def neuron_lever(lever: SparseRec, neuron: DataFrame, neuron_rate: float,
motion_params: MotionParams) -> Tuple[DataFrame, np.ndarray]:
"""Read lever and corresponding neuron data by trial. I reimplement [fold_by] here to
acheive same length of the two segments.
Args:
data_file: file including spike and response(lever)
motion_params: MotionParams, a dict but all params are optional
may have [pre_time] and [post_time] in seconds, [quiet_var] and [event_thres] in float,
[window_size] in sample_no
Retursn:
neurons: a DataFrame with folded neurons
lever: a DataFrame with folded lever, resampled to neuron sample rate
"""
params = {
x: motion_params[x]
for x in ("quiet_var", "window_size", "event_thres")
if x in motion_params
}
event_onsets = event.find_response_onset(lever, **params)[0]
lever_resample = resample(lever.values[0], lever.sample_rate, neuron_rate)
anchor = np.rint(
(event_onsets / lever.sample_rate - motion_params['pre_time']) *
neuron_rate).astype(np.int)
duration = int(
round((motion_params.get('pre_time', 0.1) +
motion_params.get('post_time', 0.9)) * neuron_rate))
lever_folded = utils.take_segment(lever_resample, anchor, duration)
neuron_trials = np.stack([
utils.take_segment(trace, anchor, duration) for trace in neuron.values
])
mask, filtered = devibrate_trials(lever_folded, motion_params['pre_time'])
return neuron_trials[:, mask, :], lever_folded[mask, :]
def get_hitrate(data_file: File) -> float:
lever = load_mat(data_file['response'])
params = {
x: y
for x, y in motion_params.items()
if x in ('quiet_var', 'window_size', 'event_thres')
}
_, _, _, correct_trials, _ = find_response_onset(lever, **params)
return correct_trials.mean()
def get_hitrate(data_file: File, params: MotionParams) -> float:
"""Get hitrate (hit / trial_no) for one session. Hit is based reanalysis of trajectory and not
whether reward was given."""
lever = load_mat(data_file['response'])
params = {
x: y
for x, y in params.items()
if x in ('quiet_var', 'window_size', 'event_thres')
}
_, _, _, correct_trials, _ = find_response_onset(lever, **params)
return correct_trials.mean()
def get_delay(data_file: File) -> float:
lever = load_mat(data_file['response'])
params = {
x: y
for x, y in motion_params.items()
if x in ('quiet_var', 'window_size', 'event_thres')
}
motion_onsets, stim_onsets, _, correct_trials, _ = find_response_onset(
lever, **params)
return np.mean(
(motion_onsets - stim_onsets[correct_trials]) / lever.sample_rate)
def get_delay(data_file: File, params: MotionParams) -> float:
"""Get delay in seconds. Delay as the time between EarlyCueTime and actual motion
onset (passing threshold)."""
lever = load_mat(data_file['response'])
params = {
x: y
for x, y in params.items()
if x in ('quiet_var', 'window_size', 'event_thres')
}
motion_onsets, stim_onsets, _, correct_trials, _ = find_response_onset(
lever, **params)
return np.mean(
(motion_onsets - stim_onsets[correct_trials]) / lever.sample_rate)