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_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_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 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')