Refactor examples with common interface, plotting and benchmarking #188
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- Implemented a new backend using Mesa with sequential updates in `examples/sugarscape_ig/backend_mesa`. - Created agent and model classes for the Sugarscape simulation, including movement and sugar management. - Added a CLI interface using Typer for running simulations and saving results. - Introduced utility classes for handling simulation results from both Mesa and Mesa-Frames backends. - Added a new backend using Mesa-Frames with parallel updates in `examples/sugarscape_ig/backend_frames`. - Implemented model-level reporters for Gini coefficient and correlations between agent traits. - Included CSV output and plotting capabilities for simulation metrics.
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Note Other AI code review bot(s) detectedCodeRabbit has detected other AI code review bot(s) in this pull request and will avoid duplicating their findings in the review comments. This may lead to a less comprehensive review. WalkthroughAdds a benchmarks CLI, example models (Boltzmann and Sugarscape) with both Mesa and mesa-frames backends, shared plotting/utilities, documentation, and minor config updates (pyproject, .gitignore). New Typer CLIs and data/result scaffolding produce timestamped CSVs and plots. Changes
Sequence Diagram(s)sequenceDiagram
actor User
participant CLI as benchmarks/cli.py
participant ModelCfg as MODELS
participant Backend as Backend Runner
participant Sim as Simulation (Mesa/Frames)
participant DC as DataCollector
participant IO as File I/O (CSV/plots)
participant Plot as examples/plotting.py
User->>CLI: run(models, agents, steps, repeats, seed, save, plot)
CLI->>CLI: parse inputs (_parse_agents/_parse_models)
CLI->>ModelCfg: select model configs
loop models
CLI->>Backend: choose backend runner
loop repeats
CLI->>Backend: runner(agents, steps, seed)
activate Backend
Backend->>Sim: instantiate & run simulation
Sim->>DC: collect metrics during run
Sim-->>Backend: return runtime & datacollector
Backend-->>CLI: return runtime, datacollector
deactivate Backend
CLI->>CLI: record runtime row
end
CLI->>CLI: aggregate runtimes into DataFrame
alt save
CLI->>IO: write CSVs to timestamped results_dir
end
alt plot
CLI->>Plot: plot_performance(df, model_name, output_dir, timestamp)
Plot->>IO: save PNG/SVG (light/dark themes)
end
end
CLI->>User: report completion and paths
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Actionable comments posted: 10
🧹 Nitpick comments (12)
examples/__init__.py (1)
1-6: Clean and simple package initializer.The module docstring and
__all__list are properly structured and follow Python conventions. The subpackage names use the correct snake_case naming style.Note: The
__all__list declares these as public API surface, but without explicit imports, users will need to access them via the full path (e.g.,import examples.boltzmann_wealth) rather thanfrom examples import boltzmann_wealth. If direct importing from the examples package is desired, consider adding explicit imports:from . import boltzmann_wealth, sugarscape_igThis is optional based on your intended API—just ensure the usage pattern is intentional and documented.
examples/sugarscape_ig/backend_frames/agents.py (2)
298-298: Redundant.with_columns(pl.col("radius"))does nothing.This line selects
radiuswithout any transformation, effectively a no-op.- .with_columns(pl.col("radius"))
311-311: Minor: Non-breaking hyphen in comment.The comment contains a non-breaking hyphen (
‑) instead of a regular hyphen-minus (-). This is flagged by Ruff (RUF003) but is purely cosmetic.- # Precompute per‑agent candidate rank once so conflict resolution can + # Precompute per-agent candidate rank once so conflict resolution canexamples/boltzmann_wealth/backend_frames.py (1)
5-13: Non-standard import order.Standard library imports (
os,time) are interleaved with third-party imports. PEP 8 recommends grouping imports: stdlib first, then third-party.from datetime import datetime, timezone +import os from pathlib import Path +from time import perf_counter from typing import Annotated import numpy as np -import os import polars as pl import typer -from time import perf_counterexamples/plotting.py (3)
71-75: Consider logging the exception instead of silently ignoring it.The
try-except-passpattern suppresses all errors during SVG export. While SVG is optional, logging the exception would aid debugging when exports fail unexpectedly.- try: - fig.savefig(output_dir / f"{stem}_{theme}.svg", bbox_inches="tight") - except Exception: - pass # SVG is a nice-to-have + try: + fig.savefig(output_dir / f"{stem}_{theme}.svg", bbox_inches="tight") + except Exception as exc: # noqa: BLE001 + import logging + logging.debug("SVG export failed for %s: %s", stem, exc)
109-114: BareExceptioncatch may mask unexpected errors.The intent is to handle missing/malformed step data, but catching
Exceptioncould hide unrelated bugs. Consider catching more specific exceptions or at minimum adding a# noqa: BLE001comment to acknowledge this is intentional.if steps is None: try: steps = int(metrics.select(pl.col("step").max()).item()) + 1 - except Exception: + except (TypeError, ValueError, pl.exceptions.ComputeError): steps = None
286-290: Sort__all__for consistency with isort conventions.Static analysis flagged this as unsorted. Sorting maintains consistency with standard Python tooling.
__all__ = [ + "plot_agent_metrics", "plot_model_metrics", - "plot_agent_metrics", "plot_performance", ]examples/sugarscape_ig/backend_mesa/model.py (2)
57-72: Docstring mismatch: function signature differs from similar functions.The docstring for
ginidoesn't follow NumPy style as required by the coding guidelines. Consider adding Parameters/Returns sections for consistency with public APIs.def gini(values: Iterable[float]) -> float: + """Compute the Gini coefficient from an iterable of wealth values. + + Parameters + ---------- + values : Iterable[float] + Iterable of wealth/sugar values. + + Returns + ------- + float + Gini coefficient in [0, 1], or 0.0 for zero-total/constant values, + or NaN for empty input. + """ array = np.fromiter(values, dtype=float)
132-134: Unused lambda parameterm– consider using_convention.The
seedreporter lambda capturesseedfrom the enclosing scope but declares an unused parameterm. Using_makes the intent explicit.- "seed": lambda m: seed, + "seed": lambda _: seed,examples/boltzmann_wealth/backend_mesa.py (2)
24-40: Consider extractingginito a shared module to reduce duplication.This
ginifunction is identical to the one inexamples/sugarscape_ig/backend_mesa/model.py(lines 57-72). Extracting it toexamples/utils.pywould improve maintainability.You could add to
examples/utils.py: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)Then import from both backends.
74-77: Unused lambda parameterm– use_convention.Same issue as in the Sugarscape model.
model_reporters={ "gini": lambda m: gini(a.wealth for a in m.agent_list), - "seed": lambda m: seed, + "seed": lambda _: seed, }benchmarks/cli.py (1)
97-100: Range generation logic could be simplified.The current approach generates
range(start, stop + step, step)then pops if the last value exceedsstop. This is correct but less idiomatic than usingrange(start, stop + 1, step)directly for inclusive ranges.- counts = list(range(start, stop + step, step)) - if counts[-1] > stop: - counts.pop() - return counts + return list(range(start, stop + 1, step))This produces the same result for an inclusive
[start, stop]range with the given step.
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📒 Files selected for processing (18)
.gitignore(1 hunks)benchmarks/README.md(1 hunks)benchmarks/cli.py(1 hunks)examples/README.md(1 hunks)examples/__init__.py(1 hunks)examples/boltzmann_wealth/README.md(1 hunks)examples/boltzmann_wealth/backend_frames.py(1 hunks)examples/boltzmann_wealth/backend_mesa.py(1 hunks)examples/plotting.py(1 hunks)examples/sugarscape_ig/README.md(1 hunks)examples/sugarscape_ig/backend_frames/__init__.py(1 hunks)examples/sugarscape_ig/backend_frames/agents.py(1 hunks)examples/sugarscape_ig/backend_frames/model.py(1 hunks)examples/sugarscape_ig/backend_mesa/__init__.py(1 hunks)examples/sugarscape_ig/backend_mesa/agents.py(1 hunks)examples/sugarscape_ig/backend_mesa/model.py(1 hunks)examples/utils.py(1 hunks)pyproject.toml(1 hunks)
🧰 Additional context used
📓 Path-based instructions (1)
**/*.py
📄 CodeRabbit inference engine (AGENTS.md)
**/*.py: Use Python 3.11 or later with 4-space indentation
Include type hints for all public APIs
Use NumPy-style docstrings (validated by Ruff/pydoclint)
UseCamelCasefor class names
Usesnake_casefor function and attribute names
Avoid usingTYPE_CHECKINGguards for type annotations because the project usesbeartypefor runtime type checking, which requires the actual type objects to be available at runtime
Treat underscored attributes as internal/private and not part of the public API
Files:
examples/utils.pyexamples/__init__.pyexamples/sugarscape_ig/backend_mesa/agents.pyexamples/plotting.pyexamples/sugarscape_ig/backend_mesa/__init__.pyexamples/boltzmann_wealth/backend_frames.pyexamples/sugarscape_ig/backend_mesa/model.pyexamples/sugarscape_ig/backend_frames/agents.pyexamples/sugarscape_ig/backend_frames/model.pyexamples/boltzmann_wealth/backend_mesa.pyexamples/sugarscape_ig/backend_frames/__init__.pybenchmarks/cli.py
🧠 Learnings (3)
📚 Learning: 2025-12-08T18:41:11.772Z
Learnt from: CR
Repo: projectmesa/mesa-frames PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-12-08T18:41:11.772Z
Learning: Use `examples/` directory for reproducible demo models and performance scripts
Applied to files:
examples/README.mdexamples/__init__.py
📚 Learning: 2025-12-08T18:41:11.772Z
Learnt from: CR
Repo: projectmesa/mesa-frames PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-12-08T18:41:11.772Z
Learning: Use `mesa_frames/` as the source package directory, with `abstract/` and `concrete/` subdirectories for core APIs and implementations, and key modules: `agents.py`, `agentset.py`, `space.py`, `datacollector.py`, `types_.py`
Applied to files:
examples/README.mdbenchmarks/README.mdexamples/utils.pyexamples/sugarscape_ig/README.mdexamples/sugarscape_ig/backend_mesa/agents.pyexamples/sugarscape_ig/backend_mesa/__init__.pyexamples/boltzmann_wealth/backend_frames.pyexamples/sugarscape_ig/backend_mesa/model.pyexamples/sugarscape_ig/backend_frames/agents.pyexamples/sugarscape_ig/backend_frames/model.pyexamples/sugarscape_ig/backend_frames/__init__.py
📚 Learning: 2025-04-29T09:25:34.183Z
Learnt from: adamamer20
Repo: projectmesa/mesa-frames PR: 143
File: mesa_frames/abstract/space.py:50-63
Timestamp: 2025-04-29T09:25:34.183Z
Learning: The project mesa-frames has been upgraded to Python 3.11, which provides native support for `Self` type in the standard typing module, eliminating the need for imports from typing_extensions.
Applied to files:
examples/utils.pyexamples/sugarscape_ig/backend_frames/__init__.py
🧬 Code graph analysis (7)
examples/utils.py (1)
mesa_frames/concrete/datacollector.py (1)
DataCollector(69-640)
examples/sugarscape_ig/backend_mesa/agents.py (3)
examples/sugarscape_ig/backend_frames/model.py (2)
Sugarscape(174-386)step(333-350)examples/sugarscape_ig/backend_mesa/model.py (2)
Sugarscape(75-187)step(172-181)examples/sugarscape_ig/backend_frames/agents.py (3)
step(55-71)move(73-80)move(131-185)
examples/boltzmann_wealth/backend_frames.py (3)
mesa_frames/concrete/datacollector.py (1)
DataCollector(69-640)examples/utils.py (1)
FramesSimulationResult(7-14)examples/boltzmann_wealth/backend_mesa.py (1)
gini(24-40)
examples/sugarscape_ig/backend_frames/agents.py (3)
mesa_frames/concrete/agentset.py (1)
AgentSet(76-686)mesa_frames/concrete/model.py (1)
Model(54-223)examples/sugarscape_ig/backend_mesa/agents.py (1)
move(75-78)
examples/sugarscape_ig/backend_frames/model.py (3)
examples/utils.py (1)
FramesSimulationResult(7-14)examples/plotting.py (1)
plot_model_metrics(81-171)mesa_frames/abstract/datacollector.py (2)
collect(118-128)flush(176-196)
examples/boltzmann_wealth/backend_mesa.py (4)
examples/utils.py (1)
MesaSimulationResult(18-25)examples/plotting.py (1)
plot_model_metrics(81-171)examples/boltzmann_wealth/backend_frames.py (3)
gini(24-42)step(52-76)step(106-108)examples/sugarscape_ig/backend_frames/model.py (2)
gini(30-71)step(333-350)
benchmarks/cli.py (3)
examples/plotting.py (1)
plot_performance(234-283)examples/boltzmann_wealth/backend_frames.py (5)
simulate(115-124)step(52-76)step(106-108)run(110-112)run(131-186)examples/boltzmann_wealth/backend_mesa.py (5)
simulate(92-97)step(50-57)step(81-85)run(87-89)run(104-177)
🪛 LanguageTool
examples/boltzmann_wealth/README.md
[grammar] ~57-~57: Use a hyphen to join words.
Context: ...rics - --results-dir Override the auto timestamped directory under results/ ...
(QB_NEW_EN_HYPHEN)
examples/sugarscape_ig/README.md
[grammar] ~50-~50: Use a hyphen to join words.
Context: ...alive` typically decreases until a quasi steady state (metabolism vs regrowth) or...
(QB_NEW_EN_HYPHEN)
🪛 Ruff (0.14.7)
examples/sugarscape_ig/backend_mesa/agents.py
21-21: Undefined name Sugarscape
(F821)
examples/plotting.py
73-74: try-except-pass detected, consider logging the exception
(S110)
73-73: Do not catch blind exception: Exception
(BLE001)
113-113: Do not catch blind exception: Exception
(BLE001)
273-273: Comment contains ambiguous – (EN DASH). Did you mean - (HYPHEN-MINUS)?
(RUF003)
286-290: __all__ is not sorted
Apply an isort-style sorting to __all__
(RUF022)
examples/sugarscape_ig/backend_mesa/model.py
134-134: Unused lambda argument: m
(ARG005)
260-260: Local variable seed_val is assigned to but never used
Remove assignment to unused variable seed_val
(F841)
examples/sugarscape_ig/backend_frames/agents.py
50-52: Avoid specifying long messages outside the exception class
(TRY003)
311-311: Comment contains ambiguous ‑ (NON-BREAKING HYPHEN). Did you mean - (HYPHEN-MINUS)?
(RUF003)
examples/sugarscape_ig/backend_frames/model.py
227-229: Avoid specifying long messages outside the exception class
(TRY003)
examples/boltzmann_wealth/backend_mesa.py
76-76: Unused lambda argument: m
(ARG005)
138-138: Comment contains ambiguous ‑ (NON-BREAKING HYPHEN). Did you mean - (HYPHEN-MINUS)?
(RUF003)
143-143: Comment contains ambiguous ‑ (NON-BREAKING HYPHEN). Did you mean - (HYPHEN-MINUS)?
(RUF003)
benchmarks/cli.py
86-86: Avoid specifying long messages outside the exception class
(TRY003)
90-90: Avoid specifying long messages outside the exception class
(TRY003)
92-92: Avoid specifying long messages outside the exception class
(TRY003)
94-94: Avoid specifying long messages outside the exception class
(TRY003)
96-96: Avoid specifying long messages outside the exception class
(TRY003)
104-104: Avoid specifying long messages outside the exception class
(TRY003)
106-106: Avoid specifying long messages outside the exception class
(TRY003)
126-126: Avoid specifying long messages outside the exception class
(TRY003)
129-129: Avoid specifying long messages outside the exception class
(TRY003)
197-197: Do not perform function call in argument defaults; instead, perform the call within the function, or read the default from a module-level singleton variable
(B008)
🔇 Additional comments (43)
.gitignore (1)
158-162: Ignore patterns updated appropriately for nested results/plots output.The glob patterns now correctly accommodate timestamped subdirectories (
benchmarks/results/TIMESTAMP/...andexamples/*/results/TIMESTAMP/...) and plot outputs, aligning with the new CLI-driven output structure described in the READMEs.examples/README.md (1)
1-106: Well-structured and comprehensive examples documentation.The README clearly explains the dual-backend approach, CLI interface, data formats, and extension patterns. The quick start, tips, and programmatic use examples are practical and well-documented.
pyproject.toml (1)
66-85: Typer dependency added appropriately to docs group.The placement in the docs dependency group aligns with using Typer for CLI tooling in examples and benchmarks. The version constraint
>=0.9.0is reasonable and not overly restrictive.Please confirm that the examples and benchmarks code actually imports and uses Typer as documented in the READMEs (e.g.,
benchmarks/cli.py,examples/boltzmann_wealth/backend_frames.py), since those files are not visible in this review context.examples/sugarscape_ig/backend_mesa/__init__.py (1)
1-1: Appropriate minimal package initializer.The module docstring correctly identifies the package purpose. No additional code or exports are needed at this stage.
benchmarks/README.md (1)
1-88: Comprehensive and well-structured benchmarking documentation.The README clearly explains purpose, CLI usage, output formats, and extension patterns. The table of CLI options and CSV schema documentation are particularly helpful for reproducibility and post-processing.
examples/sugarscape_ig/backend_frames/__init__.py (1)
1-1: Appropriate minimal package initializer.The module docstring correctly identifies the Frames backend package. No additional code or exports are needed at this stage.
examples/sugarscape_ig/backend_mesa/agents.py (3)
34-47: LGTM!The
_visible_cellsmethod correctly implements cardinal direction visibility with proper boundary checks.
49-73: LGTM!The
_choose_best_cellmethod correctly implements the Sugarscape movement rule with proper tie-breaking by sugar amount, Manhattan distance, and lexicographic coordinates.
75-78: LGTM!The
movemethod is clean and delegates appropriately to the helper methods.examples/utils.py (1)
6-25: LGTM!The dataclasses provide clean containers for simulation results with appropriate type hints. The docstrings could be updated to reference
datacollector.datainstead ofmetrics/agent_metricsfor clarity, but this is a minor documentation nit.examples/sugarscape_ig/backend_frames/agents.py (7)
30-53: LGTM!Good defensive validation of required trait columns with a clear error message. Using
clone()on the input DataFrame prevents unintended side effects.
55-71: LGTM!The step sequence (shuffle → move → eat → remove starved) correctly implements the Sugarscape update order as documented.
82-116: LGTM!The
eatmethod correctly implements vectorised sugar harvesting with proper updates to both agent sugar stocks and cell sugar values.
118-127: LGTM!Clean vectorised removal of starved agents.
131-185: LGTM!The
movemethod is well-structured with clear early exits and proper delegation to helper methods. The inline schema comments enhance readability.
187-248: LGTM!The
_build_neighborhood_framemethod correctly assembles the neighborhood data with proper joins and null handling.
348-620: LGTM!The conflict resolution algorithm is well-implemented with proper fallback handling. The iterative lottery approach with rank promotion ensures all agents are eventually assigned, either to a preferred cell or their origin. The inline schema comments significantly aid comprehension.
examples/sugarscape_ig/backend_frames/model.py (4)
30-71: LGTM with minor redundancy.The function is robust with multiple safety checks. Lines 60-65 contain redundant size checks (already verified at line 58), but this defensive approach is acceptable for example code.
74-140: LGTM!Both correlation reporters are correctly implemented with appropriate null checks. The similar structure between them is acceptable for readability in example code.
143-171: LGTM!The
_safe_corrhelper correctly handles edge cases (insufficient data, constant values) that would make Pearson correlation undefined.
174-386: LGTM!The
Sugarscapeclass is well-structured with:
- Proper validation of agent count vs grid capacity
- Clean separation of grid generation, agent creation, and data collection
- Correct instant-growback sugar regrowth logic
- Appropriate termination handling when agents die out
examples/boltzmann_wealth/backend_frames.py (4)
24-42: LGTM!The
ginifunction correctly handles edge cases and matches the pattern from the Mesa backend implementation.
45-76: LGTM!The
MoneyAgentsclass correctly implements the Boltzmann wealth exchange using vectorised Polars operations. The sampling uses the model RNG for reproducibility.
79-112: LGTM!The
MoneyModelclass correctly integrates the agent set with data collection, with appropriate storage backend selection based onresults_dir.
115-124: LGTM!Clean simulation function that correctly wires the model and returns the result container.
examples/plotting.py (5)
17-44: LGTM! Theme configuration is well-structured.The light/dark theme definitions with consistent styling (disabled top/right spines, appropriate colors) provide a good foundation for the plotting utilities.
47-56: LGTM! Seed shortening helper is correctly implemented.The regex-based approach handles edge cases (None, no match) and truncates long seeds appropriately.
81-171: LGTM!plot_model_metricsimplementation is comprehensive.The function correctly handles:
- Empty dataframes (early return)
- Missing step column (auto-generation)
- Single vs multiple metrics (legend removal, y-axis labeling)
- Title composition with optional N/T metadata
- Multi-theme output
177-228: LGTM!plot_agent_metricshandles id variable detection well.The fallback to the first column when preferred id vars aren't present is a sensible default.
234-283: LGTM!plot_performanceimplementation is solid.The function correctly uses seaborn's
estimator="mean"witherrorbar="sd"for aggregating repeated measurements, and applies appropriate legend styling per theme.examples/sugarscape_ig/backend_mesa/model.py (5)
31-42: LGTM!_safe_corrhandles edge cases correctly.The function properly handles degenerate inputs (too few elements, constant arrays) by returning NaN, matching the documented Frames helper behavior.
45-54: Forward referenceSugarscaperequires string annotation or import reordering.
Sugarscapeis used as a type hint before the class is defined. Whilefrom __future__ import annotations(line 9) enables postponed evaluation making this work, ensure this import remains at the top.
75-144: LGTM! Sugarscape model initialization is well-structured.The model correctly:
- Generates a seed if not provided
- Initializes sugar fields with NumPy RNG
- Places agents on empty cells
- Sets up comprehensive DataCollector reporters
148-181: LGTM! Step scheduling logic matches documented order.The move → eat/starve → regrow → collect sequence matches the docstring and tutorial schedule. The survivor filtering in
_harvest_and_surviveis correctly implemented.
209-311: LGTM! CLI implementation is comprehensive and consistent.The Typer CLI follows the same patterns as the Boltzmann example:
- Timestamped output directories
- Optional CSV persistence and plotting
- Per-metric plot generation
- Clear console output
examples/boltzmann_wealth/backend_mesa.py (3)
43-57: LGTM! MoneyAgent implementation is correct.The agent correctly:
- Initializes with 1 unit of wealth
- Guards against transferring with zero/negative wealth
- Randomly selects a recipient from the agent list
60-90: LGTM! MoneyModel implementation is clean.The model correctly initializes agents, sets up the DataCollector, and implements the shuffled step execution.
103-177: LGTM! CLI implementation follows consistent patterns.The run command properly:
- Creates timestamped output directories
- Measures and reports runtime
- Extracts and displays metrics
- Saves CSV and generates plots conditionally
benchmarks/cli.py (5)
27-34: LGTM! Runner protocol is well-defined.The
RunnerPprotocol cleanly abstracts the runner interface for both Mesa and Frames backends.
43-78: LGTM! Model configuration with lambda wrappers is appropriate.The lambda wrappers for Sugarscape runners correctly adapt the width/height requirements to match the simpler
RunnerPsignature. The grid sizing formula (max(20, ceil(sqrt(agents)) * 2)) ensures adequate space.
200-205: LGTM! Runtime type-checking warning is helpful.Good practice to warn users that
beartyperuntime checking can significantly impact benchmark results.
213-244: LGTM! Benchmark loop implementation is correct.The nested loop correctly iterates over models, agent counts, repeats, and backends, recording all relevant metadata for each run.
246-262: LGTM! Output handling is well-organized.The unified timestamp directory structure with per-model CSVs and a plots subfolder is clean and maintainable.
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@Ben-geo can you take the lead in reviewing this? |
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Yep, I’ve got it! - I’ll review all the new PRs this week. I’ll start going through some tonight, and I’ll wrap up by the weekend. |
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LGTM!
ran it locally as well
…tmesa/mesa-frames into split/examples-benchmarks
for more information, see https://pre-commit.ci
…rse_agents function
…te default at call time
…proved flexibility
…o split/examples-benchmarks
…tmesa/mesa-frames into split/examples-benchmarks
for more information, see https://pre-commit.ci
…tmesa/mesa-frames into split/examples-benchmarks
…rect tutorial path.
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Actionable comments posted: 4
🧹 Nitpick comments (7)
examples/sugarscape_ig/backend_frames/model.py (1)
58-71: Redundant size checks ingini()function.The function checks
sugar.size == 0at line 60-61, then immediately callsnp.sort()and checksn == 0again at line 64-65. Sincen = sorted_vals.sizeequalssugar.size, the second check is unreachable.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)) + sorted_vals = np.sort(sugar) n = sorted_vals.size if n == 0: return float("nan")examples/plotting.py (2)
73-77: Consider logging SVG export failures instead of silently passing.Silently swallowing exceptions can make debugging difficult. Even for "nice-to-have" features, logging the failure helps identify environment issues.
+import logging + +_logger = logging.getLogger(__name__) + 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 + except Exception as exc: + _logger.debug("SVG export failed for %s: %s", stem, exc) plt.close(fig)
302-306: Optional: Sort__all__alphabetically.Static analysis suggests sorting
__all__for consistency (RUF022). This is a minor style preference.__all__ = [ + "plot_agent_metrics", "plot_model_metrics", - "plot_agent_metrics", "plot_performance", ]examples/boltzmann_wealth/backend_mesa.py (2)
104-146: Variableseedshadows the function parameter.Line 146 reassigns
seed = model_pd["seed"].iloc[0], shadowing theseedparameter from line 104. While this works correctly (the extracted seed equals the passed seed or auto-generated one), the shadowing can be confusing. Consider renaming toextracted_seedor similar.- seed = model_pd["seed"].iloc[0] + extracted_seed = model_pd["seed"].iloc[0] model_pd = model_pd[["step", "gini"]] # Show a short tail in console for quick inspection ... subtitle=f"mesa backend; seed={seed}", + # or use extracted_seed if you want the value from the DataFrame
135-140: Replace non-breaking hyphens with regular hyphens in comments.Static analysis detected ambiguous non-breaking hyphens (U+2011) in comments at lines 135 and 140. These could cause issues if copy-pasted.
- # Run simulation (Mesa‑idiomatic): we only use DataCollector's public API + # 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): + # ---- Extract metrics (no helper, no monkey-patch):benchmarks/cli.py (2)
27-41: WidenRunnerPreturn type to match actual backendsimulatefunctionsThe concrete runners (e.g.
boltzmann_* .simulate,sugarscape_* .simulate) return result objects, butRunnerPis declared as returningNone. Static type-checkers will flagBackend.runner=...simulateassignments against this protocol.Since the CLI ignores the return value, you can safely widen the protocol to avoid spurious type errors:
class RunnerP(Protocol): - def __call__(self, agents: int, steps: int, seed: int | None = None) -> None: ... + def __call__(self, agents: int, steps: int, seed: int | None = None) -> object: ...This keeps the runtime behavior identical while staying consistent with the existing backends.
81-101: Agent range parsing looks solid; optional whitespace handling improvementThe range parsing logic (validation, inclusive stop, and
start = 0support for init‑time benchmarks) looks correct and robust.If you want to make the CLI a bit more forgiving, you could accept whitespace around colons (e.g.
--agents "0: 500: 100"):- parts = value.split(":") + parts = [part.strip() for part in 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)Pure ergonomics; current behavior is already fine.
📜 Review details
Configuration used: Path: .coderabbit.yaml
Review profile: CHILL
Plan: Pro
⛔ Files ignored due to path filters (1)
uv.lockis excluded by!**/*.lock
📒 Files selected for processing (11)
benchmarks/README.md(1 hunks)benchmarks/cli.py(1 hunks)examples/boltzmann_wealth/README.md(1 hunks)examples/boltzmann_wealth/backend_frames.py(1 hunks)examples/boltzmann_wealth/backend_mesa.py(1 hunks)examples/plotting.py(1 hunks)examples/sugarscape_ig/README.md(1 hunks)examples/sugarscape_ig/backend_frames/model.py(1 hunks)examples/sugarscape_ig/backend_mesa/agents.py(1 hunks)examples/sugarscape_ig/backend_mesa/model.py(1 hunks)pyproject.toml(1 hunks)
✅ Files skipped from review due to trivial changes (1)
- benchmarks/README.md
🚧 Files skipped from review as they are similar to previous changes (4)
- examples/sugarscape_ig/backend_mesa/agents.py
- pyproject.toml
- examples/sugarscape_ig/README.md
- examples/boltzmann_wealth/backend_frames.py
🧰 Additional context used
📓 Path-based instructions (1)
**/*.py
📄 CodeRabbit inference engine (AGENTS.md)
**/*.py: Use Python 3.11 or later with 4-space indentation
Include type hints for all public APIs
Use NumPy-style docstrings (validated by Ruff/pydoclint)
UseCamelCasefor class names
Usesnake_casefor function and attribute names
Avoid usingTYPE_CHECKINGguards for type annotations because the project usesbeartypefor runtime type checking, which requires the actual type objects to be available at runtime
Treat underscored attributes as internal/private and not part of the public API
Files:
examples/sugarscape_ig/backend_frames/model.pyexamples/plotting.pyexamples/sugarscape_ig/backend_mesa/model.pyexamples/boltzmann_wealth/backend_mesa.pybenchmarks/cli.py
🧬 Code graph analysis (3)
examples/sugarscape_ig/backend_frames/model.py (3)
examples/utils.py (1)
FramesSimulationResult(7-14)examples/plotting.py (1)
plot_model_metrics(83-182)mesa_frames/abstract/datacollector.py (2)
collect(118-128)flush(176-196)
examples/sugarscape_ig/backend_mesa/model.py (3)
examples/utils.py (1)
MesaSimulationResult(18-25)examples/plotting.py (1)
plot_model_metrics(83-182)examples/sugarscape_ig/backend_mesa/agents.py (2)
AntAgent(16-78)move(75-78)
benchmarks/cli.py (5)
examples/plotting.py (1)
plot_performance(250-299)examples/boltzmann_wealth/backend_mesa.py (5)
simulate(89-94)step(47-54)step(78-82)run(84-86)run(101-174)examples/sugarscape_ig/backend_mesa/model.py (4)
simulate(189-202)step(171-180)run(182-186)run(209-308)examples/boltzmann_wealth/backend_frames.py (5)
simulate(115-124)step(52-76)step(106-108)run(110-112)run(131-184)examples/sugarscape_ig/backend_frames/model.py (4)
simulate(389-409)step(333-350)run(352-365)run(416-496)
🪛 Ruff (0.14.8)
examples/sugarscape_ig/backend_frames/model.py
227-229: Avoid specifying long messages outside the exception class
(TRY003)
examples/plotting.py
75-76: try-except-pass detected, consider logging the exception
(S110)
75-75: Do not catch blind exception: Exception
(BLE001)
115-115: Do not catch blind exception: Exception
(BLE001)
289-289: Comment contains ambiguous – (EN DASH). Did you mean - (HYPHEN-MINUS)?
(RUF003)
302-306: __all__ is not sorted
Apply an isort-style sorting to __all__
(RUF022)
examples/sugarscape_ig/backend_mesa/model.py
133-133: Unused lambda argument: m
(ARG005)
examples/boltzmann_wealth/backend_mesa.py
73-73: Unused lambda argument: m
(ARG005)
135-135: Comment contains ambiguous ‑ (NON-BREAKING HYPHEN). Did you mean - (HYPHEN-MINUS)?
(RUF003)
140-140: Comment contains ambiguous ‑ (NON-BREAKING HYPHEN). Did you mean - (HYPHEN-MINUS)?
(RUF003)
benchmarks/cli.py
86-86: Avoid specifying long messages outside the exception class
(TRY003)
90-90: Avoid specifying long messages outside the exception class
(TRY003)
92-92: Avoid specifying long messages outside the exception class
(TRY003)
95-95: Avoid specifying long messages outside the exception class
(TRY003)
97-97: Avoid specifying long messages outside the exception class
(TRY003)
105-105: Avoid specifying long messages outside the exception class
(TRY003)
107-107: Avoid specifying long messages outside the exception class
(TRY003)
127-127: Avoid specifying long messages outside the exception class
(TRY003)
130-130: Avoid specifying long messages outside the exception class
(TRY003)
🔇 Additional comments (9)
examples/sugarscape_ig/backend_frames/model.py (2)
463-465: Past review issue addressed: CSV confirmation message now correctly scoped.The
typer.echo(f"Saved CSV results under {results_dir}")is now inside theif save_results:block, fixing the misleading message issue flagged in the previous review.
174-279: Sugarscape model implementation looks correct.The model properly wires the grid, agent sets, and data collector. The conditional storage backend selection (memory vs CSV based on
results_dir) is a good pattern for supporting both benchmarking and CLI use cases.examples/plotting.py (1)
82-183: Well-designed model metrics plotting with theme support.The function handles edge cases (empty data, missing step column), automatically infers step count, and generates consistent light/dark themed outputs. The legend handling for single vs. multiple metrics is a nice touch.
examples/sugarscape_ig/backend_mesa/model.py (2)
130-134: Unused lambda argumentmis intentional for DataCollector API.The static analysis flags
lambda m: seedas having an unused argument, but this is required by Mesa's DataCollector API which passes the model to all reporter lambdas. No change needed.
74-143: Sugarscape Mesa model implementation is correct.The model properly initializes the sugar field, grid, and agents with reproducible RNG seeding. The DataCollector setup mirrors the Frames backend for CSV comparability.
examples/boltzmann_wealth/backend_mesa.py (2)
21-37: Gini coefficient implementation is robust.The function handles edge cases well: empty arrays, all-zero values, and uniform distributions all return appropriate values (nan or 0.0). The formula implementation is correct.
40-55: MoneyAgent step logic is correct.The agent correctly skips if wealth is 0, selects a random peer, and transfers one unit. The
if other is Nonecheck at line 51-52 is defensive butrandom.choiceon a non-empty list won't return None, so this is safe.benchmarks/cli.py (2)
111-138: Model selection parsing is clear and robust
_parse_modelsnicely handles"all", comma-separated lists, validation againstMODELS, and order-preserving de-duplication. I don’t see any changes needed here.
141-160: Consistent reuse of shared plotting utilitiesThe
_plot_performancewrapper is minimal and does the right thing: early‑exits on empty data, selects the expected columns, and delegates to the sharedexamples.plotting.plot_performancewith a clear stem and title. Looks good.
benchmarks/cli.py
Outdated
| @app.command() | ||
| def run( | ||
| models: Annotated[ | ||
| str | list[str], | ||
| typer.Option( | ||
| help="Models to benchmark: boltzmann, sugarscape, or all", | ||
| callback=_parse_models, | ||
| ), | ||
| ] = "all", | ||
| agents: Annotated[ | ||
| str | 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 | None, | ||
| typer.Option( | ||
| help=( | ||
| "Base directory for benchmark outputs. A timestamped subdirectory " | ||
| "(e.g. results/20250101_120000) is created with CSV files at the root " | ||
| "and a 'plots/' subfolder for images. Defaults to the module's results directory." | ||
| ), | ||
| ), | ||
| ] = None, | ||
| ) -> None: | ||
| """Run performance benchmarks for the selected models.""" | ||
| # Support both CLI (via callbacks) and direct function calls | ||
| if isinstance(models, str): | ||
| models = _parse_models(models) | ||
| if isinstance(agents, str): | ||
| agents = _parse_agents(agents) | ||
| # Ensure module-relative default is computed at call time (avoids import-time side effects) | ||
| if results_dir is None: | ||
| results_dir = Path(__file__).resolve().parent / "results" | ||
|
|
||
| runtime_typechecking = os.environ.get("MESA_FRAMES_RUNTIME_TYPECHECKING", "") | ||
| if runtime_typechecking and runtime_typechecking.lower() not in {"0", "false"}: | ||
| typer.secho( | ||
| "Warning: MESA_FRAMES_RUNTIME_TYPECHECKING is enabled; benchmarks may run significantly slower.", | ||
| fg=typer.colors.YELLOW, | ||
| ) | ||
| rows: list[dict[str, object]] = [] | ||
| # Single timestamp per CLI invocation so all model results are co-located. | ||
| timestamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S") | ||
| # Create unified output layout: <results_dir>/<timestamp>/{CSV files, plots/} | ||
| base_results_dir = results_dir | ||
| timestamp_dir = (base_results_dir / timestamp).resolve() | ||
| plots_subdir: Path = timestamp_dir / "plots" | ||
| 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, | ||
| } | ||
| ) | ||
| # Report completion of this run to the CLI | ||
| typer.echo( | ||
| f"Completed {backend.name} for model={model} agents={agents_count} steps={steps} seed={run_seed} repeat={repeat_idx} in {runtime:.3f}s" | ||
| ) | ||
| # Finished all runs for this model | ||
| typer.echo(f"Finished benchmarking model {model}") | ||
|
|
||
| if not rows: | ||
| typer.echo("No benchmark data collected.") | ||
| return | ||
| df = pl.DataFrame(rows) | ||
| if save: | ||
| timestamp_dir.mkdir(parents=True, exist_ok=True) | ||
| for model in models: | ||
| model_df = df.filter(pl.col("model") == model) | ||
| csv_path = timestamp_dir / f"{model}_perf_{timestamp}.csv" | ||
| model_df.write_csv(csv_path) | ||
| typer.echo(f"Saved {model} results to {csv_path}") | ||
| if plot: | ||
| plots_subdir.mkdir(parents=True, exist_ok=True) | ||
| for model in models: | ||
| model_df = df.filter(pl.col("model") == model) | ||
| _plot_performance(model_df, model, plots_subdir, timestamp) | ||
| typer.echo(f"Saved {model} plots under {plots_subdir}") | ||
|
|
||
| typer.echo( | ||
| f"Unified benchmark outputs written under {timestamp_dir} (CSV files) and {plots_subdir} (plots)" | ||
| ) |
There was a problem hiding this comment.
Clarify final summary message based on save / plot flags
The core benchmarking loop and IO layout look good. One minor UX issue: the final message always claims that CSVs and plots were written under {timestamp_dir} / {plots_subdir}, even when save=False and/or plot=False.
To avoid misleading output, you could tailor the summary to what was actually produced:
- typer.echo(
- f"Unified benchmark outputs written under {timestamp_dir} (CSV files) and {plots_subdir} (plots)"
- )
+ destinations: list[str] = []
+ if save:
+ destinations.append(f"CSVs under {timestamp_dir}")
+ if plot:
+ destinations.append(f"plots under {plots_subdir}")
+
+ if destinations:
+ typer.echo("Unified benchmark outputs written: " + "; ".join(destinations))
+ else:
+ typer.echo("Benchmark run completed (save=False, plot=False; no files written).")This keeps the behavior unchanged while making the CLI output more accurate in all flag combinations.
🤖 Prompt for AI Agents
In benchmarks/cli.py around lines 163 to 272, the final summary always claims
CSVs and plots were written even when save=False and/or plot=False; change the
final message to reflect only what was actually produced by building a
conditional summary: if save True include the CSV timestamp_dir path, if plot
True include the plots_subdir path, otherwise note that CSVs or plots were not
generated; then print a single concise summary string that lists produced
artifacts (or states none were produced) instead of always mentioning both.
| # Boltzmann Wealth Exchange Model | ||
|
|
||
| ## Overview | ||
|
|
||
| This example implements a simple wealth exchange ("Boltzmann money") model in two | ||
| backends: | ||
|
|
||
| - `backend_frames.py` (Mesa Frames / vectorised `AgentSet`) | ||
| - `backend_mesa.py` (classic Mesa / object-per-agent) | ||
|
|
||
| Both expose a Typer CLI with symmetric options so you can compare correctness | ||
| and performance directly. | ||
|
|
||
| ## Concept | ||
|
|
||
| Each agent starts with 1 unit of wealth. At every step: | ||
|
|
||
| 1. Frames backend: all agents with strictly positive wealth become potential donors. | ||
| Each donor gives 1 unit of wealth, and a recipient is drawn (with replacement) | ||
| for every donating agent. A single vectorised update applies donor losses and | ||
| recipient gains. | ||
| 2. Mesa backend: agents are shuffled and iterate sequentially; each agent with | ||
| positive wealth transfers 1 unit to a randomly selected peer. | ||
|
|
||
| The stochastic exchange process leads to an emergent, increasingly unequal | ||
| wealth distribution and rising Gini coefficient, typically approaching a stable | ||
| level below 1 (due to conservation and continued mixing). | ||
|
|
||
| ## Reported Metrics | ||
|
|
||
| The model records per-step population Gini (`gini`). You can extend reporters by | ||
| adding lambdas to `model_reporters` in either backend's constructor. | ||
|
|
||
| Notes on interpretation: | ||
|
|
||
| - Early steps: Gini ~ 0 (uniform initial wealth). | ||
| - Mid phase: Increasing Gini as random exchanges concentrate wealth. | ||
| - Late phase: Fluctuating plateau (a stochastic steady state) — exact level | ||
| varies with agent count and RNG seed. | ||
|
|
||
| ## Running | ||
|
|
||
| Always run examples from the project root using `uv`: | ||
|
|
||
| ```bash | ||
| uv run examples/boltzmann_wealth/backend_frames.py --agents 5000 --steps 200 --seed 123 --plot --save-results | ||
| uv run examples/boltzmann_wealth/backend_mesa.py --agents 5000 --steps 200 --seed 123 --plot --save-results | ||
| ``` | ||
|
|
||
| ## CLI options | ||
|
|
||
| - `--agents` Number of agents (default 5000) | ||
| - `--steps` Simulation steps (default 100) | ||
| - `--seed` Optional RNG seed for reproducibility | ||
| - `--plot / --no-plot` Generate line plot(s) of Gini | ||
| - `--save-results / --no-save-results` Persist CSV metrics | ||
| - `--results-dir` Override the auto-timestamped directory under `results/` | ||
|
|
||
| Frames backend additionally warns if runtime type checking is enabled because it | ||
| slows vectorised operations: set `MESA_FRAMES_RUNTIME_TYPECHECKING=0` for fair | ||
| performance comparisons. | ||
|
|
||
| ## Outputs | ||
|
|
||
| Each run creates (or uses) a results directory like: | ||
|
|
||
| ```text | ||
| examples/boltzmann_wealth/results/20251016_173702/ | ||
| model.csv # step,gini | ||
| gini_<timestamp>_dark.png (and possibly other theme variants) | ||
| ``` | ||
|
|
||
| Tail metrics are printed to console for quick inspection: | ||
|
|
||
| ```text | ||
| Metrics in the final 5 steps: shape: (5, 2) | ||
| ┌──────┬───────┐ | ||
| │ step ┆ gini │ | ||
| │ --- ┆ --- │ | ||
| │ i64 ┆ f64 │ | ||
| ├──────┼───────┤ | ||
| │ ... ┆ ... │ | ||
| └──────┴───────┘ | ||
| ``` | ||
|
|
||
| ## Performance & Benchmarking | ||
|
|
||
| Use the shared benchmarking CLI to compare scaling, checkout `benchmarks/README.md`. | ||
|
|
||
| ## Programmatic Use | ||
|
|
||
| ```python | ||
| from examples.boltzmann_wealth import backend_frames as bw_frames | ||
| result = bw_frames.simulate(agents=10000, steps=250, seed=42) | ||
| metrics = result.datacollector.data["model"] # Polars DataFrame | ||
| ``` |
There was a problem hiding this comment.
Documentation is well-structured and comprehensive.
The README provides clear explanations of the model concept, CLI usage, output structure, and programmatic API. One minor grammar fix:
Line 88: "checkout" should be "check out" (verb phrase).
-Use the shared benchmarking CLI to compare scaling, checkout `benchmarks/README.md`.
+Use the shared benchmarking CLI to compare scaling, check out `benchmarks/README.md`.🤖 Prompt for AI Agents
In examples/boltzmann_wealth/README.md around lines 1–96 (specifically line 88),
the phrase "checkout `benchmarks/README.md`" is incorrect; change it to the verb
phrase "check out `benchmarks/README.md`". Update the sentence in the
Performance & Benchmarking section to use "check out" and run a quick
spell/grammar check across the README to ensure no other similar
verb/compound-word mistakes remain.
| # Keep the full model metrics (step + any model reporters) | ||
| # Keep the full model metrics (step + any model reporters) | ||
|
|
There was a problem hiding this comment.
Remove duplicate comment line.
Line 257 is a duplicate of line 256.
# Extract metrics using DataCollector API
model_pd = (
dc.get_model_vars_dataframe().reset_index().rename(columns={"index": "step"})
)
# Keep the full model metrics (step + any model reporters)
- # Keep the full model metrics (step + any model reporters)🤖 Prompt for AI Agents
In examples/sugarscape_ig/backend_mesa/model.py around lines 256 to 258, there
is a duplicated comment line ("Keep the full model metrics (step + any model
reporters)") on line 257; remove the duplicate line 257 so only a single
instance of that comment remains.
| typer.echo(f"Saved plots under {plots_dir}") | ||
|
|
||
| typer.echo(f"Saved CSV results under {results_dir}") |
There was a problem hiding this comment.
Misleading "Saved CSV results" message when save_results=False.
Line 308 unconditionally prints "Saved CSV results under {results_dir}" regardless of whether save_results is True. This should be moved inside the if save_results: block (lines 268-270) to match the backend_frames fix.
if save_results:
csv_path = results_dir / "model.csv"
model_pd.to_csv(csv_path, index=False)
+ typer.echo(f"Saved CSV results under {results_dir}")
# Plot per-metric similar to the backend_frames example...
...
typer.echo(f"Saved plots under {plots_dir}")
-
- typer.echo(f"Saved CSV results under {results_dir}")Committable suggestion skipped: line range outside the PR's diff.
🤖 Prompt for AI Agents
In examples/sugarscape_ig/backend_mesa/model.py around lines 306-308, the
message "Saved CSV results under {results_dir}" is printed unconditionally even
when save_results is False; move this typer.echo call inside the existing if
save_results: block (the block around lines 268-270 that handles saving CSV
results) so the CSV-saved log is only emitted when results are actually written,
mirroring the backend_frames fix.
for more information, see https://pre-commit.ci
…tmesa/mesa-frames into split/examples-benchmarks
This PR introduces:
runcommand with standardised options(
--agents,--steps,--seed,--plot,--save-results,--results-dir).boltzmann,sugarscape) with automated CSV output and scaling plots.Summary by CodeRabbit
New Features
Documentation
Chores
✏️ Tip: You can customize this high-level summary in your review settings.