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)

Lets filter and detect peak on it

from spikeinterface.sortingcomponents.peak_detection 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, exclude_sweep_ms=0.3,
        local_radius_um=100,
        noise_levels=None,
        random_chunk_kwargs={},
        chunk_memory='10M', n_jobs=1, progress_bar=True)

BandpassFilterRecording: 32 channels - 32.0kHz - 1 segments - 320,000 samples - 10.00s
                         float32 dtype - 39.06 MiB

detect peaks using locally_exclusive:   0%|          | 0/5 [00:00<?, ?it/s]
detect peaks using locally_exclusive:  20%|##        | 1/5 [00:00<00:01,  2.52it/s]
detect peaks using locally_exclusive:  40%|####      | 2/5 [00:00<00:01,  2.59it/s]
detect peaks using locally_exclusive:  80%|########  | 4/5 [00:00<00:00,  4.93it/s]
detect peaks using locally_exclusive: 100%|##########| 5/5 [00:00<00:00,  5.14it/s]

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

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

[('sample_index', '<i8'), ('channel_index', '<i8'), ('amplitude', '<f8'), ('segment_index', '<i8')]
(751,)
dict_keys(['sample_index', 'channel_index', 'amplitude', 'segment_index'])

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

plot_peak_activity_map()

si.plot_peak_activity_map(rec_filtred, peaks=peaks)

<spikeinterface.widgets._legacy_mpl_widgets.activity.PeakActivityMapWidget object at 0x7fcd659a7190>

can be also animated with bin_duration_s=1.

si.plot_peak_activity_map(rec_filtred, bin_duration_s=1.)


plt.show()

detect peaks using by_channel:   0%|          | 0/10 [00:00<?, ?it/s]
detect peaks using by_channel:  30%|###       | 3/10 [00:00<00:00, 25.92it/s]
detect peaks using by_channel:  60%|######    | 6/10 [00:00<00:00, 26.05it/s]
detect peaks using by_channel:  90%|######### | 9/10 [00:00<00:00, 26.12it/s]
detect peaks using by_channel: 100%|##########| 10/10 [00:00<00:00, 25.96it/s]