Note
Go to the end to download the full example code
Peaks Widgets Gallery¶
Some widgets are useful before sorting and works with the “peaks” given by the 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)
Let’s filter and detect peaks on it
from spikeinterface.sortingcomponents.peak_detection import detect_peaks
rec_filtred = si.bandpass_filter(recording=rec, freq_min=300.0, freq_max=6000.0, margin_ms=5.0)
print(rec_filtred)
peaks = detect_peaks(
recording=rec_filtred,
method="locally_exclusive",
peak_sign="neg",
detect_threshold=6,
exclude_sweep_ms=0.3,
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, 2.70it/s]
detect peaks using locally_exclusive: 40%|#### | 2/5 [00:00<00:01, 2.77it/s]
detect peaks using locally_exclusive: 80%|######## | 4/5 [00:00<00:00, 5.24it/s]
detect peaks using locally_exclusive: 100%|##########| 5/5 [00:00<00:00, 5.47it/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')]
(749,)
dict_keys(['sample_index', 'channel_index', 'amplitude', 'segment_index'])
This “peaks” vector can be used in several widgets, for instance plot_peak_activity()
si.plot_peak_activity(recording=rec_filtred, peaks=peaks)
plt.show()
can be also animated with bin_duration_s=1.
si.plot_peak_activity(recording=rec_filtred, peaks=peaks, bin_duration_s=1.0)
plt.show()
Total running time of the script: (0 minutes 1.681 seconds)