MAINT: vectorize _read_annotations_edf#8214
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cbrnr merged 3 commits intomne-tools:masterfrom Sep 4, 2020
jvdd:vectorized_annotation_reading
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MAINT: vectorize _read_annotations_edf#8214cbrnr merged 3 commits intomne-tools:masterfrom jvdd:vectorized_annotation_reading
cbrnr merged 3 commits intomne-tools:masterfrom
jvdd:vectorized_annotation_reading
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larsoner
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Sep 4, 2020
mne/io/edf/edf.py
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| triggers = re.findall(pat, annot_file.read()) | ||
| else: | ||
| tals = bytearray() | ||
| annotations = np.array(annotations) # To make sure that annotations is a ndarray |
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I would just do annotations = np.atleast_2d(annotations) (or below if annotations.ndim == 1: annotations = annotations[np.newaxis] rather than going through expand_dims)
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Great remark, glad I learned a new numpy method today 👍 ! ! I will insert this change.
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... oh and please add an entry to |
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Azure style complains: |
Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>
agramfort
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Sep 4, 2020
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Very nice improvement (although speedup with my test files ranges from 1x to 10x, but I guess this heavily depends on the length and other file properties). I'll merge as soon as CIs come back green! |
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Thanks @jvdd! |
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* upstream/master: (489 commits) MRG, DOC: Fix ICA docstring, add whitening (mne-tools#8227) MRG: Extract measurement date and age for NIRX files (mne-tools#7891) Nihon Kohden EEG file reader WIP (mne-tools#6017) BUG: Fix scaling for src_mri_t in coreg (mne-tools#8223) MRG: Set pyvista as default 3d backend (mne-tools#8220) MRG: Recreate our helmet graphic (mne-tools#8116) [MRG] Adding get_montage for montage to BaseRaw objects (mne-tools#7667) ENH: Allow setting tqdm backend (mne-tools#8177) [MRG, IO] Persyst reader into Raw object (mne-tools#8176) MRG, BUG: Fix errors in IO/loading/projectors (mne-tools#8210) MAINT: vectorize _read_annotations_edf (mne-tools#8214) FIX : events_from_annotation when annotations.orig_time is None and f… (mne-tools#8209) FIX: do not project to sphere; DOC - explain how to get EEGLAB-like topoplots (mne-tools#7455) [MRG, DOC] Added linear algebra of transform to doc (mne-tools#7087) FIX: Travis failure on python3.8.1 (mne-tools#8207) BF: String formatting in exception message (mne-tools#8206) BUG: Fix STC limit bug (mne-tools#8202) MRG, DOC: fix ica tutorial (mne-tools#8175) CSP component order selection (mne-tools#8151) MRG, ENH: Add on_missing to plot_events (mne-tools#8198) ...
marsipu
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Oct 14, 2020
* MAINT: vectorize _read_annotations_edf * MAINT: fix style complaints to + use np.atleast_2d vectorized _read_annotations_edf * Update doc/changes/latest.inc Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org> Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>
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Speedup the processing of the channel data in EDF+ TAL format through vectorizing the code.
This speedup tackles the time bottleneck when a RawEDF object is created.
Note: experimental results on private datasets yielded a speedup of 100-150x on EDF+C and EDF+D files of 20 - 360 MB when calling mne.io.read_raw_edf.
Author: Jeroen Van Der Donckt (IDLab Ghent University - imec)