Vectorize subcolumn loop, enhance parallelism, and update dependencies - #161
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In set_convective_sub_col_frac: Replace nested for-i/for-k loop with a single broadcast expression: conv_profs = (np.arange(1, N+1)[:, None, None] <= data_frac[None, :, :]) in _allocate_strat_sub_col: - np.argwhere → np.flatnonzero for overlying_locs1/2 - Replace all locals()["overlying_locs%d" % (Iover_min/max + 1)]
…lumn.py`)
In set_stratiform_sub_col_frac / set_precip_sub_col_frac / set_q_n:
- Replace lambda closures with functools.partial so the worker callable is
picklable by both Dask and multiprocessing backends.
- Add a second parallel call path: parallel='processes' uses
ProcessPoolExecutor + tqdm (lower overhead on a single workstation);
parallel=True or 'dask' (default, unchanged behaviour) uses dask.bag +
dask.diagnostics.ProgressBar (compatible with multi-node Dask schedulers on HPC).
parallel=False runs serially as before.
- Remove the manual chunked while-loop that was a workaround for Dask task-queue
freezes caused by unpicklable lambdas; no longer needed.
- Add a labelled progress indicator for each path: desc= label on tqdm bars
('processes'), and a matching print label immediately before each ProgressBar
context (Dask).
dependencies:
- Add numba and tqdm to requirements.txt, setup.py install_requires, and all five
conda environment YMLs under continuous_integration/.
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This pull request introduces several improvements to the parallel processing capabilities and dependency management for the subcolumn module. The main focus is on enhancing flexibility and performance for parallel execution, simplifying code paths, and ensuring all required dependencies are explicitly listed.
Dependency and Environment Updates:
tqdmto the dependenciesParallelization and Code Structure Improvements:
subcolumn.pyAlgorithmic and Code Quality Improvements:
These changes collectively enhance the usability, flexibility, and performance of the simulation code, especially for users running on different hardware setups or requiring better progress monitoring during parallel execution.