def setUp(self):
os.makedirs(FILEDIR, exist_ok=True)
self.Probe = hs.probe.HierlmannVisapyEmulationProbe(
DATA, inner_radius=60, neighbor_radius=100)
self.H = hs.HSDetection(self.Probe,
out_file_name=FILENAME,
file_directory_name=FILEDIR)
def _run(self, recording, output_folder):
recording = recover_recording(recording)
p = self.params
if recording.is_filtered and p['filter']:
print(
"Warning! The recording is already filtered, but Herding Spikes filter is enabled. You can disable "
"filters by setting 'filter' parameter to False")
self.H = hs.HSDetection(self.Probe,
file_directory_name=str(output_folder),
left_cutout_time=p['left_cutout_time'],
right_cutout_time=p['right_cutout_time'],
threshold=p['detect_threshold'],
to_localize=True,
num_com_centers=p['num_com_centers'],
maa=p['maa'],
ahpthr=p['ahpthr'],
out_file_name=p['out_file_name'],
decay_filtering=p['decay_filtering'],
save_all=p['save_all'],
amp_evaluation_time=p['amp_evaluation_time'],
spk_evaluation_time=p['spk_evaluation_time'])
self.H.DetectFromRaw(load=True, tInc=100000)
sorted_file = str(output_folder / 'HS2_sorted.hdf5')
if (not self.H.spikes.empty):
self.C = hs.HSClustering(self.H)
self.C.ShapePCA(pca_ncomponents=p['pca_ncomponents'],
pca_whiten=p['pca_whiten'])
self.C.CombinedClustering(
alpha=p['clustering_alpha'],
cluster_subset=p['clustering_subset'],
bandwidth=p['clustering_bandwidth'],
bin_seeding=p['clustering_bin_seeding'],
n_jobs=p['clustering_n_jobs'],
min_bin_freq=p['clustering_min_bin_freq'])
else:
self.C = hs.HSClustering(self.H)
if p['filter_duplicates']:
uids = self.C.spikes.cl.unique()
for u in uids:
s = self.C.spikes[self.C.spikes.cl == u].t.diff(
) < p['spk_evaluation_time'] / 1000 * self.Probe.fps
self.C.spikes = self.C.spikes.drop(s.index[s])
print('Saving to', sorted_file)
self.C.SaveHDF5(sorted_file, sampling=self.Probe.fps)
def _run(self, recording, output_folder):
p = self.params
self.H = hs.HSDetection(
self.Probe, file_directory_name=str(output_folder),
left_cutout_time=p['left_cutout_time'],
right_cutout_time=p['right_cutout_time'],
threshold=p['detection_threshold'],
to_localize=True,
num_com_centers=p['num_com_centers'],
maa=p['maa'],
ahpthr=p['ahpthr'],
out_file_name=p['out_file_name'],
decay_filtering=p['decay_filtering'],
save_all=p['save_all'],
amp_evaluation_time=p['amp_evaluation_time'],
spk_evaluation_time=p['spk_evaluation_time']
)
self.H.DetectFromRaw(load=True, tInc=1000000)
sorted_file = str(output_folder / 'HS2_sorted.hdf5')
if(not self.H.spikes.empty):
self.C = hs.HSClustering(self.H)
self.C.ShapePCA(pca_ncomponents=p['pca_ncomponents'],
pca_whiten=p['pca_whiten'])
self.C.CombinedClustering(
alpha=p['clustering_alpha'],
cluster_subset=p['clustering_subset'],
bandwidth=p['clustering_bandwidth'],
bin_seeding=p['clustering_bin_seeding'],
n_jobs=p['clustering_n_jobs'],
min_bin_freq=p['clustering_min_bin_freq']
)
else:
self.C = hs.HSClustering(self.H)
print('Saving to', sorted_file)
self.C.SaveHDF5(sorted_file, sampling=self.Probe.fps)
def _run_from_folder(cls, output_folder, params, verbose):
import herdingspikes as hs
recording = load_extractor(output_folder /
'spikeinterface_recording.json')
p = params
# Bandpass filter
if p['filter'] and p['freq_min'] is not None and p[
'freq_max'] is not None:
recording = st.bandpass_filter(recording=recording,
freq_min=p['freq_min'],
freq_max=p['freq_max'])
if p['pre_scale']:
recording = st.normalize_by_quantile(recording=recording,
scale=p['pre_scale_value'],
median=0.0,
q1=0.05,
q2=0.95)
print(
'Herdingspikes use the OLD spikeextractors with RecordingExtractorOldAPI'
)
old_api_recording = RecordingExtractorOldAPI(recording)
# this should have its name changed
Probe = hs.probe.RecordingExtractor(
old_api_recording,
masked_channels=p['probe_masked_channels'],
inner_radius=p['probe_inner_radius'],
neighbor_radius=p['probe_neighbor_radius'],
event_length=p['probe_event_length'],
peak_jitter=p['probe_peak_jitter'])
H = hs.HSDetection(Probe,
file_directory_name=str(output_folder),
left_cutout_time=p['left_cutout_time'],
right_cutout_time=p['right_cutout_time'],
threshold=p['detect_threshold'],
to_localize=True,
num_com_centers=p['num_com_centers'],
maa=p['maa'],
ahpthr=p['ahpthr'],
out_file_name=p['out_file_name'],
decay_filtering=p['decay_filtering'],
save_all=p['save_all'],
amp_evaluation_time=p['amp_evaluation_time'],
spk_evaluation_time=p['spk_evaluation_time'])
H.DetectFromRaw(load=True, tInc=int(p['t_inc']))
sorted_file = str(output_folder / 'HS2_sorted.hdf5')
if (not H.spikes.empty):
C = hs.HSClustering(H)
C.ShapePCA(pca_ncomponents=p['pca_ncomponents'],
pca_whiten=p['pca_whiten'])
C.CombinedClustering(alpha=p['clustering_alpha'],
cluster_subset=p['clustering_subset'],
bandwidth=p['clustering_bandwidth'],
bin_seeding=p['clustering_bin_seeding'],
n_jobs=p['clustering_n_jobs'],
min_bin_freq=p['clustering_min_bin_freq'])
else:
C = hs.HSClustering(H)
if p['filter_duplicates']:
uids = C.spikes.cl.unique()
for u in uids:
s = C.spikes[C.spikes.cl == u].t.diff(
) < p['spk_evaluation_time'] / 1000 * Probe.fps
C.spikes = C.spikes.drop(s.index[s])
if verbose:
print('Saving to', sorted_file)
C.SaveHDF5(sorted_file, sampling=Probe.fps)
