def __init__(self, tempdir):
np.random.seed(42)
self.tmp_dir = tempdir
p = Path(self.tmp_dir)
self.ns = 100
self.nsamp = 25
self.ncmax = 42
self.nc = 10
self.nt = 5
self.ncd = 1000
np.save(p / 'spike_times.npy',
.01 * np.cumsum(nr.exponential(size=self.ns)))
np.save(p / 'spike_clusters.npy',
nr.randint(low=1, high=self.nt, size=self.ns))
shutil.copy(p / 'spike_clusters.npy', p / 'spike_templates.npy')
np.save(p / 'amplitudes.npy',
nr.uniform(low=0.5, high=1.5, size=self.ns))
np.save(p / 'channel_positions.npy', np.c_[np.arange(self.nc),
np.zeros(self.nc)])
np.save(p / 'templates.npy',
np.random.normal(size=(self.nt, 50, self.nc)))
np.save(p / 'similar_templates.npy',
np.tile(np.arange(self.nt), (self.nt, 1)))
np.save(p / 'channel_map.npy', np.c_[np.arange(self.nc)])
np.save(p / 'channel_probe.npy', np.zeros(self.nc))
np.save(p / 'whitening_mat.npy', np.eye(self.nc, self.nc))
np.save(p / '_phy_spikes_subset.channels.npy',
np.zeros([self.ns, self.ncmax]))
np.save(p / '_phy_spikes_subset.spikes.npy', np.zeros([self.ns]))
np.save(p / '_phy_spikes_subset.waveforms.npy',
np.zeros([self.ns, self.nsamp, self.ncmax]))
_write_tsv_simple(p / 'cluster_group.tsv', 'group', {
2: 'good',
3: 'mua',
5: 'noise'
})
_write_tsv_simple(
p / 'cluster_Amplitude.tsv',
field_name='Amplitude',
data={str(n): np.random.rand() * 120
for n in np.arange(self.nt)})
with open(p / 'probes.description.txt', 'w+') as fid:
fid.writelines(['label\n'])
# Raw data
self.dat_path = p / 'rawdata.npy'
np.save(self.dat_path, np.random.normal(size=(self.ncd, self.nc)))
# LFP data.
lfdata = (100 * np.random.normal(size=(1000, self.nc))).astype(
np.int16)
with (p / 'mydata.lf.bin').open('wb') as f:
lfdata.tofile(f)
self.files = os.listdir(self.tmp_dir)
def write_cluster_data(self):
"""We load all cluster metadata from TSV files, renumber the clusters,
merge the dictionaries, and save in a new merged TSV file. """
cluster_data = [
'cluster_Amplitude.tsv', 'cluster_ContamPct.tsv',
'cluster_KSLabel.tsv'
]
for fn in cluster_data:
metadata = {}
for subdir, offset in zip(self.subdirs, self.cluster_offsets):
try:
field_name, metadata_loc = _read_tsv_simple(subdir / fn)
except ValueError:
# Skipping non-existing file.
continue
for k, v in metadata_loc.items():
metadata[k + offset] = v
if metadata:
_write_tsv_simple(self.out_dir / fn, field_name, metadata)
def __init__(self, tempdir):
self.tmp_dir = tempdir
p = Path(self.tmp_dir)
self.ns = 100
self.nc = 10
self.nt = 5
self.ncd = 1000
np.save(p / 'spike_times.npy',
.01 * np.cumsum(nr.exponential(size=self.ns)))
np.save(p / 'spike_clusters.npy',
nr.randint(low=10, high=10 + self.nt, size=self.ns))
shutil.copy(p / 'spike_clusters.npy', p / 'spike_templates.npy')
np.save(p / 'amplitudes.npy',
nr.uniform(low=0.5, high=1.5, size=self.ns))
np.save(p / 'channel_positions.npy', np.c_[np.arange(self.nc),
np.zeros(self.nc)])
np.save(p / 'templates.npy',
np.random.normal(size=(self.nt, 50, self.nc)))
np.save(p / 'similar_templates.npy',
np.tile(np.arange(self.nt), (self.nt, 1)))
np.save(p / 'channel_map.npy', np.c_[np.arange(self.nc)])
_write_tsv_simple(p / 'cluster_group.tsv', 'group', {
2: 'good',
3: 'mua',
5: 'noise'
})
# Raw data
self.dat_path = p / 'rawdata.npy'
np.save(self.dat_path, np.random.normal(size=(self.ncd, self.nc)))
# LFP data.
lfdata = (100 * np.random.normal(size=(1000, self.nc))).astype(
np.int16)
with (p / 'mydata.lf.bin').open('wb') as f:
lfdata.tofile(f)
self.files = os.listdir(self.tmp_dir)
def save_metadata(filename, field_name, metadata):
"""Save metadata in a CSV file."""
return _write_tsv_simple(filename, field_name, metadata)