sorting,
folder,
load_if_exists=True,
ms_before=1,
ms_after=2.,
max_spikes_per_unit=500,
n_jobs=1,
chunk_size=30000)
##############################################################################
# plot_unit_waveforms()
# ~~~~~~~~~~~~~~~~~~~~~
unit_ids = sorting.unit_ids[:4]
sw.plot_unit_waveforms(we, unit_ids=unit_ids)
##############################################################################
# plot_unit_templates()
# ~~~~~~~~~~~~~~~~~~~~~
unit_ids = sorting.unit_ids
sw.plot_unit_templates(we, unit_ids=unit_ids, ncols=5)
##############################################################################
# plot_unit_probe_map()
# ~~~~~~~~~~~~~~~~~~~~~
unit_ids = sorting.unit_ids[:4]
sw.plot_unit_probe_map(we, unit_ids=unit_ids)
#We are also going to be setting up the unit spike features associated with each waveform
ID1_features = A_snippets_reference[cluster == 1, :]
sortingPipeline.set_unit_spike_features(unit_id=1,
feature_name='unitId1',
value=ID1_features)
print("Spike feature names: " +
str(sortingPipeline.get_unit_spike_feature_names(unit_id=1)))
#Comparing sorter with ground truth
cmp_gt_SP = sc.compare_sorter_to_ground_truth(gtOutput,
sortingPipeline,
exhaustive_gt=True)
sw.plot_agreement_matrix(cmp_gt_SP, ordered=True)
#Some more comparision metrics
perf = cmp_gt_SP.get_performance()
#print('well_detected', cmp_gt_SP.get_well_detected_units(well_detected_score=0))
print(perf)
#We will try to get the SNR and firing rates
#firing_rates = st.validation.compute_firing_rates(sortingPipeline, duration_in_frames=recordingInput.get_num_frames())
#Raster plots
w_rs_gt = sw.plot_rasters(sortingPipeline, sampling_frequency=sampleRate)
w_wf_gt = sw.plot_unit_waveforms(recordingInput,
sortingPipeline,
max_spikes_per_unit=100)
def test_unitwaveforms(self):
w = sw.plot_unit_waveforms(self._we)
unit_ids = self._sorting.unit_ids[:6]
sw.plot_unit_waveforms(self._we, max_channels=5, unit_ids=unit_ids)
sw.plot_unit_waveforms(self._we, radius_um=60, unit_ids=unit_ids)
def test_unitwaveforms(self):
sw.plot_unit_waveforms(self._we)
sw.plot_unit_waveforms(self._we, max_channels=5)
sw.plot_unit_waveforms(self._we, radius_um=60)
def test_unitwaveforms(self):
sw.plot_unit_waveforms(self._we)
11, 12, 14, 147, 239, 119, 241, 120, 245, 124, 246, 127, 249, 250, 1, 2,
133, 135, 5, 6, 140, 10, 141, 143, 13, 144, 17, 238, 117
]) + 1
recordings_folder = Path.cwd().parent / 'recordings'
recording_file = recordings_folder / "2019-11-12T15-38-27McsRecording.h5"
recording = se.MCSH5RecordingExtractor(recording_file)
recording_prb = recording.load_probe_file(probe_file='OrgMEA_252.prb')
#remove ground electrodes
recording_gnd_rem = st.preprocessing.remove_bad_channels(
recording_prb,
bad_channel_ids=np.array([226, 95, 196, 62, 29, 166, 136, 128]) + 1)
#filter signal
recording_f = st.preprocessing.bandpass_filter(recording_gnd_rem,
freq_min=200,
freq_max=6000)
#remove bad channels
#recording_f_gnd_bad_rem = st.preprocessing.remove_bad_channels(recording_f_gnd_rem, bad_channels=np.array([XX, XX, XX, XX])+1)
#recording_rm_noise = st.preprocessing.remove_bad_channels(recording_f, bad_channels=[5])
recording_cmr = st.preprocessing.common_reference(recording_f,
reference='median')
save_folder = Path.cwd().parent / 'saved__sort_test.npz'
sorting = se.NpzSortingExtractor(save_folder)
w_wf2 = sw.plot_unit_waveforms(recording_f, sorting, max_spikes_per_unit=100)
Here is a gallery of all the available widgets using a pair of RecordingExtractor-SortingExtractor objects. ''' import spikeinterface.extractors as se import spikeinterface.widgets as sw ############################################################################## # 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_unit_waveforms() # ~~~~~~~~~~~~~~~~~~~~~~~~ w_wf = sw.plot_unit_waveforms(recording, sorting, max_num_waveforms=100) ############################################################################## # plot_amplitudes_distribution() # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ w_ampd = sw.plot_amplitudes_distribution(recording, sorting, max_num_waveforms=300) ############################################################################## # plot_amplitudes_timeseres() # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ w_ampt = sw.plot_amplitudes_timeseries(recording, sorting, max_num_waveforms=300) ############################################################################## # plot_features()
def get_sort_info(sorting, recording, out_loc):
unit_ids = sorting.get_unit_ids()
print("Found", len(unit_ids), 'units')
print('Unit ids:', unit_ids)
spike_train = sorting.get_unit_spike_train(unit_id=unit_ids[0])
print('Spike train of first unit:', np.asarray(spike_train) / 48000)
# Spike raster plot
t_len = 10
o_loc = os.path.join(out_loc, "raster_" + str(t_len) + "s.png")
print("Saving {}s rasters to {}".format(t_len, o_loc))
w_rs = sw.plot_rasters(sorting, trange=[0, t_len])
plt.savefig(o_loc, dpi=200)
w_isi = sw.plot_isi_distribution(sorting, bins=10, window=1)
o_loc = os.path.join(out_loc, "isi.png")
print("Saving isi to {}".format(o_loc))
plt.savefig(o_loc, dpi=200)
# Can plot cross corr using - ignore for now
# w_cch = sw.plot_crosscorrelograms(
# sorting, unit_ids=[1, 5, 8], bin_size=0.1, window=5)
w_feat = sw.plot_pca_features(recording,
sorting,
colormap='rainbow',
nproj=3,
max_spikes_per_unit=100)
o_loc = os.path.join(out_loc, "pca.png")
print("Plotting pca to {}".format(o_loc))
plt.savefig(o_loc, dpi=200)
# See also spiketoolkit.postprocessing.get_unit_waveforms
num_samps = min(20, len(unit_ids))
w_wf = sw.plot_unit_waveforms(sorting=sorting,
recording=recording,
unit_ids=unit_ids[:num_samps],
max_spikes_per_unit=20)
o_loc = os.path.join(out_loc, "waveforms_" + str(num_samps) + ".png")
print("Saving {} waveforms to {}".format(num_samps, o_loc))
plt.savefig(o_loc, dpi=200)
wf_by_group = st.postprocessing.get_unit_waveforms(
recording,
sorting,
ms_before=1,
ms_after=2,
save_as_features=False,
verbose=True,
grouping_property="group",
compute_property_from_recording=True)
o_loc = os.path.join(out_loc, "chan0_forms.png")
fig, ax = plt.subplots()
wf = wf_by_group[0]
colors = ["k", "r", "b", "g"]
for i in range(wf.shape[1]):
wave = wf[:, i, :]
c = colors[i]
ax.plot(wave.T, color=c, lw=0.3)
print("Saving first waveform on the first tetrode to {}".format(o_loc))
fig.savefig(o_loc, dpi=200)