def make_catalogue_figure():
dataio = DataIO(dirname=dirname)
catalogue = dataio.load_catalogue(chan_grp=0)
clusters = catalogue['clusters']
geometry = dataio.get_geometry(chan_grp=0)
fig, ax = plt.subplots()
ax.set_title('Catalogue have 4 templates')
for i in range(clusters.size):
color = clusters[i]['color']
color = int32_to_rgba(color, mode='float')
waveforms = catalogue['centers0'][i:i + 1]
plot_waveforms_with_geometry(waveforms,
channels,
geometry,
ax=ax,
ratioY=3,
deltaX=50,
margin=50,
color=color,
linewidth=3,
alpha=1,
show_amplitude=True,
ratio_mad=8)
fig.savefig('../img/peeler_templates_for_animation.png')
def apply_peeler():
dataio = DataIO(dirname=dirname)
catalogue = dataio.load_catalogue(chan_grp=0)
peeler = Peeler(dataio)
peeler.change_params(catalogue=catalogue, chunksize=1024)
peeler.run(progressbar=True)
def preprocess_array(array_idx, output_dir, total_duration):
dataio = DataIO(dirname=output_dir, ch_grp=array_idx)
fullchain_kargs = {
'duration': total_duration,
'preprocessor': {
'highpass_freq': 250.,
'lowpass_freq': 3000.,
'smooth_size': 0,
'common_ref_removal': True,
'chunksize': 32768,
'lostfront_chunksize': 0,
'signalpreprocessor_engine': 'numpy',
}
}
cc = CatalogueConstructor(dataio=dataio, chan_grp=array_idx)
p = {}
p.update(fullchain_kargs['preprocessor'])
cc.set_preprocessor_params(**p)
# TODO offer noise esatimation duration somewhere
noise_duration = min(
10., fullchain_kargs['duration'],
dataio.get_segment_length(seg_num=0) / dataio.sample_rate * .99)
# ~ print('noise_duration', noise_duration)
t1 = time.perf_counter()
cc.estimate_signals_noise(seg_num=0, duration=noise_duration)
t2 = time.perf_counter()
print('estimate_signals_noise', t2 - t1)
t1 = time.perf_counter()
cc.run_signalprocessor(duration=fullchain_kargs['duration'],
detect_peak=False)
t2 = time.perf_counter()
print('run_signalprocessor', t2 - t1)
def test_good_events():
dataio = DataIO(dirname = 'datatest')
sigs = dataio.get_signals(seg_num=0)
peakdetector = PeakDetector(sigs)
peak_pos = peakdetector.detect_peaks(threshold=-4, peak_sign = '-', n_span = 2)
#~ peak_pos = peak_pos[:100]
#~ print(peak_pos)
waveforms = extract_peak_waveforms(sigs, peak_pos, peak_pos, -30,50)
keep = good_events(waveforms,upper_thr=5.,lower_thr=-5.)
#~ print(keep)
goods_wf = waveforms[keep]
bads_wf = waveforms[~keep]
fig, ax = pyplot.subplots()
#~ goods_wf.transpose().plot(ax =ax, color = 'g', lw = .3)
bads_wf.transpose().plot(ax =ax,color = 'r', lw = .3)
med = waveforms.median(axis=0)
mad = np.median(np.abs(waveforms-med),axis=0)*1.4826
limit1 = med+5*mad
limit2 = med-5*mad
med.plot(ax = ax, color = 'm', lw = 2)
limit1.plot(ax = ax, color = 'm')
limit2.plot(ax = ax, color = 'm')
def test_clustering():
dataio = DataIO(dirname = 'datatest')
sigs = dataio.get_signals(seg_num=0)
#peak
peakdetector = PeakDetector(sigs)
peak_pos = peakdetector.detect_peaks(threshold=-4, peak_sign = '-', n_span = 2)
#waveforms
waveformextractor = WaveformExtractor(peakdetector, n_left=-30, n_right=50)
limit_left, limit_right = waveformextractor.find_good_limits(mad_threshold = 1.1)
short_wf = waveformextractor.get_ajusted_waveforms()
print(short_wf.shape)
#clustering
clustering = Clustering(short_wf)
#PCA
features = clustering.project(method = 'pca', n_components = 5)
clustering.plot_projection(plot_density = True)
#Kmean
labels = clustering.find_clusters(7)
clustering.plot_projection(plot_density = True)
#ùake catalogue
catalogue = clustering.construct_catalogue()
clustering.plot_derivatives()
clustering.plot_catalogue()
clustering.merge_cluster(1,2)
clustering.split_cluster(1, 2)
def test_rectify_signals():
dataio = DataIO(dirname = 'datatest')
sigs = dataio.get_signals(seg_num=0)
retified_sigs = rectify_signals(normalize_signals(sigs), threshold = -4)
fig, ax = pyplot.subplots()
retified_sigs[3.14:3.22].plot(ax = ax)
ax.set_ylim(-20, 10)
def open_PeelerWindow(dirname, chan_grp):
dataio = DataIO(dirname=dirname)
initial_catalogue = dataio.load_catalogue(chan_grp=chan_grp)
app = pg.mkQApp()
win = PeelerWindow(dataio=dataio, catalogue=initial_catalogue)
win.show()
app.exec_()
def test_peakdetector():
dataio = DataIO(dirname = 'datatest')
sigs = dataio.get_signals(seg_num=0)
peakdetector = PeakDetector(sigs, seg_num=0)
peakdetector.detect_peaks(threshold=-4, peak_sign = '-', n_span = 2)
print(peakdetector.peak_pos.size)
peakdetector.detect_peaks(threshold=-5, peak_sign = '-', n_span = 5)
print(peakdetector.peak_pos.size)
def test_DataIO_probes():
# initialze dataio
if os.path.exists('test_DataIO'):
shutil.rmtree('test_DataIO')
dataio = DataIO(dirname='test_DataIO')
print(dataio)
localdir, filenames, params = download_dataset(name='olfactory_bulb')
dataio.set_data_source(type='RawData', filenames=filenames, **params)
probe_filename = 'A4x8-5mm-100-400-413-A32.prb'
dataio.download_probe(probe_filename)
dataio.download_probe('A4x8-5mm-100-400-413-A32')
#~ print(dataio.channel_groups)
#~ print(dataio.channels)
#~ print(dataio.info['probe_filename'])
assert dataio.nb_channel(0) == 8
assert probe_filename == dataio.info['probe_filename']
dataio = DataIO(dirname='test_DataIO')
print(dataio)
def test_peeler():
dataio = DataIO(dirname = 'datatest')
#~ dataio = DataIO(dirname = 'datatest_neo')
sigs = dataio.get_signals(seg_num=0)
#peak
peakdetector = PeakDetector(sigs)
peak_pos = peakdetector.detect_peaks(threshold=-4, peak_sign = '-', n_span = 5)
#waveforms
waveformextractor = WaveformExtractor(peakdetector, n_left=-30, n_right=50)
limit_left, limit_right = waveformextractor.find_good_limits(mad_threshold = 1.1)
#~ print(limit_left, limit_right)
short_wf = waveformextractor.get_ajusted_waveforms()
#~ print(short_wf.shape)
#clustering
clustering = Clustering(short_wf)
features = clustering.project(method = 'pca', n_components = 4)
clustering.find_clusters(8, order_clusters = True)
catalogue = clustering.construct_catalogue()
#~ clustering.plot_catalogue(sameax = False)
#~ clustering.plot_catalogue(sameax = True)
#~ clustering.merge_cluster(1, 2)
catalogue = clustering.construct_catalogue()
clustering.plot_catalogue(sameax = False)
#~ clustering.plot_catalogue(sameax = True)
#peeler
signals = peakdetector.normed_sigs
peeler = Peeler(signals, catalogue, limit_left, limit_right,
threshold=-5., peak_sign = '-', n_span = 5)
prediction0, residuals0 = peeler.peel()
prediction1, residuals1 = peeler.peel()
spiketrains = peeler.get_spiketrains()
print(spiketrains)
fig, axs = pyplot.subplots(nrows = 6, sharex = True)#, sharey = True)
axs[0].plot(signals)
axs[1].plot(prediction0)
axs[2].plot(residuals0)
axs[3].plot(prediction1)
axs[4].plot(residuals1)
for i in range(5):
axs[i].set_ylim(-25, 10)
peeler.plot_spiketrains(ax = axs[5])
def test_DataIO():
# initialze dataio
if os.path.exists('test_DataIO'):
shutil.rmtree('test_DataIO')
dataio = DataIO(dirname='test_DataIO')
print(dataio)
localdir, filenames, params = download_dataset(name='olfactory_bulb')
dataio.set_data_source(type='RawData', filenames=filenames, **params)
#~ dataio.set_channels(range(4))
dataio.set_manual_channel_group(range(14))
for seg_num in range(dataio.nb_segment):
for i_stop, sigs_chunk in dataio.iter_over_chunk(seg_num=seg_num, chunksize=1024):
assert sigs_chunk.shape[0] == 1024
assert sigs_chunk.shape[1] == 14
#~ print(seg_num, i_stop, sigs_chunk.shape)
#reopen existing
dataio = DataIO(dirname='test_DataIO')
print(dataio)
#~ exit()
for seg_num in range(dataio.nb_segment):
#~ print('seg_num', seg_num)
for i_stop, sigs_chunk in dataio.iter_over_chunk(seg_num=seg_num, chunksize=1024):
assert sigs_chunk.shape[0] == 1024
assert sigs_chunk.shape[1] == 14
def test_extract_noise_waveforms():
dataio = DataIO(dirname = 'datatest')
sigs = dataio.get_signals(seg_num=0)
peakdetector = PeakDetector(sigs)
peak_pos = peakdetector.detect_peaks(threshold=-4, peak_sign = '-', n_span = 2)
waveforms = extract_noise_waveforms(sigs, peak_pos, -30,50, size = 500)
print(waveforms.shape)
fig, ax = pyplot.subplots()
waveforms.median(axis=0).plot(ax =ax)
def run_peeler(dirname, chan_grp):
dataio = DataIO(dirname=dirname, ch_grp=chan_grp)
initial_catalogue = dataio.load_catalogue(chan_grp=chan_grp)
peeler = Peeler(dataio)
peeler.change_params(catalogue=initial_catalogue,
chunksize=32768,
use_sparse_template=False,
sparse_threshold_mad=1.5,
use_opencl_with_sparse=False)
t1 = time.perf_counter()
peeler.run()
t2 = time.perf_counter()
print('peeler.run', t2 - t1)
def test_filter():
dataio = DataIO(dirname = 'datatest_neo')
sigs = dataio.get_signals(seg_num=0, signal_type = 'unfiltered')
filter = SignalFilter(sigs, highpass_freq = 300.)
filterred_sigs = filter.get_filtered_data()
filter2 = SignalFilter(sigs, highpass_freq = 300., box_smooth = 3)
filterred_sigs2 = filter2.get_filtered_data()
fig, axs = pyplot.subplots(nrows=3, sharex = True)
sigs[0:.5].plot(ax=axs[0])
filterred_sigs[0:.5].plot(ax=axs[1])
filterred_sigs2[0:.5].plot(ax=axs[2])
def run_merge_clusters(subject, recording_date, ch_grp, threshold):
data_dir = os.path.join(cfg['intan_data_dir'], subject, recording_date)
print(data_dir)
if os.path.exists(data_dir):
# Compute total duration (want to use all data for clustering)
data_file_names = []
for x in os.listdir(data_dir):
if os.path.splitext(x)[1] == '.raw':
data_file_names.append(os.path.join(data_dir, x))
data_file_times = []
for idx, evt_file in enumerate(data_file_names):
fname = os.path.split(evt_file)[-1]
fparts = fname.split('_')
filedate = datetime.strptime(
'%s %s' % (fparts[4], fparts[5].split('.')[0]),
'%y%m%d %H%M%S')
data_file_times.append(filedate)
data_file_names = [
x for _, x in sorted(zip(data_file_times, data_file_names))
]
if os.path.exists(data_dir) and len(data_file_names) > 0:
output_dir = os.path.join(cfg['single_unit_spike_sorting_dir'],
subject, recording_date)
## Setup DataIO
dataio = DataIO(dirname=output_dir)
#dataio.set_data_source(type='RawData', filenames=data_file_names, dtype='float32', sample_rate=30000,total_channel=192)
merge_clusters(dataio, ch_grp, output_dir, threshold)
def preprocess_signals_and_peaks(dirname):
dataio = DataIO(dirname=dirname)
catalogueconstructor = CatalogueConstructor(dataio=dataio)
catalogueconstructor.set_preprocessor_params(chunksize=1024,
# signal preprocessor
highpass_freq=250,
lowpass_freq=5000,
smooth_size=0,
common_ref_removal=False,
lostfront_chunksize=0,
signalpreprocessor_engine='opencl',
# peak detector
peakdetector_engine='opencl',
peak_sign='-',
relative_threshold=5,
peak_span=0.0002
)
t1 = time.perf_counter()
catalogueconstructor.estimate_signals_noise(seg_num=0, duration=10)
t2 = time.perf_counter()
print('estimate_signals_noise', t2 - t1)
print(catalogueconstructor.signals_medians)
print(catalogueconstructor.signals_mads)
t1 = time.perf_counter()
catalogueconstructor.run_signalprocessor(duration=300)
t2 = time.perf_counter()
print('run_signalprocessor', t2 - t1)
print(catalogueconstructor)
def run_peeler(dirname):
dataio = DataIO(dirname=dirname)
initial_catalogue = dataio.load_catalogue(chan_grp=0)
peeler = Peeler(dataio)
peeler.change_params(catalogue=initial_catalogue)
t1 = time.perf_counter()
peeler.run()
t2 = time.perf_counter()
print('peeler.run', t2 - t1)
print()
for seg_num in range(dataio.nb_segment):
spikes = dataio.get_spikes(seg_num)
print('seg_num', seg_num, 'nb_spikes', spikes.size)
def test_dataio_with_neo():
if os.path.exists('datatest_neo/data.h5'):
os.remove('datatest_neo/data.h5')
dataio = DataIO(dirname = 'datatest_neo')
import neo
import quantities as pq
filenames = ['Tem06c06.IOT', 'Tem06c07.IOT', 'Tem06c08.IOT', ]
for filename in filenames:
blocks = neo.RawBinarySignalIO(filename).read(sampling_rate = 10.*pq.kHz,
t_start = 0. *pq.S, unit = pq.V, nbchannel = 16, bytesoffset = 0,
dtype = 'int16', rangemin = -10, rangemax = 10)
channel_indexes = np.arange(14)
dataio.append_signals_from_neo(blocks, channel_indexes = channel_indexes, signal_type = 'unfiltered')
print(dataio.summary(level=1))
def test_extract_peak_waveforms():
dataio = DataIO(dirname = 'datatest')
sigs = dataio.get_signals(seg_num=0)
peakdetector = PeakDetector(sigs)
peak_pos = peakdetector.detect_peaks(threshold=-4, peak_sign = '-', n_span = 2)
peak_index = sigs.index[peak_pos]
waveforms = extract_peak_waveforms(sigs, peak_pos,peak_index, -30,50)
print(waveforms.shape)
fig, ax = pyplot.subplots()
waveforms.median(axis=0).plot(ax =ax)
normed_waveforms = extract_peak_waveforms(peakdetector.normed_sigs, peak_pos, peak_index, -15,50)
fig, ax = pyplot.subplots()
normed_waveforms.median(axis=0).plot(ax =ax)
def open_cataloguewindow(dirname):
dataio = DataIO(dirname=dirname)
catalogueconstructor = CatalogueConstructor(dataio=dataio)
app = pg.mkQApp()
win = CatalogueWindow(catalogueconstructor)
win.show()
app.exec_()
def test_waveform_extractor():
dataio = DataIO(dirname = 'datatest')
sigs = dataio.get_signals(seg_num=0)
peakdetector = PeakDetector(sigs)
peakdetector.detect_peaks(threshold=-4, peak_sign = '-', n_span = 5)
waveformextractor = WaveformExtractor(peakdetector, n_left=-30, n_right=50)
limit_left, limit_right = waveformextractor.find_good_limits(mad_threshold = 1.1)
print(limit_left, limit_right)
long_wf = waveformextractor.long_waveforms
short_wf = waveformextractor.get_ajusted_waveforms()
assert long_wf.shape[1]>short_wf.shape[1]
waveformextractor.plot_good_limit()
def test_peeler():
dataio = DataIO(dirname = 'datatest')
sigs = dataio.get_signals(seg_num=0)
#peak
peakdetector = PeakDetector(sigs)
peak_pos = peakdetector.detect_peaks(threshold=-4, peak_sign = '-', n_span = 5)
#waveforms
waveformextractor = WaveformExtractor(peakdetector, n_left=-30, n_right=50)
limit_left, limit_right = waveformextractor.find_good_limits(mad_threshold = 1.1)
#~ print(limit_left, limit_right)
short_wf = waveformextractor.get_ajusted_waveforms(margin=2)
#~ print(short_wf.shape)
#clustering
clustering = Clustering(short_wf)
features = clustering.project(method = 'pca', n_components = 5)
clustering.find_clusters(7)
catalogue = clustering.construct_catalogue()
clustering.plot_catalogue()
#peeler
signals = peakdetector.normed_sigs
peeler = Peeler(signals, catalogue, limit_left, limit_right,
threshold=-4, peak_sign = '-', n_span = 5)
prediction0, residuals0 = peeler.peel()
prediction1, residuals1 = peeler.peel()
fig, axs = pyplot.subplots(nrows = 6, sharex = True)#, sharey = True)
axs[0].plot(signals)
axs[1].plot(prediction0)
axs[2].plot(residuals0)
axs[3].plot(prediction1)
axs[4].plot(residuals1)
colors = sns.color_palette('husl', len(catalogue))
spiketrains = peeler.get_spiketrains()
i = 0
for k , pos in spiketrains.items():
axs[5].plot(pos, np.ones(pos.size)*k, ls = 'None', marker = '|', markeredgecolor = colors[i], markersize = 10, markeredgewidth = 2)
i += 1
axs[5].set_ylim(0, len(catalogue))
def test_dataio():
if os.path.exists('datatest/data.h5'):
os.remove('datatest/data.h5')
dataio = DataIO(dirname = 'datatest')
#~ print(data)
#data from locust
sigs_by_trials, sampling_rate, ch_names = download_locust(trial_names = ['trial_01', 'trial_02', 'trial_03'])
for seg_num in range(3):
sigs = sigs_by_trials[seg_num]
dataio.append_signals_from_numpy(sigs, seg_num = seg_num,t_start = 0.+5*seg_num, sampling_rate = sampling_rate,
signal_type = 'filtered', channels = ch_names)
#~ print(data)
#~ print(data.segments)
#~ print(data.store)
print(dataio.summary(level=0))
print(dataio.summary(level=1))
def plot_interpolation():
dataio = DataIO(dirname = 'datatest')
sigs = dataio.get_signals(seg_num=0)
#peak
peakdetector = PeakDetector(sigs)
peak_pos = peakdetector.detect_peaks(threshold=-4, peak_sign = '-', n_span = 5)
#waveforms
waveformextractor = WaveformExtractor(peakdetector, n_left=-30, n_right=50)
limit_left, limit_right = waveformextractor.find_good_limits(mad_threshold = 1.1)
#~ print(limit_left, limit_right)
short_wf = waveformextractor.get_ajusted_waveforms(margin=2)
#~ print(short_wf.shape)
#clustering
clustering = Clustering(short_wf)
features = clustering.project(method = 'pca', n_components = 5)
clustering.find_clusters(7)
catalogue = clustering.construct_catalogue()
k = list(catalogue.keys())[1]
w0 = catalogue[k]['center']
w1 = catalogue[k]['centerD']
w2 = catalogue[k]['centerDD']
fig, ax = pyplot.subplots()
t = np.arange(w0.size)
colors = sns.color_palette('husl', 12)
all = []
jitters = np.arange(-.5,.5,.1)
for i, jitter in enumerate(jitters):
pred = w0 + jitter*w1 + jitter**2/2.*w2
all.append(pred)
ax.plot(t+jitter, pred, marker = 'o', label = str(jitter), color = colors[i], linestyle = 'None')
ax.plot(t, w0, marker = '*', markersize = 4, label = 'w0', lw = 1, color = 'k')
all = np.array(all)
interpolated = all.transpose().flatten()
t2 = np.arange(interpolated.size)/all.shape[0] + jitters[0]
ax.plot(t2, interpolated, label = 'interp', lw = 1, color = 'm')
def test_dataio_catalogue():
if os.path.exists('test_DataIO'):
shutil.rmtree('test_DataIO')
dataio = DataIO(dirname='test_DataIO')
catalogue = {}
catalogue['chan_grp'] = 0
catalogue['centers0'] = np.ones((300, 12, 50))
catalogue['n_left'] = -15
catalogue['params_signalpreprocessor'] = {'highpass_freq' : 300.}
dataio.save_catalogue(catalogue, name='test')
c2 = dataio.load_catalogue(name='test', chan_grp=0)
print(c2)
assert c2['n_left'] == -15
assert np.all(c2['centers0']==1)
assert catalogue['params_signalpreprocessor']['highpass_freq'] == 300.
def test_detect_peak_method_span():
dataio = DataIO(dirname = 'datatest')
sigs = dataio.get_signals(seg_num=0)
normed_sigs = normalize_signals(sigs)
retified_sigs = rectify_signals(normed_sigs, threshold = -4)
peaks_pos = detect_peak_method_span(retified_sigs, peak_sign='-', n_span = 5)
peaks_index = sigs.index[peaks_pos]
fig, ax = pyplot.subplots()
chunk = retified_sigs[3.14:3.22]
chunk.plot(ax = ax)
peaks_value = retified_sigs.loc[peaks_index]
peaks_value[3.14:3.22].plot(marker = 'o', linestyle = 'None', ax = ax, color = 'k')
ax.set_ylim(-20, 10)
fig, ax = pyplot.subplots()
chunk = normed_sigs[3.14:3.22]
chunk.plot(ax = ax)
peaks_value = normed_sigs.loc[peaks_index]
peaks_value[3.14:3.22].plot(marker = 'o', linestyle = 'None', ax = ax, color = 'k')
ax.set_ylim(-20, 10)
def test_find_good_limits():
dataio = DataIO(dirname = 'datatest')
sigs = dataio.get_signals(seg_num=0)
peakdetector = PeakDetector(sigs)
peak_pos = peakdetector.detect_peaks(threshold=-4, peak_sign = '-', n_span = 5)
peak_index = sigs.index[peak_pos]
normed_waveforms = extract_peak_waveforms(peakdetector.normed_sigs, peak_pos, peak_index, -25,50)
normed_med = normed_waveforms.median(axis=0)
normed_mad = np.median(np.abs(normed_waveforms-normed_med),axis=0)*1.4826
normed_mad = normed_mad.reshape(4,-1)
fig, ax = pyplot.subplots()
ax.plot(normed_mad.transpose())
ax.axhline(1.1)
l1, l2 = find_good_limits(normed_mad)
print(l1,l2)
ax.axvline(l1)
ax.axvline(l2)
def make_pca_collision_figure():
dataio = DataIO(dirname=dirname)
cc = CatalogueConstructor(dataio=dataio)
clusters = cc.clusters
#~ plot_features_scatter_2d(cc, labels=None, nb_max=500)
#~ plot_features_scatter_2d
fig, ax = plt.subplots()
ax.set_title('Collision problem')
ax.set_aspect('equal')
features = cc.some_features
labels = cc.all_peaks[cc.some_peaks_index]['cluster_label']
for k in [0, 1, 2, 3]:
color = clusters[clusters['cluster_label'] == k]['color'][0]
color = int32_to_rgba(color, mode='float')
keep = labels == k
feat = features[keep]
print(np.unique(labels))
ax.plot(feat[:, 0],
feat[:, 1],
ls='None',
marker='o',
color=color,
markersize=3,
alpha=.5)
ax.set_xlim(-40, 40)
ax.set_ylim(-40, 40)
ax.set_xlabel('pca0')
ax.set_ylabel('pca1')
ax.annotate('Collision',
xy=(17.6, -16.4),
xytext=(30, -30),
arrowprops=dict(facecolor='black', shrink=0.05))
#~
fig.savefig('../img/collision_proble_pca.png')
def clean_and_save_catalogue(dirname):
dataio = DataIO(dirname=dirname)
catalogueconstructor = CatalogueConstructor(dataio=dataio)
catalogueconstructor.trash_small_cluster(n=5)
# order cluster by waveforms rms
catalogueconstructor.order_clusters(by='waveforms_rms')
# put label 0 to trash
mask = catalogueconstructor.all_peaks['cluster_label'] == 0
catalogueconstructor.all_peaks['cluster_label'][mask] = -1
catalogueconstructor.on_new_cluster()
# save the catalogue
catalogueconstructor.make_catalogue_for_peeler()
def extract_waveforms_pca_cluster(dirname):
dataio = DataIO(dirname=dirname)
catalogueconstructor = CatalogueConstructor(dataio=dataio)
print(catalogueconstructor)
t1 = time.perf_counter()
# ~ catalogueconstructor.extract_some_waveforms(n_left=-35, n_right=150, nb_max=10000, align_waveform=True, subsample_ratio=20)
catalogueconstructor.extract_some_waveforms(n_left=-20, n_right=30, nb_max=20000, align_waveform=False)
t2 = time.perf_counter()
print('extract_some_waveforms', t2 - t1)
# ~ print(catalogueconstructor.some_waveforms.shape)
print(catalogueconstructor)
# ~ t1 = time.perf_counter()
# ~ n_left, n_right = catalogueconstructor.find_good_limits(mad_threshold = 1.1,)
# ~ t2 = time.perf_counter()
# ~ print('n_left', n_left, 'n_right', n_right)
# ~ print(catalogueconstructor.some_waveforms.shape)
print(catalogueconstructor)
# ~ print(catalogueconstructor.all_peaks)
# ~ exit()
t1 = time.perf_counter()
catalogueconstructor.clean_waveforms(alien_value_threshold=100.)
t2 = time.perf_counter()
print('clean_waveforms', t2 - t1)
# extract_some_noise
t1 = time.perf_counter()
catalogueconstructor.extract_some_noise(nb_snippet=300)
t2 = time.perf_counter()
print('extract_some_noise', t2 - t1)
t1 = time.perf_counter()
catalogueconstructor.project(method='pca_by_channel', n_components_by_channel=3)
# ~ catalogueconstructor.project(method='tsne', n_components=2, perplexity=40., init='pca')
t2 = time.perf_counter()
print('project', t2 - t1)
print(catalogueconstructor)
t1 = time.perf_counter()
catalogueconstructor.find_clusters(method='gmm', n_clusters=3*192)
t2 = time.perf_counter()
print('find_clusters', t2 - t1)
print(catalogueconstructor)
def make_catalogue():
if os.path.exists(dirname):
shutil.rmtree(dirname)
dataio = DataIO(dirname=dirname)
localdir, filenames, params = download_dataset(name='olfactory_bulb')
dataio.set_data_source(type='RawData', filenames=filenames, **params)
dataio.add_one_channel_group(channels=channels)
cc = CatalogueConstructor(dataio=dataio)
params = {
'duration': 300.,
'preprocessor': {
'highpass_freq': 300.,
'chunksize': 1024,
'lostfront_chunksize': 100,
},
'peak_detector': {
'peak_sign': '-',
'relative_threshold': 7.,
'peak_span': 0.0005,
#~ 'peak_span' : 0.000,
},
'extract_waveforms': {
'n_left': -25,
'n_right': 40,
'nb_max': 10000,
},
'clean_waveforms': {
'alien_value_threshold': 60.,
},
'noise_snippet': {
'nb_snippet': 300,
},
'feature_method': 'global_pca',
'feature_kargs': {
'n_components': 20
},
'cluster_method': 'kmeans',
'cluster_kargs': {
'n_clusters': 5
},
'clean_cluster': False,
'clean_cluster_kargs': {},
}
apply_all_catalogue_steps(cc, params, verbose=True)
cc.order_clusters(by='waveforms_rms')
cc.move_cluster_to_trash(4)
cc.make_catalogue_for_peeler()
def preprocess_data(subject, recording_date, data_files):
output_dir = os.path.join(cfg['single_unit_spike_sorting_dir'], subject,
recording_date, 'preprocess')
if os.path.exists(output_dir):
# remove is already exists
shutil.rmtree(output_dir)
## Setup DataIO
dataio = DataIO(dirname=output_dir)
dataio.set_data_source(type='Intan',
filenames=[x['fname'] for x in data_files])
# Setup channel groups
arrays_recorded = []
grp_idx = 0
for array_idx in range(len(cfg['arrays'])):
first_chan = ''
if array_idx == 0:
first_chan = 'A-000'
elif array_idx == 1:
first_chan = 'A-032'
elif array_idx == 2:
first_chan = 'B-000'
elif array_idx == 3:
first_chan = 'B-032'
elif array_idx == 4:
first_chan = 'C-000'
elif array_idx == 5:
first_chan = 'C-032'
found = False
for i in range(len(dataio.datasource.sig_channels)):
if dataio.datasource.sig_channels[i][0] == first_chan:
found = True
break
chan_range = []
if found:
chan_range = range(grp_idx * cfg['n_channels_per_array'],
(grp_idx + 1) * cfg['n_channels_per_array'])
grp_idx = grp_idx + 1
arrays_recorded.append(array_idx)
dataio.add_one_channel_group(channels=chan_range, chan_grp=array_idx)
print(dataio)
total_duration = np.sum([x['duration'] for x in data_files])
for array_idx in arrays_recorded:
print(array_idx)
preprocess_array(array_idx, output_dir, total_duration)
def getSortedTimes(dirName, chanGroup):
dataio = DataIO(dirname=dirName)
dataio.load_catalogue(chan_grp=chanGroup)
catalogueconstructor = CatalogueConstructor(dataio=dataio)
sample_rate = dataio.sample_rate # Just initialize sample rate, will set later
unitTimes = np.empty(dataio.nb_segment, dtype=object)
for j in range(dataio.nb_segment):
idd = {}
times = {}
try:
# List of all cluster labels
cluster_ids = np.array([i for i in catalogueconstructor.cluster_labels])
# List of all detected peaks by cluster ID
clusters = np.array([i[1] for i in dataio.get_spikes(j)])
spike_times = np.array([i[0] for i in dataio.get_spikes(j)])
except:
cluster_ids = np.array([])
clusters = np.array([])
spike_times = np.array([])
for i in cluster_ids:
idd[i] = np.argwhere(clusters == i)
for i in cluster_ids:
times[i] = spike_times[idd[i]]/sample_rate
mx = np.max([times[i].size for i in times.keys()])
for i in times.keys():
times[i].resize(mx + 1, 1)
timesArray = np.array([times[i] for i in times.keys()])
timesArray = np.roll(timesArray, 1)
timesArray[:, 0, :] = np.array(list(times.keys())).reshape(timesArray.shape[0], 1)
timesArray = np.transpose(timesArray)
unitTimes[j] = timesArray[0]
return unitTimes
def export_spikes(dirname, array_idx, chan_grp):
print('Exporting ch %d' % chan_grp)
data = {
'array': [],
'electrode': [],
'cell': [],
'segment': [],
'time': []
}
array = cfg['arrays'][array_idx]
dataio = DataIO(dirname=dirname, ch_grp=chan_grp)
catalogue = dataio.load_catalogue(chan_grp=chan_grp)
dataio._open_processed_data(ch_grp=chan_grp)
clusters = catalogue['clusters']
for seg_num in range(dataio.nb_segment):
spikes = dataio.get_spikes(seg_num=seg_num, chan_grp=chan_grp)
spike_labels = spikes['cluster_label'].copy()
for l in clusters:
mask = spike_labels == l['cluster_label']
spike_labels[mask] = l['cell_label']
spike_indexes = spikes['index']
for (index, label) in zip(spike_indexes, spike_labels):
if label >= 0:
data['array'].append(array)
data['electrode'].append(chan_grp)
data['cell'].append(label)
data['segment'].append(seg_num)
data['time'].append(index)
dataio.flush_processed_signals(seg_num=seg_num, chan_grp=chan_grp)
df = pd.DataFrame(
data, columns=['array', 'electrode', 'cell', 'segment', 'time'])
df.to_csv(os.path.join(dirname, '%s_%d_spikes.csv' % (array, chan_grp)),
index=False)
def initialize_catalogueconstructor(dirname, filenames):
# create a DataIO
if os.path.exists(dirname):
# remove is already exists
shutil.rmtree(dirname)
dataio = DataIO(dirname=dirname)
# The dataset contains 4 channels : we use them all
#dataio.set_channel_groups({'channels':{'channels':[0, 1, 2, 3]}})
# feed DataIO
dataio.set_data_source(type='Intan', filenames=filenames, channel_indexes=list(range(192)))
#dataio.set_probe_file('/home/bonaiuto/Projects/tool_learning/recordings/rhd2000/betta/default.prb')
dataio.add_one_channel_group(channels=range(192), chan_grp=0)
print(dataio)
def generate_spike_sorting_report(subject, recording_date):
data_dir = os.path.join(cfg['single_unit_spike_sorting_dir'], subject,
recording_date)
if os.path.exists(data_dir):
channel_results = []
for array_idx in range(len(cfg['arrays'])):
array = cfg['arrays'][array_idx]
print(array)
array_data_dir = os.path.join(data_dir, 'array_%d' % array_idx)
if os.path.exists(array_data_dir):
export_path = os.path.join(array_data_dir, 'figures')
if not os.path.exists(export_path):
os.makedirs(export_path)
for chan_grp in range(cfg['n_channels_per_array']):
print(chan_grp)
dataio = DataIO(array_data_dir, ch_grp=chan_grp)
dataio.datasource.bit_to_microVolt = 0.195
catalogueconstructor = CatalogueConstructor(
dataio=dataio, chan_grp=chan_grp)
catalogueconstructor.refresh_colors()
catalogue = dataio.load_catalogue(chan_grp=chan_grp)
channel_result = {
'array': array,
'channel': chan_grp,
'init_waveforms': '',
'clean_waveforms': '',
'noise': '',
'init_clusters': '',
'merge_clusters': [],
'final_clusters': [],
'all_clusters': ''
}
clusters = catalogue['clusters']
cluster_labels = clusters['cluster_label']
cell_labels = clusters['cell_label']
channel_result['init_waveforms'] = os.path.join(
'array_%d' % array_idx, 'figures',
'chan_%d_init_waveforms.png' % chan_grp)
channel_result['clean_waveforms'] = os.path.join(
'array_%d' % array_idx, 'figures',
'chan_%d_clean_waveforms.png' % chan_grp)
channel_result['noise'] = os.path.join(
'array_%d' % array_idx, 'figures',
'chan_%d_noise.png' % chan_grp)
channel_result['init_clusters'] = os.path.join(
'array_%d' % array_idx, 'figures',
'chan_%d_init_clusters.png' % chan_grp)
merge_files = glob.glob(
os.path.join(export_path,
'chan_%d_merge_*.png' % chan_grp))
for merge_file in merge_files:
[path, file] = os.path.split(merge_file)
channel_result['merge_clusters'].append(
os.path.join('array_%d' % array_idx, 'figures',
file))
for cluster_label in cluster_labels:
fig = plot_cluster_summary(dataio, catalogue, chan_grp,
cluster_label)
fname = 'chan_%d_cluster_%d.png' % (chan_grp,
cluster_label)
fig.savefig(os.path.join(export_path, fname))
fig.clf()
plt.close()
channel_result['final_clusters'].append(
os.path.join('array_%d' % array_idx, 'figures',
fname))
fig = plot_clusters_summary(dataio, catalogueconstructor,
chan_grp)
fname = 'chan_%d_clusters.png' % chan_grp
fig.savefig(os.path.join(export_path, fname))
fig.clf()
plt.close()
channel_result['all_clusters'] = os.path.join(
'array_%d' % array_idx, 'figures', fname)
channel_results.append(channel_result)
env = Environment(loader=FileSystemLoader(cfg['template_dir']))
template = env.get_template('spike_sorting_results_template.html')
template_output = template.render(subject=subject,
recording_date=recording_date,
channel_results=channel_results)
out_filename = os.path.join(data_dir, 'spike_sorting_report.html')
with open(out_filename, 'w') as fh:
fh.write(template_output)
copyfile(os.path.join(cfg['template_dir'], 'style.css'),
os.path.join(data_dir, 'style.css'))
def test_derivative_signals():
dataio = DataIO(dirname = 'datatest')
sigs = dataio.get_signals(seg_num=0)
deriv_sigs = derivative_signals(sigs)
deriv_sigs[3.14:3.22].plot()
def test_normalize_signals():
dataio = DataIO(dirname = 'datatest')
sigs = dataio.get_signals(seg_num=0)
normed_sigs = normalize_signals(sigs)
normed_sigs[3.14:3.22].plot()
def test_DataIO():
# initialze dataio
if os.path.exists('test_DataIO'):
shutil.rmtree('test_DataIO')
dataio = DataIO(dirname='test_DataIO')
print(dataio)
localdir, filenames, params = download_dataset(name='olfactory_bulb')
dataio.set_data_source(type='RawData', filenames=filenames, **params)
#with geometry
channels = list(range(14))
channel_groups = {0:{'channels':range(14), 'geometry' : { c: [0, i] for i, c in enumerate(channels) }}}
dataio.set_channel_groups(channel_groups)
#with no geometry
channel_groups = {0:{'channels':range(4)}}
dataio.set_channel_groups(channel_groups)
# add one group
dataio.add_one_channel_group(channels=range(4,8), chan_grp=5)
channel_groups = {0:{'channels':range(14)}}
dataio.set_channel_groups(channel_groups)
for seg_num in range(dataio.nb_segment):
for i_stop, sigs_chunk in dataio.iter_over_chunk(seg_num=seg_num, chunksize=1024):
assert sigs_chunk.shape[0] == 1024
assert sigs_chunk.shape[1] == 14
#~ print(seg_num, i_stop, sigs_chunk.shape)
#reopen existing
dataio = DataIO(dirname='test_DataIO')
print(dataio)
#~ exit()
for seg_num in range(dataio.nb_segment):
#~ print('seg_num', seg_num)
for i_stop, sigs_chunk in dataio.iter_over_chunk(seg_num=seg_num, chunksize=1024):
assert sigs_chunk.shape[0] == 1024
assert sigs_chunk.shape[1] == 14
def test_DataIO_probes():
# initialze dataio
if os.path.exists('test_DataIO'):
shutil.rmtree('test_DataIO')
dataio = DataIO(dirname='test_DataIO')
print(dataio)
localdir, filenames, params = download_dataset(name='olfactory_bulb')
dataio.set_data_source(type='RawData', filenames=filenames, **params)
probe_filename = 'neuronexus/A4x8-5mm-100-400-413-A32.prb'
dataio.download_probe(probe_filename)
dataio.download_probe('neuronexus/A4x8-5mm-100-400-413-A32')
#~ print(dataio.channel_groups)
#~ print(dataio.channels)
#~ print(dataio.info['probe_filename'])
assert dataio.nb_channel(0) == 8
assert probe_filename.split('/')[-1] == dataio.info['probe_filename']
dataio = DataIO(dirname='test_DataIO')
print(dataio)
def make_animation():
"""
Good example between 1.272 1.302
because collision
"""
dataio = DataIO(dirname=dirname)
catalogue = dataio.load_catalogue(chan_grp=0)
clusters = catalogue['clusters']
sr = dataio.sample_rate
# also a good one a 11.356 - 11.366
t1, t2 = 1.272, 1.295
i1, i2 = int(t1 * sr), int(t2 * sr)
spikes = dataio.get_spikes()
spike_times = spikes['index'] / sr
keep = (spike_times >= t1) & (spike_times <= t2)
spikes = spikes[keep]
print(spikes)
sigs = dataio.get_signals_chunk(i_start=i1,
i_stop=i2,
signal_type='processed')
sigs = sigs.copy()
times = np.arange(sigs.shape[0]) / dataio.sample_rate
def plot_spread_sigs(sigs, ax, ratioY=0.02, **kargs):
#spread signals
sigs2 = sigs * ratioY
sigs2 += np.arange(0, len(channels))[np.newaxis, :]
ax.plot(times, sigs2, **kargs)
ax.set_ylim(-0.5, len(channels) - .5)
ax.set_xticks([])
ax.set_yticks([])
residuals = sigs.copy()
local_spikes = spikes.copy()
local_spikes['index'] -= i1
#~ fig, ax = plt.subplots()
#~ plot_spread_sigs(sigs, ax, color='k')
num_fig = 0
fig_pred, ax_predictions = plt.subplots()
ax_predictions.set_title('All detected templates from catalogue')
fig, ax = plt.subplots()
plot_spread_sigs(residuals, ax, color='k', lw=2)
ax.set_title('Initial filtered signals with spikes')
fig.savefig('../img/peeler_animation_sigs.png')
fig.savefig('png/fig{}.png'.format(num_fig))
num_fig += 1
for i in range(local_spikes.size):
label = local_spikes['cluster_label'][i]
color = clusters[clusters['cluster_label'] == label]['color'][0]
color = int32_to_rgba(color, mode='float')
pred = make_prediction_signals(local_spikes[i:i + 1], 'float32',
(i2 - i1, len(channels)), catalogue)
fig, ax = plt.subplots()
plot_spread_sigs(residuals, ax, color='k', lw=2)
plot_spread_sigs(pred, ax, color=color, lw=1.5)
ax.set_title('Dected spike label {}'.format(label))
fig.savefig('png/fig{}.png'.format(num_fig))
num_fig += 1
residuals -= pred
plot_spread_sigs(pred, ax_predictions, color=color, lw=1.5)
fig, ax = plt.subplots()
plot_spread_sigs(residuals, ax, color='k', lw=2)
plot_spread_sigs(pred, ax, color=color, lw=1, ls='--')
ax.set_title('New residual after substraction')
fig.savefig('png/fig{}.png'.format(num_fig))
num_fig += 1
fig_pred.savefig('png/fig{}.png'.format(num_fig))
num_fig += 1
def export_spikes(dirname):
dataio = DataIO(dirname=dirname)
dataio.export_spikes(dirname,formats='csv')
dataio.export_spikes(dirname, formats='mat')
def compute_array_catalogue(array_idx, preprocess_dir, subject, recording_date,
data_files, cluster_merge_threshold):
# If data exists for this array
if os.path.exists(
os.path.join(preprocess_dir, 'channel_group_%d' % array_idx,
'catalogue_constructor')):
output_dir = os.path.join(cfg['single_unit_spike_sorting_dir'],
subject, recording_date,
'array_%d' % array_idx)
if os.path.exists(output_dir):
# remove is already exists
shutil.rmtree(output_dir)
# Compute total duration (want to use all data for clustering)
data_file_names = []
for seg in range(len(data_files)):
data_file_names.append(
os.path.join(preprocess_dir, 'channel_group_%d' % array_idx,
'segment_%d' % seg, 'processed_signals.raw'))
dataio = DataIO(dirname=output_dir)
dataio.set_data_source(type='RawData',
filenames=data_file_names,
dtype='float32',
sample_rate=cfg['intan_srate'],
total_channel=cfg['n_channels_per_array'])
dataio.datasource.bit_to_microVolt = 0.195
for ch_grp in range(cfg['n_channels_per_array']):
dataio.add_one_channel_group(channels=[ch_grp], chan_grp=ch_grp)
total_duration = np.sum([x['duration'] for x in data_files])
figure_out_dir = os.path.join(output_dir, 'figures')
os.mkdir(figure_out_dir)
for ch_grp in range(cfg['n_channels_per_array']):
print(ch_grp)
cc = CatalogueConstructor(dataio=DataIO(dirname=output_dir,
ch_grp=ch_grp),
chan_grp=ch_grp)
fullchain_kargs = {
'duration': total_duration,
'preprocessor': {
'highpass_freq': None,
'lowpass_freq': None,
'smooth_size': 0,
'common_ref_removal': False,
'chunksize': 32768,
'lostfront_chunksize': 0,
'signalpreprocessor_engine': 'numpy',
},
'peak_detector': {
'peakdetector_engine': 'numpy',
'peak_sign': '-',
'relative_threshold': 2.,
'peak_span': 0.0002,
},
'noise_snippet': {
'nb_snippet': 300,
},
'extract_waveforms': {
'n_left': -20,
'n_right': 30,
'mode': 'all',
'nb_max': 2000000,
'align_waveform': False,
},
'clean_waveforms': {
'alien_value_threshold': 100.,
},
}
feat_method = 'pca_by_channel'
feat_kargs = {'n_components_by_channel': 5}
clust_method = 'sawchaincut'
clust_kargs = {
'max_loop': 1000,
'nb_min': 20,
'break_nb_remain': 30,
'kde_bandwith': 0.01,
'auto_merge_threshold': 2.,
'print_debug': False
# 'max_loop': 1000,
# 'nb_min': 20,
# 'break_nb_remain': 30,
# 'kde_bandwith': 0.01,
# 'auto_merge_threshold': cluster_merge_threshold,
# 'print_debug': False
}
p = {}
p.update(fullchain_kargs['preprocessor'])
p.update(fullchain_kargs['peak_detector'])
cc.set_preprocessor_params(**p)
noise_duration = min(
10., fullchain_kargs['duration'],
dataio.get_segment_length(seg_num=0) / dataio.sample_rate *
.99)
# ~ print('noise_duration', noise_duration)
t1 = time.perf_counter()
cc.estimate_signals_noise(seg_num=0, duration=noise_duration)
t2 = time.perf_counter()
print('estimate_signals_noise', t2 - t1)
t1 = time.perf_counter()
cc.run_signalprocessor(duration=fullchain_kargs['duration'])
t2 = time.perf_counter()
print('run_signalprocessor', t2 - t1)
t1 = time.perf_counter()
cc.extract_some_waveforms(**fullchain_kargs['extract_waveforms'])
t2 = time.perf_counter()
print('extract_some_waveforms', t2 - t1)
fname = 'chan_%d_init_waveforms.png' % ch_grp
fig = plot_waveforms(np.squeeze(cc.some_waveforms).T)
fig.savefig(os.path.join(figure_out_dir, fname))
fig.clf()
plt.close()
t1 = time.perf_counter()
# ~ duration = d['duration'] if d['limit_duration'] else None
# ~ d['clean_waveforms']
cc.clean_waveforms(**fullchain_kargs['clean_waveforms'])
t2 = time.perf_counter()
print('clean_waveforms', t2 - t1)
fname = 'chan_%d_clean_waveforms.png' % ch_grp
fig = plot_waveforms(np.squeeze(cc.some_waveforms).T)
fig.savefig(os.path.join(figure_out_dir, fname))
fig.clf()
plt.close()
# ~ t1 = time.perf_counter()
# ~ n_left, n_right = cc.find_good_limits(mad_threshold = 1.1,)
# ~ t2 = time.perf_counter()
# ~ print('find_good_limits', t2-t1)
t1 = time.perf_counter()
cc.extract_some_noise(**fullchain_kargs['noise_snippet'])
t2 = time.perf_counter()
print('extract_some_noise', t2 - t1)
# Plot noise
fname = 'chan_%d_noise.png' % ch_grp
fig = plot_noise(cc)
fig.savefig(os.path.join(figure_out_dir, fname))
fig.clf()
plt.close()
t1 = time.perf_counter()
cc.extract_some_features(method=feat_method, **feat_kargs)
t2 = time.perf_counter()
print('project', t2 - t1)
t1 = time.perf_counter()
cc.find_clusters(method=clust_method, **clust_kargs)
t2 = time.perf_counter()
print('find_clusters', t2 - t1)
# Remove empty clusters
cc.trash_small_cluster(n=0)
if cc.centroids_median is None:
cc.compute_all_centroid()
# order cluster by waveforms rms
cc.order_clusters(by='waveforms_rms')
fname = 'chan_%d_init_clusters.png' % ch_grp
cluster_labels = cc.clusters['cluster_label']
fig = plot_cluster_waveforms(cc, cluster_labels)
fig.savefig(os.path.join(figure_out_dir, fname))
fig.clf()
plt.close()
# save the catalogue
cc.make_catalogue_for_peeler()
gc.collect()
def run_compare_catalogues(subject, date, similarity_threshold=0.7):
bin_min = 0
bin_max = 100
bin_size = 1.
bins = np.arange(bin_min, bin_max, bin_size)
if os.path.exists(
os.path.join(cfg['single_unit_spike_sorting_dir'], subject, date)):
# Get previous days to compare to
sorted_dates, sorted_files = read_previous_sorts(subject)
new_date = datetime.strptime(date, '%d.%m.%y')
# Results for report
channel_results = []
for array_idx in range(len(cfg['arrays'])):
new_output_dir = os.path.join(cfg['single_unit_spike_sorting_dir'],
subject, date,
'array_%d' % array_idx)
if os.path.exists(new_output_dir):
# Create directory for plots
plot_output_dir = os.path.join(new_output_dir, 'figures',
'catalogue_comparison')
if not os.path.exists(plot_output_dir):
os.mkdir(plot_output_dir)
for ch_grp in range(cfg['n_channels_per_array']):
channel_result = {
'array': cfg['arrays'][array_idx],
'channel': ch_grp,
'merged': [],
'unmerged': [],
'final': ''
}
# load catalogue for this channel
new_dataio = DataIO(dirname=new_output_dir, ch_grp=ch_grp)
catalogueconstructor = CatalogueConstructor(
dataio=new_dataio, chan_grp=ch_grp)
# Waveform time range
time_range = range(
catalogueconstructor.info['waveform_extractor_params']
['n_left'],
catalogueconstructor.info['waveform_extractor_params']
['n_right'])
# refresh
if catalogueconstructor.centroids_median is None:
catalogueconstructor.compute_all_centroid()
catalogueconstructor.refresh_colors()
# cell labels and cluster waveforms for this day
nn_idx = np.where(
catalogueconstructor.clusters['cell_label'] > -1)[0]
# If there are any clusters for this day
if len(nn_idx) > 0:
new_cluster_labels, new_cell_labels, new_wfs, new_wfs_stds, new_isis = get_cluster_info(
catalogueconstructor, new_dataio, ch_grp, bins,
nn_idx)
# Load cell labels and waveforms for all previous days
all_old_cluster_labels = []
all_old_cell_labels = []
all_old_wfs = np.zeros((0, 50))
all_old_wfs_stds = np.zeros((0, 50))
all_old_isis = np.zeros((0, len(bins) - 1))
recent_sorted_files = copy.copy(sorted_files)
recent_sorted_dates = copy.copy(sorted_dates)
if len(recent_sorted_files) > 20:
recent_sorted_files = recent_sorted_files[-20:]
recent_sorted_dates = recent_sorted_dates[-20:]
for old_date, old_file in zip(recent_sorted_dates,
recent_sorted_files):
if old_date < new_date:
print('loading %s' % old_date)
old_output_dir = os.path.join(
cfg['single_unit_spike_sorting_dir'],
subject, old_file, 'array_%d' % array_idx)
if os.path.exists(old_output_dir):
old_dataio = DataIO(
dirname=old_output_dir,
ch_grp=ch_grp,
reload_data_source=False)
old_catalogueconstructor = CatalogueConstructor(
dataio=old_dataio,
chan_grp=ch_grp,
load_persistent_arrays=False)
old_catalogueconstructor.arrays.load_if_exists(
'clusters')
old_catalogueconstructor.arrays.load_if_exists(
'centroids_median')
old_catalogueconstructor.arrays.load_if_exists(
'centroids_std')
if old_catalogueconstructor.centroids_median is None:
old_catalogueconstructor.compute_all_centroid(
)
old_cell_labels = old_catalogueconstructor.clusters[
'cell_label']
to_include = np.where(
np.bitwise_and(
np.isin(
old_cell_labels,
all_old_cell_labels) == False,
old_cell_labels > -1))[0]
if len(to_include):
old_cluster_labels, old_cell_labels, old_wfs, old_wfs_stds, old_isis = get_cluster_info(
old_catalogueconstructor,
old_dataio, ch_grp, bins,
to_include)
all_old_wfs = np.concatenate(
(all_old_wfs,
copy.deepcopy(old_wfs)))
all_old_wfs_stds = np.concatenate(
(all_old_wfs_stds,
copy.deepcopy(old_wfs_stds)))
all_old_cell_labels.extend(
copy.deepcopy(old_cell_labels))
all_old_cluster_labels.extend(
copy.deepcopy(old_cluster_labels))
all_old_isis = np.concatenate(
(all_old_isis,
copy.deepcopy(old_isis)))
#old_dataio.arrays.detach_array('clusters', mmap_close=True)
#old_dataio.arrays.detach_array('centroids_median', mmap_close=True)
#old_dataio.arrays.detach_array('centroids_std', mmap_close=True)
if len(all_old_cell_labels):
# Compute cluster similarity
wfs = np.concatenate((new_wfs, all_old_wfs))
cluster_similarity = metrics.cosine_similarity_with_max(
wfs)
new_old_cluster_similarity = cluster_similarity[
0:new_wfs.shape[0], new_wfs.shape[0]:]
# Plot cluster similarity
fname = 'chan_%d_similarity.png' % ch_grp
plot_new_old_cluster_similarity(
all_old_cluster_labels, all_old_cell_labels,
new_cluster_labels, new_cell_labels,
new_old_cluster_similarity, plot_output_dir,
fname)
channel_result['similarity'] = os.path.join(
'array_%d' % array_idx, 'figures',
'catalogue_comparison', fname)
# Go through each cluster in current day
for new_cluster_idx in range(new_wfs.shape[0]):
# Find most similar cluster from previous days
most_similar = np.argmax(
new_old_cluster_similarity[
new_cluster_idx, :])
# Merge if similarity greater than threshold
similarity = new_old_cluster_similarity[
new_cluster_idx, most_similar]
if similarity >= similarity_threshold:
print(
'relabeling unit %d-%d as unit %d-%d' %
(ch_grp,
new_cell_labels[new_cluster_idx],
ch_grp,
all_old_cell_labels[most_similar]))
fname = 'chan_%d_merge_%d-%d.png' % (
ch_grp,
new_cell_labels[new_cluster_idx],
all_old_cell_labels[most_similar])
plot_cluster_merge(
all_old_cell_labels[most_similar],
all_old_cluster_labels[most_similar],
all_old_isis[most_similar, :],
all_old_wfs[most_similar, :],
all_old_wfs_stds[most_similar, :],
new_cell_labels[new_cluster_idx],
new_cluster_labels[new_cluster_idx],
new_isis[new_cluster_idx, :],
new_wfs[new_cluster_idx, :],
new_wfs_stds[new_cluster_idx, :],
similarity, time_range, bins,
plot_output_dir, fname)
channel_result['merged'].append(
os.path.join('array_%d' % array_idx,
'figures',
'catalogue_comparison',
fname))
new_cell_labels[
new_cluster_idx] = all_old_cell_labels[
most_similar]
# Otherwise, add new cluster
else:
new_label = np.max(all_old_cell_labels) + 1
print('adding new unit %d-%d' %
(ch_grp, new_label))
all_old_cell_labels.append(new_label)
new_cell_labels[
new_cluster_idx] = new_label
fname = 'chan_%d_nonmerge_%d.png' % (
ch_grp,
new_cell_labels[new_cluster_idx])
plot_cluster_new(
all_old_cluster_labels,
all_old_cell_labels, all_old_isis,
all_old_wfs, all_old_wfs_stds,
new_cluster_labels[new_cluster_idx],
new_cell_labels[new_cluster_idx],
new_isis[new_cluster_idx, :],
new_wfs[new_cluster_idx, :],
new_wfs_stds[new_cluster_idx, :],
new_old_cluster_similarity[
new_cluster_idx:new_cluster_idx +
1, :], time_range, bins,
plot_output_dir, fname)
channel_result['unmerged'].append(
os.path.join('array_%d' % array_idx,
'figures',
'catalogue_comparison',
fname))
catalogueconstructor.clusters['cell_label'][
nn_idx] = new_cell_labels
# Merge clusters with same labels
cluster_removed = True
while cluster_removed:
for cell_label in catalogueconstructor.clusters[
'cell_label']:
cluster_idx = np.where(
catalogueconstructor.clusters['cell_label']
== cell_label)[0]
if len(cluster_idx) > 1:
cluster_label1 = catalogueconstructor.cluster_labels[
cluster_idx[0]]
cluster_label2 = catalogueconstructor.cluster_labels[
cluster_idx[1]]
print('auto_merge', cluster_label2, 'with',
cluster_label1)
mask = catalogueconstructor.all_peaks[
'cluster_label'] == cluster_label2
catalogueconstructor.all_peaks[
'cluster_label'][mask] = cluster_label1
catalogueconstructor.remove_one_cluster(
cluster_label2)
break
else:
cluster_removed = False
catalogueconstructor.make_catalogue_for_peeler()
fig = plot_clusters_summary(new_dataio,
catalogueconstructor,
ch_grp)
fname = 'chan_%d_final_clusters.png' % ch_grp
fig.savefig(os.path.join(plot_output_dir, fname))
fig.clf()
plt.close()
channel_result['final'] = os.path.join(
'array_%d' % array_idx, 'figures',
'catalogue_comparison', fname)
channel_results.append(channel_result)
env = Environment(loader=FileSystemLoader(cfg['template_dir']))
template = env.get_template('spike_sorting_merge_template.html')
template_output = template.render(subject=subject,
recording_date=date,
channel_results=channel_results)
out_filename = os.path.join(cfg['single_unit_spike_sorting_dir'],
subject, date,
'spike_sorting_merge_report.html')
with open(out_filename, 'w') as fh:
fh.write(template_output)
copyfile(
os.path.join(cfg['template_dir'], 'style.css'),
os.path.join(cfg['single_unit_spike_sorting_dir'], subject, date,
'style.css'))