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_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 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)