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Replace Python-level loops with NumPy vectorized operations: grids.py: - cartesian_grid: itertools.product → np.meshgrid - extract_inscribed_ball: per-point loop → vectorized norm + boolean mask - cartesian2polar: math.fmod loop → np.fmod datasets.py: - set_arrays: per-point loop → array slicing + broadcasting - get_random_radial_params: itertools.product double loop → outer product matrix op - generate_learning_data: per-sample params_to_boxdata → batch computation - params_to_boxdata: np.put loop → fancy indexing Results (l_max=3, rn_max=4, n_div=32): - BoxData init: 1.47s → 0.028s (53x speedup) - generate_learning_data(1000): 1.20s → 0.59s (2x speedup) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Summary
grids.pyanddatasets.pyChanges
grids.py:
cartesian_grid:itertools.product→np.meshgridextract_inscribed_ball: per-point loop → vectorized norm + boolean maskcartesian2polar:math.fmodloop →np.fmoddatasets.py:
set_arrays: per-point loop → array slicing + broadcastingget_random_radial_params: double loop → outer product matrix opgenerate_learning_data: per-sample computation → batch computationparams_to_boxdata:np.putloop → fancy indexingTest plan
test_grids.py+test_datasets.py)🤖 Generated with Claude Code