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Restore parallelism to load_as_concatenated #134

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Restore parallelism to load_as_concatenated #134

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419e508
Restore parallelism to load_as_concatenated using starmap_async
justinrporter Oct 6, 2018
02bf36a
Merge branch 'master' into restore-load-parallel
justinrporter Oct 12, 2018
c58d74e
Merge branch 'master' into restore-load-parallel
justinrporter Nov 19, 2018
d350e39
Merge branch 'master' into restore-load-parallel
justinrporter Nov 30, 2018
5521e9d
Merge branch 'master' into restore-load-parallel
justinrporter Dec 21, 2018
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6 changes: 3 additions & 3 deletions enspara/util/load.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,8 +10,6 @@
import numpy as np
import mdtraj as md

# from sklearn.externals.joblib import Parallel, delayed

from .. import exception

logger = logging.getLogger(__name__)
Expand Down Expand Up @@ -116,11 +114,13 @@ def load_as_concatenated(filenames, lengths=None, processes=None,
logger.debug("Sounding %s trajectories with %s processes.",
len(filenames), processes)
with mp.Pool(processes=processes) as pool:
lengths = pool.starmap(
proc = pool.starmap_async(
sound_trajectory,
[(f, kw.get('stride', 1)) for f, kw
in zip(filenames, args) if 'frame' not in kw])

shapes = proc.get()

# trjs with frame are always length 1, add that to lengths now
for i, kw in enumerate(args):
if 'frame' in kw:
Expand Down