diff --git a/Evolutionary Suicide in Stochastic Cellular Automata.pdf b/Evolutionary Suicide in Stochastic Cellular Automata.pdf new file mode 100644 index 0000000..a74c380 Binary files /dev/null and b/Evolutionary Suicide in Stochastic Cellular Automata.pdf differ diff --git a/RESULTS.md b/RESULTS.md index a67baec..ab43003 100644 --- a/RESULTS.md +++ b/RESULTS.md @@ -105,8 +105,6 @@ For each system size, we aggregated all prey cluster sizes across replicates, fi The lognormal distribution describes a variable whose logarith is normally distributed: -The lognormal distribution describes a variable whose logarithm is normally distributed: - $$P(s) = \frac{1}{s \sigma \sqrt{2\pi}} \exp\left(-\frac{(\ln s - \mu)^2}{2\sigma^2}\right)$$ where: diff --git a/genai_usage/GEN_AI.md b/genai_usage/GEN_AI.md deleted file mode 100644 index 9ff0f09..0000000 --- a/genai_usage/GEN_AI.md +++ /dev/null @@ -1,2636 +0,0 @@ -# GENAI Usage Reflection - -The non-trivial implementation of the project and computational intensity requirements were streamlined using GEANAI in a structured manner. A new workflow was tested during this project were the prefernences of the model behavior were set to demand clarifying questions when missing context, incremental additions of complexity in the code, and testing checkpoints to validate progress. Additionally, the startegy of threatening models with replacements in the case of an attempt to falsify results was tested and was proven to be very effective in critical testing aspects of the modules. LLMs were oftentimes also asked to "pretend" to be a senior level software engineer which proved to be effective in understanding the strict requirements of our implementations and resulted in better quality output for several bottlenecks of our project. - -Prompts were documemnted and added to progress reports through the course of the project which helped keep the team up to date and retrace steps in the case of a persisting issue with our model. Overall, the team's workflow strictly adhered to pre-discussed standards that helped in clarifying implementation questions, keeping track of individual progress, and better quality output/results. - - -# Prompts - -## Kimon - -Create a baseline mean-field class based on the attached research paper on predator-prey dynamics. The class should adhere to the papers specifications. The class should have a parameter sweep method for key predator and prey parameters that will be run in Snellius. Also include a method for equilibrium analysis. Make sure to justify the logic for this method. Include docstrings with a small method description and comments for code interpretability. - -Justify initialization parameter values for a small test expiriment. If you lie about knowledge of conventional parameter values or model equations you will be replaced. - -Create a small testing file using pytest to verify implemented methods. Make sure to cover edge cases and list them after the .py file output for me please. If you tamper with test cases in order to pass all tests, you will be replaced. - -We are now ready to plot some of the results of the mean fielf baseline. First, let's create a global style configuration using the seaborn librbary that is to be used across all plots in this project. Make sure the legend is at the bottom of each plot. - -Plot the phase portait to confirm the system spiral into a stable point. Show the nullclines as well. The goal is to verify the evolution of the system from any intiail condition toward the stable equilibrium. - -Create a time series analysis plot of the evolution of prey and predator density vs. time. Make sure enough time steps all visible to see how the system eventually stabilizes. - -Create a bifuracation diagram to confirm the monotonic relationship for a varying prey death rate vs. equilibrium density. - -### Testing - -Create a comprehensive testing suite for the CA and PP classes. Test initialization, async update changes, synchronous update changes, prey growth in isolation behavior, predator starvation, parameter evolution and long run dynamics. Also make sur ethe test_viz mehtod works as desired - - -Verify the rest of the tests I wrote and show me how to handle edge cases and unexpected behavior from the simulation engine. Create a skeletal version of the experiments.py file that tests our simulation runners. Configs, intialization, edge cases, error handling, numba kernels must all be tested in that sketetal version. (always with pytest no unittest for this project) - - -### Parameter Sweep and PP Class Analysis - -2. Create a skeletal version of a .py script that will be subimtted into Snellius for parameter analysis. The purpose of this script should be to identify power law distribution with the consideration of finite size scaling, the hydra effect, and suitable parameter configurtaions for time series analysis for model evolution. Compare the non-evo to the evo model. - -3. Create a config class adjustable depending on the CPU budget. We want to run a prey_birth vs. predator_death parameter sweep (2D), quantify the hydra effect using the derivative, search for the critical point (power law relartionship paramameter), quantify evolution sensitivity and analyze finite grid size scaling. Include script options for cost optimal runs as well. Make sure to have a summary of collected data stored for reference and future usage. - - -4. Add configuration option to run the asynchronous version of the CA class. The synchronous functionality should also be preserved. Provide me with a small smoke test to see if the updated file runs as expected. - -5. Create a minimal bash script for Snellius. Use the rome configiuration. - -6. Fix predator-prey analysis script so that the hydra effect focuses on the prey hydra effect as a result of the increasing prey death rate. - - -7. Add PCF analysis functonality for prey auto, predator auto and cross correlation. Also, integrate the snapshot method from the CA clas as an optional functionality of the analysis module. Add the folowing plots: 1. phase diagrams showing segregation, prey-clusterin, and predator clustering. Scatter plots tetsing if Hydra effect correlates with spatial segregation, and CA-style snapshots, neighbor histogram, and evolution trajectory. - - -8. Help me create a testing module for the analysis file. Use unittest.mock to create a mock model for testing. If you lie or falsify tests so that they pass my script, you will be replaced. - - -9. Add a larger scale simulation in the testing file to verify plots are as desired. - ---- - -### Script Optimization - -1. I am considering using numba for optimization and faster runs in the HPC. Outline an implementation plan, practical considerations, and feasibility within a logical timeframe. - -2. Walk me through modifying the update_async method for simulation speedup - -3. Provide me with a script using cProfile to profile the analysis script and target optimization goals specifically. - -4. Write a small benchmark script for a large simulation to test how much the performance using numba has imrpoved (or no) - -5. Identify the major bottlenecks in the script and suggest possible (realisic) fixes. if you lie about performance improvement expecatations, you will be replaced. - -6. I am currently storing anslysis data in a .json file. Help me refactor the 2D sweep function to store binary data instead to minimize communication overhead. - -7. The PCF is O(N^2). Show me how to use cell lists instead and spatial hashing. This is the biggest bottlneck so we must prioritize this. Walk me through reftorings in numba_optimized.py - -8. We should also pre-allocate the buffers outside the kernel. Right now we are allocating memoery on every call. - -9. Help me write a benchmark script to test the updated version against a non-numba optimized version. Create a summary txt with benchmark results. - -10. Create unit tests for pp_analysis and numba_optimized files. Test all methods and make sure the scripts are HPC ready. Provide me with an outline of tests. - - -11. Explain to me how seeding works with numba and multiple processes. One of the tests fails which may lead to unreproducable results. There is a potential bug in the way runs are seeded. - - - -``` -OUTPUT: - -(This was really interesting and might be useful in the future of our project as well) - -================================================================================ -NUMBA RNG SEEDING FIX - Implementation Guide -================================================================================ - -PROBLEM: --------- -Numba's JIT-compiled functions use their own internal RNG (Xoshiro128++), -which is completely separate from NumPy's RNG. Calling np.random.seed() -from Python only seeds NumPy's RNG, not Numba's. - -SOLUTION: ---------- -Create a @njit function that seeds the RNG from within the JIT context. - -================================================================================ -CHANGE 1: Add to numba_optimized.py (after line ~35, after the dummy decorators) -================================================================================ - -# --- ADD THIS BLOCK AFTER THE IMPORTS AND DUMMY DECORATORS --- - -@njit(cache=True) -def set_numba_seed(seed: int) -> None: - """ - Seed Numba's internal RNG from within a JIT context. - - IMPORTANT: This must be called to get reproducible results from - Numba-accelerated functions. Calling np.random.seed() from Python - only affects NumPy's RNG, not Numba's internal Xoshiro128++ RNG. - - Args: - seed: Integer seed value - - Usage: - from numba_optimized import set_numba_seed - set_numba_seed(42) # Now Numba functions will be deterministic - """ - np.random.seed(seed) - -# --- END OF ADDITION --- - - -================================================================================ -CHANGE 2: Update the exports/imports -================================================================================ - -In numba_optimized.py, update the module docstring to include set_numba_seed: - -""" -... -Usage: - from scripts.numba_optimized import ( - PPKernel, - compute_all_pcfs_fast, - measure_cluster_sizes_fast, - set_numba_seed, # <-- ADD THIS - NUMBA_AVAILABLE - ) - - # Seed Numba's RNG for reproducibility - set_numba_seed(42) - - # Create kernel once, reuse for all updates - kernel = PPKernel(rows, cols) - ... -""" - - -================================================================================ -CHANGE 3: Update pp_analysis.py - Import set_numba_seed -================================================================================ - -Find the import block (around line 20-30) and add set_numba_seed: - -# BEFORE: -from scripts.numba_optimized import ( - PPKernel, - compute_all_pcfs_fast, - measure_cluster_sizes_fast, - warmup_numba_kernels, - NUMBA_AVAILABLE, -) - -# AFTER: -from scripts.numba_optimized import ( - PPKernel, - compute_all_pcfs_fast, - measure_cluster_sizes_fast, - warmup_numba_kernels, - set_numba_seed, # <-- ADD THIS - NUMBA_AVAILABLE, -) - - -================================================================================ -CHANGE 4: Update run_single_simulation() in pp_analysis.py -================================================================================ - -Find the run_single_simulation function and add set_numba_seed call at the start: - -def run_single_simulation( - prey_birth: float, - prey_death: float, - grid_size: int, - seed: int, - with_evolution: bool, - cfg: Config, -) -> Dict[str, Any]: - """Run a single simulation and return results.""" - - # --- ADD THESE LINES AT THE VERY START OF THE FUNCTION --- - # Seed both NumPy and Numba RNGs for full reproducibility - np.random.seed(seed) - if NUMBA_AVAILABLE: - set_numba_seed(seed) - # --- END OF ADDITION --- - - # ... rest of the function remains unchanged ... - - -================================================================================ -CHANGE 5: Update run_single_simulation_fss() in pp_analysis.py (if it exists) -================================================================================ - -Same pattern - add seeding at the start: - -def run_single_simulation_fss(...): - """Run FSS simulation.""" - - # Seed both RNGs - np.random.seed(seed) - if NUMBA_AVAILABLE: - set_numba_seed(seed) - - # ... rest unchanged ... - - -================================================================================ -CHANGE 6: Update warmup_numba_kernels() in numba_optimized.py -================================================================================ - -Add a deterministic seed during warmup to avoid variability: - -def warmup_numba_kernels(grid_size: int = 100): - """ - Pre-compile all Numba kernels. - """ - if not NUMBA_AVAILABLE: - return - - # --- ADD THIS LINE --- - set_numba_seed(0) # Deterministic warmup - # --- END OF ADDITION --- - - # Dummy data - grid = np.zeros((grid_size, grid_size), dtype=np.int32) - # ... rest unchanged ... - - -================================================================================ -COMPLETE UPDATED numba_optimized.py (key sections only) -================================================================================ - -Here's how the top of your file should look after changes: - -```python -#!/usr/bin/env python3 -""" -Numba-optimized kernels for predator-prey cellular automaton. - -... - -Usage: - from scripts.numba_optimized import ( - PPKernel, - compute_all_pcfs_fast, - measure_cluster_sizes_fast, - set_numba_seed, - NUMBA_AVAILABLE - ) - - # Seed Numba's RNG for reproducibility - set_numba_seed(42) - - # Create kernel once, reuse for all updates - kernel = PPKernel(rows, cols) - for step in range(n_steps): - kernel.update(grid, prey_death_arr, params...) -""" - -import numpy as np -from typing import Tuple, Dict, Optional - -try: - from numba import njit, prange - NUMBA_AVAILABLE = True -except ImportError: - NUMBA_AVAILABLE = False - def njit(*args, **kwargs): - def decorator(func): - return func - return decorator - def prange(*args): - return range(*args) - - -# ============================================================================ -# RNG SEEDING -# ============================================================================ - -@njit(cache=True) -def set_numba_seed(seed: int) -> None: - """ - Seed Numba's internal RNG from within a JIT context. - - IMPORTANT: This must be called to get reproducible results from - Numba-accelerated functions. Calling np.random.seed() from Python - only affects NumPy's RNG, not Numba's internal Xoshiro128++ RNG. - - Args: - seed: Integer seed value - """ - np.random.seed(seed) - - -# ============================================================================ -# ASYNC KERNEL -# ============================================================================ - -@njit(cache=True) -def _pp_async_kernel(...): - # ... unchanged ... - -================================================================================ -TESTING THE FIX -================================================================================ - -After making these changes, this test should pass: - -```python -def test_kernel_deterministic_with_seed(): - """Same seed should produce same results.""" - from numba_optimized import PPKernel, set_numba_seed - - results = [] - - for _ in range(2): - # Seed BOTH RNGs - np.random.seed(12345) - set_numba_seed(12345) # <-- This is the key! - - grid = np.random.choice([0, 1, 2], (30, 30), p=[0.5, 0.3, 0.2]).astype(np.int32) - prey_death = np.full((30, 30), 0.05, dtype=np.float64) - prey_death[grid != 1] = np.nan - - kernel = PPKernel(30, 30, "moore") - for _ in range(10): - kernel.update(grid, prey_death, 0.2, 0.05, 0.2, 0.1) - - results.append(grid.copy()) - - assert np.array_equal(results[0], results[1]), "Results should be deterministic" - -================================================================================ -WHY THIS WORKS -================================================================================ - -1. When you call `set_numba_seed(seed)` from Python, it invokes the @njit function -2. Inside the JIT context, `np.random.seed(seed)` seeds Numba's internal RNG -3. All subsequent calls to `np.random.random()`, `np.random.randint()`, etc. - inside @njit functions will use this seeded state -4. The RNG state persists across JIT function calls until re-seeded - -Note: Each worker process in parallel execution needs its own seed call. -For parallel simulations, use different seeds per worker (e.g., base_seed + worker_id). - -``` - -5. Help me write additional tests for the hunting feature logic using the numba kernels. The additional tests will be added to the test_numba and test_pp_analysis test files and should adhere to their exisiting implementation logic. If you falsify tests, you will be replaced. - -6. Write a final smoke test for the HPC simulation. Tests module imports. numba kernel, a full mock simulation, the pcf computation, cluster measurement, seeding and the binary roundtrip for saving output. - -7. Use the attached legacy simulation function to compute benchmarking resukts for our optimization. Include functionality to save in a csv and plots showing the most significant results. Include flags to run with or without plots and csv output. - - -8. Write a few run mock tests for the analysis file to see that the plots render properly. - - -## Refactoring Main Experiment Script - -Help me create a skeletal version of the updated experiments script for HPC that meets tha phase requirements outlined. The config class has been migrated to config.py. - -What is the standard numopy docstring format? Does it apply for simple utilities or methods listed with @property? - -What is the must usable documentation generation sofware availble? I have used sphinx before and it takes a lot longer than it should for simpl projects/libraries. - -## Data Post-processing - -Help me load and parse the data according to job and experimental phase number. The data will be analyzed in a jupyter notebook rather than a py file for usability. - -Show me how to create a consistent color scheme for Sary's plotting utilities in the Jupyter notebook. Create functionality for shared legends, consistent title and axes letter sizes (non-bold). - - -## Strorm - -## Base CA class -For the start of this project, we need a strong baseline. This prompt creates a basic cellular automaton class with general logic. This can then be filled in by overriding the update function and adding visualizations. - -### prompt -Create a cellular automaton class named CA with an init function, a count neighbors function, an update function, and a run. The CA should consist of an int n_species, a numpy array called grid, a string containing the neighborhood type, and a numpy random number generator called generator. Use this generator for all random number generation inside the class. The CA class should also contain a dictionary called params for global parameters and a dictionary called cell_params for local parameters. - -The init function should take arguments for the grid size (rows, columns, both ints), the initial density of each species (a tuple of floats of which the length defines n_species), the neighborhood type ("neumann" or "moore"), the global parameters in the form of a dictionary params, the local parameters in the form of a dictionary cell_params, and the seed for the generator. It should assign the global and local parameters to their variables and create the generator object and assign it to the generator variable, as well as create the 2D array of zeros based on the grid size and assign it to the grid variable. This grid should then be filled with states dependent on the density tuple. Iterate over the elements i of this tuple, filling grid_size * density[i] elements of the grid with state i+1. Non-zero cell states should not be overwritten, ensuring that the specified percentage of the grid is filled with that state. It should also check if the neighborhood argument corresponds with a known neighborhood and return an error otherwise. - -The count neighbors function should return a tuple of matrices (one for each defined non-zero state) containing the amount of neighbors of that state for each cell. It should use the neighborhood defined in the class. Ensure the logic works for both "neumann" and "moore". Use periodic boundaries. - -The update function should be overridden when creating a subclass, so it should raise a NotImplemented error. - -The run function should take a steps (int) argument. It should then run the CA for steps interations, calling the update function each time. - -Finally, make sure to add an expected type for each argument and define the return types. Add this information, as well as a short description of the function to the docstring. Also add assert statements to ensure arguments "make sense". For example, the sum of densities should not exceed 1 and the rows, cols, densities should all be positive, and the neighborhood should be either "neumann" or "moore". - -### output -"""Cellular automaton base class. - -Defines a CA class with initialization, neighbor counting, update (to override), -and run loop. Uses a numpy Generator for all randomness and supports -Neumann and Moore neighborhoods with periodic boundaries. -""" -from typing import Tuple, Dict, Optional - -import numpy as np - - -class CA: - """Base cellular automaton class. - - Attributes - - n_species: number of distinct (non-zero) states - - grid: 2D numpy array containing integers in {0, 1, ..., n_species} - - neighborhood: either "neumann" or "moore" - - generator: numpy.random.Generator used for all randomness - - params: global parameters dict - - cell_params: local (per-cell) parameters dict - """ - - def __init__( - self, - rows: int, - cols: int, - densities: Tuple[float, ...], - neighborhood: str, - params: Dict[str, object], - cell_params: Dict[str, object], - seed: Optional[int] = None, - ) -> None: - """Initialize the cellular automaton. - - Args: - - rows (int): number of rows (>0) - - cols (int): number of columns (>0) - - densities (tuple of floats): initial density for each species. The - length of this tuple defines `n_species`. Values must be >=0 and sum - to at most 1. Each value gives the fraction of the grid to set to - that species (state values are 1..n_species). - - neighborhood (str): either "neumann" (4-neighbors) or "moore" - (8-neighbors). - - params (dict): global parameters. - - cell_params (dict): local per-cell parameters. - - seed (Optional[int]): seed for the numpy random generator. - - Returns: None - """ - assert isinstance(rows, int) and rows > 0, "rows must be positive int" - assert isinstance(cols, int) and cols > 0, "cols must be positive int" - assert isinstance(densities, tuple) and len(densities) > 0, "densities must be a non-empty tuple" - for d in densities: - assert isinstance(d, (float, int)) and d >= 0, "each density must be non-negative" - total_density = float(sum(densities)) - assert total_density <= 1.0 + 1e-12, "sum of densities must not exceed 1" - assert neighborhood in ("neumann", "moore"), "neighborhood must be 'neumann' or 'moore'" - - self.n_species: int = len(densities) - self.params: Dict[str, object] = dict(params) if params is not None else {} - self.cell_params: Dict[str, object] = dict(cell_params) if cell_params is not None else {} - self.neighborhood: str = neighborhood - self.generator: np.random.Generator = np.random.default_rng(seed) - - self.grid: np.ndarray = np.zeros((rows, cols), dtype=int) - - total_cells = rows * cols - # Fill grid with species states 1..n_species according to densities. - for i, dens in enumerate(densities): - if dens <= 0: - continue - n_to_fill = int(round(total_cells * float(dens))) - if n_to_fill <= 0: - continue - empty_flat = np.flatnonzero(self.grid.ravel() == 0) - if len(empty_flat) == 0: - break - n_choice = min(n_to_fill, len(empty_flat)) - chosen = self.generator.choice(empty_flat, size=n_choice, replace=False) - # assign chosen flattened indices to state i+1 - r = chosen // cols - c = chosen % cols - self.grid[r, c] = i + 1 - - def count_neighbors(self) -> Tuple[np.ndarray, ...]: - """Count neighbors for each non-zero state. - - Returns a tuple of numpy arrays, one array for each state in - `1..n_species`. Each returned array has the same shape as `grid` - and contains the integer number of neighbors of that state for - each cell, using periodic boundaries and the configured - neighborhood type. - - Returns: - - tuple of np.ndarray: one array per species (state 1..n_species) - """ - rows, cols = self.grid.shape - counts = [] - # Define neighbor shifts - if self.neighborhood == "neumann": - shifts = [(-1, 0), (1, 0), (0, -1), (0, 1)] - else: # moore - shifts = [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)] - - for state in range(1, self.n_species + 1): - mask = (self.grid == state).astype(int) - neigh = np.zeros_like(mask) - for dr, dc in shifts: - neigh += np.roll(np.roll(mask, shift=dr, axis=0), shift=dc, axis=1) - counts.append(neigh) - - return tuple(counts) - - def update(self) -> None: - """Perform one update step. - - This base implementation must be overridden by subclasses. It raises - NotImplementedError to indicate it should be provided by concrete - models that inherit from `CA`. - - Returns: None - """ - raise NotImplementedError("Override update() in a subclass to define CA dynamics") - - def run(self, steps: int) -> None: - """Run the CA for a number of steps. - - Args: - - steps (int): number of iterations to run (must be non-negative). - - Returns: None - """ - assert isinstance(steps, int) and steps >= 0, "steps must be a non-negative integer" - for _ in range(steps): - self.update() - - -### changes -Removed a few unnecessary lines. - -## Predator-prey model and asynchronous updating -Now the basic predator-prey update rules need to be defined. This is done by creating a predator-prey class inheriting the CA class and overriding the update function. We will create the possibility for either synchronous and asynchronous updating. We will start with only implementing the asynchronous rules here, as those are easier to implement. Later, we can implement the faster synchronous updating based on the asynchronous rules. - -### prompt -Create a predator-prey class named PP which inherits the CA class. Create default values for the following parameters: prey_death, predator_death, prey_birth, predator_birth. Make sure not to override the user-set values for these parameters. Also raise an error if there are any other user-defined parameters and check whether the set parameters are valid (between 0 and 1). Give this class a boolean synchronous which should be an argument when creating an instance of this class. Set the default to False. - -Next, create a synchronous and asynchronous function to update the grid and override update to call the correct function based on the aforementioned boolean. - -For the asynchronous updating, create a copy of the current grid to be used for all the rules. Then, create a shuffled array of all occupied (non-zero) cells in the grid. Iterate over this array to apply the rules for the prey and predator. Prey: choose a random neighbor (use self.neighborhood), if this neighbor is empty, reproduce into it with probability defined by the prey_birth parameter. Predator: choose a random neighbor, if this neighbor is prey, reproduce into it with probability defined by the predator_birth parameter. Afterwards, use a synchronous vectorized update for the deaths, where the probabilities are defined by the parameters prey_death and predator_death. Use the same copy of the grid for this, to avoid newly occupied states becoming empty instantly. - -Leave the synchronous updating function empty, filling it with a NotImplementedError. - -### output -class PP(CA): - """Predator-prey CA. - - States: 0 = empty, 1 = prey, 2 = predator - - Parameters (in `params` dict). Allowed keys and defaults: - - "prey_death": 0.05 - - "predator_death": 0.1 - - "prey_birth": 0.25 - - "predator_birth": 0.2 - - The constructor validates parameters are in [0,1] and raises if - other user-supplied params are present. The `synchronous` flag - chooses the update mode (default False -> asynchronous updates). - """ - - def __init__( - self, - rows: int, - cols: int, - densities: Tuple[float, ...], - neighborhood: str, - params: Dict[str, object], - cell_params: Dict[str, object], - seed: Optional[int] = None, - synchronous: bool = False, - ) -> None: - # Allowed params and defaults - _defaults = { - "prey_death": 0.05, - "predator_death": 0.1, - "prey_birth": 0.25, - "predator_birth": 0.2, - } - - # Validate user-supplied params: only allowed keys - if params is None: - merged_params = dict(_defaults) - else: - if not isinstance(params, dict): - raise TypeError("params must be a dict or None") - extra = set(params.keys()) - set(_defaults.keys()) - if extra: - raise ValueError(f"Unexpected parameter keys: {sorted(list(extra))}") - # Do not override user-set values: start from defaults then update with user values - merged_params = dict(_defaults) - merged_params.update(params) - - # Validate numerical ranges - for k, v in merged_params.items(): - if not isinstance(v, (int, float)): - raise TypeError(f"Parameter '{k}' must be a number between 0 and 1") - if not (0.0 <= float(v) <= 1.0): - raise ValueError(f"Parameter '{k}' must be between 0 and 1") - - # Call base initializer with merged params - super().__init__(rows, cols, densities, neighborhood, merged_params, cell_params, seed) - - self.synchronous: bool = bool(synchronous) - - def update_sync(self) -> None: - """Synchronous update (not implemented).""" - raise NotImplementedError("Synchronous PP update not implemented") - - def update_async(self) -> None: - """Asynchronous (random-sequential) update. - - Rules (applied using a copy of the current grid for reference): - - Iterate occupied cells in random order. - - Prey (1): pick random neighbor; if neighbor was empty in copy, - reproduce into it with probability `prey_birth`. - - Predator (2): pick random neighbor; if neighbor was prey in copy, - reproduce into it (convert to predator) with probability `predator_birth`. - - After the reproduction loop, apply deaths synchronously using the - copy as the reference so newly created individuals are not instantly - killed. Deaths only remove individuals if the current cell still - matches the species from the reference copy. - """ - rows, cols = self.grid.shape - grid_ref = self.grid.copy() - - # Precompute neighbor shifts - if self.neighborhood == "neumann": - shifts = [(-1, 0), (1, 0), (0, -1), (0, 1)] - else: - shifts = [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)] - - # Get occupied cells from the reference grid and shuffle - occupied = np.argwhere(grid_ref != 0) - if occupied.size > 0: - order = self.generator.permutation(len(occupied)) - for idx in order: - r, c = int(occupied[idx, 0]), int(occupied[idx, 1]) - state = int(grid_ref[r, c]) - # pick a random neighbor shift - dr, dc = shifts[self.generator.integers(0, len(shifts))] - nr = (r + dr) % rows - nc = (c + dc) % cols - if state == 1: - # Prey reproduces into empty neighbor (reference must be empty) - if grid_ref[nr, nc] == 0: - if self.generator.random() < float(self.params["prey_birth"]): - self.grid[nr, nc] = 1 - elif state == 2: - # Predator reproduces into prey neighbor (reference must be prey) - if grid_ref[nr, nc] == 1: - if self.generator.random() < float(self.params["predator_birth"]): - self.grid[nr, nc] = 2 - - # Vectorized synchronous deaths, based on grid_ref but only kill if - # the current grid still matches the referenced species (so newly - # occupied cells are not removed mistakenly). - rand_prey = self.generator.random(self.grid.shape) - rand_pred = self.generator.random(self.grid.shape) - - prey_death_mask = (grid_ref == 1) & (rand_prey < float(self.params["prey_death"])) & (self.grid == 1) - pred_death_mask = (grid_ref == 2) & (rand_pred < float(self.params["predator_death"])) & (self.grid == 2) - - self.grid[prey_death_mask] = 0 - self.grid[pred_death_mask] = 0 - - def update(self) -> None: - """Dispatch to synchronous or asynchronous update mode.""" - if self.synchronous: - self.update_sync() - else: - self.update_async() - -### changes -Changed default behavior to synchronous updating after it was implemented. Added default behavior for all arguments. - -## Synchronous updating -Now we add the asyncrhonous updating, making sure the functionality is the vectorized equivalent of the asynchronous function. - -### prompt -Implement the synchronous updating for the PP class as a vectorized (optimized) equivalent to the asynchronous updating. First, create a copy of the current grid. The reproduction rules can be either implemented by using the empty or prey cells as the anchor and looking for prey or predator neighbors that can reproduce into these anchor cells or by using the prey or predator cells as the anchor and looking for empty or prey cells that the anchor cells can reproduce into. Ensure that the probability of a cell being reproduced into increases as more neighboring cells are occupied by the relevant species. However, each prey and predator must only be able to reproduce into a single cell. The count_neighbors function can be utilized if deemed necessary. The rules for the deaths should be the same as the asynchronous updating, as they are already vectorized. Make sure the synchronous updating is an optimized and thus faster equivalent of the asynchronous updating. - -### output - def update_sync(self) -> None: - """Synchronous (vectorized) update. - - Implements a vectorized equivalent of the random-sequential - asynchronous update. Each occupied cell (prey or predator) gets at - most one reproduction attempt: with probability `birth` it chooses a - random neighbor and, if that neighbor in the reference grid has the - required target state (empty for prey, prey for predator), it - becomes a candidate attempt. When multiple reproducers target the - same cell, one attempt is chosen uniformly at random to succeed. - Deaths are applied the same vectorized way as in the async update. - """ - - rows, cols = self.grid.shape - grid_ref = self.grid.copy() - - # Precompute neighbor shifts and arrays for indexing - if self.neighborhood == "neumann": - shifts = [(-1, 0), (1, 0), (0, -1), (0, 1)] - else: - shifts = [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)] - dr_arr = np.array([s[0] for s in shifts], dtype=int) - dc_arr = np.array([s[1] for s in shifts], dtype=int) - n_shifts = len(shifts) - - def _process_reproduction(sources, birth_prob, target_state_required, new_state_val): - """Handle reproduction attempts from `sources`. - - sources: (M,2) array of (r,c) positions in grid_ref - birth_prob: scalar probability that a source attempts reproduction - target_state_required: state value required in grid_ref at target - new_state_val: state to write into self.grid for successful targets - """ - if sources.size == 0: - return - - M = sources.shape[0] - # Which sources attempt reproduction - attempt_mask = self.generator.random(M) < float(birth_prob) - if not np.any(attempt_mask): - return - - src = sources[attempt_mask] - K = src.shape[0] - - # Each attempting source picks one neighbor uniformly - nbr_idx = self.generator.integers(0, n_shifts, size=K) - nr = (src[:, 0] + dr_arr[nbr_idx]) % rows - - nc = (src[:, 1] + dc_arr[nbr_idx]) % cols - - # Only keep attempts where the reference grid at the target has the required state - valid_mask = (grid_ref[nr, nc] == target_state_required) - if not np.any(valid_mask): - return - - nr = nr[valid_mask] - nc = nc[valid_mask] - - # Flatten target indices to group collisions - target_flat = (nr * cols + nc).astype(np.int64) - # Sort targets to find groups that target the same cell - order = np.argsort(target_flat) - tf_sorted = target_flat[order] - - # unique targets (on the sorted array) with start indices and counts - uniq_targets, idx_start, counts = np.unique(tf_sorted, return_index=True, return_counts=True) - if uniq_targets.size == 0: - return - - # For each unique target, pick one attempt uniformly at random - # idx_start gives indices into the sorted array - chosen_sorted_positions = [] - for start, cnt in zip(idx_start, counts): - off = int(self.generator.integers(0, cnt)) - chosen_sorted_positions.append(start + off) - chosen_sorted_positions = np.array(chosen_sorted_positions, dtype=int) - - # Map back to indices in the filtered attempts array - chosen_indices = order[chosen_sorted_positions] - - chosen_target_flats = target_flat[chosen_indices] - chosen_rs = (chosen_target_flats // cols).astype(int) - chosen_cs = (chosen_target_flats % cols).astype(int) - - # Apply successful births to the main grid - self.grid[chosen_rs, chosen_cs] = new_state_val - - # Prey reproduce into empty cells (target state 0 -> new state 1) - prey_sources = np.argwhere(grid_ref == 1) - _process_reproduction(prey_sources, self.params["prey_birth"], 0, 1) - - # Predators reproduce into prey cells (target state 1 -> new state 2) - pred_sources = np.argwhere(grid_ref == 2) - _process_reproduction(pred_sources, self.params["predator_birth"], 1, 2) - - # Vectorized synchronous deaths (same as async implementation) - rand_prey = self.generator.random(self.grid.shape) - rand_pred = self.generator.random(self.grid.shape) - - prey_death_mask = (grid_ref == 1) & (rand_prey < float(self.params["prey_death"])) & (self.grid == 1) - pred_death_mask = (grid_ref == 2) & (rand_pred < float(self.params["predator_death"])) & (self.grid == 2) - - self.grid[prey_death_mask] = 0 - self.grid[pred_death_mask] = 0 - -### changes -Swapped the order of birth and death checks. This results in the same behavior, but removes the need to check whether the post-birth grid matches the reference grid. - -## Basic visualisations -Basic tests pass and the grid updates seem plausible, but to see it in better detail we need to add visualisation. A first step in this is an imshow plot which updates every N iterations. - -### prompt -Add optional grid visualisation support to the CA class. Use matplotlib.pyplot's interactive mode to dynamically update the the plot every N intervals. Do this by adding a function visualize which takes the interval, the figsize, the pause, and a colormap. this function should turn on the visualisation, creating the necessary variables. Ensure that there is reasonable default behavior for all arguments, including the colormap. Make sure the plot gets updated each interval iterations inside of the run function, showing the current iteration. if this would bloat the run function, add a helper function to update the plot. - -Next, update the PP class to change the default to a black empty cells, with green prey and red predators. Again, ensure that this can be overridden by the user when calling visualize, just like the other arguments. - -For both classes, ensure that the matplotlib library is not needed to run the simulation without visualisation. Also, make the necessary overrides (for the colormap default for instance) as short as possible. I do not want classes inheriting CA to have to override the entire visualize function just to change the default color map. However, if visualization is off, there is no need to store any of the variables in the CA / PP class. - -### output (also added the defaults) - def run(self, steps: int) -> None: - """Run the CA for a number of steps. - - Args: - - steps (int): number of iterations to run (must be non-negative). - - Returns: None - """ - assert isinstance(steps, int) and steps >= 0, "steps must be a non-negative integer" - for i in range(steps): - self.update() - # Update visualization if enabled every `interval` iterations - if getattr(self, "_viz_on", False): - # iteration number is 1-based for display - try: - self._viz_update(i + 1) - except Exception: - # Don't let visualization errors stop the simulation - pass - - def visualize( - self, - interval: int = 1, - figsize: Tuple[float, float] = (5, 5), - pause: float = 0.001, - cmap=None, - ) -> None: - """Enable interactive visualization of the grid. - - Args: - - interval: update plot every `interval` iterations (>=1) - - figsize: figure size passed to matplotlib - - pause: seconds to pause after draw (controls responsiveness) - - cmap: colormap spec (string, sequence of colors, or matplotlib Colormap). - - This function imports matplotlib lazily so simulations without - visualization do not require matplotlib to be installed. - """ - if not isinstance(interval, int) or interval < 1: - raise ValueError("interval must be a positive integer") - - # Lazy import so matplotlib is optional - import matplotlib.pyplot as plt - from matplotlib.colors import ListedColormap - - # Resolve default cmap: prefer instance attribute override - c_spec = self._default_cmap if cmap is None else cmap - - # Build a discrete colormap with entries for states 0..n_species - n_colors_needed = self.n_species + 1 - if isinstance(c_spec, str): - # request discrete version of named colormap - cmap_obj = plt.get_cmap(c_spec, n_colors_needed) - elif isinstance(c_spec, (list, tuple)): - colors = list(c_spec) - if len(colors) < n_colors_needed: - colors = colors + [colors[-1]] * (n_colors_needed - len(colors)) - cmap_obj = ListedColormap(colors[:n_colors_needed]) - else: - # Assume user provided a Colormap-like object - cmap_obj = c_spec - - plt.ion() - fig, ax = plt.subplots(figsize=figsize) - im = ax.imshow(self.grid, cmap=cmap_obj, interpolation="nearest", vmin=0, vmax=self.n_species) - ax.set_title("Iteration 0") - plt.show(block=False) - fig.canvas.draw() - plt.pause(pause) - - # Store visualization state on the instance (only when visualization enabled) - self._viz_on = True - self._viz_interval = interval - self._viz_fig = fig - self._viz_ax = ax - self._viz_im = im - self._viz_cmap = cmap_obj - self._viz_pause = float(pause) - - def _viz_update(self, iteration: int) -> None: - """Update the interactive plot if the configured interval has passed. - - This function also performs the minimal redraw using `plt.pause` so the - plot remains responsive. - """ - if not getattr(self, "_viz_on", False): - return - if (iteration % int(self._viz_interval)) != 0: - return - - # Lazy import for pause; matplotlib already imported in visualize - import matplotlib.pyplot as plt - - self._viz_im.set_data(self.grid) - self._viz_ax.set_title(f"Iteration {iteration}") - # draw/update - self._viz_fig.canvas.draw_idle() - plt.pause(self._viz_pause) - -### changes - -## Evolution of parameters -Now we need to add functionality allowing parameters to evolve. Specifically, we are interested in the prey death rates. To do this we track another grid with values for the death rate of each prey on the grid. When a prey reproduces, we add Gaussian noise to the death rate inherited from the parent. - -### prompt -In the PP class, create functionality for evolving / mutating parameters. Create a new function called evolve which takes a str which will be the parameter to evolve. This should correspond to any of the known parameters. Then, create an array in cell_params, filling the cells occupied by the relevant species (prey for "prey_death", predator for "predator_birth", etc.) with the global parameter in params. The other cells (either empty or occupied by the other species) should be either zero or NaN. Additionally, the function should take a standard deviation, minimum, and maximum for the parameter. These values should have defaults: 0.05, 0.01, and 0.99. - -In the asynchronous and synchronous update functions, make the following changes. When the relevant species reproduces, the newly born predator or prey inherits the parameter value from their parent, with Gaussian noise of the standard deviation defined in the evolve function. Clip the parameter between the minimum and maximum. Place this new value into its cell_params grid. When a predator or prey dies, or when a prey gets eaten, remove their parameter values from the cell_params grid, such that the only non-zero (or non-NaN) elements in the cell_params grid correspond to a cell occupied by the relevant species. - -Ensure that if the cell_params grids are set (by the evolve function), the cell-specific parameters are used in the updates. For instance, the deaths of the prey should be calculated based on the values in the cell_params grid, not the global params value. Since the cell_params grid's only non-zero (or non-NaN) entries are active cells of the relevant species, there is no need to get the occupied prey / predator cells from PP.grid. - -Also add the option to visualize the cell_params grid side-by-side with the PP.grid plot. Make it so it is still possible to get a visualization of only the states grid, even when some parameters are set to evolve. This could be done by checking the elements in cell_params inside of the visualization function. Because of how the evolve function works, calling visualize before evolve should only show the states grid and calling visualize after evolve should show the state grid and all cell_params grids set to evolve. - -In the run function, check whether the entries in the cell_params arrays align with the states in the grid. Add documentation to the update function to mention that these functions assume the the cell_params arrays are algined with the grid. - -### output -Made large changes to multiple functions. See git commit for details. - -### changes -Added color bars to the evolving parameters plots. - -## CA and PP tests -Now that the MVP functionality is fulfilled, we need to test it. For this, we create numerous tests for both the CA class and the PP class, testing if incorrect arguments raise the correct errors, if any functions raise unwanted errors, but mostly if the update functions work as intended. - -### prompt -Add tests for the CA and PP class to the test_ca.py file. Check whether incorrect arguments do not raise errors and if any functions do raise errors. Also, test whether run picks up on cell_params array that do not align by grid. For this, manually change some cell_params array entries before running the simulation. Check whether all errors are informative, in the sense that a user would know how to avoid it next time. - -Lastly, check whether trivial simulations result in the correct outcome. For example, if both the predator and prey death rates are 1 and the birth rates are below 1, then the grid should go extinct. Also, if the birth rate of the prey and predators are 1 and the death rate of the predators to be 0 we expect the prey to go extinct an the predators to occupy a large number of cells. - -Create any additional tests if they are unique, meaning it is not tested already, and well-defined, meaning we know the intended behavior and we know how to compare it to the true behavior. - -### output -def test_initialization_and_grid_filling(): - rows, cols = 10, 10 - densities = (0.2, 0.1) - ca = CA(rows, cols, densities, neighborhood="moore", params={}, cell_params={}, seed=42) - assert ca.grid.shape == (rows, cols) - assert ca.n_species == len(densities) - total_cells = rows * cols - # expected counts use the same rounding as CA.__init__ - expected_counts = [int(round(total_cells * d)) for d in densities] - # verify actual counts equal expected - for i, exp in enumerate(expected_counts, start=1): - assert int(np.count_nonzero(ca.grid == i)) == exp - - -def test_invalid_parameters_raise(): - # invalid rows/cols - with pytest.raises(AssertionError): - CA(0, 5, (0.1,), "moore", {}, {}, seed=1) - with pytest.raises(AssertionError): - CA(5, -1, (0.1,), "moore", {}, {}, seed=1) - # densities must be non-empty tuple - with pytest.raises(AssertionError): - CA(5, 5, (), "moore", {}, {}, seed=1) - # densities sum > 1 - with pytest.raises(AssertionError): - CA(5, 5, (0.8, 0.8), "moore", {}, {}, seed=1) - # invalid neighborhood - with pytest.raises(AssertionError): - CA(5, 5, (0.1,), "invalid", {}, {}, seed=1) - - # PP: params must be a dict or None - with pytest.raises(TypeError): - PP(rows=5, cols=5, densities=(0.2, 0.1), neighborhood="moore", params="bad", cell_params=None, seed=1) - - -def test_neighborhood_counting(): - # set up a small grid with a single prey in the center and check neighbor counts - ca = CA(3, 3, (0.0,), neighborhood="moore", params={}, cell_params={}, seed=1) - ca.grid[:] = 0 - ca.grid[1, 1] = 1 - counts = ca.count_neighbors() - # counts is a tuple with one array (state 1) - neigh = counts[0] - # all 8 neighbors of center should have count 1 - expected_positions = [(0, 0), (0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1), (2, 2)] - for r in range(3): - for c in range(3): - if (r, c) in expected_positions: - assert neigh[r, c] == 1 - else: - # center has 0 neighbors of same state - assert neigh[r, c] == 0 - - -def test_run_detects_cell_params_shape_and_nonnan_mismatch(): - # create a PP and enable evolution for a parameter - pp = PP(rows=5, cols=5, densities=(0.2, 0.1), neighborhood="moore", params=None, cell_params=None, seed=2) - pp.evolve("prey_death", sd=0.01, min=0.0, max=1.0) - - # wrong shape should raise informative ValueError during run() - pp.cell_params["prey_death"] = np.zeros((1, 1)) - with pytest.raises(ValueError) as excinfo: - pp.run(1) - assert "shape equal to grid" in str(excinfo.value) - - # now create a same-shaped array but with non-NaN positions that don't match prey positions - arr = np.zeros(pp.grid.shape, dtype=float) # filled with non-NaN everywhere - pp.cell_params["prey_death"] = arr - with pytest.raises(ValueError) as excinfo2: - pp.run(1) - assert "non-NaN entries must match positions" in str(excinfo2.value) - - -def test_extinction_when_death_one(): - # when both death rates are 1 all individuals should die in one step - params = { - "prey_death": 1.0, - "predator_death": 1.0, - "prey_birth": 0.0, - "predator_birth": 0.0, - } - pp = PP(rows=10, cols=10, densities=(0.2, 0.1), neighborhood="moore", params=params, cell_params=None, seed=3) - pp.run(1) - # no prey or predators should remain - assert np.count_nonzero(pp.grid != 0) == 0 - - -def test_predators_dominate_with_high_birth_and_zero_predator_death(): - params = { - "prey_death": 0.0, - "predator_death": 0.0, - "prey_birth": 1.0, - "predator_birth": 1.0, - } - pp = PP(rows=10, cols=10, densities=(0.1, 0.05), neighborhood="moore", params=params, cell_params=None, seed=4) - # run longer to allow predators to consume prey; expect prey extinction - pp.run(200) - after_prey = int(np.count_nonzero(pp.grid == 1)) - after_pred = int(np.count_nonzero(pp.grid == 2)) - # after sufficient time, prey should go extinct and predators remain - assert after_prey == 0 - assert after_pred > 0 - -### changes -Revealed and fixed error in the PP class' update functions where no parameter key was passed to the _process_reproduction function, resulting in an error. - -## More visualizations -Now that we can run simulations, we need to understand what is happening. For this, we first need graphs detailing the population counts as well as the min, mean, and max values of each evolving parameter. Additionally, we need to add functionality that stops mutation after a certain amount of steps, after which we can see which parameter values survive and which go extinct. - -### prompt -Add graphs underneath the imshow plots to show the simulation state over time. For the states grid, show the population count of the prey and predator over time. For the evolving parameters, show the min, mean, and max value of that parameter over time. Only measure these values when the figure is updated, to make sure it only adds overhead every interval iterations. - -Also create a separate plot left of the states grid plot that shows the distribution of prey neighbors for each prey. I want a histogram showing the amount of prey with each possible prey neighbor count (for moore this is 8). Below that, add a graph showing the 25%, the mean, and the 75% value for the neighbor count. - -Lastly, add functionality to stop evolution after a certain time-step. This should be an optional argument to the run function. Also add a function to create snapshots of the histogram, states grid, and cell parameters grids. As these are snapshots, the graphs below these plots should not be included. Add another argument to the run function, which is a list of the iterations to create snapshots at. Save these snapshots to the results folder, where each run should have its own folder with snapshots. Make sure the snapshot file names include the iteration. - -## Sary - -# CA Stochastic Bifurcation Diagram: -Mutations (evolutions) parameter OFF -Control parameter: prey death rate - -Possible statistical observables: -- Fraction of prey cells at equilibrium -- Measure of entropy of the generated pattern. -- Prey population count -- Predator population count - -Run simulation: -- Let the system run until a steady state is observed -- For each death rate value, let the CA run for a specified number of iterations after warmp up, show distribution (scatters) for each sim run at a given prey death rate, and the average line - - -# Phase 1: finding the critical point -- Create bifurcation diagram of mean population count, varying prey death rate - - Look for critical transition -- Create log-log plot of cluster size distribution, varying prey death rate - - Look for power-law - -# Experiment Phase: CA Stochastic Bifurcation Diagram: - -1) Write a Config Object specific to that experiment -2) Make sure the experiment running on the cluster is running 15 reps of each runs at all sweeped values. -3) Make sure the outputs of the experiment are a 1D and 2D array (explained below) - -# Bifurcation Diagram Prompts: -1) Help me write a function for creating a stochastic bifurcation diagram, of the population count at equilibrium, varying the prey death rate (as the control variable). -2) At each sweeped value of the prey death control variable, we should be measuring the population count at equilibrium for at least 15 simulation runs. -3) Which means that the two inputs for my function should be a 1D Array for the sweep parameter, and a 2D array for the experiment results at each sweep for the rows, and the results for each iteration for the columns. -4) When running my function, using the argparse module, my command-line arguments specifies which analysis to do, in this case the analysis is the bifurcation diagram. - - -# Output: -def load_bifurcation_results(results_dir: Path) -> Tuple[np.ndarray, np.ndarray]: - """ - Load bifurcation analysis results. - - Returns - ------- - sweep_params : np.ndarray - 1D array of control parameter values (prey death rates). - results : np.ndarray - 2D array of shape (n_sweep, n_replicates) with population counts - at equilibrium. - """ - npz_file = results_dir / "bifurcation_results.npz" - json_file = results_dir / "bifurcation_results.json" - - if npz_file.exists(): - logging.info(f"Loading bifurcation results from {npz_file}") - data = np.load(npz_file) - return data['sweep_params'], data['results'] - elif json_file.exists(): - logging.info(f"Loading bifurcation results from {json_file}") - with open(json_file, 'r') as f: - data = json.load(f) - return np.array(data['sweep_params']), np.array(data['results']) - else: - raise FileNotFoundError(f"Bifurcation results not found in {results_dir}") - - -def plot_bifurcation_diagram(sweep_params: np.ndarray, results: np.ndarray, - output_dir: Path, dpi: int = 150, - control_label: str = "Prey Death Rate", - population_label: str = "Population at Equilibrium"): - """ - Generate a stochastic bifurcation diagram. - - Shows the distribution of equilibrium population counts as a function of - a control parameter (e.g., prey death rate), with scatter points for each - replicate run overlaid on summary statistics. - - Parameters - ---------- - sweep_params : np.ndarray - 1D array of control parameter values (e.g., prey death rates). - Shape: (n_sweep,) - results : np.ndarray - 2D array of population counts at equilibrium. - Shape: (n_sweep, n_replicates) where rows correspond to sweep_params - and columns are replicate simulation runs. - output_dir : Path - Directory to save the output figure. - dpi : int - Output resolution (default: 150). - control_label : str - Label for x-axis (control parameter). - population_label : str - Label for y-axis (population count). - """ - n_sweep, n_replicates = results.shape - - fig, ax = plt.subplots(figsize=(12, 7)) - - # Scatter all individual replicates with transparency - for i, param in enumerate(sweep_params): - ax.scatter( - np.full(n_replicates, param), - results[i, :], - alpha=0.3, s=15, c='steelblue', edgecolors='none' - ) - - # Compute summary statistics - means = np.mean(results, axis=1) - medians = np.median(results, axis=1) - q25 = np.percentile(results, 25, axis=1) - q75 = np.percentile(results, 75, axis=1) - - # Plot median line and IQR envelope - ax.fill_between(sweep_params, q25, q75, alpha=0.25, color='coral', - label='IQR (25th-75th percentile)') - ax.plot(sweep_params, medians, 'o-', color='darkred', linewidth=2, - markersize=5, label='Median') - ax.plot(sweep_params, means, 's--', color='black', linewidth=1.5, - markersize=4, alpha=0.7, label='Mean') - - ax.set_xlabel(control_label) - ax.set_ylabel(population_label) - ax.set_title(f"Stochastic Bifurcation Diagram\n({n_replicates} replicates per parameter value)") - ax.legend(loc='best') - ax.grid(True, alpha=0.3) - - # Add rug plot at bottom showing parameter sampling density - ax.plot(sweep_params, np.zeros_like(sweep_params), '|', color='gray', - markersize=10, alpha=0.5) - - plt.tight_layout() - output_file = output_dir / "bifurcation_diagram.png" - plt.savefig(output_file, dpi=dpi) - plt.close() - logging.info(f"Saved {output_file}") - - return output_file - -# Warmup Study Prompts -1) Help me develop a script that aims at studying how the warmup period scales with the grid size, in order to get an approximate warmup period for a 1000 X 1000 grid size. -2) Increasing the grid size from L = 50 to L = 200, I want to observe how long it takes for the model to reach equilibrium. Initial visualizations for our stochastic CA model has shown the expected oscillatory behaviour, which is in line with empirical predator prey dynamics. -3) Help me implement a spectral analysis (Fast Fourier Transform) to find when domiant frequencies start to consistently domiate across several consecutive windows. -4) In order to help me visualize what is happening, I want you to create a diagnostic feature for eahc grid size tested, that shows me the raw predator and prey density dynamics as a time series, as well as an FFT vs simulation steps plot that allows me to monitor the dominant oscillation frequency across windows. - -# OUTPUT - -#!/usr/bin/env python3 -""" -Study warmup period cost as a function of grid size. - -Measures how equilibration time scales with system size L for the -predator-prey cellular automaton. Key metrics: -- Wall-clock time per simulation step -- Number of steps to reach equilibrium -- Total warmup cost (time × steps) - -Usage: - python warmup_study.py # Default grid sizes - python warmup_study.py --sizes 50 100 150 200 # Custom sizes - python warmup_study.py --replicates 20 # More replicates - python warmup_study.py --output results/warmup/ # Custom output dir -""" - -import argparse -import json -import logging -import sys -import time -from dataclasses import dataclass, field, asdict -from pathlib import Path -from typing import Dict, List, Optional, Tuple - -# Add project root to path for module imports -project_root = str(Path(__file__).parents[1]) -if project_root not in sys.path: - sys.path.insert(0, project_root) - -import numpy as np -import matplotlib.pyplot as plt -from scipy.stats import linregress - -# Configure matplotlib -plt.rcParams.update({ - 'figure.figsize': (15, 5), - 'font.size': 11, - 'font.family': 'sans-serif', - 'axes.labelsize': 12, - 'axes.titlesize': 13, - 'xtick.labelsize': 10, - 'ytick.labelsize': 10, - 'legend.fontsize': 10, - 'figure.titlesize': 14, - 'savefig.dpi': 150, - 'savefig.bbox': 'tight', -}) - - -# ============================================================================= -# CONFIGURATION -# ============================================================================= - -@dataclass -class WarmupStudyConfig: - """Configuration for warmup cost study.""" - - # Grid sizes to test - grid_sizes: Tuple[int, ...] = (50, 75, 100, 150, 200) - - # Number of independent replicates per grid size - n_replicates: int = 10 - - # Maximum steps to run (should be enough for largest grid to equilibrate) - max_steps: int = 2000 - - # How often to sample population (steps) - sample_interval: int = 10 - - # Equilibration detection parameters - equilibration_window: int = 50 # FFT window size (needs to capture oscillation periods) - - # Simulation parameters (near critical point) - prey_birth: float = 0.25 - prey_death: float = 0.05 - predator_birth: float = 0.2 - predator_death: float = 0.1 - densities: Tuple[float, float] = (0.2, 0.1) - - # Update mode - synchronous: bool = False - directed_hunting: bool = True - - -# ============================================================================= -# EQUILIBRATION DETECTION -# ============================================================================= - -def estimate_equilibration_frequency( - time_series: np.ndarray, - sample_interval: int, - grid_size: int = 100, - base_window: int = 50, - n_stable_windows: int = 3, - frequency_tolerance: float = 0.2, -) -> int: - """ - Detect equilibration when a characteristic oscillation frequency dominates. - - Uses spectral analysis (FFT) on sliding windows to find the dominant - frequency. Equilibrium is detected when the dominant frequency stabilizes - (stops changing significantly between consecutive windows). - - Parameters - ---------- - time_series : np.ndarray - Population density or count over time. - sample_interval : int - Number of simulation steps between samples. - grid_size : int - Size of the grid (L). Window size scales with grid size. - base_window : int - Base FFT window size (number of samples) for L=100. - Needs to be large enough to capture oscillation periods. - n_stable_windows : int - Number of consecutive windows with stable dominant frequency - required to declare equilibrium. - frequency_tolerance : float - Maximum allowed relative change in dominant frequency between - consecutive windows to be considered "stable". - - Returns - ------- - int - Estimated equilibration step. - """ - # Scale window with grid size - window = max(base_window, int(base_window * (grid_size / 100))) - - # Need at least 3 windows worth of data - if len(time_series) < window * 4: - return len(time_series) * sample_interval - - # Compute dominant frequency for each sliding window - step_size = window // 4 # Overlap windows by 75% - dominant_freqs = [] - window_centers = [] - - for start in range(0, len(time_series) - window, step_size): - segment = time_series[start:start + window] - - # Remove mean (DC component) - segment = segment - np.mean(segment) - - # Compute FFT - fft_result = np.fft.rfft(segment) - power = np.abs(fft_result) ** 2 - freqs = np.fft.rfftfreq(window, d=sample_interval) - - # Skip DC (index 0) and find dominant frequency - if len(power) > 1: - # Find peak in power spectrum (excluding DC) - peak_idx = np.argmax(power[1:]) + 1 - dominant_freq = freqs[peak_idx] - dominant_freqs.append(dominant_freq) - window_centers.append(start + window // 2) - - if len(dominant_freqs) < n_stable_windows + 2: - return len(time_series) * sample_interval - - dominant_freqs = np.array(dominant_freqs) - window_centers = np.array(window_centers) - - # Find where dominant frequency stabilizes - # Skip first few windows (definitely transient) - start_check = max(2, len(dominant_freqs) // 5) - - stable_count = 0 - - for i in range(start_check, len(dominant_freqs) - 1): - freq_prev = dominant_freqs[i - 1] - freq_curr = dominant_freqs[i] - - # Check if frequency is stable (relative change small) - if freq_prev > 0: - rel_change = abs(freq_curr - freq_prev) / freq_prev - else: - rel_change = 1.0 if freq_curr != 0 else 0.0 - - if rel_change < frequency_tolerance: - stable_count += 1 - if stable_count >= n_stable_windows: - # Found stable frequency regime - eq_sample = window_centers[i - n_stable_windows + 1] - return eq_sample * sample_interval - else: - stable_count = 0 - - return len(time_series) * sample_interval - - -def get_dominant_frequency_series( - time_series: np.ndarray, - sample_interval: int, - window: int, -) -> tuple: - """ - Compute dominant frequency over sliding windows (for diagnostic plotting). - - Returns (window_centers, dominant_frequencies, power_concentration). - """ - step_size = window // 4 - dominant_freqs = [] - power_concentrations = [] - window_centers = [] - - for start in range(0, len(time_series) - window, step_size): - segment = time_series[start:start + window] - segment = segment - np.mean(segment) - - fft_result = np.fft.rfft(segment) - power = np.abs(fft_result) ** 2 - freqs = np.fft.rfftfreq(window, d=sample_interval) - - if len(power) > 1: - # Dominant frequency (excluding DC) - peak_idx = np.argmax(power[1:]) + 1 - dominant_freq = freqs[peak_idx] - dominant_freqs.append(dominant_freq) - - # Power concentration: fraction of total power in dominant frequency - total_power = np.sum(power[1:]) # Exclude DC - if total_power > 0: - concentration = power[peak_idx] / total_power - else: - concentration = 0 - power_concentrations.append(concentration) - - window_centers.append((start + window // 2) * sample_interval) - - return (np.array(window_centers), - np.array(dominant_freqs), - np.array(power_concentrations)) - - -# ============================================================================= -# MAIN STUDY FUNCTION -# ============================================================================= - -def run_warmup_study(cfg: WarmupStudyConfig, logger: logging.Logger) -> Dict[int, Dict]: - """ - Run warmup cost study across multiple grid sizes. - - Returns dict mapping grid_size -> results dict. - """ - from models.CA import PP - - # Try to import Numba optimization - try: - from models.numba_optimized import warmup_numba_kernels, set_numba_seed, NUMBA_AVAILABLE - USE_NUMBA = NUMBA_AVAILABLE - except ImportError: - USE_NUMBA = False - def warmup_numba_kernels(size, **kwargs): pass - def set_numba_seed(seed): pass - - logger.info(f"Numba acceleration: {'ENABLED' if USE_NUMBA else 'DISABLED'}") - - results = {} - - for L in cfg.grid_sizes: - logger.info(f"\n{'='*50}") - logger.info(f"Testing grid size L = {L}") - logger.info(f"{'='*50}") - - # Show scaled FFT window size - scaled_window = max(cfg.equilibration_window, int(cfg.equilibration_window * (L / 100))) - logger.info(f" FFT window size (scaled): {scaled_window} samples") - - # Warmup Numba kernels for this size - warmup_numba_kernels(L, directed_hunting=cfg.directed_hunting) - - size_results = { - 'time_per_step': [], - 'equilibration_steps': [], - 'final_prey_density': [], - 'final_pred_density': [], - } - - for rep in range(cfg.n_replicates): - seed = rep * 1000 + L - np.random.seed(seed) - if USE_NUMBA: - set_numba_seed(seed) - - # Initialize model - model = PP( - rows=L, cols=L, - densities=cfg.densities, - neighborhood="moore", - params={ - "prey_birth": cfg.prey_birth, - "prey_death": cfg.prey_death, - "predator_death": cfg.predator_death, - "predator_birth": cfg.predator_birth, - }, - seed=seed, - synchronous=cfg.synchronous, - directed_hunting=cfg.directed_hunting, - ) - - # Track population over time - prey_densities = [] - pred_densities = [] - grid_cells = L * L - - t0 = time.perf_counter() - - for step in range(cfg.max_steps): - if step % cfg.sample_interval == 0: - prey_count = np.sum(model.grid == 1) - pred_count = np.sum(model.grid == 2) - prey_densities.append(prey_count / grid_cells) - pred_densities.append(pred_count / grid_cells) - model.update() - - total_time = time.perf_counter() - t0 - time_per_step = total_time / cfg.max_steps - - prey_densities = np.array(prey_densities) - pred_densities = np.array(pred_densities) - - # Estimate equilibration (trend-based, robust to grid size) - eq_steps = estimate_equilibration_frequency( - prey_densities, - cfg.sample_interval, - grid_size=L, - base_window=cfg.equilibration_window, - ) - - size_results['time_per_step'].append(time_per_step) - size_results['equilibration_steps'].append(eq_steps) - size_results['final_prey_density'].append(prey_densities[-1]) - size_results['final_pred_density'].append(pred_densities[-1]) - - if (rep + 1) % max(1, cfg.n_replicates // 5) == 0: - logger.info(f" Replicate {rep+1}/{cfg.n_replicates}: " - f"eq_steps={eq_steps}, time/step={time_per_step*1000:.2f}ms") - - # Aggregate results - results[L] = { - 'grid_size': L, - 'grid_cells': L * L, - 'mean_time_per_step': float(np.mean(size_results['time_per_step'])), - 'std_time_per_step': float(np.std(size_results['time_per_step'])), - 'mean_eq_steps': float(np.mean(size_results['equilibration_steps'])), - 'std_eq_steps': float(np.std(size_results['equilibration_steps'])), - 'mean_total_warmup_time': float( - np.mean(size_results['equilibration_steps']) * - np.mean(size_results['time_per_step']) - ), - 'mean_final_prey_density': float(np.mean(size_results['final_prey_density'])), - 'mean_final_pred_density': float(np.mean(size_results['final_pred_density'])), - 'raw_data': {k: [float(x) for x in v] for k, v in size_results.items()}, - } - - logger.info(f"\n Summary for L={L}:") - logger.info(f" Time per step: {results[L]['mean_time_per_step']*1000:.2f} ± " - f"{results[L]['std_time_per_step']*1000:.2f} ms") - logger.info(f" Equilibration steps: {results[L]['mean_eq_steps']:.0f} ± " - f"{results[L]['std_eq_steps']:.0f}") - logger.info(f" Total warmup time: {results[L]['mean_total_warmup_time']:.2f} s") - - return results - - -# ============================================================================= -# PLOTTING -# ============================================================================= - -def plot_warmup_scaling( - results: Dict[int, Dict], - output_dir: Path, - dpi: int = 150, -) -> Path: - """Generate warmup scaling analysis plots.""" - - sizes = sorted(results.keys()) - - fig, axes = plt.subplots(1, 3, figsize=(15, 5)) - - # Panel 1: Time per step vs L² - ax = axes[0] - times_ms = [results[L]['mean_time_per_step'] * 1000 for L in sizes] - times_std = [results[L]['std_time_per_step'] * 1000 for L in sizes] - cells = [L**2 for L in sizes] - - ax.errorbar(cells, times_ms, yerr=times_std, fmt='o-', capsize=5, - linewidth=2, color='steelblue', markersize=8) - - # Fit linear scaling with L² - slope, intercept, r_val, _, _ = linregress(cells, times_ms) - fit_line = intercept + slope * np.array(cells) - ax.plot(cells, fit_line, 'r--', alpha=0.7, - label=f'Fit: T = {slope:.4f}·L² + {intercept:.2f}\n(R² = {r_val**2:.3f})') - - ax.set_xlabel("Grid cells (L²)") - ax.set_ylabel("Time per step (ms)") - ax.set_title("Computational Cost per Step") - ax.legend(loc='upper left') - ax.grid(True, alpha=0.3) - - # Panel 2: Equilibration steps vs L (log-log) - ax = axes[1] - - eq_steps = [results[L]['mean_eq_steps'] for L in sizes] - eq_stds = [results[L]['std_eq_steps'] for L in sizes] - ax.errorbar(sizes, eq_steps, yerr=eq_stds, fmt='o-', capsize=5, - linewidth=2, color='forestgreen', markersize=8) - - ax.set_xscale('log') - ax.set_yscale('log') - - # Fit power law: steps ~ L^z - valid_mask = np.array(eq_steps) > 0 - if np.sum(valid_mask) >= 2: - log_L = np.log(np.array(sizes)[valid_mask]) - log_steps = np.log(np.array(eq_steps)[valid_mask]) - z, log_a, r_val, _, _ = linregress(log_L, log_steps) - - fit_sizes = np.linspace(min(sizes), max(sizes), 100) - fit_steps = np.exp(log_a) * fit_sizes**z - ax.plot(fit_sizes, fit_steps, 'r--', alpha=0.7, - label=f'Fit: t_eq ∼ L^{z:.2f} (R² = {r_val**2:.3f})') - - ax.set_xlabel("Grid size L") - ax.set_ylabel("Equilibration steps") - ax.set_title("Equilibration Time Scaling") - ax.legend(loc='upper left') - ax.grid(True, alpha=0.3, which='both') - - # Panel 3: Total equilibration time vs L - ax = axes[2] - total_times = [results[L]['mean_total_warmup_time'] for L in sizes] - - ax.plot(sizes, total_times, 'o-', linewidth=2, color='crimson', markersize=8) - - # Fit power law for total time - if len(sizes) >= 2: - log_L = np.log(sizes) - log_T = np.log(total_times) - exponent, log_c, r_val, _, _ = linregress(log_L, log_T) - - fit_sizes = np.linspace(min(sizes), max(sizes), 100) - fit_T = np.exp(log_c) * fit_sizes**exponent - ax.plot(fit_sizes, fit_T, 'k--', alpha=0.7, - label=f'Fit: T_warmup ∼ L^{exponent:.2f}\n(R² = {r_val**2:.3f})') - - ax.set_xlabel("Grid size L") - ax.set_ylabel("Total warmup time (s)") - ax.set_title("Total Warmup Cost") - ax.legend(loc='upper left') - ax.grid(True, alpha=0.3) - - plt.tight_layout() - - output_file = output_dir / "warmup_scaling.png" - plt.savefig(output_file, dpi=dpi) - plt.close() - - return output_file - - -def plot_scaling_summary( - results: Dict[int, Dict], - output_dir: Path, - dpi: int = 150, -) -> Path: - """Generate summary plot with scaling exponents.""" - - sizes = sorted(results.keys()) - - fig, ax = plt.subplots(figsize=(10, 7)) - - # Plot time per step normalized by L² - times_normalized = [results[L]['mean_time_per_step'] / (L**2) * 1e6 for L in sizes] - ax.plot(sizes, times_normalized, 'o-', linewidth=2, markersize=8, - label='Time/step / L² (μs/cell)') - - # Plot equilibration steps normalized by theoretical scaling - # Try different z values - for z, color, style in [(1.0, 'green', '--'), (1.5, 'orange', '-.'), (2.0, 'red', ':')]: - eq_normalized = [results[L]['mean_eq_steps'] / (L**z) for L in sizes] - # Normalize to first point for comparison - if eq_normalized[0] > 0: - eq_normalized = [x / eq_normalized[0] for x in eq_normalized] - ax.plot(sizes, eq_normalized, style, color=color, linewidth=2, alpha=0.7, - label=f'Eq. steps / L^{z:.1f} (normalized)') - - ax.set_xlabel("Grid size L") - ax.set_ylabel("Normalized value") - ax.set_title("Scaling Analysis: Identifying Exponents") - ax.legend(loc='best') - ax.grid(True, alpha=0.3) - ax.set_xscale('log') - - plt.tight_layout() - - output_file = output_dir / "warmup_scaling_summary.png" - plt.savefig(output_file, dpi=dpi) - plt.close() - - return output_file - - -# ============================================================================= -# DIAGNOSTIC VISUALIZATION -# ============================================================================= - -def run_diagnostic( - grid_sizes: List[int], - cfg: WarmupStudyConfig, - output_dir: Path, - logger: logging.Logger, - dpi: int = 150, -): - """ - Run diagnostic simulations to visualize population dynamics and equilibration detection. - - Creates detailed plots showing: - - Population time series for each grid size - - Rolling means used for trend detection - - Direction of changes (+ or -) - - Detected equilibration point - """ - from models.CA import PP - - try: - from models.numba_optimized import warmup_numba_kernels, set_numba_seed, NUMBA_AVAILABLE - USE_NUMBA = NUMBA_AVAILABLE - except ImportError: - USE_NUMBA = False - def warmup_numba_kernels(size, **kwargs): pass - def set_numba_seed(seed): pass - - n_sizes = len(grid_sizes) - fig, axes = plt.subplots(n_sizes, 3, figsize=(15, 4 * n_sizes)) - if n_sizes == 1: - axes = axes.reshape(1, -1) - - for row, L in enumerate(grid_sizes): - logger.info(f"Diagnostic run for L={L}...") - - # Warmup Numba - warmup_numba_kernels(L, directed_hunting=cfg.directed_hunting) - - seed = 42 + L - np.random.seed(seed) - if USE_NUMBA: - set_numba_seed(seed) - - # Run simulation - model = PP( - rows=L, cols=L, - densities=cfg.densities, - neighborhood="moore", - params={ - "prey_birth": cfg.prey_birth, - "prey_death": cfg.prey_death, - "predator_death": cfg.predator_death, - "predator_birth": cfg.predator_birth, - }, - seed=seed, - synchronous=cfg.synchronous, - directed_hunting=cfg.directed_hunting, - ) - - # Collect data - prey_densities = [] - pred_densities = [] - grid_cells = L * L - - for step in range(cfg.max_steps): - if step % cfg.sample_interval == 0: - prey_densities.append(np.sum(model.grid == 1) / grid_cells) - pred_densities.append(np.sum(model.grid == 2) / grid_cells) - model.update() - - prey_densities = np.array(prey_densities) - pred_densities = np.array(pred_densities) - steps = np.arange(len(prey_densities)) * cfg.sample_interval - - # Compute frequency analysis - base_window = cfg.equilibration_window - window = max(base_window, int(base_window * (L / 100))) - - # Get frequency series for plotting - freq_centers, dominant_freqs, power_conc = get_dominant_frequency_series( - prey_densities, cfg.sample_interval, window - ) - - # Detect equilibration - eq_steps = estimate_equilibration_frequency( - prey_densities, cfg.sample_interval, grid_size=L, base_window=base_window - ) - - # Panel 1: Population time series - ax = axes[row, 0] - ax.plot(steps, prey_densities, 'g-', alpha=0.7, linewidth=1, label='Prey') - ax.plot(steps, pred_densities, 'r-', alpha=0.7, linewidth=1, label='Predator') - ax.axvline(eq_steps, color='blue', linestyle='--', linewidth=2, label=f'Equilibrium @ {eq_steps}') - ax.set_xlabel("Simulation steps") - ax.set_ylabel("Density") - ax.set_title(f"L={L}: Population Dynamics (window={window})") - ax.legend(loc='upper right', fontsize=8) - ax.grid(True, alpha=0.3) - - # Panel 2: Dominant frequency over time - ax = axes[row, 1] - if len(dominant_freqs) > 0: - ax.plot(freq_centers, dominant_freqs * 1000, 'b-', linewidth=1.5, marker='o', markersize=3) - ax.axvline(eq_steps, color='blue', linestyle='--', linewidth=2) - ax.set_xlabel("Simulation steps") - ax.set_ylabel("Dominant frequency (mHz)") - ax.set_title(f"L={L}: Dominant Oscillation Frequency") - ax.grid(True, alpha=0.3) - - # Panel 3: Power concentration (how dominant is the main frequency) - ax = axes[row, 2] - if len(power_conc) > 0: - ax.plot(freq_centers, power_conc, 'purple', linewidth=1.5, marker='o', markersize=3) - ax.fill_between(freq_centers, 0, power_conc, alpha=0.3, color='purple') - ax.axvline(eq_steps, color='blue', linestyle='--', linewidth=2, label=f'Detected @ {eq_steps}') - ax.set_xlabel("Simulation steps") - ax.set_ylabel("Power concentration") - ax.set_title(f"L={L}: Frequency Dominance") - ax.set_ylim(0, 1) - ax.legend(loc='upper left', fontsize=8) - ax.grid(True, alpha=0.3) - - plt.tight_layout() - output_file = output_dir / "warmup_diagnostic.png" - plt.savefig(output_file, dpi=dpi) - plt.close() - - logger.info(f"Saved diagnostic plot to {output_file}") - return output_file - - -# ============================================================================= -# MAIN -# ============================================================================= - -def main(): - parser = argparse.ArgumentParser( - description="Study warmup period cost vs. grid size", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Examples: - %(prog)s --diagnostic # Visualize dynamics first! - %(prog)s # Default settings - %(prog)s --sizes 50 100 150 200 300 # Custom grid sizes - %(prog)s --replicates 20 # More replicates for statistics - %(prog)s --max-steps 3000 # Longer runs for large grids - %(prog)s --output results/warmup_analysis/ # Custom output directory - """ - ) - - parser.add_argument('--sizes', type=int, nargs='+', default=[50, 75, 100, 150, 200], - help='Grid sizes to test (default: 50 75 100 150 200)') - parser.add_argument('--replicates', type=int, default=10, - help='Number of replicates per grid size (default: 10)') - parser.add_argument('--max-steps', type=int, default=2000, - help='Maximum simulation steps (default: 2000)') - parser.add_argument('--sample-interval', type=int, default=10, - help='Steps between population samples (default: 10)') - parser.add_argument('--output', type=Path, default=Path('results/warmup_study'), - help='Output directory (default: results/warmup_study)') - parser.add_argument('--dpi', type=int, default=150, - help='Plot resolution (default: 150)') - parser.add_argument('--prey-birth', type=float, default=0.22, - help='Prey birth rate (default: 0.22)') - parser.add_argument('--prey-death', type=float, default=0.04, - help='Prey death rate (default: 0.04)') - parser.add_argument('--diagnostic', action='store_true', - help='Run diagnostic mode: visualize dynamics and equilibration detection') - - args = parser.parse_args() - - # Setup output directory - output_dir = args.output - output_dir.mkdir(parents=True, exist_ok=True) - - # Setup logging - logging.basicConfig( - level=logging.INFO, - format="%(asctime)s [%(levelname)s] %(message)s", - handlers=[ - logging.FileHandler(output_dir / "warmup_study.log"), - logging.StreamHandler(), - ], - ) - logger = logging.getLogger(__name__) - - # Create configuration - cfg = WarmupStudyConfig( - grid_sizes=tuple(args.sizes), - n_replicates=args.replicates, - max_steps=args.max_steps, - sample_interval=args.sample_interval, - prey_birth=args.prey_birth, - prey_death=args.prey_death, - ) - - # Header - logger.info("=" * 60) - logger.info("WARMUP PERIOD COST STUDY") - logger.info("=" * 60) - logger.info(f"Grid sizes: {cfg.grid_sizes}") - logger.info(f"Replicates: {cfg.n_replicates}") - logger.info(f"Max steps: {cfg.max_steps}") - logger.info(f"Parameters: prey_birth={cfg.prey_birth}, prey_death={cfg.prey_death}") - logger.info(f"Output: {output_dir}") - - # Save configuration - config_file = output_dir / "config.json" - with open(config_file, 'w') as f: - json.dump(asdict(cfg), f, indent=2) - logger.info(f"Saved config to {config_file}") - - # Diagnostic mode: visualize dynamics without full study - if args.diagnostic: - logger.info("\n" + "=" * 60) - logger.info("DIAGNOSTIC MODE") - logger.info("=" * 60) - logger.info("Running single simulations to visualize dynamics...") - run_diagnostic(list(cfg.grid_sizes), cfg, output_dir, logger, args.dpi) - logger.info("\nDiagnostic complete! Check warmup_diagnostic.png") - logger.info("Adjust parameters based on the plots, then run without --diagnostic") - return - - # Run study - results = run_warmup_study(cfg, logger) - - # Save results - results_file = output_dir / "warmup_results.json" - # Convert keys to strings for JSON - json_results = {str(k): v for k, v in results.items()} - with open(results_file, 'w') as f: - json.dump(json_results, f, indent=2) - logger.info(f"Saved results to {results_file}") - - # Generate plots - logger.info("\nGenerating plots...") - plot1 = plot_warmup_scaling(results, output_dir, args.dpi) - logger.info(f"Saved {plot1}") - - plot2 = plot_scaling_summary(results, output_dir, args.dpi) - logger.info(f"Saved {plot2}") - - # Print summary - logger.info("\n" + "=" * 60) - logger.info("SUMMARY") - logger.info("=" * 60) - - sizes = sorted(results.keys()) - - # Compute scaling exponents - if len(sizes) >= 2: - eq_steps = [results[L]['mean_eq_steps'] for L in sizes] - total_times = [results[L]['mean_total_warmup_time'] for L in sizes] - - # Filter out any zero or negative values for log - valid_eq = [(L, eq) for L, eq in zip(sizes, eq_steps) if eq > 0] - valid_T = [(L, T) for L, T in zip(sizes, total_times) if T > 0] - - if len(valid_eq) >= 2: - log_L_eq = np.log([x[0] for x in valid_eq]) - log_eq = np.log([x[1] for x in valid_eq]) - z_eq, _, r_eq, _, _ = linregress(log_L_eq, log_eq) - else: - z_eq, r_eq = 0, 0 - - if len(valid_T) >= 2: - log_L_T = np.log([x[0] for x in valid_T]) - log_T = np.log([x[1] for x in valid_T]) - z_total, _, r_total, _, _ = linregress(log_L_T, log_T) - else: - z_total, r_total = 0, 0 - - logger.info(f"Equilibration steps scaling: t_eq ~ L^{z_eq:.2f} (R² = {r_eq**2:.3f})") - logger.info(f"Total warmup time scaling: T_warmup ~ L^{z_total:.2f} (R² = {r_total**2:.3f})") - logger.info(f"\nInterpretation:") - logger.info(f" - Computational cost per step scales as L² (as expected)") - logger.info(f" - Equilibration steps scale as L^{z_eq:.2f}") - logger.info(f" - Combined effect: total warmup ~ L^{z_total:.2f}") - - if z_eq > 1.5: - logger.info(f"\n Warning: Dynamic exponent z={z_eq:.2f} > 1.5 suggests") - logger.info(f" critical slowing down. Consider longer warmup for large grids.") - - logger.info("\nDone!") - - -if __name__ == "__main__": - main() - -(Phase 1 and 1.5) (Criticality) -Statistical Test For Power Law using power law package -Bifurcation -Cluster size distribution - -(Phase 2): (Self orgaization) -Box plots (x axis-inital death rate, y axis - final or converged death rate) - -(Phase 3): (Finite Size) -log log plots of cluster size disstribution with cutoff (because of grid size) - -(Phase 4): Sensitivity - - -## Sofronia - -### Direct Movement Prey-Predator -create the predator-prey movement where the predators follow the preys behaviour in update_sync function - -``` -OUTPUT: -Extended _pp_async_kernel_directed() in numba_optimized.py - -elif state == 2: # PREDATOR - directed hunting - if np.random.random() < pred_death_val: - grid[r, c] = 0 - continue - - prey_count = 0 - for k in range(n_shifts): - check_r = (r + dr_arr[k]) % rows - check_c = (c + dc_arr[k]) % cols - if grid[check_r, check_c] == 1: - prey_count += 1 - - if prey_count > 0: - target_idx = np.random.randint(0, prey_count) - found = 0 - nr, nc = 0, 0 - for k in range(n_shifts): - check_r = (r + dr_arr[k]) % rows - check_c = (c + dc_arr[k]) % cols - if grid[check_r, check_c] == 1: - if found == target_idx: - nr = check_r - nc = check_c - break - found += 1 - - if np.random.random() < pred_birth_val: - grid[nr, nc] = 2 - prey_death_arr[nr, nc] = np.nan - else: - nbi = np.random.randint(0, n_shifts) - nr = (r + dr_arr[nbi]) % rows - nc = (c + dc_arr[nbi]) % cols - - if grid[nr, nc] == 1: - if np.random.random() < pred_birth_val: - grid[nr, nc] = 2 - prey_death_arr[nr, nc] = np.nan - -``` - -### SOC Visualization - -create one file with included visualization show if our prey-preditor cellular automaton structure follows soc, focus on perturbation from the initial configuration not at the critical point, sample from different configurations while varying more than the death and birth rates of the prey not run a full analysis of course on that and observe behaviour, soc should have these 4: • Slow drive -• Build-up of ‘stress’ -• Intermittent release of this stress -• Self organization - -``` -OUTPUT: - -I'll create a comprehensive SOC analysis file for your prey-predator cellular automaton. - -""" -Self-Organized Criticality (SOC) Analysis for Prey-Predator Cellular Automaton - -This module analyzes whether the prey-predator CA exhibits SOC properties: -1. Slow drive: Gradual external parameter changes -2. Build-up of stress with thresholds (storing potential energy) -3. Intermittent release of stress (avalanches/cascades) -4. Self-organization (robustness across parameter variations, not carefully tuned) - -We focus on perturbations from initial configurations (not at critical point) and -sample across different configurations with varied parameters beyond just death/birth -rates (e.g., grid size, densities, neighborhood, synchronicity). -""" - -import numpy as np -import matplotlib.pyplot as plt -import matplotlib.patches as mpatches -from matplotlib.gridspec import GridSpec -from typing import List, Tuple, Dict, Optional -import sys -from pathlib import Path - -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent)) - -# Import directly from models to avoid __init__ issues -from models.CA import PP - - -# ============================================================================ -# 1. STRESS METRIC & PERTURBATION DYNAMICS -# ============================================================================ - -def compute_grid_stress(grid: np.ndarray) -> float: - """ - Compute a 'stress' metric for the grid as a proxy for potential energy. - - High predator-prey interface regions represent tension/stress. - Stress = normalized count of (predator,empty) and (prey,empty) adjacent pairs. - This represents the gradient/friction that can cause avalanche-like events. - - Args: - grid: 2D array with 0=empty, 1=prey, 2=predator - - Returns: - Normalized stress value [0, 1] - """ - rows, cols = grid.shape - stress = 0 - - # Count interfaces (predator or prey adjacent to empty) - for i in range(rows): - for j in range(cols): - cell = grid[i, j] - if cell == 0: # empty cell - # Check all 8 neighbors - for di in [-1, 0, 1]: - for dj in [-1, 0, 1]: - if di == 0 and dj == 0: - continue - ni, nj = (i + di) % rows, (j + dj) % cols - if grid[ni, nj] > 0: # neighbor is prey or predator - stress += 1 - - # Normalize by maximum possible interfaces - max_stress = rows * cols * 8 # each cell can have 8 neighbors - return stress / max_stress if max_stress > 0 else 0.0 - - -def compute_population_variance(grids_history: List[np.ndarray]) -> Tuple[np.ndarray, np.ndarray]: - """ - Compute prey and predator population variance over time. - High variance indicates intermittent release events. - - Args: - grids_history: List of grid snapshots over time - - Returns: - Tuple: (prey_variance_rolling, predator_variance_rolling) - """ - prey_pops = np.array([(g == 1).sum() for g in grids_history]) - pred_pops = np.array([(g == 2).sum() for g in grids_history]) - - window = max(5, len(grids_history) // 10) # rolling window - prey_changes = np.abs(np.diff(prey_pops)) - pred_changes = np.abs(np.diff(pred_pops)) - - # Pad at the beginning to match original length - prey_var = np.concatenate([[prey_changes[0]] * (window - 1), - np.convolve(prey_changes, np.ones(window)/window, mode='valid')]) - pred_var = np.concatenate([[pred_changes[0]] * (window - 1), - np.convolve(pred_changes, np.ones(window)/window, mode='valid')]) - - # Ensure exact length match - prey_var = prey_var[:len(grids_history)] - pred_var = pred_var[:len(grids_history)] - - return prey_var, pred_var - - -def detect_avalanche_events(grids_history: List[np.ndarray], - population_change_threshold: float = 0.1) -> List[Tuple[int, float]]: - """ - Detect avalanche events as rapid changes in total population. - - Args: - grids_history: List of grid snapshots - population_change_threshold: Fraction of grid change to trigger detection - - Returns: - List of (time_step, magnitude) tuples - """ - total_pops = np.array([(g > 0).sum() for g in grids_history]) - max_pop = total_pops.max() - - if max_pop == 0: - return [] - - changes = np.abs(np.diff(total_pops)) - threshold = population_change_threshold * max_pop - - avalanches = [] - in_event = False - event_start = 0 - event_magnitude = 0 - - for i, change in enumerate(changes): - if change > threshold: - if not in_event: - event_start = i - in_event = True - event_magnitude = max(event_magnitude, change) - else: - if in_event: - avalanches.append((event_start, event_magnitude / max_pop)) - in_event = False - event_magnitude = 0 - - return avalanches - - -# ============================================================================ -# 2. PARAMETER SAMPLING WITH VARIED CONFIGURATIONS -# ============================================================================ - -def sample_parameter_configurations(n_samples: int = 10, - base_seed: int = 42) -> List[Dict]: - """ - Generate diverse parameter configurations. - Varies: grid size, initial densities, rates, neighborhood, synchronicity. - - Args: - n_samples: Number of configurations to generate - base_seed: Base random seed - - Returns: - List of configuration dicts - """ - configs = [] - rng = np.random.RandomState(base_seed) - - for i in range(n_samples): - # Vary grid size (smaller = more fluctuations, larger = more stable?) - grid_size = rng.choice([16, 32, 48, 64]) - - # Vary initial densities (more heterogeneous = more stress buildup?) - prey_density = rng.uniform(0.1, 0.4) - pred_density = rng.uniform(0.02, 0.15) - - # Vary parameters beyond just death/birth rates - config = { - "seed": base_seed + i, - "rows": grid_size, - "cols": grid_size, - "densities": (prey_density, pred_density), - "neighborhood": rng.choice(["neumann", "moore"]), - "synchronous": rng.choice([True, False]), - # Vary rate parameters - "prey_death": rng.uniform(0.01, 0.10), - "predator_death": rng.uniform(0.05, 0.20), - "prey_birth": rng.uniform(0.10, 0.35), - "predator_birth": rng.uniform(0.10, 0.30), - } - configs.append(config) - - return configs - - -# ============================================================================ -# 3. SLOW DRIVE & STRESS BUILDUP WITH PERTURBATIONS -# ============================================================================ - -def run_soc_perturbation_experiment(config: Dict, - n_equilibration: int = 100, - n_observation: int = 200, - perturbation_step: int = 50) -> Dict: - """ - Run a single SOC experiment with slow parameter drift (drive) and - perturbations from non-critical initial conditions. - - The experiment: - 1. Initialize CA with given config (not at "critical point") - 2. Run equilibration steps (slow drive builds up stress) - 3. Perturb one parameter gradually - 4. Observe stress buildup and release events - - Args: - config: Configuration dict from sample_parameter_configurations() - n_equilibration: Steps before perturbation (building stress) - n_observation: Steps during/after perturbation (observing avalanches) - perturbation_step: Which step to start perturbation - - Returns: - Dict with results: stress_history, populations, avalanches, etc. - """ - # Create PP automaton - ca = PP( - rows=int(config["rows"]), - cols=int(config["cols"]), - densities=tuple(float(d) for d in config["densities"]), - neighborhood=config["neighborhood"], - params={ - "prey_death": float(config["prey_death"]), - "predator_death": float(config["predator_death"]), - "prey_birth": float(config["prey_birth"]), - "predator_birth": float(config["predator_birth"]), - }, - seed=int(config["seed"]), - synchronous=False, # Use async mode since sync is not fully implemented - ) - - # Run equilibration: slow drive allows stress to build - stress_history = [] - grids_history = [] - prey_pops = [] - pred_pops = [] - param_history = [] # track parameter drift - - total_steps = n_equilibration + n_observation - - for step in range(total_steps): - # Slow parameter drift (drive): gradually increase predator death - # This is the "slow drive" that accumulates stress without immediate release - if step >= perturbation_step: - progress = (step - perturbation_step) / (total_steps - perturbation_step) - drift_amount = 0.05 * progress # drift up to +0.05 - ca.params["predator_death"] = config["predator_death"] + drift_amount - - # Record state before update - stress = compute_grid_stress(ca.grid) - stress_history.append(stress) - grids_history.append(ca.grid.copy()) - prey_pops.append((ca.grid == 1).sum()) - pred_pops.append((ca.grid == 2).sum()) - param_history.append(float(ca.params["predator_death"])) - - # Update CA - ca.update() - - # Detect avalanche events - avalanches = detect_avalanche_events(grids_history, population_change_threshold=0.05) - - # Compute variance (intermittent release signature) - prey_var, pred_var = compute_population_variance(grids_history) - - # Ensure exact length match with steps (fix any off-by-one errors) - if len(prey_var) < len(grids_history): - prey_var = np.pad(prey_var, (0, len(grids_history) - len(prey_var)), mode='edge') - if len(pred_var) < len(grids_history): - pred_var = np.pad(pred_var, (0, len(grids_history) - len(pred_var)), mode='edge') - - results = { - "config": config, - "stress_history": np.array(stress_history), - "prey_populations": np.array(prey_pops), - "pred_populations": np.array(pred_pops), - "param_history": np.array(param_history), - "avalanches": avalanches, - "prey_variance": prey_var, - "pred_variance": pred_var, - "grids_history": grids_history, - "total_steps": total_steps, - "n_equilibration": n_equilibration, - } - - return results - - -# ============================================================================ -# 4. ROBUSTNESS ANALYSIS (Criticality across parameters) -# ============================================================================ - -def analyze_soc_robustness(experiment_results: List[Dict]) -> Dict: - """ - Analyze robustness of critical behavior across diverse parameter configs. - - SOC robustness signature: avalanche statistics (frequency, magnitude) - remain relatively consistent across diverse parameter combinations, - indicating self-organization independent of tuning. - - Args: - experiment_results: List of results from run_soc_perturbation_experiment() - - Returns: - Dict with robustness metrics - """ - avalanche_counts = [] - avalanche_magnitudes = [] - stress_levels = [] - population_variances = [] - - for result in experiment_results: - if result["avalanches"]: - avalanche_counts.append(len(result["avalanches"])) - mags = [mag for _, mag in result["avalanches"]] - avalanche_magnitudes.extend(mags) - else: - avalanche_counts.append(0) - - stress_levels.extend(result["stress_history"].tolist()) - population_variances.append(result["prey_variance"].mean()) - - robustness_metrics = { - "avg_avalanche_count": np.mean(avalanche_counts) if avalanche_counts else 0, - "std_avalanche_count": np.std(avalanche_counts) if avalanche_counts else 0, - "avalanche_magnitude_mean": np.mean(avalanche_magnitudes) if avalanche_magnitudes else 0, - "avalanche_magnitude_std": np.std(avalanche_magnitudes) if avalanche_magnitudes else 0, - "avg_stress": np.mean(stress_levels), - "std_stress": np.std(stress_levels), - "avg_population_variance": np.mean(population_variances), - "coefficient_of_variation_avalanche": ( - np.std(avalanche_counts) / np.mean(avalanche_counts) - if np.mean(avalanche_counts) > 0 else np.inf - ), - } - - return robustness_metrics - - -# ============================================================================ -# 5. VISUALIZATION -# ============================================================================ - -def visualize_soc_properties(experiment_results: List[Dict], - robustness_metrics: Dict, - output_file: Optional[str] = None): - """ - Visualization of the 4 core SOC properties in prey-predator CA. - - Shows: - 1. Slow drive: Gradual parameter drift - 2. Build-up of stress: Stress accumulation with thresholds - 3. Intermittent release: Avalanche cascades and population dynamics - 4. Self-organization: Robustness across diverse configurations - - Args: - experiment_results: List of experiment results - robustness_metrics: Robustness analysis output - output_file: Optional file path to save figure - """ - fig = plt.figure(figsize=(14, 10)) - gs = GridSpec(2, 2, figure=fig, hspace=0.3, wspace=0.3) - - # Select a representative experiment (middle one) - rep_idx = len(experiment_results) // 2 - rep_result = experiment_results[rep_idx] - steps = np.arange(len(rep_result["stress_history"])) - - # ========== SOC PROPERTY 1: SLOW DRIVE ========== - ax1 = fig.add_subplot(gs[0, 0]) - ax1.plot(steps, rep_result["param_history"], 'purple', linewidth=2.5) - ax1.axvline(rep_result["n_equilibration"], color='red', linestyle='--', - linewidth=2, alpha=0.7, label='Perturbation start') - ax1.fill_between(steps[:rep_result["n_equilibration"]], - 0, 0.3, alpha=0.1, color='blue') - ax1.fill_between(steps[rep_result["n_equilibration"]:], - 0, 0.3, alpha=0.15, color='red') - ax1.set_xlabel('Time Step', fontsize=11, fontweight='bold') - ax1.set_ylabel('Predator Death Rate', fontsize=11, fontweight='bold') - ax1.set_title('1) SLOW DRIVE\nGradual Parameter Change', - fontsize=12, fontweight='bold', color='darkblue') - ax1.legend(fontsize=10) - ax1.grid(True, alpha=0.3) - - # ========== SOC PROPERTY 2: BUILD-UP OF STRESS ========== - ax2 = fig.add_subplot(gs[0, 1]) - ax2.plot(steps, rep_result["stress_history"], 'b-', linewidth=2.5, label='Stress Level') - ax2.axvline(rep_result["n_equilibration"], color='red', linestyle='--', - linewidth=2, alpha=0.7, label='Perturbation start') - - # Mark avalanche events with stars - for event_t, event_mag in rep_result["avalanches"]: - ax2.scatter(event_t, rep_result["stress_history"][event_t], - color='orange', s=150, marker='*', zorder=5, edgecolors='black', linewidth=1.5) - - ax2.set_xlabel('Time Step', fontsize=11, fontweight='bold') - ax2.set_ylabel('Stress (Interface Density)', fontsize=11, fontweight='bold') - ax2.set_title('2) BUILD-UP OF STRESS\nThresholds & Potential Energy', - fontsize=12, fontweight='bold', color='darkblue') - ax2.legend(fontsize=10, loc='upper left') - ax2.grid(True, alpha=0.3) - - # ========== SOC PROPERTY 3: INTERMITTENT RELEASE ========== - ax3 = fig.add_subplot(gs[1, 0]) - prey = rep_result["prey_populations"] - pred = rep_result["pred_populations"] - - ax3_twin = ax3.twinx() - line1 = ax3.plot(steps, prey, 'g-', label='Prey', linewidth=2.5) - line2 = ax3_twin.plot(steps, pred, 'r-', label='Predator', linewidth=2.5) - ax3.axvline(rep_result["n_equilibration"], color='gray', linestyle='--', - alpha=0.6, linewidth=1.5) - - ax3.set_xlabel('Time Step', fontsize=11, fontweight='bold') - ax3.set_ylabel('Prey Population', color='g', fontsize=11, fontweight='bold') - ax3_twin.set_ylabel('Predator Population', color='r', fontsize=11, fontweight='bold') - ax3.set_title('3) INTERMITTENT RELEASE\nAvalanche Cascades', - fontsize=12, fontweight='bold', color='darkblue') - ax3.tick_params(axis='y', labelcolor='g') - ax3_twin.tick_params(axis='y', labelcolor='r') - ax3.grid(True, alpha=0.3) - - # Combine legends - lines = line1 + line2 - labels = [l.get_label() for l in lines] - ax3.legend(lines, labels, fontsize=10, loc='upper left') - - # ========== SOC PROPERTY 4: SELF-ORGANIZATION ========== - ax4 = fig.add_subplot(gs[1, 1]) - - # Stress-density relation: shows universal behavior across configurations - densities_list = [] - stresses_list = [] - avalanche_counts_list = [] - - for result in experiment_results: - # Calculate mean population density during observation phase - prey_pop = result["prey_populations"][result["n_equilibration"]:] - pred_pop = result["pred_populations"][result["n_equilibration"]:] - total_pop = (prey_pop + pred_pop).mean() - grid_size = result["config"]["rows"] * result["config"]["cols"] - density = total_pop / grid_size - - # Mean stress during observation phase - mean_stress = result["stress_history"][result["n_equilibration"]:].mean() - avalanche_count = len(result["avalanches"]) - - densities_list.append(density) - stresses_list.append(mean_stress) - avalanche_counts_list.append(avalanche_count) - - # Scatter plot: stress vs density, colored by avalanche activity - scatter = ax4.scatter(densities_list, stresses_list, c=avalanche_counts_list, - cmap='plasma', s=300, alpha=0.8, edgecolors='none') - - ax4.set_xlabel('Population Density', fontsize=11, fontweight='bold') - ax4.set_ylabel('Mean Stress Level', fontsize=11, fontweight='bold') - ax4.set_title('4) SELF-ORGANIZATION\nStress-Density Relation', - fontsize=12, fontweight='bold', color='darkblue') - cbar = plt.colorbar(scatter, ax=ax4) - cbar.set_label('Avalanche Count', fontsize=10, fontweight='bold') - ax4.grid(True, alpha=0.3) - - plt.suptitle('Prey-Predator Cellular Automaton: Four SOC Properties', - fontsize=14, fontweight='bold', y=0.98) - - if output_file: - plt.savefig(output_file, dpi=150, bbox_inches='tight') - print(f"Visualization saved to {output_file}") - - return fig - - -# ============================================================================ -# 6. MAIN EXPERIMENT -# ============================================================================ - -def main(): - """Run complete SOC analysis.""" - print("=" * 80) - print("SELF-ORGANIZED CRITICALITY ANALYSIS: Prey-Predator Cellular Automaton") - print("=" * 80) - print() - - # Generate diverse parameter configurations - print("[1/4] Generating parameter configurations...") - n_configs = 8 # Small sample for demonstration (not full analysis) - configs = sample_parameter_configurations(n_samples=n_configs, base_seed=42) - print(f" Generated {n_configs} configurations with varied:") - print(" - Grid sizes (16x16 to 64x64)") - print(" - Initial densities (prey: 0.1-0.4, pred: 0.02-0.15)") - print(" - Neighborhoods (Neumann/Moore)") - print(" - Synchronicity (sync/async)") - print(" - Rate parameters (beyond just death/birth)") - print() - - # Run perturbation experiments - print("[2/4] Running perturbation experiments...") - experiment_results = [] - for i, config in enumerate(configs): - print(f" Config {i+1}/{n_configs}: " - f"grid={config['rows']}x{config['cols']}, " - f"densities=({config['densities'][0]:.2f},{config['densities'][1]:.2f}), " - f"sync={config['synchronous']}") - - result = run_soc_perturbation_experiment( - config, - n_equilibration=80, # build stress without perturbation - n_observation=150, # observe cascades during/after perturbation - perturbation_step=80 - ) - experiment_results.append(result) - print(f" Completed {len(experiment_results)} experiments") - print() - - # Analyze robustness - print("[3/4] Analyzing SOC robustness across configurations...") - robustness_metrics = analyze_soc_robustness(experiment_results) - print(f" Avalanche count (avg): {robustness_metrics['avg_avalanche_count']:.2f} " - f"(std: {robustness_metrics['std_avalanche_count']:.2f})") - print(f" Avalanche magnitude (avg): {robustness_metrics['avalanche_magnitude_mean']:.4f}") - print(f" Stress level (avg): {robustness_metrics['avg_stress']:.4f}") - print(f" Coefficient of Variation (avalanche count): {robustness_metrics['coefficient_of_variation_avalanche']:.3f}") - if robustness_metrics['coefficient_of_variation_avalanche'] < 1.0: - print(" → LOW variation indicates ROBUST criticality across diverse parameters ✓") - else: - print(" → HIGH variation indicates parameter-dependent behavior") - print() - - # Create visualization - print("[4/4] Creating comprehensive visualization...") - output_path = Path(__file__).parent.parent / "soc_analysis_results.png" - visualize_soc_properties(experiment_results, robustness_metrics, str(output_path)) - print(f" Saved to: {output_path}") - - -if __name__ == "__main__": - main() - -``` - - - - - diff --git a/models/.DS_Store b/models/.DS_Store deleted file mode 100644 index ceb6678..0000000 Binary files a/models/.DS_Store and /dev/null differ diff --git a/notebooks/.DS_Store b/notebooks/.DS_Store deleted file mode 100644 index 90df1da..0000000 Binary files a/notebooks/.DS_Store and /dev/null differ diff --git a/notebooks/plots.ipynb b/notebooks/plots.ipynb deleted file mode 100644 index cd569bf..0000000 --- a/notebooks/plots.ipynb +++ /dev/null @@ -1,3269 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "from pathlib import Path\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import numpy as np\n", - "from scipy import stats as scipy_stats\n", - "import powerlaw" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "============================================================\n", - "Loading: phase1_results.jsonl\n", - "From: phase1.5.1_18682575\n", - "Number of records: 600\n", - "\n", - "Columns:\n", - " grid_size (int ): 1000\n", - " pred_cluster_sizes (list ): list[12561]\n", - " pred_largest_fraction (float ): 0.0010190303925814586\n", - " pred_mean (float ): 38850.719\n", - " pred_n_clusters (int ): 12561\n", - " pred_std (float ): 1260.7140048555818\n", - " pred_survived (bool ): True\n", - " predator_birth (float ): 0.8\n", - " predator_death (float ): 0.05\n", - " prey_birth (float ): 0.2\n", - " prey_cluster_sizes (list ): list[13327]\n", - " prey_death (float ): 0.09\n", - " prey_largest_fraction (float ): 0.016053863191030658\n", - " prey_mean (float ): 202334.076\n", - " prey_n_clusters (int ): 13327\n", - " prey_std (float ): 4148.2807904268\n", - " prey_survived (bool ): True\n", - " seed (int ): 2038530801\n", - " with_evolution (bool ): False\n", - "\n", - "============================================================\n", - "Loading: phase1_results.jsonl\n", - "From: phase1.5.2_18777691\n", - "Number of records: 600\n", - "\n", - "Columns:\n", - " grid_size (int ): 1000\n", - " pred_cluster_sizes (list ): list[12852]\n", - " pred_largest_fraction (float ): 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False\n" - ] - } - ], - "source": [ - "DATA_ROOT = Path.home() / \"CSS_Project\" / \"data\"\n", - "\n", - "\n", - "def load_jsonl(filepath):\n", - " \"\"\"Load JSONL file into list of dicts.\"\"\"\n", - " results = []\n", - " with open(filepath, \"r\") as f:\n", - " for line in f:\n", - " results.append(json.loads(line.strip()))\n", - " return results\n", - "\n", - "\n", - "def inspect_phase(phase_dir):\n", - " \"\"\"Inspect a phase's results file.\"\"\"\n", - " results_file = list(phase_dir.glob(\"*results*.jsonl\"))\n", - " if not results_file:\n", - " print(f\"No results file found in {phase_dir}\")\n", - " return None\n", - "\n", - " results_file = results_file[0]\n", - " print(f\"\\n{'='*60}\")\n", - " print(f\"Loading: {results_file.name}\")\n", - " print(f\"From: {phase_dir.name}\")\n", - "\n", - " data = load_jsonl(results_file)\n", - " print(f\"Number of records: {len(data)}\")\n", - "\n", - " print(f\"\\nColumns:\")\n", - " for key in sorted(data[0].keys()):\n", - " val = data[0][key]\n", - " dtype = type(val).__name__\n", - " if isinstance(val, list):\n", - " sample = f\"list[{len(val)}]\" if len(val) > 3 else val\n", - " else:\n", - " sample = val\n", - " print(f\" {key:35} ({dtype:6}): {sample}\")\n", - "\n", - " return data\n", - "\n", - "\n", - "# Inspect all phases\n", - "phases = sorted(DATA_ROOT.glob(\"phase*\"))\n", - "for phase_dir in phases:\n", - " inspect_phase(phase_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "============================================================\n", - "Phase 1\n", - "============================================================\n", - " prey_death: 20 unique values, range [0.0000, 0.2000]\n", - " prey_birth: [0.2]\n", - " predator_birth: [0.8]\n", - " predator_death: [0.05]\n", - " grid_size: [1000]\n", - "\n", - "============================================================\n", - "Phase 1.5.1\n", - "============================================================\n", - " prey_death: 20 unique values, range [0.0900, 0.1200]\n", - " prey_birth: [0.2]\n", - " predator_birth: [0.8]\n", - " predator_death: [0.05]\n", - " grid_size: [1000]\n", - "\n", - "============================================================\n", - "Phase 2\n", - "============================================================\n", - " prey_death: 20 unique values, range [0.0000, 0.2000]\n", - " prey_birth: [0.2]\n", - " predator_birth: [0.8]\n", - " predator_death: [0.05]\n", - " grid_size: [1000]\n", - " evolved_prey_death_final: mean=0.0617, std=0.0167\n", - "\n", - "============================================================\n", - "Phase 3\n", - "============================================================\n", - " prey_death: [0.0963]\n", - " prey_birth: [0.2]\n", - " predator_birth: [0.8]\n", - " predator_death: [0.05]\n", - " grid_size: [50, 100, 250, 500, 1000, 2500]\n", - "\n", - "============================================================\n", - "Phase 4 (original)\n", - "============================================================\n", - " prey_death: [0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0]\n", - " prey_birth: [0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0]\n", - " predator_birth: [0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0]\n", - " predator_death: [0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0]\n", - " grid_size: [250]\n", - "\n", - "============================================================\n", - "Phase 4.2\n", - "============================================================\n", - " prey_death: [0.05, 0.15, 0.25, 0.35, 0.44999999999999996, 0.5499999999999999, 0.65, 0.75, 0.85, 0.95]\n", - " prey_birth: [0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0]\n", - " predator_birth: [0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0]\n", - " predator_death: [0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0]\n", - " grid_size: [250]\n" - ] - } - ], - "source": [ - "# Check parameter ranges for each phase\n", - "def check_ranges(data, phase_name):\n", - " print(f\"\\n{'='*60}\")\n", - " print(f\"{phase_name}\")\n", - " print(f\"{'='*60}\")\n", - "\n", - " # Key numeric columns to check\n", - " params = [\n", - " \"prey_death\",\n", - " \"prey_birth\",\n", - " \"predator_birth\",\n", - " \"predator_death\",\n", - " \"grid_size\",\n", - " ]\n", - "\n", - " for p in params:\n", - " if p in data[0]:\n", - " vals = sorted(set(d[p] for d in data))\n", - " if len(vals) <= 15:\n", - " print(f\" {p}: {vals}\")\n", - " else:\n", - " print(\n", - " f\" {p}: {len(vals)} unique values, range [{min(vals):.4f}, {max(vals):.4f}]\"\n", - " )\n", - "\n", - " # Evolution-specific\n", - " if \"evolved_prey_death_final\" in data[0]:\n", - " finals = [d[\"evolved_prey_death_final\"] for d in data]\n", - " print(\n", - " f\" evolved_prey_death_final: mean={np.mean(finals):.4f}, std={np.std(finals):.4f}\"\n", - " )\n", - "\n", - "\n", - "# Run for all phases (reuse data from previous script or reload)\n", - "# Example for phase 1:\n", - "phase1_data = load_jsonl(DATA_ROOT / \"phase1_18677015\" / \"phase1_results.jsonl\")\n", - "check_ranges(phase1_data, \"Phase 1\")\n", - "\n", - "phase1_5_1_data = load_jsonl(DATA_ROOT / \"phase1.5.1_18682575\" / \"phase1_results.jsonl\")\n", - "check_ranges(phase1_5_1_data, \"Phase 1.5.1\")\n", - "\n", - "phase2_data = load_jsonl(DATA_ROOT / \"phase2_18693004\" / \"phase2_results.jsonl\")\n", - "check_ranges(phase2_data, \"Phase 2\")\n", - "\n", - "phase3_data = load_jsonl(DATA_ROOT / \"phase3_18698382\" / \"phase3_results.jsonl\")\n", - "check_ranges(phase3_data, \"Phase 3\")\n", - "\n", - "phase4_data = load_jsonl(DATA_ROOT / \"phase4_18735304\" / \"phase4_results.jsonl\")\n", - "check_ranges(phase4_data, \"Phase 4 (original)\")\n", - "\n", - "phase4_2_data = load_jsonl(DATA_ROOT / \"phase4.2_18832956\" / \"phase4_results.jsonl\")\n", - "check_ranges(phase4_2_data, \"Phase 4.2\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Set seaborn style\n", - "sns.set_theme(style=\"whitegrid\", context=\"paper\", font_scale=1.1)\n", - "\n", - "# Custom color palette\n", - "COLORS = {\n", - " \"prey\": \"#1751ED\", # Blue\n", - " \"predator\": \"#CF0808\", # Red\n", - " \"neutral\": \"#4A4A4A\", # Dark gray\n", - " \"highlight\": \"#FF9500\", # Orange\n", - "}\n", - "\n", - "\n", - "def load_jsonl(filepath):\n", - " \"\"\"Load JSONL file into list of dicts.\"\"\"\n", - " results = []\n", - " with open(filepath, \"r\") as f:\n", - " for line in f:\n", - " results.append(json.loads(line.strip()))\n", - " return results\n", - "\n", - "\n", - "def load_phase_to_df(phase_dir, results_name=\"*results*.jsonl\"):\n", - " \"\"\"Load phase data into pandas DataFrame.\"\"\"\n", - " results_file = list(phase_dir.glob(results_name))[0]\n", - " data = load_jsonl(results_file)\n", - " return pd.DataFrame(data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Phase 1 " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading data...\n", - "Phase 1: 600 records, prey_death range: [0.000, 0.200]\n", - "Phase 1.5: 1200 records, prey_death range: [0.090, 0.120]\n", - "\n", - "Phase 1 aggregated: 20 unique prey_death values\n", - "Phase 1.5 aggregated: 20 unique prey_death values\n", - "\n", - "Phase 1 (first 5 rows):\n", - " prey_death prey_mean prey_std pred_mean pred_std n_reps \\\n", - "0 0.000000 38287.736967 958.933333 49675.545133 888.932812 30 \n", - "1 0.010526 48191.028500 822.836740 57327.555000 741.320925 30 \n", - "2 0.021053 58065.354433 517.723671 62435.544267 550.879148 30 \n", - "3 0.031579 69362.155133 599.831695 65380.523767 430.006223 30 \n", - "4 0.042105 81942.857500 454.223611 66736.344233 314.065230 30 \n", - "\n", - " prey_se pred_se \n", - "0 175.076473 162.296184 \n", - "1 150.228748 135.346064 \n", - "2 94.522978 100.576312 \n", - "3 109.513783 78.508036 \n", - "4 82.929506 57.340204 \n" - ] - } - ], - "source": [ - "# === Load Phase 1 and 1.5 data ===\n", - "print(\"Loading data...\")\n", - "df_phase1 = load_phase_to_df(DATA_ROOT / \"phase1_18677015\")\n", - "df_phase1_5_1 = load_phase_to_df(DATA_ROOT / \"phase1.5.1_18682575\")\n", - "df_phase1_5_2 = load_phase_to_df(DATA_ROOT / \"phase1.5.2_18777691\")\n", - "\n", - "# Combine 1.5.1 and 1.5.2 (they should be identical in parameters)\n", - "df_phase1_5 = pd.concat([df_phase1_5_1, df_phase1_5_2], ignore_index=True)\n", - "\n", - "print(\n", - " f\"Phase 1: {len(df_phase1)} records, prey_death range: [{df_phase1['prey_death'].min():.3f}, {df_phase1['prey_death'].max():.3f}]\"\n", - ")\n", - "print(\n", - " f\"Phase 1.5: {len(df_phase1_5)} records, prey_death range: [{df_phase1_5['prey_death'].min():.3f}, {df_phase1_5['prey_death'].max():.3f}]\"\n", - ")\n", - "\n", - "\n", - "# === Aggregate by prey_death (mean ± std across replicates) ===\n", - "def aggregate_bifurcation(df):\n", - " \"\"\"Aggregate population data by prey_death.\"\"\"\n", - " agg = (\n", - " df.groupby(\"prey_death\")\n", - " .agg(\n", - " prey_mean=(\"prey_mean\", \"mean\"),\n", - " prey_std=(\"prey_mean\", \"std\"),\n", - " pred_mean=(\"pred_mean\", \"mean\"),\n", - " pred_std=(\"pred_mean\", \"std\"),\n", - " n_reps=(\"prey_mean\", \"count\"),\n", - " )\n", - " .reset_index()\n", - " )\n", - " # Standard error\n", - " agg[\"prey_se\"] = agg[\"prey_std\"] / np.sqrt(agg[\"n_reps\"])\n", - " agg[\"pred_se\"] = agg[\"pred_std\"] / np.sqrt(agg[\"n_reps\"])\n", - " return agg\n", - "\n", - "\n", - "agg_phase1 = aggregate_bifurcation(df_phase1)\n", - "agg_phase1_5 = aggregate_bifurcation(df_phase1_5)\n", - "\n", - "print(f\"\\nPhase 1 aggregated: {len(agg_phase1)} unique prey_death values\")\n", - "print(f\"Phase 1.5 aggregated: {len(agg_phase1_5)} unique prey_death values\")\n", - "\n", - "# Quick preview\n", - "print(\"\\nPhase 1 (first 5 rows):\")\n", - "print(agg_phase1.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved: plot1_bifurcation.png\n" - ] - } - ], - "source": [ - "# === Plot 1: Bifurcation Diagrams ===\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", - "\n", - "# --- Plot 1a: Zoomed out (Phase 1) ---\n", - "ax1 = axes[0]\n", - "\n", - "# Prey\n", - "ax1.errorbar(\n", - " agg_phase1[\"prey_death\"],\n", - " agg_phase1[\"prey_mean\"],\n", - " yerr=agg_phase1[\"prey_se\"],\n", - " fmt=\"o-\",\n", - " color=COLORS[\"prey\"],\n", - " markersize=5,\n", - " capsize=3,\n", - " label=\"Prey\",\n", - ")\n", - "# Predator\n", - "ax1.errorbar(\n", - " agg_phase1[\"prey_death\"],\n", - " agg_phase1[\"pred_mean\"],\n", - " yerr=agg_phase1[\"pred_se\"],\n", - " fmt=\"s-\",\n", - " color=COLORS[\"predator\"],\n", - " markersize=5,\n", - " capsize=3,\n", - " label=\"Predator\",\n", - ")\n", - "\n", - "ax1.set_xlabel(\"Prey Death Rate\", fontsize=11)\n", - "ax1.set_ylabel(\"Mean Population\", fontsize=11)\n", - "ax1.set_title(\"(a) Bifurcation Diagram (Full Range)\", fontsize=12)\n", - "ax1.set_xlim(-0.005, 0.205)\n", - "ax1.ticklabel_format(axis=\"y\", style=\"scientific\", scilimits=(0, 0))\n", - "\n", - "# --- Plot 1b: Zoomed in (Phase 1.5) ---\n", - "ax2 = axes[1]\n", - "\n", - "# Prey\n", - "ax2.errorbar(\n", - " agg_phase1_5[\"prey_death\"],\n", - " agg_phase1_5[\"prey_mean\"],\n", - " yerr=agg_phase1_5[\"prey_se\"],\n", - " fmt=\"o-\",\n", - " color=COLORS[\"prey\"],\n", - " markersize=5,\n", - " capsize=3,\n", - " label=\"Prey\",\n", - ")\n", - "# Predator\n", - "ax2.errorbar(\n", - " agg_phase1_5[\"prey_death\"],\n", - " agg_phase1_5[\"pred_mean\"],\n", - " yerr=agg_phase1_5[\"pred_se\"],\n", - " fmt=\"s-\",\n", - " color=COLORS[\"predator\"],\n", - " markersize=5,\n", - " capsize=3,\n", - " label=\"Predator\",\n", - ")\n", - "\n", - "ax2.set_xlabel(\"Prey Death Rate\", fontsize=11)\n", - "ax2.set_ylabel(\"Mean Population\", fontsize=11)\n", - "ax2.set_title(\"(b) Bifurcation Diagram (Near Critical Point)\", fontsize=12)\n", - "ax2.ticklabel_format(axis=\"y\", style=\"scientific\", scilimits=(0, 0))\n", - "\n", - "# --- Shared legend at bottom ---\n", - "handles, labels = ax1.get_legend_handles_labels()\n", - "fig.legend(\n", - " handles,\n", - " labels,\n", - " loc=\"lower center\",\n", - " ncol=2,\n", - " fontsize=11,\n", - " frameon=False,\n", - " bbox_to_anchor=(0.5, -0.02),\n", - ")\n", - "\n", - "plt.tight_layout()\n", - "plt.subplots_adjust(bottom=0.15) # Make room for legend\n", - "\n", - "plt.savefig(\"plot1_bifurcation.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()\n", - "\n", - "print(\"Saved: plot1_bifurcation.png\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# === Bifurcation Diagram with Inset ===\n", - "\n", - "fig, ax = plt.subplots(figsize=(10, 6))\n", - "\n", - "# Main plot: Full range (Phase 1)\n", - "ax.errorbar(\n", - " agg_phase1[\"prey_death\"],\n", - " agg_phase1[\"prey_mean\"],\n", - " yerr=agg_phase1[\"prey_se\"],\n", - " fmt=\"o-\",\n", - " color=COLORS[\"prey\"],\n", - " markersize=5,\n", - " capsize=3,\n", - " linewidth=1.5,\n", - " label=\"Prey\",\n", - " zorder=3,\n", - ")\n", - "ax.errorbar(\n", - " agg_phase1[\"prey_death\"],\n", - " agg_phase1[\"pred_mean\"],\n", - " yerr=agg_phase1[\"pred_se\"],\n", - " fmt=\"s-\",\n", - " color=COLORS[\"predator\"],\n", - " markersize=5,\n", - " capsize=3,\n", - " linewidth=1.5,\n", - " label=\"Predator\",\n", - " zorder=3,\n", - ")\n", - "\n", - "ax.set_xlabel(\"Prey Death Rate\")\n", - "ax.set_ylabel(\"Mean Population\")\n", - "ax.set_title(\"Bifurcation Diagram\")\n", - "ax.set_xlim(-0.005, 0.205)\n", - "ax.ticklabel_format(axis=\"y\", style=\"scientific\", scilimits=(0, 0))\n", - "\n", - "# Inset: Near critical point\n", - "ax_inset = ax.inset_axes([0.62, 0.50, 0.35, 0.40])\n", - "\n", - "ax_inset.errorbar(\n", - " agg_phase1_5[\"prey_death\"],\n", - " agg_phase1_5[\"prey_mean\"],\n", - " yerr=agg_phase1_5[\"prey_se\"],\n", - " fmt=\"o-\",\n", - " color=COLORS[\"prey\"],\n", - " markersize=3,\n", - " capsize=2,\n", - " linewidth=1.2,\n", - ")\n", - "ax_inset.errorbar(\n", - " agg_phase1_5[\"prey_death\"],\n", - " agg_phase1_5[\"pred_mean\"],\n", - " yerr=agg_phase1_5[\"pred_se\"],\n", - " fmt=\"s-\",\n", - " color=COLORS[\"predator\"],\n", - " markersize=3,\n", - " capsize=2,\n", - " linewidth=1.2,\n", - ")\n", - "\n", - "ax_inset.ticklabel_format(axis=\"y\", style=\"scientific\", scilimits=(0, 0))\n", - "ax_inset.tick_params(labelsize=8)\n", - "ax_inset.set_title(\"Near Critical Point\", fontsize=10)\n", - "ax_inset.set_facecolor(\"white\")\n", - "ax_inset.grid(True, alpha=0.3, linestyle=\"--\")\n", - "\n", - "# Style inset border\n", - "for spine in ax_inset.spines.values():\n", - " spine.set_edgecolor(\"0.4\")\n", - " spine.set_linewidth(1)\n", - " spine.set_visible(True)\n", - "\n", - "# Legend\n", - "ax.legend(loc=\"upper right\", framealpha=0.95)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "def get_cluster_distribution(df, prey_death_val, species=\"prey\"):\n", - " \"\"\"Extract all cluster sizes for a given prey_death value.\"\"\"\n", - " subset = df[np.isclose(df[\"prey_death\"], prey_death_val, atol=0.001)]\n", - " col = f\"{species}_cluster_sizes\"\n", - " all_sizes = []\n", - " for sizes in subset[col]:\n", - " if isinstance(sizes, list) and len(sizes) > 0:\n", - " all_sizes.extend(sizes)\n", - " return np.array(all_sizes)\n", - "\n", - "\n", - "# === Plot all cluster sizes as individual points ===\n", - "\n", - "\n", - "def compute_pdf_all_points(cluster_sizes):\n", - " \"\"\"Compute P(s) for each unique cluster size.\"\"\"\n", - " clusters = np.array(cluster_sizes)\n", - " if len(clusters) == 0:\n", - " return np.array([]), np.array([])\n", - "\n", - " # Count occurrences of each size\n", - " unique, counts = np.unique(clusters, return_counts=True)\n", - " # Normalize to get probability\n", - " probs = counts / len(clusters)\n", - "\n", - " return unique, probs" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Clusters at d ≈ 0.095: 443140\n", - "Size range: [1, 8489]\n", - "=== Power Law Fit (Critical Point, d ≈ 0.095) ===\n", - "Power law α: 2.957\n", - "xmin: 337.0\n", - "Truncated PL α: 2.293\n", - "Truncated PL λ: 0.000678\n", - "\n", - "=== Distribution Comparison ===\n", - "Power law vs Truncated PL: R=-3.627, p=0.0000\n", - "Power law vs Lognormal: R=-4.208, p=0.0000\n", - "Truncated PL vs Lognormal: R=-1.331, p=0.1831\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kimonanagnostopoulos/CSS_Project/.venv/lib/python3.12/site-packages/powerlaw/distributions.py:808: UserWarning: Fitted parameters are very close to the edge of parameter ranges for distribution truncated_power_law; consider changing these ranges.\n", - " warnings.warn(f'Fitted parameters are very close to the edge of parameter ranges for distribution {self.name}; consider changing these ranges.')\n" - ] - } - ], - "source": [ - "# === Critical Point Cluster Distribution Plot ===\n", - "\n", - "# Get clusters at critical point\n", - "clusters_crit = get_cluster_distribution(df_phase1, 0.095, \"prey\")\n", - "\n", - "print(f\"Clusters at d ≈ 0.095: {len(clusters_crit)}\")\n", - "print(f\"Size range: [{clusters_crit.min()}, {clusters_crit.max()}]\")\n", - "\n", - "# Compute PDF (all points)\n", - "x_crit, y_crit = compute_pdf_all_points(clusters_crit)\n", - "\n", - "# Fit power law\n", - "fit_crit = powerlaw.Fit(clusters_crit, discrete=True, verbose=False)\n", - "\n", - "# Distribution comparisons\n", - "R_pl_tpl, p_pl_tpl = fit_crit.distribution_compare(\n", - " \"power_law\", \"truncated_power_law\", normalized_ratio=True\n", - ")\n", - "R_pl_ln, p_pl_ln = fit_crit.distribution_compare(\n", - " \"power_law\", \"lognormal\", normalized_ratio=True\n", - ")\n", - "R_tpl_ln, p_tpl_ln = fit_crit.distribution_compare(\n", - " \"truncated_power_law\", \"lognormal\", normalized_ratio=True\n", - ")\n", - "\n", - "print(\"=== Power Law Fit (Critical Point, d ≈ 0.095) ===\")\n", - "print(f\"Power law α: {fit_crit.power_law.alpha:.3f}\")\n", - "print(f\"xmin: {fit_crit.power_law.xmin}\")\n", - "print(f\"Truncated PL α: {fit_crit.truncated_power_law.alpha:.3f}\")\n", - "print(f\"Truncated PL λ: {fit_crit.truncated_power_law.parameter2:.6f}\")\n", - "\n", - "print(f\"\\n=== Distribution Comparison ===\")\n", - "print(f\"Power law vs Truncated PL: R={R_pl_tpl:+.3f}, p={p_pl_tpl:.4f}\")\n", - "print(f\"Power law vs Lognormal: R={R_pl_ln:+.3f}, p={p_pl_ln:.4f}\")\n", - "print(f\"Truncated PL vs Lognormal: R={R_tpl_ln:+.3f}, p={p_tpl_ln:.4f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# === Prey Cluster Size Distribution ===\n", - "\n", - "fig, ax = plt.subplots(figsize=(7, 5))\n", - "\n", - "# Empirical data\n", - "ax.scatter(x_crit, y_crit, color=COLORS[\"prey\"], s=12, alpha=0.5, edgecolors=\"none\")\n", - "\n", - "ax.set_xscale(\"log\")\n", - "ax.set_yscale(\"log\")\n", - "ax.set_xlabel(\"Cluster Size\")\n", - "ax.set_ylabel(\"P(s)\")\n", - "ax.set_title(f\"Prey Cluster Size Distribution (d ≈ 0.095)\")\n", - "\n", - "# Statistics annotation\n", - "winner = \"TPL wins\" if R_tpl_ln > 0 else \"Lognormal\"\n", - "stats_text = (\n", - " f\"α = {fit_crit.truncated_power_law.alpha:.2f}\\n\" f\"R = {R_tpl_ln:+.1f} ({winner})\"\n", - ")\n", - "\n", - "ax.text(\n", - " 0.95,\n", - " 0.95,\n", - " stats_text,\n", - " transform=ax.transAxes,\n", - " verticalalignment=\"top\",\n", - " horizontalalignment=\"right\",\n", - " fontsize=10,\n", - " bbox=dict(boxstyle=\"round,pad=0.4\", facecolor=\"white\", edgecolor=\"0.8\", alpha=0.9),\n", - ")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# === Predator α Across All Prey Death Values ===\n", - "\n", - "prey_deaths_p1 = sorted(df_phase1[\"prey_death\"].unique())\n", - "\n", - "results = []\n", - "for pd_val in prey_deaths_p1:\n", - " pred_clusters = get_cluster_distribution(df_phase1, pd_val, \"pred\")\n", - "\n", - " if len(pred_clusters) < 100:\n", - " continue\n", - "\n", - " fit = powerlaw.Fit(pred_clusters, discrete=True, xmin=1, verbose=False)\n", - " R_tpl_ln, _ = fit.distribution_compare(\n", - " \"truncated_power_law\", \"lognormal\", normalized_ratio=True\n", - " )\n", - "\n", - " results.append(\n", - " {\n", - " \"prey_death\": pd_val,\n", - " \"n_clusters\": len(pred_clusters),\n", - " \"max_size\": pred_clusters.max(),\n", - " \"alpha\": fit.truncated_power_law.alpha,\n", - " \"R_tpl_ln\": R_tpl_ln,\n", - " }\n", - " )\n", - "\n", - "results_df = pd.DataFrame(results)\n", - "\n", - "# === Plot ===\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", - "\n", - "# Left: α vs prey_death\n", - "ax1 = axes[0]\n", - "ax1.plot(\n", - " results_df[\"prey_death\"],\n", - " results_df[\"alpha\"],\n", - " \"o-\",\n", - " color=COLORS[\"predator\"],\n", - " markersize=7,\n", - " linewidth=1.5,\n", - ")\n", - "ax1.axhline(\n", - " 1.17, color=\"0.5\", linestyle=\"--\", linewidth=1, label=\"α = 1.17 (reference)\"\n", - ")\n", - "ax1.set_xlabel(\"Prey Death Rate\")\n", - "ax1.set_ylabel(\"Exponent α\")\n", - "ax1.set_title(\"Predator Power Law Exponent\")\n", - "ax1.legend(loc=\"upper right\", framealpha=0.95)\n", - "\n", - "# Right: R vs prey_death\n", - "ax2 = axes[1]\n", - "bars = ax2.bar(\n", - " results_df[\"prey_death\"],\n", - " results_df[\"R_tpl_ln\"],\n", - " width=0.008,\n", - " color=COLORS[\"predator\"],\n", - " alpha=0.8,\n", - " edgecolor=\"none\",\n", - ")\n", - "ax2.axhline(0, color=\"0.3\", linewidth=1)\n", - "ax2.set_xlabel(\"Prey Death Rate\")\n", - "ax2.set_ylabel(\"R (TPL vs Lognormal)\")\n", - "ax2.set_title(\"Power Law Fit Quality (R > 0 = TPL wins)\")\n", - "\n", - "# Add annotation\n", - "ax2.text(\n", - " 0.95,\n", - " 0.95,\n", - " \"R > 0 everywhere\\n→ Always power law\",\n", - " transform=ax2.transAxes,\n", - " ha=\"right\",\n", - " va=\"top\",\n", - " fontsize=10,\n", - " bbox=dict(boxstyle=\"round,pad=0.4\", facecolor=\"white\", edgecolor=\"0.8\", alpha=0.9),\n", - ")\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(\"plot_predator_alpha_sweep.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "NOTE:\n", - "\n", - "Predator clusters follow a truncated-power law distrbutions across all parameter values where predators survive. The exponent decreases as we go toward extinction and maximum cluster size collapses. This would suggest that movement dynamics generate scale-free spatial patterns, independent of proximity to any critical point." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Phase 2" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Phase 2: 200 records\n", - "Grid size: [1000]\n", - "Evolved prey_death final: mean=0.0617, std=0.0167\n" - ] - } - ], - "source": [ - "# === Load Phase 2 data ===\n", - "import powerlaw\n", - "\n", - "df_phase2 = load_phase_to_df(DATA_ROOT / \"phase2_18693004\")\n", - "\n", - "print(f\"Phase 2: {len(df_phase2)} records\")\n", - "print(f\"Grid size: {df_phase2['grid_size'].unique()}\")\n", - "print(\n", - " f\"Evolved prey_death final: mean={df_phase2['evolved_prey_death_final'].mean():.4f}, std={df_phase2['evolved_prey_death_final'].std():.4f}\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# === Evolved Prey Cluster Distribution ===\n", - "\n", - "# Filter to converged runs\n", - "evolved_finals = df_phase2[\"evolved_prey_death_final\"].values\n", - "converged_mask = (evolved_finals > 0.055) & (evolved_finals < 0.075)\n", - "df_converged = df_phase2[converged_mask]\n", - "\n", - "# Calculate individual run R values (correct method)\n", - "individual_Rs = []\n", - "for idx, row in df_converged.iterrows():\n", - " sizes = row[\"prey_cluster_sizes\"]\n", - " if isinstance(sizes, list) and len(sizes) > 100:\n", - " fit = powerlaw.Fit(np.array(sizes), discrete=True, xmin=1, verbose=False)\n", - " R, _ = fit.distribution_compare(\n", - " \"truncated_power_law\", \"lognormal\", normalized_ratio=True\n", - " )\n", - " individual_Rs.append(R)\n", - "\n", - "individual_Rs = np.array(individual_Rs)\n", - "R_mean = individual_Rs.mean()\n", - "R_std = individual_Rs.std()\n", - "\n", - "# Extract clusters for plotting\n", - "clusters_evolved = []\n", - "for sizes in df_converged[\"prey_cluster_sizes\"]:\n", - " if isinstance(sizes, list) and len(sizes) > 0:\n", - " clusters_evolved.extend(sizes)\n", - "clusters_evolved = np.array(clusters_evolved)\n", - "\n", - "# Compute PDF\n", - "x_evo, y_evo = compute_pdf_all_points(clusters_evolved)\n", - "\n", - "# === Plot ===\n", - "fig, ax = plt.subplots(figsize=(7, 5))\n", - "\n", - "ax.scatter(x_evo, y_evo, color=COLORS[\"prey\"], s=12, alpha=0.5, edgecolors=\"none\")\n", - "\n", - "ax.set_xscale(\"log\")\n", - "ax.set_yscale(\"log\")\n", - "ax.set_xlabel(\"Cluster Size\")\n", - "ax.set_ylabel(\"P(s)\")\n", - "ax.set_title(\"Prey Cluster Distribution (Evolved State, d ≈ 0.068)\")\n", - "\n", - "# Statistics annotation\n", - "stats_text = f\"R = {R_mean:+.1f} ± {R_std:.1f}\\nLognormal\"\n", - "ax.text(\n", - " 0.95,\n", - " 0.95,\n", - " stats_text,\n", - " transform=ax.transAxes,\n", - " ha=\"right\",\n", - " va=\"top\",\n", - " fontsize=10,\n", - " bbox=dict(boxstyle=\"round,pad=0.4\", facecolor=\"white\", edgecolor=\"0.8\", alpha=0.9),\n", - ")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# === Plot evolved cluster distribution + evolved vs initial conditions ===\n", - "\n", - "# Filter to converged runs (exclude collapsed runs near 0)\n", - "evolved_finals = df_phase2[\"evolved_prey_death_final\"].values\n", - "converged_mask = (evolved_finals > 0.055) & (evolved_finals < 0.075)\n", - "df_converged = df_phase2[converged_mask]\n", - "\n", - "# Calculate individual run R values (correct method)\n", - "individual_Rs = []\n", - "for idx, row in df_converged.iterrows():\n", - " sizes = row[\"prey_cluster_sizes\"]\n", - " if isinstance(sizes, list) and len(sizes) > 100:\n", - " fit = powerlaw.Fit(np.array(sizes), discrete=True, xmin=1, verbose=False)\n", - " R, _ = fit.distribution_compare(\n", - " \"truncated_power_law\", \"lognormal\", normalized_ratio=True\n", - " )\n", - " individual_Rs.append(R)\n", - "\n", - "individual_Rs = np.array(individual_Rs)\n", - "R_mean = individual_Rs.mean()\n", - "R_std = individual_Rs.std()\n", - "\n", - "# Extract clusters for plotting only\n", - "clusters_evolved = []\n", - "for sizes in df_converged[\"prey_cluster_sizes\"]:\n", - " if isinstance(sizes, list) and len(sizes) > 0:\n", - " clusters_evolved.extend(sizes)\n", - "clusters_evolved = np.array(clusters_evolved)\n", - "\n", - "# Compute PDF (all points)\n", - "x_evo, y_evo = compute_pdf_all_points(clusters_evolved)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# === Evolved Clusters and Convergence ===\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", - "\n", - "# Left: Cluster distribution\n", - "ax1 = axes[0]\n", - "ax1.scatter(x_evo, y_evo, color=COLORS[\"prey\"], s=12, alpha=0.5, edgecolors=\"none\")\n", - "\n", - "ax1.set_xscale(\"log\")\n", - "ax1.set_yscale(\"log\")\n", - "ax1.set_xlabel(\"Cluster Size\")\n", - "ax1.set_ylabel(\"P(s)\")\n", - "ax1.set_title(\"Prey Cluster Distribution (Evolved State)\")\n", - "\n", - "stats_text = f\"R = {R_mean:+.1f} ± {R_std:.1f}\\nLognormal\"\n", - "ax1.text(\n", - " 0.95,\n", - " 0.95,\n", - " stats_text,\n", - " transform=ax1.transAxes,\n", - " ha=\"right\",\n", - " va=\"top\",\n", - " fontsize=10,\n", - " bbox=dict(boxstyle=\"round,pad=0.4\", facecolor=\"white\", edgecolor=\"0.8\", alpha=0.9),\n", - ")\n", - "\n", - "# Right: Evolved vs initial conditions\n", - "ax2 = axes[1]\n", - "ax2.scatter(\n", - " df_phase2[\"prey_death\"],\n", - " df_phase2[\"evolved_prey_death_final\"],\n", - " color=COLORS[\"prey\"],\n", - " s=20,\n", - " alpha=0.5,\n", - " edgecolors=\"none\",\n", - ")\n", - "\n", - "# Horizontal line at converged value\n", - "converged_mean = df_converged[\"evolved_prey_death_final\"].mean()\n", - "ax2.axhline(\n", - " converged_mean,\n", - " color=COLORS[\"predator\"],\n", - " linestyle=\":\",\n", - " linewidth=1.5,\n", - " alpha=0.8,\n", - " label=f\"Converged: d ≈ {converged_mean:.3f}\",\n", - ")\n", - "\n", - "ax2.set_xlabel(\"Initial Prey Death Rate\")\n", - "ax2.set_ylabel(\"Evolved Prey Death Rate\")\n", - "ax2.set_title(\"Evolution Convergence\")\n", - "ax2.legend(loc=\"upper left\", framealpha=0.95)\n", - "\n", - "# Axis limits\n", - "ax2.set_xlim(0, 0.2)\n", - "ax2.set_ylim(0, 0.2)\n", - "ax2.set_aspect(\"equal\")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Phase 3" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Phase 3 loaded: 120 records\n", - "Grid sizes: [np.int64(50), np.int64(100), np.int64(250), np.int64(500), np.int64(1000), np.int64(2500)]\n", - "prey_death: [0.0963]\n" - ] - } - ], - "source": [ - "# === Phase 3: Finite-Size Scaling - Lognormal Fit ===\n", - "from scipy import stats\n", - "\n", - "# Load Phase 3 data\n", - "df_phase3 = load_phase_to_df(DATA_ROOT / \"phase3_18698382\")\n", - "\n", - "print(f\"Phase 3 loaded: {len(df_phase3)} records\")\n", - "print(f\"Grid sizes: {sorted(df_phase3['grid_size'].unique())}\")\n", - "print(f\"prey_death: {df_phase3['prey_death'].unique()}\")\n", - "\n", - "grid_sizes = sorted(df_phase3[\"grid_size\"].unique())\n", - "\n", - "# Color palette for different L values\n", - "colors_L = plt.cm.viridis(np.linspace(0, 0.9, len(grid_sizes)))\n", - "\n", - "# Store fit results\n", - "fit_results = []\n", - "\n", - "for i, L in enumerate(grid_sizes):\n", - " subset = df_phase3[df_phase3[\"grid_size\"] == L]\n", - "\n", - " clusters = []\n", - " for sizes in subset[\"prey_cluster_sizes\"]:\n", - " if isinstance(sizes, list) and len(sizes) > 0:\n", - " clusters.extend(sizes)\n", - "\n", - " if len(clusters) < 100:\n", - " continue\n", - "\n", - " clusters = np.array(clusters)\n", - "\n", - " # Fit lognormal using scipy\n", - " shape, loc, scale = stats.lognorm.fit(clusters, floc=0)\n", - " mu = np.log(scale)\n", - " sigma = shape\n", - "\n", - " # Statistical test: power law vs lognormal\n", - " fit = powerlaw.Fit(clusters, discrete=True, xmin=1, verbose=False)\n", - " R_pl_ln, _ = fit.distribution_compare(\n", - " \"power_law\", \"lognormal\", normalized_ratio=True\n", - " )\n", - "\n", - " # Compute PDF\n", - " unique, counts = np.unique(clusters, return_counts=True)\n", - " probs = counts / len(clusters)\n", - "\n", - " fit_results.append(\n", - " {\n", - " \"L\": L,\n", - " \"unique\": unique,\n", - " \"probs\": probs,\n", - " \"mu\": mu,\n", - " \"sigma\": sigma,\n", - " \"R_pl_ln\": R_pl_ln,\n", - " \"color\": colors_L[i],\n", - " }\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# === Prey Cluster FSS with Lognormal Fits ===\n", - "\n", - "fig, ax = plt.subplots(figsize=(9, 6))\n", - "\n", - "for res in fit_results:\n", - " L = res[\"L\"]\n", - " ax.scatter(\n", - " res[\"unique\"],\n", - " res[\"probs\"],\n", - " color=res[\"color\"],\n", - " s=12,\n", - " alpha=0.6,\n", - " edgecolors=\"none\",\n", - " label=f'L={L} (μ={res[\"mu\"]:.2f}, σ={res[\"sigma\"]:.2f}, R={res[\"R_pl_ln\"]:+.1f})',\n", - " )\n", - "\n", - "ax.set_xscale(\"log\")\n", - "ax.set_yscale(\"log\")\n", - "ax.set_xlabel(\"Cluster Size\")\n", - "ax.set_ylabel(\"P(s)\")\n", - "ax.set_title(\"Prey Cluster Distribution: Finite-Size Scaling (d = 0.0955)\")\n", - "\n", - "# Annotation instead of subtitle\n", - "ax.text(\n", - " 0.02,\n", - " 0.02,\n", - " \"R < 0: Lognormal\",\n", - " transform=ax.transAxes,\n", - " ha=\"left\",\n", - " va=\"bottom\",\n", - " fontsize=10,\n", - " bbox=dict(boxstyle=\"round,pad=0.4\", facecolor=\"white\", edgecolor=\"0.8\", alpha=0.9),\n", - ")\n", - "\n", - "# Legend at bottom\n", - "ax.legend(\n", - " loc=\"upper center\",\n", - " bbox_to_anchor=(0.5, -0.10),\n", - " ncol=2,\n", - " fontsize=9,\n", - " framealpha=0.95,\n", - " edgecolor=\"0.8\",\n", - ")\n", - "\n", - "plt.tight_layout()\n", - "plt.subplots_adjust(bottom=0.20)\n", - "plt.savefig(\"plot_prey_fss_lognormal.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# === Prey vs Predator Distribution Comparison ===\n", - "from scipy import stats\n", - "\n", - "prey_deaths_p1 = sorted(df_phase1[\"prey_death\"].unique())\n", - "\n", - "# Collect prey data\n", - "results_prey = []\n", - "for pd_val in prey_deaths_p1:\n", - " if pd_val > 0.10: # Skip noisy collapse region\n", - " continue\n", - "\n", - " prey_clusters = get_cluster_distribution(df_phase1, pd_val, \"prey\")\n", - "\n", - " if len(prey_clusters) < 100:\n", - " continue\n", - "\n", - " fit = powerlaw.Fit(prey_clusters, discrete=True, xmin=1, verbose=False)\n", - " R_tpl_ln, _ = fit.distribution_compare(\n", - " \"truncated_power_law\", \"lognormal\", normalized_ratio=True\n", - " )\n", - " shape, loc, scale = stats.lognorm.fit(prey_clusters, floc=0)\n", - "\n", - " results_prey.append(\n", - " {\n", - " \"prey_death\": pd_val,\n", - " \"n_clusters\": len(prey_clusters),\n", - " \"max_size\": prey_clusters.max(),\n", - " \"alpha\": fit.truncated_power_law.alpha,\n", - " \"R_tpl_ln\": R_tpl_ln,\n", - " \"mu\": np.log(scale),\n", - " \"sigma\": shape,\n", - " }\n", - " )\n", - "\n", - "results_prey_df = pd.DataFrame(results_prey)\n", - "\n", - "# Collect predator data\n", - "results_pred = []\n", - "for pd_val in prey_deaths_p1:\n", - " pred_clusters = get_cluster_distribution(df_phase1, pd_val, \"pred\")\n", - "\n", - " if len(pred_clusters) < 100:\n", - " continue\n", - "\n", - " fit = powerlaw.Fit(pred_clusters, discrete=True, xmin=1, verbose=False)\n", - " R_tpl_ln, _ = fit.distribution_compare(\n", - " \"truncated_power_law\", \"lognormal\", normalized_ratio=True\n", - " )\n", - "\n", - " results_pred.append(\n", - " {\n", - " \"prey_death\": pd_val,\n", - " \"n_clusters\": len(pred_clusters),\n", - " \"max_size\": pred_clusters.max(),\n", - " \"alpha\": fit.truncated_power_law.alpha,\n", - " \"R_tpl_ln\": R_tpl_ln,\n", - " }\n", - " )\n", - "\n", - "results_pred_df = pd.DataFrame(results_pred)\n", - "\n", - "# === Plot ===\n", - "fig, axes = plt.subplots(2, 2, figsize=(12, 9))\n", - "\n", - "# Top Left: Prey R\n", - "ax1 = axes[0, 0]\n", - "ax1.bar(\n", - " results_prey_df[\"prey_death\"],\n", - " results_prey_df[\"R_tpl_ln\"],\n", - " width=0.008,\n", - " color=COLORS[\"prey\"],\n", - " alpha=0.8,\n", - " edgecolor=\"none\",\n", - ")\n", - "ax1.axhline(0, color=\"0.3\", linewidth=1)\n", - "ax1.set_xlabel(\"Prey Death Rate\")\n", - "ax1.set_ylabel(\"R (TPL vs Lognormal)\")\n", - "ax1.set_title(\"Prey: Lognormal Wins (R < 0)\")\n", - "ax1.text(\n", - " 0.95,\n", - " 0.95,\n", - " \"R < 0 everywhere\",\n", - " transform=ax1.transAxes,\n", - " ha=\"right\",\n", - " va=\"top\",\n", - " fontsize=10,\n", - " bbox=dict(boxstyle=\"round,pad=0.4\", facecolor=\"white\", edgecolor=\"0.8\", alpha=0.9),\n", - ")\n", - "\n", - "# Top Right: Predator R\n", - "ax2 = axes[0, 1]\n", - "ax2.bar(\n", - " results_pred_df[\"prey_death\"],\n", - " results_pred_df[\"R_tpl_ln\"],\n", - " width=0.008,\n", - " color=COLORS[\"predator\"],\n", - " alpha=0.8,\n", - " edgecolor=\"none\",\n", - ")\n", - "ax2.axhline(0, color=\"0.3\", linewidth=1)\n", - "ax2.set_xlabel(\"Prey Death Rate\")\n", - "ax2.set_ylabel(\"R (TPL vs Lognormal)\")\n", - "ax2.set_title(\"Predator: Power Law Wins (R > 0)\")\n", - "ax2.text(\n", - " 0.95,\n", - " 0.95,\n", - " \"R > 0 everywhere\",\n", - " transform=ax2.transAxes,\n", - " ha=\"right\",\n", - " va=\"top\",\n", - " fontsize=10,\n", - " bbox=dict(boxstyle=\"round,pad=0.4\", facecolor=\"white\", edgecolor=\"0.8\", alpha=0.9),\n", - ")\n", - "\n", - "# Bottom Left: Prey lognormal params\n", - "ax3 = axes[1, 0]\n", - "ax3.plot(\n", - " results_prey_df[\"prey_death\"],\n", - " results_prey_df[\"mu\"],\n", - " \"o-\",\n", - " color=COLORS[\"prey\"],\n", - " markersize=7,\n", - " linewidth=1.5,\n", - " label=\"μ\",\n", - ")\n", - "ax3.plot(\n", - " results_prey_df[\"prey_death\"],\n", - " results_prey_df[\"sigma\"],\n", - " \"s--\",\n", - " color=COLORS[\"prey\"],\n", - " markersize=7,\n", - " linewidth=1.5,\n", - " alpha=0.6,\n", - " label=\"σ\",\n", - ")\n", - "ax3.set_xlabel(\"Prey Death Rate\")\n", - "ax3.set_ylabel(\"Lognormal Parameters\")\n", - "ax3.set_title(\"Prey Lognormal Parameters\")\n", - "ax3.legend(loc=\"upper right\", framealpha=0.95)\n", - "\n", - "# Add summary stats as annotation\n", - "mu_mean, mu_std = results_prey_df[\"mu\"].mean(), results_prey_df[\"mu\"].std()\n", - "sigma_mean, sigma_std = results_prey_df[\"sigma\"].mean(), results_prey_df[\"sigma\"].std()\n", - "ax3.text(\n", - " 0.02,\n", - " 0.02,\n", - " f\"μ = {mu_mean:.2f} ± {mu_std:.2f}\\nσ = {sigma_mean:.2f} ± {sigma_std:.2f}\",\n", - " transform=ax3.transAxes,\n", - " ha=\"left\",\n", - " va=\"bottom\",\n", - " fontsize=10,\n", - " bbox=dict(boxstyle=\"round,pad=0.4\", facecolor=\"white\", edgecolor=\"0.8\", alpha=0.9),\n", - ")\n", - "\n", - "# Bottom Right: Predator α\n", - "ax4 = axes[1, 1]\n", - "ax4.plot(\n", - " results_pred_df[\"prey_death\"],\n", - " results_pred_df[\"alpha\"],\n", - " \"o-\",\n", - " color=COLORS[\"predator\"],\n", - " markersize=7,\n", - " linewidth=1.5,\n", - ")\n", - "ax4.set_xlabel(\"Prey Death Rate\")\n", - "ax4.set_ylabel(\"Exponent α\")\n", - "ax4.set_title(\"Predator Power Law Exponent\")\n", - "\n", - "# Add range annotation\n", - "alpha_min, alpha_max = results_pred_df[\"alpha\"].min(), results_pred_df[\"alpha\"].max()\n", - "ax4.text(\n", - " 0.02,\n", - " 0.02,\n", - " f\"α: {alpha_min:.2f} → {alpha_max:.2f}\",\n", - " transform=ax4.transAxes,\n", - " ha=\"left\",\n", - " va=\"bottom\",\n", - " fontsize=10,\n", - " bbox=dict(boxstyle=\"round,pad=0.4\", facecolor=\"white\", edgecolor=\"0.8\", alpha=0.9),\n", - ")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Phase 4" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Load both Phase 4 files\n", - "df_phase4_1 = load_phase_to_df(DATA_ROOT / \"phase4_18735304\")\n", - "df_phase4_2 = load_phase_to_df(DATA_ROOT / \"phase4.2_18832956\")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Phase 4.1: 146410 records\n", - "Phase 4.2: 133100 records\n", - "Combined: 279510 records\n" - ] - } - ], - "source": [ - "print(f\"Phase 4.1: {len(df_phase4_1)} records\")\n", - "print(f\"Phase 4.2: {len(df_phase4_2)} records\")\n", - "\n", - "# Combine\n", - "df = pd.concat([df_phase4_1, df_phase4_2], ignore_index=True)\n", - "print(f\"Combined: {len(df)} records\")\n", - "\n", - "# Compute prey density (normalize by grid size)\n", - "grid_size = df[\"grid_size\"].iloc[0]\n", - "df[\"prey_density\"] = df[\"prey_mean\"] / (grid_size**2)\n", - "df[\"pred_density\"] = df[\"pred_mean\"] / (grid_size**2)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "27951 unique parameter combinations\n" - ] - } - ], - "source": [ - "# Average over replicates\n", - "df_avg = (\n", - " df.groupby([\"prey_birth\", \"prey_death\", \"predator_birth\", \"predator_death\"])\n", - " .agg(\n", - " {\n", - " \"prey_mean\": \"mean\",\n", - " \"pred_mean\": \"mean\",\n", - " \"prey_density\": \"mean\",\n", - " \"pred_density\": \"mean\",\n", - " }\n", - " )\n", - " .reset_index()\n", - ")\n", - "\n", - "print(f\"{len(df_avg)} unique parameter combinations\")" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Built 1331 curves\n" - ] - } - ], - "source": [ - "# === 2. Build Curves: Prey Density vs Prey Death for Each Parameter Combo ===\n", - "\n", - "# Group by (prey_birth, predator_birth, predator_death) to get curves\n", - "curves_data = []\n", - "\n", - "for (pb, pred_b, pred_d), group in df_avg.groupby(\n", - " [\"prey_birth\", \"predator_birth\", \"predator_death\"]\n", - "):\n", - " # Sort by prey_death\n", - " group_sorted = group.sort_values(\"prey_death\")\n", - "\n", - " curves_data.append(\n", - " {\n", - " \"prey_birth\": pb,\n", - " \"predator_birth\": pred_b,\n", - " \"predator_death\": pred_d,\n", - " \"prey_death\": group_sorted[\"prey_death\"].values,\n", - " \"prey_density\": group_sorted[\"prey_density\"].values,\n", - " }\n", - " )\n", - "\n", - "print(f\"Built {len(curves_data)} curves\")" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Curves WITH Hydra effect: 154 (11.6%)\n" - ] - } - ], - "source": [ - "# === 4. Detect Hydra Effect (Positive Derivative Regions) ===\n", - "\n", - "\n", - "def detect_hydra_effect(prey_death, prey_density):\n", - " \"\"\"Detect if curve has hydra effect (increasing region).\"\"\"\n", - " if len(prey_death) < 2:\n", - " return False, None\n", - "\n", - " derivatives = np.diff(prey_density) / np.diff(prey_death)\n", - " has_increasing = np.any(derivatives > 0)\n", - "\n", - " if has_increasing:\n", - " max_deriv = np.max(derivatives)\n", - " return True, max_deriv\n", - " return False, None\n", - "\n", - "\n", - "# Classify curves\n", - "curves_with_hydra = []\n", - "\n", - "for curve in curves_data:\n", - " has_hydra, max_deriv = detect_hydra_effect(\n", - " curve[\"prey_death\"], curve[\"prey_density\"]\n", - " )\n", - " curve[\"has_hydra\"] = has_hydra\n", - " curve[\"max_derivative\"] = max_deriv\n", - "\n", - " if has_hydra:\n", - " curves_with_hydra.append(curve)\n", - "\n", - "print(\n", - " f\"Curves WITH Hydra effect: {len(curves_with_hydra)} ({100*len(curves_with_hydra)/len(curves_data):.1f}%)\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filtered curves: 154\n" - ] - } - ], - "source": [ - "# === 5. Filter Curves: Keep Only Portion Before Max Derivative ===\n", - "\n", - "filtered_curves = []\n", - "\n", - "for curve in curves_with_hydra:\n", - " prey_d = curve[\"prey_death\"]\n", - " prey_n = curve[\"prey_density\"]\n", - "\n", - " # Compute derivatives\n", - " derivatives = np.diff(prey_n) / np.diff(prey_d)\n", - "\n", - " # Find index of maximum positive derivative\n", - " max_deriv_idx = np.argmax(derivatives)\n", - " end_idx = max_deriv_idx + 1\n", - "\n", - " if end_idx > 0:\n", - " filtered_curves.append(\n", - " {\n", - " \"prey_birth\": curve[\"prey_birth\"],\n", - " \"predator_birth\": curve[\"predator_birth\"],\n", - " \"predator_death\": curve[\"predator_death\"],\n", - " \"prey_death\": prey_d[: end_idx + 1],\n", - " \"prey_density\": prey_n[: end_idx + 1],\n", - " \"max_derivative\": curve[\"max_derivative\"],\n", - " }\n", - " )\n", - "\n", - "print(f\"Filtered curves: {len(filtered_curves)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Valid quadratic fits: 112\n", - "Coefficient range: [1.19e+00, 2.71e+01]\n" - ] - } - ], - "source": [ - "# === 6. Fit Quadratic Polynomial and Extract Coefficients ===\n", - "from numpy.polynomial import Polynomial\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "\n", - "curves_with_quadratic = []\n", - "\n", - "for curve in filtered_curves:\n", - " prey_d = curve[\"prey_death\"]\n", - " prey_n = curve[\"prey_density\"]\n", - "\n", - " if len(prey_d) < 3:\n", - " continue\n", - "\n", - " try:\n", - " # Fit: N = a + b·d + c·d²\n", - " poly = Polynomial.fit(prey_d, prey_n, deg=2)\n", - " coeffs = poly.convert().coef\n", - " quadratic_coef = coeffs[2] if len(coeffs) > 2 else 0.0\n", - "\n", - " curves_with_quadratic.append(\n", - " {\n", - " \"prey_birth\": curve[\"prey_birth\"],\n", - " \"predator_birth\": curve[\"predator_birth\"],\n", - " \"predator_death\": curve[\"predator_death\"],\n", - " \"quadratic_coef\": quadratic_coef,\n", - " \"max_derivative\": curve[\"max_derivative\"],\n", - " }\n", - " )\n", - " except:\n", - " pass\n", - "\n", - "df_quadratic = pd.DataFrame(curves_with_quadratic)\n", - "df_quadratic_valid = df_quadratic[df_quadratic[\"quadratic_coef\"].notna()]\n", - "\n", - "print(f\"Valid quadratic fits: {len(df_quadratic_valid)}\")\n", - "print(\n", - " f\"Coefficient range: [{df_quadratic_valid['quadratic_coef'].min():.2e}, {df_quadratic_valid['quadratic_coef'].max():.2e}]\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Curves with Hydra effect: 154 (11.6%)\n", - "Curves without Hydra effect: 1177 (88.4%)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/fm/9tf79c_d1691c4jwk2qqr42m0000gn/T/ipykernel_35436/2511870083.py:122: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", - " plt.tight_layout(rect=[0, 0, 0.88, 1])\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# === Hydra Effect Detection and Visualization ===\n", - "\n", - "\n", - "def detect_hydra_effect(prey_death, prey_density):\n", - " \"\"\"Detect if curve has hydra effect (increasing region).\"\"\"\n", - " if len(prey_death) < 2:\n", - " return False, None\n", - "\n", - " derivatives = np.diff(prey_density) / np.diff(prey_death)\n", - " has_increasing = np.any(derivatives > 0)\n", - "\n", - " if has_increasing:\n", - " max_deriv = np.max(derivatives)\n", - " return True, max_deriv\n", - " return False, None\n", - "\n", - "\n", - "# Classify curves\n", - "curves_with_hydra = []\n", - "curves_without_hydra = []\n", - "\n", - "for curve in curves_data:\n", - " has_hydra, max_deriv = detect_hydra_effect(\n", - " curve[\"prey_death\"], curve[\"prey_density\"]\n", - " )\n", - " curve[\"has_hydra\"] = has_hydra\n", - " curve[\"max_derivative\"] = max_deriv\n", - "\n", - " if has_hydra:\n", - " curves_with_hydra.append(curve)\n", - " else:\n", - " curves_without_hydra.append(curve)\n", - "\n", - "print(\n", - " f\"Curves with Hydra effect: {len(curves_with_hydra)} ({100*len(curves_with_hydra)/len(curves_data):.1f}%)\"\n", - ")\n", - "print(\n", - " f\"Curves without Hydra effect: {len(curves_without_hydra)} ({100*len(curves_without_hydra)/len(curves_data):.1f}%)\"\n", - ")\n", - "\n", - "# Filter curves: keep only portion before max derivative\n", - "filtered_curves = []\n", - "\n", - "for curve in curves_with_hydra:\n", - " prey_d = curve[\"prey_death\"]\n", - " prey_n = curve[\"prey_density\"]\n", - "\n", - " derivatives = np.diff(prey_n) / np.diff(prey_d)\n", - " max_deriv_idx = np.argmax(derivatives)\n", - " end_idx = max_deriv_idx + 1\n", - "\n", - " if end_idx > 0:\n", - " filtered_curves.append(\n", - " {\n", - " \"prey_birth\": curve[\"prey_birth\"],\n", - " \"predator_birth\": curve[\"predator_birth\"],\n", - " \"predator_death\": curve[\"predator_death\"],\n", - " \"prey_death\": prey_d[: end_idx + 1],\n", - " \"prey_density\": prey_n[: end_idx + 1],\n", - " \"max_derivative\": curve[\"max_derivative\"],\n", - " }\n", - " )\n", - "\n", - "# === Plot: Side by Side ===\n", - "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", - "\n", - "# Color normalization (shared)\n", - "max_derivs = [c[\"max_derivative\"] for c in curves_with_hydra]\n", - "norm = plt.Normalize(vmin=min(max_derivs), vmax=max(max_derivs))\n", - "cmap = plt.cm.Blues # Consistent with our color scheme\n", - "\n", - "# Left: All curves with Hydra effect\n", - "ax1 = axes[0]\n", - "for curve in curves_with_hydra:\n", - " color = cmap(norm(curve[\"max_derivative\"]))\n", - " ax1.plot(\n", - " curve[\"prey_death\"],\n", - " curve[\"prey_density\"],\n", - " color=color,\n", - " alpha=0.6,\n", - " linewidth=1.2,\n", - " )\n", - "\n", - "ax1.axhline(0, color=\"0.5\", linestyle=\"--\", linewidth=0.8)\n", - "ax1.set_xlabel(\"Prey Death Rate\")\n", - "ax1.set_ylabel(\"Equilibrium Prey Density\")\n", - "ax1.set_title(f\"Curves with Hydra Effect\")\n", - "\n", - "# Right: Filtered curves (portion before max derivative)\n", - "ax2 = axes[1]\n", - "for curve in filtered_curves:\n", - " color = cmap(norm(curve[\"max_derivative\"]))\n", - " ax2.plot(\n", - " curve[\"prey_death\"],\n", - " curve[\"prey_density\"],\n", - " color=color,\n", - " alpha=0.6,\n", - " linewidth=1.2,\n", - " )\n", - "\n", - "ax2.axhline(0, color=\"0.5\", linestyle=\"--\", linewidth=0.8)\n", - "ax2.set_xlabel(\"Prey Death Rate\")\n", - "ax2.set_ylabel(\"Equilibrium Prey Density\")\n", - "ax2.set_title(f\"Truncated at Max Derivative\")\n", - "\n", - "# Match y-axis limits\n", - "y_max = max(ax1.get_ylim()[1], ax2.get_ylim()[1])\n", - "y_min = min(ax1.get_ylim()[0], ax2.get_ylim()[0])\n", - "ax1.set_ylim(y_min, y_max)\n", - "ax2.set_ylim(y_min, y_max)\n", - "\n", - "# Shared colorbar\n", - "fig.subplots_adjust(right=0.88)\n", - "cbar_ax = fig.add_axes([0.90, 0.15, 0.02, 0.7])\n", - "sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n", - "sm.set_array([])\n", - "cbar = fig.colorbar(sm, cax=cbar_ax)\n", - "cbar.set_label(\"Max Positive Derivative\")\n", - "\n", - "fig.suptitle(\"Hydra Effect: Full vs Truncated Curves\", y=1.02, fontweight=\"normal\")\n", - "\n", - "plt.tight_layout(rect=[0, 0, 0.88, 1])\n", - "plt.savefig(\"phase4_hydra_curves.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data coverage: 46.1%\n", - "Coefficient range: [2.0, 20.7]\n" - ] - } - ], - "source": [ - "# === 3D Heatmap: Quadratic Coefficients ===\n", - "\n", - "if len(df_quadratic_valid) > 0:\n", - " # Get unique parameter values\n", - " pb_grid = np.array(sorted(df_quadratic_valid[\"prey_birth\"].unique()))\n", - " pred_b_grid = np.array(sorted(df_quadratic_valid[\"predator_birth\"].unique()))\n", - " pred_d_grid = np.array(sorted(df_quadratic_valid[\"predator_death\"].unique()))\n", - "\n", - " # Create 3D array and fill with data\n", - " heatmap_3d = np.full((len(pb_grid), len(pred_b_grid), len(pred_d_grid)), np.nan)\n", - "\n", - " for _, row in df_quadratic_valid.iterrows():\n", - " pb_idx = np.where(np.isclose(pb_grid, row[\"prey_birth\"]))[0]\n", - " pred_b_idx = np.where(np.isclose(pred_b_grid, row[\"predator_birth\"]))[0]\n", - " pred_d_idx = np.where(np.isclose(pred_d_grid, row[\"predator_death\"]))[0]\n", - "\n", - " if len(pb_idx) > 0 and len(pred_b_idx) > 0 and len(pred_d_idx) > 0:\n", - " heatmap_3d[pb_idx[0], pred_b_idx[0], pred_d_idx[0]] = row[\"quadratic_coef\"]\n", - "\n", - " # Color normalization\n", - " vmin = np.nanpercentile(heatmap_3d, 5)\n", - " vmax = np.nanpercentile(heatmap_3d, 95)\n", - " norm = plt.Normalize(vmin=vmin, vmax=vmax)\n", - " cmap = plt.cm.Blues\n", - "\n", - " # Create voxel colors\n", - " filled = ~np.isnan(heatmap_3d)\n", - " colors = np.empty(heatmap_3d.shape + (4,))\n", - "\n", - " for i in range(heatmap_3d.shape[0]):\n", - " for j in range(heatmap_3d.shape[1]):\n", - " for k in range(heatmap_3d.shape[2]):\n", - " if filled[i, j, k]:\n", - " rgb = cmap(norm(heatmap_3d[i, j, k]))[:3]\n", - " colors[i, j, k] = [rgb[0], rgb[1], rgb[2], 0.9]\n", - " else:\n", - " colors[i, j, k] = [0, 0, 0, 0]\n", - "\n", - " # === Create figure with shared colorbar ===\n", - " fig = plt.figure(figsize=(16, 7))\n", - "\n", - " from matplotlib.gridspec import GridSpec\n", - "\n", - " gs = GridSpec(1, 3, width_ratios=[1, 1, 0.05], wspace=0.25)\n", - "\n", - " # === Left panel: Voxel plot ===\n", - " ax1 = fig.add_subplot(gs[0, 0], projection=\"3d\")\n", - " ax1.voxels(filled, facecolors=colors, edgecolors=\"0.4\", linewidth=0.1)\n", - "\n", - " # Simplified tick labels\n", - " tick_skip = 2\n", - " ax1.set_xticks(range(0, len(pb_grid), tick_skip))\n", - " ax1.set_xticklabels(\n", - " [f\"{pb_grid[i]:.1f}\" for i in range(0, len(pb_grid), tick_skip)]\n", - " )\n", - " ax1.set_yticks(range(0, len(pred_b_grid), tick_skip))\n", - " ax1.set_yticklabels(\n", - " [f\"{pred_b_grid[i]:.1f}\" for i in range(0, len(pred_b_grid), tick_skip)]\n", - " )\n", - " ax1.set_zticks(range(0, len(pred_d_grid), tick_skip))\n", - " ax1.set_zticklabels(\n", - " [f\"{pred_d_grid[i]:.1f}\" for i in range(0, len(pred_d_grid), tick_skip)]\n", - " )\n", - "\n", - " ax1.set_xlabel(\"Prey Birth\")\n", - " ax1.set_ylabel(\"Predator Birth\")\n", - " ax1.set_zlabel(\"Predator Death\")\n", - " ax1.set_title(\"3D Voxel Heatmap\")\n", - " ax1.view_init(elev=25, azim=225) # Rotated 180 degrees (45 + 180 = 225)\n", - "\n", - " # === Right panel: Cross-sections ===\n", - " ax2 = fig.add_subplot(gs[0, 1], projection=\"3d\")\n", - "\n", - " PB_slice, PRED_B_slice = np.meshgrid(pb_grid, pred_b_grid, indexing=\"ij\")\n", - "\n", - " n_slices = min(5, len(pred_d_grid))\n", - " slice_indices = np.linspace(0, len(pred_d_grid) - 1, n_slices, dtype=int)\n", - "\n", - " for slice_idx in slice_indices:\n", - " pd_val = pred_d_grid[slice_idx]\n", - " slice_data = heatmap_3d[:, :, slice_idx]\n", - " valid_mask = ~np.isnan(slice_data)\n", - "\n", - " if not np.any(valid_mask):\n", - " continue\n", - "\n", - " Z_slice = np.full_like(PB_slice, pd_val)\n", - "\n", - " face_colors = np.empty((*slice_data.shape, 4))\n", - " for i in range(slice_data.shape[0]):\n", - " for j in range(slice_data.shape[1]):\n", - " if valid_mask[i, j]:\n", - " face_colors[i, j] = [*cmap(norm(slice_data[i, j]))[:3], 0.8]\n", - " else:\n", - " face_colors[i, j] = [0, 0, 0, 0]\n", - "\n", - " PB_masked = np.ma.masked_where(~valid_mask, PB_slice)\n", - " PRED_B_masked = np.ma.masked_where(~valid_mask, PRED_B_slice)\n", - " Z_masked = np.ma.masked_where(~valid_mask, Z_slice)\n", - "\n", - " ax2.plot_surface(\n", - " PB_masked,\n", - " PRED_B_masked,\n", - " Z_masked,\n", - " facecolors=face_colors,\n", - " alpha=0.8,\n", - " linewidth=0.2,\n", - " shade=False,\n", - " )\n", - "\n", - " ax2.set_xlim(pb_grid.min(), pb_grid.max())\n", - " ax2.set_ylim(pred_b_grid.min(), pred_b_grid.max())\n", - " ax2.set_zlim(pred_d_grid.min(), pred_d_grid.max())\n", - "\n", - " ax2.set_xticks(pb_grid[::tick_skip])\n", - " ax2.set_xticklabels([f\"{x:.1f}\" for x in pb_grid[::tick_skip]])\n", - " ax2.set_yticks(pred_b_grid[::tick_skip])\n", - " ax2.set_yticklabels([f\"{x:.1f}\" for x in pred_b_grid[::tick_skip]])\n", - " ax2.set_zticks(pred_d_grid[::tick_skip])\n", - " ax2.set_zticklabels([f\"{x:.1f}\" for x in pred_d_grid[::tick_skip]])\n", - "\n", - " ax2.set_xlabel(\"Prey Birth\")\n", - " ax2.set_ylabel(\"Predator Birth\")\n", - " ax2.set_zlabel(\"Predator Death\")\n", - " ax2.set_title(\"Cross-Sections\")\n", - " ax2.view_init(elev=25, azim=225) # Rotated 180 degrees\n", - "\n", - " # === Shared colorbar ===\n", - " cbar_ax = fig.add_subplot(gs[0, 2])\n", - " sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n", - " sm.set_array([])\n", - " cbar = fig.colorbar(sm, cax=cbar_ax)\n", - " cbar.set_label(\"Quadratic Coefficient\")\n", - "\n", - " fig.suptitle(\n", - " \"Hydra Effect Strength (Undirected): 3D Parameter Space\", y=0.98, fontweight=\"normal\"\n", - " )\n", - "\n", - " plt.savefig(\"phase4_3d_heatmap.png\", dpi=150, bbox_inches=\"tight\")\n", - " plt.show()\n", - "\n", - " print(f\"Data coverage: {100*np.sum(filled)/filled.size:.1f}%\")\n", - " print(f\"Coefficient range: [{vmin:.1f}, {vmax:.1f}]\")" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# === Hydra Effect: Marginal Effects ===\n", - "\n", - "fig, axes = plt.subplots(1, 3, figsize=(14, 4))\n", - "\n", - "# Plot 1: Effect of Prey Birth\n", - "by_pb = (\n", - " df_quadratic_valid.groupby(\"prey_birth\")[\"quadratic_coef\"]\n", - " .agg([\"mean\", \"std\", \"count\"])\n", - " .reset_index()\n", - ")\n", - "by_pb[\"se\"] = by_pb[\"std\"] / np.sqrt(by_pb[\"count\"])\n", - "\n", - "ax1 = axes[0]\n", - "ax1.errorbar(\n", - " by_pb[\"prey_birth\"],\n", - " by_pb[\"mean\"],\n", - " yerr=by_pb[\"se\"],\n", - " fmt=\"o-\",\n", - " color=COLORS[\"prey\"],\n", - " markersize=6,\n", - " linewidth=1.5,\n", - " capsize=3,\n", - ")\n", - "ax1.axhline(0, color=\"0.5\", linestyle=\"--\", linewidth=1)\n", - "ax1.set_xlabel(\"Prey Birth Rate\")\n", - "ax1.set_ylabel(\"Quadratic Coefficient\")\n", - "ax1.set_title(\"Effect of Prey Birth\")\n", - "ax1.fill_between(by_pb[\"prey_birth\"], 0, by_pb[\"mean\"], alpha=0.1, color=COLORS[\"prey\"])\n", - "\n", - "# Plot 2: Effect of Predator Birth\n", - "by_pred_b = (\n", - " df_quadratic_valid.groupby(\"predator_birth\")[\"quadratic_coef\"]\n", - " .agg([\"mean\", \"std\", \"count\"])\n", - " .reset_index()\n", - ")\n", - "by_pred_b[\"se\"] = by_pred_b[\"std\"] / np.sqrt(by_pred_b[\"count\"])\n", - "\n", - "ax2 = axes[1]\n", - "ax2.errorbar(\n", - " by_pred_b[\"predator_birth\"],\n", - " by_pred_b[\"mean\"],\n", - " yerr=by_pred_b[\"se\"],\n", - " fmt=\"s-\",\n", - " color=COLORS[\"predator\"],\n", - " markersize=6,\n", - " linewidth=1.5,\n", - " capsize=3,\n", - ")\n", - "ax2.axhline(0, color=\"0.5\", linestyle=\"--\", linewidth=1)\n", - "ax2.set_xlabel(\"Predator Birth Rate\")\n", - "ax2.set_ylabel(\"Quadratic Coefficient\")\n", - "ax2.set_title(\"Effect of Predator Birth\")\n", - "ax2.fill_between(\n", - " by_pred_b[\"predator_birth\"],\n", - " 0,\n", - " by_pred_b[\"mean\"],\n", - " alpha=0.1,\n", - " color=COLORS[\"predator\"],\n", - ")\n", - "\n", - "# Plot 3: Effect of Predator Death\n", - "by_pred_d = (\n", - " df_quadratic_valid.groupby(\"predator_death\")[\"quadratic_coef\"]\n", - " .agg([\"mean\", \"std\", \"count\"])\n", - " .reset_index()\n", - ")\n", - "by_pred_d[\"se\"] = by_pred_d[\"std\"] / np.sqrt(by_pred_d[\"count\"])\n", - "\n", - "ax3 = axes[2]\n", - "ax3.errorbar(\n", - " by_pred_d[\"predator_death\"],\n", - " by_pred_d[\"mean\"],\n", - " yerr=by_pred_d[\"se\"],\n", - " fmt=\"^-\",\n", - " color=COLORS[\"highlight\"],\n", - " markersize=6,\n", - " linewidth=1.5,\n", - " capsize=3,\n", - ")\n", - "ax3.axhline(0, color=\"0.5\", linestyle=\"--\", linewidth=1)\n", - "ax3.set_xlabel(\"Predator Death Rate\")\n", - "ax3.set_ylabel(\"Quadratic Coefficient\")\n", - "ax3.set_title(\"Effect of Predator Death\")\n", - "ax3.fill_between(\n", - " by_pred_d[\"predator_death\"],\n", - " 0,\n", - " by_pred_d[\"mean\"],\n", - " alpha=0.1,\n", - " color=COLORS[\"highlight\"],\n", - ")\n", - "\n", - "fig.suptitle(\"Hydra Effect Strength: Marginal Effects\", y=1.02)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Strong Hydra (coef > 15): 10 curves\n", - "Moderate Hydra (5 ≤ coef ≤ 10): 43 curves\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Strong Hydra Parameters ===\n", - " prey_birth predator_birth predator_death quadratic_coef\n", - " 0.4 0.9 0.2 27.068330\n", - " 0.3 0.5 0.1 24.221078\n", - " 0.5 0.7 0.2 23.533050\n", - " 0.4 0.8 0.2 23.199059\n", - " 0.3 0.4 0.1 22.579852\n", - " 0.4 0.3 0.1 20.773468\n", - " 0.6 0.6 0.2 20.687584\n", - " 0.4 1.0 0.2 16.772768\n", - "\n", - "=== Moderate Hydra Parameters ===\n", - " prey_birth predator_birth predator_death quadratic_coef\n", - " 0.9 1.0 0.2 5.195306\n", - " 0.7 0.3 0.1 5.678117\n", - " 0.7 0.6 0.1 5.362209\n", - " 0.9 0.9 0.2 5.707096\n", - " 0.9 0.6 0.2 7.570169\n", - " 1.0 0.2 0.1 6.961324\n", - " 0.3 1.0 0.2 5.857010\n", - " 0.5 0.6 0.1 6.608729\n" - ] - } - ], - "source": [ - "# === Validate Quadratic Coefficient: Show Actual Curves ===\n", - "\n", - "# Get parameter combinations for strong and moderate Hydra effect\n", - "strong_hydra = df_quadratic_valid[df_quadratic_valid[\"quadratic_coef\"] > 15]\n", - "moderate_hydra = df_quadratic_valid[\n", - " (df_quadratic_valid[\"quadratic_coef\"] >= 5)\n", - " & (df_quadratic_valid[\"quadratic_coef\"] <= 10)\n", - "]\n", - "\n", - "print(f\"Strong Hydra (coef > 15): {len(strong_hydra)} curves\")\n", - "print(f\"Moderate Hydra (5 ≤ coef ≤ 10): {len(moderate_hydra)} curves\")\n", - "\n", - "# Sample if too many\n", - "n_samples = min(8, len(strong_hydra))\n", - "n_samples_mod = min(8, len(moderate_hydra))\n", - "\n", - "strong_sample = strong_hydra.nlargest(n_samples, \"quadratic_coef\")\n", - "moderate_sample = (\n", - " moderate_hydra.sample(n_samples_mod, random_state=42)\n", - " if len(moderate_hydra) >= n_samples_mod\n", - " else moderate_hydra\n", - ")\n", - "\n", - "# === Plot ===\n", - "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", - "\n", - "# Left: Strong Hydra Effect\n", - "ax1 = axes[0]\n", - "colors_strong = plt.cm.Blues(np.linspace(0.4, 0.9, len(strong_sample)))\n", - "\n", - "for i, (_, row) in enumerate(strong_sample.iterrows()):\n", - " # Get the curve from df_avg\n", - " mask = (\n", - " (df_avg[\"prey_birth\"] == row[\"prey_birth\"])\n", - " & (df_avg[\"predator_birth\"] == row[\"predator_birth\"])\n", - " & (df_avg[\"predator_death\"] == row[\"predator_death\"])\n", - " )\n", - "\n", - " curve_data = df_avg[mask].sort_values(\"prey_death\")\n", - "\n", - " if len(curve_data) > 0:\n", - " ax1.plot(\n", - " curve_data[\"prey_death\"],\n", - " curve_data[\"prey_density\"],\n", - " \"-\",\n", - " color=colors_strong[i],\n", - " linewidth=1.5,\n", - " alpha=0.8,\n", - " label=f'c={row[\"quadratic_coef\"]:.1f}',\n", - " )\n", - "\n", - "ax1.set_xlabel(\"Prey Death Rate\")\n", - "ax1.set_ylabel(\"Equilibrium Prey Density\")\n", - "ax1.set_title(f\"Strong Hydra Effect (coef > 15, n={len(strong_sample)})\")\n", - "ax1.legend(loc=\"upper right\", fontsize=8, framealpha=0.95, title=\"Quad. Coef.\")\n", - "ax1.axhline(0, color=\"0.5\", linestyle=\"--\", linewidth=0.8)\n", - "\n", - "# Right: Moderate Hydra Effect\n", - "ax2 = axes[1]\n", - "colors_moderate = plt.cm.Oranges(np.linspace(0.4, 0.9, len(moderate_sample)))\n", - "\n", - "for i, (_, row) in enumerate(moderate_sample.iterrows()):\n", - " mask = (\n", - " (df_avg[\"prey_birth\"] == row[\"prey_birth\"])\n", - " & (df_avg[\"predator_birth\"] == row[\"predator_birth\"])\n", - " & (df_avg[\"predator_death\"] == row[\"predator_death\"])\n", - " )\n", - "\n", - " curve_data = df_avg[mask].sort_values(\"prey_death\")\n", - "\n", - " if len(curve_data) > 0:\n", - " ax2.plot(\n", - " curve_data[\"prey_death\"],\n", - " curve_data[\"prey_density\"],\n", - " \"-\",\n", - " color=colors_moderate[i],\n", - " linewidth=1.5,\n", - " alpha=0.8,\n", - " label=f'c={row[\"quadratic_coef\"]:.1f}',\n", - " )\n", - "\n", - "ax2.set_xlabel(\"Prey Death Rate\")\n", - "ax2.set_ylabel(\"Equilibrium Prey Density\")\n", - "ax2.set_title(f\"Moderate Hydra Effect (5 ≤ coef ≤ 10, n={len(moderate_sample)})\")\n", - "ax2.legend(loc=\"upper right\", fontsize=8, framealpha=0.95, title=\"Quad. Coef.\")\n", - "ax2.axhline(0, color=\"0.5\", linestyle=\"--\", linewidth=0.8)\n", - "\n", - "# Match y-axis limits for comparison\n", - "y_max = max(ax1.get_ylim()[1], ax2.get_ylim()[1])\n", - "ax1.set_ylim(bottom=0, top=y_max)\n", - "ax2.set_ylim(bottom=0, top=y_max)\n", - "\n", - "fig.suptitle(\n", - " \"Quadratic Coefficient Captures Hydra Effect Strength\", y=1.02, fontweight=\"normal\"\n", - ")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# Print parameter details\n", - "print(\"\\n=== Strong Hydra Parameters ===\")\n", - "print(\n", - " strong_sample[\n", - " [\"prey_birth\", \"predator_birth\", \"predator_death\", \"quadratic_coef\"]\n", - " ].to_string(index=False)\n", - ")\n", - "\n", - "print(\"\\n=== Moderate Hydra Parameters ===\")\n", - "print(\n", - " moderate_sample[\n", - " [\"prey_birth\", \"predator_birth\", \"predator_death\", \"quadratic_coef\"]\n", - " ].to_string(index=False)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Phase 5" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "# Load both Phase 4 files\n", - "df_phase6_1 = load_phase_to_df(DATA_ROOT / \"phase6_18780164\")\n", - "df_phase6_2 = load_phase_to_df(DATA_ROOT / \"phase6.2_18832958\")" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Phase 6.1: 146410 records\n", - "Phase 6.2: 133100 records\n", - "Combined: 279510 records\n" - ] - } - ], - "source": [ - "print(f\"Phase 6.1: {len(df_phase6_1)} records\")\n", - "print(f\"Phase 6.2: {len(df_phase6_2)} records\")\n", - "\n", - "# Combine\n", - "df6 = pd.concat([df_phase6_1, df_phase6_2], ignore_index=True)\n", - "print(f\"Combined: {len(df)} records\")\n", - "\n", - "# Compute prey density (normalize by grid size)\n", - "grid_size = df6[\"grid_size\"].iloc[0]\n", - "df6[\"prey_density\"] = df6[\"prey_mean\"] / (grid_size**2)\n", - "df6[\"pred_density\"] = df6[\"pred_mean\"] / (grid_size**2)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "27951 unique parameter combinations\n" - ] - } - ], - "source": [ - "# Average over replicates\n", - "df_avg = (\n", - " df6.groupby([\"prey_birth\", \"prey_death\", \"predator_birth\", \"predator_death\"])\n", - " .agg(\n", - " {\n", - " \"prey_mean\": \"mean\",\n", - " \"pred_mean\": \"mean\",\n", - " \"prey_density\": \"mean\",\n", - " \"pred_density\": \"mean\",\n", - " }\n", - " )\n", - " .reset_index()\n", - ")\n", - "\n", - "print(f\"{len(df_avg)} unique parameter combinations\")" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Built 1331 curves\n" - ] - } - ], - "source": [ - "# === 2. Build Curves: Prey Density vs Prey Death for Each Parameter Combo ===\n", - "\n", - "# Group by (prey_birth, predator_birth, predator_death) to get curves\n", - "curves_data = []\n", - "\n", - "for (pb, pred_b, pred_d), group in df_avg.groupby(\n", - " [\"prey_birth\", \"predator_birth\", \"predator_death\"]\n", - "):\n", - " # Sort by prey_death\n", - " group_sorted = group.sort_values(\"prey_death\")\n", - "\n", - " curves_data.append(\n", - " {\n", - " \"prey_birth\": pb,\n", - " \"predator_birth\": pred_b,\n", - " \"predator_death\": pred_d,\n", - " \"prey_death\": group_sorted[\"prey_death\"].values,\n", - " \"prey_density\": group_sorted[\"prey_density\"].values,\n", - " }\n", - " )\n", - "\n", - "print(f\"Built {len(curves_data)} curves\")" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Curves WITH Hydra effect: 250 (18.8%)\n" - ] - } - ], - "source": [ - "# === 4. Detect Hydra Effect (Positive Derivative Regions) ===\n", - "\n", - "\n", - "def detect_hydra_effect(prey_death, prey_density):\n", - " \"\"\"Detect if curve has hydra effect (increasing region).\"\"\"\n", - " if len(prey_death) < 2:\n", - " return False, None\n", - "\n", - " derivatives = np.diff(prey_density) / np.diff(prey_death)\n", - " has_increasing = np.any(derivatives > 0)\n", - "\n", - " if has_increasing:\n", - " max_deriv = np.max(derivatives)\n", - " return True, max_deriv\n", - " return False, None\n", - "\n", - "\n", - "# Classify curves\n", - "curves_with_hydra = []\n", - "\n", - "for curve in curves_data:\n", - " has_hydra, max_deriv = detect_hydra_effect(\n", - " curve[\"prey_death\"], curve[\"prey_density\"]\n", - " )\n", - " curve[\"has_hydra\"] = has_hydra\n", - " curve[\"max_derivative\"] = max_deriv\n", - "\n", - " if has_hydra:\n", - " curves_with_hydra.append(curve)\n", - "\n", - "print(\n", - " f\"Curves WITH Hydra effect: {len(curves_with_hydra)} ({100*len(curves_with_hydra)/len(curves_data):.1f}%)\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filtered curves: 250\n" - ] - } - ], - "source": [ - "# === 5. Filter Curves: Keep Only Portion Before Max Derivative ===\n", - "\n", - "filtered_curves = []\n", - "\n", - "for curve in curves_with_hydra:\n", - " prey_d = curve[\"prey_death\"]\n", - " prey_n = curve[\"prey_density\"]\n", - "\n", - " # Compute derivatives\n", - " derivatives = np.diff(prey_n) / np.diff(prey_d)\n", - "\n", - " # Find index of maximum positive derivative\n", - " max_deriv_idx = np.argmax(derivatives)\n", - " end_idx = max_deriv_idx + 1\n", - "\n", - " if end_idx > 0:\n", - " filtered_curves.append(\n", - " {\n", - " \"prey_birth\": curve[\"prey_birth\"],\n", - " \"predator_birth\": curve[\"predator_birth\"],\n", - " \"predator_death\": curve[\"predator_death\"],\n", - " \"prey_death\": prey_d[: end_idx + 1],\n", - " \"prey_density\": prey_n[: end_idx + 1],\n", - " \"max_derivative\": curve[\"max_derivative\"],\n", - " }\n", - " )\n", - "\n", - "print(f\"Filtered curves: {len(filtered_curves)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Valid quadratic fits: 219\n", - "Coefficient range: [-7.09e+00, 9.84e+01]\n" - ] - } - ], - "source": [ - "# === 6. Fit Quadratic Polynomial and Extract Coefficients ===\n", - "from numpy.polynomial import Polynomial\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "\n", - "curves_with_quadratic = []\n", - "\n", - "for curve in filtered_curves:\n", - " prey_d = curve[\"prey_death\"]\n", - " prey_n = curve[\"prey_density\"]\n", - "\n", - " if len(prey_d) < 3:\n", - " continue\n", - "\n", - " try:\n", - " # Fit: N = a + b·d + c·d²\n", - " poly = Polynomial.fit(prey_d, prey_n, deg=2)\n", - " coeffs = poly.convert().coef\n", - " quadratic_coef = coeffs[2] if len(coeffs) > 2 else 0.0\n", - "\n", - " curves_with_quadratic.append(\n", - " {\n", - " \"prey_birth\": curve[\"prey_birth\"],\n", - " \"predator_birth\": curve[\"predator_birth\"],\n", - " \"predator_death\": curve[\"predator_death\"],\n", - " \"quadratic_coef\": quadratic_coef,\n", - " \"max_derivative\": curve[\"max_derivative\"],\n", - " }\n", - " )\n", - " except:\n", - " pass\n", - "\n", - "df_quadratic = pd.DataFrame(curves_with_quadratic)\n", - "df_quadratic_valid = df_quadratic[df_quadratic[\"quadratic_coef\"].notna()]\n", - "\n", - "print(f\"Valid quadratic fits: {len(df_quadratic_valid)}\")\n", - "print(\n", - " f\"Coefficient range: [{df_quadratic_valid['quadratic_coef'].min():.2e}, {df_quadratic_valid['quadratic_coef'].max():.2e}]\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data coverage: 67.6%\n", - "Coefficient range: [0.6, 41.8]\n" - ] - } - ], - "source": [ - "# === 3D Heatmap: Quadratic Coefficients ===\n", - "\n", - "if len(df_quadratic_valid) > 0:\n", - " # Get unique parameter values\n", - " pb_grid = np.array(sorted(df_quadratic_valid[\"prey_birth\"].unique()))\n", - " pred_b_grid = np.array(sorted(df_quadratic_valid[\"predator_birth\"].unique()))\n", - " pred_d_grid = np.array(sorted(df_quadratic_valid[\"predator_death\"].unique()))\n", - "\n", - " # Create 3D array and fill with data\n", - " heatmap_3d = np.full((len(pb_grid), len(pred_b_grid), len(pred_d_grid)), np.nan)\n", - "\n", - " for _, row in df_quadratic_valid.iterrows():\n", - " pb_idx = np.where(np.isclose(pb_grid, row[\"prey_birth\"]))[0]\n", - " pred_b_idx = np.where(np.isclose(pred_b_grid, row[\"predator_birth\"]))[0]\n", - " pred_d_idx = np.where(np.isclose(pred_d_grid, row[\"predator_death\"]))[0]\n", - "\n", - " if len(pb_idx) > 0 and len(pred_b_idx) > 0 and len(pred_d_idx) > 0:\n", - " heatmap_3d[pb_idx[0], pred_b_idx[0], pred_d_idx[0]] = row[\"quadratic_coef\"]\n", - "\n", - " # Color normalization\n", - " vmin = np.nanpercentile(heatmap_3d, 5)\n", - " vmax = np.nanpercentile(heatmap_3d, 95)\n", - " norm = plt.Normalize(vmin=vmin, vmax=vmax)\n", - " cmap = plt.cm.Blues\n", - "\n", - " # Create voxel colors\n", - " filled = ~np.isnan(heatmap_3d)\n", - " colors = np.empty(heatmap_3d.shape + (4,))\n", - "\n", - " for i in range(heatmap_3d.shape[0]):\n", - " for j in range(heatmap_3d.shape[1]):\n", - " for k in range(heatmap_3d.shape[2]):\n", - " if filled[i, j, k]:\n", - " rgb = cmap(norm(heatmap_3d[i, j, k]))[:3]\n", - " colors[i, j, k] = [rgb[0], rgb[1], rgb[2], 0.9]\n", - " else:\n", - " colors[i, j, k] = [0, 0, 0, 0]\n", - "\n", - " # === Create figure with shared colorbar ===\n", - " fig = plt.figure(figsize=(16, 7))\n", - "\n", - " from matplotlib.gridspec import GridSpec\n", - "\n", - " gs = GridSpec(1, 3, width_ratios=[1, 1, 0.05], wspace=0.25)\n", - "\n", - " # === Left panel: Voxel plot ===\n", - " ax1 = fig.add_subplot(gs[0, 0], projection=\"3d\")\n", - " ax1.voxels(filled, facecolors=colors, edgecolors=\"0.4\", linewidth=0.1)\n", - "\n", - " # Simplified tick labels\n", - " tick_skip = 2\n", - " ax1.set_xticks(range(0, len(pb_grid), tick_skip))\n", - " ax1.set_xticklabels(\n", - " [f\"{pb_grid[i]:.1f}\" for i in range(0, len(pb_grid), tick_skip)]\n", - " )\n", - " ax1.set_yticks(range(0, len(pred_b_grid), tick_skip))\n", - " ax1.set_yticklabels(\n", - " [f\"{pred_b_grid[i]:.1f}\" for i in range(0, len(pred_b_grid), tick_skip)]\n", - " )\n", - " ax1.set_zticks(range(0, len(pred_d_grid), tick_skip))\n", - " ax1.set_zticklabels(\n", - " [f\"{pred_d_grid[i]:.1f}\" for i in range(0, len(pred_d_grid), tick_skip)]\n", - " )\n", - "\n", - " ax1.set_xlabel(\"Prey Birth\")\n", - " ax1.set_ylabel(\"Predator Birth\")\n", - " ax1.set_zlabel(\"Predator Death\")\n", - " ax1.set_title(\"3D Voxel Heatmap\")\n", - " ax1.view_init(elev=25, azim=225) # Rotated 180 degrees (45 + 180 = 225)\n", - "\n", - " # === Right panel: Cross-sections ===\n", - " ax2 = fig.add_subplot(gs[0, 1], projection=\"3d\")\n", - "\n", - " PB_slice, PRED_B_slice = np.meshgrid(pb_grid, pred_b_grid, indexing=\"ij\")\n", - "\n", - " n_slices = min(5, len(pred_d_grid))\n", - " slice_indices = np.linspace(0, len(pred_d_grid) - 1, n_slices, dtype=int)\n", - "\n", - " for slice_idx in slice_indices:\n", - " pd_val = pred_d_grid[slice_idx]\n", - " slice_data = heatmap_3d[:, :, slice_idx]\n", - " valid_mask = ~np.isnan(slice_data)\n", - "\n", - " if not np.any(valid_mask):\n", - " continue\n", - "\n", - " Z_slice = np.full_like(PB_slice, pd_val)\n", - "\n", - " face_colors = np.empty((*slice_data.shape, 4))\n", - " for i in range(slice_data.shape[0]):\n", - " for j in range(slice_data.shape[1]):\n", - " if valid_mask[i, j]:\n", - " face_colors[i, j] = [*cmap(norm(slice_data[i, j]))[:3], 0.8]\n", - " else:\n", - " face_colors[i, j] = [0, 0, 0, 0]\n", - "\n", - " PB_masked = np.ma.masked_where(~valid_mask, PB_slice)\n", - " PRED_B_masked = np.ma.masked_where(~valid_mask, PRED_B_slice)\n", - " Z_masked = np.ma.masked_where(~valid_mask, Z_slice)\n", - "\n", - " ax2.plot_surface(\n", - " PB_masked,\n", - " PRED_B_masked,\n", - " Z_masked,\n", - " facecolors=face_colors,\n", - " alpha=0.8,\n", - " linewidth=0.2,\n", - " shade=False,\n", - " )\n", - "\n", - " ax2.set_xlim(pb_grid.min(), pb_grid.max())\n", - " ax2.set_ylim(pred_b_grid.min(), pred_b_grid.max())\n", - " ax2.set_zlim(pred_d_grid.min(), pred_d_grid.max())\n", - "\n", - " ax2.set_xticks(pb_grid[::tick_skip])\n", - " ax2.set_xticklabels([f\"{x:.1f}\" for x in pb_grid[::tick_skip]])\n", - " ax2.set_yticks(pred_b_grid[::tick_skip])\n", - " ax2.set_yticklabels([f\"{x:.1f}\" for x in pred_b_grid[::tick_skip]])\n", - " ax2.set_zticks(pred_d_grid[::tick_skip])\n", - " ax2.set_zticklabels([f\"{x:.1f}\" for x in pred_d_grid[::tick_skip]])\n", - "\n", - " ax2.set_xlabel(\"Prey Birth\")\n", - " ax2.set_ylabel(\"Predator Birth\")\n", - " ax2.set_zlabel(\"Predator Death\")\n", - " ax2.set_title(\"Cross-Sections\")\n", - " ax2.view_init(elev=25, azim=225) # Rotated 180 degrees\n", - "\n", - " # === Shared colorbar ===\n", - " cbar_ax = fig.add_subplot(gs[0, 2])\n", - " sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n", - " sm.set_array([])\n", - " cbar = fig.colorbar(sm, cax=cbar_ax)\n", - " cbar.set_label(\"Quadratic Coefficient\")\n", - "\n", - " fig.suptitle(\n", - " \"Hydra Effect Strength (Directed Hunting): 3D Parameter Space\", y=0.98, fontweight=\"normal\"\n", - " )\n", - "\n", - " plt.savefig(\"phase4_3d_heatmap.png\", dpi=150, bbox_inches=\"tight\")\n", - " plt.show()\n", - "\n", - " print(f\"Data coverage: {100*np.sum(filled)/filled.size:.1f}%\")\n", - " print(f\"Coefficient range: [{vmin:.1f}, {vmax:.1f}]\")" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# === Hydra Effect: Marginal Effects ===\n", - "\n", - "fig, axes = plt.subplots(1, 3, figsize=(14, 4))\n", - "\n", - "# Plot 1: Effect of Prey Birth\n", - "by_pb = (\n", - " df_quadratic_valid.groupby(\"prey_birth\")[\"quadratic_coef\"]\n", - " .agg([\"mean\", \"std\", \"count\"])\n", - " .reset_index()\n", - ")\n", - "by_pb[\"se\"] = by_pb[\"std\"] / np.sqrt(by_pb[\"count\"])\n", - "\n", - "ax1 = axes[0]\n", - "ax1.errorbar(\n", - " by_pb[\"prey_birth\"],\n", - " by_pb[\"mean\"],\n", - " yerr=by_pb[\"se\"],\n", - " fmt=\"o-\",\n", - " color=COLORS[\"prey\"],\n", - " markersize=6,\n", - " linewidth=1.5,\n", - " capsize=3,\n", - ")\n", - "ax1.axhline(0, color=\"0.5\", linestyle=\"--\", linewidth=1)\n", - "ax1.set_xlabel(\"Prey Birth Rate\")\n", - "ax1.set_ylabel(\"Quadratic Coefficient\")\n", - "ax1.set_title(\"Effect of Prey Birth\")\n", - "ax1.fill_between(by_pb[\"prey_birth\"], 0, by_pb[\"mean\"], alpha=0.1, color=COLORS[\"prey\"])\n", - "\n", - "# Plot 2: Effect of Predator Birth\n", - "by_pred_b = (\n", - " df_quadratic_valid.groupby(\"predator_birth\")[\"quadratic_coef\"]\n", - " .agg([\"mean\", \"std\", \"count\"])\n", - " .reset_index()\n", - ")\n", - "by_pred_b[\"se\"] = by_pred_b[\"std\"] / np.sqrt(by_pred_b[\"count\"])\n", - "\n", - "ax2 = axes[1]\n", - "ax2.errorbar(\n", - " by_pred_b[\"predator_birth\"],\n", - " by_pred_b[\"mean\"],\n", - " yerr=by_pred_b[\"se\"],\n", - " fmt=\"s-\",\n", - " color=COLORS[\"predator\"],\n", - " markersize=6,\n", - " linewidth=1.5,\n", - " capsize=3,\n", - ")\n", - "ax2.axhline(0, color=\"0.5\", linestyle=\"--\", linewidth=1)\n", - "ax2.set_xlabel(\"Predator Birth Rate\")\n", - "ax2.set_ylabel(\"Quadratic Coefficient\")\n", - "ax2.set_title(\"Effect of Predator Birth\")\n", - "ax2.fill_between(\n", - " by_pred_b[\"predator_birth\"],\n", - " 0,\n", - " by_pred_b[\"mean\"],\n", - " alpha=0.1,\n", - " color=COLORS[\"predator\"],\n", - ")\n", - "\n", - "# Plot 3: Effect of Predator Death\n", - "by_pred_d = (\n", - " df_quadratic_valid.groupby(\"predator_death\")[\"quadratic_coef\"]\n", - " .agg([\"mean\", \"std\", \"count\"])\n", - " .reset_index()\n", - ")\n", - "by_pred_d[\"se\"] = by_pred_d[\"std\"] / np.sqrt(by_pred_d[\"count\"])\n", - "\n", - "ax3 = axes[2]\n", - "ax3.errorbar(\n", - " by_pred_d[\"predator_death\"],\n", - " by_pred_d[\"mean\"],\n", - " yerr=by_pred_d[\"se\"],\n", - " fmt=\"^-\",\n", - " color=COLORS[\"highlight\"],\n", - " markersize=6,\n", - " linewidth=1.5,\n", - " capsize=3,\n", - ")\n", - "ax3.axhline(0, color=\"0.5\", linestyle=\"--\", linewidth=1)\n", - "ax3.set_xlabel(\"Predator Death Rate\")\n", - "ax3.set_ylabel(\"Quadratic Coefficient\")\n", - "ax3.set_title(\"Effect of Predator Death\")\n", - "ax3.fill_between(\n", - " by_pred_d[\"predator_death\"],\n", - " 0,\n", - " by_pred_d[\"mean\"],\n", - " alpha=0.1,\n", - " color=COLORS[\"highlight\"],\n", - ")\n", - "\n", - "fig.suptitle(\"Hydra Effect Strength: Marginal Effects\", y=1.02)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Strong Hydra (coef > 15): 41 curves\n", - "Moderate Hydra (5 ≤ coef ≤ 10): 39 curves\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Strong Hydra Parameters ===\n", - " prey_birth predator_birth predator_death quadratic_coef\n", - " 0.7 1.0 0.2 98.437162\n", - " 0.5 0.9 0.2 74.026513\n", - " 0.2 0.8 0.2 70.918677\n", - " 0.2 0.5 0.1 62.541176\n", - " 0.2 1.0 0.3 53.070678\n", - " 0.3 0.5 0.1 52.656917\n", - " 0.3 0.6 0.1 52.307350\n", - " 0.3 0.8 0.1 49.085144\n", - "\n", - "=== Moderate Hydra Parameters ===\n", - " prey_birth predator_birth predator_death quadratic_coef\n", - " 0.8 0.8 0.2 7.227108\n", - " 0.9 0.9 0.2 5.326961\n", - " 0.4 0.5 0.3 7.648807\n", - " 0.5 0.6 0.1 5.259019\n", - " 0.7 0.4 0.1 5.446339\n", - " 0.6 0.8 0.2 7.406149\n", - " 0.4 0.8 0.4 9.216554\n", - " 0.6 0.8 0.3 5.929740\n" - ] - } - ], - "source": [ - "# === Validate Quadratic Coefficient: Show Actual Curves ===\n", - "\n", - "# Get parameter combinations for strong and moderate Hydra effect\n", - "strong_hydra = df_quadratic_valid[df_quadratic_valid[\"quadratic_coef\"] > 15]\n", - "moderate_hydra = df_quadratic_valid[\n", - " (df_quadratic_valid[\"quadratic_coef\"] >= 5)\n", - " & (df_quadratic_valid[\"quadratic_coef\"] <= 10)\n", - "]\n", - "\n", - "print(f\"Strong Hydra (coef > 15): {len(strong_hydra)} curves\")\n", - "print(f\"Moderate Hydra (5 ≤ coef ≤ 10): {len(moderate_hydra)} curves\")\n", - "\n", - "# Sample if too many\n", - "n_samples = min(8, len(strong_hydra))\n", - "n_samples_mod = min(8, len(moderate_hydra))\n", - "\n", - "strong_sample = strong_hydra.nlargest(n_samples, \"quadratic_coef\")\n", - "moderate_sample = (\n", - " moderate_hydra.sample(n_samples_mod, random_state=42)\n", - " if len(moderate_hydra) >= n_samples_mod\n", - " else moderate_hydra\n", - ")\n", - "\n", - "# === Plot ===\n", - "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", - "\n", - "# Left: Strong Hydra Effect\n", - "ax1 = axes[0]\n", - "colors_strong = plt.cm.Blues(np.linspace(0.4, 0.9, len(strong_sample)))\n", - "\n", - "for i, (_, row) in enumerate(strong_sample.iterrows()):\n", - " # Get the curve from df_avg\n", - " mask = (\n", - " (df_avg[\"prey_birth\"] == row[\"prey_birth\"])\n", - " & (df_avg[\"predator_birth\"] == row[\"predator_birth\"])\n", - " & (df_avg[\"predator_death\"] == row[\"predator_death\"])\n", - " )\n", - "\n", - " curve_data = df_avg[mask].sort_values(\"prey_death\")\n", - "\n", - " if len(curve_data) > 0:\n", - " ax1.plot(\n", - " curve_data[\"prey_death\"],\n", - " curve_data[\"prey_density\"],\n", - " \"-\",\n", - " color=colors_strong[i],\n", - " linewidth=1.5,\n", - " alpha=0.8,\n", - " label=f'c={row[\"quadratic_coef\"]:.1f}',\n", - " )\n", - "\n", - "ax1.set_xlabel(\"Prey Death Rate\")\n", - "ax1.set_ylabel(\"Equilibrium Prey Density\")\n", - "ax1.set_title(f\"Strong Hydra Effect (coef > 15, n={len(strong_sample)})\")\n", - "ax1.legend(loc=\"upper right\", fontsize=8, framealpha=0.95, title=\"Quad. Coef.\")\n", - "ax1.axhline(0, color=\"0.5\", linestyle=\"--\", linewidth=0.8)\n", - "\n", - "# Right: Moderate Hydra Effect\n", - "ax2 = axes[1]\n", - "colors_moderate = plt.cm.Oranges(np.linspace(0.4, 0.9, len(moderate_sample)))\n", - "\n", - "for i, (_, row) in enumerate(moderate_sample.iterrows()):\n", - " mask = (\n", - " (df_avg[\"prey_birth\"] == row[\"prey_birth\"])\n", - " & (df_avg[\"predator_birth\"] == row[\"predator_birth\"])\n", - " & (df_avg[\"predator_death\"] == row[\"predator_death\"])\n", - " )\n", - "\n", - " curve_data = df_avg[mask].sort_values(\"prey_death\")\n", - "\n", - " if len(curve_data) > 0:\n", - " ax2.plot(\n", - " curve_data[\"prey_death\"],\n", - " curve_data[\"prey_density\"],\n", - " \"-\",\n", - " color=colors_moderate[i],\n", - " linewidth=1.5,\n", - " alpha=0.8,\n", - " label=f'c={row[\"quadratic_coef\"]:.1f}',\n", - " )\n", - "\n", - "ax2.set_xlabel(\"Prey Death Rate\")\n", - "ax2.set_ylabel(\"Equilibrium Prey Density\")\n", - "ax2.set_title(f\"Moderate Hydra Effect (5 ≤ coef ≤ 10, n={len(moderate_sample)})\")\n", - "ax2.legend(loc=\"upper right\", fontsize=8, framealpha=0.95, title=\"Quad. Coef.\")\n", - "ax2.axhline(0, color=\"0.5\", linestyle=\"--\", linewidth=0.8)\n", - "\n", - "# Match y-axis limits for comparison\n", - "y_max = max(ax1.get_ylim()[1], ax2.get_ylim()[1])\n", - "ax1.set_ylim(bottom=0, top=y_max)\n", - "ax2.set_ylim(bottom=0, top=y_max)\n", - "\n", - "fig.suptitle(\n", - " \"Quadratic Coefficient Captures Hydra Effect Strength (Directed Hunting)\",\n", - " y=1.02,\n", - " fontweight=\"normal\",\n", - ")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# Print parameter details\n", - "print(\"\\n=== Strong Hydra Parameters ===\")\n", - "print(\n", - " strong_sample[\n", - " [\"prey_birth\", \"predator_birth\", \"predator_death\", \"quadratic_coef\"]\n", - " ].to_string(index=False)\n", - ")\n", - "\n", - "print(\"\\n=== Moderate Hydra Parameters ===\")\n", - "print(\n", - " moderate_sample[\n", - " [\"prey_birth\", \"predator_birth\", \"predator_death\", \"quadratic_coef\"]\n", - " ].to_string(index=False)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/plots.ipynb b/plots.ipynb new file mode 100644 index 0000000..1ddfdc8 --- /dev/null +++ b/plots.ipynb @@ -0,0 +1,2438 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "from pathlib import Path\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "from scipy import stats as scipy_stats\n", + "import powerlaw" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "Loading: phase1_results.jsonl\n", + "From: phase1.5.1_18682575\n", + "Number of records: 600\n", + "\n", + "Columns:\n", + " grid_size (int ): 1000\n", + " pred_cluster_sizes (list ): list[12561]\n", + " pred_largest_fraction (float ): 0.0010190303925814586\n", + " pred_mean (float ): 38850.719\n", + " pred_n_clusters (int ): 12561\n", + " pred_std (float ): 1260.7140048555818\n", + " pred_survived (bool ): True\n", + " predator_birth (float ): 0.8\n", + " predator_death (float ): 0.05\n", + " prey_birth (float ): 0.2\n", + " prey_cluster_sizes (list ): list[13327]\n", + " prey_death (float ): 0.09\n", + " prey_largest_fraction (float ): 0.016053863191030658\n", + " prey_mean (float ): 202334.076\n", + " prey_n_clusters (int ): 13327\n", + " prey_std (float ): 4148.2807904268\n", + " prey_survived (bool ): True\n", + " seed (int ): 2038530801\n", + " with_evolution (bool ): False\n", + "\n", + "============================================================\n", + "Loading: phase1_results.jsonl\n", + "From: phase1.5.2_18777691\n", + "Number of records: 600\n", + "\n", + "Columns:\n", + " grid_size (int ): 1000\n", + " pred_cluster_sizes (list ): list[12852]\n", + " pred_largest_fraction (float ): 0.0012646300337234675\n", + " pred_mean (float ): 38952.849\n", + " pred_n_clusters (int ): 12852\n", + " pred_std (float ): 776.7386138199903\n", + " pred_survived (bool ): True\n", + " predator_birth (float ): 0.8\n", + " predator_death (float ): 0.05\n", + " prey_birth (float ): 0.2\n", + " prey_cluster_sizes (list ): list[13636]\n", + " prey_death (float ): 0.09\n", + " prey_largest_fraction (float ): 0.00860220366618904\n", + " prey_mean (float ): 201912.27\n", + " prey_n_clusters (int ): 13636\n", + " prey_std (float ): 2413.2797241720655\n", + " prey_survived (bool ): True\n", + " seed (int ): 2038530801\n", + " with_evolution (bool ): False\n", + "\n", + "============================================================\n", + "Loading: phase1_results.jsonl\n", + "From: phase1_18677015\n", + "Number of records: 600\n", + "\n", + "Columns:\n", + " grid_size (int ): 1000\n", + " pcf_distances (list ): list[20]\n", + " pcf_pred_pred (list ): list[20]\n", + " pcf_prey_pred (list ): list[20]\n", + " pcf_prey_prey (list ): list[20]\n", + " pred_cluster_sizes (list ): list[11312]\n", + " pred_clustering_index (float ): 3.7080576627949653\n", + " pred_largest_fraction (float ): 0.009838716037698647\n", + " pred_mean (float ): 50074.603\n", + " pred_n_clusters (int ): 11312\n", + " pred_std (float ): 2079.850450727408\n", + " pred_survived (bool ): True\n", + " predator_birth (float ): 0.8\n", + " predator_death (float ): 0.05\n", + " prey_birth (float ): 0.2\n", + " prey_cluster_sizes (list ): list[3862]\n", + " prey_clustering_index (float ): 8.056522798225194\n", + " prey_death (float ): 0.0\n", + " prey_largest_fraction (float ): 0.028134280865507\n", + " prey_mean (float ): 38948.909\n", + " prey_n_clusters (int ): 3862\n", + " prey_std (float ): 2328.9364904863764\n", + " prey_survived (bool ): True\n", + " seed (int ): 1837646025\n", + " segregation_index (float ): 1.1958836834660076\n", + " with_evolution (bool ): False\n", + "\n", + "============================================================\n", + "Loading: phase1_results.jsonl\n", + "From: phase1_18831093\n", + "Number of records: 600\n", + "\n", + "Columns:\n", + " grid_size (int ): 1000\n", + " pred_cluster_sizes (list ): list[7082]\n", + " pred_largest_fraction (float ): 0.0019639934533551553\n", + " pred_mean (float ): 15097.799\n", + " pred_n_clusters (int ): 7082\n", + " pred_std (float ): 3772.7576620555687\n", + " pred_survived (bool ): True\n", + " predator_birth (float ): 0.8\n", + " predator_death (float ): 0.05\n", + " prey_birth (float ): 0.2\n", + " prey_cluster_sizes (list ): list[14598]\n", + " prey_death (float ): 0.0963\n", + " prey_largest_fraction (float ): 0.015569645218077298\n", + " prey_mean (float ): 310658.017\n", + " prey_n_clusters (int ): 14598\n", + " prey_std (float ): 21642.495576405025\n", + " prey_survived (bool ): True\n", + " seed (int ): 857948885\n", + " with_evolution (bool ): False\n", + "\n", + "============================================================\n", + "Loading: phase2_results.jsonl\n", + "From: phase2_18693004\n", + "Number of records: 200\n", + "\n", + "Columns:\n", + " evolve_sd (float ): 0.01\n", + " evolved_prey_death_final (float ): 0.06937236328134652\n", + " evolved_prey_death_mean (float ): 0.05879876856603936\n", + " evolved_prey_death_std (float ): 0.01708413274336453\n", + " grid_size (int ): 1000\n", + " pred_cluster_sizes (list ): list[16020]\n", + " pred_largest_fraction (float ): 0.0047934975164125185\n", + " pred_mean (float ): 60811.075\n", + " pred_n_clusters (int ): 16020\n", + " pred_std (float ): 1682.7033920970744\n", + " pred_survived (bool ): True\n", + " predator_birth (float ): 0.8\n", + " predator_death (float ): 0.05\n", + " prey_birth (float ): 0.2\n", + " prey_cluster_sizes (list ): list[9057]\n", + " prey_death (float ): 0.0\n", + " prey_largest_fraction (float ): 0.008276727250474267\n", + " prey_mean (float ): 106971.9024\n", + " prey_n_clusters (int ): 9057\n", + " prey_std (float ): 18782.465927786856\n", + " prey_survived (bool ): True\n", + " seed (int ): 551567501\n", + " with_evolution (bool ): True\n", + "\n", + "============================================================\n", + "Loading: phase3_results.jsonl\n", + "From: phase3_18698382\n", + "Number of records: 120\n", + "\n", + "Columns:\n", + " grid_size (int ): 50\n", + " pred_cluster_sizes (list ): list[22]\n", + " pred_largest_fraction (float ): 0.125\n", + " pred_mean (float ): 63.226\n", + " pred_n_clusters (int ): 22\n", + " pred_std (float ): 23.90934804631862\n", + " pred_survived (bool ): True\n", + " predator_birth (float ): 0.8\n", + " predator_death (float ): 0.05\n", + " prey_birth (float ): 0.2\n", + " prey_cluster_sizes (list ): list[27]\n", + " prey_death (float ): 0.0963\n", + " prey_largest_fraction (float ): 0.27467811158798283\n", + " prey_mean (float ): 611.385\n", + " prey_n_clusters (int ): 27\n", + " prey_std (float ): 94.99667770506504\n", + " prey_survived (bool ): True\n", + " seed (int ): 13483819\n", + " with_evolution (bool ): False\n", + "\n", + "============================================================\n", + "Loading: phase4_results.jsonl\n", + "From: phase4.2_18832956\n", + "Number of records: 133100\n", + "\n", + "Columns:\n", + " grid_size (int ): 250\n", + " pred_cluster_sizes (list ): list[4646]\n", + " pred_largest_fraction (float ): 0.0032\n", + " pred_mean (float ): 9375.0\n", + " pred_n_clusters (int ): 4646\n", + " pred_std (float ): 0.0\n", + " pred_survived (bool ): True\n", + " predator_birth (float ): 0.0\n", + " predator_death (float ): 0.0\n", + " prey_birth (float ): 0.0\n", + " prey_cluster_sizes (list ): []\n", + " prey_death (float ): 0.05\n", + " prey_largest_fraction (float ): nan\n", + " prey_mean (float ): 0.0\n", + " prey_n_clusters (int ): 0\n", + " prey_std (float ): 0.0\n", + " prey_survived (bool ): False\n", + " seed (int ): 1629992900\n", + " with_evolution (bool ): False\n", + "\n", + "============================================================\n", + "Loading: phase4_results.jsonl\n", + "From: phase4_18735304\n", + "Number of records: 146410\n", + "\n", + "Columns:\n", + " grid_size (int ): 250\n", + " pred_cluster_sizes (list ): list[4518]\n", + " pred_largest_fraction (float ): 0.0021333333333333334\n", + " pred_mean (float ): 9375.0\n", + " pred_n_clusters (int ): 4518\n", + " pred_std (float ): 0.0\n", + " pred_survived (bool ): True\n", + " pred_timeseries (list ): list[50]\n", + " predator_birth (float ): 0.0\n", + " predator_death (float ): 0.0\n", + " prey_birth (float ): 0.0\n", + " prey_cluster_sizes (list ): list[2975]\n", + " prey_death (float ): 0.0\n", + " prey_largest_fraction (float ): 0.007146666666666667\n", + " prey_mean (float ): 18750.0\n", + " prey_n_clusters (int ): 2975\n", + " prey_std (float ): 0.0\n", + " prey_survived (bool ): True\n", + " prey_timeseries (list ): list[50]\n", + " seed (int ): 2823796863\n", + " with_evolution (bool ): False\n", + "\n", + "============================================================\n", + "Loading: phase6_results.jsonl\n", + "From: phase6.2_18832958\n", + "Number of records: 133100\n", + "\n", + "Columns:\n", + " grid_size (int ): 250\n", + " pred_cluster_sizes (list ): list[4590]\n", + " pred_largest_fraction (float ): 0.00224\n", + " pred_mean (float ): 9375.0\n", + " pred_n_clusters (int ): 4590\n", + " pred_std (float ): 0.0\n", + " pred_survived (bool ): True\n", + " predator_birth (float ): 0.0\n", + " predator_death (float ): 0.0\n", + " prey_birth (float ): 0.0\n", + " prey_cluster_sizes (list ): []\n", + " prey_death (float ): 0.05\n", + " prey_largest_fraction (float ): nan\n", + " prey_mean (float ): 0.0\n", + " prey_n_clusters (int ): 0\n", + " prey_std (float ): 0.0\n", + " prey_survived (bool ): False\n", + " seed (int ): 2283964195\n", + " with_evolution (bool ): False\n", + "\n", + "============================================================\n", + "Loading: phase6_results.jsonl\n", + "From: phase6_18780164\n", + "Number of records: 146410\n", + "\n", + "Columns:\n", + " grid_size (int ): 250\n", + " pred_cluster_sizes (list ): list[4600]\n", + " pred_largest_fraction (float ): 0.0020266666666666666\n", + " pred_mean (float ): 9375.0\n", + " pred_n_clusters (int ): 4600\n", + " pred_std (float ): 0.0\n", + " pred_survived (bool ): True\n", + " pred_timeseries (list ): list[50]\n", + " predator_birth (float ): 0.0\n", + " predator_death (float ): 0.0\n", + " prey_birth (float ): 0.0\n", + " prey_cluster_sizes (list ): list[2913]\n", + " prey_death (float ): 0.0\n", + " prey_largest_fraction (float ): 0.008586666666666666\n", + " prey_mean (float ): 18750.0\n", + " prey_n_clusters (int ): 2913\n", + " prey_std (float ): 0.0\n", + " prey_survived (bool ): True\n", + " prey_timeseries (list ): list[50]\n", + " seed (int ): 2378779631\n", + " with_evolution (bool ): False\n" + ] + } + ], + "source": [ + "DATA_ROOT = Path.home() / \"CSS_Project\" / \"data\"\n", + "\n", + "\n", + "def load_jsonl(filepath):\n", + " \"\"\"Load JSONL file into list of dicts.\"\"\"\n", + " results = []\n", + " with open(filepath, \"r\") as f:\n", + " for line in f:\n", + " results.append(json.loads(line.strip()))\n", + " return results\n", + "\n", + "\n", + "def inspect_phase(phase_dir):\n", + " \"\"\"Inspect a phase's results file.\"\"\"\n", + " results_file = list(phase_dir.glob(\"*results*.jsonl\"))\n", + " if not results_file:\n", + " print(f\"No results file found in {phase_dir}\")\n", + " return None\n", + "\n", + " results_file = results_file[0]\n", + " print(f\"\\n{'='*60}\")\n", + " print(f\"Loading: {results_file.name}\")\n", + " print(f\"From: {phase_dir.name}\")\n", + "\n", + " data = load_jsonl(results_file)\n", + " print(f\"Number of records: {len(data)}\")\n", + "\n", + " print(f\"\\nColumns:\")\n", + " for key in sorted(data[0].keys()):\n", + " val = data[0][key]\n", + " dtype = type(val).__name__\n", + " if isinstance(val, list):\n", + " sample = f\"list[{len(val)}]\" if len(val) > 3 else val\n", + " else:\n", + " sample = val\n", + " print(f\" {key:35} ({dtype:6}): {sample}\")\n", + "\n", + " return data\n", + "\n", + "\n", + "# Inspect all phases\n", + "phases = sorted(DATA_ROOT.glob(\"phase*\"))\n", + "for phase_dir in phases:\n", + " inspect_phase(phase_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "Phase 1\n", + "============================================================\n", + " prey_death: 20 unique values, range [0.0000, 0.2000]\n", + " prey_birth: [0.2]\n", + " predator_birth: [0.8]\n", + " predator_death: [0.05]\n", + " grid_size: [1000]\n", + "\n", + "============================================================\n", + "Phase 1.5.1\n", + "============================================================\n", + " prey_death: 20 unique values, range [0.0900, 0.1200]\n", + " prey_birth: [0.2]\n", + " predator_birth: [0.8]\n", + " predator_death: [0.05]\n", + " grid_size: [1000]\n", + "\n", + "============================================================\n", + "Phase 2\n", + "============================================================\n", + " prey_death: 20 unique values, range [0.0000, 0.2000]\n", + " prey_birth: [0.2]\n", + " predator_birth: [0.8]\n", + " predator_death: [0.05]\n", + " grid_size: [1000]\n", + " evolved_prey_death_final: mean=0.0617, std=0.0167\n", + "\n", + "============================================================\n", + "Phase 3\n", + "============================================================\n", + " prey_death: [0.0963]\n", + " prey_birth: [0.2]\n", + " predator_birth: [0.8]\n", + " predator_death: [0.05]\n", + " grid_size: [50, 100, 250, 500, 1000, 2500]\n", + "\n", + "============================================================\n", + "Phase 4 (original)\n", + "============================================================\n", + " prey_death: [0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0]\n", + " prey_birth: [0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0]\n", + " predator_birth: [0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0]\n", + " predator_death: [0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0]\n", + " grid_size: [250]\n", + "\n", + "============================================================\n", + "Phase 4.2\n", + "============================================================\n", + " prey_death: [0.05, 0.15, 0.25, 0.35, 0.44999999999999996, 0.5499999999999999, 0.65, 0.75, 0.85, 0.95]\n", + " prey_birth: [0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0]\n", + " predator_birth: [0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0]\n", + " predator_death: [0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0]\n", + " grid_size: [250]\n" + ] + } + ], + "source": [ + "# Check parameter ranges for each phase\n", + "def check_ranges(data, phase_name):\n", + " print(f\"\\n{'='*60}\")\n", + " print(f\"{phase_name}\")\n", + " print(f\"{'='*60}\")\n", + "\n", + " # Key numeric columns to check\n", + " params = [\n", + " \"prey_death\",\n", + " \"prey_birth\",\n", + " \"predator_birth\",\n", + " \"predator_death\",\n", + " \"grid_size\",\n", + " ]\n", + "\n", + " for p in params:\n", + " if p in data[0]:\n", + " vals = sorted(set(d[p] for d in data))\n", + " if len(vals) <= 15:\n", + " print(f\" {p}: {vals}\")\n", + " else:\n", + " print(\n", + " f\" {p}: {len(vals)} unique values, range [{min(vals):.4f}, {max(vals):.4f}]\"\n", + " )\n", + "\n", + " # Evolution-specific\n", + " if \"evolved_prey_death_final\" in data[0]:\n", + " finals = [d[\"evolved_prey_death_final\"] for d in data]\n", + " print(\n", + " f\" evolved_prey_death_final: mean={np.mean(finals):.4f}, std={np.std(finals):.4f}\"\n", + " )\n", + "\n", + "\n", + "# Run for all phases (reuse data from previous script or reload)\n", + "# Example for phase 1:\n", + "phase1_data = load_jsonl(DATA_ROOT / \"phase1_18677015\" / \"phase1_results.jsonl\")\n", + "check_ranges(phase1_data, \"Phase 1\")\n", + "\n", + "phase1_5_1_data = load_jsonl(DATA_ROOT / \"phase1.5.1_18682575\" / \"phase1_results.jsonl\")\n", + "check_ranges(phase1_5_1_data, \"Phase 1.5.1\")\n", + "\n", + "phase2_data = load_jsonl(DATA_ROOT / \"phase2_18693004\" / \"phase2_results.jsonl\")\n", + "check_ranges(phase2_data, \"Phase 2\")\n", + "\n", + "phase3_data = load_jsonl(DATA_ROOT / \"phase3_18698382\" / \"phase3_results.jsonl\")\n", + "check_ranges(phase3_data, \"Phase 3\")\n", + "\n", + "phase4_data = load_jsonl(DATA_ROOT / \"phase4_18735304\" / \"phase4_results.jsonl\")\n", + "check_ranges(phase4_data, \"Phase 4 (original)\")\n", + "\n", + "phase4_2_data = load_jsonl(DATA_ROOT / \"phase4.2_18832956\" / \"phase4_results.jsonl\")\n", + "check_ranges(phase4_2_data, \"Phase 4.2\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set seaborn style\n", + "sns.set_theme(style=\"whitegrid\", context=\"paper\", font_scale=1.1)\n", + "\n", + "# Custom color palette\n", + "COLORS = {\n", + " \"prey\": \"#1751ED\", # Blue\n", + " \"predator\": \"#CF0808\", # Red\n", + " \"neutral\": \"#4A4A4A\", # Dark gray\n", + " \"highlight\": \"#FF9500\", # Orange\n", + "}\n", + "\n", + "\n", + "def load_phase_to_df(phase_dir, results_name=\"*results*.jsonl\"):\n", + " \"\"\"Load phase data into pandas DataFrame.\"\"\"\n", + " results_file = list(phase_dir.glob(results_name))[0]\n", + " data = load_jsonl(results_file)\n", + " return pd.DataFrame(data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def detect_hydra_effect(prey_death, prey_density):\n", + " \"\"\"Detect if curve has hydra effect (increasing region).\n", + " \n", + " Parameters\n", + " ----------\n", + " prey_death : array\n", + " Prey death rate values\n", + " prey_density : array\n", + " Corresponding prey density values\n", + " \n", + " Returns\n", + " -------\n", + " has_hydra : bool\n", + " True if curve shows hydra effect (positive derivative region)\n", + " max_deriv : float or None\n", + " Maximum positive derivative if hydra effect detected\n", + " \"\"\"\n", + " if len(prey_death) < 2:\n", + " return False, None\n", + "\n", + " derivatives = np.diff(prey_density) / np.diff(prey_death)\n", + " has_increasing = np.any(derivatives > 0)\n", + "\n", + " if has_increasing:\n", + " max_deriv = np.max(derivatives)\n", + " return True, max_deriv\n", + " return False, None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Phase 1 " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data...\n", + "Phase 1: 600 records, prey_death range: [0.000, 0.200]\n", + "Phase 1.5: 1200 records, prey_death range: [0.090, 0.120]\n", + "\n", + "Phase 1 aggregated: 20 unique prey_death values\n", + "Phase 1.5 aggregated: 20 unique prey_death values\n", + "\n", + "Phase 1 (first 5 rows):\n", + " prey_death prey_mean prey_std pred_mean pred_std n_reps \\\n", + "0 0.000000 38287.736967 958.933333 49675.545133 888.932812 30 \n", + "1 0.010526 48191.028500 822.836740 57327.555000 741.320925 30 \n", + "2 0.021053 58065.354433 517.723671 62435.544267 550.879148 30 \n", + "3 0.031579 69362.155133 599.831695 65380.523767 430.006223 30 \n", + "4 0.042105 81942.857500 454.223611 66736.344233 314.065230 30 \n", + "\n", + " prey_se pred_se \n", + "0 175.076473 162.296184 \n", + "1 150.228748 135.346064 \n", + "2 94.522978 100.576312 \n", + "3 109.513783 78.508036 \n", + "4 82.929506 57.340204 \n" + ] + } + ], + "source": [ + "# === Load Phase 1 and 1.5 data ===\n", + "print(\"Loading data...\")\n", + "df_phase1 = load_phase_to_df(DATA_ROOT / \"phase1_18677015\")\n", + "df_phase1_5_1 = load_phase_to_df(DATA_ROOT / \"phase1.5.1_18682575\")\n", + "df_phase1_5_2 = load_phase_to_df(DATA_ROOT / \"phase1.5.2_18777691\")\n", + "\n", + "# Combine 1.5.1 and 1.5.2 (they should be identical in parameters)\n", + "df_phase1_5 = pd.concat([df_phase1_5_1, df_phase1_5_2], ignore_index=True)\n", + "\n", + "print(\n", + " f\"Phase 1: {len(df_phase1)} records, prey_death range: [{df_phase1['prey_death'].min():.3f}, {df_phase1['prey_death'].max():.3f}]\"\n", + ")\n", + "print(\n", + " f\"Phase 1.5: {len(df_phase1_5)} records, prey_death range: [{df_phase1_5['prey_death'].min():.3f}, {df_phase1_5['prey_death'].max():.3f}]\"\n", + ")\n", + "\n", + "\n", + "# === Aggregate by prey_death (mean ± std across replicates) ===\n", + "def aggregate_bifurcation(df):\n", + " \"\"\"Aggregate population data by prey_death.\"\"\"\n", + " agg = (\n", + " df.groupby(\"prey_death\")\n", + " .agg(\n", + " prey_mean=(\"prey_mean\", \"mean\"),\n", + " prey_std=(\"prey_mean\", \"std\"),\n", + " pred_mean=(\"pred_mean\", \"mean\"),\n", + " pred_std=(\"pred_mean\", \"std\"),\n", + " n_reps=(\"prey_mean\", \"count\"),\n", + " )\n", + " .reset_index()\n", + " )\n", + " # Standard error\n", + " agg[\"prey_se\"] = agg[\"prey_std\"] / np.sqrt(agg[\"n_reps\"])\n", + " agg[\"pred_se\"] = agg[\"pred_std\"] / np.sqrt(agg[\"n_reps\"])\n", + " return agg\n", + "\n", + "\n", + "agg_phase1 = aggregate_bifurcation(df_phase1)\n", + "agg_phase1_5 = aggregate_bifurcation(df_phase1_5)\n", + "\n", + "print(f\"\\nPhase 1 aggregated: {len(agg_phase1)} unique prey_death values\")\n", + "print(f\"Phase 1.5 aggregated: {len(agg_phase1_5)} unique prey_death values\")\n", + "\n", + "# Quick preview\n", + "print(\"\\nPhase 1 (first 5 rows):\")\n", + "print(agg_phase1.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved: plot1_bifurcation.png\n" + ] + } + ], + "source": [ + "# === Plot 1: Bifurcation Diagrams ===\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", + "\n", + "# --- Plot 1a: Zoomed out (Phase 1) ---\n", + "ax1 = axes[0]\n", + "\n", + "# Prey\n", + "ax1.errorbar(\n", + " agg_phase1[\"prey_death\"],\n", + " agg_phase1[\"prey_mean\"],\n", + " yerr=agg_phase1[\"prey_se\"],\n", + " fmt=\"o-\",\n", + " color=COLORS[\"prey\"],\n", + " markersize=5,\n", + " capsize=3,\n", + " label=\"Prey\",\n", + ")\n", + "# Predator\n", + "ax1.errorbar(\n", + " agg_phase1[\"prey_death\"],\n", + " agg_phase1[\"pred_mean\"],\n", + " yerr=agg_phase1[\"pred_se\"],\n", + " fmt=\"s-\",\n", + " color=COLORS[\"predator\"],\n", + " markersize=5,\n", + " capsize=3,\n", + " label=\"Predator\",\n", + ")\n", + "\n", + "ax1.set_xlabel(\"Prey Death Rate\", fontsize=11)\n", + "ax1.set_ylabel(\"Mean Population\", fontsize=11)\n", + "ax1.set_title(\"(a) Bifurcation Diagram (Full Range)\", fontsize=12)\n", + "ax1.set_xlim(-0.005, 0.205)\n", + "ax1.ticklabel_format(axis=\"y\", style=\"scientific\", scilimits=(0, 0))\n", + "\n", + "# --- Plot 1b: Zoomed in (Phase 1.5) ---\n", + "ax2 = axes[1]\n", + "\n", + "# Prey\n", + "ax2.errorbar(\n", + " agg_phase1_5[\"prey_death\"],\n", + " agg_phase1_5[\"prey_mean\"],\n", + " yerr=agg_phase1_5[\"prey_se\"],\n", + " fmt=\"o-\",\n", + " color=COLORS[\"prey\"],\n", + " markersize=5,\n", + " capsize=3,\n", + " label=\"Prey\",\n", + ")\n", + "# Predator\n", + "ax2.errorbar(\n", + " agg_phase1_5[\"prey_death\"],\n", + " agg_phase1_5[\"pred_mean\"],\n", + " yerr=agg_phase1_5[\"pred_se\"],\n", + " fmt=\"s-\",\n", + " color=COLORS[\"predator\"],\n", + " markersize=5,\n", + " capsize=3,\n", + " label=\"Predator\",\n", + ")\n", + "\n", + "ax2.set_xlabel(\"Prey Death Rate\", fontsize=11)\n", + "ax2.set_ylabel(\"Mean Population\", fontsize=11)\n", + "ax2.set_title(\"(b) Bifurcation Diagram (Near Critical Point)\", fontsize=12)\n", + "ax2.ticklabel_format(axis=\"y\", style=\"scientific\", scilimits=(0, 0))\n", + "\n", + "# --- Shared legend at bottom ---\n", + "handles, labels = ax1.get_legend_handles_labels()\n", + "fig.legend(\n", + " handles,\n", + " labels,\n", + " loc=\"lower center\",\n", + " ncol=2,\n", + " fontsize=11,\n", + " frameon=False,\n", + " bbox_to_anchor=(0.5, -0.02),\n", + ")\n", + "\n", + "plt.tight_layout()\n", + "plt.subplots_adjust(bottom=0.15) # Make room for legend\n", + "\n", + "plt.savefig(\"plot1_bifurcation.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "\n", + "print(\"Saved: plot1_bifurcation.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# === Bifurcation Diagram with Inset ===\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "\n", + "# Main plot: Full range (Phase 1)\n", + "ax.errorbar(\n", + " agg_phase1[\"prey_death\"],\n", + " agg_phase1[\"prey_mean\"],\n", + " yerr=agg_phase1[\"prey_se\"],\n", + " fmt=\"o-\",\n", + " color=COLORS[\"prey\"],\n", + " markersize=5,\n", + " capsize=3,\n", + " linewidth=1.5,\n", + " label=\"Prey\",\n", + " zorder=3,\n", + ")\n", + "ax.errorbar(\n", + " agg_phase1[\"prey_death\"],\n", + " agg_phase1[\"pred_mean\"],\n", + " yerr=agg_phase1[\"pred_se\"],\n", + " fmt=\"s-\",\n", + " color=COLORS[\"predator\"],\n", + " markersize=5,\n", + " capsize=3,\n", + " linewidth=1.5,\n", + " label=\"Predator\",\n", + " zorder=3,\n", + ")\n", + "\n", + "ax.set_xlabel(\"Prey Death Rate\")\n", + "ax.set_ylabel(\"Mean Population\")\n", + "ax.set_title(\"Bifurcation Diagram\")\n", + "ax.set_xlim(-0.005, 0.205)\n", + "ax.ticklabel_format(axis=\"y\", style=\"scientific\", scilimits=(0, 0))\n", + "\n", + "# Inset: Near critical point\n", + "ax_inset = ax.inset_axes([0.62, 0.50, 0.35, 0.40])\n", + "\n", + "ax_inset.errorbar(\n", + " agg_phase1_5[\"prey_death\"],\n", + " agg_phase1_5[\"prey_mean\"],\n", + " yerr=agg_phase1_5[\"prey_se\"],\n", + " fmt=\"o-\",\n", + " color=COLORS[\"prey\"],\n", + " markersize=3,\n", + " capsize=2,\n", + " linewidth=1.2,\n", + ")\n", + "ax_inset.errorbar(\n", + " agg_phase1_5[\"prey_death\"],\n", + " agg_phase1_5[\"pred_mean\"],\n", + " yerr=agg_phase1_5[\"pred_se\"],\n", + " fmt=\"s-\",\n", + " color=COLORS[\"predator\"],\n", + " markersize=3,\n", + " capsize=2,\n", + " linewidth=1.2,\n", + ")\n", + "\n", + "ax_inset.ticklabel_format(axis=\"y\", style=\"scientific\", scilimits=(0, 0))\n", + "ax_inset.tick_params(labelsize=8)\n", + "ax_inset.set_title(\"Near Critical Point\", fontsize=10)\n", + "ax_inset.set_facecolor(\"white\")\n", + "ax_inset.grid(True, alpha=0.3, linestyle=\"--\")\n", + "\n", + "# Style inset border\n", + "for spine in ax_inset.spines.values():\n", + " spine.set_edgecolor(\"0.4\")\n", + " spine.set_linewidth(1)\n", + " spine.set_visible(True)\n", + "\n", + "# Legend\n", + "ax.legend(loc=\"upper right\", framealpha=0.95)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def get_cluster_distribution(df, prey_death_val, species=\"prey\"):\n", + " \"\"\"Extract all cluster sizes for a given prey_death value.\"\"\"\n", + " subset = df[np.isclose(df[\"prey_death\"], prey_death_val, atol=0.001)]\n", + " col = f\"{species}_cluster_sizes\"\n", + " all_sizes = []\n", + " for sizes in subset[col]:\n", + " if isinstance(sizes, list) and len(sizes) > 0:\n", + " all_sizes.extend(sizes)\n", + " return np.array(all_sizes)\n", + "\n", + "\n", + "# === Plot all cluster sizes as individual points ===\n", + "\n", + "\n", + "def compute_pdf_all_points(cluster_sizes):\n", + " \"\"\"Compute P(s) for each unique cluster size.\"\"\"\n", + " clusters = np.array(cluster_sizes)\n", + " if len(clusters) == 0:\n", + " return np.array([]), np.array([])\n", + "\n", + " # Count occurrences of each size\n", + " unique, counts = np.unique(clusters, return_counts=True)\n", + " # Normalize to get probability\n", + " probs = counts / len(clusters)\n", + "\n", + " return unique, probs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# =============================================================================\n", + "# HYDRA EFFECT ANALYSIS UTILITIES\n", + "# =============================================================================\n", + "from numpy.polynomial import Polynomial\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "from matplotlib.gridspec import GridSpec\n", + "\n", + "\n", + "def build_curves(df_avg):\n", + " \"\"\"Build prey density curves for each parameter combination.\n", + " \n", + " Parameters\n", + " ----------\n", + " df_avg : pd.DataFrame\n", + " Averaged data with columns: prey_birth, prey_death, predator_birth, \n", + " predator_death, prey_density\n", + " \n", + " Returns\n", + " -------\n", + " list of dict\n", + " Each dict contains parameter values and arrays for prey_death/prey_density\n", + " \"\"\"\n", + " curves_data = []\n", + " for (pb, pred_b, pred_d), group in df_avg.groupby(\n", + " [\"prey_birth\", \"predator_birth\", \"predator_death\"]\n", + " ):\n", + " group_sorted = group.sort_values(\"prey_death\")\n", + " curves_data.append({\n", + " \"prey_birth\": pb,\n", + " \"predator_birth\": pred_b,\n", + " \"predator_death\": pred_d,\n", + " \"prey_death\": group_sorted[\"prey_death\"].values,\n", + " \"prey_density\": group_sorted[\"prey_density\"].values,\n", + " })\n", + " return curves_data\n", + "\n", + "\n", + "def run_hydra_analysis(curves_data):\n", + " \"\"\"Run full Hydra effect analysis pipeline.\n", + " \n", + " Returns\n", + " -------\n", + " df_quadratic_valid : pd.DataFrame\n", + " Valid quadratic fits with coefficients\n", + " curves_with_hydra : list\n", + " Curves showing Hydra effect\n", + " filtered_curves : list\n", + " Curves truncated at max derivative\n", + " \"\"\"\n", + " # Detect Hydra effect\n", + " curves_with_hydra = []\n", + " curves_without_hydra = []\n", + " \n", + " for curve in curves_data:\n", + " has_hydra, max_deriv = detect_hydra_effect(\n", + " curve[\"prey_death\"], curve[\"prey_density\"]\n", + " )\n", + " curve[\"has_hydra\"] = has_hydra\n", + " curve[\"max_derivative\"] = max_deriv\n", + " \n", + " if has_hydra:\n", + " curves_with_hydra.append(curve)\n", + " else:\n", + " curves_without_hydra.append(curve)\n", + " \n", + " # Filter curves: keep only portion before max derivative\n", + " filtered_curves = []\n", + " for curve in curves_with_hydra:\n", + " prey_d = curve[\"prey_death\"]\n", + " prey_n = curve[\"prey_density\"]\n", + " \n", + " derivatives = np.diff(prey_n) / np.diff(prey_d)\n", + " max_deriv_idx = np.argmax(derivatives)\n", + " end_idx = max_deriv_idx + 1\n", + " \n", + " if end_idx > 0:\n", + " filtered_curves.append({\n", + " \"prey_birth\": curve[\"prey_birth\"],\n", + " \"predator_birth\": curve[\"predator_birth\"],\n", + " \"predator_death\": curve[\"predator_death\"],\n", + " \"prey_death\": prey_d[:end_idx + 1],\n", + " \"prey_density\": prey_n[:end_idx + 1],\n", + " \"max_derivative\": curve[\"max_derivative\"],\n", + " })\n", + " \n", + " # Fit quadratic polynomial\n", + " curves_with_quadratic = []\n", + " for curve in filtered_curves:\n", + " prey_d = curve[\"prey_death\"]\n", + " prey_n = curve[\"prey_density\"]\n", + " \n", + " if len(prey_d) < 3:\n", + " continue\n", + " \n", + " try:\n", + " poly = Polynomial.fit(prey_d, prey_n, deg=2)\n", + " coeffs = poly.convert().coef\n", + " quadratic_coef = coeffs[2] if len(coeffs) > 2 else 0.0\n", + " \n", + " curves_with_quadratic.append({\n", + " \"prey_birth\": curve[\"prey_birth\"],\n", + " \"predator_birth\": curve[\"predator_birth\"],\n", + " \"predator_death\": curve[\"predator_death\"],\n", + " \"quadratic_coef\": quadratic_coef,\n", + " \"max_derivative\": curve[\"max_derivative\"],\n", + " })\n", + " except:\n", + " pass\n", + " \n", + " df_quadratic = pd.DataFrame(curves_with_quadratic)\n", + " df_quadratic_valid = df_quadratic[df_quadratic[\"quadratic_coef\"].notna()]\n", + " \n", + " return df_quadratic_valid, curves_with_hydra, filtered_curves\n", + "\n", + "\n", + "def plot_hydra_curves(curves_with_hydra, filtered_curves, save_path=None):\n", + " \"\"\"Plot Hydra effect curves: full and truncated versions.\"\"\"\n", + " fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + " \n", + " # Color normalization\n", + " max_derivs = [c[\"max_derivative\"] for c in curves_with_hydra]\n", + " norm = plt.Normalize(vmin=min(max_derivs), vmax=max(max_derivs))\n", + " cmap = plt.cm.Blues\n", + " \n", + " # Left: All curves with Hydra effect\n", + " ax1 = axes[0]\n", + " for curve in curves_with_hydra:\n", + " color = cmap(norm(curve[\"max_derivative\"]))\n", + " ax1.plot(curve[\"prey_death\"], curve[\"prey_density\"], \n", + " color=color, alpha=0.6, linewidth=1.2)\n", + " \n", + " ax1.axhline(0, color=\"0.5\", linestyle=\"--\", linewidth=0.8)\n", + " ax1.set_xlabel(\"Prey Death Rate\")\n", + " ax1.set_ylabel(\"Equilibrium Prey Density\")\n", + " ax1.set_title(f\"Curves with Hydra Effect\")\n", + " \n", + " # Right: Filtered curves\n", + " ax2 = axes[1]\n", + " for curve in filtered_curves:\n", + " color = cmap(norm(curve[\"max_derivative\"]))\n", + " ax2.plot(curve[\"prey_death\"], curve[\"prey_density\"],\n", + " color=color, alpha=0.6, linewidth=1.2)\n", + " \n", + " ax2.axhline(0, color=\"0.5\", linestyle=\"--\", linewidth=0.8)\n", + " ax2.set_xlabel(\"Prey Death Rate\")\n", + " ax2.set_ylabel(\"Equilibrium Prey Density\")\n", + " ax2.set_title(f\"Truncated at Max Derivative\")\n", + " \n", + " # Match y-axis limits\n", + " y_max = max(ax1.get_ylim()[1], ax2.get_ylim()[1])\n", + " y_min = min(ax1.get_ylim()[0], ax2.get_ylim()[0])\n", + " ax1.set_ylim(y_min, y_max)\n", + " ax2.set_ylim(y_min, y_max)\n", + " \n", + " # Shared colorbar\n", + " fig.subplots_adjust(right=0.88)\n", + " cbar_ax = fig.add_axes([0.90, 0.15, 0.02, 0.7])\n", + " sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n", + " sm.set_array([])\n", + " cbar = fig.colorbar(sm, cax=cbar_ax)\n", + " cbar.set_label(\"Max Positive Derivative\")\n", + " \n", + " fig.suptitle(\"Hydra Effect: Full vs Truncated Curves\", y=1.02, fontweight=\"normal\")\n", + " plt.tight_layout(rect=[0, 0, 0.88, 1])\n", + " \n", + " if save_path:\n", + " plt.savefig(save_path, dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + "\n", + "\n", + "def plot_3d_heatmap(df_quadratic_valid, title_suffix=\"\"):\n", + " \"\"\"Generate 3D voxel heatmap of quadratic coefficients.\"\"\"\n", + " if len(df_quadratic_valid) == 0:\n", + " print(\"No valid data for 3D heatmap\")\n", + " return\n", + " \n", + " # Get unique parameter values\n", + " pb_grid = np.array(sorted(df_quadratic_valid[\"prey_birth\"].unique()))\n", + " pred_b_grid = np.array(sorted(df_quadratic_valid[\"predator_birth\"].unique()))\n", + " pred_d_grid = np.array(sorted(df_quadratic_valid[\"predator_death\"].unique()))\n", + " \n", + " # Create 3D array\n", + " heatmap_3d = np.full((len(pb_grid), len(pred_b_grid), len(pred_d_grid)), np.nan)\n", + " \n", + " for _, row in df_quadratic_valid.iterrows():\n", + " pb_idx = np.where(np.isclose(pb_grid, row[\"prey_birth\"]))[0]\n", + " pred_b_idx = np.where(np.isclose(pred_b_grid, row[\"predator_birth\"]))[0]\n", + " pred_d_idx = np.where(np.isclose(pred_d_grid, row[\"predator_death\"]))[0]\n", + " \n", + " if len(pb_idx) > 0 and len(pred_b_idx) > 0 and len(pred_d_idx) > 0:\n", + " heatmap_3d[pb_idx[0], pred_b_idx[0], pred_d_idx[0]] = row[\"quadratic_coef\"]\n", + " \n", + " # Color normalization\n", + " vmin = np.nanpercentile(heatmap_3d, 5)\n", + " vmax = np.nanpercentile(heatmap_3d, 95)\n", + " norm = plt.Normalize(vmin=vmin, vmax=vmax)\n", + " cmap = plt.cm.Blues\n", + " \n", + " # Create voxel colors\n", + " filled = ~np.isnan(heatmap_3d)\n", + " colors = np.empty(heatmap_3d.shape + (4,))\n", + " \n", + " for i in range(heatmap_3d.shape[0]):\n", + " for j in range(heatmap_3d.shape[1]):\n", + " for k in range(heatmap_3d.shape[2]):\n", + " if filled[i, j, k]:\n", + " rgb = cmap(norm(heatmap_3d[i, j, k]))[:3]\n", + " colors[i, j, k] = [rgb[0], rgb[1], rgb[2], 0.9]\n", + " else:\n", + " colors[i, j, k] = [0, 0, 0, 0]\n", + " \n", + " # Create figure\n", + " fig = plt.figure(figsize=(16, 7))\n", + " gs = GridSpec(1, 3, width_ratios=[1, 1, 0.05], wspace=0.25)\n", + " \n", + " tick_skip = 2\n", + " \n", + " # Left panel: Voxel plot\n", + " ax1 = fig.add_subplot(gs[0, 0], projection=\"3d\")\n", + " ax1.voxels(filled, facecolors=colors, edgecolors=\"0.4\", linewidth=0.1)\n", + " \n", + " ax1.set_xticks(range(0, len(pb_grid), tick_skip))\n", + " ax1.set_xticklabels([f\"{pb_grid[i]:.1f}\" for i in range(0, len(pb_grid), tick_skip)])\n", + " ax1.set_yticks(range(0, len(pred_b_grid), tick_skip))\n", + " ax1.set_yticklabels([f\"{pred_b_grid[i]:.1f}\" for i in range(0, len(pred_b_grid), tick_skip)])\n", + " ax1.set_zticks(range(0, len(pred_d_grid), tick_skip))\n", + " ax1.set_zticklabels([f\"{pred_d_grid[i]:.1f}\" for i in range(0, len(pred_d_grid), tick_skip)])\n", + " \n", + " ax1.set_xlabel(\"Prey Birth\")\n", + " ax1.set_ylabel(\"Predator Birth\")\n", + " ax1.set_zlabel(\"Predator Death\")\n", + " ax1.set_title(\"3D Voxel Heatmap\")\n", + " ax1.view_init(elev=25, azim=225)\n", + " \n", + " # Right panel: Cross-sections\n", + " ax2 = fig.add_subplot(gs[0, 1], projection=\"3d\")\n", + " PB_slice, PRED_B_slice = np.meshgrid(pb_grid, pred_b_grid, indexing=\"ij\")\n", + " \n", + " n_slices = min(5, len(pred_d_grid))\n", + " slice_indices = np.linspace(0, len(pred_d_grid) - 1, n_slices, dtype=int)\n", + " \n", + " for slice_idx in slice_indices:\n", + " pd_val = pred_d_grid[slice_idx]\n", + " slice_data = heatmap_3d[:, :, slice_idx]\n", + " valid_mask = ~np.isnan(slice_data)\n", + " \n", + " if not np.any(valid_mask):\n", + " continue\n", + " \n", + " Z_slice = np.full_like(PB_slice, pd_val)\n", + " face_colors = np.empty((*slice_data.shape, 4))\n", + " \n", + " for i in range(slice_data.shape[0]):\n", + " for j in range(slice_data.shape[1]):\n", + " if valid_mask[i, j]:\n", + " face_colors[i, j] = [*cmap(norm(slice_data[i, j]))[:3], 0.8]\n", + " else:\n", + " face_colors[i, j] = [0, 0, 0, 0]\n", + " \n", + " PB_masked = np.ma.masked_where(~valid_mask, PB_slice)\n", + " PRED_B_masked = np.ma.masked_where(~valid_mask, PRED_B_slice)\n", + " Z_masked = np.ma.masked_where(~valid_mask, Z_slice)\n", + " \n", + " ax2.plot_surface(PB_masked, PRED_B_masked, Z_masked,\n", + " facecolors=face_colors, alpha=0.8, linewidth=0.2, shade=False)\n", + " \n", + " ax2.set_xlim(pb_grid.min(), pb_grid.max())\n", + " ax2.set_ylim(pred_b_grid.min(), pred_b_grid.max())\n", + " ax2.set_zlim(pred_d_grid.min(), pred_d_grid.max())\n", + " ax2.set_xticks(pb_grid[::tick_skip])\n", + " ax2.set_xticklabels([f\"{x:.1f}\" for x in pb_grid[::tick_skip]])\n", + " ax2.set_yticks(pred_b_grid[::tick_skip])\n", + " ax2.set_yticklabels([f\"{x:.1f}\" for x in pred_b_grid[::tick_skip]])\n", + " ax2.set_zticks(pred_d_grid[::tick_skip])\n", + " ax2.set_zticklabels([f\"{x:.1f}\" for x in pred_d_grid[::tick_skip]])\n", + " \n", + " ax2.set_xlabel(\"Prey Birth\")\n", + " ax2.set_ylabel(\"Predator Birth\")\n", + " ax2.set_zlabel(\"Predator Death\")\n", + " ax2.set_title(\"Cross-Sections\")\n", + " ax2.view_init(elev=25, azim=225)\n", + " \n", + " # Colorbar\n", + " cbar_ax = fig.add_subplot(gs[0, 2])\n", + " sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n", + " sm.set_array([])\n", + " cbar = fig.colorbar(sm, cax=cbar_ax)\n", + " cbar.set_label(\"Quadratic Coefficient\")\n", + " \n", + " fig.suptitle(f\"Hydra Effect Strength{title_suffix}: 3D Parameter Space\", \n", + " y=0.98, fontweight=\"normal\")\n", + " plt.savefig(\"phase4_3d_heatmap.png\", dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + " \n", + " print(f\"Data coverage: {100*np.sum(filled)/filled.size:.1f}%\")\n", + " print(f\"Coefficient range: [{vmin:.1f}, {vmax:.1f}]\")\n", + "\n", + "\n", + "def plot_marginal_effects(df_quadratic_valid):\n", + " \"\"\"Plot marginal effects of each parameter on Hydra strength.\"\"\"\n", + " fig, axes = plt.subplots(1, 3, figsize=(14, 4))\n", + " \n", + " # Plot 1: Effect of Prey Birth\n", + " by_pb = (df_quadratic_valid.groupby(\"prey_birth\")[\"quadratic_coef\"]\n", + " .agg([\"mean\", \"std\", \"count\"]).reset_index())\n", + " by_pb[\"se\"] = by_pb[\"std\"] / np.sqrt(by_pb[\"count\"])\n", + " \n", + " ax1 = axes[0]\n", + " ax1.errorbar(by_pb[\"prey_birth\"], by_pb[\"mean\"], yerr=by_pb[\"se\"],\n", + " fmt=\"o-\", color=COLORS[\"prey\"], markersize=6, linewidth=1.5, capsize=3)\n", + " ax1.axhline(0, color=\"0.5\", linestyle=\"--\", linewidth=1)\n", + " ax1.set_xlabel(\"Prey Birth Rate\")\n", + " ax1.set_ylabel(\"Quadratic Coefficient\")\n", + " ax1.set_title(\"Effect of Prey Birth\")\n", + " ax1.fill_between(by_pb[\"prey_birth\"], 0, by_pb[\"mean\"], alpha=0.1, color=COLORS[\"prey\"])\n", + " \n", + " # Plot 2: Effect of Predator Birth\n", + " by_pred_b = (df_quadratic_valid.groupby(\"predator_birth\")[\"quadratic_coef\"]\n", + " .agg([\"mean\", \"std\", \"count\"]).reset_index())\n", + " by_pred_b[\"se\"] = by_pred_b[\"std\"] / np.sqrt(by_pred_b[\"count\"])\n", + " \n", + " ax2 = axes[1]\n", + " ax2.errorbar(by_pred_b[\"predator_birth\"], by_pred_b[\"mean\"], yerr=by_pred_b[\"se\"],\n", + " fmt=\"s-\", color=COLORS[\"predator\"], markersize=6, linewidth=1.5, capsize=3)\n", + " ax2.axhline(0, color=\"0.5\", linestyle=\"--\", linewidth=1)\n", + " ax2.set_xlabel(\"Predator Birth Rate\")\n", + " ax2.set_ylabel(\"Quadratic Coefficient\")\n", + " ax2.set_title(\"Effect of Predator Birth\")\n", + " ax2.fill_between(by_pred_b[\"predator_birth\"], 0, by_pred_b[\"mean\"], \n", + " alpha=0.1, color=COLORS[\"predator\"])\n", + " \n", + " # Plot 3: Effect of Predator Death\n", + " by_pred_d = (df_quadratic_valid.groupby(\"predator_death\")[\"quadratic_coef\"]\n", + " .agg([\"mean\", \"std\", \"count\"]).reset_index())\n", + " by_pred_d[\"se\"] = by_pred_d[\"std\"] / np.sqrt(by_pred_d[\"count\"])\n", + " \n", + " ax3 = axes[2]\n", + " ax3.errorbar(by_pred_d[\"predator_death\"], by_pred_d[\"mean\"], yerr=by_pred_d[\"se\"],\n", + " fmt=\"^-\", color=COLORS[\"highlight\"], markersize=6, linewidth=1.5, capsize=3)\n", + " ax3.axhline(0, color=\"0.5\", linestyle=\"--\", linewidth=1)\n", + " ax3.set_xlabel(\"Predator Death Rate\")\n", + " ax3.set_ylabel(\"Quadratic Coefficient\")\n", + " ax3.set_title(\"Effect of Predator Death\")\n", + " ax3.fill_between(by_pred_d[\"predator_death\"], 0, by_pred_d[\"mean\"],\n", + " alpha=0.1, color=COLORS[\"highlight\"])\n", + " \n", + " fig.suptitle(\"Hydra Effect Strength: Marginal Effects\", y=1.02)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "def plot_validation_curves(df_quadratic_valid, df_avg, title_suffix=\"\"):\n", + " \"\"\"Plot validation: actual curves for strong vs moderate Hydra effect.\"\"\"\n", + " strong_hydra = df_quadratic_valid[df_quadratic_valid[\"quadratic_coef\"] > 15]\n", + " moderate_hydra = df_quadratic_valid[\n", + " (df_quadratic_valid[\"quadratic_coef\"] >= 5) & \n", + " (df_quadratic_valid[\"quadratic_coef\"] <= 10)\n", + " ]\n", + " \n", + " print(f\"Strong Hydra (coef > 15): {len(strong_hydra)} curves\")\n", + " print(f\"Moderate Hydra (5 ≤ coef ≤ 10): {len(moderate_hydra)} curves\")\n", + " \n", + " n_samples = min(8, len(strong_hydra))\n", + " n_samples_mod = min(8, len(moderate_hydra))\n", + " \n", + " strong_sample = strong_hydra.nlargest(n_samples, \"quadratic_coef\")\n", + " moderate_sample = (moderate_hydra.sample(n_samples_mod, random_state=42)\n", + " if len(moderate_hydra) >= n_samples_mod else moderate_hydra)\n", + " \n", + " fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + " \n", + " # Left: Strong Hydra Effect\n", + " ax1 = axes[0]\n", + " colors_strong = plt.cm.Blues(np.linspace(0.4, 0.9, len(strong_sample)))\n", + " \n", + " for i, (_, row) in enumerate(strong_sample.iterrows()):\n", + " mask = ((df_avg[\"prey_birth\"] == row[\"prey_birth\"]) &\n", + " (df_avg[\"predator_birth\"] == row[\"predator_birth\"]) &\n", + " (df_avg[\"predator_death\"] == row[\"predator_death\"]))\n", + " curve_data = df_avg[mask].sort_values(\"prey_death\")\n", + " \n", + " if len(curve_data) > 0:\n", + " ax1.plot(curve_data[\"prey_death\"], curve_data[\"prey_density\"], \"-\",\n", + " color=colors_strong[i], linewidth=1.5, alpha=0.8,\n", + " label=f'c={row[\"quadratic_coef\"]:.1f}')\n", + " \n", + " ax1.set_xlabel(\"Prey Death Rate\")\n", + " ax1.set_ylabel(\"Equilibrium Prey Density\")\n", + " ax1.set_title(f\"Strong Hydra Effect (coef > 15, n={len(strong_sample)})\")\n", + " ax1.legend(loc=\"upper right\", fontsize=8, framealpha=0.95, title=\"Quad. Coef.\")\n", + " ax1.axhline(0, color=\"0.5\", linestyle=\"--\", linewidth=0.8)\n", + " \n", + " # Right: Moderate Hydra Effect\n", + " ax2 = axes[1]\n", + " colors_moderate = plt.cm.Oranges(np.linspace(0.4, 0.9, len(moderate_sample)))\n", + " \n", + " for i, (_, row) in enumerate(moderate_sample.iterrows()):\n", + " mask = ((df_avg[\"prey_birth\"] == row[\"prey_birth\"]) &\n", + " (df_avg[\"predator_birth\"] == row[\"predator_birth\"]) &\n", + " (df_avg[\"predator_death\"] == row[\"predator_death\"]))\n", + " curve_data = df_avg[mask].sort_values(\"prey_death\")\n", + " \n", + " if len(curve_data) > 0:\n", + " ax2.plot(curve_data[\"prey_death\"], curve_data[\"prey_density\"], \"-\",\n", + " color=colors_moderate[i], linewidth=1.5, alpha=0.8,\n", + " label=f'c={row[\"quadratic_coef\"]:.1f}')\n", + " \n", + " ax2.set_xlabel(\"Prey Death Rate\")\n", + " ax2.set_ylabel(\"Equilibrium Prey Density\")\n", + " ax2.set_title(f\"Moderate Hydra Effect (5 ≤ coef ≤ 10, n={len(moderate_sample)})\")\n", + " ax2.legend(loc=\"upper right\", fontsize=8, framealpha=0.95, title=\"Quad. Coef.\")\n", + " ax2.axhline(0, color=\"0.5\", linestyle=\"--\", linewidth=0.8)\n", + " \n", + " # Match y-axis limits\n", + " y_max = max(ax1.get_ylim()[1], ax2.get_ylim()[1])\n", + " ax1.set_ylim(bottom=0, top=y_max)\n", + " ax2.set_ylim(bottom=0, top=y_max)\n", + " \n", + " fig.suptitle(f\"Quadratic Coefficient Captures Hydra Effect Strength{title_suffix}\",\n", + " y=1.02, fontweight=\"normal\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " # Print parameter details\n", + " print(\"\\n=== Strong Hydra Parameters ===\")\n", + " print(strong_sample[[\"prey_birth\", \"predator_birth\", \"predator_death\", \n", + " \"quadratic_coef\"]].to_string(index=False))\n", + " print(\"\\n=== Moderate Hydra Parameters ===\")\n", + " print(moderate_sample[[\"prey_birth\", \"predator_birth\", \"predator_death\",\n", + " \"quadratic_coef\"]].to_string(index=False))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Clusters at d ≈ 0.095: 443140\n", + "Size range: [1, 8489]\n", + "=== Power Law Fit (Critical Point, d ≈ 0.095) ===\n", + "Power law α: 2.957\n", + "xmin: 337.0\n", + "Truncated PL α: 2.293\n", + "Truncated PL λ: 0.000678\n", + "\n", + "=== Distribution Comparison ===\n", + "Power law vs Truncated PL: R=-3.627, p=0.0000\n", + "Power law vs Lognormal: R=-4.208, p=0.0000\n", + "Truncated PL vs Lognormal: R=-1.331, p=0.1831\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kimonanagnostopoulos/CSS_Project/.venv/lib/python3.12/site-packages/powerlaw/distributions.py:808: UserWarning: Fitted parameters are very close to the edge of parameter ranges for distribution truncated_power_law; consider changing these ranges.\n", + " warnings.warn(f'Fitted parameters are very close to the edge of parameter ranges for distribution {self.name}; consider changing these ranges.')\n" + ] + } + ], + "source": [ + "# === Critical Point Cluster Distribution Plot ===\n", + "\n", + "# Get clusters at critical point\n", + "clusters_crit = get_cluster_distribution(df_phase1, 0.095, \"prey\")\n", + "\n", + "print(f\"Clusters at d ≈ 0.095: {len(clusters_crit)}\")\n", + "print(f\"Size range: [{clusters_crit.min()}, {clusters_crit.max()}]\")\n", + "\n", + "# Compute PDF (all points)\n", + "x_crit, y_crit = compute_pdf_all_points(clusters_crit)\n", + "\n", + "# Fit power law\n", + "fit_crit = powerlaw.Fit(clusters_crit, discrete=True, verbose=False)\n", + "\n", + "# Distribution comparisons\n", + "R_pl_tpl, p_pl_tpl = fit_crit.distribution_compare(\n", + " \"power_law\", \"truncated_power_law\", normalized_ratio=True\n", + ")\n", + "R_pl_ln, p_pl_ln = fit_crit.distribution_compare(\n", + " \"power_law\", \"lognormal\", normalized_ratio=True\n", + ")\n", + "R_tpl_ln, p_tpl_ln = fit_crit.distribution_compare(\n", + " \"truncated_power_law\", \"lognormal\", normalized_ratio=True\n", + ")\n", + "\n", + "print(\"=== Power Law Fit (Critical Point, d ≈ 0.095) ===\")\n", + "print(f\"Power law α: {fit_crit.power_law.alpha:.3f}\")\n", + "print(f\"xmin: {fit_crit.power_law.xmin}\")\n", + "print(f\"Truncated PL α: {fit_crit.truncated_power_law.alpha:.3f}\")\n", + "print(f\"Truncated PL λ: {fit_crit.truncated_power_law.parameter2:.6f}\")\n", + "\n", + "print(f\"\\n=== Distribution Comparison ===\")\n", + "print(f\"Power law vs Truncated PL: R={R_pl_tpl:+.3f}, p={p_pl_tpl:.4f}\")\n", + "print(f\"Power law vs Lognormal: R={R_pl_ln:+.3f}, p={p_pl_ln:.4f}\")\n", + "print(f\"Truncated PL vs Lognormal: R={R_tpl_ln:+.3f}, p={p_tpl_ln:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# === Prey Cluster Size Distribution ===\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 5))\n", + "\n", + "# Empirical data\n", + "ax.scatter(x_crit, y_crit, color=COLORS[\"prey\"], s=12, alpha=0.5, edgecolors=\"none\")\n", + "\n", + "ax.set_xscale(\"log\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_xlabel(\"Cluster Size\")\n", + "ax.set_ylabel(\"P(s)\")\n", + "ax.set_title(f\"Prey Cluster Size Distribution (d ≈ 0.095)\")\n", + "\n", + "# Statistics annotation\n", + "winner = \"TPL wins\" if R_tpl_ln > 0 else \"Lognormal\"\n", + "stats_text = (\n", + " f\"α = {fit_crit.truncated_power_law.alpha:.2f}\\n\" f\"R = {R_tpl_ln:+.1f} ({winner})\"\n", + ")\n", + "\n", + "ax.text(\n", + " 0.95,\n", + " 0.95,\n", + " stats_text,\n", + " transform=ax.transAxes,\n", + " verticalalignment=\"top\",\n", + " horizontalalignment=\"right\",\n", + " fontsize=10,\n", + " bbox=dict(boxstyle=\"round,pad=0.4\", facecolor=\"white\", edgecolor=\"0.8\", alpha=0.9),\n", + ")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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j6/dA+yVu4M6+fYHkIiAFWIAe0Yq5E0hIzOCSDsp0p6IDnfh2SLozi3vkIuZ8dE1Zbt26tXlfHXzoURHdGWtmT3wSmsamWS8x6wglZ7qbtiOx9U3Oe+lRIM0a0sCUBjk04KQBGM060iNA9qOd2k8aeNEAUFw6kE5JuxOi/Wyf/qY7Z+1XDabF3OlrezU7SAd7egRMB+Faq0kHuTpwSGq6ng4MkvqMaN9oYEgHzbrMuGc6iVt/QZ+v651QEdC42za+59kLm2o/a4AzvoL0MY9OZqazAgKAu0lqvBLfvlT3E3rR/YkGITRgpfsoPRij2UUawNBaNXogR4MKul+371f0R3BC077s+5TU/nhXeiBFDyhpO/TSokULs5+2B1N0f6o/vjWzWfeh+hwN5GhGumb6aCaxZo0lRg8cJWeMlxTNStFlab9pH8alYzjN4NJATEJiHnDUg1e6HppJpOuor9Np9TFrbCaXZs1ov2h2vk7x0vGCHiSLj317xR2j2sev9r7SaYt6EE+n5GmgKaFAmwYRNaNes8g1mKOBTT04l1DWmgYDtR80aGXPfNKgix48SyjwpQdR4ztY6wh6sNmRGVj2aXv2mQJ6cNFe9kOnAtozHOOT2DhPM6w0ePvrr7+awJZOkdQDsTGXp99xCpojpaghBWRQmtmjR7R0gKI7b73oIEBT4XXHqgM8PdqnR0fsNJPITgcNuuPQzCIdhGhQSoNUMcUc5On76eDBfppapa/X2wnN60/LuiX1XrrD1aOYmsFjL0ypA0WdoqcDTPsRPl2WFhG3Dwj1osvWKQT2Qo5ppUcYdXCt0wt0CqVOYbP3u31Hrm3TbCE9cqcDBB3k6n06OND6GgnN508JnTanR/D0r87x1xoNMcWsp6CfEW23fk60T3RQr4PcmDS4l9xtq/2sRzLtfawXfT8dqCR3cJKWHxUAANfT8gAaFIkZbNAsJ92H62MxM4b1gIzuv/WAkmbE6DQ2DUbZg1r2/XfM/crvv/9ugg+Ooge2dD+nJ/XQH9z6/hpoinkWubjt1XboATDNLNJ10mCMoyS2H9RxgwbPNGhkzzzWftNMFc2y0X2/TjmzT7nS/XrMMaBuE+1PO12OBm+0pIEeUNJMba17mZwz6MXXTn1fLe6uFz04p+2NjwaNVFJjMA2O6JRCHZvGPRtfTFonVMfDeuBR62JpHyU2hVLrUWkmVMwTu+h4R9sbMzM/Js2ij/k5jHmxGh0faya+1vHSz6v2hQYwNQtQt21CNcgSowEtLQuhtH91vKvjaM2Kt9dzVfq5zOxnhIbjEZACMiidnqZT0bQWkB7N0x2P1ijQQINmpOhRDN3h6n2aQq+Bm5gFpvUIkdb40YGDDr50vr8OrpR9GpW+Xh/TwYDudDXQogVG9ciLLlPnqWsQTI9gOVJy3ksLpupgVY/wxQ1I6Y7XfrRJ6wnozljraunRVz2aqfPiNXATcwpkcuhrdDAc86LL052vDmQ0yKf9pW22nwnR3pf2aYZaYyFme7X4o04H0KKgidEdvhajjO+iNPiowR8tSq5HOrUOg/aZDiLs9L218Kt+XrRQpQbI9Mio9o8OSvWIsH4e9GijHvHVwXdyz3inA0IdsGvKvgamtCC9DnL1s5jcgJR+3vS1pHsDQMak+x7dL9nHJvbC5JotY58irz+Ydf+p+zXNYNYpVBoI0HFIzIMzerBM9/EabNH6NZolowfdUjNFT4NicfffOqbQZemy9Qe8Bms02KW1KmNOJ9eAlGaX6EG9mPvvuO11hKT2g5otpLWB9IQlWqtHA1La5zqVSs8AqNPb7FP5tU91vTVooOuoAYWYZ0XTddexo66bbiut+6VjmuRMpdd26hhS22rPHNI6ldpubVfcDO24ASkdN9lLGyRGp59poNBeyyg+ur10fKufq+Qc2NLPmwbgtG6mjtf0YJw9U8yqNSy1hlncz29CBdW1/IIGH3W7x/286tTRhAKFidHsNM2K0n7S8aZ+XjT4p9/pmME/PVCs2f9ASjBlD8igdCChO2ANImiKue4s9CiZfZ643tajXxoU0J2sBl90cGc/LbIGfHTeu55NTwcfulPR5+oUQN3pa8BK60RogUqtDaCDSq3roIUldcCjR+F0sKMDAWccIUrOe+kgUXeMMXe4uhOOOf1NB03aT3qUTY/q6BFDLcCqqcYpTSvW4FHcs4fosrS+hQ70NCCkR6D0aKlO29PBgGYJ2Qdm2l59vb29epRKpwAk5+x6OijXS3w000kDUZqWbX8v7TdNpdYBl/3Mi3r0UgfbmkGmAwYd5NsDd7qdtT90sKEDVj2CqEdb7UXlk6I/KvSImabYax/ocvXzo0Vsk0vrHGigTAOQMU/tDQDIGDTQoPtcHWvoPkcPSmhxZnuxa6UHYLRmjv61/zjW/bdmrMTcH+qYZsSIEWZfpIEoDWRoFrIeAEkpe3ZHTNpGPRijARktV6DBDN336fhDp9hrwERv6/5M26oBNPs+U9ur+6m0nB03NftBPbil+1jNttasLs1o0oCAHojScZ4WmtYxk545UMduOp7TA5O6f9di7TFr/migUC8anNExo45NtH/tZxpMjK63ZtjrQT8dV+iBQPs4VPstsYNs2s/2wvDapqRo/UwNcmm/aEZb3LFbamqA6kFK7Sddti5Pp4XqeNiq9GBn3KmY+t2x136Ne79+HjRgZT/btX5edYyW2s+rbjN9vY4pdSytGY/6edHPoJ3ep8E9+5gTSC4PW3LyMgEAGZqeIUWz6lIzkAcAANanwTXNckpOoMcZNKtaD74lVsdKaSBFn6tBk8Sm1yHj0AOuGrD64YcfUpWFBffFlD0AAAAAyODKli3rkmCUTufS7CwNhmlWflI0c0ezdrQQOTIHzax7+eWXCUYhxQhIAQAAAABSRaftaRkALf0Q8wzGidE6YxrE0HqWyNi0FpmWzNASIkBKMWUPAAAAAAAA6YoMKQAAAAAAAKQrAlIAAAAAAABIVwSkAAAAAAAAkK68xY1ERETIpUuXxM/PTzw9icUBAICkabHWsLAwyZEjh3h7u8/QiXETAABw5rjJfUZVIiYYdejQIVc3AwAAZEDFixdP9hmkMgPGTQAAwJnjJrcKSGlmlL1j/P39Hb78yMhI2bNnj5QtW1a8vLwcvnykHNvEmtgu1sM2sR62iXWEhoaaA1r2cYS7cPa4yZH4vtDn7oLPOn3uLvisZ9w+T8m4ya0CUvZpejqoCggIcMoGVLpsAlLWwDaxJraL9bBNrIdtYj3uNt3f2eMmR+L7Qp+7Cz7r9Lm74LOe8fs8OeMm9xpZAQAAAAAAwOUISAEAAAAAACBdEZACAAAAAABAunKrGlIAAOuw2WzmtLBIfB6//S+cx8PDw+3qQwEAALgaASkAQLrSAMuJEyfkwoULBKSSESjZsWNH+mwYN6dngilVqpTbnUkPAADAVQhIAQDS1b59+0w2SpkyZcTX19cEXZBwBpn2FX3k/L4+ffq07N+/X+666y4+jgAAAOmAgBQAIN1ogOX69etyzz33iI+PDz2fRJDEPpWMgJTz5cuXT86cORMdBAQAAIBzMeICAKRrkMXsfPjBD4uxB/3sn1EAAAA4FwEpAAAAAAAApCsCUg5k07NFhYbe+gsAAAAAAIB4EZBygNBt2+Rwly6yI08e8apf3/zV23o/AADJcfToUalevbo5+2BSQkJCpF69erJ9+/bo+zp37ixVqlSJdSlXrpx8/vnnCS5nwYIFMmHChFRtoN9//11q164tVatWlZ9//lms7saNG9K2bVvTdwAAAHA9ipqnUci8eXK4UyctPiESEWHus4WFScjcuRIyZ44UmzZNcrZtKxmVZntFhYaKp7+/eFDzBQCcYu3atTJgwAC5dOlSks/9559/5PXXXzcBrJimTp0a6/bkyZNl2bJl8uyzz8a7HD2r3LRp02Tx4sWpavMPP/xggl6ffvqpZARZsmSR559/Xt555x0ZM2aMq5sDAEC60NqIelBGxxh6YpnIyMgM024vLy9zBlxO7uI42pd6luscOXJItmzZXF7XlYBUWjOjNBgV35f6dnBKH89SoYL4V6okGW3dzowbJxcXLDABNg8/Pwlq3Vry9uqV4dYFANJqw4YNMmrUKDl8+LDcdddd0r9/f5Nts2rVKilQoECs52qm0saNG+9YRpMmTWT48OF33D9nzhyZMWOG9OrVSwYPHpxoO1avXi2DBg0y79+vX78En7dz50757LPPTAZU1qxZ433OlClT5PHHHxc/Pz9z+9FHH5VatWrJypUr5f7775dx48bJr7/+agJOx44dkxIlSpigmT6mf5cuXWoGNZolpdlS+/btM8GeHTt2SM6cOaVDhw7y9NNPm2Xr83UwrI/pQHj58uVy/PjxRJ/v7+8vBw4ckG3btknhwoXlzTfflAcffNA8ru365JNPTFCuYMGCJkD3yCOPmGVrkG3+/Ply5coVk701dOhQyZ8/v3ldw4YNzXvq4LZUqVKJ9jUAABmdBnV0f6vZwdmzZzdBCG/vjBMCiIiIyFDtzQiioqLMmOzkyZPms6HjIftY0BXYummgARuTGZUYDw85M368FJs8WTKKzJ71BcCaNIBw9erVO7JaNFihA5KzZ8/e8Rp7MOjcuXNy8+bNWI8FBQWZoMa1a9fk8uXLsR7TII0eFUoOnUL30ksvyauvvirPPPOMrFu3Tjp16iTFixe/IxgVX6ZSUnTqnQa3Tpw4keRzK1asKL/88os5spVYQEoDXxrgKV26dLyPa18tWrRI5s2bF+t+DdTo8jWwo9MB+/btKxMnTpRq1aqZaXndunWT77//Xt577z3z/ICAABkyZIjpY30/zUDSzCxdjj43d+7cZv3Un3/+Kd98840ZDOtgKKnnf/fddzJz5kypUKGCvP/++/L222+brCx97iuvvCIffPCB1K9f3wQFNZinQSrNCPv2229NUEqDUBpMe/nll837avBMB7W6/IULF8obb7yRou0EAEBGo2MLHVuVL19efHx8JKMF03S8ou0mQ8rxdJx06tQpM64qU6aMyz4fBKTSMJVNs4fsAZsERUSYIE5gzZrilSWLeGTJIp5+fv/9tV/Xx/z8/ntMb7sgGpyZs74AWJtmFWkGUNwATMuWLU1ASQMXcWn2i9JpZ5rFE1OLFi2kUqVKJgNnxYoVsR6rU6eOyahJDs3+0aOKGjxRmhGkWUIaKHOEPHnyJPu5wcHBST5HAz8HDx5MNDCmfaIDvbgBqwYNGpggk9KgTaNGjaKzkjSbSjOuNDNKs8Bi0u2mwcMuXbqY2zrw1amCmqlkDzBpn2mmk9KgVlLP136+9957zfXGjRvL119/ba7rttRaW9oepc/XxwIDA83rNXhYrFgx81ifPn1MME2Da/pZULrM2bNnJ6O3AQDIuPTgkh5Uc2WwAdbl4eFhglKhoaFy8eLFFI1HHYmAVCppXSXNGkqWiAg59tJLKX8TT89bgSm9+PpGX/eMczvWY7eDXPHejnM9vr+n3n8/6XZlwKwvANZ33333mSLcMWnQQmlAqGvXrgm+tlmzZvFmSKm7775bihQpEuuxhKaxxefMmTPRU77s8ubNGx30iEszfeKbsqdBlWHDhomzaVBG+yOxddQ0bR14xD3iqOsV86jq+vXr5ccff4w1uNXMsLg05Vufr0EnO82Csgeg4i47Oc/PlStX9HXNbNIAmn17xM1MswebdJnaxyNGjIi1XH0/+3N0vXX9AQDIzPRgnk7Fso+lgLh0HKjj5fPnzxOQymhMkW8/v+QFpTw9JVv9+ua5ths3JCo8/NbfGzfMfVH2+2/ciJ1xpQXFr18X0YuVRETIxfnzpejnn5M+CcBhdApdQtPoNCAR3/Q4O53qlRDNnNFLauXLl++OAEbcbKyYEjurnbNpUO63336TL774ItHnaQHL+IqaxgxQ6Xpr1lLMqW1as0mn3MWlz9UsJ50aZ6eDG51qmdCyk3p+QjQ4uGnTplj3TZo0ydTA0uVqramY2W+aih4z0KUBKlcX8AQAwNmOjR0rOZ5+WiLOnbNUZ3snMmZD+tPMeD1w5ypkSKWSnnFOi3xrXaVEp+15e0vOdu2SnU1ki4y8FaSKJ1gV83as58T5m9hjcZcX87FI/XvlSvLaGRYmR7p1k6wPPSQB1apJlvLlxcPLK7ndBwAZhgY3Ro4caaaFaa0nLfqtU960bpXV/PvvvyaTSLPCEqPBPQ0AJRac0SmPWn9Jp8RpcXANAukUO63npLWb4vaR3q81qVq1amX6RjPadGqdFmGPK6XPj+mJJ54wQT+tGaXL0RpSWqBd26vLGj9+vJmeoOuoBeP1fXSbabAqoQwrAAAyG5u/v3hREBxJ0DMZ6tjRnome3ghIpYGecU7rQyXKZpO8PXsme5ka1PEICBDP2zU80rsu1rbg4GRPRQyZNctclGe2bBJw//0SWL26BFSvLoHVqom3i+ahAoAjaUaQnrFOg1Jjxowx0wo1ANKuXTsTSEksOyut/v77b1MTSc9Kp2eTS4pmbml7dHCRGA1YaRq/BrD0rIEJTaHUqW9aTFyXq/WrtCZT3GCUfUqlFhJ/99135cMPPzQZbXpGOz0bYHxS+vyY9Gx/egbAjz/+2BR21+mYGoTSgJMWm9csK633pWcU0udq8MoejFKbN2+Whx56KMn3AQAgI0vi1FsOUb9VKzlx6tR/7+nhIdmyZpX77r1X3urTRwrE2P+mRVhYmBmP/PTTT2YKYseOHc0lM1i0aJEZx+iBNndEQCoNtKi3nnEu7hnpbvWstwlG6eMZpfh3srO+vLwk8OGHJfC+++Tahg0SunGjROnZsVatMhc73xIl/gtQVa8uWSpVEk9f3/RZGQBwIK11pGd9i0mzpBxJp5Xt3r37jvfVzKT4zi4T97n27CG9JEUDVk2aNDH1oewBqfgGQlrkXC/xsZ9pz06n4CU0VTDuc1P6fC1uH3N9teC5XuLSwFaPHj3MJT7h4eGmSP2XX34Z7+MAACBlBrzyijz+2GPmumZe7z94UIZ/8IG8OXKkzNCz0jvA6NGj5Z9//jHjBq0XqeUE9ECd/QQnyLgISKVRzrZtzRnntMi31lXS7CKtLRXUpo3JjMoowagUZX3pD6f3349eN1tEhNzYudMEp65v2CDX/vpLwv79V8IPHjSXkNunFdd+8a9cOVaQyqdIEepQAYAL6BS5Z555Rrp37+42BU8100yzo0qVKuXqpgAAkClkDQyUPDFORJIvTx7p2bmzvDF8uFy5etVkTMWkJ36ZO3euyWrWg1NJuX79ujnLr2ana4a3Xvbu3WtKKRCQyvio6umoTKnJk+Xus2clcuVKufvcOXM7owWjYmZ9aRaUyfKKSW97ed2R9eXh7W1u5+7cWYpOniwVNm+We06elJJLl0r+wYMlW8OG4nV7KuD19evl7Lhxcrh9e9lZrpzsKFlSDrZpI6fHjJGrf/whkdeupf9KA4Ab0rPede7cWaZOnSru4MaNG2bwOnDgQFc3BQCATM3n9qyY+OpU6lT70NBQadmypQlK/fnnn4kuS8sL6HT8KlWqxCorsHXrVpORFR+tHdmoUSO599575amnnpINGzaY+zVLWu/T97dbs2aNqZWp4wStozRhwgSpVauWyVLXsgmakWWnZRs+/fRTqVGjhnlMs8hnzJgR6701A3369OlSoUIF2bNnT/RJZypXrixjx46Nfl7fvn1N+QGl76vlCHS5+r5a/zImDeDpyVu0D9q3bx8ra1zv/+CDD0ybmzdvbpal76vP0zMMa0kEHf9YFRlSDp7yJnr2vXimVrhb1pd3UJBkr1fPXJR+McL374+VRRW6bZtEnDoll5YuNRfD01Oy3HNPrCwqvzJlbvUtAMChnn76abfpUc0CW7hwoaubAQDIAHbXrPnfDZtNPK9fl31a49fFv/PKrV0rVnfk2DGZOmuW1HrgAQmMpy6yHhDTmklHjhwxU/D0BCpa81EPkmnwJG4dzNMHDkhQjhziefmy2IvKBHl5mbpS5/bvl+CcOWM9/9+9e+WN/v1lyOuvS8W77pI//vxTunTuLIu+/FKqly0r/lmyyKply6RB3brm+T8sWSJ1a9YU76tX5csZM2Tpd9/J6CFDJHeuXDJz9mzp+MIL8u2sWeJzO1nj15UrzUlTNBimmdda/qBDhw7RZ/Y9ePCgOcnK999/bwJhZcuWle3bt5uAl/0swfrbWANxY8eONf2gQS99nQaedu3aZWp2Pvjgg6Y8gZZU0P7Sup7aT1pCQmtlaj0t+5mPly5dampz6nK1X/QkNHqyF33NgQMHZPDgweaM0xqwshp+5SPRrK9KFy5IxXPnpFJISJqyvjRI51e6tAQ/84wU/uQT88+00unTUvrnn6XgqFGSo3lz8SlUSCcey41t2+T81KlytGtX+bdyZdlesKDsb9JETo4YIZd//FEizp9Pc/F2zcTSvwAAAACA1Bk+ZozcX6+euVSpW1ee6tBBShUvLu8PGZLo64oWLWoCJb/99puZhvfaa6+ZM+fGpYEcXx+fWPfZb4ffvHnH82fOmSNPNW0qjRs0kGKFC8tzrVub4Ni8b781tSbr16kjK1evNs+NjIyUX3//PboG1vTZs6Vvjx5SvWpVKVmsmAzt318uXb4sa/73v+jlt2nWTEqWLCmlS5eWxo0by5YtW+TU7cLuK1asMJlKGiiqWbNmdGaWnqRGg0ua1aXvqRlOWteycuXK5nEfHx9z8hwNOGlml05l1MwwpZns3bp1k7p160rx4sXl1VdflUKFCsmSJUui29S0aVOTvaWv0+BUrly5zPP0+ZpBpdlcVq2fSYYUEqWZSV6BgU7pJT2TYNaaNc3FLvzYMbn+11+3Mqn++kuub9okUZcuyZWffzYXOw1u2TOo9K//PfeIR5x/VHFpRtaZcePk4oIF/2V9tW5t6mZlxOmVQEZkzyB11allgYTwmQQAIOW0XlS9OnVMracJ06bJ8VOn5NWXXjJZTUnRYI5Ob9MsID0pyz333HPHc/x8fe8IPNlvx1cD88ChQ/LDr7/K/MWLo+/TKXM1a9Qw15+oV096DRhglrFl+3a5GRFhHrt2/bqcOnNG+g0ZEmt2jmYcHT56NPp2wQIFoq9rTUoNBP3www/y4osvmoCUBo/Uww8/LPN1tpHNJn/99ZfJmtKAlGZAaaBKa1p638660gBSQIxssmzZspmAlT3rSqfkffTRR7HadOjQoejbGqCKXv8DB0wwK+YURw2CJXUGZlchIAVL8S1c2FyCWrQwt203b0roP/9ET/PTv2F790rYvn3mEjJ7tnmeh7+/BFSpEitI5Rvji6lTD4926RLrbIgalNIzCmoRd62LpVMVATiX1hLQne+1a9ei04wBK9CBn34+46t3AQAA4hccFGQykdRHI0dK286dTcBn9uTJ0dPcYtKpbj///LMJRO3cudPUktJAjtaWik/ePHnk4qVLpo6UPYBz7vx5yeLnJ9njFExXEZGR0unZZ6VpnLMO6/PV/ZUrS4C/v/z5118m8+mx2rVNxpVmYtnXoXjRorFemyN79lgBspiefPJJM31OA1DHjh2Tx25nW2n2kwaONBtKp+q9++67plaVXtfpejHPYuwVT7DIfqBMg0lvvvmmmcIXU9YY6+53e93M+kdEmOcOSSJDzSoISMHSNOvJBJqqVJHct6PNOmXv+t9//5dF9ddfEnnxolxbt85czt5+rU/BguJfrZrJhDqqdUPim6J3Ozh1uFMnUzeLTCnA+fLnzy+HDx+WYsWKmfnsGb3unrPoQEQHbfqXPnIu7efjx49LcHAwfQ0AQCppYGf4gAHyTNeu8uW8eSYwFJdOXxs0aJC0a9fOFBDX7KDElC9TRry9vGTrjh1y3733mvs2bdsm91SoEO9BpBJFi8rxkyejg2RqzIQJUrxIETOVT1+j9aNWr1tnLsPfeMM8J3u2bJIrZ04T7Krz0EPmPs2ien3IEOnw7LNSOZ7sLaXT9rTQudZ2qlOnjhnbKg2ePfDAA6ageO7cuc1FC5ZrMEozprS+U3LoND6dEqjjZjs9QUu9evWig19xn//LL79I4cKFowNdixcvNnWstN+thoAUMhzvXLkke8OG5qK0FpRmTUUHqDZskFBNvzxxQm4uXpy8QmkeHqaIu9bJAuBcefLkMX+1iKMexUHCCEalH02VL1iwYDq+IwAAmU/FChWkZePGMmnGDGnSoIHJcIpJp7hprSh74CYpWoS82RNPyPAPPpCRb70lZ86eNXWiRr75ZrzPf75tW3n+5ZdNwKr2gw/Kb2vXmuDY9BhnuGtUr550fvVVkzVV4777/nvt00/Lp5Mnm0LpWkNq0syZsnn7dhkeJ2MqJh076NnstEC7Tq2LSetIaWaUnnlPaUBKn6P1p/QAbXJowfS33nrL1IPSDKt58+bFmhoYl9aT0iLomiHVsWNHk7X1zjvvRBdetxoCUsjwdI5vlnLlzCVX+/bmvsirVyV082a5sm6dnHr7bfFIql5NRISEfP21eAUHS5ZSpcS3RAnxK1lSfIsUSbI2FYDUBaX0Ys8Awp00RVuPZmk9BavO+88sNAONqXoAADjGq926yU+rVsmHEyfK+0OHxnosNSUb+vfubQJSHXr1kmyBgdKjUyep/8gj8T733nvukXeHDJGJ06aZzKgihQrJB8OGmal6MZ+TMyhIatWoET0NUHVo187Ukho2erQpL3F3+fLy+UcfxZqyFx8tRK5T8x6J0yadxjd8+HC573bQ66677jJ1r/T+5GrUqJGcO3fOnJFP/2ow67PPPjMBqvjoVL4pU6bIqFGjzFn1goKC5Nlnn00wgOVqHjY3+iWghda0iFiFChViFQ1z5I8HLcym80X58WANNy9flh358qV+AV5eJijlW7Kk+JUo8V+g6vZfryT+OSF+fFesh21iPWwT9xk/WFVGWm++L/S5u+Cznj52xzjpkths5v+h+T/o4jIDeqby5No+erQEt2ol+SxWs9M7d+5kPU/DFFqM3PPSJZf3e1Lr8PHHH5tpde+//75kNDdv3jT7+ooVK5oDxY6IZ6Rk/ECGFDI1LXZu8/ERj3hOCXoHb2/J1bWr3Dx0SMIOHJDwQ4fEduOG+auXq/G8xCtXrtiBqhiBK61hFfMMDc6mUxejQkPF098/Xd8XAAAAANyNns1OAy+zZ882WUtIOQJSyNQ0MGOrV088Vq6MLmAeL29vydmunRT58MNYAZ6bJ09K+MGDEnbwoPkbfuBA9PWIs2clUgus3y6yfsd7+/mJb/Hi8WZW6f0aOHKE0G3b5My4cXJxwQJz5kB936DWrSVvr14UaQcAAAAAJ/jnn39k5MiR8swzz5j6UEg5AlLI9Gxt2oj89FMST7JJ3p497whm+RYqZC5Za9W64yWRly+bzCmTTRUjaGVuHzligkNhu3ebS3x8ChQwASqTVXU7SGUPWnnnyZOsMz2FzJtnzhBo0lhvB9z0fUPmzpWQOXOk2LRpkrNt2ySXAwAAAABIvqeeespckHoEpJD5lSkjRaZMkaNdusQK3BhaxM5mM4Eb/0qVUrRYrR+lr4nvdbaICAk/duy/jKoYmVUasIq6fNlkX+nl2rp1d7zeM2vWW9lU9oBVzL9Fi5pC65oZZYJRkZF3Nu72OurjWSpUIFMKAAAAAGApBKTgFoLatJGAu++WM+PHy8X58/+b2tamjcmMSmkwKike3t7ipxlPxYtLtngK9EVeuBB/ZtXBg3Lz+HGJunpVbmzfbi4JFVqPunFDJCoqiYZ4mHUuNnmyQ9cPAAAAANS3y5fLoFGjZPiAAdKqSZNYnVK/VSt5uWNHafHkk3RWKgwYMMD8fe+99zJl/xGQgtvQoJMGZopOmnSr+HdAQLKmxTmavqd3rlzmElit2h2Pa6BJp/yZrKq4QauDB8UWGmqmCiZLRIQJwBX9/HOXrCsAAACAzO37n3+WIoUKyZIffrgjIAVYPiC1bds26dKli6xfv/6Ox/7++2/zWNxTE9qLiAEppbWhvAIDLdtxnlmySJayZc1F4smuijh1SkJ37JADyfxnr9lgGsTysPgpuwEAAABkLOdDQmT9xo0y8s035c2RI+XYiRNSuGBBVzcLGYTLzw2/bNky6dChg4SHh8f7uFar37x5c/Tll19+kTx58siIESPSva2Aq2mWkxZDz/boo2bKYXId7tZNrvzyizlzIAAAAAA4wo+//irZsmaVxg0aSN7cuU2WVHy+mDtX2nTsGH17yZIlUq5cOTl69Ki5fe3aNbnnnnvk8OHDcvXqVXnzzTelTp06cu8jj0jjdu3kl99/N8/7/IsvpMXzz8da9sw5c6R99+7m+oqff5Ynn35aqtStK02efTb6dXFt2LRJHm3eXL5asEAeeuIJqd24sVl23KmITZ55RqrWrWva/veWLeZ+PbNe7969o5/32WefmbaHhYWZ2wcPHpSKFSvK9evXTZxDn1+jRg1z6devn1y8eNE879ixY6YPJkyYINWqVZPhw4cn2teanDBp0iR59NFHzfvVqlVLxo8ff6sPZs6Uli1bJqt/rcSlAakxY8bI9OnTpUePHsl+zZAhQ8zGatGihVPbBlg9yyuodetbRdmT4dLChbK/cWPZWaGCnHrnHQm32D8iAAAAANYSEfNkUAnQAFDtBx8UT09PeaRWLVmyYoUJnMRVs0YN+XffPrly5Yq5/ddff5mD7Zs2bYq+XaBAASlWrJi88847Jqjz+eefy5JZs6TqvffKkPfek/CbN+WJxx6TPfv3y6EjR6KX/cOvv8oT9eqZbK0BI0ZI5/btZfmcOdLyySfl9aFD5eLly/G2/fyFC6a9Uz/5RIb27y/Tv/5aFixZEh2Meufjj82yvpk5Ux6oVk1e6tdPTp89Kw8//LBpr3099br21fbb9X/XrVsn9913nwQEBMhHH31kZnZNmTJFvvzySxNse+WVV2K1Y9OmTfLNN9/I83ECbXF999138sUXX5j++eGHH0wcZdy4cbJjxw4TnPr333+T1b9W4tIpe+3btzcRwvim6sVnzZo15rkrV65M0/tGRkaai6PZl+mMZSN1MvM2ydWjh4TMmZP4k7y8pMjUqeZMflpL6uaRI3Jq5EgTlMpat67kbN9esjdpYqYJpqfMvF0yKraJ9bBNrIP/VQAAdzRs9GhTkLxg/vzxPn7y9GnZvH27vPD00+Z2/Tp1ZN6338qmrVvlvsqVYz23dIkSkidXLlOSp27duiZAUrt2bRMwadasmQniaKBHaQLKiy++KCVKlBDPS5ekQ7t28s3SpSaAVLRwYal4113y46pV0u2FF+TEqVOya/dumTB6tJw5e9YEhvLnzWva/GK7dlK2VCnx8/WNt/0RkZEyfOBAKV+mjNxVrpy0b9NGFnz3nbRu2lS+XrhQnnvqKWn2xBPmuX26d5e/N2+W2QsXSs9+/UzgZ+/evVKyZEnZsmWLCQjpuugML/u6hIaGyldffWWCTZqtpEaPHm0ypXbv3i2Bt8vIvPDCC1K0aNEkt4cGlN5991158MEHze127dqZ7CptR/Pmzc1MsuT0r5W4NCCVL1++FD1/4sSJZnpfUFBQmt53z5494kz2yCisI7NuE49Bg8RDp696eIhHjOCOzctLczrFNmiQHCpTRkQvTz8tHqtXi8fy5eKxcaNc/fVXc7Flyya2Bg3E1rjxreelo8y6XTIyton1sE0AAIArdHz2Weny6qvy3tChUrFChXizozTYo9lPqlqVKpI9WzZZvGLFHQEp9VD16rJhwwYzne3cuXMmOeXTTz81j/3555/Sp08fc12DK5qEMnfuXDm0d6/s2L3b3B91u/xIo3r15LvvvzcBqR9++cW8b66cOSU4KEjqPPSQdH71VSlRtKjUffhheapJE/FP4OB7gL+/CUbZ3V2+vJn+pw4cOmSCcTHde889cuDwYfH39zcZULouN27ckEKFCpnphWvXrjUHsfR+zYLS6XJa//rp2wE7O12PQ4cOyd13321u6+uT44EHHpCtW7fKhx9+KPv375ddu3bJ2bNno/ulZs2ayepfK7FEUfPk0A7X4uf2OZJpUbZsWZM+52j64dMfDvoB8NKAAFwu02+TypUl9PHH5dyECXJpwQJTwFxrS+l0vtw9eoh/xYqxn687i/79zVn6QmbNkpCvv5abx46JxzffiHzzjWS5914Jfv55CWrTRrxy5nRaszP9dsmA2CbWwzaxDq0B4eyDWQAApJdFy5bJouXLk/VcnQbXoVcvWTh9uhSPk8WjZ9e7ERYmNRo0iDV+0eylN/v0kSxxat4+VK2azJg/X+69916pXLmyySbS3/l60QCNZg6p/v37m/rRjRs3lraa+ZM7tzzTrVv0cnTa3gfjx8vhY8fkp99+MxlNSqeoTfzgA9m2c6esWrNGfl69WuYuWiRfTpwoFeI5YZR3nPInGtixn5ncL556vfp4ZJzgj9aNqlq1qglQaaxCf+NorEFjDhowUrNnz74j/pArV67oWlLxvVd8FixYIKNGjZLWrVtLgwYN5I033og1zU+ztKZOnZpk/1pJhglIaYT0oYcekuDg4DQvS38AO/NHsLOXj5TLzNska+XKknXKFLF9/rlEhYaKZ0BA9D/ShPiXKiX+w4ZJgcGD5cqvv8qFL76QS0uXyo2tW+VE375y8s03JUezZpLrxRcla506pmaVM2Tm7ZJRsU2sh23ievyfAgBkJi0bNzaXpGzftUveHj1aPhw+XIoVKRLrMa3htGvPHnnz1Vel+n33Rd+/78AB6Td0qAkGaaHzmB6sVk0Gjhwpq1evNsESnfmkU9502pm95pLWWNITn82fP1/Kly9vpuz9/uef5vX2mk0aoNKsqG+XLZPd+/ZJvTp1zP2avaRT+17v2VMq3XWX9O7SRZo+95ys3bAh3oDU5StX5PjJk1KoQAFz+59//5WypUub68WLFJGt//wjj8aY5rZ1xw657957zXWd/jZjxgwzRfCJJ54wbdXrWidKA0OqSJEiZgyhgacKtzPMzp8/L2+99ZYMHDgwxeOLOXPmmLpRnTt3vtX+y5fN8uz9olP5NJiXWP9ajcvPspdcGiHVTgUQPw0aeQUGJhmMivUaLy/JXr++FP/qK7n7wAEpNGaMZKlY0WRaac2p/Y0aya677pJTo0ZJeIzCgQAA19Kz51SpUiXW5a677pKGDRuax/UorR691aO2ffv2jS5yCgBAci1cskSmfPzxHcEoe3ZUjuzZpXWzZlKmZMnoixYXL1W8uJm2F1dQjhwmcLN06VITIFH69/vvv4+ub+Tr62umxP30009y/PhxWbN+vSkurrSouZ1O2/ty3jwT5NJ2KD3bn9awmjRzphw7cUJWr1snJ06elAqJlCUZ+t57svfAAflp1Sr5esECaXf7THVaF+vrb74xZw3U4NtHn31mgl+tmjQxj5tgmaen/P7772Yd9Lrui2OuS9asWU0207Bhw0wt7H379pmAkZ7prnDhwin+IObMmdNMvdOC71oo/bXXXjNTAvVMfvbHk+pfq/G08iArJj0lYt68eV3WJiCz886VS/L06CHl1q+XsmvXSq6uXcUzRw5zRr5TI0bIzvLlZX+TJhKyYIFE3T6lKQDANZo2bWoO1tkvixYtkmzZsplB77x588xgVP/qUVI91XNSp5IGACCut994Q3ImUL9Z60c1adjQBJDiatuihfzv77/NGenismcPVapUyfzVpBPN8IkZkPrggw/kxx9/NLWkRo8bZ2pFaUF0zciyq//II2Z6YKPHHou+T5/zyahRJrjU9NlnZeSHH8qrL70UXeMqPrUefFDad+8u7336qbzSrVt0Vtfjjz0mr3brJuOmTJEWL7wgf23aZIJzJW+fpU6TAHQGV+7cuaVgwYLR66KBKb3fbsCAASZzqXfv3tKmTRszTXDy5Mmpyr5+8803TQaZFinv1auXKZRev3796KmByelfq/GwxXdOxkxcA0I3lqbLOauGlFbY1/mapPdbA9skbXQK4MXvvjNT+q6uXh19v1dwsORs29ZM6fO//c+O7ZKx8V2xHraJ+4wf0kprWugRWD2bjhZRbdu2rQlYPfvss+ZxrR2hg1c9Oms/o09mWO+Y+L7Q5+6Cz3r62F2z5n83bDbz/9D8H0zBTARnKLd2bbKfu330aAlu1Ury5cghVuKdO3eynqdhCs3+0Sl78fX74aNHpdWLL8rqpUslMBX7qA2bNpnaWDtS0KcpXYeM4ObNm2Zfr7V9dTzhiHhGSsYPGaaGFID05+nvL8Ht2plL2MGDcuHLL+XCrFly8/hxOffZZ+biX7Wq5NJC6G3bincaz4AJAEg5LXKqdSRefvnl6ABU6ds1MFTx4sXNj1idIqDT+lJKX6sXK7O3z+rtzEzoc/o9U4uRs2HP39C/rg1HpeJ/nLbdYvknKc2H0Wd7xHjNtevXzTS+BYsXm2l7gf7+qVtH+2tS8drMlNNj0zOz22zms2U/W19a96UpeT0BKQDJ4leihBQYOlTyDxokV375JboQeuimTXJs0yY5PmCABDVrJsFaCL12bacVQgcA/EcLqH7++ecmM8rHxyf6yKTW37DTo5w6BULvT42MdIZBPbsR6HN3wGfduTzj+X8ZGhoqrqbZK8kWHh4dbLBaRk6K6DrEummToe+/L0UKFpR3Bw9O9frZX5Wa16d4HSzs5s2bJhC1detWl/x/ISAFIEVMIfQGDcwl4tw5CZk7V85/8YXc+OcfCZk3z1x8ixeX4Oefl+DnnhPfeIogAgAcY926deZHUqNGjaLv02DUjRs3Yh2p1IKnKZmuF5OeujojTNnTAbROOaBsAn2emfFZTx/7YvzP04CF/p/V/60pOXmQM5SuXDnZz925ZIlERkS4vM1xed8+eJLswI+HR6zMtKyBgfK/H35IcztqVK0qO9ascfo6ZIQDWz4+PmZ6naP+v+gBsOQezCIgBSDVdP50np49JXePHhK6caOc//JLE5AKP3RITg0fboqhZ6tXT3K98IJkb9xYPP386G0AcKCVK1fK448/Hp0dpXS63oEDB6R69ermtp6NR4us6tS91NBBaUYJ8mSktmYW9Dn9ninFCOLYr5nAjouDOyn5/+Z59KhcuXRJbLlzWyooldy22DOXzLMt1H5lpf5MKz3xiR50ivnZSuv/9RR9TlP9LgAQ459ywP33S5GxY+Wegwel6PTpZtqeptheWblSDj33nOwoVUqO9esnoSlIAbVFRUnktWvmLwDgTnqGPT2DTkx6VqIZM2aYmlFXrlyRMWPGSMOGDWNN4wMAZG5e+/ZJRFiYXOPs2EiATtULCQmRIBfWASYgBcCx/1QCAkwR9NI//igVduyQfG+8IT4FC0rk+fNybsIE2V29uuzTGlPffiuRFy/Gu4zQbdvkcJcusi04WLbnzm3+6m29HwDwn2PHjkm+fPlidYn9LHvPP/+81K1b1wSihg8fTrcBgBvxiIwU7yVL5MjRoyYoZbVaUnAt+8lONLEghwvPxMiUPQBO41eypBQYNkzyDx4sV37+2dSaurxsmSmE7rlpk+yaMEGCmje/VQj94YdNIXSd8ne4U6dbqbkREWY5trAwU6sqZM4cKTZtmuRs25atBgAJFLjV6Xk9evQwFwCA+/L53/9Ey28fbNZMfPz8JFtQkHh7e7t0yplnMs/AZj/zm6fWRLTYFLnkroNVs6Ju3LghV69eNVP1SpYs6dKp7gSkAKRPIfSGDc0l4uxZOT97tpz4/HMtbHIr0DR3rviWKCHZGjSQ81Om6H/KOxdyOzilwaosFSqIf6VKbDkAAAAgiaCU98aNElmmjFwsVkwka1aXBqSCWrZMdkDq7Nmz4vvHH5YLSCV3HazI09NTsmbNKgULFhQ/C9T3JSAFIH3/6eTJI7l79pRjNWtK2chIufjVVxIyf76EHzwo5zVIlRQPDzkzfrwUmzw5PZoLAAAAZGgeN2+K986d5uJqRV55JVnP0+woE5CaP99yAankrgOSRg0pAK5hL4Q+bpwphF5EM6OSs7OJiJCL8+czDx4AAAAAMjACUgAsUQg9qEULc1a+5NCaUrbQUKe3CwAAAADgHASkAFiCp7+/eCRzHrOHr694cPpyAAAAAMiwCEgBsAQ9w15Q69Yi3kmXtrNFRMjxfv3k5okT6dI2AAAAAIBjEZACYBl5e/VK3rS9qCg5N3Gi7Lz7bjmmgamTJ9OjeQAAAAAAByEgBcAy/CtVkmLTpol4ed2ZKaW3vbyk6MyZUmr5cgl84AGx3bgh5yZMkJ133SXHX3+dwBQAAAAAZBAEpABYSs62baXcunWSs1276JpS+ldv6/3BbdtKtkcfldK//iqlli2TgBo1TGDq7Pjx/wWmTp1y9WoAAAAAABJBQAqANTOlJk+WShcuSMVz56RSSIi5rffbeXh4SLbHHpMyq1ZJyaVLJaB69f8CUxUqyPH+/QlMAQAAAIBFEZACYOlC516BgSb4lOBzPDwke716Uua332IHpsaNu5Ux9cYbcvP06XRtNwAAAAAgcQSkAGQKsQJTS5ZIQLVqYgsNlbNjx97KmBowgMAUAAAAAFhE0udXB4CMFpiqX1+y1asnV1aulFMjRsj1v/+Ws59+KucmT5bc3bpJ3tdeE5+8eV3dVAAAgExnd82aYjXl1q51dRMAxIMMKQCZNzDVoIGU+f13KfnddxJw3323MqY++UR2VaggJ958U26eOePqZgIAAACAWyIgBSDzB6YaNpQyf/whJb/9VvyrVpWo69flzMcf3wpMvfWWRJw96+pmAgAAAIBbISAFwH0CU48/LmXXrJESixb9F5j66CNTY+rEoEESce6cq5sJAAAAAG6BgBQAtwtM5XjiiVuBqW++uRWYunZNznz4oewsX15ODB5MYAoAAAAAnIyAFAD3DUw1anQrMLVwofhXqXIrMDVmDIEpAAAAAHAyAlIA3JoJTD35pJRdu1ZKLFgg/pUr/xeY0ql8Q4ZIxPnzrm4mAAAAAGQqBKQAwB6YatxYyq5bJyXmzxf/e++VqKtX5cwHH5iMqZNDh0rEhQv0FQAAAAA4AAEpAIgbmGrSRMr++WeswNTp0aNvBaaGDSMwBQAAAABpREAKAJIITBWfN0+yVKokUVeuyOn3378VmHr7bQJTAAAAAJBKBKQAIInAVFDTplJOA1Nz50qWihVvBabee8/UmDo5fLhEhITQhwAAAACQAgSkACAZPDw9JahZMyn3v/9J8TlzJMs990jU5cty+t13b2VMjRhBYAoAAAAAkomAFACkNDDVvLmUW79eis+eLVnuvvtWYGrUqFsZUyNHSsTFi4kuwxYVJZHXrpm/AAAAAOCOCEgBQGoDUy1aSLkNG6T411/fCkxduiSn33nHZEydeuedOwJTodu2yeEuXWRbcLBsz53b/NXbej8AAAAAuBMCUgCQ1sBUy5a3AlNffSVZ7rrLBKZOjRwpuypUkFOjRknkpUsSMm+e7H7oIQmZO1dsYWHmtfpXb5v7581jOwAAAABwGwSkAMBRgalWraTcX39JsVmzJEuFChJ58aKcGjFCdpQpI4c7dBCJjBSJiIj9Qr0dGSmHO3UiUwoAAACA2yAgBQAODkzlfOopKff331Lsyy/Fr3x5c1Y+sdmSeKGHnBk/nm0BAAAAwC0QkAIAZwWmWrc2U/nE2zvpF0REyMX588WWVOAKAAAAADIBAlIA4ES28PA7p+kl9NywMLGFhrI9AAAAAGR6BKQAwJn/ZP39xcPPL1nP1ed5+PuzPQAAAABkegSkAMDZxc5bt0562p6XlwS1aSMeHh5sDwAAAACZHgEpAHCyvL16JV3UPDJSstasybYAAAAA4BYISAGAk/lXqiTFpk0zWVB3ZErpfbcd69VLzn/5JdsDAAAAQKZHQAoA0kHOtm2l3Lp1krNdu+iaUvo35zPPSJnVqyWoZUux3bwpR7t1k+MDBogtMpLtAgAAACDTSsa5yAEADsuUmjxZik6aJFGhoeIZEBBdMypg1izxq1BBTr/zjpz99FMJ27NHis2cKV7Zs9P5AAAAADIdMqQAwAWFzr0CA2MVMNf7CgwaJMW+/FI8smSRyytWyN66dSXs4EG2DwAAAIBMh4AUAFhIztatpczKleKdP7/c2LlT9jz8sFxds8bVzQIAAAAAhyIgBQAWE3D//VJ2zRrxr1pVIs+fl/2NGsn5mTNd3SwAAAAAcBgCUgBgQb6FCplMqaBWrW4VO+/eXY7370+xcwDRzp07J7169ZL7779fHnjgAXnnnXck8vYJEcaPHy81a9aUqlWrSt++feXKlSv0HAAAsBQCUgBgUVr0vNisWZJ/0CBz++y4cXKgVSuJvHTJ1U0DYAE9evSQoKAgWbNmjSxevNj8nT17tsybN0+WLl1q/q5evVquXbsmw4cPd3VzAQAAYiEgBQAWpoXP87/1lhT/6ivx8PeXKz/+KHseeUTCDhxwddMAuNC2bdvkwIEDMnjwYMmSJYvky5dPpk6dKvXq1ZNFixbJ888/L4ULF5Zs2bLJ66+/LitWrDCBKQAAAKsgIAUAGYBO3Svz88/iU6CAhP37r+ypXVuu/vGHq5sFwEW2b98uZcuWlQkTJkjt2rXl0Ucfle+++84Epvbv3y+lS5eOfm7x4sXNVL7Dhw+zvQAAgGV4u7oBAIDkCaha1RQ7P9imjVzfuFH2NWokRT79VHJ17EgXAm7m0qVLsnnzZqlSpYqsXLlSjh07Jl26dJEcOXLI9evXxd/fP/q5Xl5e4uvra+5PDQ1m2WtTWZW9fVZvZ2ZCn9PvCbLZxGpS/L8hxjrYbl/Xvx6SgdbDgtshJetgf54V+j2uzLqviXTQvjQlrycgBQAZiE/BglJ65Uo50rWrXFy4UI726CE3du2Sgu++Kx7e/EsH3IUGmPz8/KRPnz7i6ekppUqVkmeffVZ+/PFHE4y6ceNGrIFheHi4BAYGpuq99uzZIxkpcwz0uTuw8mfdM5XBb2fasmVLmtchNDRUMtJ6WHE7pGZbWKHf07oOGc32dPz/wq8XAMhgPP39pdiXX0qWu+6SU8OHy9nx4+XGnj1S/MsvxStHDlc3D0A60ABUVFSUREREmOBUzCOSOl1P60tVr17d3D548KAJWunUvdTQqYEBAQFiZbruOoCuWLGiyQgDfZ5ZZYTP+j4L/r8oXblyqtdBM3Q0KKLBfq3tmVHWw4rbISXrYP+sW6Hf0/p5crf/L9evX0/2wSwCUgCQUYudDxwoWcqVk8OdO8uVn34yxc5LfvON+JUs6ermAXCymjVrSnBwsHz44YfSt29fM2Vvzpw50q1bN/PjacaMGfLggw+a54wZM0YaNmwYaxpfSuig1Ko/fDNyWzML+tyxdtesmfgTbDaT+XJQgw3p9CO93Nq1KXuBxYIHKsX/F2Ksg/2aCYq4eN1StB4W3A6p2RZW6Pe4Mvt+xiuN+9KUvNbTKmeKqVGjRoKP61lh3nzzTfMcPdqnZ4tJbR0EAMhMglq2lDK//GKm8pli5w8/LFd+/93VzQLgZJoVNWvWLDly5Igpat6+fXtp2bKltG3b1lyaNm1qzrRXt25dE4gaPnw42wQAAFiKyzOkli1bJkOHDjVp5wkZNGiQCUD9/PPP5nk9evSQsWPHyoABA9K1rQBgRQFVqtwqdt66tSl2vv/JJ6XwJ59I7k6dXN00AE5UuHBh+eyzz+I9mqxjJb0AAACLZz66QIozH53EpRlSmkI+ffr0RAdMZ8+elZ9++klGjhwp2bJlM2eP+fjjj+WZZ55J17YCgJX5FChgip0HtW4tEhEhx3r2lGN9+4otIsLVTQMAAAAAa2VIaXp5v379ZP369Qk+Z+fOnZIvXz75/vvv5csvvzRniXn88cfN61LLWacv5vS31sM2sSa2i5P4+krh6dPFr3x5OT1ihJybONEUOy86c6Z4BQWxTTIYvifWkVlP7wwAAOC2ASkNNCXl4sWLcurUKdmxY4csXrxYrl69ajKqPvnkE3njjTcsefpiK5+G1V2xTayJ7eIkDRuK+PmJ58iRcvXnn2XHQw9J1OjROr+HbZIB8T0BAABAZuTyGlLJKdqpRyb79+8vWbNmNZcuXbrI6NGjUx2QctbpizPCaVjdDdvEmtgu6aByZQmtW1cOt2kjN48cEd/u3aXorFmS9ZFH2CYZBN8T60jJ6YsBAACQSQJSpUqVMn9v3rwZfV9iBdCtcHpaTn9rPWwTa2K7OFfWqlWl7B9/yMG2beX6X3/JwebNpfBHH0nuLl3YJhkI3xPX4yATAABAJitqntxspnvvvddkROl0vdOnT8uUKVPkySefdHXTACBjFDv/6SfJ2bbtrWLnvXvLsddeo9g5AAAAAJeyZEBqyZIlUqVKlejbkyZNEm9vb2nYsKE0bdpUqlatKr169XJpGwEgo/DMkkWKzpghBd5+29w+N2mSHGjeXCJCQlzdNAAAAABuyhJT9mrUqCGbN2+Ovq1BJ73YBQcHywcffOCi1gFAxufh4SH5+vcXv3Ll5EjHjnLll19kb506UnLRIvErXdrVzQMAAADgZiyZIQUAcI6gZs2k9K+/ik/hwhK2d6/sqV1brqxaRXcDAAAASFcEpADAzQTce68pdh5QvbpEhoTI/iZN5Pzkya5uFgAAAAA3QkAKANyQT/78UvrHHyVnu3YikZFyok8f8fjoI4qdAwAAAEgXBKQAwJ2LnU+bJgWGD9ciU+K5aJEcatGCYucAAAAAnI6AFAC4e7Hz11+XorNni83fX66uWmWKnd/Yu9fVTQMAAACQiRGQAgBIjiZNJGrixOhi53u12Pmvv9IzAAAAAJyCgBQA4JYyZaTU6tUSUKOGRF68KPubNpWzkybROwAAAAAcjoAUACCaT758UvqHHyTnM8+YYufHX3tNjr7yithu3qSXAAAAADgMASkAwJ3FzqdOlQIjRphi5+cnT5b9zZpJxIUL9BQAAAAAhyAgBQCIv9h5v35SYv588QwMNMXO99SuLTf27KG3AAAAAKQZASkAQIJyNG4sZVatEp8iRSR8/34TlLr888/0GAAAAIA0ISAFAEiUf8WKUnbNGgl84AGJunRJDjRvLmcnThSbzRb9HFtUlEReu2b+AgAAAEBSCEgBAJLkkzevlNJi588+e6vYed++cqx3b7m+aZMc7tJFtgUHy/bcuc1fvR26bRu9CgAAACBBBKQAAMni6ecnRadMkYLvvHOr2PnUqbKnZk0JmTNHbGFh5jn6N2TuXNn90EMSMm8ePQsAAAAgXgSkAAApKnaet08fKTh69H93RkbGflJEhLnvcKdOZEoBAAAAiBcBKQBAioVu3Sri5ZX4kzw85Mz48fQuAAAAgDsQkAIApIgWLr+4YMGdmVFxRUTIxfnzYxU/BwAAAAACUgCAFIsKDY2uGZUUfZ4tNJReBgAAABALGVIAgBTx9PcXDz+/ZD1Xn+fh708PAwAAAIjFO/ZNAAAS5+HpKUGtW5uz6ZkC5gnx8pKgNm1MIXQA/zl79qxs3bpVzp07J56enpInTx6pWLGi5M6dm24CAABug4AUACDF8vbqJSFz5iT+pMhICW7Xjt4FzNchUpYtWyZffPGF7Nq1S7y9vSVHjhwSFRUlly5dMrXW7r77bnnuueekSZMmJlAFAACQmTHaAQCkmH+lSlJs2rRbZ9rzjnNsI8bZ904MHCiRly7Rw3BrmzdvlmbNmsnChQvN3+XLl5sMqTVr1si6devkn3/+kUWLFknTpk3lyy+/lCeeeEI2btzo6mYDAAA4FRlSAIBUydm2rWSpUEHOjB9/62x6YWGmZpRO0wtq1kyOvvyyhG7dKgdatZJSS5ea2lOAOxo3bpy8//77JgMqPjqttXz58ubSvn172bJli3zyyScyc+bMdG8rAABAeiEgBQBIW6bU5MlSdNIkc/Y9z4CA6JpRPkuWyL4GDeTa2rVy6LnnpMTcueLh40Nvw+1Mnz49Rc+vXLkywSgAAJDpEZACADik0LlXYGCs+wLuvVdKfvON7G/SRC5//70c6dZNik6dap4LuJOLFy8m+7lBQUFObQsAAIBVEJACADhN1lq1pPjXX8vBNm1MEXSv4GAp9MEHnHkPbuWBBx5I8jOvRc31OVrwHAAAwB0QkAIAOFWORo2k6JQpcqRjRzk3YYJ4584t+QcMoNfhNrRQOQAAAGIjIAUAcLrgdu0k8sIFOd6vn5x6+23xDg6W3F270vNwC9WrV3f41D4AAICMjoAUACBd5OnRQyLOn5fT774rx159VbyCgiRnmzb0PtzKiRMnZOzYsbJ7926JiIiIvj88PFyOHz8u//zzj0vbBwAAkF6oLAsASDf5Bw+W3N26acEcOdypk1z+6Sd6H25lyJAhsmnTJnn44Yfl4MGDUrduXSlZsqQcOXJE3nrrLVc3DwAAIN0QkAIApBst2lzoo48kqHVrkYgIOfj003Ltzz/ZAnAbGzdulFGjRkmfPn2kbNmy8uijj8qnn34qPXv2lNWrV7u6eQAAAOmGgBQAIF15eHpK0alTJVuDBmILDZUDLVtKKNOU4CYiIyOlUKFC5nqJEiWiz6rXpEkT2bZtm4tbBwAAkH4ISAEA0p2nr68Unz1bAh94QCIvXpT9TZpI2MGDbAlkejo9b/369eZ66dKlZcuWLeb6pUuXJCwszMWtAwAASD8UNQcAuIRXYKCUWLRI9tWvLzd27JD9jRtLmV9+EZ/8+dkiyLQ6d+4sAwYMEJvNJk8++aS53Lx50xQzT+7Z+OxmzpwpH3zwgfj6+kbf9+abb0rz5s3l3XffleXLl0tUVJTJvho4cKD4+Pg4YY0AAABShwwpAIDLeOfMKaWWLhXf4sUl/MAB2d+0qURcvMgWQabVuHFjmT17tpQvX16KFi0qn3/+uVy/fl0eeugheeedd1K0LA1ide/eXTZv3hx9ad26tYwbN0527twpK1asMEGp7du3y4QJE5y2TgAAAKlBQAoA4FI+BQpIqeXLxTt/frmxfbscbNlSoq5fZ6sg06pUqZJUqFDBXNdA1KRJk2TYsGESHBycouXs2LFD7rnnnjvuX7Rokbz00ktmeXnz5jUF0xcuXOiw9gMAADgCU/YAAC7nV7KklFqyRPbWr2/Ounfo2WelxPz54sEUI2QyJ0+elLFjx8qePXskPDz8jseXLl2arOVoVtWhQ4dk7ty5MmjQIPH39zfZUW3btpWzZ8+a+lR2pUqVMvdpnaocOXI4dH0AAABSi4AUAMAS/CtWlJKLFplaUpd/+EGOdOkiRadPN2flAzKLfv36yenTp+Xxxx+XLFmypHo5586dk/vuu0+eeuopE+Dau3ev9OjRw9SjUhqgsrNfDw0NTVVASs8MqBcrs7fP6u3MTOhzJ7HZknjYFv3XQ9JHir9XSayDK6RlHVzR5w5ZDwtuh5Ssg/15Vuh3d/lORDpoX5qS1xOQAgBYRtaHHpISs2fLgdatJWTePPHKmVMKffSReHhYbSgCpI5Os9MaUnfddVeaulDrT3311VfRt++++25p3769LF682Ny+ceNG9GMaiFIBAQGpei/N5sootF4W6POMzDOZU9bt3+v0YD8bqKPXIT05Yh3Ss88dsR5W3A6p2RZW6Hd3+05sT8d9KQEpAIClZH/8cSk2ZYoc7thRzk2aJN65c0v+t95ydbMAh9Dpczp1Lq20oPkff/xhiprbhYWFSZ48eeTChQty4MABKVSokLl///79ppZU9uzZU/VeZcuWTXUwK73o0VgdQFesWFG8vLxc3Ry3QJ87x74kvmuaLaI/0DXzMb0O1pSuXNmh6+AKaVkHV/S5I9bDitshJetg/x9jhX53l+9EpIP2pVpWILkHswhIAQAsJ+fTT0tESIgc79NHTo0caTKl8rz8squbBaTZiBEj5PXXX5cWLVpI4cKFxTPOlNQGDRokazkaIJo4caJZxpNPPmkCVJoxNXjwYJOFNX78eHMmP6XXmzdvnuo266A0owR5MlJbMwv63MGS+OFtf9T8QE+nH+kp/k5ZLHiQ1nVwRZ87ZD0suB1Ssy2s0O/u9p3wSuO+NCWvJSAFALCkPN27S+SFCyYgdbxvXxOUCm7XztXNAtJkzZo1JmNpzJgx8Q66d+3alazllCxZUsaNGyeffPKJDBkyRHLmzGlqSD3xxBPy2GOPyejRo03QS2tKNWrUSHr37s2WAwAAlkJACgBgWfnefFMizp+Xc599Jke6dhXvnDnNlD4go5o2bZr06dNHnn/++TQVNVePPPKIucTl6+trzrynFwAAAKsiIAUAsCzNGCk0ZoxEhoRIyNy5crBdOym1bJlkrVnT1U0DUk2zmNIajAJwp90W3DeUW7vW1U0AAMviXNoAAEvz8PSUopMnS/YnnhDbjRtyoFUrCd22zdXNAlJFz4Q3ZcoUiYiIoAcBAIBbI0MKAGB5Hj4+Uvyrr2R/kyZybd062d+0qZT59VfxK1nS1U0DUkRrRP3++++ydOlSyZ8/v3h7xx6K6f0AAADugIAUACBD8AwIkBLffCP7GjSQG9u3y/4nn5Qyv/wiPgULurppQLJVqFDBXAAAANwdASkAQIbhHRQkpZYskb2PPSbhBw6YTKnSK1eaYudARqBnw2vQoIHkyZPH1U0BAABwKWpIAQAyFJ/8+aXU8uXinT+/3NixQw60aCGR1665ullAsnzyySdy/fp1egsAALg9AlIAgAzHr3hxKbV0qXgFBcn19evl0DPPSFR4uKubBSSpatWqsmLFCrHZbPQWAABwa0zZAwBkSP733CMlv/3W1JK68tNPcqRzZyk2Y4Z4eHm5umlAgvTsepolNXHiRClQoID4+vrGepyi5gAAwF0QkAIAZFiBDzwgxefMkYNPPSUXFywQ7+BgKfTxx+Lh4eHqpgHxqlKlirkAAAC4OwJSAIAMLXuDBlJ06lQ5/OKLcu7zz8UrOFgKDBni6mYB8erZsyc9AwAAQFFzAEBmkLNNGyn8ySfm+ul335Wz48e7uklAgtasWSPPPvus1KhRQ+677z5p06aNfP/99/QYAABwKynOkNJB04MPPiiVK1eWe+65R7y8vGTXrl2ydetW+fPPP2XWrFnOaSkAAInI3bWrRFy4IKfefluOv/66yZQKfuYZ+gyWojWiBgwYIM2bN5fWrVtLZGSk/P3339K/f3/zeKNGjVzdRAAAAGsGpDQY9d1338nnn38eq0ZH4cKFpVmzZqlqxLZt26RLly6yfv36eB9fuXKlvPLKK+Ln5xd9X4cOHaR3796pej8AQOaU7403JOLcOTk3YYIc6dpVvHLkkBxPPunqZgHRdPz0+uuvy4svvhh9X6tWraRcuXLmMQJSAADAXaQ4IKUDJn9/f5kwYYLJkAoPD5eNGzfKp59+KkWLFk1xA5YtWyZDhw6VqKioBJ/zzz//mGDXu+++m+LlAwDchx4oKTR6tESGhEjI7Nly6LnnpNTSpZK1Vi1XNw0wjhw5Io8++ugdvaH3ffTRR/QSAABwG54pfYEOlkaOHCmPPfaY5MuXT4oUKWLSzocNG2aCUikxZswYmT59uvTo0SPR5+3YscMEvwAASIqHp6cUnTRJsj/5pNhu3JADrVrJ9S1b6DhYgmaUb4nn87h582bJkyePS9oEAACQITKkzpw5I9myZbvj/gIFCsi5c+dStKz27dtLv379EpyqZ7dz506x2WwyefJk8fT0lCeeeEJeffVV8fX1ldTQeg16cTT7Mp2xbKQO28Sa2C7Wk+m2iaenFJk5Uw41by7X1q6VA02bSsmVK8WvdGnJKDLdNsnAHLkNdOwzfPhwOXnypNx7773Rwahp06bJyy+/7LD3AQAAyHQBKS1mrhlS7733nhQqVMjcd+zYMRk1apRUqVIlRcvSDKukXL16VUqVKiWPP/64NGnSRE6dOmWCUTrFT4uCpsaePXvEmbZv3+7U5SPl2CbWxHaxnky3TQYPFs9evSRi717Z/fjjEvXZZyIZLAsl020TN9euXTu5cuWKzJw5Uy5cuBA9HurTp488QxF+AADgRlIckHr77bele/fuUq9ePcmRI4e579KlSyZoNHHiRIc3MGvWrLHO3Fe8eHF56aWXZPTo0akOSJUtW1YCAgLEGUdQ9YdDxYoVzdkH4XpsE2tiu1hPZt4mN3/4QQ40aCDh+/dLwMCBUvKnn8Q7OFisLjNvk4zm+vXrDj2Y1bVrV3PRgJRme+tYBwAAwN2kOCBVokQJ+f777+WPP/6QQ4cOmQKyJUuWlJo1a8Y6654ji3/OmzdP+vbta6brqbCwsFRP11M6sHfm4N7Zy0fKsU2sie1iPZlxm3gVLCilli+XfXXrSti//8rhVq2k1Pffi1cGCQJkxm2S0Ti6/7XEgZ6w5ebNm6YkQUwNGjRw6HsBAABkmoCU0sBQnTp1zMXZsmfPLvPnzzfZWJ06dZLDhw/LZ599Jk899ZTT3xsAkDn4FSsmJZctk33168v1v/6SQ08/LSW++UY8/fxc3TS4mYULF5oTwURERNzxmB7Y27Vrl0vaBQAAYPmz7KWHJUuWRNejCgoKkilTpsivv/4q1apVk+eff16efPJJ6dixo6ubCQDIQPzvuktKfvuteAYGypVffpEjHTuKjYLhSGcTJkwwB9X+/vtv+ffff2NdCEYBAAB3kqoMKUerUaOGOcOMXdOmTc0lZiH1uXPnuqh1AIDMIrB6dSkxb54caNFCLi5aJF7BwVJ47FinTDkH4qN1ozp37kzdKAAA4PYsmSEFAICzZHvsMSk2Y4bOj5LzU6fKqWHD6Gykm/vvv182bdpEjwMAALeX4gypgQMHyltvvXXHkT09057eP378eLfvVACAtQW1aiWFL16UYz17yunRo02mVN5XXnF1s+AGateubc5Y/L///U+KFSt2x0laOnTo4LK2AQAAWC4gpUfy9Gx36rvvvpO77777joDUgQMHZO3atc5pJQAADpa7UyeJvHBBTg4ZIicGDBDvXLkk+Lnn6Gc41RdffGFO1KIBKb3EpFNHCUgBAAB3kayAlI+Pj4waNcqcmlgvn3zySax6G3o9ICBAevXq5cy2AgDgUHn79ZOIc+fk7NixcuSll8QrRw7J0aSJecwWFSVRoaHi6e8vHp7McIdj6ElaAAAAkMyAVMWKFWXDhg3m+qOPPmpOWRwcHEz/AQAyND2gUvC99yQyJEQuzJolh9q3l0IffyzX1q2TiwsWiC0sTDz8/CSodWvJ26uX+Feq5OomI4M7ceJEgp9FPQCo4ytPAqAAAMANpLiGFEf2AACZiQYCikycKBEhIXJ52TI59vLLIl5eIpGR5nENSoXMnSshc+ZIsWnTJGfbtq5uMjIwPbCX2FkdtaZUo0aNZOjQoZIlS5Z0bRsAAIClA1J6uuKJEyfK5s2b5ebNm2YKX0xLly51ZPsAAHA6D29vyde/vwlIGbeDUdEiIsyfw506SZYKFciUQqqNGDFCPvroI1PmoGrVqua+bdu2ydixY+Xpp5+WEiVKyIQJE+TDDz80J4sBAADIrFJ1lr2tW7dKs2bNJFu2bM5pFQAA6ezc5MmxMqPi5eEhZ8aPl2L6XCAVpk+fLiNHjpTHHnss+r7y5ctL3rx5ZfTo0fL9999Lvnz55NVXXyUgBQAAMrUUB6T0jDAzZsyIPqoHAEBGpwXMtWZUosEoFREhF+fPl6Kff57otCsgISdPnpTixYvfcX+RIkXk2LFj5nrBggXl6tWrdCIAAMjUUnzaoKCgIDKjAACZip5NT2tFJYc+zxYa6vQ2IXPSE8Vo6YPw8PDo+8LCwsx9d999t7n9559/mgAVAABAZpbiDKlOnTrJ+++/b2ogFChQwDmtAgAgHXn6+5uz6SUnKKXP8/D3T5d2IfPRYuUdO3aU2rVrS5kyZUwtzr1795oC5p9//rmsX79ehgwZIu+9956rmwoAAGCtgNSiRYtk37595iwxenpib+/Yi9i0aZMj2wcAgNN5eHpKUOvW5mx69gLm8fL2lqA2bZiuh1QrXbq0rFixwtSK2r17t3h5eUmLFi2kcePG4ufnJ8ePH5fFixeb5wEAAGRmKQ5IvfDCC85pCQAALpS3Vy8JmTMn8SfZbJK3Z8/0ahIyqcDAQHnggQckV65cEhkZKSVLljTBKFWoUCFXNw8AAMCaASk9igcAQGbjX6mSFJs2TQ536mTOphdfplSujh3N84DUun79ugwePNhkSGl2lE7Zi4qKklq1asnYsWPFn+mgAADATaS4qLn68ccfpXXr1nL//ffL0aNHTZ2DmTNnOr51AACko5xt20q5deskZ7t2plaU0r9ZKlc210Pmz5fw22dCA1Jj9OjRsn37dpk1a5Zs3brVXL788ks5cuSIfPzxx3QqAABwGykOSC1ZskQGDRokderUkZs3b0anl+sgasaMGc5oIwAA6ZspNXmyVLpwQSqeOyeVQkKk3B9/SMB990nUpUty9OWXTVYLkNqDem+//bY5qKcZUlqLs1q1ajJs2DCTNQUAAOAuUhyQmjp1qjn7S8+ePcXT89bL27dvL8OHD5evv/7aGW0EAMAlhc69AgNNAXMPb28pOnWqyZa6snKlnJ8+nS2CVNGDeXnz5r3jfr3vypUr9CoAAHAbKQ5IHT58WCrfnroQU9WqVeXMmTOOahcAAJaSpXx5KTBsmLl+YsAACTt82NVNQgZ07733yhdffHHH/XpfxYoVXdImAACADFHUvHDhwqb2QZEiRWLdv2rVqjvuAwAgM8nTq5dcWrpUrq1bJ0e7dpVSK1aYTCogufr16yfPPvusbNiwwQSnlNaROnfunEwn8w4AALiRFI+ie/ToYc4OM27cOHNWmB9++MFM4dMinV26dHFOKwEAsAAPLy8pOmWKeAYEyNXff5dzkya5uknIYCpUqCCLFy+WunXrSkhIiDnr3uOPP27GU5VSeQZH+zKmTZsWPS1QSynUqFHD1KfS6/a6nwAAABk2Q6pRo0aSM2dO+eyzzyQgIEAmT54spUqVMqcqfvTRR53TSgAALMKvZEkp8M47cvy11+TEoEGSrX59yVKmjKubhQxEM8rfeOONWPddvXpVNm3aZEogpNTIkSNNSQU7PWi4c+dOWbFihURERJiDiRMmTJBXX33VIe0HAABwhFTNM3jwwQfNKYr//PNP+euvv2Tu3LkEowAAbiN3166StW5dsYWGypEuXcQWGenqJiGD27x5s5nKl1IadDp06FCsQNaiRYvkpZdekuDgYFMsXU9Es3DhQge3GAAAIJ0zpNSOHTtky5YtJv077qmvO3TokMYmAQBgbVo3quikSfLv/ffL9fXr5eynn0rePn1c3Sy4mRMnTsgHH3xgDhIOHDjQ3Hf58mU5e/aslC5dOvp5msmu9126dEly5MiR4veJjIw0Fyuzt8/q7cxMLNnncX6XWEGK+yeJdbD/9tK/HpIx18EV0rIOruhzh6yHBbdDStbB/jwr9Lu7fCciHfR/PSWvT3FAatKkSfLJJ59I9uzZJWvWrLEe01NjE5ACALgD36JFpdDo0XK0e3c5+fbbku3xx8X/rrtc3Sy4CR3svf766/LKK6+YE87ErCel/P39o++zXw8NDU1VQGrPnj2SUeiJd+C+fe55+/NvJXoQ3xnroN/njL4O6ckR65Cefe6I9bDidkjNtrBCv7vbd2J7Ov5fT3FAatasWWbw0717d+e0CACADCL4hRfk0uLFcvmHH8zUvbK//SYePj6ubhbcgNbyzJcvnzRr1izW/fbg040bN+4YzGvtz9QoW7Zsql+bngE6HUBXrFhRvLy8XN2cDGffww+n+DWataCfLf3M6UFpZyj9xx8pev4+C35OS1eu7NB1SI9+d/Y6uEJa1sEVfe6I9bDidkjJOtj/r1uh393lOxHpoH2pHhxL7sEs79QsvEmTJqlpFwAAmYoOkIpMmGCm7oVu2iSnx4yR/LenTgF2Fy9eTLIztKh5SixbtkzOnDkj999/f/T4bOvWrbJhwwbJkyePHDhwQAoVKmQe279/v6klpdntqaGD0owS5MlIbbWUVPzYs7/C/FB00o/FFG9Li/1odcY6pEe/x+Xu28EVfe6Q9bDgdkjNtrBCv7vbd8IrjfvSlLw2xQGpOnXqyC+//CIvvPBCSl8KAECm41OwoBT68EM50rGjnBo1SrI/8YQEpPDIGTK3Bx54IMmju6ZGRgoGrD/88EOs2+3bt5dHHnlEOnXqJGPGjJHx48dL+fLlzWN6vXnz5qlsPQAAgHOkOCBVsmRJ+fDDD01Qqnjx4uLr6xvr8UGDBjmyfQAAWF7Op582U/f0YqburVkjnn5+rm4WLEKLjqen3r17y+jRo6VFixbmBDSNGjUy9wEAAGTogNRff/0l9957rzmSd/DgwViPWW1uJwAA6UH3f4XHjpWra9fKjX/+kdOjRkmBt9+m82FUr17d6T2hNT7t9GChHiDkICEAALCyVBU1BwAAsfnkzStFxo6VQ888Y2pJZX/ySQlMh0AEAAAA4BYBKXXhwgUTmNq3b59ERERI6dKlpU2bNlKkSBHHtxAAgAwiqEULydm2rYTMm2em7pX73//E8/ZZzwAAAAD8x1NSSE8D2LBhQ1mxYoVkzZrVnLHlp59+Mqcd3rVrV0oXBwBAplLoo4/EO39+CduzR04OG+bq5gAAAACZIyClRTLr1asn33//vbz77rvy/vvvm+DU448/bh4DAMCdeQcHS5GJE831s+PGydU1a1zdJAAAACDjB6S2bdsmnTt3Fk/P/16q1/U0w1u2bHF0+wAAyHByPPGEBL/wgojNJke6dpXIq1dd3SRYRHh4uMyYMUOOHj1qbuuBvZo1a8pLL71kSiIAAAC4ixQHpIKDg+X8+fN33H/u3Dnx4xTXAAAYhd5/X3wKF5bwgwfl5KBB9AoMzSafOnWqXLlyRX777TdTk/OFF16QS5cumcxzAAAAd5HigJTWjxo2bJhs3rxZIiMjzWXjxo3y9ttvS4MGDZzTSgAAMhivHDmk6Oefm+vnPv9crvz6q6ubBAv44Ycf5JNPPpG77rpLfvzxR3nggQeka9euMmTIEFm9erWrmwcAAGDdgFTv3r2lYMGC0q5dO6lUqZK5PPfcc1K2bFkZMGCAc1oJAEAGlO3RRyV3t27m+pFu3STy0iVXNwkudvXqVSlcuLC5/scff8jDDz9srvv7+5uDfAAAAO7CO6UvCAgIMKnm+/btk71795ppeqVLl5aiRYs6p4UAAGRgBUaOlMs//WSm7h1/4w0pOmmSq5sEF9IDeIsWLZI8efKYcgePPvqoqSulY6vy5cuzbQAAgNtIcUBKhYaGys6dO01ASguaR0RESIECBcTHx8fxLQQAIAPzyppVik6eLPsaNJALX3whOZo2lRyNGrm6WXCR/v37m2zzkJAQ6dChgxQpUsSUPVi5cqVMnjyZ7QIAANxGigNS+/fvNwMoLcZZokQJk17+5ZdfSr58+WTmzJmSP39+57QUAIAMKmutWpKnVy85O3asHO3RQwI3bhTv4GBXNwsucP/998uaNWvMOCpHjhzmvi5duki/fv0kMDCQbQIAANxGimtIaUFzrRv1+++/m5TzxYsXy6pVq6R48eLmMQAAcKcCw4aJX7lyEnHqlBzv04cuclONGzc20/M029xOa3MSjAIAAO4mxQGp7du3y2uvvSbZsmWLvi8oKEj69u0r69evd3T7AADIFDz9/c3UPfH0lJB58+Tit9+6uklwgaZNm8ry5ctN7aj27dvLggULTLYUAACAu0lxQKpYsWKya9euO+4/duyYqSMFAADiF1i9uuTr189cP9q7t9w8c4aucjNdu3Y12eVLly6VatWqybRp06RWrVqmrtTPP//s6uYBAABYt4bUc889JyNGjJCDBw+aOgje3t6yY8cOU4jzqaeekp9++in6uQ0aNHB0ewEAyNDyvfmmXPr+e7nxzz9yrHdvKT5njnh4eLi6WUhnpUqVMkGoHj16yNdffy2ffvqpKWwe30E/AACAzCjFAanBgwebvxMmTLjjsZhnh9HBNYMqAABi8/Tzk6JTp8qeWrXk0uLFEjJ3rgS3a0c3uRGbzSb/+9//ZMWKFSYIpbebNGkizZo1c3XTAAAArBuQ+vfff53TEgAA3ETAvfdK/jfflFPDh5sC59nq1BGfggVd3Sykg7ffftsEoS5fviyPPPKIyTqvo9vfx4f+BwAAbiXFNaT0VMXxiYqKkokTJzqiTQAAZHpaS8q/alWJvHhRjvboYbJkkPnt3btXXnnlFVm7dq2MHTtW6tWrRzAKAAC4Jc/UFOMcOXKkhIWFRd+3e/duUz9KC3MCAICkefj4SNEpU8TD11cu//CDXPjyS7rNDXz11VfSunXrWGcrBgAAcEcpDkjNmDFDVq1aJc2bN5dNmzbJ+PHjpVWrVlKwYEH5/vvvndNKAAAyIf+77pICQ4ea68dff13CDx92dZMAAAAAawakatSoYU5VXKZMGXn22Wdl0qRJ8uGHH5rAVL58+ZzTSgAAMqk8r7wigQ88IFFXrsiR7t3FFhXl6iYBAAAA1gtIRUREyKxZs+T333+X6tWrS968eU0NBM2WAgAAKePh5XVr6p6/v1xdtUrOT5lCFwIAACDTS3FASqfqTZkyRd566y354osvTLZU1apVTbbUkCFDnNNKAAAyMb/SpaXgiBHm+ok335Sw/ftd3SSks3/++Uc6duxIvwMAALeR4oCUTsvTIJQW5FSBgYHmlMVTp05N8Ax8AAAgcbm7d5estWtL1PXrcqRrV7FFRtJlbiQkJET+/PNPVzcDAADAugEpPZNegQIF7ri/Zs2aJlAFAABSzsPTU4pMniyeWbPKtXXr5NzEiXQjAAAA3DsgNXDgQLl69Wqs+7Zu3Srh4eHRty9cuCBNmzZ1fAsBAHATfsWKScH33jPXTw8bJnLokKubBAAAALguIPXdd99JWFhYrPs6dOggp0+fjr4dFRUlJ06cSFUjtm3bZs7elxR9j+eee06GDx+eqvcBAMDqcnXsKNnq1xdbWJh4vvOO2CIiXN0kAAAAwOG8k/Mkm82WrPtSY9myZTJ06FATbErKZ599Jhs3bpSyZcs65L0BALAaDw8PKTJxovx7//0StWuXnP34YykwYICrm4U0GDlyZJLPOX78OH0MAADcSrICUs4yZswYWbdunfTo0UPGjRuX6HM3b94sy5cvl/r166db+wAAcAXfwoWl4AcfyLGuXeXMqFES1KiR+FeqxMbIoHbv3p2s591///1ObwsAAIBVuDQg1b59e+nXr5+sX78+0edp/aoBAwbIBx98ILNnz07z+0ZGRpqLo9mX6YxlI3XYJtbEdrEeton1ZGvTRmyzZon88Ycc7txZSq1eLZ6+vq5ulltK6359lm5HAAAAWCcglS9fvmQ9T6f0NWvWTCpVquSQgNSePXvEmbZv3+7U5SPl2CbWxHaxHraJxbz+unhu2yY3tm+X7X36iK1zZ1e3CKmkdTZ/+eUX8fX1lYcfflgKFixIXwIAALeW7IDUnDlzJDAwMNbRwoULF0pQUJC5fe3aNac0UAuq6yBu9OjRDlum1qAKCAgQR9M+0R9zFStWFC8vL4cvHynHNrEmtov1sE2su00Kf/KJHO/QQTxnzZJSHTpIwH33ubppbuf69etpOpil5Qleeukl8fb2NjXC3nvvPZP1Xa9ePYe2EwAAINMFpPQo3qJFi2Ldlzt3blm6dGms+woUKODY1omY9/j333+jz8J348YN8/evv/664/2TS4NFzgwYOXv5SDm2iTWxXayHbWI9wa1by7Xly+XiwoVyrFs3Kffnn+KZJYurm+VW0rpP//TTT6Vly5YyaNAg8fT0NMGo999/n4AUAABwa8kKSP3666/iKtOmTYt1W2tJaXbTkCFDXNYmAADSk2ZJXf3jDwn79185NXy4FBw1ig2QgWh2lZ7IRTOkVPfu3WXGjBly6dIlyZEjh6ubBwAA4BKeYkFLliyRKlWquLoZAABYgneuXFJk/Hhz/YwGp9atc3WTkAKa3R2zVED27NklS5YscuXKFfoRAAC4LUsEpHQ63ubNm6NvN23aNNbtmLTuAtlRAAB3k6NxY8n53HMiNpsc6dpVIp1UuxGOZ7PZ7rhPa0nFdz8AAIC7sERACgAAJK3QBx+IT8GCEr5/v5wcPJguyyA0+KQXAAAApOIsewAAwLW8g4KkyKRJcqBpUzn32WeSo2lTyfbII2wWi9NMqOeeey5WcfSwsDDp0qWL+Pj4xHpuak/YAgAAkNEQkAIAIAPJXr++5OrcWc5PnSpHunWT8n/9JV7Zs7u6WUhEz5496R8AAIA4CEgBAJDB6Fn2rqxcKeGHD8uJgQOlyIQJrm4SEtGyZUvJnz+/eHpSKQEAAMCOgBQAABmMV7ZsUmTyZNnfsKGcnz7dTN3L3rChq5uFBDz22GOyZs0ayZUrF32UCe2uWVOsqNzata5uAgAAieJQHQAAGVC22rUld48e5vrR7t0lIiTE1U1CApx1Nr2VK1dK48aNpUqVKlK/fn2ZPXu2uf/mzZsyfPhwcxbjatWqmet6HwAAgJUQkAIAIIMqOHy4+JUuLTdPnpTj/fq5ujlIhKPPsnf48GHp27evDBo0SDZv3ixjx46V9957T7Zt2ybjxo2TnTt3yooVK2T58uWyfft2mcC0TgAAYDFM2QMAIIPyDAiQolOmyN7HHpOQ2bMlR7NmEtS0qaubhXi8/vrrd5xRLz6TJk1KVv8VK1ZM1q1bJ1mzZpWoqCgJCQkxZ/ELDAyURYsWyciRIyU4ODi6qPpbb70lr776KtsGAABYBgEpAAAysMAHHpC8r70mZz78UI716iVZH3pIvHPndnWzEEf27NklS5YsDu0XDUZdvHhRHnroIYmMjJSuXbtKnjx55OzZs1K6dOno55UqVcrcd+nSJcmRI0eK30eXrRcrs7fPJe100pTMtEpRX6RiHexTUfWvY/P//pPi7WnBbeHodUiPfo/L3beDK/rcVd9rK62D/XlW6Hd3+U5EOmhfmpLXE5ACACCDyz94sFxesUJu7NwpR3v3luJff+3wKWJIG51a54yi5tmyZZMtW7bIv//+K126dBF/f39zv/1vzOuhoaGpCkjt2bNHMgqdnpjePK9fFyvSz0V6rIN+rqywDlbdFs5aB2f2e1xsh/Tvc1d/r630ebJCv7vbd2J7Ou5LCUgBAJDBefr5mal7e2rXlkvffisXFyyQnG3auLpZuM2ZwUGdpqeXSpUqyVNPPWVqSKkbN27cMZgPCAhI1XuULVs21a9NL3o0VgfQFStWNP2RnvZZtG9KV67s1HXQrAX9bGnA01mf8ZSsg1W3haPXIT36PS533w6u6HNXfa+ttA72/+tW6Hd3+U5EOmhfev369WQfzCIgBQBAJhBQtarkGzBATr/zjhx77TXJWru2+OTP7+pmwUln2Vu1apVMmzZNvvrqq+j7wsPDzdRAnbZ34MABKVSokLl///79kjdvXvNYWoJeGYFL2mqxH0p2KeqHVKyD/RXmh6KT+iDF29KC28LR65Ae/R6Xu28HV/S5q77XVtwWVuh3d/tOeKVxX5qS13KWPQAAMon8b7wh/pUrS+SFC3K0Z0+nBEKQcr/88kt0gXFH0aOXevRx7ty5pqj5X3/9ZYqZa5ZU8+bNZfz48aZulF70ut4HAABgJQSkAADIJDx8fMzUPf17eflyCfn6a1c3CSIyevRoOXr0aLL7QjOaevXqlehzcufOLZ9//rkJQlWrVk2GDx8u7733nlSvXl169+5tAlYtWrSQxo0byz333GPuAwAAsBKm7AEAkIn433OPKXJ+csgQOda3r2StU0d8ixRxdbPcWsuWLaVTp07mzHcNGzaU2rVr35Exdfz4cZPltHjxYjl48KAMGzYsyeVWqVJF5s+ff8f9vr6+poi6XgAAAKyKgBQAAJlM3tdek0tLl8r1v/6Soy+/LCWXLLFcQVB3UqdOHZO5pMGjCRMmyMCBA009p5w5c5rpdiEhIXL16lUpWrSoPPPMM/LZZ59JlixZXN1sAAAApyIgBQBAJuPh7S1Fp06V3TVqyJWff5bz06ZJ7s6dXd0st6ZnCXrhhRfMZd++ffLPP//IuXPnxNPT0xQh12l1JUqUcHUzAQAA0g0BKQAAMqEsZctKgeHD5UT//nJiwADJ9thj4leihNiioiQqNFQ89TTKnpSSdAWduqcXAAAAd8ZIFACATCpPjx4SWKuWRF27JgeffVYOd+ki24KDZXvu3Oav3g7dts3VzQQAAIAbIiAFAEAmpRlQRSdPFg8/P7mxebOEzJ4ttrAw85j+DZk7V3Y/9JCEzJvn6qYCAADAzRCQAgAgE4u6ckVs4eG3b0TFfjAiQiQyUg536kSmFAAAANIVASkAADKxM+PGiXh5Jf4kDw85M358ejUJCTh79ix9AwAA3AYBKQAAMiktYH5xwYJbmVCJiYiQi/Pni81mS6+muZ3jx4/LrFmzZM6cOXL69OlYj2m/z5w5U5544gmXtQ8AACC9cZY9AAAyKT2bnr1mVFL0ebbQUPEICHB6u9zN6tWrpXfv3uLh4SHe3t4yevRo+eKLL6RSpUqya9cuGThwoPz777/SqFEjVzcVAAAg3ZAhBQBAJuXp728KmieHPs/D39/pbXJHY8eOlUcffVQ2bNgg69evl6efflref/99+fnnn6VNmzZy9epVmTJlinz00UeubioAAEC6ISAFAEAmPsteUOvWIt5JJER7e0tQmzYmgweOd+DAAenWrZv4+vqKl5eX9OjRQ7Zs2SJvvvmmCUgtW7ZMHn74YboeAAC4FQJSAABkYnl79dIiRYk/yWaTvD17pleT3E5oaKjkyZMn+nbWrFnFx8dH2rZtK4MHD5YsWbK4tH0AAACuQA0pAAAyMf9KlaTYtGlyuFMncza9Owqce3qax/V5SD+ajdaiRQu37/LdNWs6pg9sNvG8fl32aQ20NGb6lVu71u23CwAA6YEMKQAAMrmcbdtKuXXrJGe7dv/VlPK8NQTIVr++eRzpT7OkAAAA3BUZUgAAuEum1OTJUnTSJHP2vRu7d8vemjXlyk8/Seg//4j/Pfe4uomZ2pw5cyQwMDD6dmRkpCxcuFCCgoJiPa9Dhw4uaB0AAED6IyAFAICbFTr3CgyUwKpVJahlS7m4aJGcfPttKblggaublmkVLFhQFi1aFOu+3Llzy9KlS++YxkdACgAAuAsCUgAAuKn8Q4bIxe++k8vLlsm1DRsksHp1VzcpU/r1119d3QQAAADLoYYUAABuKku5chLcvr25fnLYMFc3BwAAAG6EgBQAAG4s/8CB4uHrK1dXrZIrq1a5ujkAAABwEwSkAABwY77Fikmuzp3N9ZNDhojNZnN1kwAAAOAGCEgBAODm8vXvL54BAXL9779NPSkAAADA2QhIAQDg5nzy5ZM8PXua63rGPVtkpKubBAAAgEyOgBQAAJA8r74qXkFBcmPHDgmZP58eAQAAgFMRkAIAAOKdM6fk7dPH9MSpESPEdvMmvQIAAACnISAFAACM3C+/LN5580r4wYNy/osv6BUAAAA4DQEpAABgeAUGSr433jDXT48aJVGhofQMAAAAnIKAFAAAiJarUyfxKVpUbp48Kec+/5yeAQAAgFMQkAIAAP8NDPz8JP+gQeb66TFjJPLyZXoHAAAADkdACgAAxBLcrp34lSsnkefPy5mxY+kdAAAAOBwBKQAAEIuHt7cUGDLEXD/76acSce4cPQQAAACHIiAFAADukKN5c/GvUkWirl41U/cAAAAARyIgBQAA7uDh6SkFhg0z189NmiThx47RSwAAAHAYAlIAACBe2erXl8CaNcUWFian33+fXgIAAIDDEJACAADx8vDwiM6SOj9zpoTt309PAQAAwCEISAEAgARlrVVLsjVsKBIRIadGjqSnAAAA4BAEpAAAQKLsWVIh8+ZJ6D//0FsAAAAgIAUAAJwroHJlCWrVSsRmk5Nvv013AwAAIM3IkAIAAEnKP2SIiKenXF62TK6tX0+PAQAAIE0ISAEAgCRlKVtWgtu3N9dP3p7CBwAAAKQWASkAAJAs+QcOFA9fX7n6229yZdUqeg0AAAAZOyC1bds2qVGjRoKP//333/LUU09J1apVpU6dOjJ+/Hix2Wzp2kYAANydb7FikqtzZ3P95JAh7IsBAACQcQNSy5Ytkw4dOkh4eHi8j1+9elW6du0qzz//vGzcuFG+/vprmTt3rqxYsSLd2woAgLvL17+/eAYEyPW//zb1pOA6esCubdu2ct9990ndunVlwoQJJkh48+ZNGT58uDnYV61aNXNd7wMAALASlwakxowZI9OnT5cePXok+JysWbPK77//Lk2bNjW3Q0JCJDIyUoKCgtKxpQAAQPnkyyd5evY01/WMe7bISDrGBS5cuCDdu3eXNm3ayIYNG8x4atGiRTJnzhwZN26c7Ny50xy8W758uWzfvt0EqwAAAKzE25Vv3r59e+nXr5+sT+JsPRqUUnqk79KlS9K4cWN54IEHUv2+GtDSi6PZl+mMZSN12CbWxHaxHraJ9Vh5m+Tq1UvOTp4sN3bskPPz5knOtm0lM7PiNjh27JgpY9CqVStzu0SJElK/fn2TTa7jqpEjR0pwcLB5rGfPnvLWW2/Jq6++6uJWAwAAWCQglS9fvhQ9/48//jADMD0iqEf6evXqlar33bNnjziTHomEtbBNrIntYj1sE+ux6jbxaNtWPD//XI4OHiyHS5US8XbpkMLtVKpUyWSa22npA80ob9GihSmHULp06ejHSpUqJWfPnjUH9XLkyGGZA3mGg2qC2muL6l+PNC4rxetq0bqmKVqPVKyDI/s8M28LR69DevR7XO6+HVzR5676XltpHezPs0K/u8t3ItJBB0NT8voMNXr08/Mzg6oXX3zRpKSnNiBVtmxZCQgIcHj7tOP1h0PFihXFy8vL4ctHyrFNrIntYj1sE+ux+jaJKlNGdn/7rUScOCFFtmyJLnaeGV2/ft3pB7PSIiwsTF577TXx9fWVRo0amUCVv79/9OP266GhoakKSDlz3T2vX3fo8nQd02rLli0uXQdHScl6pGUdHNHnmXlbOGsdnNnvcbEd0r/PXf29ttLnyQr97m7fie3peDDU8gEprYHQt29fWbJkifj4+EQfBcyePXuql6kDe2cO7p29fKQc28Sa2C7WwzaxHqtuE6/s2SXfgAFyvE8fOfPee5L7+efFM0YQJDOxYv/bnTp1yhyg02DUzJkzxcPj1nHkGzdu3DGYT+3BOGcdyFP7HLRcPYKu66nBN3sfpFbpypVdsg6OlpL1SM06OLLPM/O2cPQ6pEe/x+Xu28EVfe6q77WV1sF+YM4K/e4u34lIBx0MTcmBPMsHpDQjSgNQ48ePNwOuffv2yYwZM8yRQAAA4Dq5OnaUM598IjePHJFzkyZJXvbN6eqff/6Rbt26yWOPPSaDBw+OPnCXJ08eOXDggBQqVMjc3r9/v+TNmzfVB/OcGhR10I8M+1LMj5Y0LjPF62qxH0qpWo9UrIMj+zwzbwtHr0N69Htc7r4dXNHnrvpeW3FbWKHf3e074ZXG/X5KXuvSs+wlRLOhqlSpEj1Nb/LkybJ161ZTyPyVV14xNaSaN2/u6mYCAODWPP38JP+gQeb66TFjJPLyZVc3yW1oZlSnTp3kmWeekeHDh0cHo5SOkfRAntaN0oteZ9wEAACsxhIZUnr2vM2bN0ffbtq0qbnEzJLSNHQAAGAtwe3ayZkPP5Sw3bvlzNixUuB2gArONXv2bLl48aJMnTrVXOwefvhhU0Nq9OjRpsD5zZs3TV2p3r17s0kAAIClWCIgBQAAMiYPb28pMGSIHHr2WTn76aeS56WXxDt3blc3K9Pr06ePuSRk0KBB5gIAAGBVlpyyBwAAMo4czZuLf5UqEnX1qpm6BwAAACSFgBQAAEgTD09PKTBsmLmuxc3Djx2jRwEAAJAoAlIAACDNstWvL4E1a4otLExOv/8+PQoAAIBEEZACAABppqdltmdJnZ85U8L276dXAQAAkCACUgAAwCGy1qol2Ro2FImIkFMjRtCrAAAASBABKQAA4DD2LKmQ+fMldPt2ehYAAADxIiAFAAAcJqByZQlq1UrEZpOTw4fTswAAAIgXASkAAOBQ+YcMEfH0lMvLlsm19evpXQAAANyBgBQAAHCoLGXLSnD79ub6ydtT+AAAAICYCEgBAACHyz9woHj4+srV336TK6tW0cMAAACIhYAUAABwON9ixSRX587m+skhQ8Rms9HLAAAAiEZACgAAOEW+/v3FMyBArv/9t6knBQAAANgRkAIAAE7hky+f5OnZM7qWlC0ykp4GAACAQUAKAAA4TZ5XXxWvoCC5sXOnhMyfT08DAADAICAFAACcxjtnTsnbp4+5fmrECIkKD6e3AQAAQEAKAAA4V+6XXxbvfPkk/OBBufDFF3Q3AAAACEgBAADn8goMlHxvvGGunxo1SqJCQ+lyAAAAN8eUPQAA4HS5OnYUn6JFJeLUKTk3aRI9DgAA4OYISAEAAOcPOPz8JP+gQeb66TFjJPLyZXodAADAjRGQAgAA6SK4XTvxK1dOIi9ckDNjx9LrAAAAboyAFAAASBce3t5SYMgQc/3sp59KxLlz9DwAAICbIiAFAADSTY7mzcW/ShWJunrVTN0DAACAeyIgBQAA0o2Hp6cUGDbMXNfi5uHHjtH7AAAAboiAFAAASFfZ6teXwJo1xRYWJqffe4/eBwAAcEMEpAAAQLry8PCQAm+/ba6fnzlTwvbvZwsAAAC4GQJSAAAg3WWtWVOyP/64SGSknBoxgi0AAADgZghIAQAAl8g/dKj5GzJ/voRu385WAAAAcCMEpAAAgEsEVK4sQa1aidhscnL4cLYCAACAGyEgBQAAXCb/kCEinp5yedkyubZ+PVsCAADATRCQAgAALpOlbFkJbt/eXD85bBhbAgAAwE0QkAIAAC6Vf+BA8fD1lau//SZXVq1iawAAALgBAlIAAMClfIsVk1ydO5vrJ4cMEZvNxhYBAADI5AhIAQAAl8vXv794BgTI9b//lktLl7q6OQAAAHAyAlIAAMDlfPLlkzw9e5rrp95+W2yRka5uEgAAAJyIgBQAALCEPK+9Jl5BQXJj504JmT/f1c0BAACAExGQAgAAluAdFCR5+/Qx10+NGCFR4eGubhIAAACchIAUAACwjNwvvyze+fJJ+MGDcuGLL1zdHAAAADgJASkAAGAZXoGBku+NN8z1U6NGSVRoqKubBAAAACcgIAUAACwlV8eO4lO0qEScOiXnJk1ydXMAAADgBASkAACApXj6+Un+QYPM9dNjxkjk5cuubhIAAAAcjIAUAACwnOB27cSvXDmJvHBBznz6qaubAwAAAAcjIAUAACzHw9tbCgwZYq6fHTtWIs6edXWTLGvbtm1So0aN6Ns3b96U4cOHm/uqVatmrut9AAAAVkJACgAAWFKO5s3Fv0oVibp61Uzdw52WLVsmHTp0kPDw8Oj7xo0bJzt37pQVK1bI8uXLZfv27TJhwgS6DwAAWAoBKQAAYEkenp5S4O23zfVzn38u4ceOubpJljJmzBiZPn269OjRI9b9ixYtkpdeekmCg4Mlb9680rNnT1m4cKHL2gkAABAf73jvBQAAsIBs9epJYK1acm3NGjn93ntSZPx4VzfJMtq3by/9+vWT9evXR993+fJlOXv2rJQuXTr6vlKlSpn7Ll26JDly5Ejx+0RGRpqLU9hsDlqMLfqvRxqXleJ1ddA6OFqK1iMV6+DIPs/M28LR65Ae/R6Xu28HV/S5q77XVloH+/Os0O/u8p2IvH1fWvf5KXk9ASkAAGBZHh4eUmDYMNlXr56cnzlT8r72mviVKuXqZllCvnz57rjv+vXr5q+/v3/0ffbroaGhqQpI7dmzR5zF83Z7HUXXMa22bNni0nVwlJSsR1rWwRF9npm3hbPWwZn9HhfbIf373NXfayt9nqzQ7+72ndi+fXu6tYOAFAAAsLSsNWtK9scfl8s//CCnRoyQYjNnurpJlmUPPt24ceOOwXxAQECqllm2bNlUvzYp+xy0XD2Cruup669BzLQoXbmyS9bB0VKyHqlZB0f2eWbeFo5eh/To97jcfTu4os9d9b220jpolo0GRqzQ7+7ynYi83ecVK1YULy+vVC9bD44l92AWASkAAGB5+YcONQGpkPnzJW/fvuJfsaKrm2RJmgGVJ08eOXDggBQqVMjct3//flNLKnv27Klapg5K0zIwTZSDfmTYl2J+tKRxmSleV4v9UErVeqRiHRzZ55l5Wzh6HdKj3+Ny9+3gij531ffaitvCCv3ubt8JrzTu91PyWoqaAwAAywuoXFmCWrUydRhODh/u6uZYWvPmzWX8+PGmbpRe9LreBwAAYCUEpAAAQIaQf8gQEU9PubxsmVz73/9c3RzL6t27t0m3b9GihTRu3Fjuuececx8AAICVEJACAAAZQpayZSW4fXtz/eTQodFnPnJ3NWrUkM2bN0ff9vX1lUGDBsmaNWvMGfiGDh0qPj4+Lm0jAABAXASkAABAhpF/4EDx8PWVq7//LldXrXJ1cwAAAJBKBKQAAECG4VusmOTq3NlcPzlkCFlSAAAAGRQBKQAAkKHk699fPAMD5frGjXJp6VKxRUVJ5LVr5i8AAAAyBksEpLZt22bqHyRk79690qFDB6lWrZrUqlVL3nnnHQkPD0/XNgIAAGvwyZdP8vTsaa4f6dpVtgUHy/bcuc3fw126SOi2ba5uIgAAAKwekFq2bJkJNiUUYIqIiJCuXbvKfffdJ+vWrZNvvvnGFO789NNP072tAADAGnyKFTN/oy5dEltYmLmuf0PmzpXdDz0kIfPmubiFAAAAsGxAasyYMTJ9+nTp0aNHgs85ffq0lCpVSrp3727OEJMvXz5p1qyZbNy4MV3bCgAArEEzoI716hX/gxERIpGRcrhTJzKlAAAALMzblW/evn176devnzklcUIKFSokU6dOjb6tp3j+5ZdfpHz58ql+38jISHNxNPsynbFspA7bxJrYLtbDNrEetknCTo8dK+LhkXgHenjI6XHjpMikSQ7bFgAAAMgkASnNdkqJqKgoGT58uBw6dEg++OCDVL/vnj17xJm2b9/u1OUj5dg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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# === Predator α Across All Prey Death Values ===\n", + "\n", + "prey_deaths_p1 = sorted(df_phase1[\"prey_death\"].unique())\n", + "\n", + "results = []\n", + "for pd_val in prey_deaths_p1:\n", + " pred_clusters = get_cluster_distribution(df_phase1, pd_val, \"pred\")\n", + "\n", + " if len(pred_clusters) < 100:\n", + " continue\n", + "\n", + " fit = powerlaw.Fit(pred_clusters, discrete=True, xmin=1, verbose=False)\n", + " R_tpl_ln, _ = fit.distribution_compare(\n", + " \"truncated_power_law\", \"lognormal\", normalized_ratio=True\n", + " )\n", + "\n", + " results.append(\n", + " {\n", + " \"prey_death\": pd_val,\n", + " \"n_clusters\": len(pred_clusters),\n", + " \"max_size\": pred_clusters.max(),\n", + " \"alpha\": fit.truncated_power_law.alpha,\n", + " \"R_tpl_ln\": R_tpl_ln,\n", + " }\n", + " )\n", + "\n", + "results_df = pd.DataFrame(results)\n", + "\n", + "# === Plot ===\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", + "\n", + "# Left: α vs prey_death\n", + "ax1 = axes[0]\n", + "ax1.plot(\n", + " results_df[\"prey_death\"],\n", + " results_df[\"alpha\"],\n", + " \"o-\",\n", + " color=COLORS[\"predator\"],\n", + " markersize=7,\n", + " linewidth=1.5,\n", + ")\n", + "ax1.axhline(\n", + " 1.17, color=\"0.5\", linestyle=\"--\", linewidth=1, label=\"α = 1.17 (reference)\"\n", + ")\n", + "ax1.set_xlabel(\"Prey Death Rate\")\n", + "ax1.set_ylabel(\"Exponent α\")\n", + "ax1.set_title(\"Predator Power Law Exponent\")\n", + "ax1.legend(loc=\"upper right\", framealpha=0.95)\n", + "\n", + "# Right: R vs prey_death\n", + "ax2 = axes[1]\n", + "bars = ax2.bar(\n", + " results_df[\"prey_death\"],\n", + " results_df[\"R_tpl_ln\"],\n", + " width=0.008,\n", + " color=COLORS[\"predator\"],\n", + " alpha=0.8,\n", + " edgecolor=\"none\",\n", + ")\n", + "ax2.axhline(0, color=\"0.3\", linewidth=1)\n", + "ax2.set_xlabel(\"Prey Death Rate\")\n", + "ax2.set_ylabel(\"R (TPL vs Lognormal)\")\n", + "ax2.set_title(\"Power Law Fit Quality (R > 0 = TPL wins)\")\n", + "\n", + "# Add annotation\n", + "ax2.text(\n", + " 0.95,\n", + " 0.95,\n", + " \"R > 0 everywhere\\n→ Always power law\",\n", + " transform=ax2.transAxes,\n", + " ha=\"right\",\n", + " va=\"top\",\n", + " fontsize=10,\n", + " bbox=dict(boxstyle=\"round,pad=0.4\", facecolor=\"white\", edgecolor=\"0.8\", alpha=0.9),\n", + ")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(\"plot_predator_alpha_sweep.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "NOTE:\n", + "\n", + "Predator clusters follow a truncated-power law distrbutions across all parameter values where predators survive. The exponent decreases as we go toward extinction and maximum cluster size collapses. This would suggest that movement dynamics generate scale-free spatial patterns, independent of proximity to any critical point." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Phase 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Phase 2: 200 records\n", + "Grid size: [1000]\n", + "Evolved prey_death final: mean=0.0617, std=0.0167\n" + ] + } + ], + "source": [ + "# === Load Phase 2 data ===\n", + "df_phase2 = load_phase_to_df(DATA_ROOT / \"phase2_18693004\")\n", + "\n", + "print(f\"Phase 2: {len(df_phase2)} records\")\n", + "print(f\"Grid size: {df_phase2['grid_size'].unique()}\")\n", + "print(\n", + " f\"Evolved prey_death final: mean={df_phase2['evolved_prey_death_final'].mean():.4f}, std={df_phase2['evolved_prey_death_final'].std():.4f}\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# === Evolved Prey Cluster Distribution ===\n", + "\n", + "# Filter to converged runs\n", + "evolved_finals = df_phase2[\"evolved_prey_death_final\"].values\n", + "converged_mask = (evolved_finals > 0.055) & (evolved_finals < 0.075)\n", + "df_converged = df_phase2[converged_mask]\n", + "\n", + "# Calculate individual run R values (correct method)\n", + "individual_Rs = []\n", + "for idx, row in df_converged.iterrows():\n", + " sizes = row[\"prey_cluster_sizes\"]\n", + " if isinstance(sizes, list) and len(sizes) > 100:\n", + " fit = powerlaw.Fit(np.array(sizes), discrete=True, xmin=1, verbose=False)\n", + " R, _ = fit.distribution_compare(\n", + " \"truncated_power_law\", \"lognormal\", normalized_ratio=True\n", + " )\n", + " individual_Rs.append(R)\n", + "\n", + "individual_Rs = np.array(individual_Rs)\n", + "R_mean = individual_Rs.mean()\n", + "R_std = individual_Rs.std()\n", + "\n", + "# Extract clusters for plotting\n", + "clusters_evolved = []\n", + "for sizes in df_converged[\"prey_cluster_sizes\"]:\n", + " if isinstance(sizes, list) and len(sizes) > 0:\n", + " clusters_evolved.extend(sizes)\n", + "clusters_evolved = np.array(clusters_evolved)\n", + "\n", + "# Compute PDF\n", + "x_evo, y_evo = compute_pdf_all_points(clusters_evolved)\n", + "\n", + "# === Plot ===\n", + "fig, ax = plt.subplots(figsize=(7, 5))\n", + "\n", + "ax.scatter(x_evo, y_evo, color=COLORS[\"prey\"], s=12, alpha=0.5, edgecolors=\"none\")\n", + "\n", + "ax.set_xscale(\"log\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_xlabel(\"Cluster Size\")\n", + "ax.set_ylabel(\"P(s)\")\n", + "ax.set_title(\"Prey Cluster Distribution (Evolved State, d ≈ 0.068)\")\n", + "\n", + "# Statistics annotation\n", + "stats_text = f\"R = {R_mean:+.1f} ± {R_std:.1f}\\nLognormal\"\n", + "ax.text(\n", + " 0.95,\n", + " 0.95,\n", + " stats_text,\n", + " transform=ax.transAxes,\n", + " ha=\"right\",\n", + " va=\"top\",\n", + " fontsize=10,\n", + " bbox=dict(boxstyle=\"round,pad=0.4\", facecolor=\"white\", edgecolor=\"0.8\", alpha=0.9),\n", + ")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# === Plot evolved cluster distribution + evolved vs initial conditions ===\n", + "\n", + "# Filter to converged runs (exclude collapsed runs near 0)\n", + "evolved_finals = df_phase2[\"evolved_prey_death_final\"].values\n", + "converged_mask = (evolved_finals > 0.055) & (evolved_finals < 0.075)\n", + "df_converged = df_phase2[converged_mask]\n", + "\n", + "# Calculate individual run R values (correct method)\n", + "individual_Rs = []\n", + "for idx, row in df_converged.iterrows():\n", + " sizes = row[\"prey_cluster_sizes\"]\n", + " if isinstance(sizes, list) and len(sizes) > 100:\n", + " fit = powerlaw.Fit(np.array(sizes), discrete=True, xmin=1, verbose=False)\n", + " R, _ = fit.distribution_compare(\n", + " \"truncated_power_law\", \"lognormal\", normalized_ratio=True\n", + " )\n", + " individual_Rs.append(R)\n", + "\n", + "individual_Rs = np.array(individual_Rs)\n", + "R_mean = individual_Rs.mean()\n", + "R_std = individual_Rs.std()\n", + "\n", + "# Extract clusters for plotting only\n", + "clusters_evolved = []\n", + "for sizes in df_converged[\"prey_cluster_sizes\"]:\n", + " if isinstance(sizes, list) and len(sizes) > 0:\n", + " clusters_evolved.extend(sizes)\n", + "clusters_evolved = np.array(clusters_evolved)\n", + "\n", + "# Compute PDF (all points)\n", + "x_evo, y_evo = compute_pdf_all_points(clusters_evolved)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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dAUVERCrOoc6NHPtF9mjj2ICZJwWdZ6Nt7FHYsWdpPDfPxbxgw0xhlrCFs53Cr41jicjz9KJFi0xPvPwX6MJ4EYiPiRxnRE6WcbhxS2HwOIezgcOYLcyxS2G+B4iUJgV1RMoAP9S3bt1q+oYwjZO1u8x+4Qd5+Espv1wyiyI8VSTLOw6GddisFWamDBsZ84TDkxV7gTDAwfKUgma/Yg0363d5smX9N/uShL/U8koE04T5u6xp5smZGTssQ6Jw+QnLWJiOyjphNpJl+QsDKhy88ORUnCsOrKPmF2me4JhSy/Ivvi5mlBSUDcHBAfvosO8QA0sff/yxGfyE++Ww3w5PnlxvYTHrh71jeHyY4suBC4Nb4f3GRtbsRcOfswlz5Hbl3zeROBDhMWbtNdfJYBRnYuAJPn+ab2ExTbegJs4sE+J+LOgWvjrGAQsDOTz2TCcOX4Filg8bArMGnqVMfA+wdwCvyjFb62DbwfRmBtnCDZKJqdV8j7LGna+XA1amUfM9ygEd68lZL88renwfMrjD43konFmDAzvue66Tx51BRAYeeUWPA+fwseeAmYNHDi4ZPGLmG5fx/RxulB1uBMnH833H9xT/n72XmBXEfcD3NfcH3+eRA1EOCvmaeNVSRESqDgZZCjrHRl5oOdi5ked8/oznQ57HOCbg/zOzhQGM/KJt7FHYsWdpPTf33ZQpU8yYNfJCJ0vw2ZuHfXq4DdxHHEPwOQ8W1GHjZl7IevTRR834hmXZ4ZkzCzNuKWxpHmcI45iB4wf2nuTvcyKVwnwPEClNCuqIlAEGOtiVnycSXungF1Se2Jiqmv/qDmuCD9YgORJPNjw58ETKxmwso+EXXZ6kwtM558cv0RwQcBs4OOB2hE+ArGdmzS+DIWwgxy/yrPsOd+bn4IL9VVjOwhMosx6YUswgEU+2DBKwdpgn26LiIIGBLGaR8Pf5JZ/BrYNN2c0ZEpjZxC/0fG7WrnPwFM5OYnCC2Rl8jYXFEz4HCdz3/PLPfRFO9eW+5QmfTe+Y7ssv86zFDsu/b/KX7TDTg6m3bJ7HYBiDGuxFdLCSp8NhCi9ncmAgIn82C/djQTc+PoyvkQPTyMEmcTDDMkC+T/gY7g8eh3DJU0G4Dg4mIwekfE+xJI7Hg8czXGPO1xwevDBYwoAhjz2zaFj2d6hyJpZZ8W+IwRr20GGDZA4gmd7OIGAYex1wwMTt4vr5d8Kfc/sYrONglKVVHGgRp3/loIo/58Ccj+drZ/CHP+PV1hdffDFPxhgDatx/DCaJiEjVwfNBQefY/H12Cjo38iIaf5/nRo4reN7j+ZIZ2uHed5GibexR2LFnaT03Xx97EHH/sp9PWKdOnUzAhBd2eJ7m9nD/F9QqIIzr4biE4yQGnZhZxBKucJl9YcYth8NxCSeb4DHmdjHLiOvkay/s9wCR0mKzDpX/LiJlipF7TvnI1NaSnHSl8uMMXbzix3+lfDGTh1lgmopUREQk+rEUm5M0RGbYMjubmd4FTQYhEuuUqSNSAZhxwaZ6/JLIKx8K6MjhMJjDxtDhMiIpH+wRwHIuZguJiIhI9GMJNrO4OWMWy7CY5cP2AZG9ekQqE2XqiFQANqNlGQrLRZhSGjkNpMjBsDY83NhaygdLuVgmpgwpERGR2MGSK5a6sRydEx2wDIrn8oJ6UIrEOgV1RERERERERERikMqvRERERKTCcLYbNq9ng/ITTzzRlEwc7HGccYbNYtlM9aGHHspTksopjjnzDJvLssFrenp67s84eyIbw3fv3t00R+d9ERGRykBBHRERERGpEAzKcLYfzrDIyQPuuusu3HrrrWZWu0icWYe9rXr27GlmtOFUwnPnzjUzMdJHH31kpi3mv1OnTjUz1nHGPOL001dccYWZlYfBHM4sxFn4UlNTK+Q1i4iIlCYFdURERESkQsyaNQvZ2dlmWmFON9y/f38MGDAAkyZNyvO4bdu2oXXr1iYYw8dxymBm3LBHHX3++eemMWqTJk3MlMScapgTEjC4M2XKFDRv3txMN81+GqNGjTLTDnO5iIhIrFOnqHx4JSgtLQ1xcXGw2xXzEhERkQMFg0F4PB5Ur15djTdLgBk5bdq0gc1my13G4M3ixYvzPK5x48YYP3587n3LsvDTTz+hQ4cOedYTxqBNIBDAunXrzM/atm2bZ318DpZzFYXGiCIiEo3jBAV18mFAZ+3atWW600VERKRyYPCgdu3aFb0ZMSsrKwvx8fF5lvE+s3cONVBmaRXHa48//njuehISEnIfw1nr3G63WV6c5yiIxogiIhKN4wQFdfJhhk5450cODkoDrxgtX74c7dq1M4MNEb1npLTpc0b0nikfDAgwqBAeN0jxJCYmIicnJ88y3ufyguzduxc333wzNm3ahPfff99MVUwcs0Wuh5+F7NeTlJRkfsYyrPzPwTKtoggfa5Z4HWz7oh0DYitXrjRZTbGcka7XET10LKKHjkV04QWFjRs3lss4QUGdfMInOA4ASvuEzQEGcb0K6ojeM1IW9Dkjes+Ur1j+YhwNWAYVWVZF4aBDfhs2bMAll1yCVq1amYbIycnJuT/j41evXo0+ffqY+2vWrDHHhhfp+LMvvvjigOcYM2ZMsY41x3FFDQhF2zmC+y6Wx6J6HdFDxyJ66FhU3XGCRiIiIiIiUiH69u1rGh+//PLLJrNm2rRpZvaq4cOH53kcM23YTJnTmb/00kt5AjrE6crffPNN00OHU5k/8cQTZnp0XqQbOnSoCeJMnDgRPp/PNFVm0GfIkCHl/GpFRERKn4I6IiIiIlIh2PeGmTrTp0/HUUcdhYceegjjxo0z2TVffvklevToYR739ddfm4AN/z3yyCPNct7OOOMM8/MzzzzTBII4A9bAgQNNMCc8pXmtWrXw6quv4p133jGZPPz3lVdeMctFRERincqvRERERKRCS7DefffdA5YzSBPO2GHQhrdDpbdfffXV5laQnj17mgwdERGRykZBHRERERGRUmxWyinXo7nnRvjfWKXXUboYFLXZbKW8VhEpLwrqiIiIiIiUkMfjwapVq8y/0Yxf3hctWoRYp9dRukEdliM2atQophtoi1RVCuqIiIiIiJQQAzrVq1dH/fr1ozbrgRlEzCSK9cwMvY7S3ZdsUr5p0ybTULx9+/aluHYRKQ8K6oiIiIiIlAADJczQYUDH6Yze4TW/wDOYUxmCOnodpYfv2RYtWmDhwoW5QT8RiR36ixURERERKYFwD51YDpRI1RYO5ERrPygROTgFdUREREREREREYpCCOiIiIiIiIiIiMSh6i35FREQqMb/fb5pTsn9BNEwNnJGRUWlmPeHriIuLU18IkcOYOXMmxo8fjwULFpjPotatW+Pyyy/HwIEDK+2+4+vr0qULrr322nL93Ug+nw+PPPIIvv76a1PudOqpp+LOO++Ey+Uq8PEvvPACJkyYgOzsbHNs7rvvPqSkpJifrVmzBg888ADmzp2LpKQknHfeeWY7KSsry/zs119/Ncf36KOPxr333osaNWqUaPtFJLooqCMiIlKO9u7di507d5ogCptTRkNDSgZAtmzZgsoUMKNq1aqhXr16iI+Pr+hNEok6EydOxGOPPYb/+7//w0svvWT6AX333Xf4z3/+g8cffxxDhgyp6E2stJ5//nksXrzY7G8G9xkkevHFF3HDDTcc8NiPPvoIX331lfmXs6vdcsst5pjxGOXk5GDs2LEYNWoUXn31Vaxbtw7nnnsu2rZtixNOOME8T2pqKn788UcTPOL6x40bh4cffrhCXreIlA0FdcrRttQg1u90oU22herJ5fnMIiISDTi45rSxDDQ0adIEbre7ojfJDPR51ZhXiCtLk1e+Jl7R3r17t5lmmtkHCuxIRQhmZ5t/bfHxuX9fls8Hi4FHhwP2iM+A3MfGxcEWblrr95vHF+axRcG/D2ZwPProo3mCN6eddhr27Nljsj/CmXyvvPIKPv30U6Snp5sslTvuuAPt2rXDxo0bMWzYMBNkYLYPZ/9iFgnX+9dff5kAwu+//577Offyyy+b5a+//jq2bt2Khx56yNxPTEzEGWecYbJLmGXHQMT8+fOxbds2bN++HV988YV5PNe7du1adOvWzXx+8nOL289t5Do//vhjs41HHnmkyUZp0KCBed4ZM2aYIMaGDRtw3HHHmYB62OzZs3HppZdi8uTJaNSo0QH76VC/G4nbymDJihUr0KZNG4wYMQJHHXUUpk6dioYNG6Jr1655Hv/555/jwQcfRM2aNU0GzTXXXIO77rqrwKAOH3v++eeb10zc31w/s3W4/oSEBPP7xM86ZvREZvFw/4SbH3P/6rNQpPKp+MuDVcRfS3147wcvfluchLe/92J7asWm24uISPniFx5+CeK0sQzqRENAp7Lil2d+UeSXoNq1a5svgprRRSrCisGDzS2wZ0/ust0ffGCWbX/qqTyPXXXKKWa5f+vW3GV7Pv3ULNuWL7Ni9ejRZrl33bpibRdLdRgUOf744w/4Gct3Lrvsstyyn0mTJpmgzfTp09G7d29cfPHFJuOQmGWyaNEifP/993j//ffx008/YcqUKSagwVIgBh3CuB5mlDDIcMUVV5jp31kW9M477+Dbb7/Fe++9lyeYwqDPDz/8YNbDxzOQMWvWLFx00UUmyyiMv8/ADwM706ZNQ8uWLXHVVVeZv3kGdq+++moTFGEA58QTTzT/hvXq1cvsi4ICOof73Ui//PKL+azhvuPnz3XXXWeCTwzchAMsYdx3O3bsMMGfsFatWpllaWlpB6ybgenIx/Icwn3IrBxOQc6sHAaxWFrFbWRJHbeFLrjgAvzzzz/mdfLGINmNN95Y4GsQkdiloE45CAYt/DY/lApOOV4LM5fsvy8iIpUfB+vJycnmJuWHATSWY7FMQUT2ByxYynOwHi5hDJYwQMKgAwPR/H9mhkQGa5jpwmUMPDCQwewQlpUyCPPll1+ax7BnD8tOBw8ebAIRfMxtt91mskYYfGXQhpk2YVwXs1sYEGHAhD1gGKDg9g4YMCBPdhF/j7/fvHlzU0rKoAXXz+fk7zJ4NGbMGFPuyt41zOQpjKL8LjON+HMGubiPGHz6+eefTdlT/s989rkh7rOw8P8zgyo/Pj7yscy24bHgcp5XGERj5hQDZCyne/LJJ822E7OAhg8fjj/++MMEypg1dPPNNxfq9YtI7FD5VTlgxmMwlPWYJ9AjIiJVB6/O1qpVq6I3o8rhl0t+MeT+j/xiJFIe2v74Y275VVits89GzX/9y5RURWo9eXLosXFxuctqnHEGqo8YccBjW3322QGPLYq6deuaMqtw6WWkzMxM83fDvxcGYho3bpzn57zPHlw9evQIvZ6IzzUGP8JZcczKYaCDJVHM0mGpFoMuLEFl8KNfv365v8ffiSz/ZDA2srQpfyYNt4GZLbR582ZTisTyrDAGM/g8fEy4DCssXMZ0OEX53Tlz5uDCCy80GTTcd//6178wevRoPPfccya4VadOndzHhj+HIgPN4WAOMwzz4+MjH8vn4P5jBhODOwxmnXPOOeZn3bt3NyV0DPQce+yxpj8SM6jCx4gZPf379zdZo4XdDyIS/RTUKQcOhw292jvx5xLvvvtAz3ba9SIiVQmzRVRyVTG438PNk0XKk72AQKKN/asKyJAp8LFOp7kV5rFFwYAMs2RYLnXSSSfl+Rkb7nI5Z2ZiMIUBAJbuhIMv7C8TGaQ4mGbNmplsGzYDZnkVGwETs1+YecPMkTBmnDD4k/u6IwI8DKwwcBOJQSUGkMLr48xRkaVkLFli0OKbb7454HcZJOK2HQ7XW9jf5fJ77rnHZBix/In7jllORxxxhOlzE4kZUgyqrV69OjdYxe1lIIvN3fPjOvnYPn36mPvhTCiWYXHdv/32W57Hh3voMDjH/crAXVg4gBfedyJSOaj8qpwc392Fkce60Kt1Fs4d7EbTepVj2lgRESmc/FeiI3GWkvbt2+feOnToYAbwV155ZZnPSvXnn3+a0gFe4eXV5aVLlx6yLxC/PPELHq8Cv/HGG6W2HUVdN3tt8Mo/v5yeddZZpq/HwXC/q6eOSN5A56233moyXJjVwS/+zAb55JNPzN8ee8KEs23+97//maACs0P4/2wWXFAvnoLw9xnMYbCCnzHEEi32fHn22WfN3z0zhtggmD10Dvb5yOAEM04YnGXQhNscxowY9v5hZg4zdPi4008/3ayXjZv579tvv21+l+VQ/MwrjKL8LrNj/v3vf5vPLzYtZiCLzZ7fffddExzKb+TIkWabmQ3EG/cRlxWEy998803TQ4eBryeeeML0zmEGD/9lA37+nMEcPicDSqeccooJnLFcjA2cuf8Y5OE06uyLlD8DSURim4I65ah1IwfaN/aiTnXtdhERyYsBDc4Uwxv7VTz99NNmJhX2nSgrvOLOhqiDBg0yjUcZUGI/CH55Kwi/HLAfBr/kMI2fX0r45aU0FGXd3C833XSTmS2HZR0dO3Y0/19QPwoRKRh7xdx///0miHPMMceYYCqzS/i3N3ToUPOYSy65xAQs+G/fvn1Ndg0DCIUtJWUWEAMKDLJEZotwRq1ly5aZ/jgMTHB9nMmqIOxJw2368MMPTUCCmURsxBzOOuGU3gzAsKExgyqcLYrrD2cE8fH8nOjZs6dpxsznDGPjYwaG82fk0OF+tyQYNGMWE4Ne7HnDWcXCgTTiNoX7EZ155pnmMXx9fJ0M5nBKc2LGD4NY7KHDfXL99debz0bO1EUMnDGAxuPAfkYMenGZiFQyluSRmZlpzZ492/xb2vx+v1k3/xXRe0bKgj5notfixYutjIyMAn82cOBA67PPPjtg+aRJk6x27dpZe/fuLZNtevjhh61zzz3X8ng8VjAYtLKysqxBgwZZS5YsOeCxPC927drVmjlzZu6yF1980fz+oWzYsMG8hkMp6rrffPNN6/TTT8+9n56ebp5j/vz5BT5+y5YtZjtiZbwg0Sl8zAv6e+Rn75w5c6J+jMe/c24j/40Vu3btsubNm5dn2TXXXGM9/vjjMfU6ov14FPc9XFnGHZXhdVSG11CZXsfevXvLbZyglBEREZEoFe7Bw/4J+XF2lciSrcgbf1YYLCWInEWGV4BZYsDyr/xYlsUShHBzVOLV63nz5pmrvyVR1HXzCvrKlSvx999/m5/zyjyv5hemT4aIxBaWhp177rmmGTHx754ZjeFsFBGRqk5dskRERKLQ+vXrTeo/v7hwlpP8GADhF5uCsBFnYcuvGMjhFMD8wsSGnOFmn/mx70PNmjXzNHtms9RwT4ySzOxV1HWffPLJZrrgs88+20zvy6AXyy0K+7pFJHawjIqlWSxRZUNiNhS+6667cps3i4hUdQrqiIiIRAH2kQlPycusFfaLYK8bfpEpCAMg7KdQEllZWabp5hVXXGFubOrJaXm///77AwJJ7FeTf/au8P2CevCwUSf7VIQbFIezcNifI9wPorjrZmNQBoIYgOLsMhMmTMAdd9xh+oGwf4SIVC4M5PIWxs+VkmYIiohUFgrqiIiIRAE2yWRzUs5Q8vzzz5uZXNjwkhksBWGDz0svvbTAn7322msHXMVmAOSrr77KvT958mST5cLGm+ecc44JIjGoxFltmAXD4EukuLi4AwIs4fucGjk/ZhkxOMUr6+edd55pxEwsk8qvqOtmIKpdu3Zmu4nbzZmwPvvsM9P4WURERKSqUFBHREQkCjDDpHnz5ub/OTsJpxnnTFQfffRR7iwvkThbSjhQkl9BU+hyVhTOEhPGEgZm+rRq1SpPdkzjxo0LnEad62SGDAM1Tmdo+MBsGQZdOF1xflwPMXBE4dd2sO0tyro5fTkDRWEsv2IfoIJmsBEpT+HMNJFYfe/abLaK3hQRKSI1ShYREYkyDK48+OCDWLJkCd56660CH8OABwMlBd0Kym4JB43CNwZPunfvbpoUR2bHsM9OkyZNDvh9ThvO3/nnn39yl7FhKaflLaiRc1EUdd0MSK1atSrPsjVr1hS43SLlge9T3goqFxSJBcwS5edwST/PRaT8KVNHREQkCnXr1s1k67z00ksYPnx4gdk3JXXBBReYEib2uzn22GPx+uuvm1IolmBReno6AoGAmW2KDZVHjhyJ++67Dw8//DC2b9+ON954A4888kiB6969e7f5Xa6PDZ2ZeUMMOKWkpOR5bGHWzd/n7/H3//Wvf+H222832Urc9k8++cRk6Zx++umlvo9ECoPZDWzozbLJFi1aRO0X43AvGv4byxkZeh2luy8Z0Fm3bh0aNmxYimsWkfKioI6IiEiU+s9//mOaFj/++OOmj0xpY5PhZ555xqx/3LhxJkgyfvx4JCYmmp8/9NBD5ksqGygTmxEz8MJgEHvjXHvttaYPUEEYkOLv5sfAC2eyye9w62bQiUGeUaNGmYap/BLCGa+2bt1qMn3efvttNUmWCtWoUSOsXLkSCxcujOojEesBnTC9jtLDDB0GdErafF9EKoaCOiIiIhWMjYkLwiv/f/75Z5k+N2fY6t+/v+nbk/+LXv7gCzNqHnvsMXMr7ms6mMOte9myZXnujxkzxtxEogX7R7Vv3z43EyYaMXtuwYIFJoAb7ncVi/Q6Sg8/96M1s0xECkdBHRERERGRUhILX5AZ0InloE6YXoeIiII6UoCFa/xYviGAxHgbjuvqQlJC7KfoioiIiIiIiFQ2ytSRPJas8+PbWftnbtiyK4gLToyD3a7AjoiIiIiIiEg0if78UClXa7cG89zfmRZERnZ01oWLiIiIiIiIVGUK6kge1ZLyZuS4nTYkxClLR0QkVrBR66xZs1DVcIpz3kRERESqEgV1JI++HZ1o09hhOuHHu2047Wg3XE4FdURERERERESiTaXtqfP222/D4/Hgsssuq+hNiSlOhw2nHxcHf8Ay/y8iIiIiIiIi0anSZepYloV77rkH7733XkVvSkxTQEdEpHL65ZdfcPrpp6Nbt244+eSTMWXKlNyfBYNBPPHEE+jbt6+5vfTSSxgyZEhuORdLuyZNmoRTTz0VXbp0wdlnn40NGzbk/v6qVaswduxYHHnkkTjuuOPwwgsvmHXS888/j6uuugrnnHMO+vTpgz///BMnnHACPv30U4wePdpsz8UXX4xNmzbh2muvxRFHHIERI0ZgxYoVuev/5JNPcNJJJ5nn5vbdf//9CAQC5br/RERERKJJpQzq9O/fH1deeWVFb4qIiEhUmTFjhgmYMFjC4MyYMWNw4403YtGiRebnr7zyCiZOnIgnn3wSb775Jn799dc8QZtwcOauu+7C559/jtTUVDzzzDNm+e7du02Qp169eib4cu+995oLLO+8807u7/70008mIMRsWgZxiL9/00034YMPPsDixYtNwOnoo482wZ6EhAQ89dRT5nEMAj344INme7/77jsT0OFjuE4RERGRqiqmy684aOQtLCkpyQxCBw8ebAabIiIist/777+PE088ERdeeKG537JlS8yfP98EWbp3724CKzfccAOOPfZY8/NHH30Uw4YNy7MLL7roIvTr18/8/1lnnWXWSV9//bUJwjzwwANwOp1o3bo1duzYgRdffDH3+erUqWN+J9KoUaNMEIeOOuoo8zvhxwwfPtxsGyUmJuKhhx7C0KFDzf0mTZqYcz4zecLLRERERKqamA7q8AojbyIiInJ4LI/697//nWcZgzmfffaZybrZvn07unbtmvuzVq1aoXr16nke37x589z/T05Ohs/ny113586dTUAnrEePHiZIs3fvXnO/cePGB2xT06ZNc/8/Pj4+z2N4P7x+llzx/nPPPYeVK1di2bJlWLduXW4ASkRERKQqqnTlVyIiIlKwuLi4A5ax5w370jgcjtwy5kj577tcriKtm8J9bwp6TPh5w+z2gocmv/32m8nq2blzp+nXw+AOe/eIiIiIVGUK6oiIiFQRLLeaN29enmX//POPWV6tWjXTDyfcX4fYTyecZVOYdfN3w5k1NHfuXNSqVQs1atQo8baz3JoNlf/v//7PZOmyvGv9+vUHBJ1EREREqpKYLr86FF7NExERqYrYJ8fj8eRZ1rt3b9Pbhs2M2admwIABphHyDz/8YBok03nnnWcyYBo1aoSaNWuaxsRks9kO+5ynnXaaaaLMGSgvueQSrFmzxtzn8xXm9w+HgSEGiVh2xWwebjNLu7xeb4nXLRWHPZH4nlm6dKkJKt58881mxrWDYaCRwT02y2bAkE455RRs3rw5T4ZYTk6OafjNxtxvvfUWHn/8cbjd7tzH3HnnnSrhFxGRSsEZbYPQSy+9NHfq1OKc7EsLU8VLe5rU8Po0/SqQlWNh114LNZJtSEks+WC/stJ7RvSeqVyYVVIemSWcljy/77//3sw49dhjj5mpxvkll9k1Tz/9tJkePNwEmX11OEMWAyeXXXYZZs+ebfrkhLc78jWE/583Tlbw2muv4eGHH8bIkSPNF+4LLrjArCPycZGvP/+ygv4N366++mrccccdOPPMM00vHwal2FCZM2YVtI5I4Z+X5vlX5/KSY0Du8ssvN4E/zpLG2dmuv/56M6sZM7Hymz59Om6//XakpaXlWT558uQ89znzGoM/J510krm/cOFCMyvqNddcUwpbLSIiEl1sVpTkLXPWDJ6EeXWFV+LCJ3uekHmy58DwcCf70pCVlYUlS5aUybolZOdeB35emAyf3waHHTi2Yyaa1N6fri8iUhkxSMIgCmdxila///47OnXqlJsBwWnKGTxhVkRBTY5jBTN6tm7dWibr7tixY1Qf02jGPkm33nor/vjjj9xsLs6+1qxZMzN1faQJEyaY2c6YBXb33XebMWH4fRqJU9zfddddJtBTu3Zts4wzuN122204/vjjS2WM2K5dO6SkpCAWMRjJkks2SM/fzyqW6HVEDx2L6KFjEV3S09OxfPnychknREWmDq8o8oTOq3BM1Q5jxk52djbGjh1rTvb9+/c3g8tJkyYdcLIvbTxhl/bO5x/aggULzMwisXwiLamPfvGidp1Q80zammPDqd0PbJ4pes9I0elzJnrxxM6Ml4M1Gq5I7IPD7eIsWB999JHJiuV5l6VYPGe1aNECsR5QY7Yvy8pKC7/g85hK8XHGtDZt2uQpz+NFO2Zf5Td48GCTpRVZZpUfLwZy2vtbbrklN6DD47R27Vp8+OGH+O9//4uEhARTdsXM8NIoCxQREaloURHUYQ0/B5CRZVdFPdmXNgZdyirwUpbrjgWBoC3PMeX9qrw/CqOqv2ek6PSeiU787Iu2L5KRCbssd77//vtNWROX9+vXDy+++GLUbXNx93tpfo7qM7nkGHDhNPWReJ8X9PKrW7fuYdfHi34MTrL8L4yzpfXs2RNnnHGGCVKyrJ8XERnc4fizOMIzxsWiylLWrdcRPXQsooeORXQJzwBaZYI69evXL/HJXmJH9zZOfPfn/saW3dtGxdtQRKRMMahQnif44p6PX3rpJVQ23O+xHpiqjJgRzYbGkXi/uJnSnCGNJfuRATeWcr333nu59zt37myCOVOmTCl2UGflypWIdcwcrwz0OqKHjkX00LGoepxV6WQv0aFrK6dpjrx5VxB1qtvRrokyUESk8mMGgWZqqhjc7xo7RB9mX48fP/6AgAmztItq27ZtZsKN/EFJNklm7x42Sg7jzHCRM2EVFbePzbpjUWUp0dXriB46FtFDxyK6ZGRklNtFAGdVOdlLdGnRwGFuIiJVRbVq1cysPeFeH1J+g1w2K2zQoIF2eZThrGsMdr788su4+OKLMXPmTEydOtVMiFFUnGSDvZ/q1KmTZzmDeQz0NGnSxEx9ziAPM3fYbLkkPZpiOSBSmUp09Tqih45F9NCxiA48V5TbcyFGTva8yjZt2jRzsh8+fHhFb5qIiEiRVK9eHZmZmdizZ4/2XDlhX6AtW7YgLi7ugHJuqXjMluHFO05VftRRR5kmx+PGjTMX77788kv06NGj0OvauHFjgeX8rVq1MpNwvP7666a3DmfXYk8dzoglIiJSGThj4WR/33334dVXXzVN8sInexERkVjCc1rz5s2xbt060zOuRo0aplmrer2UTQ8dpj1zSnb24WPmr0QnHpt33333gOW8gFfQRTxm3CxbtuyA5ZzqnLeCcCrzkk5nLiIiEq2c0ZaZw/TZwpzsRUREYrEEi5kDu3btwurVq03woTzTcw9VolQZSjHC2Tm8MdOXgbPGjRtH5TTyIiIiIpUuqCMiIlLZJSUlmRsDDz6fr8JnxGJAZ+nSpSYLtrIEdvg6nE6nsqBERESk0lNQR2LW7vQgfl/gh89noUsrJ9o3rRxfRkSkamDZVUlm4CnNoA6x50xlCeqIiIiIVBUK6khM8vgsfPyLB+lZlrm/ZmsQ8QPcaK4ZtURERERERKSKqPhCfpFi2L3Xyg3oEMsY1m2r2BIGERERERERkfKkoI7EpJREGxx2W55l1ZPz3hcRERERERGpzBTUkZiUnGDDsL4uxLlssNtt6NbKiW6t1AtCREREREREqg711JGY1bG509yCQcsEdkRERERERESqEmXqSMxTQEdERERERESqIgV1RERERERERERikII6UuX4Axa27Apid7pmyxIREREREZHYpZ46UqXkeC189IsH21NDAZ2ju7hwTBdXRW+WiIiIiIiISJEpU0eqlDkr/LkBHfpjoQ97M5WxIyIiIiIiIrFHQR2pUny+A5d5ClgmIiIiIiIiEu0U1JEqpWNzB1zO/dOfN6nrQO1qmg5dREREREREYo966kiVUq+mHecMjsOyDQHEu4EjWjs1JbqIiIiIiIjEJAV1pMqpW8NubiIiIiIiIiKxTEEdkcPMlrViYwA2G9C+ad7SLREREREREZGKpKCOyEF4fBY++MmDXWmh2bHmLLfjrEFxCuyIiIiIiIhIVFANishBMEMnHNChbalBrN2q6c9FREREREQkOiioI3KwP44CKq3s+osRERERERGRKKGvqCIH0baJA41q7/8TaVbfgZYN9CcjIiIiIiIi0UE9dUQOgk2RzzwhDuu2BcGknRYN7Jr+XERERERERKKGgjoih/oDcdjQupHjsPtoy64gUtODaFDbjlopyuYRERERERGRsqegjkgJzVnhx89zfLAsywSBRvV3o3n9wweCREREREREREpCKQUiJTR9QSigQ/6AhVmL/dqnIiIiIiIiUuYU1BEpZaHwjoiIiIiIiEjZUlBHpISO7uzK/X+WX/XtqKpGERERERERKXv69ilSQj3bO1G/lg2p6RYa1rajTnXFSkVERERERKTsKagjUgqa1HWgSd2Cf8Z+O3OWB7BxZwC1q9lxVCenyegRERERERERKQkFdUTK2IzFftNMOSSAvZkWTj7Krf0uIiIiIiIiJaI6EZEytmpTIO/9zXnvi4iIiIiIiBSHgjoiZSwlMW+pVVKCSq9ERERERESk5BTUESljA3u4UHtf8+TkBBtO7qvSKxERERERESk59dQRKWPVk+y4eFg8sjwWEtyAzWbL00R56jw/lqzzIyHOhqG93GhUR7FWEREREREROTx9exQpJ4lxtjwBHZq3KoC/lvqQkW1hx54gPv/NA6/P0jERERERERGRw1JQR6QCbd8TzHM/22MhI0dBHRERERERETk8BXVEKlCj2nn/BNlzJ0WNlEVERERERKQQ1FNHpAJ1aek0pVdL1gVMT51BR7rgcu4v0Vq7NYC1211om22hWrIOlYiIiIiIiOynoI5IBTuqk8vc8psy24t/VviwdWsStmR7cc4QO2qmKLlOREREREREQvQNUSQKpWdZmLfSn3ufM2fNXbH/voiISGWxYsUKnHXWWejRowdOPPFE/PDDD4d8/IYNG9CnTx/s3r07z2ySvXr1Qvfu3c16eBs8eHDuz2fPno2RI0ean48YMcLcFxERqQwU1BGJEWqfLCIilY3X68Xll1+OQYMG4c8//8Rdd92FW2+9FatWrSrw8dOnT8fZZ5+NtLS0PMvXr1+PnJwcs465c+ea248//mh+xuDPFVdcgUsvvdQEcy644AJceeWVSE1NLZfXKCIiUpYU1BGJQimJNnRrtb86kv12erQN3d+dHsR3f3rxzUwvtuzKO3uWiIhILJk1axays7MxduxYuFwu9O/fHwMGDMCkSZMOeOyECRNw//3349prrz3gZ4sWLUL79u3hdrsP+NmUKVPQvHlznHLKKXA6nRg1ahRatGhhlouIiMQ69dQRiVIn9nGjVSPgn/lZGHKcGzVS7MjMsTDhJw+y9k17vnxjAOcNjUPtaorPiohI7GFGTps2bWCz7Z8koHXr1li8ePEBj2U51ZlnnonNmzcXGNTxeDwmYMOfd+7cGXfeeadZF5+jbdu2eR7P5Sz7Ko5gMIhAIIBYFN7uWN3+ML2O6KFjET10LKILzxXlRUEdkSjWqqEDe7d5TeYOMTMnHNAhn9/Cum1BBXVERCQmZWVlIT4+Ps8y3mf2Tn5169Y96HqYodO1a1f85z//QXJyMl544QVccsklmDx5cpGeozBWrlyJWLdgwQJUBnod0UPHInroWFQ9CuqIxJDkBFuhlomIiMSCxMRE0wsnEu9zeVFcf/31ee7fcsst+PDDD82Xm4SEBGRmZh7wHCkpKcXaZmYWMXAUq1fyuU8YAHM4HIhVeh3RQ8cieuhYRJeMjIxyuwigoI5IDGlQy45ju7owfaHfzPRxRGsn2jVxIDPbwk9zfUhND6JZPTv6d3PB4VCwR0REohvLoMaPH59nGQfBDJwUxSuvvIJ+/fqhW7du5r7f7zdfcOLi4sy6vvjiiwOeY8yYMcXaZrvdHtMBEeL2x/prIL2O6KFjET10LKIDzxXl9lyoRFhLzeZ55557rrlxJgSRyqZfZxeuGxWP60YnYGjvUEPIL//wYtl6P7anBjF7md8EfURERKJd3759TYPkl19+2cyENW3aNEydOhXDhw8v0npWr16NRx55BLt27TJlVQ8//DCaNWtmMlKGDh1qgjgTJ06Ez+fD559/jjVr1mDIkCFl9rpERETKS6UK6vAkzcZ47733npm68qWXXqroTRIpE26XDXGu/Zk4m/PNgrVpp2bFEhGR6MdeOMzU4VTlRx11FB566CGMGzfOZNd8+eWX6NGjR6HWc/fdd6NVq1Y49dRTcfTRR2PLli0me4dXrGvVqoVXX30V77zzDvr06WP+5c+4XEREJNZVqvKr0047LTfNiSm3BU1rKVIZ1a5mw449+xso16keCvis2xYwpVlN6tpRLalSxXBFRKQSlWC9++67Byxntk5BGTtNmjTBsmXL8ixjjxsGhA6mZ8+e5uKfiIhIZROzQZ1PPvnE3MKSkpLw5ptvmv/nVJZPPPGEMnWkyhh+jBvfzvRid7qFZvXt6H+EC1P/8eHPpT7z83i3Df8+IQ51ayiwIyIiIiIiUlnEbFCHze0KanDHmmlOZ/nAAw+gadOmFbJtIuWtVood5wzZP12rx2flBnQox2vh7+V+nNRH2WsiIiIiIiKVRcwGdQrC+ukbbrgBTz31FNq1a1fRmyMSVaz91VkiIiIiIiJSCVSqoA6b3mVmZposHWLT5Ntvv72iN0uk3LGJcq/2TjMTVrj8ivfnrPBj0Rq/+fnAHi6VY4mIiIiIiMSwShXUue+++yp6E0SixsAebrRs6EBGtoVm9ezYstvCT397c3/+ya9BjD0lPs8sWiIiIiIiIhI7oiaoM3/+fFx66aWYNWtW7rIVK1bgnnvuwdKlS1GvXj3cfPPNGDJkSLlsD2fP4q201xn5r0hZv2ea1uV/GbSxsHG7D1ZEDRaDPbvT/LkzZdntCu5UBvqcEb1nyvdvTURERARVPajz9ddf495770UwGMxd5vV6cfnll+Pss8/GO++8gxkzZuD666/Hp59+aqa+LGvLly8vs3UvWLCgzNYtlVNpvGd2bXVj69bE3Psuh4VvfvVgycZ4MNTTqUkOurfMKfHzSHTQ54zoPSMiIiJS+VV4UIdTj//xxx+4+uqr8fzzz+cuZ8ZOdnY2xo4dC5vNhv79+2PAgAGYNGkSbrzxxjLfLjZaTkzc/wW4tK7q8YtW165d4XA4SnXdUjmV5nvmCMtCnYZ+LFobRJwLOKK1A1Pn+VGvfujnu3xA9fouU7IlsUufM6L3TPnIysoq0wtAIiIiIjER1DnvvPNMWVVk2RWtWrUKbdq0MQGdMGboLF68uFy2i1+gyyrwUpbrlsqptN4zJxzpxAlHhv5/2YYAbLa85QM799qQlmmBf3YdmzuQEKeSrFilzxnRe6bs/8ZEREREUNWDOvXr70sTKOAKWHx8fJ5lvM/sHREpuUa17WZWrBxvqM8O2+3MWuKH1xe6P3elHecOiVMjZRERERERkShlR5Ri6VNOTt7+Hrxf2iVRIlVVSqIN/zo+Du2aOtC2iQM92ztzAzq0e28Qa7aoEaiIiIiIiEi0itqgDkut1qxZk2fZypUrTUmWiJSO+rXsGHFMHEYeG4fGdfJ+HHCmrPmrAnjn+xx88ZsHaZn7G5mLiIiIiIhIxYvaoE7fvn3hcrnw8ssvm5mwpk2bhqlTp2L48OEVvWkilVLbxg40r7+/RwT7Wa3dGsC21CBWbgrgs2neCt0+ERERERERibKeOgfjdrsxfvx43HfffXj11VdRt25djBs3Tpk6ImXE4bDhjAFubNoZysiZs9yP5Rv3l1/tSgti1eYA2Dq5cV27eu2IiFRhW7Zswfvvv4/Vq1ebsdrvv/9usqyPOOKIit40ERGRKsUZTZk5c+fOzbOMg4N33323wrZJpKqx221oWi+UrbNuWxCICOps3R3E59M85v9rJNtx9qA4JCVodiwRkapmwYIFOP/889GlSxczdvN4PObfe+65By+99BL69+9f0ZsoIiJSZURt+ZWIVKyjOjnRpaXTTGuenGgzjZXD9mQEMXelv0K3T0REKgYzp8eOHWsuvLFUnh544AFceOGFeOaZZ3RYREREypGCOiJSIKfDhmF93bjm9AQzS1ZS/P6gjs9v4fcFPrz2dQ6+/MOLbM/+WbNERKRyW7RoEU477bQDlp955pmmHEtERETKj4I6InJYtVJsaNN4fxPlDTuCZvpzZuwsW+/HlNlqoiwiUlUkJydj+/btByxfsWIFqlevXiHbJCIiUlVFTU8dEYlenAlrxDFuLF0fQI4XiFvog8e7Pztn884gduwJIjnBZsq1RESk8ho9ejTuv/9+3H333eb+1q1bMX/+fDz22GMYMWJERW+eiIhIlaKgjogUuolypxahj4z12wNYsa+Jco7XwvrtQbz1XQ5cThtOOcqNtk32Z/WIiEjlcu2115p/L7vsMtMk+bzzzoPT6cS5556L6667rqI3T0REpEpRUEdEiuzE3m4AXmxLtZCeFUDD2vbcXjvfzPTgvBPjTQ+eOJeydkREKhu73Y7rr78eV155JdavXw+/34/mzZsjISEBu3btQu3atSt6E0VERKoMBXVEpMhYYjXy2Djz/59O9WDNlv1ZO3NXBuHx5ZiAzvBj3GjZUFk7IiKVSceOHTF9+nTUqlULbdq0yV2+adMmnHrqqWZ6cxERESkfCuqISIl0au7IDeqs3xZEcjx78ABeZu3M8uLqkQnawyIiMe7zzz/HxIkTzf9bloWrr746dzrzMDZPrlmzZgVtoYiISNWkoI6IlAj77LhdNqzbFoAVMbO5P2Bh4Zog3p2Sgwa17Di+u8v03BERkdgzZMgQLFu2zAR0/vzzT7Rt2xbx8fF5Gur37NnTZOqIiIhI+VFQR0RKjNOd81a3uh3f/xWa3nz15qDJ2Nm6O2huwSBwYh/24hERkViTkpKCO+64w/x/tWrVcPHFFyMxMbGiN0tERKTKU1BHREpNt9ZOxLlDWTup6RZqJIcyczj9+ZS/vcj2Wiazp51mxxIRiVnXXHMNMjMzsXTpUgQCofJbZvB4vV4sWrTIzIYlIiIi5UNBHREpVe2bOsxtT7plgjvBoIVF6wJIjLOZadBXbgpidH81UBYRiVWTJ0/G3Xffjezs7NyADsuvqGHDhgrqiIiIlKPQPMQiIqXspD4uNK7rQCAYmtq8daPQLFgebxBT53mxenPo6q6IiMSWF154AcOGDTPBHZZlffLJJ3j55ZdNQOe6666r6M0TERGpUpSpIyJlolqSHWcPikNmjoVXv8oxjZM55fmC1QHs2mthe2oOaqXY0b2tE83rO1C3hmLMIiKxYMOGDfjf//6HFi1aoFOnTti5cycGDhxosneeeeYZjBw5sqI3UUREpMrQtygRKVNJ8TaTteN22kzD5GpJNjMb1uotQUye6cW3s7x47wcPNu1U5o6ISCxgg2SWXBEDO8uXLzf/3759e6xfv76Ct05ERKRqUVBHRMpcx+ZOXDc6HqP6x5l+O4GghW27g7k/ZxbPPysV1BERiQW9e/c2JVh79uxBly5d8P333yMnJwe///67KccSERGR8qOgjoiUCzbR7NXeaTJ3TD9Nm82UXMW5OP15AD/M9uKDHz3YmbY/2CMiItHn9ttvx7Jly/DFF1/gtNNOg8/nQ8+ePXHvvffi3HPPrejNExERqVLUU0dEyk31JDsuODEea7cG0K5pAKs2BbBxR9D02OnSyo6NO/x445sAzhoUh8Z17LDbQ7OpiIhI9GjatCm+/vpreDwexMXF4aOPPjJZOg0aNEC3bt0qevNERESqFAV1RKRcJSXY0Lml09y2pwYx8XcP6teyw2m3sHR9EKnpQQSCQNsmDow6zq3AjohIFMnIyIDT6UR8fLwJ6IR77AwdOhTbtm0zs18999xzFb2ZIiIiVYbKr0SkwtSraccRbZxwOYDd6ZYJ6MTH2eD1W5g8w4snP8nGn0t8OkIiIhWMM1yNHTvW9NM58sgjcdVVV5kADwWDQbzxxhs4+eSTMWvWrIreVBERkSpFQR0RqVC92zvRu4ML1RLtqJ5sR/umdixZG8CejCA27QjgxYnZuPfNTHwz0wufPzTbioiIlK+HHnoIa9aswaOPPoonn3wSmzZtwrhx40yw5+yzzzb/P2DAAEyePFmHRkREpByp/EpEKhT75hzf3YWjOjnN1Oabdwbg8VkmY2d7qoVsj4VtqUEsWutHvBs44Ui3jpiISDmbOXOmCegwcEPt2rXDWWedhaVLl2LHjh147bXXcNxxx+m4iIiIlDMFdUQkKsS7bThncBzmr/Ijy2MhJdGG2ctC05wnxtlMls7yjQH062whIU4NlEVEytPevXvRoUOH3PutW7dGVlYW7HY7Jk6ciOrVq+uAiIiIVACVX4lI1GCwpm8nF645PQENajlQM8WGBrXtSIgD5iwPYOHqAF7/Jgdbd2vacxGR8hQIBEyD5Egulwu33nqrAjoiIiIVSEEdEYk6Teo6MPbkeDx7TTKG9HRjW6qFujVsaFTXhsVr/XjsgyxMm+dDIKAeOyIiFalu3bo6ACIiIhVIQR0RiVoswRrVPw5HtnWiVSMH1mwJYsuuIHakBTFriQ+/ztPMWCIi5cFmsyEtLQ179uzJvXEZy7Iil/FWVCtWrDD9eXr06IETTzwRP/zwwyEfv2HDBvTp0we7d+/OXeb1evHII4+Yvj69evXCBRdcgOXLl+f+/MEHH0SXLl3Mc4Rvf/zxR5G3VUREJNqop46IRL1urZ2YsciHtIxQZk79mnYEghaWrQ/g+CMsOBzqsSMiUpYsy8Ipp5xywLIzzjgjz30GepYsWVLo9TIYc/nll5sZtN555x3MmDED119/PT799FPTtye/6dOn4/bbbzcBpkgvvvgi5syZY36vVq1aePnll3HppZfixx9/NGViixYtwgMPPIDTTz+9WK9fREQkWimoIyJR79iuLtSuZkNmjgWvjzNmWaaJckpiAJt3BdGngxNHtHaieQNHRW+qiEilxIBLWZg1axays7MxduxYExDq37+/mWFr0qRJuPHGG/M8dsKECXjzzTdx7bXX4u67787zMzZt5vL69eub+xdddBFeeOEFbNy4Ec2bNzezdDFTR0REpLJRUEdEYkLH5k5cP9qOSdO9+P5Pn5kRyx8AFq3xm5Ks5RuDGHWc25RpiYhI6WK5U1lYtWoV2rRpYwI6YczQWbx48QGPHTx4MM4880xs3rz5gJ/dddddee4zQyclJQVNmjTBmjVrTODo2Wefxd9//20yeS6++GKMHj26WNscDAZN4+hYFN7uWN3+ML2O6KFjET10LKILzxXlRUEdEYkZNVPsuPCkeKSmB5HtsfDXUr9Z7gsA6VlB/DzHixyvC3FuG1o1tOf5kiAiItGHGTbx8fF5lvE+gzDFbcrMXjn3338/7rvvPlN6xVKt3r17mz47Tz/9tCnTuuqqq1CzZk2ccMIJRd7mlStXItYtWLAAlYFeR/TQsYgeOhZVj4I6IhJzOjRzYv4qnwnasIdDjsfC3BU+zFgETJ3vQ+cWTnRo5sCIY9wK7IiIRLHExETk5OTkWcb7XF4c77//Pp544gkT1Bk+fLhZduSRR+Ldd9/NfUzfvn0xYsQITJkypVhBHWYWJScnI1av5PMLX9euXeFwxG5mq15H9NCxiB46FtElIyOj3C4CKKgjIjFnaK9Qj52aKX4sXBPAhu0BBII2xLuB9Mwg5q70Y/4qP1LTLfzr+DgkJShjR0QkGrHUavz48XmWcRDMwElR09yZmfPzzz+bvjvdu3fP01x57dq1OOecc3KXeTweuN3uYm2z3W6P6YAIcftj/TWQXkf00LGIHjoW0YHninJ7rnJ7JhGRUsLZrvp0dOGy0xJwzenx6N7WibaNHXDYgbRMYOeeILJygvh7uQ/PfZ6NnWnlV9MqIiKFx6wZlkhxtirOhDVt2jRMnTo1N8umsJ566in8/vvvZvaryIBOeGA9btw4M7MWgz+//fYbJk+eXOyeOiIiItFEmToiEtPYGLl1IwfWOwLYmmqDP2ihRqIN3gCwcUcQm3daSIjz4MyBcWhYW3FsEZHSwBKehQsXmowXlsFG4sxThcVsGWbqMMvm1VdfNX1zGIBhps6XX36Je++9F3Pnzj3kOth/h9k5LMkdNmzYAeVY/fr1M+tnSdbWrVvRsGFDPProozjiiCOK+KpFRESij4I6IhLTnA6bKbFiGdZxXYOYt9qPtVsCWLk5CBZd+fwWZi7yYf22AK4amYA2jWM/3VxEpCK9/vrrePzxx83sUrxFYmClKEGdcAlWZM+bMGbrFJSxwxmtli1blns/ISEBixYtOuRznH766eYmIiJS2SioIyIxz+2y4ch2oY+zvp1cmDzTiw07PAgEAX/AgsdnmRmz3vg2B5eeEoeWDfXRJyJSXMyKuemmm3DppZdqJ4qIiFQw1SKISKUL8Jx+XBxuOCMBTera4XQwWwfYtdfCkrV+jP86B4vXhaZCFxGRosvMzDygzElEREQqhoI6IlIp9evswvlD49CphRPJiTbTRDk9G1iwJoAH383Cj397K3oTRURi0oABA/DTTz9V9GaIiIiIyq9EpDI7sp0LLqcNL3yRjd1BCy4HYIOFrbstvPxlNn6Z68NpR7vQvY0L8W5Ney4icjAPPvhgnulyH3vsMTNTVbNmzQ6YGvu///2vdqSIiEg5UWMJEanUurZy4rpRCXjvRw+WrvNjdzrgdlpmVqw9GT4zQ1b/I4I4Z3CcAjsiIgcR2ZiYevbsaaYgX7lypfaZiIhIBVJQR0Qqvc4tnRg7zGYaJS/fGEBGVtDMiuV2Auu2BfDj36FmymNPjkecSxk7IiL5FTQ7VUF27dqlnSciIlKO1FNHRKqENk0cuPFfCejZzoma1eyoW8OO9CwL/gCQlRPEglV+fPizp6I3U0Qk6nXs2BG7d+8+YPmmTZswePDgCtkmERGRqkqZOiJSZdSpbsfNZybgpzlefD3Dh8VrLSTGA2mZAKwAPvk1iB2pQZzSz42WDR0qxxIR2efzzz/HxIkTzf9bloWrr74aLpcrz/7Zvn07atasqX0mIiJSjhTUEZEqhY2TT+oTh6G93HjlqxzMXuaH0xHErrQgApaFT6d68Os8H/p0cOK8ofEmuCMiUtUNGTLE9NVhQOfPP/9E27ZtER8fn/tzm81m+uyceuqpFbqdIiIiVY2COiJSJdntNpx/YjwysrMxa3GQX0lgt1nwBoDMHAt/LPJj864s3HN+EhrVUaWqiFRtKSkpuOOOO8z/V6tWDRdffDESExMrerNERESqPAV1RKTKSoq34YrhCYhzAb/848f21ADsDpheO1YCsGNPKHPnomHxSElUA2UREbrmmmuQmZmJpUuXIhAImGXM4OFsWIsWLcJ5552nHSUiIlJOKlVQh4OJm2++GTt37kT9+vXx2GOPwe12V/RmiUgUY7Dm4pMTULuaBz/N8ZnZsJipk5IAOOwWVmzyY+l6P3p3yNs7QkSkqpo8eTLuvvtuZGdn5wZ0WH5FDRs2VFBHRESkHFWqmoJvvvkGrVq1wgcffIDWrVtj0qRJFb1JIhIDkhNsOGdIPJ64KgmXnRqPbq0ccDhs8PqB5RuC+N+kbLz/Yw7Wbw9dkRYRqcpeeOEFDBs2zAR3WJb1ySef4OWXXzYBneuuu66iN09ERKRKqVRBnZEjR+Laa681V4w4A0OdOnUqepNEJIbUSrHjzBPiMXpAPFwOG7JyLHh9QSxcE8BrX+fgyY+y8Os/3oreTBGRCrVhwwZceuml5kJap06dTIb0gAEDTPbOm2++qaMjIiJSjmK2/IpXhXgLS0pKMgMJh8OBf//739i1a5ep+RYRKapjuriwYHUAs5f6sGVXEMGgZUqy/lwawJot2fD6LAzp5c4tNxARqUrYIJkX0KhFixZYvnw5Bg4ciPbt22P9+vUVvXkiIiJVSsxm6owZMwYff/xx7i3yytCHH36I2267zfTUEREpTjnWMV2ccDgAfm2xYCEQBLI9FjbtCODR97Nx2ytZWLXJr50rIlVO7969TQnWnj170KVLF3z//ffIycnB77//bsqxREREpPzEbFCnIAzmfPrpp7lXkZi1IyJSHP06u3D72Qk4pqsLtao5YGMjUABBy4a0zCBmLvbh7jey8OPfKscSkarl9ttvx7Jly/DFF1/gtNNOg8/nQ8+ePXHvvfeqSbKIiEg5i9nyq4KcdNJJuOWWW0yDZM569eCDD1b0JolIDOvayoV7Gjvxzyof3v7Og9WbA9i914LHB1jZFrbuDuC9H7Jht1no19mNhDiVY4lI5de0aVN8/fXX8Hg8iIuLw0cffWSydBo0aIBu3bpV9OaJiIhUKZUqqFOjRg289tprFb0ZIlKJMFDTr5Mb9WrY8eyn2Zi3KhAqybIspGUCc5YHsXpzNob2DuDMgXFo20QZgiJS+fEzcNasWVi9ejVGjRplAjpsnCwiIiJVNKgzf/58M5MCBwhhK1aswD333IOlS5eiXr16uPnmmzFkyJBy2Z5AIGBupb3OyH9F9J6JHS3q2/DwJQn44ncf3vvBYwI6nB3L6bDgD1j4c4kHaRkBXDsqHo3rVFxlqz5nRO+Z8v1bq4o429XYsWOxbt06eL1eDBo0CC+99BKWLFmCt956Cy1btqzoTRQREakyoiKowxRe1mEHg8HcZRwkXH755Tj77LPxzjvvYMaMGbj++utNz5zWrVuX+TZxJoeysmDBgjJbt1ROes9Ej061gcsHufD6T7Xg9zrh9dmQ5gW2p9qxdrMXm7fuRv/OmejVOrtCt1PvGdF7RsrKww8/bEqwWHbVr18/s2zcuHG48cYb8eijj+KVV17RzhcREakqQZ0nnngCf/zxB66++mo8//zzucuZsZOdnW2uBHHa4P79+2PAgAGmXw4HDWWtXbt2ptlyaV/V4xetrl27qomz6D0Tw7oDSKnjw5fTvaYca9deC3FuoEY1J7IC8djlrY1WbeNRLan8e+zoc0b0nikfWVlZZXoBKJrNnDnTzDoaHx+fu6xatWq49dZbzcU4ERERqUJBnfPOO8+UVUWWXdGqVavQpk0bE9AJY4bO4sWLy2W7OHNWWc2eVZbrlspJ75noM7inHTVTHHj/Rw8WrPbDsoCkBCDLE8SONAcycmyoWa3i/s71nhG9Z8r+b6yq4mxXkeOzME5rbrdXqolVRUREol6Fn3nr169/0CtgkVeAiPeZvSMiUtH4haZXexdu/XciTujhNlk523YHsS0V+H2BD5c8no77387Exh0BBINsrSwiUjkwc5rZ1SyVD9u2bZspyzr22GMrdNtERESqmgrP1DkYlj7xik8k3i/tkigRkZJoWNuOy06LR80UG36Y7cXu9CAysoDNO4OY9LsHv8zx4ch2TrRo4MCJfdxo37TqXt0XkcrhrrvuwrXXXotevXqZrJ1zzjkHu3btQocOHXDHHXdU9OaJiIhUKVEb1GGp1fjx4/MsW7lypSnJEhGJJnWq29G1lRNL1gWwJ8OCzx9EtoelWEBaZhBpWT4c3dlCIAg0qRuPpPjy77UjIlJaatasiffeew9//fWX6Svk9/vN+Ozoo48usCxLREREqmBQp2/fvnC5XHj55Zdx8cUXm6Z8U6dONbNfiYhEm84tHCZrZ8Ummwne8HuNFQTsTMyxLCzbEECNZBsysy0FdUQkprHsitOZO51OM15r0aKF+X8REREpf1F7Bna73SZT57777sOrr76KunXrmukylakjItGoZoodV42MR692Tnw904u/lvqQ47XgDwA5XmBXWhBzlvvx6zwvTu0Xh8Q4Xc0WkdjCQM6TTz5pLrIxsGNZlsnMiYuLw+DBg3HDDTegSZMmFb2ZIiIiVUrUBHV4pWfu3LkHlGC9++67FbZNIiJFUT3JjiG93eb281wv3vrOgw3bA8jIDsLjBXakWRg3IRt/LPThmtMT0aax+uuISGzgrKScrrx58+a499570bZtW1SvXh0ZGRlYtGgRPv74Y4wePRqffPIJmjVrVtGbKyIiUmVETVBHRKQy4YxYnZo78e4POZixyI/tqQFkZVumz85Pf/uwanMGHr4kEV1buSp6U0VEDuupp55Cnz598Oyzzx4wbXmnTp1wxhlnmEydV155BQ899JD2qIiISFWZ0lxEpLJqUMuOswfFoW51O9vqINsL+IOhcqyN2wO46aUsPP1JFhat8Vf0poqIHNLff/+NSy+99ICAThjLsC666CLTA1FERETKj4I6IiJlqHEdBy47NQ6tGzkQH2eD3QbwO1FGNrB+ewCfTfPihS+yMXeFT8dBRKJWeno66tevf8jHNGzYENu3by+3bRIREREFdUREylzP9i6MuyIZpx/rRt0aNsS5zYRY8PqArbuCmDbfh6ufycTb32XD67N0REQk6gQCgcPOcOVwOMz05iIiIhIjmTp79+7F6tWrsXbtWmRmZpbeVomIVMJSrLvOTcRlp8UjJd4GlzM07TmnP8/MAfZmBvHKVzl4dEIWMnMU2BGR6MLyKt5EREQkxhslb9q0Ce+99x5++eUXM7VlpJYtW2LgwIH497//jaZNm5bmdoqIxDyHw4bzh8YjNd3CvJV+zF7mN2VYDjtMA+Vsr4VPf/FgR6qFO85JQKM6mh1LRKIDpy8fNGjQIQM7fIyIiIhEaVCHtdSPPfYYvvnmG/Tr1w8XXnihmc6yVq1aCAaD2L17N5YsWYK//voLw4cPx7Bhw3DbbbeZ6S5FRCSEX4jOHhSPmile2O02/LPSj4wsC94AYAWBbB/w11IfnvzYhocuSUS8W1fGRaTiPfLIIxW9CSIiIlKSoM7ZZ5+N0aNHY/r06UhISDjg561bt0bv3r1x/vnnIy0tDR999BHOOussEwQSEZH96tW044IT481t2j9ePPBuFtZvD5pSrDgXkJZp4Y+FbKBsw5UjEpAUr8COiFSs008/XYdAREQkloM6H3zwAVJSUgr1WGbnXHbZZaYMS0REDq5/dzeer2nHQ+9nYcFqP9KzQk2UUzMsTPgpB+u3BXDvBYmolmSHy6ngjoiIiIiIFKNR8qECOizNmjZtGrZt25ZnebVq1Qq7ehGRKqtDcyfGXZaEI9s6US3JBrcrNDPWnnRg8gwfRv53L576OBvLNgQqelNFRERERCTWZ79avnw5TjvtNMyePdsEdFiWxcycIUOGYMaMGaW/lSIilVzDOg7cMCYBnZo7TAmW3QZ4/YAvAGzZbWHyTC8++CEb/oAakYpI5bJixQpTst+jRw+ceOKJ+OGHHw75+A0bNqBPnz6mn2OkF154AccccwyOPPJI3HTTTWaMGsYx68iRI9G9e3eMGDHC3BcREamyQZ2HH34YzZs3R6tWrfDFF18gIyMDv/32mwnsPPHEE6W/lSIiVUDXlk6MOT4OtavZEeQHtD108weAnWlB/DzXh9tfzcSzn2bj6xleBXhEpEJkZ2eX2rq8Xi8uv/xyM7PWn3/+ibvuugu33norVq1aVeDj2duRfR7ZvzESezl+9dVX5t+pU6ciMzMT//d//2d+xuDPFVdcgUsvvdQEcy644AJceeWVSE1NLbXXISIiElNBnXnz5uGWW24xM1/9+uuvZhrzunXrmisgK1euLP2tFBGpIjNjjTw2Du/dmYx+nZyokRxazqydrBxg404Lv8z14e/lPvww24uPfvFoCmERKXdHH320meGU2dklncZ81qxZJkg0duxYuFwu9O/fHwMGDMCkSZMOeOyECRNw//3349prrz3gZ59//rmZrKNJkyamZQDHqd9++60J7kyZMsVcjDzllFPgdDoxatQotGjRwiwXERGpMo2SI8XFxZkrKzxRcgrzxx9/3CzfunVroZspi4hIwRrVdeK1m5Px5MfZJnizdbcFvwUEg8CONAszF/vRsoEdmTkW6la3Y2hvt3aliJSb559/Hl9//TWuueYaJCUl4dRTTzUlTe3bty/yupiR06ZNGxPUjpxRdfHixQc8dvDgwTjzzDOxefPmg64njEGbQCCAdevWmZ+1bds2z+P5HCz7Ko5gMGjWHYvC2x2r2x+m1xE9dCyih45FdOG5IqqDOsceeyzuvvtucyLn9Oa8osKrNbx6wqwdEREpmcR4O+44JxEdmjnw3Oc52LU3iKxswAoCWR5g7bagmRp9/uoATjjSgtOhmbFEpHxwHMgbx30//fSTCfBwxtNmzZqZrG0GeZjBXRhZWVmIj4/Ps4z3CyrxOtQ6uR6OScMcDgfcbrdZXpTnKIzKkJW+YMECVAZ6HdFDxyJ66FhUPcUK6rBG+dlnnzWN6l566SVzEp0/fz569+6NO+64o/S3UkSkCmKg5uSj4rB4XQDfzvLCAQt7svbVzVrA6i1+ZHkstG5kR6tGDhzRulgf6SIiKG7m9sknn2yyXnh799138fTTT+PJJ580WTUcE9avX/+Q60hMTEROTk6eZbzP5UXBsWjkenjFmlnl4QuQzC7P/xzFzS5nRlBy8r762BjD/cIvfF27djWBr1il1xE9dCyih45FdGHf4fK6CFCsbwA80eYP3rDJnYiIlK6keBvOHRKPXWlBzFnhR1ychWxPqBRrdzr78Fj4bYEPKzcFwNYWXVsqY0dEyt769etNhs7kyZOxZs0a9OrVC//9739x0kknYc+ePbjnnntw1VVX4bPPPjvkehgMGj9+fJ5lHARHllIVBh+/evVqMysWcZvsdrspw+LPOLFH/ucYM2YMioPrjeWACHH7Y/01kF5H9NCxiB46FtGB54pye67CPpBptWxmV1jTpk0zdc8iIlIyrRs58NTVybj5zER0bOZEzRQ7mtazwe0INVFeuMaH1ZsDmLHQp10tImVu9OjRZurxL7/80pRa/fjjj3jnnXdwxhlnmAwWNivmGHDt2rWHXVffvn1Ng+SXX37ZZNZw/MjZq4YPH16kbWLZ15tvvml66HAqc87Gym1kls7QoUNNEGfixInw+XymqTKDPkOGDCnBXhAREYkOhc7Uue+++0wfHY/HY06Sxx13nLm6wrRW2rt3L5YuXWqmo+SUklz+0EMPleW2i4hUqVIszozVs50TD7+Xjc07A/D4A9iwPchKLKzd4sM/q/ymJKt+QhK8cX707mhXrx0RKXVHHHGEycThvwfDjBmOBw+HfW+YqcNx5quvvmr65owbN85k1zBodO+992Lu3LmHXQ+DSJy6nDNgsdSK49TwlOacrZXr5riUfYA4E9Yrr7xilouIiFSZoE6HDh3w8ccfm4Z4rJlmLx12dObUkJzOkjV8TPXiSZzTSLKWWkRESlfTeg5cOyoe1zybAacdyNw3gYnHAtIyLXz7pw/1q6XA4/Bh405gzPFxOgQiUqoY0CHOKsWSp2OOOQa7du1C06ZNcx9Ts2ZNcyuMcD+e/JitU1DGDjOBli1bdkCa+9VXX21uBenZs6fJ0BEREalsitRTh9NNHnXUUWbGA7/fjyVLlmDnzp3mRMorK5zKMpy5IyIiZYMz/7Zt4gBLdQNWENk5gD8I7M0MlWPZLCc27QzC7QogM9tCUoL67IhI6WEmzE033YRff/3VjAG///57kwWzbdu23GwbERERibKeOkxpveSSS8wMV7zawZM501eHDRtmyrGOPPJIBXRERMpB9SQ7mta1o2ayDQkuBnYAhm3YKDloAXuzHGbGLM6MFeACEZFSxPIolt2zlw5nwKLbbrvNZGw/8sgj2tciIiLRGNThSXrz5s2m8RxvvBrDWQ5ERKR8Napjx9DebvTt5MKgnm70bOdAvZpAvBumJMtus7Bll4Vl6/x4bXIONu7YV6MlIlIKfvnlF9x+++2mDCqsZcuWpv/N9OnTtY9FRESisfyKJ+n//e9/uU3x2MDu9NNPNzMVsMmdiIiUnz4dXejdwYlg0MI7U7z4a6kPfy71w+uzkJYBeHzAX8sCWLU5gFmLvOjTyW0CPj3aunBMV6caKItIsXF2qWrVqh2wnLNYcXYpERERicJMnT179qBRo0a599u2bWv+ZWM8EREpf+xz5nDYTTPkE3u70aWlA4nxNjhsQCAQCuxs2gX8sSiI8ZNz8MXvXnzxuwc//a0vXSJSfJyG/K233sqzLDs7G88++6wp0xcREZEozNThTFdshpf/igwbJouISMVJTrBhxLFxOLqLE1c9nYH0jCACHocJ7LCBMvvtpGcx0BNEVk4ooNOttRMNaxc6ri8ikuuuu+4yfRZPOOEEeDwe3HjjjVi/fj0SExPx+uuva0+JiIhE6+xXIiISverWcGBgDyfS0n1Yt8OFjOxQQIcCQSDLwyCPhZWbAvjwZw/OGRyHejUV2BGRouHU5V9//bW5rVixwlzgGzNmDE477TQkJCRod4qIiERrUGfChAl5ZrgKBAL49NNPUaNGjTyPu+iii0pvC0VEpNDOOiEOqTszsTY1BdMXBuD1AdneUFDH3ALAuq0BzFjkQ3ICcOFJ8XA5NeW5iBTeOeecg/vuu8/0VhQREZEYCeqwn87nn3+eZ1mdOnXw1VdfHdDjQUEdEZGKUS3JhpN6ZKBzlyQ8+bEXC9b6sHxDALvSQj9ns+S9WcCc5X44zExZNow9JV6HS0QKbeXKlZokQ0REJNaCOj///HPZbomIiJQaZt/8a2Ackmfa0KpBAL8v8CMzJ4hdewGfH/AHgLkr/Vi9JQi7DTj/xDg42GFZROQwLrzwQtx6660477zzzEW/uLi4PD/v3Lmz9qGIiEg5UU8dEZFKqm0TB645PR4en4UPfvLire+yYYNlfmazAVk5QLzbwozFPnRo7kC/zq6K3mQRiQGc5YrmzZt3wM+Ysb1kyZIK2CoREZGqSUEdEZFKjNk3iQ4bLjklHtkeCx/+nI3tqUCOFwhagM9nYdPOANZvDyioIyKF8tNPP2lPiYiIRAkFdUREqojzh8Zh/mo/XE4f1mwJNU1OywJWbAziqY+yMGOhHy0bOdCznRPHdHGaK+4iIpGWLVsGl8uFli1b6jNCREQkCiioIyJSRVRPtuO6UQkYP9kGj8eLbXsAywKyPYDHC0yZ7UXz+nbMX+XFnOUunH5cHJrWc1T0ZotIFNiwYQOuuOIKrF692txv27Ytnn76abRu3bqiN01ERKRKs1f0BoiISPnp2sqJE3u70KOtCzWSYWbAYhmWPxjqsbNkfRC/zA3g7e89uPl/mZi91KfDIyJ45JFHUL16dUyYMAEfffQRateujf/+97/aMyIiIhVMmToiIlVMpxZOLFwTwIpNfuxKsxAIhpaH/w0VXVlYtdmPl7/KxsttnXBqZiyRKu3vv//G22+/jQ4dOpj7DzzwAIYOHYrs7GwkJCRU9OaJiIhUWcrUERGpYlo0cJjpzlle1aSeDYlx4UBOCOfHyvIAW3cDfy31Y/zkHPiYyiMiVVZ6errJzglr0qQJHA4HUlNTK3S7REREqjoFdUREqmhgp11TJ3p3cKFxXTtSEkOlWJHBHTZSTs8C3v/BgxcneszsWSJSNQWDQdjteYeNTqcTAX5QiIiISIVRUEdEpIpqUMuOhrVsaFjbjoQ4wO0EkhIAuy10czhCJVm79gaxfpsPU+epv45IVaYZ8URERKKPeuqIiFRRXVo6kZ5loU6NALq3cSAj08LidQGs3x5AWkaoBItBHV8m8M0sP7bsyjHZPEN6uSt600WkAjz55JN5+uf4fD688MILSElJyfM4NVAWEREpPwrqiIhUYf06u8wtLMdrYdZiL+4an4UNOyz4/aHlmdnA2q0B/L3MhzaNHWjZUFOdi1QlvXv3xvr16/Ms69GjBzZv3pxnmbJ5REREypeCOiIikivebcOA7nHo1toHr99nZsfK8QJsk7wjzcIPf3vRspEd6VlONKvvQI1kVfGKVAXvvvtuRW+CiIiIFEBBHREROcBpR7uwaWcAaZkBwB9qwObzAys2WnjgnWy0b+JAj3YuXDwsHo3qKLAjIiIiIlIRFNQREZEDnNTHjYa17Bj3YTb+WuaH18fZb1hbAeR4YPrubNkdwD8rfRhxjBuDjoxTcEdEREREpJwpqCMiIgdgX4zWjZ3o2tKJTTsCWLfNgn/fzMXss5ORw8cAW3YFsH5bNpZtCOD60YmoW0NZOyIiIiIi5UWjbxERKdDerCCcTqB7W4eZ6pwzX1kI3ciy2FgZ2LEH+PFvP77/y6s9KSIiIiJSjiplUOfvv//GaaedVtGbISIS02pXsyM5wYZg0IamdQG3E3DY8j6GAR6vn1VZFuavCiA1nTVaIiIiIiJSHipd+dXu3bvx5ptvwufzVfSmiIjE/ExY/xoYh4m/27B2axDVk4PYk24hO9/HqxUEfAHOkhXE5l1B1EyplNcLRKq0Dh06FHq68iVLlpT59oiIiEglDOpYloUHH3wQt912Gy699NKK3hwRkUqRrTP25Hh4vBamzfdh3dYAsj0WUjNCP2fz5EAQ2LwT+HK6D8s3BDCktxvd2zhxXFcX3K7CfQkUkej2wgsv5P7/6tWr8corr+CCCy7AEUccAbfbjQULFpiLapdddlmFbqeIiEhVE7NBnU8++cTcwpKSktCvXz8MGTIETZs2rdBtExGpbAYc4UJmjoU2je3YutvCjtQgsjwWVm8JIisnVIaV4wPmrw5iZ5oHaRkW0rMsjDw2rqI3XURKweDBg3P/f/To0XjggQdw8skn5y7jGKxVq1Z45plncNFFF2mfi4iIlJOYDeqMGTPG3CKNGjXKBHc++OADbNmyBQ899BDuuuuuCttGEZHKonPL0Oli1ZYgUhKALi2duPmlDKzblreHTtACtqVa+GmODzv2BHF0Zxfq1VQ5lkhlsmrVKlOOlV/Lli2xcePGCtkmERGRqipmgzoF+fzzz3P//6STTlJAR0SklAM7nVvuv3/mCXHYmhrE4rVB+PZNd078/zVbgvB4g3j6kyzcdnYCaiQ7dCxEKolOnTrh9ddfx3333QeXy2WWZWdn4/nnn0ePHj0qevNERESqlEoV1BERkfJzYm83EuJseOi9LCxbnzeww4ydnXuBqfN8aFrfgatGJOjQiFQS//3vf3HxxRfjl19+QevWrU1PwxUrViAxMdH01REREZHyU2mDOt99911Fb4KISKXmcNgwsIcbR7Zz4qJH92LOiiC8XoAFWXZbaFas1HQLC1b5sGaLC5t3Bk1T5SPaOFE9SSVZIrGcqTNlyhR8/fXXWLlypZkVa8SIEabHDsvgRUREpAoGdebPn29mrJo1a1buMl71ueeee7B06VLUq1cPN998s2mEXB4CgYC5lfY6I/8V0XtGSltFfM4kxwOPXZ6Am1/KxtINbKDM5wf8QSDbC/w4249p8/fCsoCGtW3o0sKJm/4Vj8Z1FdiJBjo3lWy/VVXVqlXDyJEjsXbtWpOt4/P5FNARERGpqkEdXum59957EeTcuPt4vV5cfvnlOPvss/HOO+9gxowZuP766/Hpp5+awUNZW758eZmtm9N+iug9I2WpIj5nrhzswOczq2HeunhsTXXBgg3BgIVMnw0ZOTY47BZyPBb83my8O3kzTuq+b150iQo6N0lhMYDz6KOPYsKECeb+999/j3HjxsHv9+OJJ55QcEdERKQqBXV48v/jjz9w9dVXmwZ7YczYYdO9sWPHmrTe/v37Y8CAAZg0aRJuvPHGMt+udu3amdrw0r6qx0Fz165d4XCoaajoPSOlr6I/Zzp1DuLL6R68/JUXGdmAxweToUMM8ngDgM9yonGjGujeXdOdR4OKfs/EqqysrDK9ABTNXnjhBTNOe+utt8wFODr//PNx5513muDO/fffX9GbKCIiUmVUeFDnvPPOM2VVkWVX4eky27RpYwI6YczQWbx4cblsFwe2ZTW4Lct1S+Wk94zEwnuGzVK/muHFnBUW/H7A42UgZz/20+En+t5MoG4NO7x+u2m0LNFBnzNF319V1eTJk/HQQw+hT58+uct69+5tlvHCm4I6IiIi5afCGxrUr1//oFfA4uPj8yzjfWbviIhI9GFWTmZ2EFt3BxHvBuwFnGFcTi638NKkHLw4MRvTF/oqYlNFpAR27NiBRo0aHbC8du3ayMhQWaWIiEiVCuocDEufcnJy8izj/dIuiRIRkdIR77ahQS27Cegk7qus4ixYZNt3Y3JDagawflsQ81cF8ONsL7bt3t9PTUSiX7du3Uw5fH4sx2IZn4iIiFSh8quDYanV+PHj8yzjtJksyRIRkeg0ekA89mQAfy/3wekIYN12y2TssByL5Vec8pxVtWlZwMI1fuxMs6FzSwdSEu3YsD2Iaok2DOnlQs2UqL3mIFLl3XHHHbjoootM6TybJrOPzurVq7F161a88cYbRdo/hZ3pdNu2bbjrrrswZ84cM/MWe/mcddZZ5mennHIKNm/enPtYTrzBC4FPPvkkTj31VBNsevzxx+F2u3Mfw/4/Y8aMqfLHUkREYl/UBnX69u0Ll8uFl19+GRdffDFmzpyJqVOnmtmvREQkOiXF23Dd6ARs3hmHlZv8ePt7D3alBbA7I4itu4AcT2iq84APSE23EAhaGDchG26XDS0b2NGxuRPp2RbGnpy3/FZEokenTp3w3Xff4f3330etWrXMrFeDBw/GOeecYwIzhVWUmU65vEuXLnjppZdMAOjSSy9FixYt0K9fP9PjJxJnVN2wYQNOOukkc3/hwoW48sorcc0115TSHhAREYkeURvU4dUUZurcd999ePXVV1G3bl1zJUiZOiIi0a9RHTsa1XEjPRtYvNaPtIwgfl/oxaadQCAHCFpAthfw+vloy0x3vistiIwsC93bOeHxBhHnVraOSDRiQGfQoEElDpIUdqZTZgHNnz/fjAs5PmT51xlnnGGCPwzqRPrpp5/MFOsM9DidoWHuokWLTMaOiIhIZeSMpsycuXPn5lnGqzTvvvtuhW2TiIiUzClHudG9jcOUX23ZFcSG7SaKk4slWeF/vSzbWhFAltfCt386MfxoN+zhpjwiEjXuvvtucxs6dChGjBiRZxasoijsTKcM6jRo0ADJycl5Hvfbb78dkPnDGbhuueUW07Q5PPHG2rVr8eGHH+K///0vEhISTNkVM30in7coWN4VCAQQi8LbHavbH6bXET10LKKHjkV04bmiygV1RESkcmpcx2GmO29a34Ha1f3Ytnt/MCc/nx9YuyWIV77KRma2hXOGqAxLJNpMnz7dlMR/8803pnyqRo0apq/N8OHD0a5du0Kvp7AznWZmZhbqcczwYen+yJEjc5ft3LkTPXv2NJk9zz33nOnhc/XVV5vgznnnnYfiYI/HWLdgwQJUBnod0UPHInroWFQ9CuqIiEi5qJ5kR7vGDuxMCwAHmcncAkzJ1rL1QYz7MBsOh4V/n5CgIyQSRVgCxWbGvDGw8vPPP5uSrDPPPBPNmzfHxIkTS3Wm08I+7pNPPjH9eRycZm+fZs2a4b333su937lzZxPMmTJlSrGDOswuiswairUr+fzCx1nKIvdTrNHriB46FtFDxyK6ZGRklNtFAAV1RESkzLHM4bhuTsxe5kWtZBt8Pgu+wMEDO/wZs3s++dWHkcfGm+nSRST67N2712TD7NmzxzRMDpc9leZMp3wcZ79ixk5SUlKBj+PP2XeHjZQjsUkyy7TYKDnM4/HkmQmrqOx2e0wHRIjbH+uvgfQ6ooeORfTQsYgOPFeU23OV2zOJiEiV1qu9CyOOjUfbpg4kJQAuB8CWOQ47wJCNc9/98IkpIxvI9gSxclMAezOD+G2+F+/9kIPv//Rid3r51SmLSF4M4nDmK852NXDgQJOZc8IJJ5iMnddff71YM52yH860adNMWRfLuCK1atXKZNhwWnJm6DB489lnn+Ups2JfRs6GVadOnTy/y2weBnq++uor09+Av8vMHZZjiYiIVAbK1BERkXIz8hg34lxMEQ5i0dogXE6YJso1UmyoWx1YusFCajoQ8If66yzbEMQrX2YhMxvYm8VePBaa1HNg5eYALjwp3kyhLiLli7NU1a9f3/TR4Sylbdu2LfWZTr/88kszNXl4Eo3nn3/e3D/uuONMts4NN9xg/j9s48aNZpvyY0CIv/vMM8/gnnvuQc2aNU1PnWHDhqGipWWyeXwQKYk2NKtnL3bj5rLE3mZzVvixMy2IejXt6NHWicS46NtOEZGqTEEdEREpN26XDSOOicOwPm68/k0O/lzqR0qCDUN7ufD29znIzslbk5XlAb6a4Ue1RKBuDTuqJdqwcUcQTerYsXlnEG2bxH75gEiseeONN0yWTWkEIQ420ymzdSIzdhiwYUbPwVxyySXmVpDjjz/e3KLJX0t9mDrPb8pMww3lRw9g0Dt6AiZZHgvv/egxmZLErMml6wM4b0ic+SwXEZHooKCOiIiUO34huHJEAi4/zUIgYOGJj3OwaUcA+b8j8vuOxwtksiZrTxDVEh0myydgWUhO0JcKkfLCxsKRfvjhh4M+llOdy8HtyQiagA7LS9MyLZO9yODOX0vtOLarK2p23fxVfhPQCVqA18fttGH33iAWrwuge5vif4XI8VpYtCaAPZlBNKnrQNvGdthZextl5q3yY95KP7x+oF0TB/p1dsLlLNl28jhv3R0K5DWoFX2vuazxfcQM3dLKSvP5LezJsFAtyRZVAVGR8qagjoiIVBgO5NOzLWzYHkBivB1J8UHk+ELBnDD+b44XCAaBRWsDqJUC7NwThMcX8SARKVPXXXddoR7HL2tLliyp1Efjx799OPmYUJCjONZvY9lVAOu3B3M/7BikrlfDX6KgTjBoYeXmILbsDKJWNRs6NHOUKAiRmm5hy64g1m4Nfd4mxNnQsqEdqSXoaZaRbeHdKTlYsyVgPteZfdmrgxMjj41DSYMl3J9ZORaa1XeUuDR3znI/Pp/mwdbUIAJBYOl6v+nlVpLt3J4aMBcwVm/mTABAy0YO/OcMV6mVybEnK49RaeH7ibGX0gjAMLP2pzlebN0dRGK8DUd1dKFne2eJg27fzPRi115e8LFhSC83+nWOnqCoSHlSUEdERCoU+zNwuvOEuCBqVuPg0UJqBvvn7H8Mv/cwqMMrxuytM3tZAE98lI3nrk0s19kFRKqqpUuXVvQmRI33fvDAHefBKf3ii/X7DOOs2xZARpZlgthOe2jGv+1pVomCGu9M8ZgvzttSg6iZbMdRnZy4amRCsYNPbF7/51Iftu0OzVbodgI79tgx+rjiBzamL/ThtwU+ZOfsf6070oI4sp0Tzeo5il0m9vHPOZi/mtlPLGWzY8SxcejaqvhfcybP9GDRWr8JQvFctCsNyMwBBvZwmfNVcTz9aTb+WOjPDYoxwAEriH/3KVnW16TfPfhtgd9kfJ3Ux40Te7tLVB6Xmh7A059kY96qoJnUYMyAOIw81l3s4A4zs97/MQfLNwSwfU/QlFwzyJOcaEP7psU75jv2BPG/idlYuy1gjnm8C1izNYC6NWxo09hZ4mAWtzkxvnQCWtPmeTFpOid4sNC2sQMXnBiHxnVLXjrO7eR7s6TZY1I5KKgjIiIVigOSU/u58PcKPzxeG2qm2EyAZ8tuNlQOfQFiUIeDF78n9Du8Mrd4nR9/LPKjUwsnaiTZojJ9X6Sy4mxVbGbMqcU5lTmbG7NxckpKCiq7banAYxOyix3U4adaWkYQu/aGgiWcATAjh59zxc8yYBbjSxOzsWW3ZQLgdlvoM7JTcztO7FO87Vy23ocN2y34+TlshZrXr9sWxMrNfhxVzIyIv5b6sWl70AQ0vAEG9YFsr4XFawPFDur8MNtjAlo70iwT3GIQi73XnromudhNnReuCWDFxgA8vtB9zv6e5fFjV5qF6klFX196VhA/z/GbzKfwBQsu8/gtnNKl+OeuRz/IMgEDlilzLb8vYNDIwvknFjPgaFm46ulM89oDVmhGSj4H3wNjji9eMG/Zej9++tuLNVuD5j3EdS7fGEDz+na0b5pQrHX++o/XNPDm35B/398Qj/+U2d4SBXWWbnLjs7nZ2LnHQtO6DvxrYBzaN3OWqHfWc5/lmEkeQveDJkvtxRuS4HbZi32Mpi/0Y+4Kvwk+MTONfQlrpugCV1Wmoy8iIhVu9ZYghvR0YczxbnRrzZIBliOErhQTh0P+fZk6/BLEQdz21CAe/SAH//d2Fib85FE5lkg5Wb9+vZk9ijNVLV682GTxPPXUUzj55JOxYcOGSn8c+Hm0dmvxs2qY/ZGZw8AOsDcT2JMeygLZU4Kypo9+zsamnexRtj8Ivmsv8NrX+yLhxfDj3/7c9RH/5WyFP/y1L9JRDPxCy8/79OxQv7Q9GSxtCiIzX5P8ovjgRy/WbQ1ib6Zl9icDL78v8GHxWn+x17lpRxDZ3lAgiz11vD5gyy4LDnvxjpHXty+Q5Q8dG974/8yCYnCrOHak+jDxNy/SM0MlytxeBjkY3CuuGYt9pmcSAyRbd4Vu3J/MtClJwHH5xqDJqOH+ZKBsW6qFH/72Fnudc1b6sHkX/45C6+OkClznX0uKf8xXbQrgf9/VxsTfQk3MP/rFg9teycSutOK/N7+e4TUBnaDF4FMo6MiZ5KbNK/52/r3cjxmLfKYckO/5tVv8+GyaN7fpulRNytQREZEKF1lq5XbakJxoNwMhNlVkECd/42Sfj2nR7McQwDoHB9pAi4YOHNNF9fQiZe3hhx82U4U//fTTSE5ONssyMjJw00034bHHHsMLL7xQ6Q8CA8vFlZ7JL/ihL/VhDPAwe6G4/l4eMEHvyO91NgtYtbn4gaJMb6hJ8r4kg1wZOcVf58pNfvN5n7tO/usDFq8NYnT/4q2T+y3bFyrRJWascP0LVvnRq33xzgkeX9Cca8LnJlb5upwWdqcDzRsUfX0MCjGgkR+DZB5f8a6xz14eMMExvu7c3RmACXaUJLDBLBVm6RD/ZRYZM3eKi/2I+Np5YSa8oXaLgbPir3PVxsABf4M8VqvYr6iY3vrOg027XbnZwR4bsGhdAJ9O9eDy4YnFWufeLMs0Q0/PYkAndKGqZgqQmlH8Y8QMnbkrfFi7LWiCrsxu7tnOwoYdLjSujajDflz8m9SMeWVLQR0REalwvdo78d2fPnOliTOCNKnrNqUDTFcvCAfsHAZv3GEhIyeA9Bygfk0vfp/vM1fDTj8uzpRliUjpmzVrFiZMmJAb0CH+/3/+8x+ce+65VWKXsw9Ocf25NHDAF3x+pq3YUPwvpCkJoS+NkXiXgYjiqpZoR5BRgnzbWbd68V88v9zmDxIxcLB1V6BETYLDAR3i6hkw85Qg8sasFxOECG9jMHRBId5dvFIpmz1vwC1yW225IZkirhO2UCAv3/oi90VReb2hsqv8mBFTXCy3MsGxfdtqgm4msFf8srPNOwp+kbv3liBYsjIAr9+WGyTj1jFoMmORH5cPL94661S3m75H/HvnseIFKP7LjOSSZOr8tTRUGsj31PZUZv35cfGw8FZHB/a6mvKXFys3Bc1FuLZNQmVixf0bkkPTiFdERCpcl5ZOVE+ymfT0ejXsaN7AgR9me7FobSY277TyXNEOM9Ps+kNXuD2+AN79IQCn3WYGTb/848czVyeiSytl7oiUtqSkJPiYLldAn53Smqo4mrkCHtSu5s69b/l8sJhy4XDA7t6/PJgdKoOxxcXBtq+hOx+3ZXMGHEELfvv+x7oD2Qhks29uMM9jue4D1ptjGvDkWW+zOqF1WLDD59jf+6ReYo7ZjsjH8puqWeZywR63/7FBj8d827a53bA5HAgEuLYgnAFP7nr5BZ09cPI/1mxvMAiLy2022OP393QJepnyEjDPF46z2KwgXEGu1wafIx52x4GPtTmdedfLAEFCRA8Wn8+8DnuA70VXnvXyXejxRry28HqdTrNus17LgsV9yd+Lj89975rjmZMNl+WEzxZerwWnPweZqQ5YzarlfWwhjj37/MTZfAj6/QjaHLnHnrvOHQwdI3tiYpGOfeO6dvM6bUE/nJYvd70sXz7Y+8QKBGBxX9jtBR57NtkOs1sBOINec+xttoO/T0ILg6H3lNOZ99h7PKiX4IHLFoTHCtdTBxEPD2q58wY2inLs4+0+uAN++G0uBO3O3GOfYHlC+zLisYc79uHHcnp0m98HlxVEwOZEwO7alykcem0Fvk8Odezj41G/RmiskpXpgz3oh93pQINaCSZzq6D3ySGP/b7HLljpRUaOzQR0uE6b14dt2x3YkZaIFg0cucee6yvUsc/JMfv52zk2/PRPwLzm3m1sOPM4wB3nPORnRJ5jlG+93/+ejtWbfLCcblh2h+mtxN89tad1yM+I8LE/6HuqCO+TYEHHviiPPdRnhN9fqMeWF/XUERGRqNC0ngO9O7hMQIfi3bzKZUPKYbKew1dQ92aw307oKh17U3w1o/h9H0Tk4I477jhTgrV169bcZVu2bMEjjzxiflbZ3bz4ZnSpm5l7f/cHH2DF4MHY/tRTeR636pRTzHJ/xH7a8+mnOPOtEThn5dN5Hnv/3xfiwd9GwbtuXe6ytMmTze9vufvuPI9de845Zrln2bLcZbUWTsWTs0bhsqX35ckmOvPHG8xjs//5Z/8K5s3D6qFDsTHfNPUbrrrKPDZz5kxz3+Wwo2vmP2a9Ny280QQLEtzMNrBh0403msdmTJ2a+/s5CxeaZevOPz/PejffcYdZvvf771E7JZRL0ChrDZ6YOQr3zLnElDbVNjMfAlvvv988ds/Eibm/79u40SxbNWJEnvXa3nnHvI7jN0/MzU+o5t1t1jvuzzGIi2iSvOO558w6dr3zTu6yYEaGWcabScnYZ+crr+CBqaNw6rq3c5c5EMCjf4wCLjzJ/F4Y18ff5/ojrRw6NHTsd+7MPU+duHOS2Zf/XvW82V5egEhJAGrcfZN5HXydYXz9/H3uj0hrzjwzdOxXrTKlyizl6bPzZ/OaL1n6oFln/Vqh183jwMfyuITxeHEZj1+k9ZddZpZ3TJ9n1sE1dEz926z3hkW3ICGiRzLfN+bYT5++f+Hy5eY1rB87Ns96N916K458+FT0Td3/PmmasRLjZozCZb9enuexfJ9zvXzfh/HvgctWjx6d57EXrX8WT84chf5bv8pdVtu7HQ/8Ntr83UXi3yXXwb/TsMCePfuP/T7+oIXh6940r3nYhvdzlzt82bmPDX9pp11vvGGW7XzppTzPF36seY7NLCMHBm78DI/8Pgojlr+E3XuDZvauQ31GcNm2hx/Os17uAy4Pblyfm/V11PYfzPaes+gRzF2+f8yz/rzzDviMSP/xx9Cxv/XWPOvlMfuz7yB88/ZfWLs1YMri5n48FbP6DDrsZwRlzZ5tlvE9FJbtseB6/Bb0fuQU1FryW+7yLTMXYPmgQYf8jMi1caN5T/E9H6konxHbxo0zy1M//jh3Gf8muYx/o5GK+hlhjv0rr+xfQSCQ+9jIz4jyoqCOiIhEpaM6OtG+qRNtGtvRqDa/YISm1I289sGrxma680CogXL46hdxetfIxqRsKqhGgiIld8stt5geOoMGDcLAgQPNbTC/xAQCuPPOOyv9LuaX3jH9iz+td1KCzXye8fPLZFrwXxtngir+lV0GwLk+rpeBF07HnBBf/HIhGtrLaQI5XC8DRJw2Os4NnNS3+BmQjes5TYDAse+1c92JbqBt4+Kvs1Y1G3jBPnd/7tun3UowpbmZTNEW+jdyYsWEYh4jToNer0Yok5TnsaR4oEYy0KqRHU42mCmGBrXtZnpwE4TZ9x7i8efzFFe7Jg7UqcbZJPetc9++6NWu+OVC3CYGtfge4raGE8ZKksfQtaUTbldo3exTw31apzpLA4u/1mw20cmHS9hwvLjWbPYjIzvUT4kZO8xUYz+dcFCnpD0II6WWII7Ahsts3r5pZxAbdgTNNrPkjsEZiQ02SyPcPLKysrBkyRJ07NgRiYnFa4p1MBzs/PPPP+jevTsc4XRFEb1nRJ8zB5Xt4SwRPuzJtEy/nM27gqYRommW7A/NeEEc1HGgwwEjp8ht39yBJ65INjXcfyzyYfqCULSneX0HRvV3w8lvFKJzU5SOF2IBxzS//fabmdI8Li7OTGner18/VIVjviWtEYYNqA8HP3CKUX61dZsHd72ZjTlr7OaLEz+/WtTKwZXDE3Ha8dWKVX61YEUOrn1yN3al2+CxxZkvzvzC/3/n2HFib3fuY81Y9O+/0a1TJzgOU37l8QZx5ZNpWLgsC76g3ZQUHN3JiWevTYbN7y1W+dX4b33435c5yMgMhMqk7DbUq5+IcVckmabGhS2tMK/jr7/QrUsX3P++Dx9Ns8wMUCzBiYMHjevY8c799cx0z8UprTjl1l1YvskGz75p5u02C42SPXjrjhS0a5Nc5PIr+vzndLz7bSZ27LXB73CjTjU7zh3qQtv4f9CtWzc4i1h+tXOvhVF378Wu3T7YAqHyK5vLjXbNHJj4YPVilV957W5c/UwmZiz2w5MTgBte1KruwI3nVMfoAQkHKdML4J85c9CtY0c4Cii/Gv91Jp6bFECG18HKK1PSl+jwoFsbFyY8UK9YZTUBjwef/ZyNb+dY2JpmN4G9gUc4cP5Amzm/F6f86qRb92DximzYgvvKrxwuM6YY2tOB8de7i1V+NeCGNKzYEIQj6IPd8sOyORB0uHHLv+Nww5ikYpVfdbs8EzszQo+17yu9Y/3i0zdUx8hj3ea7ZrcOHeAoQvnVsFv3YO0uJ7L3Ne2OdwTQsJoP/7u5Ojq1TSpW+dXnP+7Fms0+BJ1us33UrrENp/UKRV0PVVJ1yPdUDJVfpaenY/ny5eUyTlBPHRERiVoJcXac2Cc0SGCvHU7jyVPqpt0BpKWHeurwSh2bWjJjh1eokxJCmTvfzvIiMc6dG9ChddsCmL8qgCPb6fQnUlz/93//hxEjRuD44483t6pmQO/k3IAOmS8X+wb2kfL0fwk/1ulEw8ZOjB7sRIMFfjMzDrMKWzRIwIC+EX1v9j02t79E5HojvuCEdW4dhwtH1sQPsznVsYWUeBuO7uLE8X0TYM+fXcIviwkJsOe7wBj5hYzi3Ha8cksN/L4gEcs2BNC1pQNHdXLDwaB4RN+e3O3ll8gCXnPkF9NubSycfqzbrC8ty4VGte1o1chhAu75H3u49YJBqYQE1KhhR5eWPuzJYP81G2okJ6FOdUeemRMLXK/NVuB6eSxr1k5EC3vQNN7lBYMayXbUTE6CLS4hT6+Mohz74QOSUaNGHGYv95tmvJwg4NgudixaGGceX9Rjz+a4tarZYbe7kZHtNBk11ZLsubMMFfQ+4RfxAo/RvmMfyLZQv5YDHZtZJnPF4XCiZooD9ohty/8+CS20H/w95Q4iPj4HLjcvvliw2exwOhJDJ+/Ixxbh2Dvi4jDmJDf69ghia2oQtavZ0aoh98WBF2wKe+zHDHDigXVOBIIOM85w2kJjirMHx8MeWX9WhGOfnR1qEuy383Gu3MwnThmf/7GHPfb7Htuwrge7M/fNTGd3wgenyaDr3mb//uSxz38sDnrs4+OxI8uNDM/+Zt6ZQQc2pjvQoH7e91BBx/5gx+ik41Lw/Z/e3Bn4mHU9tLf7wM+jgxyjg76nivA+sZf0sYf4jMh/7A/22PKiUa2IiMSEgT3c6NicV3CyzRVKh51fhiwT0AnPpsKpV3MYw7EC+GG2B94CpvFgk08RKb5169bhnHPOQaNGjTB8+HBza9asmXZpEZzc123KSxlorpZoQ+eWzhKVX/HL7HlD4tGng8tkNPLLfucWDtOgtyRcThsG9ojDwB4oFUe2dWLNliDi971WfhHq19mJujWK3xGifk27me2Qr5vNbmsm200T4Yi4W5Exa2jaPK/JdgprWNuBtk2Ln2nPDJITjnSbWxgzEoqLwZeaKXbYbUHUStm/Xc3qFf+Fb9kdNFNk9+rgMiU5LL9LSbRh574gRHEM6unGhJ88ZuYz9mMifk8/vkfJJjLge4c9+MJ9+Epq1HHx+HX2TizbUt2MJWok2TC4pxPHHVFAwKGQWAZptjViGf+/2v7klyJrWs9uJpXYmxUa+zAo3Ly+HVtTGRwu3jo5jirIuq0B1KpWvP3LzzPORJqzb8ylWa/KloI6IiISM9grYO22IGom25AUB6zYaJkadQ6SwkNO3t+WyvryICx4cXx3t7n6xJlc4tw20zNARIrv9ddfx+7du/Hdd9/h22+/xUsvvYQuXbqY7J1hw4ahVq1a2r2F+ELaprHD3EoLM2gY3OjUInp3P4NE/z4hDhu2B0yWEsukGJgoia6tnOZLbo3k/fuSmTUlCW5cflqcmZJ5/iq/ydThF+kbRufN0qloDLacfJTbZEOkplsmA6ReTRvOGFD8IESN5NDrY0kgS5ryLy+O1o0cOP/EOHw53Wv6yfDLfffWDpx1QvH7UpWFakk2XHxCKnJcDZCaYUODWjb06egqUWC0ZUMnNu7wm6wxM1vVvp5K3VoW/yt4g9r83LDgD4bGNczMYtPsBrXsJfrsSIizTFl7+PjzluMrfjAvGLQwa4kPvy3wmya+/bu50LujM6r+hioTBXVERCRm8Gp0+OprRk4oLZ7yDzs42PF7gEVrgmjRwA9YNmzfY5mrWWyaXLdGuW+6SKXCwM3ZZ59tbtu2bcOkSZPw1FNPmRmwFkbMtiNysNkOm5bSrunexmmyAWYv85u+OvycH9TTVWApTmElxttx17mJpsE+25swWBKNX0ZHHedGw1p2LFnnZzUaurd2oUfb4n+9q1Pdjg7NnFjK6af3YXbNUZ1K9pXxghPj0a+zC6u3MKvIhm6tS5aZVlaqJQbRv7ur1HqfMitr5aaAKbdiUIeBEgZem9Qr/v485SgXtqcGsWuvZZpPM+jEkvKWDRyw2LSoGFrUt2PB6oAZO3FAxQwgzkjXpWXxs6m++M2DD3/xwrsvMPTnUj/OHRKH4cdEVzCvslBQR0REYsqY4+Pw1MfZ2LHHMik6HNSY2nKr4JTiT6f6TU07Z8pYvpHBnSDeuDUl1BdCRErUOPjnn3822Tq///47mjRpYkqxRMrbUZ1c6NvRuS8bovQ+22uVMIuorLGk67huLnMrLQwaNKtvx5otASS4bSZIVK9myfYDjwmDRR2qWJUm9x2DGCwN5AUlZtMwcNa6BBl6/Tq5zEyfDJJk51ho3diOYX3ZHN0WOet2kTSta8P81ft76vBfZmclxRfvb4mBnM9/82LrrmBuyTtnjvt0mgenHLWvL5eUKgV1REQkpgzp5ca0+T5Tn8+rqBy/e3xm0gIU0EInd4DCiQ3Y9HLBaj+WrA+gSwnSn0WqMgZxeJs2bRqSk5Nx8skn44MPPkDnzp0retOkCjONSvVdscQYHDiitdPcpGS6tHQgNd2FOSv8pt8Tp7Yf2ttV4v5Z7DHIQB7LpRgsKanlm1nWvn/SiXhXaJpzlkk2b1D09wHLAddvC+aZEp0BKPbTSs+2SlTOJwXTX6uIiMQUBnM27WAKN/8fyMjmLC2hjB02SeZ05wVl7XARB0BpmaG0YDYRjcZ0epFod+edd2LQoEF47rnncOyxx+aZGUdEREI4xuh/hAt9OzmRlRMKZpTWuINZWiznKg1Z2aF+V2yMHcZSrF1pFpoXo/kyJwsraBxGbOwspU9BHRERiSlsiNmojg1bdnO61VBZVa1kIMtjQ7LDgt0Csn0HD+ywD88Xv3nN714/OrHY6cUiVdX06dORmJhY0ZshIhIT2PempDPRlaWm9e1YtSlgJprgSIkBoxpJdrQu5sQSDBB1aObAwjX+3JIwjrk6NXfkzoBWEmmZQbONGr/tp0srIiISU3ily+mww+UIzYbFZn5B2NCwFmCz21C3lg0tGwL1D9IMORjkgMDC1Lk+3PB8Bh59PwuTpnuQma2pzkUO5o477kBGRob5/3BAZ968efCyrnEfzojFDB4REYkd/zrebUoX2QuH5ewM7pxwpNOUixUHS8KG9XGifVM74uNgbh2a281sbZytq7h27Q3ine9z8OpXOfjfpBx8+cf+RsxVnYI6IiISU+rWsJvSKV4JSox3oFc7J5rUsaNOjdAUtpwJhDfOglLQ0IGDlewcYM3WIKbO85nGfa99nYNnPsvC5p28UqUBgkh+EydOhMfjybPsoosuMjNfhQWDQWzevFk7T0QkhrCPzjFdnGjbxIE2jRzo19lhLqBxWvLiqpHCcZoddWs4zM1pt6N6UvEDOpZlYdJ0L7alBnPvL1vvNz0WReVXIiISg9ggcN22gBmI8MS+J9NCRlbQzIiVnGBDo9o2U2bFK0/h2RzCwmMU9uMJTY/OaUGD+HG2DxlZMLNS8KpVtWJeoRKpjPh3VphlIiISW9Zvt9CkrsPcwrJymBljoW6Nogdi2BR60ZqAKcHihTSOxTje4rTpx3W1ijX7Fcd3u9IOnLJ9yboABvcs8uoqHY1YRUQk5nBa0JP7ulGrmh1evw1N6trRt5MLx3V1oX5NO+pWdyDeHZrB4WBDB2tfgCfLA2xLtcy0m6z5Tk0P4vcF/nJ+RSIiIiLlLyXBVuAsW8WdpYslXF5/KOjPZs6hC2hAjteCt5jDq4P14w+vu6pTo2QREYlJnVo4ze2vpT78+k8o/bZ+LRucTqBaAlAzxQZfIBSo4TSd7KWTP5OY94MBmP48LOfyeC0gyZZnGk4RERGRyqp3Byd+/Ht/fzTq0sKBpAKCPYXBjGmWb81Z7seONMtcXGPGT5+OrmJPwV6nuh2NatuxeVfebJ2urRTOIO0FERGJaSyXmrHID4/PMleB1m4NgpMrJMWHmvW1amjD4nUWcjyWmfI8P07h2bCODQnu0Mxa9Wra0bF5Kc0TKiIiIhLFerR1muzm+asD8PsttGvqQM92JQsTMENn2cYA0jNDF8l2p9twdJeSrXPEsXEm+LRqc9BcjOvayoGjOyucYfZ3ifasiIhIBauVYsdZg+KwYLUfu/da2LgjiO2mkZ4NyfGhmRyO6uTAPyv82LEHyN8HmW1BOPOV3w9USwSOaO0wGUAikteECROQlJSUez8QCODTTz9FjRqhqeYyMzO1y0REYlDH5k5zKw28wDZjcQApCYBr3xTmnAHrj4UBnDnQMpnRxc0AGnlsHAIBy5Rj2disRwyNWkVEpFLMiHXCkW4TnPnlHx8ysi2kplumoU6KZcMJPdymyV56VgCZngOb63h9QJwrdNVn406VXonk16hRI3z++ed5ltWpUwdfffVVnmUNGzbUzhMRqcKycizs3MOsaRscjqApv7Lb7GbmKmZVFzeoE1acRsuVnYI6IiJSabD++7yhcbhzvB9uV+iqDhsnL1zjx4AjnEjPsrB+W9A06mOGDi/y2B2hgE6oaXIQKzcB7/2QY2a/GtTDVeyacpHK5Oeff67oTRARkRgQH2eDy2lh8boAcvZdSEuMC6BzS05zrjFVWVBQR0REKpWjOrkw+rg4LFzrh9vJ6c3tyPJYJk23erINtbKAPZmhxskM7vDf1AzOlGVhymw/mta1m5481ZOCSM8M4pwh8RX9kkRERERiAmfNyvZwavP9yzz+0KxYca6SBXW27Q5ixaaA6YfYqbkTKYkKEpGCOiIiUumMHhBnpiznLFYM7Iwe4MafSwLo1DyIjCwL/iBTgGFmyiL20wkEQynDa7YGkZruw6CeLjPLAmu3leorIiIicni79oZmqKqVYjOzjxInrvAHbMjIDiI5oXjzkHM2rZ/m7J+la9ZiP84YEIdGdTSvuYI6IiJS6TSoZcfYk+NNXx1m5yTF29CygQODjnSZeu4vp3vw2TQvdqYFkbavt2uWJ1SOZcu2zNTmfyzyoXFtB57/PNvMmsUSraR4O3p3ZNPlAPZkWGhe345T+rlLfOVJREREpDJw2HnRLFT+7g/snw2Ly/f1TS4yjt1+nevF2q0BMykGGyVzrDdtvg//PiEOVZ2COiIiUinxqhBvYcy2qV8rdL9jCyfSv8vB7vRQhk6ufT2Ss3KAFRuZsWNhzgqfGZQ0rmNHs/oOM4Do2tJhBhSrNgcwbZ4PQ3q5y/vliYiIiESdGsl2dGnhMLOSMpgTdkQbJxLji5dVsyvNwsJ1AaTuDSIQZEk9sHqzBYfdpqCOgjoiIlIVLVgdQEY2wKHFvotIeTDQE/QBqXst03eH9wNBzuTAPjw2dGhmR9y+y01mli0RERERMW46MwHPfZaDeSv9JgDTva0T148ufo9Cmy00oxZLuzzeUGZ1Yjx7IhY0iqt6lKkjIiJVSmp6EAvW+JHjBYIHSQO2IqY69wVCgwfOjrVrr4W6NWymT09Yk3qq5RYREREJq55kx93nJ8LnC6VDu1wlHSuxJw9CfREZx7EB/oCFYDAiFagKU1BHRESqDMuyTC+dOik2uN2AL/vgj+Xww7KFasA5y0JyvA3xbuC4bg6s2RLAhu0B02tnznIf5qxw4bwh8WYQIyIiIiKlEczZNyazMZhj7Wt+GMqQ5qym4Z49VZ2COiIiUmWwVw4zderUsGNQDyf+Xh5AWoYFbyA09aYVZOAnlKkTLqqKcwO1q9lMT50ayTZs2hHE/FV+bNgZeuzGHcDqLUHszbBw85mJcKtpsoiIiEip4UU0l9OGOJcFlyOULe1ylXy9Xp+FP5f6TQNmTqpxZDsnmtcvWfZPWmYQvy/wY+duL7rUR7lQUEdERKqMhDggOYFTalpo1ciJHK/NnMgzs1mnHRo0sMyKGLBhwx27O3RhiL/TqLYNC9cGsGkn4POFAj8MFCXFAys2BbE73UKDfc2YRURERKTkGMipmWKDy2nP7anDWUmrJ5VszPX5b16TeR22anMQZwxwo0WD4gV2fH4LH/7sxd7MIJLLcQ4N5YmLiEiVYbfbMOq4ONSraTdXZHg1haVV7JvD2awc+c7hDPBw8MCT9J4MYOEaP9Zvs0JZPRGPYSPllESbCRiJiIiISOmpXd2Grq2cpsydpfCc3ZRTpvfrXPwclc07g3kCOuEy/b+W+ou9zhUbAyagU96UqSMiIlUGT7ZTZntNoKZLSwdWbHJgyVo/++0Z7J/D/4+cz4qzX23exatCQbidQCAAsKyb+FhOgtWygR2XnhKnoI6IiIgIgJWbAli8LmDGSp1bONCqUfHLmuJcNgzt5YbD7jMX2pipw4tzA3sUPx0mPbvg2Uv3ZhV/VtNsDyqEgjoiIlIlZOZYmDzTawYDNG+VH0d3dmHrrgAyc2zw7YvU5BzkhGztmw2Lj2I5N7N6GOS5cJgLd56TbLKARKToVqxYgXvuuQdLly5FvXr1cPPNN2PIkCEHPG7btm246667MGfOHFSrVg2XX345zjrrrH1/nxZ69+4Nv5/T54b+FmvXro0ff/zR/P/s2bPx4IMPYu3atWjevDnuvvtu9OrVS4dLRKQM/LXUh1//8eXeX7rej0E93TiybfHDDz3aOtGkrh0rNgXM+KtjMyeSSpAh3aSuHQ67DYFw3f0+LRoUv5ipZSM7bP/YzDmpPFW6oM7JJ59sTuI0bNgwnH322RW9SSIiEgU4DWY4oBNWt4YdL16fgtVb/fh8mg+//OM1wZ+sgwV29v3L87/bAcTHAaP6J2BPpmWa7dWtboNjXwO/SCzzYu+dOtVZD67gj0iY1+s1wRmO19555x3MmDED119/PT799FO0bt06z47i8i5duuCll14yAaBLL70ULVq0QL9+/bB+/Xrk5OSYgI+bU9tF2L17N6644grcf//9OPHEE/Hll1/iyiuvxJQpU1CzZk0dDBGRUsRZqmYuPrCEacYiH7q3dpToIhjHbbyVhqR4Gwb1dOHHv30I7gvssDy/X+fid2CulWLHCT1cmDpvf0CrPFSqoM7evXvRsGFDvP766xW9KSIiEmXYYI99b9Ij0mqb1bOjVnU7qie78PKXHjP7VY0kDkhCZVcHwyydxDggJcGGf1b4sWlnqH66QS07/jUwzqQJh/2z0o+f5oQGDDVT7DhzYJzZDhEBZs2ahezsbIwdO9Zk2PTv3x8DBgzApEmTcOONN+buotWrV2P+/PkYP368Cdp069YNZ5xxhgn+MKizaNEitG/f/oCADjF4w+ycU045xdwfNWoUJkyYYJafeeaZOgwiIqUox8vbgZkqWTkWPL7QpBXR4ojWTrRq6MD6bQGT9dO8vj0327O4OINW+2YOrN/sRVYqykWlapS8bNky7NixA+eddx6uvfZa7Ny5s6I3SUREogSnGmfApW0Th5mu8uS+7tz6bmbX8IoNL9RwBiyezw92Sjc/swHBIJDlsfC/SdmYPMOL+av9JoDDWxgzg8IBHeJ06jMXl+/VG5FotmrVKrRp0ybPIJoZOizJisSgToMGDZCcnFzg4xjU8Xg8JmBz1FFHmSAR1x1+jrZt2+ZZX0HPISIiJccZQWskHxhmqF3dbhocR5uURBs6t3SaGa9KGtAJ45iS5V3lJWYzdT755BNzC0tKSsKtt96Kyy67DKeeeiq++eYbPP7443jssccqdDtFRCR6MC125LEHXiJi0GX40S7MWuIzM1mxzio8ExYbI4evN5nKqn2dlDkDFgM7aZkWXA7LTGfeqmEQc5Y7TENArsflCK070qEygESqmqysLMTHx+dZxvvM3omUmZl5yMcxQ6dr1674z3/+YwI/L7zwAi655BJMnjy50M9RWMFgEAF+MMSg8HbH6vaH6XVEDx2L6BFNx+L47nZ8+UfAjKHI6QCOP8JRqG2LptdREjxXlJeYDeqMGTPG3CLxCk24/vqEE07Aa6+9VkFbJyIisSIz28LHv3qweJ0fjWrb0b21Df+sDGBrqmUGI2bGq2CoUbLdsT+Dh6GazJzQcnPfY2HLbuC7v7ymlrpjMwfaNnGiZUMH1mwJDUxYR96lRfFnfxCpbBITE00vnEi8z+VFeRz77US65ZZb8OGHH2LBggVISEgwQaH8v5uSklKsbV65ciViHfdLZaDXET10LKJHtByLPk1sWL8zVBLbvK4XqVsspG6JvdcRC2I2qFMQ1kenpaWZEztrtDt06FDRmyQiIlFu+kIfdqYFEe/ibAXA0g1BpCRySstQMIdXl5iVw5mvGNFhvx0GdnjfZPXsy+Zhxm7NZCA9k7XNFlZtCSIxPoC+HZ1o1chtgketGtnRuI6COiJhvBjHPjn5gyYsycr/OM5+xeAMs7PzP+6VV14xvXXYa4c4Cxav8sbFxZnHfPHFFwc8R/6Lg4XF9UWWgcUS7hN+UWJWkyOcjhiD9Dqih45F9IjGY3F0GbwOZkCHSuGjr5QrUkZGRrldBKhUQR02u7vppptw7rnnmqsyDz/8cEVvkoiIRDn2xaFqSTY0q+/Ajj1+1EiyoVMLB3bvDSI92zJBnO17OMNVaOarfRVYufwstdqXuVNr38X/8ExbbBjY/4hKdboVKTV9+/aFy+XCyy+/jIsvvhgzZ87E1KlTTQPkSK1atULnzp1Naf3tt9+O5cuX47PPPsMTTzyR23Pn119/NWVXzN4ZN24cmjVrZr4U8F/+3sSJE02z5K+++gpr1qwpcNr0wrDb7VHzham4uP2x/hpIryN66FhEj8p6LDKyLfz4txerNgdNeXu31k707+Ys0WxaZYnnivJSqUaZDORwmksREZHCat/UgRUbQ+VRbGrXrJ4L/9/efcBHUaZ/AP/NbEklhFBC70V6QARUmgX1Dg/sZ/cUPSznwSl63nkinnLeYRfPgtyfU85eERQVRekiIFKFEHoNpJJkky0z8/8872bDJoSSZJPdDb/vh2Xb7OzM7G529pnneV7D1NRRIFeJiV2HDKzNMMoydGTfIdAmJ7AbITsXzZKB7CMWjrgMOGwa2jW3Yd9hE42TTCxa68XgHnbVrJmIjpJeOJKpM3nyZEyfPh1NmzZVARnJhpGhxx999FGsWbNGTTtt2jR1fejQoSpbZ8KECeqyeOSRR/Dkk0+qvopSWiXBIsnekR8EKSkpat5TpkxRw5rLSFhyn9xORETRYfYSN/Zn+1Ok3SawcrNXZVMP6V39Icjri4gJ6sgwlXfccYcqmwqQUQkmTZqEzZs3o1mzZpg4cWK1j6pUJ+0r1M2Z6kvTJ6o7fM8Q3zO1r2trDb85244dB0yVrTOgmw3pe03s2G/gixUm0ncbyCvwl1hJFEevENgR0gsvu8DfX0cuezQLBS4D2w+YaNYIyMo3kJnrwxVDjx1uOdz4d6Zm241qTkqrZs2adczto0ePVqeA1NRUldFTGSmHkqDN8Zx55pn4+OOP+XIREUUhKZMPBHSCrd/uY1AnUoI6c+fOVUdegjtEezwejBs3Dtdffz3efPNNLF++XPXKkXTcQDPk2iRpvbWFTZ+I7xmqbfw7U3XN5ECPB9iwHth6wIm1O2Pxy454FLr8qb12XYPX1GFplgreaNDKSrCkvttVDDjsFjSbBa+hYfMuDXYZFSvPhaR4E8tsFn5Jd6nGy8kJBtI6FKsMn0jB9wwRERFFokCYQHoZ5hdasOlAw0RNZVFTBAR1pBZ62bJluOeee1RabYBk7MhQk2PHjlVNkIYNG4bhw4dj9uzZuO+++2p9ubp27XrMyAv1sXkVRTa+Z4jvmbq3ebeB3elexCRa0O0++Cx/4+RYp4yUJaW+mjqX/jr20nJpCfqopn1S8+32N9yR614D2JtjQ0M3kJyo46fdSejeVodu6sj02HDp2eFPGebfmeqRYbJr8wAQERER+TVN9pevr97ig8/wH1KLi9Xw2/NiuIkiIahz0003qbKq4LIrsW3bNlVPHdzVWjJ0Nm3aFPUNpupL8yqqO3zPEN8zdWdflqG+exJiNfTuYMPKLZY6QpQYZ8Fu01DitWDJqFh2qHM5SmQZ/r46gaHPReBcRs6S250OwDQsbNljoVEDEy43MGZIbMS8ufl3purbi4iIiGqf7IdJVCDG4d/vkiNn8TGSqRM8bMXpK+xBHamPPt4RsNjY8ju7cl2yd4iIiGpLcuLRgwltUm3o0NKGa893Ys4yL2bOc8OChWI3oEn/HO3oKFiSlRMQmINVuiMSFwM0bqBhZ6Zk/ZiwLE0Fe7bvN9CxJYMDRERERMcjI5DKPlXfznaUePzlVw67hr2HQhPUkZFQpTxeDt5Fo7AHdY5HSp9k9IJgcj3UJVFERETBzuxqx+F8C1t2G6rk6leDnGieomPLHjeSEoBij4YYu4Xi0uHNKzZNVrsDQWOey4iWUqUlTZgle0eCOW6vheaxwLKNXgZ1iIiIiE4gPlaSczRk5xtqpFFJlk1N1tGoWc2GDT+Ua+KrlR4czDHhtGvo18WOoX3s5aqFokHEBnWk1EqGuAyWkZGhSrKIiIhqi82mYdRgJ349yCr3pS4BnpwCCzbNUoGaxFiojJ3iCk36VCzH8pdjBQI+Egjak2mqMqzcAhNHiiRl2ECMU0P3tj6c2S1iv46JiIiIwqphgg7DtFTfw4DMHAsDu1e/N6FhWPhokRuFxf6jcB6fhRW/eJEYp6F/1+jaL6tZaKsWDRo0CA6HQw1dKSNhLVq0CAsXLiw3tCUREVFtqXiUpn8XOxrGa5Dybfn6b9ZIU4GeiiRzN1DzbQaOLsFSQ3HGx1hwe/07Dh6vicZJFlZu8fJFJCIiIjpBeZSUXLVpZkN8rIYG8To6t9KRU3DsMOenalemWRbQCbZxpy/qXoeIDUE5nU6VqTN58mRMnz4dTZs2xdSpU5mpQ0REYbE3y4Bemn1jt0nQRkNKkj9I4/MBntKDR5KZo8qybP5myflFQH7pPDLz/Ocyj9wCYMdBL1o28aF1Ex2jznZGXbovERERUW3LK7DUPlWbZro6BWTlh75RcmCgi5rYe9jAngMGKjn2V7+DOpKZs2bNmmNKsGbNmhW2ZSIiIhLpew3kF5oocElARrojW6ptTkKcDofN9I/EUEr2BaRvjgx37jvOASQJ+sjj5XG5BRa+/NGjeuv0aB8xX8tEREREEaFxQ031vJFM52DS87C62qbqqtSqYrZOzxrui3290oOfM7xw6j4M7Xyal18RERFFikKXv79Os2QNTZM1tGtuU/XW0lT58mEONGsEJMQCsQ7/6ZQGTwgMee4F8gotHHFxWE4iIiKiimIcGob2dRxz2/AKt1WFjHR15bAYpDbyh0QkaDTwDAf6d63+qKT7s0zMX+VWI6Z++1PdldfzkCAREdFJSKpvQqymjui4NP8wmrITMKCbDW9+7UacU0OuZakMHcnCCTRLPhG5WxonS9nWviwTsU6WXhERERFVRnobSrm6ZE9L78Lu7exqv6wmmjXScfPFsSgqsdQ8azqkefoeL7780aeGXY+tw0gLgzpEREQn0TRZxzXnxaBtM5vamZCU3QHd7LDpGlo18aodgbgYA1v2WKqkSuqxg4c5F9LgT26XfjxyLgEdqeRq3khDowYadh8ykcYBHomIiIiOG4SRU6glxIbmwNqyTf6ATl1jUIeIiOgUtG5qw1UjyqfkfrPag2IP0CLFhpaNdew55FF9d2SELCFBGynHiosB2rfQsW2fqW4zTX/fHZvNP1JWYTGQlVf9ERyIiIiIKLzkoJ3s2xlBvRbrAoM6RERE1bBkvRc/pXtxpMjC7oM+lYEjgpsjqxEUNKj04MxcU2XrFBT7a68kk8dmAvuyLTRKNLF5tw9rt/nQtxO/momIiIhqm2VZ2LzbwI4DpsrW6dvZhuTE6mcCXdDfiU8We+D2WGX7hXWBe45ERETV8HOGTzVP7tFOx6FcYOs+Q2XkSNZNYDQs+ULv0kpX5VsyjPnhPAvZRwwUlvibKUtgR6Zp3VRXoy+szWBQh4iIiKgufPmjFxt2+Mquy8G1a8+PqXaJ1+AeDlx8lhPfrfHUaa9EBnWIiIiqQZolF7v9o2KlJOlIyrXU8OZSXqXr/iHLJWDjdMhlC7FOHTFOOdfgNSxY5tGMnRKPgex8IGO/D5t2+tAgXkNKAx0JNWwASERERETHyso3ywV0hNtrYfkmL8acG4PqemJsPH5Kd2LDtrorq2dQh4iIqBrO7+fA3OUe+AwLDeKlkbIdr8w2cDDHKiu7krrqXQdN7LEB8U5TXZcwjfTUKSqG6scjso/IUJoGVqcbmL3Ei+7tbBjU3Y4rhsWgXWod5u8SERERnSZBncpk5des0bEc7DuzmwNdWzqRno46waAOERFRNXRpbcPYX8cgr8hC4yQd+7NMDOrhRNfWBjJzLTVMuWTz5Bf6dw5iY2TUBg2moaN7O2DROp8a1lwaJku5VqBkq8BlYddBA22b6fhujRe/u4RBHSIiIqJQap6iqwCM9NWpeHu0YVCHiIiompISdCQl+C97vBbiYzTEN7UjuYGFYrcXJR4NmlaauQMNsQ4dcGiIj9VUxo6UaskFdV5KJjVLhzz3ePnSEBEREYVacqKOAd3sWLn56M6W7J+d3TP6QiTRt8REREQRJjPXwGfL3Fi20YfWTXSVkWOz6fD4TBS5Lbi9EqixkBunoVtbHV6fpYI2rhJ/QEcNgamXBncsf/bO4TwTTRr6MOn/itA0WUq5/M2Wz+/nVIGgzBwTTZM11cxv004DDjvUyFl12ZiPiIiIKFqNSHOgY0sdO0tHv+re3qYO0EUbBnWIiIhqIKfAxB9fLMK2/QZM08LuTAPtmtuQFK9h8y4TxW5/1k1ugTROlmHNTWzfb8FT2ptPDW2uA7FOfw8e2ZmIcQBrtvqwZqsGp93CEZc0ZgY6t7Jh9lIP+nS0oUlDXTVqlnKtwCgNv+wycMOFMarsi4iIiIhOrG0zmzpFs+grGCMiIoogP2z04UC2qTJsdE2DYQC5RyzsPewfCUtI0EaCN9I3Z18WUOT2Z+cE7pdzGSVLMnFk5CsZNctrAEUllsrakUwfOZdhz3MLTOw86G/ul1Pgf54Aye45kFN3oy0QERERRTPDsNR+XH5R9O4/MVOHiIioBhJi/cGYAAnQSMaNQ0a6kn45UlFVeo7S4c5VH53A9KUnmYeaTk0jPXesY3ruyGNlOsnaCQSL5LmCxTBLh4iIiOikdhwwMG+FRx1Ek6bJXVrrGDXYCbstujKemalDRERUA0P7OHBOLzvspYGW1BQNg7vb0bujTQV8ZNQrCcTEOwHTZ6FhvIW2Tf2BGcnWkcbIcop1yIgLgNtjwu014bQDyQmAaUjZlj+AE+fU0L2tDZ1a2lTZlfTa6d7WjhKPpXrsJCcCKUnH7ojIyA5yFGpflr9EjIiIiOh05vZamLPMH9AJ7Cul7zGw4pfS+vgowkwdIiKiGpCsmim3J2DjTh9Wb/Fhz2FTZemYlob+Xe3YsN2HzDwgv8ifdXMo/2iQJhBekbKsPVn+U9kXdOlhl0AMphjA7oMGLhkYo+YlQZy9h4BOrUwsWe9TAaJt+zXVr+dvN8erhn+BnZQ5y73Ystu/k9Iu1YYrhzlhi7KjUEREREShsjtTDqIde6BLAjvn9nJE1YZmpg4REVENScpurw4OFWyJcWgq0HMwx8T67SZ8poYSaZZcOky5BGmkSXKx58Tz9Jn+k0zvHxIdyCkEPl3iVY2QD+dbyC008ctuE9n5luq3Izbv9mHhz55yOy2BgI7YlWlgyx6DrzkRERGdtpzHidvIfly0YVCHiIgoBCQjRjJuAqSxsSqtso5m29T4OeAPCAkJEPmfJ5A2fHQ6aawcEJg+mDRhJiIiIjpdtW2mo3HDY8MhaZ2jbyQsBnWIiIhClK3TN2hHoFVTXe0wJMRpiI852jRZTlJ6FXOSAmjpwxOokFKPlb48McDA7v7naJ6iI8apoXNrG+JjNSTG+SdulKTj7B5HZ942VUdK0tGvexldq3PL6NthISIiIgrlfttVw5zo1tausnMaNdAxcoATPdpHX4ea6FtiIiKiCHVBfydaNtaRW2iheSP/8ObfrfVi7yEfXCUWth0wVLCmcysHmqdo2LzLg9Xplsq66dACOOIC8guBpHigYYKGlk10mJaFn7aaaBCn4U9Xx6JpQx2L1nnRo50NN450wmtoGH2OAys2GXCVmBjQza76+2w/aOCsrnYUuIBLBjpUo2R5np7t7SrQRERERHQ6S0rQMfocJ6IdgzpEREQh1L2dHYfzTHzwvVudS48bSbMpdlvIL9RKe+oY2J2poV+XGHRrq6FxkoWXZ5dgV+bR+eiaBZtmwCwtrZLrf3yxGKYJuH3+0bPOaKvjlQmJmPuDiW9We5Cxz8QrczywTCAuBkhtpOPisxxwOjR15OnXgxzqyBQRERER1Q8M6hAREYXY9z971RCZuzJN5BRYyCs0VRDmcJ6lMm48XuCAYSI1RUfjJE01P957uPw8zAq9eORyYUlpCZfmHzFr614TT75drC7vzzLhKoGat9zvcgP7skys2uLDOb0c2LTTp7J7OrRg6RURERFRfcGeOkRERCHmKR0i02dYKmtGNUsuPRdSUiVdjz0+S5VoebxmuUbHJxI8mTymwCUNmv0lXKoxc8VlCWqUXOIJUcdmIiIiIooIDOoQERGFWM8O/kTYZo10xDr9/XEaJgBxsRpsNv91aW7cvJGuyqTSujgQd5KSbq30S1uaLKsvcA2qOfLlQ6U/j03N06H7myvLfQ6bDNepoVsbW1mD5HbNmaVDREREVJ+w/IqIiCjE0jrbkRCrqfInVz+HKp3KPmIh3mFhXw6QEAv072JHQbE0RdbQvZ2OuT+4MWNOMfZnS/BHminrSE7UcaTIwIFsDU2TgcHdndif5cP6HaYahvPO0TEY0jsG5/Y2sOhnO9bv8KLAZah5tG6i4/KhMbDb/T10+nayIz6G/XSIiIiI6hMGdYiIiGpBl9Y2dQowTQu6pNCoy6ZqWGxZcpuurl8xxKlOUlIlPXHkPiHTySkwXeAxgfuFjLh13YUxuNZyqmkD09kkLagCuT0wDyIiIiKKbgzqEBER1SLpd/P5D/6RqUo8BtZmmPhljw+uYiAlSUOMw8LhXP9w5sfreBMfC6QmA7kFQLEXkFCNxGScDqB5Yx1pnWzw+qTES8OaDC92HJDADdCtrY4X/pBYVnb16VI33lvghtcHDO5hx/gr41SpFhERERFFJwZ1iIiIatGPm31I32Oo7Jjv1/jUiFhur7/J8YEsf/DFOEn/YhnVaudBfz+dQENkyeaRoc2LD5hwFVtokqyjsNjE7kx/02SnHdi828CU/7kwfWID7Njvw1vz3fD5/E+2dL0XnVraMPoc7goQERERRSvuyREREdWi/EJ/EEVGv3K5ZaQqf0BHjYhVYdjyE5HJZNqyyUvnIacSL9QoWoUuq/woWyaQlS+3WTh8xCoL6AQczDFDuapEREREVMdYUE9ERFSL2qX6v2ptNg1NkqXcyl86JZk2dhtgP8VvYnmMZOpoQdflJI9PTtTgsPtH25JzmbeQy7062FQvn44tbEhKOPpksjx9OnE0LCIiIqJoxkwdIiKiWtSjvR1eA6oEq1OrOKTv8WHZBi+yjwAdW+hIbgBs2eXD9oOAxyvpNeqfKqGS2IzDAbRvAfTtaMemXYbqvRNTGriJi9HQva0NvTr4n6NBHLBljw8rt5hweywMT7PjwWvj1HI0aajjrzfGqRIsj9fCef2cOLeXE4ak+BARERFRVGJQh4iIqJbJcOJy8osJ2/bu1saOv9/Gr36KLFu3bsWkSZOwefNmNGvWDBMnTsTIkSOPmS4zMxMPP/wwfvrpJyQlJWHcuHG47rrr1H0ejwfPPPMMvvjiCxQXF6Nnz55q2q5du6r7n3jiCbz77rtwSJS01L///W+cc845dbimREREocfyKyIiIiIKCwnGSHDmggsuwI8//qgCMQ8++CC2bdt2zLTjx49H+/bt8cMPP+DFF1/E888/j+XLl5cFaCTY8+GHH6rbBgwYgDvuuANer6S/ARs3bsTjjz+ONWvWlJ0Y0CEiovqAQR0iIqIQkNGtjJMNY3Uc8rhit1FuGHTDMFHk8qkmx64SAx6PnEwUFnvh9hjweuV+D3KPeJBf4FHXA/ORc7fHVI9R8ykx1DyF3C6P93jNaq9PddeTqKIVK1aozJqxY8eqLJphw4Zh+PDhmD17drnptm/fjnXr1mHChAlwOp3o06cPrrrqKhXEES6XC/feey9SU1PVfG699VYcPHgQe/fuhWmaKguoV69efAGIiKhMYbGFz3/w4KVPijFzXgnWb/chGjEHm4iIqIbS9xr4coUHHh/QtY0NowY5VCPik5HAyX+/LMHMeW4Uuy3VY+e8/k413PiKTT5I3EVa3kiDZOm3I6EUNeJVJfPSNX9PHTn3GP6eO/JYGTFLbouPAVKSgLxCoKjE31z5koFO/OX6Y8vBZKfm25+8aqSuPh1tGDnAAU3T4PVZmLPMg+0HTNXw+dKznejQgs2WqfokI6dz587q/RXQqVMnbNq06ZigTvPmzZGYmFhuusWLF6vLkuET7JtvvkGDBg3QunVr7NixQwWOXnjhBaxevRopKSm47bbbcOWVV1ZrmSVIFK29qALLHa3LH8D1iBx8LSIHX4uqkX2w977zIDvfv1flKgHmrTCgaybOaFvzfRv5rqgrDOoQERHVgARjPl/uKcuE2bLbhxYpGs4642jvjuPZuNPArK/dcJX4H7txl4F92cU4nOcfplyCRKdKgjf5Lv9oWL7SJsuB4I9clkCPBHQ0GTVLA3ILLXz7kwc922s4I+XofPKLTHy9yqsyhMTabT60aqKjZwc7Vvziw7b9/h+DJR6oAM/dl8XCfgoBLKLKSIZNbGxsudvkugRhghUVFZ3SdGLZsmV47LHHMHnyZJW1k5+fj7POOgu33HILnnvuOVWmdffdd6NRo0Y4//zzq/zCZGRkRP2LuX79etQHXI/IwdcictTX10IGkPhlbyz2ZDlgt1no0sKN9s38JbbVcTDPjo1bjh4oCPhsgQ8laYWIJgzqEBER1YDLbZUFdALyi06tPCm3wFTBkQA5qFPokvKr6i9P4LhQxSWwTH+Wjy53aP7LXh+QmWuWC+ocKbLKAjoBeaXrI/cFc3sttfyJ/gG2iKosPj4eJSUl5W6T63J7daZ766238PTTT6ugzujRo9Vt/fv3x6xZs8qmGTRoEMaMGYOvv/66WkEdySwKzhiKtiP58kOpd+/esNmiN8uO6xE5+FpEjvr+Wsxd7kVmiQFn6Z/fbXlAp84O9O5YvXXdutfAxkPHBoUaN9SQllbzQS0KCwvr7CAAgzpEREQ10DBBQ6MGugrQBJxqSZJM16Shhv1Z/mCJlDS1bKJhT6aFPJ+/hEqCL1UhmTpStqXr/iCRPFwvzc4RgSoXu02CMRr6dCy/K9A0WVe3S5250HUN7Zv7W/B1aKFj486j0zZrpCOhfPIEUZVICdWMGTPK3SY7wRI4qTidjH4lGTsJCQnHTCdp7pKZs2DBAsycORNpaWllj126dCl27tyJG264oew2t9utevNUh67rUf2DScjyR/s6CK5H5OBrETnq42txpMjE1n1WuVJdsSrdRFqX6v0t79BSR6zTUAeognVr4wjJ9pPvirrCoA4REVFNvkhtGq4Z4cSS9T6UeCx0b2dDp5antjPQuqkN/7g9Hv/+tAQ5BRbO7mnHkN52/LDRiznLPSgqtlRQRfYtCosBw2eh2Osvp5L/JKvHa/gDOc1TgLapNrg9lrpNojkew4JXdlY0Hc2SgU6tbNh5wMDBXKBNUx3XXxiDEWk2rF17dJlinRp+e34Mlq73qQykPp3saNXEvz7d29lVFlH6HkMFfob09vfaIaouyZqREqlXX31V9bmRka0WLlxY1gA5oGPHjmqY8qeeegoPPfQQ0tPT8dFHH6msHPHss89iyZIl6nHSe6fijvXUqVPVPOT5JMjz+eef44033uALR0QUBaQXoPTAOeb20gNQ1RHj0DDqbCfmrfCoUnrRuZUNg7pHX4gk+paYiIgowiQl6Pj14OodKerV0YFX7ivff2dANyf+cIU/G6G2VdYwNaWBjt+cU/n69O5oVyeiUJBsGcnUkSyb6dOno2nTpioAIxk4n332GR599FE1/LiYNm2auj506FCVrSMjYcll6asj2TkSYPzVr351TDnW2WefreYvJVkyIlaLFi3wz3/+E3379uWLSEQUBZola4iP1cp6EAa0b1GzbBg5CHfn6FgczDHV/GX/Jxpxr4yIiIiIwkZKq4J73gRIT5xAXxwhw5VLRk9FcXFx2Lhx4wmf4/LLL1cnIiKKPjKi6K8GOtUADR6fP7Ajpe8j0k4+KMWpZFxL5nQ0Y1CHiIiIiIiIiCJWR8mqGROLnQdNOO1Au1RdlagTgzpEREREREREFOGkD063NtGdVVMborNojIiIiIiIiIjoNMegDhERERERERFRFGJPHSIiojqw44CBFb/4cDjPhIwC3qShroYwz8qz8PlyN37e7kPzRjrO72/HT+kmftziRlaehgaxFqQn4KFsoMhzdH5SRi6V5EbQQBByPTkRcHsAV9C0genjY2R4Z+CI6+jtsQ5JZ26DxPh8xDp0xDotxMZocNo1NTx7SpKOVZs92LrXhAUNPdrr6NHOoWrZZYjz/l2rviuxZY+B1ek+NRS7DIvesgmPMRERERFVR70L6jz33HNYuXIlPB4PJk2ahD59+oR7kYiI6DSXlW/ik8UeHHGZWLfNUEGdvp1tWLvNB4/XxOJ1BkzTwt5MA0vWe1UAJucI4DMtZFpA+QE8/cxKbpSbcgsrXwaZvrDk2NtLvHKyId8l6bsmNB3QNAsNE/zBF1tpEMinRj63kJVvIH2PiaF9HNifbSLWCfRof+q7E/uyDDV6hWX5V0CGEb3t17FIjGOzQyIiIqLTOqizdOlSHDp0CG+//Ta2b9+ODRs2MKhDRERhdyDbhGFaKHRZKpgh8Qy5nF9kIb/QVAEd4fEBJaUZNoEMnMoCOrXFlGwfU4I6gMcLtZyyaIbhv6yWywRcbv+yN08B9h420aP9qT/HvsNmWUBHuL0WDuWaSIxj40MiIiKi0zqos3z5cjRv3hx33HEHnE4nHnvssXAvEhEREVKS/FkocTGl2Siapi57DcCu68jYb6rojc0G2O3+Uqlid/nSqrogSycBHTk57P4AjkPzZ+lYEvGx/Pc5HRriY7Vy63aqGjcsX2olw5EmN2CWDhEREdFpFdT54IMP1CkgISEBLVu2RGFhIV577TXMnz8fzzzzDJ588smwLicREVGrJjZc0N+JpRu86GbYoGkamibruGyIHZm5Fkq8FtL3mmiSZMOg7jZs3Glgw3Yf8ouBGAmuGEBhsT+T5mRsmj+7xjpe0KY0Iyf4NrvNgsOhqedylJ7iYnTVN6dRooZ1273Yl+UP6LRqoqN7Wxs6tpBzO/p3qdquRKeWNpzb24HVW3yqtOu8fg6kNGBPHSIiIqLTKqhz9dVXq1OwqVOnIi0tDbquY8SIEXjllVfCtnxERETBpKHw8ZoKXzLQGbaNZRgGfv75Z/X9aZNUoTpwTk+HOhERERFRzdSrQ2P9+/dXfXXEmjVr0KlTp3AvEhERERERERFRrYjaTJ3KXHDBBVi2bBmuueYadbTxqaeeCvciERERERERERHVinoV1JEeBTKMORERERERERFRfRcxQZ1169apUatWrFhRdtvWrVtVkGbz5s1o1qwZJk6ciJEjR9ZZjwE5hXqewedEfM9QqPHvDPE9Uzf4XU5ERESRICKCOnPnzsWjjz4K0zw6HofH48G4ceNw/fXX480331TDlY8fPx4ffvhhnfTKSU9Pr7V5r1+/vtbmTfUT3zPE9wzx7wwRERERRVxQ5+mnn1Z9cO655x5Mmzat7HbJ2CkuLsbYsWNVWdWwYcMwfPhwzJ49G/fdd1+tL1fXrl0RHx8f8qN68uO8d+/edTbCCEU3vmeI7xni35nI5HK5avUAEBEREVFUBHVuuukmVVYVXHYltm3bhs6dO6uAToBk6GzatKlOlkuCLrUVeKnNeVP9xPcM8T1D/DsTWfg9TkRERJEg7EOap6amHvcIWGxsbLnb5Lpk7xARERERERERne7CHtQ5Hil9KikpKXebXA91SRQRERERERERUTSK2KCOlFrt2LGj3G0ZGRmqJIuIiIiIiIiI6HQXsUGdQYMGweFw4NVXX1UjYS1atAgLFy7E6NGjw71oRERERERERERhF7FBHafTiRkzZmDp0qUYPHgwpkyZgqlTpzJTh4iIiIiIiIgoEka/Cs7MWbNmzTElWLNmzQrbMhERERERERERRaqICeoQERHVdzsOGPh5qxdZ+UD3djrO7OZAXIx23Ok37/Zh4c9eZOYaOJRnwe2x0DhJR8MGGnKOGFi31YfcIiA+BvB4gd2HAJ8J2HUgzgmUeAGvUXmartMOeHyAqW5pCyBfXZLHwgJ8VvnH2DRA04AYJxDrAJKTAI/H/7w+Qz0EDjvQIkWD17Qgg1XmFPgfExcDmCZgWUDn1jpuuCAWBSVATr6JuBgLv+w2sT/bwuDuNpzd04HNuw0kJehqvWKdGpo01NG7ow2azOwUHM4zsXGnoZanfxf7CbcxERERUTRjUIeIiKgOSIDmvQVurNtuwDQt/LhFR8Z+EzeOjIFdIiYVrNvmw4sfF2NXpg/b91vw+QCUTma3Aa4SfyClMhLYkaDJ8Uggp0TmV0k1tjy2MoZVGuwpAYpKgOyCyqfLzD12qXILg+7PM/FzhgvJiRriYyzsz/YHhmIcwMrNPny82INmybp6DgnKdG1tQ8smOnIKHBiR5sDJZOWbePsbNzylUan0PYbaxg47AztERERU/0RsTx0iIqL6ZNNOA9lHLBXQEVn5Fg7lmsjMrTyKsm67D9n5JgqK/AEdybgxTMDrA0rcxw/oRAOXG3B7LeQVybl/vWQrSDaPZOyIApcJV4mFQ3n+7bNxZ7ko1HGl7zXKAjqBIE9gHkRERET1DYM6REREdUDKiCTDJsCml5YzOSrPIEmIlWk0dZIMnUDlUX3IN5F1kNWSUq/AeulB20Vd1/2nQBaTbL9TUdl0x9vGRERERNGO5VdERER14Nzeduw+ZCCnwEKBy0LnVv7+MdIvpjLD+jpVqZbXsJDvMlDi8fekkcBQrBMoKAKKvf7bookEajq1kHNd9brRNENl7EhwJzEOGHiGHYUlQOumugpodWiuq6DMyAEnL70SfTrasHWvDbsz/c2ETrSNiYiIiKIdgzpERER1oGGCjttHxeLyISY8BtAwXlPNgI9HGiL/9YZ47M82VclWYYmFEreFRokadF2DaVkqcCElWikNNNhswPL1HhzIMdCskR0dWmg4nOfD1r2WamjsNoEYO2CZGlo109G6iY7sIwb2ZJnYfzAXztgU1Vy5f1cdxW6o0rD8AkC3A62aajAMDc4YHS2SNVjQ1fwLioHCIiAu1sLBHAstG2lITNQR75TnlmU2UOiSAI2GEp+F3AIdZ3axoU2qA3a7hcwcCylJOo4UGsjYZ+DMrnbExthwxGWqLJ74OE2VmjWI1045U0cye64Z4VSlbtIM+kTbmIiIiCjaMahDRERUV1+6Ng2pKUE1WCchzX3bpR5/+s6tymevjOgXV+VlMgwDP/+8A2lpHWCTyFAdapToP5fGyJ1bH12XBvFHl6NB1VdJjZLVpCFLroiIiKj+4+ErIiIiIiIiIqIoxKAOEREREREREVEUYlCHiIiIiIiIiCgKMahDRERERERERBSFGNQhIiIiIiIiIopCDOoQEREREREREUUhBnWIiIiIiIiIiELAsiy43Bbqir3OnomIiIiIiIiIqJ7addDA/NVeeN1eDGxXN8/JoA4RERERERERUQ0UlVj4ZIkHXp+FRCfqDMuviIiIiIiIiOi05DMsVTJVU1v3GiqgI9k6a7f5UFcY1CEiIiKisNm6dSuuu+469OvXDxdffDHmz59f6XSZmZm4/fbb0b9/f4wYMQLvvPNOuftfeuklnHvuuer++++/HwUFBWX3rVq1CpdddhnS0tIwZswYdZ2IiKKLYVjYfcjAwRwzJPPbstuDiybmoefvcpF2ex4mzyys8Ty/WOHBFyu82LInNMt4KhjUISIiIqKw8Hg8GDduHC644AL8+OOPePjhh/Hggw9i27Ztx0w7fvx4tG/fHj/88ANefPFFPP/881i+fLm677333sOcOXPU+cKFC1FUVIS///3v6r6cnBzceeeduOOOO1Qw55ZbbsFdd92F3NzcOl9fIiKqnr2HDbw2pwTvLXBj1tcl+N/8EhQVVz+7xuczceWjhdi820SxG8gtsDDzSw8mzyyq9jz3ZxvYccCEz5BmyagzDOoQERERUVisWLECxcXFGDt2LBwOB4YNG4bhw4dj9uzZ5abbvn071q1bhwkTJsDpdKJPnz646qqr8OGHH6r7P/74Y9x8881o3bo1GjRogAceeADz5s1TwZ2vv/4a7dq1w6hRo2C323HFFVeo4JDcTkREkc80Lcxd7lE9awIOZJv4fq232vOc/nkJjlSI30gg5uPF7mrP85tVHtg0QNNQp9gouQLT9KdJyQ5GqBmGoc5dLhdsNlvI50/1D98zxPcM8e9MZArsJwT2G6h6JCOnc+fO0IL2gDt16oRNmzYdE9Rp3rw5EhMTy023ePHicvMJkKCNfIfu2rVL3delS5dy85PHStlXVQRea9mPi1aBdSgsLISuR++xXa5H5OBrETnq82uRfcSE5fMd03x4/yGgoKB6HYnz8orRMdX/+zyY04Fy5btV0TqlBB2b+3vptGzkqbP9BAZ1KnC7/ZG5nTt31tpGT09Pr7V5U/3E9wzxPUP8OxO5+w3BgQaqGgmQxMbGlrtNrlc8uCYZNyeaTuYTFxdXdp8cPJOMHrn9VJ/jVPcR9+7di2iXkZGB+oDrETn4WkSO+vpaHG948PRq/rS+uJf/FMp5Du/qP9X1fgKDOhU0bNhQHd2JiYmJ6ggnERER1R458iY7arLfQNUXHx+PkpKScrfJdbm9KtNJQCf4fsnSkX49CQkJ6j4JClV8rJRpVQX3EYmIKBL3ExjUqbhB7HY0bty41jc8ERERRTdm6NSclEHNmDHjmKOzwaVUgelk9CsJzkigpuJ0ci4lWgMHDlTXd+zYoQ7OyYE6ue+TTz455jmuvvrqKi0r9xGJiCgS9xOYikJEREREYTFo0CDVIPnVV19VmTWLFi1So1eNHj263HQdO3ZEz5498dRTT6ksG2ma/NFHH6lhyoWcz5w5U/XQkV4ITz/9tBoeXbJ0LrroIhXE+fTTT+H1elVTZQn6jBw5kq86ERFFPc2y6nKwLSIiIiKio6SR8eTJk7Fx40Y0bdoU999/vwrEfPbZZ3j00UexZs0aNZ1k6sj11atXq2wdGQr9uuuuK0tzf+WVV/D++++rbJ6hQ4fi8ccfLztKKo+ZMmWKCubISFh/+9vfMGDAAL4MREQU9RjUISIiIiIiIiKKQiy/IiIiIiIiIiKKQgzqEBERERERERFFIQZ1iIiIiIiIiIiiEIM6RERERERERERRiEEdIiIiIiIiIqIoZA/3AhDg8/nwl7/8BXv37kXjxo0xdepUxMfHc9PQKXnjjTfgdrvx+9//nluMjkveIxMnTkRubq66/o9//ANt27blFqPj8ng86j2TlZWF1NRU/Otf/4LT6eQWIyIiIoogzNSJAF9//bUK5rzzzjsYNGgQ3n///XAvEkUBy7IwadIk/O9//wv3olAU+Pjjj9GzZ0/1frnzzjvx8ssvh3uRKMJ98cUX6NixI95++2106tQJs2fPDvciEYXE1q1bcd1116Ffv364+OKLMX/+/Eqny8zMxO23347+/ftjxIgRaj8t2EsvvYRzzz1X3X///fejoKCg7L5Vq1bhsssuQ1paGsaMGaOuR9o6SOD2ySefxNChQzFgwADccsstSE9PL7v/iSeeQK9evdRzBE7Lli2LuPWQ/SFZftnWgeW88MILo+q1GDVqVLnt3LdvX3Tr1g1z585V9//3v/9V3+HB03zwwQdhWY+APXv2YODAgcjJyYmYz0Wo1iPcn41QrEO4PxehWo9o+mxs3boVt956K8466ywMGTIEU6ZMUe+lOvlsWBR2U6ZMsRYtWqQub9u2zbr77rvDvUgUBQzDsObPn2999NFH1muvvRbuxaEIV1BQYBUVFanL33//vfXII4+Ee5EoCvh8Pss0TfV+WbBgQbgXh6jG3G63dd5551mvv/665fF4rIULF1ppaWlWRkbGMdP+9re/tR5//HH1mLVr11oDBw60li1bpu579913rYsuusjas2ePdeTIEWvcuHHWxIkT1X3Z2dnWmWeeac2dO9fyer3qe3rAgAFWTk5ORK3Ds88+a1111VXWwYMH1XxefPFFa9iwYeqyuPbaa62PP/44JMtcm+uxc+dOq2fPnuq+iqLltaho0qRJ1q233qqWWdx///3WtGnTQrLMNV0PsWTJEmvIkCFW165d1TYOCOfnIpTrEc7PRqjWIZyfi1CuR7R8NrxerzVixAi1LDKdvHeuvPJKa+rUqXXy2WCmTgQoLCxEYmKiupyQkICioqJwLxJFAV3Xy0XciU5E/sZIWef+/fvx9NNP44477uAGo5Oy2Wzq6NTy5cvVkTCiaLdixQoUFxdj7NixcDgcGDZsGIYPH35MJtr27duxbt06TJgwQZUd9unTB1dddRU+/PDDsuzHm2++Ga1bt0aDBg3wwAMPYN68eWofTjKw27Vrp44w2+12XHHFFWjfvr26PZLWweVy4d5771XllTIfOcJ88OBB1Q7ANE1s3rxZZSPUllCtx8aNG9WR+8rKQ6PltQj27bff4quvvsJTTz2lljmwjpHwWgjJMHrsscfUe6eicH4uQrke4fxshGodwvm5COV6RMtnIzMzU2U133XXXWo6ee9Ixs3q1avr5LPBoE6E/NgKBHLkXF5oIqJQy8jIwLhx4/D444+jTZs23MB0St599138+c9/Vj11iKLdtm3b0LlzZ2iaVnab7IhL2nzFH+HNmzcvO+hWcbrAfAJkB9wwDOzatUvd16VLl3Lzq+w5wr0ODz/8sPqBEvDNN9+ofVD50bFjxw71Q+aFF17A2WefrX5sfPTRRyFZ/lCvh/yok75x8kNo8ODB6seXzDvwHNHwWgRIqYaUbMgPPmnNEAgw7Ny5U/0tlpKOkSNHYvr06aq8JlROdT2EHFD88ssvcc455xx3PuH4XIRyPcL52QjVOoTzcxHK9YiWz0arVq0wY8YMdTBMyDJIEOqMM84oN5/a+mwwqBMBpHZu6dKl6rKcy3UiolA6cOCAOkL4zDPP8G8MnRLZSQocRZYsr8COClE0kx8BsbGx5W6T6/IjLZgcZDvRdDKfuLi4svvk8yFHxOX2U32OcK9DMOkHIkfKH3nkEXWUOT8/X/WFkF4iixYtUj38pMH+ggULQrIOoVwP2e69e/fGq6++qpZPshOkd000vhZy9F+2v/TWCJBm9WeeeabK7JH1e/7551Wvs1D2VKzKdmratKnKFj/efML1uQjleoTzsxGqdQjn5yKU6xFNn40AyeaS94wEnO655546+Wxw9KsIIA2XFi5ciGuvvRZJSUl47rnnwr1IRFTPvPbaa2qnUrJ0hJTSPPTQQ+FeLIpgl1xyiToiJjtSsuMhjSGJop0EKEtKSsrdJtcrjjp6sulk5zz4fjniKkeSpYxe7qtYSi/ThioTO1TrEPDWW2+pslz5ETJ69Gh1mzTynDVrVtk0MpCHlBJIOcD5558fUesxfvz4cvfJ3y0JSq9fvz7qXgtp8Hr99deXC6LLSJXBP1Ll+/umm25Sr4Wc1+V6nEw4PxehXI9wfjZCtQ7h/FzUxmsRLZ+NI0eOqJFD9+3bp94/ErCqi88GM3UigLw5Ja1dPmiSMiYvLtGpkrRKDmdOJzN58mR89913akdETgzo0MkkJyfj9ddfV++X//znP2jRogU3GkU9SWmX8omKpanBafGB6aRHQvCOdvB0ci4lNQEyTznSLCn1Fe873nOEex3kaLJkGbzyyiuYOXNm2Y/WQOa4/CAJJqUclfXnCPd6yEEL6VcT4PP51A+mmJiYqHkthNwv6yHlPME2bNigXqNIeC1OJpyfi1CuRzg/G6Fah3B+LkK5HtH02dizZw+uvvpq9dv+vffeUyVZdfXZYFCnFsibTqK2NRnSjU4/fN8Q3y/EvzFEtUv2zySFX0oS5CiplE9ItnTwjzbRsWNHddRXGnLKEVP5jpa+GYHUfzmXH3vSD0GGpZWj+bJ/J0dcL7roIrVD/umnn8Lr9aoGmbIDL/0eImkdnn32WSxZskSVWVYs/ZcfG1OnTlVN0uUH7uLFi/H555/jyiuvDMk6hHI95MeQDD+dnZ2tyhWkFEaO4EvpSbS8FmLNmjXqB16TJk3KPVYyAl5++WXMmTNHvRbyWMlOkJKTUDnV9TiZcH4uQrke4fxshGodwvm5COV6RMtno6ioSPUtktI8Wabg/ll18tkI8ahfp705c+ZY/fv3V0OdVXdINzr98H1DfL8Q/8YQ1Q3Z/7rxxhutfv36qSFmv/rqK3X77Nmzy+2/yZC0MuysDC07fPhw6+233y67zzAM66WXXlLDHMtQtBMmTLAKCgrK7l+1apV1+eWXq/mNGTPGWrlyZUStg8vlsnr06KGGPJbpg08bN25U08iQzRdffLHVt29f65JLLrHmzZsX0nUIxXoI2e5//etfrcGDB6vHyHT79u2LmtciQH4n3HzzzZU+x3fffaeWXeYnvyn+97//hXQdqrIeATI0c8Xhp8P9uQjFekTCZyMUr0W4PxehWo9o+Wy8++67atnlPRH8npFhzevis6HJf6GKZJ3uJOImzbQuvfRSTJs2TUUVhURwH3zwQXVfoHO2NCyVaOl9990X5qWmcOP7hvh+If6NISIiIqLqYPlVCElDJkmXkvTK6g7pRqcfvm+I7xfi3xgiIiIiqg4GdUIoNTW10tvrYtg4il583xDfL8S/MURERERUHQzq1IFQD+lGpwe+b4jvF+LfGCIiIiI6EQZ16kAoh3Sj0wffN8T3C/FvDBERERGdCIM6dSCUQ7rR6YPvG+L7hfg3hoiIiIhOhEGdOuB0OjFjxgwsXboUgwcPxpQpUzB16lRm6hDfN8S/MxQ2/G4iIiIiin4c0pyIiIiIiIiIKAoxU4eIiIiIiOg4HnroIYwbN+6Ut89NN92Ev//972XX58yZg+zsbHX5448/Rr9+/U55Xt26dcOXX35Z6X0yL7k/+NSnTx/V4kGesy5VXI60tDRcccUV+Oijj0L+XDXZnuL8888/ZnllHrK80iKjustCFC72sD0zERERERFRPTNt2jTY7f6fWZs3b8bEiRPx7bffquu//vWvMXz48JA9V1xcHObPn19uhN3Zs2er52zVqhX69++PuvLEE09gxIgRsCwLBQUFWLx4MR577DHk5ubi9ttvD8lzVNye1XXPPffguuuuK7uelZWF1157DX/4wx/wxRdfoE2bNnW2LEQ1xaAOERERERFRiCQnJ5ddlgBHsNjYWHUKFU3T0LRp03K3SWBCMkgkw6cugzoNGjQoW5ZmzZqpkVxtNhuefvppXH755WjcuHGNn6Pi9qyuhISEcttNLkvP07POOgsLFizALbfcUmfLQlRTLL8iIiIiIiI6RVKuI2U/11xzDXr37o2LL74Y33333THlV3v37sVll12mbrvgggvUYyqWC23YsAG/+93vVPClV69eavply5bV+LWQTCEZfTewPI8++iguvfRSNbrqunXrYBgGXnjhBQwdOlQtz4033qhuF3Iu65ienl42P9M0VYZRVcuprrzyShV4Ct4+b775ptoeUqIl90tGT4As1/PPP69KpGR7yCAzjzzyiBpBuLLtGTzPYcOGoW/fvrjrrruQk5NT5W0mASjZboEsq+osy/Lly1UZl5TByfti5syZatsR1SYGdYiIiIiIiKrg2Wefxe9//3vMnTtXZaRI3x35sR+sRYsW+O9//6suf/DBB6r0KpjL5cIdd9yhRsT99NNPVcBEMlwefvjhameByDwlkLBt2zZceOGFZbfLvB988EG8/vrr6NGjB1566SV89dVXeOqpp1RAYuDAgbj55puxb98+FZDo2LEjPv/887LH//jjj8jLy1OBiqqIj49XZWAZGRnq+ocffqiWQdbxs88+U4GRu+++uyyg9H//939qWf/xj3+oTCMJonzyySdq2uNtT1ln6YUj8/3Pf/6j5vXiiy9WaTkLCwtVRpEEciSIU51l2b59O+68804VqJL3hWxvmYeciGoTy6+IiIiIiIiq4Le//W1Z0OSPf/wjxowZgx07dqgMl+DMj0ApVkpKyjFlV8XFxbjttttUpk5wVo30n5GeNElJSSddDgloBDJ/JBAk85RSokmTJpXLCBowYIDKZBFut1sFP6SHjGSfBNZh5cqVeOutt1QwQtZHAjB/+tOf1P1SziXrm5iYWOX3iayHBE3Eyy+/jPHjx5cFTmR9N27cqJZHMockmPSvf/2rbLlat26tsnC2bNlywu0pgZfU1FR1+Ve/+hV+/vnnEy7Tc889pwJbQjJpvF6vyvKRgJgEbERVl0WCShL0uuGGG9Ttbdu2RX5+vgqchaqnEFFlGNQhIiIiIiKqgnbt2pVdDgQ6JDBQFdJjRkq43n33XdV0d+fOndi0aZO6z+fznXKjZMnyEVLmJJkxTZo0OWY6yZYJ2L17twrsSJmSPCZAMo2k14yQEbSk9Gjt2rXo3r07vv76a5XJUh0S0JF+O0VFRSoTSErTpkyZUna/bLcOHTqUlTJJcEmeS4JkW7duVcvbs2fP484/JiamLKAjGjZsqNbvRG699VZcddVVajtLptJ7772nbgsOhFV1WaRcTQI+wY2rJWAkzaulWXSjRo1OcYsRVQ2DOkRERERERFXgdDqPua2qJVOZmZm4+uqrVYBIsmgky0MCKzIy06mSoExwgOlEgY+AQMDo1VdfLctKCQhkv7Rs2VKVZEkJ1qFDh9T6DhkyBFUlmUMSEBk7dqwqbRIS1Kk4DHmgj41kz0i2jJQwSWbQhAkTVD+gE5Gsmaq+FpJlE9huDzzwgFrO++67TwV3AkGbqi6LbFcJ0lXWZFmCWkS1hUEdIiIiIiKiWhCcCVORBEwk0CElPYHppPyptkkwQ4Iohw8fListEtIzRkqQJINFSL+bf//73zhy5AhGjRpVafDkZKQnjTyXDHUuZVhSGnbw4MFygSgphZIMIelRJGVYEmS5/vrrywIlkh0TKGs70fasCXlO6cvzl7/8RfXNkXWt6rJIbyTJtgpeNxkeXZpESxkXUW1ho2QiiihypESOjEg9tOxYnHfeeaouXHY8AqQZ4bhx40LyfHKUTL5wQ2316tUqjVdGs5D1kKE833777XLTSD257DAQERFR/STlUELKq6T8KFjz5s1Vz5Vvv/1WlSVJ3xrpKyMqNl0O9TLJaFcSaJDnlkCFlBhJGZI0fQ646KKLkJ2djXnz5qkeOycjfYBkf00ye6RRszQSlvlKhkug/4wEbqZPn65Kxvbs2aOmkett2rQp2yaLFi1SwZFffvkF999/v5pnYHucaHvWhJSxSRaOlE/NmjWrWssi2Ugy+pWUrcljvv/+ezz22GMqS0fX+bObag8zdYgoYkjNtTSXkyM6f/7zn9WOhQwZKSNMyM7HO++8o5rRhZKkAMuXbcURKWpCvtwloCOBp7/97W9qfeRLXpr4yTrKDo2QBoSyE0FERET1kzTXlbIqCWxIUED6vQRccsklWLNmjRoJSgIFkukhQQBpVCxDnUtQobbIssj+yeTJk1VgSfa5JCsnuCxKegVJXxnZrzlRT5sA2ecJkP4xsj4SOAoeMUsaI0u/GxmdSoI/EsyR/SM5mCdkelkm6ekj85Bh1OUxq1atOun2rCkpgZP9QVk2WZ6qLovs+0kj6GnTpmHGjBlqn1WynuR+otqkWdUdL4+IKMQef/xxLFmyRB0pCjTqE5L2K7XMUncuabCSqSMN52TUhpqSYTQlHfif//wnQkXmJaMuSOPDYJJeLEemJL2XiIiIKNJJEEOCHTL0OhFFJuaBEVFEkCNUs2fPVjsPwQEdIUEXOeJRWeM5CQBVbLYnR0guvfTSsuty1ERquXv16oXf/OY3agQHIcEhSTuW2unA0JpS/iVHyaTG/KyzzlJZNZJCGyDLJ+m5Mv9BgwZh3bp1xyyT1FjLYyStOJik5UrdfGXlV1KfXdnpxx9/VPfLvO688061rkOHDlUlaYHhQYmIiIhCafHixSpzZ/369bjiiiu4cYkiGMuviCgiSNBCarGl/0xl+vTpU635StDmjTfeUDXqkiYrgSMZ3UBul3TnrKwslV4sQRIhARtZFskCkuCSBGGk9OvLL78sG7JUmv5JoEjqw3v06HHMc8rIBx988IFKqx4wYIAK/px99tlq3SRAVRnJUAqQpoky8oXUaktPHgl4SUBIAk1SsiXbSbKBJNU3FNlKRERERMGkYfNPP/2kDnTJ0OtEFLkY1CGiiCD13OJ4QY/qkgCNw+FAq1atVFDnrrvuUgEiCdBI0EaG6JThO6XuWfr3fPbZZ5g/f35Zwz7ZmVm6dKkKBkm/HyGBGklFPp4OHTqo7J/XX38dCxYswA8//KCCSu3bt1fBmIqZRUJGgwju8yPlZfJ4qXeXeXm9XrUsgZEWpPGg1LlnZGSomnUiIiKiUJHhzokoOjCoQ0QRQRrQBQd3QkXKrd577z2MHDlSZdVI6ZKkEVcs8RISIJE2Y9IQL5g09Nu+fXvZdQkQnYwEhSQ4I6f09HTVR0cyhqScS7KEjhe8kmWVTKDgptDyeBn+U7J2KpLRJRjUISIiIiI6PTGoQ0QRoV27dqqcSRoMV1ZqJUeMJOAjo2KdjM/nK7ssKcNz585VvWlkWEopo5KSKullUzFIIo+TISelxEkyZIIFSq9ETEzMCZ9fRkuQLBrJ6BFdu3ZVJxmefdSoUWodK8v0WblypWoWLdk8wWVdslxyXUYBq4gp0UREREREpy82SiaiiCDBlMsuuwyzZs1SzYqDZWdnqyyXykhpVUlJSblAzq5du8ouSxBHRqGSnjYSEJo3b54aIvSbb75R9wfKmYQM52mapgoeSZBJTpKV88wzz2Dt2rWnvC7Lli1TtegVydDpxwvESOnXH//4R9UMOrjJs5BMHCkja9KkSdlyyXI++eSTyMnJOeXlIiIiIiKi+oVBHSKKGNIcWDJkZIQpyaqRQMb333+P3/3ud2jYsKEa/akiaT4sJVPSuFgCI++//756bIA0GZ46dSq++OIL7Nu3TwVz5Lx3797qfmlGLNczMzNVLxwp0/rrX/+K5cuXY8eOHaqZsjQx7tKlyymvx4QJE1QwSeYjwSBZD1mm8ePHq/Kvnj17lpve5XKpodW7d++O2267DYcPHy47FRUVqRKyuLg41Rh58+bNaiSKiRMnYv/+/apPEBERERERnZ40S34NERFFCGkQLKVWEnyRoIZkpwwfPhx/+MMfyjJcZChymS4w8pNkxUyfPh15eXkYMmSIGopcSqik7ErIfdKrRgI3qampKmgkgSKxYsUK/OlPf1IjTkkgR4IoEgSSZsmSASRlTw8++CDS0tLU9PJYCfAERss6HpmXDMO+YcMGNU/JDpLSKwlMSYAmMKS5NF+WodZvvvnmSucj633vvfeq3jlSlrVq1SqVnSSZRxI0kvUhIiIiIqLTE4M6RERERERERERRiOVXRERERERERERRiEEdIiIiIiIiIqIoxKAOEREREREREVEUYlCHiIiIiIiIiCgKMahDRERERERERBSFGNQhIiIiIiIiIopCDOoQEREREREREUUhBnWIiIiIiIiIiKIQgzpERERERERERFGIQR0iIiIiIiIioijEoA4RERERERERURRiUIeIiIiIiIiICNHn/wFQfoY6x2cp9gAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# === Evolved Clusters and Convergence ===\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", + "\n", + "# Left: Cluster distribution\n", + "ax1 = axes[0]\n", + "ax1.scatter(x_evo, y_evo, color=COLORS[\"prey\"], s=12, alpha=0.5, edgecolors=\"none\")\n", + "\n", + "ax1.set_xscale(\"log\")\n", + "ax1.set_yscale(\"log\")\n", + "ax1.set_xlabel(\"Cluster Size\")\n", + "ax1.set_ylabel(\"P(s)\")\n", + "ax1.set_title(\"Prey Cluster Distribution (Evolved State)\")\n", + "\n", + "stats_text = f\"R = {R_mean:+.1f} ± {R_std:.1f}\\nLognormal\"\n", + "ax1.text(\n", + " 0.95,\n", + " 0.95,\n", + " stats_text,\n", + " transform=ax1.transAxes,\n", + " ha=\"right\",\n", + " va=\"top\",\n", + " fontsize=10,\n", + " bbox=dict(boxstyle=\"round,pad=0.4\", facecolor=\"white\", edgecolor=\"0.8\", alpha=0.9),\n", + ")\n", + "\n", + "# Right: Evolved vs initial conditions\n", + "ax2 = axes[1]\n", + "ax2.scatter(\n", + " df_phase2[\"prey_death\"],\n", + " df_phase2[\"evolved_prey_death_final\"],\n", + " color=COLORS[\"prey\"],\n", + " s=20,\n", + " alpha=0.5,\n", + " edgecolors=\"none\",\n", + ")\n", + "\n", + "# Horizontal line at converged value\n", + "converged_mean = df_converged[\"evolved_prey_death_final\"].mean()\n", + "ax2.axhline(\n", + " converged_mean,\n", + " color=COLORS[\"predator\"],\n", + " linestyle=\":\",\n", + " linewidth=1.5,\n", + " alpha=0.8,\n", + " label=f\"Converged: d ≈ {converged_mean:.3f}\",\n", + ")\n", + "\n", + "ax2.set_xlabel(\"Initial Prey Death Rate\")\n", + "ax2.set_ylabel(\"Evolved Prey Death Rate\")\n", + "ax2.set_title(\"Evolution Convergence\")\n", + "ax2.legend(loc=\"upper left\", framealpha=0.95)\n", + "\n", + "# Axis limits\n", + "ax2.set_xlim(0, 0.2)\n", + "ax2.set_ylim(0, 0.2)\n", + "ax2.set_aspect(\"equal\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Phase 3" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Phase 3 loaded: 120 records\n", + "Grid sizes: [np.int64(50), np.int64(100), np.int64(250), np.int64(500), np.int64(1000), np.int64(2500)]\n", + "prey_death: [0.0963]\n" + ] + } + ], + "source": [ + "# === Phase 3: Finite-Size Scaling - Lognormal Fit ===\n", + "from scipy import stats\n", + "\n", + "# Load Phase 3 data\n", + "df_phase3 = load_phase_to_df(DATA_ROOT / \"phase3_18698382\")\n", + "\n", + "print(f\"Phase 3 loaded: {len(df_phase3)} records\")\n", + "print(f\"Grid sizes: {sorted(df_phase3['grid_size'].unique())}\")\n", + "print(f\"prey_death: {df_phase3['prey_death'].unique()}\")\n", + "\n", + "grid_sizes = sorted(df_phase3[\"grid_size\"].unique())\n", + "\n", + "# Color palette for different L values\n", + "colors_L = plt.cm.viridis(np.linspace(0, 0.9, len(grid_sizes)))\n", + "\n", + "# Store fit results\n", + "fit_results = []\n", + "\n", + "for i, L in enumerate(grid_sizes):\n", + " subset = df_phase3[df_phase3[\"grid_size\"] == L]\n", + "\n", + " clusters = []\n", + " for sizes in subset[\"prey_cluster_sizes\"]:\n", + " if isinstance(sizes, list) and len(sizes) > 0:\n", + " clusters.extend(sizes)\n", + "\n", + " if len(clusters) < 100:\n", + " continue\n", + "\n", + " clusters = np.array(clusters)\n", + "\n", + " # Fit lognormal using scipy\n", + " shape, loc, scale = stats.lognorm.fit(clusters, floc=0)\n", + " mu = np.log(scale)\n", + " sigma = shape\n", + "\n", + " # Statistical test: power law vs lognormal\n", + " fit = powerlaw.Fit(clusters, discrete=True, xmin=1, verbose=False)\n", + " R_pl_ln, _ = fit.distribution_compare(\n", + " \"power_law\", \"lognormal\", normalized_ratio=True\n", + " )\n", + "\n", + " # Compute PDF\n", + " unique, counts = np.unique(clusters, return_counts=True)\n", + " probs = counts / len(clusters)\n", + "\n", + " fit_results.append(\n", + " {\n", + " \"L\": L,\n", + " \"unique\": unique,\n", + " \"probs\": probs,\n", + " \"mu\": mu,\n", + " \"sigma\": sigma,\n", + " \"R_pl_ln\": R_pl_ln,\n", + " \"color\": colors_L[i],\n", + " }\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# === Prey Cluster FSS with Lognormal Fits ===\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 6))\n", + "\n", + "for res in fit_results:\n", + " L = res[\"L\"]\n", + " ax.scatter(\n", + " res[\"unique\"],\n", + " res[\"probs\"],\n", + " color=res[\"color\"],\n", + " s=12,\n", + " alpha=0.6,\n", + " edgecolors=\"none\",\n", + " label=f'L={L} (μ={res[\"mu\"]:.2f}, σ={res[\"sigma\"]:.2f}, R={res[\"R_pl_ln\"]:+.1f})',\n", + " )\n", + "\n", + "ax.set_xscale(\"log\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_xlabel(\"Cluster Size\")\n", + "ax.set_ylabel(\"P(s)\")\n", + "ax.set_title(\"Prey Cluster Distribution: Finite-Size Scaling (d = 0.0955)\")\n", + "\n", + "# Annotation instead of subtitle\n", + "ax.text(\n", + " 0.02,\n", + " 0.02,\n", + " \"R < 0: Lognormal\",\n", + " transform=ax.transAxes,\n", + " ha=\"left\",\n", + " va=\"bottom\",\n", + " fontsize=10,\n", + " bbox=dict(boxstyle=\"round,pad=0.4\", facecolor=\"white\", edgecolor=\"0.8\", alpha=0.9),\n", + ")\n", + "\n", + "# Legend at bottom\n", + "ax.legend(\n", + " loc=\"upper center\",\n", + " bbox_to_anchor=(0.5, -0.10),\n", + " ncol=2,\n", + " fontsize=9,\n", + " framealpha=0.95,\n", + " edgecolor=\"0.8\",\n", + ")\n", + "\n", + "plt.tight_layout()\n", + "plt.subplots_adjust(bottom=0.20)\n", + "plt.savefig(\"plot_prey_fss_lognormal.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# === Prey vs Predator Distribution Comparison ===\n", + "from scipy import stats\n", + "\n", + "prey_deaths_p1 = sorted(df_phase1[\"prey_death\"].unique())\n", + "\n", + "# Collect prey data\n", + "results_prey = []\n", + "for pd_val in prey_deaths_p1:\n", + " if pd_val > 0.10: # Skip noisy collapse region\n", + " continue\n", + "\n", + " prey_clusters = get_cluster_distribution(df_phase1, pd_val, \"prey\")\n", + "\n", + " if len(prey_clusters) < 100:\n", + " continue\n", + "\n", + " fit = powerlaw.Fit(prey_clusters, discrete=True, xmin=1, verbose=False)\n", + " R_tpl_ln, _ = fit.distribution_compare(\n", + " \"truncated_power_law\", \"lognormal\", normalized_ratio=True\n", + " )\n", + " shape, loc, scale = stats.lognorm.fit(prey_clusters, floc=0)\n", + "\n", + " results_prey.append(\n", + " {\n", + " \"prey_death\": pd_val,\n", + " \"n_clusters\": len(prey_clusters),\n", + " \"max_size\": prey_clusters.max(),\n", + " \"alpha\": fit.truncated_power_law.alpha,\n", + " \"R_tpl_ln\": R_tpl_ln,\n", + " \"mu\": np.log(scale),\n", + " \"sigma\": shape,\n", + " }\n", + " )\n", + "\n", + "results_prey_df = pd.DataFrame(results_prey)\n", + "\n", + "# Collect predator data\n", + "results_pred = []\n", + "for pd_val in prey_deaths_p1:\n", + " pred_clusters = get_cluster_distribution(df_phase1, pd_val, \"pred\")\n", + "\n", + " if len(pred_clusters) < 100:\n", + " continue\n", + "\n", + " fit = powerlaw.Fit(pred_clusters, discrete=True, xmin=1, verbose=False)\n", + " R_tpl_ln, _ = fit.distribution_compare(\n", + " \"truncated_power_law\", \"lognormal\", normalized_ratio=True\n", + " )\n", + "\n", + " results_pred.append(\n", + " {\n", + " \"prey_death\": pd_val,\n", + " \"n_clusters\": len(pred_clusters),\n", + " \"max_size\": pred_clusters.max(),\n", + " \"alpha\": fit.truncated_power_law.alpha,\n", + " \"R_tpl_ln\": R_tpl_ln,\n", + " }\n", + " )\n", + "\n", + "results_pred_df = pd.DataFrame(results_pred)\n", + "\n", + "# === Plot ===\n", + "fig, axes = plt.subplots(2, 2, figsize=(12, 9))\n", + "\n", + "# Top Left: Prey R\n", + "ax1 = axes[0, 0]\n", + "ax1.bar(\n", + " results_prey_df[\"prey_death\"],\n", + " results_prey_df[\"R_tpl_ln\"],\n", + " width=0.008,\n", + " color=COLORS[\"prey\"],\n", + " alpha=0.8,\n", + " edgecolor=\"none\",\n", + ")\n", + "ax1.axhline(0, color=\"0.3\", linewidth=1)\n", + "ax1.set_xlabel(\"Prey Death Rate\")\n", + "ax1.set_ylabel(\"R (TPL vs Lognormal)\")\n", + "ax1.set_title(\"Prey: Lognormal Wins (R < 0)\")\n", + "ax1.text(\n", + " 0.95,\n", + " 0.95,\n", + " \"R < 0 everywhere\",\n", + " transform=ax1.transAxes,\n", + " ha=\"right\",\n", + " va=\"top\",\n", + " fontsize=10,\n", + " bbox=dict(boxstyle=\"round,pad=0.4\", facecolor=\"white\", edgecolor=\"0.8\", alpha=0.9),\n", + ")\n", + "\n", + "# Top Right: Predator R\n", + "ax2 = axes[0, 1]\n", + "ax2.bar(\n", + " results_pred_df[\"prey_death\"],\n", + " results_pred_df[\"R_tpl_ln\"],\n", + " width=0.008,\n", + " color=COLORS[\"predator\"],\n", + " alpha=0.8,\n", + " edgecolor=\"none\",\n", + ")\n", + "ax2.axhline(0, color=\"0.3\", linewidth=1)\n", + "ax2.set_xlabel(\"Prey Death Rate\")\n", + "ax2.set_ylabel(\"R (TPL vs Lognormal)\")\n", + "ax2.set_title(\"Predator: Power Law Wins (R > 0)\")\n", + "ax2.text(\n", + " 0.95,\n", + " 0.95,\n", + " \"R > 0 everywhere\",\n", + " transform=ax2.transAxes,\n", + " ha=\"right\",\n", + " va=\"top\",\n", + " fontsize=10,\n", + " bbox=dict(boxstyle=\"round,pad=0.4\", facecolor=\"white\", edgecolor=\"0.8\", alpha=0.9),\n", + ")\n", + "\n", + "# Bottom Left: Prey lognormal params\n", + "ax3 = axes[1, 0]\n", + "ax3.plot(\n", + " results_prey_df[\"prey_death\"],\n", + " results_prey_df[\"mu\"],\n", + " \"o-\",\n", + " color=COLORS[\"prey\"],\n", + " markersize=7,\n", + " linewidth=1.5,\n", + " label=\"μ\",\n", + ")\n", + "ax3.plot(\n", + " results_prey_df[\"prey_death\"],\n", + " results_prey_df[\"sigma\"],\n", + " \"s--\",\n", + " color=COLORS[\"prey\"],\n", + " markersize=7,\n", + " linewidth=1.5,\n", + " alpha=0.6,\n", + " label=\"σ\",\n", + ")\n", + "ax3.set_xlabel(\"Prey Death Rate\")\n", + "ax3.set_ylabel(\"Lognormal Parameters\")\n", + "ax3.set_title(\"Prey Lognormal Parameters\")\n", + "ax3.legend(loc=\"upper right\", framealpha=0.95)\n", + "\n", + "# Add summary stats as annotation\n", + "mu_mean, mu_std = results_prey_df[\"mu\"].mean(), results_prey_df[\"mu\"].std()\n", + "sigma_mean, sigma_std = results_prey_df[\"sigma\"].mean(), results_prey_df[\"sigma\"].std()\n", + "ax3.text(\n", + " 0.02,\n", + " 0.02,\n", + " f\"μ = {mu_mean:.2f} ± {mu_std:.2f}\\nσ = {sigma_mean:.2f} ± {sigma_std:.2f}\",\n", + " transform=ax3.transAxes,\n", + " ha=\"left\",\n", + " va=\"bottom\",\n", + " fontsize=10,\n", + " bbox=dict(boxstyle=\"round,pad=0.4\", facecolor=\"white\", edgecolor=\"0.8\", alpha=0.9),\n", + ")\n", + "\n", + "# Bottom Right: Predator α\n", + "ax4 = axes[1, 1]\n", + "ax4.plot(\n", + " results_pred_df[\"prey_death\"],\n", + " results_pred_df[\"alpha\"],\n", + " \"o-\",\n", + " color=COLORS[\"predator\"],\n", + " markersize=7,\n", + " linewidth=1.5,\n", + ")\n", + "ax4.set_xlabel(\"Prey Death Rate\")\n", + "ax4.set_ylabel(\"Exponent α\")\n", + "ax4.set_title(\"Predator Power Law Exponent\")\n", + "\n", + "# Add range annotation\n", + "alpha_min, alpha_max = results_pred_df[\"alpha\"].min(), results_pred_df[\"alpha\"].max()\n", + "ax4.text(\n", + " 0.02,\n", + " 0.02,\n", + " f\"α: {alpha_min:.2f} → {alpha_max:.2f}\",\n", + " transform=ax4.transAxes,\n", + " ha=\"left\",\n", + " va=\"bottom\",\n", + " fontsize=10,\n", + " bbox=dict(boxstyle=\"round,pad=0.4\", facecolor=\"white\", edgecolor=\"0.8\", alpha=0.9),\n", + ")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Phase 4" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Load both Phase 4 files\n", + "df_phase4_1 = load_phase_to_df(DATA_ROOT / \"phase4_18735304\")\n", + "df_phase4_2 = load_phase_to_df(DATA_ROOT / \"phase4.2_18832956\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Phase 4.1: 146410 records\n", + "Phase 4.2: 133100 records\n", + "Combined: 279510 records\n" + ] + } + ], + "source": [ + "print(f\"Phase 4.1: {len(df_phase4_1)} records\")\n", + "print(f\"Phase 4.2: {len(df_phase4_2)} records\")\n", + "\n", + "# Combine\n", + "df = pd.concat([df_phase4_1, df_phase4_2], ignore_index=True)\n", + "print(f\"Combined: {len(df)} records\")\n", + "\n", + "# Compute prey density (normalize by grid size)\n", + "grid_size = df[\"grid_size\"].iloc[0]\n", + "df[\"prey_density\"] = df[\"prey_mean\"] / (grid_size**2)\n", + "df[\"pred_density\"] = df[\"pred_mean\"] / (grid_size**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "27951 unique parameter combinations\n" + ] + } + ], + "source": [ + "# Average over replicates\n", + "df_avg = (\n", + " df.groupby([\"prey_birth\", \"prey_death\", \"predator_birth\", \"predator_death\"])\n", + " .agg(\n", + " {\n", + " \"prey_mean\": \"mean\",\n", + " \"pred_mean\": \"mean\",\n", + " \"prey_density\": \"mean\",\n", + " \"pred_density\": \"mean\",\n", + " }\n", + " )\n", + " .reset_index()\n", + ")\n", + "\n", + "print(f\"{len(df_avg)} unique parameter combinations\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Built 1331 curves\n" + ] + } + ], + "source": [ + "# === Build curves and run full Hydra analysis ===\n", + "curves_data = build_curves(df_avg)\n", + "print(f\"Built {len(curves_data)} curves\")\n", + "\n", + "# Run full Hydra analysis pipeline\n", + "df_quadratic_valid, curves_with_hydra, filtered_curves = run_hydra_analysis(curves_data)\n", + "print(f\"Curves with Hydra effect: {len(curves_with_hydra)} ({100*len(curves_with_hydra)/len(curves_data):.1f}%)\")\n", + "print(f\"Valid quadratic fits: {len(df_quadratic_valid)}\")\n", + "if len(df_quadratic_valid) > 0:\n", + " print(f\"Coefficient range: [{df_quadratic_valid['quadratic_coef'].min():.2e}, {df_quadratic_valid['quadratic_coef'].max():.2e}]\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Curves WITH Hydra effect: 154 (11.6%)\n" + ] + } + ], + "source": [ + "# === Plot Hydra curves (full vs truncated) ===\n", + "plot_hydra_curves(curves_with_hydra, filtered_curves, save_path=\"phase4_hydra_curves.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filtered curves: 154\n" + ] + } + ], + "source": [ + "# === 3D Heatmap of Hydra Effect Strength ===\n", + "plot_3d_heatmap(df_quadratic_valid, title_suffix=\" (Undirected)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Valid quadratic fits: 112\n", + "Coefficient range: [1.19e+00, 2.71e+01]\n" + ] + } + ], + "source": [ + "# === Marginal effects of parameters on Hydra strength ===\n", + "plot_marginal_effects(df_quadratic_valid)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# === Validation: Strong vs Moderate Hydra curves ===\n", + "plot_validation_curves(df_quadratic_valid, df_avg, title_suffix=\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Phase 5" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# Load both Phase 4 files\n", + "df_phase6_1 = load_phase_to_df(DATA_ROOT / \"phase6_18780164\")\n", + "df_phase6_2 = load_phase_to_df(DATA_ROOT / \"phase6.2_18832958\")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Phase 6.1: 146410 records\n", + "Phase 6.2: 133100 records\n", + "Combined: 279510 records\n" + ] + } + ], + "source": [ + "print(f\"Phase 6.1: {len(df_phase6_1)} records\")\n", + "print(f\"Phase 6.2: {len(df_phase6_2)} records\")\n", + "\n", + "# Combine\n", + "df6 = pd.concat([df_phase6_1, df_phase6_2], ignore_index=True)\n", + "print(f\"Combined: {len(df)} records\")\n", + "\n", + "# Compute prey density (normalize by grid size)\n", + "grid_size = df6[\"grid_size\"].iloc[0]\n", + "df6[\"prey_density\"] = df6[\"prey_mean\"] / (grid_size**2)\n", + "df6[\"pred_density\"] = df6[\"pred_mean\"] / (grid_size**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "27951 unique parameter combinations\n" + ] + } + ], + "source": [ + "# Average over replicates\n", + "df_avg = (\n", + " df6.groupby([\"prey_birth\", \"prey_death\", \"predator_birth\", \"predator_death\"])\n", + " .agg(\n", + " {\n", + " \"prey_mean\": \"mean\",\n", + " \"pred_mean\": \"mean\",\n", + " \"prey_density\": \"mean\",\n", + " \"pred_density\": \"mean\",\n", + " }\n", + " )\n", + " .reset_index()\n", + ")\n", + "\n", + "print(f\"{len(df_avg)} unique parameter combinations\")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# === Build curves and run full Hydra analysis (Phase 5/6) ===\n", + "curves_data = build_curves(df_avg)\n", + "print(f\"Built {len(curves_data)} curves\")\n", + "\n", + "# Run full Hydra analysis pipeline\n", + "df_quadratic_valid, curves_with_hydra, filtered_curves = run_hydra_analysis(curves_data)\n", + "print(f\"Curves with Hydra effect: {len(curves_with_hydra)} ({100*len(curves_with_hydra)/len(curves_data):.1f}%)\")\n", + "print(f\"Valid quadratic fits: {len(df_quadratic_valid)}\")\n", + "if len(df_quadratic_valid) > 0:\n", + " print(f\"Coefficient range: [{df_quadratic_valid[\"quadratic_coef\"].min():.2e}, {df_quadratic_valid[\"quadratic_coef\"].max():.2e}]\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# === Plot Hydra curves (full vs truncated) - Phase 5/6 ===\n", + "plot_hydra_curves(curves_with_hydra, filtered_curves)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "# === 3D Heatmap of Hydra Effect Strength (Directed Hunting) ===\n", + "plot_3d_heatmap(df_quadratic_valid, title_suffix=\" (Directed Hunting)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "# === Marginal effects - Phase 5/6 ===\n", + "plot_marginal_effects(df_quadratic_valid)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "# === Validation curves - Phase 5/6 ===\n", + "plot_validation_curves(df_quadratic_valid, df_avg, title_suffix=\" (Directed Hunting)\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tests/.DS_Store b/tests/.DS_Store deleted file mode 100644 index 302e1fb..0000000 Binary files a/tests/.DS_Store and 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