Coverage: - test_model: SimpleNet forward (parametrized over batch sizes and both unsqueezed and flat input shapes), layer dimensions, differentiability, and ONNX round-trip - test_inference: load_model resolution order (bundled, cwd override, explicit path, missing path), and predict shape/dtype/determinism plus endpoint sanity across 8 cardinal/diagonal targets - test_train: _load_csv parsing, TrajectoryDataset indexing, full train() pipeline producing a single-file ONNX, plus a smoke test against the real data shipped under data/ - test_cli: --help for the three console scripts and a real run of mouse-visualize via both the entry point and python -m Wire up pytest via dependency-groups and tool.pytest.ini_options.
23 lines
282 B
Plaintext
23 lines
282 B
Plaintext
# Byte-compiled / build artifacts
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__pycache__/
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*.py[cod]
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*.egg-info/
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build/
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dist/
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# Virtual envs / tooling
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.venv/
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.uv/
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# Generated outputs (only at repo root; bundled assets must stay)
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/trajectories.png
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/mouse.onnx
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/mouse.onnx.data
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# IDE
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.idea/
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.vscode/
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# pytest
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.pytest_cache/
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