def decode_data(self, chdata, tsdata, sync_offset):
"""
The qutau data has two arrays: one channel array containing the channel numbers
where clicks were observed. The other one contains the time stamps.
Here I assign to each event a third and fourth array. The third array contains
the sync number, and a fourth array replaces the second array with timestamps. It
contains the times with respect to the last sync. Finally we reshape the data
in the well known HydraHarp format.
"""
chdata = chdata.astype(np.uint8)
tsdata = tsdata.astype(np.uintc)
sync_mask = chdata != self.sync_chan
special = np.logical_or(chdata == self.marker1_chan,
chdata == self.marker2_chan)
sync_number = hhopt.get_sync_number(chdata, self.sync_chan,
sync_offset)
timings_wrt_sync = hhopt.get_dt_wrt_sync(chdata, tsdata,
self.sync_chan)
datastack = hhopt.stack(sync_number.astype(np.uintc),
timings_wrt_sync.astype(np.uintc),
chdata.astype(np.uintc),
special.astype(np.uintc))
#adjust the sync number offset to the last sync number
sync_offset = datastack[-1, 0]
#delete syncs from the arrays
decoded_data = hhopt.apply_filter_to_2darray(
datastack, sync_mask.astype(np.uintc))
return decoded_data, sync_offset
def filter_marker_on_counts(data, mchan, delta_syncs=[0], print_status=False):
t0 = time.time()
if print_status:
print '* filter mrkr %s on counts' % mchan
special = data[:, 3] == 1
mrkr = np.logical_and(special, data[:, 2] == mchan)
other_mrkr = np.logical_and(special, np.logical_not(mrkr))
clicks = np.logical_not(special)
good = data[mrkr, 0] < 0
for d in delta_syncs:
good = np.logical_or(good, np.in1d(data[mrkr, 0] + d, data[clicks, 0]))
for i, idx in np.ndenumerate(np.where(mrkr)[0]):
if not good[i]:
mrkr[idx] = False
if print_status:
print ' %d markers are valid.' % len(data[mrkr])
print ' processing took %.2f secs.' % (time.time() - t0)
filtered = np.logical_or(np.logical_or(other_mrkr, mrkr), clicks)
if len(np.where(filtered)[0]) == 0:
return np.zeros((0, 4), dtype=np.uint)
return hhopt.apply_filter_to_2darray(data, filtered.astype(np.uint))
def decode_data(self, chdata, tsdata, sync_offset):
"""
The qutau data has two arrays: one channel array containing the channel numbers
where clicks were observed. The other one contains the time stamps.
Here I assign to each event a third and fourth array. The third array contains
the sync number, and a fourth array replaces the second array with timestamps. It
contains the times with respect to the last sync. Finally we reshape the data
in the well known HydraHarp format.
"""
chdata = chdata.astype(np.uint8)
tsdata = tsdata.astype(np.uintc)
sync_mask = chdata != self.sync_chan
special = np.logical_or(chdata == self.marker1_chan, chdata == self.marker2_chan)
sync_number = hhopt.get_sync_number(chdata, self.sync_chan, sync_offset)
timings_wrt_sync = hhopt.get_dt_wrt_sync(chdata, tsdata, self.sync_chan)
datastack = hhopt.stack(sync_number.astype(np.uintc),
timings_wrt_sync.astype(np.uintc),
chdata.astype(np.uintc),
special.astype(np.uintc))
#adjust the sync number offset to the last sync number
sync_offset = datastack[-1,0]
#delete syncs from the arrays
decoded_data = hhopt.apply_filter_to_2darray(datastack, sync_mask.astype(np.uintc))
return decoded_data, sync_offset
def filter_timewindow(data, chan, mintime, maxtime, print_status=False):
t0 = time.time()
if print_status:
print '* filter ch %d timewindow' % chan
if len(data) == 0:
print ' empty data, cannot time-filter'
return data
notspecial = data[:, 3] == 0
ch = data[:, 2] == chan
click = np.logical_and(notspecial, ch)
clickstartbad = np.logical_and(click, data[:, 1] < mintime)
clickstopbad = np.logical_and(click, data[:, 1] > maxtime)
clickbad = np.logical_or(clickstartbad, clickstopbad)
if print_status:
print ' found %d of %d clicks out of time window' % \
(len(data[clickbad]), len(data[click]))
print ' processing took %.2f secs.' % (time.time() - t0)
filtered = np.logical_not(clickbad)
if len(np.where(filtered)[0]) == 0:
return np.zeros((0, 4), dtype=np.uint)
return hhopt.apply_filter_to_2darray(data, filtered.astype(np.uint))
def filter_marker_on_counts(data, mchan, delta_syncs=[0], print_status = False):
t0 = time.time()
if print_status:
print '* filter mrkr %s on counts' % mchan
special = data[:,3] == 1
mrkr = np.logical_and(special, data[:,2] == mchan)
other_mrkr = np.logical_and(special, np.logical_not(mrkr))
clicks = np.logical_not(special)
good = data[mrkr,0] < 0
for d in delta_syncs:
good = np.logical_or(good, np.in1d(data[mrkr,0]+d, data[clicks,0]))
for i,idx in np.ndenumerate(np.where(mrkr)[0]):
if not good[i]:
mrkr[idx] = False
if print_status:
print ' %d markers are valid.' % len(data[mrkr])
print ' processing took %.2f secs.' % (time.time()-t0)
filtered = np.logical_or(np.logical_or(other_mrkr, mrkr), clicks)
if len(np.where(filtered)[0]) == 0:
return np.zeros((0,4), dtype=np.uint)
return hhopt.apply_filter_to_2darray(data, filtered.astype(np.uint))
def filter_timewindow(data, chan, mintime, maxtime, print_status = False):
t0 = time.time()
if print_status:
print '* filter ch %d timewindow' % chan
if len(data) == 0:
print ' empty data, cannot time-filter'
return data
notspecial = data[:,3] == 0
ch = data[:,2] == chan
click = np.logical_and(notspecial, ch)
clickstartbad = np.logical_and(click, data[:,1] < mintime)
clickstopbad = np.logical_and(click, data[:,1] > maxtime)
clickbad = np.logical_or(clickstartbad, clickstopbad)
if print_status:
print ' found %d of %d clicks out of time window' % \
(len(data[clickbad]), len(data[click]))
print ' processing took %.2f secs.' % (time.time()-t0)
filtered = np.logical_not(clickbad)
if len(np.where(filtered)[0]) == 0:
return np.zeros((0,4), dtype=np.uint)
return hhopt.apply_filter_to_2darray(data, filtered.astype(np.uint))
def delete_markers(data, mchan):
"""
Delete markers from the data on marker channel "mchan".
Useful for throwing away useless clicks.
"""
special = data[:, 3] == 1
mrkr = np.logical_and(special, data[:, 2] == mchan)
filtered = np.logical_not(mrkr)
if len(np.where(filtered)[0]) == 0:
return np.zeros((0, 4), dtype=np.uint)
return hhopt.apply_filter_to_2darray(data, filtered.astype(np.uint))
def delete_markers(data, mchan):
"""
Delete markers from the data on marker channel "mchan".
Useful for throwing away useless clicks.
"""
special = data[:,3] == 1
mrkr = np.logical_and(special, data[:,2] == mchan)
filtered = np.logical_not(mrkr)
if len(np.where(filtered)[0]) == 0:
return np.zeros((0,4), dtype=np.uint)
return hhopt.apply_filter_to_2darray(data, filtered.astype(np.uint))
