def write_results(path, params, extension):
result = io.get_results(params, extension)
spikes = [numpy.zeros(0, dtype=numpy.uint64)]
clusters = [numpy.zeros(0, dtype=numpy.uint32)]
amplitudes = [numpy.zeros(0, dtype=numpy.double)]
N_tm = len(result['spiketimes'])
has_purity = test_if_purity(params, extension)
rpvs = []
if prelabelling:
labels = []
norms = io.load_data(params, 'norm-templates', extension)
norms = norms[:len(norms) // 2]
if has_purity:
purity = io.load_data(params, 'purity', extension)
for key in list(result['spiketimes'].keys()):
temp_id = int(key.split('_')[-1])
myspikes = result['spiketimes'].pop(key).astype(numpy.uint64)
spikes.append(myspikes)
myamplitudes = result['amplitudes'].pop(key).astype(numpy.double)
amplitudes.append(myamplitudes[:, 0])
clusters.append(temp_id *
numpy.ones(len(myamplitudes), dtype=numpy.uint32))
rpv = get_rpv(myspikes, params.data_file.sampling_rate)
rpvs += [[temp_id, rpv]]
if prelabelling:
if has_purity:
if rpv <= rpv_threshold:
if purity[temp_id] > 0.75:
labels += [[temp_id, 'good']]
else:
if purity[temp_id] > 0.75:
labels += [[temp_id, 'mua']]
else:
labels += [[temp_id, 'noise']]
else:
median_amp = numpy.median(myamplitudes[:, 0])
std_amp = numpy.std(myamplitudes[:, 0])
if rpv <= rpv_threshold and numpy.abs(median_amp -
1) < 0.25:
labels += [[temp_id, 'good']]
else:
if median_amp < 0.5:
labels += [[temp_id, 'mua']]
elif norms[temp_id] < 0.1:
labels += [[temp_id, 'noise']]
if export_all:
print_and_log([
"Last %d templates are unfitted spikes on all electrodes" % N_e
], 'info', logger)
garbage = io.load_data(params, 'garbage', extension)
for key in list(garbage['gspikes'].keys()):
elec_id = int(key.split('_')[-1])
data = garbage['gspikes'].pop(key).astype(numpy.uint64)
spikes.append(data)
amplitudes.append(numpy.ones(len(data)))
clusters.append((elec_id + N_tm) *
numpy.ones(len(data), dtype=numpy.uint32))
if prelabelling:
f = open(os.path.join(output_path, 'cluster_group.tsv'), 'w')
f.write('cluster_id\tgroup\n')
for l in labels:
f.write('%s\t%s\n' % (l[0], l[1]))
f.close()
# f = open(os.path.join(output_path, 'cluster_rpv.tsv'), 'w')
# f.write('cluster_id\trpv\n')
# for l in rpvs:
# f.write('%s\t%s\n' % (l[0], l[1]))
# f.close()
spikes = numpy.concatenate(spikes).astype(numpy.uint64)
amplitudes = numpy.concatenate(amplitudes).astype(numpy.double)
clusters = numpy.concatenate(clusters).astype(numpy.uint32)
idx = numpy.argsort(spikes)
numpy.save(os.path.join(output_path, 'spike_templates'), clusters[idx])
numpy.save(os.path.join(output_path, 'spike_times'), spikes[idx])
numpy.save(os.path.join(output_path, 'amplitudes'), amplitudes[idx])
return
def write_templates(path, params, extension):
max_loc_channel = get_max_loc_channel(params, extension)
templates = io.load_data(params, 'templates', extension)
N_tm = templates.shape[1] // 2
nodes, edges = get_nodes_and_edges(params)
if sparse_export:
n_channels_max = 0
for t in range(N_tm):
data = numpy.sum(
numpy.sum(templates[:, t].toarray().reshape(N_e, N_t), 1)
!= 0)
if data > n_channels_max:
n_channels_max = data
else:
n_channels_max = N_e
if export_all:
to_write_sparse = numpy.zeros((N_tm + N_e, N_t, n_channels_max),
dtype=numpy.float32)
mapping_sparse = -1 * numpy.ones(
(N_tm + N_e, n_channels_max), dtype=numpy.int32)
else:
to_write_sparse = numpy.zeros((N_tm, N_t, n_channels_max),
dtype=numpy.float32)
mapping_sparse = -1 * numpy.ones(
(N_tm, n_channels_max), dtype=numpy.int32)
has_purity = test_if_purity(params, extension)
if has_purity:
purity = io.load_data(params, 'purity', extension)
f = open(os.path.join(output_path, 'cluster_purity.tsv'), 'w')
f.write('cluster_id\tpurity\n')
for i in range(N_tm):
f.write('%d\t%g\n' % (i, purity[i]))
f.close()
for t in range(N_tm):
tmp = templates[:, t].toarray().reshape(N_e, N_t).T
x, y = tmp.nonzero()
nb_loc = len(numpy.unique(y))
if sparse_export:
all_positions = numpy.zeros(y.max() + 1, dtype=numpy.int32)
all_positions[numpy.unique(y)] = numpy.arange(
nb_loc, dtype=numpy.int32)
pos = all_positions[y]
to_write_sparse[t, x, pos] = tmp[x, y]
mapping_sparse[t, numpy.arange(nb_loc)] = numpy.unique(y)
else:
pos = y
to_write_sparse[t, x, pos] = tmp[x, y]
if export_all:
garbage = io.load_data(params, 'garbage', extension)
for t in range(N_tm, N_tm + N_e):
elec = t - N_tm
spikes = garbage['gspikes'].pop('elec_%d' % elec).astype(
numpy.int64)
spikes = numpy.random.permutation(spikes)[:100]
mapping_sparse[t, 0] = t - N_tm
waveform = io.get_stas(params,
times_i=spikes,
labels_i=np.ones(len(spikes)),
src=elec,
neighs=[elec],
nodes=nodes,
mean_mode=True)
nb_loc = 1
if sparse_export:
to_write_sparse[t, :, 0] = waveform
else:
to_write_sparse[t, :, elec] = waveform
numpy.save(os.path.join(output_path, 'templates'), to_write_sparse)
if sparse_export:
numpy.save(os.path.join(output_path, 'template_ind'),
mapping_sparse)
return N_tm