def test_discrete_cross_correlation(self):
ts1 = np.arange(10)
ts2 = ts1 + 0.5
t, cc = ta.discrete_cross_correlation(ts1,
ts2,
t_range=(-1., 1.),
bins=10,
isPlot=False)
assert (np.array_equal(
cc, np.array([0., 0., 1., 0., 0., 0., 0., 1., 0., 0.])))
t, cc = ta.discrete_cross_correlation(ts1,
ts2,
t_range=(-1., 1.),
bins=20,
isPlot=False)
assert (np.array_equal(
cc,
np.array([
0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,
0., 0., 0., 0.
])))
ts2 = np.hstack((ts2, ts2 + 0.01)).flatten()
t, cc = ta.discrete_cross_correlation(ts1,
ts2,
t_range=(-1., 1.),
bins=20,
isPlot=False)
assert (np.array_equal(
cc,
np.array([
0., 0., 0., 0., 0., 2., 0., 0., 0., 0., 0., 0., 0., 0., 0., 2.,
0., 0., 0., 0.
])))
def test_get_event_with_pre_iei(self):
ts = np.arange(100) + np.random.rand(100) * 0.4
ts2 = ta.get_event_with_pre_iei(ts, iei=0.8)
assert (np.min(np.diff(ts2)) > 0.8)
ts3 = np.arange(20)
ts3[5] = 6
ts3[7] = 6
ts3[1] = 0
ts4 = ta.get_event_with_pre_iei(ts3, iei=0.5)
assert (np.array_equal(
ts4,
np.array(
[2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])))
ts5 = ta.get_event_with_pre_iei(ts3, iei=1)
assert (np.array_equal(ts5, np.array([2, 6, 8])))
np.random.shuffle(ts3)
ts6 = ta.get_event_with_pre_iei(ts3, iei=0.5)
print(ts3)
print(ts6)
assert (np.array_equal(
ts6,
np.array(
[2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])))
ts7 = ta.get_event_with_pre_iei(ts3, iei=1)
assert (np.array_equal(ts7, np.array([2, 6, 8])))
def test_get_burst(self):
spikes = [
0.3, 0.5, 0.501, 0.503, 0.505, 0.65, 0.7, 0.73, 0.733, 0.734,
0.735, 0.9, 1.5, 1.6, 1.602, 1.603, 1.605, 1.94, 1.942
]
_, burst_ind = ta.get_burst(spikes,
pre_isi=(-np.inf, -0.1),
inter_isi=0.004,
spk_num_thr=2)
assert (np.array_equal(burst_ind, [[1, 4], [13, 4], [17, 2]]))
_, burst_ind = ta.get_burst(spikes,
pre_isi=(-np.inf, -0.01),
inter_isi=0.004,
spk_num_thr=2)
assert (np.array_equal(burst_ind, [[1, 4], [7, 4], [13, 4], [17, 2]]))
_, burst_ind = ta.get_burst(spikes,
pre_isi=(-np.inf, -0.01),
inter_isi=0.002,
spk_num_thr=2)
assert (np.array_equal(burst_ind, [[1, 2]]))
_, burst_ind = ta.get_burst(spikes,
pre_isi=(-np.inf, -0.01),
inter_isi=0.004,
spk_num_thr=3)
assert (np.array_equal(burst_ind, [[1, 4], [7, 4], [13, 4]]))
def test_haramp(self):
t = np.arange(1000) * 0.001
trace = np.sin(t * 2 * 2 * np.pi) + 1
har = ta.haramp(trace=trace, periods=1, ceil_f=3)
assert (len(har) == 3)
assert (har[2] / har[0] == 1.)
trace = np.zeros(1000)
trace[np.array([0, 249, 499, 749])] = 1.
har = ta.haramp(trace=trace, periods=4, ceil_f=4)
assert (len(har) == 4)
assert (round(1000. * har[1] / har[0]) / 1000. == 2.)
def test_threshold_to_intervals(self):
trace = np.array([2.3, 4.5, 6.7, 5.5, 3.3, 9.2, 4.4, 3.2, 1.0, 0.8, 5.5])
intvs1 = ta.threshold_to_intervals(trace=trace, thr=5.0, comparison='>=')
# print(intvs1)
for intv in intvs1:
# print(trace[intv[0]: intv[1]])
assert(np.min(trace[intv[0]: intv[1]]) >= 5.0)
intvs2 = ta.threshold_to_intervals(trace=trace, thr=3.0, comparison='<')
for intv in intvs2:
# print(trace[intv[0]: intv[1]])
assert(np.max(trace[intv[0]: intv[1]]) < 3.0)
def test_find_nearest(self):
trace = np.arange(10)
assert(ta.find_nearest(trace, 1.4) == 1)
assert(ta.find_nearest(trace, 1.6) == 2)
assert(ta.find_nearest(trace, 1.4, -1) == 1)
assert(ta.find_nearest(trace, 1.6, -1) == 1)
assert(ta.find_nearest(trace, 1.4, 1) == 2)
assert(ta.find_nearest(trace, 1.6, 1) == 2)
assert(ta.find_nearest(trace, -1, -1) is None)
assert(ta.find_nearest(trace, 11, 1) is None)
column=vasMapColumn,row=vasMapRow,frame=vasMapFrame,crop=vasMapCrop,mergeMethod=vasMapMergeMethod)
else:
print 'No vasculature map find. Taking first frame of movie as vasculature map.'
vasMap = BinarySlicer(movPath)[0,:,:]
vasMap = ia.array_nor(vasMap).astype(np.float32)
tf.imsave(os.path.join(saveFolder,dateRecorded+'_M'+mouseID+'_Trial'+trialNum+'_vasMap.tif'),vasMap)
_, jphys = ft.importRawNewJPhys(jphysPath,dtype=jphysDtype,headerLength=jphysHeaderLength,channels=jphysChannels,sf=jphysFs)
pd = jphys['photodiode']
displayOnsets = hl.segmentPhotodiodeSignal(pd, digitizeThr=pdDigitizeThr, filterSize=pdFilterSize, segmentThr=pdSegmentThr, Fs=jphysFs)
imgFrameTS = ta.get_onset_timeStamps(jphys['read'], Fs=jphysFs, threshold=readThreshold, onsetType=readOnsetType)
logPath = hl.findLogPath(date=dateRecorded,mouseID=mouseID,stimulus='KSstimAllDir',userID=userID,fileNumber=str(fileNum),displayFolder=dataFolder)
displayInfo = hl.analysisMappingDisplayLog(logPath)
sweepNum = len(displayInfo['B2U']['ind']+displayInfo['U2B']['ind']+displayInfo['L2R']['ind']+displayInfo['R2L']['ind'])
if len(displayOnsets) != sweepNum:
warningMessage = '\nNumber of detected photodiode onsets ('+str(len(displayOnsets))+') is not equal to display sweep number ('+str(sweepNum)+')!\n'
warnings.warn(warningMessage)
altPosMap,aziPosMap,altPowerMap,aziPowerMap = hl.getMappingMovies(movPath=movPath,frameTS=imgFrameTS,
displayOnsets=displayOnsets,displayInfo=displayInfo,
temporalDownSampleRate=temporalDownSampleRate,
saveFolder=saveFolder,
savePrefix=dateRecorded+'_M'+mouseID+'_Trial'+trialNum,
np.ceil((end_time - start_time) / plane_mean_frame_dur))
plane_chunk_frame_start = int(np.floor(start_time / plane_mean_frame_dur))
plane_t = (np.arange(plane_chunk_frame_dur) +
plane_chunk_frame_start) * plane_mean_frame_dur
print '{}: STRF time axis: \n{}'.format(plane_n, plane_t)
plane_roi_traces = []
trigger_ts = []
for probe_ind, probe_n in enumerate(probe_ns):
probe_ts = probe_grp[probe_n]['pd_onset_ts_sec'].value
probe_traces = []
probe_trigger_ts = []
for curr_probe_ts in probe_ts:
curr_frame_start = ta.find_nearest(
plane_trace_ts, curr_probe_ts) + plane_chunk_frame_start
curr_frame_end = curr_frame_start + plane_chunk_frame_dur
if curr_frame_start >= 0 and curr_frame_end <= len(plane_trace_ts):
probe_traces.append(
plane_traces[:, curr_frame_start:curr_frame_end])
probe_trigger_ts.append(curr_probe_ts)
plane_roi_traces.append(np.array(probe_traces))
trigger_ts.append(probe_trigger_ts)
print('probe: {} / {}; shape: {}'.format(probe_ind + 1, len(probe_ns),
np.array(probe_traces).shape))
# plane_roi_traces = np.array(plane_roi_traces)
print('saving ...')
for roi_ind in range(roi_num):
def test_get_onset_time_stamps(self):
trace = np.array(([0.] * 5 + [5.] * 5) * 5)
ts = ta.get_onset_timeStamps(trace, Fs=10000., onsetType='raising')
assert(ts[2] == 0.0025)
ts2 = ta.get_onset_timeStamps(trace, Fs=10000., onsetType='falling')
assert(ts2[2] == 0.0030)
