implement VideoViz to record model runs in a video

.gitignore

@@ -92,3 +92,6 @@ dmypy.json
 # JS dependencies
 mesa/visualization/templates/external/
 mesa/visualization/templates/js/external/
+
+# Video
+**/*.mp4

mesa/examples/basic/schelling/video.py

@@ -0,0 +1,40 @@
+"""Example of using VideoViz with the Schelling model."""
+
+from mesa.examples.basic.schelling.model import Schelling
+from mesa.visualization.video_viz import (
+    VideoViz,
+    make_measure_component,
+    make_space_component,
+)
+
+# Create model
+model = Schelling(10, 10)
+
+
+def agent_portrayal(agent):
+    """Portray agents based on their type."""
+    if agent is None:
+        return {}
+
+    portrayal = {
+        "color": "red" if agent.type == 0 else "blue",
+        "size": 25,
+        "marker": "s",  # square marker
+    }
+    return portrayal
+
+
+# Create visualization with space and some metrics
+viz = VideoViz(
+    model,
+    [
+        make_space_component(agent_portrayal=agent_portrayal),
+        make_measure_component("happy"),
+    ],
+    title="Schelling's Segregation Model",
+)
+
+# Record simulation
+if __name__ == "__main__":
+    viz.record(steps=50, filepath="schelling.mp4")
+    print("Video saved to: schelling.mp4")

mesa/visualization/video_viz.py

@@ -0,0 +1,201 @@
+"""Mesa visualization module for recording videos of model simulations.
+
+This module uses Matplotlib to create visualizations of model spaces and
+measures, and records them as videos.
+
+Please install FFmpeg to use this module:
+    - macOS: brew install ffmpeg
+    - Linux: sudo apt-get install ffmpeg
+    - Windows: download from https://ffmpeg.org/download.html
+"""
+
+import shutil
+from collections.abc import Callable, Sequence
+from pathlib import Path
+
+import matplotlib.animation as animation
+import matplotlib.pyplot as plt
+import numpy as np
+
+import mesa
+from mesa.visualization.mpl_drawing import (
+    draw_plot,
+    draw_space,
+)
+
+
+def make_space_component(
+    agent_portrayal: Callable | None = None,
+    propertylayer_portrayal: dict | None = None,
+    post_process: Callable | None = None,
+    **space_drawing_kwargs,
+) -> Callable[[mesa.Model, plt.Axes | None], plt.Axes]:
+    """Create a Matplotlib-based space visualization component.
+
+    Args:
+        agent_portrayal: Function to portray agents.
+        propertylayer_portrayal: Dictionary of PropertyLayer portrayal specifications
+        post_process : a callable that will be called with the Axes instance. Allows for fine tuning plots (e.g., control ticks)
+        space_drawing_kwargs : additional keyword arguments to be passed on to the underlying space drawer function. See
+                               the functions for drawing the various spaces for further details.
+
+    ``agent_portrayal`` is called with an agent and should return a dict. Valid fields in this dict are "color",
+    "size", "marker", and "zorder". Other field are ignored and will result in a user warning.
+
+
+    Returns:
+        function: A function that returns a Axes instance with the space drawn
+    """
+    if agent_portrayal is None:
+
+        def agent_portrayal(a):
+            return {}
+
+    def _make_space_component(model, ax=None):
+        space = getattr(model, "grid", None) or getattr(model, "space", None)
+        ax = draw_space(
+            space,
+            agent_portrayal,
+            propertylayer_portrayal,
+            ax,
+            **space_drawing_kwargs,
+        )
+        if post_process:
+            post_process(ax)
+        return ax
+
+    return _make_space_component
+
+
+def make_measure_component(
+    measure: Callable,
+    post_process: Callable | None = None,
+    **kwargs,
+) -> Callable[[mesa.Model, plt.Axes | None], plt.Axes]:
+    """Create a plotting function for a specified measure.
+
+    Args:
+        measure (str | dict[str, str] | list[str] | tuple[str]): Measure(s) to plot.
+        post_process : a callable that will be called with the Axes instance. Allows for fine tuning plots (e.g., control ticks)
+        kwargs: Additional keyword arguments to pass to the MeasureRendererMatplotlib constructor.
+
+    Returns:
+        function: A function that returns a Axes instance with the measure(s) drawn
+    """
+
+    def _make_measure_component(model, ax=None):
+        ax = draw_plot(model, measure, ax, **kwargs)
+        if post_process:
+            post_process(ax)
+        return ax
+
+    return _make_measure_component
+
+
+class VideoViz:
+    """Create high-quality video recordings of model simulations."""
+
+    def __init__(
+        self,
+        model: mesa.Model,
+        components: Sequence[Callable[[mesa.Model, plt.Axes | None], plt.Axes]],
+        *,
+        title: str | None = None,
+        figsize: tuple[float, float] | None = None,
+        grid: tuple[int, int] | None = None,
+    ):
+        """Initialize video visualization configuration.
+
+        Args:
+            model: The model to simulate and record
+            components: Sequence of component objects defining what to visualize
+            title: Optional title for the video
+            figsize: Optional figure size in inches (width, height)
+            grid: Optional (rows, cols) for custom layout. Auto-calculated if None.
+        """
+        # Check if FFmpeg is available
+        if not shutil.which("ffmpeg"):
+            raise RuntimeError(
+                "FFmpeg not found. Please install FFmpeg to save animations:\n"
+                "  - macOS: brew install ffmpeg\n"
+                "  - Linux: sudo apt-get install ffmpeg\n"
+                "  - Windows: download from https://ffmpeg.org/download.html"
+            )
+        self.model = model
+        self.components = components
+        self.title = title
+        self.figsize = figsize
+        self.grid = grid or self._calculate_grid(len(components))
+
+        # Setup figure and axes
+        self.fig, self.axes = self._setup_figure()
+
+    def record(
+        self,
+        *,
+        steps: int,
+        filepath: str | Path,
+        dpi: int = 100,
+        fps: int = 10,
+        codec: str = "h264",
+        bitrate: int = 2000,
+    ) -> None:
+        """Record model simulation to video file.
+
+        Args:
+            steps: Number of simulation steps to record
+            filepath: Where to save the video file
+            dpi: Resolution of the output video
+            fps: Frames per second in the output video
+            codec: Video codec to use
+            bitrate: Video bitrate in kbps (default: 2000)
+
+        Raises:
+            RuntimeError: If FFmpeg is not installed
+        """
+        filepath = Path(filepath)
+
+        def update(frame_num):
+            # Update model state
+            self.model.step()
+
+            # Render all visualization frames
+            for component, ax in zip(self.components, self.axes):
+                ax.clear()
+                component(self.model, ax)
+            return self.axes
+
+        # Create and save animation
+        anim = animation.FuncAnimation(
+            self.fig, update, frames=steps, interval=1000 / fps, blit=False
+        )
+
+        writer = animation.FFMpegWriter(
+            fps=fps,
+            codec=codec,
+            bitrate=bitrate,  # Now passing as integer
+        )
+
+        anim.save(filepath, writer=writer, dpi=dpi)
+
+    def _calculate_grid(self, n_frames: int) -> tuple[int, int]:
+        """Calculate optimal grid layout for given number of frames."""
+        cols = min(3, n_frames)  # Max 3 columns
+        rows = int(np.ceil(n_frames / cols))
+        return (rows, cols)
+
+    def _setup_figure(self):
+        """Setup matplotlib figure and axes."""
+        if not self.figsize:
+            self.figsize = (5 * self.grid[1], 5 * self.grid[0])
+        fig = plt.figure(figsize=self.figsize)
+        axes = []
+
+        for i in range(len(self.components)):
+            ax = fig.add_subplot(self.grid[0], self.grid[1], i + 1)
+            axes.append(ax)
+
+        if self.title:
+            fig.suptitle(self.title, fontsize=16)
+        fig.tight_layout()
+        return fig, axes