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Fix fancy indexing for electrical series with Zarr backend #283

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Fix fancy indexing for electrical series with Zarr backend #283

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2d8f72a
Fix fancy indexing for electrical series with Zarr backend
alejoe91 May 9, 2023
6afc11c
Merge branch 'master' into fix-es-zarr
bendichter Jun 16, 2023
1381e3b
Merge branch 'master' into fix-es-zarr
bendichter Aug 16, 2023
03a598a
Merge branch 'master' into fix-es-zarr
bendichter Mar 18, 2024
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9 changes: 7 additions & 2 deletions nwbwidgets/timeseries.py
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Original file line number Diff line number Diff line change
Expand Up @@ -423,10 +423,15 @@ def _prep_timeseries(time_series: TimeSeries, time_window=None, order=None):
t_ind_stop = timeseries_time_to_ind(time_series, time_window[1])

tt = get_timeseries_tt(time_series, t_ind_start, t_ind_stop)
unique_sorted_order, inverse_sort = np.unique(order, return_inverse=True)

if len(time_series.data.shape) > 1:
mini_data = time_series.data[t_ind_start:t_ind_stop, unique_sorted_order][:, inverse_sort]
unique_sorted_order, inverse_sort = np.unique(order, return_inverse=True)
# fancy indexing is not supported in zarr, so we use slice when possible
if np.all(np.diff(unique_sorted_order) == 1):
unique_sorted_order = slice(unique_sorted_order[0], unique_sorted_order[-1] + 1)
mini_data = time_series.data[t_ind_start:t_ind_stop, unique_sorted_order][:, inverse_sort]
else:
mini_data = np.array(time_series.data[t_ind_start:t_ind_stop])[:, unique_sorted_order][:, inverse_sort]
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@bendichter bendichter Aug 16, 2023 •
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can we add a check here and have this work differently only for Zarr dataset objects? I'd prefer to use the simultaneous indexing approach for h5py datasets where we can so we don't load data into memory when we don't need to. I also think this could and probably should be refactored into a data utility function that can be used in other places

if np.all(np.isnan(mini_data)):
return None, tt, None
gap = np.median(np.nanstd(mini_data, axis=0)) * 20
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