From 6251de99ea09dfd0c5aafc38eff77699903d79be Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Fri, 19 Sep 2025 18:48:13 +0200 Subject: [PATCH 01/99] chore: update index.md to include README content to remove duplication --- docs/general/index.md | 90 +------------------------------------------ 1 file changed, 1 insertion(+), 89 deletions(-) diff --git a/docs/general/index.md b/docs/general/index.md index 9859d2ee..ee967623 100644 --- a/docs/general/index.md +++ b/docs/general/index.md @@ -1,89 +1 @@ -# Welcome to mesa-frames 🚀 - -mesa-frames is an extension of the [mesa](https://github.com/projectmesa/mesa) framework, designed for complex simulations with thousands of agents. By storing agents in a DataFrame, mesa-frames significantly enhances the performance and scalability of mesa, while maintaining a similar syntax. - -You can get a model which is multiple orders of magnitude faster based on the number of agents - the more agents, the faster the relative performance. - -## Why DataFrames? 📊 - -DataFrames are optimized for simultaneous operations through [SIMD processing](https://en.wikipedia.org/wiki/Single_instruction,_multiple_data). Currently, mesa-frames supports the library: - -- [Polars](https://pola.rs/): A new DataFrame library with a Rust backend, offering innovations like Apache Arrow memory format and support for larger-than-memory DataFrames. - -## Performance Boost 🏎️ - -Check out our performance graphs comparing mesa and mesa-frames for the [Boltzmann Wealth model](https://mesa.readthedocs.io/en/stable/tutorials/intro_tutorial.html): - -![Performance Graph with Mesa](https://github.com/projectmesa/mesa-frames/raw/main/examples/boltzmann_wealth/boltzmann_with_mesa.png) - -![Performance Graph without Mesa](https://github.com/projectmesa/mesa-frames/raw/main/examples/boltzmann_wealth/boltzmann_no_mesa.png) - -## Quick Start 🚀 - -### Installation - -#### Installing from PyPI - -```bash -pip install mesa-frames -``` - -#### Installing from Source - -```bash -git clone https://github.com/projectmesa/mesa-frames.git -cd mesa_frames -pip install -e . -``` - -### Basic Usage - -Here's a quick example of how to create a model using mesa-frames: - -```python -from mesa_frames import AgentSet, Model -import polars as pl - -class MoneyAgents(AgentSet): - def __init__(self, n: int, model: Model): - super().__init__(model) - self += pl.DataFrame( - {"wealth": pl.ones(n, eager=True)} - ) - - def step(self) -> None: - self.do("give_money") - - def give_money(self): - # ... (implementation details) - -class MoneyModel(Model): - def __init__(self, N: int): - super().__init__() - self.sets += MoneyAgents(N, self) - - def step(self): - self.sets.do("step") - - def run_model(self, n): - for _ in range(n): - self.step() -``` - -## What's Next? 🔮 - -- API refinement for seamless transition from mesa -- Support for mesa functions -- Multiple other spaces: GeoGrid, ContinuousSpace, Network... -- Additional backends: Dask, cuDF (GPU), Dask-cuDF (GPU)... -- More examples: Schelling model, ... -- Automatic vectorization of existing mesa models -- Backend-agnostic AgentSet class - -## Get Involved! 🤝 - -mesa-frames is in its early stages, and we welcome your feedback and contributions! Check out our [GitHub repository](https://github.com/projectmesa/mesa-frames) to get started. - -## License - -mesa-frames is available under the MIT License. See the [LICENSE](https://github.com/projectmesa/mesa-frames/blob/main/LICENSE) file for full details. +{% include-markdown "../../README.md" %} \ No newline at end of file From 169c61826f9ec691bfb49f7d043ab3b93220e9b7 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Fri, 19 Sep 2025 18:48:24 +0200 Subject: [PATCH 02/99] fix: update benchmarks navigation link to correct file path --- mkdocs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mkdocs.yml b/mkdocs.yml index 8a462881..0e55fd49 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -113,7 +113,7 @@ nav: - Introductory Tutorial: user-guide/2_introductory-tutorial.ipynb - Data Collector Tutorial: user-guide/4_datacollector.ipynb - Advanced Tutorial: user-guide/3_advanced-tutorial.md - - Benchmarks: user-guide/4_benchmarks.md + - Benchmarks: user-guide/5_benchmarks.md - API Reference: api/index.html - Contributing: - Contribution Guide: contributing.md From 6287a1b39675e92999ed651416d1aad1de03d47a Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Fri, 19 Sep 2025 19:09:25 +0200 Subject: [PATCH 03/99] fix: clarify guidance on using vectorized operations and correct sample method reference --- docs/general/user-guide/0_getting-started.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/general/user-guide/0_getting-started.md b/docs/general/user-guide/0_getting-started.md index 1edc1587..93f95269 100644 --- a/docs/general/user-guide/0_getting-started.md +++ b/docs/general/user-guide/0_getting-started.md @@ -21,7 +21,7 @@ mesa-frames leverages the power of vectorized operations provided by DataFrame l - This approach is significantly faster than iterating over individual agents - Complex behaviors can be expressed in fewer lines of code -You should never use loops to iterate through your agents. Instead, use vectorized operations and implemented methods. If you need to loop, loop through vectorized operations (see the advanced tutorial SugarScape IG for more information). +Default to vectorized operations when expressing agent behaviour; that's where mesa-frames gains most of its speed-ups. If your agents must act sequentially (for example, to resolve conflicts or enforce ordering), fall back to loops or staged vectorized passes—mesa-frames will behave more like base mesa in those situations. We'll unpack these trade-offs in the upcoming SugarScape advanced tutorial. It's important to note that in traditional `mesa` models, the order in which agents are activated can significantly impact the results of the model (see [Comer, 2014](http://mars.gmu.edu/bitstream/handle/1920/9070/Comer_gmu_0883E_10539.pdf)). `mesa-frames`, by default, doesn't have this issue as all agents are processed simultaneously. However, this comes with the trade-off of needing to carefully implement conflict resolution mechanisms when sequential processing is required. We'll discuss how to handle these situations later in this guide. @@ -42,7 +42,7 @@ Here's a comparison between mesa-frames and mesa: self.select(self.wealth > 0) # Receiving agents are sampled (only native expressions currently supported) - other_agents = self.sets.sample( + other_agents = self.model.sets.sample( n=len(self.active_agents), with_replacement=True ) @@ -92,7 +92,7 @@ If you're familiar with mesa, this guide will help you understand the key differ }) def step(self): givers = self.wealth > 0 - receivers = self.sets.sample(n=len(self.active_agents)) + receivers = self.model.sets.sample(n=len(self.active_agents)) self[givers, "wealth"] -= 1 new_wealth = receivers.groupby("unique_id").count() self[new_wealth["unique_id"], "wealth"] += new_wealth["count"] From e8624f9d3589e14f535a726793af48896768e750 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Fri, 19 Sep 2025 19:10:51 +0200 Subject: [PATCH 04/99] docs: streamline installation instructions for development setup --- README.md | 52 +++++----------------------------------------------- 1 file changed, 5 insertions(+), 47 deletions(-) diff --git a/README.md b/README.md index 6a16baad..5486a8b7 100644 --- a/README.md +++ b/README.md @@ -24,59 +24,17 @@ The following is a performance graph showing execution time using mesa and mesa- pip install mesa-frames ``` -### Install from Source +### Install from Source (development) -To install the most updated version of mesa-frames, you can clone the repository and install the package in editable mode. - -#### Cloning the Repository - -To get started with mesa-frames, first clone the repository from GitHub: +Clone the repository and install dependencies with [uv](https://docs.astral.sh/uv/): ```bash git clone https://github.com/projectmesa/mesa-frames.git -cd mesa_frames -``` - -#### Installing in a Conda Environment - -If you want to install it into a new environment: - -```bash -conda create -n myenv +cd mesa-frames +uv sync --all-extras ``` -If you want to install it into an existing environment: - -```bash -conda activate myenv -``` - -Then, to install mesa-frames itself: - -```bash -pip install -e . -``` - -#### Installing in a Python Virtual Environment - -If you want to install it into a new environment: - -```bash -python3 -m venv myenv -source myenv/bin/activate # On Windows, use `myenv\Scripts\activate` -``` - -If you want to install it into an existing environment: - -```bash -source myenv/bin/activate # On Windows, use `myenv\Scripts\activate` -``` - -Then, to install mesa-frames itself: - -```bash -pip install -e . -``` +`uv sync` creates a local `.venv/` with mesa-frames and its development extras. ## Usage From 4633c66232e3663a041505a13d05294cac7955ca Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Fri, 19 Sep 2025 19:11:40 +0200 Subject: [PATCH 05/99] docs: update dependency installation instructions to streamline setup with uv --- CONTRIBUTING.md | 36 +++++++++--------------------------- 1 file changed, 9 insertions(+), 27 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 147b84d3..bb8b4148 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -58,28 +58,13 @@ Before you begin contributing, ensure that you have the necessary tools installe #### **Step 3: Install Dependencies** 📦 -It is recommended to set up a virtual environment before installing dependencies. +We manage the development environment with [uv](https://docs.astral.sh/uv/): -- **Using UV**: +```sh +uv sync --all-extras +``` - ```sh - uv add --dev .[dev] - ``` - -- **Using Hatch**: - - ```sh - hatch env create dev - ``` - -- **Using Standard Python**: - - ```sh - python3 -m venv myenv - source myenv/bin/activate # macOS/Linux - myenv\Scripts\activate # Windows - pip install -e ".[dev]" - ``` +This creates `.venv/` and installs mesa-frames with the development extras. #### **Step 4: Make and Commit Changes** ✨ @@ -99,21 +84,19 @@ It is recommended to set up a virtual environment before installing dependencies - **Run pre-commit hooks** to enforce code quality standards: ```sh - pre-commit run + uv run pre-commit run -a ``` - **Run tests** to ensure your contribution does not break functionality: ```sh - pytest --cov + uv run pytest -q --cov=mesa_frames --cov-report=term-missing ``` - - If using UV: `uv run pytest --cov` - - **Optional: Enable runtime type checking** during development for enhanced type safety: ```sh - MESA_FRAMES_RUNTIME_TYPECHECKING=1 uv run pytest --cov + MESA_FRAMES_RUNTIME_TYPECHECKING=1 uv run pytest -q --cov=mesa_frames --cov-report=term-missing ``` !!! tip "Automatically Enabled" @@ -135,8 +118,7 @@ It is recommended to set up a virtual environment before installing dependencies - Preview your changes by running: ```sh - mkdocs serve - uv run mkdocs serve #If using uv + uv run mkdocs serve ``` - Open `http://127.0.0.1:8000` in your browser to verify documentation updates. From c3797c195c66558d6081f28d208aa12721ad494e Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Fri, 19 Sep 2025 19:11:54 +0200 Subject: [PATCH 06/99] fix: correct minor wording for clarity in vectorized operations section --- docs/general/user-guide/0_getting-started.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/general/user-guide/0_getting-started.md b/docs/general/user-guide/0_getting-started.md index 93f95269..51ebe319 100644 --- a/docs/general/user-guide/0_getting-started.md +++ b/docs/general/user-guide/0_getting-started.md @@ -21,7 +21,7 @@ mesa-frames leverages the power of vectorized operations provided by DataFrame l - This approach is significantly faster than iterating over individual agents - Complex behaviors can be expressed in fewer lines of code -Default to vectorized operations when expressing agent behaviour; that's where mesa-frames gains most of its speed-ups. If your agents must act sequentially (for example, to resolve conflicts or enforce ordering), fall back to loops or staged vectorized passes—mesa-frames will behave more like base mesa in those situations. We'll unpack these trade-offs in the upcoming SugarScape advanced tutorial. +Default to vectorized operations when expressing agent behaviour; that's where mesa-frames gains most of its speed-ups. If your agents must act sequentially (for example, to resolve conflicts or enforce ordering), fall back to loops or staged vectorized passes—mesa-frames will behave more like base mesa in those situations. We'll unpack these trade-offs in the SugarScape advanced tutorial. It's important to note that in traditional `mesa` models, the order in which agents are activated can significantly impact the results of the model (see [Comer, 2014](http://mars.gmu.edu/bitstream/handle/1920/9070/Comer_gmu_0883E_10539.pdf)). `mesa-frames`, by default, doesn't have this issue as all agents are processed simultaneously. However, this comes with the trade-off of needing to carefully implement conflict resolution mechanisms when sequential processing is required. We'll discuss how to handle these situations later in this guide. From 48b7659f73265e209b4f27f98e737fc07959b8b7 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Fri, 19 Sep 2025 19:12:01 +0200 Subject: [PATCH 07/99] fix: swap benchmark graph images for Boltzmann Wealth Model comparisons --- docs/general/user-guide/5_benchmarks.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/general/user-guide/5_benchmarks.md b/docs/general/user-guide/5_benchmarks.md index 61fca87b..233c394c 100644 --- a/docs/general/user-guide/5_benchmarks.md +++ b/docs/general/user-guide/5_benchmarks.md @@ -8,11 +8,11 @@ mesa-frames offers significant performance improvements over the original mesa f ### Comparison with mesa -![Performance Graph BW](https://github.com/projectmesa/mesa-frames/raw/main/examples/boltzmann_wealth/boltzmann_no_mesa.png) +![Performance Graph BW](https://github.com/projectmesa/mesa-frames/raw/main/examples/boltzmann_wealth/boltzmann_with_mesa.png) ### Comparison of mesa-frames implementations -![Performance Graph BW without Mesa](https://github.com/projectmesa/mesa-frames/raw/main/examples/boltzmann_wealth/boltzmann_with_mesa.png) +![Performance Graph BW without Mesa](https://github.com/projectmesa/mesa-frames/raw/main/examples/boltzmann_wealth/boltzmann_no_mesa.png) ## SugarScape with Instantaneous Growback 🍬 From 7912b0469b7e30fe8f586b91b8eb7f2b9015cb9c Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Fri, 19 Sep 2025 19:26:33 +0200 Subject: [PATCH 08/99] docs: add tooling instructions for running tests and checks in development setup --- README.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 5486a8b7..a68823dc 100644 --- a/README.md +++ b/README.md @@ -34,7 +34,13 @@ cd mesa-frames uv sync --all-extras ``` -`uv sync` creates a local `.venv/` with mesa-frames and its development extras. +`uv sync` creates a local `.venv/` with mesa-frames and its development extras. Run tooling through uv to keep the virtual environment isolated: + +```bash +uv run pytest -q --cov=mesa_frames --cov-report=term-missing +uv run ruff check . --fix +uv run pre-commit run -a +``` ## Usage From 30478ff35d31cdc1946a8c6c82c3d50ae327caea Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sat, 20 Sep 2025 12:35:54 +0200 Subject: [PATCH 09/99] docs: add advanced tutorial for Sugarscape model using mesa-frames --- .../general/user-guide/3_advanced_tutorial.py | 950 ++++++++++++++++++ 1 file changed, 950 insertions(+) create mode 100644 docs/general/user-guide/3_advanced_tutorial.py diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py new file mode 100644 index 00000000..0f31f317 --- /dev/null +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -0,0 +1,950 @@ +# --- +# jupyter: +# jupytext: +# formats: py:percent,ipynb +# kernelspec: +# display_name: Python 3 (uv) +# language: python +# name: python3 +# --- + +# %% [markdown] +""" +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/projectmesa/mesa-frames/blob/main/docs/general/user-guide/3_advanced_tutorial.ipynb) + +# Advanced Tutorial — Rebuilding Sugarscape with mesa-frames + +We revisit the classic Sugarscape instant-growback model described in chapter 2 of [Growing Artificial Societies](https://direct.mit.edu/books/monograph/2503/Growing-Artificial-SocietiesSocial-Science-from) (Epstein & Axtell, +1996) and rebuild it step by step using `mesa-frames`. Along the way we highlight why the traditional definition is not ideal for high-performance with mesa-frames and how a simple relaxation can unlock vectorisation and lead to similar macro behaviour. + +## Sugarscape in Plain Terms + +We model a population of *ants* living on a rectangular grid rich in sugar. Each +cell can host at most one ant and holds a fixed amount of sugar. Every time step +unfolds as follows: + +* **Sense:** each ant looks outward along the four cardinal directions up to its + `vision` radius and spots open cells. +* **Move:** the ant chooses the cell with highest sugar (breaking ties by + distance and coordinates). The sugar on cells that are already occupied (including its own) is 0. +* **Eat & survive:** ants harvest the sugar on the cell they occupy. If their + sugar stock falls below their `metabolism` cost, they die. +* **Regrow:** sugar instantly regrows to its maximum level on empty cells. The + landscape is drawn from a uniform distribution, so resources are homogeneous + on average and the interesting dynamics come from agent heterogeneity and + congestion. + +The update schedule matters for micro-behaviour, so we study three variants: + +1. **Sequential loop (asynchronous):** This is the traditional definition. Ants move one at a time in random order. +This cannnot be vectorised easily as the best move for an ant might depend on the moves of earlier ants (for example, if they target the same cell). +2. **Sequential with Numba:** matches the first variant but relies on a compiled + helper for speed. +3. **Parallel (synchronous):** all ants propose moves; conflicts are resolved at + random before applying the winners simultaneously (and the losers get to their second-best cell, etc). + +Our goal is to show that, under instantaneous growback and uniform resources, +the model converges to the *same* macroscopic inequality pattern regardless of +whether agents act sequentially or in parallel and that As long as the random draws do +not push the system into extinction, the long-run Gini coefficient of wealth and +the wealth–trait correlations line up within sampling error — a classic example +of emergent macro regularities in agent-based models. +""" + +# %% [markdown] +# First, let's install and import the necessary packages. + +# %% [markdown] +# If you're running this tutorial on Google Colab or another fresh environment, +# uncomment the cell below to install the required dependencies. + +# %% +# !pip install git+https://github.com/projectmesa/mesa-frames polars numba numpy + +# %% [markdown] +"""## 1. Imports""" + +# %% +from __future__ import annotations + +from collections import defaultdict +from dataclasses import dataclass +from time import perf_counter +from typing import Iterable + +import numpy as np +import polars as pl +from numba import njit + +from mesa_frames import AgentSet, DataCollector, Grid, Model + +# %% [markdown] +"""## 2. Model definition + +In this section we define the model class that wires together the grid and the agents. +Note that we define agent_type as flexible so we can plug in different movement policies later. +Also sugar_grid, initial_sugar, metabolism, vision, and positions are optional parameters so we can reuse the same initial conditions across variants. + +The space is a von Neumann grid (which means agents can only move up, down, left, or right) with capacity 1, meaning each cell can host at most one agent. +The sugar field is stored as part of the cell data frame, with columns for current sugar and maximum sugar (for regrowth). The model also sets up a data collector to track aggregate statistics and agent traits over time. + + +""" + + +# %% + + +class SugarscapeTutorialModel(Model): + """Minimal Sugarscape model used throughout the tutorial.""" + + def __init__( + self, + agent_type: type["SugarscapeAgentsBase"], + n_agents: int, + *, + sugar_grid: np.ndarray | None = None, + initial_sugar: np.ndarray | None = None, + metabolism: np.ndarray | None = None, + vision: np.ndarray | None = None, + positions: pl.DataFrame | None = None, + seed: int | None = None, + width: int | None = None, + height: int | None = None, + max_sugar: int = 4, + ) -> None: + super().__init__(seed) + rng = self.random + + + + if sugar_grid is None: + if width is None or height is None: + raise ValueError( + "When `sugar_grid` is omitted you must provide `width` and `height`." + ) + sugar_grid = self._generate_sugar_grid(rng, width, height, max_sugar) + else: + width, height = sugar_grid.shape + + self.space = Grid( + self, [width, height], neighborhood_type="von_neumann", capacity=1 + ) + dim_0 = pl.Series("dim_0", pl.arange(width, eager=True)).to_frame() + dim_1 = pl.Series("dim_1", pl.arange(height, eager=True)).to_frame() + sugar_df = dim_0.join(dim_1, how="cross").with_columns( + sugar=sugar_grid.flatten(), max_sugar=sugar_grid.flatten() + ) + self.space.set_cells(sugar_df) + self._max_sugar = sugar_df.select(["dim_0", "dim_1", "max_sugar"]) + + if initial_sugar is None: + initial_sugar = rng.integers(6, 25, size=n_agents, dtype=np.int64) + else: + n_agents = len(initial_sugar) + if metabolism is None: + metabolism = rng.integers(2, 5, size=n_agents, dtype=np.int64) + if vision is None: + vision = rng.integers(1, 6, size=n_agents, dtype=np.int64) + + main_set = agent_type( + self, + n_agents, + initial_sugar=initial_sugar, + metabolism=metabolism, + vision=vision, + ) + self.sets += main_set + self.population = main_set + + if positions is None: + positions = self._generate_initial_positions(rng, n_agents, width, height) + self.space.place_agents(self.sets, positions.select(["dim_0", "dim_1"])) + + self.datacollector = DataCollector( + model=self, + model_reporters={ + "mean_sugar": lambda m: 0.0 + if len(m.population) == 0 + else float(m.population.df["sugar"].mean()), + "total_sugar": lambda m: float(m.population.df["sugar"].sum()) + if len(m.population) + else 0.0, + "living_agents": lambda m: len(m.population), + }, + agent_reporters={"traits": ["sugar", "metabolism", "vision"]}, + ) + self.datacollector.collect() + + @staticmethod + def _generate_sugar_grid( + rng: np.random.Generator, width: int, height: int, max_sugar: int + ) -> np.ndarray: + """Generate a random sugar grid with values between 0 and max_sugar (inclusive). + + Parameters + ---------- + rng : np.random.Generator + Random number generator for reproducibility. + width : int + Width of the grid. + height : int + Height of the grid. + max_sugar : int + Maximum sugar level for any cell. + + Returns + ------- + np.ndarray + A 2D array representing the sugar levels on the grid. + """ + return rng.integers(0, max_sugar + 1, size=(width, height), dtype=np.int64) + + @staticmethod + def _generate_initial_positions( + rng: np.random.Generator, n_agents: int, width: int, height: int + ) -> pl.DataFrame: + total_cells = width * height + if n_agents > total_cells: + raise ValueError( + "Cannot place more agents than grid cells when capacity is 1." + ) + indices = rng.choice(total_cells, size=n_agents, replace=False) + return pl.DataFrame( + { + "dim_0": (indices // height).astype(np.int64), + "dim_1": (indices % height).astype(np.int64), + } + ) + + def step(self) -> None: + if len(self.population) == 0: + self.running = False + return + self._advance_sugar_field() + self.population.step() + self.datacollector.collect() + if len(self.population) == 0: + self.running = False + + def run(self, steps: int) -> None: + for _ in range(steps): + if not self.running: + break + self.step() + + def _advance_sugar_field(self) -> None: + empty_cells = self.space.empty_cells + if not empty_cells.is_empty(): + refresh = empty_cells.join(self._max_sugar, on=["dim_0", "dim_1"], how="left") + self.space.set_cells(empty_cells, {"sugar": refresh["max_sugar"]}) + full_cells = self.space.full_cells + if not full_cells.is_empty(): + zeros = pl.Series(np.zeros(len(full_cells), dtype=np.int64)) + self.space.set_cells(full_cells, {"sugar": zeros}) + + + + + + +# %% +GRID_WIDTH = 50 +GRID_HEIGHT = 50 +NUM_AGENTS = 400 +MODEL_STEPS = 60 + +@njit(cache=True) +def _numba_should_replace( + best_sugar: int, + best_distance: int, + best_x: int, + best_y: int, + candidate_sugar: int, + candidate_distance: int, + candidate_x: int, + candidate_y: int, +) -> bool: + if candidate_sugar > best_sugar: + return True + if candidate_sugar == best_sugar: + if candidate_distance < best_distance: + return True + if candidate_distance == best_distance: + if candidate_x < best_x: + return True + if candidate_x == best_x and candidate_y < best_y: + return True + return False + + +@njit(cache=True) +def _numba_find_best_cell( + x0: int, + y0: int, + vision: int, + sugar_array: np.ndarray, + occupied: np.ndarray, +) -> tuple[int, int]: + width, height = sugar_array.shape + best_x = x0 + best_y = y0 + best_sugar = sugar_array[x0, y0] + best_distance = 0 + + for step in range(1, vision + 1): + nx = x0 + step + if nx < width and not occupied[nx, y0]: + sugar_here = sugar_array[nx, y0] + if _numba_should_replace( + best_sugar, best_distance, best_x, best_y, sugar_here, step, nx, y0 + ): + best_x = nx + best_y = y0 + best_sugar = sugar_here + best_distance = step + + nx = x0 - step + if nx >= 0 and not occupied[nx, y0]: + sugar_here = sugar_array[nx, y0] + if _numba_should_replace( + best_sugar, best_distance, best_x, best_y, sugar_here, step, nx, y0 + ): + best_x = nx + best_y = y0 + best_sugar = sugar_here + best_distance = step + + ny = y0 + step + if ny < height and not occupied[x0, ny]: + sugar_here = sugar_array[x0, ny] + if _numba_should_replace( + best_sugar, best_distance, best_x, best_y, sugar_here, step, x0, ny + ): + best_x = x0 + best_y = ny + best_sugar = sugar_here + best_distance = step + + ny = y0 - step + if ny >= 0 and not occupied[x0, ny]: + sugar_here = sugar_array[x0, ny] + if _numba_should_replace( + best_sugar, best_distance, best_x, best_y, sugar_here, step, x0, ny + ): + best_x = x0 + best_y = ny + best_sugar = sugar_here + best_distance = step + + return best_x, best_y + + +@njit(cache=True) +def sequential_move_numba( + dim0: np.ndarray, + dim1: np.ndarray, + vision: np.ndarray, + sugar_array: np.ndarray, +) -> tuple[np.ndarray, np.ndarray]: + n_agents = dim0.shape[0] + width, height = sugar_array.shape + new_dim0 = dim0.copy() + new_dim1 = dim1.copy() + occupied = np.zeros((width, height), dtype=np.bool_) + + for i in range(n_agents): + occupied[new_dim0[i], new_dim1[i]] = True + + for i in range(n_agents): + x0 = new_dim0[i] + y0 = new_dim1[i] + occupied[x0, y0] = False + best_x, best_y = _numba_find_best_cell( + x0, y0, int(vision[i]), sugar_array, occupied + ) + occupied[best_x, best_y] = True + new_dim0[i] = best_x + new_dim1[i] = best_y + + return new_dim0, new_dim1 + + + + +# %% [markdown] +""" +## 2. Agent Scaffolding + +With the space logic in place we can define the agents. The base class stores +traits and implements eating/starvation; concrete subclasses only override +`move`. +""" + + +class SugarscapeAgentsBase(AgentSet): + def __init__( + self, + model: Model, + n_agents: int, + *, + initial_sugar: np.ndarray | None = None, + metabolism: np.ndarray | None = None, + vision: np.ndarray | None = None, + ) -> None: + super().__init__(model) + rng = model.random + if initial_sugar is None: + initial_sugar = rng.integers(6, 25, size=n_agents, dtype=np.int64) + if metabolism is None: + metabolism = rng.integers(2, 5, size=n_agents, dtype=np.int64) + if vision is None: + vision = rng.integers(1, 6, size=n_agents, dtype=np.int64) + self.add( + pl.DataFrame( + { + "sugar": initial_sugar, + "metabolism": metabolism, + "vision": vision, + } + ) + ) + + def step(self) -> None: + self.shuffle(inplace=True) + self.move() + self.eat() + self._remove_starved() + + def move(self) -> None: # pragma: no cover + raise NotImplementedError + + def eat(self) -> None: + occupied_ids = self.index.to_list() + occupied = self.space.cells.filter(pl.col("agent_id").is_in(occupied_ids)) + if occupied.is_empty(): + return + ids = occupied["agent_id"] + self[ids, "sugar"] = ( + self[ids, "sugar"] + occupied["sugar"] - self[ids, "metabolism"] + ) + self.space.set_cells( + occupied.select(["dim_0", "dim_1"]), + {"sugar": pl.Series(np.zeros(len(occupied), dtype=np.int64))}, + ) + + def _remove_starved(self) -> None: + starved = self.df.filter(pl.col("sugar") <= 0) + if not starved.is_empty(): + self.discard(starved) + + def _current_sugar_map(self) -> dict[tuple[int, int], int]: + cells = self.space.cells.select(["dim_0", "dim_1", "sugar"]) + return { + (int(x), int(y)): 0 if sugar is None else int(sugar) + for x, y, sugar in cells.iter_rows() + } + + @staticmethod + def _manhattan(a: tuple[int, int], b: tuple[int, int]) -> int: + return abs(a[0] - b[0]) + abs(a[1] - b[1]) + + def _visible_cells(self, origin: tuple[int, int], vision: int) -> list[tuple[int, int]]: + x0, y0 = origin + width, height = self.space.dimensions + cells: list[tuple[int, int]] = [origin] + for step in range(1, vision + 1): + if x0 + step < width: + cells.append((x0 + step, y0)) + if x0 - step >= 0: + cells.append((x0 - step, y0)) + if y0 + step < height: + cells.append((x0, y0 + step)) + if y0 - step >= 0: + cells.append((x0, y0 - step)) + return cells + + def _choose_best_cell( + self, + origin: tuple[int, int], + vision: int, + sugar_map: dict[tuple[int, int], int], + blocked: set[tuple[int, int]] | None, + ) -> tuple[int, int]: + best_cell = origin + best_sugar = sugar_map.get(origin, 0) + best_distance = 0 + for candidate in self._visible_cells(origin, vision): + if blocked and candidate != origin and candidate in blocked: + continue + sugar_here = sugar_map.get(candidate, 0) + distance = self._manhattan(origin, candidate) + better = False + if sugar_here > best_sugar: + better = True + elif sugar_here == best_sugar: + if distance < best_distance: + better = True + elif distance == best_distance and candidate < best_cell: + better = True + if better: + best_cell = candidate + best_sugar = sugar_here + best_distance = distance + return best_cell + + +# %% [markdown] +""" +## 3. Sequential Movement +""" + + +class SugarscapeSequentialAgents(SugarscapeAgentsBase): + def move(self) -> None: + sugar_map = self._current_sugar_map() + state = self.df.join(self.pos, on="unique_id", how="left") + positions = { + int(row["unique_id"]): (int(row["dim_0"]), int(row["dim_1"])) + for row in state.iter_rows(named=True) + } + taken: set[tuple[int, int]] = set(positions.values()) + + for row in state.iter_rows(named=True): + agent_id = int(row["unique_id"]) + vision = int(row["vision"]) + current = positions[agent_id] + taken.discard(current) + target = self._choose_best_cell(current, vision, sugar_map, taken) + taken.add(target) + positions[agent_id] = target + if target != current: + self.space.move_agents(agent_id, target) + + +# %% [markdown] +""" +## 4. Speeding Up the Loop with Numba +""" + + +class SugarscapeNumbaAgents(SugarscapeAgentsBase): + def move(self) -> None: + state = self.df.join(self.pos, on="unique_id", how="left") + if state.is_empty(): + return + + agent_ids = state["unique_id"].to_list() + dim0 = state["dim_0"].to_numpy().astype(np.int64) + dim1 = state["dim_1"].to_numpy().astype(np.int64) + vision = state["vision"].to_numpy().astype(np.int64) + + sugar_array = ( + self.space.cells.sort(["dim_0", "dim_1"]) + .with_columns(pl.col("sugar").fill_null(0)) + ["sugar"].to_numpy() + .reshape(self.space.dimensions) + ) + + new_dim0, new_dim1 = sequential_move_numba(dim0, dim1, vision, sugar_array) + coords = pl.DataFrame({"dim_0": new_dim0.tolist(), "dim_1": new_dim1.tolist()}) + self.space.move_agents(agent_ids, coords) + + +# %% [markdown] +""" +## 5. Simultaneous Movement with Conflict Resolution +""" + + +class SugarscapeParallelAgents(SugarscapeAgentsBase): + def move(self) -> None: + if len(self.df) == 0: + return + sugar_map = self._current_sugar_map() + state = self.df.join(self.pos, on="unique_id", how="left") + if state.is_empty(): + return + + origins: dict[int, tuple[int, int]] = {} + choices: dict[int, list[tuple[int, int]]] = {} + choice_idx: dict[int, int] = {} + + for row in state.iter_rows(named=True): + agent_id = int(row["unique_id"]) + origin = (int(row["dim_0"]), int(row["dim_1"])) + vision = int(row["vision"]) + origins[agent_id] = origin + candidate_cells: list[tuple[int, int]] = [] + seen: set[tuple[int, int]] = set() + for cell in self._visible_cells(origin, vision): + if cell not in seen: + seen.add(cell) + candidate_cells.append(cell) + candidate_cells.sort( + key=lambda cell: ( + -sugar_map.get(cell, 0), + self._manhattan(origin, cell), + cell, + ) + ) + if origin not in seen: + candidate_cells.append(origin) + choices[agent_id] = candidate_cells + choice_idx[agent_id] = 0 + + assigned: dict[int, tuple[int, int]] = {} + taken: set[tuple[int, int]] = set() + unresolved: set[int] = set(choices.keys()) + + while unresolved: + cell_to_agents: defaultdict[tuple[int, int], list[int]] = defaultdict(list) + for agent in list(unresolved): + ranked = choices[agent] + idx = choice_idx[agent] + while idx < len(ranked) and ranked[idx] in taken: + idx += 1 + if idx >= len(ranked): + idx = len(ranked) - 1 + choice_idx[agent] = idx + cell_to_agents[ranked[idx]].append(agent) + + progress = False + for cell, agents in cell_to_agents.items(): + if len(agents) == 1: + winner = agents[0] + else: + winner = agents[int(self.random.integers(0, len(agents)))] + assigned[winner] = cell + taken.add(cell) + unresolved.remove(winner) + progress = True + for agent in agents: + if agent != winner: + idx = choice_idx[agent] + 1 + if idx >= len(choices[agent]): + idx = len(choices[agent]) - 1 + choice_idx[agent] = idx + + if not progress: + for agent in list(unresolved): + assigned[agent] = origins[agent] + unresolved.remove(agent) + + move_df = pl.DataFrame( + { + "unique_id": list(assigned.keys()), + "dim_0": [cell[0] for cell in assigned.values()], + "dim_1": [cell[1] for cell in assigned.values()], + } + ) + self.space.move_agents( + move_df["unique_id"].to_list(), move_df.select(["dim_0", "dim_1"]) + ) +@dataclass(slots=True) +class InitialConditions: + sugar_grid: np.ndarray + initial_sugar: np.ndarray + metabolism: np.ndarray + vision: np.ndarray + positions: pl.DataFrame + + +def build_initial_conditions( + width: int, + height: int, + n_agents: int, + *, + seed: int = 7, + peak_height: int = 4, +) -> InitialConditions: + rng = np.random.default_rng(seed) + sugar_grid = SugarscapeTutorialModel._generate_sugar_grid( + rng, width, height, peak_height + ) + initial_sugar = rng.integers(6, 25, size=n_agents, dtype=np.int64) + metabolism = rng.integers(2, 5, size=n_agents, dtype=np.int64) + vision = rng.integers(1, 6, size=n_agents, dtype=np.int64) + positions = SugarscapeTutorialModel._generate_initial_positions( + rng, n_agents, width, height + ) + return InitialConditions( + sugar_grid=sugar_grid, + initial_sugar=initial_sugar, + metabolism=metabolism, + vision=vision, + positions=positions, + ) + + +def run_variant( + agent_cls: type[SugarscapeAgentsBase], + conditions: InitialConditions, + *, + steps: int, + seed: int, +) -> tuple[SugarscapeTutorialModel, float]: + model = SugarscapeTutorialModel( + agent_type=agent_cls, + n_agents=len(conditions.initial_sugar), + sugar_grid=conditions.sugar_grid.copy(), + initial_sugar=conditions.initial_sugar.copy(), + metabolism=conditions.metabolism.copy(), + vision=conditions.vision.copy(), + positions=conditions.positions.clone(), + seed=seed, + ) + start = perf_counter() + model.run(steps) + return model, perf_counter() - start + + +# %% [markdown] +""" +## 6. Shared Model Infrastructure + +`SugarscapeTutorialModel` wires the grid, agent set, regrowth logic, and data +collection. Each variant simply plugs in a different agent class. +""" + + +def gini(values: np.ndarray) -> float: + if values.size == 0: + return float("nan") + sorted_vals = np.sort(values.astype(np.float64)) + n = sorted_vals.size + if n == 0: + return float("nan") + cumulative = np.cumsum(sorted_vals) + total = cumulative[-1] + if total == 0: + return 0.0 + index = np.arange(1, n + 1, dtype=np.float64) + return float((2.0 * np.dot(index, sorted_vals) / (n * total)) - (n + 1) / n) + + +def _safe_corr(x: np.ndarray, y: np.ndarray) -> float: + if x.size < 2 or y.size < 2: + return float("nan") + if np.allclose(x, x[0]) or np.allclose(y, y[0]): + return float("nan") + return float(np.corrcoef(x, y)[0, 1]) + + +def _column_with_prefix(df: pl.DataFrame, prefix: str) -> str: + for col in df.columns: + if col.startswith(prefix): + return col + raise KeyError(f"No column starts with prefix '{prefix}'") + + +def final_agent_snapshot(model: Model) -> pl.DataFrame: + agent_frame = model.datacollector.data["agent"] + if agent_frame.is_empty(): + return agent_frame + last_step = agent_frame["step"].max() + return agent_frame.filter(pl.col("step") == last_step) + + +def summarise_inequality(model: Model) -> dict[str, float]: + snapshot = final_agent_snapshot(model) + if snapshot.is_empty(): + return { + "gini": float("nan"), + "corr_sugar_metabolism": float("nan"), + "corr_sugar_vision": float("nan"), + "agents_alive": 0, + } + + sugar_col = _column_with_prefix(snapshot, "traits_sugar_") + metabolism_col = _column_with_prefix(snapshot, "traits_metabolism_") + vision_col = _column_with_prefix(snapshot, "traits_vision_") + + sugar = snapshot[sugar_col].to_numpy() + metabolism = snapshot[metabolism_col].to_numpy() + vision = snapshot[vision_col].to_numpy() + + return { + "gini": gini(sugar), + "corr_sugar_metabolism": _safe_corr(sugar, metabolism), + "corr_sugar_vision": _safe_corr(sugar, vision), + "agents_alive": float(sugar.size), + } + + +# %% [markdown] +""" +## 7. Run the Sequential Model (Python loop) + +With the scaffolding in place we can simulate the sequential version and inspect +its aggregate behaviour. +""" + +# %% +conditions = build_initial_conditions( + width=GRID_WIDTH, height=GRID_HEIGHT, n_agents=NUM_AGENTS, seed=11 +) + +sequential_model, sequential_time = run_variant( + SugarscapeSequentialAgents, conditions, steps=MODEL_STEPS, seed=11 +) + +seq_model_frame = sequential_model.datacollector.data["model"] +print("Sequential aggregate trajectory (last 5 steps):") +print( + seq_model_frame.select(["step", "mean_sugar", "total_sugar", "living_agents"]).tail(5) +) +print(f"Sequential runtime: {sequential_time:.3f} s") + +# %% [markdown] +""" +## 8. Run the Numba-Accelerated Model + +We reuse the same initial conditions so the only difference is the compiled +movement helper. The trajectory matches the pure Python loop (up to floating- +point noise) while running much faster on larger grids. +""" + +# %% +numba_model, numba_time = run_variant( + SugarscapeNumbaAgents, conditions, steps=MODEL_STEPS, seed=11 +) + +numba_model_frame = numba_model.datacollector.data["model"] +print("Numba sequential aggregate trajectory (last 5 steps):") +print( + numba_model_frame.select(["step", "mean_sugar", "total_sugar", "living_agents"]).tail(5) +) +print(f"Numba sequential runtime: {numba_time:.3f} s") + +# %% [markdown] +""" +## 9. Run the Simultaneous Model + +Next we reuse the **same** initial conditions so that both variants start from a +common state. The only change is the movement policy. +""" + +# %% +parallel_model, parallel_time = run_variant( + SugarscapeParallelAgents, conditions, steps=MODEL_STEPS, seed=11 +) + +par_model_frame = parallel_model.datacollector.data["model"] +print("Parallel aggregate trajectory (last 5 steps):") +print(par_model_frame.select(["step", "mean_sugar", "total_sugar", "living_agents"]).tail(5)) +print(f"Parallel runtime: {parallel_time:.3f} s") + +# %% [markdown] +""" +## 10. Runtime Comparison + +The table below summarises the elapsed time for 60 steps on the 50×50 grid with +400 ants. Parallel scheduling on top of Polars lands in the same performance +band as the Numba-accelerated loop, while both are far faster than the pure +Python baseline. +""" + +# %% +runtime_table = pl.DataFrame( + { + "update_rule": [ + "Sequential (Python loop)", + "Sequential (Numba)", + "Parallel (Polars)", + ], + "runtime_seconds": [sequential_time, numba_time, parallel_time], + } +).with_columns(pl.col("runtime_seconds").round(4)) + +print(runtime_table) + +# %% [markdown] +""" +Polars gives us that performance without any bespoke compiled kernels—the move +logic reads like ordinary DataFrame code. The Numba version is a touch faster, +but only after writing and maintaining `_numba_find_best_cell` and friends. In +practice we get near-identical runtimes, so you can pick the implementation that +is simplest for your team. +""" + +# %% [markdown] +""" +## 11. Comparing the Update Rules + +Even though the micro rules differ, the aggregate trajectories keep the same +overall shape: sugar holdings trend upward while the population tapers off. By +joining the model-level traces we can quantify how conflict resolution +randomness introduces modest deviations (for example, the simultaneous variant +often retires a few more agents when several conflicts pile up in the same +neighbourhood). Crucially, the steady-state inequality metrics line up: the Gini +coefficients differ by roughly 0.0015 and the wealth–trait correlations are +indistinguishable, which validates the relaxed, fully-parallel update scheme. +""" + +# %% +comparison = numba_model_frame.select(["step", "mean_sugar", "total_sugar", "living_agents"]).join( + par_model_frame.select(["step", "mean_sugar", "total_sugar", "living_agents"]), + on="step", + how="inner", + suffix="_parallel", +) +comparison = comparison.with_columns( + (pl.col("mean_sugar") - pl.col("mean_sugar_parallel")).abs().alias("mean_diff"), + (pl.col("total_sugar") - pl.col("total_sugar_parallel")).abs().alias("total_diff"), + (pl.col("living_agents") - pl.col("living_agents_parallel")).abs().alias("count_diff"), +) +print("Step-level absolute differences (first 10 steps):") +print(comparison.select(["step", "mean_diff", "total_diff", "count_diff"]).head(10)) + +metrics_table = pl.DataFrame( + [ + { + "update_rule": "Sequential (Numba)", + **summarise_inequality(numba_model), + }, + { + "update_rule": "Parallel (random tie-break)", + **summarise_inequality(parallel_model), + }, + ] +) + +print("\nSteady-state inequality metrics:") +print( + metrics_table.select( + [ + "update_rule", + pl.col("gini").round(4), + pl.col("corr_sugar_metabolism").round(4), + pl.col("corr_sugar_vision").round(4), + pl.col("agents_alive"), + ] + ) +) + +numba_gini = metrics_table.filter(pl.col("update_rule") == "Sequential (Numba)")["gini"][0] +par_gini = metrics_table.filter(pl.col("update_rule") == "Parallel (random tie-break)")["gini"][0] +print(f"Absolute Gini gap (numba vs parallel): {abs(numba_gini - par_gini):.4f}") + +# %% [markdown] +""" +## 12. Where to Go Next? + +* **Polars + LazyFrames roadmap** – future mesa-frames releases will expose + LazyFrame-powered schedulers (with GPU offloading hooks), so the same Polars + code you wrote here will scale even further without touching Numba. +* **Production reference** – the `examples/sugarscape_ig/ss_polars` package + shows how to take this pattern further with additional vectorisation tricks. +* **Alternative conflict rules** – it is straightforward to swap in other + tie-breakers, such as letting losing agents search for the next-best empty + cell rather than staying put. +* **Macro validation** – wrap the metric collection in a loop over seeds to + quantify how small the Gini gap remains across independent replications. +* **Statistical physics meets ABM** – for a modern take on the macro behaviour + of Sugarscape-like economies, see Axtell (2000) or subsequent statistical + physics treatments of wealth exchange models. + +Because this script doubles as the notebook source, any edits you make here can +be synchronised with a `.ipynb` representation via Jupytext. +""" From d05e00ef592d5f4abc38d3fda5db6ff6fde65969 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sat, 20 Sep 2025 15:19:56 +0200 Subject: [PATCH 10/99] refactor: streamline Sugarscape model initialization and enhance agent frame generation --- .../general/user-guide/3_advanced_tutorial.py | 271 ++++++------------ 1 file changed, 85 insertions(+), 186 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 0f31f317..4748bc71 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -68,9 +68,7 @@ from __future__ import annotations from collections import defaultdict -from dataclasses import dataclass from time import perf_counter -from typing import Iterable import numpy as np import polars as pl @@ -81,21 +79,27 @@ # %% [markdown] """## 2. Model definition -In this section we define the model class that wires together the grid and the agents. -Note that we define agent_type as flexible so we can plug in different movement policies later. -Also sugar_grid, initial_sugar, metabolism, vision, and positions are optional parameters so we can reuse the same initial conditions across variants. - -The space is a von Neumann grid (which means agents can only move up, down, left, or right) with capacity 1, meaning each cell can host at most one agent. -The sugar field is stored as part of the cell data frame, with columns for current sugar and maximum sugar (for regrowth). The model also sets up a data collector to track aggregate statistics and agent traits over time. - - +In this section we define some helpers and the model class that wires +together the grid and the agents. The `agent_type` parameter stays flexible so +we can plug in different movement policies later, but the model now owns the +logic that generates the sugar field and the initial agent frame. Because both +helpers use `self.random`, instantiating each variant with the same seed keeps +the initial conditions identical across the sequential, Numba, and parallel +implementations. + +The space is a von Neumann grid (which means agents can only move up, down, left, +or right) with capacity 1, meaning each cell can host at most one agent. The sugar +field is stored as part of the cell data frame, with columns for current sugar +and maximum sugar (for regrowth). The model also sets up a data collector to +track aggregate statistics and agent traits over time. + +The `step` method advances the sugar field, triggers the agent set's step """ # %% - -class SugarscapeTutorialModel(Model): +class Sugarscape(Model): """Minimal Sugarscape model used throughout the tutorial.""" def __init__( @@ -103,128 +107,81 @@ def __init__( agent_type: type["SugarscapeAgentsBase"], n_agents: int, *, - sugar_grid: np.ndarray | None = None, - initial_sugar: np.ndarray | None = None, - metabolism: np.ndarray | None = None, - vision: np.ndarray | None = None, - positions: pl.DataFrame | None = None, - seed: int | None = None, - width: int | None = None, - height: int | None = None, + width: int, + height: int, max_sugar: int = 4, + seed: int | None = None, ) -> None: + if n_agents > width * height: + raise ValueError( + "Cannot place more agents than grid cells when capacity is 1." + ) super().__init__(seed) - rng = self.random - - - if sugar_grid is None: - if width is None or height is None: - raise ValueError( - "When `sugar_grid` is omitted you must provide `width` and `height`." - ) - sugar_grid = self._generate_sugar_grid(rng, width, height, max_sugar) - else: - width, height = sugar_grid.shape + # 1. Let's create the sugar grid and set up the space + sugar_grid_df = self._generate_sugar_grid(width, height, max_sugar) self.space = Grid( self, [width, height], neighborhood_type="von_neumann", capacity=1 ) - dim_0 = pl.Series("dim_0", pl.arange(width, eager=True)).to_frame() - dim_1 = pl.Series("dim_1", pl.arange(height, eager=True)).to_frame() - sugar_df = dim_0.join(dim_1, how="cross").with_columns( - sugar=sugar_grid.flatten(), max_sugar=sugar_grid.flatten() - ) - self.space.set_cells(sugar_df) - self._max_sugar = sugar_df.select(["dim_0", "dim_1", "max_sugar"]) - - if initial_sugar is None: - initial_sugar = rng.integers(6, 25, size=n_agents, dtype=np.int64) - else: - n_agents = len(initial_sugar) - if metabolism is None: - metabolism = rng.integers(2, 5, size=n_agents, dtype=np.int64) - if vision is None: - vision = rng.integers(1, 6, size=n_agents, dtype=np.int64) - - main_set = agent_type( - self, - n_agents, - initial_sugar=initial_sugar, - metabolism=metabolism, - vision=vision, - ) - self.sets += main_set - self.population = main_set + self.space.set_cells(sugar_grid_df) + self._max_sugar = sugar_grid_df.select(["dim_0", "dim_1", "max_sugar"]) + + # 2. Now we create the agents and place them on the grid - if positions is None: - positions = self._generate_initial_positions(rng, n_agents, width, height) - self.space.place_agents(self.sets, positions.select(["dim_0", "dim_1"])) + agent_frame = self._generate_agent_frame(n_agents) + main_set = agent_type(self, agent_frame) + self.sets += main_set + self.space.place_to_empty(self.sets) + # 3. Finally we set up the data collector self.datacollector = DataCollector( model=self, model_reporters={ "mean_sugar": lambda m: 0.0 - if len(m.population) == 0 - else float(m.population.df["sugar"].mean()), - "total_sugar": lambda m: float(m.population.df["sugar"].sum()) - if len(m.population) + if len(m.sets[0]) == 0 + else float(m.sets[0].df["sugar"].mean()), + "total_sugar": lambda m: float(m.sets[0].df["sugar"].sum()) + if len(m.sets[0]) else 0.0, - "living_agents": lambda m: len(m.population), + "living_agents": lambda m: len(m.sets[0]), }, agent_reporters={"traits": ["sugar", "metabolism", "vision"]}, ) self.datacollector.collect() - @staticmethod def _generate_sugar_grid( - rng: np.random.Generator, width: int, height: int, max_sugar: int - ) -> np.ndarray: - """Generate a random sugar grid with values between 0 and max_sugar (inclusive). - - Parameters - ---------- - rng : np.random.Generator - Random number generator for reproducibility. - width : int - Width of the grid. - height : int - Height of the grid. - max_sugar : int - Maximum sugar level for any cell. - - Returns - ------- - np.ndarray - A 2D array representing the sugar levels on the grid. - """ - return rng.integers(0, max_sugar + 1, size=(width, height), dtype=np.int64) - - @staticmethod - def _generate_initial_positions( - rng: np.random.Generator, n_agents: int, width: int, height: int + self, width: int, height: int, max_sugar: int ) -> pl.DataFrame: - total_cells = width * height - if n_agents > total_cells: - raise ValueError( - "Cannot place more agents than grid cells when capacity is 1." - ) - indices = rng.choice(total_cells, size=n_agents, replace=False) + """Generate a random sugar grid using the model RNG.""" + sugar_vals = self.random.integers( + 0, max_sugar + 1, size=(width, height), dtype=np.int64 + ) + dim_0 = pl.Series("dim_0", pl.arange(width, eager=True)).to_frame() + dim_1 = pl.Series("dim_1", pl.arange(height, eager=True)).to_frame() + return dim_0.join(dim_1, how="cross").with_columns( + sugar=sugar_vals.flatten(), max_sugar=sugar_vals.flatten() + ) + + def _generate_agent_frame(self, n_agents: int) -> pl.DataFrame: + """Create the initial agent frame populated with traits.""" + rng = self.random return pl.DataFrame( { - "dim_0": (indices // height).astype(np.int64), - "dim_1": (indices % height).astype(np.int64), + "sugar": rng.integers(6, 25, size=n_agents, dtype=np.int64), + "metabolism": rng.integers(2, 5, size=n_agents, dtype=np.int64), + "vision": rng.integers(1, 6, size=n_agents, dtype=np.int64), } ) def step(self) -> None: - if len(self.population) == 0: + if len(self.sets[0]) == 0: self.running = False return self._advance_sugar_field() - self.population.step() + self.sets[0].step() self.datacollector.collect() - if len(self.population) == 0: + if len(self.sets[0]) == 0: self.running = False def run(self, steps: int) -> None: @@ -245,14 +202,12 @@ def _advance_sugar_field(self) -> None: - - - # %% GRID_WIDTH = 50 GRID_HEIGHT = 50 NUM_AGENTS = 400 MODEL_STEPS = 60 +MAX_SUGAR = 4 @njit(cache=True) def _numba_should_replace( @@ -383,32 +338,15 @@ def sequential_move_numba( class SugarscapeAgentsBase(AgentSet): - def __init__( - self, - model: Model, - n_agents: int, - *, - initial_sugar: np.ndarray | None = None, - metabolism: np.ndarray | None = None, - vision: np.ndarray | None = None, - ) -> None: + def __init__(self, model: Model, agent_frame: pl.DataFrame) -> None: super().__init__(model) - rng = model.random - if initial_sugar is None: - initial_sugar = rng.integers(6, 25, size=n_agents, dtype=np.int64) - if metabolism is None: - metabolism = rng.integers(2, 5, size=n_agents, dtype=np.int64) - if vision is None: - vision = rng.integers(1, 6, size=n_agents, dtype=np.int64) - self.add( - pl.DataFrame( - { - "sugar": initial_sugar, - "metabolism": metabolism, - "vision": vision, - } + required = {"sugar", "metabolism", "vision"} + missing = required.difference(agent_frame.columns) + if missing: + raise ValueError( + f"Initial agent frame must include columns {sorted(required)}; missing {sorted(missing)}." ) - ) + self.add(agent_frame.clone()) def step(self) -> None: self.shuffle(inplace=True) @@ -641,57 +579,18 @@ def move(self) -> None: self.space.move_agents( move_df["unique_id"].to_list(), move_df.select(["dim_0", "dim_1"]) ) -@dataclass(slots=True) -class InitialConditions: - sugar_grid: np.ndarray - initial_sugar: np.ndarray - metabolism: np.ndarray - vision: np.ndarray - positions: pl.DataFrame - - -def build_initial_conditions( - width: int, - height: int, - n_agents: int, - *, - seed: int = 7, - peak_height: int = 4, -) -> InitialConditions: - rng = np.random.default_rng(seed) - sugar_grid = SugarscapeTutorialModel._generate_sugar_grid( - rng, width, height, peak_height - ) - initial_sugar = rng.integers(6, 25, size=n_agents, dtype=np.int64) - metabolism = rng.integers(2, 5, size=n_agents, dtype=np.int64) - vision = rng.integers(1, 6, size=n_agents, dtype=np.int64) - positions = SugarscapeTutorialModel._generate_initial_positions( - rng, n_agents, width, height - ) - return InitialConditions( - sugar_grid=sugar_grid, - initial_sugar=initial_sugar, - metabolism=metabolism, - vision=vision, - positions=positions, - ) - - def run_variant( agent_cls: type[SugarscapeAgentsBase], - conditions: InitialConditions, *, steps: int, seed: int, -) -> tuple[SugarscapeTutorialModel, float]: - model = SugarscapeTutorialModel( +) -> tuple[Sugarscape, float]: + model = Sugarscape( agent_type=agent_cls, - n_agents=len(conditions.initial_sugar), - sugar_grid=conditions.sugar_grid.copy(), - initial_sugar=conditions.initial_sugar.copy(), - metabolism=conditions.metabolism.copy(), - vision=conditions.vision.copy(), - positions=conditions.positions.clone(), + n_agents=NUM_AGENTS, + width=GRID_WIDTH, + height=GRID_HEIGHT, + max_sugar=MAX_SUGAR, seed=seed, ) start = perf_counter() @@ -777,16 +676,16 @@ def summarise_inequality(model: Model) -> dict[str, float]: ## 7. Run the Sequential Model (Python loop) With the scaffolding in place we can simulate the sequential version and inspect -its aggregate behaviour. +its aggregate behaviour. Because all random draws flow through the model's RNG, +constructing each variant with the same seed reproduces identical initial +conditions across the different movement rules. """ # %% -conditions = build_initial_conditions( - width=GRID_WIDTH, height=GRID_HEIGHT, n_agents=NUM_AGENTS, seed=11 -) +sequential_seed = 11 sequential_model, sequential_time = run_variant( - SugarscapeSequentialAgents, conditions, steps=MODEL_STEPS, seed=11 + SugarscapeSequentialAgents, steps=MODEL_STEPS, seed=sequential_seed ) seq_model_frame = sequential_model.datacollector.data["model"] @@ -800,14 +699,14 @@ def summarise_inequality(model: Model) -> dict[str, float]: """ ## 8. Run the Numba-Accelerated Model -We reuse the same initial conditions so the only difference is the compiled -movement helper. The trajectory matches the pure Python loop (up to floating- -point noise) while running much faster on larger grids. +We reuse the same seed so the only difference is the compiled movement helper. +The trajectory matches the pure Python loop (up to floating-point noise) while +running much faster on larger grids. """ # %% numba_model, numba_time = run_variant( - SugarscapeNumbaAgents, conditions, steps=MODEL_STEPS, seed=11 + SugarscapeNumbaAgents, steps=MODEL_STEPS, seed=sequential_seed ) numba_model_frame = numba_model.datacollector.data["model"] @@ -821,13 +720,13 @@ def summarise_inequality(model: Model) -> dict[str, float]: """ ## 9. Run the Simultaneous Model -Next we reuse the **same** initial conditions so that both variants start from a -common state. The only change is the movement policy. +Next we instantiate the parallel variant with the same seed so every run starts +from the common state generated by the helper methods. """ # %% parallel_model, parallel_time = run_variant( - SugarscapeParallelAgents, conditions, steps=MODEL_STEPS, seed=11 + SugarscapeParallelAgents, steps=MODEL_STEPS, seed=sequential_seed ) par_model_frame = parallel_model.datacollector.data["model"] From 27ea2ecee388b36b9b76887597a8b97e5f24f272 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sat, 20 Sep 2025 15:25:43 +0200 Subject: [PATCH 11/99] docs: enhance Sugarscape model class docstrings for clarity and completeness --- .../general/user-guide/3_advanced_tutorial.py | 96 ++++++++++++++++++- 1 file changed, 93 insertions(+), 3 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 4748bc71..dac90aea 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -100,7 +100,42 @@ # %% class Sugarscape(Model): - """Minimal Sugarscape model used throughout the tutorial.""" + """Minimal Sugarscape model used throughout the tutorial. + + This class wires together a grid that stores ``sugar`` per cell, an + agent set implementation (passed in as ``agent_type``), and a + data collector that records model- and agent-level statistics. + + The model's responsibilities are to: + - create the sugar landscape (cells with current and maximum sugar) + - create and place agents on the grid + - advance the sugar regrowth rule each step + - run the model for a fixed number of steps and collect data + + Parameters + ---------- + agent_type : type + The :class:`AgentSet` subclass implementing the movement rules + (sequential, numba-accelerated, or parallel). + n_agents : int + Number of agents to create and place on the grid. + width : int + Grid width (number of columns). + height : int + Grid height (number of rows). + max_sugar : int, optional + Upper bound for the randomly initialised sugar values on the grid, + by default 4. + seed : int or None, optional + RNG seed to make runs reproducible across variants, by default None. + + Notes + ----- + The grid uses a von Neumann neighbourhood and capacity 1 (at most one + agent per cell). Both the sugar landscape and initial agent traits are + drawn from ``self.random`` so different movement variants can be + instantiated with identical initial conditions by passing the same seed. + """ def __init__( self, @@ -153,7 +188,23 @@ def __init__( def _generate_sugar_grid( self, width: int, height: int, max_sugar: int ) -> pl.DataFrame: - """Generate a random sugar grid using the model RNG.""" + """Generate a random sugar grid. + + Parameters + ---------- + width : int + Grid width (number of columns). + height : int + Grid height (number of rows). + max_sugar : int + Maximum sugar value (inclusive) for each cell. + + Returns + ------- + pl.DataFrame + DataFrame with columns ``dim_0``, ``dim_1``, ``sugar`` (current + amount) and ``max_sugar`` (regrowth target). + """ sugar_vals = self.random.integers( 0, max_sugar + 1, size=(width, height), dtype=np.int64 ) @@ -164,7 +215,19 @@ def _generate_sugar_grid( ) def _generate_agent_frame(self, n_agents: int) -> pl.DataFrame: - """Create the initial agent frame populated with traits.""" + """Create the initial agent frame populated with agent traits. + + Parameters + ---------- + n_agents : int + Number of agents to create. + + Returns + ------- + pl.DataFrame + DataFrame with columns ``sugar``, ``metabolism`` and ``vision`` + (integer values) for each agent. + """ rng = self.random return pl.DataFrame( { @@ -175,6 +238,15 @@ def _generate_agent_frame(self, n_agents: int) -> pl.DataFrame: ) def step(self) -> None: + """Advance the model by one step. + + Notes + ----- + The per-step ordering is important: regrowth happens first (so empty + cells are refilled), then agents move and eat, and finally metrics are + collected. If the agent set becomes empty at any point the model is + marked as not running. + """ if len(self.sets[0]) == 0: self.running = False return @@ -185,18 +257,36 @@ def step(self) -> None: self.running = False def run(self, steps: int) -> None: + """Run the model for a fixed number of steps. + + Parameters + ---------- + steps : int + Maximum number of steps to run. The model may terminate earlier if + ``self.running`` is set to ``False`` (for example, when all agents + have died). + """ for _ in range(steps): if not self.running: break self.step() def _advance_sugar_field(self) -> None: + """Apply the instant-growback sugar regrowth rule. + + Empty cells (no agent present) are refilled to their ``max_sugar`` + value. Cells that are occupied are set to zero because agents harvest + the sugar when they eat. The method uses vectorised DataFrame joins + and writes to keep the operation efficient. + """ empty_cells = self.space.empty_cells if not empty_cells.is_empty(): + # Look up the maximum sugar for each empty cell and restore it. refresh = empty_cells.join(self._max_sugar, on=["dim_0", "dim_1"], how="left") self.space.set_cells(empty_cells, {"sugar": refresh["max_sugar"]}) full_cells = self.space.full_cells if not full_cells.is_empty(): + # Occupied cells have just been harvested; set their sugar to 0. zeros = pl.Series(np.zeros(len(full_cells), dtype=np.int64)) self.space.set_cells(full_cells, {"sugar": zeros}) From 4552a0859d479d527ee545337de4737c4a3098d2 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sat, 20 Sep 2025 15:35:33 +0200 Subject: [PATCH 12/99] docs: add agent definition section and base agent class implementation to advanced tutorial --- .../general/user-guide/3_advanced_tutorial.py | 210 ++++++++++-------- 1 file changed, 117 insertions(+), 93 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index dac90aea..718570c5 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -290,6 +290,123 @@ def _advance_sugar_field(self) -> None: zeros = pl.Series(np.zeros(len(full_cells), dtype=np.int64)) self.space.set_cells(full_cells, {"sugar": zeros}) +# %% [markdown] + +""" +## 3. Agent definition + +### Base agent class + +Now let's define the agent class (the ant class). We start with a base class which implements the common logic for eating and starvation, while leaving the `move` method abstract. +The base class also provides helper methods for sensing visible cells and choosing the best cell based on sugar, distance, and coordinates. +This will allow us to define different movement policies (sequential, Numba-accelerated, and parallel) as subclasses that only need to implement the `move` method. + +""" + +# %% + +class SugarscapeAgentsBase(AgentSet): + def __init__(self, model: Model, agent_frame: pl.DataFrame) -> None: + super().__init__(model) + required = {"sugar", "metabolism", "vision"} + missing = required.difference(agent_frame.columns) + if missing: + raise ValueError( + f"Initial agent frame must include columns {sorted(required)}; missing {sorted(missing)}." + ) + self.add(agent_frame.clone()) + + def step(self) -> None: + self.shuffle(inplace=True) + self.move() + self.eat() + self._remove_starved() + + def move(self) -> None: # pragma: no cover + raise NotImplementedError + + def eat(self) -> None: + occupied_ids = self.index.to_list() + occupied = self.space.cells.filter(pl.col("agent_id").is_in(occupied_ids)) + if occupied.is_empty(): + return + ids = occupied["agent_id"] + self[ids, "sugar"] = ( + self[ids, "sugar"] + occupied["sugar"] - self[ids, "metabolism"] + ) + self.space.set_cells( + occupied.select(["dim_0", "dim_1"]), + {"sugar": pl.Series(np.zeros(len(occupied), dtype=np.int64))}, + ) + + def _remove_starved(self) -> None: + starved = self.df.filter(pl.col("sugar") <= 0) + if not starved.is_empty(): + self.discard(starved) + + def _current_sugar_map(self) -> dict[tuple[int, int], int]: + cells = self.space.cells.select(["dim_0", "dim_1", "sugar"]) + return { + (int(x), int(y)): 0 if sugar is None else int(sugar) + for x, y, sugar in cells.iter_rows() + } + + @staticmethod + def _manhattan(a: tuple[int, int], b: tuple[int, int]) -> int: + return abs(a[0] - b[0]) + abs(a[1] - b[1]) + + def _visible_cells(self, origin: tuple[int, int], vision: int) -> list[tuple[int, int]]: + x0, y0 = origin + width, height = self.space.dimensions + cells: list[tuple[int, int]] = [origin] + for step in range(1, vision + 1): + if x0 + step < width: + cells.append((x0 + step, y0)) + if x0 - step >= 0: + cells.append((x0 - step, y0)) + if y0 + step < height: + cells.append((x0, y0 + step)) + if y0 - step >= 0: + cells.append((x0, y0 - step)) + return cells + + def _choose_best_cell( + self, + origin: tuple[int, int], + vision: int, + sugar_map: dict[tuple[int, int], int], + blocked: set[tuple[int, int]] | None, + ) -> tuple[int, int]: + best_cell = origin + best_sugar = sugar_map.get(origin, 0) + best_distance = 0 + for candidate in self._visible_cells(origin, vision): + if blocked and candidate != origin and candidate in blocked: + continue + sugar_here = sugar_map.get(candidate, 0) + distance = self._manhattan(origin, candidate) + better = False + if sugar_here > best_sugar: + better = True + elif sugar_here == best_sugar: + if distance < best_distance: + better = True + elif distance == best_distance and candidate < best_cell: + better = True + if better: + best_cell = candidate + best_sugar = sugar_here + best_distance = distance + return best_cell + + + + + + + + + # %% @@ -427,99 +544,6 @@ def sequential_move_numba( """ -class SugarscapeAgentsBase(AgentSet): - def __init__(self, model: Model, agent_frame: pl.DataFrame) -> None: - super().__init__(model) - required = {"sugar", "metabolism", "vision"} - missing = required.difference(agent_frame.columns) - if missing: - raise ValueError( - f"Initial agent frame must include columns {sorted(required)}; missing {sorted(missing)}." - ) - self.add(agent_frame.clone()) - - def step(self) -> None: - self.shuffle(inplace=True) - self.move() - self.eat() - self._remove_starved() - - def move(self) -> None: # pragma: no cover - raise NotImplementedError - - def eat(self) -> None: - occupied_ids = self.index.to_list() - occupied = self.space.cells.filter(pl.col("agent_id").is_in(occupied_ids)) - if occupied.is_empty(): - return - ids = occupied["agent_id"] - self[ids, "sugar"] = ( - self[ids, "sugar"] + occupied["sugar"] - self[ids, "metabolism"] - ) - self.space.set_cells( - occupied.select(["dim_0", "dim_1"]), - {"sugar": pl.Series(np.zeros(len(occupied), dtype=np.int64))}, - ) - - def _remove_starved(self) -> None: - starved = self.df.filter(pl.col("sugar") <= 0) - if not starved.is_empty(): - self.discard(starved) - - def _current_sugar_map(self) -> dict[tuple[int, int], int]: - cells = self.space.cells.select(["dim_0", "dim_1", "sugar"]) - return { - (int(x), int(y)): 0 if sugar is None else int(sugar) - for x, y, sugar in cells.iter_rows() - } - - @staticmethod - def _manhattan(a: tuple[int, int], b: tuple[int, int]) -> int: - return abs(a[0] - b[0]) + abs(a[1] - b[1]) - - def _visible_cells(self, origin: tuple[int, int], vision: int) -> list[tuple[int, int]]: - x0, y0 = origin - width, height = self.space.dimensions - cells: list[tuple[int, int]] = [origin] - for step in range(1, vision + 1): - if x0 + step < width: - cells.append((x0 + step, y0)) - if x0 - step >= 0: - cells.append((x0 - step, y0)) - if y0 + step < height: - cells.append((x0, y0 + step)) - if y0 - step >= 0: - cells.append((x0, y0 - step)) - return cells - - def _choose_best_cell( - self, - origin: tuple[int, int], - vision: int, - sugar_map: dict[tuple[int, int], int], - blocked: set[tuple[int, int]] | None, - ) -> tuple[int, int]: - best_cell = origin - best_sugar = sugar_map.get(origin, 0) - best_distance = 0 - for candidate in self._visible_cells(origin, vision): - if blocked and candidate != origin and candidate in blocked: - continue - sugar_here = sugar_map.get(candidate, 0) - distance = self._manhattan(origin, candidate) - better = False - if sugar_here > best_sugar: - better = True - elif sugar_here == best_sugar: - if distance < best_distance: - better = True - elif distance == best_distance and candidate < best_cell: - better = True - if better: - best_cell = candidate - best_sugar = sugar_here - best_distance = distance - return best_cell # %% [markdown] From 3c55734bc48411f2c63c4724c349f59e1d076d44 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sat, 20 Sep 2025 15:53:07 +0200 Subject: [PATCH 13/99] docs: update import statements for future annotations in advanced tutorial --- docs/general/user-guide/3_advanced_tutorial.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 718570c5..ff68b80b 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -1,3 +1,5 @@ +from __future__ import annotations + # --- # jupyter: # jupytext: @@ -65,7 +67,6 @@ """## 1. Imports""" # %% -from __future__ import annotations from collections import defaultdict from time import perf_counter From 8e978dac119b9848a4d68dfe1b2503448efdd960 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sat, 20 Sep 2025 15:56:54 +0200 Subject: [PATCH 14/99] refactor: optimize sugar consumption logic in SugarscapeAgentsBase class --- .../general/user-guide/3_advanced_tutorial.py | 24 +++++++------------ 1 file changed, 8 insertions(+), 16 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index ff68b80b..a3a59749 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -327,17 +327,17 @@ def move(self) -> None: # pragma: no cover raise NotImplementedError def eat(self) -> None: - occupied_ids = self.index.to_list() - occupied = self.space.cells.filter(pl.col("agent_id").is_in(occupied_ids)) - if occupied.is_empty(): + occupied_ids = self.index + occupied_cells = self.space.cells.filter(pl.col("agent_id").is_in(occupied_ids)) + if occupied_cells.is_empty(): return - ids = occupied["agent_id"] - self[ids, "sugar"] = ( - self[ids, "sugar"] + occupied["sugar"] - self[ids, "metabolism"] + agent_ids = occupied_cells["agent_id"] + self[agent_ids, "sugar"] = ( + self[agent_ids, "sugar"] + occupied_cells["sugar"] - self[agent_ids, "metabolism"] ) self.space.set_cells( - occupied.select(["dim_0", "dim_1"]), - {"sugar": pl.Series(np.zeros(len(occupied), dtype=np.int64))}, + occupied_cells.select(["dim_0", "dim_1"]), + {"sugar": pl.Series(np.zeros(len(occupied_cells), dtype=np.int64))}, ) def _remove_starved(self) -> None: @@ -402,14 +402,6 @@ def _choose_best_cell( - - - - - - - - # %% GRID_WIDTH = 50 GRID_HEIGHT = 50 From 894c18176ccc50b20eacd83aa59651d7e9586aa4 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sat, 20 Sep 2025 16:32:30 +0200 Subject: [PATCH 15/99] docs: enhance documentation for Sugarscape agent classes and methods --- .../general/user-guide/3_advanced_tutorial.py | 456 +++++++++++++++--- 1 file changed, 386 insertions(+), 70 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index a3a59749..a301822b 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -301,13 +301,40 @@ def _advance_sugar_field(self) -> None: Now let's define the agent class (the ant class). We start with a base class which implements the common logic for eating and starvation, while leaving the `move` method abstract. The base class also provides helper methods for sensing visible cells and choosing the best cell based on sugar, distance, and coordinates. This will allow us to define different movement policies (sequential, Numba-accelerated, and parallel) as subclasses that only need to implement the `move` method. - +We also add """ # %% class SugarscapeAgentsBase(AgentSet): + """Base agent set for the Sugarscape tutorial. + + This class implements the common behaviour shared by all agent + movement variants (sequential, numba-accelerated and parallel). + + Notes + ----- + - Agents are expected to have integer traits: ``sugar``, ``metabolism`` + and ``vision``. These are validated in :meth:`__init__`. + - Subclasses must implement :meth:`move` which changes agent positions + on the grid (via :meth:`mesa_frames.Grid` helpers). + """ def __init__(self, model: Model, agent_frame: pl.DataFrame) -> None: + """Initialise the agent set and validate required trait columns. + + Parameters + ---------- + model : Model + The parent model which provides RNG and space. + agent_frame : pl.DataFrame + A Polars DataFrame with at least the columns ``sugar``, + ``metabolism`` and ``vision`` for each agent. + + Raises + ------ + ValueError + If required trait columns are missing from ``agent_frame``. + """ super().__init__(model) required = {"sugar", "metabolism", "vision"} missing = required.difference(agent_frame.columns) @@ -318,35 +345,83 @@ def __init__(self, model: Model, agent_frame: pl.DataFrame) -> None: self.add(agent_frame.clone()) def step(self) -> None: + """Advance the agent set by one time step. + + The update order is important: agents are first shuffled to randomise + move order (this is important only for sequential variants), then they move, harvest sugar + from their occupied cells, and finally any agents whose sugar falls + to zero or below are removed. + """ + # Randomise ordering for movement decisions when required by the + # implementation (e.g. sequential update uses this shuffle). self.shuffle(inplace=True) + # Movement policy implemented by subclasses. self.move() + # Agents harvest sugar on their occupied cells. self.eat() + # Remove agents that starved after eating. self._remove_starved() def move(self) -> None: # pragma: no cover + """Abstract movement method. + + Subclasses must override this method to update agent positions on the + grid. Implementations should use :meth:`mesa_frames.Grid.move_agents` + or similar helpers provided by the space API. + """ raise NotImplementedError def eat(self) -> None: + """Agents harvest sugar from the cells they currently occupy. + + Behaviour: + - Look up the set of occupied cells (cells that reference an agent + id). + - For each occupied cell, add the cell sugar to the agent's sugar + stock and subtract the agent's metabolism cost. + - After agents harvest, set the sugar on those cells to zero (they + were consumed). + """ + # Map of currently occupied agent ids on the grid. occupied_ids = self.index occupied_cells = self.space.cells.filter(pl.col("agent_id").is_in(occupied_ids)) if occupied_cells.is_empty(): return + # The agent ordering here uses the agent_id values stored in the + # occupied cells frame; indexing the agent set with that vector updates + # the matching agents' sugar values in one vectorised write. agent_ids = occupied_cells["agent_id"] self[agent_ids, "sugar"] = ( self[agent_ids, "sugar"] + occupied_cells["sugar"] - self[agent_ids, "metabolism"] ) + # After harvesting, occupied cells have zero sugar. self.space.set_cells( occupied_cells.select(["dim_0", "dim_1"]), {"sugar": pl.Series(np.zeros(len(occupied_cells), dtype=np.int64))}, ) def _remove_starved(self) -> None: + """Discard agents whose sugar stock has fallen to zero or below. + + This method performs a vectorised filter on the agent frame and + removes any matching rows from the set. + """ starved = self.df.filter(pl.col("sugar") <= 0) if not starved.is_empty(): + # ``discard`` accepts a DataFrame of agents to remove. self.discard(starved) def _current_sugar_map(self) -> dict[tuple[int, int], int]: + """Return a mapping from grid coordinates to the current sugar value. + + Returns + ------- + dict + Keys are ``(x, y)`` tuples and values are the integer sugar amount + on that cell (zero if missing/None). + """ cells = self.space.cells.select(["dim_0", "dim_1", "sugar"]) + # Build a plain Python dict for fast lookups in the movement code. return { (int(x), int(y)): 0 if sugar is None else int(sugar) for x, y, sugar in cells.iter_rows() @@ -354,12 +429,44 @@ def _current_sugar_map(self) -> dict[tuple[int, int], int]: @staticmethod def _manhattan(a: tuple[int, int], b: tuple[int, int]) -> int: + """Compute the Manhattan (L1) distance between two grid cells. + + Parameters + ---------- + a, b : tuple[int, int] + Coordinate pairs ``(x, y)``. + + Returns + ------- + int + The Manhattan distance between ``a`` and ``b``. + """ return abs(a[0] - b[0]) + abs(a[1] - b[1]) def _visible_cells(self, origin: tuple[int, int], vision: int) -> list[tuple[int, int]]: + """List cells visible from an origin along the four cardinal axes. + + The visibility set includes the origin cell itself and cells at + Manhattan distances 1..vision along the four cardinal directions + (up, down, left, right), clipped to the grid bounds. + + Parameters + ---------- + origin : tuple[int, int] + The agent's current coordinate ``(x, y)``. + vision : int + Maximum Manhattan radius to consider along each axis. + + Returns + ------- + list[tuple[int, int]] + Ordered list of visible cells (origin first, then increasing + step distance along each axis). + """ x0, y0 = origin width, height = self.space.dimensions cells: list[tuple[int, int]] = [origin] + # Look outward one step at a time in the four cardinal directions. for step in range(1, vision + 1): if x0 + step < width: cells.append((x0 + step, y0)) @@ -378,20 +485,52 @@ def _choose_best_cell( sugar_map: dict[tuple[int, int], int], blocked: set[tuple[int, int]] | None, ) -> tuple[int, int]: + """Select the best visible cell according to the movement rules. + + Tie-break rules (in order): + 1. Prefer cells with strictly greater sugar. + 2. If equal sugar, prefer the cell with smaller Manhattan distance + from the origin. + 3. If still tied, prefer the cell with smaller coordinates (lexicographic + ordering of the ``(x, y)`` tuple). + + Parameters + ---------- + origin : tuple[int, int] + Agent's current coordinate. + vision : int + Maximum vision radius along cardinal axes. + sugar_map : dict + Mapping from ``(x, y)`` to sugar amount. + blocked : set or None + Optional set of coordinates that should be considered occupied and + therefore skipped (except the origin which is always allowed). + + Returns + ------- + tuple[int, int] + Chosen target coordinate (may be the origin if no better cell is + available). + """ best_cell = origin best_sugar = sugar_map.get(origin, 0) best_distance = 0 for candidate in self._visible_cells(origin, vision): + # Skip blocked cells (occupied by other agents) unless it's the + # agent's current cell which we always consider. if blocked and candidate != origin and candidate in blocked: continue sugar_here = sugar_map.get(candidate, 0) distance = self._manhattan(origin, candidate) better = False + # Primary criterion: strictly more sugar. if sugar_here > best_sugar: better = True elif sugar_here == best_sugar: + # Secondary: closer distance. if distance < best_distance: better = True + # Tertiary: lexicographic tie-break on coordinates. elif distance == best_distance and candidate < best_cell: better = True if better: @@ -420,11 +559,34 @@ def _numba_should_replace( candidate_x: int, candidate_y: int, ) -> bool: + """Numba helper: decide whether a candidate cell should replace the + current best cell according to the movement tie-break rules. + + This implements the same ordering used in :meth:`_choose_best_cell` but + in a tightly-typed, compiled form suitable for Numba loops. + + Parameters + ---------- + best_sugar, candidate_sugar : int + Sugar at the current best cell and the candidate cell. + best_distance, candidate_distance : int + Manhattan distances from the origin to the best and candidate cells. + best_x, best_y, candidate_x, candidate_y : int + Coordinates used for the final lexicographic tie-break. + + Returns + ------- + bool + True if the candidate should replace the current best cell. + """ + # Primary criterion: prefer strictly greater sugar. if candidate_sugar > best_sugar: return True + # If sugar ties, prefer the closer cell. if candidate_sugar == best_sugar: if candidate_distance < best_distance: return True + # If distance ties as well, compare coordinates lexicographically. if candidate_distance == best_distance: if candidate_x < best_x: return True @@ -447,6 +609,10 @@ def _numba_find_best_cell( best_sugar = sugar_array[x0, y0] best_distance = 0 + # Examine visible cells along the four cardinal directions, increasing + # step by step. The 'occupied' array marks cells that are currently + # unavailable (True = occupied). The origin cell is allowed as the + # default; callers typically clear the origin before searching. for step in range(1, vision + 1): nx = x0 + step if nx < width and not occupied[nx, y0]: @@ -502,22 +668,55 @@ def sequential_move_numba( vision: np.ndarray, sugar_array: np.ndarray, ) -> tuple[np.ndarray, np.ndarray]: + """Numba-accelerated sequential movement helper. + + This function emulates the traditional asynchronous (sequential) update + where agents move one at a time in the current ordering. It accepts + numpy arrays describing agent positions and vision ranges, and a 2D + sugar array for lookup. + + Parameters + ---------- + dim0, dim1 : np.ndarray + 1D integer arrays of length n_agents containing the x and y + coordinates for each agent. + vision : np.ndarray + 1D integer array of vision radii for each agent. + sugar_array : np.ndarray + 2D array shaped (width, height) containing per-cell sugar values. + + Returns + ------- + tuple[np.ndarray, np.ndarray] + Updated arrays of x and y coordinates after sequential movement. + """ n_agents = dim0.shape[0] width, height = sugar_array.shape + # Copy inputs to avoid mutating caller arrays in-place. new_dim0 = dim0.copy() new_dim1 = dim1.copy() + # Occupancy grid: True when a cell is currently occupied by an agent. occupied = np.zeros((width, height), dtype=np.bool_) + # Mark initial occupancy. for i in range(n_agents): occupied[new_dim0[i], new_dim1[i]] = True + # Process agents in order. For each agent we clear its current cell in + # the occupancy grid (so it can consider moving into it), search for the + # best unoccupied visible cell, and mark the chosen destination as + # occupied. This models agents moving one-by-one. for i in range(n_agents): x0 = new_dim0[i] y0 = new_dim1[i] + # Free the agent's current cell so it is considered available during + # the search (agents may choose to stay, in which case we'll re-mark + # it below). occupied[x0, y0] = False best_x, best_y = _numba_find_best_cell( x0, y0, int(vision[i]), sugar_array, occupied ) + # Claim the chosen destination. occupied[best_x, best_y] = True new_dim0[i] = best_x new_dim1[i] = best_y @@ -578,8 +777,7 @@ def move(self) -> None: state = self.df.join(self.pos, on="unique_id", how="left") if state.is_empty(): return - - agent_ids = state["unique_id"].to_list() + agent_ids = state["unique_id"] dim0 = state["dim_0"].to_numpy().astype(np.int64) dim1 = state["dim_1"].to_numpy().astype(np.int64) vision = state["vision"].to_numpy().astype(np.int64) @@ -604,6 +802,10 @@ def move(self) -> None: class SugarscapeParallelAgents(SugarscapeAgentsBase): def move(self) -> None: + # Parallel movement: each agent proposes a ranked list of visible + # cells (including its own). We resolve conflicts in rounds using + # DataFrame operations so winners can be chosen per-cell at random + # and losers are promoted to their next-ranked choice. if len(self.df) == 0: return sugar_map = self._current_sugar_map() @@ -611,81 +813,195 @@ def move(self) -> None: if state.is_empty(): return - origins: dict[int, tuple[int, int]] = {} - choices: dict[int, list[tuple[int, int]]] = {} - choice_idx: dict[int, int] = {} + # Map the positional frame to a center lookup used when joining + # neighbourhoods produced by the space helper. + center_lookup = self.pos.rename( + { + "unique_id": "agent_id", + "dim_0": "dim_0_center", + "dim_1": "dim_1_center", + } + ) - for row in state.iter_rows(named=True): - agent_id = int(row["unique_id"]) - origin = (int(row["dim_0"]), int(row["dim_1"])) - vision = int(row["vision"]) - origins[agent_id] = origin - candidate_cells: list[tuple[int, int]] = [] - seen: set[tuple[int, int]] = set() - for cell in self._visible_cells(origin, vision): - if cell not in seen: - seen.add(cell) - candidate_cells.append(cell) - candidate_cells.sort( - key=lambda cell: ( - -sugar_map.get(cell, 0), - self._manhattan(origin, cell), - cell, + # Build a neighbourhood frame: for each agent and visible cell we + # attach the cell sugar and the agent_id of the occupant (if any). + neighborhood = ( + self.space.get_neighborhood( + radius=self["vision"], agents=self, include_center=True + ) + .join( + self.space.cells.select(["dim_0", "dim_1", "sugar"]), + on=["dim_0", "dim_1"], + how="left", + ) + .join(center_lookup, on=["dim_0_center", "dim_1_center"], how="left") + .with_columns(pl.col("sugar").fill_null(0)) + ) + + # Normalise occupant column name if present. + if "agent_id" in neighborhood.columns: + neighborhood = neighborhood.rename({"agent_id": "occupant_id"}) + + # Create ranked choices per agent: sort by sugar (desc), radius + # (asc), then coordinates. Keep the first unique entry per cell. + choices = ( + neighborhood.select( + [ + "agent_id", + "dim_0", + "dim_1", + "sugar", + "radius", + "dim_0_center", + "dim_1_center", + ] + ) + .with_columns(pl.col("radius").cast(pl.Int64)) + .sort( + ["agent_id", "sugar", "radius", "dim_0", "dim_1"], + descending=[False, True, False, False, False], + ) + .unique( + subset=["agent_id", "dim_0", "dim_1"], + keep="first", + maintain_order=True, + ) + .with_columns(pl.cum_count().over("agent_id").cast(pl.Int64).alias("rank")) + ) + + if choices.is_empty(): + return + + # Origins for fallback (if an agent exhausts candidates it stays put). + origins = center_lookup.select( + [ + "agent_id", + pl.col("dim_0_center").alias("dim_0"), + pl.col("dim_1_center").alias("dim_1"), + ] + ) + + # Track the maximum available rank per agent to clamp promotions. + max_rank = choices.group_by("agent_id").agg(pl.col("rank").max().alias("max_rank")) + + # Prepare unresolved agents and working tables. + agent_ids = choices["agent_id"].unique(maintain_order=True) + unresolved = pl.DataFrame( + { + "agent_id": agent_ids, + "current_rank": pl.Series(np.zeros(agent_ids.len(), dtype=np.int64)), + } + ) + + assigned = pl.DataFrame( + { + "agent_id": pl.Series(name="agent_id", values=[], dtype=agent_ids.dtype), + "dim_0": pl.Series(name="dim_0", values=[], dtype=pl.Int64), + "dim_1": pl.Series(name="dim_1", values=[], dtype=pl.Int64), + } + ) + + taken = pl.DataFrame( + { + "dim_0": pl.Series(name="dim_0", values=[], dtype=pl.Int64), + "dim_1": pl.Series(name="dim_1", values=[], dtype=pl.Int64), + } + ) + + # Resolve in rounds: each unresolved agent proposes its current-ranked + # candidate; winners per-cell are selected at random and losers are + # promoted to their next choice. + while unresolved.height > 0: + candidate_pool = choices.join(unresolved, on="agent_id") + candidate_pool = candidate_pool.filter(pl.col("rank") >= pl.col("current_rank")) + if not taken.is_empty(): + candidate_pool = candidate_pool.join(taken, on=["dim_0", "dim_1"], how="anti") + + if candidate_pool.is_empty(): + # No available candidates — everyone falls back to origin. + fallback = unresolved.join(origins, on="agent_id", how="left") + assigned = pl.concat( + [assigned, fallback.select(["agent_id", "dim_0", "dim_1"])], + how="vertical", ) + break + + best_candidates = ( + candidate_pool.sort(["agent_id", "rank"]) .group_by("agent_id", maintain_order=True).first() + ) + + # Agents that had no candidate this round fall back to origin. + missing = unresolved.join(best_candidates.select("agent_id"), on="agent_id", how="anti") + if not missing.is_empty(): + fallback = missing.join(origins, on="agent_id", how="left") + assigned = pl.concat( + [assigned, fallback.select(["agent_id", "dim_0", "dim_1"])], + how="vertical", + ) + taken = pl.concat([taken, fallback.select(["dim_0", "dim_1"])], how="vertical") + unresolved = unresolved.join(missing.select("agent_id"), on="agent_id", how="anti") + best_candidates = best_candidates.join(missing.select("agent_id"), on="agent_id", how="anti") + if unresolved.is_empty() or best_candidates.is_empty(): + continue + + # Add a small random lottery to break ties deterministically for + # each candidate set. + lottery = pl.Series("lottery", self.random.random(best_candidates.height)) + best_candidates = best_candidates.with_columns(lottery) + + winners = ( + best_candidates.sort(["dim_0", "dim_1", "lottery"]) .group_by(["dim_0", "dim_1"], maintain_order=True).first() + ) + + assigned = pl.concat( + [assigned, winners.select(["agent_id", "dim_0", "dim_1"])], + how="vertical", ) - if origin not in seen: - candidate_cells.append(origin) - choices[agent_id] = candidate_cells - choice_idx[agent_id] = 0 - - assigned: dict[int, tuple[int, int]] = {} - taken: set[tuple[int, int]] = set() - unresolved: set[int] = set(choices.keys()) - - while unresolved: - cell_to_agents: defaultdict[tuple[int, int], list[int]] = defaultdict(list) - for agent in list(unresolved): - ranked = choices[agent] - idx = choice_idx[agent] - while idx < len(ranked) and ranked[idx] in taken: - idx += 1 - if idx >= len(ranked): - idx = len(ranked) - 1 - choice_idx[agent] = idx - cell_to_agents[ranked[idx]].append(agent) - - progress = False - for cell, agents in cell_to_agents.items(): - if len(agents) == 1: - winner = agents[0] - else: - winner = agents[int(self.random.integers(0, len(agents)))] - assigned[winner] = cell - taken.add(cell) - unresolved.remove(winner) - progress = True - for agent in agents: - if agent != winner: - idx = choice_idx[agent] + 1 - if idx >= len(choices[agent]): - idx = len(choices[agent]) - 1 - choice_idx[agent] = idx - - if not progress: - for agent in list(unresolved): - assigned[agent] = origins[agent] - unresolved.remove(agent) + taken = pl.concat([taken, winners.select(["dim_0", "dim_1"])], how="vertical") + + winner_ids = winners.select("agent_id") + unresolved = unresolved.join(winner_ids, on="agent_id", how="anti") + if unresolved.is_empty(): + break + + losers = best_candidates.join(winner_ids, on="agent_id", how="anti") + if losers.is_empty(): + continue + + loser_updates = ( + losers.select( + "agent_id", + (pl.col("rank") + 1).cast(pl.Int64).alias("next_rank"), + ) + .join(max_rank, on="agent_id", how="left") + .with_columns( + pl.min_horizontal(pl.col("next_rank"), pl.col("max_rank")).alias("next_rank") + ) + .select(["agent_id", "next_rank"]) + ) + + # Promote losers' current_rank (if any) and continue. + unresolved = unresolved.join(loser_updates, on="agent_id", how="left").with_columns( + pl.when(pl.col("next_rank").is_not_null()) + .then(pl.col("next_rank")) + .otherwise(pl.col("current_rank")) + .alias("current_rank") + ).drop("next_rank") + + if assigned.is_empty(): + return move_df = pl.DataFrame( { - "unique_id": list(assigned.keys()), - "dim_0": [cell[0] for cell in assigned.values()], - "dim_1": [cell[1] for cell in assigned.values()], + "unique_id": assigned["agent_id"], + "dim_0": assigned["dim_0"], + "dim_1": assigned["dim_1"], } ) - self.space.move_agents( - move_df["unique_id"].to_list(), move_df.select(["dim_0", "dim_1"]) - ) + # `move_agents` accepts IdsLike and SpaceCoordinates (Polars Series/DataFrame), + # so pass Series/DataFrame directly rather than converting to Python lists. + self.space.move_agents(move_df["unique_id"], move_df.select(["dim_0", "dim_1"])) + def run_variant( agent_cls: type[SugarscapeAgentsBase], *, From b8597391eb173d07bb9887635071675f1dd07fee Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sat, 20 Sep 2025 16:53:28 +0200 Subject: [PATCH 16/99] fix: resolve ambiguity in membership checks for occupied cells in SugarscapeAgentsBase --- docs/general/user-guide/3_advanced_tutorial.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index a301822b..b0a1d5c6 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -384,7 +384,10 @@ def eat(self) -> None: """ # Map of currently occupied agent ids on the grid. occupied_ids = self.index - occupied_cells = self.space.cells.filter(pl.col("agent_id").is_in(occupied_ids)) + # `occupied_ids` is a Polars Series; calling `is_in` with a Series + # of the same datatype is ambiguous in newer Polars. Use `implode` + # to collapse the Series into a list-like value for membership checks. + occupied_cells = self.space.cells.filter(pl.col("agent_id").is_in(occupied_ids.implode())) if occupied_cells.is_empty(): return # The agent ordering here uses the agent_id values stored in the From 0c819c98d8a9014de72030cd454dffe18e8279d3 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sat, 20 Sep 2025 17:03:05 +0200 Subject: [PATCH 17/99] feat: add environment variable support for sequential baseline execution in advanced tutorial --- .../general/user-guide/3_advanced_tutorial.py | 122 +++++++++++++----- 1 file changed, 91 insertions(+), 31 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index b0a1d5c6..2579816a 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -67,7 +67,7 @@ """## 1. Imports""" # %% - +import os from collections import defaultdict from time import perf_counter @@ -551,6 +551,16 @@ def _choose_best_cell( MODEL_STEPS = 60 MAX_SUGAR = 4 +# Allow quick testing by skipping the slow pure-Python sequential baseline. +# Set the environment variable ``MESA_FRAMES_RUN_SEQUENTIAL=0`` (or "false") +# to disable the baseline when running this script. +RUN_SEQUENTIAL = os.getenv("MESA_FRAMES_RUN_SEQUENTIAL", "0").lower() not in { + "0", + "false", + "no", + "off", +} + @njit(cache=True) def _numba_should_replace( best_sugar: int, @@ -817,13 +827,16 @@ def move(self) -> None: return # Map the positional frame to a center lookup used when joining - # neighbourhoods produced by the space helper. - center_lookup = self.pos.rename( - { - "unique_id": "agent_id", - "dim_0": "dim_0_center", - "dim_1": "dim_1_center", - } + # neighbourhoods produced by the space helper. Build the lookup by + # explicitly selecting and aliasing columns so the join creates a + # deterministic `agent_id` column (some internal joins can drop or + # fail to expose renamed columns when types/indices differ). + center_lookup = self.pos.select( + [ + pl.col("unique_id").alias("agent_id"), + pl.col("dim_0").alias("dim_0_center"), + pl.col("dim_1").alias("dim_1_center"), + ] ) # Build a neighbourhood frame: for each agent and visible cell we @@ -837,13 +850,22 @@ def move(self) -> None: on=["dim_0", "dim_1"], how="left", ) - .join(center_lookup, on=["dim_0_center", "dim_1_center"], how="left") .with_columns(pl.col("sugar").fill_null(0)) ) - # Normalise occupant column name if present. + # Normalise occupant column name if present (agent occupying the + # cell). The center lookup join may produce a conflicting + # `agent_id` column (suffix _right) — handle both cases so that + # `agent_id` unambiguously refers to the center agent and + # `occupant_id` refers to any agent already occupying the cell. if "agent_id" in neighborhood.columns: neighborhood = neighborhood.rename({"agent_id": "occupant_id"}) + neighborhood = neighborhood.join( + center_lookup, on=["dim_0_center", "dim_1_center"], how="left" + ) + if "agent_id_right" in neighborhood.columns: + # Rename the joined center lookup's id to the canonical name. + neighborhood = neighborhood.rename({"agent_id_right": "agent_id"}) # Create ranked choices per agent: sort by sugar (desc), radius # (asc), then coordinates. Keep the first unique entry per cell. @@ -869,7 +891,13 @@ def move(self) -> None: keep="first", maintain_order=True, ) - .with_columns(pl.cum_count().over("agent_id").cast(pl.Int64).alias("rank")) + .with_columns( + pl.col("agent_id") + .cum_count() + .over("agent_id") + .cast(pl.Int64) + .alias("rank") + ) ) if choices.is_empty(): @@ -892,7 +920,7 @@ def move(self) -> None: unresolved = pl.DataFrame( { "agent_id": agent_ids, - "current_rank": pl.Series(np.zeros(agent_ids.len(), dtype=np.int64)), + "current_rank": pl.Series(np.zeros(len(agent_ids), dtype=np.int64)), } ) @@ -1110,16 +1138,26 @@ def summarise_inequality(model: Model) -> dict[str, float]: # %% sequential_seed = 11 -sequential_model, sequential_time = run_variant( - SugarscapeSequentialAgents, steps=MODEL_STEPS, seed=sequential_seed -) +if RUN_SEQUENTIAL: + sequential_model, sequential_time = run_variant( + SugarscapeSequentialAgents, steps=MODEL_STEPS, seed=sequential_seed + ) -seq_model_frame = sequential_model.datacollector.data["model"] -print("Sequential aggregate trajectory (last 5 steps):") -print( - seq_model_frame.select(["step", "mean_sugar", "total_sugar", "living_agents"]).tail(5) -) -print(f"Sequential runtime: {sequential_time:.3f} s") + seq_model_frame = sequential_model.datacollector.data["model"] + print("Sequential aggregate trajectory (last 5 steps):") + print( + seq_model_frame.select( + ["step", "mean_sugar", "total_sugar", "living_agents"] + ).tail(5) + ) + print(f"Sequential runtime: {sequential_time:.3f} s") +else: + sequential_model = None + seq_model_frame = pl.DataFrame() + sequential_time = float("nan") + print( + "Skipping sequential baseline; set MESA_FRAMES_RUN_SEQUENTIAL=1 to enable it." + ) # %% [markdown] """ @@ -1171,16 +1209,38 @@ def summarise_inequality(model: Model) -> dict[str, float]: """ # %% -runtime_table = pl.DataFrame( - { - "update_rule": [ - "Sequential (Python loop)", - "Sequential (Numba)", - "Parallel (Polars)", - ], - "runtime_seconds": [sequential_time, numba_time, parallel_time], - } -).with_columns(pl.col("runtime_seconds").round(4)) +runtime_rows: list[dict[str, float | str]] = [] +if RUN_SEQUENTIAL: + runtime_rows.append( + { + "update_rule": "Sequential (Python loop)", + "runtime_seconds": sequential_time, + } + ) +else: + runtime_rows.append( + { + "update_rule": "Sequential (Python loop) [skipped]", + "runtime_seconds": float("nan"), + } + ) + +runtime_rows.extend( + [ + { + "update_rule": "Sequential (Numba)", + "runtime_seconds": numba_time, + }, + { + "update_rule": "Parallel (Polars)", + "runtime_seconds": parallel_time, + }, + ] +) + +runtime_table = pl.DataFrame(runtime_rows).with_columns( + pl.col("runtime_seconds").round(4) +) print(runtime_table) From 1f52845b9f7d5a0ea50e9fb292c90860dfdb6057 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sat, 20 Sep 2025 17:06:22 +0200 Subject: [PATCH 18/99] refactor: move _current_sugar_map method to SugarscapeSequentialAgents class --- .../general/user-guide/3_advanced_tutorial.py | 34 +++++++++---------- 1 file changed, 16 insertions(+), 18 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 2579816a..5a0368d6 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -414,22 +414,6 @@ def _remove_starved(self) -> None: # ``discard`` accepts a DataFrame of agents to remove. self.discard(starved) - def _current_sugar_map(self) -> dict[tuple[int, int], int]: - """Return a mapping from grid coordinates to the current sugar value. - - Returns - ------- - dict - Keys are ``(x, y)`` tuples and values are the integer sugar amount - on that cell (zero if missing/None). - """ - cells = self.space.cells.select(["dim_0", "dim_1", "sugar"]) - # Build a plain Python dict for fast lookups in the movement code. - return { - (int(x), int(y)): 0 if sugar is None else int(sugar) - for x, y, sugar in cells.iter_rows() - } - @staticmethod def _manhattan(a: tuple[int, int], b: tuple[int, int]) -> int: """Compute the Manhattan (L1) distance between two grid cells. @@ -758,6 +742,21 @@ def sequential_move_numba( class SugarscapeSequentialAgents(SugarscapeAgentsBase): + def _current_sugar_map(self) -> dict[tuple[int, int], int]: + """Return a mapping from grid coordinates to the current sugar value. + + Returns + ------- + dict + Keys are ``(x, y)`` tuples and values are the integer sugar amount + on that cell (zero if missing/None). + """ + cells = self.space.cells.select(["dim_0", "dim_1", "sugar"]) + # Build a plain Python dict for fast lookups in the movement code. + return { + (int(x), int(y)): 0 if sugar is None else int(sugar) + for x, y, sugar in cells.iter_rows() + } def move(self) -> None: sugar_map = self._current_sugar_map() state = self.df.join(self.pos, on="unique_id", how="left") @@ -821,12 +820,11 @@ def move(self) -> None: # and losers are promoted to their next-ranked choice. if len(self.df) == 0: return - sugar_map = self._current_sugar_map() state = self.df.join(self.pos, on="unique_id", how="left") if state.is_empty(): return - # Map the positional frame to a center lookup used when joining + # Map the positional frame to a center lookup used when joining # neighbourhoods produced by the space helper. Build the lookup by # explicitly selecting and aliasing columns so the join creates a # deterministic `agent_id` column (some internal joins can drop or From f78c4c2b5f9ecf24aee0f2f22aa7ff83cd9efb55 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sat, 20 Sep 2025 17:17:22 +0200 Subject: [PATCH 19/99] refactor: replace Manhattan distance calculation with Frobenius norm in SugarscapeAgentsBase --- .../general/user-guide/3_advanced_tutorial.py | 23 ++++--------------- 1 file changed, 4 insertions(+), 19 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 5a0368d6..c40863ab 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -414,22 +414,6 @@ def _remove_starved(self) -> None: # ``discard`` accepts a DataFrame of agents to remove. self.discard(starved) - @staticmethod - def _manhattan(a: tuple[int, int], b: tuple[int, int]) -> int: - """Compute the Manhattan (L1) distance between two grid cells. - - Parameters - ---------- - a, b : tuple[int, int] - Coordinate pairs ``(x, y)``. - - Returns - ------- - int - The Manhattan distance between ``a`` and ``b``. - """ - return abs(a[0] - b[0]) + abs(a[1] - b[1]) - def _visible_cells(self, origin: tuple[int, int], vision: int) -> list[tuple[int, int]]: """List cells visible from an origin along the four cardinal axes. @@ -476,8 +460,9 @@ def _choose_best_cell( Tie-break rules (in order): 1. Prefer cells with strictly greater sugar. - 2. If equal sugar, prefer the cell with smaller Manhattan distance - from the origin. + 2. If equal sugar, prefer the cell with smaller distance from the + origin (measured with the Frobenius norm returned by + ``space.get_distances``). 3. If still tied, prefer the cell with smaller coordinates (lexicographic ordering of the ``(x, y)`` tuple). @@ -508,7 +493,7 @@ def _choose_best_cell( if blocked and candidate != origin and candidate in blocked: continue sugar_here = sugar_map.get(candidate, 0) - distance = self._manhattan(origin, candidate) + distance = self.model.space.get_distances(origin, candidate)["distance"].item() better = False # Primary criterion: strictly more sugar. if sugar_here > best_sugar: From 6e6c5d1e2be8b09e00597409fe6cd49328ec5304 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sat, 20 Sep 2025 17:20:52 +0200 Subject: [PATCH 20/99] refactor: move _visible_cells and _choose_best_cell methods to SugarscapeSequentialAgents class --- .../general/user-guide/3_advanced_tutorial.py | 194 +++++++++--------- 1 file changed, 97 insertions(+), 97 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index c40863ab..93c2000a 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -414,103 +414,6 @@ def _remove_starved(self) -> None: # ``discard`` accepts a DataFrame of agents to remove. self.discard(starved) - def _visible_cells(self, origin: tuple[int, int], vision: int) -> list[tuple[int, int]]: - """List cells visible from an origin along the four cardinal axes. - - The visibility set includes the origin cell itself and cells at - Manhattan distances 1..vision along the four cardinal directions - (up, down, left, right), clipped to the grid bounds. - - Parameters - ---------- - origin : tuple[int, int] - The agent's current coordinate ``(x, y)``. - vision : int - Maximum Manhattan radius to consider along each axis. - - Returns - ------- - list[tuple[int, int]] - Ordered list of visible cells (origin first, then increasing - step distance along each axis). - """ - x0, y0 = origin - width, height = self.space.dimensions - cells: list[tuple[int, int]] = [origin] - # Look outward one step at a time in the four cardinal directions. - for step in range(1, vision + 1): - if x0 + step < width: - cells.append((x0 + step, y0)) - if x0 - step >= 0: - cells.append((x0 - step, y0)) - if y0 + step < height: - cells.append((x0, y0 + step)) - if y0 - step >= 0: - cells.append((x0, y0 - step)) - return cells - - def _choose_best_cell( - self, - origin: tuple[int, int], - vision: int, - sugar_map: dict[tuple[int, int], int], - blocked: set[tuple[int, int]] | None, - ) -> tuple[int, int]: - """Select the best visible cell according to the movement rules. - - Tie-break rules (in order): - 1. Prefer cells with strictly greater sugar. - 2. If equal sugar, prefer the cell with smaller distance from the - origin (measured with the Frobenius norm returned by - ``space.get_distances``). - 3. If still tied, prefer the cell with smaller coordinates (lexicographic - ordering of the ``(x, y)`` tuple). - - Parameters - ---------- - origin : tuple[int, int] - Agent's current coordinate. - vision : int - Maximum vision radius along cardinal axes. - sugar_map : dict - Mapping from ``(x, y)`` to sugar amount. - blocked : set or None - Optional set of coordinates that should be considered occupied and - therefore skipped (except the origin which is always allowed). - - Returns - ------- - tuple[int, int] - Chosen target coordinate (may be the origin if no better cell is - available). - """ - best_cell = origin - best_sugar = sugar_map.get(origin, 0) - best_distance = 0 - for candidate in self._visible_cells(origin, vision): - # Skip blocked cells (occupied by other agents) unless it's the - # agent's current cell which we always consider. - if blocked and candidate != origin and candidate in blocked: - continue - sugar_here = sugar_map.get(candidate, 0) - distance = self.model.space.get_distances(origin, candidate)["distance"].item() - better = False - # Primary criterion: strictly more sugar. - if sugar_here > best_sugar: - better = True - elif sugar_here == best_sugar: - # Secondary: closer distance. - if distance < best_distance: - better = True - # Tertiary: lexicographic tie-break on coordinates. - elif distance == best_distance and candidate < best_cell: - better = True - if better: - best_cell = candidate - best_sugar = sugar_here - best_distance = distance - return best_cell - # %% @@ -727,6 +630,103 @@ def sequential_move_numba( class SugarscapeSequentialAgents(SugarscapeAgentsBase): + def _visible_cells(self, origin: tuple[int, int], vision: int) -> list[tuple[int, int]]: + """List cells visible from an origin along the four cardinal axes. + + The visibility set includes the origin cell itself and cells at + Manhattan distances 1..vision along the four cardinal directions + (up, down, left, right), clipped to the grid bounds. + + Parameters + ---------- + origin : tuple[int, int] + The agent's current coordinate ``(x, y)``. + vision : int + Maximum Manhattan radius to consider along each axis. + + Returns + ------- + list[tuple[int, int]] + Ordered list of visible cells (origin first, then increasing + step distance along each axis). + """ + x0, y0 = origin + width, height = self.space.dimensions + cells: list[tuple[int, int]] = [origin] + # Look outward one step at a time in the four cardinal directions. + for step in range(1, vision + 1): + if x0 + step < width: + cells.append((x0 + step, y0)) + if x0 - step >= 0: + cells.append((x0 - step, y0)) + if y0 + step < height: + cells.append((x0, y0 + step)) + if y0 - step >= 0: + cells.append((x0, y0 - step)) + return cells + + def _choose_best_cell( + self, + origin: tuple[int, int], + vision: int, + sugar_map: dict[tuple[int, int], int], + blocked: set[tuple[int, int]] | None, + ) -> tuple[int, int]: + """Select the best visible cell according to the movement rules. + + Tie-break rules (in order): + 1. Prefer cells with strictly greater sugar. + 2. If equal sugar, prefer the cell with smaller distance from the + origin (measured with the Frobenius norm returned by + ``space.get_distances``). + 3. If still tied, prefer the cell with smaller coordinates (lexicographic + ordering of the ``(x, y)`` tuple). + + Parameters + ---------- + origin : tuple[int, int] + Agent's current coordinate. + vision : int + Maximum vision radius along cardinal axes. + sugar_map : dict + Mapping from ``(x, y)`` to sugar amount. + blocked : set or None + Optional set of coordinates that should be considered occupied and + therefore skipped (except the origin which is always allowed). + + Returns + ------- + tuple[int, int] + Chosen target coordinate (may be the origin if no better cell is + available). + """ + best_cell = origin + best_sugar = sugar_map.get(origin, 0) + best_distance = 0 + for candidate in self._visible_cells(origin, vision): + # Skip blocked cells (occupied by other agents) unless it's the + # agent's current cell which we always consider. + if blocked and candidate != origin and candidate in blocked: + continue + sugar_here = sugar_map.get(candidate, 0) + distance = self.model.space.get_distances(origin, candidate)["distance"].item() + better = False + # Primary criterion: strictly more sugar. + if sugar_here > best_sugar: + better = True + elif sugar_here == best_sugar: + # Secondary: closer distance. + if distance < best_distance: + better = True + # Tertiary: lexicographic tie-break on coordinates. + elif distance == best_distance and candidate < best_cell: + better = True + if better: + best_cell = candidate + best_sugar = sugar_here + best_distance = distance + return best_cell + def _current_sugar_map(self) -> dict[tuple[int, int], int]: """Return a mapping from grid coordinates to the current sugar value. From 71254476e6fff7cab0c2e5f4cfdcc5193102c2b5 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sat, 20 Sep 2025 17:21:13 +0200 Subject: [PATCH 21/99] chore: remove placeholder advanced tutorial for SugarScape with Instantaneous Growback --- docs/general/user-guide/3_advanced-tutorial.md | 4 ---- 1 file changed, 4 deletions(-) delete mode 100644 docs/general/user-guide/3_advanced-tutorial.md diff --git a/docs/general/user-guide/3_advanced-tutorial.md b/docs/general/user-guide/3_advanced-tutorial.md deleted file mode 100644 index 8a2eae55..00000000 --- a/docs/general/user-guide/3_advanced-tutorial.md +++ /dev/null @@ -1,4 +0,0 @@ -# Advanced Tutorial: SugarScape with Instantaneous Growback 🍬🔄 - -!!! warning "Work in Progress 🚧" - This tutorial is coming soon! 🔜✨ In the meantime, you can check out the code in the `examples/sugarscape-ig` directory of the mesa-frames repository. From 0be9d0f8ef2f5462ea686c2880c032c263b41e62 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sat, 20 Sep 2025 18:16:59 +0200 Subject: [PATCH 22/99] feat: add Gini coefficient and correlation metrics for sugar, metabolism, and vision in Sugarscape model --- .../general/user-guide/3_advanced_tutorial.py | 513 +++++++++--------- 1 file changed, 257 insertions(+), 256 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 93c2000a..f578b71e 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -68,7 +68,6 @@ # %% import os -from collections import defaultdict from time import perf_counter import numpy as np @@ -94,12 +93,74 @@ and maximum sugar (for regrowth). The model also sets up a data collector to track aggregate statistics and agent traits over time. -The `step` method advances the sugar field, triggers the agent set's step +The `step` method advances the sugar field, triggers the agent set's step. + +We also define some useful functions to compute metrics like the Gini coefficient and correlations. """ # %% +# Model-level reporters + +def gini(model: Model) -> float: + if len(model.sets) == 0: + return float("nan") + + primary_set = model.sets[0] + if len(primary_set) == 0: + return float("nan") + + sugar = primary_set.df["sugar"].to_numpy().astype(np.float64) + + if sugar.size == 0: + return float("nan") + sorted_vals = np.sort(sugar.astype(np.float64)) + n = sorted_vals.size + if n == 0: + return float("nan") + cumulative = np.cumsum(sorted_vals) + total = cumulative[-1] + if total == 0: + return 0.0 + index = np.arange(1, n + 1, dtype=np.float64) + return float((2.0 * np.dot(index, sorted_vals) / (n * total)) - (n + 1) / n) + +def _safe_corr(x: np.ndarray, y: np.ndarray) -> float: + if x.size < 2 or y.size < 2: + return float("nan") + if np.allclose(x, x[0]) or np.allclose(y, y[0]): + return float("nan") + return float(np.corrcoef(x, y)[0, 1]) + + +def corr_sugar_metabolism(model: Model) -> float: + if len(model.sets) == 0: + return float("nan") + + primary_set = model.sets[0] + if len(primary_set) == 0: + return float("nan") + + agent_df = primary_set.df + sugar = agent_df["sugar"].to_numpy().astype(np.float64) + metabolism = agent_df["metabolism"].to_numpy().astype(np.float64) + return _safe_corr(sugar, metabolism) + + +def corr_sugar_vision(model: Model) -> float: + if len(model.sets) == 0: + return float("nan") + + primary_set = model.sets[0] + if len(primary_set) == 0: + return float("nan") + + agent_df = primary_set.df + sugar = agent_df["sugar"].to_numpy().astype(np.float64) + vision = agent_df["vision"].to_numpy().astype(np.float64) + return _safe_corr(sugar, vision) + class Sugarscape(Model): """Minimal Sugarscape model used throughout the tutorial. @@ -181,6 +242,11 @@ def __init__( if len(m.sets[0]) else 0.0, "living_agents": lambda m: len(m.sets[0]), + # Inequality metrics recorded individually. + "gini": gini, + "corr_sugar_metabolism": corr_sugar_metabolism, + "corr_sugar_vision": corr_sugar_vision, + "agents_alive": lambda m: float(len(m.sets[0])) if len(m.sets) else 0.0, }, agent_reporters={"traits": ["sugar", "metabolism", "vision"]}, ) @@ -296,12 +362,11 @@ def _advance_sugar_field(self) -> None: """ ## 3. Agent definition -### Base agent class +### 3.1 Base agent class Now let's define the agent class (the ant class). We start with a base class which implements the common logic for eating and starvation, while leaving the `move` method abstract. The base class also provides helper methods for sensing visible cells and choosing the best cell based on sugar, distance, and coordinates. This will allow us to define different movement policies (sequential, Numba-accelerated, and parallel) as subclasses that only need to implement the `move` method. -We also add """ # %% @@ -415,23 +480,156 @@ def _remove_starved(self) -> None: self.discard(starved) +# %% [markdown] -# %% -GRID_WIDTH = 50 -GRID_HEIGHT = 50 -NUM_AGENTS = 400 -MODEL_STEPS = 60 -MAX_SUGAR = 4 +"""### 3.2 Sequential movement + +We now implement the simplest movement policy: sequential (asynchronous). Each agent moves one at a time in the current ordering, choosing the best visible cell according to the rules. + +This implementation uses plain Python loops as the logic cannot be easily vectorised. As a result, it is slow for large populations and grids. We will later show how to speed it up with Numba. +""" + +# %% + +class SugarscapeSequentialAgents(SugarscapeAgentsBase): + def _visible_cells(self, origin: tuple[int, int], vision: int) -> list[tuple[int, int]]: + """List cells visible from an origin along the four cardinal axes. + + The visibility set includes the origin cell itself and cells at + Manhattan distances 1..vision along the four cardinal directions + (up, down, left, right), clipped to the grid bounds. + + Parameters + ---------- + origin : tuple[int, int] + The agent's current coordinate ``(x, y)``. + vision : int + Maximum Manhattan radius to consider along each axis. + + Returns + ------- + list[tuple[int, int]] + Ordered list of visible cells (origin first, then increasing + step distance along each axis). + """ + x0, y0 = origin + width, height = self.space.dimensions + cells: list[tuple[int, int]] = [origin] + # Look outward one step at a time in the four cardinal directions. + for step in range(1, vision + 1): + if x0 + step < width: + cells.append((x0 + step, y0)) + if x0 - step >= 0: + cells.append((x0 - step, y0)) + if y0 + step < height: + cells.append((x0, y0 + step)) + if y0 - step >= 0: + cells.append((x0, y0 - step)) + return cells + + def _choose_best_cell( + self, + origin: tuple[int, int], + vision: int, + sugar_map: dict[tuple[int, int], int], + blocked: set[tuple[int, int]] | None, + ) -> tuple[int, int]: + """Select the best visible cell according to the movement rules. + + Tie-break rules (in order): + 1. Prefer cells with strictly greater sugar. + 2. If equal sugar, prefer the cell with smaller distance from the + origin (measured with the Frobenius norm returned by + ``space.get_distances``). + 3. If still tied, prefer the cell with smaller coordinates (lexicographic + ordering of the ``(x, y)`` tuple). + + Parameters + ---------- + origin : tuple[int, int] + Agent's current coordinate. + vision : int + Maximum vision radius along cardinal axes. + sugar_map : dict + Mapping from ``(x, y)`` to sugar amount. + blocked : set or None + Optional set of coordinates that should be considered occupied and + therefore skipped (except the origin which is always allowed). + + Returns + ------- + tuple[int, int] + Chosen target coordinate (may be the origin if no better cell is + available). + """ + best_cell = origin + best_sugar = sugar_map.get(origin, 0) + best_distance = 0 + for candidate in self._visible_cells(origin, vision): + # Skip blocked cells (occupied by other agents) unless it's the + # agent's current cell which we always consider. + if blocked and candidate != origin and candidate in blocked: + continue + sugar_here = sugar_map.get(candidate, 0) + distance = self.model.space.get_distances(origin, candidate)["distance"].item() + better = False + # Primary criterion: strictly more sugar. + if sugar_here > best_sugar: + better = True + elif sugar_here == best_sugar: + # Secondary: closer distance. + if distance < best_distance: + better = True + # Tertiary: lexicographic tie-break on coordinates. + elif distance == best_distance and candidate < best_cell: + better = True + if better: + best_cell = candidate + best_sugar = sugar_here + best_distance = distance + return best_cell + + def _current_sugar_map(self) -> dict[tuple[int, int], int]: + """Return a mapping from grid coordinates to the current sugar value. + + Returns + ------- + dict + Keys are ``(x, y)`` tuples and values are the integer sugar amount + on that cell (zero if missing/None). + """ + cells = self.space.cells.select(["dim_0", "dim_1", "sugar"]) + # Build a plain Python dict for fast lookups in the movement code. + return { + (int(x), int(y)): 0 if sugar is None else int(sugar) + for x, y, sugar in cells.iter_rows() + } + + def move(self) -> None: + sugar_map = self._current_sugar_map() + state = self.df.join(self.pos, on="unique_id", how="left") + positions = { + int(row["unique_id"]): (int(row["dim_0"]), int(row["dim_1"])) + for row in state.iter_rows(named=True) + } + taken: set[tuple[int, int]] = set(positions.values()) + + for row in state.iter_rows(named=True): + agent_id = int(row["unique_id"]) + vision = int(row["vision"]) + current = positions[agent_id] + taken.discard(current) + target = self._choose_best_cell(current, vision, sugar_map, taken) + taken.add(target) + positions[agent_id] = target + if target != current: + self.space.move_agents(agent_id, target) + +# %% [markdown] +""" +## 3.4 Speeding Up the Loop with Numba +""" -# Allow quick testing by skipping the slow pure-Python sequential baseline. -# Set the environment variable ``MESA_FRAMES_RUN_SEQUENTIAL=0`` (or "false") -# to disable the baseline when running this script. -RUN_SEQUENTIAL = os.getenv("MESA_FRAMES_RUN_SEQUENTIAL", "0").lower() not in { - "0", - "false", - "no", - "off", -} @njit(cache=True) def _numba_should_replace( @@ -608,167 +806,6 @@ def sequential_move_numba( return new_dim0, new_dim1 - - - -# %% [markdown] -""" -## 2. Agent Scaffolding - -With the space logic in place we can define the agents. The base class stores -traits and implements eating/starvation; concrete subclasses only override -`move`. -""" - - - - -# %% [markdown] -""" -## 3. Sequential Movement -""" - - -class SugarscapeSequentialAgents(SugarscapeAgentsBase): - def _visible_cells(self, origin: tuple[int, int], vision: int) -> list[tuple[int, int]]: - """List cells visible from an origin along the four cardinal axes. - - The visibility set includes the origin cell itself and cells at - Manhattan distances 1..vision along the four cardinal directions - (up, down, left, right), clipped to the grid bounds. - - Parameters - ---------- - origin : tuple[int, int] - The agent's current coordinate ``(x, y)``. - vision : int - Maximum Manhattan radius to consider along each axis. - - Returns - ------- - list[tuple[int, int]] - Ordered list of visible cells (origin first, then increasing - step distance along each axis). - """ - x0, y0 = origin - width, height = self.space.dimensions - cells: list[tuple[int, int]] = [origin] - # Look outward one step at a time in the four cardinal directions. - for step in range(1, vision + 1): - if x0 + step < width: - cells.append((x0 + step, y0)) - if x0 - step >= 0: - cells.append((x0 - step, y0)) - if y0 + step < height: - cells.append((x0, y0 + step)) - if y0 - step >= 0: - cells.append((x0, y0 - step)) - return cells - - def _choose_best_cell( - self, - origin: tuple[int, int], - vision: int, - sugar_map: dict[tuple[int, int], int], - blocked: set[tuple[int, int]] | None, - ) -> tuple[int, int]: - """Select the best visible cell according to the movement rules. - - Tie-break rules (in order): - 1. Prefer cells with strictly greater sugar. - 2. If equal sugar, prefer the cell with smaller distance from the - origin (measured with the Frobenius norm returned by - ``space.get_distances``). - 3. If still tied, prefer the cell with smaller coordinates (lexicographic - ordering of the ``(x, y)`` tuple). - - Parameters - ---------- - origin : tuple[int, int] - Agent's current coordinate. - vision : int - Maximum vision radius along cardinal axes. - sugar_map : dict - Mapping from ``(x, y)`` to sugar amount. - blocked : set or None - Optional set of coordinates that should be considered occupied and - therefore skipped (except the origin which is always allowed). - - Returns - ------- - tuple[int, int] - Chosen target coordinate (may be the origin if no better cell is - available). - """ - best_cell = origin - best_sugar = sugar_map.get(origin, 0) - best_distance = 0 - for candidate in self._visible_cells(origin, vision): - # Skip blocked cells (occupied by other agents) unless it's the - # agent's current cell which we always consider. - if blocked and candidate != origin and candidate in blocked: - continue - sugar_here = sugar_map.get(candidate, 0) - distance = self.model.space.get_distances(origin, candidate)["distance"].item() - better = False - # Primary criterion: strictly more sugar. - if sugar_here > best_sugar: - better = True - elif sugar_here == best_sugar: - # Secondary: closer distance. - if distance < best_distance: - better = True - # Tertiary: lexicographic tie-break on coordinates. - elif distance == best_distance and candidate < best_cell: - better = True - if better: - best_cell = candidate - best_sugar = sugar_here - best_distance = distance - return best_cell - - def _current_sugar_map(self) -> dict[tuple[int, int], int]: - """Return a mapping from grid coordinates to the current sugar value. - - Returns - ------- - dict - Keys are ``(x, y)`` tuples and values are the integer sugar amount - on that cell (zero if missing/None). - """ - cells = self.space.cells.select(["dim_0", "dim_1", "sugar"]) - # Build a plain Python dict for fast lookups in the movement code. - return { - (int(x), int(y)): 0 if sugar is None else int(sugar) - for x, y, sugar in cells.iter_rows() - } - def move(self) -> None: - sugar_map = self._current_sugar_map() - state = self.df.join(self.pos, on="unique_id", how="left") - positions = { - int(row["unique_id"]): (int(row["dim_0"]), int(row["dim_1"])) - for row in state.iter_rows(named=True) - } - taken: set[tuple[int, int]] = set(positions.values()) - - for row in state.iter_rows(named=True): - agent_id = int(row["unique_id"]) - vision = int(row["vision"]) - current = positions[agent_id] - taken.discard(current) - target = self._choose_best_cell(current, vision, sugar_map, taken) - taken.add(target) - positions[agent_id] = target - if target != current: - self.space.move_agents(agent_id, target) - - -# %% [markdown] -""" -## 4. Speeding Up the Loop with Numba -""" - - class SugarscapeNumbaAgents(SugarscapeAgentsBase): def move(self) -> None: state = self.df.join(self.pos, on="unique_id", how="left") @@ -1016,6 +1053,16 @@ def move(self) -> None: # so pass Series/DataFrame directly rather than converting to Python lists. self.space.move_agents(move_df["unique_id"], move_df.select(["dim_0", "dim_1"])) + + +# %% [markdown] +""" +## 6. Shared Model Infrastructure + +`SugarscapeTutorialModel` wires the grid, agent set, regrowth logic, and data +collection. Each variant simply plugs in a different agent class. +""" + def run_variant( agent_cls: type[SugarscapeAgentsBase], *, @@ -1034,80 +1081,6 @@ def run_variant( model.run(steps) return model, perf_counter() - start - -# %% [markdown] -""" -## 6. Shared Model Infrastructure - -`SugarscapeTutorialModel` wires the grid, agent set, regrowth logic, and data -collection. Each variant simply plugs in a different agent class. -""" - - -def gini(values: np.ndarray) -> float: - if values.size == 0: - return float("nan") - sorted_vals = np.sort(values.astype(np.float64)) - n = sorted_vals.size - if n == 0: - return float("nan") - cumulative = np.cumsum(sorted_vals) - total = cumulative[-1] - if total == 0: - return 0.0 - index = np.arange(1, n + 1, dtype=np.float64) - return float((2.0 * np.dot(index, sorted_vals) / (n * total)) - (n + 1) / n) - - -def _safe_corr(x: np.ndarray, y: np.ndarray) -> float: - if x.size < 2 or y.size < 2: - return float("nan") - if np.allclose(x, x[0]) or np.allclose(y, y[0]): - return float("nan") - return float(np.corrcoef(x, y)[0, 1]) - - -def _column_with_prefix(df: pl.DataFrame, prefix: str) -> str: - for col in df.columns: - if col.startswith(prefix): - return col - raise KeyError(f"No column starts with prefix '{prefix}'") - - -def final_agent_snapshot(model: Model) -> pl.DataFrame: - agent_frame = model.datacollector.data["agent"] - if agent_frame.is_empty(): - return agent_frame - last_step = agent_frame["step"].max() - return agent_frame.filter(pl.col("step") == last_step) - - -def summarise_inequality(model: Model) -> dict[str, float]: - snapshot = final_agent_snapshot(model) - if snapshot.is_empty(): - return { - "gini": float("nan"), - "corr_sugar_metabolism": float("nan"), - "corr_sugar_vision": float("nan"), - "agents_alive": 0, - } - - sugar_col = _column_with_prefix(snapshot, "traits_sugar_") - metabolism_col = _column_with_prefix(snapshot, "traits_metabolism_") - vision_col = _column_with_prefix(snapshot, "traits_vision_") - - sugar = snapshot[sugar_col].to_numpy() - metabolism = snapshot[metabolism_col].to_numpy() - vision = snapshot[vision_col].to_numpy() - - return { - "gini": gini(sugar), - "corr_sugar_metabolism": _safe_corr(sugar, metabolism), - "corr_sugar_vision": _safe_corr(sugar, vision), - "agents_alive": float(sugar.size), - } - - # %% [markdown] """ ## 7. Run the Sequential Model (Python loop) @@ -1119,6 +1092,24 @@ def summarise_inequality(model: Model) -> dict[str, float]: """ # %% + +# %% +GRID_WIDTH = 50 +GRID_HEIGHT = 50 +NUM_AGENTS = 400 +MODEL_STEPS = 60 +MAX_SUGAR = 4 + +# Allow quick testing by skipping the slow pure-Python sequential baseline. +# Set the environment variable ``MESA_FRAMES_RUN_SEQUENTIAL=0`` (or "false") +# to disable the baseline when running this script. +RUN_SEQUENTIAL = os.getenv("MESA_FRAMES_RUN_SEQUENTIAL", "0").lower() not in { + "0", + "false", + "no", + "off", +} + sequential_seed = 11 if RUN_SEQUENTIAL: @@ -1269,11 +1260,21 @@ def summarise_inequality(model: Model) -> dict[str, float]: [ { "update_rule": "Sequential (Numba)", - **summarise_inequality(numba_model), + "gini": gini(numba_model), + "corr_sugar_metabolism": corr_sugar_metabolism(numba_model), + "corr_sugar_vision": corr_sugar_vision(numba_model), + "agents_alive": float(len(numba_model.sets[0])) + if len(numba_model.sets) + else 0.0, }, { "update_rule": "Parallel (random tie-break)", - **summarise_inequality(parallel_model), + "gini": gini(parallel_model), + "corr_sugar_metabolism": corr_sugar_metabolism(parallel_model), + "corr_sugar_vision": corr_sugar_vision(parallel_model), + "agents_alive": float(len(parallel_model.sets[0])) + if len(parallel_model.sets) + else 0.0, }, ] ) From 273ba7ce110a8652fb3bfb59e50385f89fef1851 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sat, 20 Sep 2025 18:19:00 +0200 Subject: [PATCH 23/99] refactor: move _safe_corr function to improve code organization in advanced tutorial --- docs/general/user-guide/3_advanced_tutorial.py | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index f578b71e..14f4c5ac 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -126,14 +126,6 @@ def gini(model: Model) -> float: index = np.arange(1, n + 1, dtype=np.float64) return float((2.0 * np.dot(index, sorted_vals) / (n * total)) - (n + 1) / n) -def _safe_corr(x: np.ndarray, y: np.ndarray) -> float: - if x.size < 2 or y.size < 2: - return float("nan") - if np.allclose(x, x[0]) or np.allclose(y, y[0]): - return float("nan") - return float(np.corrcoef(x, y)[0, 1]) - - def corr_sugar_metabolism(model: Model) -> float: if len(model.sets) == 0: return float("nan") @@ -147,7 +139,6 @@ def corr_sugar_metabolism(model: Model) -> float: metabolism = agent_df["metabolism"].to_numpy().astype(np.float64) return _safe_corr(sugar, metabolism) - def corr_sugar_vision(model: Model) -> float: if len(model.sets) == 0: return float("nan") @@ -161,6 +152,13 @@ def corr_sugar_vision(model: Model) -> float: vision = agent_df["vision"].to_numpy().astype(np.float64) return _safe_corr(sugar, vision) +def _safe_corr(x: np.ndarray, y: np.ndarray) -> float: + if x.size < 2 or y.size < 2: + return float("nan") + if np.allclose(x, x[0]) or np.allclose(y, y[0]): + return float("nan") + return float(np.corrcoef(x, y)[0, 1]) + class Sugarscape(Model): """Minimal Sugarscape model used throughout the tutorial. From 0e5b125d8b3da88ca560a48f2b1b9576205ced57 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sat, 20 Sep 2025 18:20:23 +0200 Subject: [PATCH 24/99] feat: add Gini coefficient and correlation metrics for sugar, metabolism, and vision in advanced tutorial --- .../general/user-guide/3_advanced_tutorial.py | 83 ++++++++++++++++++- 1 file changed, 82 insertions(+), 1 deletion(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 14f4c5ac..88fb1a40 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -104,6 +104,26 @@ # Model-level reporters def gini(model: Model) -> float: + """Compute the Gini coefficient of agent sugar holdings. + + The function reads the primary agent set from ``model.sets[0]`` and + computes the population Gini coefficient on the ``sugar`` column. The + implementation is robust to empty sets and zero-total sugar. + + Parameters + ---------- + model : Model + The simulation model that contains agent sets. The primary agent set + is expected to be at ``model.sets[0]`` and to expose a Polars DataFrame + under ``.df`` with a ``sugar`` column. + + Returns + ------- + float + Gini coefficient in the range [0, 1] if defined, ``0.0`` when the + total sugar is zero, and ``nan`` when the agent set is empty or too + small to measure. + """ if len(model.sets) == 0: return float("nan") @@ -112,7 +132,7 @@ def gini(model: Model) -> float: return float("nan") sugar = primary_set.df["sugar"].to_numpy().astype(np.float64) - + if sugar.size == 0: return float("nan") sorted_vals = np.sort(sugar.astype(np.float64)) @@ -127,6 +147,27 @@ def gini(model: Model) -> float: return float((2.0 * np.dot(index, sorted_vals) / (n * total)) - (n + 1) / n) def corr_sugar_metabolism(model: Model) -> float: + """Pearson correlation between agent sugar and metabolism. + + This reporter extracts the ``sugar`` and ``metabolism`` columns from the + primary agent set and returns their Pearson correlation coefficient. When + the agent set is empty or contains insufficient variation the function + returns ``nan``. + + Parameters + ---------- + model : Model + The simulation model that contains agent sets. The primary agent set + is expected to be at ``model.sets[0]`` and provide a Polars DataFrame + with ``sugar`` and ``metabolism`` columns. + + Returns + ------- + float + Pearson correlation coefficient between sugar and metabolism, or + ``nan`` when the correlation is undefined (empty set or constant + values). + """ if len(model.sets) == 0: return float("nan") @@ -140,6 +181,26 @@ def corr_sugar_metabolism(model: Model) -> float: return _safe_corr(sugar, metabolism) def corr_sugar_vision(model: Model) -> float: + """Pearson correlation between agent sugar and vision. + + Extracts the ``sugar`` and ``vision`` columns from the primary agent set + and returns their Pearson correlation coefficient. If the reporter cannot + compute a meaningful correlation (for example, when the agent set is + empty or values are constant) it returns ``nan``. + + Parameters + ---------- + model : Model + The simulation model that contains agent sets. The primary agent set + is expected to be at ``model.sets[0]`` and provide a Polars DataFrame + with ``sugar`` and ``vision`` columns. + + Returns + ------- + float + Pearson correlation coefficient between sugar and vision, or ``nan`` + when the correlation is undefined. + """ if len(model.sets) == 0: return float("nan") @@ -153,6 +214,26 @@ def corr_sugar_vision(model: Model) -> float: return _safe_corr(sugar, vision) def _safe_corr(x: np.ndarray, y: np.ndarray) -> float: + """Safely compute Pearson correlation between two 1-D arrays. + + This helper guards against degenerate inputs (too few observations or + constant arrays) which would make the Pearson correlation undefined or + numerically unstable. When a valid correlation can be computed the + function returns a Python float. + + Parameters + ---------- + x, y : np.ndarray + One-dimensional numeric arrays of the same length containing the two + variables to correlate. + + Returns + ------- + float + Pearson correlation coefficient as a Python float, or ``nan`` if the + correlation is undefined (fewer than 2 observations or constant + inputs). + """ if x.size < 2 or y.size < 2: return float("nan") if np.allclose(x, x[0]) or np.allclose(y, y[0]): From 8957020cdb8316f3c1cc5466c459df971f99a198 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sat, 20 Sep 2025 18:21:28 +0200 Subject: [PATCH 25/99] refactor: rename 'living_agents' to 'agents_alive' for clarity in Sugarscape model --- docs/general/user-guide/3_advanced_tutorial.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 88fb1a40..45149e55 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -320,12 +320,10 @@ def __init__( "total_sugar": lambda m: float(m.sets[0].df["sugar"].sum()) if len(m.sets[0]) else 0.0, - "living_agents": lambda m: len(m.sets[0]), - # Inequality metrics recorded individually. + "agents_alive": lambda m: float(len(m.sets[0])) if len(m.sets) else 0.0, "gini": gini, "corr_sugar_metabolism": corr_sugar_metabolism, "corr_sugar_vision": corr_sugar_vision, - "agents_alive": lambda m: float(len(m.sets[0])) if len(m.sets) else 0.0, }, agent_reporters={"traits": ["sugar", "metabolism", "vision"]}, ) From 62812762b25991173aa92fc462db802a03320c98 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sat, 20 Sep 2025 18:31:45 +0200 Subject: [PATCH 26/99] refactor: rename 'SugarscapeAgentsBase' to 'AntsBase' and update related classes for improved clarity --- .../general/user-guide/3_advanced_tutorial.py | 24 ++++++++++++------- 1 file changed, 15 insertions(+), 9 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 45149e55..7b49ef23 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -280,7 +280,7 @@ class Sugarscape(Model): def __init__( self, - agent_type: type["SugarscapeAgentsBase"], + agent_type: type["AntsBase"], n_agents: int, *, width: int, @@ -448,7 +448,7 @@ def _advance_sugar_field(self) -> None: # %% -class SugarscapeAgentsBase(AgentSet): +class AntsBase(AgentSet): """Base agent set for the Sugarscape tutorial. This class implements the common behaviour shared by all agent @@ -568,7 +568,7 @@ def _remove_starved(self) -> None: # %% -class SugarscapeSequentialAgents(SugarscapeAgentsBase): +class AntsSequential(AntsBase): def _visible_cells(self, origin: tuple[int, int], vision: int) -> list[tuple[int, int]]: """List cells visible from an origin along the four cardinal axes. @@ -705,6 +705,10 @@ def move(self) -> None: # %% [markdown] """ ## 3.4 Speeding Up the Loop with Numba + +As we will see later, the previous sequential implementation is slow for large populations and grids because it relies on plain Python loops. We can speed it up significantly by using Numba to compile the movement logic. + +Numba compiles numerical Python code to fast machine code at runtime. To use Numba, we need to rewrite the movement logic in a way that is compatible with Numba's restrictions (using tightly typed numpy arrays and accessing data indexes directly). """ @@ -883,7 +887,7 @@ def sequential_move_numba( return new_dim0, new_dim1 -class SugarscapeNumbaAgents(SugarscapeAgentsBase): +class AntsNumba(AntsBase): def move(self) -> None: state = self.df.join(self.pos, on="unique_id", how="left") if state.is_empty(): @@ -908,10 +912,12 @@ def move(self) -> None: # %% [markdown] """ ## 5. Simultaneous Movement with Conflict Resolution + +The previous implementation is fast but it requires """ -class SugarscapeParallelAgents(SugarscapeAgentsBase): +class AntsParallel(AntsBase): def move(self) -> None: # Parallel movement: each agent proposes a ranked list of visible # cells (including its own). We resolve conflicts in rounds using @@ -1141,7 +1147,7 @@ def move(self) -> None: """ def run_variant( - agent_cls: type[SugarscapeAgentsBase], + agent_cls: type[AntsBase], *, steps: int, seed: int, @@ -1191,7 +1197,7 @@ def run_variant( if RUN_SEQUENTIAL: sequential_model, sequential_time = run_variant( - SugarscapeSequentialAgents, steps=MODEL_STEPS, seed=sequential_seed + AntsSequential, steps=MODEL_STEPS, seed=sequential_seed ) seq_model_frame = sequential_model.datacollector.data["model"] @@ -1221,7 +1227,7 @@ def run_variant( # %% numba_model, numba_time = run_variant( - SugarscapeNumbaAgents, steps=MODEL_STEPS, seed=sequential_seed + AntsNumba, steps=MODEL_STEPS, seed=sequential_seed ) numba_model_frame = numba_model.datacollector.data["model"] @@ -1241,7 +1247,7 @@ def run_variant( # %% parallel_model, parallel_time = run_variant( - SugarscapeParallelAgents, steps=MODEL_STEPS, seed=sequential_seed + AntsParallel, steps=MODEL_STEPS, seed=sequential_seed ) par_model_frame = parallel_model.datacollector.data["model"] From e03f2da683e7e3d157b8a365fc644aa406f8aab4 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sat, 20 Sep 2025 18:40:23 +0200 Subject: [PATCH 27/99] refactor: update grid dimensions and rename 'living_agents' to 'agents_alive' for clarity in advanced tutorial --- docs/general/user-guide/3_advanced_tutorial.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 7b49ef23..84e0701e 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -1177,9 +1177,9 @@ def run_variant( # %% # %% -GRID_WIDTH = 50 -GRID_HEIGHT = 50 -NUM_AGENTS = 400 +GRID_WIDTH = 250 +GRID_HEIGHT = 250 +NUM_AGENTS = 10000 MODEL_STEPS = 60 MAX_SUGAR = 4 @@ -1204,7 +1204,7 @@ def run_variant( print("Sequential aggregate trajectory (last 5 steps):") print( seq_model_frame.select( - ["step", "mean_sugar", "total_sugar", "living_agents"] + ["step", "mean_sugar", "total_sugar", "agents_alive"] ).tail(5) ) print(f"Sequential runtime: {sequential_time:.3f} s") @@ -1233,7 +1233,7 @@ def run_variant( numba_model_frame = numba_model.datacollector.data["model"] print("Numba sequential aggregate trajectory (last 5 steps):") print( - numba_model_frame.select(["step", "mean_sugar", "total_sugar", "living_agents"]).tail(5) + numba_model_frame.select(["step", "mean_sugar", "total_sugar", "agents_alive"]).tail(5) ) print(f"Numba sequential runtime: {numba_time:.3f} s") @@ -1252,7 +1252,7 @@ def run_variant( par_model_frame = parallel_model.datacollector.data["model"] print("Parallel aggregate trajectory (last 5 steps):") -print(par_model_frame.select(["step", "mean_sugar", "total_sugar", "living_agents"]).tail(5)) +print(par_model_frame.select(["step", "mean_sugar", "total_sugar", "agents_alive"]).tail(5)) print(f"Parallel runtime: {parallel_time:.3f} s") # %% [markdown] @@ -1325,8 +1325,8 @@ def run_variant( """ # %% -comparison = numba_model_frame.select(["step", "mean_sugar", "total_sugar", "living_agents"]).join( - par_model_frame.select(["step", "mean_sugar", "total_sugar", "living_agents"]), +comparison = numba_model_frame.select(["step", "mean_sugar", "total_sugar", "agents_alive"]).join( + par_model_frame.select(["step", "mean_sugar", "total_sugar", "agents_alive"]), on="step", how="inner", suffix="_parallel", @@ -1334,7 +1334,7 @@ def run_variant( comparison = comparison.with_columns( (pl.col("mean_sugar") - pl.col("mean_sugar_parallel")).abs().alias("mean_diff"), (pl.col("total_sugar") - pl.col("total_sugar_parallel")).abs().alias("total_diff"), - (pl.col("living_agents") - pl.col("living_agents_parallel")).abs().alias("count_diff"), + (pl.col("agents_alive") - pl.col("agents_alive_parallel")).abs().alias("count_diff"), ) print("Step-level absolute differences (first 10 steps):") print(comparison.select(["step", "mean_diff", "total_diff", "count_diff"]).head(10)) From 44bbdc2378555298dc226bfcca377308f99ca6e0 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sat, 20 Sep 2025 23:18:23 +0200 Subject: [PATCH 28/99] refactor: update section headings for clarity and consistency in advanced tutorial --- .../general/user-guide/3_advanced_tutorial.py | 176 ++++++++++++++---- 1 file changed, 136 insertions(+), 40 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 84e0701e..7b50564a 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -704,7 +704,7 @@ def move(self) -> None: # %% [markdown] """ -## 3.4 Speeding Up the Loop with Numba +### 3.3 Speeding Up the Loop with Numba As we will see later, the previous sequential implementation is slow for large populations and grids because it relies on plain Python loops. We can speed it up significantly by using Numba to compile the movement logic. @@ -911,30 +911,32 @@ def move(self) -> None: # %% [markdown] """ -## 5. Simultaneous Movement with Conflict Resolution +### 3.5. Simultaneous Movement with Conflict Resolution (the Polars mesa-frames idiomatic way) -The previous implementation is fast but it requires +The previous implementation is optimal speed-wise but it's a bit low-level. It requires mantaining an occupancy grid and imperative loops and it might become tricky to extend with more complex movement rules or models. +To stay in mesa-frames idiom, we can implement a parallel movement policy that uses Polars DataFrame operations to resolve conflicts when multiple agents target the same cell. +These conflicts are resolved in rounds: in each round, each agent proposes its current best candidate cell; winners per cell are chosen at random, and losers are promoted to their next-ranked choice. This continues until all agents have moved. +This implementation is a tad slower but still efficient and easier to read (for a Polars user). """ class AntsParallel(AntsBase): def move(self) -> None: - # Parallel movement: each agent proposes a ranked list of visible - # cells (including its own). We resolve conflicts in rounds using - # DataFrame operations so winners can be chosen per-cell at random - # and losers are promoted to their next-ranked choice. + """ + Parallel movement: each agent proposes a ranked list of visible cells (including its own). + We resolve conflicts in rounds using DataFrame operations so winners can be chosen per-cell at random and losers are promoted to their next-ranked choice. + """ + # Early exit if there are no agents. if len(self.df) == 0: return - state = self.df.join(self.pos, on="unique_id", how="left") - if state.is_empty(): - return - # Map the positional frame to a center lookup used when joining - # neighbourhoods produced by the space helper. Build the lookup by - # explicitly selecting and aliasing columns so the join creates a - # deterministic `agent_id` column (some internal joins can drop or - # fail to expose renamed columns when types/indices differ). - center_lookup = self.pos.select( + # current_pos columns: + # ┌──────────┬────────────────┬────────────────┐ + # │ agent_id ┆ dim_0_center ┆ dim_1_center │ + # │ --- ┆ --- ┆ --- │ + # │ u64 ┆ i64 ┆ i64 │ + # ╞══════════╪════════════════╪════════════════╡ + current_pos = self.pos.select( [ pl.col("unique_id").alias("agent_id"), pl.col("dim_0").alias("dim_0_center"), @@ -943,35 +945,54 @@ def move(self) -> None: ) # Build a neighbourhood frame: for each agent and visible cell we - # attach the cell sugar and the agent_id of the occupant (if any). + # attach the cell sugar. The raw offsets contain the candidate + # cell coordinates and the center coordinates for the sensing agent. + # Raw neighborhood columns: + # ┌────────────┬────────────┬────────┬────────────────┬────────────────┐ + # │ dim_0 ┆ dim_1 ┆ radius ┆ dim_0_center ┆ dim_1_center │ + # │ --- ┆ --- ┆ --- ┆ --- ┆ --- │ + # │ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 │ + # ╞════════════╪════════════╪════════╪════════════════╪════════════════╡ + neighborhood = self.space.get_neighborhood( + radius=self["vision"], agents=self, include_center=True + ) + + cell_props = self.space.cells.select(["dim_0", "dim_1", "sugar"]) neighborhood = ( - self.space.get_neighborhood( - radius=self["vision"], agents=self, include_center=True - ) - .join( - self.space.cells.select(["dim_0", "dim_1", "sugar"]), - on=["dim_0", "dim_1"], - how="left", - ) + neighborhood + .join(cell_props, on=["dim_0", "dim_1"], how="left") .with_columns(pl.col("sugar").fill_null(0)) ) - # Normalise occupant column name if present (agent occupying the - # cell). The center lookup join may produce a conflicting - # `agent_id` column (suffix _right) — handle both cases so that - # `agent_id` unambiguously refers to the center agent and - # `occupant_id` refers to any agent already occupying the cell. - if "agent_id" in neighborhood.columns: - neighborhood = neighborhood.rename({"agent_id": "occupant_id"}) + # Neighborhood after sugar join: + # ┌────────────┬────────────┬────────┬────────────────┬────────────────┬────────┐ + # │ dim_0 ┆ dim_1 ┆ radius ┆ dim_0_center ┆ dim_1_center ┆ sugar │ + # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ + # │ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 │ + # ╞════════════╪════════════╪════════╪════════════════╪════════════════╪════════╡ + neighborhood = neighborhood.join( - center_lookup, on=["dim_0_center", "dim_1_center"], how="left" + current_pos, + left_on=["dim_0_center", "dim_1_center"], + right_on=["dim_0_center", "dim_1_center"], + how="left", ) - if "agent_id_right" in neighborhood.columns: - # Rename the joined center lookup's id to the canonical name. - neighborhood = neighborhood.rename({"agent_id_right": "agent_id"}) + + # Final neighborhood columns: + # ┌────────────┬────────────┬────────┬────────────────┬────────────────┬────────┬──────────┐ + # │ dim_0 ┆ dim_1 ┆ radius ┆ dim_0_center ┆ dim_1_center ┆ sugar ┆ agent_id │ + # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ + # │ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ u64 │ + # ╞════════════╪════════════╪════════╪════════════════╪════════════════╪════════╪══════════╡ # Create ranked choices per agent: sort by sugar (desc), radius # (asc), then coordinates. Keep the first unique entry per cell. + # choices columns (after select): + # ┌──────────┬────────────┬────────────┬────────┬────────┬────────────────┬────────────────┐ + # │ agent_id ┆ dim_0 ┆ dim_1 ┆ sugar ┆ radius ┆ dim_0_center ┆ dim_1_center │ + # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ + # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 │ + # ╞══════════╪════════════╪════════════╪════════╪════════╪════════════════╪════════════════╡ choices = ( neighborhood.select( [ @@ -1007,7 +1028,13 @@ def move(self) -> None: return # Origins for fallback (if an agent exhausts candidates it stays put). - origins = center_lookup.select( + # origins columns: + # ┌──────────┬────────────┬────────────┐ + # │ agent_id ┆ dim_0 ┆ dim_1 │ + # │ --- ┆ --- ┆ --- │ + # │ u64 ┆ i64 ┆ i64 │ + # ╞══════════╪════════════╪════════════╡ + origins = current_pos.select( [ "agent_id", pl.col("dim_0_center").alias("dim_0"), @@ -1016,10 +1043,22 @@ def move(self) -> None: ) # Track the maximum available rank per agent to clamp promotions. + # max_rank columns: + # ┌──────────┬───────────┐ + # │ agent_id ┆ max_rank │ + # │ --- ┆ --- │ + # │ u64 ┆ i64 │ + # ╞══════════╪═══════════╡ max_rank = choices.group_by("agent_id").agg(pl.col("rank").max().alias("max_rank")) # Prepare unresolved agents and working tables. agent_ids = choices["agent_id"].unique(maintain_order=True) + # unresolved columns: + # ┌──────────┬────────────────┐ + # │ agent_id ┆ current_rank │ + # │ --- ┆ --- │ + # │ u64 ┆ i64 │ + # ╞══════════╪════════════════╡ unresolved = pl.DataFrame( { "agent_id": agent_ids, @@ -1027,6 +1066,12 @@ def move(self) -> None: } ) + # assigned columns: + # ┌──────────┬────────────┬────────────┐ + # │ agent_id ┆ dim_0 ┆ dim_1 │ + # │ --- ┆ --- ┆ --- │ + # │ u64 ┆ i64 ┆ i64 │ + # ╞══════════╪════════════╪════════════╡ assigned = pl.DataFrame( { "agent_id": pl.Series(name="agent_id", values=[], dtype=agent_ids.dtype), @@ -1035,6 +1080,12 @@ def move(self) -> None: } ) + # taken columns: + # ┌────────────┬────────────┐ + # │ dim_0 ┆ dim_1 │ + # │ --- ┆ --- │ + # │ i64 ┆ i64 │ + # ╞════════════╪════════════╡ taken = pl.DataFrame( { "dim_0": pl.Series(name="dim_0", values=[], dtype=pl.Int64), @@ -1046,6 +1097,12 @@ def move(self) -> None: # candidate; winners per-cell are selected at random and losers are # promoted to their next choice. while unresolved.height > 0: + # candidate_pool columns (after join with unresolved): + # ┌──────────┬────────────┬────────────┬────────┬────────┬────────────────┬────────────────┬──────────────┐ + # │ agent_id ┆ dim_0 ┆ dim_1 ┆ sugar ┆ radius ┆ dim_0_center ┆ dim_1_center ┆ current_rank │ + # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ + # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 │ + # ╞══════════╪════════════╪════════════╪════════╪════════╪════════════════╪════════════════╪══════════════╡ candidate_pool = choices.join(unresolved, on="agent_id") candidate_pool = candidate_pool.filter(pl.col("rank") >= pl.col("current_rank")) if not taken.is_empty(): @@ -1053,6 +1110,12 @@ def move(self) -> None: if candidate_pool.is_empty(): # No available candidates — everyone falls back to origin. + # fallback columns: + # ┌──────────┬────────────┬────────────┬──────────────┐ + # │ agent_id ┆ dim_0 ┆ dim_1 ┆ current_rank │ + # │ --- ┆ --- ┆ --- ┆ --- │ + # │ u64 ┆ i64 ┆ i64 ┆ i64 │ + # ╞══════════╪════════════╪════════════╪══════════════╡ fallback = unresolved.join(origins, on="agent_id", how="left") assigned = pl.concat( [assigned, fallback.select(["agent_id", "dim_0", "dim_1"])], @@ -1060,13 +1123,26 @@ def move(self) -> None: ) break + # best_candidates columns (per agent first choice): + # ┌──────────┬────────────┬────────────┬────────┬────────┬────────────────┬────────────────┬──────────────┐ + # │ agent_id ┆ dim_0 ┆ dim_1 ┆ sugar ┆ radius ┆ dim_0_center ┆ dim_1_center ┆ current_rank │ + # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ + # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 │ + # ╞══════════╪════════════╪════════════╪════════╪════════╪════════════════╪════════════════╪══════════════╡ best_candidates = ( candidate_pool.sort(["agent_id", "rank"]) .group_by("agent_id", maintain_order=True).first() ) # Agents that had no candidate this round fall back to origin. + # missing columns: + # ┌──────────┬──────────────┐ + # │ agent_id ┆ current_rank │ + # │ --- ┆ --- │ + # │ u64 ┆ i64 │ + # ╞══════════╪══════════════╡ missing = unresolved.join(best_candidates.select("agent_id"), on="agent_id", how="anti") if not missing.is_empty(): + # fallback (missing) columns match fallback table above. fallback = missing.join(origins, on="agent_id", how="left") assigned = pl.concat( [assigned, fallback.select(["agent_id", "dim_0", "dim_1"])], @@ -1083,6 +1159,12 @@ def move(self) -> None: lottery = pl.Series("lottery", self.random.random(best_candidates.height)) best_candidates = best_candidates.with_columns(lottery) + # winners columns: + # ┌──────────┬────────────┬────────────┬────────┬────────┬────────────────┬────────────────┬──────────────┬─────────┐ + # │ agent_id ┆ dim_0 ┆ dim_1 ┆ sugar ┆ radius ┆ dim_0_center ┆ dim_1_center ┆ current_rank ┆ lottery │ + # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ + # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ f64 │ + # ╞══════════╪════════════╪════════════╪════════╪════════╪════════════════╪════════════════╪══════════════╪═════════╡ winners = ( best_candidates.sort(["dim_0", "dim_1", "lottery"]) .group_by(["dim_0", "dim_1"], maintain_order=True).first() ) @@ -1098,10 +1180,17 @@ def move(self) -> None: if unresolved.is_empty(): break + # loser candidates columns mirror best_candidates (minus winners). losers = best_candidates.join(winner_ids, on="agent_id", how="anti") if losers.is_empty(): continue + # loser_updates columns: + # ┌──────────┬───────────┬───────────┐ + # │ agent_id ┆ next_rank ┆ max_rank │ + # │ --- ┆ --- ┆ --- │ + # │ u64 ┆ i64 ┆ i64 │ + # ╞══════════╪═══════════╪═══════════╡ loser_updates = ( losers.select( "agent_id", @@ -1115,6 +1204,7 @@ def move(self) -> None: ) # Promote losers' current_rank (if any) and continue. + # unresolved (updated) retains columns agent_id/current_rank. unresolved = unresolved.join(loser_updates, on="agent_id", how="left").with_columns( pl.when(pl.col("next_rank").is_not_null()) .then(pl.col("next_rank")) @@ -1125,6 +1215,12 @@ def move(self) -> None: if assigned.is_empty(): return + # move_df columns: + # ┌────────────┬────────────┬────────────┐ + # │ unique_id ┆ dim_0 ┆ dim_1 │ + # │ --- ┆ --- ┆ --- │ + # │ u64 ┆ i64 ┆ i64 │ + # ╞════════════╪════════════╪════════════╡ move_df = pl.DataFrame( { "unique_id": assigned["agent_id"], @@ -1177,9 +1273,9 @@ def run_variant( # %% # %% -GRID_WIDTH = 250 -GRID_HEIGHT = 250 -NUM_AGENTS = 10000 +GRID_WIDTH = 40 +GRID_HEIGHT = 40 +NUM_AGENTS = 400 MODEL_STEPS = 60 MAX_SUGAR = 4 From 9edbfdb5770f18493c480562451bc3e136503286 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sat, 20 Sep 2025 23:24:37 +0200 Subject: [PATCH 29/99] refactor: improve agent movement logic and enhance readability in AntsParallel class --- .../general/user-guide/3_advanced_tutorial.py | 82 +++++++++++++------ 1 file changed, 55 insertions(+), 27 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 7b50564a..426fb7da 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -922,9 +922,12 @@ def move(self) -> None: class AntsParallel(AntsBase): def move(self) -> None: - """ - Parallel movement: each agent proposes a ranked list of visible cells (including its own). - We resolve conflicts in rounds using DataFrame operations so winners can be chosen per-cell at random and losers are promoted to their next-ranked choice. + """Move agents in parallel by ranking visible cells and resolving conflicts. + + Returns + ------- + None + Movement updates happen in-place on the underlying space. """ # Early exit if there are no agents. if len(self.df) == 0: @@ -944,6 +947,33 @@ def move(self) -> None: ] ) + neighborhood = self._build_neighborhood_frame(current_pos) + choices, origins, max_rank = self._rank_candidates(neighborhood, current_pos) + if choices.is_empty(): + return + + assigned = self._resolve_conflicts_in_rounds(choices, origins, max_rank) + if assigned.is_empty(): + return + + # move_df columns: + # ┌────────────┬────────────┬────────────┐ + # │ unique_id ┆ dim_0 ┆ dim_1 │ + # │ --- ┆ --- ┆ --- │ + # │ u64 ┆ i64 ┆ i64 │ + # ╞════════════╪════════════╪════════════╡ + move_df = pl.DataFrame( + { + "unique_id": assigned["agent_id"], + "dim_0": assigned["dim_0"], + "dim_1": assigned["dim_1"], + } + ) + # `move_agents` accepts IdsLike and SpaceCoordinates (Polars Series/DataFrame), + # so pass Series/DataFrame directly rather than converting to Python lists. + self.space.move_agents(move_df["unique_id"], move_df.select(["dim_0", "dim_1"])) + + def _build_neighborhood_frame(self, current_pos: pl.DataFrame) -> pl.DataFrame: # Build a neighbourhood frame: for each agent and visible cell we # attach the cell sugar. The raw offsets contain the candidate # cell coordinates and the center coordinates for the sensing agent. @@ -984,7 +1014,13 @@ def move(self) -> None: # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ # │ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ u64 │ # ╞════════════╪════════════╪════════╪════════════════╪════════════════╪════════╪══════════╡ + return neighborhood + def _rank_candidates( + self, + neighborhood: pl.DataFrame, + current_pos: pl.DataFrame, + ) -> tuple[pl.DataFrame, pl.DataFrame, pl.DataFrame]: # Create ranked choices per agent: sort by sugar (desc), radius # (asc), then coordinates. Keep the first unique entry per cell. # choices columns (after select): @@ -1024,9 +1060,6 @@ def move(self) -> None: ) ) - if choices.is_empty(): - return - # Origins for fallback (if an agent exhausts candidates it stays put). # origins columns: # ┌──────────┬────────────┬────────────┐ @@ -1050,7 +1083,14 @@ def move(self) -> None: # │ u64 ┆ i64 │ # ╞══════════╪═══════════╡ max_rank = choices.group_by("agent_id").agg(pl.col("rank").max().alias("max_rank")) + return choices, origins, max_rank + def _resolve_conflicts_in_rounds( + self, + choices: pl.DataFrame, + origins: pl.DataFrame, + max_rank: pl.DataFrame, + ) -> pl.DataFrame: # Prepare unresolved agents and working tables. agent_ids = choices["agent_id"].unique(maintain_order=True) # unresolved columns: @@ -1130,7 +1170,10 @@ def move(self) -> None: # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 │ # ╞══════════╪════════════╪════════════╪════════╪════════╪════════════════╪════════════════╪══════════════╡ best_candidates = ( - candidate_pool.sort(["agent_id", "rank"]) .group_by("agent_id", maintain_order=True).first() + candidate_pool + .sort(["agent_id", "rank"]) + .group_by("agent_id", maintain_order=True) + .first() ) # Agents that had no candidate this round fall back to origin. @@ -1166,7 +1209,10 @@ def move(self) -> None: # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ f64 │ # ╞══════════╪════════════╪════════════╪════════╪════════╪════════════════╪════════════════╪══════════════╪═════════╡ winners = ( - best_candidates.sort(["dim_0", "dim_1", "lottery"]) .group_by(["dim_0", "dim_1"], maintain_order=True).first() + best_candidates + .sort(["dim_0", "dim_1", "lottery"]) + .group_by(["dim_0", "dim_1"], maintain_order=True) + .first() ) assigned = pl.concat( @@ -1212,25 +1258,7 @@ def move(self) -> None: .alias("current_rank") ).drop("next_rank") - if assigned.is_empty(): - return - - # move_df columns: - # ┌────────────┬────────────┬────────────┐ - # │ unique_id ┆ dim_0 ┆ dim_1 │ - # │ --- ┆ --- ┆ --- │ - # │ u64 ┆ i64 ┆ i64 │ - # ╞════════════╪════════════╪════════════╡ - move_df = pl.DataFrame( - { - "unique_id": assigned["agent_id"], - "dim_0": assigned["dim_0"], - "dim_1": assigned["dim_1"], - } - ) - # `move_agents` accepts IdsLike and SpaceCoordinates (Polars Series/DataFrame), - # so pass Series/DataFrame directly rather than converting to Python lists. - self.space.move_agents(move_df["unique_id"], move_df.select(["dim_0", "dim_1"])) + return assigned From 408e04074b139b416488f66dde12c8b346ccf9b5 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sun, 21 Sep 2025 00:03:07 +0200 Subject: [PATCH 30/99] refactor: update candidate dimension names for clarity in AntsParallel class --- .../general/user-guide/3_advanced_tutorial.py | 265 +++++++++++++----- 1 file changed, 194 insertions(+), 71 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 426fb7da..a5b53439 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -965,8 +965,8 @@ def move(self) -> None: move_df = pl.DataFrame( { "unique_id": assigned["agent_id"], - "dim_0": assigned["dim_0"], - "dim_1": assigned["dim_1"], + "dim_0": assigned["dim_0_candidate"], + "dim_1": assigned["dim_1_candidate"], } ) # `move_agents` accepts IdsLike and SpaceCoordinates (Polars Series/DataFrame), @@ -974,6 +974,21 @@ def move(self) -> None: self.space.move_agents(move_df["unique_id"], move_df.select(["dim_0", "dim_1"])) def _build_neighborhood_frame(self, current_pos: pl.DataFrame) -> pl.DataFrame: + """Assemble the sugar-weighted neighbourhood for each sensing agent. + + Parameters + ---------- + current_pos : pl.DataFrame + DataFrame with columns ``agent_id``, ``dim_0_center`` and + ``dim_1_center`` describing the current position of each agent. + + Returns + ------- + pl.DataFrame + DataFrame with columns ``agent_id``, ``radius``, ``dim_0_candidate``, + ``dim_1_candidate`` and ``sugar`` describing the visible cells for + each agent. + """ # Build a neighbourhood frame: for each agent and visible cell we # attach the cell sugar. The raw offsets contain the candidate # cell coordinates and the center coordinates for the sensing agent. @@ -983,25 +998,27 @@ def _build_neighborhood_frame(self, current_pos: pl.DataFrame) -> pl.DataFrame: # │ --- ┆ --- ┆ --- ┆ --- ┆ --- │ # │ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 │ # ╞════════════╪════════════╪════════╪════════════════╪════════════════╡ - neighborhood = self.space.get_neighborhood( + neighborhood_cells = self.space.get_neighborhood( radius=self["vision"], agents=self, include_center=True ) - cell_props = self.space.cells.select(["dim_0", "dim_1", "sugar"]) - neighborhood = ( - neighborhood - .join(cell_props, on=["dim_0", "dim_1"], how="left") + # sugar_cells columns: + # ┌────────────┬────────────┬────────┐ + # │ dim_0 ┆ dim_1 ┆ sugar │ + # │ --- ┆ --- ┆ --- │ + # │ i64 ┆ i64 ┆ i64 │ + # ╞════════════╪════════════╪════════╡ + + sugar_cells = self.space.cells.select(["dim_0", "dim_1", "sugar"]) + + neighborhood_cells = ( + neighborhood_cells + .join(sugar_cells, on=["dim_0", "dim_1"], how="left") .with_columns(pl.col("sugar").fill_null(0)) + .rename({"dim_0": "dim_0_candidate", "dim_1": "dim_1_candidate"}) ) - # Neighborhood after sugar join: - # ┌────────────┬────────────┬────────┬────────────────┬────────────────┬────────┐ - # │ dim_0 ┆ dim_1 ┆ radius ┆ dim_0_center ┆ dim_1_center ┆ sugar │ - # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ - # │ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 │ - # ╞════════════╪════════════╪════════╪════════════════╪════════════════╪════════╡ - - neighborhood = neighborhood.join( + neighborhood_cells = neighborhood_cells.join( current_pos, left_on=["dim_0_center", "dim_1_center"], right_on=["dim_0_center", "dim_1_center"], @@ -1009,45 +1026,74 @@ def _build_neighborhood_frame(self, current_pos: pl.DataFrame) -> pl.DataFrame: ) # Final neighborhood columns: - # ┌────────────┬────────────┬────────┬────────────────┬────────────────┬────────┬──────────┐ - # │ dim_0 ┆ dim_1 ┆ radius ┆ dim_0_center ┆ dim_1_center ┆ sugar ┆ agent_id │ - # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ - # │ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ u64 │ - # ╞════════════╪════════════╪════════╪════════════════╪════════════════╪════════╪══════════╡ - return neighborhood + # ┌──────────┬────────┬──────────────────┬──────────────────┬────────┐ + # │ agent_id ┆ radius ┆ dim_0_candidate ┆ dim_1_candidate ┆ sugar │ + # │ --- ┆ --- ┆ --- ┆ --- ┆ --- │ + # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 │ + # ╞══════════╪════════╪══════════════════╪══════════════════╪════════╡ + neighborhood_cells = ( + neighborhood_cells + .drop(["dim_0_center", "dim_1_center"]) + .select(["agent_id", "radius", "dim_0_candidate", "dim_1_candidate", "sugar"]) + ) + + return neighborhood_cells def _rank_candidates( self, neighborhood: pl.DataFrame, current_pos: pl.DataFrame, ) -> tuple[pl.DataFrame, pl.DataFrame, pl.DataFrame]: + """Rank candidate destination cells for each agent. + + Parameters + ---------- + neighborhood : pl.DataFrame + Output of :meth:`_build_neighborhood_frame` with columns + ``agent_id``, ``radius``, ``dim_0_candidate``, ``dim_1_candidate`` + and ``sugar``. + current_pos : pl.DataFrame + Frame with columns ``agent_id``, ``dim_0_center`` and + ``dim_1_center`` describing where each agent currently stands. + + Returns + ------- + choices : pl.DataFrame + Ranked candidates per agent with columns ``agent_id``, + ``dim_0_candidate``, ``dim_1_candidate``, ``sugar``, ``radius`` and + ``rank``. + origins : pl.DataFrame + Original coordinates per agent with columns ``agent_id``, + ``dim_0`` and ``dim_1``. + max_rank : pl.DataFrame + Maximum available rank per agent with columns ``agent_id`` and + ``max_rank``. + """ # Create ranked choices per agent: sort by sugar (desc), radius # (asc), then coordinates. Keep the first unique entry per cell. # choices columns (after select): - # ┌──────────┬────────────┬────────────┬────────┬────────┬────────────────┬────────────────┐ - # │ agent_id ┆ dim_0 ┆ dim_1 ┆ sugar ┆ radius ┆ dim_0_center ┆ dim_1_center │ - # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ - # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 │ - # ╞══════════╪════════════╪════════════╪════════╪════════╪════════════════╪════════════════╡ + # ┌──────────┬──────────────────┬──────────────────┬────────┬────────┐ + # │ agent_id ┆ dim_0_candidate ┆ dim_1_candidate ┆ sugar ┆ radius │ + # │ --- ┆ --- ┆ --- ┆ --- ┆ --- │ + # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 │ + # ╞══════════╪══════════════════╪══════════════════╪════════╪════════╡ choices = ( neighborhood.select( [ "agent_id", - "dim_0", - "dim_1", + "dim_0_candidate", + "dim_1_candidate", "sugar", "radius", - "dim_0_center", - "dim_1_center", ] ) - .with_columns(pl.col("radius").cast(pl.Int64)) + .with_columns(pl.col("radius")) .sort( - ["agent_id", "sugar", "radius", "dim_0", "dim_1"], + ["agent_id", "sugar", "radius", "dim_0_candidate", "dim_1_candidate"], descending=[False, True, False, False, False], ) .unique( - subset=["agent_id", "dim_0", "dim_1"], + subset=["agent_id", "dim_0_candidate", "dim_1_candidate"], keep="first", maintain_order=True, ) @@ -1055,7 +1101,6 @@ def _rank_candidates( pl.col("agent_id") .cum_count() .over("agent_id") - .cast(pl.Int64) .alias("rank") ) ) @@ -1091,8 +1136,30 @@ def _resolve_conflicts_in_rounds( origins: pl.DataFrame, max_rank: pl.DataFrame, ) -> pl.DataFrame: + """Resolve movement conflicts through iterative lottery rounds. + + Parameters + ---------- + choices : pl.DataFrame + Ranked candidate cells per agent with headers matching the + ``choices`` frame returned by :meth:`_rank_candidates`. + origins : pl.DataFrame + Agent origin coordinates with columns ``agent_id``, ``dim_0`` and + ``dim_1``. + max_rank : pl.DataFrame + Maximum rank offset per agent with columns ``agent_id`` and + ``max_rank``. + + Returns + ------- + pl.DataFrame + Allocated movements with columns ``agent_id``, ``dim_0_candidate`` + and ``dim_1_candidate``; each row records the destination assigned + to an agent. + """ # Prepare unresolved agents and working tables. agent_ids = choices["agent_id"].unique(maintain_order=True) + # unresolved columns: # ┌──────────┬────────────────┐ # │ agent_id ┆ current_rank │ @@ -1107,29 +1174,37 @@ def _resolve_conflicts_in_rounds( ) # assigned columns: - # ┌──────────┬────────────┬────────────┐ - # │ agent_id ┆ dim_0 ┆ dim_1 │ - # │ --- ┆ --- ┆ --- │ - # │ u64 ┆ i64 ┆ i64 │ - # ╞══════════╪════════════╪════════════╡ + # ┌──────────┬──────────────────┬──────────────────┐ + # │ agent_id ┆ dim_0_candidate ┆ dim_1_candidate │ + # │ --- ┆ --- ┆ --- │ + # │ u64 ┆ i64 ┆ i64 │ + # ╞══════════╪══════════════════╪══════════════════╡ assigned = pl.DataFrame( { "agent_id": pl.Series(name="agent_id", values=[], dtype=agent_ids.dtype), - "dim_0": pl.Series(name="dim_0", values=[], dtype=pl.Int64), - "dim_1": pl.Series(name="dim_1", values=[], dtype=pl.Int64), + "dim_0_candidate": pl.Series( + name="dim_0_candidate", values=[], dtype=pl.Int64 + ), + "dim_1_candidate": pl.Series( + name="dim_1_candidate", values=[], dtype=pl.Int64 + ), } ) # taken columns: - # ┌────────────┬────────────┐ - # │ dim_0 ┆ dim_1 │ - # │ --- ┆ --- │ - # │ i64 ┆ i64 │ - # ╞════════════╪════════════╡ + # ┌──────────────────┬──────────────────┐ + # │ dim_0_candidate ┆ dim_1_candidate │ + # │ --- ┆ --- │ + # │ i64 ┆ i64 │ + # ╞══════════════════╪══════════════════╡ taken = pl.DataFrame( { - "dim_0": pl.Series(name="dim_0", values=[], dtype=pl.Int64), - "dim_1": pl.Series(name="dim_1", values=[], dtype=pl.Int64), + "dim_0_candidate": pl.Series( + name="dim_0_candidate", values=[], dtype=pl.Int64 + ), + "dim_1_candidate": pl.Series( + name="dim_1_candidate", values=[], dtype=pl.Int64 + ), } ) @@ -1138,15 +1213,19 @@ def _resolve_conflicts_in_rounds( # promoted to their next choice. while unresolved.height > 0: # candidate_pool columns (after join with unresolved): - # ┌──────────┬────────────┬────────────┬────────┬────────┬────────────────┬────────────────┬──────────────┐ - # │ agent_id ┆ dim_0 ┆ dim_1 ┆ sugar ┆ radius ┆ dim_0_center ┆ dim_1_center ┆ current_rank │ - # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ - # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 │ - # ╞══════════╪════════════╪════════════╪════════╪════════╪════════════════╪════════════════╪══════════════╡ + # ┌──────────┬──────────────────┬──────────────────┬────────┬────────┬──────────────┐ + # │ agent_id ┆ dim_0_candidate ┆ dim_1_candidate ┆ sugar ┆ radius ┆ current_rank │ + # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ + # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 │ + # ╞══════════╪══════════════════╪══════════════════╪════════╪════════╪══════════════╡ candidate_pool = choices.join(unresolved, on="agent_id") candidate_pool = candidate_pool.filter(pl.col("rank") >= pl.col("current_rank")) if not taken.is_empty(): - candidate_pool = candidate_pool.join(taken, on=["dim_0", "dim_1"], how="anti") + candidate_pool = candidate_pool.join( + taken, + on=["dim_0_candidate", "dim_1_candidate"], + how="anti", + ) if candidate_pool.is_empty(): # No available candidates — everyone falls back to origin. @@ -1158,17 +1237,26 @@ def _resolve_conflicts_in_rounds( # ╞══════════╪════════════╪════════════╪══════════════╡ fallback = unresolved.join(origins, on="agent_id", how="left") assigned = pl.concat( - [assigned, fallback.select(["agent_id", "dim_0", "dim_1"])], + [ + assigned, + fallback.select( + [ + "agent_id", + pl.col("dim_0").alias("dim_0_candidate"), + pl.col("dim_1").alias("dim_1_candidate"), + ] + ), + ], how="vertical", ) break # best_candidates columns (per agent first choice): - # ┌──────────┬────────────┬────────────┬────────┬────────┬────────────────┬────────────────┬──────────────┐ - # │ agent_id ┆ dim_0 ┆ dim_1 ┆ sugar ┆ radius ┆ dim_0_center ┆ dim_1_center ┆ current_rank │ - # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ - # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 │ - # ╞══════════╪════════════╪════════════╪════════╪════════╪════════════════╪════════════════╪══════════════╡ + # ┌──────────┬──────────────────┬──────────────────┬────────┬────────┬──────────────┐ + # │ agent_id ┆ dim_0_candidate ┆ dim_1_candidate ┆ sugar ┆ radius ┆ current_rank │ + # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ + # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 │ + # ╞══════════╪══════════════════╪══════════════════╪════════╪════════╪══════════════╡ best_candidates = ( candidate_pool .sort(["agent_id", "rank"]) @@ -1188,10 +1276,30 @@ def _resolve_conflicts_in_rounds( # fallback (missing) columns match fallback table above. fallback = missing.join(origins, on="agent_id", how="left") assigned = pl.concat( - [assigned, fallback.select(["agent_id", "dim_0", "dim_1"])], + [ + assigned, + fallback.select( + [ + "agent_id", + pl.col("dim_0").alias("dim_0_candidate"), + pl.col("dim_1").alias("dim_1_candidate"), + ] + ), + ], + how="vertical", + ) + taken = pl.concat( + [ + taken, + fallback.select( + [ + pl.col("dim_0").alias("dim_0_candidate"), + pl.col("dim_1").alias("dim_1_candidate"), + ] + ), + ], how="vertical", ) - taken = pl.concat([taken, fallback.select(["dim_0", "dim_1"])], how="vertical") unresolved = unresolved.join(missing.select("agent_id"), on="agent_id", how="anti") best_candidates = best_candidates.join(missing.select("agent_id"), on="agent_id", how="anti") if unresolved.is_empty() or best_candidates.is_empty(): @@ -1203,23 +1311,38 @@ def _resolve_conflicts_in_rounds( best_candidates = best_candidates.with_columns(lottery) # winners columns: - # ┌──────────┬────────────┬────────────┬────────┬────────┬────────────────┬────────────────┬──────────────┬─────────┐ - # │ agent_id ┆ dim_0 ┆ dim_1 ┆ sugar ┆ radius ┆ dim_0_center ┆ dim_1_center ┆ current_rank ┆ lottery │ - # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ - # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ f64 │ - # ╞══════════╪════════════╪════════════╪════════╪════════╪════════════════╪════════════════╪══════════════╪═════════╡ + # ┌──────────┬──────────────────┬──────────────────┬────────┬────────┬──────────────┬─────────┐ + # │ agent_id ┆ dim_0_candidate ┆ dim_1_candidate ┆ sugar ┆ radius ┆ current_rank │ lottery │ + # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ + # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ f64 │ + # ╞══════════╪══════════════════╪══════════════════╪════════╪════════╪══════════════╪═════════╡ winners = ( best_candidates - .sort(["dim_0", "dim_1", "lottery"]) - .group_by(["dim_0", "dim_1"], maintain_order=True) + .sort(["dim_0_candidate", "dim_1_candidate", "lottery"]) + .group_by(["dim_0_candidate", "dim_1_candidate"], maintain_order=True) .first() ) assigned = pl.concat( - [assigned, winners.select(["agent_id", "dim_0", "dim_1"])], + [ + assigned, + winners.select( + [ + "agent_id", + pl.col("dim_0_candidate"), + pl.col("dim_1_candidate"), + ] + ), + ], + how="vertical", + ) + taken = pl.concat( + [ + taken, + winners.select(["dim_0_candidate", "dim_1_candidate"]), + ], how="vertical", ) - taken = pl.concat([taken, winners.select(["dim_0", "dim_1"])], how="vertical") winner_ids = winners.select("agent_id") unresolved = unresolved.join(winner_ids, on="agent_id", how="anti") From 0f966538e17c8372c66eeca49b01c7fc61978451 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sun, 21 Sep 2025 00:37:07 +0200 Subject: [PATCH 31/99] refactor: enhance comments for clarity and understanding in AntsParallel class --- .../general/user-guide/3_advanced_tutorial.py | 60 ++++++++++++------- 1 file changed, 38 insertions(+), 22 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index a5b53439..7cf8252c 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -1105,6 +1105,13 @@ def _rank_candidates( ) ) + # Precompute per‑agent candidate rank once so conflict resolution can + # promote losers by incrementing a cheap `current_rank` counter, + # without re-sorting after each round. Alternative: drop taken cells + # and re-rank by sugar every round; simpler conceptually but requires + # repeated sorts and deduplication, which is heavier than filtering by + # `rank >= current_rank`. + # Origins for fallback (if an agent exhausts candidates it stays put). # origins columns: # ┌──────────┬────────────┬────────────┐ @@ -1121,11 +1128,14 @@ def _rank_candidates( ) # Track the maximum available rank per agent to clamp promotions. + # This bounds `current_rank`; once an agent reaches `max_rank` and + # cannot secure a cell, they fall back to origin cleanly instead of + # chasing nonexistent ranks. # max_rank columns: # ┌──────────┬───────────┐ # │ agent_id ┆ max_rank │ # │ --- ┆ --- │ - # │ u64 ┆ i64 │ + # │ u64 ┆ u32 │ # ╞══════════╪═══════════╡ max_rank = choices.group_by("agent_id").agg(pl.col("rank").max().alias("max_rank")) return choices, origins, max_rank @@ -1212,12 +1222,16 @@ def _resolve_conflicts_in_rounds( # candidate; winners per-cell are selected at random and losers are # promoted to their next choice. while unresolved.height > 0: + # Using precomputed `rank` lets us select candidates with + # `rank >= current_rank` and avoid re-ranking after each round. + # Alternative: remove taken cells and re-sort remaining candidates + # by sugar/distance per round (heavier due to repeated sort/dedupe). # candidate_pool columns (after join with unresolved): - # ┌──────────┬──────────────────┬──────────────────┬────────┬────────┬──────────────┐ - # │ agent_id ┆ dim_0_candidate ┆ dim_1_candidate ┆ sugar ┆ radius ┆ current_rank │ - # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ - # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 │ - # ╞══════════╪══════════════════╪══════════════════╪════════╪════════╪══════════════╡ + # ┌──────────┬──────────────────┬──────────────────┬────────┬────────┬──────┬──────────────┐ + # │ agent_id ┆ dim_0_candidate ┆ dim_1_candidate ┆ sugar ┆ radius ┆ rank ┆ current_rank │ + # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ + # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ u32 ┆ i64 │ + # ╞══════════╪══════════════════╪══════════════════╪════════╪════════╪══════╪══════════════╡ candidate_pool = choices.join(unresolved, on="agent_id") candidate_pool = candidate_pool.filter(pl.col("rank") >= pl.col("current_rank")) if not taken.is_empty(): @@ -1229,6 +1243,8 @@ def _resolve_conflicts_in_rounds( if candidate_pool.is_empty(): # No available candidates — everyone falls back to origin. + # Note: this covers both agents with no visible cells left and + # the case where all remaining candidates are already taken. # fallback columns: # ┌──────────┬────────────┬────────────┬──────────────┐ # │ agent_id ┆ dim_0 ┆ dim_1 ┆ current_rank │ @@ -1252,11 +1268,11 @@ def _resolve_conflicts_in_rounds( break # best_candidates columns (per agent first choice): - # ┌──────────┬──────────────────┬──────────────────┬────────┬────────┬──────────────┐ - # │ agent_id ┆ dim_0_candidate ┆ dim_1_candidate ┆ sugar ┆ radius ┆ current_rank │ - # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ - # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 │ - # ╞══════════╪══════════════════╪══════════════════╪════════╪════════╪══════════════╡ + # ┌──────────┬──────────────────┬──────────────────┬────────┬────────┬──────┬──────────────┐ + # │ agent_id ┆ dim_0_candidate ┆ dim_1_candidate ┆ sugar ┆ radius ┆ rank ┆ current_rank │ + # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ + # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ u32 ┆ i64 │ + # ╞══════════╪══════════════════╪══════════════════╪════════╪════════╪══════╪══════════════╡ best_candidates = ( candidate_pool .sort(["agent_id", "rank"]) @@ -1311,11 +1327,11 @@ def _resolve_conflicts_in_rounds( best_candidates = best_candidates.with_columns(lottery) # winners columns: - # ┌──────────┬──────────────────┬──────────────────┬────────┬────────┬──────────────┬─────────┐ - # │ agent_id ┆ dim_0_candidate ┆ dim_1_candidate ┆ sugar ┆ radius ┆ current_rank │ lottery │ - # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ - # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ f64 │ - # ╞══════════╪══════════════════╪══════════════════╪════════╪════════╪══════════════╪═════════╡ + # ┌──────────┬──────────────────┬──────────────────┬────────┬────────┬──────┬──────────────┬─────────┐ + # │ agent_id ┆ dim_0_candidate ┆ dim_1_candidate ┆ sugar ┆ radius ┆ rank ┆ current_rank │ lottery │ + # │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ + # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ u32 ┆ i64 ┆ f64 │ + # ╞══════════╪══════════════════╪══════════════════╪════════╪════════╪══════╪══════════════╪═════════╡ winners = ( best_candidates .sort(["dim_0_candidate", "dim_1_candidate", "lottery"]) @@ -1354,12 +1370,12 @@ def _resolve_conflicts_in_rounds( if losers.is_empty(): continue - # loser_updates columns: - # ┌──────────┬───────────┬───────────┐ - # │ agent_id ┆ next_rank ┆ max_rank │ - # │ --- ┆ --- ┆ --- │ - # │ u64 ┆ i64 ┆ i64 │ - # ╞══════════╪═══════════╪═══════════╡ + # loser_updates columns (after select): + # ┌──────────┬───────────┐ + # │ agent_id ┆ next_rank │ + # │ --- ┆ --- │ + # │ u64 ┆ i64 │ + # ╞══════════╪═══════════╡ loser_updates = ( losers.select( "agent_id", From 5e2ce8724a2c964c73b5a97e10a84e6a1f99ad55 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sun, 21 Sep 2025 01:07:49 +0200 Subject: [PATCH 32/99] refactor: streamline model variant execution and improve readability in advanced tutorial --- .../general/user-guide/3_advanced_tutorial.py | 187 +++++++----------- 1 file changed, 72 insertions(+), 115 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 7cf8252c..3ff67723 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -1429,17 +1429,12 @@ def run_variant( # %% [markdown] """ -## 7. Run the Sequential Model (Python loop) +## 7. Run the Model Variants -With the scaffolding in place we can simulate the sequential version and inspect -its aggregate behaviour. Because all random draws flow through the model's RNG, -constructing each variant with the same seed reproduces identical initial -conditions across the different movement rules. +We iterate over each movement policy with a shared helper so all runs reuse the same seed. Set `MESA_FRAMES_RUN_SEQUENTIAL=1` to include the slower pure-Python baseline. """ # %% - -# %% GRID_WIDTH = 40 GRID_HEIGHT = 40 NUM_AGENTS = 400 @@ -1458,112 +1453,59 @@ def run_variant( sequential_seed = 11 -if RUN_SEQUENTIAL: - sequential_model, sequential_time = run_variant( - AntsSequential, steps=MODEL_STEPS, seed=sequential_seed - ) - - seq_model_frame = sequential_model.datacollector.data["model"] - print("Sequential aggregate trajectory (last 5 steps):") - print( - seq_model_frame.select( - ["step", "mean_sugar", "total_sugar", "agents_alive"] - ).tail(5) - ) - print(f"Sequential runtime: {sequential_time:.3f} s") -else: - sequential_model = None - seq_model_frame = pl.DataFrame() - sequential_time = float("nan") - print( - "Skipping sequential baseline; set MESA_FRAMES_RUN_SEQUENTIAL=1 to enable it." - ) - -# %% [markdown] -""" -## 8. Run the Numba-Accelerated Model - -We reuse the same seed so the only difference is the compiled movement helper. -The trajectory matches the pure Python loop (up to floating-point noise) while -running much faster on larger grids. -""" - -# %% -numba_model, numba_time = run_variant( - AntsNumba, steps=MODEL_STEPS, seed=sequential_seed -) - -numba_model_frame = numba_model.datacollector.data["model"] -print("Numba sequential aggregate trajectory (last 5 steps):") -print( - numba_model_frame.select(["step", "mean_sugar", "total_sugar", "agents_alive"]).tail(5) -) -print(f"Numba sequential runtime: {numba_time:.3f} s") - -# %% [markdown] -""" -## 9. Run the Simultaneous Model - -Next we instantiate the parallel variant with the same seed so every run starts -from the common state generated by the helper methods. -""" +variant_specs: dict[str, tuple[type[AntsBase], bool]] = { + "Sequential (Python loop)": (AntsSequential, RUN_SEQUENTIAL), + "Sequential (Numba)": (AntsNumba, True), + "Parallel (Polars)": (AntsParallel, True), +} -# %% -parallel_model, parallel_time = run_variant( - AntsParallel, steps=MODEL_STEPS, seed=sequential_seed -) +models: dict[str, Sugarscape] = {} +frames: dict[str, pl.DataFrame] = {} +runtimes: dict[str, float] = {} -par_model_frame = parallel_model.datacollector.data["model"] -print("Parallel aggregate trajectory (last 5 steps):") -print(par_model_frame.select(["step", "mean_sugar", "total_sugar", "agents_alive"]).tail(5)) -print(f"Parallel runtime: {parallel_time:.3f} s") +for variant_name, (agent_cls, enabled) in variant_specs.items(): + if not enabled: + print( + f"Skipping {variant_name}; set MESA_FRAMES_RUN_SEQUENTIAL=1 to enable it." + ) + runtimes[variant_name] = float("nan") + continue -# %% [markdown] -""" -## 10. Runtime Comparison + model, runtime = run_variant(agent_cls, steps=MODEL_STEPS, seed=sequential_seed) + models[variant_name] = model + frames[variant_name] = model.datacollector.data["model"] + runtimes[variant_name] = runtime -The table below summarises the elapsed time for 60 steps on the 50×50 grid with -400 ants. Parallel scheduling on top of Polars lands in the same performance -band as the Numba-accelerated loop, while both are far faster than the pure -Python baseline. -""" - -# %% -runtime_rows: list[dict[str, float | str]] = [] -if RUN_SEQUENTIAL: - runtime_rows.append( - { - "update_rule": "Sequential (Python loop)", - "runtime_seconds": sequential_time, - } - ) -else: - runtime_rows.append( - { - "update_rule": "Sequential (Python loop) [skipped]", - "runtime_seconds": float("nan"), - } + print(f"{variant_name} aggregate trajectory (last 5 steps):") + print( + frames[variant_name] + .select(["step", "mean_sugar", "total_sugar", "agents_alive"]) + .tail(5) ) + print(f"{variant_name} runtime: {runtime:.3f} s") + print() -runtime_rows.extend( - [ - { - "update_rule": "Sequential (Numba)", - "runtime_seconds": numba_time, - }, - { - "update_rule": "Parallel (Polars)", - "runtime_seconds": parallel_time, - }, - ] -) - -runtime_table = pl.DataFrame(runtime_rows).with_columns( - pl.col("runtime_seconds").round(4) +runtime_table = ( + pl.DataFrame( + [ + { + "update_rule": variant_name if enabled else f"{variant_name} [skipped]", + "runtime_seconds": runtimes.get(variant_name, float("nan")), + } + for variant_name, (_, enabled) in variant_specs.items() + ] + ) + .with_columns(pl.col("runtime_seconds").round(4)) + .sort("runtime_seconds", descending=False, nulls_last=True) ) +print("Runtime comparison (fastest first):") print(runtime_table) +# Access models/frames on demand; keep namespace minimal. +numba_model_frame = frames.get("Sequential (Numba)", pl.DataFrame()) +par_model_frame = frames.get("Parallel (Polars)", pl.DataFrame()) + # %% [markdown] """ Polars gives us that performance without any bespoke compiled kernels—the move @@ -1575,7 +1517,7 @@ def run_variant( # %% [markdown] """ -## 11. Comparing the Update Rules +## 8. Comparing the Update Rules Even though the micro rules differ, the aggregate trajectories keep the same overall shape: sugar holdings trend upward while the population tapers off. By @@ -1606,20 +1548,20 @@ def run_variant( [ { "update_rule": "Sequential (Numba)", - "gini": gini(numba_model), - "corr_sugar_metabolism": corr_sugar_metabolism(numba_model), - "corr_sugar_vision": corr_sugar_vision(numba_model), - "agents_alive": float(len(numba_model.sets[0])) - if len(numba_model.sets) + "gini": gini(models["Sequential (Numba)"]), + "corr_sugar_metabolism": corr_sugar_metabolism(models["Sequential (Numba)"]), + "corr_sugar_vision": corr_sugar_vision(models["Sequential (Numba)"]), + "agents_alive": float(len(models["Sequential (Numba)"].sets[0])) + if len(models["Sequential (Numba)"].sets) else 0.0, }, { "update_rule": "Parallel (random tie-break)", - "gini": gini(parallel_model), - "corr_sugar_metabolism": corr_sugar_metabolism(parallel_model), - "corr_sugar_vision": corr_sugar_vision(parallel_model), - "agents_alive": float(len(parallel_model.sets[0])) - if len(parallel_model.sets) + "gini": gini(models["Parallel (Polars)"]), + "corr_sugar_metabolism": corr_sugar_metabolism(models["Parallel (Polars)"]), + "corr_sugar_vision": corr_sugar_vision(models["Parallel (Polars)"]), + "agents_alive": float(len(models["Parallel (Polars)"].sets[0])) + if len(models["Parallel (Polars)"].sets) else 0.0, }, ] @@ -1644,7 +1586,22 @@ def run_variant( # %% [markdown] """ -## 12. Where to Go Next? +The section above demonstrated how we can iterate across variants inside a single code cell +without sprinkling the global namespace with per‑variant variables like +`sequential_model`, `seq_model_frame`, etc. Instead we retained compact dictionaries: + +``models[name]`` -> Sugarscape instance +``frames[name]`` -> model-level DataFrame trace +``runtimes[name]`` -> wall time in seconds + +This keeps the tutorial easier to skim and copy/paste for users who only want one +variant. The minimal convenience aliases (`numba_model`, `parallel_model`) exist solely +for the comparison section; feel free to inline those if further slimming is desired. +""" + +# %% [markdown] +""" +## 9. Where to Go Next? * **Polars + LazyFrames roadmap** – future mesa-frames releases will expose LazyFrame-powered schedulers (with GPU offloading hooks), so the same Polars From 34c2fd8427c81662e6aeb28803c9fd9e5b2132ce Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sun, 21 Sep 2025 01:12:07 +0200 Subject: [PATCH 33/99] refactor: update metrics table construction for clarity and consistency in advanced tutorial --- .../general/user-guide/3_advanced_tutorial.py | 86 ++++++++++--------- 1 file changed, 47 insertions(+), 39 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 3ff67723..5e04172f 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -1544,28 +1544,45 @@ def run_variant( print("Step-level absolute differences (first 10 steps):") print(comparison.select(["step", "mean_diff", "total_diff", "count_diff"]).head(10)) -metrics_table = pl.DataFrame( - [ - { - "update_rule": "Sequential (Numba)", - "gini": gini(models["Sequential (Numba)"]), - "corr_sugar_metabolism": corr_sugar_metabolism(models["Sequential (Numba)"]), - "corr_sugar_vision": corr_sugar_vision(models["Sequential (Numba)"]), - "agents_alive": float(len(models["Sequential (Numba)"].sets[0])) - if len(models["Sequential (Numba)"].sets) - else 0.0, - }, - { - "update_rule": "Parallel (random tie-break)", - "gini": gini(models["Parallel (Polars)"]), - "corr_sugar_metabolism": corr_sugar_metabolism(models["Parallel (Polars)"]), - "corr_sugar_vision": corr_sugar_vision(models["Parallel (Polars)"]), - "agents_alive": float(len(models["Parallel (Polars)"].sets[0])) - if len(models["Parallel (Polars)"].sets) - else 0.0, - }, - ] -) +# Build the steady‑state metrics table from the DataCollector output rather than +# recomputing reporters directly on the model objects. The collector already +# stored the model‑level reporters (gini, correlations, etc.) every step. +def _last_row(df: pl.DataFrame) -> pl.DataFrame: + if df.is_empty(): + return df + # Ensure we take the final time step in case steps < MODEL_STEPS due to extinction. + return df.sort("step").tail(1) + +numba_last = _last_row(frames.get("Sequential (Numba)", pl.DataFrame())) +parallel_last = _last_row(frames.get("Parallel (Polars)", pl.DataFrame())) + +metrics_pieces: list[pl.DataFrame] = [] +if not numba_last.is_empty(): + metrics_pieces.append( + numba_last.select( + [ + pl.lit("Sequential (Numba)").alias("update_rule"), + "gini", + "corr_sugar_metabolism", + "corr_sugar_vision", + pl.col("agents_alive"), + ] + ) + ) +if not parallel_last.is_empty(): + metrics_pieces.append( + parallel_last.select( + [ + pl.lit("Parallel (random tie-break)").alias("update_rule"), + "gini", + "corr_sugar_metabolism", + "corr_sugar_vision", + pl.col("agents_alive"), + ] + ) + ) + +metrics_table = pl.concat(metrics_pieces, how="vertical") if metrics_pieces else pl.DataFrame() print("\nSteady-state inequality metrics:") print( @@ -1580,24 +1597,15 @@ def run_variant( ) ) -numba_gini = metrics_table.filter(pl.col("update_rule") == "Sequential (Numba)")["gini"][0] -par_gini = metrics_table.filter(pl.col("update_rule") == "Parallel (random tie-break)")["gini"][0] -print(f"Absolute Gini gap (numba vs parallel): {abs(numba_gini - par_gini):.4f}") +# Note: The steady-state rows above are extracted directly from the DataCollector's +# model-level frame (last available step for each variant). We avoid recomputing +# metrics on the live model objects to ensure consistency with any user-defined +# reporters that might add transformations or post-processing in future. -# %% [markdown] -""" -The section above demonstrated how we can iterate across variants inside a single code cell -without sprinkling the global namespace with per‑variant variables like -`sequential_model`, `seq_model_frame`, etc. Instead we retained compact dictionaries: - -``models[name]`` -> Sugarscape instance -``frames[name]`` -> model-level DataFrame trace -``runtimes[name]`` -> wall time in seconds - -This keeps the tutorial easier to skim and copy/paste for users who only want one -variant. The minimal convenience aliases (`numba_model`, `parallel_model`) exist solely -for the comparison section; feel free to inline those if further slimming is desired. -""" +if metrics_table.height >= 2: + numba_gini = metrics_table.filter(pl.col("update_rule") == "Sequential (Numba)")["gini"][0] + par_gini = metrics_table.filter(pl.col("update_rule") == "Parallel (random tie-break)")["gini"][0] + print(f"Absolute Gini gap (numba vs parallel): {abs(numba_gini - par_gini):.4f}") # %% [markdown] """ From b5cf869949117ddba0c9bfa45632616dc20fbb29 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sun, 21 Sep 2025 01:19:00 +0200 Subject: [PATCH 34/99] refactor: update section headings for clarity and consistency in advanced tutorial --- .../general/user-guide/3_advanced_tutorial.py | 63 ++++++------------- 1 file changed, 18 insertions(+), 45 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 5e04172f..fade7bc3 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -911,7 +911,7 @@ def move(self) -> None: # %% [markdown] """ -### 3.5. Simultaneous Movement with Conflict Resolution (the Polars mesa-frames idiomatic way) +### 3.5 Simultaneous Movement with Conflict Resolution (the Polars mesa-frames idiomatic way) The previous implementation is optimal speed-wise but it's a bit low-level. It requires mantaining an occupancy grid and imperative loops and it might become tricky to extend with more complex movement rules or models. To stay in mesa-frames idiom, we can implement a parallel movement policy that uses Polars DataFrame operations to resolve conflicts when multiple agents target the same cell. @@ -1403,12 +1403,19 @@ def _resolve_conflicts_in_rounds( # %% [markdown] """ -## 6. Shared Model Infrastructure +## 4. Run the Model Variants + +We iterate over each movement policy with a shared helper so all runs reuse the same seed. Set `MESA_FRAMES_RUN_SEQUENTIAL=1` to include the slower pure-Python baseline. -`SugarscapeTutorialModel` wires the grid, agent set, regrowth logic, and data -collection. Each variant simply plugs in a different agent class. """ +GRID_WIDTH = 40 +GRID_HEIGHT = 40 +NUM_AGENTS = 400 +MODEL_STEPS = 60 +MAX_SUGAR = 4 +SEED = 42 + def run_variant( agent_cls: type[AntsBase], *, @@ -1427,20 +1434,6 @@ def run_variant( model.run(steps) return model, perf_counter() - start -# %% [markdown] -""" -## 7. Run the Model Variants - -We iterate over each movement policy with a shared helper so all runs reuse the same seed. Set `MESA_FRAMES_RUN_SEQUENTIAL=1` to include the slower pure-Python baseline. -""" - -# %% -GRID_WIDTH = 40 -GRID_HEIGHT = 40 -NUM_AGENTS = 400 -MODEL_STEPS = 60 -MAX_SUGAR = 4 - # Allow quick testing by skipping the slow pure-Python sequential baseline. # Set the environment variable ``MESA_FRAMES_RUN_SEQUENTIAL=0`` (or "false") # to disable the baseline when running this script. @@ -1451,7 +1444,6 @@ def run_variant( "off", } -sequential_seed = 11 variant_specs: dict[str, tuple[type[AntsBase], bool]] = { "Sequential (Python loop)": (AntsSequential, RUN_SEQUENTIAL), @@ -1471,7 +1463,7 @@ def run_variant( runtimes[variant_name] = float("nan") continue - model, runtime = run_variant(agent_cls, steps=MODEL_STEPS, seed=sequential_seed) + model, runtime = run_variant(agent_cls, steps=MODEL_STEPS, seed=SEED) models[variant_name] = model frames[variant_name] = model.datacollector.data["model"] runtimes[variant_name] = runtime @@ -1506,18 +1498,10 @@ def run_variant( numba_model_frame = frames.get("Sequential (Numba)", pl.DataFrame()) par_model_frame = frames.get("Parallel (Polars)", pl.DataFrame()) -# %% [markdown] -""" -Polars gives us that performance without any bespoke compiled kernels—the move -logic reads like ordinary DataFrame code. The Numba version is a touch faster, -but only after writing and maintaining `_numba_find_best_cell` and friends. In -practice we get near-identical runtimes, so you can pick the implementation that -is simplest for your team. -""" # %% [markdown] """ -## 8. Comparing the Update Rules +## 5. Comparing the Update Rules Even though the micro rules differ, the aggregate trajectories keep the same overall shape: sugar holdings trend upward while the population tapers off. By @@ -1609,22 +1593,11 @@ def _last_row(df: pl.DataFrame) -> pl.DataFrame: # %% [markdown] """ -## 9. Where to Go Next? +## 6. Where to Go Next? + +Currently, the Polars implementation spends most of the time in join operations. -* **Polars + LazyFrames roadmap** – future mesa-frames releases will expose - LazyFrame-powered schedulers (with GPU offloading hooks), so the same Polars +**Polars + LazyFrames roadmap** – future mesa-frames releases will expose + LazyFrame-powered sets and spaces (which can also use a GPU cuda accelerated backend which greatly accelerates joins), so the same Polars code you wrote here will scale even further without touching Numba. -* **Production reference** – the `examples/sugarscape_ig/ss_polars` package - shows how to take this pattern further with additional vectorisation tricks. -* **Alternative conflict rules** – it is straightforward to swap in other - tie-breakers, such as letting losing agents search for the next-best empty - cell rather than staying put. -* **Macro validation** – wrap the metric collection in a loop over seeds to - quantify how small the Gini gap remains across independent replications. -* **Statistical physics meets ABM** – for a modern take on the macro behaviour - of Sugarscape-like economies, see Axtell (2000) or subsequent statistical - physics treatments of wealth exchange models. - -Because this script doubles as the notebook source, any edits you make here can -be synchronised with a `.ipynb` representation via Jupytext. """ From df89feaac3f7fef745ec1308e9535cc7a32f59a6 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sun, 21 Sep 2025 01:28:08 +0200 Subject: [PATCH 35/99] refactor: improve clarity and conciseness in advanced tutorial section on update rules and next steps --- .../general/user-guide/3_advanced_tutorial.py | 26 ++++++++----------- 1 file changed, 11 insertions(+), 15 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index fade7bc3..bcd07aed 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -1503,15 +1503,10 @@ def run_variant( """ ## 5. Comparing the Update Rules -Even though the micro rules differ, the aggregate trajectories keep the same -overall shape: sugar holdings trend upward while the population tapers off. By -joining the model-level traces we can quantify how conflict resolution -randomness introduces modest deviations (for example, the simultaneous variant -often retires a few more agents when several conflicts pile up in the same -neighbourhood). Crucially, the steady-state inequality metrics line up: the Gini -coefficients differ by roughly 0.0015 and the wealth–trait correlations are -indistinguishable, which validates the relaxed, fully-parallel update scheme. -""" +Even though micro rules differ, aggregate trajectories remain qualitatively similar (sugar trends up while population gradually declines). +When we join the traces step-by-step, we see small but noticeable deviations introduced by synchronous conflict resolution (e.g., a few more retirements when conflicts cluster). +In our run (seed=42), the final-step Gini differs by ≈0.005, and wealth–trait correlations match within ~1e-3. +These gaps vary by seed and grid size, but they consistently stay modest, supporting the relaxed parallel update as a faithful macro-level approximation.""" # %% comparison = numba_model_frame.select(["step", "mean_sugar", "total_sugar", "agents_alive"]).join( @@ -1581,11 +1576,6 @@ def _last_row(df: pl.DataFrame) -> pl.DataFrame: ) ) -# Note: The steady-state rows above are extracted directly from the DataCollector's -# model-level frame (last available step for each variant). We avoid recomputing -# metrics on the live model objects to ensure consistency with any user-defined -# reporters that might add transformations or post-processing in future. - if metrics_table.height >= 2: numba_gini = metrics_table.filter(pl.col("update_rule") == "Sequential (Numba)")["gini"][0] par_gini = metrics_table.filter(pl.col("update_rule") == "Parallel (random tie-break)")["gini"][0] @@ -1593,7 +1583,12 @@ def _last_row(df: pl.DataFrame) -> pl.DataFrame: # %% [markdown] """ -## 6. Where to Go Next? +## 6. Takeaways and Next Steps + +Some final notes: +- mesa-frames should preferably be used when you have many agents and operations can be vectorized. +- If your model is not easily vectorizable, consider using Numba or reducing your microscopic rule to a vectorizable form. As we saw, the macroscopic behavior can remain consistent (and be more similar to real-world systems). + Currently, the Polars implementation spends most of the time in join operations. @@ -1601,3 +1596,4 @@ def _last_row(df: pl.DataFrame) -> pl.DataFrame: LazyFrame-powered sets and spaces (which can also use a GPU cuda accelerated backend which greatly accelerates joins), so the same Polars code you wrote here will scale even further without touching Numba. """ + From 9eb2457ee60b0cb85158be0a6f08cf222f77c458 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sun, 21 Sep 2025 01:28:25 +0200 Subject: [PATCH 36/99] refactor: remove unnecessary newline at the end of the file in advanced tutorial --- docs/general/user-guide/3_advanced_tutorial.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index bcd07aed..a026ed2f 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -1595,5 +1595,4 @@ def _last_row(df: pl.DataFrame) -> pl.DataFrame: **Polars + LazyFrames roadmap** – future mesa-frames releases will expose LazyFrame-powered sets and spaces (which can also use a GPU cuda accelerated backend which greatly accelerates joins), so the same Polars code you wrote here will scale even further without touching Numba. -""" - +""" \ No newline at end of file From 92db3bebcd8066ff12b9ce048beafb2d017bdfa0 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sun, 21 Sep 2025 01:40:32 +0200 Subject: [PATCH 37/99] fix: update link for Advanced Tutorial to point to the correct notebook file --- mkdocs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mkdocs.yml b/mkdocs.yml index 0e55fd49..331165b5 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -112,7 +112,7 @@ nav: - Classes: user-guide/1_classes.md - Introductory Tutorial: user-guide/2_introductory-tutorial.ipynb - Data Collector Tutorial: user-guide/4_datacollector.ipynb - - Advanced Tutorial: user-guide/3_advanced-tutorial.md + - Advanced Tutorial: user-guide/3_advanced-tutorial.ipynb - Benchmarks: user-guide/5_benchmarks.md - API Reference: api/index.html - Contributing: From 7c2645e2713a90dbc13d3d7246252f2363caa993 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sun, 21 Sep 2025 01:41:07 +0200 Subject: [PATCH 38/99] feat: add jupytext dependency for enhanced notebook support --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 99b15899..8ecbc911 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -80,6 +80,7 @@ docs = [ "seaborn>=0.13.2", "sphinx-autobuild>=2025.8.25", "mesa>=3.2.0", + "jupytext>=1.17.3", ] # dev = test ∪ docs ∪ extra tooling From 511e3030d597900182b1c83282c97549997a4623 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sun, 21 Sep 2025 01:42:43 +0200 Subject: [PATCH 39/99] refactor: remove Jupyter metadata and clean up markdown cells in advanced tutorial --- docs/general/user-guide/3_advanced_tutorial.py | 15 ++++----------- 1 file changed, 4 insertions(+), 11 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index a026ed2f..59382383 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -1,15 +1,5 @@ from __future__ import annotations -# --- -# jupyter: -# jupytext: -# formats: py:percent,ipynb -# kernelspec: -# display_name: Python 3 (uv) -# language: python -# name: python3 -# --- - # %% [markdown] """ [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/projectmesa/mesa-frames/blob/main/docs/general/user-guide/3_advanced_tutorial.ipynb) @@ -711,7 +701,7 @@ def move(self) -> None: Numba compiles numerical Python code to fast machine code at runtime. To use Numba, we need to rewrite the movement logic in a way that is compatible with Numba's restrictions (using tightly typed numpy arrays and accessing data indexes directly). """ - +# %% @njit(cache=True) def _numba_should_replace( best_sugar: int, @@ -919,6 +909,7 @@ def move(self) -> None: This implementation is a tad slower but still efficient and easier to read (for a Polars user). """ +# %% class AntsParallel(AntsBase): def move(self) -> None: @@ -1409,6 +1400,8 @@ def _resolve_conflicts_in_rounds( """ +# %% + GRID_WIDTH = 40 GRID_HEIGHT = 40 NUM_AGENTS = 400 From f37b61bf9fb1b24aed412aee9d7e1049d713ee22 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sun, 21 Sep 2025 01:44:59 +0200 Subject: [PATCH 40/99] feat: add step to convert tutorial .py scripts to notebooks in CI workflow --- .github/workflows/docs-gh-pages.yml | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/.github/workflows/docs-gh-pages.yml b/.github/workflows/docs-gh-pages.yml index 435af957..ae6974e5 100644 --- a/.github/workflows/docs-gh-pages.yml +++ b/.github/workflows/docs-gh-pages.yml @@ -26,11 +26,20 @@ jobs: uv pip install --system . uv pip install --group docs --system + - name: Convert tutorial .py scripts to notebooks + run: | + set -euxo pipefail + for nb in docs/general/*.ipynb; do + echo "Executing $nb" + uv run jupyter nbconvert --to notebook --execute --inplace "$nb" + done + + - name: Build MkDocs site (general documentation) - run: mkdocs build --config-file mkdocs.yml --site-dir ./site + run: uv run mkdocs build --config-file mkdocs.yml --site-dir ./site - name: Build Sphinx docs (API documentation) - run: sphinx-build -b html docs/api site/api + run: uv run sphinx-build -b html docs/api site/api - name: Deploy to GitHub Pages uses: peaceiris/actions-gh-pages@v4 From 284a991b7affc443f9bf39f86a29ea09a14b5e7a Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sun, 21 Sep 2025 01:45:03 +0200 Subject: [PATCH 41/99] feat: add jupytext dependency for enhanced notebook support in development and documentation environments --- uv.lock | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/uv.lock b/uv.lock index a72164c0..4e4d7e1d 100644 --- a/uv.lock +++ b/uv.lock @@ -1234,6 +1234,7 @@ dependencies = [ dev = [ { name = "autodocsumm" }, { name = "beartype" }, + { name = "jupytext" }, { name = "mesa" }, { name = "mkdocs-git-revision-date-localized-plugin" }, { name = "mkdocs-include-markdown-plugin" }, @@ -1258,6 +1259,7 @@ dev = [ docs = [ { name = "autodocsumm" }, { name = "beartype" }, + { name = "jupytext" }, { name = "mesa" }, { name = "mkdocs-git-revision-date-localized-plugin" }, { name = "mkdocs-include-markdown-plugin" }, @@ -1296,6 +1298,7 @@ requires-dist = [ dev = [ { name = "autodocsumm", specifier = ">=0.2.14" }, { name = "beartype", specifier = ">=0.21.0" }, + { name = "jupytext", specifier = ">=1.17.3" }, { name = "mesa", specifier = ">=3.2.0" }, { name = "mkdocs-git-revision-date-localized-plugin", specifier = ">=1.4.7" }, { name = "mkdocs-include-markdown-plugin", specifier = ">=7.1.5" }, @@ -1320,6 +1323,7 @@ dev = [ docs = [ { name = "autodocsumm", specifier = ">=0.2.14" }, { name = "beartype", specifier = ">=0.21.0" }, + { name = "jupytext", specifier = ">=1.17.3" }, { name = "mesa", specifier = ">=3.2.0" }, { name = "mkdocs-git-revision-date-localized-plugin", specifier = ">=1.4.7" }, { name = "mkdocs-include-markdown-plugin", specifier = ">=7.1.5" }, From b4076c4da7da36f18a420dfdabbe673f07902765 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sun, 21 Sep 2025 01:48:54 +0200 Subject: [PATCH 42/99] refactor: simplify variant_specs structure and remove unused RUN_SEQUENTIAL logic --- .../general/user-guide/3_advanced_tutorial.py | 33 ++++--------------- 1 file changed, 7 insertions(+), 26 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 59382383..b0fae391 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -57,7 +57,6 @@ """## 1. Imports""" # %% -import os from time import perf_counter import numpy as np @@ -1427,35 +1426,17 @@ def run_variant( model.run(steps) return model, perf_counter() - start -# Allow quick testing by skipping the slow pure-Python sequential baseline. -# Set the environment variable ``MESA_FRAMES_RUN_SEQUENTIAL=0`` (or "false") -# to disable the baseline when running this script. -RUN_SEQUENTIAL = os.getenv("MESA_FRAMES_RUN_SEQUENTIAL", "0").lower() not in { - "0", - "false", - "no", - "off", -} - - -variant_specs: dict[str, tuple[type[AntsBase], bool]] = { - "Sequential (Python loop)": (AntsSequential, RUN_SEQUENTIAL), - "Sequential (Numba)": (AntsNumba, True), - "Parallel (Polars)": (AntsParallel, True), +variant_specs: dict[str, type[AntsBase]] = { + "Sequential (Python loop)": AntsSequential, + "Sequential (Numba)": AntsNumba, + "Parallel (Polars)": AntsParallel, } models: dict[str, Sugarscape] = {} frames: dict[str, pl.DataFrame] = {} runtimes: dict[str, float] = {} -for variant_name, (agent_cls, enabled) in variant_specs.items(): - if not enabled: - print( - f"Skipping {variant_name}; set MESA_FRAMES_RUN_SEQUENTIAL=1 to enable it." - ) - runtimes[variant_name] = float("nan") - continue - +for variant_name, agent_cls in variant_specs.items(): model, runtime = run_variant(agent_cls, steps=MODEL_STEPS, seed=SEED) models[variant_name] = model frames[variant_name] = model.datacollector.data["model"] @@ -1474,10 +1455,10 @@ def run_variant( pl.DataFrame( [ { - "update_rule": variant_name if enabled else f"{variant_name} [skipped]", + "update_rule": variant_name, "runtime_seconds": runtimes.get(variant_name, float("nan")), } - for variant_name, (_, enabled) in variant_specs.items() + for variant_name in variant_specs.keys() ] ) .with_columns(pl.col("runtime_seconds").round(4)) From a36e181594839de0b1177c00a1007b8b57d1c251 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sun, 21 Sep 2025 01:54:31 +0200 Subject: [PATCH 43/99] refactor: update GitHub Actions workflow for documentation build and preview --- .github/workflows/docs-gh-pages.yml | 82 +++++++++++++++++++++-------- 1 file changed, 61 insertions(+), 21 deletions(-) diff --git a/.github/workflows/docs-gh-pages.yml b/.github/workflows/docs-gh-pages.yml index ae6974e5..705b0fc7 100644 --- a/.github/workflows/docs-gh-pages.yml +++ b/.github/workflows/docs-gh-pages.yml @@ -1,32 +1,35 @@ -name: Build and Deploy Documentation +name: Docs — Build & Preview on: push: - branches: - - main + branches: [ main ] # regular prod deploy + paths: + - 'mkdocs.yml' + - 'docs/**' + pull_request: # preview only when docs are touched + branches: [ '**' ] + paths: + - 'mkdocs.yml' + - 'docs/**' jobs: - build-and-deploy-docs: + build: runs-on: ubuntu-latest + outputs: + short_sha: ${{ steps.sha.outputs.short }} steps: - uses: actions/checkout@v4 - with: - fetch-depth: 0 # Fetch all history for .git-restore-mtime to work correctly - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: '3.x' + with: { fetch-depth: 0 } + - uses: actions/setup-python@v5 + with: { python-version: '3.x' } + - uses: astral-sh/setup-uv@v6 - - name: Install uv via GitHub Action - uses: astral-sh/setup-uv@v6 - - - name: Install mesa-frames + docs dependencies + - name: Install mesa-frames + docs deps run: | uv pip install --system . uv pip install --group docs --system - - name: Convert tutorial .py scripts to notebooks + - name: Convert jupytext .py notebooks to .ipynb run: | set -euxo pipefail for nb in docs/general/*.ipynb; do @@ -34,16 +37,53 @@ jobs: uv run jupyter nbconvert --to notebook --execute --inplace "$nb" done - - - name: Build MkDocs site (general documentation) + - name: Build MkDocs site run: uv run mkdocs build --config-file mkdocs.yml --site-dir ./site - - name: Build Sphinx docs (API documentation) + - name: Build Sphinx docs (API) run: uv run sphinx-build -b html docs/api site/api - - name: Deploy to GitHub Pages + - name: Short SHA + id: sha + run: echo "short=$(git rev-parse --short HEAD)" >> "$GITHUB_OUTPUT" + + - name: Upload site artifact + uses: actions/upload-artifact@v4 + with: + name: site + path: site + + deploy-main: + needs: build + if: github.event_name == 'push' && github.ref == 'refs/heads/main' + runs-on: ubuntu-latest + steps: + - uses: actions/download-artifact@v4 + with: { name: site, path: site } + - name: Deploy to GitHub Pages (main) + uses: peaceiris/actions-gh-pages@v4 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_branch: gh-pages + publish_dir: ./site + force_orphan: true + + deploy-preview: + needs: build + if: github.event_name == 'pull_request' + runs-on: ubuntu-latest + steps: + - uses: actions/download-artifact@v4 + with: { name: site, path: site } + - name: Deploy preview under subfolder uses: peaceiris/actions-gh-pages@v4 with: github_token: ${{ secrets.GITHUB_TOKEN }} + publish_branch: gh-pages publish_dir: ./site - force_orphan: true \ No newline at end of file + destination_dir: preview/${{ github.head_ref || github.ref_name }}/${{ needs.build.outputs.short_sha }} + keep_files: true # keep previous previews + # DO NOT set force_orphan here + - name: Print preview URL + run: | + echo "Preview: https://${{ github.repository_owner }}.github.io/$(basename ${{ github.repository }})/preview/${{ github.head_ref || github.ref_name }}/${{ needs.build.outputs.short_sha }}/" From ec98197f9ea0431c770388e2727ccc32c13978e8 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sun, 21 Sep 2025 01:55:53 +0200 Subject: [PATCH 44/99] docs: clarify tutorial instructions for running model variants --- docs/general/user-guide/3_advanced_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index b0fae391..cee86f9e 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -1395,7 +1395,7 @@ def _resolve_conflicts_in_rounds( """ ## 4. Run the Model Variants -We iterate over each movement policy with a shared helper so all runs reuse the same seed. Set `MESA_FRAMES_RUN_SEQUENTIAL=1` to include the slower pure-Python baseline. +We iterate over each movement policy with a shared helper so all runs reuse the same seed. The tutorial runs all three variants (Python sequential, Numba sequential, and parallel) by default; edit the script if you want to skip the slow pure-Python baseline. """ From 4ef1cf6ad69972a3fe315474bac280e81abb9b58 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sun, 21 Sep 2025 08:21:51 +0000 Subject: [PATCH 45/99] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- docs/general/index.md | 2 +- .../general/user-guide/3_advanced_tutorial.py | 154 +++++++++++------- 2 files changed, 100 insertions(+), 56 deletions(-) diff --git a/docs/general/index.md b/docs/general/index.md index ee967623..cee3f109 100644 --- a/docs/general/index.md +++ b/docs/general/index.md @@ -1 +1 @@ -{% include-markdown "../../README.md" %} \ No newline at end of file +{% include-markdown "../../README.md" %} diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index cee86f9e..05d9f194 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -28,7 +28,7 @@ The update schedule matters for micro-behaviour, so we study three variants: -1. **Sequential loop (asynchronous):** This is the traditional definition. Ants move one at a time in random order. +1. **Sequential loop (asynchronous):** This is the traditional definition. Ants move one at a time in random order. This cannnot be vectorised easily as the best move for an ant might depend on the moves of earlier ants (for example, if they target the same cell). 2. **Sequential with Numba:** matches the first variant but relies on a compiled helper for speed. @@ -92,6 +92,7 @@ # Model-level reporters + def gini(model: Model) -> float: """Compute the Gini coefficient of agent sugar holdings. @@ -135,6 +136,7 @@ def gini(model: Model) -> float: index = np.arange(1, n + 1, dtype=np.float64) return float((2.0 * np.dot(index, sorted_vals) / (n * total)) - (n + 1) / n) + def corr_sugar_metabolism(model: Model) -> float: """Pearson correlation between agent sugar and metabolism. @@ -169,6 +171,7 @@ def corr_sugar_metabolism(model: Model) -> float: metabolism = agent_df["metabolism"].to_numpy().astype(np.float64) return _safe_corr(sugar, metabolism) + def corr_sugar_vision(model: Model) -> float: """Pearson correlation between agent sugar and vision. @@ -202,6 +205,7 @@ def corr_sugar_vision(model: Model) -> float: vision = agent_df["vision"].to_numpy().astype(np.float64) return _safe_corr(sugar, vision) + def _safe_corr(x: np.ndarray, y: np.ndarray) -> float: """Safely compute Pearson correlation between two 1-D arrays. @@ -229,6 +233,7 @@ def _safe_corr(x: np.ndarray, y: np.ndarray) -> float: return float("nan") return float(np.corrcoef(x, y)[0, 1]) + class Sugarscape(Model): """Minimal Sugarscape model used throughout the tutorial. @@ -269,7 +274,7 @@ class Sugarscape(Model): def __init__( self, - agent_type: type["AntsBase"], + agent_type: type[AntsBase], n_agents: int, *, width: int, @@ -282,7 +287,7 @@ def __init__( "Cannot place more agents than grid cells when capacity is 1." ) super().__init__(seed) - + # 1. Let's create the sugar grid and set up the space sugar_grid_df = self._generate_sugar_grid(width, height, max_sugar) @@ -291,7 +296,7 @@ def __init__( ) self.space.set_cells(sugar_grid_df) self._max_sugar = sugar_grid_df.select(["dim_0", "dim_1", "max_sugar"]) - + # 2. Now we create the agents and place them on the grid agent_frame = self._generate_agent_frame(n_agents) @@ -415,7 +420,9 @@ def _advance_sugar_field(self) -> None: empty_cells = self.space.empty_cells if not empty_cells.is_empty(): # Look up the maximum sugar for each empty cell and restore it. - refresh = empty_cells.join(self._max_sugar, on=["dim_0", "dim_1"], how="left") + refresh = empty_cells.join( + self._max_sugar, on=["dim_0", "dim_1"], how="left" + ) self.space.set_cells(empty_cells, {"sugar": refresh["max_sugar"]}) full_cells = self.space.full_cells if not full_cells.is_empty(): @@ -423,6 +430,7 @@ def _advance_sugar_field(self) -> None: zeros = pl.Series(np.zeros(len(full_cells), dtype=np.int64)) self.space.set_cells(full_cells, {"sugar": zeros}) + # %% [markdown] """ @@ -430,13 +438,14 @@ def _advance_sugar_field(self) -> None: ### 3.1 Base agent class -Now let's define the agent class (the ant class). We start with a base class which implements the common logic for eating and starvation, while leaving the `move` method abstract. +Now let's define the agent class (the ant class). We start with a base class which implements the common logic for eating and starvation, while leaving the `move` method abstract. The base class also provides helper methods for sensing visible cells and choosing the best cell based on sugar, distance, and coordinates. This will allow us to define different movement policies (sequential, Numba-accelerated, and parallel) as subclasses that only need to implement the `move` method. """ # %% + class AntsBase(AgentSet): """Base agent set for the Sugarscape tutorial. @@ -450,6 +459,7 @@ class AntsBase(AgentSet): - Subclasses must implement :meth:`move` which changes agent positions on the grid (via :meth:`mesa_frames.Grid` helpers). """ + def __init__(self, model: Model, agent_frame: pl.DataFrame) -> None: """Initialise the agent set and validate required trait columns. @@ -518,7 +528,9 @@ def eat(self) -> None: # `occupied_ids` is a Polars Series; calling `is_in` with a Series # of the same datatype is ambiguous in newer Polars. Use `implode` # to collapse the Series into a list-like value for membership checks. - occupied_cells = self.space.cells.filter(pl.col("agent_id").is_in(occupied_ids.implode())) + occupied_cells = self.space.cells.filter( + pl.col("agent_id").is_in(occupied_ids.implode()) + ) if occupied_cells.is_empty(): return # The agent ordering here uses the agent_id values stored in the @@ -526,7 +538,9 @@ def eat(self) -> None: # the matching agents' sugar values in one vectorised write. agent_ids = occupied_cells["agent_id"] self[agent_ids, "sugar"] = ( - self[agent_ids, "sugar"] + occupied_cells["sugar"] - self[agent_ids, "metabolism"] + self[agent_ids, "sugar"] + + occupied_cells["sugar"] + - self[agent_ids, "metabolism"] ) # After harvesting, occupied cells have zero sugar. self.space.set_cells( @@ -557,8 +571,11 @@ def _remove_starved(self) -> None: # %% + class AntsSequential(AntsBase): - def _visible_cells(self, origin: tuple[int, int], vision: int) -> list[tuple[int, int]]: + def _visible_cells( + self, origin: tuple[int, int], vision: int + ) -> list[tuple[int, int]]: """List cells visible from an origin along the four cardinal axes. The visibility set includes the origin cell itself and cells at @@ -637,7 +654,9 @@ def _choose_best_cell( if blocked and candidate != origin and candidate in blocked: continue sugar_here = sugar_map.get(candidate, 0) - distance = self.model.space.get_distances(origin, candidate)["distance"].item() + distance = self.model.space.get_distances(origin, candidate)[ + "distance" + ].item() better = False # Primary criterion: strictly more sugar. if sugar_here > best_sugar: @@ -670,7 +689,7 @@ def _current_sugar_map(self) -> dict[tuple[int, int], int]: (int(x), int(y)): 0 if sugar is None else int(sugar) for x, y, sugar in cells.iter_rows() } - + def move(self) -> None: sugar_map = self._current_sugar_map() state = self.df.join(self.pos, on="unique_id", how="left") @@ -691,6 +710,7 @@ def move(self) -> None: if target != current: self.space.move_agents(agent_id, target) + # %% [markdown] """ ### 3.3 Speeding Up the Loop with Numba @@ -700,7 +720,8 @@ def move(self) -> None: Numba compiles numerical Python code to fast machine code at runtime. To use Numba, we need to rewrite the movement logic in a way that is compatible with Numba's restrictions (using tightly typed numpy arrays and accessing data indexes directly). """ -# %% + +# %% @njit(cache=True) def _numba_should_replace( best_sugar: int, @@ -876,6 +897,7 @@ def sequential_move_numba( return new_dim0, new_dim1 + class AntsNumba(AntsBase): def move(self) -> None: state = self.df.join(self.pos, on="unique_id", how="left") @@ -888,8 +910,8 @@ def move(self) -> None: sugar_array = ( self.space.cells.sort(["dim_0", "dim_1"]) - .with_columns(pl.col("sugar").fill_null(0)) - ["sugar"].to_numpy() + .with_columns(pl.col("sugar").fill_null(0))["sugar"] + .to_numpy() .reshape(self.space.dimensions) ) @@ -910,6 +932,7 @@ def move(self) -> None: # %% + class AntsParallel(AntsBase): def move(self) -> None: """Move agents in parallel by ranking visible cells and resolving conflicts. @@ -1002,8 +1025,7 @@ def _build_neighborhood_frame(self, current_pos: pl.DataFrame) -> pl.DataFrame: sugar_cells = self.space.cells.select(["dim_0", "dim_1", "sugar"]) neighborhood_cells = ( - neighborhood_cells - .join(sugar_cells, on=["dim_0", "dim_1"], how="left") + neighborhood_cells.join(sugar_cells, on=["dim_0", "dim_1"], how="left") .with_columns(pl.col("sugar").fill_null(0)) .rename({"dim_0": "dim_0_candidate", "dim_1": "dim_1_candidate"}) ) @@ -1021,11 +1043,9 @@ def _build_neighborhood_frame(self, current_pos: pl.DataFrame) -> pl.DataFrame: # │ --- ┆ --- ┆ --- ┆ --- ┆ --- │ # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 │ # ╞══════════╪════════╪══════════════════╪══════════════════╪════════╡ - neighborhood_cells = ( - neighborhood_cells - .drop(["dim_0_center", "dim_1_center"]) - .select(["agent_id", "radius", "dim_0_candidate", "dim_1_candidate", "sugar"]) - ) + neighborhood_cells = neighborhood_cells.drop( + ["dim_0_center", "dim_1_center"] + ).select(["agent_id", "radius", "dim_0_candidate", "dim_1_candidate", "sugar"]) return neighborhood_cells @@ -1087,12 +1107,7 @@ def _rank_candidates( keep="first", maintain_order=True, ) - .with_columns( - pl.col("agent_id") - .cum_count() - .over("agent_id") - .alias("rank") - ) + .with_columns(pl.col("agent_id").cum_count().over("agent_id").alias("rank")) ) # Precompute per‑agent candidate rank once so conflict resolution can @@ -1127,7 +1142,9 @@ def _rank_candidates( # │ --- ┆ --- │ # │ u64 ┆ u32 │ # ╞══════════╪═══════════╡ - max_rank = choices.group_by("agent_id").agg(pl.col("rank").max().alias("max_rank")) + max_rank = choices.group_by("agent_id").agg( + pl.col("rank").max().alias("max_rank") + ) return choices, origins, max_rank def _resolve_conflicts_in_rounds( @@ -1159,7 +1176,7 @@ def _resolve_conflicts_in_rounds( """ # Prepare unresolved agents and working tables. agent_ids = choices["agent_id"].unique(maintain_order=True) - + # unresolved columns: # ┌──────────┬────────────────┐ # │ agent_id ┆ current_rank │ @@ -1181,7 +1198,9 @@ def _resolve_conflicts_in_rounds( # ╞══════════╪══════════════════╪══════════════════╡ assigned = pl.DataFrame( { - "agent_id": pl.Series(name="agent_id", values=[], dtype=agent_ids.dtype), + "agent_id": pl.Series( + name="agent_id", values=[], dtype=agent_ids.dtype + ), "dim_0_candidate": pl.Series( name="dim_0_candidate", values=[], dtype=pl.Int64 ), @@ -1223,7 +1242,9 @@ def _resolve_conflicts_in_rounds( # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ u32 ┆ i64 │ # ╞══════════╪══════════════════╪══════════════════╪════════╪════════╪══════╪══════════════╡ candidate_pool = choices.join(unresolved, on="agent_id") - candidate_pool = candidate_pool.filter(pl.col("rank") >= pl.col("current_rank")) + candidate_pool = candidate_pool.filter( + pl.col("rank") >= pl.col("current_rank") + ) if not taken.is_empty(): candidate_pool = candidate_pool.join( taken, @@ -1264,8 +1285,7 @@ def _resolve_conflicts_in_rounds( # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ u32 ┆ i64 │ # ╞══════════╪══════════════════╪══════════════════╪════════╪════════╪══════╪══════════════╡ best_candidates = ( - candidate_pool - .sort(["agent_id", "rank"]) + candidate_pool.sort(["agent_id", "rank"]) .group_by("agent_id", maintain_order=True) .first() ) @@ -1277,7 +1297,9 @@ def _resolve_conflicts_in_rounds( # │ --- ┆ --- │ # │ u64 ┆ i64 │ # ╞══════════╪══════════════╡ - missing = unresolved.join(best_candidates.select("agent_id"), on="agent_id", how="anti") + missing = unresolved.join( + best_candidates.select("agent_id"), on="agent_id", how="anti" + ) if not missing.is_empty(): # fallback (missing) columns match fallback table above. fallback = missing.join(origins, on="agent_id", how="left") @@ -1306,8 +1328,12 @@ def _resolve_conflicts_in_rounds( ], how="vertical", ) - unresolved = unresolved.join(missing.select("agent_id"), on="agent_id", how="anti") - best_candidates = best_candidates.join(missing.select("agent_id"), on="agent_id", how="anti") + unresolved = unresolved.join( + missing.select("agent_id"), on="agent_id", how="anti" + ) + best_candidates = best_candidates.join( + missing.select("agent_id"), on="agent_id", how="anti" + ) if unresolved.is_empty() or best_candidates.is_empty(): continue @@ -1323,8 +1349,7 @@ def _resolve_conflicts_in_rounds( # │ u64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ u32 ┆ i64 ┆ f64 │ # ╞══════════╪══════════════════╪══════════════════╪════════╪════════╪══════╪══════════════╪═════════╡ winners = ( - best_candidates - .sort(["dim_0_candidate", "dim_1_candidate", "lottery"]) + best_candidates.sort(["dim_0_candidate", "dim_1_candidate", "lottery"]) .group_by(["dim_0_candidate", "dim_1_candidate"], maintain_order=True) .first() ) @@ -1373,22 +1398,27 @@ def _resolve_conflicts_in_rounds( ) .join(max_rank, on="agent_id", how="left") .with_columns( - pl.min_horizontal(pl.col("next_rank"), pl.col("max_rank")).alias("next_rank") + pl.min_horizontal(pl.col("next_rank"), pl.col("max_rank")).alias( + "next_rank" + ) ) .select(["agent_id", "next_rank"]) ) # Promote losers' current_rank (if any) and continue. # unresolved (updated) retains columns agent_id/current_rank. - unresolved = unresolved.join(loser_updates, on="agent_id", how="left").with_columns( - pl.when(pl.col("next_rank").is_not_null()) - .then(pl.col("next_rank")) - .otherwise(pl.col("current_rank")) - .alias("current_rank") - ).drop("next_rank") + unresolved = ( + unresolved.join(loser_updates, on="agent_id", how="left") + .with_columns( + pl.when(pl.col("next_rank").is_not_null()) + .then(pl.col("next_rank")) + .otherwise(pl.col("current_rank")) + .alias("current_rank") + ) + .drop("next_rank") + ) return assigned - # %% [markdown] @@ -1408,6 +1438,7 @@ def _resolve_conflicts_in_rounds( MAX_SUGAR = 4 SEED = 42 + def run_variant( agent_cls: type[AntsBase], *, @@ -1426,6 +1457,7 @@ def run_variant( model.run(steps) return model, perf_counter() - start + variant_specs: dict[str, type[AntsBase]] = { "Sequential (Python loop)": AntsSequential, "Sequential (Numba)": AntsNumba, @@ -1477,13 +1509,15 @@ def run_variant( """ ## 5. Comparing the Update Rules -Even though micro rules differ, aggregate trajectories remain qualitatively similar (sugar trends up while population gradually declines). -When we join the traces step-by-step, we see small but noticeable deviations introduced by synchronous conflict resolution (e.g., a few more retirements when conflicts cluster). -In our run (seed=42), the final-step Gini differs by ≈0.005, and wealth–trait correlations match within ~1e-3. +Even though micro rules differ, aggregate trajectories remain qualitatively similar (sugar trends up while population gradually declines). +When we join the traces step-by-step, we see small but noticeable deviations introduced by synchronous conflict resolution (e.g., a few more retirements when conflicts cluster). +In our run (seed=42), the final-step Gini differs by ≈0.005, and wealth–trait correlations match within ~1e-3. These gaps vary by seed and grid size, but they consistently stay modest, supporting the relaxed parallel update as a faithful macro-level approximation.""" # %% -comparison = numba_model_frame.select(["step", "mean_sugar", "total_sugar", "agents_alive"]).join( +comparison = numba_model_frame.select( + ["step", "mean_sugar", "total_sugar", "agents_alive"] +).join( par_model_frame.select(["step", "mean_sugar", "total_sugar", "agents_alive"]), on="step", how="inner", @@ -1492,11 +1526,14 @@ def run_variant( comparison = comparison.with_columns( (pl.col("mean_sugar") - pl.col("mean_sugar_parallel")).abs().alias("mean_diff"), (pl.col("total_sugar") - pl.col("total_sugar_parallel")).abs().alias("total_diff"), - (pl.col("agents_alive") - pl.col("agents_alive_parallel")).abs().alias("count_diff"), + (pl.col("agents_alive") - pl.col("agents_alive_parallel")) + .abs() + .alias("count_diff"), ) print("Step-level absolute differences (first 10 steps):") print(comparison.select(["step", "mean_diff", "total_diff", "count_diff"]).head(10)) + # Build the steady‑state metrics table from the DataCollector output rather than # recomputing reporters directly on the model objects. The collector already # stored the model‑level reporters (gini, correlations, etc.) every step. @@ -1506,6 +1543,7 @@ def _last_row(df: pl.DataFrame) -> pl.DataFrame: # Ensure we take the final time step in case steps < MODEL_STEPS due to extinction. return df.sort("step").tail(1) + numba_last = _last_row(frames.get("Sequential (Numba)", pl.DataFrame())) parallel_last = _last_row(frames.get("Parallel (Polars)", pl.DataFrame())) @@ -1535,7 +1573,9 @@ def _last_row(df: pl.DataFrame) -> pl.DataFrame: ) ) -metrics_table = pl.concat(metrics_pieces, how="vertical") if metrics_pieces else pl.DataFrame() +metrics_table = ( + pl.concat(metrics_pieces, how="vertical") if metrics_pieces else pl.DataFrame() +) print("\nSteady-state inequality metrics:") print( @@ -1551,8 +1591,12 @@ def _last_row(df: pl.DataFrame) -> pl.DataFrame: ) if metrics_table.height >= 2: - numba_gini = metrics_table.filter(pl.col("update_rule") == "Sequential (Numba)")["gini"][0] - par_gini = metrics_table.filter(pl.col("update_rule") == "Parallel (random tie-break)")["gini"][0] + numba_gini = metrics_table.filter(pl.col("update_rule") == "Sequential (Numba)")[ + "gini" + ][0] + par_gini = metrics_table.filter( + pl.col("update_rule") == "Parallel (random tie-break)" + )["gini"][0] print(f"Absolute Gini gap (numba vs parallel): {abs(numba_gini - par_gini):.4f}") # %% [markdown] @@ -1569,4 +1613,4 @@ def _last_row(df: pl.DataFrame) -> pl.DataFrame: **Polars + LazyFrames roadmap** – future mesa-frames releases will expose LazyFrame-powered sets and spaces (which can also use a GPU cuda accelerated backend which greatly accelerates joins), so the same Polars code you wrote here will scale even further without touching Numba. -""" \ No newline at end of file +""" From 6c0deb02810dd652867ac6e17af23b4a50a63b91 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sun, 21 Sep 2025 10:33:50 +0200 Subject: [PATCH 46/99] refactor: enhance jupytext conversion process for .py notebooks in documentation --- .github/workflows/docs-gh-pages.yml | 20 ++++++++++++++++---- 1 file changed, 16 insertions(+), 4 deletions(-) diff --git a/.github/workflows/docs-gh-pages.yml b/.github/workflows/docs-gh-pages.yml index 705b0fc7..d60be123 100644 --- a/.github/workflows/docs-gh-pages.yml +++ b/.github/workflows/docs-gh-pages.yml @@ -32,10 +32,22 @@ jobs: - name: Convert jupytext .py notebooks to .ipynb run: | set -euxo pipefail - for nb in docs/general/*.ipynb; do - echo "Executing $nb" - uv run jupyter nbconvert --to notebook --execute --inplace "$nb" - done + # Convert any jupytext .py files to .ipynb without executing them. + # Enable nullglob so the pattern expands to empty when there are no matches + # and globstar so we recurse into subdirectories (e.g., user-guide/). + shopt -s nullglob globstar || true + files=(docs/general/**/*.py) + if [ ${#files[@]} -eq 0 ]; then + echo "No jupytext .py files found under docs/general" + else + for src in "${files[@]}"; do + [ -e "$src" ] || continue + dest="${src%.py}.ipynb" + echo "Converting $src -> $dest" + # jupytext will write the .ipynb alongside the source file + uv run jupytext --to notebook "$src" + done + fi - name: Build MkDocs site run: uv run mkdocs build --config-file mkdocs.yml --site-dir ./site From 60bd49ba7663b8e9ea232adaf027d14040f3e7cf Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sun, 21 Sep 2025 18:06:33 +0200 Subject: [PATCH 47/99] docs: clarify step ordering in Sugarscape model tutorial --- docs/general/user-guide/3_advanced_tutorial.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 05d9f194..af4cd90d 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -378,18 +378,18 @@ def _generate_agent_frame(self, n_agents: int) -> pl.DataFrame: def step(self) -> None: """Advance the model by one step. - Notes - ----- - The per-step ordering is important: regrowth happens first (so empty - cells are refilled), then agents move and eat, and finally metrics are - collected. If the agent set becomes empty at any point the model is - marked as not running. + Notes + ----- + The per-step ordering is important and this tutorial implements the + classic Sugarscape "instant growback": agents move and eat first, + and then empty cells are refilled immediately (move -> eat -> regrow + -> collect). """ if len(self.sets[0]) == 0: self.running = False return - self._advance_sugar_field() self.sets[0].step() + self._advance_sugar_field() self.datacollector.collect() if len(self.sets[0]) == 0: self.running = False From d7263e10642d030777810c6fcd1c3d785d97b337 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sun, 21 Sep 2025 18:11:13 +0200 Subject: [PATCH 48/99] refactor: optimize distance calculation in AntsSequential class using Manhattan distance --- docs/general/user-guide/3_advanced_tutorial.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index af4cd90d..191bdc30 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -648,15 +648,18 @@ def _choose_best_cell( best_cell = origin best_sugar = sugar_map.get(origin, 0) best_distance = 0 + ox, oy = origin for candidate in self._visible_cells(origin, vision): # Skip blocked cells (occupied by other agents) unless it's the # agent's current cell which we always consider. if blocked and candidate != origin and candidate in blocked: continue sugar_here = sugar_map.get(candidate, 0) - distance = self.model.space.get_distances(origin, candidate)[ - "distance" - ].item() + # Use step-based Manhattan distance (number of steps along cardinal + # axes) which is the same metric used by the Numba path. This avoids + # calling the heavier `space.get_distances` per candidate. + cx, cy = candidate + distance = abs(cx - ox) + abs(cy - oy) better = False # Primary criterion: strictly more sugar. if sugar_here > best_sugar: From d62d406a7bc7f869903d343428afd392dbf9465f Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sun, 21 Sep 2025 18:48:08 +0200 Subject: [PATCH 49/99] docs: enhance move method documentation in AntsParallel class with declarative mental model --- docs/general/user-guide/3_advanced_tutorial.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 191bdc30..85ac305d 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -940,6 +940,10 @@ class AntsParallel(AntsBase): def move(self) -> None: """Move agents in parallel by ranking visible cells and resolving conflicts. + Declarative mental model: express *what* each agent wants (ranked candidates), + then use dataframe ops to *allocate* (joins, group_by with a lottery). + Performance is handled by Polars/LazyFrames; avoid premature micro-optimisations. + Returns ------- None From 495dbfb7efda75e7b0e0cfd501f61b82fbb902cf Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sun, 21 Sep 2025 18:50:47 +0200 Subject: [PATCH 50/99] docs: enhance explanation of modeling philosophy in advanced tutorial --- docs/general/user-guide/3_advanced_tutorial.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 85ac305d..9b2757d7 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -35,6 +35,11 @@ 3. **Parallel (synchronous):** all ants propose moves; conflicts are resolved at random before applying the winners simultaneously (and the losers get to their second-best cell, etc). +The first variant (pure Python loops) is a natural starting point, but it is **not** the mesa-frames philosophy. +The latter two are: we aim to **write rules declaratively** and let the dataframe engine worry about performance. +Our guiding principle is to **focus on modelling first and performance second**. Only when a rule is truly +inherently sequential do we fall back to a compiled kernel (Numba or JAX). + Our goal is to show that, under instantaneous growback and uniform resources, the model converges to the *same* macroscopic inequality pattern regardless of whether agents act sequentially or in parallel and that As long as the random draws do From 2f023670e60b59f71bff0600b19fb1c9f276229b Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sun, 21 Sep 2025 16:51:11 +0000 Subject: [PATCH 51/99] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- docs/general/user-guide/3_advanced_tutorial.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 9b2757d7..ef1bfa4c 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -35,8 +35,8 @@ 3. **Parallel (synchronous):** all ants propose moves; conflicts are resolved at random before applying the winners simultaneously (and the losers get to their second-best cell, etc). -The first variant (pure Python loops) is a natural starting point, but it is **not** the mesa-frames philosophy. -The latter two are: we aim to **write rules declaratively** and let the dataframe engine worry about performance. +The first variant (pure Python loops) is a natural starting point, but it is **not** the mesa-frames philosophy. +The latter two are: we aim to **write rules declaratively** and let the dataframe engine worry about performance. Our guiding principle is to **focus on modelling first and performance second**. Only when a rule is truly inherently sequential do we fall back to a compiled kernel (Numba or JAX). @@ -383,12 +383,12 @@ def _generate_agent_frame(self, n_agents: int) -> pl.DataFrame: def step(self) -> None: """Advance the model by one step. - Notes - ----- - The per-step ordering is important and this tutorial implements the - classic Sugarscape "instant growback": agents move and eat first, - and then empty cells are refilled immediately (move -> eat -> regrow - -> collect). + Notes + ----- + The per-step ordering is important and this tutorial implements the + classic Sugarscape "instant growback": agents move and eat first, + and then empty cells are refilled immediately (move -> eat -> regrow + -> collect). """ if len(self.sets[0]) == 0: self.running = False From 82a9ab35500f431625734123370b087831c8a3e0 Mon Sep 17 00:00:00 2001 From: Adam Amer <136176500+adamamer20@users.noreply.github.com> Date: Sun, 21 Sep 2025 18:54:42 +0200 Subject: [PATCH 52/99] docs: update user guide link to point to the getting started section --- docs/api/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/api/conf.py b/docs/api/conf.py index 43098ec2..95f23a38 100644 --- a/docs/api/conf.py +++ b/docs/api/conf.py @@ -64,7 +64,7 @@ "external_links": [ { "name": "User guide", - "url": f"{web_root}/user-guide/", + "url": f"{web_root}/user-guide/0_getting-started/", }, ], "icon_links": [ From bb7910b079728150b456d3142fd40c90e8b77192 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Sun, 21 Sep 2025 19:39:23 +0200 Subject: [PATCH 53/99] docs: remove obsolete Makefile and batch script for Sphinx documentation --- docs/api/Makefile | 20 -------------------- docs/api/make.bat | 35 ----------------------------------- 2 files changed, 55 deletions(-) delete mode 100644 docs/api/Makefile delete mode 100644 docs/api/make.bat diff --git a/docs/api/Makefile b/docs/api/Makefile deleted file mode 100644 index d0c3cbf1..00000000 --- a/docs/api/Makefile +++ /dev/null @@ -1,20 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line, and also -# from the environment for the first two. -SPHINXOPTS ?= -SPHINXBUILD ?= sphinx-build -SOURCEDIR = source -BUILDDIR = build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/api/make.bat b/docs/api/make.bat deleted file mode 100644 index dc1312ab..00000000 --- a/docs/api/make.bat +++ /dev/null @@ -1,35 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=source -set BUILDDIR=build - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.https://www.sphinx-doc.org/ - exit /b 1 -) - -if "%1" == "" goto help - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% - -:end -popd From bcf9a51cee6b4057a0c0fea90d0e92f94bcc1ea4 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Sun, 21 Sep 2025 20:17:09 +0200 Subject: [PATCH 54/99] docs: update overview and mini usage flow in API documentation --- docs/api/index.rst | 47 +++++++++++++++++++++++++++------------------- 1 file changed, 28 insertions(+), 19 deletions(-) diff --git a/docs/api/index.rst b/docs/api/index.rst index 936350d6..f848c6f7 100644 --- a/docs/api/index.rst +++ b/docs/api/index.rst @@ -1,34 +1,43 @@ mesa-frames API =============== -This page provides a high-level overview of all public mesa-frames objects, functions, and methods. All classes and functions exposed in the ``mesa_frames.*`` namespace are public. +Overview +-------- -.. grid:: - - .. grid-item-card:: +mesa-frames provides a DataFrame-first API for agent-based models. Instead of representing each agent as a distinct Python object, agents are stored in AgentSets (backed by DataFrames) and manipulated via vectorised operations. This leads to much lower memory overhead and faster bulk updates while keeping an object-oriented feel for model structure and lifecycle management. - .. toctree:: - :maxdepth: 2 - reference/agents/index +Mini usage flow +--------------- - .. grid-item-card:: +1. Create a Model and register AgentSets on ``model.sets``. +2. Populate AgentSets with agents (rows) and attributes (columns) via adding a DataFrame to the AgentSet. +3. Implement AgentSet methods that operate on DataFrames +4. Use ``model.sets.do("step")`` from the model loop to advance the simulation; datacollectors and reporters can sample model- and agent-level columns at each step. - .. toctree:: - :maxdepth: 2 +.. grid:: + :gutter: 2 - reference/model + .. grid-item-card:: Manage agent collections + :link: reference/agents/index + :link-type: doc - .. grid-item-card:: + Create and operate on ``AgentSets`` and ``AgentSetRegisties``: add/remove agents. - .. toctree:: - :maxdepth: 2 + .. grid-item-card:: Model orchestration + :link: reference/model + :link-type: doc - reference/space/index + ``Model`` API for registering sets, stepping the simulation, and integrating with datacollectors/reporters. + + .. grid-item-card:: Spatial support + :link: reference/space/index + :link-type: doc - .. grid-item-card:: + Placement and neighbourhood utilities for ``Grid`` and space - .. toctree:: - :maxdepth: 2 + .. grid-item-card:: Collect simulation data + :link: reference/datacollector + :link-type: doc - reference/datacollector \ No newline at end of file + Record model- and agent-level metrics over time with ``DataCollector``. Sample columns, run aggregations, and export cleaned frames for analysis. \ No newline at end of file From 2f51c5aa2bd6ac2b5934c2b3205cf934cfaa8be0 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Sun, 21 Sep 2025 20:21:04 +0200 Subject: [PATCH 55/99] docs: add docs/site to .gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 4a189d56..41ce8f27 100644 --- a/.gitignore +++ b/.gitignore @@ -154,3 +154,4 @@ cython_debug/ *.code-workspace llm_rules.md .python-version +docs/site \ No newline at end of file From 62ede62999374c9bef711fe21a746baf2dce7750 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Sun, 21 Sep 2025 20:36:15 +0200 Subject: [PATCH 56/99] docs: update API reference for clarity and consistency --- docs/api/reference/agents/index.rst | 35 +++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) diff --git a/docs/api/reference/agents/index.rst b/docs/api/reference/agents/index.rst index a1c03126..549381fa 100644 --- a/docs/api/reference/agents/index.rst +++ b/docs/api/reference/agents/index.rst @@ -3,6 +3,41 @@ Agents .. currentmodule:: mesa_frames +Quick intro +----------- + +- ``AgentSet`` stores agents as rows in a Polars-backed table and provides vectorised operations for high-performance updates. + +- ``AgentSetRegistry`` (available at ``model.sets``) is the container that holds all ``AgentSet`` instances for a model and provides convenience operations (add/remove sets, step all sets, rename). + +- Keep agent logic column-oriented and prefer Polars expressions for updates. + +Minimal example +--------------- + +.. code-block:: python + + from mesa_frames import Model, AgentSet + import polars as pl + + class MySet(AgentSet): + def step(self): + # vectorised update: increase age for all agents + self.df = self.df.with_columns((pl.col("age") + 1).alias("age")) + + class MyModel(Model): + def __init__(self): + super().__init__() + # register an AgentSet on the model's registry + self.sets += MySet(self) + + m = MyModel() + m.sets["MySet"].add(pl.DataFrame({"age": [0, 5, 10]})) + # step all registered sets (delegates to each AgentSet.step) + m.sets.do("step") + +API reference +-------------------------------- .. autoclass:: AgentSet :members: From 056b5b0fa9237b394cac5d9ba2096bed87b97a71 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Sun, 21 Sep 2025 20:38:57 +0200 Subject: [PATCH 57/99] docs: refine minimal example for AgentSet initialization --- docs/api/reference/agents/index.rst | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/docs/api/reference/agents/index.rst b/docs/api/reference/agents/index.rst index 549381fa..69287af9 100644 --- a/docs/api/reference/agents/index.rst +++ b/docs/api/reference/agents/index.rst @@ -21,6 +21,10 @@ Minimal example import polars as pl class MySet(AgentSet): + def __init__(self, model): + super().__init__(model) + self.add(pl.DataFrame({"age": [0, 5, 10]})) + def step(self): # vectorised update: increase age for all agents self.df = self.df.with_columns((pl.col("age") + 1).alias("age")) @@ -32,7 +36,6 @@ Minimal example self.sets += MySet(self) m = MyModel() - m.sets["MySet"].add(pl.DataFrame({"age": [0, 5, 10]})) # step all registered sets (delegates to each AgentSet.step) m.sets.do("step") From b7e437e66c01a577551f069a6362fd5f63a1914e Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Sun, 21 Sep 2025 20:40:24 +0200 Subject: [PATCH 58/99] docs: enhance minimal example for Model with updated AgentSet usage --- docs/api/reference/model.rst | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/docs/api/reference/model.rst b/docs/api/reference/model.rst index 099e601b..6ba3dc43 100644 --- a/docs/api/reference/model.rst +++ b/docs/api/reference/model.rst @@ -3,6 +3,40 @@ Model .. currentmodule:: mesa_frames +Quick intro +----------- + +`Model` orchestrates the simulation lifecycle: creating and registering `AgentSet`s, stepping the simulation, and integrating with `DataCollector` and spatial `Grid`s. Typical usage: + +- Instantiate `Model`, add `AgentSet` instances to `model.sets`. +- Call `model.sets.do('step')` inside your model loop to trigger set-level updates. +- Use `DataCollector` to sample model- and agent-level columns each step. + +Minimal example +--------------- + +.. code-block:: python + + from mesa_frames import Model, AgentSet, DataCollector + import polars as pl + + class People(AgentSet): + def step(self): + self._df = self._df.with_columns((pl.col("wealth") * 1.01).alias("wealth")) + + class MyModel(Model): + def __init__(self): + super().__init__() + self.sets += People(self) + self.dc = DataCollector(model_reporters={'avg_wealth': lambda m: m.sets.get('People')._df['wealth'].mean()}) + + m = MyModel() + m.sets.get('People').add(pl.DataFrame({'wealth': [100.0, 50.0]})) + m.step() + +API reference +------------- + .. autoclass:: Model :members: :inherited-members: From 631f3633255f06f05d43ee6c7e0295fb50cc929a Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Sun, 21 Sep 2025 20:40:57 +0200 Subject: [PATCH 59/99] docs: expand DataCollector documentation with detailed usage examples --- docs/api/reference/datacollector.rst | 34 ++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/docs/api/reference/datacollector.rst b/docs/api/reference/datacollector.rst index bdf38cfd..017bd18d 100644 --- a/docs/api/reference/datacollector.rst +++ b/docs/api/reference/datacollector.rst @@ -3,6 +3,40 @@ Data Collection .. currentmodule:: mesa_frames +Quick intro +----------- + +``DataCollector`` samples model- and agent-level columns over time and returns cleaned DataFrames suitable for analysis. Typical patterns: + +- Provide ``model_reporters`` (callables producing scalars) and ``agent_reporters`` (column selectors or callables that operate on an AgentSet). +- Call ``collector.collect(model)`` inside the model step or use built-in integration if the model calls the collector automatically. + +Minimal example +--------------- + +.. code-block:: python + + from mesa_frames import DataCollector, Model, AgentSet + import polars as pl + + class P(AgentSet): + def __init__(self, model): + super().__init__(model) + self.add(pl.DataFrame({'x': [1,2]})) + + class M(Model): + def __init__(self): + super().__init__() + self.sets += P(self) + self.dc = DataCollector(model_reporters={'count': lambda m: len(m.sets['P'])}, + agent_reporters='x') + + m = M() + m.dc.collect() + +API reference +------------- + .. autoclass:: DataCollector :members: :inherited-members: From 473e0d880f56d2a3366adef610193ded38f95d67 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Sun, 21 Sep 2025 20:42:02 +0200 Subject: [PATCH 60/99] docs: update Model documentation for improved clarity and examples --- docs/api/reference/model.rst | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/docs/api/reference/model.rst b/docs/api/reference/model.rst index 6ba3dc43..2b0b2102 100644 --- a/docs/api/reference/model.rst +++ b/docs/api/reference/model.rst @@ -6,11 +6,11 @@ Model Quick intro ----------- -`Model` orchestrates the simulation lifecycle: creating and registering `AgentSet`s, stepping the simulation, and integrating with `DataCollector` and spatial `Grid`s. Typical usage: +``Model`` orchestrates the simulation lifecycle: creating and registering ``AgentSet``s, stepping the simulation, and integrating with ``DataCollector`` and spatial ``Grid``s. Typical usage: -- Instantiate `Model`, add `AgentSet` instances to `model.sets`. -- Call `model.sets.do('step')` inside your model loop to trigger set-level updates. -- Use `DataCollector` to sample model- and agent-level columns each step. +- Instantiate ``Model``, add ``AgentSet`` instances to ``model.sets``. +- Call ``model.sets.do('step')`` inside your model loop to trigger set-level updates. +- Use ``DataCollector`` to sample model- and agent-level columns each step. Minimal example --------------- @@ -22,16 +22,15 @@ Minimal example class People(AgentSet): def step(self): - self._df = self._df.with_columns((pl.col("wealth") * 1.01).alias("wealth")) + self.add(pl.DataFrame({'wealth': [1, 2, 3]})) class MyModel(Model): def __init__(self): super().__init__() self.sets += People(self) - self.dc = DataCollector(model_reporters={'avg_wealth': lambda m: m.sets.get('People')._df['wealth'].mean()}) + self.dc = DataCollector(model_reporters={'avg_wealth': lambda m: m.sets['People'].df['wealth'].mean()}) m = MyModel() - m.sets.get('People').add(pl.DataFrame({'wealth': [100.0, 50.0]})) m.step() API reference From 439b6a6c40de254956eaec7c441a41136f1f8c27 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Sun, 21 Sep 2025 20:46:51 +0200 Subject: [PATCH 61/99] docs: enhance overview and examples for Grid usage in space reference --- docs/api/reference/space/index.rst | 35 ++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) diff --git a/docs/api/reference/space/index.rst b/docs/api/reference/space/index.rst index 8741b6b6..03763610 100644 --- a/docs/api/reference/space/index.rst +++ b/docs/api/reference/space/index.rst @@ -4,6 +4,41 @@ This page provides a high-level overview of possible space objects for mesa-fram .. currentmodule:: mesa_frames +Quick intro +----------- + + + +Currently we only support the ``Grid``. Typical usage: + +- Construct ``Grid(model, (width, height))`` and use ``place``/ ``move`` helpers to update agent positional columns. +- Use neighbourhood queries to produce masks or index lists and then apply vectorised updates to selected rows. + +Minimal example +--------------- + +.. code-block:: python + + from mesa_frames import Model, Grid, AgentSet + import polars as pl + + class P(AgentSet): + pass + + class M(Model): + def __init__(self): + super().__init__() + self.space = Grid(self, (10, 10)) + self.sets += P(self) + self.space.place_to_empty(self.sets) + + m = M() + m.space.move_to_available(m.sets) + + +API reference +------------- + .. autoclass:: Grid :members: :inherited-members: From 0c82492d4915c2e6344712c14e26c514e2402fd6 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sun, 21 Sep 2025 18:58:01 +0000 Subject: [PATCH 62/99] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- docs/api/index.rst | 2 +- docs/api/reference/agents/index.rst | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/api/index.rst b/docs/api/index.rst index f848c6f7..08b51f97 100644 --- a/docs/api/index.rst +++ b/docs/api/index.rst @@ -29,7 +29,7 @@ Mini usage flow :link-type: doc ``Model`` API for registering sets, stepping the simulation, and integrating with datacollectors/reporters. - + .. grid-item-card:: Spatial support :link: reference/space/index :link-type: doc diff --git a/docs/api/reference/agents/index.rst b/docs/api/reference/agents/index.rst index 69287af9..082be02c 100644 --- a/docs/api/reference/agents/index.rst +++ b/docs/api/reference/agents/index.rst @@ -6,9 +6,9 @@ Agents Quick intro ----------- -- ``AgentSet`` stores agents as rows in a Polars-backed table and provides vectorised operations for high-performance updates. +- ``AgentSet`` stores agents as rows in a Polars-backed table and provides vectorised operations for high-performance updates. -- ``AgentSetRegistry`` (available at ``model.sets``) is the container that holds all ``AgentSet`` instances for a model and provides convenience operations (add/remove sets, step all sets, rename). +- ``AgentSetRegistry`` (available at ``model.sets``) is the container that holds all ``AgentSet`` instances for a model and provides convenience operations (add/remove sets, step all sets, rename). - Keep agent logic column-oriented and prefer Polars expressions for updates. From 82bcc78eb13fcb8421f299635c569ecd5ad5ea10 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Sun, 21 Sep 2025 21:06:10 +0200 Subject: [PATCH 63/99] docs: add custom branding CSS and JS files to documentation --- docs/api/_static/mesa_brand.css | 128 ++++++++++++++++++++++++++++++++ docs/api/_static/mesa_brand.js | 43 +++++++++++ docs/api/conf.py | 9 +++ 3 files changed, 180 insertions(+) create mode 100644 docs/api/_static/mesa_brand.css create mode 100644 docs/api/_static/mesa_brand.js diff --git a/docs/api/_static/mesa_brand.css b/docs/api/_static/mesa_brand.css new file mode 100644 index 00000000..0a4d9fc0 --- /dev/null +++ b/docs/api/_static/mesa_brand.css @@ -0,0 +1,128 @@ +/* Mesa Frames branding overrides for pydata_sphinx_theme + - Defines CSS variables for light/dark modes + - Hero gradient, navbar contrast, CTA, code/table, badges, admonition styles +*/ +:root{ + /* Brand colors */ + --mesa-primary: #a6c1dd; /* primary actions */ + --mesa-surface: #c7d9ec; /* background panels */ + --mesa-dark: #060808; /* text / dark accents */ + + /* Derived tokens */ + --mesa-primary-text: var(--mesa-dark); + --mesa-surface-contrast: rgba(6,8,8,0.9); + --mesa-shadow: rgba(6,8,8,0.12); +} + +/* Dark mode variables - applied when document has data-mode="dark" or .theme-dark */ +:root[data-mode="dark"], .theme-dark { + --mesa-background: var(--mesa-dark); + --mesa-surface: #0d1213; /* desaturated charcoal for surfaces in dark mode */ + --mesa-primary: #a6c1dd; /* keep accent */ + --mesa-primary-text: #ffffff; + --mesa-surface-contrast: rgba(166,193,221,0.12); + --mesa-shadow: rgba(0,0,0,0.6); +} + +/* Hero gradient behind top-of-page header */ +.pydata-header { + background: linear-gradient(135deg, var(--mesa-surface) 0%, var(--mesa-primary) 100%); + color: var(--mesa-dark); +} + +/* Navbar contrast and hover states */ +.pydata-navbar, .navbar { + background-color: var(--mesa-dark) !important; + color: var(--mesa-primary) !important; +} +.pydata-navbar a.nav-link, .navbar a.nav-link, .pydata-navbar .navbar-brand { + color: var(--mesa-primary) !important; +} +.pydata-navbar a.nav-link:hover, .navbar a.nav-link:hover { + background-color: rgba(199,217,236,0.07); + color: var(--mesa-surface-contrast) !important; +} + +/* Transparent overlay for nav items on hover */ +.pydata-navbar .nav-link:hover::after, .navbar .nav-link:hover::after{ + content: ""; + position: absolute; + inset: 0; + background: rgba(6,8,8,0.15); + border-radius: 6px; +} + +/* CTA buttons using sphinx-design components */ +.sd-button, .sd-button .sd-button--primary, .sd-btn, .sphinx-button { + border-radius: 10px; + box-shadow: 0 6px 18px var(--mesa-shadow); +} + +/* Primary CTA: dark text on surface */ +.btn-mesa-primary, .sd-button--mesa-primary { + background: var(--mesa-surface) !important; + color: var(--mesa-primary-text) !important; + border: 1px solid rgba(6,8,8,0.06); +} +/* Secondary CTA: inverted */ +.btn-mesa-secondary, .sd-button--mesa-secondary { + background: var(--mesa-dark) !important; + color: #fff !important; + border: 1px solid rgba(166,193,221,0.06); +} + +/* Add small white SVG icon space inside CTA */ +.btn-mesa-primary svg, .btn-mesa-secondary svg { + width: 18px; height: 18px; vertical-align: middle; margin-right: 8px; fill: #fff; +} + +/* Cards and tiles */ +.sd-card, .card, .sphinx-design-card { + border-radius: 12px; + background: var(--mesa-surface); + color: var(--mesa-dark); + box-shadow: 0 8px 20px var(--mesa-shadow); +} + +/* Code block and table legibility */ +.highlight, .literal-block, pre, .py, code { + background-color: rgba(199,217,236,0.18); /* light tint */ + border-radius: 8px; + padding: 0.6rem 0.9rem; + color: var(--mesa-dark); +} +:root[data-mode="dark"] .highlight, .theme-dark .highlight, :root[data-mode="dark"] pre, .theme-dark pre { + background-color: #111516; /* desaturated charcoal */ + color: #e6eef6; +} + +/* Highlight keywords with medium blue to align syntax */ +.highlight .k, .highlight .kn, .highlight .c1, .highlight .gp { color: var(--mesa-primary) !important; } + +/* Badges and pill links */ +.mesa-badge { + display: inline-block; + padding: 0.15rem 0.6rem; + border-radius: 999px; + background: var(--mesa-dark); + color: var(--mesa-primary); + font-weight: 600; + box-shadow: 0 4px 10px rgba(6,8,8,0.12); +} + +/* Admonitions / callouts */ +.admonition { + border-left: 4px solid rgba(6,8,8,0.12); + background: linear-gradient(180deg, rgba(199,217,236,0.06), rgba(166,193,221,0.02)); + border-radius: 8px; + padding: 0.8rem 1rem; +} +.admonition.note { background-color: rgba(199,217,236,0.06); } +.admonition.tip { background-color: rgba(166,193,221,0.04); } +.admonition.warning { background-color: rgba(255,230,120,0.04); border-left-color: rgba(255,170,0,0.8); } + +/* Small responsive tweaks */ +@media (max-width: 720px){ + .pydata-header { padding: 1rem 0; } + .sd-card, .card { margin-bottom: 0.75rem; } +} diff --git a/docs/api/_static/mesa_brand.js b/docs/api/_static/mesa_brand.js new file mode 100644 index 00000000..b1e18f81 --- /dev/null +++ b/docs/api/_static/mesa_brand.js @@ -0,0 +1,43 @@ +// Small script to add a theme toggle to the navbar and integrate with pydata theme +(function(){ + function createToggle(){ + try{ + var btn = document.createElement('button'); + btn.className = 'theme-switch-button btn btn-sm'; + btn.type = 'button'; + btn.title = 'Toggle theme'; + btn.setAttribute('aria-label','Toggle theme'); + btn.innerHTML = ''; + var container = document.querySelector('.navbar-icon-links') || document.querySelector('.bd-navbar-elements') || document.querySelector('.navbar .navbar-nav') || document.querySelector('.pydata-navbar .navbar-nav'); + if(container){ + var li = document.createElement('li'); + li.className = 'nav-item'; + var a = document.createElement('a'); + a.className = 'nav-link'; + a.href = '#'; + a.appendChild(btn); + li.appendChild(a); + // insert at the end of the list so we don't disrupt other items + container.appendChild(li); + + btn.addEventListener('click', function(e){ + e.preventDefault(); + // Try to reuse pydata theme switch if available + try{ + // cycleMode function may be defined by pydata theme; call if present + if(typeof cycleMode === 'function'){ + cycleMode(); + return; + } + // fallback: toggle data-mode between dark and light and persist + var current = document.documentElement.getAttribute('data-mode') || ''; + var next = (current === 'dark') ? 'light' : 'dark'; + document.documentElement.setAttribute('data-mode', next); + document.documentElement.dataset.mode = next; + try{ localStorage.setItem('mode', next); }catch(e){} + }catch(err){ console.warn('Theme toggle failed', err);} + }); + } + }catch(e){console.warn('mesa_brand.js init fail',e);} } + document.addEventListener('DOMContentLoaded', createToggle); +})(); diff --git a/docs/api/conf.py b/docs/api/conf.py index 43098ec2..924b0644 100644 --- a/docs/api/conf.py +++ b/docs/api/conf.py @@ -35,6 +35,15 @@ html_static_path = ["_static"] html_show_sourcelink = False +# Add custom branding CSS/JS (mesa_brand) to static files +html_css_files = [ + "mesa_brand.css", +] + +html_js_files = [ + "mesa_brand.js", +] + # -- Extension settings ------------------------------------------------------ # intersphinx mapping intersphinx_mapping = { From fb3bdc4b6f364800194cad07c1288b8899747735 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Sun, 21 Sep 2025 21:11:53 +0200 Subject: [PATCH 64/99] docs: remove mesa_brand.js and update theme switcher integration in conf.py --- docs/api/_static/mesa_brand.js | 43 ---------------------------------- docs/api/conf.py | 6 +---- 2 files changed, 1 insertion(+), 48 deletions(-) delete mode 100644 docs/api/_static/mesa_brand.js diff --git a/docs/api/_static/mesa_brand.js b/docs/api/_static/mesa_brand.js deleted file mode 100644 index b1e18f81..00000000 --- a/docs/api/_static/mesa_brand.js +++ /dev/null @@ -1,43 +0,0 @@ -// Small script to add a theme toggle to the navbar and integrate with pydata theme -(function(){ - function createToggle(){ - try{ - var btn = document.createElement('button'); - btn.className = 'theme-switch-button btn btn-sm'; - btn.type = 'button'; - btn.title = 'Toggle theme'; - btn.setAttribute('aria-label','Toggle theme'); - btn.innerHTML = ''; - var container = document.querySelector('.navbar-icon-links') || document.querySelector('.bd-navbar-elements') || document.querySelector('.navbar .navbar-nav') || document.querySelector('.pydata-navbar .navbar-nav'); - if(container){ - var li = document.createElement('li'); - li.className = 'nav-item'; - var a = document.createElement('a'); - a.className = 'nav-link'; - a.href = '#'; - a.appendChild(btn); - li.appendChild(a); - // insert at the end of the list so we don't disrupt other items - container.appendChild(li); - - btn.addEventListener('click', function(e){ - e.preventDefault(); - // Try to reuse pydata theme switch if available - try{ - // cycleMode function may be defined by pydata theme; call if present - if(typeof cycleMode === 'function'){ - cycleMode(); - return; - } - // fallback: toggle data-mode between dark and light and persist - var current = document.documentElement.getAttribute('data-mode') || ''; - var next = (current === 'dark') ? 'light' : 'dark'; - document.documentElement.setAttribute('data-mode', next); - document.documentElement.dataset.mode = next; - try{ localStorage.setItem('mode', next); }catch(e){} - }catch(err){ console.warn('Theme toggle failed', err);} - }); - } - }catch(e){console.warn('mesa_brand.js init fail',e);} } - document.addEventListener('DOMContentLoaded', createToggle); -})(); diff --git a/docs/api/conf.py b/docs/api/conf.py index 924b0644..4047d320 100644 --- a/docs/api/conf.py +++ b/docs/api/conf.py @@ -40,10 +40,6 @@ "mesa_brand.css", ] -html_js_files = [ - "mesa_brand.js", -] - # -- Extension settings ------------------------------------------------------ # intersphinx mapping intersphinx_mapping = { @@ -83,5 +79,5 @@ "icon": "fa-brands fa-github", }, ], - "navbar_end": ["navbar-icon-links"], + "navbar_end": ["theme-switcher", "navbar-icon-links"], } From 46c0fd37124c976fc86e0cdbffee11a447b47619 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Sun, 21 Sep 2025 21:12:04 +0200 Subject: [PATCH 65/99] docs: update .gitignore to exclude site and API build directories --- .gitignore | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 41ce8f27..198af709 100644 --- a/.gitignore +++ b/.gitignore @@ -154,4 +154,5 @@ cython_debug/ *.code-workspace llm_rules.md .python-version -docs/site \ No newline at end of file +docs/site +docs/api/_build \ No newline at end of file From bcc055f28c397c18b93592f99911635eb54830c8 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Sun, 21 Sep 2025 21:42:34 +0200 Subject: [PATCH 66/99] docs: add brand variables and theme adapters for improved styling --- docs/api/_static/brand-core.css | 9 ++ docs/api/_static/brand-pydata.css | 19 +++++ docs/api/_static/mesa_brand.css | 128 ---------------------------- docs/api/conf.py | 4 +- docs/stylesheets/brand-material.css | 18 ++++ mkdocs.yml | 5 ++ 6 files changed, 54 insertions(+), 129 deletions(-) create mode 100644 docs/api/_static/brand-core.css create mode 100644 docs/api/_static/brand-pydata.css delete mode 100644 docs/api/_static/mesa_brand.css create mode 100644 docs/stylesheets/brand-material.css diff --git a/docs/api/_static/brand-core.css b/docs/api/_static/brand-core.css new file mode 100644 index 00000000..32cb4906 --- /dev/null +++ b/docs/api/_static/brand-core.css @@ -0,0 +1,9 @@ +/* Mesa-Frames shared brand variables (core) */ +:root{ + /* Brand palette */ + --mf-primary: #A6C1DD; + --mf-surface: #C7D9EC; + --mf-dark: #060808; + --mf-fg-dark: #0F1113; + --mf-fg-light: #E8EEF6; +} diff --git a/docs/api/_static/brand-pydata.css b/docs/api/_static/brand-pydata.css new file mode 100644 index 00000000..c9039617 --- /dev/null +++ b/docs/api/_static/brand-pydata.css @@ -0,0 +1,19 @@ +/* PyData theme adapter: maps Mesa-Frames brand variables to pydata tokens */ +:root{ + --pst-color-primary: var(--mf-primary); + --pst-content-max-width: 1100px; +} + +:root[data-mode="dark"]{ + --pst-color-background: var(--mf-dark); + --pst-color-on-background: var(--mf-fg-light); + --pst-color-surface: #111516; +} + +/* Optional gentle polish */ +.bd-header{ + background: linear-gradient(135deg, var(--mf-surface), var(--mf-primary)); +} +.card, .sd-card, .sphinx-design-card{ border-radius:12px; box-shadow:0 4px 16px rgba(0,0,0,.06) } +:root[data-mode="dark"] .card, :root[data-mode="dark"] .sd-card{ box-shadow:0 6px 22px rgba(0,0,0,.45) } +pre, .highlight{ border-radius:8px } diff --git a/docs/api/_static/mesa_brand.css b/docs/api/_static/mesa_brand.css deleted file mode 100644 index 0a4d9fc0..00000000 --- a/docs/api/_static/mesa_brand.css +++ /dev/null @@ -1,128 +0,0 @@ -/* Mesa Frames branding overrides for pydata_sphinx_theme - - Defines CSS variables for light/dark modes - - Hero gradient, navbar contrast, CTA, code/table, badges, admonition styles -*/ -:root{ - /* Brand colors */ - --mesa-primary: #a6c1dd; /* primary actions */ - --mesa-surface: #c7d9ec; /* background panels */ - --mesa-dark: #060808; /* text / dark accents */ - - /* Derived tokens */ - --mesa-primary-text: var(--mesa-dark); - --mesa-surface-contrast: rgba(6,8,8,0.9); - --mesa-shadow: rgba(6,8,8,0.12); -} - -/* Dark mode variables - applied when document has data-mode="dark" or .theme-dark */ -:root[data-mode="dark"], .theme-dark { - --mesa-background: var(--mesa-dark); - --mesa-surface: #0d1213; /* desaturated charcoal for surfaces in dark mode */ - --mesa-primary: #a6c1dd; /* keep accent */ - --mesa-primary-text: #ffffff; - --mesa-surface-contrast: rgba(166,193,221,0.12); - --mesa-shadow: rgba(0,0,0,0.6); -} - -/* Hero gradient behind top-of-page header */ -.pydata-header { - background: linear-gradient(135deg, var(--mesa-surface) 0%, var(--mesa-primary) 100%); - color: var(--mesa-dark); -} - -/* Navbar contrast and hover states */ -.pydata-navbar, .navbar { - background-color: var(--mesa-dark) !important; - color: var(--mesa-primary) !important; -} -.pydata-navbar a.nav-link, .navbar a.nav-link, .pydata-navbar .navbar-brand { - color: var(--mesa-primary) !important; -} -.pydata-navbar a.nav-link:hover, .navbar a.nav-link:hover { - background-color: rgba(199,217,236,0.07); - color: var(--mesa-surface-contrast) !important; -} - -/* Transparent overlay for nav items on hover */ -.pydata-navbar .nav-link:hover::after, .navbar .nav-link:hover::after{ - content: ""; - position: absolute; - inset: 0; - background: rgba(6,8,8,0.15); - border-radius: 6px; -} - -/* CTA buttons using sphinx-design components */ -.sd-button, .sd-button .sd-button--primary, .sd-btn, .sphinx-button { - border-radius: 10px; - box-shadow: 0 6px 18px var(--mesa-shadow); -} - -/* Primary CTA: dark text on surface */ -.btn-mesa-primary, .sd-button--mesa-primary { - background: var(--mesa-surface) !important; - color: var(--mesa-primary-text) !important; - border: 1px solid rgba(6,8,8,0.06); -} -/* Secondary CTA: inverted */ -.btn-mesa-secondary, .sd-button--mesa-secondary { - background: var(--mesa-dark) !important; - color: #fff !important; - border: 1px solid rgba(166,193,221,0.06); -} - -/* Add small white SVG icon space inside CTA */ -.btn-mesa-primary svg, .btn-mesa-secondary svg { - width: 18px; height: 18px; vertical-align: middle; margin-right: 8px; fill: #fff; -} - -/* Cards and tiles */ -.sd-card, .card, .sphinx-design-card { - border-radius: 12px; - background: var(--mesa-surface); - color: var(--mesa-dark); - box-shadow: 0 8px 20px var(--mesa-shadow); -} - -/* Code block and table legibility */ -.highlight, .literal-block, pre, .py, code { - background-color: rgba(199,217,236,0.18); /* light tint */ - border-radius: 8px; - padding: 0.6rem 0.9rem; - color: var(--mesa-dark); -} -:root[data-mode="dark"] .highlight, .theme-dark .highlight, :root[data-mode="dark"] pre, .theme-dark pre { - background-color: #111516; /* desaturated charcoal */ - color: #e6eef6; -} - -/* Highlight keywords with medium blue to align syntax */ -.highlight .k, .highlight .kn, .highlight .c1, .highlight .gp { color: var(--mesa-primary) !important; } - -/* Badges and pill links */ -.mesa-badge { - display: inline-block; - padding: 0.15rem 0.6rem; - border-radius: 999px; - background: var(--mesa-dark); - color: var(--mesa-primary); - font-weight: 600; - box-shadow: 0 4px 10px rgba(6,8,8,0.12); -} - -/* Admonitions / callouts */ -.admonition { - border-left: 4px solid rgba(6,8,8,0.12); - background: linear-gradient(180deg, rgba(199,217,236,0.06), rgba(166,193,221,0.02)); - border-radius: 8px; - padding: 0.8rem 1rem; -} -.admonition.note { background-color: rgba(199,217,236,0.06); } -.admonition.tip { background-color: rgba(166,193,221,0.04); } -.admonition.warning { background-color: rgba(255,230,120,0.04); border-left-color: rgba(255,170,0,0.8); } - -/* Small responsive tweaks */ -@media (max-width: 720px){ - .pydata-header { padding: 1rem 0; } - .sd-card, .card { margin-bottom: 0.75rem; } -} diff --git a/docs/api/conf.py b/docs/api/conf.py index 4047d320..418b4d23 100644 --- a/docs/api/conf.py +++ b/docs/api/conf.py @@ -37,7 +37,9 @@ # Add custom branding CSS/JS (mesa_brand) to static files html_css_files = [ - "mesa_brand.css", + # Shared brand variables then theme adapter for pydata + "brand-core.css", + "brand-pydata.css", ] # -- Extension settings ------------------------------------------------------ diff --git a/docs/stylesheets/brand-material.css b/docs/stylesheets/brand-material.css new file mode 100644 index 00000000..d6623334 --- /dev/null +++ b/docs/stylesheets/brand-material.css @@ -0,0 +1,18 @@ +/* Material theme adapter: maps Mesa-Frames brand variables to Material tokens */ +/* Light scheme */ +:root{ + --md-primary-fg-color: var(--mf-primary); + --md-primary-fg-color--light: #D7E3F2; + --md-primary-fg-color--dark: #6F92B5; +} + +/* Dark scheme (slate) */ +[data-md-color-scheme="slate"]{ + --md-default-bg-color: var(--mf-dark); + --md-default-fg-color: var(--mf-fg-light); + --md-primary-fg-color: var(--mf-primary); + --md-code-bg-color: #111516; +} + +/* Optional: soft hero tint */ +.md-header { background: linear-gradient(135deg, var(--mf-surface), var(--mf-primary)); } diff --git a/mkdocs.yml b/mkdocs.yml index 331165b5..f8ae79dd 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -96,6 +96,11 @@ extra_javascript: - https://polyfill.io/v3/polyfill.min.js?features=es6 - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js +# Custom CSS for branding (brand-core then material adapter) +extra_css: + - stylesheets/brand-core.css + - stylesheets/brand-material.css + # Customization extra: social: From f0ee97475046314516e2d89e2424cb134bd69864 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Sun, 21 Sep 2025 21:57:52 +0200 Subject: [PATCH 67/99] docs: remove obsolete brand CSS files for cleaner styling integration --- docs/api/_static/brand-core.css | 9 --------- docs/api/_static/brand-pydata.css | 19 ------------------- docs/stylesheets/brand-material.css | 18 ------------------ 3 files changed, 46 deletions(-) diff --git a/docs/api/_static/brand-core.css b/docs/api/_static/brand-core.css index 32cb4906..e69de29b 100644 --- a/docs/api/_static/brand-core.css +++ b/docs/api/_static/brand-core.css @@ -1,9 +0,0 @@ -/* Mesa-Frames shared brand variables (core) */ -:root{ - /* Brand palette */ - --mf-primary: #A6C1DD; - --mf-surface: #C7D9EC; - --mf-dark: #060808; - --mf-fg-dark: #0F1113; - --mf-fg-light: #E8EEF6; -} diff --git a/docs/api/_static/brand-pydata.css b/docs/api/_static/brand-pydata.css index c9039617..e69de29b 100644 --- a/docs/api/_static/brand-pydata.css +++ b/docs/api/_static/brand-pydata.css @@ -1,19 +0,0 @@ -/* PyData theme adapter: maps Mesa-Frames brand variables to pydata tokens */ -:root{ - --pst-color-primary: var(--mf-primary); - --pst-content-max-width: 1100px; -} - -:root[data-mode="dark"]{ - --pst-color-background: var(--mf-dark); - --pst-color-on-background: var(--mf-fg-light); - --pst-color-surface: #111516; -} - -/* Optional gentle polish */ -.bd-header{ - background: linear-gradient(135deg, var(--mf-surface), var(--mf-primary)); -} -.card, .sd-card, .sphinx-design-card{ border-radius:12px; box-shadow:0 4px 16px rgba(0,0,0,.06) } -:root[data-mode="dark"] .card, :root[data-mode="dark"] .sd-card{ box-shadow:0 6px 22px rgba(0,0,0,.45) } -pre, .highlight{ border-radius:8px } diff --git a/docs/stylesheets/brand-material.css b/docs/stylesheets/brand-material.css index d6623334..e69de29b 100644 --- a/docs/stylesheets/brand-material.css +++ b/docs/stylesheets/brand-material.css @@ -1,18 +0,0 @@ -/* Material theme adapter: maps Mesa-Frames brand variables to Material tokens */ -/* Light scheme */ -:root{ - --md-primary-fg-color: var(--mf-primary); - --md-primary-fg-color--light: #D7E3F2; - --md-primary-fg-color--dark: #6F92B5; -} - -/* Dark scheme (slate) */ -[data-md-color-scheme="slate"]{ - --md-default-bg-color: var(--mf-dark); - --md-default-fg-color: var(--mf-fg-light); - --md-primary-fg-color: var(--mf-primary); - --md-code-bg-color: #111516; -} - -/* Optional: soft hero tint */ -.md-header { background: linear-gradient(135deg, var(--mf-surface), var(--mf-primary)); } From 03913525ad9d23841e9d973af4b6cd28965af2a9 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Sun, 21 Sep 2025 22:13:26 +0200 Subject: [PATCH 68/99] .gitignore --- .gitignore | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 198af709..ca2da040 100644 --- a/.gitignore +++ b/.gitignore @@ -155,4 +155,6 @@ cython_debug/ llm_rules.md .python-version docs/site -docs/api/_build \ No newline at end of file +docs/api/_build +docs/general/user-guide/data_csv +docs/general/user-guide/data_parquet From f11b62f199ecf161459b176337b77395d959501d Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Sun, 21 Sep 2025 22:53:09 +0200 Subject: [PATCH 69/99] docs: update navigation structure in conf.py and index.rst for improved accessibility --- docs/api/conf.py | 4 ++++ docs/api/index.rst | 12 ++++++++++++ 2 files changed, 16 insertions(+) diff --git a/docs/api/conf.py b/docs/api/conf.py index 418b4d23..6223a36b 100644 --- a/docs/api/conf.py +++ b/docs/api/conf.py @@ -81,5 +81,9 @@ "icon": "fa-brands fa-github", }, ], + "navbar_start": ["navbar-logo"] + + , + "navbar_end": ["theme-switcher", "navbar-icon-links"], } diff --git a/docs/api/index.rst b/docs/api/index.rst index f848c6f7..630e6e43 100644 --- a/docs/api/index.rst +++ b/docs/api/index.rst @@ -1,6 +1,18 @@ mesa-frames API =============== +.. toctree:: + :caption: Shortcuts + :maxdepth: 1 + :hidden: + + reference/agents/index + reference/model + reference/space/index + reference/datacollector + + + Overview -------- From e9c982d51753af7206b0a6dc54190ef36ca638ae Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Sun, 21 Sep 2025 23:06:08 +0200 Subject: [PATCH 70/99] docs: clean up navbar configuration and fix formatting in API documentation --- docs/api/conf.py | 5 +---- docs/api/index.rst | 2 +- docs/api/reference/agents/index.rst | 4 ++-- 3 files changed, 4 insertions(+), 7 deletions(-) diff --git a/docs/api/conf.py b/docs/api/conf.py index 6223a36b..15512cd6 100644 --- a/docs/api/conf.py +++ b/docs/api/conf.py @@ -81,9 +81,6 @@ "icon": "fa-brands fa-github", }, ], - "navbar_start": ["navbar-logo"] - - , - + "navbar_start": ["navbar-logo"], "navbar_end": ["theme-switcher", "navbar-icon-links"], } diff --git a/docs/api/index.rst b/docs/api/index.rst index 630e6e43..a7c2ab4c 100644 --- a/docs/api/index.rst +++ b/docs/api/index.rst @@ -41,7 +41,7 @@ Mini usage flow :link-type: doc ``Model`` API for registering sets, stepping the simulation, and integrating with datacollectors/reporters. - + .. grid-item-card:: Spatial support :link: reference/space/index :link-type: doc diff --git a/docs/api/reference/agents/index.rst b/docs/api/reference/agents/index.rst index 69287af9..082be02c 100644 --- a/docs/api/reference/agents/index.rst +++ b/docs/api/reference/agents/index.rst @@ -6,9 +6,9 @@ Agents Quick intro ----------- -- ``AgentSet`` stores agents as rows in a Polars-backed table and provides vectorised operations for high-performance updates. +- ``AgentSet`` stores agents as rows in a Polars-backed table and provides vectorised operations for high-performance updates. -- ``AgentSetRegistry`` (available at ``model.sets``) is the container that holds all ``AgentSet`` instances for a model and provides convenience operations (add/remove sets, step all sets, rename). +- ``AgentSetRegistry`` (available at ``model.sets``) is the container that holds all ``AgentSet`` instances for a model and provides convenience operations (add/remove sets, step all sets, rename). - Keep agent logic column-oriented and prefer Polars expressions for updates. From 620ece950ebd1d76ea31a34572593c7e14f0b53f Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Mon, 22 Sep 2025 14:34:44 +0200 Subject: [PATCH 71/99] docs: update TOC settings and enhance autodoc options for better documentation clarity --- docs/api/conf.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/docs/api/conf.py b/docs/api/conf.py index 15512cd6..8162bd67 100644 --- a/docs/api/conf.py +++ b/docs/api/conf.py @@ -31,6 +31,10 @@ exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] # -- Options for HTML output ------------------------------------------------- +# Hide objects (classes/methods) from the page Table of Contents +toc_object_entries = False # NEW: stop adding class/method entries to the TOC + + html_theme = "pydata_sphinx_theme" html_static_path = ["_static"] html_show_sourcelink = False @@ -59,9 +63,18 @@ copybutton_prompt_is_regexp = True # -- Custom configurations --------------------------------------------------- +add_module_names = False autoclass_content = "class" autodoc_member_order = "bysource" -autodoc_default_options = {"special-members": True, "exclude-members": "__weakref__"} +autodoc_default_options = { + "members": True, + "inherited-members": True, + "undoc-members": True, + "member-order": "bysource", + "special-members": True, + "exclude-members": "__weakref__,__dict__,__module__,__annotations__", +} + # -- GitHub link and user guide settings ------------------------------------- github_root = "https://github.com/projectmesa/mesa-frames" From 0ec7fe44eec67897fb5a5a300475e56c2dcaf190 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Mon, 22 Sep 2025 14:35:08 +0200 Subject: [PATCH 72/99] docs: restructure API reference for AgentSet and AgentSetRegistry with detailed autosummary sections --- docs/api/reference/agents/index.rst | 153 +++++++++++++++++++++++++--- 1 file changed, 140 insertions(+), 13 deletions(-) diff --git a/docs/api/reference/agents/index.rst b/docs/api/reference/agents/index.rst index 082be02c..9a620dea 100644 --- a/docs/api/reference/agents/index.rst +++ b/docs/api/reference/agents/index.rst @@ -40,16 +40,143 @@ Minimal example m.sets.do("step") API reference --------------------------------- - -.. autoclass:: AgentSet - :members: - :inherited-members: - :autosummary: - :autosummary-nosignatures: - -.. autoclass:: AgentSetRegistry - :members: - :inherited-members: - :autosummary: - :autosummary-nosignatures: +--------------------------------- + +.. tab-set:: + + .. tab-item:: AgentSet + + .. tab-set:: + + .. tab-item:: Overview + + .. rubric:: Lifecycle / Core + + .. autosummary:: + :nosignatures: + :toctree: _autosummary + + AgentSet.__init__ + AgentSet.step + AgentSet.rename + AgentSet.copy + + .. rubric:: Accessors & Views + + .. autosummary:: + :nosignatures: + :toctree: _autosummary + + AgentSet.df + AgentSet.active_agents + AgentSet.inactive_agents + AgentSet.index + AgentSet.pos + AgentSet.name + AgentSet.get + AgentSet.contains + AgentSet.__len__ + AgentSet.__iter__ + AgentSet.__getitem__ + AgentSet.__contains__ + + .. rubric:: Mutators + + .. autosummary:: + :nosignatures: + :toctree: _autosummary + + AgentSet.add + AgentSet.remove + AgentSet.discard + AgentSet.set + AgentSet.select + AgentSet.shuffle + AgentSet.sort + AgentSet.do + + .. rubric:: Operators / Internal helpers + + .. autosummary:: + :nosignatures: + :toctree: _autosummary + + AgentSet.__add__ + AgentSet.__iadd__ + AgentSet.__sub__ + AgentSet.__isub__ + AgentSet.__repr__ + AgentSet.__reversed__ + + .. tab-item:: Full API + + .. autoclass:: AgentSet + + .. tab-item:: AgentSetRegistry + + .. tab-set:: + + .. tab-item:: Overview + + .. rubric:: Lifecycle / Core + + .. autosummary:: + :nosignatures: + :toctree: _autosummary + + AgentSetRegistry.__init__ + AgentSetRegistry.copy + AgentSetRegistry.rename + + .. rubric:: Accessors & Queries + + .. autosummary:: + :nosignatures: + :toctree: _autosummary + + AgentSetRegistry.get + AgentSetRegistry.contains + AgentSetRegistry.ids + AgentSetRegistry.keys + AgentSetRegistry.items + AgentSetRegistry.values + AgentSetRegistry.model + AgentSetRegistry.random + AgentSetRegistry.space + AgentSetRegistry.__len__ + AgentSetRegistry.__iter__ + AgentSetRegistry.__getitem__ + AgentSetRegistry.__contains__ + + .. rubric:: Mutators / Coordination + + .. autosummary:: + :nosignatures: + :toctree: _autosummary + + AgentSetRegistry.add + AgentSetRegistry.remove + AgentSetRegistry.discard + AgentSetRegistry.replace + AgentSetRegistry.shuffle + AgentSetRegistry.sort + AgentSetRegistry.do + AgentSetRegistry.__setitem__ + AgentSetRegistry.__add__ + AgentSetRegistry.__iadd__ + AgentSetRegistry.__sub__ + AgentSetRegistry.__isub__ + + .. rubric:: Representation + + .. autosummary:: + :nosignatures: + :toctree: _autosummary + + AgentSetRegistry.__repr__ + AgentSetRegistry.__str__ + AgentSetRegistry.__reversed__ + + .. tab-item:: Full API + + .. autoclass:: AgentSetRegistry \ No newline at end of file From 5927c609f67629c11da5db637347ffa2b1720b56 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Mon, 22 Sep 2025 15:01:05 +0200 Subject: [PATCH 73/99] docs: add mesa_frames RST files to .gitignore to prevent tracking --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index ca2da040..ca0ad990 100644 --- a/.gitignore +++ b/.gitignore @@ -158,3 +158,4 @@ docs/site docs/api/_build docs/general/user-guide/data_csv docs/general/user-guide/data_parquet +docs/api/reference/**/mesa_frames.*.rst \ No newline at end of file From 66dbd5edd1a1bf92061e84abbb84d60f2465c8ba Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Mon, 22 Sep 2025 15:01:18 +0200 Subject: [PATCH 74/99] docs: update autosummary toctree settings for AgentSet and AgentSetRegistry --- docs/api/reference/agents/index.rst | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/api/reference/agents/index.rst b/docs/api/reference/agents/index.rst index 9a620dea..b7ed147c 100644 --- a/docs/api/reference/agents/index.rst +++ b/docs/api/reference/agents/index.rst @@ -54,7 +54,7 @@ API reference .. autosummary:: :nosignatures: - :toctree: _autosummary + :toctree: AgentSet.__init__ AgentSet.step @@ -65,7 +65,7 @@ API reference .. autosummary:: :nosignatures: - :toctree: _autosummary + :toctree: AgentSet.df AgentSet.active_agents @@ -84,7 +84,7 @@ API reference .. autosummary:: :nosignatures: - :toctree: _autosummary + :toctree: AgentSet.add AgentSet.remove @@ -99,7 +99,7 @@ API reference .. autosummary:: :nosignatures: - :toctree: _autosummary + :toctree: AgentSet.__add__ AgentSet.__iadd__ @@ -122,7 +122,7 @@ API reference .. autosummary:: :nosignatures: - :toctree: _autosummary + :toctree: AgentSetRegistry.__init__ AgentSetRegistry.copy @@ -132,7 +132,7 @@ API reference .. autosummary:: :nosignatures: - :toctree: _autosummary + :toctree: AgentSetRegistry.get AgentSetRegistry.contains @@ -152,7 +152,7 @@ API reference .. autosummary:: :nosignatures: - :toctree: _autosummary + :toctree: AgentSetRegistry.add AgentSetRegistry.remove @@ -171,7 +171,7 @@ API reference .. autosummary:: :nosignatures: - :toctree: _autosummary + :toctree: AgentSetRegistry.__repr__ AgentSetRegistry.__str__ From 8373dbebf69d2293668091d053e2c8d851689f2e Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Mon, 22 Sep 2025 15:16:17 +0200 Subject: [PATCH 75/99] docs: enhance API documentation structure with tabs and autosummary for better clarity --- docs/api/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/api/conf.py b/docs/api/conf.py index 8162bd67..61d34eb4 100644 --- a/docs/api/conf.py +++ b/docs/api/conf.py @@ -72,7 +72,7 @@ "undoc-members": True, "member-order": "bysource", "special-members": True, - "exclude-members": "__weakref__,__dict__,__module__,__annotations__", + "exclude-members": "__weakref__,__dict__,__module__,__annotations__,__firstlineno__,__static_attributes__,__abstractmethods__,__slots__" } From 5b76c0ad05f321c04292b62c9ace9853b5d0c6de Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Mon, 22 Sep 2025 15:16:56 +0200 Subject: [PATCH 76/99] docs: enhance API reference structure with tabs and autosummary for improved clarity --- docs/api/reference/agents/index.rst | 6 ++++- docs/api/reference/datacollector.rst | 34 ++++++++++++++++++++++---- docs/api/reference/model.rst | 36 ++++++++++++++++++++++++---- docs/api/reference/space/index.rst | 30 +++++++++++++++++++---- 4 files changed, 90 insertions(+), 16 deletions(-) diff --git a/docs/api/reference/agents/index.rst b/docs/api/reference/agents/index.rst index b7ed147c..8904e6ff 100644 --- a/docs/api/reference/agents/index.rst +++ b/docs/api/reference/agents/index.rst @@ -111,6 +111,8 @@ API reference .. tab-item:: Full API .. autoclass:: AgentSet + :autosummary: + :autosummary-nosignatures: .. tab-item:: AgentSetRegistry @@ -179,4 +181,6 @@ API reference .. tab-item:: Full API - .. autoclass:: AgentSetRegistry \ No newline at end of file + .. autoclass:: AgentSetRegistry + :autosummary: + :autosummary-nosignatures: \ No newline at end of file diff --git a/docs/api/reference/datacollector.rst b/docs/api/reference/datacollector.rst index 017bd18d..f1f2c68e 100644 --- a/docs/api/reference/datacollector.rst +++ b/docs/api/reference/datacollector.rst @@ -37,8 +37,32 @@ Minimal example API reference ------------- -.. autoclass:: DataCollector - :members: - :inherited-members: - :autosummary: - :autosummary-nosignatures: \ No newline at end of file +.. tab-set:: + + .. tab-item:: Overview + + .. rubric:: Lifecycle / Core + + .. autosummary:: + :nosignatures: + :toctree: + + DataCollector.__init__ + DataCollector.collect + DataCollector.conditional_collect + DataCollector.flush + DataCollector.data + + .. rubric:: Reporting / Internals + + .. autosummary:: + :nosignatures: + :toctree: + + DataCollector.seed + + .. tab-item:: Full API + + .. autoclass:: DataCollector + :autosummary: + :autosummary-nosignatures: \ No newline at end of file diff --git a/docs/api/reference/model.rst b/docs/api/reference/model.rst index 2b0b2102..74b7e4e5 100644 --- a/docs/api/reference/model.rst +++ b/docs/api/reference/model.rst @@ -36,8 +36,34 @@ Minimal example API reference ------------- -.. autoclass:: Model - :members: - :inherited-members: - :autosummary: - :autosummary-nosignatures: \ No newline at end of file +.. tab-set:: + + .. tab-item:: Overview + + .. rubric:: Lifecycle / Core + + .. autosummary:: + :nosignatures: + :toctree: + + Model.__init__ + Model.step + Model.run_model + Model.reset_randomizer + + .. rubric:: Accessors / Properties + + .. autosummary:: + :nosignatures: + :toctree: + + Model.steps + Model.sets + Model.space + Model.seed + + .. tab-item:: Full API + + .. autoclass:: Model + :autosummary: + :autosummary-nosignatures: \ No newline at end of file diff --git a/docs/api/reference/space/index.rst b/docs/api/reference/space/index.rst index 03763610..c11b140d 100644 --- a/docs/api/reference/space/index.rst +++ b/docs/api/reference/space/index.rst @@ -39,8 +39,28 @@ Minimal example API reference ------------- -.. autoclass:: Grid - :members: - :inherited-members: - :autosummary: - :autosummary-nosignatures: \ No newline at end of file +.. tab-set:: + + .. tab-item:: Overview + + .. rubric:: Lifecycle / Core + + .. autosummary:: + :nosignatures: + :toctree: + + Grid.__init__ + + .. rubric:: Sampling & Queries + + .. autosummary:: + :nosignatures: + :toctree: + + Grid.remaining_capacity + + .. tab-item:: Full API + + .. autoclass:: Grid + :autosummary: + :autosummary-nosignatures: \ No newline at end of file From eb87385e1d0e76e08aaa3c0c6e4abc9a80d4ac75 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Mon, 22 Sep 2025 15:23:16 +0200 Subject: [PATCH 77/99] docs: add logo and favicon settings to HTML output configuration --- docs/api/_static/mesa_logo.png | Bin 0 -> 10958 bytes docs/api/conf.py | 2 ++ 2 files changed, 2 insertions(+) create mode 100644 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to navigation bar for community support --- docs/api/conf.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/docs/api/conf.py b/docs/api/conf.py index 36f85c4e..f17e9108 100644 --- a/docs/api/conf.py +++ b/docs/api/conf.py @@ -95,6 +95,11 @@ "url": github_root, "icon": "fa-brands fa-github", }, + { + "name": "Matrix", + "url": "https://matrix.to/#/#project-mesa:matrix.org", + "icon": "fa-solid fa-comments", + }, ], "navbar_start": ["navbar-logo"], "navbar_end": ["theme-switcher", "navbar-icon-links"], From 7632f921b03adceee937b8e0a4a896436728afd5 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Mon, 22 Sep 2025 17:15:08 +0200 Subject: [PATCH 79/99] docs: add Meta and Chat sections with relevant badges to README --- README.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/README.md b/README.md index a68823dc..358733ef 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,12 @@ # mesa-frames 🚀 +| | | +| ------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| CI/CD | [![CI Checks](https://github.com/projectmesa/mesa-frames/actions/workflows/build.yml/badge.svg?branch=main)](https://github.com/projectmesa/mesa-frames/actions/workflows/build.yml) [![codecov](https://codecov.io/gh/projectmesa/mesa-frames/branch/main/graph/badge.svg)](https://app.codecov.io/gh/projectmesa/mesa-frames) | +| Package | [![PyPI - Version](https://img.shields.io/pypi/v/mesa-frames.svg?logo=pypi&label=PyPI&logoColor=gold)](https://pypi.org/project/mesa-frames/) [![PyPI - Downloads](https://img.shields.io/pypi/dm/mesa-frames.svg?color=blue&label=Downloads&logo=pypi&logoColor=gold)](https://pypi.org/project/mesa-frames/) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mesa-frames.svg?logo=python&label=Python&logoColor=gold)](https://pypi.org/project/mesa-frames/) | +| Meta | [![linting - Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://docs.astral.sh/ruff/) [![formatter - Ruff](https://img.shields.io/badge/formatter-Ruff-0f172a?logo=ruff&logoColor=white)](https://docs.astral.sh/ruff/formatter/) [![Hatch project](https://img.shields.io/badge/%F0%9F%A5%9A-Hatch-4051b5.svg)](https://github.com/pypa/hatch) [![Managed with uv](https://img.shields.io/badge/managed%20with-uv-5a4fcf?logo=uv&logoColor=white)](https://github.com/astral-sh/uv) | +| Chat | [![chat](https://img.shields.io/matrix/project-mesa:matrix.org?label=chat&logo=Matrix)](https://matrix.to/#/#project-mesa:matrix.org) | + mesa-frames is an extension of the [mesa](https://github.com/projectmesa/mesa) framework, designed for complex simulations with thousands of agents. By storing agents in a DataFrame, mesa-frames significantly enhances the performance and scalability of mesa, while maintaining a similar syntax. mesa-frames allows for the use of [vectorized functions](https://stackoverflow.com/a/1422198) which significantly speeds up operations whenever simultaneous activation of agents is possible. ## Why DataFrames? 📊 From b3bdf73cec3379ddd657649bff5067a3bbb14816 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Mon, 22 Sep 2025 17:36:45 +0200 Subject: [PATCH 80/99] docs: update logo and favicon URLs to use remote assets --- docs/api/_static/mesa_logo.png | Bin 10958 -> 0 bytes docs/api/conf.py | 4 ++-- mkdocs.yml | 4 ++++ 3 files changed, 6 insertions(+), 2 deletions(-) delete mode 100644 docs/api/_static/mesa_logo.png diff --git a/docs/api/_static/mesa_logo.png b/docs/api/_static/mesa_logo.png deleted file mode 100644 index 41994d7e45f355924aa07a8a04d571107568b549..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 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zWwYSjZ(t><4Tb&y3qIBZ4?NJfUq6VTB8`iSBUNV1m@#k^rF~;!V?pGkNs}nO%&ttM zqhTikEUG3;`%>VUddU!!`}Xb2u2ks-I0nhTFLmuBqoP=R04k<}-9CN$QcQd>b`fFU wL4#ue*4VLQ73^|`@f{>$Q5Ij?H!?Ev{~hlwytuk+-~a#s07*qoM6N<$f(9Z=fB*mh diff --git a/docs/api/conf.py b/docs/api/conf.py index f17e9108..8701feb4 100644 --- a/docs/api/conf.py +++ b/docs/api/conf.py @@ -38,8 +38,8 @@ html_theme = "pydata_sphinx_theme" html_static_path = ["_static"] html_show_sourcelink = False -html_logo = "_static/mesa_logo.png" -html_favicon = "_static/mesa_logo.png" +html_logo = "https://raw.githubusercontent.com/projectmesa/mesa/main/docs/images/mesa_logo.png" +html_favicon = "https://raw.githubusercontent.com/projectmesa/mesa/main/docs/images/mesa_logo.ico" # Add custom branding CSS/JS (mesa_brand) to static files html_css_files = [ diff --git a/mkdocs.yml b/mkdocs.yml index f8ae79dd..1e481037 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -40,6 +40,10 @@ theme: code: Roboto Mono icon: repo: fontawesome/brands/github + # Logo (PNG) + logo: https://raw.githubusercontent.com/projectmesa/mesa/main/docs/images/mesa_logo.png + # Favicon (ICO) + favicon: https://raw.githubusercontent.com/projectmesa/mesa/main/docs/images/mesa_logo.ico # Plugins plugins: From 3717ed23160e0f50f8fe5b6980dddad48612993e Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Mon, 22 Sep 2025 18:07:35 +0200 Subject: [PATCH 81/99] docs: update site name for clarity in project documentation --- mkdocs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mkdocs.yml b/mkdocs.yml index 1e481037..a1caa258 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,5 +1,5 @@ # Project information -site_name: mesa-frames +site_name: mesa-frames documentation site_url: https://projectmesa.github.io/mesa-frames repo_url: https://github.com/projectmesa/mesa-frames repo_name: projectmesa/mesa-frames From 9db75c02982cb2d38519a75e70cce9a9d6d87a91 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Mon, 22 Sep 2025 18:08:03 +0200 Subject: [PATCH 82/99] docs: enhance README structure and content for clarity and organization --- README.md | 37 +++++++++++++++++++++---------------- 1 file changed, 21 insertions(+), 16 deletions(-) diff --git a/README.md b/README.md index 358733ef..195d7625 100644 --- a/README.md +++ b/README.md @@ -1,35 +1,40 @@ -# mesa-frames 🚀 +

+ Mesa logo +

+ +

mesa-frames

| | | | ------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | CI/CD | [![CI Checks](https://github.com/projectmesa/mesa-frames/actions/workflows/build.yml/badge.svg?branch=main)](https://github.com/projectmesa/mesa-frames/actions/workflows/build.yml) [![codecov](https://codecov.io/gh/projectmesa/mesa-frames/branch/main/graph/badge.svg)](https://app.codecov.io/gh/projectmesa/mesa-frames) | | Package | [![PyPI - Version](https://img.shields.io/pypi/v/mesa-frames.svg?logo=pypi&label=PyPI&logoColor=gold)](https://pypi.org/project/mesa-frames/) [![PyPI - Downloads](https://img.shields.io/pypi/dm/mesa-frames.svg?color=blue&label=Downloads&logo=pypi&logoColor=gold)](https://pypi.org/project/mesa-frames/) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mesa-frames.svg?logo=python&label=Python&logoColor=gold)](https://pypi.org/project/mesa-frames/) | | Meta | [![linting - Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://docs.astral.sh/ruff/) [![formatter - Ruff](https://img.shields.io/badge/formatter-Ruff-0f172a?logo=ruff&logoColor=white)](https://docs.astral.sh/ruff/formatter/) [![Hatch project](https://img.shields.io/badge/%F0%9F%A5%9A-Hatch-4051b5.svg)](https://github.com/pypa/hatch) [![Managed with uv](https://img.shields.io/badge/managed%20with-uv-5a4fcf?logo=uv&logoColor=white)](https://github.com/astral-sh/uv) | -| Chat | [![chat](https://img.shields.io/matrix/project-mesa:matrix.org?label=chat&logo=Matrix)](https://matrix.to/#/#project-mesa:matrix.org) | - -mesa-frames is an extension of the [mesa](https://github.com/projectmesa/mesa) framework, designed for complex simulations with thousands of agents. By storing agents in a DataFrame, mesa-frames significantly enhances the performance and scalability of mesa, while maintaining a similar syntax. mesa-frames allows for the use of [vectorized functions](https://stackoverflow.com/a/1422198) which significantly speeds up operations whenever simultaneous activation of agents is possible. +| Chat | [![chat](https://img.shields.io/matrix/project-mesa:matrix.org?label=chat&logo=Matrix)](https://matrix.to/#/#project-mesa:matrix.org) | -## Why DataFrames? 📊 +--- -DataFrames are optimized for simultaneous operations through [SIMD processing](https://en.wikipedia.org/wiki/Single_instruction,_multiple_data). At the moment, mesa-frames supports the use of Polars library. +## Scale Mesa beyond its limits -- [Polars](https://pola.rs/) is a new DataFrame library with a syntax similar to pandas but with several innovations, including a backend implemented in Rust, the Apache Arrow memory format, query optimization, and support for larger-than-memory DataFrames. +Classic [Mesa](https://github.com/projectmesa/mesa) stores each agent as a Python object, which quickly becomes a bottleneck at scale. +**mesa-frames** reimagines agent storage using **Polars DataFrames**, so agents live in a columnar store rather than the Python heap. -The following is a performance graph showing execution time using mesa and mesa-frames for the [Boltzmann Wealth model](https://mesa.readthedocs.io/en/stable/tutorials/intro_tutorial.html). +You keep the Mesa-style `Model` / `AgentSet` structure, but updates are vectorized and memory-efficient. -![Performance Graph with Mesa](https://github.com/projectmesa/mesa-frames/blob/main/examples/boltzmann_wealth/boltzmann_with_mesa.png) +### Why it matters +- ⚡ **10× faster** bulk updates on 10k+ agents (see benchmarks) +- 📊 **Columnar execution** via [Polars](https://docs.pola.rs/): [SIMD](https://en.wikipedia.org/wiki/Single_instruction,_multiple_data) ops, multi-core support +- 🔄 **Declarative logic**: agent rules as transformations, not Python loops +- 🚀 **Roadmap**: Lazy queries and GPU support for even faster models -![Performance Graph without Mesa](https://github.com/projectmesa/mesa-frames/blob/main/examples/boltzmann_wealth/boltzmann_no_mesa.png) +--- -([You can check the script used to generate the graph here](https://github.com/projectmesa/mesa-frames/blob/main/examples/boltzmann_wealth/performance_plot.py), but if you want to additionally compare vs Mesa, you have to uncomment `mesa_implementation` and its label) +## Who is it for? -## Installation +- Researchers needing to scale to **tens or hundreds of thousands of agents** +- Users whose agent logic can be written as **vectorized, set-based operations** -### Install from PyPI +❌ **Not a good fit if:** your model depends on strict per-agent sequencing, complex non-vectorizable methods, or fine-grained identity tracking. -```bash -pip install mesa-frames -``` ### Install from Source (development) From 54d0f27ae6b776fee42ae4f2eb96836206c55c25 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Tue, 23 Sep 2025 09:06:08 +0200 Subject: [PATCH 83/99] docs: change .ipynb tutorials to jupytext --- .../user-guide/2_introductory-tutorial.ipynb | 449 ---------------- .../user-guide/2_introductory_tutorial.py | 277 ++++++++++ docs/general/user-guide/4_datacollector.ipynb | 501 ------------------ docs/general/user-guide/4_datacollector.py | 229 ++++++++ 4 files changed, 506 insertions(+), 950 deletions(-) delete mode 100644 docs/general/user-guide/2_introductory-tutorial.ipynb create mode 100644 docs/general/user-guide/2_introductory_tutorial.py delete mode 100644 docs/general/user-guide/4_datacollector.ipynb create mode 100644 docs/general/user-guide/4_datacollector.py diff --git a/docs/general/user-guide/2_introductory-tutorial.ipynb b/docs/general/user-guide/2_introductory-tutorial.ipynb deleted file mode 100644 index 11391f9d..00000000 --- a/docs/general/user-guide/2_introductory-tutorial.ipynb +++ /dev/null @@ -1,449 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7ee055b2", - "metadata": {}, - "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/projectmesa/mesa-frames/blob/main/docs/general/user-guide/2_introductory-tutorial.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "8bd0381e", - "metadata": {}, - "source": [ - "## Installation (if running in Colab)\n", - "\n", - "Run the following cell to install `mesa-frames` if you are using Google Colab." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "df4d8623", - "metadata": {}, - "outputs": [], - "source": [ - "# !pip install git+https://github.com/projectmesa/mesa-frames mesa" - ] - }, - { - "cell_type": "markdown", - "id": "11515dfc", - "metadata": {}, - "source": [ - " # Introductory Tutorial: Boltzmann Wealth Model with mesa-frames 💰🚀\n", - "\n", - "In this tutorial, we'll implement the Boltzmann Wealth Model using mesa-frames. This model simulates the distribution of wealth among agents, where agents randomly give money to each other.\n", - "\n", - "## Setting Up the Model 🏗️\n", - "\n", - "First, let's import the necessary modules and set up our model class:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fc0ee981", - "metadata": {}, - "outputs": [ - { - "ename": "ImportError", - "evalue": "cannot import name 'Model' from partially initialized module 'mesa_frames' (most likely due to a circular import) (/home/adam/projects/mesa-frames/mesa_frames/__init__.py)", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmesa_frames\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Model, AgentSet, DataCollector\n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mclass\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mMoneyModelDF\u001b[39;00m(Model):\n\u001b[32m 5\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, N: \u001b[38;5;28mint\u001b[39m, agents_cls):\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/projects/mesa-frames/mesa_frames/__init__.py:65\u001b[39m\n\u001b[32m 63\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmesa_frames\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mconcrete\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01magentset\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m AgentSet\n\u001b[32m 64\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmesa_frames\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mconcrete\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01magentsetregistry\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m AgentSetRegistry\n\u001b[32m---> \u001b[39m\u001b[32m65\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmesa_frames\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mconcrete\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdatacollector\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m DataCollector\n\u001b[32m 66\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmesa_frames\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mconcrete\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mmodel\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Model\n\u001b[32m 67\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmesa_frames\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mconcrete\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mspace\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Grid\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/projects/mesa-frames/mesa_frames/concrete/datacollector.py:62\u001b[39m\n\u001b[32m 60\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtempfile\u001b[39;00m\n\u001b[32m 61\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpsycopg2\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m62\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmesa_frames\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mabstract\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdatacollector\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m AbstractDataCollector\n\u001b[32m 63\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Any, Literal\n\u001b[32m 64\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mcollections\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mabc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Callable\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/projects/mesa-frames/mesa_frames/abstract/datacollector.py:50\u001b[39m\n\u001b[32m 48\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Any, Literal\n\u001b[32m 49\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mcollections\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mabc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Callable\n\u001b[32m---> \u001b[39m\u001b[32m50\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmesa_frames\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Model\n\u001b[32m 51\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpolars\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpl\u001b[39;00m\n\u001b[32m 52\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mthreading\u001b[39;00m\n", - "\u001b[31mImportError\u001b[39m: cannot import name 'Model' from partially initialized module 'mesa_frames' (most likely due to a circular import) (/home/adam/projects/mesa-frames/mesa_frames/__init__.py)" - ] - } - ], - "source": [ - "from mesa_frames import Model, AgentSet, DataCollector\n", - "\n", - "\n", - "class MoneyModel(Model):\n", - " def __init__(self, N: int, agents_cls):\n", - " super().__init__()\n", - " self.n_agents = N\n", - " self.sets += agents_cls(N, self)\n", - " self.datacollector = DataCollector(\n", - " model=self,\n", - " model_reporters={\n", - " \"total_wealth\": lambda m: m.sets[\"MoneyAgents\"].df[\"wealth\"].sum()\n", - " },\n", - " agent_reporters={\"wealth\": \"wealth\"},\n", - " storage=\"csv\",\n", - " storage_uri=\"./data\",\n", - " trigger=lambda m: m.schedule.steps % 2 == 0,\n", - " )\n", - "\n", - " def step(self):\n", - " # Executes the step method for every agentset in self.sets\n", - " self.sets.do(\"step\")\n", - "\n", - " def run_model(self, n):\n", - " for _ in range(n):\n", - " self.step()\n", - " self.datacollector.conditional_collect\n", - " self.datacollector.flush()" - ] - }, - { - "cell_type": "markdown", - "id": "00e092c4", - "metadata": {}, - "source": [ - "## Implementing the AgentSet 👥\n", - "\n", - "Now, let's implement our `MoneyAgents` using polars backends." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2bac0126", - "metadata": {}, - "outputs": [], - "source": [ - "import polars as pl\n", - "\n", - "\n", - "class MoneyAgents(AgentSet):\n", - " def __init__(self, n: int, model: Model):\n", - " super().__init__(model)\n", - " self += pl.DataFrame({\"wealth\": pl.ones(n, eager=True)})\n", - "\n", - " def step(self) -> None:\n", - " self.do(\"give_money\")\n", - "\n", - " def give_money(self):\n", - " self.select(self.wealth > 0)\n", - " other_agents = self.df.sample(n=len(self.active_agents), with_replacement=True)\n", - " self[\"active\", \"wealth\"] -= 1\n", - " new_wealth = other_agents.group_by(\"unique_id\").len()\n", - " self[new_wealth[\"unique_id\"], \"wealth\"] += new_wealth[\"len\"]" - ] - }, - { - "cell_type": "markdown", - "id": "3b141016", - "metadata": {}, - "source": [ - "\n", - "## Running the Model ▶️\n", - "\n", - "Now that we have our model and agent set defined, let's run a simulation:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "65da4e6f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shape: (9, 2)\n", - "┌────────────┬──────────┐\n", - "│ statistic ┆ wealth │\n", - "│ --- ┆ --- │\n", - "│ str ┆ f64 │\n", - "╞════════════╪══════════╡\n", - "│ count ┆ 1000.0 │\n", - "│ null_count ┆ 0.0 │\n", - "│ mean ┆ 1.0 │\n", - "│ std ┆ 1.134587 │\n", - "│ min ┆ 0.0 │\n", - "│ 25% ┆ 0.0 │\n", - "│ 50% ┆ 1.0 │\n", - "│ 75% ┆ 2.0 │\n", - "│ max ┆ 8.0 │\n", - "└────────────┴──────────┘\n" - ] - } - ], - "source": [ - "# Create and run the model\n", - "model = MoneyModel(1000, MoneyAgents)\n", - "model.run_model(100)\n", - "\n", - "wealth_dist = list(model.sets.df.values())[0]\n", - "\n", - "# Print the final wealth distribution\n", - "print(wealth_dist.select(pl.col(\"wealth\")).describe())" - ] - }, - { - "cell_type": "markdown", - "id": "812da73b", - "metadata": {}, - "source": [ - "\n", - "This output shows the statistical summary of the wealth distribution after 100 steps of the simulation with 1000 agents.\n", - "\n", - "## Performance Comparison 🏎️💨\n", - "\n", - "One of the key advantages of mesa-frames is its performance with large numbers of agents. Let's compare the performance of mesa and polars:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fbdb540810924de8", - "metadata": {}, - "outputs": [], - "source": [ - "class MoneyAgentsConcise(AgentSet):\n", - " def __init__(self, n: int, model: Model):\n", - " super().__init__(model)\n", - " ## Adding the agents to the agent set\n", - " # 1. Changing the df attribute directly (not recommended, if other agents were added before, they will be lost)\n", - " \"\"\"self.df = pl.DataFrame(\n", - " {\"wealth\": pl.ones(n, eager=True)}\n", - " )\"\"\"\n", - " # 2. Adding the dataframe with add\n", - " \"\"\"self.add(\n", - " pl.DataFrame(\n", - " {\n", - " \"wealth\": pl.ones(n, eager=True),\n", - " }\n", - " )\n", - " )\"\"\"\n", - " # 3. Adding the dataframe with __iadd__\n", - " self += pl.DataFrame({\"wealth\": pl.ones(n, eager=True)})\n", - "\n", - " def step(self) -> None:\n", - " # The give_money method is called\n", - " # self.give_money()\n", - " self.do(\"give_money\")\n", - "\n", - " def give_money(self):\n", - " ## Active agents are changed to wealthy agents\n", - " # 1. Using the __getitem__ method\n", - " # self.select(self[\"wealth\"] > 0)\n", - " # 2. Using the fallback __getattr__ method\n", - " self.select(self.wealth > 0)\n", - "\n", - " # Receiving agents are sampled (only native expressions currently supported)\n", - " other_agents = self.df.sample(n=len(self.active_agents), with_replacement=True)\n", - "\n", - " # Wealth of wealthy is decreased by 1\n", - " # 1. Using the __setitem__ method with self.active_agents mask\n", - " # self[self.active_agents, \"wealth\"] -= 1\n", - " # 2. Using the __setitem__ method with \"active\" mask\n", - " self[\"active\", \"wealth\"] -= 1\n", - "\n", - " # Compute the income of the other agents (only native expressions currently supported)\n", - " new_wealth = other_agents.group_by(\"unique_id\").len()\n", - "\n", - " # Add the income to the other agents\n", - " # 1. Using the set method\n", - " \"\"\"self.set(\n", - " attr_names=\"wealth\",\n", - " values=pl.col(\"wealth\") + new_wealth[\"len\"],\n", - " mask=new_wealth,\n", - " )\"\"\"\n", - "\n", - " # 2. Using the __setitem__ method\n", - " self[new_wealth, \"wealth\"] += new_wealth[\"len\"]\n", - "\n", - "\n", - "class MoneyAgentsNative(AgentSet):\n", - " def __init__(self, n: int, model: Model):\n", - " super().__init__(model)\n", - " self += pl.DataFrame({\"wealth\": pl.ones(n, eager=True)})\n", - "\n", - " def step(self) -> None:\n", - " self.do(\"give_money\")\n", - "\n", - " def give_money(self):\n", - " ## Active agents are changed to wealthy agents\n", - " self.select(pl.col(\"wealth\") > 0)\n", - "\n", - " other_agents = self.df.sample(n=len(self.active_agents), with_replacement=True)\n", - "\n", - " # Wealth of wealthy is decreased by 1\n", - " self.df = self.df.with_columns(\n", - " wealth=pl.when(\n", - " pl.col(\"unique_id\").is_in(self.active_agents[\"unique_id\"].implode())\n", - " )\n", - " .then(pl.col(\"wealth\") - 1)\n", - " .otherwise(pl.col(\"wealth\"))\n", - " )\n", - "\n", - " new_wealth = other_agents.group_by(\"unique_id\").len()\n", - "\n", - " # Add the income to the other agents\n", - " self.df = (\n", - " self.df.join(new_wealth, on=\"unique_id\", how=\"left\")\n", - " .fill_null(0)\n", - " .with_columns(wealth=pl.col(\"wealth\") + pl.col(\"len\"))\n", - " .drop(\"len\")\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "496196d999f18634", - "metadata": {}, - "source": [ - "Add Mesa implementation of MoneyAgent and MoneyModel classes to test Mesa performance" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9dbe761af964af5b", - "metadata": {}, - "outputs": [], - "source": [ - "import mesa\n", - "\n", - "\n", - "class MesaMoneyAgent(mesa.Agent):\n", - " \"\"\"An agent with fixed initial wealth.\"\"\"\n", - "\n", - " def __init__(self, model):\n", - " # Pass the parameters to the parent class.\n", - " super().__init__(model)\n", - "\n", - " # Create the agent's variable and set the initial values.\n", - " self.wealth = 1\n", - "\n", - " def step(self):\n", - " # Verify agent has some wealth\n", - " if self.wealth > 0:\n", - " other_agent: MesaMoneyAgent = self.model.random.choice(self.model.agents)\n", - " if other_agent is not None:\n", - " other_agent.wealth += 1\n", - " self.wealth -= 1\n", - "\n", - "\n", - "class MesaMoneyModel(mesa.Model):\n", - " \"\"\"A model with some number of agents.\"\"\"\n", - "\n", - " def __init__(self, N: int):\n", - " super().__init__()\n", - " self.num_agents = N\n", - " for _ in range(N):\n", - " self.agents.add(MesaMoneyAgent(self))\n", - "\n", - " def step(self):\n", - " \"\"\"Advance the model by one step.\"\"\"\n", - " self.agents.shuffle_do(\"step\")\n", - "\n", - " def run_model(self, n_steps) -> None:\n", - " for _ in range(n_steps):\n", - " self.step()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2d864cd3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Execution times:\n", - "---------------\n", - "mesa:\n", - " Number of agents: 100, Time: 0.03 seconds\n", - " Number of agents: 1001, Time: 1.45 seconds\n", - " Number of agents: 2000, Time: 5.40 seconds\n", - "---------------\n", - "---------------\n", - "mesa-frames (pl concise):\n", - " Number of agents: 100, Time: 1.60 seconds\n", - " Number of agents: 1001, Time: 2.68 seconds\n", - " Number of agents: 2000, Time: 3.04 seconds\n", - "---------------\n", - "---------------\n", - "mesa-frames (pl native):\n", - " Number of agents: 100, Time: 0.62 seconds\n", - " Number of agents: 1001, Time: 0.80 seconds\n", - " Number of agents: 2000, Time: 1.10 seconds\n", - "---------------\n" - ] - } - ], - "source": [ - "import time\n", - "\n", - "\n", - "def run_simulation(model: MesaMoneyModel | MoneyModel, n_steps: int):\n", - " start_time = time.time()\n", - " model.run_model(n_steps)\n", - " end_time = time.time()\n", - " return end_time - start_time\n", - "\n", - "\n", - "# Compare mesa and mesa-frames implementations\n", - "n_agents_list = [10**2, 10**3 + 1, 2 * 10**3]\n", - "n_steps = 100\n", - "print(\"Execution times:\")\n", - "for implementation in [\n", - " \"mesa\",\n", - " \"mesa-frames (pl concise)\",\n", - " \"mesa-frames (pl native)\",\n", - "]:\n", - " print(f\"---------------\\n{implementation}:\")\n", - " for n_agents in n_agents_list:\n", - " if implementation == \"mesa\":\n", - " ntime = run_simulation(MesaMoneyModel(n_agents), n_steps)\n", - " elif implementation == \"mesa-frames (pl concise)\":\n", - " ntime = run_simulation(MoneyModel(n_agents, MoneyAgentsConcise), n_steps)\n", - " elif implementation == \"mesa-frames (pl native)\":\n", - " ntime = run_simulation(MoneyModel(n_agents, MoneyAgentsNative), n_steps)\n", - "\n", - " print(f\" Number of agents: {n_agents}, Time: {ntime:.2f} seconds\")\n", - " print(\"---------------\")" - ] - }, - { - "cell_type": "markdown", - "id": "6dfc6d34", - "metadata": {}, - "source": [ - "\n", - "## Conclusion 🎉\n", - "\n", - "- All mesa-frames implementations significantly outperform the original mesa implementation. 🏆\n", - "- The native implementation for Polars shows better performance than their concise counterparts. 💪\n", - "- The Polars native implementation shows the most impressive speed-up, ranging from 10.86x to 17.60x faster than mesa! 🚀🚀🚀\n", - "- The performance advantage of mesa-frames becomes more pronounced as the number of agents increases. 📈" - ] - } - ], - "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": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/general/user-guide/2_introductory_tutorial.py b/docs/general/user-guide/2_introductory_tutorial.py new file mode 100644 index 00000000..8560034a --- /dev/null +++ b/docs/general/user-guide/2_introductory_tutorial.py @@ -0,0 +1,277 @@ +from __future__ import annotations + +# %% [markdown] +"""[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/projectmesa/mesa-frames/blob/main/docs/general/user-guide/2_introductory-tutorial.ipynb)""" + +# %% [markdown] +"""## Installation (if running in Colab) + +Run the following cell to install `mesa-frames` if you are using Google Colab.""" + +# %% +# !pip install git+https://github.com/projectmesa/mesa-frames mesa + +# %% [markdown] +""" # Introductory Tutorial: Boltzmann Wealth Model with mesa-frames 💰🚀 + +In this tutorial, we'll implement the Boltzmann Wealth Model using mesa-frames. This model simulates the distribution of wealth among agents, where agents randomly give money to each other. + +## Setting Up the Model 🏗️ + +First, let's import the necessary modules and set up our model class:""" + +# %% +from mesa_frames import Model, AgentSet, DataCollector + + +class MoneyModel(Model): + def __init__(self, N: int, agents_cls): + super().__init__() + self.n_agents = N + self.sets += agents_cls(N, self) + self.datacollector = DataCollector( + model=self, + model_reporters={ + "total_wealth": lambda m: m.sets["MoneyAgents"].df["wealth"].sum() + }, + agent_reporters={"wealth": "wealth"}, + storage="csv", + storage_uri="./data", + trigger=lambda m: m.schedule.steps % 2 == 0, + ) + + def step(self): + # Executes the step method for every agentset in self.sets + self.sets.do("step") + + def run_model(self, n): + for _ in range(n): + self.step() + self.datacollector.conditional_collect + self.datacollector.flush() + + +# %% [markdown] +"""## Implementing the AgentSet 👥 + +Now, let's implement our `MoneyAgents` using polars backends.""" + +# %% +import polars as pl + + +class MoneyAgents(AgentSet): + def __init__(self, n: int, model: Model): + super().__init__(model) + self += pl.DataFrame({"wealth": pl.ones(n, eager=True)}) + + def step(self) -> None: + self.do("give_money") + + def give_money(self): + self.select(self.wealth > 0) + other_agents = self.df.sample(n=len(self.active_agents), with_replacement=True) + self["active", "wealth"] -= 1 + new_wealth = other_agents.group_by("unique_id").len() + self[new_wealth["unique_id"], "wealth"] += new_wealth["len"] + + +# %% [markdown] +""" +## Running the Model ▶️ + +Now that we have our model and agent set defined, let's run a simulation:""" + +# %% +# Create and run the model +model = MoneyModel(1000, MoneyAgents) +model.run_model(100) + +wealth_dist = list(model.sets.df.values())[0] + +# Print the final wealth distribution +print(wealth_dist.select(pl.col("wealth")).describe()) + +# %% [markdown] +""" +This output shows the statistical summary of the wealth distribution after 100 steps of the simulation with 1000 agents. + +## Performance Comparison 🏎️💨 + +One of the key advantages of mesa-frames is its performance with large numbers of agents. Let's compare the performance of mesa and polars:""" + + +# %% +class MoneyAgentsConcise(AgentSet): + def __init__(self, n: int, model: Model): + super().__init__(model) + ## Adding the agents to the agent set + # 1. Changing the df attribute directly (not recommended, if other agents were added before, they will be lost) + """self.df = pl.DataFrame( + {"wealth": pl.ones(n, eager=True)} + )""" + # 2. Adding the dataframe with add + """self.add( + pl.DataFrame( + { + "wealth": pl.ones(n, eager=True), + } + ) + )""" + # 3. Adding the dataframe with __iadd__ + self += pl.DataFrame({"wealth": pl.ones(n, eager=True)}) + + def step(self) -> None: + # The give_money method is called + # self.give_money() + self.do("give_money") + + def give_money(self): + ## Active agents are changed to wealthy agents + # 1. Using the __getitem__ method + # self.select(self["wealth"] > 0) + # 2. Using the fallback __getattr__ method + self.select(self.wealth > 0) + + # Receiving agents are sampled (only native expressions currently supported) + other_agents = self.df.sample(n=len(self.active_agents), with_replacement=True) + + # Wealth of wealthy is decreased by 1 + # 1. Using the __setitem__ method with self.active_agents mask + # self[self.active_agents, "wealth"] -= 1 + # 2. Using the __setitem__ method with "active" mask + self["active", "wealth"] -= 1 + + # Compute the income of the other agents (only native expressions currently supported) + new_wealth = other_agents.group_by("unique_id").len() + + # Add the income to the other agents + # 1. Using the set method + """self.set( + attr_names="wealth", + values=pl.col("wealth") + new_wealth["len"], + mask=new_wealth, + )""" + + # 2. Using the __setitem__ method + self[new_wealth, "wealth"] += new_wealth["len"] + + +class MoneyAgentsNative(AgentSet): + def __init__(self, n: int, model: Model): + super().__init__(model) + self += pl.DataFrame({"wealth": pl.ones(n, eager=True)}) + + def step(self) -> None: + self.do("give_money") + + def give_money(self): + ## Active agents are changed to wealthy agents + self.select(pl.col("wealth") > 0) + + other_agents = self.df.sample(n=len(self.active_agents), with_replacement=True) + + # Wealth of wealthy is decreased by 1 + self.df = self.df.with_columns( + wealth=pl.when( + pl.col("unique_id").is_in(self.active_agents["unique_id"].implode()) + ) + .then(pl.col("wealth") - 1) + .otherwise(pl.col("wealth")) + ) + + new_wealth = other_agents.group_by("unique_id").len() + + # Add the income to the other agents + self.df = ( + self.df.join(new_wealth, on="unique_id", how="left") + .fill_null(0) + .with_columns(wealth=pl.col("wealth") + pl.col("len")) + .drop("len") + ) + + +# %% [markdown] +"""Add Mesa implementation of MoneyAgent and MoneyModel classes to test Mesa performance""" + +# %% +import mesa + + +class MesaMoneyAgent(mesa.Agent): + """An agent with fixed initial wealth.""" + + def __init__(self, model): + # Pass the parameters to the parent class. + super().__init__(model) + + # Create the agent's variable and set the initial values. + self.wealth = 1 + + def step(self): + # Verify agent has some wealth + if self.wealth > 0: + other_agent: MesaMoneyAgent = self.model.random.choice(self.model.agents) + if other_agent is not None: + other_agent.wealth += 1 + self.wealth -= 1 + + +class MesaMoneyModel(mesa.Model): + """A model with some number of agents.""" + + def __init__(self, N: int): + super().__init__() + self.num_agents = N + for _ in range(N): + self.agents.add(MesaMoneyAgent(self)) + + def step(self): + """Advance the model by one step.""" + self.agents.shuffle_do("step") + + def run_model(self, n_steps) -> None: + for _ in range(n_steps): + self.step() + + +# %% +import time + + +def run_simulation(model: MesaMoneyModel | MoneyModel, n_steps: int): + start_time = time.time() + model.run_model(n_steps) + end_time = time.time() + return end_time - start_time + + +# Compare mesa and mesa-frames implementations +n_agents_list = [10**2, 10**3 + 1, 2 * 10**3] +n_steps = 100 +print("Execution times:") +for implementation in [ + "mesa", + "mesa-frames (pl concise)", + "mesa-frames (pl native)", +]: + print(f"---------------\n{implementation}:") + for n_agents in n_agents_list: + if implementation == "mesa": + ntime = run_simulation(MesaMoneyModel(n_agents), n_steps) + elif implementation == "mesa-frames (pl concise)": + ntime = run_simulation(MoneyModel(n_agents, MoneyAgentsConcise), n_steps) + elif implementation == "mesa-frames (pl native)": + ntime = run_simulation(MoneyModel(n_agents, MoneyAgentsNative), n_steps) + + print(f" Number of agents: {n_agents}, Time: {ntime:.2f} seconds") + print("---------------") + +# %% [markdown] +""" +## Conclusion 🎉 + +- All mesa-frames implementations significantly outperform the original mesa implementation. 🏆 +- The native implementation for Polars shows better performance than their concise counterparts. 💪 +- The Polars native implementation shows the most impressive speed-up, ranging from 10.86x to 17.60x faster than mesa! 🚀🚀🚀 +- The performance advantage of mesa-frames becomes more pronounced as the number of agents increases. 📈""" diff --git a/docs/general/user-guide/4_datacollector.ipynb b/docs/general/user-guide/4_datacollector.ipynb deleted file mode 100644 index 0809caa2..00000000 --- a/docs/general/user-guide/4_datacollector.ipynb +++ /dev/null @@ -1,501 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7fb27b941602401d91542211134fc71a", - "metadata": {}, - "source": [ - "# Data Collector Tutorial\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/projectmesa/mesa-frames/blob/main/docs/general/user-guide/4_datacollector.ipynb)\n", - "\n", - "This notebook walks you through using the concrete `DataCollector` in `mesa-frames` to collect model- and agent-level data and write it to different storage backends: **memory, CSV, Parquet, S3, and PostgreSQL**.\n", - "\n", - "It also shows how to use **conditional triggers** and how the **schema validation** behaves for PostgreSQL.\n" - ] - }, - { - "cell_type": "markdown", - "id": "acae54e37e7d407bbb7b55eff062a284", - "metadata": {}, - "source": [ - "## Installation (Colab or fresh env)\n", - "\n", - "Uncomment and run the next cell if you're in Colab or a clean environment.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "9a63283cbaf04dbcab1f6479b197f3a8", - "metadata": { - "editable": true - }, - "outputs": [], - "source": [ - "# !pip install git+https://github.com/projectmesa/mesa-frames mesa" - ] - }, - { - "cell_type": "markdown", - "id": "8dd0d8092fe74a7c96281538738b07e2", - "metadata": {}, - "source": [ - "## Minimal Example Model\n", - "\n", - "We create a tiny model using the `Model` and an `AgentSet`-style agent container. This is just to demonstrate collection APIs.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "72eea5119410473aa328ad9291626812", - "metadata": { - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'model': shape: (5, 5)\n", - " ┌──────┬─────────────────────────────────┬───────┬──────────────┬──────────┐\n", - " │ step ┆ seed ┆ batch ┆ total_wealth ┆ n_agents │\n", - " │ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", - " │ i64 ┆ str ┆ i64 ┆ f64 ┆ i64 │\n", - " ╞══════╪═════════════════════════════════╪═══════╪══════════════╪══════════╡\n", - " │ 2 ┆ 332212815818606584686857770936… ┆ 0 ┆ 1000.0 ┆ 1000 │\n", - " │ 4 ┆ 332212815818606584686857770936… ┆ 0 ┆ 1000.0 ┆ 1000 │\n", - " │ 6 ┆ 332212815818606584686857770936… ┆ 0 ┆ 1000.0 ┆ 1000 │\n", - " │ 8 ┆ 332212815818606584686857770936… ┆ 0 ┆ 1000.0 ┆ 1000 │\n", - " │ 10 ┆ 332212815818606584686857770936… ┆ 0 ┆ 1000.0 ┆ 1000 │\n", - " └──────┴─────────────────────────────────┴───────┴──────────────┴──────────┘,\n", - " 'agent': shape: (5_000, 4)\n", - " ┌────────────────────┬──────┬─────────────────────────────────┬───────┐\n", - " │ wealth_MoneyAgents ┆ step ┆ seed ┆ batch │\n", - " │ --- ┆ --- ┆ --- ┆ --- │\n", - " │ f64 ┆ i32 ┆ str ┆ i32 │\n", - " ╞════════════════════╪══════╪═════════════════════════════════╪═══════╡\n", - " │ 3.0 ┆ 2 ┆ 332212815818606584686857770936… ┆ 0 │\n", - " │ 0.0 ┆ 2 ┆ 332212815818606584686857770936… ┆ 0 │\n", - " │ 2.0 ┆ 2 ┆ 332212815818606584686857770936… ┆ 0 │\n", - " │ 1.0 ┆ 2 ┆ 332212815818606584686857770936… ┆ 0 │\n", - " │ 0.0 ┆ 2 ┆ 332212815818606584686857770936… ┆ 0 │\n", - " │ … ┆ … ┆ … ┆ … │\n", - " │ 0.0 ┆ 10 ┆ 332212815818606584686857770936… ┆ 0 │\n", - " │ 0.0 ┆ 10 ┆ 332212815818606584686857770936… ┆ 0 │\n", - " │ 0.0 ┆ 10 ┆ 332212815818606584686857770936… ┆ 0 │\n", - " │ 0.0 ┆ 10 ┆ 332212815818606584686857770936… ┆ 0 │\n", - " │ 0.0 ┆ 10 ┆ 332212815818606584686857770936… ┆ 0 │\n", - " └────────────────────┴──────┴─────────────────────────────────┴───────┘}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mesa_frames import Model, AgentSet, DataCollector\n", - "import polars as pl\n", - "\n", - "\n", - "class MoneyAgents(AgentSet):\n", - " def __init__(self, n: int, model: Model):\n", - " super().__init__(model)\n", - " # one column, one unit of wealth each\n", - " self += pl.DataFrame({\"wealth\": pl.ones(n, eager=True)})\n", - "\n", - " def step(self) -> None:\n", - " self.select(self.wealth > 0)\n", - " receivers = self.df.sample(n=len(self.active_agents), with_replacement=True)\n", - " self[\"active\", \"wealth\"] -= 1\n", - " income = receivers.group_by(\"unique_id\").len()\n", - " self[income[\"unique_id\"], \"wealth\"] += income[\"len\"]\n", - "\n", - "\n", - "class MoneyModel(Model):\n", - " def __init__(self, n: int):\n", - " super().__init__()\n", - " self.sets.add(MoneyAgents(n, self))\n", - " self.dc = DataCollector(\n", - " model=self,\n", - " model_reporters={\n", - " \"total_wealth\": lambda m: m.sets[\"MoneyAgents\"].df[\"wealth\"].sum(),\n", - " \"n_agents\": lambda m: len(m.sets[\"MoneyAgents\"]),\n", - " },\n", - " agent_reporters={\n", - " \"wealth\": \"wealth\", # pull existing column\n", - " },\n", - " storage=\"memory\", # we'll switch this per example\n", - " storage_uri=None,\n", - " trigger=lambda m: m.steps % 2\n", - " == 0, # collect every 2 steps via conditional_collect\n", - " reset_memory=True,\n", - " )\n", - "\n", - " def step(self):\n", - " self.sets.do(\"step\")\n", - "\n", - " def run(self, steps: int, conditional: bool = True):\n", - " for _ in range(steps):\n", - " self.step()\n", - " self.dc.conditional_collect() # or .collect if you want to collect every step regardless of trigger\n", - "\n", - "\n", - "model = MoneyModel(1000)\n", - "model.run(10)\n", - "model.dc.data # peek in-memory dataframes" - ] - }, - { - "cell_type": "markdown", - "id": "3d3ca41d", - "metadata": {}, - "source": [ - "## Saving the data for later use \n", - "\n", - "`DataCollector` supports multiple storage backends. \n", - "Files are saved with **step number** and **batch number** (e.g., `model_step10_batch2.csv`) so multiple collects at the same step don’t overwrite. \n", - " \n", - "- **CSV:** `storage=\"csv\"` → writes `model_step{n}_batch{k}.csv`, easy to open anywhere. \n", - "- **Parquet:** `storage=\"parquet\"` → compressed, efficient for large datasets. \n", - "- **S3:** `storage=\"S3-csv\"`/`storage=\"S3-parquet\"` → saves CSV/Parquet directly to Amazon S3. \n", - "- **PostgreSQL:** `storage=\"postgresql\"` → inserts results into `model_data` and `agent_data` tables for querying. \n" - ] - }, - { - "cell_type": "markdown", - "id": "8edb47106e1a46a883d545849b8ab81b", - "metadata": {}, - "source": [ - "## Writing to Local CSV\n", - "\n", - "Switch the storage to `csv` and provide a folder path. Files are written as `model_step{n}.csv` and `agent_step{n}.csv`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5f14f38c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "\n", - "os.makedirs(\"./data_csv\", exist_ok=True)\n", - "model_csv = MoneyModel(1000)\n", - "model_csv.dc = DataCollector(\n", - " model=model_csv,\n", - " model_reporters={\n", - " \"total_wealth\": lambda m: m.sets[\"MoneyAgents\"].df[\"wealth\"].sum(),\n", - " \"n_agents\": lambda m: len(m.sets[\"MoneyAgents\"]),\n", - " },\n", - " agent_reporters={\n", - " \"wealth\": \"wealth\",\n", - " },\n", - " storage=\"csv\", # saving as csv\n", - " storage_uri=\"./data_csv\",\n", - " trigger=lambda m: m._steps % 2 == 0,\n", - " reset_memory=True,\n", - ")\n", - "model_csv.run(10)\n", - "model_csv.dc.flush()\n", - "os.listdir(\"./data_csv\")" - ] - }, - { - "cell_type": "markdown", - "id": "10185d26023b46108eb7d9f57d49d2b3", - "metadata": {}, - "source": [ - "## Writing to Local Parquet\n", - "\n", - "Use `parquet` for columnar output.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8763a12b2bbd4a93a75aff182afb95dc", - "metadata": { - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "os.makedirs(\"./data_parquet\", exist_ok=True)\n", - "model_parq = MoneyModel(1000)\n", - "model_parq.dc = DataCollector(\n", - " model=model_parq,\n", - " model_reporters={\n", - " \"total_wealth\": lambda m: m.sets[\"MoneyAgents\"].df[\"wealth\"].sum(),\n", - " \"n_agents\": lambda m: len(m.sets[\"MoneyAgents\"]),\n", - " },\n", - " agent_reporters={\n", - " \"wealth\": \"wealth\",\n", - " },\n", - " storage=\"parquet\", # save as parquet\n", - " storage_uri=\"data_parquet\",\n", - " trigger=lambda m: m._steps % 2 == 0,\n", - " reset_memory=True,\n", - ")\n", - "model_parq.run(10)\n", - "model_parq.dc.flush()\n", - "os.listdir(\"./data_parquet\")" - ] - }, - { - "cell_type": "markdown", - "id": "7623eae2785240b9bd12b16a66d81610", - "metadata": {}, - "source": [ - "## Writing to Amazon S3 (CSV or Parquet)\n", - "\n", - "Set AWS credentials via environment variables or your usual config. Then choose `S3-csv` or `S3-parquet` and pass an S3 URI (e.g., `s3://my-bucket/experiments/run-1`).\n", - "\n", - "> **Note:** This cell requires network access & credentials when actually run.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7cdc8c89c7104fffa095e18ddfef8986", - "metadata": { - "editable": true - }, - "outputs": [], - "source": [ - "model_s3 = MoneyModel(1000)\n", - "model_s3.dc = DataCollector(\n", - " model=model_s3,\n", - " model_reporters={\n", - " \"total_wealth\": lambda m: m.sets[\"MoneyAgents\"].df[\"wealth\"].sum(),\n", - " \"n_agents\": lambda m: len(m.sets[\"MoneyAgents\"]),\n", - " },\n", - " agent_reporters={\n", - " \"wealth\": \"wealth\",\n", - " },\n", - " storage=\"S3-csv\", # save as csv in S3\n", - " storage_uri=\"s3://my-bucket/experiments/run-1\", # change it to required path\n", - " trigger=lambda m: m._steps % 2 == 0,\n", - " reset_memory=True,\n", - ")\n", - "model_s3.run(10)\n", - "model_s3.dc.flush()" - ] - }, - { - "cell_type": "markdown", - "id": "b118ea5561624da68c537baed56e602f", - "metadata": {}, - "source": [ - "## Writing to PostgreSQL\n", - "\n", - "PostgreSQL requires that the target tables exist and that the expected reporter columns are present. The collector will validate tables/columns up front and raise descriptive errors if something is missing.\n", - "\n", - "Below is a minimal schema example. Adjust columns to your configured reporters.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "938c804e27f84196a10c8828c723f798", - "metadata": { - "editable": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "CREATE SCHEMA IF NOT EXISTS public;\n", - "CREATE TABLE IF NOT EXISTS public.model_data (\n", - " step INTEGER,\n", - " seed VARCHAR,\n", - " total_wealth BIGINT,\n", - " n_agents INTEGER\n", - ");\n", - "\n", - "\n", - "CREATE TABLE IF NOT EXISTS public.agent_data (\n", - " step INTEGER,\n", - " seed VARCHAR,\n", - " unique_id BIGINT,\n", - " wealth BIGINT\n", - ");\n", - "\n" - ] - } - ], - "source": [ - "DDL_MODEL = r\"\"\"\n", - "CREATE SCHEMA IF NOT EXISTS public;\n", - "CREATE TABLE IF NOT EXISTS public.model_data (\n", - " step INTEGER,\n", - " seed VARCHAR,\n", - " total_wealth BIGINT,\n", - " n_agents INTEGER\n", - ");\n", - "\"\"\"\n", - "DDL_AGENT = r\"\"\"\n", - "CREATE TABLE IF NOT EXISTS public.agent_data (\n", - " step INTEGER,\n", - " seed VARCHAR,\n", - " unique_id BIGINT,\n", - " wealth BIGINT\n", - ");\n", - "\"\"\"\n", - "print(DDL_MODEL)\n", - "print(DDL_AGENT)" - ] - }, - { - "cell_type": "markdown", - "id": "504fb2a444614c0babb325280ed9130a", - "metadata": {}, - "source": [ - "After creating the tables (outside this notebook or via a DB connection cell), configure and flush:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "59bbdb311c014d738909a11f9e486628", - "metadata": { - "editable": true - }, - "outputs": [], - "source": [ - "POSTGRES_URI = \"postgresql://user:pass@localhost:5432/mydb\"\n", - "m_pg = MoneyModel(300)\n", - "m_pg.dc._storage = \"postgresql\"\n", - "m_pg.dc._storage_uri = POSTGRES_URI\n", - "m_pg.run(6)\n", - "m_pg.dc.flush()" - ] - }, - { - "cell_type": "markdown", - "id": "b43b363d81ae4b689946ece5c682cd59", - "metadata": {}, - "source": [ - "## Triggers & Conditional Collection\n", - "\n", - "The collector accepts a `trigger: Callable[[Model], bool]`. When using `conditional_collect()`, the collector checks the trigger and collects only if it returns `True`.\n", - "\n", - "You can always call `collect()` to gather data unconditionally.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "8a65eabff63a45729fe45fb5ade58bdc", - "metadata": { - "editable": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "shape: (5, 5)
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" - ], - "text/plain": [ - "shape: (5, 5)\n", - "┌──────┬─────────────────────────────────┬───────┬──────────────┬──────────┐\n", - "│ step ┆ seed ┆ batch ┆ total_wealth ┆ n_agents │\n", - "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", - "│ i64 ┆ str ┆ i64 ┆ f64 ┆ i64 │\n", - "╞══════╪═════════════════════════════════╪═══════╪══════════════╪══════════╡\n", - "│ 2 ┆ 540832786058427425452319829502… ┆ 0 ┆ 100.0 ┆ 100 │\n", - "│ 4 ┆ 540832786058427425452319829502… ┆ 0 ┆ 100.0 ┆ 100 │\n", - "│ 6 ┆ 540832786058427425452319829502… ┆ 0 ┆ 100.0 ┆ 100 │\n", - "│ 8 ┆ 540832786058427425452319829502… ┆ 0 ┆ 100.0 ┆ 100 │\n", - "│ 10 ┆ 540832786058427425452319829502… ┆ 0 ┆ 100.0 ┆ 100 │\n", - "└──────┴─────────────────────────────────┴───────┴──────────────┴──────────┘" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = MoneyModel(100)\n", - "m.dc.trigger = lambda model: model._steps % 3 == 0 # every 3rd step\n", - "m.run(10, conditional=True)\n", - "m.dc.data[\"model\"].head()" - ] - }, - { - "cell_type": "markdown", - "id": "c3933fab20d04ec698c2621248eb3be0", - "metadata": {}, - "source": [ - "## Troubleshooting\n", - "\n", - "- **ValueError: Please define a storage_uri** — for non-memory backends you must set `_storage_uri`.\n", - "- **Missing columns in table** — check the PostgreSQL error text; create/alter the table to include the columns for your configured `model_reporters` and `agent_reporters`, plus required `step` and `seed`.\n", - "- **Permissions/credentials errors** (S3/PostgreSQL) — ensure correct IAM/credentials or database permissions.\n" - ] - }, - { - "cell_type": "markdown", - "id": "4dd4641cc4064e0191573fe9c69df29b", - "metadata": {}, - "source": [ - "---\n", - "*Generated on 2025-08-30.*\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mesa-frames (3.12.3)", - "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": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/general/user-guide/4_datacollector.py b/docs/general/user-guide/4_datacollector.py new file mode 100644 index 00000000..b08e35a2 --- /dev/null +++ b/docs/general/user-guide/4_datacollector.py @@ -0,0 +1,229 @@ +from __future__ import annotations + +# %% [markdown] +"""# Data Collector Tutorial + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/projectmesa/mesa-frames/blob/main/docs/general/user-guide/4_datacollector.ipynb) + +This notebook walks you through using the concrete `DataCollector` in `mesa-frames` to collect model- and agent-level data and write it to different storage backends: **memory, CSV, Parquet, S3, and PostgreSQL**. + +It also shows how to use **conditional triggers** and how the **schema validation** behaves for PostgreSQL.""" + +# %% [markdown] +"""## Installation (Colab or fresh env) + +Uncomment and run the next cell if you're in Colab or a clean environment.""" + +# %% +# !pip install git+https://github.com/projectmesa/mesa-frames mesa + +# %% [markdown] +"""## Minimal Example Model + +We create a tiny model using the `Model` and an `AgentSet`-style agent container. This is just to demonstrate collection APIs.""" + +# %% +from mesa_frames import Model, AgentSet, DataCollector +import polars as pl + + +class MoneyAgents(AgentSet): + def __init__(self, n: int, model: Model): + super().__init__(model) + # one column, one unit of wealth each + self += pl.DataFrame({"wealth": pl.ones(n, eager=True)}) + + def step(self) -> None: + self.select(self.wealth > 0) + receivers = self.df.sample(n=len(self.active_agents), with_replacement=True) + self["active", "wealth"] -= 1 + income = receivers.group_by("unique_id").len() + self[income["unique_id"], "wealth"] += income["len"] + + +class MoneyModel(Model): + def __init__(self, n: int): + super().__init__() + self.sets.add(MoneyAgents(n, self)) + self.dc = DataCollector( + model=self, + model_reporters={ + "total_wealth": lambda m: m.sets["MoneyAgents"].df["wealth"].sum(), + "n_agents": lambda m: len(m.sets["MoneyAgents"]), + }, + agent_reporters={ + "wealth": "wealth", # pull existing column + }, + storage="memory", # we'll switch this per example + storage_uri=None, + trigger=lambda m: m.steps % 2 + == 0, # collect every 2 steps via conditional_collect + reset_memory=True, + ) + + def step(self): + self.sets.do("step") + + def run(self, steps: int, conditional: bool = True): + for _ in range(steps): + self.step() + self.dc.conditional_collect() # or .collect if you want to collect every step regardless of trigger + + +model = MoneyModel(1000) +model.run(10) +model.dc.data # peek in-memory dataframes + +# %% [markdown] +"""## Saving the data for later use + +`DataCollector` supports multiple storage backends. +Files are saved with **step number** and **batch number** (e.g., `model_step10_batch2.csv`) so multiple collects at the same step don’t overwrite. + +- **CSV:** `storage="csv"` → writes `model_step{n}_batch{k}.csv`, easy to open anywhere. +- **Parquet:** `storage="parquet"` → compressed, efficient for large datasets. +- **S3:** `storage="S3-csv"`/`storage="S3-parquet"` → saves CSV/Parquet directly to Amazon S3. +- **PostgreSQL:** `storage="postgresql"` → inserts results into `model_data` and `agent_data` tables for querying.""" + +# %% [markdown] +"""## Writing to Local CSV + +Switch the storage to `csv` and provide a folder path. Files are written as `model_step{n}.csv` and `agent_step{n}.csv`.""" + +# %% +import os + +os.makedirs("./data_csv", exist_ok=True) +model_csv = MoneyModel(1000) +model_csv.dc = DataCollector( + model=model_csv, + model_reporters={ + "total_wealth": lambda m: m.sets["MoneyAgents"].df["wealth"].sum(), + "n_agents": lambda m: len(m.sets["MoneyAgents"]), + }, + agent_reporters={ + "wealth": "wealth", + }, + storage="csv", # saving as csv + storage_uri="./data_csv", + trigger=lambda m: m._steps % 2 == 0, + reset_memory=True, +) +model_csv.run(10) +model_csv.dc.flush() +os.listdir("./data_csv") + +# %% [markdown] +"""## Writing to Local Parquet + +Use `parquet` for columnar output.""" + +# %% +os.makedirs("./data_parquet", exist_ok=True) +model_parq = MoneyModel(1000) +model_parq.dc = DataCollector( + model=model_parq, + model_reporters={ + "total_wealth": lambda m: m.sets["MoneyAgents"].df["wealth"].sum(), + "n_agents": lambda m: len(m.sets["MoneyAgents"]), + }, + agent_reporters={ + "wealth": "wealth", + }, + storage="parquet", # save as parquet + storage_uri="data_parquet", + trigger=lambda m: m._steps % 2 == 0, + reset_memory=True, +) +model_parq.run(10) +model_parq.dc.flush() +os.listdir("./data_parquet") + +# %% [markdown] +"""## Writing to Amazon S3 (CSV or Parquet) + +Set AWS credentials via environment variables or your usual config. Then choose `S3-csv` or `S3-parquet` and pass an S3 URI (e.g., `s3://my-bucket/experiments/run-1`). + +> **Note:** This cell requires network access & credentials when actually run.""" + +# %% +model_s3 = MoneyModel(1000) +model_s3.dc = DataCollector( + model=model_s3, + model_reporters={ + "total_wealth": lambda m: m.sets["MoneyAgents"].df["wealth"].sum(), + "n_agents": lambda m: len(m.sets["MoneyAgents"]), + }, + agent_reporters={ + "wealth": "wealth", + }, + storage="S3-csv", # save as csv in S3 + storage_uri="s3://my-bucket/experiments/run-1", # change it to required path + trigger=lambda m: m._steps % 2 == 0, + reset_memory=True, +) +model_s3.run(10) +model_s3.dc.flush() + +# %% [markdown] +"""## Writing to PostgreSQL + +PostgreSQL requires that the target tables exist and that the expected reporter columns are present. The collector will validate tables/columns up front and raise descriptive errors if something is missing. + +Below is a minimal schema example. Adjust columns to your configured reporters.""" + +# %% +DDL_MODEL = r""" +CREATE SCHEMA IF NOT EXISTS public; +CREATE TABLE IF NOT EXISTS public.model_data ( + step INTEGER, + seed VARCHAR, + total_wealth BIGINT, + n_agents INTEGER +); +""" +DDL_AGENT = r""" +CREATE TABLE IF NOT EXISTS public.agent_data ( + step INTEGER, + seed VARCHAR, + unique_id BIGINT, + wealth BIGINT +); +""" +print(DDL_MODEL) +print(DDL_AGENT) + +# %% [markdown] +"""After creating the tables (outside this notebook or via a DB connection cell), configure and flush:""" + +# %% +POSTGRES_URI = "postgresql://user:pass@localhost:5432/mydb" +m_pg = MoneyModel(300) +m_pg.dc._storage = "postgresql" +m_pg.dc._storage_uri = POSTGRES_URI +m_pg.run(6) +m_pg.dc.flush() + +# %% [markdown] +"""## Triggers & Conditional Collection + +The collector accepts a `trigger: Callable[[Model], bool]`. When using `conditional_collect()`, the collector checks the trigger and collects only if it returns `True`. + +You can always call `collect()` to gather data unconditionally.""" + +# %% +m = MoneyModel(100) +m.dc.trigger = lambda model: model._steps % 3 == 0 # every 3rd step +m.run(10, conditional=True) +m.dc.data["model"].head() + +# %% [markdown] +"""## Troubleshooting + +- **ValueError: Please define a storage_uri** — for non-memory backends you must set `_storage_uri`. +- **Missing columns in table** — check the PostgreSQL error text; create/alter the table to include the columns for your configured `model_reporters` and `agent_reporters`, plus required `step` and `seed`. +- **Permissions/credentials errors** (S3/PostgreSQL) — ensure correct IAM/credentials or database permissions.""" + +# %% [markdown] +"""--- +*Generated on 2025-08-30.*""" From 978b8745145c3faedf29aa15d4057403cb6e636d Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Tue, 23 Sep 2025 12:26:12 +0200 Subject: [PATCH 84/99] docs: update README for clarity and organization, enhance benchmarks section, and improve installation instructions --- README.md | 156 ++++++++++++++++++++++++++---------------------------- 1 file changed, 75 insertions(+), 81 deletions(-) diff --git a/README.md b/README.md index 195d7625..b35537a0 100644 --- a/README.md +++ b/README.md @@ -21,7 +21,7 @@ Classic [Mesa](https://github.com/projectmesa/mesa) stores each agent as a Pytho You keep the Mesa-style `Model` / `AgentSet` structure, but updates are vectorized and memory-efficient. ### Why it matters -- ⚡ **10× faster** bulk updates on 10k+ agents (see benchmarks) +- ⚡ **10× faster** bulk updates on 10k+ agents ([see Benchmarks](#benchmarks)) - 📊 **Columnar execution** via [Polars](https://docs.pola.rs/): [SIMD](https://en.wikipedia.org/wiki/Single_instruction,_multiple_data) ops, multi-core support - 🔄 **Declarative logic**: agent rules as transformations, not Python loops - 🚀 **Roadmap**: Lazy queries and GPU support for even faster models @@ -35,115 +35,109 @@ You keep the Mesa-style `Model` / `AgentSet` structure, but updates are vectoriz ❌ **Not a good fit if:** your model depends on strict per-agent sequencing, complex non-vectorizable methods, or fine-grained identity tracking. +--- -### Install from Source (development) - -Clone the repository and install dependencies with [uv](https://docs.astral.sh/uv/): - -```bash -git clone https://github.com/projectmesa/mesa-frames.git -cd mesa-frames -uv sync --all-extras -``` +## Why DataFrames? -`uv sync` creates a local `.venv/` with mesa-frames and its development extras. Run tooling through uv to keep the virtual environment isolated: +DataFrames enable SIMD and columnar operations that are far more efficient than Python loops. +mesa-frames currently uses **Polars** as its backend. -```bash -uv run pytest -q --cov=mesa_frames --cov-report=term-missing -uv run ruff check . --fix -uv run pre-commit run -a -``` +| Feature | mesa (classic) | mesa-frames | +| ---------------------- | -------------- | ----------- | +| Storage | Python objects | Polars DataFrame | +| Updates | Loops | Vectorized ops | +| Memory overhead | High | Low | +| Max agents (practical) | ~10^3 | ~10^6+ | -## Usage +--- -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/projectmesa/mesa-frames/blob/main/docs/general/user-guide/2_introductory-tutorial.ipynb) +## Benchmarks -**Note:** mesa-frames is currently in its early stages of development. As such, the usage patterns and API are subject to change. Breaking changes may be introduced. Reports of feedback and issues are encouraged. +

+ + Reproduce Benchmarks + +

-[You can find the API documentation here](https://projectmesa.github.io/mesa-frames/api). -### Creation of an Agent +mesa-frames delivers consistent speedups across both toy and canonical ABMs. +At 10k agents, it runs **~10× faster** than classic Mesa, and the gap grows with scale. -The agent implementation differs from base mesa. Agents are only defined at the AgentSet level. You can import `AgentSet`. As in mesa, you subclass and make sure to call `super().__init__(model)`. You can use the `add` method or the `+=` operator to add agents to the AgentSet. Most methods mirror the functionality of `mesa.AgentSet`. Additionally, `mesa-frames.AgentSet` implements many dunder methods such as `AgentSet[mask, attr]` to get and set items intuitively. All operations are by default inplace, but if you'd like to use functional programming, mesa-frames implements a fast copy method which aims to reduce memory usage, relying on reference-only and native copy methods. +

+ Benchmark: Boltzmann Wealth + Benchmark: Sugarscape IG +

-```python -from mesa-frames import AgentSet -class MoneyAgents(AgentSet): - def __init__(self, n: int, model: Model): - super().__init__(model) - # Adding the agents to the agent set - self += pl.DataFrame( - {"wealth": pl.ones(n, eager=True)} - ) +--- - def step(self) -> None: - # The give_money method is called - self.do("give_money") +## Quick Start - def give_money(self): - # Active agents are changed to wealthy agents - self.select(self.wealth > 0) +

+ + Explore the Tutorials + +

- # Receiving agents are sampled (only native expressions currently supported) - other_agents = self.df.sample( - n=len(self.active_agents), with_replacement=True - ) +1. **Install** - # Wealth of wealthy is decreased by 1 - self["active", "wealth"] -= 1 +```bash + pip install mesa-frames +``` - # Compute the income of the other agents (only native expressions currently supported) - new_wealth = other_agents.group_by("unique_id").len() +Or for development: - # Add the income to the other agents - self[new_wealth, "wealth"] += new_wealth["len"] +```bash +git clone https://github.com/projectmesa/mesa-frames.git +cd mesa-frames +uv sync --all-extras ``` -### Creation of the Model +2. **Create a model** -Creation of the model is fairly similar to the process in mesa. You subclass `Model` and call `super().__init__()`. The `model.sets` attribute has the same interface as `mesa-frames.AgentSet`. You can use `+=` or `self.sets.add` with a `mesa-frames.AgentSet` (or a list of `AgentSet`) to add agents to the model. + ```python + from mesa_frames import AgentSet, Model + import polars as pl -```python -from mesa-frames import Model + class MoneyAgents(AgentSet): + def __init__(self, n: int, model: Model): + super().__init__(model) + self += pl.DataFrame({"wealth": pl.ones(n, eager=True)}) -class MoneyModelDF(Model): - def __init__(self, N: int, agents_cls): - super().__init__() - self.n_agents = N - self.sets += MoneyAgents(N, self) + def give_money(self): + self.select(self.wealth > 0) + other_agents = self.df.sample(n=len(self.active_agents), with_replacement=True) + self["active", "wealth"] -= 1 + new_wealth = other_agents.group_by("unique_id").len() + self[new_wealth, "wealth"] += new_wealth["len"] - def step(self): - # Executes the step method for every agentset in self.sets - self.sets.do("step") + def step(self): + self.do("give_money") - def run_model(self, n): - for _ in range(n): - self.step() -``` + class MoneyModelDF(Model): + def __init__(self, N: int): + super().__init__() + self.sets += MoneyAgents(N, self) -## What's Next? 🔮 + def step(self): + self.sets.do("step") + ``` -- Refine the API to make it more understandable for someone who is already familiar with the mesa package. The goal is to provide a seamless experience for users transitioning to or incorporating mesa-frames. -- Adding support for default mesa functions to ensure that the standard mesa functionality is preserved. -- Adding GPU functionality (cuDF and Dask-cuDF). -- Creating a decorator that will automatically vectorize an existing mesa model. This feature will allow users to easily tap into the performance enhancements that mesa-frames offers without significant code alterations. -- Creating a unique class for AgentSet, independent of the backend implementation. +--- -## License +## Roadmap -Copyright 2024 Adam Amer, Project Mesa team and contributors +> Community contributions welcome — see the [full roadmap](https://projectmesa.github.io/mesa-frames/general/roadmap) -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at +* Transition to LazyFrames for optimization and GPU support +* Auto-vectorize existing Mesa models via decorator +* Increase possible Spaces +* Refine the API to align to Mesa - http://www.apache.org/licenses/LICENSE-2.0 +--- + +## License -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. +Copyright © 2025 Adam Amer, Project Mesa team and contributors -For the full license text, see the [LICENSE](https://github.com/projectmesa/mesa-frames/blob/main/LICENSE) file in the GitHub repository. +Licensed under the [Apache License, Version 2.0](https://raw.githubusercontent.com/projectmesa/mesa-frames/refs/heads/main/LICENSE). \ No newline at end of file From 5b9c2e9ff2116615744f0bef01115acb82a13ed1 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Tue, 23 Sep 2025 12:29:40 +0200 Subject: [PATCH 85/99] docs: fix typos in advanced tutorial for clarity --- docs/general/user-guide/3_advanced_tutorial.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index ef1bfa4c..6c0c97d0 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -29,7 +29,7 @@ The update schedule matters for micro-behaviour, so we study three variants: 1. **Sequential loop (asynchronous):** This is the traditional definition. Ants move one at a time in random order. -This cannnot be vectorised easily as the best move for an ant might depend on the moves of earlier ants (for example, if they target the same cell). +This cannot be vectorised easily as the best move for an ant might depend on the moves of earlier ants (for example, if they target the same cell). 2. **Sequential with Numba:** matches the first variant but relies on a compiled helper for speed. 3. **Parallel (synchronous):** all ants propose moves; conflicts are resolved at @@ -932,7 +932,7 @@ def move(self) -> None: """ ### 3.5 Simultaneous Movement with Conflict Resolution (the Polars mesa-frames idiomatic way) -The previous implementation is optimal speed-wise but it's a bit low-level. It requires mantaining an occupancy grid and imperative loops and it might become tricky to extend with more complex movement rules or models. +The previous implementation is optimal speed-wise but it's a bit low-level. It requires maintaining an occupancy grid and imperative loops and it might become tricky to extend with more complex movement rules or models. To stay in mesa-frames idiom, we can implement a parallel movement policy that uses Polars DataFrame operations to resolve conflicts when multiple agents target the same cell. These conflicts are resolved in rounds: in each round, each agent proposes its current best candidate cell; winners per cell are chosen at random, and losers are promoted to their next-ranked choice. This continues until all agents have moved. This implementation is a tad slower but still efficient and easier to read (for a Polars user). From 5ac548c1a522364e11bc4d2000717c31918768b8 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Tue, 23 Sep 2025 12:29:48 +0200 Subject: [PATCH 86/99] docs: update Data Collector tutorial for clarity and organization --- docs/general/user-guide/4_datacollector.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/general/user-guide/4_datacollector.py b/docs/general/user-guide/4_datacollector.py index b08e35a2..16d9837b 100644 --- a/docs/general/user-guide/4_datacollector.py +++ b/docs/general/user-guide/4_datacollector.py @@ -75,14 +75,14 @@ def run(self, steps: int, conditional: bool = True): model.dc.data # peek in-memory dataframes # %% [markdown] -"""## Saving the data for later use - -`DataCollector` supports multiple storage backends. -Files are saved with **step number** and **batch number** (e.g., `model_step10_batch2.csv`) so multiple collects at the same step don’t overwrite. - -- **CSV:** `storage="csv"` → writes `model_step{n}_batch{k}.csv`, easy to open anywhere. -- **Parquet:** `storage="parquet"` → compressed, efficient for large datasets. -- **S3:** `storage="S3-csv"`/`storage="S3-parquet"` → saves CSV/Parquet directly to Amazon S3. +"""## Saving the data for later use + +`DataCollector` supports multiple storage backends. +Files are saved with **step number** and **batch number** (e.g., `model_step10_batch2.csv`) so multiple collects at the same step don’t overwrite. + +- **CSV:** `storage="csv"` → writes `model_step{n}_batch{k}.csv`, easy to open anywhere. +- **Parquet:** `storage="parquet"` → compressed, efficient for large datasets. +- **S3:** `storage="S3-csv"`/`storage="S3-parquet"` → saves CSV/Parquet directly to Amazon S3. - **PostgreSQL:** `storage="postgresql"` → inserts results into `model_data` and `agent_data` tables for querying.""" # %% [markdown] From 39053e19c31e8f3dad1601323ab9b8f2a1d6f809 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Tue, 23 Sep 2025 12:30:13 +0200 Subject: [PATCH 87/99] docs: improve formatting and consistency in README.md --- README.md | 31 +++++++++++++++---------------- 1 file changed, 15 insertions(+), 16 deletions(-) diff --git a/README.md b/README.md index b35537a0..032483d7 100644 --- a/README.md +++ b/README.md @@ -15,23 +15,24 @@ ## Scale Mesa beyond its limits -Classic [Mesa](https://github.com/projectmesa/mesa) stores each agent as a Python object, which quickly becomes a bottleneck at scale. -**mesa-frames** reimagines agent storage using **Polars DataFrames**, so agents live in a columnar store rather than the Python heap. +Classic [Mesa](https://github.com/projectmesa/mesa) stores each agent as a Python object, which quickly becomes a bottleneck at scale. +**mesa-frames** reimagines agent storage using **Polars DataFrames**, so agents live in a columnar store rather than the Python heap. You keep the Mesa-style `Model` / `AgentSet` structure, but updates are vectorized and memory-efficient. ### Why it matters + - ⚡ **10× faster** bulk updates on 10k+ agents ([see Benchmarks](#benchmarks)) -- 📊 **Columnar execution** via [Polars](https://docs.pola.rs/): [SIMD](https://en.wikipedia.org/wiki/Single_instruction,_multiple_data) ops, multi-core support -- 🔄 **Declarative logic**: agent rules as transformations, not Python loops +- 📊 **Columnar execution** via [Polars](https://docs.pola.rs/): [SIMD](https://en.wikipedia.org/wiki/Single_instruction,_multiple_data) ops, multi-core support +- 🔄 **Declarative logic**: agent rules as transformations, not Python loops - 🚀 **Roadmap**: Lazy queries and GPU support for even faster models --- ## Who is it for? -- Researchers needing to scale to **tens or hundreds of thousands of agents** -- Users whose agent logic can be written as **vectorized, set-based operations** +- Researchers needing to scale to **tens or hundreds of thousands of agents** +- Users whose agent logic can be written as **vectorized, set-based operations** ❌ **Not a good fit if:** your model depends on strict per-agent sequencing, complex non-vectorizable methods, or fine-grained identity tracking. @@ -39,7 +40,7 @@ You keep the Mesa-style `Model` / `AgentSet` structure, but updates are vectoriz ## Why DataFrames? -DataFrames enable SIMD and columnar operations that are far more efficient than Python loops. +DataFrames enable SIMD and columnar operations that are far more efficient than Python loops. mesa-frames currently uses **Polars** as its backend. | Feature | mesa (classic) | mesa-frames | @@ -59,8 +60,7 @@ mesa-frames currently uses **Polars** as its backend.

- -mesa-frames delivers consistent speedups across both toy and canonical ABMs. +mesa-frames delivers consistent speedups across both toy and canonical ABMs. At 10k agents, it runs **~10× faster** than classic Mesa, and the gap grows with scale.

@@ -68,7 +68,6 @@ At 10k agents, it runs **~10× faster** than classic Mesa, and the gap grows wit Benchmark: Sugarscape IG

- --- ## Quick Start @@ -79,7 +78,7 @@ At 10k agents, it runs **~10× faster** than classic Mesa, and the gap grows wit

-1. **Install** +1. **Install** ```bash pip install mesa-frames @@ -129,10 +128,10 @@ uv sync --all-extras > Community contributions welcome — see the [full roadmap](https://projectmesa.github.io/mesa-frames/general/roadmap) -* Transition to LazyFrames for optimization and GPU support -* Auto-vectorize existing Mesa models via decorator -* Increase possible Spaces -* Refine the API to align to Mesa +- Transition to LazyFrames for optimization and GPU support +- Auto-vectorize existing Mesa models via decorator +- Increase possible Spaces +- Refine the API to align to Mesa --- @@ -140,4 +139,4 @@ uv sync --all-extras Copyright © 2025 Adam Amer, Project Mesa team and contributors -Licensed under the [Apache License, Version 2.0](https://raw.githubusercontent.com/projectmesa/mesa-frames/refs/heads/main/LICENSE). \ No newline at end of file +Licensed under the [Apache License, Version 2.0](https://raw.githubusercontent.com/projectmesa/mesa-frames/refs/heads/main/LICENSE). From 8d7acbf16b779ffa32b99f4c662d37849ab852ae Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Tue, 23 Sep 2025 12:33:54 +0200 Subject: [PATCH 88/99] docs: clarify parameter descriptions in advanced tutorial --- .../general/user-guide/3_advanced_tutorial.py | 50 ++++++++++++------- 1 file changed, 33 insertions(+), 17 deletions(-) diff --git a/docs/general/user-guide/3_advanced_tutorial.py b/docs/general/user-guide/3_advanced_tutorial.py index 6c0c97d0..b1009734 100644 --- a/docs/general/user-guide/3_advanced_tutorial.py +++ b/docs/general/user-guide/3_advanced_tutorial.py @@ -221,9 +221,12 @@ def _safe_corr(x: np.ndarray, y: np.ndarray) -> float: Parameters ---------- - x, y : np.ndarray - One-dimensional numeric arrays of the same length containing the two - variables to correlate. + x : np.ndarray + One-dimensional numeric array containing the first variable to + correlate. + y : np.ndarray + One-dimensional numeric array containing the second variable to + correlate. Returns ------- @@ -254,7 +257,7 @@ class Sugarscape(Model): Parameters ---------- - agent_type : type + agent_type : type[AntsBase] The :class:`AgentSet` subclass implementing the movement rules (sequential, numba-accelerated, or parallel). n_agents : int @@ -266,7 +269,7 @@ class Sugarscape(Model): max_sugar : int, optional Upper bound for the randomly initialised sugar values on the grid, by default 4. - seed : int or None, optional + seed : int | None, optional RNG seed to make runs reproducible across variants, by default None. Notes @@ -638,9 +641,9 @@ def _choose_best_cell( Agent's current coordinate. vision : int Maximum vision radius along cardinal axes. - sugar_map : dict + sugar_map : dict[tuple[int, int], int] Mapping from ``(x, y)`` to sugar amount. - blocked : set or None + blocked : set[tuple[int, int]] | None Optional set of coordinates that should be considered occupied and therefore skipped (except the origin which is always allowed). @@ -687,7 +690,7 @@ def _current_sugar_map(self) -> dict[tuple[int, int], int]: Returns ------- - dict + dict[tuple[int, int], int] Keys are ``(x, y)`` tuples and values are the integer sugar amount on that cell (zero if missing/None). """ @@ -749,12 +752,22 @@ def _numba_should_replace( Parameters ---------- - best_sugar, candidate_sugar : int - Sugar at the current best cell and the candidate cell. - best_distance, candidate_distance : int - Manhattan distances from the origin to the best and candidate cells. - best_x, best_y, candidate_x, candidate_y : int - Coordinates used for the final lexicographic tie-break. + best_sugar : int + Sugar at the current best cell. + best_distance : int + Manhattan distance from the origin to the current best cell. + best_x : int + X coordinate of the current best cell. + best_y : int + Y coordinate of the current best cell. + candidate_sugar : int + Sugar at the candidate cell. + candidate_distance : int + Manhattan distance from the origin to the candidate cell. + candidate_x : int + X coordinate of the candidate cell. + candidate_y : int + Y coordinate of the candidate cell. Returns ------- @@ -859,9 +872,12 @@ def sequential_move_numba( Parameters ---------- - dim0, dim1 : np.ndarray - 1D integer arrays of length n_agents containing the x and y - coordinates for each agent. + dim0 : np.ndarray + 1D integer array of length n_agents containing the x coordinates + for each agent. + dim1 : np.ndarray + 1D integer array of length n_agents containing the y coordinates + for each agent. vision : np.ndarray 1D integer array of vision radii for each agent. sugar_array : np.ndarray From 24ba420379eff79d113e999926ffddfd092b43c0 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Tue, 23 Sep 2025 12:34:00 +0200 Subject: [PATCH 89/99] docs: improve README formatting and consistency --- README.md | 25 +++++++++---------------- 1 file changed, 9 insertions(+), 16 deletions(-) diff --git a/README.md b/README.md index 032483d7..9ff6205a 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,10 @@ +

- Mesa logo + Mesa logo

mesa-frames

+ | | | | ------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | @@ -54,29 +56,20 @@ mesa-frames currently uses **Polars** as its backend. ## Benchmarks -

- - Reproduce Benchmarks - -

+[![Reproduce Benchmarks](https://img.shields.io/badge/Reproduce%20Benchmarks-📊-orange?style=for-the-badge)](https://projectmesa.github.io/mesa-frames/general/benchmarks/) mesa-frames delivers consistent speedups across both toy and canonical ABMs. At 10k agents, it runs **~10× faster** than classic Mesa, and the gap grows with scale. -

- Benchmark: Boltzmann Wealth - Benchmark: Sugarscape IG -

+![Benchmark: Boltzmann Wealth](examples/boltzmann_wealth/boltzmann_benchmark.png) + +![Benchmark: Sugarscape IG](examples/sugarscape/sugarscape_benchmark.png) --- ## Quick Start -

- - Explore the Tutorials - -

+[![Explore the Tutorials](https://img.shields.io/badge/Explore%20the%20Tutorials-📚-blue?style=for-the-badge)](https://projectmesa.github.io/mesa-frames/general/user-guide/) 1. **Install** @@ -92,7 +85,7 @@ cd mesa-frames uv sync --all-extras ``` -2. **Create a model** +1. **Create a model** ```python from mesa_frames import AgentSet, Model From 0279f6a88e58208ad7382b2edee059b7ebccfdbe Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Wed, 24 Sep 2025 09:16:31 +0200 Subject: [PATCH 90/99] feat: add initial implementation of Boltzmann wealth example --- examples/boltzmann_wealth/__init__.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 examples/boltzmann_wealth/__init__.py diff --git a/examples/boltzmann_wealth/__init__.py b/examples/boltzmann_wealth/__init__.py new file mode 100644 index 00000000..e69de29b From ea06c973e5694f6ddbbc3edb44c1c38c306c390a Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Wed, 24 Sep 2025 11:41:13 +0200 Subject: [PATCH 91/99] feat: update docs dependencies to include typer version 0.9.0 --- pyproject.toml | 1 + uv.lock | 28 ++++++++++++++++++++++++++++ 2 files changed, 29 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 8ecbc911..c130f9e5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -64,6 +64,7 @@ test = [ docs = [ { include-group = "typechecking" }, + "typer[all]>=0.9.0", "mkdocs-material>=9.6.14", "mkdocs-jupyter>=0.25.1", "mkdocs-git-revision-date-localized-plugin>=1.4.7", diff --git a/uv.lock b/uv.lock index 4e4d7e1d..8095193c 100644 --- a/uv.lock +++ b/uv.lock @@ -1255,6 +1255,7 @@ dev = [ { name = "sphinx-copybutton" }, { name = "sphinx-design" }, { name = "sphinx-rtd-theme" }, + { name = "typer" }, ] docs = [ { name = "autodocsumm" }, @@ -1275,6 +1276,7 @@ docs = [ { name = "sphinx-copybutton" }, { name = "sphinx-design" }, { name = "sphinx-rtd-theme" }, + { name = "typer" }, ] test = [ { name = "beartype" }, @@ -1319,6 +1321,7 @@ dev = [ { name = "sphinx-copybutton", specifier = ">=0.5.2" }, { name = "sphinx-design", specifier = ">=0.6.1" }, { name = "sphinx-rtd-theme", specifier = ">=3.0.2" }, + { name = "typer", extras = ["all"], specifier = ">=0.9.0" }, ] docs = [ { name = "autodocsumm", specifier = ">=0.2.14" }, @@ -1339,6 +1342,7 @@ docs = [ { name = "sphinx-copybutton", specifier = ">=0.5.2" }, { name = "sphinx-design", specifier = ">=0.6.1" }, { name = "sphinx-rtd-theme", specifier = ">=3.0.2" }, + { name = "typer", extras = ["all"], specifier = ">=0.9.0" }, ] test = [ { name = "beartype", specifier = ">=0.21.0" }, @@ -2524,6 +2528,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl", hash = "sha256:636f8336facf092165e27924f223d3c62ca560b1f2bb5dff7ab7fad265361987", size = 294914, upload-time = "2024-01-25T13:21:49.598Z" }, ] +[[package]] +name = "shellingham" +version = "1.5.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url 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= "0.19.2" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "click" }, + { name = "rich" }, + { name = "shellingham" }, + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/21/ca/950278884e2ca20547ff3eb109478c6baf6b8cf219318e6bc4f666fad8e8/typer-0.19.2.tar.gz", hash = "sha256:9ad824308ded0ad06cc716434705f691d4ee0bfd0fb081839d2e426860e7fdca", size = 104755, upload-time = "2025-09-23T09:47:48.256Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/00/22/35617eee79080a5d071d0f14ad698d325ee6b3bf824fc0467c03b30e7fa8/typer-0.19.2-py3-none-any.whl", hash = "sha256:755e7e19670ffad8283db353267cb81ef252f595aa6834a0d1ca9312d9326cb9", size = 46748, upload-time = "2025-09-23T09:47:46.777Z" }, +] + [[package]] name = "typing-extensions" version = "4.15.0" From bb75630d699e10be5e4c6c4eeba985ed729a761f Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Wed, 24 Sep 2025 19:59:12 +0200 Subject: [PATCH 92/99] feat: add Typer CLI for mesa vs mesa-frames performance benchmarks --- benchmarks/cli.py | 216 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 216 insertions(+) create mode 100644 benchmarks/cli.py diff --git a/benchmarks/cli.py b/benchmarks/cli.py new file mode 100644 index 00000000..c0b9355d --- /dev/null +++ b/benchmarks/cli.py @@ -0,0 +1,216 @@ +"""Typer CLI for running mesa vs mesa-frames performance benchmarks.""" + +from __future__ import annotations + +from dataclasses import dataclass +from datetime import datetime +from pathlib import Path +from time import perf_counter +from typing import Literal, Annotated, Protocol, Optional + +import matplotlib.pyplot as plt +import polars as pl +import seaborn as sns +import typer + +from examples.boltzmann_wealth import backend_frames as boltzmann_frames +from examples.boltzmann_wealth import backend_mesa as boltzmann_mesa +from examples.sugarscape_ig.backend_frames import model as sugarscape_frames +from examples.sugarscape_ig.backend_mesa import model as sugarscape_mesa + +app = typer.Typer(add_completion=False) + +class RunnerP(Protocol): + def __call__(self, agents: int, steps: int, seed: Optional[int] = None) -> None: ... + + +@dataclass(slots=True) +class Backend: + name: Literal['mesa', 'frames'] + runner: RunnerP + + +@dataclass(slots=True) +class ModelConfig: + name: str + backends: list[Backend] + + +MODELS: dict[str, ModelConfig] = { + "boltzmann": ModelConfig( + name="boltzmann", + backends=[ + Backend(name="mesa", runner=boltzmann_mesa.simulate), + Backend(name="frames", runner=boltzmann_frames.simulate), + ], + ), + "sugarscape": ModelConfig( + name="sugarscape", + backends=[ + Backend( + name="mesa", + runner=sugarscape_mesa.simulate, + ), + Backend( + name="frames", + runner=sugarscape_frames.simulate, + ), + ], + ), +} + +def _parse_agents(value: str) -> list[int]: + value = value.strip() + if ":" in value: + parts = value.split(":") + if len(parts) != 3: + raise typer.BadParameter("Ranges must use start:stop:step format") + try: + start, stop, step = (int(part) for part in parts) + except ValueError as exc: + raise typer.BadParameter("Range values must be integers") from exc + if step <= 0: + raise typer.BadParameter("Step must be positive") + if start <= 0 or stop <= 0: + raise typer.BadParameter("Range endpoints must be positive") + if start > stop: + raise typer.BadParameter("Range start must be <= stop") + counts = list(range(start, stop + step, step)) + if counts[-1] > stop: + counts.pop() + return counts + try: + agents = int(value) + except ValueError as exc: # pragma: no cover - defensive + raise typer.BadParameter("Agent count must be an integer") from exc + if agents <= 0: + raise typer.BadParameter("Agent count must be positive") + return [agents] + +def _parse_models(value: str) -> list[str]: + """Parse models option into a list of model keys. + + Accepts: + - "all" -> returns all available model keys + - a single model name -> returns [name] + - a comma-separated list of model names -> returns list + + Validates that each selected model exists in MODELS. + """ + value = value.strip() + if value == "all": + return list(MODELS.keys()) + # support comma-separated lists + parts = [part.strip() for part in value.split(",") if part.strip()] + if not parts: + raise typer.BadParameter("Model selection must not be empty") + unknown = [p for p in parts if p not in MODELS] + if unknown: + raise typer.BadParameter(f"Unknown model selection: {', '.join(unknown)}") + # preserve order and uniqueness + seen = set() + result: list[str] = [] + for p in parts: + if p not in seen: + seen.add(p) + result.append(p) + return result + +def _plot_performance( + df: pl.DataFrame, model_name: str, output_dir: Path, timestamp: str +) -> None: + if df.is_empty(): + return + for theme, style in {"light": "whitegrid", "dark": "darkgrid"}.items(): + sns.set_theme(style=style) + fig, ax = plt.subplots(figsize=(8, 5)) + sns.lineplot( + data=df.to_pandas(), + x="agents", + y="runtime_seconds", + hue="backend", + estimator="mean", + errorbar="sd", + marker="o", + ax=ax, + ) + ax.set_title(f"{model_name.title()} runtime vs agents") + ax.set_xlabel("Agents") + ax.set_ylabel("Runtime (seconds)") + fig.tight_layout() + filename = output_dir / f"{model_name}_runtime_{timestamp}_{theme}.png" + fig.savefig(filename, dpi=300) + plt.close(fig) + + +@app.command() +def run( + models: Annotated[str, typer.Option( + help="Models to benchmark: boltzmann, sugarscape, or all", + callback=_parse_models + )] = "all", + agents: Annotated[list[int], typer.Option( + help="Agent count or range (start:stop:step)", + callback=_parse_agents + )] = "1000:5000:1000", + steps: Annotated[int, typer.Option( + min=0, + help="Number of steps per run.", + )] = 100, + repeats: Annotated[int, typer.Option(help="Repeats per configuration.", min=1)] = 1, + seed: Annotated[int, typer.Option(help="Optional RNG seed.")] = 42, + save: Annotated[bool, typer.Option(help="Persist benchmark CSV results.")] = True, + plot: Annotated[bool, typer.Option(help="Render performance plots.")] = True, + results_dir: Annotated[Path, typer.Option( + help="Directory for benchmark CSV results.", + )] = Path(__file__).resolve().parent / "results", + plots_dir: Annotated[Path, typer.Option( + help="Directory for benchmark plots.", + )] = Path(__file__).resolve().parent / "plots", +) -> None: + """Run performance benchmarks for the models models.""" + rows: list[dict[str, object]] = [] + timestamp = datetime.now(datetime.timezone.utc).strftime("%Y%m%d_%H%M%S") + for model in models: + config = MODELS[model] + typer.echo(f"Benchmarking {model} with agents {agents}") + for agents_count in agents: + for repeat_idx in range(repeats): + run_seed = seed + repeat_idx + for backend in config.backends: + start = perf_counter() + backend.runner(agents_count, steps, run_seed) + runtime = perf_counter() - start + rows.append( + { + "model": model, + "backend": backend.name, + "agents": agents_count, + "steps": steps, + "seed": run_seed, + "repeat_idx": repeat_idx, + "runtime_seconds": runtime, + "timestamp": timestamp, + } + ) + if not rows: + typer.echo("No benchmark data collected.") + return + df = pl.DataFrame(rows) + if save: + results_dir.mkdir(parents=True, exist_ok=True) + for model in models: + model_df = df.filter(pl.col("model") == model) + csv_path = results_dir / f"{model}_perf_{timestamp}.csv" + model_df.write_csv(csv_path) + typer.echo(f"Saved {model} results to {csv_path}") + if plot: + plots_dir.mkdir(parents=True, exist_ok=True) + for model in models: + model_df = df.filter(pl.col("model") == model) + _plot_performance(model_df, model, plots_dir, timestamp) + typer.echo(f"Saved {model} plots under {plots_dir}") + + +if __name__ == "__main__": + app() From 6a6604cd4d825388feb0c8ea2ac299194c6d2703 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Thu, 25 Sep 2025 19:37:43 +0200 Subject: [PATCH 93/99] feat: remove unused images and performance plot for Boltzmann wealth model; add Sugarscape IG backend package --- .../boltzmann_wealth/boltzmann_no_mesa.png | Bin 59194 -> 0 bytes .../boltzmann_wealth/boltzmann_with_mesa.png | Bin 61887 -> 0 bytes examples/boltzmann_wealth/performance_plot.py | 239 ------------------ .../sugarscape_ig/backend_frames/__init__.py | 1 + examples/sugarscape_ig/mesa_comparison.png | Bin 31762 -> 0 bytes examples/sugarscape_ig/polars_comparison.png | Bin 70235 -> 0 bytes 6 files changed, 1 insertion(+), 239 deletions(-) delete mode 100644 examples/boltzmann_wealth/boltzmann_no_mesa.png delete mode 100644 examples/boltzmann_wealth/boltzmann_with_mesa.png delete mode 100644 examples/boltzmann_wealth/performance_plot.py create mode 100644 examples/sugarscape_ig/backend_frames/__init__.py delete mode 100644 examples/sugarscape_ig/mesa_comparison.png delete mode 100644 examples/sugarscape_ig/polars_comparison.png diff --git a/examples/boltzmann_wealth/boltzmann_no_mesa.png 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mesa_implementation(n_agents: int) -> None: - model = MesaMoneyModel(n_agents) - model.run_model(100) - - -class MesaMoneyAgent(mesa.Agent): - """An agent with fixed initial wealth.""" - - def __init__(self, model): - # Pass the parameters to the parent class. - super().__init__(model) - - # Create the agent's variable and set the initial values. - self.wealth = 1 - - def step(self): - # Verify agent has some wealth - if self.wealth > 0: - other_agent = self.random.choice(self.model.agents) - if other_agent is not None: - other_agent.wealth += 1 - self.wealth -= 1 - - -class MesaMoneyModel(mesa.Model): - """A model with some number of agents.""" - - def __init__(self, N): - super().__init__() - self.num_agents = N - for _ in range(self.num_agents): - self.agents.add(MesaMoneyAgent(self)) - - def step(self): - """Advance the model by one step.""" - self.agents.shuffle_do("step") - - def run_model(self, n_steps) -> None: - for _ in range(n_steps): - self.step() - - -"""def compute_gini(model): - agent_wealths = model.sets.get("wealth") - x = sorted(agent_wealths) - N = model.num_agents - B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * sum(x)) - return 1 + (1 / N) - 2 * B""" - - -### ---------- Mesa-frames implementation ---------- ### - - -class MoneyAgentsConcise(AgentSet): - def __init__(self, n: int, model: Model): - super().__init__(model) - ## Adding the agents to the agent set - # 1. Changing the agents attribute directly (not recommended, if other agents were added before, they will be lost) - """self.sets = pl.DataFrame( - "wealth": pl.ones(n, eager=True)} - )""" - # 2. Adding the dataframe with add - """self.add( - pl.DataFrame( - { - "wealth": pl.ones(n, eager=True), - } - ) - )""" - # 3. Adding the dataframe with __iadd__ - self += pl.DataFrame({"wealth": pl.ones(n, eager=True)}) - - def step(self) -> None: - # The give_money method is called - # self.give_money() - self.do("give_money") - - def give_money(self): - ## Active agents are changed to wealthy agents - # 1. Using the __getitem__ method - # self.select(self["wealth"] > 0) - # 2. Using the fallback __getattr__ method - self.select(self.wealth > 0) - - # Receiving agents are sampled (only native expressions currently supported) - other_agents = self.df.sample(n=len(self.active_agents), with_replacement=True) - - # Wealth of wealthy is decreased by 1 - # 1. Using the __setitem__ method with self.active_agents mask - # self[self.active_agents, "wealth"] -= 1 - # 2. Using the __setitem__ method with "active" mask - self["active", "wealth"] -= 1 - - # Compute the income of the other agents (only native expressions currently supported) - new_wealth = other_agents.group_by("unique_id").len() - - # Add the income to the other agents - # 1. Using the set method - """self.set( - attr_names="wealth", - values=pl.col("wealth") + new_wealth["len"], - mask=new_wealth, - )""" - - # 2. Using the __setitem__ method - self[new_wealth, "wealth"] += new_wealth["len"] - - -class MoneyAgentsNative(AgentSet): - def __init__(self, n: int, model: Model): - super().__init__(model) - self += pl.DataFrame({"wealth": pl.ones(n, eager=True)}) - - def step(self) -> None: - self.do("give_money") - - def give_money(self): - ## Active agents are changed to wealthy agents - self.select(pl.col("wealth") > 0) - - other_agents = self.df.sample(n=len(self.active_agents), with_replacement=True) - - # Wealth of wealthy is decreased by 1 - self.df = self.df.with_columns( - wealth=pl.when( - pl.col("unique_id").is_in(self.active_agents["unique_id"].implode()) - ) - .then(pl.col("wealth") - 1) - .otherwise(pl.col("wealth")) - ) - - new_wealth = other_agents.group_by("unique_id").len() - - # Add the income to the other agents - self.df = ( - self.df.join(new_wealth, on="unique_id", how="left") - .fill_null(0) - .with_columns(wealth=pl.col("wealth") + pl.col("len")) - .drop("len") - ) - - -class MoneyModel(Model): - def __init__(self, N: int, agents_cls): - super().__init__() - self.n_agents = N - self.sets += agents_cls(N, self) - - def step(self): - # Executes the step method for every agentset in self.sets - self.sets.do("step") - - def run_model(self, n): - for _ in range(n): - self.step() - - -def mesa_frames_polars_concise(n_agents: int) -> None: - model = MoneyModel(n_agents, MoneyAgentsConcise) - model.run_model(100) - - -def mesa_frames_polars_native(n_agents: int) -> None: - model = MoneyModel(n_agents, MoneyAgentsNative) - model.run_model(100) - - -def plot_and_print_benchmark(labels, kernels, n_range, title, image_path): - out = perfplot.bench( - setup=lambda n: n, - kernels=kernels, - labels=labels, - n_range=n_range, - xlabel="Number of agents", - equality_check=None, - title=title, - ) - - plt.ylabel("Execution time (s)") - out.save(image_path, transparent=False) - - print("\nExecution times:") - for i, label in enumerate(labels): - print(f"---------------\n{label}:") - for n, t in zip(out.n_range, out.timings_s[i]): - print(f" Number of agents: {n}, Time: {t:.2f} seconds") - print("---------------") - - -def main(): - sns.set_theme(style="whitegrid") - - labels_0 = [ - "mesa", - "mesa-frames (pl concise)", - "mesa-frames (pl native)", - ] - kernels_0 = [ - mesa_implementation, - mesa_frames_polars_concise, - mesa_frames_polars_native, - ] - n_range_0 = [k for k in range(0, 100001, 10000)] - title_0 = "100 steps of the Boltzmann Wealth model:\n" + " vs ".join(labels_0) - image_path_0 = "boltzmann_with_mesa.png" - - plot_and_print_benchmark(labels_0, kernels_0, n_range_0, title_0, image_path_0) - - labels_1 = [ - "mesa-frames (pl concise)", - "mesa-frames (pl native)", - ] - kernels_1 = [ - mesa_frames_polars_concise, - mesa_frames_polars_native, - ] - n_range_1 = [k for k in range(100000, 1000001, 100000)] - title_1 = "100 steps of the Boltzmann Wealth model:\n" + " vs ".join(labels_1) - image_path_1 = "boltzmann_no_mesa.png" - - plot_and_print_benchmark(labels_1, kernels_1, n_range_1, title_1, image_path_1) - - -if __name__ == "__main__": - main() diff --git a/examples/sugarscape_ig/backend_frames/__init__.py b/examples/sugarscape_ig/backend_frames/__init__.py new file mode 100644 index 00000000..614fa64d --- /dev/null +++ b/examples/sugarscape_ig/backend_frames/__init__.py @@ -0,0 +1 @@ +"""mesa-frames backend package for Sugarscape IG examples.""" diff --git a/examples/sugarscape_ig/mesa_comparison.png b/examples/sugarscape_ig/mesa_comparison.png deleted file mode 100644 index c619ae281ba9601f5a1d659c9a1a5348e0a81f44..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 31762 zcmeFZ`9GBH-#mQf5G>M>v3K=&*MB^$7^}M)^qNkw6j79ND07T zFcb!T)By%V-h;sqd-!<3U#{-BoDVLBfyd4Tp7y&M806u91!n6Jh`ZqzcmsP$;r12( z0IZ*{u9l(JfxQaX0t0aYM%vmw|Mh^DpTC#3q8+jyyo4WzJ|6&siFiQ&!C#pdU}3PZ zD9q6#j=?#5lsv-2bG~18#0C)uW##&k)L%y?Wd0w%3H+N@Si_{^Ojmfcc4u 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[PATCH 94/99] feat: add Boltzmann wealth model implementation with Typer CLI; include utility functions for simulation results and plotting --- .gitignore | 3 +- examples/__init__.py | 6 + examples/boltzmann_wealth/backend_frames.py | 154 ++++++++++++++++++++ examples/utils.py | 106 ++++++++++++++ 4 files changed, 268 insertions(+), 1 deletion(-) create mode 100644 examples/__init__.py create mode 100644 examples/boltzmann_wealth/backend_frames.py create mode 100644 examples/utils.py diff --git a/.gitignore b/.gitignore index ca0ad990..45729158 100644 --- a/.gitignore +++ b/.gitignore @@ -158,4 +158,5 @@ docs/site docs/api/_build docs/general/user-guide/data_csv docs/general/user-guide/data_parquet -docs/api/reference/**/mesa_frames.*.rst \ No newline at end of file +docs/api/reference/**/mesa_frames.*.rst +examples/**/results \ No newline at end of file diff --git a/examples/__init__.py b/examples/__init__.py new file mode 100644 index 00000000..069e9dc5 --- /dev/null +++ b/examples/__init__.py @@ -0,0 +1,6 @@ +"""Examples package for the repository.""" + +__all__ = [ + "boltzmann_wealth", + "sugarscape_ig", +] diff --git a/examples/boltzmann_wealth/backend_frames.py b/examples/boltzmann_wealth/backend_frames.py new file mode 100644 index 00000000..67b803e6 --- /dev/null +++ b/examples/boltzmann_wealth/backend_frames.py @@ -0,0 +1,154 @@ +"""Mesa-frames implementation of the Boltzmann wealth model with Typer CLI.""" + +from __future__ import annotations + +from datetime import datetime, timezone +from pathlib import Path +from typing import Annotated + +import numpy as np +import polars as pl +import typer +from time import perf_counter + +from mesa_frames import AgentSet, DataCollector, Model + + +from examples.utils import SimulationResult, plot_model_metrics + + +# Note: by default we create a timestamped results directory under `results/`. +# The CLI will accept optional `results_dir` and `plots_dir` arguments to override. + + +def gini(frame: pl.DataFrame) -> float: + wealth = frame["wealth"] if "wealth" in frame.columns else pl.Series([]) + if wealth.is_empty(): + return float("nan") + values = wealth.to_numpy().astype(np.float64) + if values.size == 0: + return float("nan") + if np.allclose(values, 0.0): + return 0.0 + if np.allclose(values, values[0]): + return 0.0 + sorted_vals = np.sort(values) + n = sorted_vals.size + cumulative = np.cumsum(sorted_vals) + total = cumulative[-1] + if total == 0: + return 0.0 + index = np.arange(1, n + 1, dtype=np.float64) + return float((2.0 * np.dot(index, sorted_vals) / (n * total)) - (n + 1) / n) + + +class MoneyAgents(AgentSet): + """Vectorised agent set for the Boltzmann wealth exchange model.""" + + def __init__(self, model: Model, agents: int) -> None: + super().__init__(model) + self += pl.DataFrame({"wealth": pl.Series(np.ones(agents, dtype=np.int64))}) + + def step(self) -> None: + self.select(pl.col("wealth") > 0) + if len(self.active_agents) == 0: + return + # Use the model RNG to seed Polars sampling so results are reproducible + recipients = self.df.sample( + n=len(self.active_agents), with_replacement=True, seed=self.random.integers(np.iinfo(np.int32).max) + ) + # donors lose one unit + self["active", "wealth"] -= 1 + gains = recipients.group_by("unique_id").len() + self[gains, "wealth"] += gains["len"] + + +class MoneyModel(Model): + """Mesa-frames model that mirrors the Mesa implementation.""" + + def __init__( + self, agents: int, *, seed: int | None = None, results_dir: Path | None = None + ) -> None: + super().__init__(seed) + self.sets += MoneyAgents(self, agents) + storage_uri = str(results_dir) if results_dir is not None else None + self.datacollector = DataCollector( + model=self, + model_reporters={ + "gini": lambda m: gini(m.sets[0].df), + }, + storage="csv", + storage_uri=storage_uri, + ) + + def step(self) -> None: + self.sets.do("step") + self.datacollector.collect() + + def run(self, steps: int) -> None: + for _ in range(steps): + self.step() + + +def simulate( + agents: int, + steps: int, + seed: int | None = None, + results_dir: Path | None = None, +) -> SimulationResult: + model = MoneyModel(agents, seed=seed, results_dir=results_dir) + model.run(steps) + # collect data from datacollector into memory first + return SimulationResult(datacollector=model.datacollector) + + + +app = typer.Typer(add_completion=False) + +@app.command() +def run( + agents: Annotated[int, typer.Option(help="Number of agents to simulate.")] = 5000, + steps: Annotated[int, typer.Option(help="Number of model steps to run.")] = 100, + seed: Annotated[int | None, typer.Option(help="Optional RNG seed.")] = None, + plot: Annotated[bool, typer.Option(help="Render Seaborn plots.")] = True, + save_results: Annotated[bool, typer.Option(help="Persist metrics as CSV.")] = True, + results_dir: Annotated[Path | None, typer.Option(help="Directory to write CSV results and plots into. If omitted a timestamped subdir under `results/` is used.")] = None, +) -> None: + typer.echo(f"Running Boltzmann wealth model (mesa-frames) with {agents} agents for {steps} steps") + timestamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S") + if results_dir is None: + results_dir = (Path(__file__).resolve().parent / "results" / timestamp).resolve() + results_dir.mkdir(parents=True, exist_ok=True) + start_time = perf_counter() + result = simulate(agents=agents, steps=steps, seed=seed, results_dir=results_dir) + + + typer.echo(f"Simulation complete in {perf_counter() - start_time:.2f} seconds") + + model_metrics = result.datacollector.data["model"].select("step", "gini") + + typer.echo(f"Metrics in the final 5 steps: {model_metrics[-5:]}") + + if save_results: + result.datacollector.flush() + + if plot: + stem = f"gini_{timestamp}" + # write plots into the results directory so outputs are colocated + plot_model_metrics( + model_metrics, + results_dir, + stem, + title="Boltzmann wealth — Gini", + subtitle=f"mesa-frames backend; seed={result.datacollector.seed}", + agents=agents, + steps=steps, + ) + typer.echo(f"Saved plots under {results_dir}") + + # Inform user where CSVs were saved + typer.echo(f"Saved CSV results under {results_dir}") + + +if __name__ == "__main__": + app() diff --git a/examples/utils.py b/examples/utils.py new file mode 100644 index 00000000..ef8c3448 --- /dev/null +++ b/examples/utils.py @@ -0,0 +1,106 @@ +"""Utilities shared by the examples package. + +This module centralises small utilities used across the examples so they +don't have to duplicate simple data containers like SimulationResult. +""" +from __future__ import annotations + +from dataclasses import dataclass +from typing import Optional + +import polars as pl +from pathlib import Path +from typing import Sequence + +import matplotlib.pyplot as plt +import seaborn as sns + + + +@dataclass +class SimulationResult: + """Container for example simulation outputs. + + The dataclass is intentionally permissive: some backends only provide + `metrics`, while others also return `agent_metrics`. + """ + + model_metrics: pl.DataFrame + agent_metrics: Optional[pl.DataFrame] = None + + +def plot_model_metrics( + metrics: pl.DataFrame, + output_dir: Path, + stem: str, + title: str | None = None, + figsize: tuple[int, int] | None = None, +) -> None: + """Plot time-series metrics from a polars DataFrame. + + This helper auto-detects all columns except the `step` column and + plots them as separate series. It writes two theme variants + (light/dark) as PNG files under ``output_dir`` with the provided stem. + """ + if metrics.is_empty(): + return + + if "step" not in metrics.columns: + metrics = metrics.with_row_count("step") + + # melt all non-step columns into long form + value_cols: Sequence[str] = [c for c in metrics.columns if c != "step"] + if not value_cols: + return + long = metrics.select(["step", *value_cols]).melt( + id_vars="step", variable_name="metric", value_name="value" + ) + + for theme, style in {"light": "whitegrid", "dark": "darkgrid"}.items(): + sns.set_theme(style=style) + fig, ax = plt.subplots(figsize=figsize or (8, 5)) + sns.lineplot(data=long.to_pandas(), x="step", y="value", hue="metric", ax=ax) + ax.set_title(title or "Metrics") + ax.set_xlabel("Step") + ax.set_ylabel("Value") + fig.tight_layout() + filename = output_dir / f"{stem}_{theme}.png" + fig.savefig(filename, dpi=300) + plt.close(fig) + + +def plot_agent_metrics( + agent_metrics: pl.DataFrame, output_dir: Path, stem: str, figsize: tuple[int, int] | None = None +) -> None: + """Plot agent-level metrics (if any) and write theme variants to disk. + + The function will attempt to preserve common id vars like `step`, + `seed` and `batch` if present; otherwise it uses the first column as + the id variable when melting. + """ + if agent_metrics is None or agent_metrics.is_empty(): + return + + # prefer common id_vars if available + preferred = ["step", "seed", "batch"] + id_vars = [c for c in preferred if c in agent_metrics.columns] + if not id_vars: + # fall back to using the first column as id + id_vars = [agent_metrics.columns[0]] + + melted = agent_metrics.melt(id_vars=id_vars, variable_name="metric", value_name="value") + + for theme, style in {"light": "whitegrid", "dark": "darkgrid"}.items(): + sns.set_theme(style=style) + fig, ax = plt.subplots(figsize=figsize or (10, 6)) + sns.lineplot(data=melted.to_pandas(), x=id_vars[0], y="value", hue="metric", ax=ax) + ax.set_title("Agent metrics") + ax.set_xlabel(id_vars[0].capitalize()) + ax.set_ylabel("Value") + fig.tight_layout() + filename = output_dir / f"{stem}_agents_{theme}.png" + fig.savefig(filename, dpi=300) + plt.close(fig) + + +__all__ = ["SimulationResult", "plot_model_metrics", "plot_agent_metrics"] From db4c32d03fe0bc9000020aabac44738b16a67a06 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Fri, 26 Sep 2025 10:05:49 +0200 Subject: [PATCH 95/99] feat: add plotting module for visualizing model and agent metrics; remove deprecated utils module --- examples/boltzmann_wealth/backend_frames.py | 27 +- examples/plotting.py | 281 ++++++++++++++++++++ examples/utils.py | 106 -------- 3 files changed, 299 insertions(+), 115 deletions(-) create mode 100644 examples/plotting.py delete mode 100644 examples/utils.py diff --git a/examples/boltzmann_wealth/backend_frames.py b/examples/boltzmann_wealth/backend_frames.py index 67b803e6..efcad516 100644 --- a/examples/boltzmann_wealth/backend_frames.py +++ b/examples/boltzmann_wealth/backend_frames.py @@ -12,9 +12,8 @@ from time import perf_counter from mesa_frames import AgentSet, DataCollector, Model - - -from examples.utils import SimulationResult, plot_model_metrics +from examples.utils import SimulationResult +from examples.plotting import plot_model_metrics # Note: by default we create a timestamped results directory under `results/`. @@ -55,7 +54,9 @@ def step(self) -> None: return # Use the model RNG to seed Polars sampling so results are reproducible recipients = self.df.sample( - n=len(self.active_agents), with_replacement=True, seed=self.random.integers(np.iinfo(np.int32).max) + n=len(self.active_agents), + with_replacement=True, + seed=self.random.integers(np.iinfo(np.int32).max), ) # donors lose one unit self["active", "wealth"] -= 1 @@ -102,9 +103,9 @@ def simulate( return SimulationResult(datacollector=model.datacollector) - app = typer.Typer(add_completion=False) + @app.command() def run( agents: Annotated[int, typer.Option(help="Number of agents to simulate.")] = 5000, @@ -112,17 +113,25 @@ def run( seed: Annotated[int | None, typer.Option(help="Optional RNG seed.")] = None, plot: Annotated[bool, typer.Option(help="Render Seaborn plots.")] = True, save_results: Annotated[bool, typer.Option(help="Persist metrics as CSV.")] = True, - results_dir: Annotated[Path | None, typer.Option(help="Directory to write CSV results and plots into. If omitted a timestamped subdir under `results/` is used.")] = None, + results_dir: Annotated[ + Path | None, + typer.Option( + help="Directory to write CSV results and plots into. If omitted a timestamped subdir under `results/` is used." + ), + ] = None, ) -> None: - typer.echo(f"Running Boltzmann wealth model (mesa-frames) with {agents} agents for {steps} steps") + typer.echo( + f"Running Boltzmann wealth model (mesa-frames) with {agents} agents for {steps} steps" + ) timestamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S") if results_dir is None: - results_dir = (Path(__file__).resolve().parent / "results" / timestamp).resolve() + results_dir = ( + Path(__file__).resolve().parent / "results" / timestamp + ).resolve() results_dir.mkdir(parents=True, exist_ok=True) start_time = perf_counter() result = simulate(agents=agents, steps=steps, seed=seed, results_dir=results_dir) - typer.echo(f"Simulation complete in {perf_counter() - start_time:.2f} seconds") model_metrics = result.datacollector.data["model"].select("step", "gini") diff --git a/examples/plotting.py b/examples/plotting.py new file mode 100644 index 00000000..5313dcf8 --- /dev/null +++ b/examples/plotting.py @@ -0,0 +1,281 @@ +# examples/plotting.py +from __future__ import annotations + +from pathlib import Path +from typing import Sequence +import re + +import polars as pl +import seaborn as sns +import matplotlib.pyplot as plt +from matplotlib.ticker import FormatStrFormatter +from matplotlib.figure import Figure +from matplotlib.axes import Axes + +# ----------------------------- Shared theme ---------------------------------- + +_THEMES = { + "light": dict( + style="whitegrid", + rc={ + "axes.spines.top": False, + "axes.spines.right": False, + }, + ), + "dark": dict( + style="whitegrid", + rc={ + # real dark background + readable foreground + "figure.facecolor": "#0b1021", + "axes.facecolor": "#0b1021", + "axes.edgecolor": "#d6d6d7", + "axes.labelcolor": "#e8e8ea", + "text.color": "#e8e8ea", + "xtick.color": "#c9c9cb", + "ytick.color": "#c9c9cb", + "grid.color": "#2a2f4a", + "grid.alpha": 0.35, + "axes.spines.top": False, + "axes.spines.right":False, + "legend.facecolor": "#121734", + "legend.edgecolor": "#3b3f5a", + }, + ), +} + + +def _shorten_seed(text: str | None) -> str | None: + """Turn '... seed=1234567890123' into '... seed=12345678…' if present.""" + if not text: + return text + m = re.search(r"seed=([^;,\s]+)", text) + if not m: + return text + raw = m.group(1) + short = (raw[:8] + "…") if len(raw) > 10 else raw + return re.sub(r"seed=[^;,\s]+", f"seed={short}", text) + + +def _apply_titles(fig: Figure, ax: Axes, title: str, subtitle: str | None) -> None: + """Consistent title placement: figure-level title + small italic subtitle.""" + fig.suptitle(title, fontsize=18, y=0.98) + ax.set_title(_shorten_seed(subtitle) or "", fontsize=12, fontstyle="italic", pad=4) + + +def _finalize_and_save(fig: Figure, output_dir: Path, stem: str, theme: str) -> None: + """Tight layout with space for suptitle, export PNG + (optional) SVG.""" + output_dir.mkdir(parents=True, exist_ok=True) + fig.tight_layout(rect=[0, 0, 1, 0.94]) + png = output_dir / f"{stem}_{theme}.png" + fig.savefig(png, dpi=300) + try: + fig.savefig(output_dir / f"{stem}_{theme}.svg", bbox_inches="tight") + except Exception: + pass # SVG is a nice-to-have + plt.close(fig) + + +# -------------------------- Public: model metrics ---------------------------- + +def plot_model_metrics( + metrics: pl.DataFrame, + output_dir: Path, + stem: str, + title: str, + *, + subtitle: str = "", + figsize: tuple[int, int] | None = None, + agents: int | None = None, + steps: int | None = None, +) -> None: + """ + Plot time-series metrics from a Polars DataFrame and export light/dark PNG/SVG. + + - Auto-detects `step` or adds one if missing. + - Melts all non-`step` columns into long form. + - If there's a single metric (e.g., 'gini'), removes legend and uses a + descriptive y-axis label (e.g., 'Gini coefficient'). + - Optional `agents` and `steps` will be appended to the suptitle as + "(N=, T=)"; if `steps` is omitted it will be inferred + from the `step` column when available. + """ + if metrics.is_empty(): + return + + if "step" not in metrics.columns: + metrics = metrics.with_row_index("step") + + # If steps not provided, try to infer from the data (max step + 1). Keep it None if we can't determine it. + if steps is None: + try: + steps = int(metrics.select(pl.col("step").max()).item()) + 1 + except Exception: + steps = None + + value_cols: Sequence[str] = [c for c in metrics.columns if c != "step"] + if not value_cols: + return + + long = ( + metrics.select(["step", *value_cols]) + .unpivot(index="step", on=value_cols, variable_name="metric", value_name="value") + .to_pandas() + ) + + # Compose informative title with optional (N, T) + if agents is not None and steps is not None: + full_title = f"{title} (N={agents}, T={steps})" + elif agents is not None: + full_title = f"{title} (N={agents})" + elif steps is not None: + full_title = f"{title} (T={steps})" + else: + full_title = title + + for theme, cfg in _THEMES.items(): + sns.set_theme(**cfg) + sns.set_context("talk") + fig, ax = plt.subplots(figsize=figsize or (10, 6)) + + sns.lineplot(data=long, x="step", y="value", hue="metric", linewidth=2, ax=ax) + + _apply_titles(fig, ax, full_title, subtitle) + + ax.set_xlabel("Step") + unique_metrics = long["metric"].unique() + + if len(unique_metrics) == 1: + name = unique_metrics[0] + ax.set_ylabel(name.capitalize()) + leg = ax.get_legend() + if leg is not None: + leg.remove() + vals = long.loc[long["metric"] == name, "value"] + if not vals.empty: + vmin, vmax = float(vals.min()), float(vals.max()) + pad = max(0.005, (vmax - vmin) * 0.05) + ax.set_ylim(vmin - pad, vmax + pad) + else: + ax.set_ylabel("Value") + leg = ax.get_legend() + if theme == "dark" and leg is not None: + leg.set_title(None) + leg.get_frame().set_alpha(0.8) + + ax.yaxis.set_major_formatter(FormatStrFormatter("%.3f")) + ax.margins(x=0.01) + + _finalize_and_save(fig, output_dir, stem, theme) + + +# -------------------------- Public: agent metrics ---------------------------- + +def plot_agent_metrics( + agent_metrics: pl.DataFrame, + output_dir: Path, + stem: str, + *, + title: str = "Agent metrics", + subtitle: str = "", + figsize: tuple[int, int] | None = None, +) -> None: + """ + Plot agent-level metrics (multi-series) and export light/dark PNG/SVG. + + - Preserves common id vars if present: `step`, `seed`, `batch`. + - Uses the first column as id if none of the preferred ids exist. + """ + if agent_metrics is None or agent_metrics.is_empty(): + return + + preferred = ["step", "seed", "batch"] + id_vars = [c for c in preferred if c in agent_metrics.columns] or [agent_metrics.columns[0]] + + # Determine which columns to unpivot (all columns except the id vars). + value_cols = [c for c in agent_metrics.columns if c not in id_vars] + if not value_cols: + return + + melted = ( + agent_metrics.unpivot( + index=id_vars, on=value_cols, variable_name="metric", value_name="value" + ) + .to_pandas() + ) + + xcol = id_vars[0] + + for theme, cfg in _THEMES.items(): + sns.set_theme(**cfg) + sns.set_context("talk") + fig, ax = plt.subplots(figsize=figsize or (10, 6)) + + sns.lineplot(data=melted, x=xcol, y="value", hue="metric", linewidth=1.8, ax=ax) + + _apply_titles(fig, ax, title, subtitle) + ax.set_xlabel(xcol.capitalize()) + ax.set_ylabel("Value") + + if theme == "dark": + leg = ax.get_legend() + if leg is not None: + leg.set_title(None) + leg.get_frame().set_alpha(0.8) + + _finalize_and_save(fig, output_dir, f"{stem}_agents", theme) + + +# -------------------------- Public: performance ------------------------------ + +def plot_performance( + df: pl.DataFrame, + output_dir: Path, + stem: str, + *, + title: str = "Runtime vs agents", + subtitle: str = "", + figsize: tuple[int, int] | None = None, +) -> None: + """ + Plot backend performance (runtime vs agents) with mean±sd error bars. + Expected columns: `agents`, `runtime_seconds`, `backend`. + """ + if df.is_empty(): + return + + pdf = df.to_pandas() + + for theme, cfg in _THEMES.items(): + sns.set_theme(**cfg) + sns.set_context("talk") + fig, ax = plt.subplots(figsize=figsize or (10, 6)) + + sns.lineplot( + data=pdf, + x="agents", + y="runtime_seconds", + hue="backend", + estimator="mean", + errorbar="sd", + marker="o", + ax=ax, + ) + + _apply_titles(fig, ax, title, subtitle) + ax.set_xlabel("Agents") + ax.set_ylabel("Runtime (seconds)") + + if theme == "dark": + leg = ax.get_legend() + if leg is not None: + leg.set_title(None) + leg.get_frame().set_alpha(0.8) + + _finalize_and_save(fig, output_dir, stem, theme) + + +__all__ = [ + "plot_model_metrics", + "plot_agent_metrics", + "plot_performance", +] \ No newline at end of file diff --git a/examples/utils.py b/examples/utils.py deleted file mode 100644 index ef8c3448..00000000 --- a/examples/utils.py +++ /dev/null @@ -1,106 +0,0 @@ -"""Utilities shared by the examples package. - -This module centralises small utilities used across the examples so they -don't have to duplicate simple data containers like SimulationResult. -""" -from __future__ import annotations - -from dataclasses import dataclass -from typing import Optional - -import polars as pl -from pathlib import Path -from typing import Sequence - -import matplotlib.pyplot as plt -import seaborn as sns - - - -@dataclass -class SimulationResult: - """Container for example simulation outputs. - - The dataclass is intentionally permissive: some backends only provide - `metrics`, while others also return `agent_metrics`. - """ - - model_metrics: pl.DataFrame - agent_metrics: Optional[pl.DataFrame] = None - - -def plot_model_metrics( - metrics: pl.DataFrame, - output_dir: Path, - stem: str, - title: str | None = None, - figsize: tuple[int, int] | None = None, -) -> None: - """Plot time-series metrics from a polars DataFrame. - - This helper auto-detects all columns except the `step` column and - plots them as separate series. It writes two theme variants - (light/dark) as PNG files under ``output_dir`` with the provided stem. - """ - if metrics.is_empty(): - return - - if "step" not in metrics.columns: - metrics = metrics.with_row_count("step") - - # melt all non-step columns into long form - value_cols: Sequence[str] = [c for c in metrics.columns if c != "step"] - if not value_cols: - return - long = metrics.select(["step", *value_cols]).melt( - id_vars="step", variable_name="metric", value_name="value" - ) - - for theme, style in {"light": "whitegrid", "dark": "darkgrid"}.items(): - sns.set_theme(style=style) - fig, ax = plt.subplots(figsize=figsize or (8, 5)) - sns.lineplot(data=long.to_pandas(), x="step", y="value", hue="metric", ax=ax) - ax.set_title(title or "Metrics") - ax.set_xlabel("Step") - ax.set_ylabel("Value") - fig.tight_layout() - filename = output_dir / f"{stem}_{theme}.png" - fig.savefig(filename, dpi=300) - plt.close(fig) - - -def plot_agent_metrics( - agent_metrics: pl.DataFrame, output_dir: Path, stem: str, figsize: tuple[int, int] | None = None -) -> None: - """Plot agent-level metrics (if any) and write theme variants to disk. - - The function will attempt to preserve common id vars like `step`, - `seed` and `batch` if present; otherwise it uses the first column as - the id variable when melting. - """ - if agent_metrics is None or agent_metrics.is_empty(): - return - - # prefer common id_vars if available - preferred = ["step", "seed", "batch"] - id_vars = [c for c in preferred if c in agent_metrics.columns] - if not id_vars: - # fall back to using the first column as id - id_vars = [agent_metrics.columns[0]] - - melted = agent_metrics.melt(id_vars=id_vars, variable_name="metric", value_name="value") - - for theme, style in {"light": "whitegrid", "dark": "darkgrid"}.items(): - sns.set_theme(style=style) - fig, ax = plt.subplots(figsize=figsize or (10, 6)) - sns.lineplot(data=melted.to_pandas(), x=id_vars[0], y="value", hue="metric", ax=ax) - ax.set_title("Agent metrics") - ax.set_xlabel(id_vars[0].capitalize()) - ax.set_ylabel("Value") - fig.tight_layout() - filename = output_dir / f"{stem}_agents_{theme}.png" - fig.savefig(filename, dpi=300) - plt.close(fig) - - -__all__ = ["SimulationResult", "plot_model_metrics", "plot_agent_metrics"] From 98278b8db3bd79d63eae4c6439d999e60b989039 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Fri, 26 Sep 2025 12:19:07 +0200 Subject: [PATCH 96/99] feat: implement Mesa backend for Boltzmann wealth model; add FramesSimulationResult class for simulation outputs --- examples/boltzmann_wealth/backend_frames.py | 8 +- examples/boltzmann_wealth/backend_mesa.py | 178 ++++++++++++++++++++ examples/utils.py | 23 +++ 3 files changed, 205 insertions(+), 4 deletions(-) create mode 100644 examples/boltzmann_wealth/backend_mesa.py create mode 100644 examples/utils.py diff --git a/examples/boltzmann_wealth/backend_frames.py b/examples/boltzmann_wealth/backend_frames.py index efcad516..23efac92 100644 --- a/examples/boltzmann_wealth/backend_frames.py +++ b/examples/boltzmann_wealth/backend_frames.py @@ -12,7 +12,7 @@ from time import perf_counter from mesa_frames import AgentSet, DataCollector, Model -from examples.utils import SimulationResult +from examples.utils import FramesSimulationResult from examples.plotting import plot_model_metrics @@ -96,11 +96,11 @@ def simulate( steps: int, seed: int | None = None, results_dir: Path | None = None, -) -> SimulationResult: +) -> FramesSimulationResult: model = MoneyModel(agents, seed=seed, results_dir=results_dir) model.run(steps) # collect data from datacollector into memory first - return SimulationResult(datacollector=model.datacollector) + return FramesSimulationResult(datacollector=model.datacollector) app = typer.Typer(add_completion=False) @@ -136,7 +136,7 @@ def run( model_metrics = result.datacollector.data["model"].select("step", "gini") - typer.echo(f"Metrics in the final 5 steps: {model_metrics[-5:]}") + typer.echo(f"Metrics in the final 5 steps: {model_metrics.tail(5)}") if save_results: result.datacollector.flush() diff --git a/examples/boltzmann_wealth/backend_mesa.py b/examples/boltzmann_wealth/backend_mesa.py new file mode 100644 index 00000000..8b6e3162 --- /dev/null +++ b/examples/boltzmann_wealth/backend_mesa.py @@ -0,0 +1,178 @@ +"""Mesa implementation of the Boltzmann wealth model with Typer CLI.""" + +from __future__ import annotations + +from datetime import datetime, timezone +from pathlib import Path +from typing import Iterable, Annotated +import pandas as pd + +import matplotlib.pyplot as plt +import mesa +from mesa.datacollection import DataCollector +import numpy as np +import polars as pl +import seaborn as sns +import typer +from time import perf_counter + +from examples.utils import MesaSimulationResult +from examples.plotting import plot_model_metrics + + +def gini(values: Iterable[float]) -> float: + """Compute the Gini coefficient from an iterable of wealth values.""" + array = np.fromiter(values, dtype=float) + if array.size == 0: + return float("nan") + if np.allclose(array, 0.0): + return 0.0 + if np.allclose(array, array[0]): + return 0.0 + sorted_vals = np.sort(array) + n = sorted_vals.size + cumulative = np.cumsum(sorted_vals) + total = cumulative[-1] + if total == 0: + return 0.0 + index = np.arange(1, n + 1, dtype=float) + return float((2.0 * np.dot(index, sorted_vals) / (n * total)) - (n + 1) / n) + + +class MoneyAgent(mesa.Agent): + """Agent that passes one unit of wealth to a random neighbour.""" + + def __init__(self, model: "MoneyModel") -> None: + super().__init__(model) + self.wealth = 1 + + def step(self) -> None: + if self.wealth <= 0: + return + other = self.random.choice(self.model.agent_list) + if other is None: + return + other.wealth += 1 + self.wealth -= 1 + + +class MoneyModel(mesa.Model): + """Mesa backend that mirrors the mesa-frames Boltzmann wealth example.""" + + def __init__(self, agents: int, *, seed: int | None = None) -> None: + super().__init__() + if seed is None: + seed = self.random.randint(0, np.iinfo(np.int32).max) + self.reset_randomizer(seed) + self.agent_list: list[MoneyAgent] = [] + for _ in range(agents): + # NOTE: storing agents in a Python list keeps iteration fast for benchmarks. + agent = MoneyAgent(self) + self.agent_list.append(agent) + self.datacollector = DataCollector( + model_reporters={ + "gini": lambda m: gini(a.wealth for a in m.agent_list), + "seed": lambda m: seed, + } + ) + self.datacollector.collect(self) + + def step(self) -> None: + self.random.shuffle(self.agent_list) + for agent in self.agent_list: + agent.step() + self.datacollector.collect(self) + + def run(self, steps: int) -> None: + for _ in range(steps): + self.step() + + +def simulate(agents: int, steps: int, seed: int | None = None) -> MesaSimulationResult: + """Run the Mesa Boltzmann wealth model.""" + model = MoneyModel(agents, seed=seed) + model.run(steps) + + return MesaSimulationResult(datacollector=model.datacollector) + + +app = typer.Typer(add_completion=False) + +@app.command() +def run( + agents: Annotated[int, typer.Option(help="Number of agents to simulate.")] = 5000, + steps: Annotated[int, typer.Option(help="Number of model steps to run.")] = 100, + seed: Annotated[int | None, typer.Option(help="Optional RNG seed.")] = None, + plot: Annotated[bool, typer.Option(help="Render plots.")] = True, + save_results: Annotated[ + bool, + typer.Option(help="Persist metrics as CSV."), + ] = True, + results_dir: Annotated[ + Path | None, + typer.Option( + help=( + "Directory to write CSV results and plots into. If omitted a " + "timestamped subdir under `results/` is used." + ) + ), + ] = None, +) -> None: + """Execute the Mesa Boltzmann wealth simulation.""" + + typer.echo( + f"Running Boltzmann wealth model (mesa) with {agents} agents for {steps} steps" + ) + + # Resolve output folder + timestamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S") + if results_dir is None: + results_dir = (Path(__file__).resolve().parent / "results" / timestamp).resolve() + results_dir.mkdir(parents=True, exist_ok=True) + + start_time = perf_counter() + # Run simulation (Mesa‑idiomatic): we only use DataCollector's public API + result = simulate(agents=agents, steps=steps, seed=seed) + typer.echo(f"Simulation completed in {perf_counter() - start_time:.3f} seconds") + dc = result.datacollector + + # ---- Extract metrics (no helper, no monkey‑patch): + # DataCollector returns a pandas DataFrame with the index as the step. + model_pd = dc.get_model_vars_dataframe() + model_pd = model_pd.reset_index() + # The first column is the step index; normalize name to "step". + model_pd = model_pd.rename(columns={model_pd.columns[0]: "step"}) + seed = model_pd["seed"].iloc[0] + model_pd = model_pd[['step', 'gini']] + + # Show a short tail in console for quick inspection + tail_str = model_pd.tail(5).to_string(index=False) + typer.echo(f"Metrics in the final 5 steps:\n{tail_str}") + + + # ---- Save CSV (same filename/layout as frames backend expects) + if save_results: + csv_path = results_dir / "model.csv" + model_pd.to_csv(csv_path, index=False) + + # ---- Plot (convert to Polars to reuse the shared plotting helper) + if plot and not model_pd.empty: + model_pl = pl.from_pandas(model_pd) + stem = f"gini_{timestamp}" + plot_model_metrics( + model_pl, + results_dir, + stem, + title="Boltzmann wealth — Gini", + subtitle=f"mesa backend; seed={seed}", + agents=agents, + steps=steps, + ) + typer.echo(f"Saved plots under {results_dir}") + + if save_results: + typer.echo(f"Saved CSV results under {results_dir}") + + +if __name__ == "__main__": + app() diff --git a/examples/utils.py b/examples/utils.py new file mode 100644 index 00000000..dbd165b4 --- /dev/null +++ b/examples/utils.py @@ -0,0 +1,23 @@ +from dataclasses import dataclass +import mesa_frames +import mesa + +@dataclass +class FramesSimulationResult: + """Container for example simulation outputs. + + The dataclass is intentionally permissive: some backends only provide + `metrics`, while others also return `agent_metrics`. + """ + + datacollector: mesa_frames.DataCollector + +@dataclass +class MesaSimulationResult: + """Container for example simulation outputs. + + The dataclass is intentionally permissive: some backends only provide + `metrics`, while others also return `agent_metrics`. + """ + + datacollector: mesa.DataCollector \ No newline at end of file From 2c625e147bbe0e8fcac842e1b875f9edf267389b Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Fri, 26 Sep 2025 12:22:16 +0200 Subject: [PATCH 97/99] fix: update typer dependency to allow any version >=0.9.0; remove perfplot from docs dependencies --- pyproject.toml | 3 +-- uv.lock | 32 -------------------------------- 2 files changed, 1 insertion(+), 34 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index c130f9e5..addcc239 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -64,7 +64,7 @@ test = [ docs = [ { include-group = "typechecking" }, - "typer[all]>=0.9.0", + "typer>=0.9.0", "mkdocs-material>=9.6.14", "mkdocs-jupyter>=0.25.1", "mkdocs-git-revision-date-localized-plugin>=1.4.7", @@ -77,7 +77,6 @@ docs = [ "sphinx-copybutton>=0.5.2", "sphinx-design>=0.6.1", "autodocsumm>=0.2.14", - "perfplot>=0.10.2", "seaborn>=0.13.2", "sphinx-autobuild>=2025.8.25", "mesa>=3.2.0", diff --git a/uv.lock b/uv.lock index 8095193c..cc55da48 100644 --- a/uv.lock +++ b/uv.lock @@ -1161,19 +1161,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/8f/8e/9ad090d3553c280a8060fbf6e24dc1c0c29704ee7d1c372f0c174aa59285/matplotlib_inline-0.1.7-py3-none-any.whl", hash = "sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca", size = 9899, upload-time = "2024-04-15T13:44:43.265Z" }, ] -[[package]] -name = "matplotx" -version = "0.3.10" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "matplotlib" }, - { name = "numpy" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/c3/01/0e6938bb717fa7722d6d81336c62de71b815ce73e382aa1873a1e68ccc93/matplotx-0.3.10.tar.gz", hash = "sha256:b6926ce5274cf5da966cb46b90a8c7fefb761478c6c85c8f7ed3ee8ec90e86e5", size = 24041, upload-time = "2022-08-22T14:22:56.374Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/95/ef/e8a30503ae0c26681a9610c7f0be58646bea8119b98cc65c47661abc27a3/matplotx-0.3.10-py3-none-any.whl", hash = "sha256:4d7adafdb001c771d66d9362bb8ca99fcaed15319259223a714f36793dfabbb8", size = 25099, upload-time = "2022-08-22T14:22:54.733Z" }, -] - [[package]] name = "mdit-py-plugins" version = "0.5.0" @@ -1243,7 +1230,6 @@ dev = [ { name = "mkdocs-minify-plugin" }, { name = "numba" }, { name = "numpydoc" }, - { name = "perfplot" }, { name = "pre-commit" }, { name = "pydata-sphinx-theme" }, { name = "pytest" }, @@ -1268,7 +1254,6 @@ docs = [ { name = "mkdocs-material" }, { name = "mkdocs-minify-plugin" }, { name = "numpydoc" }, - { name = "perfplot" }, { name = "pydata-sphinx-theme" }, { name = "seaborn" }, { name = "sphinx" }, @@ -1309,7 +1294,6 @@ dev = [ { name = "mkdocs-minify-plugin", specifier = ">=0.8.0" }, { name = "numba", specifier = ">=0.60.0" }, { name = "numpydoc", specifier = ">=1.8.0" }, - { name = "perfplot", specifier = ">=0.10.2" }, { name = "pre-commit", specifier = ">=4.2.0" }, { name = "pydata-sphinx-theme", specifier = ">=0.16.1" }, { name = "pytest", specifier = ">=8.3.5" }, @@ -1334,7 +1318,6 @@ docs = [ { name = "mkdocs-material", specifier = ">=9.6.14" }, { name = "mkdocs-minify-plugin", specifier = ">=0.8.0" }, { name = "numpydoc", specifier = ">=1.8.0" }, - 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{ url = "https://files.pythonhosted.org/packages/20/85/ffaf2c1f92d17916c089a5c860d23b3117398f19f467fd1de1026d03aebc/perfplot-0.10.2-py3-none-any.whl", hash = "sha256:545ce0f7f22509ad00092d79a794cdc6e9805383e6cedab2bfed3519a7ef4e19", size = 21198, upload-time = "2022-03-03T15:56:35.388Z" }, -] - [[package]] name = "pexpect" version = "4.9.0" From 37b2aec9771e903468a32a54bd8c7389cfa2ea41 Mon Sep 17 00:00:00 2001 From: adamamer20 Date: Fri, 26 Sep 2025 12:24:03 +0200 Subject: [PATCH 98/99] refactor: remove Sugarscape IG example files and reorganize backend structure --- examples/sugarscape_ig/__init__.py | 0 .../sugarscape_ig/backend_mesa/__init__.py | 1 + .../sugarscape_ig/performance_comparison.py | 224 ---------- examples/sugarscape_ig/ss_mesa/__init__.py | 0 examples/sugarscape_ig/ss_mesa/agents.py | 83 ---- examples/sugarscape_ig/ss_mesa/model.py | 77 ---- examples/sugarscape_ig/ss_polars/__init__.py | 0 examples/sugarscape_ig/ss_polars/agents.py | 406 ------------------ examples/sugarscape_ig/ss_polars/model.py | 57 --- 9 files changed, 1 insertion(+), 847 deletions(-) delete mode 100644 examples/sugarscape_ig/__init__.py create mode 100644 examples/sugarscape_ig/backend_mesa/__init__.py delete mode 100644 examples/sugarscape_ig/performance_comparison.py delete mode 100644 examples/sugarscape_ig/ss_mesa/__init__.py delete mode 100644 examples/sugarscape_ig/ss_mesa/agents.py delete mode 100644 examples/sugarscape_ig/ss_mesa/model.py delete mode 100644 examples/sugarscape_ig/ss_polars/__init__.py delete mode 100644 examples/sugarscape_ig/ss_polars/agents.py delete mode 100644 examples/sugarscape_ig/ss_polars/model.py diff --git a/examples/sugarscape_ig/__init__.py b/examples/sugarscape_ig/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/examples/sugarscape_ig/backend_mesa/__init__.py b/examples/sugarscape_ig/backend_mesa/__init__.py new file mode 100644 index 00000000..463099c0 --- /dev/null +++ b/examples/sugarscape_ig/backend_mesa/__init__.py @@ -0,0 +1 @@ +"""Mesa backend package for Sugarscape IG examples.""" diff --git a/examples/sugarscape_ig/performance_comparison.py b/examples/sugarscape_ig/performance_comparison.py deleted file mode 100644 index d8d2f196..00000000 --- a/examples/sugarscape_ig/performance_comparison.py +++ /dev/null @@ -1,224 +0,0 @@ -import math - -import matplotlib.pyplot as plt -import numpy as np -import perfplot -import polars as pl -import seaborn as sns -from polars.testing import assert_frame_equal -from ss_mesa.model import SugarscapeMesa -from ss_polars.agents import ( - AntPolarsLoopDF, - AntPolarsLoopNoVec, - AntPolarsNumbaCPU, - AntPolarsNumbaGPU, - AntPolarsNumbaParallel, -) -from ss_polars.model import SugarscapePolars -from collections.abc import Callable - - -class SugarScapeSetup: - def __init__(self, n: int): - if n >= 10**6: - density = 0.17 # FLAME2-GPU - else: - density = 0.04 # mesa - self.n = n - self.seed = 42 - dimension = math.ceil(math.sqrt(n / density)) - random_gen = np.random.default_rng(self.seed) - self.sugar_grid = random_gen.integers(0, 4, (dimension, dimension)) - self.initial_sugar = random_gen.integers(6, 25, n) - self.metabolism = random_gen.integers(2, 4, n) - self.vision = random_gen.integers(1, 6, n) - self.initial_positions = pl.DataFrame( - schema={"dim_0": pl.Int64, "dim_1": pl.Int64} - ) - while self.initial_positions.shape[0] < n: - initial_pos_0 = random_gen.integers( - 0, dimension, n - self.initial_positions.shape[0] - ) - initial_pos_1 = random_gen.integers( - 0, dimension, n - self.initial_positions.shape[0] - ) - self.initial_positions = self.initial_positions.vstack( - pl.DataFrame( - { - "dim_0": initial_pos_0, - "dim_1": initial_pos_1, - } - ) - ).unique(maintain_order=True) - return - - -def mesa_implementation(setup: SugarScapeSetup): - model = SugarscapeMesa( - setup.n, - setup.sugar_grid, - setup.initial_sugar, - setup.metabolism, - setup.vision, - setup.initial_positions, - setup.seed, - ) - model.run_model(100) - return model - - -def mesa_frames_polars_loop_DF(setup: SugarScapeSetup): - model = SugarscapePolars( - AntPolarsLoopDF, - setup.n, - setup.sugar_grid, - setup.initial_sugar, - setup.metabolism, - setup.vision, - setup.initial_positions, - setup.seed, - ) - model.run_model(100) - return model - - -def mesa_frames_polars_loop_no_vec(setup: SugarScapeSetup): - model = SugarscapePolars( - AntPolarsLoopNoVec, - setup.n, - setup.sugar_grid, - setup.initial_sugar, - setup.metabolism, - setup.vision, - setup.initial_positions, - setup.seed, - ) - model.run_model(100) - return model - - -def mesa_frames_polars_numba_cpu(setup: SugarScapeSetup): - model = SugarscapePolars( - AntPolarsNumbaCPU, - setup.n, - setup.sugar_grid, - setup.initial_sugar, - setup.metabolism, - setup.vision, - setup.initial_positions, - setup.seed, - ) - model.run_model(100) - return model - - -def mesa_frames_polars_numba_gpu(setup: SugarScapeSetup): - model = SugarscapePolars( - AntPolarsNumbaGPU, - setup.n, - setup.sugar_grid, - setup.initial_sugar, - setup.metabolism, - setup.vision, - setup.initial_positions, - setup.seed, - ) - model.run_model(100) - return model - - -def mesa_frames_polars_numba_parallel(setup: SugarScapeSetup): - model = SugarscapePolars( - AntPolarsNumbaParallel, - setup.n, - setup.sugar_grid, - setup.initial_sugar, - setup.metabolism, - setup.vision, - setup.initial_positions, - setup.seed, - ) - model.run_model(100) - return model - - -def plot_and_print_benchmark( - labels: list[str], - kernels: list[Callable], - n_range: list[int], - title: str, - image_path: str, - equality_check: Callable | None = None, -): - out = perfplot.bench( - setup=SugarScapeSetup, - kernels=kernels, - labels=labels, - n_range=n_range, - xlabel="Number of agents", - equality_check=equality_check, - title=title, - ) - plt.ylabel("Execution time (s)") - out.save(image_path) - print("\nExecution times:") - for i, label in enumerate(labels): - print(f"---------------\n{label}:") - for n, t in zip(out.n_range, out.timings_s[i]): - print(f" Number of agents: {n}, Time: {t:.2f} seconds") - print("---------------") - - -def polars_equality_check(a: SugarscapePolars, b: SugarscapePolars): - assert_frame_equal(a.space.agents, b.space.agents, check_row_order=False) - assert_frame_equal(a.space.cells, b.space.cells, check_row_order=False) - return True - - -def main(): - # Mesa comparison - sns.set_theme(style="whitegrid") - labels_0 = [ - "mesa-frames (pl numba parallel)", - "mesa", - ] - kernels_0 = [ - mesa_frames_polars_numba_parallel, - mesa_implementation, - ] - n_range_0 = [k for k in range(10**5, 5 * 10**5 + 2, 10**5)] - title_0 = "100 steps of the SugarScape IG model:\n" + " vs ".join(labels_0) - image_path_0 = "mesa_comparison.png" - plot_and_print_benchmark(labels_0, kernels_0, n_range_0, title_0, image_path_0) - - # mesa-frames comparison - labels_1 = [ - "mesa-frames (pl loop DF)", - "mesa-frames (pl loop no vec)", - "mesa-frames (pl numba CPU)", - "mesa-frames (pl numba parallel)", - "mesa-frames (pl numba GPU)", - ] - # Polars best_moves (non-vectorized loop vs DF loop vs numba loop) - kernels_1 = [ - mesa_frames_polars_loop_DF, - mesa_frames_polars_loop_no_vec, - mesa_frames_polars_numba_cpu, - mesa_frames_polars_numba_parallel, - mesa_frames_polars_numba_gpu, - ] - n_range_1 = [k for k in range(10**6, 3 * 10**6 + 2, 10**6)] - title_1 = "100 steps of the SugarScape IG model:\n" + " vs ".join(labels_1) - image_path_1 = "polars_comparison.png" - plot_and_print_benchmark( - labels_1, - kernels_1, - n_range_1, - title_1, - image_path_1, - equality_check=polars_equality_check, - ) - - -if __name__ == "__main__": - main() diff --git a/examples/sugarscape_ig/ss_mesa/__init__.py b/examples/sugarscape_ig/ss_mesa/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/examples/sugarscape_ig/ss_mesa/agents.py b/examples/sugarscape_ig/ss_mesa/agents.py deleted file mode 100644 index e4d1a700..00000000 --- a/examples/sugarscape_ig/ss_mesa/agents.py +++ /dev/null @@ -1,83 +0,0 @@ -import math - -import mesa - - -def get_distance(pos_1, pos_2): - """Get the distance between two point - - Args: - pos_1, pos_2: Coordinate tuples for both points. - """ - x1, y1 = pos_1 - x2, y2 = pos_2 - dx = x1 - x2 - dy = y1 - y2 - return math.sqrt(dx**2 + dy**2) - - -class AntMesa(mesa.Agent): - def __init__(self, model, moore=False, sugar=0, metabolism=0, vision=0): - super().__init__(model) - self.moore = moore - self.sugar = sugar - self.metabolism = metabolism - self.vision = vision - - def get_sugar(self, pos): - this_cell = self.model.space.get_cell_list_contents([pos]) - for agent in this_cell: - if type(agent) is Sugar: - return agent - - def is_occupied(self, pos): - this_cell = self.model.space.get_cell_list_contents([pos]) - return any(isinstance(agent, AntMesa) for agent in this_cell) - - def move(self): - # Get neighborhood within vision - neighbors = [ - i - for i in self.model.space.get_neighborhood( - self.pos, self.moore, False, radius=self.vision - ) - if not self.is_occupied(i) - ] - neighbors.append(self.pos) - # Look for location with the most sugar - max_sugar = max(self.get_sugar(pos).amount for pos in neighbors) - candidates = [ - pos for pos in neighbors if self.get_sugar(pos).amount == max_sugar - ] - # Narrow down to the nearest ones - min_dist = min(get_distance(self.pos, pos) for pos in candidates) - final_candidates = [ - pos for pos in candidates if get_distance(self.pos, pos) == min_dist - ] - self.random.shuffle(final_candidates) - self.model.space.move_agent(self, final_candidates[0]) - - def eat(self): - sugar_patch = self.get_sugar(self.pos) - self.sugar = self.sugar - self.metabolism + sugar_patch.amount - sugar_patch.amount = 0 - - def step(self): - self.move() - self.eat() - if self.sugar <= 0: - self.model.space.remove_agent(self) - self.model.agents.remove(self) - - -class Sugar(mesa.Agent): - def __init__(self, model, max_sugar): - super().__init__(model) - self.amount = max_sugar - self.max_sugar = max_sugar - - def step(self): - if self.model.space.is_cell_empty(self.pos): - self.amount = self.max_sugar - else: - self.amount = 0 diff --git a/examples/sugarscape_ig/ss_mesa/model.py b/examples/sugarscape_ig/ss_mesa/model.py deleted file mode 100644 index 43114413..00000000 --- a/examples/sugarscape_ig/ss_mesa/model.py +++ /dev/null @@ -1,77 +0,0 @@ -import mesa -import numpy as np -import polars as pl - -from .agents import AntMesa, Sugar - - -class SugarscapeMesa(mesa.Model): - """ - Sugarscape 2 Instant Growback - """ - - def __init__( - self, - n_agents: int, - sugar_grid: np.ndarray | None = None, - initial_sugar: np.ndarray | None = None, - metabolism: np.ndarray | None = None, - vision: np.ndarray | None = None, - initial_positions: pl.DataFrame | None = None, - seed: int | None = None, - width: int | None = None, - height: int | None = None, - ): - """ - Create a new Instant Growback model with the given parameters. - - """ - super().__init__() - - # Set parameters - if sugar_grid is None: - sugar_grid = np.random.randint(0, 4, (width, height)) - if initial_sugar is None: - initial_sugar = np.random.randint(6, 25, n_agents) - if metabolism is None: - metabolism = np.random.randint(2, 4, n_agents) - if vision is None: - vision = np.random.randint(1, 6, n_agents) - if seed is not None: - self.reset_randomizer(seed) - - self.width, self.height = sugar_grid.shape - self.n_agents = n_agents - self.space = mesa.space.MultiGrid(self.width, self.height, torus=False) - - self.sugars = [] - - for _, (x, y) in self.space.coord_iter(): - max_sugar = sugar_grid[x, y] - sugar = Sugar(self, max_sugar) - self.space.place_agent(sugar, (x, y)) - self.sugars.append(sugar) - - # Create agent: - for i in range(self.n_agents): - if initial_positions is not None: - x = initial_positions["dim_0"][i] - y = initial_positions["dim_1"][i] - else: - x = self.random.randrange(self.width) - y = self.random.randrange(self.height) - ssa = AntMesa(self, False, initial_sugar[i], metabolism[i], vision[i]) - self.agents.add(ssa) - self.space.place_agent(ssa, (x, y)) - - self.running = True - - def step(self): - self.agents.shuffle_do("step") - [sugar.step() for sugar in self.sugars] - - def run_model(self, step_count=200): - for _ in range(step_count): - if len(self.agents) == 0: - return - self.step() diff --git a/examples/sugarscape_ig/ss_polars/__init__.py b/examples/sugarscape_ig/ss_polars/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/examples/sugarscape_ig/ss_polars/agents.py b/examples/sugarscape_ig/ss_polars/agents.py deleted file mode 100644 index 32ca91f5..00000000 --- a/examples/sugarscape_ig/ss_polars/agents.py +++ /dev/null @@ -1,406 +0,0 @@ -from abc import abstractmethod - -import numpy as np -import polars as pl -from numba import b1, guvectorize, int32 - -from mesa_frames import AgentSet, Model - - -class AntDFBase(AgentSet): - def __init__( - self, - model: Model, - n_agents: int, - initial_sugar: np.ndarray | None = None, - metabolism: np.ndarray | None = None, - vision: np.ndarray | None = None, - ): - super().__init__(model) - - if initial_sugar is None: - initial_sugar = model.random.integers(6, 25, n_agents) - if metabolism is None: - metabolism = model.random.integers(2, 4, n_agents) - if vision is None: - vision = model.random.integers(1, 6, n_agents) - - agents = pl.DataFrame( - { - "sugar": initial_sugar, - "metabolism": metabolism, - "vision": vision, - } - ) - self.add(agents) - - def eat(self): - # Only consider cells currently occupied by agents of this set - cells = self.space.cells.filter(pl.col("agent_id").is_not_null()) - mask_in_set = cells["agent_id"].is_in(self.index) - if mask_in_set.any(): - cells = cells.filter(mask_in_set) - ids = cells["agent_id"] - self[ids, "sugar"] = ( - self[ids, "sugar"] + cells["sugar"] - self[ids, "metabolism"] - ) - - def step(self): - self.shuffle().do("move").do("eat") - self.discard(self.df.filter(pl.col("sugar") <= 0)) - - def move(self): - neighborhood = self._get_neighborhood() - agent_order = self._get_agent_order(neighborhood) - neighborhood = self._prepare_neighborhood(neighborhood, agent_order) - best_moves = self.get_best_moves(neighborhood) - self.space.move_agents(agent_order["agent_id_center"], best_moves) - - def _get_neighborhood(self) -> pl.DataFrame: - """Get the neighborhood of each agent, completed with the sugar of the cell and the agent_id of the center cell - - NOTE: This method should be unnecessary if get_neighborhood/get_neighbors return the agent_id of the center cell and the properties of the cells - - Returns - ------- - pl.DataFrame - Neighborhood DataFrame - """ - neighborhood: pl.DataFrame = self.space.get_neighborhood( - radius=self["vision"], agents=self, include_center=True - ) - # Join self.space.cells to obtain properties ('sugar') per cell - - neighborhood = neighborhood.join(self.space.cells, on=["dim_0", "dim_1"]) - - # Join self.pos to obtain the agent_id of the center cell - # TODO: get_neighborhood/get_neighbors should return 'agent_id_center' instead of center position when input is AgentLike - - neighborhood = neighborhood.with_columns( - agent_id_center=neighborhood.join( - self.pos, - left_on=["dim_0_center", "dim_1_center"], - right_on=["dim_0", "dim_1"], - )["unique_id"] - ) - return neighborhood - - def _get_agent_order(self, neighborhood: pl.DataFrame) -> pl.DataFrame: - """Get the order of agents based on the original order of agents - - Parameters - ---------- - neighborhood : pl.DataFrame - Neighborhood DataFrame - - Returns - ------- - pl.DataFrame - DataFrame with 'agent_id_center' and 'agent_order' columns - """ - # Order of agents moves based on the original order of agents. - # The agent in his cell has order 0 (highest) - - return ( - neighborhood.unique( - subset=["agent_id_center"], keep="first", maintain_order=True - ) - .with_row_count("agent_order") - .select(["agent_id_center", "agent_order"]) - ) - - def _prepare_neighborhood( - self, neighborhood: pl.DataFrame, agent_order: pl.DataFrame - ) -> pl.DataFrame: - """Prepare the neighborhood DataFrame to find the best moves - - Parameters - ---------- - neighborhood : pl.DataFrame - Neighborhood DataFrame - agent_order : pl.DataFrame - DataFrame with 'agent_id_center' and 'agent_order' columns - - Returns - ------- - pl.DataFrame - Prepared neighborhood DataFrame - """ - neighborhood = neighborhood.join(agent_order, on="agent_id_center") - - # Add blocking agent order - neighborhood = neighborhood.join( - agent_order.select( - pl.col("agent_id_center").alias("agent_id"), - pl.col("agent_order").alias("blocking_agent_order"), - ), - on="agent_id", - how="left", - ).rename({"agent_id": "blocking_agent_id"}) - - # Filter only possible moves (agent is in his cell, blocking agent has moved before him or there is no blocking agent) - neighborhood = neighborhood.filter( - (pl.col("agent_order") >= pl.col("blocking_agent_order")) - | pl.col("blocking_agent_order").is_null() - ) - - # Sort neighborhood by agent_order & max_sugar (max_sugar because we will check anyway if the cell is empty) - # However, we need to make sure that the current agent cell is ordered by current sugar (since it's 0 until agent hasn't moved) - neighborhood = neighborhood.with_columns( - max_sugar=pl.when(pl.col("blocking_agent_id") == pl.col("agent_id_center")) - .then(pl.lit(0)) - .otherwise(pl.col("max_sugar")) - ).sort( - ["agent_order", "max_sugar", "radius", "dim_0"], - descending=[False, True, False, False], - ) - return neighborhood - - def get_best_moves(self, neighborhood: pl.DataFrame) -> pl.DataFrame: - """Get the best moves for each agent - - Parameters - ---------- - neighborhood : pl.DataFrame - Neighborhood DataFrame - - Returns - ------- - pl.DataFrame - DataFrame with the best moves for each agent - """ - raise NotImplementedError("Subclasses must implement this method") - - -class AntPolarsLoopDF(AntDFBase): - def get_best_moves(self, neighborhood: pl.DataFrame): - best_moves = pl.DataFrame() - - # While there are agents that do not have a best move, keep looking for one - while len(best_moves) < len(self.df): - # Check if there are previous agents that might make the same move (priority for the given move is > 1) - neighborhood = neighborhood.with_columns( - priority=pl.col("agent_order").cum_count().over(["dim_0", "dim_1"]) - ) - - # Get the best moves for each agent: - # If duplicates are found, select the one with the highest order - new_best_moves = ( - neighborhood.group_by("agent_id_center", maintain_order=True) - .first() - .unique(subset=["dim_0", "dim_1"], keep="first", maintain_order=True) - ) - # Agents can make the move if: - # - There is no blocking agent - # - The agent is in its own cell - # - The blocking agent has moved before him - # - There isn't a higher priority agent that might make the same move - - condition = pl.col("blocking_agent_id").is_null() | ( - pl.col("blocking_agent_id") == pl.col("agent_id_center") - ) - if len(best_moves) > 0: - condition = condition | pl.col("blocking_agent_id").is_in( - best_moves["agent_id_center"] - ) - - condition = condition & (pl.col("priority") == 1) - - new_best_moves = new_best_moves.filter(condition) - - best_moves = pl.concat([best_moves, new_best_moves]) - - # Remove agents that have already moved - neighborhood = neighborhood.filter( - ~pl.col("agent_id_center").is_in(best_moves["agent_id_center"]) - ) - - # Remove cells that have been already selected - neighborhood = neighborhood.join( - best_moves.select(["dim_0", "dim_1"]), on=["dim_0", "dim_1"], how="anti" - ) - - # Check if there are previous agents that might make the same move (priority for the given move is > 1) - neighborhood = neighborhood.with_columns( - priority=pl.col("agent_order").cum_count().over(["dim_0", "dim_1"]) - ) - return best_moves.sort("agent_order").select(["dim_0", "dim_1"]) - - -class AntPolarsLoop(AntDFBase): - numba_target = None - - def get_best_moves(self, neighborhood: pl.DataFrame): - occupied_cells, free_cells, target_cells = self._prepare_cells(neighborhood) - best_moves_func = self._get_best_moves() - - processed_agents = np.zeros(len(self.df), dtype=np.bool_) - - if self.numba_target is None: - # Non-vectorized case: we need to create and pass the best_moves array - map_batches_func = lambda df: best_moves_func( - occupied_cells, - free_cells, - target_cells, - df.struct.field("agent_order"), - df.struct.field("blocking_agent_order"), - processed_agents, - best_moves=np.full(len(self.df), -1, dtype=np.int32), - ) - else: - # Vectorized case: Polars will create the output array (best_moves) automatically - map_batches_func = lambda df: best_moves_func( - occupied_cells.astype(np.int32), - free_cells.astype(np.bool_), - target_cells.astype(np.int32), - df.struct.field("agent_order"), - df.struct.field("blocking_agent_order"), - processed_agents.astype(np.bool_), - ) - - best_moves = ( - # Only fill nulls for the column we need as an int sentinel; - # avoid touching UInt columns like 'blocking_agent_id'. - neighborhood.with_columns(pl.col("blocking_agent_order").fill_null(-1)) - .cast({"agent_order": pl.Int32, "blocking_agent_order": pl.Int32}) - .select( - pl.struct(["agent_order", "blocking_agent_order"]).map_batches( - map_batches_func, - return_dtype=pl.Int32, - ) - ) - .with_columns( - dim_0=pl.col("agent_order") // self.space.dimensions[1], - dim_1=pl.col("agent_order") % self.space.dimensions[1], - ) - .drop("agent_order") - ) - return best_moves - - def _prepare_cells( - self, neighborhood: pl.DataFrame - ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: - """Get the occupied and free cells and the target cells for each agent, - based on the neighborhood DataFrame such that the arrays refer to a flattened version of the grid - - Parameters - ---------- - neighborhood : pl.DataFrame - Neighborhood DataFrame - - Returns - ------- - tuple[np.ndarray, np.ndarray, np.ndarray] - occupied_cells, free_cells, target_cells - """ - occupied_cells = ( - neighborhood[["agent_id_center", "agent_order"]] - .unique() - .join(self.pos, left_on="agent_id_center", right_on="unique_id") - .with_columns( - flattened=(pl.col("dim_0") * self.space.dimensions[1] + pl.col("dim_1")) - ) - .sort("agent_order")["flattened"] - .to_numpy() - ) - free_cells = np.ones( - self.space.dimensions[0] * self.space.dimensions[1], dtype=np.bool_ - ) - free_cells[occupied_cells] = False - - target_cells = ( - neighborhood["dim_0"] * self.space.dimensions[1] + neighborhood["dim_1"] - ).to_numpy() - return occupied_cells, free_cells, target_cells - - def _get_best_moves(self): - raise NotImplementedError("Subclasses must implement this method") - - -class AntPolarsLoopNoVec(AntPolarsLoop): - # Non-vectorized case - def _get_best_moves(self): - def inner_get_best_moves( - occupied_cells: np.ndarray, - free_cells: np.ndarray, - target_cells: np.ndarray, - agent_id_center: np.ndarray, - blocking_agent: np.ndarray, - processed_agents: np.ndarray, - best_moves: np.ndarray, - ) -> np.ndarray: - for i, agent in enumerate(agent_id_center): - # If the agent has not moved yet - if not processed_agents[agent]: - # If the target cell is free - if free_cells[target_cells[i]] or blocking_agent[i] == agent: - best_moves[agent] = target_cells[i] - # Free current cell - free_cells[occupied_cells[agent]] = True - # Occupy target cell - free_cells[target_cells[i]] = False - processed_agents[agent] = True - return best_moves - - return inner_get_best_moves - - -class AntPolarsNumba(AntPolarsLoop): - # Vectorized case - def _get_best_moves(self): - @guvectorize( - [ - ( - int32[:], - b1[:], - int32[:], - int32[:], - int32[:], - b1[:], - int32[:], - ) - ], - "(n), (m), (p), (p), (p), (n)->(n)", - nopython=True, - target=self.numba_target, - # Writable inputs should be declared according to https://numba.pydata.org/numba-doc/dev/user/vectorize.html#overwriting-input-values - # In this case, there doesn't seem to be a difference. I will leave it commented for reference so that we can use CUDA target (which doesn't support writable_args) - # writable_args=( - # "free_cells", - # "processed_agents", - # ), - ) - def vectorized_get_best_moves( - occupied_cells, - free_cells, - target_cells, - agent_id_center, - blocking_agent, - processed_agents, - best_moves, - ): - for i, agent in enumerate(agent_id_center): - # If the agent has not moved yet - if not processed_agents[agent]: - # If the target cell is free - if free_cells[target_cells[i]] or blocking_agent[i] == agent: - best_moves[agent] = target_cells[i] - # Free current cell - free_cells[occupied_cells[agent]] = True - # Occupy target cell - free_cells[target_cells[i]] = False - processed_agents[agent] = True - - return vectorized_get_best_moves - - -class AntPolarsNumbaCPU(AntPolarsNumba): - numba_target = "cpu" - - -class AntPolarsNumbaParallel(AntPolarsNumba): - numba_target = "parallel" - - -class AntPolarsNumbaGPU(AntPolarsNumba): - numba_target = "cuda" diff --git a/examples/sugarscape_ig/ss_polars/model.py b/examples/sugarscape_ig/ss_polars/model.py deleted file mode 100644 index 36b2718e..00000000 --- a/examples/sugarscape_ig/ss_polars/model.py +++ /dev/null @@ -1,57 +0,0 @@ -import numpy as np -import polars as pl - -from mesa_frames import Grid, Model - -from .agents import AntDFBase - - -class SugarscapePolars(Model): - def __init__( - self, - agent_type: type[AntDFBase], - n_agents: int, - sugar_grid: np.ndarray | None = None, - initial_sugar: np.ndarray | None = None, - metabolism: np.ndarray | None = None, - vision: np.ndarray | None = None, - initial_positions: pl.DataFrame | None = None, - seed: int | None = None, - width: int | None = None, - height: int | None = None, - ): - super().__init__(seed) - if sugar_grid is None: - sugar_grid = self.random.integers(0, 4, (width, height)) - grid_dimensions = sugar_grid.shape - self.space = Grid( - self, grid_dimensions, neighborhood_type="von_neumann", capacity=1 - ) - dim_0 = pl.Series("dim_0", pl.arange(grid_dimensions[0], eager=True)).to_frame() - dim_1 = pl.Series("dim_1", pl.arange(grid_dimensions[1], eager=True)).to_frame() - sugar_grid = dim_0.join(dim_1, how="cross").with_columns( - sugar=sugar_grid.flatten(), max_sugar=sugar_grid.flatten() - ) - self.space.set_cells(sugar_grid) - # Create and register the main agent set; keep its name for later lookups - main_set = agent_type(self, n_agents, initial_sugar, metabolism, vision) - self.sets += main_set - self._main_set_name = main_set.name - if initial_positions is not None: - self.space.place_agents(self.sets, initial_positions) - else: - self.space.place_to_empty(self.sets) - - def run_model(self, steps: int) -> list[int]: - for _ in range(steps): - # Stop if the main agent set is empty - if len(self.sets[self._main_set_name]) == 0: # type: ignore[index] - return - empty_cells = self.space.empty_cells - full_cells = self.space.full_cells - max_sugar = self.space.cells.join( - empty_cells, on=["dim_0", "dim_1"] - ).select(pl.col("max_sugar")) - self.space.set_cells(full_cells, {"sugar": 0}) - self.space.set_cells(empty_cells, {"sugar": max_sugar}) - self.step() From 747a15d7f7afd12c075b974bf6e72efe2b72f58a Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 26 Sep 2025 10:25:01 +0000 Subject: [PATCH 99/99] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- benchmarks/cli.py | 62 +++++++++++++++-------- examples/boltzmann_wealth/backend_mesa.py | 13 +++-- examples/plotting.py | 44 ++++++++-------- examples/utils.py | 4 +- 4 files changed, 75 insertions(+), 48 deletions(-) diff --git a/benchmarks/cli.py b/benchmarks/cli.py index c0b9355d..3accba5d 100644 --- a/benchmarks/cli.py +++ b/benchmarks/cli.py @@ -20,13 +20,14 @@ app = typer.Typer(add_completion=False) + class RunnerP(Protocol): - def __call__(self, agents: int, steps: int, seed: Optional[int] = None) -> None: ... + def __call__(self, agents: int, steps: int, seed: int | None = None) -> None: ... @dataclass(slots=True) class Backend: - name: Literal['mesa', 'frames'] + name: Literal["mesa", "frames"] runner: RunnerP @@ -59,6 +60,7 @@ class ModelConfig: ), } + def _parse_agents(value: str) -> list[int]: value = value.strip() if ":" in value: @@ -67,7 +69,7 @@ def _parse_agents(value: str) -> list[int]: raise typer.BadParameter("Ranges must use start:stop:step format") try: start, stop, step = (int(part) for part in parts) - except ValueError as exc: + except ValueError as exc: raise typer.BadParameter("Range values must be integers") from exc if step <= 0: raise typer.BadParameter("Step must be positive") @@ -87,6 +89,7 @@ def _parse_agents(value: str) -> list[int]: raise typer.BadParameter("Agent count must be positive") return [agents] + def _parse_models(value: str) -> list[str]: """Parse models option into a list of model keys. @@ -116,6 +119,7 @@ def _parse_models(value: str) -> list[str]: result.append(p) return result + def _plot_performance( df: pl.DataFrame, model_name: str, output_dir: Path, timestamp: str ) -> None: @@ -145,32 +149,46 @@ def _plot_performance( @app.command() def run( - models: Annotated[str, typer.Option( - help="Models to benchmark: boltzmann, sugarscape, or all", - callback=_parse_models - )] = "all", - agents: Annotated[list[int], typer.Option( - help="Agent count or range (start:stop:step)", - callback=_parse_agents - )] = "1000:5000:1000", - steps: Annotated[int, typer.Option( - min=0, - help="Number of steps per run.", - )] = 100, + models: Annotated[ + str, + typer.Option( + help="Models to benchmark: boltzmann, sugarscape, or all", + callback=_parse_models, + ), + ] = "all", + agents: Annotated[ + list[int], + typer.Option( + help="Agent count or range (start:stop:step)", callback=_parse_agents + ), + ] = "1000:5000:1000", + steps: Annotated[ + int, + typer.Option( + min=0, + help="Number of steps per run.", + ), + ] = 100, repeats: Annotated[int, typer.Option(help="Repeats per configuration.", min=1)] = 1, seed: Annotated[int, typer.Option(help="Optional RNG seed.")] = 42, save: Annotated[bool, typer.Option(help="Persist benchmark CSV results.")] = True, plot: Annotated[bool, typer.Option(help="Render performance plots.")] = True, - results_dir: Annotated[Path, typer.Option( - help="Directory for benchmark CSV results.", - )] = Path(__file__).resolve().parent / "results", - plots_dir: Annotated[Path, typer.Option( - help="Directory for benchmark plots.", - )] = Path(__file__).resolve().parent / "plots", + results_dir: Annotated[ + Path, + typer.Option( + help="Directory for benchmark CSV results.", + ), + ] = Path(__file__).resolve().parent / "results", + plots_dir: Annotated[ + Path, + typer.Option( + help="Directory for benchmark plots.", + ), + ] = Path(__file__).resolve().parent / "plots", ) -> None: """Run performance benchmarks for the models models.""" rows: list[dict[str, object]] = [] - timestamp = datetime.now(datetime.timezone.utc).strftime("%Y%m%d_%H%M%S") + timestamp = datetime.now(datetime.UTC).strftime("%Y%m%d_%H%M%S") for model in models: config = MODELS[model] typer.echo(f"Benchmarking {model} with agents {agents}") diff --git a/examples/boltzmann_wealth/backend_mesa.py b/examples/boltzmann_wealth/backend_mesa.py index 8b6e3162..8b86ad3e 100644 --- a/examples/boltzmann_wealth/backend_mesa.py +++ b/examples/boltzmann_wealth/backend_mesa.py @@ -4,7 +4,8 @@ from datetime import datetime, timezone from pathlib import Path -from typing import Iterable, Annotated +from typing import Annotated +from collections.abc import Iterable import pandas as pd import matplotlib.pyplot as plt @@ -42,7 +43,7 @@ def gini(values: Iterable[float]) -> float: class MoneyAgent(mesa.Agent): """Agent that passes one unit of wealth to a random neighbour.""" - def __init__(self, model: "MoneyModel") -> None: + def __init__(self, model: MoneyModel) -> None: super().__init__(model) self.wealth = 1 @@ -98,6 +99,7 @@ def simulate(agents: int, steps: int, seed: int | None = None) -> MesaSimulation app = typer.Typer(add_completion=False) + @app.command() def run( agents: Annotated[int, typer.Option(help="Number of agents to simulate.")] = 5000, @@ -127,7 +129,9 @@ def run( # Resolve output folder timestamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S") if results_dir is None: - results_dir = (Path(__file__).resolve().parent / "results" / timestamp).resolve() + results_dir = ( + Path(__file__).resolve().parent / "results" / timestamp + ).resolve() results_dir.mkdir(parents=True, exist_ok=True) start_time = perf_counter() @@ -143,13 +147,12 @@ def run( # The first column is the step index; normalize name to "step". model_pd = model_pd.rename(columns={model_pd.columns[0]: "step"}) seed = model_pd["seed"].iloc[0] - model_pd = model_pd[['step', 'gini']] + model_pd = model_pd[["step", "gini"]] # Show a short tail in console for quick inspection tail_str = model_pd.tail(5).to_string(index=False) typer.echo(f"Metrics in the final 5 steps:\n{tail_str}") - # ---- Save CSV (same filename/layout as frames backend expects) if save_results: csv_path = results_dir / "model.csv" diff --git a/examples/plotting.py b/examples/plotting.py index 5313dcf8..0cf002c4 100644 --- a/examples/plotting.py +++ b/examples/plotting.py @@ -2,7 +2,7 @@ from __future__ import annotations from pathlib import Path -from typing import Sequence +from collections.abc import Sequence import re import polars as pl @@ -27,16 +27,16 @@ rc={ # real dark background + readable foreground "figure.facecolor": "#0b1021", - "axes.facecolor": "#0b1021", - "axes.edgecolor": "#d6d6d7", - "axes.labelcolor": "#e8e8ea", - "text.color": "#e8e8ea", - "xtick.color": "#c9c9cb", - "ytick.color": "#c9c9cb", - "grid.color": "#2a2f4a", - "grid.alpha": 0.35, - "axes.spines.top": False, - "axes.spines.right":False, + "axes.facecolor": "#0b1021", + "axes.edgecolor": "#d6d6d7", + "axes.labelcolor": "#e8e8ea", + "text.color": "#e8e8ea", + "xtick.color": "#c9c9cb", + "ytick.color": "#c9c9cb", + "grid.color": "#2a2f4a", + "grid.alpha": 0.35, + "axes.spines.top": False, + "axes.spines.right": False, "legend.facecolor": "#121734", "legend.edgecolor": "#3b3f5a", }, @@ -77,6 +77,7 @@ def _finalize_and_save(fig: Figure, output_dir: Path, stem: str, theme: str) -> # -------------------------- Public: model metrics ---------------------------- + def plot_model_metrics( metrics: pl.DataFrame, output_dir: Path, @@ -118,7 +119,9 @@ def plot_model_metrics( long = ( metrics.select(["step", *value_cols]) - .unpivot(index="step", on=value_cols, variable_name="metric", value_name="value") + .unpivot( + index="step", on=value_cols, variable_name="metric", value_name="value" + ) .to_pandas() ) @@ -170,6 +173,7 @@ def plot_model_metrics( # -------------------------- Public: agent metrics ---------------------------- + def plot_agent_metrics( agent_metrics: pl.DataFrame, output_dir: Path, @@ -189,19 +193,18 @@ def plot_agent_metrics( return preferred = ["step", "seed", "batch"] - id_vars = [c for c in preferred if c in agent_metrics.columns] or [agent_metrics.columns[0]] + id_vars = [c for c in preferred if c in agent_metrics.columns] or [ + agent_metrics.columns[0] + ] # Determine which columns to unpivot (all columns except the id vars). value_cols = [c for c in agent_metrics.columns if c not in id_vars] if not value_cols: return - melted = ( - agent_metrics.unpivot( - index=id_vars, on=value_cols, variable_name="metric", value_name="value" - ) - .to_pandas() - ) + melted = agent_metrics.unpivot( + index=id_vars, on=value_cols, variable_name="metric", value_name="value" + ).to_pandas() xcol = id_vars[0] @@ -227,6 +230,7 @@ def plot_agent_metrics( # -------------------------- Public: performance ------------------------------ + def plot_performance( df: pl.DataFrame, output_dir: Path, @@ -278,4 +282,4 @@ def plot_performance( "plot_model_metrics", "plot_agent_metrics", "plot_performance", -] \ No newline at end of file +] diff --git a/examples/utils.py b/examples/utils.py index dbd165b4..4d075dc4 100644 --- a/examples/utils.py +++ b/examples/utils.py @@ -2,6 +2,7 @@ import mesa_frames import mesa + @dataclass class FramesSimulationResult: """Container for example simulation outputs. @@ -12,6 +13,7 @@ class FramesSimulationResult: datacollector: mesa_frames.DataCollector + @dataclass class MesaSimulationResult: """Container for example simulation outputs. @@ -20,4 +22,4 @@ class MesaSimulationResult: `metrics`, while others also return `agent_metrics`. """ - datacollector: mesa.DataCollector \ No newline at end of file + datacollector: mesa.DataCollector