def test_spikesorter():
spikesorter = SpikeSorter(dirname = 'datatest')
print(spikesorter)
spikesorter.detect_peaks_extract_waveforms(seg_nums = 'all', threshold=-4, peak_sign = '-', n_span = 2, n_left=-30, n_right=50)
print(spikesorter.summary(level=1))
spikesorter.project(method = 'pca', n_components = 5)
spikesorter.find_clusters(7)
def test_spikesorter_neo():
if os.path.exists('datatest_neo/data.h5'):
os.remove('datatest_neo/data.h5')
spikesorter = SpikeSorter(dirname = 'datatest_neo')
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(10)
spikesorter.dataio.append_signals_from_neo(blocks, channel_indexes = channel_indexes, signal_type = 'unfiltered')
print('### after data import ###')
print(spikesorter.summary(level=1))
spikesorter.apply_filter(highpass_freq = 150., box_smooth= 3)
print('### after filtering ###')
print(spikesorter.summary(level=1))
spikesorter.detect_peaks_extract_waveforms(seg_nums = 'all', threshold=-5., peak_sign = '-', n_span = 2, n_left=-30, n_right=30)
print('### after peak detection ###')
print(spikesorter.summary(level=1))
spikesorter.project(method = 'pca', n_components = 5)
spikesorter.find_clusters(10)
print('### after clustering ###')
print(spikesorter.summary(level=1))
spikesorter.construct_catalogue(save = True)
spikesorter.appy_peeler()
for seg_num in range(3):
spiketrains = spikesorter.dataio.get_spiketrains(seg_num=seg_num)
print(spiketrains)
def test_spikesorter():
if os.path.exists('datatest/data.h5'):
os.remove('datatest/data.h5')
spikesorter = SpikeSorter(dirname = 'datatest')
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]
spikesorter.dataio.append_signals_from_numpy(sigs, seg_num = seg_num,
t_start = 0.+5*seg_num, sampling_rate = sampling_rate,
signal_type = 'unfiltered', channels = ch_names)
print('### after data import ###')
print(spikesorter)
print('### after filtering ###')
spikesorter.apply_filter(highpass_freq = 0., box_smooth = 3)
spikesorter.detect_peaks_extract_waveforms(seg_nums = 'all', threshold=-4, peak_sign = '-', n_span = 2, n_left=-60, n_right=100)
print('### after peak detection ###')
print(spikesorter.summary(level=1))
spikesorter.project(method = 'pca', n_components = 5)
spikesorter.find_clusters(7)
print('### after clustering ###')
print(spikesorter.summary(level=1))
spikesorter.construct_catalogue(save = True)
spikesorter.appy_peeler()
for seg_num in range(3):
spiketrains = spikesorter.dataio.get_spiketrains(seg_num=seg_num)
print(spiketrains)
