def test_peeler_several_chunksize():
dataio = DataIO(dirname='test_peeler')
print(dataio)
catalogue = dataio.load_catalogue(chan_grp=0)
all_spikes = []
sig_length = dataio.get_segment_length(0)
chunksizes = [174, 512, 1024, 1023, 10000, 150000]
#~ chunksizes = [512, 1024,]
for chunksize in chunksizes:
print('**', chunksize, '**')
peeler = Peeler(dataio)
peeler.change_params(engine='geometrical',
catalogue=catalogue,
chunksize=chunksize,
argmin_method='numba')
t1 = time.perf_counter()
peeler.run(progressbar=False)
t2 = time.perf_counter()
print('extra_size', peeler.peeler_engine.extra_size, 'n_span',
peeler.peeler_engine.n_span, 'peak_width',
peeler.peeler_engine.peak_width)
print('peeler.run_loop', t2 - t1)
# copy is need because the memmap is reset at each loop
spikes = dataio.get_spikes(seg_num=0, chan_grp=0).copy()
all_spikes.append(spikes)
print(spikes.size)
# clip to last spike
last = min([spikes[-1]['index'] for spikes in all_spikes])
for i, chunksize in enumerate(chunksizes):
spikes = all_spikes[i]
all_spikes[i] = spikes[spikes['index'] <= last]
previsous_spikes = None
for i, chunksize in enumerate(chunksizes):
print('**', chunksize, '**')
spikes = all_spikes[i]
is_sorted = np.all(np.diff(spikes['index']) >= 0)
assert is_sorted
labeled_spike = spikes[spikes['cluster_label'] >= 0]
unlabeled_spike = spikes[spikes['cluster_label'] < 0]
print('labeled_spike.size', labeled_spike.size, 'unlabeled_spike.size',
unlabeled_spike.size)
print(spikes)
# TODO: Peeler chunksize influence the number of spikes
if previsous_spikes is not None:
assert previsous_spikes.size == spikes.size
np.testing.assert_array_equal(previsous_spikes['index'],
spikes['index'])
np.testing.assert_array_equal(previsous_spikes['cluster_label'],
spikes['cluster_label'])
previsous_spikes = spikes
def test_peeler_several_chunksize():
dataio = DataIO(dirname='test_peeler')
print(dataio)
catalogue = dataio.load_catalogue(chan_grp=0)
all_spikes = []
sig_length = dataio.get_segment_length(0)
chunksizes = [174, 512, 1024, 1023, 10000, 150000]
#~ chunksizes = [512, 1024,]
for chunksize in chunksizes:
print('**', chunksize, '**')
peeler = Peeler(dataio)
peeler.change_params(catalogue=catalogue, chunksize=chunksize)
t1 = time.perf_counter()
peeler.run_offline_loop_one_segment(seg_num=0, progressbar=False)
t2 = time.perf_counter()
print('n_side', peeler.n_side, 'n_span', peeler.n_span, 'peak_width',
peeler.peak_width)
print('peeler.run_loop', t2 - t1)
spikes = dataio.get_spikes(seg_num=0, chan_grp=0)
all_spikes.append(spikes)
# clip to last spike
last = min([spikes[-1]['index'] for spikes in all_spikes])
for i, chunksize in enumerate(chunksizes):
spikes = all_spikes[i]
all_spikes[i] = spikes[spikes['index'] <= last]
previsous_spikes = None
for i, chunksize in enumerate(chunksizes):
print('**', chunksize, '**')
spikes = all_spikes[i]
is_sorted = np.all(np.diff(spikes['index']) >= 0)
assert is_sorted
labeled_spike = spikes[spikes['cluster_label'] >= 0]
unlabeled_spike = spikes[spikes['cluster_label'] < 0]
print('labeled_spike.size', labeled_spike.size, 'unlabeled_spike.size',
unlabeled_spike.size)
if previsous_spikes is not None:
assert previsous_spikes.size == spikes.size
np.testing.assert_array_equal(previsous_spikes['index'],
spikes['index'])
np.testing.assert_array_equal(previsous_spikes['cluster_label'],
spikes['cluster_label'])
previsous_spikes = spikes
def test_peeler_empty_catalogue():
"""
This test peeler with empty catalogue.
This is like a peak detector.
Check several chunksize and compare to offline-one-buffer.
"""
dataio = DataIO(dirname='test_peeler')
#~ print(dataio)
catalogue = dataio.load_catalogue(chan_grp=0)
# empty catalogue for debug peak detection
s = catalogue['centers0'].shape
empty_centers = np.zeros((0, s[1], s[2]), dtype='float32')
catalogue['centers0'] = empty_centers
catalogue['centers1'] = empty_centers
catalogue['centers2'] = empty_centers
catalogue['cluster_labels'] = np.zeros(0, dtype=catalogue['cluster_labels'].dtype)
sig_length = dataio.get_segment_length(0)
chunksizes = [ 101, 174, 512, 1024, 1023, 10000, 150000]
#~ chunksizes = [1024,]
previous_peak = None
for chunksize in chunksizes:
print('**', chunksize, '**')
peeler = Peeler(dataio)
peeler.change_params(engine='classic', catalogue=catalogue,chunksize=chunksize)
t1 = time.perf_counter()
#~ peeler.run(progressbar=False)
peeler.run_offline_loop_one_segment(seg_num=0, progressbar=False)
t2 = time.perf_counter()
#~ print('n_side', peeler.n_side, 'n_span', peeler.n_span, 'peak_width', peeler.peak_width)
#~ print('peeler.run_loop', t2-t1)
spikes = dataio.get_spikes(seg_num=0, chan_grp=0)
labeled_spike = spikes[spikes['cluster_label']>=0]
unlabeled_spike = spikes[spikes['cluster_label']<0]
assert labeled_spike.size == 0
is_sorted = np.all(np.diff(unlabeled_spike['index'])>=0)
assert is_sorted
online_peaks = unlabeled_spike['index']
engine = peeler.peeler_engine
i_stop = sig_length-catalogue['signal_preprocessor_params']['lostfront_chunksize']-engine.n_side+engine.n_span
sigs = dataio.get_signals_chunk(signal_type='processed', i_stop=i_stop)
offline_peaks = detect_peaks_in_chunk(sigs, engine.n_span, engine.relative_threshold, engine.peak_sign)
offline_peaks = offline_peaks[offline_peaks<=online_peaks[-1]]
assert offline_peaks.size == online_peaks.size
np.testing.assert_array_equal(offline_peaks, online_peaks)
if previous_peak is not None:
last = min(previous_peak[-1], online_peaks[-1])
previous_peak = previous_peak[previous_peak<=last]
online_peaks_cliped = online_peaks[online_peaks<=last]
assert previous_peak.size == online_peaks_cliped.size
np.testing.assert_array_equal(previous_peak, online_peaks_cliped)
previous_peak = online_peaks