def make(self, key):
print(f'Populating Sync for {key}')
# Get olfactory h5 path and filename
olfactory_path = (OdorSession & key).fetch1('odor_path')
local_path = lab.Paths().get_local_path(olfactory_path)
filename_base = (OdorRecording & key).fetch1('filename')
analog_filename = os.path.join(local_path, filename_base + '_%d.h5')
# Load olfactory data
analog_data = h5.read_analog_olfaction_file(analog_filename)
scan_times = h5.ts2sec(analog_data['ts'], is_packeted=True)
binarized_signal = analog_data['scanImage'] > 2.7 # TTL voltage low/high threshold
rising_edges = np.where(np.diff(binarized_signal.astype(int)) > 0)[0]
frame_times = scan_times[rising_edges]
# Correct NaN gaps in timestamps (mistimed or dropped packets during recording)
if np.any(np.isnan(frame_times)):
# Raise exception if first or last frame pulse was recorded in mistimed packet
if np.isnan(frame_times[0]) or np.isnan(frame_times[-1]):
msg = ('First or last frame happened during misstamped packets. Pulses '
'could have been missed: start/end of scanning is unknown.')
raise PipelineException(msg)
# Fill each gap of nan values with correct number of timepoints
frame_period = np.nanmedian(np.diff(frame_times)) # approx
nan_limits = np.where(np.diff(np.isnan(frame_times)))[0]
nan_limits[1::2] += 1 # limits are indices of the last valid point before the nan gap and first after it
correct_fts = []
for i, (start, stop) in enumerate(zip(nan_limits[::2], nan_limits[1::2])):
correct_fts.extend(frame_times[0 if i == 0 else nan_limits[2 * i - 1]: start + 1])
num_missing_points = int(round((frame_times[stop] - frame_times[start]) /
frame_period - 1))
correct_fts.extend(np.linspace(frame_times[start], frame_times[stop],
num_missing_points + 2)[1:-1])
correct_fts.extend(frame_times[nan_limits[-1]:])
frame_times = np.array(correct_fts)
# Check that frame times occur at the same period
frame_intervals = np.diff(frame_times)
frame_period = np.median(frame_intervals)
if np.any(abs(frame_intervals - frame_period) > 0.15 * frame_period):
raise PipelineException('Frame time period is irregular')
self.insert1({**key, 'signal_start_time': frame_times[0],
'signal_duration': frame_times[-1] - frame_times[0],
'frame_times': frame_times})
print(f'ScanImage sync added for animal {key["animal_id"]}, '
f'olfactory session {key["odor_session"]}, '
f'recording {key["recording_idx"]}\n')
def make(self, key):
print(f'Populating Respiration for {key}')
# Get olfactory h5 path and filename
olfactory_path = (OdorSession & key).fetch1('odor_path')
local_path = lab.Paths().get_local_path(olfactory_path)
filename_base = (OdorRecording & key).fetch1('filename')
analog_filename = os.path.join(local_path, filename_base + '_%d.h5')
# Load olfactory data
analog_data = h5.read_analog_olfaction_file(analog_filename)
breath_times = h5.ts2sec(analog_data['ts'], is_packeted=True)
breath_trace = analog_data['breath']
# Correct NaN gaps in timestamps (mistimed or dropped packets during recording)
if np.any(np.isnan(breath_times)):
# Raise exception if first or last frame pulse was recorded in mistimed packet
if np.isnan(breath_times[0]) or np.isnan(breath_times[-1]):
msg = (
'First or last breath happened during misstamped packets. Pulses '
'could have been missed: start/end of collection is unknown.'
)
raise PipelineException(msg)
# Linear interpolate between nans
nans_idx = np.where(np.isnan(breath_times))[0]
non_nans_idx = np.where(~np.isnan(breath_times))[0]
breath_times[nans_idx] = np.interp(nans_idx, non_nans_idx,
breath_times[non_nans_idx])
print(
f'Largest NaN gap found: {np.max(np.abs(np.diff(breath_times[non_nans_idx])))} seconds'
)
# Check that frame times occur at the same period
breath_intervals = np.diff(breath_times)
breath_period = np.median(breath_intervals)
if np.any(
abs(breath_intervals - breath_period) > 0.15 * breath_period):
raise PipelineException('Breath time period is irregular')
# Error check tracing and timing match
if breath_trace.shape[0] != breath_times.shape[0]:
raise PipelineException('Breath timing and trace mismatch!')
breath_key = {**key, 'trace': breath_trace, 'times': breath_times}
self.insert1(breath_key)
print(f'Respiration data for {key} successfully inserted.\n')