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])
