forked from moreo/QuaPy
36 lines
885 B
Python
36 lines
885 B
Python
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import itertools
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import multiprocessing
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from joblib import Parallel, delayed
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import contextlib
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import numpy as np
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def get_parallel_slices(n_tasks, n_jobs=-1):
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if n_jobs == -1:
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n_jobs = multiprocessing.cpu_count()
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batch = int(n_tasks / n_jobs)
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remainder = n_tasks % n_jobs
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return [slice(job * batch, (job + 1) * batch + (remainder if job == n_jobs - 1 else 0)) for job in
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range(n_jobs)]
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def parallelize(func, args, n_jobs):
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args = np.asarray(args)
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slices = get_parallel_slices(len(args), n_jobs)
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results = Parallel(n_jobs=n_jobs)(
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delayed(func)(args[slice_i]) for slice_i in slices
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)
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return list(itertools.chain.from_iterable(results))
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@contextlib.contextmanager
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def temp_seed(seed):
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state = np.random.get_state()
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np.random.seed(seed)
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try:
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yield
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finally:
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np.random.set_state(state)
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