Add HDF5 checkpoint support for analysis pipeline Add support f…#18
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darshanmandge wants to merge 2 commits into
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Add HDF5 checkpoint support for analysis pipeline Add support f…#18darshanmandge wants to merge 2 commits into
darshanmandge wants to merge 2 commits into
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…or reading optimisation checkpoints from HDF5 files in addition to pickle. This enables storing portable, reproducible checkpoint summaries that are not tied to specific Python/library versions. Changes: - New module bluepyemodel/tools/checkpoint_hdf5.py with: - read_checkpoint_h5(): reads HDF5 and returns DEAP-compatible shims - convert_checkpoint(): pickle → HDF5 conversion utility - Shim classes (_Individual, _Logbook, _CMAStatus, _History) that implement the exact API surface used by the analysis pipeline - Updated read_checkpoint() in utils.py to auto-detect format (.h5/.pkl) - Updated existing_checkpoint_paths() to glob both .pkl and .h5 - Updated access_point.py to use read_checkpoint() instead of inline pickle.load, and to discover .h5 checkpoint files HDF5 layout stores: param_names, halloffame (genes+fitness), population (genes+fitness), logbook (per-gen stats), and history/genealogy_genes for parameter evolution plots. Tested end-to-end: store_optimisation_results, optimisation plot, evolution_parameters_density, and check_optimisation_state all work from HDF5 with pickle removed.
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| p = Path(checkpoint_path) | ||
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| # HDF5 format | ||
| if p.suffix in (".h5", ".hdf5"): |
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If you write .h5 it won't become .hdf5, no? Wouldn't checking only for .h5 be sufficient?
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