def test_get_chunks(self):
x = np.random.normal(0, 1, (100, 80))
starts = [20, 50]
ends = [30, 70]
# test dimension 0
chunks = time_series.get_chunks(x, starts, ends, axis=0)
self.assertEqual(len(chunks), 2)
for chunk, start, end in zip(chunks, starts, ends):
np.testing.assert_array_almost_equal(chunk, x[start:end, :])
# test dimension 1
chunks = time_series.get_chunks(x, starts, ends, axis=1)
self.assertEqual(len(chunks), 2)
for chunk, start, end in zip(chunks, starts, ends):
np.testing.assert_array_almost_equal(chunk, x[:, start:end])
def variability_stim_conditioned(nwb_file, detrend_window, neurons_per_stim):
"""
Calculate the variability of neural responses conditioned on a specific stimulus, averaged over
basically everything, for a single experiment.
:param nwb_file: path to experiment's .nwb file
:param detrend_window: window length (number of timesteps) to use when detrending fluorescence traces
:return data frame where rows are stimuli and which has columns: temporal_frequency, orientation, variability,
most_active_neurons
"""
print(nwb_file)
# get and detrend fluorescence traces
_, traces = api.get_fluorescence_traces(nwb_file)
traces -= time_series.windowed_mean(traces, detrend_window)
traces /= np.tile(traces.std(axis=1)[:, None], (1, traces.shape[1]))
# get stimulus data
stim_data = api.get_stimulus_table(nwb_file)
# get unique stimulus conditions as list of tuples
stim_conditions_df = api.get_stimulus_conditions(nwb_file, include_blanks=False)
stim_conditions = [tuple(cond) for cond in stim_conditions_df.values]
df = pd.DataFrame(columns=['temporal_frequency', 'orientation', 'variability', 'most_active_neurons'])
for tf, ori in stim_conditions:
# get all start and end times for this stimulus
mask = (stim_data['temporal_frequency'] == tf) & (stim_data['orientation'] == ori)
starts, ends = stim_data[['start', 'end']][mask].values.T
min_dur = (ends - starts).min()
diffs = ends - starts - min_dur
ends[diffs > 0] -= diffs[diffs > 0]
# get all neural responses to all repetitions of this stimulus
responses_all = time_series.get_chunks(traces, starts, ends, axis=1)
# find most responsive neurons
responses_all = [time_series.subtract_first(response, axis=1) for response in responses_all]
responsivenesses_trial = [np.abs(response).max(axis=1)[:, None] for response in responses_all]
responsivenesses_stim = np.concatenate(responsivenesses_trial, axis=1).mean(axis=1)
most_active_neurons = np.argsort(responsivenesses_stim)[-neurons_per_stim:]
# get responses of active neurons only
responses = [response[most_active_neurons, :] for response in responses_all]
time_averaged_stds_neuron = []
for ctr in range(neurons_per_stim):
std = np.concatenate([response[ctr, :][None, :] for response in responses], axis=0).std(axis=0)
time_averaged_stds_neuron.append(std.mean())
data = {
'temporal_frequency': tf, 'orientation': ori, 'variability': np.mean(time_averaged_stds_neuron),
'most_active_neurons': most_active_neurons
}
df = df.append(data, ignore_index=True)
return df
def variability_stim_conditioned(nwb_file, detrend_window, neurons_per_stim):
"""
Calculate the variability of neural responses conditioned on a specific stimulus, averaged over
basically everything, for a single experiment.
:param nwb_file: path to experiment's .nwb file
:param detrend_window: window length (number of timesteps) to use when detrending fluorescence traces
:return data frame where rows are stimuli and which has columns: temporal_frequency, orientation, variability,
most_active_neurons
"""
print(nwb_file)
# get and detrend fluorescence traces
_, traces = api.get_fluorescence_traces(nwb_file)
traces -= time_series.windowed_mean(traces, detrend_window)
traces /= np.tile(traces.std(axis=1)[:, None], (1, traces.shape[1]))
# get stimulus data
stim_data = api.get_stimulus_table(nwb_file)
# get unique stimulus conditions as list of tuples
stim_conditions_df = api.get_stimulus_conditions(nwb_file,
include_blanks=False)
stim_conditions = [tuple(cond) for cond in stim_conditions_df.values]
df = pd.DataFrame(columns=[
'temporal_frequency', 'orientation', 'variability',
'most_active_neurons'
])
for tf, ori in stim_conditions:
# get all start and end times for this stimulus
mask = (stim_data['temporal_frequency']
== tf) & (stim_data['orientation'] == ori)
starts, ends = stim_data[['start', 'end']][mask].values.T
min_dur = (ends - starts).min()
diffs = ends - starts - min_dur
ends[diffs > 0] -= diffs[diffs > 0]
# get all neural responses to all repetitions of this stimulus
responses_all = time_series.get_chunks(traces, starts, ends, axis=1)
# find most responsive neurons
responses_all = [
time_series.subtract_first(response, axis=1)
for response in responses_all
]
responsivenesses_trial = [
np.abs(response).max(axis=1)[:, None] for response in responses_all
]
responsivenesses_stim = np.concatenate(responsivenesses_trial,
axis=1).mean(axis=1)
most_active_neurons = np.argsort(
responsivenesses_stim)[-neurons_per_stim:]
# get responses of active neurons only
responses = [
response[most_active_neurons, :] for response in responses_all
]
time_averaged_stds_neuron = []
for ctr in range(neurons_per_stim):
std = np.concatenate(
[response[ctr, :][None, :] for response in responses],
axis=0).std(axis=0)
time_averaged_stds_neuron.append(std.mean())
data = {
'temporal_frequency': tf,
'orientation': ori,
'variability': np.mean(time_averaged_stds_neuron),
'most_active_neurons': most_active_neurons
}
df = df.append(data, ignore_index=True)
return df
