def get_fall(data_file: File, params: MotionParams):
lever = devibrate_rec(get_trials(data_file, params))
pre_value = lever.values[:, :lever._pre // 2].mean(axis=1, keepdims=True)
lever_off = np.argmax(lever.values[:, lever._pre:] <= pre_value, axis=1) + lever._pre
lever_top = int(np.median([np.argmax(x[lever._pre: y]) for x, y in zip(lever.values, lever_off) if y > lever._pre])) + lever._pre
lever_off = int(np.median(lever_off))
return reliability(lever.values[:, lever_top: lever_off])
def make_sample_neurons(spike_framerate: Tuple[Dict[str, np.ndarray], float],
log: SparseRec, params: Dict[str, float]) -> SparseRec:
lever = log.center_on("motion", **params).fold_trials()
lever.values = np.squeeze(lever.values, 0)
lever.axes = lever.axes[1:]
filtered = devibrate_rec(lever, params)
spikes, frame_rate = spike_framerate
return fold_by(DataFrame.load(spikes), filtered, frame_rate, True)
def make_trial_log(log: SparseRec, params: Dict[str, float], center: str = "motion") -> SparseRec:
lever = log.center_on(center, **params).fold_trials()
lever.values = np.squeeze(lever.values, 0)
lever.axes = lever.axes[1:]
return devibrate_rec(lever, params)
def fall_spread(data_file: File, params: MotionParams):
lever = devibrate_rec(get_trials(data_file, params))
pre_value = lever.values[:, :lever._pre // 2].mean(axis=1, keepdims=True)
lever_off = np.argmax(lever.values[:, lever._pre:] <= pre_value, axis=1)
return np.std(lever_off)
def get_rise(data_file: File, params: MotionParams):
lever = devibrate_rec(get_trials(data_file, params))
lever_top = int(np.median(np.argmax(lever.values[:, lever._pre:], axis=1))) + lever._pre
return reliability(lever.values[:, lever._pre // 2: lever_top])
def get_initial(data_file: File, params: MotionParams):
lever = devibrate_rec(get_trials(data_file, params))
pre_value = lever.values[:, :lever._pre // 2].mean(axis=1, keepdims=True)
lever_off = int(np.median(np.argmax(lever.values[:, lever._pre:] <= pre_value, axis=1))) + lever._pre
return reliability(lever.values[:, lever._pre // 2: lever_off])