Peaks Widgets Gallery

Some widgets are useful before sorting and works with “peaks” given by detect_peaks() function.

They are useful to check drift before running sorters.

import matplotlib.pyplot as plt

import spikeinterface.full as si

First, let’s download a simulated dataset from the repo ‘https://gin.g-node.org/NeuralEnsemble/ephy_testing_data’

local_path = si.download_dataset(remote_path='mearec/mearec_test_10s.h5')
rec, sorting = si.read_mearec(local_path)

Out:

/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/conda/0.93.0/lib/python3.8/site-packages/MEArec/tools.py:339: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  if StrictVersion(mearec_version) >= '1.5.0':
/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/conda/0.93.0/lib/python3.8/site-packages/MEArec/tools.py:339: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  if StrictVersion(mearec_version) >= '1.5.0':

Lets filter and detect peak on it

from spikeinterface.sortingcomponents import detect_peaks

rec_filtred = si.bandpass_filter(rec, freq_min=300., freq_max=6000., margin_ms=5.0)
print(rec_filtred)
peaks = detect_peaks(
        rec_filtred, method='locally_exclusive',
        peak_sign='neg', detect_threshold=6, n_shifts=7,
        local_radius_um=100,
        noise_levels=None,
        random_chunk_kwargs={},
        chunk_memory='10M', n_jobs=1, progress_bar=True)

Traceback (most recent call last):
  File "/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/checkouts/0.93.0/examples/modules/widgets/plot_4_peaks_gallery.py", line 30, in <module>
    peaks = detect_peaks(
  File "/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/conda/0.93.0/lib/python3.8/site-packages/spikeinterface/sortingcomponents/peak_detection.py", line 196, in detect_peaks
    raise ModuleNotFoundError('"locally_exclusive" need numba which is not installed')
ModuleNotFoundError: "locally_exclusive" need numba which is not installed

peaks is a numpy 1D array with structured dtype that contains several fields:

sample_ind/channel_ind/amplitude/segment_ind

print(peaks.dtype)
print(peaks.shape)
print(peaks.dtype.fields.keys())

This “peaks” vector can be used in several widgets, for instance

plot_peak_activity_map()

si.plot_peak_activity_map(rec_filtred, peaks=peaks)

can be also animated with bin_duration_s=1.

si.plot_peak_activity_map(rec_filtred, bin_duration_s=1.)

plot_drift_over_time’()

heatmap mode here bin_duration_s=1. because the rec is short (10s). a better value could 60s

si.plot_drift_over_time(rec_filtred, peaks=peaks, bin_duration_s=1.,
        weight_with_amplitudes=True, mode='heatmap')

plot_drift_over_time’()

in scatter mode

si.plot_drift_over_time(rec_filtred, peaks=peaks, weight_with_amplitudes=False, mode='scatter')


plt.show()