Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fix the curation tutorial #2103

Merged
merged 2 commits into from
Oct 16, 2023
Merged

Fix the curation tutorial #2103

Changes from 1 commit
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
0d29a42
add kwargs and make keep_unit_ids list of strings
zm711 Oct 14, 2023
4d83610
Merge branch 'main' into curation-tutorial
samuelgarcia Oct 16, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
22 changes: 13 additions & 9 deletions examples/modules_gallery/qualitymetrics/plot_4_curation.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,8 @@
quality metrics.

"""
#############################################################################
# Import the modules and/or functions necessary from spikeinterface

import spikeinterface as si
import spikeinterface.extractors as se
Expand All @@ -15,22 +17,21 @@


##############################################################################
# First, let's download a simulated dataset
# from the repo 'https://gin.g-node.org/NeuralEnsemble/ephy_testing_data'
# Let's download a simulated dataset
# from the repo 'https://gin.g-node.org/NeuralEnsemble/ephy_testing_data'
#
# Let's imagine that the ground-truth sorting is in fact the output of a sorter.
#

local_path = si.download_dataset(remote_path='mearec/mearec_test_10s.h5')
recording, sorting = se.read_mearec(local_path)
recording, sorting = se.read_mearec(file_path=local_path)
print(recording)
print(sorting)

##############################################################################
# First, we extract waveforms and compute their PC scores:
# First, we extract waveforms (to be saved in the folder 'wfs_mearec') and
# compute their PC scores:

folder = 'wfs_mearec'
we = si.extract_waveforms(recording, sorting, folder,
we = si.extract_waveforms(recording, sorting, folder='wfs_mearec',
ms_before=1, ms_after=2., max_spikes_per_unit=500,
n_jobs=1, chunk_size=30000)
print(we)
Expand All @@ -47,12 +48,15 @@
##############################################################################
# We can now threshold each quality metric and select units based on some rules.
#
# The easiest and most intuitive way is to use boolean masking with dataframe:
# The easiest and most intuitive way is to use boolean masking with a dataframe.
#
# Then create a list of unit ids that we want to keep

keep_mask = (metrics['snr'] > 7.5) & (metrics['isi_violations_ratio'] < 0.2) & (metrics['nn_hit_rate'] > 0.90)
print(keep_mask)

keep_unit_ids = keep_mask[keep_mask].index.values
keep_unit_ids = [unit_id for unit_id in keep_unit_ids]
print(keep_unit_ids)

##############################################################################
Expand All @@ -61,4 +65,4 @@

curated_sorting = sorting.select_units(keep_unit_ids)
print(curated_sorting)
se.NpzSortingExtractor.write_sorting(curated_sorting, 'curated_sorting.pnz')
se.NpzSortingExtractor.write_sorting(sorting=curated_sorting, save_path='curated_sorting.npz')