def run(self):
code=''.join(random.choice(string.ascii_uppercase) for x in range(10))
tmpdir=os.environ.get('TEMPDIR','/tmp')+'/ironclust-tmp-'+code
num_workers=os.environ.get('NUM_WORKERS',2)
try:
recording=si.MdaRecordingExtractor(self.recording_dir)
if len(self.channels)>0:
recording=si.SubRecordingExtractor(parent_recording=recording,channel_ids=self.channels)
if not os.path.exists(tmpdir):
os.mkdir(tmpdir)
sorting=sf.sorters.spyking_circus(
recording=recording,
output_folder=tmpdir,
probe_file=None,
file_name=None,
detect_sign=self.detect_sign,
adjacency_radius=self.adjacency_radius,
spike_thresh=self.spike_thresh,
template_width_ms=self.template_width_ms,
filter=self.filter,
merge_spikes=True,
n_cores=num_workers,
electrode_dimensions=None,
whitening_max_elts=self.whitening_max_elts,
clustering_max_elts=self.clustering_max_elts
)
si.MdaSortingExtractor.writeSorting(sorting=sorting,save_path=self.firings_out)
except:
if os.path.exists(tmpdir):
shutil.rmtree(tmpdir)
raise
shutil.rmtree(tmpdir)
def run(self):
code = ''.join(random.choice(string.ascii_uppercase)
for x in range(10))
tmpdir = os.environ.get('TEMPDIR', '/tmp')+'/yass-tmp-'+code
#num_workers = os.environ.get('NUM_WORKERS', 1)
#print('num_workers: {}'.format(num_workers))
try:
recording = se.MdaRecordingExtractor(self.recording_dir)
if len(self.channels) > 0:
recording = se.SubRecordingExtractor(
parent_recording=recording, channel_ids=self.channels)
if not os.path.exists(tmpdir):
os.mkdir(tmpdir)
sorting, yaml_file = yass_helper(
recording=recording,
output_folder=tmpdir,
probe_file=None,
file_name=None,
detect_sign=self.detect_sign,
adjacency_radius=self.adjacency_radius,
template_width_ms=self.template_width_ms,
filter=self.filter)
se.MdaSortingExtractor.writeSorting(
sorting=sorting, save_path=self.firings_out)
#shutil.copyfile(yaml_file, self.paramfile_out)
except:
if os.path.exists(tmpdir):
shutil.rmtree(tmpdir)
raise
if not getattr(self, '_keep_temp_files', False):
shutil.rmtree(tmpdir)
def test_remove_bad_channels():
rec, sort = se.example_datasets.toy_example(dump_folder='test', dumpable=True, duration=2, num_channels=4, seed=0)
rec_rm = remove_bad_channels(rec, bad_channel_ids=[0])
assert 0 not in rec_rm.get_channel_ids()
rec_rm = remove_bad_channels(rec, bad_channel_ids=[1, 2])
assert 1 not in rec_rm.get_channel_ids() and 2 not in rec_rm.get_channel_ids()
check_dumping(rec_rm)
shutil.rmtree('test')
timeseries = np.random.randn(4, 60000)
timeseries[1] = 10 * timeseries[1]
rec_np = se.NumpyRecordingExtractor(timeseries=timeseries, sampling_frequency=30000)
rec_np.set_channel_locations(np.ones((rec_np.get_num_channels(), 2)))
se.MdaRecordingExtractor.write_recording(rec_np, 'test')
rec = se.MdaRecordingExtractor('test')
rec_rm = remove_bad_channels(rec, bad_channel_ids=None, bad_threshold=2)
assert 1 not in rec_rm.get_channel_ids()
check_dumping(rec_rm)
rec_rm = remove_bad_channels(rec, bad_channel_ids=None, bad_threshold=2, seconds=0.1)
assert 1 not in rec_rm.get_channel_ids()
check_dumping(rec_rm)
rec_rm = remove_bad_channels(rec, bad_channel_ids=None, bad_threshold=2, seconds=10)
assert 1 not in rec_rm.get_channel_ids()
check_dumping(rec_rm)
shutil.rmtree('test')
def run(self):
ironclust_src=os.environ.get('IRONCLUST_SRC',None)
if not ironclust_src:
raise Exception('Environment variable not set: IRONCLUST_SRC')
code=''.join(random.choice(string.ascii_uppercase) for x in range(10))
tmpdir=os.environ.get('TEMPDIR','/tmp')+'/ironclust-tmp-'+code
try:
recording=si.MdaRecordingExtractor(self.recording_dir)
if len(self.channels)>0:
recording=si.SubRecordingExtractor(parent_recording=recording,channel_ids=self.channels)
if not os.path.exists(tmpdir):
os.mkdir(tmpdir)
sorting=sf.sorters.ironclust(
recording=recording,
tmpdir=tmpdir, ## TODO
detect_sign=self.detect_sign,
adjacency_radius=self.adjacency_radius,
detect_threshold=self.detect_threshold,
merge_thresh=self.merge_thresh,
freq_min=self.freq_min,
freq_max=self.freq_max,
pc_per_chan=self.pc_per_chan,
prm_template_name=self.prm_template_name,
ironclust_src=ironclust_src
)
si.MdaSortingExtractor.writeSorting(sorting=sorting,save_path=self.firings_out)
except:
if os.path.exists(tmpdir):
shutil.rmtree(tmpdir)
raise
shutil.rmtree(tmpdir)
def run(self):
recording=si.MdaRecordingExtractor(dataset_directory=self.recording_dir,download=False)
if len(self.channels)>0:
recording=si.SubRecordingExtractor(parent_recording=recording,channel_ids=self.channels)
sorting=si.MdaSortingExtractor(firings_file=self.firings)
sw.CrossCorrelogramsWidget(samplerate=recording.getSamplingFrequency(),sorting=sorting).plot()
fname=save_plot(self.plot_out)
def run(self):
R0=si.MdaRecordingExtractor(dataset_directory=self.recording_dir,download=True)
if (self.channel_ids) and (len(self.channel_ids)>0):
R0=si.SubRecordingExtractor(parent_recording=R0,channel_ids=self.channel_ids)
recording=sw.lazyfilters.bandpass_filter(recording=R0,freq_min=300,freq_max=6000)
sorting=si.MdaSortingExtractor(firings_file=self.firings)
ef=int(1e6)
recording_sub=si.SubRecordingExtractor(parent_recording=recording,start_frame=0,end_frame=ef)
recording_sub=MemoryRecordingExtractor(parent_recording=recording_sub)
sorting_sub=si.SubSortingExtractor(parent_sorting=sorting,start_frame=0,end_frame=ef)
unit_ids=self.unit_ids
if (not unit_ids) or (len(unit_ids)==0):
unit_ids=sorting.getUnitIds()
channel_noise_levels=compute_channel_noise_levels(recording=recording)
print('computing templates...')
templates=compute_unit_templates(recording=recording_sub,sorting=sorting_sub,unit_ids=unit_ids)
print('.')
ret=[]
for i,unit_id in enumerate(unit_ids):
template=templates[i]
info0=dict()
info0['unit_id']=int(unit_id)
info0['snr']=compute_template_snr(template,channel_noise_levels)
peak_channel_index=np.argmax(np.max(np.abs(template),axis=1))
info0['peak_channel']=int(recording.getChannelIds()[peak_channel_index])
train=sorting.getUnitSpikeTrain(unit_id=unit_id)
info0['num_events']=int(len(train))
info0['firing_rate']=float(len(train)/(recording.getNumFrames()/recording.getSamplingFrequency()))
ret.append(info0)
write_json_file(self.json_out,ret)
def real(name='franklab_tetrode', download=True):
if name == 'franklab_tetrode':
dsdir = 'kbucket://b5ecdf1474c5/datasets/neuron_paper/franklab_tetrode'
IX = se.MdaRecordingExtractor(dir_path=dsdir)
return (IX, None)
else:
raise Exception('Unrecognized name for real dataset: ' + name)
def run(self):
code = ''.join(
random.choice(string.ascii_uppercase) for x in range(10))
tmpdir = os.environ.get('TEMPDIR', '/tmp') + '/kilosort-tmp-' + code
try:
recording = si.MdaRecordingExtractor(self.recording_dir)
if len(self.channels) > 0:
recording = si.SubRecordingExtractor(
parent_recording=recording, channel_ids=self.channels)
if not os.path.exists(tmpdir):
os.mkdir(tmpdir)
sorting = sorters.kilosort(recording=recording,
tmpdir=tmpdir,
detect_sign=self.detect_sign,
adjacency_radius=self.adjacency_radius,
detect_threshold=self.detect_threshold,
merge_thresh=self.merge_thresh,
freq_min=self.freq_min,
freq_max=self.freq_max,
pc_per_chan=self.pc_per_chan)
si.MdaSortingExtractor.writeSorting(sorting=sorting,
save_path=self.firings_out)
except:
if os.path.exists(tmpdir):
shutil.rmtree(tmpdir)
raise
shutil.rmtree(tmpdir)
def run(self):
ret={}
recording=si.MdaRecordingExtractor(dataset_directory=self.recording_dir,download=False)
ret['samplerate']=recording.getSamplingFrequency()
ret['num_channels']=len(recording.getChannelIds())
ret['duration_sec']=recording.getNumFrames()/ret['samplerate']
write_json_file(self.json_out,ret)
def spikeforest_sort(
recording_dirname, # The recording extractor
sorter,
sorting_params,
_force_run=False,
_force_save=False
):
recording_signature=kb.computeDirHash(recording_dirname)
signature_obj=dict(
sorter_name=sorter.name,
sorter_version=sorter.version,
recording=recording_signature,
sorting_params=sorting_params
)
if not _force_run:
print('Looking up in cache...')
firings=kb.realizeFile(key=signature_obj)
if firings:
print('Found')
if _force_save:
print('Saving')
kb.saveFile(fname=firings,key=signature_obj)
return si.MdaSortingExtractor(firings_file=firings)
recording=si.MdaRecordingExtractor(recording_dirname)
sorting=sorter(recording=recording,**sorting_params)
si.MdaSortingExtractor.writeSorting(sorting=sorting,save_path='tmp_firings.mda')
kb.saveFile(fname='tmp_firings.mda',key=signature_obj)
return sorting
def run(self):
recording=si.MdaRecordingExtractor(dataset_directory=self.recording_dir)
if len(self.channels)>0:
recording=si.SubRecordingExtractor(parent_recording=recording,channel_ids=self.channels)
sorting=si.MdaSortingExtractor(firings_file=self.firings)
sw.UnitWaveformsWidget(recording=recording,sorting=sorting).plot()
fname=save_plot(self.plot_out)
def recordingExtractor(self, download=False):
X = si.MdaRecordingExtractor(dataset_directory=self.directory(),
download=download)
if 'channels' in self._obj:
if self._obj['channels']:
X = si.SubRecordingExtractor(parent_recording=X,
channel_ids=self._obj['channels'])
return X
def run(self): R0=si.MdaRecordingExtractor(dataset_directory=self.recording_dir,download=False) R=st.filters.bandpass_filter(recording=R0,freq_min=300,freq_max=6000) N=R.getNumFrames() N2=int(N/2) channels=R.getChannelIds() if len(channels)>20: channels=channels[0:20] sw.TimeseriesWidget(recording=R,trange=[N2-4000,N2+0],channels=channels,width=12,height=5).plot() save_plot(self.jpg_out)
def createSession(self):
recording = se.MdaRecordingExtractor(
dataset_directory=self._recording_directory, download=False)
recording = se.SubRecordingExtractor(parent_recording=recording,
start_frame=0,
end_frame=10000)
recording = se.NumpyRecordingExtractor(
timeseries=recording.getTraces(),
samplerate=recording.getSamplingFrequency())
W = SFW.TimeseriesWidget(recording=recording)
return W
def test_mda_extractor(self):
path1 = self.test_dir + '/mda'
path2 = path1 + '/firings_true.mda'
se.MdaRecordingExtractor.write_recording(self.RX, path1)
se.MdaSortingExtractor.write_sorting(self.SX, path2)
RX_mda = se.MdaRecordingExtractor(path1)
SX_mda = se.MdaSortingExtractor(path2)
self._check_recording_return_types(RX_mda)
self._check_recordings_equal(self.RX, RX_mda)
self._check_sorting_return_types(SX_mda)
self._check_sortings_equal(self.SX, SX_mda)
def create_dumpable_extractors_from_existing(folder, RX, SX):
folder = Path(folder)
if 'location' not in RX.get_shared_channel_property_names():
RX.set_channel_locations(np.random.randn(RX.get_num_channels(), 2))
se.MdaRecordingExtractor.write_recording(RX, folder)
RX_mda = se.MdaRecordingExtractor(folder)
se.NpzSortingExtractor.write_sorting(SX, folder / 'sorting.npz')
SX_npz = se.NpzSortingExtractor(folder / 'sorting.npz')
return RX_mda, SX_npz
def run(self):
R0=si.MdaRecordingExtractor(dataset_directory=self.recording_dir,download=True)
R=st.filters.bandpass_filter(recording=R0,freq_min=300,freq_max=6000)
S=si.MdaSortingExtractor(firings_file=self.firings)
channels=R.getChannelIds()
if len(channels)>20:
channels=channels[0:20]
units=S.getUnitIds()
if len(units)>20:
units=units[::int(len(units)/20)]
sw.UnitWaveformsWidget(recording=R,sorting=S,channels=channels,unit_ids=units).plot()
save_plot(self.jpg_out)
def yass_example(download=True, set_id=1):
if set_id in range(1, 7):
dsdir = 'kbucket://15734439d8cf/groundtruth/visapy_mea/set{}'.format(
set_id)
IX = se.MdaRecordingExtractor(dataset_directory=dsdir,
download=download)
path1 = os.path.join(dsdir, 'firings_true.mda')
print(path1)
OX = se.MdaSortingExtractor(path1)
return (IX, OX)
else:
raise Exception(
'Invalid ID for yass_example {} is not betewen 1..6'.format(
set_id))
def toy_example(duration=10,
num_channels=4,
sampling_frequency=30000.0,
K=10,
dumpable=False,
dump_folder=None,
seed=None):
upsamplefac = 13
waveforms, geom = synthesize_random_waveforms(K=K,
M=num_channels,
average_peak_amplitude=-100,
upsamplefac=upsamplefac,
seed=seed)
times, labels = synthesize_random_firings(
K=K,
duration=duration,
sampling_frequency=sampling_frequency,
seed=seed)
labels = labels.astype(np.int64)
SX = se.NumpySortingExtractor()
SX.set_times_labels(times, labels)
X = synthesize_timeseries(sorting=SX,
waveforms=waveforms,
noise_level=10,
sampling_frequency=sampling_frequency,
duration=duration,
waveform_upsamplefac=upsamplefac,
seed=seed)
SX.set_sampling_frequency(sampling_frequency)
RX = se.NumpyRecordingExtractor(timeseries=X,
sampling_frequency=sampling_frequency,
geom=geom)
RX.is_filtered = True
if dumpable:
if dump_folder is None:
dump_folder = 'toy_example'
dump_folder = Path(dump_folder)
se.MdaRecordingExtractor.write_recording(RX, dump_folder)
RX = se.MdaRecordingExtractor(dump_folder)
se.NpzSortingExtractor.write_sorting(SX, dump_folder / 'sorting.npz')
SX = se.NpzSortingExtractor(dump_folder / 'sorting.npz')
return RX, SX
def run(self):
recording = si.MdaRecordingExtractor(self.dataset_dir)
num_workers = int(os.environ.get('NUM_WORKERS', -1))
if num_workers <= 0: num_workers = None
sorting = sf.sorters.mountainsort4(
recording=recording,
detect_sign=self.detect_sign,
adjacency_radius=self.adjacency_radius,
freq_min=self.freq_min,
freq_max=self.freq_max,
whiten=self.whiten,
clip_size=self.clip_size,
detect_threshold=self.detect_threshold,
detect_interval=self.detect_interval,
noise_overlap_threshold=self.noise_overlap_threshold,
num_workers=num_workers)
si.MdaSortingExtractor.writeSorting(sorting=sorting,
save_path=self.firings_out)
def run(self):
import spikeextractors as se
import spiketoolkit as st
import ml_ms4alg
print('MountainSort4......')
recording = se.MdaRecordingExtractor(self.recording_dir)
num_workers = os.environ.get('NUM_WORKERS', None)
if num_workers:
num_workers = int(num_workers)
# Bandpass filter
if self.freq_min or self.freq_max:
recording = st.preprocessing.bandpass_filter(
recording=recording,
freq_min=self.freq_min,
freq_max=self.freq_max)
# Whiten
if self.whiten:
recording = st.preprocessing.whiten(recording=recording)
# Sort
sorting = ml_ms4alg.mountainsort4(
recording=recording,
detect_sign=self.detect_sign,
adjacency_radius=self.adjacency_radius,
clip_size=self.clip_size,
detect_threshold=self.detect_threshold,
detect_interval=self.detect_interval,
num_workers=num_workers)
# Curate
# if self.noise_overlap_threshold is not None:
# sorting=ml_ms4alg.mountainsort4_curation(
# recording=recording,
# sorting=sorting,
# noise_overlap_threshold=self.noise_overlap_threshold
# )
se.MdaSortingExtractor.writeSorting(sorting=sorting,
save_path=self.firings_out)
def create_dumpable_extractors(folder,
duration=10,
num_channels=4,
sampling_frequency=30000.0,
K=10,
seed=None):
RX, SX = toy_example(duration=duration,
num_channels=num_channels,
K=K,
sampling_frequency=sampling_frequency,
seed=seed)
folder = Path(folder)
se.MdaRecordingExtractor.write_recording(RX, folder)
RX_mda = se.MdaRecordingExtractor(folder)
se.NpzSortingExtractor.write_sorting(SX, folder / 'sorting.npz')
SX_npz = se.NpzSortingExtractor(folder / 'sorting.npz')
return RX_mda, SX_npz
def test_dump_load_multi_sub_extractor(self):
# generate dumpable formats
path1 = self.test_dir + '/mda'
path2 = path1 + '/firings_true.mda'
se.MdaRecordingExtractor.write_recording(self.RX, path1)
se.MdaSortingExtractor.write_sorting(self.SX, path2)
RX_mda = se.MdaRecordingExtractor(path1)
SX_mda = se.MdaSortingExtractor(path2)
RX_multi_chan = se.MultiRecordingChannelExtractor(recordings=[RX_mda, RX_mda, RX_mda])
check_dumping(RX_multi_chan)
RX_multi_time = se.MultiRecordingTimeExtractor(recordings=[RX_mda, RX_mda, RX_mda], )
check_dumping(RX_multi_time)
RX_multi_chan = se.SubRecordingExtractor(RX_mda, channel_ids=[0, 1])
check_dumping(RX_multi_chan)
SX_sub = se.SubSortingExtractor(SX_mda, unit_ids=[1, 2])
check_dumping(SX_sub)
SX_multi = se.MultiSortingExtractor(sortings=[SX_mda, SX_mda, SX_mda])
check_dumping(SX_multi)
def run(self):
import spikewidgets as sw
R0=si.MdaRecordingExtractor(dataset_directory=self.recording_dir,download=True)
if (self.channel_ids) and (len(self.channel_ids)>0):
R0=si.SubRecordingExtractor(parent_recording=R0,channel_ids=self.channel_ids)
recording = R0
# recording=sw.lazyfilters.bandpass_filter(recording=R0,freq_min=300,freq_max=6000)
sorting=si.MdaSortingExtractor(firings_file=self.firings)
unit_ids=self.unit_ids
if (not unit_ids) or (len(unit_ids)==0):
unit_ids=sorting.getUnitIds()
channel_noise_levels=compute_channel_noise_levels(recording=recording)
# No longer use subset to compute the templates
templates=compute_unit_templates(recording=recording,sorting=sorting,unit_ids=unit_ids,max_num=100)
ret=[]
for i,unit_id in enumerate(unit_ids):
template=templates[i]
max_p2p_amps_on_channels=np.max(template,axis=1)-np.min(template,axis=1)
peak_channel_index=np.argmax(max_p2p_amps_on_channels)
peak_channel=recording.getChannelIds()[peak_channel_index]
R1=si.SubRecordingExtractor(parent_recording=recording,channel_ids=[peak_channel_index])
R1f=sw.lazyfilters.bandpass_filter(recording=R1,freq_min=300,freq_max=6000)
templates2=compute_unit_templates(recording=R1f,sorting=sorting,unit_ids=[unit_id],max_num=100)
template2=templates2[0]
info0=dict()
info0['unit_id']=int(unit_id)
info0['snr']=np.max(np.abs(template2))/channel_noise_levels[peak_channel_index]
#info0['snr']=compute_template_snr(template,channel_noise_levels)
#peak_channel_index=np.argmax(np.max(np.abs(template),axis=1))
info0['peak_channel']=int(recording.getChannelIds()[peak_channel])
train=sorting.getUnitSpikeTrain(unit_id=unit_id)
info0['num_events']=int(len(train))
info0['firing_rate']=float(len(train)/(recording.getNumFrames()/recording.getSamplingFrequency()))
ret.append(info0)
write_json_file(self.json_out,ret)
def run(self):
recording=si.MdaRecordingExtractor(self.recording_dir)
if len(self.channels)>0:
recording=si.SubRecordingExtractor(parent_recording=recording,channel_ids=self.channels)
num_workers=os.environ.get('NUM_WORKERS',None)
if num_workers:
num_workers=int(num_workers)
sorting=sf.sorters.mountainsort4(
recording=recording,
detect_sign=self.detect_sign,
adjacency_radius=self.adjacency_radius,
freq_min=self.freq_min,
freq_max=self.freq_max,
whiten=self.whiten,
clip_size=self.clip_size,
detect_threshold=self.detect_threshold,
detect_interval=self.detect_interval,
noise_overlap_threshold=self.noise_overlap_threshold,
num_workers=num_workers
)
si.MdaSortingExtractor.writeSorting(sorting=sorting,save_path=self.firings_out)
def run(self):
ironclust_path = os.environ.get('IRONCLUST_PATH', None)
if not ironclust_path:
raise Exception('Environment variable not set: IRONCLUST_PATH')
code = ''.join(
random.choice(string.ascii_uppercase) for x in range(10))
tmpdir = os.environ.get('TEMPDIR', '/tmp') + '/ironclust-tmp-' + code
try:
recording = se.MdaRecordingExtractor(self.recording_dir)
params = read_dataset_params(self.recording_dir)
if len(self.channels) > 0:
recording = se.SubRecordingExtractor(
parent_recording=recording, channel_ids=self.channels)
if not os.path.exists(tmpdir):
os.mkdir(tmpdir)
sorting = ironclust_helper(
recording=recording,
tmpdir=tmpdir,
detect_sign=self.detect_sign,
adjacency_radius=self.adjacency_radius,
detect_threshold=self.detect_threshold,
merge_thresh=self.merge_thresh,
freq_min=self.freq_min,
freq_max=self.freq_max,
pc_per_chan=self.pc_per_chan,
prm_template_name=self.prm_template_name,
ironclust_path=ironclust_path,
params=params,
)
se.MdaSortingExtractor.writeSorting(sorting=sorting,
save_path=self.firings_out)
except:
if os.path.exists(tmpdir):
if not getattr(self, '_keep_temp_files', False):
shutil.rmtree(tmpdir)
raise
if not getattr(self, '_keep_temp_files', False):
shutil.rmtree(tmpdir)
def getRecording(self,download=True): ret=si.MdaRecordingExtractor(dataset_directory=self._kbucket_path,download=download) return ret
def toy_example(duration: float = 10.,
num_channels: int = 4,
sampling_frequency: float = 30000.,
K: int = 10,
dumpable: bool = False,
dump_folder: Optional[Union[str, Path]] = None,
seed: Optional[int] = None):
"""
Create toy recording and sorting extractors.
Parameters
----------
duration: float
Duration in s (default 10)
num_channels: int
Number of channels (default 4)
sampling_frequency: float
Sampling frequency (default 30000)
K: int
Number of units (default 10)
dumpable: bool
If True, objects are dumped to file and become 'dumpable'
dump_folder: str or Path
Path to dump folder (if None, 'test' is used
seed: int
Seed for random initialization
Returns
-------
recording: RecordingExtractor
The output recording extractor. If dumpable is False it's a NumpyRecordingExtractor, otherwise it's an
MdaRecordingExtractor
sorting: SortingExtractor
The output sorting extractor. If dumpable is False it's a NumpyRecordingExtractor, otherwise it's an
NpzSortingExtractor
"""
upsamplefac = 13
waveforms, geom = synthesize_random_waveforms(K=K,
M=num_channels,
average_peak_amplitude=-100,
upsamplefac=upsamplefac,
seed=seed)
times, labels = synthesize_random_firings(
K=K,
duration=duration,
sampling_frequency=sampling_frequency,
seed=seed)
labels = labels.astype(np.int64)
SX = se.NumpySortingExtractor()
SX.set_times_labels(times, labels)
X = synthesize_timeseries(sorting=SX,
waveforms=waveforms,
noise_level=10,
sampling_frequency=sampling_frequency,
duration=duration,
waveform_upsamplefac=upsamplefac,
seed=seed)
SX.set_sampling_frequency(sampling_frequency)
RX = se.NumpyRecordingExtractor(timeseries=X,
sampling_frequency=sampling_frequency,
geom=geom)
RX.is_filtered = True
if dumpable:
if dump_folder is None:
dump_folder = 'toy_example'
dump_folder = Path(dump_folder)
se.MdaRecordingExtractor.write_recording(RX, dump_folder)
RX = se.MdaRecordingExtractor(dump_folder)
se.NpzSortingExtractor.write_sorting(SX, dump_folder / 'sorting.npz')
SX = se.NpzSortingExtractor(dump_folder / 'sorting.npz')
return RX, SX