.mp4``. This hook copies the
+gallery videos straight from ``examples/output/`` into the built site so they are
+published to the ``gh-pages`` branch -- and only there -- by ``mkdocs gh-deploy``.
+
+Only the named gallery videos are copied (not, e.g., the large benchmark
+artifacts that ``scaling_test.py`` writes alongside them). Missing videos are
+skipped with a warning so the build still succeeds on a fresh checkout where the
+examples have not been rendered yet.
+"""
+
+from __future__ import annotations
+
+import logging
+import shutil
+from pathlib import Path
+
+log = logging.getLogger("mkdocs.hooks.embed_example_videos")
+
+_SOURCE_DIR = Path("examples/output")
+_GALLERY_VIDEOS = (
+ "simple_wave_animation.mp4",
+ "multi_panel_animation.mp4",
+ "very_complex_animation.mp4",
+ "nondeterministic_video_loader.mp4",
+)
+
+
+def on_post_build(config): # noqa: ARG001
+ dest_dir = Path(config["site_dir"]) / "output"
+ dest_dir.mkdir(parents=True, exist_ok=True)
+ for name in _GALLERY_VIDEOS:
+ src = _SOURCE_DIR / name
+ if src.exists():
+ shutil.copy2(src, dest_dir / name)
+ else:
+ log.warning("Example video missing, not published: %s", src)
diff --git a/docs/examples.md b/docs/examples.md
new file mode 100644
index 0000000..4f02587
--- /dev/null
+++ b/docs/examples.md
@@ -0,0 +1,83 @@
+# Examples
+
+Runnable example scripts live in [`examples/`](https://github.com/sibocw/parallel-matplotlib-animation/tree/main/examples).
+
+---
+
+## `simple_wave_animation.py`
+
+A single oscillating cosine wave — the [quick example](index.md#quick-example).
+
+
+
+
+
+
+
+---
+
+## `multi_panel_animation.py`
+
+Five subplots with different plot types updating together.
+
+
+
+
+
+
+
+---
+
+## `very_complex_animation.py`
+
+A 14-subplot `GridSpec` layout — the figure used by the
+[benchmark](benchmark.md). It showcases how much the setup-once / update-many
+design saves when the layout is expensive to build.
+
+
+
+
+
+
+
+---
+
+## `nondeterministic_video_loader.py`
+
+Handling frames whose parameters arrive out of order, using
+[`IndexedFrameParams`](api/animator.md). Useful when frames are decoded by a
+parallel dataloader that returns them non-deterministically.
+
+
+
+
+
+
diff --git a/docs/gen_benchmark.py b/docs/gen_benchmark.py
new file mode 100644
index 0000000..c4fd108
--- /dev/null
+++ b/docs/gen_benchmark.py
@@ -0,0 +1,23 @@
+"""MkDocs hook: ensure the benchmark figure exists before the docs build.
+
+The strong-scaling figure is generated by ``examples/scaling_test.py`` and
+written to ``examples/output/scaling_test/scaling_graph.html``, then embedded
+into ``benchmark.md`` via
+pymdownx.snippets. When the benchmark has not been run (e.g. on a fresh CI
+checkout) the file is missing and the build would fail, so this hook writes a
+placeholder in its place.
+"""
+
+from pathlib import Path
+
+_FIGURE = Path("examples/output/scaling_test/scaling_graph.html")
+_PLACEHOLDER = (
+ "Run python examples/scaling_test.py to generate this "
+ "figure.
\n"
+)
+
+
+def on_pre_build(config): # noqa: ARG001
+ if not _FIGURE.exists():
+ _FIGURE.parent.mkdir(parents=True, exist_ok=True)
+ _FIGURE.write_text(_PLACEHOLDER)
diff --git a/docs/index.md b/docs/index.md
new file mode 100644
index 0000000..e224101
--- /dev/null
+++ b/docs/index.md
@@ -0,0 +1,76 @@
+# parallel-matplotlib-animation
+
+Create matplotlib animations rendered to video **in parallel**, with efficient
+reuse of figure resources.
+
+## What it does
+
+Rendering a matplotlib animation frame-by-frame in a single process is slow.
+This library speeds it up by:
+
+1. Spawning a pool of worker processes and creating the matplotlib resources
+ (`plt.Figure`, `plt.Axes`, artists, ...) **once per worker**.
+2. Distributing frames across workers via a dynamic queue.
+3. Rendering each assigned frame by updating only the data that changes (rather
+ than redrawing the whole plot from scratch).
+4. Encoding the rendered frames into an H.264 MP4 with
+ [parallel-video-io](https://github.com/sibocw/parallel-video-io) (FFmpeg
+ under the hood, with automatic GPU/NVENC acceleration when available).
+
+**Key design: figure reuse.** In each worker process, `setup()` runs once to
+build the figure, then `update()` mutates it repeatedly. This combines the best
+of both worlds:
+
+- *Serial processing* — avoids the overhead of recreating complex layouts for
+ every frame.
+- *Parallel processing* — achieves speedup by using multiple CPU cores.
+
+See the [benchmark](benchmark.md) for how this scales.
+
+## Quick example
+
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+from parallel_animate import Animator
+
+# Step 1: Create a child class of parallel_animate.Animator
+class WaveAnimation(Animator):
+
+ # Step 2: Define how the plot should be set up
+ def setup(self):
+ fig, ax = plt.subplots()
+ self.x = np.linspace(0, 4 * np.pi, 200)
+ (self.line,) = ax.plot(self.x, np.cos(self.x))
+ ax.set_xlim(0, 4 * np.pi)
+ ax.set_ylim(-1.5, 1.5)
+ ax.set_xlabel("x")
+ ax.set_ylabel("y")
+ ax.set_title("Cosine Wave")
+ return fig # <- return a plt.Figure object
+
+ # Step 3: Define how plot elements should be updated for each frame
+ # (given parameters that you define later)
+ def update(self, frame_idx, params):
+ phase = params["phase"]
+ self.line.set_ydata(np.cos(self.x + phase))
+
+# Step 4: Define a list of input parameters, one for each frame
+params = [{"phase": 2 * np.pi * i / 60} for i in range(60)]
+
+# Step 5: Make the video in parallel
+anim = WaveAnimation()
+anim.make_video("wave.mp4", param_by_frame=params, fps=30, num_workers=4)
+```
+
+
+
+
+
+Continue to [Usage](usage.md) for a more detaield explanation, [API Reference] for the full API, or browse the [examples](examples.md).
+
+!!! note "Linux only"
+ parallel-video-io (the encoding backend) is currently Linux-only.
diff --git a/docs/installation.md b/docs/installation.md
new file mode 100644
index 0000000..d6898a8
--- /dev/null
+++ b/docs/installation.md
@@ -0,0 +1,43 @@
+# Installation
+
+!!! warning "Linux only"
+ This package depends on [parallel-video-io](https://github.com/sibocw/parallel-video-io)
+ for encoding, which is currently Linux-only.
+
+## Install with `pip`
+
+```bash
+pip install parallel-matplotlib-animation
+```
+
+## Install from a local copy
+
+```bash
+git clone https://github.com/sibocw/parallel-matplotlib-animation.git
+cd parallel-matplotlib-animation
+pip install -e . --config-settings editable_mode=compat
+```
+
+## Optional dependency groups
+
+| Extra | Installs | For |
+|-------|----------|-----|
+| `dev` | pytest, ruff, mkdocs-material, mkdocstrings | running tests, formatting, building the docs |
+| `benchmark` | plotly, pandas | running the [strong-scaling benchmark](benchmark.md) |
+
+```bash
+pip install -e '.[dev]' # development
+pip install -e '.[benchmark]' # benchmarking
+```
+
+## Requirements
+
+Make sure `ffmpeg` is available on your `$PATH` (required by parallel-video-io
+via imageio-ffmpeg). On Debian/Ubuntu:
+
+```bash
+sudo apt-get install ffmpeg
+```
+
+GPU (NVENC) encoding is used automatically when a CUDA-capable NVIDIA GPU and the
+matching drivers are present; it falls back to the CPU encoder otherwise.
diff --git a/docs/usage.md b/docs/usage.md
new file mode 100644
index 0000000..660d5a5
--- /dev/null
+++ b/docs/usage.md
@@ -0,0 +1,86 @@
+# Usage
+
+!!! tip "Examples"
+ See [Examples](examples.md) to get started fast.
+
+
+This library has a single class: [`parallel_animate.Animator`](api/animator.md).
+To make an animation you create your own subclass and define two methods.
+
+## Define your animator
+
+- **`setup(self)`** — no arguments except `self`. Build your figure however you
+ like and **return the `plt.Figure` object**. Store the things you will need to
+ update later (axes, the return values of plotting calls like `ax.plot`, etc.)
+ as instance attributes so you can access them in `update()`.
+- **`update(self, frame_idx, params)`** — given the frame index and the
+ per-frame parameters, mutate the artists you created in `setup()`. Typically
+ you call methods like `line.set_data(...)` on attributes you stored. `params`
+ can be any picklable Python object (a dict, tuple, single value, ...).
+- *(Optional)* **`__init__(self, ...)`** — add any custom construction logic. This
+ is handy when you want to create many animators from the same class with
+ different settings, e.g. `anim = RecordingAnimator(dataset_path=...)`.
+
+!!! warning "Keep heavy data out of instance attributes"
+ In parallel mode the whole `Animator` instance is pickled into **every**
+ worker process. Stashing a large array (e.g. a full video tensor) on `self`
+ multiplies its memory across workers. Pass per-frame data through
+ `param_by_frame` instead, or load it lazily inside `setup()`.
+
+## Render the video
+
+Once your animator is defined, a single call renders the video:
+
+```python
+anim = MyAnimation()
+anim.make_video(
+ output_file="out.mp4",
+ param_by_frame=params,
+ fps=30,
+ num_workers=-1,
+)
+```
+
+Key arguments to `make_video`:
+
+- **`output_file`** (`Path | str`) — output video path.
+- **`param_by_frame`** (`Iterable`) — one element per frame, each passed to
+ `update()` as `params`. Can be a list, tuple, or generator. Generators are
+ useful for large per-frame data (e.g. bitmaps) to avoid loading everything
+ into memory at once.
+- **`fps`** (`int`) — frame rate of the output video.
+- **`n_frames`** (`int | None`) — number of frames to render. If `None`, the
+ length of `param_by_frame` is used (when available).
+- **`num_workers`** (`int`) — worker processes to spawn. `-1` uses all CPU cores,
+ `-2` all but one, etc. `1` runs in the main process (no child processes).
+- **`video_mode`** (`str`) — encoder selection passed to parallel-video-io:
+ `"auto"` (default) uses GPU/NVENC when a CUDA device is available and falls
+ back to CPU/libx264 otherwise; `"gpu"` forces NVENC and `"cpu"` forces
+ libx264. Output is always an H.264 MP4.
+- **`video_quality`**, **`video_preset`**, **`video_extra_ffmpeg_params`** —
+ optional encoding-quality controls forwarded to parallel-video-io. Leave as
+ `None` for sensible defaults.
+
+See the [API reference](api/animator.md) for the remaining, less commonly used
+options (logging intervals, figure reuse, prefetching).
+
+## Out-of-order frame parameters
+
+Sometimes the frames in `param_by_frame` arrive out of order — for example when
+the parameters are decoded from a video by a parallel dataloader that returns
+frames non-deterministically. Wrap each item in
+[`parallel_animate.IndexedFrameParams`](api/animator.md), which carries an
+explicit `frame_id` that overrides the iterator ordering:
+
+```python
+from parallel_animate import IndexedFrameParams
+
+param_by_frame = [
+ IndexedFrameParams(frame_id=5, params={"frame": ...}),
+ IndexedFrameParams(frame_id=0, params={"frame": ...}),
+ # ...
+]
+```
+
+See [`examples/nondeterministic_video_loader.py`](examples.md) for a full
+example.
diff --git a/src/parallel_animate/examples/multi_panel_animation.py b/examples/multi_panel_animation.py
similarity index 98%
rename from src/parallel_animate/examples/multi_panel_animation.py
rename to examples/multi_panel_animation.py
index fcf7e5d..3069c48 100644
--- a/src/parallel_animate/examples/multi_panel_animation.py
+++ b/examples/multi_panel_animation.py
@@ -137,7 +137,7 @@ def update(self, frame_idx, params):
# Create animation
anim = MultiPanelAnimation()
- output_path = Path("example_output/multi_panel_animation.mp4")
+ output_path = Path("examples/output/multi_panel_animation.mp4")
output_path.parent.mkdir(parents=True, exist_ok=True)
anim.make_video(
output_file=output_path, param_by_frame=params, fps=30, num_workers=8
diff --git a/src/parallel_animate/examples/nondeterministic_video_loader.py b/examples/nondeterministic_video_loader.py
similarity index 95%
rename from src/parallel_animate/examples/nondeterministic_video_loader.py
rename to examples/nondeterministic_video_loader.py
index 0bbc5dc..9356caa 100644
--- a/src/parallel_animate/examples/nondeterministic_video_loader.py
+++ b/examples/nondeterministic_video_loader.py
@@ -36,7 +36,7 @@ def fake_video_loader(n_frames=64, frame_size=(128, 128)):
# Create animation
anim = VideoFrameAnimation()
- output_path = Path("example_output/nondeterministic_video_loader.mp4")
+ output_path = Path("examples/output/nondeterministic_video_loader.mp4")
anim.make_video(
output_file=output_path,
param_by_frame=frame_loader,
diff --git a/examples/output/scaling_test/scaling_graph.html b/examples/output/scaling_test/scaling_graph.html
new file mode 100644
index 0000000..defe820
--- /dev/null
+++ b/examples/output/scaling_test/scaling_graph.html
@@ -0,0 +1,2 @@
+
\ No newline at end of file
diff --git a/examples/run_all_except_benchmarks.sh b/examples/run_all_except_benchmarks.sh
new file mode 100755
index 0000000..e0ff2ab
--- /dev/null
+++ b/examples/run_all_except_benchmarks.sh
@@ -0,0 +1,47 @@
+#!/usr/bin/env bash
+# Run all examples except the benchmark (scaling_test.py).
+#
+# Each example is executed with the current Python interpreter, exactly as if
+# invoked directly from the command line.
+#
+# Usage:
+# ./examples/run_all_except_benchmarks.sh
+
+set -euo pipefail
+
+EXAMPLES_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+PYTHON="${PYTHON:-python}"
+
+# Examples to skip (e.g. the benchmark, which is slow and produces no artifact
+# meant for the README gallery).
+SKIP=("scaling_test.py")
+
+failures=()
+count=0
+
+for script in "$EXAMPLES_DIR"/*.py; do
+ name="$(basename "$script")"
+
+ skip=false
+ for s in "${SKIP[@]}"; do
+ if [[ "$name" == "$s" ]]; then
+ skip=true
+ break
+ fi
+ done
+ [[ "$skip" == true ]] && continue
+
+ printf '\n%s\nRunning %s\n%s\n' "======================================================================" "$name" "======================================================================"
+ count=$((count + 1))
+ if ! "$PYTHON" "$script"; then
+ failures+=("$name")
+ fi
+done
+
+printf '\n%s\nSummary\n%s\n' "======================================================================" "======================================================================"
+printf 'Ran %d example(s).\n' "$count"
+if [[ ${#failures[@]} -gt 0 ]]; then
+ printf 'Failed: %s\n' "${failures[*]}"
+ exit 1
+fi
+printf 'All examples completed successfully.\n'
diff --git a/examples/scaling_test.py b/examples/scaling_test.py
new file mode 100644
index 0000000..aa75901
--- /dev/null
+++ b/examples/scaling_test.py
@@ -0,0 +1,234 @@
+"""Strong-scaling benchmark for parallel matplotlib animation.
+
+Measures how render-to-video throughput scales with the number of worker
+processes, with and without figure reuse, and across both parallel-video-io
+encoding backends: CPU (libx264) and GPU (NVENC). The GPU backend is benchmarked
+only when a CUDA device is available; otherwise it is skipped automatically.
+
+See https://hpc-wiki.info/hpc/Scaling_tests#Strong_Scaling for background on
+strong scaling.
+
+Run from the repository root::
+
+ python examples/scaling_test.py # full sweep
+ python examples/scaling_test.py --quick # small, fast smoke run
+
+The ``benchmark`` optional dependencies (plotly, pandas) are required::
+
+ pip install -e '.[benchmark]'
+
+Outputs:
+
+* ``examples/output/scaling_test/results.csv`` -- one row per configuration
+* ``examples/output/scaling_test/results.json`` -- same data as JSON
+* ``examples/output/scaling_test/scaling_graph.html`` -- interactive Plotly
+ figure (embedded in the documentation site)
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import time
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+
+from very_complex_animation import VeryComplexAnimation
+
+# Repository-root-relative locations (this script lives in examples/).
+_REPO_ROOT = Path(__file__).resolve().parent.parent
+_OUTPUT_DIR = _REPO_ROOT / "examples" / "output" / "scaling_test"
+_FIGURE_PATH = _OUTPUT_DIR / "scaling_graph.html"
+
+# Two-tone colour scheme: CPU encode in blue, GPU encode in magenta; the darker
+# shade is "with figure reuse" (the mode this library is built around).
+_COLOR = {
+ ("cpu", True): "#0c2e6e", # CPU encode, with cache -- dark blue
+ ("cpu", False): "#90c4e4", # CPU encode, no cache -- light blue
+ ("gpu", True): "#7a0042", # GPU encode, with cache -- dark magenta
+ ("gpu", False): "#f0a0c4", # GPU encode, no cache -- light pink
+}
+
+
+def _gpu_available() -> bool:
+ """True when a CUDA device is present, so NVENC encoding can be benchmarked."""
+ try:
+ import torch
+
+ return bool(torch.cuda.is_available())
+ except Exception:
+ return False
+
+
+def run_scaling_test(
+ num_frames: int,
+ num_workers_list: list[int],
+ video_modes: list[str],
+ output_dir: Path,
+) -> pd.DataFrame:
+ """Run the strong-scaling sweep and return one row per configuration.
+
+ Args:
+ num_frames: Number of frames to render per run.
+ num_workers_list: Worker counts to sweep (e.g. ``[1, 2, 4, 8, 16]``).
+ video_modes: parallel-video-io encode backends to sweep, a subset of
+ ``["cpu", "gpu"]``.
+ output_dir: Directory for the rendered ``.mp4`` artifacts.
+
+ Returns:
+ A DataFrame with columns ``num_workers``, ``reuse_fig_obj``,
+ ``video_mode``, and ``time_seconds``.
+ """
+ params = [{"phase": 2 * np.pi * i / num_frames} for i in range(num_frames)]
+
+ rows: list[dict] = []
+ for video_mode in video_modes:
+ for num_workers in num_workers_list:
+ for reuse_figure_object in (True, False):
+ label = f"{num_workers}w_reuse{reuse_figure_object}_{video_mode}"
+ print(f"Running test: {label} ...")
+ output_file = output_dir / f"output_{label}.mp4"
+ anim = VeryComplexAnimation()
+ start_time = time.perf_counter()
+ anim.make_video(
+ output_file=output_file,
+ param_by_frame=params,
+ fps=30,
+ num_workers=num_workers,
+ disable_progress_bar=True,
+ reuse_figure_object=reuse_figure_object,
+ video_mode=video_mode,
+ )
+ elapsed_time = time.perf_counter() - start_time
+ rows.append(
+ {
+ "num_workers": num_workers,
+ "reuse_fig_obj": reuse_figure_object,
+ "video_mode": video_mode,
+ "time_seconds": elapsed_time,
+ }
+ )
+ print(f" {label} completed in {elapsed_time:.2f} seconds")
+
+ return pd.DataFrame(rows)
+
+
+def add_speedup(df: pd.DataFrame) -> pd.DataFrame:
+ """Add a ``speedup`` column, normalised to the serial no-reuse CPU baseline.
+
+ All configurations are normalised to a single baseline -- one worker, no
+ figure reuse, CPU encode -- so the plot shows both the parallel speedup and
+ the extra gains from figure reuse and GPU encoding on a common scale.
+ """
+ df = df.copy()
+ baseline_mode = (
+ "cpu" if "cpu" in df["video_mode"].values else df["video_mode"].iloc[0]
+ )
+ baseline = df[
+ (df["num_workers"] == df["num_workers"].min())
+ & (~df["reuse_fig_obj"])
+ & (df["video_mode"] == baseline_mode)
+ ]["time_seconds"]
+ baseline_time = baseline.iloc[0]
+ df["speedup"] = baseline_time / df["time_seconds"]
+ return df
+
+
+def plot_scaling_results(df: pd.DataFrame, output_path: Path) -> None:
+ """Write an interactive Plotly speedup-vs-workers figure."""
+ df = add_speedup(df)
+ num_workers_list = sorted(df["num_workers"].unique())
+
+ fig = go.Figure()
+ for video_mode in sorted(df["video_mode"].unique()):
+ for reuse in (True, False):
+ g = df[(df["video_mode"] == video_mode) & (df["reuse_fig_obj"] == reuse)]
+ if g.empty:
+ continue
+ g = g.sort_values("num_workers")
+ cache = "with cache" if reuse else "without cache"
+ fig.add_trace(
+ go.Scatter(
+ x=g["num_workers"],
+ y=g["speedup"],
+ mode="lines+markers",
+ name=f"{video_mode.upper()} encode, {cache}",
+ line=dict(color=_COLOR.get((video_mode, reuse), "#888888")),
+ marker=dict(color=_COLOR.get((video_mode, reuse), "#888888")),
+ hovertemplate=(
+ f"{video_mode.upper()} encode, {cache}
"
+ "%{x} workers
%{y:.2f}x speedup"
+ ),
+ )
+ )
+
+ # Ideal (zero-overhead) linear scaling.
+ fig.add_trace(
+ go.Scatter(
+ x=[1, max(num_workers_list)],
+ y=[1, max(num_workers_list)],
+ mode="lines",
+ name="ideal scaling",
+ line=dict(color="black", dash="dash"),
+ hoverinfo="skip",
+ )
+ )
+
+ fig.update_layout(
+ title="Strong scaling test",
+ xaxis_title="# workers",
+ yaxis_title="speedup",
+ xaxis=dict(type="log", dtick="L0.30103"), # log base-2 ticks
+ yaxis=dict(type="log"),
+ hovermode="closest",
+ legend_title_text="Configuration",
+ )
+ fig.update_xaxes(
+ tickvals=num_workers_list, ticktext=[str(n) for n in num_workers_list]
+ )
+
+ output_path.parent.mkdir(parents=True, exist_ok=True)
+ fig.write_html(str(output_path), include_plotlyjs="cdn", full_html=False)
+
+
+def main() -> None:
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--quick", action="store_true", help="small, fast smoke run")
+ parser.add_argument(
+ "--no-gpu", action="store_true", help="skip the GPU (NVENC) encode backend"
+ )
+ args = parser.parse_args()
+
+ if args.quick:
+ num_frames_to_draw = 24
+ num_workers_to_test = [1, 2, 4]
+ else:
+ num_frames_to_draw = 320
+ num_workers_to_test = [1, 2, 4, 8, 16]
+
+ video_modes = ["cpu"]
+ if not args.no_gpu and _gpu_available():
+ video_modes.append("gpu")
+ print("CUDA device detected: benchmarking both CPU and GPU encoding.")
+ else:
+ print("No CUDA device (or --no-gpu): benchmarking CPU encoding only.")
+
+ _OUTPUT_DIR.mkdir(exist_ok=True, parents=True)
+
+ df = run_scaling_test(
+ num_frames_to_draw, num_workers_to_test, video_modes, _OUTPUT_DIR
+ )
+ df.to_csv(_OUTPUT_DIR / "results.csv", index=False)
+ with open(_OUTPUT_DIR / "results.json", "w") as f:
+ json.dump(df.to_dict(orient="records"), f, indent=4)
+
+ plot_scaling_results(df, output_path=_FIGURE_PATH)
+ print(f"\nWrote results to {_OUTPUT_DIR}")
+ print(f"Wrote figure to {_FIGURE_PATH}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/src/parallel_animate/examples/simple_wave_animation.py b/examples/simple_wave_animation.py
similarity index 93%
rename from src/parallel_animate/examples/simple_wave_animation.py
rename to examples/simple_wave_animation.py
index 2547c2c..e8cb654 100644
--- a/src/parallel_animate/examples/simple_wave_animation.py
+++ b/examples/simple_wave_animation.py
@@ -30,7 +30,7 @@ def update(self, frame_idx, params):
# Create and render
anim = WaveAnimation()
- output_path = Path("example_output/simple_wave_animation.mp4")
+ output_path = Path("examples/output/simple_wave_animation.mp4")
output_path.parent.mkdir(parents=True, exist_ok=True)
anim.make_video(
output_file=output_path, param_by_frame=params, fps=30, num_workers=4
diff --git a/src/parallel_animate/examples/very_complex_animation.py b/examples/very_complex_animation.py
similarity index 99%
rename from src/parallel_animate/examples/very_complex_animation.py
rename to examples/very_complex_animation.py
index f7c2848..cd363d8 100644
--- a/src/parallel_animate/examples/very_complex_animation.py
+++ b/examples/very_complex_animation.py
@@ -281,7 +281,7 @@ def setup(self):
self.artists["stats_text"] = self.ax9.text(
0.95,
0.95,
- f"μ = 0.00\nσ = 1.00\nN = 1000",
+ "μ = 0.00\nσ = 1.00\nN = 1000",
transform=self.ax9.transAxes,
fontsize=9,
verticalalignment="top",
@@ -627,7 +627,7 @@ def update(self, frame_idx, params):
# Create and render
anim = VeryComplexAnimation()
- output_path = Path("example_output/very_complex_animation.mp4")
+ output_path = Path("examples/output/very_complex_animation.mp4")
output_path.parent.mkdir(parents=True, exist_ok=True)
anim.make_video(
output_file=output_path, param_by_frame=params, fps=30, num_workers=8
diff --git a/mkdocs.yml b/mkdocs.yml
new file mode 100644
index 0000000..f91f5d6
--- /dev/null
+++ b/mkdocs.yml
@@ -0,0 +1,66 @@
+site_name: Parallel Matplotlib Animation
+site_url: https://sibocw.github.io/parallel-matplotlib-animation/
+repo_url: https://github.com/sibocw/parallel-matplotlib-animation
+repo_name: sibocw/parallel-matplotlib-animation
+
+theme:
+ name: material
+ features:
+ - navigation.top
+ - search.suggest
+ - search.highlight
+ - content.code.copy
+ palette:
+ - scheme: default
+ primary: indigo
+ accent: indigo
+ toggle:
+ icon: material/brightness-7
+ name: Switch to dark mode
+ - scheme: slate
+ primary: indigo
+ accent: indigo
+ toggle:
+ icon: material/brightness-4
+ name: Switch to light mode
+
+hooks:
+ - docs/gen_benchmark.py
+ - docs/embed_example_videos.py
+
+plugins:
+ - search
+ - mkdocstrings:
+ handlers:
+ python:
+ paths: ["src"]
+ options:
+ docstring_style: google
+ merge_init_into_class: true
+ show_root_heading: true
+ show_symbol_type_heading: true
+ show_symbol_type_toc: true
+ separate_signature: true
+ show_signature_annotations: true
+ members_order: source
+ filters:
+ - "!^_"
+
+markdown_extensions:
+ - admonition
+ - pymdownx.details
+ - pymdownx.superfences
+ - pymdownx.highlight:
+ anchor_linenums: true
+ - pymdownx.inlinehilite
+ - pymdownx.snippets:
+ base_path: ["."]
+
+nav:
+ - Home: index.md
+ - Installation: installation.md
+ - Usage: usage.md
+ - Examples: examples.md
+ - API Reference: api/animator.md
+ - Benchmark: benchmark.md
+ - Developer Info: developer.md
diff --git a/pyproject.toml b/pyproject.toml
index 28cab6d..d75a67b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,22 +1,42 @@
[project]
name = "parallel-matplotlib-animation"
-version = "0.1.2"
+version = "0.1.3"
description = "Animate matplotlib figures into videos, in parallel but with efficient caching"
authors = [
{ name = "Sibo Wang-Chen", email = "sibo.wang@epfl.ch" }
]
readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.10,<3.15"
dependencies = [
"matplotlib>=3.10.0",
"numpy>=2,<3",
"tqdm>=4.67",
- "av>=14.0",
- "Pillow>=11.0",
+ "parallel-video-io==0.1.8",
+]
+
+[project.optional-dependencies]
+dev = [
+ # Unit testing
+ "pytest>=9.0,<10.0",
+ # Formatting & linting
+ "ruff>=0.15",
+ # Documentation
+ "mkdocs-material>=9.5,<10.0",
+ "mkdocstrings[python]>=0.29",
+]
+benchmark = [
+ # Reporting and plotting for the strong-scaling benchmark
+ # (examples/scaling_test.py). matplotlib is already a core dependency and
+ # renders the animation frames; these add an interactive Plotly figure and a
+ # pandas results table. The benchmark also exercises parallel-video-io's GPU
+ # (NVENC) encoder when a CUDA device is available; no extra dependency is
+ # needed for it, as parallel-video-io is already a core dependency.
+ "plotly>=6.8,<7.0",
+ "pandas>=2.0,<3.0",
]
[project.urls]
-Repository = "https://github.com/sibocw/parallel-matplotlib-animation"
+Repository = "https://github.com/nely-epfl/parallel-matplotlib-animation"
[build-system]
requires = ["hatchling"]
@@ -24,3 +44,9 @@ build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["src/parallel_animate"]
+
+[tool.pytest.ini_options]
+# The package lives under src/ and is not installed into the env, so put it on
+# the import path for test collection.
+pythonpath = ["src"]
+testpaths = ["tests"]
diff --git a/src/parallel_animate/__init__.py b/src/parallel_animate/__init__.py
index a070988..2deaa41 100644
--- a/src/parallel_animate/__init__.py
+++ b/src/parallel_animate/__init__.py
@@ -1 +1,3 @@
from .animator import Animator, IndexedFrameParams
+
+__all__ = ["Animator", "IndexedFrameParams"]
diff --git a/src/parallel_animate/animator.py b/src/parallel_animate/animator.py
index ab84bf4..f6ca557 100644
--- a/src/parallel_animate/animator.py
+++ b/src/parallel_animate/animator.py
@@ -2,13 +2,15 @@
matplotlib.use("Agg")
+import sys
import tempfile
import itertools
import logging
+import queue
import multiprocessing as mp
+import numpy as np
import matplotlib.pyplot as plt
-import av
-from PIL import Image
+import pvio
from tqdm import tqdm
from pathlib import Path
from abc import ABC, abstractmethod
@@ -36,6 +38,25 @@ def __post_init__(self):
class Animator(ABC):
"""
Base class for creating matplotlib animations with efficient parallel rendering.
+
+ Performance note: every frame is fully rasterised — ``update()`` mutates the
+ figure and the whole canvas is then re-drawn and captured. Matplotlib's
+ blitting optimisation (re-drawing only the artists that changed via
+ ``draw_artist``/``restore_region``) is intentionally not used: frames are
+ rendered independently and, in parallel mode, in separate worker processes,
+ so there is no persistent inter-frame canvas state to blit against. Speedups
+ come from rendering frames concurrently across workers rather than from
+ incremental redraws. To keep each frame cheap, do expensive, frame-invariant
+ setup once in ``setup()`` and only touch what changes in ``update()``.
+
+ Pickling note: in parallel mode the whole Animator instance is pickled and
+ copied into every worker process. Anything stored on ``self`` (e.g. in
+ ``__init__``) is therefore serialised once per worker — stashing a large
+ array such as an entire video tensor on an instance attribute multiplies its
+ memory across workers and adds pickling overhead. Keep heavy, per-frame data
+ out of instance attributes: pass it through ``param_by_frame`` (the intended
+ channel), or load/construct it lazily inside ``setup()`` so each worker
+ builds its own copy instead of receiving one over the pickle boundary.
"""
@abstractmethod
@@ -81,9 +102,10 @@ def make_video(
plotting_log_interval: int | None = None,
saving_log_interval: int | None = None,
savefig_params: dict[str, Any] | None = None,
- video_codec: str = "libx264",
- video_pixfmt: str = "yuv420p",
- video_params: dict[str, Any] | None = None,
+ video_mode: str = "auto",
+ video_quality: int | None = None,
+ video_preset: str | None = None,
+ video_extra_ffmpeg_params: list[str] | None = None,
reuse_figure_object: bool = True,
preload_factor: int = 8,
) -> None:
@@ -106,20 +128,38 @@ def make_video(
worker (None = no interval logging).
saving_log_interval (int | None): Log progress every N frames when merging
frames into video (None = no interval logging).
- savefig_params (dict[str, Any]): Additional keyword arguments to
- pass to plt.Figure.savefig() when saving frames (default: {}).
- video_codec (str): Codec to use for video encoding (default: "libx264").
- video_pixfmt (str): Pixel format for video encoding (default: "yuv420p").
- video_params (dict[str, Any]): Additional parameters to set on the video
- stream (default: {"crf": "23", "preset": "slow"}).
+ savefig_params (dict[str, Any]): Rendering options for each frame
+ (default: {}). For speed, frames are grabbed directly from the
+ Agg canvas buffer as raw RGB pixels (no PNG encode/decode or
+ image-file round-trip), so only ``dpi`` is honoured here — it
+ sets the raster resolution. Other ``savefig``-specific options
+ such as ``bbox_inches`` do not apply to a direct buffer grab.
+ video_mode (str): Encoding backend selection passed to parallel-video-io,
+ one of "auto", "gpu", or "cpu" (default: "auto"). "auto" uses the GPU
+ encoder (FFmpeg/NVENC) when a CUDA device is available and transparently
+ falls back to CPU (libx264) otherwise. "gpu" forces NVENC and "cpu"
+ forces libx264. Output is always an H.264 MP4.
+ video_quality (int | None): H.264 quantiser scale in the range 0-51, where
+ lower means higher quality / larger files. If None (default), use
+ parallel-video-io's visually-lossless default.
+ video_preset (str | None): Encoder preset. If None (default), use the
+ encoder's own default. Accepts libx264 presets ("ultrafast" … "placebo")
+ in CPU mode or NVENC presets ("p1" … "p7") in GPU mode.
+ video_extra_ffmpeg_params (list[str] | None): Extra raw FFmpeg arguments
+ appended to the encode command for advanced tuning (default: None).
reuse_figure_object (bool): If False, the figure will be re-created for each
frame (i.e. setup() called every frame). There is basically no reason to
set this to False. Use only for testing and benchmarking.
preload_factor (int): Number of workloads to prefetch in the task queue per
worker (approximately). I.e. each worker will have up to this many
- frames (on average) queued ahead of time to work on.
+ frames (on average) queued ahead of time to work on. Higher values
+ smooth throughput but hold more frames' params in the queue at once;
+ with large per-frame params (e.g. images) this raises peak memory by
+ roughly num_workers * preload_factor frames' worth.
"""
- # Convert param_by_frame to list if it's not already
+ # Use the length of param_by_frame as the frame count when not given. Some
+ # iterables (e.g. generators) have no length; that's fine, but the progress
+ # bar then can't show a completion percentage.
if n_frames is None:
try:
n_frames = len(param_by_frame)
@@ -129,6 +169,12 @@ def make_video(
"Progress bar won't show completion percentage."
)
+ # Resolve auto (None) progress-bar disabling to a concrete bool up front so
+ # tqdm and pvio agree: when output isn't a TTY, both stay quiet. tqdm writes
+ # to stderr by default, so mirror its auto-detection on stderr.
+ if disable_progress_bar is None:
+ disable_progress_bar = not sys.stderr.isatty()
+
# Determine number of workers
if num_workers == 0:
raise ValueError(
@@ -146,8 +192,6 @@ def make_video(
# Avoid mutable default arguments by creating defaults here
if savefig_params is None:
savefig_params = {}
- if video_params is None:
- video_params = {"crf": "23", "preset": "slow"}
if num_workers == 1:
_logger.info("Running in serial mode")
@@ -174,14 +218,15 @@ def make_video(
preload_factor,
)
- _logger.info("Creating video with PyAV")
+ _logger.info("Creating video with parallel-video-io")
_merge_frames_into_video(
frames_dir,
output_file,
fps,
- video_codec,
- video_pixfmt,
- video_params,
+ video_mode,
+ video_quality,
+ video_preset,
+ video_extra_ffmpeg_params,
disable_progress_bar,
_logger,
log_interval=saving_log_interval,
@@ -215,28 +260,18 @@ def _render_serial(
if reuse_figure_object:
fig = self._setup_and_check()
- seen_frame_ids: set[int] = set()
frames_processed = 0
for frame_idx, params in tqdm(
- enumerate(itertools.islice(param_by_frame, n_frames)),
+ _resolved_frames(param_by_frame, n_frames),
total=n_frames,
disable=disable_progress_bar,
+ desc="Rendering frames",
):
- if isinstance(params, IndexedFrameParams):
- if params.frame_id in seen_frame_ids:
- raise ValueError(
- f"Duplicate frame_id {params.frame_id} in param_by_frame"
- )
- seen_frame_ids.add(params.frame_id)
- frame_idx = params.frame_id
- params = params.params
-
if not reuse_figure_object:
fig = self._setup_and_check()
self.update(frame_idx, params)
- frame_path = Path(frames_dir) / f"frame_{frame_idx:09d}.png"
- fig.savefig(frame_path, **savefig_params)
+ _save_frame(fig, frames_dir, frame_idx, savefig_params)
if not reuse_figure_object:
plt.close(fig)
@@ -288,34 +323,35 @@ def _render_parallel(
# workers by ~preload_factor frames. Note: tqdm here tracks enqueue speed,
# which approximates render progress but hits 100% while workers finish the
# last queued frames.
- seen_frame_ids: set[int] = set()
- for frame_idx, params in tqdm(
- enumerate(itertools.islice(param_by_frame, n_frames)),
- total=n_frames,
- disable=disable_progress_bar,
- ):
- if isinstance(params, IndexedFrameParams):
- if params.frame_id in seen_frame_ids:
- raise ValueError(
- f"Duplicate frame_id {params.frame_id} in param_by_frame"
- )
- seen_frame_ids.add(params.frame_id)
- frame_idx = params.frame_id
- params = params.params
-
- task_queue.put((frame_idx, params))
-
- # Send sentinel values to signal workers to exit
- for _ in range(num_workers):
- task_queue.put(None)
-
- # Wait for all workers to finish and check for errors
- failed = []
- for p in workers:
- p.join()
- if p.exitcode != 0:
- failed.append((p.pid, p.exitcode))
-
+ #
+ # Because the queue is bounded, a worker that dies (e.g. update() raised)
+ # stops draining it; without the liveness check in _put_or_abort the queue
+ # would fill and this loop would block forever instead of surfacing the
+ # error. _put_or_abort polls worker health while it waits for space.
+ # Any failure once workers are running (a validation error here, or a
+ # worker crash surfaced by _put_or_abort) must tear the workers down,
+ # otherwise they block forever on the queue and leak as orphans.
+ try:
+ for frame_idx, params in tqdm(
+ _resolved_frames(param_by_frame, n_frames),
+ total=n_frames,
+ disable=disable_progress_bar,
+ desc="Rendering frames",
+ ):
+ _put_or_abort(task_queue, (frame_idx, params), workers)
+
+ # Send sentinel values to signal workers to exit
+ for _ in range(num_workers):
+ _put_or_abort(task_queue, None, workers)
+
+ # Wait for all workers to finish and check for errors
+ for p in workers:
+ p.join()
+ except BaseException:
+ _abort_workers(workers)
+ raise
+
+ failed = _failed_workers(workers)
if failed:
details = ", ".join(
f"pid {pid} exited with code {code}" for pid, code in failed
@@ -325,6 +361,129 @@ def _render_parallel(
_logger.info("All workers completed")
+def _resolved_frames(param_by_frame: Iterable[Any], n_frames: int | None):
+ """Yield ``(frame_idx, params)`` for each frame, shared by serial and parallel.
+
+ Resolves :class:`IndexedFrameParams` (its ``frame_id`` overrides the positional
+ index) and rejects duplicate frame indices: each frame is written to one file
+ keyed by its index, so a repeat — whether two identical ``frame_id``s or a
+ ``frame_id`` colliding with a positional (enumerate) index — would silently
+ overwrite an earlier frame.
+ """
+ seen_frame_ids: set[int] = set()
+ for frame_idx, params in enumerate(itertools.islice(param_by_frame, n_frames)):
+ if isinstance(params, IndexedFrameParams):
+ frame_idx = params.frame_id
+ params = params.params
+
+ if frame_idx in seen_frame_ids:
+ raise ValueError(f"Duplicate frame index {frame_idx} in param_by_frame")
+ seen_frame_ids.add(frame_idx)
+
+ yield frame_idx, params
+
+
+def _figure_to_rgb_array(fig: plt.Figure, savefig_params: dict[str, Any]) -> np.ndarray:
+ """Rasterise ``fig`` to a contiguous ``(H, W, 3)`` uint8 RGB array.
+
+ Pixels are read straight from the Agg canvas buffer, skipping PNG
+ encode/decode and any image-file round-trip. Only the ``dpi`` entry of
+ ``savefig_params`` affects the result (it sets the raster resolution); other
+ ``savefig``-specific options do not apply to a direct buffer grab.
+ """
+ dpi = savefig_params.get("dpi")
+ if dpi is not None and dpi != "figure" and fig.get_dpi() != dpi:
+ fig.set_dpi(dpi)
+ fig.canvas.draw()
+ rgba = np.asarray(fig.canvas.buffer_rgba())
+ # Drop the alpha channel and return a contiguous array the encoder can consume.
+ return np.ascontiguousarray(rgba[:, :, :3])
+
+
+def _save_frame(
+ fig: plt.Figure,
+ frames_dir: Path | str,
+ frame_idx: int,
+ savefig_params: dict[str, Any],
+) -> None:
+ """Render ``fig`` and persist it as a raw ``.npy`` frame in ``frames_dir``."""
+ frame = _figure_to_rgb_array(fig, savefig_params)
+ frame_path = Path(frames_dir) / f"frame_{frame_idx:09d}.npy"
+ np.save(frame_path, frame)
+
+
+class _NpyFrameSequence:
+ """Re-iterable, lazily-loading view over frames saved as ``.npy`` files.
+
+ pvio's ``write_frames_to_video`` accepts any re-iterable yielding uint8
+ ``(H, W, C)`` arrays and may iterate it more than once (GPU→CPU fallback),
+ so frames are loaded from disk on demand rather than held in memory. It also
+ indexes ``frames[0]`` to determine the output size, hence ``__getitem__``.
+ """
+
+ def __init__(self, paths: list[Path | str]):
+ self._paths = list(paths)
+
+ def __len__(self) -> int:
+ return len(self._paths)
+
+ def __getitem__(self, index: int) -> np.ndarray:
+ return np.load(self._paths[index])
+
+ def __iter__(self):
+ for path in self._paths:
+ yield np.load(path)
+
+
+def _failed_workers(workers: list[mp.Process]) -> list[tuple[int | None, int | None]]:
+ """Return (pid, exitcode) for every worker that has exited with a non-zero code.
+
+ Workers that are still running (exitcode is None) or exited cleanly (0) are
+ excluded.
+ """
+ return [
+ (p.pid, p.exitcode)
+ for p in workers
+ if p.exitcode is not None and p.exitcode != 0
+ ]
+
+
+def _abort_workers(workers: list[mp.Process]) -> None:
+ """Terminate any still-running workers and reap them all.
+
+ Called when one worker has already failed: the survivors are blocked waiting
+ on a queue that will never be drained, so there is nothing to salvage.
+ """
+ for p in workers:
+ if p.is_alive():
+ p.terminate()
+ for p in workers:
+ p.join()
+
+
+def _put_or_abort(task_queue: mp.Queue, item: Any, workers: list[mp.Process]) -> None:
+ """Put ``item`` on ``task_queue``, aborting if a worker dies while we wait.
+
+ The queue is bounded, so ``put`` blocks once it is full. If a worker has
+ crashed it will never drain the queue again, which would block the producer
+ forever. Instead we put with a timeout and, whenever the queue stays full,
+ check whether any worker has died; if so we tear down the remaining workers
+ and raise rather than hang.
+ """
+ while True:
+ try:
+ task_queue.put(item, timeout=1.0)
+ return
+ except queue.Full:
+ failed = _failed_workers(workers)
+ if failed:
+ _abort_workers(workers)
+ details = ", ".join(
+ f"pid {pid} exited with code {code}" for pid, code in failed
+ )
+ raise RuntimeError(f"One or more worker processes failed: {details}")
+
+
def _worker_process(
animator: Animator,
worker_id: int,
@@ -341,6 +500,10 @@ def _worker_process(
1. Calls setup() once to initialize the figure (unless reuse_figure_object is False)
2. Repeatedly pulls individual frames from the task queue
3. Renders each frame
+
+ The Animator instance is pickled and sent to every worker. See the Animator
+ class docstring for the implications of stashing large data on instance
+ attributes.
"""
fig = None
if reuse_figure_object:
@@ -356,8 +519,7 @@ def _worker_process(
if not reuse_figure_object:
fig = animator._setup_and_check()
animator.update(frame_idx, params)
- frame_path = Path(frames_dir) / f"frame_{frame_idx:09d}.png"
- fig.savefig(frame_path, **savefig_params)
+ _save_frame(fig, frames_dir, frame_idx, savefig_params)
frames_processed += 1
if not reuse_figure_object:
plt.close(fig)
@@ -375,67 +537,51 @@ def _merge_frames_into_video(
frames_dir: Path | str,
output_file: Path | str,
fps: int,
- video_codec: str,
- video_pixfmt: str,
- video_params: dict[str, Any],
+ video_mode: str,
+ video_quality: int | None,
+ video_preset: str | None,
+ video_extra_ffmpeg_params: list[str] | None,
disable_progress_bar: bool | None,
logger: logging.Logger,
log_interval: int | None,
) -> None:
- """Use PyAV to merge frames into video."""
- # Gather frame files in sorted order
- frame_files = sorted(Path(frames_dir).glob("frame_*.png"))
+ """Use parallel-video-io (pvio) to merge frames into an H.264 MP4.
+
+ Frames are stored as raw ``.npy`` RGB arrays (see :func:`_save_frame`) and
+ streamed to pvio lazily, so no PNG decode step is needed. pvio handles
+ even-dimension and pixel-format requirements internally, and picks the GPU
+ (NVENC) or CPU (libx264) encoder according to ``video_mode``.
+ """
+ # Gather frame files in sorted (i.e. frame-index) order
+ frame_files = sorted(Path(frames_dir).glob("frame_*.npy"))
if not frame_files:
raise RuntimeError("No frames found in temporary directory")
- # Open first image to determine size and ensure even dimensions
- with Image.open(frame_files[0]) as first_img:
- width, height = first_img.size
- # Make width/height even (required by many codecs)
- width = (width // 2) * 2
- height = (height // 2) * 2
+ Path(output_file).parent.mkdir(parents=True, exist_ok=True)
- try:
- Path(output_file).parent.mkdir(parents=True, exist_ok=True)
- container = av.open(str(output_file), mode="w")
- stream = container.add_stream(video_codec, rate=fps)
- stream.width = width
- stream.height = height
- stream.pix_fmt = video_pixfmt
- stream.options.update(video_params)
-
- # Encode each frame
- for i, frame_path in tqdm(
- enumerate(frame_files),
- total=len(frame_files),
- desc="Merging frames",
- disable=disable_progress_bar,
- ):
- with Image.open(frame_path) as img:
- img = img.convert("RGB")
- # Ensure image has the right size
- if img.size != (width, height):
- img = img.resize((width, height))
-
- video_frame = av.VideoFrame.from_image(img)
- video_frame = video_frame.reformat(width, height, format=video_pixfmt)
- for packet in stream.encode(video_frame):
- container.mux(packet)
+ encode_kwargs: dict[str, Any] = {
+ "mode": video_mode,
+ "preset": video_preset,
+ "extra_ffmpeg_params": video_extra_ffmpeg_params,
+ "log_interval": log_interval,
+ "quiet": bool(disable_progress_bar),
+ }
+ # Only override pvio's default quality when the user requested a specific value.
+ if video_quality is not None:
+ encode_kwargs["quality"] = video_quality
- # Optional interval logging
- if log_interval and (i + 1) % log_interval == 0:
- logger.info(f"Frame written {i + 1}/{len(frame_files)} to video")
-
- # Flush encoder
- for packet in stream.encode(None):
- container.mux(packet)
-
- container.close()
+ try:
+ pvio.write_frames_to_video(
+ output_file,
+ _NpyFrameSequence(frame_files),
+ fps,
+ **encode_kwargs,
+ )
size_mb = Path(output_file).stat().st_size / (1024 * 1024)
logger.info(f"Video created: {output_file} ({size_mb:.2f} MB)")
except Exception as e:
- logger.critical(f"PyAV failed to create video: {e}")
+ logger.critical(f"parallel-video-io failed to create video: {e}")
raise
diff --git a/src/parallel_animate/examples/__init__.py b/src/parallel_animate/examples/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/parallel_animate/examples/scaling_test.py b/src/parallel_animate/examples/scaling_test.py
deleted file mode 100644
index b5987b6..0000000
--- a/src/parallel_animate/examples/scaling_test.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import time
-import json
-from pathlib import Path
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-from parallel_animate.examples.very_complex_animation import VeryComplexAnimation
-
-
-def run_scaling_test(
- num_frames: int, num_workers_list: list[int], output_dir: Path
-) -> dict[str, dict]:
- """Run strong scaling test for different parallelization strategies."""
- # Generate parameters
- params = [{"phase": 2 * np.pi * i / num_frames} for i in range(num_frames)]
-
- # Run tests for all configurations
- results_all = {}
- for num_workers in num_workers_list:
- for reuse_figure_object in [True, False]:
- config_name = f"{num_workers}workers_reusefig{reuse_figure_object}"
- print(f"Running test: {config_name}...")
- output_file = output_dir / f"output_{config_name}.mp4"
- start_time = time.time()
- anim = VeryComplexAnimation()
- anim.make_video(
- output_file=output_file,
- param_by_frame=params,
- fps=30,
- num_workers=num_workers,
- disable_progress_bar=True,
- reuse_figure_object=reuse_figure_object,
- )
- elapsed_time = time.time() - start_time
- results_all[config_name] = {
- "num_workers": num_workers,
- "reuse_fig_obj": reuse_figure_object,
- "time_seconds": elapsed_time,
- }
- print(f"Test {config_name} completed in {elapsed_time:.2f} seconds")
-
- return results_all
-
-
-def plot_scaling_results(results: dict[str, dict], output_path: Path) -> None:
- """Create plots showing scaling performance."""
- num_workers_list = list(set(result["num_workers"] for result in results.values()))
-
- fig, ax = plt.subplots(figsize=(4, 4), tight_layout=True)
- baseline_time = results[f"1workers_reusefigFalse"]["time_seconds"]
- _max_speedup = len(num_workers_list)
- for reuse_figure_object in [True, False]:
- times = [
- results[f"{n}workers_reusefig{reuse_figure_object}"]["time_seconds"]
- for n in num_workers_list
- ]
- speedups = [baseline_time / t for t in times]
- _max_speedup = max(_max_speedup, *speedups)
- ax.plot(
- num_workers_list,
- speedups,
- marker="o",
- label="with cache" if reuse_figure_object else "without cache",
- )
- ax.plot(
- [1, max(num_workers_list)],
- [1, max(num_workers_list)],
- color="black",
- label="ideal scaling",
- zorder=0,
- )
- ax.legend()
- ax.set_xscale("log", base=2)
- ax.set_yscale("log", base=2)
- ax.set_xlabel("# workers")
- ax.set_ylabel("speedup")
- ax.set_title("Strong scaling test")
- ax.set_aspect("equal")
- fig.savefig(output_path)
- plt.close(fig)
-
-
-if __name__ == "__main__":
- output_dir = Path("example_output/scaling_test")
- output_dir.mkdir(exist_ok=True, parents=True)
-
- num_frames_to_draw = 320
- num_workers_to_test = [1, 2, 4, 8, 16]
- results = run_scaling_test(num_frames_to_draw, num_workers_to_test, output_dir)
- with open(output_dir / "results.json", "w") as f:
- json.dump(results, f, indent=4)
-
- with open(output_dir / "results.json", "r") as f:
- results = json.load(f)
- plot_scaling_results(results, output_path=output_dir / "scaling_graph.png")
diff --git a/tests/test_animator.py b/tests/test_animator.py
index 1226bc9..1044fba 100644
--- a/tests/test_animator.py
+++ b/tests/test_animator.py
@@ -1,5 +1,6 @@
"""AI-generated unit tests"""
+import threading
import unittest
import tempfile
import numpy as np
@@ -9,6 +10,18 @@
from parallel_animate import Animator, IndexedFrameParams
+class FailingAnimation(Animator):
+ """Animation whose update() always raises, to simulate a crashing worker."""
+
+ def setup(self):
+ fig, ax = plt.subplots(figsize=(2, 2))
+ (self.line,) = ax.plot([0, 1], [0, 1])
+ return fig
+
+ def update(self, frame_idx, params):
+ raise RuntimeError("boom")
+
+
class SimpleTestAnimation(Animator):
"""Minimal animation for testing."""
@@ -453,6 +466,65 @@ def test_mixed_indexed_and_regular_params_succeeds(self):
self.assertTrue(output_path.exists())
+ def test_duplicate_frame_id_raises_serial(self):
+ """Repeated IndexedFrameParams frame_id must raise, not overwrite a frame."""
+ params = [
+ IndexedFrameParams(frame_id=1, params={"phase": 0.0}),
+ IndexedFrameParams(frame_id=1, params={"phase": 1.0}),
+ ]
+ anim = SimpleTestAnimation()
+ with tempfile.TemporaryDirectory() as tmpdir:
+ with self.assertRaises(ValueError):
+ anim.make_video(
+ output_file=Path(tmpdir) / "dup.mp4",
+ param_by_frame=params,
+ fps=10,
+ num_workers=1,
+ disable_progress_bar=True,
+ )
+
+ def test_duplicate_frame_id_raises_parallel(self):
+ """Duplicate frame ids must also be rejected (and not hang) in parallel mode."""
+ params = [
+ IndexedFrameParams(frame_id=1, params={"phase": 0.0}),
+ IndexedFrameParams(frame_id=1, params={"phase": 1.0}),
+ ]
+ anim = SimpleTestAnimation()
+ with tempfile.TemporaryDirectory() as tmpdir:
+ with self.assertRaises(ValueError):
+ anim.make_video(
+ output_file=Path(tmpdir) / "dup_parallel.mp4",
+ param_by_frame=params,
+ fps=10,
+ num_workers=2,
+ disable_progress_bar=True,
+ )
+
+ def test_indexed_and_positional_index_collision_raises(self):
+ """An explicit frame_id colliding with a positional (enumerate) index raises.
+
+ Before validation covered all indices, the positional frame at index 1
+ and the IndexedFrameParams with frame_id=1 would map to the same output
+ file and one would silently overwrite the other.
+ """
+ params = [
+ {"phase": 0.0}, # positional index 0
+ {"phase": 0.1}, # positional index 1
+ IndexedFrameParams(
+ frame_id=1, params={"phase": 0.2}
+ ), # collides with index 1
+ ]
+ anim = SimpleTestAnimation()
+ with tempfile.TemporaryDirectory() as tmpdir:
+ with self.assertRaises(ValueError):
+ anim.make_video(
+ output_file=Path(tmpdir) / "collision.mp4",
+ param_by_frame=params,
+ fps=10,
+ num_workers=1,
+ disable_progress_bar=True,
+ )
+
def test_indexed_params_with_generator(self):
"""IndexedFrameParams should work with generators."""
with tempfile.TemporaryDirectory() as tmpdir:
@@ -517,5 +589,45 @@ def test_savefig_params(self):
self.assertTrue(output_path.exists())
+class TestWorkerFailure(unittest.TestCase):
+ """Parallel rendering must surface worker crashes instead of deadlocking."""
+
+ def test_parallel_worker_failure_raises_without_hanging(self):
+ """A crashing worker should raise RuntimeError promptly, not hang.
+
+ With a bounded task queue, a dead worker stops draining frames; the
+ producer must detect the failure rather than block forever filling a
+ queue nobody is consuming. We enqueue far more frames than the queue can
+ hold (preload_factor * num_workers) so the producer is guaranteed to hit
+ a full queue, and guard the call with a watchdog thread.
+ """
+ result = {}
+
+ def run():
+ with tempfile.TemporaryDirectory() as tmpdir:
+ output_path = Path(tmpdir) / "fail.mp4"
+ try:
+ FailingAnimation().make_video(
+ output_file=output_path,
+ param_by_frame=[{"phase": 0.0}] * 200,
+ fps=10,
+ num_workers=2,
+ preload_factor=2,
+ disable_progress_bar=True,
+ )
+ result["error"] = None
+ except Exception as exc: # noqa: BLE001 - we assert the type below
+ result["error"] = exc
+
+ worker = threading.Thread(target=run)
+ worker.start()
+ worker.join(timeout=60)
+
+ self.assertFalse(
+ worker.is_alive(), "make_video hung instead of reporting worker failure"
+ )
+ self.assertIsInstance(result["error"], RuntimeError)
+
+
if __name__ == "__main__":
unittest.main()
diff --git a/tests/test_examples.py b/tests/test_examples.py
index 73fe4b8..d89c08e 100644
--- a/tests/test_examples.py
+++ b/tests/test_examples.py
@@ -1,11 +1,20 @@
+import sys
import unittest
import tempfile
import numpy as np
from pathlib import Path
-from parallel_animate.examples.simple_wave_animation import WaveAnimation
-from parallel_animate.examples.multi_panel_animation import MultiPanelAnimation
-from parallel_animate.examples.very_complex_animation import VeryComplexAnimation
+# The example scripts live in the top-level examples/ directory (they are
+# standalone scripts, not part of the installable package), so make them
+# importable by module name.
+_EXAMPLES_DIR = Path(__file__).resolve().parent.parent / "examples"
+if str(_EXAMPLES_DIR) not in sys.path:
+ sys.path.insert(0, str(_EXAMPLES_DIR))
+
+from simple_wave_animation import WaveAnimation
+from multi_panel_animation import MultiPanelAnimation
+from very_complex_animation import VeryComplexAnimation
+from nondeterministic_video_loader import VideoFrameAnimation, fake_video_loader
class TestProvidedExamples(unittest.TestCase):
@@ -42,6 +51,28 @@ def test_multi_panel_animation_setup_serial(self):
def test_very_complex_animation_setup_serial(self):
self._run_animation(VeryComplexAnimation, num_workers=1)
+ def _run_video_frame_animation(self, num_workers):
+ # This example feeds frames through a generator that yields them in
+ # nondeterministic order (wrapped in IndexedFrameParams), so it needs an
+ # explicit n_frames (a generator has no __len__).
+ n_frames = 8
+ with tempfile.TemporaryDirectory() as tmpdir:
+ anim = VideoFrameAnimation()
+ anim.make_video(
+ output_file=Path(tmpdir)
+ / f"VideoFrameAnimation_{num_workers}_workers.mp4",
+ param_by_frame=fake_video_loader(n_frames=n_frames),
+ n_frames=n_frames,
+ fps=30,
+ num_workers=num_workers,
+ )
+
+ def test_video_frame_animation_setup_parallel(self):
+ self._run_video_frame_animation(num_workers=4)
+
+ def test_video_frame_animation_setup_serial(self):
+ self._run_video_frame_animation(num_workers=1)
+
if __name__ == "__main__":
unittest.main()
diff --git a/tests/test_pvio_options.py b/tests/test_pvio_options.py
new file mode 100644
index 0000000..39532c8
--- /dev/null
+++ b/tests/test_pvio_options.py
@@ -0,0 +1,216 @@
+"""Pytest tests for the parallel-video-io (pvio) encoding options exposed by
+``Animator.make_video``.
+
+These focus on the contract between ``make_video`` and
+``pvio.write_frames_to_video``: that the user-facing ``video_*`` options are
+forwarded correctly and that frames reach pvio as raw arrays, plus one real
+end-to-end CPU encode.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+import pytest
+
+import parallel_animate.animator as animator_mod
+from parallel_animate import Animator
+
+
+class _SineAnimation(Animator):
+ """Minimal animation used across the tests in this module."""
+
+ def setup(self):
+ fig, ax = plt.subplots(figsize=(4, 3))
+ self.x = np.linspace(0, 2 * np.pi, 50)
+ (self.line,) = ax.plot(self.x, np.sin(self.x))
+ ax.set_xlim(0, 2 * np.pi)
+ ax.set_ylim(-1.5, 1.5)
+ return fig
+
+ def update(self, frame_idx, params):
+ self.line.set_ydata(np.sin(self.x + params["phase"]))
+
+
+def _params(n):
+ return [{"phase": 2 * np.pi * i / n} for i in range(n)]
+
+
+@pytest.fixture
+def captured_encode(monkeypatch, tmp_path):
+ """Patch pvio's writer with a recorder that still creates the output file.
+
+ Returns a dict that, after ``make_video`` runs, holds the positional args
+ (``video_path``, the materialised ``frames`` arrays, their backing
+ ``frame_paths``, and ``fps``) and keyword args passed to
+ ``pvio.write_frames_to_video``.
+ """
+ captured = {}
+
+ def fake_write(video_path, frames, fps, **kwargs):
+ # make_video reads the output file size afterwards, so it must exist.
+ from pathlib import Path
+
+ Path(video_path).parent.mkdir(parents=True, exist_ok=True)
+ Path(video_path).write_bytes(b"fake mp4")
+ captured["video_path"] = video_path
+ # Backing .npy paths of the lazy sequence, captured before the temp dir
+ # is cleaned up; materialise the arrays too.
+ captured["frame_paths"] = [str(p) for p in getattr(frames, "_paths", [])]
+ captured["frames"] = list(frames)
+ captured["fps"] = fps
+ captured["kwargs"] = kwargs
+
+ monkeypatch.setattr(animator_mod.pvio, "write_frames_to_video", fake_write)
+ return captured
+
+
+def _run(tmp_path, **make_video_kwargs):
+ out = tmp_path / "out.mp4"
+ _SineAnimation().make_video(
+ output_file=out,
+ param_by_frame=_params(make_video_kwargs.pop("n", 5)),
+ fps=make_video_kwargs.pop("fps", 12),
+ num_workers=1,
+ disable_progress_bar=True,
+ **make_video_kwargs,
+ )
+ return out
+
+
+def test_defaults_forwarded_to_pvio(captured_encode, tmp_path):
+ """With no encoding options set, pvio receives the documented defaults."""
+ _run(tmp_path)
+
+ kwargs = captured_encode["kwargs"]
+ assert kwargs["mode"] == "auto"
+ assert kwargs["preset"] is None
+ assert kwargs["extra_ffmpeg_params"] is None
+ # quality is left out entirely so pvio applies its own default.
+ assert "quality" not in kwargs
+
+
+@pytest.mark.parametrize("mode", ["auto", "gpu", "cpu"])
+def test_video_mode_forwarded(captured_encode, tmp_path, mode):
+ """video_mode (the GPU/CPU selector) is passed straight through as `mode`."""
+ _run(tmp_path, video_mode=mode)
+ assert captured_encode["kwargs"]["mode"] == mode
+
+
+def test_quality_omitted_when_none(captured_encode, tmp_path):
+ _run(tmp_path, video_quality=None)
+ assert "quality" not in captured_encode["kwargs"]
+
+
+def test_quality_forwarded_when_set(captured_encode, tmp_path):
+ _run(tmp_path, video_quality=18)
+ assert captured_encode["kwargs"]["quality"] == 18
+
+
+def test_preset_and_extra_params_forwarded(captured_encode, tmp_path):
+ _run(
+ tmp_path,
+ video_preset="p5",
+ video_extra_ffmpeg_params=["-tune", "film"],
+ )
+ kwargs = captured_encode["kwargs"]
+ assert kwargs["preset"] == "p5"
+ assert kwargs["extra_ffmpeg_params"] == ["-tune", "film"]
+
+
+def test_logging_and_progress_options_mapped(captured_encode, tmp_path):
+ """saving_log_interval -> log_interval, disable_progress_bar -> quiet."""
+ out = tmp_path / "out.mp4"
+ _SineAnimation().make_video(
+ output_file=out,
+ param_by_frame=_params(5),
+ fps=12,
+ num_workers=1,
+ disable_progress_bar=True,
+ saving_log_interval=2,
+ )
+ kwargs = captured_encode["kwargs"]
+ assert kwargs["log_interval"] == 2
+ assert kwargs["quiet"] is True
+
+
+def test_quiet_false_when_progress_explicitly_enabled(captured_encode, tmp_path):
+ _SineAnimation().make_video(
+ output_file=tmp_path / "out.mp4",
+ param_by_frame=_params(5),
+ fps=12,
+ num_workers=1,
+ disable_progress_bar=False,
+ )
+ assert captured_encode["kwargs"]["quiet"] is False
+
+
+def test_quiet_auto_follows_tty(captured_encode, tmp_path, monkeypatch):
+ """disable_progress_bar=None resolves via TTY detection for both tqdm and pvio.
+
+ On a TTY the progress bar shows and pvio is not quiet; off a TTY both go
+ silent, instead of pvio always staying noisy.
+ """
+ import sys
+
+ monkeypatch.setattr(sys.stderr, "isatty", lambda: True, raising=False)
+ _SineAnimation().make_video(
+ output_file=tmp_path / "tty.mp4",
+ param_by_frame=_params(3),
+ fps=12,
+ num_workers=1,
+ disable_progress_bar=None,
+ )
+ assert captured_encode["kwargs"]["quiet"] is False
+
+ monkeypatch.setattr(sys.stderr, "isatty", lambda: False, raising=False)
+ _SineAnimation().make_video(
+ output_file=tmp_path / "notty.mp4",
+ param_by_frame=_params(3),
+ fps=12,
+ num_workers=1,
+ disable_progress_bar=None,
+ )
+ assert captured_encode["kwargs"]["quiet"] is True
+
+
+def test_frames_passed_as_sorted_raw_arrays(captured_encode, tmp_path):
+ """All rendered frames reach pvio as raw uint8 RGB arrays in frame order."""
+ n = 7
+ _run(tmp_path, n=n, fps=24)
+
+ frames = captured_encode["frames"]
+ assert len(frames) == n
+ assert all(isinstance(f, np.ndarray) and f.dtype == np.uint8 for f in frames)
+ assert all(f.ndim == 3 and f.shape[2] == 3 for f in frames)
+
+ paths = captured_encode["frame_paths"]
+ assert len(paths) == n
+ assert all(p.endswith(".npy") for p in paths)
+ assert paths == sorted(paths)
+ assert captured_encode["fps"] == 24
+
+
+def test_dpi_in_savefig_params_changes_raster_resolution(captured_encode, tmp_path):
+ """``dpi`` is honoured by the raw-buffer renderer (higher dpi -> bigger frames)."""
+ _run(tmp_path, n=2, savefig_params={"dpi": 50})
+ low_h, low_w = captured_encode["frames"][0].shape[:2]
+
+ _run(tmp_path, n=2, savefig_params={"dpi": 100})
+ high_h, high_w = captured_encode["frames"][0].shape[:2]
+
+ assert high_h > low_h
+ assert high_w > low_w
+
+
+def test_cpu_encode_creates_real_video(tmp_path):
+ """End-to-end: a real CPU encode through pvio produces a non-empty file."""
+ out = tmp_path / "real.mp4"
+ _SineAnimation().make_video(
+ output_file=out,
+ param_by_frame=_params(8),
+ fps=10,
+ num_workers=1,
+ disable_progress_bar=True,
+ video_mode="cpu",
+ )
+ assert out.exists()
+ assert out.stat().st_size > 0
diff --git a/uv.lock b/uv.lock
index fe52eb5..3eeb39c 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,40 +1,158 @@
version = 1
revision = 3
-requires-python = ">=3.10"
+requires-python = ">=3.10, <3.15"
resolution-markers = [
- "python_full_version >= '3.11'",
+ "python_full_version >= '3.12'",
+ "python_full_version == '3.11.*'",
"python_full_version < '3.11'",
]
[[package]]
-name = "av"
-version = "17.0.1"
+name = "babel"
+version = "2.18.0"
source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/4e/f0/8c8dca97ae0cf00e8e2a53bb5cb9aca5fd484f585ef3e9b412200aff3ebd/av-17.0.1.tar.gz", hash = "sha256:fbcbd4aa43bca6a8691816283112d1659a27f407bbeb66d1397023691339f5d4", size = 4411938, upload-time = "2026-04-18T17:12:34.29Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/7d/b2/51899539b6ceeeb420d40ed3cd4b7a40519404f9baf3d4ac99dc413a834b/babel-2.18.0.tar.gz", hash = "sha256:b80b99a14bd085fcacfa15c9165f651fbb3406e66cc603abf11c5750937c992d", size = 9959554, upload-time = "2026-02-01T12:30:56.078Z" }
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+]
+
## Usage
This library has a single class: `parallel_animate.Animator`. To make an animation, you must create your own class inheriting from it and define the following methods:
@@ -79,37 +79,41 @@ Once you have defined your animator class, there is a single method that you nee
- `fps` (int): Frame rate of the output video
- `n_frames` (int or None): Number of frames to render. If None, use the length of `param_by_frame`. If param_by_frame does not have `__len__` implemented and `n_frames` is None, the progress bar won't show completion percentage.
- `num_workers` (int): Number of worker processes to be spawned. If -1, use all CPU cores. If -2, use all but one CPU cores, etc. If 1, no child process is created and the video is made in the main process itself. Default is -1.
-- See the docstring for `parallel_animate.animator` directly for less commonly used, optional parameters. These control logging, rendering quality, etc.
+- `video_mode` (str): Encoder selection passed to parallel-video-io: `"auto"` (default) uses the GPU encoder (FFmpeg/NVENC) when a CUDA device is available and falls back to CPU (libx264) otherwise; `"gpu"` forces NVENC and `"cpu"` forces libx264. Output is always an H.264 MP4.
+- `video_quality` (int or None), `video_preset` (str or None), `video_extra_ffmpeg_params` (list of str or None): Optional encoding-quality controls forwarded to parallel-video-io. Leave as `None` to use its sensible defaults.
+- See the docstring for `parallel_animate.animator` directly for the remaining less commonly used, optional parameters. These control logging, figure reuse, prefetching, etc.
+
+> **Note:** parallel-video-io is currently Linux-only.
### Special case: frame params arriving out-of-order in `param_by_frame`
In some cases, frames in `param_by_frame` might be out of order. We can handle these scenarios by populating `param_by_frame` with a special `parallel_animate.IndexedFrameParams` dataclass, which specifies the frame index that overrides the ordering in `param_by_frame`. This can be useful when, for example, the animator needs to draw frames that are decoded from a video, and the dataloader for that video might return frames in nondeterministic order because it's parallelized.
-See `src/parallel_animate/examples/nondeterministic_video_loader.py` for details.
+See `examples/nondeterministic_video_loader.py` for details.
## Examples
-See [`src/parallel_animate/examples/`](https://github.com/sibocw/parallel-matplotlib-animation/blob/main/src/parallel_animate/examples/):
+See [`examples/`](https://github.com/sibocw/parallel-matplotlib-animation/blob/main/examples/). Run all of them (except the benchmark) with `./examples/run_all.sh`.
`simple_wave_animation.py`: The example above
`multi_panel_animation.py`: 5 subplots with different plot types
-
+
`very_complex_animation.py`: 14 subplots with GridSpec layout
-
+
`nondeterministic_video_loader.py`: handling frames that arrive out of order
-
+
## Performance test
-A [strong scaling test](https://hpc-wiki.info/hpc/Scaling_tests#Strong_Scaling) is implemented in `src/parallel_animate/examples/scaling_test.py`. Here's the result on my 8-core (16-thread) Intel Core i9-11900K Processor:
+A [strong scaling test](https://hpc-wiki.info/hpc/Scaling_tests#Strong_Scaling) is implemented in `examples/scaling_test.py`. Here's the result on my 8-core (16-thread) Intel Core i9-11900K Processor:
-
+See the [interactive scaling figure](https://sibocw.github.io/parallel-matplotlib-animation/benchmark/) on the documentation site.
The left-most blue dot indicates serial processing with resources reuse. The black line indicates ideal scaling (zero overhead) if all frames are rendered completely independently in parallel (as is the case in all parallel matplotlib animation libraries I found). Blue dots at 1+ workers are what's implemented in this library.
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diff --git a/assets/scaling_graph.png b/assets/scaling_graph.png
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diff --git a/assets/simple_wave_animation.gif b/assets/simple_wave_animation.gif
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diff --git a/assets/very_complex_animation.gif b/assets/very_complex_animation.gif
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diff --git a/docs/api/animator.md b/docs/api/animator.md
new file mode 100644
index 0000000..93d2be9
--- /dev/null
+++ b/docs/api/animator.md
@@ -0,0 +1,10 @@
+# `parallel_animate` — API Reference
+
+The public API consists of the `Animator` base class and the
+`IndexedFrameParams` helper, both importable directly from `parallel_animate`.
+
+::: parallel_animate.animator
+ options:
+ members:
+ - Animator
+ - IndexedFrameParams
diff --git a/docs/benchmark.md b/docs/benchmark.md
new file mode 100644
index 0000000..5aca57c
--- /dev/null
+++ b/docs/benchmark.md
@@ -0,0 +1,41 @@
+# Benchmark
+
+A [strong-scaling test](https://hpc-wiki.info/hpc/Scaling_tests#Strong_Scaling)
+measures how render-to-video throughput scales with the number of worker
+processes. It sweeps:
+
+- **worker count** — 1, 2, 4, 8, 16;
+- **figure reuse** — with vs without (`reuse_figure_object`); and
+- **encoding backend** — parallel-video-io's CPU (libx264) and GPU (NVENC)
+ encoders. The GPU backend is benchmarked only when a CUDA device is present.
+
+The animation rendered is
+[`very_complex_animation.py`](examples.md#very_complex_animationpy), a 14-subplot
+`GridSpec` figure that is expensive to build — exactly the case where the
+setup-once / update-many design pays off.
+
+## How to run
+
+```bash
+pip install -e '.[benchmark]'
+python examples/scaling_test.py # full sweep
+python examples/scaling_test.py --quick # small, fast smoke run
+```
+
+Results are written to `examples/output/scaling_test/` (`results.csv`,
+`results.json`, and the interactive figure embedded below as
+`scaling_graph.html`).
+
+## Result
+
+Speedup is normalised to the serial (one worker), no-reuse, CPU-encode baseline,
+so the curves capture both the parallel speedup and the extra gains from figure
+reuse and GPU encoding. The dashed black line is ideal (zero-overhead) linear
+scaling.
+
+--8<-- "examples/output/scaling_test/scaling_graph.html"
+
+The left-most dark-blue point is serial processing with figure reuse. Points at
+2+ workers show the parallel speedup; reuse curves (darker) sit above the
+no-reuse curves (lighter) because the expensive layout is built once per worker
+instead of once per frame.
diff --git a/docs/developer.md b/docs/developer.md
new file mode 100644
index 0000000..9e47038
--- /dev/null
+++ b/docs/developer.md
@@ -0,0 +1,49 @@
+# Developer Info
+
+Clone the repository and install the development extra:
+
+```bash
+git clone https://github.com/sibocw/parallel-matplotlib-animation.git
+cd parallel-matplotlib-animation
+pip install -e '.[dev]'
+```
+
+The `dev` extra installs everything needed for testing, formatting, and building
+the documentation site.
+
+## Testing
+
+The test suite uses pytest. Run it from the repository root:
+
+```bash
+pytest
+```
+
+A few tests write small MP4 files via parallel-video-io / FFmpeg, so make sure
+`ffmpeg` is on your `$PATH`.
+
+## Documentation
+
+The docs are built with [MkDocs](https://www.mkdocs.org/) and the Material theme,
+with API pages generated from docstrings by
+[mkdocstrings](https://mkdocstrings.github.io/).
+
+```bash
+mkdocs serve # live-preview at http://127.0.0.1:8000
+mkdocs build # build the static site into site/
+mkdocs gh-deploy # build and push to the gh-pages branch
+```
+
+The [benchmark page](benchmark.md) embeds
+`examples/output/scaling_test/scaling_graph.html`, produced
+by `python examples/scaling_test.py`. If that file is missing at build time, a
+placeholder is substituted by the `docs/gen_benchmark.py` hook so the build still
+succeeds.
+
+## Code style
+
+- Follow the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html).
+- Use ASCII-only characters in `.py` docstrings and comments.
+- Format with [ruff](https://docs.astral.sh/ruff/).
+- On a new GitHub release, the package is published to PyPI and the docs are
+ deployed to GitHub Pages automatically.
diff --git a/docs/embed_example_videos.py b/docs/embed_example_videos.py
new file mode 100644
index 0000000..a56c511
--- /dev/null
+++ b/docs/embed_example_videos.py
@@ -0,0 +1,40 @@
+"""MkDocs hook: publish the gallery example videos into the built site.
+
+The example scripts render their animations to ``examples/output/`` (git-ignored,
+so the MP4s are not committed to ``main``/``dev`` branches). The docs embed them
+via ``