###############################################################################
# Now let's check what we have loaded:
print('Channel ids:', recording_tetrodes.get_channel_ids())
print('Loaded properties',
recording_tetrodes.get_shared_channel_property_names())
print(
'Label of channel 0:',
recording_tetrodes.get_channel_property(channel_id=0,
property_name='label'))
###############################################################################
# and let's plot the probe layout:
import spikeinterface.widgets as sw
w_el_tetrode = sw.plot_electrode_geometry(recording_tetrodes)
##################################################################################
# Alternatively, one can use a '.csv' file to load the electrode locations. Let's create a '.csv' file
# with 2D locations in a circular layout:
delta_deg = 2 * np.pi / recording.get_num_channels()
with open('circular_layout.csv', 'w') as f:
for i in range(recording.get_num_channels()):
angle = i * delta_deg
radius = 50
x = radius * np.cos(angle)
y = radius * np.sin(angle)
f.write(str(x) + ',' + str(y) + '\n')
#######################################################################################
for i in range(n_spike_frames):
frame_buff = np.array([])
#
for j in range(n_units):
frame_buff = np.append(frame_buff, (trains_buff[j])[pointers[j]])
min_id = np.argmin(frame_buff)
# print(min_id)
spike_labels_GT = np.append(spike_labels_GT, min_id)
spike_times_GT = np.append(spike_times_GT, frame_buff[min_id])
pointers[min_id] += 1
info_dict['spike_labels_GT'] = spike_labels_GT.astype(np.int)
info_dict['spike_times_GT'] = spike_times_GT.astype(np.int)
return info_dict, recording_f
if __name__ == "__main__":
# recgen = mr.load_recordings(globvar.h5data_path)
# mr.plot_recordings(recgen)
# mr.plot_templates(recgen)
# mr.plot_waveforms(recgen)
# mr.plot_amplitudes(recgen)
recording, sorting = load_data(globvar.h5data_path)
info_dict, recording_f = preprocessing(recording, sorting)
# sw.plot_unit_waveforms(recording_f, sorting, max_spikes_per_unit=10000000,ms_before=globvar.snippet_ms_before, ms_after=globvar.snippet_ms_after)
channel_loc = recording_f.get_channel_locations()
print(f'channel location:{channel_loc}')
sw.plot_electrode_geometry(recording_f)
plt.show()
print(recording)
import numpy as np
import spikeinterface.extractors as se
import spikeinterface.widgets as sw
import matplotlib.pyplot as plt
recording, sorting_true = se.example_datasets.toy_example(duration=10,
num_channels=256,
seed=0)
recording_OrgMEA = recording.load_probe_file(probe_file='OrgMEA_252.prb')
print('Channel ids:', recording_OrgMEA.get_channel_ids())
print('Loaded properties',
recording_OrgMEA.get_shared_channel_property_names())
print(
'Label of channel 60:',
recording_OrgMEA.get_channel_property(channel_id=60,
property_name='label'))
w_el_OrgMEA = sw.plot_electrode_geometry(recording_OrgMEA)
plt.show()
##############################################################################
# First, let's create a toy example with the `extractors` module:
recording, sorting = se.example_datasets.toy_example(duration=10,
num_channels=4,
seed=0)
##############################################################################
# plot_timeseries()
# ~~~~~~~~~~~~~~~~~
w_ts = sw.plot_timeseries(recording)
w_ts1 = sw.plot_timeseries(recording, trange=[5, 8])
recording.set_channel_groups(channel_ids=recording.get_channel_ids(),
groups=[0, 0, 1, 1])
w_ts2 = sw.plot_timeseries(recording, trange=[5, 8], color_groups=True)
##############################################################################
# **Note**: each function returns a widget object, which allows to access the figure and axis.
w_ts.figure.suptitle("Recording by group")
w_ts.ax.set_ylabel("Channel_ids")
##############################################################################
# plot_electrode_geometry()
# ~~~~~~~~~~~~~~~~~~~~~~~~~
w_el = sw.plot_electrode_geometry(recording)