def get_laser_train_rasters(h5, clu,
amplitude='',
onset_delay=None,
n_pulses=-1,
time_window_ms=1000,
pre_pad_ms=500,
include_odor_trials=False,
baseline_nsniffs=5,
skip_last_trials=0,
*args, **kwargs):
"""
:param h5:
:param clu:
:param amplitude:
:param onset_delay:
:param time_window_ms:
:param pre_pad_ms:
:return:
"""
pre_pad_samples = np.int(pre_pad_ms * h5.root.events._v_attrs['sample_rate_Hz'] / 1000.)
train_events = find_laser_trains(h5, amplitude=amplitude, onset_delay=onset_delay,
n_pulses=n_pulses, include_odor_trials=include_odor_trials,
skip_last_trials=skip_last_trials, *args, **kwargs)
train_starts = train_events[:, 0]
rasters = get_rasters(h5, clu, train_starts-pre_pad_samples, time_window_ms, convert_to_ms=True)
rasters -= pre_pad_ms
# now find a pre - stimulus sniff to calculate baseline from and get a raster centered around this sniff at the
# same latency as the laser stimulus.
baseline_starts = []
sniff_events = h5.root.events.sniff.inh_events
sniff_starts = sniff_events[:, 0]
for start in train_starts:
last_inh = sniff_starts[sniff_starts < start][-1]
latency = start - last_inh
for i in xrange(baseline_nsniffs):
two_sniffs_back = sniff_starts[sniff_starts < start][-3-i] # find the inhalation 2 prior to the triggering one.
baseline_starts.append(two_sniffs_back+latency) # add the latency, because we want to center around the same point in the sniff as we did in the laser trial.
baseline_starts = np.asarray(baseline_starts)
baseline_rasters = get_rasters(h5, clu, baseline_starts - pre_pad_samples, time_window_ms, convert_to_ms=True)
baseline_rasters -= pre_pad_ms
return rasters, baseline_rasters
def get_pre_odor_baseline_psths(h5, clu, odor, bin_size, n_sniffs=5,
include_laser_trials=False,
time_window_ms=1000,
pre_pad_ms=0,
skip_last_trials=0):
"""
Gets the first inhalations before every finalvalve opening and averages them all to get a baseline psth.
:param h5:
:param clu:
:param bin_size:
:param time_window_ms:
:param pre_pad_ms:
:return:
"""
pre_pad_samples = np.int(pre_pad_ms * h5.root.events._v_attrs['sample_rate_Hz'] / 1000.)
sniff_events = h5.root.events.sniff.inh_events
sniff_starts = sniff_events[:, 0]
fv_events = find_odor_events(h5, odor,
include_laser_trials=include_laser_trials,
skip_last_trials=skip_last_trials)
starts = []
for fv_ev in fv_events:
fv_start = fv_ev[0]
for i_sn in xrange(1, n_sniffs + 1):
last_inh = sniff_starts[sniff_starts < fv_start][-i_sn]
starts.append(last_inh)
starts = np.array(starts)
rasters = get_rasters(h5, clu, starts - pre_pad_samples, time_window_ms, convert_to_ms=True)
rasters -= pre_pad_ms
spike_array, time_array = make_spike_array(rasters, bin_size, -pre_pad_ms, time_window_ms-pre_pad_ms)
# psths = np.sum(spike_array, axis=1)
n_trials = len(starts)
psths_hz = (1000 / bin_size) * spike_array
return psths_hz, time_array, n_trials
def get_odor_rasters_sniff(h5, clu, odor, odor_conc, time_window_ms=1000, pre_pad_ms=500, include_laser_trials=False,
only_laser_trials=False, *args, **kwargs):
"""
:param h5:
:param clu:
:param odor:
:param odor_conc:
:param time_window_ms:
:param pre_pad_ms:
:return:
"""
fs = h5.root.events._v_attrs['sample_rate_Hz']
pre_pad_samples = np.int(pre_pad_ms * fs / 1000.)
fv_events = find_odor_events(h5, odor, odor_conc, include_laser_trials=include_laser_trials,
only_laser_trials=only_laser_trials)
inh_starts = list()
sniff_events_all_trials = list()
for i, ev in enumerate(fv_events):
sn_events = find_sniff_events(h5, ev, *args, **kwargs)
try:
first_sn_event = sn_events[0] # the first sniff after odor onset.
first_inh = first_sn_event[0] # this is the first inhalation after odor onset. THIS IS TIME 0!!!
inh_starts.append(first_inh)
sn_events_in_window = find_sniff_events(h5, [first_inh - pre_pad_samples,
first_inh - pre_pad_samples + (time_window_ms*fs)/1000.])
sn_events_in_window = sn_events_in_window - first_inh # must make relative to first inh = 0.
sn_events_in_window_ms = sn_events_in_window / fs * 1000
sniff_events_all_trials.append(sn_events_in_window_ms)
except TypeError: # empty array, no sniffs found
print 'no sniffs found'
pass
inh_starts = np.array(inh_starts)
rasters = get_rasters(h5, clu, inh_starts-pre_pad_samples, time_window_ms=time_window_ms, convert_to_ms=True)
rasters -= pre_pad_ms
assert len(rasters) == len(sniff_events_all_trials)
return rasters, sniff_events_all_trials
def get_odor_rasters(h5, clu, odor, odor_conc,
time_window_ms=1000,
pre_pad_ms=500,
include_laser_trials=False,
only_laser_trials=False,
skip_last_trials=0,
*args, **kwargs):
"""
:param h5:
:param clu:
:param odor:
:param odor_conc:
:param time_window_ms:
:param pre_pad_ms:
:return:
"""
pre_pad_samples = np.int(pre_pad_ms * h5.root.events._v_attrs['sample_rate_Hz'] / 1000.)
fv_events = find_odor_events(h5, odor, odor_conc,
include_laser_trials=include_laser_trials,
only_laser_trials=only_laser_trials,
skip_last_trials=skip_last_trials,
*args, **kwargs)
starts = []
for i, ev in enumerate(fv_events):
sn_events = find_sniff_events(h5, ev)
try:
sn_event = sn_events[0] # the first inhalation after odor onset.
starts.append(sn_event[0])
except TypeError: # empty array, no sniffs found
print 'no sniffs found'
pass
starts = np.array(starts)
rasters = get_rasters(h5, clu, starts-pre_pad_samples, time_window_ms=time_window_ms, convert_to_ms=True)
rasters -= pre_pad_ms
return rasters
def get_laser_train_rasters(h5,
clu,
amplitude='',
onset_delay=None,
n_pulses=-1,
time_window_ms=1000,
pre_pad_ms=500,
include_odor_trials=False,
baseline_nsniffs=5,
skip_last_trials=0,
*args,
**kwargs):
"""
:param h5:
:param clu:
:param amplitude:
:param onset_delay:
:param time_window_ms:
:param pre_pad_ms:
:return:
"""
pre_pad_samples = np.int(pre_pad_ms *
h5.root.events._v_attrs['sample_rate_Hz'] / 1000.)
train_events = find_laser_trains(h5,
amplitude=amplitude,
onset_delay=onset_delay,
n_pulses=n_pulses,
include_odor_trials=include_odor_trials,
skip_last_trials=skip_last_trials,
*args,
**kwargs)
train_starts = train_events[:, 0]
rasters = get_rasters(h5,
clu,
train_starts - pre_pad_samples,
time_window_ms,
convert_to_ms=True)
rasters -= pre_pad_ms
# now find a pre - stimulus sniff to calculate baseline from and get a raster centered around this sniff at the
# same latency as the laser stimulus.
baseline_starts = []
sniff_events = h5.root.events.sniff.inh_events
sniff_starts = sniff_events[:, 0]
for start in train_starts:
last_inh = sniff_starts[sniff_starts < start][-1]
latency = start - last_inh
for i in xrange(baseline_nsniffs):
two_sniffs_back = sniff_starts[sniff_starts < start][
-3 - i] # find the inhalation 2 prior to the triggering one.
baseline_starts.append(
two_sniffs_back + latency
) # add the latency, because we want to center around the same point in the sniff as we did in the laser trial.
baseline_starts = np.asarray(baseline_starts)
baseline_rasters = get_rasters(h5,
clu,
baseline_starts - pre_pad_samples,
time_window_ms,
convert_to_ms=True)
baseline_rasters -= pre_pad_ms
return rasters, baseline_rasters