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 - 1 segments - 32.0kHz - 10.000s

detect peaks:   0%|          | 0/5 [00:00<?, ?it/s]
detect peaks:  20%|##        | 1/5 [00:00<00:01,  2.55it/s]
detect peaks:  40%|####      | 2/5 [00:00<00:01,  2.63it/s]
detect peaks:  80%|########  | 4/5 [00:00<00:00,  4.97it/s]
detect peaks: 100%|##########| 5/5 [00:00<00:00,  5.19it/s]

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())

[('sample_ind', '<i8'), ('channel_ind', '<i8'), ('amplitude', '<f8'), ('segment_ind', '<i8')]
(751,)
dict_keys(['sample_ind', 'channel_ind', 'amplitude', 'segment_ind'])

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 0x7fc1bfad8a00>

can be also animated with bin_duration_s=1.

si.plot_peak_activity_map(rec_filtred, bin_duration_s=1.)

detect peaks:   0%|          | 0/10 [00:00<?, ?it/s]
detect peaks:  30%|###       | 3/10 [00:00<00:00, 26.06it/s]
detect peaks:  60%|######    | 6/10 [00:00<00:00, 26.26it/s]
detect peaks:  90%|######### | 9/10 [00:00<00:00, 26.12it/s]
detect peaks: 100%|##########| 10/10 [00:00<00:00, 26.03it/s]

<spikeinterface.widgets._legacy_mpl_widgets.activity.PeakActivityMapWidget object at 0x7fc1bfad8e50>

plot_drift_over_time()

Plots detected peaks over time in scatter mode heatmap mode. Here bin_duration_s=1.0 because the recording is short (10s). A better value could 60s for normal recordings

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

<spikeinterface.widgets._legacy_mpl_widgets.drift.DriftOverTimeWidget object at 0x7fc1c0155c10>

Plots detected peaks over time in scatter mode

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


plt.show()