DeepLabCut · n-poulsen · Feb 10, 2025 · Feb 5, 2025 · Feb 7, 2025 · Jan 29, 2025
diff --git a/_config.yml b/_config.yml
@@ -5,7 +5,7 @@ only_build_toc_files: true
 
 sphinx:
   config:
-    autodoc_mock_imports: ["wx"]
+    autodoc_mock_imports: ["wx", "matplotlib", "qtpy", "PySide6", "napari", "shiboken6"]
     mermaid_output_format: raw
   extra_extensions:
     - numpydoc

diff --git a/_toc.yml b/_toc.yml
@@ -46,10 +46,8 @@ parts:
   chapters:
   - file: docs/ModelZoo
   - file: docs/recipes/UsingModelZooPupil
-  - file: docs/recipes/MegaDetectorDLCLive
 - caption: 🧑‍🍳 Cookbook (detailed helper guides)
   chapters:
-  - file: docs/tutorial
   - file: docs/convert_maDLC
   - file: docs/recipes/OtherData
   - file: docs/recipes/io

diff --git a/deeplabcut/create_project/modelzoo.py b/deeplabcut/create_project/modelzoo.py
@@ -13,14 +13,17 @@
 from pathlib import Path
 
 import yaml
-
-import deeplabcut
-from deeplabcut.utils import auxiliaryfunctions
 from dlclibrary.dlcmodelzoo.modelzoo_download import (
     download_huggingface_model,
     MODELOPTIONS,
 )
 
+import deeplabcut
+from deeplabcut import Engine
+from deeplabcut.generate_training_dataset.metadata import TrainingDatasetMetadata, ShuffleMetadata, DataSplit
+from deeplabcut.pose_estimation_pytorch.config import read_config_as_dict
+from deeplabcut.utils import auxiliaryfunctions
+
 Modeloptions = MODELOPTIONS  # backwards compatibility for COLAB NOTEBOOK
 
 
@@ -300,6 +303,9 @@ def create_pretrained_project(
 
         MakeTest_pose_yaml(pose_cfg, keys2save, path_test_config)
 
+        # create fake metadata to read TF Engine in scorer (temporary fix until Pytorch version is implemented)
+        _create_and_save_fake_metadata(cfg)
+
         video_dir = os.path.join(config["project_path"], "videos")
         if analyzevideo == True:
             print("Analyzing video...")
@@ -320,3 +326,28 @@ def create_pretrained_project(
 
     else:
         return "N/A", "N/A"
+
+def _create_and_save_fake_metadata(config_path: str | Path):
+    # First create the metadata object
+    metadata = TrainingDatasetMetadata.create(config_path)
+
+    # Create a new shuffle with TensorFlow engine
+    new_shuffle = ShuffleMetadata(
+        name="project_name-trainset95shuffle1",
+        # This will be overwritten with proper name
+        train_fraction=read_config_as_dict(config_path)["TrainingFraction"][0],
+        index=1,
+        engine=Engine.TF,
+        split=DataSplit(
+            train_indices=(0, 1, 2, 3, 4),  # Example indices
+            test_indices=(5,)  # Example indices
+        )
+    )
+
+    # Add the shuffle to metadata
+    metadata = metadata.add(new_shuffle)
+
+    # Save the metadata
+    metadata.save()
+
+    return metadata
diff --git a/deeplabcut/generate_training_dataset/metadata.py b/deeplabcut/generate_training_dataset/metadata.py
@@ -290,11 +290,15 @@ def create(config: str | Path | dict) -> TrainingDatasetMetadata:
             cfg = config
 
         trainset_path = TrainingDatasetMetadata.path(cfg).parent
-        shuffle_docs = [
-            f
-            for f in trainset_path.iterdir()
-            if re.match(r"Documentation_data-.+shuffle[0-9]+\.pickle", f.name)
-        ]
+        if trainset_path.exists():
+            shuffle_docs = [
+                f
+                for f in trainset_path.iterdir()
+                if re.match(r"Documentation_data-.+shuffle[0-9]+\.pickle", f.name)
+            ]
+        else:
+            trainset_path.mkdir(parents=True)
+            shuffle_docs = []
 
         prefix = cfg["Task"] + cfg["date"]
         shuffles = []

diff --git a/deeplabcut/refine_training_dataset/outlier_frames.py b/deeplabcut/refine_training_dataset/outlier_frames.py
@@ -235,7 +235,7 @@ def extract_outlier_frames(
     outlieralgorithm: str, optional, default="jump".
         String specifying the algorithm used to detect the outliers.
 
-        * ``'Fitting'`` fits a Auto Regressive Integrated Moving Average model to the
+        * ``'fitting'`` fits an Auto Regressive Integrated Moving Average model to the
           data and computes the distance to the estimated data. Larger distances than
           epsilon are then potentially identified as outliers
         * ``'jump'`` identifies larger jumps than 'epsilon' in any body part

diff --git a/deeplabcut/utils/visualization.py b/deeplabcut/utils/visualization.py
@@ -17,6 +17,7 @@
 https://github.com/DeepLabCut/DeepLabCut/blob/master/AUTHORS
 Licensed under GNU Lesser General Public License v3.0
 """
+from __future__ import annotations
 
 import os
 from pathlib import Path

diff --git a/docs/Governance.md b/docs/Governance.md
@@ -1,6 +1,6 @@
 (governance-model)=
 # Governance Model of DeepLabCut
-(adapted from https://napari.org/docs/_sources/developers/GOVERNANCE.md.txt)
+(adapted from https://napari.org/stable/community/governance.html)
 
 ## Abstract
 
@@ -30,7 +30,7 @@ project in concrete ways, such as:
   [GitHub pull request](https://github.com/DeepLabCut/DeepLabCut/pulls);
 - reporting issues on our
   [GitHub issues page](https://github.com/DeepLabCut/DeepLabCut/issues);
-- proposing a change to the documentation (http://docs.deeplabcut.org) via a
+- proposing a change to the [documentation](https://deeplabcut.github.io/DeepLabCut/README.html) via a
   GitHub pull request;
 - discussing the design of the `DeepLabCut` or its tutorials on in existing
   [issues](https://github.com/DeepLabCut/DeepLabCut/issues) and
@@ -43,7 +43,7 @@ among other possibilities. Any community member can become a contributor, and
 all are encouraged to do so. By contributing to the project, community members
 can directly help to shape its future.
 
-Contributors are encouraged to read the [contributing guide](https://github.com/DeepLabCut/DeepLabCut/CONTRIBUTING.md).
+Contributors are encouraged to read the [contributing guide](https://github.com/DeepLabCut/DeepLabCut/blob/main/CONTRIBUTING.md).
 
 ### Core developers
 

diff --git a/docs/MISSION_AND_VALUES.md b/docs/MISSION_AND_VALUES.md
@@ -3,54 +3,74 @@
 
 This document is meant to help guide decisions about the future of `DeepLabCut`, be it in terms of
 whether to accept new functionality, changes to the styling of the code or graphical user interfaces (GUI),
-or whether to take on new dependencies, when to break into other repos, among other things. It serves as a point of reference for core developers actively working on the project, and an introduction for
+or whether to take on new dependencies, when to break into other repos, among other things. It serves as a point of
+reference for core developers actively working on the project, and an introduction for
 newcomers who want to learn a little more about where the project is going and what the team's
-values are. You can also learn more about how the project is managed by looking at our [governance model](governance-model).
+values are. You can also learn more about how the project is managed by looking at our
+[governance model](governance-model).
 
 ## Our founding principles
 
-The founding DeepLabCut team came together around a shared vision for building the first open-source animal pose estimation framework
-that is:
+The founding DeepLabCut team came together around a shared vision for building the first open-source animal pose
+estimation framework that is:
 
 - user defined pose estimation - i.e. species or object agnostic.
 - access to SOTA deep learning models that can be swiftly re-trained for customized applications
 - fast (GPU-powered)
 - scalable (project focused for ease of portability and sharability)
 
 
-As the project has grown we've turned these original principles into the mission statement and set of values that we described below.
+As the project has grown we've turned these original principles into the mission statement and set of values that we
+described below.
 
 ## Our mission
 
-DeepLabCut aims to be **the animal pose software package for Python** and to **provide access to deep learning-based pose estimation for people to use in their daily work** without the need to be able to program in a deep learning framework.
-We hope to accomplish this by:
+DeepLabCut aims to be **the animal pose software package for Python** and to **provide access to deep learning-based
+pose estimation for people to use in their daily work** without the need to be able to program in a deep learning
+framework. We hope to accomplish this by:
 
-- being **easy to use and install**. We are careful in taking on new dependencies, sometimes making them optional, and aim support a fully (Python) packaged installation that works cross-platform.
+- being **easy to use and install**. We are careful in taking on new dependencies, sometimes making them optional, and
+aim support a fully (Python) packaged installation that works cross-platform.
 
-- being **well-documented** with **comprehensive tutorials and examples**. All functions in our API have thorough docstrings clarifying expected inputs and outputs, and we maintain a separate [tutorials and information website](http://deeplabcut.org).
+- being **well-documented** with **comprehensive tutorials and examples**. All functions in our API have thorough
+docstrings clarifying expected inputs and outputs, and we maintain a separate
+[tutorials and information website](http://deeplabcut.org).
 
 - providing **GUI access** to all critical functionality so DeepLabCut can be used by people without coding experience.
 
 - being **interactive** and **highly performant** in order to support large data pipelines.
 
-- providing a **consistent and stable API** to enable plugin developers to build on top of DeepLabCut without their code constantly breaking and to enable advanced users to build out sophisticated Python workflows, if needed.
+- providing a **consistent and stable API** to enable plugin developers to build on top of DeepLabCut without their
+code constantly breaking and to enable advanced users to build out sophisticated Python workflows, if needed.
 
-- **ensuring correctness**. We strive for complete test coverage of both the code and GUI, with all code reviewed by a core developer before being included in the repository.
+- **ensuring correctness**. We strive for complete test coverage of both the code and GUI, with all code reviewed by a
+core developer before being included in the repository.
 
 ## Our values
 
-- We are **inclusive**. We welcome newcomers who are making their first contribution and strive to grow our most dedicated contributors into [core developers](https://github.com/orgs/DeepLabCut/teams/core-developers). We have a [Code of Conduct](https://github.com/DeepLabCut/DeepLabCut/CODE_OF_CONDUCT.md) to make DeepLabCut
+- We are **inclusive**. We welcome newcomers who are making their first contribution and strive to grow our most
+dedicated contributors into [core developers](https://github.com/orgs/DeepLabCut/teams/core-developers).
+We have a [Code of Conduct](https://github.com/DeepLabCut/DeepLabCut/blob/main/CODE_OF_CONDUCT.md) to make DeepLabCut
 a welcoming place for all.
 
-- We are **community-engaged**. We respond to feature requests and proposals on our [issue tracker](https://github.com/DeepLabCut/DeepLabCut/issues).
+- We are **community-engaged**. We respond to feature requests and proposals on our
+- [issue tracker](https://github.com/DeepLabCut/DeepLabCut/issues).
 
-- We serve **scientific applications** primarily, over “consumer or commercial” pose estimation tools. This often means prioritizing core functionality support, and rejecting implementations of “flashy” features that have little scientific value.
+- We serve **scientific applications** primarily, over “consumer or commercial” pose estimation tools. This often means
+prioritizing core functionality support, and rejecting implementations of “flashy” features that have little
+scientific value.
 
-- We are **domain agnostic** within the sciences. Functionality that is highly specific to particular scientific domains belongs in plugins, whereas functionality that cuts across many domains and is likely to be widely used belongs inside DeepLabCut.
+- We are **domain agnostic** within the sciences. Functionality that is highly specific to particular scientific
+domains belongs in plugins, whereas functionality that cuts across many domains and is likely to be widely used belongs
+inside DeepLabCut.
 
-- We value **education and documentation**. All functions should have docstrings, preferably with examples, and major functionality should be explained in our [tutorials](http://deeplabcut.org). Core developers can take an active role in finishing documentation examples.
+- We value **education and documentation**. All functions should have docstrings, preferably with examples, and major
+functionality should be explained in our [tutorials](http://deeplabcut.org). Core developers can take an active role
+in finishing documentation examples.
 
 
 ## Acknowledgements
 
-We share a lot of our mission and values with [`napari`](https://napari.org/docs/developers/MISSION_AND_VALUES.html) and [`scikit-image`](https://scikit-image.org/docs/dev/values.html) and acknowledge the influence of their mission and values statements on this document.
+We share a lot of our mission and values with [`napari`](https://napari.org/stable/community/mission_and_values.html)
+and [`scikit-image`](https://scikit-image.org/docs/stable/about/values.html) and acknowledge the influence of their
+mission and values statements on this document.