdKyV7U1KlHBdXA59zKGdx2Jr3/9Cnq93rDGl0KhwPCuI3Ep9zIu5V42GYO3lzea1GsKT6Wn1T+fnJiouCgptSVS51qR2in3zv4wOr3e5HTvRET2pNfrUFZeBg8PD5t+gUcERVS5HpcoiogMikBEUAQe6vUIVu1cCZ1eB0EhQNSLUCqUGNfrYYQHRQAA2sQnIT46AX9kHMTNopsI9Q9Fu4T28Pfxr/L6Gp0GZeVlt2uOmKiQM5BSW1I/MljyXCtc84eInJmn0hNeHpZ17P/XhndRWHILAb6BeH7ICybLdG7aGVv+MN38o1Qo0alpF3h5eKFNXBLi68TjwNkDuFl4A6EBYWjfqAMCfIxrn8MCwtA3qb9ZcWq11p9x3BEwUXFRUmpLpDbrEJFtlGvVHJ7sBApLbqGgpKDaMgE+gRjf51Gs2LasUm3J+D6PGiUiAT6BSG3V29ZhuwwmKi5K6sy0Upt1iMi6bhRmG1ZQrujbcOH6KSTGJCM0IEru8MgCLRq0xOzRc2qsLSHzMFFxUebUlrBZh8i+yrVqQ5ICwNABUxT1OHH5IDo07sOaFSdVU21JYekt7D+zH3lFNxHiH4oOjTsgwCfQjhE6HyYqLoy1JUSOKSf/ssl1foDbyUpO/mVOBOeCjl08WqlpaPPBTRjf51G0aNBS7vAcFtf6cXEVtSUJdcNRLzyYSQqRA1BrSgzr+9xNgAC1psTOEZGtFZbewopty6DVayFChF6vhwgRWr0WK7YtQ2HpLblDdFhMVIiI7Ezl6Wtyvg3gdjOQytPXzhGRre0/sx+6Kiad0+l1OHD2gJ0jch5Om6jMnz8fHTp0QEBAACIjI/HAAw/g1KlTcodFRFSjyOAYw8rJdxMEBSKDpa/nRY6lsPQWth/Zhm/2rMX2I9sMNSV5RTchKKqoRVMIuFl4w55hOhWn7aOya9cuTJ48GR06dIBWq8XLL7+M/v374/jx4/Dz85M7PCKiKnl5qJAYk4wTd436EQQFEmOS2ZHWSVXXByXEPxSivopaNL2I0IAwO0frPJw2Udm8ebPR++XLlyMyMhIHDx5Ez549ZYqKiEia0IAodGjch/OouIg7+6AAMCQlFX1Q/jFkGjYf3FTlhHDtG3Wwa7zOxGmbfu5WUHB7Mp7Q0FCZIyEiksbLQ4WY8AQkRLdCTHgCkxQnVlMflJNXTmJ8n0fhofCAAAEKhQICBHgoPCpNCEfGnLZG5U56vR7PPfccunXrhpYtqx7ipVaroVarDe+LiorsER4REbm4ij4oppp3KvqgpLbqzQnhLOASicrkyZNx9OhR/PLLL9WWmz9/PubNm2enqIiIyF1I7YPC6fPN5/RNP8888wx+/PFH7NixAzEx1feUnzlzJgoKCgyvXbt22SlKIiJyZR0ad4BSYXqeKvZBqR2nTVREUcQzzzyD9evXY/v27YiLi6vxGJVKhcDAQMPL37/qJbOJiIikqliUkH1QrM9pm34mT56M1atXY8OGDQgICEBWVhYAICgoCD4+PjJHR0RE7oaLEtqG0yYqH3/8MQCgV69eRtuXLVuGCRMm2D8gIiJye+yDYn1Om6iIoulOS0REROQ6nLaPChEREbk+p61RISIicmaFpbew/8x+5BXdRIh/KDo07oAAn0C5w3I4TFSIiIjsrLp1gVo0qHriUnfEph8iIiI7unNdIBEi9Ho9RIiGdYEqVlym25ioEBER2VFN6wIdOHvAzhE5Njb9EBER2UBVfVCkrAtEf2OiQkREZGXV9UGRui4Q3camHyIiIiuqqQ9KYkwi1wUyAxMVIiKiagT4BiLINwgBvtKGDtfUB+XklZNcF8gMbPohIiKqxvNDXjCrvJQ+KKmtenNdIImYqBAREVmR1D4oXBdIGjb9EBERWVGHxh3YB8WKmKgQERFZUYBPIPugWBGbfoiIiKysRYOW7INiJUxUiIiIrIQLDVofExUiIiIr4EKDtsE+KkRERLXEhQZth4kKERFRLXGhQdthokJERFRLFZO8mcKFBmuHiQoREVEtcaFB22GiQkREVEuc5M12mKgQERHVEid5sx0OTyYiIrICTvJmG0xUiIiIrIQLDVofExUiIiI74uy15mGiQkREZCecvdZ87ExLRERkJYWlt7D9yDZ8s2ctth/ZZjQjLWevtQxrVIiIiKygptoSKbPXsn9LZaxRISIiqiUptSWcvdYyTp2o7N69G/fddx/q1q0LQRDw3XffyR0SERG5ISm1JZy91jJOnagUFxejTZs2+M9//iN3KERE5Mak1JZw9lrLOHUflYEDB2LgwIFyh0FERG5OSm1Jxey1d/djUSqUnL22Gk6dqJhLrVZDrVYb3hcVFckYDRERuYoOjTtg88FN0Oq1lfbdWVvC2WvN51aJyvz58zFv3jy5wyAiIhdjTm0JZ681j1slKjNnzsTzzz9veH/48GGkpKTIGBEREbkK1pbYhlslKiqVCiqVyvDe399fxmiIiMjVsLbE+px61A8RERG5NqeuUSkqKsLZs2cN7zMzM3H48GGEhoaiQYMGMkZGRERE1uDUicqBAweQmppqeF/R/2T8+PFYvny5TFERERGRtTh1otKrVy+Ioulx62QsK+sasrKy7HY9vV6ETq9HkK8PlErXbWFUa7Q4czkH3p4e8PQwPZGTLURERiEyKspu1yPbysrKQnZWtt2up9ProddrEeR7FUqlU38N1Khcq0bGtQyoPFXwVHra7boRURGIjIq02/VcmSC68Tf9tWvXsGjRIqSlpSE6OlrucGxGrVZjwIAB2LVrl9yhkJWkpKQgPT3dqHM4OSc+n66Jz6j1uHWi4i5u3bqFoKAg7Nq1iyOdXEBRURFSUlJQUFCAwMBAucOhWuLz6Xr4jFqXa9f5kZGkpCQ+NC7g1q1bcodANsDn03XwGbUu1+08QERERE6PiQoRERE5LCYqbkClUmHOnDns1OUieD9dC++n6+E9tS52piUiIiKHxRoVIiIiclhMVIiIiMhhMVEhIiIih8VEhcxy/vx5CILAtZSIHBSfUXI1TFRsKCMjA2lpaYiPj4e3tzcCAwPRrVs3LFiwAKWlpTa77vHjxzF37lycP3/eZteQ4o033sD999+PqKgoCIKAuXPnyhqPPQmCIOm1c+fOWl+rpKQEc+fONetc7nxv7uTOz+jJkycxffp0JCUlISAgANHR0Rg0aBAOHDggW0z24sjPpzvfl6pwZlob2bhxI0aMGAGVSoVHHnkELVu2RHl5OX755Re8+OKLOHbsGBYvXmyTax8/fhzz5s1Dr169EBsba5NrSDFr1izUqVMHbdu2RXp6umxxyGHlypVG7z///HNs2bKl0vbExMRaX6ukpATz5s0DcHuhTinc+d5UcPdn9NNPP8XSpUvx4IMP4umnn0ZBQQEWLVqEzp07Y/Pmzejbt68scdmDIz+f7nxfqsJExQYyMzMxevRoNGzYENu3bzda8HDy5Mk4e/YsNm7cKGOEfxNFEWVlZfDx8bH6uTMzMxEbG4vc3FxERERY/fyO7KGHHjJ6v3fvXmzZsqXSdrm4870B+IwCwJgxYzB37lyj9YUmTpyIxMREzJ0716W/EB35+XTn+1IVNv3YwNtvv42ioiIsXbrU5KrMjRo1wrPPPmt4r9Vq8dprryEhIQEqlQqxsbF4+eWXoVarjY6LjY3F4MGD8csvv6Bjx47w9vZGfHw8Pv/8c0OZ5cuXY8SIEQCA1NTUSlWYFedIT09H+/bt4ePjg0WLFgEAzp07hxEjRiA0NBS+vr7o3LlzrX5Zy1mb4wz0ej3ef/99tGjRAt7e3oiKikJaWhry8vKMyh04cAADBgxAeHg4fHx8EBcXh4kTJwK43R+hItGYN2+e4X7X1JTj7veGzyiQnJxcaRHEsLAw9OjRAydOnLDonK5ErueT96Uy1qjYwA8//ID4+Hh07dpVUvlJkyZhxYoVGD58OKZNm4bff/8d8+fPx4kTJ7B+/XqjsmfPnsXw4cPx2GOPYfz48fjss88wYcIEJCcno0WLFujZsyemTp2KDz74AC+//LKh6vLOKsxTp05hzJgxSEtLw+OPP46mTZsiOzsbXbt2RUlJCaZOnYqwsDCsWLEC999/P9atW4ehQ4da7wdEAIC0tDQsX74cjz76KKZOnYrMzEx89NFH+OOPP/Drr7/C09MTOTk56N+/PyIiIjBjxgwEBwfj/Pnz+PbbbwEAERER+Pjjj/HUU09h6NChGDZsGACgdevWcn40h8dntGpZWVkIDw+3yrmcmaM9n259X0SyqoKCAhGAOGTIEEnlDx8+LAIQJ02aZLT9hRdeEAGI27dvN2xr2LChCEDcvXu3YVtOTo6oUqnEadOmGbatXbtWBCDu2LGj0vUqzrF582aj7c8995wIQPz5558N2woLC8W4uDgxNjZW1Ol0oiiKYmZmpghAXLZsmaTPJ4qieP36dRGAOGfOHMnHuJrJkyeLdz5uP//8swhAXLVqlVG5zZs3G21fv369CEDcv39/leeuzc/XHe8Nn9Gq7d69WxQEQZw9e7bZxzozR30+K7jrfanAph8rq1jeOyAgQFL5TZs2AQCef/55o+3Tpk0DgErVus2bN0ePHj0M7yMiItC0aVOcO3dOcoxxcXEYMGBApTg6duyI7t27G7b5+/vjiSeewPnz53H8+HHJ56earV27FkFBQejXrx9yc3MNr4pq3x07dgAAgoODAQA//vgjNBqNjBG7Dj6jpuXk5GDs2LGIi4vD9OnTa3UuZ+dIzyfvC/uoWF1gYCAAoLCwUFL5CxcuQKFQoFGjRkbb69Spg+DgYFy4cMFoe4MGDSqdIyQkpFK7aXXi4uJMxtG0adNK2yuqo++Og2rnzJkzKCgoQGRkJCIiIoxeRUVFyMnJAQCkpKTgwQcfxLx58xAeHo4hQ4Zg2bJllfpGkHR8RisrLi7G4MGDUVhYiA0bNlTqI+FuHOX55H25jX1UrCwwMBB169bF0aNHzTpOEARJ5ZRKpcntohlrS9pihA+ZR6/XIzIyEqtWrTK5v6IDniAIWLduHfbu3YsffvgB6enpmDhxIt577z3s3bvXbX9x1QafUWPl5eUYNmwYjhw5gvT0dLRs2dJu13ZUjvB88r78jYmKDQwePBiLFy/Gb7/9hi5dulRbtmHDhtDr9Thz5oxRZ7rs7Gzk5+ejYcOGZl9f6i/Uu+M4depUpe0nT5407CfrSUhIwNatW9GtWzdJX0qdO3dG586d8cYbb2D16tUYN24cvvrqK0yaNMmi++3u+Izeptfr8cgjj2Dbtm34+uuvkZKSYvY5XJHczyfvizE2/djA9OnT4efnh0mTJiE7O7vS/oyMDCxYsAAAcO+99wIA3n//faMy//rXvwAAgwYNMvv6fn5+AID8/HzJx9x7773Yt28ffvvtN8O24uJiLF68GLGxsWjevLnZcVDVRo4cCZ1Oh9dee63SPq1Wa7h3eXl5lf4ST0pKAgBD9bKvry8A8+63u+MzetuUKVOwZs0aLFy40DAiheR/PnlfjLFGxQYSEhKwevVqjBo1ComJiUazXu7Zswdr167FhAkTAABt2rTB+PHjsXjxYuTn5yMlJQX79u3DihUr8MADDyA1NdXs6yclJUGpVOKtt95CQUEBVCoVevfujcjIyCqPmTFjBr788ksMHDgQU6dORWhoKFasWIHMzEx88803UCjMz2lXrlyJCxcuoKSkBACwe/duvP766wCAhx9+2K1raVJSUpCWlob58+fj8OHD6N+/Pzw9PXHmzBmsXbsWCxYswPDhw7FixQosXLgQQ4cORUJCAgoLC7FkyRIEBgYavkB9fHzQvHlzrFmzBk2aNEFoaChatmxZbVWxu98bPqO3E6+FCxeiS5cu8PX1xRdffGG0f+jQoYaEyt3I+Xzyvpgg76Aj13b69Gnx8ccfF2NjY0UvLy8xICBA7Natm/jhhx+KZWVlhnIajUacN2+eGBcXJ3p6eor169cXZ86caVRGFG8PWxw0aFCl66SkpIgpKSlG25YsWSLGx8eLSqXSaBhkVecQRVHMyMgQhw8fLgYHB4ve3t5ix44dxR9//NGojDlDH1NSUkQAJl+mhmW6sruHP1ZYvHixmJycLPr4+IgBAQFiq1atxOnTp4tXr14VRVEUDx06JI4ZM0Zs0KCBqFKpxMjISHHw4MHigQMHjM6zZ88eMTk5WfTy8pI0FJL35jZ3fkbHjx9f5b8BAGJmZma1x7sSR3o+eV8qE0TRjB5eRERERHbEPipERETksJioEBERkcNiokJEREQOi4kKEREROSwmKkREROSwmKjI6O2330azZs2g1+vlDqXWZsyYgU6dOskdhqx4P10P76lr4f10UnKPj3ZXBQUFYmhoqPjZZ58ZtuF/4+TffffdSuWXLVtW43LiUn3zzTfiyJEjxbi4ONHHx0ds0qSJ+Pzzz4t5eXkmy2/YsEFs27atqFKpxPr164uvvPKKqNFojMpcu3ZNVKlU4oYNG2odnzPi/XQ9vKeuhffTeTFRkcm///1vMTAwUCwtLTVsq3hooqKixOLiYqPy1nxowsLCxFatWomzZ88WlyxZIk6dOlX08vISmzVrJpaUlBiV3bRpkygIgpiamiouXrxYnDJliqhQKMQnn3yy0nlHjhwp9ujRo9bxOSPeT9fDe+paeD+dFxMVmbRu3Vp86KGHjLYBEJOSkkQA4nvvvWe0z5oPjamZR1esWCECEJcsWWK0vXnz5mKbNm2Msvn/+7//EwVBEE+cOGFUdt26daIgCGJGRkatY3Q2vJ+uh/fUtfB+Oi/2UZFBZmYmjhw5gr59+1ba161bN/Tu3Rtvv/02SktLbXL9Xr16Vdo2dOhQAMCJEycM244fP47jx4/jiSeegIfH38tCPf300xBFEevWrTM6R8Xn2bBhgw2idly8n66H99S18H46NyYqMtizZw8AoF27dib3z507F9nZ2fj444+rPY9arUZubq6kV02ysrIAAOHh4YZtf/zxBwCgffv2RmXr1q2LmJgYw/4KQUFBSEhIwK+//lrj9VwJ76fr4T11Lbyfzo2rJ8vg5MmTAIC4uDiT+3v06IHU1FS88847eOqpp+Dj42Oy3JdffolHH31U0jXFGpZ0euutt6BUKjF8+HDDtmvXrgEAoqOjK5WPjo7G1atXK22Pj4/H8ePHJcXkKng/XQ/vqWvh/XRuTFRkcOPGDXh4eMDf37/KMnPnzkVKSgo++eQT/OMf/zBZZsCAAdiyZUut41m9ejWWLl2K6dOno3HjxobtFdWgKpWq0jHe3t64detWpe0hISGVsn5Xx/vpenhPXQvvp3NjouKgevbsidTUVLz99tt48sknTZaJjo42mXmb4+eff8Zjjz2GAQMG4I033jDaV/FXhVqtrnRcWVmZyb86RFGEIAi1iskV8X66Ht5T18L76biYqMggLCwMWq0WhYWFCAgIqLLcnDlz0KtXLyxatAjBwcGV9peWlqKgoEDSNevUqVNp259//on7778fLVu2xLp164w6bwF/Vz9eu3YN9evXN9p37do1dOzYsdI58/LyjNpc3QHvp+vhPXUtvJ/OjZ1pZdCsWTMAt3uiVyclJQW9evXCW2+9ZbI3+po1awwZfk2vu2VkZOCee+5BZGQkNm3aZLJKNCkpCQBw4MABo+1Xr17F5cuXDfvvlJmZicTExGo/l6vh/XQ9vKeuhffTubFGRQZdunQBcPsfY+vWrastO3fuXPTq1QuLFy+utM/S9tKsrCz0798fCoUC6enpiIiIMFmuRYsWaNasGRYvXoy0tDQolUoAwMcffwxBEIw6gQFAQUEBMjIy8NRTT5kdkzPj/XQ9vKeuhffTyckzfQu1bNlSHDNmjNE2AOLkyZMrlU1JSTHMoGiNyYfatGkjAhCnT58urly50uj1008/GZX94YcfREEQxN69e4uLFy8Wp06dKioUCvHxxx+vdN5169aJAMSzZ8/WOkZnw/vpenhPXQvvp/NioiKTf/3rX6K/v7/R9MlVPTQ7duyw6kNTcS5Tr5SUlErl169fLyYlJYkqlUqMiYkRZ82aJZaXl1cqN2rUKLF79+61js8Z8X66Ht5T18L76byYqMgkPz9fDA0NFT/99FO5Q7GKa9euid7e3uJ3330ndyiy4P10PbynroX303mxM61MgoKCMH36dLzzzjsuseT4+++/j1atWmHIkCFyhyIL3k/Xw3vqWng/nZcgijVMn0dEREQkE9aoEBERkcNiokJEREQOi4kKEREROSwmKkREROSwmKgQERGRw2KiQkRERA6LiQoRERE5LCYqRERE5LCYqBAREZHDYqJCREREDouJChERETksJipERETksJioEBERkcNy60Tl2rVrmDt3Lq5duyZ3KERERGSC2ycq8+bNY6JCRETkoJw6Udm9ezfuu+8+1K1bF4Ig4LvvvpM7JCIiIrIip05UiouL0aZNG/znP/+ROxQiIiKyAQ+5A6iNgQMHYuDAgXKHQURERDbi1DUqRERE5NqcukbFXGq1Gmq12vC+qKhIxmiIiIioJm5VozJ//nwEBQUZXikpKXKHRERERNVwq0Rl5syZKCgoMLx27dold0hERERUDbdq+lGpVFCpVIb3/v7+MkZDZLmcW2WIDPSWOwwiIptz6kSlqKgIZ8+eNbzPzMzE4cOHERoaigYNGsgYGZFtXS1gokJE7sGpE5UDBw4gNTXV8P75558HAIwfPx7Lly+XKSoi2ysp10IURQiCIHcoREQ25dSJSq9evSCKotxhENmdRieiuFwHf5VTP8JERDVyq860RK4kq6BU7hCIiGyOiQqRkzqZVSh3CERENsdEhchJ7cm4IXcIREQ2x0SFyEn9daUAWQVlcodBRGRTTFSInNiPR67KHQIRkU0xUSFyYv89loXcInXNBYmInBQTFSIn0759ewzv2QY/vz0J5Vo9Pt6ZwWH6ROSymKgQOZmsrCzkZl+DuvAmAGDf+ZtYe/CyzFEREdkGExUiF7By7wV8ue8ia1aIyOUwUSFyEav3XcSbm0+isEwjdyhERFbDRIXIhezJuIGnVx/CjpM50OtZu0JEzo+JCpGLyS/R4F9bT+MfXx/G75k32BxERE6NiQqRizqXW4zXN57AtLV/4tDFPLnDISKyCBMVIhd3JqcIc74/htd+PM45V4jI6TBRIXIT+87fxDNfHsKu09fZHEREToOJCpEbKVbr8O5Pp/D6xhO4VlAqdzhERDXykDsAIpLu4sWLKC4uBgBo1WUovZkNn9Aos8+z7/xNHLiQhz7NIjGsXT3EhPhaO1QiIqtgjQqRE9i3bx/uu+8+xMbGIj8/HwCgLS3EtrkjsH/RDORfOGH2OfWiiC0nsvH0qkOY98MxHLqYxyHNRORwWKNC5OC+/fZbjBo1CqIoVu5bIorIOf4bco7vRbtH5yE6KcXs84sADlzIw4ELeagb7I1Breqib2IkfL1s8+vhVnEZ/rN+B/Yey4QgAD1aN8bTQ1Pgo/KqOVZRxP8t+Q77T17A3EcHo1urRoZ9py5m4dONv+LMpWwIgoCmDaLw+OAeSKgXYZPPQUT2wRoVIge2b98+jBo1CjqdDjqdzmQZUa+HqNfj0LI5FtWs3OlqfhmW/HwOE5btx2e/ZiKvuNyi80z7z1qk7ztmct+bq/6L81k38OaTQ/H6pCE4cu4K/v31Nknn/Xb3HwCESttL1eWYufg7RAYH4MPnRuPfU0bCV+WFmYvXQ1vFz42InAMTFSIH9vrrr5uuSalEBCDiTPoKq1y3VKPD+j+u4PGVB/D1gUvQWalJ6EL2Tew/eQHPj+qHxIbRaBlfD88M7YWdh08ht6Co2mPPXsnBup2H8MLofpX2XczJQ2FJGcbf0wX1I0MRWycMD/fvjLzCEmTfLLRK7EQkDyYqRA7q4sWL+PHHH6usSbmbqNcj++gelN7MtloMaq0eK/dewNwfjqFcq6/1+U6cvwZ/HxWa1v+7A3C7Jg0gCAJOXsiq8riycg3mf7EZUx5MRWigX6X99SNCEOjnjc2/H4VGq4O6XIv//n4UDaJCUSc0sNZxE5F8atUIrVarcejQIeTk5KBbt24IDw+3VlxETkOj1UGnr/2X+N02p/9k/nwnoojrpw8iptNAq8Zy6GIefvjzCh5Mrl+r89wsLEawv/EII6VSgUBfb+QVFld53Cff7ULz2Gh0bZlgcr+vtxfefXo45n72A1Zt2QcAqBcRjPlPDIVSyb/HiJyZxYnKBx98gLlz56KgoAAAsGXLFvTu3Ru5ublo1qwZ3n77bUycONFqgRI5Io1Wh1MXs1Citv6KxacyL0GhUEBvThIkCCgtuoVStWV9S6rz86lruL9NXXh6KCvtW711H77cut/wvlyjxYkLWfjo252GbUtfetii6+45moE/zl7GJ9PGVllGXa7Fv9ZsQYu4unj54YHQ6UWs23kQsz7dgI+eGwOVjToGE5HtWfT0Llu2DM899xxGjx6N/v37GyUk4eHh6N27N7766ismKuTydHo9StQaeHoo4Kms/AVeG8HBQeYlKQAgivDw9oVCqNzhtDZEEfD1EKDT6+GJyp9zcJfWSGnTxPD+zVX/RffWjdH9jlE5YYH+CA3wQ35RidGxOp0et0rKEBJQuUkHAA6fuYRrN/LxwP99bLT91eUb0TK+Lt6bPALbD51E1s1CLJg6GgrF7c8+86GBGDbrY+w5loHUtk0t/uxEJC+LEpX33nsPQ4YMwerVq3Hjxo1K+5OTk/HBBx/UOjgiZ+GpVMLL07p/tffomQJBEMxr/hEEhDVuB8HKiUqASolejYKr3B/o541AP2/Dey9PDwT7+6BehPExibHRKCpV4/SlbDT5Xz+VP85egiiKaNawjslzj+7TAQM7tzTa9sQ7X+DJIT3RuUU8AECt0UIhAHd+7NvJmgCRc8MQOTWLGm/Pnj2LgQOrbgMPDQ01mcAQkXR168WgV59+UEqsqREUCoQ37wLvEPNnqq1OnUAVpvasj2Afz1qfq2FUKDo0a4h/f70VJy9k4WjmVXz07Q70SmqK8CB/AEBufhEmvrnC0Lk2NNAPcdHhRi8AiAwJQHRYEIDbHXILS9X48JsduJB9E+ezbuCdr36CUiGgTePa9ashInlZ9CdgcHAwcnNzq9x//Phx1Klj+q8jIpLu6Wf/gZ93bpdYsyIgru9DVr1+cv1AjE2OhgKi1frhzBg3EB99uwPTP/kGgiCgR+tGmDy0l2G/Vq/DpZw8qDXSr9cgKhSvPXY/Vv70O55d8BUUgoCEmEj884mhCDMxSoiInIcgWrCM6sSJE7F9+3YcPnwYOp0OERER2Lp1K3r37o1jx46hU6dOmDhxosM3/xw6dAjJyck4ePAg2rVrJ3c45ITKyjU4knEFvipPqzf9VPjpvxvxj8lpEEXR5FBlQaEAIKDVI68gslUPq1xTIQgY1iYKvRqFQBAElGu0KFFr0DqhHry9al+zQkQklUVNP6+//jp0Oh1atmyJWbNmQRAErFixAg899BDat2+PyMhIvPLKK9aOlcgt9R84CF+u/wE9U/tU7nsiCAhL7Iz2Uz60WpISHajCC71jkdo41Op9XYiIzGXRn4B169bFwYMH8fLLL2PNmjUQRRErV65EQEAAxowZgzfffJNzqhBZUes2bfHJZ5/j6pXLGDKgD27dKoCHjz86T1titT4pvl5KDGgWjl6NQuDBuUeIyEFYXFcdGRmJTz/9FJ9++imuX78OvV6PiIgIKBT8BUdkK3XrxcDH1xe3bhVA6eVtlSTF11OJ1Cah6NUoFL5e1h1iTURUW1ZpVI+I4OqkRM4m2McDqY3D0D0+GN6eTFCIyDFZVP0xa9YsJCUlVbm/bdu2mDdvnqUxEZENxYb64NFO9fDqvY3Rt2kYkxQicmgWJSrr1q2rdh6Ve++9F2vWrLE4KCKyLk+FgE4NgzC9Txxe7BOH9g2CoFSwoywROT6Lmn4uXryIhATTi4MBQFxcHC5cuGBxUERkHX5eSvRqHIoe8SEI8OZ6N0TkfCz6zeXv719tIpKZmQlvb+8q9xORbXkqBfRvFo7ejUPZtENETs2ipp9evXph0aJFuHLlSqV9ly5dwuLFi5Gamlrr4IjIfPFhPpjVPwH3No9gkkJETs+iGpXXXnsNHTt2RIsWLfDYY4+hRYsWAICjR4/is88+gyiKeO2116waKBHVrH39QDzcoS7nQSEil2FRotK0aVP8/PPPmDJlCv79738b7evZsyc++OADJCYmWiVAIpKmeR1/PNKxHjvJEpFLsbh3XevWrbFr1y7k5ubi3LlzAID4+HjOSEtkY+ERESjX6aFVBRm2RQV44dFOTFKIyPXUehhAeHg4kxMiO/p24084cLEAy36/3UfMX6XEk90acFZZInJJFicqOp0O6enpOHfuHPLy8iotQS8IAmbPnl3rAImoah4KAU92q4/IAC+5QyEisgmLEpUDBw7gwQcfxOXLlyslKBWYqBDZ3qAWEYgL85U7DCIim7EoUXn66adRWlqK7777Dj169EBwcLCVwyJruXjxIrZt24bCwkIEBASgT58+aNCggdxhkRUEensgtXGo3GFQLfD5JKqZRYnKkSNH8MYbb+C+++6zdjxkJfv27cNrr72GjRs3QhRFKBQK6PV6CIKAwYMHY/bs2ejQoYPcYVIttG8QCE8OQ3ZKfD6JpLPot1xMTEyVTT4kv2+//RbdunXDf//7X8N90uv1AABRFLFp0yZ07doV3377rZxhUi01jfSTOwSyAJ9PIvNYlKi89NJLWLJkCW7dumXteMz2n//8B7GxsfD29kanTp2wb98+uUOS1b59+zBq1CjodDrodDqTZSr2jRo1Cvv377dzhGQt0YEquUMgM/H5JDKfRU0/hYWF8Pf3R6NGjTB69GjUr18fSqXx0EhBEPCPf/zDKkFWZc2aNXj++efxySefoFOnTnj//fcxYMAAnDp1CpGRkTa9tqN6/fXXIYpijTVeFWVef/11bNiwwU7RkbUIAhDs4yl3GGQmPp9E5hNEC9pwFIqaK2IEQajyLwZr6dSpEzp06ICPPvoIwO3q0/r162PKlCmYMWNGjccfOnQIycnJOHjwINq1a2fTWO3h4sWLiI2NNatZThAEnD9/nh34LFRWrsGRjCvwVXnCy9N+qxOfuV6MxhH2a/op12hRotagdUI9eHsxQbIEn08iy1j0mzUzM9PacZitvLwcBw8exMyZMw3bFAoF+vbti99++83kMWq1Gmq12vC+qKgIAKDVaqHRaGwbsB2kp6eb3XdIFEX89NNPGD9+vI2icm0ajQZajQYlog4ajf0mXPPQa1BcUmq362l0Omi1emg0Gig5+a1F+HzKR6PTQK+37R/OjkChUMJTad8/JDw97XA90UlduXJFBCDu2bPHaPuLL74oduzY0eQxc+bMEQHwxRdffPHFF19WeNlDreqqr1y5gt27dyMnJwcPPvggYmJioNPpUFBQgKCgoEr9VuQ2c+ZMPP/884b3hw8fRkpKCn7//Xe0bdtWxsisY/ny5XjiiSfMPm7JkiX8i60WNFoddP8btWEvRWVa+Hvbr6kJAJQKBTw9HOuZdiZ8PuWh1pTh2IVjUHrYv7bBnjQ6DXRaHVo0bAGVp7fc4ViVRb/pRFHEtGnT8NFHH0Gr1UIQBLRq1QoxMTEoKipCbGwsXn31VTz33HNWDvdv4eHhUCqVyM7ONtqenZ2NOnXqmDxGpVJBpfp7pIS/vz8AwMPDwz7VVzY2YMAACIJgdht4//79XeLzy0WOn53SUwtfL/smKlQ7fD7loYcOHp4e8FH5wMvDdZeaKNeWo1RdCk9PT5f792LR8OR33nkHCxYswAsvvIAtW7YYPXhBQUEYNmwYvvnmG6sFaYqXlxeSk5Oxbds2wza9Xo9t27ahS5cuNr22o2rQoAEGDx4suSZLqVTivvvuY0c9J6QQ2FHE2fD5JLKMRYnKkiVL8Mgjj+Cf//wnkpKSKu1v3bo1Tp8+XdvYavT8889jyZIlWLFiBU6cOIGnnnoKxcXFePTRR21+bUc1e/ZsCIIAoYYvsooys2bNslNkZE3enmyCcUZ8PonMZ1GicunSJXTt2rXK/X5+fnaZDG7UqFF499138corryApKQmHDx/G5s2bERUVZfNrO6oOHTpgzZo1UCqVVf7lVrHv66+/5jTdRHbE55PIfBYlKpGRkbh06VKV+w8ePGi36spnnnkGFy5cgFqtxu+//45OnTrZ5bqObNiwYdizZw/uvfdew19uFXPfCIKAQYMGYc+ePRg6dKicYRK5JT6fROaxqDfesGHD8Mknn2DChAkICgoCAMMD99NPP2H58uWYPn269aIks3Xo0AHff/89Ll68iO3bt+PWrVsIDAxE79692eZNJDM+n0TSWTQzbUFBAXr27InMzEz06NEDmzdvRr9+/VBUVITffvsNbdu2xe7du+Hr62uLmK3G1WamJSIiY2pNGY5eOOo2o35aNmzpcsOTLWr6CQoKwt69ezF9+nRcuXIF3t7e2LVrF/Lz8zFnzhz8/PPPDp+kEBERkeMzu+mnrKwMixcvRlJSEmbNmsVe6URERGQzZteoeHt746WXXsKpU6dsEQ8RERGRgUVNPy1btsT58+etHAoRERGRMYsSlTfeeAOLFi3C1q1brR0PERERkYFFw5M/+ugjhIaGYsCAAYiLi0NcXBx8fHyMygiCgA0bNlglSCIiImei1WvgoXCtNXfkYlGicuTIEQiCgAYNGkCn0+Hs2bOVytQ0RTQRERFRTSxKVNg/hYiIqGqCZT0ryAT+JImIiKyMrQrWY3GiotPp8NVXXyEtLQ1Dhw7FX3/9BeD2rLXffvstsrOzrRYkERERuSeLEpX8/Hx069YNY8eOxZdffonvv/8e169fBwD4+/tj6tSpWLBggVUDJSIiIvdjUaIyY8YMHDt2DOnp6Th37hzuXC5IqVRi+PDh2LRpk9WCJCIiIvdkUaLy3XffYcqUKejXr5/JdrgmTZqwwy0REbkxs9f7pSpYlKgUFBQgLi6uyv0ajQZardbioIiIiJyZXmSiYi0WJSoJCQk4dOhQlft/+uknNG/e3OKgiIiInJnIGhWrsShRmTRpEj777DOsWbPG0D9FEASo1Wr83//9HzZv3oy0tDSrBkpEROQsRFEvdwguw6IJ35599lkcO3YMY8aMQXBwMABg7NixuHHjBrRaLdLS0vDYY49ZM04iIiKnwfoU67EoUREEAUuWLMH48eOxbt06nDlzBnq9HgkJCRg5ciR69uxp7TiJiIichk7UyR2Cy5CUqAwbNgz/+Mc/0KNHDwDA7t27kZiYiO7du6N79+42DZCIiMjZiOxMazWS+qhs2LABFy9eNLxPTU3Fli1bbBYUERGRM2ONivVISlTq1auHP/74w/BeFEWuY0BERFQFPTvTWo2kpp/Ro0fj3Xffxddff23oPDtjxgzMnz+/ymMEQcCff/5plSCJiIicCRMV65GUqMyfPx+NGjXCjh07kJOTA0EQ4Ofnh7CwMFvHR0RE5HT0bPqxGkmJilKpxBNPPIEnnngCAKBQKDBr1iyMHTvWpsERERE5I9aoWI+kPirt2rXD5s2bDe+XLVuGtm3b2iwoIiIiZ6YVuYyMtUhKVI4cOYLc3FzD+4kTJxp1riUiIqK/qXVlcofgMiQlKg0bNsTWrVuh091uc+OoHyIioqoVaYrlDsFlSEpUnnzySXz++efw9vZGYGAgBEHAY489hsDAwCpfQUFBto6diIjIIRWU58sdgsuQ1Jn2xRdfRJs2bbBjxw5kZ2djxYoV6NChA+Lj420dHxERkdO5WXZD7hBchuS1fvr374/+/fsDAJYvX460tDSO+iEiIjIht+y63CG4DIsWJdTrOeyKiIioKvnqfKh1aqiUKrlDcXqSEpWKdX4aNGhg9L4mFeWJiIjcTU5pNur783uwtiQlKrGxsRAEAaWlpfDy8jK8r0nFKCEiIiJ3c634KhMVK5CUqHz22WcQBAGenp5G74mIiMi0i0UX0DGqs9xhOD1JicqECROqfU9ERETGzhVkcN4xK5A0jwoRERGZJ788D9dLc+QOw+lJqlF59dVXzT6xIAiYPXu22ccRERG5ir9uHkEf335yh+HUJCUqc+fOrbStoipLFMVK2yuqupioEBGROzucexCp9fpAIbABw1KSfnJ6vd7odenSJbRq1QpjxozBvn37UFBQgIKCAvz+++8YPXo02rRpg0uXLtk6diIiIod2o+wGTuefkjsMp2ZRijd58mQ0btwYX3zxBdq3b4+AgAAEBASgQ4cOWLVqFRISEjB58mRrx0pEROR0tl5Oh17kRKmWsihR2b59O3r37l3l/j59+mDbtm0WB0VEROQqrhZfwe/Zv8kdhtOyKFHx9vbGb79V/UPfs2cPvL29LQ6KiIjIlWy68COySrLkDsMpWZSojBs3DqtWrcLUqVNx5swZQ9+VM2fOYMqUKVi9ejXGjRtn7ViJiIgc3tB7huHzh1dj64s7Ddt0oharTq9AqbZUvsCclEWLEr711lvIzc3FRx99hP/85z9QKG7nO3q9HqIoYsyYMXjrrbesGigREZEzuH79OopvlEAvGrcs3CjLxZqzq/BI04kcBWQGixIVLy8vrFy5Ei+++CI2bdqECxcuAAAaNmyIgQMHok2bNlYN0pQ33ngDGzduxOHDh+Hl5YX8/HybX5OIiKg2TuefwuaLG3Fvw/vkDsVpWJSoVGjdujVat25trVjMUl5ejhEjRqBLly5YunSpLDEQERGZ65druxHpE4X2kR3lDsUp1CpRkdO8efMAAMuXL5c3ECIiIjOtP/cNQlShSAhqJHcoDs+tGsnUajVu3bpleBUVFckdEhERuSERenxxejmuFl+VOxSH51aJyvz58xEUFGR4paSkyB0SERG5KbVOjaUnFuEak5VqOVSiMmPGDAiCUO3r5MmTFp9/5syZhun+CwoKsGvXLitGT0REZJ5SbQmWHP8E529lyh2Kw3KoPirTpk3DhAkTqi0THx9v8flVKhVUKpXhvb+/v8XnIiIisoYyXSmWnliM4Qkj0Sa8rdzhOByHSlQiIiIQEREhdxhERER2pRO1WHN2NbJLstC3/gDOs3IHixOV9PR0LF26FOfOnUNeXh5EUTTaLwgCMjIyah1gVS5evIibN2/i4sWL0Ol0OHz4MACgUaNGrCkhIiKntPPqdlwruYZRjcbC24NL0QAWJirvvPMOZsyYgaioKHTs2BGtWrWydlw1euWVV7BixQrD+7Ztb1eX7dixA7169bJ7PERERNZwKv8EPjn2ER5pOhGh3qFyhyM7ixKVBQsWoHfv3ti0aRM8PT2tHZMky5cv5xwqRETkknJKs/HJsY8wodkk1PWrK3c4srKoESwvLw/Dhw+XLUkhIiJydUWaQiw9/gmuFF2WOxRZWZSodOzYEadOnbJ2LERERHSHUl0pPjuxGFklWXKHIhuLEpWFCxfi22+/xerVq60dDxEREd2hIlnJLcuVOxRZWNRHZdSoUdBqtXj44Yfx1FNPISYmBkql0qiMIAj4888/rRIkERGRO6toBprU/EmEeYfLHY5dWZSohIaGIiwsDI0bN7Z2PERERGRCQXkBlhz/BI82m4Qo3zpyh2M3FiUqO3futHIYREREVJNb5QVYfGwhHmk2EQ0DYuUOxy449R0REZETKdWVYumJRTiZd0LuUOyiVlPoazQanDx5EgUFBdDr9ZX29+zZszanJyIiIhO0ei1WnlqOkY3GoE14ktzh2JRFiYper8fMmTOxcOFClJSUVFlOp9NZHBgREZGzuXr5KkpLSgEAWrUWJddL4Bvha5NridDj67Or4aFQonFgU5tcwxFYlKj885//xDvvvIO0tDR0794dDz/8MN566y0EBwdj4cKFEAQBb7/9trVjJSIickh//vEn/vPvhdi5badh7TtNsRYbn9yC6OQoNB/RFKGNQ6x+XREivj77JR5PfAoBikCLzrHn5K/YfHATMrLOorC0EO9P+hDxdRJqPGbdr2tw7eY1aPVa1A2thwc6DUVq6z6GMqXlpVixfRl+P/UbCksLERUchcEd7sfA5EFmxWdRorJ8+XKMHDkSH3/8MW7cuAEASE5ORu/evTF+/Hh06dIF27dvR9++fS05PRERkdNI35SO5578B0RRrLRAL0Qg61AOsv7IQedp7RHT2frT4Wv0Gmy+uBEjYsdYdLy6vAzN67dA9+Y98NHGDyQdE+AdgBHdRiMmPAYeCk/sP/s7FvzwbwT5BaNdQjIAYOmWJThy/k88P+RFRAZH4Y9zh/DJf/+D0IAwdGrSWXJ8FnWmvXz5Mnr37g0AUKlUAICysjIAgJeXFx566CGsXLnSklMTERE5jT//+BPPPfkP6HS6Krs7iHoRok7E3vcO4OaZPJvEcfbWWRRriiw6NrV1H4zuORZt4tpKPqZVbGt0adYV9cMbIDo0Gvd3fACxUXE4fumYoczJyyfQu3UftIptjajgKNzTbiDiouJx5op5M9tblKiEhYWhqOj2D8Tf3x+BgYE4d+6cUZm8PNvcDCIiIkex8P2PTdekmCICJ9adtlksxdpim527OqIo4s/Mw7hy4zJaNGhp2N4sJhH7Tv+OG7dyIYoijpz/E1dvXkFSfDuzzm9R00/btm2xf/9+w/vU1FS8//77aNu2LfR6PT744AO0adPGklMTERE5hauXr2LH1h3SkhTcrlm5eiDLJh1slYICwV7BVj1nTYrLivHogoeh0WmgEBR4cuBktL0jCUkb8BQ+2vgBHv3gESgVSgiCgGcGPYuWDVuZdR2LEpUnnngCy5cvh1qthkqlwhtvvIGePXuiZ8+eEEURISEh+PLLLy05NRERkdVpdBqrn/Pn3T9LTlIMRCDryHXE9m5g1VgSAuPhpVTVWG7nXzuwcNOHhvdzxrxqVAtiDh+VD95//COUlZfiz/N/4rMtS1AnuA5axbYGAPy4/3ucvnISs0bOQURQJI5dPIpFmxci1D8USfHSm5ksSlTuv/9+3H///Yb3zZs3R0ZGBnbu3AmlUomuXbsiNDTUklMTERFZjUKhhLeXN8rKy6DVaq167vz8fCgUCpPziFVJADQlGvOOkaBFUCt4e3lDoVBWW65jk05oUu/vocxhAWEWX1MhKFA39Hbn4Pg6CbicexHr9nyNVrGtodaosXLHCswcMQsdGncEAMRFxSEzOwPr935r+0TFlKCgIAwZMsRapyMiIqo1T6UnmtRrCr3e+vN6NW7YxPyEQwR8A33ho/KxWhwqpQoPtBoGH09feCo9qy3rq/KFr8o287roRREa7e2aK51eB61eC4UgGJVRCEqIonk/M4sTFZ1Oh7Vr12LHjh3IycnBq6++ilatWqGgoADbtm1Dt27dEBUVZenpiYiIrMJT6QnU8AVuiXv63wNBEMxr/hGA6NaRlb7AayMpqi0CvYMsPr6wtBDXC3Jws+gmAODKjcsAgBD/EIT4324d+feGdxEaEIbxvR8FAKz9dQ0aRTdGdEg0NDoNDpw9gJ1/bcdTAycDuJ0QtWzQCsu2fQYvD9X/mn7+wo6/tmFiv8fNis+iRCU/Px/33HMP9u3bB39/fxQXF2PKlCkAbo8Cmjp1Kh555BH885//tOT0REREDq9BgwYYPHgwNm3aJGkmdkEhoF77aPhF+lk1jo7R0uckMWXf6b1Y8MO/De/fWf8WAGB0j7EYm/IQAOB6wXUIwt8DhdXlZfjkvwtxozAXXh5eiAmvj+eHvIAeLVIMZV4c9hI+374c7214B0WlhYgIisRDvR7BwHb3mhWfIJrdEwh48sknsWrVKnz77bdo27YtIiMjsXXrVsPcKs899xx27tyJw4cPm3tquzp06BCSk5Nx8OBBtGtn3nApIiKi/fv3o2vXrtDpdDXWrAhKAQPe7oPwJtbrw+nvGYAFfRfCS+lltXM6GovmUfnuu+8wZcoU9OvXD4KJ6qsmTZrg/PnztY2NiIjIoXXo0AFr1qyBUqmEUmm6I6ugECAoBfR4qYtVkxQAuCd+kEsnKYCFiUpBQQHi4uKq3K/RaKzeu5qIiMgRDRs2DHv27MG9995b+Y93AajXPhoD3u6DBl1irHrdcJ9IDIw3b90cZ2RRH5WEhAQcOnSoyv0//fQTmjdvbnFQREREzqRDhw74/vvvcfHiRbRp0wb5+fnw8vPEoA/6W71PCgAIEJCW9JTL16YAFtaoTJo0CZ999hnWrFljaJMTBAFqtRr/93//h82bNyMtLc2qgRIRETm6Bg0awM/vdmLi4e1hkyQFAO5vNBTNwtyjQsCiGpVnn30Wx44dw5gxYxAcHAwAGDt2LG7cuAGtVou0tDQ89thj1oyTiIiIALQIb4VhTUfIHYbdWJSoCIKAJUuWYPz48Vi3bh3OnDkDvV6PhIQEjBw5Ej179rR2nERERG6vjl80nmn3HBSCRQ0iTqlWM9N2794d3bt3t1YsREREVIVAryC80HEG/L385Q7FrtwnJSMiInJS/p4BmNF5FqL86sgdit1JrlG5cxFCKQRBwIYNG8wOiIiIiP4W4BWAlzrNQv1A66647CwkJyo//vgjvL29UadOHUnrGpiaCI6IiIikC1aF4KXO/4eYgPpyhyIbyYlKvXr1cOXKFYSHh2Ps2LEYPXo06tRxvyooIiIiewj3icSMzv/nls09d5LcR+XSpUvYsWMH2rZti9deew3169dH3759sWzZMhQWFtoyRiIiIrdS178eZned6/ZJCmBmZ9qUlBQsWrQIWVlZWLduHcLCwvDMM88gMjISw4YNw7p166BWq20VKxERkcuLD26E2V3nIdQnTO5QHIJFo348PT0xZMgQrFmzBtnZ2YbkZdSoUXj77betHSMREZFbaBneGjM7z4K/V4DcoTiMWs2jolarkZ6ejg0bNuCPP/6At7c3YmNjrRQaERGR++gU3QVPtp0MD4Wn3KE4FLMTFb1ejy1btuDLL7/Ed999h5KSEvTt2xdLlizB0KFDDWscEBERkTQ9YnphUps0t5pxVirJicqePXuwevVqrF27Fjdu3EDnzp3xz3/+EyNHjkR4eLgtYyQiInJZ3WNSmKRUQ3Ki0r17d/j4+ODee+/FmDFjDE08Fy9exMWLF00e065dO6sESURE5IraRiVjUmsmKdUxq+mntLQU33zzDb799ttqy4miCEEQoNPpahUcERGRq4oNisfTbadCqVDKHYpDk5yoLFu2zJZxEBERuY1gVQj+0f4FeHt4yx2Kw5OcqIwfP96WcRAREbkFT4Unnmv/AudJkYiNYkRERHY0sfUTSAhpJHcYToOJChERkZ0MjB+M7jE95Q7DqTBRISIisoOmoYkY1Wys3GE4HSYqRERENubj4YOn2j7DET4WYKJCRERkY6MTH0KYDydHtYRTJirnz5/HY489hri4OPj4+CAhIQFz5sxBeXm53KEREREZiQlogF4NessdhtOq1aKEcjl58iT0ej0WLVqERo0a4ejRo3j88cdRXFyMd999V+7wiIiIDB5oPIwzz9aCUyYq99xzD+655x7D+/j4eJw6dQoff/wxExUiIpJVnTp1UKAugFewJ0K8Q9G+Tke5Q3JqTpmomFJQUIDQ0NBqy6jVaqjVasP7oqIiW4dFRERu5sCBA3hxxz+QVXwVnet2ZQfaWnKJuqizZ8/iww8/RFpaWrXl5s+fj6CgIMMrJSXFThESEZE7Sork4ry15VCJyowZMyAIQrWvkydPGh1z5coV3HPPPRgxYgQef/zxas8/c+ZMFBQUGF67du2y5cchIiI3JkDBGWitwKGafqZNm4YJEyZUWyY+Pt7w/1evXkVqaiq6du2KxYsX13h+lUoFlUpleO/v729xrERERNWJ9o+GSqmquSBVy6ESlYiICEREREgqe+XKFaSmpiI5ORnLli2DQuFQlUNEROTm6vrHyB2CS3CoREWqK1euoFevXmjYsCHeffddXL9+3bCvTp06MkZGRER0W13/unKH4BKcMlHZsmULzp49i7NnzyImxjhjFUVRpqiIiIj+Vte/ntwhuASnbC+ZMGECRFE0+SIiInIE9QLY9GMNTpmoEBEROTJBEFDHL1ruMFwCExUiIiIrC/UOhbeHt9xhuAQmKkRERFYW7iNtBCvVjIkKERGRlYX6VL+kC0nHRIWIiMjKglUhcofgMpioEBERWVmAV4DcIbgMJipERERWxkTFepioEBERWVmT0GZyh+AymKgQERFZmULg16u18CdJREREDouJChERETksJipERETksJioEBERkcNiokJEREQOi4kKEREROSwPuQMg+7h27RquXbsmdxhkJdHR0YiO5hLyroLPp+vhM2o9bp2oREdHY86cOS7/j0mtVmPMmDHYtWuX3KGQlaSkpCA9PR0qlUruUKiW+Hy6Jj6j1iOIoijKHQTZ1q1btxAUFIRdu3bB399f7nColoqKipCSkoKCggIEBgbKHQ7VEp9P18Nn1LrcukbF3SQlJfGhcQG3bt2SOwSyAT6froPPqHWxMy0RERE5LCYqRERE5LCYqLgBlUqFOXPmsFOXi+D9dC28n66H99S62JmWiIiIHBZrVIiIiMhhMVEhIiIih8VEhYiIiBwWExUiIiJyWExUiGxAEARJr507d9b6WiUlJZg7d65Z53rjjTdw//33IyoqCoIgYO7cubWOg8hZOPLzefLkSUyfPh1JSUkICAhAdHQ0Bg0ahAMHDtQ6FmfFmWmJbGDlypVG7z///HNs2bKl0vbExMRaX6ukpATz5s0DAPTq1UvSMbNmzUKdOnXQtm1bpKen1zoGImfiyM/np59+iqVLl+LBBx/E008/jYKCAixatAidO3fG5s2b0bdv31rH5GyYqBDZwEMPPWT0fu/evdiyZUul7XLJzMxEbGwscnNzERERIXc4RHblyM/nmDFjMHfuXKN1nyZOnIjExETMnTvXLRMVNv0QyUSv1+P9999HixYt4O3tjaioKKSlpSEvL8+o3IEDBzBgwACEh4fDx8cHcXFxmDhxIgDg/PnzhkRj3rx5hirrmppyYmNjbfGRiFyGXM9ncnJypcUpw8LC0KNHD5w4ccK6H9JJsEaFSCZpaWlYvnw5Hn30UUydOhWZmZn46KOP8Mcff+DXX3+Fp6cncnJy0L9/f0RERGDGjBkIDg7G+fPn8e233wIAIiIi8PHHH+Opp57C0KFDMWzYMABA69at5fxoRE7P0Z7PrKwshIeHW/UzOg2RiGxu8uTJ4p2P288//ywCEFetWmVUbvPmzUbb169fLwIQ9+/fX+W5r1+/LgIQ58yZY3ZctTmWyFU46vNZYffu3aIgCOLs2bMtPoczY9MPkQzWrl2LoKAg9OvXD7m5uYZXRbXvjh07AADBwcEAgB9//BEajUbGiInchyM9nzk5ORg7dizi4uIwffp0m1zD0TFRIZLBmTNnUFBQgMjISERERBi9ioqKkJOTAwBISUnBgw8+iHnz5iE8PBxDhgzBsmXLoFarZf4ERK7LUZ7P4uJiDB48GIWFhdiwYUOlvivugn1UiGSg1+sRGRmJVatWmdxf0QFPEASsW7cOe/fuxQ8//ID09HRMnDgR7733Hvbu3eu2v7iIbMkRns/y8nIMGzYMR44cQXp6Olq2bGnxuZwdExUiGSQkJGDr1q3o1q0bfHx8aizfuXNndO7cGW+88QZWr16NcePG4auvvsKkSZMgCIIdIiZyH3I/n3q9Ho888gi2bduGr7/+GikpKZZ8DJfBph8iGYwcORI6nQ6vvfZapX1arRb5+fkAgLy8PIiiaLQ/KSkJAAzVy76+vgBgOIaIakfu53PKlClYs2YNFi5caBgp5M5Yo0Ikg5SUFKSlpWH+/Pk4fPgw+vfvD09PT5w5cwZr167FggULMHz4cKxYsQILFy7E0KFDkZCQgMLCQixZsgSBgYG49957AQA+Pj5o3rw51qxZgyZNmiA0NBQtW7astqp45cqVuHDhAkpKSgAAu3fvxuuvvw4AePjhh9GwYUPb/xCIHJScz+f777+PhQsXokuXLvD19cUXX3xhtH/o0KHw8/Oz+c/Aocg97IjIHdw9/LHC4sWLxeTkZNHHx0cMCAgQW7VqJU6fPl28evWqKIqieOjQIXHMmDFigwYNRJVKJUZGRoqDBw8WDxw4YHSePXv2iMnJyaKXl5ekoZApKSkiAJOvHTt2WOtjEzkFR3o+x48fX+WzCUDMzMy05kd3CoIo3lVvRUREROQg2EeFiIiIHBYTFSIiInJYTFSIiIjIYTFRISIiIofFRIWIiIgcFhMVIiIiclhMVIgczPnz5yEIApYvXy53KERkAp9R+2KiQkRERA6LE74RORhRFKFWq+Hp6QmlUil3OER0Fz6j9sVEhYiIiBwWm36IbGDu3LkQBAGnT5/GQw89hKCgIERERGD27NkQRRGXLl3CkCFDEBgYiDp16uC9994zHGuq/XvChAnw9/fHlStX8MADD8Df3x8RERF44YUXoNPpDOV27twJQRCwc+dOo3hMnTMrKwuPPvooYmJioFKpEB0djSFDhuD8+fM2+qkQOQ4+o86DiQqRDY0aNQp6vR5vvvkmOnXqhNdffx3vv/8++vXrh3r16uGtt95Co0aN8MILL2D37t3Vnkun02HAgAEICwvDu+++i5SUFLz33ntYvHixRbE9+OCDWL9+PR599FEsXLgQU6dORWFhIS5evGjR+YicEZ9RJyDXaohErmzOnDkiAPGJJ54wbNNqtWJMTIwoCIL45ptvGrbn5eWJPj4+4vjx40VRFMXMzEwRgLhs2TJDmYoVVV999VWj67Rt21ZMTk42vN+xY4fJFZDvPmdeXp4IQHznnXes84GJnAyfUefBGhUiG5o0aZLh/5VKJdq3bw9RFPHYY48ZtgcHB6Np06Y4d+5cjed78sknjd736NFD0nF38/HxgZeXF3bu3Im8vDyzjydyFXxGHR8TFSIbatCggdH7oKAgeHt7Izw8vNL2mn4ZeXt7IyIiwmhbSEiIRb/EVCoV3nrrLfz3v/9FVFQUevbsibfffhtZWVlmn4vImfEZdXxMVIhsyNTQxaqGM4o1DMCTMgxSEAST2+/szFfhueeew+nTpzF//nx4e3tj9uzZSExMxB9//FHjdYhcBZ9Rx8dEhciFhISEAADy8/ONtl+4cMFk+YSEBEybNg0//fQTjh49ivLycqPRDURkXXxGzcdEhciFNGzYEEqlstLohIULFxq9LykpQVlZmdG2hIQEBAQEQK1W2zxOInfFZ9R8HnIHQETWExQUhBEjRuDDDz+EIAhISEjAjz/+iJycHKNyp0+fRp8+fTBy5Eg0b94cHh4eWL9+PbKzszF69GiZoidyfXxGzcdEhcjFfPjhh9BoNPjkk0+gUqkwcuRIvPPOO2jZsqWhTP369TFmzBhs27YNK1euhIeHB5o1a4avv/4aDz74oIzRE7k+PqPm4RT6RERE5LDYR4WIiIgcFhMVIiIiclhMVIiIiMhhMVEhIiIih8VEhYiIiBwWExUiN3b+/HkIgoDly5fLHQoRmcBnlIkKkWQZGRlIS0tDfHw8vL29ERgYiG7dumHBggUoLS212XWPHz+OuXPn4vz58za7hhRvvPEG7r//fkRFRUEQBMydO1fWeIju5s7P6MmTJzF9+nQkJSUhICAA0dHRGDRoEA4cOCBbTNbCCd+IJNi4cSNGjBgBlUqFRx55BC1btkR5eTl++eUXvPjiizh27BgWL15sk2sfP34c8+bNQ69evRAbG2uTa0gxa9Ys1KlTB23btkV6erpscRCZ4u7P6KeffoqlS5fiwQcfxNNPP42CggIsWrQInTt3xubNm9G3b19Z4rIGJipENcjMzMTo0aPRsGFDbN++HdHR0YZ9kydPxtmzZ7Fx40YZI/ybKIooKyuDj4+P1c+dmZmJ2NhY5ObmVlrKnkhOfEaBMWPGYO7cufD39zdsmzhxIhITEzF37lynTlTY9ENUg7fffhtFRUVYunSp0S/ACo0aNcKzzz5reK/VavHaa68hISEBKpUKsbGxePnllystJBYbG4vBgwfjl19+QceOHeHt7Y34+Hh8/vnnhjLLly/HiBEjAACpqakQBAGCIGDnzp1G50hPT0f79u3h4+ODRYsWAQDOnTuHESNGIDQ0FL6+vujcuXOtflnLWZtDVB0+o0BycrJRkgIAYWFh6NGjB06cOGHROR0FExWiGvzwww+Ij49H165dJZWfNGkSXnnlFbRr1w7//ve/kZKSgvnz55tcSOzs2bMYPnw4+vXrh/feew8hISGYMGECjh07BgDo2bMnpk6dCgB4+eWXsXLlSqxcuRKJiYmGc5w6dQpjxoxBv379sGDBAiQlJSE7Oxtdu3ZFeno6nn76abzxxhsoKyvD/fffj/Xr11vhp0LkOPiMVi0rKwvh4eFWO58sRCKqUkFBgQhAHDJkiKTyhw8fFgGIkyZNMtr+wgsviADE7du3G7Y1bNhQBCDu3r3bsC0nJ0dUqVTitGnTDNvWrl0rAhB37NhR6XoV59i8ebPR9ueee04EIP7888+GbYWFhWJcXJwYGxsr6nQ6URRFMTMzUwQgLlu2TNLnE0VRvH79ughAnDNnjuRjiGyFz2jVdu/eLQqCIM6ePdvsYx0Ja1SIqnHr1i0AQEBAgKTymzZtAgA8//zzRtunTZsGAJWqdZs3b44ePXoY3kdERKBp06Y4d+6c5Bjj4uIwYMCASnF07NgR3bt3N2zz9/fHE088gfPnz+P48eOSz0/kyPiMmpaTk4OxY8ciLi4O06dPr9W55MZEhagagYGBAIDCwkJJ5S9cuACFQoFGjRoZba9Tpw6Cg4Nx4cIFo+0NGjSodI6QkBDk5eVJjjEuLs5kHE2bNq20vaI6+u44iJwVn9HKiouLMXjwYBQWFmLDhg2V+q44G476IapGYGAg6tati6NHj5p1nCAIksoplUqT20VRlHwtW4zwIXIWfEaNlZeXY9iwYThy5AjS09PRsmVLu13bVlijQlSDwYMHIyMjA7/99luNZRs2bAi9Xo8zZ84Ybc/OzkZ+fj4aNmxo9vWl/kK9O45Tp05V2n7y5EnDfiJXwWf0Nr1ej0ceeQTbtm3D6tWrkZKSYvY5HBETFaIaTJ8+HX5+fpg0aRKys7Mr7c/IyMCCBQsAAPfeey8A4P333zcq869//QsAMGjQILOv7+fnBwDIz8+XfMy9996Lffv2Gf3iLi4uxuLFixEbG4vmzZubHQeRo+IzetuUKVOwZs0aLFy4EMOGDTP7eEfFph+iGiQkJGD16tUYNWoUEhMTjWa93LNnD9auXYsJEyYAANq0aYPx48dj8eLFyM/PR0pKCvbt24cVK1bggQceQGpqqtnXT0pKglKpxFtvvYWCggKoVCr07t0bkZGRVR4zY8YMfPnllxg4cCCmTp2K0NBQrFixApmZmfjmm2+gUJj/N8rKlStx4cIFlJSUAAB2796N119/HQDw8MMPs5aGZMNn9HbitXDhQnTp0gW+vr744osvjPYPHTrUkFA5HbmHHRE5i9OnT4uPP/64GBsbK3p5eYkBAQFit27dxA8//FAsKyszlNNoNOK8efPEuLg40dPTU6xfv744c+ZMozKieHvY4qBBgypdJyUlRUxJSTHatmTJEjE+Pl5UKpVGwyCrOocoimJGRoY4fPhwMTg4WPT29hY7duwo/vjjj0ZlzBn6mJKSIgIw+TI1LJPI3tz5GR0/fnyVzycAMTMzs9rjHZkgimb0CCIiIiKyI/ZRISIiIofFRIWIiIgcFhMVIiIiclhMVIiIiMhhMVEhIiIih8VEhYiIiBwWExUiIiJyWExUiIiIyGExUSEiIiKHxUSFiIiIHBYTFSIiInJYTFSIiIjIYTFRISIiIof1/9wwHQvqDmqMAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(custom_palette=\"viridis\");\n", - "multi_2group.mean_diff.plot(custom_palette=\"Paired\");" - ] - }, - { - "cell_type": "markdown", - "id": "9062fecb", - "metadata": {}, - "source": [ - "#### A user-defined dictionary\n", - "\n", - "There are [many ways](https://matplotlib.org/users/colors.html) to specify matplotlib colours. Find one example below using accepted colour names, hex strings (commonly used on the web), and RGB tuples." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8b81a893", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "my_color_palette = {\"Control 1\" : \"blue\",\n", - " \"Test 1\" : \"purple\",\n", - " \"Control 2\" : \"#cb4b16\", # This is a hex string.\n", - " \"Test 2\" : (0., 0.7, 0.2) # This is a RGB tuple.\n", - " }\n", - "\n", - "multi_2group.mean_diff.plot(custom_palette=my_color_palette);" - ] - }, - { - "cell_type": "markdown", - "id": "4d0e3fd1", - "metadata": {}, - "source": [ - "For proportion plots (barplots and sankey), a color palette dict can also be supplied via `{1: first_color, 0, second_color}` where first_color and second_color are valid matplotlib colours." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6581fc9f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_prop_paired.mean_diff.plot(custom_palette={1: \"red\", 0: \"blue\"});" - ] - }, - { - "cell_type": "markdown", - "id": "b4687df4", - "metadata": {}, - "source": [ - "#### Color palette changes also now affect the effect size curve colors in paired plots\n", - "\n", - "Note: The first color in the custom palette is used for the control group. As in the example below, if `show_baseline_ec` is set to `False`, it wont be represented in the plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d1e34f90", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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82wx2j0MWqrTbbUilUrRaLXi93om9x5VKBbFYjI4D3ojBYIBsNou1tTUIBAI4HA622/8WwYryGMlkEr/3e7+HH/3oRzveE/qpT30Kv//7v0//fenSJTz22GO36hJZdki9Xkc4HIZYLIbdbkc8HketVqNpur2kcmu1GjKZDJrNJhQKBRYWFnadhuZyuXA4HJBIJEilUmi323A6nTdt20kE3+/301Q2qSWLRCK43W40Gg2kUikEAgGo1WpYrVZ2H+49Btl33Ol0oNFoUCwWYbPZJg5h1WoVkUgEarX6hoI8Go1QKBSQy+UAABaLZVelkF6vt/dv5g0KK8pjXLhwAYVCAQ888AD9v+FwiBdeeAH/43/8D3S73U03T5FINHEC3a+tQSx7p1wuIxqNQqFQQKfTIRAIgMPhwOPx7MkusNFoIJPJoF6vQyaT7cvIlMlkgkQiQSQSwerqKjwez8TraC8IhUJ4vV4EAgEEg0H4fL6J16tcLsfCwgLW19fZZrB7kHFBNpvNyGQyMBgME2sYyWFVpVLB6XRumaVhGAbr6+vIZDLo9/swGAyYmZnZ1eukVqthfX2d7cDeJexv4hhvfetbcfXq1Yn/+w//4T9gYWEBn/zkJ2/pEgKW/WFtbQ2JRAIqlQp8Ph/RaJRGBLsVnlarhUwmg2q1Sn2x99MDWKlUYmFhgTaAuVyumzb9F4vFm4R5Y1Sj1WqhVqvpmki2GezuZ1yQ7XY7nUcerxU3Gg2EQqGpDV/j1Go1msXRaDSwWq27PjAOh0PEYjGIRCJWlHcJK8pjKBQKHDlyZOL/ZDIZdDrdpv+/G2AYBul0GlKpFDKZ7KYjsTsd4tIll8vR6XTQ7/dht9t37UzU6XSQyWRQLpchEoluelxqO8RiMRYWFhCNRhEMBvfFZ5scIILBIEKhEDwezyax5XK5MJlMdE1kJpPB2toarFbrLfteWW4Ng8GAThe43W4kEgnweLwJ4W02mwiFQpDJZHC73VOFst1uI5VKoVar0azKXqcEEokERqMRlEol6vX6TX1/bzRYUb6H6fV6qFQqyOfzAK43BBGBlslkkEql98RCA4ZhkEwmUSgUwOfz0Wg0IJPJNjW33Ihxe0ChUAi73Q6dTnfLT/o8Hg9utxuZTIZGKONe3HtBJpNRYY5EIlveiEkzmMFgQDqdRjQapc5gbCnmzmdckL1eL003Lyws0MwQaXgkh7WNB7SNtphutxtqtXrP11Qul7G+vr4vvRJvRFhRvgHPPffcQV/CnhGJRDhy5AgGgwGazSZarRaazSZNWwLX65BEoMmfd9MvEsMwtKsYuB4BWiwWmM3mHYtav99HLpfD2toaeDwe9f3dj1TuTs0TOBwOrFYrJBLJvhmNyOVyuN1uhEIhRKPRbWuI5GZcr9eRSqXg9/uhVqsxOzt7z2dY7lYGgwECgQD6/T58Ph9KpRKq1Sq8Xi9t4Gu32wgGgxCJRJsE+VbYYvb7fSQSCWg0GjbjskdYUX4DwOfzoVKpJuqhvV4PzWaTinU2m8VoNAJw/QY9Hk1LpdI7si5EXLqSySQEAgG0Wi2cTueOU27En7pQKIDD4ey6s3Qn7NY8Yb+NRshKykgkgng8DofDse3jFQoFFhcXaTPY8vLynpp8WG4tGwW51WrRRRKkL6Hb7SIYDEIgEMDr9dLDNsMw9GA+Go1gMplgMpn25TAej8fB4XBgs9lu+nO9UWF/y96gCIVCCIVCuiuVYRh0u10q1M1mE+vr62AYBhwOh4ozEeuDHqXp9/tYXV1FNBqFRqOBzWbD3NzcjgR1OByiUCggn8+DYZh9vSntB8RoJBwOIxAI3PQKPbVaDYfDgWg0SiOiG0Gawcg4TKlUwszMDIxG4x15QHsj0e/3EQgEMBwOMT8/j8FggHg8Dr1eTxdJ9Ho9BAIB8Hg8+Hw+eqCqVCpIpVJb2mLeDMViEdVqFR6Phz3A3QTsM8cC4Hr6VCwWQywWU1MAhmFoyrvVaqFer2NtbQ3A9TroeMpbJpPdtvVs3W4XFy9eRCqVgs1mw/z8/I5qYNP8qe/UCHC/jUa0Wi1GoxHi8Th4PN6OLBW5XC7MZjP0ej0ymQzS6TStN7OL7w+GcUEmnfXhcBgymYwetoggczgcKsjNZhOpVAqNRgNKpXLHhjc7pdfrIZVKQa/X7+uEwhuRO+9uxHLHwOFwaGRMGA6HVKhJNE2MBQQCwaZGsv0WvGaziRdffBGVSgVHjhyBz+e74Umf+FNnMhkMBoOp/tR3IvttNKLX6zEajZBMJsHlcne8fIDP58Nms8FoNCKdTiMSiUAmk7HNYLeZjRGyQCDA6uoquFwubeQjPu8Mw2B+fp5uRiuXy5BKpfB6vTc9djeNWCwGHo9Hd5Oz7B1WlO9xSqXSvqabibH9uHlGv9+fEOp8Po/hcAjgerPZxkayvdZs8/k8XnzxRQDAmTNnbigqDMOgXC4jk8mg2+1Cq9XCYrHcdY1LG41G3G73nn+eRqMRw+EQmUwGPB5vV3tzpzWD7XWOlWV3kOiXiC3pO+h2u7TTmnRiD4dDuN1u5PP522KLWSgUUK/XN5nVsOwNVpTvYbrdLmKxGIDrYiqTySCXy2kku1+/QAKBYFMj2Xh9utVqoVKp0EYyiUQyEU1LJJJt07IMw8Dv9+PChQvQarU4c+bMDZufxv2p1Wr1vqfrbjfjRiOrq6s3ZTQyMzMzETHvdo6bNIOR7APbDHZrGRdkn88HkUiETCaDSqVCX9fEPKTb7UKj0SAYDALYvS3mbul0Okin0zAajayf+j7B/gbdw4hEItx///0TzVvjUex4l7VMJruhOO72a4tEItqgxDAMOp3OxLWUSiUwDAMul7upkYxEXt1uF6+//jpCoRDcbjcefvjhbW8w4/7USqUSdrt9X9Yk3gnsp9GI1WrFcDhEPB4Hl8vdUyOZTqeDRqOhayLZZrD9Z2OELBQKsb6+jmw2C6vVCrVajeFwiEAggEKhAKlUivX19dtySCLjiEKhcFdrH1m2hxXlexwejwelUjkRVW0UR9JlTcRxPJrer85MDocDiUQCiURCI7PRaIRWq0VT39VqFYVCgV53r9dDJBJBq9XCiRMncPTo0S1v9rfCn/pOZJrRiM1m21MkZLPZMBqNEIvFwOVy92QYQZrBiDNYOp2mzmBsM9jN0e12acMWEeRms4l4PA6dTgez2YzRaIRLly4hFovRn8PtKifkcjm0223Mz8+z9qz7CCvKb0A2dlkTcRyPYEnzFjEXGU8379cvIJfLhVwun2gWGgwGqNVqCAQCuHr1KtrtNjweD/r9PpaWljY1knW73VvqT30nsp9GI3a7nTYDeTyePafEBQIBbQZLpVK0GWxubu6eyVTcTsYFmez87vf7CIfDkEgksNlsaDabePnll1EoFLC4uIj5+fnb9lwTbwOz2cz+fPcZVpRZporjuLlIs9lEJpPBaDSiEe94NL2fp/JWq4VkMolSqQSPx4MjR45ApVJNHBpyuRxarRbW1tbQ7XbpxhsiVG8U9sNohMPhwOl0IhwOIxwOw+v13lRHtVgshsfjoc1gq6urbDPYLtkYIQsEAoxGI4RCIXA4HMzNzSEej+Py5cvo9/s4c+bMxOKJW81oNEI0GoVEItlxBz/LzmFFmWUq08xF2u02FcbxVDOfz5+IpvfSRDYajZBOp5HNZlGtVmEwGOD1eunXFwqFUKvV1Ke31WpBq9VCqVRCKBSi0WhgdXWVHhrGo2mxWHzP1jj3w2iEw+HA5XIhFAohFArB5/NBKpXe1HWNN4MRZzCj0YiZmRm2Q3cbOp3OhOkHyX7EYjE0m02o1Wr4/X5ks1koFAqcOHHitmeGyDTD4uLiPft7dZCwosyyI8Zdvch+VuKpPa2JbFwYyUjWVr/A7XYb0WgUjUYDvV4PBoMBbrd709hVNptFsVgEj8eDx+OBwWCgn3PjoWHc6ITUysev506fUd4N+2E0wuVy4fF4JlY+7kfWgTSD5fP5CWew8Z/dzcIwDEajEYbD4aY/ORwOlErlXSEeWwlyJpNBJBKBSCQCl8tFr9eD0WiEz+e77YLcaDSQz+cxOzv7hspK3U5YUWbZM9M8tcebyBqNBorFIoDrN/2N0bRAIEA+n0c6nQaXy6VNaR6Ph0ZqO/WnnnZoIEYnJPVdLpcnNmaNR9MymeyuHufZD6MRLpe7aRfzfsy3E6MS4gwWj8eRTqcxMzMDpVK5paDu5E/yth06ne6Gnt8HDRFkPp8/YYsZj8dx7tw5qNVqzM3Nod/vo1arweVy3XZBJjuS5XL5rubbWXbH3XsXYrkjmdZEtnEMKpfLod/vo1gsYjQa0V9w0jUtEon2xZ96mtHJxui+UChgMBgAuLVNbbeLmzUa4fF48Hq98Pv9CAaD8Hq94PP5OxLHnQppr9ejKW2JREKvmcDlcukhbeOfAoFg6v9v9TG1Wg2xWAwikeiOrX+2220EAgGa8SC2mMFgEMvLyzCbzXjTm96EtbU11Go1OByOA+lsT6VS6Pf78Hq9d0Xm4W6FFWWWWwqXy90kjLlcDoFAgM4yR6NR2r0bDofR6XTQaDQgEolgtVphNpv3LYqdFt13u92JRjLS1Ab8NA1Poun9nOXeb0gaVywWw+FwIBgM4sKFC5ibm4NcLt9VFNrtdhGNRnHt2jU4HI4tn38Oh7OlSAqFwi3fd+TIETQaDeRyOXS7XSgUCpoS3c/nV6fT0T6E8bn5O4WNgjwcDpFIJOgWJ5/PhwcffBCZTAZra2t0x/ftplqtolgswm63sw17txhWlFluG+SGs76+jrm5OahUKsTjcTzwwAMwm81Ip9OIxWJoNBqQSqXQ6/VYX19Ht9udiF73u1GIHA7Gm9pIGp6INTE6IWnyjY1ke2E3KdqdPJZhmInPT1b0RaNRGI1G6HS6LUVSIBBAJBJN/P/MzAyi0ShGoxFcLteEyJLH3IyAqlQqWCwWuiZydXX1ljSDzczMUHc7oVB4x/h1t1otBINBCIVCOJ1OZLNZutN7NBrB6XTi0KFDyOVydC3jbt3X9gOyhUqlUh3I13+jwYoyy22hXq8jFothOBzC6XSCYRjEYjEolUqoVCokk0na0WmxWMDj8SZq02SzEzDZRCaXy/d9jeS40QlhNBpNNJLVarUJo5PxaJrUy+v1OiqVCiqVCgaDAZaWliaE9kZslcLl8XgQCoU3TN3yeDwcO3aM1uR1Ot2ujUb0ej38fj9yudwt8TbmcDi3pRnMbrej1+shHA5jYWHhwKO9VqtFI2SlUonV1VUA120xW60WhsMhPB4PSqUSde86qDpuIpHAaDSC3W7f9cdOOyyybA8ryiy3FIZhkMlkkMvlIJfL4XQ6aWQkEAjQ6XRQrVan+lOPp5mn2XSSJjIiiuNv+920Nd6oRhgOh5ui6Vwuh2aziUqlQg0WeDwe+Hw+NBrNluI57c/9wm63Q6FQ7MloRCwWw+fzwe/3IxQKwev13pI6+8ZmsGQySddE7sVpbCNk7IvUyskSh4OACHK73QZwfdEKscUslUool8twOp1oNpu0Ic5sNh/ItZbLZXo9O33NtFotVKtVVKtVrK2t3XV9GQcNK8ost4x2u41YLIZ2uw2r1QqTyYR0Oo1wOAwAkMvlUCqVcDqdNzS9mGbTOb5GstFo0Doc8NPtVMTk5FbUgjdamNbrdUSjUVSrVWSzWfR6PbTbbTAMA6VSiV6vB71efyD2nzdjNCKRSGhXdigUgsfjuWU3WoFAALvdTp3BwuEw5HI5Zmdnb9o5is/nw+PxYHV1FeFwGD6f77b3BzSbTbz++uuoVCowGo1QKpXUWKVarSKVSsFsNmM4HCKZTMJsNsNisdzWayT0+30kEgloNJpta/Gj0Qi1Wo0Kcb/fp78bKpWKHj5YdgYryiy3hEKhgFQqBZFIhIWFBUgkEiwvL8Pv90OlUmFubg4Wi+WmBGpad/X4dioyBjXu673XWWWGYTAYDOiIFUllt9ttFItFpNNprK+vo9Fo0AOEVqsFn89HpVJBuVxGKpVCqVSCWCyGXq+HTqfbN2/xnXAzRiMymQwejwehUAiRSITu771VkINArVajzmBarRZWq/WmZsxFIhHcbjcCgQDi8fhtHZUqFot46aWXMBgMsLi4OLEspd1uIxKJQK1WQywWIxaLwWg0Huiih3g8Dg6HA5vNtul93W6XinC9XgfDMBCLxdBqtVCpVJDL5fT3oNvtHsDV372wosyyr/T7fcRiMdRqNXpTabVaeOaZZ5DP5+FyuXDo0KFbNmO5cTvVeC240WjQWWXStEVGuAQCAfh8Pu3EJsLb6XTon4PBYKI+RvZIE5MKjUYDt9sNm81GI7uLFy9iNBqh2+0il8vBbDaj0+lgfX0dUqkUarUaer0eKpXqtkRtN2M0olAo4Ha7EQqFEI1G4XQ6b/k1K5XKiTWRS0tLMJlMMJvNe65vy+VyOBwORKPR2zIq1e/3EQgEcPnyZSgUCrz5zW+e6KAeDAYIh8MQiURQq9WIxWLQ6/W31TpzI8ViEdVqFR6PB3w+HwzDoNFoUCHudDrgcDi0a16lUh14nf5egRVlln2jUqnQ07XX64VQKEQoFMLVq1fBMAweffTRPTWL7JTBYIDBYIB+vz/1791uF91ulzZg1et1Ktb9fh+j0Yg2kUkkEqjValrXJsYkEokEo9EIlUoF3W4XvV6PRgkGgwFmsxnNZhOxWAyJRALpdBqDwQBSqRSdTgfFYpGKQK1Ww/r6OpLJJDQaDQwGA/R6/S2/ud2M0YhSqYTL5UIkEkEikbilP08Ch8OBXq+HVqulncjFYhEWiwV6vX5PBwOtVkuXmdyqUanhcIhcLodYLIZUKgWXy4WTJ09OPM8MwyAcDmM4HMJoNNINULfjed2KbreLZDJJ10JGIhHUajUMh0O6O91qtUKhULCWqbcAVpRZbhpS/yqVSlCr1ZiZmUGhUEAul0M2m4Ver8eJEyd2XRMcDodbCuw08d34RiLb0WhEI2PSdKVUKmEymajQcrlcMAyDXq+Hfr9Po1/SRCaXy6kYkzrxaDSintwKhQL5fB5XrlxBsViko1ykxkZq2tVqFb1eD2azGVwuFxwOB41GA+vr67QbnewkJo1ht4q9Go2o1WoaaXK53NsW0XG5XFgsFhgMBqTTaSQSCRQKBbpXeLfcqlEphmFQLBbpKtFms4ljx45NXXGYSCTQbDZhNpuRSqWgVqsPVJBbrRYuXryI9fV1mM1mVCoVSKVSGI1GqFQqdiPUbYAVZZabotFoIBaLod/vw2q1otfrYXV1FcPhkHb6ErvG4XC4Y4HdmCoejUbo9/v0/4jQMgxDxy7IuJBYLIZKpYJEIoFIJKLLNcbfblTLJZ3VxNN7ZWUFtVptYqZXLBZDKpUim81idXUVhUIBtVoNzWYTYrEYXC4X9Xod7XabbrV68MEHweVyMRqNYLFYMBwOaUqwWq0iEonA7/dDJBLBaDTC4/HAZrPd9IKIrVAqlVhYWEA4HMbq6ipcLteO1jdqtVo6d87lcm9r7VMgEMDhcMBkMtFmMJJG3e3ztN+jUpVKBel0Gp1Oh772TCYT3G73JkEuFAooFovQ6XTI5/O06fF2Np+NRiPU63X6+iOz0kePHoXFYoFKpbqtfQ8srCiz7BGGYZBOp5FKpcDlciESibCyskIdpSqVCr0JBYNBDAaDqbO5fD5/YjSFPIbH403M9DIMA6FQSG9Y04R2/O1m02rEGKNer2M4HMLhcKDb7SKbzSKXyyGRSCCZTGJtbQ3NZhMymQxKpZLWqMdvwOTvL774Il555RW85z3vwZEjR9DpdOiuXFLLrlQqtEkuGo3i6tWrtO7p9XrpeJNIJNq3m7dYLMbCwgKi0SiCwSBmZ2dhMplu+HEGgwGj0QipVAo8Hu+2j+2QZjDStbyysrLrZjAOhwO3243V1dWbGpVqNptIpVJoNBpQKpV0tIv0GWwU5FqthmQyCblcjnK5DLlcDpfLdVsEudfrTTRpjUYjesgUi8X7ugqSWNiy7BxWlFkoDMPsKIJtNpuIx+NoNBq0CQS4bmkokUiQyWQgl8vhdrshEonoY4i4jottv99Hr9ebEGxi0SiVSjeJrUgkgkAguKU3r3q9jmw2i3q9DoFAgFarhcuXL9NouFKp0IUWWq0WOp2OPke1Wg0ajQYOhwMLCwtoNpuIRCJotVrQaDQol8v44he/iMXFRbz97W8HwzA0k0C6V10uF4DrN/psNotwOIxoNIqnn34aAoEABoMBNpsNZrOZpt/H0/B7gcfjwe12I5PJ0DrzToxGTCYTXbvJ5XIPxOBCpVJBqVTSNZFk3GinzWBk69heRqW63S7S6TTK5TI9JABAKBSi9feNz2Gn00EkEqGvLZlMNlW49wuGYei61Wq1ina7DQ6HA7lcTqNhkUiE1dVV6HS6fct6EGvOO9WW9k6FFeV7nK1qstP+ThyzxiHpWhLRtlot6mRFapImkwlGoxHlchnRaBQWiwVms5mKVK/Xm/icfD6fiqxEIpkQXKFQeGCmDo1GA5lMBvl8HolEAuFwGEtLS0in0xNbpWZmZiYiVbJGz+fzwel00v3OzWYT3/72t2Gz2SCXy+HxeBAOh8EwDJaXl6m38ZkzZ/DYY49tWoVHxpA8Hg8GgwHW1tYQDocRiUSwvLyMYDAIg8EAg8FAa8EkpT7emLbT55PD4cBqtUIikezKaGRmZob2FXC53AOxYiTNYMQZbLwZTKPR3PA5EIlE8Hg88Pv9iMVicDqd2z5+MBjQVC9Jp2u1WtRqNYTDYSiVyqljY8PhEKFQCMPhEMPh8JYJ8nhZpFarYTAYUN93sp1r/MCSyWTQbrexsLCwL9fSarUQiUT23W3vjcBdLcrD4RBf/epX8eyzz6JQKOCzn/0sjh49imq1imeeeQaPPPLIjtJw9yrdbhdf+MIX0O12IRKJ8PDDD4PL5VKBJbVRYgs5/v/k71wul47+BINBRCIR9Ho9yOVy8Hg8SKVSNBoNxONxFAoF2kU8GAxo88zGuu6d5vBD5kdff/11JBIJZDIZtFot9Pt9iMViGI1GuhOax+PBZDJREfb5fFObjMj/qdVqJJNJcDgcfOITn8DS0hK+9rWv4cqVK1hfX0coFEIoFMJLL72EX/3VX91SDPh8PmZmZjAzM4PTp08jlUohFotRMxalUgmz2QyFQoFut4tKpUKzDyTrQCJqkoHYir0YjczOzmI0GiEej4PH4x3IFiPgetRLurIzmQwSiQQSiQQkEgnkcjkUCgXkcvnUg4ZMJoPT6aS7i6eZdoxGI9rECGBilWi1WkU4HIZKpZqaimYYhmZNSKTq9Xr3rYO50+mgUqmgWq2i2WyCYRi6ypRMEEyLWpvNJnK5HGZmZvald6Hf7yMUCkEsFkOhUKBWq93053wjcdeKcqVSwTve8Q68+uqrkMvlaDab+J3f+R0A1+cQf/d3fxcf+chH8F/+y3854Cs9OIRCIQKBAGq1GrRaLX7rt35rkyCSjmPy1mq1Jv5N6k+BQACVSgUajYbO4SoUCgiFQqytrUEikeCRRx6Bw+G449NVnU4HoVAIFy5cwLlz5xCJRGiWgHReSyQSmM1myOVyzM3NTYjwtBnrfr8/MddM/l4ulwFcv/GFw2EcPXoUR48exauvvor/9b/+F1KpFAwGAwKBAP7oj/4Ib3/72/HzP//z24oaj8eD3W6nTUrxeByxWAzhcBixWAwmkwl2ux0ajQYMw9CZ67W1NVrj4/P5EyItlUonov9xoxG/308jwe2w2WwYjUa0K/t27/sdRygUwuFwwGKxoF6vo9FooFarYW1tDcD1jIJcLqdCTQ4pGo0GVqsV6XQaIpFoYp6YzEr3+31qi0ki8EqlgkgksqUgA6DmMQzDQKVS3bQgj0YjOjtcqVTQ6/XoVjabzQalUnnD2vpoNEIsFqOv95ul3+/j9ddfR7Vahdlspgs2WHbOXSvK/+k//ScsLy/jBz/4AY4fPz5Ry+LxePiFX/gFfO9733tDizKHw6H118FggFKpRIWWzNj2+/2JjyGpZZFIBIVCgWg0img0CpVKhUceeQQul4umWRmGQTweR6/Xw/z8/B27+LzdbiMUCiEQCODatWtYWVlBpVJBp9MBwzAQCAS05i2VSjE/P48TJ05gYWEBPp9vohu53+/TjupxESaizuVyaX2YzDl3Oh0AoNHxyZMncerUKTz44IP4/Oc/jx/+8Id0d/P3v/99vPrqq3jb296Gd7zjHTcc0xEKhfB6vfB6vWg2m4hGo4jH4zh37hxEIhHMZjPsdjusViukUim1/iQGKcRMhVz7eH1aKpXC4/HQprOdGI3Y7XYMh0OEw2F4PJ4ddXLfSoRCIXQ6HRXXfr+PRqNBhZr4p5OsjkKhgFqtRrfbRTweh1AopE2NpC+A2GISiCCr1eotu6fX1taQTqfR7Xah1+vpnurd0u/3J9LSZCyPzNMrFIpdZaLINR06dGhPh+nhcEifz3q9jkAggEajAa/XC7lcztps7oG7VpS/8Y1v4Hd+53fw5JNPolQqbXq/z+fDF7/4xdt/YXcQ3W4XhUIB9Xodg8EAyWSSppDFYjE9SU9LLWezWbz22muo1+s4dOgQjhw5MpHCHI1G1FTA6XTeUXtqW60WFeFAIIBoNEqXWTQaDRpRkG5upVIJj8eDRx99FG9605ugUqkwGAyo6CYSiQlXLwDUDUwikUCpVFJf7vEO8dFoROt4MpkMPp8PV69eRaFQwIkTJ2AymfDRj34UR48exdNPP41EIgGGYVAqlfC9730PP/nJT/Dkk0/iiSee2FFtTiaT4ciRIzh06BDK5TJisRht3JLL5XQtokajmXCxIh7iJKImYkUa+Mg42/LyMtbW1rC4uLjl6BBZ/BAKhRAOh+nN+U5BIBBAo9HQTMRgMECj0aDCsr6+DuD64TSXy2FpaQlzc3OYmZnB/Pz8pu+F9FFsJ8jEE73RaMBsNsPn8+1qzGi8SavVagG4ng2cmZmho397oV6v06UfO639DodDeqCp1+v0eoRCIer1OuRyOU6fPk0P6KzN5u65a0W5Wq1u24xBmpfeyJATNI/Hg1arxfHjx294Gq7X67h06RI1on/b294Gg8Ew8RjSrNJqte6IaIjUu4kIJ5NJ9Pt9mg3odDrodDro9/vgcrmw2+2w2WwwmUzweDxwu91QKBTo9XrI5/OIxWKbxJccYiQSCcRi8aaRJFJ3L5fLVOC63S5KpRIdq1KpVDhx4gSWl5fxyiuvwGazwWg04vjx4xAIBLTB7LXXXkO1WgXDMPj2t7+N5557Du985zvx2GOP7Si64nK5NDo8cuQIisUi4vE48vk8kskkFAoF9Ho9bYySyWSbPMRHoxE6nQ79Xng8HrrdLq5du4ZAIACXywWNRjO1Tk3GjEi93Ofz3bI565uFz+dDrVbTHoDhcIhKpYJQKIRms4m1tTX0+31qDtNsNiGXyyGVSlGpVBCNRmm3/bTfrW63C7/fj2KxiNnZ2R0J8nA4nFjwMBgMwOPxoFKpYDKZoFQqb7oZcjgcIhaLQaFQbNt3Qw4tRIjHRVihUMBgMEChUKBer6PX68Hj8dyxGbO7hbtWlN1uNy5evLjl+3/4wx/i0KFDt/GK7jzGDeFvtJC+1WohGo1idXUVDMPgwQcfxPz8/KaP6ff7CAaD6Pf78Pl8B+Lw02w2J0Q4lUrRcS6Smifdrd1ul3odk+5oUjeWyWTQaDSo1Wqo1+sQiUSQSCQwGAw08t0ovmSFJBFfkgomIk6a30hjDckgCAQCusDCYDAgHo8jEAigWCxCLBZDJpNBrVZDq9XixIkTeOmll3D+/Hkamf3Lv/wLnn76afzsz/4sTp06teMUpUgkgtVqhcVioTXVbDZL3+RyORUljUZDFwmQBR7jYjo/P49qtYpr165hfX0dPB4PjUZjyzq1xWJBMplEMBiEz+fbc0R3uxgOh7RzWywW47HHHoNcLselS5fQbrchl8uRyWSo4Ua5XIbVat2y23w4HGJ1dRXpdBo2mw3z8/Nb1njJCtNqtYpGowGGYehGNJJp2c9ejVQqhcFggPn5+Yn/HxdhUqYBfirCRqNxogYPXD/IJxIJGAwGVpD3gbtWlP/jf/yP+OQnP4k3v/nNeOtb3wrgugh1u1189rOfxb//+7/j7//+7w/4Ku98Op0OMpkMotEoyuUyZmZmcN99901NOXY6HQSDQQDXb9C3a9yh2WxSAQ4EAkin09TNi3ha93o9DIdDcLlcqNVq2i2t1+tpmq/dbqNcLkOr1dJGKBL5isXiqeMrREhJxNjpdDZ1NRuNRipGG2+6pFSgUqlw6NAhmiq2WCxYWlqiN71cLodyuYzhcAiz2YwzZ87g4Ycfxo9//GMkk0mIRCKsr6/jS1/6En74wx/ife97H+6///4d36g5HA6tO9rtdqyvr6NYLKJcLlNnKVJLJiKtVConPj+Hw4FarcbDDz9MV1SSkaOt6tSj0Qj5fB65XA6HDx+mz/md1IE/bos5Go02zTjfd999CAQCEIlEmJ+fRyqVQrFYhEajgUAgQCAQoN3U5E0mk9EU/uzs7KaUP8MwE05a3W6XllLm5uagUqluahvWdpD5YbvdDi6Xi3K5TIWYvB5FIhHkcjlMJtMmER6n0+lQR7WDXKBxL3HXivLv/d7vYXl5GR/60Ido6unDH/4wSqUSBoMBfvM3fxO/9mu/drAXeQdD3Kny+TxKpRJEIhEefPBB2Gy2qd2SzWYToVAIAoEAHo/nlt0wgOsn7/FIOJ1O0/eRiJg4YA2HQ+h0OrjdbiiVSgyHQ/R6PUgkEmpRSdLXVqsVLpdr6ggTST+PR7+kFja+y5kYpEil0j11lY6vmzQajQiFQhAKhXjggQeQy+Xwyiuv4Nq1a4jFYpDL5XjggQfg9XoRiUSoQUc2m8Xf/u3fwuFw4AMf+AAWFhZ2dQ0CgQAmkwkmk4nWj0nkT7ya19bWJlK7SqWSCulWRiPj3dbjdWq1Wo2VlRW89NJLsNvtEAgENzVPvZ+M22LqdDpYLJZNr22ZTAaHw4FIJIJms4lut4uFhQXY7XYwDEPr8PV6fSITEQ6HMTc3B5vNBoFAgH6/j1qtRpehkAUPpCFwt01ae6HT6eDq1at0tCsejwMAbey8kQiPMxgM6Ov3drmRvRG4a0WZw+HgH/7hH/DLv/zL+Nd//VcEg0GMRiO43W784i/+Is6cOXPQl3hH0u/3kc1mUSwW0el00Ov1YLFYaOQ4DWKIIJVK4Xa79/3mSUTY7/cjEAggk8nQ95FomNQ3u90uNUBwuVywWq30BjMajWA2m3HkyBE4nU4UCgWsra1BLpdjfn6eppJJ5/RO0s9SqXRqFL0fKBQKuqO4Uqng8OHDOHz4MF577TUkEgk0Gg0kEgmIRCK4XC44nU5cunQJuVwOAoEAwWAQ//2//3csLi7iAx/4wJ52A5PIbm5uDuVyGcViEc1mkz4f5XIZpVKJjjhpNBqoVCrqd72V0cj44YOMaK2srNDXGzkE3cw89c2w0RZzfKpgGhqNBmKxGK+//jqOHDlCl0ZwOBx6rSR1m0wmcfXqVboz+/Lly5tS0mSW+lan9Me7zev1Ou0FWVhYgEwmo2N/u32eR6MR3W7l8/mmHlDJAXrcw57lxty1okx49NFH8eijjx70ZdzxEI/itbU1mvollo0Oh2PL5pPx7UXTLAP3Qq1Wm0hHZ7NZar85vrSCRLgqlQpmsxkOhwNHjx6l4y3lchnlchkcDgf33XcfFd61tTWsrKxgMBjQRqZGo4FCoUA3PAGgNeTt0s+3mnFhDofDcLvdOH78OD0INBoNRKNR9Ho9yGQyfPjDH0Y2m8X3v/995PN5cDgcnDt3DufPn8cDDzyAX/iFX9jTliEej0ebv9rtNt10NRgMIBAIwOPxaPqfy+VCqVRSgZ6fn7+h0YhQKMTCwgL8fj/K5TJ8Ph+1XyVZj/HFHVvVqUm5Ya+HpGm2mDtpVCSHWJfLRWvK441xhEqlghdeeAEMw9BDChFishVsOBzS5SXEzGQvwjgNMrJHhJiM44nFYgwGAygUionu6L0Sj8fRarWodzsAOhNPDgCNRoMe6Fh2zl0vyizbMxqNqCHI2toalEoljYTsdvu2v5yFQgHJZJLud93rjZCYjwQCAfj9fqTTaSq+438SYSZLBex2OxYXF2Gz2ejsaKlUonU4Pp8Pt9sNu90OmUyGTCaDK1euoNFo0FEl4nA0nn4mN/f9NDUgKXVS3+52u7Re3+12wTAMotHoVOc0kv6LRCJUmN1uNwKBAGZnZ2Gz2XDlyhUUCgX4/X4sLi7ir//6r/Haa6/h61//OgqFAnq9Hl566SW8/PLLOH78ON75znfCarVSa9Dd3BglEgnm5uYwOzuLSqWCYrGIWq1Go18+n49er4doNErroEajEcVicVujEZFIBJ/PB7/fT5u/yFavjf0J4+UEElFvnKfeKNbbfY/jtph8Pp9e405e02tra0gkEjAajZidnUUwGKRbpcRiMV3RWSgU8PTTT6Pf7+PMmTOwWq1QqVS0eY7Q6/UmxrCIoQmp45J56Z1srCIiTERwXIQVCgVmZmbo4WF5eRlOp/OmBTmbzWJ9fZ1Ov+RyOfr1R6MRuFwuZDIZtYYldXqWnXHXivJOVpxxOByEw+Edf87Pf/7z+PznP49YLAYAOHz4MP70T/8U73znO2/mUg+MTqeDYrGIVqsFo9EIg8GAQqEAsVgMr9e7beosnU4jl8vBZDJhdnZ2V1+3UqkgEAhgaWkJKysryGazNAom4guAWn5aLBb65nA4YDAYaB2z2WzSaJ2MN5EImMxZr6ysIJPJ0Kja6XROjOvsR/p5MBhQsR0XXvLneIqOLO0olUqoVCpgGAZqtZo6ppHu74202216wydLKZaXl2nnrkQiQT6fx9LSEorFItxuNz796U/j8uXL+M53voNKpYLBYIDV1VWsrKzg2LFjOH36NGQyGaRSKRVo0gR3IzgcDp3p7fV6KBaLdMSLeJ6T9ZSk+a5er+P111+n9fyNzzt57QUCAYRCIXi93qliKhAIaGMagdSpSURNnuPxeeqNdWoul7ulLeZOIAdTo9FIG5lcLhcuXryIF198kVrKjkYjXL16FTKZDO95z3sm5sA3IhQKodVq6cFl49gR8V0QCAQTkbREIqGCToSY9D0QEbZYLFNtRIPB4E3vvmYYhm7jksvlSCQStLmSzE0PBgNcvXoVr7zyCtLpNOLxOPh8Po4dO7bnr/tG464V5ccee2xqt2w8HsdLL72EI0eO4Pjx47v6nLOzs/jLv/xLeL1eMAyDL33pS3jf+96H119/HYcPH97Py78tCIVC9Ho9uo0mnU7DarXCarVuKVIMwyCRSNC5yht5hzMMg3w+j6tXr8Lv99NNStPEVyAQQCaT0Tlhs9kMjUZD3ZRIOrTT6aBUKiGZTNL5YhIFkMUDIpGImqP0+33YbDZ4PJ49m5iMRqMJkd0ovOMiyuPxqOuZUqmkm6sajQat15MRGplMhkKhgEajgbm5OTpiRGrlG5eDGAwGhEIhJJNJzMzMQCAQ4MqVK7BareDz+RiNRlS8ycyxQqHAz//8z8Pv9+PFF1+kN0qy9eiRRx7BqVOnJqIyHo8HmUxGr1Emk22bORAKhbBYLJiZmUG9XkexWKSLSdRqNRwOB0ajETUt+clPfgKTyYQjR45Ar9dPiIRUKqXCTJy/dnJoGq9Tj//cxuep2+32RJ2ay+WCYRhax91NPwQRZJPJBLPZTLM0xEmLpPLvu+8+XL58GWKxGI8//vi2gjyNabPSJJIulUqIRqO0n4Kk83U6HfR6PaxW65Ze3oS1tTXUarVdu4iRJjZyACgUCtQohcwnk8zJpUuX8N3vfherq6vU/rNWq6HVakEkEqFer+/qOXkjw2HuwSr85cuX8fa3vx3/9E//hCeeeOKmPpdWq8V/+2//bced3BcvXsSDDz6ICxcu4IEHHripr32z9Ho9/Pqv/zrS6TSkUin+8A//kNZYSeQ03kVMfIur1SrsdvuE7y+p/XU6HWSzWayurtKbPnFBIuLL4/HA4/GoELvdbjidTpjNZhoxkhESjUYDtVpNDw3ZbBaNRgPAT0fcer0eFAoFHA4HzGYzBAIBKpUKyuUyBAIBrFbrDcWYeHxvJbzjRjMcDocu0RCJRBN/37jFimzDicfjNE1uNBphs9mg0Wjw2c9+FolEAq1WC7/xG7+BxcXFGxr/k4YcsmM3Eomg0WjA4/Gg3+9jdXUV+XwefD4fcrkcDMOgVqvRvdZXr17Fq6++OvE9SSQSnDlzBidOnACAiZluDodDRZpEpyqV6oYZBmLdSuqtQqGQztXm83lcuXIFvV6P/nxI1E1qkOT7JP0K+9VMN16n/tGPfkRd1XZzL8jn8wiFQjTb0mw2AWDiOWIYhjrGtVot/MzP/Mye6vnj9Hq9iXR0t9uduoecy+WCx+NtGsPa+BwSwxedTgebzbbt194owo1Ggx7whEIhstkstFotHcULBoM4e/YsLly4gHa7PRHFDwYDOJ1O5PN56HQ6mM1m/NVf/dVNPTdvFO7aSHk77rvvPvzmb/4mPvnJT+LChQt7+hxkA1Wz2cTp06e3fBy5uROIoNwJkF9mYq949OhRNJtNtFotZLNZ+n5S0yM1O5fLBR6Ph1wuh3a7jWw2i2AwiFgshng8TuuLRIDVajV4PB69UTidTvh8PjgcDmg0GppqJO5YarUaQqGQOnHlcjnqGqXVamE0Gmm62Gq10nQ0icrT6TRdM2g0GumNaKOv97jwblwfSUSWzBCPC++NHJd6vR4SiQSi0SgKhQK4XC7MZjMOHToEk8k0IbhkPpg0k0WjUVQqFdpBPq0parz5KxKJwOFwIBAIIJFIYGFhASdPnkQoFEImk6HPt1arRT6fR7lcxpNPPol3v/vdOHfuHF566SW6v/rFF1/ElStX8Nhjj+G+++6j+6zJz4fUTkk6nqRPFQoFlEollEolxGLxRD1cpVJBr9fTUkkul0Mmk4FSqcRDDz2EQqGAcrmMTqeDdDqNVCoFqVRKBdrlctFFGvu1zGTche3ixYuoVCpQq9U3FGXSwEWcyJRKJaRSKV3NqFQqN702Wq0WlpeXcebMmT0J8rgIE1cs4PohioxJyeXyiYMg2Y9MBJD8LpNaLvkYqVSKWCwGgUCwZQmq1WpNpMOJCMtkMjoeJRaL4ff7YTabodVq8d3vfhcvvfQScrkc+v0++v0+9YBXKBR48skn8bM/+7Pw+Xz41Kc+hUqlsuvn5Y3MPSnKwPVdv9euXdv1x129ehWnT59Gp9OBXC7H17/+9W2dwT73uc/hM5/5zM1c6i2DmGc0Gg3w+fwJz18SXa2vryOfz+PcuXPUWOPKlSsolUrUYKLT6UAkElERHo+g+Xw+FWGfzweLxYJms0lnMZPJJF0RyePxUK1WkUqlUK1W0ev16Io80pCyvr6OarUKpVIJi8VCU3rr6+uIx+Not9tQqVRQq9V02xMR3vGkj0AgoCJL1keOr5Dc7c2/3+8jl8shGo0ik8lgMBhAp9PhxIkTsNvtN6zRSiQSHDt2DPF4nKbeV1dXoVQqMTMzs8msZVyYyX5fEpW53W7avZxIJBAMBuHxeOByudDtduns+f3334/7778fr776Kl5//XWaMn/mmWewtLSE973vfXjggQdoRzBJo5PNYLVajbqdra2t0Rs26Sge74TmcDhUrJvNJl1UIhAI6Oid0+mkG92y2SzS6TT9PJlMhlqg3k7I91qtVmmKtlKpwOVyYX5+HgqFYsvXCjmoPvzww5DL5SiVShO/G9PodrsT3dFEhKVSKdRq9VQR3si4UYnZbJ6IcMmUQSaTofX/o0ePUl/qjYeA4XBIPx8R4fGIm2EYLC0t4dVXX6U7xsnrhPy+cTgcHD58GGfOnMHP/MzP3FFe53cj96Qol0olfOELX9h1gxJw3anq0qVLqFar+Nd//Vf88i//Mp5//vkthflTn/oUfv/3f5/++9KlS3jsscf2fO37DZ/Pp3aT+Xx+YrsRqaOurKygWCxiOBzi/PnzKJfLdGsSSYGSJelisRgej4fO1LpcLvT7fdpME4/H0e12qUEE2btMmp86nQ4EAgG8Xi+MRiM0Gg01MgmFQjRaJnuaY7EYkskkvakYjUZ0u10Ui0UqsiTyHhfe/RjD6Ha79DBAlsArFAosLi7C5XJtGokhKeHxaL1SqdBMisFgAMMwSCaTsFgsEIvFyGaz8Pv9mzplgUlhTqfTcDgcCIfDyGQysFqt8Hg8EAgECIfD1B7VZrPBZrPBYrFgbW2NLr84cuQIXnnlFfj9fgDX07N///d/D7vdjve///1YXFycOFhsLAf0ej0anTWbTdRqNbq0gzzvpJOcCAypiZKFDJcvX4bFYoHH4wGfz0en08H6+joVlCtXrsDlcuHQoUM0Kh3f670fkCiTCHG73aaiRGrWx44du2FdOBaL4cKFC3A4HDh9+jSSySTdKjX+MyQiTARzXISJralCobipSYDxWWnSA7K+vk5XrabTaaysrKDVatG+DmKJScpZG5/f0WiEixcv4utf/zouXbpED+TkUCaVSjE3N4eFhQUcO3YMTqfzQFd13kvctaL8lre8Zer/VyoVrK6uotfr4f/8n/+z688rFArh8XgAAA8++CBee+01/M3f/A3+7u/+burjyQ2JcCedEkkjVLVaRb/fRyaTgVgsRqvVQiaTQTgcxuuvv452uw2lUklvDONzmzweD7Ozs5ibm5uwVKxUKjh//jyef/55GvGSOVe73U5P+yQ12uv1Jk7jHA4HlUoFV69exfr6Ou30VSqVWF9fp7aTvV4PKpUKR44cgVarpeJ7q3a0kvEbEm3U63WIxWJYLBa6Q5qMoYyvwtwYqZNmMLJcgMfjoVwuw2AwYDQaIZ1OY3Z2FocOHUKlUkE2m0UgEKBdrORnMC7MwPXuYfJz1Ol0cDgcEAqFWF1dxdLSEobDIZxOJ/h8PmZmZmAymVAqlZDP5/HYY4/h2LFjOHfuHJLJJIDr86Z/8zd/A5/Phw984AO063sjJMMwnmkhP1si1uTAJxAIIJfLMTs7S+1Y19fX4ff7aTPgwsICTCYTdboql8uIRCK4cOECAoEA9VgmS0BIuWQ8db7d38ejW9IcF41G6WGCpN7Jc022qTmdzhsKciaTwfnz52EymfDQQw+By+XCZrOh1+thZWWFmqPU63W6GpWIMImEb9Xrt91uo1arUcOSubk5OoNOnhMyhri2toZGo0GjbpFIhGg0ihdeeAGvvvoq1tbWaGaKzFFbrVb4fD7Mzc1Bo9HAYrFAp9NNzSYQ97JpkwYsW3PXivJoNNr0QuBwOHA6nXjiiSfwq7/6q7u2H9zq69ytq8dI1NJut9HtdvH6668jEAigXC7Tvazk5kROyqQxy+v1wuPxwGQyod/v0/GTXC6HSqVCa8Qk3UW6kHk8HvL5PMLhMI2KSUMZcfjJZrMolUq0ROD1emEwGCAWi8Hlcmm3qM1mw+zs7FRbzP2CRE4kqiwUClhfX6e7lUmXKZfLpcsKyPNEhIqYjoynx8lNV6fTYTQagcfjIRKJQCwWU7FMpVLgcrl0BKxardL6vUwmo57dG4VZo9EgHo/TRRYWiwUCgQBLS0vUQtHtdtPFEgaDAXq9ns76vuMd70AqlcKrr76KYrEIDoeDQCCAv/qrv8J9992H97///bBYLNs+bxwOh3ZtE8jrhLwRL2liU+pwOGCxWOgSkX6/D61WC71eD4fDgZMnT9LMAcl2kIYtmUwGoVAIgUCA4XCIdrtN06jTGqF4PB4EAgEtwygUCnQ6HRgMBrrggZDJZJDNZmG1WmE2m7f9vguFAl577TUolUo8/PDDdKsU6ZaOx+OIRqM4fPgwtFrtLRfhTqczkY4eDAbUle2+++6D0WicGgmTtH02m8WlS5fw2muv0T3jpG+Aw+HQ1+qJEydo9zaHw4HZbJ46WjYajVAqlVAoFOj44j3YS3xLuWtF+bnnntv3z/mpT30K73znO2Gz2VCv1/GVr3wFzz33HH7wgx/s+9e61QwGA3zpS1/C+fPnUavVIBKJaHqJpNQEAgF0Oh2tR87NzcFgMKDX61E/33w+j0ajgX6/Dz6fD5FIhIWFBWo7SFK1+Xwe8XicjgMR1yeSWiYHqG63Cy6XC7fbTSNvUtMkokcij/HT/c0wvkGKXG+pVKKdw2Qv7HA4hEgkot2iOp2OrmncuHd6N9dF/I0XFhaQzWYRjUYhFoshFAoRj8fpqkXS1Vur1Wg6XyqVYmZmBmq1mgqzTCaDRCKhBhZCoRAGgwEPPPAALl++jCtXrlD7Q3Kd4zPHjUYDarUas7OzCAQCeO211+ghi3z8ww8/jJ/92Z/dcgPSdt8nOUSRWicR6Vqthk6nA61Wi06ng2QySQ97JPLX6/Xw+XzIZrOYm5uDRCKhnfbkNatWq2EymagpBymzjI+Wkb/z+Xy6YnBxcXHTNZN5/J0IcrFYxLlz59BqtWC32xEKhdDv92n6mJiLpFIpOn+/325W00SYHJAMBgN9Pqal4BmGmdgbffnyZVy9ehXRaJR2WBuNRnQ6HTpC9fjjj8Nut1OBNZvNmJmZ2VTz7na7WFtbo2UwtVpNf4fZRq/dcdeK8q2gUCjgIx/5CLLZLFQqFY4dO4Yf/OAHePLJJw/60nYNn89HNBrdFOUPBgOoVCo8+OCDuP/++6HT6egcLsMw9IRLurRHoxGtk5FVhgzDYH19fcLMgWEYWCwWHD9+nDYuEbOMYrGIaDSKYrFIrT05HA69qRD7RoZhNm3o2Qmj0WgijTzecd3r9egNutls0k04ZH6Vx+PRLmCTyTSROt5vZDIZPB4PLR9Uq1Xa2Hb8+HFaxyWdzqSzNhwOQyKRYGZmBm63G+FwmP4cwuEw5ufnqTf1gw8+iNdffx1XrlzBYDDA4uLiJmGQy+XweDw0cvR6vbh69SrOnz+PXq8HHo+Hc+fO4bXXXsOZM2fwrne9a0/PyXitk+zkJj8Hq9WKYDCIbDZLO8Cz2Sw9zPH5fEQiEXi9XszNzWFubg6NRoMK9PjCDJIWnmZTSUaXpr2eiCBvN49PRDCRSOD1119Hs9mkTl6kM33jjLdUKkUgEEAsFtuyHLBTyNcnYjp+CNDr9TQS53K5GI1GWFlZgUKhoAcMYntJGvbS6TSWl5cRDAaphep4Jmp2dhYzMzM4duwYDAYDIpEIUqkUHXdbX19Hv9+nKW+SBq9UKuDz+TAYDDAYDLfdrvZe4q4R5f/9v//3nj7uIx/5yI4f+4UvfGFPX+NOxeVy0Q5YjUaDI0eOoN/v0xsZiT5ImpDUBBmGoaNLBoMBGo2GRos8Ho/+onc6HajVarhcLuh0uk3jPaPRCOvr66jX65idncWJEycgFotp9JTNZpFMJtHtdunJejQaoVKpTLhOjbtpjYsteRufxwV+Wv8kYkRGNoRCIW3+I4vjSYSh0WhuiUfvk08+iXa7PeGeJpVKqThns1lcu3YNTz/9NB588MGJeV1y0yemJCT9rdVqUSqVaM0+Ho9Ty0OZTIaTJ0/i0qVLWFpawmAwwNGjR6d+b2KxGHa7nbqpHT16FK+++urEnvJnn30WL7/8Mt761rfiySef3Ha+eieQcolKpcLs7CwtdZCsDeknIOsFybz/4uIirVPPzs7SFZFkiQYZzdu40WorUqkU8vk85ubmJiwgxzc+kUNjvV5HJpOBUCjEQw89hCNHjmz7+WUyGZxOJ8LhMDXs2SnjjWEbRVin002I8EbS6TSazSYsFgtisRitnzcaDQSDQZqeJnV3Eu2SdZwPP/ww7biXSqVYX1/H3NwcTp8+DbFYTFP01WoV0WiUbuTTaDSw2+2Ym5vbZCnKsnvuGvOQvdwwSVr0dnInmYdkMhn86q/+KprNJrhcLh555BFaw+NyubTW3O/36XYkjUYDrVY7MZZBmnqIQw+Xy4VCoYBWq4VKpZrozORyueh0OrSJRCKRwGKxQKvV0scQT+hms0mFp9/vU5/qZrOJfr9Pa7FknphE6htTyeP1XIZhUK1W6UgWAFpTI+JMxrpux5aendBqtfDqq68inU7D5XLB6/VObZ4hB5lqtYrBYIBWq0UbC+fm5ibSr/1+H5cvX6Ye0/fff/8Nsw/D4RDFYhHxeBw/+clPcOnSJQgEAjqbK5VK8Y53vAOPP/74vkZCtVoNkUiE9jMQd7RkMokLFy4gnU7TLIbRaIRer6f1bIlEgk6nQwW60+lMbLT63Oc+h2q1CrVaTc0rkskkCoUC5ubmoFAoJrqjx9PBCoUCg8EAmUwG5XIZdrsdCwsLO74X5fN5pFIp2O32LcsA00QYwMS88XY1aXJoyGazuHr1KrXvFAgEiMfjWF5eRjQa3fRxAoEADzzwAE6dOoWFhQVwOBxcu3YNkUiEGr3Mzs5OZEjGU9T9fp8udAEw4Xs9bmjyZ3/2Z5uef5btuWsi5WkvLJbtIX7XxPT/F3/xFyGTyehOWBIpKpVKKq6j0Yi+kZEW0mWsVCrpLyqPx6OPI5Fss9lEPp9HrVYDn8+HVqsFn89HJpOhrlakq5q4EYlEIjrnSsYtyGGA1MdIzXd8zIrcqHg8Hvr9Pk0Dk5q1Wq2G0Wik3c/9fp/uxb1VUfFekUqleOyxx+gNdDAYQK1Ww2w2T9TVx9PfZM43FotBIpFMZDeAn950hUIhlpeX0e/3cfLkyW2FmcfjwWQywWg00vr1008/jatXr9J0+de+9jX8+Mc/xrvf/W488sgj+9LApFQqsbCwQEe7XC4XjaYPHz6MlZUVhEIhcDgc5PN5FAoFuv+XjOfI5XJYrVbweDya5o5EIigUCrRkA/x0tlilUtFMzXhNlqSjuVwu3WZWr9dhsVi29OneCpPJhG63S9dvKhSKbUekdtIYNhqNaH2eHJKHwyHS6TTtWL9y5Qpef/31qQ2qXq8Xp0+fxoMPPkgzUf1+HxcvXkQoFILT6cT8/PzEvPX4/Dbp5DYajRMHM3JwJ1mGfD6PTCaDQqHARs675K4R5dttKnAvwOFwaFOSXC7HaDRCrVaDXC6nxhwbIx4icKVSiY63eDwe6HS6qVEliXCTySRKpRIdKZNIJLSWSzpU19fXIRKJcP/990Ov10+k0cjIC3D9xkNcqMhbt9ulde5isYhAIEBvSkSQSJRA3MkajQaNmkj0kM1mkc/nJyL7jW+7fd9WNx1ScydR/I1+VocOHYJIJKLr7hKJBLLZLBVnIghkr7XFYkEwGMTVq1eRzWZRq9XoAgrgenbp6NGjEAqFuHjxInq9Ht70pjfd0P+Yw+FAq9XioYcewsLCAlZXV/Hd734XKysrEIvFYBgGX/7yl/GjH/0I733ve3HixImbvvGKxWIsLCwgGo0iGAzSOi+Hw8Hi4iIEAgHq9TqMRiMdyev1ejQDQtLewPXyBfH15vP51ADnq1/9Krrd7kRX+7gIj1Ov1xEOh1Gv12kz5I2c3qZhMpmQy+Xwk5/8ZMJ9bjcjUiRLNb6Jic/n0+1cpH5NJiM2otfrcfr0aZw6dWoiYifNlaurq8jlcrjvvvtw6NAhWp9eX1+n604lEgnsdju0Wu3Ug8l4N77JZALDMOh0OrTrnWXn3DWizLJ7ms0mrbcJhULMzc1BrVZvurmQOm6pVEKtVgOHw4FaraYGF2TUhcwNkzdS9yNdsSaTCQaDgYqQQCCgrmEzMzM4efIkzGbznqJU0jlaqVRQqVRgtVppepusEszlcjSqJ/VHk8lEnafIashxsSdWk9MOAtNGbaZBonxi0kLMWYhokOefiACZv5VKpfRAAoB2pZO5WqfTiVqthmQyiVwut0mciUvYzMwMzp49i2vXrqFcLuORRx6hzzOHw6Ed2q+88gqee+45PPbYYzsWGGKXeeTIEVy5cgXf/OY3EQwGIRaLkcvl8I//+I/4wQ9+gPe///04fPjwTYkzj8eD2+1GJpNBKpVCu92GzWajz0swGESxWITP54Pdbqfd891uF2KxGEajka5SJAYhZGqg0+kgn8/j8OHDOHXq1LZli0ajgVAoRJeKOByOqXao09jKNnM4HKJcLuP48ePQaDTbijCJpokQDwYDWjKyWCxQKpVgGAYXLlzAj3/8Y1y+fJnaYRLEYjFOnjyJU6dO0fE4AsMwKBaLtNmw2+3iTW96ExwOB3q9HgqFwkQXNUnz7wYyBieVSjdZ3LJsz11TU55GLpfDF77wBVy8eBHVanXTTZTD4eCZZ565rdd0J9WUAeB3f/d30W63odVqN9V0yJzi2toabYQidTpiuzgOmc0lN5h2uw2ZTEY3Po1HYOVyGel0Gt1ul27o2W2kQSJ7UmseDAbUwUutVkMmk1ELyGq1Sk/r5IbbbDZp4xq5SZDT/E5XOo4L9EbRHo9gyBo90q1OauBCoXBijKXdbtPGNFJWIMtBSIc7h8NBKpXCcDjE/Pw8BAIB3WksEomo5/f44aZer+PSpUtYXV2FVquFx+OB2WyGwWCgj0ulUnjppZegVCrxlre8ZUf7ejfS6/Xw6quv4mtf+xq1DCVLTbxeLz7wgQ/A7Xbv+vNuhKzrJFkBMp8cCATQ6/WoKQlwvSZdLBZplKhWq6HX6yGXy/GJT3wC2WwWAoEA/9//9//RfgVSo954QGy1WggEAvS1b7Vat53ZnrZAArh+YCIHMJKlWl1dhVAohM/nm/i6pC5MXktkSYhUKqWd+DKZDAzD4Nq1azh79iwuXbqEXq9Hl7IolUqabTl9+jTuv//+qb9v47+X5PdHo9HAaDTSLmqSoiYH7Jvhk5/8JPUeZ2vKO+OujZSvXLmCN7/5zWi325ifn8fVq1epO1I6nYbb7b6p3aH3AiSSJJFuIpGYWLtH7DCJlzRx7dlohEHeyHKKZrMJtVqNxcXFTYviG40GUqkUms0mVCoV3G73rpqphsMhbdQiBy3i4a3RaCCVStHv91EsFumOZalUCpvNRpvJNj4HpNubdI2T9YVcLndiz7BUKt10EyIpajLrTDpQNzYFmc1mWuveLj1MVg0Sb+nxG3q1WgVw/fDD5XKRy+WQz+fhcrloGYJs6OLz+bT+SzZXGQwGOi5E3Mji8Tj1Fp+dncXjjz9OZ++feOKJXXdTC4VCPProozh16hReeukl/Nu//RtSqRREIhGuXbuGYDCIo0eP4v3vf/+ebG4JWq0WIpEI4XAYKysrcLvdkMlkdOVjIBDA/Pw8XZ+pVCoxGAyoX3swGIRQKKSz0Xq9Hi6XC6PRCLlcDrlcjnYXkzp8u91GIBAA8NO57o2CTJy6Nu4z3m6BBMHj8cDv9yMSicBoNFIhbrVaAECXoxAxJ6/lVCqF73//+3j11VdRq9Xo5yPLb7xeLx599FE89NBDWxrtjP9eKpVK2O12RCIR6kkQDAYhkUhgs9mg0+nuqJ6LNxp3baT8rne9C0tLS3jxxRfp4P7TTz+Nt7zlLfjqV7+Kj370o/je976Hhx566LZe150UKZPVjbVaDWKxGL/0S79Eo029Xg+TyQSdTkdTzVtBGosqlQpEIhFmZmY2iTHZAlSpVCCVSqkl5U4g7mJkRIthGMhkMhoRk9QziYhIVEw6yXeaWiSQWi8RamIDClwf2SFRNICJevb4Jp7xDtP9uIGRgxPZC0zmv+PxOBiGgc/no41NwHUji7W1NTAMQ+1JSef51atXUa/XqVlGpVIBh8OB0Wik5hCvvvoqBAIB3vzmN0Oj0UzU93fjNz0cDvHcc8/hX//1X5HP5+myCpI+fe9730tnlPf6vITDYbRaLTgcDmi1WvT7ffj9fjAMg/n5+an1elK6+fSnPw2GYWAymSYitW63i2QySZefGAwGauQyHA4hFAqxsLCA4XC4pQiPR8LbHcRIE1S9XkcqlYLf74dWq4XVap3YwDX+O1iv1/Hqq6/i7Nmz1BJ1HB6Ph7m5ObznPe/BkSNHtsz4dDodugCG/F4KBAKcO3cOhUIBNpuN+mDvNkW9E/7wD/8Q9XqdjZR3wV0bKb/00kv4oz/6I9hsNrrPl6SvP/jBD+LFF1/EJz7xCTz//PMHeZkHjkAgoA1fPp8POp0OarV6RzfcjWJMborjN4DBYEBT4AKBgK4RvBHdbpfWh8mJX6FQUFtNcqMltpzFYhG9Xg8SiQRzc3NTo+KdQhYPjN+E2u02tdnMZrMol8sYDofg8XhQqVTQ6XTUrnKjWcR+sNENC7j+vbtcLtrINV6e4fP5sNlsEweL2dlZLCws4MiRI/jJT36CZrOJRx55BHw+H9lslvp5q9VqnDp1CmfPnsV3vvMdPPjgg1NvyGQ/9o3e3vSmN+HRRx/Fj3/8Y3z9619HsVhEq9XCCy+8QA1I3v3ud+9pYYFAIIDP56OrMtvtNiwWC3w+H/x+Px352nioJNkPo9E4tflJJBLB4/GgWq0iHA7j4sWL1O600WhgZmYGKysrtElJIpFAqVTS182NmuW63e7Eli2yYUutVuO+++5DtVqFxWKZaLwaDAa4cuUKzp49i6WlpU3lOB6Ph2PHjtHGOq1WS+fTN0K87sniFqfTCYFAgEKhgKWlJbTbbTzwwAOw2+03naLeClKbZruvd8ddK8qj0Yi68JB9vkScAeDo0aP3nBnIbiHCQcY9vF7vjj5uJ2I8Go3oZijg+qKEaV6445CuWVKP5nA4UCqVcDgcdCSLUKvVJmrFGo0GBoNh11HxVoyv0NtYC1xYWKCp/OFwSIWvVquhXC4DwEQtmNSx9zvlR9zPHn30Ufj9fnC5XLqekUTTpLu9UCggHA7T7T2HDx/G+fPnceHCBTz55JPUwzyfz9P0/Tvf+U4sLS0hn8/TMRhiUzntrd/v05r4NE9jo9GIX//1X8crr7yCZ599FrlcDlwuF//2b/+GH/7wh3j88cfxtre9jTp2jTe5bQeXy4XD4YBEIqENYGRd6Lgw30gop8Hn89FsNgEAy8vLaDQaOHbsGDXqsFgsOxLhwWBABbhWq6HX69HSxrSViIlEAolEgvpzEwc1ksoeh2yiOnnyJGQyGYLBIG2C28hwOEQul6N7vsmYGNmNXqvVIJPJ8Oijj95wzeRe6fV6iMViqNfrtD+FZefctaLsdDrp7DKXy4XT6cTTTz+NX/zFXwQAvPzyy7d0kcHdADHJIM0bN6LdbiOTyWwrxsD11ZiZTAb9fh8Gg2GqFy6BdG2TFYYk8iRjKeNC1u/3JzpqJRIJZmdnodPpbioyJR7M4/Vg0shDaoEkFT0tjU82IwGgFqREqIk96Hi3KRHqnTSS7QSS5fD7/YhGo5ifn594bRM702q1ikQigUAggOFwCIFAgNXVVaytreGJJ56ASqWiNqZk3ndmZgaBQADPPPMMHnvssV31YZBtQxuFe2ZmBk8++SSefvppPPPMM6jVaqhWq/jnf/5nfP/738eb3vQmnDx5ktbJdxKNk33gIpEIsVgMq6urdHFKIBBAKBSC1+u94euENFUR/2eyylKv10MoFEKpVNLXAenmngaZ4yfRcLvdBnD99URcxbYbdZLL5fjhD3+Iv/3bv8VwONz0+0OyGadOnZrwsCbLWjZ+rwzDYG1tjWZUSKCSz+fp3Ds50Fqt1lsmyKVSCclkEjweDz6fD0qlkvW+3iV3lSiXy2V6g3zb296Gr371q/iLv/gLAMBHP/pR/MEf/AEikQgYhsFzzz2HP/iDPzjIy71r2KkYk5pYq9WCRqOB1WrdlPpiGAb1ep2mpslIChmtIF2iGz8v6fwkUbHD4djzGkxisDAuwmRbkUwmg06nu6FT0laIxWK6PIF8v+NLF0gtE/hpIxn5GPK224UWwPV0KxGgYDA4cVMmjXhqtRp2ux39fh/JZBKpVIr6IX/ve9/Dgw8+SLcnEdvGZrMJj8eDy5cv4xvf+AaefPLJHW9XI+YtW6U/5+fn8ZGPfATf+c538IMf/IBmJF588UWsrq7ine98J06cOAEAE8Le7XapwE9reen3+4hGo/D7/XA6nVSoybgUaXwb3yVeq9WwvLxM09F8Ph9ra2uwWq10DGt+fh6zs7OoVqtIJpO4du0ajEYj7dLeOC/MMAztfDabzRM1/2mQTW1nz56F3++no4gMw9DeiePHj+P06dPUz3zjx6dSKRgMhgmnrfX1dWQyGToaRrKG413U/X4fgUAAer3+hos39kK/30cikUClUoFOp6MrI1l2z10lymazGe9617vw1FNP4Q/+4A/woQ99CP1+HwKBAB//+MfRbDbxb//2b+DxePjP//k/44//+I8P+pLvaHYqxu12G+l0GtVqFTKZDPPz8xOCSUaXyuUyqtUqbZTRarX0hL7xcw4GA5RKJaytrdGbyezsLHUB2w3E35e8EUMR4hpGFmRMu46bZdrSBTIuNd64RdZBko8h1qHkjfx7u+9dIpFsigynpcwFAgFcLhdsNhvy+TxUKhUuXLiAaDSKxx9/nFp0lstl6gHudrtx/vx5fPGLX8Tp06fx1re+dV92gysUCnzoQx/C29/+dnzrW9/Cs88+S53dvvzlL+P555/HBz/4QZw8eXLq90Ic4za+WSwWRKNRJBIJ6PV66HQ6RCIRrK2tYW5ujv6c19bWqL87eS1IJBJEo1GYTCbY7XZEo1GoVCrqUU06oOPxOAKBAC5fvjxxiCO9D0qlcstImsAwDAKBAM6ePYuLFy9OuGyR8o1KpcKJEyfwvve9b8tueIZhEI1GIRAIaFc7OSSTAwKZxxaLxRNd1N1uF+FwGHK5fGrK+2Ypl8tIJBIAALfb/YbPUN4sd5Uo/8Iv/AK+9a1v4Vvf+hYUCgV+7ud+Dk899RTe8pa3gMPh4E/+5E/wJ3/yJwd9mXc8ZLSpXC5vK8Ybm0VcLhfNVAwGAzq6VKvVMBqNIJFIYDQaoVart7y5kJEsUpvdS1RMZkOJCJPoRygUQi6X00j4oHytx/1/xyFLNTqdDn1bX1+fMFfg8/kTIj0u2uQAQIQ5HA7D7XZvWcvm8/mwWq20pvn888+j3+/jzJkzsNvtEAgENPXdarVgsVjw4x//GD/60Y+wtLSE48ePw+Fw0J+nTCbbk6sVcH3E6Vd+5Vfw9re/Hd/85jfpCsRIJIK//uu/hsvlwoc+9CEcO3Zs4nVIVgpO67Cem5tDJpNBLpeDTqeDz+dDOByms/PD4RAajYYuu7Db7XTWud/vw+12IxaL0QbFjfPCvV4PMpkMw+EQnU4HKpUKPp9vR2Nk+Xwe586dw7lz5yZ6XQhGo5GmpyUSCfx+P7LZ7JYz3vl8Hs1mE/Pz8zRiLpVK6Ha79PkhDl/jUfRwOEQoFAKfz59YdrIfDIdDJBIJrK+v0yzNXur6LJPcVc/gl7/8ZbTbbXzjG9/AV77yFXz5y1/Gl770JZhMJnzoQx/CU089deBjSHcyG8XYbrdPXXxAZjnJbmOyZ3l8Tdv46BKx7NwqjUmiYmJ3KBaLaV3rRr/ExK6PCPC4aT8ZSyGR8J2+Lo7cPDd2OpOxq3GxJk1xpEmGdNATkdZoNEin09RgZLubLZ/PxyOPPAKZTIaXXnoJ586dg9vtpvVl0vVtsViwuLiIV155BWfPnkUsFkOr1YJUKqU/X5L6JiItlUp3JdQzMzP4rd/6LbzjHe/AN77xDVy5coUadvzZn/0ZFhcX8dRTT+0ohc7hcGC1WiGRSBCLxdDpdGCz2ZBIJJDL5Whqm9R7R6MRQqEQut0uvF4vkskkKpUKZmZm4Pf7J+rCxAJToVBQH+xEIoHV1VWa0t6Ynm21Wjh//jzOnj2LSCSy6XolEglOnDiB06dPbxJIslUqlUptmu8mGS2tVotisYhkMkmXvRCjlGlGH2S152AwwMLCwr4KZq1WQywWw2g0gsPh2LJG3ev1ppYgWLbmrp1TBq6nTf7lX/4FX/nKV/Diiy8CuD5I/0u/9Ev48Ic/fNO7TPfCnTSnDFx31CkWi+Dz+fiN3/gNiEQimM3mqWLMMAxt4hoMBjCZTNBoNNRVq9lsgsPhQKFQ0Bv5djfkRqOBtbW1iaiY7IDdCpL6HU9HD4dDGiWOzwe/EU7lZLNVp9OZEO5ut0sNIcZNWjZG2RvtFS9evIhAIDBh12gwGGAymeihZjQaYWlpCcvLyzT6Gg6H9BBE5ryJM9nNCPXq6iq+/vWvU1tL4mZ17Ngx/NIv/dKOf4ebzSbC4TCA61F5Pp+HwWDA//yf/xOVSgUqlQq/9mu/hlKpBJPJhEQigUwmg7m5OSrAZORpq2tnGAb5fB7ZbBY8Ho+O7y0vL+Ps2bO4fPnypjWiHA4Hhw8fxunTp3Hfffdt+7wUCgUkk8mJrVIMw9AOeQ6HQ+eqzWYz9RnYKlMSj8dRKpXg8/n2pRQBXH9tpFIprK2tUROSaYdhsrnqL/7iL8DhcDbNibNszV0tyuOk02l85Stfwf/9v/8Xly5dAofDwcMPP4yXX375tl7HnSTK/X4fv/3bv421tTWo1Wr81//6X6eKMXDdcjOdTlPzealUSg0syOJ5IsTbNXAQVyVi3SkSiWAwGLaMiofD4YQAN5tNMAyzaQXctKUBb2RIBqFQKMDv99Ol951OZ2IEhQj0eM16aWkJhUIBhw4dgkqlQqFQwGg0ok1AZAXm6uoqlpaWYDKZYLPZaEpXo9HQSHFj7XwvQs0wDC5duoRvfvOb9DXY6XTAMAxOnDiBD3/4wztaSDNuNKJQKFCr1fCP//iPdCvaBz7wAVitVurKNT8/D4fDccO68EZ6vR5ee+01vPjii/D7/bSDfByr1YrTp0/j4Ycfnkgn34hEIoFisQiPxwO5XI7z58/jtddeg0KhgNFohNPpxMzMzA0/Zy6XQzqd3jaK3S2NRgOxWAz9fh+zs7NTTWGIGNfrdUilUvzt3/4t3bvOivLOuGdCDavVik984hN4xzvegT/90z/FN7/5TbzyyisHfVkHCnEnUigU0Ov1U3e6tlotul+W1IXb7TZ6vR5Nae5kaXyj0aC1YtJNarPZNkXF5IZIRJikDAUCAV0iQerBrOnA1pARLLvdDoVCgWg0CqlUioWFBQwGg4lUOLH1JA5gXC4X1WoVzz77LBYXF+HxeOiu5kKhQMfcFhYWwOPxsLS0BIZhcOzYMToHe+3aNajVaszMzEwdzyJCTcZ0gJ8KNRFpItQcDgfHjx/Hfffdh3PnzuHb3/42SqUSOp0OLly4gAsXLuD06dP44Ac/uG2j0rjRSKlUojPI+XweUqmUmqSk02l4vd5d2/DWajXqspVKpei60uFwCLFYDJPJhFOnTuH06dOYnZ3d0+t3bm4OvV4PL774Il0/6XK5cOrUqanTDtMgVsMzMzP7IsgMw9DaPbE63XgdG8WYNHyJxWJ2S9QuuSdEOZFI0CiZ3EDe9KY34amnnjroSztQxueUN94gut0u/H4/EokEXRqh0WhoNKxQKG54UxkOh7RW3G63qQWnTqejUdHGejBpaiJ7kU0mE92rzLI3tFotGIZBLBYDl8ulB5uNKUuGYWgKXK/X49KlS4hEItTBisvlolQqIRaLUW/t2dlZuFwuhEIhvP766zh69CgOHz6M9fV1ZLNZrKys0LlzmUw2MZ5F2CjUZMcxsFmoT548iYceeggvvPACvve976FWq6Hb7eKVV17BK6+8gkcffRQ/93M/B6vVOvX1udFohIzB2Ww22Gw2rKys0L3gO6Hf71OXreXl5QmXLaFQCIlEAofDAbvdDp/PR7ue98JoNEIikcDS0hJWVlbQbDbx2GOP4fHHH99xqabVaiEajU717d4L7XYb0WgUnU6HNg2OP+/1eh2ZTAaNRgNSqRQej2dPzm0sP+WuFeVisUjryWfPngXDMFhYWMBnP/tZPPXUU3A4HAd9iXccZLsTWfTO4XBgsVioNeZO3bKazSatFZOomHhdk/GfjUsbSFMKcTbaaxcvy3R0Oh29qXO53Kk3ZA6HQ2vNxOAiEAjQfdtWq5UKaCaTQTqdRiQSocsRlpaWEIvFsLi4CLvdjpmZGbTbbZTLZayurkKpVNKmu3G2EurxtPdGobbZbPjYxz6G1157DT/5yU8gFovR6/Xw8ssv4+zZs/iZn/kZvPe974XFYpmaxSErO8neYYFAgFAoRMfFtjtwkvGjs2fP4vz581NdtpxOJ06fPo0TJ05AJpOh1+shlUrReWmbzbbj7v9er4dEIoFr166hVqvBaDTiscceQzKZvKFL3sbPEwqF6EHhZiD180wmA7FYjMXFxYnvhxXjW8ddJcrNZhNf//rX8ZWvfAXPPPMMdQ/6+Mc/znZeb8Fb3vIWFItF9Pt9PPvss1hbW6Nm+16vd8cNIMPhkNaK2+02hELhxP7aXC6HcDg8sbTBYDDQejBrJHDrMRgMtBGHy+Xe0CSCiGo6ncb6+jr4fD7cbjc1fxiNRlhbW0MymUSz2QSfz0ehUMDq6ioqlQoMBgM4HA6tb+fzeQQCAWi1WrroYKuOeCLU425pG4W6VqvB7XbDYrHg1VdfxcWLF+lh7vnnn8fZs2dx5swZvP3tb4fFYtkUTSqVSuj1ejo/3+/3aUp+GqVSCa+88grOnTuHfD6/6f0ajYaOMW18boVCIVwuF91/TYxHLBbLll+v0WggmUwiFAqhXq/DaDTi4YcfhkQiQTAYxKlTp7C+vo5oNHrDgwQZfeJwONuOye2ETqeDWCyGZrMJs9kMi8VCv3atVkM2m2XF+BZyV4ky2XAjl8vx4Q9/mM4osw1A0yFpSpFIhHq9jtFohAcffJCa0+8E4lC1vr5O3blIvTefz1PTArlcDovFArlcDqlUytaDDwiTyYTRaIR0Og0ulwuj0bjt4x0OB10OQuZp3W43OBwOuFwuTCYTDAYDisUi9bKuVCoYjUb0Z046xLvdLgqFAtLpNMLhMJ1b1+l0U41SNorVdkLtdDpx5swZ/PCHP8TFixcxGo3Q7/fx7W9/G8888wzOnDmDJ598EjabbVMppNVq0TndjU1d3W4XFy9epC5bGxEKhXjggQeoy9aNXtdKpRKHDh2iyz/W19epVSxwPUVdLpeRyWSQSCTQarWg1+vx0EMPwWg0YjQa0a53u90OtVqNUCiEdDq9ZcqdRPZkz/TNZKHIz08gEGBhYYFmz8bFWCaTsWJ8C7mrRPmJJ57AU089hfe+97277ph8I8LlctHv9yGRSGC1WjE7O7ujlBqJiolxSL/fp8vsGYahN2QSCbM/izuLmZkZDIdDJJNJcLncqQ1+BC6XC7fbjZWVFWoIQ0xJiAARcScNYJcvX0Y0GsX6+joOHz6Mo0ePUtFxuVzUhzkej6NSqSCXy0GlUkEgENAZc+B6mnqaUcq4Dem4UFutVpw4cQKJRAJf+9rXcP78eQwGA/R6PXznO9/BM888g5MnT+LkyZNwOBzQ6/U06rZarbRjmWEY+P1+nD17Fq+//vqEyxZhfn4ep0+fxgMPPLDrfgcyAqTVamlKO5PJQCqVolar0V3mWq0WDzzwAEwmEw0skskkRqMR7TZXqVSYm5tDMpmkkwwbSaVSqNVq8Hg8ezbMGV8iYTAYMDs7S+ezWTG+vdxVovzNb37zoC/hroLP50On08FkMt1whILMKMfjcWQyGTSbTWpSQXx9ye5Yth585zM7O4vRaER3BG+3TlMoFMLtdiMQCEAoFKJWq20SZuC62BgMBrz1rW/F8vIyLl68iFdeeQX5fB6nTp2iEe747ubxm7pAIKBmH+Od4c1m84Y2pOSNx+PBZrPh4x//OBKJBL7xjW9geXkZw+EQ/X4fFy5cwOrqKo4cOQKn04lisUg3euXzeZw9exbnzp2js/PjGI1GOsa0H13LZMtXvV7HysoKyuUyZDIZHA4HDh06tGmRS6VSQalUgsPhmEj7G41Guv9ZJBJN/C6vra3Rvci7Gb0aZ3yJhNfrhVKpRK1Wo/cB0nG918/PsjvuKlFm2R1cLnfLdY1kaUOtVkMqlaIr8QQCAWZmZnDkyBFotdo9LW1guTOw2Wy0K5ss+tgKuVwOu92OWCwGtVo9NWImcDgcHDlyBDqdDpcvX0Ymk8H3v/99HDlyBB6PZ8KGUqlUQqlU0pGZWCwGiUSCmZmZTc1ovV5vQqy73e6WNqTk7SMf+QiSySS++93vIhqN0qwNmcUmtpkkBSwQCOgYFgBIpVLqsuV0Ovel7EJS1IVCAa1WC71ejx5sh8MhRCIRbUIj9Pt9xONxqNXqqQeC2dlZdLtdRCIRzM/PQyKRUJcxksXYLYPBgGYzSB9Bs9nE6uoqK8YHCCvKbxA2Lm0olUqoVCpoNBoQiUSwWCyYm5ubah/Icvdis9kwGo0QjUbB5XK3TT3qdDq0223k83mYTCa6o3maMAOgrxWNRoN8Po/V1VWUSiX6Ohrv5ieZlkajgWw2i0gkArFYjJmZGWg0GmojSjycxxmNRpsczTYu+XjyyScRi8XwwgsvoFQqgcfjoVgsIh6Po9Pp0GUNrVYLXC4XCwsLdEcx8ce+Wfr9PtbW1rC2tobBYAA+nw+GYSAUCqHT6WC1WiEUCpFKpRCPx2mXtlQqpQsdtjJJ4XA4cLlc8Pv9CIVCsNvtiEQiUKlUOx7vGqdSqSAejwMAbQwLBoOsGN8BsKJ8D0MWSpClDcTCktT1DAYDjh8/TnfJstx7cDgcOBwOjEYjhMNheL3ebW1OrVYr2u02isUirWVuJ8xGoxEcDgc8Hg/tdpu+5qrV6sT8MkEul8Pr9VKzkmg0imw2C7PZPHUpCvDTFZjTFkGM25AajUYcOXIE58+fxzPPPEN3eJOxPKPRiMXFRSpCnU4HL7zwAk2XjzvIicVicLlccLlc2vRG/tz491arhVKphGq1St3vgOuNllKpFHNzcxPjYE6nE3q9HslkEisrK/TAMD8/v+08MpfLhcfjwZUrV/Dss8/C4/HseskE6TUolUrUkyCXy7FifAfBivI9DJfLpaMsAoEA3W4XcrkcSqUSBoMBKpWK7ZJ+A0CirFAoRFc+bjUKRx67urqKQqEAp9OJaDS6rTCT0ah4PI5erwc+nw+hUIhut7vl/DJpGmq1WjStTcR5KyvYaZB09PhBY35+Hh/84Afx7LPP4s///M/B4/Hgcrnw6U9/mjYqjkYjaqZCyjiNRoN2lo+nyYnzGMMw9OMZhkG1WkW5XEan04FAIIBKpUKv10MkEgGfz4derwePx6Oe3NNEvlar4dKlS5DL5eBwONDr9VPFn/wJXLfELRaLMJvNqNVq4PF4234M+bharYZ4PI7hcAitVotut4tYLEYPSqwY3xmwonwPMxgMwOVyaZONxWKhI1IsbyzI/CoR5u1WEPJ4PHg8HqysrKBYLMLtdiMcDm8rzHq9HlwuF9FolG690ul0mJmZQS6Xg9/vh0KhoGNzBGLJSHYsx+NxKs5EoPaCUCjE29/+dvz4xz9GpVKBWq2+4XgYsLUXO8Mw1HmOuNSR70er1dLrB4BDhw5NLJQYPwRs/Pva2hq8Xi9kMhmy2SzW19ep8cm0j0kkEqjX65idnUUwGKQLNrZjNBqhUCigWq2Cw+FAIBBgNBpBJpPRj83n81hbW7thVmC3779HVivcVlhRvochUYTL5YJarWaj4jc4ZPwpGAwiEAjQhqFpkP3ZxCHK4/EgFAptK8wk/RyNRsHhcOhs+/z8PO3CJuI8MzMzEd1KpVK4XC60223kcjkkEokJcb5dXgQ8Hg8qlYrW3klDZKFQQDweRy6Xm3CxGwwGCIfDEAgEmJubg9ls3rElZqFQgEKhoJ7cjUYDiUSCLoWxWq0Tn4us6nS73VAqlcjn80gkEjCZTNDr9ZsEn2EY1Ot1xGIxDIdDeqgg8+MymWzTxwyHw4mPn3ag2A1k/SvLzmFF+R6G3IRZWAhk7CUQCFBh3mrOnHhEJ5PJHQszadoiKdxmswm/3w+Px4NDhw6hUqkgm80iEAhALpdv2ngkkUjoJqRcLodUKoVcLkdNTG7nDZ5hGFQqFRQKBTSbTRiNRhw+fBhSqRS5XA6hUAiVSgUymQxGo5HuGCd16e2axzqdDtLpNIxGIz2cyOVyLC4uYm1tDZlMBpVKhWa31tfXkcvl6LpI4Hqj3WAwQD6fh0KhmGjiI0skiLe5Xq+HTqeDxWLZtqdgp8/LRvGfdiAYjUZQqVR06QzLzmBFmYXlDcY0Yd6qpGE0GtFutxGPx+Hz+XYkzGq1Gh6Ph0aQ/X4fq6ur8Hq9tLmoWq0ik8kgGAxOFWexWAyHw0HFOZ1OT4jzrZwQIF3UxDhHqVRS04xGo4F0Oo1GowGPxwOr1UpLRI1GA9VqFYVCgX4P481j5DkmY2pCoRBWq3Xia5OGNI1Gg3Q6jUQigXg8jm63i9nZ2U2p6vFRqYWFBbrl7fLly0in05DL5XC73bBarTctxuPXuNPnXyKRTDVnYdkaVpRZWN6A8Pl8eL1e+P1+KsxbdeDbbDZ0Oh1649+JMBMhC4VC9PP6/X643W4a1alUKlSrVWSzWQSDQchkMszMzExEfCKRiC6/yOVyyGQyyOfz1JxkP8WZpKnL5TI4HA50Oh31d+90OgiHw6hUKpBKpZsao8RiMa0j93q9ibp0sVgE8NP1pK1WC9VqFffff/+Wkb9AIIDD4YBcLscLL7wA4HpnPBm1IoyPSgWDQXC5XPj9foxGI8zPz8PpdO6bGLPcHlhRZmF5g0L2D48L87SUK2kSW1lZQTgcxvz8PBVcsu93mjArFAp4vV4Eg0Hq0BUMBuFwOKjDGBFnUnMOhUKQSqWb9jQLhULYbDaYzWbk83lks9kJcd5pHXcjDMNQo49mswmRSES9qnk8HjX1KJVKEAgEdKPadgiFQmi1Wvq48eaxQqGAa9euQavV0iwBeZPJZBPP42AwQC6Xg8/ng16vRy6XQ6VSgdVqnWiC43K5UCgU+OY3v4lGo4FTp07h+PHjbDf1XQoryiwsb2CEQiEV5mAwCJ/PN1Xg+Hw+PB4PVldXEYvF4HK5aFf2dsIsl8vh8/moMKtUKkSjUfT7/YlU7LjzVyaTQTgc3lKcSUNVPp9HPp9HoVCAwWCAyWTasTj3+30Ui0Wsra1NpKiVSiU4HA6GwyGNyjkcDqxW655r2qR5TKFQoFqt4r777oPNZkOz2aRmKuPb1YhAZzIZDIdDLCwsQCQSQa/X05Q2MR7p9/tYXl5GLBajrmH7mapmuf2woszC8gZHJBJtEuZpaWHShBUOh5HNZjEzM7MjYZbJZFSYgetzzalUCr1eD7OzsxMfo1AoMD8/T205ybYp4vxFEAgEmJ2dpeJcKBQmxHmrJqvtUtTA9ci5UChQoTQajTCbzfuSJs9kMuh0OnQ38fiCjHa7jXq9jkajgbW1NVy4cAG1Wg3z8/O0S5tYoer1eiwtLeFb3/oWarUaXWxBnrdQKIRUKoW5ubmbvmaW2w8ryiwsLBCLxZuEeVpUqFarYbFYkMlk6MKSnQizVCqFz+dDIBAAwzCYmZlBNptFv9+Hw+HY9LWm2XIScR4f7+Pz+bBardQWtFAoYG1tDXq9nu48JqJHPJ1FIhFNAY+LbblcRjqdpjPWFotl35zuGo0G8vk8XcgxDofDoY5lJpOJOmwtLCyAz+fTDnDg+qpJUpNutVr0OSGGKyqVCjabDYlEAiKRaEez2Sx3Fqwos7CwALgeCZMacCgUgsfjmSrMMzMzaLfbiMViNCW9E2GWSCSYn59HIBBAuVyGzWZDKpVCMBiE2+2emnomblPbeWYD18XZYrFQcSZmGGRPtEwmA4/Hg9vt3uRkV6/XkU6n0Ww26fey1xWI0xiNRtQ560ZGH+Rg4HA4JhZ25PN5hMNhrK2toVQq0Sh+ZmYG3W4XV65cgVarhc/ng8FgmNgqxa5avLtgRZmFhYVC7C+DwSDC4TA8Hs9UgXU4HPD7/QiHw1hcXNyxMIvFYirM+XweDocDiUQCfr8fXq93y8h0p57ZPB4PMzMzMBqNdDEEqcdu3JjWbreRTqdRrVZpJH8rarGpVAr9fh9er3dbA59ms4lYLAatVksFuVwuI5vNot1u08jX4XDAarVCIBDQlHepVEI0GsXq6iosFgtcLheEQiHC4TAWFha2dG9jufNgRZmFhWUCuVxOhXkrgSXGNKurqwiHw/D5fDsWZpFIRIU5lUrB6XQikUhgdXV10+rHjWzlmT0zM7NJnIkbWKVSmYjCe70estksisUidS7bbq3lzVCr1bC2tgabzbatvW2v16PNbXa7fUKMZTIZHcsyGo2w2Wy0Zr7ReSwejyMSieDll1+GRqNBvV5HKpXCfffdB41GA7lczjps3eGwP50xPve5z+HkyZNQKBQwGo14//vfD7/ff9CXxcJy21EoFHC73ahWq4jFYlM9jIVCIdxuN5rNJl09SIS5Wq0iEols6X1Mur45HA5isRjsdjsEAgH8fj9qtdoNr494ZpOmqVgshuXlZRSLxS2/5nA4RDqdxvLyMiqVCubm5nD48OFbJsjD4RCxWIwugNnucaFQCBwOB1qtFqurq4hEItSvvt/v09q72+2e2sRGxqKOHDmCd73rXTh9+jRkMhm0Wi36/T4uXrwIv9+PS5cuYWVlBalUCpVKBYPB4JZ874Qnn3wS73nPe/Dkk0/e0q9zL8FGymM8//zz+NjHPoaTJ09iMBjgj//4j/G2t70N165dm1g/x8LyRkClUsHpdCISiYDL5U7d9SuTyWC32xGLxain8k4jZqFQSCPmaDQKt9tNZ5Xtdjt0Ot0Nr5GIc7vdRjab3bTQArje6NVsNrG0tITRaASTyQSTyXTL94YnEgmMRiM4HI4tH8MwDK0VKxQKJBIJKJVKzM3NoVKpIJPJQKFQwOFw7LjpjM/nw263w2Aw0IavSqVCRb7ZbKJcLiOfzwOYdB5TKBT7usb1iSee2LfP9UaBFeUx/v3f/33i31/84hdhNBpx4cIFnDlz5oCuioXl4NBoNHR9I5fLnTpmo9Pp0G63kUwmIRaLoVQqdyzMAoGACjNZKykQCBCLxdDv92kH9Y2QSCR0oUU2m0UikUAul0Oj0UC5XIZUKqWd49t5Uu8X5XIZ6+vrcDqdW349hmFw9epV+P1+6oFNasmxWAy9Xg9zc3N77qCWSqVYWFhAsViE3+/HtWvXMBqNcPz4cXA4nAnnsXq9Tp3HhELhhKnJfja9sdwYVpS3oVqtAsC2Dj7dbnfC27XRaNzy62JhuZ1otVqMRiPE43FwudxNfs3AdQvIcStOsVi8Y2Hm8/l0jjkQCMDn80EoFCKdTlNh2umGMyLOnU4H2WwWjUaD7jaeFunfCvr9PhKJBDQazdR7B3ERW1paQiKRgM/no4suMpkMcrkcZDIZFhcXt1wWshv0ej3UajWkUimWl5fRaDRw/PhxKBSKCeexwWAwYQ9aLpfBMAz4fD5kMhmdlZZKpezGuVsIh2EXXk5lNBrhve99LyqVCl588cUtH/fpT38an/nMZzb9/4ULF/DAAw/cyktkYbmtFAoFJJNJWCwWzMzMbHr/cDjE6uoqAGBhYYGmh6vVKsLhMFQq1ZbCTD4+GAyi0+nA6/Wi3W7TdK7L5dpTg9If/dEfoVqtQq1W46/+6q92/fF7IRQKodVq4dChQxMNZkSMSZNZuVzGoUOH4PP50G63EY1G0el0MDMzA7PZfEuELxgM4sqVK9Dr9bDZbJidnd0ykifNY6TDu9lsbnIeI29s89j+wYryFnz0ox/F97//fbz44ouYnZ3d8nEbI+VLly7hscceY0WZ5Z6EbGyatrEIuP77sLKyQrukibDsRpiJqHk8HoxGI2oc4vF4du1x/clPfhKVSuW2iXKxWEQ8HqdbpYBJMe50OhCJRKjVajCZTHA6nSgUCtSMxeFw3NLxJYZhEIlEkEgkIJFIIBKJYLFYYDQab3gIYBgGrVZrIpoeDAbU/GRcpPfqRc7Cpq+n8tu//dv4zne+gxdeeGFbQQauj3eMjzrI5fJbfXksLAeG2WzGaDRCKpUCl8vd1FVMRoxCoRAVbwA7TmWTtZKhUAihUAhutxvz8/MIBoN0/eN2o0UHSa/XQzKZhE6ng0ql2iTGKpUKVqsVyWQSGo0GFosFwWAQjUYDZrMZFovllqeFORwOnE4ner0eOp0OlEolUqkU9dLebk6bw+FAJpNBJpPRA1m73Z5Id29sHlMoFFAqlaxI7wI25zAGwzD47d/+bXz961/Hj3/8YzidzoO+JBaWOw7inJVIJFAqlTa9X6lUYnZ2Fvl8njYPATsfl+JyufB4PFAoFAiFQuj3+1hYWAAAapV5JxKLxcDn8zE7O4tSqYTl5WVEo1GIRCIsLi7C5XIhm82CYRioVCqsrq6i3+9jfn4eVqv1ttVpyfPL5/PR7XYxPz8PPp+PQCCASCSCXq+3488lkUhgMBjgdDpx9OhRHD16FE6nE3K5HI1GA9FoFO12+xZ+N/cerCiP8bGPfQz/9E//hK985StQKBTI5XLI5XLsi4qFZQOzs7MwGAyIxWJYX1/f9H6j0Qi9Xo9EIjHR/LgbYSaWmOFwGO12m25LCgQCtAnzTiGfz6NWq0GhUNBNWhKJBIuLi9QQJRqNol6vAwCy2Sx0Oh0OHTp0INk1gUAAj8eDXq9H10M6HA40Gg0sLy8jl8tt+bPZDrK20m634/Dhw7jvvvvY7OEuYUV5jM9//vOoVqt485vfjJmZGfr2//7f/zvoS2NhueOw2WzQ6XSIxWKoVCpT3y+TyRAOhyeir50KM4fDgcvlglqtRiQSQb1eh8/ng1KpRDgcnojCD5J2u00NSUqlEhVjt9tN68PpdBrxeJz2n3i9XthstgNtkCKd6rVajabdDx8+DL1ej0wmg2vXru3IyGU7+Hw+26m9S1hRHoNhmKlvv/Irv3LQl8bCckdit9upaG68gXM4HLjdbnC5XIRCIYxGI/q+3Qiz0+mERqNBJBJBuVyGy+WCXq9HPB5HJpO5pd/fdjAMg2KxiB/96EcoFouw2+2bxBi43hx3/vx5DAYDzM7O4vDhw3Rt40GjVCphs9mwtraGQqEAHo+Hubk5LC4ugs/nUw/03aS0WW4OVpRZWFj2DBFNEr2S9CyBz+fD4/Gg2+0iFotNvG83wuxwOKDX6xGLxVAqlWCz2WC1WqmL1+0cImEYhtaML1y4AIZh8Pjjj8Pr9W7qnE4mk3juuecgEolw8uRJOJ3OW+4ktlv0ej1MJhOSySTNeJCNXk6nE81m86ZS2iy7gxVlFhaWm4KkmeVyOUKh0KZGLIlEAqfTSTuRx9mNMBPryHg8jrW1NZjNZjgcDpRKJYTD4YlI/FZAIuPl5WV6wJDL5Th58uSmLvThcAi/34+f/OQn0Ov1eMtb3nLLPLb3g9nZWajVakSjUbRaLfr/Wq0Whw8fhsFgQCaTwfLy8k2ntFm2hxVlFhaWm4Y0ZkmlUgSDwYkbOwBqcZnJZFAulyfeNy7M0Wh022jMZrPRzu98Pg+dTgePx4NGowG/349+v7/v39u4GMfjcUgkEiwsLNAFEhuNVOr1Oq5cuYKrV6/Cbrfj8ccf31c/6VuF0+mERCJBKBSaSFfzeDzMzs5icXERQqEQgUAAfr8fjUYDnU6HGoxUq1Wsr6+jWCyiUCggm80inU5P+Diw3Bh2eIyFhWVfIKM2wWAQwWAQPp9vwjd5ZmYGnU4HsVgMIpFoItU7PsccjUbhdDq3bBCanZ0Fh8NBKpUCwzAwm83w+XwIhULw+/3weDz7Yk9J0tTZbBa9Xg8ajQZutxsSiQSpVArdbheLi4v0OkejEdLpNB0FczgcOHr06IGlqxmGwWg0wmg0wnA43NHf+Xw+otEo0uk0XaSx8bG1Wg2hUAjnz5+HXq+HVqvd1LDG5XLB4/HA5XKhUqnu2NnyOxFWlFlYWPYNHo8Hj8eDQCCAYDCI+fn5iRuy3W5Hp9NBOBzGwsLChMUjcfuKRCI3FGar1Qoul4t0Oo3RaASLxYKFhQUEg0EqzHvd7DZNjD0eDz1g1Ot15PN5zM7O0v9rNpt0iQRwfSRs4/e+HRuFb6ciut37d5LO53A4VDzJn1arlW7bcrlc4PP54HK59DHEQ7xQKKBUKoHD4WBubg4ajYZ+Dpa9w4oyCwvLvkIWTPj9fgQCAczPz9P0LUlzk53BZKcyQa1W71iYZ2ZmwOFwkE6nwTAMrFYr5ufnEQ6HEQgEdm3+cyMxBq4vbQiFQhAKhVAqlWg0GshkMtQmUyAQYH19na5eLJVKOxLUG8HhcCaEcfxPPp9P/z7+/9Meu/HvWz23DocDoVAIXC53S1dDnU6HTqeDRCKBZDKJWq2Gubk5Niq+SVhRZmFh2XemCTOJioVCIdxuN/x+P+Lx+KZ9w7sRZrPZDC6Xi2QyidFohLm5OXi9XsRiMYTDYbRaLQyHQ/T7fTQajanR5WAwQLFYRD6fR6fTgUKhgF6vR7fbRSQSmXh8JpNBtVqFy+XCxYsXkc1m0e12odfrMRwOkc1mYTQa0e/3US6XN4mgQCDYViS3EtTbPetLRqXi8ThEItFUn3Pgup2mz+dDuVxGMpnE8vIyzGYz/bmw7B5WlFlYWG4JAoFgkzATD2SZTAa73U6drzbe9HcjzGSZQiKRAMMwsNlsdI9xrVZDvV5Hv9+H3++f+DgOh0Obk0ajEdRqNZxOJ2Qy2VRhJP7OCwsLYBgG+XweLpeLXlsoFKL/vhcgB5NUKgWRSAS1Wr3lYzUaDVQqFbLZLHK5HM0WkKUcLDuHFWUWFpZbhlAonBBmn89HhVmn06HdbiOVSkEikWwy1NiNMBsMBnC5XMRiMYxGI9jtdszNzUGn04HH40GtVuPw4cM06lxfX0c+n4dSqYTD4cDMzMy2zWGDwQD5fB5msxnD4RCNRgMOhwMWiwX9fh+rq6tQKBSbov67HavVim63i2g0ivn5+W03WJF6tE6nQzKZRCgUgkqlYlPau4TNL7CwsNxSRCIRvF4v+v0+QqEQhsMhfZ/VaoVKpUIkEkGn09n0sUSYK5XKDceldDodnE4n1tfXEYvFwDAMBAIBfROJRKhUKlhZWUEymYRcLsfhw4fhdDpv2K2dSCRQLBbRbDbpEonZ2VkwDINQKAQejwe3231PWko6HI6po1JbIRaL4fV64Xa70W63J37eLDeG3ae8z1y8eBEPPvggu0+ZhWUDrVYLgUAAEokEXq+X1hyHwyFWV1fBMAwWFxenjhBVKhVEIhGaYt5O/MrlMqLRKBX7VqtFhbTX69HZ4p2OTeXzebz88stQqVTweDyYnZ0Fl8ulgtxsNrGwsLAvY1h3KiQbwOPxMD8/v+MxL4Zh7smDyq2EjZRZWFhuC1KpFF6vF61Wa8KBi4xRDQaDLV29dhMxk3niarVK68tkneBOI2NCPp+nNpknTpyYWCKRTCZRr9fhdrvvaUEGrvcHeL1e9Hq9bZ3XNsIK8u5hRZmFheW2IZPJqAPX+M1dJBLB7XajXq8jlUpN/djdCDOJatvtNhQKxa7FmBwQzp49C4VCgbe85S0TNe9CoYC1tTXYbDYoFIpdPAN3L2KxGC6XC/V6Hclk8qAv556FFWUWFpbbikKhgNvtRq1WmxBXhUKBubk5FAqFLdcy7kaYlUol7r///l2JMQBUq1Vcu3YNiUQCKpUKjzzyyMTHV6tVJJNJmM1m6PX6HX/ee4HxrVL5fP6gL+eehBVlFhaW245SqaTiGo/H6f8bDAYYDAYkEgk0Go2pH7sbYd4Nw+EQsVgMoVAIfD4fUqkULpdrYqyn1WrR2rbVat2Xr3u3of//tXfncVGV+x/AP2eGmWHYt2GVHUEQFQVJU0TM3M3cMs1yabH0alldf1quV81bmWl1MzVDsyzLSstKM68KamikVoLIvimr7IzMen5/cBkdmYFhPQf4vl+veb2cc86c+c4j8J3nOc/5Pk5OcHV1RX5+vsF1tEnbUFImhHCiYdLW7du3kZubq9vu6ekJKyurJtfxbe/EXF1djeTkZJSXl8PLywtCoRBSqVSvmlXD7HFzc/Nucy9ya3l4eMDe3h5ZWVmNVgUjbUNJmRDCGXt7e/j4+KCkpER3nbJhKUiBQID09HSjZSjbIzFrtVrk5eUhNTUVEokEffv2hVar1d2H3DDLWKvVIj09HQzDICAggKpV4e6tUk19eSItRz9ZhBBOOTo6wsvLC8XFxbh16xaA+jKdAQEBusIVxrQlMdfW1uL69esoLS2Fp6cnAgMDodFocPPmTbi4uOgmcLEsi6ysLNTV1SEgIEBvEY2erGFVsIYvT3Q/cvugpEwI4ZxMJkOvXr10ZRoBQCqVwtfXFxUVFbpkbUhLEzPLsrh16xZu3LgBgUCA4OBgODs7g2VZ3bKS7u7uuuNv3ryJiooK+Pn56S1OQe5+eWrprVLEOErKhBBecHFxgbu7O27evIni4mIA0E2oKigoQHl5udHXmpqY79y5g5SUFBQWFsLNzU2v6EdBQQHu3LkDX19f3fB0w0IVVMfZOHNzc93tbPfODSCtQ0mZEMIbbm5ucHV1RV5enu62KFdXVzg4OCA7Oxtyudzoa5tKzA0LSFy/fh1arRZ9+vTRLf0I1A9lNyTqhvrOVVVVyM3NhUwmg7Ozcwd+6q7P2toa3t7eui8xpPUoKRNCeMXDwwPOzs7IyclBWVkZAMDb2xvm5ubIyMiASqUy+lpDiVmhUCA1NRX5+flwdnZGcHCw3sIKWq1Wt1qVq6srAKCurg6ZmZm6e6dJ8xwdHXW3SjU1qkGaRkmZEMI7np6ecHJyQlZWlm5d4oCAALAsi4yMjCavXd6bmFNTU5GcnKy3iMT9M6dv3rwJhUKhq6mtVquRnp4OsVgMPz8/KhXZAg23SmVnZ9OtUq1ESZkQwkteXl5wcHBAVlYWKisrIRKJ4O/vD7lcrldwxJCGxFxbWwsHBwcEBwfDysqq0XHV1dUoLi6Gh4cHzM3NodVqkZGRAY1Gg4CAAJMXXiB3+fj4wMLCwuRVpYg+SsqEEF5iGAY+Pj6wtbVFRkYGqqqqYGlpCR8fH9y+fbvZa5d2dnYYOHAgvL29DSbXhgpe1tbWcHFxAQDk5ORALpcjICAAYrG4Qz5XdycQCODv7w+hUIi0tDS6VaqFKCkTQniroZCItbU1MjIyUFNTAwcHB921y8rKymZfb0x+fj7UajV8fHwA1M++Lisrg4+PDywtLdvzY/Q4DbdKaTSaJifnkcYoKRNCeI1hGPj7+8PS0lK3frG7uztsbW11RT1aqrKyUlc0RCwWo6ysDLdu3dJdEyVtZ25ujtDQ0B6zilZ7oaRMCOG9hole5ubmSEtLQ11dHXx9fSEWi1tcTUqtViMnJwe2trZwcnJCTU0NsrOzdbOHSfuhcqQtRy1GCOkSBAIBevfuDYlEgtTUVKhUKvj7+0Oj0bSomlRubi5YloW3tzcUCgUyMjJgZWUFb2/vDv4EhDSPkjIhpMsQCoXo3bs3RCIRUlNTAQB+fn6orq5Gfn5+s68vKyvTrQTVULNZKBTSrU+ENygpE0K6FDMzM/Tu3RtCoVC3upOnpyeKi4t1VcAMUalUyM3NhYODA+zs7JCRkQG1Wo3evXvDzMysEz8BIcZRUiaEdDkikQi9e/cGAKSmpsLOzg4ymQy5ubmoqakx+Jrs7GwIBAJ4enrqjvPz84NEIunM0AlpEiVlQkiXJBaLERgYCK1Wi7S0NLi5ucHKysrg+r4lJSWoqqrS3eNcWloKb29vmhlMeIeSMiGky5JIJAgMDIRKpUJ6erquUEh6ejq0Wi0AQKFQID8/HzKZDFqtFvn5+XB1dYWjoyPH0RPSGCVlQkiXZm5ujsDAQCiVSmRlZcHHxwcKhUK3IEV2djZEIpGuZKe9vT08PDy4DpsQgygpE0K6PKlUit69e6Ourg63bt2Cj48PKioqkJKSgpqaGri7uyMzMxNSqVRXwYsQPqKkTAjpFiwsLBAQEIDa2lqUlpbC3d0dcrkcMpkMRUVFuspgVNCC8Bn9dBJCug0rKysEBASguroacrkcgYGBUCgUUCgUCAgIgEgk4jpEQppEN+cRQroVa2tr+Pv7IyMjA3fu3IFCoUDv3r0hlUq5Do2QZlFP+T5xcXGYPHky3N3dwTAMjhw5wnVIhJAWsrW1ha+vL5RKJby8vGBjY8N1SISYhJLyfWprazFgwAD85z//4ToUQkgb2NvbY+DAgZDJZFyHQojJaPj6PuPHj8f48eO5DoMQ0g6onjXpaigpt1HDJJIGxkr8EUIIIc2h4es22rJlC2xtbXWP6OhorkMihBDSRVFSbqNVq1ahsrJS9zh79izXIRFCCOmiaPi6jSQSid4qM1ZWVhxGQwghpCujnjIhhBDCE9RTvk9NTQ3S09N1z7OysnD16lU4ODjAy8uLw8gIIYR0d5SU75OYmIiYmBjd85dffhkAMG/ePOzbt4+jqNqmoKAABQUFXIdBCOmB3Nzc4ObmxnUYXQbDsizLdRDdSUFBAXbt2oVFixbx4gdRoVBg7NixNAGNEMKJ6OhonDhxQm/uDTGOknI3V1VVBVtbW5w9e5YmoXGgpqYG0dHR1P4cofbnVkP7V1ZWUqlTE9HwdQ8RFhZGvxQcqKqqAkDtzxVqf241tD8xHc2+JoQQQniCkjIhhBDCE5SUuzmJRIJ169bRJAuOUPtzi9qfW9T+LUcTvQghhBCeoJ4yIYQQwhOUlAkhhBCeoKRMCCGE8AQlZWKy7OxsMAzTZcuNEkII31FS7iAZGRlYtGgR/Pz8YG5uDhsbGwwbNgw7duzAnTt3Oux9k5OTsX79emRnZ3fYe5hi8+bNeOSRR+Di4gKGYbB+/XpO4zGGYRiTHmfOnGnze8nlcqxfv75F5+oq7dhafG7/lJQUrFixAmFhYbC2toabmxsmTpyIxMTENsfCF3xu/1u3bmHu3LkICgqCtbU17OzsEBkZif3796M7z0+mil4d4Mcff8TMmTMhkUjw1FNPITQ0FEqlEufOncM///lPJCUlYffu3R3y3snJydiwYQNGjhwJHx+fDnkPU6xevRqurq4YOHAgTpw4wVkczTlw4IDe808//RQnT55stD04OLjN7yWXy7FhwwYAwMiRI016TVdpx9bic/t//PHH2Lt3L6ZPn47FixejsrISu3btwpAhQ3D8+HGMHj26zTFxjc/tX1paivz8fMyYMQNeXl5QqVQ4efIk5s+fjxs3buCNN95oc0y8xJJ2lZmZyVpZWbF9+vRhb9261Wh/Wloau3379g57/6+//poFwJ4+fbrZY7VaLSuXy00+d1ZWFguAjY2NNelYlmXZkpISFgC7bt06k9+HS0uWLGE76teiNW3RVduxtfjU/omJiWx1dbXettLSUlYmk7HDhg3rgAi5x6f2N2bSpEmspaUlq1ar2ycwnqHh63b21ltvoaamBnv37jW4SlRAQABefPFF3XO1Wo2NGzfC398fEokEPj4+eO2116BQKPRe5+Pjg0mTJuHcuXOIjIyEubk5/Pz88Omnn+qO2bdvH2bOnAkAiImJaTT01HCOEydOICIiAlKpFLt27QIAZGZmYubMmXBwcICFhQWGDBmCH3/8sdXtwGUvvb1ptVps374dffv2hbm5OVxcXLBo0SKUl5frHZeYmIixY8fCyckJUqkUvr6+WLhwIYD66/EymQwAsGHDBt3/TXPD0d2pHVuLq/YPDw9vtIiFo6MjoqKicP369fb9kDzG5c+/IT4+PpDL5VAqlW3+bHxEw9ft7IcffoCfnx8efPBBk45/5plnsH//fsyYMQOvvPIKLl68iC1btuD69ev47rvv9I5NT0/HjBkz8PTTT2PevHn45JNPMH/+fISHh6Nv374YMWIEli1bhvfeew+vvfaabsjp3qGnGzduYPbs2Vi0aBGeffZZBAUFoaioCA8++CDkcjmWLVsGR0dH7N+/H4888ggOHz6MqVOntl8DdUGLFi3Cvn37sGDBAixbtgxZWVn44IMPcOXKFZw/fx4ikQjFxcUYM2YMZDIZVq5cCTs7O2RnZ+Pbb78FAMhkMuzcuRMvvPACpk6dimnTpgEA+vfvz+VH6xL41v6FhYVwcnJq18/IZ1y3/507d1BbW4uamhqcPXsWsbGxGDp0KKRSaYd+bs5w3VXvTiorK1kA7JQpU0w6/urVqywA9plnntHb/uqrr7IA2P/+97+6bd7e3iwANi4uTretuLiYlUgk7CuvvKLb1tTwdcM5jh8/rrf9pZdeYgGw8fHxum3V1dWsr68v6+Pjw2o0GpZlWzZ83aCrDbveP3wXHx/PAmA///xzveOOHz+ut/27775jAbC///670XO3pS26Wju2Fl/bv0FcXBzLMAy7Zs2aVp+Dz/jY/lu2bGEB6B4PPfQQm5ub26JzdCU0fN2OGpYps7a2Nun4n376CQDw8ssv621/5ZVXAKDR8HFISAiioqJ0z2UyGYKCgpCZmWlyjL6+vhg7dmyjOCIjIzF8+HDdNisrKzz33HPIzs5GcnKyyefvbr7++mvY2tri4YcfRmlpqe7RMLR5+vRpAICdnR0A4NixY1CpVBxG3L3wqf2Li4sxZ84c+Pr6YsWKFR3yHnzDh/afPXs2Tp48iYMHD2LOnDkA0KF3sHCNknI7alivtbq62qTjc3JyIBAIEBAQoLfd1dUVdnZ2yMnJ0dvu5eXV6Bz29vaNru00xdfX12AcQUFBjbY3DHvfH0dPkpaWhsrKSjg7O0Mmk+k9ampqUFxcDACIjo7G9OnTsWHDBjg5OWHKlCmIjY1tNDeAtAxf2r+2thaTJk1CdXU1jh492uhac3fFh/b39vbG6NGjMXv2bHz++efw8/PD6NGju21ipmvK7cjGxgbu7u64du1ai17HMIxJxwmFQoPb2Rbcs9dtr8N0EK1WC2dnZ3z++ecG9zdMXmEYBocPH0ZCQgJ++OEHnDhxAgsXLsQ777yDhISEHvNHvL3xof2VSiWmTZuGv/76CydOnEBoaGirz9XV8KH97zdjxgzs2bMHcXFxjUb9ugNKyu1s0qRJ2L17N3777TcMHTq0yWO9vb2h1WqRlpamNxmrqKgIFRUV8Pb2bvH7m5rg74/jxo0bjbanpKTo9vdU/v7++PXXXzFs2DCTvtAMGTIEQ4YMwebNm3Hw4EE88cQT+PLLL/HMM8+06v+mp+O6/bVaLZ566imcOnUKX331FaKjo1vzMbosrtvfkIYecmVlZbucj29o+LqdrVixApaWlnjmmWdQVFTUaH9GRgZ27NgBAJgwYQIAYPv27XrHbNu2DQAwceLEFr+/paUlAKCiosLk10yYMAGXLl3Cb7/9pttWW1uL3bt3w8fHByEhIS2Oo7t47LHHoNFosHHjxkb71Gq1rp3Ly8sbjViEhYUBgG4Iz8LCAkDL/m96Oq7bf+nSpTh06BA+/PBD3YzhnoTL9i8pKTG4fe/evWAYBoMGDTLpPF0N9ZTbmb+/Pw4ePIhZs2YhODhYr6LXhQsX8PXXX2P+/PkAgAEDBmDevHnYvXs3KioqEB0djUuXLmH//v149NFHERMT0+L3DwsLg1AoxJtvvonKykpIJBKMGjUKzs7ORl+zcuVKfPHFFxg/fjyWLVsGBwcH7N+/H1lZWfjmm28gELT8u9uBAweQk5MDuVwOAIiLi8OmTZsAAE8++WSX6X1HR0dj0aJF2LJlC65evYoxY8ZAJBIhLS0NX3/9NXbs2IEZM2Zg//79+PDDDzF16lT4+/ujuroae/bsgY2Nje7Ll1QqRUhICA4dOoTAwEA4ODggNDS0yeHQ7tKOrcVl+2/fvh0ffvghhg4dCgsLC3z22Wd6+6dOnar7Etxdcdn+mzdvxvnz5zFu3Dh4eXmhrKwM33zzDX7//XcsXbq00VycboPbyd/dV2pqKvvss8+yPj4+rFgsZq2trdlhw4ax77//PltXV6c7TqVSsRs2bGB9fX1ZkUjEenp6sqtWrdI7hmXrb2eaOHFio/eJjo5mo6Oj9bbt2bOH9fPzY4VCod7tUcbOwbIsm5GRwc6YMYO1s7Njzc3N2cjISPbYsWN6x7Tklqjo6Gi92xjufZhSbYwrxioa7d69mw0PD2elUilrbW3N9uvXj12xYoWuatvly5fZ2bNns15eXqxEImGdnZ3ZSZMmsYmJiXrnuXDhAhseHs6KxWKTbg/pqu3YWnxq/3nz5hltewC6amvdCZ/a/5dffmEnTZrEuru7syKRSPc3NDY2ltVqte36ufmEYdluXNmbEEII6ULomjIhhBDCE5SUCSGEEJ6gpEwIIYTwBCVlQgghhCcoKRNCCCE8QUmZI2+99Rb69OkDrVbLdShttnLlSjzwwANch9Ei1P7covbnFrU/j3F9T1ZPVFlZyTo4OLCffPKJbhv+d+/j1q1bGx0fGxvb7LJopvrmm2/Yxx57jPX19WWlUikbGBjIvvzyy2x5ebnB448ePcoOHDiQlUgkrKenJ7t27VpWpVLpHVNQUMBKJBL26NGjbY6vM1D7c4van1vU/vxGSZkD7777LmtjY8PeuXNHt63hl8LFxYWtra3VO749fykcHR3Zfv36sWvWrGH37NnDLlu2jBWLxWyfPn1YuVyud+xPP/3EMgzDxsTEsLt372aXLl3KCgQC9vnnn2903scee4yNiopqc3ydgdqfW9T+3KL25zdKyhzo378/O3fuXL1tANiwsDAWAPvOO+/o7WvPXwpDVaD279/PAmD37Nmjtz0kJIQdMGCA3jfT119/nWUYhr1+/bresYcPH2YZhmEzMjLaHGNHo/bnFrU/t6j9+Y2uKXeyrKws/PXXXxg9enSjfcOGDcOoUaPw1ltvddhaoSNHjmy0berUqQCA69ev67YlJycjOTkZzz33HMzM7pZIX7x4MViWxeHDh/XO0fB5jh492gFRtx9qf25R+3OL2p//KCl3sgsXLgCA0RVO1q9fj6KiIuzcubPJ8ygUCpSWlpr0aE5hYSEAwMnJSbftypUrAICIiAi9Y93d3dGrVy/d/ga2trbw9/fH+fPnm30/LlH7c4van1vU/vxHq0R1soY1in19fQ3uj4qKQkxMDN5++2288MILRtcw/eKLL7BgwQKT3pNtprz5m2++CaFQiBkzZui2FRQUAADc3NwaHe/m5oZbt2412u7n54fk5GSTYuIKtT+3qP25Re3Pf5SUO9nt27dhZmYGKysro8esX78e0dHR+Oijj7B8+XKDx4wdOxYnT55sczwHDx7E3r17sWLFCvTu3Vu3vWH4SiKRNHqNubk5qqqqGm23t7dv9A2Wb6j9uUXtzy1qf/6jpMxDI0aMQExMDN566y08//zzBo9xc3Mz+C2yJeLj4/H0009j7Nix2Lx5s96+hm/IDQuU36uurs7gN2iWZcEwTJti4gNqf25R+3OL2p9blJQ7maOjI9RqNaqrq2FtbW30uHXr1mHkyJHYtWsX7OzsGu2/c+cOKisrTXpPV1fXRtv+/PNPPPLIIwgNDcXhw4f1JlMAd4eNCgoK4OnpqbevoKAAkZGRjc5ZXl6ud12Ij6j9uUXtzy1qf/6jiV6drE+fPgDqZ0E2JTo6GiNHjsSbb75pcCbkoUOHdN9Wm3vcLyMjA+PGjYOzszN++ukng0NZYWFhAIDExES97bdu3UJ+fr5u/72ysrIQHBzc5OfiGrU/t6j9uUXtz3/UU+5kQ4cOBVD/w9a/f/8mj12/fj1GjhyJ3bt3N9rX2ms6hYWFGDNmDAQCAU6cOAGZTGbwuL59+6JPnz7YvXs3Fi1aBKFQCADYuXMnGIbRm5QBAJWVlcjIyMALL7zQ4pg6E7U/t6j9uUXt3wVwc3t0zxYaGsrOnj1bbxsAdsmSJY2OjY6O1lXbaY+b9wcMGMACYFesWMEeOHBA7/HLL7/oHfvDDz+wDMOwo0aNYnfv3s0uW7aMFQgE7LPPPtvovIcPH2YBsOnp6W2OsaNR+3OL2p9b1P78RkmZA9u2bWOtrKz0ysoZ+6U4ffp0u/5SNJzL0CM6OrrR8d999x0bFhbGSiQStlevXuzq1atZpVLZ6LhZs2axw4cPb3N8nYHan1vU/tyi9uc3SsocqKioYB0cHNiPP/6Y61DaRUFBAWtubs4eOXKE61BMQu3PLWp/blH78xtN9OKAra0tVqxYgbfffrtbLJ22fft29OvXD1OmTOE6FJNQ+3OL2p9b1P78xrBsM+VWCCGEENIpqKdMCCGE8AQlZUIIIYQnKCkTQgghPEFJmRBCCOEJSsqEEEIIT1BSJoQQQniCkjIhhBDCE5SUCSGEEJ6gpEwIIYTwBCVlQgghhCcoKRNCCCE8QUmZEEII4QlKyoQQQghPUFJuZwUFBVi/fj0KCgq4DoUQQkgXQ0m5nRUUFGDDhg2UlAkhhLQYJWVCCCGEJygpE0IIITxBSZkQQgjhCUrKhBBCCE9QUiaEENIhlEolLly4AKVSyXUoXQYlZUIIIR0iISEBsbGxuHjxItehdBmUlAkhhLQ7hUKBEydOICsrC8ePH4dCoeA6pC6BkjIhhJB2d/HiRaSmpqJ///5ITU3FpUuXuA6pS6CkTAghpF019JLFYjFsbGwgFoupt2wiSsqEEELa1ZUrV5CRkYHa2lokJSWhtrYWGRkZuHLlCteh8Z4Z1wEQQgjpXjw9PfHEE08Y3E6aRkmZEEJIu/Lw8ICHhweUSiUSExMREREBsVjMdVhdAg1fE0II6RB0S1TLUVImhBDS7uiWqNahpEwIIaTd0S1RrUNJmRBCSLuiW6Jaj5IyIYSQdkW3RLUezb4mhBDSruiWqNajpHyPLVu24Ntvv0VKSgqkUikefPBBvPnmmwgKCuI6NEII6TIabokiLUfD1/c4e/YslixZgoSEBJw8eRIqlQpjxoxBbW0t16ERQgjpAainfI/jx4/rPd+3bx+cnZ3xxx9/YMSIERxFRQghpKegpNyEyspKAICDg4PRYxQKhd6Mwpqamg6PixBCSPdEw9dGaLVavPTSSxg2bBhCQ0ONHrdlyxbY2trqHtHR0Z0YJSGEkO6EkrIRS5YswbVr1/Dll182edyqVatQWVmpe5w9e7aTIiSEENLd0PC1Af/4xz9w7NgxxMXFoVevXk0eK5FIIJFIdM+trKw6OjxCCCHdFCXle7Asi6VLl+K7777DmTNn4Ovry3VIhBBCehBKyvdYsmQJDh48iKNHj8La2hqFhYUAAFtbW0ilUo6jI4QQ0t3RNeV77Ny5E5WVlRg5ciTc3Nx0j0OHDnEdGiGEkB6Aesr3YFmW6xAIIYT0YNRTJqQDKZVKXLhwAUqlkutQCCFdACVlQjpQQkICYmNjcfHiRa5DIYR0AZSUCekgDWvKZmVl0VqyhBCT8DIpFxQU4M8//6SFIEiXdvHiRaSmpqJ///5ITU3FpUuXuA6JEMJzvErKR48eRZ8+fdCrVy8MGjRIN+RXWlqKgQMH4siRI9wGSIiJGnrJYrEYNjY2EIvF1FsmhDSLN0n5hx9+wLRp0+Dk5IR169bpzYR2cnKCh4cHYmNjOYyQENNduXIFGRkZqK2tRVJSEmpra5GRkYErV65wHRohhMd4c0vUv/71L4wYMQKnT5/G7du3sX79er39Q4cOxa5du7gJjpAW8vT0xBNPPGFwOyGEGMObpHzt2jVs27bN6H4XFxcUFxd3YkSEtJ6Hhwc8PDy4DoMQ0sXwZvjawsKiyYldmZmZcHR07MSICCGEkM7Fm6QcExOD/fv3Q61WN9pXWFiIPXv2YMyYMRxERgghhHQO3iTlzZs3Iz8/H4MHD8auXbvAMAxOnDiB1atXo1+/fmBZFuvWreM6TEIIIaTD8CYpBwUF4dy5c3B0dMSaNWvAsizefvttvPHGG+jXrx/i4+Ph4+PDdZiEEEJIh+HNRC8A6Nu3L3799VeUl5cjPT0dWq0Wfn5+kMlkXIdGCCGknSiVSiQmJiIiIgJisZjrcHiFV0m5gb29PQYPHsx1GIQQQtrAWPJNSEjAgQMHoNFoEBUVxWGE/MOb4ev33nsPY8eONbp//Pjx2LlzZydGRAghpC0MLchCNeGbxpukvHfvXoSEhBjdHxISgt27d3diRIS0HS3dSHoqY8mXasI3jTdJOSMjA8HBwUb39+nTBxkZGZ0YESFtR0s3kp7KUPKlmvDN401SFovFKCwsNLq/oKAAAgFvwiWkWTRMR3oqY8n34sWLVBO+GbyZ6DVkyBDs27cPy5cvh7W1td6+yspKxMbGYsiQIRxFR0jLGeop0KQW0hM0LMhSV1eHpKQkqFQqZGRkoKKigmrCN4M3SXndunWIjo5GWFgYXnrpJfTt2xdAfU3s7du3o6CgAAcPHuQ4SkJMY6ynEBkZCYlEwnV4hHQoYwuyDBo0iGrCN4M3SfmBBx7ADz/8gEWLFuHFF18EwzAAAJZl4evri++//x5Dhw7lOEpCTGOsp3DlyhUa8SHdHi3I0nq8ScoA8PDDDyM9PV33Bw0A/P39MWjQIF2SJqQroKUbCSGtwaukDAACgQDh4eEIDw/nOhRCWo16CoSQ1uBdUk5OTkZmZibKy8vBsmyj/U899RQHURFCCCEdjzdJOSMjA3PnzsWlS5cMJmMAYBiGkjIhhJBuizdJedGiRfj777+xfft2REVFwd7enuuQCCGEkE7Fm6R8/vx5vPbaa1i6dCnXoRDCKVpBp/1QW5KuhjclspycnGBra8t1GIRwjkpzth9qS24plUqcOXmGar+3AG+S8vPPP4/PPvsMGo2G0zji4uIwefJkuLu7g2EYHDlyhNN4SPdkbKEKKs3ZfqgtuZeQkIBPP/uUvhS1AG+GrwMDA6HRaDBgwAAsXLgQnp6eEAqFjY6bNm1ah8ZRW1uri6Gj34v0XMbWk6XSnO2H2pJb938pomp2puFNUp41a5bu36+++qrBYxiG6fCe9Pjx4zF+/PgOfQ/Scxi6pmnsjxWV5mw/1Jbca/hS1LdPX/pS1AK8ScqnT5/mOoRWUSgUesNiNTU1HEZD+MZQj9hYD45Kc7Yfaktu3fulyFJqCXEtfSkyFW+ScnR0NNchtMqWLVuwYcMGrsMgPNTwhykz826PGIDRHhyV5mw/1JbcuvdL0fXS6xBIBfSlyES8ScoNFAoFLl++jOLiYgwbNgxOTk5ch9SkVatW4eWXX9Y9v3r1apf9gkHaV0OPODT0bo9YJBI12YOj0pztg8qccuveL0UVWRWw87XTbSdN41VSfu+997B+/XpUVlYCAE6ePIlRo0ahtLQUffr0wVtvvYWFCxdyHKU+iUSiNxxjZWXFYTSEL/SG7yzv9oiffvpp6sGRbu/eL0V5v+XBcyj9fJuKN0k5NjYWL730Eh5//HGMGTNGL/k6OTlh1KhR+PLLL3mXlAkx5N7hu7/+SoKZWX2PuLi4GJMnT+Y6PEI6TV15HdchdCm8ScrvvPMOpkyZgoMHD+L27duN9oeHh+O9997r8DhqamqQnp6ue56VlYWrV6/CwcEBXl5eHf7+pHu4d/guNxdo+NGhHjHpaapvVXMdQpfCm6Scnp6OZcuWGd3v4OBgMFm3t8TERMTExOieN1wvnjdvHvbt29fh70+6h3uH7778Ehg7tv7WKJlMxnFkhHSuipwKrkPoUniTlO3s7FBaWmp0f3JyMlxdXTs8jpEjRxpdpYqQ1vjlF8DNLQGffda4WAgh3V15ejnXIXQpvCmzOWHCBOzevRsVFRWN9iUlJWHPnj145JFHOj8wQtooK0uBgwep3CPpmWqLa2kIuwV4k5Q3bdoEjUaD0NBQrF69GgzDYP/+/Zg7dy4iIiLg7OyMtWvXch0mIS0SERGB8+d7Yd++D9Gv391bo4wxVhObkK4s89dMvef0c24cb5Kyu7s7/vjjD4wbNw6HDh0Cy7I4cOAAfvjhB8yePRsJCQm8v2eZkPsVFBRApSqFSqXAtWs2EInETfaWaVUj0h39/fnfUMlVuuf0c24cL64pN9zT6ePjg48//hgff/wxSkpKoNVqIZPJIBDw5rsDIS2iUjX8IWKRmJgEOzsVamsNVzaiAv6kuxk0aBDSr6XDzswO/rv8MXT5UPo5bwYvsp1YLMbMmTNx4cIF3TaZTAYXFxdKyKRLa1jpTCCwgKPjUxAKn0Za2hM4c8YFp0/rD98ZqolNSFeWl5eHalU1ypXluHbwGgouF9DPeTN4kfEYhkHv3r2bnH1NSFfU8KWSYcxhZzcZdnaTYWExGbGxeZg7NxZr115EaanxVY1oUhjpqhQKBerq6guHqFgVlBolTqw6gR+P/kg/503gRVIGgNdeew0ffPABbty4wXUohLSL3NxcVFVVAQA0mioolbkAAK1WgaqqE6iuzsL+/ccxbpwCTzxxBXFxGaisrEVSUhJqa2t1NbEJ6YouXrwItVoNANCyWmTXZiMlJwW/ff8bamvo59wYXlxTBuov/Ds6OiI0NBQjR46Ej48PpFKp3jEMw2DHjh0cRUiIaS5duoSNGzfixx9/1N3zzrI1+PtvH9jaToKt7UTU1aVCKu2PurpUVFdfQmqqH+TyJ1BSAvTrB0RHA/7+HVcBrKpKhffeS8Nvv90GwwAjRsiwdGkApNLm/ySwLIuVK//GpUtl2LixL4YPv1sQJSWlCrt3ZyI1tRoMw6BPH2ssWuSPgACqCd+TNIz8MAwDAGDBIqkqCcMch6Gfuh88bTwRMjNEt58q3d3Fm6T8wQcf6P596tQpg8dQUiZ89+2332LWrFlgWdZAERoWlZU/obLyR1haDoNU2hcMI0ZV1XG4ua2FnV19TeysrPpHcDBgaQm4uQGtmVrx0ktXMG6cK8aNc2u0b/Pm67h9W4G33x4AjUaLN9+8ga1bU7FmTUiz5z18OB//+1uq584dNf7v//7Cgw864aWXAqHRsNi3LwsrVvyJr74aCjMz3gzMkQ7WUPv93t+BEkUJqtXVGGA3ALgOuOW7YfDiwRxGyU8tSsq+vr66bzamYhgGGRkZzR6n1WpbdF5C+ObSpUuYNWsWNBpNE1XhNACA2tpzYBgRhEJrKBQZkMuvwMpKfzb29evA//1ffd3sefOA8eMBsbjtcebk1OLSpTJ89NEgBAXZAACWLQvAypV/44UX/OHkZHwmbHp6Nb76Kg+7doVj+vTf9Pbl5spRVaXGggU+cHY2BwDMm+eDp59ORFFRHTw8LNoePOkSGmq/nzx5EnV1dZAIJIh0iIS92B4AoNaqcfi9w1Br1RiyZEiL80p31qKkHB0d3ajxEhMTkZSUhJCQEAQFBQEAbty4geTkZISGhiI8PLz9oiWExzZt2mSkh2wIA622Bi4uLwEAxGLjw3e5ucDGjcCuXcBTTwFTpwJtuYMkKakKVlZmuoQMAOHh9mAY4Pr1KkRFGa7PXVenwaZN1/Hii4FwcGgcgKenBWxszPDTTwV44glvaLUsfvqpAN7eFnB1NW99wKTLaaj93nAJUiQQ1feQ/yezNhMXb1+E9n0toASGvDQEjIASM9DCpHz/ggxHjhzBkSNHcPLkSTz00EN6+06ePInHHnsMGzdubFFACQkJOH36NIqLi7F48WL07t0bcrkcKSkpCAwMpPWKCS/l5ubi2LFjLaibroVc/jssLAZALDZt9bHiYmDrVuCTT4C5c4EZMwCLVnQ+y8qUsLcX6W0TCgWwsRGhrMx4haX//CcdffvaYPhww0V8LCzMsH17GFavvoYDB3IAAB4eUrz11gAIhTR0TeqptCokVyWjVFmKpKok+Hzmg7qKOoxYMwJCkZDr8DjXpmvKa9euxdKlSxslZAB4+OGH8Y9//AOrV6/GlClTmj2XUqnE448/jqNHj4JlWTAMg8mTJ6N3794QCAQYM2YMli9fjtdff70tIRPSiEqlhUbT8kVINBoNiooKcfNmPj777EArFjJhUVV1Co6O81v0qtu3gR076pPz889rMX06A5FIgM8+y8Hnn+fojlMqtUhOrsKOHWm6bfv2RbYwxnrnz5fiypUK7NljfORLodDgrbduIDTUFmvWhECrZXHoUB5WrfoLH30UDomE/uD2NHcnOt793ciqzUJRXRE8pB4oqitCdm02RD+JIL8tx8NvPQyxZTtco+nC2pSU09LS4OjoaHS/o6OjSdeTAWDNmjU4duwYdu7ciZiYGN1QOACYm5tj5syZOHr0KCVl0q5UKi1SUqogl2uaPE6tVqK8vAi3bxegrKwAZWW3UF5eDK22/paPpKS/wTBMCxMzA7m8FFJp0+9tjFwObN3KIiioFqGhNnjkEXfExNwdet606TpGjJBhxIi7PVsnJzEcHMQoL1fpnUuj0aKqSgUHB8N/EK9cKcetW3cwadI5ve3r1iWhXz9bbN8+EL/+Woyiojr85z+DIPjfUOTq1dZ45JFzOH++FKNGubTqc5Kuq6E4joat/xlv6CULGSGkQimqmKr63rKlD25evIkfnv0BY98dCyuXnjsi2qak7O/vj9jYWDz99NONhpWrq6vxySefwM/Pz6RzffHFF3jhhRfw3HPPGVw3OTg4GF9//XVbwiWkEY2GhVyugUgkgEhUP8RaVydHaWkBSktvorT0FkpLC1BRUdJkwhWLxa3qKdfV3YRWq4aZWct/FYOCtHj6aTmUSg00GhY2NiLY2NwdlpZIBLC3FzWaYNW3rw1qatS4caMaQUHWAIDLlyvAskBwsA0MmTPHCxMn6s/iXrgwEYsXB+DBB+u/mCsUGjAMozczu2HWOM3j7HnuLx6i0qqQJ89DiaIEKq0Kt+7cgobVoERRgjx5Hvys/HA79Ta+e/I7jH5zNNwGNr5roCdoU1LetGkTZsyYgT59+mD+/PkICAgAUN+D3r9/P4qKikxOpMXFxejXr5/R/UKhEHK5vC3hEqKHZVlUVJQjL+8GqquLUFZWgOLiW6iuNmX9Vwb29k6Qydwhk7lj2LCJOHPmTIsTs1JZjrKyXIjFNpBKLSGRSJudiSoSAU8+qcT48Wqo1Qxa+mvh7W2JyEgHvPPODSxfHgi1msV776UhJsZZN/O6pESBV165ilWrghEcbAMHB4nByV0uLhK4udVP5omIsMdHH2Vg+/Y0TJvmAa0W+OKLXAiFDAYOtGtZkBxSKpVITExEREQExO0x3b2HMlQ8xEnihEiHxpdQGmZlA8Cdsjs4tugYIv8Rif5z+/e4CWBtSsqPPvoofvrpJ/zf//0f3njjDb19YWFh2Lt3L8aOHWvSuTw9PZGSkmJ0//nz53VJn5CW0mq1KCkpQV5eHvLz85GXl4e8vDzU1NQ0+1qBQAhHRxfIZO5wdvaATOYOJyc3iMX6SWrkyHGIi/sFGo0pw9EMGMYJLFsGtToJDDMIKpUCAoEA5uaWkEotIRQ2/vUMCNDiH/9QwNNTP/krlUpcvnzF5ETy+uvB2LEjDa+88icEAiAqSoZly+7+fmk0WuTl3YFCYfrQupeXJd54ox/278/GkiWXIRAwCAiwwltv9YejI78WHGgq8SYkJODAgQPQaDSIioriKMKuzVjxkIluE/VmYRui1qqRU5MD1XYVCq4UIGZDDCQ2/Pr56UhtLh4yZswYjBkzBoWFhcjJqZ9k4u3tDVdX1xadZ86cOdi2bRumT5+OwMBAAND9h+7ZswdfffUV/v3vf7c1XNIDqNVqFBQU6CXf/Px8k+rrikRiXe+3IQk7ODgbTJD3W7x4BeLjT5p4bVkAmexVMIwXtNq7s6+1Wi3k8mrI5dUQi80hlVrC1tYcgwZpEBOjQViYxmAhkUuXEnDo0EG9RLJ9+0Cj725jI2qyUIirqxSnT49s8hMY2h8R4YCICIcmX9eZjCVfY4mXVjBqH1euXMFff/2lWyVNy2qRfydfN0zdFN3tUo5amMWb4ci8Ixj77ljY+dh1QuTca7eKXq6uri1OxPd6/fXXkZCQgBEjRiA4OBgMw2D58uUoKytDfn4+JkyYgOXLl7dXuKSbqKurw82bN/WSb0FBgW7YrClWVlbw8OgFkUgGN7decHPzgJ2dIximdbfv9OsXjm3b9uHll+eDZVkjPWYhAAaenl/CxuZR3VaVSgG5vBYKxR0wjAaOjjfh7JwDmSwXXl41GDAgAoGBkRAIGl/zVSoVOHnyF0okBhhKvk0lXkMrGFFvuWUuXbqENWvW4NSpU7ovp1po8Xfl31Br1XjU41H0kvZCjjwH3hbeMBPcTUONbpey9EFlXiWOLjyKcTvGwaVf958s2OaknJubizfeeAOnT59GSUkJjhw5ghEjRqC0tBT/+te/sGDBAgwcaPwbe4OG1UI+//xzHD58GBqNBgqFAv3798emTZvw5JNPUtWXHq6mpkYv+ebl5aG4uNik67gODg7w9PSEp6cnevXqBU9PT9jZ2UGh0OLPPytgYWEGsbjt99KOGfMIvvjiJP7979fwxx+/NdpvYTEUrq5vQyIZALn8AszNIyAQiGFjI8aYMUIMGCAAy8bj7NkjsLSUQCgU4s4dICHhJC5c+AUikSXGjZsBf/9g3ZeHpKQ/kJaWRonkPsaSr7HEa2ylLvqSY7qmy8wCN6pv4M2UNzHJbRLKlGXQOmrR27q3br+h26V6W/eGokqBnxb/hLHbx8I93L3NcdZU1ODz9Z/j6qmrYBgGEeMjMGftHJhbGi9yc+bgGSR8n4CcpBzU1dThP3/+BxY2dydRpiSk4M3Zbxp87Zoja+A3wLRJz21KysnJyYiKioJWq8UDDzyA9PR0XQ/FyckJ586dQ21tLfbu3dvotS+//DKefPJJXcLOzc2FTCbD3LlzMXfu3LaERbo4lmVRXl7eKAGXlzc/AYthGLi4uOglX09PT1haWnZC5PXqe8yxiI8/hc2bV+DOnVowjCXc3N6FldVYiEQeqK2NQ0XF5/D11WD+/AcQHa2GuTkAiPH77+ZQKFgMHBgBllUgK+s6WJZFYWEBUlNTkZeXjuDgfujffwi8vEKQkPBfiMUiSiT3MZR8IyMjjSbehnrNdXV1SEpKgkql0q1gNGTIkObfsIczpcysFvXT8H8o+AFeFl6wrrKGj6UPRAJRk7dLiQQiqO6o8PPSnzFm6xh4Ptj8Ahb/fvzfGD5jOIbPGN5o3+6XdqOiuAKvfvoqNGoN9q7Yi32v7cPzO543ej5lnRL9ovuhX3Q/HH7rcKP9AYMCsP3Sdr1t377zLa5fuA7f/r7NxtugTUl5xYoVsLOzQ0JCAhiGgbOzs97+iRMn4tChQwZfu337dkREROiSsq+vLw4cOIA5c+a0JSTSxWi1WhQXFzdKwLW1tc2+1szMDO7u7roE7OXlBXd3d14kIxcXd8yY8STef38z7typhVBoB3v7hQDql27UaH6Bs3MGIiKOISYmVDdpTKlU4Ny5X5Gfn4O0NBmWLFmJujo5rl69gMuXd6Cqqgp5eXlwcXFBfPyPKCnZj7S0TDg62kOtVkGtVlMigfH1qVmWNZp4G+o1349WMDJNS8rMsmBRq67V6w03d7sUAGiUGpxYfgIxG2PgP8a/VXHeSr+Fv8/+jbVH1+qS5dz1c/Hugncx67VZsHexN/i6MQvHAKjvERtiJjaDrcxW91ytUuPKr1cw+qnRLRrlbVNSjouLw9q1ayGTyQzeW+zl5YWbN28afK2LiwsyMzN1z1t+jyfpalQqFQoKCnQJOC8vDzdv3tQVGGiKubm5Xs+3V69ecHNzg1DI7ypR91c0kkhYDBp0HtnZ1+Hh0Rc5Oen4668/EBHxIADgzz8TkZWVjqCgUGRl3d0nldrBxsYZERHeSE+/jqKiIri7u0MqNYe7e/2XYalUipCQEAQGBvb4RGKs11tRUWE08TbUayYt1/Iys0CpshTuUnddb9hebN/s7VIAoNVo8d/X/wsArUrM6ZfTYWFjodd7DRkWAkbAIPNqJsLHts96DVd/vYqa8hoMn9m4p96UNiVlrVYLiyaK75aUlBjttUycOBH/+te/8Msvv8DOzg4A8M477+DLL780ej6GYXD06NG2hEw6SV1dXaPZz7du3TJpNTBra2td8m1IwE5OThC0Zv1CjllYWIFhLCAUWqF3by2WLKnC4cMnIJGIYWVlDZFIjPj4k+jfv/4Pwblzv0Ik0t/Xp08ozp37FWKxBL16+UKt1sLOzgmhoUOQlvanXuGehuvsSqUSUVFR6NWrF1cfnVPGer2DBg2ixHsftVJt4m18xh3/+XirOlblinKIGBFya3PhZ+kHOxs7g8exWv1zs2Bxet1p2PjawN7bHmZi01NZVUkVbBz1J0wKzYSwtLNEZUlliz+DMXFfxSF0RCgc3Fp2N0KbkvKgQYPw448/YvHixY32qdVqfPnll0aH0Hbs2AFnZ2ecPn0aSUlJYBgGeXl5KCsrM/p+NNGrZXJzc3Hq1ClUV1fD2toaDz30ELy8TFv8oCWqq6v1er/5+fkoLi426bWOjo6NJmDZ2tp2i/9rpVKBsWMfwRdfXIGLy2C8/nolMjL+RF5eNhSKOqSlXYdGo0ZeXjaSk/8EAIP7fv7520bbS0qK4ejogaFDJ+D69avIy7uMnJxsAPVDt/Hx8YiPj4evry+ioqIwaNCgHlUIg3q9plEr1cj8MxN18rpWn0Oj0eCP3/5oRZlZwEPsgVCLUFioLFBX28IYaoEzb5/BgGcHwG+AH47vOY5jHx7T7VbWKZFxJQOfrftMt23zL5tb9h6tVFZQhmtx17D4g8a5sTltSsqrVq3CpEmT8MILL+Dxxx8HABQVFeHXX3/FG2+8gevXr+ODDz4w+FpLS0u9giMCgQDbt2+na8rt4NKlS9i4cSN+/PFHsCwLgUAArVYLhmEwadIkrFmzBoMHt3xxcZZlUVZWppeA8/LyUFnZ/LdLhmHg6uraKAE3NdLS1TUMRVta9oOLyw3cuPEHPD19MGnSY42OdXOr79Ea2ufs7ApHx8a3gri59YKZmQj+/gMxbVoMSkpuIT4+Hr///rvunuysrCxkZWXhm2++wZAhQxAVFdVo7kdP0lTRkJ5YyUuj0aBOXgeRSNSi3mZldSWy87ORlZ+FvII8pOWmtaqn7G/pj/62/Vv8ugblSeWok9dBo9Fg5BMjMXji3b9ru1/ajfBx4Qgfd3c42s7FDjYyG1TdrtI7j0atQW1Frd414bY49/U5WNlbIWx0WItf26akPH78eOzbtw8vvvgidu/eDQCYO3cuWJaFjY0NPv30U4wYMcLga6dNm4bly5frbt04ffo0QkKMFzMgpjF0S0LDkDHLsvjpp5/w888/49ChQ5g2bZrR82i1WhQVFTXqAZtS6lQkEhmcgNVT/tABdydsiURiuLpawdZWhPj4k1iyZCVGjRpv9HUuLi273UOpvHs5wNPTE3PmzMHUqVPx+++/Iy4uDrdu3QIA1NbW4tSpUzh16hT69OmDqKgo9O/fn/fX5NtbU9W6enIlLzOxGUQSkdH9ao0a+bfykZmbiczcTJRV6I9otmZUggGDQKvANo2KKcoVUNfV3/FjZWcFK7u7l3JE5iLYONnAxUf/C23AoADIq+TI/jsbPv18AADXL1wHq2XhF2babUtNYVkW5w6fw4NTH4SZqOUpts33KT/55JOYNm0aTp48ibS0NGi1Wvj7+2Ps2LGwtrY2+rqjR49i+vTpuuejRo3izezr//znP3j77bdRWFiIAQMG4P3330dkZOuWvOtMptySoNHULxowa9YsXLhwAYMHD4ZKpcLNmzf1rgHfvHlTV42nKVKpVG8ClqenJ1xcXHrcH/v7JSffHabWapNRV6fUDVOHhXXsz5JUKsWIESMQFRWFzMxMxMfH4/Lly7rbFVNSUpCSkgJbW1s8+OCDGDZsGBwc+FOFq6M0VTSEKnk1VllVqUvCuTdzoVIb/ntgKbVEaFAo0lLSkHgl0aTr0wII0NeqLxxEbfu5Y4QMBC1cq9s9wB39ovshdlUs5m2aB41ag8/WfYbIyZG6mdflheV464m38Ow7z+oSdWVJJSpLKlGUXQQAyE/Jh7mVORzcHfS+DFy/cB0leSWIfjy6VZ+p1UlZLpfD09MTK1euxD//+U88+uijLXq9h4cHrly5opuM0bCGMtcOHTqEl19+GR999BEeeOABbN++HWPHjsWNGzd4P+xn6i0JLMtCq9Vi0aJFmDZtGgoLC02agGVjY6OXfD09PeHo6MiL/ze+cXPrpRuKzs5m4OPD6rZ3FoZh4O/vD39/f8yYMQO//fYbzp07h5KSEgBAZWUlfv75Zxw/fhz9+vVDVFQUgoODu+SEuqakl6UjwCGgyWpdVMnrf73hgvrecFZuFm6XN76jBqj/uXJ3cYeflx98vXzh4uQChmHg4eyBGU/NMPna8liZaesiNMUzxhMCUct/Xp/b/hw+W/cZ3p77NhgBg/Bx4Xhi3d2JgRq1BoWZhVDW3b0z5PTnp3F0x92JxltmbQEAPP3203r3Qsd9FYeA8AC4+bdulSuGbcO9SC4uLli3bp3BiV7NWbFiBbZu3YpevXrBzs4O165d003yMRosw+DPP/9sbbgmeeCBBzB48GDdtXCtVgtPT08sXboUK1eubPb1ly9fRnh4OP744w8MGjSoQ2O9V25uLnx8fFp8XWfu3LkGRzRkMlmjW5Ca+r/pqurqNO1a0cuQnBwG3t4dc8ufUqmFXK7GgAF2MDdvfnRCq9Xixo0biIuLw99//93oy5ijoyOioqIwdOjQJke6upK/i/5GoF0g/vWvfyE7Oxu9e/dGWloafHx8sHbtWgAwuq+795YLbhbgzPEzuFlyE3m38oz2hi2kFrok7NPLB1JzqcHjjv96HEtXLDVaZlaA+t+xhb0WYoBN0wtTNEc2QIYBSwdAqVQiKDIIEmn3+L9q0/D19OnTcfjwYbzwwgst7i1t2bIFAQEBOH36NIqLi8EwDCwtLeHo6NiWkNpEqVTijz/+wKpVq3TbBAIBRo8ejd9+a1wyEagf9rp3oYOGVYfUarVJw7/t5cSJE62aaJGfn4+RI0fqErCHhwc8PT0hlTb+pevMz9NZVCoN1GoV5HINVKqOSco2NoAJtVBaRaXSQq3WQqVSQSg0bdHigIAABAQEoKKiAhcuXMCFCxdQUVEBoH4J1W+++QZHjx7FgAEDEBUVBX9//w4dDVGqlW2+JcfouZVKnPrvKdx0uImUGymoq6vD1atXoVKrkHIjBefOnwMAo/sGRzY/IVIoFEJs1jXmSzTcr52cnIzk5GQUFhYaPI5hGLjKXOHj4QOfXj6QOcrurvikYSGvNTy3ZMTQEfjso8/wUexHOHP+TKP9wZbBeNjpYXhLvaHWNl+f3hjXIa4IWRgCpUoJlVoFlUoFgVnH/P6KRMavtXeENvWU4+LisHjxYjg5OeHZZ5+Fj4+PwT/mpvQYBQIBPvvsM06vKd+6dQseHh64cOEChg4dqtu+YsUKnD17FhcvXmz0mvXr12PDhg2dGSYhhJBO0tmFrdrUUx45cqTu3/Hx8Y32N1wnNuVbcFZWFmQyWVvC4cSqVavw8ssv655fvXoV0dHRuHjxokkLcbSXffv24bnnnmvx6/bs2YN58+Z1QERdh0qlhUbTMb9458/H48svv8Djj8/BsGEtq+xjKqGQgagV19UMKSkpwblz55CQkNCo1KlYLEZERASGDx/ebve731Hcwe83fodIJGr33qZSqcQnH36CY+eOYeKwiXh6ydPtfgeAUq2ESqXC4KDBkEoMD+l2NrVajfT0dCQnJ+P69esoKCgweBzDMPDx8UGfoD4ICgyCh4dHu88nCOoThFsFt2BrZos3Qt9o/gVNkDpJMWzVMLhH6N+hIBQKW3Q7F9+16ZPExsa2Vxzw9vZut3O1lpOTE4RCIYqKivS2FxUVGV2WUiKR6F13aqiuZGZm1qnDHmPHjm3xzfsMw2DMmDGdPjzDNx318euLeJxBfn4u4uNPIyYmivfXKN3d3fHYY49h6tSpuHLlCuLj45GRkQGgfub+xYsXcfHiRXh7e2PEiBEIDw9vU6JTa9UwE5nBQmoBiah92+b639dxM/8mzNzNkJqViszUTIRHtk8JxQZmKjPUohYikYjT36OysjIkJSUhKSkJN27c0Lukdu+dEFZWVujbty/69u2LPn366FWD6wiMoH7Im2EYmJmwJrmxc4TMCEHECxGQWPP796c9tCkpt6WHJRAIIBAIIJfLIRaLIRAImr1uxTCMSevktpZYLEZ4eDhOnTqlm02u1Wpx6tQp/OMf/+iw920PXl5emDRpEn766SeTRiaEQiEmTpzYIRW+SL2uPKNXJBIhMjISkZGRuHnzJuLj43Hp0iXU1dVXXcrJycGBAwdw+PBhDBkyBMOHD4ebW+tmm3YEpUKJC3EXIBKJIBQIUVxVjPNnz6PfgH4QS7rG9d+mNCw80pCIm+sNNyRiT0/PLjW73nOYJ4a8OAT2foYXieiOOOvzr127tv7bk5mZ3nOuvfzyy5g3bx4iIiIQGRmJ7du3o7a2FgsWLOA6tGatWbMGP//8c7M9ZoZhwDAMVq9e3YnR9SzdaW1eDw8PPP7443j00UeRmJiIuLg45OfnAwDu3LmD06dP4/Tp0+jduzeioqIQFham+73myvXk68jPzYdCocCd0juoVlcjJSMF15OvY8DAts365UpZWRmSk5Nx7dq1Rr3he1lZWSEkJAR9+/ZFcHBwh/eGm3L/giymkoXIMOSlIXAbxJ8vep2lRb85CxcuBMMw2L17N4RCIRYuXNjsaxiGMbie8vr165t8zpVZs2ahpKQEa9euRWFhIcLCwnD8+HG4uDQuc8g3gwcPxqFDh3QVvQz1mIVCIRiGwVdffdWqUpvENN1xbV5zc3MMHz4cw4YNQ3Z2NuLj4/HHH3/oZuWnpaUhLS0NNjY2GDp0KIYPH87Z3RSubq6YMGUCACD/Sj7q1HWokFRA6sCP676maE1vOCQkBF5eXrzpDTesAKdhTZtdL7WXInJZJAInBuqGvnuaFs2+9vHxgUAgwI0bNyASieDj42PSkPO9SzR2d1zdp3yv33//HRs3btQtpXZv7evJkydj9erVlJA72M2bN3H58uVG27vbKkW1tbW4ePEi4uPjG83FYBgGffv2RVRUFPr27WswUdxR3MGlG5dgKbVs92vKAPD4I48jLS8NQmsh/Fb4wdnCGf8X+X/wsDL+f6BSqpD0dxL69usLkbjp68QKlQK1d2oRGRTZLhO9GnrDSUlJSElJ6RK9YWMUCgWcnJxQU1MDiUCCdwa8A5HAeHv6j/HHsP8bBnNb806Mkn9a1FPOzs5u8nlLfPrpp6163VNPPdXq9+wpBg8ejO+//x65ubn473//i6qqKtjY2GDUqFF0DbmT9JRViiwtLTFq1CjExMQgNTUV8fHxuHr1KrRaLViWxbVr13Dt2jU4ODhg+PDhGDp0aKcWoSktKYWqQgUW9X2PYnkxXo9/HYvDFiPSzXC50z+v/Ilj3x2DRqNp94lh97u3N5ycnKyrVX4/hmHg7e2tuzbMp96wMRcvXtTNAdKyWmTXZqO3de9GxwnFQgxfORyBk9tWB7u74OzCz/z58xtt092cfl/n/d7/KErKpvPy8jLYzoS0N4ZhEBQUhKCgIFRWVuLChQs4f/68binWsrIyfP/99zh27BjCwsIQFRWFwMBATmKt09ThnYvvIEwdhhcnvQgL87srlTVMELuZd7PDJoaVl5frzZRumDx3v67QGzamYU6F7m86WCRVJcHH0kevtyx1kGLsu2Ph3JffJYw7E2dJOSsrS+95RUUF5s2bB1tbWyxduhRBQUEA6ovnv//++6iursb+/fu5CJUQ0gK2trYYP348xo4di2vXriE+Ph7Jycm6muuXL1/G5cuX4eLigiFDh0BgL4Cl1LJTY5Rny/HzpZ+RXZGNNbPWoJd1fU3yv67+heysbPQO7o3srGz8/effbe4tazQaZGRk4Nq1a92uN2xMw5yKeztYJYoS5Mnz4GdVv8CDlasVJu6cCFvP7le+ty3anJR//vlnbNu2DZcvX0ZlZaXBWXaGJhzdf1/y+vXrIZPJ8Msvv+j1jPv164fp06djzJgxePfdd9v13mhCSMcRCATo378/+vfvj9LSUpw/fx4XLlxAdXU1gPr7/48eOQqhmRB+QX7oN6gfnF2d23UI09DsX61Ki5rrNVDdViH191SskK3AtOBpGNdrnO42KisrK4hEolb3llvSGw4ODtb1hrtLvXFPT0888cQTOHnyJOrq6iARSBDpEAl7cf2tTZbOlpi8ezKs3bvH521PbUrK33zzDR577DH07dsXjz/+OHbu3Ik5c+aAZVkcPXoUvXv3Nnn1qCNHjmDz5s0GfyEFAgGmTZtGt/AQ0kU5OTlhypQpmDhxIq5evYr4+HikpaUBqF+RJy0pDWlJaXBycULfsL7oHdy72UlWptDVa7+nX3An5w6UxUqIPcRQFitRnVWNw8LD+P7M9xCkCGAjtEF6ajo0ag3yc/NNuo1Ko9EgNTVVl4ib6w03DEt7e3t32d5wUxrmVDSUXRYJRBhgV9+GIgsRJnwwgRKyEW1Kylu2bEFkZCTOnTuH8vJy7Ny5EwsXLsSoUaOQnZ2NIUOGwNfX16RzsSyLlJQUo/sbhr8IIV2XmZkZIiIiEBERgYKCApw5cwYJFxOgUtYnz9KiUpw9cRa/nf4NgX0D0XdgXzg4tW7NXaVCCaWi/pYcrUoLrap+wY6a6zVghAyE5kJohBrUXK+B1FuKWota1AXUARZAjGcMvG3qR/Nc3QxX86uprkFmWiZyMnJwJPaI0ZnSlpaWeteGu0tvuLWiXovqUcVAWqpNSTk5ORlbtmyprz36v2IBDd9MfXx8sHjxYrz55psmTc569NFHsXPnTvj4+OD555+HhUX95Au5XI6dO3di165durWXCekqlEolEhMTERERoVeO0tj25vZ1J25ubnh02qNwDnRGUW4Rbvx9AyWF9Ws9K5VKXLtyDdeuXINbLzeEhIXAP9AfQrPml6ds8NfVv6BR/6+LrK3vITNCBqpSFbQqLRQFCrAaFqpSFery62DhawGRnQh1qMPP+BkRthGYHzofTlInAPW94aKbRcjNykVOZg7KSsoMvi/DMPDy8tJdG+6uvWFTuLq6Ql4qh7Ww/otIryG94D/Wn+Oo+K1NSdnCwkL3R8POzg4SiUTvBncXF5dGE7qM2bFjB7KysvDqq69i1apVupJ9BQUFUKlUGDZsGLZv396WcAnpdAkJCThw4AA0Go1eiU1j25vb1x2ZicwQGBqIfgP7obigGElXk5B+PV13O01BfgEK8gtwXnoeffr1Qd+wvrCxs2nynEqFEr/8+ItuPgurYVF5uRKOMY6wjWg8sUhk33ioPLEoEX8W/YlhlsPQq6IXinKKdMUw7mdhaYG+IfXFO0JCQnp8b7hBYmIiPn3oU9RV1oFhGDzw4gN021Mz2pSUg4KCkJycrHseFhaGAwcOYO7cuVCr1Th48KDJ98Xa2tri7NmzOHr0KH7++Wfk5OQAAMaNG4cJEyZg8uTJ9J9JupSG20KysrL0Smwa297Ua3oKZzdnOLs548GYB5GalIqkq0kov10OAKi7U4erl67i6qWr8PT1RN+wvvD2b9wL/fvq33h789u4mnj17kYtUBFfAcVNBVynuULqbbjQB8uyUKvUuqFvlUKFgwUHYeNigz5MH/jAB0LU99adXZ3h7u0OR3dHjBs+rtkZ5CzL4t0j7+LLs1+iSl6FiN4R2PjkRvi6Gr/Et/3Iduw4ukNvm5+rH05tOWXw/AveXYCzf5/FrqW7MGbQmCbj6Wx+D/vBsTc3Fd66kjYl5WnTpuG9997D1q1bIZFI8Prrr2PKlCmws7MDwzCora3FJ5980qJzTpkyBVOmTGlLWITwgrEFKZpaqKIrL2LRniTmEvQL74fQQaEoyC9A0pUkZKZmQqutvy6cl5WHvKw8WFpbIqR/CIL7B8PS2hK/Hv8V/1z6T7Baw/NP7mTdQda2LPRa0As2YfW9ba1GW5+ElfWJ+N65K6qbKtT9XQe2H4ur3leR7ZSNUR6jMDNsJmR2Ml1FL1OGp3f9tAv7Tu7D1me2wlPmiW3fbsO8bfNwcvPJJquZBXoE4rN/fqZ7LhQYHsL/5JdPwICfHReGYTDoWW4qHHY1rUrKdXV1OHr0KFQqFVavXo2ysjK4ublh0qRJOHPmDL799lvdKkQxMTHtHTMhvGdsQYoBAwYYXagCQLdZxKK9MAwDd093uHu6Q14rR8rfKUj+MxnVlfW3VdVW1+L3878j8UIiBGIBdr6/U1dNzCC2/pEfmw/X513ByIyvPMeqWaiz1EA1ILglgEOYAwRiAeJq4pCYkIjpgdMxqtcokz4Hy7L45OQn+Mfkf+h6sO88+w4GvzgYv1z+BZMfmGz0tUKBEDLbpteaT85NxscnPsb3675H5EuGK5VxyXukN+x9aXKXKVqclIuLi/Hggw8iKysLLMuCYRhIpVIcOXIEo0ePRlRUVI/8Zk/IvYwtSPHVV18ZXagCQLdbxKI9WVhaYNCQQQiLDENedh6SriQhNzMXLMuCZVn88N0PTSfke7FA2cky2D6mf32ZYRiIJWKIJWKo89RQ1Chg4WUB9W017uTegWVA/RC1XC3HgeQDqKyrxLhe45p9u7ySPJRUlmB43+G6bTYWNgjzD8Pl9MtNJuXsomw8sPwBSEQSDPIfhH/O+Cc8HO+WcL2juIMXd72IDXM3NJu8udJ3Zl+uQ+gyWpyUN27ciOzsbCxfvhyjRo1Ceno6Nm7ciEWLFukWQyekp2sonnA/Nzc3gyuOeXp6AoDB1zTsI/UEAgG8/bzh7eeN6qpqXP/zOi6eu6ibh2ISFlCmKaGp1EDiJIFYLIZIKIK6QA2poxQsy6LyRqXBW6cEortD1YlFiSYl5ZLK+lnlTjZOetudbJx0+wwJ8wvD28+8DT9XPxRXFOO9o+/hsS2P4cTGE7CS1pfd3PjFRgzyH8S7a8gNzO3M4R7hznUYXUaLk/Ivv/yCp556Clu3btVtc3FxwZw5c3Djxg1deUxCerLWLkjRExaxMESpNjyruTliqRgDhgxAdl52615/Wwy7QDsAgDxdjorfK+pHAAUMlCVKsGoWilsKsFoWyhIl6vLqIPW9O0msvK7c4HmP/HYEr+9/Xfd870uNl681xcj+I3X/DvYMxkD/gRj+6nD8+PuPmDViFk5eOYnfrv+GYxuOter8ncG5v3OPXYaxNVqclHNzc/F///d/etuGDx8OlmVRVFRESZkQYjKhUAgLcwvI6+R3q2+1QmVlJRgBY3SCl0EMoKnTQKPVQKvWojq5GqpSFaqTqmEz1AbW4Y1va2JsGWi0d8uDVSurITATQCjUn3w1Omw0wvzCdM8bvnSUVpXC2e7u4gulVaUI8QwxOWQbCxv4uvgip6h+VOC3678hpyQHA5boVxx74YMXMDhwML5c+aXJ5+4odj52XIfQpbQ4KSsUCpib66932fDc2IQJQggxRGwmxgC/AQbr47dEin9KyxIyALCA1FoKS3NL1KTVQFOmgaWXJZRlSojkIthHND8xydPGE4N7D4bYTL/Ii5XUSje8DNRP9JLZynA++TxCvOqTcPWdalzNuIq5MXNNDrm2rhY5JTl41O5RAMALE1/ArBGz9I4Zt2YcVs9ejdFho00+b0eydO7cxUa6ulbNvs7OztZbwL2yshIAkJaWBjs7u0bHDxpk2lT469evIzY2FpmZmSgvLze4hOOpU43vzyOEdF1iM3Gbl8YZP248GIZpWSleBrANtgXUQHVyNQRCAcykZlBXqVGdXA0rXyu968f3c7Fywbvj3oW5yNzoMbq3YhgsfHghPvjhA/i4+MDTyRPbvtsGF3sXvWvBT7z1BMYMGoN5o+cBADZ/uRkPhT2EXk69UFRehHePvAshI8QjDzwCAJDZygxO7vJw9ICnjB9zESwcLZo/iOi06ldhzZo1WLNmTaPtixcv1nveMDvblG/BBw4cwIIFCyASiRAUFAR7+8bfUqn2NSHEEC8vL0yaNAk//fSTab1uBrD0t4SZjRnkuXIoShTQqrS4c+sOWA0LRYkC8jw5rPwMr2Ec7R2NNdFrYGduZ3KMiyYsglwpx2v7XkOVvAqDAwdj38v79O5RzinOQXnN3evUheWFeHHXi6ioqYCDtQMiekfg2zXfwtGm6xThkNj0zNv5WothW5jpWrOm8bx585o9xt/fHw4ODvj555/h5OTU7PF8dfnyZYSHh+OPP/4weYSAENJ2v//+Ox588EFoNJrmv8AzgG1/W7g87AKJkwTyXHmjQyy8LCC21x+WFgvFeHnoy5gePJ0qDJqoMrcStl60ZrKpWtxTNiXBtsatW7fw6quvdumETAjhzuDBg3Ho0CHMmjULLMsa7jH/bzTaboAdNHc0qEqqgttEN9gNsGv2/M6Wztg6ZitCZKZPzOrplEolEv9ORJRrVLdeXKU98Wbpkv79+xtdg5QQQkwxbdo0XLhwARMmTGjck2UA23628JrtBTCA1EOKuqI61GbXNnveBz0fxOfTPqeE3EIJCQn47NBnuHjxItehdBm8Scrbtm3D3r17ceHCBa5DIYR0YYMHD8b333+PQ4cO6ZaUhRDwfdYXfs/6QVmmrC8KIhWCETKoSqrSrbV8PxcrF/wr5l/YMW4H7KVUJrIlGkrN5uTm4Pjx40bXmyb62jjnsf28+eabsLW1RVRUFEJCQuDl5dXo3j+GYXD06FGOIiSEdBUKhQJXr16FSCSCWq0GwzC4k3cHImuRSZO6vGy9MLf/XEwKnASxkIZdW6NhcZV+/fr16MVVWoo3Sfmvv/7SLQ5eU1OjtyRkA5pYQQgxRUPt8XsnfClKFFDfUcMh0qHR8Q0Tuga6DsSTA57EcK/hEDC8GUjscu5dkMXO3g6lZaU9fnEVU/EmKWdnZ3MdAiGkm2ioPX7y5EnU1dVBIBHAIdLB4IxqABjgMgBLH1iKMNewzg+2G7p3QZbk68m0uEoL8CYpE0JIe2moPS6V1tepFogEBmdYmwnM8MrQVzAjZAaNxLUjYwuy0OIqzeNlUq6urkZlZaVuQfN7eXl5cRARIaS7EQvF2D5uOyI9+Lf+cFfX2gVZCM+S8s6dO7Ft2zZkZmYaPaatNXKbsnnzZvz444+4evUqxGIxKioqOuy9CCHcEQvFeGfMO5SQCe/wZibDRx99hCVLliAgIACbNm0Cy7J46aWXsHLlSri6umLAgAHYu7d1y5+ZSqlUYubMmXjhhRc69H0IIdwxNzPHu2PfxVDPoVyHQgxQKpW4cOEClMrWLefZ1fEmKb///vsYO3Ysfv75Zzz33HMAgIkTJ2Lz5s1ITk5GdXU1bt++3aExbNiwAcuXL0e/fv069H0IIdwwNzPH++PfxwO9HuA6lB6tqcSbkJCA2NjYHltwhDdJOSMjA5MnTwYAiEQiAND9h9na2uKZZ57Bhx9+yFl8xigUClRVVekeNTU1XIdECDFiY8xGDHQbyHUYPYax5Gss8TbcSpWVldVjC47wJinb2trq1mO2sbGBhYUF8vLydPutra1RWFjIVXhGbdmyBba2trpHdHQ01yERQgwY5TsKMb4xXIfRoxhKvk0l3oaCI/3799cVHOlpeJOUQ0ND8eeff+qeDxkyBDt37sTNmzeRl5eHXbt2ITAwsMXnXblyJRiGafKRkpLS6rhXrVqFyspK3ePs2bOtPhchpH25urpCYi+ByFaEhf0X9uhrlZ3NWPI1lnjvLThiY2MDsVjcI3vLvJl9PXfuXHz00UdQKBSQSCTYsGEDRo8erbsFSiQS4ZtvvmnxeV955RXMnz+/yWP8/PxaEzIAQCKR6FWosbIyvP4qIaTzJSYm4qFPH8Ig10EovlGMAwcOQKPRULnHTmAo+UZGRhpMvJGRkXoFR5KSknpswRHeJOUFCxZgwYIFuufDhg1DUlISfvjhBwiFQowZM6ZVPWWZTAaZTNaeoRJCupipvafix49/1PXaqNxjxzLW62VZ1mjipYIj9XiTlA3x8/PDiy++2Gnvl5ubi7KyMuTm5kKj0eDq1asAgICAAOoBE9JFOUgdoMxVNuq1UW+54xjr9VZUVBhNvFRwpB7vknJCQgJOnz6N4uJiLF68GL1794ZcLkdKSgoCAwM7NDmuXbsW+/fv1z0fOLB+lubp06cxcuTIDntfQkjH6efQDyd/OWlwyJR6yx3DWK83NDQUBQUFiIiIgFhMq28ZwpukrFQq8fjjj+Po0aNgWRYMw2Dy5Mno3bs3BAIBxowZg+XLl+P111/vsBj27duHffv2ddj5CSGdjylk6FplJzPW642Li6Pr+s3gTVJes2YNjh07hp07dyImJgZBQUG6febm5pg5cyaOHj3aoUmZENK9KJVKoAZ47LHHdPUPGvS0a5Vcu382No1UGMabpPzFF1/ghRdewHPPPWewcldwcDC+/vprDiIjhHRVCQkJuBJ3BSOfH0k9M44Zmo1N/yeN8eY+5eLi4ibLWwqFQsjl8k6MiBDSlTX0zIryi3rk/a58Qvcgm443SdnT07PJIh7nz59HQEBAJ0ZECOnKdD2zAT23OhTXGspsXrp0CRkZGaitrUVSUhJqa2t11/WJPt4MX8+ZMwfbtm3D9OnTdfcjNyw6vmfPHnz11Vf497//zWWIhJAu4t6emYuDC6puV9F1TA4kJCTgwIEDGD9+PN2DbCLeJOXXX38dCQkJGDFiBIKDg8EwDJYvX46ysjLk5+djwoQJWL58OddhEkK6AKoOxb17J3b98ccfWLt2LX0hMgFvknLDNYbPP/8chw8fhkajgUKhQP/+/bFp0yY8+eSTup4zIYQ0hapDcY8mdrUOb5IyUD9cPXfuXMydO5frUAghXRhVh+KWsYlddPmgebyZ6EUIIaR7aLh8QBO7Wo5XPeVz587hk08+QWZmJsrLy8GyrN5+hmH0lnckhBDCP3T5oPV4k5S3bduGf/7znzA3N0dQUBAcHBy4DokQQkgr0OWD1uNNUn777bcxbNgw/PDDD7C1teU6HEIIIaTT8eaaslwuxxNPPEEJmRBCSI/Fm6QcExODv//+m+swCCGEEM7wJim///77OHXqFLZu3YqysjKuwyGEENJGDWU2lUol16F0GbxJyp6enli0aBFWrlwJmUwGS0tL2NjY6D1oaJsQQrqOhIQExMbG4uLFi1yH0mXwZqLX2rVrsXnzZnh4eCAiIoISMCGEdGG0fnLr8CYpf/TRR5g4cSKOHDkCgYA3HXhCCCGtQGU2W4c32U+pVGLixImUkAkhpIuj9ZNbjzcZcNKkSYiPj+c6DEIIIW1EZTZbjzfD1+vWrcOsWbOwePFiPP300/Dy8oJQKGx0HFX6IoQQfqMym63HsPcXmObIvcPWTS3RqNFoOiOcVrt8+TLCw8Pxxx9/YNCgQVyHQwghpAvhTU957dq1tF4yIYSQHo03SXn9+vVch0AIIYRwijcTvQghhJCejpIyIYQQwhOUlAkhhBCeoKRMCCGE8AQl5f/Jzs7G008/DV9fX0ilUvj7+2PdunW0ugkhhJBOw5vZ11xLSUmBVqvFrl27EBAQgGvXruHZZ59FbW0ttm7dynV4hBBCegBKyv8zbtw4jBs3Tvfcz88PN27cwM6dOykpE0II6RSUlJtQWVnZbFlPhUKhV2S9pqamo8MihBDSTdE1ZSPS09Px/vvvY9GiRU0et2XLFtja2uoe0dHRnRQhIYSQ7qbbJ+WVK1eCYZgmHykpKXqvuXnzJsaNG4eZM2fi2WefbfL8q1atQmVlpe5x9uzZjvw4hJAWUCqVuHDhAk3YJF1Gtx++fuWVVzB//vwmj/Hz89P9+9atW4iJicGDDz6I3bt3N3t+iUQCiUSie25lZdXqWAkh7SshIQEHDhyARqNBVFQU1+EQ0qxun5RlMhlkMplJx968eRMxMTEIDw9HbGys3spVhJCuRaFQ4MSJE8jKysLx48cRGRmp9wWaED6irPM/N2/exMiRI+Hl5YWtW7eipKQEhYWFKCws5Do0QkgrXLx4Eampqejfvz9SU1Nx6dIlrkMipFndvqdsqpMnTyI9PR3p6eno1auX3j6eLDlNCDFRQy9ZLBbDxsYGYrGYesukS6Ce8v/Mnz8fLMsafBBCupYrV64gIyMDtbW1SEpKQm1tLTIyMnDlyhWuQyOkSdRTJoR0O56ennjiiScMbieEzygpE0K6HQ8PD3h4eBjdr1QqkZiYiIiICIjF4k6MjJCm0fA1IaTHSUhIQGxsLC5evMh1KITooaRMCOlR7r9V6t4yuYRwjZIyIaTbMlTRi26VInxGSZkQ0m3dP0xt7FYp6i0TvqCkTAjplgwNU9OtUoTvaPY1IaRbMjRM7efnR7dKEV6jpEwI6XaMDVOvXbsWkydP5jo8Qoyi4WtCSLdDw9Skq6KeMiGk26GKXqSroqRMCOl2mqvoRQhf0fA1IYQQwhOUlAkhhBCeoKRMCCGE8ARdU+4BCgoKUFBQwHUYhJAeyM3NDW5ublyH0WVQUm5nbm5uWLduHW9+CBUKBWbPno2zZ89yHQohpAeKjo7GiRMnIJFIuA6lS2BYlmW5DoJ0nKqqKtja2uLs2bOwsrLiOpwep6amBtHR0dT+HKH251ZD+1dWVsLGxobrcLoE6in3EGFhYfRLwYGqqioA1P5cofbnVkP7E9PRRC9CCCGEJygpE0IIITxBSbmbk0gkWLduHU2y4Ai1P7eo/blF7d9yNNGLEEII4QnqKRNCCCE8QUmZEEII4QlKyoQQQghPUFImhBBCeIKSMunRGIYx6XHmzJk2v5dcLsf69etbdK7NmzfjkUcegYuLCxiGwfr169scB5/wuf1TUlKwYsUKhIWFwdraGm5ubpg4cSISExPbHAtf8Ln9b926hblz5yIoKAjW1taws7NDZGQk9u/fj+48P5kqepEe7cCBA3rPP/30U5w8ebLR9uDg4Da/l1wux4YNGwAAI0eONOk1q1evhqurKwYOHIgTJ060OQa+4XP7f/zxx9i7dy+mT5+OxYsXo7KyErt27cKQIUNw/PhxjB49us0xcY3P7V9aWor8/HzMmDEDXl5eUKlUOHnyJObPn48bN27gjTfeaHNMvMQSQnSWLFnCdtSvRUlJCQuAXbduncmvycrKavVruyI+tX9iYiJbXV2tt620tJSVyWTssGHDOiBC7vGp/Y2ZNGkSa2lpyarV6vYJjGdo+JqQZmi1Wmzfvh19+/aFubk5XFxcsGjRIpSXl+sdl5iYiLFjx8LJyQlSqRS+vr5YuHAhACA7OxsymQwAsGHDBt2wYHPD0T4+Ph3xkboUrto/PDy80SIWjo6OiIqKwvXr19v3Q/IYlz//hvj4+EAul0OpVLb5s/ERDV8T0oxFixZh3759WLBgAZYtW4asrCx88MEHuHLlCs6fPw+RSITi4mKMGTMGMpkMK1euhJ2dHbKzs/Htt98CAGQyGXbu3IkXXngBU6dOxbRp0wAA/fv35/KjdQl8a//CwkI4OTm162fkM67b/86dO6itrUVNTQ3Onj2L2NhYDB06FFKptEM/N2e47qoTwif3D9/Fx8ezANjPP/9c77jjx4/rbf/uu+9YAOzvv/9u9NxtGb7rqcPXfGn/BnFxcSzDMOyaNWtafQ4+42P7b9myhQWgezz00ENsbm5ui87RldDwNSFN+Prrr2Fra4uHH34YpaWlukfD0Obp06cBAHZ2dgCAY8eOQaVScRhx98Kn9i8uLsacOXPg6+uLFStWdMh78A0f2n/27Nk4efIkDh48iDlz5gCo7z13V5SUCWlCWloaKisr4ezsDJlMpveoqalBcXExACA6OhrTp0/Hhg0b4OTkhClTpiA2NhYKhYLjT9C18aX9a2trMWnSJFRXV+Po0aONrjV3V3xof29vb4wePRqzZ8/G559/Dj8/P4wePbrbJma6pkxIE7RaLZydnfH5558b3N8weYVhGBw+fBgJCQn44YcfcOLECSxcuBDvvPMOEhISeswf8fbGh/ZXKpWYNm0a/vrrL5w4cQKhoaGtPldXw4f2v9+MGTOwZ88exMXFYezYse12Xr6gpExIE/z9/fHrr79i2LBhJk0sGTJkCIYMGYLNmzfj4MGDeOKJJ/Dll1/imWeeAcMwnRBx98J1+2u1Wjz11FM4deoUvvrqK0RHR7fmY3RZXLe/IQ095MrKynY5H9/Q8DUhTXjssceg0WiwcePGRvvUajUqKioAAOXl5Y2qDIWFhQGAbgjPwsICAHSvIc3juv2XLl2KQ4cO4cMPP9TNGO5JuGz/kpISg9v37t0LhmEwaNAgk87T1VBPmZAmREdHY9GiRdiyZQuuXr2KMWPGQCQSIS0tDV9//TV27NiBGTNmYP/+/fjwww8xdepU+Pv7o7q6Gnv27IGNjQ0mTJgAAJBKpQgJCcGhQ4cQGBgIBwcHhIaGNjkceuDAAeTk5EAulwMA4uLisGnTJgDAk08+CW9v745vBA5x2f7bt2/Hhx9+iKFDh8LCwgKfffaZ3v6pU6fC0tKyw9uAS1y2/+bNm3H+/HmMGzcOXl5eKCsrwzfffIPff/8dS5cuRUBAQGc2RefhePY3IbxirKLR7t272fDwcFYqlbLW1tZsv3792BUrVrC3bt1iWZZlL1++zM6ePZv18vJiJRIJ6+zszE6aNIlNTEzUO8+FCxfY8PBwViwWm3R7SHR0tN7tIPc+Tp8+3V4fmzf41P7z5s0z2vYAdNXWuhM+tf8vv/zCTpo0iXV3d2dFIhFrbW3NDhs2jI2NjWW1Wm27fm4+YVi2G1f2JoQQQroQuqZMCCGE8AQlZUIIIYQnKCkTQgghPEFJmRBCCOEJSsqEEEIIT1BSJoQQQniCkjIhbZCdnQ2GYbBv3z6uQ+mRqP25Re3f/igpE0IIITxBxUMIaQOWZaFQKCASiSAUCrkOp8eh9ucWtX/7o6RMCCGE8AQNX5Meb/369WAYBqmpqZg7dy5sbW0hk8mwZs0asCyLvLw8TJkyBTY2NnB1dcU777yje62ha2rz58+HlZUVbt68iUcffRRWVlaQyWR49dVXodFodMedOXMGDMPgzJkzevEYOmdhYSEWLFiAXr16QSKRwM3NDVOmTEF2dnYHtUrnofbnFrU/v1BSJuR/Zs2aBa1Wi3//+9944IEHsGnTJmzfvh0PP/wwPDw88OabbyIgIACvvvoq4uLimjyXRqPB2LFj4ejoiK1btyI6OhrvvPMOdu/e3arYpk+fju+++w4LFizAhx9+iGXLlqG6uhq5ubmtOh8fUftzi9qfJ7hZB4MQ/li3bh0LgH3uued029RqNdurVy+WYRj23//+t257eXk5K5VK2Xnz5rEsy7JZWVksADY2NlZ3TMPqQv/617/03mfgwIFseHi47vnp06cNrvZ0/znLy8tZAOzbb7/dPh+YZ6j9uUXtzy/UUybkf5555hndv4VCISIiIsCyLJ5++mnddjs7OwQFBSEzM7PZ8z3//PN6z6Oiokx63f2kUinEYjHOnDmD8vLyFr++q6D25xa1Pz9QUibkf7y8vPSe29rawtzcHE5OTo22N/fHwdzcHDKZTG+bvb19q/6oSCQSvPnmm/j555/h4uKCESNG4K233kJhYWGLz8Vn1P7covbnB0rKhPyPoVs6jN3mwTZz04Ipt4cwDGNw+72TYRq89NJLSE1NxZYtW2Bubo41a9YgODgYV65cafZ9ugpqf25R+/MDJWVCOGJvbw8AqKio0Nuek5Nj8Hh/f3+88sor+OWXX3Dt2jUolUq9mbCkZaj9uUXtbxglZUI44u3tDaFQ2Ggm64cffqj3XC6Xo66uTm+bv78/rK2toVAoOjzO7oran1vU/oaZcR0AIT2Vra0tZs6ciffffx8Mw8Df3x/Hjh1DcXGx3nGpqal46KGH8NhjjyEkJARmZmb47rvvUFRUhMcff5yj6Ls+an9uUfsbRkmZEA69//77UKlU+OijjyCRSPDYY4/h7bffRmhoqO4YT09PzJ49G6dOncKBAwdgZmaGPn364KuvvsL06dM5jL7ro/bnFrV/Y1RmkxBCCOEJuqZMCCGE8AQlZUIIIYQnKCkTQgghPEFJmRBCCOEJSsqEEEIIT1BSJqSLMLTOLOk81P7c6intT0mZdEsZGRlYtGgR/Pz8YG5uDhsbGwwbNgw7duzAnTt3Oux9k5OTsX79es4XX9+8eTMeeeQRuLi4gGEYrF+/vlPfn9qf2p9LXLd/W1DxENLt/Pjjj5g5cyYkEgmeeuophIaGQqlU4ty5c/jnP/+JpKSkVi+23pzk5GRs2LABI0eOhI+PT4e8hylWr14NV1dXDBw4ECdOnOjU96b2p/bvye3fVpSUSbeSlZWFxx9/HN7e3vjvf/8LNzc33b4lS5YgPT0dP/74I4cR3sWyLOrq6iCVStv93FlZWfDx8UFpaWmjJfQ6ErV/PWr/5nXH9m8PNHxNupW33noLNTU12Lt3r94fpAYBAQF48cUXdc/VajU2btwIf39/SCQS+Pj44LXXXmtU6N7HxweTJk3CuXPnEBkZCXNzc/j5+eHTTz/VHbNv3z7MnDkTABATEwOGYcAwDM6cOaN3jhMnTiAiIgJSqRS7du0CAGRmZmLmzJlwcHCAhYUFhgwZ0qY/nlz1Uqj978bLBWr/u/F2WSwh3YiHhwfr5+dn8vHz5s1jAbAzZsxg//Of/7BPPfUUC4B99NFH9Y7z9vZmg4KCWBcXF/a1115jP/jgA3bQoEEswzDstWvXWJZl2YyMDHbZsmUsAPa1115jDxw4wB44cIAtLCzUnSMgIIC1t7dnV65cyX700Ufs6dOn2cLCQtbFxYW1trZmX3/9dXbbtm3sgAEDWIFAwH777be6GLKyslgAbGxsrMmfr6SkhAXArlu3zuTXtAW1vz5q/57V/u2BkjLpNiorK1kA7JQpU0w6/urVqywA9plnntHb/uqrr7IA2P/+97+6bd7e3iwANi4uTretuLiYlUgk7CuvvKLb9vXXX7MA2NOnTzd6v4ZzHD9+XG/7Sy+9xAJg4+Pjdduqq6tZX19f1sfHh9VoNCzL8v+PErV/Y9T+bKNzdNf2by80fE26jaqqKgCAtbW1Scf/9NNPAICXX35Zb/srr7wCAI2Gz0JCQhAVFaV7LpPJEBQUhMzMTJNj9PX1xdixYxvFERkZieHDh+u2WVlZ4bnnnkN2djaSk5NNPj+XqP25Re3fPVBSJt2GjY0NAKC6utqk43NyciAQCBAQEKC33dXVFXZ2dsjJydHb7uXl1egc9vb2KC8vNzlGX19fg3EEBQU12h4cHKzb3xVQ+3OL2r97oKRMug0bGxu4u7vj2rVrLXodwzAmHScUCg1uZ1uw+mlHzDTlC2p/blH7dw+UlEm3MmnSJGRkZOC3335r9lhvb29otVqkpaXpbS8qKkJFRQW8vb1b/P6m/oG7P44bN2402p6SkqLb31VQ+3OL2r/ro6RMupUVK1bA0tISzzzzDIqKihrtz8jIwI4dOwAAEyZMAABs375d75ht27YBACZOnNji97e0tAQAVFRUmPyaCRMm4NKlS3p/SGtra7F79274+PggJCSkxXFwhdqfW9T+XR8VDyHdir+/Pw4ePIhZs2YhODhYr6LRhQsX8PXXX2P+/PkAgAEDBmDevHnYvXs3KioqEB0djUuXLmH//v149NFHERMT0+L3DwsLg1AoxJtvvonKykpIJBKMGjUKzs7ORl+zcuVKfPHFFxg/fjyWLVsGBwcH7N+/H1lZWfjmm28gELT8u/OBAweQk5MDuVwOAIiLi8OmTZsAAE8++WSH9T6o/etR+/fM9m8XHM/+JqRDpKamss8++yzr4+PDisVi1tramh02bBj7/vvvs3V1dbrjVCoVu2HDBtbX15cViUSsp6cnu2rVKr1jWLb+do6JEyc2ep/o6Gg2Ojpab9uePXtYPz8/VigU6t0eYuwcLFt/j+eMGTNYOzs71tzcnI2MjGSPHTumd0xLbgmJjo5mARh8GLpdpb1R+1P79+T2bwuGZVtwlZ4QQgghHYauKRNCCCE8QUmZEEII4QlKyoQQQghPUFImhBBCeIKSMiGEEMITlJQJIYQQnqCkTAghhPAEJWVCCCGEJygpE0IIITxBSZkQQgjhCUrKhBBCCE9QUiaEEEJ4gpIyIYQQwhP/D41avffdI0qJAAAAAElFTkSuQmCC", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "repeated_measures.mean_diff.plot(custom_palette=[\"red\", \"blue\", \"green\", \"purple\"]);" - ] - }, - { - "cell_type": "markdown", - "id": "18e6c0e9", - "metadata": {}, - "source": [ - "## Color saturation\n", - "\n", - "By default, ``dabest.plot()`` [desaturates](https://en.wikipedia.org/wiki/Colorfulness#Saturation)\n", - "the colour of the dots in the swarmplot by 50%. This draws attention to the effect size bootstrap curves.\n", - "\n", - "You can alter the default values with the parameters ``raw_desat`` and ``contrast_desat``.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6d2cc09a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(custom_palette=my_color_palette,\n", - " raw_desat=0.75,\n", - " contrast_desat=0.25);" - ] - }, - { - "cell_type": "markdown", - "id": "976d3d7a", - "metadata": {}, - "source": [ - "## Alpha (transparency)\n", - "It is possible change the transparency of the raw data by using the `raw_alpha` parameter. This can also be achieved by adding\n", - "`alpha` to the relevant rawdata kwargs (`barplot_kwargs`, or `swarmplot_kwargs`, or `slopegraph_kwargs`, or `sankey_kwargs`)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "09345d42", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(raw_alpha=0.2);\n", - "\n", - "multi_2group.mean_diff.plot(swarmplot_kwargs={'alpha': 0.2});" - ] - }, - { - "cell_type": "markdown", - "id": "f454d2bc", - "metadata": {}, - "source": [ - "It is also possible change the transparency of the effect size curves by using the `contrast_alpha` parameter. This can also be \n", - "achieved via adding `alpha` to the `contrast_kwargs` parameter." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9ad0dfc7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(contrast_alpha=0.2);\n", - "\n", - "multi_2group.mean_diff.plot(contrast_kwargs={'alpha':0.2});" - ] - }, - { - "cell_type": "markdown", - "id": "13da3fc1", - "metadata": {}, - "source": [ - "## Marker size\n", - "It is possible change the size of the dots used in the rawdata swarmplot, as well as those to indicate the effect sizes, by using the parameters `raw_marker_size` and `contrast_marker_size` respectively.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7239686e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(raw_marker_size=3,\n", - " contrast_marker_size=12);" - ] - }, - { - "cell_type": "markdown", - "id": "482b1497", - "metadata": {}, - "source": [ - "## Axes" - ] - }, - { - "cell_type": "markdown", - "id": "ff0d7fb7", - "metadata": {}, - "source": [ - "### Lims\n", - "\n", - "To change the y-limits for the rawdata axes, and the contrast axes, use the parameters `raw_ylim` and `contrast_ylim`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1c4f9835", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(raw_ylim=(0, 5),\n", - " contrast_ylim=(-2, 2));" - ] - }, - { - "cell_type": "markdown", - "id": "4643eb83", - "metadata": {}, - "source": [ - "If the effect size is qualitatively inverted (ie. a smaller value is a\n", - "better outcome), you can simply invert the tuple passed to\n", - "``contrast_ylim``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a5c27d52", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(contrast_ylim=(2, -2));" - ] - }, - { - "cell_type": "markdown", - "id": "59414b18", - "metadata": {}, - "source": [ - "The contrast axes share the same y-limits as those of the delta-delta plot. Thus, the y axis of the delta-delta plot changes as well." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f1606464", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "paired_delta2.mean_diff.plot(contrast_ylim=(3, -3));" - ] - }, - { - "cell_type": "markdown", - "id": "c5013e84", - "metadata": {}, - "source": [ - "You can also change the y-limit of the delta-delta axes and the regular delta axes via the `delta2_ylim` parameter." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e87009b1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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LlvnSl77EZz/7WUZGRtDr9czMzFwy3Hslhw8fpr6+HoBoNMrIyMglc/z27dvHBz/4Qb72ta/xK7/yK2+7fVciAqAgCLcFR+Nm8rEgucgCJl8jkdGjaE129DbPm56rt3uxN2wiPn0Wg6MKg+ONC0msJgOP7uhi/+lRXjgxyJ0bmuio96/XWxEEYR1IkvS2izrejo9+9KN8/vOf54knnuBzn/scn/nMZ6hUKtxzzz3E43EOHDiAzWbjk5/85No5f/iHf4jb7cbv9/N7v/d7eDwePvzhDwOrw74f/OAH+fSnP81HPvIRgsEgADqdDpfL9Y7bK2YxC4JwW5BkBfeGeygXskio6CwuVs69QrlwbQuz2uq70Nu8hIcPUC7m3/R4nVbDg72ddNT7OXJugqODk1QqYvs4QXiv0mg0/NZv/RZ/+qd/yhe/+EW+/OUv8/jjj7NhwwYeeeQRnnzySZqami4550/+5E/49Kc/zbZt2wgGg/zoRz9Cp9MBq0UhmUyGxx9/nKqqqrWPn/qpn1qX9kqq2PByXfX19bFt2zZOnDhBb2/vjW6OINw2vvCFL6xVAX/ta1+76nHppUlWhg7iaO4lOXcOrcmGb9OD11QUUsqlWTzxJAZnFZ4N91zzci/DM0GODk5R7XFw75ZWdBoxuCIIwtXt27eP3bt3E41GcTgcN6QNogdQEITbitnfhNnfRGL6DI7mHvLxZWKTJ6/pXI3BjLt9J5nlGdLB8Wt+zY76AO/b1slyNMkzhwdIZtZvOyhBEITrQQRAQRBuCaqqXvNK/a7WO1B0RlLzIziaekjMDZNemrymc03eeixVrUTHj1PMxK+5fdUeB4/s7KJcqfDUoX5C0cQ1nysIgvBuEwFQEIRbQjCSYCEcp1KpvOmxskaLu/MuCqkI5UIWS6CZ8MgRCslr287N2dKLojezMngAtVK+5jY6LCYe3dmN3WLkuWODTCwsX/O5giC8dzzwwAOoqnrDhn9BBEBBEG4RFqOeTK7A9FKEQqn0psfrbR4cTVtJzA1idNeiM9tZPrefcvHNh2dlRYtnw90UM3Fik6feUjsNOi0P3bGB5ioPr54Z4+To7FveY1QQBOF6EwFQEIRbgtVkoNbrIJcv8vzRQXKF4pufU7sBo7OKyOhRnK13opZLq7166pv3IuosrvPDx0NkIwtvqa2KLLOru5ne9gb6x+fZf2qUUvnaexIFQRCuNxEABUG4ZRj1OhqrPKRzeZ49eo507o2Xa5EkCXfn6lZL8ekzuDvvJh9bIjZ5+ppez1rTgdFVTXj4EOVC9i21VZIkupurub+nnbmVGM8eOUcmV3hL1xAEQbheRAAUBOGWUFFVyuUKRr2WR3Z0USqVefbIAIn0Gw/pKjoj7o6dZCMLFDNxHM09JGbPkV6eftPXXN1reCcA4eFDb2sot97v4pE7u8gWijx1uJ9wPP2WryEIgrDeRAAUBOGWML8cY34lRqVSwWY28siOLmRZ5tmjA0STbxyqjK4abLWdxCZPorf5MPsaiQwfppCOvenrrgbIXWQjiyTnh95W2912M4/t7Mao0/LM0QFmlq6tGEUQBOF6EQFQEIRbgsduIV8sMb8SR1VVzEY9D9/ZhVGv5dkj5whFk294vqNpK1qTg/DwARwtvWiMFlYG9l/Trh9GV/X5AHnqmiuJX89k0PHwjo3Uehy8fHKE/ol5URwiCMINIwKgIAi3BKNeS8BlI57KcGZ8fu2599+xEYfVxPPHB1lYiV31fElW8Gy4m3I+S3zyFN6N91Ep5QkPHbymIHYhQK4MHaBSfvMClCvRKAr3bW2ju6WGvpEZDvaPUy6/eUGKIAjCehMBUBCEW4bVZMDntHF6bJapxTCwuifvnu2dBFw2XuobZvoNhle1JhvO1u2kghPkk2HcnXeTiy4Snz7zpq+9GiDvopxPEx0/8bbfgyRJ9LTVcc/mViYXwzx//NoqmgVBENaTCICCINxSvA4LjQEPB/rHWYmngNWetQd62qn3udh/coSx+dBVzzf7m1fnAI4eRWu0Ym/cQny6n8zK7Ju+ttZkx9myndTiOJnlmXf0Ppqrvbz/jo0k0lmeOtRPLJV5R9cTBEF4K0QAFAThliJJEndtasZpMbLv5Mja0iqKLHPP5lba6nwcPDvOuanFq57varsDWatnZegg1poOTN56wsOHrmnrN3OgZfX4kcOUcu+sotfntPLYrm40iswzhwfecAhbEARhPYkAKAjCLWe1x68DgH0nh9cWWZZliR0bm+huquH40BSnrrILh6zR4em8m0IyTGKmH3f7ThS9ieWB/VRKbzwcuxog70TWaM/PH3xnc/gsRgOP7OzC67Ty4okhhmeC7+h6giAI10IEQEEQbkkmg44HetqJprIcPDuxFvQkSaK3o57e9nrOjM9xbGjqiiFQb/Ngb9xCYvYchWQY78b7KBeyhIffvChE0epxd9xNPrFMYmbgHb8XnUbD7p4OOusDHDk3ydHBSSoVUSEsCML1IwKgIAi3hEwuTyp76ZItHruFu7tbmAqurFUGX9DdXMOOjc0MTy9x4Oz4FQOVrW4jeruPleGDyNrVXsHMytw1hTqDw4etvov49Fny8eV39uZY7b28Y0Pjaptnlnipb4hC8c33PBYEQXg7RAAUBOGWEE1mWVyJXVYs0VjlZktrHafHZpkOhi/5Xke9n3u2tDK5uMLLp0YuW3Jldau4u6BSITJ8GIOrGnvDJuLTZ8hGLg2UV2Jv2ITO5lldGqa0Ptu8ddT72bNtAyuxFE8fGSCZeeOdTgRBEN4OEQAFQbglVLltWM0G5kMxRmaXLvne5pYaGgMeXj07ftlWa01VHnb3drCwEuPFE0MUSpf2qmn0Jtwdu8iE50ktjGBv2ITRVc3K4AGK2TdeXFqSZDydd1EpFYiMHl23hZ2rPHYe3dlNpVLhqUP9hKKJdbmuIAjCBSIACoJw01NVlVOnTpEMh7BbDBwemLikyvdCZbDDbGTvyeG1yuALar1O9mzfQDiR4oVjg+QLl4ZAo7sGa00H0YmTFNNR3J13oegMrAy8/KaLPmsMFlxtd5IOTZNemli392y3GHl0Zzd2i5Hnjg0yPv/Oh5kFQRAuEAFQEISb3r59+wiFQiwuLjI1cg6fSeH40BRnxufWjtEoCrt7O1BV9ZLK4Av8Lhvvv6OLZCbPs0cHLguJzuYetCYbK4MHQJLwbryfUi5NZPjwm/bsmX2NWALNRMeOU8ysX2+dQafloTs20Fzl4cDZMfpGZsT2cYIgrAsRAAVBuOnpdDrUQoZSNkUqleLfvv33FMJznByZuSQUmQw6dvd2XFYZfIHbbuaRHV0USmWeed38ute2iksTHe9Da7bj7txFenmG5Nzgm7bR2bodRW8kPHQAtVJ+0+OvlSLL7OpuZltHAwMTC7x8aoRiaf2uLwjCe5MIgIIg3PTuvvtuervb0cklCskwhVyGY/tfYOz4Po4PjF6y1MvFlcFnJy4v5LBbjDyyYyOyJPHMkQGiydeKSl7b6WOM9PI0Jk899vouYpOnyEWvvLD0BbKixd15N4V0jNjUm28t91ZIkkRXUzUP9LazsBLn2aMDpHP5Nz9REAThKkQAfAN/8id/giRJ/Kf/9J9udFME4T3P19TFtjt2EnCaScz0U0hFCc1Nc+y5H7D31SMc6p9YW+pltTK4llOjl1cGw+riyw/v2IhBp+XZowMsx14r9jAHWjB764mMHKGUS2Fv3IzBGWBl8FVKudQbtlFvdeM4v7ZgNvLGgfHtqPO5eGRHF7lCiacO9V9W8CIIgnCtRAC8imPHjvHEE0+wefPmG90UQRBY7QUzOnxsu/dhfv4nHqQSmSK9PI1UynF6/9P83+98l5dPDlOurC71srmlloaA+4qVwQBGvY6H79yIw2Li+WODLK7E117H1b4DWaNjZeggAO7Ou5E0utWdQspvvDaftXYDRmcV4eGDlAvrv4SLy2bmsZ3dmA06njk6wPRSZN1fQxCE258IgFeQSqX4+Mc/zt/8zd/gdDpvdHMEQbiIojfy6K//EV/4zG9SbSqRnB/GKJdZnhrkG//9v/HDvYcplytIksTdm1quWhkMoNNqeN+2TnxOKy/2DTFzPkytbRWXWCE+fRZFq8e78T5K2QSR0SNvWIixurbgLgDCI29eQPJ2mAw63n/nRmq9Dl4+OUz/xLwoDhEE4S0RAfAKfvM3f5MPfOAD7Nmz50Y3RRCE89KJGJn06lCtotGy4aGf53e//P/nwd4W0sExtIUY2kKKJ/7yv/Lfv/kdSuXy6p7Bve1XrQwG0GpWq4frfE5ePjWyttyK3u7F3rCZxMwAudgSOosTV/tO0ktTpBaG37Ctis6Iu30n2fD8mx77dmkUhfu2tLG5pZa+kRkOnB2/bKFrQRCEqxEB8HW+/e1v09fXx+OPP35Nx+fzeRKJxNpHKvXGc4QEQXh7IvNjhKfOMj9xjkwygSRJeDp28jO/9WV+/Wc+gKmcgOQCboPED7//f/n8V/6YRCqF2aBfrQxOZjjUf3llMKxW2t67uY2WGi8Hzo4xOL06f89WvxG93Ut46CDlYg6zrxFb7Qai433kYkuXXedir60teIpCKnpd7okkSWxtq+Oeza1MB8M8f3yQbP6N1y0UBEEAEQAvMTs7y6c//Wn+8R//EYPBcE3nPP7449jt9rWP+++//zq3UhDem7w1jegNZorRBfb/85/Sf+wVyqUSlkALOz78q/ynX/0kmxo9qKklPPoSZ06e4Jd/8zMMDY+uVgZvWt0S7kqVwbC6F++urmY2NlZzbHCK02NzgIS7827USonIyOrQr6N56+r+wYOvUsq9cRHG6tqC1tWt4t5k7uA70Vzt5aE7NpJIZ3n6cP9l2+UJgiC8nqSKiSNr/vVf/5Wf/MmfRFGUtefK5TKSJCHLMvl8/pLvwWoPYD7/2nIMp06d4v777+fEiRP09va+a20XhNvdb//Sx1kJLWK12vnEns1kklFkVyMb7/0JahpaKGaThM7u5eiRw/zb3qOkChAuGdAbTfzqpz7Bv/vgBzk7Mc/psTnu7+mgwe+64uuoqkr/xAInR2fY0FDF9s4GsuE5lgf242q7A2t1O+VCjuDJp1G0RvxbH0KSlSteC6CYjhM8+TRmfzOutjuv1+0BIJXN8dKJYdK5PPdtaafG67iurycIwq1L9ABe5H3vex9nz57l1KlTax/bt2/n4x//OKdOnbos/AHo9XpsNtvah8ViuQEtF4Tbn8FVjc7kQFJLtPXeQ333TjTJBc7++Bu88uQ/kyuUCfQ8zF33Pch//PmfpLXOj1vJkE3G+B9/8/f86Z/9OfVuy2pl8Jmxqy6hIkkSm1pq2LGxicHpRQ72j2Nw1WKtbiM63kchHUPRGfBsvI9COvamewBrzXYczdtILoySWZm5XrcHWF3e5pGdXfidNl46McTQdPC6vp4gCLcuzY1uwM3EarXS3d19yXNmsxm3233Z84IgvLtKZRWNxY3JIFNMhrHbbPgf/gSTJ/cSmTzCq/PDBDY/QHfPPWgMZn7FbuXlk+M8ve8AS8kIL+zbz9zcHJ/61C+sVQY/trMbk0F3xdfrqA+g1SgcODtOsVTm7q6t5OMhwoOv4u95BL3VjavtDsLDh9FZPVir267adktVK7noIpGRI+isHjR60/W6Teg0Gh7o6eDEyDRHByeJp7Pc0dmILEvX7TUFQbj1iB7A29RsKCK2ixJuK8FInKlgmGRZi3fz+0GtkF4cof2uD9F9309g16uEjv8bL373r4nLDlwt29i9rZ1P/9LPsaEhQCmbpH9wkL/8b/+N0EgfxWKRfadG3rBytrnaywM97cwvx9h3ehx72y6K2RSxiT4ALIEWrDXtRMePk48vX/U6q2sL3okka1a3ilOvb7WuLEvc0dnIzq5mRmaXePHEEIXi9ZuDKAjCrUfMAVxnfX19bNu27YbOAUxmcvxg/0kUWabG66DB76bG50CnER2+wq3rs7/zOcZn5iih4Vc//Xm2tVWjWxkkszKLvW4jeoef2ZMvsjB+lmxJxlC3mc4NXZSCgxQkA/+yt4+nXtxPWYX66gA1DS3UbtrJ1q5O7tnciiRdvYcsGInz0olhHFYTO6sUklN9eLvuxeSpR62UWTrzIqVskkDvo2/Yu5eLLRE68yL2xs3Y69+dUYXFlTgvnxrBaNDxYG8HVtO1FbgJgnB7Ez2AtyGrycBP3d/D1rY6MrkCr5wZ5bsvnWBv3zATC8sUSqInQLj1JDM5NIpCc7UXnVbDiyfHOJvzoqveSGJukMTsOZrv/km27PlZAgE/5dk++vb9Gwt5A4qa52f29PD5z/wWRqOR6bkFpoZOc+SZ7/PUs89xdvzKlcEXBFx23n/nRpLpHAdm82gcVee3iksjyQrejfeCJLFy7hXUytV73g0OP7a6LuJTZ8gnVtb7Fl1RlcfOozu7qVQqPHWon6VI4g2PVytlCukYpbyoJBaE25noAVxnN0MP4OulsjlmliJMBSOsxJIoskyVx06D302dz4lOK3oGhZvfL/7abzI5M4/f5+X//K9vML8cp29kmnSuQIdHiz87gaJR8G64F1mjIzR4gNnB48STKUpmPzVuG26HjYytma/+v3/N7NQ4fouGkt6GNdDMFz/322xsrn/DNsRSGZ4/NoiGMls0U5itTnxb3ockyeQTKyydfh5LoOUNq33VSpml0y9QLuao6n0MWaNd71t1RblCkZdPjbAcS7Gjs44Gl5FSLkkpm6KYTVLKJinlkpTzWVRVxd6wCUej2ApTEG5XIgCus5sxAF4snc0zvRRmOhhhOZZElmWq3XYaAiIMCje3L3zhC8wtLpHIV/i1T3+e3b0d6LQK56YW6Z9YQKMWaVcWcGqKuFp7MVe1kwlNsti/n8XpUVIFMBiMBAJ+PB27+K//+DSv7n0Ot65MFh2K1cOXf/d32HnHG/+7TWZyvHB8EDUdoVs7h7+tF3vDJgBSi6OER47i7tiJJdBy1WuUskkW+57G6K7B03n3ut6nCyqlIqVc8rVwl01SyCSZmJomEgnjddqoctuQNTo0BitaowWN0br6YbCiNdtQtGK4WBBuVyIArrObPQBeLJ3LMx2MMLMUIRRNIMsyVW47DQEXdV4Xep0Ig8LN43Of+zyJRByDycJ9H/4EALt7OnDbzWRyBU6OzjA+t4S3uEC9NoG/sQNX+w7Ucpno+AkWho6yHFqiWCxhsVpp7t3DUwPLfPef/gFjKUYiV0bSW/ilT/4cH/vov7/isk8XZHIFXjwxSGlphA2WFI07PoDB7gMgPHKE9NIk/i170Ns8V71GOjTFyuABPJ13YfY3va17UikVLgp4qUsCX7mQWztO1mjRGm1ojFYUg4WZaI6BuRjVVVXctXWj+MVPEN6DRABcZ7dSALxYOpdnZimy2jMYTSLJ0moY9Luo84kwKNx4H//Ur7C8Eqahtor/8ud/zv7To8TSWe7d3Er9+UWdV+Ipjg9NE10Yp6o4Q31NNXU970NrspOLLrI8dIjg5CCxyApUyjhbelg0dfLtf/5n0otjRBIpdHoD9999F7/2G7+J1+u9anvyhRIvnjhHafIQ7VU22u75KRSt/rUh3nyGQO+jKLqr96KtDB0kuzJLYNtjaI3WKx5TLubPD8+mKGYSa8O2pWyScvG1RegVrf58D57lfI+ede3xlXryZkMRXjk9hs1sYHdvB2aD/lr/KARBuA2IALjObtUAeLFMrnB+zmCY5WgSJM6HQTd1ficG3bszZ0kQLvbpz3yWydkFKpKWT/7Gf6KzPkAolmBhOU5vRz0bG6uQJAlVVZkKhjnZP4gSPI3PomHDjvfjqG6mUi6RmBlgZew4welRcskIWPxkGx/klaNnGO17heWVFcxaiabGej75i7/Grl27rtqmYqnMvqOnyI3so61jIx07H0GSJEr5DMG+p9GabPg2PXjVnUIqpSKLfU8hSTKu1jspFdKv9eZdmJNXLKwdr+gMaAwWtCYbGsP5sGe0ojFYULRvPcBFEmn29g1TUVV293bgsYuF7AXhvUIEwHV2MwTASkXlxMg0TVWed/wf+oUwOL0UJhRZDYMBl53GgAiDwrvrC1/4ArFYDKPZwkc/9etMLKyg0yhoNQrxdJaO+gA7NjahyKuLG5TKZQbGZ5no24c+t0xdRy8b77gfRaOhmI4THjlMaPwM0YUx8qqGqHMLUwUbZw7uZWZqHJumhNtu5d49j/Fzn/zUVfcHL5crvHLgFZJjh2nb/iCdm+8AIBcLETrzAtaadhzN26gUc5cO12YTFLMpcvEgyblh9DYvBmcARW9Ee3G4M1rPP7Zel4KRbL7A3r5hoqks92xqoSHgXvfXEATh5iMC4Dq7GQJgMpPj+WPnSGXzeBxWOuv9NATcaz8Y365svrA2TLwUSZwPg7bzBSQujHoRBoXr50IAdDgcfO1rXyOZyTEwucDY/DLxVIZsvsiGhir2bN9wyZSFdDbPyaOvEBk/gd7mofuuD1BTHUBVVdLBcVaGDxMaPUEqnSYk+1kwtDI+Pkn/yaO4NAVMWqhubOfXfvt3aGm5cmFHpaJy8IUfEp4dpqFnN61VLkrZJImFEZJzgxgcATQXDfFq9KbzAc+G1mglFwuuzhvseRiTu/a638vXK5XLHDw7wVRwhZ62erqbq99wXURBEG59IgCus5shAMLqD6S55SjDM0EWw3GMeh1ttT7a6/xX3frqrVgNg1FmlsIEw6+FwXq/m3q/CIPC+nt9ALwgnctzbnKRvpEZpoJhqj0OPrZ7O37XpXPqFmenGDj4FOlsDlvbLrZt3YrNbKRcyBEZO8bSmZeIrywSKhqZw89MWsfpY4dwaXLIxTQ6g4WPfPxTPPzQHir59PmCi/NDtdkEhXSckeFzLKcr+Orb6KxxojFayUUWKGbi+DbtxuiuRWOwICuXzqlVVZXQmRcpZhNUbXvshlTfqqrKmfE5To/N0VztZVdXM4oilooVhNuVCIDr7GYJgBeLpTIMTy8xvrBMuVKhwe+ms8GP12Fdl9/ys/kis6EI08EwwUgCVPC7bDQEXOfD4DsPnIJwtQB4QTZf4PjQNM8ePUepXGZ3bwf3bm675BeeUj7D0OFnmZueIGluoHHjdra01qLTashGFlk48gNi88OspMtMZkzMFUycGxxFlwlRycZQyyVamxv5mQ8/gsNmRTGYLxmuVSsqQ8deYqZoo2nr/WzrOL9TyKnnqZTyBHofuWq4K+UzBE88hd7mwdN1/w3rgZtcWOFg/zgbGqro7XjjdREFQbh1iQC4zm7GAHhBoVhibH6Z4ZkgyUwOl81MZ32Axio3mjdY8uKtyBWKa8PEwUgcVPC5rDT43TQERBgU3r43C4AXJNJZvvPSCUbnlqjxONixsZnu5mrMxtUiCVWtEB7vY/zUAZYyEmVrDa0BOz6LQikTJzZ1mkxoikReZTpnIlIyMhpMkg4vQTFDqZDD7nTzy//xd9m+867LXj85P8zIib2MVGppaN3Azo3NVAppgn3PoLO48G56AEm6cs9aZmWW5YH9uNruwFrdvj437m1YiaewmQxieRhBuI2JALjObuYAeIGqqsyvxBieWWJ+OYpeq10dHq73YTGu39BTrlBkdinK9FKYxfBqGPQ6rTQGVoeJ12MoWnjvuNYACFCuVDh4ZpSDZ0ahUsJlUmhwaml1yhgqOUq5FIV0jNTyDKlcmbDWh97uo62pCZ/PTz4ZYen0c6SSSSaTCpFMhVhZy+TcCuV8hkI8hKpWeN/7H+MTv/xb6HSv/V1WVZWVgZdZmJvmbKWRuuoq7tncSjEeInT2JWy1G3A091y17ZHRo6SCEwR6H0FndqzX7RMEQbiE+PXuPUiSJGq9Tmq9ThLpHMMzQYZngwxMLlDnd9JRHyDgsr3jISiDTktbnY+2Oh/5Qml1mHgpwrGhKY4NTuF1WteGicUaZMLboVbKq1uZ5V7b7eLC4/psiqK1yNmlArmCxFRGz0RQR6PfRXdzM7VuNyoQHT+BJxZhmioOLGmpU2FbRw9tdRuZP/w9lNkxphUJbaZEoMPLyfFlihU/SiHJC8/+mKGBM/zm73yJhpbVHjtJknB17KSQitJbjNG3pGdv3zD3b23H0bSV6MRJtFYXZm/DFd+To7mXfDxEePBV/D2PXDZfUBAEYT2IHsB1div0AF5JsVRmYmGFoZkg8VQGh8VER32A5moPWs36DA9fkC+UmF1eHSZeDMepVCr4nDbq/S4aAiIMClf2O5/+j0RWlrCa9Pzeb33ykn1rAWRFWdvGTGM8v7WZwUowVebg0Bxmg556v5vxhWWyuQL1fhebWmpwmPVERo+SXpogY6jiXMZOtlhmQ0OAjXUeYoMvszhygslQikRJwWU1MTAV5PjQPFaTkXJqBUWW+NjPfIJHP/Jx5PPV9rlokNDZlyg5WzgclHDZzOzuaSc5foRseB5/z8NX7eErpGME+57BUtWCq/WOd+sWC4LwHiIC4Dq7VQPgBaqqEowkGJ4JMrsURatRaKnx0lEfwGZe/8rEQrHEbCjKdDDMwvkw6HWs9gw2+N1r87YE4bd/+ecIhxZxOJx89Qu/fcm+tRqjBUVnvGqvdTSZ5qUTqwse37eljXg6S//EAqlsjlqfk01NNRiyi0THT6AxOQgZWxmYj6JVZLa01ODKTLIwfIzBqUUKqkJrXRWTs4s8efAsBfSY5CKlbIJNXV38+me/hMNbBUBs8jSJ2QG0zXfz8nAIs0HPgz0tJAb3oZbLBHofQdZceSpEcn6YyNhxvN3335ClYQRBuL2JALjObvUAeLFUNsfITIiRuSUKxRI1Xicd9X5qPI7rUqG4FgaXwiysrIZBj8NK4/lh4vWcnyjcep5/7lmyuTwmk4k9e/a85fOz+QJ7T44QTaS5Z3MrtT4nU4thzk7Mk0hnqXI76PQbkRdPopbLmJrv4FyoyMTCMk6LkS5HnvTMGfpGZpElmW0ddYTjaX609zBnJ0PYHS40+RgWk4Ff/rVfp/feR0CtrG4LV8hiaH+Al06No1UUHthUR2rwJXRWD97uB6747+nCXMJ8YoXAtsfQ6E3rcRsFQRAAEQDX3e0UAC8olctMLYYZmgkSSaSxmYy01/tprfFetyrBQrHE3HKU6WCEhZUY5UoFj91CQ2C1mliEQeHtuNKCx6oK00thzo7PE0tl8NqM1KmLWEsRHE1byFvrOT48w0osSb2pgC0+wvGxRXR6I3e3eUCjY//R0/xo71FKGjM2gwapkOJ9D9zDx3/lMygaDYsnnsLoqkZXv40Xjg9RUVXubXWSnziErb4LR+OWK7a3XMyxeOIptCb76pZyYnFmQRDWyS0VAHft2sXf/M3f0N3dfaObclW3YwC8QFVVlmNJhqaXmF4Ko8gyLdVeOhr8OCzXr3eiUCoxH4oxFQyvhUG37bUwaDWJMChcu6steKyqKrOhKGfH51mJJ7GU49RKIZrq63F17GJ2JcWJkRmKqTCe5DATi2FkRy0PNhvRyLAQSfHN7/yYyWAUo9WJScpR53fxq7/xH/EFqgkPHcTdsRPZUccLxwfJ5ovsqJKQlofwdt2HyVN3xfbmoouEzu7F0bQVW93Gd/luCYJwu7qlAmBVVRWRSITf+Z3f4fd///evujfnjXQ7B8CLZXIFRmaXGJ0Lkc0XqHLb6agPUOt1IsvXr5eiUCoxvxxjOhhmfvniMOiiIeAWYfA2NheKki+WqPU6L9nq7e26sOCx227hgZ72tX2tVVVlYSXOmfE5FhYW0Kbm6ayysGXngygmBwOTCwyMTsLscWKxCNqazTzS7aMSm0dVtPzopcM8ve8QZcWI2WTEphT52Ic/yPaeTeTjSwR6H6WsMfHSiSFiqSxbrXGs5SiBnofRmuxXbGts4iSJuUH8W9+P3uZ5x+9dEAThlgqAiUSCL37xizzxxBM0NTXxjW98423NBbqe3isB8IJypcJMMMLgTJCVWBKLUU97XYDWWu/aD9TrpVgqM7ccZSYYYW4lRrlcxmUzn1902n1dilaEG+fVM2OMzy8jyxJ+p406v5N6n+sdFQqFokn2nRxGoyg8uK3jkp7sCwVRp4bGmRgewCwXuGNbLxu6tpItFOgbnGTo0NPkYkH0dZv58O67KC2cpZRLMrOS5m/+8V8IhuNIegtWHdy5sYkP3teDI9BAoOdhihWVfX0jhCJxNuoWCVi1BHoeQdZc/u/mtd1ECgR6H73iMYIgCG/FLRUALzh27Bi/9mu/xqlTp/jZn/1Z/st/+S94vd4b3SzgvRcAL7YSTzE8E2RyMYwENFV56GwI4LKZr/trF0vl1Z7BpTBzy6th0Gk103h+mNhmNl73NgjX14GzY0wHI9jNBiRZJpJIU6lUcNnM1Ptd1PlcOCxXrwS+mlQ2x0snhsnkCty3tY1qj+OyY5bCMQ4fepXpuQVcHh877txJc62PlViCf/uX/0s2OIbkbuJDP/FTOPJLJGYHKKLw3Wde5ZXDxymqClqDiVq7hp+6u4Pe9/0U3o33Ui5X2H96lJmFIO3yHK0NtXg23nfF91DMJgmeeAqTtx53x663exsFQRCAWzQAAlQqFf7yL/+SL3/5yyiKQl3d5fNnJEni9OnT72q73ssB8IJsvsjYXIjh2SUyuTw+p42Oej/1fheKfP03ly+WyiysxJgORphbjlIql3FaTatzBv1u7BYRBm9FkUSaofO/YKiqSq3Xgd1sJJnJM78SpVgqYzUZqPO5qPc78dit1zwdoVAq8crpMRZWYty5oZGO+sAVj5sZ6efI8WMs5zV469rY3N5Ivc/F9//1B8Qn+ygY3Gy55zF2tAdITvaRjy9zbnqFf/iXp4jG4pRlHXpZ5eEtVXz0U7+Br+s+VFXiYP84I+MTtMgLbN26HXvDlec5p5YmCA8dwrPhbsy+xrd7Ky9TKRUp5zOUCxlK+QzlQha9zYvB4V+31xAE4eZyywbAQqHAH/7hH/Jnf/ZnuN1uOjo6rnjc3r1739V2iQD4mkpFZTYUYWgmyFIkgVGvo73OT3ud713bE7hULjO/Emd6MXxJGKw/vzfx9SxeEa6PfKHE2HyI4ZkgqWwej91Ca60Po07L3HKMueUo2XwBg05LrW91mLjKbUdR3viXj0pF5fjwFEPTQTobAmzvaLxigCwkI4z17WV4KU1UG8DqcNNS7WHoXD+ZySPk0WNu3cVdW7sIaBLEJ08STaT49jMHGRgcJlcoUiyrdFbZ+I+/9h9o3fkYGqONY0NTnDp9mhZdjLseeAijq+ay11ZVlfDQQbKReap6H0VjtL7he1LVCuVCbi3clfMZyvkspcLq53IhSzmfoVIuXXKeotVjq+/GVtt5DX8igiDcim7JAPjCCy/wG7/xG0xMTPAbv/Eb/NEf/RFW6xv/R/huEQHwyqLJNEPTS0wsrqCqKg0BN531fryOd+/P7UIYnAmGmQ2thkGHxbRWQCLC4K2lUlGZX4kyNL3EYjiGQaeltdZHe62PTL7IbCjC7FKURCaLRlGo8Tqo97mo8TrecPmi4ZkgRwenqPY4uHdLKzrN5ceWi3nCw4dYXpxjXq5iqWimWC6TjITwpIcplcpkPZupqWtgW6sfXWSMZHCCl0+N8dwrx0nEImSzWQwmM//hox/goQ98GEtNB6fH5jl8+ABNlhIPPvIT6Ey2y993qcDC8SeRNTrcnbuoFPLnw132tZBXyFLKZ6gU81z8X7wkyyh6E4rOiOb8Z0VnPP+cCY1+9WtJXt/dfwRBuPncUgFweXmZz3zmM/zzP/8zmzZt4n/+z//JnXfeeaObdQkRAN/Ya703S6SyOTx2Cx31ARoD7jftoVlP5XKF+ZUY00sR5kIRiqUydouJBv+FMPjW55IJN048lWV4Nsj4/DKlcoV6n4uOBj8+h5VEJsfsUpTZUISVeApJkgi47NT7ndT6nFfcenB+Ocb+0yOrO3ds67jiupOqqpKYGSA+fYayycOCtp6+0QUWgkt0KAtY5AIl/yYKhtWpB11+Hfm504yNjfP9l04wPz9HMhalpOi5s3cLv/rxD1PbfS/DC1Fe2fc8jXaFu++9H8rF1Z66i8JdPrFCcn4Evd2LwbE6XK1o9Wvh7pKQp38t5Mkavfh7LQgCcIsFQJfLRaFQ4Ctf+Qqf/exnUZSb77dUEQCvTaWisrASY3A6uNZ703Z+ePjd3gu4XK6wEF6dMzh7PgzazMa1AhKHxSR+aN4iCqUSkwsrDM0sXXFP63Q2z2xoNQwGIwlUVcVjt6wVkVw8PzSWyvDSiWFK5TIP9HTgc165tzobWSQ8dABJ0WBq3sEzp+foG5qkQZ3FJaUJdGwnovGTKxTprHFSS5Dl8dP8cO9xzg6OkkolKVQUTDY7P71nG70dDSRLMlNT0zisZlo7OtEZLOdDnBFFtxrqsuF50kuT+LbsweytF712giC8JbdUAHzsscf4H//jf9DY2Hijm3JVIgC+dVfqvelsCOBzWt/14FUuV1gMx5kKhpkLRSmUStjMxvMFJC6cVhEGbwWqqhIMJxiaCTIXOr+nda2XjrrX9rTOF0rMr0SZXYquLSNkMxup97mo8zvx2C3kCiVePjVCOJ7iru4WmqqvvAZfMZMkdPYl8ollrNVtnJzLcG4qiJpawpQP4XT7qHLbiScSKIqM32pAlw9z/Nw4zxzqJ19SKVQUiircuX0bH3v0PiRbFftPjdDQ1MYje3ajed0vvKpaIXTmRUrZFIFtj6Foxb7ZgiBcu1sqAN4KRAB8+wqlEhPzKwzNBEmkszitr/XevP6H37uhXK6wGIkzHQwzu3Q+DJqM1AdcNPjduGwiDN4KrmVP61K5TDCcYGYpwmwoSr5YxKjXUet1UOUwMTEfZHJhma5qGx0+I5Vibq2AYnV4NgdqhWx0kUIyjNbiZiZvIVPREM+VyC9PobO4qO3eBbKWpUQeh8PBJo9EaOBVvvnt7xHJy5TQkErE8Pr8fPAD76ch4OPYdJyGti7ef8+Oy+YulnJpgn1Pobf78Wy8V/x9FAThmokAuM5EAHznVFVlMRxnaCbIfCiGVqvQWuOjo95/w3b6KFdWewZnghFmQhEKxRJWk2Ft0WkRBm9+r9/T2mrU0haw0+CxIJfza4GumMsQi0UIRyLEY1GKhQKSJBOr6FnMaGjxW9nR4kFnMK0VT1w8NJuLLRGb6KOosXAs6cZus2Ejw1Tf86AxQE0vLreXbK5AKpenwW3BnzzLj77/fzk5m0JndpKILqOWS9y5awcdjbUEi2ZqO3t5aOdWjPpLF4HOrMywPPAK7vY7sVS13aC7KwjCrUYEwHUmAuD6SmZyDM8sMTYXolgqU+Nz0FkfoMptv2GBq1ypEAwnmF4KM7O0Omfwo7u3XfedT4Rro6oqleL5ytjzoa60ViG7Wh2biMdYDoeJpbJIkoTTasLrdGCx2daCnEZnQtYZSZckgokC87EsE0sJZpfjVLls7LljA221V17SqJCKsnLuFZbjaU6mPbQ0NWJRSkwefxaTVibl6iIjmTDpdWTyBRRZprU0wnz/QZ4+PklRMVMul4iHQ7Q2VNPSsYG8uYqm7h28f8emy3Y/CY8cIROaJNDzKFrzlbeTEwRBuJgIgOvsZgiAlYrKX/3dt7DoFWp8Hh5++P03pB3rqVQuM7GwwvBMkGgyg81spLM+QHON54rLdLxbypUK0WQGj91yw9rwXlIpl86HukurYsv5DKWLhmTVSmXtHEmSkHUGNBdVx174XETL1EqKsaUE2UIFv9tOZ32AOp/rimsAprI5zozP89KJIdLZAk1Vbur8rrUikou3H6yUCoSHDzEyPsVA2sZdO+6AcpGRI8/gN6pYm+9gJK6QyuSQJIlCIU918iwWNc2LR/qZWFhBZ3KQSUbRU2BTVzspezst3dv4iQd2XLK7TaVcInjyGSRJJtDzsCgIEQThTYkAuM5uhgCYzub5uV/6NaLRKFarjS/8P79PY8BDwG17V3biuJ5UVSUUTTI0HWQmFEGjyLTUrE7uFzt83N7Cw4dIBScueU7WaC9d9uTC12tDsiYUnQFJeuO/9+VKhZmlCMMzS4SiCUwGPR11/tUFpvWX9+yms3meO3aOueUY1W47uUKRcqWCw2Kizu+izufEfX4LxOTcIAePHGUyrePRPQ+SK1YYOPQcfk2SDb33sqINMDC5QCSRoZhNYo+exeWtYmFqnL37X0XWW5C1OtLhBbqbfMhV3eirN/LJn3wEn8ux1qZCKkLw5LNYq9twtmx/R/f6hRdeIJvNYjQab7r91gVBWB8iAK6zmyEAAnzhC19gaXkFNDo+/IlfJZHJotdqqfe7aAy48bts17xN1s0qnc0zMhdidHaJXKFIldvBhoYANV6HmI93G8pFFykVshf15JmQNes/7B6OpxmeDTK5sIIKNAbcdNYH8Dgu7eUtlsq8cmaU+VCMre112EwGZkNR5pajFIolTAY9ded3IrGS4tkXX2I5XebDj7yPVEXPyUN7CahLdHT34mrfwfRSjLPj88xMj6NGpzF6m7BLWV556rvEEil0FheZ+DIBMzR2biZqauSDDz/E9s0b1/6+J+aGiI6fwLfpgSvuJHKtvvCFLxCLxXA4HHzta197J7dTEISblAiA6+xmCICqqvI7n/4NEqksLreXr/3pnxJJZJgOhpkKrpDK5jHotDT43TRWuW/IcivrqVyuMBUMMzQdJJxIYTEa6Gzw01LtQ6+7ccPDwq0tVygyNrfMyOxrW851NgRo8L+2aHmlotI3MsO5qQU66gJs39CAhMRSNLG2E0k6l0en1eCzGhgcOI22kuMnH9xF2uDnyJFDBArTtLe24eu+H0mjZyq4wsFX9rKwHKForsJh1BA6+Syj5/rR6I2Ui3l0ap7uje2ULQGq23v44COP4LBZUFWV5f59FFIRqrY9hqJ7e73iIgAKwu1PBMB1djMEwFIuzW/9h58mkUpjs5j58m//BzRGCxqDBcVgIVHWMh8rMhfNkS2WMOp15xc9duN1WG7pMLgcSzI0s8R0MIwkSTRXeehs8OO0mm9004Rb1JW2nHv9ouWjsyEOn5sg4LJz/9a2teVaVFUlksishsFQhGA4zvD4BHY5x0Nbm/A0beb4mX78mVFa66sIbH4QrclOKZ/h5L5/YzQuM1u0kckVUJdHGTvyHJSKVMoFZEVLS2Mt1X432KrZcvf72b6lG6VSYvHEk+jMTrybdr+tf88iAArC7U90j9yGFL0Ja3U7pfAyRrMRs7+ZUi5JMR0nszKLWipSDVQpKnG0BNMaBs5McvK0BqvZQmONj5b6GjwuJ/ItNmfQ67DidVjZ3lHPyGyIkdklRueW8Ltsbzi5XxCuRpYl6nyrRR4XFi0fml6kf2J+bcu51lovFpOel0+O8PSRAR7s7cBqMiBJEm67GbfdzNa2OhLpHGcbq3n6wAl+dHyS6rEgzpoW+ssN5KdmKRefJtB9PwZnFd133If3zEvk3B6GYgpnFYWqez0ETzxDOTxLpZBgcn6Z5QxsqS8z8tI/MT3czR33vp/q9l0s9+8lOT+ErXbDjb6FgiDchEQAvA1JkoSsaFB0RnRmB46mLZd8v1zMU8omKeVSOLNJarJJCtkkoXCM2XCEM32THDuqYjFoqPfYaAy4cLucaE02NAYrGuPqtlQ3c0+hUa9jS2st3c3Va5P7Xz61urdr+xtM7heEN2K3GLlzQxNb2+rWFi1/7ui5tUXLH7pjA/tPj/LUoX4e6GnH77Jdcr7NbODuza1Ueey8eOQM1nwQJTqGjJd9YSfz0QW6ln9A3eZ7qW7pxl63EXlhmEe3vJ8dG5s5cHaMk2YbE6cOEBs6gCmyiFqp8HK2zOYmHx2aUxz54TS2jrvZ6GsmNnkKg92Pzuq6QXdMEISblQiA70GKVo+i1aO3XbqtVTWwuVSkkEmwEAwxMb9aaTvSv4hZmSVgrFBj02DVy8iKgsZgQWO0ng+FVrTG1ceK3vSmVZfvFkWWaary0FTlWZ3cPxPkzPgcZ8bnaKxy01EfEEu4CG+ZTqOhsyFAR71/bcu5IwOTaDUKdX4ny7EUzx8fZFdXMy013svOb672Eutqo3/MwFZHhp7cEks11bwybeXY4jjB8I9R+gfxtW3DgZ6VwQNUbX+Uf//ANu7d3MqLzdXsPdDI+P7vY1gJYrPnOX4uzVxtPY9syFEZfIqDM404nW5KZ/fTuOMDyIr4hUcQhNeIAChcQtZoMdjcNNvcNLdvWNsBYzoYZjoYZiGbxapK1Ng1VBsklEqObHiOUi7FhemkkiyjMZgvC4YagxWNwXzD1ihz283ctamF3o76tcn94/PLeB1WOusD1Adct/wyOcK7S5Ikqjx2qjx2UtnVRctH50LkCyUy+QJPH+7nru5metrrL+sx72mrI57K0h+OcU+9j7qlAT7S5uR4vJdcYoHq/CKhgVcYNVTjjPYzFf8x9Vvvp8pt52cf2sGDvZ38YFMnP/rWE8wvjOGwFAhOZPjfywF+5qFe6sszhObm2bvoYkNxLzvu3SP+fguCsEYEQOENKbJMrddJrdfJzo3NLIRjTC2GmQxFGY2Vcdk8NAY6qPc5MCqV1aHl88PLpWySXCxIajGFWikDqz8wFb3pfDC0vtaLeP5rWbn+fyUNOi3dzdVsbKxibjnK0HSQV86MYhzW0V7no63Wj8lw+e4OgvBGLEYD2zoa2NJay9RimMHpRYZnlvjevj4Gp4P85H1bMRle28FDkiTu2dzKM0cGOB4s8eDGB0iNH6ZHn+SUJUAUO12GEKo2TczTQ3S8j1dflalYq6jx2KnzufgPH3qAR3ds4C//6//LgYOHMORimDIp/vY7Szz86Id4f32B+blpRo+GmI8XuPuue6j2OG7cTRIE4aYhAqBwzRRFXpsMXyqXmV+OMRUMc3p8nr6RGTx2Cw0BN42BRpwXbVWlqurqTg0XBcNiLkU+sUJ6aZJKufTaa+iNaM/PM7w4GGqNVmTN+oYyWZaoP7+LQyyVYXh6iYHJRc6Mz9Pgd9PZELjlq6KFd59GUWit9dFS42XHxhQHzoxxcGCCkdklHuztZHNrLU6rCQCtRuHB3g6eOtzP4YkoD255GO3IQbaWZziZ9nBarmWLEqJKE6VuUzfJyArZagdzsQIHzo4hSRJ+p41PfuqXuWdDDX/7owMsLwUx5sL86PvfYrL3Lv7jx/ZgO/s8S8NPsi+2QNXGu9m+seWG7astCMLNQQRA4W3RKAoN55eOKZbKzC1HmVoMc2p0lhPD03gdVhqr3DT43ZgMuvNDwpcvxbK6b2uO4kXhsJRNUUzHyYbnKBcLa8cqWv35UGhBY7i0B1HW6t9RUHNYTOzoaqKnvY6x+WWGZ4I8c6Qfl81MZ32ApirP2tpvgnAtJEnC57Tyk/f3cMeGRv5lfx8vnBhkaGaRxioPnQ1V1HmdmI167t/aznPHznFsbJFdmx5EN3WG3ol+TsRM9BFgmyZKJRFGI5XwZSbYdOdDZAtlZkNRZkMRzswnyNk6+akPmTk7OsvBw0eRczFOHd7LZ8eG+OJnf5vmuZcJx/pI9C3x1NwW2jZuZVNzDVqN2DZOEN6LRAC8DalqhVwsSDmvoqrXf2N4rUZZK7QolErMLkWZDoY5MTzD8cFpfC4rjQE39X73ZZW3kiStbuWlM4Ldd9m1L65YLmVTq8vZZJPkokHKhdzacbJGez4UXtpzqDFa31LFsk6rYWNjFRsaAsyvxBieWeJg/zgnhmdoq/XRUe/HfFHvZq5QJJnJ4XVY3+bdE94Lan1OfumD9/LiiSFmliKE42lePjl8yZZzd3U38+qZMRwWE13NPehtbpSBgxyZn+FwqZZdXgPEZ0gujKB3+HE299BR76ej3k+hWGJ+Jca5U5CvaHAEGtn7wnMkw4uUQot88f/5Mu9/+GEe62zAkY8RXz7E5KFpJqe30tu9gcaAW/R0C8J7jFgI+iLf+MY3+MY3vsHU1BQAXV1d/P7v/z6PPvroNV/j5lgIOsWvf+pnSadTOOx2/uirv4/Z14jO4nxX25EvlJgNRZhcDBOMxAEIuGw0BjzU+13veJeOSrl4STgsrs0/TFLOZ9eKUi6vWLashUXFYH7TiuV4KsuZ8TnOTS2SzuWxGg3YLEZQVfLF1eHrn9lzp+hJEd5UqVzm1TNjzC5Faanxoaqru9hc2HKuWCozF4qyu7eDWp+TYibBwtl9vDqyQslSw73NZkozx1FLRerv/9nLtntTK2UWTj5PMJogbGjm/3zrW5wb6EfNRDHKRar8Pn7q0QfpafGRW54mXNAQNbdgb9zEnV2tawumi4WgBeH2JwLgRX70ox+hKAptbW2oqso3v/lN/uzP/oyTJ0/S1dV1Tde4GQJgIp3jZ37hlyhnE9Q4jXzl1/49aqmAzmzH5GvC7GtAY3h3lz7J5ovMLEWYDoZZiiRAgmqPg4aAi3qfa23nhPWiVsqXBsNccm14+WoVy4rBQkHSk60opEoKiaJEIlMgns5SLJUpVyok0jmSmRyqulpVvLGhmq6mAG67VSwwLVwTVVU5OTpL/8Q8rbU+trTWMrkQZmQ2SDKTI5LMoNUo/MyDd+B2WKiUi4SGDrO3b4S01sXuTfXkzz2NrNHS8vCvozVdutZgKZ8h2Pc0WpMNd9dufvBvT/K//+EfmVtYwFBOYdFU2Lr9Tu548EO0q5OUUmHC2ElY22js2ERPWz2//+XfEwFQEG5zIgC+CZfLxZ/92Z/xi7/4i9d0/M0QAMuVCh/7xC+SSCbQGYz84m/+JzrcGjxygkJ0gUq5jMHuw+RrxOStR9Hq3/yi6yibLzAdjDAVDBOKJpBlmWqPnaaAhxqfA53m+s5MKJVKxKJhYpEwiViYZDxKNhUjn04gFTNIagVZltHrtBjMVkwWB2a7E5vdjdXhQmu0sJKpMDIxycrMKIZSnPd95D9cUuEpCG9mbD7E4YFJvA4LD2ztQKtRmF+JMjCxwKvnCzw+eNdmupurMel1xOeHeW7/QSJFHbt72in2/xidxUHTQ7+M1njpFIRcLETozAtYazpwtmxjenqa//4/vsHBvrMUkitYygmqauup2fUT+G16NmrmMWshqvGTd7fz/A+/j1Qu4HQ6RQAUhNuUmAN4FeVyme9+97uk02l27dp11ePy+Tz5fH7tcSqVejea94Zy+SKSIlEqlzEpCkgKJ+azGHQmOmp3UG8qUYzOEh07RnT8OEZnFSZ/E0ZXzbuyDItRr6OzIUBnQ4B0Ls90MMxUMMIrZ0bXlp1pCLip9TnQKG9/WLVQKpFI5YilM2uf46ksqUweldXfeww6I3aHC0eNEbvFiN1kxKJT0VbylC+qWC5lk5SWR1ieSVPIxCimY3gqZVwaI1mzF51UAkQAFK5da40Pq9HAvpMjPH24nwe3daxV2W9qreWfnz/K04f7GZoO0hhw09lQzQce/QDPvfA8Lxzt574Nd1OYfpWZl79F7V0fvWRhd4PDh7Oll8jYCXRWNw0NjfznP/wq/+cfvsX3/u1psokVwsF50s/9A7pd7+MFRxPmcpJO4zyWzArBuUm0RhtO57s7bUQQhHeP6AF8nbNnz7Jr1y5yuRwWi4V/+qd/4rHHHrvq8X/wB3/AV7/61cuev5E9gAC/+7u/y+LSMnlV5oM/+0sY9Vr0Wi2JdA5FkWiv89Ne5YDkIumlKfLJMLJGi8lTh8nbiMHpf9d380hlc0wtRpgKrhBJpNEoCrVeJ41Vbmo8jqtW4WbzRRLpLLFUlng6QzyVI57Oksm9FszNBv1qwLMYsZuNOCxGbGYjBt2b745QLmTJLE+TWpoiH1tCrZTRWhxoTXZkjY5yIYt/y0NiEr3wtiTSOV7qGyJXKHL/1naq3KuFW6FokqcP92PUa9FqFJKZHE6ridaAg7H+40wFI+yo0WLJzGJ01+Db9CBmX+PadVVVJTx8iOzKDP6t70dnWd0O7tDhw3zt639FankOfTlFRVLo3dJF9eb7WIiX0BXCHHnuh+hMdjq6NoseQEG4TYkA+DqFQoGZmRni8Tjf+973+F//63/x8ssvs3Hjxise//oewFOnTnH//fff8AD4+d/5DPFkGqfTye/8/36PM+PzLKzEMOi1mPQ6EukcFVWlpdpLV1M1RqlAOjRFZnmKYiaJojNg9jVg8jWhs7je9XCTSOeYCq4wHQwTTWbQKAp+pxWHxYRepyGZyRNPZ4mnsuSLRWC1othqMmA3rwa9CyHPbja+5QKNSqlIJjxLJjRFLhoEwOCqxuxrxOiuWdtW64UXXiCbzWI0GtmzZ8/63gThPaNQLPHyqRGCkQQ7NzbTVrdaET8+v8yBs2P0ttfjspkZmgkyH4qhkaEYmyO8HOIORwKfVY/O5sbZ1Iutvmvt32ulXGLp1PNUygUCPQ+jaFfX/lsKhfjyf/4TpvuPYNRrKVRkAg4jex56iKKtjr/9myew6jV0d28SAVAQblNiCPh1dDodra2tAGzbto1jx47xF3/xFzzxxBNXPF6v16PXvzb0Z7Hc+H1ly4UsycUxMtk8BjWDqbDCA5saiGXrODMxx1woikGnw2LSM70UZmwuREPATVdTC1UNmygkw2SWp0iHpknMDaM12TD7GjH5Gi+ba7TeKhWVZGa1B09Cwmk1kyuUmFmK0D8xT75YQqsoBDx2mgJu2uv9uKwm7BYjVpPhHW11pVbKZCMLZEJTZMJzlMslNBYv+trNKPZqKrKGWKnCcihBqVymWC7zj9/9FxKxOC0NNSIACm+bTqvhwW2dHBuc4tDAOIlMlp62elpqvMRTWU6OzLK7t4MHeztJZnKMzC4xIsuUUxVeWcywPRGk1ewkNnWaYjaBu30HkqwgKxq8XfcS7HuG8OBBvJseQJJk/D4f//3rf8qff/2/sf/J72BxeEhj4Dv/8kN279yK327EYHbc6NsiCMJ1JALgm6hUKpf08N0SZA1mTx255dW18sJDhwDQmqxsdQTY4HQxEikys5xCp9Pgc1oJxRJMHVqh2uOgu7kaf/M2HM295KJLpEOTJGbPEZs6g97mWe0Z9Dai6N7+TgKlcplEOkc8lV3ryYunsyQyOSqVCgA6jQabxUiNx8HGxipsJgNIEIokmQlFiKezjMws0RBwodUoGHU6CmqJYqlCsVReC2nFUplSqfLa1+XVx4VymVKxSDkVphKfR00uoZYKFDUmsnoveUMANWeAlSQwfNl7UBSFSDxNNptDdKQL75Qiy+zY2ITNbOTE0DSJdI57NrfS015HPJ3llTOjPLKjG6fVtLbl3ORCAy8eOc3xsSOkTh6nbuMdVJamKOfSeLruRdEa0BgseDbcTejsXuKTp3E09wCg1Wj4wu98huYaN9/9h/9NRWvAU9PCyyeGmZuepLquAaoCN/iuCIJwvYgAeJEvfvGLPProo9TX15NMJvmnf/on9u3bx7PPPnujm/aWfPv/fofDJ06j0Wjw+/0knV34rHo0pSS52BLFzCiNQJ3NzEJGYTYoo5jdVLntpDJ5njt6Dq/DSndzNbXeAEZXFZVyiWx4jnRoiuh4H9HxPgzOAGZfE0ZP7dqQ6OsViqW1gBdLZdfm6qUyOcpqhUpFRafVYNbrMBn01PucGPRaDDotiiyvhrhSZXV5jESa4vnwplUUFFlmMRzn7MQc2XwRjaLgOD/0azLoLhu21igKWo2CRpbQVTLosstoMyH05TyywYymtgOtqxad2YlWI68eqyhoL5x3/jmtsvq8LEuceeH7xGIx5HfQ8ygIF0iStPbLzv7TozxzZIAHt3Wc3zO4n719wzy6sxujXotGUWir89Na+xCvnqpjYO93KfYfYy7Qgc+SJJmM07DtIbQmOwZnFY6mrUQnTqKzujF564HV7RA/+tM/R5VF4dvf/yGhZApPoJaRiWmm55eork/e4DsiCML1IgLgRUKhED//8z/P4uIidrudzZs38+yzz/LQQw/d6Ka9JdPT06TTafL5PPF4nL/8798AwOFwUF9fT5Xfi99hxKvX0GjJ4iPJcmyS5aCCanRR5a0lVyqxt294dVeCpmrq/S60zlrMtiq02Syp0BTR5WmWZl+kjETZ5CWtcRPHRDxdIJlZ7c3L5YtUVJVKRV0NU9rXApVeq0Gv06CqKqlcntRFRRsSEhqNfHkAUxSMei1WkwG/20Z3czWKLJPKFliJJQlGE+QLJUCiwe+mqdqD32VFq2go51NkQlOkQ1MUMwkUpwFTey9mbyM6m0cUcQg3jVqfk0d3dvHSiWGeOtTP7t4Odvd28NShfl4+NcJDd2xYm+4gSRL39mzA6/gUZ5/9P1Ri80SoYjk+w8zCP9DQu4emlg6stRsopCKEhw+hMdnQnR/ilWWZux/5CEYpz7NHhzg9voSiaMjlcsRisRt3EwRBuK5EALzI3/7t397oJrxjqqqiKAq5QpFyuYKmUiFfLFGpqGSCIeYWl6hUKmuhTKvT43E78TjMOPQKemmO2NRZJEWhoHdzDhuv9vWj6Ax4HDasRj2FUplcoUi+qKWUs6HPLmMpncKo5qnIWgpGH1ircNk82AIGbCYjNosBvXa1mlF7Idhd6E3TyGgVzSXPaxT5bQUyVVVZjiWZXAwzsxRhcmEJXSmNV0nj06RxmnWYvPU4W7ZhcAbe9UpnQbhWTquZx3Z1s/fkCM8ePcc9m1p4oKeD546d48jAJLu6my/5N9LZVIe8+yMM7v8+UqVIvc9JOJlj4JUf0j/cRfOGHpobeilm4qyc20+g5xFkjQ4ARWek++5HUNQS3vp2hofO4fHYqK2tvVFvXxCE60wEwNuMJEl86ld+gx88/wrxWAxVUShbA+RTceLhZQqF1V42idXf/GUpSzgWZ1SSkGUJGQmJMjqNgkmv4LdpcTudVKxeglErQa0Bh8OJy+OjscqN09qMy2rCYTVhlQtIyQVyK7OU8hk0xghmbyNmfx1a0/Xfk/jC+/dYDZjyFRqKMeZzCyzky8yWTEzrfbgMXhplL01aOwZEj59wczPqdbz/jg0cPDvBy6dG6GmrZ+fGJg72j+OwGtnYWH3J8e0dnZDdzdCJ/cxlNHT5bRQrdpbDk4z0JTlja6LBU4s3cRbN0EG8XfevhUijq4amrm0omtP8W2MTWkURveKCcBsTAfA2ZNNW6LQXCWsMaOwB7nnwYWRJotpjx2PWIuVTLC4uMD0zw/jEFKHlZQqlMoViiUKxTLFUQqXEckJldLFEuTiPjhIBpwW7w07C4iTn81HT1UZ3fTcuvx2Nybb6w8JfjdrSSz4WIr08RXJhmPhMPzqLC7O/EZO3AY3etO7vebWCd550aJpseB7UCnq7l46tO9nqqQdFy1IkweTiCsPTi5wdm8VuMdLgd1Lvc+IwGwB1tZhDVUGtrC4VrVYuek5dXUD6wteqSimXppzPoKrvTsAV3ns0isK9W1qxWwycHJ2hudrLhoYqTgzNYDMbqfVeulhz25adVFLLjIxPMpBpY6MpSp3fRVUxR1xZYipRzUzSiW/pJDV5mfat96ytselo6iEXC1GlL6CaPFdqjiAItwkRAG9DepMFZ6CR4tw4FinMI3Ulpos2RueDnImnUCsqFr2M2V/Pjrpm8tkMyXiUTCJGIh4lFg4Tj8XQKBKKLFGpWEhmciQyWWKZEEZlmZmRQfa+8Dw6rYZGn53uBg+tTfU0NjYTqGtEozMgKxpM3noKyTC56CLxmbOgqmjNdvQ27+r6gooG1MpaoLo0hKmo6mpF8GoQA3jtWFWtUMwkKCTDFNMxKuUiGp1pbZHmQjJCIRkmMnZ87ZxaoFqrEiqUWZgvc3i4zCtlFZtBpsaqUG3TYNFd+7BwenmaTCqNyyeqJYXrR5IktrTWYTMZOdg/jstmxuey8srp1yqDXztWpvWOh6jk/oWRlSXOGdrp1iwgy0WclQgBh45C61bGB1XGTh3kXDBDc3sX7XU+zAY9ns670cp/TTYbB0QIFITblQiAt6FMJs3y/ATxZJpiDkb2fw9V1uA3uHEqZmJFDeGUTFaS8Zg0NLn01Leb0CpmoAZYrd5dWomyEAqzGAqzuBxhIZQllc2TzRVRVRWnXksZldH5MEMzyxgPnsWilLDoFAIeJzXVAWpra6ivb6Smugq9w08hFaGYDJOYPYcky+jMLvR2L1qLC1lRQJKRASQZJF6boyetzglUgXIuRT4eIhdfplLMo9GbMTVuxuisQmOwgiSt9kZe+Ix0/vzz1wV8kky3JFFWVYLRDNPLCRYiaWZSFVwWIw0+O/V+JxaDfu1aF19POt8+y78dpxyLr76GIFxnTdUezEY9+04OI52ftrG3b5jHdnVfsquNRm+isecBOP4cg8kV+l3NbLEsUU4skQvPoS/luGfX/QTdeuZnxhkeNdA/MU+930VnfQCTp45y5hZb/koQhLdEBMDbkFZnIKf3ItssmJwO6h/6ZeTlYeRCCmtVC/aGTaBomQ8nGF+MMrsSZz4vU+930lLtpcptR5JlWi8EH1aDT6VSIRQKMTk5xZFT/ZzqH2QpuIiDMlqNQiqbJ1EsUdBAKQdL40scH54FDqPR6qmuqaOhpY2m9o3Udlbj1pchFaKQikIqjNFTj9nXhN7uvWzuUTGTuGynEnfHLsy+RnRW9zuaq2TzQ3vn6tqEc6EY08Ewg0tR+hfTeBxWGgMuGgJuzIbL9/pVNDpkjVbMlRLeNT6nlcd2dfPSiWGiqQzJYo59Jy+tDAYwuWsJtGxCmRrkTDrFKbmG7dUu8ovDZMPzLJ16Fk/HLrRqnrpynJS3heH5MM8eHWBqJYNBURE7AQvC7UsEwNuQTqejpbGOWCyGw+Ggo6sHVd1CamGU2OQpls/tx9W6neb6eprr68jkCkwsrDA2H+KlU2OYDXpaary01Hixml5b7FmWZQKBAIFAgF27dlIuVxidW+Lo2RFmZmdQihlS0TCj4xOEVlYw6qw4bAYMcplKIcvc3AwzU2O88uLTKBo9it6I119NbV0dPrsBt34On/0kHp8Pi68JvcNPMRMnE5oin4ys7VXsbLnjuuxVrFEUGqvcNFa5KZRKzIdiTAZX6BuZ5fjQND6njcaAm4aAC6Net66vLQhvhcVo4JGdXew/NcrYXIih6SA2s4FdXZdWBjube8nHQ/RkI5zMmzgaNnJX606yUyfIxUIsn92LvXEzqeA4nuwkHXffQzCS4KlvK1RKhRv4DgVBuN5EALwNVSoqi+E4pXwOs6UMrA6lWms6MLpriYwdY3ngldUw1bodk8FEd3M1XU1VrMRTjM0tMzi9yJnxOfwuG601vtV1AF+3n66iyHQ2VNFW52dyYYWzE/MkMzke/MBPYjVqOHtulJGxcfKpGFIhTTYZo1wqUC5kKeezlLIp5ifOsTAxiKzVo+iMIIGmUsBjqOCxaKkOeGjp3EL7He/DXtuOrLw7f2V1Gg1N1R6aqj0UiiVmQhGmgxGODU1xbHAKv8tGQ8BNqVx+V9ojCK+n02h4sLcTm9mwWiV8cgSb2UB3U83aMZKs4Om8h9LJp9nlK3AkrOHVqSz3b3wQzcRR0qEJIqNHMfkaSS/PoLOeo7q+mwa/W6wBKAi3OREAb0PZfIFUJk80FieeK/OD/SfxOa34nTZ8ThuejfeRDc8SHTvO4vEf42zuwRxoRZIkvA4rXoeVOzY0MLMUYWxudTP6o4MKDQE3rTU+vA7LJb0MiizTWuujudrLdDDMmYl55ldi1Dc2smP7FpbCCeZXYhi1Ch6TglLKsDA/z+zsLDNTE6TiEYqpKNnIPOViDgmJlM7IrM7Eqfkc6tFJlH/8N2pr62hq30Dzhi00NrVQW1t7yT7M14tOq6G1xkdrjY9cocjsUpSp4ApHz00yPLOEWspjsV7fPZIF4UpkWeLODU3YzUb+9ZXTfH/vScwGPU1VrxVvaM12nC3bCI8c5YHWneyfSLB3YJH39TyAzmwnNnWW5NwgWouT2MRJdBbXjXtDgiC8a0QAvA0ZdTJVmgRmC5jseqrdVkKxDBPzK6ioGPU6fE4rXt8d6BOTrAwfIb00hav9zrX1+jSKQnO1l+ZqL8lMjomFZcbnlxmbC2EzG2mtWf2eyfDaUKgsSzRVe2iscjOzFOHM+Dx9wzP4nDa2dzSyEk8yHYyg1xnZsP0uHt0tUYzOMjd2jvnFIKFEgaVYhsVQmGhkhXIhS6VcWF2ZsKIyNTXF1MQ4+55/BsVgRmu0UlXbSENjI7W1tdTX11NXV4fNZrtu99ag09JW56Otzkc2X2T/D+0sLa9cMvdKEN5tHfUBfvYhPd98+iD/+8kD/Mq/u49a32sz+MyBVrLRIJnpE+zZ8hB7z87w/IlR9mzfjs/mJTz4KrnIIrJGS+jMS5TF8K8g3PZEALwNqeUyisGMmgwipctUR0/Q7KpG0+AnKVtZSRYJxZL0haJUKjqkYjWmUBDH5A9pbNtIQ+cWNJrXKgqtJgNbWuvY3FJLMJxgbGGZ02NznByZpdrroLXGS63PecnWVA0BN/V+F/PLMc5MzHN8eAqPzUxvg434wjgzB15hrlLE4/XR2N5L14MtlywWnUwmmZmZZnL4HJOjg0xNjLG0tEhFrVAu5SknchQSK0xEFpgdG0BrtCLrjEiShM1mo66ujvr6+rVg6PVeXljyThn1Wlw2M3KlKIpAhBuuzufkV3/iPv76X1/miX97hV/9d/euhUBJknC13Umw7ykyk8d56I77ePHEMM8dG2TPtg1U3+li6cyLpIPjJOeHSAfHUc3+G/yOBEG4nkQAvA0pOgNGZxUmDFjMBmx1XWTDc2SWjyFJEtVWNy1Vteg2tBIrKizHUgTDPqYnxzh3oA/d8XPUNXdQXVWN32nD47Cg1azuClDlsVPlsVPY0MjkYpjxhWVePjWCXqulqXp1iNhlMwOrP3RqfU58Zom5sXNMjhxiYjCK3mShoa2bvNHH2EqO0akiLYUwXU16bObVohOr1UpXVzddXd1r7yuXzTA53M/EUD9T48NMTY6zsBgkG54jK8soWgM6s5NyMUc8HmdgYGDtXL1eT21t7SXBsKamBo1G/BMQbh9eh5Vf/8n7+cYPXuZ//fhVfu7hnbTUeAFQtHrcnXcTOv0C+cVh3n/HRl48McTzx8/xYG8nNTt/itCZl4iMHiUXWaCUK+F0iuFgQbhdiZ9+tzFJklB0RuwN3dgbuikXsmTD82Qj8yRmzlKZPIXGaKHWVUN7Ww3a3k6WQkHGzx4lODfAyeAissWLrCi4bWZ8Tht+lxWfw4Zep6Gj3k9HvZ9YKsPY3DKTiysMTQdx2cw0+Wz45CSl6ByFVAStRsvWzZvJ6jwMLhc4FUlgK6v0djSQL5QYmgkyNheiIeCmu7l6LURezGA0sWHrnWzYeicAlXKRbDTE9Eg/4wN9TIyeY34hyOJikCIatGYHeqsbRWckn88zPj7O+Pj42vVkWaaqqoq6urpLgqHJtP47lQjCu8Vjt/JLH7qHv3vyIN9+8Rgf2LWJLa21SJKEwe7D3rCJ+PRZDM4Ae7ZvYG/fMC+cGGJ3TztV2x5Fb/cif/vF80tACYJwuxIB8D1E0RmxVLViqWpFrZTJxYKrgXBlluT8MLJGi9FZRc+WLZRyKRKz50hXFim6O4iXDUwFw5ybWgDAYTGdLyqx4nNZ2d7ZwOYmH5OjwwyOjrC3P4qERH21jw3tPTQ1t6Oc722rbYLlWJKzE/McOTeJxWhgc3MtFVSGpoP8+OAZqj0ONjXX4HNarzq8KitazJ4aNnpq2HjXw1RKBbLRRRKzg8wNn2R6YozFcIyVdJJITiZd0aIxWJDk1WrmSqXC/Pw88/PzHD58eO26brd7LRReCIYOh0MM8wq3jCq3g489uI1/3X+KF08MkczkuKu7BUWRsdV3kYsGCQ8dIND7GO/b1snLp0Z4qW+Y+7a0Ud++A3vDJpIZMQ9QEG5nIgC+R0mygtFVg9FVg9qqrlXhZsNzhIcPI0kSGoMFUzpOefEoVTUd7Nq1jVxFYimSJBRNshiJMzw9jzYfwVaKYpdSmPVaemrruLt3E0tFIxPBKAcn4pxaOENztYfWGi82sxGvw8qDvZ1EEmnOjs9zbGgKk0HHhsYqdBqFwelFnj06gNdhpbu5mlqv800DmKzRYfY2YPY2UNX7CFuzCeLTZ0nMDpJdmSWVShHOlIkU9USLOkKJLMuR5OqevxcJh8OEw2FOnTq19pzZbL4kFNbV1eH3+6lUVMqV8/sFC8JNpK3Wz4PbOjl4dnx1iaZsnt097Rj1OtyddxHse4rI6FE8G+7h/p52Xj0zxsunRrhnUytakx25ELvRb0EQhOtIBEABSZLQWV3orC7sDZso5TPkIvNkw/OU8hkq2TjLZ18iOn4Md+fdNDZtpdpYJK0JkqjMkFLTZPQmolIDUxUrlbAOYyqFzyHRWutFp9GwHEsxMrNE/8Q8PqeN1hov9QEXLpuZ+3vaiaUynJ1Y4MTQNAa9lo2NAUwGPcMzS+ztG8ZhWV2rsCHgvqTi9kIAW/son39crlCuQNnThda1ATWfo7g4iXlxFCm6hL2Qp8FvQdXUEy9piSTzhONpliMxQktBCvnC+f2GVSqqiqqG6R+bPv8cVFQVWVGYnRxDlhV6ujpu4J+gIFxZT1s98VSWicVlVmJJnj7cz+7eTpxWM672HSwPvELaOYalqo17N7dxaGCcV8+MEU1mxOaGgnCbEwFQuIxGb8JS1Yalqo1KuUQ+tkQqOMHK4CtMvfT3q3OJHH6Mnjoc9Zup9rcg6S2UKxWyhQKhSIqlaJylaJJz04uUyxUUWcJmNiFLEpMLy/RPzKPIMl6HhYDbjtWkRyPL+F02phbDfG/fSRRZIuCyY9BrGZ5Z4tDABDqNgtdhxWE1oqq8xZ43GfQdSN5mStk4peQKlVQCSc1g1ck4a5xsaKpCb7qTXAkS6RzReIKVUIjl0CKZdBoJ6fy2wBKyJDFfKVHMZZidnb1ufx6C8HbJssQ9W1pJZfOkcwUkSeKZI/3cu6WNWm891uo2ImMn0Nm86MwO7upuQSMrfHs5ikUn43A4bvRbEAThOhEB8DZUrlTIFYrk8kXS2TwLK7FLe8fWessqV3n+ose5BCQWkRKLUNBR1FSTy6RRF1NoVyaRJkOU9EcpGtwU9W4q2ksLKIw6LZl8gVQ2TzCSJJPLr4U2VZKYXoogSxJ2i5Eqtw2/00ZTtYc6v4OF5TjBaAKtolDvd7G1rY7FSJxQJEE0odJU7aGl2otBr0WR5dUPRXrt6ys8lmVpbShZVVeHvtOhKdKhydU9idUyijaLyuowuax40Ns3YnD4KcgmFiNJ5ubmmJ2dZXZ2lsH+01CWr+vag4LwTug0Gh7c1sFTh/rRazVYTQb2nhhm+4YG2pt6yMdDq/MBex5BkhXu3NiIz2mlUszf6KYLgnAdiQB4GyoUS4zNhcikU5jSBV44PnjJ9yUkZEVGkS8KRxc/LufRZENoU0H0hSSyokP2VKM4a1AsHjSSRDY0QW5pHJ1UwWyzIxVTSGoCrd6G3lWF3lWDwe5Do2guCWESErF0hlA0QSiaJBhOEEmmiSYzzC/HiCQyNFa56Wmr4/13dJEvlhiYWmBkNsRyLEl3YzV1d3at7V18amyW9jo/GxqqLlmU+lpcPPTtaN5KPhYiHZokszJLpVRA1uqQtXoK6SjJxTEqxRwGVDp0Jja2G5E3dBEcOUFwJUptfd16/hEKwrqyGA080NPOc8cGaary0NkY4NjgFIlUji0ddxM69SzRiT5crXcgSRI+p01sBScItzkRAG9DOq1mdQePhA6Hw8lP3d+DIitrQUyWpMsKKsrFPJnlGTKhKXLxEJJewVTdisnXiNFVvVY5u6a9gXxiE5HRIxTTcSyNm9HbPGuVxcXYDGWNdrXQxF2D1lmFcn5xaY/dgsduYWPjai9cPJ0lFE0yvxJjeDrIuckFjg1OYTbqaa3xsqm5hvffsYHZpShDM0EGZ4K01/p4eEcXM0sRhmeCDE4Haan20tVUvbaW4JtRVZVKMUcpl6aUT1POpZEULXqbl2xkgfTSJIVUBFTQGEwoWiOyVk8pm6CQKCApGmS1jN1ux2K+fNkaQbiZ+Jw2dnY1cfDsONs7G9nZ1cyRc5MkMjl6G7aQnOzD4KzC5K690U0VBOFdIALgbUiRZUwGHYWcFr1Og8V45UBUKRfJhufJhKbIRhdBVTE4A7g7d2Fy1yFftBvIlehtHgI9j5CYGyI+fZZsZB5325242nZQSIbPVxUvkA5NIUkSersXo7sWo6sGrWl1yFSSJBwWEw6LifY6P7t7Okhlc0zML3NmfIHR+RCnx+Yw6LTUeB20VHsplcsMTC4wNBOkrdbHw3duZGElzuD0pWsJOsx6yvk0pVxm9XM+TTmfoZQ7/zmfRq2UgdUwKMsyis6ErDNictdgqWpBkhUKqRiF5AqFdBxZVjA4fOjsXiRkNMbDqNHo5QFZEG5CrTU+YsksJ4ameXBbJ3u2b+DlkyO8mtOy2RogMnwI3bYP3OhmCoLwLhAB8D3mwvp/6dAU2ZU5KuUSepsHZ3MPJm8Dis74lq4nyQr2+i5Mnjoio0dYOvMSlkAz9qat2Ou7sdVupJhLkg3PkwvPERk5QqVSRmuwoLf70Nv96Cz2822roKoVpEqFZkuFpk0uKl02pkNxzkyHmQwFmZmdRStLWPUyOjVPZPQ4A0qeOruejQ4t8VSW4HSSF1/JYdWquE0ajOdzrCxrkDRaZEWDpGiQZM35r7VIipYKEmo+A/kMV1oBTdHqKabjRMdPUi5mkWQN+cQKlVIFxDIwwi2it72eRDrL/tMjPLqzm8d2dfPiiSGOhM1slCNohw6wZ8/7yOXyGI1v7f8DQRBuHSIA3obUSpn7d24jk81gMOhJh6YppCJkVmbJRReoFPMoehMGRwCT3YeiNVDKZ0nMDqyFMLVSgfOfVbUClQqqWn7t+cu+V0GtlClm4iwc+zELx3+M0VmN1myHixeUkGQqxQzpxAqx6bOo5SKSrEFjtKIxWtEarUiysnrtSgm1UsJcLrFDX2arr8RcosR0vEwkUiGnlrErRWQ1w1y8xNSMBovZjM/jRNZVsZKDhaKCw2CjvdZHlduKrCggyUiyjHT+8+pjBUmS4cLzF38ty0iScsnjYiZBZmUW3bPnyJQTq/dBEG4BFyqDnzk8wN6+YR7d2c2jO7t5+dQIfYsu2jJz3Ll5G/aG7je/mCAItywRAG9DlVKBLleRcqFMMb3A5Av/a7WoQdGiNTvQWd0oehOVYp5sZP6ikCODpLz29cUBSKMFWX/+8fkQdUmAeu1xpVQkOT9ENraErNHjaNyCxmheuz6ShFouUirkKMRCZCJz5KKLFFMRCollZK0eRWdEY7Sh6FarihW9EaveTEBvYofeTLKsMB0pMBPJUqiAQW8gVygwsRLnZDSL1WhYW15mJZNnYaZETUZlS1vgsrUE3w5FZ8Tg8GOpaqOgDSEr4p+ScOu4UBn85MF+Xj41wp7tG9izfQNHBgz0DyRJHT/O/XYfBofvRjdVEITrRPzUug1VyiUUnYFKuYjRXY27YycmXyMGZ+B879b1XeJVrZQxeRtILY0THT1OsO/p1UIQs4NyIUMpl1mbewcgKwpmXyOSrKFczFLKpijlkqjlIrLJhsXfiNFTj97mWQ2RgBOoB8rlCnPLUcbml1lYjtFY5UGrKESSKRLpDIViCZNBR7kscWZinhMjM3jsZra21bG9owGL6doKRq5GkiQUrf4dXUMQbgSL0cDu3tXK4KPnptjZ1cSu7mZsZj2vvPIytqEJdu0UAVAQblciAN6GNAYzRncNLnctRmfVuhYorFbO5s8XVFxcYJGhfKGatpBbO17W6inl0yRmz6G1OLE3bMZS1Y7GYEajN6MYTMga/WWhtFIukosukg3Pkw5Nk5gbRtHqMbiqMbprMDqrkTVaFEWmIeCmIeAmncszubDC2NwyJr0eRVYolsuUy2WqPQ562uuJpzKcmVjgRwfO8PThfhoCbjY111DjdeB32jAbRZgT3jt8Ths7NzZxsH8ch9XIhoYquptrsZsfxW233OjmCYJwHYkAeBuSJBl3+863da5aKV8a5s5Xy5ZzGUr51GW9d5KsoDGY0OgtaC0OjO4aFL3pfLgzo9GbkGSFfHyZ8OgRcpF5dGYHxuq2NwymsqLF5KnH5KlHVdXVquLwHNnIPOmlSSRZRm/3YXKv7mesMVoxG/R0N9fQ1VTNcizJ2PwyU4srrMTTDM8sMRUM01rr4+MP3YkkwdFz0wxOL/LcsXOYDXq8DgsOiwmfy4bfacXntGI3G697j6kg3EittT5iqSzHB6exmYzUeB3U+V03ulmCIFxnIgC+h6iqSqWUPx/mLvTgXb33DkDRGdbCnNFcg6I3r66Jp1/twZO1l/feXYne7qWq91HiMwMkZgfIrEzjatt5TXOMJElCb/Ogt3lwNG2llEuRDa/uVRydOLm6lZXZfn7NwVp0Njc+pw2f08YdnY1ML4UZn1tmZG6JY4NTnBqdpbXGy67uZh66o5OhmSUGJudJpFe3y1qKJJhaXEFVVQw6Lb7zYdDntOGympFlEQiF28vrK4MdFtObnyQIwi1NBMDbUKVcJLM887reu9XAVylf3nun6M1ozXYMrurzQ7MmFINlrfduvUiygqNxM2ZvA+GRwyydfh5LVSvO5h5kzbXv4qExWLDWdGCt6aBSOj9UHJknFRwnPnsORWfA6KrG6KrB4KyitcZHa42PXd0tq7uHjM4yMLnImfE5Gvwe7u9p42O7tzM6F2JwOkg2V6DG6yDgtpMvlghFE5wcmaVcqaBRlLVA6HfaqFTE8i/Cre9KlcEG3RuvAyoIwq1NUlWxgNl66uvrY9u2bZw4cYLe3t4b0oZyMcfcwe+f770zXdRrZ3lbvXfXg6qqpBZHiU2eQlI0uFq3Y/LUv8NrVigkXhsqLqTjSLKMwe5fnTforkFjsFCpqAQjcY4PTXNydJZkOovfbefu7mZ62uuZWYoyMLlAKpuj2uNgU3MNbpuZSDLNUiRJKJYkFE1QLJX5zt/9DyrFPBtbGvjzP/+zdbo7gnBjJDM5njrUj8NqZM/2De+4Wl4QhJuXCIDr7GYIgJVKheT8MHqrG63ZflNXqZbyGaKjR8mE5zF56nC2bkejX5/hp1I2STYyTyY8Tz4eQq1U0Fkcrw0VW90UimX6RmY42D/OwkoMs0HPto56dnW3kM7m6Z+cJ5rM4HVY6f7/2LvvsKbO9g/g35MAISQh7CHKEBQUN05EUUSpu0urvlarVq0db7VvbWuto1prbdWf1ra2tXXUaofauq11ICruPVFB9p5hBAJJnt8flJQYQGYSyP25Lq6ac55zcnN4mnPnOc9o2wqtHW3BcRzUaobcAjnef/89ZGZnw9fLHZ9//nmjxE2IIaXn5OPYlfvw92qF7u1ojWtCWip6BNwCqUuLIYu7UT5pM8rn0LOwsoG5SApzkQ3MraQwF0nB4xv+EY+ZwAoO/sEozkpETvRlpF45CBuv7hC7+jS4ddJMKIHEzQ8SNz+olWUozk1BcXYyClOjIUu4q3lU3NXFDX06DEJsWh5OXX+Ac3dicP7uY7Rr7YS+/m0hFJjjYWIGwq89gI3YCp3atoKHiz3spSLYS8XgMyUNFCEthrOdNQZ1aw8HGxoFTEhLRglgC2RmKUKb/uOhLC5EqTwPZUV5KCuSoTgnGQXJD1DR6GsmFMPC6p+ksCIxtLLW+7q2HMfBytEdAhtn5MVeR86jSyjKiIV9+z4wt5I2ynvwzMwhcvSAyNEDjKmhyM8qH0iSk4zCtMfgeHxIbJwwvkdrFPduj8j7ybj9OBnbj16As501uvm0Qfd2bZCRW4Czt6Jx41EiOnq2glpNK4CQlqe1k62hQyCENDFKAFsojsf/p8VPCjh6aLarVUoo5fn/JIYylBXloSgjFsoSeflxHAczK+tKiaEU5lY2MBOKNZMwNxW+uQD27ftC5OSFnEcXkXr1cPl6wm06Nu5gFI4HS6kTLKVOsG3bHWXFBf+MKk5CbvQVMMbQV2yLPn2d8CCX4Vq8DKeuP4BYaAl3Fzt4udqjuFSJy1FxeJiYAXNOBYm1daPFRwghhDQ1SgBNDI9vBguJHSwk2vN8qZWl5QmhPA+lReWthiXJD6AqUwD4J6H859GxRaXEkC+wavTHn5Y2znDpMQL5CXcgi78NeWY87Nr1gUDq2KjvU8FcKIF5az9Yt/aDWlmK4pwUFOckoyQnHm3LSuHhZoY0hQAPcoqQkKpEcmYubMRWsLMWgc/jIS+/oEniIoQQQpoKJYAEAMAzs4BA6qiVZJWv+lFS/ghZLkPpPy2GxdlJUCvL/jnO/J/E0AYW/ySF5iIb8C0atsQaj28GG69usHL0QM7Di+VTxrRqDxvPruCZNV3fRZ6ZBUROnhA5eZY/KpZloTg7CZY5yXDi5SBTJkdqiRny8sTILnVEcWkp1IyBxlIRQghpTigBJNXiOA58CyH4FkJY2rpqtjPGoFLIUfbPY+TSojyUFmSjKD1Ws0oI38KyisRQWqf5/gDAQmwL5+7DUJD8ELK4GyjOToSdT28I7d0a9XetCsfxYGnjBEsbJ9h690CZPB9OOclonR6PlIRYZOUlQlCcAag4gBJAQgghzQglgKTOOI4rnzDaUgSh3b+JGGNqKIsLNElhmVyGktxUFKY8/HfgiaWVppXQXCSFhZUNzKysweNXXxU5jgfr1n6wcmiNnEeXkHHnFEROHrD1DgDfQtjkv28FcytrmFtZw7p1B7TqooAsIwG/H72AzMwM8GgUMCGEkGaEEkDSaDiO989IYimsHP+d1JmpVSiTyzSDTkrlMsgz46FMLPrnOA5mQrGmldBcZFOeGArFWoM/zCzFcOw0GPLMeORGXymfMqZtD4ic2+p9Gha+uQB2bu3g4tEOAmsH8Pj6HTlNCCGENAQlgKTJcTw+LMR2sBA/OfCk7J/EMO+fwScyFKZGa9Yj5ni8f1rdKieGUlg5esDS1gW5MdeQ/eACitJjYde+D8yFEr3/bkOHDkVxcTGEQv21RBJCCCENRQkgMRiemTkE1g4QWDtobVeVlWhaC8sHn+ShOCf534EnfL4mKbRy9EBRRiySL+yFrXcPWLfp0OTT1VQWGhqqt/cihBBCGgslgMTo8M0twbexhKWNs2YbYwyq0uJ/JrXOq9RyKAPAoJBlIPHMTpiL7cpHDzu0+SdJtAbfvGEjkgkhhJCWhhJA0ixwHAczgRXMBFYQ2rXSbGeMQVlSiLKiPMizEpAbfRVZ987ATCiBQOoIjuMb9VJ4hBBCiCFQAljJypUr8ccffyAqKgpCoRCBgYFYtWoVfH19DR0aqQbHceUTOQslsHJoA/v2fZGfFIW8uJvgOB7ELj7gmZmjTJ6H4mzjXAqPEEII0TdKACuJiIjAG2+8gV69ekGpVOLDDz/EsGHDcO/ePYhEIkOHR2qB4/EhdfeHlaM7ch5eREHKA4icvWDXvg/45pZQq5T/Pj7+Z+WTovRYKBWGXwqPEEII0ReO0RIG1crMzISTkxMiIiIwcODAWh1z7do1BAQE4OrVq+jRo0cTR0hqwhhDUfpj5D2+BoCDrU9PWDl6VDlljFpZ+s8SeLJ/Jrgu/7chlsIjhBBCmhq1ANZAJpMBAOzs7J5SkhgjjuMgdvGG0K4VcmOuIut+JITpj2HXrjfMLMVaZXlmFrCUOsFS6qTZVnkpvFJNYihDcVYi1CrlP8eVL4Xn1DmkSZeoI4QQQhoTJYDVUKvVmDt3Lvr3749OnTpVW06hUEChUGheFxYW6iM8Ugd8CyEcOgRB5OSFnEeXkHrlEKSeXSFxa1/jY92al8Ir0qx4oiwpAFfDSiaEEEKIsaG7VjXeeOMN3LlzB2fPnq2x3MqVK/Hxxx/rKSrSEEJ7N7hKRyEv7gbyHl+DPCMOdu37wEJsW6fzlC+FJ4aZpVgvaxITQgghjY36AFbhzTffxL59+3D69Gl4eXnVWPbJFsAbN24gODiY+gAaOYUsEzmPLqJMng/rNh0h9ehMo38JIYSYDGoBrIQxhrfeegt//vknTp069dTkDwAEAgEEAoHmtVgsrqE0MRYCqSNcegxHfuI9yBLuQJ6VALt2fbQmnyaEEEJaKkoAK3njjTewc+dO7Nu3DxKJBGlpaQAAqVRKa722QByPD6lHZ1g5uiP74UWk3zwOsas3bLy6g28uePoJCCGEkGaKHgFXUt10Hlu2bMErr7xSq3PQNDDNE2MMhanRyIu9Do7Hh51PLwgd2tAUL4QQQlokagGshHJh08VxHCSt2kFo74bc6MvIvHcGVg6tYevTC2YCK0OHRwghhDQqWtqAkErMBFZw9A+Go/8AKPKzkHrlIApSHtKXA0IIIS0KtQASUgUrB3dY2rgg9/F15Dy6jKKMONi36wNzkdTQoRFCCCENRi2AhFSDZ2YB+/Z94Nx1KNRlJUi9dhh5cbfA1CpDh0YIIYQ0CCWAhDyFpY0TXANGwrp1R+Qn3EHqtSNQyDINHRYhhBBSb5QAElILHI8PG6+ucOkxHDy+OdJvHkNO9GWolWWGDo0QQgipM0oACakDC7EtnLsNha13DxSlPUbqlYOQZycZOixCCCGkTigBJKSOOI4HiZsfXHuOhLnIBln3zkKpkBs6LEIIIaTWaBQwIfVkZimGY6dBUBbn01yBhBBCmhVqASSkATiOg7kVTQ1DCCGkeaEEkBBCCCHExFACSAghhBBiYigBJIQQQggxMZQAEkIIIYSYGEoACSGEEEJMDCWAhBBCCCEmhuYBbMFSU1ORmppq6DBaPFdXV7i6uho6DJNAdVp/qF4T0rJRAtjIXF1dsWTJEoN/cCoUCkycOBEREREGjcMUBAcH4+jRoxAIBIYOpUWjOq1fVK8Jadk4xhgzdBCk8eXn50MqlSIiIgJisdjQ4bRYhYWFCA4Ohkwmg7W1taHDadGoTusP1WtCWj5qAWzhunXrRh/gTSg/P9/QIZgcqtNNj+o1IS0fDQIhhBBCCDExlAASQgghhJgYSgBbKIFAgCVLllAH7iZG11l/6FrrD11rQlo+GgRCCCGEEGJiqAWQEEIIIcTEUAJICCGEEGJiKAEkhBBCCDExlAA2kVOnToHjOJw6dcoo4ti9e7dB4yAtB9VtQghp/igBrKOtW7eC4zjNj6WlJdq3b48333wT6enphg7PIC5dugSO4/B///d/OvvGjh0LjuOwZcsWnX0DBw6Em5tbo8VR1d+mVatWCAsLw5dffomCgoJGe6/G8vvvv4PjOPz55586+7p27QqO4xAeHq6zz93dHYGBgY0aC9XtqlUkmhzH4eeff66yTP/+/cFxHDp16tQkMTTHug1ox3327Fmd/YwxtGnTBhzHYdSoUQaIkBDTRQlgPS1btgzbt2/HV199hcDAQGzcuBH9+vWDXC43dGh616NHD1hZWVX5AX/u3DmYmZkhMjJSa3tpaSkuX76M/v37N3o8FX+bjRs34q233gIAzJ07F507d8atW7ca/f0aIigoCAB0rl1+fj7u3LlT5bVLTExEYmKi5tjGRnW7apaWlti5c6fO9ri4OJw7dw6WlpZNHkNzqtuVVXftIiIikJSURNPNEGIAtBRcPQ0fPhw9e/YEALz66quwt7fH2rVrsW/fPkycONHA0emXmZkZ+vTpo5OoPHjwAFlZWZg0aZJOgnP16lWUlJQ0SRJT+W8DAAsWLMDJkycxatQojBkzBvfv34dQKKz2+KKiIohEokaPqyqtWrWCl5eXzvU5f/48GGMYN26czr6K102VAFLdrtqIESOwf/9+ZGVlwcHBQbN9586dcHZ2Rrt27ZCbm9ukMTSnul3ZiBEjsGvXLnz55ZcwM/v3trNz504EBAQgKytL7zERYuqoBbCRhISEAABiY2OrLXPmzBmMGzcO7u7uEAgEaNOmDebNm4fi4mKdslFRURg/fjwcHR0hFArh6+uLhQsXapVJTk7G9OnT4ezsDIFAAH9/f2zevLnK91apVPjwww/h4uICkUiEMWPGIDExUafcrl27EBAQAKFQCAcHB0yePBnJyclP/f2DgoKQnp6O6OhozbbIyEhYW1tj1qxZmmSw8r6K4/QhJCQEixYtQnx8vNZjvFdeeQVisRgxMTEYMWIEJBIJ/vOf/wAAPD098corr+ica9CgQRg0aJDWtvj4eIwZMwYikQhOTk6YN28ejh49Wqu+ckFBQbh+/bpWPYiMjIS/vz+GDx+OCxcuQK1Wa+3jOK5JWk+rYup1u8LYsWMhEAiwa9cure07d+7E+PHjwefza32uxmTMdbvCxIkTkZ2djWPHjmm2lZaWYvfu3Zg0aVKdf2dCSMNRAthIYmJiAAD29vbVltm1axfkcjnmzJmDDRs2ICwsDBs2bMCUKVO0yt26dQt9+vTByZMnMXPmTKxfvx7PPvssDhw4oCmTnp6Ovn374vjx43jzzTexfv16+Pj4YMaMGVi3bp3Oe69YsQKHDh3C+++/j//+9784duwYQkNDtW7QW7du1dzIVq5ciZkzZ+KPP/5AUFAQ8vLyavz9q3qUGRkZib59+6JPnz4wNzfHuXPntPZJJBJ07dq1xvM2ppdffhkA8Pfff2ttVyqVCAsLg5OTE1avXo0XXnihTuctKipCSEgIjh8/jv/+979YuHAhzp07h/fff79WxwcFBaGsrAwXL17UbIuMjERgYCACAwMhk8lw584drX1+fn411rXGZOp1u4KVlRXGjh2LX375RbPt5s2buHv3rsGTGGOt2xU8PT3Rr18/rWt35MgRyGQyTJgwoU7nIoQ0EkbqZMuWLQwAO378OMvMzGSJiYns119/Zfb29kwoFLKkpCTGGGPh4eEMAAsPD9ccK5fLdc63cuVKxnEci4+P12wbOHAgk0gkWtsYY0ytVmv+PWPGDObq6sqysrK0ykyYMIFJpVLNe1XE4ebmxvLz8zXlfv/9dwaArV+/njHGWGlpKXNycmKdOnVixcXFmnIHDx5kANjixYtrvC75+fmMz+ezGTNmaLb5+vqyjz/+mDHGWO/evdn8+fM1+xwdHdnQoUNrPGddVfxtLl++XG0ZqVTKunfvrnk9depUBoB98MEHOmU9PDzY1KlTdbYHBwez4OBgzes1a9YwAGzv3r2abcXFxczPz0+nDlTl7t27DABbvnw5Y4yxsrIyJhKJ2LZt2xhjjDk7O7Ovv/6aMfbvdZ45c2aN56wPqttVq3ifXbt2sYMHDzKO41hCQgJjjLH58+eztm3bMsbK64W/v3+N56qv5lq3K8f91VdfMYlEovn7jRs3jg0ePFgTz8iRI2s8FyGkcVELYD2FhobC0dERbdq0wYQJEyAWi/Hnn3/WOKq1ct+coqIiZGVlITAwEIwxXL9+HQCQmZmJ06dPY/r06XB3d9c6nuM4AOUj5/bs2YPRo0eDMYasrCzNT1hYGGQyGa5du6Z17JQpUyCRSDSvX3zxRbi6uuLw4cMAgCtXriAjIwOvv/66Vmf2kSNHws/PD4cOHarxekgkEnTp0kXTApiVlYUHDx5oRqr2799f89j34cOHyMzM1Nvj38rEYnGVIybnzJlT73P+9ddfcHNzw5gxYzTbLC0tMXPmzFod36FDB9jb22uu3c2bN1FUVKS5doGBgZprd/78eahUqia9dlS3qzds2DDY2dnh119/BWMMv/76q9H0izTGul3Z+PHjUVxcjIMHD6KgoAAHDx40eMspIaaMBoHU09dff4327dvDzMwMzs7O8PX1BY9Xcz6dkJCAxYsXY//+/TqdxWUyGQDg8ePHAFDjdBKZmZnIy8vD999/j++//77KMhkZGVqv27Vrp/Wa4zj4+PggLi4OQHk/HwDw9fXVOZefn1+VI3yfFBQUhA0bNiArKwvnzp0Dn89H3759AZQnMd988w0UCoXe+/9VVlhYCCcnJ61tZmZmaN26db3PGR8fD29vb00SU8HHx6dWx3Mch8DAQJw+fRpqtRqRkZFwcnLSHB8YGIivvvoKgH76TlLdrp65uTnGjRuHnTt3onfv3khMTDSaJMYY63Zljo6OCA0Nxc6dOyGXy6FSqfDiiy/WOzZCSMNQAlhPvXv31hqN9zQqlQpDhw5FTk4O3n//ffj5+UEkEiE5ORmvvPKKVif/p6koO3nyZEydOrXKMl26dKn1+RpLRQIYGRmJc+fOoXPnzhCLxQDKkxiFQoHLly/j7NmzMDMz0ySH+pKUlASZTKZz8xIIBFUmOE/e9CqoVKpG7/AfFBSEAwcO4Pbt25r+fxUCAwMxf/58JCcn4+zZs2jVqhXatm3bqO9fGdXtmk2aNAnffvstli5diq5du6Jjx44GjQcw7rpd2aRJkzBz5kykpaVh+PDhsLGxabL3IoTUjBJAPbl9+zYePnyIbdu2aXWMrzwqDoDmxl650/+THB0dIZFIoFKpEBoaWqv3f/TokdZrxhiio6M1N1MPDw8A5VO3VIz6rPDgwQPN/ppUHghy/vx5rVGqrVq1goeHByIjIxEZGYnu3bvDysqqVrE3lu3btwMAwsLCalXe1ta2ygEC8fHxWgmYh4cH7t27B8aY1o218ojop6l87SIjIzF37lzNvoCAAAgEApw6dQoXL17EiBEjan1efTCFul1ZUFAQ3N3dcerUKaxatapOxzYVY67blT333HOYPXs2Lly4gN9++61e5yCENA7qA6gnFd+qGWOabYwxrF+/Xquco6MjBg4ciM2bNyMhIUFrX8WxfD4fL7zwAvbs2VPlzTQzM1Nn208//aTVP2j37t1ITU3F8OHDAQA9e/aEk5MTvv32WygUCk25I0eO4P79+xg5cuRTf8eKOe1OnDiBK1eu6KxUERgYiL179+LBgwd6f/x78uRJLF++HF5eXpqpMJ7G29sbFy5cQGlpqWbbwYMHdaYYCQsLQ3JyMvbv36/ZVlJSgk2bNtU6vp49e8LS0hI7duxAcnKy1rUTCATo0aMHvv76axQVFRnk0XlNTKFuV8ZxHL788kssWbJEM/rWkIy9blcmFouxceNGLF26FKNHj67XOQghjYNaAPXEz88P3t7eePfdd5GcnAxra2vs2bOnyoljv/zySwQFBaFHjx6YNWsWvLy8EBcXh0OHDuHGjRsAgM8++wzh4eHo06cPZs6ciY4dOyInJwfXrl3D8ePHkZOTo3VOOzs7BAUFYdq0aUhPT8e6devg4+Oj6cxtbm6OVatWYdq0aQgODsbEiRORnp6O9evXw9PTE/PmzavV7xkUFKRpjXhynrrAwEDNNBBNmcQcOXIEUVFRUCqVSE9Px8mTJ3Hs2DF4eHhg//79tV6x4dVXX8Xu3bvxzDPPYPz48YiJicHPP/8Mb29vrXKzZ8/GV199hYkTJ+Ltt9+Gq6srduzYoXmf6h63VWZhYYFevXrhzJkzEAgECAgI0NofGBiINWvWADBM38mamErdrmzs2LEYO3Zs3S9WAzXHuv2k6h7tE0L0TO/jjpu52kzHwFjVU2Xcu3ePhYaGMrFYzBwcHNjMmTPZzZs3GQC2ZcsWrePv3LnDnnvuOWZjY8MsLS2Zr68vW7RokVaZ9PR09sYbb7A2bdowc3Nz5uLiwoYMGcK+//57nTh++eUXtmDBAubk5MSEQiEbOXKkzlQcjDH222+/se7duzOBQMDs7OzYf/7zH830H7Xx3XffaabmeNK1a9cYAAaApaen1/qctVXxt6n4sbCwYC4uLmzo0KFs/fr1WlOFVJg6dSoTiUTVnnPNmjXMzc2NCQQC1r9/f3blyhWdqTIYY+zx48ds5MiRTCgUMkdHR/a///2P7dmzhwFgFy5cqFX8CxYsYABYYGCgzr4//viDAWASiYQplcpana+uqG7X/Pvu2rWrxnL6mAamudXt2tYpmgaGEP3jGKv03IYQ0mjWrVuHefPmISkpqcYpVAhpbqhuE9L8UQJISCMoLi7WmguvpKQE3bt3h0qlwsOHDw0YGSENQ3WbkJaJ+gAS0gief/55uLu7o1u3bpDJZPj5558RFRWFHTt2GDo0QhqE6jYhLRMlgIQ0grCwMPzwww/YsWMHVCoVOnbsiF9//RUvvfSSoUMjpEGobhPSMtEjYEIIIYQQE0PzABJCCCGEmBhKAPXs888/h5+fX52WxzJWH3zwAfr06WPoMKpF11o/6DrrD11rQkijMeQcNKZGJpMxOzs7tnnzZs02/DOv1+rVq3XK13YOrdrYs2cPGz9+PPPy8mJCoZC1b9+evfPOOyw3N7fK8vv27dPMmdamTRu2ePFiVlZWplUmNTWVCQQCtm/fvgbH19joWusHXWf9oWtNCGlMlADq0f/93/8xa2trVlxcrNlW8QHu7OzMioqKtMo35ge4vb0969y5M1u0aBHbtGkT++9//8ssLCyYn58fk8vlWmUPHz7MOI5jgwcPZt9//z176623GI/HY6+99prOecePH88GDBjQ4PgaG11r/aDrrD90rQkhjYkSQD3q0qULmzx5stY2AKxbt24MAFuzZo3Wvsb8AK+8akOFbdu2MQBs06ZNWts7duzIunbtqvWNfeHChYzjOHb//n2tsrt372Ycx7GYmJgGx9iY6FrrB11n/aFrTQhpTNQHUE9iY2Nx69YthIaG6uzr378/QkJC8Pnnn6O4uLhJ3n/QoEE625577jkAwP379zXb7t27h3v37mHWrFkwM/t3lqDXX38djDHs3r1b6xwVv8++ffuaIOr6oWutH3Sd9YeuNSGksVECqCfnzp0DAPTo0aPK/UuXLkV6ejo2btxY43kUCgWysrJq9fM0aWlpAAAHBwfNtuvXrwMAevbsqVW2VatWaN26tWZ/BalUCm9vb0RGRj71/fSFrrV+0HXWH7rWhJDGRhNB60lUVBQAwMvLq8r9AwYMwODBg/HFF19gzpw5WksvVfbLL79g2rRptXpP9pQpHletWgU+n48XX3xRsy01NRUA4OrqqlPe1dUVKSkpOtvbtm2Le/fu1SomfaBrrR90nfWHrjUhpLFRAqgn2dnZMDMzg1gsrrbM0qVLERwcjG+//Rbz5s2rskxYWBiOHTvW4Hh27tyJH3/8Ee+99x7atWun2V7xCEkgEOgcY2lpifz8fJ3ttra2Ot/sDYmutX7QddYfutaEkMZGCaARGThwIAYPHozPP/8cr732WpVlXF1dq/x2XRdnzpzBjBkzEBYWhhUrVmjtq2g5UCgUOseVlJRU2bLAGAPHcQ2KSd/oWusHXWf9oWtNCKkLSgD1xN7eHkqlEgUFBZBIJNWWW7JkCQYNGoTvvvsONjY2OvuLi4shk8lq9Z4uLi46227evIkxY8agU6dO2L17t1ZHbeDfRzepqalo06aN1r7U1FT07t1b55y5ubla/YAMja61ftB11h+61oSQxkaDQPTEz88PQPlovpoEBwdj0KBBWLVqVZUj+n777TfNt/in/TwpJiYGzzzzDJycnHD48OEqHyd169YNAHDlyhWt7SkpKUhKStLsryw2NhYdOnSo8ffSJ7rW+kHXWX/oWhNCGhu1AOpJv379AJR/MHbp0qXGskuXLsWgQYPw/fff6+yrbx+etLQ0DBs2DDweD0ePHoWjo2OV5fz9/eHn54fvv/8es2fPBp/PBwBs3LgRHMdpdfgGAJlMhpiYGMyZM6fOMTUVutb6QddZf+haE0IaGyWAetK2bVt06tQJx48fx/Tp02ssGxwcjODgYEREROjsq28fnmeeeQaPHz/Ge++9h7Nnz+Ls2bOafc7Ozhg6dKjm9RdffIExY8Zg2LBhmDBhAu7cuYOvvvoKr776qs439ePHj4MxhrFjx9Y5pqZC11o/6DrrD11rQkij0//c06Zr7dq1TCwWay2dBIC98cYbOmXDw8M1yzw1xkz+Feeq6ic4OFin/J9//sm6devGBAIBa926Nfvoo49YaWmpTrmXXnqJBQUFNTi+xkbXWj/oOusPXWtCSGOiBFCP8vLymJ2dHfvhhx8MHUqjSE1NZZaWlmzv3r2GDkUHXWv9oOusP3StCSGNiQaB6JFUKsV7772HL774Amq12tDhNNi6devQuXNno3x8Q9daP+g66w9da0JIY+IYe8p074QQQgghpEWhFkBCCCGEEBNDCSAhhBBCiImhBJAQQgghxMRQAkgIIYQQYmIoASSEEEIIMTGUABJCCCGEmBhKAAkhhBBCTAwlgIQQQgghJoYSQEIIIYQQE0MJYCVjxoyBu7s7LC0t4erqipdffhkpKSmGDosQQgghDUT3eG2UAFYyePBg/P7773jw4AH27NmDmJgYvPjii4YOixBCCCENRPd4bbQWcA3279+PZ599FgqFAubm5oYOhxBCCCGNxNTv8WaGDsBY5eTkYMeOHQgMDKyxYigUCigUCq1tAoEAAoGgqUMkhBBCWhx93Fdre49vyegR8BPef/99iEQi2NvbIyEhAfv27aux/MqVKyGVSrV+wsLCkJqaqqeICSGEkJYhNTUVYWFhOvfVlStXNsr563qPb8lafAL4wQcfgOO4Gn+ioqI05efPn4/r16/j77//Bp/Px5QpU1DTU/IFCxZAJpNpfiIiIhAREUEJICGEEFJHqampmvto5XvrggULqizf1Pf4lqzF9wHMzMxEdnZ2jWXatm0LCwsLne1JSUlo06YNzp07h379+tXq/a5du4aAgABcvXoVPXr0qFfMhBBCiCmq6z1U3/f4lqTF9wF0dHSEo6NjvY5Vq9UAoNMXgRBCCCGGR/f4+mvxCWBtXbx4EZcvX0ZQUBBsbW0RExODRYsWwdvb2yS/GRBCCCEtBd3jdbX4PoC1ZWVlhT/++ANDhgyBr68vZsyYgS5duiAiIoJG9BJCCCHNGN3jdVEL4D86d+6MkydPGjoMQgghhDQyusfrohZAQgghhBATQwkgIYQQQoiJoQSQEEIIIcTEUAJICCGEEGJiKAEkhBBCCDExlAASQgghhJgYSgAJIYQQQkwMJYCEEEIIISaGEkBCCCGEEBNDCSAhhBBCiImhBJAQQgghxMRQAkgIIUSLPCsBypJCrW2MMTDGDBQRIaSxUQJICCFEo6xIhtQrh5B1P1KzjTGGjNsnkPPokgEjI4Q0JkoACSGEaOTF30JJXjryE++hJC8dAKDIS4Ms7jZyoy+jrEhm4AgJIY2BEkBCCCEAylv/ZHE3YSG2g1JRhLzYG2CMIffxDagURSgtykNewi1Dh0kIaQSUABJCCAFQ3vpXWpQHc7EtLMR2yE+8h/yEOyhIjoK52B7mQmvIYm9SKyAhLQAlgIQQQqAsLoAs7hbUZaUozkpEaWEuFPlZSLtxDGVyGTgeHzwzART5WdQKSEgLYGboAAghhBgexzeHrXcPMJVSs02tUiI35irMRTZQlcoBAOYiG8gzE4EOhoqUENIYKAEkhBACvoUl7H37AQCUCjn45gJwPD7sfftCrSzTKssztzBEiISQRkQJICGEEA1VWQmSzu2GuFV7OPj2Bd9CCL6F0NBhEUIaGfUBJIQQolGQFAV5ZhzyYq9DWVxg6HAIIU2EEkBCCCEAylv/cmOugmcuRFlBNvLi72j20SoghLQslAD+Iy4uDjNmzICXlxeEQiG8vb2xZMkSlJaWGjo0QgjRi4KkKJTkpUEgdQLfUoy8uBtQFhcgNiULf56+jlKl8uknIcQI0T1eF/UB/EdUVBTUajW+++47+Pj44M6dO5g5cyaKioqwevVqQ4dHCCFNqqL1T60sQ1lhDhhjUOSmISf2Ns4l8RGTnIl2rZ3R2dvN0KESUmd0j9dFCeA/nnnmGTzzzDOa123btsWDBw+wceNGk60chBDToVaWwcxSBCsnD802C4ktYjPzEZemhpoxXI6Kg6+HMyzM6NZBmhe6x+ui/4trIJPJYGdnV2MZhUIBhUKheV1YWNjUYRFCSKMzF0rQJmiC1jaVWo1zxy+DsSy0drJBUkYuHsSnUysgaXKFhYXIz8/XvBYIBBAIBI36HrW5x7dk1AewGtHR0diwYQNmz55dY7mVK1dCKpVqfoKDg/UUISGENK2Y5EzEpmZBLBRAqfy3FZD6ApKmFhwcrHVvXblyZaOev7b3+JbMKBPA1NRU3Lx5E0VFRQ0+1wcffACO42r8iYqK0jomOTkZzzzzDMaNG4eZM2fWeP4FCxZAJpNpfiIiIhocMyGEGIOE9BwIzM1RqlQhX14CK0sLyEtKkZlLTzpI04qIiNC6ty5YsKDKck19j2/JOGZEY/v37duH999/H48ePQIAHDt2DCEhIcjKysLQoUOxZMkSPPvss3U6Z2ZmJrKzs2ss07ZtW1hYlM9sn5KSgkGDBqFv377YunUreLy65cjXrl1DQEAArl69ih49etTpWEIIMSalSiUK5QqtbTweB6lICI7jDBQVacnqeg/V9z2+JTGaPoAHDhzA888/j379+mHSpElYunSpZp+DgwPc3NywZcuWOieAjo6OcHR0rFXZ5ORkDB48GAEBAdiyZYtJVwxCCLEwM4OdtdHcJgjRQff4+jOa337ZsmUYOHAgzp49izfeeENnf79+/XD9+vUme//k5GQMGjQI7u7uWL16NTIzM5GWloa0tLQme09CCCGEND26x+symq92d+7cwdq1a6vd7+zsjIyMjCZ7/2PHjiE6OhrR0dFo3bq11j4jekpOCCGEkDqie7wuo2kBtLKyqnHQx+PHj2Fvb99k7//KK6+AMVblDyGEEEKaL7rH6zKaBHDw4MHYtm0blFVML5CWloZNmzZh2LBhBoiMEEIIIaRlMZoEcMWKFUhKSkKvXr3w3XffgeM4HD16FB999BE6d+4MxhiWLFli6DAJIYQQQpo9o0kAfX19cfbsWdjb22PRokVgjOGLL77Ap59+is6dO+PMmTPw9PQ0dJiEENLiMcZQVlZm6DAIIU3IaAaBAIC/vz+OHz+O3NxcREdHQ61Wo23btrUe4k0IIaRhVCo1DkTeQmsHCXp29DZ0OISQJmJUCWAFW1tb9OrVy9BhEEKIyYlOzsSd2GTEJvPQpZ0HLMzLbxPnbsfA0UaCdm2cDBwhIaQxGM0j4C+//BJhYWHV7h8+fDg2btyox4gIIcS0qFRqXImKg1KlRkJaNu7HpwIA0nJkOHcnBhE3HqK0jNYBJqQlMJoE8Mcff0THjh2r3d+xY0d8//33eoyIEEJMS3RyJmJTs9HKQQpVWSnO34lBaZkS1x8mokBeguSsPE1SSAhp3owmAYyJiUGHDh2q3e/n54eYmBg9RkQIIaajovWvtEwJlUoNCzMOdx7GIvJ2NO7GpsBBKobAnI/L9+OpFZCQFsBoEkALC4sal2RJTU01+XX7CCGkqciKipFfVAJrkSWKSkqhVDEo5IU4fvE28otKIBJawFYiQnJmeSsgU6sMHTIhpAGMZhBI3759sXXrVsybNw8SiURrn0wmw5YtW9C3b18DRUcIIS2bnbUIU57pC6VKjdDQUCSlpsHBqwMCB4dBaueIlCw+AMCMz8OjB/dhm3Iazt3CILB2AACo1GrcjU1Bu9ZOEAosDPmrEEJqwWgSwCVLliA4OBjdunXD3Llz4e/vD6B8jeB169YhNTUVO3fuNHCUhBDScomEAgBAalI8lBZSmEudYS/kwQ55cBYK0LlzZ9jY2kIZE4H8hIcQ2LjAucsQAMDj5Cwcu3wfhXIFAjvT9DGEGDujSQD79OmDAwcOYPbs2Xj77bfBcRyA8glJvby8sH//fvTr18/AURJCSMvHMxfAzt0XZhaWyFMwtLbmUJyXiUtnTsLNzgouihiIhELkJ9yBjWdXmIntcDkqDhm5Bbj6IAGdvd0gsbI09K/RbOTn58Pa2trQYRATYzQJIAAMHToU0dHRuH79umbAh7e3N3r06KFJCAkhhDSdhIQElIEPZWYKCtPj4d7KCVkiJ7iI+QBjkCfdRVppJpjQDrYWSpjdOw+lWx/EpmbB09UO6TkFuB2TTK2AdVBUVEQJINE7o0oAAYDH4yEgIAABAQGGDoUQQkzGpUuXsHz5chw6dAiMMc32PVHXccXXHy89Owp+7o4QluWAcXyoFUXIKy5D1pnDuMxPQQlfDBdbMSRWAlx9kIBObVuhLPkmLG1cIHLyNNwv1gwUFhYaOgRigowuAbx37x4eP36M3NxcrQ+hClOmTDFAVIQQ0nL98ccfeOmll8AYq+JzlyHh4T2s/uIe3pw2AUP8bMCBgQODmmeGnDJLZOYXogQKZGfnQCi0grW1BFH378Im5TwE1vYQ2ruBxzevUzzffvstrl69ipycHFy/fh3dunWr8ZhNmzbhp59+wp07dwAAAQEB+PTTT9G7d28AQFlZGT766CMcPnwYjx8/hlQqRWhoKD777DO0atWqLper0WVnZ6Ndu3YGjYGYHqNJAGNiYjB58mRcunSpysQPADiOowSQEEIa0aVLl/DSSy9BpVJV+9nLmBqMAV9t+RXi996Fp6eHZl+pCnATVO6iUwqUZCHx8gNAUABJaTEKUx7Buk31E/0/qaioCEFBQRg/fjxmzpxZq2NOnTqFiRMnIjAwEJaWlli1ahWGDRuGu3fvws3NDXK5HNeuXcOiRYvQtWtX5Obm4u2338aYMWNw5cqVWsfWFGJiYtC9e3cIBAKDxkFMi9EkgLNnz8bt27exbt06DBgwALa2toYOiRBCWrxPPvmkmpa/qh06chSvv/665rW5GeAi1i5jriqCoDAZaUVmyJEVooj9hW4u3jAzr12C8/LLLwMA4uLialUeAHbs2KH1+ocffsCePXtw4sQJTJkyBVKpFMeOHdMq89VXX6F3795ISEiAu7t7rd+rsZWUlODUqVMYNmwY9XcnemM0CWBkZCQ+/PBDvPXWW4YOhRBCTEJCQgIOHjxY6+RPrVbj1q1byMnJgZ2dXdWFGIOoNBM8VoYyniVK1Gqkx93D4V9/xIBRE/X25V4ul6OsrKz6OFE+xyzHcbCxsdFLTFXp2bMnHj9+DIlEgt27d6NXr14Gi4WYFqNJAB0cHCCVSg0dBiGENBulpaVQKuu/LNvhw4drnfxVYIzh/v371U7LxVeXwqIsHwwcLFT/Dm5QZMZiz549CAkJQdu2besdc229//77aNWqFUJDQ6vcX1JSgvfffx8TJ0406AjctLQ0TZ/369evw9raGr6+vgaLh5gOo0kAX3vtNfz888944403wOfzDR0OIYQYtdLSUly6dKlBI0hv3rwJjuPqlARyHAeZTFb9+zIGBXMCB7XWZqXKHGV5eTh06BBmz54NC4vy1UJ27NiB2bNna8odOXIEAwYMqPsvU8lnn32GX3/9FadOnYKlpe58hGVlZRg/fjwYY9i4cWOD3quxnT59GoWFhejevTstf2rE0tLS8OOPP+LatWuQyWRQq7XrO8dxOHHihIGiqx2jSQDbt28PlUqFrl27Yvr06WjTpk2VieDzzz9vgOgIqR5Tq8Dx6EsL0S+lUonCwkJYWFjUa/BAWVkZFApFvVoALS0ta0xOSiGucjsPgJ2dHZRKpSYBHDNmDPr06aMp4+bmVqd4nrR69Wp89tlnOH78OLp06aKzvyL5i4+Px8mTJ41u/j3GGK5evYrY2Fj069evwdeDNL5bt25h0KBBKC4uhq+vL27fvo2OHTsiLy8PycnJ8Pb2Rps2bQwd5lMZTQL40ksvaf797rvvVlmG4zioVLQAOTEehWkxyHl0Ga16j4GZwMrQ4RATJBAIqmzlqo5cLse9e/dw//59lJWV1fn9OI6Dr69vnVun7Ozs4OrqqvMZLpFIdNZ/r6/PP/8cK1aswNGjR9GzZ0+d/RXJ36NHjxAeHg57e/tGed+mkJOTg0OHDqFNmzbo3bu3Ucdqaj744AOIxWLcuHEDVlZWcHJywvr16xESEoJdu3Zhzpw5OoOSjJHRJIDh4eGGDoGQOmFqFXKiL6Mg5QHyE+/Bzkf3hkOIscjJycGdO3cQExOjeVwlkUjg4eGBhISEWrUE8ngcOnToWKeBHFKpFF5eXpBIJCgpKUFBQUGtYk1ISEBKSgoA4MGDBwAAFxcXuLi4ACifE9bNzQ0rV64EAKxatQqLFy/Gzp074enpibS0NACAWCyGWCxGWVkZXnzxRVy7dg0HDx6ESqXSlLGzs9O0SBqbxMREJCUlwdvbGwEBAdRX3ghERkbivffeg7u7O3JycgBA8//UuHHjcPbsWcyfPx8RERGGDPOpjCYBDA4ONnQIWLFiBQ4dOoQbN27AwsICeXl5hg6JGLHCtMeQZ8SDZyZA3uNrsG7TkVoBiVFhjCElJQV37txBUlKS1j4ejwdvb2+8++67mDt3LtRq9VOTQA5A2NAhFSeHtSoHcr4ESu7f5EnNysvx+Tx4eXnVa5Ll/fv3Y9q0aZrXEyZMAAAsWbIES5cuBVA+grlyK+TGjRtRWlqKF198UetcFcckJydj//79AKAzqXR4eDgGDRpU5zgbKiEhAUVFRQAAhUJR7ehqxhiio6MRExMDb29vdO3alVoE66Gx7vFqtRrOzs4AABsbG/D5fE0iCACdO3fGjz/+2BghNymj62GqUChw/vx57Nu3D1lZWXp979LSUowbNw5z5szR6/uS5oepVch9fBVgDEK7VlDIMpCfeM/QYRECoPwGFR0djb179+Kvv/7SSv4sLCzQpUsXjB8/HgMHDkS/fv2wcuVK8Hg88HhVz0HH43Hg8zjMnxiMjm7lfeaE6kLYKVMhVf77Oa1mQGw+UGohQffu3TXJn6JMiTJl7bvvvPLKK5q5CSv/VCR/QPnEz1u3btW8jouLq/EYT0/PKvczxvSe/F26dAmjR4+Gp6enJgkpLi7Ghx9+iK+//rra+Q8rEsE9e/bgwIEDePToUYO7RTHGsHjxYri6ukIoFCI0NBSPHj2q8ZiCggLMnTsXHh4eEAqFCAwMxOXLlxt83qbWWPd4Ly8vxMbGAij/IuXl5YXjx49r9p87d86gUwvVllElgF9++SVcXV0RFBSE559/Hrdu3QIAZGVlwcHBAZs3b27S9//4448xb948dO7cuUnfhzR/5a1/ceCZW0JZUgQGDnmPr0GpkBs6NGLCSktLcevWLfz222+IiIjQapUQi8Xo27cvJkyYgF69ekEkEmn2hYSE4MdN3yGwZ1ediYg5Dujp2xrLXnsBffy9Ya3KBk+thLUqG+asFGJVLszVJbCysoKVnQt4YlsUm0nANy9vFVSrGc49SMWVmPQ6Dzhpif744w/0798fR44c0bkejDHcuXMHq1atwrVr12o8T2pqKsLDw/Hzzz8jMjISubm59Yrn888/x5dffolvv/0WFy9ehEgkQlhYGEpKSqo95tVXX8WxY8ewfft23L59G8OGDUNoaCiSk5MbdN6m1lj3+GHDhmHXrl2a13PmzMEPP/yA0NBQDBkyBNu2bcOkSZMaGm6TM5oEcMuWLZg7dy6eeeYZ/Pjjj1r/Yzg4OCAkJAS//vqrASOsmkKhQH5+vuaHFvU2DYr8TPAFInB8Hpi6DOZWEjDGUFZYvw9hQhqioKAAFy5cwK+//orLly9DLv/3i0jF5+e4cePg7+8Pc/Oq1+Tt1KUb1n3zAw4cOKAZlGEvFePb9yZi0cuD4d/aGjyoYakugp0yFVaqfJSaWUNsaYbObhJ0694D+WoBeDwe8ooUiMvMBwCk5BYiNbcQCVkFyClUNP3FMGKVl92rruVOrVZDrVZj06ZNtVoJRaFQ4O7du9i1axeOHj2K/Pz8WsfDGMO6devw0UcfYezYsejSpQt++uknpKSkYO/evVUeU1xcjD179uDzzz/HwIED4ePjg6VLl8LHx0czpU59zvukwsJCrXurQmE8dWfhwoX45ZdfNIOo5s6di2XLliE7OxsymQyLFi3CJ598YuAon85o+gCuWbMGY8eOxc6dO5Gdna2zPyAgAF9++aUBIqvZypUr8fHHHxs6DKJn9r59Ydu2u/ZGjgPfvPajMQlpqLy8PNy8ebPKQRzu7u7o3LkznJ2d67S8mIuLC4RCIQoKCmAtEkJs54RCVDo3Y5DySyARSSCwaw2UFgLyNCSnpiFdVgRbkSWKSsrwMCUX7vYSPEzJBWNAqVKFmHQZ/JyEjfXrNzt1XXbv8OHDWsvuPU18fDxSUlIwZMiQWi1tFxsbi7S0NK3JsqVSKfr06YPz589r+l5WplQqoVKpdEaeC4VCnD17tt7nfdKT4wIq9/80NFtbWwQEBGhecxyHjz76CB999JEBo6o7o2kBjI6OxvDhw6vdb2dnV2Vi+DQffPABOI6r8ScqKqrecS9YsAAymUzzY+yjfkjj4Dge+BZC7R9K/ogeqNVq3L59Gxs3bsTp06cRHx+vSSj4fD78/PzwwgsvYOjQoXBxcWnQ2rI5RaXIsPBEmoUX0iy8IJN2hGPHAXCwt4OFuTlYYQZYqRzKsmI8iEtCqVKFMqUaZnwecgpKcC02A2l5ctiIBJBaCZCUU4Q8ed2nnmkJKpbdq22fvcrL7tVFWVkZjh49qulCVZOKUdAVAxoqODs7a/Y9SSKRoF+/fli+fDlSUlKgUqnw888/4/z580hNTa33eZ8UERGhdW9dsGBBleWa+h5flZCQkBoneQ4PD0dISEijvmdTMJoWQBsbmxoHfdy7d08z/L8u/ve//+GVV16psUxDliUSCARak7CKxVVPgEpapjKlCll5hXB1oKkZSNMqKyvDpUuXcPz4caSnp2vts7S0RIcOHdChQwcIhQ1vYTO3koBnpt2doVWrVvDy8gKnVkJtyQMqtWKVlqpQmlgGa74Z1P9sF1uaIzotF0oVA/+fwSVFJWWIy26e3WSMcdm9mpw7dw5qtVprxPOTq64cOnSozucFgO3bt2P69Olwc3MDn89Hjx49MHHiRFy9erVe56uKWCyu1STdTX2Pr8qpU6fw6quvVrs/IyOjWTQGGU0COGLECHz//fdVNnffvXsXmzZtwvTp0+t8XkdHRzg6OjZGiITouP4oAZfvx2Hc4AA42ZZ/WJUqlYhNyYKPmxP4fKNpZCfNVGFhIU6fPo2IiAidOfREIhH8/f3RoUMHmJk1zsd5cakSDr69oBRIUZIWA47j0K5du39bc3gW4Nt5ah0jARDmpNYkfwCgUqtxNSYDirJ/kyap0Ax8rvlN5m+0y+49xa1bt9CxY8dqV12p6FeXnp4OV1dXzfb09HSdqXIq8/b2RkREBIqKipCfnw9XV1e89NJLmkSrorGmruetD0Pd42tqWY+Ojm60yc2bktEkgJ988gn69OmDTp06YfTo0eA4Dtu2bcPmzZuxZ88euLq6YvHixU0aQ0JCgmYCUpVKhRs3bgAAfHx8qGWP6JCXlOJKVDwSM3Jx/VEiwnr7AwDuxqYi4voDDO/bCb7udW+1JgQob0U4ceIELly4oLNih4+PDwYMGICsrCxYW1s3WvIHALHpeRDbu8CFb448VRF8fHx0HuVVxUznyw4fQR20lzGr7UTQxqahy+4B5f3GmmLZvZpIJBKtZfeeXHWFMQYXFxecOHFCk5jl5+fj4sWLtZoqRSQSQSQSITc3F0ePHsXnn38OoHyalIact6k05B6/bds2bNu2TfP6k08+waZNm3TK5eXl4datWxgxYkSjxt4UjCYBbNWqFa5evYoPP/wQv/32Gxhj2L59OyQSCSZOnIjPPvsMDg4OTRrD4sWLtf7A3buXd/I31CShxLjdiU1GRm4BXOyscTsmGd3btYFUZIUrUXFIyZLhclQ8tQKSOmGM4fHjxzh+/Dhu3bqllTBwHIfu3bsjNDQUnp6ekMvlOH36dKO+f3GpEo9S8xDUtxcysnPRekj/enW9aanquuxeZf37969XC2B9lt0DgDZt2jy1OwDHcZg7dy4++eQTtGvXDl5eXli0aBFatWqFZ599VlNuyJAheO655/Dmm28CAI4ePQrGGHx9fREdHY358+fDz89PM3l3bc+rbw25x8vlcmRmZmpeFxQU6PxdOI6DSCTCa6+91uQNVo3BKBJAhUKBo0ePwtPTEz/88AN++OEHZGZmQq1Ww9HRsd7ffupq69atWhOLElKditY/kaUFbCVWiE3NxvVHiXCytUZqlgyervaIS81GdHIGfN1d8CgxAzEpGRjWy7/ayXaJ6VKr1bh58yaOHz+umWC2gkAgQGBgIAYPHtzkX4Jj0/OQX6yAk7UI+WYciiBAvrwUhYpS5BSUwL+NfYMGlZgyFxcXDBgwAJGRkbUaCMLj8dChQ4c6LbsHlCch7u7ucHJyqlVr63vvvYeioiLMmjULeXl5CAoKwl9//aWV6MbExGj10a8YlJGUlAQ7Ozu88MILWLFihdYUQ7U5r7415B4/Z84cTeull5cX1q9fjzFjxjRidPpnFAmghYUFxo0bh/Xr16NLly4AQP32iFG7F5eK1CwZzM34SMzIhVKlwo1HibC0MIeFuRnEQgFy8uW4HBUPD2d7nL0VjdQcGdq3cUbbVlS3SbmKlY9OnjypMwhOKpVi8ODBCAoKgpVV0y8xWPJP61+ZkiG7oBhK8FBcBjxKzUFWQQlkcgVcbUWwl5juNC4NNWPGDERGRta6JXDYsGG1Pjefz4ejoyNat24NoVBY6wmXOY7DsmXLsGzZsmrLPDkf4fjx4zF+/PgGn7e5evJLWnNlFAlgRSdjfS/9Rkh9iYUC9O7opbUtNVuGhPRs8Hk8xKVmQ6lSIyEtB6euP0BSZnmSeDkqHp4uDuDxOOQWyMHncbAW0Q3V1MhkMpw6dQpnzpzRmrQZKO8OExoaip49ezZq377aaGUnhpO0PNmUy81hZWWFolIlMvPlUKkZHqXmwU5sSa2A9eTv74+VK1dqpjSpqiWw4onX1KlTazWXn5WVFVxdXeHk5KT3+mIqEhIS6nVcbf5+hmQ0teXDDz/EO++8g3HjxsHX19fQ4RBSIz8PF/h5lPeNYoyB4zik5cgQnZSpVU6lUuNubAr4PA6ONlLEJGciLi0L7s52OBB5EwJzM4wb3JMeC5uIlJQUnDhxApcvX9aZUqRDhw4YMmQIOnToYJAEy9LCDL18/u3vV1RUBIGlECduJ4DP8SCxMkNCVj7audpQK2ADhISEYPPmzfjxxx9x5swZnX6eHTp0wLBhw56aPNja2sLNza3Oj4hJ3Xl6etbr/8mGrtPc1IwmAbxw4QLs7e3RqVMnDBo0CJ6enjodWDmOw/r16w0UISG65JkJyIm+DNeAkXCxk8LFrnw+QKVKBTM+H3ceJyNTVggHqQg8HgdFaRkuR8WjWFGG+LQc8HkcYlOz4O1Gj4VbKsYYHjx4gBMnTuDu3bta+/h8Pnr16oWQkBC0bt3aQBFWzcLCAknZBciUyWEjsoQZn4OsWEGtgI3A398fa9euRVpaGiZOnIiCggIIhULMnz+/xoSOz+fDyckJrq6uWms5k6a1efPmFlnfjSYB/OqrrzT/rm6GbUoAiTFhTI3sRxdRkBQFsXNb2PyzNFxSZi6OX7mPUYFd8DglCwJzMxTIy+fbEgoskJlbgLP50eBxHFRqhitRcfByLe/cf/txErzdHCEW0qoizZ1KpcLVq1dx/PhxJCUlae0TCoUYMGAABg0aBBsbG8ME+BTm5uZIzC4Ej8dB9k/95XM8ZMjkkJcqIRJUvaYwqb3Ky+5ZWFhUm/yZmZmhTZs2cHFxoce8BvC0iaabqzrVJC8vrzpnwRzHISYm5qnl1Gp1nc5LiKEVpceiKD0WHMdDbsw1SFp3AM9cgMv34/AwIR03HBIR2rMD+jzRVzA2JQvHrt6Hi601VEyNmORMxKZmgeOAY5fvI6dAjsHdqRtEc1VcXIyzZ88iPDwceXl5Wvvs7OwQEhKCwMBAg46GfJqErHzYiizRzdMR7V1ttPaZ8XmwsqAkRF/s7OzQvn17rRG2xLjIZDKIxWLw+XxDh1Indfq/ODg4WCcBvHLlCu7evYuOHTtq+u49ePAA9+7dQ6dOnbQWTCakpWBMjdzH18DUKnAiexRkJaIg6T4KxB54mJAOkVCAWzFJ6Na+DZztrFGckwpZwm3Y+wfjaFI6CuUKpCMfAFAgV+Dy/VhwPB6yZIW48TAR3XzawFbS9CM/SePJyclBeHg4IiMjdUZgenh4IDQ0FN26dTP6m4SsSIEr0elwtrFCoG8rSIQWhg7JJPF4PHh4eMDNza1FPn5s7q5cuYKPPvoIp0+fRmlpKf7++2+EhIQgKysLM2bMwLx584x+/uA6JYBPzp+zd+9e7N27F8eOHcOQIUO09h07dgzjx4/H8uXL6xTQhQsXEB4ejoyMDLz++uto164d5HI5oqKi0L59e1qRgxiFitY/vsAKSmUp8vMLkBN9FTfMlSgpU8LTxQ6xqdm48TARQ3r6IefRBeQnRcHSzg1tHG1hK9Huv1OsKEV0ciY8nO2QllOAG9GJ1ArYTCQkJOD48eO4du2azpOMLl26YMiQIfDx8Wk2N/HotDzI5AqUqVTIyi+Go5S+iOibra0tvL29G2VdZ9L4zp07h5CQELi5uWHy5Mn44YcfNPscHBwgk8nw3XfftawE8EmLFy/GW2+9pZP8AcDQoUPx5ptv4qOPPsLYsWOfeq7S0lJMmDAB+/bt04yqHD16NNq1awcej4dhw4Zh3rx5WLhwYUNCJqRRFOemIi1XjvM3ryO/oAjFRQXo3sscSba2EAisICsqhhmfh1sxSehgDxSmRoMpyyB7fA3BAyaAxzdHmTwfKkURLKQu2HXqKlQqFURCAWwkSmoFNHJqtRr37t3D8ePH8fDhQ619ZmZm6Nu3L0JCQprdKhqyIgViM2SwEQlQVFKGh6m5cLAWNpvktbnjOA4+Pj7Nrt6Ymg8//BAdOnTAhQsXUFBQoJUAAsDgwYO1VhwxVg1KAB89egR7e/tq99vb29eq/x8ALFq0CAcPHsTGjRsxePBgralgLC0tMW7cOOzbt48SQGJwly5dwvJln+HQ4cPaUzjsioB3x24YPn4q2vp1glhoCTMeh4yHl2GpLIPQoQ3k2UkoTHkESesOyLgdDkV+Jsw6jkRieg7KVGrEpWWDsfKRo/fjUhHY2duAvyl5UllZGS5duoQTJ04gLS1Na59YLMbAgQMRHBzcLBaCr0p0Wh7kpUq4SK3A5/GQlF1ArYBNzN7eHgqFAhKJRLOGLjFuly9fxsqVKyEQCFBYWKiz383NTefzwRg1KAH09vbGli1bMGPGDJ1HswUFBdi8eTPatm1bq3P98ssvmDNnDmbNmoXs7Gyd/R06dMCuXbsaEi4hDfbHH3/gpZdeAmNMZyZ/xhge37+Jrz9+B++99z5emjABjlZA4d2r4FlJNeVyY66CZ2GJwtRHUJUWwzY/HiEBfjrna+VgU6uYGGNYsmQJNm3ahLy8PPTv3x8bN25Eu3btqj1m48aN2Lhxo2aGf39/fyxevBjDhw8HUN6fbcmSJfj777+RkJAAR0dHPPvss1i+fDmkUmm1522pCgsLcebMGZw6dUpneS0nJycMGTIEffr0gYVF8+0vV1RShsSsfDA1Q0Z++eTUSqUacZkySgCb0Pbt2xEZGQlLS0u0atXK0OGQWjA3N69x4GpycnKz6K7WoATwk08+wYsvvgg/Pz+88sor8PHxAVDeMrht2zakp6fXOmnLyMhA586dq93P5/N1ZswnRJ8uXbqEl156CSqVqtplnCo+FD7/fBU4DujibAZblgOh0BJWVlYQCoUolecj8+5pqJWl4AtEKE68ha6DuoJvUb/+Pp9//jm+/PJLbNu2TbPoelhYGO7du1ftSNPWrVvjs88+Q7t27cAYw7Zt2zB27Fhcv34d/v7+SElJQUpKClavXo2OHTsiPj4er732GlJSUrB79+56xdkcZWRk4OTJkzh//jzKysq09vn4+GDIkCHo3Lmz3tYrb0oW5nx09nCESq1dt62tmm9S25zUd7Jhon99+/bF7t27MXfuXJ19RUVF2LJlC4KDg/UfWB01KAF89tlncfjwYbz//vv49NNPtfZ169YNP/74I8LCwmp1rjZt2iAqKqra/ZGRkZoEkxBD+OSTT6ps+avO4cOH4fXaLBSrbQEVgILyHxtLBezLYiG0tofYygoKWSbyE+/B1rvuI+YZY1i3bp1WX9uffvoJzs7O2Lt3LyZMmFDlcaNHj9Z6vWLFCmzcuBEXLlyAv78/OnXqhD179mj2e3t7Y8WKFZg8eTKUSmWLn4ssJiYGJ06cwM2bN3VWaujevTtCQ0Ph6elpuACbgDmfB28XG0OHYZIsLS1hZ2dn6DBILX388ccIDg7GyJEjMXHiRADAzZs38fjxY6xevRqZmZlYtGiRgaN8ugZ/ig8bNgzDhg1DWloa4uPjAZRPeVDXfgyTJk3C2rVr8cILL6B9+/YAoPk2tGnTJvz+++/47LPPGhouIfWSkJCAgwcP1jr5U6vVuHXrFrLz8nU+2JX5jyFXZKIgPw8ZSYA51EiX7UWrUku4e3nXaX642NhYpKWlITQ0VLNNKpWiT58+OH/+fLUJYGUqlQq7du1CUVER+vXrV205mUwGa2vrFpv8qdVq3Lx5E8ePH9dZ7F0gEKBfv34ICQmBg4ODgSIkLZWdnR21/jUjffr0weHDhzFnzhxMmTIFAPC///0PQPmX5cOHD6NLly6GDLFWGu2T3MXFpUGdVxcuXIgLFy5g4MCBmrUw582bh5ycHCQlJWHEiBGYN29eY4VLTFBpaanO+qu1oVarsWfPnlonfxUYY7h//75OUlXCE0Npqb3Op7qIhwenTgGnz8Le3h5ubm5wd3eHo6Njjf3KKjoaOzs7a213dnZ+aifk27dvo1+/figpKYFYLMaff/6Jjh07Vlk2KysLy5cvx6xZs2o8Z3OkUChw4cIFnDx5EpmZ2ms5S6VSDBo0CAMGDICVFfWDI02juQ4aMmUhISF48OABbty4gUePHkGtVsPb2xsBAQHNJplvcAKYkJCATz/9FOHh4cjMzMTevXsxcOBAZGVlYdmyZZg2bRq6d+/+1PNYWFjgr7/+wo4dO7B7926oVCooFAp06dIFn3zyCV5++eVmc1GJ8SktLcWlS5eqHLGlUqlQXFwMuVyO4uJizU/l1zdv3gTHcXVKAjmOg0wmQ2FhIazUBeBBjUKeFIWwBFBFK19ZMYDyAVRxcXGabg8jRozQJIE7duzA7NmzNYccOnSobheiEl9fX9y4cQMymQy7d+/G1KlTERERoZME5ufnY+TIkejYsSOWLl1a7/czNjKZDBEREThz5gyKioq09rVq1QqhoaEICAigFRhIkzPmVWFIzbp164Zu3boZOox6aVACeO/ePQwYMABqtRp9+vRBdHS0poXFwcEBZ8+eRVFREX788UedY9955x28/PLLmuSwYqTh5MmTMXny5IaERYgGYwyFhYVISUnBw4cPUVpaitLSUhQVFWl+FArFU89jYWFRrxZAK0sLmHEMDuoM8KCCgi+Gint6QsFxHOzs7GBhYQGlUqlJAMeMGYM+ffpoylXEnp6eDldXV8329PT0p34oWVhYaPrVBgQE4PLly1i/fj2+++47TZmCggI888wzkEgk+PPPP1tEMpSamooTJ07g0qVLOi3Cfn5+CA0N1TyFIEQfWmq3ipbi9OnT9Tpu4MCBjRxJ42pQrXvvvfdgY2ODCxcugOM4ODk5ae0fOXIkfvvttyqPXbduHXr27KlJAL28vLB9+3ZMmjSpISERE1NWVoa8vDzk5OQgJycHubm5yM7ORm5urub1k6M368LCwgJisRi9evXCqVOn6tgCCHT3doaE5cOSlYADg7VaBpm5U7XHSCQS2Nvbw8nJCYwxnSlHJBKJ1uMixhhcXFxw4sQJTcKXn5+PixcvYs6cOXX6XdVqtVYynJ+fj7CwMAgEAuzfv79Zt1IwxvDw4UMcP34cd+/e1drH4/HQs2dPhIaGonXr1gaKkJgyY18e0NQNGjRI6wthxWIVT6NSqZoyrAZrUAJ4+vRpLF68GI6OjlXO3efu7o7k5OQqj3V2dsbjx481r+vaukJaPsYY5HK5JrmrnORV/Fcmk9X7/BzHwcrKCmKxGGKxGCKRSOfflfvfXbhwAZGRkbX6n5rHcejp5wYvSSmgzIIaPDDwYK3ORiGz1bQCmpmZwcbGBra2tpoWvwpPridb3e8wd+5cfPLJJ2jXrp1mGphWrVrh2Wef1ZQbMmQInnvuObz55psAgAULFmD48OFwd3dHQUEBdu7ciVOnTuHo0aMAypO/YcOGQS6X4+eff0Z+fj7y88vXLnZ0dGw2NyyVSoVr167h+PHjSExM1NonFAoRFBSEQYMGwdbW1kAREoIWMY1QSxYeHq71WqFQ4L333oNcLsesWbM0C1dERUVh06ZNEIlE+Pzzzw0Rap00KAFUq9U1dozOzMyEQCCoct/IkSOxbNky/P3337CxsQEArFmzBr/++mu15+M4Dvv27WtIyCYhISEBJ06cQEFBASQSCYYMGQJ3d/enH6hnKpVKq/WucnJX8e/aPJ6tjkAggJ2dHezs7CCRSJCXlwc7OzvY2tpCJBJBJBLV6YN3xowZiIyMrF1fQI5D2NBQlHFCiNR5UHAiMHAQoRitxWqYuXhDKpXCysqqwY8a33vvPRQVFWHWrFnIy8tDUFAQ/vrrL60Wu5iYGGRlZWleZ2RkYMqUKUhNTYVUKkWXLl1w9OhRDB06FABw7do1XLx4EQB0pl+KjY3V+xQoda3TxcXFiIyMRHh4OHJzc7X22dnZISQkBIGBgc26VZMQoh9Pzun3zjvvwMLCAhcuXND6DBk9ejTeeOMNBAcH46+//tJ8nhqrBiWAPXr0wKFDh/D666/r7FMqlfj111/Rt2/fKo9dv349nJycEB4ejrt374LjOCQmJiInJ6fa96M+OTW7dOkSli9fjkOHDoExBh6PB7VaDY7jMGrUKCxatAi9evXSWzzFxcXVttxlZ2dDJpM1qOVXKpVqErqKRK/itb29PYTCf9cwlcvlOH36NCQSSb1v+v7+/li5ciUWLFgAoOrm/YqEcurUqbDz6gKBOh6WnCWsLfgwMzMHj2cJnlAFMxdncLzGaUXjOA7Lli3DsmXLqi1TseJHhar65VY2aNAgo2iVr2udzsnJQXh4OCIjI3VaUN3d3REaGoru3bs3mxZMYhqM4f81Uns7duzARx99VOW9xMrKCi+//DJWrFiBNWvWGCC62mtQArhgwQKMGjUKc+bM0cw3lp6ejuPHj+PTTz/F/fv38dVXX1V5rEgk0po8msfjYd26ddQHsJ6qWqKsYlUKxhgOHz6MI0eO4LfffsPzzz/f4PdTq9WQyWQ6rXaVk73i4uJ6n9/c3LzG5E4qlRpkQEJISAg2b96MH3/8EWfOnNGZJLhDhw4YMWIE+vbtCycnJwhUfoDyiUe5fAuAo0c+T1OXOt2rVy8cP34cV69e1VmiqVOnTggNDUW7du3oSyQxSvSFpHkpKipCampqtftTU1ObxcplDUoAhw8fjq1bt+Ltt9/G999/DwCYPHkyGGOwtrbGTz/9VO0omOeffx7z5s3DgAEDAJQ/Y69uDjJ9+/rrr/HFF18gLS0NXbt2xYYNG9C7d29Dh1Wt2ixRplKpwHEcXnrpJZw7d+6pLYEKhaLG5C43N7fGtRCfRiKRVJvc2draQiwWG+3N2t/fH2vXrkVaWhomTpyIgoICCIVCzJ8/Hz4+Pmjfvn2lUX31W97N1NWlTo8bNw7PPfec1iA0MzMz9OnTB0OGDGnQ/KSE6IOxftY1F/q+Z4eGhmL9+vXo2bOnToPKnj17sH79+lqvgmZIDR57/vLLL+P555/HsWPHtCZDDAsLq3Fyy3379uGFF17QvA4JCTGKUcC//fYb3nnnHXz77bfo06cP1q1bh7CwMDx48EBnlLOxqO0SZRVlli9fju3bt1eb3OXk5OjMi1YXZmZmsLW11UrwKid3tra2NU5u3Fy4uLigdevWyMvJBt/cAl27doWHh4ehw2oR6lKnAeDq1asYPnw4RCIRgoODERwcTJPrEmICDHHP/vrrrxESEoJx48bB1dVV0086JiYGKSkp8Pb2xoYNG5rkvRtTvRNAuVyONm3a4IMPPsD8+fO1RhzWhpubG65fv47//Oc/AGo/rLqprV27FjNnzsS0adMAAN9++y0OHTqEzZs344MPPjBwdLrqukSZSqXCgQMH8Oabb9b7BikSiapN7ioGXJjKqLZtGz5DcfJtxCkdNckfU6vACjPASZzBVXrUayx13NjVtU4zxhAXF4fBgwdj7NixLeLLBSGkdgxxz3Zzc8PNmzfx3Xff4ciRI5plcP39/TF//nzMnDkTQqHxP/2pdwJoZWUFMzMziESieh0/YcIErF69Gr///rtmFPAHH3yAlStXVnsMx3G4efNmvd6vNkpLS3H16lVNJ3+gvG9iaGgozp8/X+UxCoVCa6RqxUoTSqWyQfPP1dbRo0fr1YE4MTFRM3S9Mh6PBxsbG01iV5HcVd72tEEUKpXK6OY/Kisrg1KpRFFRUb2Wg6sKU6uAxFtQ5SXByUGq+duzvAQg/R7g2gWcdSsAQEmZEldiMuHnZgsHyb/Xr6akUKFQaOqRPuqSsahvnZbJZOA4zmSuVVPU6abWkDrN4/Eapa+cSqWqc/cVU7vWhlRxfQsLCzVTTwHlszo8OatIfe7ZjcXS0hJvv/023n777SZ9n6bUoEfAL7zwAnbv3o05c+bUuWVj5cqV8PHxQXh4ODIyMsBxHEQiEezt7RsSUoNkZWVBpVJVua5qVFRUlcesXLkSH3/8sc72yqs1GKOIiAhEREQYOgxCGs3MmTMxc+ZMQ4dBmsiSJUsaZSnC5cuXV/mZTYzLk1OvVPX3r889uykUFRVhzZo1mDJlit6nyGoIjjVg/Pnp06fx+uuvw8HBATNnzoSnp2eVzZ49evR46rl4PB5+/vlng/YBTElJgZubG86dO4d+/fpptr/33nuIiIjQzItW2ZMtgDdu3EBwcDAuXrxYqzWQG2rr1q2YNWtWnY/btGkTpk6d2gQRGa/S0tLGa/1TKZF6+U8oclIBoS3UhRmw7zgIZgIRMm4cgZnIFkq5DM4BIwEbd/x64gpy8uUwN+Pj+eBucHeyxb3YVBw8fwcervZ4cWA38Pm6j83NzMxM7pEm1enaa8w6rS/1rdOGbAEETOtaG9L169fRp08fREREaC1nWVULYH3u2U0hPT0drVq1wrFjxxASEqKX92wMDWoBHDRokObfZ86c0dlf8XirNo8DY2Nj4ejo2JBwGszBwQF8Ph/p6ela29PT06sdSfhkpRSLxQDK/8fTxzQlYWFhtZuYuBKO4zBs2LAWsa5rXTTm71uQHAUUZkJoJQJ4aqgEFihLvw+VmQBCKysIbR1QrC6GMuMhkkqtkV+ihLe7CxIzcvEwOQftPd1wPzkbFgIBMmRyZBQo4OdBo1UBqtN1YWq/b2Pg8/n1SiTpWutHxQwKYrEY1tbWNZatzz27qTTHuRwblABu2bKlseIwitGTFhYWCAgIwIkTJzSDWtRqNU6cOKFZQsvYuLu7Y9SoUTh8+HCtEm0+n4+RI0ca5cogzUlpYR7MraQAyv+n54ttoSwuhLIkDTy+GeQZcVCrypCdGo9LcULweeYoVpTBSmCBh0npOH3DEkkZuXBzskF6dgEuR8WhXWsncFCD4/G0Bo+YGqrThsUYQ+bdCIicPCFy8jR0OIRUqznes41JgxLAhjxu4fF44PF4kMvlsLCwAI/He2o/Qo7jmrwJ/p133sHUqVPRs2dP9O7dG+vWrUNRUZFmhJExWrRoEY4cOfLUVhOO48BxHD766CM9Rtcy2bXvA7t22nMpluSmoTAtRmtbQk4xuEQVrMzNUKwoBccBZnw+zt+NhZoxlJWpIBJaIC41Gw8T02CdfgkWEns4dhygz1/H6FCdNhx5VgJyo6+gJCcFVg5tGm3FGkKagjHcs/l8Pjw8PJrFyN/KGjwPYH0tXrwYHMdpmnsrXhvaSy+9hMzMTCxevBhpaWno1q0b/vrrL51OpsakV69e+O233zSrJlTVasLn88FxHH7//Xe9LgfXUnEcB3DaN0ahvRuE9m5a2xwYQ/tiBSrnMOk5+Th66S5KSstQVFJafqzAHCmx98HlPQTfQgipeydYiG2b/PcwVlSnDYMxhryYq1CVyiHPTEBh6iNI3PzA1CpkRZ2DxM0XllLjnA+VmCZjuGc7ODggNjZWb+/XWOo0CGT69OngOA7ff/89+Hw+pk+f/vQ34Linrjvakly7dg0BAQG4evVqrQa/NKbLly9j+fLlmjnUKq+bOnr0aHz00Ud0o2xkssJiPErKQI/27uDxav8FpkBeAlWljuiMqZF/8wjkaY/AmBqOHQbAsVNwDWcwDVSn9asoMx5Jkb/DzEqKssJcWDm0QZugl1CU/hhJF/bCurUvXHuONoov66RlMuQ91NTUqQXw5MmTmg9gPp+PkydP1uqxLdGPXr16Yf/+/UhISMDJkyeRn58Pa2trhISEUP+oJnLpfiyuP0qEjUQIH7fat4xIrLTnUixKj0VxZiwE1o5QlZUgL/4WpJ5dTLoVEKA6rU8VrX/qMgV4ZhYwF9miKDMeBckPkBd/CypFEQpSHsEmJxlW9q0NHS4hBnXr1i1s2LAB165dg0wm0xlZznEcYmJiqjnaONQpAYyLi6vxdV389NNP9TpuypQp9X5PU+Hu7o5XXnnF0GG0eFl5hbgVk4zcAjmuRsWjratjnVoBKzCmRu7ja1CWFIEvsALAQVGQCVncLWoF/AfV6aZXWpiDkrwMcHxzKPLKR1VyPD6yH16EQpYBoX0bKGRpyHt8DUI7N/pyT0zWqVOn8Mwzz8DW1hY9e/bE9evXERISgpKSEpw/fx7+/v4ICAgwdJhPZbA+gFV9mFd8oDz5VLryBw0lgMRYXH+UgAJ5CTyc7RCTnInHqZl1agWsoFaWQqmQ/9u3igMsrZ1QWpTTyBETUj0LsR3c+jxbvsLNP5hajYw7J8HUKvDMLWAutqdWQGLyFi9ejLZt2+LChQsoLS2Fk5MTPvzwQ4SEhODixYsYPnw4Vq1aZegwn8pgCeCTHSbz8vIwdepUSKVSvPXWW5plyqKiorBhwwYUFBRg27ZthgiVEB0VrX+2EitYWVogM6+w3q2AfHNLeAycpPvFx0TWUybGgeM4WNpqz51WnJMMZXFB+b+zEsvL8XgoTH1ECSAxWdeuXcPHH38Ma2tr5ObmAoBmoFqfPn0we/ZsLFq0CMOHDzdkmE/V4ATwyJEjWLt2reY5eFVjSqoawffkvH9Lly6Fo6Mj/v77b60Wv86dO+OFF17AsGHD8H//93+NOvcgIfV1Pz4VRcXlK8DICouhZgzJmXlIycpDa6e699vjeHzQAzVibARSZ7j2Gg080b/JQmJnoIgIMTwzMzNIJBIAgI2NDczNzZGRkaHZ37ZtW9y7d89Q4dVagxLAPXv2YPz48fD398eECROwceNGTJpU3pKxb98+tGvXTjM549Ps3bsXK1asqLJfCY/Hw/PPP09zfRGj4evuAqlYe84nHsfBXio2UESEND4e3wwiR8NP0k+IMfHx8cGjR48AlLec+/n54c8//8R//vMfAMChQ4f0vhJJfTQoAVy5ciV69+6Ns2fPIjc3Fxs3bsT06dMREhKCuLg49O3bF15eXrU6F2OsxsWb79271yyXWiEtk5OtBE62EkOHQQghRM9GjBiBzZs3Y+XKlTAzM8M777yDadOmoV27dgCAmJgYrFy50sBRPl2DOhndu3cPEyZMAJ/P10zoXFZWBgDw9PTE66+/XuuOkM8++yw2btyItWvXQi6Xa7bL5XKsWbMG3333HcaOHduQcAkhhBBCGmTRokW4efOmZk3pqVOn4qeffkKnTp3QtWtXbN68Ge+//76Bo3y6BrUAWllZwcLCAkD5c3CBQIDU1FTNfmdn51rPjr1+/XrExsbi3XffxYIFC+Dq6goASE1NRVlZGfr3749169Y1JFxCCCGEkAYxNzeHvb291rbJkydj8uTJBoqofhrUAujr66vV0bFbt27Yvn07lEolSkpKsHPnzlpP1iqVShEREYE///wT06ZNQ4cOHdChQwdMmzYNe/fuxenTp2FjY9OQcAkhhBBCGqRt27bYv39/tfsPHjyItm3b6jGi+mlQC+Dzzz+PL7/8EqtXr4ZAIMDChQsxduxY2NjYgOM4FBUVYfPmzXU659ixY+lRL2k2FPlZkCXehYNff/D4BptViRBCiJ7ExcWhsLCw2v2FhYWIj4/XY0T1U687VklJCfbt24eysjJ89NFHyMnJgaurK0aNGoVTp07hjz/+AJ/Px8iRIzF48ODGjpkQo8AYQ86jS5Al3IGl1BnWrf0MHRIhhBA9qGklnMuXLzeLJ5Z1TgAzMjIQGBiI2NhYMMbAcRyEQiH27t2L0NBQDBgwAAMGDGiKWAkxKiW5achPioJKIUduzFWIXX2oFZAQQlqg9evXY/369QDKk7+5c+di4cKFOuVkMhny8vIwadIkfYdYZ3W+Wy1fvhxxcXGYN28eQkJCEB0djeXLl2P27NlGv/AxIY2FMYa82OtQlcohdHBHcVYCClOjqRWQEEJaICcnJ/j7+wMofwTs5uYGNzc3rTIcx0EkEiEgIACvv/66IcKskzongH///TemTJmC1atXa7Y5Oztj0qRJePDggWYJN0JasorWPwuxHXh8c4DjqBWQEEJaqIkTJ2LixIkAgMGDB+Ojjz7CkCFDDBxVw9R5FHBCQgKCgoK0tgUFBYExhvT09EYLjBBjVpgWDaZWokwuQ3FOEhhjKC3MQUlOiqFDI4QQ0oTCw8ObffIH1KMFUKFQwNLSUmtbxWulUtk4URFi5Gy8usHKoY32Ro6DpV0rwwRECCGkSZw+fbpexw0cOLCRI2lc9XpWFRcXh2vXrmley2QyAMCjR4+qHPnSo0ePWp33/v372LJlCx4/fozc3Fydpd84jsOJEyfqEzIhjcpcKIG5kJaCI4SQlm7QoEFao34rBsBWp2K/SqXSR3j1Vq8EcNGiRVi0aJHO9ic7PdblImzfvh3Tpk2Dubk5fH19YWtrq1OG1gImhBBCiD6Fh4cbOoQmUecEcMuWLU0RB5YuXYru3bvjyJEjcHBwaJL3IIQQQgipi+DgYEOH0CTqnABOnTq1KeJASkoK3n33XUr+CCGEENIspKamIiMjAz4+PhCJRIYOp04atBZwY+rSpQtSUmgEJSGEEEKM2759++Dn54fWrVujR48euHjxIgAgKysL3bt3x969ew0bYC0YTQK4du1a/Pjjjzh37pyhQyGEEEIIqdKBAwfw/PPPw8HBAUuWLNEan+Dg4AA3N7cm6y7XmIwmAVy1ahWkUikGDBiAzp07Y+TIkRgzZozWz9ixY5s8jtOnT2P06NFo1aoVOI5rFlk8IYQQYooMcc9etmwZBg4ciLNnz+KNN97Q2d+vXz9cv369yeNoKKNZsuDWrVvgOA7u7u4oLCzEvXv3dMrUNOy6sRQVFaFr166YPn06nn/++SZ/P0IIIYTUjyHu2Xfu3MHatWur3e/s7IyMjAy9xNIQRpMAxsXFGToEAMDw4cMxfPhwQ4dBCCGEkKcwxD3bysoKRUVF1e5//Pgx7O3t9RhR/RjNI+DmSqFQID8/X/NTWFho6JAIIYSQZq2wsFDr3qpQKAwdksbgwYOxbdu2Klc/S0tLw6ZNmzBs2DADRFY3RtMCWFlBQQFkMhnUarXOPnd3dwNEVL2VK1fi448/NnQYhBBCSIvx5Nx7S5YswdKlSw0TzBNWrFiBvn37olevXhg3bhw4jsPRo0dx8uRJfPfdd2CMYcmSJYYO86mMKgHcuHEj1q5di8ePH1dbxtiWVlmwYAHeeecdzesbN2602EkjCSGEEH2IiIhAt27dNK8FAoHhgnmCr68vzp49i7fffhuLFi0CYwxffPEFgPJl477++mt4enoaNshaMJoE8Ntvv8Ubb7yBsLAwTJ8+HQsXLsS8efNgaWmJrVu3wtnZGf/9738NHaYOgUCgVTHFYrEBoyGEEEKaP7FYDGtra0OHUS1/f38cP34cubm5iI6OhlqtRtu2beHo6Gjo0GrNaBLADRs2ICwsDEeOHEF2djYWLlyIkSNHIiQkBO+99x569uyJ7OxsQ4dJCCGEEBOlUCjw888/4++//0ZMTAwKCgogkUjg4+ODZ555BpMmTYKFhYWhw6wVoxkEEhMTg9GjRwMAzM3NAQClpaUAAKlUildffRXffPNNk8dRWFiIGzdu4MaNGwCA2NhY3LhxAwkJCU3+3oQQQgipPX3es2/fvo0OHTpg1qxZ2LVrF2JiYiCXyxETE4Pff/8dM2bMgL+/P+7fv9/o790UjCYBlEqlmhE11tbWsLKyQmJioma/RCJBWlpak8dx5coVdO/eHd27dwcAvPPOO+jevTsWL17c5O9NCCGEkNrT1z27sLAQY8aMQXp6OlasWIHExETk5uZq/feTTz5BSkoKRo8eXeM0McbCaB4Bd+rUCTdv3tS87tu3LzZu3IgRI0ZArVbju+++Q/v27Zs8jkGDBmkt60IIIYQQ46Sve/aWLVuQkJCAEydOYNCgQTr73dzcsGDBAvTp0wdDhw7F1q1bq1wlxJgYTQvg5MmTcefOHc1cPx9//DHu378Pd3d3eHp64sGDB/jkk08MHCUhhBBCTM2hQ4cwbNiwKpO/ykJCQjB06FAcOHBAP4E1gNG0AE6bNg3Tpk3TvO7fvz/u3r2LAwcOgM/nY9iwYXppASSEEEIIqez27du1nokkJCQE69evb+KIGs5oEsCqtG3bFm+//bahwyCEEEKICcvJyYGLi0utyjo7OyMnJ6eJI2o4o0sAL1y4gPDwcGRkZOD1119Hu3btIJfLERUVhfbt29M8e4QQQgjRK4VCoZmh5GnMzMw0s5gYM6NJAEtLSzFhwgTs27cPjDFwHIfRo0ejXbt24PF4GDZsGObNm4eFCxcaOlRCCCGEmJi4uDhcu3btqeViY2P1EE3DGU0CuGjRIhw8eBAbN27E4MGD4evrq9lnaWmJcePGYd++fZQAEkIIIUTvFi1ahEWLFj21XEUjlrEzmgTwl19+wZw5czBr1qwqV/zo0KEDdu3aZYDICCGEEGLKtmzZYugQGp3RJIAZGRno3Llztfv5fD7kcrkeIyKEEEIIAaZOnWroEBqd0cwD2KZNG0RFRVW7PzIyEj4+PnqMiBBCCCGkZTKaBHDSpEn47rvvcP78ec22imfomzZtwu+//44pU6YYKjxCCCGEkBbDaB4BL1y4EBcuXMDAgQPRoUMHcByHefPmIScnB0lJSRgxYgTmzZtn6DAJIYQQQpo9o2kBtLCwwF9//YUtW7agbdu28PPzg0KhQJcuXbB161bNiiCEEEIIIaRhjKYFECh/5Dt58mRMnjzZ0KEQQgghhLRYRtMCSAghhBBC9MOoWgDPnj2LzZs34/Hjx8jNzQVjTGs/x3G4efOmgaIjhBBCCGkZjCYBXLt2LebPnw9LS0v4+vrCzs7O0CERQgghhLRIRpMAfvHFF+jfvz8OHDgAqVRq6HAIIYQQQloso+kDKJfL8Z///IeSP0IIIYSQJmY0CeDgwYNx+/ZtQ4dBCCGEENLiGU0CuGHDBpw4cQKrV69GTk6OocMhhBBCCGmxjCYBbNOmDWbPno0PPvgAjo6OEIlEsLa21vqhx8OEEEIIIQ1nNINAFi9ejBUrVsDNzQ09e/akZI8QQgghpIkYTQL47bffYuTIkdi7dy94PMM0TK5cuRJ//PEHoqKiIBQKERgYiFWrVsHX19cg8RBCCCGkanTPbhijeQRcWlqKkSNHGiz5A4CIiAi88cYbuHDhAo4dO4aysjIMGzYMRUVFBouJEEIIIbront0wRtMCOGrUKJw5cwazZ882WAx//fWX1uutW7fCyckJV69excCBAw0UFSGEEEKeRPfshjGaBHDJkiV46aWX8Prrr2PGjBlwd3cHn8/XKafPFUJkMtlT31OhUEChUGheFxYWNnlchBBCSEtWWFiI/Px8zWuBQACBQFDjMbW5Z5N/cezJBXcNpPKjX47jqi2nUqn0EQ7UajXGjBmDvLw8nD17ttpyS5cuxccff6yz/erVq+jRo0dThkgIIYS0KNeuXUNAQIDO9iVLlmDp0qXVHlfbezb5l9G0AC5evLjGxE/f3njjDdy5c+epFWnBggV45513NK9v3LiB4ODgpg6PEEIIabEiIiLQrVs3zeuntf7V9p5N/mU0CWBNmb2+vfnmmzh48CBOnz6N1q1b11j2yWZpsVjc1OERQgghLZpYLIa1tXWtytblnk3+ZTQJoDFgjOGtt97Cn3/+iVOnTsHLy8vQIRFCCCGkCnTPbhhKACt54403sHPnTuzbtw8SiQRpaWkAAKlUCqFQaODoCCGEEFKB7tkNYzTzABqDjRs3QiaTYdCgQXB1ddX8/Pbbb4YOjRBCCCGV0D27YagFsBIjGRBNCCGEkKege3bDUAsgIYQQQoiJoQSQEEIIIcTEUAJICCGEEGJiKAEkhBBCCDExlAASQgghhJgYSgAJIYQQQkwMJYCEEEIIISaGEkBCCCGEEBNDCSAhhBBCiImhBJAQQgghxMRQAkgIIYQQYmIoASSEEEIIMTGUABJCCCGEmBhKAAkhhBBCTAwlgIQQQgghJoYSQEIIIYQQE0MJICGEEEKIiaEEkBBCCCHExFACSAghhBBiYigBJIQQQggxMZQAEkIIIYSYGEoACSGEEEJMDCWAhBBCCCEmhhLASjZu3IguXbrA2toa1tbW6NevH44cOWLosAghhBDyBLpnNwwlgJW0bt0an332Ga5evYorV64gJCQEY8eOxd27dw0dGiGEEEIqoXt2w5gZOgBjMnr0aK3XK1aswMaNG3HhwgX4+/sbKCpCCCGEPInu2Q1DCWA1VCoVdu3ahaKiIvTr16/acgqFAgqFQvO6sLBQH+HVSmpqKlJTUw0dRovn6uoKV1dXQ4dhEqhO6w/Va2JIhYWFyM/P17wWCAQQCATVlq/tPZtUwoiWW7duMZFIxPh8PpNKpezQoUM1ll+yZAkDoPUTHBzMUlJS9BRx1UpKSlhwcLBObPTT+D/BwcGspKTEoH9vU0B1mup1S1RSUsKWLFlC1/ofKSkpVf5/vmTJkirL1/WeTf7FMcYYiEZpaSkSEhIgk8mwe/du/PDDD4iIiEDHjh2rLP9kCyDw9G8q+pCfnw+pVIqIiAiIxWKDxtKSFRYWIjg4GDKZDNbW1oYOp0WjOq0/VK/1p6Je07X+V13uq3W9Z5N/UQL4FKGhofD29sZ3331n6FDqhD5U9IOus/7QtdYfutb6Q9e6cTXXe7Yh0Cjgp1Cr1TrfRAghhBBifOieXXs0CKSSBQsWYPjw4XB3d0dBQQF27tyJU6dO4ejRo4YOjRBCCCGV0D27YSgBrCQjIwNTpkxBamoqpFIpunTpgqNHj2Lo0KGGDq3OBAIBlixZYvC+iC0dXWf9oWutP3St9Yeudf21pHu2IVAfQEIIIYQQE0N9AAkhhBBCTAwlgIQQQgghJoYSQEIIIYQQE0MJICGEEPIUp06dAsdxOHXqVLOMYevWreA4DnFxcY0eF2meKAEkLUbFB1zFj6WlJVq1aoWwsDB8+eWXKCgoMHSIOn7//XdwHIc///xTZ1/Xrl3BcRzCw8N19rm7uyMwMFAfIRIj0BzrNqAd99mzZ3X2M8bQpk0bcByHUaNGNfp7NvW12rlzJ9atW9do59O3b775Blu3bjV0GMRAKAEkLc6yZcuwfft2bNy4EW+99RYAYO7cuejcuTNu3bpl4Oi0BQUFAYDOzTE/Px937tyBmZkZIiMjtfYlJiYiMTFRcywxHc2pbldmaWmJnTt36myPiIhAUlJSk0yBoo9rRQkgac5oHkDS4gwfPhw9e/bUvF6wYAFOnjyJUaNGYcyYMbh//z6EQmG1xxcVFUEkEukjVLRq1QpeXl46CeD58+fBGMO4ceN09lW8pgTQ9DSnul3ZiBEjsGvXLnz55ZcwM/v3trNz504EBAQgKyur0d+zodeKkJaOWgCJSQgJCcGiRYsQHx+Pn3/+WbP9lVdegVgsRkxMDEaMGAGJRIL//Oc/AABPT0+88sorOucaNGgQBg0apLUtPj4eY8aMgUgkgpOTE+bNm4ejR4/Wqr9OUFAQrl+/juLiYs22yMhI+Pv7Y/jw4bhw4QLUarXWPo7j0L9//7pfCNLiGHPdrjBx4kRkZ2fj2LFjmm2lpaXYvXs3Jk2aVOffub6qu1ZRUVF48cUXYWdnB0tLS/Ts2RP79++v8VyDBg3CoUOHEB8fr3nc7OnpCaD8d1u8eDECAgIglUohEokwYMCAKrtzVCcpKQnPPvus1nWvbomzixcv4plnnoFUKoWVlRWCg4N1nhw8ydPTE3fv3kVERIQm/oq/fU5ODt5991107twZYrEY1tbWGD58OG7evFnr+InxowSQmIyXX34ZAPD3339rbVcqlQgLC4OTkxNWr16NF154oU7nLSoqQkhICI4fP47//ve/WLhwIc6dO4f333+/VscHBQWhrKwMFy9e1GyLjIxEYGAgAgMDIZPJcOfOHa19fn5+sLe3r1OcpOUy1rpdwdPTE/369cMvv/yi2XbkyBHIZDJMmDChTudqqCev1d27d9G3b1/cv38fH3zwAdasWQORSIRnn322yr65FRYuXIhu3brBwcEB27dvx/bt2zWPg/Pz8/HDDz9g0KBBWLVqFZYuXYrMzEyEhYXhxo0bT42xuLgYQ4YMwdGjR/Hmm29i4cKFOHPmDN577z2dsidPnsTAgQORn5+PJUuW4NNPP0VeXh5CQkJw6dKlat9j3bp1aN26Nfz8/DTxL1y4EADw+PFj7N27F6NGjcLatWsxf/583L59G8HBwUhJSXlq/KSZYIS0EFu2bGEA2OXLl6stI5VKWffu3TWvp06dygCwDz74QKesh4cHmzp1qs724OBgFhwcrHm9Zs0aBoDt3btXs624uJj5+fkxACw8PLzGuO/evcsAsOXLlzPGGCsrK2MikYht27aNMcaYs7Mz+/rrrxljjOXn5zM+n89mzpxZ4zlJy9Jc63bluL/66ismkUiYXC5njDE2btw4NnjwYE08I0eOrPFctVXXazVkyBDWuXNnVlJSotmvVqtZYGAga9eunWZbeHi4zu88cuRI5uHhoXN+pVLJFAqF1rbc3Fzm7OzMpk+f/tTfYd26dQwA+/333zXbioqKmI+Pj1YMarWatWvXjoWFhTG1Wq0pK5fLmZeXFxs6dKhmW8V1iY2N1Wzz9/fX+ntXKCkpYSqVSmtbbGwsEwgEbNmyZU+NnzQP1AJITIpYLK5yFOCcOXPqfc6//voLbm5uGDNmjGabpaUlZs6cWavjO3ToAHt7e03fvps3b6KoqEgzyjcwMFDzOOf8+fNQqVTU/4/oMMa6Xdn48eNRXFyMgwcPoqCgAAcPHtTr49/KKq5VTk4OTp48ifHjx6OgoABZWVnIyspCdnY2wsLC8OjRIyQnJ9f5/Hw+HxYWFgAAtVqNnJwcKJVK9OzZE9euXXvq8YcPH4arqytefPFFzTYrKyvMmjVLq9yNGzfw6NEjTJo0CdnZ2Zr4i4qKMGTIEJw+fVqr+0htCQQC8Hjl6YFKpUJ2djbEYjF8fX1rFT9pHmgQCDEphYWFcHJy0tpmZmaG1q1b1/uc8fHx8Pb2BsdxWtt9fHxqdTzHcQgMDNR8WEdGRsLJyUlzfGBgIL766isA0CSClACSJxlj3a7M0dERoaGh2LlzJ+RyOVQqlVaCo08V1yo6OhqMMSxatAiLFi2qsmxGRgbc3Nzq/B7btm3DmjVrEBUVhbKyMs12Ly8vzb8zMzOhUqk0r8ViMcRiMeLj4+Hj46Nz3X19fbVeP3r0CAAwderUauOQyWSwtbWtU+xqtRrr16/HN998g9jYWK0YqetJy0EJIDEZSUlJkMlkOjevyt92K3vyw7eCSqUCn89v1NiCgoJw4MAB3L59W9P/r0JgYCDmz5+P5ORknD17Fq1atULbtm0b9f1J82bMdbuySZMmYebMmUhLS8Pw4cNhY2PTZO9VncrXqqJ17N1330VYWFiV5euT7P7888945ZVX8Oyzz2L+/PlwcnICn8/HypUrERMToynXq1cvxMfHa14vWbIES5curfX7VMT/xRdfoFu3blWWEYvFdY7/008/xaJFizB9+nQsX74cdnZ24PF4mDt3br1aFIlxogSQmIzt27cDQLUf9E+ytbVFXl6ezvb4+HitBMzDwwP37t0DY0zrxhodHV3r2CrPBxgZGYm5c+dq9gUEBEAgEODUqVO4ePEiRowYUevzEtNgzHW7sueeew6zZ8/GhQsX8Ntvv9XrHA1V+VpV/K7m5uYIDQ2t87mqS6R3796Ntm3b4o8//tAqs2TJEq1yO3bs0Br9XxGPh4cH7ty5o3PdHzx4oHW8t7c3AMDa2rrR4x88eDB+/PFHre15eXlwcHCo8/sQ40R9AIlJOHnyJJYvXw4vLy/NVBhP4+3tjQsXLqC0tFSz7eDBg0hMTNQqFxYWhuTkZK1pI0pKSrBp06Zax9ezZ09YWlpix44dSE5O1moBFAgE6NGjB77++msUFRXR41+ixdjrdmVisRgbN27E0qVLMXr06HqdoyGevFZOTk4YNGgQvvvuO6SmpuqUz8zMrPF8IpEIMplMZ3tFKypjTLPt4sWLOH/+vFa5/v37IzQ0VPNTkQCOGDECKSkp2L17t6asXC7H999/r3V8QEAAvL29sXr1ahQWFtYr/qq+CPD5fK3YAWDXrl316g9JjBe1AJIW58iRI4iKioJSqUR6ejpOnjyJY8eOwcPDA/v374elpWWtzvPqq69i9+7deOaZZzB+/HjExMTg559/1nzrrjB79mx89dVXmDhxIt5++224urpix44dmvep7lt2ZRYWFujVqxfOnDkDgUCAgIAArf2BgYFYs2YNAOr/Z8qaY91+Uk391RpTba/V119/jaCgIHTu3BkzZ85E27ZtkZ6ejvPnzyMpKanGue8CAgLw22+/4Z133kGvXr0gFosxevRojBo1Cn/88Qeee+45jBw5ErGxsfj222/RsWPHKhO1J82cORNfffUVpkyZgqtXr8LV1RXbt2+HlZWVVjkej4cffvgBw4cPh7+/P6ZNmwY3NzckJycjPDwc1tbWOHDgQI3xb9y4EZ988gl8fHzg5OSEkJAQjBo1CsuWLcO0adMQGBiI27dvY8eOHdT1pKUx5BBkQhpTxTQHFT8WFhbMxcWFDR06lK1fv57l5+frHDN16lQmEomqPeeaNWuYm5sbEwgErH///uzKlSs6U2Uwxtjjx4/ZyJEjmVAoZI6Ojux///sf27NnDwPALly4UKv4FyxYwACwwMBAnX1//PEHA8AkEglTKpW1Oh9pOZpr3a7NlCyMNc00MHW5VjExMWzKlCnMxcWFmZubMzc3NzZq1Ci2e/duTZmqpoEpLCxkkyZNYjY2NgyAZkoYtVrNPv30U+bh4cEEAgHr3r07O3jwIJs6dWqV08ZUJT4+no0ZM4ZZWVkxBwcH9vbbb7O//vqryul3rl+/zp5//nlmb2/PBAIB8/DwYOPHj2cnTpzQuS6Vp4FJS0tjI0eOZBKJhAHQ/O1LSkrY//73P+bq6sqEQiHr378/O3/+fJX1gzRfHGNPtPMSQhrFunXrMG/ePCQlJdVrFCEhxorqNiHNHyWAhDSC4uJirXVFS0pK0L17d6hUKjx8+NCAkRHSMFS3CWmZqA8gIY3g+eefh7u7O7p16waZTIaff/4ZUVFR2LFjh6FDI6RBqG4T0jJRAkhIIwgLC8MPP/yAHTt2QKVSoWPHjvj111/x0ksvGTo0QhqE6jYhLRM9AiaEEEIIMTE0DyAhhBBCiImhBJAQQgghxMRQAkjIU8TFxYHjOGzdutXQoRDSKKhOE0IoASSEEEIIMTE0CISQp2CMQaFQwNzcXLPGJyHNGdVpQgglgIQQQgghJoYeAROTsHTpUnAch4cPH2Ly5MmQSqVwdHTEokWLwBhDYmIixo4dC2tra7i4uGDNmjWaY6vqL/XKK69ALBYjOTkZzz77LMRiMRwdHfHuu+9CpVJpyp06dQocx+HUqVNa8VR1zrS0NEybNg2tW7eGQCCAq6srxo4di7i4uCa6KqQ5ozpNCGkISgCJSXnppZegVqvx2WefoU+fPvjkk0+wbt06DB06FG5ubli1ahV8fHzw7rvv4vTp0zWeS6VSISwsDPb29li9ejWCg4OxZs0afP/99/WK7YUXXsCff/6JadOm4ZtvvsF///tfFBQUICEhoV7nI6aB6jQhpF4YISZgyZIlDACbNWuWZptSqWStW7dmHMexzz77TLM9NzeXCYVCNnXqVMYYY7GxsQwA27Jli6bM1KlTGQC2bNkyrffp3r07CwgI0LwODw9nAFh4eLhWuSfPmZubywCwL774onF+YdLiUZ0mhDQEtQASk/Lqq69q/s3n89GzZ08wxjBjxgzNdhsbG/j6+uLx48dPPd9rr72m9XrAgAG1Ou5JQqEQFhYWOHXqFHJzc+t8PDFdVKcJIfVBCSAxKe7u7lqvpVIpLC0t4eDgoLP9aTctS0tLODo6am2ztbWt181OIBBg1apVOHLkCJydnTFw4EB8/vnnSEtLq/O5iGmhOk0IqQ9KAIlJqWrKi+qmwWBPGSBfm+kzOI6rcnvlTvUV5s6di4cPH2LlypWwtLTEokWL0KFDB1y/fv2p70NMF9VpQkh9UAJISBOytbUFAOTl5Wltj4+Pr7K8t7c3/ve//+Hvv//GnTt3UFpaqjV6kxBDozpNSMtACSAhTcjDwwN8Pl9n9OU333yj9Voul6OkpERrm7e3NyQSCRQKRZPHSUhtUZ0mpGUwM3QAhLRkUqkU48aNw4YNG8BxHLy9vXHw4EFkZGRolXv48CGGDBmC8ePHo2PHjjAzM8Off/6J9PR0TJgwwUDRE6KL6jQhLQMlgIQ0sQ0bNqCsrAzffvstBAIBxo8fjy+++AKdOnXSlGnTpg0mTpyIEydOYPv27TAzM4Ofnx9+//13vPDCCwaMnhBdVKcJaf5oKThCCCGEEBNDfQAJIYQQQkwMJYCEEEIIISaGEkBCCCGEEBNDCSAhhBBCiImhBJAQQgghxMRQAkiavVOnToHjOJw6dcoo4ti9e7dB4yAtB9VtQkhToQSQGK2tW7eC4zjNj6WlJdq3b48333wT6enphg7PIC5dugSO4/B///d/OvvGjh0LjuOwZcsWnX0DBw6Em5ubPkIktUB1u2oViSbHcfj555+rLNO/f39wHKc15yAhpO4oASRGb9myZdi+fTu++uorBAYGYuPGjejXrx/kcrmhQ9O7Hj16wMrKCmfPntXZd+7cOZiZmSEyMlJre2lpKS5fvoz+/fvrK0xSS1S3q2ZpaYmdO3fqbI+Li8O5c+dgaWlpgKgIaVloJRBi9IYPH46ePXsCAF599VXY29tj7dq12LdvHyZOnGjg6PTLzMwMffr00UnyHjx4gKysLEyaNEknObx69SpKSkoQFBSkz1BJLVDdrtqIESOwf/9+ZGVlwcHBQbN9586dcHZ2Rrt27ZCbm2vACAlp/qgFkDQ7ISEhAIDY2Nhqy5w5cwbjxo2Du7s7BAIB2rRpg3nz5qG4uFinbFRUFMaPHw9HR0cIhUL4+vpi4cKFWmWSk5Mxffp0ODs7QyAQwN/fH5s3b67yvVUqFT788EO4uLhAJBJhzJgxSExM1Cm3a9cuBAQEQCgUwsHBAZMnT0ZycvJTf/+goCCkp6cjOjpasy0yMhLW1taYNWuWJhmsvK/iOGLcTL1uVxg7diwEAgF27dqltX3nzp0YP348+Hx+rc9FCKkatQCSZicmJgYAYG9vX22ZXbt2QS6XY86cObC3t8elS5ewYcMGJCUlad1Ubt26hQEDBsDc3ByzZs2Cp6cnYmJicODAAaxYsQIAkJ6ejr59+4LjOLz55ptwdHTEkSNHMGPGDOTn52Pu3Lla771ixQpwHIf3338fGRkZWLduHUJDQ3Hjxg0IhUIA5X3Apk2bhl69emHlypVIT0/H+vXrERkZievXr8PGxqba360ikTt79ix8fHwAlCd5ffv2RZ8+fWBubo5z585hzJgxmn0SiQRdu3at24UmemfqdbuClZUVxo4di19++QVz5swBANy8eRN3797FDz/8gFu3btXlshJCqsIIMVJbtmxhANjx48dZZmYmS0xMZL/++iuzt7dnQqGQJSUlMcYYCw8PZwBYeHi45li5XK5zvpUrVzKO41h8fLxm28CBA5lEItHaxhhjarVa8+8ZM2YwV1dXlpWVpVVmwoQJTCqVat6rIg43NzeWn5+vKff7778zAGz9+vWMMcZKS0uZk5MT69SpEysuLtaUO3jwIAPAFi9eXON1yc/PZ3w+n82YMUOzzdfXl3388ceMMcZ69+7N5s+fr9nn6OjIhg4dWuM5iX5R3a5axfvs2rWLHTx4kHEcxxISEhhjjM2fP5+1bduWMcZYcHAw8/f3r/FchJCa0SNgYvRCQ0Ph6OiINm3aYMKECRCLxfjzzz9rHNVa0RoBAEVFRcjKykJgYCAYY7h+/ToAIDMzE6dPn8b06dPh7u6udTzHcQAAxhj27NmD0aNHgzGGrKwszU9YWBhkMhmuXbumdeyUKVMgkUg0r1988UW4urri8OHDAIArV64gIyMDr7/+ulZn9pEjR8LPzw+HDh2q8XpIJBJ06dJF09cvKysLDx48QGBgIIDyUZIVj30fPnyIzMxMevxrpKhuV2/YsGGws7PDr7/+CsYYfv31V5PuF0lIY6NHwMToff3112jfvj3MzMzg7OwMX19f8Hg1f3dJSEjA4sWLsX//fp3O4jKZDADw+PFjAKhxOonMzEzk5eXh+++/x/fff19lmYyMDK3X7dq103rNcRx8fHwQFxcHAIiPjwcA+Pr66pzLz8+vyhG+TwoKCsKGDRuQlZWFc+fOgc/no2/fvgCAwMBAfPPNN1AoFNT/z8hR3a6eubk5xo0bh507d6J3795ITEzEpEmTan08IaRmlAASo9e7d2/NSMnaUKlUGDp0KHJycvD+++/Dz88PIpEIycnJeOWVV6BWq2t9roqykydPxtSpU6ss06VLl1qfr7FUJICRkZE4d+4cOnfuDLFYDKA8AVQoFLh8+TLOnj0LMzMzTXJIjAvV7ZpNmjQJ3377LZYuXYquXbuiY8eOBo2HkJaEEkDS4ty+fRsPHz7Etm3bMGXKFM32Y8eOaZVr27YtAODOnTvVnsvR0RESiQQqlQqhoaG1ev9Hjx5pvWaMITo6WnMz9fDwAFA+dUvFqM8KDx480OyvSeWBIOfPn9ea469Vq1bw8PBAZGQkIiMj0b17d1hZWdUqdmLcTKFuVxYUFAR3d3ecOnUKq1atqtOxhJCaUR9A0uJUTBHBGNNsY4xh/fr1WuUcHR0xcOBAbN68GQkJCVr7Ko7l8/l44YUXsGfPnipvppmZmTrbfvrpJxQUFGhe7969G6mpqRg+fDgAoGfPnnBycsK3334LhUKhKXfkyBHcv38fI0eOfOrv2KpVK3h5eeHEiRO4cuWKpv9fhcDAQOzduxcPHjygx78tiCnU7co4jsOXX36JJUuW4OWXX67TsYSQmlELIGlx/Pz84O3tjXfffRfJycmwtrbGnj17qpw49ssvv0RQUBB69OiBWbNmwcvLC3FxcTh06BBu3LgBAPjss88QHh6OPn36YObMmejYsSNycnJw7do1HD9+HDk5OVrntLOzQ1BQEKZNm4b09HSsW7cOPj4+mDlzJoDyvk2rVq3CtGnTEBwcjIkTJ2qmyvD09MS8efNq9XsGBQVh+/btAKCzykdgYCB++eUXTTnSMphK3a5s7NixGDt2bN0vFiGkZgYYeUxIrVRMlfH/7dwxiiJRFIbRN4GoYCAiYmJs7Bo0MzI2NnIFBi5EE0MRlyIIgriHWsU/WTM9PUjDwATzzolvFVVQwQevuLfb7e3cn1ZlvF6vLBaL9Hq9DIfDbDabPB6PlFJyOp0+Xf98PrNardLv99PpdDKdTrPf7z/NNE2T7XabyWSSVquV8Xic+Xye4/H45TnO53N2u11Go1G63W6Wy+WXVRxJcrlcMpvN0m63MxgMsl6vP9Z/fMfhcPhYzfG7+/2eUkpKKWma5tv35N/wbb9/3+v1+nbOGhj4ez+SX84SAAD47/kHEACgMgIQAKAyAhAAoDICEACgMgIQAKAyAhAAoDICEACgMgIQAKAyAhAAoDICEACgMgIQAKAyAhAAoDICEACgMj8BLonvWDdachAAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "paired_delta2.mean_diff.plot(delta2_ylim=(3, -3));" - ] - }, - { - "cell_type": "markdown", - "id": "6c8ce9d5", - "metadata": {}, - "source": [ - "### Labels\n", - "\n", - "- `raw_label` - label the raw data y-axis\n", - "- `contrast_label` - label the contrast y-axis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e7e095c7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired.mean_diff.plot(raw_label=\"This is my\\nrawdata\", \n", - " contrast_label=\"The bootstrap\\ndistribtions!\"\n", - " );" - ] - }, - { - "cell_type": "markdown", - "id": "97230328", - "metadata": {}, - "source": [ - "Unique for delta-delta:\n", - "- `delta2_ylim` - to label the delta-delta y-axis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "07225a03", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "paired_delta2.mean_diff.plot(delta2_label='delta-delta label');" - ] - }, - { - "cell_type": "markdown", - "id": "1db8d529", - "metadata": {}, - "source": [ - "### Axes ticks\n", - "You can add minor ticks and also change the tick frequency by accessing\n", - "the axes directly.\n", - "\n", - "Each estimation plot produced by ``dabest`` has two axes. The first one\n", - "contains the rawdata swarmplot while the second one contains the bootstrap\n", - "effect size differences.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1dfc1df3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.ticker as Ticker\n", - "\n", - "f = two_groups_unpaired.mean_diff.plot()\n", - "\n", - "rawswarm_axes = f.axes[0]\n", - "contrast_axes = f.axes[1]\n", - "\n", - "rawswarm_axes.yaxis.set_major_locator(Ticker.MultipleLocator(1))\n", - "rawswarm_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.5))\n", - "\n", - "contrast_axes.yaxis.set_major_locator(Ticker.MultipleLocator(0.5))\n", - "contrast_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.25))" - ] - }, - { - "cell_type": "markdown", - "id": "a2d0461a", - "metadata": {}, - "source": [ - "### Add counts to tick labels\n", - "By default, the tick labels include the sample size for each group. This can be switched off via\n", - "setting `show_sample_size=False` in the `.plot()` method. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "40fa2021", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired.mean_diff.plot(show_sample_size=False\n", - " );" - ] - }, - { - "cell_type": "markdown", - "id": "2ec11b77", - "metadata": {}, - "source": [ - "## Changing swarm side\n", - "In `dabest`, swarmplots are, by default, plotted asymmetrically to the right side. You may change this by using the parameter `swarm_side`. \n", - "\n", - "There are only three valid values: `\"right\"` (default), `\"left\"`, `\"center\"`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c98edf2b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(swarm_side=\"right\");\n", - "multi_2group.mean_diff.plot(swarm_side=\"left\");\n", - "multi_2group.mean_diff.plot(swarm_side=\"center\");" - ] - }, - { - "cell_type": "markdown", - "id": "bfb6a084", - "metadata": {}, - "source": [ - "## Creating estimation plots in existing axes" - ] - }, - { - "cell_type": "markdown", - "id": "c2cbd8b2", - "metadata": {}, - "source": [ - "*Implemented in v0.2.6 by Adam Nekimken*.\n", - "\n", - "``dabest.plot`` has an ``ax`` parameter that accepts Matplotlib\n", - "``Axes``. The entire estimation plot will be created in the specified\n", - "``Axes``.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "093bbd83", - "metadata": {}, - "outputs": [], - "source": [ - "two_groups_paired_baseline = dabest.load(df, idx=(\"Control 1\", \"Test 1\"),\n", - " paired=\"baseline\", id_col=\"ID\")\n", - "multi_2group_paired = dabest.load(df,\n", - " idx=((\"Control 1\", \"Test 1\"),\n", - " (\"Control 2\", \"Test 2\")),\n", - " paired=\"baseline\", id_col=\"ID\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "65f86ec5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "f, axx = plt.subplots(nrows=2, ncols=2,\n", - " figsize=(15, 15),\n", - " gridspec_kw={'wspace': 0.25} # ensure proper width-wise spacing.\n", - " )\n", - "\n", - "two_groups_unpaired.mean_diff.plot(ax=axx.flat[0]);\n", - "\n", - "two_groups_paired_baseline.mean_diff.plot(ax=axx.flat[1]);\n", - "\n", - "multi_2group.mean_diff.plot(ax=axx.flat[2]);\n", - "\n", - "multi_2group_paired.mean_diff.plot(ax=axx.flat[3]);" - ] - }, - { - "cell_type": "markdown", - "id": "091e817d", - "metadata": {}, - "source": [ - "In this case, to access the individual rawdata axes, use\n", - "``name_of_axes`` to manipulate the rawdata axes, and\n", - "``name_of_axes.contrast_axes`` to gain access to the effect size axes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "babb6587", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "topleft_axes = axx.flat[0]\n", - "topleft_axes.set_ylabel(\"New y-axis label for rawdata\")\n", - "topleft_axes.contrast_axes.set_ylabel(\"New y-axis label for effect size\")\n", - "f" - ] - }, - { - "cell_type": "markdown", - "id": "91106d74", - "metadata": {}, - "source": [ - "## Legend\n", - "For plots with a `color_col` specified, a legend will be created. Utilise the `legend_kwargs` parameter to adjust the legend." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1228fc1d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(color_col=\"Gender\", \n", - " legend_kwargs={'bbox_to_anchor': [0, 1], 'fontsize':8});" - ] - }, - { - "cell_type": "markdown", - "id": "7dd00ed6", - "metadata": {}, - "source": [ - "## Hiding options \n", - "For mini-meta plots, it is possible to hide the weighted average plot by setting the parameter ``show_mini_meta=False`` in the ``.plot()`` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "012a6452", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mini_meta_paired.mean_diff.plot(show_mini_meta=False);" - ] - }, - { - "cell_type": "markdown", - "id": "9d0b2d3f", - "metadata": {}, - "source": [ - "Similarly, you can hide the delta-delta effect size by setting \n", - "``show_delta2=False`` in the ``.plot()`` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9306729c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "paired_delta2.mean_diff.plot(show_delta2=False);" - ] - }, - { - "cell_type": "markdown", - "id": "81c87af0", - "metadata": {}, - "source": [ - "## Effect size error bar and marker\n", - "\n", - "Modifying the effect size marker can be done via `contrast_marker_kwargs`. This parameter accepts a dictionary of keyword arguments.\n", - "\n", - "The available options are:\n", - "\n", - "- `'marker'` - type of the marker \n", - "- `'markersize'` - size of the marker\n", - "- `'color'` - color of the marker \n", - "- `'alpha'` - alpha of the marker (transparency)\n", - "- `'zorder'` - zorder of the marker (the layering relative to other plot elements)\n", - "\n", - "**Note:\n", - "markersize can also be modified directly via the `contrast_marker_size` argument**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "62b74c3e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired.mean_diff.plot(contrast_marker_kwargs={\"marker\": \"x\", 'markersize': 15, 'color': 'green', 'alpha':0.8, 'zorder': 5});" - ] - }, - { - "cell_type": "markdown", - "id": "47140b62", - "metadata": {}, - "source": [ - "Modifying the appearance of the effect size error bar can be done via the `contrast_errorbar_kwargs` parameter. This parameter accepts a dictionary of keyword arguments.\n", - "\n", - "The relevant inputs to `contrast_errorbar_kwargs` are:\n", - "\n", - "- `'lw'` - width of the error bar\n", - "- `'linestyle'` - line style of the error bar\n", - "- `'color'` - color of the error bar \n", - "- `'zorder'` - zorder of the error bar (the layering relative to other plot elements)\n", - "- `'alpha'` - alpha of the error bar (transparency)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7d9dbb13", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired.mean_diff.plot(contrast_errorbar_kwargs={'lw': 4, 'color': 'green', 'alpha':0.5, 'zorder': 2, 'linestyle': ':'});" - ] - }, - { - "cell_type": "markdown", - "id": "f3d6bc15", - "metadata": {}, - "source": [ - "## Group summaries\n", - "\n", - "Group summaries represent the summary statistics of the sample and are included by default. \n", - "\n", - "In swarmplots and proportion plots, these are represented by gapped lines.\n", - "\n", - "In slopegraphs, these are represented by a solid line connecting the group mean/median with error bars." - ] - }, - { - "cell_type": "markdown", - "id": "ff2fd6cc", - "metadata": {}, - "source": [ - "The type of group summary can be specified via `group_summaries` in the `.plot()` method and must be one of these: `'median_quartiles'`, `'mean_sd'`, `None`.\n", - "\n", - "By default, the group summary is set to `'mean_sd'`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e61f77c2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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cQceOHdXbkyZNAgCMGDECkZGRSEhIUCdtoKAGu2fPHvznP//BsmXL4OnpibVr1xpkaBYAdOjQAatWrcLbb7+N2rVraxy7d+8eVq9erRF/eQiiqOOlpZk4d+4cQkNDcfbsWYSEhJg6HCIi3R7eAmrUM3UU5VYVf97euHEDLVq0gCAIGD16NAIDAwEAly9fxvr16yGKIk6ePAl///K/WmSNmoioslDlmToCek6DBg3w66+/4r333sOSJUs0jrVv3x7Lly+vUJIGmKiJiCoPZa6pIyAtgoKCEBMTg4cPH+L27dsAAF9f3wrPSFaIiZqIqLJQ5ps6AipGjRo19Jacn8VETURUWbDpW5KUSiX279+P27dvIyUlBc93/RIEAR9++GG5789ETURUWeTrb05s0o8zZ87gtddew/3794sk6EIVTdQcR01EVFkwUUvOu+++i+zsbOzatQuPHz+GSqUq8lEqlRUqgzVqIqLKIu+JqSOg51y8eBELFy5E7969DVYGa9RERJVFToapI6DneHp66mzy1hcmajOUkpGF746cwfIdR/DdkTNch5qosnjKFe6kZtq0aVizZg3S09MNVgabvs3Micu3sWDjHuQrVZAJAlSiiMj/ncDskb3QKtDX1OERUXGy/jZ1BPScjIwM2NnZoV69ehg0aBDq1KkDuVyucY4gCBWaa5yJ2oykZGRhwcY9yMsv6Nig/Ke5Ji9fifmRe7B59ptwtrc1ZYhEVJzMZFNHQM/54IMP1P/91VdfaT2HiZpK7eDpa8hXqrQey1eqcOjMdQzoGGrkqIio1NLvmzoCes6dO3cMXgYTtRlJfJwOmSCoa9LPkgkCEh7x/ReRpD1NL3hPbe1o6kjoH15eXgYvg53JzIi7iwNUOnonqkQRHtX5j59I8lLjSz6HjO7BgwfYsmULli1bhvv3C1o+lEolHj9+XOFx1EzUZqRrc39YyLU/cgu5DF2aVWyFFyIygpQ4U0dAzxBFEZMmTYKPjw+GDh2KSZMm4c8//wQAZGZmwtvbG19++WWFymCiNiPO9raYPbIXLC3kEAQBcpkMgiDA0kKO2SN7wdnevBakJ6qUHsWaOgJ6xuLFi7Fs2TJ88MEHOHjwoMaYakdHR7z66qv4/vvvK1QG31GbmVaBvtg8+00cOnMdCY/S4FHdEV2a+TNJE1UWj26aOgJ6xpo1azB8+HB8/PHHePToUZHjQUFB+N///lehMpiozZCzvS17dxNVVg9vFix3KeePbym4d+8e2rRpo/O4ra1thSdDYdM3EVFlkp/DWrWEuLq64t69ezqPnz17FnXr1q1QGUzURESVzV8XTB0B/ePVV1/FypUrcfv2bfU+QRAAAAcOHEBkZCQGDBhQoTKYqImIJK5Zs2bw9PREs4/PFey497tpAyK1efPmwcPDA8HBwRg+fDgEQcCiRYvQrl079OjRA0FBQZg5c2aFymCiJiKSuMTERDx48ACJ6bn/7LgIZKeaNCYq4OjoiJMnT2Lq1Kl48OABrK2tERMTg9TUVMyZMwe//vorqlWrWGdd9kYgIqpsVErg9i9A4CumjsSsPX36FKtXr0ZwcDBmzZqFWbNmGaQc1qiJiCqja7sBA6+DTMWztrbGtGnTcOPGDYOWw0RNRFQZPboF/HXO1FGYvUaNGiEuLs6gZTBRExFVVmc3slZtYgsXLsSqVatw6NAhg5XBd9RERJVVwh/AvVNA3ZamjsRsffXVV3BxcUG3bt3g4+MDHx8f2NjYaJwjCAJ+/PHHcpfBRE1EVJkdXw7U2gBYWJk6ErN08eJFCIKAunXrQqlU4tatW0XOKRxXXV5M1ERElVnafeDsBqDlWFNHYpYM/X4aqMA7aqVSia1bt2Ls2LF45ZVXcOnSJQBAWloafvjhByQlJektSCIiKsYfW4D7Z00dBRlIuRJ1amoq2rZtiyFDhmDLli346aef8PfffwMA7OzsMGHCBCxbtkyvgRIRkQ6iCByZD2SwgmQKhq64litRT58+HVeuXMH+/ftx+/ZtjfU35XI5+vfvj71791YoMCIiKoPsVODArIJFO8hojFFxLVei3rVrF9577z107dpV60vyF154wSjt9kRE9IyHfwJHP+OQLSMyRsW1XIk6LS0NPj4+Oo/n5eUhPz+/3EEREVE53TwA3GCLprEYo+JarkTt5+eHc+d0z4hz4MABBAQElDsoIiKqgONfAekJpo7CLBij4lquRB0eHo7169dj27Zt6mq+IAjIycnB//3f/2Hfvn0YO5ZDBYiITCLvCXD8S1NHYRaMUXEt1zjq999/H1euXMHgwYPh5OQEABgyZAgePXqE/Px8jB07FqNHj65QYERU9eVmpiD50hHkpCZB4eQG18adYGXnbOqwqoa7vxXMWlanhakjqdLCw8Mxbdo0dOjQAZ07dwbwb8V1/vz52LdvH1avXl2hMsqVqAVBwJo1azBixAjs2LEDN2/ehEqlgp+fH15//XW0b9++XMGsWLECK1asULfnBwYGYvbs2ejRo4fW8yMjIzFq1CiNfQqFAk+fPi1X+URkPI/+/B3Xv4+AqMqHIMggiircjd4E//4z4VKfyUUvTnwF1FoHyC1NHUmVZYyKa4VmJmvXrh3atWtXoQCe5enpiU8++QT169eHKIrYuHEj+vbti/PnzyMwMFDrNQ4ODhpLjFV0qjYi0h9dNebczJSCJK3MAwCIorLgT2Ueru34GM3f28CatT6k3AX+2AqEvGHqSKosQ1VcnyWpKUR79+6tsb1w4UKsWLECJ0+e1JmoBUGAu7u7McIjqhKM1dxcXI35ycN7EFXaO9iIqnwkXzoCz9av6T0ms3Q2EqjTEqj5gqkjqRJeffVV/Oc//8GLL74IADh69Cj8/f31XnF9VrkStY+PT4k1V0EQEBsbW66ggIKZXrZv346srCy0bt1a53mZmZnw8vKCSqVCSEgIPv74Y51JHQBycnKQk5OjcT2RuTBWc3NJNeaagWH/lK8scq0gyJCTyhm29EaVDxycDby6CrB2NHU0ld6PP/6I117795fIjh07YtOmTRgyZIjByixXog4LCyuSqJVKJe7evYvffvsNjRo1QtOmTcsV0KVLl9C6dWs8ffoUdnZ22Llzp84ecw0aNMD69esRFBSEtLQ0fPbZZ2jTpg2uXLkCT09PrddERERg3rx55YqNqDIzVHOzthp68qUjxdaY856kQRRV2o+LKiic3MocBxUjIwE48CHQ8zOuslVBtWvXxvnz5zF06FAAgCiKBn/lKoiifqew+eOPP9CtWzf897//RZcuXcp8fW5uLuLj45GWloYdO3Zg7dq1iImJKVX39ry8PPj7+2Pw4MFYsGCB1nOer1FfuHABYWFhOHv2LEJCQsocL1Flcf/E94g7skH7rFWCAO9Oo7Q2NxfXVK6thi7ILODk0wSpt89DVGmpMcvkqNmoI/6+EqP+pUHjuNwSzSdEwsrWqcLfuarw9PTEgwcPUNvJCvc/aVX+G/m0B7rMBWRyvcVWFufOnUNoaGil/nk7depUfPbZZ/D09ISTkxMuX76MOnXqwNFRd2uFIAj4448/yl2m3t9RN2nSBGPHjsW0adNw9mzZV3OxsrJCvXr1AAChoaE4ffo0li1bhlWrVpV4raWlJZo2bap1PdBCCoUCCoVCvW1nZ1fmGIkqo5zUpDI3NxfXVG7nUV9nDT0l9hwA7XUAUVShWs268O8/E9d2fFwkyfv3n2mQJJ2XnYHb+1bi8c3fAUGGGg3bwLfbWMitbEq8VhRFXN06BymxZ+E/YBaqN/j3dVzGX38i7kgkMhNuAQJgX6sBvDuPgp2br96/Q4XdOQocXQy0nwrIyr14olmLiIhAvXr18MsvvyA5ORmCIMDW1hbVq1c3WJkG6Uzm5uaGq1ev6uVeKpVKowZcHKVSiUuXLqFnz556KZuoKlE4uZWpubmkpnLP1q/pbN6GqAIEGbQla0FmAdegzrCydULz9zZo1tb/2V9eF7+dDrcmneHWpGuRY3/uWozczMdoNPQjqJRK3Px5KW7t+RINXpla4n3/OrULQNHmTWVuNq5smQ2X+i3h1+NdiCol4mM240rUh2g+YSNkckn11y1w43+AhQJoOxHgKJkyk8vleOutt/DWW28BAGQyGWbNmmXQd9R6/5Xq0aNHWLdunc53xMWZMWMGjh49iri4OFy6dAkzZsxAdHS0+l3A8OHDMWPGDPX58+fPx4EDB3D79m2cO3cOw4YNw927dxEeHq6370NUVbg27gRBpj1xFCbPZ5X0njn1zgUIgvYfIYJMDmffEAhyS0AQIMjkBX/KLTVqzFZ2zvBs/Rr8erwLz9avGay5+8nDeKTEnkW9Xu/DvnZDONYNhF/3sfj7ylHkZDwq9trMxFg8OLkT9Xu/r+W+95GfnQGvsGGoVt0TtjW9ULf9EORlpSInLdkg30UvruwCfl/FxTvKISQkBPv27VNvb9iwodx9skqrXL/uderUSev+1NRUXL9+Hbm5udi0aVOZ75ucnIzhw4cjISEBjo6OCAoKwv79+9G1a8Fvx/Hx8ZA901yTkpKCMWPGIDExEc7OzggNDcXx48c5zziRFlZ2zmVqbi6pqRxAsTV0R+8g1O89Ua815vJKv38dcmtb2Neqr97n5NMUEARkPLgBRcM2Wq9T5j3FjV2L4df9HVjZuRQ5blO9NixsHJB44QDqtHsdokqFpAsHYFOjDqyl3iHujy2Awh5oOtTUkVQqFy9exMOHD9Xbb775JjZt2gR/f3+DlVmuRK1SqYr0chMEAT4+PujSpQvefPNNNGzYsMz3XbduXbHHo6OjNbaXLFmCJUuWlLkcInPzbIcwz9b9AQHIf5KuTp4QRdw/8b1Gh7GSmsqdfJoiMzFWe4ewZ5q3pTAeOi8zBVbVnDT2CTI5LG3skZeVovO6OwfWwMHTX+Od9LMsFNXQ+I0IXNv+Ee4d2woAsHGphcDBCwpaEaTu1GrAtibwwkumjqTS8PLywqFDhzB48GDI5XKj9PouV6J+PmESkXTp6pldOHZaV4ex+i9PgCCz0JmIPZq/DPvaLxi1Q9jz7h3bhnu/fafeVuXnIuPBdcTuW6neF/L2inLd+9GfJ5EadxFNxyzXeY4yLwc3dy+Dg2cAGrwyFaJKhQcnf8DVbXPR5M0lkFsqdF4rGUcXA051AdeyV67M0dtvv41p06Zh8+bNsLGxgSAIGD16dLELUQmCgLS0tHKXKcGeDkSkLyV1CAt+c6nO4zd3L0e9XhNwa89ynYnYpX4LvXcIKwv30J6oEfCievvGrsWo0bAtqj/TlK2wrw5LO2fkPknVuFZUKZGXnQFLW+1jx9PiLuJpSgJOLH5dY/+1HR/DoU4ggoZ/gr8vRyMnLRlNRn2ufh1g98oUnPxsIB7/eRI1A8Mq/B3j4+ORlZUFAMjKUSL+8VPUdbGu8H3VlLnAkQXAa2sBy5J7wJvC119/jcWLFyMxMRFNmjTBl19+iRYttE/QY+g1IKZMmYImTZrgl19+QVJSEjZu3IjmzZvD19dwvfxLlai//fbbct18+PDh5bqOiPSjpA5hcb9EFj8xSVZKiYm4sEOYKVja2MPSxl69LbNQwNLWETYutTTOc/BsCOXTLGQm3ISdR8F76tQ7fwCiCPvaDbTe27NNf7gFazYJn189Dr5dx6hncVPl5/zTc/rfps+ChC2golNUnDp1CgsWLMCePXvU90rNVsL7/07h5cYu+LCnF5p725dwl1JKuw9c3AaEjtTP/fRo27ZtmDRpElauXImWLVti6dKl6NatG27cuAFXV1et1xh6DYiXXnoJL71U8HcjMjISY8eONf3MZCNHjizzjQVBYKImMrESx06nJZc4ttqUiVhfqtWoC2e/UNzc8yXq9RgHUaVE7P4VqBnYHgr7gvGvOekPcXnz/+GFPpNgX7sBrOxctHYgUzjWhLVzwfoCTj5NcefQesTu+wa1mveGKIq4/9t2CDI5nLyCyh3vDz/8gIEDB0IUxSIJXxSBvZcf43+XU7BtjD9ebVqj3OVouLgdCBoEWOqxtq4HX3zxBcaMGaOuJa9cuRJ79uzB+vXrMX36dK3XGHMNCJVKez8OfSpVor5z546h4yAiAyhx7LSjK548jNd9XOo9l8vghX5TcHvfClze/H+AIKB6w7bw6/bve0VRpUT2o/tQ5pVu3gYAqFajDgIGzsG9o1H4Y8MHBZNfuPshcPB8WNkXTfKlcerUKQwcOBBKpVJnrVypAgSIGLjmGo5PDdZPzTo3E4g/Afh1rPi99CQ3Nxdnz57VGJYrk8nQpUsXnDhxQud1ZV0Doizi4wv+vdStW1djuySF55dHqRK1l5dXuQsgItNxbdwJd6M36ewQ5t1pFFLvXCi253ZlEjT8E53HLG3si53cxNrJDe1m7Sn2/tqOO/s2hbOv/sbRfvTRR1pr0s8TAYgQ8dHeu/jx3Ub6KTzhglESdWZmJtLT09Xbz88YWejhw4dQKpVwc9P8hdHNzQ3Xr1/Xeu/yrAFRFt7e3hAEAdnZ2bCyslJvl0SpLNpqVVrsTEZUhZU0dtrW1cvoU3mSbvHx8di9e3ep328rVcDPlx7rr4NZyt2K36MUwsI0O9nNmTMHc+fO1cu9W7durbHiYps2beDv749Vq1bpXAOiLNavXw9BEGBpaamxbUjlTtSJiYlYt24dzp07h7S0tCLt9IIg4PDhwxUOkKiqUSnztC5WYSiO3kEIeWclHl6OQdbjRFg51EDNwDBY2jri6ZMMVKvtj8bhX+LhlaN4mvY3rB1rahzXF0Emh0xuqbf7SY0+nuv/9u4pcyc0UQT2X03BiFbaO1aVScZDCHlFW1f0JT+/oONiTEwMgoOD1fu11aYBoEaNGpDL5UhK0pyHPikpqdTvoEuzBkRZPN9nqzx9uMqqXIn64sWL6NChA7Kzs9GgQQNcunQJAQEBSE1NxYMHD+Dn54c6deroO1aiSk+lzEPGgz+hzM02etm27n746rvD+PTLNUYvmwzrrf/exFv/vamHO/0GDN2sh/sUz87ODg4ODiWeZ2VlhdDQUBw+fBj9+vUDUNB56/Dhwxg/fnypyqoKa0CUK1FPnz4ddnZ2uHDhAqpVqwZXV1csW7YMnTp1wvbt2/HOO+9g82bDP2yiykZUKaHMzYbMwsIktcvpk8Zj6sR3jV6uSpkHVb4Sjl6BkEusV7E+KPOeIu3uFcgsKtZqELXjR0yYNrfM160a7I3hLSvW+1sURShta8Fi6GaDPaPz58+jZcuWZbpm0qRJGDFiBJo1a4YWLVpg6dKlyMrKUvcCHz58OGrXro2IiAgABWtAtGrVCvXq1UNqaioWL16s1zUg5s+fX+ZrBEHAhx9+WO4yy5Wof/vtN0ydOhV169bF48ePAfzbRX3AgAE4duwYpkyZgpiYmHIHRlSVyeSWkFlYGb9cE/VKUeXnIj8nG5aWlpBbVr3mbxmUsLS0gIXCpkLPtUvHDhCEso3BFgSga0MnWMorOmWpiDz7GgZ9RhYWZf8LOHDgQPz999+YPXs2EhMTERwcjH379qk7mBl7DQht79IL31E//9wKn6VJErVKpVL/T3JycoJcLlcnbABo3LhxifN2E5E05WVnIOXmaeRmPoaVnQuc6zfXmFSEDKeOZy1079IRB47ElKqXsFwG9Ax0Ql0X/UxVmu/kAyn+GjV+/HidTd3GXgPi+f5YDx48QK9evdCoUSNMnDgRDRoUTKBz/fp1LF26FFevXsWePcWPJihJuZa59PHxUY+tlslk8PHxwaFDh9THjx8/DicnpwoFRkTGlxZ/GVe3zsNfp3/GwxvH8dfpn3F16zykxV8xdWhmY9p/3oUgCCX2JC6YD03AzG619VZ2vrthl2usisaNG4f69evjv//9L5o1awZ7e3vY29ujefPm2Lx5M/z8/DBu3LgKlVHqRJ2S8u8KMy+99BK2b9+u3n7nnXewdu1adOnSBZ07d8bGjRsNOp0aEelfXnYG4g4XTikqAioVALFgqtHDG5CXrb8e4KRbaHAQIlcugVwuh1xHc7ZcBshlAra+WQ/Nvez0Uq6yRkOo7Iwzm1dVcuTIEZ1LPwNA586dKzwCqtSJ2t3dHa+88gp27NiByZMnY8uWLcj7pxv/xIkTMX/+fDx69AhpaWn48MMP8dFHH1UoMDKulIwsfHfkDJbvOILvjpxBSkaWqUMiI0u5eVrn8CJRpUTKrdNGjsh89e3ZDYd+2oqXOoVpWVK4oLn710kB6NekfLOfaZP7wst6u5c5sba2LnaWtOPHj8PaumKd80r9jrp///746aef8NNPP8He3h6vvvoqhg4dik6dOkEQBMyaNQuzZs2qUDBkGicu38aCjXuQr1RBJghQiSIi/3cCs0f2QqtAw60IQ9KSm/kYkAmASktHJpmA3IzHRfeTwYQGB+G7jStx7/5faNO1D1LT0uFkI8e56Y319k66kMo1EKqaAUCO8YcNVnZDhw7F8uXL4eTkhPfeew9+fn4AgNjYWCxfvhxRUVGYMGFChcoodaLevHkzsrOzsWvXLkRFRWHz5s3YuHEj3NzcMHjwYAwdOhQhISEVCoaMLyUjCws27kFefkFNSvlPr8W8fCXmR+7B5tlvwtne1pQhkoE832lMrrDVnqQBQCWWe+5qqpg6nrVQrZoNUtPSYauQ6T1JA0Be4AC939NcLFq0CA8fPsRXX32Fr7/+Wt0DXaVSQRRFDB48GIsWLapQGWXq9W1jY4PBgwdj8ODBSElJwXfffYeoqCgsXboUS5cuRf369TFs2DAMGTLEoGtzkv4cPH0N+UrtizbkK1U4dOY6BnQMNXJUZGhp8Zf/eR+t/LcWLch01qgFmRzO9ZqbIFIyNGWtEKhc/ID8XFOHUilZWVlh06ZNmDJlCvbu3Yu7dwumYfXy8kKPHj3QpEmTCpdR7lGVzs7OGDt2LMaOHYsHDx4gKioKW7ZswezZszFnzhy0bNkSx48fr3CAZFiJj9MhEwR1TfpZMkFAwqM0E0RFhqTZaQz/JmZRCUAGQSaHqFKpk7Ygk8O78ygO0aqKBAF5AZV7CVOpCAoKQlBQ+Zc2LY5epj+oXbs2pkyZgu7du2P27Nn48ccf8fvvv+vj1mRg7i4OUOmYXEElivCo7mjkiMjQius0BlGEa3AXyC2tkZvxGFb2LnCux3HUVZWyThuIjuVffpGMo8KJOj4+Xl2bvnz5MkRRRJs2bTB06FB9xEcG1rW5PyL/d0L9jvpZFnIZujTzN0FUZEgldRpTPn0Cj5DKOy8ylZKFgu+mK4lyJeqHDx+q30+fOHECoiiiYcOGmD9/PoYOHQpvb289h0n6lJKRhYOnryHxcTrcXRwwaWBnfLHtsEavbwu5DLNH9oKzfTVTh0t6ZmXnwk5jhLyA/hCrVTd1GFQKpU7UWVlZ2LlzJ6KionD48GHk5eXBw8MDEydOZI/vSkTbUCwLuQyTXu+MlMxsJDxKg0d1R3Rp5s8kXUU512+OhLN7/31H/Qx2GjMPqpoNkV/vJVOHQaVU6kTt6uqKp0+fws7ODkOGDFGPoX52MnSStuKGYn3x3WEOxarCnh+KVefFQbj361aNXt/sNGYeRIU9cpu/U9DLnyqFUifqLl26YOjQoejTp0+FZ1kh0+BQLPOkbSiWIJOjTruByH+awU5j5kQmQ27L8RBt+HqjMil1ov7xxx8NGQcZAYdimR9dQ7FEVT7uHduGgEFzmJzNSG6T4QUzkJFe7d+/H+vWrcPt27eRkpKidbnL2NjYct/fRKvTkilwKJb5Kc383a6NdS8oQFVHfv3uUPp2NnUYVc7ixYsxffp0uLm5oUWLFmjcuLHey2CiNiMcimV+OH83AYDSswXyGg82dRhV0rJly9CpUyfs3bsXlpaGWc2bvQnMiLO9LWaP7AVLCzkEQYBcJoMgCLC0kHMoVhXFoVikcg1EbrO32XnMQFJSUtC/f3+DJWmANWqz0yrQF5tnv4lDZ65zKJYZ4FAs86Zy8UVOq4mA3HBJxNy1aNECN27cMGgZTNRmyNnelr27zYSljT28O49C3OENHIplZlROdZHTdgpgyVE6hvTNN9+gR48eaNasGYYMGWKQMpioiao4x7qBCBg0Bym3TnMolpkQHWohp900wMrO1KFUeQMHDkR+fj7eeOMNvPPOO/D09IRcLtc4RxAE/PHHH+Uug4mayAxY2tizd7eZEG1rFCRphYOpQzELLi4uqF69OurXr2+wMpioiYiqCNHKDjltp3FCEyOKjo42eBnsBkhEVBUIAnJbvQ/R3t3UkZCesUZNRFQF5AUOgKpmQ1OHYbby8vJw/fp1pKWlQaUqOlVz+/bty31vJmoiokpO5eKH/Bd6mToMs6RSqTBjxgx88803ePLkic7zlErtMwSWBpu+iYgqM0FAbvAITmhiIh9//DEWL16MYcOG4dtvv4Uoivjkk0+wcuVKBAUFoUmTJti/f3+FyuCTJSKqxJS1W0B09jF1GGYrMjISr7/+OlasWIHu3bsDAEJDQzFmzBj8/vvvEAQBR44cqVAZTNREVKK87AwkXzyC+8d3IPniEeRlZ5g6JPpHXoOXTR2CWbt//z46dSoY+qhQKAAAT58+BQBYWVlh2LBh2LRpU4XK4DtqIkJedgZSbp5GbuZjWNm5wLn+vxOiaFvPOuHsXnh3HgXHuoEmjty8qVzqQXTyNnUYZq169erIzMwEANjZ2cHBwQG3b9/WOCclJaVCZTBRU4WkZGTh4OlrSHycDncXB3Rt7g9ne1tTh0VlUFwirlazrs71rOMOb+B61iaWX7etqUMwe02bNsXp06fV2x07dsTSpUvRtGlTqFQqLF++HE2aNKlQGUzUVG4nLt/Ggo17kK9UQSYIUIkiIv93ArNH9kKrQF9Th0fP0FVjzsvOKDYRuwZ15nrWUiUIUNbmoiqm9tZbbyEyMhI5OTlQKBRYuHAh2rdvj/bt20MURTg7O2PLli0VKoOJmsolJSMLCzbuUa9trRQLfsDn5SsxP3IPNs9+kzVriSiuxpyTmlRsIs746wbXs5YAt5o1AQDuFpnqfaoaDQBrR1OFRP/o06cP+vTpo94OCAhAbGwsoqOjIZfL0aZNG7i4VGymOCZqKpaupu2Dp68hX1l0UD8A5CtVOHTmOlfokoCSasxOfiHFJuJnrymC61kbzdF9PwAAbL5/Q71PWbuFqcKhEjg6OqJv3756ux8TNelUXNN24uN0yARBXZN+lkwQkPAozQQR0/NSbp4utsasfJpZbCK2r/0Csh/e53rWUiMIyGeilgylUont27fjl19+QXJyMubPn4/GjRsjLS0Nhw8fRtu2beHm5lbu+3N4Fmn1bNO2KIpQqlQQRVHdtO1oaw2VliQNACpRhEd1NslJQW7m439rxs+TCbCwtoMgk2s9LMjkqOH/Irw7j4IgswAgADIZAAGCzILrWZuQ0q0xm70lIjU1FW3btsWQIUOwZcsW/PTTT/j7778BFPQCnzBhApYtW1ahMpioSauSmrYBARZy7X99LOQydGnmb8DoqLSs7FyKrTFbO7uXmIgL17Ou1aI3ajRog1oteiNg0BwOzTIhZZ3Wpg6B/jF9+nRcuXIF+/fvx+3btyE+U4GRy+Xo378/9u7dW6Ey2PRNWt9Dl9S0nZaVjdkje2F+pGbTuIVchtkje8HZvpoJvgk9z7l+cySc3Vts07WljT0CBs1Byq3TyM14DCt7F/X+QlzPWkJkFlB6sP+HVOzatQvvvfceunbtikePHhU5/sILLyAyMrJCZUgqUa9YsQIrVqxAXFwcACAwMBCzZ89Gjx49dF6zfft2fPjhh4iLi0P9+vWxaNEi9OzZ00gRV3663kN3CmlQYtN2q0BfbJ79Jg6duY6ER2nwqO6ILs38maQlxNLGHt6dRyHu8AaNXt+CTK7RdM1EXHkoazYELG1MHQb9Iy0tDT4+uqdwzcvLQ35+0V+Uy0JSidrT0xOffPIJ6tevD1EUsXHjRvTt2xfnz59HYGDRZrbjx49j8ODBiIiIwMsvv4yoqCj069cP586dQ6NGjUzwDSqX4oZYHT57HRYyAXnKosn62aZtZ3tb9u6WuMKm6+JqzFR5qFz5ykFK/Pz8cO7cOZ3HDxw4gICAgAqVIal31L1790bPnj1Rv359vPDCC1i4cCHs7Oxw8uRJrecvW7YM3bt3x5QpU+Dv748FCxYgJCQEX331lZEjr5yKew+tVInoFNoQlhZyCIIAuUwGQRBgaSFn03YlVFhj9mzTH66NOzFJV2Iql3qmDoGeER4ejvXr12Pbtm3q99OCICAnJwf/93//h3379mHs2LEVKkNSNepnFXZ3z8rKQuvW2jtOnDhxApMmTdLY161bN+zatcsIEVZ+Jb2HtrK0YNM2kcSoHL1MHQI94/3338eVK1cwePBgODk5AQCGDBmCR48eIT8/H2PHjsXo0aMrVIbkEvWlS5fQunVrPH36FHZ2dti5c6fOZoPExMQiY9Pc3NyQmJio8/45OTnIyclRbxdOpm6O3F0cSnwPzaZtIukQbZz5flpiBEHAmjVrMGLECOzYsQM3b96ESqWCn58fXn/9dbRv377CZUguUTdo0AAXLlxAWloaduzYgREjRiAmJqbCbfyFIiIiMG/ePL3cq7Lr2twfkf87oX5H/SwOsapailsdiyoP0bb8k2aQYbVr1w7t2rUzyL0l9Y4aKFi/s169eggNDUVERASaNGmic7C4u7s7kpKSNPYlJSXB3d1d5/1nzJiBtLQ09ScmJkav8Vcmzva2mD2yF99DV3Fp8Zdxdes8/HX6Zzy8cRx/nf4ZV7fOQ1r8FVOHRmUkVqtu6hDIBCRXo36eSqXSaKp+VuvWrXH48GFMnDhRve/gwYM632kDBQt7Fy7uDRTMHGNunh83/fWkQThzPZ7voaugkub65jKVlYvI2cgk4dlFOEpDEAT8+OOP5S5PUol6xowZ6NGjB+rWrYuMjAxERUUhOjoa+/fvBwAMHz4ctWvXRkREBICCl/hhYWH4/PPP0atXL2zduhVnzpzB6tWrTfk1JK24+bv5LrrqKWmuby5TWbmIVuZXsZCi3bt3w9raGu7u7hozkekiCDqm8S0lSSXq5ORkDB8+HAkJCXB0dERQUBD279+Prl27AgDi4+Mhk/3bWt+mTRtERUVh1qxZmDlzJurXr49du3ZxDLUOXJrS/Kjn+uYylVWCaMmWLimoXbs2Hjx4gBo1amDIkCEYNGhQsa9cK0pSiXrdunXFHo+Oji6yb8CAARgwYICBIqpauDSl+Slprm8uU1nJWChKPocM7t69e4iJiUFUVBQWLFiAKVOmICwsDEOHDkX//v1hb6/f10mS60xGhlM4blobLk1ZNTnXb17s6lhcprKSsbA2dQT0j7CwMKxatQqJiYnYsWMHqlevjvHjx8PV1RWvvvoqduzYobN/VVkxUZuR0oybpqqlcK5vLlNZNYhyJmqpsbS0RN++fbFt2zYkJSWpk/fAgQPx6aef6qUMJmoz0rW5P5emNENcprIKMdOm76+//hre3t6wtrZGy5YtcerUqWLP3759Oxo2bAhra2s0bty4wstMlkZOTg7279+PH3/8EefPn4e1tTW8vb31cm8majPCcdPmi3N9Vw2iGSbqbdu2YdKkSZgzZw7OnTuHJk2aoFu3bkhOTtZ6fuFiTaNHj8b58+fRr18/9OvXD5cvX9Z7bCqVCvv378fIkSPh5uaGwYMHIzs7G2vWrEFycjLeeOMNvZQjiKXpW16FnTt3DqGhoTh79ixCQkJMHY5RpGRkcf5uE1HmPUXa3cuwUNhAZmFl6nCMRpWfi/ycbDh6NYLcsuo13xrruQoZiRDtDdO72BjPqDw/b1u2bInmzZurF1tSqVSoU6cO3nvvPUyfPr3I+QMHDkRWVhZ2796t3teqVSsEBwdj5cqVevkex48fR1RUFLZv345Hjx6hVatWGDJkCF5//XXUqFFDL2U8S1K9vs1RXr4SSpX2ntiGYqOwQu+2QVAqlVD9U3ZG1hOjxiCXyWBpob2TU1WmzMtDXl4+RFkeZGLFxlZWJqr8POTn5SMvLw8qVL3nXvhcleITyOR5BitHyMuHmJ1lkHurlHlQ5SsN+owK12XOzMxEenq6ev/zE1EVys3NxdmzZzFjxgz1PplMhi5duuDEiRNayzDGYk3t2rWDjY0NevbsicGDB6ubuOPj4xEfH6/1mopUBJmoTSgvX4kb8Yl4kmO4f9jFWfPVUqz9Rvv0rEREhhIWFqaxPWfOHMydO7fIeQ8fPoRSqdS6+NL169e13rs8izWVR3Z2Nr7//nv88MMPxZ4niiIEQYBSqX3iodJgojYhpUqFJzl5sJSbpnY5fuIkvDthIjKfPMWZG/F4nJ4FFwdbNGtQF3bVyt/0dSUuAZv2nYRSpYIgCBBFEXKZDMO7t0KAtwfy8pXIV6rQ2K82rK0s9fiNpK+gifQKLBTWZtj0/RSOXoFVsukbKKiR6poFTm9yMgCF4foXCDI5ZHLD/Zs8f/48WrZsiZiYGAQHB6v3a6tNS9mGDRuMWh4TtQRYWshhZWmCR2FpgUuxD7B29zGNpHrgzHWE926Hxr61y3zL9KxsbD54GioI6vG7ggCoAPz34GksCO+DajbWBb+gWFrC0tK8ErUMSlhaWsDC0hIyC/P57ipBhKDKh6WlJeRV9Zkb43tZyACryjt7oIVFwc85Ozs7ODg4lHh+jRo1IJfLy7T4UnkWayqrESNG6O1epcFe32YsPSsba3cfQ75SBVEEVCoRolgwS9nan48hPSu7xOsPnr6GbYfP4ODpa0jPysbvV+N0vnNXqlQ4dS3OAN+EyFyYT78GoGA1xdDQUBw+fFi9T6VS4fDhwzoXXypcrOlZJS3WJHWsUZux0iRVXWOrtdXEdx+/iAZ13dTbz5MJAh6lGaYjDBFVTZMmTcKIESPQrFkztGjRAkuXLkVWVhZGjRoFwDwWa2KiNmOP07PKlVSfrYkDUF+fr1ThWlwiROie/ay6Y+VttiMyuQquwlQZDRw4EH///Tdmz56NxMREBAcHY9++feoOY+awWBMTtRlzcbDVuURbcUm1uJq4ShQhEwStyVouk6FlgE/5AyYye+aXqAFg/PjxGD9+vNZj5rBYE99Rm7GWAd6Qy7T/FSguqRbWxLVfJ8Df2x0WchkEoWBbEAqmKA3v3Q72FehNTmT2zLBGTaxRmzUHWxuE926HtT8XvGuWCQJU/wylKkyqhR3ECodutQzwLrEm/kIdNwx7qSVOXYvDo7QsVHe0RcsAHyZpCcvLzkDKzdPIzXwMKzsXONdvzmlGpUhuPkP66F9M1Gbo+eQ7dchLuHY3sUhS1dVhbEjXFpDLZFrXti6sidtXs+YiH5VEWvxlxB2OLBgDLBMAlYiEs3vh3XkUF+6QGtaozRITtZnRnnwLatDPJtbiOoxFHTyFIV1bIOrgKZ01cZIWXTXmvOyMf5J0wdSOUBU8Z1GVj7jDGxAwaA5r1kQmxkRtRopLvmt/PoYF4X3gYGsDoOShWxlPnmJBeB82b1cCxdWYc1KTdM6mJaqUSLl1Gq6NOxk5YiJ6FhO1GSnLuOnSDN1ysLVh87bElVRjdvILUSfvImQCcjMeGzFaItKGvb7NSHG9tZ8fN13eoVskLSk3TxdbY1Y+zdSepAFAJcLK3sWA0RFRaTBRm5GyJN/yDt0iacnNfFxQY9ZGJsDC2k49J/vzBJkczvWaGzA6IioNJmozUpbkWzh0i+OhKzcrO5dia8zWzu7w7jwKgswCgADIZAAECDILeHcexY5kRBLAd9RmpDTjpgHN4VtdmvlDEICs7Fx2GKuEnOs3R8LZvf++o35GYY3Z0sYeAYPmIOXWaeRmPIaVvYt6PxGZHhO1mWnsW7vY3trahm8VJvLyLHtJpmVpYw/vzqMQd3iDRq9vQSbXqDFb2tizdzeRRDFRmyFdvbXLMnyLKg/HuoGsMRNVYkzUpFaRZS9J2lhjJqq8mKjNkLb5ux1sbcq97CURERkOE7WZ0TV/d3jvdhw7TUQkQRyeZUaefQctioBKJUIU/30HHeDtzrHTREQSw0RtRkp6B33tbiLHThMRSQybvs1Iad5Bd2nmz8U2iIgkhInajJT2HTQX2yAikg42fZsRzt9NRFT5MFGbEc7fbb7ysjOQfPEI7h/fgeSLR5CXnWHqkIiolNj0bWZKmkKUqp60+Mv/rEn97xSiCWf3wrvzKDjWDTR1eERUAiZqM8R30OYjLzvjnyT9z6Ic/6ykJaryEXd4AwIGzeFUokQSx6Zvoios5ebpgpq0FqJKiZRbp40cERGVFRM1URWWm/m4oLlbG5mA3IzHxg2IiMqMiZqoCrOyc1E3dxehEmFl72LcgIiozJioiaow5/rNIcjkWo8JMjmc6zU3ckREVFZM1ERVmKWNPbw7j4IgswAgADIZAAGCzALenUexIxlRJcBe30RVnGPdQAQMmoOUW6eRm/EYVvYucK7XnEmaqJJgoiYyA5Y29nBt3MnUYRBRObDpm4iISMKYqImIiCSMTd9mKD0rG79fjcPj9Cy4ONiiZYA3HGxtTB0WERFpwURtZi7FPsDa3cegVKnUa1PvPn4R4b3bobFvbVOHR0REz2HTtxlJz8rG2t3HkK9UQRQBlUqEKAL5ShXW/nwM6VnZpg6RiIiew0RtRn6/GgelSqX1mFKlwqlrccYNiIiISsREbUYep2dBELTP+ywTBDxKyzJyREREVBJJJeqIiAg0b94c9vb2cHV1Rb9+/XDjxo1ir4mMjIQgCBofa2uurayNi4MtRFH7vM8qUUR1R1sjR0RERCWRVKKOiYnBuHHjcPLkSRw8eBB5eXl46aWXkJVVfE3PwcEBCQkJ6s/du3eNFHHl0jLAG3KZ9kcul8nQMsDHyBEREVFJJNXre9++fRrbkZGRcHV1xdmzZ9G+fXud1wmCAHd3d0OHV+k52NogvHc7rP25oNe3TBCgEkXIZTKE924H+2psiSAikhpJJernpaWlAQBcXIpfii8zMxNeXl5QqVQICQnBxx9/jMDAQK3n5uTkICcnR+Nac9LYtzYWhPfBqWtxeJSWheqOtmgZ4MMkTUQkUZJN1CqVChMnTkTbtm3RqFEjnec1aNAA69evR1BQENLS0vDZZ5+hTZs2uHLlCjw9PYucHxERgXnz5hkydMlzsLVBl2b+pg6DiIhKQVLvqJ81btw4XL58GVu3bi32vNatW2P48OEIDg5GWFgYfvjhB9SsWROrVq3Sev6MGTOQlpam/sTExBgifCIiIr2QZI16/Pjx2L17N44ePaq1VlwcS0tLNG3aFLdu3dJ6XKFQQKFQqLft7OwqFGtVxClGiYikQ1KJWhRFvPfee9i5cyeio6Ph41P2XshKpRKXLl1Cz549DRBh1ccpRomIpEVSTd/jxo3Df//7X0RFRcHe3h6JiYlITExEdva/U1sOHz4cM2bMUG/Pnz8fBw4cwO3bt3Hu3DkMGzYMd+/eRXh4uCm+QqXGKUaJiKRHUjXqFStWAAA6dOigsX/Dhg0YOXIkACA+Ph6yZ8YCp6SkYMyYMUhMTISzszNCQ0Nx/PhxBAQEGCvsSkdX03ZpphhlJzQiIuOSVKLWNWvWs6KjozW2lyxZgiVLlhgooqqnuKbtwilGtT0HTjFKRGQakmr6JsMqqWnb1tqKU4wSEUkME7UZKalpGwI4xSgRVUqPHz/G0KFD4eDgACcnJ4wePbrECa06dOhQZK2It99+20gRlx4TtRkpafWsrOxchPduBwu5DIIAyGUCBAGwkHOKUSKStqFDh+LKlSs4ePCgenjvW2+9VeJ1Y8aM0Vgr4tNPPzVCtGUjqXfUZFilWT2LU4wSUWVz7do17Nu3D6dPn0azZs0AAF9++SV69uyJzz77DLVq1dJ5bbVq1SS/VgRr1GaktKtnFU4xOrBzM3Rp5s8kTUSSduLECTg5OamTNAB06dIFMpkMv//+e7HXbt68GTVq1ECjRo0wY8YMPHnyxNDhlhlr1GaEq2cRkRRkZmYiPT1dvf38jJFllZiYCFdXV419FhYWcHFxQWJios7rhgwZAi8vL9SqVQsXL17EtGnTcOPGDfzwww/ljsUQmKjNDJu2icjUwsLCNLbnzJmDuXPnFjlv+vTpWLRoUbH3unbtWrnjePYdduPGjeHh4YHOnTsjNjYWfn5+5b6vvjFRmyGunkVEphQTE4Pg4GD1tq7a9OTJk9WTXeni6+sLd3d3JCcna+zPz8/H48ePy/T+uWXLlgCAW7duMVETEZH5srOzg4ODQ4nn1axZEzVr1izxvNatWyM1NRVnz55FaGgoAODIkSNQqVTq5FsaFy5cAAB4eHiU+hpjYGcy0pCelY2Dp69h2+EzOHj6Guf3JiLJ8/f3R/fu3TFmzBicOnUKv/32G8aPH49Bgwape3w/ePAADRs2xKlTpwAAsbGxWLBgAc6ePYu4uDj89NNPGD58ONq3b4+goCBTfp0iWKMmNa6cRUSV1ebNmzF+/Hh07twZMpkMr732GpYvX64+npeXhxs3bqh7dVtZWeHQoUNYunQpsrKyUKdOHbz22muYNWuWqb6CTkzUBEBzelHg33nXC6cXXRDeh2tSE5Fkubi4ICoqSudxb29vjXkk6tSpg5iYGGOEVmFs+iYAJU8veupanHEDIiIiAKxRmyVty1xy5SwiImliojYzut5DN2voxZWziIgkiE3fZqS4ZS5PX4uDjCtnERFJDhO1GSnuPbRKFNG8oRdXziIikhg2fZuRkt5DW1lYcHpRIiKJYaI2I6VZ5pLTixIRSQubvs1IaZe5JCIi6WCiNiOFy1zyPTQRUeXBpm8zw2UuiYgqFyZqM8T30ERElQebvomIiCSMiZqIiEjCmKiJiIgkjImaiIhIwpioiYiIJIyJmoiISMKYqImIiCSM46j/ce3aNaOXmZOXj5v3k2FtaQFLC7nRyzeVvHwlnublQ5meDIWlef0VVObnIDMhFnJLBWRyS1OHYzQqZR6UeTmwe5wHuYVC45iHhwc8PDxMFFn5JCQkICEhwdRhVDqm+DlbFZjXT0ktPDw8EBYWhmHDhpk6FCKzNGfOHMydO9fUYZTJqlWrMG/ePFOHUSmFhYVVul/MTE0QdS2nZEbM8bfjzMxMhIWFISYmBnZ2dqYOh4xAqs+cNeqSSfXZlUdlfN6mxkRtptLT0+Ho6Ii0tDQ4ODiYOhwyAj7zyovPzryxMxkREZGEMVETERFJGBO1mVIoFJgzZw4UCkXJJ1OVwGdeefHZmTe+oyYiIpIw1qiJiIgkjImaiIhIwpioqcLi4uIgCAIiIyNNHQoRUZXDRG1ksbGxGDt2LHx9fWFtbQ0HBwe0bdsWy5YtQ3Z2tsHKvXr1KubOnYu4uDiDlVEaCxcuRJ8+feDm5gZBECrdjFSGJAhCqT7R0dEVLuvJkyeYO3dume7FZ1c8Pj8yFLOfQtSY9uzZgwEDBkChUGD48OFo1KgRcnNzcezYMUyZMgVXrlzB6tWrDVL21atXMW/ePHTo0AHe3t4GKaM0Zs2aBXd3dzRt2hT79+83WRxStGnTJo3tb7/9FgcPHiyy39/fv8JlPXnyRD0FZocOHUp1DZ9d8fj8yFCYqI3kzp07GDRoELy8vHDkyBGNKfTGjRuHW7duYc+ePSaM8F+iKOLp06ewsbHR+73v3LkDb29vPHz4EDVr1tT7/Suz5+ebP3nyJA4ePCiZeej57IrH50eGwqZvI/n000+RmZmJdevWaZ3ntl69enj//ffV2/n5+ViwYAH8/PygUCjg7e2NmTNnIicnR+M6b29vvPzyyzh27BhatGgBa2tr+Pr64ttvv1WfExkZiQEDBgAAOnbsWKQJrvAe+/fvR7NmzWBjY4NVq1YBAG7fvo0BAwbAxcUF1apVQ6tWrSr0C4Upa/NVgUqlwtKlSxEYGAhra2u4ublh7NixSElJ0TjvzJkz6NatG2rUqAEbGxv4+PjgzTffBFDQp6DwB/W8efPUfx9Kagrls6s4Pj8qD9aojeTnn3+Gr68v2rRpU6rzw8PDsXHjRvTv3x+TJ0/G77//joiICFy7dg07d+7UOPfWrVvo378/Ro8ejREjRmD9+vUYOXIkQkNDERgYiPbt22PChAlYvnw5Zs6cqW56e7YJ7saNGxg8eDDGjh2LMWPGoEGDBkhKSkKbNm3w5MkTTJgwAdWrV8fGjRvRp08f7NixA6+88or+/gdRqYwdOxaRkZEYNWoUJkyYgDt37uCrr77C+fPn8dtvv8HS0hLJycl46aWXULNmTUyfPh1OTk6Ii4vDDz/8AACoWbMmVqxYgXfeeQevvPIKXn31VQBAUFCQKb+aWeDzo3IRyeDS0tJEAGLfvn1Ldf6FCxdEAGJ4eLjG/g8++EAEIB45ckS9z8vLSwQgHj16VL0vOTlZVCgU4uTJk9X7tm/fLgIQf/nllyLlFd5j3759GvsnTpwoAhB//fVX9b6MjAzRx8dH9Pb2FpVKpSiKonjnzh0RgLhhw4ZSfT9RFMW///5bBCDOmTOn1NeYm3HjxonP/hP99ddfRQDi5s2bNc7bt2+fxv6dO3eKAMTTp0/rvHdF/v/z2ZUOnx/pC5u+jSA9PR0AYG9vX6rz9+7dCwCYNGmSxv7JkycDQJGm54CAALz44ovq7Zo1a6JBgwa4fft2qWP08fFBt27disTRokULtGvXTr3Pzs4Ob731FuLi4nD16tVS358qbvv27XB0dETXrl3x8OFD9Sc0NBR2dnb45ZdfAABOTk4AgN27dyMvL8+EEdOz+PyovJiojaBwWbqMjIxSnX/37l3IZDLUq1dPY7+7uzucnJxw9+5djf1169Ytcg9nZ+ci772K4+PjozWOBg0aFNlf2GT+fBxkWDdv3kRaWhpcXV1Rs2ZNjU9mZiaSk5MBAGFhYXjttdcwb9481KhRA3379sWGDRuK9G8g4+Lzo/LiO2ojcHBwQK1atXD58uUyXScIQqnOk8vlWveLZZjG3RA9vEm/VCoVXF1dsXnzZq3HCzsYCYKAHTt24OTJk/j555+xf/9+vPnmm/j8889x8uRJ2NnZGTNs+gefH5UXE7WRvPzyy1i9ejVOnDiB1q1bF3uul5cXVCoVbt68qdHhKykpCampqfDy8ipz+aVN+s/HcePGjSL7r1+/rj5OxuPn54dDhw6hbdu2pfrFqlWrVmjVqhUWLlyIqKgoDB06FFu3bkV4eHi5/j5QxfD5UXmx6dtIpk6dCltbW4SHhyMpKanI8djYWCxbtgwA0LNnTwDA0qVLNc754osvAAC9evUqc/m2trYAgNTU1FJf07NnT5w6dQonTpxQ78vKysLq1avh7e2NgICAMsdB5ff6669DqVRiwYIFRY7l5+ern21KSkqR1pTg4GAAUDefVqtWDUDZ/j5QxfD5UXmxRm0kfn5+iIqKwsCBA+Hv768xM9nx48exfft2jBw5EgDQpEkTjBgxAqtXr0ZqairCwsJw6tQpbNy4Ef369UPHjh3LXH5wcDDkcjkWLVqEtLQ0KBQKdOrUCa6urjqvmT59OrZs2YIePXpgwoQJcHFxwcaNG3Hnzh18//33kMnK/nvepk2bcPfuXTx58gQAcPToUXz00UcAgDfeeIO19GKEhYVh7NixiIiIwIULF/DSSy/B0tISN2/exPbt27Fs2TL0798fGzduxDfffINXXnkFfn5+yMjIwJo1a+Dg4KD+JdDGxgYBAQHYtm0bXnjhBbi4uKBRo0Zo1KiRzvL57CqGz4/KzcS9zs3On3/+KY4ZM0b09vYWraysRHt7e7Ft27bil19+KT59+lR9Xl5enjhv3jzRx8dHtLS0FOvUqSPOmDFD4xxRLBha1atXryLlhIWFiWFhYRr71qxZI/r6+opyuVxjqJaue4iiKMbGxor9+/cXnZycRGtra7FFixbi7t27Nc4py/CssLAwEYDWj7ahY+bs+eE9hVavXi2GhoaKNjY2or29vdi4cWNx6tSp4l9//SWKoiieO3dOHDx4sFi3bl1RoVCIrq6u4ssvvyyeOXNG4z7Hjx8XQ0NDRSsrq1IN1+GzKxs+P9IXQRTL0OOIiIiIjIrvqImIiCSMiZqIiEjCmKiJiIgkjImaiIhIwpioiYiIJIyJmoiISMKYqCXm008/RcOGDaFSqUwdSoVNnz4dLVu2NHUYksdnTgAQFxcHQRAQGRlp6lBIYpioJSQ9PR2LFi3CtGnT1LN+CYIAQRDw+eefFzk/MjISgiDgzJkzFS77hx9+wMCBA+Hr64tq1aqhQYMGmDx5ss4pCn/66SeEhITA2toadevWxZw5c5Cfn69xzsSJE/HHH3/gp59+qnB8VRWfORGVyNQzrtC/lixZIjo4OIjZ2dnqffhn5iA3NzcxKytL4/wNGzaUuMB8aVWvXl1s3Lix+OGHH4pr1qwRJ0yYIFpZWYkNGzYUnzx5onHu3r17RUEQxI4dO4qrV68W33vvPVEmk4lvv/12kfu+/vrr4osvvljh+KoqPnMqpFKpxOzsbDE/P9/UoZDEMFFLSFBQkDhs2DCNfQDE4OBgEYD4+eefaxzT5w9tbVMIbty4UQQgrlmzRmN/QECA2KRJEzEvL0+97//+7/9EQRDEa9euaZy7Y8cOURAEMTY2tsIxVkV85kRUEjZ9S8SdO3dw8eJFdOnSpcixtm3bolOnTvj000+RnZ1tkPI7dOhQZN8rr7wCALh27Zp639WrV3H16lW89dZbsLD4d02Xd999F6IoYseOHRr3KPw+P/74owGirtz4zKueuXPnQhAE/Pnnnxg2bBgcHR1Rs2ZNfPjhhxBFEffu3UPfvn3h4OAAd3d3jdcb2t5Rjxw5EnZ2dnjw4AH69esHOzs71KxZEx988AGUSqX6vOjoaAiCgOjoaI14tN0zMTERo0aNgqenJxQKBTw8PNC3b1/ExcUZ6P8KVRQTtUQcP34cABASEqL1+Ny5c5GUlIQVK1YUe5+cnBw8fPiwVJ+SJCYmAgBq1Kih3nf+/HkAQLNmzTTOrVWrFjw9PdXHCzk6OsLPzw+//fZbieWZGz7zqmvgwIFQqVT45JNP0LJlS3z00UdYunQpunbtitq1a2PRokWoV68ePvjgAxw9erTYeymVSnTr1g3Vq1fHZ599hrCwMHz++edYvXp1uWJ77bXXsHPnTowaNQrffPMNJkyYgIyMDMTHx5frfmR4XOZSIq5fvw4A8PHx0Xr8xRdfRMeOHbF48WK88847Ohee37JlC0aNGlWqMsUS1mNZtGgR5HI5+vfvr96XkJAAAPDw8ChyvoeHB/76668i+319fXH16tVSxWRO+MyrrhYtWmDVqlUAgLfeegve3t6YPHkyIiIiMG3aNADA4MGDUatWLaxfvx7t27fXea+nT59i4MCB+PDDDwEAb7/9NkJCQrBu3Tq88847ZYorNTUVx48fx+LFi/HBBx+o98+YMaOsX5GMiIlaIh49egQLCwvY2dnpPGfu3LkICwvDypUr8Z///EfrOd26dcPBgwcrHE9UVBTWrVuHqVOnon79+ur9hc2wCoWiyDXW1tZIT08vst/Z2blIrYv4zKuy8PBw9X/L5XI0a9YM9+/fx+jRo9X7nZyc0KBBA9y+fbvE+7399tsa2y+++CI2bdpU5rhsbGxgZWWF6OhojB49Gs7OzmW+BxkfE3Ul0r59e3Ts2BGffvppkX+4hTw8PLTWfMri119/xejRo9GtWzcsXLhQ41hhrS4nJ6fIdU+fPtVa6xNFEYIgVCgmc8VnXjnVrVtXY9vR0RHW1tYarxQK9z969KjYe1lbW6NmzZoa+5ydnZGSklLmuBQKBRYtWoTJkyfDzc0NrVq1wssvv4zhw4fD3d29zPcj4+A7aomoXr068vPzkZGRUex5c+bMQWJiorpZ7XnZ2dlITEws1UebP/74A3369EGjRo2wY8cOjc5DwL/Nn4XNoc9KSEhArVq1iuxPSUkp8gOK+MyrMrlcXqp9QMmvI3Rd9yxdvxQ92+Gs0MSJE/Hnn38iIiIC1tbW+PDDD+Hv72/WLSBSx0QtEQ0bNgRQ0BO4OGFhYejQoQMWLVqktTfwtm3b1DWskj7Pi42NRffu3eHq6oq9e/dqbZINDg4GgCITbvz111+4f/+++viz7ty5A39//2K/lzniMyd9KWzCfn6ymrt372o938/PD5MnT8aBAwdw+fJl5Obmap1gh6SBTd8S0bp1awAFPwyDgoKKPXfu3Lno0KGD1l6f5X1fmZiYiJdeegkymQz79+8v0tRWKDAwEA0bNsTq1asxduxY9W/7K1asgCAIGp2QACAtLQ2xsbFl7vRiDvjMSV+8vLwgl8tx9OhR9OvXT73/m2++0TjvyZMnkMlksLa2Vu/z8/ODvb291lcbJA1M1BLh6+uLRo0a4dChQ3jzzTeLPTcsLAxhYWGIiYkpcqy87yu7d++O27dvY+rUqTh27BiOHTumPubm5oauXbuqtxcvXow+ffrgpZdewqBBg3D58mV89dVXCA8PL1KLOnToEERRRN++fcscU1XHZ0764ujoiAEDBuDLL7+EIAjw8/PD7t27kZycrHHen3/+ic6dO+P1119HQEAALCwssHPnTiQlJWHQoEEmip5KZKqZVqioL774QrSzs9OYvhGAOG7cuCLn/vLLL+qpJvUxS1XhvbR9wsLCipy/c+dOMTg4WFQoFKKnp6c4a9YsMTc3t8h5AwcOFNu1a1fh+KoqPvOqZc6cOSIA8e+//9bYP2LECNHW1rbI+WFhYWJgYKAoiqJ4584dEYC4YcOGEq8rLOdZf//9t/jaa6+J1apVE52dncWxY8eKly9f1rjnw4cPxXHjxokNGzYUbW1tRUdHR7Fly5bid999V8FvToYkiGIJPRnIaNLS0uDr64tPP/1UYxhHZZWYmAgfHx9s3bqVtSsd+MyJqCTsTCYhjo6OmDp1KhYvXlwlljxcunQpGjduzB/YxeAzJ6KSsEZNREQkYaxRExERSRgTNRERkYQxURMREUkYEzUREZGEMVETEZmZuLg4CIKAyMhIU4dCpcBETURUjNjYWIwdOxa+vr6wtraGg4MD2rZti2XLlmmde11frl69irlz5yIuLs5gZZTGwoUL0adPH7i5uUEQBMydO9ek8ZgjTiFKRKTDnj17MGDAACgUCgwfPhyNGjVCbm4ujh07hilTpuDKlSta51/Xh6tXr2LevHno0KEDvL29DVJGacyaNQvu7u5o2rQp9u/fb7I4zBkTNRGRFnfu3MGgQYPg5eWFI0eOaMynPm7cONy6dQt79uwxYYT/EkVR59rgFXXnzh14e3vj4cOHOhduIcNi0zcRkRaffvopMjMzsW7dOq2LntSrVw/vv/++ejs/Px8LFiyAn58fFAoFvL29MXPmzCKrUnl7e+Pll1/GsWPH0KJFC1hbW8PX1xfffvut+pzIyEgMGDAAANCxY0cIggBBEBAdHa1xj/3796NZs2awsbFRr1d++/ZtDBgwAC4uLqhWrRpatWpVoV8oTFmbpwJM1EREWvz888/w9fVFmzZtSnV+eHg4Zs+ejZCQECxZsgRhYWGIiIjQuirVrVu30L9/f3Tt2hWff/45nJ2dMXLkSFy5cgUA0L59e0yYMAEAMHPmTGzatAmbNm3SWKnsxo0bGDx4MLp27Yply5YhODgYSUlJaNOmDfbv3493330XCxcuxNOnT9GnTx/s3LlTD/9XyCRMuiQIEZEEpaWliQDEvn37lur8CxcuiADE8PBwjf0ffPCBCEA8cuSIep+Xl5cIQDx69Kh6X3JysqhQKMTJkyer923fvl0EIP7yyy9Fyiu8x759+zT2T5w4UQQg/vrrr+p9GRkZoo+Pj+jt7S0qlUpRFLWv1FWSv//+WwQgzpkzp9TXkH6wRk1E9Jz09HQAgL29fanO37t3LwBg0qRJGvsnT54MAEWangMCAvDiiy+qt2vWrIkGDRrg9u3bpY7Rx8cH3bp1KxJHixYt0K5dO/U+Ozs7vPXWW4iLi8PVq1dLfX+SDiZqIqLnODg4AAAyMjJKdf7du3chk8lQr149jf3u7u5wcnLC3bt3NfbXrVu3yD2cnZ2RkpJS6hh9fHy0xtGgQYMi+wubzJ+PgyoHJmoiouc4ODigVq1auHz5cpmuEwShVOfJ5XKt+8UyLGZoiB7eJE1M1EREWrz88suIjY3FiRMnSjzXy8sLKpUKN2/e1NiflJSE1NRUeHl5lbn80ib95+O4ceNGkf3Xr19XH6fKh4maiEiLqVOnwtbWFuHh4UhKSipyPDY2FsuWLQMA9OzZEwCwdOlSjXO++OILAECvXr3KXL6trS0AIDU1tdTX9OzZE6dOndL45SIrKwurV6+Gt7c3AgICyhwHmR4nPCEi0sLPzw9RUVEYOHAg/P39NWYmO378OLZv346RI0cCAJo0aYIRI0Zg9erVSE1NRVhYGE6dOoWNGzeiX79+6NixY5nLDw4Ohlwux6JFi5CWlgaFQoFOnTrB1dVV5zXTp0/Hli1b0KNHD0yYMAEuLi7YuHEj7ty5g++//x4yWdnrZps2bcLdu3fx5MkTAMDRo0fx0UcfAQDeeOMN1tKNwdTdzomIpOzPP/8Ux4wZI3p7e4tWVlaivb292LZtW/HLL78Unz59qj4vLy9PnDdvnujj4yNaWlqKderUEWfMmKFxjigWDK3q1atXkXLCwsLEsLAwjX1r1qwRfX19RblcrjFUS9c9RFEUY2Njxf79+4tOTk6itbW12KJFC3H37t0a55RleFZYWJgIQOtH29Ax0j9BFMvQe4GIiIiMiu+oiYiIJIyJmoiISMKYqImIiCSMiZqIiEjCmKiJiIgkjImaiIhIwpioiYiIJIyJmoiISMKYqImIiCSMiZqIiEjCmKiJiIgkjImaiIhIwpioiYiIJOz/AXO10dC7NxWKAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "two_groups_unpaired.mean_diff.plot(group_summaries=\"mean_sd\");\n", - "two_groups_unpaired.mean_diff.plot(group_summaries=\"median_quartiles\");\n", - "two_groups_unpaired.mean_diff.plot(group_summaries=None);" - ] - }, - { - "cell_type": "markdown", - "id": "b7908353", - "metadata": {}, - "source": [ - "For slopegraphs, the group summary is represented by a solid line connecting the group mean/median with error bars." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "862d87b1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "repeated_measures.mean_diff.plot(group_summaries=\"mean_sd\");\n", - "repeated_measures.mean_diff.plot(group_summaries=\"median_quartiles\");\n", - "repeated_measures.mean_diff.plot(group_summaries=None);" - ] - }, - { - "cell_type": "markdown", - "id": "00176729", - "metadata": {}, - "source": [ - "**Group summaries** have an associated kwargs `group_summaries_kwargs`\n", - "\n", - "The relevant inputs to `group_summaries_kwargs` are:\n", - "\n", - "- `'zorder'` - zorder of the gapped lines (the layering relative to other plot elements)\n", - "- `'lw'` - linewidth of the gapped lines\n", - "- `'alpha'` - alpha of the gapped lines (transparency)\n", - "- `'gap_width_percent'` - gap size (for gapped lines only)\n", - "- `'offset'` - location adjustment of the gapped lines (x-axis; for gapped lines only)\n", - "- `'color'` - the shared color of the gapped lines" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "441e5147", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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rQB9sm/Mmjp67ifjHqfCo7oiuLfyZpIkqg9yngJV5/Vs9d+4cOnXqpN6ePHkyAGDkyJEIDw9HfHy8OmkDBTXY/fv34z//+Q9WrlyJOnXq4OuvvzbI0CwA6NixI9atW4e3334btWvX1jh2//59rF+/XiP+imCiNkPO9rbs3U1UGaX9BdSob+oojKpjx44aK1I9T9usYx07dsTFixcNGNW/Fi5ciJCQEAQGBmLMmDEIDAwEAFy9ehWbNm2CKIpYuHChTmUwURMRVRaqPFNHQM/x8/PDL7/8gvfeew/Lly/XONahQwesWrUK/v669f9hoiYiqiyUuaaOgLQICgpCVFQUHj16hLt37wIAfHx8dJ6RrBATNRFRZaHMN3UEVIIaNWroLTk/i4maiKiyYNO3JCmVShw6dAh3795FcnJykXfqgiDgww8/rPD9maiJiCqLfP3NiU36ce7cObz22mt48OBBsZ3edE3UHEdNRFRZMFFLzrvvvousrCzs2bMHT548gUqlKvJRKpU6lcEaNRFRZZH31NQR0HMuX76MRYsWoU+fPgYrgzVqIqLKIifd1BHQc+rUqVPiOG99YKI2Q8npmfju+Dms2nUc3x0/x3WoiSqLbK5wJzXTp0/Hhg0bkJaWZrAy2PRtZk5fvYuFW/YjX6mCTBCgEkWE/+805ozqjdaBPqYOj4hKkvm3qSOg56Snp8POzg7169fH4MGDUbduXcjlco1zBEHQaa5xJmozkpyeiYVb9iMvv6BjQ+G61Hn5SiwI349tc96Es72tKUMkopJkJJk6AnrOBx98oP7v1atXaz2HiZrK7MjZG8hXqrQey1eqcPTcTc4BTiRlaQ9MHQE9JyYmxuBlMFGbkYQnaZAJgrom/SyZICD+Md9/EUladlrBe2prR1NHQv/w9PQ0eBnsTGZG3F0coCqmd6JKFOFRnf/4iSQvJa70c8joHj58iG+//RYrV67EgwcFLR9KpRJPnjzReRw1E7UZ6dbSHxZy7Y/cQi5D1xa6rfBCREaQHGvqCOgZoihi8uTJ8Pb2xrBhwzB58mT8+eefAICMjAx4eXnhiy++0KkMJmoz4mxvizmjesPSQg5BECCXySAIAiwt5Jgzqjec7c1rQXqiSulxtKkjoGcsXboUK1euxAcffIAjR45ojKl2dHTEq6++iu+//16nMviO2sy0DvTBtjlv4ui5m4h/nAqP6o7o2sKfSZqosnh829QR0DM2bNiAESNG4OOPP8bjx4+LHA8KCsL//vc/ncpgojZDzva27N1NVFk9ul2w3KWcP76l4P79+2jbtm2xx21tbXWeDIVN30RElUl+DmvVEuLq6or79+8Xe/z8+fOoV6+eTmUwURMRVTZ/XTJ1BPSPV199FWvXrsXdu3fV+wRBAAAcPnwY4eHhGDhwoE5lMFETEUlcixYtUKdOHbT4+ELBjvu/mzYgUps/fz48PDwQHByMESNGQBAELFmyBO3bt0fPnj0RFBSEWbNm6VQGEzURkcQlJCTg4cOHSEjL/WfHZSArxaQxUQFHR0f89ttvmDZtGh4+fAhra2tERUUhJSUFc+fOxS+//IJq1XTrrMveCERElY1KCdz9GQh8xdSRmLXs7GysX78ewcHBmD17NmbPnm2QclijJiKqjG7sAwy8DjKVzNraGtOnT8etW7cMWg4TNRFRZfT4DvDXBVNHYfYaNWqE2NhYg5bBRE1EVFmd38JatYktWrQI69atw9GjRw1WBt9RExFVVvF/APfPAPVamToSs7V69Wq4uLige/fu8Pb2hre3N2xsbDTOEQQBP/74Y4XLYKImIqrMTq0Cam0GLKxMHYlZunz5MgRBQL169aBUKnHnzp0i5xSOq64oJmoiosos9QFwfjPQapypIzFLhn4/DejwjlqpVGL79u0YN24cXnnlFVy5cgUAkJqaih9++AGJiYl6C5KIiErwx7fAg/OmjoIMpEKJOiUlBe3atcPQoUPx7bffYu/evfj7778BAHZ2dpg4cSJWrlyp10CJiKgYoggcXwCks4JkCoauuFYoUc+YMQPXrl3DoUOHcPfuXY31N+VyOQYMGIADBw7oFBgREZVDVgpweHbBoh1kNMaouFYoUe/ZswfvvfceunXrpvUl+QsvvGCUdnsiInrGoz+BE59xyJYRGaPiWqFEnZqaCm9v72KP5+XlIT8/v8JBERFRBd0+DNxii6axGKPiWqFE7evriwsXip8R5/DhwwgICKhwUEREpINTq4G0eFNHYRaMUXGtUKIOCwvDpk2bsGPHDnU1XxAE5OTk4L///S8OHjyIceM4VICIyCTyngKnvjB1FGbBGBXXCo2jfv/993Ht2jUMGTIETk5OAIChQ4fi8ePHyM/Px7hx4zBmzBidAiOiqi83IxlJV44jJyURCic3uDbuDCs7Z1OHVTXc+7Vg1rK6IaaOpEoLCwvD9OnT0bFjR3Tp0gXAvxXXBQsW4ODBg1i/fr1OZVQoUQuCgA0bNmDkyJHYtWsXbt++DZVKBV9fX7z++uvo0KFDhYJZs2YN1qxZo27PDwwMxJw5c9CzZ0+t54eHh2P06NEa+xQKBbKzsytUPhEZz+M/f8fN7xdDVOVDEGQQRRXuRW6F/4BZcGnA5KIXp1cDtTYCcktTR1JlGaPiqtPMZO3bt0f79u11CuBZderUwSeffIIGDRpAFEVs2bIF/fr1w8WLFxEYGKj1GgcHB40lxnSdqo2I9Ke4GnNuRnJBklbmAQBEUVnwpzIPN3Z9jJbvbWbNWh+S7wF/bAeavWHqSKosQ1VcnyWpKUT79Omjsb1o0SKsWbMGv/32W7GJWhAEuLu7GyM8oirBWM3NJdWYnz66D1GlvYONqMpH0pXjqNPmNb3HZJbOhwN1WwE1XzB1JFXCq6++iv/85z948cUXAQAnTpyAv7+/3iuuz6pQovb29i615ioIAqKjoysUFFAw08vOnTuRmZmJNm3aFHteRkYGPD09oVKp0KxZM3z88cfFJnUAyMnJQU5Ojsb1RObCWM3NpdWYawaG/lO+ssi1giBDTgpn2NIbVT5wZA7w6jrA2tHU0VR6P/74I1577d9fIjt16oStW7di6NChBiuzQok6NDS0SKJWKpW4d+8efv31VzRq1AhNmzatUEBXrlxBmzZtkJ2dDTs7O+zevbvYHnN+fn7YtGkTgoKCkJqais8++wxt27bFtWvXUKdOHa3XLF68GPPnz69QbESVmaGam7XV0JOuHC+xxpz3NBWiqNJ+XFRB4eRW7jioBOnxwOEPgV6fcZUtHdWuXRsXL17EsGHDAACiKBr8lWuFEnV4eHixx/744w90795d/SXKy8/PD5cuXUJqaip27dqFkSNHIioqSmuybtOmjUZtu23btvD398e6deuwcOFCrfefOXMmJk+erN6+dOkSQkNDKxQrUWVSWvIsrrm5pKby4mroTt5NSqwxW1ZzhCCzUP/SoHFcZgHXoC46flsqIv4P4PhCoOs8QCY3dTSV1uDBg/HZZ5/hu+++U3cemzFjBhYvXlzsNYIg4I8//qhwmXp/R92kSROMGzcO06dPx/nz5V/NxcrKCvXr1wcANG/eHGfPnsXKlSuxbt26Uq+1tLRE06ZNta4HWkihUEChUKi37ezsyh0jUWWUk5JY7ubmkprK7TwaFFtDT46+AED7NJaiqEK1mvXgP2AWbuz6WOPegswC/gNmwcrWSW/fu1BeVjruHlyLJ7d/BwQZajRsC5/u4yC3sin1WlEUcX37XCRHn4f/wNmo7vdvBSH9rz8RezwcGfF3AAGwr+UHry6jYefmo/fvoLOYE8CJpUCHaYCswosnmrXFixejfv36+Pnnn5GUlARBEGBra4vq1asbrEyDdCZzc3PD9evX9XIvlUql8U65JEqlEleuXEGvXr30UjZRVaJwcitXc3NpTeV12rxWbA0dogoQZNCWrAtrzFa2Tmj53mbN2vo/+yvq8jcz4NakC9yadCty7M89S5Gb8QSNhn0ElVKJ2z+twJ39X8DvlWml3vevM3sAFG3eVOZm4dq3c+DSoBV8e74LUaVEXNQ2XIv4EC0nboFMLqn+ugVu/Q+wUADtJgEcJVNucrkcb731Ft566y0AgEwmw+zZsw36jlrvv1I9fvwYGzduLPYdcUlmzpyJEydOIDY2FleuXMHMmTMRGRmpbkYfMWIEZs6cqT5/wYIFOHz4MO7evYsLFy5g+PDhuHfvHsLCwvT2fYiqCtfGnSHItCcObc3NpTWVp8RcgiBo/xEiyORw9mkGQW4JCAIEmbzgT7mlRo3Zys4Zddq8Bt+e76JOm9cMUpMGgKeP4pAcfR71e78P+9oN4VgvEL49xuHvayeQk/64xGszEqLx8LfdaNDnfS33fYD8rHR4hg5Htep1YFvTE/U6DEVeZgpyUpMM8l304toe4Pd1XLyjApo1a4aDBw+qtzdv3lzhPlllVaFf9zp37qx1f0pKCm7evInc3Fxs3bq13PdNSkrCiBEjEB8fD0dHRwQFBeHQoUPo1q3gt+O4uDjInmmuSU5OxtixY5GQkABnZ2c0b94cp06d4jzjRFpY2TmXq7m5tKZyACXW0B29gtCgzyS91pgrKu3BTcitbWFfq4F6n5N3U0AQkP7wFhQN22q9TpmXjVt7lsK3xzuwsnMpctymem1Y2Dgg4dJh1G3/OkSVComXDsOmRl1YS71D3B/fAgp7oGnF+hOZq8uXL+PRo0fq7TfffBNbt26Fv7+/wcqsUKJWqVRFerkJggBvb2907doVb775Jho2bFju+27cuLHE45GRkRrby5cvx/Lly8tdDpG5ebZDWJ02AwAByH+apk6eEEU8OP29Roex0prKnbybIiMhusQOYVa2TpIYD52XkQyrak4a+wSZHJY29sjLTC72upjDG+BQx1/jnfSzLBTV0PiNxbix8yPcP7kdAGDjUguBQxYWtCJI3Zn1gG1N4IWXTB1JpeHp6YmjR49iyJAhkMvl0u31/XzCJCLp0tYhrLAW7dIgpNgOYw1enlhiz2yPli/DvvYLRu0Q9rz7J3fg/q/fqbdV+blIf3gT0QfXqvc1e3tNhe79+M/fkBJ7GU3Hrir2HGVeDm7vWwmHOgHwe2UaRJUKD3/7Add3zEOTN5dDbqko9lrJOLEUcKoHuJa/cmWO3n77bUyfPh3btm2DjY0NBEHAmDFjSlyIShAEpKamVrhMCfZ0ICJ9Ka1DWPCbK4o9fnvfKtTvPRF39q8qNhG7NAjRe4cwrVxdte5zb94JNQJeVO+6tWcpajRsh+rPNGUr7KvD0s4ZuU9TNC4XVUrkZaXD0lb72PHU2MvITo7H6aWva+y/setjONQNRNCIT/D31UjkpCahyehl6tcBdq9MxW+fDcKTP39DzUDdh37GxcUhMzMTAJCZo0Tck2zUc7HW+b5qytyCYVuvfQ1Ylt4D3hS+/PJLLF26FAkJCWjSpAm++OILhIRon6DH0GtATJ06FU2aNMHPP/+MxMREbNmyBS1btoSPj+F6+ZcpUX/zzTcVuvmIESMqdB0R6UdpHcJifw4veWKSzORSE3FhhzCD6ttX625LAJY29uptmYUClraOsHGppXGeQ52GUGZnIiP+Nuw8Ct5Tp8T8AYgi7Gv7ab13nbYD4Bas2SR8cf14+HQbq57FTZWf80/P6X+bPgsStqBeAriizpw5g4ULF2L//v3qe6VkKeH13zN4ubELPuzliZZe9qXcpYxSHwCXdwDNR+nnfnq0Y8cOTJ48GWvXrkWrVq2wYsUKdO/eHbdu3YKrtl/gYPg1IF566SW89FLB343w8HCMGzfO9DOTjRo1qtw3FgSBiZrIxEodO52aVOrYaqMkYgOrVqMenH2b4/b+L1C/53iIKiWiD61BzcAOUNgXjH/NSXuEq9v+ixf6ToZ9bT9Y2blo7UCmcKwJa+eC9QWcvJsi5ugmRB/8CrVa9oEoinjw604IMjmcPIMqHO8PP/yAQYMGQRTFIglfFIEDV5/gf1eTsWOsP15tWqPC5Wi4vBMIGgxY6rG2rgeff/45xo4dq64lr127Fvv378emTZswY8YMrdcYcw0IlUp7Pw59KlOijomJMXQcRGQApY6ddnTF00dxxR+Xes/lcnih/1TcPbgGV7f9FxAEVG/YDr7d/32vKKqUyHr8AMq8ss3bAADVatRFwKC5uH8iAn9s/qBg8gt3XwQOWQAr+6JJvizOnDmDQYMGQalUFlsrV6oAASIGbbiBU9OC9VOzzs0A4k4Dvp10v5ee5Obm4vz58xrDcmUyGbp27YrTp08Xe11514Aoj7i4gn8v9erV09guTeH5FVGmRO3p6VnhAojIdFwbd8a9yK3Fdgjz6jwaKTGXqsxUnkEjPin2mKWNfYmTm1g7uaH97P0l3l/bcWefpnD20d842o8++khrTfp5IgARIj46cA8/vttIP4XHXzJKos7IyEBaWpp6+/kZIws9evQISqUSbm6avzC6ubnh5s2bWu9dkTUgysPLywuCICArKwtWVlbq7dIolUVbrcqKncmIqrDSxk7bunoafSpPKl5cXBz27dtX5vfbShXw05Un+utglnxP93uUwfPrK8ydOxfz5s3Ty70rsgZEeWzatAmCIMDS0lJj25AqnKgTEhKwceNGXLhwAampqUXa6QVBwLFjx3QOkKiqUSnzIKoq/tt1eTl6BaHZO2vx6GoUMp8kwMqhBmoGhsLS1hHZT9NRrbY/God9gUfXTiA79W9YO9bUOK4vgkwOmdxSb/eTGn081/8d2F/uTmiiCBy6noyRrbV3rCqX9EcQ8oq2ruhLfn5Bx8WoqCgEBwer92urTQNAjRo1IJfLkZioOQ99YmJimd9Bl2UNiPJ4vs9WRfpwlVeFEvXly5fRsWNHZGVlwc/PD1euXEFAQABSUlLw8OFD+Pr6om7duvqOlajSUynzkP7wTyhzs4xetq27L1Z/dwyffrHB6GWTYb31f7fx1v/d1sOdfgWGbdPDfUpmZ2cHBweHUs+zsrJC8+bNcezYMfTv3x9AQeetY8eOYcKECWUqqyqsAVGhRD1jxgzY2dnh0qVLqFatGlxdXbFy5Up07twZO3fuxDvvvINt2wz/sIkqG1GlhDI3CzILC5PULmdMnoBpk941erkqZR5U+Uo4egZCLrFexfqgzMtG6r1rkFno1moQsetHTJw+r9zXrRvihRGtdOv9LYoilLa1YDFsm8Ge0cWLF9GqVatyXTN58mSMHDkSLVq0QEhICFasWIHMzEx1L/ARI0agdu3a6mUmFyxYgNatW6N+/fpISUnB0qVL9boGxIIFC8p9jSAI+PDDDytcZoUS9a+//opp06ahXr16ePLkCYB/u6gPHDgQJ0+exNSpUxEVFVXhwIiqMpncEjILK+OXa6JeKar8XOTnZMHS0hJyy6rX/C2DEpaWFrBQ2Oj0XLt26ghBKN8YbEEAujV0gqVc1ylLReTZ1zDoM7KwKP9fwEGDBuHvv//GnDlzkJCQgODgYBw8eFDdwczYa0Boe5de+I76+edW+CxNkqhVKpX6f5KTkxPkcrk6YQNA48aNS523m4ikKS8rHcm3zyI34wms7Fzg3KClxqQiZDh169RCj66dcPh4VJl6CctlQK9AJ9Rz0c9UpflO3pDir1ETJkwotqnb2GtAPN8f6+HDh+jduzcaNWqESZMmwc+vYAKdmzdvYsWKFbh+/Tr27y95NEFpKrTMpbe3t3pstUwmg7e3N44ePao+furUKTg5OekUGBEZX2rcVVzfPh9/nf0Jj26dwl9nf8L17fORGnfN1KGZjen/eReCIJTak7hgPjQBs7rX1lvZ+e6GXa6xKho/fjwaNGiA//u//0OLFi1gb28Pe3t7tGzZEtu2bYOvry/Gjx+vUxllTtTJyf+uMPPSSy9h586d6u133nkHX3/9Nbp27YouXbpgy5YtBp1OjQh79wJff6352bvX1FFVanlZ6Yg9VjilqAioVADEgqlGj21GXpb+eoBT8ZoHByF87XLI5XLIi2nOlssAuUzA9jfro6WnnV7KVdZoCJWdcWbzqkqOHz9e7NLPANClSxedR0CVuenb3d0dvXr1wrBhwzBlyhQMGTIEeXl5sLS0xKRJk5CZmYnvv/8ecrkcH374IWbNmqVTYGRcyemZOHL2BhKepMHdxQHdWvrD2d7W1GEVLykJePDA1FFUKcm3zxY7vEhUKZF85yxcGxf/A8mg9u4teObPcnUtdg7wyq5fr+44unc7liz/CgeP/qzx7lMQCpq7Z3WvrbckDQC5L7yst3uZE2tra5w+fRrvvPOO1uOnTp2CtbVunfPKnKgHDBiAvXv3Yu/evbC3t8err76KYcOGoXPnzhAEAbNnz8bs2bN1CoZM4/TVu1i4ZT/ylSrIBAEqUUT4/05jzqjeaB1ouBVhSFpyM54AMgFQaenIJBOQm/6k6H5jMcNfzJoHB+G7LWtx/8FfaNutL1JS0+BkI8eFGY319k66kMo1EKqaAUCO8YcNVnbDhg3DqlWr4OTkhPfeew++vr4AgOjoaKxatQoRERGYOHGiTmWUOVFv27YNWVlZ2LNnDyIiIrBt2zZs2bIFbm5uGDJkCIYNG4ZmzZrpFAwZX3J6JhZu2Y+8/IKalPKf39zz8pVYEL4f2+a8Ke2aNVXY853G5Apb7UkaAFRiheeuJt3UrVML1arZICU1DbYKmd6TNADkBQ7U+z3NxZIlS/Do0SOsXr0aX375pboHukqlgiiKGDJkCJYsWaJTGeXq9W1jY4MhQ4ZgyJAhSE5OxnfffYeIiAisWLECK1asQIMGDTB8+HAMHTrUoGtzkv4cOXsD+UrtizbkK1U4eu4mBnZqbuSoyNBS467+8z5a+W8tWpAVW6MWZHI4129pgkjJ0JS1mkHl4gvk55o6lErJysoKW7duxdSpU3HgwAHcu1cwDaunpyd69uyJJk2a6FxGhUdVOjs7Y9y4cRg3bhwePnyIiIgIfPvtt5gzZw7mzp2LVq1a4dSpUzoHSIaV8CQNMkFQ16SfJRMExD9ONUFUZEiancbwb2IWlQBkEGRyiCqVOmkLMjm8uozmEK2qSBCQF1C5lzCViqCgIAQFVXxp05LoZfqD2rVrY+rUqejRowfmzJmDH3/8Eb///rs+bk0G5u7iAFUxkyuoRBEe1R2NHBEZWkmdxiCKcA3uCrmlNXLTn8DK3gXO9TmOuqpS1m0L0bHiyy+SceicqOPi4tS16atXr0IURbRt2xbDhg3TR3xkYN1a+iP8f6fV76ifZSGXoWsLfxNERYZUWqcxZfZTeDSrvPMiUxlZKPhuupKoUKJ+9OiR+v306dOnIYoiGjZsiAULFmDYsGHw8vLSc5ikT88PxZo8qAs+33FMo9e3hVyGOaN6w9m+mqnDJT2zsnNhpzFCXsAAiNWqmzoMKoMyJ+rMzEzs3r0bEREROHbsGPLy8uDh4YFJkyaxx3clom0oloVchsmvd0FyRhbiH6fCo7ojurbwZ5KuopwbtET8+QP/vqN+BjuNmQdVzYbIr/+SqcOgMipzonZ1dUV2djbs7OwwdOhQ9RjqZydDJ2kraSjW598d41CsKuz5oVh1XxyM+79s1+j1zU5j5kFU2CO35TsFvfypUihzou7atSuGDRuGvn376jzLCpkGh2KZJ21DsQSZHHXbD0J+djo7jZkTmQy5rSZAtOHrjcqkzIn6xx9/NGQcZAQcimV+ihuKJarycf/kDgQMnsvkbEZym4womIGM9OrQoUPYuHEj7t69i+TkZK3LXUZHR1f4/iZanZZMgUOxzI+k5+8mo8pv0ANKny6mDqPKWbp0KWbMmAE3NzeEhISgcePGei+DidqMcCiW+ZH0/N1kNMo6IchrPMTUYVRJK1euROfOnXHgwAFYWhpmNW/2JjAjzva2mDOqNywt5BAEAXKZDIIgwNJCzqFYVRSHYpHKNRC5Ld5m5zEDSU5OxoABAwyWpAHWqM1O60AfbJvzJo6eu8mhWGaAQ7HMm8rFBzmtJwFywyURcxcSEoJbt24ZtAwmajPkbG/L3t1mwtLGHl5dRiP22GYOxTIzKqd6yGk3FbDkKB1D+uqrr9CzZ0+0aNECQ4cONUgZTNREVZxjvUAEDJ6L5DtnORTLTIgOtZDTfjpgZWfqUKq8QYMGIT8/H2+88Qbeeecd1KlTB3K5XOMcQRDwxx9/VLgMJmoiM2BpY8/e3WZCtK1RkKQVDqYOxSy4uLigevXqaNCggcHKYKImIqoiRCs75LSbzglNjCgyMtLgZbAbIBFRVSAIyG39PkR7d1NHQnrGGjURURWQFzgQqpoNTR2G2crLy8PNmzeRmpoKlaroVM0dOnSo8L2ZqImIKjmViy/yX+ht6jDMkkqlwsyZM/HVV1/h6dOnxZ6nVGqfIbAs2PRNRFSZCQJyg0dyQhMT+fjjj7F06VIMHz4c33zzDURRxCeffIK1a9ciKCgITZo0waFDh3Qqg0+WiKgSU9YOgejsbeowzFZ4eDhef/11rFmzBj169AAANG/eHGPHjsXvv/8OQRBw/PhxncpgoiaiUuVlpSPp8nE8OLULSZePIy8r3dQh0T/y/F42dQhm7cGDB+jcuWDoo0KhAABkZ2cDAKysrDB8+HBs3bpVpzL4jpqIkJeVjuTbZ5Gb8QRWdi5wbvDvhCja1rOOP38AXl1Gw7FeoIkjN28ql/oQnbxMHYZZq169OjIyMgAAdnZ2cHBwwN27dzXOSU5O1qkMJmrSSXJ6Jo6cvYGEJ2lwd3FAt5b+cLa3NXVYVA4lJeJqNesVu5517LHNXM/axPLrtTN1CGavadOmOHv2rHq7U6dOWLFiBZo2bQqVSoVVq1ahSZMmOpXBRE0VdvrqXSzcsh/5ShVkggCVKCL8f6cxZ1RvtA70MXV49Iziasx5WeklJmLXoC5cz1qqBAHK2lxUxdTeeusthIeHIycnBwqFAosWLUKHDh3QoUMHiKIIZ2dnfPvttzqVwURNFZKcnomFW/ar17ZWigU/4PPylVgQvh/b5rzJmrVElFRjzklJLDERp/91i+tZS4BbzZoAAHeLDPU+VQ0/wNrRVCHRP/r27Yu+ffuqtwMCAhAdHY3IyEjI5XK0bdsWLi66zRTHRE0lKq5p+8jZG8hXFh3UDwD5ShWOnrvJFbokoLQas5NvsxIT8bPXFMH1rI3mxMEfAAA237+h3qesHWKqcKgUjo6O6Nevn97ux0RNxSqpaTvhSRpkgqCuST9LJgiIf5xqgojpecm3z5ZYY1ZmZ5SYiO1rv4CsRw+4nrXUCALymaglQ6lUYufOnfj555+RlJSEBQsWoHHjxkhNTcWxY8fQrl07uLm5Vfj+HJ5FWj3btC2KIpQqFURRVDdtO9paQ6UlSQOAShThUZ1NclKQm/Hk35rx82QCLKztIMjkWg8LMjlq+L8Iry6jIcgsAAiATAZAgCCz4HrWJqR0a8xmb4lISUlBu3btMHToUHz77bfYu3cv/v77bwAFvcAnTpyIlStX6lQGEzVpVVrTNiDAQq79r4+FXIauLfwNGB2VlZWdS4k1Zmtn91ITceF61rVC+qCGX1vUCumDgMFzOTTLhJR125g6BPrHjBkzcO3aNRw6dAh3796F+EwFRi6XY8CAAThw4IBOZbDpm7S+hy6taTs1MwtzRvXGgnDNpnELuQxzRvWGs301E3wTep5zg5aIP3+gxKZrSxt7BAyei+Q7Z5Gb/gRW9i7q/YW4nrWEyCyg9GD/D6nYs2cP3nvvPXTr1g2PHz8ucvyFF15AeHi4TmVIKlGvWbMGa9asQWxsLAAgMDAQc+bMQc+ePYu9ZufOnfjwww8RGxuLBg0aYMmSJejVq5eRIq78insP3bmZX6lN260DfbBtzps4eu4m4h+nwqO6I7q28GeSlhBLG3t4dRmN2GObNXp9CzK5RtM1E3HloazZELC0MXUY9I/U1FR4exc/hWteXh7y84v+olwekkrUderUwSeffIIGDRpAFEVs2bIF/fr1w8WLFxEYWLSZ7dSpUxgyZAgWL16Ml19+GREREejfvz8uXLiARo0ameAbVC4lDbE6dv4mLGQC8pRFk/WzTdvO9rbs3S1xhU3XJdWYqfJQufKVg5T4+vriwoULxR4/fPgwAgICdCpDUu+o+/Tpg169eqFBgwZ44YUXsGjRItjZ2eG3337Tev7KlSvRo0cPTJ06Ff7+/li4cCGaNWuG1atXGznyyqmk99BKlYjOzRvC0kIOQRAgl8kgCAIsLeRs2q6ECmvMddoOgGvjzkzSlZjKpb6pQ6BnhIWFYdOmTdixY4f6/bQgCMjJycF///tfHDx4EOPGjdOpDEnVqJ9V2N09MzMTbdpo7zhx+vRpTJ48WWNf9+7dsWfPHiNEWPmV9h7aytKCTdtEEqNy9DR1CPSM999/H9euXcOQIUPg5OQEABg6dCgeP36M/Px8jBs3DmPGjNGpDMkl6itXrqBNmzbIzs6GnZ0ddu/eXWyzQUJCQpGxaW5ubkhISCj2/jk5OcjJyVFvF06mbo7cXRxKfQ/Npm0i6RBtnPl+WmIEQcCGDRswcuRI7Nq1C7dv34ZKpYKvry9ef/11dOjQQecyJJeo/fz8cOnSJaSmpmLXrl0YOXIkoqKidG7jL7R48WLMnz9fL/eq7Lq19Ef4/06r31E/i0OsqpaSVseiykO0rfikGWRY7du3R/v27Q1yb0m9owYK1u+sX78+mjdvjsWLF6NJkybFDhZ3d3dHYmKixr7ExES4u7sXe/+ZM2ciNTVV/YmKitJr/JWJs70t5ozqzffQVVxq3FVc3z4ff539CY9uncJfZ3/C9e3zkRp3zdShUTmJ1aqbOgQyAcnVqJ+nUqk0mqqf1aZNGxw7dgyTJk1S7zty5Eix77SBgoW9Cxf3BgpmjjE3z4+b/nLyYJy7Gcf30FVQaXN9c5nKykXkbGSS8OwiHGUhCAJ+/PHHCpcnqUQ9c+ZM9OzZE/Xq1UN6ejoiIiIQGRmJQ4cOAQBGjBiB2rVrY/HixQAKXuKHhoZi2bJl6N27N7Zv345z585h/fr1pvwaklbS/N18F131lDbXN5eprFxEK/OrWEjRvn37YG1tDXd3d42ZyIojCMVM41tGkkrUSUlJGDFiBOLj4+Ho6IigoCAcOnQI3bp1AwDExcVBJvu3tb5t27aIiIjA7NmzMWvWLDRo0AB79uzhGOpicGlK86Oe65vLVFYJoiVbuqSgdu3aePjwIWrUqIGhQ4di8ODBJb5y1ZWkEvXGjRtLPB4ZGVlk38CBAzFw4EADRVS1VKmlKV1dy7bPzJU21zeXqaxkLBSln0MGd//+fURFRSEiIgILFy7E1KlTERoaimHDhmHAgAGwt9fv6yRJJWoyrCq1NGU53xGZq7LM9U2ViIW1qSOgf4SGhiI0NBSrV6/GgQMHEBERgQkTJuDdd99Fz549MXToUPTp00ejT1RFSa7XNxlOWcZNU9VSONc3l6msGkQ5E7XUWFpaol+/ftixYwcSExOxbt06JCQkYNCgQfj000/1UgYTtRnp1tKfS1OaIS5TWYWYadP3l19+CS8vL1hbW6NVq1Y4c+ZMiefv3LkTDRs2hLW1NRo3bqzzMpNlkZOTg0OHDuHHH3/ExYsXYW1tDS8vL73cm4najHDctPniXN9Vg2iGiXrHjh2YPHky5s6diwsXLqBJkybo3r07kpKStJ5fuFjTmDFjcPHiRfTv3x/9+/fH1atX9R6bSqXCoUOHMGrUKLi5uWHIkCHIysrChg0bkJSUhDfeeEMv5fAdtZnh0pRElZjMytQRGN3nn3+OsWPHYvTo0QCAtWvXYv/+/di0aRNmzJhR5PxnF2sCgIULF+LIkSNYvXo11q5dq5eYTp06hYiICOzcuROPHz9G69at8fHHH+P1119HjRo19FLGs5ioTSwvXwmlSntPbEOxUVihT7sgKJVKqP4pOz3zqVFjkMtksLSQG7VMKVDm5SEvLx+iLA8yUbexlZWJKj8P+Xn5yMvLgwrlf+6CUglBqTkeXFQqIebl6StEnRQ+V6X4FDK54WIS8vIhZmUa5N4qZR5U+coKP6OyKFyXOSMjA2lpaer9z09EVSg3Nxfnz5/HzJkz1ftkMhm6du2K06dPay3DGIs1tW/fHjY2NujVqxeGDBmibuKOi4tDXFyc1muaNWtW4fKYqE0oL1+JW3EJeJpjmh82G1avwNdfaZ+elUhKxgCo89y+BwA2vv22CaIhXYWGhmpsz507F/PmzSty3qNHj6BUKrUuvnTz5k2t967IYk0VkZWVhe+//x4//PBDieeJoghBEKBUap94qCyYqE1IqVLhaU4eLOWmqV1OmDQZ706chIyn2Th3Kw5P0jLh4mCLFn71YFet4r1Lr8XGY+vB36BUqSAIAkRRhFwmw4gerRHg5YG8fCXylSo09q0NaytLPX4j6VPmZSP13jVYKKwhszCfZkxVfi7yc7Lh6BkIuWX5/24JmzZBePBAY59Ypw7WvPmmvkLUmUqZV+wscHqTkw4oDNe/QJDJIZMb7t/kxYsX0apVK0RFRSE4OFi9Xx9DmIxp8+bNRi2PiVoCLC3ksLI0waOwtMCV6If4et9JjaR6+NxNhPVpj8Y+tct9y7TMLGw7chYqCBBkBb98CAKgAvB/R85iYVhfVLOxLvgFxdISlpbmlahlUMLS0gIWlpaQWZjPd1cJIgRVPiwtLSGvyDOXyws+z++T0t8fY8RiIQOsKu/sgRYWBT/n7Ozs4ODgUOr5NWrUgFwuL9fiSxVZrKm8Ro4cqbd7lQV7fZuxtMwsfL3vJPKVKogioFKJEMWCWcq+/ukk0jKzSr3+yNkb2HHsHI6cvYG0zCz8fj222HfuSpUKZ27EGuCbEJkL8+nXABSspti8eXMcO3ZMvU+lUuHYsWPFLr5UuFjTs0pbrEnqWKM2Y2VJqsWNrdZWE9936jL86rmpt58nEwQ8TjVMRxiq4jhlrNmaPHkyRo4ciRYtWiAkJAQrVqxAZmamuhe4OSzWxERtxp6kZVYoqT5bEwegvj5fqcKN2ASIKH72s+qOlbfZjkyIU8YW0HEVpspo0KBB+PvvvzFnzhwkJCQgODgYBw8eVHcYM4fFmpiozZiLg22xS7SVlFRLqomrRBEyQdCarOUyGVoFeFc84Gft3Qs8P+GBqyt/oFMVZ36JGgAmTJiACRMmaD1mDos18R21GWsV4AW5TPtfgZKSamFNXPt1Avy93GEhl0EQCrYFoWCK0rA+7WGvQ29yDUlJwIMHmp9iZioiqjLMsEZNrFGbNQdbG4T1aY+vfyp41ywTBKj+GUpVmFQLO4gVDt1qFeBVak38hbpuGP5SK5y5EYvHqZmo7miLVgHe+kvSpHd5WelIvn0WuRlPYGXnAucGLTnNqBTJzWdIH/2LidoMPZ98pw19CTfuJRRJqsV1GBvaLQRymUzr2taFNXH7atZc5KOSSI27ithj4QVjgGUCoBIRf/4AvLqM5sIdUsMatVliojYz2pNvQQ362cRaUoexiCNnMLRbCCKOnCm2Jk7SUlyNOS8r/Z8k/c961aqC5yyq8hF7bDMCBs9lzZrIxJiozUhJyffrn05iYVhfONjaACh96Fb602wsDOvL5u1KoKQac05KYrGzaYkqJZLvnIVr485GjpiInsVEbUbKM266LEO3HGxt2LwtcaXVmJ18m6mTdxEyAbnpT4wYLRFpw17fZqSk3trPj5uu6NAtkpbk22dLrDErszO0J2kAUImwsncxYHREVBZM1GakPMm3okO3SFpyM54U1Ji1kQmwsLZTz8n+PEEmh3P9lgaMjojKgonajJQn+RYO3TL4eGgyKCs7lxJrzNbO7vDqMhqCzAKAAMhkAAQIMgt4dRnNjmREEsB31GakLOOmAc3hW11b+EMQgMysXHYYq4ScG7RE/PkD/76jfkZhjdnSxh4Bg+ci+c5Z5KY/gZW9i3o/EZkeE7WZaexTu8Te2tqGbxUm8oose0mmZWljD68uoxF7bLNGr29BJteoMVva2LN3N5FEMVGboeJ6a5dn+BZVHo71AlljJqrEmKhJTZdlL0naWGMmqryYqM2Qtvm7HWxtKrzsJRERGQ4TtZkpbv7usD7tOXaaiEiCODzLjDz7DloUAZVKhCj++w46wMudY6eJiCSGidqMlPYO+sa9BI6dJiKSGDZ9m5GyvIPu2sKfi20QEUkIE7UZKes7aC62QUQkHWz6NiOcv5uIqPJhojYjnL/bfOVlpSPp8nE8OLULSZePIy8r3dQhEVEZsenbzJQ2hShVPalxV/9Zk/rfKUTjzx+AV5fRcKwXaOrwiKgUTNRmiO+gzUdeVvo/SfqfRTn+WUlLVOUj9thmBAyey6lEiSSOTd9EVVjy7bMFNWktRJUSyXfOGjkiIiovJmqiKiw340lBc7c2MgG56U+MGxARlRsTNVEVZmXnom7uLkIlwsrexbgBEVG5MVETVWHODVpCkMm1HhNkcjjXb2nkiIiovJioiaowSxt7eHUZDUFmAUAAZDIAAgSZBby6jGZHMqJKgL2+iao4x3qBCBg8F8l3ziI3/Qms7F3gXL8lkzRRJcFETWQGLG3s4dq4s6nDIKIKYNM3ERGRhDFRExERSRibvs1QWmYWfr8eiydpmXBxsEWrAC842NqYOiwiItKCidrMXIl+iK/3nYRSpVKvTb3v1GWE9WmPxj61TR0eERE9h03fZiQtMwtf7zuJfKUKogioVCJEEchXqvD1TyeRlpll6hCJiOg5TNRm5PfrsVCqVFqPKVUqnLkRa9yAiIioVEzUZuRJWiYEQfu8zzJBwOPUTCNHREREpZFUol68eDFatmwJe3t7uLq6on///rh161aJ14SHh0MQBI2PtTXXVtbGxcEWoqh93meVKKK6o62RIyIiotJIKlFHRUVh/Pjx+O2333DkyBHk5eXhpZdeQmZmyTU9BwcHxMfHqz/37t0zUsSVS6sAL8hl2h+5XCZDqwBvI0dERESlkVSv74MHD2psh4eHw9XVFefPn0eHDh2KvU4QBLi7uxs6vErPwdYGYX3a4+ufCnp9ywQBKlGEXCZDWJ/2sK/GlggiIqmRVKJ+XmpqKgDAxaXkpfgyMjLg6ekJlUqFZs2a4eOPP0ZgYKDWc3NycpCTk6NxrTlp7FMbC8P64syNWDxOzUR1R1u0CvBmkiYikijJJmqVSoVJkyahXbt2aNSoUbHn+fn5YdOmTQgKCkJqaio+++wztG3bFteuXUOdOnWKnL948WLMnz/fkKFLnoOtDbq28Dd1GEREVAaSekf9rPHjx+Pq1avYvn17iee1adMGI0aMQHBwMEJDQ/HDDz+gZs2aWLdundbzZ86cidTUVPUnKirKEOETERHphSRr1BMmTMC+fftw4sQJrbXiklhaWqJp06a4c+eO1uMKhQIKhUK9bWdnp1OsVRGnGCUikg5JJWpRFPHee+9h9+7diIyMhLd3+XshK5VKXLlyBb169TJAhFUfpxglIpIWSTV9jx8/Hv/3f/+HiIgI2NvbIyEhAQkJCcjK+ndqyxEjRmDmzJnq7QULFuDw4cO4e/cuLly4gOHDh+PevXsICwszxVeo1DjFKBGR9EiqRr1mzRoAQMeOHTX2b968GaNGjQIAxMXFQfbMWODk5GSMHTsWCQkJcHZ2RvPmzXHq1CkEBAQYK+xKp7im7bJMMSqZTmiurmXbR0RUyUkqURc3a9azIiMjNbaXL1+O5cuXGyiiqqekpu3CKUa1PQfJTTHat6+pIyAiMgpJNX2TYZXWtG1rbcUpRomIJIaJ2oyU1rQNAZxilIgqpSdPnmDYsGFwcHCAk5MTxowZU+qEVh07diyyVsTbb79tpIjLjonajJS2elZmVi7C+rSHhVwGQQDkMgGCAFjIOcUoEUnbsGHDcO3aNRw5ckQ9vPett94q9bqxY8dqrBXx6aefGiHa8pHUO2oyrLKsnsUpRomosrlx4wYOHjyIs2fPokWLFgCAL774Ar169cJnn32GWrVqFXtttWrVJL9WBGvUZqSsq2cVTjE6qEsLdG3hzyRNRJJ2+vRpODk5qZM0AHTt2hUymQy///57iddu27YNNWrUQKNGjTBz5kw8ffrU0OGWG2vUZoSrZxGRFGRkZCAtLU29/fyMkeWVkJAA1+eGZ1pYWMDFxQUJCQnFXjd06FB4enqiVq1auHz5MqZPn45bt27hhx9+qHAshsBEbWbYtE1EphYaGqqxPXfuXMybN6/IeTNmzMCSJUtKvNeNGzcqHMez77AbN24MDw8PdOnSBdHR0fD19a3wffWNidoMcfUsIjKlqKgoBAcHq7eLq01PmTJFPdlVcXx8fODu7o6kpCSN/fn5+Xjy5Em53j+3atUKAHDnzh0maiIiMl92dnZwcHAo9byaNWuiZs2apZ7Xpk0bpKSk4Pz582jevDkA4Pjx41CpVOrkWxaXLl0CAHh4eJT5GmNgZzLSkJaZhSNnb2DHsXM4cvYG5/cmIsnz9/dHjx49MHbsWJw5cwa//vorJkyYgMGDB6t7fD98+BANGzbEmTNnAADR0dFYuHAhzp8/j9jYWOzduxcjRoxAhw4dEBQUZMqvUwRr1KTGlbOIqLLatm0bJkyYgC5dukAmk+G1117DqlWr1Mfz8vJw69Ytda9uKysrHD16FCtWrEBmZibq1q2L1157DbNnzzbVVygWEzUB0JxeFPh33vXC6UUXhvXlmtREJFkuLi6IiIgo9riXl5fGPBJ169ZFVFSUMULTGZu+CUDp04ueuRFr3ICIiAgAa9RmSdsyl5Vq5SwiIjPCRG1minsP3aKhJ1fOIiKSIDZ9m5GSlrk8eyMWMq6cRUQkOUzUZqSk99AqUUTLhp5cOYuISGLY9G1GSnsPbWVhwelFiYgkhonajJRlmUtOL0pEJC1s+jYjZV3mkoiIpIOJ2owULnPJ99BERJUHm77NDJe5JCKqXJiozRDfQxMRVR5s+iYiIpIwJmoiIiIJY6ImIiKSMCZqIiIiCWOiJiIikjAmaiIiIgljoiYiIpIwjqP+x40bN4xeZk5ePm4/SIK1pQUsLeRGL99U8vKVyM7LhzItCQpL8/orqMzPQUZ8NOSWCsjklqYOx2hUyjwo83Jg9yQPcguFxjEPDw94eHiYKLKKiY+PR3x8vKnDqHRM8XO2KjCvn5JaeHh4IDQ0FMOHDzd1KERmae7cuZg3b56pwyiXdevWYf78+aYOo1IKDQ2tdL+YmZogFreckhkxx9+OMzIyEBoaiqioKNjZ2Zk6HDICqT5z1qhLJ9VnVxGV8XmbGhO1mUpLS4OjoyNSU1Ph4OBg6nDICPjMKy8+O/PGzmREREQSxkRNREQkYUzUZkqhUGDu3LlQKBSln0xVAp955cVnZ974jpqIiEjCWKMmIiKSMCZqIiIiCWOiJp3FxsZCEASEh4ebOhQioiqHidrIoqOjMW7cOPj4+MDa2hoODg5o164dVq5ciaysLIOVe/36dcybNw+xsbEGK6MsFi1ahL59+8LNzQ2CIFS6GakMSRCEMn0iIyN1Luvp06eYN29eue7FZ1cyPj8yFLOfQtSY9u/fj4EDB0KhUGDEiBFo1KgRcnNzcfLkSUydOhXXrl3D+vXrDVL29evXMX/+fHTs2BFeXl4GKaMsZs+eDXd3dzRt2hSHDh0yWRxStHXrVo3tb775BkeOHCmy39/fX+eynj59qp4Cs2PHjmW6hs+uZHx+ZChM1EYSExODwYMHw9PTE8ePH9eYQm/8+PG4c+cO9u/fb8II/yWKIrKzs2FjY6P3e8fExMDLywuPHj1CzZo19X7/yuz5+eZ/++03HDlyRDLz0PPZlYzPjwyFTd9G8umnnyIjIwMbN27UOs9t/fr18f7776u38/PzsXDhQvj6+kKhUMDLywuzZs1CTk6OxnVeXl54+eWXcfLkSYSEhMDa2ho+Pj745ptv1OeEh4dj4MCBAIBOnToVaYIrvMehQ4fQokUL2NjYYN26dQCAu3fvYuDAgXBxcUG1atXQunVrnX6hMGVtvipQqVRYsWIFAgMDYW1tDTc3N4wbNw7Jycka5507dw7du3dHjRo1YGNjA29vb7z55psACvoUFP6gnj9/vvrvQ2lNoXx2uuPzo4pgjdpIfvrpJ/j4+KBt27ZlOj8sLAxbtmzBgAEDMGXKFPz+++9YvHgxbty4gd27d2uce+fOHQwYMABjxozByJEjsWnTJowaNQrNmzdHYGAgOnTogIkTJ2LVqlWYNWuWuunt2Sa4W7duYciQIRg3bhzGjh0LPz8/JCYmom3btnj69CkmTpyI6tWrY8uWLejbty927dqFV155RX//g6hMxo0bh/DwcIwePRoTJ05ETEwMVq9ejYsXL+LXX3+FpaUlkpKS8NJLL6FmzZqYMWMGnJycEBsbix9++AEAULNmTaxZswbvvPMOXnnlFbz66qsAgKCgIFN+NbPA50cVIpLBpaamigDEfv36len8S5cuiQDEsLAwjf0ffPCBCEA8fvy4ep+np6cIQDxx4oR6X1JSkqhQKMQpU6ao9+3cuVMEIP78889Fyiu8x8GDBzX2T5o0SQQg/vLLL+p96enpore3t+jl5SUqlUpRFEUxJiZGBCBu3ry5TN9PFEXx77//FgGIc+fOLfM15mb8+PHis/9Ef/nlFxGAuG3bNo3zDh48qLF/9+7dIgDx7Nmzxd5bl///fHZlw+dH+sKmbyNIS0sDANjb25fp/AMHDgAAJk+erLF/ypQpAFCk6TkgIAAvvviiertmzZrw8/PD3bt3yxyjt7c3unfvXiSOkJAQtG/fXr3Pzs4Ob731FmJjY3H9+vUy3590t3PnTjg6OqJbt2549OiR+tO8eXPY2dnh559/BgA4OTkBAPbt24e8vDwTRkzP4vOjimKiNoLCZenS09PLdP69e/cgk8lQv359jf3u7u5wcnLCvXv3NPbXq1evyD2cnZ2LvPcqibe3t9Y4/Pz8iuwvbDJ/Pg4yrNu3byM1NRWurq6oWbOmxicjIwNJSUkAgNDQULz22muYP38+atSogX79+mHz5s1F+jeQcfH5UUXxHbURODg4oFatWrh69Wq5rhMEoUznyeVyrfvFckzjboge3qRfKpUKrq6u2LZtm9bjhR2MBEHArl278Ntvv+Gnn37CoUOH8Oabb2LZsmX47bffYGdnZ8yw6R98flRRTNRG8vLLL2P9+vU4ffo02rRpU+K5np6eUKlUuH37tkaHr8TERKSkpMDT07Pc5Zc16T8fx61bt4rsv3nzpvo4GY+vry+OHj2Kdu3alekXq9atW6N169ZYtGgRIiIiMGzYMGzfvh1hYWEV+vtAuuHzo4pi07eRTJs2Dba2tggLC0NiYmKR49HR0Vi5ciUAoFevXgCAFStWaJzz+eefAwB69+5d7vJtbW0BACkpKWW+plevXjhz5gxOnz6t3peZmYn169fDy8sLAQEB5Y6DKu7111+HUqnEwoULixzLz89XP9vk5OQirSnBwcEAoG4+rVatGoDy/X0g3fD5UUWxRm0kvr6+iIiIwKBBg+Dv768xM9mpU6ewc+dOjBo1CgDQpEkTjBw5EuvXr0dKSgpCQ0Nx5swZbNmyBf3790enTp3KXX5wcDDkcjmWLFmC1NRUKBQKdO7cGa6ursVeM2PGDHz77bfo2bMnJk6cCBcXF2zZsgUxMTH4/vvvIZOV//e8rVu34t69e3j69CkA4MSJE/joo48AAG+88QZr6SUIDQ3FuHHjsHjxYly6dAkvvfQSLC0tcfv2bezcuRMrV67EgAEDsGXLFnz11Vd45ZVX4Ovri/T0dGzYsAEODg7qXwJtbGwQEBCAHTt24IUXXoCLiwsaNWqERo0aFVs+n51u+Pyowkzc69zs/Pnnn+LYsWNFLy8v0crKSrS3txfbtWsnfvHFF2J2drb6vLy8PHH+/Pmit7e3aGlpKdatW1ecOXOmxjmiWDC0qnfv3kXKCQ0NFUNDQzX2bdiwQfTx8RHlcrnGUK3i7iGKohgdHS0OGDBAdHJyEq2trcWQkBBx3759GueUZ3hWaGioCEDrR9vQMXP2/PCeQuvXrxebN28u2tjYiPb29mLjxo3FadOmiX/99ZcoiqJ44cIFcciQIWK9evVEhUIhurq6ii+//LJ47tw5jfucOnVKbN68uWhlZVWm4Tp8duXD50f6IohiOXocERERkVHxHTUREZGEMVETERFJGBM1ERGRhDFRExERSRgTNRERkYQxURMREUkYE7XEfPrpp2jYsCFUKpWpQ9HZjBkz0KpVK1OHIXl85gQAsbGxEAQB4eHhpg6FJIaJWkLS0tKwZMkSTJ8+XT3rlyAIEAQBy5YtK3J+eHg4BEHAuXPndC77hx9+wKBBg+Dj44Nq1arBz88PU6ZMKXaKwr1796JZs2awtrZGvXr1MHfuXOTn52ucM2nSJPzxxx/Yu3evzvFVVXzmRFQqU8+4Qv9avny56ODgIGZlZan34Z+Zg9zc3MTMzEyN8zdv3lzqAvNlVb16dbFx48bihx9+KG7YsEGcOHGiaGVlJTZs2FB8+vSpxrkHDhwQBUEQO3XqJK5fv1587733RJlMJr799ttF7vv666+LL774os7xVVV85lRIpVKJWVlZYn5+vqlDIYlhopaQoKAgcfjw4Rr7AIjBwcEiAHHZsmUax/T5Q1vbFIJbtmwRAYgbNmzQ2B8QECA2adJEzMvLU+/773//KwqCIN64cUPj3F27domCIIjR0dE6x1gV8ZkTUWnY9C0RMTExuHz5Mrp27VrkWLt27dC5c2d8+umnyMrKMkj5HTt2LLLvlVdeAQDcuHFDve/69eu4fv063nrrLVhY/Lumy7vvvgtRFLFr1y6NexR+nx9//NEAUVdufOZVz7x58yAIAv78808MHz4cjo6OqFmzJj788EOIooj79++jX79+cHBwgLu7u8brDW3vqEeNGgU7Ozs8fPgQ/fv3h52dHWrWrIkPPvgASqVSfV5kZCQEQUBkZKRGPNrumZCQgNGjR6NOnTpQKBTw8PBAv379EBsba6D/K6QrJmqJOHXqFACgWbNmWo/PmzcPiYmJWLNmTYn3ycnJwaNHj8r0KU1CQgIAoEaNGup9Fy9eBAC0aNFC49xatWqhTp066uOFHB0d4evri19//bXU8swNn3nVNWjQIKhUKnzyySdo1aoVPvroI6xYsQLdunVD7dq1sWTJEtSvXx8ffPABTpw4UeK9lEolunfvjurVq+Ozzz5DaGgoli1bhvXr11cottdeew27d+/G6NGj8dVXX2HixIlIT09HXFxche5HhsdlLiXi5s2bAABvb2+tx1988UV06tQJS5cuxTvvvFPswvPffvstRo8eXaYyxVLWY1myZAnkcjkGDBig3hcfHw8A8PDwKHK+h4cH/vrrryL7fXx8cP369TLFZE74zKuukJAQrFu3DgDw1ltvwcvLC1OmTMHixYsxffp0AMCQIUNQq1YtbNq0CR06dCj2XtnZ2Rg0aBA+/PBDAMDbb7+NZs2aYePGjXjnnXfKFVdKSgpOnTqFpUuX4oMPPlDvnzlzZnm/IhkRE7VEPH78GBYWFrCzsyv2nHnz5iE0NBRr167Ff/7zH63ndO/eHUeOHNE5noiICGzcuBHTpk1DgwYN1PsLm2EVCkWRa6ytrZGWllZkv7Ozc5FaF/GZV2VhYWHq/5bL5WjRogUePHiAMWPGqPc7OTnBz88Pd+/eLfV+b7/9tsb2iy++iK1bt5Y7LhsbG1hZWSEyMhJjxoyBs7Nzue9BxsdEXYl06NABnTp1wqefflrkH24hDw8PrTWf8vjll18wZswYdO/eHYsWLdI4Vliry8nJKXJddna21lqfKIoQBEGnmMwVn3nlVK9ePY1tR0dHWFtba7xSKNz/+PHjEu9lbW2NmjVrauxzdnZGcnJyueNSKBRYsmQJpkyZAjc3N7Ru3Rovv/wyRowYAXd393Lfj4yD76glonr16sjPz0d6enqJ582dOxcJCQnqZrXnZWVlISEhoUwfbf744w/07dsXjRo1wq5duzQ6DwH/Nn8WNoc+Kz4+HrVq1SqyPzk5ucgPKOIzr8rkcnmZ9gGlv44o7rpnFfdL0bMdzgpNmjQJf/75JxYvXgxra2t8+OGH8Pf3N+sWEKljopaIhg0bAijoCVyS0NBQdOzYEUuWLNHaG3jHjh3qGlZpn+dFR0ejR48ecHV1xYEDB7Q2yQYHBwNAkQk3/vrrLzx48EB9/FkxMTHw9/cv8XuZIz5z0pfCJuznJ6u5d++e1vN9fX0xZcoUHD58GFevXkVubq7WCXZIGtj0LRFt2rQBUPDDMCgoqMRz582bh44dO2rt9VnR95UJCQl46aWXIJPJcOjQoSJNbYUCAwPRsGFDrF+/HuPGjVP/tr9mzRoIgqDRCQkAUlNTER0dXe5OL+aAz5z0xdPTE3K5HCdOnED//v3V+7/66iuN854+fQqZTAZra2v1Pl9fX9jb22t9tUHSwEQtET4+PmjUqBGOHj2KN998s8RzQ0NDERoaiqioqCLHKvq+skePHrh79y6mTZuGkydP4uTJk+pjbm5u6Natm3p76dKl6Nu3L1566SUMHjwYV69exerVqxEWFlakFnX06FGIooh+/fqVO6aqjs+c9MXR0REDBw7EF198AUEQ4Ovri3379iEpKUnjvD///BNdunTB66+/joCAAFhYWGD37t1ITEzE4MGDTRQ9lcpUM61QUZ9//rloZ2enMX0jAHH8+PFFzv3555/VU03qY5aqwntp+4SGhhY5f/fu3WJwcLCoUCjEOnXqiLNnzxZzc3OLnDdo0CCxffv2OsdXVfGZVy1z584VAYh///23xv6RI0eKtra2Rc4PDQ0VAwMDRVEUxZiYGBGAuHnz5lKvKyznWX///bf42muvidWqVROdnZ3FcePGiVevXtW456NHj8Tx48eLDRs2FG1tbUVHR0exVatW4nfffafjNydDEkSxlJ4MZDSpqanw8fHBp59+qjGMo7JKSEiAt7c3tm/fztpVMfjMiag07EwmIY6Ojpg2bRqWLl1aJZY8XLFiBRo3bswf2CXgMyei0rBGTUREJGGsURMREUkYEzUREZGEMVETERFJGBM1ERGRhDFRExGZmdjYWAiCgPDwcFOHQmXARE1EVILo6GiMGzcOPj4+sLa2hoODA9q1a4eVK1dqnXtdX65fv4558+YhNjbWYGWUxaJFi9C3b1+4ublBEATMmzfPpPGYI04hSkRUjP3792PgwIFQKBQYMWIEGjVqhNzcXJw8eRJTp07FtWvXtM6/rg/Xr1/H/Pnz0bFjR3h5eRmkjLKYPXs23N3d0bRpUxw6dMhkcZgzJmoiIi1iYmIwePBgeHp64vjx4xrzqY8fPx537tzB/v37TRjhv0RRLHZtcF3FxMTAy8sLjx49KnbhFjIsNn0TEWnx6aefIiMjAxs3btS66En9+vXx/vvvq7fz8/OxcOFC+Pr6QqFQwMvLC7NmzSqyKpWXlxdefvllnDx5EiEhIbC2toaPjw+++eYb9Tnh4eEYOHAgAKBTp04QBAGCICAyMlLjHocOHUKLFi1gY2OjXq/87t27GDhwIFxcXFCtWjW0bt1ap18oTFmbpwJM1EREWvz000/w8fFB27Zty3R+WFgY5syZg2bNmmH58uUIDQ3F4sWLta5KdefOHQwYMADdunXDsmXL4OzsjFGjRuHatWsAgA4dOmDixIkAgFmzZmHr1q3YunWrxkplt27dwpAhQ9CtWzesXLkSwcHBSExMRNu2bXHo0CG8++67WLRoEbKzs9G3b1/s3r1bD/9XyCRMuiQIEZEEpaamigDEfv36len8S5cuiQDEsLAwjf0ffPCBCEA8fvy4ep+np6cIQDxx4oR6X1JSkqhQKMQpU6ao9+3cuVMEIP78889Fyiu8x8GDBzX2T5o0SQQg/vLLL+p96enpore3t+jl5SUqlUpRFLWv1FWav//+WwQgzp07t8zXkH6wRk1E9Jy0tDQAgL29fZnOP3DgAABg8uTJGvunTJkCAEWangMCAvDiiy+qt2vWrAk/Pz/cvXu3zDF6e3uje/fuReIICQlB+/bt1fvs7Ozw1ltvITY2FtevXy/z/Uk6mKiJiJ7j4OAAAEhPTy/T+ffu3YNMJkP9+vU19ru7u8PJyQn37t3T2F+vXr0i93B2dkZycnKZY/T29tYah5+fX5H9hU3mz8dBlQMTNRHRcxwcHFCrVi1cvXq1XNcJglCm8+Ryudb9YjkWMzRED2+SJiZqIiItXn75ZURHR+P06dOlnuvp6QmVSoXbt29r7E9MTERKSgo8PT3LXX5Zk/7zcdy6davI/ps3b6qPU+XDRE1EpMW0adNga2uLsLAwJCYmFjkeHR2NlStXAgB69eoFAFixYoXGOZ9//jkAoHfv3uUu39bWFgCQkpJS5mt69eqFM2fOaPxykZmZifXr18PLywsBAQHljoNMjxOeEBFp4evri4iICAwaNAj+/v4aM5OdOnUKO3fuxKhRowAATZo0wciRI7F+/XqkpKQgNDQUZ86cwZYtW9C/f3906tSp3OUHBwdDLpdjyZIlSE1NhUKhQOfOneHq6lrsNTNmzMC3336Lnj17YuLEiXBxccGWLVsQExOD77//HjJZ+etmW7duxb179/D06VMAwIkTJ/DRRx8BAN544w3W0o3B1N3OiYik7M8//xTHjh0renl5iVZWVqK9vb3Yrl078YsvvhCzs7PV5+Xl5Ynz588Xvb29RUtLS7Fu3brizJkzNc4RxYKhVb179y5STmhoqBgaGqqxb8OGDaKPj48ol8s1hmoVdw9RFMXo6GhxwIABopOTk2htbS2GhISI+/bt0zinPMOzQkNDRQBaP9qGjpH+CaJYjt4LREREZFR8R01ERCRhTNREREQSxkRNREQkYUzUREREEsZETUREJGFM1ERERBLGRE1ERCRhTNREREQSxkRNREQkYUzUREREEsZETUREJGFM1ERERBLGRE1ERCRh/w+n/tOGQ3KveQAAAABJRU5ErkJggg==", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "repeated_measures.mean_diff.plot(group_summaries_kwargs={'gap_width_percent': 3, 'alpha': 0.5, 'lw': 4, 'offset': 0.6, 'color':'red'});" - ] - }, - { - "cell_type": "markdown", - "id": "4033f7c2", - "metadata": {}, - "source": [ - "## Raw bars" - ] - }, - { - "cell_type": "markdown", - "id": "d5eed93f", - "metadata": {}, - "source": [ - "**Raw bars** are included in swarmplots by default. It can be turned off by setting `raw_bars=False` in the `.plot()` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "afe820f4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Jm+Xp6oz5EwbgrVjN9Xzs5DLO7CEysszcTGTklt+96uXipTXZAABRJcLb1dugx5Hx5aenlyQpRUUAAFFZ8kuiqqgIRz/8EANWrmTLip4sdq0fqlyHZg04s4fIDIU3Csfuk7u1VqCVy+QIbxhu0OPI+G4eOgRVOa1UquJi3Dx0CI2HDDFxVNbBJIlKUFCQ1vopjxMEAdevXzdFODbF09WZM3uIzIybkxsmRk7Euvh1UKqU6u4cuUyOiZET4erkWu3jMvMyceLqCTzKfgQvFy+ENwqHm5ObxvnSc9Lx68VfOc3ZAHJSU0u6e5Rlu9sFmQw5qakSRGUdTJKoRERElElUlEolbt68id9++w2hoaFo3bq1KUIhIjNQmJ2G1LP7UZCeAoWHP/ya94SDjf2ADK0XigVjFiDhWgIeZj2Et6s3whuGa01Snkw6ZgyZgUt3LpV73Llb58okM7tP7sbEyIkIrVeyFtqlO5ewaMsirtBsIM5+fhDLGegsqlRw9vMzcUTWwySJSmxsbLmf/fnnn+jbty/GjRtnilCILJqpfsAb8zoPr/yOS98thagqhiDIIIoq3Dy4ASEj5sCrUTuDXMNSuDm5oWfznhXuU1HSoe3YzLxMrItfp+4eKh3TUqwqxrr4dVgwZgGKiouw+bfNWqc5L9m6BLHTYtmyUkX1IyJw7ptv1GNUHiezs0P97t1NH5SVMEkJ/Yq0bNkS0dHR+Pe//y11KERm7eGV33Hi44lI2r8OyX/sQdL+dTjx8UQ8unrcYq5TmJ1WkqQoiwBRhKhSlvypLMLFrUtsblHGzLxMxJ+Jx5YjWxB/Jh6ZeZllPi9NOkSIUKlUECGqk44n9weAE1dPlFuwTqlSIuFaAk5eP1nuNOfSFZqpahw9PNBxxgzI7O0BQYAglwOCAJm9PTrOmAFHd5aE0JdZDKb19/fHhQsXpA6DyGxp/IAHIP79G3DpD/jwaet0bvGoqLXEENep6PypZ/dD1DIQFABEVTFSz+5HnY7Ddfo6LJ0u3TO6JB1Ptqo8yn5U4RTmh1kPS64pCBoLxJbiCs36q9W2LQasXKlZR6V7dyYp1SR5ovLw4UOsXbsWderUkToUIrNlqB/wlXW7VPc6lZ2/ID3l7+1aBhwKMhSkp1T6NVgDXbpn3JzcdEo6nqTLFOZiZbHWJAXgCs3V5ejhwdk9BmaSRKVnT+19sOnp6bh06RIKCwuxYcMGU4RCZJEM8QNel9aS6lxHl/MrPPwhiuUMOBRVUHj4V/p1WANdW0r0qZuiyxTmwuJC7Pljj9YYuEIzmRuTjFFRqVQQRVHjBZRMW37llVdw7tw5REVFmSIUIrPk4OIJB1fvcrtVqvIDvjA7DbePfofrP63A7aPfqcd96NJaUp1EQpfz+zXvCUGm/fcjQWYHPxv5AVnaUqLN4y0l4Y3CIZfJte5XmnQ8Oc4FACZGToSdzA4CBMhkMggQYCezU09hdnVyxZjOY2Avt4cgCJDL5BAEAfZye/UKzUTmwiQtKgcPHjTFZUhPaVk52HviIpIfZSLAyw29w0Pg6eosdVgWQ6UsKhkUWg3Nn3tX/XelltV2vUM64+bBDerWiscJMjv4hHSBsigfj66dwJUdH5bpemn8rzeQ9+huha0leY/uok7nkRVep6JEQpfWGAcXT4SMmIOLW5doxCjI7BAyYg4cbOQHpK4tJZXVTbl5/2a541wqm/rcuHZjrJqyCr9d/A3J6ckI8AhAZItIJinVxLV+DE/yMSokraPnbmDRes0y+7E/HcX8CQPQoVkDqcMzeyplEbLuXIGyMM/o16rTaSRu/7a5pFaDIACiCEEmQ51OI5H74C8U376Iq9//V500Pd71cnn7e/AO6VJhnQdRVCHvwe1yr1MvYhzsHJ3LHSyra2uMV6N2CJ+2TvMcLSJtJkkBqlZhtrx6KyJELPxmYYXjXCqb+uzh7IHhNjJ42RS41o9xGCVR+fLLL/U67rnnnjNwJFSRtKwcLFr/o3rhQuXfXXJFxUq8FfsjNs6fxJaVSogqJZSFeZDZ2UFm5FVoPRu0gkvNYKRfP4XC7EdwcPGCZ3AY7JxcAACPrv5eYSIik9tDkMm1ds8IMjl8nuoAO4WT1uu4B7WAzM4RD68c09piEzJiDvya99S5NcbBxdNmZvdoU9XKtNrqrcSfia/yjCAyHq71YzxGSVQmTJhQ5WMEQWCioqOXP9yEtKxceLrWwIoZYyvct6Junb0nLqJYWU4tBaUK+xIusfy+jmRye8iqsdru5Z0fojg3E3Y13NB4yIxy91O4esO/VW+tnxXnZgIyAdDWpSAToCrKR2DkRCTFrytpdfl7X0EmR2DkRDi4epV7HVVxIfIz7uPqD8sqHCzLbh3d6VKZtqIy+PrMCCLj4Vo/xmOURCUxMdEYp6W/pWXl4kFGdqX7Vdatk/woEzJBULekPE4mCLj3sPzVX8mwinMzUVTBarulivKykHb1xD8tKo3CYf/3DzYHFy/tSQoAqEQ4uHrBvV4zNB2zAGnXTqAw6xEcXL3g2fCfc1QkPfG0TlOXbb1bpyoqqkxbWZ0VrqRsXrjWj/EYJVGpX7++MU5LVaBLt06AlxtU5dZSEFHTm0WKzEnGrXNIio/VaA25d3I3AiMnwr1eM3g2Cse9k7vL7drx/Hvcg72TK/z06BIoyknXaeqyrXfrGIIudVa4krJ54Vo/xiN5CX0yDl26dXqHh8BOrv2/gJ1chl5tQ4wZIlVBUV7W30lKMQARUKkAiBBVxUiKX4eivCzYO7kiMHLi39N/BUAmAyBAkNkhMHKiTq0mFbF39mANFBPRpc5K6TiXiqYhk+nUj4iAzE777/5c66d6TDbrJzk5GWvXrsWpU6eQkZFRZp0JQRAQHx9vqnCsni7dOp6uzpg/YQDeitXsHrKTyzB/wgB4utaQIHLSJu3qiXKnQIsqJdKunYBf857V6tqpjEdQK9w/d1CvqctUNbqOP6nKCsxkXKVr/Tw560dmZ8e1fqrJJInKmTNn0L17d+Tl5aFx48Y4e/YsmjZtivT0dNy5cwfBwcGoW7euKUKxGbp263Ro1gAb50/CvoRLuPcwAzW93dGrbQiTFDNTmP2owoGyhVmP1G/17dqpjJ2jCxr/6w1c3vEBB8saSHmDZasy/kSXFZjJNLjWj3GYJFGZNWsWXFxccPr0adSoUQN+fn5Yvnw5evbsiS1btmDKlCnYuHGjKUKxGb3DQxD701H1GJXHPdmt4+nqbLLZPSwupx9dBspWRUWDcrXtY1fDDW51m8G7cQcOljWQigbLcvyJ5eJaP4ZnkkTlt99+w8yZM1GvXj08elTym19p18/IkSPx66+/4s0338ShQ4dMEY5NkKJbp7IkhMXl9KfrQFmg8iSkskG55e2T8kccZENnwiekCwfLVpMug2WrUmeFLAcr11adSRIVlUoFf/+SgXYeHh6Qy+XqhAUAmjdvjrVr15oiFJtiyG6d6iYhLC5XPaUDZcurgVKaiFSWhGgOyoW6laZ0UG7TMQsAoJx9lLi84wO41W1W7ppEpBtdFyXk+BPrwsq1+jFJohIUFKSurSKTyRAUFIR9+/Zh1KhRAIAjR47Ao4oZ5cqVK7Fy5UokJSUBAJo1a4b58+ejX79+hgzd4hmiW8cQSQiLy1VfZQNldUlCdBmUCxEV7PNPvRTSn66DZTn+xHqwcq3+jDY9OS0tTf33Pn36YMuWLer3U6ZMweeff45evXohMjIS69evx9ixFVdYfVKdOnXwzjvv4OTJk0hISEDPnj0xZMgQnD9/3mBfA2nWYxFFEcq/V8IuTUJKW1oqS0JKZyFpw+JyVSQ+8effdElC1INytfl7UG5F+zxeL4X0x2JtlmXfzJnY9eKL2Ddzpt7n0KVyLWlntBaVgIAA9O/fH+PGjcOMGTMQFRWFoqIi2Nvb47XXXkNOTg6+++47yOVyzJs3D3PmzKnS+QcNGqTxfvHixVi5ciWOHTuGZs2aGfJLsXrVLbOvy1RoFpervsq6dXSZGaTToFwR5e7DeimGwcGyliU/PR15jw1X0Acr1+rPaInKiBEj8P333+P777+Hq6srhg0bhnHjxqFnz54QBAFz587F3LlzDXItpVKJLVu2ICcnBx07dix3v4KCAhQUFKjfZ2dXXobe2hmizL4uSUivtk10noVEZenSraNLEuLZULdBueXvw3ophlDVRQnJcpQ3WJaVa/VntK6fjRs3IjU1FV999RW6du2KjRs3ok+fPqhduzZmzJiBU6dOVfsaZ8+ehYuLCxQKBV566SVs374dTZs2LXf/pUuXwt3dXf2KiIiodgyWTJduHV2SEF0q3JbOQrK3k0MQBMhlMgiCAHs7OYvL6UCXbh3PRuEQZHKt+5QmIbpUry1/Hzka/+sNTkU2kNJibYPbDUanxp0wuN1gxIyJQWi9UKlDIz3dTUjAj1Om4MxXX+HGvn0489VX+HHKFNxNSGDl2mow6mBaJycnREVFISoqCmlpafj222+xadMmLFu2DMuWLUOjRo3wzDPPYOzYsWjQoOrTUxs3bozTp08jIyMDW7duxfjx43Ho0KFyk5XZs2fj9ddfV78/ffq01Scr1e3W0aUei6drDZ2mQrO4nP506dbRdWaQLtVrn9zHvoY7XOs2hWdDzkwwJA6WtR66DJZl5Vr9mKyEvqenJ6KjoxEdHY07d+5g06ZN+PrrrzF//nwsWLAA7du3x5EjR6p0TgcHBzRs2BAAEBYWhhMnTmD58uVYtWqV1v0VCgUUCoX6vYuLi/5fkIRKf7BX9gPeEN06utZj0TUJqWwWEgvCaadrwTddS+jrUr328X1UxYUoLsir/hdCZKV0GSzbeMgQVq7Vg8kSlcfVrl0bb775Jp5++mnMnz8fO3fuxO+//17t86pUKo0xKOaqqFgJZTl9lbr477SR6r/nF5ZddwUA0rJyK5wyvHbWc/Bxd66wW8fH3QX5hUVo1agu1s56DgdOXUZKWib8Pd3QM6wxPFxqaFzfSeGAQZ1bqN/LZVXrWWRBuPJVpeCbsUroE1H5dB0sa6jKtbZUOM7kicqtW7fUrSnnzp2DKIro1KkTxo0bV6XzzJ49G/369UO9evWQlZWFTZs24eDBg4iLizNS5IZRVKzE5VvJyC3QnmAYyi9/XkWxlgcGAIqVSmzaexytG9WtsEUlwNsdZ67fUW9rWMcPDeuUDPi6lZKGWyklU9Czc/Pxx9W/kJaVC0/XGmjdqC5cajiihsIejesFwN5OXmlLCQvCVUzXbh1d6FI+n4iqxpCDZStLQmytcJxJEpUHDx6ox6ccPXoUoiiiSZMmeOuttzBu3DgEBgZW+Zypqal47rnncO/ePbi7u6NFixaIi4tD7969Df8FGJBSpUJuQRHs5TLY22kf+GgIWbn5EAQBopYkRBAEZOXmw8/TFRP7d0TsT0ehVKnU+8tlMkzo1xF+HpV3jZ1LvIv1Txy/L+EinunTHo3q+kOpUiHh3M1KW0pYEK5yunTrGKJ8PlmO8hY1JNOrHxGBc998ox6j8riqDJatLAmxxcJxRktUcnJysH37dmzatAnx8fEoKipCzZo18dprr2HcuHFo06ZNtc5v6SX37e3kcLDX75//3Y1xyMzJg5uzE/49rq/WfXw9XLUmKQAgiiL8PF3hYG+H1k/VQ3BtXxy/mISHGTnwdndG+6ZBcK3hWGkcmTl5WP/TUXWCUXq9YqUKX/38O96M6lNpF1RpS4ku42WsmV0NN40/y1NRt44hyuezZcVyVLSoIWcOmZ6jh4dOg2Urai3RJQnRdSyMNTFaouLn54f8/Hy4uLhg7Nix6hoqsiqOW6CyMnPykJ5d8cDG9k0DsevIGa2tFHKZDO2bBqnfuzk7lVvHJDMnD79fSMKjzBx4uTmjfdNAuDk7AQB+v5BU7lgbpUqFP67+heRHGTq1lNh6QbjGQ2ZU63hDlc/n2BbLoMuihmxZMb1abdtWOFi2stYSXZIQWywcZ7REpVevXhg3bhwGDx4MR8fKfzsnw3JzdsLkQV3w+Q+/Qqn6p8tFLpNh8qAucK3hWGESAgBnr9/B57t+1ejW2XXkDCYP6oLmDWrjUWZOhd1LaVm5kMkEnVpKdJkGTeWrUvn8CqY4k2XQdVFDMr3yBsvq0lqiSxJii4XjjJao7Ny501inJh01b1AbiyYP1tqtU1kSkpmTh893/aq1W+fzH37FosmD4eXmXGH3kqdrDfh7uurUUqLrNGjSzmDl88ki6LqoIZkPXVpLdElCDDUWxpKwH8bKlXbrjI5si15tQ9QtKaVJiCgCKpUIUfwnCSltaamoW+f4xSS0bxpY7hRkuUyG1k/VQ482TSqtWluqtBbLC4O6oH/HULwwqAs2zn/e5qcmP64oLwupZ/bj9pGtSD2zH0V5WQB0q7OiS+Vasgxc1NDylLaWaFPaWqJL9drSsTAye3tAECDI5YAgQGZvb7WF4ySpo0LS0iUJqahbRyYIeJiRU2H30oR+HeHipNC5am2pygrC2bKKBsvqUmfFkFOcqWJufw+KdqtkcLS+uKih+arOWj+6DsitbCyMtWGiYoN0SUIq6tZRiSK83UtqmpTXvaSwt1PXimHp/OrTZbCsocrnU/W9MeQNg55P2zRkLmpofioaLKtrl42uSYihCsdZAiYqNkiXJKRdSPVmDRUWaf6mx5aS6tF1xo6hyueT+ahoGvKCMQuQcC0BD7MewtvVG+ENw5mkSMSQa/3YUhKiCyYqVk7bzB5dpi671nCsdNYQmY6uM3aYhFiWygq26TINmbN7zAPX+jEeJipWrKKZPbokIRXNGiLT4owd66NLwTZOQ7Ycpl7rR1fWsCYQExUrpcv0Yl2SkIqKwZHpVGVRQjJ/uhZs4zRkyyFFfRNbWROI05OtlC4ze7RNXSbzVDpjR5DZARAAmQyAAEFmxxk7FkiXlhKA05AtiS5Tiw3pbkICfpwyBWe++go39u3Dma++wo9TpuBuQsn/HY0xM6JY0tIjiuoxM/np6QaNx5iYqFip0pk92pTO7DG0zJw87D1xEZvjE7D/1GVk5+Yb/Bq2rHTGTq12g+DTuBNqtRuEpmMWcDFBC1TaUqLN4y0l4Y3CIS+n9g2nIZsXU9Y30SUJ0WXMjKVg14+V0nV6sa70Kbe/++g5zHmuH7q1bFStr4X+wcGy1kHXlhI3JzdOQ7YghqxvUlG3jq2tCcRExUpVZVHCyuhbbl8pqvDOV3vQvEEteLpWLTEismZVKdgWWi+U05AtiC6DZas7tsTW1gRi14+VKq0aayeXQRAAuUyAIJSUra/K9OLqltsvXSGZiP5R2lJiJ7ODAAEymQwCBNjJ7LS2lLg5uaFn854Y2WkkejbvySTFghlibImuawKZcsyMMbFFxQKVdrk83vWija7Tiyvq1jFEuf3SFZKJ6B9sKbFOFbWW6FIUTpduHV2q3Dq6u+tcYM7cMVGxQP8e11fnfSubXlxZt44hyu2XrpBMRJpKW0rIOlTWZWOosSW2tiYQExUrV1FriS61Vqpbbv/JFZKJiCxNaYtIRYXSdGktMeTYEltaE4iJihWrrLVEl26d6pTblwkyzHrmaS4+SESSUhUVQVXO9zpddF+0SP334oICrfvciI+vsLUkcf9+OHp5VZiEOHl7o3bHjjj39ddazyWTy1GnY0d1DHZOTgh++ul/PpdZ57BTJipWSpfWEl26dUoH5Va13L6HixOaNaiNdiGBJvuaiYiepCoqwsNr11Ccb9y6To+uXCn3+6kgCHh4+TLqdulSYYtKDT8/ZN25gybDhuHid99BfOyXTEEmQ5Nhw5B55w5w547WGOwcHeHdsGFJLRcrwkTFSunSWqJrrRVdB+U+Ph6msKgYuQVlB3oREZmSSqVCcX4+5HZ25c6CMYQaPj7lfj8VRRE1fHzg7OOD0KgonPv66zJJSGhUFJy9S+rn+DdvDs+gICSfPo38tDQ4enoioFUrOLi4lHt9VXExivPzoVKprG46LxMVK6VLa0m/Ds10rrXCNX+IyJLJ7Owgd3DQ69jfP/oIhVlZcHB1Rfvp07XuUys8HDfi4yFq6bIR5HLUbtcOcgcH+DdvDo/AQCSfOoW8tDQ4eXqiZlhYmSTEycsLQT2rNtBaWU7Xk6VjomKldGkt0bVbh4iMIzMvEyeunsCj7EfwcvFCeKNwuDm5SR0WPaEwKwsFmZkV7qNwdUWLZ57Bma++gqhU/tNaIpejxTPPaCQiCldX1I+IMHbYVoOJipXStTKtrt06RGRY526dK1Maf/fJ3ZgYORGh9UKlDo/04BsSgi6zZlXaWkJVw0TFSlWltYTdOkSmlZmXiXXx69Ql9EvX/SlWFWNd/DosGLOALSsWqrLWkoKsLNw7dUo99qRmmzZQuLLIX0WYqFgxtpYQmacTV09AqSo78wMAlColEq4lsBCcFbp/4QLObNyo0TV0/eef0eKZZ+Abwl8Wy8NExcqxtYTI/DzKfqTu7nmSIBPwMOuhBFGRMRVkZZUkKX8PeC0dQygWF+PMV1+hy6xZbFkph7XNYiIiMnteLl5akxSgpBvI29XbxBGRsd07dUpr/RSgpIpt8qlTJo7IclhsorJ06VKEh4fD1dUVfn5++Ne//oXLly9LHRYRUaXCG4VDLpNr/UwukyO8YbiJIyJDKcjKQtKhQ7i0YweSDh1CQVYWACA/LQ2CIGg9RhAE5KWlmTJMi2KxXT+HDh3C1KlTER4ejuLiYsyZMwd9+vTBhQsX4OzsLHV4RETlcnNyw8TIiWVm/chlckyMnMgVlC1URWNQHD09KywI5+TpaeJoLYfFJip79uzReB8bGws/Pz+cPHkS3bp1kygqIiLdhNYLxYIxC5BwLQEPsx7C29Ub4Q3DmaRYqMrGoLR75RVc//nncgvC1QwLM2m8lsRiE5UnZWRkAAC8vLwkjoSISDduTm6c3WMlKhuD8ujKFZ0LwpEmq0hUVCoVXnvtNXTu3BmhoeUXSiooKEDBYytfZmdnmyI8IiKycqVjUMpblDAvLQ31IyJYEE4PVpGoTJ06FefOncOvv/5a4X5Lly7FwoULTRQVERHZCl3HoLB8ftVZ7KyfUq+88gp27dqFAwcOoE6dOhXuO3v2bGRkZKhfhw4dMlGURERkzWq2aQNBrn0mF8egVI/FJiqiKOKVV17B9u3bsX//fgQFBVV6jEKhgJubm/rlwuY2IiIygNJFCQU7O0AQIMhkJX/a2XEMSjVZbNfP1KlTsWnTJuzcuROurq5ITk4GALi7u8PJyUni6IiIyNZwUULjsNhEZeXKlQCA7t27a2xft24dJkyYYPqAiIjI5nEMiuFZbKJS3qAlIiIish4WO0aFiIiIrJ/FtqgQERFZsoKsLNw7dQr5aWlw9PREzTZtuIKyFkxUiIiITKyidYF8Q0KkDs+ssOuHiIjIhDTWBRJFiCpVyZ9/rwtUuuIylWCiQkREZEKVrQuUfOqUiSMyb+z6ISIiMoLyxqDosi4Q/YOJChERkYFVNAZF13WBqAS7foiIiAyosjEo3k89xXWBqoCJChERUQUcXF2hcHODg45Thysbg/LoyhWuC1QF7PohIiKqQPvp06u0vy5jUOpHRHBdIB0xUSEiIjIgXcegcF0g3bDrh4iIyIBqtmnDMSgGxESFiIjIgBSurhyDYkDs+iEiIjIw35AQjkExECYqREREBsKFBg2PiQoREZEBcKFB4+AYFSIiomriQoPGw0SFiIiomrjQoPEwUSEiIqqm0iJv2nChwephokJERFRNXGjQeJioEBERVROLvBkPExUiIqJqYpE34+H0ZCIiIgNgkTfjYKJCRERkIFxo0PCYqBAREZkQq9dWDRMVIiIiE2H12qrjYFoiIiIDKcjKQtKhQ7i0YweSDh3SqEjL6rX6YYsKERGRAVTWWqJL9VqObymLLSpERETVpEtrCavX6seiE5XDhw9j0KBBqFWrFgRBwI4dO6QOiYiIbJAurSWsXqsfi05UcnJy0LJlS3z66adSh0JERDZMl9YSVq/Vj0WPUenXrx/69esndRhERGTjdGktKa1ee+arrzTGsQhyOavXVsCiE5WqKigoQEFBgfp9dna2hNEQEZG1qNmmDa7//HPJGJUnPN5awuq1VWdTicrSpUuxcOFCqcMgIiIrU5XWElavrRqbSlRmz56N119/Xf3+9OnTiOB/FiIiMgC2lhiHTSUqCoUCCoVC/d6F/3mIiMiA2FpieBY964eIiIism0W3qGRnZ+PatWvq94mJiTh9+jS8vLxQr149CSMjIiIiQ7DoRCUhIQE9evRQvy8dfzJ+/HjExsZKFBUREREZikUnKt27dy933jppSk1Jwf3UFJNdr6hYifyiYigzU6Gwt+j/ZhVSFhcg+951yO0VkMntTXbdAH9fBPj7mex6ZFypKam4n3LfZNcrUhahoKgARY+KoLBTVH6ABSsuLETajRuwUyggtzPd9yJ/Pz8E+PEZNQRBtOGf9Pfu3cOqVasQHR2NmjVrSh2O0RQUFKBv3744dOiQ1KGQgURERCAuLk5jcDhZJj6f1onPqOHYdKJiKzIzM+Hu7o5Dhw5xppMVyM7ORkREBDIyMuDm5iZ1OFRNfD6tD59Rw7LeNnkqo1WrVnxorEBmZqbUIZAR8Pm0HnxGDYvTk4mIiMhsMVEhIiIis8VExQYoFAosWLCAg7qsBO+ndeH9tD68p4bFwbRERERkttiiQkRERGaLiQoRERGZLSYqREREZLaYqFCVJCUlQRAErqVEZKb4jJK1YaJiRNevX0d0dDQaNGgAR0dHuLm5oXPnzli+fDny8vKMdt0LFy4gJiYGSUlJRruGLhYvXozBgwfD398fgiAgJiZG0nhMSRAEnV4HDx6s9rVyc3MRExNTpXPZ8r15nC0/o5cuXcLMmTPRqlUruLq6ombNmhgwYAASEhIki8lUzPn5tOX7Uh5WpjWSH3/8ESNHjoRCocBzzz2H0NBQFBYW4tdff8Wbb76J8+fPY/Xq1Ua59oULF7Bw4UJ0794dgYGBRrmGLubOnYuAgAC0bt0acXFxksUhhQ0bNmi8//LLL7F3794y20NCQqp9rdzcXCxcuBBAyUKdurDle1PK1p/Rzz//HGvXrsXw4cPx8ssvIyMjA6tWrUKHDh2wZ88e9OrVS5K4TMGcn09bvi/lYaJiBImJiRgzZgzq16+P/fv3ayx4OHXqVFy7dg0//vijhBH+QxRF5Ofnw8nJyeDnTkxMRGBgIB48eABfX1+Dn9+cPfPMMxrvjx07hr1795bZLhVbvjcAn1EAiIqKQkxMjMb6QpMmTUJISAhiYmKs+geiOT+ftnxfysOuHyN47733kJ2djbVr12pdlblhw4Z49dVX1e+Li4uxaNEiBAcHQ6FQIDAwEHPmzEFBQYHGcYGBgRg4cCB+/fVXtGvXDo6OjmjQoAG+/PJL9T6xsbEYOXIkAKBHjx5lmjBLzxEXF4e2bdvCyckJq1atAgDcuHEDI0eOhJeXF2rUqIEOHTpU65u1lK05lkClUmHZsmVo1qwZHB0d4e/vj+joaKSlpWnsl5CQgL59+8LHxwdOTk4ICgrCpEmTAJSMRyhNNBYuXKi+35V15dj6veEzCoSFhZVZBNHb2xtdu3bFxYsX9TqnNZHq+eR9KYstKkbwww8/oEGDBujUqZNO+0+ePBnr16/HiBEjMGPGDPz+++9YunQpLl68iO3bt2vse+3aNYwYMQLPP/88xo8fjy+++AITJkxAWFgYmjVrhm7dumH69On46KOPMGfOHHXT5eNNmJcvX0ZUVBSio6PxwgsvoHHjxkhJSUGnTp2Qm5uL6dOnw9vbG+vXr8fgwYOxdetWDB061HD/QAQAiI6ORmxsLCZOnIjp06cjMTERn3zyCf744w/89ttvsLe3R2pqKvr06QNfX1/MmjULHh4eSEpKwrZt2wAAvr6+WLlyJaZMmYKhQ4di2LBhAIAWLVpI+aWZPT6j5UtOToaPj49BzmXJzO35tOn7IpJBZWRkiADEIUOG6LT/6dOnRQDi5MmTNba/8cYbIgBx//796m3169cXAYiHDx9Wb0tNTRUVCoU4Y8YM9bYtW7aIAMQDBw6UuV7pOfbs2aOx/bXXXhMBiL/88ot6W1ZWlhgUFCQGBgaKSqVSFEVRTExMFAGI69at0+nrE0VRvH//vghAXLBggc7HWJupU6eKjz9uv/zyiwhA3Lhxo8Z+e/bs0di+fft2EYB44sSJcs9dnX9fW7w3fEbLd/jwYVEQBHHevHlVPtaSmevzWcpW70spdv0YWOny3q6urjrtv3v3bgDA66+/rrF9xowZAFCmWbdp06bo2rWr+r2vry8aN26MGzdu6BxjUFAQ+vbtWyaOdu3aoUuXLuptLi4uePHFF5GUlIQLFy7ofH6q3JYtW+Du7o7evXvjwYMH6ldps++BAwcAAB4eHgCAXbt2oaioSMKIrQefUe1SU1MxduxYBAUFYebMmdU6l6Uzp+eT94VjVAzOzc0NAJCVlaXT/jdv3oRMJkPDhg01tgcEBMDDwwM3b97U2F6vXr0y5/D09CzTb1qRoKAgrXE0bty4zPbS5ugn46DquXr1KjIyMuDn5wdfX1+NV3Z2NlJTUwEAERERGD58OBYuXAgfHx8MGTIE69atKzM2gnTHZ7SsnJwcDBw4EFlZWdi5c2eZMRK2xlyeT96XEhyjYmBubm6oVasWzp07V6XjBEHQaT+5XK51u1iFtSWNMcOHqkalUsHPzw8bN27U+nnpADxBELB161YcO3YMP/zwA+Li4jBp0iR8+OGHOHbsmM1+46oOPqOaCgsLMWzYMJw5cwZxcXEIDQ012bXNlTk8n7wv/2CiYgQDBw7E6tWrcfToUXTs2LHCfevXrw+VSoWrV69qDKZLSUlBeno66tevX+Xr6/oN9ck4Ll++XGb7pUuX1J+T4QQHB2Pfvn3o3LmzTj+UOnTogA4dOmDx4sXYtGkTxo0bh2+++QaTJ0/W637bOj6jJVQqFZ577jnEx8fj22+/RURERJXPYY2kfj55XzSx68cIZs6cCWdnZ0yePBkpKSllPr9+/TqWL18OAOjfvz8AYNmyZRr7/Pe//wUADBgwoMrXd3Z2BgCkp6frfEz//v1x/PhxHD16VL0tJycHq1evRmBgIJo2bVrlOKh8o0aNglKpxKJFi8p8VlxcrL53aWlpZX4Tb9WqFQCom5dr1KgBoGr329bxGS0xbdo0bN68GStWrFDPSCHpn0/eF01sUTGC4OBgbNq0CaNHj0ZISIhG1csjR45gy5YtmDBhAgCgZcuWGD9+PFavXo309HRERETg+PHjWL9+Pf71r3+hR48eVb5+q1atIJfL8e677yIjIwMKhQI9e/aEn59fucfMmjULX3/9Nfr164fp06fDy8sL69evR2JiIr777jvIZFXPaTds2ICbN28iNzcXAHD48GG8/fbbAIBnn33WpltpIiIiEB0djaVLl+L06dPo06cP7O3tcfXqVWzZsgXLly/HiBEjsH79eqxYsQJDhw5FcHAwsrKysGbNGri5ual/gDo5OaFp06bYvHkznnrqKXh5eSE0NLTCpmJbvzd8RksSrxUrVqBjx46oUaMGvvrqK43Phw4dqk6obI2UzyfvixbSTjqybleuXBFfeOEFMTAwUHRwcBBdXV3Fzp07ix9//LGYn5+v3q+oqEhcuHChGBQUJNrb24t169YVZ8+erbGPKJZMWxwwYECZ60RERIgREREa29asWSM2aNBAlMvlGtMgyzuHKIri9evXxREjRogeHh6io6Oj2K5dO3HXrl0a+1Rl6mNERIQIQOtL27RMa/bk9MdSq1evFsPCwkQnJyfR1dVVbN68uThz5kzx7t27oiiK4qlTp8SoqCixXr16okKhEP38/MSBAweKCQkJGuc5cuSIGBYWJjo4OOg0FZL3poQtP6Pjx48v9/8AADExMbHC462JOT2fvC9lCaJYhRFeRERERCbEMSpERERktpioEBERkdliokJERERmi4kKERERmS0mKkRERGS2mKhI6L333kOTJk2gUqmkDqXaZs2ahfbt20sdhqR4P60P76l14f20UFLPj7ZVGRkZopeXl/jFF1+ot+HvefIffPBBmf3XrVtX6XLiuvruu+/EUaNGiUFBQaKTk5P41FNPia+//rqYlpamdf+dO3eKrVu3FhUKhVi3bl1x/vz5YlFRkcY+9+7dExUKhbhz585qx2eJeD+tD++pdeH9tFxMVCTyv//9T3RzcxPz8vLU20ofGn9/fzEnJ0djf0M+NN7e3mLz5s3FefPmiWvWrBGnT58uOjg4iE2aNBFzc3M19t29e7coCILYo0cPcfXq1eK0adNEmUwmvvTSS2XOO2rUKLFr167Vjs8S8X5aH95T68L7abmYqEikRYsW4jPPPKOxDYDYqlUrEYD44YcfanxmyIdGW+XR9evXiwDENWvWaGxv2rSp2LJlS41s/j//+Y8oCIJ48eJFjX23bt0qCoIgXr9+vdoxWhreT+vDe2pdeD8tF8eoSCAxMRFnzpxBr169ynzWuXNn9OzZE++99x7y8vKMcv3u3buX2TZ06FAAwMWLF9XbLly4gAsXLuDFF1+End0/y0K9/PLLEEURW7du1ThH6dezc+dOI0Rtvng/rQ/vqXXh/bRsTFQkcOTIEQBAmzZttH4eExODlJQUrFy5ssLzFBQU4MGDBzq9KpOcnAwA8PHxUW/7448/AABt27bV2LdWrVqoU6eO+vNS7u7uCA4Oxm+//Vbp9awJ76f14T21Lryflo2rJ0vg0qVLAICgoCCtn3ft2hU9evTA+++/jylTpsDJyUnrfl9//TUmTpyo0zXFSpZ0evfddyGXyzFixAj1tnv37gEAatasWWb/mjVr4u7du2W2N2jQABcuXNApJmvB+2l9eE+tC++nZWOiIoGHDx/Czs4OLi4u5e4TExODiIgIfPbZZ/i///s/rfv07dsXe/furXY8mzZtwtq1azFz5kw0atRIvb20GVShUJQ5xtHREZmZmWW2e3p6lsn6rR3vp/XhPbUuvJ+WjYmKmerWrRt69OiB9957Dy+99JLWfWrWrKk1866KX375Bc8//zz69u2LxYsXa3xW+ltFQUFBmePy8/O1/tYhiiIEQahWTNaI99P68J5aF95P88VERQLe3t4oLi5GVlYWXF1dy91vwYIF6N69O1atWgUPD48yn+fl5SEjI0OnawYEBJTZ9ueff2Lw4MEIDQ3F1q1bNQZvAf80P967dw9169bV+OzevXto165dmXOmpaVp9LnaAt5P68N7al14Py0bB9NKoEmTJgBKRqJXJCIiAt27d8e7776rdTT65s2b1Rl+Za8nXb9+HU8//TT8/Pywe/durU2irVq1AgAkJCRobL979y5u376t/vxxiYmJCAkJqfDrsja8n9aH99S68H5aNraoSKBjx44ASv4ztmjRosJ9Y2Ji0L17d6xevbrMZ/r2lyYnJ6NPnz6QyWSIi4uDr6+v1v2aNWuGJk2aYPXq1YiOjoZcLgcArFy5EoIgaAwCA4CMjAxcv34dU6ZMqXJMloz30/rwnloX3k8LJ035FgoNDRWjoqI0tgEQp06dWmbfiIgIdQVFQxQfatmypQhAnDlzprhhwwaN188//6yx7w8//CAKgiD27NlTXL16tTh9+nRRJpOJL7zwQpnzbt26VQQgXrt2rdoxWhreT+vDe2pdeD8tFxMVifz3v/8VXVxcNMonl/fQHDhwwKAPTem5tL0iIiLK7L99+3axVatWokKhEOvUqSPOnTtXLCwsLLPf6NGjxS5dulQ7PkvE+2l9eE+tC++n5WKiIpH09HTRy8tL/Pzzz6UOxSDu3bsnOjo6ijt27JA6FEnwflof3lPrwvtpuTiYViLu7u6YOXMm3n//fatYcnzZsmVo3rw5hgwZInUokuD9tD68p9aF99NyCaJYSfk8IiIiIomwRYWIiIjMFhMVIiIiMltMVIiIiMhsMVEhIiIis8VEhYiIiMwWExUiIiIyW0xUiIiIyGwxUSEiIiKzxUSFiIiIzBYTFSIiIjJbTFSIiIjIbDFRISIiIrPFRIWIiIjMlk0nKvfu3UNMTAzu3bsndShERESkhc0nKgsXLmSiQkREZKYsOlE5fPgwBg0ahFq1akEQBOzYsUPqkIiIiMiALDpRycnJQcuWLfHpp59KHQoREREZgZ3UAVRHv3790K9fP6nDICIiIiOx6BYVIiIism4W3aJSVQUFBSgoKFC/z87OljAaIiIiqoxNtagsXboU7u7u6ldERITUIREREVEFbCpRmT17NjIyMtSvQ4cOSR0SERERVcCmun4UCgUUCoX6vYuLi4TREFVDVjLgGiB1FERERmfRiUp2djauXbumfp+YmIjTp0/Dy8sL9erVkzAyIiPLuMNEhYhsgkUnKgkJCejRo4f6/euvvw4AGD9+PGJjYyWKisgECrMBUQQEQepIiIiMyqITle7du0MURanDIDI9VVFJsqJwlToSIiKjsqnBtERWJfOu1BEQERkdExUiS5VyXuoIiIiMjokKkaVKPCx1BERERsdEhchS3f2D3T9EZPWYqBBZsnPbpI6AiMiomKgQWbKL3wPZ96WOgojIaJioEFmYtm3bok6XKLRdcgooLgB+/V9JTRUiIivERIXIwiQnJ+NOygMkZxaWbLj5G/DHV9IGRURkJExUiKzBic+Bk7FsWSEiq8NEhchaJKwD9s4H8jOljoSIyGCYqBBZk8TDwJbxwJWfAZVK6miIiKqNiQqRtcl9BBxYDGx/EUj6jd1BRGTRmKgQWasHV4G4OcD2l4C/TkgdDRGRXpioEFm7+5eA3W8Ae+aw5goRWRwmKkS24uZvwNaJwLV97A4iIovBRIXIlhRkAfGLgLj/ABl3pI6GiKhSTFSILMitW7eQk5MDAMgpUOLWo3z9TnTzN+DbZ4FD7wHptwwYIRGRYTFRIbIAx48fx6BBgxAYGIj09HQAQHqeEoH/OY7BK87hRFJW1U+qUgKXfgS+fQ74aVbJgFtOaSYiM2MndQBEVLFt27Zh9OjREEUR4hNjS0QR2H3uEX46l4bNL4RgWGufql9AFIFbR0te7nWAZkOBxv0AB2cDfQWaivKycGPPZ3h09XdAkMGnSSc06BsNuYOTDqGKuPDNAqRdP4mQkXPh3bij+rOsu1eQtD8W2feuAQLgWqsxAiMnwsW/gVG+DiIyDbaoEJmx48ePY/To0VAqlVAqlVr3UaoApUrE6DUX9WtZeVzGbeDIx8BXI4BjK0tqsujhzJezkPLnXq2fXdnxPnIf3ETouLfRdPQCZNw6j2s/fqzTee8e3wFAKLNdWZiH81/Ph8LNFy0n/Rctxr8PuYMTzm+aB5WyWK+vgYjMAxMVIjP29ttva21JeZIIQISIt3ffNMyFi3KBP78Bvo4CTm0o6SYygNwHt5B2/SQaDngVrrWbwL1eMwQ/HY375w+jIOthhcdmJ1/HnWPb0WjQq1rOexvFeVmoH/EManjXgbNvfdTrNhZFOekoyEg1SOxEJA0mKkRm6tatW9i1a1e5LSlPUqqAH84+0n+ArTbF+SULHv40EygurPbpMm9fgtzRGa61Gqm3eQS1BgQBWXcul3ucsigfl3e8j+Cnp8DBxavM507etWHn5Ibk0z9DpSyCsqgAKad/hpNPXTh6+Fc7biKSTrUSlYKCAhw9ehQ7d+7EgwcPDBUTEQGIj4+vtCXlSaII7L+UbvhgbicAF3ZW+zRF2WlwqOGhsU2QyWHv5IqinLRyj0v8eQ3c6oRojEl5nJ2iBpo/uxT3zx3AkXeG4eh7I5B2/SSajXkLgkxe7biJSDp6D6b96KOPEBMTg4yMDADA3r170bNnTzx48ABNmjTBe++9h0mTJhksUCJbk5WVBZlMBlUVZuLIBCAz3zDdNGXcOw20GKn1o79+3Yy/fvtW/V5VXIisO5dwfc9n6m1tXlqp12UfXjmG9KQzaP3CR+XuoywqwNVdy+FWpykaD50JUaXCnWPbcGFzDFpO+h/k9gq9rk1E0tMrUVm3bh1ee+01jBkzBn369NFISHx8fNCzZ0988803TFSIqsHV1bVKSQoAqETAzdFILQg1vMv9KCCsP3yadlW/v7zjffg06QzvJp3U2xSu3rB38URhbrrGsaJKiaK8LNg7e2o9d0bSGeSn3cPR90dpbL+4dQnc6jZDi+fewf1zB1GQkYqWEz+EIJQ0FLsMfRPHPhiNR1eOwbdZRFW/WiIyE3olKh9++CGGDBmCTZs24eHDsgPgwsLC8NFH5f/2Q0SVi4yMhCAIVer+EQSgZxMPwwfj5Am0Glfux/ZOrrB3clW/l9kpYO/sDievWhr7udVpAmV+DrLvXYVLzZJxKumJfwKiCNfajbWeu06nEfBv1Udj2x+rp6JB7xfg1agdAEBVXFDyxT82I6gkYanavx8RmR+9xqhcu3YN/fr1K/dzLy8vrQkMEemuXr16GDhwIORy3VpI5DJgUHMv1PNyNGwgnvWBIZ8ArtUflFrDpx48g8Nw9cePkXXnMjL/uoDrcSvh26wbFK4lLTYFmQ9wcmW0enCtg4sXnP0CNV4AoHD3haNnAICSAbnFedm4vmcFch/cQs79m7jy/f8gyOTwqN+i2nETkXT0SlQ8PDwqHDx74cIFBAQE6B0UEZWYN28eBEGAIJStHfK4krYEAXP71zdsAME9gX99VlIIzkCe+tebqOFdB+c2/gfnv1kAt7rN0HDANPXnokqJvIe3oSwq0PmcNXzqounoBchNScKf697A2fUzUZj9CM2i3oKDa9lZQkRkOQRRj3bRSZMmYf/+/Th9+jSUSiV8fX2xb98+9OzZE+fPn0f79u0xadIks+/+OXXqFMLCwnDy5Em0adNG6nCItHq8Mq22qcpyWUmS8u0LIRiqT2VabWRyoMPLQOjwv7tUiIikoVeLyttvvw2lUonQ0FDMnTsXgiBg/fr1eOaZZ9C2bVv4+flh/vz5ho6VyCYNGzYMR44cQf/+/cu0rAgCMCDUC0dmtjJckuIZCAxZATQfwSSFiCSnV4sKAKSmpmLOnDnYtm2bepE0V1dXDB8+HO+88w78/PwMGadRsEWFLM2tW7fQsmVLpKenw8NJjj/nhRluTIrCFWj9LBA6DJDbG+acRETVpHcdFT8/P3z++ef4/PPPcf/+fahUKvj6+kImY7FbImOpV68enJ2dkZ6eDmeF3DBJisK1pPUkdHjJ34mIzIhBVk/29fU1xGmIyJScfYHmI4GQQYBDDamjISLSSq/mj7lz56JVq1blft66dWssXLhQ35iIyJj8mgKR84Cob4CWo5mkEJFZ0ytR2bp1a4V1VPr374/NmzfrHRQRGZjcAXjqaWDoKmDoSqBhL0BukAZVIiKj0us71a1btxAcHFzu50FBQbh500DLzROR/hzdSsaeNB1SUl2WiMjC6JWouLi4VJiIJCYmwtHRwNUxiUh3dgqg1Vig+Sh27RCRRdOr66d79+5YtWoV7ty5U+azv/76C6tXr0aPHj2qHRwR6cE/FBgZC4RNYJJCRBZPrxaVRYsWoV27dmjWrBmef/55NGvWDABw7tw5fPHFFxBFEYsWLTJooESkg4aRQPfZrINCRFZDr0SlcePG+OWXXzBt2jT873//0/isW7du+OijjxASEmKQAIlIR3XbAz3+U1L+nojISug97L9FixY4dOgQHjx4gBs3bgAAGjRoAB8fA5XxJiKtAgICgOICBCjy/9noUbdkyjGTFCKyMtWen+jj48PkhMiEEhISgGv7gPi/u1cd3YGn32FVWSKySnonKkqlEnFxcbhx4wbS0tLw5JJBgiBg3rx51Q6QiCogtweeXgq415E6EiIio9ArUUlISMDw4cNx+/btMglKKSYqRCbQdhLg30zqKIiIjEavROXll19GXl4eduzYga5du8LDw8PAYZGh3Lp1C/Hx8cjKyoKrqysiIyNRr149qcMiQ6jhBYSOkDoKqgY+n0SV0ytROXPmDBYvXoxBgwYZOh4ykOPHj2PRokX48ccfIYoiZDIZVCoVBEHAwIEDMW/ePISHh0sdJlVHw16AnYPUUZAe+HwS6U6vgm916tQpt8uHpLdt2zZ07twZP/30k/o+qVQqAIAoiti9ezc6deqEbdu2SRkmVVetNlJHQHrg80lUNXolKv/+97+xZs0aZGZmGjqeKvv0008RGBgIR0dHtG/fHsePH5c6JEkdP34co0ePhlKphFKp1LpP6WejR4/GiRMnTBwhGYxnoNQRUBXx+SSqOr26frKysuDi4oKGDRtizJgxqFu3LuRyzfoNgiDg//7v/wwSZHk2b96M119/HZ999hnat2+PZcuWoW/fvrh8+TL8/PyMem1z9fbbb0MUxUpbvEr3efvtt7Fz504TRUcGI8gAZ1+po6Aq4vNJVHWCqEcfjkxWeUOMIAjl/sZgKO3bt0d4eDg++eQTACXNp3Xr1sW0adMwa9asSo8/deoUwsLCcPLkSbRpY/nN6Ldu3UJgYGCVuuUEQUBSUhIH8Fmau6eBWq2kjoKqgM8nkX70alFJTEw0dBxVVlhYiJMnT2L27NnqbTKZDL169cLRo0e1HlNQUICCggL1++zsbABAcXExioqKjBuwCcTFxVV57JAoivj5558xfvx4I0VFRmHnAljB/1lbwueTrJG9vQnWFRMt1J07d0QA4pEjRzS2v/nmm2K7du20HrNgwQIRAF988cUXX3zxZYCXKVSrhP6dO3dw+PBhpKamYvjw4ahTpw6USiUyMjLg7u5eZtyK1GbPno3XX39d/f706dOIiIjA77//jtatW0sYmWHExsbixRdfrPJxa9as4W9sliY/E3B0kzoKqgI+n0T60StREUURM2bMwCeffILi4mIIgoDmzZujTp06yM7ORmBgIN566y289tprBg73Hz4+PpDL5UhJSdHYnpKSUrJomxYKhQIKhUL93sXFBQBgZ2dnmuYrI+vbty8EQahyH3ifPn2s4uu3KaIjwHtmUfh8EulHr+nJ77//PpYvX4433ngDe/fu1Xjw3N3dMWzYMHz33XcGC1IbBwcHhIWFIT4+Xr1NpVIhPj4eHTt2NOq1zVW9evUwcOBAnVuy5HI5Bg0axIF6lkgwr9ZKqhyfTyL96JWorFmzBs899xyWLFmCVq1alfm8RYsWuHLlSnVjq9Trr7+ONWvWYP369bh48SKmTJmCnJwcTJw40ejXNlfz5s2DIAgQBKHC/Ur3mTt3rokiI4Oyd5Q6AtIDn0+iqtMrUfnrr7/QqVOncj93dnY2STG40aNH44MPPsD8+fPRqlUrnD59Gnv27IG/v7/Rr22uwsPDsXnzZsjl8nJ/cyv97Ntvv2WZbiIT4vNJVHV6JSp+fn7466+/yv385MmTJmuufOWVV3Dz5k0UFBTg999/R/v27U1yXXM2bNgwHDlyBP3791f/5lZa+0YQBAwYMABHjhzB0KFDpQyTyCbx+SSqGr0G0w4bNgyfffYZJkyYAHd3dwBQP3A///wzYmNjMXPmTMNFSVUWHh6O77//Hrdu3cL+/fuRmZkJNzc39OzZk33eRBLj80mkO70q02ZkZKBbt25ITExE165dsWfPHvTu3RvZ2dk4evQoWrdujcOHD6NGjRrGiNlgrK0yLRERkbXRq+vH3d0dx44dw8yZM3Hnzh04Ojri0KFDSE9Px4IFC/DLL7+YfZJCRERE5q/KXT/5+flYvXo1WrVqhblz53JUOhERERlNlVtUHB0d8e9//xuXL182RjxEREREanp1/YSGhiIpKcnAoRARERFp0itRWbx4MVatWoV9+/YZOh4iIiIiNb2mJ3/yySfw8vJC3759ERQUhKCgIDg5OWnsIwgCdu7caZAgiYiILIlYWAjBwUHqMKyCXonKmTNnIAgC6tWrB6VSiWvXrpXZp7IS0URERESV0StR4fgUIiKiCsj0GllBWvBfkoiIyNCYqBiM3v+SSqUS33zzDaKjozF06FCcPXsWQEnV2m3btiElJcVgQRIREZFt0itRSU9PR+fOnTF27Fh8/fXX+P7773H//n0AgIuLC6ZPn47ly5cbNFAiIiKyPXolKrNmzcL58+cRFxeHGzdu4PHlguRyOUaMGIHdu3cbLEgiIiKyTXolKjt27MC0adPQu3dvrbN7nnrqKQ64JSIi21X19X6pHHolKhkZGQgKCir386KiIhQXF+sdFBERkUVTqaSOwGrolagEBwfj1KlT5X7+888/o2nTpnoHRUREZMlEtqgYjF6JyuTJk/HFF19g8+bN6pshCAIKCgrwn//8B3v27EF0dLRBAyUiIrIYbFExGL0Kvr366qs4f/48oqKi4OHhAQAYO3YsHj58iOLiYkRHR+P55583ZJxERESWgy0qBqNXoiIIAtasWYPx48dj69atuHr1KlQqFYKDgzFq1Ch069bN0HESERFZDFGplDoEq6FTojJs2DD83//9H7p27QoAOHz4MEJCQtClSxd06dLFqAESERFZHHb9GIxOY1R27tyJW7duqd/36NEDe/fuNVpQRERElowtKoajU6JSu3Zt/PHHH+r3oihydWQiIqLyMFExGJ26fsaMGYMPPvgA3377rXrw7KxZs7B06dJyjxEEAX/++adBgiQiIrIkopJdP4aiU6KydOlSNGzYEAcOHEBqaioEQYCzszO8vb2NHR8REZHlUbLoqaHolKjI5XK8+OKLePHFFwEAMpkMc+fOxdixY40aHBERkSUSOZjWYHQao9KmTRvs2bNH/X7dunVo3bq10YIiIiKyZGJhkdQhWA2dEpUzZ87gwYMH6veTJk3SGFxLRERE/1Dl5UodgtXQKVGpX78+9u3bB+Xfo5g564eIiKh8qsxMqUOwGjolKi+99BK+/PJLODo6ws3NDYIg4Pnnn4ebm1u5L3d3d2PHTkREZJaKHzyUOgSrodNg2jfffBMtW7bEgQMHkJKSgvXr1yM8PBwNGjQwdnxEREQWpzglWeoQrIbOa/306dMHffr0AQDExsYiOjqas36IiIi0KLp7V+oQrIZeixKqOO2KiIioXMUPHkKVlweZk5PUoVg8nRKV0nV+6tWrp/G+MqX7ExER2RRRRNHt21A0aiR1JBZPp0QlMDAQgiAgLy8PDg4O6veVUXKtAyIislEFiYlMVAxAp0Tliy++gCAIsLe313hPRERE2hVcuQr8PbaT9KdTojJhwoQK3xMREZGm/HPnWHfMAHSqo0JERERVU3z/Poru3JE6DIunU4vKW2+9VeUTC4KAefPmVfk4IiIia5Fz5AgcRo2SOgyLplOiEhMTU2ZbaVOWKIpltpc2dTFRISIiW5Z9+DA8RoyAIGMHhr50+pdTqVQar7/++gvNmzdHVFQUjh8/joyMDGRkZOD333/HmDFj0LJlS/z111/Gjp2IiMisFd9LRh4X8a0WvVK8qVOnolGjRvjqq6/Qtm1buLq6wtXVFeHh4di4cSOCg4MxdepUQ8dKRERkcdK+2QyRhVL1pleisn//fvTs2bPczyMjIxEfH693UERERNai8MYNZMXFSR2GxdIrUXF0dMTRo0fL/fzIkSNwdHTUOygiIiJr8mj9lyjUsao7adIrURk3bhw2btyI6dOn4+rVq+qxK1evXsW0adOwadMmjBs3ztCxEhERmb22bdsifN0XGLz/n54FsagIqe+/D2V2joSRWSa9FiV899138eDBA3zyySf49NNPIft7NLNKpYIoioiKisK7775r0ECJiIgsQXJyMpJzcgBHzQUJi+7ew/1ly+A/ZzZnAVWBXomKg4MDNmzYgDfffBO7d+/GzZs3AQD169dHv3790LJlS4MGqc3ixYvx448/4vTp03BwcEB6errRr0lERFQdeX/8gbQNG+A1frzUoVgMvRKVUi1atECLFi0MFUuVFBYWYuTIkejYsSPWrl0rSQxERERVlfH9D7CvWxeuFUxKoX9UK1GR0sKFCwEAsbGx0gZCRERURQ8+WwU7Xz84NQ+VOhSzZ1OdZAUFBcjMzFS/srOzpQ6JiIhskVKJ1HffRUFiotSRmD2bSlSWLl0Kd3d39SsiIkLqkIiIyEap8vKQvPAtFCYlSR2KWTOrRGXWrFkQBKHC16VLl/Q+/+zZs9Xl/jMyMnDo0CEDRk9ERFQ1qqws3Ju/APkXL0oditkyqzEqM2bMwIQJEyrcp0GDBnqfX6FQQKFQqN+7uLjofS4iIiJDUOXkIHnhW/CZOhUuXbtIHY7ZMatExdfXF76+vlKHQUREZFJiURHuL1uGwr9uwXPMGNZZeYzeiUpcXBzWrl2LGzduIC0tDaIoanwuCAKuX79e7QDLc+vWLTx69Ai3bt2CUqnE6dOnAQANGzZkSwkREVmkjO+2oejmTfi++ipkNWpIHY5Z0CtRef/99zFr1iz4+/ujXbt2aN68uaHjqtT8+fOxfv169fvWrVsDAA4cOIDu3bubPB4iIiJDyE04ibtz/gP/2bNh7+8ndTiSE8Qnm0J0UKdOHYSEhGD37t2wt7c3RlwmcerUKYSFheHkyZNo06aN1OEQEZEVqFOnDu7cuYMARycc6d9f7/PIPTzgP/c/UAQFGTA6y6NXJ1haWhpGjBhh0UkKERGROVOmpyM5ZiEKjDiMwhLolai0a9cOly9fNnQsRERE9BhVdjaS31qEwlu3pA5FMnolKitWrMC2bduwadMmQ8dDREREj1FlZyN54VsoundP6lAkoddg2tGjR6O4uBjPPvsspkyZgjp16kAul2vsIwgC/vzzT4MESUREZMuU6em4FxODmgsXwj4gQOpwTEqvRMXLywve3t5o1KiRoeMhIiIiLZQPHuLe/PkImDcPDnXrSh2OyeiVqBw8eNDAYRAREVFllA8f4d7cefCfMxuOjRtLHY5JsPQdERGRBVFlZyM5ZiFyT56UOhSTqFYJ/aKiIly6dAkZGRlQqVRlPu/WrVt1Tk9ERERaiIWFSHn3PfhOnw6XLp2lDseo9EpUVCoVZs+ejRUrViA3N7fc/ZRKpd6BERERWZpbt24hJycHAJBbXIw7ubmobaxS+Eol7i9fDsHODs4d2hvnGmZAr0RlyZIleP/99xEdHY0uXbrg2WefxbvvvgsPDw+sWLECgiDgvffeM3SsREREZun48eNYtGgRfvzxR/Xad5nFRei25yf0DKiJV5o0QUsvL8NfWKXC/Y8+gn3td/QeYHv72DHc+PlnpN24gcLsbPR+/314VFIN9/axY7i0bRuyk5OhUirhUrMmGg8ahPoREep9ivPycGbjRtw9fhwF2dlw9vNDo379ENy3b5Xi0ytRiY2NxahRo7By5Uo8fPgQABAWFoaePXti/Pjx6NixI/bv349evXrpc3oiIiKLsW3bNowePRqiKJZZoFcEcDAlGYdSkvFRu/Z4unZtg19fLCjAo3WxCJg/T6/jlQUF8AkJQZ1OnXDys890OsbBxQUhw4fDtXZtyOzscO/kSZz49FMo3N0R0KoVAOD0+vVIPXcO7aZPh7OfH1L+/BOn1qyBk5cXaoWH6xyfXoNpb9++jZ49ewIAFAoFACA/P78keAcHPPPMM9iwYYM+pyYiIrIYx48fx+jRo6FUKssd7qAURShFEdOP/44/Hz0yShx5f/4JZXq6XsfWj4hA05Ej4d+ihc7H+IWGonb79nCrUwcuAQFoNGAA3OvXx4OLF9X7PLx8GYEREfALDYWznx8a9O4N98BAPLp2rUrx6ZWoeHt7Izs7GwDg4uICNzc33LhxQ2OftLQ0fU5NRERkMd5++22tLSlPEv9+fXr5ktFiUWZkGO3cFRFFESlnziDr7l34Nm2q3u7duDHuJiQg7+FDiKKI1HPnkH33LvxbtqzS+fXq+mndujVOnDihft+jRw8sW7YMrVu3hkqlwkcffYSWVQyEiIjIkty6dQu7du2qNEkppRRFxN+7Z5wBtnZy2Pn5GfaclSjKycEP0dFQFRVBkMnQZvJkjSSk9fPP4+Rnn2FXdDQEuRyCICDspZc0khld6JWovPjii4iNjUVBQQEUCgUWL16Mbt26oVu3bhBFEZ6envj666/1OTUREZFFiI+P1zlJKSUCOHo/FSPqBxo0FqfmLSBzcqp0v5uHD+Pk6tXq913nzKly4lDKzskJfd5/H8X5+Ug5exZ/rl8PZ39/+IWGAgCu7d6Nh1evovOsWajh44MHFy/ij88/h5OXV5W6mfRKVAYPHozBgwer3zdt2hTXr1/HwYMHIZfL0alTJ3gZY3QzERGRmcjKyoJMJtNaR6w8MgDZRcUGj8Xlsdk2FakVHg7vx5a/carGz2pBJoNLzZoAAI+gIGTduYNL27fDLzQUyoICnP36a3R+803UDAsr2ScwEOlJSbj8/ffGT1S0cXd3x5AhQwx1OiIiIrPm6upapSQFAFQAXOwN9qMXACA4OqJGO91m0dg7OcFeh5YXfYgqFVRFRQAAlVIJsbgYEASNfQSZDKjiv5neJfSVSiW++eYbREdHY+jQoTh79iwAICMjA9u2bUNKSoq+pyYiIjJ7kZGREJ74QVwZAUBHX8OOJanRuhVkf8/A1UdhVhbSExORefs2ACDr7l2kJyYi/7FJMcc/+ghnN25Uv7+4bRtS/vwT2SkpyLx9G5e//x43Dx9Gvb8r0tvXqAHfpk1xZsMGpJ47h5yUFCQdOICkQ4dQu33VitPpldalp6fj6aefxvHjx+Hi4oKcnBxMmzYNQMksoOnTp+O5557DkiVL9Dk9ERGR2atXrx4GDhyI3bt361SJXS4I6BEQYPCBtDU6dqzW8XcTEnDi00/V74/9738AgKYjR6LZ6NEAgNwHDwDZP20byoICnFqzBrmPHkHu4AC3WrXQfvp01O38Tzn/Dv/3fzi7aRN+/+gjFGZnw9nHB82jotCgT58qxSeIVR0JBOCll17Cxo0bsW3bNrRu3Rp+fn7Yt2+furbKa6+9hoMHD+L06dNVPbVJnTp1CmFhYTh58iTatGkjdThERGRhTpw4gU6dOkGpVFY4sFZASaKyJaK7QSvUylxdUXf1KsgcHAx2TnOjV9fPjh07MG3aNPTu3Vtrs9dTTz2FpKSk6sZGRERk1sLDw7F582bI5XLI5XKt+8gFAXJBwMft2hu8jL77oIFWnaQAeiYqGRkZCKpgHYCioiIUFxt+VDMREZG5GTZsGI4cOYL+/fuX+eVdANAjIABbIrqjr4HL59v5+cFt0CCDntMc6TVGJTg4GKdOnSr3859//hlN9ZyXTUREZGnCw8Px/fff49atW2jZsiXS09PhZmePH3v1Ms7qyYIAn1emWn1rCqBni8rkyZPxxRdfYPPmzeo+OUEQUFBQgP/85z/Ys2cPoqOjDRooERGRuatXrx6cnZ0BADXs7IyTpADwGD4MTs2aGeXc5kavFpVXX30V58+fR1RUFDw8PAAAY8eOxcOHD1FcXIzo6Gg8//zzhoyTiIiIADi2aA6Pv2fj2AK9EhVBELBmzRqMHz8eW7duxdWrV6FSqRAcHIxRo0ah29/zqImIiMhw7GvVhN+MGSWF02xEtcrjdenSBV26dDFULERERFQOubs7/P/zH8hdXKQOxaRsJyUjIiKyUDJXVwQsmA/7gACpQzE5nVtUHl+EUBeCIGDnzp1VDoiIiIj+IXNzRcD8+XCoX1/qUCShc6Kya9cuODo6IiAgQKdlrau6/gERERFpknt6ImDBfDjUrSt1KJLROVGpXbs27ty5Ax8fH4wdOxZjxoxBgA02QREREZmCnZ+fzXb3PE7nMSp//fUXDhw4gNatW2PRokWoW7cuevXqhXXr1iErK8uYMRIREdkU+zp1UPPtRTafpABVHEwbERGBVatWITk5GVu3boW3tzdeeeUV+Pn5YdiwYdi6dSsKCgqMFSsREZHVUzRsiJpvL4Kdt7fUoZgFvWb92NvbY8iQIdi8eTNSUlLUycvo0aPx3nvvGTpGIiIim+DUsiUCYhZA7uoqdShmo1p1VAoKChAXF4edO3fijz/+gKOjIwIDAw0UGhERke1w7tQJvtOnQbC3lzoUs1LlREWlUmHv3r34+uuvsWPHDuTm5qJXr15Ys2YNhg4dql7jgIiIiHTj0qMHfF6eYlMVZ3Wlc6Jy5MgRbNq0CVu2bMHDhw/RoUMHLFmyBKNGjYKPj48xYyQiIrJaLt27M0mpgM6JSpcuXeDk5IT+/fsjKipK3cVz69Yt3Lp1S+sxbdq0MUiQRERE1qhG27ZMUipRpa6fvLw8fPfdd9i2bVuF+4miCEEQoFQqqxUcERGRtXIIbgDf/3sNglwudShmTedEZd26dcaMg4iIyGbIPT3h/+9/Q+boKHUoZk/nRGX8+PHGjIOIiMgmCPb28P/3TNZJ0RE7xYiIiEzIZ8pLUDRqJHUYFoOJChERkYm4Dx4El4gIqcOwKExUiIiITMCxaQg8n3lG6jAsDhMVIiIiI5M5OcH31Vc5w0cPTFSIiIiMzPO5Z2HH4qh6schEJSkpCc8//zyCgoLg5OSE4OBgLFiwAIWFhVKHRkREpMGhfj249uoldRgWq1qLEkrl0qVLUKlUWLVqFRo2bIhz587hhRdeQE5ODj744AOpwyMiIlLzGDGClWerwSITlaeffhpPP/20+n2DBg1w+fJlrFy5kokKERFJKiAgAMr0dPjY20Pu7YUa7dtLHZJFs8hERZuMjAx4eXlVuE9BQQEKCgrU77Ozs40dFhER2ZiEhATcnjYdRXfvwqVzZw6grSaraIu6du0aPv74Y0RHR1e439KlS+Hu7q5+RXAuOxERGZFTmzCpQ7B4ZpWozJo1C4IgVPi6dOmSxjF37tzB008/jZEjR+KFF16o8PyzZ89GRkaG+nXo0CFjfjlERGTLZDIonmIF2uoyq66fGTNmYMKECRXu06BBA/Xf7969ix49eqBTp05YvXp1pedXKBRQKBTq9y4uLnrHSkREVBH7WrUge+xnDunHrBIVX19f+Pr66rTvnTt30KNHD4SFhWHdunWQcUQ1ERGZEfs6taUOwSqYVaKiqzt37qB79+6oX78+PvjgA9y/f1/9WUBAgISRERERlbCvzUTFECwyUdm7dy+uXbuGa9euoU6dOhqfiaIoUVRERET/cHji5xPpxyL7SyZMmABRFLW+iIiIzIF9nbpSh2AVLDJRISIiMmuCAPtaNaWOwiowUSEiIjIwubcXZI6OUodhFZioEBERGZi9n5/UIVgNJipEREQGJvfyljoEq8FEhYiIyMDknh5Sh2A1mKgQEREZmNzNTeoQrAYTFSIiIgOTubpKHYLVYKJCRERkYI4hIVKHYDWYqBARERmYwPXnDIb/kkRERGS2mKgQERGR2WKiQkRERGaLiQoRERGZLSYqREREZLaYqBAREZHZspM6ADKNe/fu4d69e1KHQQZSs2ZN1KzJJeStBZ9P68Nn1HBsOlGpWbMmFixYYPX/mQoKChAVFYVDhw5JHQoZSEREBOLi4qBQKKQOhaqJz6d14jNqOIIoiqLUQZBxZWZmwt3dHYcOHYKLi4vU4VA1ZWdnIyIiAhkZGXDjeiIWj8+n9eEzalg23aJia1q1asWHxgpkZmZKHQIZAZ9P68Fn1LA4mJaIiIjMFhMVIiIiMltMVGyAQqHAggULOKjLSvB+WhfeT+vDe2pYHExLREREZostKkRERGS2mKgQERGR2WKiQkRERGaLiQoRERGZLSYqREYgCIJOr4MHD1b7Wrm5uYiJianSuRYvXozBgwfD398fgiAgJiam2nEQWQpzfj4vXbqEmTNnolWrVnB1dUXNmjUxYMAAJCQkVDsWS8XKtERGsGHDBo33X375Jfbu3Vtme0hISLWvlZubi4ULFwIAunfvrtMxc+fORUBAAFq3bo24uLhqx0BkScz5+fz888+xdu1aDB8+HC+//DIyMjKwatUqdOjQAXv27EGvXr2qHZOlYaJCZATPPPOMxvtjx45h7969ZbZLJTExEYGBgXjw4AF8fX2lDofIpMz5+YyKikJMTIzGuk+TJk1CSEgIYmJibDJRYdcPkURUKhWWLVuGZs2awdHREf7+/oiOjkZaWprGfgkJCejbty98fHzg5OSEoKAgTJo0CQCQlJSkTjQWLlyobrKurCsnMDDQGF8SkdWQ6vkMCwsrszilt7c3unbtiosXLxr2i7QQbFEhkkh0dDRiY2MxceJETJ8+HYmJifjkk0/wxx9/4LfffoO9vT1SU1PRp08f+Pr6YtasWfDw8EBSUhK2bdsGAPD19cXKlSsxZcoUDB06FMOGDQMAtGjRQsovjcjimdvzmZycDB8fH4N+jRZDJCKjmzp1qvj44/bLL7+IAMSNGzdq7Ldnzx6N7du3bxcBiCdOnCj33Pfv3xcBiAsWLKhyXNU5lshamOvzWerw4cOiIAjivHnz9D6HJWPXD5EEtmzZAnd3d/Tu3RsPHjxQv0qbfQ8cOAAA8PDwAADs2rULRUVFEkZMZDvM6flMTU3F2LFjERQUhJkzZxrlGuaOiQqRBK5evYqMjAz4+fnB19dX45WdnY3U1FQAQEREBIYPH46FCxfCx8cHQ4YMwbp161BQUCDxV0Bkvczl+czJycHAgQORlZWFnTt3lhm7Yis4RoVIAiqVCn5+fti4caPWz0sH4AmCgK1bt+LYsWP44YcfEBcXh0mTJuHDDz/EsWPHbPYbF5ExmcPzWVhYiGHDhuHMmTOIi4tDaGio3ueydExUiCQQHByMffv2oXPnznBycqp0/w4dOqBDhw5YvHgxNm3ahHHjxuGbb77B5MmTIQiCCSImsh1SP58qlQrPPfcc4uPj8e233yIiIkKfL8NqsOuHSAKjRo2CUqnEokWLynxWXFyM9PR0AEBaWhpEUdT4vFWrVgCgbl6uUaMGAKiPIaLqkfr5nDZtGjZv3owVK1aoZwrZMraoEEkgIiIC0dHRWLp0KU6fPo0+ffrA3t4eV69exZYtW7B8+XKMGDEC69evx4oVKzB06FAEBwcjKysLa9asgZubG/r37w8AcHJyQtOmTbF582Y89dRT8PLyQmhoaIVNxRs2bMDNmzeRm5sLADh8+DDefvttAMCzzz6L+vXrG/8fgchMSfl8Llu2DCtWrEDHjh1Ro0YNfPXVVxqfDx06FM7Ozkb/NzArUk87IrIFT05/LLV69WoxLCxMdHJyEl1dXcXmzZuLM2fOFO/evSuKoiieOnVKjIqKEuvVqycqFArRz89PHDhwoJiQkKBxniNHjohhYWGig4ODTlMhIyIiRABaXwcOHDDUl01kEczp+Rw/fny5zyYAMTEx0ZBfukUQRPGJdisiIiIiM8ExKkRERGS2mKgQERGR2WKiQkRERGaLiQoRERGZLSYqREREZLaYqBAREZHZYqJCZGaSkpIgCAJiY2OlDoWItOAzalpMVIiIiMhsseAbkZkRRREFBQWwt7eHXC6XOhwiegKfUdNiokJERERmi10/REYQExMDQRBw5coVPPPMM3B3d4evry/mzZsHURTx119/YciQIXBzc0NAQAA+/PBD9bHa+r8nTJgAFxcX3LlzB//617/g4uICX19fvPHGG1Aqler9Dh48CEEQcPDgQY14tJ0zOTkZEydORJ06daBQKFCzZk0MGTIESUlJRvpXITIffEYtBxMVIiMaPXo0VCoV3nnnHbRv3x5vv/02li1bht69e6N27dp499130bBhQ7zxxhs4fPhwhedSKpXo27cvvL298cEHHyAiIgIffvghVq9erVdsw4cPx/bt2zFx4kSsWLEC06dPR1ZWFm7duqXX+YgsEZ9RCyDVaohE1mzBggUiAPHFF19UbysuLhbr1KkjCoIgvvPOO+rtaWlpopOTkzh+/HhRFEUxMTFRBCCuW7dOvU/piqpvvfWWxnVat24thoWFqd8fOHBA6wrIT54zLS1NBCC+//77hvmCiSwMn1HLwRYVIiOaPHmy+u9yuRxt27aFKIp4/vnn1ds9PDzQuHFj3Lhxo9LzvfTSSxrvu3btqtNxT3JycoKDgwMOHjyItLS0Kh9PZC34jJo/JipERlSvXj2N9+7u7nB0dISPj0+Z7ZV9M3J0dISvr6/GNk9PT72+iSkUCrz77rv46aef4O/vj27duuG9995DcnJylc9FZMn4jJo/JipERqRt6mJ50xnFSibg6TINUhAErdsfH8xX6rXXXsOVK1ewdOlSODo6Yt68eQgJCcEff/xR6XWIrAWfUfPHRIXIinh6egIA0tPTNbbfvHlT6/7BwcGYMWMGfv75Z5w7dw6FhYUasxuIyLD4jFYdExUiK1K/fn3I5fIysxNWrFih8T43Nxf5+fka24KDg+Hq6oqCggKjx0lkq/iMVp2d1AEQkeG4u7tj5MiR+PjjjyEIAoKDg7Fr1y6kpqZq7HflyhVERkZi1KhRaNq0Kezs7LB9+3akpKRgzJgxEkVPZP34jFYdExUiK/Pxxx+jqKgIn332GRQKBUaNGoX3338foaGh6n3q1q2LqKgoxMfHY8OGDbCzs0OTJk3w7bffYvjw4RJGT2T9+IxWDUvoExERkdniGBUiIiIyW0xUiIiIyGwxUSEiIiKzxUSFiIiIzBYTFSIiIjJbTFSIbFhSUhIEQUBsbKzUoRCRFnxGmagQ6ez69euIjo5GgwYN4OjoCDc3N3Tu3BnLly9HXl6e0a574cIFxMTEICkpyWjX0MXixYsxePBg+Pv7QxAExMTESBoP0ZNs+Rm9dOkSZs6ciVatWsHV1RU1a9bEgAEDkJCQIFlMhsKCb0Q6+PHHHzFy5EgoFAo899xzCA0NRWFhIX799Ve8+eabOH/+PFavXm2Ua1+4cAELFy5E9+7dERgYaJRr6GLu3LkICAhA69atERcXJ1kcRNrY+jP6+eefY+3atRg+fDhefvllZGRkYNWqVejQoQP27NmDXr16SRKXITBRIapEYmIixowZg/r162P//v2oWbOm+rOpU6fi2rVr+PHHHyWM8B+iKCI/Px9OTk4GP3diYiICAwPx4MGDMkvZE0mJzygQFRWFmJgYuLi4qLdNmjQJISEhiImJsehEhV0/RJV47733kJ2djbVr12p8AyzVsGFDvPrqq+r3xcXFWLRoEYKDg6FQKBAYGIg5c+aUWUgsMDAQAwcOxK+//op27drB0dERDRo0wJdffqneJzY2FiNHjgQA9OjRA4IgQBAEHDx4UOMccXFxaNu2LZycnLBq1SoAwI0bNzBy5Eh4eXmhRo0a6NChQ7W+WUvZmkNUET6jQFhYmEaSAgDe3t7o2rUrLl68qNc5zQUTFaJK/PDDD2jQoAE6deqk0/6TJ0/G/Pnz0aZNG/zvf/9DREQEli5dqnUhsWvXrmHEiBHo3bs3PvzwQ3h6emLChAk4f/48AKBbt26YPn06AGDOnDnYsGEDNmzYgJCQEPU5Ll++jKioKPTu3RvLly9Hq1atkJKSgk6dOiEuLg4vv/wyFi9ejPz8fAwePBjbt283wL8KkfngM1q+5ORk+Pj4GOx8khCJqFwZGRkiAHHIkCE67X/69GkRgDh58mSN7W+88YYIQNy/f796W/369UUA4uHDh9XbUlNTRYVCIc6YMUO9bcuWLSIA8cCBA2WuV3qOPXv2aGx/7bXXRADiL7/8ot6WlZUlBgUFiYGBgaJSqRRFURQTExNFAOK6det0+vpEURTv378vAhAXLFig8zFExsJntHyHDx8WBUEQ582bV+VjzQlbVIgqkJmZCQBwdXXVaf/du3cDAF5//XWN7TNmzACAMs26TZs2RdeuXdXvfX190bhxY9y4cUPnGIOCgtC3b98ycbRr1w5dunRRb3NxccGLL76IpKQkXLhwQefzE5kzPqPapaamYuzYsQgKCsLMmTOrdS6pMVEhqoCbmxsAICsrS6f9b968CZlMhoYNG2psDwgIgIeHB27evKmxvV69emXO4enpibS0NJ1jDAoK0hpH48aNy2wvbY5+Mg4iS8VntKycnBwMHDgQWVlZ2LlzZ5mxK5aGs36IKuDm5oZatWrh3LlzVTpOEASd9pPL5Vq3i6Ko87WMMcOHyFLwGdVUWFiIYcOG4cyZM4iLi0NoaKjJrm0sbFEhqsTAgQNx/fp1HD16tNJ969evD5VKhatXr2psT0lJQXp6OurXr1/l6+v6DfXJOC5fvlxm+6VLl9SfE1kLPqMlVCoVnnvuOcTHx2PTpk2IiIio8jnMERMVokrMnDkTzs7OmDx5MlJSUsp8fv36dSxfvhwA0L9/fwDAsmXLNPb573//CwAYMGBAla/v7OwMAEhPT9f5mP79++P48eMa37hzcnKwevVqBAYGomnTplWOg8hc8RktMW3aNGzevBkrVqzAsGHDqny8uWLXD1ElgoODsWnTJowePRohISEaVS+PHDmCLVu2YMKECQCAli1bYvz48Vi9ejXS09MRERGB48ePY/369fjXv/6FHj16VPn6rVq1glwux7vvvouMjAwoFAr07NkTfn5+5R4za9YsfP311+jXrx+mT58OLy8vrF+/HomJifjuu+8gk1X9d5QNGzbg5s2byM3NBQAcPnwYb7/9NgDg2WefZSsNSYbPaEnitWLFCnTs2BE1atTAV199pfH50KFD1QmVxZF62hGRpbhy5Yr4wgsviIGBgaKDg4Po6uoqdu7cWfz444/F/Px89X5FRUXiwoULxaCgINHe3l6sW7euOHv2bI19RLFk2uKAAQPKXCciIkKMiIjQ2LZmzRqxQYMGolwu15gGWd45RFEUr1+/Lo4YMUL08PAQHR0dxXbt2om7du3S2KcqUx8jIiJEAFpf2qZlEpmaLT+j48ePL/f5BCAmJiZWeLw5E0SxCiOCiIiIiEyIY1SIiIjIbDFRISIiIrPFRIWIiIjMFhMVIiIiMltMVIiIiMhsMVEhIiIis8VEhYiIiMwWExUiIiIyW0xUiIiIyGwxUSEiIiKzxUSFiIiIzBYTFSIiIjJbTFSIiIjIbP0/4/ThIboz5LIAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(raw_bars=True, contrast_bars=False);" - ] - }, - { - "cell_type": "markdown", - "id": "684b8010", - "metadata": {}, - "source": [ - "Raw bar kwargs can be utilised via `raw_bars_kwargs` in the `.plot()` method.\n", - "\n", - "Pass any keyword arguments accepted by matplotlib.patches.Rectangle here, as a string." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d207d2bf", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(raw_bars=True, contrast_bars=False,\n", - " raw_bars_kwargs={'color': \"red\", 'alpha': 0.2}, \n", - " );" - ] - }, - { - "cell_type": "markdown", - "id": "d0aa1055", - "metadata": {}, - "source": [ - "## Contrast bars\n", - "**Contrast bars** are included in all plots by default. It can be turned off by setting `contrast_bars=False` in the `.plot()` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "61626d46", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(contrast_bars=True, raw_bars=False);" - ] - }, - { - "cell_type": "markdown", - "id": "0667877a", - "metadata": {}, - "source": [ - "Contrast bar kwargs can be utilised via `contrast_bars_kwargs` in the `.plot()` method.\n", - "\n", - "Pass any keyword arguments accepted by matplotlib.patches.Rectangle here, as a string." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1087421b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(contrast_bars=True, raw_bars=False, \n", - " contrast_bars_kwargs={'color': \"red\", 'alpha': 0.1}\n", - " );" - ] - }, - { - "cell_type": "markdown", - "id": "ecdfcb2e", - "metadata": {}, - "source": [ - "## Reference band\n", - "A **reference band** can be added for each relevant contrast object as desired via supplying a list to the argument `reference_band` in the `.plot()` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "31b7831a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(reference_band=[0, 1], contrast_bars=False, raw_bars=False);" - ] - }, - { - "cell_type": "markdown", - "id": "d76dc0f7", - "metadata": {}, - "source": [ - "Reference band kwargs can be utilised via `reference_band_kwargs` in the `.plot()` method.\n", - "\n", - "The relevant inputs to `reference_band_kwargs` are:\n", - "\n", - "- `'span_ax'` - Whether the reference band(s) should span the entire x-axis or start from the relevant effect size curve\n", - "- `'color'` - Color of the reference band(s). If color is not specified, the color of the effect size curve will be used.\n", - "- `'alpha'` - Alpha of the reference band(s) (transparency)\n", - "- `'zorder'` - Zorder of the reference band(s) (the layering relative to other plot elements)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "173c109c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(reference_band=[0,1], contrast_bars=False, raw_bars=False,\n", - " reference_band_kwargs={\"alpha\": 0.2, \"color\": 'black', 'span_ax': True}\n", - " );" - ] - }, - { - "cell_type": "markdown", - "id": "319bf446", - "metadata": {}, - "source": [ - "## Delta text\n", - "**Delta text** is included in all plots by default. It can be turned off by setting `delta_text=False` in the `.plot()` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7ce8da49", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(delta_text=True);" - ] - }, - { - "cell_type": "markdown", - "id": "5d8ec997", - "metadata": {}, - "source": [ - "Delta text kwargs can be utilised via `delta_text_kwargs` in the `.plot()` method.\n", - "\n", - "The relevant inputs to `delta_text_kwargs` are:\n", - "\n", - "- `'color'` - Color. If color is not specified, the color of the effect size curve will be used. \n", - "- `'alpha'`- Alpha (transparency)\n", - "- `'fontsize'` - Font size\n", - "- `'ha'` - Horizontal alignment\n", - "- `'va'` - Vertical alignment \n", - "- `'rotation'` - Text rotation\n", - "- `'x_coordinates'` - Specify the x-coordinates of the text\n", - "- `'y_coordinates'` - Specify the y-coordinates of the text\n", - "- `'offset'` - Am x-axis coordinate adjuster for minor movement of all text\n", - "\n", - "Otherwise, pass any keyword arguments accepted by matplotlib.text.Text, as a string. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cd25ccc7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(delta_text=True, \n", - " delta_text_kwargs={\"color\":\"red\", \"rotation\":45, \"va\":\"bottom\", \"alpha\":0.7});" - ] - }, - { - "cell_type": "markdown", - "id": "5179c353", - "metadata": {}, - "source": [ - "`'x_coordinates'` and/or `'y_coordinates'` if you would like to specify the text locations manually. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8501251a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(delta_text=True, \n", - " delta_text_kwargs={\"x_coordinates\":(0.5, 2.75), \n", - " \"y_coordinates\":(0.5, -1.7)});" - ] - }, - { - "cell_type": "markdown", - "id": "ae1a40ff", - "metadata": {}, - "source": [ - "`'offset'` to adjust the x location of all the texts (positive moves right, negative left)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "00d65bcd", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(delta_text=True, \n", - " delta_text_kwargs={\"offset\":0.1});" - ] - }, - { - "cell_type": "markdown", - "id": "feaec766", - "metadata": {}, - "source": [ - "## Adding jitter to slopegraph plots\n", - "\n", - "For paired plots, you can add jitter to the slopegraph by adding a value for `jitter` in the `slopegraph_kwargs` parameter.\n", - "\n", - "This can be useful for specific paired plots when there are many overlapping points.\n", - "\n", - "Currently, jitter is only available for slopegraphs and only in the x-direction (vertical plots) or y-direction (horizontal plots)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4a4bf97f", - "metadata": {}, - "outputs": [], - "source": [ - "# Jitter tests\n", - "np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n", - "Ns = 20 # The number of samples taken from each population\n", - "# Create samples\n", - "c1 = [0.5]*Ns + [1.5]*Ns\n", - "c2 = [2]*Ns + [1]*Ns\n", - "t1 = [1]*Ns + [2]*Ns\n", - "t2 = [1.5]*Ns + [2.5]*Ns\n", - "t3 = [2]*Ns + [1]*Ns\n", - "t4 = [1]*Ns + [2]*Ns\n", - "t5 = [1.5]*Ns + [2.5]*Ns\n", - "id_col = pd.Series(range(1, 2*Ns+1))\n", - "df_jittertest= pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n", - " 'Control 2' : c2, 'Test 2' : t2, 'Test 3' : t3,\n", - " 'Test 4' : t4, 'Test 5' : t5, 'ID' : id_col})" - ] - }, - { - "cell_type": "markdown", - "id": "6cd63ad4", - "metadata": {}, - "source": [ - "For the example below, there are many overlapping points for the paired plot, which makes it look like only one sample." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6275895f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group = dabest.load(df_jittertest, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\", \"Test 3\", \"Test 4\", \"Test 5\")), paired='baseline', id_col='ID')\n", - "multi_2group.mean_diff.plot(horizontal=False, group_summaries=None);" - ] - }, - { - "cell_type": "markdown", - "id": "121516bd", - "metadata": {}, - "source": [ - "Adding jitter can help to visualize the data better." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "81a91b34", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(horizontal=False, slopegraph_kwargs={'jitter': 1}, group_summaries=None);" - ] - }, - { - "cell_type": "markdown", - "id": "54c58acc", - "metadata": {}, - "source": [ - "## Gridkey\n", - "\n", - "You can utilise a gridkey table format for representing the index groupings. This can be reached via `gridkey` in the `.plot()` method. \n", - "\n", - "You can either use `gridkey='auto'` to automatically generate the gridkey, or pass a list of indexes to represent the groupings (e.g., `gridkey=['Control', 'Test']`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ca1012f1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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3fgeXy8WHPvQhYG2Y+/3vfz+PPfYYH/7whwkGgwBoNJorsjBH9FAKgiC8iUprwN66jVwsiMHdQCo4TXr50kozSpKEvXU7cqVMbOr42x6rUavYs60Tr8PCS/2jzATCV6L5giBcRZIkodToLvlxKQv73opKpeKTn/wkf/Inf8IXvvAFfu/3fo8nnniCrq4uHn74YZ5++mmamprOOefLX/4yjz32GH19fQSDQX74wx+urxT/5je/SSaT4YknnsDr9a4/fuqnfuqy7slZkixKONzQ+vv76evr4/jx42zduvV6N0cQbiqf//zn13sov/KVr6x/XZZlQoP7KKRjaMwO8qsreLc+gkpvvqTrpoJTRMYOUdV7D3pnzdseW65UODg4zYw/zI7uRjrqPZf1mgRBuPXs27ePe++9l1gshs1muy5tED2UgiAIP0aSJBztO5HLJSRJiVKte0elGY3VzegdPiITh6mUCm97rFKh4PYNLXQ2eDg8PMPJyUVRqlEQhPccESgFQRAuQKU1YG/pIxOax1jdTCEVJT576pLOfXMgjU33X9Lx2zob2NxWx8nJBY6OzopQKQjCe4oIlIIgCG9hrafRSzIwjqW+h8TiCLlY4JLOVWkN2Ju3kApMkY1e/BxJktjYUsvO7mbG5pZ5TdT/FgThEt1zzz3IsnzdhrtBBEpBEIS3tLbh+VpPYymXQWfzEB498Jabl/84o6cVnd1DdOLQJW8/1FFfzZ2b2pgLRtg3IOp/C4Lw3iACpSAIwttQ6YzYm7eSXp7GUNUAQGT80CUNSUuShLN9J5VigdWZgUt+zkavk/u2dhCMivrfgiC8N4hAKQiCcBFGTwt6u5f47CnsLX1kI0uk/GOXdK5KZ8LWtJmkf4JcLHjJz+lz2XhwexerqQzPHRkik3v7xT2CIAjXkwiUgiDcssrlS5ujuLbIZgeVUoHc6jKW2k5i0wMUUtFLOt/ka0dnrVpb9V2+tKFvALfNzMM7e8gXy6L+tyAINzQRKAVBuCUViiUml0JEEulLOl6lM2Fr3koqMInWVo3aYCM88volBURJknB07KJSyBKfPfmO2mkzGXh4ZzcKSeLZw0PEkpfWXkEQhGtJBEpBEG5JGrUKu9lAJJ4itJq6pHNM3lZ0tmpik0dxtG2nnE9ftCLOWWq9GWvjJpJL4+TiK++orSa9jod29qDXqnnu8DArscQ7Ol8QBOFqE4FSEIRblttmwmExshyNMzx7aVv7nF1kk16ext66nVRginRo7pKez1zTgcbsJDp+iEr5nS200WvVPLijG7vFwAvHRlkMxd7R+YIgCFeTCJSCINyyJEnCaTHispk4NjrL6NzFF82o9GZszVtI+idQagwYqxqIjh+mlLt4L6ckKXB27KKUyxCfO/2O26tRqdjT14XPaeXl/jGm/aF3fA1BEISrQQRKQRBuaZIkUW230NXg5cjIDOMLyxc9x+RtQ2erIjpxGFvzFhQqzVppRvnii3zUBivWhg0kF0fIJ8LvuL1KpYK7N7fT7HPx2qlJRuYubaN1QRCEq0kESkEQbknFUpnFlRjZfHG99GFHnYfDQzNMLr39HMe1Vd+7qBRzJBaGcXXdQSEZIT57ab2OlrouNCb72n6Wl1gf/M0UConbelvobvRxdGSWExMLolSjIAjXlQiUgiDckhQKiUKpRDASp1QuI0kSO7obaa2t4uDp6YsOJ6v15rX9JZfGkStlrI0bSSwMkVu9eA+nJClwdOymlE0Snxt8V+0/G4K3ttdzamqRI8Oi/rcgCNePCJSCINySlAoFdVUOKrLMUmgVWZaRJIldPU0017h4/dQUs4HI217D5OtY219y/BAmXxtaaxWR0QOUixffL1JjtGGt7yWxMEQheWn7WV5Ib3MNu3qaGV9Y5tWTk6L+tyAI14UIlIIg3LLUKiUeh4VUJs/gtB9Y6/nb3dNCo9fJq6cmmFt+67C3NvS988z+kqdxdt6GXCkTHT98Sb2Flrpu1Ebbux76Pqu9rpq7NrcxvxLl5f4xiiVR/1sQhGtLBEpBEG5pRr0Wt93EiYkFApE4sDYcfvuGVuqrHbx6coKFlbcOlWqDBWvjZpJLY5SyKRwdu8iEF0kFJi763JJCibN9F8X0KomF4ct6HQ0eJ/dv7WQllmTvsRHyBVH/WxCEa0cESkEQbnlum5lqh4XXTk2u18xWKCTu2NhKrdvG/hMTLIVW3/J8c007WouL6PghdDYP5pp2YlP9FFIX3ytSY3ZgqeshPj94Sce/Ha/LyoPbu0mkc6L+tyAI15QIlIIg3PIkSeLOTa0AvHpqgkplbbhaqVBw56Y2fE4r+wbGCITjb3H+mf0l8xnisyewN29FbTATGX39kjYwtzb0otabiY4fuqSth96Oy2bi4Z09FEplnj08SCIt6n8LgnD1iUApCIIA6LUa7trUxkosyYnJhfWvKxVr+z5WOyy8NDBGMHrhUKk2WLE1biS5NE4+GcHVeQelXIrV6f6LPrekUOJo30UhFSOxMHLZr8Vq0vPIzh4UCgXPHh4kEhf1vwVBuLpEoBQEQTij2mFhS1sdg9NL55Q2VCoV3LOlnSqbiZeOj71lLW1zbed6aUWlzoi9pY+kf4JMeP6iz621uDDXdhGfO00xc+HQ+k4Y9Voe2tGDUafl+aNDLEdF/W9BEK4eESgFQbhlpbL59eHts3qafNS67bx2apJ0Nr/+dZVSyb1bO3Bajbx4fJTQavK860mSAmf7mdKKs6cweloxuOvPlGa8eC+htWEDKp2ByNjlD33DWv3vB3Z04bSY2Hts5G0XFwmCIFwOESgFQbglFYolFpaj+COr54RKSVpb4a1WKtl/cuKcfR1VSiX3be3Ebjby4rFRwvHz63erjWeHvkcpJMI42nYgKVVERg9cNCQqlKq1oe9khOTS2BV5nRqVivv7Oqlx29g3MM7Ukqj/LQjClScCpSAItySNWkV9tYNsvshCKHpOcNRqVNy9uZ1oIk3/2LnD1WqVkvv6OrCY9Ow9NkI0cX7Po7m2E43JQWT80NrWQJ23k0+ESMwPXbRdOmsV5pp24rMnKWbP7wV9N5RKBXdtaqelxs3rpycZnvVfkesKgiCcJQKlIAi3rMRqFI/DTCqT57VTk+f0VLpsJvo66hmZCzAXPLdijkalYk9fJ2a9jheOjhBLnhsq11Z976aUSxGfO43OWoWlvpf43Gly8bevEw5gbdyEQqMnOnboipVTVCgkdvc009tUw7HROQZE/W9BEK4gESgFQbglHTx4kFOnTjE5Norbomd+OcrBoalzQlZnvYcGj5MDg1Pnbb+jUavYs60Lg07DC0dHWE1lzvm+2mjF2rCB5OII+UQYa0MvWoubyOjrlIt53o5CqcbZvotcfIWUf/yKvWZJktjaUU9fRwOnpxY5PDxz3hxSQRCEd0MESkEQbjm5XI7vfOc7VMpFkskkgycH8OplppfCHBmeXQ+VkiSxu7cZvUbD/hPjlMrnljTUalQ8sL0LnUbNC0dHSKSz53zfUteNxmQnMn4IZHmtNGO5RHTiyEV7B3W2asy+NlZnTlC6QkPfZ/U0+bitt4WJhRVePTVBuSzqfwuCcHlEoBQE4Zaj0+l47JO/iiKfpJhJUCgU+O6/fJNSZJ7R+QDHx+bXA59GpeLuLW0k0lmOjsydfy2Nmge2d6NRq3j+6Mg5PZmSpMDRsZtSNkl87jQqnRFH+04yoXnSwcmLttPWtAWFWkPkEgLoO9VaW8XdW9pZXInxkqj/LQjCZRKBUhCEW1JdQxM7d/Rh1koUkmHKxQKHXnmRuZMHODk+y6mpxfVj7WYjO7ubmFhcvuAqab1WzQPbulApFLxwdJhU9o1QqTHasNZvILEwTD4ZweCqx+xrIzZ1nGL67febVKjUONp2kYsFLymAvlP11Q7u39ZJOJ7khaPD5ArFK/4cgiDcGkSgvIa+/OUvI0kSv/Vbv3W9myIItzyFUoWtpp2NfTupcZqJzw9SzKZYmpmg/6X/5NVjpxiaeWM1dEuNm5YaN4eGZ4glM+ddz6DT8MCOLiRJ4vkjI+fsYWmp60JttBMdO4hcKWNr3opSZyI8+hpy5e17BvUOLyZvC7HpgUvay/Kd8jisPLi9h1Q2z3NHhknn3n5+pyAIwoWIQHmNHD16lCeffJKNGzde76YIgvAmOouLTbvv5+ce3k1hZYJ8PEQxk+Tgs9/j+8++yNh8EFibT7mzuwmzXssrJycuOERs1Gl5cEcXIPP80TfCmaRQ4uzYSTGTID43iEKpwtV5O8VMktgllGa0N29FoVRf0tzLd8NpNfLwzh5KpTLPHho6by6oIAjCxYhAeQ2kUil+/ud/nq9//evY7fbr3RxBEH6MSm/i0V/7X3zyYx9GXwiRCc1jUEH/vmf4m7///5hYXF47Tqnk7s3tpLN5Dg1NXzDcmfQ6HtjeTbki88LREbL5AgAakwNLfS+JhSEKySgakx17y1aSS+NkIovnXefNFCoNjrYdZKN+0svTV/4GABajnod39aBSKXn28JCo/y0IwjsiAuU18Ou//us8+uij7Nmz53o3RRCEN0kn48hnNjRX681s/+nH+NSv/0+anUrSwWnsOgUzw/38ryf+lOGptQ3OrSY9u3ubmQmEGV+48J6SZoOOB7d3UyyVef7oCNn82txEa30PaqONyPja0LfJ24bBVUt07BCl/PnD6G+md9ZgrG4iNnX8ose+W0adlod2dGMy6Hj+6BDB6OXXFBcE4dYgAuVV9q1vfYv+/n6eeOKJSzo+n8+TSCTWH6nU+aXdBEG4fNlMmpXJfvxTp8mkEgAo1Tpa7/6v/Mqv/jr3bm4kE57DpsgTW17kC7/3OEdPrVW6afK66KjzcHR09i178izGtVBZKJbYe2xtwYukUOJs30UxHSc+P4QkSTjadyIplJdUmtHe0oekUBK7SkPfcGbV+rYuXFYzLx4bZX5Z1P8WBOHiRKC8ihYWFnjsscf453/+Z3Q63SWd88QTT2C1Wtcfd99991VupSDcmrRaDVaXDwoZghPHOfryDykW8ihUarxbHuKD//Vj/OL770SRj2OpxClnk/zO7/0Bz+x9GYBtnQ3YTQb2nxinUCxd8DmsJj17tnWRyRXZe2yEfKGExuzAUt9DYn6QQiqKUq3D2Xkb+fgKiYXht22zUq3F0badTGSJTOj8LYyuFLVKyX1bO6itsrN/YJzJxYtX9xEE4dYmAuVVdPz4cVZWVti6dSsqlQqVSsX+/fv5i7/4C1QqFeXy+ZP6v/CFLxCPx9cf+/fvvw4tF4RbgCyjVoDV7UOn0xMbfIGXv/UXLM5OIimUuHvuYsd9H+CTv/ABqqxaLOVVlOU8f/rVv+Ab//D/IUlw1+Y2CsUSr5+eesseQ7vZwAPbu0hl87x4fIRCsYS1vhe10Upk7BBypYzOVo2lrof47CnyifDbNtvgqsdY1UBs8hjlQu5tj70cSqWCOze20VZXxYHBqXNWvAuCIPw4ESivovvvv5/Tp09z4sSJ9ce2bdv4+Z//eU6cOIFSqTzvHK1Wi8ViWX+YTKbr0HJBuPkpVBq0VjeUi1gd1TRvvht1ZoXBp/+WAy98n3w+h6N9F02b7+BXf/4n6Otpw0YKVSnDv3zn3/njL/8JlIvcvrGFhZUoI3OBt3wuh8XIA9u6SaRzvHh8lGJFxtG+i2J6db1X0tq4AY3ZSXjkNSqlwtu23d6yDYDY5NErd0MuQKFYW9m+oaWW42Nz9L9pw3dBEIQ3E4HyKjKbzfT29p7zMBqNOJ1Oent7r3fzBOGWpzE50Dm8lIt5DHotmx78BZxuN6nR/bz8rb9idmIEW3MfVW3b+fBDt/PTP/EQbl0FcgleevUAv/f4H1DJJulp8nF8bJ6V2FuXSHRajezZ1sVqKsNLx8dQ6G1Y6rqJzw9SSMWQJAWurtuplAoX3R5IqdFhb91GOjRPJjR/NW7NOkmS2NJWx7bOBgZnljg4NC3qfwuCcB4RKAVBuGWFVlNUFFqM1Y3I5RKZ5Um67/kIbX13oy9GGd37TV595juonE0427axraOWx375Y7T67Mi5OCcHh/mj//XHZEILuG1mXjk5sb6i+0JcNhP393URTaR5uX8MY203Kr15fehbpTPhaNtBemXuotsDGdwNGFx1RCePUi5evaHvs7obfdy+oZWppRCvnBwX9b8FQTiHCJTX2L59+/i///f/Xu9mvGPheIoTEwtiw2PhplEolVhNZphbjhJJFXBvfBCVzkR0/CBVrVvY/NBHqa6qIj9ziH3f/iuWYmtD4B6Tgs/+6se5o28jciHD5PQU3/i7v8M/fJRCocDrpyfftnexym7m/r5OQvEU+05MYWvdSTEdI7E4AoCxqnGtMs7kMYqZt962R5IkHG3bQa4Qmzp+xe/PhbTUuLlnSztLoVVePD5KoXThxUiCINx6RKAULkk8lWVkLsB/vHqCHx0aZHQuKOr+Cu9pGpWKlhoXDrOB0GqKHx0dp1K7Hb2zlvDI60jI9Dz832jbcgcWOcnMK9/i6JGDaOs2oSmn+NWf+wl+7iM/CZUys7Oz7H/xeYZee5bJ2UVOTS297XNXOyzct7WDlViCg1MRjL5O4nOnKaRXgbXtgZRaA+GRA29bmlGp0WNr6SO9PHvRzdGvlLoqB3u2dRFJpHjhyMjb9sgKgnDrEIFSuCQtNW5+5t4+7trUjk6t4ujoLN95+Tgv9Y8yG4iI4S/hPadYKjMbjJwJlm50WjV7j48xWqxG7+tideYkqaVR6ne8nw33fwSf10PFf5KjL/+QqGSlmI7wE7u7+MLnPoNSo2VucYn50ZMcfOa7PL//IIHw228K7nVauXdrB4FInJOrWhRaI9GxQ8hyBYVSjavrdoqZVVZnTrztdYxVTeidNUQnjlx0Mc+VUu2w8NCOHtK5PM8dGTqnbvmPk+UKxWySbGTponXLBUF475JksWTvhtbf309fXx/Hjx9n69at17s567L5IrPBMDP+MOF4Co1KRb3HQYvPTZXdjCRJ17uJgvC2KhWZj/73XyYQXKG5vpavf+0vmVwMcXxsDkmS2FglYYyNobW6cXXfSSmbYnlwP4vjJ1nNllHb66h36rA43KyoavndL/1vUtFlvFY9cclE3x17+PyvfRyjXvu27VgMxdg3ME6dSaKxOIGtaTPW+h4AEoujxKaOU7XhHvSOmre8RimfIXDsKQyuOpwdu6/ofXo7iXSOvceGqchw3+ZmjIoSxWyCUiZBMZOglE1QzCbXqxF5+x5BY3Jcs/YJgnDtiEB5g7tRA+WbxVNZpgNhZvwhUtk8Jr2WRq+LFp8bq0l/vZsnCG/pc5/7HJNzi+QrCv74iS+zsaWGXKHIsdE5ZgJhvPoKTfI8BqMRd889KLUG4rMnWBw8wEowQFbS47QaqG1opeLZxO8+8Wf4J4dx6iFc1LF5x27++Hc+h0739qFyfjnK/hPjNChCNGhT+Preh9poRZZlQkP7KSQjeLY+gkpreMtrpAKTRMYPU7XhXvQO35W+VQDIskw5n6b4psCYjkcZGZ+kVMjS5HVh0GlQaQ2oDBbUejNqgwWV3oraYEGpNYg3m4JwkxKB8gb3XgiUZ8myzEosybQ/zFwwQqFUwmkx0VzjotHjQq9VX+8mCsI5Pve5z7G6ukpJUnHPT36UzgYP2zsbkSQJf3iVQ0Mz5NJx2lig2qKlqvdudLZqcrEgK8Ov4Z8ZIZbMoJZkfE0dODY8wFee/CcGD+3DpMgTySvp6d3IH/3+F3G5XG/blrlghFcGRmnKj9FS78Oz5UEkSUG5kCNw/Gk0RjvuDfe+ZSCTZZnQ6ZcpZuN4+96PQvXu/71VysUzgTFJMRNf63HMJihmkuvD1pJCidpgRq23IKuN9M9FWM0ruHPbJnzVb/9aBUG4+YhAeYN7LwXKNyuXKyyGYkz5QyyFVgGocdlo8rmoq7KjusCm7oJwLVUqMh/52CegWKCxzsv/+I3PcGR4hkafi9t6m1EqFJTKZU5NLTE8NY8zNUGDVaJ+452YPC1USgViU8dZnugnGFiimE1hcnqpue2n+ecfHeDQiz9ETkVYzct0tDTz25/5DN3d3W/bphl/mIPHjtOQn6B3131Y69aOz8UCrJx+GVvTZix1b32NUi5F4NjTGKubcLTteNvnWuttzJwJjnGK2eTanzMJSvnM+nFKrf5MT6MVld6C2mA509toPCfcFktl9p8YJxhNcOemNhqqxdC2INxKRKC8wb1XA+Wb5QpF5oIRpvxhwqtJ1ColDdVOmnwuPA6LGAITrotSucx/++VfZzGwjNls4ctf+TIatYqDg9P4nFbu2ty2/sYnlsxwaHCS9NwAHmWC9k27cbf1IUkSmcgi4dEDBOcmiIeWQK3HtuUDHJ5Lc/C5/yC0NEM2X6DRW8XP/uLHeeSR973tz/zUUoiB157Hp4qz7eGfQ2O0AbA6PUBicYTqzQ+itbx1D2DSP0504ijVG+9HZ/dQKZfW5jK+eV5jJkkpG6dSPtvbqECtN6MyWM98tKA+Ex4VKs0l39NypcLrp6aYC0bY1dNMW13VJZ8rCMJ7mwiUN7gbJVCWymVK5Qo6zeUNWyfSWab9Yab9YVLZHEadliavi+YaFzbTW88PE4Sr4fOf/zyhcISCrOShj3wco16Dz2VjcimEy2rivq0daNQqYK1Hb2x+meH+A+gSUzS0dNO560GUKjXlQo7o5FEiM6dYnh0lXyxR8m5jXtXA0OF9DJ04BsUsNQ4Tu+59iE/89/+JTqd7y3ZNzPsZevk7VLtd7Hz4v6JQKJErZZZPvEClVMCz9ZFzhrRlWaZcyFLKJChk4oSHXqGYiWOsbqRceGMFtlKjQ623rAXGM6FRZbCg0hmRpCuz6YcsyxwZmWVsPsjW9np6mnziTaMg3AJEoLzB3SiBcmQuwMD4Am21VXQ3ei+6cvViZFkmtLo233I2GKFQLOGwGGn2uWnyOtFrL71XRBDerc9//vOsrq5is9n4wu89zomJBeaXo6iUCpKZPD6XlT3bus75eczmCxw9eoTV8YOYbE623PuT2O12ZFkmszJLZPwQyxMDJBKrxHV1+M29hAN+9r/wDDo5g9OgpL6tl9/87c/j8Xjesm3Dw0NMHnqK6o7t7Lhtbe5kIb2K//APUJtsmKpbKGbj6yuqK+W1TcYlSUJSqkkvT2GsasLeuu3McLUZpfry/t1eKlmWOTW1yMnJRbobffR11ItQKQg3OREob3A3SqDM5ouMzgcZmw9SLJVp8rroafJhN19+r2K5XGExvMqMP8RiaBVZlvG5bDR7XdRVi/mWwtXz5kD5la98BYDQapL+8XlmAxFCq0nqqx381N1bMOnP7VGcnZ1m5PWnKZYr1G/dw4burrV5l/kMkbEDrAwfIB7yE6qYWVA3kVZYeOmZH2CR02jJYbZX8SuPfZYtW/vOua4sy1SKOYqZBKcO7mVhehR7wwba3Roq+Qz5VIxseB5TdTOG6qY35jee7XHUGZEUShKLo6xO91O1aQ866/UZeh6ZC3B0ZJbWmip29TSjUIhQKQg3KxEob3A3SqA8q1gqM7G4zPBskEwuT63bTm+zjyq75YpcP18oMRuMMO0PEVpNolIqafA4aPa6qXZYxC8k4Yq6UKCEtVDnD8c5ODTF8bF5LAYdP7tnO43ec+cu5jJJTu77AcvLQaSaLezcvp0quwVZlkn6xwkef5rV4CwreRXzeStRbQ39Rw5jKK5SyURRKBQ8+v4P8vCeuyjnUuurqSulM9Vn5Apzs1P4M0pqem5jS3sDKoOFxOII+VgQT9/7UOvNF3xtslw5M0Sex7P1fSiUqqt2H9/OtD/E66enqHXbuWtTG0qlqKchCDcjEShvcDdaoDyrXKkwEwgzNBMgnsrgtpnpbfZR67ZfsaGtRDrHTCDEtD9MMpPDoNPS5HXS7HNfkZ5RQXirQHmWLMuMzgf57r5+Uukcd21p565NbZgNb/RWVspF5gdeZnJ8iIimlrqOrfR1NKDVqChkEiy+/m+szp4mnJGZSalZrpiZnJqnEJqmnEuBXKGro42P/cLPYXNVv7GSWm9BpTeRT4Q5ve97TJfcdG3ZxcaWWiqlIsH+Z1Co1FRvfhBJceFe/GImTuD4M5h97dhbrt//H4uhGAdOT/HA9m7xb1cQblIiUN7gbtRAeZYsyyyGYgzNBFiJJbCaDPQ0eWnyulAqrtwk/9Bqihl/mJlgmEKxhN1spOXM/pYGnZhvKbw7FwuUZ2XzBf7txWOMLyxTW+VgW2c9G5pr1udWVsolwqMHmR8+TCCrRNa7aHYbsKorlIt50sFpMqEZEmUNM2kdSVlLMKMi7F9Y2+cxm6LaV8tv/T+P09DUct7zRyePMjk0wLiylc3dnfQ2+8gnIyyfeB5zTSf25i1v2fbEwjCrMycuujr8aiuVy2L6iiDcxESgvMHd6IHyzVZiCQan/SyGYhh0WrobPbTWVqFRXbmhtnKlwlJolRl/mIVQDLki43VZafa5qKtyoFaJX1jCpbvUQAlrgWjvkUFOTcyjVVYwqWRaHCoazWUUhTSyLFNIRclE/MQrelbUPuxuDxu7OrA63KSXZ/Ef/QHxVI6JOGTyZdIqK5PTSxTTcXLxZdQaDZ/41d/kzvsePue5K+UigWNPsxTPM1quY1tXE92NXhILw8SmB6jacB96h/eC7ZblCssDz1OplPBufeQtezMFQRAux/WZVCPclKrsFu7rsxBLZhia8XN8bJ5Tk0t0NHjorPdckUo5SoWC+moH9dUO8oUSc8sRpv1hXjs1iUqppL7aQbPPhcdhFfMthXdFliuUcukzq6fjZyrErFWN6chnyagKzCdKqEw6xoJqZlf19Da20tVci9Zko5iOEx55FVcBRst2nh+JsqnVQFfjRrQWF4sHvoPMClMJNepyml2d1RwarSCr1BQTIf7m//4pUyOD/MIvP4bqzNZACqUaZ/suSqf2ImmKHBudRSFJdNR3kYsFiYwdwNv3KErN+VsRSZICR8cugv3PEJ87ja1p8zW+o4Ig3ApED+UN7r3UQ/nj0tk8w7MBJhZXkGWZ1poqupu858w/u1KSmRzT/jAz/jCJTBa9VkOT10VLjQu72XjFn09475Nlmc9+6jGi0RBmvY7f/c2PU8yuBUe5UgFAoVSdUx1mbc9GMwNzUcYXQ3Q1eikWy0z5Qxi0Gja11tLsc1POJVgZ3EepWGBF38poKI/VqGdXTzMWRXatt3FuiqlYGbVOj0GlZP/pOYKxDFo5RzEVo7W1hce+8CUc7je2FopOHCG1PEPQvIHRQJxdPc20VFsIHP8RGpMdd+9bl2aMzw0SnztF9ZaH0Jqd1+QeC4Jw6xCB8gb3Xg6UZ+ULJUbng4zOBSkUSzR4nPQ0+XBar3zQk2WZSDzNlD/EbCBCvljEbjac2d9SzLcU3iDLMr/2Cx8knkxhs1r5g8/+BqoztanXwqMVpUZ/wYD24/ssttW6OTG5yFwwgtVkYHNbHTV2PeHhVykkoyhrNnFyuUQknqa9rpoen4no0EtMTY4zG0rjdrsw6dQcODnO4ZFFbGYT5WQIi0nPJz/1Obr7bgegUioSOP4UKp2ZGWUzE4sr7N7QTI2+xMrpl7G3bMVS23Xh11spExx4Hqjg2fKwGPoWBOGKEoHyBnczBMqzSuUyk4shhmf9pLJ5vE4bvc2+q1Z+sVyp4A+vMu0Ps7gSo1KR8TgtNPvc1FeL+ZYCPPvUD8iXKhhNZvbs2fOOz//xfRajyTQD4wsEIqu4bGa2tNagDg+TXp7FUr+BgOTixMQiapWSrY1ONMF+BkcnWIgk6a6vxmqzcvTEEE+9dgqVwYqunIFSlv/yMz/NI//lEyiUKrLRACunX8Leup2hmIrJxRVu39iCPbtA0j+GZ/NDaMwXrqNdSEUJ9j+Lpb4XW+PGy719giAI60SgvMHdTIHyrEpFZi4YYXDGTyyZxmkx0dvso67KcdXmPRaKJeaCUab8IVZiCZRn51t6XXidYr6l8O6d3WexrsrOnRvX9lkMhOMMTMwTjqfwOm20GTNIoVGM1Y3o6rdwbGytIk+NTU9jeYqB4QkCSZnbWh04HU7GZub51lMvsZKqYDcbkTMxdm3byH/7zS9gtLmJjB8mszKLp+99HJlcZnopzB0bm9EFjiNXimulGZUXnrO8OnuSxPwQnq2PoDHZr/HdEgThZiUC5Q3uZgyUZ53dPHpoxk8wGsdi0NPd5KXF576qmx+nsjlm/BGm/CES6bX5lo1eJ81eNw6LQZSIE96xxZUY+0+M47aZuWdrOxqVClmWmV+JMTA+TyKdxWuoUFOYw+Fy4e6+i6VYhiMjsxRyWVrlWSZmZlmu2Hiow4rFoCaRlfmX7z/NsZE5jHY3hkqamiobv/7YZ6hp20iw/2lUeguunns5MDTNbCDMbR0+NIsHMbjrcXbsvmBb5UqZYP8zICnxbH3oitXwFgTh1nZLB8rdu3fz9a9/nd7e3uvdlLd0MwfKNwuvphicWWJhOYZOq6arwUN7XTUa9dXbiECWZSKJNNNn5lvmCkVsJgPNPhdNPhdG3bWpeyxcP7IsX7E3EMvRBC/1j2Ix6Lmvr3N9V4NKRWZqKcTJqUVSiVVcRT+dHiMNm+9D1ho5ObHI6MwitvgwK8FFEqZWPrSlGkU6jNJg4bl9B/nes/upaMxYDRosqjL/7Rd/jt7ttxMZeR1n+w4M1a28emqChZUY22t06MKDuLpux1jVeMG25hNhlk88j7VxI9b6G/f/P0EQ3jtu6UDp9XqJRqP89m//Nr//+7+PTnflVx9frlslUJ4VT2UZmvUz7Q+jVEi011XT1eC96otpypUKgXCcaX+YhZUolYpMtcNCc42L+mrHFd1LU7gxyLLMD18/hcNipNHrxOu0XvZm/NFEmr3HRtCoVTywrQuj/o03JaVymbH5ZU6Oz5AMTNFgkdh9xz1Yq+oIx1McOj1BbOwAyfAiCu9GPnTnZor+00gKBTPBOF/7h38lnMyjN9swS3nef99t3HX7Tsq5JN6+R5E0el49uRYqt1jTWMsRvFsfQfUWpRlXpwdILI3i3fo+1EbrZb1uQRCEWzpQJhIJvvCFL/Dkk0/S1NTE1772tXc1Mf9qutUC5VmZXIGRuQDjC8uUKzLNPhe9TT4sRv1Vf+5CscT88tp8y+XomfmWVXaafC58TpuYb3mTKJcr7BsYJ57OkMrm0ahV1Fc7aPK4LqtufCKdZe+xEWQZ9mzrwmo692e2UCwxODXPsaOHqeRSbN28ka1bt6GQFIzM+jnwwvfJhufR1W/mZx59kMLiSXKxICW1ma99898YHJtC1pgw61Rs76jh/XdswNW0gaoN91GRZfYPjOMPxejRLOG1m6netOeCK7rlSpnA8R+hUGmo3vyAGPoWBOGy3NKB8qyjR4/yK7/yK5w4cYKf+7mf4//8n/+D2+2+3s0Cbt1AeVahWGJ8YZmRuSC5fJG6aju9TTW4bKZr8vzpbJ5pf5jpQJh4KoNOo6bJuzYk7rQYxXzL97Biqcx39x1HlmVqXHY0ahWBSJxUNodeq1kLl14nbpv5Hf89p3N5Xjw2SjZf5P5tnbis5/+8ZnI5Dr62n5GpOSxODzt37KC1top0Js+3v/WPZJcnwd3Bf/npj6DPBlidGQCFmmcPnuYHP3qeQkWBWm+i2aHmI3d3s/HBj2Jt6KVcrvDywBj+QJBe1SLNnRvecjPzfDzE8skXsDVtxlLX/W5uoyAIAiAC5bpKpcJf/uVf8nu/93solUrq6urOO0aSJE6ePHlN23WrB8qzyuUKU/4QQzN+kpkc1Q4LvU01+FzWaxLqZFkmmsgw7Q8xEwiTKxSxmgw0e100+1znDG0K7x0/3hPe5HXiddqIJFLMBaNkcnmMOi0NHieNXuc7ehORL5R4sX+UeCrDvVs78DguPKzsnzzFoaPHWC7q8TS0s6W9EafFwL9/79tkFofJm+u4+4H301NjJzZxiEIywsRKhm/883eJxVYpq/RY1WV+9u4uHvjopzHXdFAql3m5f4yFuWm2mGJ07XwAnd1zweePTR0n6Z/A2/c+1AbLu76Xb1YpFynnM5RyGcr5NKX82kdb05YLVvMRBOG9TwTKMwqFAl/60pf40z/9U5xOJx0dHRc87uWXX76m7RKB8lyViszCSpTBaT+RRAq72Uhvk48Gj/OaDUVXKjL+yFo98fmVGOVymWqHhRafm3qPmG/5XnShnvDuRi+yDLOBCHPLa4u2zAYdjV4XjR4ndrPhotctlsrsGxhnJZbgzk1t1FdfeH/IbGSJqYH9jK0qSOp8uBw2at12Th59jUpwmITaRcOmO7h9YxuaxDzxudNE0yW++R8vMjU5Qa5UoVIu84HdHfz8J34dR+s2Kki8eGyU+YlT7PQp6bnzJ1Cqzw9zlXKJ4PEfodToqdq056KBWa6UKReya2GxkKGUS1POp9cC5JngWC4WzjlHqdGh0hpxdt52xUKrIAg3FhEogb179/Jrv/ZrTE9P82u/9mv87//9vzGbLzyR/VoTgfLCZFkmGE0wOO0nEFnFpNfS3eijtdaNSnntNiwvlNbmW874wwQjCRQKibqqtXriXtflL/IQrq2zPeHDMwESmSzVDgs9TT68DisrsSQzwTDzwSiFUgmbyUCj10mjx/m2c3vL5QqvnZ5kPhhl94ZmWmuqLnhcIRUjNLSPUKrIgrKBWK5CoVSmElvAlp4ijhV13RZ6mmvp8hpJTh4muRrhP18f5vCxfpKJVXKFMhu7WvjNj/8X6rfcj6Sz8PyhkyyMneC+3jradlw4MOZWl1k+uRd7Sx/Gqob1YLjWw/jmXsYM5UKWN//aUKjUqHRGlBrD2ketAaXWgEprRHXmz6IqjyDc/G7pQBkKhfjUpz7Fv/7rv7Jhwwb+9m//lh07dlzvZp1DBMqLi8TTDM34mQtG0KhVdDZ46KivRqe58MbOV0s6l2fGH2HaH2L1zHzLs/tbOq1ivuV7SaUisxCKMTS9RDiewm420HOmJxwgEI4zE4ywsBylVC7jtJjWw+WFpj9UKjKHh2eYWFxmW2cD3Y2+Cz5vKZ8hPPQKhXSMXNUmRkIF+scWMJZitEpLlHV2su4NGPR6tnfUYc0uEl8Y5rXT0zz/Wj+R4ALZfAl7tY9P/uyj9N2xB01VM8/uP4R/ZpRHbt+Kr6HljR7FXIbSmT8nlkbIRYOYvC0oVGuvQVIo10PhG6HRgFJrRKVdC48K1bX9dyYIwo3plg6UDoeDQqHA448/zqc//WmU17Bn61KJQHnpkpkcwzMBJpdWkCSJttoquhu913x+oyzLxJKZ9Xri2XwBi1FPs29tvqVJL+aQvVfIssxyLMHQTIClUAyjTktXo5e22irUKiWlcpml0CqzgQiLoRjlSgW3zbweLvVazTnXGphYYHB6id7mGra01V3wTUalXCIydpBseAFL4yaWynb+7aVjqHJRmuUF7C4P2sYd+Fcz1FU52FxjJDvXz/DYGN95/gjLC1NkCyVkjZH33b2dR3d1oLF6GBoaIpeO09nWitG0Vu5UqdGf6U00oFBric2cQGOw4d54H2qdCYVaK94ICYJwSW7pQPm+972Pv/7rv6axsfF6N+UtiUD5zmXzRUbng4zNBymWyjR5XfQ0+S5pztuVVqnIBCJxpgNh5pejlMtlquxr8y0bPI6runG7cGXFkmkGZwLMBsKoVUo66z101HvWNzAvlEosrsSYDUTwR+LIZ/YybfQ6qa92rPeYD834OT42R3tdNTu6mi4491eWZeKzJ4nPD2HytpCztfHtFw9TTIaxrI5hsxjp6NrM4vIKlWKWGqsWYyHESmCR77x8Cv9ymIKsJFeW6Ozq5KMP76K2YxOvD4yQKSt49AM/hcvlOm8oOhcLsHzqJZztOzB5267+TRUE4aZxSwfK9wIRKN+9QqnE5OIKw7NBMrk8tW47vc0+quzXZ1FAsVRmfjnKtD9MMBJHoZCoddtprnHhc9nEfMv3iFQ2x8hskPHFFQBaa9x0N3oxG97oec4X1ubWzgbX5tYigc9lo9HjpK7aznwwysHBaeo9Dm7f0IJUKZ2Zq/jGEHQpnya9MktyaQyFSktaZWMxnCQp60iEFtGq1TR3bUKj0RGM5zBZbfTU2Cksj/Fv3/tPTk8HQWMkmYjjcLn44PsfYvumjbx8YhIMDn7q0YcvOPczMn6YTGgOb9+jqHTGa3ZfBUF4bxOB8gYnAuXlK1cqzATCDM0EiKcyuG1mept91Lrt1204L5MrMBMIM+0PEUtm0KrPzLf0uXBZTWKY8T0gVygyNr/M6FyQQrFEg8dJT5MPp/XcEJbJZJhd9LOw5CcWi6CqFHCbVJRKMmMraZzaCtt8alRneiolSTpnYUullCe5NIpCa2JK0UQ4r6S9xs7g/h8gyxUMLbdTW1fHSixBIpWjo85BbWmRZ779DV46sYDa6iERXUaSZO646262dzczHs6jr27j0Xtvx2I8dwpGpVQgcOxp1EYr7t57xc+iIAiXRATKG5wIlFeOLMsshmIMTvsJrSaxmgz0NHlp8rqua+9gLJlmainMTCC8Nt/SoKe9vpruRu91a5NwaWS5Qj6TYmpukcnZOXLpJC6jijqHHrNaXut1LObXjy+WSsRzFaLZMokCJMsa/BklNVVOfuL2jZitNpQa3XlVa4qZBKGhfeRzeY6mqzFa7LR5LZze9wOUcpG0qxedxY3ZoGNlNYleo6bXGGNi/3f5weFpyjo7+UyKdDzKpo09tLU1k5LMOFv7eOT2LefN681Gl1g5vQ9nxy5MnpZrcSsFQXiPE4HyBnejBEpZlm+qnoqV2NqWQ4uhGAadlu5GD621Vdd1D8lKRWY5mmDKH0KnUbOts+G6tUVY+5mvlPKUc28ehj53o+5y/o0tdGRZJpErEkwUSBUljGYrjbW1+Lwe1DojSt3aNjpn5y0m0jnmghEGZ5Y4MbGAVq3m3q0ddNRX43FYz5tbWS7mCQ+/QnB5meMpFz2dHdj1aoZe+0+cOpmSdzOLKQV6rQZZlsnmCzSWZlBHRvnP10cJJIoo1TrikSANHjvNbR1kDT4aN+7kfbs3n7d4LTJ2kEx4Ae+296PSXvv5x4IgvLeIQHmDu1EC5T9/+3uMz/lprKni5376p9Bqbo7FJLFkhqEZPzNnFlp01HvofNNCC+HmJcsypWziTdVcfmxz7nyGSrm8frykUKyviFau77FofNM2OgYUqrUwF4jEGZrxE4jEMel19DR5aal56z1S54JR/uPVAVaTGXwuGxajngaPg0avk2q7Zf3NnFwpE504wumRMcazNvbcfTu5XI7Rgz+i1gS+DfcwGCqdqUGvoJjP4o72U2U3c/DoKY4OjqExuyhkUujJsqG7lYixlfYN2/jQfbsw6t4IleVinsCxp9Canbh67r6p3lAKgnDliUB5g7tRAuVvPPZbzCwEQKXhv/73T1LnttNS48bnsl2zCjVXUzqbZ3g2wMTiCrIs01pTRXfTuQsthJuLLMssvPZvyJUykiShOFPN5fzQuLYHo0Kte8ehKhxPMTTjZz4YRas5s0dqneeCb8jS2TwvHB1hNZWl0esgEk+TzuXRazU0nin9eLYmeHx+iJdeP0IUMx9+/8MsrsQYP/ICDaYSnTv2sKp0MDC+QHg1SSEVwbY6jLW+l8T8CE89+zwKrRmlRkdhdYm+jlri1i4cjT18/KcewfSmn/lMeIHQ0Cu4um7HWNX4ru/13r17yWaz6PV69uzZ866vIwjCjUsEyhvcjRIoP//5z7O6uorJbOa//fqn1xeT6LUaGr1OWmvc2M3v/RWh+UKJ0fngOQstept9OCzv/dcmnC+fCKPU6FBq9Fe1mksinWN41s/UUmhtj9S6Krobzt8jNZsv8tLxUZKZHPdubUeSFMwGw8wFo2TzBUx6LY0eF41eJ4r0Cv/x3EuotAZ+5kMfYGghwnT/PpoNOdr67sbk62A2EGFgYp6FyRGUuQj6mh5c5RDP/vu/kMwV0FpcpENzbGp0ofG0k7U08/H/+pPUVL9RzSc88hq5WADvtvej1Lx1RaC3c/b/D5vNxle+8pXLupeCINyYRKC8wd0ogfKzn/k0sWgEh9PNn/zp/4ssy0QTGab8K8z4I+SLRRwWI80+N01e13t+yLhULjO5GGJ41k8qm8frtNHb7MPjsIihP+Fdy+YLjM4vMzYXpFi+8B6phVKJff3jhOIp7t7cRq3bvja/NpZgNhBhfjlKvljEYtRj1cicPNFPi0vPgw89wsn5VeZOv06rMUPzht1Y6nupyDJjMwu8tv9FVgsKZLMHr77C8EvfYWF+HpXBQiEVpcljo66xkZi6mrvufZDdfVtRKhWUizkCx55Ga63C3X3nu3rdIlAKws1PBMob3I0SKH/r1/47K4vTWExGfvc3P45ab0ZtsKLSm1HozKykZWYjaZZCqwBn9ld0U+t+b++vWKnIZxZO+Ikl0zgtJnqbfdRVOW6KoX7h+rjQHqk9TT6q7GYkSaJcrvDKyQkWQzFu39BCs8+9fm65UiEYSTAbXNssPxiKsrQ4zxavmgfuuYfJWIXF0eN0GOLUd27B1tyHJEmsBqY5emA/s7KHQKqCRgG5idcYGziMpAC5VKLK66WzoYqS2oizZSv33/8g1U4b6dAc4eHXcHfficFd/45frwiUgnDzuzlWVghXncZow+Csw2DUYapuoZRNkE+ESAWnkStllECrQkmz1UAgq2ZxMcrU9BR6g5HmWi9tDT6clvdePWuFQqLJtzbE6A/HGZxZYv+JcSwGPd1NXlp8bpTK925gFq4PjUpFd6OPjnrP+h6pzx0Zwm0z09Pko67Kzt2b2zk4NMVrpyYpFMt0NngAUCoU1Lht1Lht7OqusBRe5blDeo6OT+D/wfM0N7dQtjcxGJ1FMX6CSqmIo30nVk8TmzsXaI6uEOnczJGxRZJ1O6nXuZg78hxyKcPy0iLpiobuegX5mYP86NsLtO96iG29HRhcdUQnj6K1VaFUi7nFgiCcSwRK4ZJICiVKjQ610YatadP612VZppxPU8wkKWXjFDMJ9Nkk9foE0USKhfgyp4+Mc/ywEpvZQLPXSUttNWabA7XegkpnvKpz164USZLWf4mHV1MMzixxeGiGk5OLdDV4aK+rFmUUhXdMqVDQWlNFi8/NUmiVwRk/+wbGsBj19Db52NnVhEat4sjIDPliiY0tNee8KVMqFdRXO/j4++/gqdctLM1PUQzPklTaCRb0+GNpdqeO05TN4tt4N462HeSPPUWzcoWeR2/j+Og8r57S4FKY8R99Gml1gaR/ghOlVupqPGxQB5h/5V9YnN3Brh07UcaXiU0ex9V1+3W8a4Ig3IjEb0DhskiShEpnQqUzAeduxO0tF+nIJMln4iz6l5nyhzg1s0z/2Bxug0SdVYXXrEJrtKDWm1EZrGc+WlDrLSjU2huyR9NlM3HPlg7iqSxDs35OTC5yenqJ9rpquhq8GHSa691E4T1GkiRqq+zUVtlZiSUZmvFzYHCKgYkFOus99DbXcHJygXyxyPbOxvP+XaiUSu7r6+TpQhG70s02eYGwQseh5Rr2BhfojR3GMbtI3Zb7cTdsIT55GIO7jrs2t7GptZZ9A2O8ZrYysv8/yQUGkZcnmS3kWE7W8FCPCf3iq7wSnqO6ZSON/hkMVQ0YnLXX6W4JgnAjEoFSuGoUSjUaswON2UFXdRNdW9ZWUc8Gw0zM+RkNR5iIlfAVFdSWcphTMSqFNzaKVqo1qPQW1AbLmY9m1HorKr3phujVtJr03NbbwubWOkbmAowvLDMyF6TZ56K3yXfBOsmCcDFVdjNV9g5WUxmGZwKcnFpEpVBgMxkYnPKTL5a5rbf5vLnJFuPaz+P+E+N4ajZRkxjhw80yA96N+JcWMSZnGHrlB6SdG6ipSMSP76P9rp/GajLwwTs3s6unmacaa3jm+//G8uBr2MuLlPNpvrVazU/et40GeYHl4RCv62vpKLzGjvs/hEojhr4FQVgjAqVwTWk1KjrqPXTUe4inskz5Q0z7wwTieSwGD00eO/UOHVo5TzGToJiNU0zHyYQXqJSKwJt6RfXmN4XNtce72Svwchl0Gvo6GtjQXLMeKqcWQ9RV2+ltqsFlM13T9gg3B5vJwG0bWtjUVsvIbJCJxWXS+QKvnZwgkcrw0M6e8zZKb/A46WzwcHphhfs23I48f5SNilkkTy3JnIVelZ9cZZ6goZH4zEEm//M7ODt20+h1UeOy8on338GdG1t58m//hoMvP4+hGMWcy/Av/xHl/R/8EHd6wywFJhgbMbCc1/LAQ4+cV7ZREIRbkwiUwnVjNenZ2l7P5tY6lqMJJv0hBueWOTVdweO00Oyrpr61C7VKuVYGr5ijmElQyibOhM0k2cgSpdzYeq+mQqVem5t5tjfTYF77XG9Goby6P+4atYre5hq6GrxM+UMMzfj50aHTVDss9DbV4HNZb8ghfOHGZtRp2dbZwMaWtTcsh4Zm2DcwwcTiCh+5bxs+l+2c4/s6GgjH0xwaX+bhbXtIjL9OT2KGEwovw5U6tuiXqdaGUNx+L4vDRwmFFtgfjKBWKamrWqvO879/57P8oNnO3/7bcwTjUczqIN/7l38g8shP8bHbb8N08hWWJl7iP3IFtu66i856j9j1QBBucSJQCtedQiHhdVnxuqwUSiXmg1Eml0K8fnqSw8NKGjwOWmrcVNst6Gx6sFWfc75cKVPKptZ6MzPJ9cCZi/opFwvAWq+mUms4EzbP9Giembep1OivaNBTKhW011XTWlPFwkqUwWk/Lx4fwW420tvko8HjFL98hXfszW9Yjo3N8fSB0/z19/exs7uJLe311Lhsaz/nCgV3b2rjhwdOcWh0kXs23090/BC9/lkGMk4GFDVsVS6jicxSU1OLt7SKsauP+XCKmUCYaX8IjVqFr+dOHv9lPf/8wgn6Tw+hz6Z48T/+Gf/8Ln73N/8b+lf+kZWllzi9P8Jsyy52b+q4KYobCILw7ohAKdxQNCoVrbVVtNZWkczkmPaHmFoKM7UUwqTX0uxz0+xzYzG+McwmKZSojVbURut51yuv92om1z5m4uRiAVL+8Td6NZWq9Tma68PnZ3o5Fcp3v0G7QiHR4HFSX+0gGE0wOO3n1VMTDEzM093oo7X2rWs7C8JbUSoV7Oxuoq2miu/u7+fU5BLL0QTVDgs9Z96wGPVa7tjQykv9owzPrdDTeTsqvZnNk6c4uprjhMJLnylEORunUixQDg6zqfM2NrbUsJrKMBOIMBuIEC5UsbVvC76mNp577nnkdITTR17lNz47y5ce/z1qh/6DRGKY2GiU50JLdPRuZmNzjdhKSxBuQSJQCpeklMuQi69Q1muRZfmaDN2aDTo2tdaxsaWWlViSqaUQI3MBTk0tUmW30OJz0+B1oFG99Y+xUq1DadWBteqcr8uVMqVcmmI2QSmTOPMxSW51mXIht36cSms4s+r83PmaSu2l76kpSRJepxWv00oknmZoxs/RkVlOTi7S2eChs/6N2s6lcpnVVBZkxNxL4W05rEZ+ds/2M/W/M1RkmddOTTIwvkB3k5fWmip6m2sYmJjHbTNR3bgJtd6CPHSAQ8FZ+qVGtlnUFEMzrM6ewuhuQO+swW42Yjcb2dJWR3i1hYEDe1GpNbz/p3+eF57+DxKRIIGleX7zsd/kF37uZ9npcWDOrhKLHGLqcIC5pc3s3thJtcNyvW+RIAjXkKiUc5V97Wtf42tf+xqzs7MA9PT08Pu///s88sgjl3T+DVMp55O/zPL8FFaTnsd/+1cweVowVDWhVGsvfvIVVCqXmV+OMrUUIhhJoFAqqK+y01LjxuOwXpGh5HIxf6ZHM/5Gz2Z2bc6mXKkAZ3pFz/Zorq9EX6sepFBdvFczkc5yfGye4dkA+UIRm8mA0aClUCghI+N12nhge9dlvxbh5pfNF9h7bJR0Ls+2jnoC0SSzgTBqlZL2umr8oVWyhSLvv20Deq2GXHyF2YF9vD6fw1nTynZHhsTUYbQ2D433fBSF6txtr0q5FP5jT5NWO5kvO/i7v/1bxifGUeVi6JQVtmzs5cOP3EudWSYenCMsm4maO2lq38DWzno0KpWolCMItwDRQ3mV1dbW8uUvf5m2tjZkWeab3/wmH/zgBxkYGKCnp+d6N++SJYoqlksmFAoTJYWO2FQ/sekTGFx1mDwtaG3V16TXUqVUrg97p7N5pv1hpvwhZgJhDDotzV4XLTVurKZ3v2WPUq1FqdaitbjO+bosVyif6dV8Y65mnFQiRDmffeN8je6c3syyUk+6oiZegHg6x2oyQyyVpVwuo9OoKBRLLIZjaFQqWnxutnTU0VDtfNftF24teq2Gh3Z081L/GEdG5rhnSztb2moZng0wMhekWCoTiad44dgI79+9EZ21itadj4DyRV6ZGOWY3Ma2lh1Eh17Gf+wpanf91DnXV+lMODt2IQ+/xu3tjdzz5F/wtb/9O7731LOsRpfpP3Ga4NICt7/vp9neuZPa1dOYUicJnF7hh8EedmzsvE53RhCEa0n0UF4HDoeDP/3TP+UTn/jERY+9UXooH/utTzM+u4Baq+cjn/h1WqutNJlylKPzFDNJVHoTpuoWjJ5mVFrDNW2bLMuE4ymmlkLMBiIUSiVcNjMtPheNHtf6cPLVVCkVyaViRCMh4rEwydUImUSMfCpO6ezCIIUSjdGCwWLHZHFgdbiwO6ow2RxUFComF1cYmZylHJ2jxuPm9rv2XPV2CzePUrnMKycm8Efi3LGhlUavk1yhyNj8MkdHZhmdD7KxpYb337YRh8VIpVRg/NhL7BtcxFPXTJ8lTmziEN6+R3H33HXe9SPjh8mszODZ+ggqvYVXX32Vv37yG0zML2IshDFrJbpuewRby2a6NSHqlRHikpmQrpH//OEz6FUyLqdT9FAKwk1K9FBeQ+Vyme985zuk02l27959wWPy+Tz5fH7981Qqda2a97ay+SL5QhGNzoDLYmQmlGRquUJb3QY6GrWUonMkFgaJz51C5/Bh8rSgd/iuyQbkkiThtplx28xs72xkYSXGlD/EkZFZjo7OUedeGxL3uWxXZEi8UpFJZXPEUllWk2lWk1liyQzJTA4ZGQktJkMTtsYePCY9Vq2ESVlCK+cp598YQi8tLxNfHmJVrlAp5FAVMnTnsyRRoZXFalnhnVEpldy9pZ0Dg9O8enKCQqlEe101m1pr6Wny8uyhIV4fnCKZydFe56G32Uf79j0oNAfYe2wUyVtDr6+D5RPPo9QasLf0nTPqYG/pI58IER55Hc+Wh7jrrruora3liT/9P4xMzlLIBhh65Sl6szGONO7iSFlNtzaEzzRENjLPSlmL0STmVQrCzUoEymvg9OnT7N69m1wuh8lk4vvf/z7d3d0XPPaJJ57gD//wD69xCy/ObjHQ4HGSlxWsrCYx6rWY9EYmF0NMLkJnQw1dWzdRWl0iHZwkNPQKSo0OU3UzRk8LasO1+UWiVCpo9Dpp9DrJ5gtrQ+JLIV7qH0Wv1dDoddJa477k7U2y+eKZIeoMq8kMq6k3hqsBtGo1drMBn9uK3eTDZtZjMxlQqy4epHOJEIm5QVKBCUq5FAqVFq3Dh1GpQu+0Xc5tEG5RSoWCOza0oFUrOTQ0TaFYoqfJh0qp5NHbNqDVqJlcXCGezvLC0WGcFhPdjd08sNvI8wf7kdxWOixuVk69CHIFe+s2JGltxbZCqcLVeRvBgedYnRnA3rKN5uZm/uwrf8z/+vL/y9H+ftTZZQYHjtKRWqXrjocYS9YzsRImn0miKMaQS6JcoyDcrMSQ9zVQKBSYn58nHo/z3e9+l7/7u79j//79FwyVP95DeeLECe6+++7rPuT95kn1n/3i7zEwvkAgsorVZMCo07AcS6KQoLvRR1ejB3IJUsEp0sszVEpFdNYqjN4WDK76q77B+I+TZZloIsOUf4UZf4R8sYjDYqTZ56bJ60KvVa+vrn5zeIwlM+QKa9V5lAoFVpMeu9mAzWTAbl576DTqdzR3tFIqkF6ZIx2cJJ+MotToMFY1YfK0rG979MILL5BJpzCazOzZI4a9hXdOlmVOTS1ycnKR7kYffR31SJJErlDkqQOnMWjVbGypYXg2SDAax2zQYVdmGRwaoslUpNWUQ6U1Yq3vwdmx+5yRhuTSKNHJ41T13oPeWQNAqVTiz7/2dZ75z3/HIGcoK7Q4zVoefd8j5O0tfP2v/5JSJsG2rVv4y7/66+t1WwRBuIpED+U1oNFoaG1tBaCvr4+jR4/y53/+5zz55JPnHavVatFq31g5bTLdGFvHlPMZ8okwJZ0Kp1nPA9u7CETiDIzP4w+v4rKa0GnUDE4vMTIXoLepho7GrdiatpCNLJIKTBIZPUhMdQxjVSMmTysas+OatF2SJJxWI05rE1va6plYXGZoJsDTB0+TyxfQadToNBrMRi1KSYHJoMVmMtBeV70eIM0G3bseLpdlmXx8hVRwkkxoAeQKOocPd89dF5wWsHfv3vXwLgKl8G5IksSm1jo0ahVHR2YpFEvs6mlGp1Fz16Y2njsyRDCa5MEd3YRXUwzN+pkP5tE6ajntn0GRTtFWoyO9MkulVMTVfef6G0GTr4NsLEhk7CCevvetba2lUvHbv/GreGtq+Kev/RlaJeSUZr717z/g4bt24HOYWZFkVGpRplEQblYiUF4HlUrlnF7I94JKuUgpmyQdKrB44LvobNWYHD4e2NSIP1lkYHyecDyF12FFpVIyMDG/FiybfbTX1mOsaqSYTZIOTpFanibpn0BjcmDytmCsajxvq5Ir4Y3h6jfmOa6m3xiu9jmtFEtlUtk8xVKZSkWmvcFNe101Tsul7zP5ZpWKTLFUplgukUsnSS3PkFmepphNgtoAthpkcw1FSU0xWKC4OLV2/JseI7MB5FIem812he+IcKvpavCiVat4/fQU+WKJuza1UWU309dRz7HROarsZuqrHdy9uZ1EOsfwrJ9jFQWHZnIU0hP0bNiKIr5M6PTLuHvvRqHSrL1B69hF4PiPiIwdpGrDfev/Vn7up34Cl9XM1//PH1HOZ7B7GnnmtRNEVvxYbE5kuXyd74ggCFeLCJRX2Re+8AUeeeQR6uvrSSaT/Mu//Av79u3jueeeu95Ne0cKsgqdqx6T2YClvpdczE9s8hiyLKM2mLnT42WlYGRoOU22WKbWbQMkjo3MMTQTYGNLDS01bmxNm7E2biQb9ZMOTBKbPEZsqh+Duw6TpxWtteodB7lSucxqMrs2v/HsPMcfG662mQzYzHqafM71Ieuzw9WyLBONpxlbWGZqKcSpySVMBg0+lw2Pw4JSoaBYKlP4seB3zqO89v1yqYg6F0WTCaLORwEFBb2LgqGWksKKOqFCnUmiVinXHkolGpUSvVZ95msqXDYz+ayoNCJcGc0+NxqViv0nxnnx+Cj3bG2nq8HLSizJgdNT2M1rPfAWo45dPc1saq3lhSNuRg4+S/H4Qao7d1BdWKFyci9VG+5DqdGhVOtwddzGyumXSCwMY61/Ywu0B++/FwNZvvl3XyOWTFFd08D41Czx1BItHe+drdIEQXhnRKC8ylZWVvjFX/xFAoEAVquVjRs38txzz/HAAw9c76ZdskqlwsGDB8nlcrjdbp45cJrGxkbqmzuxamXyq0Gy0UUMuQzbVBJRdMzO+8lrHDR4fZTlCoeHZhic9rOptZYmrwuDsxaDs5ZSPkN6eYZ0cIrl5VnUejNGTwvG6qbzth9aX119JjRGE2mi8Qyr6QzlSoVKRUanUWPUazDqtOvD8GqVklK5QrFUJhBJML8co1gqnQmDFYrlMpUzG5bLskypXGbGH+bExCKyLGMx6nHbTFTZzGg1KtQqFWqVEp1GjdmgQ61SoixlUCSWILGEpCmgcToxVm/HWN2IVqdfD4+XEpbdNhOrlK7K36Vwa6qtsrNnWxcv9Y/ywpER7uvr5LbeFp46cJr9J8Z5ZGfverlEvVbDT9y5lQavi/4ffRPGT7BS3YEzEieR+k9adj6KSmdEZ/dgqesmPnsSna36nH1bb7/3IVS5CN9/7hVmQmk0Wh35fJ6BEyeJRqM4HNdmuosgCNeOCJRX2Te+8Y3r3YTLFgwGKZ8JXaurq7z88svr39PpdDQ0NNDQ0ECdp5Yqi4aaSga7IshKbJjlwVNU9E6aalrIqdS8fnqSU1OLdDd68TqtlMoyRVMd5QYvhdVl4isz+E+8TqG4j4zSRlzlIFwykMzmSWbzlEolyhUZhSSthzqdRo1eq0arUaNQSGTzRbL5IsozvX9qpfKNHkGVEpNei1q1thJbozr3e2vBby0wVmSZQHiVmUCYcDxFqVyh1mam2efGbTMhl0tkQnOkghPkE2GUai3Gth6M1S1oTPbr+DcmCOerdlh4aEcPLx4f5bkjQzywrYu7N7fz7OFBjo3OsbOn6ZzjN7XWw57/wtQr/4o2t0JW42V0ZpGF4Ddp2fUojfX1WBs2kFtdJjz6Ot6tj6xPXZEUSrbc9SgUUuwbizE0eApYW7yj04l5lIJwMxKBUrgoSZJwOt0sBgKUymUKxRKVikxFlkll86xETnD42AAVWV7rJdQbcHs8uG0mnEYl2tVZYvMjICkp6BwsyFZeHxhGqzdQbbegVinJFUrkCkVyhTLloh1jMYq9ModeHsOkNWCy+KivacBqc2M36zEZtOvB742H4pxAeCX2nLSZ9HQ1eomnskz5Q0wthRiemEZbSlKtTFJrUeCo8uHqvgODs/aa7LspCO+Ww2LkoR097D02zLOHh9izrYvtXY0cGpqmym6myXdudahN3e0UE/eydHIfdlWF+poqAtE4p176Lqfrd9Dd0UFd+25WTjxLdOIozs7b1nvhtWYHnVtuQ5Zf58W6RpaXFtiyZQsGw7UtfCAIwrUhAqVwUV6vF1dNHZFcBYVWh7Glj3ImTno1wupKkEw6hSRJKCQJhUIinc8Ti8eZVKx9TalQYDLqUasUmDQBah06Op0uwkkTCzEdWoOJKreH1lo3LqsJh8W4Ps9RU86QWZ46s9p0EZ2uiMnVisFVfU3Dm0kDrfoUVcYlgtlVlkpK5ks2lrI2fDkXzQUD9RVQi6mPwg3OYtTx0M4eXjw2yrOHh7i/r4Mmn5uDQ9M4LMbzypZu3XEHpdgSgYAfpcFKe42LdK7IcuQUR46nGLBU0WFtpBgcQ+fwYqpuXj/XWt9LQ3iRakOFoq8OlVr7480RBOEmIQKlcElc2gpFcwmNzczu7VtJZQtYjXo6GzxUmbWElgPMzs4yMzPD1PQ0sXiCQrFEvrjW8xhPZ5FlmXK5wqFCAak8iM9ppsHjQDLZCcdduORGal1tNFRXoTbZz/R06NFZnNiat5IJz5MOTBEeeR2lWoOhqhGTpwWN6erMx5LlytrioeAU2cgSSAoMrjp627bTZ/NQLJeZD0aZXArx+ulJDg8rafQ4aa5xUW23vO18SVmWQa6sfUQGWUaWKyCvraiXK2I1rHD1GHXateHv/lGePzrCnZtaiSbSa/Mpd/WeszG/QqFg613v4+iz/4J/tYBWZ8etLtHsc+HLhwnpbJwKKzCtyngPvkD3PR/GbFvr6ZQUSpwduzBp/gYPCnRa9fV6yYIgXGUiUAqXxGh3Y0kl0Utp7ncnqHg3MuaPc2R4llyxiNWow2ytxdvrwd7WRzKxSmwlSCYeJRENsRpZQZJlNGolyjPzHKOJFKdmVtCrQqgVYxzY/xL/rDfQ4HGwrc3Lpp5OGpo70NmqUKhUSAolJl8bOruHTHiB1ekBImMHUest6J21aK3VKJTKC4a0N4e3twtzyBVKuQy5eJDc6gqVUh6V1ojW4kZjtlPKpVidPQXyCWRZxojMRqVM2lxkLppnbGCU/sNlDGqJepuaOqsKg1o68xxvft63lvRPkCsCDudV/3sVbl1ajYoHtnWxb2CcV05MsLmtjhOTixwenuH2DS3nvCHSGK307riHysG9TCSq0FRXY8kF0ColaoszdHRtYyZ5O8H+HxF86tu4Nz1Ib3MNFqMerdmJ1uxCGZinUipcx1csCMLVJAKlcElyiRjpRIyyWmL8yPMUi0+TU9twSAbCeQVLeSVlFDgMSlocGnY4Nei9eiSpFqilUqmwElllMRhmMRhiaTmNlEqRk4pkMgVy5QoGtRJyRYZGowwMjqH7j704dBUa3BZqvW7qa3zU19fhdlehUChQaPRUyiWy0SWSS2NICgVqox2txYlSa0JSKNbKxkkSSNLaL0hJgQRrHyUJWPseskwhFSWXCFHKJlEo1WgtbnR2D2q9+U3XOHM93nQ9CfQocHsl+oBwqsBsOM1CNM30skyVRU9jlYV6lxm1WnXO816ofUbX65RS6ev4ty3cKtQqJfdt7eC105P0j81TX21n2h+i2m6hra7qnGMttR20ts5TmZ3nZKybnTUdaKLjyMUc6ekjdLbtoOvR/8LoK99naeI4P1gKU1/toKfJh9bqQpNKoVCKHkpBuFmJQClcknQJYgUFFq2JYsOdGLJBrPkYJpePqtbNqDRa/LEMk8E4i7EMoZyKFpudVp8Nk16HJEl4ULBJAs6EqFKxxKI/wNz8Av2Do5wYGiMcDmN1adGoVSTSeSLFItkU+GfTHJ86jSyfxGg00tjUTEtHN+29fTTv7sCk15AOTpNanqKUTa3VET+z/ZBSo7/ga5JlmUIyQio4RSY0hyzLWOt7MXla0Dtr33WJSDvQxtr+mPPLUaaWQpwOJRiKpqivdtDic+FxWN9y0ZBKb0aZF0PewrWhVCq4c2MbR9QzjC8so1WrOTIyg9NqxGF5o+a9JClwde6ikIpSzoQ4uuzj9satKAInKeWSREYPYm/po23rnbim+sm4DYxF0vzo0Glmg1E0SuOZN2OCINyMRKAULondZgckXE4H9z/8QQDSoTliE0fIBCdwdeym09dOZw/EU1nGz2wSPhUNU1Nlo6POg89lPW9eYafLR+fGPh56P5TLFU5PzrHvUD9B/xJSIUV4OcDcop+VfBmTwYxNr6RYzjM6PsnQ0CB879soVToc7mqa2ztp6eimxlmLQ1FkdfYkqzMn0DtrMXlb0Nm9SJKCcjG3vvdlIR1HpTVgrunAVN2MSm++YvdMpVTS7HPT7HOTzuaZ9oeZWgox4w9h0Glp9rlo8bnPWwQhCNeaQiGxs7sJjVrFqclFMrkC+wbGeP9tG9Go3/g1oTZYsTdtonmqn7JU5OBcirs6b0cxf4R8Ikx04jDWxk3oHV5UiQnev+MR/KtZ/vOfZJaiqzgcYjstQbhZiUApXBKFQkL5Yz1qRncDWoubyNhBlk+9hKW2A2vjZqwmPdu7GtncVsdMIMzY/DIvHh/BbNDRUV9Ni68Kreb8Hz2lUsHmjia6W+oYmQ0wNBNAkqDeaSa0HOBQ/yn8SwsU0nF0eht6CUqFNOVchsjyIqHAHEdeeQGl1oBKa6DaV0uNy4rbOEO19ShepwWdXodcLqNQazE4a7E1b0Vn96wNPV9FRr2WDS019Db7CMdTTC2FGJ9fZnB6CZfNTIvPRaPHdcH7IgjXgiRJbG2vR6tWceD0FBOLKxw4PcXdW9rPeSNoru0kE16gMx9mUN3Iq2PL3L/xLlRzR9beZE4dw1LbhSzLxCYOUd97L80+F0b12qbpgiDcnMRvL+GSrKayzATCpIswubSC12nFqNOi0hqo2nAfyaUxVmdOkI0FcXXehsbkQK1S0l5XTVttFaHVFGPzQfrHFxiYWKTJ46Sj3oPTajzvuTQqFZta62iv8zA4vcTYwjJarZWP/tePUEFmeMZPLBZDTx5NKUswsMTMzAyZZJxyPkMpnyYfDzEXX2FBoUJGplLII5fzuKxG6mu8tPVspmtrPTaz66qHyTeTJAm3zYzbZmZ7ZyMLKzGm/CGODM9ydHSOuio7yUzuogt3BOFq6WnyoVWreObwEK+emsBtN9HTVLP+fUlS4GzfRaD/GbZVFzgc1vLy6Xke6LsTpdZAYmGE+Nwp9M56MpElkosjSJKETiPmTwrCzUwESuGS6NRKzHot+WKRA6enALAY9XgdVjwuK56qVjx2D5HRAwQHnsPasBFLXRfSmcUvVXYzVXYz2/JFJhdXGF9YZnJpBZfNTEddNY0e53rpt7P0WjXbuxrpavRwcnKR/vF5jHotW9vryRa8DM8EKFcq3Ll5J7/c6CWxGmV2dpbp6SkmhgaYGjlFPhWnUi6hUKpQavQkKgYGFxOcmnuRyn8+g1qtprG5jbaeLbR29tDQ0IDH43nH9cTfDaVSQaPXSaPXSSZXYCawNiQ+F4wglQvY7WJ4ULg+Wmur+KBaxb/uPcp39w3gsBjxOm3r31cbrdgaN7I6c4K7uu/h5dEQLw5M8eD2Xaj0ZqITx0gHp1AZrcSm+ykXstfvxQiCcE1IsugKuaH19/fT19fH8ePH2bp163Vrx6d+/X+ysjiNw1XFl770JVZlM8vxNIFInGQmh4SEw2LE4zBhzAbQxGcw2qpwdu5GpTOdd71KRWYxFGNsfplAZBWtWk1bbRXt9VWY9BcuzRZLZjgxscDCShS72Uhvs4/VZIbR+SCyDB3VBuo0KfKReSqlImqTk4RkIpgoMjM+yuTYEIvzc5TyGWTk9Z5JuVwCSYFSo0NtsGCyuWlobKSpqYnGxkYaGhpwOBzXJGTKssxjn/ptVldjeKur+MpXvnLVn1MQ3spSKMbXf/gaKqWC3/jp+7Aa35jvK8sVlgeep1IuYu2+n+ePjyHL8NDObiqRGUJDr1BIRpAUSv783w9S0ruwOxziZ1oQblKih1K4JCqdCY3JgVwukpg8jFKhpMXhZUNHPWWDi5V4lkAkznQgSjYvUcl70AcDOGe/T2vPVuqau87pgVQoJOqrHdRXO0iks4zNLzO+sMzQjJ8at42O+vMX8djNBu7d2sFKLEn/+DyvnpygyqJnu0dBZH6EwPEAAaUWX0s3HVv7MJod+IBO4J577gWgUCgwNzPF+NAAkyODzExOsBKOIJeLFDMFiulVcqvLxFcWGBk8iUK9tkLdbDbT0NBAY2Pj+sNsvnILeM6SJAm9Vk1ezDUTbgA1bjufePR2vvYfr/C3P3iVX/vJuzHq16rdSJICR8cugv3PUFge48Ht3Tx7eIgXjo7w0I5uvH1mgieeJxOaJZdYppQvgePqFCEQBOH6E4FSuCQKlRqNyY7RZqNm5wfJhObJhBcIjx5AUiiw2r14PPXou3tJ5MoEInECIQ8zkyMMv/g6hqNDNLZ2UVO9tmWOzaRfD4sW4xuLeGYDEUbng+uLeNrrqmmtOXcRj9tm4u42B/MTi8xPHmY4n8dSVc+GO3+CYEHN8GKYiSPT9Dbn6KivRqV8o+qHRqOhraOLto4uYK1HMB4OMHH6OBOjg8xMjDI7t0A8ukQuFkCpM6I1OUmUywwmkwwODq5fy+FwnBMwGxoa0Oku3LsqCO9VddUOPvrwTr75zEH+4ZkDfPSh3ViMaz/nGqMNa8MG4rOnqHbV8cD2bp47MsTeYyM8uL2Hmp0fIjjwDHL5WTKRBYqe2uv8agRBuFpEoBTeMZXOhKWuG0tdN6Vcmkx4gUx4nsjoQSSFAp3NQ727no7Nrdzb1838zChTg8cJTQ2w4Peh1BnRazV4nFa8DsvaAh+9FrVKSVtdFa217rVFPAvLDEwscGJigSavi5ZqM7rsMqngFKVcGrPBwo47HyAsWzk5G2JhcpXmGhcPbO9keilC//g8w7MBNjTX0FZbdd4cTVjrEbS5fWy/z8f2+z5ApVwkt7rM8uwYIwOHmJ4YZdEfJBD0U1Lo0FpcqAxrPafRaJRoNEp/f//6taqrq88JmHV1dajVYjGC8N7W1eDlJ+7YzNOvn+I7+47xk3duWd+j0lLXTTa8QGT8EN6tj/DA9i6eOzzMi8dH2LO9C9+OD6F3fJd0YP6aLoATBOHaEoFSuCwqnRFLbSeW2k5K+QzZ8DyZ0ALR8cPAYXR2Dy5XPd77HyU+PUAy4idvaSBtqGYllmbWH0ZGxmzQ4XVa8TqtVDss64t4+tpqGRweYmTwGMcPxHEY1HQ119G5YTd6exWSJGEFGut8TC6ucGpqiZlAhI66ah7e0cP44jJHR2YZmvGzsaWW5hoXSsVb/1JTKNUYnLU0OWtp3HofpWyS9Mosq7MnWRw/zUIgSDAeJ5xXE8lKVJQ6pDPXk2WZYDBIMBjk0KFDa9dTKKitrT0nZPp8PhRv0wZBuBHt6m4imkjRPzbP0wdP8+D2bqodlrWh7/ZdBAeeJT53GlvTZvZs6+KFo8O8dHyM+/s6MXlayFZUKHXn7+ogCMLNQQRK4YpZ2yC8E3NNJ+VCdq3nMjRPdOIIAFprFSZHNcrYLDY5zqYNtyFrLQSjcYKRBIFInPGF5bUFProybimOsRDBrYLaDjdJTQdzaRWDsSQTAwu01eZpq6vCbNChVCjoqPfQ7HMzMhdgaMbPxOIKPU1eHt7dw8hMkINDUwzOLLGptZZGj+stK9WcJUkSaoMFW+NGbI0bqbujyAb/2vZIqeAUxWyGeB4iFSORgoZgNIV/JUKlUlm/RqVSYX5+nvn5eV555RVgbdi9rq7unOFyt9t9TRb9CMK7pVBI3N/XSSqTxx9e5YWjI9yzpZ3aKjsakx1r/Ya17YJcdTitTu7r62TvsRH2DYwjI6Ex2cXPuCDcxESgFK4KpUaP2deO2ddOuZBbHxbPr64glwokl8ZJL8/g7LyN+uYtNHicVEoFIgsTBGaGia8ECecrzGmcFE0+HIZqvBYrm2usqFUKJpdC5yziaa+vpsZlQ61SsrGllva6agan/QxO+xmdW2ZDSw3djV5OTy/x2qlJTk/72dxaS331pa/eVqrUWOt7sdb3UikVSS6OEJ0ewBlepFhII9VYUZhbiZf1BFbzBCIJ5hcWWV5eplKpUJFlZFlGltMEwzEO95888zlotFqqPD6qPD4GhsbRalTYbLar+5ckCO+QXqvhnq3tPHdomGK5zMsDY9y+oYVmnxtLXReZ8ALRsYN4tj5Cld3MfX0dvHhslIVQFItG9MoLws1MBErhqlNqdJh9bZh9bZSLObLhRdKhWeIzJ1l8/TssDzyPwV0HsoykUOKp8tG6cSc6u49EtrDeezkyG+Dk5AIqpRKPw0J3k4diqYI/vMpLx0cx6XW01VbR6HOiUijobKjG57RyemaJ/QPj6LUq2us89DbVMDof4Hv7+zHqdbTWuLGbDZQrMuVKZe1RXvtYKl/oc5lyuUKpUqFS7qCkq6dQipJbWaG8GAVkJIUKpUZHS1cvzb19xFMZYrEYkZVloqEgqWQchSQhSRKSBLlCkWRqgpmpSaYnRqmUS6jk0vX+qxOE83gcVrZ21NM/PofNZOC1U5MUimU6Gzw4O3YS7H+W+NwgtqZNeBxW7tnSwb/+XZ5MqijeJAnCTUwESuGqqVRkSpUy5bJMef1jhZKumpLbhFTSUCwcIRGcIrw0hcpSjdrTiUIjUQlmqIQW1gOcJIHLaiKezhJNpDk2OkcinaVUqaCQJFRKJfliiVdPTqBUKrCb9TgtJgy6te13lEoF88sxTk/70WnUeJwWtBo1/vAqU0srmAw66qrsOMxGVEoFSqUCpUKBSqlAq1ahVJz7tQt9rpBkiokV8uF5Cqt+KGZRa/IolAVUSjVaXQ16xzYMjhpKajOLgWVmZ2fXH4lEAoBFvZZ8Hkym8/fvFIQbQW+zj5XVJKFYkmafmyMjM+SLRTa21GKp7yUxP4jBVYfG7KDGbaO2ys7EzAK5gniTJAg3KxEohUuSzuZZjiZIFWT2D4yf03t39mPlx7523p75cgV1LoomE0SdjwIKCnoX+ZpOUskEquQS5gU/puXgWt1wnQ3MHmSzB6V2Lei5bWa8TuvawhoJUpkcsVSW1WSGZCZHuVKhUCpTKpWJJjMY9Vq6G700VDvRalSsJjOcnl5iJZakym7mp+7agizDwOQC4dUkGrWKLW11VDss7+5GeVzQ3k0plyIVnCK9PE0pm0JSqlFptBSSUTIrc0iShNNkp6anlj137EBltBJZDjA1MYZ/fpqVUASr1Xq5f22CcFVIksTtG1p4+sBpEuksm1rrODm5Fhi3d3STjSwQGT+IZ8vDSAolVqOeRq8LvVbseCAINysRKIVLUixXyBdLqEtlCqUySuVabV6lUrpgb916L55CAcU0pcgc5dgCsqKAxlONsWo3pupG1Brd+jWy4QWiE4eRAYOrgUoxSzYaQC6PoVU6MbjrMbh8qPQX3lC8UCwRjK4Nj/vDqyyGYkwtrTA6F8RhWauss72zkUd29RKIxOkfn+f5o8PUuO3s7Gokmy8yMLHAc0eG8DptbGmrw2V7d72EKp0JW+MmrA0byMWCJAOTZJanqZSLa5WDJIlMZInY9AClfAbkCiqdCZfeTI1Ng8Nch9EoVsQKNy6dRs1dm9t47sgwbruZXT3NHB6aoVAqs611J6GTzxOfH8LWuBEA1QW27RIE4eYhAqVwSWwmPVQ7sNlsPLC966LHV8pFMqF5UoFJ8okwerUGY2MHxuoWNKYL16g2uOvRWFxExw6RCkxgrmnHt+OD5OPLZMILxOdOEZseQGNyYHDXYXDVoza80ZOoUavWq+8ApHN5gpEE0/4QgzN+Xjw2ygtHR6hx2djQ4qOvvZ5svsipqSWePniaJq+Luza1sZrKcGJykR8dOk1tlZ3NrXXre+69FVmWqRRzlHJpSrnUmx5rn5dzaUBa37dTrlRQ600YqhvRmd1UKmVKmfiZmsdiJazw3uC2mdnaXs+x0Vnu3tLBnZvaeO30JMViiY01XWeGvsVm5oJwKxCBUrhiZFmmkAiTCk6SCc0jV8pr+1B234HBWYukUF70GiqtAfeGe0n5x4hNnyAXC+LqvB13951UykWyUT/Z0DyJ+UFWZ06iMdkxuOoxuOtQG84dIjbqtLTUuGmpcbNnWxfRRJqB8XlOTS/x/JERXjw+RpXdTEddNVV2M9P+MDOBMB11Hvb0dbIcS3BycpGnDpyiweNkQ2M1JlX5TaHxzMdsklIuRaVUBNZWciuUKpRaA0qNHrXegs5WjVKtR6nRIim1FDOrpEOz5KIBCokwGpMTnd2LWm9Gpf0RZPJX6W9JEK6srgYPoViSA6cneXT3Ru7b2sHLA+PkC3q6tSYiY4fYc/995PIF9Hr9xS8oCMJ7kgiUwmX7/9m77/im6vWB45+TpOlKd0tbSkuBMsreS9nIdHtRVETQK17HvQp6VVTEgZefV0VF78XJuIqKW1QQEQGZMsretLSF0r3TkaTJ+f1RiVRa6ErSlOf9euVec1aenIacJ9/z/T5fq7mckswkjJlJWEoKK4udR3fGN7wtunoUMlYUBb+oTngFRpJzdCsZe37Ev3U3/Ft1wjuoJV6B4fhbemDKO0tpzmnyE3eRe2wbOm8/vIIi8AqMQOfli6rawGZDtVlRVRseNhv9I6BvWDjZRX7sS87jeEYWWzPPotMo+Hsp6Cyl7Dy+lWO/WmgdqKeTj5Z8YylZicX8ssFCgJeGEB8dep2CRuuBovVA0erQ6PRVHqBgNZVhNZVd9L3qvAxUlBVRkplE0elDaHR6zMZ8VMWznn8NIZxLURQGdW3LD9sO8Ou+44wf0JUx/eJZt/soCUoYXUikf8fK7h9CiOZLEkpRKyOHD6OkOB8vTz3l+RnYbBWUF2RSmpVMeUEmAJ4BYfi0iEXvGwiqijH9ZGVSp1pRz0vszk/yzi1HtVV5fm67yqkQMyhI3o/W0wef0FZodH9KtjRabNZSSrKSKUzZj2qzovHwwsMnAA8ffzQeXpUz0ygaVFsFNmsFWmsFvfQWOoWbSS2oILnQSmGOFUVjw09nQjGXcbQQjuo8CQ0KpFVsJywaPSmFKqmqB7HhIXRuHYLB26tyOjmNBkWjRVE0KBoNiqKtXHbu+e/r0Gjt65XfYzpXB9NszMeYkYju+z1gsjr5LyxE/ek9dAzr2YHV2w+y82gyA7u0ZWz/Lvy86wi7CgLpdXI/3iGtauzuIoRwf5JQiloZ3L0deSd3YbUUk7JxOeaSfFSrpfKWriEID59A1AoL5bmnMeWfrUyUzk+kfk+gqiRbWg80Os0fidX5CZlG+8cx2vXBXFJAYcoBbBYzflGd8AmLQaPRViZov28HGmwV5ZTknKEsK4WyvDOYi3NQNFp0nr5oPDxRdJ5otB6g9UDrGYyvpy8RHQ0M1PuSXaaQlFNGZrEJjd6LCB9PsvKLScjIZedpG+FB/rSODMHTZiOxqJQzx0uIj/WjW9sovD31DT7HekMQwXF9MbTsgCUvt+F/NCGcKNjfl/7xbdh2KJEWQX60bRnGuAFdWLtTYWtqPpp9m+kwaEKtur4IIdyPJJSiVjQeXui8fLFZyvEKDCe4Q38MEXF4+of8nvwplYmjAwW16UXu8e0UnT5MeUEmhoi22Cosvw96qezTaLP+UefOMzAc1aZiNZdSUVaMzVqB3uCBb4u2GCLb4xnQososOcFAR6CopJxjpzNIPJONl4cHgzq3paTcTHpuAckZufj5eKLTacguKCZ5Wy6/7j1Jj7goBndth693w29VK4qmMukVws3EtQojM7+IbYdOEezvS6DBh/EDu/JjRTkbjh0lqF0GLSKiXB2mEMIBJKEUtaJoNJWlcNr0xDukFRqtYz46qs36p5HSJecljEasFjO2CjP5J3dSkJSAX1RHvIOj8AwMx9fTF523AZ2nAZ234fe+jH8ct7wgi9KcVMpyTlOSlYLOy/f3AT0x6P1C7Mmlv68X/TrF0qt9NMnpuRxNzcBksRAS4IfVasVqsxLo603HmAjKys2/D/I5zM+7jtIhugV9Oram1e9F0i81X7gQzYmiKAzo3Ia8ohI27j3BhIFd8fbUM3Fof/aeCCc4LMLVIQohHEQSSlErPqHR+IRGN/g4qmrDaiqrsbSO1VxmL4iuKApaL190nr54GILwDon+PWH0BY2GglOVo8B1Xr4Etul10SRX0WjxDo7EOzgStX0/TAWVpYhKsk5RdOYIOi+fyuQytLJ0kfL77DtxrVrQLiqMnEIjx1IzSc7IpbzUwpmcArIKimkX1YI7xg7AbLGy/VASh06d5cSZLEL8DUSFBRIVGkhEiD+RIQH4+XjVet5wIdyVh07L0J7tWbXtINsPn+LKbu3Qe+jo37mdq0MTQjiQJJSiUVXWYzTVmDBWmEpQbTb79lq9FzovAzovA14B4faEUedtQOvpc9Hb6C26jcR49jj5SXsoL8gkpNNgPP1CLhmjomjwCorEKyiSoLi+mAqzKc1JpSQrmaIzR9F5+uAdWlnn0jMgFEWpnKEnLNCPPh1bk5iWxbHUTNJyCth5NJlDp9LoHteKq/rFM35gV/adPMO+xDNk5RdRZjKTnJGLolSWMTqXXEYEB9inhRSiuQk0+DCoS1s27T9BeJAfHaLDXR2SEMLBJKEUdXau32KVWow19GPU6DzQefmh8/JFH9IKndcft6W1Xr4NunVeWV6oI16BEeQc3UrmnjUEtO6Of0znWvfnVBQNXoHheAWGE9SuD6bCHPtt8eK0Y2j1Xr/P0BODZ0AY3p4edG0bRefYlqTl5HM0JZNDp9L4Zfcxdh5Jpl+nWAZ0aUO3di3Zn5hGYlo2HlotLcMC8PTwICu/iMS0bAACDD5EhvgTERxARLA/eg/55yiajzYtQ8nML2LnkWRC/A2EBMjMT0I0Z3IFE7VSmp1K0elDVJSXYLX8UXRb0WjtrYqX6sfoKB6+AUT0GkNhygEKU/ZTlpdGaKfBNU7RWJPK5LIFXoEtUNv1wVxUmVyW5qRSnHa8Mrn8/da/Z2A40S2CiW4RzIDObTiaks72w0ms2XGYzftPMqBzG4b26EDXNlHsTzzDqbM5+Hrr6d6uFS1DAsksqJwi8kxWPkdTMlBQCAk0EBHsj7HMdOE86EK4oX6dYskpNPLrvuNMHNRNfjQJ0Ywpqly5mrSEhAT69OnD7t276d27t8viKM9PpyQr2d7aqPOqbGHU6r2bVL/A8sIsco9tw2YuJyiuL77hbRscn6qqmItzKc2uTC4rykvQenjab4t7BbZA0WipsFo5cTqLDXuPk5SWjafeg/7xsYzs05EKq419J8+QkpGLv483PeJa0ToiBI1Gobi0nPTcQjJyK5PM/739OljNdG3fhpdeeqlxTowQLlJcWs4PWw8QEeLPsJ4dmtT3hRCi8cjPRVE7Gi0anSc6L9/f6076O7xMUH14BbQgsveE32fP2U5Z7hmC2w9Aq/eq9zEVRcHTPxRP/1AC2/bCbMyjLDuVkpzKucq1Hnq8Q1rhExZDp5gIOrWO4Ex2Pj/vPMKm/SfZdiiJXu1jGNarPd3aRrH35Gk27T/BgaQ0erSPJqZFEB2iw+kQHY6qqmz5rgUFBQWNd1KEcCE/Hy8Gd2vHxr3HyS0qITTA4OqQhBAOIAmlqBWrqZTSnFSKzhwBKm91630D8DAEofcNqvx/Q1CTqJ+o0XkQ0nEQ3iFR5B3fQfruHwjpOBDv4IbXv1MUBU+/EDz9Qgho0xOLMd9+W9yYkYRG54FPaDShoTFMGz+QnKJSftp5hD3HU9lzIpVOMRFc0a0d8a0jOZh0lo17jhHs70vP9tFEhQaiKArenh6YPF1/HoVoLDHhwdwwtCcG7/r/sBNCNG2SUIpa8W0Ri2+LWGwVZszGfMzGfCwl+ZiL8yjJSEJV1cpSO96GKgmmh2+Qy26L+4TGoPcLJe/4drIObMCvZXsC2/ZutBqaiqKg9wtG7xdMQGwPLCUFlcll9h/JpXdIFDf0imFs346s33vy97JCmcSEh9CrQzRtW4Zw4kw2v+w+SligHz3bN7w0kxBNkSSTQjRvklCKOtHo9PZR0eeoNiuW0sLzksx8yk4fxlZhASpLA52fZOoNQei8/Zxyy1zn6UNY1xH1Ki9UF4qi2N9bYGwPzCUFlGanUpaTSnZmMhqtjhERrRgYHceuM6UcO5PF6u0HaRHoR1yrFnRt05L03ELW7jzMqfQcvDQ2Ahs1QiGEEMJxJKEUDaZotOgNwegNwfZlqqpiLTdiLvm9NdOYT2l2MkWnDwOg0Wrx8P2jFbPy/wMdMgOPvbxQUAS59SwvVFd630D0voEExnbHUlJIac5pSnNSsBmT6aPT0TkugGP5Ok4VlbM/8QwGb09CAwy0ahGEpcJKmamMSCndJ4QQwk1IQikcovL2tx86bz98QmPsy62WcizGAnuiaSrMwph+8rxb5n5Vkky9IbhBA2rO5+ETQHjPMRSmHLSXFwrpNBiPOpYXqvPr+gYQ4BtAQOuuWEqLKM1JxSP7NN29c4lRTZwp0ZFTaqBAtVFoLMdSYUWnbXoDnoQQQoiaSEIpnErr4YU2KAKvoD/m9FVtVswlBViM+fZEsyw3zV4gXevpjf5cK+bvg4Aqb5nXvV+motES2KYHXsGR5B7bRsbuVQS164NvRDun9PP08PEnIKYrATFdsZQVE5xzmhZZKaSnpZCZn0yZzh+9raxJDG4SQgghaksSSuFyikZrHzl9jqqqVJQbK5PM3xNNY2YS1tQyADRaHR6+gfZ+ix6+dbtlXqW80PHfKMtLa3B5obry8PbDI7oz/tGdadHFSGHGKZKOHUJTmkO5VQFiLnkMIYQQoimQhFI0SYqiVCZc3n74hJ13y9xc/sfgH2M+5X+6Ze7h429vxTzXoqn1qD5JdGR5obrSeRkIie1GSGw3oldvIzcv1+kxCCGEEPUlCaVwK1q9F97BkXgHR9qX2awVWEoK7Emm2ZhPWc5pbFYrUDnS+/wkU28IQutlsN/i/qO80G/nlRfq5bLbzhOvvoaysjK8vb1d8vpCCCFEXUlCKdyeRquzz2RzjqraqCgrto8wN5fkY8w4idVcXrmPzqNqKSPfIEI7D6EkM4mCpATKCzII6Ti4yjGdZfTo0U5/TSGEEKIhJKEUzZKiaPDwCcDDJwBaxNqXW81lVQqzl+efxXj2+B+3zH0D0PuFUpqTStq2rwiK60tg255NcppJIYQQoqmQhFJcVrR6b7yDvfEObmlfZrNaqpQyshjz0Xp4UWZM5/SWz8jc9xOBbXrhFRT5xy1zT1+XzP4jhBBCNEWSUIrLnkbrgWdAGJ4BYfZlqmqjorQYY0Yiuce3kZ+4G71fCDovX0CpvGVu+FNhdp8AFI3WdW9ECCGEcBFJKB1s/vz5fPXVVxw9ehRvb28GDx7MSy+9RMeOHV0dmrgIRdHg4RtAULveBLTuRn7SborPnsTTPwxDZHusplLMJfmU5Z6l6Myxyn00Gjx8AtEbAn9PNIPRGwLR6PQufjdCCCGEY0lC6WAbN27kgQceoF+/flRUVPDkk08yZswYDh8+jK+vr6vDE7Wg0XkQ0qGynFDe8d8oOLWHkA4DCWjdFQBbhQVzyR+Df8zF+ZRkJaPabADovA1VC7MbgtHqveWWuRBCiGZDUVVVdXUQl5Ps7GxatGjBxo0bGTp06CW3T0hIoE+fPuzevZvevXs7IUJxMVZzGbnHtlOWd/ai5YVUmxVLadEfiebvg4CsFjMAWg/PC+plevj4y+AfIYQQbklaKJ2ssLAQgODgYBdHIupDq/cmrOtwjOknKUjaXWN5IUWjtfexJLxymaqq9lvl55LM0pxUis4c+WMf3wA8DEF4BUbge97odCGEEKIpk4TSiWw2Gw8//DBXXHEFXbt2rXYbk8mEyWSyPzcajc4KT9SSoij4tWyPV2A4uUe3krn3J/xjuhLQuutFWxgVRUHn5Vs5sCeklX25rcJcpZSRuTgP1WaThFIIIYTbkITSiR544AEOHjzI5s2ba9xm/vz5PPfcc06MStSXh48/4T2vojD1IEWpBynPTyek4yA8fPzrdByNTo9XYDhegeH2ZdITRQghhDuRPpRO8uCDD/Ltt9/y66+/0qZNmxq3+3ML5d69exk2bJj0oWziTEU55B7ditVcSlC7PvhGxMmgGyGEEJcNaaF0MFVV+fvf/87XX3/Nhg0bLppMAnh6euLp6Wl/bjAYHB2iaASe/qFE9BlPfuJuco/voCw3jeAOA9HqvVwdmhBCCOFwMqTUwR544AE++ugjPv74Y/z8/MjIyCAjI4OysjJXhyYamUZbWV4orOswTEU5pO/+gdLcM64OSwghhHA4SSgdbNGiRRQWFjJ8+HAiIyPtjxUrVrg6NOEgPiGtiOw7Eb1fCNkHN5J7/DdsVourwxJCCCEcRm55O5h0Ub08afXehHUZZi8vZCrIJKTTheWFhBBCiOZAWiiFcJBz5YUiek9Ao9OTufcnCpL3o9qsrg5NCCGEaFSSUArhYOfKC/nHdKEo9SCZe9diKS1ydVhCCCFEo5GEUggnUDRaAmN7EN5zDLYKMxkJqyg+e0K6RAghhGgWJKEUwonOlRfyadGGvBM7yD60EatZRvwLIYRwb5JQCuFkleWFBhDWdRjm4lzSd6+S8kJCCCHcmiSUQriIT0grIvtMkPJCQggh3J4klEK40LnyQiEd+lOadYqM3asxFeW4OiwhhBCiTiShFMLFFEXBEPl7eSEPTzL3/kRhygFXhyWEEELUmiSUQjQRf5QX6urqUIQQQog6kZlyhGhCFEVDYGx3V4chhBBC1Im0UAohhBBCiAaRhFIIIYQQQjSIJJRCCCGEEKJBJKEUQgghhBANIgmlEEIIIYRoEEkohRBCCCFEg0hCKYQQQgghGkQSSiGEEEII0SCSUDZxkZGRzJ07l8jISFeHIoQQQghRLUVVVdXVQQghhBBCCPclLZRCCCGEEKJBJKEUQgghhBANIgmlEEIIIYRoEEkohRBCCCFEg0hCKYQQQgghGkQSSiGEEEII0SCSUAohhBBCiAaRhFIIIYQQQjSIJJRCCCGEEKJBJKEUQgghhBANIgmlEEIIIYRoEEkohRBCCCFEg0hCKYQQQgghGkQSSiGEEEII0SCSUAohhBBCiAaRhFIIIYQQQjSIJJRCCCGEEKJBJKEUQgghhBANIgmlEEIIIYRoEEkohRBCCCFEg0hC2cSlp6fz7LPPkp6e7upQhBBCCLci11DnkYSyiUtPT+e5556TfwxCCCFEHck11HkkoRRCCCGEEA0iCaUQQgghhGgQSSiFEEIIIUSDSEIphBBCCCEaRBJKIZoYVVVRVZurwxBCCCFqTRJKIZqY7IMbyDqw3tVhCCGEELWmc3UAQog/mAqzKUjeB0BATDe8Alu4OCIhhBDi0qSFUogmJP/UXirKjVSUl1CQvNfV4QghhBC1IgmlEE2EqTCbotOH0PsGozcEUXT6MOUFWa4OSwghhLgkSSiFaCLyT+3FYswHRQEULCX50kophBDCLUhCWQ//+c9/iI2NxcvLiwEDBrBjx44at126dCmKolR5eHl5OTFa4Q5UVcVUkIHeLwTVakG1WtAbQjAVZMqIbyGEcDK5ztedDMqpoxUrVjBr1izefvttBgwYwOuvv87YsWM5duwYLVpUP4DC39+fY8eO2Z8riuKscIWbUBSF6CsnY7NaqizXaD1QFPndJ4QQziLX+fqRK1UdLViwgHvuuYfp06fTuXNn3n77bXx8fFi8eHGN+yiKQkREhP0RHh7uxIiFu9DoPNB5+lR5aHQerg5LCCEuK3Kdrx9JKOvAbDaze/duRo8ebV+m0WgYPXo027Ztq3E/o9FI69atiY6O5rrrruPQoUPOCFcIIYQQdSDX+fqThLIOcnJysFqtF/zyCA8PJyMjo9p9OnbsyOLFi/n222/56KOPsNlsDB48mDNnzlS7vclkoqioyP4wGo2N/j6EEEKIy4nRaKxybTWZTNVu54zrfHMlCaWDDRo0iKlTp9KzZ0+GDRvGV199RVhYGO+8806128+fP5+AgAD7Y9iwYU6OWAghhGhehg0bVuXaOn/+/EY7dl2v882VJJR1EBoailarJTMzs8ryzMxMIiIianUMDw8PevXqxcmTJ6tdP3v2bAoLC+2PjRs3NjhuIYQQ4nK2cePGKtfW2bNnV7udM67zzZUklHWg1+vp06cP69atsy+z2WysW7eOQYMG1eoYVquVAwcOEBkZWe16T09P/P397Q+DwdAosQshhBCXK4PBUOXa6unpWe12zrjON1dSNqiOZs2axZ133knfvn3p378/r7/+OiUlJUyfPh2AqVOnEhUVZW9Of/755xk4cCBxcXEUFBTw8ssvk5KSwl//+ldXvg0hhBBCVEOu8/UjCWUd3XLLLWRnZ/PMM8+QkZFBz549+fHHH+0deFNTU9Fo/mj4zc/P55577iEjI4OgoCD69OnD1q1b6dy5s6veghBCCCFqINf5+lFUVVVdHYSoWUJCAn369GH37t307t3b1eEIIYQQbkOuoc4jfSiFEEIIIUSDSEIphBBCCCEaRBJKIYQQQgjRIJJQCiGEEEKIBpGEUgghhBBCNIgklEI0cTabFGIQQgjRtElCKUQTdjangE/X7aTQWObqUIQQQogaSUIpRBOlqio7jyRzODmdfSfPuDocIYQQokaSUArRRKVlF3A0NQNPDy17TqRKK6VoNirKS8g+vAmrWT7TQjQXklAK0QSpqsruYymUmy1Etwgmv6hUWilFs1GQcoDco1spOn3Y1aEIIRqJJJRCNDGqauNMVh5HUzPw1ntQUm5Cp9NIK6VoFirKiik4tYeK8hLyExOklVKIZkISSiGamKwDGzi6+1c8PXRotVrKzRV4e+rRKAqZ+UWuDk+IBilIPYSlOBff8LaYCrOklVKIZkLn6gCEEH8oL8yiMGU/0TaVXkP/gldAmH2dgoKPl96F0QlRP7YKM8Vnj+Md3IqCU3vQehnQ6DzQ6L3JT0zAP7ozWr23q8MUQjSAJJRCNCEFp/ZSUW4EoCL9MIaIsS6OSIiGKzp9mOxDG/Ft0QZreQlWiwmrqQRVBUWBksxT+Ed3dnWYQogGkIRSiCai/Pfbf3pDMFB5EQ5s0xOvwHAXRyZE/Vkt5eQn7sZUmIXOy5fIvhPRaM+/9Ch4h0S5LD4hROOQhFKIJqLg1F4sxnw8f08gLSUFFJzaS0QvaaUU7qv4zFHKCzLwadGG8sJsUFUMke1dHZYQopHJoBwhmgBVVTEVZqH3D0W1WVFtVvT+oZgKs1BVm6vDE6JezrVOanReaPXeaDRa8hN3YbNaXB2aEKKRSQulEE2AoihEX3kLqtVadblWi6LI7z7hnozpJykvzEa1WSjNTkG1WSnLz6Ak8xR+LTu4OjwhRCOShFKIJkKj9QCtR4OOUWG1UmG14aVv2HGEaAxegRGE9xj1p6UKnv5h1W4vhHBfklAK0Yys232UvKISbh7ZF61GWjaFa3n6h+LpH+rqMIQQTuB2V5z09HT27dtHSUmJq0MRwqVUVcVsqbA/z8ov4kBiGklpOSSl5bgwMiGEEJcbt0kov/32Wzp16kSrVq3o3bs3v/32GwA5OTn06tWLb775xrUBNmOpqaksWbKEhQsXsmTJElJTU10d0mXHZq3AajFVWfbb4VN8vn43lorKfpcJx1MxlpmwqSo7jyZjtclgnouRz7UQQjQet7jl/d1333HjjTcyaNAgbrvtNp599ln7utDQUKKioliyZAnXX3+9y2Jsjnbs2MELL7zADz/8gKqqaDQabDYbiqJw9dVXM2fOHPr16+fqMC8LWQd+oaKsmKiBN6AoGopLy9l1NIW8ohKOn84kLNDAwaSzBPv74Onhwan0ylbK9tEtXB16kyOfayGEaHxu0UL5/PPPM3ToUDZv3swDDzxwwfpBgwaxZ88ep8Xzn//8h9jYWLy8vBgwYAA7duy46Paff/45nTp1wsvLi27durFq1SonRVp/X331FVdccQWrV69GVVUAbL+3eKmqyqpVqxg8eDBfffWVK8NslvISd5F74o/PVHlBJkWphzBmJFKSeQqAA4lp5BQa0Wg07DqazM4jyeQWlmC2WCkuLcdYZpJWymrI51oIURuXw3W+sblFQnnw4EFuvvnmGteHh4eTlZXllFhWrFjBrFmzmDt3LgkJCfTo0YOxY8fW+Ppbt27l1ltv5e6772bPnj1cf/31XH/99Rw8eNAp8dbHjh07uOWWW7BarVj/VMbmnHPrbrnlFnbu3OnkCJsvS0khece2k3N0G+aSAuD36RhNJag2K/lJCRSVlJJwPBWDtycRwX6kZuaRll1A26hQAgzeBBi8adsyFK1GwWqVhPIc+VwLIWrjcrjOO4JbJJQ+Pj4XHYSTlJRESEiIU2JZsGAB99xzD9OnT6dz5868/fbb+Pj4sHjx4mq3f+ONNxg3bhz//Oc/iY+P54UXXqB379689dZbTom3PubNm4eqqvYWnJqc22bevHlOiqz5K0jdj7mkALMxn8KU/ZWtk79Px+jpH0ZJ5il27k4gI68QRVEoLjNhMlfgqdcxbfxg7rlmiP1xy6h+6D3coleLU8jnWghRG5fDdd4R3CKhHDFiBMuWLaOiouKCdRkZGbz33nuMGTPG4XGYzWZ2797N6NGj7cs0Gg2jR49m27Zt1e6zbdu2KtsDjB07tsbtXS01NZXvv/++xhacP7NarXz33XcyoKERWEoKKTy1Dw9vfzReBgqT95NzbBtmYx62CjMV5UYqyorJPH2S8CB/9B5adFoN4SH+WG0q5WaZfaQm8rkWQtTG5XCddxS3aL548cUXGThwIP369WPSpEkoisKaNWv45ZdfeOedd1BVlblz5zo8jpycHKxWK+Hh4VWWh4eHc/To0Wr3ycjIqHb7jIyMarc3mUyYTH+M5jUajQBUVFRgsTg+YVizZs0lW3D+TFVVvvjiCx588EEURXFQZE2P2Wyu9kdOfeUf305hTjpeQVGUWysw5aRTUlKCzj+Sc395xT+SPt4aWvTsjkZXtXi5zWKisNB04YH/RKfTodfrGy1ud1Dfz/VPP/3EnXfe6aComqbG/lw7Q30/0xqNBq1W2+DXt1qt9r64dXE5nWtXOXd+jUYjRUVF9uWenp54enpesL0zrvPNluomDh48qI4aNUrVaDSqoij2x4gRI9TDhw87JYa0tDQVULdu3Vpl+T//+U+1f//+1e7j4eGhfvzxx1WW/ec//1FbtGhR7fZz585VAXnIQx7ykIeDH3Pnzm2Ua4N8b7vfo6a/vTOu882VW7RQAnTp0oWff/6Z/Px8Tp48ic1mo23btoSFOW8Kr9DQULRaLZmZmVWWZ2ZmEhERUe0+ERERddp+9uzZzJo1y/587969DBs2jN9++41evXo18B1c2tKlS5kxY0ad9xs2bBgdO3assszLy4t27drZHzExMXh4NI8pAUtLS9m0aRN6vb7aX7n1oZpLwWoGoKSkBF9fX9B6oOh9G+X4UNkCbjabGTJkCD4+Po123Kauvp/r995777JqoXTE59rRGvKZ1jTSbFJz5szhqaeeqtM+l9u5dpU9e/YwYMAANm7cSM+ePe3LazrnzrjON1duk1CeExQU5LIacXq9nj59+rBu3Tp7zUubzca6det48MEHq91n0KBBrFu3jocffti+bO3atQwaNKja7f/cDG8wGIDK2wzOSMbGjh2Loih1uj2oKAqjRo0iPz+f8vJy+3KLxcLRo0fttwk8PDyIjY0lLi6OuLg42rRpg5eXV6O/B2fw8PBAp9Ph6+vbiO/BYP+vMjULv7DGryGp0+mw2Wx4eHg0m+S+Nur7uR4zZsxldZ4c87l2rKbwmdZqtXW+dS7n2jl0uso0x2Aw4O/vf8ntnXGdb67cIqFcuHAhP/zwA2vWrKl2/fjx47n22mu57777HB7LrFmzuPPOO+nbty/9+/fn9ddfp6SkhOnTpwMwdepUoqKimD9/PgAPPfQQw4YN49VXX2XixIl8+umn7Nq1i3fffdfhsdZHTEwMV199NatWrarVAAatVsvEiROZM2cONpuNtLQ0Tp48aX8UFxfbt7VYLJw4cYITJ04Ala0D0dHR9gSzXbt29gT6cldWVobJZHKbloumrq6f63M/kmJiYpwQnRCiKWnu13lHcYuE8oMPPmDkyJE1ru/cuTPvvvuuUxLKW265hezsbJ555hkyMjLo2bMnP/74o71DbmpqapXbKIMHD+bjjz/m6aef5sknn6R9+/Z88803dO3a1eGx1tecOXNYvXr1JVt0FEVBURSefvpp4I8EMTo6mhEjRqCqKllZWVUSzNzcXPv+NpuNlJQUUlJSWLduHQCRkZH2BDMuLo6goCDHvtkmrKCg4IKO3qL+avu5Pic0NJSjR4/SqVMnJ0QnhGgqLofrvCMoal3uAbmIwWBgwYIFNfaBeu+993jkkUeqjOBqLhISEujTpw+7d++md+/eTnvdr776iltuuQVVVatt0dFqtSiKwmeffcYNN9xQ6+Pm5eWRmJhoTzDT09Mvun1ISAjt27enXbt2xMXF0aJFiyYxkry0tJRff/0VPz8/h9yuSklJoaysrNGTmfLycoqLixk6dKjb9IFqTLX5XKuqylVXXUXbtm3R6XRMmzbNqf/2XMnRn2tHcNfPtJxr53DVNfRy5BYtlHq9/qLD79PT0xutc7WodOONN7J161ZeeOEFvv/++wvmPJ44cSJPP/10nfuzBgcHExwcbN/PaDRWSTBPnz5dpfxGbm4uubm5bN++HQB/f397chkXF0dUVFSz+9vfcccdZGZm4uvryyeffOI2Fxt3UJvP9eOPP87+/fvZv38/FRUVfPDBBxiNRoYOHerq8IUQoslyi4Ry4MCBLF26lJkzZ+Ln51dlXWFhIUuWLGHgwIEuiq756tevHytXriQ1NZVffvmFoqIi/P39GTlyZKP1LTMYDPTo0YMePXoAlb+AT506xcmTJzlx4gTJyclV6rQVFRWxZ88e+9zt3t7etG3b1t6K2bp1a3snbHeVm5tLXl4eVquVpKQkOnfu7OqQmpXafK4HDBjA8uXL2b59O6qq8umnn2I0Ghk/fnyTaCEXQoimxi2uvHPnzmXYsGH07NmThx9+mC5dugCVc3y//vrrpKen8/HHH7s4yuYrJiaGadOmOeW1vLy8iI+PJz4+HqgcyJOSkmJvwUxKSqoykrysrIxDhw5x6NAh4I+R5O3bt7ePJHfngS25ubnk5OQQGhrq6lCanYt9rrVaLXfccQd+fn6sXbsWgO+//57i4mImTZrU7FrFhRCiodwioRwwYADfffcd9957Lw899JC9hUBVVdq0acPKlSsvu+H5lwsPDw/77W3APpL8xIkTJCYmcuLECftsQlD9SPKYmJgqI8l9fRuvrqMznDx5Ej8/P7dOjN2RoijccMMNGAwGvv76awA2btxISUkJU6dOdfuWcCGEaExu84141VVXcfLkSfbs2UNiYiIA7dq1o3fv3nIL6jJy/kjykSNH2keSnzhxwt6KmZeXZ9/eZrORnJxMcnIyP//8MwAtW7asMpI8MDDQRe+mdiwWCwcPHqRz5854e3u7OpzLzlVXXYXBYGD58uXYbDZ27dpFaWkp99xzjyT5QgjxO7dJKKEymejTpw99+vRxdSiiiVAUhfDwcMLDw7nyyiuBypHk55cq+vOArrNnz3L27Fl+/fVXoLI8zPkJZlhYWJP7kVJaWsqePXto06YNERERTS6+5m7QoEH4+vrywQcfYLFYOHz4MG+88Qb333+/1E4VTY7Vam2UOcqFqAu3SigPHz5MUlIS+fn51daRmzp1qguiEk1NcHAw/fv3p3///gAUFxdfMJL8/M9PTk4OOTk5VUaSn59gtmzZ0qV95jRqBXq1nHIM9lJLrVu3Jjg4WBJLJ+revTt///vfWbRoEWVlZSQnJ7NgwQIefPBBgoODXR2eEHZuUA1QNENukVAmJiYyZcoUduzYUeM/FEVRJKEU1fLz86Nnz572eVzLysrsI8lPnjxZ7UjyhIQEEhISgMqR5OeXKnL27CkBFTn4WfNJ17fBovGipKSEw4cP4+vrS3R0NKGhoZJYOklcXBwzZ87krbfeoqioiIyMDF599VX+/ve/X3bz9gohxPncIqG89957OXDgAK+//jpDhgy5rGdPEQ3n7e1N586d7eV4ajOS/ODBgxw8eBCoHCgUExODVqslOjqaqKioRpvXNiMjg7KyMgDMZjNFuZm09s1Dr5bjZ80jT9PSvm1JSQlHjx7Fy8uLqKgowsPD5TaXE7Rq1YpHH32UN998k+zsbPLz83n11Vd54IEHiI2NdXV4QgjhEm6RUG7ZsoUnn3ySv//9764ORTRDfx5JbrVa7XOSnxtN/ueR5OcGhh0/fhxFUQgNDSUiIoKIiAjCw8PrPFjj4MGDfPDBB2zevNneCl9WVsazL/4ffTu24i8j+9I+2pNibTAWTdVC5+Xl5SQmJpKamkpUVBQtW7ZsUGKpqipz587lvffeo6CggCuuuIJFixbRvn37GvdZtGgRixYtIjk5GYAuXbrwzDPPMH78eKCyX+vcuXP56aefSE1NJSwsjOuvv54XXniBgICAesfqKqGhoTzyyCO89dZbnDlzhpKSEt544w1mzJhhL3klhKvIHQvhCm6RUIaGhrrlRUe4J61WS0xMDDExMfaR5BkZGfYyRYmJiVVGkquqSnZ2NtnZ2Rw4cACAoKCgKgnmxUoV/fLLL8yePdt+rPOpKuw+nkbC8bP8c/JQ4nsGVWmlPJ/FYiE5OZm0tDRiYmKIiIioV9/Pf//73yxcuJBly5bRpk0b5syZw9ixYzl8+HCNs/a0atWK//u//6N9+/aoqsqyZcu47rrr2LNnD126dLEPhHrllVfo3LkzKSkp/O1vf+Ps2bN88cUXdY6xKfD392fmzJm8/fbbnDhxApPJxH//+1/uvPNO+vbt6+rwhBDCqdxiLu8XX3yRb7/9lm3btl12t/RkHtKm6cyZM6xevZri4mKys7MpLCy86PZ+fn72BDMiIgI/Pz8UReHgwYPcfffd2Gy2S3ak12oU/vW3a/FpN4gKzaVbQH18fIiLi6vyY+xSc/GqqkrLli155JFHePTRR4HK2ajCw8NZunQpkydPvuTrnhMcHMzLL7/M3XffXe36zz//nClTplBSUuLWNR0tFguLFy9m3759QGXr0M0338ywYcNcHFndyfzSzuPIc+2oUd7ueK6b+zU0IyODDz74gISEBAoLC6tMXQyV30fr1q1zSixu8S3eoUMHrFYrPXr04K677iI6Orrafyw33nijC6ITl6Pg4GCio6PtF4OysjIyMzPJyMggIyODvLy8KglicXExxcXF9oLrPj4+hIeH88knn6Cqaq1GZarApxsOMT3uilrFWFpayv79+4mIiKBNmza1StpOnTpFRkYGo0ePti8LCAhgwIABbNu2rVYJpdVq5fPPP6ekpOSiEw4UFhbi7+/v1skkVHaZ+Otf/8qnn37Kli1bUFWVFStWUFxczMSJE+X2o3A6N2gnEo1g//79DB8+nLKyMjp27MiBAwfo3LkzBQUFpKWl0a5dO6Kjo50Wj1t8k99yyy32/z7XavJniqJgtVqdFZIQVXh7exMbG2sflGE2m8nKyrInmNnZ2VV+OZ5L9s7NSV4bNpvK3iOJ5BQY6zQwLSMjg/z8fDp37nzJ5O1czc7w8PAqy8PDwy+o5/lnBw4cYNCgQZSXl9tnl6lpHvKcnBxeeOEFZsyYUev30ZRptVpuu+02DAYDa9asAWDVqlUYjUZuvvlmmapRONWfW6lE8/TEE09gMBjYu3cvPj4+tGjRgjfeeIORI0fy+eefc99997F8+XKnxeMWCeX69etdHYIQ1TKZTDWuCw0NJTQ0lK5du2K1WsnJySE7O5usrCyys7NJS0ur8+upqsqxY8fsNTZrq6ysjH379tG+ffsqLWbLly/n3nvvtT//4Ycf6hzTOR07dmTv3r0UFhbyxRdfcOedd7Jx48YLksqioiImTpxI586defbZZ+v9ek2Noihcd911GAwGvvzySwB+/fVXjEYjd955Z6NVAhDiUqSF8vKwZcsWHnvsMWJiYuz9+s/9mJg0aRKbN2/mn//8Jxs3bnRKPG6RULpjXyTRvOl0OgwGA0ajEbPZXKt9fHx8aN26Na1bt8Zms1FUVISiKHX68lcUhfLy8nq1QJjNZpKTk+nWrZu9pfLaa69lwIAB9m3OJciZmZlERkbal2dmZtrreNZEr9fbR8r36dOHnTt38sYbb/DOO+/YtykuLmbcuHH4+fnx9ddfN8ska9SoURgMBj788ENsNhsJCQmUlpYyY8YMt+mXKNybtFBeHmw2m/1uUmBgIFqttsqA0W7duvHBBx84LR63SCjPMZlMJCQkkJWVxRVXXEFoaKirQxKXKb1eT//+/asURK+r1NRUli1bVqd9VFUlICCgQdP9xcfHo9frgcrBQn5+flWOHxERwbp16+wJZFFREb/99hv33XdfnV7HZrNVacEtKipi7NixeHp6snLlymadXA0YMAAfHx/ef/99LBYLR48etU/VeP75FsIRJKG8PLRp04ZTp04BlVNTt2nThp9//pmbb74ZgK1btxIYGOi0eNymY8/ChQuJjIzkyiuv5MYbb2T//v1AZV+s0NBQFi9e7OIIxeVGr9fj4+NT78eECRPqPGBDURTi4+PRaDT1egQFBV20BJeiKDz88MPMmzePlStXcuDAAaZOnUrLli25/vrr7duNGjWKt956y/589uzZ/PrrryQnJ3PgwAFmz57Nhg0buP3224HKZHLMmDGUlJTwwQcf2GeZycjIaLZ9n7t168Y//vEPvL29AUhJSWHBggVVWhCEcITm+m9KVDVmzBg+//xz+/P77ruP999/n9GjRzNq1CiWLVvGbbfd5rR43CKhXLJkCQ8//DDjxo3jgw8+qHKLMDQ0lJEjR/Lpp5+6MEIh6i4mJoarr7661uU9FI2G7t2713ve6LZt23LttddecmDOY489xt///ndmzJhBv379MBqN/Pjjj1VaFBMTE8nJybE/z8rKYurUqXTs2JFRo0axc+dO1qxZw1VXXQVUlu747bffOHDgAHFxcURGRtofp0+frtf7cQft2rXjkUcesSfxmZmZvPLKK6Snp7s4MtGcVVRUSD/Ky8BTTz3FJ598gsViAeDhhx/m+eefJzc3l8LCQubMmcO8efOcFo9b1KHs2rUr7du35+uvvyY3N5ewsDB+/vlnRo4cCcBLL73EwoUL6zXIoalr7jW0Lnc7d+5k8ODBWK3WS14AFI2GRx99jLh2ber0GqGhoQwaNKhKn0jhXLm5ubz55ptkZWUBlf1p77//ftq2beviyKqSOpTO48hznZubi5+fn71rS2Nxx3Mt11DncYsWypMnT9qncKtOcHAwubm5ToxIiMbRr18/VqxYgVarrbGlUtFoUDQaht04HUN4TK2PrdfrufLKK7nhhhskmXSxkJAQHnnkEXtNuNLSUhYuXMihQ4dcHJlormo7WFC4r5EjR160aPn69evtDW/O4BYJZWBgYJXba392+PBhIiIinBiREI3nxhtvZOvWrVX6VJ7rWakoCtFxnbn+rzNp16UHGSVWLNZLtGQqCh07duSWW26hc+fOUli7ifDz8+Phhx+mY8eOQOUFf9GiRezcudPFkYnm6GIlzUTzsGHDBjIzM2tcn5WV5bSSQeAmCeWECRN49913KSgouGDdoUOHeO+997j22mudH5gQjaRfv36sXLmSPeu+4pYr45jQO4ourUO59/HnGDbpHiJbtUavgRKzSm5Z9SM4dTodnTt3tk/7d24wiGg6vL29uf/+++nVqxdQORp3yZIlUmtXNLqysjJXhyCc4GINBidPnnRqVQm3SCjnzZuH1Wqla9euPP300yiKwrJly5gyZQp9+/alRYsWPPPMMw6NIS8vj9tvvx1/f38CAwO5++67MRqNF91n+PDhKIpS5fG3v/3NoXEK91VRbsRgSqdLTAhdooMZ37cNGm9/PLQK5RVgsoJOA/nlVUdwent7079/f26//XauvPLKi47iFq7n4eHB3XffzZVXXmlf9vnnn/Pdd9/JQArRaC51fRIN54q8YNmyZYwcOdJ+K3vevHn25+c/evfuzdy5cxk6dGiD3mNduEUdypYtW7J7926efPJJVqxYgaqqfPjhh/j5+XHrrbfyf//3fw6vSXn77beTnp7O2rVrsVgsTJ8+nRkzZvDxxx9fdL977rmH559/3v7cXToyC+crTDmIqSiHgrIKio0m2kf5EOxXSLG+RZXtdJrKX6ReXl706tWL+Ph4t58P+3Kj0Wi49dZb8fPzY/Xq1QCsXr2a4uJiJk+eLFM1igYrLi52dQjNnivygtLSUrKzs+3Pi4uLL/i+UBQFX19f/va3vzm8se18Tf4qZDKZWLNmDbGxsbz//vu8//779nmRw8LCnPLFe+TIEX788Ud27txJ3759AXjzzTeZMGECr7zyCi1btqxxXx8fH+nfKS5JVVWKzhxB0eqICPAkQG+jwqYSouaiUfzwNWdR6B2NqujQ6XR07dqVnj17NvooTuE8iqJwzTXXYDAY7LXkNm/eTElJCdOmTWuWswgJ5ykvL6e8vNxtRuu7G1flBffdd599kok2bdrwxhtvNJkuf03+Z7Ber2fSpEls3brVviwsLIzw8HCn/Yrftm0bgYGB9g8NwOjRo9FoNPz2228X3Xf58uX2+Zxnz55NaWnpRbc3mUwUFRXZH3Lb4vKgKAqRfSYQM+RWvtqdybINJ1mx/Qz5XrEYTJkYzNn4WvLtg2369+8vyWQzMWLECKZPn27/PtuzZw///e9/m1UfOKtNJaOgBJvc0neq/Px8V4fQZBiNxirX1oYOWnJmXlCTU6dONZlkEtyghVJRFNq3b3/RUd6OlpGRQYsWf7rtqNMRHBxMRkZGjfvddttttG7dmpYtW7J//34ef/xxjh07xldffVXjPvPnz+e5555rtNiF+/AKrJyT9XReOWmZxQSatICKd0U+Hh5aukf7ET9oAFq9tDg0N/369cPHx4f33nsPs9nMsWPHeP3113nwwQebxVSNp3OK2JucTd924bQKcf/34y4KCgqkZNjvhg0bVuX53LlzefbZZ+t9PGfmBeekpqbWK9aYmNqXm2uIJp9QAjz55JPMmjWLSZMm2UtuNIYnnniCl1566aLbHDlypN7HnzFjhv2/u3XrRmRkJKNGjSIxMZF27dpVu8/s2bOZNWuW/fnevXsv+IcgmrfIVq2xaXR4KCoGczbenh60ju+FpTCDojNHCGrby9UhCgfo0qULDz30EP/9738pKSnh9OnTvPLKK/zjH/8gJCTE1eHVW4XVxvGz+WQXlnL8bD4tgwxoNFLKyhkKCgpQVVVKhwEbN26kZ8+e9ueenp7VbtcU84JzYmNj6/W3dNZUnG6RUG7fvp2QkBC6du3K8OHDiY2NvaAkiqIovPHGG3U67iOPPMK0adMuuk3btm2JiIiwz3BxTkVFBXl5eXXqBzFgwACgcih/TR8cT0/PKh90g8FQ6+ML92e12Zj5wmucSUsjtDQJf/Mpolq3Q6MooGjJT9yFf6v4C1opswuK8fTwwN9XWi/dWZs2bZg1axZvvvkmBQUFZGdn88orr/Dggw8SFRXl6vDq5UxuMdlFpbQI8CGjoJSz+UZppXSSiooKCgoKCAoKcnUoLmcwGPD397/kdk0xLzhn8eLFTfrHgVsklG+99Zb9v2uqCl+fhDIsLIywsLBLbjdo0CAKCgrYvXs3ffr0AeCXX37BZrPZPwy1sXfvXgC5BSFqlJiWzan0HPIKSgisKCIsKhxFtWIx5qHR6rBZzJTlpWGI+OOLx2Su4NtN+wjy9+XGoT2b9BeOuLTIyEgeffRR3nzzTTIzMyksLGTBggXcf//9l7zgNDXnWic1Gg0+nh4Yyy3SSulgd9xxB9nZ2Xh5efHII4+QlpYmCWUdNOW84FKJrqs1+UE5UFn891IPRzbpxsfHM27cOO655x527NjBli1bePDBB5k8ebJ9JFdaWhqdOnVix44dACQmJvLCCy+we/dukpOTWblyJVOnTmXo0KF0797dYbEK92W12dh1NAVVVVGB09poOo75K62HT7U/Wl0xCUWjpTw/3b7fkZR00rLzOXE6k9TMPNe9AdFogoODeeSRR2jdujVQWaR64cKFHDhwwMWR1c3ZPCNZhaWYLVYyCkqwWG1kFJSQUVDi6tCardzcXHJycuxlg/Lz8106BqG5aop5QWFhodNub1en0Voo27RpU+eWEUVRSExMbKwQHGr58uU8+OCDjBo1Co1Gw0033cTChQvt6y0WC8eOHbOP1tLr9fz888+8/vrrlJSUEB0dzU033cTTTz/tqrcgmrhzrZNhgX7YTKWUmHWcKdHQMeaPjt9Wcxlnf/sWrd6L6CG3YrHCzqPJeOp1mCwV7DqWSkx4sLRSNgMGg4GHHnqId999l6NHj2KxWHjnnXeYMmUKAwcOdHV4teLj5UHn6Kr9PxXAW+8WN8eajePHj+Pr6yuzZzWyppAX7Nq1i6effppff/0Vs9nMTz/9xMiRI8nJyeHuu+9m5syZDB8+vKFvtVYa7V/1sGHDLriI7dq1i0OHDtG5c2f7YJpjx45x+PBhunbtam8mrq3t27ezfv16srKyuP/++2nfvj2lpaUcPXqUDh06OLS/YXBw8EWLlcbGxlaZ5SI6Otqpc2gK96aqKruOplBYUoaqqhSUmNB6eLLraDIdolvY/20VnT5MeWEmikaLMf0ESWUG0nMKiQoLxGypqGylzMqndXiwi9+RaAxeXl7cd999LFu2jISEBGw2G//73/8oKSlh1KhRrg7vkkL9vAn1kyTG1axWK4cOHaJHjx5S37QRuTov2Lp1KyNHjiQqKoopU6bw/vvv29eFhoZSWFjIO++8434J5dKlS6s8/+abb/jmm29Yu3btBV98a9eu5eabb+aFF16o1bHNZjOTJ0/m22+/tY9Yu+aaa2jfvj0ajYYxY8Ywc+ZMnnrqqcZ6O0I4XcvQAAJ+H1STnu5BZGQEFVYbm/ef5IpucagV5eQnJqDV+6DaKsg+sYsdRa0pKTdzNqcAgOIyEwnHUiShbEY8PDy46667MBgM/PrrrwB8+eWXFBcXc91110lrtKiVsrIyjh07RpcuXeQz00w8+eSTxMfHs337doqLi6sklFBZ43bZsmVOi8dhfSifeeYZ/v73v1f7K/qqq67iwQcfrHUz75w5c/j+++9ZtGgRx44dq5Lxe3l5MWnSJL799ttGi10IZ1MUheG9OjJxcHcmDu7OhEFdmTCoGzZVZcuBRE6l51B0+jCmwiw8/UPx9AujNOcM0YYKRvbpSP/ObejfuQ2j+nSiZWigq9+OaGQajYZbbrmFiRMn2pf99NNPLF++3KV9poR7yc/PJz09/dIbCrewc+dOpk+fjqenZ7U/EqKioi5aE7OxOawjy4kTJy5aOy0kJKTW/Sc/+eQT7rvvPmbMmEFubu4F6+Pj4+1TlwnRHISFhXE6K5/jqZkUl5Wz63Ai/ZRD2KwWynLPAKCxmumizySm/0gXRyucQVEUJk6ciMFg4LPPPkNVVbZu3UpJSQl33XWX3MoUtZKcnExISEiNdRiF+/Dw8MBms9W4Pi0tzamlBx3WQtmuXTuWLFlS7dSBxcXFLF68mLZt29bqWFlZWXTr1q3G9Vqttt5TFwnRFOl0OnYfS6HcUkF0iyASz+ZiCupIy35XE9FrDBG9xtCy39UEtpEi55ebYcOGMX36dLRaLQD79u3jrbfealZTNQrHsVqtnDx5ssqdPuGeBg4cyBdffFHtupKSEpYsWeLUiVEc1kI5b948/vKXv9CpUyemTZtGXFwcUNlyuWzZMjIzM2vdqhgdHc3Ro0drXL9lyxb78YVoDs61ToYFGvDWe1Chwv4CTzr26iP1+wR9+/bF19eXd999F5PJxIkTJ3jttdd48MEHa1W8WVze8vLySE1NtZelEu7pueeeY9iwYUycOJFbb70VqPyBmZSUxCuvvEJ2djZz5sxxWjwOa6G8/vrrWbVqFWFhYfzrX//irrvu4q677mL+/Pm0aNGC77//nuuvv75Wx7rtttt455132LZtm33Zuf4C7733Hp999hlTp051xNsQwumS03PZcuAkZquV7IJikjNyUVUbGXlFZOYVuTo80UTEx8fz0EMP4evrC8CZM2d49dVXm2TNwRKThd2JmZSbK1wdivhdamoqqamp0lLpxgYMGMCqVas4efKkPQd65JFHmDFjBlarlVWrVjm17rVDi4GNGTOGMWPGkJGRQUpKCgCtW7eu07REAE899RTbt29n6NChxMfHoygKM2fOJC8vjzNnzjBhwgRmzpzpiLcghFOVmy38tPMQZSYLNwzphafHH/9EtVqFsCCZilP8ITY2lkceeYQ333yT/Pz8KlM1tmrVytXh2SVmFHDkTC7eei2do0NdHY74XUpKCmazmXbt2snIbzc1cuRIjh07xt69ezlx4gQ2m4127drRp08fp/9NnVJdNiIios5J5Pn0ej0//vgjy5cv54svvsBqtWIymejevTvz5s3jjjvukH8Molk4nJxOem5lK6TJYqFr25Yujkg0dREREfapGjMyMigqKuK1117jvvvuaxJdgYzlZhIzCrBYbRxLy6NteCBeUti8yUhPT6esrIz4+Hh0Ovm7uKuePXvSs2dPl8bg0E9Pamoq//rXv1i/fj3Z2dl88803DB06lJycHJ5//nmmT59Or14XDiqYNWsWd9xxh31damoqYWFhTJkyhSlTpjgyZCFcptxsYdfRZPvFdtfRFDrHtsTbU0bviosLCgpi1qxZ/Pe//yU5OZmysjLefPNN7r77bpdP9ZqUWUhxmZmIQB8yC0pIziqiUyupk9qUFBQUsH//frp06SKjv5uwc3Vo62ro0KGNHEn1HJZQHj58mCFDhtgnSj958iQVFZX9Z0JDQ9m8eTMlJSV88MEHF+z7+uuv07dvX3tC2aZNGz788ENuu+02R4UrhMuda52MbhEIwOmsAo4kp9O7Y4xrAxNu4dxUje+99x6HDx/GYrHw7rvvcvvttzNo0CCXxHSuddJb7wEoeGg1HE/PJ7aFv7RSNjElJSXs27ePbt26yRSNTdTw4cOr3I09N9HLpTirVq3D/kU/9thjBAYGsn37dhRFoUWLFlXWT5w4kRUrVlS7b3h4OElJSfbn0mlYNHeqqrL3xGkqrFbOZBcAUGG1sufEaXq2j6a4tBxFUfD/fSYdIarj6enJ3/72N/73v/+xa9cubDYbH374IUajkauuusrp8WTkl2Cx2rBU2MgzVlBhtWKusJJRUEJsiwCnxyMuzmQysW/fPjp37izVApqg9evXV3luMpl47LHHKC0tZcaMGfYpro8ePcp7772Hr68v//73v50Wn8MSyl9//ZVnnnmGsLCwaouRx8TEkJaWVu2+EydO5Pnnn+enn34iMDAQgFdffZVPP/20xtdTFEVmyxFuS1EUrugWR58/tUZ66fXYbDa+27ofnVbDzSP6StkgcVE6nY5p06ZhMBjYsGEDAF9//TXFxcXccMMNTu1vHhPmj5+3HgCbqlJcVERgYCBBBvlh1FRZLBYOHDhAXFwc4eHhrg5HnOfPNSVnzZqFXq9n+/bteHn98W/qmmuu4YEHHmDYsGH8+OOPTvsx6bCE0maz4ePjU+P67OzsGvtqvPHGG7Ro0YL169dz6NAhFEXh9OnT5OXl1Xg8GZQj3F3HmMov79TMPFSwz8d9LDWD5PRcFAWS0rOJi2pxkaMIUTlV46RJk/Dz8+O7774D4Oeff8ZoNHL77bfbi6I7ml6nJTzQF3OFleSsQrx10CKg5uuCaBpsNhvHjx+npKSENm3ayPW1iVq+fDlPP/10lWTyHB8fH+644w5efPFFXn31VafE47CEsnfv3vzwww/cf//9F6yrqKjg008/ZeDAgdXu6+vry7/+9S/7c41Gw+uvvy59KEWzZzJXsGbHYVBV7hg3EA+tlp1HUwAVmwq7j6bQNjJMWinFJSmKwvjx4zEYDHz66aeoqsr27dspKSnh7rvvRq/XOy2W5Kwi9p7KoqWPjegoqVzgLtLS0igvL6dTp05oNA4rWy3qqaSk5KJzs6enpzt1FkGHfUJmz57Njz/+yH333cfBgwcByMzM5Oeff2bMmDEcOXKEJ554otp9b7zxRjZt2mR/vn79epf0/xHC2Y6kpJOeU8DZ3EKOpGRwMi2L5PRcWgT50SLQQGJaNknp2a4OU7iRIUOGcPfdd9tLwhw4cIA333zTaRcak8XK8bN5FJaaScwswmKtee5h0TAZGRn2KTjNZjP5+fkNPmZubi4nTpxo8HFE4xs9ejRvvPEGX3311QXrvvzyS9544w1Gjx7ttHgcllCOHz+epUuXsmLFCkaOHAnAlClTGDNmDAkJCfzvf/+rcSj7t99+S2pqqv35yJEjWbt2raNCFaJJMJkr2Hk0Gb2HFk8PHTsPn2LbwSSKS8vJLigmp9BIYUk5u4+myEA1USe9e/fm/vvvt3czSkxM5LXXXqOwsNDhr52SXUR+iYnIIF/ySy2kZjv+NS83Bw8eZObMmVxzzTUUFxcDUFZWxgsvvMD7779f5XpaH1lZWXWagUlVVZ555hkiIyPx9vZm9OjRl0xKi4uLefjhh2ndujXe3t4MHjyYnTt3Nvi4zdl//vMfWrVqxaRJk2jVqhXDhw9n+PDhREdHc/PNN9OqVSvefPNNp8Xj0DbsO+64g9OnT/Pll1/y0ksv8a9//YvPPvuM06dP2+edrE5UVBR79uyxP6/t0Hgh3Fll62QhYYF+hAUaOJtbiFVVGdy1HV3btKJrm1YM7tqOFoF+8u9B1FmnTp2YOXMmBkPlbEtpaWm88sorZGVlOew1z7VO6nVa9DotGuBQcpa0UjaiX375hbvvvputW7de8ENTVVWOHDnCG2+8wf79+xv0OnWZpvHf//43Cxcu5O233+a3337D19eXsWPHUl5eXuM+f/3rX1m7di0ffvghBw4cYMyYMYwePbrK4N36HLc5i4qKYt++fSxYsICuXbuSmZlJZmYmXbp04bXXXmPfvn1OnTFLUR3Q1FFaWkp0dDRPPPEE//znP+u8/2OPPcYrr7xCq1atCAwM5ODBg0RHRxMQUHOZCUVR2LdvX0PCbpISEhLo06cPu3fvpnfv3q4ORziIqqosXbWNxLPZ6D0qB0yYLVbatgxl2vjB0mdSNJrMzEzefPNN+yBHPz8/HnzwQaKjoyktLeXXX3/Fz8+v2o7+dZWUWcD24+lYbZWXmZycXAL8fBndO46Y0MYpS1NeXk5xcTFDhw696EDQpqYxzvXBgwe5++67sdlsl0z2NBoNDz30EDEx9a9r27lzZ2w220XPtaqqtGzZkkceeYRHH30UgMLCQsLDw1m6dCmTJ0++YJ+ysjL8/Pz49ttvmThxon15nz59GD9+PPPmzavXcUGuoc7kkBZKHx8fdDodvr6+9dp//vz5vP3221xxxRWEhYWhKAq+vr6EhITU+AgOlpkXhHsb0KUNNwztycRB3Zg4qBs3DO3JwC5tkMZI0ZjCw8N59NFHiYyMBCpvNb722mscP3680V8rzN+HfnERDOwQyYD2EcT6QZSvSohBCmc3hsWLFwO1r9X8008/Nej1srMv3X/71KlTZGRkVOm7FxAQwIABA9i2bVu1+1RUVGC1Wi9IrL29vdm8eXO9j3u5KSkp4fnnnyc5Odklr++wUd433XQTX3zxBffdd1+db89ptVpmzJjBjBkzgMpfVk8//bSM8hbNlqIodI6NdHUY4jIRGBjIrFmzWLRoEUlJSZSXl/PWW29x++23N+rr+Hnr/6hDabORmQjYStFiBWRK0YbIyMhg06ZNtU4mbTYbhw8fJj8/n6CgoHq9Zn5+/iUbbzIyMgAuqGEZHh5uX/dnfn5+DBo0iBdeeIH4+HjCw8P55JNP2LZtm30++voc93JjNBp57rnnuPLKK4mNjXX66zusD+XkyZPJyspixIgRLF++nC1btpCQkHDBozZOnTrF9ddf76hQhRDisuPr68s//vEPunTpAlS2Ev3vf/8jJSWl0V/rjjvu4Oqrr7bXwztz5kyjv4a7MplMlJeX1/mxZcuWOg/OU1WVY8eOYbPZ6vUwmUyYTKYqx1y+fDkGg8H+sFgs9ToPH374IaqqEhUVhaenJwsXLuTWW2+VckV15MoBmw5roRw+fLj9v88vAXTOuYE2tZljsnXr1o0ZmhBCCECv1/O3v/2NDz/8kB07dqCqKvv27UNRFHr37t1og79yc3PJysqy94PPyMggKirqsp4zWqfTYTAYMBqNmM3mOu+fn5+Poih1SiAURaG8vBybrf6DohRFsZegArj22msZMGCA/fm5hDMzM9PereLc8549e9Z43Hbt2rFx40ZKSkooKioiMjKSW265hbZt2wIQERFRr+MK53FYQrlkyZJ676vRaNBoNJSWlqLX69FoNJf8YlMUhYqKinq/phBCXI60Wi1Tp07FYDDwyy+/ALB3714sFgsDBgxwSEUBVVVJTEykS5cuNR7fZLGSmFFAXGQgep1zZvZxJr1eT//+/et93UpOTq5XC2VAQIB9pH99dOrUqUpRfD8/P/z8/Kq8RkREBOvWrbMnekVFRfz222/cd999lzy+r68vvr6+5Ofns2bNGvtc1G3atGnQcS8HWq3WXnbJFRyWUN5555313veZZ56p8ivo3HNXevHFF/nhhx/Yu3cver2egoKCS+6jqipz587lvffeo6CggCuuuIJFixbRvn17xwcshBC1pNFouOmmm/Dy8mLVqlUAHDp0CJPJxJAhQxrltqOiVD1Gfn4+WVlZNc4XnZxVyP6UbHRaDR1a1q/PX1On1+vrPWPRhAkT6tVCGR8fX++/p6IolyxDoygKDz/8MPPmzaN9+/a0adOGOXPm0LJlyypd10aNGsUNN9zAgw8+CMCaNWtQVZWOHTty8uRJ/vnPf9KpUyemT59ep+M6U1PLC0JDQzl16lSDj1NfDksoG+LZZ5+96HNXMJvNTJo0iUGDBvHBBx/Uap9zNbOWLVtm//CPHTuWw4cPN0pJDiGEaCyKojBy5EjS0tLYv38/qqpy8uRJysvLGTVqVJXbnHWl9w2kTdteFKUcrrI8MTERf3//C1pUTJYKjqfnU2KycPxsHq3D/PH0aH6tlA0RExPD1VdfzapVq2rVdUyj0dCtW7cGVUSJi4urVfWWxx57jJKSEmbMmEFBQQFXXnklP/74Y5XrXmJiYpVi6YWFhcyePZszZ84QHBzMTTfdxIsvvoiHh0edjutMkhdU1Wh1KO+66y4UReHdd99Fq9Vy1113XfrFFaXWf4SmYunSpTz88MOX/CVS35pZfyY1tIQQznKuNmJBQQFbtmyxJyotWrRgzJgx9pl26kJVVe546Gm8wmIoOn2Mv99xQ5X1vr6+dOvWDSXzIBq/cDQBLTmWlseOkxmE+HmTW1zGgPaRNbZSumsdysawc+dOBg8ejNVqrVUdyscff7zeo389PDy4+eab610O0FWccQ11dl5wvv379/Pmm2+SkJBAYWHhBf1jFUUhMTGxzsetj0Zrofzll1/QaDTYbDa0Wi2//PJLrfo9Vud///tfvWKYOnVqvfZzhEvVzKrpg/PnUXRGo9HhsQohxPmio6MZO3Ysa9euxWKxkJWVxQ8//MDYsWPrnFDkGsvxCW2J2VyGX0RryirA+7wrT0lJCYn7txOryUBbkovZM4Tj6fl46rR4aDXodVpppaxBv379WLFiBbfccguqqlbbUnnu9vaMGTMaVEqmT58+bpdMns9oNFJUVGR/7unpWa8fSA1R37ygJhs2bGDcuHEEBQXRt29f9uzZw8iRIykvL2fbtm106dKFPn36NPbbqFGjJZR/LqTZkMKa06ZNu2DZueTzz7/Czk9Km1JCWd+aWfPnz+e5555zaGxCCHEpkZGRTJgwgTVr1lBeXk5+fj7ff/8948aNu+isZedTVZUT6flodHpKs84SFNmanHKINlTZCKUghSLFiL+tgjNnkikpV7FYbWQWlv7+na+Snm8ktkXtXvdycuONN7J161ZeeOEFvv/++yrXSEVR6NatGxMmTGhQMhkQEGAvL+Wuhg0bVuX53Llznd6drrFraT7zzDO0bduW7du3YzabadGiBU8++SQjR47kt99+Y/z48bz00kuNEnttNMkCT6dOnary2LNnD926dePKK6/ks88+Y9++fezbt48VK1ZwxRVX0L179ypzf9fWE088gaIoF30cPXrUAe+wZrNnz6awsND+2Lhxo1NfXwghzgkNDeWaa66xj+I1Go18//33Vfq+XUyusZzT2cWo1go8vH2xVVSQV17ZR9JgLQBVxctWgq+1mBKrB/kFhQQakxjYPpyhnVsxJD6KoZ1bMbBDS8ID3bd1zNH69evHypUrSU5OJjAwEKicZeZf//oX999/f4OLXA8ZMgSt1r1bhzdu3Fjl2jp79uxqt2uKeUFNEhISuPvuu/H397f/fc61Ug8YMIB7772XOXPmOC2eJjko5891J5999lnCwsL46aefqrRIduvWjZtuuokxY8bw2muv1blU0SOPPFJta+j5ztXAqqv61sz6czN8Q8o7CPd0roXB1ZUNhADw9/fn6quv5scffyQ/P5/y8nJWrVrF6NGjadmy5UX3LS4zo/fQYqswoygarJZytArozIWEaDOo0Gnxt+WiwYqKgsmmRc1Owsc3nLC4XvJvoI5iYmLw9fWloKAAT0/PRpmSuF+/fpf8O7sDg8GAv/+l549vinlBTXQ6nf3HXmBgIB4eHmRlZVWJ8/DhwzXt3ugcmlCuXr2aBQsW2DuLVtdpuDaj07755htefPHFar9cNBoNN954I08//XSd4wsLCyMsLKzO+9WG1MwS9ZV96FdQbbToNsLVoQgBgI+PDxMnTmTt2rVkZmZisVhYs2YNw4cPp02bNjXuFxvmT6sQP957/meys7PxDwjgrqt6EG3OQq+WE2jNxEOtwIYWvVrZd9yGQl7qEXIsXsTFxdW7rI5ouD59+lx2RcPdKS+Ii4vjxIkTQGUDRKdOnfj666/tU6j+8MMP9iTWGRx2y/vLL7/k6quvJjMzk8mTJ2Oz2bj11luZPHky3t7edO/enWeeeaZWx1JV9aJNzIcPH3b4dEOpqans3buX1NRUrFYre/fuZe/evVUGzZz7Y0LVmlkrV67kwIEDTJ061aU1s0TTZyrKoeDUXgqS91FemHXpHYRwEk9PT8aNG0dMTAxQOTf0L7/8ctHvZkVR8NBqUG1WbNYKVGsFAdZ8PFQT5Yov3tZS8nVhpHnGVXnkeUSSm5tLQkICeXl5znqL4ndarZbhw4fTp08faSW+CFfnBRMmTOCTTz6xF8efNWsWX331Fe3bt6d9+/asXLmSe++9t1Hea204rIVy/vz59O/fn82bN5Ofn8+iRYu46667GDlyJMnJyQwcOPCiv2zPd/3117No0SJiY2P529/+Zi8NUVpayqJFi3jnnXfsGbmjPPPMMyxbtsz+vFevXgCsX7/ePs3ksWPHKCwstG/T1Gpmiaav4NReKsqKQFEoOLWPiJ5XuTokIex0Oh2jRo1i8+bN9paRLVu2UFZWRs+ePS+afPh66vD31uFvzaMCD6yKBx6KCR9bMUZtMFSzr8Vi4dChQ7Ru3Zro6GhJbpzAz8+Pq666itDQUFeH0uS5Oi+YM2cODz30kL3/5J133olWq+XLL79Eq9Xy1FNPXfL2fWNqtDqUf+bj48P8+fN56KGHKCgoIDg4mNWrVzN27FgAnn/+eVasWMGhQ4cueazCwkKuvfZaNm3ahIeHh73vQXp6OhaLhSuuuILvvvvO3hm5OZE6lJcPU1EOKRuXo9HqAAWb1UzM0NvwCmjh6tDEZeJcHUo/P7+LXuBUVWXnzp0cOHDAvqxz584MHDiw2qTv3ik30j/Gh9xSG1MmDAIU1N//16roSNe3waS5+KCbqKioavuuXc51KKvTqlUr0tLSCAwMrPMI36ioKEaNGtWsGj3kGuo8Drvl7ePjY+/7EhgYiKenJ+np6fb14eHhtZ4iKCAggI0bN/L1118zffp04uPjiY+PZ/r06XzzzTf8+uuvzTKZFJeXglN7sRjzQKMFjQZLST4Fp/a5OiwhLqAoCv3796dfv372ZYcPH2bDhg0X9ItXVRs9Y/yJi/QjyNeDHF0UOR4tyfVoSY5HFHm6CCqUS/eTTEtLIzMzs9Hfi6jUuXNnxo8f36ySyeaubdu2rFy5ssb133//fb0HENWHw255d+zYscroop49e/Lhhx8yZcoUKioq+Pjjj+19cWrruuuu47rrrmvsUIVwOVVVKc8/i94vGLWicnCC3hCMqSAdVbVdMA+yEE1B9+7d8fLyYvPmzaiqSlJSEiaTiVGjRtmnzFON2bRt4Ut2USkRgd6Ua30xaerXkpiYmGhvoBCNp0+fPvTu3Vu6FLiZ5OTki05+YjQaSUlJcVo8Dksob7zxRhYuXMgrr7yCp6cnTz31FNdddx2BgYEoikJJSQmLFy921MsL4VYURSH6ysnYKixVlmt0HpJMiiatQ4cOeHp6sn79eqxWK2lpaaxevfr3qRr1pB3ZwZ6kbE5nFxMdaiCwzSlo0bnaPpOXYrVaOXbsWOVUjZL8NIp+/frZ+/4J93Oxfwc7d+506t3bRr9SlZeXs2LFCiwWC08//bR9hN7VV1/Nhg0buOeee7j33ntZt26dUzuLCtHUaXR6dF6+VR4anZRMEU1f69atGTt2rL1VMjs7m/fee49/PHAfNz7wHF9sTWLHiWy+2HaKWS+9z+L33yU1NbVer1VYWEhiYmKdK3t89dVXjBkzhpCQEBRFYe/evZfc57333mPIkCEEBQURFBTE6NGj2bFjh329xWLh8ccfp1u3bvj6+tKyZUumTp3K2bNn6/q2XKJPnz6STLqZN954g7Zt29K2bVv7qPFzz89/hISE8PrrrzNhwgSnxdaoLZRZWVkMHjyYU6dOoaoqiqLg7e3NN998w+jRoxkyZAhDhgxpzJcUQgjRBERGRjJx4kTWrFnDoUOH+OmnnwA4l/edS/9UFQ4dPcaho8e588476d69e51fKz09HZ1OV6cZYEpKSrjyyiu5+eabueeee2q1z4YNG7j11lsZPHgwXl5evPTSS4wZM4ZDhw4RFRVFaWkpCQkJzJkzhx49epCfn89DDz3Etddey65du+r8vpypW7duMkjFDbVo0cI+DWZycjJRUVFERUVV2UZRFHx9fenTpw/333+/02Jr1ITyhRdeIDk5mZkzZzJy5EhOnjzJCy+8wL333ktiYmJjvpQQQogmJiQkhHbt2vH6669ftAXRZqucn3vZsmU89NBDde5PD3D69Gm0Wm2ti1DfcccdQOVFuLaWL19e5fn777/Pl19+ybp165g6dSoBAQGsXbu2yjZvvfUW/fv3JzU1tV7vyxnat29f44h80bTdeuut3HrrrQCMGDGCp59+mlGjRrk4qkqNmlD+9NNPTJ06lVdeecW+LDw8nNtuu41jx47RsWPHxnw5IYQQTcyKFSvqtP1PP/3EX//613q9VnJyMhqNxmmz6ZSWlmKxWC46pWFhYSGKojTZyiPR0dEMGzZMkslmYP369a4OoYpGTShTU1N5/PHHqyy78sorUVWVzMxMSSiFEKIZy8jIYNOmTbXu32iz2Th8+DD5+fkEBQXV6zVPnTrltNIojz/+OC1btmT06NHVri8vL+fxxx/n1ltvrdW80c4WFhbG6NGj0WhkoJ87+vXXX+u139ChQxs5kuo1akJpMpkuqGF17vm5qYHq68iRIyxZsoSkpCTy8/Mv+MJSFIV169Y16DWEEEJUfpfXx5YtW+o8WEZVVY4dO0b//v3r9ZqqqpKfn19l2fLly6tMObd69eoG99//v//7Pz799FM2bNhQba1Gi8XCzTffjKqqLFq0qEGv5Qg+Pj6MGTPGPnBKuJ/hw4dXaVk+N1alJufW/7k2rKM0etmg5ORkEhIS7M/PTTl04sSJam8B1KZT8Icffsj06dPx8PCgY8eO1f6SdfRc3kII0dzpdDoMBgNGoxGz2Vzn/fPz81EUpU7fx4qiUF5ejs1mq/PrnaOqKjrdH5eza6+9lgEDBtif/3nQQl298sor/N///R8///xztYOIziWTKSkp/PLLL02ydXLEiBH4+l58NiLRtDW1W9x/1ugJ5Zw5c5gzZ84Fy/880qgumfOzzz5Lr169WL16tcwvKoQQDqLX6+nfv3+97yglJyfXq4UyICAAg8FQr9eEygLr5/ej9PPzw8/Pr97HO9+///1vXnzxRdasWUPfvn0vWH8umTxx4gTr168nJCSkUV63MXXo0KHBSbVwvWHDhrk6hItq1IRyyZIljXk4u7Nnz/Loo49KMimEEA6m1+vrPchlwoQJ9WqhjI+Pr3e/Pr1eT+fOnS+5XV5eHqmpqfYakceOHQMgIiKCiIgIAKZOnUpUVBTz588H4KWXXuKZZ57h448/JjY2loyMDAAMBgMGgwGLxcJf/vIXEhIS+P7777FarfZtgoODnTZY6GIURaFPnz6uDkM4WHp6OllZWcTFxbmsJbpRE8o777yzMQ9n1717d7cpFCuEEJermJgYrr76alatWlWru08ajYZu3bpddNT0pfTp06dWUzGuXLmS6dOn259PnjwZgLlz5/Lss88ClQNLz09sFy1ahNls5i9/+UuVY53bJy0tzT6Xcs+ePatss379eoYPH16Pd9QwERERWK1WezIbGxvbaK21oun59ttvefzxxzlx4gQAa9euZeTIkeTk5HDVVVcxd+5crr/+eqfE4rCpFxvTggULmDRpEuPHj2fw4MGuDkcIIUQN5syZw+rVq2vdUtmQmTzOL/J8KdOmTbvk7GwbNmyo8vxSNStjY2ObXP/9Xbt2kZKSwpo1awBq1Xor3NN3333HjTfeyKBBg7jtttvsP4wAQkNDiYqKYsmSJZJQnu+ll14iICCAIUOG0LlzZ2JiYtBqtVW2URSFb7/91kURCiGEgMq5oVesWMEtt9yCqqrVtlSeawWcMWNGnWa7OZ9er2fEiBFSAuciAgICaNmypavDEA7y/PPPM3ToUNavX09ubm6VhBJg0KBBvPPOO06Lxy0Syv3796MoCjExMRiNRg4fPnzBNlKkVQghmoYbb7yRrVu38sLzz/P9Dz9UacVTFOjWtSsTJk6sdzKp1WoZM2YMAQEBjRRx89SpUye5NjZjBw8eZMGCBTWuDw8PJysry2nxuEVCWZepsoQQQrhev379+PjdBexY2YV3P/qSguJS/H29GDdsAJpWvTHr6ldaR6fTMWbMGGl5uwRFUejQoYOrwxAO5OPjQ0lJSY3rk5KSnFp1QO4VCCGEcAhF60GH7v0J9DMQaPDE19sT/7BW1PfS4+XlxdVXX02rVq0aN9BmqGXLlnh7e7s6DOFAI0aMYNmyZdWW+crIyOC9995jzJgxTovHLVooz1dcXExhYWG1RXBjYmJcEJEQQojq+LaIxbdFLN/v/wdpaWkEBgYy6Oa4+h3L15cJEybUe4rGy43UnWz+XnzxRQYOHEi/fv2YNGkSiqKwZs0afvnlF9555x1UVWXu3LlOi8dtEspFixaxYMECkpKSatzGWdMLCSGEaBhVVSm1qPh4KPZ+fqqqkl1qw0+v4O3xRytmUFAQ48ePb1Dx88tNUyywLhpXx44d2bx5Mw899BBz5sxBVVVefvlloHKaxv/85z/17qdcH26RUL799ts88MADjB07lrvuuounnnqKmTNn4uXlxdKlSwkPD+cf//iHq8MUQghRDU+dhoHtw0jK/+NHf4lF5USehVZ+OsJ8tfZlKYUVBHppiAuqTDRbt27NiBEjmkSRcHcitScvD126dOHnn38mPz+fkydPYrPZaNu2LWFhYU6PxS0SyjfffJOxY8eyevVqcnNzeeqpp5g4cSIjR47kscceo2/fvuTm5ro6TCGEENWIb+lLz8gg9CcKgMqWyAyjlSKTSrrGSrC3Bo0CGUYr5RUqeWU2Sv0URg0ZSLdu3WSkcj3Upti7cF8mk4mPPvqIn376icTERIqLi/Hz8yMuLo5x48Zx2223Of1HmFsMyklMTOSaa64BwMPDAwCz2QxU1tn661//yn//+1+XxSeEEKJ6FaZSurfyI8jgSa/YQBTVSomlMmk06BWM5sr/Pn+ZRudBi7adJZlsAGnRbb4OHDhAfHw8M2bM4PPPPycxMZHS0lISExP57LPPuPvuu+nSpQtHjhxxalxukVAGBATYRzH5+/vj4+PD6dOn7ev9/Pzs86c6yosvvsjgwYPx8fEhMDCwVvtMmzYNRVGqPMaNG+fQOIUQoikpOn2IUIMHJ9KLiAj0wsucR4bRSoUNPLWgVVTOGitIL66gwgYRYaF0j+/AmTwjadkFrg7fLWm12gsm/xCNzxV5gdFo5NprryUzM5MXX3yR06dPk5+fX+X/582bx9mzZ7nmmmsuWlaosblFQtm1a1f27dtnfz5w4EAWLVpEWloap0+f5p133nF4vS2z2cykSZO477776rTfuHHjSE9Ptz8++eQTB0UohBBNS4WplPzEBErNVswVNswWG9ayPPLKrNhQMZpVrCoUltvIKlMJDg1B8fIjv7iU0nIzh5LPuvotuCVpnXQOV+QFS5YsITU1lR9++IEnnnjigtH8UVFRzJ49m++++45Tp06xdOnSOsXWEG7Rh3LKlCm8/fbbmEwmPD09ee655xg9erS9TJCHhwdffvmlQ2N47rnnAOr8x/H09CQiIsIBEQkhRNNmTD+JpbQQb72WNi0MeHpo0VvLCdebMGt97NtptTr69OxGx7ZVS7+FBsio7vo41zVMOJYr8oIffviBMWPGMHz48ItuN3LkSK666iq+++47HnjggXq9Vl25RUI5ffp0pk+fbn9+xRVXcOjQIb777jv7FFxNdUaADRs20KJFC4KCghg5ciTz5s27aDkHk8mEyWSyPzcajc4IUwghGp1vi9ZE9pnIqhc/IDs7Gz8/A3/rfx1tdN7YNJVJj7+/P+PHj5dpFBuRl5eXq0NocoxGI0VFRfbnnp6eLhu4VNe84HwHDhyodVWbkSNH8sYbbzQk1Dpxi4SyOm3btuWhhx5ydRgXNW7cOG688UbatGlDYmIiTz75JOPHj2fbtm019m+ZP3++/VePEEK4Mw+fAAJiAjiWUUJaWj6BgSpl+j8unAEBAVx99dX4+vq6MMrmR2bIudCwYcOqPJ87dy7PPvus0+OoT15wvry8vFq3boaHh5OXl9fQkGvNLfpQnrN9+3bmz5/PzJkzOXHiBAClpaUkJCTUqyXviSeeuKBz7J8fR48erXe8kydP5tprr6Vbt25cf/31fP/99+zcuZMNGzbUuM/s2bMpLCy0PzZu3Fjv1xdCiKbK29ubCRMmSDIpnGLjxo1Vrq2zZ8+udrummBecz2Qy1bpLg06ns1fEcQa3aKE0m81MnjyZb7/9FlVVURSFa665hvbt26PRaBgzZgwzZ87kqaeeqtNxH3nkEaZNm3bRbdq2bduAyC88VmhoKCdPnmTUqFHVbvPnZniZGUII4e7Otaic3wIzfPhwfH3l+004h8FgwN/f/5LbNcW84M+Sk5NJSEi45HanTp1qaHh14hYJ5Zw5c/j+++9ZtGgRI0aMoGPHjvZ1Xl5eTJo0iW+//bbOCWVYWJhTq8mfOXOG3NxcIiMjnfaaQgjhart27cJqtfLBBx8AEBsbSzmefPbLLq4f2hMvvQwiEU2DO+QFc+bMYc6cOZfc7lwDnLO4RUL5ySefcN999zFjxoxqZ8SJj4/n888/d2gMqamp5OXlkZqaitVqZe/evQDExcXZWxE7derE/PnzueGGGzAajTz33HPcdNNNREREkJiYyGOPPUZcXBxjx451aKxCCNGUde/Rg7V7kzh5JosOyeH07hBz6Z2EaGJckRcsWbLEUW+nwdwioczKyqJbt241rtdqtZSWljo0hmeeeYZly5bZn/fq1QuA9evX24fvHzt2jMLCQntM+/fvZ9myZRQUFNCyZUvGjBnDCy+8IFNiCSEuK2UmM3uOp2K1qUSEtyC/3EZKRi5arYZdR1PoHBsprZTC7bgiL7jzzjsb9000IrdIKKOjoy/aCXbLli3ExcU5NIalS5destaUqqr2//b29mbNmjUOjUkIIdzB/pNpbNx7AmuZjSvi4th1NAUFhajQAM5kF3A4OV1aKYXbkbygKrcY5X3bbbfxzjvvsG3bNvuyc/0C3nvvPT777DOmTp3qqvCEEELUoKTMxK5jKRQYy0g3WinDm+T0XLw9PSg3W1BVlV1HUyg3W1wdqhCiAdyihfKpp55i+/btDB06lPj4eBRFYebMmeTl5XHmzBkmTJjAzJkzXR2mEEKIPzmYdJbsgmLatgxld04Wh09n4eutBxVM5gr8fbyosFrJLyolMlSKmwvhrtwiodTr9fz4448sX76cL774AqvVislkonv37sybN4877rjDqSOZhBBCXNq51klfbz2eHjp8vLwwmyu4c9ygKn0mNRoFb0+Zf1oId+YWCSVU3uKeMmUKU6ZMcXUoQgghauHEmSyKSsowV1hJKctF1WgoKCklM7+YzrFSPk2I5sRtEkohhBDupW3LUK65ogcANpuV06dP07p1LDHhQS6OTAjR2Nwmody8eTOLFy8mKSmJ/Pz8KiOnoLIFc9++fS6KTgghxJ/5+3rTtW3lvNJWq5UADxutW7d0cVRCCEdwi4RywYIF/POf/8TLy4uOHTsSHBzs6pCEEELUkczbLUTz5RYJ5csvv8wVV1zBd999R0CAjAIUQgh35OXl5eoQhBAO4hZ1KEtLS7n99tslmRRCCDfm4SGz4QjRXLlFQjlixAgOHDjg6jCEEEI0gE7nFjfFhBD14BYJ5Ztvvsm6det45ZVXyMvLc3U4Qggh6kGjcYtLjhCiHtziX3d0dDT33nsvTzzxBGFhYfj6+uLv71/lIbfDhRCi6dJqtTIBhRDNmFvcf3jmmWd48cUXiYqKom/fvpI8CiGEEEI0IW6RUL799ttMnDiRb775Rm6ZCCGEEEI0MW6RnZnNZiZOnCjJpBBCCCFEE+QWGdrVV1/Npk2bXB2GEEIIIYSohlsklHPnzuXw4cPcf//97N69m+zsbPLy8i54CCGEEEII53OLPpQdO3YEYO/evbzzzjs1bme1Wp0VkhBCCCGE+J1bJJTPPPOMlJsQQgghhGii3CKhfPbZZ10dghBCCCGEqIFb9KEUQgghhBBNlySUQgghhBCiQSShFEIIIYQQDSIJZS0kJydz991306ZNG7y9vWnXrh1z587FbDZfdL/y8nIeeOABQkJCMBgM3HTTTWRmZjopaiGEEEI4guQFF5KEshaOHj2KzWbjnXfe4dChQ7z22mu8/fbbPPnkkxfdb+bMmXz33Xd8/vnnbNy4kbNnz3LjjTc6KWohhBBCOILkBRdSVFVVXR2EO3r55ZdZtGgRSUlJ1a4vLCwkLCyMjz/+mL/85S9A5QcwPj6ebdu2MXDgwFq9TkJCAn369GH37t307t270eIXQgghmjtnXkOdlRc0VdJCWU+FhYUEBwfXuH737t1YLBZGjx5tX9apUydiYmLYtm1bjfuZTCaKiorsD6PR2KhxCyGEK2TlF2GuqHB1GOIyZTQaq1xbTSZTo7+Go/ICdyEJZT2cPHmSN998k3vvvbfGbTIyMtDr9QQGBlZZHh4eTkZGRo37zZ8/n4CAAPtj2LBhjRW2EEK4RH5xKV9sSGDH4WRXhyIuU8OGDatybZ0/f36jHt+ReYG7uKwTyieeeAJFUS76OHr0aJV90tLSGDduHJMmTeKee+5p9Jhmz55NYWGh/bFx48ZGfw0hhHCm/SfPcDornz3HUykqKXd1OOIytHHjxirX1tmzZ1e7XVPMC9yFW8yU4yiPPPII06ZNu+g2bdu2tf/32bNnGTFiBIMHD+bdd9+96H4RERGYzWYKCgqq/BrJzMwkIiKixv08PT3x9PS0PzcYDBd/E0II0YTlF5ey58RpQgMM5BaVsD/xDFd2j3N1WOIyYzAY8Pf3v+R2TTEvcBeXdUIZFhZGWFhYrbZNS0tjxIgR9OnThyVLlqDRXLxxt0+fPnh4eLBu3TpuuukmAI4dO0ZqaiqDBg1qcOxCCOEO9p88Q35xKW1ahgCw53gq3du1wt/Xy8WRCXEhyQvq77K+5V1baWlpDB8+nJiYGF555RWys7PJyMio0uchLS2NTp06sWPHDgACAgK4++67mTVrFuvXr2f37t1Mnz6dQYMGuf1ILiGEqI1CYxl7TpymwmolLbuA0nIz6XlFHEg64+rQhGgQyQsudFm3UNbW2rVrOXnyJCdPnqRVq1ZV1p2rumSxWDh27BilpaX2da+99hoajYabbroJk8nE2LFj+e9//+vU2IUQwlUUBdpHt6BNZGiV5X4+0jop3JvkBReSOpRNnNShFEK4u4pyI/lJewiO64tW7+3qcMRlRK6hziO3vIUQQjhUQfJ+co9upej0YVeHIoRwEEkohRBCOIylrJiCU3upMJWQn7gbq7nM1SEJIRxAEkohhBAOU5hyALMxD98WbTEVZksrpRDNlCSUQgghHOJc66TOy4Ci1aHRe0srpRDNlCSUQgghHKIkIwmrqRRreQll2SnYzGVUlJdQknnK1aEJIRqZlA0SQgjhEIaW7dF5Xzjbl3dwlAuiEUI4kiSUQgghHELn6YMhop2rwxBCOIHc8hZCCCGEEA0iCaUQQgghhGgQSSiFEEIIIUSDSEIphBBCCCEaRBJKIYQQQgjRIJJQCiGEcAlVtbk6BCFEI5GEUgghhMOpNmuV55aSQk5v+YyyvHQXRSSEaEySUAohhHCo8oJMTm/5DHNxnn1ZQep+is8cJT9xF6qqujA6IURjkIRSCCGEw6iqSn5iAsVpRylI3gtUtk4WntqHRudJ8dnjlOeddW2QQogGk4RSCCGEw5gKMihOO4rGw5vClAOYinMpSN2PuaQA79BW2Czl5CclSCulEG5OEkohhBAOoaoq+Ul7sZpK8A5phaW0mNxj2yg8tQ9Fo8NaXoLGw0taKYVoBiShFEII4RDnWicVD08qyovR6PQUJO3BWmFB5+mNrcKERqtD4+FJeUGmq8MVQjSAztUBCCGEaJ5MRTloPPQoVi1qhQWt3gutly9h8VdgiGxfZVutp7eLohRCNAZJKIUQQjiEf0xXfCPaXbBcq/dGURQXRCSEcBRJKIUQQjiEoijoPH1cHYYQwgmkD6UQQgghhGgQSSiFEEIIIUSDSEIphBBCCCEaRBJKIYQQQgjRIJJQCiGEEEKIBpGEsomLjIxk7ty5REZGujoUIYQQwq3INdR5FFUmUBVCCCGEEA0gLZRCCCGEEKJBJKEUQgghhBANIgmlEEIIIYRoEEkohRBCCCFEg0hCKYQQQgghGkQSSiGEEEII0SCSUAohhBBCiAaRhFIIIYQQQjSIztUBiEtLTU0lJyfH1WE0SyaTCU9PT1eH0WzJ+XUcObeOExoaSkxMTJ33k+/q6tX3fAr3IgllE5eamkp8fDylpaWuDqVZ0mq1WK1WV4fRbMn5dRw5t47j4+PDkSNH6pQEyXd1zepzPoX7kYSyicvJyaG0tJSPPvqI+Ph4V4fTrKxatYo5c+bIuXUQOb+OI+fWcY4cOcKUKVPIycmpUwIk39XVq+/5FO5HEko3ER8fT+/evV0dRrNy5MgRQM6to8j5dRw5t02X/E3E5UoG5QghhBBCiAZpcgnls88+S8+ePV0dxiUtXbqUwMBAV4chhBBCCOFyjZZQTps2DUVRUBQFDw8PwsPDueqqq1i8eDE2m61Bx73++usbJcZnn33WHqNOpyM0NJShQ4fy+uuvYzKZGnxsd0iEhRBCCCEaW6O2UI4bN4709HSSk5NZvXo1I0aM4KGHHuLqq6+moqKiMV+q3rp06UJ6ejqpqamsX7+eSZMmMX/+fAYPHkxxcbGrwxNCCCGEcDuNmlB6enoSERFBVFQUvXv35sknn+Tbb79l9erVLF26FICCggL++te/EhYWhr+/PyNHjmTfvn3VHu/ZZ59l2bJlfPvtt/aWxQ0bNgDw+OOP06FDB3x8fGjbti1z5szBYrFcMkadTkdERAQtW7akW7du/P3vf2fjxo0cPHiQl156yb6dyWTi0UcfJSoqCl9fXwYMGGB/7T9bunQpzz33HPv27bPHee79LliwgG7duuHr60t0dDT3338/RqOx1udUCCGEqK1Vq1YxZswYQkJCCAwMZOjQoaxYscLVYYnLgMP7UI4cOZIePXrw1VdfATBp0iSysrJYvXo1u3fvpnfv3owaNYq8vLwL9n300Ue5+eab7S2f6enpDB48GAA/Pz+WLl3K4cOHeeONN3jvvfd47bXX6hVjp06dGD9+vD1GgAcffJBt27bx6aefsn//fiZNmsS4ceM4ceLEBfvfcsstPPLII/bWz/T0dG655RYANBoNCxcu5NChQyxbtoxffvmFxx57rF5xCiGEEDWZNWsWEydOZO3ateTl5VFYWMimTZuYPHkyU6ZMaVD3MyEuxSmDcjp16kRycjKbN29mx44dfP755/Tt25f27dvzyiuvEBgYyBdffHHBfgaDAW9vb3vLZ0REBHq9HoCnn36awYMHExsbyzXXXMOjjz7KZ5991uAYobJA7ZIlS/j8888ZMmQI7dq149FHH+XKK69kyZIlF+zr7e2NwWCwt35GRETg7e0NwMMPP8yIESOIjY1l5MiRzJs3r0FxCiGEEH+2ePHiizaqLF++vMpdOCEam1PqUKqqiqIo7Nu3D6PRSEhISJX1ZWVlJCYm1umYK1asYOHChSQmJmI0GqmoqMDf3x+oTAg7d+5s3/bJJ5/kySefrFWMAAcOHMBqtdKhQ4cq25hMpgtiv5Sff/6Z+fPnc/ToUYqKiqioqKC8vJzS0lJ8fHzqdCwhhBDiz2w2G/Pmzbvkdi+//DIPP/ywvcFDiMbklITyyJEjtGnTBqPRSGRkZLV9EetSgmfbtm3cfvvtPPfcc4wdO5aAgAA+/fRTXn31VQBatmzJ3r177dsHBwfXOkYAo9GIVqtl9+7daLXaKtsZDIZax5mcnMzVV1/Nfffdx4svvkhwcDCbN2/m7rvvxmw2S0IphBCiwZKSkjh16tQlt8vPzychIYErrrjCCVGJy43DE8pffvmFAwcOMHPmTFq1akVGRgY6nY7Y2Nha7a/X6y+Yr3br1q20bt2ap556yr4sJSXF/t86nY64uLhax3j06FF+/PFHZs+eDUCvXr2wWq1kZWUxZMiQese5e/dubDYbr776KhpNZe8Cud0thBCiMeXm5jpkWyHqolETSpPJREZGBlarlczMTH788Ufmz5/P1VdfzdSpU9FoNAwaNIjrr7+ef//733To0IGzZ8/yww8/cMMNN9C3b98LjhkbG8uaNWs4duwYISEhBAQE0L59e1JTU/n000/p168fP/zwA19//XWtYqyoqCAjIwObzUZubi4bNmxg3rx59OzZk3/+858AdOjQgdtvv52pU6fy6quv0qtXL7Kzs1m3bh3du3dn4sSJ1cZ56tQp9u7dS6tWrfDz8yMuLg6LxcKbb77JNddcw5YtW3j77bcbdpKFEEKI89S2gaau2wpRF406KOfHH38kMjKS2NhYxo0bx/r161m4cCHffvstWq0WRVFYtWoVQ4cOZfr06XTo0IHJkyeTkpJCeHh4tce855576NixI3379iUsLIwtW7Zw7bXXMnPmTB588EF69uzJ1q1bmTNnTq1iPHToEJGRkcTExDB8+HA+++wzZs+ezaZNm6rczl6yZAlTp07lkUceoWPHjlx//fXs3Lmzxsntb7rpJsaNG8eIESMICwvjk08+oUePHixYsICXXnqJrl27snz5cubPn1/3EyuEEELUIDw8nGHDhl1yu/j4eLp37+6EiMRlSRVN2u7du1VA3b17t6tDaXY++ugjObcOJOfXceTcOk59v3Nd/V29e/du1dPTUwWqfWg0GnXt2rUuiUs+q5eHJjeXtxBCCCHqpnfv3qxcubLaSiQGg4GPP/6Y0aNHuyAycblwyihvIYQQQjjWmDFjSEpK4sMPP2T37t1YrVZ69OjB1KlTCQ0NdXV4opmThFIIIYRoJvz9/XnggQdcHYa4DMktbyGEEEII0SCSUAohhBBCiAaRW95uYtWqVRw5csTVYTQrW7ZsAeTcOoqcX8eRc+s4tZlx5mLkb1JVQ8+ncB+Kqqqqq4MQNdu2bRtDhgy5YBYe0Tg0Gg02m83VYTRbcn4dR86t42i1WjZt2sSgQYNqvY98V9esPudTuB9poWziPD09sVqtfPTRR8THx7s6nGZl1apVzJkzR86tg8j5dRw5t45z5MgRpkyZgqenZ532k+/q6tX3fAr3Iwmlm4iPj6d3796uDqNZOXdbSs6tY8j5dRw5t02X/E3E5UoG5QghhBBCiAaRhFIIIYQQQjSIJJSN5O2338bPz4+Kigr7MqPRiIeHB8OHD6+y7YYNG1AUhcTERCdHKYQQQgjR+CShbCQjRozAaDSya9cu+7JNmzYRERHBb7/9Rnl5uX35+vXriYmJoV27dq4IVQghhBCiUUlC2Ug6duxIZGQkGzZssC/bsGED1113HW3atGH79u1Vlo8YMcIFUTrWqlWrGDNmDCEhIQQGBjJ06FBWrFjh6rCaBTm3QgghmjJJKBvRiBEjWL9+vf35+vXrGT58OMOGDbMvLysr47fffmt2CeWsWbOYOHEia9euJS8vj8LCQjZt2sTkyZOZMmWK1MtrADm3QgghmjpJKBvRiBEj2LJlCxUVFRQXF7Nnzx6GDRvG0KFD7S2X27Ztw2QyNauEcvHixbz22ms1rl++fDkvvfSSEyNqPuTcCiGEcAeSUDai4cOHU1JSws6dO9m0aRMdOnQgLCyMYcOG2ftRbtiwgbZt2xITE+PqcBuFzWZj3rx5l9zu5ZdfpqyszAkRNR9yboUQQrgLSSgbUVxcHK1atWL9+vWsX7+eYcOGAdCyZUuio6PZunUr69evZ+TIkS6OtPEkJSXVaq7W/Px8EhISnBBR8yHnVgghhLuQhLKRjRgxgg0bNrBhw4Yq5YKGDh3K6tWr2bFjR7O63Z2bm+uQbYWcWyGEEO5DEspGNmLECDZv3szevXvtLZQAw4YN45133sFsNjerhDI2NtYh2wo5t0IIIdyHJJSNbMSIEZSVlREXF0d4eLh9+bBhwyguLraXF2ouwsPDqyTONYmPj6d79+5OiKj5kHMrhBDCXUhC2chiY2NRVZUjR45UWd66dWtUVeXo0aMuisxxFixYgKenZ43rNRoNCxcudGJEzYecWyGEEO5AEkrRYL1792blypWEhIRcsM5gMPDxxx8zevRoF0Tm/uTcCiGEcAc6VwcgmocxY8aQlJTEhx9+yO7du7FarfTo0YOpU6cSGhrq6vDcmpxbIYQQTZ0klKLR+Pv788ADD7g6jGZJzq0QQoimTG55CyGEEEKIBpGEUgghhBBCNIjc8nYTq1atumDkuGiYLVu2AHJuHUXOr+PIuXWc2sxOdTHyN6mqoedTuA9FVVXV1UGImm3bto0hQ4ZgtVpdHUqzpNFosNlsrg6j2ZLz6zhybh1Hq9WyadMmBg0aVOt95Lu6ZvU5n8L9SAtlE+fp6YnVauWjjz4iPj7e1eE0K6tWrWLOnDlybh1Ezq/jyLl1nCNHjjBlypSL1n+tjnxXV6++51O4H0ko3UR8fDy9e/d2dRjNyrnbUnJuHUPOr+PIuW265G8iLlcyKEcIIYQQQjSIJJRCCCGEEKJBJKF0gGnTpqEoCn/7298uWPfAAw+gKArTpk1zfmBCCCGEEA4gCaWDREdH8+mnn1JWVmZfVl5ezscff0xMTIwLIxNCCCGEaFySUDpI7969iY6O5quvvrIv++qrr4iJiaFXr14ujEwIcb5Vq1YxZswYQkJCCAwMZOjQoaxYscLVYTULcm6FuHxIQulAd911F0uWLLE/X7x4MdOnT3dhREKI882aNYuJEyeydu1a8vLyKCwsZNOmTUyePJkpU6ZInccGkHMrxOVFEkoHmjJlCps3byYlJYWUlBS2bNnClClTXB2WEILKH3ivvfZajeuXL1/OSy+95MSImg85t0JcfiShdKCwsDAmTpzI0qVLWbJkCRMnTiQ0NNTVYQlx2bPZbMybN++S27388stV+kGLS5NzK8TlSRJKB7vrrrtYunQpy5Yt46677nJ1OEIIICkpqVZzDOfn55OQkOCEiJoPObdCXJ5kphwHGzduHGazGUVRGDt2rKvDEUIAubm5DtlWyLkV4nIlCaWDabVa+zRpWq3WxdEIIQBiY2Mdsq2QcyvE5UpueTuBv78//v7+rg5DCPG78PBwhg0bdsnt4uPj6d69uxMiaj7k3ApxeZIWSgdYunTpRdd/8803TolDCFGzBQsWMHjwYEwmU7XrNRoNCxcudHJUzYOcWyEuP9JCKYS4LPXu3ZuVK1cSEhJywTqDwcDHH3/M6NGjXRCZ+5NzK8TlR1oohRCXrTFjxpCUlMSHH37I7t27sVqt9OjRg6lTp0qJrwaScyvE5UUSSiHEZc3f358HHnjA1WE0S3Juhbh8yC1vIYQQQgjRIJJQCiGEEEKIBpFb3m5i1apV9nqWonFs2bIFkHPrKHJ+HUfOrePUZpafi5G/SVUNPZ/CfSiqqqquDkLUbNu2bQwZMgSr1erqUJoljUaDzWZzdRjNlpxfx5Fz6zharZZNmzYxaNCgWu8j39U1q8/5FO5HWiibOE9PT6xWKx999BHx8fGuDqdZWbVqFXPmzJFz6yByfh1Hzq3jHDlyhClTpuDp6Vmn/eS7unr1PZ/C/UhC6Sbi4+Pp3bu3q8NoVs7dlpJz6xhyfh1Hzm3TJX8TcbmSQTlCCCGEEKJBmk1CuXTpUgIDA536msOHD+fhhx926msKIYQQQjQ1bpVQTps2DUVRUBQFvV5PXFwczz//PBUVFa4OTQghhBDisuV2fSjHjRvHkiVLMJlMrFq1igceeAAPDw8iIyNdHZoQQgghxGXJrVoooXIkXUREBK1bt+a+++5j9OjRrFy58oLtEhMTue666wgPD8dgMNCvXz9+/vnnKtuYTCYef/xxoqOj8fT0JC4ujg8++MC+/uDBg4wfPx6DwUB4eDh33HEHOTk5VY5RUVHBgw8+SEBAAKGhocyZM4fzKzHl5+czdepUgoKC8PHxYfz48Zw4caKRz0rTsGrVKsaMGUNISAiBgYEMHTqUFStWuDosIS5KPrdCCNFwbpdQ/pm3tzdms/mC5UajkQkTJrBu3Tr27NnDuHHjuOaaa0hNTbVvM3XqVD755BMWLlzIkSNHeOeddzAYDAAUFBQwcuRIevXqxa5du/jxxx/JzMzk5ptvrvI6y5YtQ6fTsWPHDt544w0WLFjA+++/b18/bdo0du3axcqVK9m2bRuqqjJhwgQsFouDzohrzJo1i4kTJ7J27Vry8vIoLCxk06ZNTJ48mSlTpki9PNEkyedWCCEah9vd8j5HVVXWrVvHmjVr+Pvf/37B+h49etCjRw/78xdeeIGvv/6alStX8uCDD3L8+HE+++wz1q5dy+jRowFo27atffu33nqLXr168a9//cu+bPHixURHR3P8+HE6dOgAQHR0NK+99hqKotCxY0cOHDjAa6+9xj333MOJEydYuXIlW7ZsYfDgwQAsX76c6OhovvnmGyZNmuSQc+Nsixcv5rXXXqtx/fLly+nSpQuzZ892YlRCXJx8boUQovG4XQvl999/j8FgwMvLi/Hjx3PLLbfw7LPPXrCd0Wjk0UcfJT4+nsDAQAwGA0eOHLG3UO7duxetVsuwYcOqfZ19+/axfv16DAaD/dGpUyeg8nb6OQMHDkRRFPvzQYMGceLECaxWK0eOHEGn0zFgwAD7+pCQEDp27Nhspuay2WzMmzfvktu9/PLLlJWVOSEiIS5NPrdCCNG43K6FcsSIESxatAi9Xk/Lli3R6ap/C48++ihr167llVdeIS4uDm9vb/7yl7/Yb497e3tf9HWMRiPXXHMNL7300gXrZADQH5KSkmo1V2t+fj4JCQlcccUVTohKiIuTz60QQjQut0sofX19iYuLu+R2W7ZsYdq0adxwww1AZYKYnJxsX9+tWzdsNhsbN2603/I+X+/evfnyyy+JjY2tMWkF+O2336o83759O+3bt0er1RIfH09FRQW//fab/ZZ3bm4ux44do3PnzrV5u01ebm6uQ7YVwpHkcyuEEI3L7W5511b79u356quv2Lt3L/v27eO2226r0sE+NjaWO++8k7vuuotvvvmGU6dOsWHDBj777DMAHnjgAfLy8rj11lvZuXMniYn/3969B0VV9nEA/y68sNyWqwZIEZoGOIEOmgalbxmuNpJgmGUQYl7GwgvZxaxBnZSEcmjCHGoKI0XsMkmirZAXJLSNbMnC2phKjCkujanAgBIu5/2jYd9WWIXdPZy9fD8zzLznnOc87+/89pnHX+f6KyoqKrB48WLodDp9P42NjVi7di3q6+uxd+9ebN++HWvWrNHHkJiYiGXLluHEiRP47rvvkJqaipCQECQmJg5vQkQSFhYmSlsiMXHcEhFZlt0WlHl5efDz80NcXBwefPBBzJo1q9/3VQsKCjB//nw89dRTiIiIwLJly9DZ2QkAGDVqFE6ePAmdTgelUomoqChkZmbC19cXTk7/T1taWhouX76MKVOmICMjA2vWrMHy5cv129977z1MmjQJCQkJiI2NhSAIUKlUcHFxGZ5EiCwwMNDofaj/FhkZiejo6GGIiOjGOG6JiCzLpi55FxUVGd2Wnp6O9PR0/XJYWBiOHTtm0CYjI8Ng2c3NDXl5ecjLyxuwz76znMYcP35c/78LCgoGbOPn54ddu3YZ7cMe5OXlIS4uDt3d3QNud3JyQn5+/jBHRXR9HLdERJZjt2coafjExMSgrKwMAQEB/bZ5eXmhpKRkwPtUiaTEcUtEZDk2dYaSrJdSqcTZs2exe/duaDQa6HQ6TJgwAWlpaRgxYoTU4RENiOOWiMgyWFCSxXh7e/e7rYDI2nHcEhGZj5e8iYiIiMgsLCiJiIiIyCy85G0jVCqV3Xyu0VqcPHkSAHMrFuZXPMyteAbzBaXr4W9iyNx8ku2QCYIgSB0EGadWqzFt2jSDl6mT5Tg5ORm88J4si/kVD3MrHmdnZ1RXVyM2NnbQ+3CuNs6UfJLt4RlKKyeXy6HT6VBcXIzIyEipw7ErKpUKWVlZzK1ImF/xMLfi0Wq1SE1NhVwuH9J+nKsHZmo+yfawoLQRkZGR/b70Q+bpuyzF3IqD+RUPc2u9+JuQo+JDOURERERkFhaURERERGQWhy4o09PTIZPJIJPJ4OLigsDAQMycORM7d+7kze5EREREg+TQBSUAzJ49G83NzTh37hwOHTqE++67D2vWrEFCQgKuXr064D49PT3DHCURERGR9XL4glIulyMoKAghISGIiYnBiy++iP379+PQoUMoKioCAMhkMhQUFGDu3Lnw9PREdnY2ioqK4Ovra9DXp59+CplMZrBuy5YtuOmmm6BQKLB06VK88MILmDhx4vAcHNkNlUoFpVKJgIAA+Pr6Yvr06fjwww+lDovoujhuiRyHwxeUA5kxYwYmTJiAffv26ddt2rQJ8+bNQ11dHZ544olB9bNnzx5kZ2cjNzcXGo0GoaGhKCgoECtsslNr167FnDlzcPjwYVy4cAFtbW2orq7Go48+igoykuIAABDlSURBVNTUVN6eQVaJ45bIsbCgNCIiIgLnzp3TLz/22GNYvHgxxowZg9DQ0EH1sX37dixZsgSLFy/G7bffjg0bNiAqKkqkiMke7dy5E6+//rrR7Xv27EFubu4wRkR0Yxy3RI6HBaURgiAYXL6ePHnykPuor6/HlClTDNZdu0xkTG9vL7Zs2XLDdq+99houX748DBER3RjHLZFjYkFphFarxejRo/XLnp6eBtudnJxw7Vcr+bAOWdLZs2cH9R3cixcvora2dhgiIroxjlsix8SCcgDHjh1DXV0dkpOTjbYZOXIkOjo60NnZqV93+vRpgzbh4eE4deqUwbprl4mM+euvv0RpSyQmjlsix+Twn17s7u5GS0sLdDodWltbUV5ejq1btyIhIQFpaWlG95s6dSo8PDzw4osvYvXq1aipqdE/Fd5n1apVWLZsGSZPnoy4uDh8+OGH+P777zFmzBiRj4rsQVhYmChticTEcUvkmBz+DGV5eTmCg4MRFhaG2bNno7KyEvn5+di/fz+cnZ2N7ufv74/i4mKoVCpERUVh79692LRpk0GblJQUrF+/Hs8++yxiYmLQ0NCA9PR0uLm5iXxUZA8CAwPx3//+94btIiMjER0dPQwREd0Yxy2RY3LoM5RFRUX9zioO5Np7JfskJSUhKSnJYN2yZcsMlrOyspCVlaVfnjlzJsaOHTvkWMkx5eXlIS4uDt3d3QNud3JyQn5+/jBHRXR9HLdEjsfhz1CKqaurC3l5efjhhx/w008/YePGjThy5AgWLVokdWhkI2JiYlBWVoaAgIB+27y8vFBSUoL4+HgJIiMyjuOWyPE49BlKsclkMqhUKmRnZ+PKlSsIDw/HJ598womUhkSpVOLs2bPYvXs3NBoNdDodJkyYgLS0NIwYMULq8IgGxHFL5FhYUIrI3d0dR44ckToMsgPe3t7IyMiQOgyiIeG4JXIcvORNRERERGZhQUlEREREZuElbxuhUqmg1WqlDsOunDx5EgBzKxbmVzzMrXgG85Wf6+FvYsjcfJLtkAnG3olDVkGtVmPatGnQ6XRSh2KXnJyc0NvbK3UYdov5FQ9zKx5nZ2dUV1cjNjZ20PtwrjbOlHyS7eEZSisnl8uh0+lQXFyMyMhIqcOxKyqVCllZWcytSJhf8TC34tFqtUhNTYVcLh/SfpyrB2ZqPsn2sKC0EZGRkYiJiZE6DLvSd1mKuRUH8yse5tZ68TchR8WHcoiIiIjILCwoiYiIiMgsLCjNlJ6eDplMhpycHIP1n376KWQymURRiWvr1q248847oVAocNNNNyEpKQn19fUGba5cuYKMjAwEBATAy8sLycnJaG1tlShi28Hcioe5FRfzS+TYWFBagJubG3Jzc3Hx4kWpQxkWVVVVyMjIwFdffYXDhw+jp6cHSqUSnZ2d+jZPP/00Dhw4gI8//hhVVVVoamrCQw89JGHUtoG5FQ9zKy7ml8jBCWSWRYsWCQkJCUJERITw3HPP6deXlpYKfek9d+6ckJCQIPj6+goeHh7C+PHjhc8++2xQ/Ws0GgGAoNFoRInfEv78808BgFBVVSUIgiBcunRJcHFxET7++GN9G61WKwAQ1Gq1VGH2U1xczNyKyNrzy9yKy1bza+qcawtztRSYF8fBM5QW4OzsjFdeeQXbt2/H77//3m97RkYGuru78cUXX6Curg65ubnw8vKSIFJxtLW1AQD8/f0BABqNBj09PYiPj9e3iYiIQGhoKNRqtSQx2irmVjzMrbiYXyLHwtcGWci8efMwceJEbNy4EYWFhQbbGhsbkZycjKioKADAmDFjpAhRFL29vcjMzMTdd9+NO+64AwDQ0tICV1dX+Pr6GrQNDAxES0uLBFHaJuZWPMytuJhfIsfDgtKCcnNzMWPGDDz77LMG61evXo0nn3wSn3/+OeLj45GcnIzo6GiJorSsjIwMnDlzBidOnJA6FLvD3IqHuRUX80vkeHjJ24KmT5+OWbNmYf369Qbrly5dirNnz+Lxxx9HXV0dJk+ejO3bt0sUpeWsXLkSBw8eRGVlJW6++Wb9+qCgIPz999+4dOmSQfvW1lYEBQUNc5S2ibkVD3MrLuaXyDGxoLSwnJwcHDhwoN89QbfccgtWrFiBffv24ZlnnsE777wjUYTmEwQBK1euRGlpKY4dO4bRo0cbbJ80aRJcXFxw9OhR/br6+no0NjbyW643wNyKh7kVF/NL5Nh4ydvCoqKikJKSgvz8fP26zMxMPPDAA7j99ttx8eJFVFZW2vS3XjMyMlBSUoL9+/dDoVDo73/y8fGBu7s7fHx8sGTJEqxduxb+/v7w9vbGqlWrEBsbi7vuukvi6K0bcyse5lZczC+Rg5P2IXPbt2jRIiExMdFgXUNDg+Dq6qp/bdDKlSuF2267TZDL5cLIkSOFxx9/XDh//vyg+rfGVy4AGPDvvffe07e5fPmy8NRTTwl+fn6Ch4eHMG/ePKG5uVm6oAdgja9esZfcCoL15Ze5FZe95JevDbIs5sVx8AylmYqKivqtCwsLQ3d3t37ZHu6X/DdBEG7Yxs3NDTt27MCOHTuGISL7wdyKh7kVF/NL5Nh4DyURERERmYUFJRERERGZhQUlEREREZmFBSURERERmYUFJRERERGZhU952wiVSgWtVit1GHbl5MmTAJhbsTC/4mFuxdPQ0GDW/vxNDJmbT7IdMmEw73ogyajVakybNg06nU7qUOySk5MTent7pQ7DbjG/4mFuxePs7Izq6uohfcGHc7VxpuSTbA/PUFo5uVwOnU6H4uJim/66jjVSqVTIyspibkXC/IqHuRWPVqtFamoq5HL5kPbjXD0wU/NJtocFpY2IjIxETEyM1GHYlb7LUsytOJhf8TC31ou/CTkqPpRDRERERGZhQUlEREREZmFBOQzUajVkMhnmzJkjdShkZQRBwIYNGxAcHAx3d3fEx8fj559/vu4+BQUFiI6Ohre3N7y9vREbG4tDhw7pt1+4cAGrVq1CeHg43N3dERoaitWrV6OtrU3swyEHsW/fPiiVSgQEBEAmk+H06dM33Oedd97BtGnT4OfnBz8/P8THx+Prr7/Wb+/p6cG6desQFRUFT09PjBo1CmlpaWhqahLxSGyHKXNFR0cHMjMzceutt8Ld3R1xcXE4deqU2f0SDYQF5TAoLCzEwoULcfToUU6OZODVV19Ffn4+3nrrLdTU1MDT0xOzZs3ClStXjO5z8803IycnBxqNBt988w1mzJiBxMRE/PDDDwCApqYmNDU1Ydu2bThz5gyKiopQXl6OJUuWDNdhkZ3r7OzEPffcg9zc3EHvc/z4cSxcuBCVlZVQq9W45ZZboFQq8ccffwAAurq6UFtbi6ysLNTW1mLfvn2or6/H3LlzxToMm2LKXLF06VIcPnwYu3fvRl1dHZRKJeLj4/U5N7VfogEJJKqOjg7By8tLqKmpEWbPni1kZ2cPaX+NRiMAEDQajUgROq7i4mJJc9vb2ysEBQUJr732mn7dpUuXBLlcLuzdu3dIffn5+Qnvvvuu0e0fffSR4OrqKvT09Jgc71BJnV97Zi25bWhoEAAI33777ZD3vXr1qqBQKIT333/faJuvv/5aACD89ttvZkQ5NKbOuWLO1abMFV1dXYKzs7Nw8OBBg/UxMTHCSy+9ZHK/Q8V/wxwHz1CK7KOPPkJQUBCmTJmClJQU7Ny5EwJf/Un454W/LS0tiI+P16/z8fHB1KlToVarB9WHTqfDBx98gM7Ozuu+462trQ3e3t74z3/4YgeyDl1dXejp6YG/v7/RNm1tbZDJZPD19R2+wKyQKXPF1atXodPp4ObmZrDe3d0dJ06cMLlfImNYUIqssLAQKSkpAICkpCQ0NzejqqpK4qjEsWfPHnh5een/qqurpQ7JqrW0tAAAAgMDDdYHBgbqtxlTV1cHLy8vyOVyrFixAqWlpRg/fvyAbc+fP4/Nmzdj+fLllgncznDcSmPdunUYNWqUQTHzb1euXMG6deuwcOFCeHt7D3N01sWUuUKhUCA2NhabN29GU1OT/h2ZarUazc3NJvdLZAwLShHV19fjyy+/1BeUXl5eSExMRGFhocSRiWPu3Lk4ffq0/m/y5MlSh2RVri1cenp6TO4rPDwcp0+fRk1NDZ588kksWrQIP/74Y7927e3tmDNnDsaPH49NmzaZEb394ri9PjEK7pycHHzwwQcoLS3tdwYN+OcBnQULFkAQBBQUFJj9/2drLDVX7N69G4IgICQkBHK5HPn5+Vi4cCGcnPhPP1ker3+JqLCwEHfeeSfGjRunX5eSkoKHH34Yb775Jnx8fCSMzvIUCgUUCoXUYVituXPnYurUqfrl7u5uAEBrayuCg4P161tbWzFx4sTr9uXq6oqxY8cCACZNmoRTp07hjTfewNtvv61v09HRgdmzZ0OhUKC0tBQuLi4WPBr7wXF7fdeO25CQELP627ZtG3JycnDkyBFER0f3295XTP722284duyYQ56dtNRccdttt6GqqgqdnZ1ob29HcHAwHnnkEYwZMwYAEBQUZFK/RAPhf6aI5OrVq9i1axcee+wxg/VKpRIeHh7Yu3evRJGRVBQKBcaOHav/Gz9+PIKCgnD06FF9m/b2dtTU1Az5m7e9vb36f3T6+lEqlXB1dUVZWdmAZ4GIBuPacevu7m5yX6+++io2b96M8vLyAc8E9xWTP//8M44cOYKAgABzQrdZlp4rPD09ERwcjIsXL6KiogKJiYkAgNGjR1tsDiLiGUqRHDx4EK2trbjjjjtw5swZg23Tp09HYWEhVqxYIVF0ZA1kMhkyMzOxZcsWjBs3DqNHj0ZWVhZGjRqFpKQkfbv7778f8+bNw8qVKwEA69evxwMPPIDQ0FB0dHSgpKQEx48fR0VFBYD/F5NdXV0oLi5Ge3s72tvbAQAjR46Es7PzsB8r2ZcLFy6gsbFR/xq0+vp6AP+c8eo765WWloaQkBBs3boVAJCbm4sNGzagpKQEYWFh+nv0/n1Zd/78+aitrcXBgweh0+n0bfz9/eHq6jrch2k1TJ0rKioqIAgCwsPD8csvv+C5555DREQEFi9ePKR+iQaDBaVI+u6TnDlzptE233///YCXfMhxPP/88+js7MTy5ctx6dIl3HPPPSgvLzc4o/jrr7/i/Pnz+uU///wTaWlpaG5uho+PD6Kjo1FRUaEfa7W1taipqQEA/WXxPg0NDQgLCxP/wMiulZWV6YsSAHj00UcBABs3btTfq9vY2Ghwr15BQQH+/vtvzJ8/36Cvvn3++OMPlJWVAUC/y62VlZW49957LX8gNsSUuaKtrQ3r16/H77//Dn9/fyQnJyM7O9vg9pfB9Es0GCwoRXLgwAGpQyAbIJPJ8PLLL+Pll1822ubcuXMGyzd6qOvee+/lq6lIVOnp6UhPT79um+PHjxssXzuOrxUWFsZxex2mzBULFizAggULzO6XaDB4DyURERERmYUFJRERERGZhQUlEREREZmFBSURERERmYUFJRERERGZhU952witVit1CHanoaEBAHMrFuZXPMyteMzNKX8TQ8yH45AJfE+DVWtsbERkZCS6urqkDsUuOTs7Q6fTSR2G3WJ+xcPcisfDwwNarRahoaGD3odztXGm5JNsDwtKG9DY2GjwslqynO7ubsjlcqnDsFvMr3iYW/GMGDHCpOKHc/XATM0n2RYWlERERERkFj6UQ0RERERmYUFJRERERGZhQUlEREREZmFBSURERERmYUFJRERERGZhQUlEREREZmFBSURERERm+R9CtPChL3a1uAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "paired_delta2.mean_diff.plot(gridkey='auto');" - ] - }, - { - "cell_type": "markdown", - "id": "e0136f07", - "metadata": {}, - "source": [ - "Gridkey kwargs can be utilised via `gridkey_kwargs` in the `.plot()` method.\n", - "\n", - "The relevant inputs to `gridkey_kwargs` are:\n", - "\n", - "- `'show_es'` - Whether to show the effect size in the gridkey\n", - "- `'show_Ns'` - Whether to show the sample sizes in the gridkey\n", - "- `'merge_pairs'` - Whether to merge the pairs in the gridkey (paired data only)\n", - "- `'delimiters'` - Delimiters to use for the autoparser. E.g., [';', '>', '_']\n", - "- `'marker'` - Marker to use for filling the gridkey\n", - "- `'fontsize'` - Font size of the gridkey text\n", - "- `'labels_fontsize'` - Font size of the labels in the gridkey" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5d20e84a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group = dabest.load(df, idx=((\"Control 1\", \"Control 2\"), (\"Test 1\", \"Test 2\")),paired='baseline', id_col='ID')\n", - "multi_2group.mean_diff.plot(gridkey=['Control', 'Test'], \n", - " gridkey_kwargs={'merge_pairs': True, 'show_es': False, 'show_Ns': False, 'marker': '√',\n", - " 'fontsize': 8, 'labels_fontsize': 12});" - ] - }, - { - "cell_type": "markdown", - "id": "4e5b0d2f", - "metadata": {}, - "source": [ - "## Delta dot\n", - "\n", - "By default, delta dots are included in paired experiment plots (excluding proportion plots). \n", - "\n", - "This feature can be turned off by setting `delta_dot=False` in the `.plot()` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "74b02990", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Ui8xI/uE4Dnq9HnK5HG1tbaitrYVUKl3U97BareB5Hr29veA4DkVFRVmPCz3hvr4+9PT0IBqNori4mP2N6fV62O12uN1uqNXqSVMuM/1sLpcLEokEPT09SKVSKCgoWNSfjSw9CtaETGFoaAh9fX0s53PmwrBwOAy3243x8XFoNBq2NYcsLxzHoaKiAk1NTWhvb0d1dfWij4bYbDY2OsNxHAoLCye1oaSkBAqFAr29vYhGoygvL2dTLEVFRQiFQujs7ERdXd2c9u+XlJRALBbD7XYjmUyiuLh4UX82srRonI6QDIlEAm1tbejp6YHZbEZdXR0L1NOt8KZAvbzEYjGWV1smk6GyshKRSGRO88NzYbfbUVRUBI/HM22ZS6vVisrKSoRCoaw900IvOZ1Oz6t9hYWFrADIqXKVk/xGwZqQ/+P3+9HQ0IBQKITKyko4nU6IRKJJK7xLS0tphfcyxfM8xsfHMTo6ygKiSqViSU36+/tPy/sWFBSwMpcDAwNTHqPT6VBbWwue57MWnslkMpSVlcHv90/73JnYbDaWq3xiWlSyfNAwOFnx0uk0+vr6MDQ0BL1ej7KyMkgkEqTTaXi9XgwMDIDneTgcDtjtdlo4toxxHAeTyYRwOIzR0VG43W4UFhayNQdutxsKheK01B13OBzgeR5utxscx01ZMlWhUKC2thYdHR1obW2F0+mExWKBXq9HQUEB+vv7odFo5jyaYzabIRaL0dHRgba2NlRUVNDf8TJDwZqsaOFwGJ2dnYjH43A6nWxR0PDwMPr7+5FIJGCxWOBwOBa8ACkWi0Emk9FWrhwTi8XQ6/WQSqUYHBxEIBCAy+VCQUEBotEourq6IJPJTsv0RmFhYdYcts1mm3SMRCJBVVUVenp60N3djWg0iqKiIhQWFrI62vX19XPOP28wGFBVVYW2tja0tLSgsrKSctgvI3RpNYP/+q//AsdxuO2223LdFLLIeJ7HwMAAmpqaIBKJUFdXB6vVynJ4d3V1Qa1WY9WqVXA6nfMO1DzPY2xsDK2trTh+/Djtfc0jarUaNTU1SCaTaGxsZIVY1Go12tvbT1uubWFnQW9v77RbqziOQ2lpKZtvbm9vRzqdRnl5OXieR2dn57zeW6vVoqamhqXLpQIgywcF62m8++67+NWvfoW1a9fmuilkkcXjcbS0tMDtdsNut6O2thbpdBotLS1obW2FWCxGbW0tysvLIZfL5/0e/f39OHbsGNrb25FKpVBWVkbz3HlGrVajrq4Oer0enZ2d6O7uRllZGcRi8WmtF11cXAybzYaenh74fL5pj7PZbKiqqkIwGERzczN4nofL5cL4+Pi0i9VORSgAkkql0NzcTAVAlgkK1lMIBoO48cYb8dBDD8FoNOa6OWQRjYyMoKGhgeX1tlgs6OrqQmNjIxKJBFvhrVar5/zaPM/D7/ejra0Nx44dg9frhcFgQH19PWpra2E2m2meMA+JxWK4XC6UlZVhdHQUra2tKCwsRCKRQEdHx2lbkFVSUgKr1Yru7m4MDw9Pe5yw8CydTqOxsREikQgOhwP9/f2Tsp/NllAABAAVAFkm6JtjCrfccguuuuoqXHLJJac8NhaLYXx8nN0oiX5+SqVS6OzsRGdnJ/R6PaqrqzE2NoYTJ04seIV3IpGAx+PB8ePH0dbWhkQigdLSUqxduxZOpxNKpXLxfyCy6MxmM+rr6yEWi9HV1QWtVotAIDBlytDFIiwg6+rqwsjIyLTHCQvPFAoFWlpaIJPJoNVq0dnZOe+hbCGHgFAA5HQUNyGLh1YXTPDHP/4Rhw4dwrvvvjur4++77z7ce++9p7lVZCECgQC6urqQSqVQWlrK5igXusJ7fHwcPp8PY2NjbJWx1WqlylrLmJAG1O12Y3BwEMlkEh6PBwqFYsrFYIuhtLQUANg89HQr0YViHd3d3eju7obZbGZZ9qqqqua1cFF4TWHRWUVFBXQ63fx/GHLaULDO0Nvbi6997Wt49dVXZ5028q677sLtt9/O/n348GFs3br1dDWRzAHP82xfq1qthsVigcfjQSKRgNVqhcPhmPNq2GQyCZ/PB5/Ph1gsBqVSiZKSEphMpqzCHmT5EspZ6nQ6dHZ2wuv1orGxEXK5HHq9/rS8p9PpBM/z6OrqAsdx006/cRyHsrIyKJVK9PX1QSKRwO/3w+PxTMqONltisRhVVVVsW5fL5aLpvzxEwTrDwYMH4fV6sWHDBnZfKpXCvn378POf/xyxWGzSF7JcLs9ahETZrPJDZl5vrVaLRCKB/v5+GI1GFBUVzXnhWCAQgM/nY6u5hVKI9HmfuXQ6Herr66FUKtHY2Ii3334bF1544WkZORFWfwsrvTmOm3FKxm63Q6FQoKOjA5FIBD09PdBoNPPuFQsFQLq6utDR0YHS0lJYLJZ5/jTkdKBgnWHHjh04duxY1n2f+cxnUFtbi29961vLsucUjUYxODgIpVIJhUIBhUIBmUyW62adVkJe71QqBalUikAgAI1Gw6oczVYymcTIyAiGhoYQjUahUChQVFQEs9lM+1NXCKlUiurqami1Wrz55pt47bXXsH379tNykSb0mnmeR0dHByoqKmbsyev1etTW1qK1tRXd3d1oaGjAxo0b573NUEhtKhaL0d3djWQySQVA8siy/sZJpVJ46qmnsHv3bni9Xnz3u9/FmjVr4Pf7sXPnTmzZsmXKLEHT0Wq1WL16ddZ9arUaZrN50v3LRTKZRCgUwsjICMuHLBaLWeBWKBQskC/3hB3JZBJdXV0YGhpCKpWCXC6HRCJBWVnZnIYvQ6EQhoaGMDo6Cp7nYTAY4HQ6odVqT2PryVKJRCJIJpNzeo7D4cCOHTuwc+dO/P3vf8dFF110WnqeQsDs6OhAe3s7KisrZ+wtK5VK1NfXQyQS4cSJExCJRDjnnHMWdB47nU5IJBK43W6kUqlJ1cJIbizbYD02NoYrrrgCBw4cgEajQSgUwq233grg5FD0V7/6Vdx00034z//8zxy3NLc0Gg3q6+vB8zzi8Tii0SgikQii0Sii0SjGxsbYXlKRSAS5XJ7VC1cqlZDL5XkfxIUtUz6fDwqFAkajEYWFhTCbzbNqeyqVYr3oSCQCuVwOh8MBi8WyaL3o1157DZFIBEqlclY7Dcji43ke4XAYY2Njc96eZzAYcNlll2Hv3r3Yv38/1q1bxypbLSaO41BeXo729na0tbWdMmBLJBJ2jh85cgQSiSRrKm8+CgsLIRaL0dfXh2QyCafTmfffAWe6ZRus77zzTpw4cQKvvPIKzjrrrKyVmmKxGNdddx1eeumlBQfrPXv2LLCl+YHjODa/PrGXKQTxzEA+Pj7Oeh/CczN74cIt1/uG0+k0enp6WHIHh8OB4uLiWa/wDofDGBoawsjICHieh16vZ4uLFturr76KsbExGAwGCtY5Iqzaj0Qi8Pv9bH52tgFXq9XiggsuwMGDB9HS0oJgMIjy8vJFn8cWyne2t7ezHvZMIzsikQhr1qwBz/M4evQoxGIx1qxZs6Dz0263QyKRsJ0ULpeLAnYOLdtg/de//hW33norLr300ikTClRXV+PRRx9d+oYtQzKZDDKZbFKASiaTWb3waDQKn8+Xta9TCOITA/lSzO+HQiEcPnwYbreb7ZGdzQrvdDrNetHhcBgymQwFBQWwWCwLzv9N8h/HcdBoNFAoFCy97FwCrsFgwKpVq9DZ2YlAIICmpiaWQnSx25nZw66qqjrlXPnatWuRSCTQ1NQEAKivr1/Q3zQVAMkfyzZY+/1+uFyuaR9PJBJznpci2SQSCbRa7aQr+lQqNeVw+uDgIDtGKpVOGk5XKBSLMqTM8zxaW1tx9OhRAMCaNWtmlRo0EomwXnQqlYJer2dDjNRjWBnS6TTa2tqg1WphMBhQV1eHjo4ONDU1sRSgs1FQUIBYLAafzwe1Wo2+vj6Mj4+jrKxsUS/4hFXabW1taG1tnVXAXrduHZLJJLq7uwEAVVVVC+r5ZxYAaW1tRWVl5bJcbLvcLdtgXVFRgUOHDk37+N///nfU19cvYYvyUyAQQGdnJ5RKJQuYwn/ne8KJxWKo1epJc37pdDqrFx6JRDA+Po6hoSGWslEikUw5nD7bFepjY2N45513MDQ0BKfTiQ0bNsz45ZVOpzE6OoqhoSGEQiFIpVLYbDZYLJYzflU8meztt99Ge3s7kskkS7cpJEHp7e1l2exmc1HpdDoRi8UQiUTgdDrR39+PhoYGuFyuRZ1GEYlEqKysRGtrK+thzzTfLpFIsGrVKnAch7GxMTQ3N6OsrGxBe6e1Wi1LntLc3IyqqioahVpiyzZYf/7zn8e3vvUtbNu2DTt27ABwctgoFovhu9/9Ll5++WU8+OCDOW5l7kmlUpbpaGxsLCtpv0wmywrgwv/Pd5hLJBJBpVJNuorneZ59qQmBPBgMwufzsSCeuUI9M5ALveVYLIaGhgY0NjZCqVRi27ZtKC4unrYt0WgUQ0NDGB4eRiqVgk6nY1thqBe9MvE8j1dffRU8zyOVSqG9vR3f+c53cPXVV2PLli3QaDQsT7zL5TplD1YYpm5qaoLX60VNTQ16e3vR2toKu92OoqKiRftbEwK20Lutrq6esbes0WjgdDrR29sLkUiEjo4OFBYWwuFwzLsNQpWylpYWFrDnW+iGzB3Hn64s9acZz/P44he/iIcffhgGgwFjY2Ow2+0YHh5GMpnEzTffjAceeGDJ23Xo0CFs3LgRBw8eXPCKzNNB6P0KgTMSiSASiSAej7NjMleEZwbOxZ6rmm6FeiQSYdvM0uk0/H4/Ojs7kUwmUVtbi3POOQdqtXrSFyHP8xgdHYXP50MgEIBEIoHFYoHFYsmLL5VvfetbbIHZD37wg1w3Z0UKBAK44oor4PF4oNVqcd555wE4ufr52muvRVVVFbq6uhAKhVBYWDirfcaxWAyNjY1QqVSoqqqC1+uF2+2GUqlcUOW2qaRSKbS2tiIajZ4yYANAW1sbQqEQDAYDfD4fTCYTSktLF3QuC1Xr0uk0qqqqKPf9Elm2wVrwxhtv4Omnn0ZrayvS6TQqKipw/fXX46KLLspJe/I9WE9n4jy0EMQzF5Nl9nwzg/jp6KlGo1H09vaipaUF3d3dUCqVcLlcbP5cWKGuVCrBcRxCoRCCwSDEYjH0ej0sFguMRmNe9aIpWOcez/P4p3/6J3R0dCCdTrPhYkFdXR2uueYaiMViDAwMQKfTweVynXJYPBAIoLW1FWazGaWlpQiHw+jo6GBFXabL9z0fqVQKLS0tiMfjqK6unjFYCnnwhekf4VyqqKhY0DB2IpFAa2srEokEKisr51WljszNsg/W+Wa5BuvppFKpSQE8Go2yIJ4ZNDN74/Pdm83zPIaHh+F2u+HxeFgd6KqqKshkMrZCPRKJwOv1or+/nyV80ev1MBqN0Ol0OVuhPhMK1vnhm9/8JqtV/ZWvfAWHDh1iRTSAk3/TmzdvxrZt21iRlswLxekMDw+jq6uLbR1MpVLo7e3F8PAwzGbzou7JzgzYNTU1M9YyCIVCaG5uhs1mg9FoRHt7O9satpCFZ6lUCm1tbQiHw1QAZAks2zlrsjTEYjE0Gs2k+btkMjkpgHu93qy92ZnBUgjmM2VJ8/v9cLvdGBsbY0N3paWlKCgoYM9Jp9MsT3cikYDT6cTGjRuh0+kQj8ezhtNHR0ezVqjLZLIpM7dR6tCVRSQSwWKxIBKJYGRkBNdccw3Gx8fxzDPPsHUUb775Jt59911cfPHFqK6uRktLCxsWn+7vV1gb0tfXB4VCAb1ej7KyMmi1WvT09Czqnmyh+EZLSwtaWlpQXV09bcBWq9UoKipCX18ftFot6urq2EIxl8s1r7KwmW0QtpaVl5dnvRbP80in0zPexGIxBflZWrbfUrPZoM9xHNrb25eoRSuLRCKZNohnBnAh+URmlrSJw+mpVIrNMws9dofDAZfLBbVaDZ7n4ff7MTQ0BL/fD7FYzMpRZg4BSqXSGVeoZyZ8OdUKdaVSSatdz2AikQgOhwMlJSU4dOgQNm3ahHvvvRd79uzBiy++iHA4jEQigb///e948803ccEFF7ALRZfLNe3fRlFREaLRKDo6OlBTUwOVSgWz2QyNRsO2iC3WnmyhvKUQsGtqaqadH7fb7QgGg+js7ER9fT2qq6vR0dGB5uZmFBQUwGaznTKwZt5SqVTW/w8NDaG1tRUFBQXQ6/VIp9OYzaDtQoqPrDTLNlhv3bp1UrBOpVLo7u7G/v37sXr1apx11lk5at3KNd3e7EQiMSmIDw0NwePxIBAIQCqVQiaTQSKRsCxkAODxeODz+RCPx6FSqdj832wXyMy0Qn3iNrO5rlAny5tIJMK5556LZDKJAwcOQCwW45JLLsHmzZvx0ksvYffu3UilUggGg3j55ZdhMpmwceNGhMNhlJeXTxtkXC4Xmpub0d7ejtraWkil0qw62afakz2bHmlmoFSr1Whra8PAwABKS0shlUqnPDaRSKCtrQ3t7e0oLS0Fx3EYHR1Fa2sr9Ho9HA7HpO9UjuMgEommvUkkEshkMtTV1WFgYACjo6Mwm80sg+BsbmR2lm2wnik72ZEjR3D55ZfjxhtvXLoGkRlJpVJIpVLodDokk0l4PB6Ew2EUFxdDIpFgeHgY0WgUZrMZkUgEe/fuZQvGrFYrioqKYDKZIJPJkEqlFnyScxzHevaZhBXqE1enj46OshXqmaMDmYF8OeRQJ9mkUinOP/98vP7663jrrbcgFotRVFSEj33sY9i2bRueeeYZHDx4EAAwMjKCV155Be+99x42b96MNWvWoKCggG0FywyMOp0Ozc3NGBkZYcmbhMdEIhEaGxtx4sQJFBYWQqVSZT13tsuIMgOpyWRiaXcrKiqgUCgglUonBUa9Xo/Ozk7IZDKUlJSgsrISY2Nj6Ovrg1QqRWVlJWQyGTt+Ln/PTqcTbrcbAwMDiMfjVABkkS3bYD2TdevW4eabb8a3vvUtdqKR3Eun0/B6vRgYGABwMgtUIpGAz+eDw+GARqOB3+/P6p0rFAq2vauvry+r1ztxUZtSqVzw/HNmDvWJptpmljnEn7nYLjOQy2QyBINBjI6OsmNJ7ghTLiKRCH19fUin0yguLobb7cbTTz+Ns88+G2azGel0Gueffz6Ki4vx97//Hb29vWwB5LFjx1BUVIQtW7agpqZmUg9ZGNHp6elBJBJh26VEIhF0Oh1qa2vR39+PoaEh2Gw2FBUVQSwWz7o3OlUgXbVqFVpaWsDzPEpKSqZN+qPRaNDb2wuO46DX69nuifb2dnR0dKCysnLe27GKioogkUhYiVqn0zmv1yGTnZHBGjg5R9PQ0JDrZhD8Y4V3f38/kskkrFYr9Ho9ent7MTIyArlczobITSYTysrKpkxIkdnrFQLmxKFriUQyZaKXxVhENl0O9UQiMWle3OfzIRQKwe/3Y3h4GMFgEF1dXTCbzQtuB1kYYRHj6Ogo9uzZA6vVCpvNhg0bNuDAgQN4//33sWXLFlgsFohEIpjNZmzYsAEnTpzASy+9hOHhYXAch0gkghdeeAGDg4P4xCc+gcLCQhZEhUBaW1uL9vZ26HS6ST3N6upqDA4Owu12w+/3L3hPtkwmQ3V1NZqbm9kc9lTD7Dabjf091tXVQS6XQ61Wo7a2Fm1tbWhqakJ5efmcyspmstvtrCa2sJuDRpwW7owM1sPDw3j44YdnzHBFloawwlsIxIWFhfD5fHjnnXcQiURgNpuhVqthtVphMplmDKqZvd6Jq06FDGlCsBRWjAtBPDNXeWYQX4ytNGKxGBKJhPWMQqEQBgYG0N/fD6/Xi0gkAo7jEI1GKV99HqiqqsLQ0BAkEgl0Oh18Ph+GhoagUqlQUlKCjo4OHD58GJdeeikcDgebcikoKMDWrVuxd+9evPjiiwiFQlAqlXj//fdx7NgxfOhDH8I111yT9TdsMBhQXFzMVohPvFiz2+3QarXo6OhAQ0MDnE7ngi7oZDIZampqWMCurq6eMmCXlpaisbERHR0dqK2tBcdxkMlkqK2tRWdnJ9ra2tgWtPmwWCwQi8Xo7OxEKpVCeXk5zU8v0LIN1tu3b5/y/rGxMTQ1NSEej+Pxxx9f4lYRQSgUgtvtRiAQYNtFotEo9u/fD4/HA5PJhNWrV8NqtZ5y/+qpCNvEhDrWAmER2cSV6V6vlx0jbOeSy+WQyWRsbj1zAVosFkMsFsv6/3g8zv4/kUggFoshGAwiEAggHA4jnU5DJpOxkQKbzYaWlhZaYZ4HEokEpFIpHA4HPvShD7E9+4ODgxgaGoJSqcTRo0fR0tKCCy64AE6nE2q1ml3kXXDBBdi8eTP+9re/YdeuXdDpdIhEInjyySexZ88efOYzn8HZZ5/NepN2ux3RaBTd3d2QyWST/t5VKhXq6urQ29uLrq4ujI+Pw+l0zvtCcmIPe6qALRaLWarUvr4+lJSUAPhH4RBhIVw0Gp13LWuj0QixWIz29nYqALIIlm2wTqfTU65cdLlcuOSSS/DZz34WtbW1OWrdyhWLxeB2uzE6OsqyjqVSKRw5cgSdnZ2Qy+XYtGkTysrKFn1/cyqVYrdkMpn1/8DJLzGe59lWMCG4jo+PIxKJIBaLsecJPY3MvdkqlYolfNFqtawuspCuVafTQSqVwmAwwGazwWq1Ih6PY3BwEAMDA/D7/ZDJZAvKz0wWhud5cBzH6pj7fD5YLBaUlZWhrKwMPM8jFArh7LPPxosvvoj33nsP0WgUer0eUqmUTamIRCKsWrUK5eXl2L17N44fPw6dToehoSH84Ac/wNq1a3HTTTehrKwMwD+Kfgg92YnD3WKxGGVlZdDpdOju7kYoFGJbF+dDLpezbV1CLvGJ55swktDT0wONRpN1oVtUVASFQoHu7m5Eo1FUVFTM63zV6XSorq5Ga2srFQBZIMpgtsjOtAxmsyWs8BaGF41GI9s/PTAwwOauVq1aNe1JL6yqnS7gTvf/Qs82kUiw+4SbcJwQpDmOY1+2IpGIbRnLXFQmHAuArfIVniOXy5FOpxEOhxGNRtnqXmGFuEwmQyKRgNvtRmtrKwYHB+H3+xGJRHDw4EHIZDJcccUVlMEsh771rW9heHgYEokEX/jCFyCXy1FYWDgpJWggEMBLL72ESCSCdevWIZ1OI5lMQiKRQC6Xsy1SkUgEfX192LlzJ/r6+hCLxdiWqosvvhjXX389rFYrUqkUqzNdW1s7bS8zFouhs7MT4XB4wXuyo9EompubIZVKpwzYANDR0YHx8XE2f50pGAyivb0dYrEYlZWVM2ZKm0kkEkFraytEIhGqq6up4t08ULBeZCstWKfTaQwODmJwcJAN/aZSKYRCIcTjcQQCASgUCjidTuh0ulMGXwHP8+z+ZDLJtk0JgVTYi5q5OlwkErH548wesbB/Wxjilkql7N+zGZZLpVIIBAIsBer4+DgCgQC7QBgdHYXf72eLloLBIICTq27VajXb0vXOO+8gFovhiiuuwE9+8pPT8GmQ2chM+3rPPfegv78fY2NjUCqVKCoqylpYNT4+jldffRVSqRTbtm1DPB7H2NgYAoEA0uk0y1Qm5Kh/77338Nxzz7GMZUKGrgsvvBCXXnop1Go1ent7YTQaJ+Ulz8TzPPr7+1l+8oXUyY5EImhpaWHD4xP/5lOpFBobGyEWi1FTUzNpbjkWi6G9vR3xeHzG/eWnEovFWA2HmTKukaktm2D929/+dl7Pu+mmmxa5JTM7E4K1kGxhpt5sMpnE0NAQBgYGEAgEWK+V53mo1WrW+1SpVCgsLIRcLodIJMo6LjPgZiaCAE5+WQnBV/hyyQy4E4Nv5n2LNbzO8zzGx8fh8/lY77ivrw9dXV3o6elhSSCSyWRWL13Y38pxHGu/UqlkX5hr167F/fffvyhtJHN32223IRQKwWQysRGOUCiEvr4+BINBaDQaFBUVsR0Jo6Oj2LlzJxQKBS699FI2ujI+Po6xsTH4/X4kk0lIpVLo9XpoNBocPnwYzzzzDPr7+xGPx6FQKKDVarFlyxZUVFSgv78fBoMBFRUVbC58qu2H4+Pj6OrqAs/zKCsrm/cKbSFgy+VyVFVVTQrY4XAYTU1NsFgsU263SqVS6OzshN/vR0lJCWw227zaQQVA5m/ZBOv5rCTkOG7J97XmW7AW0n/OZVh5uj8JIXiGw2F4vV5W5ECYyzUYDFCpVPB4PCzBiTC0KGRQmkgsFk8bgCcG56Xa/hGLxTAwMIDjx4+jvb0dfX196O7uhsfjQSKRYMFXyIwmJKAQ2qdUKmG322G322G1WmE0GqFWq3H//feD4zjY7XYaBs8Rnufxuc99DuPj4ygsLMTPfvazrMfHx8fhdrsRDoeh1+tRVFQEpVKJ4eFh7Ny5E1qtFpdccklWL1eY5x4bG2M140UiEWQyGQ4cOICdO3ciHA5DoVBAo9HAYrFgy5YtbLpIo9EgGo1O2rkg3KRSKbsoXkid7HA4jJaWFigUiikDts/nQ3d3N1wu15RVwnieh9vtxuDgIKxWK0pKSubVjmQyiba2NkQiEVRWVi54gelKsWwWmGVWxSGz9+KLL6Kjo4Mt7BJ6epnbjYRFM5n3A5jU4w0Gg2hra2OrRLVaLXQ6HQwGA0toImRHKi0thV6vP2UAzoftHJFIBN3d3Thx4gSOHj2K9vZ2eL1exONxNg8uFouhUqlgs9mgUqkgk8kgFothMBjgdDrhdDpRUlKCkpISmEwm9iWWSqXYqnG1Wo1YLJbjn3Zl4ziOFdzw+/1oa2uD0+lkc6g6nQ46nQ6jo6Nwu91oaGhgWw4vvvhi7Nq1C7t27cKOHTvYecJxHMuTX1xcjGg0ygL32rVrUVpail27duHw4cNsPvu5556DxWLBpk2bcNFFF0Gv1yMWiyEcDrNFi5mFaDiOQzAYhNvtZnnHjUbjnOZ+hXrbra2taGtrQ1VVVdb5Z7FYEAgE0N3dzS5CJ/7uiouLoVAo0NPTg2g0ivLy8jmPZAk5zdvb29Hf34+ampo5PX+lWjbBurS0NNdNWJb279+PkZERGI1G3HzzzUilUkgkEkgkEmxxlrBAK/PfwnA08I8FL16vFxzHweFwYM2aNbDZbGyo0OPxQKVS4fzzz19wcfvTaXx8HL29vejp6UF3dzeamprY/GI8Hs9K+SgSiaBWq6HX66FWq1FcXIzS0lIWlJ1OJzQaTdZWrkgkgs7OThagM0d2wuFwDn9yIhCJRCw7XjgcRkNDA4qKimC1WtkxRqMRBoOBJfM5ceIELBYLzj//fOzfvx+7d+/G9u3bp1zzoFAoUFBQwDL0+f1+OJ1OHD16FH/961/R09MDg8GARCKBP/zhD3j33XfxhS98AU6nc1KAFErUCreRkRG0tbWht7cXBQUFMJvNk4bRlUrltGsx1Gp1VsCurKzMOlcza3HX1tZOeR5bLBbI5XJWmGQ+C89EIhEqKyspo98cLJtgTeYnkUhgfHwcqVQKhw8fnvR4Zi9XLpdDo9Gwf8fjcbS2tqKnpwcSiQTnn38+qqqqYDQaWc8xc06trq5u3uX2FhvP8xgZGUFPTw8Lzj09PRgaGkI8HkcoFEIoFEIsFgPP8+zLTehBl5aWYs2aNairq4PD4YDNZgPHcVn7q7u6uhCPx9l7Zm73UqlUMBqNbP+2TCaD2WxGIBDI1a+E/J/h4WEEAgGYzWYUFxdjbGwMPT09GB0dRWlpKVsRzXEcLBYLTCYTKzrD8zwqKyvR1NSEvXv3YuvWrTMuUpRKpbBYLLBYLCgvL8fWrVvx/PPP47nnnoPb7YZarcb777+P2267Dddffz2uvvrqrKIzE0vUOp1OrF27Fm1tbfB4PIjH49BoNJMy+clkskkBXKFQgOM4qNVqVFZWThmwRSIR23/d29s7bSdJq9VmZTyrqKiY83C2sDODzM6y/k0NDAzg4YcfxqFDh+D3+7N6g8DJP4adO3fmqHX5QZg7U6vVbEWpEIynmgcWglxjYyO6u7shFotRV1eH2trarC+RdDoNt9sNr9e74NWqCyWsSBeCsvDfUCjERgqElKBCghTg5BehWq1mX8glJSVs76xYLGZBWRgmB8AuaoS91pnbviQSCXsfIYlKIBBANBpFPB6Hz+ejtIt5QKlUIhQKsVEQ4OTfvbBosKKiAoWFheyzEolEsNvtsFgsGBgYgNfrhcViQWNjI0Qi0ZQVAKcipC79p3/6J1x11VX44x//iL1790IikWB8fBy/+c1v8Morr+Dqq6/GBz7wgWlTjwrbnywWCxuOdrlcUKlUWTXmhZ64cEGZWWNeqVTCarWiv78f7e3tqKioYAFbqVSipKQE3d3dLJ/AVIRKYh0dHWhtbUVJSUnW6ARZXMs2WB89ehTbtm1DJBJBTU0Njh07hvr6eoyNjcHtdqOiooJl5VnJJBIJ1Go11Gr1jGkM4/E4hoaG2BW7TCbDmjVrUFNTM2leTPiSi0ajC1oZOh/JZBL9/f1ZQbmvrw/xeBw8z2cFZ2HhnBCgOY6DyWRCTU0NrFYrdDod9Ho9dDodTCYTtFotxGIxUqkUS0UpBGIhKE/sXUciEYyNjbGALPRshC9GuVwOk8nE/hsKhZbsd0WmplKp2FqLVatWseQ4UqkUbrcbu3fvhl6vZ38nwmiTUJHLZrPBbDYjHo/jrbfeQjAYxFVXXTWnCzGr1YovfvGLuPDCC7Fz504cOnQIY2Nj8Hq9ePzxx/Hiiy/iqquuwvnnnz/t3LTJZIJarUZnZyeam5tRWFiIgoKCSUU4Jg6lZ9aYj0ajOHjwIDo7O1n9baVSCaPRiGAwOO38tUDYf93X18cuHIqLi+mi9DRYtsH6zjvvZFskhIU/P/3pT7F9+3Y89dRT+PKXv4zf//73uW5mXhO2Jg0NDaGvrw/Dw8NQKBRYt27dtEUFhMIDCoUCdXV1867OMxuxWIx9CQjBub+/n81zCXuxheAZj8fZqnZh4ZxarUZhYSGKi4tRXFzMfiaZTAabzQaHwwGDwZDVOwbAXlPoHWemG80MyJm5yuVyeVbq0olkMhnNW+cZYS++xWIBAFaXuaWlBYcPH4ZOp4PFYmFbrzQaDbRaLZxOJ8vr/cYbb2B8fBxXXHHFtL3Qqcjlcqxbtw4WiwUbNmzAvn37cPjwYVZp7sknn8Tu3buxY8cOVFVVwWAwsB0Xma9RU1OD/v5+lt534ijXxKF0gVAUx+FwoKmpCY2NjVk9Y4lEgoGBAYyNjWHNmjVQq9VsKD0Tx3EoKSmBQqFAb28vW3hGqUUX17IN1vv378cdd9wBp9OJkZERAGDD4B/72Mfwxhtv4Jvf/Cb27t2by2bmJaEspbB/eHx8HHK5HKtWrYLT6cz6Msh8jpC32G63swpDiyUYDE4axvZ6vSwwCoFZ6NFmBmetVguz2cxSQgrbqsxmM0pLS2GxWNiqbLVaDZvNBovFwgqACKt3M3N/Z06pCAFZ6GlnBmTqQZxZZDIZW9k/MDCAnp4eNi8cCoXg8/nYcRqNhgWx119/HS+++CI2btw4KbHKTIQV1sJwc21tLV5//XUEAgHo9XpEo1E888wzqKqqwubNm9nfuBC4tVotOI5DUVERtFoturq60NDQMKs92cJaCr1eD5vNhvb2duj1ejgcDjacLpFIcOLECbz11ltsaiBzKF24yWQyWK3WSQvPFlJFjGRbtsE6nU6zNHwGgwFisZgFbQBYs2YNHn744Vw1Ly8JvWi/349EIoF4PA6RSISysjIUFxdPm5lodHQUPT094DgOVVVV885gBJwMusKCnsyFXyMjIywZS2ZSFqGHK2yhEr5YioqK4HA4YLfbkUwmEQwGkU6nWVKH4uJixGIxeDweBAIBSCQS6PV6yOVyjI6OshSoAiHjmUajgdlsZgFZGPpeKOFnWiZpDc5oIyMj8Pv9bJ5aCFpCUhvhIkwYdRFyddtsNlRWViIcDrOh85GREZbXu6WlBclkks0nZyZWORW9Xo+6ujo2GuR2u9HV1cVKyArTPZs3b8amTZvg9/sxNDTEMqQZDAbo9XrU19ejq6sLbW1tc9qTrdfrUVFRgfb2dlZjwWQyoaioCAUFBWhvb4fFYoFKpZo0lA5k15c3m83weDw4ceIEqqqqaB/1Ilm2wdrlcrHFISKRCC6XC6+99hquv/56AMCbb74555XJDzzwAB544AF0dXUBOFnM/d///d9x5ZVXLmbTl5SQSUzIHCQk7xCJRDAYDCwn8lQndCqVQm9vL4aHh2EwGFBaWjqn1Zs8z8Pr9bKg3NnZie7ubrY6PTMwCye9kB2N4zgYjUYUFRWhsLAQlZWVKCkpgVKpRDqdZpmjhOxQhYWFbE5+YGAAra2tiMVi0Gq1MBqNkEqlLK+zSqVi88jCbb6jBEKyl5lu4XCY7emdWDCBLD1hyxYAtmVv4hYiIWWtsLI/kUiwfcEVFRUsCAqpdQsKCiCVSnH06FEAJ6eLmpqaYLfbUVlZCbPZfMqgKaQD1Wg0iMfjsNlsUCqVeO2111h60/379+P999/HBz7wAZx33nkIBoMYGxtDZ2cn2+9tMBigUCjg9XoRCATgcrlmtbVKr9ejvLwcHR0d6OrqYnWozWYzgsEgRkZGWIEaQWZ9+XA4zKaMeJ5HV1cXWltb4XK5WHIZlUq1aBfAK82yCtajo6Psi+6yyy7DU089he9///sAgC9/+cv4xje+gY6ODvA8jz179uAb3/jGnF6/uLgY//Vf/4WqqirwPI/HHnsMH/7wh/H+++9j1apVi/7zLIVYLIbR0VHYbDZotVqEQiFwHAen0wmr1TptkAqFQujs7EQikUBZWdkpa+ymUin09fWho6MDHR0d6O7uRm9vLyKRyJS9SqH4Bc/zkEgkKCgogMVigcPhgNPpRHl5OVQqFQuGY2NjLCmEUIJS6IVoNBqEw2G20EytVqOkpAQOh4PNs801IM8mCE/1Jc9xHCuxGQgEMDw8jPHxcXYRMnHHAll6KpUK8XgcBoMB9fX1AE5+3vF4nCXCEf4/Ho8jGAwilUpBLpezAi16vR7FxcUsw5hMJsOmTZug0WjQ0NCAkpISaLVa9PT0oK2tDQaDAS6XC2azmeWMnypgCUPaQgKWUCiEb3zjGzhw4ABeffVVdvH39NNPY8+ePfjoRz+KjRs3sv3cY2Nj6OvrY+eax+PB6OgoampqZlUnW2hnR0cHOI5jVcNKSkoQCoXQ0dGBuro6di5lDqULhPKyFRUVaGtrg9vtht/vZ1s+M4fStVotWy9AZrZs0o0CJ+cOP/CBD+DGG2/Eli1b0N/fj7Vr17Ivx+9///v485//DLFYjA9+8IP49re/veDqLiaTCT/60Y/wuc99blbH51u60a997Wtoa2uDWCzGxz/+cTaErNFo2N5LIV0mcPJEGxgYYElOXC5XVjUqoThHV1cXurq60N3djb6+PvT392elExXyYmfm9xaJRFCpVGwbjFBK0mKxQKvVsi9RYW4wEAiwL89oNIpUKgWZTAadToeCggIYDAZEIhEEAgHWExeGLqe7cl9oEM6seT3xFg6H2e9udHSUzXWaTCY4HA787Gc/QyQSycpJTZZeZiGPuXwOQkIhj8eDrq4upFIp1vsVAns6ncbx48fR09OD1atXw+VyscpzsViMbZkSku0YDAbodDqo1epJF5PBYBC7du1CMpnEpk2boFQq8fzzz+Ott97KuvB1uVy47rrrUFlZydop5C0XMrEJFbxWrVo14/khGBkZQWdnJywWC9trHY1G0djYyAL6bAmja2q1GgUFBaw3vnPnTqRSKVbWmMxsWfWsr7vuOjz33HN47rnnoNVqcc011+DGG2/E9u3bwXEcvvOd7+A73/nOorxXKpXCU089hVAohM2bN097nLAgSSBUXMoncrkcRqMRO3bsQDqdZiucM5MoCMHU4/EgFovBZrPBaDSis7OTFa7o6+vDwMAAhoeHWSIR4XlCEgYhOItEIhiNRpZlSZhTE4axhaAHnPydCUUx0uk0S0wiDEWqVCo2HO5wOMDzPIaGhjA8PAzg5FW/2WyGVCplvfD5BmHhwmWqIiETxeNx9Pf3w+PxwOPxIBwOQywWw2w2o7a2FgUFBdDr9eyCUalUUrrRZUz4e3e5XCguLkZPTw/Gxsag0WhQXV0NqVSKVCqF+vp67N+/H21tbSgoKGBTOAMDA+jv70dvby+7SI7H4+xvUKvVwmAwsOxpSqUSF1xwAd599128//77qK6uxic/+Uns2LEDTz/9NBobGwGcTMX8ox/9CGeddRauueYadu4ajUaUlZWhqqoKXV1daGpqgtvthtPpZH+ber1+ylXbJpOJDWULI3EKhQKlpaXo7OycU4/YZrNBoVCgo6MD/f39qKyshM1mQ3NzM8bGxtDW1kbBehaWVbD+/e9/j0gkgr/+9a944okn8Pvf/x6PPfYY7HY7brjhBtx4440L7s0eO3YMmzdvRjQahUajwTPPPMOGyqZy33334d57713Qe55OCoUCRqMRFosFBQUFSKfTWSuqhXKPvb29OHHiBIaHh9mQ2sjICCKRSFbQEnq2mYUrhOIUxcXFsNvt7IsmGo0iGo2yikTCe6fTaYyMjLDhbJlMBoPBwLZ/JJNJhMNhcBzH9kLL5XL4fD68//778Pv97DG1Wo3R0VG2SlcgZBMT2j2XIDydeDwOv9+P/v5+9Pf3s4sLoUqTw+FAQUHBpNGczN48DYOfGaRSKSoqKtjiyxMnTrCLRqVSiR07drCV0Q6HAzU1NaipqUEqlYLb7Ybb7UYymWRZ7oSecE9PD1paWsDzPBsqTqVS7OK0ra0NZWVluOGGG9DR0YEXX3wRg4ODEIlEeP/993HkyBFs27YNV111FTQaDTiOg1arxZo1a1BdXY2GhgZ4vV4kEgmMjIywx4VefubfrtlsBs/z6O7uZtuzTCYT+75Qq9Wz3rqp0+lYxrPGxkZUVFScro/mjLWshsEnGh0dxZ/+9Cc88cQTeOONNwAAVVVV+NSnPoVPfvKTKC8vn/NrxuNx9PT0wO/34+mnn8avf/1r7N27d9qAPbFnffjwYWzdujVvhsFvv/12DAwMQKVS4dZbb2VVtYQEDIODg2hvb2epFLVaLQtgU5XKFFIwOhwOOBwOVjVIWGgj5NYWFpNoNBqoVCq2dSoej7M90DqdDiqVCiKRCMPDw/B6vYhGo5BIJOx5yWSSFUVIJBJsGN1sNk87JC0U2VioWCzGFtZ4PB4MDw8jHA5DJpPBZDLBarWyYdCp8q1nJmYBgJ/97GdUdSsPzHcYfDrJZBK9vb0YGRmBTqdDaWkpZDIZeJ7H3r174Xa7cdFFF2UlaRKG030+HyQSCRwOBywWCziOY+tMhJswvTI8PMzyftvtdhQUFIDneRw9ehR79+5FJBJhI1sqlQqXXXYZLr74YqjValbTPbNOtnAhHwqFWJlblUrFRsGELZxCNS7hgjydTqOpqQk8z6O2tnZO51oymUR7eztCoRAeffRRxGKxRfscznTLOlhncrvdeOKJJ/CHP/wBhw8fBsdx2LRpE958880Fve4ll1yCiooK/OpXv5rV8fk2Z/31r38dHR0dEIlEuOKKK+D1ejEwMIB4PI5kMslyVWs0mkl7IuVyOestCqtARSIRfD4fvF4v/H4/m0tWKpXQ6XSsEpdQs1rYdiUWi7P2KEskEgQCAYyNjbHhY4vFwjKLJZNJ+P1+llnKZrOhsLDwtG4DiUajGB0dxdjYGAYHB9k2t2QyyRbRCJnOpprzm6netkQiwb333otAIACj0UhfTjm02MFa4Pf70dPTg2QyyTKdpdNp7N69G16vF9u2bYPD4ch6TiwWQ39/P9uiJezOyCQscjtx4gQGBwchk8kQCASgUqlQUVEBjUaDdDqNN954A7t370Y0GmUXzXq9Hlu3bsWqVatYOVqZTMbKwEokElRUVMBkMiEcDiMUCrEtWcKIl7A2RCgeUlRUhFgshsbGRuj1+jnNXwNgvfW7774bqVQKRUVFdD7MwrIaBp9JUVERvvnNb+KKK67Av//7v+PZZ5/FO++8s+DXFYZul6Ph4WF2xS2Xy3Ho0CEAJ08WYauFkDdcqVSyoGyz2WAymaBSqRCLxRAKhRAMBtHe3s7mqi0WC1wuF1uoFgqFMDQ0xAoeCPPDUqmUBXGpVMp6q4lEAgqFAmVlZWz4kOM41uMPBoNQKBQoKSmBxWJZUMJ/YSX2xB6vsO1FSAwjXDikUim2GE7oPQvbYRZSb3spa3KTpSfsc3a73ejt7WWFQbZu3Yrdu3dj37592LZtG8sPAZy8IHa5XCgoKIDb7UZnZycGBwdRWFjIVlgLIzlbtmxBS0sLQqEQzGYzWltb0draCrPZDK1Wi40bN+Kss87CG2+8gcOHD0MkEiGdTmPv3r1oa2vDVVddBavVytLiCrnF9+3bB6PRCLvdzgJ6Op1GIBCA1+tlbQCAxsZGpNNplJSUoLS0FB0dHdBoNHPKCS6sMtdqtRgbG1u8D+AMd0YE656eHtarPn78OHiex/nnn48bb7xxTq9z11134corr4TT6UQgEMATTzyBPXv24JVXXjlNLT+9hG1uwnB2JBJh86bCF4uwLUuYGxOG9QcGBti2K6HghRDAhYVkQs98aGgIiUQCEokElZWVMJlMbFGZSCRCOBxmC7HGxsbYMLler2dX2U1NTWye2mQysfnv6YKbMDw/MQBP9W9h8CizyIGwcjeRSLB951arNWuFutFopKpAZE7EYjGcTidMJhPLJlZYWIitW7di586d2LNnD3bs2DFpcZZSqURlZSXbnijsyc9MrCISiVBRUYGmpiZEIhFs27YNPT098Pl8kMvl0Ov1bEGsy+XC3r170dPTA6lUiv7+fjzyyCNYt24drr32WnbBsH79evT396OrqwvJZBI2mw0ikShrG5uwriUYDGJwcBDvvPMOnE4nqqurEY1GcejQIVZxby7TUGq1OmsHCZnZsv0m8vl8bL5a2MpQW1uL7373u7jxxhvZ/sC58Hq9uOmmm+DxeKDX67F27Vq88soruPTSSxf/B1gCQt3eYDAIiUQCo9GIdDrNUnHK5XK2QjVzTlpYmSqsFtVqtWyfciKRQCgUYsPfEomEpT0UFoIJwW9wcBCjo6MYHx8HcLJ4QV1dHdtv6ff74Xa7MTAwgEQiwYplRKNRNDU1oaGhARKJJGuFudA7nTh7I7Rb6OUK892ZOb2F34dWq2U/r1wuZ/N0BoNh2v2vi+HSSy9FJBI5rfnUSX7QaDSor6+Hx+NhixE3b96MN998k+X7niqPuEajQU1NDTs3mpubodfrWVIRqVTKSnT29PSgvLwcOp0Ovb29CAaDKC8vZxfN69atw+HDh/HCCy+wRWj79u3DO++8g23btuHaa6+FTqdDUVER2/kxPDzMMgAKMgvkBINBNDY2oqWlBU1NTVCr1WwrW319PZvnzhxynypD3EK31K5Ey2rOOhQK4ZlnnsETTzyBnTt3IpFIwOFw4BOf+MSirARfDPk2Z/2Vr3wFra2tEIvFuOaaa2AymWAwGLJyYAsVpoThaiHbEc/zCAaDbO5YWEAmJCMRqvHwPA+e57MSmAgLwqRSaVYRAWGluc/nQzAYZAFUWGjGcRx7HWExnPDaQrAWhu5VKhWbI1epVOA4DtFoFKFQCOFwGDzPs21lIpGIZUwTRgoyt8iQleN0zVlPJxwOo6urC9FoFDqdDg0NDUgkEtixY8cpsyyOjIygv78fsVgMJpMJhYWFkMvlLCOesOgrHA6jo6MDyWSS9ewFyWQSe/bswV/+8heMjIyw0SaFQoEdO3bg0ksvhclkgkKhgNvths/ng8lkgtPpnLaH7PF40NvbC51OB57nceTIEUgkEpSUlEClUkGlUrGtlEIPfWIv+uc//zmi0SicTifNWc/CsupZ22w2tqXqk5/8JNtjvZgFJc40IpGIbV0Scg8LBeiFXmZmQoZkMonx8XE2j5tMJqFUKmG321k5SSF1Z+aQ88jICAYHBzEyMoJUKsUCqEwmywrio6OjSKVSMBgMKC8vZ0NnYrGYFaMXetFChrN0Os0SsgjlLoXSlF1dXQgEAmzIXhgVUCgUWb1wobctVE4SXmdwcJCNBAgXCzP9/1yOFX6nQn7zSCQCu91OFwcrjEqlQl1dHQYHB9Hf34/CwkK0t7dj165duOSSS2bMtW8ymWA0GuHz+Vi+bavVioKCApSUlLA92xaLBXV1dSytbyAQQElJCbvAveSSS3DhhRfitddew8svv8xqvb/66qt44403sHXrVqxZswYajYYF7UAggIqKCqjV6kntEvIdeDweFBcX47LLLsOxY8dYgqqxsTGIxWLo9XoUFBSwRafChbQw5bWM+oo5t6yC9SWXXIIbb7wRV1999axy3ZKTkskkW7U5MTgDYLmrhQIHwupnIR2gkOzB6/Wy/aFC8BQWaAn7ja1WKyspKJVK2TGBQACFhYVYv379vAIWz/NsoZswf2YymSASiaBUKiGRSBCLxdgc+vj4OEs9KmSLEq72heF2YW5NuCAQLgom/r/QE5npGOH3nDnsLtyELySpVIrzzjuPZYQiuSEEDJlMhrGxsaxFg6drCoTjOJZ1r7u7G4WFhWhqasJrr72Gyy67bMaCHxzHwWq1wmw2s90cPp8PdrsdJpMJPT09kMvl0Gq17Bzv7e1FKBRCeXk5+66Uy+W46qqrcOGFF+K5557DG2+8wUauXn/9dTQ3N2P79u2w2WwQi8VsAZvL5WKrzjN72oWFhawYSkFBAex2OwYGBlBWVoZkMonh4WE0NTVlpWsVOggSiYRyDszRsgrWzz77bK6bsOwoFAoUFRWxnq6wPUnYojQ6OsqGw4V90RqNhp1MwmNSqZQF+XA4zIpxmM1m1NXVwWazsfmqzH3cwWAQMpkMFRUVc1rVnU6ns4KzMAQv1OYV8n6nUimWEzmZTEKlUmHt2rVs/llIVSr0pIVhciHTnLDnW0hAIfz/TOUvhS1p4XCYLVgLhULsC0l4vrBNLTM3+VS9FLK0hBEakUiE9vb2rMcmbrmbahueMBI0HwqFAjU1NTAajeB5HocOHcILL7yAq6++esrStJlEIhEKCgpgtVoxMDCAwcFBACe3f7W2tqK+vp71stVqNTo6OtDY2Ain05mVF1yn0+FTn/oUtm/fjj//+c84fvw4JBIJRkZG8PTTT2Pt2rX4wAc+AJfLhaamJhw5cgTHjh2D1Wpl5TCFm5BNsLm5GXa7HePj4/B6vaiurobBYIDVamVZE0OhEGKxGJLJJLuIpprXs7esgjWZu3g8jtHRUUSjUbzzzjsIhUKIx+OQSCRQq9UssYmQFjPzyynzRAqHw6wGtjDMLQxjZw6h+3w+DA0NsbzYwjGn6rEIwTkQCLDgLGwT02g0bI+1TCZj26w8Hg/S6TTkcjnLNT4xGEokEiiVyqxKVxOH1IVgLmQkA05+MQq9b6F9PM+zFfPCcVKpFCqVipUPVCqVVFUoz11//fWIRCJQKBRYu3bttDsKMrcZTuwFCtNLMwX0mbb0CdsBVSoVdu3ahT/84Q+4/vrrZ5VHQCwWs33cHo8Hg4OD6OrqQigUwnnnncdqutfV1aG3t5dNFTmdTrZ2I5FIQKvV4sYbb0RDQwOeffZZuN1upFIp7NmzB3v27MG6detw4YUXoqKiAt3d3ejv74fJZGJbu4TkRUVFRTAYDEgkEjj33HMxPDwMrVY7ZZYyYZptbGwMoVAIkUiECnnMEgXrM1zmMK3ZbEZlZSWbez7VisxUKoWRkRH4fD6Ew2FIpVI2zJ2ZQCUcDsPr9bL0hSaTiWX2mk46nc4a0haCs/AFUFxczPZwC/PdfX19CAaD4HmeXWgI+5/nguM4VhozUywWY4vj/H4/GxkQtnkJvWQhh7PBYMgqiEJrJ5aHiXmohRz1MxEC3FRbBIXRG+GxiWYK6rW1tZDL5XjppZfw29/+Ftdcc82kxCnTkUqlcDqdsNvt0Gq1eO+99+D1enH22WdDo9EgkUiwNRTvv/8+Dh8+DLvdPum812g0+PSnP42Ghgbs3LkToVAIIpEIbW1t6OvrwxVXXIGPfOQjGBwchN/vZ7kYhIvrYDDI6sS/+eabcDgcGB4ehkwmy8raBpy8eDaZTOw7YmRkZFY/K6FgfcYTVmNbrVZs3LhxVj0+ocjH2NgYeJ6HXq9HYWEhWyQCTD3UXVhYOO1QdyqVygrOwmptiUQCrVaL4uJiaLVaFuCFBWTd3d1sMYpWq0VJSQlblDZf6XSaJYXJ/K9wUSNscyssLGS9ZWFOPLM3Pj4+npWTXBj6zhxOFxa6keVNKOJxqgtDYZvTVAFdCOqBQCBr/z8ArF27Fnv27MHPf/5znH/++aisrGRrLCYuxMrcSpV5s9vtOHr0KHp6elBSUoKCggKWDljYRhYKhVi6YOGCQbjIXL16NT7ykY9g586d+Nvf/sbWW/ztb3/DW2+9hY985CNwuVzo7+9HMBiEy+Viq86TySTKy8tZsZBUKoWBgQFWtEOj0WTtNAH+UZmPzA59i5zhhG1KM83BAie3VA0PD8Pn8yEWi7FUo0I1K8Fsh7pTqRQLzIFAAOFwGADYam0h61LmNrFQKIS+vj6MjY0hFotBLBZDp9OxlejzObHj8XjW3HI4HGYZ6TLr6gpDksJe1qkIFz6ZUqkUC97CTWi/QBiKF+YTTzU3SZYvoYDMqUathJrPwt+ksMti9+7d2LVrF9ra2ljegomV4iQSCRvlUSqV7FZWVoaCggL09PRklb4tKiqCWq1GVVUV+vr6MDQ0BLFYjNLS0kmjQTKZDFdeeSW2bNmCF154Aa+//jrS6TTGxsbw6KOPoqSkBFdddRXS6XTWfLhwgbt582Z0dXVhcHAQwWAQ4+PjrNhO5siZVquddMFCZkbBegXjeR5+vx/Dw8OskpVQVm9iUDrVULeQvlOYc45EIgBOnvxCz15IriIQvgQyF4hJpVI2xDxdDu6pCL3lzKAciUTYF50QMIVV4cIX3ELnloWLoYlz5cLivMw58WAwmDV3Ts480/V6p7pNZLPZsGPHDhw5coQVzBHqSWf2SKfrtQvFOMRiMSvZ2t3djcOHD8NkMqGoqIhtXezt7cXQ0BAqKythMBgmXQjrdDp88pOfxMUXX4y//OUvOHr0KACgt7cXv/zlL7Fq1Sqcd9556Orqwvj4eNae7NLSUhaE4/E4FAoF6uvrEQwG2XdEX18fhoeHEY1G6ZyYJQrWK1AsFoPP52PlMFUqFSt/l3nSzjTULSRMGRoaQjAYZMFZLpdDo9HAbrdPWRxEKNAxNjaG8fFxlmRlugViU0kkEpOGsKPRKHtc6HEI9bOFFaxLSdhSRnuq809HRwdbfzHb9Q7pdJoFycwkHxNvE+eshdwBwpCzWq0+5UK08vJyvPPOOywB0MDAAAoLC2Gz2U55cSm0saWlBSMjIygtLWUjVg0NDdBoNDAajZDJZOjr60NbWxusViusVuu0i+Q+9alPobOzE8899xz6+vrAcRxOnDiBhoYGrF+/HqtXr2bZ04QMgEIGyZ6eHvT19UGr1cJut7M95el0GiaTiXKDzwEF6xWC53m2WCwQCEAsFsNsNsNsNk8alp041C3MFYvFYgSDQTQ3N7PgKOydFBa5TBUUhb3WY2Njc1ogJhQcmdhbFr4QxWIx2wsu7N0W8pYTMh2hNrrX64VKpYJer2dlXqcKwEJRmkyZ6W2F6ZG5rAafibBP+b333mOrrfv6+lhhkJkuACUSCSQSCdasWYOWlhYEg0HU1tZi9erVLLFKMpmE3W7H6tWrWW32eDwOk8nERgaEVfCZFx8f+MAHcOLECezdu5dlH9y3bx/eeustrFq1CuvXr0dFRQWKioogk8lQVlYGnuezUpNm5jmntKNzQ8H6DCcs7IrH4+js7GSJEzK3XAkyh7qFxCgajQbxeBy9vb0AwFZDOxwOljBlKsICMaGS1akWiCWTySl7y8Jwmlwuh1KpZEPvQnY0QuZKWA8hlLQUVj9nlkAVgu3ESmuZQfh0qqysRCqVwuHDhyGRSFBbWwu3243GxkZWtnami4DMoh/t7e2oqamZlFhlZGQEdrsd69atQ29vL8bHx1klPQHP81nD7mVlZbj44ouxZ88e7Nq1ixUHevvtt3HgwAHU19dj7dq1KCkpYTW0RSIRGhoaMDo6irPPPputC0kmk5QYZQ4oWJ/hhLlTvV6P1atXTxqWFoa6+/r64PV6kUgkWHELodiFkDNc6D1MRRgWFwJ0PB6flG5QyBYmrIjNXPglzOEJw8fCPLfQW6ZVo2SxyOVyNgwu7BkW0usK+4fNZjPLkJcr1dXVSKVSOHr0KMRiMdavX89K0Aq97JmmjTKLfnR0dKCiooIlVrFYLBgcHGQFPkwmE4LBIFpaWlBYWIiCggIA2SMImW666SZ89KMfxQsvvIB9+/axPARdXV3weDy48MILsXnzZiiVSqjVaiSTSTQ0NCAWi6GyshLAyWJM0Wh0yoImZDIK1mc4qVTKeguZgToYDKK7uxvd3d0IBALsOKHHLATnmXoQ6XSaJTgQ0pRmLhBTKpVscVVvby8LzEJvWUhparFYWG954sUEIYutqKho0n3COgy/34+hoSF0d3ejr68PJpMpZyv4OY5DXV0d0uk0jh8/DpFIhPXr18NoNKKrqwtNTU2w2+0oLCyc9qJCqVSivLwcbW1tcLvdrJqWRCLJSqwirBAX5rIDgQDKyspm3CKp1Wpxww03YPv27fjLX/6Cw4cPQ6lUIp1O46WXXsKxY8dw/fXX4+yzz2bFRY4fP45EIgGr1UrTVXNEwXoF4DgOyWQSQ0ND8Hq96OnpYckI7HY7NmzYwFZrn6oHO9UCMWFRmZC5KxKJoLu7G/F4HMDJ3rJCoWClOYXATL1lkk84jmMXmvF4nGXsGxoagkqlgtVqhdFoXNK/W47jUF9fj1QqhaamJojFYqxduxa1tbUYHBxkNeJLS0unzX6m1+snFf0QZCZW6e/vZ4V4hHr2Qq7xmdjtdnz5y19GY2Mj/vSnP6GzsxNSqRTt7e34zne+g5qaGlx11VWsB71r1y5s2bKF5ekns7OsSmQuB/lWIvO5555DZ2cnEokEysvLWZITp9OJ0tLSWfVkY7EYC9DCHuLMlaNCUQvgH+k3heFrobdM6TfJcsTzPMbHxzE0NAS/38+GjIX820sllUrhyJEjLAf4qlWrIBKJEI1G0d3djWAwCIvFguLi4mkvJnp6euDz+VBVVTVtAA6Hw+jv78fw8DBGRkagUqlQVVXFsqoJFeSmuglD4Q0NDWwRmrBIVCKR4KqrrsLHPvYxvP322xgbG8Nbb72FSCSyZKVKlzvqWZ/htmzZwrYu2e12FBQUQK/XnzJ4hsNhNqclBGipVMp60cLiGyEwZ2b6IuRMwXEc9Ho99Ho94vE4Sxzk8/lYTviJWx5PB6FHnU6n0dDQAJFIxAp31NTUYGhoCG63G36/H6WlpdDr9ZNeo6SkBLFYDO3t7aitrZ1yF4ZcLmc1s4VFqceOHYPNZkNpaSk7v4XkL8L3gdlsZil8zzrrLNxwww3YvXs3XnrpJYTDYQSDQbz00kt45513cP3110Mmk6Gnp+eU9bzJP1DPepHlW886Ho/D4/HMmKs7nU4jHA5jaGgIg4ODbF81ALYv02q1sjzYKpWK1cUmZKURets+ny8rmZDVaj3tve1EIoFDhw6hp6cHq1evRm1tLTsP4/E4enp64Pf7YTKZUFJSMuniOZlM4vjx44jFYigtLWWFaWKxGOLxeNZWLbFYjHg8zrZ36fV6bNq0CU6nc9blREOhEF544QXs2bOHJQbiOA5OpxPvv/8+1Go16uvrqWc9CxSsF1m+BeuJhGpT4XAYoVAIXq8Xw8PDCAaDrEym1WqF3W5nZS8XkoebkDOZkKZXyEkgLJg0m82nrbcdj8dx8OBB9PX1Yd26daiqqsoKnD6fD+3t7YjH47BarVCpVJOGr7u6uiCTyVBVVcVKuApV5oT/FwK9UAbz3XffxejoKCoqKrBhw4Y5LQb1er145pln8O677yIQCCCZTOLYsWNQq9U477zz8MADDyz67+lMQ8F6keVbsI5GoxgaGsrKiy1UuRLqVBuNRtjtdpZ1jBAyd0Jve2xsjC1WE0akFlswGMRbb70Ft9uN6upqOBwOFpCFZCaDg4MIBAIwmUwshbAQiBOJBLq7u2E2m1m2sVMR5qOF+tfr16/PGhqfjfb2dvzpT3/CiRMncODAAaRSKbhcLrz22mvz/E2sHDTBeIYTspEJ9XnT6TTUajWKi4vZylfaLkXIwul0Ouh0uqyiOCMjI2wFtlDwYjaEmuszLeZSqVRIJBJ45513sHr1alRWVrItmnK5HBs2bEAoFEJPTw9isRgsFgusVit7D5FIhM7OTigUCraveiYcx2HVqlVwOp1477338N5778HtdqOyshJ2u31WIwkVFRW48847cfDgQfzzP/8z/H4/qqurZ/U7WekoWJ/hhCxBQh1Zg8EAvV5PQ9uEnCZSqRQFBQUoKChg5WbdbjfcbjeMRiMsFgu0Wm3WfPHEm7DtEZh5MdfatWtx8OBBjI6OguM4FBYWZrVFKIjT19fHtmyWlZVBLpfDZDIhGo3C7XZDLpfPuqCGVqvFRRddhLa2NnR0dODIkSMoKChAYWHhrPZPcxyHs88+G1u3bkV3d/cpt4aRkyhYn+G0Wi3Ky8uh1+spCQEhS4jnecjlcpb0Z2BgAM3NzTh48CCAk+emwWCARCKBWCxmAVitVrP/F7KtzbSYa+PGjXjvvfdw5MgRiEQiOJ3OrMeFcpgmkwnd3d1oaGiAw+FgCVUy57Bnu0BOLBajpqYGJpMJnZ2dGBkZQSwWg9frZaV1T7UATSQSUcWtOaBgfYYTi8V0QhBymgjpfKfrHWcuCZLJZCgvL2flZMPhMJLJJEwmU1ZFqrnSarXYsGED3nvvPRw+fBgikYhlKpt4XH19Pfr7++F2uzE6OoqysjKUlpZmbemaS6ISYQV8R0cHQqEQmwsfHBxEYWEhffcsIgrWhBAyA2G9x1TBOLMutUgkYr1hYS1I5urqiT3NZDLJKuG1trayXrjZbJ7zNJVer2cBW+hhTxwSF9pYXFwMo9GI7u5uNDY2oqCgAOXl5WhubkZbWxtqamrmtJJdpVKhrq6ODbMLZTI7OjqgUqlQVFQ07wsR8g8UrAkhKxrP85OC8MTFXAIhMZBCoYBOp5s0XD0XEokENpsNNpsNwWCQlbAU9jRbLBbodLpZ5zMwGo0466yzcOjQIRw+fBgcx7HMYxOp1WrU1dVhYGCAFQYpKCiA2+1GZ2cnKioq5pRHQSwWw+VyQafToaenB3K5HE6nE8PDw2htbYVWq0VRUdGSZn0701CwJoSsKF6vF5FIZM6LueRy+Wlb96HRaKDRaFBSUoKRkREMDQ2hra0NMpmM9bZnMzxtsViwfv16vP/++zh69Cg4jpt2pbcQzA0GA7q7u9HT0wOlUonR0VH09fWhpKRkzj+H2WyGSqVCZ2cn+vr64HQ6UVBQgP7+fjQ1NcFgMKCoqGjaGvZkehSsCSErytjYGFKp1LwWc51uYrEYVqsVVqsVoVAIPp8PAwMDrLdttVpP2du22WxYu3Ytjhw5gmPHjkEkEsFms017vFKpRE1NDbxeL/r7+xEKhdDR0QGFQpG11Wu2lEolamtr0dvbi66uLphMJlRXV8Pv96O/vx8nTpyA2WxGKpWa82uvZBSsCSErynLZ16tWq1lOhNHRUdbblkqlsFgssFgs0/a2HQ4HUqkUjh07hmPHjmHt2rUzBl6O42C321kvu7W1Fe+88w62bNkyr0ViIpGIVQLr7u5GOByGy+XC6tWrWU1un8+HZDJJ+cFnifbyZLjvvvtwzjnnQKvVwmaz4SMf+Qiam5tz3SxCyAomFothsVhQV1eHuro6GAwGeL1eHDt2DK2trRgbG8NUiSiLi4uxatUqRKNRHD9+HD6f75TvJZfLUV1djXPPPRepVAqvvvoqBgYG5t12k8mE+vp6iEQiNDU1wefzwWazYc2aNTR/PUcUrDPs3bsXt9xyC95++228+uqrSCQSuOyyyxAKhXLdNEIIgUqlgtPpxNq1a1khjvb2dhw7dgxutxuxWCzreKfTifr6eoTDYZw4cYLVsT8Vq9WKyy+/HDqdDnv37kVLS0vWyve5kMvlqKmpgdVqRU9PDzo6OsDzPDQaDQXsOaBh8Awvv/xy1r8fffRR2Gw2HDx4EBdddFGOWkUIIdlEIhEbCo9EIvD5fBgaGsLAwAB0Oh0sFgsMBgM4jkNZWRnS6TSamppw/PhxrFmzZlZD2wqFAtu3b8c777yDxsZGhEIhOJ1OmM3mebW3pKQEWq0WXV1daGhomHfwX6moZz0Dv98P4ORQDiGE5COlUomSkhKsXbuWBeaOjg4cPXoUfX19iMViKC8vR1VVFcbHx9HQ0ICxsbFZvbZMJsOGDRvgcDgQDAbR1dWF1tbWrBX0c2EwGFBXVwepVIqRkRFEIpF5vc5KRD3raaTTadx2223YsmULVq9ePe1xwvYPgVAHmhBClpJIJILZbIbZbGbV9nw+HwYHB6HVamEymVBeXo6Ojg40NDRg1apV0Ov1p3xdtVqNyspKdHZ2QqPRIBqN4sSJEygqKppxlfl0hGFxlUo164sGQsF6WrfccguOHz+ON954Y8bj7rvvPtx7771L1CpCCDk1hUKBkpISFBUVYWxsDD6fD93d3eA4DiqVCoODgxCJRKivr59VdjGTyYRYLIb+/n6UlpYiHA6jt7cXo6OjKC0tnfO+aY7jWDETMjs0DD6Fr3zlK3jhhRewe/fuKXPsZrrrrrvg9/vZbe/evUvUSkIImZlIJGL7nFetWgWbzQa9Xo94PI4jR47g7bffZtN9p+JwOGAymdDb2wuz2YyamhokEgk0NDTA4/FMuSKdLB7qWWfgeR633nornnnmGezZswcul+uUzxGSKQhOR6F5QghZKIVCgeLiYhQVFaGkpARvv/02Ghoa4PV6cfbZZ6O0tBRKpXLG1ygtLUU8HkdbWxvq6upQX18Pj8fDUpaWlZVBpVIt0U+0slDPOsMtt9yC3/3ud3jiiSeg1WoxMDCAgYEBWgRBCDljcBwHm82Gyy+/HOeffz54nsf777+PgwcPsr3QmfnQM4lEIlRUVEAkEqGtrQ08z6OoqAi1tbXgOA5NTU1wu93TPp/MHwXrDA888AD8fj+2bdsGh8PBbk8++WSum0YIIYtKLpdj7dq1WL16NfR6PZLJJOLxOLq7u3H06FH09PQgHA5Pep5EIkFlZSXi8Tg6OzvB8zxUKhVqa2tRWFiIwcFBNDQ00GLbRUbD4BlozoUQspIoFArU1NQgnU7D6/UilUqhsrKS5SUfGhqCSqWC1WqF0WhkpTOVSiXKy8vR1tbGin4IRUOElKXNzc2wWq0oKiqaU8lNMjUK1oQQsoKpVCrU1NSA53kMDg6C4zjU1tbC4XDA7/ezleS9vb0wmUywWCxQq9XQ6XQoKSlBT09PVtEP4QLA6/XC7XbD7/fD6XTOapsYmR4Fa0IIWeE0Gg2qq6uRTqezArbBYIDBYEA8Hsfw8DB8Ph98Ph9UKhUsFgtMJhOi0Sh6e3shl8uztoHZbDbWy25ra4PJZEJJSQkkEgo780Fz1oQQQqDT6VBdXQ2j0Qiv14uWlhaW8Ekmk8HhcGD16tWorKyETCZDb28vjh49imQyCYlEgo6OjkmLcWUyGaqqqlBWVga/348TJ05gdHQ0Fz/eskeXOIQQQgCcTAdaWVmJ1tZWDA4OAgBqampYKU6O46DX66HX65FIJFhPOxqNYmBgAGNjY9i8eXPWdlYAMJvN0Ol06O3tRUdHBwwGAyVEmSPqWRNCCGGEtKQajYb1sKcquiGVSuFwOLBmzRrU1taiuroag4ODePHFF9He3j5pNbhUKkV5eTkqKioQCoUwPDyMaDS6VD/Wskc9a0IIIVmsVitSqRQ6Ozvh8/nAcRyqq6shlUqnPF6n00Gn06GgoADvvvsuq7OtUChgsVhgNpvZXLXBYIBWq4VcLp919jRCwZoQQsgUCgoKkE6n0d3dnRWwZ1ogZjAYcPbZZ6OjowM6nQ5isRhutxtutxtGoxEWiwVarRZisRh6vZ6Sp8wBBWtCCCFTKiwsRCqVQm9vb1bAnmnftNFoRGFhIfr7++FyueB0OtlK8paWFsjlclgsFmzfvh3xePyUKU7JSRSsCSGETKukpASpVAr9/f0sYFdVVc0YsB0OB6LRKLq6ulBTUwO73Q673Y5gMIihoSH09/fDarXCbDajtLR0CX+a5YuCNSGEkBmVlpYinU7D4/GwgF1ZWTljwC4rK8sq+iGTyaDRaKDRaFBSUoKRkREaBp8DWg1OCCFkRhzHweVywW63QyQSYXh4GO3t7TMGW47jUFFRAbFYjLa2tqytWhKJBDabDQUFBUvR/DMCBWtCCCGnxHEcysvLYbFYAAA+n++UATuz6EdHRwfVX1gACtaEEEJmRSiRaTKZAADDw8OnDMIKhQLl5eUIBALo6+tbqqaecShYE0IImTWxWIzKykoYDAak02n4fL5TBmyh6IfX64XX613C1p45KFgTQgiZE4lEgqqqKuh0OvA8D5/Px2pbT8dqtcJms6G3txfj4+NL2NozAwVrQgghcyaVSlFdXQ2NRsN62F1dXTMG7OLiYuj1+imLfpCZUbAmhBAyL0JVLZVKhXQ6jaGhIfT09Ex7vLCqXCaToa2tbcqc42RqFKwJIYTMm0KhQHV1NRQKBXieh9frnTFgC3PePM+jo6NjCVu6vFGwJoQQsiBKpRJVVVWQyWRIp9Pwer3o7e2d9niZTIaKigoUFhYuYSuXNwrWhBBCFkytVqOyshJSqRTpdBqDg4Nwu90zHq/VapewhcsbBWtCCCGLQqvVoqKiglXmGhgYQH9/f45bdWagYE0IIWTR6PV6uFwucBwHnufh8Xjg8Xhy3axlj4I1IYSQRWU0GlFaWgqO48BxHPr7+zE4OJjrZi1rFKwJIYQsOovFgpKSEvA8D7FYjL6+PspetgAUrAkhhJwWNpsNhYWFSKVSEIvF6O3thc/ny3WzliUK1oQQQk4bh8MBu92OVCoFqVSK7u5uDA8P57pZy44k1w0ghBByZisuLkYqlYLP54NMJkNXVxc4jmPVu8ipUbAmhBBy2jmdTqTTaYyOjmYFbKPRmOumLQs0DE4IIeS04zgOZWVl0Ov1SCQSkMlkNH89BxSsJ9i3bx8+9KEPobCwEBzH4a9//Wuum0QIIWcEoZCHRqNBIpFAQUFBrpu0bFCwniAUCmHdunX4xS9+keumEELIGUckEqGyshJKpXLGdKQkG81ZT3DllVfiyiuvzHUzCCHkjCUSiVBVVYV0Op3rpiwbFKwXKBaLIRaLsX8Hg8EctoYQQpYHsVgMsVic62YsGzQMvkD33Xcf9Ho9u23dujXXTSKEEHKGoWC9QHfddRf8fj+77d27N9dNIoQQcoahYfAFksvlkMvl7N8ajSaHrSGEEHImop41IYQQkueoZz1BMBhEW1sb+3dnZycOHz4Mk8kEp9OZw5YRQghZqShYT/Dee+/h4osvZv++/fbbAQCf/vSn8eijj+aoVYQQQlYyCtYTbNu2DTzP57oZi8rj8cDj8eS6GQQnKxA5HI5cN2PFo3MiP9D5MHsUrBeZw+HA3XffnTd/gLFYDDfccAOtUs8TW7duxSuvvJK1KJEsLTon8gedD7PH8WdaN5JkGR8fh16vx969e2mleo4Fg0Fs3boVfr8fOp0u181ZseicyA90PswN9axXiPXr19MJkWPj4+O5bgLJQOdEbtH5MDe0dYsQQgjJcxSsCSGEkDxHwfoMJ5fLcffdd9MCjjxAn0V+oM8hP9DnMDe0wIwQQgjJc9SzJoQQQvIcBWtCCCEkz1GwJoQQQvIcBWsyJ11dXeA4jvKkEwI6H8jSoWB9GrW3t+Pmm29GeXk5FAoFdDodtmzZgp/+9KeIRCKn7X0bGhpwzz33oKur67S9x2x8//vfx9VXXw273Q6O43DPPffktD2nwnHcrG579uxZ8HuFw2Hcc889c3qt5fb7nGglnw9NTU244447sH79emi1WjgcDlx11VV47733ctam2cjnc2K5/k7nizKYnSYvvvgiPvaxj0Eul+Omm27C6tWrEY/H8cYbb+Cb3/wmTpw4gQcffPC0vHdDQwPuvfdebNu2DWVlZaflPWbjO9/5DgoKCnDWWWfhlVdeyVk7Zuvxxx/P+vdvf/tbvPrqq5Pur6urW/B7hcNh3HvvvQBOFo+ZjeX2+8y00s+HX//613j44Ydx7bXX4p//+Z/h9/vxq1/9Cueddx5efvllXHLJJTlp16nk8zmxXH+n88aTRdfR0cFrNBq+traW7+/vn/R4a2srf//995+293/qqad4APzu3btPeWw6nebD4fCsX7uzs5MHwD/yyCOzOpbneX5oaIgHwN99992zfp98cMstt/Cn6xSZz+9kuf4+6Xzg+ffee48PBAJZ9/l8Pt5qtfJbtmyZ9fvlWj6dE2fK73S2KFifBl/60pd4APz+/ftndXwikeC/+93v8uXl5bxMJuNLS0v5u+66i49Go1nHlZaW8ldddRX/+uuv8+eccw4vl8t5l8vFP/bYY+yYRx55hAcw6SZ8UQmv8fLLL/MbN27k5XI5/5Of/ITneZ5vb2/nr7vuOt5oNPJKpZLftGkT/8ILL2S1YS7BWrDcgotgqi+mVCrF/+QnP+Hr6+t5uVzO22w2/otf/CI/MjKSddy7777LX3bZZbzZbOYVCgVfVlbGf+Yzn+F5/h+/w4m32f5+ltvvk86H6V1zzTW8yWSa13NzIV/PiUzL7Xc6WxSsT4OioiK+vLx81sd/+tOf5gHw1113Hf+LX/yCv+mmm3gA/Ec+8pGs40pLS/mamhrebrfz3/72t/mf//zn/IYNG3iO4/jjx4/zPH/yC+arX/0qD4D/9re/zT/++OP8448/zg8MDLDXqKys5I1GI3/nnXfyv/zlL/ndu3fzAwMDvN1u57VaLf+v//qv/I9//GN+3bp1vEgk4v/yl7+wNqz0YP35z3+el0gk/Be+8AX+l7/8Jf+tb32LV6vV/DnnnMPH43Ge53l+cHCQNxqNfHV1Nf+jH/2If+ihh/h//dd/5evq6nie5/lgMMg/8MADPAD+ox/9KPuMjhw5Mqt2LbffJ50P0zv//PP56urqeT03F/L1nMi03H6ns0XBepH5/X4eAP/hD394VscfPnyYB8B//vOfz7r/X/7lX3gA/K5du9h9paWlPAB+37597D6v18vL5XL+G9/4BrtvpmE/4TVefvnlrPtvu+02HgD/+uuvs/sCgQDvcrn4srIyPpVK8Ty/soP166+/zgPgf//732cd9/LLL2fd/8wzz/AA+HfffXfa117I72Q5/T7pfJjevn37eI7j+H/7t3+b83NzJV/PCcFy/J3OFq0GX2RC2TetVjur41966SUAwO233551/ze+8Q0AJxfmZKqvr8eFF17I/m21WlFTU4OOjo5Zt9HlcuHyyy+f1I5zzz0XF1xwAbtPo9Hgi1/8Irq6utDQ0DDr1z9TPfXUU9Dr9bj00kvh8/nYbePGjdBoNNi9ezcAwGAwAABeeOEFJBKJHLY49+h8mJrX68UnP/lJuFwu3HHHHQt6rVzKp3PiTPmdToeC9SIT6uMGAoFZHd/d3Q2RSITKysqs+wsKCmAwGNDd3Z11v9PpnPQaRqMRo6Ojs26jy+Wash01NTWT7hdWeU5sx0rU2toKv98Pm80Gq9WadQsGg/B6vQCArVu34tprr8W9994Li8WCD3/4w3jkkUcQi8Vy/BMsPTofJguFQvjgBz+IQCCAZ599FhqNZt6vlWv5ck6cSb/T6dDWrUWm0+lQWFiI48ePz+l5HMfN6jixWDzl/fwc6rEolcpZH0v+IZ1Ow2az4fe///2Uj1utVgAnP8unn34ab7/9Np5//nm88sor+OxnP4v//u//xttvv31GfpFMh86HbPF4HNdccw2OHj2KV155BatXr16y9z4d8uGcONN+p9OhYH0afPCDH8SDDz6It956C5s3b57x2NLSUqTTabS2tmbtVRwcHMTY2BhKS0vn/P6z/aKb2I7m5uZJ9zc1NbHHV7qKigq89tpr2LJly6y+4M877zycd955+P73v48nnngCN954I/74xz/i85///Lw+o+WKzoeT0uk0brrpJuzcuRN/+tOfsHXr1jm/Rr7J9TlxJv5Op0PD4KfBHXfcAbVajc9//vMYHByc9Hh7ezt++tOfAgA+8IEPAADuv//+rGN+/OMfAwCuuuqqOb+/Wq0GAIyNjc36OR/4wAdw4MABvPXWW+y+UCiEBx98EGVlZaivr59zO840119/PVKpFP7jP/5j0mPJZJL9vkdHRyf17NavXw8AbNhPpVIBmNtntFzR+XDSrbfeiieffBL/+7//i2uuuWbOz89HuT4nzsTf6XSoZ30aVFRU4IknnsDHP/5x1NXVZWVsevPNN/HUU0/hn/7pnwAA69atw6c//Wk8+OCDGBsbw9atW3HgwAE89thj+MhHPoKLL754zu+/fv16iMVi/OAHP4Df74dcLsf27dths9mmfc6dd96JP/zhD7jyyivx1a9+FSaTCY899hg6Ozvx5z//GSLR3K/rHn/8cXR3dyMcDgMA9u3bh+9973sAgP/v//v/ll1vfevWrbj55ptx33334fDhw7jssssglUrR2tqKp556Cj/96U9x3XXX4bHHHsP//u//4qMf/SgqKioQCATw0EMPQafTsWCkVCpRX1+PJ598EtXV1TCZTFi9evWMQ3jL9fdJ58PJi4///d//xebNm6FSqfC73/0u6/GPfvSj7KJiOcnlOXGm/k6nldvF6Ge2lpYW/gtf+AJfVlbGy2QyXqvV8lu2bOH/53/+JyvBQyKR4O+9917e5XLxUqmULykpmTEJxERbt27lt27dmnXfQw89xJeXl/NisXjKJBBTEZJAGAwGXqFQ8Oeee+6CkkBs3bp1ykQHmGYbTb6ZLlvTgw8+yG/cuJFXKpW8Vqvl16xZw99xxx0sO9ehQ4f4G264gXc6nSxJxAc/+EH+vffey3qdN998k9+4cSMvk8lmtWVluf8+V/L5IOwdn+4mZKfLd/l0Tpwpv9PZ4nh+DisxCCGEELLkaM6aEEIIyXMUrAkhhJA8R8GaEEIIyXMUrAkhhJA8R8GaEEIIyXMUrHPohz/8IWpra5FOp3PdlAW78847sWnTplw3Y17oc8gP9DnkB/oc8lSu946tVH6/nzeZTPxvfvMbdh/+b3/g//t//2/S8Y888sgpS8zN1p///Gf++uuv510uF69UKvnq6mr+9ttv50dHR6c8/tlnn+XPOussXi6X8yUlJfy///u/84lEIusYj8fDy+Vy/tlnn11w+5YSfQ75gT6H/ECfQ/6iYJ0jP/nJT3idTsdHIhF2n3BS2O12PhQKZR2/mCeF2Wzm16xZw//bv/0b/9BDD/Ff/epXeZlMxtfW1vLhcDjr2JdeeonnOI6/+OKL+QcffJC/9dZbeZFIxH/pS1+a9LrXX389f+GFFy64fUuJPof8QJ9DfqDPIX9RsM6RtWvX8p/61Key7gPAr1+/ngfA//d//3fWY4t5UkyV7eqxxx7jAfAPPfRQ1v319fX8unXrsq5Y//Vf/5XnOI5vbGzMOvbpp5/mOY7j29vbF9zGpUKfQ36gzyE/0OeQv2jOOgc6Oztx9OhRXHLJJZMe27JlC7Zv344f/vCHiEQip+X9t23bNum+j370owCAxsZGdl9DQwMaGhrwxS9+ERLJP9LI//M//zN4nsfTTz+d9RrCz/Pss8+ehlYvPvoc8gN9DvmBPof8RsE6B958800AwIYNG6Z8/J577sHg4CAeeOCBGV8nFovB5/PN6nYqAwMDAACLxcLue//99wEAZ599dtaxhYWFKC4uZo8L9Ho9KioqsH///lO+Xz6gzyE/0OeQH+hzyG9UdSsHhJq4LpdryscvvPBCXHzxxfjRj36EL3/5y9PWif3DH/6Az3zmM7N6T/4UKeB/8IMfQCwW47rrrmP3eTweAIDD4Zh0vMPhQH9//6T7y8vL0dDQMKs25Rp9DvmBPof8QJ9DfqNgnQPDw8OQSCTQaDTTHnPPPfdg69at+OUvf4mvf/3rUx5z+eWX49VXX11we5544gk8/PDDuOOOO1BVVcXuF4a75HL5pOcoFAqMj49Put9oNE66ss1X9DnkB/oc8gN9DvmNgnWeuuiii3DxxRfjhz/8Ib70pS9NeYzD4Zjy6nIuXn/9dXzuc5/D5Zdfju9///tZjwlXzkJx+EzRaHTKK2ue58Fx3ILalE/oc8gP9DnkB/occoeCdQ6YzWYkk0kEAgFotdppj7v77ruxbds2/OpXv4LBYJj0eCQSgd/vn9V7FhQUTLrvyJEjuPrqq7F69Wo8/fTTWYs1gH8MM3k8HpSUlGQ95vF4cO655056zdHR0az5pXxGn0N+oM8hP9DnkN9ogVkO1NbWAji5+nImW7duxbZt2/CDH/xgyhWYTz75JLuKPdVtovb2dlxxxRWw2Wx46aWXphz6Wr9+PQDgvffey7q/v78ffX197PFMnZ2dqKurm/Hnyhf0OeQH+hzyA30O+Y161jmwefNmACf/2NauXTvjsffccw+2bduGBx98cNJj850bGhgYwGWXXQaRSIRXXnkFVqt1yuNWrVqF2tpaPPjgg7j55pshFosBAA888AA4jsta9AEAfr8f7e3t+PKXvzznNuUCfQ75gT6H/ECfQ57LzfZusnr1av6GG27Iug8Af8stt0w6duvWrSyL0GIkH1i3bh0PgL/jjjv4xx9/POv297//PevY559/nuc4jt++fTv/4IMP8l/96ld5kUjEf+ELX5j0uk8//TQPgG9ra1twG5cKfQ75gT6H/ECfQ/6iYJ0jP/7xj3mNRpOVRm+6k2L37t2LelIIrzXVbevWrZOOf+aZZ/j169fzcrmcLy4u5r/zne/w8Xh80nEf//jH+QsuuGDB7VtK9DnkB/oc8gN9DvmLgnWOjI2N8SaTif/1r3+d66YsCo/HwysUCv6vf/1rrpsyJ/Q55Af6HPIDfQ75ixaY5Yher8cdd9yBH/3oR2dEKbr7778fa9aswYc//OFcN2VO6HPID/Q55Af6HPIXx/OnSCFDCCGEkJyinjUhhBCS5yhYE0IIIXmOgjUhhBCS5yhYE0IIIXmOgjUhhBCS5yhYE0IIIXmOgjUhhBCS5yhYE0IIIXmOgjUhhBCS5yhYE0IIIXmOgjUhhBCS5yhYE0IIIXmOgjUhhBCS5yhYLzKPx4N77rkHHo8n100hhBByhqBgvcg8Hg/uvfdeCtaEEEIWDQVrQgghJM9RsCaEEELyHAXrCfbt24cPfehDKCwsBMdx+Otf/5rrJhFCCFnhKFhPEAqFsG7dOvziF7/IdVMIIYQQAIAk1w3IN1deeSWuvPLKXDeDEEIIYShYL1AsFkMsFmP/DgaDOWwNIYSQMxENgy/QfffdB71ez25bt27NdZMIIYScYShYL9Bdd90Fv9/Pbnv37s11k0geS6fTuW4CIWQZomHwBZLL5ZDL5ezfGo0mh60h+Y7n+Vw3gRCyDFHPmpAlRD1rQsh8UM96gmAwiLa2Nvbvzs5OHD58GCaTCU6nM4ctI2eCZDIJqVSa62YQQpYZCtYTvPfee7j44ovZv2+//XYAwKc//Wk8+uijOWoVOVMkEgkolcpcN4MQssxQsJ5g27ZtNK9ITpt4PJ7rJhBCliGasyZkCdE+fELIfFCwJmQJeb3eXDeBELIMUbAmZAl5PB6aZiGEzBkFa0KWUDgcxvj4eK6bQQhZZihYE7LEenp6ct0EQsgyQ8GakCXW2NhIQ+GEkDmhrVuELJGzzz4bnZ2d0Gg0WLVqFVatWpXrJhFClgnqWROyRAYGBjAyMoLx8XG8/fbb8Hg8uW4SIWSZoGBNSA6kUim8/PLLcLvduW4KIWQZoGBNSI4kEgn87W9/Q1NTU66bQgjJcxSsCcmhdDqNffv2Ye/evUgkErluDiEkT9ECM0LyQHNzM/r6+nDuueeisrISHMflukmEkDxCPWtC8kQoFMLu3bvx1FNPobW1lWpfE0IYCtaE5JmxsTHs3r0bf/rTn9DS0kJ7sgkhFKwJyVfj4+PYs2cPnnnmGSoAQsgKR8GakCXQ09ODUCgEAIjFYhgZGZn1c30+H5599lm89dZbSCaTp6uJhJA8RsGakNPowIED+NCHPoSysjKMjY0BACKRCL797W/jF7/4Bbq6umb1OjzP49ixY/jzn/98WpKpjIyM4MYbb4ROp4PBYMDnPve5Wdfe5nkeV155JTiOw1//+tesx959913s2LEDBoMBRqMRl19+OY4cObLo7SfkTEfBmpDT5C9/+Qu2bNmCv/3tb5PmnXmex/Hjx/GDH/wAhw4dmvVr+v1+PP/889i/f/+ct3pt27YNjz766JSP3XjjjThx4gReffVVvPDCC9i3bx+++MUvzup177///ilXrweDQVxxxRVwOp1455138MYbb0Cr1eLyyy+nbWqEzBEFa0JOgwMHDuDjH/84UqkUUqnUlMek02mk02k89NBDs+5hC06cOIE///nPGBwcXHBbGxsb8fLLL+PXv/41Nm3ahAsuuAD/8z//gz/+8Y/o7++f8bmHDx/Gf//3f+M3v/nNpMeampowMjKC7373u6ipqcGqVatw9913Y3BwEN3d3QtuNyErSV4Ga4/HgyNHjrA5PkKWm+9973vgeX7WK7lfeumlOb/H+Pg4nnvuORw4cGDaC4LZeOutt2AwGHD22Wez+y655BKIRCK888470z4vHA7jk5/8JH7xi1+goKBg0uM1NTUwm814+OGHEY/HEYlE8PDDD6Ourg5lZWXzbi8hK1FeBetnn30WtbW1KC4uxoYNG9gXhc/nw1lnnTVpPoyQfNTT04MXXnhh1gE0nU7j6NGjc1p0JuB5HocPH8Zf/vIX+Hy+OT8fOFlgxGazZd0nkUhgMpkwMDAw7fO+/vWv4/zzz8eHP/zhKR/XarXYs2cPfve730GpVEKj0eDll1/G3/72N0gklI+JkLnIm2D9/PPP45prroHFYsHdd9+d1SOxWCwoKirCI488ksMWkjNdPB5HOBxe8O3Pf/7znPdG8zyPxsZGNjQ+19vw8DCeeeaZrOH0//zP/4RGo2G3119/HV/60pey7uvp6ZnX7+q5557Drl27cP/99097TCQSwec+9zls2bIFb7/9Nvbv34/Vq1fjqquuQiQSmdf7ErJS5c3l7Xe/+11cdNFF2L17N4aHh3HPPfdkPb5582b86le/yk3jyBkvHo/jwIEDs14BnSmZTGJ4eBhDQ0MYGhrC/v37wXHcnAI2x3Hw+/3zev9M+/btQ2FhIWQyGb70pS/h+uuvZ4/deOONuPbaa3HNNdew+woLC1FQUDBpH3cymcTIyMiUw9sAsGvXLrS3t8NgMGTdf+211+LCCy/Enj178MQTT6CrqwtvvfUWRKKT/YInnngCRqMRzz77LD7xiU8s6GclZCXJm2B9/Phx/PjHP572cbvdTokhyGmTTCYRDAYhk8kgl8tnPDadTmNkZAQDAwPweDzw+XxZqUFlMtm8etYKhYIFtfkQiUTQ6/VIJpOQyWQwmUwwmUzscaVSCZvNhsrKyqznbd68GWNjYzh48CA2btwI4GQwTqfT2LRp05Tvdeedd+Lzn/981n1r1qzBT37yE3zoQx8CcHJOWyQSZa0UF/5NqVQJmZu8CdYqlWrGBWUdHR0wm81L2CKyEsnlcigUikn3j4+Pw+12o7+/H/39/YjH41M+n+M4rF27Fnv27Jlzz7qmpmbewVoul6OqqmpeSVPq6upwxRVX4Atf+AJ++ctfIpFI4Ctf+Qo+8YlPoLCwEADgdruxY8cO/Pa3v8W5556LgoKCKXvdTqcTLpcLAHDppZfim9/8Jm655RbceuutSKfT+K//+i9IJBJcfPHF8/o5CVmp8iZYX3zxxXjsscdw2223TXpsYGAADz30ED74wQ8ufcPIihSLxdDf388CdCAQmPZYrVaLoqIiFBUVweFwQC6X4/3338f+/ftntchMJBKhrq4ORqNxXm1VKpVYs2YNeJ6fsZ0z+f3vf4+vfOUr2LFjB0QiEa699lr87Gc/Y48nEgk0NzcjHA7P+jVra2vx/PPP495778XmzZshEolw1lln4eWXX4bD4ZhXOwlZqfImWH//+9/Heeedh3POOQcf+9jHwHEcXnnlFezatQu/+tWvwPM87r777lw3k5yhkskkfD4f2tvbMTg4OOPKaplMhsLCQhagtVrtpGM+97nPzWnu+rLLLptXu4VALZfLEY1GZzx2z5490z5mMpnwxBNPTPt4WVnZKX+OqR6/9NJLcemll874PELIqXF8HpX0OXHiBL72ta9h9+7dWSf+tm3b8Itf/AJ1dXU5bN3sHDp0CBs3bsTBgwexYcOGXDeHTIPneXg8HjQ2NqKpqQktLS3TZtUSiUSw2+0sQJvN5lkNV+/atQt33XUXAEzZwxZe49Of/jTWrl0755/BYDCgpqYGMpkMABCNRhEIBHDRRRdBpVLN+fUIIfkrb3rWALBq1Sq89tprGB0dRVtbG9LpNMrLy2G1WnPdNHIG8Pv9aGpqQmNjI5qbm+H3+6c91mg0suBcUFAAqVQ65/fbvn07fvOb3+Dhhx/G66+/nnUBynEc6urqcNlll8HpdM7pdVUqFUpKSmC1WqdM80kIOfPkVbAWGI1GnHPOObluBlnmYrEYWltb0dTUhKamphlTZ+p0Ouh0OpSUlKCsrGzReqarVq3Cj3/8YwwMDOCGG25AIBCAUqnEN7/5zTnNUUskElgsFthsNuh0OgrShKwweROsf/azn+HFF1/EK6+8MuXjV155Ja6++mp8+ctfXuKWkeUinU6jp6eHDW13dHRMu8BLLpejsrISdXV1qK2thV6vx+uvvw6tVjvlavCFKigogFKpRCAQgEwmm3WgNplMsNvtMJlMC9rWRQhZ3vImWD/88MPYvn37tI/X19fjwQcfpGBNsvh8PhacZ1qtzHEcSktLUVtbi9raWpSXl2elvJzLKufTjeM4OBwOFBUVnZYLB0LI8pM3wbq9vR233HLLtI/X1tbioYceWsIWkXwUDofR3NzMAvRMq7YtFgtqa2tRV1eHmpqaZbHoymg0oqKiAkqlMtdNIYTkkbwJ1jKZbMaiAR6Ph4YBV6BkMomOjg4279zd3T3tFiKVSoWamhoWoC0WyxK3dv5EIhHKy8tRUFBA89GEkEnyJlifd955ePTRR/H1r3990r5Vv9+PRx55BOedd16OWkeWysQtVa2trdNmCxOLxSgvL2fzzk6nc1le0KlUKtTW1kKtVue6KYSQPJU3wfruu+/G1q1bsX79etx2221YtWoVgJM5w++//354PJ4ZkzaQ5WsuW6oKCwtZz7mysvKUebzzndVqRVVVFcRica6bQgjJY3kTrDdt2oTnn38eN998M772ta+xoUCe5+FyufDcc89h8+bNOW4lWQyxWAxtbW2s93yqLVVCcBZWbS9XZrMZ8XgcGo0GAFBSUoLS0lIa9iaEnFLeBGvgZGrCtrY2vP/++2hvbwcAVFRUYMOGDfSFtowJW6qE3nNnZ+e0BSdkMhmqqqpYgHY4HGfMZ//444/j3XffRTQaRVFREcrKynLdJELIMpFXwRo4udBm48aNrFQfWZ58Pl/W0PZst1S5XK55ZQtbTpRKJQVqQsic5F2wbmhoQEdHB0ZHR6dc9XvTTTfloFXkVIQtVcKq7aGhoWmPXY5bqhZTcXHxslwIRwjJnbwJ1u3t7fjUpz6FAwcOTLs1h+M4CtZ5Yi5bqpRKJes5L7ctVYuN47gV/fMTQuYnb4L1zTffjGPHjuH+++/HhRdeOO/avuQfenp6sHPnTgQCAWi1WuzYsWPORSMEwpYqYWi7ra0NsVhsymOFLVVCcF6uW6pOB61Wm5U5jSytxTwnCFlKefOtsX//fnz729/GrbfemuumLHsHDhzAf/zHf+DFF18Ez/MQiURIp9PgOA4f/OAH8W//9m+zKpQibKkSbjNtqXI4HGzFdmVlJaXJnAbtpc6NxTonCMmVvAnWFotlWW/LyRd/+ctf8PGPfxw8z7Nh6XQ6DeBk7/ill17C3/72Nzz55JO45pprsp4bj8dZlarGxsYVs6VqKS33feHL0ULOCULyRd4E6y996Uv43e9+h1tuuSXnCSJ+8Ytf4Ec/+hEGBgawbt06/M///A/OPffcnLZpNg4cOICPf/zjSKVS084fp1IpcByHj3/843jjjTdQUFCQVaVqJW6pWkpn+kr3fDPXc+LNN9+kHjbJS3kTrKurq5FKpbBu3Tp89rOfRUlJyZRB+3Rf+T755JO4/fbb8ctf/hKbNm3C/fffj8svvxzNzc2w2Wyn9b0X6nvf+15W72E6PM8jnU7jxhtvxGWXXTblMRzHwel0suC8ErZULQWar15aczkneJ7H9773PTz77LNL1DpCZo/jT/VXvERmswCJ47hp6xMvlk2bNuGcc87Bz3/+cwAnh8tKSkpw66234s477zzl8w8dOoSNGzfi4MGD2LBhw2lta6aenh6UlZWd8ktpok996lMsF7vZbGbD2jU1NStqfjUcDmPfvn2nrZ61IBaLnbah8Gg0ikAggIsuumjFbYebynzOCY7j0NXVRYvOSN7Jm8v83bt357oJiMfjOHjwIO666y52n0gkwiWXXIK33npryufEYrGsVdHBYBDAya1NiUTi9DY4wyuvvDLnQA2c/HK67rrrUFtbC6vVmvXYUrY/1xKJBJLJJEKh0LRTAYv5XqdDLBZjf3cr6bObznzOCZ7n8fe//x2f/vSnT1Orlpd4PH7az4fTTSKRQCaTzfv5eTOiyBPG7XbzAPg333wz6/5vfvOb/Lnnnjvlc+6++24eAN3oRje60e0MvOWLvOlZC2KxGA4dOgSv14stW7bkfQKJu+66C7fffjv79+HDh7F161a88847OOuss5asHY8++ii++MUvzvl5Dz30EPUi/g/1Is4sdE4sTDgcxuuvvw6ZTLZsdzHEYjHE43FceOGFy35qKK+C9c9+9jPcc889bD/vq6++iu3bt8Pn86G2thY//OEP8dnPfva0vb/FYoFYLMbg4GDW/YODgygoKJjyOXK5POsPWaioJJFIlnT45PLLLwfHcXOen7vsssvyZ5gnx+j3cGahc2JhpFIpJBIJ1Gr1ss2bIJFIkE6nIZVKl/1nmjdppR555BHcdtttuOKK/7+9O4+rot7/B/4aUFbZFERUdk3hCkGYkqSg5gYoipr7mkuSlnuYqXjNiFKvZF8X0EBRU7vmmvueordMqasgioDgBimIbALC/P7wcn6dgDhwgBng9Xw8zuPR+czMZ96H/Jz3mZnP0h9btmxRamCmpqbo1asXdu3aVasxaGlpwc3NDadPn1aUlZSU4PTp07JfntPKygq+vr4qD3vT1NTEwIED2ZGGGqyqtglBEODu7g5LS8tajoyo6mSTrFevXg0/Pz/s3LkTAwcOLLPdzc0NN2/erPU45s6di/DwcGzduhVxcXGYMWMGcnNzMWnSpFo/t7qWLFkCQRAqHf9cus+nn35aR5ERSUPVNlGqdevWCAsLQ35+fi1HRlQ1sknWCQkJGDBgQIXbmzdvjqdPn9Z6HCNGjMCqVauwdOlSuLi4ICYmBseOHYO5uXmtn1tdb775Jnbv3g1NTc0KryZKt+3Zs4eTP1CDV5U20bdvX7Rs2RK//fYbQkJC8ODBgzqOlqhisknWxsbGePLkSYXbY2NjK3xuXNNmzpyJe/fuoaCgAP/5z3/QtWvXOjlvTfD390d0dDS8vb0VVxOlY9gFQYCPjw+io6MxZMgQKcMkqjOqtInLly9j1apVik5I6enp+PLLL/Gf//xHsriJ/kw2Hcy8vb0RFhaGgICAMttu3ryJ8PDwWu1c1pC8+eabOHjwIFJSUnDmzBk8f/4choaG6NWrF59RU6OkapsIDAxEeHg4UlNTUVRUhK1btyIxMRHDhg2r9x2UqH6TzQxmDx8+RNeuXSGKIgYOHIiwsDCMHTsWxcXF2Lt3LywsLPDzzz/LfiiXVDOYEVHNKCoqwp49e3Dp0iVFmY2NDaZMmYLmzZtLGFndqqtZ/WpTQ5rVTza3wVu3bo1ff/0V/fv3x+7duyGKIqKionDo0CGMGjUKV65ckX2iJqL6r2nTphgzZgzGjh2ruJpOTk5GcHAwYmNjJY6OGitZJOuCggIcPHgQjx8/xubNm5GRkYG0tDQ8evQImZmZ+Pbbb2W/iAYRNSzdunXD/Pnz0aJFCwBAbm4u/u///g9HjhxRLLFJVFdkkay1tLQwfPhwREdHK8rMzMxgbm6u0gIfRES1wdLSEosWLUKnTp0AAKIo4vDhw9iwYQNyc3Mljo4aE1lkQkEQ0L59+7/tDU5EJAU9PT28//77GDRokKI3+c2bN/HFF18gJSVF4uiosZBFsgaATz75BN988w3i4+OlDoWISImGhgb69++PWbNmKaYUfvr0KVatWqXUEY2otshm6NaVK1fQokULdOrUCV5eXrCxsYGurq7SPoIgIDQ0VKIIiaix69ixIwIDA7FlyxYkJSXh5cuX2LFjBxITEzFixAguokK1RjZDt1R5Ni0IAoqLi+sgmurj0C2ihu/ly5fYu3cvzp8/ryhr27Ytpk2b1mBGrXDolrxU6cra1tZW5Tl2SwmCgLt371a6H3tXElF90aRJE4wYMQJ2dnbYsWMHCgsLcf/+fQQHB2PChAlwdnaWOkRqYKqUrD09Pcsk66tXr+LmzZtwdHREhw4dAADx8fGIjY1Fp06d4ObmVnPREhHJyJtvvok2bdogLCwM6enpyM/Px8aNG9GvXz8MHDiQo1moxlQpWUdGRiq9379/P/bv34+TJ0+id+/eSttOnjyJd999FytWrKhSQFeuXMHZs2eRnp6OgIAAtG/fHnl5ebh16xZee+01RecOIiI5aN26NT7++GNs374d169fBwAcP34cycnJmDx5MgwMDCSOkBoCtX72LV26FLNmzSqTqAGgT58+mDlzpsrLMBYWFsLf3x8eHh5YvHgxvv76a6Smpr4KUkMDffv2ZecyIpIlXV1dTJkyBUOHDlVcTcfHxyM4OBhJSUkSR0cNgVrJ+s6dO4rZfcrTokULlZ5XA6/WnS2dbCA+Ph5/7vemo6OD4cOH48CBA+qES0RUawRBQO/evTF79mwYGhoCAJ49e4Y1a9bg/PnzkElfXqqn1ErW9vb2iIiIQE5OTplt2dnZ+Pbbb2FnZ6dSXd999x1mzJiBadOmlTtZvoODAxITE9UJl4io1rVr1w6LFi1Cu3btAADFxcXYvXs3IiMjUVBQIHF0VF+pNc76s88+w7Bhw9CxY0dMnDhR8Y/zzp072Lp1K9LS0vD999+rVFd6ejqcnJwq3K6pqYm8vDx1wiUiqhNGRkb46KOPcODAAZw6dQoA8Msvv+D+/fuYNm0azM3NJY6Q6hu1kvXgwYNx5MgRfPzxx/j888+Vtrm4uGDLli3o16+fSnVZWlri1q1bFW6/dOmS4scAEZHcaWpqwt/fH7a2toiKisKLFy/w6NEjhISEYNy4cXB1dZU6RKpH1B5X0LdvX1y/fh0PHz7E5cuXcfnyZTx8+BDXrl1TOVEDwOjRo7Fp0yZcvnxZUVY6TCw8PBx79uzB+PHj1Q2XiKhOubq64uOPP4aFhQWAVxN1hIeHY+/evbKf5InkQzYzmBUWFmLgwIE4c+YMHBwccPPmTTg5OSEjIwP379+Ht7c3Dhw4AE1NTalD/VucwYyIylNQUICdO3fil19+UZS1a9cO7733HoyMjCSMrHycwUxe1L6yTklJwfvvv48OHTqgefPmuHDhAgDgyZMn+PDDDxXjDiujpaWFY8eOISIiAnZ2dujYsSMKCgrg7OyMyMhIHDp0SPaJmoioItra2pg4cSJGjBih+C5LSEhAcHAw7ty5I3F0JHdqPbOOjY1F9+7dUVJSgq5duyIhIQEvX74EAJiamuLixYvIzc3Fli1byhw7d+5cpec2KSkpMDMzw9ixYzF27Fh1wiIikiVBEODp6QkrKyuEh4fj2bNneP78OUJDQzF48GD07t27ylM6U+Og1pX1woULYWxsjNu3b2P79u1lxhH6+Pjgp59+KvfYtWvXIi4uTvHe1tYW+/btUyccIqJ6wdbWFosWLULHjh0BvFob4YcffsDmzZuRn58vcXQkR2ol6wsXLmDGjBkwMzMr99eglZUVHjx4UO6x5ubmSuOmZfLonIioThgYGGDmzJno37+/ouz69esICQnBw4cPJYys7hQVFUkdQr2h1m3wkpKSv31o/8cff0BbW7vcbT4+PvjnP/+JEydOwNjYGACwevVq7Nq1q8L6BEHgLGZE1GBoaGhg0KBBsLW1RWRkJPLz85Geno4vv/wSo0ePRpcuXaQOsVZxtUXVqZWs33jjDfz4448ICAgos+3ly5fYtWsX3N3dyz02NDQULVu2xNmzZ3Hz5k0IgoDU1FRkZGRUeD4+yyGihsjJyQmLFi1CWFgY7t+/j8LCQkRGRiIpKQlDhw5FkyZqfVXLFpO16tT6F7Bo0SL4+vpixowZGDlyJAAgLS0Np06dwueff464uDh888035R6rr6+vNJGKhoYG1q5di9GjR6sTEhFRvWRqaor58+dj9+7divkmzp8/j3v37mHKlCnlTsNc33GcuerUemY9YMAAREZGYvfu3ejVqxcAYOzYsejbty+uXbuGbdu2oUePHuUe6+/vr9T57OzZs+jTp4864RAR1WtaWloYN24cxowZo7iaTk5OxhdffPG3MzzWV6Wjh6hyao+zHjduHFJTU7F3716EhITg888/x549e5CamopRo0ZVeNyBAweQkpKieN+rVy+cPHlS3XCIiOo9Dw8PzJ8/X7GqYU5ODtatW4ejR482qFvH7GCmumrfBs/Ly4OlpSUCAwOxYMECDB48uErHt2nTBtevX8eYMWMAvOoNzmfSRESvWFlZITAwEFu3bsWNGzcgiiIOHTqEpKQkTJw4sd7PyAW8mrmypKREsQY4VazafyE9PT00adIE+vr61Tp+5MiRWLNmDaysrODs7AwACAwMhLOzc4Wv119/vbrhEhHVO/r6+nj//fcxcOBAxcXMjRs3EBwcjNTUVImjqxkvXryQOoR6Qa2fM0OHDsW///3vao2RDg4OxsaNG+Hh4aEYp62vr48WLVpU+GqIHSyIiP6OhoYGBgwYgJkzZyoujp4+fYqvvvoK0dHREkenPi59rBq1FvK4cOECAgICYGpqiqlTp8LGxga6urpl9lNlQQsNDQ1s37693vcG50IeRFRbMjIysHnzZiQnJyvKunXrhhEjRqBp06Y1eq66WMjjwYMHKCkpgaWlZa3U35AW8lBr6JaXl5fiv8ubVrT0ObQq3fOTkpJgZmamTjhERA1a8+bNMWfOHOzdu1exaFJ0dDRSU1MxdepUmJqaShxh1fHKWjVqJeuIiIiaigPW1tY1VhcRUUPVtGlTjBw5Era2tti5cyeKioqQmpqK4OBgTJw4EU5OTlKHWCW5ublSh1AvqJWsJ0yYUO1jNTQ0oKGhgby8PGhpaUFDQ6PS3uCCIHBcHhERgK5du6Jt27YIDw9Heno68vPzsWHDBgwYMAA+Pj71pod1Xl4ee4SrQLI57JYuXQpBEBQD/0vfExGRatq0aYOPP/4Y27Ztw2+//QYAOHr0KJKSkjB58mQ0a9ZM4ggrJ4oicnNzYWBgIHUoslalDmaTJ0+GIAgICwuDpqYmJk+eXPkJBKHc9awbKnYwI6K6JooiTp06hQMHDigmTTExMcGUKVNga2tbrTrrqoNZYmIi7O3t0bp16xqvv9F2MDtz5gw0NDRQUlICTU1NnDlzRqVb10REVHsEQUCfPn1gbW2Nb7/9Fs+fP0dmZibWrFmDYcOGoUePHrL+Ls7OzpY6BNmrUrL+83CB8t5XxbZt26p13Pjx46t9TiKihuy1115DYGAgtmzZgrt376K4uBi7d+9GUlISRo0aVeGSxVJjsq6cZM+sJ06cWKas9JffX+/M//kXIZM1EVHFjI2NMXv2bOzfvx+nT58GAPz888+4f/8+pk6dCnNzc4kjLCs/Px+FhYXQ0tKSOhTZkixZJyUlKb1/9uwZJkyYACMjI8yaNQsdOnQAANy6dQvr1q1DdnY2tm7dKkWoRET1iqamJoYOHQpbW1tERUWhoKAADx8+REhICMaNGwdXV1epQ8S4ceOQnp4OXV1dzJs3DxkZGWjVqpXUYcmW2n3ljx49ij59+qBFixZo0qQJNDU1y7zKY21trfRau3YtzMzMcO7cOQwbNgxOTk5wcnLC8OHDce7cObRo0QL/+te/1A2XiKjReOONNxAYGAgLCwsArzpchYeH44cffpB8LemnT5/i6dOnilvgaWlpksYjd2ol671798LX1xdpaWkYOXIkSkpKMGrUKIwcORK6urpwdnbG0qVLVapr//79GDJkSLmdIDQ0NODv748DBw6oEy4RUaNjbm6OBQsWoHPnzoqyU6dOITQ0FFlZWRJGpuz58+d4/vy51GHIllrJOjg4GF26dMH169exfPlyAK+Gd+3YsQM3btzAo0ePVB42IIri3y6uHhsbW60FQ4iIGjsdHR1MmjQJ7777rmLykYSEBHzxxRdISEiQOLr/LzExkd/zFVArWcfGxmLkyJHQ1NRUTG5Supi4jY0NAgICEBISolJdgwcPxoYNG7BmzRqluWLz8vKwevVqbNq0CX5+fuqEW6mVK1eiW7du0NPTg7Gxca2ei4ioLgmCAC8vL8ydO1fx/ZaVlYW1a9fi9OnTskiS2dnZePDggdRhyJJayVpPT0/Re8/Y2Bja2tp49OiRYru5uXmZjmQVCQ0NRbdu3TB//nyYmJjAxsYGNjY2MDExwYIFC+Du7o61a9eqE26lCgsLMXz4cMyYMaNWz0NEJBU7OzssWrRI0Ym3pKQEe/fuxebNm2WxtnRycjKHcpVDrWTdoUMHxMbGKt67uLggKioKL1++xIsXL7Bz505YWVmpVJeRkRHOnz+Pffv2YdKkSXBwcICDgwMmTZqE/fv348KFC7V+tbt8+XLMmTOn3k2ET0RUFQYGBpg1axb69eunKLt+/TpCQkLw8OFDCSN79Ug0NjYWBQUFksYhN2oN3fL398fXX3+NVatWQVtbG4sXL4afnx+MjY0hCAJyc3Px7bffVqlOPz+/Wr/dXZMKCgqU/lHl5ORIGA0RkWo0NDTg5+cHW1tbbN26Ffn5+UhLS8OXX36JMWPG4B//+IdksRUWFuLmzZtwdnZWPGJt7Kp1Zf3ixQvs3r0bRUVF+PTTT5GRkQEA8PX1xblz5zB16lRMnz4dp0+fLnfyk4YkODgYRkZGipenp6fUIRERqczZ2RmBgYFo27YtgFeJMiIiAvv371fMMy6F3NxcxMbGSj7ETC6q/JMlPT0d3bp1Q1JSEkRRhCAI0NXVxf79+/HOO++ge/fu6N69e23EWi2BgYGVdnKLi4tDx44dq1X/okWLMHfuXMX7mJgYJmwiqlfMzMwwf/587Nq1C1euXAEAXLx4ESYmJvDy8qq1hTwqk5WVhbi4ODg6Ojb6JTSrnKxXrFiB5ORkzJkzB7169UJCQgJWrFiB6dOn4+7du7URo1rmzZtX6dW9nZ1dtevX1tZWmm+3PixJR0T0V1paWhg3bhzs7OywZ88evHz5EpmZmThy5Ah69epVK6tiqSIzMxOxsbGNPmFXOVmfOHEC48ePx6pVqxRl5ubmGD16NOLj4xU9DOXCzMwMZmZmUodBRCR7giDg7bffhqWlJcLCwpCZmYmCggIcO3YMbm5ucHZ2lmT1rszMTNy8eROOjo4VzorZ0FX5Z0pKSgrefvttpbK3334boijW++niUlJSEBMTg5SUFBQXFyMmJgYxMTHsNEZEjYq1tTXmzJmDli1bAnjVQ/vq1as4deqUZL20nz171qifYVc5WRcUFJR5flH6/uXLlzUTlUSWLl0KV1dXLFu2DDk5OXB1dYWrqyuuXr0qdWhERHVKT08PXbt2hbOzs6IsJSUFBw4cwNOnTyWJqTRhS9nxTSrV6hOfnJyMa9euKd6Xzi97586dcsdCv/HGGyrVGxcXh4iICCQmJiIzM7PcpTJLl3yrDZGRkYiMjKy1+omI6hNBEODk5AQLCwucO3cOBQUFyM7OxqFDh9CtWze89tprdR7Ts2fPFJ3OpLglL5VqJeslS5ZgyZIlZcoDAgKU3pf2FlfltkVUVBQmTZqEpk2bokOHDjAxMSmzjxymwyMiamzatm0LPz8/nDlzBk+ePEFxcTF++uknpKenw93dvc7HQmdkZODOnTto3759o0nYVf4LR0RE1EYcCAoKgqurK44ePQpTU9NaOQcREVWPgYEBfH19ceXKFcWiS/Hx8Xjy5Al69+4NAwODOo0nLS0NWlpasLGxqdPzSqXKyXrChAm1EQcePnyI+fPnM1ETEcmUpqYmPDw80LJlS1y6dAnFxcV4+vQp9u/fDy8vL1haWtZpPKmpqWjatCnatGlTp+eVgmwGrTk7O0s+Jy0REVWuffv2GDRoEAwNDQG8mvXsxIkTuHbtWp13/kpMTGwUK3XJJlmvWbMGW7ZsQXR0tNShEBFRJZo3bw4/Pz9YW1sryq5fv44TJ07U+epdiYmJSElJqdNz1jXZzJAeEhICIyMjdO/eHY6OjrCysioz+F0QBBw4cECiCImI6M+0tLTQu3dv/Pe//8XVq1chiiIePHiA/fv3o3fv3nU6IdW9e/dQUlLSYJ9hyyZZ//777xAEAVZWVsjJyVFaerNUY+n1R0RUXwiCAGdnZ5iZmeHMmTN48eIFcnNzcfjwYbi7u6Njx4519t2dmpqKJk2aKBYlaUhkk6yTk5OlDoGIiKrJwsICgwcPxtmzZ5GWloaSkhJER0cjPT0dHh4eSsO7Hj9+jPz8fACvnndnZmaWO1y3OpKSkqCnp4fmzZvXSH1yIZtn1kREVL/p6+vD29tbaS3shIQEHDx4EFlZWbhx4wbmzJmDgQMHIjs7GwCQn5+PFStWYPPmzTX23Pn27dsoKiqqkbrkQpbJOjs7G/fv30dKSkqZFxERyZeGhgbc3d3Rs2dPxdV0ZmYmVq9ejffeew/R0dFlJrgSRRFxcXEIDQ3F77//rnYMRUVFSEpKqtIxP/zwA/r27YsWLVpAEATExMSodEznzp1hbGwMfX19uLi4ICoqSmmfnJwczJw5E23btoWuri4cHR2xcePGKsUGyCxZb9iwAe3bt4exsTGsra1ha2tb5kVERPJnZ2cHPz8/GBsbIy0tDUePHkVxcXGFM1qWlJSgpKQEW7durZELs7S0NOTl5am8f25uLt5++22EhISofEzz5s2xePFiXL58Gb///jsmTZqESZMm4fjx44p95s6di2PHjmH79u2Ii4vD7NmzMXPmTBw8eLBKn0c2yXrjxo344IMP0K5dO3z22WcQRRGzZ89GYGAgWrVqhddffx1btmyROkwiIlKRsbExBg0ahLi4uCodd+LEiRo5/+PHj1Xed9y4cVi6dCneeecdlY/x8vLCkCFD4ODgAHt7e3z00UdwdnbGxYsXFftER0djwoQJ8PLygo2NDaZNm4bXX38dP//8c5U+i2yS9bp169CvXz8cPXoU06ZNAwD4+Phg5cqViI2NRXZ2tmQrvRARUfU8ffoUt27dUnlth5KSEsTGxiIzM1Ptc5e3IFRtEUURp0+fRnx8PHr06KEo79atGw4ePIgHDx5AFEWcPXsWt2/fRt++fatUv2x6g9+9excffPABAKBp06YAXvUSBAAjIyNMmTIF69evx7x58ySLkYiosVF3/epLly5VOWGKooj4+Hh06dJFrXMXFxfX+tLNWVlZaNOmDQoKCqCpqYn169ejT58+iu3r1q3DtGnT0LZtWzRp0gQaGhoIDw9XSuiqkE2yNjIyUvxRDQ0Noaenh9TUVMV2AwODKt3SICKi6mvSpAmaNWuGnJwcxYVTdWRmZkIQhColbEEQ8OLFixqZulRfX7/MqmA7duzA9OnTFe+PHj2K7t27V6t+AwMDxMTEICcnB6dPn8bcuXNhZ2cHLy8vAK+S9ZUrV3Dw4EFYW1vjwoUL+OCDD9C6desq3XKXTbLu1KkTfvvtN8V7d3d3bNiwAd7e3igpKcGmTZskWTuViKgx0tLSQpcuXdS+Mk1OTq7WlbWRkRGaNWum1rkB4K233oKWlpZS2aBBg9C1a1fFe3UWAtHQ0EC7du0AAC4uLoiLi0NwcDC8vLyQn5+PTz75BPv27YOPjw+AV+tgxMTEYNWqVfUzWY8dOxYbN25EQUEBtLW1sXz5crzzzjuwsrIC8OrW+N69eyWOkoio8dDS0iqT6KrK29u7WlfWDg4O0NBQr1uVvr4+dHV1y5QbGBjU2pKeJSUlikcHRUVFKCoqKvM5NDU1q3zXQDbJurTLeykPDw/cvHkThw4dgqamJvr27csrayKiesbKygq+vr44cuRIhcO2/kxDQwNOTk41MgNZ6apgqsjIyEBKSopi9cf4+HgAQKtWrdCqVSsAwPjx49GmTRsEBwcDAIKDg9G5c2fY29ujoKAAR44cQVRUFDZs2KA4v6enJxYsWABdXV1YW1vj/Pnz2LZtG9asWVOlzyKbZF0eOzs7fPTRR1KHQUREaliyZAmOHj2q8hW2t7d3jZy3Ksn64MGDSheMI0eOBAAsW7YMQUFBAICUlBSlq+Tc3FwEBATg/v370NXVRceOHbF9+3aMGDFCsc+uXbuwaNEijBkzBhkZGbC2tsbKlSvx/vvvV+mzCGJd9WtX0ZUrV3D27Fmkp6cjICAA7du3R15eHm7duoXXXnutRp5h1KZr167Bzc0Nv/76K9544w2pwyEikoUffvgBI0aMgCiK5V5hlybBadOmwdXVtUbO6e7uDmdn5xqpS2qyGWddWFgIf39/eHh4YPHixfj6668VvcE1NDTQt29fhIaGShwlERFVh7+/P6KjoxXPsP9MEAQ4OTnh448/rrFEDbxaXKShkE2yXrJkCQ4fPowNGzYgPj5e6VaJjo4Ohg8fzrWsiYjqsTfffBMHDx5EcnIyjI2NAQC6urr4/PPPERAQUKNrUZuamsLU1LTG6pOabJL1d999hxkzZmDatGnldixwcHBAYmKiBJEREVFNsrKygr6+PgBAW1u7xpezFAQBb731Vp2to10XZJOs09PT4eTkVOF2TU3NKk3KTkREjZObm1uDugUOyChZW1pa4tatWxVuv3TpkmLgORERUXkcHR1r9Lm3XMgmWY8ePRqbNm3C5cuXFWWltzDCw8OxZ88ejB8/XqrwiIhI5pydneHh4dGgbn+Xks0468WLF+PKlSvo0aMHHBwcIAgC5syZg4yMDNy/fx/e3t6YM2eO1GESEZHMaGhooFu3bnB0dJQ6lFojmytrLS0tHDt2DBEREbCzs0PHjh1RUFAAZ2dnREZGKmYyIyIiKqWnpwdfX98GnagBGV1ZA69ue48dOxZjx46VOhQiIpK5Nm3aoFevXuXO/93QyCpZExERVUYQBHTu3BkuLi4N8vl0eWSVrC9evIhvv/0WiYmJyMzMLDOHrCAISstoEhFR46Kjo4PevXurtaxlfSSbZL1mzRosWLAAOjo66NChQ40PkiciovrNxMQE/fv3r7XlLeVMNsn6q6++goeHBw4dOgQjIyOpwyEiIhlp2bIlBgwYAG1tbalDkYRsknVeXh7GjBnDRE1EREpMTU3h7e0NLS0tqUORjGyGbvXs2RP//e9/pQ6DiIhkxMDAAAMGDGjUiRqQUbJet24dTp8+jVWrViEjI0PqcIiISGJaWlro379/oxiaVRnZJGtLS0tMnz4dgYGBMDMzg76+PgwNDZVevEVORNQ4CIKA3r17w8TEROpQZEE2z6yXLl2KlStXok2bNujcuTMTMxFRI+bh4QFLS0upw5AN2STrjRs3wsfHB/v374eGhmwu+ImIqI45Ozs3+OlDq0o2WbGwsBA+Pj5M1EREjZiVlRW6du0qdRiyI5vM6Ovri59++knqMIiISCL6+vro2bNno5lCtCpkk6yXLVuG2NhYBAQE4Ndff8Uff/yBjIyMMi8iIqr/WrVqhZYtW8LQ0FBR5uHh0WgnPamMbJ5Zd+jQAQAQExODTZs2VbhfcXFxXYVERES15OrVq/jvf/+Ly5cvAwDMzMxgbW0tcVTyJZtkvXTpUt76ICJqpBwcHJgD/oZsknVQUJCk509OTsaKFStw5swZPH78GK1bt8bYsWOxePHiRj9zDhFRbRIEAba2tlKHIWuySdZSu3XrFkpKSrBp0ya0a9cON27cwNSpU5Gbm4tVq1ZJHR4RUYPVqlUrPquuBJP1//Tv3x/9+/dXvLezs0N8fDw2bNjAZE1EVItsbGykDkH2mKz/RlZWVqXrahcUFKCgoEDxPicnp7bDIiJqUJisKyeboVtyk5CQgHXr1mH69Ol/u19wcDCMjIwUL09PzzqKkIio/jMxMYGBgYHUYcheg0/WgYGBEAThb1+3bt1SOubBgwfo378/hg8fjqlTp/5t/YsWLUJWVpbidf78+dr8OEREDUrbtm2lDqFeaPC3wefNm4eJEyf+7T52dnaK/3748CF69uyJbt26ISwsrNL6tbW1lTpGNGvWrNqxEhE1Nubm5lKHUC80+GRtZmYGMzMzlfZ98OABevbsCTc3N0RERHCeciKiWlZZvyB6pcEna1U9ePAAXl5esLa2xqpVq/DHH38otrVq1UrCyIiIGi4+r1YNk/X/nDx5EgkJCUhISCjzDEUURYmiIiJquLS1taGpqSl1GPUC7/P+z8SJEyGKYrkvIiKqeZwIRXVM1kREJAlO5aw6JmsiIpIEk7XqmKyJiEgSTZqw25SqmKyJiEgSTNaqY7ImIiJJcC4L1fEvRUREkhAEQeoQ6g0mayIikgSTteqYrImIiGSOyZqIiEjmmKyJiIhkjsmaiIgkwWfWqmOyJiIiSTBZq47JmoiIJMFJUVTHZE1ERJLglbXqmKyJiIhkjsmaiIhI5pisiYiIZI7JmoiISOaYrImIiGSOyZqIiEjmOMitEXj06BEePXokdRgEwMLCAhYWFlKH0eixTcgD24PqmKxrmIWFBZYtWyabf4AFBQUYNWoUzp8/L3UoBMDT0xPHjx+Htra21KE0WmwT8sH2oDpBFEVR6iCo9jx//hxGRkY4f/48mjVrJnU4jVpOTg48PT2RlZUFQ0NDqcNptNgm5IHtoWp4Zd1IuLi4sEFI7Pnz51KHQH/CNiEttoeqYQczIiIimWOyJiIikjkm6wZOW1sby5YtYwcOGeD/C3ng/wd54P+HqmEHMyIiIpnjlTUREZHMMVkTERHJHJM1ERGRzDFZExERyRyTNdH/CIKg0uvcuXNqnysvLw9BQUFVqmvlypUYNGgQzM3NIQgCgoKC1I6D6O/IuU3cunULCxcuhIuLCwwMDGBhYQEfHx9cvXpV7VjkiDOYEf1PVFSU0vtt27bh5MmTZcodHBzUPldeXh6WL18OAPDy8lLpmE8//RStWrWCq6srjh8/rnYMRJWRc5vYvHkztmzZgqFDhyIgIABZWVnYtGkT3N3dcezYMbzzzjtqxyQnTNZE/zN27Fil91euXMHJkyfLlEslKSkJNjY2ePLkCczMzKQOhxoBObeJUaNGISgoSGl+98mTJ8PBwQFBQUENLlnzNjhRFZSUlGDt2rX4xz/+AR0dHZibm2P69OnIzMxU2u/q1avo168fTE1NoaurC1tbW0yePBkAkJycrEi2y5cvV9xKrOy2to2NTW18JCK1SNUm3NzcyizE0qJFC3Tv3h1xcXE1+yFlgFfWRFUwffp0REZGYtKkSfjwww+RlJSEb775BtevX8elS5fQtGlTpKeno2/fvjAzM0NgYCCMjY2RnJyMH374AQBgZmaGDRs2YMaMGRgyZAj8/f0BAM7OzlJ+NKJqkVubePz4MUxNTWv0M8qCSETl+uCDD8Q/N5GffvpJBCDu2LFDab9jx44ple/bt08EIP7yyy8V1v3HH3+IAMRly5ZVOS51jiVSh1zbRKkLFy6IgiCIS5YsqXYdcsXb4EQq+v7772FkZIQ+ffrgyZMnilfp7bizZ88CAIyNjQEAhw8fRlFRkYQRE9UuObWJ9PR0jB49Gra2tli4cGGtnENKTNZEKrpz5w6ysrLQsmVLmJmZKb1ycnKQnp4OAPD09MTQoUOxfPlymJqaws/PDxERESgoKJD4ExDVLLm0idzcXPj6+iI7OxsHDhwo8yy7IeAzayIVlZSUoGXLltixY0e520s7yAiCgH//+9+4cuUKDh06hOPHj2Py5MlYvXo1rly50iC/SKhxkkObKCwshL+/P37//XccP34cnTp1qnZdcsZkTaQie3t7nDp1Ch4eHtDV1a10f3d3d7i7u2PlypXYuXMnxowZg127dmHKlCkQBKEOIiaqXVK3iZKSEowfPx6nT5/Gnj174OnpWZ2PUS/wNjiRit59910UFxdjxYoVZba9fPkSz549AwBkZmZC/MvKsy4uLgCguO2np6cHAIpjiOojqdvErFmzsHv3bqxfv17Rg7yh4pU1kYo8PT0xffp0BAcHIyYmBn379kXTpk1x584dfP/99wgNDcWwYcOwdetWrF+/HkOGDIG9vT2ys7MRHh4OQ0NDeHt7AwB0dXXh6OiI3bt347XXXkPz5s3RqVOnv72FFxUVhXv37iEvLw8AcOHCBXz22WcAgHHjxsHa2rr2/whEfyJlm1i7di3Wr1+Pt956C3p6eti+fbvS9iFDhkBfX7/W/wZ1Ruru6ERy9ddhKqXCwsJENzc3UVdXVzQwMBCdnJzEhQsXig8fPhRFURSvXbsmjho1SrSyshK1tbXFli1bir6+vuLVq1eV6omOjhbd3NxELS0tlYaseHp6igDKfZ09e7amPjZRheTUJiZMmFBhewAgJiUl1eRHl5wgin+5N0FERESywmfWREREMsdkTUREJHNM1kRERDLHZE1ERCRzTNZEREQyx2RNREQkc0zWRDUkOTkZgiAgMjJS6lCIJMf2ULOYrImIiGSOk6IQ1RBRFFFQUICmTZtCU1NT6nCIJMX2ULOYrImIiGSOt8GJ/iQoKAiCIOD27dsYO3YsjIyMYGZmhiVLlkAURaSmpsLPzw+GhoZo1aoVVq9erTi2vGd0EydORLNmzfDgwQMMHjwYzZo1g5mZGebPn4/i4mLFfufOnYMgCDh37pxSPOXV+fjxY0yaNAlt27aFtrY2LCws4Ofnh+Tk5Fr6q1BjxfYgH0zWROUYMWIESkpK8MUXX6Br16747LPPsHbtWvTp0wdt2rRBSEgI2rVrh/nz5+PChQt/W1dxcTH69euHFi1aYNWqVfD09MTq1asRFhZWrdiGDh2Kffv2YdKkSVi/fj0+/PBDZGdnIyUlpVr1EVWG7UEGpFpBhEiOli1bJgIQp02bpih7+fKl2LZtW1EQBPGLL75QlGdmZoq6urrihAkTRFEUxaSkJBGAGBERodindGWgf/7zn0rncXV1Fd3c3BTvz549W+7qWX+tMzMzUwQgfvXVVzXzgYn+BtuDfPDKmqgcU6ZMUfy3pqYmOnfuDFEU8d577ynKjY2N0aFDByQmJlZa3/vvv6/0vnv37iod91e6urrQ0tLCuXPnkJmZWeXjiaqD7UF6TNZE5bCyslJ6b2RkBB0dHZiampYpr+xLQkdHB2ZmZkplJiYm1fpy0dbWRkhICI4ePQpzc3P06NEDX375JR4/flzluohUxfYgPSZronKUN9SkouEnYiUDKlQZtiIIQrnlf+50U2r27Nm4ffs2goODoaOjgyVLlsDBwQHXr1+v9DxE1cH2ID0mayIZMDExAQA8e/ZMqfzevXvl7m9vb4958+bhxIkTuHHjBgoLC5V64hLVZ2wPZTFZE8mAtbU1NDU1y/SkXb9+vdL7vLw8vHjxQqnM3t4eBgYGKCgoqPU4ieoC20NZTaQOgIhePesbPnw41q1bB0EQYG9vj8OHDyM9PV1pv9u3b6N3795499134ejoiCZNmmDfvn1IS0vDyJEjJYqeqGaxPZTFZE0kE+vWrUNRURE2btwIbW1tvPvuu/jqq6/QqVMnxT6WlpYYNWoUTp8+jaioKDRp0gQdO3bEnj17MHToUAmjJ6pZbA/KON0oERGRzPGZNRERkcwxWRMREckckzUREZHMMVkTERHJHJM1ERGRzDFZE9VD5a3rS9RYNYb2wGRNDd7du3cxffp02NnZQUdHB4aGhvDw8EBoaCjy8/Nr7byxsbEICgpCcnJyrZ1DFStXrsSgQYNgbm4OQRAQFBQkaTwkrcbcHm7duoWFCxfCxcUFBgYGsLCwgI+PD65evSpZTKripCjUoP34448YPnw4tLW1MX78eHTq1AmFhYW4ePEiFixYgJs3b1Z70fvKxMbGYvny5fDy8oKNjU2tnEMVn376KVq1agVXV1ccP35csjhIeo29PWzevBlbtmzB0KFDERAQgKysLGzatAnu7u44duwY3nnnHUniUgWTNTVYSUlJGDlyJKytrXHmzBlYWFgotn3wwQdISEjAjz/+KGGE/58oinjx4gV0dXVrvO6kpCTY2NjgyZMnZZYmpMaD7QEYNWoUgoKC0KxZM0XZ5MmT4eDggKCgIFkna94Gpwbryy+/RE5ODrZs2aL0xVSqXbt2+OijjxTvX758iRUrVsDe3h7a2tqwsbHBJ598UmZBABsbG/j6+uLixYvo0qULdHR0YGdnh23btin2iYyMxPDhwwEAPXv2hCAIEAQB586dU6rj+PHj6Ny5M3R1dbFp0yYAQGJiIoYPH47mzZtDT08P7u7uan2JSnlVT/LB9gC4ubkpJWoAaNGiBbp37464uLhq1VlXmKypwTp06BDs7OzQrVs3lfafMmUKli5dijfeeAP/+te/4OnpieDg4HIXBEhISMCwYcPQp08frF69GiYmJpg4cSJu3rwJAOjRowc+/PBDAMAnn3yCqKgoREVFwcHBQVFHfHw8Ro0ahT59+iA0NBQuLi5IS0tDt27dcPz4cQQEBGDlypV48eIFBg0ahH379tXAX4UaK7aHij1+/BimpqY1Vl+tEIkaoKysLBGA6Ofnp9L+MTExIgBxypQpSuXz588XAYhnzpxRlFlbW4sAxAsXLijK0tPTRW1tbXHevHmKsu+//14EIJ49e7bM+UrrOHbsmFL57NmzRQDiTz/9pCjLzs4WbW1tRRsbG7G4uFgURVFMSkoSAYgREREqfT5RFMU//vhDBCAuW7ZM5WOoYWB7qNiFCxdEQRDEJUuWVPnYusQra2qQnj9/DgAwMDBQaf8jR44AAObOnatUPm/ePAAoc9vN0dER3bt3V7w3MzNDhw4dkJiYqHKMtra26NevX5k4unTpgrfffltR1qxZM0ybNg3JycmIjY1VuX6iUmwP5UtPT8fo0aNha2uLhQsXqlVXbWOypgbJ0NAQAJCdna3S/vfu3YOGhgbatWunVN6qVSsYGxvj3r17SuVWVlZl6jAxMUFmZqbKMdra2pYbR4cOHcqUl94u/GscRKpgeygrNzcXvr6+yM7OxoEDB8o8y5Yb9ganBsnQ0BCtW7fGjRs3qnScIAgq7aepqVluuViFFWdro+c3UXnYHpQVFhbC398fv//+O44fP660RrZc8cqaGixfX1/cvXsXly9frnRfa2trlJSU4M6dO0rlaWlpePbsGaytrat8flW/6P4aR3x8fJnyW7duKbYTVQfbwyslJSUYP348Tp8+jZ07d8LT07PKdUiByZoarIULF0JfXx9TpkxBWlpame13795FaGgoAMDb2xsAsHbtWqV91qxZAwDw8fGp8vn19fUBAM+ePVP5GG9vb/z8889KX6i5ubkICwuDjY0NHB0dqxwHEcD2UGrWrFnYvXs31q9fD39//yofLxXeBqcGy97eHjt37sSIESPg4OCgNGNTdHQ0vv/+e0ycOBEA8Prrr2PChAkICwvDs2fP4OnpiZ9//hlbt27F4MGD0bNnzyqf38XFBZqamggJCUFWVha0tbXRq1cvtGzZssJjAgMD8d1332HAgAH48MMP0bx5c2zduhVJSUnYu3cvNDSq/vs6KioK9+7dQ15eHgDgwoUL+OyzzwAA48aN49V6I8H28OrHx/r16/HWW29BT08P27dvV9o+ZMgQxY8K2ZG6OzpRbbt9+7Y4depU0cbGRtTS0hINDAxEDw8Pcd26deKLFy8U+xUVFYnLly8XbW1txaZNm4qWlpbiokWLlPYRxVfDTHx8fMqcx9PTU/T09FQqCw8PF+3s7ERNTU2lYSsV1SGKonj37l1x2LBhorGxsaijoyN26dJFPHz4sNI+VRmq4unpKQIo91XeMBpq2Bpze5gwYUKFbQGAmJSU9LfHS0kQxSr0ACAiIqI6x2fWREREMsdkTUREJHNM1kRERDLHZE1ERCRzTNZEREQyx2RNREQkc0zWREREMsdkTUREJHNM1kRERDLHZE1ERCRzTNZEREQyx2RNREQkc0zWREREMvf/AGLjYvnE727ZAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group_paired.mean_diff.plot(delta_dot=False);" - ] - }, - { - "cell_type": "markdown", - "id": "8108826e", - "metadata": {}, - "source": [ - "Delta dot kwargs can be utilised via `delta_dot_kwargs` in the `.plot()` method.\n", - "\n", - "The relevant inputs to `delta_dot_kwargs` are:\n", - "\n", - "- `'color'` - Specify the color of the delta dots. If color is not specified, the color of the effect size curve will be used.\n", - "- `'marker'` - Marker of the dots. The default are triangles ('^')\n", - "- `'alpha'` - Alpha (Transparency)\n", - "- `'zorder'` - Zorder (the layering relative to other plot elements)\n", - "- `'size'` - Marker size\n", - "- `'side'` - Which side to plot the delta dots. The options are `'left'`, `'right'`, or `'center'`. This functions like the `swarm_side` parameter." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "58acb413", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group_paired.mean_diff.plot(delta_dot_kwargs={\"color\":'red', \"alpha\":0.1, 'zorder': 2, 'size': 5, 'side': 'center'});" - ] - }, - { - "cell_type": "markdown", - "id": "ed845c6f", - "metadata": {}, - "source": [ - "## Effect size paired lines\n", - "\n", - "By default, effect size paired lines are included in paired experiment plots (excluding proportion plots). \n", - "\n", - "This feature can be turned off by setting `contrast_paired_lines=False` in the `.plot()` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c302afd2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "repeated_measures.mean_diff.plot(contrast_paired_lines=True);" - ] - }, - { - "cell_type": "markdown", - "id": "88d55d19", - "metadata": {}, - "source": [ - "Effect size line kwargs can be utilised via `contrast_paired_lines_kwargs` in the `.plot()` method.\n", - "\n", - "By default, the following keywords are passed:\n", - "\n", - "- `'linestyle'` - Linestyle\n", - "- `'linewidth'` - Linewidth\n", - "- `'zorder'` - Zorder (the layering relative to other plot elements)\n", - "- `'color'` - Color. Default is 'dimgray'\n", - "- `'alpha'` - Alpha (transparency)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "41d08b48", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "repeated_measures.mean_diff.plot(contrast_paired_lines=True, \n", - " contrast_paired_lines_kwargs={\"color\": \"red\", \"alpha\": 0.5, \"linestyle\": \"-\"}); " - ] - }, - { - "cell_type": "markdown", - "id": "e3969990", - "metadata": {}, - "source": [ - "## Baseline error curve\n", - "\n", - "In DABEST **v2025.03.27**, we introduce a new aspect to the contrast axes: the baseline dot and error curve. \n", - "While the baseline dot is always present, the error curve can be turned on by setting `show_baseline_ec=True` in the `.plot()` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "081d9d27", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "repeated_measures.mean_diff.plot(show_baseline_ec=True); " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/tutorials/09-forest_plot.ipynb b/nbs/tutorials/09-forest_plot.ipynb deleted file mode 100644 index 4a7401fb..00000000 --- a/nbs/tutorials/09-forest_plot.ipynb +++ /dev/null @@ -1,1161 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9f60d8c2", - "metadata": {}, - "source": [ - "# Forest Plots: Visualizing Multiple Contrasts\n", - "\n", - "> Explanation of how to use forest_plot for contrast objects e.g delta-delta and mini-meta or regular deltas.\n", - "\n", - "- order: 9" - ] - }, - { - "cell_type": "markdown", - "id": "73c68853", - "metadata": {}, - "source": [ - "In DABEST **v2025.03.27**, we introduce a new function to plot separately calculated effect sizes in the same axes to allow direct visual comparisons. \n", - "\n", - "Currently you can make a forest plot for delta-delta, mini-meta, or standard delta effect sizes. In addition, for delta-delta and mini-meta experiments, you can also plot the effect sizes of the original comparisons alongside the delta-delta/mini-meta measurement." - ] - }, - { - "cell_type": "markdown", - "id": "0113d4b4", - "metadata": {}, - "source": [ - "## Load libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3919becd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pre-compiling numba functions for DABEST...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 23.74it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Numba compilation complete!\n", - "We're using DABEST v2025.10.20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import dabest\n", - "import matplotlib.pyplot as plt\n", - "import dabest \n", - "print(\"We're using DABEST v{}\".format(dabest.__version__))" - ] - }, - { - "cell_type": "markdown", - "id": "f0b0907e", - "metadata": {}, - "source": [ - "## Delta-delta effects" - ] - }, - { - "cell_type": "markdown", - "id": "3c77114d", - "metadata": {}, - "source": [ - "First please revisit the notebook [Delta-Delta Tutorial](06-delta_delta.html) for how to generate a delta-delta effect size. We will generate three of them plot them into the same axes. Here we test the efficacy of 3 drugs named ``Drug1``, ``Drug2`` , and ``Drug3`` on a disease-causing mutation ```M``` based on disease metric ```Tumor Size```. We want to know how the three drugs fare in ameliorating the phenotype metric ```Tumor Size```. " - ] - }, - { - "cell_type": "markdown", - "id": "e7ded244", - "metadata": {}, - "source": [ - "| | Wildtype | Mutant |\n", - "|-------|---------|----------|\n", - "| Drug1 | XD1, W | XD1, M |\n", - "| Placebo | XP1, W | XP1, M |" - ] - }, - { - "cell_type": "markdown", - "id": "7fd677d0", - "metadata": {}, - "source": [ - "| | Wildtype | Mutant |\n", - "|-------|---------|----------|\n", - "| Drug2 | XD2, W | XD2, M |\n", - "| Placebo | XP2, W | XP2, M |" - ] - }, - { - "cell_type": "markdown", - "id": "95cca8ae", - "metadata": {}, - "source": [ - "| | Wildtype | Mutant |\n", - "|-------|---------|----------|\n", - "| Drug3 | XD3, W | XD3, M |\n", - "| Placebo | XP3, W | XP3, M |" - ] - }, - { - "cell_type": "markdown", - "id": "5e1f9a3e", - "metadata": {}, - "source": [ - "In each scenario, there are two ``Treatment`` conditions, ``Placebo`` (control group) and ``Drug`` (test group). There are two ``Genotype``\\'s: ``W`` (wild type population) and ``M`` (mutant population). Additionally, each experiment was conducted twice (``Rep1`` and ``Rep2``). We will perform several analyses to visualise these differences in a simulated dataset. We will simulate three separte datasets below. " - ] - }, - { - "cell_type": "markdown", - "id": "273579c0", - "metadata": {}, - "source": [ - "### Creating a demo dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f0467048", - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.stats import norm\n", - "def create_delta_dataset(N=20, \n", - " seed=9999, \n", - " second_quarter_adjustment=3, \n", - " third_quarter_adjustment=-0.1):\n", - " np.random.seed(seed) # Set the seed for reproducibility\n", - "\n", - " # Create samples\n", - " y = norm.rvs(loc=3, scale=0.4, size=N*4)\n", - " y[N:2*N] += second_quarter_adjustment\n", - " y[2*N:3*N] += third_quarter_adjustment\n", - "\n", - " # Treatment, Rep, Genotype, and ID columns\n", - " treatment = np.repeat(['Placebo', 'Drug'], N*2).tolist()\n", - " rep = ['Rep1', 'Rep2'] * (N*2)\n", - " genotype = np.repeat(['W', 'M', 'W', 'M'], N).tolist()\n", - " id_col = list(range(0, N*2)) * 2\n", - "\n", - " # Combine all columns into a DataFrame\n", - " df = pd.DataFrame({\n", - " 'ID': id_col,\n", - " 'Rep': rep,\n", - " 'Genotype': genotype,\n", - " 'Treatment': treatment,\n", - " 'Tumor Size': y\n", - " })\n", - "\n", - " return df\n", - "\n", - "# Generate the first dataset with a different seed and adjustments\n", - "df_delta2_drug1 = create_delta_dataset(seed=9999, second_quarter_adjustment=1, third_quarter_adjustment=-0.5)\n", - "\n", - "# Generate the second dataset with a different seed and adjustments\n", - "df_delta2_drug2 = create_delta_dataset(seed=9999, second_quarter_adjustment=0.1, third_quarter_adjustment=-1)\n", - "\n", - "# Generate the third dataset with the same seed as the first but different adjustments\n", - "df_delta2_drug3 = create_delta_dataset(seed=9999, second_quarter_adjustment=3, third_quarter_adjustment=-0.1)" - ] - }, - { - "cell_type": "markdown", - "id": "0faf0466", - "metadata": {}, - "source": [ - "### Loading data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "706cc02b", - "metadata": {}, - "outputs": [], - "source": [ - "unpaired_delta_01 = dabest.load(data = df_delta2_drug1, \n", - " x = [\"Genotype\", \"Genotype\"], \n", - " y = \"Tumor Size\", delta2 = True, \n", - " experiment = \"Treatment\")\n", - "unpaired_delta_02 = dabest.load(data = df_delta2_drug2, \n", - " x = [\"Genotype\", \"Genotype\"], \n", - " y = \"Tumor Size\", delta2 = True, \n", - " experiment = \"Treatment\")\n", - "unpaired_delta_03 = dabest.load(data = df_delta2_drug3, \n", - " x = [\"Genotype\", \"Genotype\"], \n", - " y = \"Tumor Size\", \n", - " delta2 = True, \n", - " experiment = \"Treatment\")\n", - "contrasts = [unpaired_delta_01, unpaired_delta_02, unpaired_delta_03]" - ] - }, - { - "cell_type": "markdown", - "id": "52381d78", - "metadata": {}, - "source": [ - "### Generate delta-delta plots for each datasets " - ] - }, - { - "cell_type": "markdown", - "id": "3013cd12", - "metadata": {}, - "source": [ - "To create a delta-delta plot, you simply need to set ``delta2=True`` in the \n", - "``dabest.load()`` function and ``mean_diff.plot()``\n", - "\n", - "In this case,``x`` needs to be declared as a list consisting of 2 elements, unlike most cases where it is a single element. \n", - "The first element in ``x`` will represent the variable plotted along the horizontal axis, and the second one will determine the \n", - "color of dots for scattered plots or the color of lines for slope graphs. We use the ``experiment`` input to specify the grouping of the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5621a8b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "f1 = unpaired_delta_01.mean_diff.plot(\n", - " contrast_label='Mean Diff',\n", - " fig_size = (7, 4),\n", - " raw_marker_size = 1,\n", - " contrast_marker_size = 5,\n", - ");\n", - "f1.suptitle('Delta-delta plot for Drug 1');\n", - "\n", - "\n", - "f2 = unpaired_delta_02.mean_diff.plot( \n", - " contrast_label='Mean Diff',\n", - " fig_size = (7, 4),\n", - " raw_marker_size = 1,\n", - " contrast_marker_size = 5,\n", - ");\n", - "f2.suptitle('Delta-delta plot for Drug 2');\n", - "\n", - "\n", - "f3 = unpaired_delta_03.mean_diff.plot( \n", - " contrast_label='Mean Diff',\n", - " fig_size = (7, 4),\n", - " raw_marker_size = 1,\n", - " contrast_marker_size = 5,\n", - ");\n", - "f3.suptitle('Delta-delta plot for Drug 3');\n" - ] - }, - { - "cell_type": "markdown", - "id": "6ec8759b", - "metadata": {}, - "source": [ - "### Generate a forest plot" - ] - }, - { - "cell_type": "markdown", - "id": "f8c731a4", - "metadata": {}, - "source": [ - "This will allow for comparisons of different ``Drug`` effects.\n", - "\n", - "Key Parameters:\n", - "\n", - "- ``data``: A list of dabest objects \n", - "\n", - "- ``labels``: A list of labels for the dabest objects. E.g., ``['Drug1', 'Drug2', 'Drug3']``\n", - "\n", - "- ``effect_size``: For delta-delta experiments, you can select the effect size metric from ``\"mean_diff\", or \"hedges_g\" / \"delta_g\"``. The default is ``\"mean_diff\"``.\n", - "\n", - "- ``ci_type``: A string specifying the confidence interval type to use. The options are either `bca` or `pct`. Default is `bca`.\n", - " \n", - " **Note: \"hedges_g\" and \"delta_g\" can be used interchangeably for delta-delta experiments - both plot hedges_g regular effect sizes and our `Delta g` delta-delta effect size.**\n", - "\n", - "- ``horizontal``: A boolean input (``True``/ ``False``) to adjust the plot orientation. The default is vertical orientation (``False``) \n", - "\n", - "- ``ax``: Optional argument to specify an existing matplotlib axes (otherwise a standalone figure will be created) \n", - "\n", - "See the [Controlling aesthetics](#controlling-aesthetics) section for more information on how to alter the aesthetics of the plots.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "adfdf4c5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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dO3Hq0F7kpR9G1vUb2HueRxNE5FqsGpMICgqqMATOnTsHPz8/q4vyBkeOHMFDXTqb7rd6cQ7WHgpFi1pV4evDwX8icg1WfRt17twZX331FTIzM+/ZlpGRgXnz5qFLly42F+dtbtwswZpDl6Uug4jIxKojiWnTpqFNmzZo1KgRRo4ciUaNGgEAjh49igULFkAURUybNs2uhXqLzSeyULdaEJpFh0hdChGRdSFRr1497NixA2PGjMF///vfMts6deqEOXPmoEGDBnYp0Bst2HkerzxcF7XCAqQuhYi8nNXzJJo2bYrt27fj+vXrSE9PBwDEx8dzprUdaHV6zN16BhMeqYtqwRzbISLp2DyZLjw8nMHgAPnFpfjvb6cx4ZG6COckOyKSiNUhodfrkZqaivT0dOTm5pa5Oh0ACIKAN954w+YCvVleYQk+3nQK47rVRUQVBgUROZ9VIfH3339jwIABuHTp0j3hcAtDwj5u3CzBRxtPYkzXOqhZ1V/qcojIy1h1Cuzzzz+PoqIirF69Gjdu3IDBYLjnptfzamv2kldUig9TT2L/xVypSyEiL2NVSBw+fBivvfYa+vTpg5CQEDuXROXR6vSY93s6lu6+gCJe7pSInMSq7qbo6Giz3UzkWDtOX8eRS2r0bxGN1nFVIQiC1CURkQez6kjitddew/z586HRaOxdD1kgr6gUC/44h5mpp3Du+k2pyyEiD2bVkUR+fj6CgoKQmJiIwYMHIyYmBnK5vMxzBEHAuHHj7FIklS/9egE+2JCG1nGheKx5TYQG+kpdEhF5GKtCYsKECaY/f/rpp+U+hyHhPHvP38DBjDz0alwd3RpWg0LOBQKJyD6sColz587Zuw6vc/eYjq1jPKV6A9Ycuozd52/gqbaxiI8Isml/RESAlSERGxtr7zq8Rl5eHhYvXoyZM2eWefzokmmI6zoYNVp3h8K/itX7v6ouxsyNp9CjUTX8q2kU5DIObBOR9QTRhp+wmZmZ+P3333Ht2jUMGDAA0dHR0Ov1UKvVUKlU94xTuBqNRgOVSgW1Wo3g4GCHv19qaioGDBiAwsJCAOUdPQiQ+yrRdMQ7CK/fxub3iwsLxDMdayM8SIkSnQHFpXo0iAqG0qfy7SKKIv676RT+tzcDmqJStIqrincfbYLa4YFmX1Og1eGjjSex8VgWrhdo0SgqGG/1aYRmMSE27ZfIEiX6EqTdSIO/3B8KucKh71WqL0WRvgj1Q+vDV+5ZY4NWdV6Loojx48ejdu3aGDp0KMaPH49Tp04BAAoKChAXF4e5c+fatVB3l5qait69e6OoqAiiKJrpXhKhL9XiwPxJuJ62x+b3PJ9zE+//mmaXM6C+3J6OhX+ex3uPNsbqFzrAX+GDpxbsRnEFczZeW3kYO09fx8eDmiH15U7oWCcCw77ejavqYpv2S0TOY1VIzJw5E7Nnz8aECROwadOmMl94KpUK/fv3x8qVK+1WpLvLy8vDgAEDIIoiDAZDxU8WRQAiDi96E6VF+Ta/d4FWh9mbTyM7X2v1PkRRxII/zmFM10Q80qg6GtQIxsdPNEOWRouNx7PKfU1xqR4bjl7F5F718UB8GOLCAzHu4bqIDQ/Ad7suWL1fInIuq0Ji/vz5eOqppzB9+nQkJSXds71p06amIwsCFi9ejMLCwvsHxC2iCH2JFlf2brTL+xeX6rHuyBWrX59xowjZ+Vp0SLy92m+wnwJJMSHYf6H8pUJ0BhF6g3hP15afj9x0LW9r9ktEzmVVSGRkZKB9+/ZmtwcGBnKi3T9EUbS66+3ijpV2m9l+/IoGBiv3lV1g7B6KuGvJ8oggJbILyj9CCVL6oEWtEMzZfBpZmmLoDSJ+OnAJ+y/mmo5qrNkvETmXVWc3RUZGIiMjw+z2ffv2oVatWlYX5UlycnJw9uxZK14poijnMkpuauAbqLK5jkKtzuIup9UHMvH6T0dM9xeMaG3Ve/73iSS8uuIwHpi+GXKZgMZRwejbLApHMtVW7Y+InM+qkOjfvz++/PJLjBgxAiqV8Qvs1hpCGzduxKJFizBx4kT7VenGCgoKbHp9aXEhfALsc+bVhRuFkFuw1lO3htWQdMcZSCV6YzdZdoEWkXdcKS+7QIuGNczXFhsWiB9Gt0NhiQ4FxTpEBvvhhWX7USvUeFnWiCA/q/ZLRM5jVXfT22+/jRo1aiApKQlPPfUUBEHABx98gOTkZPTs2RNNmzbF66+/bu9ay3jvvffQvn17BAQEuPRKtEFBtk1qCwwKgp9CbpdboVYPHwtmYwcpfRAXHmi61YkMQkQVJf48k2N6Tn5xKQ5m5KFFbNX77i/A1weRwX5QF5bi91PZeLhhdQBATKi/TfslIsezKiRUKhV27dqFiRMnIjMzE35+fti+fTvy8vLw1ltvYceOHQgICLB3rWWUlJTg8ccfx7///W+Hvo+twsLCkJCQYMVqrQICwmtCGaiCTBDscgvys+5ChIIg4P861MbcLaex6XgW0q5qMP6HQ6gWrMQjDauZnjdk/i4s/vO86f72U9nYdvIaMm4UYsfpbAyevwsJEUF4vFV0pfZLRNKp9LdGcXEx5s2bh6SkJEydOhVTp051RF339fbbbwMAFi1aJMn7W0oQBIwZM8aqdaxqPzjQbkuBywTgoQbWf/E+92A8ikp0mLzqCDTFpWgdVxWLn24DP8Xts5cu5BTixs0S0/384lJ8uOEkrqqLoQpQoGfj6pjQvV6ZtaUs2S8RSceqGdf+/v6YM2cORo0a5YiaKmXRokV4+eWXkZeXV+nXOmvGdV5eHqKjo1FUVGTZabCCALnCD93eWQlFgPVLdNzpybaxGNQ6xi77InIHnHFtH1Z1NzVu3Bjnz5+3cymOp9VqodFoytycISQkBCtXroQgCJDJ7vNXLggQIKDVyHftFhC9mtQwdfEQEVWGVSHx3nvv4auvvsJvv/1m12ImTZoEQRAqvKWlpVm9/xkzZkClUpluMTHO+2XdvXt3rFu3Dv7+/qb/l3sZjyDaPDcTEQ1sX7sJAB5rXhPPPRjPK9gRkVWs6m7q27cv0tLScPbsWdSuXRu1a9eGv79/2R0LAtasWVOp/WZnZyMnJ6fC58THx8PX9/bhXGW6m7RaLbTa23MFNBoNYmJinLbAH2Dsevr222/x4YcfIjMz0/S4UhWOxG5DEd2mBxT+ti/zLROAZzsloHfTGjbvi8gdsbvJPqw63eXw4cMQBAG1atWCXq/HmTNn7nmONb9cIyIiEBERYU1JFlEqlVAqlfd/ogOFhIRg7NixaN68OTp16mR6vMXw/yAssZld3qOKnw8m9qhfZq4DEZE1rAoJVxiPuHjxIm7cuIGLFy9Cr9fj4MGDAIDExESb5yY4w90haq/uoPrVq+DVHvUQWcXv/k8mIroP606cdwFvvvkmFi9ebLrfvHlzAMDWrVvRuXNniaqSjkIuYOgDsXiseU3IeKEhIrITqy+GrNfr8f3332P06NF47LHHcOSIca0ftVqNVatWISvLsUs9L1q0yHRdhjtv3hgQD9QOxedDW2JAy2gGBBHZlVVHEnl5eejRowf27NmDoKAg3Lx5E2PGjAFgXIZi7NixpqXEyXFiwwLwTMd4jj0QkcNYdSQxadIkHDt2DKmpqUhPTy+znLVcLsfAgQOxfv16uxVJZQUpffB85wTMGdycAUFEDmVVSKxevRpjxozBww8/XO6Aa926dV1icNsTdakfiS+fbImeTWqwa4mIHM6q7ia1Wo3atWub3V5aWgqdTmd1UXSvIKUPxj5UB+0SwqQuhYi8iFUhkZCQgP3795vdvnHjRjRs2NDqoqisuPBATO3dANWCeVorETmXVd1NzzzzDBYsWIDly5ebxiMEQYBWq8WUKVOwYcMGjB492q6Feqv2iWGYObApA4KIJGHVkcRLL72EY8eOISUlxXTBnyFDhiAnJwc6nQ6jR4/GyJEj7VmnV0ppUwspbWK47hIRScaqkBAEAfPnz8fw4cOxYsUKnD59GgaDAQkJCRg0aFCZ5Sao8mQC8HK3uuhSP1LqUojIy1kUEv3798e4cePQsWNHAMDvv/+OBg0aIDk5GcnJyQ4t0NsIAvDKI/XQqa7j1rAiIrKURWMSa9aswcWLF033u3Tpgk2bNjmsKG82qFUMA4KIXIZFIVGzZk0cOHDAdF8URfaTO0BsWAAG8+pxRORCLLqexMSJEzFr1ixER0cjJCQER48eRUxMDFQqlfkdCwIOHTpk12LtzVmXLy2PWq3GkSNHcCgjF8t2Z6BKVDzeHdQGreJCnVoHkafi9STsw6IxiRkzZiAxMRFbt27FtWvXIAgCAgMDERbGiV3WUqlUSE5Ohng6GxuuV0VceCBaxlaVuiwiojIsCgm5XI5nn30Wzz77LABAJpNh6tSpGDJkiEOL8ybdG1VjFx4RuRyLxiRatGiBDRs2mO4vXLjQdP0Gso/kxHCpSyAiuodFIXH48GFcv37ddP///u//ygxkk21qhwciJMCz+jGJyDNYFBKxsbH47bffoNfrAfDsJntrGOXcQXMiIktZFBLPPfccvv32W/j5+SE4OBiCIGDkyJEIDg42e6vozCcqq241178mNxF5J4sGrl999VU0a9YMW7duRVZWFhYvXozWrVsjPj7e0fV5hVqhgVKXQERULovmSdxNJpPhu+++c/uzm6ScJ3HLjtPZSIoJQRU/x57HTeRtOE/CPqxa4M9gMNi7Dq8llwkIUlrVDEREDmfRt9OtdZtq1apV5v793Ho+mRek9OFJAETksiwKibi4OAiCgKKiIvj6+pru38+ts6HIPH+FXOoSiIjMsigkFixYAEEQoFAoytwn2/kxJIjIhVk1cO0pXGHg+qq6GNVVvDQpkb1x4No+rLrGNdmPjC1ARC7Mou6md955p9I7FgQBb7zxRqVf523CA5VSl0BEZJZF3U2ycn7u3hqTuPvlgiCYlu1w9YFrV+huIiLHYHeTfVjU2WEwGMrcMjIy0KRJE6SkpGDPnj1Qq9VQq9XYvXs3Bg8ejGbNmiEjI8PRtRMRkYNZNXD96KOPQqFQ4Mcffyx3+8CBA6HX6/HTTz/ZXKAj8UiCyHPxSMI+rBo23bJlC7p27Wp2+0MPPYTNmzdbXRQREbkGq0LCz88Pf/31l9ntf/75J/z8eFonEZG7syokhg4diqVLl2Ls2LE4ffq0aazi9OnTGDNmDJYtW4ahQ4fau1YiInIyq8YkSkpKMHLkSCxduhSCIJjOfjIYDBBFESkpKVi4cCF8fV27b45jEkSei2MS9mHTjOvDhw9j/fr1uHDhAgDjFex69uyJZs2a2a1AR2JIEHkuhoR92LRGddOmTdG0aVN71UJERC6Gi0IQEZFZDAkiIjKLIUFERGYxJIiIyCyGBBERmcWQICIis6w+BTY1NRXffPMN0tPTkZubW+6S4WfPnrW5QCIiko5VITFz5kxMmjQJ1apVQ5s2bdCkSRN710VERC7AqpCYPXs2unbtivXr10OhcOxMxvKcP38e06ZNw5YtW3D16lVERUVh2LBhmDJlissvBUJE5E6sConc3FwMHDhQkoAAgLS0NBgMBnz11VdITEzE0aNHMWrUKNy8eROzZs2SpCYiIk9kVUi0adMGJ0+etHctFuvRowd69Ohhuh8fH4+TJ0/iiy++YEgQEdmRVWc3ff7551i1ahWWLVtm73qsplarERoaKnUZREQexaojiSeeeAI6nQ5PPvkk/v3vfyM6OhpyubzMcwRBwKFDh+xS5P2cOXMGc+fOve9RhFarhVarNd3XaDSOLo2IyK1ZFRKhoaEICwtDnTp17FrMpEmT8MEHH1T4nBMnTqB+/fqm+5mZmejRowcef/xxjBo1qsLXzpgxA2+//bZdaiUi8gY2XU/C3rKzs5GTk1Phc+Lj401nMF2+fBmdO3dG27ZtsWjRItPFj8wp70giJiaG15Mg8kC8noR92HQ9CXuLiIhARESERc/NzMxEly5d0LJlSyxcuPC+AQEASqUSSqXS1jKJiLyGTSFRWlqKtLQ0qNVqGAyGe7Z36tTJlt2blZmZic6dOyM2NhazZs1Cdna2aVv16tUd8p5ERN7IqpAwGAyYPHkyPv/8cxQWFpp9nl6vt7qwimzatAlnzpzBmTNnEB0dXWabC/WeERG5PatOgZ0+fTpmzpyJYcOG4dtvv4Uoinj//ffx5ZdfomnTpmjWrBlSU1PtXavJiBEjIIpiuTciIrIfq0Ji0aJFGDRoEL744gvTpLaWLVti1KhR2L17NwRBwJYtW+xaKBEROZ9VIXHp0iV07doVAEwDwcXFxQAAX19fDBs2DEuWLLFTiUREJBWrQiIsLAwFBQUAgKCgIAQHByM9Pb3Mc3Jzc22vjoiIJGXVwHXz5s2xd+9e0/0uXbrgk08+QfPmzWEwGDBnzhw0a9bMbkUSEZE0rDqSePbZZ8tMTHvvvfeQl5eHTp064cEHH4RGo8FHH31k10KJiMj57DbjWq1WY9u2bZDL5Wjfvr1bLLan0WigUqk445rIA3HGtX3Ybca1SqVCv3797LU7IiJyAVZ1NwHGiXLff/89Ro8ejcceewxHjhwBYDyiWLVqFbKysuxWJBERScOqkMjLy0OHDh0wZMgQ/O9//8PatWtNS2MEBQVh7NixmD17tl0LJSIi57MqJCZNmoRjx44hNTUV6enpZWY6y+VyDBw4EOvXr7dbkUREJA2rQmL16tUYM2YMHn74YQiCcM/2unXr4vz587bWRkREErMqJNRqNWrXrm12e2lpKXQ6ndVFERGRa7AqJBISErB//36z2zdu3IiGDRtaXRQREbkGq0LimWeewYIFC7B8+XLTeIQgCNBqtZgyZQo2bNiA0aNH27VQIiJyPqvmSbz00ks4duwYUlJSEBISAgAYMmQIcnJyoNPpMHr0aIwcOdKedRIRkQSsCglBEDB//nwMHz4cK1aswOnTp2EwGJCQkIBBgwY57Ip0RETkXDbNuE5OTkZycrK9aiEiIhdj9YxrIiLyfBYfSfTt27dSOxYEAWvWrKl0QURE5DosDolffvkFfn5+qF69ukXXki5vkh0REbkXi0OiZs2ayMzMRHh4OIYMGYLBgwejevXqjqyNiIgkZvGYREZGBrZu3YrmzZtj2rRpiImJQbdu3bBw4ULk5+c7skYiIpJIpQauH3zwQXz11Ve4evUqVqxYgbCwMLz44ouIjIxE//79sWLFCtPV6oiIyP1ZdXaTQqFAv379sHz5cmRlZZmC44knnsCHH35o7xqJiEgiNp0Cq9VqkZqaijVr1uDAgQPw8/NDXFycnUojIiKpVTokDAYDUlNTMWLECFSrVg0pKSkoKirC/Pnzce3aNTz55JOOqJOIiCRg8dlNf/75J5YtW4Yff/wROTk5aNu2LaZPn45BgwYhPDzckTUSEZFELA6J5ORk+Pv7o1evXkhJSTF1K128eBEXL14s9zUtWrSwS5FERCQNQbRkZhwAmex2z9T9JsqJoghBEKDX622rzsE0Gg1UKhXUajWCg4OlLoeI7KhEX4K0G2nwl/tDIVc49L1K9aUo0hehfmh9+Mp9HfpezmbxkcTChQsdWQcREbkgi0Ni+PDhjqyDiIhcEFeBJSIisxgSRERkFkOCiDzWyRsnpS7B7TEkiMhj5RTnoNRQKnUZbo0hQUQeSxRFqLVqqctwawwJIvJoDAnbMCSIyKPlFOdIXYJbY0gQkUfLyM+QugS3xpAgIo92MvckLFx9iMrBkCAij5ZbnIsLmgtSl+G2GBJE5PF2XdkldQluiyFBRB5v37V9yC/Jl7oMt8SQICKPo1ar8cfOP3DmwBnkHM9BUX4RNl3YJHVZbsniVWCJiNzFkSNH0LVzV9P9jtM7YmfgTjxQ4wHUDKopYWXux22PJPr27YtatWrBz88PNWrUwJNPPonLly9LXRYRuSgRIpYcXwKtXit1KW7FbUOiS5cu+OGHH3Dy5EmsXLkSZ8+excCBA6Uui4hcWFZhFpadWAaDaJC6FLfhtt1N48aNM/05NjYWkyZNwqOPPorS0lIoFI69VCERua/D1w/jpzM/oX9i//teipncOCTudOPGDSxduhTt27evMCC0Wi202tuHmhqNxhnlEZGL2Zm5E35yP/SO7y11KS7PbbubAOC1115DYGAgwsLCcPHiRaxZs6bC58+YMQMqlcp0i4mJcVKlRORqfrv4Gzac3yB1GS7PpUJi0qRJEAShwltaWprp+a+++ioOHDiAjRs3Qi6X46mnnqpw+v3kyZOhVqtNt4wMrulC5M1Sz6diXfo6LttRAZfqbnrllVcwYsSICp8THx9v+nN4eDjCw8NRt25dNGjQADExMdi1axfatWtX7muVSiWUSqU9SyYiN/fbxd+g1qrxeL3HoZBxPPNuLhUSERERiIiIsOq1BoPxbIU7xxyIiCyxN2svrty8gmENhqFaYDWpy3EpLtXdZKndu3fj008/xcGDB3HhwgVs2bIFKSkpSEhIMHsUQURUkUsFlzBr3yxsurCJlzy9g1uGREBAAFatWoWHHnoI9erVw8iRI9G0aVNs376d3UlEZDWdQYf159bjw70f4sj1IxyrgIt1N1mqSZMm2LJli9RlEJGHul50HQuOLkBCSAIeTXgU0VWipS5JMm55JEFE5Axn887io30fYfnJ5SgoKZC6HEkwJIiI7mPXlV2YsWcGdl/Z7XVdUAwJIvI4d3+R2+OLvVBXiO9Pfo+vDn+FPG2ezftzFwwJIvIYeXl5mD17NlJSUso8/vdHf+Psz2dRUlBi83uczD2JmXtn4lD2IZv35Q4E0duOne6g0WigUqmgVqsRHBwsdTlEZIPU1FQMGDAAhYWFAMo5ehAAuVKONq+1QbXm9pkL8WD0g+ib0Bd6gx5F+iLUD60PX7lvpffz+cHP8eu5X5FVmAUfmQ8ahjXE2OZj0TSiqdnXLE9bjuWnluNygfESCQkhCXiu6XPoGN3R9BytXouZe2diw/kNKNGXoENUB0xpOwXh/uEW18aQYEgQub3U1FT07t0boiiaJtaWSwAEQUDbN9raLShaVmuJQXUGodhQbHVIrEtfh1C/UERXiYZWp8WSE0uw8fxGrOtvfLw82zK2QSbIEBscC1EUsfbsWiw8thA//utHJFZNBABM+2safs/8He92eBdBvkGYvns6ZJBhSa8lFtfGkGBIELm1vLw8REdHo6ioqOKAuOWfI4ruX3eHb1Dlv9DLM7juYDQIb2B1SNytoKQA7f7XDvMfmY+2Ndpa/LoO/+uAV1q9gv51+iO/JB+dlnfCBx0/wCNxjwAA0tXp6Le6H77r9R2aRTSzaJ8ckyAit7Z48WIUFhZaFhAAIAJ6rR4Z2+y3wOferL1221epvhQrTq1AFUUV1Ktaz6LX6A16/HruVxTpikxf/sdzjkNn0KFt1O2QiVfFo0ZgDRy6Zvl4iltOpiMiAozjDnPnzrXqtem/pCO+d7xdLjyUkZ9h89Xutmdsx6u/v4piXTEi/CMw75F5qOpXtcLXnMo9hWHrh6FEX4IAnwB80uUTJIQkADBOCFTIFAj2LdtLEuYXhuvF1y2ui0cSROS2cnJycPbs2cqf4ioCN6/eRImmBKIo2nwr1hebBpDv55f0X9BmaRvTbV/WPgBA6+qtsaLPCizptQQdanbAhO0TkFOUU+G+agfXxoo+K7C091IMqjcIU3dOxdm8s5X7u7gPHkkQkdsqKLBtFnRJUQl8qtjna/BywWXIhPv/7u4S0wVNw2+ftRQZEAkACFAEoJaiFmqhFppFNEPvVb3x05mf8EyTZ8zuSyFXoFZwLQBAo7BGOJpzFN+d+A5vtXsL4f7hKDWUQlOiKXM0kVOcg3A/y89uYkgQkdsKCgqy6fWBQYFQ+thnUdBA30D4yO7/lRqoCESgIvC+zzOIBpToKzevQxRF02sahjWEj8wHu6/sxsOxDwMAzqnP4crNK2gWadmgNcCQICI3FhYWhoSEBKSnp1euy0kAAqsFQhmstMuYBIAyRweVUVhaiPlH5qNzTGdE+EcgV5uL79O+x7XCa3gk9hHT855JfQZda3XFkAZDAACf7PsEyTWTUSOoBm6W3sT69PXYe3Uvvnz4SwBAFd8q6J/YHzP3zoTKV4VA30DM2D0DzSKaWXxmE8CQICI3JggCxowZg3HjxlX6tXX61LFbQLSp3gYNwxpa9Vq5TI5z6nNYe2YtcrW5CFGGoFF4Iyzuudg03wEwDo7fuRzIjeIbmLJzCrKLslHFtwrqVK2DLx/+Eu2j2pueM7HNRAh7BYzbNg6lhlK0j2qPqW2nVqo+zpPgPAkit2btPIk+C/vYZZ5EbHAs/tPuPwhQBNi8L1fEs5uIyK2FhIRg5cqVEAQBMtl9vtL+mXHdYXIHuwREvCoeb7R9w2MDAmBIEJEH6N69O9atWwd/f38IglB+N9I/RxAd3+qI6i2q2/yeSRFJeLPdm6jiW8Xmfbkydjexu4nIY+Tl5eHbb7/Fhx9+iMzMTNPj/qH+qDegHuIeioNvoO1HEL1q98KwBsMgl8lt3perY0gwJIg8zo4dO9CpUyfT/c4zOiOycaTN+1XKlXimyTPoFN3p/k/2EDy7iYg8zt3dTfY4iykxJBEvJL2AqKAom/flThgSREQV8PfxxxP1nkD3uO4Wzaj2NAwJIqJyCBDQJaYLBtcfDJVSJXU5kmFIEBHdpWFYQwxvOBxxqjipS5EcQ4KI6B8hyhAMbzgc7aLa2W02trtjSBARAUiumYynGz2NIF/bFg30NAwJIvJqCpkCIxuPRJdaXaQuxSUxJIjIa1X1q4pXW71qupob3YshQUReKTEkERNaTbjvJUK9HUOCiLxOuxrt8ELSC1DIFVKX4vIYEkTkVR6q9RCeafKMV06Mswb/lojIa7SIbMGAqCT+TRGRV1D5qvB80vMMiEpidxMReZwmTZpgx44d+PPyn9hwbgNUcSo8Ue8Jj7/2gyMwJIjI46hUKiQnJ0Odrsa+gH2I8I9A55jOUpfllnjcRUQe75G4R7ziAkGOwJAgIo/XIaqD1CW4LYYEEXm02OBYhPmHSV2G22JIEJFHaxTWSOoS3BpDgog8Wp2QOlKX4NYYEkTk0XjhINswJIjIYwkQEBkQKXUZbo0hQUQeK1gZDB8Zp4PZgiFBRB4rSMGrzNmKIUFEHitAESB1CW7P7UNCq9UiKSkJgiDg4MGDUpdDRC7ET+4ndQluz+1DYuLEiYiKipK6DCJyQQoZLypkK7cOiV9//RUbN27ErFmzpC6FiFwQB61t57Z/g1lZWRg1ahRWr16NgADL+h21Wi20Wq3pvkajcVR5ROQCgnw5cG0rtzySEEURI0aMwHPPPYdWrVpZ/LoZM2ZApVKZbjExMQ6skoik5u/jL3UJbs+lQmLSpEkQBKHCW1paGubOnYv8/HxMnjy5UvufPHky1Gq16ZaRkeGg/xMiIs8giKIoSl3ELdnZ2cjJyanwOfHx8Rg0aBB+/vlnCIJgelyv10Mul2Po0KFYvHixRe+n0WigUqmgVqsRHBxsU+1ERJ7IpULCUhcvXiwznnD58mV0794dK1aswAMPPIDo6GiL9sOQICKqmFsOXNeqVavM/aAg4+BUQkKCxQFBRET351JjEkRE5Frc8kjibnFxcXDDXjMiIpfHIwkiIjKLIUFERGYxJIiIyCyGBBERmcWQICIisxgSRERkFkNCQlqtFv/5z3/KrExLrovt5V7YXvbhlsty2IsoisjPz0eVKlXKrAPlLFwWxL2wvdwL28s+PGIynbUEQeCHh4ioAuxuIiIisxgSRERkFkNCQkqlEm+99RaUSqXUpZAF2F7uhe1lH149cE1ERBXjkQQREZnFkCAiIrMYEkREZBZDgoiIzGJIEBGRWQwJIiIyiyFBRG7DYDCUuc8z+B3Pq9dusrdTp06hatWqiIiIkLoUssD27dtx/PhxXLx4EUOHDkXdunXh6+srdVlkxi+//ILNmzfjwoULeOyxx9CvXz+uveYEPJKwk++++w7t27fHggULcP36danLoftYsGABBg0ahDVr1mDx4sV49NFH8ddffwG499cqSW/BggUYNmwY9Ho9rl+/jvfffx+HDx+WuizvIJLNtm7dKiYmJootW7YU69SpI3700Udidna21GWRGT///LMYHh4urlq1StTr9aIoimLnzp3FHj16SFwZlWfVqlViaGiouGrVKtNjjRo1Er///ntRFEXRYDCIoiia2pLsi0cSNiotLUVaWhratm2LTZs2oX///pgzZw6+/fZbHlG4ILVajTVr1uC5555Dnz59oNPpAAAvvvgirly5Ar1eL3GFdKe8vDzs2LEDEydORN++fU2Pq1QqrFy5Ep07d8aECRNw/vx5yGQyjlE4AMckbKRQKNCjRw+0atUKVatWxfvvvw+DwYA5c+YAAJ588knTGIXBYDB9kKW4yBEZv1wiIiLQokUL+Pjc/viHhITg0qVLyMvLQ9WqVSGT8feTKwgJCUFKSgoiIiIgl8sBAI888ggyMjIwZMgQnDt3Dnv37sXkyZPxzTffICAgQOKKPQ9Dwkq3vvABIC4uDnFxcaYv/w8//BCCIJiCYsSIEVAoFHj//ffx0ksvITIyUsrSvdKd7fXee++ZQvpWmwUFBaFKlSrw8/MzPW/NmjXo1q0bAgMDJavbW93ZXq1btzY9fuHCBej1emzZsgWJiYkAgOnTp2PevHnQaDQMCQdgSFjp1gf45MmTqFevnulxvV4PuVyODz74AAAwd+5c3Lx5E2vXrkVJSQneeecdSer1drfa68SJE2jQoAGA220FAL6+vvD19TUFQteuXaFQKMp0cZDzlPfvS6fTITY2FqmpqfDx8YFOp4OPjw9q166NWrVqwd/fX8qSPRaPqSvpzjNftm3bhs6dO2PDhg0AjJdDlclkpn7tDz74AH369MFbb70FvV6Pv//+G3K5nGfPONHd7dW1a1dTe8nlclMfdmFhIfLz83Ht2jX07t0bmZmZ+OWXXyAIAvu5naiif18+Pj4QRdEU7D4+PiguLsZ3332HhIQEqFQqSWr2dAyJSrjzEHj58uX48ccfkZubi5deegnr168HcDsoDAYDcnNzcejQIbRu3Rp79uyBQqGATqdjf7eTWNpegPFIQqFQoE+fPjh16hSOHj1qai+OHzmHpe0lCAKKi4tx5swZ9O/fH5mZmZg/fz4ATq5zBH5bVcKtD/DEiRMxYcIE1KlTB+PGjUOVKlUwYcIErF27FsDtD/KKFStw9uxZ7Ny5s8zhMTmHpe0FGLsyMjIyoFQqcfz4cVNAsL2cpzLttXbtWrz88svQarXYu3cvfHx8oNfrGeiOIN3Zt+7p+PHjYmJiorhu3TrTY3/88YeYkpIi1qtXT9ywYYPpcZ1OZzp3u7S01Om1kuXtpdPpxAkTJpjaie0lDUvbS6/Xixs2bBB1Op0oimwvR+KRxH2Idx2+yuVyZGVlmc6vB4D27dvjueeeg0ajwQsvvGA6NJbL5aZTXvmL1Dmsaa+1a9dCLpdj5syZPOJzMmva6+eff4ZMJkP37t1NY3xsL8dhSNzH3YevgYGBSEpKwsGDB6HRaEyPd+rUCUlJSYiMjMS0adOwZ88es/sgx7GmvWbMmFGmvfiF4zzWtNf06dPLtBfH+ByLf7sW+Oijj9CnTx8AQM2aNdGxY0d8+umn+Omnn0wfZLVajcDAQAwdOhR6vR7bt2+XsmSvxvZyL2wv1yaIdx/vURkGgwE//vgjRo4cib59+2LZsmUAgOeffx6rV69Gx44dERsbiz///BM6nQ67du3Cv/71L8jlcqxZs0bi6r0P28u9sL1cH48k7nL3HAaZTIbHHnsMS5cuxfr16zFo0CAAwOeff46pU6eiSpUq+Ouvv1C3bl1s27YNgLFftXHjxjwdzwnYXu6F7eWGJBsyd3GbNm0qc1+r1Yo//fSTqFKpxCeeeML0uE6nM51hkZeXJ06dOlUMCwsTT5w44dR6vR3by72wvdwHQ+Ifdy4zfODAAdHHx0ccN25cmecUFxeLixYtEgVBEJ9//vky265evSoOGTJEjIuLEw8cOOCMkr0a28u9sL3cF0PiLvPnzxf/97//iZ9++qkYGRkpTpgwocz2M2fOiDExMaIgCOLUqVPLbDt27Jh4/vx5Z5br9dhe7oXt5X68PiTu/IUzZ84cMSIiQjxx4oSYk5MjfvbZZ2JoaGiZD/LVq1fFkSNHitu2bTMdBpPzsL3cC9vL/Xn9CeG3zrE+cOAArly5gk8++QT169cHAAwZMgQA8Prrr+PSpUvo168fFixYAIVCgU6dOkEQBE68cjK2l3the3kAqVPKFezevVsUBEGUy+XiwoULy2zLz88X165dKyYkJIjNmzcXu3btKpaUlIiiePuyieRcbC/3wvZyb14ZEleuXBEPHz4sLlmyRDxy5Igoisbr6AqCID799NPitWvX7nlNcXGxeOXKFdMHl2vFOA/by72wvTyL14XEypUrxV69eonVq1cXg4ODRV9fX7Fv375iRkaGuHLlSlEQBPHdd98V8/LyTK+5u2+UF1x3HraXe2F7eR6vCol58+aJVatWFWfNmiX+9ttvYm5urvjOO++IderUEevWrStmZGSIS5cuFQVBEKdPny6q1WqpS/ZqbC/3wvbyTF4TEvPmzRN9fX3FlStX3rPthx9+EBs3bix27NhRNBgM4ueffy7K5XLx9ddfFwsKCiSolthe7oXt5bm8IiS2bt0qCoIgvv3226IoGgfEDAZDmX7PTz/9VFQqleKKFStEURTFadOmie3bt+fgmQTYXu6F7eXZvGKBv9OnT2PkyJEIDQ3FK6+8go4dO5q23XnJxKZNm6JDhw744osvABjXur91jWMu9+08bC/3wvbybF6xwF+dOnXwzTffQKvV4r333sPOnTtN2259ODUaDYqKihAVFVVmGz/Azsf2ci9sL8/mFSEBGD/Ic+bMgSAIePfdd/HHH3+U2Z6eno7o6Gi0bdsWwO0rZvEDLA22l3the3kur+huutPp06cxduxYiKKIKVOmoGPHjtDpdOjXrx9kMhnWrFnDK125ELaXe2F7eR6vCwng9gdZJpPh9ddfx8cff4y0tDQcPHgQCoWiTD8qSY/t5V7YXp7FK0MCMH6Qx40bh40bNyI+Ph5HjhyBQqHgWjEuiu3lXthensNrQwIA0tLS8Pnnn+Pjjz+Gj48PP8Auju3lXthensGrQ+JO/AC7F7aXe2F7uS+GBBERmcXRIyIiMoshQUREZjEkiIjILIYEERGZxZAgIiKzGBJERGQWQ4KIiMxiSBARkVkMCSIiMoshQUREZv0/BCkEas6n3XIAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_forest_delta2 = dabest.forest_plot(\n", - " data = contrasts, \n", - " labels = ['Drug1', 'Drug2', 'Drug3']\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "aa104dab", - "metadata": {}, - "source": [ - "### Generate a forest plot with delta effect sizes alongside the delta-delta effect sizes\n", - "\n", - "If you want to plot the original effect sizes alongside the delta-delta effect sizes, you can do so by utilising the `idx` parameter. This parameter takes a tuple/list of indices of the original effect sizes you want to plot. \n", - "\n", - "For example, if you want to plot only the first effect size and the delta-delta effect size for each of the three dabest object supplied, you can do so by setting `idx=[[0, 2],[0, 2],[0, 2]]`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a3b8885f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_forest_delta2 = dabest.forest_plot(\n", - " data = contrasts, \n", - " labels = ['Drug1 Delta1', 'Drug1 Delta-Delta', 'Drug2 Delta1', 'Drug2 Delta-Delta', 'Drug3 Delta1', 'Drug3 Delta-Delta'],\n", - " idx=[[0, 2], [0, 2], [0, 2]]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "63926f6a", - "metadata": {}, - "source": [ - "### Selecting normalised effect sizes via `hedges_g` or `delta_g`\n", - "\n", - "Remember, `hedges_g` and `delta_g` are interchangeable for delta-delta experiments. However, when plotting the original effect sizes alongside the delta-delta effect sizes, you should note that hedges_g effect sizes will be plotted alongside the Delta g effect sizes. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "11cdbfb1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_forest_delta2 = dabest.forest_plot(\n", - " data = contrasts, \n", - " labels = ['Drug1', 'Drug2', 'Drug3'],\n", - " effect_size='hedges_g');\n", - "f_forest_delta2 = dabest.forest_plot(\n", - " data = contrasts, \n", - " labels = ['Drug1', 'Drug2', 'Drug3'],\n", - " effect_size='delta_g');\n", - "\n", - "f_forest_delta2 = dabest.forest_plot(\n", - " data = contrasts, \n", - " labels = ['Drug1 Delta1', 'Drug1 Delta-Delta', 'Drug2 Delta1', 'Drug2 Delta-Delta', 'Drug3 Delta1', 'Drug3 Delta-Delta'],\n", - " effect_size='hedges_g',\n", - " idx=[[0, 2], [0, 2], [0, 2]]);" - ] - }, - { - "cell_type": "markdown", - "id": "3497e3c4", - "metadata": {}, - "source": [ - "## Mini-meta effects\n", - "Next we will generate a similar forest plot for mini-meta effect sizes. Please revisit the notebook [Mini-Meta Tutorial](05-mini_meta.html) on how to generate a mini-meta effect size. We will generate three mini-meta effect sizes for three separate mini-meta analyses:\n", - "\n", - "**Note: the only effect size metric currently available for mini-meta is ``\"mean_diff\"``.**" - ] - }, - { - "cell_type": "markdown", - "id": "1e66885f", - "metadata": {}, - "source": [ - "### Creating a demo dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3727d3b6", - "metadata": {}, - "outputs": [], - "source": [ - "def create_mini_meta_dataset(N=20, seed=9999, control_locs=[3, 3.5, 3.25], control_scales=[0.4, 0.75, 0.4], \n", - " test_locs=[3.5, 2.5, 3], test_scales=[0.5, 0.6, 0.75]):\n", - " np.random.seed(seed) # Set the seed for reproducibility\n", - "\n", - " # Create samples for controls and tests\n", - " controls_tests = []\n", - " for loc, scale in zip(control_locs + test_locs, control_scales + test_scales):\n", - " controls_tests.append(norm.rvs(loc=loc, scale=scale, size=N))\n", - "\n", - " # Add a `Gender` column for coloring the data\n", - " gender = ['Female'] * (N // 2) + ['Male'] * (N // 2)\n", - "\n", - " # Add an `ID` column for paired data plotting\n", - " id_col = list(range(1, N + 1))\n", - "\n", - " # Combine samples and gender into a DataFrame\n", - " df_columns = {f'Control {i+1}': controls_tests[i] for i in range(len(control_locs))}\n", - " df_columns.update({f'Test {i+1}': controls_tests[i + len(control_locs)] for i in range(len(test_locs))})\n", - " df_columns['Gender'] = gender\n", - " df_columns['ID'] = id_col\n", - "\n", - " df = pd.DataFrame(df_columns)\n", - "\n", - " return df\n", - "\n", - "# Customizable dataset creation with different arguments\n", - "df_mini_meta01 = create_mini_meta_dataset(seed=9999, \n", - " control_locs=[3, 3.5, 3.25], \n", - " control_scales=[0.4, 0.75, 0.4], \n", - " test_locs=[3.5, 2.5, 3], \n", - " test_scales=[0.5, 0.6, 0.75])\n", - "\n", - "df_mini_meta02 = create_mini_meta_dataset(seed=9999, \n", - " control_locs=[4, 2, 3.25], \n", - " control_scales=[0.3, 0.75, 0.45], \n", - " test_locs=[2, 1.5, 2.75], \n", - " test_scales=[0.5, 0.6, 0.4])\n", - "\n", - "df_mini_meta03 = create_mini_meta_dataset(seed=9999, \n", - " control_locs=[6, 5.5, 4.25], \n", - " control_scales=[0.4, 0.75, 0.45], \n", - " test_locs=[4.5, 3.5, 3], \n", - " test_scales=[0.5, 0.6, 0.9])" - ] - }, - { - "cell_type": "markdown", - "id": "53b9bdf6", - "metadata": {}, - "source": [ - "### Loading data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "be7881aa", - "metadata": {}, - "outputs": [], - "source": [ - "contrast_mini_meta01 = dabest.load(data = df_mini_meta01,\n", - " idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), \n", - " mini_meta=True)\n", - "contrast_mini_meta02 = dabest.load(data = df_mini_meta02,\n", - " idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), \n", - " mini_meta=True)\n", - "contrast_mini_meta03 = dabest.load(data = df_mini_meta03,\n", - " idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")),\n", - " mini_meta=True)\n", - "contrasts_mini_meta = [contrast_mini_meta01, contrast_mini_meta02, contrast_mini_meta03] \n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "dde6d98a", - "metadata": {}, - "source": [ - "### Generate a forest plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3720e1ad", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_forest_minimeta = dabest.forest_plot(\n", - " data = contrasts_mini_meta, \n", - " labels=['mini_meta1', 'mini_meta2', 'mini_meta3']\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "d09c48a4", - "metadata": {}, - "source": [ - "### Generate a forest plot with delta effect sizes alongside the mini-meta effect sizes\n", - "\n", - "If you want to plot the original effect sizes alongside the mini-meta effect sizes, you can do so by utilising the `idx` parameter. This parameter takes a tuple/list of indices of the original effect sizes you want to plot. \n", - "\n", - "For example, if you want to plot only the first effect size and the mini-meta effect size for each of the three dabest object supplied, you can do so by setting `idx=[[0, final_idx],[0, final_idx],[0, final_idx]]` (where `final_idx` is the index of the last contrast object which will be the mini-meta effect size.)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d7c60fe5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_forest_minimeta = dabest.forest_plot(\n", - " data = contrasts_mini_meta, \n", - " idx = [[0, 3],[0, 3], [0, 3]],\n", - " labels=['Contrast 1A', 'mini_meta1', 'Contrast 2A', 'mini_meta2', 'Contrast 3A', 'mini_meta3']\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "5c9ada3f", - "metadata": {}, - "source": [ - "## Delta effects\n", - "Next we will generate a similar forest plot of regular delta effect sizes. In the example below, we will generate three regular `mean_diff` experiments. Here, we will only plot the effect size between the first group (Test 1 - Control 1) for each of the three dabest object supplied." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9e5dc341", - "metadata": {}, - "outputs": [], - "source": [ - "delta1 = dabest.load(data = df_mini_meta01,\n", - " idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")))\n", - "delta2 = dabest.load(data = df_mini_meta02,\n", - " idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")))\n", - "delta3 = dabest.load(data = df_mini_meta03,\n", - " idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")))\n", - "contrasts_deltas = [delta1, delta2, delta3] " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "54aa5353", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dabest.forest_plot(contrasts_deltas, idx=((0,),(0,), (0,)), \n", - " labels=['Drug1 \\nTest 1 - Control 1', 'Drug2 \\nTest 2 - Control 2', 'Drug3 \\nTest 3 - Control 3']);" - ] - }, - { - "cell_type": "markdown", - "id": "110264fa", - "metadata": {}, - "source": [ - "Unlike delta-delta and mini-meta experiments, here you can choose between more effect size metrics (where applicable): `mean_diff`, `cohens_d`, `cohens_h`, `hedges_g`, and `cliffs_delta`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9105f80e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dabest.forest_plot(contrasts_deltas, idx=((0,),(0,), (0,)), effect_size = 'cohens_d',\n", - " labels=['Drug1 \\nTest 1 - Control 1', 'Drug2 \\nTest 2 - Control 2', 'Drug3 \\nTest 3 - Control 3']);" - ] - }, - { - "cell_type": "markdown", - "id": "5d48c72c", - "metadata": {}, - "source": [ - "## Controlling aesthetics" - ] - }, - { - "cell_type": "markdown", - "id": "be8c4949", - "metadata": {}, - "source": [ - "The main aesthetic parameters for the forest_plot function are:\n", - "\n", - "- `fig_size`: The size of the figure\n", - "\n", - "- ``horizontal``: A boolean input (``True``/ ``False``) to adjust the plot orientation. The default is vertical orientation (``False``) \n", - "\n", - "- `custom_palette`: A list or dictionary of colors, one for each contrast object. E.g., `['gray', 'blue', 'green']` or `{'Drug1':'gray', 'Drug2':'blue', 'Drug3':'green'}` or a set of colors from seaborn color palettes.\n", - "\n", - "- `marker_size`: The size of the markers for the effect sizes. The default is 10.\n", - "\n", - "- `contrast_alpha`: Transparency level for violin plots. The default is 0.8.\n", - "\n", - "- `contrast_desat`: Saturation level for violin plots. The default is 1.\n", - "\n", - "- `labels_rotation`: Rotation angle for contrast labels. The default is 45 (for `horizontal=False`).\n", - "\n", - "- `labels_fontsize`: Font size for contrast labels. The default is 10.\n", - "\n", - "- `title`: The plot title. The default is None.\n", - "\n", - "- `title_fontsize`: Font size for the plot title. The default is 16.\n", - "\n", - "- `ylabel`: The axis label of dependent variable (Y-axis for vertical layout, X-axis for horizontal layout). The default will be given via the effect size selected. (eg., `\"Mean Difference\"` for `\"mean_diff\"`)\n", - "\n", - "- `ylabel_fontsize`: Font size for the axis label (Y-axis for vertical layout, X-axis for horizontal layout). The default is 12.\n", - "\n", - "- `ylim`: Limits for the dependent variable (Y-axis for vertical layout, X-axis for horizontal layout). The default is None.\n", - "\n", - "- `yticks`: Custom ticks (Y-axis for vertical layout, X-axis for horizontal layout) for the plot. The default is None.\n", - "\n", - "- `yticklabels`: Custom tick labels (Y-axis for vertical layout, X-axis for horizontal layout) for the plot. The default is None.\n", - "\n", - "- `remove_spines`: If True, removes plot spines (except the relevant dependent variable spine). The default is True.\n", - "\n", - "- `violin_kwargs`: A dictionary of keyword arguments for the violin plots. \n", - " \n", - " The default violin_kwargs = {\"widths\": 0.5, \"showextrema\": False, \"showmedians\": False, \"vert\": not horizontal}\n", - "\n", - "- `zeroline_kwargs`: A dictionary of keyword arguments for the zero line. The default is None.\n", - " \n", - " The default zeroline_kwargs = {\"linewidth\": 1, \"color\": \"black\"}\n", - "\n", - "- `marker_kwargs`: A dictionary of keyword arguments for the effect size markers. The default is None.\n", - " \n", - " The default marker_kwargs = {'marker': 'o', 'markersize': 12, 'color': 'black', 'alpha': 1, 'zorder': 2}\n", - "\n", - "- `errorbar_kwargs`: A dictionary of keyword arguments for the effect size error bars. The default is None.\n", - " \n", - " The default errorbar_kwargs = {'color': 'black', 'lw': 2.5, 'linestyle': '-', 'alpha': 1, 'zorder': 1}" - ] - }, - { - "cell_type": "markdown", - "id": "ee5b7648", - "metadata": {}, - "source": [ - "### Changing layout with `horizontal`\n", - "Forest plot assumes a vertical layout by default, but you can change it to a horizontal layout by setting ```horizontal``` to be ```True```:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "48f672bc", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_forest_minimeta = dabest.forest_plot(\n", - " data = contrasts_mini_meta, \n", - " labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n", - " horizontal=True,)" - ] - }, - { - "cell_type": "markdown", - "id": "dbec9054", - "metadata": {}, - "source": [ - "### Using a custom palette \n", - "You can color the half-violins with ```custom_palette```:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "226e0ad9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_forest_minimeta = dabest.forest_plot(\n", - " data = contrasts_mini_meta, \n", - " labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n", - " custom_palette=['#FF0000', '#00FF00', '#0000FF'],)" - ] - }, - { - "cell_type": "markdown", - "id": "5108d27f", - "metadata": {}, - "source": [ - "### Plotting other effect sizes \n", - "Forest plots can be drawn for effect sizes other than mean_difference, such as `hedges_g`, by setting `effect_size`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3810d975", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_forest_hedgesg = dabest.forest_plot(\n", - " data = contrasts, \n", - " labels =['Drug1', 'Drug2', 'Drug3'], \n", - " effect_size='hedges_g',\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "5817dc8b", - "metadata": {}, - "source": [ - "### Delta text\n", - "You can add/remove delta text via the `delta_text` argument. It is on by default." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4b8ef827", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_forest_minimeta = dabest.forest_plot(\n", - " data = contrasts_mini_meta, \n", - " labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n", - " custom_palette=['#FF0000', '#00FF00', '#0000FF'],\n", - " delta_text=True)" - ] - }, - { - "cell_type": "markdown", - "id": "636d8142", - "metadata": {}, - "source": [ - "You can set a variety of kwargs to customize the delta text via `delta_text_kwargs`.\n", - "\n", - "The relevant inputs to `delta_text_kwargs` are:\n", - "\n", - "- `'color'` - Color. If color is not specified, the color of the effect size curve will be used. \n", - "- `'alpha'`- Alpha (transparency)\n", - "- `'fontsize'` - Font size\n", - "- `'ha'` - Horizontal alignment\n", - "- `'va'` - Vertical alignment \n", - "- `'rotation'` - Text rotation\n", - "- `'x_coordinates'` - Specify the x-coordinates of the text\n", - "- `'y_coordinates'` - Specify the y-coordinates of the text\n", - "- `'offset'` - Am x-axis coordinate adjuster for minor movement of all text\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5d32f9e2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_forest_minimeta = dabest.forest_plot(\n", - " data = contrasts_mini_meta, \n", - " labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n", - " custom_palette=['#FF0000', '#00FF00', '#0000FF'],\n", - " delta_text=True,\n", - " delta_text_kwargs={'color': 'red', 'offset': 0.1,\n", - " 'fontsize': 8, 'rotation': 45,\n", - " 'va': 'bottom',\n", - " 'x_coordinates': [1.4,2.4,3.4], \n", - " 'y_coordinates': [0,-1.4,-1.6]}) " - ] - }, - { - "cell_type": "markdown", - "id": "adf2a443", - "metadata": {}, - "source": [ - "### Contrast bars\n", - "You can add/remove contrast bars via the `contrast_bars` argument. It is on by default." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "84fcd0c0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_forest_minimeta = dabest.forest_plot(\n", - " data = contrasts_mini_meta, \n", - " labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n", - " custom_palette=['#FF0000', '#00FF00', '#0000FF'],\n", - " contrast_bars=True,) " - ] - }, - { - "cell_type": "markdown", - "id": "6e0002ae", - "metadata": {}, - "source": [ - "You can set a variety of kwargs to customize the delta text via `contrast_bars_kwargs`.\n", - "\n", - "Pass any keyword arguments accepted by matplotlib.patches.Rectangle here, as a string.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "df1fa100", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_forest_minimeta = dabest.forest_plot(\n", - " data = contrasts_mini_meta, \n", - " labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n", - " custom_palette=['#FF0000', '#00FF00', '#0000FF'],\n", - " contrast_bars=True,\n", - " contrast_bars_kwargs={'color': 'red', 'alpha': 0.4}) " - ] - }, - { - "cell_type": "markdown", - "id": "5db33a33", - "metadata": {}, - "source": [ - "### Reference band\n", - "You can add reference bands by supplying a list/tuple to the `reference_band` argument, indicating the contrast to highlight. None are displayed by default." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c16b4146", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_forest_minimeta = dabest.forest_plot(\n", - " data = contrasts_mini_meta, \n", - " labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n", - " custom_palette=['#FF0000', '#0000FF', '#00FF00'],\n", - " reference_band=[1,]) " - ] - }, - { - "cell_type": "markdown", - "id": "3dbfc1d4", - "metadata": {}, - "source": [ - "You can set a variety of kwargs to customize the reference bands via `reference_band_kwargs`.\n", - "\n", - "Pass any keyword arguments accepted by matplotlib.patches.Rectangle here, as a string.\n", - "\n", - "In addition, the `span_ax` keyword argument can be used to expand the reference band across the whole plot. \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8833e72c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_forest_minimeta = dabest.forest_plot(\n", - " data = contrasts_mini_meta, \n", - " labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n", - " custom_palette=['#FF0000', '#0000FF', '#00FF00'],\n", - " reference_band=[1,],\n", - " reference_band_kwargs={'span_ax': True, 'color': 'grey', 'alpha': 0.2}) " - ] - }, - { - "cell_type": "markdown", - "id": "05d55fdd", - "metadata": {}, - "source": [ - "### Embedding forest plots into an existing Axes \n", - "\n", - "You can plot a forest plot into an existing Axes as a subplot by using the with the ``ax`` parameter. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dce18882", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_forest_drug_profiles, axes = plt.subplots(2, 2, figsize=[15, 14])\n", - "f_forest_drug_profiles.subplots_adjust(hspace=0.3, wspace=0.3)\n", - "\n", - "for ax, contrast in zip(axes.flatten(), [unpaired_delta_01, unpaired_delta_02, unpaired_delta_03]):\n", - " contrast.mean_diff.plot( \n", - " contrast_label='Mean Diff',\n", - " raw_marker_size = 1,\n", - " contrast_marker_size = 5,\n", - " color_col='Genotype',\n", - " ax = ax\n", - " )\n", - "\n", - "dabest.forest_plot(\n", - " data = contrasts, \n", - " labels = ['Drug1', 'Drug2', 'Drug3'], \n", - " ax = axes[1,1], \n", - " )\n", - "\n", - "for ax, title in zip(axes.flatten(), ['Drug 1', 'Drug 2', 'Drug 3', 'Forest plot']):\n", - " ax.set_title(title, fontsize = 12)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/tutorials/10-whorlmap.ipynb b/nbs/tutorials/10-whorlmap.ipynb deleted file mode 100644 index e2bae374..00000000 --- a/nbs/tutorials/10-whorlmap.ipynb +++ /dev/null @@ -1,524 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cfe53651", - "metadata": {}, - "source": [ - "# Whorlmaps: Visualizing Even More Contrasts\n", - "\n", - "> Explanation of how to use forest_plot for contrast objects e.g delta-delta and mini-meta or regular deltas.\n", - "\n", - "- order: 10" - ] - }, - { - "cell_type": "markdown", - "id": "fb7414d4", - "metadata": {}, - "source": [ - "In DABEST **v2025.10.20**, we introduce a new and more compact way of visualizing bootstrap distributions:\n", - "- whorlmap" - ] - }, - { - "cell_type": "markdown", - "id": "b8d232aa", - "metadata": {}, - "source": [ - "## Load libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d4b8d786", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pre-compiling numba functions for DABEST...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 37.69it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Numba compilation complete!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy.stats import norm\n", - "import dabest\n", - "from dabest.multi import combine, whorlmap\n" - ] - }, - { - "cell_type": "markdown", - "id": "d4874fc4", - "metadata": {}, - "source": [ - "## Create a simulated dataset and generate a list of corresponding dabest objects" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e7a5e74a", - "metadata": {}, - "outputs": [], - "source": [ - "def create_delta_dataset(N=50, \n", - " seed=9999, \n", - " second_quarter_adjustment=3, \n", - " third_quarter_adjustment= -0.5,\n", - " fourth_quarter_adjustment= -3, \n", - " scale4=1, initial_loc = 10):\n", - " \"\"\"Create a sample dataset for delta-delta analysis.\"\"\"\n", - " np.random.seed(seed)\n", - "\n", - " # Create samples\n", - " y = norm.rvs(loc=initial_loc, scale=0.4, size=N*4)\n", - " y[N:2*N] = norm.rvs(loc=initial_loc + second_quarter_adjustment, scale= 1, size=N) \n", - " y[2*N:3*N] = norm.rvs(loc=initial_loc + third_quarter_adjustment, scale=0.4, size=N)\n", - " y[3*N:4*N] = norm.rvs(loc=initial_loc + fourth_quarter_adjustment, scale=scale4, size=N)\n", - "\n", - " # Treatment, Rep, Genotype, and ID columns\n", - " treatment = np.repeat(['Placebo', 'Drug'], N*2).tolist()\n", - " genotype = np.repeat(['W', 'M', 'W', 'M'], N).tolist()\n", - " id_col = list(range(0, N*2)) * 2\n", - "\n", - " # Combine all columns into a DataFrame\n", - " df = pd.DataFrame({\n", - " 'ID': id_col,\n", - " 'Genotype': genotype,\n", - " 'Treatment': treatment,\n", - " 'Transcript Level': y\n", - " })\n", - " return df" - ] - }, - { - "cell_type": "markdown", - "id": "46c0292f", - "metadata": {}, - "source": [ - "## Working with many many Dabest objects\n", - "Let's say you have a transcriptomics experiment where you investigate the effects of administering 6 different drugs on oncogene transcripts 1 to 10. You want to find the drug that reduces all the transcripts the most effectively. In a 2x2 experiment, drug is compared to its placebo, so we will be tabulating delta-delta effect sizes. You may simulate the data as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "af3ade87", - "metadata": {}, - "outputs": [], - "source": [ - "dabest_objects_2d = [[None for _ in range(8)] for _ in range(6)]\n", - "labels_2d = [\"Transcript 1\", \"Transcript 2\", \"Transcript 3\", \"Transcript 4\", \"Transcript 5\", \"Transcript 6\", \"Transcript 7\", \"Transcript 8\"]\n", - "row_labels_2d = [\"Drug A\", \"Drug B\", \"Drug C\", \"Drug D\", \"Drug E\", \"Drug F\"]\n", - "drug_effect_2d = [[.9, 2, 2, .5, 1.2, 1, 3,2, 3, 4], \n", - " [0.1, -.3, .1, -0.3, -2, 1.2, 1,.1,-4, 2],\n", - " [4, 4, 1, 5, 1, 3, 6.5,.5, -1.2, .4],\n", - " [6, 2, 2, 4, 1.4, -0.5, -.5,1.1, 3, .4],\n", - " [0.1, -.3, .1, -0.3, -2, 1.2, 1,.1,-4, 2],\n", - " [-.3, -1, 2, 7, 1, -0.5, 4,1, 2.3, -.4],\n", - " ]\n", - "drug_effect_scale_2d = [[5, 10, 1, 5, 1, 2, 1,1, .1, 2], \n", - " [7, .2, 8, 3, 1, 4, 7,1, 5, 2],\n", - " [15, 3, 1, 2, 1, 1, 11,1, 7, 2],\n", - " [8, .1, 1, 5, 1, 6,1,1, 3, .4],\n", - " [9, 10, 7, 12, 4, 2,14,10, 9, 20],\n", - " [4, 3, 1, 4, 1, 4,4,1, 3, 4],\n", - " ]\n", - "seeds = [1, 1000, 20, 9999, 1000, 5320]\n", - "\n", - "for i in range(len(row_labels_2d)):\n", - " for j in range(len(labels_2d)):\n", - " df = create_delta_dataset(seed=seeds[i], \n", - " fourth_quarter_adjustment=drug_effect_2d[i][j],\n", - " scale4=drug_effect_scale_2d[i][j],\n", - " initial_loc = 20)\n", - " dabest_objects_2d[i][j] = dabest.load(data=df, \n", - " x=[\"Genotype\", \"Genotype\"], \n", - " y=\"Transcript Level\", \n", - " delta2=True, \n", - " experiment=\"Treatment\")\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "c9946643", - "metadata": {}, - "source": [ - "We are going to create a new object called MultiContrast which will contain the array of contrast objects and information about them." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "486e60e4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "multi_2d_mean_diff is a MultiContrast(2D: 6x8, effect_size='mean_diff', contrast_type='delta2')\n" - ] - } - ], - "source": [ - "multi_2d_mean_diff = combine(dabest_objects_2d, labels_2d, row_labels=row_labels_2d, effect_size=\"mean_diff\")\n", - "print(\"multi_2d_mean_diff is a \" + str(multi_2d_mean_diff))" - ] - }, - { - "cell_type": "markdown", - "id": "bc2e2d31", - "metadata": {}, - "source": [ - "As we have seen in the previous tutorial, we can visualize these effect sizes with forest plot as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ed4ac7f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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NzTXNlHj8+HFGjhxZ6pd7ZYWGhvLcc8/x8ssv079/f4YMGcLhw4f58MMP6dixo0UTwtx99918/fXXPPjggyQkJNClSxcMBgOHDh3i66+/Zs2aNXTo0IFXXnmFjRs3MmjQIOrXr8+lS5f48MMPiYqKMrW0x8XFERAQwEcffYSvry/e3t7ccMMNdrv9f5mrqytvvfUWEyZMoEePHowaNco0bF5MTAxTpkwxbdu+fXsAJk2aRL9+/dBqtYwcObLU49rjelpq8uTJFW5j6Xtzyy234ObmxuDBg5k4cSLZ2dnMnz+fsLAwzp8/b7eYLzty5AhffPEFqqqSmZnJ7t27+eabb8jOzua9996jf//+djuXn58fc+fO5e6776Zdu3aMHDmS0NBQzpw5w08//USXLl2YPXt2ucfo0aMHEydO5I033mDXrl3ccsstuLq6cvToUb755htmzpzJsGHDKhXXU089xfLly7nzzju55557aN++PWlpaXz//fd89NFHtG7d2uL3TwhhJSeOMCKEsNLlYefKepS3rY+Pj9qoUSN1zJgx6q+//mrxOceNG6d6e3tXuN3s2bPVpk2bqq6urmp4eLj60EMPqenp6SViKmsov8LCQvWtt95SW7Roobq7u6uBgYFq+/bt1ZdfflnV6XSqqqrqb7/9pt56661qnTp1VDc3N7VOnTrqqFGj1CNHjpgda9WqVWrz5s1VFxeXCofQK2v4tGtdO2zeZcuWLVPbtm2ruru7q0FBQepdd91lGmrvsqKiIvWxxx5TQ0NDVUVRLBpCz5Lrae2weeWhlCHpLHlvVFVVv//+e7VVq1aqh4eHGhMTo7711lvqggULVEA9efKkabv69eurgwYNKnHuHj16mIZjrCjGyw+NRqMGBASobdu2VSdPnqzu37+/xPa2Dpt3WUJCgtqvXz/V399f9fDwUOPi4tTx48er27ZtM21T0b+XefPmqe3bt1c9PT1VX19fNT4+Xn366afVc+fOmbapzPVJTU1VH330UbVu3bqqm5ubGhUVpY4bN05NSUkxbWPp+yeEqDxFVe3Q80MIIYQQQohaSmqohRBCCCGEsIEk1EIIIYQQQthAEmohhBBCCCFsIAm1EEIIIYQQNpCEWgghhBBCCBtIQi2EEEIIIYQNJKEWQgghhBDCBpJQCyGEEEIIYQNJqIUQQgghhLCBJNRCCCGEEELYQBJqIYQQQgghbCAJtRBCCCGEEDaQhFoIIYQQQggbSEIthBBCCCGEDSShFkIIIYQQwgaSUAshhBBCCGEDSaiFEEIIIYSwgSTUQgghhBBC2EASaiGEEEIIIWwgCbUQQgghhBA2kIRaCCGEEEIIG0hCLYQQQgghhA0koRZCCCGEEMIGklALIYQQQghhA0mohRBCCCGEsIEk1EIIIYQQQthAEmohhBBCCCFsIAm1EEIIIYQQNpCEWgghhBBCCBtIQi2EEEIIIYQNJKEWQgghhBDCBpJQCyGEEEIIYQNJqIUQQgghhLCBJNRCCCGEEELYQBJqIYQQQgghbCAJtRBCCCGEEDaQhFoIIYQQQggbSEIthBBCCCGEDSShFkIIIYQQwgaSUAshhBBCCGEDSaiFEEIIIYSwgSTUQgghhBBC2EASaiGEEEIIIWwgCbUQQgghhBA2kIRaCCGEEEIIG0hCLYQQQgghhA0koRZCCCGEEMIGklALIYQQQghhA0mohRBCCCGEsIEk1EIIIYQQQthAEmohhBBCCCFsIAm1EEIIIYQQNpCEWgghhBBCCBtIQi2EEEIIIYQNJKEWQgghhBDCBpJQCyGEEEIIYQNJqIUQQgghhLCBJNRCCCGEEELYQBJqIYQQQgghbCAJtRBCCCGEEDaQhFoIIYQQQggbSEIthBBCCCGEDSShFkIIIYQQwgaSUAshhBBCCGEDSaiFEEIIIYSwgSTUQgghhBBC2EASaiGEEEIIIWwgCbUQQgghhBA2kIRaCCGEEEIIG0hCLYQQQgghhA0koRZCCCGEEMIGklALIYQQQghhA0mohRBCCCGEsIEk1EIIIYQQQthAEmohhBBCCCFsIAm1EEIIIYQQNpCEWgghhBBCCBtIQi2EEEIIIYQNJKEWQgghhBDCBpJQCyGEEEIIYQNJqIUQQgghhLCBJNRCCOFgMTExKIpiemg0Gnx9fYmKiqJXr148+eSTbNmyxdlhVqmLFy/i5uaGoii0b9/e2eEIIYRNFFVVVWcHIYQQtUlMTAynT5+mS5cuNGzYEIC8vDxSUlLYuXMn6enpAPTo0YMFCxbQoEEDZ4ZbJd555x2efvpp0/Ndu3bRunVrJ0YkhBDWk4RaCCEc7HJCvXDhQsaPH2+2TlVVfvnlFx5//HGOHj1KeHg4mzdvJjY21jnBVpFmzZpx6NAh6taty9mzZ3nssceYNWuWs8MSQgirSMmHEEJUI4qiMHDgQLZs2UKjRo24ePEi9913n7PDsqs///yTQ4cOERgYyIIFCwBYsmQJBQUFTo5MCCGsIwm1EEJUQwEBAcyYMQOA9evXs337drP1l+uwT506xapVq+jduzdBQUEoisKGDRsA6Nmzp9nza02bNg1FUZg2bVqJdUVFRUyfPp2WLVvi4eFBWFgYd955JwcOHGDRokUoilKidd1Sn3zyCQB33XUXffv2pWHDhqSlpfHdd99ZdTwhhHA2SaiFEKKaGjBgAEFBQQCsXbu21G2mT5/ObbfdRlZWFv3796dHjx5otVqbzms0Grn99tt58sknOXr0KD169KB3797s2LGDjh07lkjuKyMrK4tvvvkGgHvuuQdFUZgwYQKAqbVaCCGuNy7ODkAIIUTpFEWhXbt2rFu3jv3795e6zdy5c1m1ahVDhgyx23lnz57Njz/+SGRkJAkJCTRp0gQAg8HAE088wcyZM60+9ldffUVOTg5t2rShbdu2AIwbN44XX3yR3377jdOnT1O/fn27vA4hhHAUaaEWQohqLCQkBIDU1NRS148bN86uyTRgSpinTZtmSqYBtFotb7/9NnXr1rX62J9++ikA9957r2lZ3bp16devH0ajkYULF1p9bCGEcBZJqIUQohozGo1AcWt1aYYNG2bX8yUlJXHixAkARo8eXWK9m5ub1efct28f//zzD+7u7tx1111m6+655x4AFi1aZHrNQghxvZCEWgghqrGUlBQAUy31tWJiYux6vqSkJKC4ZdzHx8eu57zcOn3bbbcRGBhotm7IkCGEhIRw+vRpfvvtN6uOL4QQziI11EIIUU2pqsrOnTsBiI+PL3UbT09Pq49fXktwWS3iFa0rS2FhIV988QUAW7dupWvXriW2MRgMQHHi3bdv30qfQwghnEUSaiGEqKZ+/vln06yJt9xyS6X3d3NzA4pH1ijN6dOnSyy7XB+dnJxMTk4O3t7eJbY5depUpWNZtWqVqbX9xIkTprKS0qxcuZK0tLQyW+WFEKK6kZIPIYSohnQ6HVOmTAGgb9++tGnTptLHuJwcHzx4sMS63NxcEhISSiyPjo42lXQsXbq0xPrCwkK+/fbbSsdyeezpZ555BlVVy3x06tSJgoICU2u2EEJcDyShFkKIauTy1OOdOnXi6NGjREZGMn/+fKuO1adPHwDmzJnD2bNnTctzcnJ44IEHSExMLHW/SZMmAfDSSy9x5MgR03Kj0chzzz1X5n5lOXPmDOvWrQOKRyUpz9ixYwEZk1oIcX2Rkg8hhHCSTz75xDSLYUFBASkpKezYsYO0tDSgeKbDBQsWWD0u8/Dhw5kxYwbbtm2jRYsWdO3aFaPRyLZt23Bzc+Oee+4pNXGdNGkSa9eu5ZdffqFVq1b06tWLgIAAtm7dyrlz53j44Yf58MMPTSUlFVm4cCFGo5GOHTvSrFmzcrcdOXIkU6dOZffu3Wzfvp327dtb9dqFEMKRpIVaCCGc5M8//+Szzz7js88+44cffuDAgQPEx8fzxBNPsGXLFhISEoiNjbX6+K6urqxdu5ZHH30UX19ffv31V/bs2cPtt9/Ojh07iI6OLnU/rVbLqlWrePvtt4mLiyMhIYF169bRqlUrtmzZQkREBHBljOzyqKpqGlu6otZpgODgYAYOHAhcGRVECCGqO0VVVdXZQQghhLh+9O7dm4SEBL799luGDh3q7HCEEMLppIVaCCFECbt27aKwsNBsWWFhIdOmTSMhIYGwsDBTS7IQQtR2UkMthBCihMcff5xdu3bRunVrIiMjSU9PZ+/evZw/fx4PDw8+++wzPDw8nB2mEEJUC1LyIYQQooQlS5awZMkS9uzZQ2pqKqqqUqdOHXr16sUTTzxB8+bNnR2iEEJUG5JQCyGEEEIIYQOpoRZCCCGEEMIGklALIYQQQghhA0moK0lVVTIzM5FKGSGEEEIIAU5IqAsKCti8eTOrVq0iJSXF0ae3WVZWFv7+/mRlZTk7FCGEEEIIUQ04NKGeNWsWkZGRdO3alaFDh7Jnzx4AUlJSCAkJKXUKXCGEEEIIIaozhyXUCxcu5PHHH6d///58+umnZiUTISEh9O7dm6+++spR4QghhBBCCGEXDkuop0+fzq233sqXX37J4MGDS6xv3749+/fvd1Q4QgghhBBC2IXDEupjx44xYMCAMtcHBQWRmprqqHCEEEIIIYSwC4cl1AEBAeV2Qjxw4AARERGOCkcIIYQQQgi7cFhCPXDgQObNm0dGRkaJdfv372f+/PkMGTLEUeGImspocHYEQgghhKhlHDb1+Llz57jhhhtQVZXBgwczb948xowZg8Fg4NtvvyUyMpItW7YQEhLiiHCslpmZib+/PzqdDj8/P2eHI65lNIBG6+wohBBCCFGLOCyhBrh06RL//e9/WbFihaml2tfXlzvuuIM333yTsLAwR4ViNUmoqzmjETQyX5EQQgghHMehCfXVkpOTMRqNhIaGormOEiBJqKs5SaiFEEII4WAuzjpxaGios04tajJJpoUQQgjhYA7LPv73v//Rpk2bMte3bduWl19+2VHhCCGEEEIIYRcOS6iXL19e7jjUAwcOZNmyZY4KRwghhBBCCLtwWEJ95swZ4uLiylwfGxvL6dOnHRWOEEIIIYQQduGwhNrHx6fchPnkyZN4eHg4KhwhhBBCCCHswmEJdc+ePfn44485e/ZsiXWJiYnMmzePXr16OSocIYQQQggh7MJhw+YdPnyYTp06oSgK9957Ly1atABg3759LFiwAFVV+fvvv2nWrJkjwrGaDJsnhBBCCCGu5tBxqPfs2cNjjz3Gpk2bzJZ3796dWbNm0apVK0eFYjVJqIUQQgghxNWcMrFLSkoKJ06cAKBBgwbVfrrxq0lCLYQQQgghruaUiV1CQkKuqyRaXEdUFRTF2VEIIYQQohZxaEJtMBhYs2YNJ06cID09nWsbxxVF4YUXXnBkSKKmkYRaCCGEEA7msJKPbdu2cccdd5CUlFQikTYFoygYDIYqi2HatGklZmNs0qQJhw4dsvgYUvJRzRkNoNE6OwohhBBC1CIOa6F++OGHycvLY+XKlXTr1o2AgABHndpMixYtWLdunem5i4tTql5EVXF8lwAhhBBC1HIOyyb37NnDa6+9xuDBgx11ylK5uLgQERHh1BhEFVKNzo5ACCGEELWMwyZ2iYqKKrPUw5GOHj1KnTp1aNCgAXfddRdnzpwpd/uCggIyMzPNHqI6c/5nTAghhBC1i8MS6meeeYb58+c7NSG94YYbWLRoEatXr2bu3LmcPHmSbt26kZWVVeY+b7zxBv7+/qZHdHS0AyMWlVYNfrQJIYQQonZxWKfE9957jyVLlpCYmMjIkSOJjo5GqzXvPKYoClOmTHFEOABkZGRQv3593nvvPe69995StykoKKCgoMD0PDMzk+joaOmUWF0VFYCLu7OjEEIIIUQt4rCEWqOpuDG8qkf5KE3Hjh3p06cPb7zxhkXbyygf1VxRIbi4OTsKIYQQQtQiDuuUePLkSUedymLZ2dkcP36cu+++29mhCCGEEEKI65TDEur69es76lRlevLJJxk8eDD169fn3LlzvPTSS2i1WkaNGuXs0IQQQgghxHXK4YMwnz17lo0bN3Lp0iXuuOMOoqKiMBgM6HQ6/P39S9RV21NSUhKjRo0iNTWV0NBQunbtyt9//01oaGiVnVM4mnRKFEIIIYRjOSyhVlWVJ554gtmzZ1NUVISiKMTHxxMVFUV2djYxMTG88sorPP7441UWw1dffVVlxxbVhNGxNfhCCCGEEA4bNu+dd95h5syZPPnkk6xdu9ZsTGp/f3+GDh3Kt99+66hwRE1lLHJ2BEIIIYSoZRyWUM+fP5+xY8fy+uuv06ZNmxLrW7VqxZEjRxwVjqipJKEWQgghhIM5LKFOTEzkpptuKnO9t7e3zEIobFdUUPE2QgghhBB25LCEOiwsjMTExDLXb9++nXr16jkqHFFTFeU5OwIhhBBC1DIOS6iHDh3KRx99xIkTJ0zLFEUB4Ndff2XRokXceeedjgpH1FQFZU8jL4QQQghRFRw2U6JOp6N79+6cPHmSbt26sXr1avr27Ut2djabN2+mbdu2bNy4ES8vL0eEYzWZKbGaO/E7NOjh7CiEEEIIUYs4rIXa39+fv//+m6effpqzZ8/i4eHB77//TkZGBi+99BKbNm2q9sm0uA4UZoM+39lRCCGEEKIWcUgLdX5+PvPmzaNNmzZ07969qk9XpaSFupo79BOEt4RA58/MKYQQQojawSEt1B4eHjzzzDMcPnzYEacTtV3mOWdHIIQQQohaxGElHy1btuTUqVOOOp2ozXRJzo5ACCGEELWIwxLq1157jY8//ph169Y56pSitko/5ewIhBBCCFGLuDjqRLNnzyYoKIh+/foRGxtLbGwsnp6eZtsoisKqVascFZKoqSShFkIIIYQDOSyh3rNnD4qiUK9ePQwGA8eOHSuxzeVxqYWwSdoJMBpB47AbMEIIIYSoxRyWUEv9tHAYfS5knQP/KGdHIoQQQohaQJrwRM106ZCzIxBCCCFELeHQhNpgMPDVV18xceJEbr/9dvbu3QsUz6K4YsUKLl686MhwRE12fpezIxBCCCFELeGwhDojI4MuXbowevRoli5dyvfff09ycjIAPj4+TJo0iZkzZzoqHFHD6HQ6/vjjD/7Yvp8/junQHfkTqn7OIiGEEEIIxyXUzz77LPv372fNmjWcOHGCqydo1Gq1DBs2jJ9//tlR4VRrRqMkgpW1d+9eunXrRrcxz9Dt3d3sPXwSUo46OywhhBBC1AIOS6hXrlzJY489Rt++fUsdzaNx48bScfFfRmlZtY+jvzo7AiGEEELUAg5LqHU6HbGxsWWu1+v1FBUVOSqcak0jwwfax5HVUJjr7CiEEEIIUcM5LKGOi4tjx44dZa7/9ddfad68uaPCqdYkn7aTgiw49KOzoxBCCCFEDeewhPq+++5jwYIFLFu2zFQ/rSgKBQUFPP/886xevZqJEyc6KpxqTSa4saOdiyE/09lRiFpANRicHYIQQggncdjELpMnT2b//v2MGjWKgIAAAEaPHk1qaipFRUVMnDiRe++911HhiNoiPxO2LYCujzs7ElHTyQ9hIYSotRyWUCuKwvz58xk3bhzLly/n6NGjGI1G4uLiGD58ON27d3dUKKK2ObASGvWF8BbOjkQIIYQQNVCVJdRDhw5lypQpdOvWDYCNGzfSrFkzunbtSteuXavqtEKUpKrw+9swdD64uDk7GlFDKRqZeFYIIWqrKvsGWLVqFWfOnDE979WrF2vXrq2q09UoMg51FUg/BdsXOTsKIYQQ1YxqkO9cYbsqS6jr1q3Lzp07Tc9VVZXOdhYqkoS6auxeCud2OTsKIYQQ1YmkJsIOqqzkY+TIkbz77rt8/fXXpk6Izz77LG+88UaZ+yiKwu7du6sqpOuGTOxSRVQj/PYK3PEJeAU5OxohhBDVgFpoQPFwWJcyUUNV2SfojTfeoGHDhiQkJHDp0iUURcHb25vg4OCqOqUQFctNhV9fgP+8L/XUwq5Uo1HqqIW4juh0Ovbu3UtBYhbu0b7Ex8fj7+/v0BiMuXrSvz9O/sE0UMCzZQgBg+PQuGsr3FdVVVIW7qfgSDrBdzfDs0WIaV1hYha61ScpPJsNKLhF++A/IBa3Oj5V+GpqtypLqLVaLQ888AAPPPAAABqNhv/973+MHj26qk5ZY0hlTBW7uA82vgO9/isXW9iP0QiSUAtx3di1axc9e/Y0Pd+wYQM9evSw+3kufbwH7/bheHcIL7Eu9avDGLMKCbm3JRhV0r85QvqKowSPalrhcbP/OFfqV5ixwEDKwn14NAsm7LaGYFTJXHualAX7iHyuE4pW/k5VhSq7qu3atWP16tWm5wsXLqRt27ZVdboaxWh0dgS1wNFfYe9yZ0chahBV/uEKcd1YsWIFd955p9myYbcOZemszzBkFpgmoKtK+ku5FBxJJ/CORrjX88M9xp+AIXHk7UnGkFlQ7r6F57LJ3pRE4LDGJdYVJedizC3Cr299XEO9cA33xq9PfYzZegzp5R9XWK/KEuo9e/aQkpJien7PPfeYdVIUZSsski9mh/j7Q7h00NlRiJpCEmohrgsrVqxg2LBhJCcnmy1P0aVx1+QJfDb1A1IW7CPjh+PkbLlAwelMjLl6u8dReDoTxcMFtyhf0zL3hoGgQOGZrDL3MxYaSPvqEAG3NkTrW7J00SXUE42XCzlbL6AWGVH1BnK2XsAlzBNtoIfdX4coVmUlH/Xr12fdunWMGjUKrVYro3xUQn6RAX9cnR1GzacaIeE1uGOB1FML20m5hxDVnsFgYPLkyeW2QL/w7bv0j++JMbeIglOZpuUaLxdcgjxwCfbEJcQTlzAvXII8UDTW5TaG7EK0Pubf9YpWQePpiiG7sMz9dD+ewK2eH54tSu+TpnF3IfSBVqQsPkDW+uLhi11CPAm5pyWKVvKwqlJlCfWDDz7IM888w5IlS/D09ERRFO69914mTpxY5j6KoqDT6aoqpOtGem4h4X7yK9IhMhJhx2fQ6X5nRyKudzI6jxDV3qZNm0hKSipzvYrKuYyL/H1sB10adTBbZ8zRU5ijpzDxSuux4qLBNcIbr3ZhxS3NikLWxkSyEhKvHFNvpDAxk4zvj5mWhU9tb1X8eQdSKTieQdikdmW/Br2BtG+P4l7fD5+RTVFVleyNSaQs2k/4o21QXCvu8Cgqr8oS6qeeeorWrVuTkJDAxYsX+eyzz+jYsSMNGjSoqlNabM6cObzzzjtcuHCB1q1b88EHH9CpUydnh2VyOjWXphF+zg6j9ti9FOJ6Q3CcsyNxuiJjES4aGT7KGqq+CNzdnR2GEKIc58+ft2i7i+nJqPqKy7hUvZGCkzoKTupwCfHEPc4f7w4ReMWHmrZJXXYYr5bBZqNwaH3d0fq4Ycg2LyVRDSrGPD1an9LvmhYcz6AoLZ9zL/9ltjz1i4O4xfgTNrEVubuSMaTnE/ZQa1PrudvIppx7eTN5B1Lxah1m0TWwhMFgQKuVBB2qMKEGuOWWW7jlllsAWLRoERMnTnT6KB/Lli1j6tSpfPTRR9xwww3MmDGDfv36cfjwYcLC7Pchs8X+szr6tYhwdhi1h9EACa/D7R+BtnaX2mgUKVuwlqovBLydHYYQohyRkZEWbRfuF2LRhC+KqxbXCC9cwrzQBrij9XNH4+mCclVts+KiQePthkuIp9m+bvX9UPOLKEzKMtVRFxzPABXc6vlSGt+e0Xh3NM8PLs7Ygf9/GuDZrLgERC00FMd+dfyKUvzczjfSpJT3Cod9exqNRqcn0wDvvfce999/PxMmTKB58+Z89NFHeHl5sWDBAmeHZnLwQhYZuWXXT4kqkHoMtn7i7CicLlef6+wQrlvGHLl2wjkcMSJFTdGtWzeioqLKTAQVoE5AODfEtf03Cb3y0Hi44BrhjUfTILxvrIP/oAYEjmiMb89oPJsH4xrmVakaZdcwL9wbB5K+4iiFiVkUnNKR8f1xPFuFovUrvttl0BVwYfo2U5mJ1tcN1whvsweAS4A7LkHFpaLujQIx5hWRseo4+ku56C/mkL78CGgU3BsEWH/xSlFYKLnKZVXWQn3mTHEhfL169cyeV+Ty9lWhsLCQ7du389xzz5mWaTQa+vTpw+bNm0vdp6CggIKCK8PMZGYWd1DYtWsXPj5VM0D6xRMH+Ojbi/RsYnuLuUZRcKkFY04ePnzY/PnFHLzcKvm6z8yHZFcIb3llmaIBbc0vgcjKyuLYsWMcTT9KrH8sDRs2tPnzrShKueUjqqry3aXv2JC2gVxDLo28GjGu7jgi3Mu+O/Pdxe9YeWml2bJI90jebPwmANlF2Xx36Tv2Ze0jVZ+Kr4sv7f3aMzR8KF5aL5teT0Xy9h/AvXEjux1PURQUl5r/2bvMaDDarQxdUUBTC/7uXWYoKrJrw6NGUdDU4Nv4kydP5qmnnip1nQrc23UEBy4eK25xDnBH6+eG1tcNxUOLQnbxhln/Pk5dta9RBaOK6wUvFJcrn7+A7DxSTueRrzlb4nxKMxXfnQUUfLwLFCio60J2gxxO79gBgCbHSEhyHof3H0KfXPp7EgYcP3GCwoIreZbrTe4UHrhI1vbzoEBRgIbsLm5cPLbP8gtVjsvfGZcuXSIoKIhGjRrZJSdSFKXcEhJVVTl8+DCnT59Gr9cTFBREq1atyjx3u3btSEhI4PfffzdbHhwczGOPPQZAbm4uGzZs4Pjx4+h0Ory8vGjatCm9e/fGw8Py/myKWkU/bTUaDYqikJeXh5ubm+l5RQwGQ1WEA8C5c+eoW7cuf/31F507dzYtf/rpp/n999/5559/Suwzbdo0Xn755SqLSYjaKmRgCKH/CSVpfhKFyYWEDw3HI8qDo88fRdWX/mcp7LYw/Dr4ceqdU6ZlqkHFkF38d8O9rjtht4eR8UcGBWcLcA1xpc64OuQn5pM4J7HUYwohhLg+dOnSha5du7Jy5UrS09Pp1asX4eHhzJkzp9T8UVVVEhISOHDgAGPHjjUt12g0eHsXt+5fvHiRDRs20KZNG0JDQ8nIyODHH38kPDycESNGWBxblTV/LFiwAEVRcHV1NXt+vXnuueeYOnWq6XlmZibR0dH8/vvvdm+h3rlzJ/fdd5/p+cip/8ewW7pZfbwio4rRqBIb6o2bS81tbYCS1+6T0TG0jbaynjWyDTS/DYz64vrq4IY1eli99evX89prr5GRkWFa5uHjwe0Tb+eWPrfg7+Zf6X+7RcYiDBiI8YvBtZS6dFVVmXxoMv1D+jPwk4EA5BpymXRwErNXz+bGgBtLPe53F79jR+YOlq1fVn4AE6787xbdFj6O/JhvJ3yLVqmafwdGvR7dqu/ReHnh0by5zcdTi4pQjQbcY2PRuNb8un5DkZGMizloNIrNs7ipBiNGo0pAuDdal5rfSl1UVET62UQUrYtdOocZDAZUo4HAOlG41PA7JNu3bzfN5gzw0ey5dOjYoZw9LKAoNX5outK+M7y8vBgzZgw333wz/v7+aKwYRtRoNGI0GgkODi71s6eqKr/++itxcXEMHz4cAL1ez5o1a/juu++oW7dumcfWaDT4+pZel35t4hwUFMTNN9/MihUrKtXpssr+tYwfP77c584QEhKCVqvl4sWLZssvXrxIRETpt5nd3d1xL6Xnfps2bfDzs+9IHLm55jWYJ4sCiGzYnAgrh9ArLDKSrzfQrI4f7jU8ob722jUJ96JdtJU/eJTj0CAYfMJBnwsR8eBSxugNqlrcoXHHZ5Cvg+gb4D/vlz9iiNEAG96APcsg+xL4RkCbu6D7U8X3qg16WP8qHF0L6afA3Q8a9IQ+08DPsg41llqxYgVPP/10iRrM/Ox8lk5fynGX4zTs1pB6fvWo71efer71iPKNws+t/M++3qAnz5BH06CmuGlL/hhJzEpEt0/HsI7DaBp0ZYrdVqmtyPTJpF270oeE+nvX3ySnJfPksSdx07rROrQ1j7d7nEifsq/LySMn8b3oS8f2HcuN2RbGwkJSd+5Cf+oUwW3b2Hw8tVCPMT8Pj2bN0LjV3B9zlxn0RlKSsnBx09qcBBuKjBQVGgiJ8kXrWgsSar2eCz5euPv4oHWx/ceXoUhPUUEBoTENcKnhP+bi4uJo1qwZuftS8GoZQnx8PP7+/s4Oq1or6zsjNzeXefPmYTQaadeuHf7+/gQFBREcHExgYCB+fn4VJtkGgwG9Xk9ERESpCXVaWhoFBQV069bNrHPpgQMHcHFxKfN74/K+7777Li4uLkRHR3PzzTcTEBBQ5vb5+fm4u7tX6kdqzf75eQ03Nzfat2/Pb7/9xm233QYU/yL67bffePTRR50bXCkMRiPzN57gyX5N8JRxI8t17a0eg9GGSiZVhcO/QPvxFW/75wz452O4fS4E1C+eKGbx7fDIFnAt44fQH+/D1k+LRxUJbQrndsKqR4oT5xsfLE7iz+8uTrAj4iEvA1Y/A0tHwsTfSz+mFSyZ4GDvp3uJ7BTJobRDHEo7ZFru4+pDuHc4kd6R1PGuQ7RvNJHekWg1ln1OU/NSAQj2MJ+YINgjmJS8lNJ2ASA+JJ5Xu7xKjF8MKXkpzN09l3Grx/Hdrd/h7VryjkR6fjof7/mYYY2HWRSXNXQ6Hbt37EB37Bi5m/+i+8ABBEVHV9n5airpWGc9YxWWStZk/v7+dO3aFUNbPVrvmv3jwR4s+c5Yvnw5rVq1Ij09nfT0dI4fPw4UtxD7+fnh5+eHv78/gYGBBAUFldpgWZbs7OL69WurA7y9vU3rShMVFcVtt91GcHAw2dnZbNiwgYULF/Lwww+Xev6cnBw2btxI+/aVGyu8yhLqV155pdL7KIrCCy+8UAXRXDF16lTGjRtHhw4d6NSpEzNmzCAnJ4cJEyZUvLMTnM3I4/21R5jYvQHBPjLGbWlWrFjBgw8+aLbszk+P8tHIWIa2CbLuoGc2Q6sKaqdUFf6eC92fhKaDipfd/hG80wgO/QjxZSRxiVug6UBo3K/4eWB92Lcczm4vfu7hD2NXme8z8B2Y37t4IpoA+yRrFU1wAJCXkkfKgRRCWoaYLc8qzCKrMItj6VcmKnDRuFDfrz49onrQMKCh2fY/nviRVzZf+Zsw5+Y5VsXcLepKCVQTmhAfGk+/5f1Yc2oNQxsNNds2uzCbR357hAYBDXiozUNWnc8Se/fupUfv3qbnq5Yupd/TT1fZ+YS4lmrvsdBqGUmmLWPJd0ZGRgbHjh2jcePGZsuNRiMZGRlmZSJQnBxHRkbSsmVLrrVnzx5++OEH0/O77rrLqrgbNTLvLF63bl1mzJjB/v37S7Rq5+fn8+WXXxIaGkrPnj0rdZ4qS6inTZtWYtnlOsxrf90oimKamryqE+oRI0aQnJzMiy++yIULF2jTpg2rV68mPDy8Ss9rizNpufzfTwe5s0MUnRsEX5e16FVlxYoVDBs2rMRnKjm7iGGfHGX5fY2sS6oNeji7DeqW8ws1/RRkXywux7jMwx+iOkDS1rIT6uhOsP0zSDkGIQ3hwl448zf0e73sc+VnAkrx8e3E0gkO8tLyMKoVT3BQaCjkaPpRjqYfpZ5fPdqFtaN5cHE9ca/oXrQKaWW2LUBqfiqhXldNgJCfalYCUhE/Nz/q+9XnTKb5KEI5+hweXPcgXq5ezOw1E1eN474w8/fspfDMGdyqcMSimshQpOIqbQZWyc/Oxss/wNlhiBrO0u8MnU5n8R2nrKwssrKyOH36NOHh4cTHx5tynCZNmpjVRV++E52dnW1WD52Tk1Nm2W5pPD09CQ4OJi0tzWx5QUEBX3zxBW5ubowYMaLSfRKqLKE2Gs2/gM+ePcugQYNo2bIljz/+OE2aNAHg0KFDzJgxgwMHDvDTTz9VVThmHn300WpZ4lGePL2BzzefZneijrs718fHvVZV65TKkttPjy8/za2tAtFqrPgRcnZ7+Ql19qXi//pcM7yhd2hxol2WrlOhIAtmdwCNtrim+uYXoNXw0rfX58O6l4oTdA/71e1bOsGBb4gvHi4V1/F7uHjQPLg5zYKa0TCgIQ38G5iGzfN29TYryVBVlRDPEP45/48pgc4uzGZv8l5GNLG8V3WuPpfErEQGxw02LcsuzGbiuom4adz4oPcHuGsdm6WpqKQvWULYE0+iuEnLV0V0Oh27du4m6VAaAeFetGwZj7+f1LFaQqfTsXPnTg79sQHf4FC69Opt9749Qlxm6XdGUFCQaUAKS7atW7cuYWFhhIWF4e3tbUpkr+3DpqoqPj4+nDx50hRLfn4+SUlJdOhgeWfSgoIC0tLSaNXqSiNPfn4+X3zxBVqtllGjRlkc/9UclpU98sgjNGrUiC+++MJseceOHVmyZAnDhg3jkUce4bvvvnNUSNel3UkZnP4pl/u7xRIXWjXjYF8vKrr9pAKJGYVsOpZJz8ZWfMlc3F/cUn3Znq/hh8evPL/r68ofE2D/Ctj7DdzxCYQ1K26hXv0s+EZCm2smPzLo4ZvxxeUlg96z7nxluDzBwdmzZ8v8UeIZ4kloi9ASd0V8XH2I9o2mnl896vnWo4F/A+r71be4hlpRFMY0G8PHez6mnm896vrWZfbO2YR6hdK73pXyifvW3Efver0Z3az4ury79V16RPegjk8dknOTmbNrDlpFy4DYAcC/yfTaieQZ8niz55vk6HPI0ecAEOgeaHF8ttKfP0/a558TNG4sSg3v2GWrXbt20bNXT9PzH1asputN1o9uZInjOy+xf+NZLp3JoiCniOHPdyQ0uvQRAK7eZ/svp9El52E0GPEP86Jtn2ia3Fj8xW4wGPln1QlO70slMyUPN08XopsG0fn2OLwDquZH3d69e+nVq5fp+eezZzHo9qHl7CGE9Sz5zggMDCxRYgHFNdSXOyoGBQUREhJCWFhY5cZ5VhRuvPFGNm7cSFBQEIGBgaxfvx5fX1+aNr1yZ/Ozzz6jadOm3HDDDQCsWbOGJk2a4O/vT1ZWFhs2bECj0RAfHw8UJ9OLFy9Gr9czcuRIs/lHvL29LR6xxGEJ9fr163nrrbfKXH/zzTfzzDPPOCqc61pGbiHv/nqEQfERDGgZaV3raw1g6e2n87oCsKBkoQRDIWSeg6h/R4doMsC8xfrfsgXTSB2X5SQXdyYsy9oXoeuUKyUh4S2Ka6M3vWeeUF9OpnWJMO4Hu7ZOA2i1WmbOnMmwYWV32Gt7f1u83LxoHNiYRoGNaODfgFj/WII8gmwuPbqn5T3kFeXx8uaXySrMom14Wz7q85FZi3JiViIZBRmm5xdzL/LMxmfIKMgg0COQdmHtWDJwCUEexWU9B9MOsidlDwADvxtodr7Vd6ymrk/ZwyrZW96e3SR/8AFBY8fiEhJS8Q61UGn9H8ZOuIs3X5nOncOHVVl5W1GBgciGATRsH07CF4cq3gHw8HKlw4AYAiK80LoonNqTym+fH8LT1416LYIpKjSSfCaLDgNjCInyoSC3iE1fH+WnD/cw/L9VN8LM1Q5uWk9MkD9Nu/RAW8OHvBOOZ8l3xvDhw9FqtQQGBhIeHk5YWBghISEEBATYZRjGLl26UFhYyA8//EB+fj716tVjzJgxZi3KaWlpZiN/ZWZmsnz5cvLy8vDy8qJevXrcd999pnGoz58/z9mzxZPuzJo1y+x8kydPJjAw0KLYHPYvzsPDg82bN/PQQ6V3Dvrrr78q9UultlNVlR/3nGdPko7xN8VQJ8DT2SE5nKW3nyKDfMDVyuujKFdmSnT3LX5cpqrFQ+ud/B0i/711lJ8JSdugwz1lH1OfW3zcq2k05kn/5WQ69TiM/xG8rOxcWYGhQ4eyfPlyHnzwQZKTk03LPQI8mPjyRB65+xEa+DeokpZdRVF4tO2jPNq27PKrNcPWmD1/p8c75R6zY0RH9o7ba5f47KHw1CkuvfkWfrfeinfXLtL/4Spl9X9I16UycfJ4jm69SL/e/8E/1BP/ME/8w7xw97TPV9blVuXMlDyL96nbxPxLtfXNXhz6+zznj+uo1yIYd08Xbn28rdk23Uc2Zvmb28hKy8c3yP7fb9eObmQ0qhzf9jfnDh8gumVrIhs2xi803O6fu6P//MXudb9w8cQx8rOzuPutWYTFNCh3n30b1rFm7gyzZVpXVx7/wvyudGpSIhu/XEjSgX0YjQaC69ZjyBPP4Rdi+8zBwnZlfWf4+voyefJkxo0bR1RUVJXlc4qi0Lt3b3pf1RH8WlOmTDF7fuedd5Z7zNjY2FL7/VWWwxLqu+66i1mzZhEQEMBjjz1GXFzxOL3Hjx9n1qxZfPnll0yaNMlR4dQYZ9Jyef3nQwxtV5deTUremq/JKrr9pABRge50axTw7zMrFBWUvU5R4MaHYOM7EBRXPFrH+teKW6ub/ufKdp8NhqaD4YZ/JxBoPAA2Tgf/6OJh8y7sgc1zoO2Y4vUGPXw9tnjovNHLimuss/6tyfYMtPskM0OHDiU4ONisR/MP3/5An9597Hqe2spYWEDGN1+Tf+AAQWPuQuNt5YRDNYgl/R8Wfv8ebeK6kpyYZVrm4e2Kb5AHPoHu+AR54BfsgU+gBxoHT6ShqipJh9PJuJhLndsDytyuMK8IFOz2Q+BqpbXuf/jjGsb26UGHRg04snkTRzZvwsPHl7DYOMJi4wiNjsHVDomOviCfuk2a0/jGrqyd94HF+7l5enHPjI/LXJ9x4TxfvfQ0LXv15aY778Ld04uUpDO4uNb8sdivJ6V9ZyxevJhbb73VeUFVAw5LqN966y1SUlKYPXs2c+bMMdWkGI1GVFVl1KhR5ZaE1AbXtjaoRsvKFIqMRr7elsiBc5mM7VwfP0/LazZX7zvPkn/OsPesjoxcPT9N6kqLOuV3CBrx8Wb+OZlWYnmvJqEsnNAJgCe+3s23O8zrm7s3DuXzezpZHFtFLLn9NGN4nG0lMdd2OLxWl8ehMBd+mFw8sUu9G2HMCvMxqNNOQW7qlecD3y5OvH96org8xDcC2k+AHv+WPGWeg8M/F///R13NzzfuR4i1f31pmzZt2LRpE2tPr6Vv/b6m2jJhP/n793Hp7bcJHDcO9wblt+bVdJYMv5WaeZGDp3bRPPbKsFZ52YXkZRdy6apBXTRaDX7BHgRGeBHVNAgPr6r7WivIK2LRs39i1BtRNArdRzUmunnpd4+K9AY2f3ecRh3CcbNzQl1W635WXj5zfljDI4P70aFR8WcsPzuLM3t3cWbvLhSNhsDIuoTFxhEe2xC/0DCrGmGady9uHdRdKqfzdSkURcE7oOzb53989TmxbTvQY8yVO3wBEfadzErYx+XvjBMnThAREWGqV67NHJZQu7m5sXjxYp566il+/vlnTp8+DUD9+vUZMGAArVu3dlQo1VJprQ17Pp9Gs2FTCW/V3aJj7Dun49UfDzChSyzN61hWb5tbaKBD/SAGxUfy7ArLbpV/fHd7Cg1Xkv2MXD0DZm5iYLz5H74ejUN5584rvWjd7TAt7rXKuv0U5uvK3NGNGNrWhtpVF/cr9dNlURTo/XzxoyxTrrmu7r4w4M3iR2kC68M0XeVitdHlCQ5adGhBoIdl9WKi8orS00mZOQufPjfj179/re2waGn/h4ysFCoaYtlYZCTjYi4ZF3NJPJhORAM/wmP9CK1XXJ51+J8LbPjysGn7wY+2pk6jAKvidnPXMuL5jugLDCQdSufP5cfwD/EsUQ5iMBhZM38/qqrSc3QTq85VFkta95du+JN2cTElOlOpRiNpZxNJO5vIoT824OXnT0SjJkQ3b4V/WNUPHVuYn8e8RyagqirhsXF0HTmWkOj6pthO7NxGxyFDWf7aC1w6dQL/sHA63XYnjTp2rvLYROVc/s5o27atqRa5tnN4r4VWrVqZDVUiym5t0GdnsGfRi7Qa/4rFSXVWQREfJBxjXOf6tKtXcWI0tF0UAIlpuRVseUWAl/nttx92n8fTVcugVuYJtZuLhjDfqq+LL+3209f3N6NH4wDbDhx/p907AlZ3kkxXPVU1krV2Lfl79xI4dixuUVHODsnhLO3/EBwQiouF04d7+rkRGu1LUB1vwur7otEW7xfbOoTw2Cv/jn1sGHFD0SgEhHkBEBrtS/qFHLavOW2WUBsMRtbM20dWaj63TWlr99ZpS1r307KyOXL2HE2jy++Em5uZwYnt/3Bi+z9ENmpKk5u64+FT/mgn1gqqU5d+D04mtH4sBbk5bPthBUtfeIrx0z/ENziE3Ewd+vw8tqxaTtcRd9P9rgmc3LWd76e/zvAXXye6udw1q44kmb5CugE7mSWtDYdXziasZRcUCzuGqarK4r9PE+rrTqgDZlf8emsig1tH4uVm/nH6+0Qq7V9di7+nK53jgnnyliYEeldNLdy1A7DbPPJJSCNoN862YwhRDv2FCyRPfw//IYPx7tEDxcKhmQBUvZ7kmTPJ/n0jhUlJaH188L6pM6FTn8A1vOwypdytW0n9dAH5+/dTlJxM1OwP8O1jXit/7tnn0K1cabbMu2tX6n0yv1KvrzyWDL8V7B9Os9i2JTvwAi5umn87K3oRFOFNSD0ffIM8Si1fcPNwwc2jar7qVBUM+it36y4n07rkPG6b0hYPH/vfgbC0dT89KxfVaPkMiucOH+TckUPUj29Nix59TK3bBzclsHb+lZlNhz43jahmJWe1q0idxs2o07iZ2fNFUx9iz7pf6DLiblOJY8MON9J+0G0AhMU04NyRg+xe+4sk1KLak4TaySxpbSjIuETa8T0ENWxb7nZXKzKofLUlkUd7xdkaYrl2JWZw+GIWbw0zv+vQo0ko/VtGEB3kyenUXN5Zc5jxC7ew4uEu1X+YP+/Q4lkL7dz5T9RMJUZasHCGMADVUETGd9+Rf/AggaPvQhtg2YQmxvx88g8cIOThh3Bv0hRjpo4Lr79B0sMPE/vt8rL3y8vDvWkT/O8YytnHyu4E7t2tG3Vef830XHGz778FS/o/jBs0FY1Gi7uXC8F1fAiM9CIg3IuAMC+8/N1s6oCdn6MnKy2fnIziTscZF4vv0Hn5ueHtX9wIsW7hAbwD3Ol8e/Hf0O2rTxFWzw+/UE8MRUZO70vlyN8X6PFvSYfBYGT1x/tIScxi0COtMBpVcnTFx/fwdkXrYvkPpvJY3Lof6I+Le+UbVM4ePkjDjjeh+beRIq7DDUQ0ulK24hMUXOljlkbr4kJYTAPSLxT/QPD080Oj1RJcN9psu+C60Zw9dMAu5xSiKklC7WSWtjbkZ6ZW6osa4FRqDvvPZxEfFQDAyp1n+e93V+p5F03oRKdY24ZjW7Y1kaYRvrSJDjBbPqR1HdP/N43wo1mEH93fSeDvE6l0aViNx+R19y3uNFhRZ0QhKL3vw6Pbt/NqfDz9LEx8APIPHeLS228RdM+9uDes+Eew1teXegsWmC2LeOF/nLpzOPpz53CtU6fU/Xy6d8ene3H52Nlyjq+4ueESGlrOFrYrq/+Dn3cgTz3wCqPGDCcsxg+fQHe7j150cncK6z8/aHr+6yf7Aeg4KIZOg4s782Wl5Zs1jusLDPy+9DDZGQW4uGoIjPCizz3NadShuPY4J72AU3tSAFj2f1vNznfblLYl6qytZUnrfpCvD02j61p83Tx8fAmJrkdo/QbUadKMwIgrnx83Ty/cPL3sEvvVjEYDyYmnadC2eGx/rYsr4XGNSDtv/slMP38Wv1D5eyyqP0monczi6Z8DQ/FwrXynvk1HkxlzY3Gnjz7Nw80S3wh/2+qbcwuL+HH3Oab0bVzhtvWCvQjyduNUak71TahdPWHgOxBUu0dgEJYpq+9DWmEhj27fzuz27SuVVBuys0mZ+yGhkybhGll6QlweY1YWKAoaO0w9nbtlC0du6oLWzw+vG28gdPJkXCyc3KAySuv/8NVXXzHgP7fY/VxXa3ZTJM1uKv+9uf2JdmbPb7w1jhtvLfvHjl+IJ498VPbYuPZiSev+mD49yp3dTePiQt0mzanbtAXhDeLwCQyu1I+WvOwsslKSyU4vHr0o7VzxXVbvgEDTKB6/zJ6OT1Aw3UaPB2Dz8qVENmpCQEQdCnKy2frDCrKSLxHfu5/puB0HD+XHGW8T1awF0S1acWrXdo5v38Lwl96wODYhnEUSaiezpLXBIyCMkIatrWqlUa4af9nH3QUfd/u95T/tOU+BwcjtbSuefe68Lo/03EKHdFK0iqKBPi8XTwUuRAUs6fvwf/v30yciAm0l/t2qej1pn31G2BNPVioeY0EBl96djt+gQWh9fCq177W8u3XF95a+uNaNQp94hkvvzyDxgYnEfLUUpQpG6rm2/4NvgP1bQ2uaMlv3vTwZ3683HZs0LLGP1tWNsJgGRLeIp358W9y9rL/Ox7f9YzZJy08z3wag87BR3HTnXQBkpiab9QvIz8nm13kfkJuRjru3D+ENGjLy1XcIjqpn2qZRp5voc//DbFn5DQkL5xFYpy5Dpv6XqKYtrI5VCEeRhNrJLGltaHHHJIs7JJrtV8ePqRW0HmfkFnI2I49LmcW1fieScwAI9XU3Jb9Tl+0i3N+DZ/o3Ndv3622J3NI8vERHw5yCImb+dpT+LSMI9XHnTFoub/xykJhgb7o3rqat0x3vg3oyjqawTEV9H1TgfH4+W1NSuTG4cjWnRZeSyd6wAe9uV8Yg1/3wA+dfmmZ6Xm/ex3h16FB8Lr2es49PQUUlYtpLlTpXafwHDTL9v0eTxrg3acLxvreQu2UL3p3tP3xZfHw8mzZtIiejAO8AdxkD3UKlte4/eutAmtW/MmpMSL0Y6jZtTkSDRgTVjbbbdOQte/ahZc/yJ34a8ZL5sKC9xt1Pr3H3V3js+F63EN+rau9QCFEVHJpQr1mzhk8//ZQTJ06Qnp5eonVHURSOHz/uyJCqhbJaG9x8Aokf8QSRrXtU6nh1Ajy4+8YYujSs+Dbe2gMXeWr5HtPzx5buBGDyzY1MpRxnM/JKHOd4cjZbT6Wz+N6SE7VoNQoHz2fy7fYkMvP1hPl60L1xCFP7NsHdxf4tXDar0xZaj3J2FOI6Ymnfh+T8/OKhICop67ff8LrxRlMLn0+v3jS4arhRl/Diul1VrydpyhT0585Rb9FCm1unS+MWHY02MJDC02eqJKG+PJ6tqLxrW/c1GgVFUWjYsTPNuvXCL6Rq6+CFEFc4LKF+5513ePbZZwkPD6dTp07SCnGN0lob2k94heBGbSw+ho+7C2NurE+/FuG4aC3rUX5nh2ju7BBd7jbLJpb8Eo0L9eHUm4NK2Ro8XLUsvvc6ae118SieobASQ5YJYWnfhzBfXxQrp3o2JCej/NuiqPXxRutjPt6rKZk+fZp6n31WJTXOUDy8nyEjA5cwSc6qO//QcAZNfoaA8AhnhyJEreOwhHrmzJn07t2bn3/+GddaOjtYRa5tbajMuLSdYoN4rHfDEpOuiArcMBH8ZGpbUTkV9X1QgAhPTzqFhlrV90EbEIBnu7KHyVT1epImP07+gQNEfzQXDAaK/r27pfX3Nw1zd3r8BHz79CFoTHFdqzEnh8IzV+btLkxKIv/gQbT+/rjWqYMxJ4fkOR/id0tftCGhxTXU77yLW716eEsrcrXX4dZhkkwL4SQOS6jT09MZNmyYJNN2plFgbOcYhrazfIgk8a96N0KL250dhbgOWdL34YVWrSvVIfEynx7dCbz77nJbnPUXL5G9fj0AJ28z/wzX++wzvG8oLsXSnzmDIT3dtC5v337OjLsyYdGlN98CwP+226jz5hug1VJw+DCJK1diyMrCNTQU7y5dCJ08CY2dx6IWtrtcf35qz078wyNo29byuQqEEPblsIS6U6dOHD582FGnqxXqBHjweJ/GNIusXdNj24V3KPR8rtRZ2ISwRFl9H4Ld3Xm1TVv616149JurebZtS+Bdo3GPja1wW7eoujQ7dLDC7Rqu/83sufcNncrdT+PhQb1PP6k4WFEtXK4/bxAWTGSjptKoIoQTOSyh/vDDDxkwYAAdOnRg9OjRjjptjeSqVbizQzR3tIvCzU6zb9UqGhfo+wp4Bjg7EnGdK63vw+xON3BDJSZFcQkLI/j++/Eqp8RDiPKE1IuRZFoIJ3NYQj1ixAiKioq4++67eeihh4iKiipZM6wo7N6921EhXZfa1w/koZ5xhPtV0/GcrwddJkN4c2dHIWqIEiMtVCKx8e7WlZCJE9F4eto7LFGLuHnI50cIZ3NYQh0UFERwcDCNGjVy1ClrFE9XLQ/2bECvJmHSEmGLZoOh+RBnRyFqOcXVlaBxY/Ht31/+PQshRA3gsIR6w4YNjjpVjRMd5Mnzg5pTN0BaIWwS0bK4dVoIJ3Jv1pSQBx/ELSqq4o2FEEJcF2SmxGquaYQvLw1pYdcpw2sl7xDo+ypoZZQZ4Rxaf38Cx96NT48e0iothBA1jMOzNL1ez6FDh9DpdBiNxhLru3fv7uiQqq0QH3emDWmBtyTTtlE00Odl8ApydiSilvK5uTdBY8dWyUyGQgghnM9hmZrRaOS5557jww8/JDc3t8ztDAaDo0Kq9oa0rSPJtD20H1dc7iGEg2l8fQl99BG8OnRwdihCCCGqkMOytddff5133nmHiRMn0rVrV+6++27eeustAgIC+PDDD1EUhbfffttR4VRLlwfpf+PngxQZjQztXXLKb1FJgTHQZoyzoxC1kGt0NOHPPYdreJizQxFCCFHFHDaI8aJFixg+fDhz586lf//+ALRv357777+ff/75B0VRWP/vzF+11eVB+iMat2FQ394EBgY4O6Tr340Pg1Za+YVjudWvT+Srr0gyLYQQtYTDEuqkpCR69+4NgLu7OwD5+fkAuLm5MWbMGBYvXuyocKq9AS0jnB3C9S+kMUR3cnYUopZR3N0Ie/YZtL6+zg5FCCGEgzis6S44OJjs7GwAfHx88PPz48SJE2bbpKenOyqcaq1xhC+NwuXL2GbNh8jU4qLKXS7Vyvn7b3Q//EiHwUNwDZOWaSGEqE0cllC3bduWrVu3mp736tWLGTNm0LZtW4xGI7NmzaJ169aOCqda6xQjo1HYTFEgppuzoxC1wOVSrczsbFI3/01EDxmpSAghahuHJdQPPPAAixYtoqCgAHd3d1577TW6d+9O9+7dUVWVwMBAli5d6qhwqrXoIJnApbIutxJy+i/Y9SXx8W3AM8DZYYlaRhsQgEdzmdZeCCFqG4cl1EOGDGHIkCtTPjdv3pzjx4+zYcMGtFotN910E0FB0jIL4Oshk49U1uVWQkJ0kO0Pce2cHZKohXz790NxkU6wQghR2zj1L7+/vz+33nqrM0Oollw0Uvdrs+BGzo5A1EK+vXo5OwQhhBBO4LBRPqB40pavvvqKiRMncvvtt7N3714AdDodK1as4OLFi44Mp9py0Tj0bamZQiShFo7lEhaOS0iIs8MQQgjhBA7L3DIyMujSpQujR49m6dKlfP/99yQnJwPFo35MmjSJmTNnVmkMMTExKIpi9njzzTer9JzW8HbXOjuE65vWrXhCFyEcyCVUkmkhhKitHJZQP/vss+zfv581a9Zw4sQJVFU1rdNqtQwbNoyff/65yuN45ZVXOH/+vOnx2GOPVfk5K8tFKy3UNglpDBr5USIcS+Ph4ewQhBBCOInDMreVK1fy2GOP0bdvX5RSxgZu3Lgxp06dqvI4fH19iYiIMD28vb2r/JzCwcKaOTsCURtJZ0QhhKi1HJZQ63Q6YmNjy1yv1+spKiqq8jjefPNNgoODadu2Le+8806F5ywoKCAzM9PsIaq5kMbOjkDUQoqrjM4jhBC1lcOaVOLi4tixY0eZ63/99VeaV/H4rZMmTaJdu3YEBQXx119/8dxzz3H+/Hnee++9Mvd54403ePnll6s0LmFnwXHOjkDUQlofH2eHIIQQwkkc1kJ93333sWDBApYtW2aqn1YUhYKCAp5//nlWr17NxIkTK33cZ599tkRHw2sfhw4dAmDq1Kn07NmTVq1a8eCDDzJ9+nQ++OADCgoKyjz+c889h06nMz0SExOtuwDCQRTwj3Z2EEIIIYSoRRT16t6BVUhVVR544AE+/fRTAgICyMjIIDw8nNTUVIqKipg4cSJz586t9HGTk5NJTU0td5sGDRrg5uZWYvn+/ftp2bIlhw4dokmTJhadLzMzE39/f3Q6HX5+fpWOV1SxM39DvRudHYUQQgghahGHlXwoisL8+fMZN24cy5cv5+jRoxiNRuLi4hg+fDjdu3e36rihoaGEhoZate+uXbvQaDSEhYVZtb+ohjwCnB2BEEIIIWoZh3dL79q1a/EU0Q62efNm/vnnH3r16oWvry+bN29mypQpjBkzhsDAQIfHI6qIm5ezIxBCCCFELVNrxnlyd3fnq6++Ytq0aRQUFBAbG8uUKVOYOnWqs0MT9qSRkRaEEEII4VhVWkM9ZMiQSm2vKAqrVq2qomjsQ2qoq7msi+Ab7uwohBBCCFGLVGkL9Y8//oiHhwcRERFYkreXNuGLEJUinyEhhBBCOFiVJtR169bl7NmzhISEMHr0aEaOHElERERVnlLUepJQCyGEEMKxqnQc6sTERBISEmjbti2vvvoq0dHR9OnTh4ULF5KVlVWVpxa1leKwodWFEEIIIQAHjkOt1+v5+eef+fLLL/nxxx8xGo0MGDCA0aNHM3jwYNzd3R0Rhs2khrqay8sAzwBnRyGEEEKIWsRhzXmurq7ceuutLFu2jIsXL/Lxxx9z4cIFRowYwdtvv+2oMERN5y4/coQQQgjhWA6/P15QUMCaNWtYtWoVO3fuxMPDg5iYGEeHIWoqjZR8CCGEEMKxHJJ9GI1G1qxZw/jx4wkPD2fUqFHk5eUxf/58Ll26xN133+2IMIQQQgghhLC7Kh3l46+//uLLL7/km2++ITU1lRtvvJHXX3+d4cOHExISUpWnFkIIIYQQwiGqtFOiRqPB09OTgQMHMmrUKItKO9q1a1dV4diFdEoUQgghhBBXq/KE2nSiCibcUFUVRVEwGAxVFY5dSEIthBBCCCGuVqUlHwsXLqzKwwshhBBCCOF0DhuHuqZQVZWsrCx8fX1lqnQhhBBCCOH4YfOud4qi4OfnJ8m0EMJqMTExKIpiemg0Gnx9fYmKiqJXr148+eSTbNmyxdlh2tWpU6fMXvPVrz0oKIiuXbsyZ84cioqKnB2qEEJUmrRQCyGEg8XExHD69Gm6dOlCw4YNAcjLyyMlJYWdO3eSnp4OQI8ePViwYAENGjRwZrh2cerUKWJjYwG444478PHxAaCwsJCTJ0/yzz//oKoq3bt3Z+3atbi5uTkzXCGEqBRJqIUQwsEuJ9QLFy5k/PjxZutUVeWXX37h8ccf5+jRo4SHh7N582ZTMnq9ujqhPnnyZIlRn/766y969+5NQUEBH3zwAY8++qgTohRCCOtIyYcQQlQjiqIwcOBAtmzZQqNGjbh48SL33Xefs8OqcjfddBPDhg0D4LfffnNyNEIIUTmSUAshRDUUEBDAjBkzAFi/fj3bt283W3+5DvvUqVOsWrWK3r17ExQUhKIobNiwAYCePXuaPb/WtGnTUBSFadOmlVhXVFTE9OnTadmyJR4eHoSFhXHnnXdy4MABFi1ahKIoJVrXbRUREWE6txBCXE8koRZCiGpqwIABBAUFAbB27dpSt5k+fTq33XYbWVlZ9O/fnx49eqDVam06r9Fo5Pbbb+fJJ5/k6NGj9OjRg969e7Njxw46duxYIrm3l8sdMVu0aFElxxdCiKpSpeNQCyGEsJ6iKLRr145169axf//+UreZO3cuq1atYsiQIXY77+zZs/nxxx+JjIwkISGBJk2aAGAwGHjiiSeYOXOm3c5VWFjIqVOnmDlzJps2bSIkJIRHHnnEbscXQghHkIRaCCGqsZCQEABSU1NLXT9u3Di7JtOAKWGeNm2aKZkG0Gq1vP322yxfvpyzZ89affyyOliOGjWK119/nejoaKuPLYQQziAJtRBCVGNGoxGgzLHvL3fks5ekpCROnDgBwOjRo0usd3NzY9iwYTa1Ul89bJ7RaOTcuXNs27aNr7/+GoB58+aZ1gshxPVAEmohhKjGUlJSAEy11Ne6dvg5WyUlJQHFLeNlJbW2nvPdd98tcYyMjAyGDx/O0qVLycrK4ocffrDpHEII4UjSKVEIIaopVVXZuXMnAPHx8aVu4+npafXxL7d+l6a82WCrYqbYgIAApk+fDsCPP/5YZs24EEJUR5JQCyFENfXzzz+bZk285ZZbKr3/5dkGs7KySl1/+vTpEsvq1q0LQHJyMjk5OaXud+rUqUrHYomrZ4Q8ePBglZxDCCGqgiTUQghRDel0OqZMmQJA3759adOmTaWPcTk5Li05zc3NJSEhocTy6OhoUznG0qVLS6wvLCzk22+/rXQsljh+/Ljp/6WGWghxPZGEWgghqpHLU4936tSJo0ePEhkZyfz58606Vp8+fQCYM2eO2agcOTk5PPDAAyQmJpa636RJkwB46aWXOHLkiGm50WjkueeeK3M/W2RkZPDkk08CxfXi3bp1s/s5hBCiqkinRCGEcJJPPvnENIthQUEBKSkp7Nixg7S0NKB4psMFCxZQv359q44/fPhwZsyYwbZt22jRogVdu3bFaDSybds23NzcuOeee1iwYEGJ/SZNmsTatWv55ZdfaNWqFb169SIgIICtW7dy7tw5Hn74YT788ENTSUllPfnkk2ajfJw/f56tW7ei0+nw8PDgs88+w9vb26pjCyGEM0hCLYQQTvLnn3/y559/AuDt7Y2/vz/x8fF06NCBESNG0LFjR5uO7+rqytq1a3nhhRdYuXIlv/76K2FhYdx+++28+uqrfPjhh6Xup9VqWbVqFTNmzGDRokUkJCTg6+tLt27dWLlyJd999x1wZYzsyrq2ZMTb25t69epx9913M3nyZBo2bGjVcYUQwlkUVVVVZwchhBDi+tG7d28SEhL49ttvGTp0qLPDEUIIp5MaaiGEECXs2rWLwsJCs2WFhYVMmzaNhIQEwsLCGDhwoJOiE0KI6kVKPoQQQpTw+OOPs2vXLlq3bk1kZCTp6ens3buX8+fPm+qcPTw8nB2mEEJUC1LyIYQQooQlS5awZMkS9uzZQ2pqKqqqUqdOHXr16sUTTzxB8+bNnR2iEEJUG5JQCyGEEEIIYQOpoRZCCCGEEMIGklALIYQQQghhA0moK0lVVTIzM5FKGSGEEEIIAZJQV1pWVhb+/v5kZWU5OxQhhBBCCFEN2DRsXkFBATt27ODSpUt06dLF6lmzhBBCCCGEuF5Z3UI9a9YsIiMj6dq1K0OHDmXPnj0ApKSkEBISwoIFC+wWpBBCCCGEENWVVQn1woULefzxx+nfvz+ffvqpWT1xSEgIvXv35quvvrJbkEIIIYQQQlRXViXU06dP59Zbb+XLL79k8ODBJda3b9+e/fv32xycEEIIIYQQ1Z1VCfWxY8cYMGBAmeuDgoJITU21OighhBBCCCGuF1Yl1AEBAaSkpJS5/sCBA0RERFgdlBBCCCGEI6h6o7NDEDWAVQn1wIEDmTdvHhkZGSXW7d+/n/nz5zNkyBBbYxNCCCGEqFLG/CJnhyBqAEW1YoaSc+fOccMNN6CqKoMHD2bevHmMGTMGg8HAt99+S2RkJFu2bKmRw+hlZmbi7++PTqfDz8/P2eEIIYQQwgZFafm4BHk4OwxxnbMqoQa4dOkS//3vf1mxYoWppdrX15c77riDN998k7CwMHvGWW1IQi2EEELUHPqLObiGezs7DHGdszqhvlpycjJGo5HQ0FA0mpo9+aIk1EIIIUTNUZiYhVu0r7PDENc5m2ZKvCw0NNQehxFCCCGEcChjocHZIYgawKrm5P/973+0adOmzPVt27bl5ZdftjYmIYQQQgiHkFE+hD1YlVAvX7683HGoBw4cyLJly6wOSgghhBDCEVRpoRZ2YFVCfebMGeLi4spcHxsby+nTp60OSgjhHKl5MiGTEKJ2UQskoRa2syqh9vHxKTdhPnnyJB4eMgSNENebQkOhs0MQQgiHMkpCLezAqoS6Z8+efPzxx5w9e7bEusTERObNm0evXr1sDk4I4VgFhgJnhyCEEA6lysQuwg6sGuXj1VdfpVOnTrRo0YJ7772XFi1aALBv3z4WLFiAqqq8+uqrdg1UCFH1JKEWQtQ2hhy9s0MQNYBVCXWTJk3YtGkTjz32GO+//77Zuu7duzNr1iyaNWtmlwCFEI6TV5Tn7BCEEMKhjJlS6iZsZ/U41K1ateL3338nJSWFEydOANCgQYMaOd24ELVFviHf2SEIIYRDGXL0GAsNaNy0zg5FXMdsntglJCREkmg7KzIYcdHW7BknRfVUUCQlH0KI2seQno9Gph8XNrA6oTYYDKxZs4YTJ06Qnp7OtTOYK4rCCy+8YHOAtVFGnp4QH3dnhyFqIWmhFkLURkXJebhKQi1sYFVCvW3bNu644w6SkpJKJNKXSUJtvYIimbVJOId0ShRC1EaF53PwbCl324X1rKorePjhh8nLy2PlypWkpaVhNBpLPAwGGdfRWnkya5Nwklx9rrNDEEJUklG+b22mP5tdZgOhEJawKqHes2cPzzzzDIMHDyYgIMDOIYl8vfxxFM6RWyQJtRDXm8I8GZ3HVoasQgwZcodOWM+qko+oqCj5JVeFcgpkkHnhHFmFWc4OQQhRSfqCfDx8fJwdxnVHp9Oxd+9e0o8dQy0y0mGPD3V6NHJ2WOI6ZVVC/cwzz/Duu+/ywAMP4OfnZ++Yar1caaEWTqIr0Dk7BCFEJeXnZOMbLPW/lbV37166detmev5T4BJJqIXVrEqos7Ky8PHxoWHDhowcOZLo6Gi0WvPxGxVFYcqUKXYJsraRGmrhLGn5aaiqiqIozg5FCGGhXF2Gs0OoEYrS8ylKzcMl2NPZoYjrkFUJ9ZNPPmn6/9mzZ5e6jSTU1suVhFo4SX5RPtn6bHzdfJ0dihDCQnlZmRiKitC62Dy1RK2XuzcFv57Rzg5DXIes+td38uRJe8chriI11MKZLuRckIRaiOuIajSSnZaKf1i4s0O57uUfTMOnUwQaL1dnhyKuM1Yl1PXr17d3HOIqWfl6Z4cgarFz2edoFCh1hEJcTzIunJeE2g7UIiM5Oy/h26Wus0MR1xmb5rc+e/YsS5cuZebMmSQlJQHFMyimpaXJONQ2SMuRhFo4z6nMU84OQQhRSalJp50dQo2RtzsZQ1ahs8MQ1xmrEmpVVZk6dSqxsbHcddddTJ06lSNHjgCQnZ1NTEwMH3zwgV0DrU2SswtkWELhNMcyjjk7BCFEJZ0/dsTZIdQYqkEl+69zzg5DXGesSqjfeecdZs6cyZNPPsnatWvNkj9/f3+GDh3Kt99+a7cgyzNnzhxiYmLw8PDghhtuYMuWLeVu/80339C0aVM8PDyIj4/n559/dkiclZFbUERmvtRRC+c4oTtBoUFaZ4S4nqSfP4vu0gVnh1Fj5B9JpzBJxuUXlrMqoZ4/fz5jx47l9ddfp02bNiXWt2rVytRiXZWWLVvG1KlTeemll9ixYwetW7emX79+XLp0qdTt//rrL0aNGsW9997Lzp07ue2227jtttvYt29flcdaWYlpMmOdcI4iYxGH0w47OwwhRAV0Oh1//PEH2/fs5XDiWbb9utrZIdUomQmJqEVGZ4chrhNWJdSJiYncdNNNZa739vYmMzPT6qAs9d5773H//fczYcIEmjdvzkcffYSXlxcLFiwodfuZM2fSv39/nnrqKZo1a8arr75Ku3btyhz6z5lOpOQ4OwRRi1z+Yj6/7zzJ+5P568Rfzg5JCFGByxOT3P3IJP5vyXI2rl0tY1LbkSGjgOx/zjs7DHGdsGqUj7CwMBITE8tcv337durVq2d1UJYoLCxk+/btPPfcc6ZlGo2GPn36sHnz5lL32bx5M1OnTjVb1q9fP1auXFnmeQoKCigoKDA9v/xDYdeuXfhU0VSvyacOsjbrDGEFtl9DjaLgorWp7+n1xVAEqh1bFBQNaGv+2K47d+7kvvvuu7JgKrRWWts0wYuiKLhoav61u0wtKrJr3wdFUVBq0bjCRoMRe10+RQFNLfi7d/iw+Z2kpOQUln38IS169rHpuBpFQXPNZG010bXX7/jFU3i6uptvlHgQ34woXEO9LDuooqBoa8/EWAaDwe5/966dKNAW7dq1s9uxKqRaYfLkyWpoaKh6/PhxNSUlRVUURV2/fr2qqqq6Zs0a1dXVVX3++eetObTFzp49qwLqX3/9Zbb8qaeeUjt16lTqPq6uruqXX35ptmzOnDlqWFhYmed56aWXVEAe8pCHPOQhD3nIQx7X0cORrGr+ePnll0lISKBNmzZ069YNRVF46623eOGFF9i8eTNt27blv//9rzWHrnaee+45s1btzMxMoqOj+f3336ukhbqwyMDz3+0jK1/Pfd0aEOztZvWxiowqRqNKbKg3bi41v7WBokJIPQYaLWjsMCi/UQ9GAwQ3BBfr34frwbUt1G0ebkPPG3vSI6qHVccrMhZhwECMXwyu2po/QYJRr6fg5EkUjdYurcpqURGq0YB7bCwa15p//QxFRjIu5qDRKCg2tiyrBiNGo0pAuDdal5rdSn3tv9sJfXtQLywEDx8/2vQbhKubezl7l85gMKAaDQTWicKlht8hufb6TR/+P1rWbVLqttogD7w7hJd71041qmBUcQ31Qqnhnz2AoqIiUlNT0Wg0aDS2v16j0YjRaCQ4OPi6/OxZFbG/vz9///0306dPZ/ny5Xh4ePD7778TFxfHSy+9xFNPPYWnp6e9YzUTEhKCVqvl4sWLZssvXrxIREREqftERERUansAd3d33N1L/lFq06YNfn5+VkRevoIiA4Hb9Wjy9BiD6tK2RdmxVaSwyEi+3kCzOn6414qEugAuuICrF2jtkAAbCkGfCxHx4FL5L6brSW6ueSdY3yhfLgVdokXrFlYlxHqDnjxDHk2DmuJmj/eimjMWFpLv4YHGwxPFzfYEWC3UY8zPw6NZMzRuNf/6GfRGUpKycHHT2pwEG4qMFBUaCInyRetas5Oaa//dRgQFEhMeBkDhicO0u2MULpX8/BiK9BQVFBAa0wCXGv5j7trrFxcWQ6voZmVu72EIxLtT2d/JapERVW/ErY5PrUmoL1y4gKurq13KNAwGA3q9noiIiOsyoa70O56fn8+sWbPYunUr//vf/9i1axc5OTnk5eWxb98+XnzxxSpPpgHc3Nxo3749v/32m2mZ0Wjkt99+o3PnzqXu07lzZ7PtAdauXVvm9o6m0+n4848/SDm2m4wTe9h8KMnZIYlaLLcolx2Xdjg7DCGEFdLOJfHXN0soyJEO7vaSfySdguMZzg5DVFOVTqg9PDx45plnShTzO8PUqVOZP38+n332GQcPHuShhx4iJyeHCRMmADB27FizTouTJ09m9erVTJ8+nUOHDjFt2jS2bdvGo48+6qyXYGbv3r3c3Ksnv7/3MNvmTOLA/r1c0OU7OyxRiyUkJmC0ZydPIYTDZFw4x+9LFpB+/qyzQ6kxcrZcoCijoOINRa1j1T2Jli1bcurUKTuHUnkjRozg3Xff5cUXX6RNmzbs2rWL1atXEx4eDsCZM2c4f/7KkDc33XQTX375JfPmzaN169YsX76clStX0rJlS2e9hAptOprs7BBELXYx9yK7knc5OwwhhJXyszL546vPObplM6pRfhzbSjWo5Px5FtWgOjsUUc1YVaTy2muvMXr0aHr16kWfPrYNz2OrRx99tMwW5g0bNpRYduedd3LnnXdWcVT28+fxVP7Tqg6ebrWgBlpUS6tPrqZ1aGu0inwGhbgeqUYjBzetJ/nUCdoOGIynr/37/9QmRekF5O1NxqtNmLNDEdWIVQn17NmzCQoKol+/fsTGxhIbG1uiblpRFFatWmWXIGuzfL2B9YcuMahVpLNDEbVUcl4yW85voXOd6tHXQAhhnZTEU/y++FPaDRxCWEycs8O5ruXtT8U10gfXcAvHpxY1nlUJ9Z49e1AUhXr16mEwGDh27FiJbWyZEEKYW3vwIj0ah+Ljcf31ehU1w+pTq2kX3g53bc0e7USImq4wL5d/Viyj0Q1daNy5m12GO6uVVMjedBb/gTFovGr2aCjCMlZlaNWhfro2ydcb+HHPOUZ2qtrZJ4UoS2ZhJgmJCfSP6e/sUIQQNlJVlSN//0HKmVO06T8Yn8AgZ4d0XTLmF5H52xn8+tZHIw1etZ78NL1O/H40hTNpuRVvKEQV+e3Mb6TmpTo7DCGEnaSdS2LD5/M5uuUvjAaDs8O5Lhl0hWSuO4Mxv8jZoQgns/onlcFg4JtvviEhIYFLly7xyiuvEB8fj06n47fffqNLly6m0TaE7VRVZck/Z3imfxM0Uk4jnKDIWMS3R7/l/vj7paRLiGrAcE0SbDRWfuQJY1ERBzclcPbQAdr2H4xPUMWt1Uf/+Yvd637h4olj5GdncfdbswiLaVDhfoc3/8GfX39BZvJFAiPq0O2u8TRo29G0/q9vlnDor01kpSajdXEhPLYhXUeOJbJR6bMXVheGjAKyfjuDT69o+dtYi1nVQp2RkUGXLl0YPXo0S5cu5fvvvyc5uXh4Nx8fHyZNmsTMmTPtGqiA06k5rD90ydlhiFrsYNpBtl/c7uwwhKj1VqxYUWLEqg9/XMO2oyesOl5m8kU2LV3EheNHKtxWX5BP3SbN6TZ6vMXHP3v4ID/Nepv4Xn25+81ZNOx4I6veeY2UM6dM2wRG1uXmCQ8y7p05jHz5bfxCw1n+2gvkZuqseEWOVZReQM7m86iqDKdXW1mVUD/77LPs37+fNWvWcOLECbMPkFarZdiwYfz88892C1JcsXLnOc5l5Dk7DFGLfXv0W9Ly05wdhhC11ooVKxg2bJipIeuyrLx85vxgfVJtLCpi5y8/kp+dVe52zbv3pvOwUdSPb2PxsXf88j2xbdrTccgdBEdF02XE3YTHxrFzzY+mbZp17Un9Vm0ICI8gJLo+PcfeR2FeLsmnT1r1ehxNfy4bfVJ2udvk7Ush+dO9nHtlM0nPbqLwXPnbA2RvOc+lj3Zzdtpmzk7bTPIneylMNH+PVFVF9+spzr32N0n/+5PkT/aiT5FcwZGsSqhXrlzJY489Rt++fUu9vdG4cWPpuFhFioxG5m86SWGRDNAvnCPfkM+Sg0tkBkUhnMBgMDB58uRyW0KXbvgTo5WTuBgNelIST1sbXpnOHzlEvZZtzJbVb92O80cOlbq9oUjPnt9W4+7lTWj9WLvHAyVLZgx2mPim8FRmueuNhQbc6/vh39/y11RwQodX61BCH4gn7OHWaP3dSf50LwbdlRkbs35PIvuvcwTe1oiwR9qguGpIWbAPVS9/px3FqoRap9MRG1v2h0Gv11NUJAX6VeW8Lo/Ff5+WW0vCaU7oTrD+zHpnhyFErbNp0yaSkpLK3SYtK5sjZ88BaqUfqkqVdFDMyUjHKyDAbJm3fwA5ugyzZce3b2HW2GHMGDOUHT+tZNjzr+Ll52/3eEormXng82f4aY9tf9euTnJL490uHL8+9XFvGGDxMYNHNsWncx3c6vjgGuZF4B2NQIX8YxlAcet09p9n8etdD88WwbhFehM0ogmGzALyDqTY8GpEZViVUMfFxbFjx44y1//66680b97c6qBExbaeSmPtQamnFs6z+tRqLuRccHYYQtQq58+ft2i79KxcVKNa6Qeqipd/gGl86oObEpg1dpjpkXRwX1W+POq1aMXdb89i1CvvENOmPT/MeIvca5JuW5VVMpOanc79i562KalWregYWulz6A2oBhWNV/G4Eoa0fIxZerMkXePhglu0L4Wnyy/fEfZj1Sgf9913H8888ww9e/bk5ptvBooncikoKOCVV15h9erVzJs3z66BipK+23mW6EBPmkXKNLLC8Qyqga+PfM1jbR6Tnu2AMT8fjYdnxRsKYYPISMtmzQ0O9MfF3bqJmOrHt0Gj1QIQ1+EGIq4aZcMnKNiqY3oHBJKbkWG2LEeXgbd/gNkyVw8PAiPqEBhRhzqNm/Lp5PvZu/5Xbrh9uFXnvZYlJTMvrpxO/5Y90Gq0lT6+oqn6v4W6X06h9XPDo2EgAIZsPQBaHzez7bQ+bhiyC6s8HlHMqoR68uTJ7N+/n1GjRhHw7y2c0aNHk5qaSlFRERMnTuTee++1Z5yiFKqq8skfJ/nfoGYEerlVvIMQdnZSd5Ltl7bTIbyDs0NxOmNuLgQEOjsMUcN169aNqKgozp49W2ZSGOTrQ9Poulb90PULDcM3JNT03M3TCzdP26fXjmzclDP7dtF+0K2mZaf37iSycdNy91NVFUOR3ubzX1ZRyYwKnMu4yD/Hd3JTw/aVO7gKuFy58Z+78xLp3x01PQ+Z0BL3WNvKVzI3JJK7O5nQB1qhuMpUItWJVQm1oijMnz+fcePGsXz5co4ePYrRaCQuLo7hw4fTvXt3e8cpypBTUMSiv04x+eZGMj61cIofjv9Ay+CWeLh4ODsUpzJk6KBOXWeHIWo4rVbLzJkzGTZsWJnbjOnTw8opxRXaD7q9wkQ8LzuLrJRkstOLJ3pKO1ecoHoHBOL974/KX2ZPxyco2DS0XrsBQ/j65WfZ9sMKYtt15PBfG7l4/Bi33P8oAPr8fP7+bhlx7W/AJzCIvKxMdq75key0VBrf2NWK11I6S0tmLmamFCfIlaRx08C/18+jeRDh0e1M67T+tjV8ZW1MImtDIqH3xeMW6X3luD7FU58bsgvR+l05hyG7ELdIH5vOKSxnUUI9dOhQpkyZQrdu3QDYuHEjzZo1o2vXrnTtar8PurDO4QtZ/H44mV5Nw5wdiqiFMgsz+eHED9zZ+M6KN67BDKkpqAYDCq7ODkXUcEOHDmX58uU8+OCDZnXAfl6ejO/Xm45NGlb6mIpGw41DR1K3SbMKtz2+7R/WzJ1hev7TzLcB6DxsFDfdeRcAmanJKFcl9XWbNGPgY0/x57LF/PHV5wRE1OHWp54npF6M6fxpZ5M48Ptv5GVl4uHrR0RcI0ZOe4uQ6PqVfj1lsbRkJjww1KoWYK2/B4q2OKHWuLugcbfPlORZvyeSuT6RkHtb4hbla37OIA80vq4UHMvArU5xAm3ML6IwMQufGy17vcJ2Fr3Tq1at4o477jA979WrF4sXL2b06NFVFpionBU7z9I00pdIf6nhFI7317m/aBrYlPjQeGeH4jRqkYGilBTcoqOcHYqoBYYOHUpwcDA9e/Y0LXv01oE0q1/5z5+Hjy/dRo0jvIFliXjLnn1o2bNPuduMeOnNEsuadO5Kk86lN8K5uLlx65PPW3R+W1RUMqOgEBkQxo0N25lamitDG1B+K7QxV09RRgGGzOLa5qLk4rGitb5uaH2L901bdhitv5tpaL3MDYlkrj1N0MimuAR6YMgq3ldx06Jx16IoCj5d6pK5PhGXEE9cgjzQ/XoarZ87ns1DKv0aKkOv1+PqKo0IYOEoH3Xr1mXnzp2m56qqSiekakZvMPLxxhPk6e0/3JEQllhyaEmtH/VDf+aMs0MQtYhWa95pTmNFh7igOlEMeGSqxcn09e5yyUx5Xr3jSas6JAK4hnuXuz7vQBqXZu0kddF+ANKWHuLSrJ1k/32lFOXqhBsg5+/zYFBJW3KQ86/9Y3pkbbxSC+7bIwqfmyJJX3GUi7N3ohYaCJnQosrrrK8dy7s2s6iFeuTIkbz77rt8/fXXpk6Izz77LG+88UaZ+yiKwu7du+0SpLDMBV0+H/9+gkd6xeGqlc4KwrEKDAV8vOdjprSfgqe2dt4pyT90CO8uNzk7DCEsEtWsBV1GjsXVzbrRQK5XZZXMhPgE8daI5xjU+marjqtolAo7HXp3CMe7Q3i524RNbGX2PPLZThWfW1HwvyUG/1tiKtzWngoKCvD19a14w1rAooT6jTfeoGHDhiQkJHDp0iUURcHb25vgYOuGzxFV59CFTD7+/QQTezRwdiiiFsooyODTvZ8ysdVEZ4fiFPkHDvw7fF7t7qApqr+mXXrQfuCtZnXOtUlpJTPzJrzFTY0qObLHVdwbB6LxtE/N9PUiMzOTkJCqLSu5Xlj0zmu1Wh544AEeeOABADQaDf/73/+khrqa2ndOx/xNJxjfOcbZoYha6EzWGVafWk2f+uXXWNYUOp2O3Tt2oDt2jNzz5+n461rqDhns7LCEKJXGxYUbbx9Bg3YdnR2K011bMqO14ceFolXwuSHC1pCuGzqdjp07d7Jnzx4SExPp0KEDfn61e04MixLqdu3a8frrr9O/f38AFi5cSNu2bas0MGGbPUk6lm9P4ra2MoyXcLxNZzfRJqyNs8NwiL1799Kjd2/T82VuboTf1BkXabUR1Yy7lzc9x91P6L8jawj78e4Uidav9pTO7N27l169epmef/XVV6aJ/mori36O7dmzh5SUK/PB33PPPWadFEX19OfxVA6dz3R2GKIWUlWVbRe2lbleb9Tz3vb3uH3V7XRa0oneX/fmv5v+y6XcSxUee+mhpfRb3o/2i9sz+qfR7E3ea7Y+MTORyesn0/2r7tz45Y08seEJUvJSyjia/alFetIWfYaqt99kFELYytPXj1smTpJkugq41fXBq13tHrZ2+/btFo/xXVNZlFDXr1+fdevWmXpzyigfVePa3rKq0WjzMTceKzuR0BuMvPHLQfq9v5FmL6ym02vrmLpsFxcz88s95j8nUrl30VY6vbaOmGd/Ys3+kiM7qKrKe78epuNr62jyv1+465O/OZmSY/PrEVWj5GfPihkNrnFCd6LMdflF+RxMPcjE1hNZ9p9lvN/rfU5lnuKx9Y+Ve8zVJ1fzztZ3eLD1g3w9+GsaBzZm4rqJpOYVTzCRq8/lgbUPoCgKn/T7hM8HfI7eqOex3x7DqNr+78lShWdOk750qV3+DQthK09fP/re/yj+YeV3hhOVp/F2xa9fjEOmHK/OioqK2LhxI9u2bSMvL8/Z4TiFRSUfDz74IM888wxLlizB09MTRVG49957mTix7I5HiqKg0+nsFmhNt2LFCh588EGzZXs+n0azYVMJb2X9zJNnUnPLXJenN7D/bCaP3dyQZpF+6PL0vPzDAe77bBs/PFb2hD25egPNIv24s0M0D36xvdRtPvr9BAv/OsX0O1sTHeTF9F+PMHbBP6yd0gMPV+uGIxJVo7TP3pa3t9DmoTbU6VzH6uNmFGSUuc7XzZf5t8w3W/bfG/7LqJ9GcT77PJE+pU9G8PmBz7mj0R3c3uh2AF7s/CKbkjbx3bHvuC/+PnZd2sW5nHN8M/gbfNyKJzh4retrdFnahX/O/0PnOp2tfj2VlbttG2pBIYFj7kLjWTtHPRHO5xscQu97HsQ3SEqQ7E3RKgQMikXrXfvGYb62Ecb4b+PB8ePHOXnyJHXq1CE6OprIyEi7jVNtMBhYv349R48eJT09HXd3dxo0aECfPn3Krd8+deoUf/31F+fOnSM7O5sRI0bQrFnJCYySk5NZu3Ytp0+fxmg0EhoayvDhw02j21XEooT6qaeeonXr1iQkJHDx4kU+++wzOnbsSIMGMpKEPaxYsYJhw4aVGGRen53BnkUv0mr8K1Yn1eWNS+3n4coX991gtuyVIS24dc6fnM3Io25A6UlAryZh9GpS9u0tVVVZ8OdJHuvdkFtaFHfSeG9Eazr83zp+PXCRIa2tT9IqpM8DV6+qO34NU9ZnrzCzkC1vbaHTM52sTqoLDYUVb3SVrMIsFBR83Uofgklv0HMg9QD3xt9rWqZRNNxY50Z2JxcP0VloLERBwU17ZXIFd607GkXDzks7HZpQA+Tt3YP+zUQCRozAo3lzh55biLDYOHqMuQd3r/LHRhbW8etbv8Jxp2ui0hphPv30U0aOHEmbNm0wGo0kJSWRlJSEVqslLCyMOnXqEBERgY+P9VOh6/V6zp8/T/fu3YmIiCAvL4/Vq1ezdOnScht49Xo94eHhtG3blmXLlpW6TVpaGgsWLKBt27b06tULd3d3Ll26hIuL5aO2WLzlLbfcwi233ALAokWLmDhxoozyYQcGg4HJkyeXOmPTZYdXziasZRcUKwaad6nkbais/CIUBfw8rB/6JzEtj+SsAro0vNIi4ufhSpvoAHacTq/ahFpYzJLP3t5P9xLZKdI0lW6lVGKXAkMB729/nwGxA0wty9dKL0jHoBoI9jAfrjPYI5iTupMAtApthaeLJ+9vf59J7SahqiozdszAoBpIzksu7bBVrig9nZSPPsKzdWv8b78dl6Agp8Qhapf68W24afhdaF1qX+upI/jcEIlHo0Bnh+FwZTXCZGdn88knn3DffffRpk0b03KDwcD58+dN9dW+vr6EhYUREhJCSEgI3t7eFpcQe3h4MHbsWLNlAwcOZP78+WRkZJTZktyoUSMaNWpU7rF/++03GjVqZMpzAYIq+bfaqqzJKHWBdrNp0yaSkpLK3aYg4xJpx/cQ1LByI6uoqopbJcor8vUG3lx9kCGt6+DrYf0f4eTs4hrsUB/zHs+hPu4kZxdYfVyL6BLBUxIWS1jy2ctLySPlQAohLSt3u1hFxUW58uflxxM/8srmV0zP5/aZS/vw4vFe9UY9T254EoAXbnyhUue5VpBHENN7TOfVv19lycElaBQNA2IH0CyoGRrLuoxUmbzdu8k/cADfW27Bt3dvFJmuV1SR4glb7kZj5Wx/onzuDfzx6lj76tEtaYRZvnw5rVq1QlPGEIRZWVlkZWVx/PhxALy8vAgPD6du3bqEhVW+Y2d+fnG+4WHD2P9Go5GjR4/SpUsXFi9ezPnz5wkMDKRr166lloaUxaKE+sy/0+nWq1fP7HlFLm8vymZpr9j8zFSM5XyIy+Lp6oL2319/K3ee5b/fXRkRYdGETnSKLU4+9QYjj365A1WF/7utZaXP42w6nY69O7fDgX/A6zjx3f+Dv5/M3lQeSz97eWl5VnXou1xqAdAruhetQq7M/hXmVfyH83IyfS7nHJ/e8mmZrdMAge6BaBUtqfmpZstT81MJ9rzSan1T3Zv45Y5fSM9PR6vR4ufmR89lPYnyjar0a7A3Va8n86efyP1rM36D/4Nnu3a1dmINUTXcvX3oPOwuSaariNbXDb8+9WrlwAyWNMJkZGRw7NgxGjdubNExc3NzOXnyJCdPnsTX15ebbrJ8plm9Xs+6deuIj4+3KaHOycmhsLCQP/74g969e9OnTx+OHTvGsmXLGD9+PDExMRYdx6KEOiYmBkVRyMvLw83NzfS8IjLHe8UiI0vvfHUt38BQqzrzhfm54/LvNOR9mofTJjrAtC7Cv/gDqDcYeWTJDpLS81h6/402tU4DhPoUHzc5u4Awvysf8uTsAppHVs3A73v37qVbrytjYG5aUZeu3azvzFkbWPzZC/HFw6Xyf6zCvMJw0RT/ifF29cbb1bzW8HIyfSbrDJ/2+5QAj4Byj+eqdaV5cHP+Of8PN9crfq+NqpG/z//NqKajSmwf6FF8O/af8/+Qlp9Gz+ielX4NVaUoPY20zz/Hde06/Ab0x6NVK0mshV20urkf7l7Sj6RKKMV10xr32jUb4mWWNsLodLpyW7HLkpmZSXp6OrGxsUDxkM0//PCDaf2YMWOoX78+UJxffvPNN6iqyqBBgyp9rqtdjrVJkyZ07lzczyYyMpLExES2bdtm34R6wYIFKIpi6ql5+bmznDp1ildffZX169dz4cIF6tSpw5gxY3j++edxc3Mrc7+ePXvy+++/my2bOHEiH330UVWHXKZu3boRFRXF2bNny/wAegSEEdKwtVXXPOSqsgsfdxd8rvlDcDmZPpWaw9L7byTQu+zrZ6noIE9Cfd3561gqLer4A5CVr2dXYgZjbqxv8/EtsmUeRCtQrzNoquiPn0EP61+Fo2sh/RS4+0GDntBnGviVk6wmvAG/v2m+LLgRPPbvuM3pp2Fmq5L7Ady5CFrcbnvsWPbZ8wzxJLRFqFWfvVCv0DLX6Y16pm6YysHUg8y5eQ5G1WgaK9rfzR9XbfHfmvvW3Efver0Z3ay4v8bY5mN5/o/naRHcgviQeBYfXExeUR63NbzNdOzvjn5Hg4AGBLkHsSt5F29tfYu7m99NrH9spV+DJUr0dq/EF4n+/DlSFyzANTwC37598WzfDkVb8Q9nVa8neeZMsn/fSGFSElofH7xv6kzo1CdwDS/7tmnyB7NJmTPHbJlbbCxxv/xc8hyqSuIDE8nZtImo2R/g26d2zHx5PfP09aNhxxudHUaN5dUqFLe61nequ95Z2ggTFBRU6ZE9tFotrVq1om3btqYZLJs0aULdulcmp7s8ksflZFqn0zFu3DibWqehuOxEo9EQGmr+nRUaGmpxRQZYmFCPHz++3OeOdujQIYxGIx9//DENGzZk37593H///eTk5PDuu++Wu+/999/PK69cqeX0cvIvea1Wy8yZMxk2bFiZ27S4Y5JVHRIB4kLL7oGsNxh56Isd7D+n49NxHTGoKpeyiuuRAjzdcHMpbjEbPf9v+rWIYNxNMQDkFBRxKvXKmNKJabnsP6cjwMuNugHFwyre0yWWD9YfJSbEm+ggT6b/eoRwP3duaV41dWfXJjWGvEz452PY+w3U7wJ12kFQA7DnbVB9LpzfDd2fgoh4yMuA1c/A0pEw8ffy9w1tBmNXXXl+ddLvHwVPHDHffvsi+GsWNOxrr+gt+uy1vb8tGq11LafRvtFlrruUe4kNiRsAGPaD+fkX9FtAx4jiaZETsxLNht/rH9uftPw05uyaQ0peCk2DmvJRn48I8bxS430q8xQzd8xEV6ijrk9d7o+/n7HNzTuy2Etpvd0f3b6dV+Pj6Wfhlw+A/uIF0r5YjMuvawi4YxjucXHlbm/Mzyf/wAFCHn4I9yZNMWbquPD6GyQ9/DCx3y4vd1/3Rg2pt2DBlQVl9GJP++yzSnUsFY4XHx/Ppk2bOLVnJ4f/2sh/Ro+VTohVROvrhveNlv+broksaYQJDAyssAPg1RRFoVGjRnTs2LHECCDu7u64u5v3xbqcTKempjJ+/Hi75HAuLi7UqVOH1NRryglTU/H397f8ODZH4gT9+/c3TYMO0KBBAw4fPszcuXMrTKi9vLyIiIio6hArZejQoSxfvpwHH3yQ5OQrIxG4+QQSP+IJIlv3sPrYHWPL7qB3QZfPuoMXARg4a5PZuqX330jnuOK61NOpuaTlXBkCbU+SjlHz/zY9/7+fDgJwR7sopg9vDcCDPRqQV1jEcyv2kpmvp2NMIJ9N6FQlY1CXltTc+elRPhoZy9A2wMEfih9uXhDeEiJaQVgL8A4BW+60ePibJ8UAA9+B+b0hIxECyk4o0biAbxk/LjTakusO/QAtbgN3+7aOlPXZc/d3p/3D7Ym6yfq64+bBZQ8TV9enLnvH7S1z/WVrhq0psWx0s9GmFuvSTGk/hSntp1gWpA3K6u2eVljIo9u3M7t9+0ol1QBFly6RMvdD/P8zGK8bbyhzO62vr3lSDES88D9O3Tkc/blzuNYpZyQdrQsuoWXfPQDIP3iQtIWLiF3+DUeldKra8vf3p2vXrgRThF9eFvFdujk7pBrLt2cUGrfaXZduSSPM8OHDy+yQeJmiKISGhlKvXj0aNmxocdJqMBj4+uuvOX/+PKNHj8ZoNJKVlQWAp6enaYi7zz77jKZNm3LDDcV/QwsKCkhLSzMdJyMjg/Pnz+Pp6WkaGaRLly5888031K9fn5iYGI4dO8bhw4cr1YBsUUJ9dYuupRRF4YUXbOuxXxk6nc6iIU6WLFnCF198QUREBIMHD+aFF15weis1FCc2wcHB9OzZ07Ss/YRXCG7UxupjxoZ40yS87I550UFenHqz4tqjP5/tbfa8c1zw/7N33+FNlusDx79v0ibpTPeelA1l771UQHHUCoIgHlHEwRAHeH56BD2CW8ANR8CFCxEUGaIgoiLIEmTvUUrp3jPJ749KoHSQrrwd9+e6cp2Td97BtL3z5H7u55rnKYrC9OtbMP36FrYFW0XlJTWJWUXE/u8oy+9rRkyHf94XBTlwdnvxA4oTYq9I8AgDj3DwjAAXv+ol2XkZgFJ87YqkHIdXW4CDHkK7weBny0/Az++GC/tg+GtVj6sCZb33es7oiV901ZfSdXF0oa13/ZvcaitbZrv/d/9+hgQEWCcFV0b66tXomts+ygNgzswERUFTwQIHAAWnT3O0bz8UvR6nDh3wm/5oiQTcnJtL3ONPEPCfZ66ZeIu6I7JTF6nDryX6KA/0EbaPVDZk5Q3CuLm5MWbMGDp2LLsbmV6vJywsjPDwcIKDg6tUppGRkcHhw4cBSpXqjh8/3lp7nZKSQk7O5UXtzp8/z4cffmh9vn598UBN+/btue224hLKVq1acdNNN/Hrr7+ydu1avL29GTVqlLVm2xY2JdSzZs0qte1STeXVf1AURbEuTW6vhPrYsWO8+eab1xydHjNmDOHh4QQFBbF3715mzJjB4cOHWbFiRbnn5Ofnk59/udVbRkZGjcV9Ne1VtZPV/eV4V/eGPRPZlqRm2vLT3NLOE21Z/bjz0uH8nuLHJXo38GsFIV0hoH3lAirMgx+fhehYMFSQ1IR0gVvfKa6bzroAP78ES4bBQ1uL73+1XR+DTwsIK3/EsrpKv/eq977pH9LfWgfdEF1rtrsFiM/L48+kZHp4e5d7XPkXMFNw9Bj07m3T4eb8fC6++hruN96ItoKFE5zatyNo7hx0kZEUXUwk6e23OTV2LE2+/Q6ta3F5WMLcF3Hq2AG3wYPLvY6oewKibOuqICpHcdTg1jf42gc2ImUNwtx///2lOnu4u7tbk+jAwMBSf2cqy9PTs8x89GqPPlryG8rIyEibzuvUqROdOnWqYnQ2JtRX952Oi4vjxhtvpG3btkybNo0WLYpHIQ8dOsS8efM4cOAA33//faWDmTlzJi+99FKFxxw8eJCWLVuWiGXo0KHccccd3H///RWeO3HiROv/j46OJjAwkMGDB3P8+HGiyqlZnDt3LrNnz67Eq6gbukR4WlviNVS2JDVn0wrYciyDAc1t7C6SnwFntxU/nH2gx0PF9dEAe7+E76ZdPnbscgj/p8WPqRC+ugcsFrjx9Yrv0ezKOui2ENwF5kXD/m+g01W1voW5sG859H/CtvjrAEeNIzc1uUntMGqVrbPdE/Pyit8TVWGxWD9Up3/3HfHPzrLuClv4Ps5duhQfVlhI3LRHsWAhYNazFV7Std8V5RstWuDUvh3HBg0mc91aPGJjydy4kextf9CkgkEGUTd5+NetUsaGwqVrAFq36k/Wb2iuTo4vlXl4eHjQvHlzIiMjbV6yu6GoUg31ww8/TLNmzfjkk09KbO/atSuffvopsbGxPPzww3zzzTeVuu5jjz12zXqVK5c7P3/+PAMHDqRXr14sXLiwUvcCrPU1x44dKzehfuqpp5g+fbr1eUZGBqGhFdTG1gFuBgceGdi0QY9Og+1JTXx6PlShjzLmwuKH8s8vjhbDILjz5f3u/3xNfimZTj8L47+reHS6LE4e4B0FKSdK7zuwqnjyY/vSbeHqquGRw0v0hW6IbJ3t7ufmhlLFGeguvXuj/FMT6DpwEE3aXe784uBfXGNvKSzk3KOPUnj+PGFLl1Q4Ol0Wrbs7uogICk4Xz2TP/uMPCs+c5XC3kt+GnJsyFefOnQn/+KMqvRZRuxwNTjjqq9fpQJTm4GXAuYOUPdmqV69etGnT5po11A1VlRLqjRs3VjiSPHjwYGbMmFHp6/r6+pZqW1KeuLg4Bg4cSOfOnVmyZEmV/gPu2bMHqPiPY1mzTOsyRYHHb2iBt2v9ibmqbE1qAr1cwdHJ9gt7hEP0HRA1CBwMoP3nx0TvVrok41IynXwc7lkNzlX4ViA/C1JOQrs7S+/b9XFxIu9SuZUK1eLn5EdMsxi1w6h115rtrgABTk50861ay0Hj7TE4tW1jfa51dbGWZFxiTaZPnybsww9x8Kz8Msjm7GwKzp7FePPNAPjcfz8eV004OnnzLfjPnInroIGVvr6wD10124aJsrkNCEWpYpejxqZNmzZER0erHYaqqpRQGwwGtm7dyoMPPljm/t9//73afQErEhcXx4ABAwgPD+fVV18tURh/qYNHXFwcgwcP5qOPPqJbt24cP36cZcuWMXz4cLy9vdm7dy+PPvoo/fr1o127cnr+1kP/6h1Bp7DK/2Gtj2xJakI89fRt5kG5/b8UTfFkRL9WxY+AdsWTFG1JgkyF8OXdxa3zxnwBZhNkFndNwckTHP75mvDDEdByBHT/p+Ro/f8VJ8nGUMi8AD/PKe7sEX3VzOnk43D6N7ir4jZodYWCwqQOk6q0CEx9Y8ts92fata/0hESt0R3viRNx6VFxL2FLYSHnpk4j78ABQt97F0wmiv75Pag1GlH+6cd/+p5/4TZkCF5j7wIg4aWXcR04AMeg4OKOIm+9iaLR4H5T8SRjB1/fMiciOgYFogtRf6VJUTYZna55Tq29G3XP6Wu51LLx6NGjmEwm6+S+xqxKCfVdd93FggUL8PDwYPLkydZyiePHj7NgwQKWLVvGlClTajTQK23YsIFjx45x7NgxQq76JX8psSosLOTw4cPWmZ46nY4ff/yRefPmkZ2dTWhoKLfffjtPP/10rcVpb9e39ufWDo1n8oQtSc28kVElJyRqtODbEgLbQ2AHCGgLuvJ7dVco4zwc/mdBjPf6lNw3fjVE/tPCKuUU5CSXPG/5BMhNKa7TDusB9/1YehR69yfgHlw8Ul4PjIgaQRvvNtc+sIEob7a7t17P8x06MjS4cj+LLr164X3/fWiv0aUDoDDhIlkbNwJw8taSf8jCPvwQl+7dio87cwZTaqp1X1HCBc4/9jimtDS0Xl44d+5ExBef42BDhyRRdznWo29R6wONQYtr7wpaTwpry0YfHx+aNWtW7QmHDYFiqcL6kAUFBUyYMIFPP/0URVGs5RZmsxmLxcLo0aNZsmRJhasW1lcZGRkYjUbS09Otq/bUlF9//ZW+fS/3Ee019W28omwbPW8bbOT5W9pYlxlvTC71ob4yqfFzc+TdMc2I6egDLr7FyW1oj+IJhjr12yTWNVe/9wa+OBDfNrbXDjb3bM6zPZ+1LjXemGzevLnEbPfP+vajeyXazSkODnjdNwG3IUMa/LyH8pgKzSSdy8RBp0XrUL3fYaYiM0UFJnxC3NA6No7fh+kXL2D0k0mJlXX1771VUz+ge1RH3AaE4BwttdO2SE5OxrsqnYwaoCr99dPpdHz88cc88cQTrFmzhtOnTwMQHh7OsGHDaN++ku3GRLX4uumZOaxlo0ymoewWPl/e34r+AwdBh7uKV0lspJMk7MHV0ZWpnaY2ymQaypjtXomk2DEoEN9HH0V/xWRrISpL51zFb9lEKVp3HU6t68eclbrg6tUNG7Nq/QVs165dg6o/ro80GoUZQ1tidGq4PX9tcXVSo+00Fm58vHqLtAibTGo/qcTS38IGGg3GESPwuHMUmgb4TZ6wLyfX8hfwEpXj3N4XRSt/N2xVn5o21LbGOaTUgNzVLYwWAfLL9NIECU7/DmYz0TdNlGTaDgaHDaZrQFe1w6hXHIOD8Z0yGX3TpmqHIoS4gqJRMLSU+QSiaiShrsea+blye2eZeQ+XJ0gQkA8RfS932BC1xsvgxdhWY9UOo15xHTAA7/vvQyNtzoSocxz8nNEYJC0SVSPvnHpKo1GYMrhZ2UtqN2a+LSWZtpN72tyDs6NM8LSFxtkZ7/sm4Nq/v9qhCCHK4Rggv89E1UlCXU/FdgomwkcmopTiIjOz7SHaJ5puAd3UDqNecO7SGe8HHpDWdELUMZdKBVNXHsNSZKbjoO7XPkmIckhCXQ/5uxsY2bVuL3+uGhmdrnUKCuNaj2u0Ld5s5qDF+94JuF1/nfxbCVEHXSoVvPi3GxpnR7zD/NQOSdRjklDXQ5P6N0HvIE3UhToGhA4g3D1c7TDqNgct/jNm4typo9qRCCFsoAuTyf2ieqqcUK9fv54PPviAEydOkJqaWmrpZ0VROH78eLUDFCV1i/SiS4R8dSzUodfqGdlipNph1Hne990nyXQlmQrNOOhkoECoQ5YZF9VVpYT6lVdeYebMmfj7+9OtWzeio6NrOi5RBp2Dhon9ZAEIoZ7hkcPxMsgHuoo4tW2D25AhaodR75hMZrVDEI2YY6DMSRLVU6WEev78+QwaNIg1a9bg6Ni4FxSxpzu7huLvLu22hDpcHV25OepmtcOocy5NbMr+4w8y1q6j1+OPS810FRTmm9QOQTRSGoMDGhfJZUT1VCmhTk1NJTY2VpJpOwr3dua2jsFqhyEasVua3iJt8spwaWJTRlYWhR4eeEdGqh1SvZSdlo/RV95fwv607jr5ECyqTVOVk7p168bhw4drOhZRgYcHNsVBW6X/XEJUm6fekxsiblA7jDrPdeBAtUOot7LTCtQOQTRSGmcZHBTVV6UM7Z133mHFihUsW7aspuMRZRjU0o9Wge5qhyEasdjmsei1erXDqNMUnQ6djE5XWnp6Or/+9isb1m9i7bc/kp6RrnZIopFRDDIZVlRflUo+Ro0aRVFREePGjePBBx8kJCQErbbkG1JRFP76668aCbIxc9AqjO0hLcqEeoJdgxkYKiOv16J1c0PRyLdIlbVv3z4GDLy8guT/XvmSmLHD5St4YTcaR/m5FdVXpYTay8sLb29vmjVrVtPxiKsMaO6Hr5uMDAr1jGk5Bq1GRnCuRTHIhOGacOZgCrvWnaZV7yCc3WWhJlH7FGnXKGpAlRLqn3/+uYbDEOUZFh2gdgiiEWvu2ZzO/p3VDqNe0LhKH9uqMJlKdvcwm81cPJNJ4rkjBES6E9zcE+9gVzTa2hmx/vnTQ+zfcp4+dzSj/eDyV6Ddue4UJ3YnknohBwedhoAmRnreFoVnQMl2axdOpPPHquMknMxA0Sj4hLhy85QO0mO7DlP08t9GVJ+slFiHebo40sxP/kgL9cQ2j5Wv3m2klYS60lasWMGkSZNKbFvw5b+5d8QMurUeSPzxdOKPp+Oo0+Ib7oZ/hDs+Ia41lpye2J3IhZMZuBivPRJ+/kgabfuH4BfhhsVs4Y+VJ/h2wR7GPNsDx38Ssgsn0vluwR46DQ2n76jmaDQKSeey5GeojpOEWtSEaiXUhYWFHDp0iPT0dMzm0k35+/XrV53LN3qtAtzlF7FQTZhbGO182qkdRr2h9fBQO4R6ZcWKFcTGxpZaZTcjJ415XzzFtFFz6da6uHa/sMDE+aNpnD+ahqKAh78z3sGueAW54OHnjNah8jWwWan5/PLFEUZMac/3b+295vEjpnQo8Xzw+FYsfuJXEs9kENTME4BfvzpKu0GhdB4aYT3u6hFsUfdoHCWhFtVXpYTabDbz1FNP8c4775CTk1PucVd/lScqdmmBiLlrDpKWU8hNA7qrHZJoxAaFDZIPdJWgSF9+m5lMJqZOnVoqmb7Sx2vn0aVlPzRX1e9bLJB6IYfUCzmwExSNgtHXCa9AF3xD3XD3vXYtu8Vs4cel++l4XRjeQVX7ZiE/twgA/T8t13IyCkg4mUHzbv58/fIO0hNz8QxwofstTQhq6lGlewj7UGRSoqgBVXoXzZkzh1deeYWxY8fy0UcfYbFYePHFF3nvvfdo164d7du3Z/369TUda4N3aYGIgOYd8G/enu4ty6/nE6K29QjsoXYIooHasmUL586dq/CY5IwEDp7ag8ViqfBhNplJvZDN8d0X+ePb42z5/AjJcVkVXnvXD6fRaBTaDQqpUvwWs4VfvzpKYJQR7+DihDwjKReA7atP0rpPECMmd8A31I1V83aTllD+wJOoA6rwDYcQV6vSCPXSpUsZOXIk7777LsnJyQB07tyZQYMGMX78eHr27MnGjRsZMmRIjQbbmLQJMmKQr6GESoJcg/A0eKodhmig4uPjbTouLTMJyh/ELlNuViGHtl6gZc9AAA5vu8DPyy4vRHbTw+34a+M5Rv27a5W/gdn8+RFS4rKJeaKTddulwfY2fYNp1SsIAN8wN84dTuHg7/H0vC2qSvcStU9xkG/iRPVVKaE+d+4cTz75JAB6fXFLt7y8PAB0Oh1jx47l9ddfZ86cOTUUZuPTLsSodgiiEWtibKJ2CKIBCwwMtOk4bw9fHCr5dbzWUUP7IaFo/llZNrK9D/6RlxfGOr7rIrmZBXz479+t2yxmC78tP8pfP53l7jm9Krz+L58d5vS+JG57rBOunpfLSy5NbPQKLFkz7RngQmZKXqVeg7AvjUxKFDWgSgm1t7c3WVnFX6m5urri7u7OiRMnShyTmppa/egaMVkZUdjTpfr9V/58hcyCTIb0kG+XRO3p27cvISEhxMXFlVtH7W30p1VkR7BxFFlRILK9L236BuHkdrlrh87ggM5w+U9dmz7BRET7lDj3uwV7aNEjwDqqXRaLxcKWz49wYk8it07vhLuPU4n9bt4GXIy6UuUdaRdzCGvjbdNrEOrQustaD6L6qpRQd+zYkT///NP6fODAgcybN4+OHTtiNptZsGAB7du3r7EgGxtFgShfacEl7OdS/f4nuZ/glO9El/AuaockGjCtVsv8+fOJjY0t95jxN04vNSGxLE5uOoKbexDVyQ+jr9M1jze4OmJwLTmBVKPV4OyuL9GRY+Ubu2nSwZd2A4vrrH/57AhH/kxg+IPROBq0ZKfnA6B3csBBp0VRFDpeH872707gHeyKT6grh/+4QOqFHIZObHvNuIQQ9VuVEuqJEyeydOlS8vPz0ev1vPDCC/Tr149+/fphsVjw9PTks88+q+lYGw0PZx1OsgiAUEmYexjOjs5qhyEauJiYGJYvX86kSZNITEy0bnd38eS+W2bSvU3Zy90rGgW/MDcCmxrxjzTi7mOolW40GYm55GUVWJ///UscACtf313iuEF3t6JVr+KR7faDQykqNPHb8qPkZRcWL+oytQNGX/l5EqKhUywV9S2qhPT0dH7++We0Wi29evXCy8urJi5b52RkZGA0GklPT8fdvXbKMuauOchTw1vVyrWFqMikDZPoGtCVCdET1A5FNBKbN29mwIAB1ufPTniP1k06lTjGUa/FP8KdoOYeBDX1QOcka5IJIeqWGvutZDQaueWWW2rqco2aq17+WAj1NPNspnYIohHRakt+G6fRFE8mdHLTEdrKk8CmHviGulonGQohRF1U5czNZDLx1VdfsWnTJi5evMhzzz1HdHQ06enp/PTTT/Tu3Rt/f/+ajLXRkHIPoaYI9wi1QxCNmINOQ/dbmhDWygtFI+3MhBD1Q5U+8qelpdG7d2/GjBnDZ599xrfffmutgXN1dWXKlCnMnz+/RgO9WkREBIqilHi8+OKLFZ6Tl5fHww8/jLe3N66urtx+++0kJCTUapxV4SIj1EIliqIQ6GJbSzMhakP0gBDC23hLMi2EqFeqlFDPnDmT/fv3s379ek6cOFGi7ZFWqyU2NpY1a9bUWJDlee6554iPj7c+Jk+eXOHxjz76KN999x1fffUVmzdv5vz588TExNR6nJXlLCPUQiWeek8ctbKEtlDPlZ02hBCivqjSUOjKlSuZPHky1113nXWlxCs1b96cpUuXVje2a3JzcyMgIMCmY9PT0/nggw9YtmwZgwYNAmDJkiW0atWKP/74gx496s4yyy46GaEW6jDqZUEhYV+XeqCf/CuRyPa+REdHqx2SEEJUWpVGqNPT04mMjCx3f2FhIUVFRVUOylYvvvgi3t7edOzYkVdeeaXCe+7cuZPCwsISy6G3bNmSsLAwtm7dWu55+fn5ZGRklHjUNmdZtUmoxMVRRgeFfV3qgT78tuvo06cPRqN8qBNC1D9VGgqNiopi165d5e7/4YcfaN26dZWDssWUKVPo1KkTXl5e/P777zz11FPEx8fz+uuvl3n8hQsX0Ol0eHh4lNju7+/PhQsXyr3P3LlzmT17dk2Gfk0GR0mohTqcHK69MIYQtcFFVqsTQtRjVRqhvu+++1i8eDFffPGFtX5aURTy8/P5v//7P9atW8cDDzxQ6evOnDmz1ETDqx+HDh0CYPr06QwYMIB27doxadIkXnvtNd58803y8/Or8pLK9dRTT5Genm59nD17tkavXxa9g7SHEuowOBjUDkE0UlevXiiEEPVJlUaop06dyv79+xk9erR1xHfMmDEkJydTVFTEAw88wIQJlV8Y4rHHHuOee+6p8JgmTZqUub179+4UFRVx6tQpWrRoUWp/QEAABQUFpKWllRilTkhIqLAOW6/Xo9fbd+REEmqhFhmhFkIIISqvSgm1oigsWrSI8ePHs3z5co4ePYrZbCYqKoqRI0fSr1+/KgXj6+uLr69vlc7ds2cPGo0GPz+/Mvd37twZR0dHfvrpJ26//XYADh8+zJkzZ+jZs2eV7llbdFop+RDqMGhlhFoIIYSorGq1k+jTpw99+vSpqVhstnXrVrZt28bAgQNxc3Nj69atPProo4wdOxZPT08A4uLiGDx4MB999BHdunXDaDQyYcIEpk+fjpeXF+7u7kyePJmePXvWqQ4fACGeMkoo1CEj1EIIIUTl1cv+bHq9ns8//5xZs2aRn59PZGQkjz76KNOnT7ceU1hYyOHDh8nJybFue+ONN9BoNNx+++3k5+dzww038M4776jxEiqkkQUNhEqcHZ3VDkEIIYSodxTLlauyVODmm2+u3IUVhVWrVlUpqLosIyMDo9FIeno67u7uaocjRI3ac3EPHfw6qB2GEEIIUa/YPEK9evVqDAYDAQEB2JKDK4qMsgpR3+i0OrVDEEIIIeodmxPq4OBg4uLi8PHxYcyYMdx55502r1IohKgfHDXSukwIIYSoLJv7s509e5ZNmzbRsWNHnn/+eUJDQxkyZAhLliwhMzOzNmMUQtiJjFALIYQQlWdzDfWVCgsLWbNmDcuWLWP16tWYzWaGDRvGmDFjGDFihN37NtuT1FCLhiw9Px2jXpZ+FkIIISqjSiuIODo6csstt/DFF1+QkJDA+++/z4ULFxg1ahQvv/xyTccohLATSaaFEEKIyqvWknz5+fmsX7+eVatWsXv3bgwGAxERETUUmhBCCCGEEHVfpRNqs9nM+vXrueeee/D392f06NHk5uayaNEiLl68yLhx42ojTiGEEEIIIeokm7t8/P777yxbtoyvvvqK5ORkevTowZw5cxg5ciQ+Pj61GaMQQgghhBB1ls2TEjUaDU5OTgwfPpzRo0fbVNrRqVOn6sZX58ikRCGEEEIIcaVKJdTWk66xaIvFYkFRFEwmU/Wiq4MkoRZCCCGEEFeyueRjyZIltRlHveHm5kZ6ejpubm5qhyKEEEIIIeqAKvWhFkIIUXURERGcPn3a+lxRFFxcXDAajTRr1ozOnTszcuRIunXrpmKUtaewsJBPPvmEVatWsWvXLhITE1EUhYCAADp16sRtt93GHXfcgU4nCw0JIeoHSaiFEMLOLiXUvXv3pmnTpgDk5uaSlJTE7t27SU1NBaB///4sXryYJk2aqBlujdq1axexsbGcPHkSRVFo3749UVFRaDQaTp06xa5duzCZTISHh3PgwAGcnZ3VDlkIIa7J5pIPIYQQNeu+++7jnnvuKbHNYrGwdu1apk2bxubNm+nVqxdbt24lMjJSnSBr0K5du+jbty85OTncdNNNLFiwoNTrSkxM5I033uC1116joKBAEmohRL1QrYVdhBBC1CxFURg+fDjbt2+nWbNmJCQkcN9996kdVrUVFhZyxx13kJOTw6233sqqVavK/JDg6+vLnDlz+PXXX9Hr9SpEKoQQlScJtRBC1EEeHh7MmzcPgI0bN7Jz584S+yMiIlAUhVOnTrFq1SoGDRqEl5cXiqLw888/AzBgwIASz682a9YsFEVh1qxZpfYVFRXx2muv0bZtWwwGA35+ftxxxx0cOHCApUuXoihKqdH1iixbtowTJ06g0+l49913S3SOKkvXrl1xcnKy+fpCCKEmSaiFEKKOGjZsGF5eXgBs2LChzGNee+01br31VjIzMxk6dCj9+/dHq9VW675ms5nbbruNxx9/nKNHj9K/f38GDRrErl276Nq1a6nk3harVq0C4IYbbiAgIKBa8QkhRF0jNdRCCFFHKYpCp06d+PHHH9m/f3+Zx7z77rusWrWKm2++ucbu+9Zbb7F69WoCAwPZtGkTLVq0AMBkMvHYY48xf/78Sl9zx44dQPHIsxBCNDQyQi2EEHWYj48PAMnJyWXuHz9+fI0m04A1YZ41a5Y1mQbQarW8/PLLBAcHV/qaiYmJAPj5+dVMkEIIUYdIQi2EEHWY2WwGyl+hNjY2tkbvd+7cOU6cOAHAmDFjSu3X6XQ1fk8hhKjvJKEWQog6LCkpCcBaS321iIiIGr3fuXPngOKRcVdX1xq7p6+vLwAXL16scmxCCFFXSUIthBB1lMViYffu3QBER0eXeUx1OmFcGv0uS3kj4tfaV57OnTsD8Oeff1b6XCGEqOskoRZCiDpqzZo11lUTr7/++kqff2np7szMzDL3X7n8+SWX6qMTExPJzs4u87xTp05VOpZbbrkFgPXr15OQkFDp84UQoi6ThFoIIeqg9PR0Hn30UQCuu+46OnToUOlrXEqODx48WGpfTk4OmzZtKrU9NDTUWtLx2WefldpfUFDA119/XelY7rrrLiIiIigoKODBBx+scHQcYOfOneTm5lb6PkIIoQZJqIUQog65tPR4t27dOHr0KIGBgSxatKhK1xoyZAgAb7/9NnFxcdbt2dnZTJw4kbNnz5Z53pQpUwB49tlnOXLkiHW72WzmqaeeKve8ijg6OvLll19iMBj45ptvuPXWWzl58mSp41JSUnjmmWfo3bs3+fn5lb6PEEKoQfpQCyGESv73v/9ZVzHMz88nKSmJXbt2kZKSAhSvdLh48WLCw8OrdP2RI0cyb948duzYQZs2bejTpw9ms5kdO3ag0+m49957Wbx4canzpkyZwoYNG1i7di3t2rVj4MCBeHh48Oeff3L+/Hkeeugh3nnnHWtJia26du3KL7/8wh133MF3333H6tWr6dixI02aNEGj0XD69Gl27NiByWSiSZMmsvS4EKLekIRaCCFU8ttvv/Hbb78B4OLigtFoJDo6mi5dujBq1KhqL4Li6OjIhg0beOaZZ1i5ciU//PADfn5+3HbbbTz//PO88847ZZ6n1WpZtWoV8+bNY+nSpWzatAk3Nzf69u3LypUr+eabb4DLPbIro2vXrhw5coSPP/6YVatWsWvXLvbv34+iKAQGBnLbbbdx++23c/vtt+Po6Fit1y+EEPaiWCwWi9pBCCGEqD8GDRrEpk2b+Prrr4mJiVE7HCGEUJ3UUAshhChlz549FBQUlNhWUFDArFmz2LRpE35+fgwfPlyl6IQQom6Rkg8hhBClTJs2jT179tC+fXsCAwNJTU1l3759xMfHYzAY+PDDDzEYDGqHKYQQdYKUfAghhCjl008/5dNPP2Xv3r0kJydjsVgICgpi4MCBPPbYY7Ru3VrtEIUQos6QhFoIIYQQQohqkBpqIYQQQgghqkESaiGEEEIIIapBEupKslgsZGRkIJUyQgghhBACJKGutMzMTIxGI5mZmWqHIoQQQggh6gBV2+bl5+eza9cuLl68SO/evau06pYQQgghhBBqUm2EesGCBQQGBtKnTx9iYmLYu3cvAElJSfj4+LB48WK1QhNCCCGEEMJmqiTUS5YsYdq0aQwdOpQPPvigRD2yj48PgwYN4vPPP1cjNCGEEEIIISpFlYT6tdde45ZbbmHZsmWMGDGi1P7OnTuzf/9+FSITQgghhBCiclRJqI8dO8awYcPK3e/l5UVycrIdIxJCCCGEEKJqVEmoPTw8SEpKKnf/gQMHCAgIsGNEdYvZLC35hBBCCCHqC1US6uHDh7Nw4ULS0tJK7du/fz+LFi3i5ptvtn9gdURiVr7aIQghhBBCCBspFhVWKDl//jzdu3fHYrEwYsQIFi5cyNixYzGZTHz99dcEBgayffv2OtlGLyMjA6PRSHp6Ou7u7rVyj7MpOYR6OdfKtYUQQgghRM1SZYQ6KCiInTt3MnToUL744gssFgsff/wx3333HaNHj+aPP/6ok8m0veQXmdUOQQghhBBC2EiVEeqrJSYmYjab8fX1RaOp24s32mOE+sD5DFoH1c61hRBCCHGZpciM4lC3cw9R96m6UuIlvr6+aodQpxSYZIRaCCGEsAdzThFad53aYYh6TpWPZE8//TQdOnQod3/Hjh2ZPXu2/QKqY4okoRZCCCHswlJoUjsE0QCoklAvX768wj7Uw4cP54svvrBjRHWLjFALIYQQ9mGReUuiBqiSUJ85c4aoqKhy90dGRnL69Gk7RlS3FJlUL2sXQgghGgVLgSTUovpUSahdXV0rTJhPnjyJwWCwY0R1S6GMUAshhBB2YSmUv7mi+lRJqAcMGMD7779PXFxcqX1nz55l4cKFDBw4UIXI6oZcqecSQggh7EJKPkRNUKXLx/PPP0+3bt1o06YNEyZMoE2bNgD8/fffLF68GIvFwvPPP69GaHVCZl6R2iEIIYQQjYKMUIuaoEpC3aJFC7Zs2cLkyZN54403Suzr168fCxYsoFWrVmqEViekZBeoHYIQQgjRKJhzZRBLVJ9qfajbtWvH5s2bSUpK4sSJEwA0adKkUa+QeElcWq7aIQghhBCNgik9X+0QRAOg+sIuPj4+kkRf5WJGHrkFJpx0WrVDEUIIIRq0wos5aocgGgDVEmqTycT69es5ceIEqampXL0CuqIoPPPMMypFpy6LBY4nZtE22Kh2KEIIIUSDZkrPx5SRj9Zdr3Yooh5TJaHesWMHt99+O+fOnSuVSF/SmBNqgCMJmZJQCyGEEHaQfyoD53a+aoch6jFV2uY99NBD5ObmsnLlSlJSUjCbzaUeJlPjbh13JCFL7RCEEEKIRqHgdIbaIYh6TpUR6r179/LCCy8wYsQINW5fLxxJyFQ7BCGEEKJRKDiXhaXQjOKoyjijaABUeeeEhISUW+pRHb/88gsjRowgKCgIRVFYuXJlif0Wi4X//Oc/BAYG4uTkxJAhQzh69GiNx1ETEjPzScqSmcdCCCFEbbMUmck/ma52GKIeUyWhnjFjBosWLSIjo2a/YsnOzqZ9+/a8/fbbZe5/+eWXWbBgAe+99x7btm3DxcWFG264gby8vBqNo6bsOyc/3EIIIURtSE9P59dff2Xbsd1sO76bC78fr5XBPtE4qFLykZmZiaurK02bNuXOO+8kNDQUrbZkizhFUXj00Ucrdd1hw4YxbNiwMvdZLBbmzZvH008/zS233ALARx99hL+/PytXruTOO++s2oupRdtOpjCwpZ/aYQghhBANzr59++jbt6/1+aqpH+DfswlOLbxUjErUV6ok1I8//rj1/7/11ltlHlOVhLoiJ0+e5MKFCwwZMsS6zWg00r17d7Zu3VpuQp2fn09+/uXSi5oeVb9Seno6+/bt48KRg6TlFPJbQRQZA6NwNzjW2j2FEEIIUSxr8zl0wa5oXXVqhyLqGVUS6pMnT9r9nhcuXADA39+/xHZ/f3/rvrLMnTuX2bNn12psl1z9abnX1LdZt681I7uG2uX+QgghRGNmzjeRvu4Unrc1Q9Eqaocj6hFVEurw8HA1blslTz31FNOnT7c+z8jIIDTUfgnuN7vjuLFdIC561Re1FEIIIRq8wvhssn6Lw61fiNqhiHpE1f4wcXFxfPbZZ8yfP59z584BxSsopqSk1Hgf6oCAAAASEhJKbE9ISLDuK4ter8fd3b3Ew56y8ov4asdZu95TCCGEaMxy/kok70iq2mGIekSVhNpisTB9+nQiIyO56667mD59OkeOHAEgKyuLiIgI3nzzzRq9Z2RkJAEBAfz000/WbRkZGWzbto2ePXvW6L1q2qq/znMhvW52IhFCCCEaooyfzlCUlKt2GKKeUCWhfuWVV5g/fz6PP/44GzZsKNGmxmg0EhMTw9dff13p62ZlZbFnzx727NkDFNdq79mzhzNnzqAoCtOmTeO///0v3377Lfv27ePuu+8mKCiIW2+9tYZeWe0oMln435YTaochhBBCNBqWIjNp35/AnFOodiiiHlClMHfRokXcfffdzJkzh+Tk5FL727Vrx9q1ayt93R07djBw4EDr80u1z+PHj2fp0qU8+eSTZGdnM3HiRNLS0ujTpw/r1q3DYDBU/cXYybaTKew5m0aHUA+1QxFCCCEaBVNGAWmrT+Bxa1M0Ou21TxCNlioj1GfPnqVXr17l7ndxcalSe7oBAwZgsVhKPZYuXQoUt+J77rnnuHDhAnl5efz44480b968qi/D7j749SRmszSdF0IIIeylMCGHtG+PY86v2bldomFRJaH28/Pj7NnyJ9rt3LmTsLAwO0ZUP5xKyuaHAwnXPlAIIYQQNaYwPpvUFUcxZUv5hyibKgl1TEwM7733HidOXK4LVpTifo8//PADS5cu5Y477lAjtDrv4z9OkZknP9BCCCGEPRUl5ZL61RGZqCjKpEpCPXv2bAIDA+nQoQN33303iqLw0ksv0adPH4YNG0a7du3497//rUZodV5GbhGLfz2ldhhCCCFEo2PKLCBl+RHyjqepHYqoY1RJqI1GI3/88QdPPvkkcXFxGAwGNm/eTFpaGs8++yxbtmzB2dlZjdDqhR8PJvDHidKTOYUQQghRuyyFZtLXnCTrj/NYZF6T+Ifdu3zk5eWxcOFCOnTowNNPP83TTz9t7xAahPk/HiXyThf83et+hxIhhBCiocn+M4GipDzcrw+XDiDC/iPUBoOBGTNmcPjwYXvfukHJyi/ihe8Pklsgs46FEEIINeSfTCdt5THM+UVqhyJUpkrJR9u2bTl16pQat25QTiZl89oPh6WVnhBCCKGSwoQc0teclPKPRk6VhPqFF17g/fff58cff1Tj9g3KtpMpLNxyosRqk0IIIYSwn4JzWeTuTVQ7DKEiVVZKfOutt/Dy8uKGG24gMjKSyMhInJycShyjKAqrVq1SI7x65/u98bgbHBnTXXp3CyGEELYwmUqWTJrM5mpdL3tnAk7RPiha28Yq846nkbRoX5n7/B7ugC7UrcLzLRYLSUv2k38kFe9xrXBq4wOAKbuQlC8OUxifjTmnEK2rI4bW3hhviEBjUCXtaxRU+Zfdu3cviqIQFhaGyWTi2LFjpY651Jda2Oaz7Wdw0Wu5pUOw2qEIIYQQddqKFSuYNGlSiW33L3mSl0f9mxvbD67SNc05RRScyUQfaSyx/eL7e3Hp7I9LF/8S2/Xh7gT+X/cS2zJ+OE3e8TQcQ1yveb+sX89TVqqkKODU2gvj9eFoXBwpSs4lbdVxUnOO4T26ZeVfmLCJKgm11E/Xjv9tOYmzzoHrWvtf+2AhhBCiEVqxYgWxsbGlSiWTs1K574Mn+d+El6ucVOefziiVUJdHcdCgddNZn1tMZnIPJOPaK+iag4oF57PI2nIOv8kdiX9hW4l9GmdHXHsEWZ87eBpw6RFI1i/nKvFKRGWpUkMtas9bG4+y60yq2mEIIYQQdY7JZGLq1KkVzjt65utXMZmr1kGrKCGnqqGReyAFc04hzl0qHhQzF5hI+fwQHrc0LZGQl8eUkU/u38k2J/qialRLqE0mE59//jkPPPAAt912G/v2FdcRpaens2LFChISEtQKrV4zW+CVdYdJzMxXOxQhhBCiTtmyZQvnzpU/UmvBwvm0BP44tgsslko/TFkFVY4tZ8cFDM09cTDqKzwuffUJdGHuOLXxrvC45M8OEffMb8TP2Y7GoMXz9uZVjk1cmyolH2lpaQwdOpTt27fj6upKdnY2kydPBsDV1ZUpU6Zw9913M2fOHDXCq/ey8ot45+djPDuijdqhCCGEEHVGfHy8TcclpCZiKaz8JEVLgYmMn86Qufns5W2FZgrOZpD27eX5Yv7TO+PgcXlhtqL0fPKOpOI1plWF1889kEz+8TT8pnS6ZiweNzXBPDiMoqRc0tedIu37E3je2rTSr0nYRpWEeubMmezfv5/169fTsWNH/Pz8rPu0Wi2xsbGsWbNGEupq2HEqlYPxGbQKdFc7FCGEEKJOCAwMtOk4f3cfqGxvBAsoTg64dPXHub2vdXPyF4dxbutt7cIBoHUrOQqdsyMBjbMjTq29KrxF/vE0ilLyOD/79xLbkz85iC7CiN8D7a64hw6tmw5HP2c0zg4kvrcX90FhaN2vXSYiKk+VhHrlypVMnjyZ6667juTk5FL7mzdvztKlS+0fWAOzdl+8JNRCCCHEP/r27UtISAhxcXFl1lErQKCHP92jOlJmC40KWXDw0KNxdkRxv1xRqzho0LjocPBxKvssi4XsnQk4d/K7Zss9twGhuHQNKLEtYd4ujDc1walVBSUg/wy2W4qq1xpQlE+VGur09HQiIyPL3V9YWEhRkSzjWV1/nEzBJCs3CSGEEEDxt+Dz58+v8Jjnbn0MrUZbpeuXlzRXJP94GqaUvFKJMoApPZ8Lr+2g4GwmUDzq7BjgUuIB4OChx8GruIQk91AK2TsuUHghm6KUPHIPpZC68ii6cHfrMaLmqZJQR0VFsWvXrnL3//DDD7Ru3dqOETVMuQUmjiRkqh2GEEIIUWfExMSwfPlyfH19S2z3cfVk0T0vc2O7QVW+toOvc6XPyf4zAV24O45+pc+1mCwUJeZiLrC964jiqCF7+wUuvvcXF17fSfrqEzi18sbnHplXVZtUKfm47777mDFjBgMGDGDw4OJej4qikJ+fz3PPPce6detYuHChGqGp6upVmyzVXLUJ4MD5ytdRbzyUwPyfjnEoPgO9g4buTbxZdHeXco9f93c8n247w764dNJyCvl+Sh/aBJVuz7PzdCqvrj/MnrNpaDUKrQPd+WhCNwyOVRsJEEIIIaoiJiYGb29vBgwYYN32/t0v0avptSf7lUfrYUDjVDqturKuuSwVLbbi4GUg5MW+FZ5/9X5DlAeGhzpUeI6oeaok1FOnTmX//v2MHj0aDw8PAMaMGUNycjJFRUU88MADTJgwQY3QVFPWqk07lzxD9KjHCWzfv8rXLWuEetT7W4ntHMIdXUJL7Vu7L56ZK/bxxA0t6BXljcls4fA1RrlzCkx0CffixuhAZq4oexnVnadTuWfxdh4cGMXsW9qg1SgcjM+ofImaEEIIUQO02pKDOVpN9b601zeVPs+NmSoJtaIoLFq0iPHjx7N8+XKOHj2K2WwmKiqKkSNH0q9fPzXCUk15qzYVZKWx84On6Tzhv1VOqk8lZ9t8bJHJzOzvDvDv4S0Z1TXMur2Zv1uF58V0CgHgbEr5De2fX32Ae3pH8NCAyy17onyvvbSqEEIIUdc5eBvQR3mASeYtNVZ2SahjYmJ49NFH6du3+GuJX375hVatWtGnTx/69OljjxDqLFtWbdr/9QICovugVGGSRFIlmsz/fT6DCxl5KIrC8PlbSMzKp3WgO/8e3ooWARUn1RXHkM+es2nc2iGImHd+40xKDk18XXnihhZ0jai4RZAQQghRl2nddLj1D0HRKFgkoW607DIpcdWqVZw5c8b6fODAgWzYsMEet67zrrVqE0Be2kWSjv2F2WKp9KOgEi1yzvwzwjz/x6NMHtSUxeO7YnRy5M6FW0nLqfrqT5euO++no9zZLYyl/+pG2yAjdy3axskk20fQhRBCiLrEwccJ9+vD0Tg7qh2KUJldRqiDg4PZvXs3d911F1Dcc1GR4lnA9lWbMlMTcSm0fZbvJXoHLQt+Osp7m49bt+UVmth9No1nv91v3bZhen/rKPnDA5syLLq4+f0rd7Sj59yNfL8vnru6h1f6/oD1umO6hTHyn7rttsFGfj+exJc7zjJjaPkTMoQQQoi6SB/lgUs3/2v2jhaNg10S6jvvvJNXX32VL7/80joJcebMmcydO7fccxRF4a+//rJHeKqyddUmg7s3mkp+CLFYLLjqtYzqGsLN7YOs26d+sYdhbQMY2uZyz0t/Nz2+/6zc1Mz/cm2z3kFLqJcz59NyK3XvK/m5GUpdFyDKz7Va1xVCCCHsTdEoOHcLwNDUQ+1QRB1il4R67ty5NG3alE2bNnHx4kUURcHFxQVv7wpW9WkkrrVqE4Deww+vqHZV6Iih4O2qx8NZh7/75fprg4MGbxcdET4uJY6ODjaic9BwIjHLWttcaDITl5pDsEfpjiC2CvF0wt9dz4nEkuUdJxOzGdDCt5yzhBBCiLpF4+yAW/8QHLwrv4CLaNjsklBrtVomTpzIxIkTAdBoNDz99NOMGTPGHrev0y6t2hQbG1vuMS1ufaRKExIB66izLdwMjtzVPYw3Nhwl0OhEsKcTCzefAODG6Msj6YNe+5knb2jJ0LbFI9xpOQXEpeVyMSMfwJo4+7rp8XMzoCgKE/tFMW/DEVoFutM60J2vd53jeGIW746tes9PIYQQwl60Rj3ug0OlXlqUyS4JdadOnZgzZw5Dhw4FYMmSJXTs2NEet64XLq3aNGnSJBITE63bHV09aBU7Hf92VW8j6FeJhBrg38Nb4aBRmP7lHvIKzXQI9WDZ/T0wXvEL5ERiNpl5hdbnGw4k8MTyvdbnkz/bDcDUwc149LrmAEzoE0l+kYnnVx8gLaeQVoFufHJfd8K9S46SCyGEEHWN1qjH/bowNAZVug2LekCxVNSvrYY4ODiwdOlSxo4dCxSPyn788cd2H6GeNWsWs2fPLrGtRYsWHDp0yOZrZGRkYDQaSU9Px929cisQXsvmzZtLrNrU+aF5eDXtUOXrWSxwf99IbukYjN5BViMUQgghLvn111+t7XwBVj7yP7o36VDqOI2zA8ahEdccmbYUmbEUmtEFuaI4yETFxsYu/8XDw8P58ccfrUtrq9nlo02bNsTHx1sfv/76qypxlOXqVZuUaq7aBBBklDovIYQQoioUrYJbfynzENdml4R60qRJfPTRRxgMBtzd3VEUhQkTJuDu7l7uw2isnSU8HRwcCAgIsD58fHxq5T51ga+bHlf5ekoIIYSoEpeeQTh4G9QOQ9QDdsm2nnjiCdq3b8+mTZtISEjgww8/pGvXrjRp0sQety/h6NGjBAUFYTAY6NmzJ3PnziUsLKzc4/Pz88nPz7c+z8jIsEeYNaJdSO18KBFCCCEaOkNLL/QRNVvaKRouuw1fXn/99Vx//fUALF26lAceeMDuNdTdu3dn6dKltGjRgvj4eGbPnk3fvn35+++/cXMre2ntuXPnlqq7rg80ikLfZtKSTgghhKgsB28Dzh391A5D1COq1AOYzbYvh12Thg0bZv3/7dq1o3v37oSHh/Pll18yYcKEMs956qmnmD59uvV5RkYGoaFV78lsL9e19sfbRUdeFVZXFEIIIRorRavg2jsYRSsrOgvb2SWhPnPmDIC1tOLS82upqBSjJnh4eNC8eXOOHTtW7jF6vR69vnKt59QW4e3CTe0Cqf3+LUIIIUTD4tTBF627Tu0wRD1jl4Q6IiICRVHIzc1Fp9NZn1/Lpa4gtSUrK4vjx48zbty4Wr2PPXm56JjUvwmOWg0FRep8EyCEEELUR1qjHkMLL7XDEPWQXRLqxYsXoygKjo6OJZ7b2+OPP86IESMIDw/n/PnzPPvss2i1WkaPHm33WGqDm8GRKYOb4eEsn6yFEEKIynJu74OikVIPUXl2SajvueeeCp/by7lz5xg9ejTJycn4+vrSp08f/vjjD3x96//kPRe9A9OGNCPAXdr7CCGEEJWlcXbAMbTsBgVCXEujalL8+eefqx1CrXBy1DJ1cDOCPWQRFyHUYjGba2QxJiGEOnShbqotOifqP7sk1M8991ylz1EUhWeeeaYWomlYHDQaHhnYlDAvZ7VDEaJRM6Wl4eAltZdC1BfR0dH8smkzKd8cBTN0HNhV7ZBEPWaXhHrWrFmltl36FGi5qhWFoijWpcklob62e3tHEOXnqnYYdYfZDDJKKOwoPT2dffv2kXvgAPqoKNp36VJrK70KIWqO0WikT58+JPzliqOfM+5+3mqHJOoxuyTUV/edjouL48Ybb6Rt27ZMmzaNFi1aAHDo0CHmzZvHgQMH+P777+0RWr12Q5sAOoV7qh1G3ZKXBs4ySijsZ9++ffTt29f6fPNPP9Fv0CC7xnD2wYfIO3QIU3IyGqM7Lj174vfY4zj6l78wReoXX5KxejV5Bw5gzs6m+fZtaN1LrwqX+fPPJL3zLvmHD6Po9Th37Uro22/V5ssRwu4cg1zUDkHUc6oM5T388MM0a9aMTz75hC5duuDm5oabmxtdu3bl008/JSoqiocffliN0OqNMC9nbm4fpHYYdU9hrtoRiEbm6vaeF159lZRlyyg8f75G73N63N2krfimzH3O3bsR8sbrRK1dQ8j8BRSeOUvc1KkVXs+Sl4tL3754P/BAucdkrP+B8zNm4hFzG5ErvyFi2acYb7qxWq9DiLrIwVfmIInqUWVS4saNG3nppZfK3T948GBmzJhhx4jqF0VRGNcjHK209iktYT84GMBFvroTtW/FihVMmjSpxLYHf/qJ/6akMvTrFeibNcO5axec2rVD16QJilZbK3F4X9E5yTE4GO+J93Pu4UewFBai/NOu9Gpe48cDkL1te5n7LUVFJMyZg/8Tj+MRG2vdrm/atOYCF6IOULQKWmP9WsBN1D2qJNQGg4GtW7fy4IMPlrn/999/x2CQ9m/lGdTCl1CZhFjCpTpWTu+BNV8QPf5VjJ5SDiNqz4oVK4iNjS01DySloICHt/3B2917MBTIP3qU1GWfoRgM6Js3Q9+0KfqoKHSRTXDw863xrgKmtDTSv/sOp44dy02mbZF34ABFCQmgaDhxWwxFSYkYWrbC74nHMTRvXoMRC6EurZtOek+LalMlob7rrrtYsGABHh4eTJ48maioKACOHz/OggULWLZsGVOmTFEjtDrPzeDITe2k1ONqV9exbinKp8+D82WkWtQKk8nE1KlTSyXTV3p+719cFxSE9tIE7Lw88vbuI2/vPusxWqM7+hYtcYpuiyE6GseQkCon2BdffZWUT5dhyc3FqX17Qt57t0rXuaTg7FkAEt9+C/8ZM3EMDiZlyRLO3D2eqHVr0Xp4VOv6QtQVGpeqf/AU4hJVEuqXXnqJpKQk3nrrLd5++200/3RlMJvNWCwWRo8eXWFJSGN2a4cgnHS187Vxg5LwN3w5DjqOhda3gk5G9EXN2bJlC+fOnSt3vwWIz81le2IiPSpYOKooLZ2ibdvI3rYNAK27O/qWLXHq0J78I0dIWfrh5Wvm5ZH7119c+O9/rduiVn+HY1DxB2yvCRMw3n47hefPk/T2O5yfOZPQ996r+gi4ufjDgs8Dk3C/4XoAAufO4Vj/AWSsW4/nnaOqdl0h6hiNc6NakkPUElXeRTqdjo8//pgnnniCNWvWcPr0aQDCw8MZNmwY7du3VyOsOi/E05meUTLiWparJ4aZzBYoyIZt78Ouj6HJAGh2HQR2qJ22eqmn4ZeX4eQvkHUR3AKg3Sjo+zg42LAUvMUCn8bCsR9h1KfQ6qbi7bs/hVUPlX3O48fAtf6v8lkfxcfH23TcxcxMLG62r7xWlJdH0cWL5B85gvHWW4lc/pW17jruiSdxv/463K67znq8g9/lLh4Onp44eHqij4xEHxXFsQEDyd2zB+eOHW2+/5Uc/vkgoG8aZd2m0elwDA2l0MbXL0R9oBgkoRbVp+q7qF27drRr107NEOqV2zsFo5FVnEopa2LYHQsP8t5dzYjp6AOFOXB4TfHDxac4uQ7vDf5tbUt2r7TkRugwBjreVXJ70tHipPimeeDVBC4ehO+mFCf1N7xw7ev+8Q5Qxn/btjHQdEjJbSsfhKJ8SaZVFBgYaNNxvgYD2Pgz6+Djg6FNG5zat8chKAjMJhxDQtDoit+jGr0erZc3uvDwa1/sn1alloJCm+5dFkPbNig6HQUnT+LcuXPx9QoLKYyLs46KC9EQaHSydoGoPvlYVk+0DHCnVWDpHrGNXXkTwxKzCol9/wDLH2hdnFRfkp0E+5YXPxz0xUl1YHsI7gx+ras+et1sSPHjEq9ISD4Kf35w7YQ6fi/8/hZM/Bleu2qyl6NT8ePK+E/+ArdIH2A19e3bl5CQEOLi4sqso1aAAIOBrj7eZSbUGidnHIOCcAwOwjE0FH1UFA4+l9+nloJCzHm2tYDM/esvcvf9jXPnTmjd3Sk4e5bE+QtwDAvDqWMHAAoTEjhzz78IeulFnP4ZxChKTKQoKYmCM8XfEOYfOYLGxQXHwEC0Hh5oXV3xuHMUiW++hUNAII5BQSQv/gAA96E3VOafS4g6TXGUMkpRfZJQ1xM3t7dtRKwxsWVi2LQvj3NLe++yWwwW5UPczuLHjsXg5PlPacj14NfK5pHFcuVlFF+zIgU58PV9cOOr4OZ/7Wv+9Rk4OkPrW6oXm6gWrVbL/Pnzib2indzVnm7Txjoh0cHXD0OLFuiiotBFhKP18qqx7h6KwYnMDRtIevNNzLm5OPj64tK3D8EPPmgd3bYUFlFw8iTm3Dzreamff0HS229bn58eOw6AwDlz8Ii5DQD/J55A0TpwfsYMLHl5OLVvR/jSJWhlJUjRgCgyQi1qgCTU9UDLAHea+Mry4lezZWLY2dR8thxNY0ALj2tfMDcF9q8ofhhDoeVNxYlrVSY0Jh+H7Qvh+ucrPm79UxDaDVrauFjGro8hOrbkqLVQRUxMDMuXL2fSpEkkJiZat3vrdDzfrj03DxqEU3Rb9C1b4uBd/bkP4R9/VOZ2Q4vmhH+4tMJzdSHBtDp0sMQ238mP4Dv5kQrPUxwd8Z/xJP4znqxUrELUJ4qDJNSi+iShrgeub23DyGUjZOvEsPiULCisZNP+pCPw6+tw8Ds48fPl7UW5cO5PWPPE5W0PbwOP0MvPM87DJ7cXJ+Od7yn/HofWFJdvPLDFtpjOboekwxDzfmVeiahFMTExeHt7M2DAAOu2Rffcww0PPVTmMt5CiLpHcZSEWlSfJNR1nL+7gVaBtncJaExsnRgWaNSDUslfmBoHiL4DAtrB4GdA+8/kxRX3Q6ubodWIy8e6XRFHRjwsvQlCu8OIBRXf4+QvkHISXgwruf3LcRDWC/71fcntuz6EgGgIqlrXBlE7tFetfmgcPFiSaSHqE1nURdQASajruN5R3jW+klpDYcvEsBAPHX2bulNmB42yaHUQ3rM4aXbyLO4Q4tWkeAIjgIMTuPiCd1TpczPOFyfTQR3g1neuPcGxz6PQ6e6S297tCTfMhRZDS27Pz4L9K2Hws7a9DmE30dHRbN64kcwff8TQoiVtW7RQOyQhRGVIQi1qgGoJ9fr16/nggw84ceIEqamppRIiRVE4fvy4StHVDQoKXSO91A6jzrJlYti82PCyJyReydG5eOQ3uHPx6O+l+mRTge3BZJyHpTcW115f/9/ibhyXXJpsmHEePrwZbnsfQjoXby9rIqIxBDwjSm7bvwLMRdBupO0xCbswGo306d2bbI0Gx4BAFJ2suiZEfSLLjouaoEpC/corrzBz5kz8/f3p1q0b0dHRaoRR5wV5OOHpXMk+yY1MeRPD/FwdePfOSGI6lPGBRNEUjzoHtC0u6fCKAk012yYd3wQpJ4ofr7cquW9WevH/mgqLW+kV5lT++rs+Li4zcfKoXpyi1mik84UQ9ZJMShQ1QZWEev78+QwaNIg1a9bg6CijOeVp6ueidgj1QlkTw76c0Iz+za6oY3U0/DMC3Qn824Cuil1Trq5rvqTjXaUXe7maZ/jl5Lo85e2/b8O1YxOqktIsIYRovFRJqFNTU4mNjZVk+irR0dH8tOlnnvvuANn5RQzu1VXtkOqNqyeGWcs8XP2K29+F975cBy2EEEIIUYNUSai7devG4cOH1bh1nWY0Gundpw8+Bx3xMFtoHuqndkj1l0ZbvER40+tAKx/chBCiNuRmZbJx8Xuc2LUdRdHQrHsvBt4zEZ2h/F752WmpbP5kMaf37qYgLxevwBC6x4ykeffeJY47setPtn79GUmnT6HVORLSKppbn3i6tl+SEFWiSkL9zjvvMGzYMLp06cKYMWPUCKHOi/Rxka+Qq6PTeGgxXO0ohBCi1qSkpPDpp59y9sQJQiIjGTt2LF5eNT+R/YvZM2nTfwhtBwwptW/Nm6+SnZpC7P/9F5OpiPXvzmPDwre4ccoTZVyp2Nq3Xyc/O4tbn3wGJzcjh379mdVvvMRdc9/AP7K4g9KRbb+x4f036TP6bkLbtMdiNpF09nSNvzYhaooqCfWoUaMoKipi3LhxPPjgg4SEhJT6yl5RFP766y81wqsTwryqsDpfIxYdHc2WTT/BgVXFz3sMUjkiIYSoPStWrCg1Gfu//32e9957n5iYGLvEkHzuLKf27OSuOW8QENUMgEH/msSKF2fRf+y9uHqVvULo+cMHGXLfQwQ2LW4x2eP2O9m5ZhUJJ47hHxmF2WRi09KF9Bt7L9GDrree5x0SVub1hKgLVEmovby88Pb2plmzZmrcvl4I8jCoHUK9YjQa6dOnNzj+XbyUt7MshiOEaJhWrFhBbGxsqXaziYlJ3H777cx/fhZ3jLoTo38ATm7utfZt5/mjB9G7uFiTaYDw6A4oikL8scM069arzPOCWrTi8NYtRHbqisHZhcNbt1BUWEBom+KOXwknj5GVkoyiKHw0Ywo5aan4RjSh/13/wicsolZeixDVpUpC/fPPP6tx23olwF0S6ipxDwK9rFInhGiYTCYTU6dOLXMxq0tmv/wqHpnJaDQadAYn3P38cffxw+jnj9HXH3c/f1y9vNBUs11oTloazu4eJbZptFoMrm5kp6WVe95N02awet5LvDNhNBqtFgednlse+z88A4IASE+4AMDvy5cx4O77MPr6s2P1N3zx3L+5d977OLnKgImoe2SlxDpIo4C3i/SfrhIXmcgphGi4tmzZwrlz5yo8JiUzi0Nn42gVFkJ+bg6Jp0+SePpkiWM0Gi2uXt64efvg5uOLm7cvnkHBeAYGsWv1SravWm49tqiggPijh9m4+D3rtntef6fKr+G3Lz4hPyeb2Kf/i5ObO8f+/IPV815i1OyX8A2LsH5Y6HHbKOtExRsenMbCB8dzZOuvtL9uWJXvLURtUTWhLiws5NChQ6Snp2M2m0vt79evnwpRqc/o5IiDVhrNV4lOas+FEA1XfHy8Tcclp6ZT5O9b4TEpcTmkxJ0tsc0zMIjW/QYxdu48lH/+Dq1581WadetFs+6XSzhcPb1x9vAgJyOtxPlmk4m8rExcPDzKvGfahXj2rF/N+Fffxic0HAC/iCbEHdrPnvWrue7+R3DxKJ5Y6RUSaj3PwdERo38AmcmJZV5XCLWpklCbzWaeeuop3nnnHXJyyl81zmQy2TGqusNDVkcUQghRhsDAQJuO83RzLndJbUXRYHBzw8nNHSd3Iy4eXrj7+OIZGITWUYfFZMLdzx+Hf9aKcNDpcDZ6WEsyLglq1or87OziyYRNmgJw5u+/sFgs1gmHVyssyLfGUCImjcY6Mu3fpClaR0dSz8cR0rINAKaiIjISL+LuI99CirpJlYR6zpw5vPLKKzzwwAP06dOHcePG8dJLL+Hh4cE777yDoii8/PLLtXb/t99+m1deeYULFy7Qvn173nzzTbp161Zr96ssV71U4gghhCitb9++hISEEBcXV24dtZebK82Dg9A7u+Lu61dc0uHlg4unNy4eHhhcXFE0ZX8LaioqpMjGwSzvkFAiOnTmh/ffZMj9D2EuMrFxyXu07NXP2uEjMyWJr55/mmEPP0pg0xZ4BYXgERDIhkVv0X/cvTi5unPsz62c3reH22b8BwC9szPthwzj968+xc3bB3dfP/78dgUAzXv0qew/mRB2oUrmtnTpUkaOHMm7775LcnIyAJ07d2bQoEGMHz+enj17snHjRoYMKd3zsrq++OILpk+fznvvvUf37t2ZN28eN9xwA4cPH8bPr2588nXRV2+iiBBCiIZJq9Uyf/58YmNjyz3m2ZkzGDZyJHpnl1qPZ/jkx9m4+D2+ev5pFEWhWfdeDPrXA9b95iITqefPUZRfPDKtdXAgZuYstiz7kJUvP09BXi6e/oEMe+hRmnS8vDpwv7H3omi1rH37dYoK8glo2oI7nnkBg6trrb8mIapCsVQ0VbiWGAwGFixYwMSJE8nKysLd3Z3Vq1czfHjxQhxvvPEGr7/+OmfPnr3GlSqve/fudO3albfeegsoLj8JDQ1l8uTJzJw585rnZ2RkYDQaSU9Px9295rtJ5BeZWPTLCYa08kfnUL066oIiM3mFJloFuaN3aARJelE+XNgHjs6grYGyGVMBFOZAQLQsWy4qZC4oIO/gQTQGJxRd9VfmtBQUYs7LxdCqFRqdlICJ0srqQ+3j7c2rL85lxPCqL2plKiqkKD8f34gm1pKPhsxSZKbgfBaKowalmn9zLUVmLIVmdEGu1b6WqH9UGaH29vYmKysLAFdXV9zd3Tlx4kSJY1JTU2v8vgUFBezcuZOnnnrKuk2j0TBkyBC2bt1a4/erKoNjI0h+hRBCVFlMTAwDBgzgo48+4ti+vQSFhTEqNhZPT0+1QxOiUVIloe7YsSN//vmn9fnAgQOZN28eHTt2xGw2s2DBAtq3b1/j901KSsJkMuHv719iu7+/P4cOHSrznPz8fPL/+aoKikeoAfbs2YNrLXz1VFBk4sLxBP7KcsOhnAkltioyWzCbLeRdcEHXKEaoCyD5GGi0oKmBkRVzIZhNcL4IHBrHKGGRuajC/raVoSgKDprGMR/AXFhI/smTKBotikP1X7OlqAiL2YQ+Lw9NIxglBDCbzNTU96WKAppG0impV69etAoLRXFw4NSpU5w6dapa1zOZTFjMJjxT03GogfdyXWcpMlOYmAMapdxJnDZfy2wBswXHC86NZoTaZDLV2N8MKP67cfXK2dXRqVOnGrvWtajy0zJx4kSWLl1Kfn4+er2eF154gX79+tGvXz8sFguenp589tlnaoRWyty5c5k9e3ap7f3791chGiGEEEIIYQt7VjWrUkNdlvT0dH7++We0Wi29evXCy8urxu9RUFCAs7Mzy5cv59Zbb7VuHz9+PGlpaaxatarUOWWNUIeGhrJ58+ZaGaEGOJGYSYhnzfRT1ihK4+ppbSoCS+me5lWmaEDb8EdpAApNhZzKOIUWbbVHlovMRZgwEeEegaO2cYywWopqbnQfikdqamK0uz4wFZlJS8hGo1GsvY+rymIyYzZb8PB3QdtIRgnNJhPmGnzvaRQFTQ2OEtZ1FpOFmvx6RNHWzlLvdU1RURHJycUrcmrK6RpTGWazGbPZjLe3d419O9LgR6jLYjQaueWWW2r1Hjqdjs6dO/PTTz9ZE2qz2cxPP/3EI488UuY5er0evb70hLQOHTrUyqREAK+kbCJ8an92thBXKjAV4JTihJPWqdpJcKGpkFxTLi29WqKriQmiokEzFZpJOpeJg05b7STYVGSmqMCET4gbWsfGkVALoYaioiIuXLiAo6NjjZRpmEwmCgsLCQgIqJflRqpFbDKZ+Oqrr9i0aRMXL17kueeeIzo6mvT0dH766Sd69+5dqta5JkyfPp3x48fTpUsXunXrxrx588jOzuZf//pXjd+rqqrb3UOIqjLX5Oj+Nfx4+ke+PPwlB1IOkJ6fzlcjvqKlV8sKzzmWeoy397zNgeQDnM8+z5Ndn2Rc63HlHv+/ff9j/q75jG01lhndZtT0SxA1yJ5flh7ffZH9v8Rx8Uwm+dlFjPy/rviGulV4TvL5LLZ/d5LE05lkpuTR545mtB8cWu7xO9ed4o+VJ2g3KIS+I5vX9EsQQtQxqiTUaWlpDB06lO3bt+Pq6kp2djaTJ08Girt+TJkyhbvvvps5c+bU+L1HjRpFYmIi//nPf7hw4QIdOnRg3bp1tZK8V5Uk1MLeUlJS+PCjD9l0eBPtw9sTMyoGT6/a7RaQW5RLR/+O3BBxA7O2zrLpnDxTHiFuIVwfcT0v/1nx4k9/J/3N8iPLae4pyUxdlpKSwkcffcyBHSeIbBHCqDtG4+VZ8yV/VyrKNxHY1IOmnf3Z9EnZE9JLnVNgxt3Hiaad/Pj1q6MVHptwKoP9W87jHSw9k4VoLFRJqGfOnMn+/ftZv349HTt2LLGgilarJTY2ljVr1tRKQg3wyCOPlFviURdUt7uHEJVxdT/b7/iOt199mzlvzGHozUNr7b4jokYAEJcVZ/M5bX3a0tanLQDzds4r97icwhxmbpnJsz2fZeHehdWKU9SesnopvzbvZd54ZQEjbqy9EsAWPYqX785IyrX5HP8Id/wjisv8tn5zvNzjCvKK2LB4PwPHtmTHmlPVilMIUX+oMhS6cuVKJk+ezHXXXYeilE4emzdvXu3WP/WZVhJqYScrVqwgNja2REIDkJqcyoN3P8gTbz3B9ye+Z9fFXcRlxVFoKlQp0sp5YdsL9A3uS8+gnmqHIspR3nsvOSWJuyeMYf4L/+PI9gvEH0sjIzkXU5H9ypGq45fPjxDR1ofQVrU7yi6EqFtUGaFOT08nMjKy3P2FhYUUFRXZMaK6pawPGULUNJPJxNSpUyusXf1+3vcUtiosMWvdy+CFn7Mf/s7++Dv74+vsi5+zH26ObnXivbv25FoOJB/g85s+VzsUUQ5b3ntvfjiHpl5d0GguT3ZyctPh6qHH1VOPi8c/D6MenZO2Trz3jv6ZQOKZTO54qovaoQhRq9LT09m9ezfJyck4OjrStm3bWmvUcKUDBw6wY8cO4uPjyc3N5YEHHiAwMPCa5+Xm5rJx40YOHjxIbm4uRqORoUOH0rx56ZLALVu28NNPP9G9e3eGDRtmc2yqJNRRUVHs2rWr3P0//PADrVu3tmNEdYuUfAh72LJlC+fOnavwmNykXJIOJOHT1se6LTk3meTcZA4mHyxxrF6rx9vJmwDnAIJdg4n0uPyhefWJ1Ty39Tnr83eHvEtn/8419Eouu5B9gRe3v8jC6xai18py8XWVLe+95IwEDp7aQ+vIy22vcjLyycnI5+KZksc6OGpxdtfh6qnH3duAZ+DlLkmHt13g52WHrc9HPNKeoGYeNfI6rpSZkseWL49w89SOOMhqt6KB27NnDwMHDrQ+X7lyJb179671+xYWFhIWFkabNm347rvvbDqnqKiIjz/+GBcXF0aOHImbmxvp6ekYDIZSx8bFxbFz584qzatTJaG+7777mDFjBgMGDGDw4MFA8ahsfn4+zz33HOvWrWPhwsZb9yhLjwt7iI+Pt+m43JRcm7p/5Bblci7zHNmF2RRZinDRuaBRiqvKBoYOpJ1PO+uxfs5+5V2mWvYn7yclL4VRq0dZt5ksJnYm7OSzQ5+xc+xOtBr5+VKbre+9tMwksKH5R1GBiYykXAryijCbLOicHfjnrUdkex/8Iy+PnLl61M4HrcQzmeRmFvLlnMurAFvMFs4fS2Pfz3FMemsAGhksEQ3ApbkPV/rXv/7Fyy+/XGKNj9pwaRXt1NRUm8/ZvXs3ubm5TJgwwdrez9Oz9KT7/Px8vv76a0aMGMEvv/xS6dhUSainTp3K/v37GT16NB4eHgCMGTOG5ORkioqKeOCBB5gwYYIaoQnRaNjyNRmAm48bBofLn+Q1igZfZ198nXzxNnjj7eSNj5MP/s7+BLkG4aZzsx53aYEYF0cXXBxrv7d6j8AerLh5RYltz/z2DJHGSO5te68k03WEre89bw9fHK7oJa1oFVzc9TgbdTi7X3oUl364exvQORW/3xTN5aXHdQYHdIba/1MX0tKTO5/pVmLbxo8O4hHgTKfrwyWZFg3CpbkPV5drpaamcv/997N3714GDx6M0WjE3d0dd3d33NzcanQ58co6fPgwISEhfP/99xw+fBhnZ2eio6Pp06dPiQVp1qxZQ/PmzYmKiqo/CbWiKCxatIjx48ezfPlyjh49itlsJioqipEjR9KvXz81whKiUenbty8hISHExcWVW8vq6uvKTYNvIswjjDD3MMLcwvB39q+RxDQ9P5347Hgu5lwE4FT6KQB8nHzwcSouMfn3ln/j5+zHtM7TgOIFY46nF3dYKDQXcjHnIodSDuHs4EyYexguji4082xW4j5ODk546D1KbRfqseW95+MZwJChg/AKcMXo64TRzxkXD32NJKZ52YVkpuSRnVa8Cm5aQg4Azu46XIzFI9g/LjmAi4eenrdFAcULxqTEZxf/f5OZrLR8Es9m4qjX4uHnjM7gUKpNnoNOi8HFUdrniQbBlrkPH3/8Mc2aNSuRqCqKgouLS4kE29XVFTc3NwwGQ63Pf0hNTeXkyZO0a9eOu+66i5SUFL7//nvMZjMDBgwAYN++fcTHx3P//fdX+T6qLkXTp08f+vTpo2YIQjRaWq2W+fPnExsbW+Z+RVH48L0PiekeUyv333R2E8/89oz1+RO/PAHAg+0f5KEODwEQnx1f4pftxdyL3PHdHdbnS/cvZen+pXTx78KSoUtqJU5R82x5773/v7fpG9OiVu5/8q8kNn50eQ7AD//bD0DXGyPoNqIJUFwTfeXf+ey0fL584XI5x54NZ9iz4QxBzTy47TH7LW8shFpsmfuQlpbGsWPHSkz2s1gsZGVlkZWVxfnz50sc7+DggJubGx4eHnh5eREQEGDdt3fv3hJ10mPHjiU8PLzScVssFlxcXBgxYgQajYagoCAyMjL4/fffGTBgAOnp6axbt45x48bh6Fj1VYLr39qOQogaExMTw/Lly0v1Avbz8+Pdd98lJqZ2kmmAW5veyq1Nb63wmKuT5GDXYPaN31ep+0iiXTep+d5r1SuQVr0qLju5Okl293Hi4fcGVeo+kmiLhsTWuQ/p6ek2r3xaWFhITk4OTk5OFBYWoiiKdRClRYsWBAcHW4+tahcRNzc3NBpNiVFzX19fsrKyKCoq4vz582RnZ/P+++9b91ssFk6fPs327dt55plnSpxbHrsl1DfffHOljlcUhVWrVtVSNEKIS2JiYhgwYACffvopcQlxBPsHc9ddd+HlJX10Re268r13If4iAYF+8t4Too6yde6Dl5dXiZFejUZTotTDxcXFWvJhNBrR6y9PFFYUxVpvrdfrS+yrqtDQUPbt24fZbLYmxsnJybi6uuLg4ECTJk148MEHS5yzatUqfHx86N27t03JNNgxoV69ejUGg4GAgACbPrnUhZ6iQjQWXl5eTJ48GYvFIj97wq7kvSdE/WDL3Advb2+uu+46fH198fLywsvLC3d3d5uT0mvJyckhPT2dzMxMoDgxBqwJOhRPnHR3d2fIkCEAdO3ale3bt7Nu3Tq6detGSkoKW7ZsoXv37kBx4n51mzxHR0ecnJwq1T7Pbgl1cHAwcXFx+Pj4MGbMGO68884StTJCCPVJQiPUIu89Ieo2W+Y+LFy4sFKLoVTW4cOHS1QvLF++HID+/ftb+2Knp6eX+H1iNBoZN24c69at491338Xd3Z3u3bvX+Bw+xWJroUsN2Lx5M8uWLWP58uVkZGTQv39/7rrrLmJjY62fLOq6jIwMjEYj6enpdlkVSAghhBCirrjUh9recx/qOrsm1JcUFhayZs0ali1bxurVqzGbzQwbNowxY8YwYsSIGqmZqS2SUAshhBCiMUtJSSme+3DhAgEBATL3AZUS6itlZWWxYsUK3nvvPbZt28asWbN45plnrn2iSiShFkIIIYQQV6qZKvEqys/PZ/369axatYrdu3djMBiIiIhQMyQhhBBCCCEqxe4JtdlsZv369dxzzz34+/szevRocnNzWbRoERcvXmTcuHH2DkkIIYQQQogqs1uXj99//51ly5bx1VdfkZycTI8ePZgzZw4jR47Ex8fHXmEIIYQQQghRo+xWQ63RaHBycmL48OGMHj3aptKOTp3q3ipTUkMthBBCCCGuZNeE2nrTa/QbvdTg32Qy1XZYlSYJtRBCCCGEuJLdSj6WLFlir1vVKjc3N9LT0+tN32whhBBCCFG7VG+bJ4QQQgghRH2mats8IYRobCIiIlAUxfrQaDS4ubkREhLCwIEDefzxx9m+fbvaYdaoU6dOlXjNiqKg1Wrx8PCgSZMmjBgxgjlz5nD69Gm1QxVCiCqREWohhLCjiIgITp8+Te/evWnatCkAubm5JCUlsXv3blJTUwHo378/ixcvpkmTJmqGWyNOnTpFZGQkALfffjuurq4AZGZmEh8fz+7du8nLy0NRFCZOnMirr75qPUYIIeoDSaiFEMKOLiXUS5Ys4Z577imxz2KxsHbtWqZNm8bRo0fx9/dn69at1mS0vroyoT558mSpLk+5ubksWbKEmTNnkpmZSd++fdmwYQN6vV6FaIUQovKk5EMIIeoIRVEYPnw427dvp1mzZiQkJHDfffepHVatc3Jy4qGHHuLnn3/GYDCwZcsWXn75ZbXDEkIIm0lCLYQQdYyHhwfz5s0DYOPGjezcubPE/kt12KdOnWLVqlUMGjQILy8vFEXh559/BmDAgAElnl9t1qxZKIrCrFmzSu0rKiritddeo23bthgMBvz8/Ljjjjs4cOAAS5cuRVGUUqPrNaFTp05MnjwZgDfeeIOioqIav4cQQtQGSaiFEKIOGjZsGF5eXgBs2LChzGNee+01br31VjIzMxk6dCj9+/dHq9VW675ms5nbbruNxx9/nKNHj9K/f38GDRrErl276Nq1a6nkvqaNHTsWgNTUVHbs2FGr9xJCiJpitz7UQgghbKcoCp06deLHH39k//79ZR7z7rvvsmrVKm6++eYau+9bb73F6tWrCQwMZNOmTbRo0QIAk8nEY489xvz582vsXmVp27YtOp2OgoIC9u/fT48ePWr1fkIIURNkhFoIIeooHx8fAJKTk8vcP378+BpNpgFrwjxr1ixrMg2g1Wp5+eWXCQ4OrtH7XU2j0VhH5st73UIIUddIQi2EEHWU2WwGikeryxIbG1uj9zt37hwnTpwAYMyYMaX263S6Gr9nWa71uoUQoq6RhFoIIeqopKQkAOuI7dWubj9XXefOnQOKR8bL6wNd0/e8mslkIi0tDSj/dQshRF0jCbUQQtRBFouF3bt3AxAdHV3mMU5OTlW+/qVR4LJUNDJc26PGf//9NwUFBUD5r1sIIeoaSaiFEKIOWrNmjXXVxOuvv77S5+t0OqB4NcKylLXM96X66MTERLKzs8s879SpU5WOpTI++eQTALy9vencuXOt3ksIIWqKJNRCCFHHpKen8+ijjwJw3XXX0aFDh0pf41JyfPDgwVL7cnJy2LRpU6ntoaGh1pKOzz77rNT+goICvv7660rHYqtdu3bx1ltvATB9+vRqtwAUQgh7kYRaCCHqiEtLj3fr1o2jR48SGBjIokWLqnStIUOGAPD2228TFxdn3Z6dnc3EiRM5e/ZsmedNmTIFgGeffZYjR45Yt5vNZp566qlyz6uO3Nxc3n33XQYMGEBeXh4DBgzg8ccfr/H7CCFEbZE+1EIIoYL//e9/1lUM8/PzSUpKYteuXaSkpADFKx0uXryY8PDwKl1/5MiRzJs3jx07dtCmTRv69OmD2Wxmx44d6HQ67r33XhYvXlzqvClTprBhwwbWrl1Lu3btGDhwIB4eHvz555+cP3+ehx56iHfeecdaUlJZjz/+uHXCY3Z2NufPn2fXrl3k5eWh0WiYNGkSr776apWvL4QQapCEWgghVPDbb7/x22+/AeDi4oLRaCQ6OpouXbowatQounbtWq3rOzo6smHDBp555hlWrlzJDz/8gJ+fH7fddhvPP/8877zzTpnnabVaVq1axbx581i6dCmbNm3Czc2Nvn37snLlSr755hvgco/syrpUMqLRaHB1dcXLy4shQ4bQs2dPxo4dS1hYWNVesBBCqEixWCwWtYMQQghRPwwaNIhNmzbx9ddfExMTo3Y4QghRJ0gNtRBCiBL27NljbV13SUFBAbNmzWLTpk34+fkxfPhwlaITQoi6R0o+hBBClDBt2jT27NlD+/btCQwMJDU1lX379hEfH4/BYODDDz/EYDCoHaYQQtQZUvIhhBCihE8//ZRPP/2UvXv3kpycjMViISgoiIEDB/LYY4/RunVrtUMUQog6RRJqIYQQQgghqkFqqIUQQgghhKgGSaiFEEIIIYSoBkmoK8lisZCRkYFUygghhBBCCFAhoc7Pz2fr1q2sWrWKpKQke9++2jIzMzEajWRmZqodihBCCCGEqAPsmlAvWLCAwMBA+vTpQ0xMDHv37gUgKSkJHx+fMpfBFUIIIYQQoi6zW0K9ZMkSpk2bxtChQ/nggw9KlEz4+PgwaNAgPv/8c3uFI4QQQgghRI2wW0L92muvccstt7Bs2TJGjBhRan/nzp3Zv3+/vcIRQgghhBCiRtgtoT527BjDhg0rd7+XlxfJycn2CkcIIYQQQogaYbeE2sPDo8JJiAcOHCAgIMBe4QghhBBCCFEj7JZQDx8+nIULF5KWllZq3/79+1m0aBE333yzvcKp03ILTGqHIIQQQgghbGS3pcfPnz9P9+7dsVgsjBgxgoULFzJ27FhMJhNff/01gYGBbN++HR8fH3uEU2UZGRkYjUbS09Nxd3evlXukZhfg6aKrlWsLIYQQQoiaZbeEGuDixYv8+9//ZsWKFdaRajc3N26//XZefPFF/Pz87BVKldkjob6QnkeA0VAr1xZCCCGEEDXLrgn1lRITEzGbzfj6+qLR1J8FG+2RUB+7mEVTP9daubYQQgghhKhZDmrd2NfXV61b13lpOQVqhyCEEEIIIWxkt6Hhp59+mg4dOpS7v2PHjsyePdte4dRpFzLy1A5BCCGEEELYyG4J9fLlyyvsQz18+HC++OILe4VTp51OzlE7BCGEEEIIYSO7JdRnzpwhKiqq3P2RkZGcPn3aXuHUaYcvZGI2q1LaLoQQQgghKsluCbWrq2uFCfPJkycxGKSzBUB2fhGHEzLVDkMIIYQQQtjAbgn1gAEDeP/994mLiyu17+zZsyxcuJCBAwfaK5w677dj5a8qKYQQQggh6g67tc07fPgw3bp1Q1EUJkyYQJs2bQD4+++/Wbx4MRaLhT/++INWrVrZI5wqs0fbvAlL/yS/yMySf3XFUVt/WgoKIYQQQjRGdmub16JFC7Zs2cLkyZN54403Suzr168fCxYsqPPJdG1LT09n3759XDhykLScQtbt9mZEl6ZqhyWEEEIIISpg1z7U7dq1Y/PmzSQlJXHixAkAmjRpUueXG7eXffv20bdvX+vzTzwXc1PnKBRFUTEqIYQQQghREVUWdvHx8ZEk2gbnUnPZey6d9qEeaocihBBCCCHKYdeE2mQysX79ek6cOEFqaipXl28risIzzzxjz5DqvGXbztAuxCij1EIIIYQQdZTdEuodO3Zw++23c+7cuVKJ9CWSUJd2ID6DzUcSGdDCT+1QhBBCCCFEGezWQuKhhx4iNzeXlStXkpKSgtlsLvUwmUz2CqdeeX/zCZKz8tUOQwghhBBClMFuCfXevXuZMWMGI0aMwMPDw163LWHWrFkoilLi0bJlS1ViqYys/CJe/eGIrJ4ohBBCCFEH2S2hDgkJKbfUw57atGlDfHy89fHrr7+qHZJN/o5L58Otp9QOQwghhBBCXMVuCfWMGTNYtGgRGRkZ9rplmRwcHAgICLA+6lO3kRW74vh65zm1wxBCCCGEEFew26TEzMxMXF1dadq0KXfeeSehoaFotdoSxyiKwqOPPlqrcRw9epSgoCAMBgM9e/Zk7ty5hIWFlXt8fn4++fmX65fV/kCw9PdTJGblc2/vSHQOsoqiEEIIIYTa7Lb0uEZz7eRPUZRanZi4du1asrKyaNGiBfHx8cyePZu4uDj+/vtv3Nzcyjxn1qxZzJ49u9T22lh6/Ndffy2xsEuvqW/jFdWuzGMjfVyYMrgZTf1cazQGIYQQQghROXZLqE+fPm3TceHh4bUcyWVpaWmEh4fz+uuvM2HChDKPKWuEOjQ0VPWEGkCjwIj2QdzVPRwnnbbc44QQQgghRO2xW8mHPRNlW3l4eNC8eXOOHTtW7jF6vR69Xm/HqGxntsCqPef57VgSjwxqRudwT7VDEkIIIYRodOxehBsXF8dnn33G/PnzOXeueIKdyWQiJSXF7n2os7KyOH78OIGBgXa9b01Lyipg1rf7WfLbSWmtJ4QQQghhZ3ZLqC0WC9OnTycyMpK77rqL6dOnc+TIEaA4sY2IiODNN9+s1Rgef/xxNm/ezKlTp/j999+57bbb0Gq1jB49ulbvay8rdsUx76ejklQLIYQQQtiR3RLqV155hfnz5/P444+zYcOGEj2pjUYjMTExfP3117Uaw7lz5xg9ejQtWrRg5MiReHt788cff+Dr61ur97WnTYcu8s3uOLXDEEIIIYRoNOxWQ71o0SLuvvtu5syZQ3Jycqn97dq1Y+3atbUaw+eff16r168rPtl2mq4RXoR5O6sdihBCCCFEg2e3hPrs2bP06tWr3P0uLi6q93hW29U15BazuUrXKTJZmP/TUV6JbYdGo1Tq3Ps+/JMD5zNIyi7A6ORIn6Y+zBzWEn93wzXPtVgs3LPkTzYfSeT9cZ25oU2Add9fZ9N4ad0h9sWlowDtQz14algrWgfVbKcUIYQQQgh7s1vJh5+fH2fPni13/86dOytcYKWhW7FiBXfccUeJbTuXPEP8X5urdL0jCZnlln6Men8rX+0o+79FjybevHVXJzY+1p/3xnbidHI2D36y06Z7fvDrSZQy8vfs/CLGL9lOkIcTKx/uzfIHe+Gqd+DuxdspNFXtQ4MQdY05J0ftEIQQQqjEbgl1TEwM7733HidOnLBuU/7Jvn744QeWLl1aKqFsLFasWEFsbCyJiYklthdkpbHzg6ernFR/uu00iZn51z7wCvf1bUKnME9CPJ3pHO7FgwOasvts2jUT3/3n0/nflpO8HFu6b/bxxCzScgqZfl1zonxdae7vxtQhzUjKyicuNbdS8QlRV5lSU9UOQQghhErsllDPnj2bwMBAOnTowN13342iKLz00kv06dOHYcOG0a5dO/7973/bK5w6w2QyMXXqVCpaX2f/1wuwmCvfUrDQZGH13vNVji0tp4CVe+LoHOaJo7b8t0pugYmpn+/huVva4OdWujSkia8rns6OfPHnWQqKzOQVmvjiz7M09XMlxNOpyvEJUZcUXryodghCCCFUYreE2mg08scff/Dkk08SFxeHwWBg8+bNpKWl8eyzz7JlyxacnRvfJLotW7ZY+3GXJy/tIknH/sJssVT6sftMWqVjmrv2IK2eWUeH5zZwPi2XRXd3qfD451YfoHOYJ9dfUTN9JVe9A59P7MnKPXG0fGYtrf+zjs1HEln6r644VJCoC1GfFCVcxJSVpXYYQgghVGCXSYl5eXksXLiQDh068PTTT/P000/b47b1Qnx8vE3HZaYm4lJY+VHq08nZLPjpKO9tPm7dlldoYvfZNJ79dr9124bp/Qn2KB4tfqBfFKO6hBKXlsv8H48y/cs9LL6nq7VE50obDiSw9XgS30/pW2rflfd78uu9dA73ZMGdHTFZLCz65QT3Lv2Tbx/pg8FRlk0XDYGF3L/+wrV3b7UDEUIIYWd2SagNBgMzZsxgwYIF9OvXzx63rDdsXaXR4O6NpqwZfxWwWCzkFJgY1TWEm9sHWbdP/WIPw9oGMPSKEWV/t8vLq3u56PBy0dHE15Wmfq70nLuRXWfSylza/PfjSZxOyaHd7B9KbH/wk510jfDiiwd6smpPHHGpOXzzYC9r15H5d3ak/ewf+OFAQonYhKjPMlZ/j0uvXmV++BRCCNFw2a1tXtu2bTl16pS9bldv9O3bl5CQEOLi4sqto9Z7+OEV1a7MDhoVU1AU8HDW4e9+eRTY4KDB20VHhI/LNa9wadHFgqKyJyU+OCCKO7uW7M5yw7xfeOam1gxp5Q8U11grilIifo0CikKFteNC1Afp6ens27eP7L//Jv333+n17beE3HKL2mEJIYSwI7sl1C+88AJjxoxh4MCBDBkyxF63rfO0Wi3z588nNja23GNa3PoIiqZqZRG+rvprH/SP3WdS2XsunS4RnhidHDmTnMNrG44Q7u1Mp3APAC6k5zHmf3/w+sgOdAj1wM/NUOZExCAPJ0K9imvi+zTzZc7aQzyz6m/u6RWB2QLv/nwcrUahZxPvKr0uIeqKffv20bfv5ZKnL3U6bu3aFccg+eZFCCEaC7sl1G+99RZeXl7ccMMNREZGEhkZiZNTyQ4PiqKwatUqe4VUZ8TExLB8+XImTZpUonWeo6sHrWKn49+u6mUyXSJKl2mUx0mnZd3fF3jjxyPkFJjwc9PTv7kvkwd1Qu9QnNAXmsycSMwmt8D2eu6mfq58ML4L8388ym3v/I5GUWgT5M6H93bDz4YFY4SoTywFBVx87XUC585Bo9OpHY4QQgg7UCx2+s49IiLimnWFiqKU6FNdF2VkZGA0GklPT8fdvWZX+du8eTMDBgywPu/80Dy8mnao8vWCPZx4aEAU7UI9rAmxEKJm/frrryVGqL/o15+uPj649OqJ76OPomikk40QQjR0dhuhlvrpa9NqSya91flD7Odm4IF+TSrsHy2EqD3Zv29FMRjwefBBSaqFEKKBs1tCLeynmZ8bD/Rvgk6rIa8KrfaEEDUja+MmzBmZ+Ex+BK2rq9rhCCGEqCV2HTYxmUx8/vnnPPDAA9x2223s27cPKJ4lv2LFChISEuwZToN0fWt/pg1phqtePisJURfk7NhB3NRpZG7ciKWoSO1whBBC1AK7JdRpaWn07t2bMWPG8Nlnn/Htt99aJ+C5uroyZcoU5s+fb69wGhxXvQOPDGxKTKcQtBrpgStEXWJKSyPp7Xc498hk0letwpSRoXZIQgghapDdEuqZM2eyf/9+1q9fz4kTJ0r0H9ZqtcTGxrJmzRp7hdOgNPd34+kbW9E22Kh2KEKIChQlJpLy0cecvX8iF19/g9y/90svdiGEaADsllCvXLmSyZMnc91115XZ7aN58+YycbGSFBRujA5k2pBmeDhLey4h6gtLURHZv/3GhWef5fzjT5CzY0e1rpf03nucunM0hzp05HDXbpU+P/7ZWRxs2YqUDz8ssT13/37O3Hsvh7t240j3HsQ/8x/M2dnVilUIIRoiuyXU6enpREZGlru/sLCQIqkvtJnBUcvDA6MY0T6o0kuSCyFqjslUcuKvuZIjzgWnTpEw90Uuzp+PuaCg3ONOj7ubtBXflLnPUlCI29Ab8LzzzkrdGyBjwwZy//oLBz+/EtsLEy5y5t4JOIaFEfHFF4T+bxH5x45x/ql/V/oeQgjR0NktoY6KimLXrl3l7v/hhx9o3bq1vcKp19wNjjxxfQsp8RBCZStWrOCOO+4ose3hbX+wLi6u0tfK/mULCc//F3NubqXP9Z0yGe977kHfvHmlzitMSCDhvy8Q/MrLKA4lJzJn/fwzioMDAf/5D/omkThFRxMwaxaZP/xAwenTlY5RCCEaMrsl1Pfddx+LFy/miy++sNYMKopCfn4+//d//8e6det44IEH7BVOveWmd2D6dc0J9nS69sFCiFqzYsUKYmNjS6xuCpBSUFDlpDrvwAES5szFUsFIdU2xmM2cf3IG3hPuRd+sWen9BQUojo4lemhrDHoAcnaWPzgihBCNkd0S6qlTp3L33XczevRomv8zijJmzBjc3NyYO3cuEydOZMKECfYKp17SO2h5ZFBTAoyyXLcQajKZTEydOrXCCYXP7/0LUxUmHOYdOEDKRx9VJzybJC/6H4pWi+e4cWXud+nRnaKkJJI/+ABLQQGm9HQuvvY6UDy5UgghxGV2a1asKAqLFi1i/PjxLF++nKNHj2I2m4mKimLkyJH069fPXqHUS4qiMLFfJOHeLmqHIkSjt2XLFs6dO1fufgsQn5vL9sREevj6Vvr6GevWUZSWRuqyzy5fMy+P3L/+4sJ//2vdFrX6OxyDgip9/dy/95Py8cdEfv11mZPEAfTNmhE0dy4JL73ExdffQNFo8Bw3Dq2PD0hrTiGEKKHWEuqYmBgeffRR+vbtC8Avv/xCq1at6NOnD3369Kmt2zZYY7uH0SZIaqaFqAvi4+NtOu5iZiYWN7dKX98CaD08iVz+FYpWC0DcE0/ifv11uF13nfW4qycS2ip35w5MyckcGzTo8kaTiYSXXiblw49ouvEnAIwjbsI44iaKkpLQODmBopCydCm60NAq3VcIIRqqWkuoV61axe233259PnDgQD7++GPGjBlTW7dssG5oE0Dvpj5qhyGE+EdgYKBNx/kaDFCVLjwWC5b8PBxDQtDoiltiavR6tF7e6MLDK3+9q7jffDPOPXuW2Hb2vvsx3nIzxttiSh3v4FP8+yft669R9HpcevWqdgxCCNGQ1FoNdXBwMLt377Y+t1gs5X61KMrXIsCNWzpU/itdIUTt6du3LyEhIeX+TlOAQIOBrj7exeURlXwojo7omja1OZ7C8+fJO3iQwvjzYDKRd/AgeQcPlugZfXzYcDI2bADAwdMTQ/PmJR6KgwMOPj7om1xub5ryyafk7t9P/smTpHz6KRee/y9+jz6K1t29av9wQgjRQNXaCPWdd97Jq6++ypdffomHhwdQvFri3Llzyz1HURT++uuv2gqp3nHUaBjfM0L6TAtRx2i1WubPn09sbGy5xzzdpg3aKvzs6sLCMMbcjoOPt83nJC54k/SVK63PT/4zyhz24Ye4dC9e6KXg5EnMmVmViiVv316S3nwTc04OuiZNCJw9C+Mtt1TqGkII0Rgollpa99ZkMvHBBx+wadMmLl68yM8//0yLFi3w9/ev8LxNmzbVRjg1JiMjA6PRSHp6Ou41PErz66+/WmvOAR569RMeu7vqf7wKiszkFWtDwskAAM0hSURBVJpoFeSO3kFbEyEKIa6wYsUKJk2aVKJ1nrdOx3PR0dxgY1nIJbqwcNyuvx5DdFsoLMKcl4uhVStryYcQQoi6q9ZGqLVaLRMnTmTixIkAaDQann76aamhroT2oR5qhyCEqEBMTAze3t4MGDDAuu3Nzp3p5m376LI+qiluQ29A37y5tYSkVkY5hBBC1JpaS6g7derEnDlzGDp0KABLliyhY8eOtXW7BiE6OpqfNv3Mc98dwFmnpXvnDmqHJIS4Bq225Lc/tpZoGVq2xO2GG9BHRdVGWEIIIeyo1hLqvXv3kpSUZH1+77338vHHH9OqVavaumW9ZzQa6d2nDz4HHekQ6oGbu7TJE6Kh0UVEYrz1FvRNmqgdihBCiBpSa10+wsPD+fHHHzGZTEDd6vLx9ttvExERgcFgoHv37mzfvl3tkEoJlaXFhWhQNDo9nqNG4TttqiTTQgjRwNRaQj1p0iQ++ugjDAYD7u7uKIrChAkTcHd3L/dhNNb+iOwXX3zB9OnTefbZZ9m1axft27fnhhtu4OLFi7V+78oI8pCEWoiGQhcRid+MJ3Hp3RtFU2u/doUQQqik1ko+nnjiCdq3b8+mTZtISEjgww8/pGvXrjRReWTm9ddf5/777+df//oXAO+99x7ff/89ixcvZubMmarGdolGoxBgNKgdhmiEcgpzcHZ0VjuMBkNx1GEccRMu/fpJIi2EEA1YrSXUANdffz3XX389AEuXLuWBBx5QtctHQUEBO3fu5KmnnrJu02g0DBkyhK1bt5Z5Tn5+Pvn5+dbnGRkZAOzZswdXV9eaj7HIBIknOLCvAAdN9UpkiswWzGYLeRdc0DXwtnkmk4ndu3eTlHABH19vOnbogFZTA69Z0YC2gh8Ti4XAw0vwOf092sIssrzacrbdo+S7hth0ef+jywg+uIiLTW7nXNtHrNt12XGE7H8Pl5R9aMyFZPh15WzbKRQZvKr7isqUnp7O2rVr2XN6D9Eh0dww/AaM1azhVxQFB035/3YWi4VvLn7Dzyk/k2PKoZlzM8YHjydAH2DT9VdfXM1XCV9xvff13BV0l3V7Qn4Cn1/4nKPZRym0FBLtFs24wHEYHWvnG7DDhw+XeH4yMxODosHB2xvjjTeSYPSAv/ZW6pqWoiIsZhP6vDw0jo41GG3dZTaZqakmrooCGm3j+QBjNpkw12AHXI2ioNE27L8ZV7KYLNTkm0/R1o3yVnswmUzUZPdlRVFKTfSujk6dOtXYta6l1vpQ10Xnz58nODiY33//nZ5XLLv75JNPsnnzZrZt21bqnFmzZjF79mx7hinqkSd763iqj57xK3M5mWrm+YF6ov21tH47i3xTxed2CdLwZawzGfkWNp0q4tH1xR/cnB1h7yRX/kow8ezPxdueH6gnyE1Dj/9lN5iWaj7DffC9yZdzi85RkFiAf4w/hhADR//vKJbCil+lU6QToQ+FYso1kX0omwvLLgCg6BSa/bcZuWdyubiyuIzLP8YfBw8HTjx/QvrRCSFEI2LPFLfWEuozZ84AEBYWVuL5tVw6vjZUJaEua4Q6NDSUzZs318oINcC2E0l0DPOskWtpFAWHBjxSs3HjRp544oly97/yn8cY1KdH1S5uLgSzCbybgkMZi2tYLET/EEtC1EguNh0FgKYwi3brYzjdcSapwYPKvbSmKJeWmydytt00Ao58TK6xqXWE2u3inzT9YyZ/DfsWs6OL9brt197MsZ6vkOnbuWqvpwzX+veLmRbDdUOuw8fgU+Fo89WKzEWYMBHhHoGjtvQIq8ViYeqhqQz1Gcpw3+EA5JhymHJwCveF3EcPj/L/m+WZ8vjPsf8wPng83178ljBDmHWEel/mPl479Rrvtn4XJ62T9boPHXiIJyKfoI1rG5tfg612797NfffdZ33+Uu/eDJg9G001fz8oioLiUKtfItYZpiIzaQnZaDQKSjV/X1lMZsxmCx7+LmgdGu7vvkuKiopIOHYEvatbjYzsmUwmLGYTnkEhODSC95+lyExhYg5oFJRqfitsMVvAbMHR1xmlkbz3kpOT0Wg0aGqgpM1sNmM2m/H29q6x9549R6hr7aclIiICRVHIzc1Fp9NZn1/Lpa4gtcHHxwetVktCQkKJ7QkJCQQElP01s16vR6/Xl9reoUOHGl8p8RKnwAxaBdbOtRsSk8nELRUsg6wAC95+l0d7OqP1aQJugeDsDQb34lKOa96gAApzICAaHEq/B0g5CfkphPS9i5DAdpe3H+hGpEMikRX9IH8zCaJvptkN98P5lbj5+uF36fjDCbBNoUPnbpfvW5QP6zQ00yVBDf2CuNa/H8DaT9Zi6mtCU6TB28kbXydffJ188XH2wc/JDz9nPzz0HqV+tgtNheSacmnp1RKdtvSHkbOZZ0n/O53YrrG09Gpp3d4uuR0ZrhkV/hL8v1//j+uirmNct3FsXLcRPy8/6/EZZzPQnNbQtVNX630LTAVoDmrIdM+kU4ea/+Wak5NT4nmHO0bSbfDgGr9PQ2YqNJN0LhMHnbbaSbCpyExRgQmfEDe0jo0gqSks5LipAK/gELQO1S8PMhUVUpSfj29EExwaQbmRpchMwfksFEdNtZNgS5EZS6EZXZBro0moL1y4gKOjY419mCssLCQgIKBefpirtYgXL16Moig4/vMDeem5mnQ6HZ07d+ann37i1ltvBYo/Ef3000888sgjFZ9sRx7ODf+XWE3YsmUL586dK3e/BTibnMOWtV8xoPkVH1AULTh5gJMnOHn98/+9ip87e4N7IDg6FyfUFcn6pzOMq1/J7S6+kJVQ+vhL9i2H+L/g/k1l7w/pCjoX2PAsDP5P8Sv5cRZYTJB1oeKYKuFa/34AuUm5JB1IwqetD4k5iSTmJJY6Rq/VE+gSSLBbMMEuwYS4heB1jVrv5NxkALwNJVcU9DZ4k5SbVNYpAKw9uZYDyQf4/KbPy9zfzrcdTg5OvLHzDaZ0moLFYmHernmYLCYSc0vHXhOio6PZsmUL2X/8QcYPP9BtzOhauU9DV5hvwkHXeOp2a1JeZobaIQjR6NVaQn3PPfdU+Fwt06dPZ/z48XTp0oVu3boxb948srOzrV0/6gJnx/r3yUwN8fHxth2Xng8W8+UNFjNkJxY/yuMWBIHtoeng4gQcYO+X8N20y8fc9WXlg04/B+tmwriV4FhOJxcXH7hjKXw/Hba9VzyaHh1bHI8tI+s2svXfLzclF/OV/35X7y/K5UT6CU6kn7BuM+qNjGwxktberQFYfWI1z219zrr/7cFvVzreC9kXeHH7iyy8biF6bRnfGABeBi9e6/8az//xPJ8e/BSNomFY5DBaebVCU0tdQo1GI3369CEjK4tciwUPX99auU9DlZKSwkcffczffxwnqk0oo+4YjZdn7Uy+veT47ovs/yWOi2cyyc8uYuT/dcU31O2a5x3beZFt354gMzkPo58TPW+LIiLax7o/J6OArSuOceZgCgU5RQQ186DvqOZ4+Ndu55yczAyKCgpqZIRaCFE1jS5zGzVqFImJifznP//hwoULdOjQgXXr1uHv7692aFa6RvBVUU0IDAy07TgvV3C8Rl9vFz/wCAP3oOLSEFf/4pFqzyaXu3y0GAbBV9QvXxrBzroIbleUDGUnFpeJlOX8nuL97/e7vM1igtO/wfaF8EwiaLTFifzUvyA7ufi5kwe80gzaRtj0mm1h67+fm48bBgfb2jj6OvkS6hZKmHsYQa5B1rrrgaEDaedzuSym4J9/u+S8ZHydLyegyXnJJUpArrQ/eT8peSmMWj3Kus1kMbEzYSefHfqMnWN3otVo6RXci7W3ryU1LxWtRou7zp0BXwwgxM22zivV4dylS63foyFZsWIFkyZNIjHx8ofb1954iddfWcDNN91aa/ctyjcR2NSDpp392fTJIZvOiT+ezg8f7KfHrU2IiPbh6J8JrH1vHyP/3RXvYFcsFgtr3t2LRqsw/MF26Axa9vx0llXzdzPm2R446mtx9N1iIeX8OQKbtqi9ewghKlRrCfVzzz137YOuoigKzzzzTC1EU9IjjzxSp0o8rqat5sSIxqJv376EhIQQFxdX5kxeBQjx1NO3pR94hv6TKPsVjwC7+IKzT/H/dwsou0b6anq34sclFktx4n1yM1yqoc7LgHM7oMu9ZV+jSX948KoWjaseAp/m0HtacfJ8JZd/SiJObC5OxFsMv3acNrrWvx+Ak48Tvm18S5VreRu8CXINItg1mFC3UOujvB7WLo4uuPwzwRKKJyX6OPmwLX6bNYHOKshiX+I+RrUYVeY1egT2YMXNK0pse+a3Z4g0RnJv23tLtUn0NBRP7N0Wv42UvBQGhA4o/x+jRig4d+xYy/doOFasWEFsbGyp915yajLj7xvLS/9+mztGxuLm7YRDDddCt+hR/GEyIynX5nP2bjxLWBsvOl0fDkD3m5tw9mAK+34+x4C7WpJ+MZeEkxnc+Z9ueAcVT0gdMLoFS2b8ytE/E2jdJ6hGX8PVzh85KAm1sJv09HR2797NqVOn8PLyom3btrU2r+xKBw4cYMeOHcTHx5Obm8sDDzxwzcGhAwcOsGXLFlJSUjCbzXh5edGrVy/at29vPSYrK4sNGzZw/Phx8vLyCA8PZ/jw4Xh7e1dw5ZJqLaGeNWtWqW2X/ihf/QtUURTr0uT2SKjrOsmnbaPVapk/fz6xsbFlH6DAvDfeQDtuYulEtSYoCvR4EH55BbyiwDMcNr5QnKC3vOnycR+OgJYjoPvE4oTcv3XJ6zi6FNdwX7l99yfg06I4oT77J6ybAT0fBp9mNRb+Nf/9gC4Tu9DcuzmRxkgijZFEuEcQ7Bps84h1eRRFYWyrsby/933C3MIIdgvmrd1v4evsy6Cwy91R7lt/H4PCBjGm1RhcHF1o5lny9Ts5OOGh9yix/Zuj39DEowleei/2JO7hpT9fYlzrcUQaI6sV87U4Bgag9fCo1Xs0FCaTialTp1bQ0srCy2/NJtixPRqNFidXR5yNelyMuuL/9dDjatTh5KardmcGW104kU6HISW7UIW29uLknuKaf1NRcVnUlcm/olHQOmiIP5ZW+wn14YO06jMQ52r2jxfCFnv27GHgwIHW59988w19+vSp9fsWFhYSFhZGmzZt+O6772w6x8nJiX79+lkbUxw5coSVK1fi4uJC06ZNsVgsfP7552g0GkaPHo1er2fr1q189NFHPPzww+h0ZXT5KkOtJdRmc8may7i4OG688Ubatm3LtGnTaNGi+JP0oUOHmDdvHgcOHOD777+vrXDqFY3Kkzfrk5iYGJYvX17qa2M/Pz/effddYmJiajeA3tOgIAe+mwp56RDWA8auKFkfnXIKcpIrd92ko/DjbMhNLS5F6ft4cUJdw8r793PzcuOF119g0rhJOGpqpy7z3rb3kluUy+yts8ksyKSjf0feG/Jeifros5lnSctPq9R1T2WcYv6u+aQXpBPsGsz90fdzd+u7azj60hxsLKERtk2ITc5I4OCpPbSO7EROZgE5mQUkXXWKVqvB1UuPu7cT7j4G3H2ccDHa9sevsnIyCnByL/mz4OymIyejuK2qR4Azrl56tn5zggF3tcBRr+Wvn86SlZpPdsY1JjhX0aVRwkMHD3Hm7Dk8flhL39g7a+VeQlxyqVTrSnfffTePPfYYY8eOxWisvQ91l0aVU1NTbT4nMrLkYEqPHj3Ys2cPZ86coWnT/2fvvuOjqNY+gP9m+ybZ7Kb3HnoaoUNoAZQiCCGAIE1AwQJKseDVK+q1oOIFXsSCIlwURDCCIIIgAUGaQEJCL6ElhJC66dtm3j9iFkKyySbZnU15vvezn+vOnJl5Nmyyz5455zmhyM3NRXp6Op577jm4u1cUGRgxYgQ++eQTpKamoksX80rV8jaG+vnnn0ebNm3w3XffVdnerVs3fP/994iPj8fzzz+Pn3/+ma+QmizKp+snLi4OAwYMwPfff4/c3Fy4uLjgySefhLOzdSc2Aaj4x4r9V8XDlPmptZ/jqRq+SA55u+LBg7i4OLi4uGDAgAHGbTsSdqB///5WvS7DMHih8wt4obPp4Vd74vfUeo5vh35bbdv8LvMxv8v8RsdXX0Ir1aVvicydEFtQlFPrYjwGPQv1vTKo790fuiG1E6FTPx+4+VcMz7p0/C4ObLy/muXIFyLh3UbVoLhrIxQKMGx2OPZvuIhvFh4CI2Dg194J/p1cYK0VhVJTU6v0Ei4Gg469+sDFx88q1yPE1FCtoqIiLFmyBOnp6ejbty9cXFygVCqhUCjg4OAABwcHi66A2FAcx+H69evIzc1FQEDF8K3Kcs0PluoTCAQQiUS4detW00uo9+/fj6VLl5rcP2jQILz66qt8hUNaGGdnZ8ydO9fWYTRbUVFROHToEHLLcuEid0F4uIlJlcSk1rIIiyWYOyHWReVm3vhphoGDkxRKNzlU7nZw8rAzLj0eFOkKj6D7YzsdVGbMl6iBnaMEZYW6KttKi7Swc7x/PvcARzzxRndoyvRg9SzkCgm2fHgS7gF1VxCxlJS9v6Hf5BkQ0vuRWFjdQ7WArVu3IiIiAkVFRVW2MwwDOzs7KBQKODo6QqVSQaVSwdHRkZdEu7y8HMuWLYPBYADDMBgxYgRCQkIAVKxRolQqsW/fPowcORJisRjHjh1DYWEhiouLzb4Gb79xMpkMR48exbPPPlvj/iNHjkAma9y4zJaC46iXmvCrsvSbntXXa0VE8gBKYMxmzoRYF6UHOgR1rvLHkGEABycZFM4yKFxkcHSTQ+kmh9JVbnIRF4lMBIms8f82nsFKpF/MQ+Sg+72/6Rfy4BlcfSKWVF5xvYKsUmTfLESPUdYdv/+gotxsXDiUiLCBQ3i7JmkdzBmqVVBQgKtXr6Jt27ZVtnMch5KSEpSUlODu3fvrKTAMA6VSCRcXFwQHB8PO7v7E9pSUlCrjpCdPnmzsVa4viUSCOXPmQKvV4vr169izZw+cnJwQFBQEoVCICRMmYPv27Vi6dCkYhkFwcDBCQ0PrdQ3ePgGefPJJrFy5EiqVCnPnzjV+M7h27RpWrlyJjRs3Yt68eXyF06SxHAcBKKMm/KNkuhHoW7DZzJkQO+PxhfAIVMHJ0w4qj4qHwkUGYSOXJgeA8hIdivLKUVJQMf65IKtitUs7RwnslRU9zvu+PQ97lRS9xlR8VkXE+mHbstNI2nsLgeEuuPJ3Fu7dLMKAJ++Xebx66h7kDmI4OMuQm1GMwz9eQVCUG/w7ml8pwBLSTp+AwtUNAeFRvF6XtGzmDtVSq9W19mI/iOM45OfnIz8/H9nZ2ejVq5exgEW7du3g4+NjbNuYKiICgcBYscPLywvZ2dk4fPiwcXy1t7c3nn32WZSXl8NgMMDe3h5r1qyBt7f5k4l5+/RcunQpcnJysGrVKnz22WfGdd9ZlgXHcZg4cWKtQ0JaEwPHtb4C4YQ0d3RrqV5MTYh1Vrngv5+sxJSnJlqtgsf1MznY/78Lxue/f30OANBtRCC6jwwGABTllVf55/QKUWLIzE44/ksajm2/BpW7HYbNCYeLz/2x8yVqDf7aegWlhVrYKSVo39MLXYcHWuU11CVl328QS6XwbtvBJtcnLY+5Q7WcnZ2Nq2TXRCwWw8nJCU5OTnB2djY+JBIJGIYxDgGRSqWQShs2RKsuHMdBr9dX2145UiI3Nxd37typMkehLrzlbRKJBBs2bMDLL7+MXbt24ebNmwCAgIAADBs2rEo9wNbOzC92hJCmRK8HzCyvRCrUNCE2YdtPVp8Q26G3Fzr0rj05GLMwutq20C7uCO3ibvKYyFg/RMY2jQmBHMvi1K/boC0tRUBkdLVa8oTUlzlDtZycnNCmTUUZU4lEAldXVzg7OxvHTCuVStjb2zf4/VhaWgq1Wm0co52bW1FBy8HBAQpFxVyFhIQEODo6YvDgwQAqhqp4e3vDyckJBoMBV65cQUpKCkaMGGE877lz52BnZwelUol79+7ht99+Q/v27es17IP3jtCIiAhERETU3bAVk4ltPxOWEFI/HEADtRqgckJsWZEWcoWEJsRaEMeySPljN+7dvI6wAUNgZ8VyZqTlM2eo1iuvvIJHHnkErq6ucHR0tPgXuUuXLmH79u3G51u3bgUA9O/f39ibrFarq1xXq9Xi119/RWFhIUQiEVxdXREXF4ewsDBjm6KiIuzZswfFxcVQKBSIjIxEv34PrGhsBoYzd6ALAQAUFhZCqVRCrVbzsioQIYS0BqyBNVbmIOY5fPgw+vbta3y+eMJotPWpueddIBTCPzwKoV17wk6pMnlOg14HvUYDt8BgiGq5bd9ScHoW2jvFYMQCMKLGvf84PQtOx0Li7dDoczVllXWoHxyq5eLigi+//BJjx461YWS2RUN1CSGE2Bwl09bFGgy4kXwKN1OS4N22A0K794LSzcPWYZFm6MG1H9LS0hAQEICpU6fys/ZDE0YJNSGEENJKcCyLjIvnkHHxHLzbdUSn/oMgV9DdVlI/lWs/aDQaq00cbG4ooSaEEEJaoTuXziP7Rhq6PDYG7oHBtg6Hd2q1GilJZ6BJL4LQQYzw8HAoHa03zpwzsFD/fhPlF/NgyCsHIxNBFqqCclgghI6mk1JWo0fh7zdRdi4XhmIdJN72UI0MgcTv/oJBhiIt1L9dR/mVAnDlekiClFCNCoHYVW611wOAkukHUEJNCCGENEOVSyZXYtn6T4nSacpxYvsW9IybCFc/f7OOObLle1w8cghFudkQikTwCApFzBNT4dWmXa3HJe3ZiZM7ElBSkA+3gCDEPjUbXqH3j0nZtxsX/jqAe9evQVtWhufX/gCZvUMtZ2yc5ORkDBg4wPh838+70a9PX5PtG4vTsdBlFMNxkD/EXvZgy/Qo2JGGnPXn4TG3s8nj8n+6At3dUjiPbwehowQlSfeQ/XUqPBd0gVApBcdxyN1wHhAwcJ3aEYxMiOJDGcj5OhUeC7pAIKFCB3ygQWuEEEJIM5OQkIBx48ZV2bZ65x6cvJJW73Oxej3+/mULinKy624MwMnLB4OemoNpH3+GJ97+CI5uHtj63psoLVSbPObikT9x8H9fo9fYiZjy4Qq4BQThp/f/jVJ1gbGNTqNBYGQXdB89vt6vob5q+vk98dSTSEhIsNo1BTIR3GaFwy7CDWI3O0j9HeE0KgS6jGLoC8prPIbTGVB2NgfK4UGQBishcpVDOSQAIlc5io9VLLSizymD9lYRnMaEQuKngNjNDqrRoeB0LMqSzfs3JY3Ha0K9Z88ejB8/Hl27dkVISAiCg4OrPCpXTySEEEJIzRISEhAfH1+lygIAFJWV47MdDUuqdeXlOL7tR2jLyups2yFmAAIioqDy8ISrXwAGTJ0FbVkpsm9eN3nMqV+3IXzQowgbOAQuvv4YMut5iCVSpCbuNbbpMuJx9Bg9Dt519HQ3lqmfX05BLibOmYKN73+NsnO50N4phqFEZ/aqfw3BlusBpiLZrgnHcgALMKKq5ecYkQCaG4UVTwyccZtxv4ABI2KguWH6Sw6xLN6GfHz88cd47bXX4OHhge7du1OtUUIIIaSeDAYDXnzxxVqTvE0H/kJ0SKBxRWJzlaoLcP7P/QgbMNj8ePQ6pPyxG1I7e7gFBJlsk5V2Fd1H3+8RZgQC+IdHIfPKxXrF2Fjm/PxeX/c+Bvv2hFBQMVSCETIQOEggdJRAqJBAqJRAqJJCqJQ2qjwep2Oh3n0D8kg3kwm1QCqCxF+Bwj9uQ+xuB4GDBKVnsqG9VQiRS8X4aJGbHEKVFOrdN+A0JhSMRIiiwxkwqLUwFGkbHB+pH94S6hUrViA2Nha7du2qdUlKQgghhNTs0KFDSE9Pr7VNXlExLmfcQXs/n3qfP+PiObTt0bvOdtdOncCvKz6CTquBg8oJ8f96F3YmJvSVFRaCY1nYP1T/2k6pQt6d2l+LpdX18+MA3CnIwvFrSegd2qVim56DoaAchoeHZTBMRZLtJIPIWQqRhz2E9vfzm9Kke8j/+YrxuetTYZAGVfyMOAOL3I0XAI6D0+jaV+NzntAOeVsvI/P9E4AAEHs7wC7SDdqM4oowhAK4TO6A/J+u4M47xwABIA11gqydE628zCPeEur8/HzEx8dTMk0IIYQ0UGZmplnt8otKK4YL1BuHorxcY+/2hUOJ2LvmM+PeuMVL4NshDP6dIjDlo5UoKyxE6v492LF8KZ58b1mti8Y0Beb+/LIKcyqy69pwHAwFGhgKNNBeB8AA8ig3SHwqqm/IOjrDw+/+EvZCpaTiMAOL3O8vwpCvgevT4SZ7pyuJXORwnx0JVmsAV26A0FGC3I0XIHKWGdtIfBXweDEabLkenJ6F0EGCrM+SIfGx3qROUhVvCXX37t1x6dIlvi5HCCGEtDheXjWvhPgwFyclRA0saSaWSiEQVgx3COnaA54PjGl2cHapaCOTwcnTG06e3vBu2x7fvPg0Uvf/jh5jqk8olDs6ghEIUPLABESgYoiJvcqpQTE2lLk/Pw8nNzDi2odzMGIhRC4yiJxlELrIIXKVQexmD0ZYMd5ZIBVBIK2aZlUm0/rcMrg9HV6lR7suAokQkAjBlupQfjkfymHVh9hUJue6nDLo0ougHBJg9vlJ4/CWUK9evRrDhg1D165dMWnSJL4uSwghhLQYffv2ha+vLzIyMkyOA3ZWOKC9nw8Yhqlxf11EkvuJuERuB4ncrs5jOI6DQa+rcZ9QJIZHcChupZ5Bm269KtqzLG6dPYOoRx9rUIwNVdfPjwEDL5U7eoZGA0xlYiyEyKVinLLQSQqRkwwiFxkECkm9fsacgUXudxegu1MMl2mdAA7GMc4Cucg4Hjt7TQrknVzh0NsbAFB+OR/gOIjc7KDPLYN613WI3exg3/X+SpelKdkQ2IshUkmhu1uKgh3XIO/oAllbfr+wtGa8JdQTJkyAXq/HlClT8Oyzz8LX1xdCYdXaiAzD4MyZM3yFRAghhDQrQqEQK1asQHx8vMk2kwf3r/eExEp2jkr4dQwzuV9XXo5jP29GSJcecHByRllRIZL27ERxXi7a9owxttvy7usI7dYLnYeOBAB0GTEau1f/F54hbeAZ0hand22HTlNeZQJkSUE+SgrykX+3YlhGzq0bkMjtoHB1g9xBAUsw5+f34Zx/w7GHD0Ru8n8mAoob/OXkQQa1FuUX8gAA91YmVdnn+nQ4ZCEqAIA+txyGkvtfTthyPdS7b8Cg1kBgJ4I8zBXKRwPBCO//GxuKtFD/mgZDsQ5ChQR20e5wjDWvrjixDIazZj2YBwwYMMCsN2RiYiIP0TRcYWEhlEol1Go1HB1puVZCCCH8S0hIwJw5c6qUfnO0k2P6o7Ho1q72SW6mOHn5oN+TT0Hh4mqyjV6rxa8rP8bdq5dQVlQImcIRniFt0HPMBHiGtjW2W/PCDHTqPwi9xz1p3Ja0ewf+3pGA0oJ8uAUGI3b67CqLwRzZ8j2Obt1U7ZqPPvtSvSqPmKOmn5+bixs+/+JzjI0fa9FrkdaBt4S6paCEmhBCSFNw8OBBDBgwwPj89Ylj0SHAt97nEUmkCI99BB1iBhjHTrcGD//8Dhw4gP79+9suINKs0dLjhBBCSDP08LBJgaC+wxIYhHTpjshHhpssedeSRUVF4dChQ2DL9RDIRLQ+BmkU3hNqnU6HixcvQq1Wg2XZavv79evHd0iEEEJIq+LqH4huI+Pg4tt6x9kqlUrExMSAYzkw9f4yQkhVvCXULMti8eLFWL16NUpLS022MxgMfIVECCGEtCpyRyWih45EYFQXi0y0awkomSaWwFtC/f777+Pjjz/G7NmzERMTgylTpmDp0qVQqVRYvXo1GIbBRx99xFc4hBBCSCvCoF3vvoh6ZDjEUlndzQkh9dLwRejrad26dRg/fjw+//xzDB06FADQpUsXPP300zh+/DgYhsH+/fv5CocQQghpFaR29hg0Yw66jYyjZJoQK+EtoU5PT0dsbCwAQPrP6k3l5eUAAIlEgsmTJ2PDhg18hUMIIYS0eEoPLwx7fkGV8nSEEMvjLaF2cXFBcXExAMDBwQGOjo5IS0ur0iY/P9+qMQQGBoJhmCqPDz/80KrXJIQQQmzBt0MnDH32ReNy4YQQ6+FtDHXnzp3x999/G58PHDgQy5cvR+fOncGyLFauXInIyEirx/HOO+/g6aefNj5XKCyz+hIhhBDSVHQaMBhRQ4aDaeCKiYSQ+uEtoX7mmWewbt06aDQaSKVSvPfee+jXrx/69esHjuPg5OSETZuqr5BkaQqFAp6enla/DiGEEGILXUaMRoeYAbYOg5BWxaYrJarVahw4cABCoRC9e/eGs7OzVa8XGBiI8vJy6HQ6+Pv7Y9KkSZg/fz5EIvO/V9BKiYQQQpqCw4cPo2/fvsbnbzwZj9ETJ6PLiMdtGBUhrZNNV0pUKpV4/HH+fvHnzZuH6OhoODs748iRI1i8eDEyMzPx6aefmjxGo9FAo9EYnxcWFvIRKiGEEFIv9ionRD06wtZhENIq8dpDbTAYsGXLFiQmJuLevXt45513EB4eDrVajT/++AN9+vSBh4dHvc752muvYenSpbW2uXDhAtq3b19t+9q1azF79mwUFxcbK488bMmSJXj77berbaceakIIIbakVquRmpqKGylJuHTkT8Q//Rwi+w+0dViEtEq8JdQFBQUYOnQoTpw4AQcHB5SUlGDv3r2IjY2FwWBAQEAApk6divfff79e583OzkZubm6tbYKDgyGRSKptP3fuHMLCwnDx4kW0a1dzSaGaeqj9/PwooSaEENIkXDh8AEm7d2Dcm+9RnWlCbIS3IR+vvfYazp07hz179qBz585wd3c37hMKhYiPj8euXbvqnVC7ubnBzc2tQTElJydDIBBUieVhUqnUZO81IYQQ0hS4+gVQMk2IDfGWUG/btg1z587FkCFDauxRbtu2LdatW2e16x89ehTHjx/HwIEDoVAocPToUcyfPx+TJ0+Gk5OT1a5LCCGEWJt7YIitQyCkVeMtoVar1QgKCjK5X6fTQa/XW+36UqkUP/zwA5YsWQKNRoOgoCDMnz8fCxYssNo1CSGEED64B1FCTYgt8ZZQh4SE4PTp0yb3//777+jYsaPVrh8dHY1jx45Z7fyEEEKILTACAdwCAm0dBiGtGm9LKM2aNQtr167F5s2bUTkPkmEYaDQa/Otf/8Lu3bsxe/ZsvsIhhBBCWgRHVzcaP02IjfFW5YPjODzzzDP45ptvoFKpUFBQAA8PD+Tm5kKv12P27Nn4/PPP+QilUWhhF0IIIU1JYU42HF0bNjmfEGIZvK+UePjwYWzduhVXrlwBy7IICQnB+PHj0a9fPz7DaDBKqAkhhDQlmtJSSO3sbB0GIa2aTZceb44ooSaEENKU6LVaiGpYa4EQwh/exlATQgghxPIEQqGtQyCk1bNqlY9Ro0bVqz3DMNi+fbuVoiGEEEJaHoZhbB0CIa2eVRPqnTt3QiaTwdPTE+aMLKE/CoQQQkj9MAK62UyIrVk1ofbx8UFGRgZcXV0xadIkPPHEE/D09LTmJQkhhJBWhWNZSqoJsTGr/gbevn0biYmJ6Ny5M9599134+flh8ODB+Pbbb1FUVGTNSxNCCCGtAsuytg6BkFaPtyofOp0Ou3btwsaNG7Fz506wLIthw4Zh0qRJGDlyJKRSKR9hNBpV+SCEENKU6HU6iMRiW4dBSKvG2z0isViMxx9/HJs3b0ZWVha+/PJL3L17FxMmTMBHH33EVxiEEEJIiyIQ0nAPQmyN999CjUaDPXv2YPv27UhKSoJMJkNgYCDfYRBCCCEtgkBAZfMIsTVeEmqWZbFnzx5Mnz4dHh4emDhxIsrKyrBmzRrcu3cPU6ZM4SMMQgghhBBCLM6qVT6OHDmCjRs3YsuWLcjNzUXPnj3x/vvvY/z48XB1dbXmpQkhhBBCCOGFVSclCgQCyOVyDB8+HBMnTjRraEd0dLS1wrEImpRICCGEEEIeZPWE2nihOhZt4TgODMPAYDBYKxyLoISaEEIIIYQ8yKpDPr799ltrnp4QQgghhBCb460OdUvBcRyKioqgUChoqXRCCCGEEMJ/2bzmjmEYODo6UjJNCGmUwMBAMAxjfAgEAigUCvj6+mLgwIFYtGgRTpw4YeswLerGjRtVXnNtjxs3btg6XEIIMZtVh3wQQgipXZ8+fRAaGgoAKCsrQ05ODpKSknDgwAEsW7YM/fv3x9q1axEcHGzjSC1r7NixcHBwMLm/tn2EENLU0JAPQgixgcDAQNy8eRPffvstpk+fXmUfx3H47bff8NJLL+HKlSvw8PDA0aNHERQUZJtgLeTGjRvG13D9+nVa1IsQ0mLQkA9CCGliGIbB8OHDceLECbRp0wZZWVmYNWuWrcMihBBiAiXUhBDSRKlUKixfvhwAsH//fpw6darK/spx2Ddu3MD27dsRGxsLZ2dnMAyDAwcOAAAGDBhQ5fnDlixZAoZhsGTJkmr79Ho9li1bhrCwMMhkMri7u2PcuHE4f/481q1bB4ZhqvWuE0JIa0QJNSGENGHDhg2Ds7MzAGDv3r01tlm2bBlGjx6NoqIiDB06FP3794dQKGzUdVmWxZgxY7Bo0SJcuXIF/fv3R2xsLE6fPo1u3bpVS+4JIaQ1o0mJhBDShDEMg+joaOzbtw/nzp2rsc3nn3+O7du3Y9SoURa77qpVq7Bz5054eXkhMTER7dq1AwAYDAYsXLgQK1assNi1CCGkuaMeakIIaeJcXV0BALm5uTXunzZtmkWTaQDGhHnJkiXGZBoAhEIhPvroI/j4+DTq/EFBQSZL5kVFRTXq3IQQwjfqoSaEkCaOZVkAMFn/Pj4+3qLXS09PR1paGgBg0qRJ1fZLJBLEx8c3qpe6trJ5/v7+DT4vIYTYAiXUhBDSxOXk5ACAcSz1wyxdfi49PR1ARc+4qaS3sdf85JNPqGweIaTFoCEfhBDShHEch6SkJABAeHh4jW3kcnmDz1/Z+12T2laEpdViCSHkPkqoCSGkCdu1axfy8/MBAI888ki9j5dIJACAoqKiGvffvHmz2rbK8dHZ2dkoKSmp8ThaGpwQQu6jhJoQQpootVqN+fPnAwCGDBnSoMl6lcnxhQsXqu0rLS1FYmJite1+fn7G4RibNm2qtl+r1eKnn36qdyyEENJSUUJNCCFNTOXS4927d8eVK1fg5eWFNWvWNOhcgwcPBgB89tlnyMjIMG4vKSnBM888g9u3b9d43Lx58wAAb731Fi5fvmzczrIsFi9ebPI4QghpjWhSIiGE2NDXX39tXMVQo9EgJycHp0+fRl5eHoCKlQ7Xrl2LgICABp1//PjxWL58OU6ePIlOnTohJiYGLMvi5MmTkEgkmDFjBtauXVvtuHnz5mHv3r347bffEBERgYEDB0KlUuHvv//GnTt38Nxzz2H16tXGISX1tWjRIpMTHiuvHx0d3aBzE0II3yihJoQQG/rrr7/w119/AQDs7e2hVCoRHh6Orl27YsKECejWrVujzi8Wi7F37168+eab2LZtG37//Xe4u7tjzJgxePfdd7F69eoajxMKhdi+fTuWL1+OdevWITExEQqFAn379sW2bdvw888/A7hfI7u+6hoyMnr0aEqoCSHNBsNxHGfrIAghhDQvsbGxSExMxE8//YS4uDhbh0MIITZFY6gJIYTUKDk5GVqttso2rVaLJUuWIDExEe7u7hg+fLiNoiOEkKaDhnwQQgip0UsvvYTk5GRERkbCy8sL+fn5SE1NRWZmJmQyGdavXw+ZTGbrMAkhxOZoyAchhJAaff/99/j++++RkpKC3NxccBwHb29vDBw4EAsXLkTHjh1tHSIhhDQJlFATQgghhBDSCDSGmhBCCCGEkEaghJoQQgghhJBGoIS6njiOQ2FhIWikDCGEEEIIASihrreioiIolUoUFRXZOhRCCCGEENIE8F42T6PR4PTp07h37x769OnT4FW2CCGEEEIIaQp47aFeuXIlvLy8EBMTg7i4OKSkpAAAcnJy4OrqirVr11r1+kuWLAHDMFUe7du3t+o1CSGEEEJIy8ZbQv3tt9/ipZdewtChQ/HNN99UGYPs6uqK2NhY/PDDD1aPo1OnTsjMzDQ+Dh8+bPVrEkIIIYSQlou3IR/Lli3D448/jo0bNyI3N7fa/i5dumDlypVWj0MkEsHT09Pq1yGEEEIIIa0Dbz3UV69exbBhw0zud3Z2rjHRtrQrV67A29sbwcHBePLJJ3Hr1q1a22s0GhQWFlZ5EEIIIYQQUom3hFqlUiEnJ8fk/vPnz1u957hHjx5Yt24ddu/ejc8//xzXr19H3759a63Y8cEHH0CpVBoffn5+Vo2REEIIIfzhdKytQyAtAG9Lj8+YMQP79+9HcnIyDAYD3NzcsG/fPsTGxuLcuXPo0aMHZsyYwcuwj0oFBQUICAjAp59+ipkzZ9bYRqPRQKPRGJ8XFhbCz88ParUajo6OfIVKCCGEECvQ55RB5Cq3dRikmeNtDPV//vMf9OjRA2FhYRg5ciQYhsH69euxdu1a/PTTT/Dy8sK///1vvsIBUNFr3rZtW1y9etVkG6lUCqlUymNUhBBCCOELp6ceatJ4vA358Pb2xqlTpzB06FBs3rwZHMdhw4YN2LFjByZOnIhjx47xXpO6uLgY165dg5eXF6/XJYQQQkjTQEM+iCXwNuTjYdnZ2WBZFm5ubhAI+MnrFy1ahJEjRyIgIAB37tzBW2+9heTkZJw/fx5ubm5mnaOwsBBKpZKGfBBCCCEtgCZNDWmw0tZhkGaO95USK5mbwFpSeno6Jk6ciNzcXLi5uSEmJgbHjh2zSSyEEEIIsT0a8kEsgbeE+o033sDOnTuRnJxc4/7OnTtj9OjReOutt6wWAx8LxxBCCCGk+aCEmlgCb2Oot27dWmsd6uHDh2Pz5s18hUMIIYQQAs5ACTVpPN4S6lu3biEkJMTk/qCgINy8eZOvcAghhBBCwOltMpWMtDC8JdQODg61JszXr1+HTCbjKxxCCCGEEHBag61DIC0Abwn1gAED8OWXXyIjI6Pavtu3b+Orr77CwIED+QqHEEIIIYQSamIRvJXNu3TpErp37w6GYTBz5kx06tQJAHD27FmsXbsWHMfh2LFj6NChAx/hNBiVzSOEEEJaDvXvN6B8JNDWYZBmjrcqH+3atcOhQ4cwd+5c/Pe//62yr1+/fli5cmWTT6YJIYQQ0rJw5dRDTRqP1zrUEREROHjwIHJycpCWlgYACA4O5n2FREIIIYQQAGBLdbYOgbQANlnYxdXVlZJoQgghhNicoUQHjuPAMIytQyHNGK8JtcFgwJ49e5CWlob8/Hw8PHybYRi8+eabfIZECCGEkFaM07HgNAYwMpstHk1aAN7ePSdPnsTYsWORnp5eLZGuRAk1IYQQQvhmKNJCQAk1aQTeyuY999xzKCsrw7Zt25CXlweWZas9DAaaGEAIIYQQfhnyNbYOgTRzvCXUKSkpePXVVzFy5EioVCq+LksIIYQQUit9TpmtQyDNHG8Jta+vr8mhHoQQQgghtqLLKrF1CKSZ4y2hfvXVV7FmzRoUFhbydUlCCCGEkDrpMkvA6Vlbh0GaMd5G4BcVFcHBwQGhoaF44okn4OfnB6FQWKUNwzCYP38+XyERQojF6O7dg9jd3dZhEEIagDNw0GYUQxpAKyCThuFt6XGBoO7OcIZhmvzERFp6nBBSE821a5CGhNg6DEKImdRqNVJTU5G/7So4PYvOsd3hNyrM1mGRZoq3Hurr16/zdSlCCOEdW1Zu6xAIIfWQmpqKvn37Gp//IvkWviM6ghHyNhqWtCC8JdQBAQF8XYoQQnjHlVOVAEKaM07LQnuzENJgla1DIc0Q71/DMjIysGnTJqxYsQLp6ekAKlZQzMvLa/LDPQghxBS2nOrYEtLclV3Mt3UIzYpGQ3/3KvGWUHMchwULFiAoKAhPPvkkFixYgMuXLwMAiouLERgYiP/7v//jKxxCCLEoTksfLIQ0d9rrarClOluH0WyUlFC5wUq8JdQff/wxVqxYgUWLFmHv3r1ValIrlUrExcXhp59+4iscQgixKE6rBUd32Qhp1jiWQ9n5PFuH0WxotVpbh9Bk8JZQr1mzBlOnTsX777+PqKioavsjIiKMPdaEENIcsWU0jpqQ5q4sNRucgWpSm4MS6vt4S6hv376N3r17m9xvb29Pi778o0xLvVwNVko9C8R22JJSW4dACGkkQ7EO5Rfps8QclFDfx1tC7e7ujtu3b5vcf+rUKfj7+/MVTpOWU0xjMRusjCaUENthC9W2DoEQYgElf2fRyolmoIT6Pt4S6ri4OHzxxRdIS0szbmMYBgDw+++/Y926dRg3bhxf4TRpxRq9rUNovjRFto6AtGKGggJbh0AIsQBDkRZlqTm2DqPJ02q1YFn64gHwmFC//fbb8PLyQlRUFKZOnQqGYbB06VLExMRg2LBhiIiIwOuvv85XOE0aJdSNoKUZx8R29Lm5tg6BEGIhJX/fBVtOn8d1oV7qCrwl1EqlEseOHcMrr7yCjIwMyGQyHDx4EAUFBXjrrbdw6NAh2NnZ8RVOk0YJdSNQQk1sSH8v29YhEEIshNUYUPL3XVuH0eSV0WRsADytlFheXo6vvvoKUVFReOONN/DGG2/wcdlmq4QS6oZjdYBBBwjFto6EtEK6u/ThS0hLUpaSA3m4G0Qqqa1DabKKi4vh5ORk6zBsjpceaplMhldffRWXLl3i43LNXhHdYmocGkdNbER3+5atQyCEWBDHcig+csfWYTRpVKGtAm9DPsLCwnDjxg2+LtesFdAqTY2joV9uYhu6zLswFNOwI0JaEs21AmjTqaPGlPx8qq4F8JhQv/fee/jyyy+xb98+vi7ZbGUXUdm8RimliWENpTPQl7lG4ThoLpy3dRSEEAsrOpgOzsDV3bAVysmhaigAT2OoAWDVqlVwdnbGo48+iqCgIAQFBUEul1dpwzAMtm/fzldITVamugwsy0EgYGwdSvNUfM/WETRbueW58LT3tHUYzVrpqdOw69bN1mEQQixIn1eO0qR7sO/qYetQmpzs7GxotVpIJBJbh2JTvCXUKSkpYBgG/v7+MBgMuHr1arU2lXWpWzutnkVGQRn8nKnqSYOo020dQbNVrC0G7G0dRfNWeuI4uFkzwYh4+/NKCOFByYlMSENVNEHxISzLIj09HcHBwbYOxaZ4+4tP46fr5+LdIkqoGyrvuq0jaLYKNAW2DqHZUavVSE1NRcnZs1Dn5KCdTgeX06dh3727rUMjhFgQZ+BQlHgLqtGh1AH4kCtXrlBCbesASM1O3czHkI50a6lBsi8AHAfQH7x6U2tp6ez6Sk1NRd++fY3PN/frD4/f91JCTUgLpE0vRvmlfMjbO9s6FJuq7Ei4cuUKrl69ivLycvTp0wcODg62Ds1meJuUCAAGgwE//PADZs+ejTFjxiA1NRVAxT9MQkICsrKy+AynSTt5Iw9lWoOtw2ieSvOAAipf1hBqDSXUllCWlATdHSq1RUhLVPxXBlhN6/58ruxImDFjBj755BOkp6cjJSXF1mHZFG8JdUFBAfr06YNJkyZh06ZN+OWXX5CdXbGqmIODA+bNm4cVK1bwEstnn32GwMBAyGQy9OjRAydOnODlunVRq9U4fPgw7l5ORualZOw8WX2cOTHTjcO2jqBZyi6llf4sRb3zV1uHQAixArZUj5KTtIjTwy5cuIDS0lJbh2EzvCXUr732Gs6dO4c9e/YgLS0NHHe//IxQKER8fDx27dpl9Tg2b96MBQsW4K233sLp06cRGRmJRx99FPfu2b4yROU3vl0fPIMjK57H+l8PwcBSmZ4Guby7YtgHqZfbRbdtHUKLUbx/P/S5VMKRkJaoLDkb+vxyW4fRpOj1epw6dcrWYdgMbwn1tm3bMHfuXAwZMqTGwfxt27blZeLip59+iqeffhpPPfUUOnbsiC+++AJ2dnZYu3at1a9dX/klWuw9T9+CzVHZu3/41DkcvqqGOjMNyDht67CanZyyHOSWURJoCZxOh4ItW20dBiHECjiWq6hNTR03VVy4cKFJdFDaAm+TEtVqNYKCgkzu1+l00Outu+S2VqvFqVOnsHjxYuM2gUCAwYMH4+jRozUeo9FooNHcX2ilconN5ORkiw++f3hp9qKsW1jxw++QDGsHO7GwQecUMAxEQl6HyttEUlISZs2aZXz+9ZRQdMYnQO8XG3diRgAIW8/c3ewr2fi+7Hv08OrR6HMxDAORoOX/7B7+vU0rKoTsn985ZsuPcPVwh9jbu97nZRimVZXeYw2sxW4qMQwgaAV/9yqxBgNYCyZ2AoaBQNiwz5zm5OHf3WtZNyAX16Mk3i3ATnMTsiDl/W0MA0bY8ifEP/yzy8rKMtahXrt2LQYMGACBoP6/gwzDQGjB9150dLTFzlUnjiedOnXi5syZw3Ecx+Xk5HAMw3B//PGHcf/w4cO5bt26WTWGjIwMDgB35MiRKttffvllrnv37jUe89Zbb3EA6EEPetCDHvSgBz3o0YwefOKt+2PWrFl49dVXMWDAAAwaNAhAxTcRjUaDd955B7t378ZXX33FVzhmW7x4MRYsWGB8XlhYCD8/Pxw8eNDiPdQP97J2GP8yFD5tAAAjI73RwVNRr/PpWQ4syyHIzR4SUcvubajWQz0pEJ397AFHH6DrzIqe5vpidQBrAFxCAVHLXgGqqKgIFy9fxNqza6HRazC572SEeoQ2+Hx6Vg8DDAh0DIRYKLZgpE3Pw++9D8LD0cFRWaWNQ2ws7KKizD4np9eDYw2QBgVBIG7ZPz8AMOhZFGSVQCBgwDSyZ5kzsGBZDioPewhFLb+XWq/X4+qJI3DxC4BI1Pj3isFgAMca4OTtC1ELv0Py8O/usvFvIMynXb3PI3SUwL6HV8UTloPYzQ5MC3/vPfyzmzRpEvz8/Kq0iYiIgJubm9nnZFkWLMvCxcWlWb73eIv4xRdfxLlz5zBx4kSoVCoAFf8Aubm50Ov1mD17NmbOnGnVGFxdXSEUCquV58vKyoKnZ83LLUulUkil1W8BRUVFwdHR0aLxPTw71t7dD0q/tgCA44UC9O/dHj4qeU2H1kirZ1GuM6CDtyOkLTyhfvhn187DDtF+DgDUgLIACImt/0kNWkBXCniGA6KWvTLW4cOHMeeZOcbnbdu1xdihYxu8eIHOoEOZoQztndtDImzZX0Yefu8FKRQIc1JV2SY4fw4eY0ZDqKq63RROqwNbXgZZhw4QtILlfA06FjnpRRBJhI1Ogg16FnqtAa6+CgjFLTupUavVSEpKQmF+PjihCH0GDmr055JBr4Neo4FbYDBELfzL3MO/uyHugYjw69Cgc8lFLpCHuYLTsZB4O7T4hPrhn52Hh0e1hLqkpAS9e/c2u/PRYDBAp9PB09OzWSbUvP2LMwyDNWvW4M8//8TUqVMxbNgwREVF4ZlnnsGBAwfw+eefWz0GiUSCLl264I8//jBuY1kWf/zxB3r16mX16zeGRs/i8wPXUKyx7jjzFunMRqCYysHVx92SuziaWfO8AlJ/rEaD/M0/0gQmYlGpqakYOHAgnv33O5j83Fzj2g6Ef2XncmFQa+pu2IpotVocOXIEBkPrqNlttYQ6Li4Ohw4dMj7/888/kZ2djZiYGCxfvhy//vorfvvtN6xatQr9+vWzVhjVLFiwAGvWrMH69etx4cIFPPvssygpKcFTTz3FWwwNlVOswVd/pkFvYG0dSvOiKweOrwZY+jJSH9uubkN6Ubqtw2gxys+dRdnfJ20dBmnBUv7Yjdx0WtTKJjigNCXH1lE0Ofn5+Th+/Hir6EywWp/69u3bMXbsWOPzgQMHYsOGDZg0aZK1LmmWCRMmIDs7G//+979x9+5dREVFYffu3fDwaB7LfF/OKsL6ozfxVJ9ACGhpbfPlXAFStwKRT9g6kmZDx+rw9dmvMa/zPDjLWvcyu5ZSsHUrJKEhEDnTz5M03sM9f0V5efhr8waoPL0RENEZ3m3aQyyTWfy6V44fwZl9vyEr7SrKi4swZelKuAcG13ncpaOH8deP36EwOwtOnt7o++R0BHfuZty/e/V/ce7gH1WOCYyMxtjX37H4a7AGXXoxDCW6WtuUnc1B8fFM6DKKwZbq4T6vMyTetQ+J0GWVoPD3m9BmFMNQoIHysWAoYnyqtCk+dgclxzKhz6/oJRd72EExyB/ydrb/W3P79m3I5XJERUU1eBhhc2C1HmofHx8kJSUZn3Mc12R+kC+88AJu3rwJjUaD48ePo0ePxpcI49PfN/Lw/bFbFi2T1Cpc3AncSaq7HTFSa9RYnbwaBZoCW4fSIrDlZchbtx5cK7kFSqwnISEB48aNq7Jt9c49OHklDQV37+DM779izxfLcXTrRlz9+xjU97LAsZa5u6nTlMOnXUf0nTTd7GMyLl3Arys/QvjAIZjy4UqEduuJ7R+/h5xbN6q0C4zqgjlfbjA+Rsx7xSIx1+ThLySGRv98OOgyimttwWoNkAY4QjnUdBnhamfVshC6yKAcFgiBouZx7UJHKRyHBsF9bme4vxAFaYgKuf87D11WSb1egbVcvnwZFy5csHUYVmW1HuonnngCn3zyCX788UfjJMTXXnsNH3zwgcljGIbBmTNnrBVSi/LXtRzoWRZTewVCKGgaX1SaheOfA4PfARQ1T0Il1eWW5+L/kv4Pz0Y+C1e5q63Dafa0N66j8JcdUI4ZbetQSDOVkJCA+Pj4arfRi8rK8dmOPXh+5KPo2iYYrMGA7JvXkX3zOgBAIreDW0AgXP2D4OoXADulqkEdXR37VUzyVt/LqqPlfad/+wVBUV3QbVTFnes+E6bgZkoykvbsxJCnXzC2E4rEsFc51Tum+kpISMCcOXOqbHvmf69iafxijIhowCT2f+hzymrdbx9dcTdcn2f+KosSPwUkfhVVvtS/3aixjbyjS5XnykcDUXwsE9pbRRB72Jt9LWtKTU2FVCpFSEiIrUOxCqsl1B988AFCQ0ORmJiIe/fugWEY2Nvbw8XFpe6DiVmOX8+DnuUwo08QJdXm0pYChz4BBr0FSOtXhrA1yyvPw4rTKzAjbAaClOb3rJCaFSXuhyQ0BPLwcFuHQpoZg8GAF198sdYxqZsO/IXokMBqC2toy0qRcfE8Mi6eBwDIHBRw8vKBk5c3VJ4V/28tmZcvosuI0VW2BURG49rfVSc/p59Pxeqnn4TM3gH+YRHoM2EK5ArLVtQy9YUktzgfT697BWumf9TgpJotrn3IBx84lkNZajY4rQES/6b1OXfq1Ck4ODg0m2G29WG1hFooFOKZZ57BM888A6BiRcI33njD5mOoW5pTN/MhEjCY1pvGVJut6C5waBkw4DVAZPnxhS1Vsa4YnyV/hjGhY9Dbu3eTGcLVXOV//z0kixdDqFTW3ZiQfxw6dAjp6bVPFs4rKsbljDto7+dTa7vy4kJkXilE5pWKW/ECkRjebTugfW/LFwooKciH3UNlI+2VKpSoC4zPAyOjEdq9N5TuHijIysThTf9DwgdvYeJ/PoFAYJnSr+Z8Ifn3tmUYGtYfwgZck9XabjiX7m4J7q1OBqdnwUiEcJnSscn0TlfiOA5Hjx7FkCFDYG/ftGJrLKuNoY6Ojsbu3buNz7/99lt07tzZWpdr1Y5fz8P25Du2DqN5yb0K/LUcMNi+N6E5MXAGbL2yFf87/z+U6Wu/tUlqx5aWIn/TD61i9juxnMzMTLPa5ReVgmO5ej0MWi1unz2D3PSbxt7tC4cSsXJqvPGRfuGs1V5b+z79Edq1B9z8A9GmWy+MefUt3L12BbfPWa4cYF1fSDgAdwqycPxaEsBx9Xz8c4J/lCbdQ8a//zI+NNfVFnsdNRG5yuExLxruz0XBoacX8rdcajJjqB+k0Wjw119/QadrWZ+/VuuhTklJQU7O/RIyM2bMwIYNG9ChQ8OKppPa7Tl3F44yEQZ1aHm3Uazm7lng7zVAj2cB6m2tl+TsZKQXp2Nm2Ex42tN49IYqP38OpSdOwL6ZTYwmtuPl5WVWOxcnJUQ1LEpmisxBAe+27eHbMRy+7TpCIKzonQ3p2gOebe6vHujg3LBhm/YqJ5QWFFTZVqIugL1SZfIYlYcn5ApHFNzNREB4VIOu+zBzv5BkFeZUSY7NJZAKjZ8nso7O8PCLNu4TKq27SBMjEkDkWrH4m8RXAW16MYr/ugOnuDZWvW5D5Ofn46+//kJMTEyzXMSlJlZ7FQEBAdi3bx8mTpwIoVDYpKp8tFRbTqVDo2cxLMyTftbmunmkYnnyjo/bOpJmJ6csB8tPL8eMsBlo69TW1uE0W+qtP0EaGgoRzS8hZujbty98fX2RkZFh8u6Gs8IB7f186vwcYAQC+HWKQLueMXAPDAYjqH7TWiK3g0Ru1+i4vdq2x62zyegy4v7f2pupSfBq297kMUW5OSgrLoK9k+VKv5n7hcTDyQ1MA1baFLnbgRFW/NwFUhEEUhsmiywHTt90163IysrCwYMH0bdvX0hawIqwVhvyMWfOHPzvf/+DTCaDo6MjGIbBzJkz4ejoaPKhpLGEjfbLmTv45vB1aHRUlstsZ7cCmVRdpiE0Bg2+SvkKR+4coaELDcRqypH39TdgtVpbh0KaAaFQiBUrVtTaZvLg/tUmJFYSSSTw7dAJPUaPR9zit9Fv0nR4BIfWmEybUlZchHs30pCbUbGITN6ddNy7kYaSgnxjm99WLcOhjeuMz6OHjcKNM6dxckcCcjNu48iW75F17So6P/oYAEBbXoaD363FncsXob6XhZupydj28btw8vRCYGQ0LKXyC4mpLxsMGHirPNAzNLqip7meD7FH7V8+2FIdtHeKobtXsXS3PrsM2jvFMBTd//3P23wJ6t3Xjc85PQvtnWJo7xSDM3AwFGqgvVNcpaKIevd1aNLU0OeVQ3e3pOL5dTXsOrs35sdldTk5Odi/fz/Kypr/EEKrfXV6+eWXERkZicTERGRlZWH9+vXo1q0bgoPrLv5OGufkzXxkFWowu18wJCLTfyR1Bhaf/H4JBy5m41ZeKRQyEWJCXfHqsPbwcKx9st7/jt7AlwfTkF2sQQcvR7w9qhOi/FTG/feKyvHBros4dCUHJRo9gt3s8cLAUAwLN693gFccBxz9DBj0JqD0s3U0zY6BM2DL5S24lHcJcW3ioJTW/cVYx+rwf0n/h0Pph5BRnAEHsQN6evXES11egrud6Q+Ak3dPYt25dTifex7ZZdlYPnA5BvkPqtKG4zh8lvwZfrryE4q0RYhyj8KbPd9EgGNAo1+rtWgz0pG/YQOcn3qqXokNaZ3i4uKwdetWzJkzB9nZ2cbtjnZyTH80Ft3ahVZpb69ygk/7TvDrGA73oGAIRTXXMjbXtZPHsefz5cbnv674CADQK34ieo97EgBQmJtd5b3s064Dhs99GX9t3oDDP/wPKk9vPP7yv+DqHwigorc8++Z1nDv4BzQlJXBwdkZARGf0GT8ZInHj4n1Q5ReS+Ph4k23eHbuoQRMSAUDiU/siLWXn85C/9bLxed6miwAAxSB/KIdU/I3SF2iAB/J9Q6EW91beX0Oh+M8MFP+ZAUmQEu6zIyraFOuQ9+MlGIq0EMhEEHvZw3VGGGRtrF+CsLHUajX+/PNP9O/fv9Z2BoMB+/fvx5UrV5Cfnw+pVIrg4GAMHjwYjo61V4I5ceIE/vrrLxQXF8PT0xPDhg2Dr68vgIrhJ6a+pI4bNw6dOnUy63VY9V7EI488gkceeQQAsG7dOsyePZuqfNTi4SLzjSnCfzu/FP+XeBUvDDBd77FMZ8C5jELMHRSKDl6OUJfp8PaO85i1/iR2zI0xedyOM3fwn50X8J8xYejsp8Lav65j6jfHsX/RALg6VIzZW/jjGRSW6fD1tK5wtpNge3IGnt94Gr+8EIMwnyZ4J0JXChz4EBiwGFD62joa3lV/79W/tzklJwWX8i9haOBQ9PTqWWvbcn05LuRewOzI2Wjn1A6F2kIsPbEUc/fPxebHNps8rkxfhrZObTEmdAxeOvBSjW3Wnl2LjRc24j8x/4GPgw9WJa/C7L2zsX30dkiF5o8p5VvZmTNQb98O1Zgxtg6FNANxcXFwcXHBgAEDjNteeHw4OgRU/P1SunsiIDwKfp3CofL0tugwwLABgxE2YHCtbSa89WG1be16xaBdr5o/W8QSKeL/9a5F4quLqS8krg7OWDphMUZEDqrlaNMYiRBir9oTavuuHrDvWvtcp8okuZLIWQbfD/vWeoxzPL/D7h7+zGAbuShOQUEBzp49i7CwMJNtdDodMjMz0a9fP3h6eqKsrAy7d+/Gpk2bMHv2bJPHnT17Fnv27MFjjz0GHx8fHDt2DN999x1eeOEFODg4QKlUYuHChVWOOXXqFI4cOYLQ0FATZ62Ot64QlmUpma5FTatepfxvCbJS/mzwOe+qy/HHxXsm9zvKxPhuVg88FuGNEDcHRPs74Z1RnZCaoUZGgenbL18fvo4nuvthfFc/tPFQ4L3R4ZBLhPjx5G1jm1M38zGtdyCi/FTwd7HD3EFt4CgX42yGdWc5N0q5Gkj8T0UFkFakpvfeiY9O4M7R+leO0Rg02H5tO75I+QKlulKT7RQSBdY8sgZDA4ciSBmESLdIvN7jdZzPPY/MYtOThvr69sW86HkYFFDzBx7Hcfjuwnd4JuIZxPrHop1zO7wf8z6yS7Ox/9b+er8evhUnJqLk+HFbh0GaCaGwai+qUCRESJceGD53EUbOfw0Rg4fCyavusdStUVxcHLZs2VJl21dPLW1wMg0AshClcfx0S1bTZ8Y333yD5OTkRp33+vXrtVb+kMlkmDp1KsLCwuDq6go/Pz8MHz4cmZmZKHhowuuDjh49iujoaHTu3Bnu7u547LHHIBaLjat5CwQCKBSKKo+LFy+iU6dOkNZjYq/VEupbt27h1q1b1Z7X9WiNKovMP/hNGQB0xQVIWffvRiXVKen1S2CLyvVgGMBRVvPNC62exdkMNfqE3l8xTyBg0CfUFadvFhi3dQlwws6UTBSUasGyHH45cwcaHYuewU184pWmGDjwAXCvZS+RWsnUe09bqMWJpQ1LqgHghvoGEq4m1OuYIm0RGDBQSBq+EEF6cTpyynLQ0/t+D7lCokC4WzjOZDePcfLqLVuhz8+vuyEhD+kVPwm94ifC2bv13WVriGpfSBo53ErWsYl/vlmAqc+M4uJifP31141KqlmWRV5eXr2OKS+vWHFSJqt5mKper8edO3eqDDcWCAQIDg42WT7xzp07uHv3br1LPVttyEdgYCAYhkFZWRkkEonxeV0evo3Q0plTZP7StlVwD+sDpgFjuvJLzZ/oVK4z4MPdFzAq0hsKWc1j1vJLtTCwnHFoRyU3BymuZd+vd7lqUjRe2HgaUe/shUjAQC4W4sspXRDo2gwKues1wOFlwMB/AfJaxp8ZdMD+d4Ere4H8G4DUEQgeAAxeAjiaOVb80KfAH29XlO4b9s8t0vybwIqImtuPWwd0ssyQAHPee6nfpMKru1eDel3SCtLMbqsxaPDfU//FsKBhcJDUfsu0NrlluQAAF1nVDzYXmQtyynJqOqTRqt36bOTkTFarQdGePVCONl15htPpkL1iBYoP/gltejqEDg6w790LbgsWQuxh3iSknK/WIPvTT+E0dQo8X3/duP3mlKko/fvvKm1VEybA6+0lDXo9xHrCw8Nx6NAh3EhJgouPH3r3H2DrkFotsbsdxF7N4POtEcz5zNi6dSsiIiJMToqtS2WCbA6dTod9+/YhPDzcZEJdWloKjuPg4FD1c8Xe3r5KaecHnT59Gq6urvD39zc/cFgxoV67di0YhoH4n8kElc9JVeaseqUpuIe8aylwDq3ftyWO48A+MLNhW1IGXv/5foH8dU91R/eginJEOgOLFzaeBscB/xltegyTuT79/RIKy/X4flYPONlJ8Pv5u3h+42lsmdML7T0tu4wsUD2pMTRgDHAVunLg1LdAn/m1tCmtqA7S72XAMxwoKwB2vwpsegKYfbDua2ScqriGx0M/b6UvsPBy1W2n1gFHVgKhQ+r7Skwy571XllOGnPM5cA1zrbXdwzhwVSb17EzbiXeOvmN8/vngz9HFowuAigmKiw4sAgC82fPNel3H1hISEjBnzpwq2144dQrvhofjUTPLc9WkLCkJjsOHmdzPlpej/Px5uD73LKTt2oMtVOPu+x8g/bnnEPTT1rrPn5qKgs2bIW3Xrsb9qnHj4DZvrvE5I5fX/0UQq1MqlYiJiYG/yhH+YSa+hBNe2Pdo+eVqzfnMKCgowNWrV9G2bf3HdHMcVyVZT0lJwY4dO4zPJ0+ejICAiombBoMBW7ZsAcdxGDFiRL2vZYpOp0NqamqdEyRrYrWEevr06bU+JxXMLTJfXpjboJ4vB6kIwn9+yQd39KhSicNTWfGNTmdg8fz3p5GeX4ZNT/c02TsNAE52EggFDHKKNVW2Zxdr4PZPr/XN3BKsP3oTv8/vh7YeFbfvO3o74u8befjf0Zt4f0x4vV9HbWpKasZ9cwVfPBGEuKhG1C/NvQYUZQJeJj6oZEpg6vaq24Z/DKyJBQpuA6paKoZoioGfngZGrgT+/KTqPoEQUDw0aeXiDqDTaEDa8N7bh5n73ivLKwPL1X/CiYPYAQKmopdioN9ARLje/zlWVvKoTKbvlNzBN49806jeaQBwkVf0TOeW58LNzs24Pbc8F+2dTde7bYjKW58P99bkabV44dQprOrSpcFJNafTQZueDnlkZI37hQoF/NeurbLN8803cGPceOju3IHY29vkudmSEtxZ9DK83n0HOZ9/UWMbRi6DyM2txn2k6XH1q19PGrEsiZ8CkgDLdxQ1NeZ+ZqjV6gaXUZXL5cYvJu3atYOPj49xX2Ulj8pkWq1WY9q0aSZ7pwHAzs4ODMOguLi4yvaSkpJqvdYAcP78eeh0OkSa+NtbG6rPZGPmFplXOLlBJhbW+9HWwwEiYcU/s4NUhEBXe+NDJhYak+kbuSUVvcn2tRdXl4gECPNR4sjV+7dKWJbDkau5iA5QAaioHgIAgoe+rAsYxuK1ik2N58ou1iP+6ytISK7feKxq8q/X3eZB5YUAmIpkuza7FgFtHwVCBtZ9zjtJwN1UoPPU+sVSB7Pfe64KyESyej+ClEEQCSq+s9uL7eHv6G98yEQyYzJ9q+gW1jyyBiqZqtGvydfBF65yVxzPvD+xr1hbjNTsVES61f8PpCnm3Pr8z7lzMDTi/W7Iza1Xe7aoCGAYCOooH3X3nXfhMKA/7Hv3NtmmcMdOXO7ZC2kjR+Lesk/BtoAasS2ZXS2rDRLrYoQMFAP8WnzvNGD+Z4azszPEYnGDHoGBgcax7VKpFC4uLsaHWCw2JtO5ubmYOnUq7Oxqr/stEong7e2N69fvf5azLIu0tDRj2bwHnT59Gu3atYO9ff2H71ith/qdd96pu9FDGIbBm282r1u+jWXOqlcylTtcQyMb9AvbNcB0D63OwOLZ707j3B01vpnWDQaOw72iivFLKrnEWMN60ppjeLSTJ6b1DgQAzIoJwsItZxDuq0KUnxLfHL6BUq0e47pU9MiGuDkg0MUOryecxesjOsDJTozfz2Xh8NUcrJ3Wrd6vwRRzkpqXtt7E4xFOED6c3ZtLU2R+W105sO8tIDwekNWS1KT+s5DM04nmnff0BsC1HeBv2eWpzXnvyV3lcOvk1qD3Xh/vPib36VgdFhxYgAu5F/DZoM/AcqxxjLNSooRYWHGXZNaeWYj1j8WkDhUVgkp1pbhVdH/yckZRBi7mXYRSooSXgxcYhsHkDpPxZcqX8Ff4w0fhg1VJq+Bm54ZY/9h6vwZT6rr1yQHILC/H3zm56NmQFRA5Dmw9xhKyGg3ufbIMjiNGQFhDr0sl9a+/ovz8eQRu3WKyjeNjj0Hs7Q2Ruzs0ly/h3ifLoL1xHb7/93/1egn11ZBSjYTYmkNvb4hUTbccpyWZ85nh5OSENm0attS5vb09nJ1N5ywGgwE//vgjMjMzMWnSJLAsi6Kiis9ouVxuXMJ8/fr1aN++PXr0qPjM7NWrF37++Wd4e3sby+bpdLpqkw5zc3Nx8+ZNPPnkkw2K32oJ9ZIlS6ptq/xQfvgfgvmn57I1JtTmFJnvNHZegyYkSkQCDOlout7lXXU59l3IAgAMX3moyr5NT/dEr5CKROBmbinySu5PbhwZ6Y28Ei3+u/cysos06ODtiPUzusNNUfFHRSwU4NunumPpbxcxa/3fKNEYEOBih2XjIjGwveVWbTInqbldoMWhq4UY0LYBt+M4DmAeuImT8iOw46X7zydvBQL+6eUz6IAt0yuOGfGp6XOq04HdrwFTtgHi2hfPAQDoyioS8P4v1z/+Opjz3uv8dGcIhPW/kdXBuQP6+fYzuf9e6T0cuH0AABC/o+r11z66Ft08K7543S66jQJNgXHfudxzmLFnhvH5xyc/BgCMChmF92LeAwDMCJuBMn0Z3j76Noq0Rejs0RlfDP7CojWozb31mV1eXvGeaACBTG5cGEO9Ywcy31pi3Of/1Zew69oVQMXwkIyX5oMDB88lb5k8ny4zE1nvfwD/td9AUEspKKcJ443/LWvXFiI3N9ya/hS0t25BUs9JOvWhKdNDbKK6ECFNkcRPAXlE6xkaZc5nxvjx4xs8IbFt27a1dt4UFhbi0qVLAIAvvqg6XG3atGkICgoCAOTl5aG09H7Z1rCwMJSUlCAxMdG4sMvkyZOrDflISkqCo6MjQkJMr99RG4bjab3gjIwMjBgxAmFhYXjppZfQ7p/JMBcvXsTy5ctx/vx5/Prrr/CuZexfU1BYWAilUgm1Wl3nyjz1UTkO+MGhCxIHJ4RPWAivyPoPjgeA6b0DMbZLyy2ftGnTJrNqm2+cFoSJXRtYzqjbLKDrTEAoquitLn6grrejNyCW30+m828A03YAdrWM276wE9j8JMA88AWJMwBgKpL3N7MrxlBXOvMDsP0FYOFFwL5+EwPNVdN7T6qUostzXeDbu/7vnxBlCF7v8Xqjx0M3ZQcOHMDAgXUP1/k+pi96NnAssvvCBXDoU9HLbygugSH3/jArkYcHBDIZOJ0O6fPnQ3c7Hf7rvoXIyXRVmqJ9+5D+wlzgwVJhBkPFkskCAdqnnAEjrP7FnS0txaXoLvBbswYOfU0v+NQYBh2Lq6ez4OqrgLCW1V3NOpeehV5rqDiXmEY1EtMOHz6Mvn3vL5iy/cVv0CPEvMn/ApkQzhPbQ+hQ+zDJlqimzwyFQoFJkybVu9RcJYlEgieeeALyZjwBmrfugOeffx5t2rTBd999V2V7t27d8P333yM+Ph7PP/88fv75Z75CalJqWvWqy1PvwKVNVIPO1zXQCWM6+9TdsBkzdzyXl7NDReLbEEH9K5JpAJAqKh4Pqkymc68B03fWnkwDQHB/4NmjVbdtfw5wbQv0ealqMg1UDPdoN8xqyTRQ83uv16u94B5e/7sJPb164tnIZyETmdH73ozVdeuTAeApl6O7W8OGywCA9IFeEqGDPYQOVcf0GZPpmzfhv359rck0ANj17IWgX6pOos18/V+QBAfBZdasGpNpACi/WLE0ssjduj1xRbnlcPVteA1yQvikiPVvlck0UPNnxtNPP92gyh6Vevbs2ayTaYDHSYn79+9HbKzpMYyDBg3CH3/8wVc4TdLDReaZBt42ae+pwCuPtoegoeOGm4nKpMZUwsIA8HOSom8b1T/P6vmwd6tIdE0x6IAfp1ZMGhy7BmANQFFWxUP/QP3v9SOB419V/LdUAXh0rPoQ2wNy54r/flDuNeDmX0D0tPr+aOqt+nuvfu8dsUCMGWEz8FL0Sy0+mQbu3/qszZsRkcYKO/UlcnWFyMP0cC1Op0P6iy+h/Ow5eH/8MWAwQJ+dDX12Njjt/ffezelPIe+77ytidrCHrG3bKg+BXA6hSgXZPx+E2lu3kL16NcrOnoM2PQNF+/fjzquvwa5rV8hMlNizlIIs0ytrEtKUyDs4QxaisnUYNvXwZ0ZDh3kAQJs2bdC+vWWrMNkCbz3UMpkMR48exbPPPlvj/iNHjtRa+oSYJ8zHEf9+rBPkkvqPuW5uzBnPtXx8SMMnJLYfAdT2R6LwDnBpV8V/f/HQrfBpO4Ggf24l5t0ASutXsQEAkPQd4OgDhFhuMp01BDgGYG7nufBT1FImsAWKi4vD1q1bq936dJFK8W5UZwz1afgdIruePWvt2dZl3UPx/oql1K+PrrrQj//69bDv0b2i3a1bMNRj1UVGLEbpkaPIX/8/sGVlEHl5QvHIELia+LttSflZpdCU6mDn2DomeJHmSWAngkPfln33l09eXl7o169fi6iSwltC/eSTT2LlypVQqVSYO3eucdD3tWvXsHLlSmzcuBHz5s3jK5wWqVeICxY90s5YnaM1MJXUuCvE+HxSG8R1buBQCZkSCB9XexunAGCJGUu7z0+tff9Tv9a8ffBbFY8mbJD/IDzV6SljVY7WpqZbn6u690CPRtZwVgweVOt+ia8POly8UOd5QvfXftcvYMP/qjwXe3kh4LsNdQdoQWq1GslJZ3Ah7RKyN13BiIkDoXSso+wkITaiiPGBQEqTZy3Bzc0Njz76qLE6R3PH26tYunQpcnJysGrVKnz22WfG2wMsy4LjOEycOBFLly7lK5wWZ2iYJ57tH9Lih3nUpKak5senO6B/W1XDT9p3QfXx0sSIAYNpnaZhWJDp1fxai2q3PhvZ02LXtSskfq2ntz81NRUDBt6feK3y2Ibhoy23IighliL2sIO0be1zFYh53NzcMHz4cEhrqTjU3PCWUEskEmzYsAEvv/wydu3ahZs3bwIAAgICMGzYsAatSkMqxEX7YHrvwBZxy6ShHk5qGjzMA6jomQ4e0LiAWjARI8ILnV9AL+9etg6l5REJ4TRlsq2jsKmrp+5B3bsUSvfaF2wghG8OMT6t+nPWUiqT6ZY2zJf3fvaIiAhERJhYypnU22MRXq0+mbao4P5Az+dsHUWTJRaIsajrIkS5R9k6lBZJFR8PSQ2rd7UmBgOLEzuvIyLWDx6BLX85Z9I8SIOUkHi33FKgfHFycmqRyTRAS483az2DnfF032BKpi3Fvxcw8I3aJyK2YmKBGK90e4WSaSuRdmgPVVycrcNoEvQ6Fqf33ETyH7dRVqSt+wBCrImpWBGRNI5cLsewYcNaZDIN2KCHmlhGkKs9Fj7SrlWOmbYK/17AkHcAUeusK1oXiUCCV7q9gnC3cFuH0iIJnZzgvnChyVrQrVXm1QJkpanh084JgeEucHBqmR/EpGmTd3CByJnee43BMAwGDx4MhaLlzk2ihLoZUsrFeGNEB8jE9OFrEf49KZmuhUwow6vdX0VHl451Nyb1xojFcH/l5ToXZmmtWJbD7Qt5uH0hD66+DggMd4Wrr0O9a6UT0hCMkIF9D09bh9HsdevWrcmvhN1YlFA3MwIGeG1Ye7g70rdli/CKoGS6FnKRHK/3eB1tnRq+AhapnevzzxsXVmmNDAZDlecsy5psm5NejJz0YjiopAiMdIV3qKrRS5Uf+P4izh26g5hxbRA5yHR1lVO7byAtKRv5d0shkgjgGaxErzEhcPK8v4Jl4vcXkX4hDyVqLcRSITyDlegdV7UNaV7kYa6tdkVES/Hz82sVhSdosGgz81SfIIT5UI1Wi3D0Bh55DxC1nLI9liQWiPFa99combYi5dg4OPSNqbthC5WQkIBx46rWe1/54+s4cT6x1uOKCzQ4ezADBzZewuUTd1Fa2LBx1mlJ2bh7vRD2yroTpjuXCxDW3xdjX+2CUS9GgTVw+GVlMnSa+18I3P0VGDStAya91QOj5kUBAH5ZkQyWrb48PWn6GCEDu2h3W4fRrNnZ2WHgwIGNWkmxuWj5r7AF6R3qgsejWvYtE94IJcCQdwEZVREwZXKHyWjv3PyXg22q5JGRcHriCVuHYTMJCQmIj4+vsiATABSWFmD55sV1JtUAoC3T41pSNg5uuoTjv6Qh43I+DAbTPdwPKs7X4M/NlzFkRkcIhHV/FI6cF4UOvb3g4u0AV9+KxLk4T4PsW4XGNp36+sC7jRMcXeVw81egx6hgFOdrUJRbZlZMpGmRdXSh3ulGGjBgAORyua3D4AWvQz727NmDb775BmlpacjPzwfHVf3WzjAMrl27xmdIzYa/ix1eGtSWKnpYSp8XAddQW0fRZHnaeeKRwEdsHUaLJXBUwG3eXDCtoNemJgaDAS+++GK1z4AHbfhtObq27weBwLy5InmZJcjLLIHUToQOvbzg6mt68hPHcti37hw6D/GHSwNLoWnK9AAAqV3Nq4TqNAZcPJIJR1cZTaZshhgBA/suHrYOo1nr1KkT/FrRIlW8JdQff/wxXnvtNXh4eKB79+4ID6dqAeZykIrw5oiOkEtoEqJFhA4G2o+wdRRNWj+/fhAwrTPZ44PLzFkQqlS2DsNmDh06hPT09Frb5BZm4cKNZHQMiq7XucuLdTj/1x207W56Itnp329CIGAQEduwmt8cy+HwlivwClHCxadqQp56IB1Hfr4GvcYAlYcdRr0Y1ehx3oR/sk4uECqod7qhVCoVevToYesweMVbQr1ixQrExsZi165dEItr/kZPavbS4DbwVFIPh0U4+gB9FwLU019FeHg4Dh06hI///hgSgQQTBkywdUgtlrxLNOz79LZ1GDaVmZlpVruCohygAcOPhWIBKr8PXjp+Fwc2XjLue+z5CJzZn44Jr3dr8B2/gz9cRl5GCeJerp7st+3hCb8Ozigt1CBp723sWXMOcS9HQ0RVmZoNRsjAviv1TjeUUCjEoEGDWl2ux1tCnZ+fj/j4+Fb3A26s/m3d0CPYxdZhtAyMAIj9FyChJY0fplQqERMTg+/KvsPo0NFwUlEJN2tgpFK4zJrV6odueXl5mdXOReUGkbj+vbtBEa7GcdFBka7wCLo/V+La6XsoK9Ji/etHjNs4lsNfW6/gzB+3MfX92r/s/LnpEm6m5mDMwugah3JI5SJI5SKoPOzgEaTE1wv+RFpyNtp2o9JrzQVV9micnj17wtXV1dZh8I63hLp79+64dOlS3Q2JEcMAk3sG2DqMlqPzk4BHJ1tH0aQxDIPePq2799SanCY+AbE7VQ3o27cvfH19kZGRYXIctYvSAx2COtf7bpJfR2d06udjfC6RiSCR3f+o6xTjg8Dwqh/2O1Ymo11PT7TvZTrR5zgOh364jLTkbIxeEA1HVzMmWnEVD4OOqnw0GwIGdp3pd7ShfHx80KlT6/yc5W1g1+rVq5GQkICNGzfydclqAgMDwTBMlceHH35os3jq0s5TQUM9LMWtPRA93dZRNHk+Dj5wlFDlE2uQBATAcfhwW4fRJAiFQqxYsaLWNtNGLDB7QiIAeAQ5YuDk9ug1OqTW4RUyBzFcfByqPARCAewcpVXqRW/7bxJSEu+P8/5z02VcOpGFITM7QSwTokStQYlaA722omyeOrsMp3bfwL2bhSjKK0fmNTV2rzkLoUSAgDC6y9hcSIOVNHa6gRiGQUxMTKu9A8dbD/WECROg1+sxZcoUPPvss/D19YXwoWV2GYbBmTNnrBrHO++8g6efftr4vCkvg9klgG67W4TYDhj0b0BI6xjVxd2OemasxWXWTFpa/AFxcXHYunUr5syZU6V0nqO9E2Y9/hp6dBpo1nk8Q5To1Ne7wdU6TCnMLkN58f361mf/zAAAbPs0qUq72Kkd0KG3F0RiAe5cUePMH7ehKdXDzlECr1AVxr7cBXaOlKA1F/L2zrYOodkKDQ2FqhVPtuYtw3B2doaLiwvatGnD1yVrpFAo4OnZNMeyVU4M+2DXBZRpDYgfQrfeLaLvAkDpU3c7ApVUZesQWiT73r0h60hLtz8sLi4OLi4uGDBggHHb/Cc+QMfguit7eAQ6omNfb7j5Nb5TpKZx0w9ve/6L2FrPYa+SYuTclr8aXEvGiASQWOD91Fq1b9+61y3gLaE+cOAAX5eq1Ycffoh3330X/v7+mDRpEubPnw+RqGn0XFZODPO8KkUnb0e4u9A35UYLjKkok0fM4iSjuyKWxojFcJoy2dZhNFkP36msa0U1z+B/eqR9LNsjTYjITQ6GShw2iEwma7KdlXxpGpkkT+bNm4fo6Gg4OzvjyJEjWLx4MTIzM/Hpp5+aPEaj0UCj0RifFxYWmmxrSVTZwwIEIqDXC1Qirx6oh9rylGPG0EREC1A4yxA1xB9eIUpbh0JaKJEbVYBqKC8vr1axvHhteE+odTodLl68CLVaDZatvkRsv3796nW+1157DUuXLq21zYULF9C+fXssWLDAuC0iIgISiQSzZ8/GBx98AKlUWuOxH3zwAd5+++16xWQJYT70odFoIbGAo3nluUgFe7F93Y2I2USenlCNGW3rMJo1qZ0IHXp7ISTanRZIIVYldqEiAOaqHKJ65coVnD9/HgMHmjfnoSXjLaFmWRaLFy/G6tWrUVpaarKdwWCo13kXLlyI6dOn19omODi4xu09evSAXq/HjRs30K5duxrbLF68uEoiXlhYaPWlNJ3tJVDKqV53o9FqiPVGCbVlucycAUZCE9IaQiQRoH0vL7Tp5gExrRJLrKAyKczfdhVgOUT17WrrkJqNyiGqDg4O4DgObdu2tXVINsdbQv3+++/j448/xuzZsxETE4MpU6Zg6dKlUKlUWL16NRiGwUcffVTv87q5ucHNza1BMSUnJ0MgEMC9ltuxUqnUZO+1tbgq+L1eiyR1BDwjbB1Fs+Nt723rEFoMeWQE7KLrt2w2qeDsbY/ecaFUHYNYVWVSeO+sAiIXOVTONIekIby9vavNhWiNeEuo161bh/Hjx+Pzzz9Hbm4uAKBLly6IjY3FtGnT0KtXL+zfvx+DB1tnAtnRo0dx/PhxDBw4EAqFAkePHsX8+fMxefJkODk1rV8iRxn1TtdXZU8Dbh4BkjciPGYo0MrHczWEWEjvPUtRTaDl2xvCXiVFvyfaVlmMhRBrE3vQ+OmGqq1TsjXhLeNIT09HbGxF2aHKHt/y8nIAgEQiweTJk7FhwwarXV8qleKHH35A//790alTJ7z33nuYP38+vvrqK6tds6EU9EFSb5U9DTFdOiEmVAllpyG2Dom0YpLgYMhMDCMjtYsc5EfJNOGdyJyVL0mNWnPt6Qfx9lfLxcUFxcXFAAAHBwc4OjoiLS2tSpv8/HyrXT86OhrHjh2z2vkticZPN5JIBnhRPVhiOw71nFzdmlXeXTr042VExvqhW08aJkP4J6IJiQ1mb09zbwAeE+rOnTvj77//Nj4fOHAgli9fjs6dO4NlWaxcuRKRkZQEAdRD3WieYYCIxl4S27Hr3t3WITQblXeXRLle6Pl4iK3DIa2UUEUJdUPxPc+sqeJtyMczzzxTpabze++9h4KCAvTr1w/9+/dHYWEhli1bxlc4TRol1I3k1rpXayK2JXJ1gdiDxhTWlyVWPCSkIQRSIQRSmlTXUJRQV+Atcxs1ahRGjRplfN6xY0dcu3YNBw4cgFAoRO/eveHsTCsDAoCdhBLqRlH62joC0opUDlkoOXYM6h07EUXDPRpErqC7SsQ2BPY0zLIxKKGuYNPMTalU4vHHH7dlCE2SXEzflBvFjlaZJPypHLJQWFyM3KPH4E7DPRpEak8dCcQ2BHRXuMEkEkmrXyGxEq8/BYPBgB9++AGzZ8/GmDFjkJqaCgBQq9VISEhAVlYWn+E0WYGuNMC/UaSOto6AtGKyDh1sHUKzJHegHmpiGwwN92gwmYzGnlfiLaEuKChAnz59MGnSJGzatAm//PILsrOzAVRU/Zg3bx5WrFjBVzikJZM62DoC0kqJPD0hpBJSDUKLuBBbYSTUw9pQEloJ1oi3d9Frr72Gc+fOYc+ePUhLSwPHccZ9QqEQ8fHx2LVrF1/hkJaMeqiJjUiDg2wdAiGknhgR9VA3lFhM488r8ZZQb9u2DXPnzsWQIUPAMEy1/W3btsWNGzf4Coe0VIwAkFAPNbENsQ9NiCWkuaEe6oajHur7eHsXqdVqBAWZ7r3R6XTQ6/V8hUNaKok9LTlObEbk7mbrEAgh9cRIqIe6oZycnGwdQpPBW+YREhKC06dPm9z/+++/o2PHjnyFQ1oqCU3oJLZD46cJaX4EYuqEIY3H27to1qxZWLt2LTZv3mwcP80wDDQaDf71r39h9+7dmD17Nl/hkJZKbGfrCEgrJqAleAlpdqiHmlgCb8UXX3zxRZw7dw4TJ06E6p9enEmTJiE3Nxd6vR6zZ8/GzJkz+QqHtFRyuv1EbEdgR1/oCGluGOqhJhbAW0LNMAzWrFmDadOmYevWrbhy5QpYlkVISAjGjx+PfrS6GLEEhaetIyCtGCXUhDQ/jIgSatJ4vC8PFBMTg5iYGL4vSwghVkcJNSHND/VQE0ugdxEhhFiCQACGVg0jpPmhHmpiAVbtoR41alS92jMMg+3bt1spGkIIsR6BnX2NNfYJIU2bQEaTEknjWTWh3rlzJ2QyGTw9PausjGgKfRgRQporgZ3c1iEQQhpA6ECLk5DGs2pC7ePjg4yMDLi6umLSpEl44okn4OlJk8YIIS0PI5XaOgRCCCE2YtWBQ7dv30ZiYiI6d+6Md999F35+fhg8eDC+/fZbFBUVWfPShBDCKwEl1IQQ0moxnDljMSxAp9Nh165d2LhxI3bu3AmWZTFs2DBMmjQJI0eOhLSZfBgVFhZCqVRCrVbD0dHR1uEQQpoIXdY9iD3cbR0GIYQQG+BtaqtYLMbjjz+OzZs3IysrC19++SXu3r2LCRMm4KOPPuIrDEIIsQpKpgkhpPXivVaMRqPBnj17sH37diQlJUEmkyEwMJDvMAghhBBCCLEIXhJqlmWxZ88eTJ8+HR4eHpg4cSLKysqwZs0a3Lt3D1OmTOEjDEIIIYQQQizOqlU+jhw5go0bN2LLli3Izc1Fz5498f7772P8+PFwdXW15qUJIYQQQgjhhVUnJQoEAsjlcgwfPhwTJ040a2hHdHS0tcKxCJqUSAghhBBCHmT1hNp4oToWbeE4DgzDwGAwWCsci6CEmhBCCCGEPMiqQz6+/fZba57eJhQKBdRqNRQKha1DIYQQQgghTQBvdagJIYQQQghpiXgvm0cIIaRCYGAgGIYxPgQCARQKBXx9fTFw4EAsWrQIJ06csHWYFnXjxo0qr9nUIzk52dahEkKI2aw65IMQQkjd+vTpg9DQUABAWVkZcnJykJSUhAMHDmDZsmXo378/1q5di+DgYBtHalljx46Fg4NDjfucnZ15joYQQhqOhnwQQoiNBAYG4ubNm/j2228xffr0Kvs4jsNvv/2Gl156CVeuXIGHhweOHj2KoKAg2wRrITdu3DC+huvXr9PCXoSQFoGGfBBCSBPEMAyGDx+OEydOoE2bNsjKysKsWbNsHRYhhJAaUEJNCCFNmEqlwvLlywEA+/fvx6lTp6rsrxyHfePGDWzfvh2xsbFwdnYGwzA4cOAAAGDAgAFVnj9syZIlYBgGS5YsqbZPr9dj2bJlCAsLg0wmg7u7O8aNG4fz589j3bp1YBimWu86IYS0NpRQE0JIEzds2DDjmOK9e/fW2GbZsmUYPXo0ioqKMHToUPTv3x9CobBR12VZFmPGjMGiRYtw5coV9O/fH7GxsTh9+jS6detWLbknhJDWiiYlEkJIE8cwDKKjo7Fv3z6cO3euxjaff/45tm/fjlGjRlnsuqtWrcLOnTvh5eWFxMREtGvXDgBgMBiwcOFCrFixwmLXIoSQ5ox6qAkhpBlwdXUFAOTm5ta4f9q0aRZNpgEYE+YlS5YYk2kAEAqF+Oijj+Dj49Oo8wcFBdVYMq+moSeEENKUUQ81IYQ0AyzLAqjora5JfHy8Ra+Xnp6OtLQ0AMCkSZOq7ZdIJIiPj29UL7WpsnlRUVENPichhNgCJdSEENIM5OTkADBdn9nS5efS09MBVPSMm6oV3dhrfvLJJ1Q2jxDSItCQD0IIaeI4jkNSUhIAIDw8vMY2crm8weev7P2uiake8br2EUJIa0IJNSGENHG7du1Cfn4+AOCRRx6p9/ESiQQAUFRUVOP+mzdvVttWOT46OzsbJSUlNR5348aNesdCCCEtESXUhBDShKnVasyfPx8AMGTIkAaNL65Mji9cuFBtX2lpKRITE6tt9/PzMw7H2LRpU7X9Wq0WP/30U71jIYSQlogSakIIaYIqlx7v3r07rly5Ai8vL6xZs6ZB5xo8eDAA4LPPPkNGRoZxe0lJCZ555hncvn27xuPmzZsHAHjrrbdw+fJl43aWZbF48WKTxxFCSGtDkxIJIcTGvv76a+MqhhqNBjk5OTh9+jTy8vIAVKx0uHbtWgQEBDTo/OPHj8fy5ctx8uRJdOrUCTExMWBZFidPnoREIsGMGTOwdu3aasfNmzcPe/fuxW+//YaIiAgMHDgQKpUKf//9N+7cuYPnnnsOq1evNg4pIYSQ1op6qAkhxMb++usvrF+/HuvXr8eOHTtw/vx5hIeHY+HChThx4gQSExMRFBTU4POLxWLs3bsXL7zwAhQKBX7//XekpKRgzJgxOH36NPz8/Go8TigUYvv27fjoo48QEhKCxMRE7Nu3DxEREThx4gQ8PT0B3K+RTQghrRXDcRxn6yAIIYQ0P7GxsUhMTMRPP/2EuLg4W4dDCCE2Qz3UhBBCTEpOToZWq62yTavVYsmSJUhMTIS7uzuGDx9uo+gIIaRpoDHUhBBCTHrppZeQnJyMyMhIeHl5IT8/H6mpqcjMzIRMJsP69eshk8lsHSYhhNgUDfkghBBi0vfff4/vv/8eKSkpyM3NBcdx8Pb2xsCBA7Fw4UJ07NjR1iESQojNUUJNCCGEEEJII9AYakIIIYQQQhqBEmpCCCGEEEIagRLqeuI4DoWFhaCRMoQQQgghBLBBQq3RaHD06FFs374dOTk5fF++0YqKiqBUKlFUVGTrUAghhBBCSBPAa0K9cuVKeHl5ISYmBnFxcUhJSQEA5OTkwNXVtcalbwkhhBBCCGnKeEuov/32W7z00ksYOnQovvnmmypDJlxdXREbG4sffviBr3AIIYQQQgixCN4S6mXLluHxxx/Hxo0bMXLkyGr7u3TpgnPnzvEVDiGEEEIIIRbBW0J99epVDBs2zOR+Z2dn5Obm8hUOIYQQQgghFsFbQq1SqWqdhHj+/Hl4enryFQ4hhBBCCCEWwVtCPXz4cHz11VcoKCiotu/cuXNYs2YNRo0axVc4hBBiUaxGY+sQCCGE2AhvS4/fuXMHPXr0AMdxGDlyJL766itMnjwZBoMBP/30E7y8vHDixAm4urryEU6DFRYWQqlUQq1Ww9HR0dbhEEKaCLasDAK53NZhEEIIsQHeEmoAuHfvHl5//XUkJCQYe6oVCgXGjh2LDz/8EO7u7nyF0mCUUBNCamIoLISQ/iYQQkirxGtC/aDs7GywLAs3NzcIBM1nwUZKqAkhNdHduQOxt7etwyCEEGIDIltd2M3NzVaXJoQQizMUFUNs6yAIIYTYBG9dw2+88QaioqJM7u/cuTPefvttvsIhhBCL0mdnw0Y3/AghhNgYbwn11q1ba61DPXz4cGzevJmvcAghxKLY4iLobt60dRiEEEJsgLeE+tatWwgJCTG5PygoCDfpw4gQ0owV/bHf1iEQQgixAd4SagcHh1oT5uvXr0Mmk/EVDiGEWIRarcbhw4dx9OxZ7P/hB+RRxwAhhLQ6vCXUAwYMwJdffomMjIxq+27fvo2vvvoKAwcO5CscQgixiNTUVPTt2xdDX34Z4/ftxeH3PwBnMNg6LEIIITzircrHu+++i+7du6NTp06YOXMmOnXqBAA4e/Ys1q5dC47j8O677/IVDiGEWEX5xYvIXr4CbvPmghFT3Q9CCGkNeEuo27Vrh0OHDmHu3Ln473//W2Vfv379sHLlSnTo0IGvcAghxGpKjhwBW1IM90WLILCzs3U4hBBCrIzXOtQRERE4ePAgcnJykJaWBgAIDg5u8suNE0JIfZWdScGd1xbDbd5cSENDbR0OIYQQK7LJwi6urq6URBNCWjxdRgbuvLYYikGxUI0fD5GLi61DIoQQYgW8JtQGgwF79uxBWloa8vPzqy2CwDAM3nzzTT5DIoQQ6+I4FO37A8V/HoLjyMegGjMGArnc1lERQgixIN4S6pMnT2Ls2LFIT083uZoYJdSEkJaK02qh/ikBxfsToRo/DooBA8BIJLYOixBCiAXwllA/99xzKCsrw7Zt29C3b1+oVCq+Lk0IIVZjeKhEHlvH8uOG/HzkfvkVCn7YDIfBg6AYNAhiD49GxXA1dhB0d+5U2ea2YAFcn3na5DH5m39E4c6dKD9/HmxJCdqeOA6ho2PVWAsKcPc/76E4MREQCKB4ZAg8X38dAnv7RsVLCCEtDcOZ6i62MJlMhvfeew8LFy7k43JWU1hYCKVSCbVaDceHPnwIIa1LQkIC5syZg+zsbOM2Z4kE/+kcjaE+PmafRx4VBWXcGMj/KSdak5tTpkI5ZgxUcWOq7bsaOwjK+LFwGjfOuE1gb19rhZG89evBarQAgOxPP60xob719DPQZ2fD6+0l4PR63Hn9dcjDwuGz7BOzXxshhLQGvC3s4uvra3KoB1+WLFkChmGqPNq3b2/TmAghzVNCQgLi4+OrJNMAkKfV4vnjx7C7hkWsTClLTsbdf7+F3K+/BseyDYpHaG8PkZub8VFXuT7nadPg+szTkEdG1rhfc+0aSg4dgte770IeGQm7Ll3g+cYbKNy1C7qsew2KkRBCWireEupXX30Va9asQWFhIV+XrFGnTp2QmZlpfBw+fNim8RBCmh+DwYAXX3yx1k6Cd1POwFDPToTC33aj4MctDYopZ83XuNyjJ9LGxCH3m2/A6fUNOk+lsuRkCBwdIQ8PM26z79ULEAhQlnKmUecmhJCWhrcx1EVFRXBwcEBoaCieeOIJ+Pn5QSgUVmnDMAzmz59v1ThEIhE8PT2teg1CSMt26NAhpKenm9zPAcgsK8OJ7Gz0dHOr17nzf/wRQjc3KPr3AyMy70+005QpkHXsCKFKibKkJNz79L/Q38uGx+LX6nXtB+mzcyBydq6yjRGJIFQqYcjJafB5CSGkJeItoV60aJHxv1etWlVjGz4S6itXrsDb2xsymQy9evXCBx98AH9/f5PtNRoNNBqN8bmte9gJIbaXmZlpVrt7RUXgFIp6nz9n1SqU/Pkn1L/8AjAMAIArL0fZmTO4+5//GNuF7NwBsbc3XJ6abtwma9cOjFiMzLeWwG3hAgiokgghhFgdbwn19evX+bqUST169MC6devQrl07ZGZm4u2330bfvn1x9uxZKEx86H3wwQd4++23eY6UENKUeXl5mdXOTSYzJsT1wWk0EHq4I3DLjxCIxQCAjJdfgeMjQ6AYMsTYTuTuXuPx8ogIQK+HLj0D0uCgel8fAERurtDn5VWNS6+HQa2GkBbmIoSQKnhLqAMCAvi6lEnDhg0z/ndERAR69OiBgIAA/Pjjj5g5c2aNxyxevBgLFiwwPi8sLISfn5/VYyWENF19+/aFr68vMjIyahxHzQDwlMnQzdWlQQk1WEDs5gaJv7+xh1kglULo7AKJGX9Lyy9eBAQCiFyc62xrijwqCmxhIcrOnoM8rKL6SMmx4wDLQh5R80RGQghprXiblFgpIyMDmzZtwooVK4xjEA0GA/Ly8qrVc7U2lUqFtm3b4urVqybbSKVSODo6VnkQQlo3oVCIFStW1NrmjU6dIGxAMi2QyeE0eTKkbdua1b40KQl569ej/OJFaG/fhnrHDmR98CGUI0dCqFQCAHRZWbg2bDjKUlKMx+mzs1F+4QK0t24CADSXL6P8wgUYCgoAANKQENj37YvMf7+JspQUlJ4+jax334Xj8OEQe9TcM04IIa0Vbz3UHMdh4cKFWLVqFfR6PRiGQXh4OHx9fVFcXIzAwEC88847eOmll/gKCcXFxbh27RqmTJnC2zUJIS1DXFwctm7dWq0OtYtEgnfCw/GomcNCHmQXHQ3lmDEQyO3AlpeZdQwjkUC9axeyV30GTquF2NcXztOmwfmBcdWcTg/t9etgy8qN2/J/2Iyczz4zPr85ueLvoNf77xtrXft8/BHuvvsf3Jr+1D8LuzwCz3+9Xu/XRQghLR1vC7t89NFHWLx4MV599VUMGjQIQ4YMwb59+xAbGwsAmD59Oq5du4ZDhw5ZLYZFixZh5MiRCAgIwJ07d/DWW28hOTkZ58+fh5uZM/FpYRdCyIMOHjyIAQMGGJ9v7NUL3V1c6nUOSWAQlKNGQhoaCgDgtDqw5WWQdehAkwoJIaQZ4K2Hes2aNZg6dSref/995ObmVtsfERGB3377zaoxpKenY+LEicjNzYWbmxtiYmJw7Ngxs5NpQgh52MPlPwVmDvNgBELIIyNg368fJMHBYBoy1poQQkiTwFtCffv2bfTu3dvkfnt7e6uXpPvhhx+sen5CCKmLyMkJ9n36wK5XLwgbUFKPEEJI08NbQu3u7o7bt2+b3H/q1Kla60ETQkhzJvHzg8PgwZBHRIB5qFebEEJI88ZblY+4uDh88cUXSEtLM26rvMX5+++/Y926dRg3bhxf4RBCCC/EPj5wfWY23BYtgl3nzpRME9LEsFp+K4yRlom3Huq3334biYmJiIqKQt++fcEwDJYuXYo333wTR48eRefOnfH66zR7nBDSMjACIZSPj4J9//5gBLxXKCWEEMIj3v7KK5VKHDt2DK+88goyMjIgk8lw8OBBFBQU4K233sKhQ4dgZ2fHVziEEGI1AqkUrs8/B4eBAymZJqSp07O2joC0ALz0UJeXl+Orr75CVFQU3njjDbzxxht8XJYQQnjHCIVwmT3bWAKPENK0sRoDBHZiW4dBmjleuk5kMhleffVVXLp0iY/LEUIIb8LDw3Fw/378snARfujZC90mPEHJNCHNCFumt3UIpAXgbQx1WFgYbty4wdflCCGEF0qlEjF9+iD30mWUZGXB89FHbB0SIaQeDIVawNvWUZDmjrfBfe+99x6+/PJL7Nu3j69LEkIIr+x79qQqHoQ0M9pb1l0Dg7QOvPVQr1q1Cs7Oznj00UcRFBSEoKAgyOXyKm0YhsH27dv5CokQQixKHhlp6xAIIfWkvVEIQ7EWQgeJrUMhzRhvCXVKSgoYhoG/vz8MBgOuXr1arQ0tvUsIaa4EdnIIXV1tHQYhxExqtRqpqanIv3wV0nVX0WPKICiVSluHRZop3hJqGj9NCGnJhE5O1ClASDOSmpqKvn37Gp//4fEbYscNtWFEpDmjAqmEEGIBAgcHW4dACGmEkqOZKL9WYOswSDPFa0JtMBjwww8/YPbs2RgzZgxSU1MBVNx2SUhIQFZWFp/hEEKIxQgemhNCCGleOJaD+rfrKDubY+tQSDPEW0JdUFCAPn36YNKkSdi0aRN++eUXZGdnAwAcHBwwb948rFixgq9wCCHEohgJTWgipNnjgMLE21DvvQm2nOpTE/PxllC/9tprOHfuHPbs2YO0tDRwHGfcJxQKER8fj127dvEVDiGEWBaNnyakxSi/mIfc/51HafI9cLQ0OTEDbwn1tm3bMHfuXAwZMqTGiTtt27aliYuEEEIIaRJYjQFFhzKQ+/0FlF/Jr9IRSMjDeKvyoVarERQUZHK/TqeDXk+3VwghzRMjoDnehLREhkIt1LtvQOxlD4ceXhD7OlBFH1INbwl1SEgITp8+bXL/77//jo4dO/IVDiGEEEJaMYPBUPU5W/vQDl1mCfK3XYXIWQZ5JxfI2jtDIGt4GqXPK0fh/lvQXCuAoUgHoaMEdp3d4TjQD4zI9Bf04uOZKE3Ohu5OMTiNAd5v9YJAXjUOtlSH/F+uofxCHsAA8jBXqEaGQCCllVythbculVmzZmHt2rXYvHmz8bYJwzDQaDT417/+hd27d2P27Nl8hUMIIYSQViohIQHjxo2rsu3pb1/Br2f+qPNYfV45ig5lIGftWRT+cQuGIm2t7e99mYKSk9WrmOmzSwEOcBrTBp7zo6F6LBglxzOh3nOj1vNxOhaydk5QDPQz2Sb3h0vQZ5XCdWYYXKd3gva6GvkJV+p8baTheOuhfvHFF3Hu3DlMnDgRKpUKADBp0iTk5uZCr9dj9uzZmDlzJl/hEEIIIaQVSkhIQHx8fLUx0bnF+Zj1zSv4euZHGBE5qM7zcAYOZedzUX41H6rHQiDxqV8telk7Z8jaORufi1zk0GeXofh4JlQjgk0ep4jxAQCTNbN190qhuZwP9xeiIPFVAABUo0KQs+4cDCOCIHSU1itOYh7eEmqGYbBmzRpMmzYNW7duxZUrV8CyLEJCQjB+/Hj069ePr1AIIYQQ0goZDAa8+OKLtU4wfPOnTzA0fACEAvOGR3BaFupf0+A0vi1EKlmj4mPL9dWGb9SX9mYhGJnImEwDgDTUCWAA7a0iyMMoobYGqyXUcXFxmD9/vnFZzz///BMdOnRATEwMYmJirHVZQgghhJAaHTp0COnp6Sb3c+BwpyALx66eRp82Xc0+L1uuR8H2a3Aa1w5Cu4alVvqcMhQfuQPlCNMFHMxhKNZC6CCuso0RMhDIxTAU1z48hTSc1RLq7du3Y+zYscbnAwcOxIYNGzBp0iRrXZIQQgghxKTMzEyz2mXlZ4PT1a/+tD6nDEV/3oZALETRwdvG7ZyOhfZ2IQp+uWrc5rGgS5XebINag+xvz0Ie7gqH7l71ui5pGqyWUPv4+CApKQlPPvkkAIDjOCozQwghhBCb8fIyL1n1cHQF6puycBXDLVwmtoddpJtxc+7mS7ALc4G8k6txm1Bxf9iFoVCD7DWpkPo7wimuTT0vWp3QQQJDsa5qaAYObJkOQgda0dVarJZQP/HEE/jkk0/w448/Gichvvbaa/jggw9MHsMwDM6cOWOtkAghhBDSivXt2xe+vr7IyMiocRw1A8BL5YEeIZ0bsPopB6FKCoGdGIzj/SJqjEgAgb0EIld5tSMM6opkWuzjAKdxbcEIGt/xKAlwBFeuhza9yDiOWnOtAOAAib+i9oNJg1ktof7ggw8QGhqKxMRE3Lt3DwzDwN7eHi4uLta6JCGEEEKISUKhECtWrEB8fLzJNu+MXmj2hMQHydo5Q9bRue6G/zCoNcj+KgVCJxlUw4PAltzvVRYqJPfbfJ0K5/HtIPGrSIYNRdqKR245AEB3twSMVAjRP8m82N0O0rZOyE+4AqcxbcAZWBT8cg3yCDeq8GFFDMfTWpoCgQDfffddsx9DXVhYCKVSCbVaDUdHR1uHQwhpAlitFuUXLkAgk4ORiOs+oA6cVge2vAyyDh0gkNAtWkIsLSEhAXPmzEF2drZxm6uDEz6MX4wREbH1OpfIWQb7Hp4QKqXgdCwk3g5VFma592UK7Lt4wL6rR5XjSk5mIX/r5RrP6fthRUEHfV457n70N1yfDocsRAUAUO+9iaI/blU7xim+rfEabKkO+dsfWthlFC3sYk1WS6ijo6Px/vvvY+jQoQCA9evXo3v37ujQoYM1LscbSqgJIQ+jhJqQ5ufgwYMYMGCA8flPz32F3qHRZh8vdBBDHu4KSbASDMOA07M1JtSkdbDav3hKSgpycnKMz2fMmIGkpCRrXY4QQgghxGxCYdXeWqHAvJRI7GUPRX9fKB8PgTRERQUXCAArJtQBAQHYt28fDAYDAKryQQghhJDmiZEIIe/oAtXjIXAc5A+Jn4JyGlKF1RLqOXPm4H//+x9kMhkcHR3BMAxmzpwJR0dHkw+lUmmtcAghhBBC6kVgL4Z9N084xYXCLtrdOFmQkIdZrcrHyy+/jMjISCQmJiIrKwvr169Ht27dEBxsen16QgghhBBbE8iEkEe6VQzpsEApO9LyWS2hBoBHHnkEjzzyCABg3bp1mD17drOv8kEIIYSQlksS4Aj7Hp4QSKgiBjGfVRPqB7Fs/ZbwJIQQQgjhk7yTC+RRbjQ+mtSb1RLqW7cqaiT6+/tXeV6XyvaEEEIIIXyRBCgomSYNZrWEOjAwEAzDoKysDBKJxPi8LpVVQQghhBBCrCU8PBx/Jh5E3s9XILATo+vjMZRMkwazWkK9du1aMAwDsVhc5TkhpOmi8paEkNZCqVQiJiYGWWcc4NDHG1IVLdZGGs5qCfX06dNrfU4IaXpYjoWQoYk4hJDWQ2gngtjb3tZhkGaO1sYkhBhpDBpbh0AIIbwSezvQnTnSaFbroX7nnXfqfQzDMHjzzTetEE1Vn332GT7++GPcvXsXkZGR+L//+z90797d6tclpKlSq9VITU3FrcJb8Hf0R3h4OC20RAhpFYROUluHQFoAhuM4zhonFgiqd35XfgN8+JIMwxjHblp7UuLmzZsxdepUfPHFF+jRoweWL1+OLVu24NKlS3B3d6/z+MLCQiiVSqjVajg60ngr0jIcPHgQAwYMMD4/cOAA+vfvb9VrchyHz5I/w09XfkKRtghR7lF4s+ebCHAMMHnM16lfY9/Nfbiuvg6ZSIZIt0jM7zIfQcogY5u3j76NY3eOIbssG3YiO0S6V7QJVlpvUSlWq0X5hQsQyORgJOJGn4/T6sCWl0HWoQMEElqZjRBr4fQsSs/mQOxhB0bUuJv2nJ4Fp2Mh8XZo9LlI82O1f3GWZas8bt++jfDwcEycOBEnTpyAWq2GWq3G8ePH8cQTTyAyMhK3b9+2VjhGn376KZ5++mk89dRT6NixI7744gvY2dlh7dq1Vr82IU1RQkICxo0bV2XbY2Mew+oNq6t9+bWktWfXYuOFjXiz55v4fvj3kIvkmL13dq3DTk7ePYkn2j+B74d/j6+GfAU9q8fsvbNRqis1tuno0hHv9nkX20dvxxdDvgA4YPbe2TCwVEGIEFIdI6V5I6TxrNZD/bDRo0dDLBZjy5YtNe6Pj4+HwWDAzz//bLUYtFot7OzssHXrVowePdq4fdq0aSgoKMD27durHaPRaKDR3P+ALywshJ+fHw4ePAgHBwerxKk3sGAt9M8iYBiIhK3om7JBD3AWXESIEQBC3tY/4t3+/fvx8ssvm9zf6+leGDRoEEJVofB18IWDxPz3PMMwEAlq/tlxHIcXL76Ioa5DMdxtOACg1FCKeRfmYZbvLPRU9TTrGoX6Qsy9MBeLgxejvX37GtvcKruFN6++iY/afgQPqYfZ8dcHq9NBc/06GIEQjKjx7xdOrwfHGiANCoJA3Pge7+aANbCw1KcRwwCCVvR3jzUYLPaZAVR8bgiErSPJ5PQstJnFYCTCRi8xzrEcwHIQuzW+t7u5MBgMFu14YRgGQgu+96Kjoy12rjpxPFEoFNzq1atN7l+9ejWnUCisGkNGRgYHgDty5EiV7S+//DLXvXv3Go956623OAD0oAc9LPgQu4m5sHVhnMxfVmV70GtBnOckT7PPI3GXcGHrwjipj7TG/YyE4TwnenJtP2rLMULG5q+bHvSgBz3owd+DT7x1vclkMhw9ehTPPvtsjfuPHDkCmUzGVzhmW7x4MRYsWGB8bu0eaq3egEuZhZBJRBA18tuynuXAshyC3OwhEbWC3ga9Fsi9CgiEgMACvXqsDmANgEsoIGp541hPnjyJ2bNn19ku4tkIqEJUJvdLhVK4yl3hJneDu707vO29IRfJwTAMAh0DIRZW/7e4UnIF/0n7D3798VeoxPfPverWKjA9GDy/8Pk642I5FstvLkepoRTrf1lfZd8fuX9g893N0LAaeEm9MD9gPjxOWKd3uhKn11u8p8YSvd3NgUHPoiCrBAIBA6aRPcucgQXLclB52EPYCnoJ9Xo98u+kgxEILdKzZzAYwLEGOHn7QtRK3n+snoXFanwwDBhh66gYotfrkZubC4FAUOO8ufqqHCLs4uLSLN97vEX85JNPYuXKlVCpVJg7dy5CQkIAANeuXcPKlSuxceNGzJs3z6oxuLq6QigUIisrq8r2rKwseHp61niMVCqFVFp9BnBUVJRVJiVq9AbgZj5UdhJIGvlhoNWzKNcZ0MHbEdJWkVBrgLsiQGwHCC2QABu0gK4U8AwHRC1vFvilS5fMaieSiaAMVtbapvif/13HdaAM8HbwRi+vXujSoQtEAhF2pu3EO0fvV/75bNBnQFrFSmVudm7G7U6FTmAYxqzbdO8efRfZbDbWD18PT/uqv79ttG0wvnw8skuzsf7cenyb8y02DN8AqbDl/Tu2BAYdi5z0IogkwkYnwQY9C73WAFdfBYTiVpBQ63S4p1RALJdDKGp8R4JBr4Neo4FbYDBErWS4EcdyjR7u0Rrp9XrcvXsXYrHYYl/mdDodPD09KaGuzdKlS5GTk4NVq1bhs88+M36bYVkWHMdh4sSJWLp0qVVjkEgk6NKlC/744w/jGGqWZfHHH3/ghRdesOq160Ort+AYYEJM8PLyMqudwlUBmcj8u0dCgRB+Cj+EqkKNY6gH+g1EhGuEsY3WoAUA5JbnVkmoc8tz0d655rHQD3rv2Hs4mH4Q64auq5ZMA4BCooBCokCAYwAi3SLR54c++OPmHxgePNzs10H4Zcne/dbGoNejdaS+1kHJdMPR7+19vCXUEokEGzZswMsvv4xdu3bh5s2bAICAgAAMGzYMkZGRvMSxYMECTJs2DV27dkX37t2xfPlylJSU4KmnnuLl+uYo1VI1AmJ9ffv2ha+vLzIyMkz+UZS7yuHWya3WRQ+cZE7wV/gjwDEAoapQhLmGwV5cddUxe7F9lW0cx8FV7orjmceNCXSxthip2amY0G6CyWtxHIf3j7+P/bf2Y+3QtfBV+Nb5Ojlw4DgOWlZbZ1tiQ/S53Aj0wyO2QQn1fbz3qUdERCAiIqLuhlYyYcIEZGdn49///jfu3r2LqKgo7N69Gx4e1h1fWR/FGr2tQyCtgFAoxIoVKxAfH2+yTeenOxurJYgEIvg4+BiT52BlMAIcA+pV+aMSwzCY3GEyvkz5Ev4Kf/gofLAqaRXc7NwQ6x9rbDdrzyzE+sdiUodJAID3jr+HXWm7sCJ2BezF9sgpywEAOIgdIBPJcLvoNvbc2INe3r3gLHVGVmkWvkn9BlKRFH19+tY7TsIfvY6FuOlNo2kW9DqdrUMgrZRer69xWGxr1PwGqVjACy+80KSGeDwsu4iWfyb8iIuLw9atWzFnzhxkZ2cbt0uVUvR8oSdGjh6JCLcItHVqCx8HH5Nl8BpiRtgMlOnL8PbRt1GkLUJnj874YvAXVcY53y66jQJNgfH55kubK47dM6PKud7t8y5Gh46GVCjFqaxT2HB+Awq1hXCRuaCLRxdsGLYBLnIXi8VOLEetViM56QzSL+VB5W6HsLBwKB1rH7NPqtJr6DOD8EutViMpKQnXr1+Hs7MzwsPDW/1id60yoW7qzmcWYkA7t7obEmIBcXFxcHFxqbJS4tKvlmLO2DlWncTHMAxe6PwCXuhs+svtnvg9VZ6nTkut9Zzudu74fPDnFomP8CM5ORkDBg4wPt+RsBsxva17N+Fa0j2c+zMD924VQVOix/h/dYObn6LWY84dysCl43eRd6cEAODmr0DPx0PgEXQ/iTixIw1XTt5DcX45hCIB3PwV6PF4MDyDrPsFQVtWWncjQiwoOTkZAwcOND5PSEhA377Wvwt4/vx5nDx5EpmZmSgrK8Ps2bPrnA906tQpnDlzBvfu3QNQMX9o0KBB8PW9P2QwMTERZ8+eRWFhIYRCYY1t6tLyp0A3Q5nqclzNLrF1GKQViYqKwqFDh/DiNy9iX+I+TH90OlXEIFZX0yqdU6ZPwqbvNlcskmEleo0BXqEq9B4TavYxGZcL0KarBx6f3xljX+kCBycpflmZjOL8+73DKg879HuiLZ54swfGLIqGwkWGHSuSUVZk3fH7JeoCsCxNZif8qOn3dtq0afj666+h1Vr3va7T6eDv74/BgwebfcyNGzcQFhaGadOmYebMmVAqldiwYQMKCwuNbVxcXDB8+HA8++yzmDFjBlQqFTZs2ICSEvNzMeqhbkLUajVOJSUj5+p5fHPnApZMexSOSpWtwyKtgFKpRExMDLzCvBCiCrF1OKQVSEhIQHx8fLVJTQWFeXhu0QxcT8nByBGPw9XHASoPO8gcxLVOjq2Pdj0rerQKc8rMPuaRmZ2qPB84pQOuJf2J9Et5aP/P+dp2r1pxJia+DS78lYmcjGL4tXduZNSmGXQ65KXfgkdwG6tdgxDA9O9tUVERFi9ejLS0NDzyyCPw9PSEq6srnJycLLryYWUBi/z8fLOPGTt2bJXno0aNwvnz55GWloaoqCgAqDa379FHH0VSUhKysrIQHBxs1nUooW5CUlNTMWjgAONzP+fv8dKTIy32IUJIXTzsms7kXNJyGQwGvPjii7VWCFi7bRkig/og/WLFB6dEJoLCRQZHFxkUrjI4ushhr5JCYKOSZ3qtAayBg8yu5oJ1Bj2Lc4fuQCIXwdXX8ouAPexGShIl1MSqzPm93bJlC8LDw43rfQgEAjg6OkKlUhkfTk5OkEhst1iaTqcDy7KQy+U17tfr9Th16hSkUmm9ClZQQt2EHUvLxU+nMzA22oeSasILuajmPzCEWNKhQ4eQnp5ea5vcwixcuJGMjkEVi/xoynTQpOuQk15kbCMQCqBwlsLRRQ6lmxxOXvaQ2fHzsXY04RrslRL4dnCqsv1GSg72fHMOeq0B9o4SjHoxCnIH6yQPlRPDLl64iFvnUuAdFg2/UPOHsRBSH+b83hYUFODq1ato27YtgIq1PgoKClBQUFClnZ2dnTG5dnFxgYsLf5PG9+7dC4VCUa3n+dKlS9i6dSt0Oh0UCgWmTp0Ke3t7E2epjteEes+ePfjmm2+QlpaG/Pz8at9yGIbBtWvX+Aypydt3IQuF5TpM6RkAcSOX5CWkLkJBK1hRk9hcZmamWe0KinJqLbHM6lmo75VBfa8Mty9UbLNXSuHX0Qlu/hWTDC8dv4sDG++vCjryhUh4t1E1NHQAwKndN3DlZBZGL4iGSFz1d8annRMm/Ksbyot1OH/4DvasOYv4V7vCztHySXVqamqViWFyBwVmvvI6ZA61T7AkpCHM/b1Vq9V11qcuKSlBSUkJMjIyAMA4ETA8PNzYgZiSkoIdO3YYj5k8eTICAgIaGH2FQ4cO4ezZs5g+fTrED60EGhQUhDlz5qD0/9u787io6vUP4J8zw76vsomA4sqioOKC+1ZqZpmaYWrXTG+lptY1/WW2eG/WtTJNK9GbZBqZ5lJmKqmguC+4hpTmLsq+C8zy/P6YGBkFGc6RWZjn/Xrx6jVntsdPZ+CZc77n+y0rw8mTJ7FhwwZMmjQJTk76nWEyWEO9aNEizJkzBz4+PoiJiUFERISh3trsHb2chxt5dzG2azO08G74U4dmjSeZZ8zk6btKp6ebN6zqsXy43FoO72bO8G3hqp0/PaS9l85MHE5u0i62Tdt1DSd3XsPwGR1qHMphbSuHWxMHoAng29wVa98+hPSDt9Dx8WBJ76uPuyXFSF3/Lbo98xwc3dzrfgJj9aDv59bDw+OBZlWf5wQGBsLPz0875rp169YICAjQPkbqtHwHDhxAamoqxo8fD1/fB1fYtbGx0R4tDwwMxNKlS5GWlqb37CUGa6iXLFmCfv36Yfv27fUOmgG3Cu9i0c4MRDdzx/AO/vBx4RUQaqSqBKD/KRrGmOHps0qnp6sP2oZEAbUMdxMEwMnDDq5e9nBtYg/PACd4BzpDfl8DbmNnBRu7R/On7uTOqzjx6xUMm94BTYL0++NORFApDDcDR1lBPlITv0HMU6Pg7hdQ9xMY05M+n1t3d3e0bPnwsfy2trbw8PCAh4cHvLy84O/vX2OzbGtr+8gWjUlNTcX+/fvx/PPP6zTpD0NEUCr1X2jPYA11fn4+Ro4cyc20RCev5ePU9QL0be2NIRF+cLTlYfA6iKeOYszU6bNK54ShsyD7ewiSta0crt6axtnNx0Hz4+3wQPOsr/JSBYrzylFaoJnyruCOZh5nBxcbOLpq/oD/tvp3OLrZotvTmllvTu68iiM//4VBE8Pg7GmH0sIKbW02dlZQVKhw/NcrCIn0goOrDcpLFDibchOlBZVo0bGJqDrFqigrxYH136JlTHeExnSH3Ir/TjDp9Pncjh49GjKZ5nMpCAJcXFzg4eGhPfLr5eUFR0dH0deFlZWVobCwEMXFmmspcnNzAQBOTk5wdtYMddq0aRNcXFy0U+ulpqZi7969eOaZZ+Dm5qZ9ro2NDWxtbVFZWYl9+/ahdevWcHZ2RllZGY4ePYqioiKEhYXVUEXNDPYpi4mJQUZGRt0PZHVSE2H3hSwc+isP/ds0Qe9W3nAScQRGoVLj410ZSL6QjWt5ZXC2s0KPUC+8ObjNQ4+AL997ETvP38alrBLYWcsRHeSOOYPb6AxHKVeo8J9f0vHzmVuoVKrRq6U3FjwVDm/nBp7buLIEcOAV8RgzdbWt0uni6I5/jvo/jBgxAj7BLvBq6gxHN5tHemH25dM52LMmXXt716rzAIDOQ4MRM0xzoVJxXrnOwfFzKTehVhJ2xJ/Tea2q5wgyoOB2GXYcOou7pQrYOVqjSZALnn4jGp7+hh+qp1apkHFoP66dP4M23XujadswCDK+DodJU9vn1tnZGXFxcXj88cfRrFkz+Pv7w9PT85EfRM3IyMDWrVu1tzdu3AgA6N27t/Z6gsLCQp3fF8eOHYNKpcIPP/yg81pVzxEEATk5OTh9+jTKyspgb2+PgIAATJw4EU2a6P9lWKC6Ro4/Iunp6Rg8eDA++OADxMXFGeItG0RRURFcXV1RWFj4yJfZTElJ0VmtruMrn8EjtEOdz7OSyRDVzA09W3qhZRMn7Y5UqVSjXKFCW38X2Fo9eLFZUbkCr6w9iTExgWjr54LCuwq89/PvUKsJP0/rUev7jf/6KIZF+qF9oBuUKsKinRfwx50SJM3qBQcbTWP/1uaz2HshCx+Pag9nO2vM/+kcZIKAH1/uXr9Q6kNZAaStAwK7AFaPoHFXVQKKMsA34tG8HmPsAff/3vthzU94Jm6odgw0q11qaqrO+M65zz6FVgE1j3N1beKDsD6D4BXY7KGvqVIqoKyogHdwc1jxGWVWi/s/t8uXL8f48eP1voCvMTJYQx0ZGYm8vDxkZmbCyckJTZs2fWCyb0EQcPr0aUOUI1pDNdSbNm164BuftZMb2o6cBZ/IXnq/ToCbPZ6OCkB4gGudDXVNTl8vwPDlB3BgTj8EuOk3hVpuSQU6/vs3rJ/cFV2ae6KoXIGOC5KwZEwUhkRofrlfzCrBgE9TsOmV7ohu1kAXyygrgCNfAd5tAQ/9JmJ/KG6oGWtwhYWFOHv2LNIP3kKrGF90iGoPV9eGXaq7sahPQ12lSUgoWsZ0g0dAYI1H/etqqFVKJQ6s/xaX046jIOs2bB0cERTeHj3jXoCTh35nB49s2YDUxG8QPfhJ9H1hsna7srISyd/+DxkH90GlUCC4fTT6v/gyX2Bpgqo+t5cuXYK7uzt69+5t8Z9bgw35qBpDU9dgdUtU28pDipICnEmYj8gX3te7qb5ZcBfL9l7EqI5N0bOld71rKS5XQhAAl3oMISku1wzad3PQTAt17kYhFCpCbKiX9jGhTZwQ4GaPk1fzG6ShLiwsxNm0E8DvfwFlRxEx4g24uvDUUYyZuqpVOsNCS+HuyxcU14dKpdK5rdZjufasyxeRdfkinL28ERTRAU3bRcDGTv/555WVFbhz+RK6PjMG3kEhKC8pwd5v4rFl0QI8v/CzOp9/++IfOPPbDng3C37gvuQ1K/HXyeMYNnMObB0csfvrL/HTJx/guQWL9K6PGUbV57Z169bw9q5/r9EYGayhTk5ONtRbmRV9Vh7K2LIMTcJjIdRjjuCNJ27Cxd4arX30byrLFSp8uCMdT7b3h7Odfqf61GrC+9t+R6cgd7T21bxXdkkFbOQyuNrrvoaXkw2ySyr0rqc+zp49i559+2tv7/duhx5Dn22Q92KMPXqOEqezszRVZzWr+2LbTowf0BudWtZ9hq44Jxvn9iYhPTUZwe2j0bpbL1jpsXqdrYMjRs37t862/v/4J9a9NQtFOVlw8ap9zGll+V1sX/YxBk2ehsObv9e5r6KsFGf3JGHo9DfQLFyzvPRjL89AwqyXceuPC/Bv1abO2pjhPeqhr+aMB6kZmT4rD1UUZCHv0hkQQe8fNRESDlzB7cJy7etsSbuJdvN3aH+OXs7T3qdQqTH1u5MgAv79VLje9b+99Rwybhfj87io+v/jG9LZ9cC1w8augjGmJ2sbXlRIX1VnNasPEQSA4rvlWP7zThz/8y+9X0ulUODS8SNI/f4bVJbfFVVPRVkZIAiwdXj4+Nnd//sSIVGdERTZ4YH77vx1EWqVEs0i7t3nGRAIZy9vZP55QVRdrOHxzG33GHwuHYVCgQsXLqCwsBBq9YNTnPXqpf944cZA35WHyotyoa7ncPdKFSEp/Q4GtNOsRT+gnQ86BLpp7/d11czkoVCp8eq6k7iRfxeJL3XV++j0/K3nsOdCFn6Y0g1+rvdOGXo72aJSpUbhXYXOUeqckkp4OxnoKJRaBRxaBuRfBsJHAnL+0DNmygTZo5vFozHT56xmYvIBRLcI1k5fpo+i7Cyc2LYZnYaNqFc9yspK7PtuNdp07wVbB4daH3fhQAqyLl/C2A8W13h/aUE+5FZWsHPUbcodXd1QWpBfr5qY4dRnH2vsDNZQq9VqzJ07F1988QXKyspqfdz9Y8IaO31XHnJ294addf2P4FzNLYPV31fLO9lawem+eaurmukruaVIfKkr3B3rPuVHRHjnp/PYef42vp/cDYEeur9Ew5u6wlou4ODFHAz++6LES9kluFlwF9FBBr645MIvwM0TQNsngWbduLFmjJk1fc5q5hWX4I+bt9AmsH4Lu2Rf/QvnU3ajbWxv7bb0/XuRtHK59vaIue+iaVvNWUyVUomfP/sQIGDApFdrfd2inGzs/WYlRr61QK9hJYyZI4M11B988AEWLVqEKVOmoEePHhg3bhw++ugjuLm54YsvvoAgCPjvf/9rqHJMhj4rD9m5NYFXaHtR87D2alX7xQIKlRovrz2J87cK8b8JnaEiQlaxZoiIm70NbKw0jXjcysN4LMwXE7oHA9AM89h66hZWju8ER1u59jkudtaws5bDxc4aozsF4t+/pMPVwRrOttZ456dziG7m1nAzfDxM8W3gaDxwah0Q3BNo0R9w0e+LTJ32LgTO/QgU3dQ0634dgP7zgaadHv68oyuBA0uBkjuAbzgweBHQtOO9+4+vBs5uBDJPA5XFwJtXAXu3R1MzY8xs6XtWM7+4DKTHRYr3u3omDS2iO2uPPLbo1AW+LVtr76+ayUOlVGLbZx+iODsLo+Z/8NCj03cuX0RZYQG+nfOadhup1biRfh5pO7dhxrrNcHRzh0qpRHlpic5R6tLCAp7lg5kFgzXUCQkJGD16NL788kvtyjYdO3ZEv379MGHCBHTr1g179uzRrmxjKfRZeSjsmen1uiARAOyt5RjXLQhDI2pvHG8XluO39DsAgCFL9+vcl/hSV3RrofnFeTW3DHmlldr71h6+BgAYE687RnnRyEiM6hQIAHj7iXaQCel4ee1JzcIurbywoB5js+vr/jMbqpr+kFSWAn/s0Pz4dwBCB2qmxBMknLLyDAWGLALcgwFlOXBoOfDt08D0NMDRq+bnnPsR2Pl/wBOLgYBOwOEvgLVPA1NPAE5/fwFS3AVC+2t+dr8nvj7GWKOi71lNT3dXWIlctrny7l3I/p7W1sbeATb2us1yVTOdn3kLo99ZCHvnh1+YFhTeHhMWLdPZtuPLJfAIaIqYJ5+BTCaHT/NQyORWuHbuNFp1iQUA5N26geKcbPi15AsSmekz2DzUdnZ2WLp0KSZPnoySkhK4uLhg27ZtGDJkCABg8eLF+PTTT3H9+nVDlCOaIeehtnFyR8Szr8Ovfe+HPPNB3UM9MalH84ZfldBE1JSdt5MVvhoTghEdPB7+ZDtXoGlnoGkM0KSNbnMtZh7q8iLgw0Bg/FageZ+aH7OyH+AfDQz9WHNbrQYWtwNiJgM9Z+k+9vJ+4Jsn+Ag1YwyA5uBBcHDwQ89qejg7YfHL/xA1vtXOyRmPvzyj1jmlVUolfl68EHcuX8LTs+frHD22c3KC3EozrG7Dgv9DaOduiHp8WI2vs/69OWgS1FxnHurfVi3HX2nH8fjLM2Hr4IDdq78CAMQt+Lje/w7GDM1gR6g9PT1RUlICQLPmuouLC/76S/dK5Px8y73wYMSIEfD09NRdKfEf78OzZQe9XyO6mRue7dwM7fwtZxqb2ubwzi5RYuSqP7FxUsuHN9XlhcDF3zQ/Dh5Ay8eAloPEjbVWVgInEgBbV8AnovbH3DoF9KjWOMtkmub7xrH6vydjzKLoc1bz+QG9RTXTAW3C0OWpUXBwdav1MSV5ubh0/AgA4Ns3p+vcN3r+BwgMiwQAFNy5jbvFRfV6/z7jXwIEGX7+9AMolQoER0ZjwKRX6vePYMxIDNZQR0VF4dixew1D37598dlnnyEqKgpqtRpLly5F+/btDVWOSXpg5Ug9fiE62MjRr00TDA73QzPP2sewNUb6XO0+Y+NVDI90h1yfGQTK8oDTicDlFKDzS4B7kH6FZOwANk7UHM129gXGbwYca1kxrCwXIBXgdN9crY7eQM4f+r0fY8yijRgxAhs3bnzgzJyLgz1eeKwfOrcOrdfrObq5o+PQpxEYFlHntTquTXzw+vptdb7mS8u+fuj9z77z4QPbrGxsMODFlzHgxZfrfH3GTI3BGurJkycjISEBFRUVsLW1xX/+8x/06tULvXr1AhHB3d0diYmJhirH7AV7OWJYpB96tRI3+0djUNfV7gTgekEl9l8sQp9W9ThqX3RTM2655UDN7CBVzvwA/Dzj3u3nNwJB3YGQnsA/92sa8pMJwIYXgEl77o2HZoyxR6yms5pThw9B26Cmer+Gg4sr2vbsi5ZdYmtcZpwxpj+DNdRPPvkknnzyXnPSrl07XLp0CcnJyZDL5ejevTs8POoY78oQHuCCUZ0CERXoJmrWj8ZE36vdMwsrAHpwzvM6/bED8GgO+P+9aE3rwUBAtZk4XPw1/7VxBDxbaH4COwNLo4C0NUDP1x98TQdPQJADJVm620uzASef+tfIGLNY95/VlOk5l7dvi5ZoGROLwLAI7cWHjDFpDL6wS3Wurq4YPny4MUswG0GeDpjUs7nOwiyWTt+r3f08nABr+7ofWBMHL0D+98fE1lnzUxdSa8ZK18TKRjPDyOUUoO0Tmm1qNfBXChDzkrgaGWOsDlY2tgjt1AWtuvV46PLgjDFxDNpQq1QqbNiwAXv37kVWVhbef/99REREoLCwELt370ZsbCx8fPgo3f2eiQ7A812DtAu0MI265vAWADR1t0XPlm5/36oHuTXQYSzQZmjtj6ksBfZ9DLQeAjj7aMZHH10FFGUCYU/de9w3w4A2w4Auf1/N3u1VYPPLmiPfAR010+YpSoGo5+89p/iOZo7qvL8v3M36HbBxAlybai6eZIwxPdg6OKJtz75o1TUWNnYiDywwxupksIa6oKAAjz/+OI4ePQonJyeUlpZi2rRpADSzfkyfPh3jx4/HBx98YKiSzMKU3s3xRKS/scswSfpc7f7Z6Bb6XZBYxcpOM7SjQ9yDFw7eT5BrLiQ8nahppu09gIBoYOKvQJO29x6Xd0Vzf5XwZ4DSXGDvB38v7BIBPL9J9/2Ofw2kVLtoZ/VgzX+HfwFEjdX/38MYs0gyKyuE9x6Atj37wNrWztjlMNboGayhnjNnDs6fP4+dO3ciKioKTZrcax7kcjlGjhyJ7du3c0NdzWNhPtxM16G2q92bOFvjy7iWGBFVy+Iq9/MJ1yyi0nKgfsM6AMDaDhizru7HzTz74LYuk+8dsa5J37maH8YYqyfXJr7oPW4iD+1gzIAM1lBv2bIF06ZNw8CBA7UrJVbXqlUrJCQkGKockxQREYH9+/dj4fZ0ONtbY9rI/sYuySzUdLX7Dy+1Re9WbrU/SRAA30jN/M/BPXlGDsZYo+DZtBn6T/znA6sbMsYalsEa6sLCQoSEhNR6v0KhgFKpNFQ5JsnV1RU9evSA70VbTOndAu7ubsYuyWzcf7V7rcM8PJprFm4JHcBNNGOsUXFwdUO/f3AzzZgxGKyhbtGiBU6ePFnr/bt27UK7du0MVY5J83SyQacg97ofyPRjbQ+0ekxzYaBX/RY8YIwxU1V1VvPKmTRkHNyHZ1+eBlsHbqYZMwaDNdSTJk3Cm2++iT59+qB/f81QBkEQUFFRgffffx87duxAfHy8ocoxaZ2CPPSeT5Q9hCAAYU8DHV8A7FyNXQ1jjD1SVWc1PaGEn50VmodHGrskxiyWwRrq1157DefPn8dzzz0HNzc3AEBcXBxyc3OhVCoxZcoUvPjii4Yqx6QFWdgS4g1CZg0M+g8QHGvsShhjrMGFdupm7BIYs2gGa6gFQcDKlSsxYcIEbNy4EX/++SfUajVatGiB0aNHo1evXoYqxeR5OdsauwTz134MN9OMMYsgCAL8W7cxdhmMWTSBaloRg9WqqKgIrq6uKCwshIuLS4O8R05JBbycuKmuj8LCQpw9exa4ehC4cRwRU+Lh+veZEMYYa8xu/H4OTduFG7sMxiyaUZceZzVzsuX/LfVVNZYQXoVA734AN9OMMQvhwL/vGDO6Bu3cnnzyyXo9XhAEbN26tYGqMR/WvMS4eFa2gF97Y1fBGGMGY+foZOwSGLN4DdpQb9u2DXZ2dvD19YU+I0sEgWe2APB3VpyFKJ6hgNza2FUwxpjBWNvbG7sExixegzbUAQEBuHnzJry8vBAXF4cxY8bA19e3Id+yUZDxFwvx7D2MXQFjjBmUXM7DBBkztgYdW3D9+nXs3bsXUVFRWLBgAQIDAzFgwACsXr0axcXFDfnWNQoODoYgCDo/H374ocHrqAvPQS2BlZ2xK2CMMYPiuQUYMz6DzfKhUCiwfft2fPfdd9i2bRvUajUGDx6MuLg4DBs2DLa2DT+rRXBwMF588UW89NJL2m3Ozs5wdHTU+zUMMcsHk0ClBPhoDWPMgpBaDUHG194wZkwG+wRaW1tj+PDhWL9+Pe7cuYMVK1bg9u3bePbZZ/Hf//7XUGXA2dkZvr6+2p/6NNPMDMjkxq6AMcYMiptpxozP4J/CiooK7Ny5E1u3bkVaWhrs7OwQHBxssPf/8MMP4enpiaioKCxatAhKpfKhj6+oqEBRUZHODzNhPP6cMcYYYwZmkHPjarUaSUlJSExMxJYtW1BWVoYBAwZg5cqVePrppw12lHj69OmIjo6Gh4cHDh48iLlz5yIzMxOffvpprc9ZuHAh3nvvPYPUxxhjjDHGzE+DjqE+ePAgvvvuO2zYsAG5ubno2rUr4uLiMHr0aHh5eT2S95gzZw4++uijhz4mPT0dbdo8uCzr119/jSlTpqCkpKTWMdwVFRWoqKjQ3i4qKkJgYCCPoWaMMcYYYwAauKGWyWSwt7fHkCFD8Nxzz+k1tCM6Orpe75GdnY3c3NyHPqZ58+awsbF5YPv58+cRHh6OCxcuoHXr1nq9H1+UyBhjjDHGqmvwhlr7RnWMbSUiCIIAlUrVUOU8YN26dRg/fjxycnLg7u6u13O4oWaMMcYYY9U16Bjq1atXN+TL18uhQ4dw5MgR9O3bF87Ozjh06BBmzpyJ559/Xu9m2hAqKiqwcOFCzJ071yBTCTYmnJ00nJ94nJ00nJ94nJ00nJ94nJ0ug81DbWwnT57EK6+8ggsXLqCiogIhISEYN24cZs2aVa8dgYhQXFwMZ2fnBlkqnY+Ai8fZScP5icfZScP5icfZScP5icfZ6bKYFTCio6Nx+PBhya8jCALvOIwxxhhjTItng2eMMcYYY0wCbqgZY4wxxhiTgBtqE2Nra4t33nmHB/iLwNlJw/mJx9lJw/mJx9lJw/mJx9npspiLEhljjDHGGGsIfISaMcYYY4wxCbihZowxxhhjTAJuqBljjDHGGJOAG2rGGGOMNSpqtdrYJTALww01Mxt8/SwzpPLycmOXYNauXLli7BLM1u+//27sEszanj17kJycDID/bjDD4Ya6gSmVSgD8bVmM9PR0HDlyBLt27QKgWaWSfznq79ChQ/j++++xZMkSqFQqAPzHRV8bNmzAW2+9hevXrxu7FLO0YsUKTJgwAbdv3zZ2KWYnISEBjz/+ODZv3mzsUsxSQkICBgwYgEWLFgHQ/N1g+vn1118xb948TJw4ESkpKVAoFMYuybwQazBff/01de7cmfLy8oiISKVSGbki87F69Wpq27YthYSEkKenJ40YMcLYJZmV//3vf9S0aVPq1q0bOTo6Uq9evYxdkllQqVR048YNcnd3J0EQaObMmZSZmWnssszKihUrSBAE+vHHHx+4T61WG6Ei87Fu3Tqyt7enb775hgoKCoxdjtn56quvyMrKiiZPnkytWrWipKQkY5dkNlatWkUuLi70wgsvUKtWrahVq1Z069YtY5dlVrihbiC//voreXh4kJ2dHXXo0IHy8/OJiJtqfaxfv56cnJzo+++/pzNnztBvv/1G/v7+tHz5cmOXZhYSExPJycmJNm3aRDk5OXT27Fny8PCgjIwMY5dmNl599VWaPXs2yWQymjJlCl2/ft3YJZmFhIQEEgSBtm3bRkREeXl5dOPGDUpLSzNuYWagtLSUhgwZQp988gkREV29epU2b95MH330EZ0+fZoKCwuNXKFpi4+PJysrK9q2bRsVFhZSSEgIvfnmm0TEX+TqcuDAAQoICKCff/5Zu83f35+Sk5ONWJX54Ya6AWRlZdFLL71Er776Kh04cICio6MpLCyMm2o9XL58mXr27EnLli3TbisrK6OhQ4fSa6+9ZrzCzERGRgZ17dqVVq5cqd2Wk5ND3bp1oyVLltC8efMoLS2NysvLjVil6VKr1aRQKGjo0KG0Y8cO2rt3L8lkMpo5cybduHGDZs6cSbm5ucYu0ySdOnWK7O3tafTo0USk+Sw/9thjFBoaSp6entS3b186c+aMkas0XdnZ2RQUFETHjh2ja9euUUhICPXu3Zs8PDyoXbt2NG3aNMrOzjZ2mSZpw4YNJAgCbdmyRbttyZIl5OrqSmfPnjViZebh22+/pZiYGJ3fbTExMTR16lR64okn6PPPP6c7d+4YsULzwGOoG4CXlxd69OiBMWPGoHv37li3bh1sbGzQo0cPFBQUQCaT8VjWWtjb26Np06Zo06aNzrZOnTrh4sWLAMDjuh6iRYsWePHFF9GnTx/ttnHjxuHixYtISUnBli1b8OSTTyI1NdV4RZowQRBgZWWFQYMGIS0tDX369MHevXuxZMkSRERE4MSJE7zMbi3CwsIwduxYZGdnY+bMmejRowfatGmDhQsXYsuWLcjLy8Nzzz2HnJwcY5dqkpydnRESEoLLly9j9uzZGD58ODZv3ozc3FyMHz8eR44cwaZNm4xdpkmKjIzE7t27MXz4cO31Sv369UNAQACSkpIAQHsdCXtQQUEBMjMzkZaWhvz8fIwYMQI3b96Ev78/HBwcsGbNGnz55Zfaa8JYLYzd0Tc2NZ1aUqvV9Pvvv1NUVBSFhYVpx8ZlZ2dTamoqVVZWGrpMk6VWq+nGjRva21VH89977z0aMmSIzmNLS0sNWpupq+nMx5o1aygmJob++OMP7bbo6Gh66qmnDFma2Vm5ciX17NlTezskJIRkMhn94x//oKysLCNWZpqUSiURESkUCpoyZQo1adKEZsyYoXMmpLS0lHx9femdd94xUpWmb+jQodSlSxcaOHAg/frrrzr3jRo1ivr06WOkyszTxIkTqXnz5qRQKIxdikmrqKigzp07U0hICPXs2ZP8/PzoypUr2vtfffVVCgsLo6KiIiNWafr4CPUjdv8VxUQEQRDQtm1brF27FjY2NujZsyf++OMPDB06FEuXLoW1tbWRqjU9giAgICAAgO6MFGq1Wud2bGws5s6da/D6TJlM9uDHuU+fPti5cydatmypPbrQsWNHuLi4GLo8sxIREYHAwEAAQIcOHdCyZUusW7cO69atw2uvvYbc3FwjV2ha5HI51Go1rKyssGzZMsyZMwejRo3SHs2v+uz6+/sbs0yTVZVPfHw88vPz8dtvvz0ww0zfvn3h5OTEM0bpoSqj119/HUSEFStWGLki06VSqWBjY4ODBw9i165diIuLQ/v27eHn54fKykoAmr+3Tk5OfHa4DtxQN7DqDXa7du2QmJgIuVyONm3aoKSkBGvXrjVidaZNEARtk2hnZ6f9o/PYY48hJydHOy0Sq11gYCDc3NwAAFZWVigpKcGlS5fQunVr4xZm4lq0aIHTp0/Dzs4OTk5OWLNmDcaMGYP169fj+vXrcHd3N3aJJkcmk0GlUsHKygozZsxA9+7dtfcJgoC7d+/C3t4eISEhRqzSNAmCALVaDX9/f3z55ZcICgrC0qVLkZycjKKiIlRUVGDz5s3w8fGp8Ysz01WVUVBQEFq3bo0dO3YYuSLTJZfLtZ/b0NBQKBQKFBUVwcbGBjY2NlCr1UhISEBISAj/3quDQMSDeaWoOgJdRaVSQS6X1/r4O3fuYODAgXB2dkZKSgqsrKygVCphZWVliHJNSn2y++STT7Br1y7Y29vj3LlzSE9Ph7W1tcVmB9QvP4VCgeLiYowbNw537tzB4cOHLTY3oO7sysrK8MYbb6C8vBwLFy6Ej48P1Gq1TjNz/21LUt99r6SkBOPGjUNubi5SU1Mf+juysasrOyLCqVOnMHbsWFRUVEAQBHh7e6OsrAzHjx+HtbX1A69hSfTd96oed+jQIcTGxmLnzp0YOHCgIUs1Ofpkd/XqVURGRiIqKgphYWHIyMhATk4Ojh07ZvH7Xl0s86/BI5KZmandsdatWwcAD/1DUV5ejvfeew+lpaVITk626GZa3+yqvu8VFxcjKSkJt2/f5mYa9dv3VCoVfvjhBwwbNgy5ubk4dOgQrKysLPYiHX2yc3BwwNtvv42lS5fCx8cHwL2zTVX7pKU20/Xd9xITEzFgwABkZWVh37592iNilkif7ARBQFRUFM6cOYMFCxZg2rRpmDx5Mk6cOKH9vWepDU199r2qhcACAwMxd+5c9OvXz2B1miJ9swsKCkJKSgrkcjkyMzMRHh6u/SJnyfueXgw3XLtx2bVrF3Xv3p2OHj1KM2bMIEEQdAbx10StVtP27du1F0hY6oUSYrI7dOgQjR492uKzI6p/fiqVii5cuECLFy+2+PzE7HvsHjH73vnz52nhwoW879Uju6qLPPXdbgkexWfXUvPj7AyDh3yIlJmZiSFDhiA/Px+FhYVITk5G+/btaz0NTPUcGtKY1Tc7QHPauOriTUs+Mg2Iy6863vfEZ2fpeN8Tj/c9aTg/8aT2K/ffZjXjvVAEpVIJPz8/DBs2DJmZmQgNDUVJSYl256zpO8r9O6Ol/lERkx0AnZlQLLmZFptfdbzvic/OkvG+Jx7ve9JwfuI9in6Fm2n9cENdD1U7XtUfhdjYWCQlJUEmk2HevHnYvXt3jd/kLHW8YHWcnTScn3icnTScn3icnTScn3icnREYZGBJI1B90Yxbt27R3bt3qaysjIiIrly5Qh07dqQ+ffrQ7t27tY/75JNPDF6nKeLspOH8xOPspOH8xOPspOH8xOPsjIPHUOuBqn2Lmz9/PrZv347i4mJERkbilVdeQd++fXHt2jU888wzcHBwwJAhQ5Camor9+/cjNzfXYk9zApydVJyfeJydNJyfeJydNJyfeJyd8fCQjzqo1Wrtzrlq1SosW7YMU6dOxdixYyGTyTB48GD88ssvaNasGTZv3gxPT0/s3LkTSqUS2dnZ2hXELBFnJw3nJx5nJw3nJx5nJw3nJx5nZ2TGOjRubg4cOEATJ06k1atXa7dlZmbS1KlTydXVlY4dO0ZERHfv3qW8vDxSq9VEZLlTRFXH2UnD+YnH2UnD+YnH2UnD+YnH2RkHH6GuwdSpU5GSkqK9vWfPHkyYMAE//fSTzmwTvr6+eP311xEZGYk9e/YAAGxtbeHu7q5dStbSZqTg7KTh/MTj7KTh/MTj7KTh/MTj7EwHN9T3OXv2LGxsbBAbG6vd1q9fP4wdOxZqtRqJiYnIzMzU3hccHAxbW1tkZGQA0J1extLmxuTspOH8xOPspOH8xOPspOH8xOPsTIyxD5GboqrTH2vWrKHvvvtOu/3dd9+l8PBw+te//kW5ublEpDllEhMTQ7NnzzZKraaGs5OG8xOPs5OG8xOPs5OG8xOPszMdPMtHNdVX4MvMzMTzzz+PyspKvP7663jqqacAAG+99Ra+//57ODg4oGvXrsjLy8OFCxdw6tQpndMrloazk4bzE4+zk4bzE4+zk4bzE4+zM0HG7uhN0cyZM2nHjh2UkpJCI0eOpN69e9OPP/6ovX/BggXk7u5O/fv3py+++EK7nQf0c3ZScX7icXbScH7icXbScH7icXamgwfNADpLbyYnJyM+Ph6Ojo7o1asXZs2aBQ8PD3z++efYtGkTAGDevHl45ZVXUF5ejps3b6KoqAiAZS6ry9lJw/mJx9lJw/mJx9lJw/mJx9mZLh7yUU18fDzy8/Mhl8vxxhtvaLcfPHgQH3/8MQoKCjB9+nTt6ZR58+Zh165diI2NxVtvvQUvLy8jVW58nJ00nJ94nJ00nJ94nJ00nJ94nJ0JMtahcVNz8+ZN6tSpEwmCoB2wX1FRob3/wIEDNHLkSAoLC6O9e/dqt8+cOZN69+5NWVlZhi7ZZHB20nB+4nF20nB+4nF20nB+4nF2psliG+rqa90XFRUREdHBgwdp0KBB5OvrS7du3SIiosrKSu3j9u7dS3PnziWlUqnzWpa2c3J20nB+4nF20nB+4nF20nB+4nF25sEih3yo1WrtnIsff/wxcnNzERcXh4iICBw+fBizZs1Cbm4uUlJS4OvrC4VC8cAVsSqVCoIgWNzcjZydNJyfeJydNJyfeJydNJyfeJydGTF2R29Ms2fPJi8vL1q7di3duHFDu/3QoUPUu3dvatu2Ld2+fZuI+IrY+3F20nB+4nF20nB+4nF20nB+4nF2ps9iG+odO3ZQUFAQHTlyRLutaoJ0Is1O2qdPH/Lw8NBOis40ODtpOD/xODtpOD/xODtpOD/xODvzYLHH/2/fvg1XV1eEhobqTENTpWvXrvj3v/+NZ599Fq6urkao0HRxdtJwfuJxdtJwfuJxdtJwfuJxdubBytgFGEL1MUhVrl+/jvz8fHh4eAC4t+oQEeG3336Dn58fYmNjERsbC0AzBskS523k7KTh/MTj7KTh/MTj7KTh/MTj7MxXoz9CXX3nTEpKwpkzZwAAcXFxUCgUmDZtGgBol/AsKirCZ599hsOHD+u8jiXunJydNJyfeJydNJyfeJydNJyfeJydmTP8KBPDqT7G6M0336Tw8HBasWIFFRQUUElJCX3yySfUokULGjduHJ0/f56SkpJoyJAh1KFDB4sf1M/ZScP5icfZScP5icfZScP5icfZmb9G3VBXee+998jb25v27dtH5eXl2u0VFRWUmJhIoaGh5OnpSW3atKFBgwZp53K8f/5GS8TZScP5icfZScP5icfZScP5icfZma9G31Bfu3aNOnXqRFu3biUiolu3btH+/ftpxowZlJCQQESab4YnT56kv/76SzuBOn/j4+yk4vzE4+yk4fzE4+yk4fzE4+zMW6O/KNHNzQ1EhMOHD8PNzQ1fffUVMjIy4ODggCVLluDOnTuYPXs2oqKitM9Rq9XaMUqWjLOThvMTj7OThvMTj7OThvMTj7Mzb43qokS1Wv3ANplMhgEDBmDnzp3o378//Pz88OGHH2L//v0YO3Ysbty4UeNzLA1nJw3nJx5nJw3nJx5nJw3nJx5n1/g0mqXHiQiCIAAAfvjhB1y6dAnh4eEYMmQIVCoVbt68iZKSEkRERGif06NHDwwaNAjz5883VtkmgbOThvMTj7OThvMTj7OThvMTj7NrpAw9xqQhVL86du7cueTk5EQxMTEkk8lo0qRJdPLkSe39xcXFdOrUKXrssceoffv2Fj/2iLOThvMTj7OThvMTj7OThvMTj7NrvBrFuYKqb3ppaWk4deoUkpKScOTIEWzduhUHDhzAZ599huPHjwMAtm7dinfffRcqlQrHjh2DlZUVVCqVMcs3Ks5OGs5PPM5OGs5PPM5OGs5PPM6uETN2R/+oLF++nEaNGkUjRozQmWrmp59+onbt2tGECRMoPT2dysvLKSUlha+OrYazk4bzE4+zk4bzE4+zk4bzE4+za5waTUO9bNkycnBwoODgYDp//rzOfT///DNFRETQ0KFD6dKlS9rtVTuppePspOH8xOPspOH8xOPspOH8xOPsGiezbKirj0Gq7rvvviMfHx+aPn06Xbx4Uee+jRs30tixYy1+p+TspOH8xOPspOH8xOPspOH8xOPsLIfZNdTVd7CsrCy6cuWKzv0rV66kgIAAmjVrls63u9pew5JwdtJwfuJxdtJwfuJxdtJwfuJxdpbFrBrq6jvWu+++S126dCFnZ2caO3YsbdmyRXtffHw8NW3alN544w3KyMgwRqkmh7OThvMTj7OThvMTj7OThvMTj7OzPGbVUFeZP38++fj40Lp16+jEiRMUGRlJ3bt3p9WrV2sfs2rVKpLL5fT5558br1ATxNlJw/mJx9lJw/mJx9lJw/mJx9lZDrNoqKuPQdq/fz+FhYVRcnIyERGlpqaSra0tRUREUHR0NK1du1b72J9++omUSqXB6zUlnJ00nJ94nJ00nJ94nJ00nJ94nJ3lMvmGuvppk9zcXLp06RKtWLGC1Go17dq1izw9PSkhIYHy8/PJ39+funTpQkuXLtV5DUvdSTk7aTg/8Tg7aTg/8Tg7aTg/8Tg7y2bSC7uUlpZq16mfNm0a5s2bB2dnZ4waNQoKhQLLli3Dq6++inHjxsHNzQ1hYWG4ceMGLl++DKq2orpcLjfWP8FoODtpOD/xODtpOD/xODtpOD/xODtmsg31N998g8WLFwMA/vzzTyQlJSEuLg7e3t5wd3eHWq3G7du3YW1tDZlMBqVSCT8/P6xatQoff/wxBEHQ2UktCWcnDecnHmcnDecnHmcnDecnHmfHAEAgE/y/GB8fj3/+85/Yt28fjh07hvT0dMjlcixfvlz7DbCwsBDPPfcciAhRUVE4duwYcnJycOLECchkMqjVau1jLQlnJw3nJx5nJw3nJx5nJw3nJx5nx7QMNbZEX2vWrCFra2v65ZdfiIjojTfeIEEQKDY2VjvYv2qc0rlz52jYsGHUp08fGj58OFVWVurcb2k4O2k4P/E4O2k4P/E4O2k4P/E4O1adSTXUq1evJkEQaODAgdpt2dnZtHDhQhIEgVasWEFEmqtoqwbul5aWUmVlpXbntdS17jk7aTg/8Tg7aTg/8Tg7aTg/8Tg7dj+Taajj4+NJJpPRpEmTyN/fn6ZOnaq9Lz8/n95++20SBIHWrFlDRJqd9P4lPWtb4rOx4+yk4fzE4+yk4fzE4+yk4fzE4+xYTUyioV68eDEJgkDbt28nIqKvvvqKvLy8aNq0adrHFBQU0Lx580gul9O3335rrFJNDmcnDecnHmcnDecnHmcnDecnHmfHamMSDXVycjIlJiZqbxcUFNCKFStq3Ennz59PgiDQr7/+aoxSTQ5nJw3nJx5nJw3nJx5nJw3nJx5nx2pjEg11leqnQAoLC2vcSfPy8mjlypU89ug+nJ00nJ94nJ00nJ94nJ00nJ94nB27n0k11Per2km9vb3ptddee+B+3klrx9lJw/mJx9lJw/mJx9lJw/mJx9kxk26oiTQ7aXx8PAmCQIsXLzZ2OWaFs5OG8xOPs5OG8xOPs5OG8xOPs7NsJrmwy/0KCgqQkpKCJ554gpflrCfOThrOTzzOThrOTzzOThrOTzzOznKZRUNdnVKphJWVlbHLMEucnTScn3icnTScn3icnTScn3icnWUxu4aaMcYYY4wxU8KLxzPGGGOMMSYBN9SMMcYYY4xJwA01Y4wxxhhjEnBDzRhjjDHGmATcUDPGGGOMMSYBN9SMMcYYY4xJwA01Y4wxxhhjEnBDzRhjjDHGmATcUDPGGGOMMSYBN9SMMcYYY4xJwA01Y4wxxhhjEnBDzRhjjDHGmAT/D1P5I30LgRtrAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2d_mean_diff.forest_plot(forest_plot_title = \"2D Forest Plot of Mean Difference\", forest_plot_kwargs = { 'marker_size': 6});" - ] - }, - { - "cell_type": "markdown", - "id": "e3a3e186", - "metadata": {}, - "source": [ - " This data would require a stack of forest plots to visualize. So instead, we plot a whorlmap for a concise representation and use color to represent the dimension of effect size. For each effect size, the full bootstrap distribution is binned by quantiles and ranked by value, and then each bin is represented by a pixel. All the pixels correponding to the bins of effects are arranged in a spiral in a cell. The redness and the blueness of the cells represent the magnitude of the effects in the positive and negative direction." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e1beb9b1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2d_mean_diff.whorlmap(\n", - " title=\"Mean Difference Gene Expression Whorlmap\",\n", - " cmap=\"vlag\",\n", - " chop_tail=2.5, # Remove 5% extreme values\n", - " fig_size = (10, 4)\n", - ");\n" - ] - }, - { - "cell_type": "markdown", - "id": "311bf96e", - "metadata": {}, - "source": [ - "The resulting graphic is easy to interpret. Drug B and E induces the most broad spectrum reduction. However the data for Drug E seems a little less precise, mixing blue and red colored pixels. We can say Drug B is a surer bet. \n", - "\n", - "## Plotting whorlmaps with standardized effect sizes\n", - "We can also visualize the same array of effects in terms of standardized effect delta g. Let's plot them together in the same figure by specifying the axes to plot in:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7ee951a8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "multi_2d_delta_g is a MultiContrast(2D: 6x8, effect_size='delta_g', contrast_type='delta2')\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "figure, axes = plt.subplots(1, 2, figsize = (9, 2))\n", - "multi_2d_mean_diff.whorlmap(\n", - " cmap=\"vlag\",\n", - " chop_tail=2.5, # Remove 5% extreme values\n", - " title=\"Mean Difference\", ax = axes[0]\n", - ");\n", - "\n", - "multi_2d_delta_g = combine(dabest_objects_2d, labels_2d, row_labels=row_labels_2d, effect_size=\"delta_g\")\n", - "print(\"multi_2d_delta_g is a \" + str(multi_2d_delta_g))\n", - "\n", - "multi_2d_delta_g.whorlmap(\n", - " cmap=\"vlag\",\n", - " chop_tail=2.5, # Remove 5% extreme values\n", - " title=\"Delta g\", ax = axes[1]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "95695191", - "metadata": {}, - "source": [ - "## MultiContrast object can also handle 1-D dabest object arrays" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "350000c0", - "metadata": {}, - "outputs": [], - "source": [ - "multi_1d = combine(dabest_objects_2d[0], labels_2d, row_labels=\"Drug A\", effect_size=\"mean_diff\")" - ] - }, - { - "cell_type": "markdown", - "id": "fe7a8d27", - "metadata": {}, - "source": [ - "You can plot a forest plot from this MultiContrast object" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "184aa227", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig_forest = multi_1d.forest_plot(forest_plot_kwargs = {\"title\":\"Forest Plot from Multi Contrast (1D)\", \"marker_size\": 6})\n" - ] - }, - { - "cell_type": "markdown", - "id": "5d54f722", - "metadata": {}, - "source": [ - "## 1-D whorlmap also works" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dd82e1ad", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_1d.whorlmap(\n", - " n=21, # Larger spiral size\n", - " chop_tail=2.5 # Remove 5% extreme values\n", - ")\n", - "# plt.title(\"Customized whorlmap\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4c6176a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "multi_2d_mean_diff is a MultiContrast(2D: 6x8, effect_size='mean_diff', contrast_type='delta')\n" - ] - } - ], - "source": [ - "dabest_objects_2d_2group_delta = [[None for _ in range(8)] for _ in range(6)]\n", - "for i in range(len(row_labels_2d)):\n", - " for j in range(len(labels_2d)):\n", - " df = create_delta_dataset(seed=seeds[i], \n", - " fourth_quarter_adjustment=drug_effect_2d[i][j],\n", - " scale4=drug_effect_scale_2d[i][j],\n", - " initial_loc = 20)\n", - " dabest_objects_2d_2group_delta[i][j] = dabest.load(data=df, \n", - " x=\"Treatment\", \n", - " y=\"Transcript Level\", \n", - " idx = (\"Placebo\", \"Drug\"))\n", - "multi_2d_2group_delta_mean_diff = combine(dabest_objects_2d_2group_delta, labels_2d, row_labels=row_labels_2d, effect_size=\"mean_diff\")\n", - "print(\"multi_2d_mean_diff is a \" + str(multi_2d_2group_delta_mean_diff))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ad60757d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2d_2group_delta_mean_diff.whorlmap(\n", - " title=\"Mean Difference Treatment Whorlmap\",\n", - " cmap=\"vlag\",\n", - " chop_tail=2.5, # Remove 5% extreme values\n", - " fig_size = (10, 4)\n", - ");\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a1053e97", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2d_2group_delta_mean_diff.whorlmap(\n", - " title=\"Mean Difference Treatment Whorlmap\",\n", - " cmap=\"vlag\",\n", - " chop_tail=2.5, # Remove 5% extreme values\n", - " fig_size = (10, 4),\n", - " heatmap_kwargs={'cbar_kws':{'pad':0.17}}\n", - ");\n" - ] - }, - { - "cell_type": "markdown", - "id": "ad82941f", - "metadata": {}, - "source": [ - "## Heatmap and plot kwargs\n", - "You can customize the whorlmap further by passing in `heatmap_kwargs` and `plot_kwargs`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a7c7e109", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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9ycR8oyU6/4t6MoC3CmsYczMLygB8Yvh6ZuFPFL8I9sk0i4pOiVNhAvZiOsSmTZvQsWNHAIDb7UbXrl2xfPlyDB06FP369cO7776Lhx56CDwf+NKyLIv8/HzMmjULO3bswLx58yKOdU2ePBnjx49XbGs5YzFQqnMAAF9a8BYTE0ZFTy1CAAbwZKXKDI5owMJprS+bd5VtJxZEqUEJgT/VpXmOSLwnXpX7Rmh0bZt3E+PUetl55BANtHy74FbWt6SXhg7yPL9onkEkqAjelRHi3njJE1D37NmDrl27YsyYMXjhhRcAlBmw//znPxg2bBhq1KiBV155RRpRPHHiBMaPH49vv/0WmzZtQuPGjSPWJSEhAQkJCYptjMFopT9B9bZaNJqSdwPAmyZPAdeqrPO/FogyCzxaCAmMlTxOQ1CTo4Dm5VnKMTWElN5iRl64k9r8DCKBY7xiDNFwvf7660hNTcX8+fPx9NNPg2VZPPfccwACRuW2225D3bp1sWjRIlSvXh0zZ84EAGRlZWHq1KlISUlBvXr1LprevNpwBWG5S4dgFwoIMX4h7cNsPpJBN8sqeHnmvKlGbVxPLzk2LLS8GnXbjPB+AaqM/nAfDzPyZAnJ5QVnAY4YQe5xvfTSS5g5cya2bduGzMxMEEIwbdo0AJAMWGpqKjp16oTBgwfjtttuU8g6deoUtm7dir///hsDBw5Eo0aN4HbbM9NV6/lLuU0674aVrpQUEzGUJ7MIYUYb7fzYhiSgGl2vmbiaXpglgjZGGXNdRXOy7JEjyYsTh8fxvGIE0XDt3r0bx48fx/z586Uunzi6OGXKFBw5cgS9evXCsmXL4HK50LZtWxBCpMz5ZcuWYfLkybjhhhvwv//9D6tXr8bSpUtx++23RzwRWxPBhqI1V9EUKIKtnChsD5+g3witfjgtvathjE3IdZroEodL9NQ/MKxopSw726SGXtE4LHY+g2jh15rUWYEQt8YLAHbu3ImWLVuCYRi8/vrr0vaqVatiyJAhuPzyyzFu3DgcOHAA1apVw2effQZCiJQ5v27dOjz22GN47bXX0KtXLzAMg/bt22Pu3Lm4/fbb7TNcQWhNPtaC2ZdfHNUyfIdNyrLTe1B4XDZ4N/Z6XGEq6Awq6N1jO9t3vA3g8Tb2WefMmYM1a9bg119/RVJSElq1aoV58+YpaKnsRlzdzry8POzduxcrVqzAvn378I9//AMffvghBEHAtm3bFCsApaamokePHvj555+xfv16bNq0CS6XC36/H4QQ5Ofn4+OPP8bIkSPRo0cPadrQ6NGjcfz4cWk00i4IXLC4whfKlRU9+BPLCp8I8Ek6RbUPgOaUE71CWf0iQdYrDbkerqzwLqJbRIR4X0b6MPpFC0bXYiQzRI6oLkOlQllzRRcR3P+QZ2Az/AJrWKxg69atyMnJwffff4+NGzfC5/OhU6dOKCoqsl/xIOLG81qzZg2WLl2KXbt2obi4GKWlpejSpQteeeUVfPDBB7j77rtRv359jB49GlWqVAEQIBhMSEhArVq1AATiZOJcxSpVqiAjIwM33nijYv5i1apVceTIEZw/fx7VqlWzxfvyphvs1Pi4aXpeQVYJb1UBqGosI+R4uUWQjbdTDuAtPOFwHqE/CUASEdXVh86MA8W5OEDr82G1S8bwwVHAMNdpRS7lqLn7ZpSNH6NnYCfsXLfx888/V/xevnw5MjMz8eOPP6Jt27a2nUeOuDBeS5YswWOPPYapU6di/PjxaNGiBV566SW8/fbbuOOOO7B582asWLECgwYNAsMwyMnJQXp6upQOIULk6xIN0qxZs6ScLnEid2pqKtLS0pCYmCjV+/jjj9GxY0ekpKRY1r2wkQ/uUy5pTl4IFTJVBqy1Jh+L9avuZwJyZMcrKHa0WA8ooNWKTncoBTmTEJzSEpy4LTIkyHTUonBWUygTGmDyZD1Bcj31RGX1cWpdVXonnQ1MJyJSsq38nGWVzVBC/9WHBVvEBK5DpOKG7J5r6aYjj1DAVbsY3rOJgJ+A8IGikKF4psoEU/V9c59X3S+NZ25ELBlrhJu/6PF44PF4FNu00oW0kJ+fDwCoXr165AqGQbkbryVLlmD06NFYtWoV+vTpI22fNm0aGjdujKeffhoDBgzA1q1bkZ+fjzFjxqCwsBBTpkzRNDaiQdq/fz+uueYaAGUU0EAgL8ztdkvHdujQAS6XCz179oxI/6o/hUbow758Gvt9aYD7QvRvLaGBJM/0/9NJA9c7hcGpfelASp7VXADtzXwCQeIpj/ZOWG+4KbkWR40N5PNJALcntaxRGOlisI/QwPNMyLfnngGA4LbfJfOHMV5z5szBU089pdg2ffp0zJgxw/A4QRDwyCOPoHXr1rjuuuuiVVMX5Wq8tmzZggcffBAzZsxAnz59pGk7PM+D4zj861//wsmTJzFhwgSsWbMGo0aNwpkzZ7BhwwY888wzCllGNNAsy0qeV3FxMS5cuICTJ09i2LBhOHr0KPbt2ycF+i1n3xOAKwlTJ8w7LCcRJEKYqTXhTiVN1wE4HUZWOQzPQ5VTYSKiNJZ5UvJAvz4nmPkLp1wgOZYNQ8OtPEh7s3z01CyPmp5cQeaY2DG5PFaB/nBxLa0ZJWa8rpycHOzbty+qecRmUK4B+zp16uC2227Drl27sH37dhBCQAgBx3HSXMacnBw0atQImzYF5hA+8cQT+OabbyRjA4TSQK9evVqigV6/fj0ASEbJ7XbD5XKhR48e+O2337Bv3z5FoD8SsF6qXzwG+7zaLUNiG4igqMF5qGFhDYoEeXeHp5aKyF6hBcILoUWgpkqoLHOF0Siaz8AbLD7jwnqDxadimZXfMyHyoiXPLgiUGJaEhASkp6crSjjjNXr0aKxbtw5ff/016tata7/SMpSr8WrYsCGWLl0Kj8eDWbNmKSy1aEgKCgpQUlKCyy67TLFP7iXJaaAnTpyIhg0bYty4cUhLS8PEiRPxySefSMf6/X4cPnwYCQkJ+OWXXyTDFS0poZy/S1H8Sj4vLW4vzSROqiyWXnRJsFI3TQMq18nAqIrhHdEYmS1GE9IpIYFJ5laLDgyNlqhPGGMvh2iQjIramOnqFo3hihH8lDEsVkApxejRo7F27Vp89dVXuPLKK2OkdRnKPebVsGFDLFiwAGPHjsUzzzyDadOmoXXr1tL+P//8E3Xr1kXLli0BlAXe1V6SWRrof/zjH5gwYQLmzJljG3+9CF2KGTUoheAW2U9leQgqaHkYugjjNQbWNjSWJ+rOu4nudVCiwVJqINbMXEUimG+pNMzosKYnpXct6l4ThWLajkgbBKgGTnQQjvLayvMUu/+xhF+wz3fJycnBO++8g48//hhpaWnIy8sDEBj1T0pKCnN0ZIiLPC/RgBFCMHPmTKkL6ff7MXXqVKSmpuKOO+4AAMXooRx6NNAjR45EQUEBcnJy8Mknn0hzIu02XCIYPwXjE3QL6w3DjQNljMPOL3SI96fywkLoq0VojYjJu4cWune6oDRQIvC4QnTzq4qJ7rUexHsleZM63cfQ69HRzcLzVFB7xwDhuo1WsGjRIuTn56Ndu3aoXbu2VN57770YaR8HnpcIuQc2d+5cMAyDF154AYcOHcKePXtC0iCMaKDbtWuH9PRA8pVIA33+/HnMmTMHtWrVwj/+8Q8AsN1wiWB8VHcI3OqokTL9QKM7F+YLHULb7FFaOyXdi7VvmUglrXtuM+IYSAtTlKVOaF2nBd2I0YCAjodkcB2Gy73RIOWOWSNDVMZT7cRexBk7VruGRoiUIy8axIXnJULugbVv3x4///wz9uzZI8Wl1Aml8UIDDSCk28b6hEDxyEtkQ09lnowFj0vPphFSpk8pL5WIwVMQf2jRhdYu+b3jeUAQlCVCyGNdotcERObNsB4lRz/roWBLdYyaCfkBfv0IvVSb4BcYwxLviDsNGzZsiOeffx4jR45UjASqvaS4oYHm/ZpZ0ZQQsEV+cMU+cEVecBdU4/kaCZJGIP5gFy2ccTADAnCF3kDJL9XUx5JuPAXjFwLFq+Lqt/hFJn6hrHh5U8uFyRcrUSNgtIxHd8sEGSlGAgugFAdLkbbBl6tBw8QhA4uM6I94xhp2dhvLA3FnvACgcePGWLBggSIuFa800Cf/z5gGminygCn1B4LS0dgchgSMl0YcJNxvEfL3kbIEzIXSgGejk8VPqLL7q+QBU77cjIcH8QllHoRWOoOZ6ycE8PkBP2/Z8MkhGg5KAK6UBgZT9FYPUnfdiOp/8VoZAleRH2wpD4Y35tgPR+st1RMCsT6zz9BOODTQMYboccUrDXTrKYsB9dxTpmyCob9qEkCCNDcqY2AV1BXhC0XK9CkbTSPw1wjQSgdiV7LUDas9NQLwSZxSfjRwu8J6LVbgS7EWL9OCyGrrreqSfodWCiebhBhkrfibYvX0GIKvAF1DI8S98aoYNNDab60/RXl7xRfSzDy7kN2MUoYp6DZEAn9yMKVc/v4KOmkbYXQzsyq2Ui/Z7HH1qTgLDYrSsJ6NmncsGj58f2JQNzkjUBT5WPI0kvLopVWErqER4t542UUDnZSUpEici2gqkAmI74OQwGhuDz0AJgyXdT1DYsjymLhctyhvAbXI7RU2X8rMqKJebpxqU1imVwtWTBwljszjChZ5F1yLisfo0mPQjXQ8rxjBThroP//8E5s3b0Zubi569+6NXr16SakUtiL4YvNapIQRGgm7Qw+Cm+gmtFpnZrXX+JtKaKUU1MTNlFIOLKgY4hFLzzO8LCsxqnhhX+Ydzys2sJMGevz48RgyZAhOnz6NuXPn4sorrwwxcHYhHJuqVWOkCBhHArnHpZXHFaFo2xmEbWK1pQT2rMojetAazzMqGuiLkDlvFtQxXrFDtDTQa9euxaOPPoo33ngDvXv3BgBcd911OHr0KICyrmM0XPby5y+4SMQvttaX284XXZ0cG1ZP9Wic/DrDeUjl6MFZ9mrCxfNMrElgdhWneBvA4wXHeNmGvLw8nDp1Cj/99BOaN28u0UD37dsX27ZtQ/fu3ZGRkQGgjAa6U6dOOHfuHLKyshQe1/nz57F9+3ZMmjRJwdVVpUoVfPjhh1i0aBFatGiBMWPGIDs7O+oYmHwB13AwY+Ds7FrwBpRXYZcXVKcR2PzGWMqcF6E3EGHinoXr3Sk/RuHrmEW8GS4AEJyYlz2wmwa6atWq6N+/PzIyMqSRx06dOuHw4cMYMGAA/vrrL/zwww+YPHkyli5diuTk5Ih1D1mj0YxxMqhj+kU3YWxF3cI2OJMNMqzXZUEWtTpKaYCwazSGgdpIGxl8EWaNWLSGK1Y5X85oow2IFQ30zTffLO3Lzc0Fz/P46quv0KBBAwDA7Nmz8dprr6GgoCAq4+VPgr43oLVRXVeVPKo43ur7Javv07oklTxN+TrnDNeFMtRVPRKolbNmtbsZNN6C3NCEkWHmfvrl902vvkE+WOCfwB9dYx/lB84OCE63MTrEigb6wIED0rJLfr8f9erVwxdffKHI2r/yyitxxRVXREfZQYDiWgDlaHB1muAqMgwCq84E/xKCwCeUAYBAfhIhyr/4MQme6gEvgrJUGmIPWV1Z+qtKPJWBKyRlFD3BEpZ3XrZdve3C5QTe6hQCFxQkDv+L1yXTS5mJH5qLlV8/UV83qjw0nG6+5kXwe1lQXrxZqrpBZajGtWvdOC6fBeqUgmEFcByPBHeApcTF8GAZCkIoGBL4yxFB+s0EleUYAUzwBAc3XxmkIjJxjRHk/kULx/OKAheLBprjOFBKJU+N4ziUlpZixYoVqF+/vtQNjQQZPQ5L/zOq1ib/rfgfofV+OVobBY395vsIBu8dU8Qi4aw5MYanCKrCuwFv4wDXte4Si2baAaHgi1226lZ80tyHR25j9UATBPzrzm/BqiZUqp8rq8pMlT9Pcd/3Z69EwjlTqin11NGRj3QxYwM4o41RQE0D3aZNm4BSwbmMYhdx8eLF2LRpE/r27YsnnngCU6dOVbCpqmmgt23bJtFAv/jii+jWrZsUjC8tLcWRI0cwduxYHDt2DB9//DGAyJNWBUrQqdb+kO2szmeTUb34h0ur48/CmiBMYDtxUTCcyZm6GuryJZzkERENW2gpH0m+riEFuAQeHGusm94t9PMM/D5W0ttO3RgeAF92YqsxIip6ukHUTzyJy11nQuqpjZa0Xfasj/qr4S9PhmTwGHFVKc0Tm9CNiV1eGK2M3cbDhw+DECJxVO/cuRPvvPMOmjRpggceeMC0HJEGeuzYsZg1axaeeOIJKf8qUhroVatWYcKECRg3bhw2btyIiRMnwu/3SyOOn3zyCd566y14PB788MMP4DhOsbpQpKjrCnUnWPUXW/VFP+arpiuP4czPOxENBu/X9ou0llsLCy1RFHCZNaziuQng8+vf26h1I2V5HGZIBkNGTzVUE4LyanEXAOh/iLT2HfVXA0OEEG9Nix0jrCcY48HAShnzGjBgAB544AEMHjwYeXl5uPPOO3Httddi5cqVyMvLw5NPPmla1sWmgb777rtRpUoVdOzYESzLRs2mSimBQBkIYJDtOg0AYFRvqvyLLb7sf/vL1rMLxFCUMgGANWnACACeF0katWNgVoxEuEbjDuN9yeFTDwNqtFg7dSO8hQbJhJ5QjANJBizokTEqN5fVcHtPhFmF3WgepHralDrHLyZmpoJ3GyOy7fv27ZPYSN9//31cd911+Pbbb7Fy5UosX77csryLRQP96aefgmEYdO7cGSzLKlIr7MLl3AXU4YpwebBku4pwOVeCy7kSZHOluJzTXrOQ6HyGWUYAxxoXs7CDUlo0rOK53RxvWC6mbgFBxjKs8mfVYFJQg0lBNSZZUdKZJEVhQCC3NyGel3SdOnTZ1OJ12gAqGJd4R0Qt1+fzSewKmzZtkrpkjRs3xvHjxyNS5GLQQM+ePRtZWVmS4Y00q14PfPD7mM2l6dY5yRcqfuu95JQSUEqkbppeLInnGVjpyBmNbpntpoheSQJb9qHQMr5eK+vci7rZlJ1u2IWMYXdMHRrQgp6BIvTiJrNW9JhXRLfq2muvxauvvort27dj48aN6NKlCwDg2LFjqFGjRsTKVEQaaEHDoRfCvMCaBsvgPUrgeLjZQEnk/FKJFIRXeSRhrJ9RbCaB8yPJ5ZN0SuZ8SOYCfMt63qThudTeUhQso5a8Nw3DGU0qAQNjuh61J1gebKriB1KvxDsiMl7z5s3D4sWL0a5dO/Tv3x/NmjUDEAiGi15NpKhwNNBepRHhTUYnWAghI496oBRIdPmQ7PIikfMhyRWglNbz2swMtwWom6n+QhUWkMz5kMp5ke7yIDWcbhb0ikq34GNQr/oTTi0BF49FlPFT6XrLBSLDol6Jc0TUbWzXrh1Onz6NgoICVKtWNmL2wAMPRJWpLkKkgQagoIGWe14iDXRiYiLuu+8+3HPPPXj//fexcOFCXHfdddi1a5dEA71w4UIAShroSNIi5syZg6eeekqx7YrBt4KfVFPxwvNUAEO0GwBLiGYekRkvpVpiCWon5eNAfmZEXo0ISgJtm/EBgk0hv2ruYtRNPIf9hbWMK5q47YwvsOCFLyXKBhRsg2wp4C4EPPqDu1GDjWReLCFwFfLwJ5fTHMMKENcyQsR3jWVZheECgOzsbGRmZkatlBxaNNAi5DTQX375Jfr16wcAeOihh7B48WJs2bIFy5Ytg8fjwbRp07Bjxw4MHjw4Khro/Px8RanTr6W0P5IvtjydwsgeCZTAy7M4eKGmea8m3FQZS6Sl+sIESuAROPxelKnINo9UN8oA/mQbv/wM4FHlIRtx2AMAH2HTYIgAFibvAQB/EqPZZb0oqIye1w033KBpAAghSExMRIMGDTB06FC0b98+agXjngbarerOUkAweBPVQ+4izLwq+d5EQyNiySYTc3QvRhDjfQIlOO9Nsme6iQ16qcG7oX+DbTQa8pwvsyEB0xTaMTBuFWFE0QgRfV66dOmCP//8EykpKWjfvj3at2+P1NRUHDx4EDfffDOOHz+Ojh07StnrUSkoo4H+9ddfMX/+fKxcuRKPPvqoVEdNAy2yrAJlNNBbtmxB8+bNQ1YhKi/IPa5wX2kBBCU+F0p8LpT6OZT6OXj80fX3BJZIJZoQj0AJiv0uFPtdKOU5lPIciv2RWx+5XtHqBgQofCgnZtFbO9Zs/DJSCGyZXuXBrkoEYljiHRG1gNOnT2PChAkKIwEAzzzzDHJzc/Hll19i+vTpmDlzJnr16hWRYnbSQDdp0gS5ubkAQpknogWlRHPEsYQG8rl4WT6aAAoWRDNjWy+GRSkxzFDXRLj3TnXp0ThMHo10CLkHJnYjCUyOPkagmxjDC9mu9YjDyOMpiajLyKiOCfdBigsq6HIaJ7ALERmv999/Hz/++GPI9n79+qFFixZYsmQJ+vfvL3XtIoFdNNAAsGrVKowbNw5Tp07FmDFjbDdgQMA7EkBwyF8Vh/yhMROteBgLQcrG12vYpR5zXox0vEGjUcxV1Ktj0pB5eTYy709HfsS6aWwz9GSMjIrGCfKCeXm8RkvnZZvk/jxDhJDJ99IpGANv8mI7OxXAuzJCRMYrMTER3377rcSLJeLbb79FYmIigIDnJP4fKaKlgRaxfft2pKamYt26dSCEYPTo0WAYxpY5jXIv47CvhqaRUhsyrdiQFK+SvfMhcxWjcJHCdr+MRAd1EuNtWp5gNHlBUemmqiefYB0J1M/mqAErodZzVI8ky73CiK8zVh5SDCIor7zyCp577jnk5eWhWbNmeOmll6JOn9JDRMZrzJgxGDlyJH788UeJ8O+HH37A66+/jilTpgAAvvjiCzRv3tySXDtpoOXIyspCgwYNcMUVV+Dtt9+GIAgYO3Zs1IZLjlxPTcVvrRdbz4hpfaUF0XBF8uISC43Gos2xw3CZ0i0CW6iQFaVTwVMGR/1VFdsEje6k6RV4dK6zXAf1bD75e++9h/Hjx+PVV1/FLbfcgvnz56Nz5844cOCA7VkIQITG64knnsCVV16Jl19+GW+//TYA4Oqrr8aSJUukLt3IkSMxatQo0zLtpoEGyiZxX3XVVSguLsZjjz2GiRMnYvXq1SCE4P3338eLL76IFi1aRMVff7AoYFA1s+1VL4gQbGHq7fIYCdVhh4jEkEUc8Na5HX4T8bdwSzNK9WzUjXL2uie53poh27RiYYLqIsQgv/x52r7Skk2wex7lCy+8gBEjRmDYsGEAgFdffRWfffYZli1bhscff9zekyEKPq+BAwdi4MCBuvutsJPaSQMth2iQmjVrhsWLF6N69eqYM2cOpkyZgmnTpiExMRFNmjSJagUhSgl+Px80XjKDRBXxEOV0C7EelXXHiLiUs8dAB0Pi+9BNphNQzdhtCvg92q3Q0PPSsSl26qZruCx0N+XYX1RGvyQfcRQUz1DlRQfrqT2xkNibhW9krL2ycOMnHo8HHo+SSEArXQgAvF4vfvzxR0yePFnaxjAMOnbsiO+++84WfdW45GigRSMkJypMTk7G8ePHwfM8srKysGPHDiQnJ+Pyyy/H22+/jQcffDDi4H3JhsxAAiQTfNmI7C+j/A1CpW2AbHsQDAEYD4FEGUwRNplSvU1iGK0rgPEEh7zlcmhoXTNyq21PBJ8YNDok9Fr16KrV2ygBvFURoEcGpLiLWhdT9NQA3GdYcKXB+Zp8oJ44H1IxhiFLBDU61zc//iMwXUd9rFonrempqm2ljRCWBlrv+SkQq5hXGOuoNaNk+vTpmDFjRkjd06dPS+1LjqysLPz6669Rq6qFiIwXwzCG3Sw+DK+RCDtpoOW6AcBvv/2Gq6++GgzDoEGDBmjZsiX27NmD4cOHo06dOnj77bfx+uuv47nnnkNGRobCcFpB/g1ena+pOW9AHCUkZxLgPqsRyI/gxRVcQMJpa8bY6Dy8Gyi4CsZeQ7jUgOCxrgvEEg10uOtPPWJcIez9k+33phEknLfWl9KlbU4glmigw+kZCxrocAH7yZMnY/z48YptWl5XeSEi47V27VrFb5/Ph927d+PNN98MsdRGsIsGGjDmsBcEAUePHsXNN9+M9u3bY9WqVcjMzESVKlVw5ZVXRpyLJsKdHHQjDBq3UY6Tt7jszWQiJ4sAoOymRCtLlCeqLiRouW1hjpd7lp6yH1psEVZ7SaIXy5VQ096Jntr+RFluGh/BF0N2iHwFqGgYO8rkWVfHDMKdW6+LqIWaNWtKnHpynDhxQopJ242IjJdWY7/77rtx7bXX4r333sPw4cNNybGLBtqIw/7f//43/vnPf+LFF1/EypUrMXHiRGRmZoJSikaNGkmBxGjSJhITzFPAqB1WKY9LTmcc7uNvlKokvwRi0HgibDSCy4RnomGFiJ+E7GOiCBirg/2sT3VBZkcNoL0MmzF9jlEuHZHVCQbvo6C7iSnBhY0Be7fbjRYtWmDz5s246667AuIFAZs3b8bo0aPtO5EMtt6ali1bYvPmzZaOkXN4PfPMM9ixY4divxYNNABFt1XOYT9x4kQ0bNgQ48aNQ1paGh599FF88sknuOGGGzB37lzpK6Du9kabNpGS4EWy26dZUhO8UklxlxVDCGVFjOdIxSLTaMjxBjLC8l5xFHCFKVygUFegmL1OM8XIk2G8Qlnx0bLi1S9G3xs91lMSjF9ZegYmmGIjYo6NAnZPDxo/fjyWLFmCN998E/v378eoUaNQVFQkjT7aDdsC9iUlJViwYAHq1Klj+Vg5i+rMmTMxdepUtGnTxpAGWg2zHPYAIqbEMQKlBCluj+G0EPm+Qq/SHdeKnVr5YofldrfQGMJ+7eWjewbXS2CQ9iGvZ8AsalU3RuWBqbnh5eBVHpcmR6SZ+yYEDlQvGBxaz4QsEReDJcfmgYB7770Xp06dwpNPPom8vDw0b94cn3/+eUgQ3y5EZLyqVaumaPyUUly4cAHJyclYsWJFRIpYpYFWGyA9Dnu/349+/fohJycHCxYsUCyDFgsYkfGJyagFPoM4gkw1atTtUwoOLwcw13jMNhqNxUG0us0hxks1wipt1tAtZKUfk7ox3tCbFrKYj9ukMLPxL5NTl0ytE6C1mHgMRhxj4d2NHj06Zt1ENSIyXvPnz1f8ZhgGGRkZuOWWW0I4vqxANGDjxo1D+/btcdVVV2Hv3r2abKqRcNjPnDkTNWvWtHe6gsZLle4qRUZCYE6cfCUhhlAcLMyQyAclA6fToIGy7onWuShrPReIUKVBFFUIrPatCueEaTAMJ4Bz8wqjJarj87EQfBZHPDW6hYRSCCxRGlUTDZnxCmA8PuVGAaAJLIQELtAFFeP8lILQQEqJtsGhioYu9+YCcxWJcQoeUYoV5WkZZyGc92YjLuZiH7GAZePl9/uRm5uL+++/X1q30U6INNALFy7ECy+8AI7jdJcn+/e//40tW7bg008/VXDY16tXD71790Z6errEYd+9e3e8+eab2Lp1q+1zrfSSNGu5zweJ6QSwEHDcWzViemTFyCE1SPI0KV6UR4xkyWC0OEaCy1+W8kHkgxBqISb0Ej0dmVE1gvZk7aC1IARMsTdgkQUKUAo+IcVSMF+hm19Q6maWi0sHRICU/yV/BpJRjTUuykliB8s9a47j8Nxzzym6Z3ZDpIE2MlzxwmFPfYH7IK33R8syrQXKoFlSLnKqHoIrTP9P98stS+5kfMG0AHFXNC8fAVhvFALkqQGUoHpyMW6p9bdknBVdSFUXNhxYL+AqMg6mm1aTEKCgEPT4CSBKLjdKCBivAK4wzGCL5rHa292FSp2MYnR2Qxp40CnxjojCgh06dLB9FR49iIZLTSIoctivXLkS69evxz333AMAWLhwIZ544gmkpaVJHPZbtmwBoOSwjwRz5sxBlSpVFCX/s6+l/SEGDATn+WSsvJAJF+E1vS69Rq4FSgDeTWx7uShDov/6Bo8v9buw53QdEJN8/MZ6Af4ka14NJQhMUdA6jGVBalSTAutR6cYS+FNcEXtvavhEuutyMBblPdoZLSKKeXXt2hWPP/449u7dixYtWoRM0xHXcbQTcg77jh07AlBy2A8dOhT9+vXDu+++i4ceekjK8mdZFvn5+Zg1axZ27NiBefPmRcVhr844vv7dlwFoLyQrUAb7S+tINDmszhtBCAU10eAFF0x1vcxCYnsxOrWJRiVQgoKSRO32LMtfMwNKbMwmJ4FYGa2aavLk4asInOx7L0D386/5ONXTp2BAA30xjFkF8K6MEJHxeuihhwBAk2yQEGJ6epAZ2M1h37Bhw4h10co4Ji4OWsZLoAQnvWmKCbzySb5WY1/UypPSSjGQOyVEI85lIVBPqdIc+XyseVocrVFQuWyNgHW4GJDRKCRNdAe6jiKiSYxldTw7KyCQLkZgYSwvxsalInhXRojIeF1MHng5h31qairmz5+Pp59+GizLShTQag776tWrY+bMmQDKOOx//vlny/xi0eC0N/RrL1BiyLKpB6OgtawtmJcnmxweFSjg98aImDCcCIMBhMDxJDCqqCdfLiccCLF9ik480EBXOuMlCAKWL1+ONWvW4NChQxJfVt++faNaVkzrPHZx2H/zzTcYOXIkGjZsGHMDJq6ko7XdEHq9BztHzom98gKEifYJtJNMUHAxtqmmZv+IWh4TgbBYeGGVqdtIKUXPnj2xfv16NGvWDNdffz0opdi/fz+GDh2KNWvW4KOPPrJFMbs47BcvXowxY8Zg9erVmoYr2mx7dZyn0Oc2XuNQ1grMBLbtnNsWlSxRVRrw9WwjTBTj1ZHaQY1j5AmohsZaNv9Q/r+iOyvrxtph+CN6BjEyMpXK81q+fDm2bduGzZs3h6zJ+NVXX+Guu+7CW2+9hSFDhtiiXLQc9itWrMCoUaOwadMmdOjQAUePHsVvv/2G3377Dbfffjsuu+wypKenR23ABEpAKVDoMZ6Br8W0CkA3SdVO+mbTjcaEXGIm+dRCg7OzC6WVOW86HGdhOlIkhiymk6wjQWUyXqtWrcKUKVM0F5Pt0KEDHn/8caxcuTJi42Unh/2ZM2ewatUqJCcno0OHDjh58iQ6d+4MlmXx+++/o27duujUqROmTZtmy9yrYq/+Qg1WR9UpAWBnTESkw7fBcyA+q1ZTfrDGbhsbNO9SnSDK69Xq3kV6DyO+zhh27SpCLpcRLBmv//3vf3j22Wd193ft2hULFiyISBG7Oexr1KiBJ554Aj6fD7Vr10ZiYiIGDBiAIUOG4Oqrr8bcuXPx3nvv4d1338XDDz8ckc4iSksNxvbVtMBmXpgImaA162sYQcsNkJb9DWe8rDQIO2NJfEJQkAl5Zq4/Kt55eXcU8RGc10JF7zYSaiFj0+12Izc3F7Vr19bcf+zYMVx55ZUhvNfhIOewb968uYLDnhCCzZs3Y9u2bRg0aBBmzZolcdhrQc1F/91332HWrFmoUqUKFi5ciLS0NGl/r169cPbsWWzbti3ibmOjD2bCfywZ4ElZcp9AlDTOspExTVphcej8+kJ485LB+AmIP5gs6CcKGfJjFFN2VPKLs31wneVAeCJR4IRQEdOyF1hLllzHqh3ycPR4daCIBeFJ8FgizRGU/gbnCErTNoXQ60/LpXAVU5leVKGDkm5Ztk+O4O9jrTkwPmjPAdW4jnC0y/cP/BwbT16DcyXJ8PIsvD4WvMBA4BkIQmDEg1KABum1pfim+Fco+z+9ZiHyT6SBeEngOQTvj0RXHXxXpPsEnfcliAPTxsFOXD/+RcP9e1+w93x2w5LnJdIz64FlWcvThuzmsBcN0/79+3HNNdfg1ltvxYwZM8CyrOSxiV3LRo0a4Y8//ogq3pXwbSoUq1OG+RToNUJPNSBpayqSTNQ1lAegtDpQ9Sdtb9AKLbIIXxpwYUMtpOvUMcU3L2biZwDpuSa4zEwi8YzFAXOD6xcSgFc/6SL9Vl8XY3Tdqn2e2j7QrdWQbuYZmtDVU12vUuS4eFPAYwPLo41Dhw7VpYa16nHZyWGvRQO9dOlSdOvWDTfddJOiLsdx8Hg82L17N2688UZLOmuBKzbYqdNY5C++P6msnquI6tYzI9ufTKSkdq7E4Ngw+ongEwJ1KAD3BbleOgcaNHBvqixJ16tjoSwECGkw2130ZKKB1LUjgDtf3Kiso/ssVNt9shQ/rsT8cXr7/MkG9aJARe82WjJe9913X9g6VoL1dnHY69FAjxsXcHtFckIAKC0txZEjRzBmzBicPXsWs2fPtnILNMGVmmg5OlWkWA0gfQpdJeYMgxbUcwINJ1+HM1zu0G+zu8gMIVV4vQCA+My1Hi1DKVI3i2kNVoPP8vqCRozRVRT8x8CL1JLpSw6tw5aamGSus1/i1o9FcL0yGa833njD1pPbxWEvp4FetWoVJkyYgHHjxmHjxo2YOHEi/H6/NN9y7dq1WLp0KXw+H/773/+C47io+OuBQAPiPBpvlwXjoH65WR2DaLzKj/JFF8MwanZRM/LEkTtFLIwoWS1CoGFkCAX8iTLroPZm/IKqfvhWSjntEQ093jNDGPCESR61gTy1vmUTrXU+IkayVMZEvihILFCpRhtjATmD6jPPPINp06ahdevW0n4tDntCSEicyiwN9L333ivxe4kxOqM4nhWE40SXv7gKj0sOWRY865G9zWG6n3yi8Vi8GNQOC0ohSMwVwf6nfDcBuBJBUV9PJy29CIVyriEAIs6FNeMJ6BiuMlkmZARhJn1Bbay111UMbAzHhMF6qOm4pfrDVlGYVC8m4iJtTr4Ix8yZM7F9+3YpZ0uPw149SKpHAz1y5EgUFBQgJycHn376KRiGQc+ePcGybNgBiEjAeql2KRXAegTJIGkxaWrKKxUUx3KlvKKwnmBrld8PHbmMn4LxCZqF9QaKLlTtki3xB0opD65EWaR9JbLBmzANkPh4EN64mIUexQvDlxX9g6niWl3FgqJwsuIqCpbiUAMXMNKh4sXnKD3XUgGM6rdaVqxQKSlxYgGrHPaR0EDPnj0bWVlZEpNqtCsGBRRR/qQEGi9g2RvIa3Gni10zuSzJ++INDZOQYO0aGF/o119al1EjxmUEttgfPF7sEsn1pOBT9BN3Q0AIiE9lVeR0y2E8rhBxRgaKgWm2UkoAV5Gg0EU9yuhPYaX/zUB8P4jGc+UTWSlPzGg00xZU8G5jXHheIuQeWPv27fHzzz9jz549Eoe9PH8LCNBA9+jRAwAUNNBr165FQUEBAEg00AMHDgTP8zEjUaQqw8OW+sGW+sEV+8GW8GCL/YHGrvZAjF6gYIY3U+yTClvsDZQij3nWTfUybz4hUEQvwBM5hRFbWApGLEUeMBdKwRQYDHOGU9nnDy0aCOsZELXXRS0vJit/pq4LPqlwF3xwFXjhyi8bXZfy2sRThDmV5J0W+wKlyGd8QAzgeF42wyyHvZoG+p133sGsWbNw7tw5TJ48GevXr0e9evXw7bffwu/3IycnBxs2bMA333yDRx99NCLdPB5PSDqI4PdDby4PV+AJUA9TCggAXyWUbcIUGIA5VwRQAeAD8mi1NM2qUnxEO24c3EbAFfoCxo+n4FNVOWFhUgQCmfEkQPRHCMiZc4BAQfmAl0hqBpKSImoAniDnvGiYE6NbXp7xB+RI3qVJRzXkvhEC7tSFwPPkBcDvB18rmHylERs0Vir4p6AUxOcPeKnVUmXXTS6KV0RsYJYtT8SV5yVCi8M+XmmgT+3cpF2ZkGDDzgf942+QMBxoYWMbDIGQdwr+I0dDudgjuR4CsPlBD8nM4UZtk+PgP3MG/PnzoD6VB6GWLR8JDelyB+OZhUUQzpwLua6I+N0JwPoAV6GgP+ih0CF0v7SNZcDnHoH/UK70DPTz3XRumDhfkgBgAP8ffwYMfrhri1W30ajEOeLSeMkhelxyGmgRchroL7/8Ev369QMQYHpdvHgxtmzZgmXLlsHj8WDatGnYsWNHVJxjkydPRn5+vqJk/KOj/gEMABcHpm5tQDDxgmpAGhFjGBC3C1yNGkDQw4mKR50QUI4pM6o2cLITziV5hpojh2ZOwRAQjgOTnhowEHbwzhPAnxyMlZocDZYgGlQGoCwBk5IMLqMmwPPhnycpK3qrHHF16wAxXMzGCE63McawmwZa5ASLBFo00IyLg/TWEyK1DkoAf1oCkJYREnPShGp0ShlDIxCqJANV6kFQy1LNCxRl6YIhACj8KRz8KWnKc+mNCOp5S2LXsUYVsDWqBK+fBMYe5MZVJjesh5mQEChyyA2YGYMh+1/giO7SbvKgvaSX3NDI/xICX40UoMZVkJZWA2RzOsM/A/Uz5dOTQKsmSb8171EMvaCKYKCMEPfGyy4a6JSUFNSrVy82SmqMOPKJsuCKzOAEXnBljET+slKpYchGKJNdIQbQFDOEhl4AICSYJeuDfsMRZaUmhuRtaZ3bLCgnu29R9gs087jM6qWq50tXxgXD3v8wBsefLF+oUfavXtc2BgbMSVKNEeykgW7SpAm2bduG3NxctGzZEm63hSF8syABjwZQGgdA50U38UUNGEHtl1z/IB25wWNt4bwSZSWV6aZpwPSMj9455ekQRt6qzKPTrWIjQ7UvRSPKL/eSLfLi+5N0Rg3U+sbYuDieV4xgFw00ACxduhQTJkzAiy++iCZNmqBmzZpRs6fqQSuPy+grTcKMLulmzkeguqA2XDKEyzZXX0NIAzTBy2606jZ12Ud6FRV/lqpr7Esx6fFqGB6tun71zIoYvINmUdFHG+PWeAHR00ADwPr16zFhwgQsWrQI/fv3l2SI2fVq/q9ooDflJ1L2TWmKiIkXXH0O9W/BRSLWQ50tzicyxrLU03/CeZh2Gi4bqbOB0Ck/xtdtUJEQ8AZzFSPuvkeDcrBdhw4dwsyZM/HVV18hLy8Pl112GQYNGoSpU6da7hHFlfGykwZa9KzEPLD+/fvj999/x2uvvYa//voLWVlZGDt2LK6++uqoDJj40vHu0G2aMNmARI8rEllqY8FbWKw23Pl8yYzit2k5uh6XiftuZLxlQXW7GUt9aioaE89CMycOgF8vXc3wHsbWK7MyD9Qu/PrrrxAEAYsXL0aDBg2wb98+jBgxAkVFRXj++ectyYob42U3DbSIffv2oVu3bigtLUXbtm3RtWtXMAyD3377DTfddBO2b9+O5s2bR9WNDDch16oB8onydI4z3XWBhjdoZuDToI6iG2VWP52AtKBnuCJ4DtqpCNZkyD1MkZPL6FrMnktimjB5jK3L3RmgPLqNXbp0QZcuZYSPV111FQ4cOIBFixZVTOMlp4EeP368ggb6jjvuwObNm7FixQoMGjRI4vhKT08PmZso955EQ9SwYUOsW7cOJ0+eRJcuXfD666+DYRgcO3YM48ePx/Dhw7F582ZUrVo1Yv2lL7TGSxcy7C6HulGTAGupdKxOXb1RRPU+f1LoNsVxWhv1GisBvOnG51Ovhm10bq05mVa6ogpZBksIKGWYq+aprp5iIP9fuU/PuInVfCka59W6T1r6mRiFjAbhZGrNKNFKF4oW+fn5qF7dOlVsuRsvu2mgxS6gyNHVtm1b7N27F+vWrUP37t0lA3fZZZehf//+eOSRR3Du3LmIjZcvHWBLAi54CAc7VWY9KN5NnSk4xA8w/jI50tdRL09K9lu+72RLAUwJo8+JLnGvax+vBp8sQKjmA+sSQBghmNJFpYtiggeTkL+h+44mVgPrLbtGxb3Qy3XSAgX4JIAthYKnH1R2r9W5azp5WGIdxkPgTwkaRCLLbRNvl2hEiY7hIbTMe0uDtevUG/0rpzyvOXPm4KmnnlJsmz59OmbMmGGbDn/88Qdeeukly14XUM7Gy04aaBGicfr999/RuHFj9O7dG+vWrcPWrVuRlpaGCxcuIC0t4N7Uq1cP1atXD5l6ZAUpR8zXNTIO/mQg+YSKjSIStWiACjrhlEEAyGJjEFzAFdceV+pmcDGMwb5ThalIOGvt/EYOU9LhcKMB5s/jTyYovEr7ppvLqys7GVvM2HqdvFnv0sr5wnQbJ0+ejPHjxyu26Xldjz/+OObNm2cob//+/Yok8aNHj6JLly7417/+hREjRpjUugyWVg+yG7///juGDx+O6tWrY8KECRINNKDM82ratClat26NRYsWAYAUn5LHqbQ47F9//XV0794dPM/jvvvuwxdffIE77rgDTz75JCilePzxx+H3+7F+/fqI4103jnzRmGpZhEEVwUUk42VKlgEElsCfRJDfILACUbSgbMB41fnHUVRJKJW2GxkoOeT1CrwJOHEhDfTramAiNMxq3dwFFEwYQgYzqgpcIJ6Xf7UA6haX8rGgmryL5yVgixkkHyO2XSfvAn6eNy4CYfpo2/M5w/3bPjFPYHDq1CmcOXPGsM5VV10ljSgeO3YM7dq1Q8uWLbF8+fKIBszK1fOyiwZaj8N+/Pjx4HkePXv2xJtvvomnn34aX3zxBa677jo0bdoUSUlJ0rJn0Yw4MpEYieALqjV1hVikbpGOowHjpdim0XjMxk+0GBhSXaGLrDAm3Jvi4HAsIbSstko3K3EddUqEyB4RDlrnEDhZF1rc79LpV+rJFfXya7xDNl6nnYj0PdNCRkaGlAkQDkePHkX79u3RokULvPHGGxG3u3KPedlBA23EYS96V3369MGMGTPw2GOPYc+ePahevToaNWoEhmFsoYImPNU2YnopApzM0MgajbSghAAQgwYZMiTPEd0XXYoHmQRlEJIdLwSbZwrrRQrnAWuiBTIQUMgn4IIvUbeO0SRgrVNQRtmgqWxmA+NXNshwKgosATV67KwAhhPMp5nwpMx4qQd5o7zOWKA8MuyPHj2Kdu3aoV69enj++edx6tQpaZ+YNWAW5W68AKUBmzlzJqZOnYo2bdoY0kCrYcRhP2XKFCQlJaFr165ITk5Gq1atpOO00iuiAeHDrxSj9o6MYIZAT2tZ+oAyqp9mFv4x2WCSmMDai6yOUNEbK+QTTHUxdXWTH2pSNzNeGDX5DBiuTDGjyAKv5XFpINLrjMk8xHKIGG3cuBF//PEH/vjjD9StW1eljjV94oYSR86iOnfuXOzYsQP33nsvDh06hDVr1kg00CKsctiPGTMG69evDzmvXdn1chCeahazXRvFRG6BBopfu1jl4tKlQNFIWAzHBJHGliKZ8SKVLZVKFbYEVdgSpLGlmscY5ptq6aUeLTQJOV+9VHwUjE//46K3neMEcJwAlg0UjuMVJeS6wthFO68zGpQHJc7QoUNBKdUsVhE3xguwRgNtxGEvUkADZRz2mZmZmDlzJnbu3Gm73lqsm2JDkYqXghgsQWa4JJZPAOHLCuMPFN36WrLkOuq9qBrHKZJKNYbcqnOFqMoWozpbFChcIapzhfoXo9ZFvUsINfqWUwWC7wbxCVJhvIL+x8OEfBfHw+3yw+3yK/4PnE7PGhqoKH6UornOKKH3kZX0iXPElfECymigR44ciX379kmGS6trF08c9iFgygwY6w1wxgOI+AWVVvrxCqYXazUC4YPFH7lOIjK4C8jgClDLdR61uPMBfSP8dDN+k6v8mATrDfD0M14BjJyrP4JrTnT5kez2ISXBi2S3ryzXTQsmeqXyay0PkCCDq16Jd8Sd8QK0aaDVUHPYA8CsWbNw9913Y/LkyRgxYgQmTZqE7t274/Dhw8jJyUFmZia++eabiPXyeDwoKChQFIE3GGokgdV/3HkXoludWFxc18uDO31BerGifcFMr+UYBjwluII7h/aJRWBAwWhcrBgDCxf/oiTQveNKovNEJJ59AIyHhzuvwPgAI1my/6skluCqKmdMp4oYyiWI+jqjgkCNS5wjLo2XHKLhilcO+xO7dTjsg6AE8NVMDX7NQvebbgNBTny+egoipYA2PU/PJHgQ8JQBDwZesNjtJXAbzPY12+ApQ6Q5mVbuj6asQIo/fBmpURtqSgG/wOBoYRWwjGCLAbN8nTZC6rrqlHhHXIw2moGcw75jxwBvvJzDfujQoejXrx/effddPPTQQ+CDi5SyLIv8/HzMmjULO3bswLx586LisFdnHLeduBgITX0CACWjqopNVfPVCKMWdbP6o4ECNcWpJT+X5pxAnXdWHe+S/xYogz+9GeCD30J5GgUbgVshcLBmWMPU5eXcY0bqyPZRGipWAMH50qTgfntmT+tRVF8MOGSEMUbcc9izQQ57Wb6RCOoKw3slQ9j0Cpd2/lCkEEQjqCHP7IKsAMCDwRk+QMEgN2gsBN00CiNQ1l4qGMGt/wysXKdACUq99s7RsftaLaMCeFdGiHvjVSE47DUgZW1DZ16clSzroCw7XnRKoLfMpMFByp9yI5XPJ8m2h49CGM2JBKzlwAUEGsjSYo41I56WfYhED8vnN7hpIawT4YkfLV9nDOxMuOX44h1xa7zs5rC/qLqzME5oMgsS5iXXO4dRRkaUUU5B1lIL/aGZ87xGGFVr5FGcSaCpmw23Tp6EGpalNAz8vjDW3uAEhtcJ2OZJR4SKbbvi13jZyWF/sRCY0mHv22g2E9wUbByeKeT1OZ3MeGBqhBwSxWXb9vEAIJjJnI9mrmI5Gi/H84oh7OCwv1gQWABahsbsyxnCJEAMDZdRHCekrt48OSusCZRAoARFfrfhXEU1tBJb7dZNOpeLAAaLjAAGTpJGhjv1M6DqTBiLMxqkw2y8TttQARJRjRBXxstODvuLiZARoyhfSInNwY4Yl1GPJwLxBV5twyWEESYaMTkDRdSc8/Lsf51HHvGiIz4djyuC9m7nddqJipCIaoS4MV6x4rCPNSjRXzVIgtaXWOcQwR0m2BvGoClW3Wb195mCqv55T1KIJ6WXMqDlccnbSjRxH7VocZWlSBbiDZm8TgHisfDAAG3jEqxu53XajgrebQSNA7z22mu0WrVq9Pnnn6ebNm2i586do08//TRt2LAhbdSoET18+DBduXIlJYTQ2bNn0/z8/PJW2TRKS0vp9OnTaWlpadzJi1dZdsurTLpZQecmUwxLvKNcmVQBfQ57AFi9ejWefvppVKtWDVu3bsWrr76KMWPG4LHHHtPlsI83FBQUoEqVKsjPz0d6enpcyYtXWY5uFwddm0wx3L/hl9kXSZPIcMlx2Dtw4MAk+IrdbSzXuY116tTBbbfdhl27dmH79u0SQ6q4kjUA5OTkoFGjRti0KTCH8IknnsA333wjjSo6cOAgQgiCcYlzlKvxEjnsPR4PZs2apWB8sMJh78CBgwggTvDXK3GOcmeVkBMQPvPMM9ixY4divxaHPRBKRhivSEhIwPTp021bqNNOefEqy255lUk3S+B54xLnKPeAvYjff/8dY8eOBaVUwWHfq1cvMAyDjz/+OCaUzQ4cVFZ0veIRw/0b/p5/UfSIFHFjDaxy2Dtw4CBKON1G+2CFw96BAwdRwgnY2wsrHPYOHDiIAk7MK7ZwDJcDB7FB16xRhvs3nFh0kTSJDHFvFRzD5cBBbEArgHdlBMcyOHBQWeFk2Dswi6KiImzbtq3czs+rvrT//e9/sW3bNvh8PlvkDxs2DMeOHYtajs/nw++//478/PyoZZ0/fx5LlizBtGnT8Prrr1uS+eOPP0Z9fjlOnjyJr776StLhxIkTePbZZzF37lzs3bvX1nOZQgUP2McFq0RlwZ49eyjDMKbqer1e+uijj9L69evTm2++mS5dulSxPy8vz7SsY8eO0datW1OWZWnbtm3p2bNnaffu3SkhhBJCaKNGjeixY8dMX8dPP/2kWVwuF127dq302wzmzZtHi4uLKaWU+v1+OmHCBOp2uynDMJTjODps2DDq9XpN69a7d2+6evVqSiml+/btozVr1qQZGRn0lltuoVlZWbRWrVr0l19+MSWLEELr169PZ82aRY8ePWpaBy18/fXXNCUlhRJCaK1ateiePXto3bp1acOGDenVV19NExIS6BdffBHVOayiU8oQwxLvcIzXRYQV4zV9+nSalZVFn3vuOTp16lRapUoV+sADD0j78/LyKCHElKzBgwfTVq1a0U8++YTee++9tFWrVrRNmzb0yJEjNDc3l7Zu3Zrm5OSYvg5CCGUYRjJ+8iJuN3udDMPQEydOUEopfe6552i1atXosmXL6M8//0xXrFhBMzMz6bx580zrVq1aNbp//35KKaVdu3alAwYMoB6Ph1Ia+CAMHz6cdurUyfR1jhgxgmZmZlKO42j37t3p2rVrqd/vN62PiNtuu43m5OTQCxcu0Oeee47WqVNHcc8nTpxIW7VqZVluNOiUOMiwxBqlpaW0WbNmFADdvXu35eMd42UjqlWrZljS09NNN+oGDRrQTz/9VPr9+++/0wYNGtChQ4dSQRAseV61a9em3333HaWU0jNnzlBCCN20aZO0f/PmzfSqq64yfZ3NmjWj3bt3p/v376eHDh2ihw4don/99RflOI5u3LhR2mYGhBDJeN1www108eLFiv0rVqyg1157rWndkpKS6B9//EEpDVz3rl27FPsPHDhAq1SpYkk3n89HP/jgA9qtWzfKsizNysqikyZNogcOHDCtV3p6uqSXz+ejHMcpGuxvv/1mWi+70ClhgGGJNcaOHUu7du0asfFyAvY2wuPxYNSoUbj++us19+fm5uKpp54yJevo0aO47rrrpN8NGjTAli1b0KFDBwwePBjPPvusab3OnTuHOnXqAACqV6+O5ORkxTJwDRo0wPHjx03L27lzJyZNmoS+fftixYoVuOGGG6R9l112meUl5sR5qn///TdatWql2NeqVSv89ddfpmU1bdoUX331FerXr49atWohNzdXoV9ubi6SkpIMJISC4zj07dsXffv2xdGjR7Fs2TIsX74czz//PFq3bm0qjul2u1FaWgoA8Hq9EARB+g0AJSUlcLnsXRcyHMpztHHDhg348ssv8eGHH2LDhg0RyXCMl41o3rw5Lr/8ctx3332a+3/66SfTxqtWrVo4ePAgsrOzpW116tTB119/jfbt22Po0KGm9crMzMTx48dx+eWXAwBGjx6N6tWrS/vPnTtnidjR7XZj/vz52LBhA3r27ImHHnoIjz32mOnj1ViyZAlSU1Phdrtx9uxZxb4LFy5YmrQ8bdo0DBkyBC6XC2PHjsW4ceNw5swZXHPNNThw4ACmT5+OwYMHm5KlNfm/Tp06mDZtGqZNm4bNmzdj2bJlpmS1bt0ajz/+OB5//HG89dZbuPHGG/HMM8/gvffeAyEEM2fOxE033WT6Ou1AOOPl8Xjg8SiXg9daeNkqTpw4gREjRuCjjz5CcnJy5ILsdwYrL2bNmkVnzJihu//vv/+mQ4cONSVr+PDh9P7779fcd+TIEdqgQQPT3caePXvS+fPn6+5/+eWXaYcOHUzJUiMvL4927dqVtmnThnIcR3/++WdLx9erV49mZ2dL5cUXX1Tsnz9/Pm3ZsqUlmR988AGtW7duSFwuMTGRPvLII9Tn85mSI+/SRovffvuNNmzYkBJC6DXXXEOPHDlCe/bsSTmOoxzH0YyMDPrjjz/aci67MH36dIoAQ79Upk+fHpVMQRBoly5d6MyZMymllP71118RdxvjPsO+siI3Nxe//vorOnfurLn/2LFj2Lhxo66XZwU7d+5EcnKyoptqFQsWLMDXX3+Nl156CXXr1o1aJxHff/89EhISFF0/M+B5Hrt27cKff/4JQRBQu3ZttGjRAmlpaaZlbN26Fa1bt7Y1UfrMmTOoUaOG9Hvz5s0oKSnBrbfeqtgeD7DieT3++OOYN2+eobz9+/fjyy+/xPvvv4+tW7eCZVkcOnQIV155JXbv3o3mzZtbUzAqM+pAE1u3btX8uvt8Prp169ZLQpajW/nLiiecPHmS7t+/37B4PB7aq1cvyjAMZVlWKgAoy7J0yBBr6RmO5xUDsCyL48ePIzMzU7H9zJkzyMzMDEkWrYiyHN3KX1ZFxN9//42CggLp97Fjx9C5c2d88MEHuOWWWyx57U7APgagOhTVZ86csbziUbzKcnQrf1kVEVdccYXid2pqKgCgfv36lsMNjvGyEeLSbYQQDB06VBEb4Hke//vf/0JSASqaLEe38pflIADHeNkIcSVvSinS0tIU+URutxstW7bEiBEjKrQsR7fyl3UpITs7O/JVwOwI1jlQYsaMGbSwsPCSlmW3vMqim93XWZnhBOxjgJKSElBKpQS83NxcrF27Fk2aNEGnTp0uCVmObuUvq9KjvKzmpYw777yTLlq0iFJK6blz52hmZiatW7cuTUxMpAsXLrwkZDm6lb+syg7HeMUANWrUoPv27aOUUrpkyRLatGlTyvM8ff/992njxo0vCVmObuUvq7LDISOMAYqLi6VM7i+//BJ9+vQBwzBo2bIlcnNzLwlZjm7lL6uywzFeMUCDBg3w0Ucf4fDhw/jiiy+kWMbJkyeRnp5+SchydCt/WZUe5e36XYpYvXo1dblclGEY2rFjR2n77NmzaZcuXS4JWY5u5S+rssMZbYwR8vLycPz4cTRr1kxaLHfnzp1IT09H48aNLwlZjm7lL6sywzFeMcQff/yBgwcPom3btkhKStKdGlKRZTm6lb+sSovycfgubZw+fZp26NBB4nI/ePAgpZTSYcOG0fHjx18Sshzdyl9WZYcTsI8Bxo0bB5fLhb///lvBFHnvvffi888/vyRkObqVv6xKj/K2npcisrKy6J49eyillKampkpf14MHD9KUlJRLQpajW/nLquxwPK8YoKioSJOb++zZs5b5v+NVlqNb+cuq7HCMVwzQpk0bvPXWW9JvQggEQcCzzz6L9u3bXxKyHN3KX1alR3m7fpci9u7dSzMzM2mXLl2o2+2md999N73mmmtoVlaWtHZfRZfl6Fb+sio7HM8rBkhPT8f+/ftx2223oVevXigqKkKfPn2we/duy2vzxassR7fyl1XpUd7W81KEfAl7OU6fPm16ubJ4l+XoVv6yKjsczysGoDp5v4WFhUhMTLwkZDm6lb+syg6HBtpGjB8/HkAgCPvkk08qRpV4nsd///tf02vTxassR7fyl+UgAMd42Yjdu3cDCHxd9+7dC7fbLe1zu91o1qwZJk6cWKFlObqVvywHQVz8nuqlj6FDh9L8/PxLWpbd8iqLbnZfZ2WGMzHbgQMHFRJOwN6BAwcVEo7xcuDAQYWEY7wcOHBQIeEYLwcOHFRIOMbLgQMHFRKO8XJgCnl5eXj44YfRoEEDJCYmIisrC61bt8aiRYtQXFxc3uo5qIRwklQdhMWff/6J1q1bo2rVqpg9ezauv/56JCQkYO/evXjttddQp04d9OzZs7zVdFDZUN6JZg7iH507d6Z169alhYWFmvsFQaCUBpavHz58OK1ZsyZNS0uj7du3l1hDKaV0+vTptFmzZvStt96i9erVo+np6fTee++lBQUFUh2e5+ns2bNpdnY2TUxMpE2bNqWrV6+W9p89e5YOGDCA1qxZkyYmJtIGDRrQZcuWxejKHcQznG6jA0OcOXMGX375JXJycpCSkqJZR1z15l//+hdOnjyJDRs24Mcff8SNN96IO+64A2fPnpXqHjx4EB999BHWrVuHdevWYevWrZg7d660f86cOXjrrbfw6quv4ueff8a4ceMwaNAgbN26FQAwbdo0/PLLL9iwYQP279+PRYsWoWbNmjG8Aw7iFuVtPR3EN77//nsKgK5Zs0axvUaNGjQlJYWmpKTQSZMm0e3bt9P09HRaWlqqqFe/fn26ePFiSmnA80pOTlZ4Wo8++ii95ZZbKKWUlpaW0uTkZPrtt98qZAwfPpz279+fUkppjx496LBhw2y/TgcVD07My0FE2LlzJwRBwMCBA+HxePDTTz+hsLAQNWrUUNQrKSnBwYMHpd/Z2dlIS0uTfteuXRsnT54EEFjLsLi4GHfeeadChtfrxQ033AAAGDVqFPr27Ytdu3ahU6dOuOuuu9CqVatYXaaDOIZjvBwYokGDBiCE4MCBA4rtV111FQAgKSkJQICPqnbt2tiyZUuIjKpVq0r/q9lCRQ53UQYAfPbZZ6hTp46inrg4RdeuXZGbm4v169dj48aNuOOOO5CTk4Pnn38+8ot0UCHhGC8HhqhRowbuvPNOvPzyyxgzZoxu3OvGG29EXl4eOI5DdnZ2ROdq0qQJEhIS8Pfff+P222/XrZeRkYH77rsP9913H9q0aYNHH33UMV6VEI7xchAWCxcuROvWrXHTTTdhxowZaNq0KRiGwQ8//IBff/0VLVq0QMeOHXHrrbfirrvuwrPPPotGjRrh2LFj+Oyzz9C7d2/cdNNNYc+TlpaGiRMnYty4cRAEAbfddhvy8/OxY8cOpKen47777sOTTz6JFi1a4Nprr4XH48G6detwzTXXXIS74CDe4BgvB2FRv3597N69G7Nnz8bkyZNx5MgRJCQkoEmTJpg4cSIeeughEEKwfv16TJ06FcOGDcOpU6dQq1YttG3bFllZWabPNXPmTGRkZGDOnDn4888/UbVqVdx4442YMmUKgABx3+TJk3Ho0CEkJSWhTZs2ePfdd2N16Q7iGA6flwMHDioknDwvBw4cVEg4xsuBAwcVEo7xcuDAQYWEY7wcOHBQIeEYLwcOHFRIOMbLgQMHFRKO8XLgwEGFhGO8HDhwUCHhGC8HDhxUSDjGy4EDBxUSjvFy4MBBhcT/A72sW4M98PHiAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2d_2group_delta_mean_diff.whorlmap(\n", - " heatmap_kwargs={'cbar_kws':{'pad':0.10}, \"cmap\":'viridis', \"vmax\":4, \"vmin\":-4},\n", - " plot_kwargs={'xlabel':\"Genes\", 'ylabel':\"Drugs\", \n", - " \"xticklabels\":['test1', 'test2', 'test3', 'test4', 'test5', 'test6', 'test7', 'test8'],\n", - " \"xticklabels_rotation\": 90, \"xticklabels_ha\": 'center',\n", - " \"yticklabels\": ['Drug1', 'Drug2', 'Drug3', 'Drug4', 'Drug5', 'Drug6'],\n", - " \"yticklabels_rotation\": 45, 'yticklabels_ha': \"right\", 'title': 'My Title!'}\n", - ");" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/tutorials/index.qmd b/nbs/tutorials/index.qmd deleted file mode 100644 index 46d74058..00000000 --- a/nbs/tutorials/index.qmd +++ /dev/null @@ -1,11 +0,0 @@ ---- -order: 1 -title: Tutorials -listing: - fields: [title, description] - type: table - sort-ui: false - filter-ui: false ---- - -Click through to any of these tutorials to get started with dabest's features. \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml deleted file mode 100644 index 533ba62c..00000000 --- a/pyproject.toml +++ /dev/null @@ -1,38 +0,0 @@ -[build-system] -requires = ["setuptools>=64", "packaging"] -build-backend = "setuptools.build_meta" - -[project] -name = "dabest" -dynamic = ["version"] -description = "Data Analysis and Visualization using Bootstrap-Coupled Estimation." -readme = "README.md" -requires-python = ">=3.10" -license = {text = "Apache-2.0"} -authors = [{name = "Joses W. Ho", email = "joseshowh@gmail.com"}] -keywords = ['nbdev', 'jupyter', 'notebook', 'python'] -classifiers = ["Natural Language :: English", "Intended Audience :: Developers", "Development Status :: 3 - Alpha", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3 :: Only"] -dependencies = ['fastcore', 'pandas~=2.2.3', 'numpy~=2.1.0', 'matplotlib~=3.10.0', 'seaborn~=0.13.2', 'scipy~=1.15.2', 'numba~=0.61.0', 'datetime', 'statsmodels', 'lqrt', 'tqdm'] - -[project.urls] -Repository = "https://github.com/acclab/DABEST-python" -Documentation = "https://acclab.github.io/DABEST-python" - -[project.entry-points.nbdev] -dabest = "dabest._modidx:d" - -[project.optional-dependencies] -dev = ['pytest~=8.3.4', 'pytest-mpl~=0.17.0'] - -[tool.setuptools.dynamic] -version = {attr = "dabest.__version__"} - -[tool.setuptools.packages.find] -include = ["dabest"] - -[tool.nbdev] -branch = 'master' -readme_nb = 'read_me.ipynb' -jupyter_hooks = true -custom_sidebar = true -title = 'dabest' diff --git a/read_me.html b/read_me.html new file mode 100644 index 00000000..21abcfc5 --- /dev/null +++ b/read_me.html @@ -0,0 +1,948 @@ + + + + + + + + + +DABEST-Python – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
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+ +
+ + +
+ + + +
+ +
+
+

DABEST-Python

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+ + + +
+ + + + +
+ + + +
+ + + +

minimal Python version PyPI version Downloads Free-to-view citation License

+
+

Recent Version Update

+

✨ DABEST “Bingka” v2025.10.20 for Python is now released! ✨

+

Dear DABEST users, The latest version of the DABEST Python library brings new visualizations, refined plots, and improved accuracy.

+
    +

  1. Whorlmap 🌀: Compact visualization for multi-dimensional effects

    +

    Introducing Whorlmap, a new way to visualize effect sizes from multiple comparisons in a compact, grid-based format.

    +

    Whorlmaps condense information from the full bootstrap distributions of many contrast objects into a 2D heatmap-style grid of “whorled” cells. This provides an overview of the entire dataset while preserving the underlying distributional detail.

    +

    They are especially useful for large-scale or multi-condition experiments, serving as a space-efficient alternative to stacked forest plots.

    +

    You can generate a Whorlmap directly from multi-dimensional DABEST objects using the .whorlmap() method. See the Whorlmap tutorial for more details.

    2. +

  2. Slopegraphs 📈: Enhanced summaries for paired data

    +

    Slopegraphs for paired continuous data now display group summary statistics.

    +
      +

    • By default, a thick trend line connects group means, with vertical bars showing standard deviation.

      • +

    • Choose the summary type via the group_summaries argument in .plot() — options include 'mean_sd', 'median_quartiles', or None.

      • +

    • Customize appearance with group_summaries_kwargs.

      • +
    +

    See the Group Summaries section in the Plot Aesthetics tutorial for more details.

    3. +

  3. Mini-meta Weighted Delta Fix 🧮

    +

    The weighted delta calculation in mini-meta plots has been updated for greater accuracy and consistency.

    4. +

  4. Expanded custom_palette functionality 🎨

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      +

    • Barplots (unpaired, proportional): custom_palette can now take 1 and 0 as dictionary keys to color the filled and unfilled portions of the plot.

      • +

    • Slopegraphs (paired, non-proportional): custom_palette can now color contrast bars and effect-size curves.

      • +
    5. +
+

See the Custom Palette section in the Plot Aesthetics tutorial for examples.

+

Thank you for your continued support!

+

The DABEST Development Team

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+
+

Contents

+ + + +
+
+

About

+

DABEST is a package for Data Analysis using Bootstrap-Coupled ESTimation.

+

Estimation statistics are a simple framework that avoids the pitfalls of significance testing. It employs familiar statistical concepts such as means, mean differences, and error bars. More importantly, it focuses on the effect size of one’s experiment or intervention, rather than succumbing to a false dichotomy engendered by P values.

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An estimation plot comprises two key features.

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    +

  1. It presents all data points as a swarm plot, ordering each point to display the underlying distribution.

    2. +

  2. It illustrates the effect size as a bootstrap 95% confidence interval on a separate but aligned axis.

    3. +
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+
+

+
The five kinds of estimation plots
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+
+

DABEST powers estimationstats.com, allowing everyone access to high-quality estimation plots.

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+
+

Installation

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This package is tested on Python 3.11 and onwards. It is highly recommended to download the Anaconda distribution of Python in order to obtain the dependencies easily.

+

You can install this package via pip.

+

To install, at the command line run

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pip install dabest
+

You can also clone this repo locally.

+

Then, navigate to the cloned repo in the command line and run

+
pip install .
+
+
+

Usage

+
import pandas as pd
+import dabest
+
+# Load the iris dataset. This step requires internet access.
+iris = pd.read_csv("https://github.com/mwaskom/seaborn-data/raw/master/iris.csv")
+
+# Load the above data into `dabest`.
+iris_dabest = dabest.load(data=iris, x="species", y="petal_width",
+                          idx=("setosa", "versicolor", "virginica"))
+
+# Produce a Cumming estimation plot.
+iris_dabest.mean_diff.plot();
+
+
+

+
A Cumming estimation plot of petal width from the iris dataset
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+
+

Please refer to the official tutorial for more useful code snippets.

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+
+

How to cite

+

Getting over ANOVA: Estimation graphics for multi-group comparisons

+

Zinan Lu, Jonathan Anns, Yishan Mai, Rou Zhang, Kahseng Lian, Nicole MynYi Lee, Shan Hashir, Lucas Wang Zhuoyu, A. Rosa Castillo Gonzalez, Joses Ho, Hyungwon Choi, Sangyu Xu, Adam Claridge-Chang

+

bioRxiv preprint 2026. 10.64898/2026.01.26.701654

+

PDF

+

Moving beyond P values: Everyday data analysis with estimation plots

+

Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang

+

Nature Methods 2019, 1548-7105. 10.1038/s41592-019-0470-3

+

Paywalled publisher site; Free-to-view PDF

+
+
+

Bugs

+

Please report any bugs on the issue page.

+
+
+

Contributing

+

All contributions are welcome; please read the Guidelines for contributing first.

+

We also have a Code of Conduct to foster an inclusive and productive space.

+
+

A wish list for new features

+

If you have any specific comments and ideas for new features that you would like to share with us, please read the Guidelines for contributing, create a new issue using Feature request template or create a new post in our Google Group.

+
+
+
+

Acknowledgements

+

We would like to thank alpha testers from the Claridge-Chang lab: Sangyu Xu, Xianyuan Zhang, Farhan Mohammad, Jurga Mituzaitė, and Stanislav Ott.

+
+
+

Testing

+

To test DABEST, you need to install pytest and nbdev.

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    +
  • Run pytest in the root directory of the source distribution. This runs the test suite in the folder dabest/tests/mpl_image_tests.
    • +
  • Run nbdev_test in the root directory of the source distribution. This runs the value assertion tests in the folder dabest/tests
    • +
+

The test suite ensures that the bootstrapping functions and the plotting functions perform as expected.

+

For detailed information, please refer to the test folder

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+
+

DABEST in other languages

+

DABEST is also available in R (dabestr) and Matlab (DABEST-Matlab).

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+ +
+ +
+ + + + + \ No newline at end of file diff --git a/robots.txt b/robots.txt new file mode 100644 index 00000000..0dffcb7f --- /dev/null +++ b/robots.txt @@ -0,0 +1 @@ +Sitemap: https://acclab.github.io/DABEST-python/sitemap.xml diff --git a/search.json b/search.json new file mode 100644 index 00000000..753e8f5e --- /dev/null +++ b/search.json @@ -0,0 +1,1541 @@ +[ + { + "objectID": "tutorials/index.html", + "href": "tutorials/index.html", + "title": "Tutorials", + "section": "", + "text": "Click through to any of these tutorials to get started with dabest’s features.\n\n\n\n\n\n\n\n\n\n\nTitle\n\n\n\nDescription\n\n\n\n\n\n\n\n\nBasics\n\n\nAn end-to-end tutorial on how to use the dabest library.\n\n\n\n\n\n\nTwo-Group Experiments\n\n\nExplanation of how to use dabest for two-group and multi two-group analysis.\n\n\n\n\n\n\nShared Control & Repeated Measures\n\n\nExplanation of how to use dabest for shared control and repeated measures analyses.\n\n\n\n\n\n\nProportion Plots\n\n\nA guide to plot proportion plots with binary data.\n\n\n\n\n\n\nMini-Meta\n\n\nExplanation of how to compute the meta-analyzed weighted effect size using dabest.\n\n\n\n\n\n\nDelta-Delta\n\n\nExplanation of how to calculate delta-delta using DABEST.\n\n\n\n\n\n\nHorizontal Plots\n\n\nA guide to plot data in a horizontal format.\n\n\n\n\n\n\nControlling Plot Aesthetics\n\n\nA guide to various plot aesthetic changes that can be done.\n\n\n\n\n\n\nForest Plots: Visualizing Multiple Contrasts\n\n\nExplanation of how to use forest_plot for contrast objects e.g delta-delta and mini-meta or regular deltas.\n\n\n\n\n\n\nWhorlmaps: Visualizing Even More Contrasts\n\n\nExplanation of how to use forest_plot for contrast objects e.g delta-delta and mini-meta or regular deltas.\n\n\n\n\n\n\nNo matching items" + }, + { + "objectID": "blog/index.html", + "href": "blog/index.html", + "title": "DABEST Blog", + "section": "", + "text": "Preprint: Getting over ANOVA\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBootstrap Confidence Intervals\n\n\nExplanation of the bootstrap method and its application in hypothesis testing using DABEST.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRobust and Beautiful Statistical Visualization\n\n\n\n\n\n\n\n\n\n\n\nNo matching items" + }, + { + "objectID": "tutorials/10-whorlmap.html", + "href": "tutorials/10-whorlmap.html", + "title": "Whorlmaps: Visualizing Even More Contrasts", + "section": "", + "text": "In DABEST v2025.10.20, we introduce a new and more compact way of visualizing bootstrap distributions: - whorlmap", + "crumbs": [ + "Get Started", + "Tutorials", + "Whorlmaps: Visualizing Even More Contrasts" + ] + }, + { + "objectID": "tutorials/10-whorlmap.html#load-libraries", + "href": "tutorials/10-whorlmap.html#load-libraries", + "title": "Whorlmaps: Visualizing Even More Contrasts", + "section": "Load libraries", + "text": "Load libraries\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats import norm\nimport dabest\nfrom dabest.multi import combine, whorlmap\n\nPre-compiling numba functions for DABEST...\n\n\nCompiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 37.69it/s]\n\n\nNumba compilation complete!", + "crumbs": [ + "Get Started", + "Tutorials", + "Whorlmaps: Visualizing Even More Contrasts" + ] + }, + { + "objectID": "tutorials/10-whorlmap.html#create-a-simulated-dataset-and-generate-a-list-of-corresponding-dabest-objects", + "href": "tutorials/10-whorlmap.html#create-a-simulated-dataset-and-generate-a-list-of-corresponding-dabest-objects", + "title": "Whorlmaps: Visualizing Even More Contrasts", + "section": "Create a simulated dataset and generate a list of corresponding dabest objects", + "text": "Create a simulated dataset and generate a list of corresponding dabest objects\n\ndef create_delta_dataset(N=50, \n seed=9999, \n second_quarter_adjustment=3, \n third_quarter_adjustment= -0.5,\n fourth_quarter_adjustment= -3, \n scale4=1, initial_loc = 10):\n \"\"\"Create a sample dataset for delta-delta analysis.\"\"\"\n np.random.seed(seed)\n\n # Create samples\n y = norm.rvs(loc=initial_loc, scale=0.4, size=N*4)\n y[N:2*N] = norm.rvs(loc=initial_loc + second_quarter_adjustment, scale= 1, size=N) \n y[2*N:3*N] = norm.rvs(loc=initial_loc + third_quarter_adjustment, scale=0.4, size=N)\n y[3*N:4*N] = norm.rvs(loc=initial_loc + fourth_quarter_adjustment, scale=scale4, size=N)\n\n # Treatment, Rep, Genotype, and ID columns\n treatment = np.repeat(['Placebo', 'Drug'], N*2).tolist()\n genotype = np.repeat(['W', 'M', 'W', 'M'], N).tolist()\n id_col = list(range(0, N*2)) * 2\n\n # Combine all columns into a DataFrame\n df = pd.DataFrame({\n 'ID': id_col,\n 'Genotype': genotype,\n 'Treatment': treatment,\n 'Transcript Level': y\n })\n return df", + "crumbs": [ + "Get Started", + "Tutorials", + "Whorlmaps: Visualizing Even More Contrasts" + ] + }, + { + "objectID": "tutorials/10-whorlmap.html#working-with-many-many-dabest-objects", + "href": "tutorials/10-whorlmap.html#working-with-many-many-dabest-objects", + "title": "Whorlmaps: Visualizing Even More Contrasts", + "section": "Working with many many Dabest objects", + "text": "Working with many many Dabest objects\nLet’s say you have a transcriptomics experiment where you investigate the effects of administering 6 different drugs on oncogene transcripts 1 to 10. You want to find the drug that reduces all the transcripts the most effectively. In a 2x2 experiment, drug is compared to its placebo, so we will be tabulating delta-delta effect sizes. You may simulate the data as follows:\n\ndabest_objects_2d = [[None for _ in range(8)] for _ in range(6)]\nlabels_2d = [\"Transcript 1\", \"Transcript 2\", \"Transcript 3\", \"Transcript 4\", \"Transcript 5\", \"Transcript 6\", \"Transcript 7\", \"Transcript 8\"]\nrow_labels_2d = [\"Drug A\", \"Drug B\", \"Drug C\", \"Drug D\", \"Drug E\", \"Drug F\"]\ndrug_effect_2d = [[.9, 2, 2, .5, 1.2, 1, 3,2, 3, 4], \n [0.1, -.3, .1, -0.3, -2, 1.2, 1,.1,-4, 2],\n [4, 4, 1, 5, 1, 3, 6.5,.5, -1.2, .4],\n [6, 2, 2, 4, 1.4, -0.5, -.5,1.1, 3, .4],\n [0.1, -.3, .1, -0.3, -2, 1.2, 1,.1,-4, 2],\n [-.3, -1, 2, 7, 1, -0.5, 4,1, 2.3, -.4],\n ]\ndrug_effect_scale_2d = [[5, 10, 1, 5, 1, 2, 1,1, .1, 2], \n [7, .2, 8, 3, 1, 4, 7,1, 5, 2],\n [15, 3, 1, 2, 1, 1, 11,1, 7, 2],\n [8, .1, 1, 5, 1, 6,1,1, 3, .4],\n [9, 10, 7, 12, 4, 2,14,10, 9, 20],\n [4, 3, 1, 4, 1, 4,4,1, 3, 4],\n ]\nseeds = [1, 1000, 20, 9999, 1000, 5320]\n\nfor i in range(len(row_labels_2d)):\n for j in range(len(labels_2d)):\n df = create_delta_dataset(seed=seeds[i], \n fourth_quarter_adjustment=drug_effect_2d[i][j],\n scale4=drug_effect_scale_2d[i][j],\n initial_loc = 20)\n dabest_objects_2d[i][j] = dabest.load(data=df, \n x=[\"Genotype\", \"Genotype\"], \n y=\"Transcript Level\", \n delta2=True, \n experiment=\"Treatment\")\n\nWe are going to create a new object called MultiContrast which will contain the array of contrast objects and information about them.\n\nmulti_2d_mean_diff = combine(dabest_objects_2d, labels_2d, row_labels=row_labels_2d, effect_size=\"mean_diff\")\nprint(\"multi_2d_mean_diff is a \" + str(multi_2d_mean_diff))\n\nmulti_2d_mean_diff is a MultiContrast(2D: 6x8, effect_size='mean_diff', contrast_type='delta2')\n\n\nAs we have seen in the previous tutorial, we can visualize these effect sizes with forest plot as follows:\n\nmulti_2d_mean_diff.forest_plot(forest_plot_title = \"2D Forest Plot of Mean Difference\", forest_plot_kwargs = { 'marker_size': 6});\n\n\n\n\n\n\n\n\nThis data would require a stack of forest plots to visualize. So instead, we plot a whorlmap for a concise representation and use color to represent the dimension of effect size. For each effect size, the full bootstrap distribution is binned by quantiles and ranked by value, and then each bin is represented by a pixel. All the pixels correponding to the bins of effects are arranged in a spiral in a cell. The redness and the blueness of the cells represent the magnitude of the effects in the positive and negative direction.\n\nmulti_2d_mean_diff.whorlmap(\n title=\"Mean Difference Gene Expression Whorlmap\",\n cmap=\"vlag\",\n chop_tail=2.5, # Remove 5% extreme values\n fig_size = (10, 4)\n);\n\n\n\n\n\n\n\n\nThe resulting graphic is easy to interpret. Drug B and E induces the most broad spectrum reduction. However the data for Drug E seems a little less precise, mixing blue and red colored pixels. We can say Drug B is a surer bet.", + "crumbs": [ + "Get Started", + "Tutorials", + "Whorlmaps: Visualizing Even More Contrasts" + ] + }, + { + "objectID": "tutorials/10-whorlmap.html#plotting-whorlmaps-with-standardized-effect-sizes", + "href": "tutorials/10-whorlmap.html#plotting-whorlmaps-with-standardized-effect-sizes", + "title": "Whorlmaps: Visualizing Even More Contrasts", + "section": "Plotting whorlmaps with standardized effect sizes", + "text": "Plotting whorlmaps with standardized effect sizes\nWe can also visualize the same array of effects in terms of standardized effect delta g. Let’s plot them together in the same figure by specifying the axes to plot in:\n\nfigure, axes = plt.subplots(1, 2, figsize = (9, 2))\nmulti_2d_mean_diff.whorlmap(\n cmap=\"vlag\",\n chop_tail=2.5, # Remove 5% extreme values\n title=\"Mean Difference\", ax = axes[0]\n);\n\nmulti_2d_delta_g = combine(dabest_objects_2d, labels_2d, row_labels=row_labels_2d, effect_size=\"delta_g\")\nprint(\"multi_2d_delta_g is a \" + str(multi_2d_delta_g))\n\nmulti_2d_delta_g.whorlmap(\n cmap=\"vlag\",\n chop_tail=2.5, # Remove 5% extreme values\n title=\"Delta g\", ax = axes[1]\n);\n\nmulti_2d_delta_g is a MultiContrast(2D: 6x8, effect_size='delta_g', contrast_type='delta2')", + "crumbs": [ + "Get Started", + "Tutorials", + "Whorlmaps: Visualizing Even More Contrasts" + ] + }, + { + "objectID": "tutorials/10-whorlmap.html#multicontrast-object-can-also-handle-1-d-dabest-object-arrays", + "href": "tutorials/10-whorlmap.html#multicontrast-object-can-also-handle-1-d-dabest-object-arrays", + "title": "Whorlmaps: Visualizing Even More Contrasts", + "section": "MultiContrast object can also handle 1-D dabest object arrays", + "text": "MultiContrast object can also handle 1-D dabest object arrays\n\nmulti_1d = combine(dabest_objects_2d[0], labels_2d, row_labels=\"Drug A\", effect_size=\"mean_diff\")\n\nYou can plot a forest plot from this MultiContrast object\n\nfig_forest = multi_1d.forest_plot(forest_plot_kwargs = {\"title\":\"Forest Plot from Multi Contrast (1D)\", \"marker_size\": 6})", + "crumbs": [ + "Get Started", + "Tutorials", + "Whorlmaps: Visualizing Even More Contrasts" + ] + }, + { + "objectID": "tutorials/10-whorlmap.html#d-whorlmap-also-works", + "href": "tutorials/10-whorlmap.html#d-whorlmap-also-works", + "title": "Whorlmaps: Visualizing Even More Contrasts", + "section": "1-D whorlmap also works", + "text": "1-D whorlmap also works\n\nmulti_1d.whorlmap(\n n=21, # Larger spiral size\n chop_tail=2.5 # Remove 5% extreme values\n)\n# plt.title(\"Customized whorlmap\")\nplt.show()\n\n\n\n\n\n\n\n\n\ndabest_objects_2d_2group_delta = [[None for _ in range(8)] for _ in range(6)]\nfor i in range(len(row_labels_2d)):\n for j in range(len(labels_2d)):\n df = create_delta_dataset(seed=seeds[i], \n fourth_quarter_adjustment=drug_effect_2d[i][j],\n scale4=drug_effect_scale_2d[i][j],\n initial_loc = 20)\n dabest_objects_2d_2group_delta[i][j] = dabest.load(data=df, \n x=\"Treatment\", \n y=\"Transcript Level\", \n idx = (\"Placebo\", \"Drug\"))\nmulti_2d_2group_delta_mean_diff = combine(dabest_objects_2d_2group_delta, labels_2d, row_labels=row_labels_2d, effect_size=\"mean_diff\")\nprint(\"multi_2d_mean_diff is a \" + str(multi_2d_2group_delta_mean_diff))\n\nmulti_2d_mean_diff is a MultiContrast(2D: 6x8, effect_size='mean_diff', contrast_type='delta')\n\n\n\nmulti_2d_2group_delta_mean_diff.whorlmap(\n title=\"Mean Difference Treatment Whorlmap\",\n cmap=\"vlag\",\n chop_tail=2.5, # Remove 5% extreme values\n fig_size = (10, 4)\n);\n\n\n\n\n\n\n\n\n\nmulti_2d_2group_delta_mean_diff.whorlmap(\n title=\"Mean Difference Treatment Whorlmap\",\n cmap=\"vlag\",\n chop_tail=2.5, # Remove 5% extreme values\n fig_size = (10, 4),\n heatmap_kwargs={'cbar_kws':{'pad':0.17}}\n);", + "crumbs": [ + "Get Started", + "Tutorials", + "Whorlmaps: Visualizing Even More Contrasts" + ] + }, + { + "objectID": "tutorials/10-whorlmap.html#heatmap-and-plot-kwargs", + "href": "tutorials/10-whorlmap.html#heatmap-and-plot-kwargs", + "title": "Whorlmaps: Visualizing Even More Contrasts", + "section": "Heatmap and plot kwargs", + "text": "Heatmap and plot kwargs\nYou can customize the whorlmap further by passing in heatmap_kwargs and plot_kwargs.\n\nmulti_2d_2group_delta_mean_diff.whorlmap(\n heatmap_kwargs={'cbar_kws':{'pad':0.10}, \"cmap\":'viridis', \"vmax\":4, \"vmin\":-4},\n plot_kwargs={'xlabel':\"Genes\", 'ylabel':\"Drugs\", \n \"xticklabels\":['test1', 'test2', 'test3', 'test4', 'test5', 'test6', 'test7', 'test8'],\n \"xticklabels_rotation\": 90, \"xticklabels_ha\": 'center',\n \"yticklabels\": ['Drug1', 'Drug2', 'Drug3', 'Drug4', 'Drug5', 'Drug6'],\n \"yticklabels_rotation\": 45, 'yticklabels_ha': \"right\", 'title': 'My Title!'}\n);", + "crumbs": [ + "Get Started", + "Tutorials", + "Whorlmaps: Visualizing Even More Contrasts" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html", + "href": "tutorials/08-plot_aesthetics.html", + "title": "Controlling Plot Aesthetics", + "section": "", + "text": "Since v2024.03.29, swarmplots are, by default, plotted asymmetrically to the right side. For detailed information, please refer to Swarm Side.\nSince v2025.03.27, further aesthetic changes were added/updated which include:", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#load-libraries", + "href": "tutorials/08-plot_aesthetics.html#load-libraries", + "title": "Controlling Plot Aesthetics", + "section": "Load libraries", + "text": "Load libraries\n\nimport numpy as np\nimport pandas as pd\nimport dabest\nimport seaborn as sns\n\nprint(\"We're using DABEST v{}\".format(dabest.__version__))\n\nPre-compiling numba functions for DABEST...\n\n\nCompiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 50.20it/s]\n\n\nNumba compilation complete!\nWe're using DABEST v2025.10.20", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#creating-a-demo-dataset", + "href": "tutorials/08-plot_aesthetics.html#creating-a-demo-dataset", + "title": "Controlling Plot Aesthetics", + "section": "Creating a demo dataset", + "text": "Creating a demo dataset\n\nfrom scipy.stats import norm # Used in generation of populations.\n\nnp.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n\nNs = 20 # The number of samples taken from each population\n\n# Create samples\nc1 = norm.rvs(loc=3, scale=0.4, size=Ns)\nc2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\nc3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n\nt1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\nt2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\nt3 = norm.rvs(loc=3, scale=0.75, size=Ns)\nt4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\nt5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\nt6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n\n\n# Add a `gender` column for coloring the data.\nfemales = np.repeat('Female', Ns/2).tolist()\nmales = np.repeat('Male', Ns/2).tolist()\ngender = females + males\n\n# Add an `id` column for paired data plotting.\nid_col = pd.Series(range(1, Ns+1))\n\n# Combine samples and gender into a DataFrame.\ndf = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n 'Control 2' : c2, 'Test 2' : t2,\n 'Control 3' : c3, 'Test 3' : t3,\n 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6,\n 'Gender' : gender, 'ID' : id_col\n })\n\nnp.random.seed(9999) # Fix the seed so the results are replicable.\n\n# Create samples\nN = 20\ny = norm.rvs(loc=3, scale=0.4, size=N*4)\ny[N:2*N] = y[N:2*N]+1\ny[2*N:3*N] = y[2*N:3*N]-0.5\n\n# Add a `Treatment` column\nt1 = np.repeat('Placebo', N*2).tolist()\nt2 = np.repeat('Drug', N*2).tolist()\ntreatment = t1 + t2 \n\n# Add a `Rep` column as the first variable for the 2 replicates of experiments done\nrep = []\nfor i in range(N*2):\n rep.append('Rep1')\n rep.append('Rep2')\n\n# Add a `Genotype` column as the second variable\nwt = np.repeat('W', N).tolist()\nmt = np.repeat('M', N).tolist()\nwt2 = np.repeat('W', N).tolist()\nmt2 = np.repeat('M', N).tolist()\n\ngenotype = wt + mt + wt2 + mt2\n\n# Add an `id` column for paired data plotting.\nid = list(range(0, N*2))\nid_col = id + id \n\n# Combine all columns into a DataFrame.\ndf_delta2 = pd.DataFrame({'ID' : id_col,\n 'Rep' : rep,\n 'Genotype' : genotype, \n 'Treatment': treatment,\n 'Y' : y\n })\n\ndef create_demo_prop_dataset(seed=9999, N=40):\n import numpy as np\n import pandas as pd\n\n np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n # Create samples\n n = 1\n c1 = np.random.binomial(n, 0.2, size=N)\n c2 = np.random.binomial(n, 0.2, size=N)\n c3 = np.random.binomial(n, 0.8, size=N)\n\n t1 = np.random.binomial(n, 0.6, size=N)\n t2 = np.random.binomial(n, 0.2, size=N)\n t3 = np.random.binomial(n, 0.3, size=N)\n t4 = np.random.binomial(n, 0.4, size=N)\n t5 = np.random.binomial(n, 0.5, size=N)\n t6 = np.random.binomial(n, 0.6, size=N)\n t7 = np.ones(N)\n t8 = np.zeros(N)\n t9 = np.zeros(N)\n\n # Add a `gender` column for coloring the data.\n females = np.repeat('Female', N / 2).tolist()\n males = np.repeat('Male', N / 2).tolist()\n gender = females + males\n\n # Add an `id` column for paired data plotting.\n id_col = pd.Series(range(1, N + 1))\n\n # Combine samples and gender into a DataFrame.\n df = pd.DataFrame({'Control 1': c1, 'Test 1': t1,\n 'Control 2': c2, 'Test 2': t2,\n 'Control 3': c3, 'Test 3': t3,\n 'Test 4': t4, 'Test 5': t5, 'Test 6': t6,\n 'Test 7': t7, 'Test 8': t8, 'Test 9': t9,\n 'Gender': gender, 'ID': id_col\n })\n\n return df\ndf_prop = create_demo_prop_dataset()\n\n\ntwo_groups_prop_paired = dabest.load(df_prop, idx=(\"Control 1\", \"Test 1\"), proportional=True, paired=\"baseline\", id_col=\"ID\")\ntwo_groups_prop = dabest.load(df_prop, idx=(\"Control 1\", \"Test 1\"), proportional=True)\ntwo_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"))\nmulti_2group = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\")))\nrepeated_measures = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\"),paired=\"baseline\", id_col=\"ID\")\ntwo_groups_paired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), paired=\"baseline\", id_col=\"ID\")\nmini_meta_paired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), mini_meta=True, id_col=\"ID\", paired=\"baseline\")\npaired_delta2 = dabest.load(data = df_delta2, \n paired = \"baseline\", id_col=\"ID\",\n x = [\"Treatment\", \"Rep\"], y = \"Y\", \n delta2 = True, experiment = \"Genotype\")", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#changing-the-graph-colours", + "href": "tutorials/08-plot_aesthetics.html#changing-the-graph-colours", + "title": "Controlling Plot Aesthetics", + "section": "Changing the graph colours", + "text": "Changing the graph colours\n\nColor categories from another variable\nUse the parameter color_col to specify which column in the dataframe will be used to create the different colours for your graph.\n\nmulti_2group.mean_diff.plot(color_col=\"Gender\");\n\ntwo_groups_paired.mean_diff.plot(color_col=\"Gender\");\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCustom palette\nThe colour palette for the graph can be changed using the parameter custom_palette. Multiple types of color palettes can be used:\n\nA list of colors (named colors, hex, rgb, etc) e.g. ['red', 'blue', 'green']\nA seaborn color palette e.g. 'Set1'\nA matplotlib color map e.g. 'viridis'\n\n'paired' is an interesting option for two-group (or multi two-group) comparisons\n\nA dictionary with the keys as the column names and the values as the colors e.g. {'Control 1': 'red', 'Test 1': 'blue', 'Test 2': 'green'}\n\nOr, a dictionary with the keys as the binary options for proportion plots (barplots and sankey) and the values as the colors e.g. {0: 'red', 1: 'blue'}\n\n\n\nA list of colors\n\nmulti_2group.mean_diff.plot(custom_palette=['red', 'blue', 'green', 'purple', 'orange', 'brown']);\n\n\n\n\n\n\n\n\n\n\nSeaborn color palette\n\nmulti_2group.mean_diff.plot(custom_palette=sns.color_palette(\"husl\", 6));\n\n\n\n\n\n\n\n\n\n\nMatplotlib color map/palette\n\nmulti_2group.mean_diff.plot(custom_palette=\"viridis\");\nmulti_2group.mean_diff.plot(custom_palette=\"Paired\");\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nA user-defined dictionary\nThere are many ways to specify matplotlib colours. Find one example below using accepted colour names, hex strings (commonly used on the web), and RGB tuples.\n\nmy_color_palette = {\"Control 1\" : \"blue\",\n \"Test 1\" : \"purple\",\n \"Control 2\" : \"#cb4b16\", # This is a hex string.\n \"Test 2\" : (0., 0.7, 0.2) # This is a RGB tuple.\n }\n\nmulti_2group.mean_diff.plot(custom_palette=my_color_palette);\n\n\n\n\n\n\n\n\nFor proportion plots (barplots and sankey), a color palette dict can also be supplied via {1: first_color, 0, second_color} where first_color and second_color are valid matplotlib colours.\n\ntwo_groups_prop.mean_diff.plot(custom_palette={1: \"red\", 0: \"blue\"});\n\n\n\n\n\n\n\n\n\ntwo_groups_prop_paired.mean_diff.plot(custom_palette={1: \"red\", 0: \"blue\"});\n\n\n\n\n\n\n\n\n\n\nColor palette changes also now affect the effect size curve colors in paired plots\nNote: The first color in the custom palette is used for the control group. As in the example below, if show_baseline_ec is set to False, it wont be represented in the plot.\n\nrepeated_measures.mean_diff.plot(custom_palette=[\"red\", \"blue\", \"green\", \"purple\"]);", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#color-saturation", + "href": "tutorials/08-plot_aesthetics.html#color-saturation", + "title": "Controlling Plot Aesthetics", + "section": "Color saturation", + "text": "Color saturation\nBy default, dabest.plot() desaturates the colour of the dots in the swarmplot by 50%. This draws attention to the effect size bootstrap curves.\nYou can alter the default values with the parameters raw_desat and contrast_desat.\n\nmulti_2group.mean_diff.plot(custom_palette=my_color_palette,\n raw_desat=0.75,\n contrast_desat=0.25);", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#alpha-transparency", + "href": "tutorials/08-plot_aesthetics.html#alpha-transparency", + "title": "Controlling Plot Aesthetics", + "section": "Alpha (transparency)", + "text": "Alpha (transparency)\nIt is possible change the transparency of the raw data by using the raw_alpha parameter. This can also be achieved by adding alpha to the relevant rawdata kwargs (barplot_kwargs, or swarmplot_kwargs, or slopegraph_kwargs, or sankey_kwargs)\n\nmulti_2group.mean_diff.plot(raw_alpha=0.2);\n\nmulti_2group.mean_diff.plot(swarmplot_kwargs={'alpha': 0.2});\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt is also possible change the transparency of the effect size curves by using the contrast_alpha parameter. This can also be achieved via adding alpha to the contrast_kwargs parameter.\n\nmulti_2group.mean_diff.plot(contrast_alpha=0.2);\n\nmulti_2group.mean_diff.plot(contrast_kwargs={'alpha':0.2});", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#marker-size", + "href": "tutorials/08-plot_aesthetics.html#marker-size", + "title": "Controlling Plot Aesthetics", + "section": "Marker size", + "text": "Marker size\nIt is possible change the size of the dots used in the rawdata swarmplot, as well as those to indicate the effect sizes, by using the parameters raw_marker_size and contrast_marker_size respectively.\n\nmulti_2group.mean_diff.plot(raw_marker_size=3,\n contrast_marker_size=12);", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#axes", + "href": "tutorials/08-plot_aesthetics.html#axes", + "title": "Controlling Plot Aesthetics", + "section": "Axes", + "text": "Axes\n\nLims\nTo change the y-limits for the rawdata axes, and the contrast axes, use the parameters raw_ylim and contrast_ylim.\n\nmulti_2group.mean_diff.plot(raw_ylim=(0, 5),\n contrast_ylim=(-2, 2));\n\n\n\n\n\n\n\n\nIf the effect size is qualitatively inverted (ie. a smaller value is a better outcome), you can simply invert the tuple passed to contrast_ylim.\n\nmulti_2group.mean_diff.plot(contrast_ylim=(2, -2));\n\n\n\n\n\n\n\n\nThe contrast axes share the same y-limits as those of the delta-delta plot. Thus, the y axis of the delta-delta plot changes as well.\n\npaired_delta2.mean_diff.plot(contrast_ylim=(3, -3));\n\n\n\n\n\n\n\n\nYou can also change the y-limit of the delta-delta axes and the regular delta axes via the delta2_ylim parameter.\n\npaired_delta2.mean_diff.plot(delta2_ylim=(3, -3));\n\n\n\n\n\n\n\n\n\n\nLabels\n\nraw_label - label the raw data y-axis\ncontrast_label - label the contrast y-axis\n\n\ntwo_groups_unpaired.mean_diff.plot(raw_label=\"This is my\\nrawdata\", \n contrast_label=\"The bootstrap\\ndistribtions!\"\n );\n\n\n\n\n\n\n\n\nUnique for delta-delta: - delta2_ylim - to label the delta-delta y-axis\n\npaired_delta2.mean_diff.plot(delta2_label='delta-delta label');\n\n\n\n\n\n\n\n\n\n\nAxes ticks\nYou can add minor ticks and also change the tick frequency by accessing the axes directly.\nEach estimation plot produced by dabest has two axes. The first one contains the rawdata swarmplot while the second one contains the bootstrap effect size differences.\n\nimport matplotlib.ticker as Ticker\n\nf = two_groups_unpaired.mean_diff.plot()\n\nrawswarm_axes = f.axes[0]\ncontrast_axes = f.axes[1]\n\nrawswarm_axes.yaxis.set_major_locator(Ticker.MultipleLocator(1))\nrawswarm_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.5))\n\ncontrast_axes.yaxis.set_major_locator(Ticker.MultipleLocator(0.5))\ncontrast_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.25))\n\n\n\n\n\n\n\n\n\n\nAdd counts to tick labels\nBy default, the tick labels include the sample size for each group. This can be switched off via setting show_sample_size=False in the .plot() method.\n\ntwo_groups_unpaired.mean_diff.plot(show_sample_size=False\n );", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#changing-swarm-side", + "href": "tutorials/08-plot_aesthetics.html#changing-swarm-side", + "title": "Controlling Plot Aesthetics", + "section": "Changing swarm side", + "text": "Changing swarm side\nIn dabest, swarmplots are, by default, plotted asymmetrically to the right side. You may change this by using the parameter swarm_side.\nThere are only three valid values: \"right\" (default), \"left\", \"center\".\n\nmulti_2group.mean_diff.plot(swarm_side=\"right\");\nmulti_2group.mean_diff.plot(swarm_side=\"left\");\nmulti_2group.mean_diff.plot(swarm_side=\"center\");", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#creating-estimation-plots-in-existing-axes", + "href": "tutorials/08-plot_aesthetics.html#creating-estimation-plots-in-existing-axes", + "title": "Controlling Plot Aesthetics", + "section": "Creating estimation plots in existing axes", + "text": "Creating estimation plots in existing axes\nImplemented in v0.2.6 by Adam Nekimken.\ndabest.plot has an ax parameter that accepts Matplotlib Axes. The entire estimation plot will be created in the specified Axes.\n\ntwo_groups_paired_baseline = dabest.load(df, idx=(\"Control 1\", \"Test 1\"),\n paired=\"baseline\", id_col=\"ID\")\nmulti_2group_paired = dabest.load(df,\n idx=((\"Control 1\", \"Test 1\"),\n (\"Control 2\", \"Test 2\")),\n paired=\"baseline\", id_col=\"ID\")\n\n\nfrom matplotlib import pyplot as plt\nf, axx = plt.subplots(nrows=2, ncols=2,\n figsize=(15, 15),\n gridspec_kw={'wspace': 0.25} # ensure proper width-wise spacing.\n )\n\ntwo_groups_unpaired.mean_diff.plot(ax=axx.flat[0]);\n\ntwo_groups_paired_baseline.mean_diff.plot(ax=axx.flat[1]);\n\nmulti_2group.mean_diff.plot(ax=axx.flat[2]);\n\nmulti_2group_paired.mean_diff.plot(ax=axx.flat[3]);\n\n\n\n\n\n\n\n\nIn this case, to access the individual rawdata axes, use name_of_axes to manipulate the rawdata axes, and name_of_axes.contrast_axes to gain access to the effect size axes.\n\ntopleft_axes = axx.flat[0]\ntopleft_axes.set_ylabel(\"New y-axis label for rawdata\")\ntopleft_axes.contrast_axes.set_ylabel(\"New y-axis label for effect size\")\nf", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#legend", + "href": "tutorials/08-plot_aesthetics.html#legend", + "title": "Controlling Plot Aesthetics", + "section": "Legend", + "text": "Legend\nFor plots with a color_col specified, a legend will be created. Utilise the legend_kwargs parameter to adjust the legend.\n\nmulti_2group.mean_diff.plot(color_col=\"Gender\", \n legend_kwargs={'bbox_to_anchor': [0, 1], 'fontsize':8});", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#hiding-options", + "href": "tutorials/08-plot_aesthetics.html#hiding-options", + "title": "Controlling Plot Aesthetics", + "section": "Hiding options", + "text": "Hiding options\nFor mini-meta plots, it is possible to hide the weighted average plot by setting the parameter show_mini_meta=False in the .plot() method.\n\nmini_meta_paired.mean_diff.plot(show_mini_meta=False);\n\n\n\n\n\n\n\n\nSimilarly, you can hide the delta-delta effect size by setting show_delta2=False in the .plot() method.\n\npaired_delta2.mean_diff.plot(show_delta2=False);", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#effect-size-error-bar-and-marker", + "href": "tutorials/08-plot_aesthetics.html#effect-size-error-bar-and-marker", + "title": "Controlling Plot Aesthetics", + "section": "Effect size error bar and marker", + "text": "Effect size error bar and marker\nModifying the effect size marker can be done via contrast_marker_kwargs. This parameter accepts a dictionary of keyword arguments.\nThe available options are:\n\n'marker' - type of the marker\n'markersize' - size of the marker\n'color' - color of the marker\n'alpha' - alpha of the marker (transparency)\n'zorder' - zorder of the marker (the layering relative to other plot elements)\n\nNote: markersize can also be modified directly via the contrast_marker_size argument\n\ntwo_groups_unpaired.mean_diff.plot(contrast_marker_kwargs={\"marker\": \"x\", 'markersize': 15, 'color': 'green', 'alpha':0.8, 'zorder': 5});\n\n\n\n\n\n\n\n\nModifying the appearance of the effect size error bar can be done via the contrast_errorbar_kwargs parameter. This parameter accepts a dictionary of keyword arguments.\nThe relevant inputs to contrast_errorbar_kwargs are:\n\n'lw' - width of the error bar\n'linestyle' - line style of the error bar\n'color' - color of the error bar\n'zorder' - zorder of the error bar (the layering relative to other plot elements)\n'alpha' - alpha of the error bar (transparency)\n\n\ntwo_groups_unpaired.mean_diff.plot(contrast_errorbar_kwargs={'lw': 4, 'color': 'green', 'alpha':0.5, 'zorder': 2, 'linestyle': ':'});", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#group-summaries", + "href": "tutorials/08-plot_aesthetics.html#group-summaries", + "title": "Controlling Plot Aesthetics", + "section": "Group summaries", + "text": "Group summaries\nGroup summaries represent the summary statistics of the sample and are included by default.\nIn swarmplots and proportion plots, these are represented by gapped lines.\nIn slopegraphs, these are represented by a solid line connecting the group mean/median with error bars.\nThe type of group summary can be specified via group_summaries in the .plot() method and must be one of these: 'median_quartiles', 'mean_sd', None.\nBy default, the group summary is set to 'mean_sd'.\n\ntwo_groups_unpaired.mean_diff.plot(group_summaries=\"mean_sd\");\ntwo_groups_unpaired.mean_diff.plot(group_summaries=\"median_quartiles\");\ntwo_groups_unpaired.mean_diff.plot(group_summaries=None);\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFor slopegraphs, the group summary is represented by a solid line connecting the group mean/median with error bars.\n\nrepeated_measures.mean_diff.plot(group_summaries=\"mean_sd\");\nrepeated_measures.mean_diff.plot(group_summaries=\"median_quartiles\");\nrepeated_measures.mean_diff.plot(group_summaries=None);\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGroup summaries have an associated kwargs group_summaries_kwargs\nThe relevant inputs to group_summaries_kwargs are:\n\n'zorder' - zorder of the gapped lines (the layering relative to other plot elements)\n'lw' - linewidth of the gapped lines\n'alpha' - alpha of the gapped lines (transparency)\n'gap_width_percent' - gap size (for gapped lines only)\n'offset' - location adjustment of the gapped lines (x-axis; for gapped lines only)\n'color' - the shared color of the gapped lines\n\n\ntwo_groups_unpaired.mean_diff.plot(group_summaries_kwargs={'gap_width_percent': 3, 'alpha': 0.5, 'lw': 4, 'offset': 0.6, 'color':'red'});\n\n\n\n\n\n\n\n\n\nrepeated_measures.mean_diff.plot(group_summaries_kwargs={'gap_width_percent': 3, 'alpha': 0.5, 'lw': 4, 'offset': 0.6, 'color':'red'});", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#raw-bars", + "href": "tutorials/08-plot_aesthetics.html#raw-bars", + "title": "Controlling Plot Aesthetics", + "section": "Raw bars", + "text": "Raw bars\nRaw bars are included in swarmplots by default. It can be turned off by setting raw_bars=False in the .plot() method.\n\nmulti_2group.mean_diff.plot(raw_bars=True, contrast_bars=False);\n\n\n\n\n\n\n\n\nRaw bar kwargs can be utilised via raw_bars_kwargs in the .plot() method.\nPass any keyword arguments accepted by matplotlib.patches.Rectangle here, as a string.\n\nmulti_2group.mean_diff.plot(raw_bars=True, contrast_bars=False,\n raw_bars_kwargs={'color': \"red\", 'alpha': 0.2}, \n );", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#contrast-bars", + "href": "tutorials/08-plot_aesthetics.html#contrast-bars", + "title": "Controlling Plot Aesthetics", + "section": "Contrast bars", + "text": "Contrast bars\nContrast bars are included in all plots by default. It can be turned off by setting contrast_bars=False in the .plot() method.\n\nmulti_2group.mean_diff.plot(contrast_bars=True, raw_bars=False);\n\n\n\n\n\n\n\n\nContrast bar kwargs can be utilised via contrast_bars_kwargs in the .plot() method.\nPass any keyword arguments accepted by matplotlib.patches.Rectangle here, as a string.\n\nmulti_2group.mean_diff.plot(contrast_bars=True, raw_bars=False, \n contrast_bars_kwargs={'color': \"red\", 'alpha': 0.1}\n );", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#reference-band", + "href": "tutorials/08-plot_aesthetics.html#reference-band", + "title": "Controlling Plot Aesthetics", + "section": "Reference band", + "text": "Reference band\nA reference band can be added for each relevant contrast object as desired via supplying a list to the argument reference_band in the .plot() method.\n\nmulti_2group.mean_diff.plot(reference_band=[0, 1], contrast_bars=False, raw_bars=False);\n\n\n\n\n\n\n\n\nReference band kwargs can be utilised via reference_band_kwargs in the .plot() method.\nThe relevant inputs to reference_band_kwargs are:\n\n'span_ax' - Whether the reference band(s) should span the entire x-axis or start from the relevant effect size curve\n'color' - Color of the reference band(s). If color is not specified, the color of the effect size curve will be used.\n'alpha' - Alpha of the reference band(s) (transparency)\n'zorder' - Zorder of the reference band(s) (the layering relative to other plot elements)\n\n\nmulti_2group.mean_diff.plot(reference_band=[0,1], contrast_bars=False, raw_bars=False,\n reference_band_kwargs={\"alpha\": 0.2, \"color\": 'black', 'span_ax': True}\n );", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#delta-text", + "href": "tutorials/08-plot_aesthetics.html#delta-text", + "title": "Controlling Plot Aesthetics", + "section": "Delta text", + "text": "Delta text\nDelta text is included in all plots by default. It can be turned off by setting delta_text=False in the .plot() method.\n\nmulti_2group.mean_diff.plot(delta_text=True);\n\n\n\n\n\n\n\n\nDelta text kwargs can be utilised via delta_text_kwargs in the .plot() method.\nThe relevant inputs to delta_text_kwargs are:\n\n'color' - Color. If color is not specified, the color of the effect size curve will be used.\n'alpha'- Alpha (transparency)\n'fontsize' - Font size\n'ha' - Horizontal alignment\n'va' - Vertical alignment\n'rotation' - Text rotation\n'x_coordinates' - Specify the x-coordinates of the text\n'y_coordinates' - Specify the y-coordinates of the text\n'offset' - Am x-axis coordinate adjuster for minor movement of all text\n\nOtherwise, pass any keyword arguments accepted by matplotlib.text.Text, as a string.\n\nmulti_2group.mean_diff.plot(delta_text=True, \n delta_text_kwargs={\"color\":\"red\", \"rotation\":45, \"va\":\"bottom\", \"alpha\":0.7});\n\n\n\n\n\n\n\n\n'x_coordinates' and/or 'y_coordinates' if you would like to specify the text locations manually.\n\nmulti_2group.mean_diff.plot(delta_text=True, \n delta_text_kwargs={\"x_coordinates\":(0.5, 2.75), \n \"y_coordinates\":(0.5, -1.7)});\n\n\n\n\n\n\n\n\n'offset' to adjust the x location of all the texts (positive moves right, negative left).\n\nmulti_2group.mean_diff.plot(delta_text=True, \n delta_text_kwargs={\"offset\":0.1});", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#adding-jitter-to-slopegraph-plots", + "href": "tutorials/08-plot_aesthetics.html#adding-jitter-to-slopegraph-plots", + "title": "Controlling Plot Aesthetics", + "section": "Adding jitter to slopegraph plots", + "text": "Adding jitter to slopegraph plots\nFor paired plots, you can add jitter to the slopegraph by adding a value for jitter in the slopegraph_kwargs parameter.\nThis can be useful for specific paired plots when there are many overlapping points.\nCurrently, jitter is only available for slopegraphs and only in the x-direction (vertical plots) or y-direction (horizontal plots).\n\n# Jitter tests\nnp.random.seed(9999) # Fix the seed to ensure reproducibility of results.\nNs = 20 # The number of samples taken from each population\n# Create samples\nc1 = [0.5]*Ns + [1.5]*Ns\nc2 = [2]*Ns + [1]*Ns\nt1 = [1]*Ns + [2]*Ns\nt2 = [1.5]*Ns + [2.5]*Ns\nt3 = [2]*Ns + [1]*Ns\nt4 = [1]*Ns + [2]*Ns\nt5 = [1.5]*Ns + [2.5]*Ns\nid_col = pd.Series(range(1, 2*Ns+1))\ndf_jittertest= pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n 'Control 2' : c2, 'Test 2' : t2, 'Test 3' : t3,\n 'Test 4' : t4, 'Test 5' : t5, 'ID' : id_col})\n\nFor the example below, there are many overlapping points for the paired plot, which makes it look like only one sample.\n\nmulti_2group = dabest.load(df_jittertest, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\", \"Test 3\", \"Test 4\", \"Test 5\")), paired='baseline', id_col='ID')\nmulti_2group.mean_diff.plot(horizontal=False, group_summaries=None);\n\n\n\n\n\n\n\n\nAdding jitter can help to visualize the data better.\n\nmulti_2group.mean_diff.plot(horizontal=False, slopegraph_kwargs={'jitter': 1}, group_summaries=None);", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#gridkey", + "href": "tutorials/08-plot_aesthetics.html#gridkey", + "title": "Controlling Plot Aesthetics", + "section": "Gridkey", + "text": "Gridkey\nYou can utilise a gridkey table format for representing the index groupings. This can be reached via gridkey in the .plot() method.\nYou can either use gridkey='auto' to automatically generate the gridkey, or pass a list of indexes to represent the groupings (e.g., gridkey=['Control', 'Test']).\n\npaired_delta2.mean_diff.plot(gridkey='auto');\n\n\n\n\n\n\n\n\nGridkey kwargs can be utilised via gridkey_kwargs in the .plot() method.\nThe relevant inputs to gridkey_kwargs are:\n\n'show_es' - Whether to show the effect size in the gridkey\n'show_Ns' - Whether to show the sample sizes in the gridkey\n'merge_pairs' - Whether to merge the pairs in the gridkey (paired data only)\n'delimiters' - Delimiters to use for the autoparser. E.g., [‘;’, ‘>’, ’_’]\n'marker' - Marker to use for filling the gridkey\n'fontsize' - Font size of the gridkey text\n'labels_fontsize' - Font size of the labels in the gridkey\n\n\nmulti_2group = dabest.load(df, idx=((\"Control 1\", \"Control 2\"), (\"Test 1\", \"Test 2\")),paired='baseline', id_col='ID')\nmulti_2group.mean_diff.plot(gridkey=['Control', 'Test'], \n gridkey_kwargs={'merge_pairs': True, 'show_es': False, 'show_Ns': False, 'marker': '√',\n 'fontsize': 8, 'labels_fontsize': 12});", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#delta-dot", + "href": "tutorials/08-plot_aesthetics.html#delta-dot", + "title": "Controlling Plot Aesthetics", + "section": "Delta dot", + "text": "Delta dot\nBy default, delta dots are included in paired experiment plots (excluding proportion plots).\nThis feature can be turned off by setting delta_dot=False in the .plot() method.\n\nmulti_2group_paired.mean_diff.plot(delta_dot=False);\n\n\n\n\n\n\n\n\nDelta dot kwargs can be utilised via delta_dot_kwargs in the .plot() method.\nThe relevant inputs to delta_dot_kwargs are:\n\n'color' - Specify the color of the delta dots. If color is not specified, the color of the effect size curve will be used.\n'marker' - Marker of the dots. The default are triangles (‘^’)\n'alpha' - Alpha (Transparency)\n'zorder' - Zorder (the layering relative to other plot elements)\n'size' - Marker size\n'side' - Which side to plot the delta dots. The options are 'left', 'right', or 'center'. This functions like the swarm_side parameter.\n\n\nmulti_2group_paired.mean_diff.plot(delta_dot_kwargs={\"color\":'red', \"alpha\":0.1, 'zorder': 2, 'size': 5, 'side': 'center'});", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#effect-size-paired-lines", + "href": "tutorials/08-plot_aesthetics.html#effect-size-paired-lines", + "title": "Controlling Plot Aesthetics", + "section": "Effect size paired lines", + "text": "Effect size paired lines\nBy default, effect size paired lines are included in paired experiment plots (excluding proportion plots).\nThis feature can be turned off by setting contrast_paired_lines=False in the .plot() method.\n\nrepeated_measures.mean_diff.plot(contrast_paired_lines=True);\n\n\n\n\n\n\n\n\nEffect size line kwargs can be utilised via contrast_paired_lines_kwargs in the .plot() method.\nBy default, the following keywords are passed:\n\n'linestyle' - Linestyle\n'linewidth' - Linewidth\n'zorder' - Zorder (the layering relative to other plot elements)\n'color' - Color. Default is ‘dimgray’\n'alpha' - Alpha (transparency)\n\n\nrepeated_measures.mean_diff.plot(contrast_paired_lines=True, \n contrast_paired_lines_kwargs={\"color\": \"red\", \"alpha\": 0.5, \"linestyle\": \"-\"});", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/08-plot_aesthetics.html#baseline-error-curve", + "href": "tutorials/08-plot_aesthetics.html#baseline-error-curve", + "title": "Controlling Plot Aesthetics", + "section": "Baseline error curve", + "text": "Baseline error curve\nIn DABEST v2025.03.27, we introduce a new aspect to the contrast axes: the baseline dot and error curve. While the baseline dot is always present, the error curve can be turned on by setting show_baseline_ec=True in the .plot() method.\n\nrepeated_measures.mean_diff.plot(show_baseline_ec=True);", + "crumbs": [ + "Get Started", + "Tutorials", + "Controlling Plot Aesthetics" + ] + }, + { + "objectID": "tutorials/06-delta_delta.html", + "href": "tutorials/06-delta_delta.html", + "title": "Delta-Delta", + "section": "", + "text": "Since v2023.02.14, DABEST also supports the calculation of delta-delta, an experimental function that facilitates the comparison between two bootstrapped effect sizes computed from two independent categorical variables.\nSince v2025.03.27, DABEST also supports the calculation of delta-delta for binary data (proportion plots).\nMany experimental designs investigate the effects of two interacting independent variables on a dependent variable. The delta-delta effect size enables us distill the net effect of the two variables. To illustrate this, let’s explore the following problem.\nConsider an experiment where we test the efficacy of a drug named Drug on a disease-causing mutation M based on disease metric Y. The greater the value Y has, the more severe the disease phenotype is. Phenotype Y has been shown to be caused by a gain-of-function mutation M, so we expect a difference between wild type (W) subjects and mutant subjects (M). Now, we want to know whether this effect is ameliorated by the administration of Drug treatment. We also administer a placebo as a control. In theory, we only expect Drug to have an effect on the M group, although in practice, many drugs have non-specific effects on healthy populations too.\nEffectively, we have four groups of subjects for comparison.\nThere are two Treatment conditions, Placebo (control group) and Drug (test group). There are two Genotype: W (wild type population) and M (mutant population). Additionally, each experiment was conducted twice (Rep1 and Rep2). We will perform several analyses to visualise these differences in a simulated dataset.", + "crumbs": [ + "Get Started", + "Tutorials", + "Delta-Delta" + ] + }, + { + "objectID": "tutorials/06-delta_delta.html#load-libraries", + "href": "tutorials/06-delta_delta.html#load-libraries", + "title": "Delta-Delta", + "section": "Load libraries", + "text": "Load libraries\n\nimport numpy as np\nimport pandas as pd\nimport dabest\n\nprint(\"We're using DABEST v{}\".format(dabest.__version__))\n\nWe're using DABEST v2025.10.20", + "crumbs": [ + "Get Started", + "Tutorials", + "Delta-Delta" + ] + }, + { + "objectID": "tutorials/06-delta_delta.html#creating-a-demo-dataset", + "href": "tutorials/06-delta_delta.html#creating-a-demo-dataset", + "title": "Delta-Delta", + "section": "Creating a demo dataset", + "text": "Creating a demo dataset\n\nfrom scipy.stats import norm # Used in generation of populations.\nnp.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n\n# Create samples\nN = 20\ny = norm.rvs(loc=3, scale=0.4, size=N*4)\ny[N:2*N] = y[N:2*N]+1\ny[2*N:3*N] = y[2*N:3*N]-0.5\n\n# Add a `Treatment` column\nt1 = np.repeat('Placebo', N*2).tolist()\nt2 = np.repeat('Drug', N*2).tolist()\ntreatment = t1 + t2 \n\n# Add a `Rep` column as the first variable for the 2 replicates of experiments done\nrep = []\nfor i in range(N*2):\n rep.append('Rep1')\n rep.append('Rep2')\n\n# Add a `Genotype` column as the second variable\nwt = np.repeat('W', N).tolist()\nmt = np.repeat('M', N).tolist()\nwt2 = np.repeat('W', N).tolist()\nmt2 = np.repeat('M', N).tolist()\n\n\ngenotype = wt + mt + wt2 + mt2\n\n# Add an `id` column for paired data plotting.\nid = list(range(0, N*2))\nid_col = id + id \n\n\n# Combine all columns into a DataFrame.\ndf_delta2 = pd.DataFrame({'ID' : id_col,\n 'Rep' : rep,\n 'Genotype' : genotype, \n 'Treatment': treatment,\n 'Y' : y\n })\ndf_delta2.head(5)\n\n\n\n\n\n\n\n\nID\nRep\nGenotype\nTreatment\nY\n\n\n\n\n0\n0\nRep1\nW\nPlacebo\n2.793984\n\n\n1\n1\nRep2\nW\nPlacebo\n3.236759\n\n\n2\n2\nRep1\nW\nPlacebo\n3.019149\n\n\n3\n3\nRep2\nW\nPlacebo\n2.804638\n\n\n4\n4\nRep1\nW\nPlacebo\n2.858019", + "crumbs": [ + "Get Started", + "Tutorials", + "Delta-Delta" + ] + }, + { + "objectID": "tutorials/06-delta_delta.html#loading-data", + "href": "tutorials/06-delta_delta.html#loading-data", + "title": "Delta-Delta", + "section": "Loading data", + "text": "Loading data\nTo create a delta-delta plot, you simply need to set delta2=True in the dabest.load() method. However, in this case,x needs to be declared as a list consisting of 2 elements, unlike most cases where it is a single element. The first element in x will represent the variable plotted along the horizontal axis, and the second one will determine the color of dots for scattered plots or the color of lines for slope graphs. We use the experiment input to specify the grouping of the data.\n\nunpaired_delta2 = dabest.load(data = df_delta2, x = [\"Genotype\", \"Genotype\"], y = \"Y\", delta2 = True, experiment = \"Treatment\")\nunpaired_delta2\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:50 2025.\n\nEffect size(s) with 95% confidence intervals will be computed for:\n1. M Placebo minus W Placebo\n2. M Drug minus W Drug\n3. Drug minus Placebo (only for mean difference)\n\n5000 resamples will be used to generate the effect size bootstraps.\n\n\n\nunpaired_delta2.mean_diff\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:51 2025.\n\nThe unpaired mean difference between W Placebo and M Placebo is 1.23 [95%CI 0.937, 1.51].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\nThe unpaired mean difference between W Drug and M Drug is 0.326 [95%CI 0.0956, 0.574].\nThe p-value of the two-sided permutation t-test is 0.0122, calculated for legacy purposes only. \n\nThe delta-delta between Placebo and Drug is -0.903 [95%CI -1.27, -0.522].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing the effect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.mean_diff.statistical_tests`", + "crumbs": [ + "Get Started", + "Tutorials", + "Delta-Delta" + ] + }, + { + "objectID": "tutorials/06-delta_delta.html#generating-delta-delta-plots", + "href": "tutorials/06-delta_delta.html#generating-delta-delta-plots", + "title": "Delta-Delta", + "section": "Generating delta-delta plots", + "text": "Generating delta-delta plots\n\nunpaired_delta2.mean_diff.plot();\n\n\n\n\n\n\n\n\nIn the above plot, the horizontal axis represents the Genotype condition and the dot colour is also specified by Genotype. The left pair of scattered plots is based on the Placebo group while the right pair is based on the Drug group. The bottom left axis contains the two primary deltas: the Placebo delta and the Drug delta. We can easily see that when only the placebo was administered, the mutant phenotype is around 1.23 [95%CI 0.948, 1.52]. This difference was shrunken to around 0.326 [95%CI 0.0934, 0.584] when the drug was administered. This gives us some indication that the drug is effective in amiliorating the disease phenotype. Since the Drug did not completely eliminate the mutant phenotype, we have to calculate how much net effect the drug had. This is where delta-delta comes in. We use the Placebo delta as a reference for how much the mutant phenotype is supposed to be, and we subtract the Drug delta from it. The bootstrapped mean differences (delta-delta) between the Placebo and Drug group are plotted at the right bottom with a separate y-axis from other bootstrap plots. This effect size, at about -0.903 [95%CI -1.28, -0.513], is the net effect size of the drug treatment. That is to say that treatment with drug A reduced disease phenotype by 0.903.\nThe mean difference between mutants and wild types given the placebo treatment is:\n\\(\\Delta_{1} = \\overline{X}_{P, M} - \\overline{X}_{P, W}\\)\nThe mean difference between mutants and wild types given the drug treatment is:\n\\(\\Delta_{2} = \\overline{X}_{D, M} - \\overline{X}_{D, W}\\)\nThe net effect of the drug on mutants is:\n\\(\\Delta_{\\Delta} = \\Delta_{2} - \\Delta_{1}\\)\nwhere \\(\\overline{X}\\) is the sample mean, \\(\\Delta\\) is the mean difference.\nThe configuration of comparison we performed above is reminiscent of a two-way ANOVA. In fact, the delta - delta is an effect size estimated for the interaction term between Treatment and Genotype. Main effects of Treatment and Genotype, on the other hand, can be determined by simpler, univariate contrast plots.", + "crumbs": [ + "Get Started", + "Tutorials", + "Delta-Delta" + ] + }, + { + "objectID": "tutorials/06-delta_delta.html#specifying-grouping-for-comparisons", + "href": "tutorials/06-delta_delta.html#specifying-grouping-for-comparisons", + "title": "Delta-Delta", + "section": "Specifying grouping for comparisons", + "text": "Specifying grouping for comparisons\nIn the example above, we used the convention of test - control but you can manipulate the orders of the experiment groups as well as the horizontal axis variable by setting the paremeters experiment_label and x1_level.\n\nunpaired_delta2_specified = dabest.load(data = df_delta2, \n x = [\"Genotype\", \"Genotype\"], y = \"Y\", \n delta2 = True, experiment = \"Treatment\",\n experiment_label = [\"Drug\", \"Placebo\"],\n x1_level = [\"M\", \"W\"])\n\nunpaired_delta2_specified.mean_diff.plot();\n\n\n\n\n\n\n\n\nUtilising the show_delta2 argument within the .plot() method allows for control of whether the delta-delta effect size is displayed on the plot. By default, this is set to True.\n\nunpaired_delta2_specified.mean_diff.plot(show_delta2=False);\n\n\n\n\n\n\n\n\nThe delta-delta function also supports paired data, providing a useful alternative visualization of the data. Assuming that the placebo and drug treatment were administered to the same subjects, our data is paired between the treatment conditions. We can specify this by using Treatment as x and Genotype as experiment, and we further specify that id_col is ID, linking data from the same subject with each other. Since we have conducted two replicates of the experiments, we can also colour the slope lines according to Rep.\n\npaired_delta2 = dabest.load(data = df_delta2, \n paired = \"baseline\", id_col=\"ID\",\n x = [\"Treatment\", \"Rep\"], y = \"Y\", \n delta2 = True, experiment = \"Genotype\")\npaired_delta2.mean_diff.plot();\n\n\n\n\n\n\n\n\nWe see that the drug had a non-specific effect of -0.321 [95%CI -0.498, -0.131] on wild type subjects even when they were not sick, and it had a bigger effect of -1.22 [95%CI -1.52, -0.906] in mutant subjects. In this visualisation, we can see the delta-delta value of -0.903 [95%CI -1.21, -0.587] as the net effect of the drug accounting for non-specific actions in healthy individuals.\nThe mean difference between drug and placebo treatments in wild type subjects is:\n\\[\\Delta_{1} = \\overline{X}_{D, W} - \\overline{X}_{P, W}\\]\nThe mean difference between drug and placebo treatments in mutant subjects is:\n\\[\\Delta_{2} = \\overline{X}_{D, M} - \\overline{X}_{P, M}\\]\nThe net effect of the drug on mutants is:\n\\[\\Delta_{\\Delta} = \\Delta_{2} - \\Delta_{1}\\]\nwhere \\(\\overline{X}\\) is the sample mean, \\(\\Delta\\) is the mean difference.", + "crumbs": [ + "Get Started", + "Tutorials", + "Delta-Delta" + ] + }, + { + "objectID": "tutorials/06-delta_delta.html#standardising-delta-delta-effect-sizes-with-delta-g", + "href": "tutorials/06-delta_delta.html#standardising-delta-delta-effect-sizes-with-delta-g", + "title": "Delta-Delta", + "section": "Standardising delta-delta effect sizes with Delta g", + "text": "Standardising delta-delta effect sizes with Delta g\nStandardized mean difference statistics like Cohen’s d and Hedges’ g quantify effect sizes in terms of the sample variance. We have introduced a metric, Delta g, to standardize delta-delta effects. This metric enables the comparison between measurements of different dimensions.\nThe standard deviation of the delta-delta value is calculated from a pooled variance of the 4 samples:\n\\[s_{\\Delta_{\\Delta}} = \\sqrt{\\frac{(n_{D, W}-1)s_{D, W}^2+(n_{P, W}-1)s_{P, W}^2+(n_{D, M}-1)s_{D, M}^2+(n_{P, M}-1)s_{P, M}^2}{(n_{D, W} - 1) + (n_{P, W} - 1) + (n_{D, M} - 1) + (n_{P, M} - 1)}}\\]\nwhere \\(s\\) is the standard deviation and \\(n\\) is the sample size.\nA delta g value is then calculated as delta-delta value divided by pooled standard deviation \\(s_{\\Delta_{\\Delta}}\\):\n\\(\\Delta_{g} = \\frac{\\Delta_{\\Delta}}{s_{\\Delta_{\\Delta}}}\\)\nThis metric can be accessed via the ‘hedges_g’ effect size when utilising delta2=True in dabest.load().\n\nunpaired_delta2.hedges_g\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:53 2025.\n\nThe unpaired Hedges' g between W Placebo and M Placebo is 2.54 [95%CI 1.71, 3.31].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\nThe unpaired Hedges' g between W Drug and M Drug is 0.793 [95%CI 0.17, 1.33].\nThe p-value of the two-sided permutation t-test is 0.0122, calculated for legacy purposes only. \n\nThe delta g between Placebo and Drug is -2.11 [95%CI -2.97, -1.22].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing the effect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.hedges_g.statistical_tests`\n\n\nWe see the standardised delta-delta (delta g) value of -2.11 standard deviations [95%CI -2.98, -1.2] as the net effect of the drug accounting for non-specific actions in healthy individuals.\n\nunpaired_delta2.hedges_g.plot();", + "crumbs": [ + "Get Started", + "Tutorials", + "Delta-Delta" + ] + }, + { + "objectID": "tutorials/06-delta_delta.html#delta-delta-for-binary-data", + "href": "tutorials/06-delta_delta.html#delta-delta-for-binary-data", + "title": "Delta-Delta", + "section": "Delta-delta for binary data", + "text": "Delta-delta for binary data\nSince v2025.03.27, the delta-delta function also supports binary data (proportion plots). In this case, the delta-delta value is calculated as the difference between the two proportions. This can be used for both unpaired and paired binary data.\n\ndef create_demo_dataset_delta(seed=9999, N=20):\n \n import numpy as np\n import pandas as pd\n from scipy.stats import norm # Used in generation of populations.\n\n np.random.seed(seed) # Fix the seed so the results are replicable.\n # pop_size = 10000 # Size of each population.\n\n from scipy.stats import norm # Used in generation of populations.\n\n # Create samples\n y = norm.rvs(loc=3, scale=0.4, size=N*4)\n y[N:2*N] = y[N:2*N]+1\n y[2*N:3*N] = y[2*N:3*N]-0.5\n ind = np.random.binomial(1, 0.5, size=N*4)\n ind[N:2*N] = np.random.binomial(1, 0.2, size=N)\n ind[2*N:3*N] = np.random.binomial(1, 0.1, size=N)\n\n # Add drug column\n t1 = np.repeat('Placebo', N*2).tolist()\n t2 = np.repeat('Drug', N*2).tolist()\n treatment = t1 + t2 \n\n # Add a `rep` column as the first variable for the 2 replicates of experiments done\n rep = []\n for i in range(N*2):\n rep.append('Rep1')\n rep.append('Rep2')\n\n # Add a `genotype` column as the second variable\n wt = np.repeat('WT', N).tolist()\n mt = np.repeat('Mut', N).tolist()\n wt2 = np.repeat('WT', N).tolist()\n mt2 = np.repeat('Mut', N).tolist()\n\n\n genotype = wt + mt + wt2 + mt2\n\n # Add an `id` column for paired data plotting.\n id = list(range(0, N*2))\n id_col = id + id \n\n\n # Combine all columns into a DataFrame.\n df_prop = pd.DataFrame({'ID' : id_col,\n 'Rep' : rep,\n 'Genotype' : genotype, \n 'Treatment' : treatment,\n 'Y' : y,\n 'Cat' :ind\n })\n return df_prop\n\ndf_prop = create_demo_dataset_delta()\n\nunpaired_prop = dabest.load(data = df_prop, proportional=True,\n # id_col=\"index\", paired='baseline', \n x = [\"Treatment\", \"Treatment\"], \n y = \"Cat\", delta2=True,\n experiment=\"Genotype\",)\n\nunpaired_prop.mean_diff.plot();", + "crumbs": [ + "Get Started", + "Tutorials", + "Delta-Delta" + ] + }, + { + "objectID": "tutorials/06-delta_delta.html#statistics", + "href": "tutorials/06-delta_delta.html#statistics", + "title": "Delta-Delta", + "section": "Statistics", + "text": "Statistics\nYou can find all outputs of the delta-delta calculation by assessing the attribute named delta_delta of the effect size object.\n\nunpaired_delta2.mean_diff.delta_delta\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:54 2025.\n\nThe delta-delta between Placebo and Drug is -0.903 [95%CI -1.27, -0.522].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing the effect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\n\nThe delta_delta object has its own attributes, containing various information of delta-delta.\n\ndifference: the mean bootstrapped differences between the 2 groups of bootstrapped mean differences\nbootstraps: the 2 groups of bootstrapped mean differences\nbootstraps_delta_delta: the bootstrapped differences between the 2 groups of bootstrapped mean differences\npermutations: the mean difference between the two groups of bootstrapped mean differences calculated based on the permutation data\npermutations_var: the pooled group variances of two groups of bootstrapped mean differences calculated based on permutation data\npermutations_delta_delta: the delta-delta calculated based on the permutation data\n\nA dataframe of this delta delta dabest object can also be called via the delta_delta.results attribute.\n\nunpaired_delta2.mean_diff.delta_delta.results\n\n\n\n\n\n\n\n\ncontrol\ntest\ndifference\nci\nbca_low\nbca_high\nbca_interval_idx\npct_low\npct_high\npct_interval_idx\nbootstraps_control\nbootstraps_test\nbootstraps_delta_delta\npermutations_control\npermutations_test\npermutations_delta_delta\npvalue_permutation\npermutation_count\nbias_correction\njackknives\n\n\n\n\n0\nPlacebo\nDrug\n-0.903179\n95\n-1.271483\n-0.521935\n(124, 4874)\n-1.270426\n-0.519652\n(125, 4875)\n[1.0890043559982234, 1.1472720447282119, 1.072...\n[0.6003430615628478, 0.6547912656551773, 0.294...\n[-0.43421309034304567, -0.7324573148122022, -1...\n[-0.15899787281865496, 0.23958268043726694, 0....\n[-0.036113268018566735, -0.05491466432013192, ...\n[0.12288460480008823, -0.29449734475739886, -0...\n0.0\n5000\n-0.000501\n[-0.9006797310317582, -0.9006200702547091, -0....\n\n\n\n\n\n\n\nSimilarly, for the standardised delta-delta effect size, the hedges_g object has its own delta delta (Delta g) results attribute.\n\nunpaired_delta2.hedges_g.delta_delta.results\n\n\n\n\n\n\n\n\ncontrol\ntest\ndifference\nci\nbca_low\nbca_high\nbca_interval_idx\npct_low\npct_high\npct_interval_idx\nbootstraps_control\nbootstraps_test\nbootstraps_delta_delta\npermutations_control\npermutations_test\npermutations_delta_delta\npvalue_permutation\npermutation_count\nbias_correction\njackknives\n\n\n\n\n0\nPlacebo\nDrug\n-2.106681\n95\n-2.965759\n-1.217424\n(124, 4874)\n-2.963294\n-1.212099\n(125, 4875)\n[2.3610871907095192, 2.7764672664031567, 2.350...\n[1.549355181508767, 1.7247260954921417, 0.6471...\n[-1.0128104949604284, -1.708470960574333, -2.7...\n[-0.1986457235842243, 0.3014021841951519, 0.31...\n[-0.08117530110708499, -0.12358349103957916, -...\n[0.11747042247713932, -0.4249856752347311, -0....\n0.0\n5000\n-0.000501\n[-2.1008530246437576, -2.10071386471865, -2.10...\n\n\n\n\n\n\n\nFor further aesthetic changes, the Plot Aesthetics Tutorial provides detailed examples of how to customize the plot.", + "crumbs": [ + "Get Started", + "Tutorials", + "Delta-Delta" + ] + }, + { + "objectID": "tutorials/04-proportion_plot.html", + "href": "tutorials/04-proportion_plot.html", + "title": "Proportion Plots", + "section": "", + "text": "As of v2023.02.14, DABEST can be used to generate Cohen’s h and the corresponding proportion plot for binary data. It’s important to note that the code we provide only supports numerical proportion data, where the values are limited to 0 (failure) and 1 (success). This means that the code is not suitable for analyzing proportion data that contains non-numeric values, such as strings like ‘yes’ and ‘no’.", + "crumbs": [ + "Get Started", + "Tutorials", + "Proportion Plots" + ] + }, + { + "objectID": "tutorials/04-proportion_plot.html#load-libraries", + "href": "tutorials/04-proportion_plot.html#load-libraries", + "title": "Proportion Plots", + "section": "Load libraries", + "text": "Load libraries\n\nimport numpy as np\nimport pandas as pd\nimport dabest\n\nprint(\"We're using DABEST v{}\".format(dabest.__version__))\n\nPre-compiling numba functions for DABEST...\n\n\nCompiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 61.07it/s]\n\n\nNumba compilation complete!\nWe're using DABEST v2025.10.20", + "crumbs": [ + "Get Started", + "Tutorials", + "Proportion Plots" + ] + }, + { + "objectID": "tutorials/04-proportion_plot.html#creating-a-demo-dataset", + "href": "tutorials/04-proportion_plot.html#creating-a-demo-dataset", + "title": "Proportion Plots", + "section": "Creating a demo dataset", + "text": "Creating a demo dataset\n\ndef create_demo_prop_dataset(seed=9999, N=40):\n import numpy as np\n import pandas as pd\n\n np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n # Create samples\n n = 1\n c1 = np.random.binomial(n, 0.2, size=N)\n c2 = np.random.binomial(n, 0.2, size=N)\n c3 = np.random.binomial(n, 0.8, size=N)\n\n t1 = np.random.binomial(n, 0.6, size=N)\n t2 = np.random.binomial(n, 0.2, size=N)\n t3 = np.random.binomial(n, 0.3, size=N)\n t4 = np.random.binomial(n, 0.4, size=N)\n t5 = np.random.binomial(n, 0.5, size=N)\n t6 = np.random.binomial(n, 0.6, size=N)\n t7 = np.ones(N)\n t8 = np.zeros(N)\n t9 = np.zeros(N)\n\n # Add a `gender` column for coloring the data.\n females = np.repeat('Female', N / 2).tolist()\n males = np.repeat('Male', N / 2).tolist()\n gender = females + males\n\n # Add an `id` column for paired data plotting.\n id_col = pd.Series(range(1, N + 1))\n\n # Combine samples and gender into a DataFrame.\n df = pd.DataFrame({'Control 1': c1, 'Test 1': t1,\n 'Control 2': c2, 'Test 2': t2,\n 'Control 3': c3, 'Test 3': t3,\n 'Test 4': t4, 'Test 5': t5, 'Test 6': t6,\n 'Test 7': t7, 'Test 8': t8, 'Test 9': t9,\n 'Gender': gender, 'ID': id_col\n })\n\n return df\ndf = create_demo_prop_dataset()\ndf.head()\n\n\n\n\n\n\n\n\nControl 1\nTest 1\nControl 2\nTest 2\nControl 3\nTest 3\nTest 4\nTest 5\nTest 6\nTest 7\nTest 8\nTest 9\nGender\nID\n\n\n\n\n0\n1\n0\n0\n0\n1\n0\n0\n1\n0\n1.0\n0.0\n0.0\nFemale\n1\n\n\n1\n0\n1\n0\n1\n1\n1\n0\n0\n0\n1.0\n0.0\n0.0\nFemale\n2\n\n\n2\n0\n1\n0\n0\n1\n0\n1\n1\n0\n1.0\n0.0\n0.0\nFemale\n3\n\n\n3\n0\n1\n0\n0\n1\n0\n0\n1\n0\n1.0\n0.0\n0.0\nFemale\n4\n\n\n4\n0\n0\n0\n0\n1\n0\n0\n0\n1\n1.0\n0.0\n0.0\nFemale\n5\n\n\n\n\n\n\n\n\nHelper function to create a binary table - dabest.prop_dataset\nIn DABEST v2024.3.29, we incorporated feedback from biologists who may not have tables of 0’s and 1’s readily available. As a result, a convenient function - dabest.prop_dataset - to generate a binary dataset based on the specified sample sizes is provided. Users can generate a pandas.DataFrame containing the sample sizes for each element in the groups and the group names (optional if the sample sizes are provided in a dict).\n\nsample_size_1 = {'a':[3, 4], 'b':[2, 5]}\nsample_size_2 = [3, 4, 2, 5]\nnames = ['a', 'b']\nsample_df_1 = dabest.prop_dataset(sample_size_1)\nsample_df_2 = dabest.prop_dataset(sample_size_2, names)\nprint(all(sample_df_1 == sample_df_2))\nsample_df_1.head()\n\nTrue\n\n\n\n\n\n\n\n\n\na\nb\nID\n\n\n\n\n0\n0\n0\n1\n\n\n1\n0\n0\n2\n\n\n2\n0\n1\n3\n\n\n3\n1\n1\n4\n\n\n4\n1\n1\n5", + "crumbs": [ + "Get Started", + "Tutorials", + "Proportion Plots" + ] + }, + { + "objectID": "tutorials/04-proportion_plot.html#loading-data", + "href": "tutorials/04-proportion_plot.html#loading-data", + "title": "Proportion Plots", + "section": "Loading data", + "text": "Loading data\nWhen loading data, you need to set the parameter proportional=True.\n\ntwo_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), proportional=True)\ntwo_groups_unpaired\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:22 2025.\n\nEffect size(s) with 95% confidence intervals will be computed for:\n1. Test 1 minus Control 1\n\n5000 resamples will be used to generate the effect size bootstraps.", + "crumbs": [ + "Get Started", + "Tutorials", + "Proportion Plots" + ] + }, + { + "objectID": "tutorials/04-proportion_plot.html#effect-sizes", + "href": "tutorials/04-proportion_plot.html#effect-sizes", + "title": "Proportion Plots", + "section": "Effect sizes", + "text": "Effect sizes\nTo generate a proportion plot, the dabest library features two effect sizes:\n\nMean difference (mean_diff)\nCohen’s h (cohens＿h)\n\nThese are attributes of the Dabest object.\n\ntwo_groups_unpaired.mean_diff\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:23 2025.\n\nThe unpaired mean difference between Control 1 and Test 1 is 0.575 [95%CI 0.35, 0.725].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.mean_diff.statistical_tests`\n\n\nLet’s compute the Cohen’s h for our comparison.\n\ntwo_groups_unpaired.cohens_h\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:23 2025.\n\nThe unpaired Cohen's h between Control 1 and Test 1 is 1.24 [95%CI 0.784, 1.66].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.cohens_h.statistical_tests`", + "crumbs": [ + "Get Started", + "Tutorials", + "Proportion Plots" + ] + }, + { + "objectID": "tutorials/04-proportion_plot.html#generating-proportion-plots", + "href": "tutorials/04-proportion_plot.html#generating-proportion-plots", + "title": "Proportion Plots", + "section": "Generating proportion plots", + "text": "Generating proportion plots\nTo generate an estimation plot, simply use the .plot() method.\nEach effect size instance has access to the .plot() method, allowing you to quickly create plots for different effect sizes with ease.\n\nUnpaired proportion plots\nUnpaired proportion plots utilise the common bar plot. The bar plot displays the proportion of observations in the dataset that belong to the category of interest:\n\nThe white portion represents the proportion of observations that do not belong to the category (proportion of 0s in the data).\nThe colored portion represents the proportion of observations belonging to the category (proportion of 1s in the data).\n\n\nTwo-Group\n\ntwo_groups_unpaired.mean_diff.plot();\n\n\n\n\n\n\n\n\n\ntwo_groups_unpaired.cohens_h.plot();\n\n\n\n\n\n\n\n\nInstead of a Gardner-Altman plot, you can generate a Cumming estimation plot by setting float_contrast=False in the .plot() method. This will plot the bootstrap effect sizes below the raw data.\n\ntwo_groups_unpaired.mean_diff.plot(float_contrast=False);\n\n\n\n\n\n\n\n\n\n\nMulti Two-Group, Shared-Control, and Multi Groups\nAs with regular (non-binary) unpaired data, multi two-group, shared-control, and multi group plots can be generated for binary data.\n\nmulti_two_groups_unpaired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\")),\n proportional=True)\nmulti_two_groups_unpaired\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:24 2025.\n\nEffect size(s) with 95% confidence intervals will be computed for:\n1. Test 1 minus Control 1\n2. Test 2 minus Control 2\n3. Test 3 minus Control 3\n\n5000 resamples will be used to generate the effect size bootstraps.\n\n\n\nmulti_two_groups_unpaired.mean_diff\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:25 2025.\n\nThe unpaired mean difference between Control 1 and Test 1 is 0.575 [95%CI 0.35, 0.725].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\nThe unpaired mean difference between Control 2 and Test 2 is 0.025 [95%CI -0.15, 0.15].\nThe p-value of the two-sided permutation t-test is 0.535, calculated for legacy purposes only. \n\nThe unpaired mean difference between Control 3 and Test 3 is -0.6 [95%CI -0.75, -0.425].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.mean_diff.statistical_tests`\n\n\n\nmulti_two_groups_unpaired.mean_diff.plot();\n\n\n\n\n\n\n\n\n\nshared_control = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\", \"Test 4\"),\n proportional=True)\nshared_control\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:25 2025.\n\nEffect size(s) with 95% confidence intervals will be computed for:\n1. Test 1 minus Control 1\n2. Test 2 minus Control 1\n3. Test 3 minus Control 1\n4. Test 4 minus Control 1\n\n5000 resamples will be used to generate the effect size bootstraps.\n\n\n\nshared_control.mean_diff\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:26 2025.\n\nThe unpaired mean difference between Control 1 and Test 1 is 0.575 [95%CI 0.35, 0.725].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\nThe unpaired mean difference between Control 1 and Test 2 is 0.025 [95%CI -0.15, 0.15].\nThe p-value of the two-sided permutation t-test is 0.539, calculated for legacy purposes only. \n\nThe unpaired mean difference between Control 1 and Test 3 is 0.125 [95%CI -0.025, 0.325].\nThe p-value of the two-sided permutation t-test is 0.0936, calculated for legacy purposes only. \n\nThe unpaired mean difference between Control 1 and Test 4 is 0.15 [95%CI -0.05, 0.3].\nThe p-value of the two-sided permutation t-test is 0.0604, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.mean_diff.statistical_tests`\n\n\n\nshared_control.mean_diff.plot();\n\n\n\n\n\n\n\n\n\nmulti_groups_unpaired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\", \"Test 3\", \"Test 4\")),\n proportional=True)\nmulti_groups_unpaired\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:26 2025.\n\nEffect size(s) with 95% confidence intervals will be computed for:\n1. Test 1 minus Control 1\n2. Test 2 minus Control 2\n3. Test 3 minus Control 2\n4. Test 4 minus Control 2\n\n5000 resamples will be used to generate the effect size bootstraps.\n\n\n\nmulti_groups_unpaired.mean_diff\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:26 2025.\n\nThe unpaired mean difference between Control 1 and Test 1 is 0.575 [95%CI 0.35, 0.725].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\nThe unpaired mean difference between Control 2 and Test 2 is 0.025 [95%CI -0.15, 0.15].\nThe p-value of the two-sided permutation t-test is 0.535, calculated for legacy purposes only. \n\nThe unpaired mean difference between Control 2 and Test 3 is 0.125 [95%CI -0.05, 0.325].\nThe p-value of the two-sided permutation t-test is 0.099, calculated for legacy purposes only. \n\nThe unpaired mean difference between Control 2 and Test 4 is 0.15 [95%CI -0.05, 0.3].\nThe p-value of the two-sided permutation t-test is 0.0604, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.mean_diff.statistical_tests`\n\n\n\nmulti_groups_unpaired.mean_diff.plot();\n\n\n\n\n\n\n\n\n\n\n\nPaired proportion plots\nFor the paired version of the proportion plot, we adopt the style of a Sankey Diagram. The width of each bar in each xtick represents the proportion of the corresponding label in the group, and the strip denotes the paired relationship for each observation.\nStarting from v2024.3.29, the paired version of the proportion plot receives a major upgrade. We introduce the sankey and flow parameters to control the plot. By default, both sankey and flow are set to True to cater the needs of repeated measures. When sankey is set to False, DABEST will generate a bar plot with a similar aesthetic to the paired proportion plot. When flow is set to False, each group of comparsion forms a Sankey diagram that does not connect to other groups of comparison.\nSimilar to the unpaired version, the .plot() method is used to produce an estimation plot.\n\nTwo-Group\n\ntwo_groups_paired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), \n proportional=True, paired=\"baseline\", id_col=\"ID\")\ntwo_groups_paired\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:27 2025.\n\nPaired effect size(s) for repeated measures against baseline \nwith 95% confidence intervals will be computed for:\n1. Test 1 minus Control 1\n\n5000 resamples will be used to generate the effect size bootstraps.\n\n\n\ntwo_groups_paired.mean_diff\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:27 2025.\n\nThe paired mean difference for repeated measures against baseline \nbetween Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.mean_diff.statistical_tests`\n\n\n\ntwo_groups_paired.mean_diff.plot();\n\n\n\n\n\n\n\n\nThe Sankey plots for paired proportions also supports the float_contrast parameter, which can be set to False to produce a Cumming estimation plot.\n\ntwo_groups_paired.mean_diff.plot(float_contrast=False);\n\n\n\n\n\n\n\n\n\n\nMulti Two-Group, Repeated Measures, and Multi Groups\nAs with regular (non-binary) unpaired data, multi two-group, repeated-measures, and multi group plots can be generated for binary data.\n\nmulti_two_groups_paired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\")),\n proportional=True, paired=\"baseline\", id_col=\"ID\")\nmulti_two_groups_paired\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:27 2025.\n\nPaired effect size(s) for repeated measures against baseline \nwith 95% confidence intervals will be computed for:\n1. Test 1 minus Control 1\n2. Test 2 minus Control 2\n3. Test 3 minus Control 3\n\n5000 resamples will be used to generate the effect size bootstraps.\n\n\n\nmulti_two_groups_paired.mean_diff\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:28 2025.\n\nThe paired mean difference for repeated measures against baseline \nbetween Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\nThe paired mean difference for repeated measures against baseline \nbetween Control 2 and Test 2 is 0.025 [95%CI -0.15, 0.175].\nThe p-value of the two-sided permutation t-test is 0.571, calculated for legacy purposes only. \n\nThe paired mean difference for repeated measures against baseline \nbetween Control 3 and Test 3 is -0.6 [95%CI -0.775, -0.425].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.mean_diff.statistical_tests`\n\n\n\nmulti_two_groups_paired.mean_diff.plot();\n\n\n\n\n\n\n\n\n\nrepeated_measures_baseline = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\", \"Test 4\"),\n proportional=True, paired=\"baseline\", id_col=\"ID\")\nrepeated_measures_baseline\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:28 2025.\n\nPaired effect size(s) for repeated measures against baseline \nwith 95% confidence intervals will be computed for:\n1. Test 1 minus Control 1\n2. Test 2 minus Control 1\n3. Test 3 minus Control 1\n4. Test 4 minus Control 1\n\n5000 resamples will be used to generate the effect size bootstraps.\n\n\n\nrepeated_measures_baseline.mean_diff\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:29 2025.\n\nThe paired mean difference for repeated measures against baseline \nbetween Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\nThe paired mean difference for repeated measures against baseline \nbetween Control 1 and Test 2 is 0.025 [95%CI -0.15, 0.175].\nThe p-value of the two-sided permutation t-test is 0.555, calculated for legacy purposes only. \n\nThe paired mean difference for repeated measures against baseline \nbetween Control 1 and Test 3 is 0.125 [95%CI -0.075, 0.275].\nThe p-value of the two-sided permutation t-test is 0.277, calculated for legacy purposes only. \n\nThe paired mean difference for repeated measures against baseline \nbetween Control 1 and Test 4 is 0.15 [95%CI -0.05, 0.325].\nThe p-value of the two-sided permutation t-test is 0.075, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.mean_diff.statistical_tests`\n\n\n\nrepeated_measures_baseline.mean_diff.plot();\n\n\n\n\n\n\n\n\n\nrepeated_measures_sequential = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\", \"Test 4\"),\n proportional=True, paired=\"sequential\", id_col=\"ID\")\nrepeated_measures_sequential\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:29 2025.\n\nPaired effect size(s) for the sequential design of repeated-measures experiment \nwith 95% confidence intervals will be computed for:\n1. Test 1 minus Control 1\n2. Test 2 minus Test 1\n3. Test 3 minus Test 2\n4. Test 4 minus Test 3\n\n5000 resamples will be used to generate the effect size bootstraps.\n\n\n\nrepeated_measures_sequential.mean_diff\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:30 2025.\n\nThe paired mean difference for the sequential design of repeated-measures experiment \nbetween Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\nThe paired mean difference for the sequential design of repeated-measures experiment \nbetween Test 1 and Test 2 is -0.55 [95%CI -0.725, -0.4].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\nThe paired mean difference for the sequential design of repeated-measures experiment \nbetween Test 2 and Test 3 is 0.1 [95%CI -0.075, 0.225].\nThe p-value of the two-sided permutation t-test is 0.342, calculated for legacy purposes only. \n\nThe paired mean difference for the sequential design of repeated-measures experiment \nbetween Test 3 and Test 4 is 0.025 [95%CI -0.2, 0.2].\nThe p-value of the two-sided permutation t-test is 0.624, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.mean_diff.statistical_tests`\n\n\n\nrepeated_measures_sequential.mean_diff.plot();\n\n\n\n\n\n\n\n\n\nmulti_groups_baseline = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\", \"Test 3\", \"Test 4\")),\n proportional=True, paired=\"baseline\", id_col=\"ID\")\nmulti_groups_baseline\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:30 2025.\n\nPaired effect size(s) for repeated measures against baseline \nwith 95% confidence intervals will be computed for:\n1. Test 1 minus Control 1\n2. Test 2 minus Control 2\n3. Test 3 minus Control 2\n4. Test 4 minus Control 2\n\n5000 resamples will be used to generate the effect size bootstraps.\n\n\n\nmulti_groups_baseline.mean_diff\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:31 2025.\n\nThe paired mean difference for repeated measures against baseline \nbetween Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\nThe paired mean difference for repeated measures against baseline \nbetween Control 2 and Test 2 is 0.025 [95%CI -0.15, 0.175].\nThe p-value of the two-sided permutation t-test is 0.571, calculated for legacy purposes only. \n\nThe paired mean difference for repeated measures against baseline \nbetween Control 2 and Test 3 is 0.125 [95%CI -0.075, 0.3].\nThe p-value of the two-sided permutation t-test is 0.309, calculated for legacy purposes only. \n\nThe paired mean difference for repeated measures against baseline \nbetween Control 2 and Test 4 is 0.15 [95%CI -0.025, 0.3].\nThe p-value of the two-sided permutation t-test is 0.0362, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.mean_diff.statistical_tests`\n\n\n\nmulti_groups_baseline.mean_diff.plot();", + "crumbs": [ + "Get Started", + "Tutorials", + "Proportion Plots" + ] + }, + { + "objectID": "tutorials/04-proportion_plot.html#aesthetic-adjustments", + "href": "tutorials/04-proportion_plot.html#aesthetic-adjustments", + "title": "Proportion Plots", + "section": "Aesthetic adjustments", + "text": "Aesthetic adjustments\nHere we demonstrate a few proportion plot specific aesthetic adjustments.\n\nBar Width\nYou can modify the width of the bar plot bars (unpaired data) by setting the parameter bar_width in the .plot() method.\n\ntwo_groups_unpaired.mean_diff.plot(bar_width=0.3);\n\n\n\n\n\n\n\n\n\n\nBar desaturation\nThe raw_desat is used to control the amount of desaturation applied to the bar plot bar colors (specific to unpaired data). A value of 0.0 means full desaturation (i.e., grayscale), while a value of 1.0 means no desaturation (i.e., full color saturation). The default one is 0.8.\n\ntwo_groups_unpaired.mean_diff.plot(raw_desat=1.0);\n\n\n\n\n\n\n\n\n\n\nRaw Label and Contrast Label\nThe parameters raw_label and contrast_label can be used to set labels for the y-axis of the bar plot and the contrast plot.\n\ntwo_groups_unpaired.mean_diff.plot(raw_label=\"success\",contrast_label=\"difference\");\ntwo_groups_paired.mean_diff.plot(raw_label=\"success\",contrast_label=\"difference\");\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBarplot kwargs\nThe parameters barplot_kwargs can be used to alter the aesthetics of the bar plot. This is a dictionary that can be used to pass additional arguments to the bar plot.\n\ntwo_groups_unpaired.mean_diff.plot(barplot_kwargs={\"alpha\":0.5, \"edgecolor\":\"red\", \"linewidth\":2, 'errorbar': ('sd', 0.1)});\n\n\n\n\n\n\n\n\n\n\nSankey and Flow\nBy changing the sankey and flow parameters, you can generate different types of Sankey plots for paired proportions.\n\nseparate_control = dabest.load(df, idx=(((\"Control 1\", \"Test 1\"),\n (\"Test 2\", \"Test 3\"),\n (\"Test 4\", \"Test 7\", \"Test 6\"))),\n proportional=True, paired=\"sequential\", id_col=\"ID\")\n\nseparate_control.mean_diff.plot();\nseparate_control.mean_diff.plot(sankey_kwargs={'sankey':False});\nseparate_control.mean_diff.plot(sankey_kwargs={'flow':False});\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSankey kwargs\nSeveral exclusive parameters can be provided to the .plot() method to customize the Sankey plots for paired proportions. By modifying the sankey_kwargs parameter, you can customize the Sankey plot. The following parameters are supported:\n\nalign: The alignment of each Sankey bar. Default is “center”.\nalpha: The transparency of each Sankey bar. Default is 0.4.\nbar_width: The width of each bar on the side in the plot. Default is 0.1.\n\n\nrepeated_measures_baseline.mean_diff.plot(sankey_kwargs = {\"alpha\": 0.2,\n \"bar_width\": 0.4});\n\n\n\n\n\n\n\n\n\n\nCustom Palette\nThe custom_palette parameter functions in a similar way for proportion plots as for other plots - however, there are some differences!\nA custom_palette dict can be passed for sankey plots, whereby two keys used are 0 and 1. The color associated with these keys will be used to color the bars in the sankey plot.\nFor bar plots, the custom_palette dict can be passed like a regular plot, with a color associated to each group. The chosen color will then be used to color the filled portion of the bar plot.\n\nrepeated_measures_baseline.mean_diff.plot(custom_palette={0: \"red\", 1: \"blue\"});\nshared_control.mean_diff.plot(custom_palette={'Control 1': \"red\", 'Test 1': \"blue\", 'Test 2': \"green\", 'Test 3': \"purple\", 'Test 4': \"orange\"});\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSimilarly, premade matplotlib/seaborn color palette can be passed. For sankey plots, the first two colors in the palette will be used to color the bars in the sankey plot. For bar plots, the colors will be used to color the filled portion of the bar plot.\n\nrepeated_measures_baseline.mean_diff.plot(custom_palette='Set1');\nshared_control.mean_diff.plot(custom_palette='Set1');\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPassing a custom palette list functions differently for bar plots and sankey plots:\n\nFor bar plots, the list should contain the colors associated with each group.\nFor sankey plots, the list should contain two colors, the first color will be used to color the binary ’1’s, and the second color will be used to color the ’0’s.\n\n\nrepeated_measures_baseline.mean_diff.plot(custom_palette=['red', 'blue']);\nshared_control.mean_diff.plot(custom_palette=['red', 'blue', 'green', 'purple', 'orange']);\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAdd counts to proportion plots\nBy default, the sample counts for each bar in proportion plots are not shown.\nThis feature can be turned on by setting prop_sample_counts=True in the .plot() method.\nNote: This feature is not compatible with flow=False in sankey_kwargs.\n\ntwo_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), proportional=True)\ntwo_groups_unpaired.mean_diff.plot(prop_sample_counts=True);\n\n\n\n\n\n\n\n\nThe sample counts kwargs can be utilised via prop_sample_counts_kwargs in the .plot() method.\n\ntwo_groups_unpaired.mean_diff.plot(prop_sample_counts=True, prop_sample_counts_kwargs={\"color\":\"red\"});\n\n\n\n\n\n\n\n\nFor further aesthetic changes, the Plot Aesthetics Tutorial provides detailed examples of how to customize the plot.", + "crumbs": [ + "Get Started", + "Tutorials", + "Proportion Plots" + ] + }, + { + "objectID": "tutorials/02-two_group.html", + "href": "tutorials/02-two_group.html", + "title": "Two-Group Experiments", + "section": "", + "text": "import numpy as np\nimport pandas as pd\nimport dabest\n\nprint(\"We're using DABEST v{}\".format(dabest.__version__))\n\nPre-compiling numba functions for DABEST...\n\n\nCompiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 41.28it/s]\n\n\nNumba compilation complete!\nWe're using DABEST v2025.10.20", + "crumbs": [ + "Get Started", + "Tutorials", + "Two-Group Experiments" + ] + }, + { + "objectID": "tutorials/02-two_group.html#load-libraries", + "href": "tutorials/02-two_group.html#load-libraries", + "title": "Two-Group Experiments", + "section": "", + "text": "import numpy as np\nimport pandas as pd\nimport dabest\n\nprint(\"We're using DABEST v{}\".format(dabest.__version__))\n\nPre-compiling numba functions for DABEST...\n\n\nCompiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 41.28it/s]\n\n\nNumba compilation complete!\nWe're using DABEST v2025.10.20", + "crumbs": [ + "Get Started", + "Tutorials", + "Two-Group Experiments" + ] + }, + { + "objectID": "tutorials/02-two_group.html#creating-a-demo-dataset", + "href": "tutorials/02-two_group.html#creating-a-demo-dataset", + "title": "Two-Group Experiments", + "section": "Creating a demo dataset", + "text": "Creating a demo dataset\nHere, we create a dataset to illustrate how to perform Two-Group analyses using dabest.\n\nfrom scipy.stats import norm # Used in generation of populations.\n\nnp.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n\nNs = 20 # The number of samples taken from each population\n\n# Create samples\nc1 = norm.rvs(loc=3, scale=0.4, size=Ns)\nc2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\nc3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n\nt1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\nt2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\nt3 = norm.rvs(loc=3, scale=0.75, size=Ns)\nt4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\nt5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\nt6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n\n\n# Add a `gender` column for coloring the data.\nfemales = np.repeat('Female', Ns/2).tolist()\nmales = np.repeat('Male', Ns/2).tolist()\ngender = females + males\n\n# Add an `id` column for paired data plotting.\nid_col = pd.Series(range(1, Ns+1))\n\n# Combine samples and gender into a DataFrame.\ndf = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n 'Control 2' : c2, 'Test 2' : t2,\n 'Control 3' : c3, 'Test 3' : t3,\n 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6,\n 'Gender' : gender, 'ID' : id_col\n })\ndf.head(5)\n\n\n\n\n\n\n\n\nControl 1\nTest 1\nControl 2\nTest 2\nControl 3\nTest 3\nTest 4\nTest 5\nTest 6\nGender\nID\n\n\n\n\n0\n2.793984\n3.420875\n3.324661\n1.707467\n3.816940\n1.796581\n4.440050\n2.937284\n3.486127\nFemale\n1\n\n\n1\n3.236759\n3.467972\n3.685186\n1.121846\n3.750358\n3.944566\n3.723494\n2.837062\n2.338094\nFemale\n2\n\n\n2\n3.019149\n4.377179\n5.616891\n3.301381\n2.945397\n2.832188\n3.214014\n3.111950\n3.270897\nFemale\n3\n\n\n3\n2.804638\n4.564780\n2.773152\n2.534018\n3.575179\n3.048267\n4.968278\n3.743378\n3.151188\nFemale\n4\n\n\n4\n2.858019\n3.220058\n2.550361\n2.796365\n3.692138\n3.276575\n2.662104\n2.977341\n2.328601\nFemale\n5", + "crumbs": [ + "Get Started", + "Tutorials", + "Two-Group Experiments" + ] + }, + { + "objectID": "tutorials/02-two_group.html#loading-data", + "href": "tutorials/02-two_group.html#loading-data", + "title": "Two-Group Experiments", + "section": "Loading data", + "text": "Loading data\nFirst, we need to load the data and specify the relevant groups.\nWe can achieve this by supplying the dataframe to dabest.load(). Additionally, we must provide the groups to be compared in the idx argument as a tuple or list.\nFor this tutorial, we will create two separate analyses:\n\nA singular two-group comparison between Control 1 and Test 1.\nA multi two-group comparison between Control 1 and Test 1, and between Control 2 and Test 2.\n\nThe multi two-group estimation plot tiles two or more Cumming plots horizontally, and is created by passing a nested tuple to idx when dabest.load() is first invoked.\n\ntwo_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"))\nmulti_two_groups_unpaired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\")))\n\nIn addition, we can specify the paired argument to indicate paired data.\npaired can be set as 'baseline' or 'sequential' or left as None (unpaired).\nNote: For two-group, both 'baseline' and 'sequential' are equivalent.\n\ntwo_groups_paired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), paired='baseline', id_col='ID')\nmulti_two_groups_paired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\")), \n paired='baseline', id_col='ID')\n\nThe dabest library features a range of effect sizes. In this case, we shall proceed with the default effect size, which is the mean difference.\nHere we will show the two-group unpaired analysis as an example.\n\ntwo_groups_unpaired.mean_diff\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 15:59:53 2025.\n\nThe unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].\nThe p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.mean_diff.statistical_tests`\n\n\nA dataframe of the mean_diff results can be extracted by calling the results attribute of the dabest.mean_diff object.\n\ntwo_groups_unpaired.mean_diff.results\n\n\n\n\n\n\n\n\ncontrol\ntest\ncontrol_N\ntest_N\neffect_size\nis_paired\ndifference\nci\nbca_low\nbca_high\n...\npvalue_mann_whitney\nstatistic_mann_whitney\nbec_difference\nbec_bootstraps\nbec_bca_interval_idx\nbec_bca_low\nbec_bca_high\nbec_pct_interval_idx\nbec_pct_low\nbec_pct_high\n\n\n\n\n0\nControl 1\nTest 1\n20\n20\nmean difference\nNone\n0.48029\n95\n0.205161\n0.773647\n...\n0.001625\n83.0\n0.0\n[-0.09732932551566487, 0.08087009665445155, -0...\n(127, 4877)\n-0.256862\n0.259558\n(125, 4875)\n-0.25826\n0.25759\n\n\n\n\n1 rows × 35 columns", + "crumbs": [ + "Get Started", + "Tutorials", + "Two-Group Experiments" + ] + }, + { + "objectID": "tutorials/02-two_group.html#producing-estimation-plots", + "href": "tutorials/02-two_group.html#producing-estimation-plots", + "title": "Two-Group Experiments", + "section": "Producing estimation plots", + "text": "Producing estimation plots\nWe can now call the .plot() method to generate the estimation plot.\n\ntwo_groups_unpaired.mean_diff.plot();\n\n\n\n\n\n\n\n\nFor singular two-group comparisons, the plot will display the effect size curve by default to the right of the raw data. We term this a Gardner-Altman plot.\nThis can be changed by setting the float_contrast argument to False. Here, the effect size curve will be displayed below the raw data - a Cumming estimation plot.\n\ntwo_groups_unpaired.mean_diff.plot(float_contrast=False);\n\n\n\n\n\n\n\n\nFor multi two-group comparisons, the effect size curves will always be displayed below the raw data.\nThe lower axes in the Cumming plot is effectively a forest plot, commonly used in meta-analyses to aggregate and to compare data from different experiments.\nNote: If you’re interested in just plotting the contrast ax (the violin plots), you may be interested in the new forest plot feature added in v2025.03.27!\n\nmulti_two_groups_unpaired.mean_diff.plot();\n\n\n\n\n\n\n\n\nFor paired data, we use slopegraphs (another innovation from Edward Tufte) to connect paired observations. Both Gardner-Altman and Cumming plots support this.\n\ntwo_groups_paired.mean_diff.plot();\n\n\n\n\n\n\n\n\n\ntwo_groups_paired.mean_diff.plot(float_contrast=False);\n\n\n\n\n\n\n\n\n\nmulti_two_groups_paired.mean_diff.plot();\n\n\n\n\n\n\n\n\nFor further aesthetic changes, the Plot Aesthetics Tutorial provides detailed examples of how to customize the plot.", + "crumbs": [ + "Get Started", + "Tutorials", + "Two-Group Experiments" + ] + }, + { + "objectID": "read_me.html#recent-version-update", + "href": "read_me.html#recent-version-update", + "title": "DABEST-Python", + "section": "Recent Version Update", + "text": "Recent Version Update\n✨ DABEST “Bingka” v2025.10.20 for Python is now released! ✨\nDear DABEST users, The latest version of the DABEST Python library brings new visualizations, refined plots, and improved accuracy.\n\nWhorlmap 🌀: Compact visualization for multi-dimensional effects\nIntroducing Whorlmap, a new way to visualize effect sizes from multiple comparisons in a compact, grid-based format.\nWhorlmaps condense information from the full bootstrap distributions of many contrast objects into a 2D heatmap-style grid of “whorled” cells. This provides an overview of the entire dataset while preserving the underlying distributional detail.\nThey are especially useful for large-scale or multi-condition experiments, serving as a space-efficient alternative to stacked forest plots.\nYou can generate a Whorlmap directly from multi-dimensional DABEST objects using the .whorlmap() method. See the Whorlmap tutorial for more details.\nSlopegraphs 📈: Enhanced summaries for paired data\nSlopegraphs for paired continuous data now display group summary statistics.\n\nBy default, a thick trend line connects group means, with vertical bars showing standard deviation.\nChoose the summary type via the group_summaries argument in .plot() — options include 'mean_sd', 'median_quartiles', or None.\nCustomize appearance with group_summaries_kwargs.\n\nSee the Group Summaries section in the Plot Aesthetics tutorial for more details.\nMini-meta Weighted Delta Fix 🧮\nThe weighted delta calculation in mini-meta plots has been updated for greater accuracy and consistency.\nExpanded custom_palette functionality 🎨\n\nBarplots (unpaired, proportional): custom_palette can now take 1 and 0 as dictionary keys to color the filled and unfilled portions of the plot.\nSlopegraphs (paired, non-proportional): custom_palette can now color contrast bars and effect-size curves.\n\n\nSee the Custom Palette section in the Plot Aesthetics tutorial for examples.\nThank you for your continued support!\nThe DABEST Development Team" + }, + { + "objectID": "read_me.html#contents", + "href": "read_me.html#contents", + "title": "DABEST-Python", + "section": "Contents", + "text": "Contents\n\n\nAbout\nInstallation\nUsage\nHow to cite\nBugs\nContributing\nAcknowledgements\nTesting\nDABEST in other languages" + }, + { + "objectID": "read_me.html#about", + "href": "read_me.html#about", + "title": "DABEST-Python", + "section": "About", + "text": "About\nDABEST is a package for Data Analysis using Bootstrap-Coupled ESTimation.\nEstimation statistics are a simple framework that avoids the pitfalls of significance testing. It employs familiar statistical concepts such as means, mean differences, and error bars. More importantly, it focuses on the effect size of one’s experiment or intervention, rather than succumbing to a false dichotomy engendered by P values.\nAn estimation plot comprises two key features.\n\nIt presents all data points as a swarm plot, ordering each point to display the underlying distribution.\nIt illustrates the effect size as a bootstrap 95% confidence interval on a separate but aligned axis.\n\n\n\n\nThe five kinds of estimation plots\n\n\nDABEST powers estimationstats.com, allowing everyone access to high-quality estimation plots." + }, + { + "objectID": "read_me.html#installation", + "href": "read_me.html#installation", + "title": "DABEST-Python", + "section": "Installation", + "text": "Installation\nThis package is tested on Python 3.11 and onwards. It is highly recommended to download the Anaconda distribution of Python in order to obtain the dependencies easily.\nYou can install this package via pip.\nTo install, at the command line run\npip install dabest\nYou can also clone this repo locally.\nThen, navigate to the cloned repo in the command line and run\npip install ." + }, + { + "objectID": "read_me.html#usage", + "href": "read_me.html#usage", + "title": "DABEST-Python", + "section": "Usage", + "text": "Usage\nimport pandas as pd\nimport dabest\n\n# Load the iris dataset. This step requires internet access.\niris = pd.read_csv(\"https://github.com/mwaskom/seaborn-data/raw/master/iris.csv\")\n\n# Load the above data into `dabest`.\niris_dabest = dabest.load(data=iris, x=\"species\", y=\"petal_width\",\n idx=(\"setosa\", \"versicolor\", \"virginica\"))\n\n# Produce a Cumming estimation plot.\niris_dabest.mean_diff.plot();\n\n\n\nA Cumming estimation plot of petal width from the iris dataset\n\n\nPlease refer to the official tutorial for more useful code snippets." + }, + { + "objectID": "read_me.html#how-to-cite", + "href": "read_me.html#how-to-cite", + "title": "DABEST-Python", + "section": "How to cite", + "text": "How to cite\nGetting over ANOVA: Estimation graphics for multi-group comparisons\nZinan Lu, Jonathan Anns, Yishan Mai, Rou Zhang, Kahseng Lian, Nicole MynYi Lee, Shan Hashir, Lucas Wang Zhuoyu, A. Rosa Castillo Gonzalez, Joses Ho, Hyungwon Choi, Sangyu Xu, Adam Claridge-Chang\nbioRxiv preprint 2026. 10.64898/2026.01.26.701654\nPDF\nMoving beyond P values: Everyday data analysis with estimation plots\nJoses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang\nNature Methods 2019, 1548-7105. 10.1038/s41592-019-0470-3\nPaywalled publisher site; Free-to-view PDF" + }, + { + "objectID": "read_me.html#bugs", + "href": "read_me.html#bugs", + "title": "DABEST-Python", + "section": "Bugs", + "text": "Bugs\nPlease report any bugs on the issue page." + }, + { + "objectID": "read_me.html#contributing", + "href": "read_me.html#contributing", + "title": "DABEST-Python", + "section": "Contributing", + "text": "Contributing\nAll contributions are welcome; please read the Guidelines for contributing first.\nWe also have a Code of Conduct to foster an inclusive and productive space.\n\nA wish list for new features\nIf you have any specific comments and ideas for new features that you would like to share with us, please read the Guidelines for contributing, create a new issue using Feature request template or create a new post in our Google Group." + }, + { + "objectID": "read_me.html#acknowledgements", + "href": "read_me.html#acknowledgements", + "title": "DABEST-Python", + "section": "Acknowledgements", + "text": "Acknowledgements\nWe would like to thank alpha testers from the Claridge-Chang lab: Sangyu Xu, Xianyuan Zhang, Farhan Mohammad, Jurga Mituzaitė, and Stanislav Ott." + }, + { + "objectID": "read_me.html#testing", + "href": "read_me.html#testing", + "title": "DABEST-Python", + "section": "Testing", + "text": "Testing\nTo test DABEST, you need to install pytest and nbdev.\n\nRun pytest in the root directory of the source distribution. This runs the test suite in the folder dabest/tests/mpl_image_tests.\nRun nbdev_test in the root directory of the source distribution. This runs the value assertion tests in the folder dabest/tests\n\nThe test suite ensures that the bootstrapping functions and the plotting functions perform as expected.\nFor detailed information, please refer to the test folder" + }, + { + "objectID": "read_me.html#dabest-in-other-languages", + "href": "read_me.html#dabest-in-other-languages", + "title": "DABEST-Python", + "section": "DABEST in other languages", + "text": "DABEST in other languages\nDABEST is also available in R (dabestr) and Matlab (DABEST-Matlab)." + }, + { + "objectID": "blog/posts/bootstraps/bootstraps.html", + "href": "blog/posts/bootstraps/bootstraps.html", + "title": "Bootstrap Confidence Intervals", + "section": "", + "text": "In a typical scientific experiment, we are interested in two populations (Control and Test), and whether there is a difference between their means \\((\\mu_{Test}-\\mu_{Control})\\).\n\nWe go about this by collecting observations from the control population and from the test population.\n\nWe can easily compute the mean difference in our observed samples. This is our estimate of the population effect size that we are interested in.\nBut how do we obtain a measure of the precision and confidence about our estimate? Can we get a sense of how it relates to the population mean difference?" + }, + { + "objectID": "blog/posts/bootstraps/bootstraps.html#sampling-from-populations", + "href": "blog/posts/bootstraps/bootstraps.html#sampling-from-populations", + "title": "Bootstrap Confidence Intervals", + "section": "", + "text": "In a typical scientific experiment, we are interested in two populations (Control and Test), and whether there is a difference between their means \\((\\mu_{Test}-\\mu_{Control})\\).\n\nWe go about this by collecting observations from the control population and from the test population.\n\nWe can easily compute the mean difference in our observed samples. This is our estimate of the population effect size that we are interested in.\nBut how do we obtain a measure of the precision and confidence about our estimate? Can we get a sense of how it relates to the population mean difference?" + }, + { + "objectID": "blog/posts/bootstraps/bootstraps.html#the-bootstrap-confidence-interval", + "href": "blog/posts/bootstraps/bootstraps.html#the-bootstrap-confidence-interval", + "title": "Bootstrap Confidence Intervals", + "section": "The bootstrap confidence interval", + "text": "The bootstrap confidence interval\nWe want to obtain a 95% confidence interval (95% CI) around our estimate of the mean difference. The 95% indicates that any such confidence interval will capture the population mean difference 95% of the time.\nIn other words, if we were to repeat our experiment 100 times, gathering 100 independent sets of observations and computing a 95% confidence interval for the mean difference each time, 95 of these intervals would capture the population mean difference. That is to say, we can be 95% confident the interval contains the true mean of the population.\nWe can calculate the 95% CI of the mean difference with bootstrap resampling.\n\nThe bootstrap in action\nThe bootstrap[1] is a simple but powerful technique. It was first described by Bradley Efron.\nIt creates multiple resamples (with replacement) from a single set of observations, and computes the effect size of interest on each of these resamples. The bootstrap resamples of the effect size can then be used to determine the 95% CI.\nWith computers, we can perform 5000 resamples very easily.\n\nThe resampling distribution of the difference in means approaches a normal distribution. This is due to the Central Limit Theorem: a large number of independent random samples will approach a normal distribution even if the underlying population is not normally distributed.\nBootstrap resampling gives us two important benefits:\n\nNon-parametric statistical analysis. There is no need to assume that our observations, or the underlying populations, are normally distributed. Thanks to the Central Limit Theorem, the resampling distribution of the effect size will approach a normality.\nEasy construction of the 95% CI from the resampling distribution. In the context of bootstrap resampling or other non-parametric methods, the 2.5th and 97.5th percentiles are often used to define the lower and upper limits, respectively. The use of these percentiles ensures that the resulting interval contains the central 95% of the resampled distribution. Such an interval construction is known as a percentile interval." + }, + { + "objectID": "blog/posts/bootstraps/bootstraps.html#adjusting-for-asymmetrical-resampling-distributions", + "href": "blog/posts/bootstraps/bootstraps.html#adjusting-for-asymmetrical-resampling-distributions", + "title": "Bootstrap Confidence Intervals", + "section": "Adjusting for asymmetrical resampling distributions", + "text": "Adjusting for asymmetrical resampling distributions\nWhile resampling distributions of the difference in means often have a normal distribution, it is not uncommon to encounter a skewed distribution. Thus, Efron developed the bias-corrected and accelerated bootstrap (BCa bootstrap) to account for the skew, and still obtain the central 95% of the distribution.\nDABEST applies the BCa correction to the resampling bootstrap distributions of the effect size." + }, + { + "objectID": "blog/posts/bootstraps/bootstraps.html#estimation-plots-incorporate-bootstrap-resampling", + "href": "blog/posts/bootstraps/bootstraps.html#estimation-plots-incorporate-bootstrap-resampling", + "title": "Bootstrap Confidence Intervals", + "section": "Estimation plots incorporate bootstrap resampling", + "text": "Estimation plots incorporate bootstrap resampling\nThe estimation plot produced by DABEST presents the raw data and the bootstrap confidence interval of the effect size (the difference in means) side-by-side as a single integrated plot.\n\nThus, it tightly couples a visual presentation of the raw data with an indication of the population mean difference plus its confidence interval.\n [1]: The name is derived from the saying “pull oneself by one’s bootstraps”, often used as an exhortation to achieve success without external help." + }, + { + "objectID": "API/precompile.html", + "href": "API/precompile.html", + "title": "precompile", + "section": "", + "text": "source\n\nprecompile_all\n\ndef precompile_all(\n \n):\n\nPre-compile all numba functions with dummy data", + "crumbs": [ + "Get Started", + "API", + "precompile" + ] + }, + { + "objectID": "API/plot_tools.html", + "href": "API/plot_tools.html", + "title": "plot_tools", + "section": "", + "text": "source\n\nadd_counts_to_prop_plots\n\ndef add_counts_to_prop_plots(\n plot_data:pd.DataFrame, # Dataframe of the plot data.\n xvar:str, # Column name of the x variable.\n yvar:str, # Column name of the y variable.\n rawdata_axes:axes.Axes, # Matplotlib axis object to plot on.\n horizontal:bool, # If the plot is horizontal.\n is_paired:bool, # Whether the data is paired.\n prop_sample_counts_kwargs:dict, # Keyword arguments for the sample counts.\n):\n\nAdd counts to the proportion plots.\n\nsource\n\n\ntable_for_horizontal_plots\n\ndef table_for_horizontal_plots(\n effectsize_df:object, # Effect size DABEST object.\n ax:axes.Axes, # Matplotlib axis object to plot the table axes.\n contrast_axes:axes.Axes, # Matplotlib axis object to plot the contrast axes.\n ticks_to_plot:list, # List of indices of the contrast objects.\n show_mini_meta:bool, # Whether to show the mini meta-analysis.\n show_delta2:bool, # Whether to show the delta-delta.\n table_kwargs:dict, # Keyword arguments for the table.\n ticks_to_skip:list\n):\n\nAdd table axes for showing the deltas for horizontal plots.\n\nsource\n\n\nbarplotter\n\ndef barplotter(\n xvar:str, # Column name of the x variable.\n yvar:str, # Column name of the y variable.\n all_plot_groups:list, # List of all plot groups.\n rawdata_axes:axes.Axes, # Matplotlib axis object to plot on.\n plot_data:pd.DataFrame, # Dataframe of the plot data.\n raw_colors:str, # Color of the bar.\n plot_palette_raw:dict, # Dictionary of colors used in the bar plot.\n color_col:str, # Column name of the color column.\n barplot_kwargs:dict, # Keyword arguments for the barplot.\n horizontal:bool, # If the plot is horizontal.\n):\n\nAdd bars to the raw data plot.\n\nsource\n\n\ngridkey_plotter\n\ndef gridkey_plotter(\n is_paired:bool, # Whether the data is paired.\n idx:list, # List of indices of the contrast objects.\n all_plot_groups:list, # List of all plot groups.\n gridkey:list, # List of gridkey rows.\n rawdata_axes:axes.Axes, # Matplotlib axis object for the raw data.\n contrast_axes:axes.Axes, # Matplotlib axis object for the contrast data.\n plot_data:pd.DataFrame, # Dataframe of the plot data.\n xvar:str, # Column name of the x variable.\n yvar:str, # Column name of the y variable.\n results:pd.DataFrame, # Dataframe of contrast object comparisons.\n show_delta2:bool, # Whether to show the delta-delta.\n show_mini_meta:bool, # Whether to show the mini meta-analysis.\n x1_level:list, # List of x1 levels.\n experiment_label:list, # List of experiment labels.\n float_contrast:bool, # Whether the DABEST plot uses Gardner-Altman or Cummings\n horizontal:bool, # If the plot is horizontal.\n delta_delta:object, # delta-delta object.\n mini_meta:object, # Mini meta-analysis object.\n effect_size:str, # Type of effect size to plot\n gridkey_kwargs:dict, # Keyword arguments for the gridkey.\n):\n\nAdd gridkey to the contrast plot.\n\nsource\n\n\neffect_size_curve_plotter\n\ndef effect_size_curve_plotter(\n ticks_to_plot:list, # List of indices of the contrast objects.\n ticks_for_baseline_ec:list, # List of indices of the baseline effect curve objects.\n results:pd.DataFrame, # Dataframe of contrast object comparisons.\n ci_type:str, # Type of confidence interval to plot.\n contrast_axes:axes.Axes, # Matplotlib axis object to plot on.\n contrast_kwargs:dict, # Keyword arguments for the violinplot.\n bootstraps_color_by_group:bool, # Whether to color the bootstraps by group.\n plot_palette_contrast:dict, # Dictionary of colors used in the contrast plot.\n horizontal:bool, # If the plot is horizontal.\n contrast_marker_kwargs:dict, contrast_errorbar_kwargs:dict, idx:list, # List of indices of the raw groups.\n is_paired:bool, # Whether the data is paired.\n contrast_paired_lines:bool, # Whether to add lines for repeated measures data.\n contrast_paired_lines_kwargs:dict, # Keyword arguments for the repeated measures lines.\n show_baseline_ec:bool=False, # Whether to show the baseline effect curve.\n):\n\nAdd effect size curves to the contrast plot.\n\nsource\n\n\nplot_minimeta_or_deltadelta_violins\n\ndef plot_minimeta_or_deltadelta_violins(\n dabest_obj:object, # DABEST Effectsize object delta-delta or mini_meta\n type:str, ci_type:str, # Type of confidence interval to plot.\n rawdata_axes:axes.Axes, # Matplotlib axis object to plot on.\n contrast_axes:axes.Axes, # Matplotlib axis object to plot on.\n contrast_kwargs:dict, # Keyword arguments for the violinplot.\n contrast_xtick_labels:list, # List of xtick labels for the contrast plot.\n effect_size:str, # Type of effect size to plot.\n plot_kwargs:dict, # Keyword arguments for the plot.\n horizontal:bool, # If the plot is horizontal.\n show_pairs:bool, # Whether the data is paired and shown in pairs.\n contrast_marker_kwargs:dict, contrast_errorbar_kwargs:dict\n):\n\nAdd mini meta-analysis or delta-delta violin plots to the contrast plot.\n\nsource\n\n\nslopegraph_plotter\n\ndef slopegraph_plotter(\n dabest_obj:object, # DABEST object.\n plot_data:pd.DataFrame, # Dataframe of the plot data.\n xvar:str, # Column name of the x variable.\n yvar:str, # Column name of the y variable.\n color_col:str, # Column name of the color column.\n plot_palette_raw:dict, # Dictionary of colors used in the plot.\n slopegraph_kwargs:dict, # Keyword arguments for the slopegraph.\n rawdata_axes:axes.Axes, # Matplotlib axis object to plot on.\n ytick_color:str, # Color of the yticks.\n temp_idx:list, # List of indices of the contrast objects.\n horizontal:bool, # If the plotting will be in horizontal format.\n temp_all_plot_groups:list, # List of all plot groups.\n plot_kwargs:dict, # Keyword arguments for the plot.\n group_summaries_kwargs:dict, # Keyword arguments for group summaries, if applicable.\n):\n\nAdd slopegraph to the rawdata axes.\n\nsource\n\n\ndelta_dots_plotter\n\ndef delta_dots_plotter(\n plot_data:pd.DataFrame, # Dataframe of the plot data.\n contrast_axes:axes.Axes, # Matplotlib axis object to plot on.\n delta_id_col:str, # Column name of the delta id column.\n idx:list, # List of indices of the contrast objects.\n xvar:str, # Column name of the x variable.\n yvar:str, # Column name of the y variable.\n is_paired:bool, # Whether the data is paired.\n color_col:str, # Column name of the color column.\n float_contrast:bool, # Whether the DABEST plot uses Gardner-Altman or Cummings\n plot_palette_raw:dict, # Dictionary of colors used in the plot.\n delta_dot_kwargs:dict, # Keyword arguments for the delta dots.\n horizontal:bool, # If the rawplot is horizontal.\n):\n\n\nsource\n\n\ndelta_text_plotter\n\ndef delta_text_plotter(\n results:pd.DataFrame, # Dataframe of contrast object comparisons.\n ax_to_plot:object, # Matplotlib axis object to plot on.\n ticks_to_plot:list, # List of indices of the contrast objects.\n delta_text_kwargs:dict, # Keyword arguments for the delta text.\n color_col:str, # Column name of the color column.\n plot_palette_raw:dict, # Dictionary of colors used in the plot.\n show_pairs:bool, # Whether the data is paired and show pairs.\n float_contrast:bool, # Whether the DABEST plot uses Gardner-Altman or Cummings.\n extra_delta:float, # The extra mini-meta or delta-delta value if applicable.\n bootstraps_color_by_group:bool=False, # Whether to color the bootstraps by group. Default is False.\n):\n\nAdd delta text to the contrast plot.\n\nsource\n\n\nadd_bars_to_plot\n\ndef add_bars_to_plot(\n bar_dict:dict, # Dictionary of bar values.\n ax:axes.Axes, # Matplotlib axis object to plot on.\n bar_kwargs:dict, # Keyword arguments for the bars.\n):\n\nAdd bars to the relevant axes.\n\nsource\n\n\nsankeydiag\n\ndef sankeydiag(\n data:pd.DataFrame, xvar:str, # x column to be plotted.\n yvar:str, # y column to be plotted.\n temp_all_plot_groups:list, idx:list, temp_idx:list,\n left_labels:list=None, # labels for the left side of the diagram. The diagram will be sorted by these labels.\n right_labels:list=None, # labels for the right side of the diagram. The diagram will be sorted by these labels.\n palette:str | dict=None, ax:NoneType=None, # matplotlib axes to be drawn on\n flow:bool=True, # if True, draw the sankey in a flow, else draw 1 vs 1 Sankey diagram for each group comparison\n sankey:bool=True, # if True, draw the sankey diagram, else draw barplot\n one_sankey:bool=False, # determined by the driver function on plotter.py, if True, draw the sankey diagram across the whole raw data axes\n width:float=0.4, # the width of each sankey diagram\n right_color:bool=False, # if True, each strip of the diagram will be colored according to the corresponding left labels\n align:str='center', # the alignment of each sankey diagram, can be 'center' or 'left'\n alpha:float=0.65, # the transparency of each strip\n horizontal:bool=False, # if True, the horizontal format for the sankey diagram will be used\n kwargs:VAR_KEYWORD\n):\n\nRead in melted pd.DataFrame, and draw multiple sankey diagram on a single axes using the value in column yvar according to the value in column xvar left_idx in the column xvar is on the left side of each sankey diagram right_idx in the column xvar is on the right side of each sankey diagram\n\nsource\n\n\nsingle_sankey\n\ndef single_sankey(\n left:np.array, # data on the left of the diagram\n right:np.array, # data on the right of the diagram, len(left) == len(right)\n xpos:float=0, # the starting point on the x-axis\n left_weight:np.array=None, # weights for the left labels, if None, all weights are 1\n right_weight:np.array=None, # weights for the right labels, if None, all weights are corresponding left_weight\n colorDict:dict=None, # input format: {'label': 'color'}\n left_labels:list=None, # labels for the left side of the diagram. The diagram will be sorted by these labels.\n right_labels:list=None, # labels for the right side of the diagram. The diagram will be sorted by these labels.\n ax:NoneType=None, # matplotlib axes to be drawn on\n flow:bool=True, # if True, draw the sankey in a flow, else draw 1 vs 1 Sankey diagram for each group comparison\n sankey:bool=True, # if True, draw the sankey diagram, else draw barplot\n width:float=0.5, alpha:float=0.65, bar_width:float=0.2,\n error_bar_on:bool=True, # if True, draw error bar for each group comparison\n strip_on:bool=True, # if True, draw strip for each group comparison\n one_sankey:bool=False, # if True, only draw one sankey diagram\n right_color:bool=False, # if True, each strip of the diagram will be colored according to the corresponding left labels\n align:str='center', # if 'center', the diagram will be centered on each xtick, if 'edge', the diagram will be aligned with the left edge of each xtick\n horizontal:bool=False, # if True, the horizontal format for the sankey diagram will be used\n):\n\nMake a single Sankey diagram showing proportion flow from left to right\nOriginal code from: https://github.com/anazalea/pySankey\nChanges are added to normalize each diagram’s height to be 1\n\nsource\n\n\nwidth_determine\n\ndef width_determine(\n labels, data, pos:str='left'\n):\n\nCalculates normalized width positions for a set of labels based on their associated data.\nThis function is designed to determine width positions for plotting or graphical representation. It takes into account the cumulative weight of each label in the data and adjusts their positions accordingly. The function allows for adjusting the position of labels to either the ‘left’ or ‘right’.\nParameters: labels (list): A list of labels whose width positions are to be calculated. data (DataFrame): A pandas DataFrame containing the data used for calculating width positions. The DataFrame should have columns corresponding to the ‘pos’ and ‘posWeight’. pos (str, optional): The position of labels. It can be either ‘left’ or ‘right’. Defaults to ‘left’.\nReturns: defaultdict: A dictionary where each key is a label and the value is another dictionary with keys ‘bottom’, ‘top’, and ‘pos’, representing the calculated width positions.\nNote: The function assumes that the data DataFrame contains columns named after the value of ‘pos’ and an additional column named ‘posWeight’ which represents the weight of each label.\n\nsource\n\n\nnormalize_dict\n\ndef normalize_dict(\n nested_dict, target\n):\n\nNormalizes the values in a nested dictionary based on a target dictionary.\nThis function iterates through a nested dictionary, calculates the sum of values for each key across all sub-dictionaries, and then normalizes these values according to a target dictionary. The normalization is performed such that the values in each sub-dictionary are proportionally scaled to match the corresponding ‘right’ values in the target dictionary.\nParameters: nested_dict (dict of dict): A nested dictionary where each key maps to another dictionary. The values in these inner dictionaries are subject to normalization. target (dict): A dictionary with the target values for normalization. Each key in nested_dict should have a corresponding key in target, and each target[key] should be a dictionary with a ‘right’ key containing the target normalization value.\nReturns: dict: The normalized nested dictionary. The original nested_dict is modified in place.\nNote: - If the sum of values for a particular key in nested_dict is zero, the normalized value is set to 0. - If a key in a sub-dictionary of nested_dict does not exist in the target dictionary, the corresponding ‘right’ value from the target dictionary is directly assigned. - The function modifies the input nested_dict in place and also returns it.\n\nsource\n\n\ncheck_data_matches_labels\n\ndef check_data_matches_labels(\n labels, # list of input labels\n data, # Pandas Series of input data\n side:str, # 'left' or 'right' on the sankey diagram\n):\n\nFunction to check that the labels and data match in the sankey diagram. And enforce labels and data to be lists. Raises an exception if the labels and data do not match.\n\nsource\n\n\nerror_bar\n\ndef error_bar(\n data:pd.DataFrame, # This DataFrame should be in 'long' format.\n x:str, # x column to be plotted.\n y:str, # y column to be plotted.\n type:str='mean_sd', # Choose from ['mean_sd', 'median_quartiles']. Plots the summary statistics for each group. If 'mean_sd', then the mean and standard deviation of each group is plotted as a gapped line. If 'median_quantiles', then the median and 25th and 75th percentiles of each group is plotted instead.\n offset:float=0.2, # Give a single float (that will be used as the x-offset of all gapped lines), or an iterable containing the list of x-offsets.\n ax:NoneType=None, # If a matplotlib Axes object is specified, the gapped lines will be plotted in order on this axes. If None, the current axes (plt.gca()) is used.\n line_color:str='black', # The color of the gapped lines.\n gap_width_percent:int=1, # The width of the gap in the gapped lines, as a percentage of the y-axis span.\n pos:list=[0, 1],\n method:str='gapped_lines', # The method to use for drawing the error bars. Options are: 'gapped_lines', 'proportional_error_bar', and 'sankey_error_bar'.\n horizontal:bool=False, # If True, the error bars will be horizontal. If False, the error bars will be vertical.\n kwargs:dict\n):\n\nFunction to plot the standard deviations as vertical errorbars. The mean is a gap defined by negative space.\nThis function combines the functionality of gapped_lines(), proportional_error_bar(), and sankey_error_bar().\n\nsource\n\n\nget_swarm_spans\n\ndef get_swarm_spans(\n coll\n):\n\nGiven a matplotlib Collection, will obtain the x and y spans for the collection. Will return None if this fails.\n\nsource\n\n\nhalfviolin\n\ndef halfviolin(\n v, half:str='right', fill_color:str='k', alpha:int=1, line_color:str='k', line_width:int=0\n):\n\nCall self as a function.\n\nsource\n\n\nSwarmPlot\n\ndef SwarmPlot(\n data:pd.DataFrame, # The input data as a pandas DataFrame.\n x:str, # The column in the DataFrame to be used as the x-axis.\n y:str, # The column in the DataFrame to be used as the y-axis.\n ax:axes.Axes, # Matplotlib axes.Axes object for which the plot would be drawn on.\n order:List=None, # The order in which x-axis categories should be displayed. Default is None.\n hue:str=None, # The column in the DataFrame that determines the grouping for color.\nIf None (by default), it assumes that it is being grouped by x.\n palette:Union[Iterable, str]='black', # The color palette to be used for plotting. Default is \"black\".\n zorder:float=1, # The z-order for drawing the swarm plot wrt other matplotlib drawings. Default is 1.\n size:float=5,\n side:str='center', # The side on which points are swarmed (\"center\", \"left\", or \"right\"). Default is \"center\".\n jitter:float=1, # Determines the distance between points. Default is 1.\n horizontal:bool=False, # If True, the swarm plot is drawn horizontally. Default is False.\n):\n\nInitialize a SwarmPlot instance.\n\nsource\n\n\nswarmplot\n\ndef swarmplot(\n data:pd.DataFrame, # The input data as a pandas DataFrame.\n x:str, # The column in the DataFrame to be used as the x-axis.\n y:str, # The column in the DataFrame to be used as the y-axis.\n ax:axes.Axes, # Matplotlib axes.Axes object for which the plot would be drawn on. Default is None.\n order:List=None, # The order in which x-axis categories should be displayed. Default is None.\n hue:str=None, # The column in the DataFrame that determines the grouping for color.\nIf None (by default), it assumes that it is being grouped by x.\n palette:Union[Iterable, str]='black', # The color palette to be used for plotting. Default is \"black\".\n zorder:float=1, # The z-order for drawing the swarm plot wrt other matplotlib drawings. Default is 1.\n size:float=5,\n side:str='center', # The side on which points are swarmed (\"center\", \"left\", or \"right\"). Default is \"center\".\n jitter:float=1, # Determines the distance between points. Default is 1.\n filled:Union[bool, List, Tuple]=True, # Determines whether the dots in the swarmplot are filled or not. If set to False,\ndots are not filled. If provided as a List or Tuple, it should contain boolean values,\neach corresponding to a swarm group in order, indicating whether the dot should be\nfilled or not.\n is_drop_gutter:bool=True, # If True, drop points that hit the gutters; otherwise, readjust them.\n gutter_limit:float=0.5, # The limit for points hitting the gutters.\n horizontal:bool=False, # If True, the swarm plot is drawn horizontally. Default is False.\n kwargs:VAR_KEYWORD\n): # Matplotlib axes.Axes object for which the swarm plot has been drawn on.\n\nAPI to plot a swarm plot.", + "crumbs": [ + "Get Started", + "API", + "plot_tools" + ] + }, + { + "objectID": "API/misc_tools.html", + "href": "API/misc_tools.html", + "title": "misc_tools", + "section": "", + "text": "source\n\nprepare_bars_for_plot\n\ndef prepare_bars_for_plot(\n bar_type, bar_kwargs, horizontal, plot_palette_raw, color_col, show_pairs, bootstraps_color_by_group,\n plot_data:NoneType=None, xvar:NoneType=None, yvar:NoneType=None, # Raw data\n results:NoneType=None, ticks_to_plot:NoneType=None, extra_delta:NoneType=None, # Contrast data\n reference_band:NoneType=None, summary_axes:NoneType=None, ci_type:NoneType=None, # Summary data\n):\n\nCall self as a function.\n\nsource\n\n\ncolor_picker\n\ndef color_picker(\n color_type:str, kwargs:dict, elements:list, color_col:str, show_pairs:bool, color_palette:dict,\n bootstraps_color_by_group:bool\n)->list:\n\nCall self as a function.\n\nsource\n\n\nextract_group_summaries\n\ndef extract_group_summaries(\n proportional:bool, # A boolean flag to determine if the plot is for proportional data.\n rawdata_axes:axes.Axes, # The raw data axes.\n asymmetric_side:str, # The side of the asymmetric error bars.\n horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.\n bootstraps_color_by_group:bool, # A boolean flag to determine if the bootstraps are colored by group.\n plot_palette_raw:list, # A list of the plot palette colors.\n all_plot_groups:list, # A list of all the plot groups.\n n_groups:int, # The number of groups.\n color_col, # The name of the color column.\n ytick_color, # The color of the y-ticks.\n group_summaries_kwargs:dict, # Kwargs passed to the group summaries.\n):\n\nExtract the group summaries for the plotter function.\n\nsource\n\n\nredraw_dependent_spines\n\ndef redraw_dependent_spines(\n rawdata_axes:axes.Axes, # The raw data axes.\n contrast_axes:axes.Axes, # The contrast axes.\n redraw_axes_kwargs:dict, # Kwargs passed to the redraw axes.\n float_contrast:bool, # A boolean flag to determine if the plot is GA or Cum\n horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.\n show_delta2:bool, # A boolean flag to determine if the plot will have a delta-delta effect size.\n delta2_axes:axes.Axes, # The delta2 axes.\n):\n\nDraw the dependent axis spine lines.\n\nsource\n\n\nredraw_independent_spines\n\ndef redraw_independent_spines(\n rawdata_axes:axes.Axes, # The raw data axes.\n contrast_axes:axes.Axes, # The contrast axes.\n horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.\n two_col_sankey:bool, # A boolean flag to determine if the plot is for two-col sankey.\n ticks_to_start_twocol_sankey:list, # A list of ticks to start for sankey plot.\n idx:list, # A list of indices.\n is_paired:str, # A boolean flag to determine if the data is paired.\n show_pairs:bool, # A boolean flag to determine if pairs should be shown.\n proportional:bool, # A boolean flag to determine if the plot is proportional/binary.\n ticks_to_skip:list, # A list of ticks to be skipped in the raw data axes.\n temp_idx:list, # A temporary list of indices to be used for skipping ticks in the raw data axes.\n ticks_to_skip_contrast:list, # A list of ticks to be skipped in the contrast axes.\n redraw_axes_kwargs:dict, # Kwargs passed to the redraw axes.\n):\n\nDraw the independent axis spine lines.\n\nsource\n\n\ndraw_zeroline\n\ndef draw_zeroline(\n ax:axes.Axes, # The contrast data axes.\n horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.\n reflines_kwargs:dict, # Additional keyword arguments to be passed to the zeroline.\n extra_delta:bool, # A boolean flag to determine if the plot includes an extra delta (delta-delta or mini-meta).\n):\n\nDraw the independent axis spine lines.\n\nsource\n\n\ngardner_altman_adjustments\n\ndef gardner_altman_adjustments(\n effect_size_type:str, # The type of effect size.\n plot_data:pd.DataFrame, # A dataframe of plot data.\n xvar:str, # The name of the x-axis variable.\n yvar:str, # The name of the y-axis variable.\n current_control:str, # The name of the current control group.\n current_group:str, # The name of the current test group.\n rawdata_axes:axes.Axes, # The raw data axes.\n contrast_axes:axes.Axes, # The contrast axes.\n results:pd.DataFrame, # A dataframe of the results.\n current_effsize:float, # The current effect size.\n is_paired:bool, # A boolean flag to determine if the plot is for paired data.\n one_sankey:bool, # A boolean flag to determine if the plot is for a single sankey diagram.\n reflines_kwargs:dict, # Kwargs passed to the reference lines.\n redraw_axes_kwargs:dict, # Kwargs passed to the redraw axes.\n):\n\nAesthetic adjustments specific to Gardner-Altman plots (float_contrast=True).\n\nsource\n\n\nshow_legend\n\ndef show_legend(\n legend_labels:list, # A list of legend labels.\n legend_handles:list, # A list of legend handles.\n rawdata_axes:axes.Axes, # The raw data axes.\n contrast_axes:axes.Axes, # The contrast axes.\n table_axes:axes.Axes, # The table axes.\n float_contrast:bool, # A boolean flag to determine if the plot is GA or Cumming format.\n show_pairs:bool, # A boolean flag to determine if the plot will show the paired data.\n horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.\n legend_kwargs:dict, # Kwargs passed to the legend function.\n table_kwargs:dict\n):\n\nShow the legend for the plotter function.\n\nsource\n\n\nset_xaxis_ticks_and_lims\n\ndef set_xaxis_ticks_and_lims(\n show_delta2:bool, # A boolean flag to determine if the plot will have a delta-delta effect size.\n show_mini_meta:bool, # A boolean flag to determine if the plot will have a mini-meta effect size.\n rawdata_axes:axes.Axes, # The raw data axes.\n contrast_axes:axes.Axes, # The contrast axes.\n show_pairs:bool, # A boolean flag to determine if the plot will show the paired data.\n float_contrast:bool, # A boolean flag to determine if the plot is a GA or Cumming design.\n ticks_to_skip:list, # A list of ticks to skip.\n contrast_xtick_labels:list, # A list of contrast xtick labels.\n plot_kwargs:dict, # Kwargs passed to the plot function.\n proportional:bool, horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.\n):\n\nSet the x-axis/yaxis ticks and limits for the plotter function.\n\nsource\n\n\nextract_contrast_plotting_ticks\n\ndef extract_contrast_plotting_ticks(\n is_paired:bool, # A boolean flag to determine if the plot is for paired data.\n show_pairs:bool, # A boolean flag to determine if the plot will show the paired data.\n two_col_sankey:bool, # A boolean flag to determine if the plot will show a two-column sankey diagram.\n plot_groups:list, # A list of the plot groups.\n idx:list, # A list of tuples containing the group names.\n sankey_control_group:list, # A list of the control group names.\n):\n\nExtract the contrast plotting ticks from the idx object for use in the plotter function.\n\nsource\n\n\nadd_counts_to_ticks\n\ndef add_counts_to_ticks(\n plot_data:pd.DataFrame, # A dataframe of plot data.\n xvar:str, # The name of the x-axis variable.\n yvar:str, # The name of the y-axis variable.\n rawdata_axes:axes.Axes, # The raw data axes.\n plot_kwargs:dict, # Kwargs passed to the plot function.\n flow:bool, # Whether sankey flow is enabled or not.\n horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.\n):\n\nAdd the counts to the raw data axes labels.\n\nsource\n\n\nget_plot_groups\n\ndef get_plot_groups(\n is_paired:bool, # A boolean flag to determine if the plot is for paired data.\n idx:list, # A list of tuples containing the group names.\n proportional:bool, # A boolean flag to determine if the plot is for proportional data.\n all_plot_groups:list, # A list of all the group names.\n):\n\nExtract the plot groups from the idx object for use in the plotter function.\n\nsource\n\n\ninitialize_fig\n\ndef initialize_fig(\n plot_kwargs:dict, # Kwargs passed to the plot function.\n dabest_obj:object, # A `dabest` EffectSizeDataFrame object.\n show_delta2:bool, # A boolean flag to determine if the plot will have a delta-delta effect size.\n show_mini_meta:bool, # A boolean flag to determine if the plot will have a mini-meta effect size.\n is_paired:bool, # A boolean flag to determine if the plot is for paired data.\n show_pairs:bool, # A boolean flag to determine if the plot will show the paired data.\n proportional:bool, # A boolean flag to determine if the plot is for proportional data.\n float_contrast:bool, # A boolean flag to determine if the plot is for floating contrast data.\n effect_size_type:str, # The type of effect size to be plotted.\n yvar:str, # The name of the y-axis variable.\n horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.\n show_table:bool, # A boolean flag to determine if the table will be shown in horizontal plot.\n color_col:str, # The column name for coloring the data points.\n):\n\nInitialize the figure and axes for the plotter function.\n\nsource\n\n\nget_color_palette\n\ndef get_color_palette(\n plot_kwargs:dict, # Kwargs passed to the plot function.\n plot_data:pd.DataFrame, # A dataframe of plot data.\n xvar:str, # The name of the x-axis variable.\n show_pairs:bool, # A boolean flag to determine if the plot is for paired data.\n idx:list, # A list of tuples containing the group names.\n all_plot_groups:list, # A list of all the group names.\n delta2:bool, # A boolean flag to determine if the plot will have a delta-delta effect size.\n proportional:bool, # A boolean flag to determine if the plot is for a proportional plot.\n):\n\nCreate the color palette to be used in the plotter function.\n\nsource\n\n\nget_kwargs\n\ndef get_kwargs(\n plot_kwargs:dict, # Kwargs passed to the plot function.\n ytick_color, # Color of the yticks.\n is_paired:bool=False, # A boolean flag to determine if the plot is for paired data. Default is False.\n):\n\nExtracts the kwargs from the plot_kwargs object for use in the plotter function.\n\nsource\n\n\nget_params\n\ndef get_params(\n effectsize_df:object, # A `dabest` EffectSizeDataFrame object.\n plot_kwargs:dict, # Kwargs passed to the plot function.\n sankey_kwargs:dict, barplot_kwargs:dict, # Kwargs relating to the barplot\n):\n\nExtracts parameters from the effectsize_df and plot_kwargs objects for use in the plotter function.\n\nsource\n\n\nget_unique_categories\n\ndef get_unique_categories(\n names\n):\n\nExtract unique categories from various input types.\n\nsource\n\n\nget_varname\n\ndef get_varname(\n obj\n):\n\nCall self as a function.\n\nsource\n\n\nprint_greeting\n\ndef print_greeting(\n \n):\n\nGenerates a greeting message based on the current time, along with the version information of DABEST.\nThis function dynamically generates a greeting (‘Good morning’, ‘Good afternoon’, ‘Good evening’) based on the current system time. It also retrieves and displays the version of DABEST (Data Analysis using Bootstrap-Coupled ESTimation). The message includes a header with the DABEST version and the current time formatted in a user-friendly manner.\nReturns: str: A formatted string containing the greeting message, DABEST version, and current time.\n\nsource\n\n\nunpack_and_add\n\ndef unpack_and_add(\n l, c\n):\n\nConvenience function to allow me to add to an existing list without altering that list.\n\nsource\n\n\nmerge_two_dicts\n\ndef merge_two_dicts(\n x:dict, y:dict\n)->dict: # A dictionary containing a union of all keys in both original dicts.\n\nGiven two dicts, merge them into a new dict as a shallow copy. Any overlapping keys in y will override the values in x.\nTaken from here", + "crumbs": [ + "Get Started", + "API", + "misc_tools" + ] + }, + { + "objectID": "API/forest_plot.html", + "href": "API/forest_plot.html", + "title": "Forest plot", + "section": "", + "text": "source\n\nforest_plot\n\ndef forest_plot(\n data:list, # List of contrast objects.\n idx:Optional[list[int]]=None, # List of indices to select from the contrast objects if delta-delta experiment. \nIf None, only the delta-delta objects are plotted.\n ax:Optional[plt.Axes]=None, # Matplotlib Axes object for the plot; creates new if None.\nadditional_plotting_kwargs : Optional[dict], default=None\nFurther customization arguments for the plot.\n fig_size:tuple[int, int]=None, # Figure size for the plot.\n effect_size:str='mean_diff', # Type of effect size to plot (e.g., 'mean_diff', [`hedges_g`](https://acclab.github.io/DABEST-python/API/effsize.html#hedges_g) or 'delta_g').\n ci_type:str='bca', # Type of confidence interval to plot (bca' or 'pct')\n horizontal:bool=False, # If True, the plot will be horizontal.\n marker_size:int=10, # Marker size for plotting effect size dots.\n custom_palette:Optional[Union[dict, list, str]]=None, # Custom color palette for the plot.\n contrast_alpha:float=0.8, # Transparency level for violin plots.\n contrast_desat:float=1, # Saturation level for violin plots.\n labels:list[str]=None, # Labels for each contrast. If None, defaults to 'Contrast 1', 'Contrast 2', etc.\n labels_rotation:int=None, # Rotation angle for contrast labels.\n labels_fontsize:int=10, # Font size for contrast labels.\n title:str=None, # Plot title, summarizing the visualized data.\n title_fontsize:int=16, # Font size for the plot title.\n ylabel:str=None, # Label for the y-axis, describing the plotted data or effect size.\n ylabel_fontsize:int=12, # Font size for the y-axis label.\n ylim:Optional[list[float, float]]=None, # Limits for the y-axis.\n yticks:Optional[list[float]]=None, # Custom y-ticks for the plot.\n yticklabels:Optional[list[str]]=None, # Custom y-tick labels for the plot.\n remove_spines:bool=True, # If True, removes plot spines (except the relevant dependent variable spine).\n delta_text:bool=True, # If True, it adds text next to each curve representing the effect size value.\n delta_text_kwargs:dict=None, # Additional keyword arguments for the delta_text.\n contrast_bars:bool=True, # If True, it adds bars from the zeroline to the effect size curve.\n contrast_bars_kwargs:dict=None, # Additional keyword arguments for the contrast_bars.\n reference_band:list | tuple=None,\n reference_band_kwargs:dict=None, # Additional keyword arguments for the reference_band.\n violin_kwargs:Optional[dict]=None, # Additional arguments for violin plot customization.\n zeroline_kwargs:Optional[dict]=None, # Additional arguments for the zero line customization.\n marker_kwargs:Optional[dict]=None, # Additional arguments for the effect size marker customization.\n errorbar_kwargs:Optional[dict]=None, # Additional arguments for the effect size error bar customization.\n)->plt.Figure: # The matplotlib figure object with the generated forest plot.\n\nCustom function that generates a forest plot from given contrast objects, suitable for a range of data analysis types, including those from packages like DABEST-python.\n\nsource\n\n\ncolor_palette\n\ndef color_palette(\n custom_palette, labels, number_of_curves_to_plot, contrast_desat\n):\n\nCall self as a function.\n\nsource\n\n\nget_kwargs\n\ndef get_kwargs(\n violin_kwargs, zeroline_kwargs, horizontal, marker_kwargs, errorbar_kwargs, delta_text_kwargs,\n contrast_bars_kwargs, reference_band_kwargs, marker_size\n):\n\nCall self as a function.\n\nsource\n\n\ncheck_for_errors\n\ndef check_for_errors(\n kwargs:VAR_KEYWORD\n):\n\nCall self as a function.\n\nsource\n\n\nload_plot_data\n\ndef load_plot_data(\n data:List, effect_size:str='mean_diff', contrast_type:str=None, ci_type:str='bca', idx:Optional[List[int]]=None\n)->List:\n\nLoads plot data based on specified effect size and contrast type.", + "crumbs": [ + "Get Started", + "API", + "Forest plot" + ] + }, + { + "objectID": "API/effsize.html", + "href": "API/effsize.html", + "title": "effsize", + "section": "", + "text": "source\n\ntwo_group_difference\n\ndef two_group_difference(\n control:list | tuple | np.ndarray, # Accepts lists, tuples, or numpy ndarrays of numeric types.\n test:list | tuple | np.ndarray, # Accepts lists, tuples, or numpy ndarrays of numeric types.\n is_paired:NoneType=None, # If not None, returns the paired Cohen's d\n effect_size:str='mean_diff', # Any one of the following effect sizes: [\"mean_diff\", \"median_diff\", \"cohens_d\", \"hedges_g\", \"cliffs_delta\"]\n)->float: # The desired effect size.\n\nComputes the following metrics for control and test:\n- Unstandardized mean difference\n- Standardized mean differences (paired or unpaired)\n * Cohen's d\n * Hedges' g\n- Median difference\n- Cliff's Delta\n- Cohen's h (distance between two proportions)\nSee the Wikipedia entry here\neffect_size:\nmean_diff: This is simply the mean of `control` subtracted from\n the mean of `test`.\n\ncohens_d: This is the mean of control subtracted from the\n mean of test, divided by the pooled standard deviation\n of control and test. The pooled SD is the square as:\n\n (n1 - 1) * var(control) + (n2 - 1) * var(test)\n sqrt ( ------------------------------------------- )\n (n1 + n2 - 2)\n\n where n1 and n2 are the sizes of control and test\n respectively.\n\nhedges_g: This is Cohen's d corrected for bias via multiplication\n with the following correction factor:\n\n gamma(n/2)\n J(n) = ------------------------------\n sqrt(n/2) * gamma((n - 1) / 2)\n\n where n = (n1 + n2 - 2).\n\nmedian_diff: This is the median of `control` subtracted from the\n median of `test`.\n\nsource\n\n\nfunc_difference\n\ndef func_difference(\n control:list | tuple | np.ndarray, # NaNs are automatically discarded.\n test:list | tuple | np.ndarray, # NaNs are automatically discarded.\n func, # Summary function to apply.\n is_paired:str, # If not None, computes func(test - control). If None, computes func(test) - func(control).\n)->float:\n\nApplies func to control and test, and then returns the difference.\n\nsource\n\n\ncohens_d\n\ndef cohens_d(\n control:list | tuple | np.ndarray, test:list | tuple | np.ndarray,\n is_paired:str=None, # If not None, the paired Cohen's d is returned.\n)->float:\n\nComputes Cohen’s d for test v.s. control. See here\nIf is_paired is None, returns:\n\\[\n\\frac{\\bar{X}_2 - \\bar{X}_1}{s_{pooled}}\n\\]\nwhere\n\\[\ns_{pooled} = \\sqrt{\\frac{(n_1 - 1) s_1^2 + (n_2 - 1) s_2^2}{n_1 + n_2 - 2}}\n\\]\nIf is_paired is not None, returns:\n\\[\n\\frac{\\bar{X}_2 - \\bar{X}_1}{s_{avg}}\n\\]\nwhere\n\\[\ns_{avg} = \\sqrt{\\frac{s_1^2 + s_2^2}{2}}\n\\]\nNotes:\n\nThe sample variance (and standard deviation) uses N-1 degrees of freedoms. This is an application of Bessel’s correction, and yields the unbiased sample variance.\n\nReferences:\n- https://en.wikipedia.org/wiki/Bessel%27s_correction\n- https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation\n\nsource\n\n\ncohens_h\n\ndef cohens_h(\n control:list | tuple | np.ndarray, test:list | tuple | np.ndarray\n)->float:\n\nComputes Cohen’s h for test v.s. control. See here for reference.\nNotes:\n\nAssuming the input data type is binary, i.e. a series of 0s and 1s, and a dict for mapping the 0s and 1s to the actual labels, e.g.{1: “Smoker”, 0: “Non-smoker”}\n\n\nsource\n\n\nhedges_g\n\ndef hedges_g(\n control:list | tuple | np.ndarray, test:list | tuple | np.ndarray, is_paired:str=None\n)->float:\n\nComputes Hedges’ g for for test v.s. control. It first computes Cohen’s d, then calulates a correction factor based on the total degress of freedom using the gamma function.\nSee here\n\nsource\n\n\ncliffs_delta\n\ndef cliffs_delta(\n control:list | tuple | np.ndarray, test:list | tuple | np.ndarray\n)->float:\n\nComputes Cliff’s delta for 2 samples. See here\n\nsource\n\n\nweighted_delta\n\ndef weighted_delta(\n difference, bootstrap_dist_var\n):\n\nCompute the weighted deltas where the weight is the inverse of the pooled group difference.", + "crumbs": [ + "Get Started", + "API", + "effsize" + ] + }, + { + "objectID": "API/dabest_object.html", + "href": "API/dabest_object.html", + "title": "Dabest object", + "section": "", + "text": "Dabest\n\ndef Dabest(\n data, idx, x, y, paired, id_col, ci, resamples, random_seed, proportional, delta2, experiment, experiment_label,\n x1_level, mini_meta, ps_adjust\n):\n\nClass for estimation statistics and plots.\n\nExample: mean_diff\n\ncontrol = norm.rvs(loc=0, size=30, random_state=12345)\ntest = norm.rvs(loc=0.5, size=30, random_state=12345)\nmy_df = pd.DataFrame({\"control\": control,\n \"test\": test})\nmy_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\nmy_dabest_object.mean_diff\n\nDABEST v2025.03.27\n==================\n \nGood morning!\nThe current time is Tue Mar 25 10:08:38 2025.\n\nThe unpaired mean difference between control and test is 0.5 [95%CI 0.00172, 1.04].\nThe p-value of the two-sided permutation t-test is 0.0758, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.mean_diff.statistical_tests`\n\n\nThis is simply the mean of the control group subtracted from the mean of the test group.\n\\[\\text{Mean difference} = \\overline{x}_{Test} - \\overline{x}_{Control}\\]\nwhere \\(\\overline{x}\\) is the mean for the group \\(x\\).\n\n\nExample: median_diff\n\ncontrol = norm.rvs(loc=0, size=30, random_state=12345)\ntest = norm.rvs(loc=0.5, size=30, random_state=12345)\nmy_df = pd.DataFrame({\"control\": control,\n \"test\": test})\nmy_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\nmy_dabest_object.median_diff\n\n/Users/jonathananns/GitHub/DABEST-python/dabest/_stats_tools/effsize.py:82: UserWarning: Using median as the statistic in bootstrapping may result in a biased estimate and cause problems with BCa confidence intervals. Consider using a different statistic, such as the mean.\nWhen plotting, please consider using percetile confidence intervals by specifying `ci_type='pct'`. For detailed information, refer to https://github.com/ACCLAB/DABEST-python/issues/129 \n\n warnings.warn(message=mes1+mes2, category=UserWarning)\n\n\nDABEST v2025.03.27\n==================\n \nGood morning!\nThe current time is Tue Mar 25 10:08:39 2025.\n\nThe unpaired median difference between control and test is 0.5 [95%CI -0.0401, 1.04].\nThe p-value of the two-sided permutation t-test is 0.103, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.median_diff.statistical_tests`\n\n\nThis is the median difference between the control group and the test group.\nIf the comparison(s) are unpaired, median_diff is computed with the following equation:\n\\[\\text{Median difference} = \\widetilde{x}_{Test} - \\widetilde{x}_{Control}\\]\nwhere \\(\\widetilde{x}\\) is the median for the group \\(x\\).\nIf the comparison(s) are paired, median_diff is computed with the following equation:\n\\[\\text{Median difference} = \\widetilde{x}_{Test - Control}\\]\n\nThings to note\nUsing median difference as the statistic in bootstrapping may result in a biased estimate and cause problems with BCa confidence intervals. Consider using mean difference instead.\nWhen plotting, consider using percentile confidence intervals instead of BCa confidence intervals by specifying ci_type = 'percentile' in .plot().\nFor detailed information, please refer to Issue 129.\n\n\n\nExample: cohens_d\n\ncontrol = norm.rvs(loc=0, size=30, random_state=12345)\ntest = norm.rvs(loc=0.5, size=30, random_state=12345)\nmy_df = pd.DataFrame({\"control\": control,\n \"test\": test})\nmy_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\nmy_dabest_object.cohens_d\n\nDABEST v2025.03.27\n==================\n \nGood morning!\nThe current time is Tue Mar 25 10:08:39 2025.\n\nThe unpaired Cohen's d between control and test is 0.471 [95%CI -0.0405, 0.973].\nThe p-value of the two-sided permutation t-test is 0.0758, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.cohens_d.statistical_tests`\n\n\nCohen’s d is simply the mean of the control group subtracted from the mean of the test group.\nIf paired is None, then the comparison(s) are unpaired; otherwise the comparison(s) are paired.\nIf the comparison(s) are unpaired, Cohen’s d is computed with the following equation:\n\\[d = \\frac{\\overline{x}_{Test} - \\overline{x}_{Control}} {\\text{pooled standard deviation}}\\]\nFor paired comparisons, Cohen’s d is given by\n\\[d = \\frac{\\overline{x}_{Test} - \\overline{x}_{Control}} {\\text{average standard deviation}}\\]\nwhere \\(\\overline{x}\\) is the mean of the respective group of observations, \\({Var}_{x}\\) denotes the variance of that group,\n\\[\\text{pooled standard deviation} = \\sqrt{ \\frac{(n_{control} - 1) * {Var}_{control} + (n_{test} - 1) * {Var}_{test} } {n_{control} + n_{test} - 2} }\\]\nand\n\\[\\text{average standard deviation} = \\sqrt{ \\frac{{Var}_{control} + {Var}_{test}} {2}}\\]\nThe sample variance (and standard deviation) uses N-1 degrees of freedoms. This is an application of Bessel’s correction, and yields the unbiased sample variance.\nReferences:\nhttps://en.wikipedia.org/wiki/Effect_size#Cohen's_d\nhttps://en.wikipedia.org/wiki/Bessel%27s_correction\nhttps://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation\n\n\nExample: cohens_h\n\ncontrol = randint.rvs(0, 2, size=30, random_state=12345)\ntest = randint.rvs(0, 2, size=30, random_state=12345)\nmy_df = pd.DataFrame({\"control\": control,\n \"test\": test})\nmy_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\nmy_dabest_object.cohens_h\n\nDABEST v2025.03.27\n==================\n \nGood morning!\nThe current time is Tue Mar 25 10:08:41 2025.\n\nThe unpaired Cohen's h between control and test is 0.0 [95%CI -0.563, 0.474].\nThe p-value of the two-sided permutation t-test is 0.799, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.cohens_h.statistical_tests`\n\n\nCohen’s h uses the information of proportion in the control and test groups to calculate the distance between two proportions.\nIt can be used to describe the difference between two proportions as “small”, “medium”, or “large”.\nIt can be used to determine if the difference between two proportions is “meaningful”.\nA directional Cohen’s h is computed with the following equation:\n\\[h = 2 * \\arcsin{\\sqrt{proportion_{Test}}} - 2 * \\arcsin{\\sqrt{proportion_{Control}}}\\]\nFor a non-directional Cohen’s h, the equation is:\n\\[h = |2 * \\arcsin{\\sqrt{proportion_{Test}}} - 2 * \\arcsin{\\sqrt{proportion_{Control}}}|\\]\nReferences:\nhttps://en.wikipedia.org/wiki/Cohen%27s_h\n\n\nExample: hedges_g\n\ncontrol = norm.rvs(loc=0, size=30, random_state=12345)\ntest = norm.rvs(loc=0.5, size=30, random_state=12345)\nmy_df = pd.DataFrame({\"control\": control,\n \"test\": test})\nmy_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\nmy_dabest_object.hedges_g\n\nDABEST v2025.03.27\n==================\n \nGood morning!\nThe current time is Tue Mar 25 10:08:41 2025.\n\nThe unpaired Hedges' g between control and test is 0.465 [95%CI -0.04, 0.96].\nThe p-value of the two-sided permutation t-test is 0.0758, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.hedges_g.statistical_tests`\n\n\nHedges’ g is cohens_d corrected for bias via multiplication with the following correction factor:\n\\[\\frac{ \\Gamma( \\frac{a} {2} )} {\\sqrt{ \\frac{a} {2} } \\times \\Gamma( \\frac{a - 1} {2} )}\\]\nwhere\n\\[a = {n}_{control} + {n}_{test} - 2\\]\nand \\(\\Gamma(x)\\) is the Gamma function.\nReferences:\nhttps://en.wikipedia.org/wiki/Effect_size#Hedges'_g\nhttps://journals.sagepub.com/doi/10.3102/10769986006002107\n\n\nExample: cliffs_delta\n\ncontrol = norm.rvs(loc=0, size=30, random_state=12345)\ntest = norm.rvs(loc=0.5, size=30, random_state=12345)\nmy_df = pd.DataFrame({\"control\": control,\n \"test\": test})\nmy_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\nmy_dabest_object.cliffs_delta\n\nDABEST v2025.03.27\n==================\n \nGood morning!\nThe current time is Tue Mar 25 10:08:41 2025.\n\nThe unpaired Cliff's delta between control and test is 0.28 [95%CI -0.0111, 0.544].\nThe p-value of the two-sided permutation t-test is 0.061, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.cliffs_delta.statistical_tests`\n\n\nCliff’s delta is a measure of ordinal dominance, ie. how often the values from the test sample are larger than values from the control sample.\n\\[\\text{Cliff's delta} = \\frac{\\#({x}_{test} > {x}_{control}) - \\#({x}_{test} < {x}_{control})} {{n}_{Test} \\times {n}_{Control}}\\]\nwhere \\(\\#\\) denotes the number of times a value from the test sample exceeds (or is lesser than) values in the control sample.\nCliff’s delta ranges from -1 to 1; it can also be thought of as a measure of the degree of overlap between the two samples. An attractive aspect of this effect size is that it does not make an assumptions about the underlying distributions that the samples were drawn from.\nReferences:\nhttps://en.wikipedia.org/wiki/Effect_size#Effect_size_for_ordinal_data\nhttps://psycnet.apa.org/record/1994-08169-001\n\n\nExample: delta_g via hedges_g\n\nrandom.seed(12345) # Fix the seed so the results are replicable.\nN=20\ny = norm.rvs(loc=3, scale=0.4, size=N*4)\ny[N:2*N] = y[N:2*N]+1\ny[2*N:3*N] = y[2*N:3*N]-0.5\nt1 = repeat('Placebo', N*2).tolist()\nt2 = repeat('Drug', N*2).tolist()\ntreatment = t1 + t2\nrep = []\nfor i in range(N*2):\n rep.append('Rep1')\n rep.append('Rep2')\nwt = repeat('W', N).tolist()\nmt = repeat('M', N).tolist()\nwt2 = repeat('W', N).tolist()\nmt2 = repeat('M', N).tolist()\ngenotype = wt + mt + wt2 + mt2\nid = list(range(0, N*2))\nid_col = id + id\ndf_delta2 = pd.DataFrame({'ID' : id_col,\n 'Rep' : rep,\n 'Genotype' : genotype,\n 'Treatment': treatment,\n 'Y' : y})\nunpaired_delta2 = dabest.load(data = df_delta2, x = [\"Genotype\", \"Genotype\"], y = \"Y\", delta2 = True, experiment = \"Treatment\")\nunpaired_delta2.hedges_g\n\nDABEST v2025.03.27\n==================\n \nGood morning!\nThe current time is Tue Mar 25 10:08:42 2025.\n\nThe unpaired Hedges' g between W Placebo and M Placebo is 1.74 [95%CI 1.09, 2.33].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\nThe unpaired Hedges' g between W Drug and M Drug is 1.33 [95%CI 0.632, 1.98].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\nThe delta g between Placebo and Drug is -0.651 [95%CI -1.53, 0.21].\nThe p-value of the two-sided permutation t-test is 0.0694, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing the effect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.hedges_g.statistical_tests`\n\n\nDelta g is an effect size that only applied on experiments with a 2-by-2 arrangement where two independent variables, A and B, each have two categorical values, 1 and 2, which calculates hedges_g for delta-delta statistics.\n\\[\\Delta_{1} = \\overline{X}_{A_{2}, B_{1}} - \\overline{X}_{A_{1}, B_{1}}\\]\n\\[\\Delta_{2} = \\overline{X}_{A_{2}, B_{2}} - \\overline{X}_{A_{1}, B_{2}}\\]\nwhere \\(\\overline{X}_{A_{i}, B_{j}}\\) is the mean of the sample with A = i and B = j, \\(\\Delta\\) is the mean difference between two samples.\nA delta-delta value is then calculated as the mean difference between the two primary deltas:\n\\[\\Delta_{\\Delta} = \\Delta_{2} - \\Delta_{1}\\]\nand the standard deviation of the delta-delta value is calculated from a pooled variance of the 4 samples:\n\\[s_{\\Delta_{\\Delta}} = \\sqrt{\\frac{(n_{A_{2}, B_{1}}-1)s_{A_{2}, B_{1}}^2+(n_{A_{1}, B_{1}}-1)s_{A_{1}, B_{1}}^2+(n_{A_{2}, B_{2}}-1)s_{A_{2}, B_{2}}^2+(n_{A_{1}, B_{2}}-1)s_{A_{1}, B_{2}}^2}{(n_{A_{2}, B_{1}} - 1) + (n_{A_{1}, B_{1}} - 1) + (n_{A_{2}, B_{2}} - 1) + (n_{A_{1}, B_{2}} - 1)}}\\]\nwhere \\(s\\) is the standard deviation and \\(n\\) is the sample size.\nA delta g value is then calculated as delta-delta value divided by pooled standard deviation \\(s_{\\Delta_{\\Delta}}\\):\n\\(\\Delta_{g} = \\frac{\\Delta_{\\Delta}}{s_{\\Delta_{\\Delta}}}\\)", + "crumbs": [ + "Get Started", + "API", + "Dabest object" + ] + }, + { + "objectID": "API/confint_1group.html", + "href": "API/confint_1group.html", + "title": "confint_1group", + "section": "", + "text": "source\n\nsummary_ci_1group\n\ndef summary_ci_1group(\n x:np.array, # An numerical iterable.\n func, # The function to be applied to x.\n resamples:int=5000, # The number of bootstrap resamples to be taken of func(x).\n alpha:float=0.05, # Denotes the likelihood that the confidence interval produced _does not_ include the true summary statistic. When alpha = 0.05, a 95% confidence interval is produced.\n random_seed:int=12345, # `random_seed` is used to seed the random number generator during bootstrap resampling. This ensures that the confidence intervals reported are replicable.\n sort_bootstraps:bool=True, args:VAR_POSITIONAL, kwargs:VAR_KEYWORD\n): # `summary`: float.\n The outcome of func(x).\n`func`: function.\n The function applied to x.\n`bca_ci_low`: float\n`bca_ci_high`: float.\n The bias-corrected and accelerated confidence interval, for the\n given alpha.\n`bootstraps`: array.\n The bootstraps used to generate the confidence interval.\n These will be sorted in ascending order if `sort_bootstraps`\n was True.\n\nGiven an array-like x, returns func(x), and a bootstrap confidence interval of func(x).\n\nsource\n\n\ncompute_1group_bias_correction\n\ndef compute_1group_bias_correction(\n x, bootstraps, func, args:VAR_POSITIONAL, kwargs:VAR_KEYWORD\n):\n\nCall self as a function.\n\nsource\n\n\ncompute_1group_bootstraps\n\ndef compute_1group_bootstraps(\n x, func, resamples:int=5000, random_seed:int=12345, args:VAR_POSITIONAL, kwargs:VAR_KEYWORD\n):\n\nBootstraps func(x), with the number of specified resamples.\n\nsource\n\n\ncompute_1group_acceleration\n\ndef compute_1group_acceleration(\n jack_dist\n):\n\nReturns the accaleration value based on the jackknife distribution.\n\nsource\n\n\ncompute_1group_jackknife\n\ndef compute_1group_jackknife(\n x, func, args:VAR_POSITIONAL, kwargs:VAR_KEYWORD\n):\n\nReturns the jackknife bootstraps for func(x).\n\nsource\n\n\ncreate_bootstrap_indexes\n\ndef create_bootstrap_indexes(\n array, resamples:int=5000, random_seed:int=12345\n):\n\nGiven an array-like, returns a generator of bootstrap indexes to be used for resampling.", + "crumbs": [ + "Get Started", + "API", + "confint_1group" + ] + }, + { + "objectID": "03-citation.html", + "href": "03-citation.html", + "title": "Citing DABEST", + "section": "", + "text": "If your publication features a graphic generated with this software library, please cite the following publication.\nMoving beyond P values: Everyday data analysis with estimation plots Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang\nNature Methods 2019, 1548-7105. doi:10.1038/s41592-019-0470-3\nFree-to-view PDF\nPaywalled publisher site", + "crumbs": [ + "Get Started", + "Citing DABEST" + ] + }, + { + "objectID": "01-getting_started.html", + "href": "01-getting_started.html", + "title": "Getting Started", + "section": "", + "text": "DABEST is a package for Data Analysis with Bootstrapped ESTimation\nEstimation statistics is a simple framework that avoids the pitfalls of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one’s experiment/intervention, as opposed to a false dichotomy engendered by P values.\nAn estimation plot has two key features.\n\nIt presents all datapoints as a swarmplot, which orders each point to display the underlying distribution.\nIt presents the effect size as a bootstrap 95% confidence interval on a separate but aligned axes.\n\nDABEST powers estimationstats.com, allowing everyone access to high-quality estimation plots.", + "crumbs": [ + "Get Started", + "Getting Started" + ] + }, + { + "objectID": "01-getting_started.html#introduction", + "href": "01-getting_started.html#introduction", + "title": "Getting Started", + "section": "", + "text": "DABEST is a package for Data Analysis with Bootstrapped ESTimation\nEstimation statistics is a simple framework that avoids the pitfalls of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one’s experiment/intervention, as opposed to a false dichotomy engendered by P values.\nAn estimation plot has two key features.\n\nIt presents all datapoints as a swarmplot, which orders each point to display the underlying distribution.\nIt presents the effect size as a bootstrap 95% confidence interval on a separate but aligned axes.\n\nDABEST powers estimationstats.com, allowing everyone access to high-quality estimation plots.", + "crumbs": [ + "Get Started", + "Getting Started" + ] + }, + { + "objectID": "01-getting_started.html#requirements", + "href": "01-getting_started.html#requirements", + "title": "Getting Started", + "section": "Requirements", + "text": "Requirements\nPython 3.11 is recommended. DABEST has also been tested with Python 3.10 and onwards.\nIn addition, the following packages are also required (listed with their minimal versions):\n\nnumpy 2.1.3\nscipy 1.15.2\nmatplotlib 3.10.0\npandas 2.2.3\nseaborn 0.13.2\nnumba 0.61.0\nlqrt 0.3.3\n\nTo obtain these package dependencies easily, it is highly recommended to download the Anaconda distribution of Python.", + "crumbs": [ + "Get Started", + "Getting Started" + ] + }, + { + "objectID": "01-getting_started.html#installation", + "href": "01-getting_started.html#installation", + "title": "Getting Started", + "section": "Installation", + "text": "Installation\n\nUsing pip\n\nAt the command line, run\n$ pip install dabest\n\nUsing Github\n\nClone the DABEST-python repo locally (see instructions here).\nThen, navigate to the cloned repo in the command line and run\n$ pip install .", + "crumbs": [ + "Get Started", + "Getting Started" + ] + }, + { + "objectID": "01-getting_started.html#testing", + "href": "01-getting_started.html#testing", + "title": "Getting Started", + "section": "Testing", + "text": "Testing\nTo test DABEST, you will need to install pytest and nbdev.\nRun nbdev_export && nbdev_test in the root directory of the source distribution. This runs the value assertion tests in dabest/tests folder\nRun pytest in the root directory of the source distribution. This runs the image-based tests in dabest/tests/mpl_image_tests sub folder.\nThe test suite will ensure that the bootstrapping functions and the plotting functions perform as expected.", + "crumbs": [ + "Get Started", + "Getting Started" + ] + }, + { + "objectID": "01-getting_started.html#bugs", + "href": "01-getting_started.html#bugs", + "title": "Getting Started", + "section": "Bugs", + "text": "Bugs\nPlease report any bugs on the Github issue tracker for DABEST-python.", + "crumbs": [ + "Get Started", + "Getting Started" + ] + }, + { + "objectID": "01-getting_started.html#contributing", + "href": "01-getting_started.html#contributing", + "title": "Getting Started", + "section": "Contributing", + "text": "Contributing\nAll contributions are welcome. Please fork the Github repo and open a pull request.", + "crumbs": [ + "Get Started", + "Getting Started" + ] + }, + { + "objectID": "02-about.html", + "href": "02-about.html", + "title": "About", + "section": "", + "text": "DABEST is written in Python by Joses W. Ho, with design and input from Adam Claridge-Chang and other lab members.\nFeatures in v2025.10.20 were added by Jonathan Anns, Zinan Lu, Yishan Mai, and Sangyu Xu.\nFeatures in v2025.03.27 were added by Jonathan Anns, Zinan Lu, Kah Seng Lian, Yishan Mai, Sangyu Xu, and Lucas Wang Zhuoyu.\nFeatures in v2024.03.29 were added by Zinan Lu, Kah Seng Lian, Ana Rosa Castillo.\nFeatures in v2023.02.14 were added by Yixuan Li, Zinan Lu and Rou Zhang.\nTo find out more about the authors’ research, please visit the Claridge-Chang lab webpage.", + "crumbs": [ + "Get Started", + "About" + ] + }, + { + "objectID": "02-about.html#authors", + "href": "02-about.html#authors", + "title": "About", + "section": "", + "text": "DABEST is written in Python by Joses W. Ho, with design and input from Adam Claridge-Chang and other lab members.\nFeatures in v2025.10.20 were added by Jonathan Anns, Zinan Lu, Yishan Mai, and Sangyu Xu.\nFeatures in v2025.03.27 were added by Jonathan Anns, Zinan Lu, Kah Seng Lian, Yishan Mai, Sangyu Xu, and Lucas Wang Zhuoyu.\nFeatures in v2024.03.29 were added by Zinan Lu, Kah Seng Lian, Ana Rosa Castillo.\nFeatures in v2023.02.14 were added by Yixuan Li, Zinan Lu and Rou Zhang.\nTo find out more about the authors’ research, please visit the Claridge-Chang lab webpage.", + "crumbs": [ + "Get Started", + "About" + ] + }, + { + "objectID": "02-about.html#contributors", + "href": "02-about.html#contributors", + "title": "About", + "section": "Contributors", + "text": "Contributors\n\nStatistics supervision by Hyungwon Choi\nAlpha testers from the Claridge-Chang lab: Sangyu Xu, Xianyuan Zhang, Farhan Mohammad, Jurga Mituzaitė, Stanislav Ott, Tayfun Tumkaya, Jonathan Anns, Nicole Lee and Yishan Mai.\nDizietAsahi (DizietAsahi) with PR #86: Fix bugs in slopegraph and reference line keyword parsing.\nAdam Li (@adam2392) with PR #85: Implement Lq-Likelihood-Ratio-Type Test in statistical output.\nMason Malone (@MasonM) with PR #30: Fix plot error when effect size is 0.\nMatthew Edwards (@mje-nz) with PR #71: Specify dependencies correctly in setup.py.\nAdam Nekimken (@anekimken) with PR #73: Implement inset axes so estimation plots can be plotted on a pre-determined :py:mod:matplotlib :py:class:Axes object.\nMarin Manuel (@MarinManuel) with PR #109: Fixed bug preventing non-string columns from being used.\nMike Lotinga (@mlotinga): Helped with addition of jitter and the adjusted p-value calculation, both of which are included in the v2025.03.27 release.", + "crumbs": [ + "Get Started", + "About" + ] + }, + { + "objectID": "02-about.html#typography", + "href": "02-about.html#typography", + "title": "About", + "section": "Typography", + "text": "Typography\nThis documentation uses Spectral for the body text, Merriweather Sans for the side bar and titles, and IBM Plex Mono for the monospace code font.", + "crumbs": [ + "Get Started", + "About" + ] + }, + { + "objectID": "02-about.html#license", + "href": "02-about.html#license", + "title": "About", + "section": "License", + "text": "License\nThe DABEST package in Python is licenced under the BSD 3-clause Clear License.\nCopyright (c) 2016-2023, Joses W. Ho All rights reserved.\nRedistribution and use in source and binary forms, with or without modification, are permitted (subject to the limitations in the disclaimer below) provided that the following conditions are met:\n * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.\n\n * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.\n\n * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.\n\nNO EXPRESS OR IMPLIED LICENSES TO ANY PARTY’S PATENT RIGHTS ARE GRANTED BY THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.", + "crumbs": [ + "Get Started", + "About" + ] + }, + { + "objectID": "API/bootstrap.html", + "href": "API/bootstrap.html", + "title": "Bootstrap", + "section": "", + "text": "bootstrap\n\ndef bootstrap(\n x1:array, # The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded.\n x2:array=None, # The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded.\n paired:bool=False, # Whether or not x1 and x2 are paired samples. If 'paired' is None then the data will not be treated as paired data in the subsequent calculations. If 'paired' is 'baseline', then in each tuple of x, other groups will be paired up with the first group (as control). If 'paired' is 'sequential', then in each tuple of x, each group will be paired up with the previous group (as control).\n stat_function:callable=mean, # The summary statistic called on data.\n smoothboot:bool=False, # Taken from seaborn.algorithms.bootstrap. If True, performs a smoothed bootstrap (draws samples from a kernel destiny estimate).\n alpha_level:float=0.05, # Denotes the likelihood that the confidence interval produced does not include the true summary statistic. When alpha = 0.05, a 95% confidence interval is produced.\n reps:int=5000, # Number of bootstrap iterations to perform.\n):\n\nComputes the summary statistic and a bootstrapped confidence interval.\n\n\n\nbca\n\ndef bca(\n data, alphas, stat_array, stat_function, ostat, reps\n):\n\nSubroutine called to calculate the BCa statistics. Borrowed heavily from scikits.bootstrap code.\n\n\n\njackknife_indexes\n\ndef jackknife_indexes(\n data\n):\n\nFrom the scikits.bootstrap package. Given an array, returns a list of arrays where each array is a set of jackknife indexes.\nFor a given set of data Y, the jackknife sample J[i] is defined as the data set Y with the ith data point deleted.", + "crumbs": [ + "Get Started", + "API", + "Bootstrap" + ] + }, + { + "objectID": "API/confint_2group_diff.html", + "href": "API/confint_2group_diff.html", + "title": "confint_2group_diff", + "section": "", + "text": "source\n\ncalculate_weighted_delta\n\ndef calculate_weighted_delta(\n bootstrap_dist_var, differences\n):\n\nCompute the weighted deltas.\n\nsource\n\n\ncalculate_bootstraps_var\n\ndef calculate_bootstraps_var(\n bootstraps\n):\n\nCall self as a function.\n\nsource\n\n\ncalculate_group_var\n\ndef calculate_group_var(\n control_var, control_N, test_var, test_N\n):\n\nCall self as a function.\n\nsource\n\n\ncompute_interval_limits\n\ndef compute_interval_limits(\n bias, acceleration, n_boots, ci:int=95\n):\n\nReturns the indexes of the interval limits for a given bootstrap.\nSupply the bias, acceleration factor, and number of bootstraps.\n\nsource\n\n\ncompute_meandiff_bias_correction\n\ndef compute_meandiff_bias_correction(\n bootstraps, # An numerical iterable, comprising bootstrap resamples of the effect size.\n effsize, # The effect size for the original sample.\n): # The bias correction value for the given bootstraps\nand effect size.\n\nComputes the bias correction required for the BCa method of confidence interval construction.\n\nsource\n\n\ncompute_delta2_bootstrapped_diff\n\ndef compute_delta2_bootstrapped_diff(\n x1:np.ndarray, # Control group 1\n x2:np.ndarray, # Test group 1\n x3:np.ndarray, # Control group 2\n x4:np.ndarray, # Test group 2\n is_paired:str=None, resamples:int=5000, random_seed:int=12345, proportional:bool=False\n)->tuple:\n\nBootstraps the effect size deltas’ g or proportional delta-delta\n\nsource\n\n\ndelta2_bootstrap_loop\n\ndef delta2_bootstrap_loop(\n x1, x2, x3, x4, resamples, pooled_sd, rng_seed, is_paired, proportional:bool=False\n):\n\nCompute bootstrapped differences for delta-delta, handling both regular and proportional data\n\nsource\n\n\ncompute_bootstrapped_diff\n\ndef compute_bootstrapped_diff(\n x0, x1, is_paired, effect_size, resamples:int=5000, random_seed:int=12345\n):\n\nBootstraps the effect_size for 2 groups.\n\nsource\n\n\nbootstrap_indices\n\ndef bootstrap_indices(\n is_paired, x0_len, x1_len, resamples,\n random_seed, # parallelization must be turned off for random number generation\n):\n\nCall self as a function.\n\nsource\n\n\ncompute_meandiff_jackknife\n\ndef compute_meandiff_jackknife(\n x0, x1, is_paired, effect_size\n):\n\nGiven two arrays, returns the jackknife for their effect size.\n\nsource\n\n\ncreate_repeated_indexes\n\ndef create_repeated_indexes(\n data\n):\n\nConvenience function. Given an array-like with length N, returns a generator that yields N indexes [0, 1, …, N].\n/opt/hostedtoolcache/Python/3.12.13/x64/lib/python3.12/site-packages/fastcore/docscrape.py:259: UserWarning: Unknown section Keywords\n else: warn(msg)\n\nsource\n\n\ncreate_jackknife_indexes\n\ndef create_jackknife_indexes(\n data\n):\n\nGiven an array-like, creates a jackknife bootstrap.\nFor a given set of data Y, the jackknife bootstrap sample J[i] is defined as the data set Y with the ith data point deleted.", + "crumbs": [ + "Get Started", + "API", + "confint_2group_diff" + ] + }, + { + "objectID": "API/delta_objects.html", + "href": "API/delta_objects.html", + "title": "Delta objects", + "section": "", + "text": "DeltaDelta\n\ndef DeltaDelta(\n effectsizedataframe, permutation_count, bootstraps_delta_delta, ci:int=95\n):\n\nA class to compute and store the delta-delta statistics for experiments with a 2-by-2 arrangement where two independent variables, A and B, each have two categorical values, 1 and 2. The data is divided into two pairs of two groups, and a primary delta is first calculated as the mean difference between each of the pairs:\n\\[\\Delta_{1} = \\overline{X}_{A_{2}, B_{1}} - \\overline{X}_{A_{1}, B_{1}}\\]\n\\[\\Delta_{2} = \\overline{X}_{A_{2}, B_{2}} - \\overline{X}_{A_{1}, B_{2}}\\]\nwhere \\(\\overline{X}_{A_{i}, B_{j}}\\) is the mean of the sample with A = i and B = j, \\(\\Delta\\) is the mean difference between two samples.\nA delta-delta value is then calculated as the mean difference between the two primary deltas:\n\\[\\Delta_{\\Delta} = \\Delta_{2} - \\Delta_{1}\\]\nand a delta g value is calculated as the mean difference between the two primary deltas divided by the standard deviation of the delta-delta value, which is calculated from a pooled variance of the 4 samples:\n\\[\\Delta_{g} = \\frac{\\Delta_{\\Delta}}{s_{\\Delta_{\\Delta}}}\\]\n\\[s_{\\Delta_{\\Delta}} = \\sqrt{\\frac{(n_{A_{2}, B_{1}}-1)s_{A_{2}, B_{1}}^2+(n_{A_{1}, B_{1}}-1)s_{A_{1}, B_{1}}^2+(n_{A_{2}, B_{2}}-1)s_{A_{2}, B_{2}}^2+(n_{A_{1}, B_{2}}-1)s_{A_{1}, B_{2}}^2}{(n_{A_{2}, B_{1}} - 1) + (n_{A_{1}, B_{1}} - 1) + (n_{A_{2}, B_{2}} - 1) + (n_{A_{1}, B_{2}} - 1)}}\\]\nwhere \\(s\\) is the standard deviation and \\(n\\) is the sample size.\nand the standard deviation of the delta-delta value is calculated from a pooled variance of the 4 samples:\n\\[s_{\\Delta_{\\Delta}} = \\sqrt{\\frac{(n_{A_{2}, B_{1}}-1)s_{A_{2}, B_{1}}^2+(n_{A_{1}, B_{1}}-1)s_{A_{1}, B_{1}}^2+(n_{A_{2}, B_{2}}-1)s_{A_{2}, B_{2}}^2+(n_{A_{1}, B_{2}}-1)s_{A_{1}, B_{2}}^2}{(n_{A_{2}, B_{1}} - 1) + (n_{A_{1}, B_{1}} - 1) + (n_{A_{2}, B_{2}} - 1) + (n_{A_{1}, B_{2}} - 1)}}\\]\nwhere \\(s\\) is the standard deviation and \\(n\\) is the sample size.\n\nExample: delta-delta\n\nnp.random.seed(9999) # Fix the seed so the results are replicable.\nN = 20\n# Create samples\ny = norm.rvs(loc=3, scale=0.4, size=N*4)\ny[N:2*N] = y[N:2*N]+1\ny[2*N:3*N] = y[2*N:3*N]-0.5\n# Add a `Treatment` column\nt1 = np.repeat('Placebo', N*2).tolist()\nt2 = np.repeat('Drug', N*2).tolist()\ntreatment = t1 + t2 \n# Add a `Rep` column as the first variable for the 2 replicates of experiments done\nrep = []\nfor i in range(N*2):\n rep.append('Rep1')\n rep.append('Rep2')\n# Add a `Genotype` column as the second variable\nwt = np.repeat('W', N).tolist()\nmt = np.repeat('M', N).tolist()\nwt2 = np.repeat('W', N).tolist()\nmt2 = np.repeat('M', N).tolist()\ngenotype = wt + mt + wt2 + mt2\n# Add an `id` column for paired data plotting.\nid = list(range(0, N*2))\nid_col = id + id \n# Combine all columns into a DataFrame.\ndf_delta2 = pd.DataFrame({'ID' : id_col,\n 'Rep' : rep,\n 'Genotype' : genotype, \n 'Treatment': treatment,\n 'Y' : y\n })\nunpaired_delta2 = dabest.load(data = df_delta2, x = [\"Genotype\", \"Genotype\"], y = \"Y\", delta2 = True, experiment = \"Treatment\")\nunpaired_delta2.mean_diff.plot();\n\nC:\\Users\\maiyi\\anaconda3\\Lib\\site-packages\\dabest\\plot_tools.py:2537: UserWarning: 5.0% of the points cannot be placed. You might want to decrease the size of the markers.\n warnings.warn(err)\nC:\\Users\\maiyi\\anaconda3\\Lib\\site-packages\\dabest\\plot_tools.py:2537: UserWarning: 5.0% of the points cannot be placed. You might want to decrease the size of the markers.\n warnings.warn(err)\nC:\\Users\\maiyi\\anaconda3\\Lib\\site-packages\\dabest\\plot_tools.py:2537: UserWarning: 20.0% of the points cannot be placed. You might want to decrease the size of the markers.\n warnings.warn(err)\n\n\n\n\n\n\n\n\n\n\n\n\n\nMiniMetaDelta\n\ndef MiniMetaDelta(\n effectsizedataframe, permutation_count, ci:int=95\n):\n\nA class to compute and store the weighted delta. A weighted delta is calculated if the argument mini_meta=True is passed during dabest.load().\nThe weighted delta is calcuated as follows:\n\\[\\theta_{\\text{weighted}} = \\frac{\\Sigma\\hat{\\theta_{i}}w_{i}}{{\\Sigma}w_{i}}\\]\nwhere:\n\\[\\hat{\\theta_{i}} = \\text{Mean difference for replicate }i\\]\n\\[w_{i} = \\text{Weight for replicate }i = \\frac{1}{s_{i}^2} \\]\n\\[s_{i}^2 = \\text{Pooled variance for replicate }i = \\frac{(n_{test}-1)s_{test}^2+(n_{control}-1)s_{control}^2}{n_{test}+n_{control}-2}\\]\n\\[n = \\text{sample size and }s^2 = \\text{variance for control/test.}\\]\n\nExample: mini-meta-delta\n\nNs = 20\nc1 = norm.rvs(loc=3, scale=0.4, size=Ns)\nc2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\nc3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\nt1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\nt2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\nt3 = norm.rvs(loc=3, scale=0.75, size=Ns)\nmy_df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n 'Control 2' : c2, 'Test 2' : t2,\n 'Control 3' : c3, 'Test 3' : t3})\nmy_dabest_object = dabest.load(my_df, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), mini_meta=True)\nmy_dabest_object.mean_diff.mini_meta\n\nDABEST v2025.03.27\n==================\n \nGood afternoon!\nThe current time is Mon Sep 1 16:03:47 2025.\n\nThe weighted-average unpaired mean differences is 0.0336 [95%CI -0.136, 0.236].\nThe p-value of the two-sided permutation t-test is 0.736, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\n\nAs of version 2023.02.14, weighted delta can only be calculated for mean difference, and not for standardized measures such as Cohen’s d.\nDetails about the calculated weighted delta are accessed as attributes of the mini_meta class. See the minimetadelta for details on usage.\nRefer to Chapter 10 of the Cochrane handbook for further information on meta-analysis: https://training.cochrane.org/handbook/current/chapter-10", + "crumbs": [ + "Get Started", + "API", + "Delta objects" + ] + }, + { + "objectID": "API/effsize_objects.html", + "href": "API/effsize_objects.html", + "title": "Effectsize objects", + "section": "", + "text": "TwoGroupsEffectSize\n\ndef TwoGroupsEffectSize(\n control, test, effect_size, proportional:bool=False, is_paired:NoneType=None, ci:int=95, resamples:int=5000,\n permutation_count:int=5000, random_seed:int=12345, ps_adjust:bool=False\n):\n\nA class to compute and store the results of bootstrapped mean differences between two groups.\nCompute the effect size between two groups.\n\nExample\n\nrandom.seed(12345)\ncontrol = norm.rvs(loc=0, size=30)\ntest = norm.rvs(loc=0.5, size=30)\neffsize = dabest.TwoGroupsEffectSize(control, test, \"mean_diff\")\neffsize\n\nThe unpaired mean difference is -0.253 [95%CI -0.782, 0.241].\nThe p-value of the two-sided permutation t-test is 0.348, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\n\n\neffsize.to_dict()\n\n{'alpha': 0.05,\n 'bca_high': 0.2413346581369784,\n 'bca_interval_idx': (109, 4858),\n 'bca_low': -0.7818088458343655,\n 'bec_bca_high': 0.5352403905584314,\n 'bec_bca_interval_idx': (130, 4880),\n 'bec_bca_low': -0.4982839949134528,\n 'bec_bootstraps': array([-0.48953946, -0.18565285, -0.23896785, ..., -0.55130928,\n 0.16037238, -0.07364879]),\n 'bec_difference': 0.0,\n 'bec_pct_high': 0.5280564736117328,\n 'bec_pct_interval_idx': (125, 4875),\n 'bec_pct_low': -0.5041777340626885,\n 'bootstraps': array([-0.23923425, -0.66013733, -0.42672232, ..., -0.33191074,\n -0.16543251, -0.34179536]),\n 'ci': 95,\n 'difference': -0.25315417702752846,\n 'effect_size': 'mean difference',\n 'is_paired': None,\n 'is_proportional': False,\n 'pct_high': 0.25135646125431527,\n 'pct_interval_idx': (125, 4875),\n 'pct_low': -0.763588353717278,\n 'permutation_count': 5000,\n 'permutations': array([ 0.17221029, 0.03112419, -0.13911387, ..., -0.38007941,\n 0.30261507, -0.09073054]),\n 'permutations_var': array([0.07201642, 0.07251104, 0.07219407, ..., 0.07003705, 0.07094885,\n 0.07238581]),\n 'proportional_difference': nan,\n 'pvalue_brunner_munzel': nan,\n 'pvalue_kruskal': nan,\n 'pvalue_mann_whitney': 0.5201446121616038,\n 'pvalue_mcnemar': nan,\n 'pvalue_paired_students_t': nan,\n 'pvalue_permutation': 0.3484,\n 'pvalue_students_t': 0.34743913903372836,\n 'pvalue_welch': 0.3474493875548964,\n 'pvalue_wilcoxon': nan,\n 'random_seed': 12345,\n 'resamples': 5000,\n 'statistic_brunner_munzel': nan,\n 'statistic_kruskal': nan,\n 'statistic_mann_whitney': 494.0,\n 'statistic_mcnemar': nan,\n 'statistic_paired_students_t': nan,\n 'statistic_students_t': 0.9472545159069105,\n 'statistic_welch': 0.9472545159069105,\n 'statistic_wilcoxon': nan}\n\n\n\n\n\n\nEffectSizeDataFrame\n\ndef EffectSizeDataFrame(\n dabest, effect_size, is_paired, ci:int=95, proportional:bool=False, resamples:int=5000,\n permutation_count:int=5000, random_seed:int=12345, x1_level:NoneType=None, x2:NoneType=None, delta2:bool=False,\n experiment_label:NoneType=None, mini_meta:bool=False, ps_adjust:bool=False\n):\n\nA class that generates and stores the results of bootstrapped effect sizes for several comparisons.\n\nExample: plot\nCreate a Gardner-Altman estimation plot for the mean difference.\n\nrandom.seed(9999) # Fix the seed so the results are replicable.\n# pop_size = 10000 # Size of each population.\nNs = 20 # The number of samples taken from each population\n\n# Create samples\nc1 = norm.rvs(loc=3, scale=0.4, size=Ns)\nc2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\nc3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n\nt1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\nt2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\nt3 = norm.rvs(loc=3, scale=0.75, size=Ns)\nt4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\nt5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\nt6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n\n\n# Add a `gender` column for coloring the data.\nfemales = repeat('Female', Ns/2).tolist()\nmales = repeat('Male', Ns/2).tolist()\ngender = females + males\n\n# Add an `id` column for paired data plotting.\nid_col = pd.Series(range(1, Ns+1))\n\n# Combine samples and gender into a DataFrame.\ndf = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n 'Control 2' : c2, 'Test 2' : t2,\n 'Control 3' : c3, 'Test 3' : t3,\n 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6,\n 'Gender' : gender, 'ID' : id_col\n })\nmy_data = dabest.load(df, idx=(\"Control 1\", \"Test 1\"))\n\n\nfig1 = my_data.mean_diff.plot();\n\n\n\n\n\n\n\n\nCreate a Gardner-Altman plot for the Hedges’ g effect size.\n\nfig2 = my_data.hedges_g.plot();\n\n\n\n\n\n\n\n\nCreate a Cumming estimation plot for the mean difference.\n\nfig3 = my_data.mean_diff.plot(float_contrast=True);\n\n\n\n\n\n\n\n\nCreate a paired Gardner-Altman plot.\n\nmy_data_paired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"),\n id_col = \"ID\", paired='baseline')\nfig4 = my_data_paired.mean_diff.plot();\n\n\n\n\n\n\n\n\nCreate a multi-group Cumming plot.\n\nmy_multi_groups = dabest.load(df, id_col = \"ID\", \n idx=((\"Control 1\", \"Test 1\"),\n (\"Control 2\", \"Test 2\")))\nfig5 = my_multi_groups.mean_diff.plot();\n\n/Users/jonathananns/GitHub/DABEST-python/dabest/plot_tools.py:2778: UserWarning: 5.0% of the points cannot be placed. You might want to decrease the size of the markers.\n warnings.warn(err)\n/Users/jonathananns/GitHub/DABEST-python/dabest/plot_tools.py:2778: UserWarning: 10.0% of the points cannot be placed. You might want to decrease the size of the markers.\n warnings.warn(err)\n\n\n\n\n\n\n\n\n\nCreate a shared control Cumming plot.\n\nmy_shared_control = dabest.load(df, id_col = \"ID\",\n idx=(\"Control 1\", \"Test 1\",\n \"Test 2\", \"Test 3\"))\nfig6 = my_shared_control.mean_diff.plot();\n\n/Users/jonathananns/GitHub/DABEST-python/dabest/plot_tools.py:2778: UserWarning: 10.0% of the points cannot be placed. You might want to decrease the size of the markers.\n warnings.warn(err)\n\n\n\n\n\n\n\n\n\nCreate a repeated meausures (against baseline) Slopeplot.\n\nmy_rm_baseline = dabest.load(df, id_col = \"ID\", paired = \"baseline\",\n idx=(\"Control 1\", \"Test 1\",\n \"Test 2\", \"Test 3\"))\nfig7 = my_rm_baseline.mean_diff.plot();\n\n\n\n\n\n\n\n\nCreate a repeated meausures (sequential) Slopeplot.\n\nmy_rm_sequential = dabest.load(df, id_col = \"ID\", paired = \"sequential\",\n idx=(\"Control 1\", \"Test 1\",\n \"Test 2\", \"Test 3\"))\nfig8 = my_rm_sequential.mean_diff.plot();\n\n\n\n\n\n\n\n\n\n\n\n\nPermutationTest\n\ndef PermutationTest(\n control:array, test:array, # These should be numerical iterables.\n effect_size:str, # Any one of the following are accepted inputs: 'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta'\n is_paired:str=None, permutation_count:int=5000, # The number of permutations (reshuffles) to perform.\n random_seed:int=12345, # `random_seed` is used to seed the random number generator during bootstrap resampling. This ensures that the generated permutations are replicable.\n ps_adjust:bool=False, kwargs:VAR_KEYWORD\n):\n\nA class to compute and report permutation tests.\nNotes:\nThe basic concept of permutation tests is the same as that behind bootstrapping. In an “exact” permutation test, all possible resuffles of the control and test labels are performed, and the proportion of effect sizes that equal or exceed the observed effect size is computed. This is the probability, under the null hypothesis of zero difference between test and control groups, of observing the effect size: the p-value of the Student’s t-test.\nExact permutation tests are impractical: computing the effect sizes for all reshuffles quickly exceeds trivial computational loads. A control group and a test group both with 10 observations each would have a total of \\(20!\\) or \\(2.43 \\times {10}^{18}\\) reshuffles. Therefore, in practice, “approximate” permutation tests are performed, where a sufficient number of reshuffles are performed (5,000 or 10,000), from which the p-value is computed.\nMore information can be found here.\n\nExample: permutation test\n\ncontrol = norm.rvs(loc=0, size=30, random_state=12345)\ntest = norm.rvs(loc=0.5, size=30, random_state=12345)\nperm_test = dabest.PermutationTest(control, test, \n effect_size=\"mean_diff\", \n is_paired=None)\nperm_test\n\n5000 permutations were taken. The p-value is 0.0758.", + "crumbs": [ + "Get Started", + "API", + "Effectsize objects" + ] + }, + { + "objectID": "API/load.html#example", + "href": "API/load.html#example", + "title": "Loading Data", + "section": "Example", + "text": "Example\n\nimport numpy as np\nimport pandas as pd\nimport scipy as sp\nimport dabest\n\nCreate dummy data for demonstration.\n\nnp.random.seed(88888)\nN = 10\nc1 = sp.stats.norm.rvs(loc=100, scale=5, size=N)\nt1 = sp.stats.norm.rvs(loc=115, scale=5, size=N)\ndf = pd.DataFrame({\"Control 1\": c1, \"Test 1\": t1})\n\nLoad the data.\n\nmy_data = dabest.load(df, idx=(\"Control 1\", \"Test 1\"))\nmy_data\n\nDABEST v2024.03.29\n==================\n \nGood afternoon!\nThe current time is Tue Mar 19 15:34:58 2024.\n\nEffect size(s) with 95% confidence intervals will be computed for:\n1. Test 1 minus Control 1\n\n5000 resamples will be used to generate the effect size bootstraps.\n\n\nFor proportion plot.\n\nnp.random.seed(88888)\nN = 10\nc1 = np.random.binomial(1, 0.2, size=N)\nt1 = np.random.binomial(1, 0.5, size=N)\ndf = pd.DataFrame({\"Control 1\": c1, \"Test 1\": t1})\nmy_data = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), proportional=True)", + "crumbs": [ + "Get Started", + "API", + "Loading Data" + ] + }, + { + "objectID": "API/multi.html", + "href": "API/multi.html", + "title": "multi", + "section": "", + "text": "The MultiContrast class enables visualization of multiple contrast objects in grid-based layouts.\n\nsource\n\n\n\ndef MultiContrast(\n dabest_objs:Union, # Raw dabest objects. Can be:\n- 1D: [dabest_obj1, dabest_obj2, ...] \n- 2D: [[dabest_obj1, dabest_obj2], [dabest_obj3, dabest_obj4]]\n labels:Optional=None, # Labels matching the contrast array structure. If None, defaults will be generated.\n row_labels:Optional=None, effect_size:str='mean_diff', # Effect size to extract from dabest objects\n ci_type:str='bca', # Confidence interval type\n):\n\nUnified multiple contrast object for forest plots and whorlmaps.\nTakes raw dabest objects and provides validated, processed data for downstream visualizations.", + "crumbs": [ + "Get Started", + "API", + "multi" + ] + }, + { + "objectID": "API/multi.html#multicontrast-class", + "href": "API/multi.html#multicontrast-class", + "title": "multi", + "section": "", + "text": "The MultiContrast class enables visualization of multiple contrast objects in grid-based layouts.\n\nsource\n\n\n\ndef MultiContrast(\n dabest_objs:Union, # Raw dabest objects. Can be:\n- 1D: [dabest_obj1, dabest_obj2, ...] \n- 2D: [[dabest_obj1, dabest_obj2], [dabest_obj3, dabest_obj4]]\n labels:Optional=None, # Labels matching the contrast array structure. If None, defaults will be generated.\n row_labels:Optional=None, effect_size:str='mean_diff', # Effect size to extract from dabest objects\n ci_type:str='bca', # Confidence interval type\n):\n\nUnified multiple contrast object for forest plots and whorlmaps.\nTakes raw dabest objects and provides validated, processed data for downstream visualizations.", + "crumbs": [ + "Get Started", + "API", + "multi" + ] + }, + { + "objectID": "API/multi.html#loading-function", + "href": "API/multi.html#loading-function", + "title": "multi", + "section": "Loading Function", + "text": "Loading Function\n\nsource\n\ncombine\n\ndef combine(\n dabest_objs:Union, # Raw dabest objects in 1D or 2D structure\n labels:Optional=None, # Labels for dabest_objs\n row_labels:Optional=None, effect_size:str='mean_diff', # Effect size to extract\n ci_type:str='bca', # Confidence interval type\n allow_mixed_types:bool=False, # If True, allows different contrast types in different rows (whorlmap only)\nIf False, enforces homogeneous types (forest_plot compatible)\n)->MultiContrast: # Validated multi-contrast object ready for visualization\n\nCreate a MultiContrast object from raw dabest objects.\nThis is the main entry point that users should use to create multi-contrast visualizations.", + "crumbs": [ + "Get Started", + "API", + "multi" + ] + }, + { + "objectID": "API/multi.html#whorlmap-visualization", + "href": "API/multi.html#whorlmap-visualization", + "title": "multi", + "section": "Whorlmap Visualization", + "text": "Whorlmap Visualization\nThe whorlmap creates spiral heatmaps showing the distribution of bootstrap samples for each contrast.\n\nsource\n\nwhorlmap\n\ndef whorlmap(\n multi_contrast, # Object containing multiple dabest objects\n n:int=21, # Size of each spiral (n x n grid per contrast)\n sort_by:NoneType=None, # Order to sort contrasts by\n cmap:str='vlag', vmax:NoneType=None, vmin:NoneType=None,\n reverse_neg:bool=True, # Whether to reverse negative values\n abs_rank:bool=False, # Whether to rank by absolute value\n chop_tail:int=0, # Percentage of extreme values to exclude\n ax:NoneType=None, # Existing axes to plot on\n fig_size:NoneType=None, # Figure size (width, height) in inches\n title:NoneType=None, # Plot title\n heatmap_kwargs:NoneType=None, # Additional keyword arguments passed to sns.heatmap().\nCommon options include:\n- 'cmap': colormap (overrides direct cmap parameter)\n- 'vmin', 'vmax': color scale limits (override direct parameters)\n- 'center': center value for colormap\n- 'annot': whether to annotate cells with values\n- 'fmt': format string for annotations\n- 'linewidths': width of lines between cells\n- 'linecolor': color of lines between cells\n- 'cbar': whether to show colorbar\n- 'cbar_kws': colorbar customization dict\n- 'square': whether to make cells square\n- 'xticklabels', 'yticklabels': tick label control\n- 'mask': boolean array to mask cells\n plot_kwargs:NoneType=None, # Additional keyword arguments for plot styling and layout.\nAvailable options (WIP):\n- 'title': plot title\n- 'xlabel', 'ylabel': axis labels\n- 'xticklabels', 'yticklabels': tick labels\n- 'xticklabels_rotation', 'yticklabels_rotation': tick label rotation angles\n- 'xticklabels_ha', 'yticklabels_ha': horizontal alignment \n):\n\nCreate a whorlmap visualization of multiple contrasts.", + "crumbs": [ + "Get Started", + "API", + "multi" + ] + }, + { + "objectID": "API/plotter.html", + "href": "API/plotter.html", + "title": "Plot", + "section": "", + "text": "source\n\neffectsize_df_plotter\n\ndef effectsize_df_plotter(\n effectsize_df:object, plot_kwargs:VAR_KEYWORD\n)->matplotlib.figure.Figure:\n\nCustom function that creates an estimation plot from an EffectSizeDataFrame. Keywords ——– Parameters ———- effectsize_df A dabest EffectSizeDataFrame object. plot_kwargs color_col=None raw_marker_size=6, contrast_marker_kwargs=9, raw_label=None, contrast_label=None, delta2_label=None, raw_ylim=None, contrast_ylim=None, delta2_ylim=None, custom_palette=None, swarm_side=None, empty_circle=False, face_color=None, raw_desat=0.5, contrast_desat=1, raw_alpha=None, contrast_alpha=0.8, bar_width = 0.5, ci_type=‘bca’, float_contrast=True, show_pairs=True, show_sample_size=True, show_delta2=True, show_mini_meta=True, group_summaries=“mean_sd”, fig_size=None, dpi=100, ax=None, swarmplot_kwargs=None, slopegraph_kwargs=None, barplot_kwargs=None, sankey_kwargs=None, contrast_kwargs=None, reflines_kwargs=None, group_summaries_kwargs=None, legend_kwargs=None, title=None, fontsize_title=16, fontsize_rawxlabel=12, fontsize_rawylabel=12, fontsize_contrastxlabel=12, fontsize_contrastylabel=12, fontsize_delta2label=12,\nraw_bars=True, raw_bars_kwargs=None, contrast_bars=True, contrast_bars_kwargs=None, reference_band=None, reference_band_kwargs=None, delta_text=True, delta_text_kwargs=None, delta_dot=True, delta_dot_kwargs=None,\nhorizontal=False, horizontal_table_kwargs=None, gridkey=None, gridkey_merge_pairs=False, gridkey_show_Ns=True, gridkey_show_es=True, gridkey_delimiters=[‘;’, ‘>’, ’_’], gridkey_kwargs=None, contrast_marker_kwargs=None, contrast_errorbar_kwargs=None, prop_sample_counts=False, prop_sample_counts_kwargs=None, contrast_paired_lines=True, contrast_paired_lines show_baseline_ec=False,\nFor details on how to control the aesthetic of the generated estimation plot by modifying the plot_kwargs, please refer to Controlling Plot Aesthetics\n\neffectsize_df: A dabest EffectSizeDataFrame object.\nplot_kwargs:\n\ncolor_col=None\nraw_marker_size=6, contrast_marker_size=9,\nraw_label=None, contrast_label=None, delta2_label=None,\nraw_ylim=None, contrast_ylim=None, delta2_ylim=None,\ncustom_palette=None, swarm_side=None, empty_circle=False,\nface_color = None,\nraw_desat=0.5, contrast_desat=1,\nraw_alpha=None, contrast_alpha=0.8,\nbar_width=0.5,\nci_type=‘bca’,\nfloat_contrast=True,\nshow_pairs=True,\nshow_sample_size=True\nshow_delta2=True, show_mini_meta=True,\ngroup_summaries=“mean_sd”,\nfig_size=None, dpi=100,\nax=None,\nswarmplot_kwargs=None,\nslopegraph_kwargs=None,\nbarplot_kwargs=None,\nsankey_kwargs=None,\ncontrast_kwargs=None,\nreflines_kwargs=None,\ngroup_summaries_kwargs=None,\nlegend_kwargs=None,\ntitle=None, fontsize_title=16,\nfontsize_rawxlabel=12, fontsize_rawylabel=12,\nfontsize_contrastxlabel=12, fontsize_contrastylabel=12,\nfontsize_delta2label=12,\nraw_bars=True, raw_bars_kwargs=None,\ncontrast_bars=True, contrast_bars_kwargs=None,\nreference_band=None, reference_band_kwargs=None,\ndelta_text=True, delta_text_kwargs=None,\ndelta_dot=True, delta_dot_kwargs=None,\nhorizontal=False, horizontal_table_kwargs=None,\ngridkey=None, gridkey_merge_pairs=False,\ngridkey_show_Ns=True, gridkey_show_es=True,\ngridkey_delimiters=[‘;’, ‘>’, ’_’],\ngridkey_kwargs=None,\ncontrast_marker_kwargs=None, contrast_errorbar_kwargs=None\nprop_sample_counts=False, prop_sample_counts_kwargs=None\ncontrast_paired_lines=True, contrast_paired_lines_kwargs=None,\nshow_baseline_ec=False", + "crumbs": [ + "Get Started", + "API", + "Plot" + ] + }, + { + "objectID": "blog/posts/a-dabest2-preprint/a-dabest2-preprint.html", + "href": "blog/posts/a-dabest2-preprint/a-dabest2-preprint.html", + "title": "Preprint: Getting over ANOVA", + "section": "", + "text": "Here’s a dirty secret about ANOVA: it tests a null hypothesis that nobody cares about. When you run a one-way ANOVA, you’re testing whether “all group means are equal.” But even if you reject this hypothesis, you learn nothing about which groups differ, in which direction, or by how much.\nSo you embark on a second analytical step: multiple two-group comparisons. A modest six-group experiment suddenly requires testing 15 hypotheses. To manage this multiplicity, you apply corrections like Bonferroni, which undermine your statistical power. What you posed as a focused research question has sprawled into a complex web of subsidiary tests, forced by the ANOVA ritual.\nOur new preprint, “Getting over ANOVA: Estimation graphics for multi-group comparisons,” makes the case for a better approach. Estimation statistics encourages you to compare each test group to a single control, focusing on the effect sizes that actually matter. A six-group experiment focuses attention on just five effect sizes with confidence intervals, showing magnitude and precision directly.\nThe preprint introduces estimation methods for a range of multi-group designs: repeated-measures experiments, 2×2 factorial designs, binary outcome data, and mini-meta analysis for internal replicates. Each can replace data-analysis practices used in thousands of studies every year.\nRead our new preprint here: https://doi.org/10.64898/2026.01.26.701654\nAlso posted on LinkedIn #Statistics #OpenScience #DataVisualization #Research" + }, + { + "objectID": "blog/posts/robust-beautiful/robust-beautiful.html", + "href": "blog/posts/robust-beautiful/robust-beautiful.html", + "title": "Robust and Beautiful Statistical Visualization", + "section": "", + "text": "What is data visualization? Battle-Baptiste and Rusert (2018) give a cogent and compelling definition:\nData visualization[1] is the rendering of information in a visual format to help communicate data while also generating new patterns and knowledge through the act of visualization itself.\nSadly, too many figures and visualizations in modern academic publications seemingly fail to “generate new patterns and knowledge through the act of visualization itself”. Here, we propose a solution: the estimation plot.\n\n\nBy only displaying the mean and standard deviation, barplots do not accurately represent the underlying distribution of the data.\n\nIn the above figure, four different samples with wildly different distributions–as seen in the swarmplot on the left panel–look exactly the same when visualized with a barplot on the right panel. (You can download the dataset to see for yourself.)\nWe’re not the first ones (see these articles: article 1, article 2, or article 3) to point out the barplot’s fatal flaws. Indeed, it is both sobering and fascinating to realise that the barplot is a 17th century invention initially used to compare single values, not to compare summarized and aggregated data.\n\n\n\nBoxplots are another widely used visualization tool. They arguably do include more information for each sample (medians, quartiles, maxima, minima, and outliers), but they do not convey to the viewer the size of each sample.\n\nThe figure above visualizes the same four samples as a swarmplot (left panel) and as a boxplot. If we did not label the x-axis with the sample size, it would be impossible to definitively distinguish the sample with 5 observations from the sample with 50.\nEven if the world gets rid of barplots and boxplots, the problems plaguing statistical practices will remain unsolved. Null-hypothesis significance testing–the dominant statistical paradigm in basic research–does not indicate the effect size, or its confidence interval." + }, + { + "objectID": "blog/posts/robust-beautiful/robust-beautiful.html#current-plots-do-not-work", + "href": "blog/posts/robust-beautiful/robust-beautiful.html#current-plots-do-not-work", + "title": "Robust and Beautiful Statistical Visualization", + "section": "", + "text": "What is data visualization? Battle-Baptiste and Rusert (2018) give a cogent and compelling definition:\nData visualization[1] is the rendering of information in a visual format to help communicate data while also generating new patterns and knowledge through the act of visualization itself.\nSadly, too many figures and visualizations in modern academic publications seemingly fail to “generate new patterns and knowledge through the act of visualization itself”. Here, we propose a solution: the estimation plot.\n\n\nBy only displaying the mean and standard deviation, barplots do not accurately represent the underlying distribution of the data.\n\nIn the above figure, four different samples with wildly different distributions–as seen in the swarmplot on the left panel–look exactly the same when visualized with a barplot on the right panel. (You can download the dataset to see for yourself.)\nWe’re not the first ones (see these articles: article 1, article 2, or article 3) to point out the barplot’s fatal flaws. Indeed, it is both sobering and fascinating to realise that the barplot is a 17th century invention initially used to compare single values, not to compare summarized and aggregated data.\n\n\n\nBoxplots are another widely used visualization tool. They arguably do include more information for each sample (medians, quartiles, maxima, minima, and outliers), but they do not convey to the viewer the size of each sample.\n\nThe figure above visualizes the same four samples as a swarmplot (left panel) and as a boxplot. If we did not label the x-axis with the sample size, it would be impossible to definitively distinguish the sample with 5 observations from the sample with 50.\nEven if the world gets rid of barplots and boxplots, the problems plaguing statistical practices will remain unsolved. Null-hypothesis significance testing–the dominant statistical paradigm in basic research–does not indicate the effect size, or its confidence interval." + }, + { + "objectID": "blog/posts/robust-beautiful/robust-beautiful.html#introducing-the-estimation-plot", + "href": "blog/posts/robust-beautiful/robust-beautiful.html#introducing-the-estimation-plot", + "title": "Robust and Beautiful Statistical Visualization", + "section": "Introducing the Estimation Plot", + "text": "Introducing the Estimation Plot\n\nThis is a Gardner-Altman estimation plot. The plot draws its name from Martin J. Gardner and Douglas Altman, who are credited with creating the design in 1986.\nThis plot has two key features:\n\nIt presents all data points as a swarmplot, ordering each point to display the underlying distribution.\nIt presents the effect size as a bootstrap 95% confidence interval (95% CI) on a separate but aligned axis. The effect size is displayed to the right of the raw data, and the mean of the test group is aligned with the effect size.”\n\n\nThus, estimation plots are robust, beautiful, and convey important statistical information elegantly and efficiently.\n\nAn estimation plot obtains and displays the 95% CI through nonparametric bootstrap resampling. This enables visualization of the confidence interval as a graded sampling distribution.\nThis is one important difference between estimation plots created by DABEST, and the original Gardner-Altman design. Here, the 95% CI is computed through parametric methods, and displayed as a vertical error bar.\nRead more about this technique at bootstraps.\n\nIntroducing Estimation Statistics\nEstimation plots emerge from estimation statistics, a simple framework that avoids the pitfalls of significance testing. It focuses on the effect sizes of one’s experiment/interventions, and uses familiar statistical concepts: means, mean differences, and error bars.\nSignificance testing calculates the probability (the P value) that the experimental data would be observed, if the intervention did not produce a change in the metric measured (i.e. the null hypothesis). This leads analysts to apply a false dichotomy on the experimental intervention.\nEstimation statistics, on the other hand, focuses on the magnitude of the effect (the effect size) and its precision. This encourages analysts to gain a deeper understanding of the metrics used, and how they relate to the natural processes being studied." + }, + { + "objectID": "blog/posts/robust-beautiful/robust-beautiful.html#an-estimation-plot-for-every-experimental-design", + "href": "blog/posts/robust-beautiful/robust-beautiful.html#an-estimation-plot-for-every-experimental-design", + "title": "Robust and Beautiful Statistical Visualization", + "section": "An Estimation Plot For Every Experimental Design", + "text": "An Estimation Plot For Every Experimental Design\nFor each of the most routine significance tests, there is an estimation replacement:\n\nUnpaired Student’s t-test –> Two-group estimation plot\n\n\n\nPaired Student’s t-test –> Paired estimation plot\nThe Gardner-Altman estimation plot can also display effect sizes for repeated measures (aka a paired experimental design) using a Tufte slopegraph instead of a swarmplot.\n\n\n\nOne-way ANOVA + multiple comparisons –> Multi two-group estimation plot\nFor comparisons between three or more groups that typically employ analysis of variance (ANOVA) methods, one can use the Cumming estimation plot, named after Geoff Cumming, and draws its design heavily from his 2012 textbook “Understanding the New Statistics”. This estimation plot design can be considered a variant of the Gardner-Altman plot.\n\nThe effect size and 95% CIs are still plotted on a separate axis, but unlike the Gardner-Altman plot, this axis is positioned beneath the raw data.\nSuch a design frees up visual space in the upper panel, allowing the display of summary measurements (mean ± standard deviation) for each group. These are shown as gapped lines to the right of each group. The mean of each group is indicated as a gap in the line, adhering to Edward Tufte’s dictum to keep the data-ink ratio low.\n\n\nRepeated measures ANOVA –> Multi paired estimation plot\n\n\n\nOrdered groups ANOVA –> Shared-control estimation plot\n\n\n\nEstimation Plots: The Way Forward\nIn summary, estimation plots offer five key benefits relative to conventional plots:\n\n\n\n\n\n\n\n\n\n\nBarplot\nBoxplot\nEstimation Plot\n\n\n\n\nDisplays all observed values\nNO\nNO\nYes\n\n\nAvoids false dichotomy\nNO\nNO\nYes\n\n\nFocusses on effect size\nNO\nNO\nYes\n\n\nVisualizes effect size precision\nNO\nNO\nYes\n\n\nShows mean difference distribution\nNO\nNO\nYes\n\n\n\nYou can create estimation plots using the DABEST (Data Analysis with Bootstrap Estimation) packages, which are available in Matlab, Python, and R.\n [1]:W. E. B. Du Bois’s Data Portraits: Visualizing Black America. Edited by Whitney Battle-Baptiste and Britt Rusert, Princeton Architectural Press, 2018" + }, + { + "objectID": "tutorials/01-basics.html", + "href": "tutorials/01-basics.html", + "title": "Basics", + "section": "", + "text": "import numpy as np\nimport pandas as pd\nimport dabest\n\nprint(\"We're using DABEST v{}\".format(dabest.__version__))\n\nPre-compiling numba functions for DABEST...\n\n\nCompiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 57.10it/s]\n\n\nNumba compilation complete!\nWe're using DABEST v2025.10.20", + "crumbs": [ + "Get Started", + "Tutorials", + "Basics" + ] + }, + { + "objectID": "tutorials/01-basics.html#load-libraries", + "href": "tutorials/01-basics.html#load-libraries", + "title": "Basics", + "section": "", + "text": "import numpy as np\nimport pandas as pd\nimport dabest\n\nprint(\"We're using DABEST v{}\".format(dabest.__version__))\n\nPre-compiling numba functions for DABEST...\n\n\nCompiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 57.10it/s]\n\n\nNumba compilation complete!\nWe're using DABEST v2025.10.20", + "crumbs": [ + "Get Started", + "Tutorials", + "Basics" + ] + }, + { + "objectID": "tutorials/01-basics.html#create-dataset-for-demo", + "href": "tutorials/01-basics.html#create-dataset-for-demo", + "title": "Basics", + "section": "Create dataset for demo", + "text": "Create dataset for demo\nHere, we create a dataset to illustrate how dabest works. In this dataset, each column corresponds to a group of observations.\n\nfrom scipy.stats import norm # Used in generation of populations.\n\nnp.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n\nNs = 20 # The number of samples taken from each population\n\n# Create samples\nc1 = norm.rvs(loc=3, scale=0.4, size=Ns)\nc2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\nc3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n\nt1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\nt2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\nt3 = norm.rvs(loc=3, scale=0.75, size=Ns)\nt4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\nt5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\nt6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n\n# Add a `gender` column for coloring the data.\nfemales = np.repeat('Female', Ns/2).tolist()\nmales = np.repeat('Male', Ns/2).tolist()\ngender = females + males\n\n# Add an `id` column for paired data plotting.\nid_col = pd.Series(range(1, Ns+1))\n\n# Combine samples and gender into a DataFrame.\ndf = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n 'Control 2' : c2, 'Test 2' : t2,\n 'Control 3' : c3, 'Test 3' : t3,\n 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6,\n 'Gender' : gender, 'ID' : id_col\n })\ndf.head()\n\n\n\n\n\n\n\n\nControl 1\nTest 1\nControl 2\nTest 2\nControl 3\nTest 3\nTest 4\nTest 5\nTest 6\nGender\nID\n\n\n\n\n0\n2.793984\n3.420875\n3.324661\n1.707467\n3.816940\n1.796581\n4.440050\n2.937284\n3.486127\nFemale\n1\n\n\n1\n3.236759\n3.467972\n3.685186\n1.121846\n3.750358\n3.944566\n3.723494\n2.837062\n2.338094\nFemale\n2\n\n\n2\n3.019149\n4.377179\n5.616891\n3.301381\n2.945397\n2.832188\n3.214014\n3.111950\n3.270897\nFemale\n3\n\n\n3\n2.804638\n4.564780\n2.773152\n2.534018\n3.575179\n3.048267\n4.968278\n3.743378\n3.151188\nFemale\n4\n\n\n4\n2.858019\n3.220058\n2.550361\n2.796365\n3.692138\n3.276575\n2.662104\n2.977341\n2.328601\nFemale\n5\n\n\n\n\n\n\n\nNote that we have 9 groups (3 Control samples and 6 Test samples). Our dataset has also a non-numerical column indicating gender, and another column indicating the identity of each observation.\nThis is known as a wide dataset. See this writeup for more details.", + "crumbs": [ + "Get Started", + "Tutorials", + "Basics" + ] + }, + { + "objectID": "tutorials/01-basics.html#loading-data", + "href": "tutorials/01-basics.html#loading-data", + "title": "Basics", + "section": "Loading data", + "text": "Loading data\nBefore creating estimation plots and obtaining confidence intervals for our effect sizes, we need to load the data and specify the relevant groups.\nWe can achieve this by supplying the dataframe to dabest.load(). Additionally, we must provide the two groups to be compared in the idx argument as a tuple or list.\n\ntwo_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), resamples=5000)\n\nCalling this Dabest object gives you a gentle greeting, as well as the comparisons that can be computed.\n\ntwo_groups_unpaired\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:03 2025.\n\nEffect size(s) with 95% confidence intervals will be computed for:\n1. Test 1 minus Control 1\n\n5000 resamples will be used to generate the effect size bootstraps.\n\n\n\nChanging statistical parameters\nYou can change the width of the confidence interval by manipulating the ci argument.\n\ntwo_groups_unpaired_ci90 = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), ci=90)\ntwo_groups_unpaired_ci90\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:03 2025.\n\nEffect size(s) with 90% confidence intervals will be computed for:\n1. Test 1 minus Control 1\n\n5000 resamples will be used to generate the effect size bootstraps.", + "crumbs": [ + "Get Started", + "Tutorials", + "Basics" + ] + }, + { + "objectID": "tutorials/01-basics.html#effect-sizes", + "href": "tutorials/01-basics.html#effect-sizes", + "title": "Basics", + "section": "Effect sizes", + "text": "Effect sizes\nThe dabest library now features a range of effect sizes:\n\nMean difference (mean＿diff)\nMedian difference (median＿diff)\nCohen’s d (cohens＿d)\nHedges’ g (hedges＿g)\nCohen’s h (cohens＿h)\nCliff’s delta (cliffs＿delta)\n\nEach of these are attributes of the Dabest object.\n\ntwo_groups_unpaired.mean_diff\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:03 2025.\n\nThe unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].\nThe p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.mean_diff.statistical_tests`\n\n\nFor each comparison, the type of effect size is reported (here, it’s the “unpaired mean difference”). The confidence interval is reported as: [confidenceIntervalWidth LowerBound, UpperBound]\nThis confidence interval is generated through bootstrap resampling. See bootstraps for more details.\nSince v0.3.0, DABEST will report the p-value of the non-parametric two-sided approximate permutation t-test. This is also known as the Monte Carlo permutation test.\nFor unpaired comparisons, the p-values and test statistics of Welch’s t test, Student’s t test, and Mann-Whitney U test can be found. For paired comparisons, the p-values and test statistics of the paired Student’s t and Wilcoxon tests are presented.\n\npd.options.display.max_columns = 50\ntwo_groups_unpaired.mean_diff.results\n\n\n\n\n\n\n\n\ncontrol\ntest\ncontrol_N\ntest_N\neffect_size\nis_paired\ndifference\nci\nbca_low\nbca_high\nbca_interval_idx\npct_low\npct_high\npct_interval_idx\nbootstraps\nresamples\nrandom_seed\npermutations\npvalue_permutation\npermutation_count\npermutations_var\npvalue_welch\nstatistic_welch\npvalue_students_t\nstatistic_students_t\npvalue_mann_whitney\nstatistic_mann_whitney\nbec_difference\nbec_bootstraps\nbec_bca_interval_idx\nbec_bca_low\nbec_bca_high\nbec_pct_interval_idx\nbec_pct_low\nbec_pct_high\n\n\n\n\n0\nControl 1\nTest 1\n20\n20\nmean difference\nNone\n0.48029\n95\n0.205161\n0.773647\n(145, 4893)\n0.197427\n0.758752\n(125, 4875)\n[0.6148498102262239, 0.6752095203445543, 0.300...\n5000\n12345\n[-0.17259843762502491, 0.03802293852634886, -0...\n0.001\n5000\n[0.26356588154404337, 0.2710249543904699, 0.26...\n0.002094\n-3.308806\n0.002057\n-3.308806\n0.001625\n83.0\n0.0\n[-0.09732932551566487, 0.08087009665445155, -0...\n(127, 4877)\n-0.256862\n0.259558\n(125, 4875)\n-0.25826\n0.25759\n\n\n\n\n\n\n\n\ntwo_groups_unpaired.mean_diff.statistical_tests\n\n\n\n\n\n\n\n\ncontrol\ntest\ncontrol_N\ntest_N\neffect_size\nis_paired\ndifference\nci\nbca_low\nbca_high\npvalue_permutation\npvalue_welch\nstatistic_welch\npvalue_students_t\nstatistic_students_t\npvalue_mann_whitney\nstatistic_mann_whitney\n\n\n\n\n0\nControl 1\nTest 1\n20\n20\nmean difference\nNone\n0.48029\n95\n0.205161\n0.773647\n0.001\n0.002094\n-3.308806\n0.002057\n-3.308806\n0.001625\n83.0\n\n\n\n\n\n\n\nNote: A research paper Phipson & Smyth (2010) suggested that permutation p-values should never be zero, and provided a slightly adjusted formula to compute permutation p-values.\nSince v2025.03.27, DABEST provides a ps_adjust parameter in the .load() function. This parameter allows you to adjust the permutation p-values using the formula suggested by Phipson & Smyth (2010). By default, DABEST uses the unadjusted p-values.\n\ntwo_groups_unpaired_adjusted = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), resamples=5000, ps_adjust=True)\ntwo_groups_unpaired_adjusted.mean_diff.statistical_tests\n\n\n\n\n\n\n\n\ncontrol\ntest\ncontrol_N\ntest_N\neffect_size\nis_paired\ndifference\nci\nbca_low\nbca_high\npvalue_permutation\npvalue_welch\nstatistic_welch\npvalue_students_t\nstatistic_students_t\npvalue_mann_whitney\nstatistic_mann_whitney\n\n\n\n\n0\nControl 1\nTest 1\n20\n20\nmean difference\nNone\n0.48029\n95\n0.205161\n0.773647\n0.0012\n0.002094\n-3.308806\n0.002057\n-3.308806\n0.001625\n83.0\n\n\n\n\n\n\n\nLet’s compute the Hedges’g for our comparison.\n\ntwo_groups_unpaired.hedges_g\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:04 2025.\n\nThe unpaired Hedges' g between Control 1 and Test 1 is 1.03 [95%CI 0.317, 1.62].\nThe p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.hedges_g.statistical_tests`\n\n\n\ntwo_groups_unpaired.hedges_g.results\n\n\n\n\n\n\n\n\ncontrol\ntest\ncontrol_N\ntest_N\neffect_size\nis_paired\ndifference\nci\nbca_low\nbca_high\nbca_interval_idx\npct_low\npct_high\npct_interval_idx\nbootstraps\nresamples\nrandom_seed\npermutations\npvalue_permutation\npermutation_count\npermutations_var\npvalue_welch\nstatistic_welch\npvalue_students_t\nstatistic_students_t\npvalue_mann_whitney\nstatistic_mann_whitney\nbec_difference\nbec_bootstraps\nbec_bca_interval_idx\nbec_bca_low\nbec_bca_high\nbec_pct_interval_idx\nbec_pct_low\nbec_pct_high\n\n\n\n\n0\nControl 1\nTest 1\n20\n20\nHedges' g\nNone\n1.025525\n95\n0.316506\n1.616235\n(42, 4725)\n0.44486\n1.745146\n(125, 4875)\n[1.469217954462509, 1.5972518056777079, 0.6051...\n5000\n12345\n[-0.329508986559053, 0.07158401210924781, -0.2...\n0.001\n5000\n[0.26356588154404337, 0.2710249543904699, 0.26...\n0.002094\n-3.308806\n0.002057\n-3.308806\n0.001625\n83.0\n0.0\n[-0.2669450878059954, 0.21187593591106418, -0....\n(127, 4877)\n-0.642387\n0.629464\n(125, 4875)\n-0.643604\n0.627968", + "crumbs": [ + "Get Started", + "Tutorials", + "Basics" + ] + }, + { + "objectID": "tutorials/01-basics.html#producing-estimation-plots", + "href": "tutorials/01-basics.html#producing-estimation-plots", + "title": "Basics", + "section": "Producing estimation plots", + "text": "Producing estimation plots\nTo generate a Gardner-Altman estimation plot, simply use the .plot() method. You can learn more about its genesis and design inspiration at robust-beautiful.\nEach instance of an effect size has access to the .plot() method. This allows you to quickly create plots for different effect sizes with ease.\n\ntwo_groups_unpaired.mean_diff.plot();\n\n\n\n\n\n\n\n\n\ntwo_groups_unpaired.hedges_g.plot();\n\n\n\n\n\n\n\n\nInstead of a Gardner-Altman plot, you can generate a Cumming estimation plot by setting float_contrast=False in the .plot() method. This will plot the bootstrap effect sizes below the raw data, and also displays the the mean (gap) and ± standard deviation of each group (vertical ends) as gapped lines. This design was inspired by Edward Tufte’s dictum to maximise the data-ink ratio.\n\ntwo_groups_unpaired.hedges_g.plot(float_contrast=False);\n\n\n\n\n\n\n\n\nThe confidence interval shown on the contrast axis is a BCa confidence interval by default. This can be modified using the ci_type parameter in the .plot() method, whereby you can select between bca and pct (percentile).\n\ntwo_groups_unpaired.mean_diff.plot(ci_type='pct');\n\n\n\n\n\n\n\n\n\nUsing long (aka ‘melted’) data frames\ndabest can also handle ‘melted’ or ‘long’ data. This term is used because each row now corresponds to a single data point, with one column carrying the value and other columns containing ‘metadata’ describing that data point.\nFor more details on wide vs long or ‘melted’ data, refer to this Wikipedia article. The pandas documentation provides recipes for melting dataframes.\n\nx='group'\ny='metric'\n\nvalue_cols = df.columns[:-2] # select all but the \"Gender\" and \"ID\" columns.\n\ndf_melted = pd.melt(df.reset_index(),\n id_vars=[\"Gender\", \"ID\"],\n value_vars=value_cols,\n value_name=y,\n var_name=x)\n\ndf_melted.head() # Gives the first five rows of `df_melted`.\n\n\n\n\n\n\n\n\nGender\nID\ngroup\nmetric\n\n\n\n\n0\nFemale\n1\nControl 1\n2.793984\n\n\n1\nFemale\n2\nControl 1\n3.236759\n\n\n2\nFemale\n3\nControl 1\n3.019149\n\n\n3\nFemale\n4\nControl 1\n2.804638\n\n\n4\nFemale\n5\nControl 1\n2.858019\n\n\n\n\n\n\n\nWhen your data is in this format, you need to specify the x and y columns in dabest.load().\n\nanalysis_of_long_df = dabest.load(df_melted, idx=(\"Control 1\", \"Test 1\"),\n x=\"group\", y=\"metric\")\n\nanalysis_of_long_df\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:04 2025.\n\nEffect size(s) with 95% confidence intervals will be computed for:\n1. Test 1 minus Control 1\n\n5000 resamples will be used to generate the effect size bootstraps.", + "crumbs": [ + "Get Started", + "Tutorials", + "Basics" + ] + }, + { + "objectID": "tutorials/01-basics.html#dabest-estimation-plot-designs", + "href": "tutorials/01-basics.html#dabest-estimation-plot-designs", + "title": "Basics", + "section": "Dabest estimation plot designs", + "text": "Dabest estimation plot designs\nThe dabest package implements a range of estimation plot designs aimed at depicting common experimental designs:\n\nTwo-Group\nShared Control (Unpaired) and Repeated Measures (Paired)\nProportion Plots\nMini-Meta\nDelta-Delta\nForest Plots\n\nIn addition, as of Dabest v2025.03.27, we introduce a new plotting orientation: Horizontal Plots.\nLastly, we have a whole tutorial page for making aesthetic changes to dabest plots.", + "crumbs": [ + "Get Started", + "Tutorials", + "Basics" + ] + }, + { + "objectID": "tutorials/03-shared_control_and_repeated_measures.html", + "href": "tutorials/03-shared_control_and_repeated_measures.html", + "title": "Shared Control & Repeated Measures", + "section": "", + "text": "The shared control plot and repeated measures plot display common experimental paradigms, where several test samples are compared against a common reference sample. The shared control plot is for unpaired data, while the repeated measures plot is for paired data.\nThese types of Cumming plots are automatically generated if the tuple passed to idx has more than two data columns.", + "crumbs": [ + "Get Started", + "Tutorials", + "Shared Control & Repeated Measures" + ] + }, + { + "objectID": "tutorials/03-shared_control_and_repeated_measures.html#load-libraries", + "href": "tutorials/03-shared_control_and_repeated_measures.html#load-libraries", + "title": "Shared Control & Repeated Measures", + "section": "Load libraries", + "text": "Load libraries\n\nimport numpy as np\nimport pandas as pd\nimport dabest\n\nprint(\"We're using DABEST v{}\".format(dabest.__version__))\n\nPre-compiling numba functions for DABEST...\n\n\nCompiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 62.10it/s]\n\n\nNumba compilation complete!\nWe're using DABEST v2025.10.20", + "crumbs": [ + "Get Started", + "Tutorials", + "Shared Control & Repeated Measures" + ] + }, + { + "objectID": "tutorials/03-shared_control_and_repeated_measures.html#creating-a-demo-dataset", + "href": "tutorials/03-shared_control_and_repeated_measures.html#creating-a-demo-dataset", + "title": "Shared Control & Repeated Measures", + "section": "Creating a demo dataset", + "text": "Creating a demo dataset\n\nfrom scipy.stats import norm # Used in generation of populations.\n\nnp.random.seed(9999) # Fix the seed so the results are reproducible.\nNs = 20 # The number of samples taken from each population\n\n# Create samples\nc1 = norm.rvs(loc=3, scale=0.4, size=Ns)\nc2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\nc3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n\nt1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\nt2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\nt3 = norm.rvs(loc=3, scale=0.75, size=Ns)\nt4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\nt5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\nt6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n\n\n# Add a `gender` column for coloring the data.\nfemales = np.repeat('Female', Ns/2).tolist()\nmales = np.repeat('Male', Ns/2).tolist()\ngender = females + males\n\n# Add an `id` column for paired data plotting.\nid_col = pd.Series(range(1, Ns+1))\n\n# Combine samples and gender into a DataFrame.\ndf = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n 'Control 2' : c2, 'Test 2' : t2,\n 'Control 3' : c3, 'Test 3' : t3,\n 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6,\n 'Gender' : gender, 'ID' : id_col\n })", + "crumbs": [ + "Get Started", + "Tutorials", + "Shared Control & Repeated Measures" + ] + }, + { + "objectID": "tutorials/03-shared_control_and_repeated_measures.html#shared-control-plot", + "href": "tutorials/03-shared_control_and_repeated_measures.html#shared-control-plot", + "title": "Shared Control & Repeated Measures", + "section": "Shared control plot", + "text": "Shared control plot\n\nshared_control = dabest.load(df, idx=(\"Control 1\", \"Test 1\",\n \"Test 2\", \"Test 3\",\n \"Test 4\", \"Test 5\", \"Test 6\")\n )\nshared_control\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:11 2025.\n\nEffect size(s) with 95% confidence intervals will be computed for:\n1. Test 1 minus Control 1\n2. Test 2 minus Control 1\n3. Test 3 minus Control 1\n4. Test 4 minus Control 1\n5. Test 5 minus Control 1\n6. Test 6 minus Control 1\n\n5000 resamples will be used to generate the effect size bootstraps.\n\n\n\nshared_control.mean_diff\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:12 2025.\n\nThe unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].\nThe p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n\nThe unpaired mean difference between Control 1 and Test 2 is -0.542 [95%CI -0.915, -0.206].\nThe p-value of the two-sided permutation t-test is 0.0042, calculated for legacy purposes only. \n\nThe unpaired mean difference between Control 1 and Test 3 is 0.174 [95%CI -0.273, 0.647].\nThe p-value of the two-sided permutation t-test is 0.479, calculated for legacy purposes only. \n\nThe unpaired mean difference between Control 1 and Test 4 is 0.79 [95%CI 0.325, 1.33].\nThe p-value of the two-sided permutation t-test is 0.0042, calculated for legacy purposes only. \n\nThe unpaired mean difference between Control 1 and Test 5 is 0.265 [95%CI 0.0115, 0.497].\nThe p-value of the two-sided permutation t-test is 0.0404, calculated for legacy purposes only. \n\nThe unpaired mean difference between Control 1 and Test 6 is 0.288 [95%CI 0.00913, 0.524].\nThe p-value of the two-sided permutation t-test is 0.0324, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.mean_diff.statistical_tests`\n\n\n\nshared_control.mean_diff.plot();\n\n\n\n\n\n\n\n\ndabest allows for combining both two-group and shared control experiments into the same plot. This empowers you to perform robust analyses and present complex visualizations of your statistics elegantly.\n\nmulti_groups = dabest.load(df, idx=((\"Control 1\", \"Test 1\",),\n (\"Control 2\", \"Test 2\",\"Test 3\"),\n (\"Control 3\", \"Test 4\",\"Test 5\", \"Test 6\")\n ))\nmulti_groups\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:12 2025.\n\nEffect size(s) with 95% confidence intervals will be computed for:\n1. Test 1 minus Control 1\n2. Test 2 minus Control 2\n3. Test 3 minus Control 2\n4. Test 4 minus Control 3\n5. Test 5 minus Control 3\n6. Test 6 minus Control 3\n\n5000 resamples will be used to generate the effect size bootstraps.\n\n\n\nmulti_groups.mean_diff\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:13 2025.\n\nThe unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].\nThe p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n\nThe unpaired mean difference between Control 2 and Test 2 is -1.38 [95%CI -1.93, -0.905].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\nThe unpaired mean difference between Control 2 and Test 3 is -0.666 [95%CI -1.29, -0.0788].\nThe p-value of the two-sided permutation t-test is 0.0352, calculated for legacy purposes only. \n\nThe unpaired mean difference between Control 3 and Test 4 is 0.362 [95%CI -0.111, 0.901].\nThe p-value of the two-sided permutation t-test is 0.161, calculated for legacy purposes only. \n\nThe unpaired mean difference between Control 3 and Test 5 is -0.164 [95%CI -0.398, 0.0747].\nThe p-value of the two-sided permutation t-test is 0.208, calculated for legacy purposes only. \n\nThe unpaired mean difference between Control 3 and Test 6 is -0.14 [95%CI -0.4, 0.0937].\nThe p-value of the two-sided permutation t-test is 0.282, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.mean_diff.statistical_tests`\n\n\n\nmulti_groups.mean_diff.plot();", + "crumbs": [ + "Get Started", + "Tutorials", + "Shared Control & Repeated Measures" + ] + }, + { + "objectID": "tutorials/03-shared_control_and_repeated_measures.html#repeated-measures-plot", + "href": "tutorials/03-shared_control_and_repeated_measures.html#repeated-measures-plot", + "title": "Shared Control & Repeated Measures", + "section": "Repeated measures plot", + "text": "Repeated measures plot\nDABEST v2023.02.14 expands the repertoire of plots for experiments with repeated-measures designs. DABEST now allows the visualization of paired experiments with one control and multiple test groups, as well as repeated measurements of the same group. This is an improved version of paired data plotting in previous versions, which only supported computations involving one test group and one control group.\nThe repeated-measures function supports the calculation of effect sizes for paired data, either based on sequential comparisons (group i vs group i + 1) or baseline comparisons (control vs group i). To use these features, you can simply declare the argument paired = \"sequential\" or paired = \"baseline\" correspondingly while running dabest.load(). As in the previous version, you must also pass a column in the dataset that indicates the identity of each observation, using the id_col keyword.\n\n(Please note that paired = True and paired = False are no longer valid since v2023.02.14)\n\n\nbaseline_repeated_measures = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\"),\n paired=\"baseline\", id_col=\"ID\")\nbaseline_repeated_measures\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:13 2025.\n\nPaired effect size(s) for repeated measures against baseline \nwith 95% confidence intervals will be computed for:\n1. Test 1 minus Control 1\n2. Test 2 minus Control 1\n3. Test 3 minus Control 1\n\n5000 resamples will be used to generate the effect size bootstraps.\n\n\n\nbaseline_repeated_measures.mean_diff\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:14 2025.\n\nThe paired mean difference for repeated measures against baseline \nbetween Control 1 and Test 1 is 0.48 [95%CI 0.241, 0.749].\nThe p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n\nThe paired mean difference for repeated measures against baseline \nbetween Control 1 and Test 2 is -0.542 [95%CI -0.977, -0.179].\nThe p-value of the two-sided permutation t-test is 0.014, calculated for legacy purposes only. \n\nThe paired mean difference for repeated measures against baseline \nbetween Control 1 and Test 3 is 0.174 [95%CI -0.303, 0.702].\nThe p-value of the two-sided permutation t-test is 0.505, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.mean_diff.statistical_tests`\n\n\n\nbaseline_repeated_measures.mean_diff.plot();\n\n\n\n\n\n\n\n\n\nsequential_repeated_measures = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\"),\n paired=\"sequential\", id_col=\"ID\")\nsequential_repeated_measures\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:14 2025.\n\nPaired effect size(s) for the sequential design of repeated-measures experiment \nwith 95% confidence intervals will be computed for:\n1. Test 1 minus Control 1\n2. Test 2 minus Test 1\n3. Test 3 minus Test 2\n\n5000 resamples will be used to generate the effect size bootstraps.\n\n\n\nsequential_repeated_measures.mean_diff\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:15 2025.\n\nThe paired mean difference for the sequential design of repeated-measures experiment \nbetween Control 1 and Test 1 is 0.48 [95%CI 0.241, 0.749].\nThe p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n\nThe paired mean difference for the sequential design of repeated-measures experiment \nbetween Test 1 and Test 2 is -1.02 [95%CI -1.35, -0.709].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\nThe paired mean difference for the sequential design of repeated-measures experiment \nbetween Test 2 and Test 3 is 0.716 [95%CI 0.153, 1.2].\nThe p-value of the two-sided permutation t-test is 0.022, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.mean_diff.statistical_tests`\n\n\n\nsequential_repeated_measures.mean_diff.plot();\n\n\n\n\n\n\n\n\nSimilar to unpaired data, DABEST empowers you to perform complex visualizations and statistics for paired data.\n\nmulti_baseline_repeated_measures = dabest.load(df, idx=((\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\"),\n (\"Control 2\", \"Test 4\", \"Test 5\", \"Test 6\")),\n paired=\"baseline\", id_col=\"ID\")\nmulti_baseline_repeated_measures.mean_diff.plot();\n\n\n\n\n\n\n\n\nFor further aesthetic changes, the Plot Aesthetics Tutorial provides detailed examples of how to customize the plot.", + "crumbs": [ + "Get Started", + "Tutorials", + "Shared Control & Repeated Measures" + ] + }, + { + "objectID": "tutorials/05-mini_meta.html", + "href": "tutorials/05-mini_meta.html", + "title": "Mini-Meta", + "section": "", + "text": "When scientists conduct replicates of the same experiment, the effect size of each replicate often varies, complicating the interpretation of the results. Starting from v2023.02.14, DABEST can now compute the meta-analyzed weighted effect size given multiple replicates of the same experiment. This can help resolve differences between replicates and simplify interpretation.\nFor this function, the generic inverse-variance weighting method is used to calculate a weighted mean difference, as follows:\n\\(\\hat{\\Delta}_w = \\frac{\\sum\\hat{\\Delta}_i\\,w_i}{\\sum w_i},\\quad w_i =\\frac{1}{\\hat{\\sigma}_i^{2}},\\quad \\hat{\\sigma}_i^{2} =\\operatorname{var}\\!\\big(\\hat{\\Delta}_i\\big)\\)\nThe variance used is calculated empirically as the sample variance of the bootstrapped values of the mean difference.\n\\(\\hat{\\Delta}_w=\\frac{\\sum\\hat{\\Delta}_i\\,\\hat{w}_i}{\\sum\\hat{w}_i},\\quad \\hat{w}_i=\\frac{n_i-1}{\\sum_{r=1}^{n_i}\\bigl(\\hat{\\Delta}_i^{(r)}-\\bar{\\Delta}_i^{\\mathrm{b}}\\bigr)^2},\\quad \\bar{\\Delta}_i^{\\mathrm{b}}=\\frac{1}{n_i}\\sum_{r=1}^{n_i}\\hat{\\Delta}_i^{(r)}.\\)\nWhere \\(\\hat{\\Delta}_w\\): estimated weighted delta;\n\\(w_i\\): weight for replicate \\(i\\);\n\\(\\hat{\\sigma}_i^2\\): sampling variance of the mean-difference estimator for replicate \\(i\\);\n\\(\\hat{\\Delta}_i\\): estimated mean difference for replicate \\(i\\);\n\\(\\hat{w}_i\\): estimated weight for replicate \\(i\\);\n\\(n_i\\): number of bootstrap replicates used for replicate \\(i\\);\n\\(\\hat{\\Delta}_i^{(r)}\\): the \\(r\\)-th bootstrap estimate of the mean difference for replicate \\(i\\);\n\\(\\bar{\\Delta}_i^{\\mathrm{b}}\\): bootstrap mean of the mean differences for replicate \\(i\\)\nNote that this utilizes the fixed-effects model of meta-analysis, in contrast to the random-effects model. In the fixed-effects model, all variation between the results of each replicate is assumed to be solely due to sampling error. Therefore, we recommend using this function exclusively for replications of the same experiment, where it can be safely assumed that each replicate estimates the same population mean \\(\\mu\\).\nAdditionally, be aware that as of v2023.02.14, DABEST can only compute weighted effect size for mean difference only, and not for standardized measures such as Cohen’s d.\nFor more information on meta-analysis, please refer to Chapter 10 of the Cochrane handbook.", + "crumbs": [ + "Get Started", + "Tutorials", + "Mini-Meta" + ] + }, + { + "objectID": "tutorials/05-mini_meta.html#load-libraries", + "href": "tutorials/05-mini_meta.html#load-libraries", + "title": "Mini-Meta", + "section": "Load libraries", + "text": "Load libraries\n\nimport numpy as np\nimport pandas as pd\nimport dabest\n\nprint(\"We're using DABEST v{}\".format(dabest.__version__))\n\nPre-compiling numba functions for DABEST...\n\n\nCompiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 62.75it/s]\n\n\nNumba compilation complete!\nWe're using DABEST v2025.10.20", + "crumbs": [ + "Get Started", + "Tutorials", + "Mini-Meta" + ] + }, + { + "objectID": "tutorials/05-mini_meta.html#creating-a-demo-dataset", + "href": "tutorials/05-mini_meta.html#creating-a-demo-dataset", + "title": "Mini-Meta", + "section": "Creating a demo dataset", + "text": "Creating a demo dataset\n\nfrom scipy.stats import norm # Used in generation of populations.\n\nnp.random.seed(9999) # Fix the seed to ensure reproducibility of results.\nNs = 20 # The number of samples taken from each population\n\n# Create samples\nc1 = norm.rvs(loc=3, scale=0.4, size=Ns)\nc2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\nc3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n\nt1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\nt2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\nt3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n\n\n# Add a `gender` column for coloring the data.\nfemales = np.repeat('Female', Ns/2).tolist()\nmales = np.repeat('Male', Ns/2).tolist()\ngender = females + males\n\n# Add an `id` column for paired data plotting.\nid_col = pd.Series(range(1, Ns+1))\n\n# Combine samples and gender into a DataFrame.\ndf = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n 'Control 2' : c2, 'Test 2' : t2,\n 'Control 3' : c3, 'Test 3' : t3,\n 'Gender' : gender, 'ID' : id_col\n })\ndf.head()\n\n\n\n\n\n\n\n\nControl 1\nTest 1\nControl 2\nTest 2\nControl 3\nTest 3\nGender\nID\n\n\n\n\n0\n2.793984\n3.420875\n3.324661\n1.707467\n3.816940\n1.796581\nFemale\n1\n\n\n1\n3.236759\n3.467972\n3.685186\n1.121846\n3.750358\n3.944566\nFemale\n2\n\n\n2\n3.019149\n4.377179\n5.616891\n3.301381\n2.945397\n2.832188\nFemale\n3\n\n\n3\n2.804638\n4.564780\n2.773152\n2.534018\n3.575179\n3.048267\nFemale\n4\n\n\n4\n2.858019\n3.220058\n2.550361\n2.796365\n3.692138\n3.276575\nFemale\n5\n\n\n\n\n\n\n\nWe now have three Control and three Test groups, simulating three replicates of the same experiment. Our dataset has also a non-numerical column indicating gender, and another column indicating the identity of each observation.\nThis is known as a ‘wide’ dataset. See this writeup for more details.", + "crumbs": [ + "Get Started", + "Tutorials", + "Mini-Meta" + ] + }, + { + "objectID": "tutorials/05-mini_meta.html#loading-data", + "href": "tutorials/05-mini_meta.html#loading-data", + "title": "Mini-Meta", + "section": "Loading data", + "text": "Loading data\nNext, we load data as usual using dabest.load(). However, this time, we also specify the argument mini_meta=True. Since we are loading data from three experiments, idx is passed as a tuple of tuples, as shown below.\nWhen this dabest object is invoked, it should indicate that effect sizes will be calculated for each group, along with the weighted delta. It is important to note once again that the weighted delta will only be calculated for mean differences.\n\nunpaired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), mini_meta=True)\nunpaired\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:46 2025.\n\nEffect size(s) with 95% confidence intervals will be computed for:\n1. Test 1 minus Control 1\n2. Test 2 minus Control 2\n3. Test 3 minus Control 3\n4. weighted delta (only for mean difference)\n\n5000 resamples will be used to generate the effect size bootstraps.\n\n\nBy calling the mean_diff attribute, you can view the mean differences for each group as well as the weighted delta.\n\nunpaired.mean_diff\n\nDABEST v2025.10.20\n==================\n \nGood afternoon!\nThe current time is Sun Oct 19 16:00:47 2025.\n\nThe unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].\nThe p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n\nThe unpaired mean difference between Control 2 and Test 2 is -1.38 [95%CI -1.93, -0.905].\nThe p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n\nThe unpaired mean difference between Control 3 and Test 3 is -0.255 [95%CI -0.696, 0.208].\nThe p-value of the two-sided permutation t-test is 0.293, calculated for legacy purposes only. \n\nThe weighted-average unpaired mean differences is -0.00983 [95%CI -0.225, 0.213].\nThe p-value of the two-sided permutation t-test is 0.941, calculated for legacy purposes only. \n\n5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\nAny p-value reported is the probability of observing theeffect size (or greater),\nassuming the null hypothesis of zero difference is true.\nFor each p-value, 5000 reshuffles of the control and test labels were performed.\n\nTo get the results of all valid statistical tests, use `.mean_diff.statistical_tests`\n\n\nYou can view the details of each experiment by accessing the property mean_diff.results as follows.\n\npd.options.display.max_columns = 50\nunpaired.mean_diff.results\n\n\n\n\n\n\n\n\ncontrol\ntest\ncontrol_N\ntest_N\neffect_size\nis_paired\ndifference\nci\nbca_low\nbca_high\nbca_interval_idx\npct_low\npct_high\npct_interval_idx\nbootstraps\nresamples\nrandom_seed\npermutations\npvalue_permutation\npermutation_count\npermutations_var\npvalue_welch\nstatistic_welch\npvalue_students_t\nstatistic_students_t\npvalue_mann_whitney\nstatistic_mann_whitney\nbec_difference\nbec_bootstraps\nbec_bca_interval_idx\nbec_bca_low\nbec_bca_high\nbec_pct_interval_idx\nbec_pct_low\nbec_pct_high\n\n\n\n\n0\nControl 1\nTest 1\n20\n20\nmean difference\nNone\n0.480290\n95\n0.205161\n0.773647\n(145, 4893)\n0.197427\n0.758752\n(125, 4875)\n[0.6148498102262239, 0.6752095203445543, 0.300...\n5000\n12345\n[-0.17259843762502491, 0.03802293852634886, -0...\n0.0010\n5000\n[0.26356588154404337, 0.2710249543904699, 0.26...\n0.002094\n-3.308806\n0.002057\n-3.308806\n0.001625\n83.0\n0.0\n[-0.09732932551566487, 0.08087009665445155, -0...\n(127, 4877)\n-0.256862\n0.259558\n(125, 4875)\n-0.258260\n0.257590\n\n\n1\nControl 2\nTest 2\n20\n20\nmean difference\nNone\n-1.381085\n95\n-1.934192\n-0.905164\n(94, 4838)\n-1.901802\n-0.877098\n(125, 4875)\n[-1.7266697532252988, -1.7990605927248775, -1....\n5000\n12345\n[0.015164519971271773, 0.017231919606192303, -...\n0.0000\n5000\n[1.2241741427801065, 1.2241565174150129, 1.128...\n0.000011\n5.138840\n0.000009\n5.138840\n0.000026\n356.0\n0.0\n[-0.7109511916465152, -0.3436697507223183, -0....\n(126, 4876)\n-0.578621\n0.598647\n(125, 4875)\n-0.579306\n0.598009\n\n\n2\nControl 3\nTest 3\n20\n20\nmean difference\nNone\n-0.254831\n95\n-0.696379\n0.207659\n(123, 4873)\n-0.694790\n0.208585\n(125, 4875)\n[0.3059887140714319, -0.22727011648745288, 0.0...\n5000\n12345\n[-0.05901068591042824, -0.13617667681797307, 0...\n0.2934\n5000\n[0.5835889750166371, 0.5796253365278035, 0.581...\n0.294766\n1.069798\n0.291459\n1.069798\n0.285305\n240.0\n0.0\n[0.07996849455952271, 0.24534680794041375, 0.0...\n(124, 4874)\n-0.243754\n0.240283\n(125, 4875)\n-0.243713\n0.240490\n\n\n\n\n\n\n\nNote, however, that this does not contain the relevant information for our weighted delta. The details of the weighted delta are stored as attributes of the mini_meta object, such as:\n\ngroup_var: the pooled group variances of each set of 2 experiment groups.\ndifference: the weighted mean difference calculated based on the raw data.\nbootstraps: the deltas of each set of 2 experiment groups calculated based on the bootstraps.\nbootstraps_weighted_delta: the weighted deltas calculated based on the bootstraps.\npermutations: the deltas of each set of 2 experiment groups calculated based on the permutation data.\npermutations_var: the pooled group variances of each set of 2 experiment groups calculated based on permutation data.\npermutations_weighted_delta: the weighted deltas calculated based on the permutation data.\n\nA dataframe of this mini meta dabest object can also be called via the mini_meta.results attribute.\n\nunpaired.mean_diff.mini_meta.results\n\n\n\n\n\n\n\n\ncontrol\ntest\ncontrol_N\ntest_N\ncontrol_var\ntest_var\ngroup_var\ndifference\nci\nbca_low\nbca_high\nbca_interval_idx\npct_low\npct_high\npct_interval_idx\nbootstraps_deltas\nbootstraps_weighted_delta\npermutations\npermutations_var\npermutations_weighted_delta\npvalue_permutation\npermutation_count\nbias_correction\njackknives\n\n\n\n\n0\n[Control 1, Control 2, Control 3]\n[Test 1, Test 2, Test 3]\n[20, 20, 20]\n[20, 20, 20]\n[0.17628013404546258, 0.9584767911266554, 0.16...\n[0.24512071870152594, 0.48609989925165153, 0.9...\n[0.2107004263734943, 0.7222883451891535, 0.567...\n-0.00983\n95\n-0.225073\n0.213221\n(133, 4883)\n-0.227199\n0.210616\n(125, 4875)\n[[0.6148498102262239, 0.6752095203445543, 0.30...\n[0.1383993266160009, 0.040698566036827026, -0....\n[[-0.17259843762502491, 0.03802293852634886, -...\n[[0.26356588154404337, 0.2710249543904699, 0.2...\n[-0.11757207833491819, -0.01292867970093462, -...\n0.9412\n5000\n0.014539\n[-0.010633066723935882, -0.010613522663007862,...", + "crumbs": [ + "Get Started", + "Tutorials", + "Mini-Meta" + ] + }, + { + "objectID": "tutorials/05-mini_meta.html#generating-mini-meta-plots", + "href": "tutorials/05-mini_meta.html#generating-mini-meta-plots", + "title": "Mini-Meta", + "section": "Generating mini meta plots", + "text": "Generating mini meta plots\n\nunpaired.mean_diff.plot();\n\n\n\n\n\n\n\n\nYou can also hide the weighted delta by passing the argument show_mini_meta=False. In this case, the resulting graph would be identical to a multiple two-groups plot.\n\nunpaired.mean_diff.plot(show_mini_meta=False);\n\n\n\n\n\n\n\n\nAs with regular two-groups plots, you can also analyse paired mini meta experiments via the paired=baseline argument.\n\npaired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), mini_meta=True, id_col=\"ID\", paired=\"baseline\")\npaired.mean_diff.plot();\n\n\n\n\n\n\n\n\nFor further aesthetic changes, the Plot Aesthetics Tutorial provides detailed examples of how to customize the plot.", + "crumbs": [ + "Get Started", + "Tutorials", + "Mini-Meta" + ] + }, + { + "objectID": "tutorials/07-horizontal_plot.html", + "href": "tutorials/07-horizontal_plot.html", + "title": "Horizontal Plots", + "section": "", + "text": "In DABEST v2025.03.27, we introduce a new plotting orientation: horizontal plots.\nTo access this, provide horizontal=True to the .plot() method.", + "crumbs": [ + "Get Started", + "Tutorials", + "Horizontal Plots" + ] + }, + { + "objectID": "tutorials/07-horizontal_plot.html#load-libraries", + "href": "tutorials/07-horizontal_plot.html#load-libraries", + "title": "Horizontal Plots", + "section": "Load libraries", + "text": "Load libraries\n\nimport numpy as np\nimport pandas as pd\nimport dabest\n\nprint(\"We're using DABEST v{}\".format(dabest.__version__))\n\nPre-compiling numba functions for DABEST...\n\n\nCompiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 42.06it/s]\n\n\nNumba compilation complete!\nWe're using DABEST v2025.10.20", + "crumbs": [ + "Get Started", + "Tutorials", + "Horizontal Plots" + ] + }, + { + "objectID": "tutorials/07-horizontal_plot.html#creating-a-demo-dataset", + "href": "tutorials/07-horizontal_plot.html#creating-a-demo-dataset", + "title": "Horizontal Plots", + "section": "Creating a demo dataset", + "text": "Creating a demo dataset\n\nfrom scipy.stats import norm # Used in generation of populations.\n\nnp.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n\nNs = 20 # The number of samples taken from each population\n\n# Create samples\nc1 = norm.rvs(loc=3, scale=0.4, size=Ns)\nc2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\nc3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n\nt1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\nt2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\nt3 = norm.rvs(loc=3, scale=0.75, size=Ns)\nt4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\nt5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\nt6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n\n\n# Add a `gender` column for coloring the data.\nfemales = np.repeat('Female', Ns/2).tolist()\nmales = np.repeat('Male', Ns/2).tolist()\ngender = females + males\n\n# Add an `id` column for paired data plotting.\nid_col = pd.Series(range(1, Ns+1))\n\n# Combine samples and gender into a DataFrame.\ndf = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n 'Control 2' : c2, 'Test 2' : t2,\n 'Control 3' : c3, 'Test 3' : t3,\n 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6,\n 'Gender' : gender, 'ID' : id_col\n })", + "crumbs": [ + "Get Started", + "Tutorials", + "Horizontal Plots" + ] + }, + { + "objectID": "tutorials/07-horizontal_plot.html#generating-two-group-plots", + "href": "tutorials/07-horizontal_plot.html#generating-two-group-plots", + "title": "Horizontal Plots", + "section": "Generating two-group plots", + "text": "Generating two-group plots\n\ntwo_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"))\ntwo_groups_unpaired.mean_diff.plot(horizontal=True);\n\ntwo_groups_paired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), paired='baseline', id_col='ID')\ntwo_groups_paired.mean_diff.plot(horizontal=True);", + "crumbs": [ + "Get Started", + "Tutorials", + "Horizontal Plots" + ] + }, + { + "objectID": "tutorials/07-horizontal_plot.html#generating-shared-control-and-repeated-measures-plots", + "href": "tutorials/07-horizontal_plot.html#generating-shared-control-and-repeated-measures-plots", + "title": "Horizontal Plots", + "section": "Generating shared-control and repeated-measures plots", + "text": "Generating shared-control and repeated-measures plots\n\nshared_control = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\", \"Test 4\", \"Test 5\"))\nshared_control.mean_diff.plot(horizontal=True);\n\nrepeated_measures_baseline = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\", \"Test 4\", \"Test 5\"), paired='baseline', id_col='ID') \nrepeated_measures_baseline.mean_diff.plot(horizontal=True);\n\nrepeated_measures_sequential = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\", \"Test 4\", \"Test 5\"), paired='sequential', id_col='ID') \nrepeated_measures_sequential.mean_diff.plot(horizontal=True);", + "crumbs": [ + "Get Started", + "Tutorials", + "Horizontal Plots" + ] + }, + { + "objectID": "tutorials/07-horizontal_plot.html#generating-multi-group-plots", + "href": "tutorials/07-horizontal_plot.html#generating-multi-group-plots", + "title": "Horizontal Plots", + "section": "Generating multi-group plots", + "text": "Generating multi-group plots\n\nmulti_2group = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\", \"Test 4\", \"Test 5\")))\nmulti_2group.mean_diff.plot(horizontal=True);", + "crumbs": [ + "Get Started", + "Tutorials", + "Horizontal Plots" + ] + }, + { + "objectID": "tutorials/07-horizontal_plot.html#generating-proportion-plots", + "href": "tutorials/07-horizontal_plot.html#generating-proportion-plots", + "title": "Horizontal Plots", + "section": "Generating proportion plots", + "text": "Generating proportion plots\n\ndef create_demo_prop_dataset(seed=9999, N=40):\n import numpy as np\n import pandas as pd\n\n np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n # Create samples\n n = 1\n c1 = np.random.binomial(n, 0.2, size=N)\n c2 = np.random.binomial(n, 0.2, size=N)\n c3 = np.random.binomial(n, 0.8, size=N)\n\n t1 = np.random.binomial(n, 0.6, size=N)\n t2 = np.random.binomial(n, 0.2, size=N)\n t3 = np.random.binomial(n, 0.3, size=N)\n t4 = np.random.binomial(n, 0.4, size=N)\n t5 = np.random.binomial(n, 0.5, size=N)\n t6 = np.random.binomial(n, 0.6, size=N)\n t7 = np.ones(N)\n t8 = np.zeros(N)\n t9 = np.zeros(N)\n\n # Add a `gender` column for coloring the data.\n females = np.repeat('Female', N / 2).tolist()\n males = np.repeat('Male', N / 2).tolist()\n gender = females + males\n\n # Add an `id` column for paired data plotting.\n id_col = pd.Series(range(1, N + 1))\n\n # Combine samples and gender into a DataFrame.\n df = pd.DataFrame({'Control 1': c1, 'Test 1': t1,\n 'Control 2': c2, 'Test 2': t2,\n 'Control 3': c3, 'Test 3': t3,\n 'Test 4': t4, 'Test 5': t5, 'Test 6': t6,\n 'Test 7': t7, 'Test 8': t8, 'Test 9': t9,\n 'Gender': gender, 'ID': id_col\n })\n\n return df\ndf_prop = create_demo_prop_dataset()\n\n\nmulti_two_groups_unpaired = dabest.load(df_prop, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Test 3\", \"Test 4\"), (\"Test 5\", \"Test 6\")), proportional=True)\nmulti_two_groups_unpaired.mean_diff.plot(horizontal=True);\n\nmulti_group_baseline = dabest.load(df_prop, idx=(((\"Control 1\", \"Test 1\",\"Test 2\", \"Test 3\"),(\"Test 4\", \"Test 5\", \"Test 6\"))),proportional=True, paired=\"baseline\", id_col=\"ID\")\nmulti_group_baseline.mean_diff.plot(horizontal=True);", + "crumbs": [ + "Get Started", + "Tutorials", + "Horizontal Plots" + ] + }, + { + "objectID": "tutorials/07-horizontal_plot.html#generating-delta-delta-plots", + "href": "tutorials/07-horizontal_plot.html#generating-delta-delta-plots", + "title": "Horizontal Plots", + "section": "Generating delta-delta plots", + "text": "Generating delta-delta plots\n\nfrom scipy.stats import norm # Used in generation of populations.\nnp.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n\n# Create samples\nN = 20\ny = norm.rvs(loc=3, scale=0.4, size=N*4)\ny[N:2*N] = y[N:2*N]+1\ny[2*N:3*N] = y[2*N:3*N]-0.5\n\n# Add a `Treatment` column\nt1 = np.repeat('Placebo', N*2).tolist()\nt2 = np.repeat('Drug', N*2).tolist()\ntreatment = t1 + t2 \n\n# Add a `Rep` column as the first variable for the 2 replicates of experiments done\nrep = []\nfor i in range(N*2):\n rep.append('Rep1')\n rep.append('Rep2')\n\n# Add a `Genotype` column as the second variable\nwt = np.repeat('W', N).tolist()\nmt = np.repeat('M', N).tolist()\nwt2 = np.repeat('W', N).tolist()\nmt2 = np.repeat('M', N).tolist()\n\n\ngenotype = wt + mt + wt2 + mt2\n\n# Add an `id` column for paired data plotting.\nid = list(range(0, N*2))\nid_col = id + id \n\n\n# Combine all columns into a DataFrame.\ndf_delta2 = pd.DataFrame({'ID' : id_col,\n 'Rep' : rep,\n 'Genotype' : genotype, \n 'Treatment': treatment,\n 'Y' : y\n })\n\n\nunpaired_delta2 = dabest.load(data = df_delta2, x = [\"Genotype\", \"Genotype\"], y = \"Y\", delta2 = True, experiment = \"Treatment\")\nunpaired_delta2.mean_diff.plot(horizontal=True);", + "crumbs": [ + "Get Started", + "Tutorials", + "Horizontal Plots" + ] + }, + { + "objectID": "tutorials/07-horizontal_plot.html#generating-mini-meta-plots", + "href": "tutorials/07-horizontal_plot.html#generating-mini-meta-plots", + "title": "Horizontal Plots", + "section": "Generating mini-meta plots", + "text": "Generating mini-meta plots\n\nunpaired = dabest.load(df, idx=((\"Control 1\", \"Test 2\"), (\"Control 2\", \"Test 3\"), (\"Test 4\", \"Test 5\")), mini_meta=True)\nunpaired.mean_diff.plot(horizontal=True);", + "crumbs": [ + "Get Started", + "Tutorials", + "Horizontal Plots" + ] + }, + { + "objectID": "tutorials/07-horizontal_plot.html#controlling-aesthetics", + "href": "tutorials/07-horizontal_plot.html#controlling-aesthetics", + "title": "Horizontal Plots", + "section": "Controlling aesthetics", + "text": "Controlling aesthetics\nAs with the vertical plots, horizontal plots can be customized using the same options. Shown below are a few cases where the aesthetics are modified, added functionality, or just less intuitive.\n\nSwarm side\nAs with the vertical plots, you can specify the side of the swarms via swarm_side in the .plot() method.\nIn this case, swarm_side='left' would plot the swarms upwards, and swarm_side='right' would plot the swarms downwards.\nDefault is swarm_side='left'\n\ntwo_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\", 'Test 2'), resamples=5000)\ntwo_groups_unpaired.mean_diff.plot(swarm_side='left', horizontal=True);\n\n\n\n\n\n\n\n\nswarm_side='center'\n\ntwo_groups_unpaired.mean_diff.plot(swarm_side='center', horizontal=True);\n\n\n\n\n\n\n\n\nswarm_side='right'\n\ntwo_groups_unpaired.mean_diff.plot(swarm_side='right', horizontal=True);\n\n\n\n\n\n\n\n\n\n\nTable kwargs\nThe table axis can be customized using the horizontal_table_kwargs argument. A dict of keywords can be passed to customize the table.\nIf None, the following keywords are passed:\n\n'show' - Whether to show the table. Default is True.\n'color' - The color of the table. Default is ‘yellow’.\n'alpha' - The transparency of the table. Default is 0.2.\n'fontsize' - The fontsize of the table. Default is 12.\n'text_color' - The color of the text in the table. Default is ‘black’.\n'text_units' - The units of the text in the table. Default is None.\n'control_marker' - The marker for the control group. Default is ‘-’.\n'fontsize_label' - The fontsize of the table x-label. Default is 12.\n'label' - The table x-label.\n\n\nmulti_2group = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\"),(\"Test 4\", \"Test 5\")))\nmulti_2group.mean_diff.plot(horizontal=True, \n horizontal_table_kwargs={'color': 'red', \n 'alpha': 0.5, \n 'text_color': \n 'white',\n 'text_units':'mm', \n 'label': 'delta mm',\n 'control_marker': 'o',\n });\n\n\n\n\n\n\n\n\nThe table axis can be hidden using the 'show':False in the horizontal_table_kwargs dict.\n\nmulti_2group = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\"),(\"Test 4\", \"Test 5\")))\nmulti_2group.mean_diff.plot(horizontal=True, horizontal_table_kwargs={'show': False});\n\n\n\n\n\n\n\n\n\n\nGridkey\nAs with the vertical plots, you can utilise a gridkey table for representing the groupings. This can be reached via gridkey in the .plot() method.\nYou can either use gridkey='auto' to automatically generate the gridkey, or pass a list of indexes to represent the groupings (e.g., gridkey=['Control', 'Test']).\nSee the examples in the Plot Aesthetics Tutorial for more information with regards to kwargs.\n\nmulti_2group = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\"),(\"Test 4\", \"Test 5\")))\nmulti_2group.mean_diff.plot(horizontal=True, gridkey=['Control', 'Test']);", + "crumbs": [ + "Get Started", + "Tutorials", + "Horizontal Plots" + ] + }, + { + "objectID": "tutorials/09-forest_plot.html", + "href": "tutorials/09-forest_plot.html", + "title": "Forest Plots: Visualizing Multiple Contrasts", + "section": "", + "text": "In DABEST v2025.03.27, we introduce a new function to plot separately calculated effect sizes in the same axes to allow direct visual comparisons.\nCurrently you can make a forest plot for delta-delta, mini-meta, or standard delta effect sizes. In addition, for delta-delta and mini-meta experiments, you can also plot the effect sizes of the original comparisons alongside the delta-delta/mini-meta measurement.", + "crumbs": [ + "Get Started", + "Tutorials", + "Forest Plots: Visualizing Multiple Contrasts" + ] + }, + { + "objectID": "tutorials/09-forest_plot.html#load-libraries", + "href": "tutorials/09-forest_plot.html#load-libraries", + "title": "Forest Plots: Visualizing Multiple Contrasts", + "section": "Load libraries", + "text": "Load libraries\n\nimport numpy as np\nimport pandas as pd\nimport dabest\nimport matplotlib.pyplot as plt\nimport dabest \nprint(\"We're using DABEST v{}\".format(dabest.__version__))\n\nPre-compiling numba functions for DABEST...\n\n\nCompiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 23.74it/s]\n\n\nNumba compilation complete!\nWe're using DABEST v2025.10.20", + "crumbs": [ + "Get Started", + "Tutorials", + "Forest Plots: Visualizing Multiple Contrasts" + ] + }, + { + "objectID": "tutorials/09-forest_plot.html#delta-delta-effects", + "href": "tutorials/09-forest_plot.html#delta-delta-effects", + "title": "Forest Plots: Visualizing Multiple Contrasts", + "section": "Delta-delta effects", + "text": "Delta-delta effects\nFirst please revisit the notebook Delta-Delta Tutorial for how to generate a delta-delta effect size. We will generate three of them plot them into the same axes. Here we test the efficacy of 3 drugs named Drug1, Drug2 , and Drug3 on a disease-causing mutation M based on disease metric Tumor Size. We want to know how the three drugs fare in ameliorating the phenotype metric Tumor Size.\n\n\n\n\nWildtype\nMutant\n\n\n\n\nDrug1\nXD1, W\nXD1, M\n\n\nPlacebo\nXP1, W\nXP1, M\n\n\n\n\n\n\n\nWildtype\nMutant\n\n\n\n\nDrug2\nXD2, W\nXD2, M\n\n\nPlacebo\nXP2, W\nXP2, M\n\n\n\n\n\n\n\nWildtype\nMutant\n\n\n\n\nDrug3\nXD3, W\nXD3, M\n\n\nPlacebo\nXP3, W\nXP3, M\n\n\n\nIn each scenario, there are two Treatment conditions, Placebo (control group) and Drug (test group). There are two Genotype's: W (wild type population) and M (mutant population). Additionally, each experiment was conducted twice (Rep1 and Rep2). We will perform several analyses to visualise these differences in a simulated dataset. We will simulate three separte datasets below.\n\nCreating a demo dataset\n\nfrom scipy.stats import norm\ndef create_delta_dataset(N=20, \n seed=9999, \n second_quarter_adjustment=3, \n third_quarter_adjustment=-0.1):\n np.random.seed(seed) # Set the seed for reproducibility\n\n # Create samples\n y = norm.rvs(loc=3, scale=0.4, size=N*4)\n y[N:2*N] += second_quarter_adjustment\n y[2*N:3*N] += third_quarter_adjustment\n\n # Treatment, Rep, Genotype, and ID columns\n treatment = np.repeat(['Placebo', 'Drug'], N*2).tolist()\n rep = ['Rep1', 'Rep2'] * (N*2)\n genotype = np.repeat(['W', 'M', 'W', 'M'], N).tolist()\n id_col = list(range(0, N*2)) * 2\n\n # Combine all columns into a DataFrame\n df = pd.DataFrame({\n 'ID': id_col,\n 'Rep': rep,\n 'Genotype': genotype,\n 'Treatment': treatment,\n 'Tumor Size': y\n })\n\n return df\n\n# Generate the first dataset with a different seed and adjustments\ndf_delta2_drug1 = create_delta_dataset(seed=9999, second_quarter_adjustment=1, third_quarter_adjustment=-0.5)\n\n# Generate the second dataset with a different seed and adjustments\ndf_delta2_drug2 = create_delta_dataset(seed=9999, second_quarter_adjustment=0.1, third_quarter_adjustment=-1)\n\n# Generate the third dataset with the same seed as the first but different adjustments\ndf_delta2_drug3 = create_delta_dataset(seed=9999, second_quarter_adjustment=3, third_quarter_adjustment=-0.1)\n\n\n\nLoading data\n\nunpaired_delta_01 = dabest.load(data = df_delta2_drug1, \n x = [\"Genotype\", \"Genotype\"], \n y = \"Tumor Size\", delta2 = True, \n experiment = \"Treatment\")\nunpaired_delta_02 = dabest.load(data = df_delta2_drug2, \n x = [\"Genotype\", \"Genotype\"], \n y = \"Tumor Size\", delta2 = True, \n experiment = \"Treatment\")\nunpaired_delta_03 = dabest.load(data = df_delta2_drug3, \n x = [\"Genotype\", \"Genotype\"], \n y = \"Tumor Size\", \n delta2 = True, \n experiment = \"Treatment\")\ncontrasts = [unpaired_delta_01, unpaired_delta_02, unpaired_delta_03]\n\n\n\nGenerate delta-delta plots for each datasets\nTo create a delta-delta plot, you simply need to set delta2=True in the dabest.load() function and mean_diff.plot()\nIn this case,x needs to be declared as a list consisting of 2 elements, unlike most cases where it is a single element. The first element in x will represent the variable plotted along the horizontal axis, and the second one will determine the color of dots for scattered plots or the color of lines for slope graphs. We use the experiment input to specify the grouping of the data.\n\nf1 = unpaired_delta_01.mean_diff.plot(\n contrast_label='Mean Diff',\n fig_size = (7, 4),\n raw_marker_size = 1,\n contrast_marker_size = 5,\n);\nf1.suptitle('Delta-delta plot for Drug 1');\n\n\nf2 = unpaired_delta_02.mean_diff.plot( \n contrast_label='Mean Diff',\n fig_size = (7, 4),\n raw_marker_size = 1,\n contrast_marker_size = 5,\n);\nf2.suptitle('Delta-delta plot for Drug 2');\n\n\nf3 = unpaired_delta_03.mean_diff.plot( \n contrast_label='Mean Diff',\n fig_size = (7, 4),\n raw_marker_size = 1,\n contrast_marker_size = 5,\n);\nf3.suptitle('Delta-delta plot for Drug 3');\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGenerate a forest plot\nThis will allow for comparisons of different Drug effects.\nKey Parameters:\n\ndata: A list of dabest objects\nlabels: A list of labels for the dabest objects. E.g., ['Drug1', 'Drug2', 'Drug3']\neffect_size: For delta-delta experiments, you can select the effect size metric from \"mean_diff\", or \"hedges_g\" / \"delta_g\". The default is \"mean_diff\".\nci_type: A string specifying the confidence interval type to use. The options are either bca or pct. Default is bca.\n\nNote: “hedges_g” and “delta_g” can be used interchangeably for delta-delta experiments - both plot hedges_g regular effect sizes and our Delta g delta-delta effect size.\n\nhorizontal: A boolean input (True/ False) to adjust the plot orientation. The default is vertical orientation (False)\nax: Optional argument to specify an existing matplotlib axes (otherwise a standalone figure will be created)\n\nSee the Controlling aesthetics section for more information on how to alter the aesthetics of the plots.\n\nf_forest_delta2 = dabest.forest_plot(\n data = contrasts, \n labels = ['Drug1', 'Drug2', 'Drug3']\n);\n\n\n\n\n\n\n\n\n\n\nGenerate a forest plot with delta effect sizes alongside the delta-delta effect sizes\nIf you want to plot the original effect sizes alongside the delta-delta effect sizes, you can do so by utilising the idx parameter. This parameter takes a tuple/list of indices of the original effect sizes you want to plot.\nFor example, if you want to plot only the first effect size and the delta-delta effect size for each of the three dabest object supplied, you can do so by setting idx=[[0, 2],[0, 2],[0, 2]].\n\nf_forest_delta2 = dabest.forest_plot(\n data = contrasts, \n labels = ['Drug1 Delta1', 'Drug1 Delta-Delta', 'Drug2 Delta1', 'Drug2 Delta-Delta', 'Drug3 Delta1', 'Drug3 Delta-Delta'],\n idx=[[0, 2], [0, 2], [0, 2]]\n);\n\n\n\n\n\n\n\n\n\n\nSelecting normalised effect sizes via hedges_g or delta_g\nRemember, hedges_g and delta_g are interchangeable for delta-delta experiments. However, when plotting the original effect sizes alongside the delta-delta effect sizes, you should note that hedges_g effect sizes will be plotted alongside the Delta g effect sizes.\n\nf_forest_delta2 = dabest.forest_plot(\n data = contrasts, \n labels = ['Drug1', 'Drug2', 'Drug3'],\n effect_size='hedges_g');\nf_forest_delta2 = dabest.forest_plot(\n data = contrasts, \n labels = ['Drug1', 'Drug2', 'Drug3'],\n effect_size='delta_g');\n\nf_forest_delta2 = dabest.forest_plot(\n data = contrasts, \n labels = ['Drug1 Delta1', 'Drug1 Delta-Delta', 'Drug2 Delta1', 'Drug2 Delta-Delta', 'Drug3 Delta1', 'Drug3 Delta-Delta'],\n effect_size='hedges_g',\n idx=[[0, 2], [0, 2], [0, 2]]);", + "crumbs": [ + "Get Started", + "Tutorials", + "Forest Plots: Visualizing Multiple Contrasts" + ] + }, + { + "objectID": "tutorials/09-forest_plot.html#mini-meta-effects", + "href": "tutorials/09-forest_plot.html#mini-meta-effects", + "title": "Forest Plots: Visualizing Multiple Contrasts", + "section": "Mini-meta effects", + "text": "Mini-meta effects\nNext we will generate a similar forest plot for mini-meta effect sizes. Please revisit the notebook Mini-Meta Tutorial on how to generate a mini-meta effect size. We will generate three mini-meta effect sizes for three separate mini-meta analyses:\nNote: the only effect size metric currently available for mini-meta is \"mean_diff\".\n\nCreating a demo dataset\n\ndef create_mini_meta_dataset(N=20, seed=9999, control_locs=[3, 3.5, 3.25], control_scales=[0.4, 0.75, 0.4], \n test_locs=[3.5, 2.5, 3], test_scales=[0.5, 0.6, 0.75]):\n np.random.seed(seed) # Set the seed for reproducibility\n\n # Create samples for controls and tests\n controls_tests = []\n for loc, scale in zip(control_locs + test_locs, control_scales + test_scales):\n controls_tests.append(norm.rvs(loc=loc, scale=scale, size=N))\n\n # Add a `Gender` column for coloring the data\n gender = ['Female'] * (N // 2) + ['Male'] * (N // 2)\n\n # Add an `ID` column for paired data plotting\n id_col = list(range(1, N + 1))\n\n # Combine samples and gender into a DataFrame\n df_columns = {f'Control {i+1}': controls_tests[i] for i in range(len(control_locs))}\n df_columns.update({f'Test {i+1}': controls_tests[i + len(control_locs)] for i in range(len(test_locs))})\n df_columns['Gender'] = gender\n df_columns['ID'] = id_col\n\n df = pd.DataFrame(df_columns)\n\n return df\n\n# Customizable dataset creation with different arguments\ndf_mini_meta01 = create_mini_meta_dataset(seed=9999, \n control_locs=[3, 3.5, 3.25], \n control_scales=[0.4, 0.75, 0.4], \n test_locs=[3.5, 2.5, 3], \n test_scales=[0.5, 0.6, 0.75])\n\ndf_mini_meta02 = create_mini_meta_dataset(seed=9999, \n control_locs=[4, 2, 3.25], \n control_scales=[0.3, 0.75, 0.45], \n test_locs=[2, 1.5, 2.75], \n test_scales=[0.5, 0.6, 0.4])\n\ndf_mini_meta03 = create_mini_meta_dataset(seed=9999, \n control_locs=[6, 5.5, 4.25], \n control_scales=[0.4, 0.75, 0.45], \n test_locs=[4.5, 3.5, 3], \n test_scales=[0.5, 0.6, 0.9])\n\n\n\nLoading data\n\ncontrast_mini_meta01 = dabest.load(data = df_mini_meta01,\n idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), \n mini_meta=True)\ncontrast_mini_meta02 = dabest.load(data = df_mini_meta02,\n idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), \n mini_meta=True)\ncontrast_mini_meta03 = dabest.load(data = df_mini_meta03,\n idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")),\n mini_meta=True)\ncontrasts_mini_meta = [contrast_mini_meta01, contrast_mini_meta02, contrast_mini_meta03]\n\n\n\nGenerate a forest plot\n\nf_forest_minimeta = dabest.forest_plot(\n data = contrasts_mini_meta, \n labels=['mini_meta1', 'mini_meta2', 'mini_meta3']\n);\n\n\n\n\n\n\n\n\n\n\nGenerate a forest plot with delta effect sizes alongside the mini-meta effect sizes\nIf you want to plot the original effect sizes alongside the mini-meta effect sizes, you can do so by utilising the idx parameter. This parameter takes a tuple/list of indices of the original effect sizes you want to plot.\nFor example, if you want to plot only the first effect size and the mini-meta effect size for each of the three dabest object supplied, you can do so by setting idx=[[0, final_idx],[0, final_idx],[0, final_idx]] (where final_idx is the index of the last contrast object which will be the mini-meta effect size.)\n\nf_forest_minimeta = dabest.forest_plot(\n data = contrasts_mini_meta, \n idx = [[0, 3],[0, 3], [0, 3]],\n labels=['Contrast 1A', 'mini_meta1', 'Contrast 2A', 'mini_meta2', 'Contrast 3A', 'mini_meta3']\n);", + "crumbs": [ + "Get Started", + "Tutorials", + "Forest Plots: Visualizing Multiple Contrasts" + ] + }, + { + "objectID": "tutorials/09-forest_plot.html#delta-effects", + "href": "tutorials/09-forest_plot.html#delta-effects", + "title": "Forest Plots: Visualizing Multiple Contrasts", + "section": "Delta effects", + "text": "Delta effects\nNext we will generate a similar forest plot of regular delta effect sizes. In the example below, we will generate three regular mean_diff experiments. Here, we will only plot the effect size between the first group (Test 1 - Control 1) for each of the three dabest object supplied.\n\ndelta1 = dabest.load(data = df_mini_meta01,\n idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")))\ndelta2 = dabest.load(data = df_mini_meta02,\n idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")))\ndelta3 = dabest.load(data = df_mini_meta03,\n idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")))\ncontrasts_deltas = [delta1, delta2, delta3]\n\n\ndabest.forest_plot(contrasts_deltas, idx=((0,),(0,), (0,)), \n labels=['Drug1 \\nTest 1 - Control 1', 'Drug2 \\nTest 2 - Control 2', 'Drug3 \\nTest 3 - Control 3']);\n\n\n\n\n\n\n\n\nUnlike delta-delta and mini-meta experiments, here you can choose between more effect size metrics (where applicable): mean_diff, cohens_d, cohens_h, hedges_g, and cliffs_delta\n\ndabest.forest_plot(contrasts_deltas, idx=((0,),(0,), (0,)), effect_size = 'cohens_d',\n labels=['Drug1 \\nTest 1 - Control 1', 'Drug2 \\nTest 2 - Control 2', 'Drug3 \\nTest 3 - Control 3']);", + "crumbs": [ + "Get Started", + "Tutorials", + "Forest Plots: Visualizing Multiple Contrasts" + ] + }, + { + "objectID": "tutorials/09-forest_plot.html#controlling-aesthetics", + "href": "tutorials/09-forest_plot.html#controlling-aesthetics", + "title": "Forest Plots: Visualizing Multiple Contrasts", + "section": "Controlling aesthetics", + "text": "Controlling aesthetics\nThe main aesthetic parameters for the forest_plot function are:\n\nfig_size: The size of the figure\nhorizontal: A boolean input (True/ False) to adjust the plot orientation. The default is vertical orientation (False)\ncustom_palette: A list or dictionary of colors, one for each contrast object. E.g., ['gray', 'blue', 'green'] or {'Drug1':'gray', 'Drug2':'blue', 'Drug3':'green'} or a set of colors from seaborn color palettes.\nmarker_size: The size of the markers for the effect sizes. The default is 10.\ncontrast_alpha: Transparency level for violin plots. The default is 0.8.\ncontrast_desat: Saturation level for violin plots. The default is 1.\nlabels_rotation: Rotation angle for contrast labels. The default is 45 (for horizontal=False).\nlabels_fontsize: Font size for contrast labels. The default is 10.\ntitle: The plot title. The default is None.\ntitle_fontsize: Font size for the plot title. The default is 16.\nylabel: The axis label of dependent variable (Y-axis for vertical layout, X-axis for horizontal layout). The default will be given via the effect size selected. (eg., \"Mean Difference\" for \"mean_diff\")\nylabel_fontsize: Font size for the axis label (Y-axis for vertical layout, X-axis for horizontal layout). The default is 12.\nylim: Limits for the dependent variable (Y-axis for vertical layout, X-axis for horizontal layout). The default is None.\nyticks: Custom ticks (Y-axis for vertical layout, X-axis for horizontal layout) for the plot. The default is None.\nyticklabels: Custom tick labels (Y-axis for vertical layout, X-axis for horizontal layout) for the plot. The default is None.\nremove_spines: If True, removes plot spines (except the relevant dependent variable spine). The default is True.\nviolin_kwargs: A dictionary of keyword arguments for the violin plots.\n The default violin_kwargs = {\"widths\": 0.5, \"showextrema\": False, \"showmedians\": False, \"vert\": not horizontal}\nzeroline_kwargs: A dictionary of keyword arguments for the zero line. The default is None.\n The default zeroline_kwargs = {\"linewidth\": 1, \"color\": \"black\"}\nmarker_kwargs: A dictionary of keyword arguments for the effect size markers. The default is None.\n The default marker_kwargs = {'marker': 'o', 'markersize': 12, 'color': 'black', 'alpha': 1, 'zorder': 2}\nerrorbar_kwargs: A dictionary of keyword arguments for the effect size error bars. The default is None.\n The default errorbar_kwargs = {'color': 'black', 'lw': 2.5, 'linestyle': '-', 'alpha': 1, 'zorder': 1}\n\n\nChanging layout with horizontal\nForest plot assumes a vertical layout by default, but you can change it to a horizontal layout by setting horizontal to be True:\n\nf_forest_minimeta = dabest.forest_plot(\n data = contrasts_mini_meta, \n labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n horizontal=True,)\n\n\n\n\n\n\n\n\n\n\nUsing a custom palette\nYou can color the half-violins with custom_palette:\n\nf_forest_minimeta = dabest.forest_plot(\n data = contrasts_mini_meta, \n labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n custom_palette=['#FF0000', '#00FF00', '#0000FF'],)\n\n\n\n\n\n\n\n\n\n\nPlotting other effect sizes\nForest plots can be drawn for effect sizes other than mean_difference, such as hedges_g, by setting effect_size:\n\nf_forest_hedgesg = dabest.forest_plot(\n data = contrasts, \n labels =['Drug1', 'Drug2', 'Drug3'], \n effect_size='hedges_g',\n);\n\n\n\n\n\n\n\n\n\n\nDelta text\nYou can add/remove delta text via the delta_text argument. It is on by default.\n\nf_forest_minimeta = dabest.forest_plot(\n data = contrasts_mini_meta, \n labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n custom_palette=['#FF0000', '#00FF00', '#0000FF'],\n delta_text=True)\n\n\n\n\n\n\n\n\nYou can set a variety of kwargs to customize the delta text via delta_text_kwargs.\nThe relevant inputs to delta_text_kwargs are:\n\n'color' - Color. If color is not specified, the color of the effect size curve will be used.\n'alpha'- Alpha (transparency)\n'fontsize' - Font size\n'ha' - Horizontal alignment\n'va' - Vertical alignment\n'rotation' - Text rotation\n'x_coordinates' - Specify the x-coordinates of the text\n'y_coordinates' - Specify the y-coordinates of the text\n'offset' - Am x-axis coordinate adjuster for minor movement of all text\n\n\nf_forest_minimeta = dabest.forest_plot(\n data = contrasts_mini_meta, \n labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n custom_palette=['#FF0000', '#00FF00', '#0000FF'],\n delta_text=True,\n delta_text_kwargs={'color': 'red', 'offset': 0.1,\n 'fontsize': 8, 'rotation': 45,\n 'va': 'bottom',\n 'x_coordinates': [1.4,2.4,3.4], \n 'y_coordinates': [0,-1.4,-1.6]})\n\n\n\n\n\n\n\n\n\n\nContrast bars\nYou can add/remove contrast bars via the contrast_bars argument. It is on by default.\n\nf_forest_minimeta = dabest.forest_plot(\n data = contrasts_mini_meta, \n labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n custom_palette=['#FF0000', '#00FF00', '#0000FF'],\n contrast_bars=True,)\n\n\n\n\n\n\n\n\nYou can set a variety of kwargs to customize the delta text via contrast_bars_kwargs.\nPass any keyword arguments accepted by matplotlib.patches.Rectangle here, as a string.\n\nf_forest_minimeta = dabest.forest_plot(\n data = contrasts_mini_meta, \n labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n custom_palette=['#FF0000', '#00FF00', '#0000FF'],\n contrast_bars=True,\n contrast_bars_kwargs={'color': 'red', 'alpha': 0.4})\n\n\n\n\n\n\n\n\n\n\nReference band\nYou can add reference bands by supplying a list/tuple to the reference_band argument, indicating the contrast to highlight. None are displayed by default.\n\nf_forest_minimeta = dabest.forest_plot(\n data = contrasts_mini_meta, \n labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n custom_palette=['#FF0000', '#0000FF', '#00FF00'],\n reference_band=[1,])\n\n\n\n\n\n\n\n\nYou can set a variety of kwargs to customize the reference bands via reference_band_kwargs.\nPass any keyword arguments accepted by matplotlib.patches.Rectangle here, as a string.\nIn addition, the span_ax keyword argument can be used to expand the reference band across the whole plot.\n\nf_forest_minimeta = dabest.forest_plot(\n data = contrasts_mini_meta, \n labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n custom_palette=['#FF0000', '#0000FF', '#00FF00'],\n reference_band=[1,],\n reference_band_kwargs={'span_ax': True, 'color': 'grey', 'alpha': 0.2})\n\n\n\n\n\n\n\n\n\n\nEmbedding forest plots into an existing Axes\nYou can plot a forest plot into an existing Axes as a subplot by using the with the ax parameter.\n\nf_forest_drug_profiles, axes = plt.subplots(2, 2, figsize=[15, 14])\nf_forest_drug_profiles.subplots_adjust(hspace=0.3, wspace=0.3)\n\nfor ax, contrast in zip(axes.flatten(), [unpaired_delta_01, unpaired_delta_02, unpaired_delta_03]):\n contrast.mean_diff.plot( \n contrast_label='Mean Diff',\n raw_marker_size = 1,\n contrast_marker_size = 5,\n color_col='Genotype',\n ax = ax\n )\n\ndabest.forest_plot(\n data = contrasts, \n labels = ['Drug1', 'Drug2', 'Drug3'], \n ax = axes[1,1], \n )\n\nfor ax, title in zip(axes.flatten(), ['Drug 1', 'Drug 2', 'Drug 3', 'Forest plot']):\n ax.set_title(title, fontsize = 12)", + "crumbs": [ + "Get Started", + "Tutorials", + "Forest Plots: Visualizing Multiple Contrasts" + ] + }, + { + "objectID": "API/index.html", + "href": "API/index.html", + "title": "API", + "section": "", + "text": "This section contains API details for each of dabest’s python submodules. This reference documentation is mainly useful for people looking to customise or build on top of dabest, or wanting detailed information about how dabest works.\n\n\n\n\n\n\n\n\n\n\nTitle\n\n\n\nDescription\n\n\n\n\n\n\n\n\nLoading Data\n\n\nLoading data and relevant groups\n\n\n\n\n\n\nDabest object\n\n\nMain class for estimating statistics and generating plots.\n\n\n\n\n\n\nBootstrap\n\n\n\n\n\n\n\n\n\nForest plot\n\n\nCreating forest plots from contrast objects.\n\n\n\n\n\n\nPlot\n\n\nCreating estimation plots.\n\n\n\n\n\n\nplot_tools\n\n\nA set of convenience functions used for producing plots in dabest.\n\n\n\n\n\n\neffsize\n\n\nA range of functions to compute various effect sizes.\n\n\n\n\n\n\nconfint_1group\n\n\nA range of functions to compute bootstraps for a single sample.\n\n\n\n\n\n\nconfint_2group_diff\n\n\nA range of functions to compute bootstraps for the mean difference\n\n\n\n\n\n\nDelta objects\n\n\nAuxiliary delta classes for estimating statistics and generating plots.\n\n\n\n\n\n\nmisc_tools\n\n\nConvenience functions that don’t directly deal with plotting or bootstrap computations are placed here.\n\n\n\n\n\n\nEffectsize objects\n\n\nThe auxiliary classes involved in the computations of bootstrapped effect sizes.\n\n\n\n\n\n\nprecompile\n\n\nA tool to pre-compile Numba functions for speeding up DABEST bootstrapping\n\n\n\n\n\n\nmulti\n\n\n\n\n\n\n\n\n\nNo matching items", + "crumbs": [ + "Get Started", + "API" + ] + } +] \ No newline at end of file diff --git a/settings.ini b/settings.ini deleted file mode 100644 index 9b856d0d..00000000 --- a/settings.ini +++ /dev/null @@ -1,46 +0,0 @@ -[DEFAULT] -### Python library ### -repo = DABEST-python -lib_name = dabest -version = 2025.10.20 -min_python = 3.10 -license = apache2 - -### nbdev ### -doc_path = _docs -lib_path = dabest -nbs_path = nbs -recursive = True -tst_flags = notest -put_version_in_init = True -readme_nb = read_me.ipynb -jupyter_hooks = True - -### Docs ### -branch = master -custom_sidebar = True -doc_host = https://%(user)s.github.io -doc_baseurl = /%(repo)s -git_url = https://github.com/%(user)s/%(repo)s -title = %(lib_name)s - -### PyPI ### -audience = Developers -author = Joses W. Ho -author_email = joseshowh@gmail.com -maintainer = Adam Claridge-Chang -maintainer_email = estimationstats@gmail.com -copyright = 2023 onwards, %(author)s -description = Data Analysis and Visualization using Bootstrap-Coupled Estimation. -keywords = nbdev jupyter notebook python -language = English -status = 3 -user = acclab - -requirements = fastcore pandas~=2.2.3 numpy~=2.1.0 matplotlib~=3.10.0 seaborn~=0.13.2 scipy~=1.15.2 numba~=0.61.0 datetime statsmodels lqrt tqdm -dev_requirements = pytest~=8.3.4 pytest-mpl~=0.17.0 - -### Optional ### -# requirements = fastcore pandas -# dev_requirements = -# console_scripts = diff --git a/setup.py b/setup.py deleted file mode 100644 index bb8943b0..00000000 --- a/setup.py +++ /dev/null @@ -1,59 +0,0 @@ -from packaging.version import parse as parse_version -from configparser import ConfigParser -import setuptools, shlex -assert parse_version(setuptools.__version__)>=parse_version('36.2') - -# note: all settings are in settings.ini; edit there, not here -config = ConfigParser(delimiters=['=']) -config.read('settings.ini') -cfg = config['DEFAULT'] - -cfg_keys = 'version description keywords author author_email'.split() -expected = cfg_keys + "lib_name user branch license status min_python audience language".split() -for o in expected: assert o in cfg, "missing expected setting: {}".format(o) -setup_cfg = {o:cfg[o] for o in cfg_keys} - -licenses = { - 'apache2': ('Apache Software License 2.0','OSI Approved :: Apache Software License'), - 'mit': ('MIT License', 'OSI Approved :: MIT License'), - 'gpl2': ('GNU General Public License v2', 'OSI Approved :: GNU General Public License v2 (GPLv2)'), - 'gpl3': ('GNU General Public License v3', 'OSI Approved :: GNU General Public License v3 (GPLv3)'), - 'bsd3': ('BSD License', 'OSI Approved :: BSD License'), -} -statuses = [ '1 - Planning', '2 - Pre-Alpha', '3 - Alpha', - '4 - Beta', '5 - Production/Stable', '6 - Mature', '7 - Inactive' ] -py_versions = '3.10 3.11 3.12 3.13'.split() -requirements = shlex.split(cfg.get('requirements', '')) -if cfg.get('pip_requirements'): requirements += shlex.split(cfg.get('pip_requirements', '')) -min_python = cfg['min_python'] -lic = licenses.get(cfg['license'].lower(), (cfg['license'], None)) -dev_requirements = (cfg.get('dev_requirements') or '').split() -project_urls = {} -if cfg.get('doc_host'): project_urls["Documentation"] = cfg['doc_host'] + cfg.get('doc_baseurl', '') - -setuptools.setup( - name = cfg['lib_name'], - license = lic[0], - classifiers = [ - 'Development Status :: ' + statuses[int(cfg['status'])], - 'Intended Audience :: ' + cfg['audience'].title(), - 'Natural Language :: ' + cfg['language'].title(), - ] + ['Programming Language :: Python :: '+o for o in py_versions[py_versions.index(min_python):]] + (['License :: ' + lic[1] ] if lic[1] else []), - url = cfg['git_url'], - packages = setuptools.find_packages(), - include_package_data = True, - install_requires = requirements, - extras_require={ 'dev': dev_requirements }, - dependency_links = cfg.get('dep_links','').split(), - python_requires = '>=' + cfg['min_python'], - long_description = open('README.md').read(), - long_description_content_type = 'text/markdown', - zip_safe = False, - entry_points = { - 'console_scripts': cfg.get('console_scripts','').split(), - 'nbdev': [f'{cfg.get("lib_path")}={cfg.get("lib_path")}._modidx:d'] - }, - project_urls = project_urls, - **setup_cfg) - - diff --git a/site_libs/bootstrap/bootstrap-302b3f79daa8585235c194e0fecd03e8.min.css b/site_libs/bootstrap/bootstrap-302b3f79daa8585235c194e0fecd03e8.min.css new file mode 100644 index 00000000..0ff6ab2d --- /dev/null +++ b/site_libs/bootstrap/bootstrap-302b3f79daa8585235c194e0fecd03e8.min.css @@ -0,0 +1,12 @@ +/*! + * Bootstrap v5.3.1 (https://getbootstrap.com/) + * Copyright 2011-2023 The Bootstrap Authors + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) + 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23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(252.84, 253.73, 254.72, 0);--bs-navbar-toggler-border-radius: 0.25rem;--bs-navbar-toggler-focus-width: 0.25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-navbar-padding-y) 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.navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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!important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: rgb(252.84, 253.73, 254.72);--bs-navbar-hover-color: rgba(252.84, 253.42, 254.72, 0.8);--bs-navbar-disabled-color: rgba(252.84, 253.73, 254.72, 0.75);--bs-navbar-active-color: rgb(252.84, 253.42, 254.72);--bs-navbar-brand-color: rgb(252.84, 253.73, 254.72);--bs-navbar-brand-hover-color: rgb(252.84, 253.42, 254.72);--bs-navbar-toggler-border-color: rgba(252.84, 253.73, 254.72, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgb%28252.84, 253.73, 254.72%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgb%28252.84, 253.73, 254.72%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #fff;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #343a40;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23343a40'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%2815.6, 51.2, 90.8%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: rgb(147, 191.5, 241);--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: rgb(15.6, 51.2, 90.8);--bs-accordion-active-bg: rgb(211.8, 229.6, 249.4)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28125.4, 178.8, 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var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: rgb(31.2, 77.6, 181.6);--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: 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576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 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var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: 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203);background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) rgb(107.4,101.6,203);color:#fff}.bg-gradient-cyan-indigo{--bslib-color-fg: #fff;--bslib-color-bg: rgb(132.6, 56.8, 209);background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) rgb(132.6,56.8,209);color:#fff}.bg-gradient-cyan-purple{--bslib-color-fg: #fff;--bslib-color-bg: rgb(130.6, 74.8, 161.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) rgb(130.6,74.8,161.8);color:#fff}.bg-gradient-cyan-pink{--bslib-color-fg: #fff;--bslib-color-bg: rgb(184.6, 75.2, 168.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) rgb(184.6,75.2,168.2);color:#fff}.bg-gradient-cyan-red{--bslib-color-fg: 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189.4, 113.4);--quarto-scss-export-orange-500: #f0ad4e;--quarto-scss-export-orange-600: rgb(192, 138.4, 62.4);--quarto-scss-export-orange-700: rgb(144, 103.8, 46.8);--quarto-scss-export-orange-800: rgb(96, 69.2, 31.2);--quarto-scss-export-orange-900: rgb(48, 34.6, 15.6);--quarto-scss-export-yellow-100: rgb(255, 227.4, 208.8);--quarto-scss-export-yellow-200: rgb(255, 199.8, 162.6);--quarto-scss-export-yellow-300: rgb(255, 172.2, 116.4);--quarto-scss-export-yellow-400: rgb(255, 144.6, 70.2);--quarto-scss-export-yellow-500: #ff7518;--quarto-scss-export-yellow-600: rgb(204, 93.6, 19.2);--quarto-scss-export-yellow-700: rgb(153, 70.2, 14.4);--quarto-scss-export-yellow-800: rgb(102, 46.8, 9.6);--quarto-scss-export-yellow-900: rgb(51, 23.4, 4.8);--quarto-scss-export-green-100: rgb(216.6, 240.4, 208.8);--quarto-scss-export-green-200: rgb(178.2, 225.8, 162.6);--quarto-scss-export-green-300: rgb(139.8, 211.2, 116.4);--quarto-scss-export-green-400: rgb(101.4, 196.6, 70.2);--quarto-scss-export-green-500: #3fb618;--quarto-scss-export-green-600: rgb(50.4, 145.6, 19.2);--quarto-scss-export-green-700: rgb(37.8, 109.2, 14.4);--quarto-scss-export-green-800: rgb(25.2, 72.8, 9.6);--quarto-scss-export-green-900: rgb(12.6, 36.4, 4.8);--quarto-scss-export-teal-100: rgb(210.4, 244.2, 234.2);--quarto-scss-export-teal-200: rgb(165.8, 233.4, 213.4);--quarto-scss-export-teal-300: rgb(121.2, 222.6, 192.6);--quarto-scss-export-teal-400: rgb(76.6, 211.8, 171.8);--quarto-scss-export-teal-500: #20c997;--quarto-scss-export-teal-600: rgb(25.6, 160.8, 120.8);--quarto-scss-export-teal-700: rgb(19.2, 120.6, 90.6);--quarto-scss-export-teal-800: rgb(12.8, 80.4, 60.4);--quarto-scss-export-teal-900: rgb(6.4, 40.2, 30.2);--quarto-scss-export-cyan-100: rgb(234.6, 220.8, 241.4);--quarto-scss-export-cyan-200: rgb(214.2, 186.6, 227.8);--quarto-scss-export-cyan-300: rgb(193.8, 152.4, 214.2);--quarto-scss-export-cyan-400: rgb(173.4, 118.2, 200.6);--quarto-scss-export-cyan-500: #9954bb;--quarto-scss-export-cyan-600: rgb(122.4, 67.2, 149.6);--quarto-scss-export-cyan-700: rgb(91.8, 50.4, 112.2);--quarto-scss-export-cyan-800: rgb(61.2, 33.6, 74.8);--quarto-scss-export-cyan-900: rgb(30.6, 16.8, 37.4);--quarto-scss-export-default: #343a40;--quarto-scss-export-primary-text-emphasis: rgb(15.6, 51.2, 90.8);--quarto-scss-export-secondary-text-emphasis: rgb(20.8, 23.2, 25.6);--quarto-scss-export-success-text-emphasis: rgb(25.2, 72.8, 9.6);--quarto-scss-export-info-text-emphasis: rgb(61.2, 33.6, 74.8);--quarto-scss-export-warning-text-emphasis: rgb(102, 46.8, 9.6);--quarto-scss-export-danger-text-emphasis: rgb(102, 0, 22.8);--quarto-scss-export-light-text-emphasis: #495057;--quarto-scss-export-dark-text-emphasis: #495057;--quarto-scss-export-primary-bg-subtle: rgb(211.8, 229.6, 249.4);--quarto-scss-export-secondary-bg-subtle: rgb(214.4, 215.6, 216.8);--quarto-scss-export-success-bg-subtle: rgb(216.6, 240.4, 208.8);--quarto-scss-export-info-bg-subtle: rgb(234.6, 220.8, 241.4);--quarto-scss-export-warning-bg-subtle: rgb(255, 227.4, 208.8);--quarto-scss-export-danger-bg-subtle: rgb(255, 204, 215.4);--quarto-scss-export-light-bg-subtle: rgb(251.5, 252, 252.5);--quarto-scss-export-dark-bg-subtle: #ced4da;--quarto-scss-export-primary-border-subtle: rgb(168.6, 204.2, 243.8);--quarto-scss-export-secondary-border-subtle: rgb(173.8, 176.2, 178.6);--quarto-scss-export-success-border-subtle: rgb(178.2, 225.8, 162.6);--quarto-scss-export-info-border-subtle: rgb(214.2, 186.6, 227.8);--quarto-scss-export-warning-border-subtle: rgb(255, 199.8, 162.6);--quarto-scss-export-danger-border-subtle: rgb(255, 153, 175.8);--quarto-scss-export-light-border-subtle: #e9ecef;--quarto-scss-export-dark-border-subtle: #adb5bd;--quarto-scss-export-body-text-align: ;--quarto-scss-export-body-bg: #fff;--quarto-scss-export-body-secondary-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-body-secondary-bg: #e9ecef;--quarto-scss-export-body-tertiary-color: rgba(52, 58, 64, 0.5);--quarto-scss-export-body-tertiary-bg: #f8f9fa;--quarto-scss-export-body-emphasis-color: #000;--quarto-scss-export-link-hover-color: rgb(31.2, 77.6, 181.6);--quarto-scss-export-link-hover-decoration: ;--quarto-scss-export-border-color-translucent: rgba(0, 0, 0, 0.175);--quarto-scss-export-component-active-bg: #2780e3;--quarto-scss-export-component-active-color: #fff;--quarto-scss-export-focus-ring-color: rgba(39, 128, 227, 0.25);--quarto-scss-export-headings-font-family: ;--quarto-scss-export-headings-font-style: ;--quarto-scss-export-display-font-family: ;--quarto-scss-export-display-font-style: ;--quarto-scss-export-blockquote-footer-color: #6c757d;--quarto-scss-export-blockquote-border-color: #e9ecef;--quarto-scss-export-hr-bg-color: ;--quarto-scss-export-hr-height: ;--quarto-scss-export-hr-border-color: ;--quarto-scss-export-legend-font-weight: ;--quarto-scss-export-mark-bg: rgb(255, 227.4, 208.8);--quarto-scss-export-table-color: #343a40;--quarto-scss-export-table-bg: #fff;--quarto-scss-export-table-accent-bg: transparent;--quarto-scss-export-table-th-font-weight: ;--quarto-scss-export-table-striped-color: #343a40;--quarto-scss-export-table-striped-bg: rgba(0, 0, 0, 0.05);--quarto-scss-export-table-active-color: #343a40;--quarto-scss-export-table-active-bg: rgba(0, 0, 0, 0.1);--quarto-scss-export-table-hover-color: #343a40;--quarto-scss-export-table-hover-bg: rgba(0, 0, 0, 0.075);--quarto-scss-export-table-caption-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-input-btn-font-family: ;--quarto-scss-export-input-btn-focus-color: rgba(39, 128, 227, 0.25);--quarto-scss-export-btn-color: #343a40;--quarto-scss-export-btn-font-family: ;--quarto-scss-export-btn-white-space: ;--quarto-scss-export-btn-link-color: #2761e3;--quarto-scss-export-btn-link-hover-color: rgb(31.2, 77.6, 181.6);--quarto-scss-export-btn-link-disabled-color: #6c757d;--quarto-scss-export-form-text-font-style: ;--quarto-scss-export-form-text-font-weight: ;--quarto-scss-export-form-text-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-form-label-font-size: ;--quarto-scss-export-form-label-font-style: ;--quarto-scss-export-form-label-font-weight: ;--quarto-scss-export-form-label-color: ;--quarto-scss-export-input-font-family: ;--quarto-scss-export-input-disabled-color: ;--quarto-scss-export-input-disabled-bg: #e9ecef;--quarto-scss-export-input-disabled-border-color: ;--quarto-scss-export-input-color: #343a40;--quarto-scss-export-input-focus-bg: #fff;--quarto-scss-export-input-focus-border-color: rgb(147, 191.5, 241);--quarto-scss-export-input-focus-color: #343a40;--quarto-scss-export-input-placeholder-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-input-plaintext-color: #343a40;--quarto-scss-export-form-check-label-color: ;--quarto-scss-export-form-check-transition: ;--quarto-scss-export-form-check-input-bg: #fff;--quarto-scss-export-form-check-input-focus-border: rgb(147, 191.5, 241);--quarto-scss-export-form-check-input-checked-color: #fff;--quarto-scss-export-form-check-input-checked-bg-color: #2780e3;--quarto-scss-export-form-check-input-checked-border-color: #2780e3;--quarto-scss-export-form-check-input-indeterminate-color: #fff;--quarto-scss-export-form-check-input-indeterminate-bg-color: #2780e3;--quarto-scss-export-form-check-input-indeterminate-border-color: #2780e3;--quarto-scss-export-form-switch-color: rgba(0, 0, 0, 0.25);--quarto-scss-export-form-switch-focus-color: rgb(147, 191.5, 241);--quarto-scss-export-form-switch-checked-color: #fff;--quarto-scss-export-input-group-addon-color: #343a40;--quarto-scss-export-input-group-addon-bg: #f8f9fa;--quarto-scss-export-input-group-addon-border-color: #dee2e6;--quarto-scss-export-form-select-font-family: ;--quarto-scss-export-form-select-color: #343a40;--quarto-scss-export-form-select-bg: #fff;--quarto-scss-export-form-select-disabled-color: ;--quarto-scss-export-form-select-disabled-bg: #e9ecef;--quarto-scss-export-form-select-disabled-border-color: ;--quarto-scss-export-form-select-indicator-color: #343a40;--quarto-scss-export-form-select-border-color: #dee2e6;--quarto-scss-export-form-select-focus-border-color: rgb(147, 191.5, 241);--quarto-scss-export-form-range-track-bg: #f8f9fa;--quarto-scss-export-form-range-thumb-bg: #2780e3;--quarto-scss-export-form-range-thumb-active-bg: rgb(190.2, 216.9, 246.6);--quarto-scss-export-form-range-thumb-disabled-bg: rgba(52, 58, 64, 0.75);--quarto-scss-export-form-file-button-color: #343a40;--quarto-scss-export-form-file-button-bg: #f8f9fa;--quarto-scss-export-form-file-button-hover-bg: #e9ecef;--quarto-scss-export-form-floating-label-disabled-color: #6c757d;--quarto-scss-export-form-feedback-font-style: ;--quarto-scss-export-form-feedback-valid-color: #3fb618;--quarto-scss-export-form-feedback-invalid-color: #ff0039;--quarto-scss-export-form-feedback-icon-valid-color: #3fb618;--quarto-scss-export-form-feedback-icon-invalid-color: #ff0039;--quarto-scss-export-form-valid-color: #3fb618;--quarto-scss-export-form-valid-border-color: #3fb618;--quarto-scss-export-form-invalid-color: #ff0039;--quarto-scss-export-form-invalid-border-color: #ff0039;--quarto-scss-export-nav-link-font-size: ;--quarto-scss-export-nav-link-font-weight: ;--quarto-scss-export-nav-link-color: #2761e3;--quarto-scss-export-nav-link-hover-color: rgb(31.2, 77.6, 181.6);--quarto-scss-export-nav-link-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-nav-tabs-border-color: #dee2e6;--quarto-scss-export-nav-tabs-link-hover-border-color: #e9ecef #e9ecef #dee2e6;--quarto-scss-export-nav-tabs-link-active-color: #000;--quarto-scss-export-nav-tabs-link-active-bg: #fff;--quarto-scss-export-nav-pills-link-active-bg: #2780e3;--quarto-scss-export-nav-pills-link-active-color: #fff;--quarto-scss-export-nav-underline-link-active-color: #000;--quarto-scss-export-navbar-padding-x: ;--quarto-scss-export-navbar-light-contrast: #fff;--quarto-scss-export-navbar-dark-contrast: #fff;--quarto-scss-export-navbar-light-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-navbar-dark-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-dropdown-color: #343a40;--quarto-scss-export-dropdown-bg: #fff;--quarto-scss-export-dropdown-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-link-color: #343a40;--quarto-scss-export-dropdown-link-hover-color: #343a40;--quarto-scss-export-dropdown-link-hover-bg: #f8f9fa;--quarto-scss-export-dropdown-link-active-bg: #2780e3;--quarto-scss-export-dropdown-link-active-color: #fff;--quarto-scss-export-dropdown-link-disabled-color: rgba(52, 58, 64, 0.5);--quarto-scss-export-dropdown-header-color: #6c757d;--quarto-scss-export-dropdown-dark-color: #dee2e6;--quarto-scss-export-dropdown-dark-bg: #343a40;--quarto-scss-export-dropdown-dark-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-box-shadow: ;--quarto-scss-export-dropdown-dark-link-color: #dee2e6;--quarto-scss-export-dropdown-dark-link-hover-color: #fff;--quarto-scss-export-dropdown-dark-link-hover-bg: rgba(255, 255, 255, 0.15);--quarto-scss-export-dropdown-dark-link-active-color: #fff;--quarto-scss-export-dropdown-dark-link-active-bg: #2780e3;--quarto-scss-export-dropdown-dark-link-disabled-color: #adb5bd;--quarto-scss-export-dropdown-dark-header-color: #adb5bd;--quarto-scss-export-pagination-color: #2761e3;--quarto-scss-export-pagination-bg: #fff;--quarto-scss-export-pagination-border-color: #dee2e6;--quarto-scss-export-pagination-focus-color: rgb(31.2, 77.6, 181.6);--quarto-scss-export-pagination-focus-bg: #e9ecef;--quarto-scss-export-pagination-hover-color: rgb(31.2, 77.6, 181.6);--quarto-scss-export-pagination-hover-bg: #f8f9fa;--quarto-scss-export-pagination-hover-border-color: #dee2e6;--quarto-scss-export-pagination-active-color: #fff;--quarto-scss-export-pagination-active-bg: #2780e3;--quarto-scss-export-pagination-active-border-color: #2780e3;--quarto-scss-export-pagination-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-pagination-disabled-bg: #e9ecef;--quarto-scss-export-pagination-disabled-border-color: #dee2e6;--quarto-scss-export-card-title-color: ;--quarto-scss-export-card-subtitle-color: ;--quarto-scss-export-card-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-card-box-shadow: ;--quarto-scss-export-card-cap-color: ;--quarto-scss-export-card-height: ;--quarto-scss-export-card-color: ;--quarto-scss-export-card-bg: #fff;--quarto-scss-export-accordion-color: #343a40;--quarto-scss-export-accordion-bg: #fff;--quarto-scss-export-accordion-border-color: #dee2e6;--quarto-scss-export-accordion-button-color: #343a40;--quarto-scss-export-accordion-button-bg: #fff;--quarto-scss-export-accordion-button-active-bg: rgb(211.8, 229.6, 249.4);--quarto-scss-export-accordion-button-active-color: rgb(15.6, 51.2, 90.8);--quarto-scss-export-accordion-button-focus-border-color: rgb(147, 191.5, 241);--quarto-scss-export-accordion-icon-color: #343a40;--quarto-scss-export-accordion-icon-active-color: rgb(15.6, 51.2, 90.8);--quarto-scss-export-tooltip-color: #fff;--quarto-scss-export-tooltip-bg: #000;--quarto-scss-export-tooltip-margin: ;--quarto-scss-export-tooltip-arrow-color: ;--quarto-scss-export-form-feedback-tooltip-line-height: ;--quarto-scss-export-popover-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-popover-header-bg: #e9ecef;--quarto-scss-export-popover-body-color: #343a40;--quarto-scss-export-popover-arrow-color: #fff;--quarto-scss-export-popover-arrow-outer-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-color: ;--quarto-scss-export-toast-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-header-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-toast-header-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-header-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-badge-color: #fff;--quarto-scss-export-modal-content-color: ;--quarto-scss-export-modal-content-bg: #fff;--quarto-scss-export-modal-content-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-modal-backdrop-bg: #000;--quarto-scss-export-modal-header-border-color: #dee2e6;--quarto-scss-export-modal-footer-bg: ;--quarto-scss-export-modal-footer-border-color: #dee2e6;--quarto-scss-export-progress-bg: #e9ecef;--quarto-scss-export-progress-bar-color: #fff;--quarto-scss-export-progress-bar-bg: #2780e3;--quarto-scss-export-list-group-color: #343a40;--quarto-scss-export-list-group-bg: #fff;--quarto-scss-export-list-group-border-color: #dee2e6;--quarto-scss-export-list-group-hover-bg: #f8f9fa;--quarto-scss-export-list-group-active-bg: #2780e3;--quarto-scss-export-list-group-active-color: #fff;--quarto-scss-export-list-group-active-border-color: #2780e3;--quarto-scss-export-list-group-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-list-group-disabled-bg: #fff;--quarto-scss-export-list-group-action-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-list-group-action-hover-color: #000;--quarto-scss-export-list-group-action-active-color: #343a40;--quarto-scss-export-list-group-action-active-bg: #e9ecef;--quarto-scss-export-thumbnail-bg: #fff;--quarto-scss-export-thumbnail-border-color: #dee2e6;--quarto-scss-export-figure-caption-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-font-size: ;--quarto-scss-export-breadcrumb-bg: ;--quarto-scss-export-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-active-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-border-radius: ;--quarto-scss-export-carousel-control-color: #fff;--quarto-scss-export-carousel-indicator-active-bg: #fff;--quarto-scss-export-carousel-caption-color: #fff;--quarto-scss-export-carousel-dark-indicator-active-bg: #000;--quarto-scss-export-carousel-dark-caption-color: #000;--quarto-scss-export-btn-close-color: #000;--quarto-scss-export-offcanvas-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-offcanvas-bg-color: #fff;--quarto-scss-export-offcanvas-color: #343a40;--quarto-scss-export-offcanvas-backdrop-bg: #000;--quarto-scss-export-code-color-dark: white;--quarto-scss-export-kbd-color: #fff;--quarto-scss-export-kbd-bg: #343a40;--quarto-scss-export-nested-kbd-font-weight: ;--quarto-scss-export-pre-bg: #f8f9fa;--quarto-scss-export-pre-color: #000;--quarto-scss-export-bslib-page-sidebar-title-bg: #2780e3;--quarto-scss-export-bslib-page-sidebar-title-color: #fff;--quarto-scss-export-bslib-sidebar-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.05);--quarto-scss-export-bslib-sidebar-toggle-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.1);--quarto-scss-export-sidebar-color: rgb(89.25, 89.25, 89.25);--quarto-scss-export-sidebar-hover-color: rgba(32.76, 81.48, 190.68, 0.8);--quarto-scss-export-sidebar-disabled-color: rgba(89.25, 89.25, 89.25, 0.75);--quarto-scss-export-valuebox-bg-primary: #5397e9;--quarto-scss-export-valuebox-bg-secondary: #343a40;--quarto-scss-export-valuebox-bg-success: #3aa716;--quarto-scss-export-valuebox-bg-info: rgba(153, 84, 187, 0.7019607843);--quarto-scss-export-valuebox-bg-warning: #fa6400;--quarto-scss-export-valuebox-bg-danger: rgba(255, 0, 57, 0.7019607843);--quarto-scss-export-valuebox-bg-light: #f8f9fa;--quarto-scss-export-valuebox-bg-dark: #343a40;--quarto-scss-export-mermaid-bg-color: #fff;--quarto-scss-export-mermaid-edge-color: #343a40;--quarto-scss-export-mermaid-node-fg-color: #343a40;--quarto-scss-export-mermaid-fg-color: #343a40;--quarto-scss-export-mermaid-fg-color--lighter: rgb(74.8620689655, 83.5, 92.1379310345);--quarto-scss-export-mermaid-fg-color--lightest: rgb(97.724137931, 109, 120.275862069);--quarto-scss-export-mermaid-label-bg-color: #fff;--quarto-scss-export-mermaid-label-fg-color: #2780e3;--quarto-scss-export-mermaid-node-bg-color: rgba(39, 128, 227, 0.1);--quarto-scss-export-code-block-border-left-color: #dee2e6;--quarto-scss-export-callout-color-note: #2780e3;--quarto-scss-export-callout-color-tip: #3fb618;--quarto-scss-export-callout-color-important: #ff0039;--quarto-scss-export-callout-color-caution: #f0ad4e;--quarto-scss-export-callout-color-warning: #ff7518} \ No newline at end of file diff --git a/site_libs/bootstrap/bootstrap-icons.css b/site_libs/bootstrap/bootstrap-icons.css new file mode 100644 index 00000000..82b40f57 --- /dev/null +++ b/site_libs/bootstrap/bootstrap-icons.css @@ -0,0 +1,2106 @@ +/*! + * Bootstrap Icons v1.13.1 (https://icons.getbootstrap.com/) + * Copyright 2019-2024 The Bootstrap Authors + * Licensed under MIT (https://github.com/twbs/icons/blob/main/LICENSE) + */ + +@font-face { + font-display: block; + font-family: 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W{static get NAME(){return"button"}toggle(){this._element.setAttribute("aria-pressed",this._element.classList.toggle("active"))}static jQueryInterface(t){return this.each((function(){const e=Y.getOrCreateInstance(this);"toggle"===t&&e[t]()}))}}N.on(document,"click.bs.button.data-api",X,(t=>{t.preventDefault();const e=t.target.closest(X);Y.getOrCreateInstance(e).toggle()})),m(Y);const U=".bs.swipe",G=`touchstart${U}`,J=`touchmove${U}`,Z=`touchend${U}`,tt=`pointerdown${U}`,et=`pointerup${U}`,it={endCallback:null,leftCallback:null,rightCallback:null},nt={endCallback:"(function|null)",leftCallback:"(function|null)",rightCallback:"(function|null)"};class st extends H{constructor(t,e){super(),this._element=t,t&&st.isSupported()&&(this._config=this._getConfig(e),this._deltaX=0,this._supportPointerEvents=Boolean(window.PointerEvent),this._initEvents())}static get Default(){return it}static get DefaultType(){return nt}static get 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t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&N.on(this._element,ft,(t=>this._keydown(t))),"hover"===this._config.pause&&(N.on(this._element,pt,(()=>this.pause())),N.on(this._element,mt,(()=>this._maybeEnableCycle()))),this._config.touch&&st.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of z.find(".carousel-item img",this._element))N.on(t,gt,(t=>t.preventDefault()));const t={leftCallback:()=>this._slide(this._directionToOrder(ct)),rightCallback:()=>this._slide(this._directionToOrder(ht)),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((()=>this._maybeEnableCycle()),500+this._config.interval))}};this._swipeHelper=new st(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=Tt[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=z.findOne(wt,this._indicatorsElement);e.classList.remove(yt),e.removeAttribute("aria-current");const i=z.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(yt),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===at,s=e||b(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>N.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(dt).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const 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t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get 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file mode 100644 index 00000000..25101c33 --- /dev/null +++ b/site_libs/quarto-html/quarto-syntax-highlighting-845c23b38eaddc0f92fda52bfe77a8c8.css @@ -0,0 +1,236 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +/* syntax highlight based on Pandoc's rules */ +pre > code.sourceCode > span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +/* Normal */ +code span { + color: #003B4F; +} + +/* Alert */ +code span.al { + color: #AD0000; + font-style: inherit; +} + +/* Annotation */ +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +/* Attribute */ +code span.at { + color: #657422; + font-style: inherit; +} + +/* BaseN */ +code span.bn { + color: #AD0000; + font-style: inherit; +} + +/* BuiltIn */ +code span.bu { + font-style: inherit; +} + +/* ControlFlow */ +code span.cf { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +/* Char */ +code span.ch { + color: #20794D; + font-style: inherit; +} + +/* Constant */ +code span.cn { + color: #8f5902; + font-style: inherit; +} + +/* Comment */ +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +/* CommentVar */ +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +/* Documentation */ +code span.do { + color: #5E5E5E; + font-style: italic; +} + +/* DataType */ +code span.dt { + color: #AD0000; + font-style: inherit; +} + +/* DecVal */ +code span.dv { + color: #AD0000; + font-style: inherit; +} + +/* Error */ +code span.er { + color: #AD0000; + font-style: inherit; +} + +/* Extension */ +code span.ex { + font-style: inherit; +} + +/* Float */ +code span.fl { + color: #AD0000; + font-style: inherit; +} + +/* Function */ +code span.fu { + color: #4758AB; + font-style: inherit; +} + +/* Import */ +code span.im { + color: #00769E; + font-style: inherit; +} + +/* Information */ +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +/* Keyword */ +code span.kw { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +/* Operator */ +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +/* Other */ +code span.ot { + color: #003B4F; + font-style: inherit; +} + +/* Preprocessor */ +code span.pp { + color: #AD0000; + font-style: inherit; +} + +/* SpecialChar */ +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +/* SpecialString */ +code span.ss { + color: #20794D; + font-style: inherit; +} + +/* String */ +code span.st { + color: #20794D; + font-style: inherit; +} + +/* Variable */ +code span.va { + color: #111111; + font-style: inherit; +} + +/* VerbatimString */ +code span.vs { + color: #20794D; + font-style: inherit; +} + +/* Warning */ +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // dispatch for htmlwidgets + // they use slideenter event to trigger resize + function fireSlideEnter() { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // dispatch for shiny + // they use BS shown and hidden events to trigger rendering + function distpatchShinyEvents(previous, current) { + if (window.jQuery) { + if (previous) { + window.jQuery(previous).trigger("hidden"); + } + if (current) { + window.jQuery(current).trigger("shown"); + } + } + } + + // tabby.js listener: Trigger event for htmlwidget and shiny + document.addEventListener( + "tabby", + function (event) { + fireSlideEnter(); + distpatchShinyEvents(event.detail.previousTab, event.detail.tab); + }, + false + ); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id="${anchor}"]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + // This is the no-scroll case where last section should be the active one + sectionIndex = 0; + } else { + // This finds the last section visible on screen that should be made active + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + // Categories search with listing only use path without query + const currentPagePath = offsetAbsoluteUrl( + window.location.origin + window.location.pathname + ); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + const encodedItem = encodeURI(item); + if ( + encodedItem === currentPagePath || + encodedItem === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + child.style.pointerEvents = "none"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.pointerEvents = null; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + child.style.pointerEvents = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (const child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + // toc-expand: false + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +tabsets.init(); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/site_libs/quarto-html/tabsets/tabsets.js b/site_libs/quarto-html/tabsets/tabsets.js new file mode 100644 index 00000000..51345d0e --- /dev/null +++ b/site_libs/quarto-html/tabsets/tabsets.js @@ -0,0 +1,95 @@ +// grouped tabsets + +export function init() { + window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } + }); +} diff --git a/site_libs/quarto-html/tippy.css b/site_libs/quarto-html/tippy.css new file mode 100644 index 00000000..e6ae635c --- /dev/null +++ b/site_libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 10px)}.tippy-box{position:relative;background-color:#333;color:#fff;border-radius:4px;font-size:14px;line-height:1.4;white-space:normal;outline:0;transition-property:transform,visibility,opacity}.tippy-box[data-placement^=top]>.tippy-arrow{bottom:0}.tippy-box[data-placement^=top]>.tippy-arrow:before{bottom:-7px;left:0;border-width:8px 8px 0;border-top-color:initial;transform-origin:center top}.tippy-box[data-placement^=bottom]>.tippy-arrow{top:0}.tippy-box[data-placement^=bottom]>.tippy-arrow:before{top:-7px;left:0;border-width:0 8px 8px;border-bottom-color:initial;transform-origin:center bottom}.tippy-box[data-placement^=left]>.tippy-arrow{right:0}.tippy-box[data-placement^=left]>.tippy-arrow:before{border-width:8px 0 8px 8px;border-left-color:initial;right:-7px;transform-origin:center 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b/site_libs/quarto-listing/list.min.js new file mode 100644 index 00000000..43dfd15a --- /dev/null +++ b/site_libs/quarto-listing/list.min.js @@ -0,0 +1,2 @@ +var List;List=function(){var t={"./src/add-async.js":function(t){t.exports=function(t){return function e(r,n,s){var i=r.splice(0,50);s=(s=s||[]).concat(t.add(i)),r.length>0?setTimeout((function(){e(r,n,s)}),1):(t.update(),n(s))}}},"./src/filter.js":function(t){t.exports=function(t){return t.handlers.filterStart=t.handlers.filterStart||[],t.handlers.filterComplete=t.handlers.filterComplete||[],function(e){if(t.trigger("filterStart"),t.i=1,t.reset.filter(),void 0===e)t.filtered=!1;else{t.filtered=!0;for(var r=t.items,n=0,s=r.length;nv.page,a=new g(t[s],void 0,n),v.items.push(a),r.push(a)}return v.update(),r}m(t.slice(0),e)}},this.show=function(t,e){return this.i=t,this.page=e,v.update(),v},this.remove=function(t,e,r){for(var n=0,s=0,i=v.items.length;s-1&&r.splice(n,1),v},this.trigger=function(t){for(var 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+ if (categoriesLoaded) { + activateCategory(category); + setCategoryHash(category); + } +}; + +window["quarto-listing-loaded"] = () => { + // Process any existing hash + const hash = getHash(); + + if (hash) { + // If there is a category, switch to that + if (hash.category) { + // category hash are URI encoded so we need to decode it before processing + // so that we can match it with the category element processed in JS + activateCategory(decodeURIComponent(hash.category)); + } + // Paginate a specific listing + const listingIds = Object.keys(window["quarto-listings"]); + for (const listingId of listingIds) { + const page = hash[getListingPageKey(listingId)]; + if (page) { + showPage(listingId, page); + } + } + } + + const listingIds = Object.keys(window["quarto-listings"]); + for (const listingId of listingIds) { + // The actual list + const list = window["quarto-listings"][listingId]; + + // Update the handlers for pagination events + refreshPaginationHandlers(listingId); + + // Render any visible items that need it + renderVisibleProgressiveImages(list); + + // Whenever the list is updated, we also need to + // attach handlers to the new pagination elements + // and refresh any newly visible items. + list.on("updated", function () { + renderVisibleProgressiveImages(list); + setTimeout(() => refreshPaginationHandlers(listingId)); + + // Show or hide the no matching message + toggleNoMatchingMessage(list); + }); + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Attach click handlers to categories + const categoryEls = window.document.querySelectorAll( + ".quarto-listing-category .category" + ); + + for (const categoryEl of categoryEls) { + // category needs to support non ASCII characters + const category = decodeURIComponent( + atob(categoryEl.getAttribute("data-category")) + ); + categoryEl.onclick = () => { + activateCategory(category); + setCategoryHash(category); + }; + } + + // Attach a click handler to the category title + // (there should be only one, but since it is a class name, handle N) + const categoryTitleEls = window.document.querySelectorAll( + ".quarto-listing-category-title" + ); + for (const categoryTitleEl of categoryTitleEls) { + categoryTitleEl.onclick = () => { + activateCategory(""); + setCategoryHash(""); + }; + } + + categoriesLoaded = true; +}); + +function toggleNoMatchingMessage(list) { + const selector = `#${list.listContainer.id} .listing-no-matching`; + const noMatchingEl = window.document.querySelector(selector); + if (noMatchingEl) { + if (list.visibleItems.length === 0) { + noMatchingEl.classList.remove("d-none"); + } else { + if (!noMatchingEl.classList.contains("d-none")) { + noMatchingEl.classList.add("d-none"); + } + } + } +} + +function setCategoryHash(category) { + setHash({ category }); +} + +function setPageHash(listingId, page) { + const currentHash = getHash() || {}; + currentHash[getListingPageKey(listingId)] = page; + setHash(currentHash); +} + +function getListingPageKey(listingId) { + return `${listingId}-page`; +} + +function refreshPaginationHandlers(listingId) { + const listingEl = window.document.getElementById(listingId); + const paginationEls = listingEl.querySelectorAll( + ".pagination li.page-item:not(.disabled) .page.page-link" + ); + for (const paginationEl of paginationEls) { + paginationEl.onclick = (sender) => { + setPageHash(listingId, sender.target.getAttribute("data-i")); + showPage(listingId, sender.target.getAttribute("data-i")); + return false; + }; + } +} + +function renderVisibleProgressiveImages(list) { + // Run through the visible items and render any progressive images + for (const item of list.visibleItems) { + const itemEl = item.elm; + if (itemEl) { + const progressiveImgs = itemEl.querySelectorAll( + `img[${kProgressiveAttr}]` + ); + for (const progressiveImg of progressiveImgs) { + const srcValue = progressiveImg.getAttribute(kProgressiveAttr); + if (srcValue) { + progressiveImg.setAttribute("src", srcValue); + } + progressiveImg.removeAttribute(kProgressiveAttr); + } + } + } +} + +function getHash() { + // Hashes are of the form + // #name:value|name1:value1|name2:value2 + const currentUrl = new URL(window.location); + const hashRaw = currentUrl.hash ? currentUrl.hash.slice(1) : undefined; + return parseHash(hashRaw); +} + +const kAnd = "&"; +const kEquals = "="; + +function parseHash(hash) { + if (!hash) { + return undefined; + } + const hasValuesStrs = hash.split(kAnd); + const hashValues = hasValuesStrs + .map((hashValueStr) => { + const vals = hashValueStr.split(kEquals); + if (vals.length === 2) { + return { name: vals[0], value: vals[1] }; + } else { + return undefined; + } + }) + .filter((value) => { + return value !== undefined; + }); + + const hashObj = {}; + hashValues.forEach((hashValue) => { + hashObj[hashValue.name] = decodeURIComponent(hashValue.value); + }); + return hashObj; +} + +function makeHash(obj) { + return Object.keys(obj) + .map((key) => { + return `${key}${kEquals}${obj[key]}`; + }) + .join(kAnd); +} + +function setHash(obj) { + const hash = makeHash(obj); + window.history.pushState(null, null, `#${hash}`); +} + +function showPage(listingId, page) { + const list = window["quarto-listings"][listingId]; + if (list) { + list.show((page - 1) * list.page + 1, list.page); + } +} + +function activateCategory(category) { + // Deactivate existing categories + const activeEls = window.document.querySelectorAll( + ".quarto-listing-category .category.active" + ); + for (const activeEl of activeEls) { + activeEl.classList.remove("active"); + } + + // Activate this category + const categoryEl = window.document.querySelector( + `.quarto-listing-category .category[data-category='${btoa( + encodeURIComponent(category) + )}']` + ); + if (categoryEl) { + categoryEl.classList.add("active"); + } + + // Filter the listings to this category + filterListingCategory(category); +} + +function filterListingCategory(category) { + const listingIds = Object.keys(window["quarto-listings"]); + for (const listingId of listingIds) { + const list = window["quarto-listings"][listingId]; + if (list) { + if (category === "") { + // resets the filter + list.filter(); + } else { + // filter to this category + list.filter(function (item) { + const itemValues = item.values(); + if (itemValues.categories !== null) { + const categories = decodeURIComponent( + atob(itemValues.categories) + ).split(","); + return categories.includes(category); + } else { + return false; + } + }); + } + } + } +} diff --git a/site_libs/quarto-nav/headroom.min.js b/site_libs/quarto-nav/headroom.min.js new file mode 100644 index 00000000..b08f1dff --- /dev/null +++ b/site_libs/quarto-nav/headroom.min.js @@ -0,0 +1,7 @@ +/*! + * headroom.js v0.12.0 - Give your page some headroom. 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b/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,325 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +const announceDismiss = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + annEl.remove(); + + const annId = annEl.getAttribute("data-announcement-id"); + window.localStorage.setItem(`quarto-announce-${annId}`, "true"); + } +}; + +const announceRegister = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + const annId = annEl.getAttribute("data-announcement-id"); + const isDismissed = + window.localStorage.getItem(`quarto-announce-${annId}`) || false; + if (isDismissed) { + announceDismiss(); + return; + } else { + annEl.classList.remove("hidden"); + } + + const actionEl = annEl.querySelector(".quarto-announcement-action"); + if (actionEl) { + actionEl.addEventListener("click", function (e) { + e.preventDefault(); + // Hide the bar immediately + announceDismiss(); + }); + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + announceRegister(); + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function dashboardOffset() { + const dashboardNavEl = window.document.getElementById( + "quarto-dashboard-header" + ); + if (dashboardNavEl !== null) { + return dashboardNavEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset() + dashboardOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + window.addEventListener( + "hashchange", + function (e) { + if ( + getComputedStyle(document.documentElement).scrollBehavior !== "smooth" + ) { + window.scrollTo(0, window.pageYOffset - headerOffset()); + } + }, + false + ); + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver(() => { + setTimeout(updateDocumentOffsetWithoutAnimation, 0); + }); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + if (links[i].href) { + links[i].dataset.originalHref = links[i].href; + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item, a.quarto-navigation-tool, a.quarto-navbar-tools, a.quarto-navbar-tools-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git a/site_libs/quarto-search/autocomplete.umd.js b/site_libs/quarto-search/autocomplete.umd.js new file mode 100644 index 00000000..6090a552 --- /dev/null +++ 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0===i?I.isCaseSensitive:i,c=n.includeMatches,a=void 0===c?I.includeMatches:c,s=n.minMatchCharLength,u=void 0===s?I.minMatchCharLength:s,h=n.ignoreLocation,l=void 0===h?I.ignoreLocation:h,f=n.findAllMatches,d=void 0===f?I.findAllMatches:f,v=n.location,g=void 0===v?I.location:v,y=n.threshold,p=void 0===y?I.threshold:y,m=n.distance,k=void 0===m?I.distance:m;r(this,e),this.query=null,this.options={isCaseSensitive:o,includeMatches:a,minMatchCharLength:u,findAllMatches:d,ignoreLocation:l,location:g,threshold:p,distance:k},this.pattern=o?t:t.toLowerCase(),this.query=Z(this.pattern,this.options)}return o(e,[{key:"searchIn",value:function(e){var t=this.query;if(!t)return{isMatch:!1,score:1};var n=this.options,r=n.includeMatches;e=n.isCaseSensitive?e:e.toLowerCase();for(var i=0,o=[],c=0,a=0,s=t.length;a-1&&(n.refIndex=e.idx),t.matches.push(n)}}))}function ve(e,t){t.score=e.score}function ge(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{},r=n.includeMatches,i=void 0===r?I.includeMatches:r,o=n.includeScore,c=void 0===o?I.includeScore:o,a=[];return i&&a.push(de),c&&a.push(ve),e.map((function(e){var n=e.idx,r={item:t[n],refIndex:n};return a.length&&a.forEach((function(t){t(e,r)})),r}))}var ye=function(){function e(n){var i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},o=arguments.length>2?arguments[2]:void 0;r(this,e),this.options=t(t({},I),i),this.options.useExtendedSearch,this._keyStore=new S(this.options.keys),this.setCollection(n,o)}return o(e,[{key:"setCollection",value:function(e,t){if(this._docs=e,t&&!(t instanceof $))throw new Error("Incorrect 'index' type");this._myIndex=t||F(this.options.keys,this._docs,{getFn:this.options.getFn,fieldNormWeight:this.options.fieldNormWeight})}},{key:"add",value:function(e){k(e)&&(this._docs.push(e),this._myIndex.add(e))}},{key:"remove",value:function(){for(var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:function(){return!1},t=[],n=0,r=this._docs.length;n1&&void 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s={keyId:j(c),pattern:a};return r&&(s.searcher=re(a,t)),s}var u={children:[],operator:i[0]};return i.forEach((function(t){var r=n[t];v(r)&&r.forEach((function(t){u.children.push(e(t))}))})),u};return se(e)||(e=le(e)),i(e)}(e,this.options),r=function e(n,r,i){if(!n.children){var o=n.keyId,c=n.searcher,a=t._findMatches({key:t._keyStore.get(o),value:t._myIndex.getValueForItemAtKeyId(r,o),searcher:c});return a&&a.length?[{idx:i,item:r,matches:a}]:[]}for(var s=[],u=0,h=n.children.length;u1&&void 0!==arguments[1]?arguments[1]:{},n=t.getFn,r=void 0===n?I.getFn:n,i=t.fieldNormWeight,o=void 0===i?I.fieldNormWeight:i,c=e.keys,a=e.records,s=new $({getFn:r,fieldNormWeight:o});return s.setKeys(c),s.setIndexRecords(a),s},ye.config=I,function(){ne.push.apply(ne,arguments)}(te),ye},"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).Fuse=t(); \ No newline at end of file diff --git a/site_libs/quarto-search/quarto-search.js b/site_libs/quarto-search/quarto-search.js new file mode 100644 index 00000000..437e38f1 --- /dev/null +++ b/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1457 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +// Capture search params and clean ?q= from URL at module load time, before +// any DOMContentLoaded handlers run. quarto-nav.js resolves all hrefs +// against window.location during DOMContentLoaded — if ?q= is still present, +// every link on the page gets the query param baked into its href. +const currentUrl = new URL(window.location); +const kQuery = currentUrl.searchParams.get(kQueryArg); +if (kQuery) { + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); +} + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const query = kQuery; + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query && mainEl) { + highlight(query, mainEl); + + // Activate tabs on pageshow — after tabsets.js restores localStorage state. + // tabsets.js registers its pageshow handler during module execution (before + // DOMContentLoaded). By registering ours during DOMContentLoaded, listener + // ordering guarantees we run after tabsets.js — so search activation wins. + window.addEventListener("pageshow", function (event) { + if (!event.persisted) { + for (const mark of mainEl.querySelectorAll("mark")) { + openAllTabsetsContainingEl(mark); + } + // Only scroll to first match when there's no hash fragment. + // With a hash, the browser already scrolled to the target section. + if (!currentUrl.hash) { + requestAnimationFrame(() => scrollToFirstVisibleMatch(mainEl)); + } + } + }, { once: true }); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + placeholder: language["search-text-placeholder"], + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // If this is a file URL, note that + + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + if (item.text && item.href && !item.href.includes('?q=')) { + const [main, hash] = item.href.split('#') + const hashAppend = hash ? '#' + hash : '' + item.href = main + '?q=' + encodeURIComponent(state.query) + hashAppend + } + + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + document.addEventListener("keyup", (event) => { + const { key } = event; + const kbds = quartoSearchOptions["keyboard-shortcut"]; + const focusedEl = document.activeElement; + + const isFormElFocused = [ + "input", + "select", + "textarea", + "button", + "option", + ].find((tag) => { + return focusedEl.tagName.toLowerCase() === tag; + }); + + if ( + kbds && + kbds.includes(key) && + !isFormElFocused && + !document.activeElement.isContentEditable + ) { + event.preventDefault(); + window.quartoOpenSearch(); + } + }); + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = throttle(() => { + // Only do this if we're not detached + // Bug #7117 + // This will happen when the keyboard is shown on ios (resulting in a scroll) + // which then closed the search UI + if (!window.matchMedia(detachedMediaQuery).matches) { + setIsOpen(false); + } + }, 50); + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + // Check if cookie consent is enabled from search options + const cookieConsentEnabled = algoliaOptions["cookie-consent-enabled"] || false; + + // Generate random session token only when cookies are disabled + const userToken = cookieConsentEnabled ? undefined : Array.from(Array(20), () => + Math.floor(Math.random() * 36).toString(36) + ).join(""); + + window.aa("init", { + appId, + apiKey, + useCookie: cookieConsentEnabled, + userToken: userToken, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.flatMap((event) => { + // This API limits the number of items per event to 20 + const chunkSize = 20; + const itemChunks = []; + const eventItems = event.items; + for (let i = 0; i < eventItems.length; i += chunkSize) { + itemChunks.push(eventItems.slice(i, i + chunkSize)); + } + // Split the items into multiple events that can be sent + const events = itemChunks.map((items) => { + return { + ...event, + items, + }; + }); + return events; + }); + + for (const event of events) { + insights.viewedObjectIDs(event); + } + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +var shownWarning = false; + +// fuse index options +const kFuseIndexOptions = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, +}; + +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + if (window.location.protocol === "file:" && !shownWarning) { + window.alert( + "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server." + ); + shownWarning = true; + return; + } + const fuse = new window.Fuse([], kFuseIndexOptions); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href, + item.crumbs, + quartoSearchOptions + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard( + createElement, + icon, + title, + section, + text, + href, + crumbs, + quartoSearchOptions +) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContents = [iconEl, titleEl]; + const showParent = quartoSearchOptions["show-item-context"]; + if (crumbs && showParent) { + let crumbsOut = undefined; + const crumbClz = ["search-result-crumbs"]; + if (showParent === "root") { + crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined; + } else if (showParent === "parent") { + crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined; + } else { + crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined; + crumbClz.push("search-result-crumbs-wrap"); + } + + const crumbEl = createElement( + "p", + { class: crumbClz.join(" ") }, + crumbsOut + ); + titleContents.push(crumbEl); + } + + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + titleContents + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +/** Get all html nodes under the given `root` that don't have children. */ +function getLeafNodes(root) { + let leaves = []; + + function traverse(node) { + if (node.childNodes.length === 0) { + leaves.push(node); + } else { + node.childNodes.forEach(traverse); + } + } + + traverse(root); + return leaves; +} +/** create and return `${txt}` */ +const markEl = txt => { + const el = document.createElement("mark"); + el.appendChild(document.createTextNode(txt)); + return el +} +/** get all ancestors of an element matching the given css selector */ +const matchAncestors = (el, selector) => { + let ancestors = []; + while (el) { + if (el.matches?.(selector)) ancestors.push(el); + el = el.parentNode; + } + return ancestors; +}; + +const isWhitespace = s => s.trim().length === 0 +// ================= +// MATCHING CODE +// ================= +const initMatch = () => ({ + i: 0, + lohisByNode: new Map() +}) +/** + * keeps track of the start (lo) and end (hi) index of the match per node (leaf) + * note: mutates the contents of `matchContext` + */ +const advanceMatch = (leaf, leafi, matchContext) => { + matchContext.i++ + + const curLoHi = matchContext.lohisByNode.get(leaf) + + matchContext.lohisByNode.set(leaf, { lo: curLoHi?.lo ?? leafi, hi: leafi }) +} +/** + * Finds all non-overlapping matches for a search string in the document. + * The search string may be split between multiple consecutive leaf nodes. + * + * Whitespace in the search string must be present in the document to match, but + * there may be addititional whitespace in the document that is ignored. + * + * e.g. searching for `dogs rock` would match `dogs \n rock`, + * and would contribute the match + * `{ i:9, els: new Map([[textNode, {lo:0, hi:8}],[spanNode,{lo:0,hi:5}]]) }` + * + * @returns {Map[]} + */ +function searchMatches(inSearch, el) { + // searchText has all sequences of whitespace replaced by a single space + const searchText = inSearch.toLowerCase().replace(/\s+/g, ' ') + const leafNodes = getLeafNodes(el) + + /** @type {Map[]} */ + const matches = [] + /** @type {{i:number; els:Map}[]} */ + let curMatchContext = initMatch() + + for (const leaf of leafNodes) { + const leafStr = leaf.textContent.toLowerCase() + // for each character in this leaf's text: + for (let leafi = 0; leafi < leafStr.length; leafi++) { + + if (isWhitespace(leafStr[leafi])) { + // if there is at least one whitespace in the document + // we advance over a search text whitespace. + if (isWhitespace(searchText[curMatchContext.i])) advanceMatch(leaf, leafi, curMatchContext) + // all sequences of whitespace are otherwise ignored. + } else { + if (searchText[curMatchContext.i] === leafStr[leafi]) { + advanceMatch(leaf, leafi, curMatchContext) + } else { + curMatchContext = initMatch() + // if current character in the document did not match at i in the search text, + // reset the search and see if that character matches at 0 in the search text. + if (searchText[curMatchContext.i] === leafStr[leafi]) advanceMatch(leaf, leafi, curMatchContext) + } + } + + const isMatchComplete = curMatchContext.i === searchText.length + if (isMatchComplete) { + matches.push(curMatchContext.lohisByNode) + curMatchContext = initMatch() + } + } + } + + return matches +} + +/** + * e.g. `markMatches(myTextNode, [[0,5],[12,15]])` would wrap the + * character sequences in myTextNode from 0-5 and 12-15 in marks. + * Its important to mark all sequences in a text node at once + * because this function replaces the entire text node; so any + * other references to that text node will no longer be in the DOM. + */ +function markMatches(node, lohis) { + const text = node.nodeValue + + const markFragment = document.createDocumentFragment(); + + let prevHi = 0 + for (const [lo, hi] of lohis) { + markFragment.append( + document.createTextNode(text.slice(prevHi, lo)), + markEl(text.slice(lo, hi + 1)) + ) + prevHi = hi + 1 + } + markFragment.append( + document.createTextNode(text.slice(prevHi, text.length)) + ) + + const parent = node.parentElement + parent?.replaceChild(markFragment, node) + return parent +} + +// Activate ancestor tabs so a search match inside an inactive pane becomes visible. +// When multiple panes in the same tabset contain matches, avoid switching away from +// the currently active pane — the user already sees a match there. +function openAllTabsetsContainingEl(el) { + for (const pane of matchAncestors(el, '.tab-pane')) { + const tabContent = pane.closest('.tab-content'); + if (!tabContent) continue; + const activePane = tabContent.querySelector(':scope > .tab-pane.active'); + if (activePane?.querySelector('mark')) continue; + const tabButton = document.querySelector(`[data-bs-target="#${pane.id}"]`); + if (tabButton) new bootstrap.Tab(tabButton).show(); + } +} + +function scrollToFirstVisibleMatch(mainEl) { + for (const mark of mainEl.querySelectorAll("mark")) { + const isMarkVisible = matchAncestors(mark, '.tab-pane').every(markTabPane => + markTabPane.classList.contains("active") + ) + if (isMarkVisible) { + mark.scrollIntoView({ behavior: "smooth", block: "center" }); + return; + } + } +} + +/** + * e.g. + * ```js + * const m = new Map() + * + * arrayMapPush(m, 'dog', 'Max') + * console.log(m) // Map { dog->['Max'] } + * + * arrayMapPush(m, 'dog', 'Samba') + * arrayMapPush(m, 'cat', 'Scruffle') + * console.log(m) // Map { dog->['Max', 'Samba'], cat->['Scruffle'] } + * ``` + */ +const arrayMapPush = (map, key, item) => { + if (!map.has(key)) map.set(key, []) + map.set(key, [...map.get(key), item]) +} + +// copy&paste any string from a quarto page and +// this should find that string in the page and highlight it. +// exception: text that starts outside/inside a tabset and ends +// inside/outside that tabset. +function highlight(searchStr, el) { + const matches = searchMatches(searchStr, el); + + const matchesGroupedByNode = new Map() + for (const match of matches) { + for (const [mel, { lo, hi }] of match) { + arrayMapPush(matchesGroupedByNode, mel, [lo, hi]) + } + } + + for (const [node, lohis] of matchesGroupedByNode) { + markMatches(node, lohis) + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text", "crumbs"].forEach( + (keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + } + ); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +let subSearchTerm = undefined; +let subSearchFuse = undefined; +const kFuseMaxWait = 125; + +async function fuseSearch(query, fuse, fuseOptions) { + let index = fuse; + // Fuse.js using the Bitap algorithm for text matching which runs in + // O(nm) time (no matter the structure of the text). In our case this + // means that long search terms mixed with large index gets very slow + // + // This injects a subIndex that will be used once the terms get long enough + // Usually making this subindex is cheap since there will typically be + // a subset of results matching the existing query + if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) { + // Use the existing subSearchFuse + index = subSearchFuse; + } else if (subSearchFuse !== undefined) { + // The term changed, discard the existing fuse + subSearchFuse = undefined; + subSearchTerm = undefined; + } + + // Search using the active fuse + const then = performance.now(); + const resultsRaw = await index.search(query, fuseOptions); + const now = performance.now(); + + const results = resultsRaw.map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + crumbs: result.item.crumbs, + }; + }); + + // If we don't have a subfuse and the query is long enough, go ahead + // and create a subfuse to use for subsequent queries + if ( + now - then > kFuseMaxWait && + subSearchFuse === undefined && + resultsRaw.length < fuseOptions.limit + ) { + subSearchTerm = query; + subSearchFuse = new window.Fuse([], kFuseIndexOptions); + resultsRaw.forEach((rr) => { + subSearchFuse.add(rr.item); + }); + } + return results; +} diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 00000000..7cdb198e --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,143 @@ + + + + https://acclab.github.io/DABEST-python/tutorials/index.html + 2026-03-26T04:07:11.632Z + + + https://acclab.github.io/DABEST-python/blog/index.html + 2026-03-26T04:07:11.490Z + + + https://acclab.github.io/DABEST-python/tutorials/10-whorlmap.html + 2026-03-26T04:08:25.335Z + + + https://acclab.github.io/DABEST-python/tutorials/08-plot_aesthetics.html + 2026-03-26T04:08:25.368Z + + + https://acclab.github.io/DABEST-python/tutorials/06-delta_delta.html + 2026-03-26T04:08:25.236Z + + + https://acclab.github.io/DABEST-python/tutorials/04-proportion_plot.html + 2026-03-26T04:08:25.202Z + + + https://acclab.github.io/DABEST-python/tutorials/02-two_group.html + 2026-03-26T04:08:25.058Z + + + https://acclab.github.io/DABEST-python/read_me.html + 2026-03-26T04:08:24.563Z + + + https://acclab.github.io/DABEST-python/blog/posts/bootstraps/bootstraps.html + 2026-03-26T04:08:24.372Z + + + https://acclab.github.io/DABEST-python/API/precompile.html + 2026-03-26T04:08:24.957Z + + + https://acclab.github.io/DABEST-python/API/plot_tools.html + 2026-03-26T04:08:25.448Z + + + https://acclab.github.io/DABEST-python/API/misc_tools.html + 2026-03-26T04:08:25.272Z + + + https://acclab.github.io/DABEST-python/API/forest_plot.html + 2026-03-26T04:08:24.847Z + + + https://acclab.github.io/DABEST-python/API/effsize.html + 2026-03-26T04:08:24.700Z + + + https://acclab.github.io/DABEST-python/API/dabest_object.html + 2026-03-26T04:08:24.666Z + + + https://acclab.github.io/DABEST-python/API/confint_1group.html + 2026-03-26T04:08:24.574Z + + + https://acclab.github.io/DABEST-python/03-citation.html + 2026-03-26T04:08:24.320Z + + + https://acclab.github.io/DABEST-python/01-getting_started.html + 2026-03-26T04:08:24.319Z + + + https://acclab.github.io/DABEST-python/02-about.html + 2026-03-26T04:08:24.422Z + + + https://acclab.github.io/DABEST-python/API/bootstrap.html + 2026-03-26T04:08:24.560Z + + + https://acclab.github.io/DABEST-python/API/confint_2group_diff.html + 2026-03-26T04:08:24.656Z + + + https://acclab.github.io/DABEST-python/API/delta_objects.html + 2026-03-26T04:08:24.701Z + + + https://acclab.github.io/DABEST-python/API/effsize_objects.html + 2026-03-26T04:08:24.841Z + + + https://acclab.github.io/DABEST-python/API/load.html + 2026-03-26T04:08:24.768Z + + + https://acclab.github.io/DABEST-python/API/multi.html + 2026-03-26T04:08:24.906Z + + + https://acclab.github.io/DABEST-python/API/plotter.html + 2026-03-26T04:08:25.133Z + + + https://acclab.github.io/DABEST-python/blog/posts/a-dabest2-preprint/a-dabest2-preprint.html + 2026-03-26T04:08:24.403Z + + + https://acclab.github.io/DABEST-python/blog/posts/robust-beautiful/robust-beautiful.html + 2026-03-26T04:08:24.366Z + + + https://acclab.github.io/DABEST-python/tutorials/01-basics.html + 2026-03-26T04:08:25.004Z + + + https://acclab.github.io/DABEST-python/tutorials/03-shared_control_and_repeated_measures.html + 2026-03-26T04:08:25.111Z + + + https://acclab.github.io/DABEST-python/tutorials/05-mini_meta.html + 2026-03-26T04:08:25.184Z + + + https://acclab.github.io/DABEST-python/tutorials/07-horizontal_plot.html + 2026-03-26T04:08:25.268Z + + + https://acclab.github.io/DABEST-python/tutorials/09-forest_plot.html + 2026-03-26T04:08:25.453Z + + + https://acclab.github.io/DABEST-python/API/index.html + 2026-03-26T04:07:11.487Z + + + https://acclab.github.io/DABEST-python/index.html + 2026-03-26T04:07:56.556Z + + diff --git a/nbs/styles.css b/styles.css similarity index 100% rename from nbs/styles.css rename to styles.css diff --git a/tutorials/01-basics.html b/tutorials/01-basics.html new file mode 100644 index 00000000..8df88b24 --- /dev/null +++ b/tutorials/01-basics.html @@ -0,0 +1,1570 @@ + + + + + + + + + + +Basics – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + +
    +
    + An end-to-end tutorial on how to use the dabest library. +
    +
    + + +
    + + + + +
    + + + +
    + + + +
    +

    Load libraries

    +
    +
    import numpy as np
+import pandas as pd
+import dabest
+
+print("We're using DABEST v{}".format(dabest.__version__))
    +
    +
    Pre-compiling numba functions for DABEST...
    +
    +
    +
    Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 57.10it/s]
    +
    +
    +
    Numba compilation complete!
+We're using DABEST v2025.10.20
    +
    +
    +
    +
    +
    +
    +
    +

    Create dataset for demo

    +

    Here, we create a dataset to illustrate how dabest works. In this dataset, each column corresponds to a group of observations.

    +
    +
    from scipy.stats import norm # Used in generation of populations.
+
+np.random.seed(9999) # Fix the seed to ensure reproducibility of results.
+
+Ns = 20 # The number of samples taken from each population
+
+# Create samples
+c1 = norm.rvs(loc=3, scale=0.4, size=Ns)
+c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
+c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
+
+t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)
+t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)
+t3 = norm.rvs(loc=3, scale=0.75, size=Ns)
+t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
+t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
+t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
+
+# Add a `gender` column for coloring the data.
+females = np.repeat('Female', Ns/2).tolist()
+males = np.repeat('Male', Ns/2).tolist()
+gender = females + males
+
+# Add an `id` column for paired data plotting.
+id_col = pd.Series(range(1, Ns+1))
+
+# Combine samples and gender into a DataFrame.
+df = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,
+                 'Control 2' : c2,     'Test 2' : t2,
+                 'Control 3' : c3,     'Test 3' : t3,
+                 'Test 4'    : t4,     'Test 5' : t5, 'Test 6' : t6,
+                 'Gender'    : gender, 'ID'  : id_col
+                })
+df.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Control 1Test 1Control 2Test 2Control 3Test 3Test 4Test 5Test 6GenderID
    02.7939843.4208753.3246611.7074673.8169401.7965814.4400502.9372843.486127Female1
    13.2367593.4679723.6851861.1218463.7503583.9445663.7234942.8370622.338094Female2
    23.0191494.3771795.6168913.3013812.9453972.8321883.2140143.1119503.270897Female3
    32.8046384.5647802.7731522.5340183.5751793.0482674.9682783.7433783.151188Female4
    42.8580193.2200582.5503612.7963653.6921383.2765752.6621042.9773412.328601Female5
    + +
    +
    +
    +

    Note that we have 9 groups (3 Control samples and 6 Test samples). Our dataset has also a non-numerical column indicating gender, and another column indicating the identity of each observation.

    +

    This is known as a wide dataset. See this writeup for more details.

    +
    +
    +

    Loading data

    +

    Before creating estimation plots and obtaining confidence intervals for our effect sizes, we need to load the data and specify the relevant groups.

    +

    We can achieve this by supplying the dataframe to dabest.load(). Additionally, we must provide the two groups to be compared in the idx argument as a tuple or list.

    +
    +
    two_groups_unpaired = dabest.load(df, idx=("Control 1", "Test 1"), resamples=5000)
    +
    +

    Calling this Dabest object gives you a gentle greeting, as well as the comparisons that can be computed.

    +
    +
    two_groups_unpaired
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:03 2025.
+
+Effect size(s) with 95% confidence intervals will be computed for:
+1. Test 1 minus Control 1
+
+5000 resamples will be used to generate the effect size bootstraps.
    +
    +
    +
    +

    Changing statistical parameters

    +

    You can change the width of the confidence interval by manipulating the ci argument.

    +
    +
    two_groups_unpaired_ci90 = dabest.load(df, idx=("Control 1", "Test 1"), ci=90)
+two_groups_unpaired_ci90
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:03 2025.
+
+Effect size(s) with 90% confidence intervals will be computed for:
+1. Test 1 minus Control 1
+
+5000 resamples will be used to generate the effect size bootstraps.
    +
    +
    +
    +
    +
    +

    Effect sizes

    +

    The dabest library now features a range of effect sizes:

    + +

    Each of these are attributes of the Dabest object.

    +
    +
    two_groups_unpaired.mean_diff
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:03 2025.
+
+The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].
+The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`
    +
    +
    +

    For each comparison, the type of effect size is reported (here, it’s the “unpaired mean difference”). The confidence interval is reported as: [confidenceIntervalWidth LowerBound, UpperBound]

    +

    This confidence interval is generated through bootstrap resampling. See bootstraps for more details.

    +

    Since v0.3.0, DABEST will report the p-value of the non-parametric two-sided approximate permutation t-test. This is also known as the Monte Carlo permutation test.

    +

    For unpaired comparisons, the p-values and test statistics of Welch’s t test, Student’s t test, and Mann-Whitney U test can be found. For paired comparisons, the p-values and test statistics of the paired Student’s t and Wilcoxon tests are presented.

    +
    +
    pd.options.display.max_columns = 50
+two_groups_unpaired.mean_diff.results
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    controltestcontrol_Ntest_Neffect_sizeis_paireddifferencecibca_lowbca_highbca_interval_idxpct_lowpct_highpct_interval_idxbootstrapsresamplesrandom_seedpermutationspvalue_permutationpermutation_countpermutations_varpvalue_welchstatistic_welchpvalue_students_tstatistic_students_tpvalue_mann_whitneystatistic_mann_whitneybec_differencebec_bootstrapsbec_bca_interval_idxbec_bca_lowbec_bca_highbec_pct_interval_idxbec_pct_lowbec_pct_high
    0Control 1Test 12020mean differenceNone0.48029950.2051610.773647(145, 4893)0.1974270.758752(125, 4875)[0.6148498102262239, 0.6752095203445543, 0.300...500012345[-0.17259843762502491, 0.03802293852634886, -0...0.0015000[0.26356588154404337, 0.2710249543904699, 0.26...0.002094-3.3088060.002057-3.3088060.00162583.00.0[-0.09732932551566487, 0.08087009665445155, -0...(127, 4877)-0.2568620.259558(125, 4875)-0.258260.25759
    + +
    +
    +
    +
    +
    two_groups_unpaired.mean_diff.statistical_tests
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    controltestcontrol_Ntest_Neffect_sizeis_paireddifferencecibca_lowbca_highpvalue_permutationpvalue_welchstatistic_welchpvalue_students_tstatistic_students_tpvalue_mann_whitneystatistic_mann_whitney
    0Control 1Test 12020mean differenceNone0.48029950.2051610.7736470.0010.002094-3.3088060.002057-3.3088060.00162583.0
    + +
    +
    +
    +

    Note: A research paper Phipson & Smyth (2010) suggested that permutation p-values should never be zero, and provided a slightly adjusted formula to compute permutation p-values.

    +

    Since v2025.03.27, DABEST provides a ps_adjust parameter in the .load() function. This parameter allows you to adjust the permutation p-values using the formula suggested by Phipson & Smyth (2010). By default, DABEST uses the unadjusted p-values.

    +
    +
    two_groups_unpaired_adjusted = dabest.load(df, idx=("Control 1", "Test 1"), resamples=5000, ps_adjust=True)
+two_groups_unpaired_adjusted.mean_diff.statistical_tests
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    controltestcontrol_Ntest_Neffect_sizeis_paireddifferencecibca_lowbca_highpvalue_permutationpvalue_welchstatistic_welchpvalue_students_tstatistic_students_tpvalue_mann_whitneystatistic_mann_whitney
    0Control 1Test 12020mean differenceNone0.48029950.2051610.7736470.00120.002094-3.3088060.002057-3.3088060.00162583.0
    + +
    +
    +
    +

    Let’s compute the Hedges’g for our comparison.

    +
    +
    two_groups_unpaired.hedges_g
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:04 2025.
+
+The unpaired Hedges' g between Control 1 and Test 1 is 1.03 [95%CI 0.317, 1.62].
+The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.hedges_g.statistical_tests`
    +
    +
    +
    +
    two_groups_unpaired.hedges_g.results
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    controltestcontrol_Ntest_Neffect_sizeis_paireddifferencecibca_lowbca_highbca_interval_idxpct_lowpct_highpct_interval_idxbootstrapsresamplesrandom_seedpermutationspvalue_permutationpermutation_countpermutations_varpvalue_welchstatistic_welchpvalue_students_tstatistic_students_tpvalue_mann_whitneystatistic_mann_whitneybec_differencebec_bootstrapsbec_bca_interval_idxbec_bca_lowbec_bca_highbec_pct_interval_idxbec_pct_lowbec_pct_high
    0Control 1Test 12020Hedges' gNone1.025525950.3165061.616235(42, 4725)0.444861.745146(125, 4875)[1.469217954462509, 1.5972518056777079, 0.6051...500012345[-0.329508986559053, 0.07158401210924781, -0.2...0.0015000[0.26356588154404337, 0.2710249543904699, 0.26...0.002094-3.3088060.002057-3.3088060.00162583.00.0[-0.2669450878059954, 0.21187593591106418, -0....(127, 4877)-0.6423870.629464(125, 4875)-0.6436040.627968
    + +
    +
    +
    +
    +
    +

    Producing estimation plots

    +

    To generate a Gardner-Altman estimation plot, simply use the .plot() method. You can learn more about its genesis and design inspiration at robust-beautiful.

    +

    Each instance of an effect size has access to the .plot() method. This allows you to quickly create plots for different effect sizes with ease.

    +
    +
    two_groups_unpaired.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    two_groups_unpaired.hedges_g.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +

    Instead of a Gardner-Altman plot, you can generate a Cumming estimation plot by setting float_contrast=False in the .plot() method. This will plot the bootstrap effect sizes below the raw data, and also displays the the mean (gap) and ± standard deviation of each group (vertical ends) as gapped lines. This design was inspired by Edward Tufte’s dictum to maximise the data-ink ratio.

    +
    +
    two_groups_unpaired.hedges_g.plot(float_contrast=False);
    +
    +
    +
    +

    +
    +
    +
    +
    +

    The confidence interval shown on the contrast axis is a BCa confidence interval by default. This can be modified using the ci_type parameter in the .plot() method, whereby you can select between bca and pct (percentile).

    +
    +
    two_groups_unpaired.mean_diff.plot(ci_type='pct');
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +

    Using long (aka ‘melted’) data frames

    +

    dabest can also handle ‘melted’ or ‘long’ data. This term is used because each row now corresponds to a single data point, with one column carrying the value and other columns containing ‘metadata’ describing that data point.

    +

    For more details on wide vs long or ‘melted’ data, refer to this Wikipedia article. The pandas documentation provides recipes for melting dataframes.

    +
    +
    x='group'
+y='metric'
+
+value_cols = df.columns[:-2] # select all but the "Gender" and "ID" columns.
+
+df_melted = pd.melt(df.reset_index(),
+                    id_vars=["Gender", "ID"],
+                    value_vars=value_cols,
+                    value_name=y,
+                    var_name=x)
+
+df_melted.head() # Gives the first five rows of `df_melted`.
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    GenderIDgroupmetric
    0Female1Control 12.793984
    1Female2Control 13.236759
    2Female3Control 13.019149
    3Female4Control 12.804638
    4Female5Control 12.858019
    + +
    +
    +
    +

    When your data is in this format, you need to specify the x and y columns in dabest.load().

    +
    +
    analysis_of_long_df = dabest.load(df_melted, idx=("Control 1", "Test 1"),
+                                     x="group", y="metric")
+
+analysis_of_long_df
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:04 2025.
+
+Effect size(s) with 95% confidence intervals will be computed for:
+1. Test 1 minus Control 1
+
+5000 resamples will be used to generate the effect size bootstraps.
    +
    +
    +
    +
    +
    +

    Dabest estimation plot designs

    +

    The dabest package implements a range of estimation plot designs aimed at depicting common experimental designs:

    +
      +

    1. Two-Group

      2. +

    2. Shared Control (Unpaired) and Repeated Measures (Paired)

      3. +

    3. Proportion Plots

      4. +

    4. Mini-Meta

      5. +

    5. Delta-Delta

      6. +

    6. Forest Plots

      7. +
    +

    In addition, as of Dabest v2025.03.27, we introduce a new plotting orientation: Horizontal Plots.

    +

    Lastly, we have a whole tutorial page for making aesthetic changes to dabest plots.

    + + +
    + +
    + +
    + + + + + \ No newline at end of file diff --git a/tutorials/01-basics_files/figure-html/cell-13-output-1.png b/tutorials/01-basics_files/figure-html/cell-13-output-1.png new file mode 100644 index 00000000..a41ebfea Binary files /dev/null and b/tutorials/01-basics_files/figure-html/cell-13-output-1.png differ diff --git a/tutorials/01-basics_files/figure-html/cell-14-output-1.png b/tutorials/01-basics_files/figure-html/cell-14-output-1.png new file mode 100644 index 00000000..e410b30c Binary files /dev/null and b/tutorials/01-basics_files/figure-html/cell-14-output-1.png differ diff --git a/tutorials/01-basics_files/figure-html/cell-15-output-1.png b/tutorials/01-basics_files/figure-html/cell-15-output-1.png new file mode 100644 index 00000000..cfc7f795 Binary files /dev/null and b/tutorials/01-basics_files/figure-html/cell-15-output-1.png differ diff --git a/tutorials/01-basics_files/figure-html/cell-16-output-1.png b/tutorials/01-basics_files/figure-html/cell-16-output-1.png new file mode 100644 index 00000000..5dd42bed Binary files /dev/null and b/tutorials/01-basics_files/figure-html/cell-16-output-1.png differ diff --git a/tutorials/02-two_group.html b/tutorials/02-two_group.html new file mode 100644 index 00000000..8661d22d --- /dev/null +++ b/tutorials/02-two_group.html @@ -0,0 +1,1187 @@ + + + + + + + + + + +Two-Group Experiments – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + +
    + +
    + + +
    + + + +
    + +
    +
    +

    Two-Group Experiments

    +
    + +
    +
    + Explanation of how to use dabest for two-group and multi two-group analysis. +
    +
    + + +
    + + + + +
    + + + +
    + + + +
    +

    Load libraries

    +
    +
    import numpy as np
+import pandas as pd
+import dabest
+
+print("We're using DABEST v{}".format(dabest.__version__))
    +
    +
    Pre-compiling numba functions for DABEST...
    +
    +
    +
    Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 41.28it/s]
    +
    +
    +
    Numba compilation complete!
+We're using DABEST v2025.10.20
    +
    +
    +
    +
    +
    +
    +
    +

    Creating a demo dataset

    +

    Here, we create a dataset to illustrate how to perform Two-Group analyses using dabest.

    +
    +
    from scipy.stats import norm # Used in generation of populations.
+
+np.random.seed(9999) # Fix the seed to ensure reproducibility of results.
+
+Ns = 20 # The number of samples taken from each population
+
+# Create samples
+c1 = norm.rvs(loc=3, scale=0.4, size=Ns)
+c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
+c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
+
+t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)
+t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)
+t3 = norm.rvs(loc=3, scale=0.75, size=Ns)
+t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
+t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
+t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
+
+
+# Add a `gender` column for coloring the data.
+females = np.repeat('Female', Ns/2).tolist()
+males = np.repeat('Male', Ns/2).tolist()
+gender = females + males
+
+# Add an `id` column for paired data plotting.
+id_col = pd.Series(range(1, Ns+1))
+
+# Combine samples and gender into a DataFrame.
+df = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,
+                 'Control 2' : c2,     'Test 2' : t2,
+                 'Control 3' : c3,     'Test 3' : t3,
+                 'Test 4'    : t4,     'Test 5' : t5, 'Test 6' : t6,
+                 'Gender'    : gender, 'ID'  : id_col
+                })
+df.head(5)
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Control 1Test 1Control 2Test 2Control 3Test 3Test 4Test 5Test 6GenderID
    02.7939843.4208753.3246611.7074673.8169401.7965814.4400502.9372843.486127Female1
    13.2367593.4679723.6851861.1218463.7503583.9445663.7234942.8370622.338094Female2
    23.0191494.3771795.6168913.3013812.9453972.8321883.2140143.1119503.270897Female3
    32.8046384.5647802.7731522.5340183.5751793.0482674.9682783.7433783.151188Female4
    42.8580193.2200582.5503612.7963653.6921383.2765752.6621042.9773412.328601Female5
    + +
    +
    +
    +
    +
    +

    Loading data

    +

    First, we need to load the data and specify the relevant groups.

    +

    We can achieve this by supplying the dataframe to dabest.load(). Additionally, we must provide the groups to be compared in the idx argument as a tuple or list.

    +

    For this tutorial, we will create two separate analyses:

    +
      +

    • A singular two-group comparison between Control 1 and Test 1.

      • +

    • A multi two-group comparison between Control 1 and Test 1, and between Control 2 and Test 2.

      • +
    +

    The multi two-group estimation plot tiles two or more Cumming plots horizontally, and is created by passing a nested tuple to idx when dabest.load() is first invoked.

    +
    +
    two_groups_unpaired = dabest.load(df, idx=("Control 1", "Test 1"))
+multi_two_groups_unpaired = dabest.load(df, idx=(("Control 1", "Test 1"),("Control 2", "Test 2"),("Control 3", "Test 3")))
    +
    +

    In addition, we can specify the paired argument to indicate paired data.

    +

    paired can be set as 'baseline' or 'sequential' or left as None (unpaired).

    +

    Note: For two-group, both 'baseline' and 'sequential' are equivalent.

    +
    +
    two_groups_paired = dabest.load(df, idx=("Control 1", "Test 1"), paired='baseline', id_col='ID')
+multi_two_groups_paired = dabest.load(df, idx=(("Control 1", "Test 1"),("Control 2", "Test 2"),("Control 3", "Test 3")), 
+                                      paired='baseline', id_col='ID')
    +
    +

    The dabest library features a range of effect sizes. In this case, we shall proceed with the default effect size, which is the mean difference.

    +

    Here we will show the two-group unpaired analysis as an example.

    +
    +
    two_groups_unpaired.mean_diff
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 15:59:53 2025.
+
+The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].
+The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`
    +
    +
    +

    A dataframe of the mean_diff results can be extracted by calling the results attribute of the dabest.mean_diff object.

    +
    +
    two_groups_unpaired.mean_diff.results
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    controltestcontrol_Ntest_Neffect_sizeis_paireddifferencecibca_lowbca_high...pvalue_mann_whitneystatistic_mann_whitneybec_differencebec_bootstrapsbec_bca_interval_idxbec_bca_lowbec_bca_highbec_pct_interval_idxbec_pct_lowbec_pct_high
    0Control 1Test 12020mean differenceNone0.48029950.2051610.773647...0.00162583.00.0[-0.09732932551566487, 0.08087009665445155, -0...(127, 4877)-0.2568620.259558(125, 4875)-0.258260.25759
    + +

    1 rows × 35 columns

    +
    +
    +
    +
    +
    +

    Producing estimation plots

    +

    We can now call the .plot() method to generate the estimation plot.

    +
    +
    two_groups_unpaired.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +

    For singular two-group comparisons, the plot will display the effect size curve by default to the right of the raw data. We term this a Gardner-Altman plot.

    +

    This can be changed by setting the float_contrast argument to False. Here, the effect size curve will be displayed below the raw data - a Cumming estimation plot.

    +
    +
    two_groups_unpaired.mean_diff.plot(float_contrast=False);
    +
    +
    +
    +

    +
    +
    +
    +
    +

    For multi two-group comparisons, the effect size curves will always be displayed below the raw data.

    +

    The lower axes in the Cumming plot is effectively a forest plot, commonly used in meta-analyses to aggregate and to compare data from different experiments.

    +

    Note: If you’re interested in just plotting the contrast ax (the violin plots), you may be interested in the new forest plot feature added in v2025.03.27!

    +
    +
    multi_two_groups_unpaired.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +

    For paired data, we use slopegraphs (another innovation from Edward Tufte) to connect paired observations. Both Gardner-Altman and Cumming plots support this.

    +
    +
    two_groups_paired.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    two_groups_paired.mean_diff.plot(float_contrast=False);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    multi_two_groups_paired.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +

    For further aesthetic changes, the Plot Aesthetics Tutorial provides detailed examples of how to customize the plot.

    + + +
    + +
    + +
    + + + + + \ No newline at end of file diff --git a/tutorials/02-two_group_files/figure-html/cell-10-output-1.png b/tutorials/02-two_group_files/figure-html/cell-10-output-1.png new file mode 100644 index 00000000..aff8df17 Binary files /dev/null and b/tutorials/02-two_group_files/figure-html/cell-10-output-1.png differ diff --git a/tutorials/02-two_group_files/figure-html/cell-11-output-1.png b/tutorials/02-two_group_files/figure-html/cell-11-output-1.png new file mode 100644 index 00000000..c969bef7 Binary files /dev/null and b/tutorials/02-two_group_files/figure-html/cell-11-output-1.png differ diff --git a/tutorials/02-two_group_files/figure-html/cell-12-output-1.png b/tutorials/02-two_group_files/figure-html/cell-12-output-1.png new file mode 100644 index 00000000..18ddae5a Binary files /dev/null and b/tutorials/02-two_group_files/figure-html/cell-12-output-1.png differ diff --git a/tutorials/02-two_group_files/figure-html/cell-13-output-1.png b/tutorials/02-two_group_files/figure-html/cell-13-output-1.png new file mode 100644 index 00000000..7cb559ff Binary files /dev/null and b/tutorials/02-two_group_files/figure-html/cell-13-output-1.png differ diff --git a/tutorials/02-two_group_files/figure-html/cell-8-output-1.png b/tutorials/02-two_group_files/figure-html/cell-8-output-1.png new file mode 100644 index 00000000..a41ebfea Binary files /dev/null and b/tutorials/02-two_group_files/figure-html/cell-8-output-1.png differ diff --git a/tutorials/02-two_group_files/figure-html/cell-9-output-1.png b/tutorials/02-two_group_files/figure-html/cell-9-output-1.png new file mode 100644 index 00000000..aa1d3831 Binary files /dev/null and b/tutorials/02-two_group_files/figure-html/cell-9-output-1.png differ diff --git a/tutorials/03-shared_control_and_repeated_measures.html b/tutorials/03-shared_control_and_repeated_measures.html new file mode 100644 index 00000000..b41b3e0c --- /dev/null +++ b/tutorials/03-shared_control_and_repeated_measures.html @@ -0,0 +1,1187 @@ + + + + + + + + + + +Shared Control & Repeated Measures – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + +
    + +
    + + +
    + + + +
    + +
    +
    +

    Shared Control & Repeated Measures

    +
    + +
    +
    + Explanation of how to use dabest for shared control and repeated measures analyses. +
    +
    + + +
    + + + + +
    + + + +
    + + + +

    The shared control plot and repeated measures plot display common experimental paradigms, where several test samples are compared against a common reference sample. The shared control plot is for unpaired data, while the repeated measures plot is for paired data.

    +

    These types of Cumming plots are automatically generated if the tuple passed to idx has more than two data columns.

    +
    +

    Load libraries

    +
    +
    import numpy as np
+import pandas as pd
+import dabest
+
+print("We're using DABEST v{}".format(dabest.__version__))
    +
    +
    Pre-compiling numba functions for DABEST...
    +
    +
    +
    Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 62.10it/s]
    +
    +
    +
    Numba compilation complete!
+We're using DABEST v2025.10.20
    +
    +
    +
    +
    +
    +
    +
    +

    Creating a demo dataset

    +
    +
    from scipy.stats import norm # Used in generation of populations.
+
+np.random.seed(9999) # Fix the seed so the results are reproducible.
+Ns = 20 # The number of samples taken from each population
+
+# Create samples
+c1 = norm.rvs(loc=3, scale=0.4, size=Ns)
+c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
+c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
+
+t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)
+t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)
+t3 = norm.rvs(loc=3, scale=0.75, size=Ns)
+t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
+t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
+t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
+
+
+# Add a `gender` column for coloring the data.
+females = np.repeat('Female', Ns/2).tolist()
+males = np.repeat('Male', Ns/2).tolist()
+gender = females + males
+
+# Add an `id` column for paired data plotting.
+id_col = pd.Series(range(1, Ns+1))
+
+# Combine samples and gender into a DataFrame.
+df = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,
+                   'Control 2' : c2,     'Test 2' : t2,
+                   'Control 3' : c3,     'Test 3' : t3,
+                   'Test 4'    : t4,     'Test 5' : t5, 'Test 6' : t6,
+                   'Gender'    : gender, 'ID'  : id_col
+                  })
    +
    +
    +
    +

    Shared control plot

    +
    +
    shared_control = dabest.load(df, idx=("Control 1", "Test 1",
+                                          "Test 2", "Test 3",
+                                          "Test 4", "Test 5", "Test 6")
+                                 )
+shared_control
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:11 2025.
+
+Effect size(s) with 95% confidence intervals will be computed for:
+1. Test 1 minus Control 1
+2. Test 2 minus Control 1
+3. Test 3 minus Control 1
+4. Test 4 minus Control 1
+5. Test 5 minus Control 1
+6. Test 6 minus Control 1
+
+5000 resamples will be used to generate the effect size bootstraps.
    +
    +
    +
    +
    shared_control.mean_diff
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:12 2025.
+
+The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].
+The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. 
+
+The unpaired mean difference between Control 1 and Test 2 is -0.542 [95%CI -0.915, -0.206].
+The p-value of the two-sided permutation t-test is 0.0042, calculated for legacy purposes only. 
+
+The unpaired mean difference between Control 1 and Test 3 is 0.174 [95%CI -0.273, 0.647].
+The p-value of the two-sided permutation t-test is 0.479, calculated for legacy purposes only. 
+
+The unpaired mean difference between Control 1 and Test 4 is 0.79 [95%CI 0.325, 1.33].
+The p-value of the two-sided permutation t-test is 0.0042, calculated for legacy purposes only. 
+
+The unpaired mean difference between Control 1 and Test 5 is 0.265 [95%CI 0.0115, 0.497].
+The p-value of the two-sided permutation t-test is 0.0404, calculated for legacy purposes only. 
+
+The unpaired mean difference between Control 1 and Test 6 is 0.288 [95%CI 0.00913, 0.524].
+The p-value of the two-sided permutation t-test is 0.0324, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`
    +
    +
    +
    +
    shared_control.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +

    dabest allows for combining both two-group and shared control experiments into the same plot. This empowers you to perform robust analyses and present complex visualizations of your statistics elegantly.

    +
    +
    multi_groups = dabest.load(df, idx=(("Control 1", "Test 1",),
+                                         ("Control 2", "Test 2","Test 3"),
+                                         ("Control 3", "Test 4","Test 5", "Test 6")
+                                       ))
+multi_groups
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:12 2025.
+
+Effect size(s) with 95% confidence intervals will be computed for:
+1. Test 1 minus Control 1
+2. Test 2 minus Control 2
+3. Test 3 minus Control 2
+4. Test 4 minus Control 3
+5. Test 5 minus Control 3
+6. Test 6 minus Control 3
+
+5000 resamples will be used to generate the effect size bootstraps.
    +
    +
    +
    +
    multi_groups.mean_diff
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:13 2025.
+
+The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].
+The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. 
+
+The unpaired mean difference between Control 2 and Test 2 is -1.38 [95%CI -1.93, -0.905].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+The unpaired mean difference between Control 2 and Test 3 is -0.666 [95%CI -1.29, -0.0788].
+The p-value of the two-sided permutation t-test is 0.0352, calculated for legacy purposes only. 
+
+The unpaired mean difference between Control 3 and Test 4 is 0.362 [95%CI -0.111, 0.901].
+The p-value of the two-sided permutation t-test is 0.161, calculated for legacy purposes only. 
+
+The unpaired mean difference between Control 3 and Test 5 is -0.164 [95%CI -0.398, 0.0747].
+The p-value of the two-sided permutation t-test is 0.208, calculated for legacy purposes only. 
+
+The unpaired mean difference between Control 3 and Test 6 is -0.14 [95%CI -0.4, 0.0937].
+The p-value of the two-sided permutation t-test is 0.282, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`
    +
    +
    +
    +
    multi_groups.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Repeated measures plot

    +

    DABEST v2023.02.14 expands the repertoire of plots for experiments with repeated-measures designs. DABEST now allows the visualization of paired experiments with one control and multiple test groups, as well as repeated measurements of the same group. This is an improved version of paired data plotting in previous versions, which only supported computations involving one test group and one control group.

    +

    The repeated-measures function supports the calculation of effect sizes for paired data, either based on sequential comparisons (group i vs group i + 1) or baseline comparisons (control vs group i). To use these features, you can simply declare the argument paired = "sequential" or paired = "baseline" correspondingly while running dabest.load(). As in the previous version, you must also pass a column in the dataset that indicates the identity of each observation, using the id_col keyword.

    +
    +

    (Please note that paired = True and paired = False are no longer valid since v2023.02.14)

    +
    +
    +
    baseline_repeated_measures = dabest.load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3"),
+                                               paired="baseline", id_col="ID")
+baseline_repeated_measures
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:13 2025.
+
+Paired effect size(s) for repeated measures against baseline 
+with 95% confidence intervals will be computed for:
+1. Test 1 minus Control 1
+2. Test 2 minus Control 1
+3. Test 3 minus Control 1
+
+5000 resamples will be used to generate the effect size bootstraps.
    +
    +
    +
    +
    baseline_repeated_measures.mean_diff
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:14 2025.
+
+The paired mean difference for repeated measures against baseline 
+between Control 1 and Test 1 is 0.48 [95%CI 0.241, 0.749].
+The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. 
+
+The paired mean difference for repeated measures against baseline 
+between Control 1 and Test 2 is -0.542 [95%CI -0.977, -0.179].
+The p-value of the two-sided permutation t-test is 0.014, calculated for legacy purposes only. 
+
+The paired mean difference for repeated measures against baseline 
+between Control 1 and Test 3 is 0.174 [95%CI -0.303, 0.702].
+The p-value of the two-sided permutation t-test is 0.505, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`
    +
    +
    +
    +
    baseline_repeated_measures.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    sequential_repeated_measures = dabest.load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3"),
+                                               paired="sequential", id_col="ID")
+sequential_repeated_measures
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:14 2025.
+
+Paired effect size(s) for the sequential design of repeated-measures experiment 
+with 95% confidence intervals will be computed for:
+1. Test 1 minus Control 1
+2. Test 2 minus Test 1
+3. Test 3 minus Test 2
+
+5000 resamples will be used to generate the effect size bootstraps.
    +
    +
    +
    +
    sequential_repeated_measures.mean_diff
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:15 2025.
+
+The paired mean difference for the sequential design of repeated-measures experiment 
+between Control 1 and Test 1 is 0.48 [95%CI 0.241, 0.749].
+The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. 
+
+The paired mean difference for the sequential design of repeated-measures experiment 
+between Test 1 and Test 2 is -1.02 [95%CI -1.35, -0.709].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+The paired mean difference for the sequential design of repeated-measures experiment 
+between Test 2 and Test 3 is 0.716 [95%CI 0.153, 1.2].
+The p-value of the two-sided permutation t-test is 0.022, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`
    +
    +
    +
    +
    sequential_repeated_measures.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +

    Similar to unpaired data, DABEST empowers you to perform complex visualizations and statistics for paired data.

    +
    +
    multi_baseline_repeated_measures = dabest.load(df, idx=(("Control 1", "Test 1", "Test 2", "Test 3"),
+                                                      ("Control 2", "Test 4", "Test 5", "Test 6")),
+                                               paired="baseline", id_col="ID")
+multi_baseline_repeated_measures.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +

    For further aesthetic changes, the Plot Aesthetics Tutorial provides detailed examples of how to customize the plot.

    + + +
    + +
    + +
    + + + + + \ No newline at end of file diff --git a/tutorials/03-shared_control_and_repeated_measures_files/figure-html/cell-12-output-1.png b/tutorials/03-shared_control_and_repeated_measures_files/figure-html/cell-12-output-1.png new file mode 100644 index 00000000..b8a54f71 Binary files /dev/null and b/tutorials/03-shared_control_and_repeated_measures_files/figure-html/cell-12-output-1.png differ diff --git a/tutorials/03-shared_control_and_repeated_measures_files/figure-html/cell-15-output-1.png b/tutorials/03-shared_control_and_repeated_measures_files/figure-html/cell-15-output-1.png new file mode 100644 index 00000000..12389310 Binary files /dev/null and b/tutorials/03-shared_control_and_repeated_measures_files/figure-html/cell-15-output-1.png differ diff --git a/tutorials/03-shared_control_and_repeated_measures_files/figure-html/cell-16-output-1.png b/tutorials/03-shared_control_and_repeated_measures_files/figure-html/cell-16-output-1.png new file mode 100644 index 00000000..46bb6f5e Binary files /dev/null and b/tutorials/03-shared_control_and_repeated_measures_files/figure-html/cell-16-output-1.png differ diff --git a/tutorials/03-shared_control_and_repeated_measures_files/figure-html/cell-6-output-1.png b/tutorials/03-shared_control_and_repeated_measures_files/figure-html/cell-6-output-1.png new file mode 100644 index 00000000..f3b80f6e Binary files /dev/null and b/tutorials/03-shared_control_and_repeated_measures_files/figure-html/cell-6-output-1.png differ diff --git a/tutorials/03-shared_control_and_repeated_measures_files/figure-html/cell-9-output-1.png b/tutorials/03-shared_control_and_repeated_measures_files/figure-html/cell-9-output-1.png new file mode 100644 index 00000000..4f35ba38 Binary files /dev/null and b/tutorials/03-shared_control_and_repeated_measures_files/figure-html/cell-9-output-1.png differ diff --git a/tutorials/04-proportion_plot.html b/tutorials/04-proportion_plot.html new file mode 100644 index 00000000..78997eb2 --- /dev/null +++ b/tutorials/04-proportion_plot.html @@ -0,0 +1,1959 @@ + + + + + + + + + + +Proportion Plots – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + +
    + +
    + + +
    + + + +
    + +
    +
    +

    Proportion Plots

    +
    + +
    +
    + A guide to plot proportion plots with binary data. +
    +
    + + +
    + + + + +
    + + + +
    + + + +
    +As of v2023.02.14, DABEST can be used to generate Cohen’s h and the corresponding proportion plot for binary data. It’s important to note that the code we provide only supports numerical proportion data, where the values are limited to 0 (failure) and 1 (success). This means that the code is not suitable for analyzing proportion data that contains non-numeric values, such as strings like ‘yes’ and ‘no’. +
    +
    +

    Load libraries

    +
    +
    import numpy as np
+import pandas as pd
+import dabest
+
+print("We're using DABEST v{}".format(dabest.__version__))
    +
    +
    Pre-compiling numba functions for DABEST...
    +
    +
    +
    Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 61.07it/s]
    +
    +
    +
    Numba compilation complete!
+We're using DABEST v2025.10.20
    +
    +
    +
    +
    +
    +
    +
    +

    Creating a demo dataset

    +
    +
    def create_demo_prop_dataset(seed=9999, N=40):
+    import numpy as np
+    import pandas as pd
+
+    np.random.seed(9999)  # Fix the seed to ensure reproducibility of results.
+    # Create samples
+    n = 1
+    c1 = np.random.binomial(n, 0.2, size=N)
+    c2 = np.random.binomial(n, 0.2, size=N)
+    c3 = np.random.binomial(n, 0.8, size=N)
+
+    t1 = np.random.binomial(n, 0.6, size=N)
+    t2 = np.random.binomial(n, 0.2, size=N)
+    t3 = np.random.binomial(n, 0.3, size=N)
+    t4 = np.random.binomial(n, 0.4, size=N)
+    t5 = np.random.binomial(n, 0.5, size=N)
+    t6 = np.random.binomial(n, 0.6, size=N)
+    t7 = np.ones(N)
+    t8 = np.zeros(N)
+    t9 = np.zeros(N)
+
+    # Add a `gender` column for coloring the data.
+    females = np.repeat('Female', N / 2).tolist()
+    males = np.repeat('Male', N / 2).tolist()
+    gender = females + males
+
+    # Add an `id` column for paired data plotting.
+    id_col = pd.Series(range(1, N + 1))
+
+    # Combine samples and gender into a DataFrame.
+    df = pd.DataFrame({'Control 1': c1, 'Test 1': t1,
+                       'Control 2': c2, 'Test 2': t2,
+                       'Control 3': c3, 'Test 3': t3,
+                       'Test 4': t4, 'Test 5': t5, 'Test 6': t6,
+                       'Test 7': t7, 'Test 8': t8, 'Test 9': t9,
+                       'Gender': gender, 'ID': id_col
+                       })
+
+    return df
+df = create_demo_prop_dataset()
+df.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Control 1Test 1Control 2Test 2Control 3Test 3Test 4Test 5Test 6Test 7Test 8Test 9GenderID
    01000100101.00.00.0Female1
    10101110001.00.00.0Female2
    20100101101.00.00.0Female3
    30100100101.00.00.0Female4
    40000100011.00.00.0Female5
    + +
    +
    +
    +
    +

    Helper function to create a binary table - dabest.prop_dataset

    +

    In DABEST v2024.3.29, we incorporated feedback from biologists who may not have tables of 0’s and 1’s readily available. As a result, a convenient function - dabest.prop_dataset - to generate a binary dataset based on the specified sample sizes is provided. Users can generate a pandas.DataFrame containing the sample sizes for each element in the groups and the group names (optional if the sample sizes are provided in a dict).

    +
    +
    sample_size_1 = {'a':[3, 4], 'b':[2, 5]}
+sample_size_2 = [3, 4, 2, 5]
+names = ['a', 'b']
+sample_df_1 = dabest.prop_dataset(sample_size_1)
+sample_df_2 = dabest.prop_dataset(sample_size_2, names)
+print(all(sample_df_1 == sample_df_2))
+sample_df_1.head()
    +
    +
    True
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    abID
    0001
    1002
    2013
    3114
    4115
    + +
    +
    +
    +
    +
    +
    +

    Loading data

    +

    When loading data, you need to set the parameter proportional=True.

    +
    +
    two_groups_unpaired = dabest.load(df, idx=("Control 1", "Test 1"), proportional=True)
+two_groups_unpaired
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:22 2025.
+
+Effect size(s) with 95% confidence intervals will be computed for:
+1. Test 1 minus Control 1
+
+5000 resamples will be used to generate the effect size bootstraps.
    +
    +
    +
    +
    +

    Effect sizes

    +

    To generate a proportion plot, the dabest library features two effect sizes:

    +
      +
    • Mean difference (mean_diff)
      • +
    • Cohen’s h (cohens＿h)
      • +
    +

    These are attributes of the Dabest object.

    +
    +
    two_groups_unpaired.mean_diff
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:23 2025.
+
+The unpaired mean difference between Control 1 and Test 1 is 0.575 [95%CI 0.35, 0.725].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`
    +
    +
    +

    Let’s compute the Cohen’s h for our comparison.

    +
    +
    two_groups_unpaired.cohens_h
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:23 2025.
+
+The unpaired Cohen's h between Control 1 and Test 1 is 1.24 [95%CI 0.784, 1.66].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.cohens_h.statistical_tests`
    +
    +
    +
    +
    +

    Generating proportion plots

    +

    To generate an estimation plot, simply use the .plot() method.

    +

    Each effect size instance has access to the .plot() method, allowing you to quickly create plots for different effect sizes with ease.

    +
    +

    Unpaired proportion plots

    +

    Unpaired proportion plots utilise the common bar plot. The bar plot displays the proportion of observations in the dataset that belong to the category of interest:

    +
      +
    • The white portion represents the proportion of observations that do not belong to the category (proportion of 0s in the data).
      • +
    • The colored portion represents the proportion of observations belonging to the category (proportion of 1s in the data).
      • +
    +
    +

    Two-Group

    +
    +
    two_groups_unpaired.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    two_groups_unpaired.cohens_h.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +

    Instead of a Gardner-Altman plot, you can generate a Cumming estimation plot by setting float_contrast=False in the .plot() method. This will plot the bootstrap effect sizes below the raw data.

    +
    +
    two_groups_unpaired.mean_diff.plot(float_contrast=False);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Multi Two-Group, Shared-Control, and Multi Groups

    +

    As with regular (non-binary) unpaired data, multi two-group, shared-control, and multi group plots can be generated for binary data.

    +
    +
    multi_two_groups_unpaired = dabest.load(df, idx=(("Control 1", "Test 1"),("Control 2", "Test 2"),("Control 3", "Test 3")),
+                                        proportional=True)
+multi_two_groups_unpaired
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:24 2025.
+
+Effect size(s) with 95% confidence intervals will be computed for:
+1. Test 1 minus Control 1
+2. Test 2 minus Control 2
+3. Test 3 minus Control 3
+
+5000 resamples will be used to generate the effect size bootstraps.
    +
    +
    +
    +
    multi_two_groups_unpaired.mean_diff
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:25 2025.
+
+The unpaired mean difference between Control 1 and Test 1 is 0.575 [95%CI 0.35, 0.725].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+The unpaired mean difference between Control 2 and Test 2 is 0.025 [95%CI -0.15, 0.15].
+The p-value of the two-sided permutation t-test is 0.535, calculated for legacy purposes only. 
+
+The unpaired mean difference between Control 3 and Test 3 is -0.6 [95%CI -0.75, -0.425].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`
    +
    +
    +
    +
    multi_two_groups_unpaired.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    shared_control = dabest.load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4"),
+                                        proportional=True)
+shared_control
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:25 2025.
+
+Effect size(s) with 95% confidence intervals will be computed for:
+1. Test 1 minus Control 1
+2. Test 2 minus Control 1
+3. Test 3 minus Control 1
+4. Test 4 minus Control 1
+
+5000 resamples will be used to generate the effect size bootstraps.
    +
    +
    +
    +
    shared_control.mean_diff
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:26 2025.
+
+The unpaired mean difference between Control 1 and Test 1 is 0.575 [95%CI 0.35, 0.725].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+The unpaired mean difference between Control 1 and Test 2 is 0.025 [95%CI -0.15, 0.15].
+The p-value of the two-sided permutation t-test is 0.539, calculated for legacy purposes only. 
+
+The unpaired mean difference between Control 1 and Test 3 is 0.125 [95%CI -0.025, 0.325].
+The p-value of the two-sided permutation t-test is 0.0936, calculated for legacy purposes only. 
+
+The unpaired mean difference between Control 1 and Test 4 is 0.15 [95%CI -0.05, 0.3].
+The p-value of the two-sided permutation t-test is 0.0604, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`
    +
    +
    +
    +
    shared_control.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    multi_groups_unpaired = dabest.load(df, idx=(("Control 1", "Test 1"),("Control 2", "Test 2", "Test 3", "Test 4")),
+                                        proportional=True)
+multi_groups_unpaired
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:26 2025.
+
+Effect size(s) with 95% confidence intervals will be computed for:
+1. Test 1 minus Control 1
+2. Test 2 minus Control 2
+3. Test 3 minus Control 2
+4. Test 4 minus Control 2
+
+5000 resamples will be used to generate the effect size bootstraps.
    +
    +
    +
    +
    multi_groups_unpaired.mean_diff
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:26 2025.
+
+The unpaired mean difference between Control 1 and Test 1 is 0.575 [95%CI 0.35, 0.725].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+The unpaired mean difference between Control 2 and Test 2 is 0.025 [95%CI -0.15, 0.15].
+The p-value of the two-sided permutation t-test is 0.535, calculated for legacy purposes only. 
+
+The unpaired mean difference between Control 2 and Test 3 is 0.125 [95%CI -0.05, 0.325].
+The p-value of the two-sided permutation t-test is 0.099, calculated for legacy purposes only. 
+
+The unpaired mean difference between Control 2 and Test 4 is 0.15 [95%CI -0.05, 0.3].
+The p-value of the two-sided permutation t-test is 0.0604, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`
    +
    +
    +
    +
    multi_groups_unpaired.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +
    +

    Paired proportion plots

    +

    For the paired version of the proportion plot, we adopt the style of a Sankey Diagram. The width of each bar in each xtick represents the proportion of the corresponding label in the group, and the strip denotes the paired relationship for each observation.

    +

    Starting from v2024.3.29, the paired version of the proportion plot receives a major upgrade. We introduce the sankey and flow parameters to control the plot. By default, both sankey and flow are set to True to cater the needs of repeated measures. When sankey is set to False, DABEST will generate a bar plot with a similar aesthetic to the paired proportion plot. When flow is set to False, each group of comparsion forms a Sankey diagram that does not connect to other groups of comparison.

    +

    Similar to the unpaired version, the .plot() method is used to produce an estimation plot.

    +
    +

    Two-Group

    +
    +
    two_groups_paired = dabest.load(df, idx=("Control 1", "Test 1"), 
+                                  proportional=True, paired="baseline", id_col="ID")
+two_groups_paired
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:27 2025.
+
+Paired effect size(s) for repeated measures against baseline 
+with 95% confidence intervals will be computed for:
+1. Test 1 minus Control 1
+
+5000 resamples will be used to generate the effect size bootstraps.
    +
    +
    +
    +
    two_groups_paired.mean_diff
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:27 2025.
+
+The paired mean difference for repeated measures against baseline 
+between Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`
    +
    +
    +
    +
    two_groups_paired.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +

    The Sankey plots for paired proportions also supports the float_contrast parameter, which can be set to False to produce a Cumming estimation plot.

    +
    +
    two_groups_paired.mean_diff.plot(float_contrast=False);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Multi Two-Group, Repeated Measures, and Multi Groups

    +

    As with regular (non-binary) unpaired data, multi two-group, repeated-measures, and multi group plots can be generated for binary data.

    +
    +
    multi_two_groups_paired = dabest.load(df, idx=(("Control 1", "Test 1"),("Control 2", "Test 2"),("Control 3", "Test 3")),
+                                        proportional=True, paired="baseline", id_col="ID")
+multi_two_groups_paired
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:27 2025.
+
+Paired effect size(s) for repeated measures against baseline 
+with 95% confidence intervals will be computed for:
+1. Test 1 minus Control 1
+2. Test 2 minus Control 2
+3. Test 3 minus Control 3
+
+5000 resamples will be used to generate the effect size bootstraps.
    +
    +
    +
    +
    multi_two_groups_paired.mean_diff
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:28 2025.
+
+The paired mean difference for repeated measures against baseline 
+between Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+The paired mean difference for repeated measures against baseline 
+between Control 2 and Test 2 is 0.025 [95%CI -0.15, 0.175].
+The p-value of the two-sided permutation t-test is 0.571, calculated for legacy purposes only. 
+
+The paired mean difference for repeated measures against baseline 
+between Control 3 and Test 3 is -0.6 [95%CI -0.775, -0.425].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`
    +
    +
    +
    +
    multi_two_groups_paired.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    repeated_measures_baseline = dabest.load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4"),
+                                        proportional=True, paired="baseline", id_col="ID")
+repeated_measures_baseline
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:28 2025.
+
+Paired effect size(s) for repeated measures against baseline 
+with 95% confidence intervals will be computed for:
+1. Test 1 minus Control 1
+2. Test 2 minus Control 1
+3. Test 3 minus Control 1
+4. Test 4 minus Control 1
+
+5000 resamples will be used to generate the effect size bootstraps.
    +
    +
    +
    +
    repeated_measures_baseline.mean_diff
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:29 2025.
+
+The paired mean difference for repeated measures against baseline 
+between Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+The paired mean difference for repeated measures against baseline 
+between Control 1 and Test 2 is 0.025 [95%CI -0.15, 0.175].
+The p-value of the two-sided permutation t-test is 0.555, calculated for legacy purposes only. 
+
+The paired mean difference for repeated measures against baseline 
+between Control 1 and Test 3 is 0.125 [95%CI -0.075, 0.275].
+The p-value of the two-sided permutation t-test is 0.277, calculated for legacy purposes only. 
+
+The paired mean difference for repeated measures against baseline 
+between Control 1 and Test 4 is 0.15 [95%CI -0.05, 0.325].
+The p-value of the two-sided permutation t-test is 0.075, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`
    +
    +
    +
    +
    repeated_measures_baseline.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    repeated_measures_sequential = dabest.load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4"),
+                                        proportional=True, paired="sequential", id_col="ID")
+repeated_measures_sequential
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:29 2025.
+
+Paired effect size(s) for the sequential design of repeated-measures experiment 
+with 95% confidence intervals will be computed for:
+1. Test 1 minus Control 1
+2. Test 2 minus Test 1
+3. Test 3 minus Test 2
+4. Test 4 minus Test 3
+
+5000 resamples will be used to generate the effect size bootstraps.
    +
    +
    +
    +
    repeated_measures_sequential.mean_diff
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:30 2025.
+
+The paired mean difference for the sequential design of repeated-measures experiment 
+between Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+The paired mean difference for the sequential design of repeated-measures experiment 
+between Test 1 and Test 2 is -0.55 [95%CI -0.725, -0.4].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+The paired mean difference for the sequential design of repeated-measures experiment 
+between Test 2 and Test 3 is 0.1 [95%CI -0.075, 0.225].
+The p-value of the two-sided permutation t-test is 0.342, calculated for legacy purposes only. 
+
+The paired mean difference for the sequential design of repeated-measures experiment 
+between Test 3 and Test 4 is 0.025 [95%CI -0.2, 0.2].
+The p-value of the two-sided permutation t-test is 0.624, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`
    +
    +
    +
    +
    repeated_measures_sequential.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    multi_groups_baseline = dabest.load(df, idx=(("Control 1", "Test 1"),("Control 2", "Test 2", "Test 3", "Test 4")),
+                                        proportional=True, paired="baseline", id_col="ID")
+multi_groups_baseline
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:30 2025.
+
+Paired effect size(s) for repeated measures against baseline 
+with 95% confidence intervals will be computed for:
+1. Test 1 minus Control 1
+2. Test 2 minus Control 2
+3. Test 3 minus Control 2
+4. Test 4 minus Control 2
+
+5000 resamples will be used to generate the effect size bootstraps.
    +
    +
    +
    +
    multi_groups_baseline.mean_diff
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:31 2025.
+
+The paired mean difference for repeated measures against baseline 
+between Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+The paired mean difference for repeated measures against baseline 
+between Control 2 and Test 2 is 0.025 [95%CI -0.15, 0.175].
+The p-value of the two-sided permutation t-test is 0.571, calculated for legacy purposes only. 
+
+The paired mean difference for repeated measures against baseline 
+between Control 2 and Test 3 is 0.125 [95%CI -0.075, 0.3].
+The p-value of the two-sided permutation t-test is 0.309, calculated for legacy purposes only. 
+
+The paired mean difference for repeated measures against baseline 
+between Control 2 and Test 4 is 0.15 [95%CI -0.025, 0.3].
+The p-value of the two-sided permutation t-test is 0.0362, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`
    +
    +
    +
    +
    multi_groups_baseline.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +
    +
    +

    Aesthetic adjustments

    +

    Here we demonstrate a few proportion plot specific aesthetic adjustments.

    +
    +

    Bar Width

    +

    You can modify the width of the bar plot bars (unpaired data) by setting the parameter bar_width in the .plot() method.

    +
    +
    two_groups_unpaired.mean_diff.plot(bar_width=0.3);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Bar desaturation

    +

    The raw_desat is used to control the amount of desaturation applied to the bar plot bar colors (specific to unpaired data). A value of 0.0 means full desaturation (i.e., grayscale), while a value of 1.0 means no desaturation (i.e., full color saturation). The default one is 0.8.

    +
    +
    two_groups_unpaired.mean_diff.plot(raw_desat=1.0);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Raw Label and Contrast Label

    +

    The parameters raw_label and contrast_label can be used to set labels for the y-axis of the bar plot and the contrast plot.

    +
    +
    two_groups_unpaired.mean_diff.plot(raw_label="success",contrast_label="difference");
+two_groups_paired.mean_diff.plot(raw_label="success",contrast_label="difference");
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Barplot kwargs

    +

    The parameters barplot_kwargs can be used to alter the aesthetics of the bar plot. This is a dictionary that can be used to pass additional arguments to the bar plot.

    +
    +
    two_groups_unpaired.mean_diff.plot(barplot_kwargs={"alpha":0.5, "edgecolor":"red", "linewidth":2, 'errorbar': ('sd', 0.1)});
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Sankey and Flow

    +

    By changing the sankey and flow parameters, you can generate different types of Sankey plots for paired proportions.

    +
    +
    separate_control = dabest.load(df, idx=((("Control 1", "Test 1"),
+                                ("Test 2", "Test 3"),
+                                ("Test 4", "Test 7", "Test 6"))),
+                    proportional=True, paired="sequential", id_col="ID")
+
+separate_control.mean_diff.plot();
+separate_control.mean_diff.plot(sankey_kwargs={'sankey':False});
+separate_control.mean_diff.plot(sankey_kwargs={'flow':False});
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Sankey kwargs

    +

    Several exclusive parameters can be provided to the .plot() method to customize the Sankey plots for paired proportions. By modifying the sankey_kwargs parameter, you can customize the Sankey plot. The following parameters are supported:

    +
      +
    • align: The alignment of each Sankey bar. Default is “center”.
      • +
    • alpha: The transparency of each Sankey bar. Default is 0.4.
      • +
    • bar_width: The width of each bar on the side in the plot. Default is 0.1.
      • +
    +
    +
    repeated_measures_baseline.mean_diff.plot(sankey_kwargs = {"alpha": 0.2,
+                                                  "bar_width": 0.4});
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Custom Palette

    +

    The custom_palette parameter functions in a similar way for proportion plots as for other plots - however, there are some differences!

    +

    A custom_palette dict can be passed for sankey plots, whereby two keys used are 0 and 1. The color associated with these keys will be used to color the bars in the sankey plot.

    +

    For bar plots, the custom_palette dict can be passed like a regular plot, with a color associated to each group. The chosen color will then be used to color the filled portion of the bar plot.

    +
    +
    repeated_measures_baseline.mean_diff.plot(custom_palette={0: "red", 1: "blue"});
+shared_control.mean_diff.plot(custom_palette={'Control 1': "red", 'Test 1': "blue", 'Test 2': "green", 'Test 3': "purple", 'Test 4': "orange"});
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +

    Similarly, premade matplotlib/seaborn color palette can be passed. For sankey plots, the first two colors in the palette will be used to color the bars in the sankey plot. For bar plots, the colors will be used to color the filled portion of the bar plot.

    +
    +
    repeated_measures_baseline.mean_diff.plot(custom_palette='Set1');
+shared_control.mean_diff.plot(custom_palette='Set1');
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +

    Passing a custom palette list functions differently for bar plots and sankey plots:

    +
      +
    • For bar plots, the list should contain the colors associated with each group.
      • +
    • For sankey plots, the list should contain two colors, the first color will be used to color the binary ’1’s, and the second color will be used to color the ’0’s.
      • +
    +
    +
    repeated_measures_baseline.mean_diff.plot(custom_palette=['red', 'blue']);
+shared_control.mean_diff.plot(custom_palette=['red', 'blue', 'green', 'purple', 'orange']);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Add counts to proportion plots

    +

    By default, the sample counts for each bar in proportion plots are not shown.

    +

    This feature can be turned on by setting prop_sample_counts=True in the .plot() method.

    +

    Note: This feature is not compatible with flow=False in sankey_kwargs.

    +
    +
    two_groups_unpaired = dabest.load(df, idx=("Control 1", "Test 1"), proportional=True)
+two_groups_unpaired.mean_diff.plot(prop_sample_counts=True);
    +
    +
    +
    +

    +
    +
    +
    +
    +

    The sample counts kwargs can be utilised via prop_sample_counts_kwargs in the .plot() method.

    +
    +
    two_groups_unpaired.mean_diff.plot(prop_sample_counts=True, prop_sample_counts_kwargs={"color":"red"});
    +
    +
    +
    +

    +
    +
    +
    +
    +

    For further aesthetic changes, the Plot Aesthetics Tutorial provides detailed examples of how to customize the plot.

    + + +
    +
    + +
    + +
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a/tutorials/05-mini_meta.html b/tutorials/05-mini_meta.html new file mode 100644 index 00000000..457376fc --- /dev/null +++ b/tutorials/05-mini_meta.html @@ -0,0 +1,1370 @@ + + + + + + + + + + +Mini-Meta – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + +
    + +
    + + +
    + + + +
    + +
    +
    +

    Mini-Meta

    +
    + +
    +
    + Explanation of how to compute the meta-analyzed weighted effect size using dabest. +
    +
    + + +
    + + + + +
    + + + +
    + + + +

    When scientists conduct replicates of the same experiment, the effect size of each replicate often varies, complicating the interpretation of the results. Starting from v2023.02.14, DABEST can now compute the meta-analyzed weighted effect size given multiple replicates of the same experiment. This can help resolve differences between replicates and simplify interpretation.

    +

    For this function, the generic inverse-variance weighting method is used to calculate a weighted mean difference, as follows:

    +

    \(\hat{\Delta}_w = \frac{\sum\hat{\Delta}_i\,w_i}{\sum w_i},\quad w_i =\frac{1}{\hat{\sigma}_i^{2}},\quad \hat{\sigma}_i^{2} =\operatorname{var}\!\big(\hat{\Delta}_i\big)\)

    +

    The variance used is calculated empirically as the sample variance of the bootstrapped values of the mean difference.

    +

    \(\hat{\Delta}_w=\frac{\sum\hat{\Delta}_i\,\hat{w}_i}{\sum\hat{w}_i},\quad \hat{w}_i=\frac{n_i-1}{\sum_{r=1}^{n_i}\bigl(\hat{\Delta}_i^{(r)}-\bar{\Delta}_i^{\mathrm{b}}\bigr)^2},\quad \bar{\Delta}_i^{\mathrm{b}}=\frac{1}{n_i}\sum_{r=1}^{n_i}\hat{\Delta}_i^{(r)}.\)

    +

    Where \(\hat{\Delta}_w\): estimated weighted delta;

    +

    \(w_i\): weight for replicate \(i\);

    +

    \(\hat{\sigma}_i^2\): sampling variance of the mean-difference estimator for replicate \(i\);

    +

    \(\hat{\Delta}_i\): estimated mean difference for replicate \(i\);

    +

    \(\hat{w}_i\): estimated weight for replicate \(i\);

    +

    \(n_i\): number of bootstrap replicates used for replicate \(i\);

    +

    \(\hat{\Delta}_i^{(r)}\): the \(r\)-th bootstrap estimate of the mean difference for replicate \(i\);

    +

    \(\bar{\Delta}_i^{\mathrm{b}}\): bootstrap mean of the mean differences for replicate \(i\)

    +

    Note that this utilizes the fixed-effects model of meta-analysis, in contrast to the random-effects model. In the fixed-effects model, all variation between the results of each replicate is assumed to be solely due to sampling error. Therefore, we recommend using this function exclusively for replications of the same experiment, where it can be safely assumed that each replicate estimates the same population mean \(\mu\).

    +

    Additionally, be aware that as of v2023.02.14, DABEST can only compute weighted effect size for mean difference only, and not for standardized measures such as Cohen’s d.

    +

    For more information on meta-analysis, please refer to Chapter 10 of the Cochrane handbook.

    +
    +

    Load libraries

    +
    +
    import numpy as np
+import pandas as pd
+import dabest
+
+print("We're using DABEST v{}".format(dabest.__version__))
    +
    +
    Pre-compiling numba functions for DABEST...
    +
    +
    +
    Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 62.75it/s]
    +
    +
    +
    Numba compilation complete!
+We're using DABEST v2025.10.20
    +
    +
    +
    +
    +
    +
    +
    +

    Creating a demo dataset

    +
    +
    from scipy.stats import norm # Used in generation of populations.
+
+np.random.seed(9999) # Fix the seed to ensure reproducibility of results.
+Ns = 20 # The number of samples taken from each population
+
+# Create samples
+c1 = norm.rvs(loc=3, scale=0.4, size=Ns)
+c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
+c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
+
+t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)
+t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)
+t3 = norm.rvs(loc=3, scale=0.75, size=Ns)
+
+
+# Add a `gender` column for coloring the data.
+females = np.repeat('Female', Ns/2).tolist()
+males = np.repeat('Male', Ns/2).tolist()
+gender = females + males
+
+# Add an `id` column for paired data plotting.
+id_col = pd.Series(range(1, Ns+1))
+
+# Combine samples and gender into a DataFrame.
+df = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,
+                   'Control 2' : c2,     'Test 2' : t2,
+                   'Control 3' : c3,     'Test 3' : t3,
+                   'Gender'    : gender, 'ID'  : id_col
+                  })
+df.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Control 1Test 1Control 2Test 2Control 3Test 3GenderID
    02.7939843.4208753.3246611.7074673.8169401.796581Female1
    13.2367593.4679723.6851861.1218463.7503583.944566Female2
    23.0191494.3771795.6168913.3013812.9453972.832188Female3
    32.8046384.5647802.7731522.5340183.5751793.048267Female4
    42.8580193.2200582.5503612.7963653.6921383.276575Female5
    + +
    +
    +
    +

    We now have three Control and three Test groups, simulating three replicates of the same experiment. Our dataset has also a non-numerical column indicating gender, and another column indicating the identity of each observation.

    +

    This is known as a ‘wide’ dataset. See this writeup for more details.

    +
    +
    +

    Loading data

    +

    Next, we load data as usual using dabest.load(). However, this time, we also specify the argument mini_meta=True. Since we are loading data from three experiments, idx is passed as a tuple of tuples, as shown below.

    +

    When this dabest object is invoked, it should indicate that effect sizes will be calculated for each group, along with the weighted delta. It is important to note once again that the weighted delta will only be calculated for mean differences.

    +
    +
    unpaired = dabest.load(df, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), mini_meta=True)
+unpaired
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:46 2025.
+
+Effect size(s) with 95% confidence intervals will be computed for:
+1. Test 1 minus Control 1
+2. Test 2 minus Control 2
+3. Test 3 minus Control 3
+4. weighted delta (only for mean difference)
+
+5000 resamples will be used to generate the effect size bootstraps.
    +
    +
    +

    By calling the mean_diff attribute, you can view the mean differences for each group as well as the weighted delta.

    +
    +
    unpaired.mean_diff
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:47 2025.
+
+The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].
+The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. 
+
+The unpaired mean difference between Control 2 and Test 2 is -1.38 [95%CI -1.93, -0.905].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+The unpaired mean difference between Control 3 and Test 3 is -0.255 [95%CI -0.696, 0.208].
+The p-value of the two-sided permutation t-test is 0.293, calculated for legacy purposes only. 
+
+The weighted-average unpaired mean differences is -0.00983 [95%CI -0.225, 0.213].
+The p-value of the two-sided permutation t-test is 0.941, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing theeffect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`
    +
    +
    +

    You can view the details of each experiment by accessing the property mean_diff.results as follows.

    +
    +
    pd.options.display.max_columns = 50
+unpaired.mean_diff.results
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    controltestcontrol_Ntest_Neffect_sizeis_paireddifferencecibca_lowbca_highbca_interval_idxpct_lowpct_highpct_interval_idxbootstrapsresamplesrandom_seedpermutationspvalue_permutationpermutation_countpermutations_varpvalue_welchstatistic_welchpvalue_students_tstatistic_students_tpvalue_mann_whitneystatistic_mann_whitneybec_differencebec_bootstrapsbec_bca_interval_idxbec_bca_lowbec_bca_highbec_pct_interval_idxbec_pct_lowbec_pct_high
    0Control 1Test 12020mean differenceNone0.480290950.2051610.773647(145, 4893)0.1974270.758752(125, 4875)[0.6148498102262239, 0.6752095203445543, 0.300...500012345[-0.17259843762502491, 0.03802293852634886, -0...0.00105000[0.26356588154404337, 0.2710249543904699, 0.26...0.002094-3.3088060.002057-3.3088060.00162583.00.0[-0.09732932551566487, 0.08087009665445155, -0...(127, 4877)-0.2568620.259558(125, 4875)-0.2582600.257590
    1Control 2Test 22020mean differenceNone-1.38108595-1.934192-0.905164(94, 4838)-1.901802-0.877098(125, 4875)[-1.7266697532252988, -1.7990605927248775, -1....500012345[0.015164519971271773, 0.017231919606192303, -...0.00005000[1.2241741427801065, 1.2241565174150129, 1.128...0.0000115.1388400.0000095.1388400.000026356.00.0[-0.7109511916465152, -0.3436697507223183, -0....(126, 4876)-0.5786210.598647(125, 4875)-0.5793060.598009
    2Control 3Test 32020mean differenceNone-0.25483195-0.6963790.207659(123, 4873)-0.6947900.208585(125, 4875)[0.3059887140714319, -0.22727011648745288, 0.0...500012345[-0.05901068591042824, -0.13617667681797307, 0...0.29345000[0.5835889750166371, 0.5796253365278035, 0.581...0.2947661.0697980.2914591.0697980.285305240.00.0[0.07996849455952271, 0.24534680794041375, 0.0...(124, 4874)-0.2437540.240283(125, 4875)-0.2437130.240490
    + +
    +
    +
    +

    Note, however, that this does not contain the relevant information for our weighted delta. The details of the weighted delta are stored as attributes of the mini_meta object, such as:

    +
      +
    • group_var: the pooled group variances of each set of 2 experiment groups.
      • +
    • difference: the weighted mean difference calculated based on the raw data.
      • +
    • bootstraps: the deltas of each set of 2 experiment groups calculated based on the bootstraps.
      • +
    • bootstraps_weighted_delta: the weighted deltas calculated based on the bootstraps.
      • +
    • permutations: the deltas of each set of 2 experiment groups calculated based on the permutation data.
      • +
    • permutations_var: the pooled group variances of each set of 2 experiment groups calculated based on permutation data.
      • +
    • permutations_weighted_delta: the weighted deltas calculated based on the permutation data.
      • +
    +

    A dataframe of this mini meta dabest object can also be called via the mini_meta.results attribute.

    +
    +
    unpaired.mean_diff.mini_meta.results
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    controltestcontrol_Ntest_Ncontrol_vartest_vargroup_vardifferencecibca_lowbca_highbca_interval_idxpct_lowpct_highpct_interval_idxbootstraps_deltasbootstraps_weighted_deltapermutationspermutations_varpermutations_weighted_deltapvalue_permutationpermutation_countbias_correctionjackknives
    0[Control 1, Control 2, Control 3][Test 1, Test 2, Test 3][20, 20, 20][20, 20, 20][0.17628013404546258, 0.9584767911266554, 0.16...[0.24512071870152594, 0.48609989925165153, 0.9...[0.2107004263734943, 0.7222883451891535, 0.567...-0.0098395-0.2250730.213221(133, 4883)-0.2271990.210616(125, 4875)[[0.6148498102262239, 0.6752095203445543, 0.30...[0.1383993266160009, 0.040698566036827026, -0....[[-0.17259843762502491, 0.03802293852634886, -...[[0.26356588154404337, 0.2710249543904699, 0.2...[-0.11757207833491819, -0.01292867970093462, -...0.941250000.014539[-0.010633066723935882, -0.010613522663007862,...
    + +
    +
    +
    +
    +
    +

    Generating mini meta plots

    +
    +
    unpaired.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +

    You can also hide the weighted delta by passing the argument show_mini_meta=False. In this case, the resulting graph would be identical to a multiple two-groups plot.

    +
    +
    unpaired.mean_diff.plot(show_mini_meta=False);
    +
    +
    +
    +

    +
    +
    +
    +
    +

    As with regular two-groups plots, you can also analyse paired mini meta experiments via the paired=baseline argument.

    +
    +
    paired = dabest.load(df, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), mini_meta=True, id_col="ID", paired="baseline")
+paired.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +

    For further aesthetic changes, the Plot Aesthetics Tutorial provides detailed examples of how to customize the plot.

    + + +
    + +
    + +
    + + + + + \ No newline at end of file diff --git a/tutorials/05-mini_meta_files/figure-html/cell-10-output-1.png b/tutorials/05-mini_meta_files/figure-html/cell-10-output-1.png new file mode 100644 index 00000000..f0ef3a9c Binary files /dev/null and b/tutorials/05-mini_meta_files/figure-html/cell-10-output-1.png differ diff --git a/tutorials/05-mini_meta_files/figure-html/cell-8-output-1.png b/tutorials/05-mini_meta_files/figure-html/cell-8-output-1.png new file mode 100644 index 00000000..23f9ec10 Binary files /dev/null and b/tutorials/05-mini_meta_files/figure-html/cell-8-output-1.png differ diff --git a/tutorials/05-mini_meta_files/figure-html/cell-9-output-1.png b/tutorials/05-mini_meta_files/figure-html/cell-9-output-1.png new file mode 100644 index 00000000..aff8df17 Binary files /dev/null and b/tutorials/05-mini_meta_files/figure-html/cell-9-output-1.png differ diff --git a/tutorials/06-delta_delta.html b/tutorials/06-delta_delta.html new file mode 100644 index 00000000..ba9573a2 --- /dev/null +++ b/tutorials/06-delta_delta.html @@ -0,0 +1,1430 @@ + + + + + + + + + + +Delta-Delta – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + +
    + +
    + + +
    + + + +
    + +
    +
    +

    Delta-Delta

    +
    + +
    +
    + Explanation of how to calculate delta-delta using DABEST. +
    +
    + + +
    + + + + +
    + + + +
    + + + +

    Since v2023.02.14, DABEST also supports the calculation of delta-delta, an experimental function that facilitates the comparison between two bootstrapped effect sizes computed from two independent categorical variables.

    +

    Since v2025.03.27, DABEST also supports the calculation of delta-delta for binary data (proportion plots).

    +

    Many experimental designs investigate the effects of two interacting independent variables on a dependent variable. The delta-delta effect size enables us distill the net effect of the two variables. To illustrate this, let’s explore the following problem.

    +

    Consider an experiment where we test the efficacy of a drug named Drug on a disease-causing mutation M based on disease metric Y. The greater the value Y has, the more severe the disease phenotype is. Phenotype Y has been shown to be caused by a gain-of-function mutation M, so we expect a difference between wild type (W) subjects and mutant subjects (M). Now, we want to know whether this effect is ameliorated by the administration of Drug treatment. We also administer a placebo as a control. In theory, we only expect Drug to have an effect on the M group, although in practice, many drugs have non-specific effects on healthy populations too.

    +

    Effectively, we have four groups of subjects for comparison.

    + + + + + + + + + + + + + + + + + + + + +
    WildtypeMutant
    DrugXD, WXD, M
    PlaceboXP, WXP, M
    +

    There are two Treatment conditions, Placebo (control group) and Drug (test group). There are two Genotype: W (wild type population) and M (mutant population). Additionally, each experiment was conducted twice (Rep1 and Rep2). We will perform several analyses to visualise these differences in a simulated dataset.

    +
    +

    Load libraries

    +
    +
    import numpy as np
+import pandas as pd
+import dabest
+
+print("We're using DABEST v{}".format(dabest.__version__))
    +
    +
    We're using DABEST v2025.10.20
    +
    +
    +
    +
    +

    Creating a demo dataset

    +
    +
    from scipy.stats import norm # Used in generation of populations.
+np.random.seed(9999) # Fix the seed to ensure reproducibility of results.
+
+# Create samples
+N = 20
+y = norm.rvs(loc=3, scale=0.4, size=N*4)
+y[N:2*N] = y[N:2*N]+1
+y[2*N:3*N] = y[2*N:3*N]-0.5
+
+# Add a `Treatment` column
+t1 = np.repeat('Placebo', N*2).tolist()
+t2 = np.repeat('Drug', N*2).tolist()
+treatment = t1 + t2 
+
+# Add a `Rep` column as the first variable for the 2 replicates of experiments done
+rep = []
+for i in range(N*2):
+    rep.append('Rep1')
+    rep.append('Rep2')
+
+# Add a `Genotype` column as the second variable
+wt = np.repeat('W', N).tolist()
+mt = np.repeat('M', N).tolist()
+wt2 = np.repeat('W', N).tolist()
+mt2 = np.repeat('M', N).tolist()
+
+
+genotype = wt + mt + wt2 + mt2
+
+# Add an `id` column for paired data plotting.
+id = list(range(0, N*2))
+id_col = id + id 
+
+
+# Combine all columns into a DataFrame.
+df_delta2 = pd.DataFrame({'ID'        : id_col,
+                  'Rep'      : rep,
+                   'Genotype'  : genotype, 
+                   'Treatment': treatment,
+                   'Y'         : y
+                })
+df_delta2.head(5)
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    IDRepGenotypeTreatmentY
    00Rep1WPlacebo2.793984
    11Rep2WPlacebo3.236759
    22Rep1WPlacebo3.019149
    33Rep2WPlacebo2.804638
    44Rep1WPlacebo2.858019
    + +
    +
    +
    +
    +
    +

    Loading data

    +

    To create a delta-delta plot, you simply need to set delta2=True in the dabest.load() method. However, in this case,x needs to be declared as a list consisting of 2 elements, unlike most cases where it is a single element. The first element in x will represent the variable plotted along the horizontal axis, and the second one will determine the color of dots for scattered plots or the color of lines for slope graphs. We use the experiment input to specify the grouping of the data.

    +
    +
    unpaired_delta2 = dabest.load(data = df_delta2, x = ["Genotype", "Genotype"], y = "Y", delta2 = True, experiment = "Treatment")
+unpaired_delta2
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:50 2025.
+
+Effect size(s) with 95% confidence intervals will be computed for:
+1. M Placebo minus W Placebo
+2. M Drug minus W Drug
+3. Drug minus Placebo (only for mean difference)
+
+5000 resamples will be used to generate the effect size bootstraps.
    +
    +
    +
    +
    unpaired_delta2.mean_diff
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:51 2025.
+
+The unpaired mean difference between W Placebo and M Placebo is 1.23 [95%CI 0.937, 1.51].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+The unpaired mean difference between W Drug and M Drug is 0.326 [95%CI 0.0956, 0.574].
+The p-value of the two-sided permutation t-test is 0.0122, calculated for legacy purposes only. 
+
+The delta-delta between Placebo and Drug is -0.903 [95%CI -1.27, -0.522].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing the effect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`
    +
    +
    +
    +
    +

    Generating delta-delta plots

    +
    +
    unpaired_delta2.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +

    In the above plot, the horizontal axis represents the Genotype condition and the dot colour is also specified by Genotype. The left pair of scattered plots is based on the Placebo group while the right pair is based on the Drug group. The bottom left axis contains the two primary deltas: the Placebo delta and the Drug delta. We can easily see that when only the placebo was administered, the mutant phenotype is around 1.23 [95%CI 0.948, 1.52]. This difference was shrunken to around 0.326 [95%CI 0.0934, 0.584] when the drug was administered. This gives us some indication that the drug is effective in amiliorating the disease phenotype. Since the Drug did not completely eliminate the mutant phenotype, we have to calculate how much net effect the drug had. This is where delta-delta comes in. We use the Placebo delta as a reference for how much the mutant phenotype is supposed to be, and we subtract the Drug delta from it. The bootstrapped mean differences (delta-delta) between the Placebo and Drug group are plotted at the right bottom with a separate y-axis from other bootstrap plots. This effect size, at about -0.903 [95%CI -1.28, -0.513], is the net effect size of the drug treatment. That is to say that treatment with drug A reduced disease phenotype by 0.903.

    +

    The mean difference between mutants and wild types given the placebo treatment is:

    +

    \(\Delta_{1} = \overline{X}_{P, M} - \overline{X}_{P, W}\)

    +

    The mean difference between mutants and wild types given the drug treatment is:

    +

    \(\Delta_{2} = \overline{X}_{D, M} - \overline{X}_{D, W}\)

    +

    The net effect of the drug on mutants is:

    +

    \(\Delta_{\Delta} = \Delta_{2} - \Delta_{1}\)

    +

    where \(\overline{X}\) is the sample mean, \(\Delta\) is the mean difference.

    +

    The configuration of comparison we performed above is reminiscent of a two-way ANOVA. In fact, the delta - delta is an effect size estimated for the interaction term between Treatment and Genotype. Main effects of Treatment and Genotype, on the other hand, can be determined by simpler, univariate contrast plots.

    +
    +
    +

    Specifying grouping for comparisons

    +

    In the example above, we used the convention of test - control but you can manipulate the orders of the experiment groups as well as the horizontal axis variable by setting the paremeters experiment_label and x1_level.

    +
    +
    unpaired_delta2_specified = dabest.load(data = df_delta2, 
+                                            x = ["Genotype", "Genotype"], y = "Y", 
+                                            delta2 = True, experiment = "Treatment",
+                                            experiment_label = ["Drug", "Placebo"],
+                                            x1_level = ["M", "W"])
+
+unpaired_delta2_specified.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +

    Utilising the show_delta2 argument within the .plot() method allows for control of whether the delta-delta effect size is displayed on the plot. By default, this is set to True.

    +
    +
    unpaired_delta2_specified.mean_diff.plot(show_delta2=False);
    +
    +
    +
    +

    +
    +
    +
    +
    +

    The delta-delta function also supports paired data, providing a useful alternative visualization of the data. Assuming that the placebo and drug treatment were administered to the same subjects, our data is paired between the treatment conditions. We can specify this by using Treatment as x and Genotype as experiment, and we further specify that id_col is ID, linking data from the same subject with each other. Since we have conducted two replicates of the experiments, we can also colour the slope lines according to Rep.

    +
    +
    paired_delta2 = dabest.load(data = df_delta2, 
+                                paired = "baseline", id_col="ID",
+                                x = ["Treatment", "Rep"], y = "Y", 
+                                delta2 = True, experiment = "Genotype")
+paired_delta2.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +

    We see that the drug had a non-specific effect of -0.321 [95%CI -0.498, -0.131] on wild type subjects even when they were not sick, and it had a bigger effect of -1.22 [95%CI -1.52, -0.906] in mutant subjects. In this visualisation, we can see the delta-delta value of -0.903 [95%CI -1.21, -0.587] as the net effect of the drug accounting for non-specific actions in healthy individuals.

    +

    The mean difference between drug and placebo treatments in wild type subjects is:

    +

    \[\Delta_{1} = \overline{X}_{D, W} - \overline{X}_{P, W}\]

    +

    The mean difference between drug and placebo treatments in mutant subjects is:

    +

    \[\Delta_{2} = \overline{X}_{D, M} - \overline{X}_{P, M}\]

    +

    The net effect of the drug on mutants is:

    +

    \[\Delta_{\Delta} = \Delta_{2} - \Delta_{1}\]

    +

    where \(\overline{X}\) is the sample mean, \(\Delta\) is the mean difference.

    +
    +
    +

    Standardising delta-delta effect sizes with Delta g

    +

    Standardized mean difference statistics like Cohen’s d and Hedges’ g quantify effect sizes in terms of the sample variance. We have introduced a metric, Delta g, to standardize delta-delta effects. This metric enables the comparison between measurements of different dimensions.

    +

    The standard deviation of the delta-delta value is calculated from a pooled variance of the 4 samples:

    +

    \[s_{\Delta_{\Delta}} = \sqrt{\frac{(n_{D, W}-1)s_{D, W}^2+(n_{P, W}-1)s_{P, W}^2+(n_{D, M}-1)s_{D, M}^2+(n_{P, M}-1)s_{P, M}^2}{(n_{D, W} - 1) + (n_{P, W} - 1) + (n_{D, M} - 1) + (n_{P, M} - 1)}}\]

    +

    where \(s\) is the standard deviation and \(n\) is the sample size.

    +

    A delta g value is then calculated as delta-delta value divided by pooled standard deviation \(s_{\Delta_{\Delta}}\):

    +

    \(\Delta_{g} = \frac{\Delta_{\Delta}}{s_{\Delta_{\Delta}}}\)

    +

    This metric can be accessed via the ‘hedges_g’ effect size when utilising delta2=True in dabest.load().

    +
    +
    unpaired_delta2.hedges_g
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:53 2025.
+
+The unpaired Hedges' g between W Placebo and M Placebo is 2.54 [95%CI 1.71, 3.31].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+The unpaired Hedges' g between W Drug and M Drug is 0.793 [95%CI 0.17, 1.33].
+The p-value of the two-sided permutation t-test is 0.0122, calculated for legacy purposes only. 
+
+The delta g between Placebo and Drug is -2.11 [95%CI -2.97, -1.22].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing the effect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
+
+To get the results of all valid statistical tests, use `.hedges_g.statistical_tests`
    +
    +
    +

    We see the standardised delta-delta (delta g) value of -2.11 standard deviations [95%CI -2.98, -1.2] as the net effect of the drug accounting for non-specific actions in healthy individuals.

    +
    +
    unpaired_delta2.hedges_g.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Delta-delta for binary data

    +

    Since v2025.03.27, the delta-delta function also supports binary data (proportion plots). In this case, the delta-delta value is calculated as the difference between the two proportions. This can be used for both unpaired and paired binary data.

    +
    +
    def create_demo_dataset_delta(seed=9999, N=20):
+    
+    import numpy as np
+    import pandas as pd
+    from scipy.stats import norm # Used in generation of populations.
+
+    np.random.seed(seed) # Fix the seed so the results are replicable.
+    # pop_size = 10000 # Size of each population.
+
+    from scipy.stats import norm # Used in generation of populations.
+
+    # Create samples
+    y = norm.rvs(loc=3, scale=0.4, size=N*4)
+    y[N:2*N] = y[N:2*N]+1
+    y[2*N:3*N] = y[2*N:3*N]-0.5
+    ind = np.random.binomial(1, 0.5, size=N*4)
+    ind[N:2*N] = np.random.binomial(1, 0.2, size=N)
+    ind[2*N:3*N] = np.random.binomial(1, 0.1, size=N)
+
+    # Add drug column
+    t1 = np.repeat('Placebo', N*2).tolist()
+    t2 = np.repeat('Drug', N*2).tolist()
+    treatment = t1 + t2 
+
+    # Add a `rep` column as the first variable for the 2 replicates of experiments done
+    rep = []
+    for i in range(N*2):
+        rep.append('Rep1')
+        rep.append('Rep2')
+
+    # Add a `genotype` column as the second variable
+    wt = np.repeat('WT', N).tolist()
+    mt = np.repeat('Mut', N).tolist()
+    wt2 = np.repeat('WT', N).tolist()
+    mt2 = np.repeat('Mut', N).tolist()
+
+
+    genotype = wt + mt + wt2 + mt2
+
+    # Add an `id` column for paired data plotting.
+    id = list(range(0, N*2))
+    id_col = id + id 
+
+
+    # Combine all columns into a DataFrame.
+    df_prop = pd.DataFrame({'ID'        : id_col,
+                      'Rep'        : rep,
+                       'Genotype'  : genotype, 
+                       'Treatment' : treatment,
+                       'Y'         : y,
+                       'Cat'       :ind
+                    })
+    return df_prop
+
+df_prop = create_demo_dataset_delta()
+
+unpaired_prop = dabest.load(data = df_prop, proportional=True,
+                            # id_col="index", paired='baseline', 
+                            x = ["Treatment", "Treatment"], 
+                            y = "Cat", delta2=True,
+                            experiment="Genotype",)
+
+unpaired_prop.mean_diff.plot();
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Statistics

    +

    You can find all outputs of the delta-delta calculation by assessing the attribute named delta_delta of the effect size object.

    +
    +
    unpaired_delta2.mean_diff.delta_delta
    +
    +
    DABEST v2025.10.20
+==================
+                  
+Good afternoon!
+The current time is Sun Oct 19 16:00:54 2025.
+
+The delta-delta between Placebo and Drug is -0.903 [95%CI -1.27, -0.522].
+The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 
+
+5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
+Any p-value reported is the probability of observing the effect size (or greater),
+assuming the null hypothesis of zero difference is true.
+For each p-value, 5000 reshuffles of the control and test labels were performed.
    +
    +
    +

    The delta_delta object has its own attributes, containing various information of delta-delta.

    +
      +
    • difference: the mean bootstrapped differences between the 2 groups of bootstrapped mean differences
      • +
    • bootstraps: the 2 groups of bootstrapped mean differences
      • +
    • bootstraps_delta_delta: the bootstrapped differences between the 2 groups of bootstrapped mean differences
      • +
    • permutations: the mean difference between the two groups of bootstrapped mean differences calculated based on the permutation data
      • +
    • permutations_var: the pooled group variances of two groups of bootstrapped mean differences calculated based on permutation data
      • +
    • permutations_delta_delta: the delta-delta calculated based on the permutation data
      • +
    +

    A dataframe of this delta delta dabest object can also be called via the delta_delta.results attribute.

    +
    +
    unpaired_delta2.mean_diff.delta_delta.results
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    controltestdifferencecibca_lowbca_highbca_interval_idxpct_lowpct_highpct_interval_idxbootstraps_controlbootstraps_testbootstraps_delta_deltapermutations_controlpermutations_testpermutations_delta_deltapvalue_permutationpermutation_countbias_correctionjackknives
    0PlaceboDrug-0.90317995-1.271483-0.521935(124, 4874)-1.270426-0.519652(125, 4875)[1.0890043559982234, 1.1472720447282119, 1.072...[0.6003430615628478, 0.6547912656551773, 0.294...[-0.43421309034304567, -0.7324573148122022, -1...[-0.15899787281865496, 0.23958268043726694, 0....[-0.036113268018566735, -0.05491466432013192, ...[0.12288460480008823, -0.29449734475739886, -0...0.05000-0.000501[-0.9006797310317582, -0.9006200702547091, -0....
    + +
    +
    +
    +

    Similarly, for the standardised delta-delta effect size, the hedges_g object has its own delta delta (Delta g) results attribute.

    +
    +
    unpaired_delta2.hedges_g.delta_delta.results
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    controltestdifferencecibca_lowbca_highbca_interval_idxpct_lowpct_highpct_interval_idxbootstraps_controlbootstraps_testbootstraps_delta_deltapermutations_controlpermutations_testpermutations_delta_deltapvalue_permutationpermutation_countbias_correctionjackknives
    0PlaceboDrug-2.10668195-2.965759-1.217424(124, 4874)-2.963294-1.212099(125, 4875)[2.3610871907095192, 2.7764672664031567, 2.350...[1.549355181508767, 1.7247260954921417, 0.6471...[-1.0128104949604284, -1.708470960574333, -2.7...[-0.1986457235842243, 0.3014021841951519, 0.31...[-0.08117530110708499, -0.12358349103957916, -...[0.11747042247713932, -0.4249856752347311, -0....0.05000-0.000501[-2.1008530246437576, -2.10071386471865, -2.10...
    + +
    +
    +
    +

    For further aesthetic changes, the Plot Aesthetics Tutorial provides detailed examples of how to customize the plot.

    + + +
    + +
    + +
    + + + + + \ No newline at end of file diff --git a/tutorials/06-delta_delta_files/figure-html/cell-11-output-1.png b/tutorials/06-delta_delta_files/figure-html/cell-11-output-1.png new file mode 100644 index 00000000..eae0e80f Binary files /dev/null and b/tutorials/06-delta_delta_files/figure-html/cell-11-output-1.png differ diff --git a/tutorials/06-delta_delta_files/figure-html/cell-12-output-1.png b/tutorials/06-delta_delta_files/figure-html/cell-12-output-1.png new file mode 100644 index 00000000..bd322d30 Binary files /dev/null and b/tutorials/06-delta_delta_files/figure-html/cell-12-output-1.png differ diff --git a/tutorials/06-delta_delta_files/figure-html/cell-6-output-1.png b/tutorials/06-delta_delta_files/figure-html/cell-6-output-1.png new file mode 100644 index 00000000..6b48706a Binary files /dev/null and b/tutorials/06-delta_delta_files/figure-html/cell-6-output-1.png differ diff --git a/tutorials/06-delta_delta_files/figure-html/cell-7-output-1.png b/tutorials/06-delta_delta_files/figure-html/cell-7-output-1.png new file mode 100644 index 00000000..09b2a794 Binary files /dev/null and b/tutorials/06-delta_delta_files/figure-html/cell-7-output-1.png differ diff --git a/tutorials/06-delta_delta_files/figure-html/cell-8-output-1.png b/tutorials/06-delta_delta_files/figure-html/cell-8-output-1.png new file mode 100644 index 00000000..eb2dad6d Binary files /dev/null and b/tutorials/06-delta_delta_files/figure-html/cell-8-output-1.png differ diff --git a/tutorials/06-delta_delta_files/figure-html/cell-9-output-1.png b/tutorials/06-delta_delta_files/figure-html/cell-9-output-1.png new file mode 100644 index 00000000..0d57033f Binary files /dev/null and b/tutorials/06-delta_delta_files/figure-html/cell-9-output-1.png differ diff --git a/tutorials/07-horizontal_plot.html b/tutorials/07-horizontal_plot.html new file mode 100644 index 00000000..317dc87a --- /dev/null +++ b/tutorials/07-horizontal_plot.html @@ -0,0 +1,1231 @@ + + + + + + + + + + +Horizontal Plots – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + +
    + +
    + + +
    + + + +
    + +
    +
    +

    Horizontal Plots

    +
    + +
    +
    + A guide to plot data in a horizontal format. +
    +
    + + +
    + + + + +
    + + + +
    + + + +

    In DABEST v2025.03.27, we introduce a new plotting orientation: horizontal plots.

    +

    To access this, provide horizontal=True to the .plot() method.

    +
    +

    Load libraries

    +
    +
    import numpy as np
+import pandas as pd
+import dabest
+
+print("We're using DABEST v{}".format(dabest.__version__))
    +
    +
    Pre-compiling numba functions for DABEST...
    +
    +
    +
    Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 42.06it/s]
    +
    +
    +
    Numba compilation complete!
+We're using DABEST v2025.10.20
    +
    +
    +
    +
    +
    +
    +
    +

    Creating a demo dataset

    +
    +
    from scipy.stats import norm # Used in generation of populations.
+
+np.random.seed(9999) # Fix the seed to ensure reproducibility of results.
+
+Ns = 20 # The number of samples taken from each population
+
+# Create samples
+c1 = norm.rvs(loc=3, scale=0.4, size=Ns)
+c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
+c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
+
+t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)
+t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)
+t3 = norm.rvs(loc=3, scale=0.75, size=Ns)
+t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
+t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
+t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
+
+
+# Add a `gender` column for coloring the data.
+females = np.repeat('Female', Ns/2).tolist()
+males = np.repeat('Male', Ns/2).tolist()
+gender = females + males
+
+# Add an `id` column for paired data plotting.
+id_col = pd.Series(range(1, Ns+1))
+
+# Combine samples and gender into a DataFrame.
+df = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,
+                 'Control 2' : c2,     'Test 2' : t2,
+                 'Control 3' : c3,     'Test 3' : t3,
+                 'Test 4'    : t4,     'Test 5' : t5, 'Test 6' : t6,
+                 'Gender'    : gender, 'ID'  : id_col
+                })
    +
    +
    +
    +

    Generating two-group plots

    +
    +
    two_groups_unpaired = dabest.load(df, idx=("Control 1", "Test 1"))
+two_groups_unpaired.mean_diff.plot(horizontal=True);
+
+two_groups_paired = dabest.load(df, idx=("Control 1", "Test 1"), paired='baseline', id_col='ID')
+two_groups_paired.mean_diff.plot(horizontal=True);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Generating shared-control and repeated-measures plots

    +
    +
    shared_control = dabest.load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5"))
+shared_control.mean_diff.plot(horizontal=True);
+
+repeated_measures_baseline = dabest.load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5"), paired='baseline', id_col='ID')    
+repeated_measures_baseline.mean_diff.plot(horizontal=True);
+
+repeated_measures_sequential = dabest.load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5"), paired='sequential', id_col='ID')    
+repeated_measures_sequential.mean_diff.plot(horizontal=True);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Generating multi-group plots

    +
    +
    multi_2group = dabest.load(df, idx=(("Control 1", "Test 1"),("Control 2", "Test 2"),("Control 3", "Test 3", "Test 4", "Test 5")))
+multi_2group.mean_diff.plot(horizontal=True);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Generating proportion plots

    +
    +
    def create_demo_prop_dataset(seed=9999, N=40):
+    import numpy as np
+    import pandas as pd
+
+    np.random.seed(9999)  # Fix the seed to ensure reproducibility of results.
+    # Create samples
+    n = 1
+    c1 = np.random.binomial(n, 0.2, size=N)
+    c2 = np.random.binomial(n, 0.2, size=N)
+    c3 = np.random.binomial(n, 0.8, size=N)
+
+    t1 = np.random.binomial(n, 0.6, size=N)
+    t2 = np.random.binomial(n, 0.2, size=N)
+    t3 = np.random.binomial(n, 0.3, size=N)
+    t4 = np.random.binomial(n, 0.4, size=N)
+    t5 = np.random.binomial(n, 0.5, size=N)
+    t6 = np.random.binomial(n, 0.6, size=N)
+    t7 = np.ones(N)
+    t8 = np.zeros(N)
+    t9 = np.zeros(N)
+
+    # Add a `gender` column for coloring the data.
+    females = np.repeat('Female', N / 2).tolist()
+    males = np.repeat('Male', N / 2).tolist()
+    gender = females + males
+
+    # Add an `id` column for paired data plotting.
+    id_col = pd.Series(range(1, N + 1))
+
+    # Combine samples and gender into a DataFrame.
+    df = pd.DataFrame({'Control 1': c1, 'Test 1': t1,
+                       'Control 2': c2, 'Test 2': t2,
+                       'Control 3': c3, 'Test 3': t3,
+                       'Test 4': t4, 'Test 5': t5, 'Test 6': t6,
+                       'Test 7': t7, 'Test 8': t8, 'Test 9': t9,
+                       'Gender': gender, 'ID': id_col
+                       })
+
+    return df
+df_prop = create_demo_prop_dataset()
    +
    +
    +
    multi_two_groups_unpaired = dabest.load(df_prop, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Test 3", "Test 4"), ("Test 5", "Test 6")), proportional=True)
+multi_two_groups_unpaired.mean_diff.plot(horizontal=True);
+
+multi_group_baseline = dabest.load(df_prop, idx=((("Control 1", "Test 1","Test 2", "Test 3"),("Test 4", "Test 5", "Test 6"))),proportional=True, paired="baseline", id_col="ID")
+multi_group_baseline.mean_diff.plot(horizontal=True);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Generating delta-delta plots

    +
    +
    from scipy.stats import norm # Used in generation of populations.
+np.random.seed(9999) # Fix the seed to ensure reproducibility of results.
+
+# Create samples
+N = 20
+y = norm.rvs(loc=3, scale=0.4, size=N*4)
+y[N:2*N] = y[N:2*N]+1
+y[2*N:3*N] = y[2*N:3*N]-0.5
+
+# Add a `Treatment` column
+t1 = np.repeat('Placebo', N*2).tolist()
+t2 = np.repeat('Drug', N*2).tolist()
+treatment = t1 + t2 
+
+# Add a `Rep` column as the first variable for the 2 replicates of experiments done
+rep = []
+for i in range(N*2):
+    rep.append('Rep1')
+    rep.append('Rep2')
+
+# Add a `Genotype` column as the second variable
+wt = np.repeat('W', N).tolist()
+mt = np.repeat('M', N).tolist()
+wt2 = np.repeat('W', N).tolist()
+mt2 = np.repeat('M', N).tolist()
+
+
+genotype = wt + mt + wt2 + mt2
+
+# Add an `id` column for paired data plotting.
+id = list(range(0, N*2))
+id_col = id + id 
+
+
+# Combine all columns into a DataFrame.
+df_delta2 = pd.DataFrame({'ID'        : id_col,
+                  'Rep'      : rep,
+                   'Genotype'  : genotype, 
+                   'Treatment': treatment,
+                   'Y'         : y
+                })
    +
    +
    +
    unpaired_delta2 = dabest.load(data = df_delta2, x = ["Genotype", "Genotype"], y = "Y", delta2 = True, experiment = "Treatment")
+unpaired_delta2.mean_diff.plot(horizontal=True);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Generating mini-meta plots

    +
    +
    unpaired = dabest.load(df, idx=(("Control 1", "Test 2"), ("Control 2", "Test 3"), ("Test 4", "Test 5")), mini_meta=True)
+unpaired.mean_diff.plot(horizontal=True);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Controlling aesthetics

    +

    As with the vertical plots, horizontal plots can be customized using the same options. Shown below are a few cases where the aesthetics are modified, added functionality, or just less intuitive.

    +
    +

    Swarm side

    +

    As with the vertical plots, you can specify the side of the swarms via swarm_side in the .plot() method.

    +

    In this case, swarm_side='left' would plot the swarms upwards, and swarm_side='right' would plot the swarms downwards.

    +

    Default is swarm_side='left'

    +
    +
    two_groups_unpaired = dabest.load(df, idx=("Control 1", "Test 1", 'Test 2'), resamples=5000)
+two_groups_unpaired.mean_diff.plot(swarm_side='left', horizontal=True);
    +
    +
    +
    +

    +
    +
    +
    +
    +

    swarm_side='center'

    +
    +
    two_groups_unpaired.mean_diff.plot(swarm_side='center', horizontal=True);
    +
    +
    +
    +

    +
    +
    +
    +
    +

    swarm_side='right'

    +
    +
    two_groups_unpaired.mean_diff.plot(swarm_side='right', horizontal=True);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Table kwargs

    +

    The table axis can be customized using the horizontal_table_kwargs argument. A dict of keywords can be passed to customize the table.

    +

    If None, the following keywords are passed:

    +
      +
    • 'show' - Whether to show the table. Default is True.
      • +
    • 'color' - The color of the table. Default is ‘yellow’.
      • +
    • 'alpha' - The transparency of the table. Default is 0.2.
      • +
    • 'fontsize' - The fontsize of the table. Default is 12.
      • +
    • 'text_color' - The color of the text in the table. Default is ‘black’.
      • +
    • 'text_units' - The units of the text in the table. Default is None.
      • +
    • 'control_marker' - The marker for the control group. Default is ‘-’.
      • +
    • 'fontsize_label' - The fontsize of the table x-label. Default is 12.
      • +
    • 'label' - The table x-label.
      • +
    +
    +
    multi_2group = dabest.load(df, idx=(("Control 1", "Test 1"),("Control 2", "Test 2"),("Control 3", "Test 3"),("Test 4", "Test 5")))
+multi_2group.mean_diff.plot(horizontal=True, 
+                            horizontal_table_kwargs={'color': 'red', 
+                                                     'alpha': 0.5, 
+                                                     'text_color': 
+                                                     'white',
+                                                     'text_units':'mm', 
+                                                     'label': 'delta mm',
+                                                     'control_marker': 'o',
+                                                    });
    +
    +
    +
    +

    +
    +
    +
    +
    +

    The table axis can be hidden using the 'show':False in the horizontal_table_kwargs dict.

    +
    +
    multi_2group = dabest.load(df, idx=(("Control 1", "Test 1"),("Control 2", "Test 2"),("Control 3", "Test 3"),("Test 4", "Test 5")))
+multi_2group.mean_diff.plot(horizontal=True, horizontal_table_kwargs={'show': False});
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Gridkey

    +

    As with the vertical plots, you can utilise a gridkey table for representing the groupings. This can be reached via gridkey in the .plot() method.

    +

    You can either use gridkey='auto' to automatically generate the gridkey, or pass a list of indexes to represent the groupings (e.g., gridkey=['Control', 'Test']).

    +

    See the examples in the Plot Aesthetics Tutorial for more information with regards to kwargs.

    +
    +
    multi_2group = dabest.load(df, idx=(("Control 1", "Test 1"),("Control 2", "Test 2"),("Control 3", "Test 3"),("Test 4", "Test 5")))
+multi_2group.mean_diff.plot(horizontal=True, gridkey=['Control', 'Test']);
    +
    +
    +
    +

    +
    +
    +
    +
    + + +
    +
    + +
    + +
    + + + + + \ No newline at end of file diff --git a/tutorials/07-horizontal_plot_files/figure-html/cell-10-output-1.png b/tutorials/07-horizontal_plot_files/figure-html/cell-10-output-1.png new file mode 100644 index 00000000..edbedd0c Binary files /dev/null and b/tutorials/07-horizontal_plot_files/figure-html/cell-10-output-1.png differ diff --git a/tutorials/07-horizontal_plot_files/figure-html/cell-11-output-1.png b/tutorials/07-horizontal_plot_files/figure-html/cell-11-output-1.png new file mode 100644 index 00000000..11286af0 Binary files /dev/null and b/tutorials/07-horizontal_plot_files/figure-html/cell-11-output-1.png differ diff --git a/tutorials/07-horizontal_plot_files/figure-html/cell-12-output-1.png b/tutorials/07-horizontal_plot_files/figure-html/cell-12-output-1.png new file mode 100644 index 00000000..f3238579 Binary files /dev/null and b/tutorials/07-horizontal_plot_files/figure-html/cell-12-output-1.png differ diff --git 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a/tutorials/08-plot_aesthetics.html b/tutorials/08-plot_aesthetics.html new file mode 100644 index 00000000..369a14d3 --- /dev/null +++ b/tutorials/08-plot_aesthetics.html @@ -0,0 +1,1962 @@ + + + + + + + + + + +Controlling Plot Aesthetics – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + +
    + +
    + + +
    + + + +
    + +
    +
    +

    Controlling Plot Aesthetics

    +
    + +
    +
    + A guide to various plot aesthetic changes that can be done. +
    +
    + + +
    + + + + +
    + + + +
    + + + +

    Since v2024.03.29, swarmplots are, by default, plotted asymmetrically to the right side. For detailed information, please refer to Swarm Side.

    +

    Since v2025.03.27, further aesthetic changes were added/updated which include:

    + +
    +

    Load libraries

    +
    +
    import numpy as np
+import pandas as pd
+import dabest
+import seaborn as sns
+
+print("We're using DABEST v{}".format(dabest.__version__))
    +
    +
    Pre-compiling numba functions for DABEST...
    +
    +
    +
    Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 50.20it/s]
    +
    +
    +
    Numba compilation complete!
+We're using DABEST v2025.10.20
    +
    +
    +
    +
    +
    +
    +
    +

    Creating a demo dataset

    +
    +
    from scipy.stats import norm # Used in generation of populations.
+
+np.random.seed(9999) # Fix the seed to ensure reproducibility of results.
+
+Ns = 20 # The number of samples taken from each population
+
+# Create samples
+c1 = norm.rvs(loc=3, scale=0.4, size=Ns)
+c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
+c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
+
+t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)
+t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)
+t3 = norm.rvs(loc=3, scale=0.75, size=Ns)
+t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
+t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
+t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
+
+
+# Add a `gender` column for coloring the data.
+females = np.repeat('Female', Ns/2).tolist()
+males = np.repeat('Male', Ns/2).tolist()
+gender = females + males
+
+# Add an `id` column for paired data plotting.
+id_col = pd.Series(range(1, Ns+1))
+
+# Combine samples and gender into a DataFrame.
+df = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,
+                   'Control 2' : c2,     'Test 2' : t2,
+                   'Control 3' : c3,     'Test 3' : t3,
+                   'Test 4'    : t4,     'Test 5' : t5, 'Test 6' : t6,
+                   'Gender'    : gender, 'ID'  : id_col
+                  })
+
+np.random.seed(9999) # Fix the seed so the results are replicable.
+
+# Create samples
+N = 20
+y = norm.rvs(loc=3, scale=0.4, size=N*4)
+y[N:2*N] = y[N:2*N]+1
+y[2*N:3*N] = y[2*N:3*N]-0.5
+
+# Add a `Treatment` column
+t1 = np.repeat('Placebo', N*2).tolist()
+t2 = np.repeat('Drug', N*2).tolist()
+treatment = t1 + t2 
+
+# Add a `Rep` column as the first variable for the 2 replicates of experiments done
+rep = []
+for i in range(N*2):
+    rep.append('Rep1')
+    rep.append('Rep2')
+
+# Add a `Genotype` column as the second variable
+wt = np.repeat('W', N).tolist()
+mt = np.repeat('M', N).tolist()
+wt2 = np.repeat('W', N).tolist()
+mt2 = np.repeat('M', N).tolist()
+
+genotype = wt + mt + wt2 + mt2
+
+# Add an `id` column for paired data plotting.
+id = list(range(0, N*2))
+id_col = id + id 
+
+# Combine all columns into a DataFrame.
+df_delta2 = pd.DataFrame({'ID'        : id_col,
+                  'Rep'      : rep,
+                   'Genotype'  : genotype, 
+                   'Treatment': treatment,
+                   'Y'         : y
+                })
+
+def create_demo_prop_dataset(seed=9999, N=40):
+    import numpy as np
+    import pandas as pd
+
+    np.random.seed(9999)  # Fix the seed to ensure reproducibility of results.
+    # Create samples
+    n = 1
+    c1 = np.random.binomial(n, 0.2, size=N)
+    c2 = np.random.binomial(n, 0.2, size=N)
+    c3 = np.random.binomial(n, 0.8, size=N)
+
+    t1 = np.random.binomial(n, 0.6, size=N)
+    t2 = np.random.binomial(n, 0.2, size=N)
+    t3 = np.random.binomial(n, 0.3, size=N)
+    t4 = np.random.binomial(n, 0.4, size=N)
+    t5 = np.random.binomial(n, 0.5, size=N)
+    t6 = np.random.binomial(n, 0.6, size=N)
+    t7 = np.ones(N)
+    t8 = np.zeros(N)
+    t9 = np.zeros(N)
+
+    # Add a `gender` column for coloring the data.
+    females = np.repeat('Female', N / 2).tolist()
+    males = np.repeat('Male', N / 2).tolist()
+    gender = females + males
+
+    # Add an `id` column for paired data plotting.
+    id_col = pd.Series(range(1, N + 1))
+
+    # Combine samples and gender into a DataFrame.
+    df = pd.DataFrame({'Control 1': c1, 'Test 1': t1,
+                       'Control 2': c2, 'Test 2': t2,
+                       'Control 3': c3, 'Test 3': t3,
+                       'Test 4': t4, 'Test 5': t5, 'Test 6': t6,
+                       'Test 7': t7, 'Test 8': t8, 'Test 9': t9,
+                       'Gender': gender, 'ID': id_col
+                       })
+
+    return df
+df_prop = create_demo_prop_dataset()
+
+
+two_groups_prop_paired = dabest.load(df_prop, idx=("Control 1", "Test 1"), proportional=True, paired="baseline", id_col="ID")
+two_groups_prop = dabest.load(df_prop, idx=("Control 1", "Test 1"), proportional=True)
+two_groups_unpaired = dabest.load(df, idx=("Control 1", "Test 1"))
+multi_2group = dabest.load(df, idx=(("Control 1", "Test 1"),("Control 2", "Test 2")))
+repeated_measures = dabest.load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3"),paired="baseline", id_col="ID")
+two_groups_paired = dabest.load(df, idx=("Control 1", "Test 1"), paired="baseline", id_col="ID")
+mini_meta_paired = dabest.load(df, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), mini_meta=True, id_col="ID", paired="baseline")
+paired_delta2 = dabest.load(data = df_delta2, 
+                                paired = "baseline", id_col="ID",
+                                x = ["Treatment", "Rep"], y = "Y", 
+                                delta2 = True, experiment = "Genotype")
    +
    +
    +
    +

    Changing the graph colours

    +
    +

    Color categories from another variable

    +

    Use the parameter color_col to specify which column in the dataframe will be used to create the different colours for your graph.

    +
    +
    multi_2group.mean_diff.plot(color_col="Gender");
+
+two_groups_paired.mean_diff.plot(color_col="Gender");
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Custom palette

    +

    The colour palette for the graph can be changed using the parameter custom_palette. Multiple types of color palettes can be used:

    +
      +

    • A list of colors (named colors, hex, rgb, etc) e.g. ['red', 'blue', 'green']

      • +

    • A seaborn color palette e.g. 'Set1'

      • +

    • A matplotlib color map e.g. 'viridis'

      +
        +
      • 'paired' is an interesting option for two-group (or multi two-group) comparisons
        • +
      • +

    • A dictionary with the keys as the column names and the values as the colors e.g. {'Control 1': 'red', 'Test 1': 'blue', 'Test 2': 'green'}

      +
        +
      • Or, a dictionary with the keys as the binary options for proportion plots (barplots and sankey) and the values as the colors e.g. {0: 'red', 1: 'blue'}
        • +
      • +
    +
    +

    A list of colors

    +
    +
    multi_2group.mean_diff.plot(custom_palette=['red', 'blue', 'green', 'purple', 'orange', 'brown']);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Seaborn color palette

    +
    +
    multi_2group.mean_diff.plot(custom_palette=sns.color_palette("husl", 6));
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Matplotlib color map/palette

    +
    +
    multi_2group.mean_diff.plot(custom_palette="viridis");
+multi_2group.mean_diff.plot(custom_palette="Paired");
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    A user-defined dictionary

    +

    There are many ways to specify matplotlib colours. Find one example below using accepted colour names, hex strings (commonly used on the web), and RGB tuples.

    +
    +
    my_color_palette = {"Control 1" : "blue",
+                        "Test 1"    : "purple",
+                        "Control 2" : "#cb4b16",     # This is a hex string.
+                        "Test 2"    : (0., 0.7, 0.2) # This is a RGB tuple.
+                       }
+
+multi_2group.mean_diff.plot(custom_palette=my_color_palette);
    +
    +
    +
    +

    +
    +
    +
    +
    +

    For proportion plots (barplots and sankey), a color palette dict can also be supplied via {1: first_color, 0, second_color} where first_color and second_color are valid matplotlib colours.

    +
    +
    two_groups_prop.mean_diff.plot(custom_palette={1: "red", 0: "blue"});
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    two_groups_prop_paired.mean_diff.plot(custom_palette={1: "red", 0: "blue"});
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Color palette changes also now affect the effect size curve colors in paired plots

    +

    Note: The first color in the custom palette is used for the control group. As in the example below, if show_baseline_ec is set to False, it wont be represented in the plot.

    +
    +
    repeated_measures.mean_diff.plot(custom_palette=["red", "blue", "green", "purple"]);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +
    +
    +

    Color saturation

    +

    By default, dabest.plot() desaturates the colour of the dots in the swarmplot by 50%. This draws attention to the effect size bootstrap curves.

    +

    You can alter the default values with the parameters raw_desat and contrast_desat.

    +
    +
    multi_2group.mean_diff.plot(custom_palette=my_color_palette,
+                                raw_desat=0.75,
+                                contrast_desat=0.25);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Alpha (transparency)

    +

    It is possible change the transparency of the raw data by using the raw_alpha parameter. This can also be achieved by adding alpha to the relevant rawdata kwargs (barplot_kwargs, or swarmplot_kwargs, or slopegraph_kwargs, or sankey_kwargs)

    +
    +
    multi_2group.mean_diff.plot(raw_alpha=0.2);
+
+multi_2group.mean_diff.plot(swarmplot_kwargs={'alpha': 0.2});
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +

    It is also possible change the transparency of the effect size curves by using the contrast_alpha parameter. This can also be achieved via adding alpha to the contrast_kwargs parameter.

    +
    +
    multi_2group.mean_diff.plot(contrast_alpha=0.2);
+
+multi_2group.mean_diff.plot(contrast_kwargs={'alpha':0.2});
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Marker size

    +

    It is possible change the size of the dots used in the rawdata swarmplot, as well as those to indicate the effect sizes, by using the parameters raw_marker_size and contrast_marker_size respectively.

    +
    +
    multi_2group.mean_diff.plot(raw_marker_size=3,
+                                contrast_marker_size=12);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Axes

    +
    +

    Lims

    +

    To change the y-limits for the rawdata axes, and the contrast axes, use the parameters raw_ylim and contrast_ylim.

    +
    +
    multi_2group.mean_diff.plot(raw_ylim=(0, 5),
+                                contrast_ylim=(-2, 2));
    +
    +
    +
    +

    +
    +
    +
    +
    +

    If the effect size is qualitatively inverted (ie. a smaller value is a better outcome), you can simply invert the tuple passed to contrast_ylim.

    +
    +
    multi_2group.mean_diff.plot(contrast_ylim=(2, -2));
    +
    +
    +
    +

    +
    +
    +
    +
    +

    The contrast axes share the same y-limits as those of the delta-delta plot. Thus, the y axis of the delta-delta plot changes as well.

    +
    +
    paired_delta2.mean_diff.plot(contrast_ylim=(3, -3));
    +
    +
    +
    +

    +
    +
    +
    +
    +

    You can also change the y-limit of the delta-delta axes and the regular delta axes via the delta2_ylim parameter.

    +
    +
    paired_delta2.mean_diff.plot(delta2_ylim=(3, -3));
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Labels

    +
      +
    • raw_label - label the raw data y-axis
      • +
    • contrast_label - label the contrast y-axis
      • +
    +
    +
    two_groups_unpaired.mean_diff.plot(raw_label="This is my\nrawdata", 
+                                   contrast_label="The bootstrap\ndistribtions!"
+                                );
    +
    +
    +
    +

    +
    +
    +
    +
    +

    Unique for delta-delta: - delta2_ylim - to label the delta-delta y-axis

    +
    +
    paired_delta2.mean_diff.plot(delta2_label='delta-delta label');
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Axes ticks

    +

    You can add minor ticks and also change the tick frequency by accessing the axes directly.

    +

    Each estimation plot produced by dabest has two axes. The first one contains the rawdata swarmplot while the second one contains the bootstrap effect size differences.

    +
    +
    import matplotlib.ticker as Ticker
+
+f = two_groups_unpaired.mean_diff.plot()
+
+rawswarm_axes = f.axes[0]
+contrast_axes = f.axes[1]
+
+rawswarm_axes.yaxis.set_major_locator(Ticker.MultipleLocator(1))
+rawswarm_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.5))
+
+contrast_axes.yaxis.set_major_locator(Ticker.MultipleLocator(0.5))
+contrast_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.25))
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Add counts to tick labels

    +

    By default, the tick labels include the sample size for each group. This can be switched off via setting show_sample_size=False in the .plot() method.

    +
    +
    two_groups_unpaired.mean_diff.plot(show_sample_size=False
+                                );
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +
    +

    Changing swarm side

    +

    In dabest, swarmplots are, by default, plotted asymmetrically to the right side. You may change this by using the parameter swarm_side.

    +

    There are only three valid values: "right" (default), "left", "center".

    +
    +
    multi_2group.mean_diff.plot(swarm_side="right");
+multi_2group.mean_diff.plot(swarm_side="left");
+multi_2group.mean_diff.plot(swarm_side="center");
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Creating estimation plots in existing axes

    +

    Implemented in v0.2.6 by Adam Nekimken.

    +

    dabest.plot has an ax parameter that accepts Matplotlib Axes. The entire estimation plot will be created in the specified Axes.

    +
    +
    two_groups_paired_baseline = dabest.load(df, idx=("Control 1", "Test 1"),
+                                  paired="baseline", id_col="ID")
+multi_2group_paired = dabest.load(df,
+                            idx=(("Control 1", "Test 1"),
+                                 ("Control 2", "Test 2")),
+                            paired="baseline", id_col="ID")
    +
    +
    +
    from matplotlib import pyplot as plt
+f, axx = plt.subplots(nrows=2, ncols=2,
+                      figsize=(15, 15),
+                      gridspec_kw={'wspace': 0.25} # ensure proper width-wise spacing.
+                     )
+
+two_groups_unpaired.mean_diff.plot(ax=axx.flat[0]);
+
+two_groups_paired_baseline.mean_diff.plot(ax=axx.flat[1]);
+
+multi_2group.mean_diff.plot(ax=axx.flat[2]);
+
+multi_2group_paired.mean_diff.plot(ax=axx.flat[3]);
    +
    +
    +
    +

    +
    +
    +
    +
    +

    In this case, to access the individual rawdata axes, use name_of_axes to manipulate the rawdata axes, and name_of_axes.contrast_axes to gain access to the effect size axes.

    +
    +
    topleft_axes = axx.flat[0]
+topleft_axes.set_ylabel("New y-axis label for rawdata")
+topleft_axes.contrast_axes.set_ylabel("New y-axis label for effect size")
+f
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Legend

    +

    For plots with a color_col specified, a legend will be created. Utilise the legend_kwargs parameter to adjust the legend.

    +
    +
    multi_2group.mean_diff.plot(color_col="Gender", 
+                            legend_kwargs={'bbox_to_anchor': [0, 1], 'fontsize':8});
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Hiding options

    +

    For mini-meta plots, it is possible to hide the weighted average plot by setting the parameter show_mini_meta=False in the .plot() method.

    +
    +
    mini_meta_paired.mean_diff.plot(show_mini_meta=False);
    +
    +
    +
    +

    +
    +
    +
    +
    +

    Similarly, you can hide the delta-delta effect size by setting show_delta2=False in the .plot() method.

    +
    +
    paired_delta2.mean_diff.plot(show_delta2=False);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Effect size error bar and marker

    +

    Modifying the effect size marker can be done via contrast_marker_kwargs. This parameter accepts a dictionary of keyword arguments.

    +

    The available options are:

    +
      +
    • 'marker' - type of the marker
      • +
    • 'markersize' - size of the marker
      • +
    • 'color' - color of the marker
      • +
    • 'alpha' - alpha of the marker (transparency)
      • +
    • 'zorder' - zorder of the marker (the layering relative to other plot elements)
      • +
    +

    Note: markersize can also be modified directly via the contrast_marker_size argument

    +
    +
    two_groups_unpaired.mean_diff.plot(contrast_marker_kwargs={"marker": "x", 'markersize': 15, 'color': 'green', 'alpha':0.8, 'zorder': 5});
    +
    +
    +
    +

    +
    +
    +
    +
    +

    Modifying the appearance of the effect size error bar can be done via the contrast_errorbar_kwargs parameter. This parameter accepts a dictionary of keyword arguments.

    +

    The relevant inputs to contrast_errorbar_kwargs are:

    +
      +
    • 'lw' - width of the error bar
      • +
    • 'linestyle' - line style of the error bar
      • +
    • 'color' - color of the error bar
      • +
    • 'zorder' - zorder of the error bar (the layering relative to other plot elements)
      • +
    • 'alpha' - alpha of the error bar (transparency)
      • +
    +
    +
    two_groups_unpaired.mean_diff.plot(contrast_errorbar_kwargs={'lw': 4, 'color': 'green', 'alpha':0.5, 'zorder': 2, 'linestyle': ':'});
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Group summaries

    +

    Group summaries represent the summary statistics of the sample and are included by default.

    +

    In swarmplots and proportion plots, these are represented by gapped lines.

    +

    In slopegraphs, these are represented by a solid line connecting the group mean/median with error bars.

    +

    The type of group summary can be specified via group_summaries in the .plot() method and must be one of these: 'median_quartiles', 'mean_sd', None.

    +

    By default, the group summary is set to 'mean_sd'.

    +
    +
    two_groups_unpaired.mean_diff.plot(group_summaries="mean_sd");
+two_groups_unpaired.mean_diff.plot(group_summaries="median_quartiles");
+two_groups_unpaired.mean_diff.plot(group_summaries=None);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +

    For slopegraphs, the group summary is represented by a solid line connecting the group mean/median with error bars.

    +
    +
    repeated_measures.mean_diff.plot(group_summaries="mean_sd");
+repeated_measures.mean_diff.plot(group_summaries="median_quartiles");
+repeated_measures.mean_diff.plot(group_summaries=None);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +

    Group summaries have an associated kwargs group_summaries_kwargs

    +

    The relevant inputs to group_summaries_kwargs are:

    +
      +
    • 'zorder' - zorder of the gapped lines (the layering relative to other plot elements)
      • +
    • 'lw' - linewidth of the gapped lines
      • +
    • 'alpha' - alpha of the gapped lines (transparency)
      • +
    • 'gap_width_percent' - gap size (for gapped lines only)
      • +
    • 'offset' - location adjustment of the gapped lines (x-axis; for gapped lines only)
      • +
    • 'color' - the shared color of the gapped lines
      • +
    +
    +
    two_groups_unpaired.mean_diff.plot(group_summaries_kwargs={'gap_width_percent': 3, 'alpha': 0.5, 'lw': 4, 'offset': 0.6, 'color':'red'});
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    repeated_measures.mean_diff.plot(group_summaries_kwargs={'gap_width_percent': 3, 'alpha': 0.5, 'lw': 4, 'offset': 0.6, 'color':'red'});
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Raw bars

    +

    Raw bars are included in swarmplots by default. It can be turned off by setting raw_bars=False in the .plot() method.

    +
    +
    multi_2group.mean_diff.plot(raw_bars=True, contrast_bars=False);
    +
    +
    +
    +

    +
    +
    +
    +
    +

    Raw bar kwargs can be utilised via raw_bars_kwargs in the .plot() method.

    +

    Pass any keyword arguments accepted by matplotlib.patches.Rectangle here, as a string.

    +
    +
    multi_2group.mean_diff.plot(raw_bars=True, contrast_bars=False,
+                            raw_bars_kwargs={'color': "red", 'alpha': 0.2}, 
+                            );
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Contrast bars

    +

    Contrast bars are included in all plots by default. It can be turned off by setting contrast_bars=False in the .plot() method.

    +
    +
    multi_2group.mean_diff.plot(contrast_bars=True, raw_bars=False);
    +
    +
    +
    +

    +
    +
    +
    +
    +

    Contrast bar kwargs can be utilised via contrast_bars_kwargs in the .plot() method.

    +

    Pass any keyword arguments accepted by matplotlib.patches.Rectangle here, as a string.

    +
    +
    multi_2group.mean_diff.plot(contrast_bars=True, raw_bars=False, 
+                            contrast_bars_kwargs={'color': "red", 'alpha': 0.1}
+                            );
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Reference band

    +

    A reference band can be added for each relevant contrast object as desired via supplying a list to the argument reference_band in the .plot() method.

    +
    +
    multi_2group.mean_diff.plot(reference_band=[0, 1], contrast_bars=False, raw_bars=False);
    +
    +
    +
    +

    +
    +
    +
    +
    +

    Reference band kwargs can be utilised via reference_band_kwargs in the .plot() method.

    +

    The relevant inputs to reference_band_kwargs are:

    +
      +
    • 'span_ax' - Whether the reference band(s) should span the entire x-axis or start from the relevant effect size curve
      • +
    • 'color' - Color of the reference band(s). If color is not specified, the color of the effect size curve will be used.
      • +
    • 'alpha' - Alpha of the reference band(s) (transparency)
      • +
    • 'zorder' - Zorder of the reference band(s) (the layering relative to other plot elements)
      • +
    +
    +
    multi_2group.mean_diff.plot(reference_band=[0,1], contrast_bars=False, raw_bars=False,
+                            reference_band_kwargs={"alpha": 0.2, "color": 'black', 'span_ax': True}
+                            );
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Delta text

    +

    Delta text is included in all plots by default. It can be turned off by setting delta_text=False in the .plot() method.

    +
    +
    multi_2group.mean_diff.plot(delta_text=True);
    +
    +
    +
    +

    +
    +
    +
    +
    +

    Delta text kwargs can be utilised via delta_text_kwargs in the .plot() method.

    +

    The relevant inputs to delta_text_kwargs are:

    +
      +
    • 'color' - Color. If color is not specified, the color of the effect size curve will be used.
      • +
    • 'alpha'- Alpha (transparency)
      • +
    • 'fontsize' - Font size
      • +
    • 'ha' - Horizontal alignment
      • +
    • 'va' - Vertical alignment
      • +
    • 'rotation' - Text rotation
      • +
    • 'x_coordinates' - Specify the x-coordinates of the text
      • +
    • 'y_coordinates' - Specify the y-coordinates of the text
      • +
    • 'offset' - Am x-axis coordinate adjuster for minor movement of all text
      • +
    +

    Otherwise, pass any keyword arguments accepted by matplotlib.text.Text, as a string.

    +
    +
    multi_2group.mean_diff.plot(delta_text=True, 
+                            delta_text_kwargs={"color":"red", "rotation":45, "va":"bottom", "alpha":0.7});
    +
    +
    +
    +

    +
    +
    +
    +
    +

    'x_coordinates' and/or 'y_coordinates' if you would like to specify the text locations manually.

    +
    +
    multi_2group.mean_diff.plot(delta_text=True, 
+                            delta_text_kwargs={"x_coordinates":(0.5, 2.75), 
+                                               "y_coordinates":(0.5, -1.7)});
    +
    +
    +
    +

    +
    +
    +
    +
    +

    'offset' to adjust the x location of all the texts (positive moves right, negative left).

    +
    +
    multi_2group.mean_diff.plot(delta_text=True, 
+                            delta_text_kwargs={"offset":0.1});
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Adding jitter to slopegraph plots

    +

    For paired plots, you can add jitter to the slopegraph by adding a value for jitter in the slopegraph_kwargs parameter.

    +

    This can be useful for specific paired plots when there are many overlapping points.

    +

    Currently, jitter is only available for slopegraphs and only in the x-direction (vertical plots) or y-direction (horizontal plots).

    +
    +
    # Jitter tests
+np.random.seed(9999) # Fix the seed to ensure reproducibility of results.
+Ns = 20 # The number of samples taken from each population
+# Create samples
+c1 = [0.5]*Ns + [1.5]*Ns
+c2 = [2]*Ns + [1]*Ns
+t1 = [1]*Ns + [2]*Ns
+t2 = [1.5]*Ns + [2.5]*Ns
+t3 = [2]*Ns + [1]*Ns
+t4 = [1]*Ns + [2]*Ns
+t5 = [1.5]*Ns + [2.5]*Ns
+id_col = pd.Series(range(1, 2*Ns+1))
+df_jittertest= pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,
+                 'Control 2' : c2,     'Test 2' : t2, 'Test 3' : t3,
+                    'Test 4'    : t4,     'Test 5' : t5, 'ID'  : id_col})
    +
    +

    For the example below, there are many overlapping points for the paired plot, which makes it look like only one sample.

    +
    +
    multi_2group = dabest.load(df_jittertest, idx=(("Control 1", "Test 1"),("Control 2", "Test 2", "Test 3", "Test 4", "Test 5")), paired='baseline', id_col='ID')
+multi_2group.mean_diff.plot(horizontal=False, group_summaries=None);
    +
    +
    +
    +

    +
    +
    +
    +
    +

    Adding jitter can help to visualize the data better.

    +
    +
    multi_2group.mean_diff.plot(horizontal=False, slopegraph_kwargs={'jitter': 1}, group_summaries=None);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Gridkey

    +

    You can utilise a gridkey table format for representing the index groupings. This can be reached via gridkey in the .plot() method.

    +

    You can either use gridkey='auto' to automatically generate the gridkey, or pass a list of indexes to represent the groupings (e.g., gridkey=['Control', 'Test']).

    +
    +
    paired_delta2.mean_diff.plot(gridkey='auto');
    +
    +
    +
    +

    +
    +
    +
    +
    +

    Gridkey kwargs can be utilised via gridkey_kwargs in the .plot() method.

    +

    The relevant inputs to gridkey_kwargs are:

    +
      +
    • 'show_es' - Whether to show the effect size in the gridkey
      • +
    • 'show_Ns' - Whether to show the sample sizes in the gridkey
      • +
    • 'merge_pairs' - Whether to merge the pairs in the gridkey (paired data only)
      • +
    • 'delimiters' - Delimiters to use for the autoparser. E.g., [‘;’, ‘>’, ’_’]
      • +
    • 'marker' - Marker to use for filling the gridkey
      • +
    • 'fontsize' - Font size of the gridkey text
      • +
    • 'labels_fontsize' - Font size of the labels in the gridkey
      • +
    +
    +
    multi_2group = dabest.load(df, idx=(("Control 1", "Control 2"), ("Test 1", "Test 2")),paired='baseline', id_col='ID')
+multi_2group.mean_diff.plot(gridkey=['Control', 'Test'], 
+                            gridkey_kwargs={'merge_pairs': True, 'show_es': False, 'show_Ns': False, 'marker': '√',
+                                            'fontsize': 8, 'labels_fontsize': 12});
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Delta dot

    +

    By default, delta dots are included in paired experiment plots (excluding proportion plots).

    +

    This feature can be turned off by setting delta_dot=False in the .plot() method.

    +
    +
    multi_2group_paired.mean_diff.plot(delta_dot=False);
    +
    +
    +
    +

    +
    +
    +
    +
    +

    Delta dot kwargs can be utilised via delta_dot_kwargs in the .plot() method.

    +

    The relevant inputs to delta_dot_kwargs are:

    +
      +
    • 'color' - Specify the color of the delta dots. If color is not specified, the color of the effect size curve will be used.
      • +
    • 'marker' - Marker of the dots. The default are triangles (‘^’)
      • +
    • 'alpha' - Alpha (Transparency)
      • +
    • 'zorder' - Zorder (the layering relative to other plot elements)
      • +
    • 'size' - Marker size
      • +
    • 'side' - Which side to plot the delta dots. The options are 'left', 'right', or 'center'. This functions like the swarm_side parameter.
      • +
    +
    +
    multi_2group_paired.mean_diff.plot(delta_dot_kwargs={"color":'red', "alpha":0.1, 'zorder': 2, 'size': 5, 'side': 'center'});
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Effect size paired lines

    +

    By default, effect size paired lines are included in paired experiment plots (excluding proportion plots).

    +

    This feature can be turned off by setting contrast_paired_lines=False in the .plot() method.

    +
    +
    repeated_measures.mean_diff.plot(contrast_paired_lines=True);
    +
    +
    +
    +

    +
    +
    +
    +
    +

    Effect size line kwargs can be utilised via contrast_paired_lines_kwargs in the .plot() method.

    +

    By default, the following keywords are passed:

    +
      +
    • 'linestyle' - Linestyle
      • +
    • 'linewidth' - Linewidth
      • +
    • 'zorder' - Zorder (the layering relative to other plot elements)
      • +
    • 'color' - Color. Default is ‘dimgray’
      • +
    • 'alpha' - Alpha (transparency)
      • +
    +
    +
    repeated_measures.mean_diff.plot(contrast_paired_lines=True, 
+                                 contrast_paired_lines_kwargs={"color": "red", "alpha": 0.5, "linestyle": "-"});
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Baseline error curve

    +

    In DABEST v2025.03.27, we introduce a new aspect to the contrast axes: the baseline dot and error curve. While the baseline dot is always present, the error curve can be turned on by setting show_baseline_ec=True in the .plot() method.

    +
    +
    repeated_measures.mean_diff.plot(show_baseline_ec=True);
    +
    +
    +
    +

    +
    +
    +
    +
    + + +
    + +
    + +
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@@ + + + + + + + + + + +Forest Plots: Visualizing Multiple Contrasts – dabest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + +
    + +
    + + +
    + + + +
    + +
    +
    +

    Forest Plots: Visualizing Multiple Contrasts

    +
    + +
    +
    + Explanation of how to use forest_plot for contrast objects e.g delta-delta and mini-meta or regular deltas. +
    +
    + + +
    + + + + +
    + + + +
    + + + +

    In DABEST v2025.03.27, we introduce a new function to plot separately calculated effect sizes in the same axes to allow direct visual comparisons.

    +

    Currently you can make a forest plot for delta-delta, mini-meta, or standard delta effect sizes. In addition, for delta-delta and mini-meta experiments, you can also plot the effect sizes of the original comparisons alongside the delta-delta/mini-meta measurement.

    +
    +

    Load libraries

    +
    +
    import numpy as np
+import pandas as pd
+import dabest
+import matplotlib.pyplot as plt
+import dabest 
+print("We're using DABEST v{}".format(dabest.__version__))
    +
    +
    Pre-compiling numba functions for DABEST...
    +
    +
    +
    Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 23.74it/s]
    +
    +
    +
    Numba compilation complete!
+We're using DABEST v2025.10.20
    +
    +
    +
    +
    +
    +
    +
    +

    Delta-delta effects

    +

    First please revisit the notebook Delta-Delta Tutorial for how to generate a delta-delta effect size. We will generate three of them plot them into the same axes. Here we test the efficacy of 3 drugs named Drug1, Drug2 , and Drug3 on a disease-causing mutation M based on disease metric Tumor Size. We want to know how the three drugs fare in ameliorating the phenotype metric Tumor Size.

    + + + + + + + + + + + + + + + + + + + + +
    WildtypeMutant
    Drug1XD1, WXD1, M
    PlaceboXP1, WXP1, M
    + + + + + + + + + + + + + + + + + + + + +
    WildtypeMutant
    Drug2XD2, WXD2, M
    PlaceboXP2, WXP2, M
    + + + + + + + + + + + + + + + + + + + + +
    WildtypeMutant
    Drug3XD3, WXD3, M
    PlaceboXP3, WXP3, M
    +

    In each scenario, there are two Treatment conditions, Placebo (control group) and Drug (test group). There are two Genotype's: W (wild type population) and M (mutant population). Additionally, each experiment was conducted twice (Rep1 and Rep2). We will perform several analyses to visualise these differences in a simulated dataset. We will simulate three separte datasets below.

    +
    +

    Creating a demo dataset

    +
    +
    from scipy.stats import norm
+def create_delta_dataset(N=20, 
+                        seed=9999, 
+                        second_quarter_adjustment=3, 
+                        third_quarter_adjustment=-0.1):
+    np.random.seed(seed)  # Set the seed for reproducibility
+
+    # Create samples
+    y = norm.rvs(loc=3, scale=0.4, size=N*4)
+    y[N:2*N] += second_quarter_adjustment
+    y[2*N:3*N] += third_quarter_adjustment
+
+    # Treatment, Rep, Genotype, and ID columns
+    treatment = np.repeat(['Placebo', 'Drug'], N*2).tolist()
+    rep = ['Rep1', 'Rep2'] * (N*2)
+    genotype = np.repeat(['W', 'M', 'W', 'M'], N).tolist()
+    id_col = list(range(0, N*2)) * 2
+
+    # Combine all columns into a DataFrame
+    df = pd.DataFrame({
+        'ID': id_col,
+        'Rep': rep,
+        'Genotype': genotype,
+        'Treatment': treatment,
+        'Tumor Size': y
+    })
+
+    return df
+
+# Generate the first dataset with a different seed and adjustments
+df_delta2_drug1 = create_delta_dataset(seed=9999, second_quarter_adjustment=1, third_quarter_adjustment=-0.5)
+
+# Generate the second dataset with a different seed and adjustments
+df_delta2_drug2 = create_delta_dataset(seed=9999, second_quarter_adjustment=0.1, third_quarter_adjustment=-1)
+
+# Generate the third dataset with the same seed as the first but different adjustments
+df_delta2_drug3 = create_delta_dataset(seed=9999, second_quarter_adjustment=3, third_quarter_adjustment=-0.1)
    +
    +
    +
    +

    Loading data

    +
    +
    unpaired_delta_01 = dabest.load(data = df_delta2_drug1, 
+                                x = ["Genotype", "Genotype"], 
+                                y = "Tumor Size", delta2 = True, 
+                                experiment = "Treatment")
+unpaired_delta_02 = dabest.load(data = df_delta2_drug2, 
+                                x = ["Genotype", "Genotype"], 
+                                y = "Tumor Size", delta2 = True, 
+                                experiment = "Treatment")
+unpaired_delta_03 = dabest.load(data = df_delta2_drug3, 
+                                x = ["Genotype", "Genotype"], 
+                                y = "Tumor Size", 
+                                delta2 = True, 
+                                experiment = "Treatment")
+contrasts = [unpaired_delta_01, unpaired_delta_02, unpaired_delta_03]
    +
    +
    +
    +

    Generate delta-delta plots for each datasets

    +

    To create a delta-delta plot, you simply need to set delta2=True in the dabest.load() function and mean_diff.plot()

    +

    In this case,x needs to be declared as a list consisting of 2 elements, unlike most cases where it is a single element. The first element in x will represent the variable plotted along the horizontal axis, and the second one will determine the color of dots for scattered plots or the color of lines for slope graphs. We use the experiment input to specify the grouping of the data.

    +
    +
    f1 = unpaired_delta_01.mean_diff.plot(
+    contrast_label='Mean Diff',
+    fig_size = (7, 4),
+    raw_marker_size = 1,
+    contrast_marker_size = 5,
+);
+f1.suptitle('Delta-delta plot for Drug 1');
+
+
+f2 = unpaired_delta_02.mean_diff.plot(                  
+            contrast_label='Mean Diff',
+            fig_size = (7, 4),
+            raw_marker_size = 1,
+            contrast_marker_size = 5,
+);
+f2.suptitle('Delta-delta plot for Drug 2');
+
+
+f3 = unpaired_delta_03.mean_diff.plot(                  
+                    contrast_label='Mean Diff',
+                    fig_size = (7, 4),
+                    raw_marker_size = 1,
+                    contrast_marker_size = 5,
+);
+f3.suptitle('Delta-delta plot for Drug 3');
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Generate a forest plot

    +

    This will allow for comparisons of different Drug effects.

    +

    Key Parameters:

    +
      +

    • data: A list of dabest objects

      • +

    • labels: A list of labels for the dabest objects. E.g., ['Drug1', 'Drug2', 'Drug3']

      • +

    • effect_size: For delta-delta experiments, you can select the effect size metric from "mean_diff", or "hedges_g" / "delta_g". The default is "mean_diff".

      • +

    • ci_type: A string specifying the confidence interval type to use. The options are either bca or pct. Default is bca.

      • +
    +

    Note: “hedges_g” and “delta_g” can be used interchangeably for delta-delta experiments - both plot hedges_g regular effect sizes and our Delta g delta-delta effect size.

    +
      +

    • horizontal: A boolean input (True/ False) to adjust the plot orientation. The default is vertical orientation (False)

      • +

    • ax: Optional argument to specify an existing matplotlib axes (otherwise a standalone figure will be created)

      • +
    +

    See the Controlling aesthetics section for more information on how to alter the aesthetics of the plots.

    +
    +
    f_forest_delta2 = dabest.forest_plot(
+                        data = contrasts, 
+                        labels = ['Drug1', 'Drug2', 'Drug3']
+);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Generate a forest plot with delta effect sizes alongside the delta-delta effect sizes

    +

    If you want to plot the original effect sizes alongside the delta-delta effect sizes, you can do so by utilising the idx parameter. This parameter takes a tuple/list of indices of the original effect sizes you want to plot.

    +

    For example, if you want to plot only the first effect size and the delta-delta effect size for each of the three dabest object supplied, you can do so by setting idx=[[0, 2],[0, 2],[0, 2]].

    +
    +
    f_forest_delta2 = dabest.forest_plot(
+                        data = contrasts, 
+                        labels = ['Drug1 Delta1', 'Drug1 Delta-Delta', 'Drug2 Delta1', 'Drug2 Delta-Delta', 'Drug3 Delta1', 'Drug3 Delta-Delta'],
+                        idx=[[0, 2], [0, 2], [0, 2]]
+);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Selecting normalised effect sizes via hedges_g or delta_g

    +

    Remember, hedges_g and delta_g are interchangeable for delta-delta experiments. However, when plotting the original effect sizes alongside the delta-delta effect sizes, you should note that hedges_g effect sizes will be plotted alongside the Delta g effect sizes.

    +
    +
    f_forest_delta2 = dabest.forest_plot(
+                            data = contrasts, 
+                            labels = ['Drug1', 'Drug2', 'Drug3'],
+                            effect_size='hedges_g');
+f_forest_delta2 = dabest.forest_plot(
+                            data = contrasts, 
+                            labels = ['Drug1', 'Drug2', 'Drug3'],
+                            effect_size='delta_g');
+
+f_forest_delta2 = dabest.forest_plot(
+                            data = contrasts, 
+                            labels = ['Drug1 Delta1', 'Drug1 Delta-Delta', 'Drug2 Delta1', 'Drug2 Delta-Delta', 'Drug3 Delta1', 'Drug3 Delta-Delta'],
+                            effect_size='hedges_g',
+                            idx=[[0, 2], [0, 2], [0, 2]]);
    +
    +
    +
    +

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    Mini-meta effects

    +

    Next we will generate a similar forest plot for mini-meta effect sizes. Please revisit the notebook Mini-Meta Tutorial on how to generate a mini-meta effect size. We will generate three mini-meta effect sizes for three separate mini-meta analyses:

    +

    Note: the only effect size metric currently available for mini-meta is "mean_diff".

    +
    +

    Creating a demo dataset

    +
    +
    def create_mini_meta_dataset(N=20, seed=9999, control_locs=[3, 3.5, 3.25], control_scales=[0.4, 0.75, 0.4], 
+                             test_locs=[3.5, 2.5, 3], test_scales=[0.5, 0.6, 0.75]):
+    np.random.seed(seed)  # Set the seed for reproducibility
+
+    # Create samples for controls and tests
+    controls_tests = []
+    for loc, scale in zip(control_locs + test_locs, control_scales + test_scales):
+        controls_tests.append(norm.rvs(loc=loc, scale=scale, size=N))
+
+    # Add a `Gender` column for coloring the data
+    gender = ['Female'] * (N // 2) + ['Male'] * (N // 2)
+
+    # Add an `ID` column for paired data plotting
+    id_col = list(range(1, N + 1))
+
+    # Combine samples and gender into a DataFrame
+    df_columns = {f'Control {i+1}': controls_tests[i] for i in range(len(control_locs))}
+    df_columns.update({f'Test {i+1}': controls_tests[i + len(control_locs)] for i in range(len(test_locs))})
+    df_columns['Gender'] = gender
+    df_columns['ID'] = id_col
+
+    df = pd.DataFrame(df_columns)
+
+    return df
+
+# Customizable dataset creation with different arguments
+df_mini_meta01 = create_mini_meta_dataset(seed=9999, 
+                                          control_locs=[3, 3.5, 3.25], 
+                                          control_scales=[0.4, 0.75, 0.4], 
+                                          test_locs=[3.5, 2.5, 3], 
+                                          test_scales=[0.5, 0.6, 0.75])
+
+df_mini_meta02 = create_mini_meta_dataset(seed=9999, 
+                                          control_locs=[4, 2, 3.25], 
+                                          control_scales=[0.3, 0.75, 0.45], 
+                                          test_locs=[2, 1.5, 2.75], 
+                                          test_scales=[0.5, 0.6, 0.4])
+
+df_mini_meta03 = create_mini_meta_dataset(seed=9999, 
+                                          control_locs=[6, 5.5, 4.25], 
+                                          control_scales=[0.4, 0.75, 0.45], 
+                                          test_locs=[4.5, 3.5, 3], 
+                                          test_scales=[0.5, 0.6, 0.9])
    +
    +
    +
    +

    Loading data

    +
    +
    contrast_mini_meta01 = dabest.load(data = df_mini_meta01,
+                                   idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), 
+                                   mini_meta=True)
+contrast_mini_meta02 = dabest.load(data = df_mini_meta02,
+                                    idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), 
+                                    mini_meta=True)
+contrast_mini_meta03 = dabest.load(data = df_mini_meta03,
+                                   idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")),
+                                    mini_meta=True)
+contrasts_mini_meta = [contrast_mini_meta01, contrast_mini_meta02, contrast_mini_meta03]
    +
    +
    +
    +

    Generate a forest plot

    +
    +
    f_forest_minimeta = dabest.forest_plot(
+                            data = contrasts_mini_meta, 
+                            labels=['mini_meta1', 'mini_meta2', 'mini_meta3']
+);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Generate a forest plot with delta effect sizes alongside the mini-meta effect sizes

    +

    If you want to plot the original effect sizes alongside the mini-meta effect sizes, you can do so by utilising the idx parameter. This parameter takes a tuple/list of indices of the original effect sizes you want to plot.

    +

    For example, if you want to plot only the first effect size and the mini-meta effect size for each of the three dabest object supplied, you can do so by setting idx=[[0, final_idx],[0, final_idx],[0, final_idx]] (where final_idx is the index of the last contrast object which will be the mini-meta effect size.)

    +
    +
    f_forest_minimeta = dabest.forest_plot(
+                        data = contrasts_mini_meta, 
+                        idx = [[0, 3],[0, 3], [0, 3]],
+                        labels=['Contrast 1A', 'mini_meta1', 'Contrast 2A', 'mini_meta2', 'Contrast 3A', 'mini_meta3']
+);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +
    +

    Delta effects

    +

    Next we will generate a similar forest plot of regular delta effect sizes. In the example below, we will generate three regular mean_diff experiments. Here, we will only plot the effect size between the first group (Test 1 - Control 1) for each of the three dabest object supplied.

    +
    +
    delta1 = dabest.load(data = df_mini_meta01,
+                                   idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")))
+delta2 = dabest.load(data = df_mini_meta02,
+                                    idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")))
+delta3 = dabest.load(data = df_mini_meta03,
+                                   idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")))
+contrasts_deltas = [delta1, delta2, delta3]
    +
    +
    +
    dabest.forest_plot(contrasts_deltas, idx=((0,),(0,), (0,)), 
+            labels=['Drug1 \nTest 1 - Control 1', 'Drug2 \nTest 2 - Control 2', 'Drug3 \nTest 3 - Control 3']);
    +
    +
    +
    +

    +
    +
    +
    +
    +

    Unlike delta-delta and mini-meta experiments, here you can choose between more effect size metrics (where applicable): mean_diff, cohens_d, cohens_h, hedges_g, and cliffs_delta

    +
    +
    dabest.forest_plot(contrasts_deltas, idx=((0,),(0,), (0,)), effect_size = 'cohens_d',
+            labels=['Drug1 \nTest 1 - Control 1', 'Drug2 \nTest 2 - Control 2', 'Drug3 \nTest 3 - Control 3']);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Controlling aesthetics

    +

    The main aesthetic parameters for the forest_plot function are:

    +
      +

    • fig_size: The size of the figure

      • +

    • horizontal: A boolean input (True/ False) to adjust the plot orientation. The default is vertical orientation (False)

      • +

    • custom_palette: A list or dictionary of colors, one for each contrast object. E.g., ['gray', 'blue', 'green'] or {'Drug1':'gray', 'Drug2':'blue', 'Drug3':'green'} or a set of colors from seaborn color palettes.

      • +

    • marker_size: The size of the markers for the effect sizes. The default is 10.

      • +

    • contrast_alpha: Transparency level for violin plots. The default is 0.8.

      • +

    • contrast_desat: Saturation level for violin plots. The default is 1.

      • +

    • labels_rotation: Rotation angle for contrast labels. The default is 45 (for horizontal=False).

      • +

    • labels_fontsize: Font size for contrast labels. The default is 10.

      • +

    • title: The plot title. The default is None.

      • +

    • title_fontsize: Font size for the plot title. The default is 16.

      • +

    • ylabel: The axis label of dependent variable (Y-axis for vertical layout, X-axis for horizontal layout). The default will be given via the effect size selected. (eg., "Mean Difference" for "mean_diff")

      • +

    • ylabel_fontsize: Font size for the axis label (Y-axis for vertical layout, X-axis for horizontal layout). The default is 12.

      • +

    • ylim: Limits for the dependent variable (Y-axis for vertical layout, X-axis for horizontal layout). The default is None.

      • +

    • yticks: Custom ticks (Y-axis for vertical layout, X-axis for horizontal layout) for the plot. The default is None.

      • +

    • yticklabels: Custom tick labels (Y-axis for vertical layout, X-axis for horizontal layout) for the plot. The default is None.

      • +

    • remove_spines: If True, removes plot spines (except the relevant dependent variable spine). The default is True.

      • +

    • violin_kwargs: A dictionary of keyword arguments for the violin plots.

      +
        The default violin_kwargs = {"widths": 0.5, "showextrema": False, "showmedians": False, "vert": not horizontal}
      • +

    • zeroline_kwargs: A dictionary of keyword arguments for the zero line. The default is None.

      +
        The default zeroline_kwargs = {"linewidth": 1, "color": "black"}
      • +

    • marker_kwargs: A dictionary of keyword arguments for the effect size markers. The default is None.

      +
        The default marker_kwargs = {'marker': 'o', 'markersize': 12, 'color': 'black', 'alpha': 1, 'zorder': 2}
      • +

    • errorbar_kwargs: A dictionary of keyword arguments for the effect size error bars. The default is None.

      +
        The default errorbar_kwargs = {'color': 'black', 'lw': 2.5, 'linestyle': '-', 'alpha': 1, 'zorder': 1}
      • +
    +
    +

    Changing layout with horizontal

    +

    Forest plot assumes a vertical layout by default, but you can change it to a horizontal layout by setting horizontal to be True:

    +
    +
    f_forest_minimeta = dabest.forest_plot(
+                        data = contrasts_mini_meta, 
+                        labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],
+                        horizontal=True,)
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Using a custom palette

    +

    You can color the half-violins with custom_palette:

    +
    +
    f_forest_minimeta = dabest.forest_plot(
+                        data = contrasts_mini_meta, 
+                        labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],
+                        custom_palette=['#FF0000', '#00FF00', '#0000FF'],)
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Plotting other effect sizes

    +

    Forest plots can be drawn for effect sizes other than mean_difference, such as hedges_g, by setting effect_size:

    +
    +
    f_forest_hedgesg = dabest.forest_plot(
+                            data = contrasts, 
+                            labels =['Drug1', 'Drug2', 'Drug3'], 
+                            effect_size='hedges_g',
+);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Delta text

    +

    You can add/remove delta text via the delta_text argument. It is on by default.

    +
    +
    f_forest_minimeta = dabest.forest_plot(
+                        data = contrasts_mini_meta, 
+                        labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],
+                        custom_palette=['#FF0000', '#00FF00', '#0000FF'],
+                        delta_text=True)
    +
    +
    +
    +

    +
    +
    +
    +
    +

    You can set a variety of kwargs to customize the delta text via delta_text_kwargs.

    +

    The relevant inputs to delta_text_kwargs are:

    +
      +
    • 'color' - Color. If color is not specified, the color of the effect size curve will be used.
      • +
    • 'alpha'- Alpha (transparency)
      • +
    • 'fontsize' - Font size
      • +
    • 'ha' - Horizontal alignment
      • +
    • 'va' - Vertical alignment
      • +
    • 'rotation' - Text rotation
      • +
    • 'x_coordinates' - Specify the x-coordinates of the text
      • +
    • 'y_coordinates' - Specify the y-coordinates of the text
      • +
    • 'offset' - Am x-axis coordinate adjuster for minor movement of all text
      • +
    +
    +
    f_forest_minimeta = dabest.forest_plot(
+                        data = contrasts_mini_meta, 
+                        labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],
+                        custom_palette=['#FF0000', '#00FF00', '#0000FF'],
+                        delta_text=True,
+                        delta_text_kwargs={'color': 'red', 'offset': 0.1,
+                                           'fontsize': 8, 'rotation': 45,
+                                           'va': 'bottom',
+                                           'x_coordinates': [1.4,2.4,3.4], 
+                                           'y_coordinates': [0,-1.4,-1.6]})
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Contrast bars

    +

    You can add/remove contrast bars via the contrast_bars argument. It is on by default.

    +
    +
    f_forest_minimeta = dabest.forest_plot(
+                        data = contrasts_mini_meta, 
+                        labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],
+                        custom_palette=['#FF0000', '#00FF00', '#0000FF'],
+                        contrast_bars=True,)
    +
    +
    +
    +

    +
    +
    +
    +
    +

    You can set a variety of kwargs to customize the delta text via contrast_bars_kwargs.

    +

    Pass any keyword arguments accepted by matplotlib.patches.Rectangle here, as a string.

    +
    +
    f_forest_minimeta = dabest.forest_plot(
+                        data = contrasts_mini_meta, 
+                        labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],
+                        custom_palette=['#FF0000', '#00FF00', '#0000FF'],
+                        contrast_bars=True,
+                        contrast_bars_kwargs={'color': 'red', 'alpha': 0.4})
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Reference band

    +

    You can add reference bands by supplying a list/tuple to the reference_band argument, indicating the contrast to highlight. None are displayed by default.

    +
    +
    f_forest_minimeta = dabest.forest_plot(
+                        data = contrasts_mini_meta, 
+                        labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],
+                        custom_palette=['#FF0000', '#0000FF', '#00FF00'],
+                        reference_band=[1,])
    +
    +
    +
    +

    +
    +
    +
    +
    +

    You can set a variety of kwargs to customize the reference bands via reference_band_kwargs.

    +

    Pass any keyword arguments accepted by matplotlib.patches.Rectangle here, as a string.

    +

    In addition, the span_ax keyword argument can be used to expand the reference band across the whole plot.

    +
    +
    f_forest_minimeta = dabest.forest_plot(
+                        data = contrasts_mini_meta, 
+                        labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],
+                        custom_palette=['#FF0000', '#0000FF', '#00FF00'],
+                        reference_band=[1,],
+                        reference_band_kwargs={'span_ax': True, 'color': 'grey', 'alpha': 0.2})
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Embedding forest plots into an existing Axes

    +

    You can plot a forest plot into an existing Axes as a subplot by using the with the ax parameter.

    +
    +
    f_forest_drug_profiles, axes  = plt.subplots(2, 2, figsize=[15, 14])
+f_forest_drug_profiles.subplots_adjust(hspace=0.3, wspace=0.3)
+
+for ax, contrast in zip(axes.flatten(), [unpaired_delta_01, unpaired_delta_02, unpaired_delta_03]):
+    contrast.mean_diff.plot(                  
+                    contrast_label='Mean Diff',
+                    raw_marker_size = 1,
+                    contrast_marker_size = 5,
+                    color_col='Genotype',
+                    ax = ax
+    )
+
+dabest.forest_plot(
+    data = contrasts, 
+    labels = ['Drug1', 'Drug2', 'Drug3'], 
+    ax = axes[1,1], 
+    )
+
+for ax, title in zip(axes.flatten(), ['Drug 1', 'Drug 2', 'Drug 3', 'Forest plot']):
+    ax.set_title(title, fontsize = 12)
    +
    +
    +
    +

    +
    +
    +
    +
    + + +
    +
    + +
    + +
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    +
    + + +
    + +
    + + +
    + + + +
    + +
    +
    +

    Whorlmaps: Visualizing Even More Contrasts

    +
    + +
    +
    + Explanation of how to use forest_plot for contrast objects e.g delta-delta and mini-meta or regular deltas. +
    +
    + + +
    + + + + +
    + + + +
    + + + +

    In DABEST v2025.10.20, we introduce a new and more compact way of visualizing bootstrap distributions: - whorlmap

    +
    +

    Load libraries

    +
    +
    import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.stats import norm
+import dabest
+from dabest.multi import combine, whorlmap
    +
    +
    Pre-compiling numba functions for DABEST...
    +
    +
    +
    Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 37.69it/s]
    +
    +
    +
    Numba compilation complete!
    +
    +
    +
    +
    +
    +
    +
    +

    Create a simulated dataset and generate a list of corresponding dabest objects

    +
    +
    def create_delta_dataset(N=50, 
+                        seed=9999, 
+                        second_quarter_adjustment=3, 
+                        third_quarter_adjustment= -0.5,
+                        fourth_quarter_adjustment= -3, 
+                        scale4=1, initial_loc = 10):
+    """Create a sample dataset for delta-delta analysis."""
+    np.random.seed(seed)
+
+    # Create samples
+    y = norm.rvs(loc=initial_loc, scale=0.4, size=N*4)
+    y[N:2*N] = norm.rvs(loc=initial_loc + second_quarter_adjustment, scale= 1, size=N) 
+    y[2*N:3*N] = norm.rvs(loc=initial_loc + third_quarter_adjustment, scale=0.4, size=N)
+    y[3*N:4*N] = norm.rvs(loc=initial_loc + fourth_quarter_adjustment, scale=scale4, size=N)
+
+    # Treatment, Rep, Genotype, and ID columns
+    treatment = np.repeat(['Placebo', 'Drug'], N*2).tolist()
+    genotype = np.repeat(['W', 'M', 'W', 'M'], N).tolist()
+    id_col = list(range(0, N*2)) * 2
+
+    # Combine all columns into a DataFrame
+    df = pd.DataFrame({
+        'ID': id_col,
+        'Genotype': genotype,
+        'Treatment': treatment,
+        'Transcript Level': y
+    })
+    return df
    +
    +
    +
    +

    Working with many many Dabest objects

    +

    Let’s say you have a transcriptomics experiment where you investigate the effects of administering 6 different drugs on oncogene transcripts 1 to 10. You want to find the drug that reduces all the transcripts the most effectively. In a 2x2 experiment, drug is compared to its placebo, so we will be tabulating delta-delta effect sizes. You may simulate the data as follows:

    +
    +
    dabest_objects_2d = [[None for _ in range(8)] for _ in range(6)]
+labels_2d = ["Transcript 1", "Transcript 2", "Transcript 3", "Transcript 4", "Transcript 5", "Transcript 6", "Transcript 7", "Transcript 8"]
+row_labels_2d = ["Drug A", "Drug B", "Drug C", "Drug D", "Drug E", "Drug F"]
+drug_effect_2d = [[.9, 2, 2, .5, 1.2, 1, 3,2, 3, 4], 
+             [0.1, -.3, .1, -0.3, -2, 1.2, 1,.1,-4, 2],
+             [4, 4, 1, 5, 1, 3, 6.5,.5, -1.2, .4],
+             [6, 2, 2, 4, 1.4, -0.5, -.5,1.1, 3, .4],
+             [0.1, -.3, .1, -0.3, -2, 1.2, 1,.1,-4, 2],
+             [-.3, -1, 2, 7, 1, -0.5, 4,1, 2.3, -.4],
+                                ]
+drug_effect_scale_2d = [[5, 10, 1, 5, 1, 2, 1,1, .1, 2], 
+             [7, .2, 8, 3, 1, 4, 7,1, 5, 2],
+             [15, 3, 1, 2, 1, 1, 11,1, 7, 2],
+             [8, .1, 1, 5, 1, 6,1,1, 3, .4],
+             [9, 10, 7, 12, 4, 2,14,10, 9, 20],
+             [4, 3, 1, 4, 1, 4,4,1, 3, 4],
+             ]
+seeds = [1, 1000, 20, 9999, 1000, 5320]
+
+for i in range(len(row_labels_2d)):
+    for j in range(len(labels_2d)):
+        df = create_delta_dataset(seed=seeds[i], 
+                                  fourth_quarter_adjustment=drug_effect_2d[i][j],
+                                  scale4=drug_effect_scale_2d[i][j],
+                                 initial_loc = 20)
+        dabest_objects_2d[i][j] = dabest.load(data=df, 
+                       x=["Genotype", "Genotype"], 
+                       y="Transcript Level", 
+                       delta2=True, 
+                       experiment="Treatment")
    +
    +

    We are going to create a new object called MultiContrast which will contain the array of contrast objects and information about them.

    +
    +
    multi_2d_mean_diff = combine(dabest_objects_2d, labels_2d, row_labels=row_labels_2d, effect_size="mean_diff")
+print("multi_2d_mean_diff is a " + str(multi_2d_mean_diff))
    +
    +
    multi_2d_mean_diff is a MultiContrast(2D: 6x8, effect_size='mean_diff', contrast_type='delta2')
    +
    +
    +

    As we have seen in the previous tutorial, we can visualize these effect sizes with forest plot as follows:

    +
    +
    multi_2d_mean_diff.forest_plot(forest_plot_title = "2D Forest Plot of Mean Difference", forest_plot_kwargs = { 'marker_size': 6});
    +
    +
    +
    +

    +
    +
    +
    +
    +

    This data would require a stack of forest plots to visualize. So instead, we plot a whorlmap for a concise representation and use color to represent the dimension of effect size. For each effect size, the full bootstrap distribution is binned by quantiles and ranked by value, and then each bin is represented by a pixel. All the pixels correponding to the bins of effects are arranged in a spiral in a cell. The redness and the blueness of the cells represent the magnitude of the effects in the positive and negative direction.

    +
    +
    multi_2d_mean_diff.whorlmap(
+    title="Mean Difference Gene Expression Whorlmap",
+    cmap="vlag",
+    chop_tail=2.5,  # Remove 5% extreme values
+    fig_size = (10, 4)
+);
    +
    +
    +
    +

    +
    +
    +
    +
    +

    The resulting graphic is easy to interpret. Drug B and E induces the most broad spectrum reduction. However the data for Drug E seems a little less precise, mixing blue and red colored pixels. We can say Drug B is a surer bet.

    +
    +
    +

    Plotting whorlmaps with standardized effect sizes

    +

    We can also visualize the same array of effects in terms of standardized effect delta g. Let’s plot them together in the same figure by specifying the axes to plot in:

    +
    +
    figure, axes = plt.subplots(1, 2, figsize = (9, 2))
+multi_2d_mean_diff.whorlmap(
+    cmap="vlag",
+    chop_tail=2.5,  # Remove 5% extreme values
+    title="Mean Difference", ax = axes[0]
+);
+
+multi_2d_delta_g = combine(dabest_objects_2d, labels_2d, row_labels=row_labels_2d, effect_size="delta_g")
+print("multi_2d_delta_g is a " + str(multi_2d_delta_g))
+
+multi_2d_delta_g.whorlmap(
+    cmap="vlag",
+    chop_tail=2.5,  # Remove 5% extreme values
+    title="Delta g", ax = axes[1]
+);
    +
    +
    multi_2d_delta_g is a MultiContrast(2D: 6x8, effect_size='delta_g', contrast_type='delta2')
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    MultiContrast object can also handle 1-D dabest object arrays

    +
    +
    multi_1d = combine(dabest_objects_2d[0], labels_2d, row_labels="Drug A", effect_size="mean_diff")
    +
    +

    You can plot a forest plot from this MultiContrast object

    +
    +
    fig_forest = multi_1d.forest_plot(forest_plot_kwargs = {"title":"Forest Plot from Multi Contrast (1D)", "marker_size": 6})
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    1-D whorlmap also works

    +
    +
    multi_1d.whorlmap(
+    n=21,  # Larger spiral size
+    chop_tail=2.5  # Remove 5% extreme values
+)
+# plt.title("Customized whorlmap")
+plt.show()
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    dabest_objects_2d_2group_delta = [[None for _ in range(8)] for _ in range(6)]
+for i in range(len(row_labels_2d)):
+    for j in range(len(labels_2d)):
+        df = create_delta_dataset(seed=seeds[i], 
+                                  fourth_quarter_adjustment=drug_effect_2d[i][j],
+                                  scale4=drug_effect_scale_2d[i][j],
+                                 initial_loc = 20)
+        dabest_objects_2d_2group_delta[i][j] = dabest.load(data=df, 
+                       x="Treatment", 
+                       y="Transcript Level", 
+                       idx = ("Placebo", "Drug"))
+multi_2d_2group_delta_mean_diff = combine(dabest_objects_2d_2group_delta, labels_2d, row_labels=row_labels_2d, effect_size="mean_diff")
+print("multi_2d_mean_diff is a " + str(multi_2d_2group_delta_mean_diff))
    +
    +
    multi_2d_mean_diff is a MultiContrast(2D: 6x8, effect_size='mean_diff', contrast_type='delta')
    +
    +
    +
    +
    multi_2d_2group_delta_mean_diff.whorlmap(
+    title="Mean Difference Treatment Whorlmap",
+    cmap="vlag",
+    chop_tail=2.5,  # Remove 5% extreme values
+    fig_size = (10, 4)
+);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    multi_2d_2group_delta_mean_diff.whorlmap(
+    title="Mean Difference Treatment Whorlmap",
+    cmap="vlag",
+    chop_tail=2.5,  # Remove 5% extreme values
+    fig_size = (10, 4),
+    heatmap_kwargs={'cbar_kws':{'pad':0.17}}
+);
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Heatmap and plot kwargs

    +

    You can customize the whorlmap further by passing in heatmap_kwargs and plot_kwargs.

    +
    +
    multi_2d_2group_delta_mean_diff.whorlmap(
+    heatmap_kwargs={'cbar_kws':{'pad':0.10}, "cmap":'viridis', "vmax":4, "vmin":-4},
+    plot_kwargs={'xlabel':"Genes", 'ylabel':"Drugs", 
+                 "xticklabels":['test1', 'test2', 'test3', 'test4', 'test5', 'test6', 'test7', 'test8'],
+                 "xticklabels_rotation": 90, "xticklabels_ha": 'center',
+                 "yticklabels": ['Drug1', 'Drug2', 'Drug3', 'Drug4', 'Drug5', 'Drug6'],
+                 "yticklabels_rotation": 45, 'yticklabels_ha': "right", 'title': 'My Title!'}
+);
    +
    +
    +
    +

    +
    +
    +
    +
    + + +
    + +
    + +
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    +
    + + +
    + +
    + + +
    + + + +
    + +
    +
    +

    Tutorials

    +
    + + + +
    + + + + +
    + + + +
    + + +

    Click through to any of these tutorials to get started with dabest’s features.

    + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +Title + +Description +
    +Basics + +An end-to-end tutorial on how to use the dabest library. +
    +Two-Group Experiments + +Explanation of how to use dabest for two-group and multi two-group analysis. +
    +Shared Control & Repeated Measures + +Explanation of how to use dabest for shared control and repeated measures analyses. +
    +Proportion Plots + +A guide to plot proportion plots with binary data. +
    +Mini-Meta + +Explanation of how to compute the meta-analyzed weighted effect size using dabest. +
    +Delta-Delta + +Explanation of how to calculate delta-delta using DABEST. +
    +Horizontal Plots + +A guide to plot data in a horizontal format. +
    +Controlling Plot Aesthetics + +A guide to various plot aesthetic changes that can be done. +
    +Forest Plots: Visualizing Multiple Contrasts + +Explanation of how to use forest_plot for contrast objects e.g delta-delta and mini-meta or regular deltas. +
    +Whorlmaps: Visualizing Even More Contrasts + +Explanation of how to use forest_plot for contrast objects e.g delta-delta and mini-meta or regular deltas. +
    +
    No matching items
    +
    + +
    + + + + + \ No newline at end of file