def __init__(self, session_id):
self.session_id = session_id
save_path_head = core.get_save_path()
self.save_path = os.path.join(save_path_head, 'LocallySparseNoise')
self.l0_events = core.get_L0_events(self.session_id)
self.stim_table_sp, _, _ = core.get_stim_table(self.session_id,
'spontaneous')
self.dxcm = core.get_running_speed(self.session_id)
try:
lsn_name = 'locally_sparse_noise'
self.stim_table, self.numbercells, self.specimen_ids = core.get_stim_table(
self.session_id, lsn_name)
self.LSN = core.get_stimulus_template(self.session_id, lsn_name)
self.sweep_events, self.mean_sweep_events, self.sweep_p_values, self.running_speed, self.response_events_on, self.response_events_off = self.get_stimulus_response(
self.LSN)
except:
lsn_name = 'locally_sparse_noise_4deg'
self.stim_table, self.numbercells, self.specimen_ids = core.get_stim_table(
self.session_id, lsn_name)
self.LSN_4deg = core.get_stimulus_template(self.session_id,
lsn_name)
self.sweep_events_4deg, self.mean_sweep_events_4deg, self.sweep_p_values_4deg, self.running_speed_4deg, self.response_events_on_4deg, self.response_events_off_4deg = self.get_stimulus_response(
self.LSN_4deg)
lsn_name = 'locally_sparse_noise_8deg'
self.stim_table, _, _ = core.get_stim_table(
self.session_id, lsn_name)
self.LSN_8deg = core.get_stimulus_template(self.session_id,
lsn_name)
self.sweep_events_8deg, self.mean_sweep_events_8deg, self.sweep_p_values_8deg, self.running_speed_8deg, self.response_events_on_8deg, self.response_events_off_8deg = self.get_stimulus_response(
self.LSN_8deg)
self.peak = self.get_peak(lsn_name)
self.save_data(lsn_name)
def __init__(self, session_id):
self.session_id = session_id
save_path_head = core.get_save_path()
self.save_path = os.path.join(save_path_head, 'NaturalScenes')
self.l0_events = core.get_L0_events(self.session_id)
self.stim_table, self.numbercells, self.specimen_ids = core.get_stim_table(self.session_id, 'natural_scenes')
self.stim_table_sp,_,_ = core.get_stim_table(self.session_id, 'spontaneous')
self.dxcm = core.get_running_speed(self.session_id)
self.sweep_events, self.mean_sweep_events, self.sweep_p_values, self.running_speed, self.response_events, self.response_trials = self.get_stimulus_response()
self.peak = self.get_peak()
self.save_data()
def __init__(self, session_id):
self.session_id = session_id
save_path_head = core.get_save_path()
self.save_path = os.path.join(save_path_head, 'DriftingGratings')
self.l0_events = core.get_L0_events(self.session_id)
self.stim_table, self.numbercells, self.specimen_ids = core.get_stim_table(
self.session_id, 'drifting_gratings')
self.dxcm = core.get_running_speed(self.session_id)
self.stim_table_sp, _, _ = core.get_stim_table(self.session_id,
'spontaneous')
self.orivals = range(0, 360, 45)
self.tfvals = [0, 1, 2, 4, 8, 15]
self.sweep_events, self.mean_sweep_events, self.sweep_p_values, self.running_speed, self.response_events, self.response_trials = self.get_stimulus_response(
)
self.peak = self.get_peak()
self.save_data()
def __init__(self, session_id):
self.session_id = session_id
save_path_head = core.get_save_path()
self.save_path = os.path.join(save_path_head, 'StaticGratings')
self.l0_events = core.get_L0_events(self.session_id)
self.stim_table, self.numbercells, self.specimen_ids = core.get_stim_table(
self.session_id, 'static_gratings')
self.stim_table_sp, _, _ = core.get_stim_table(self.session_id,
'spontaneous')
self.dxcm = core.get_running_speed(self.session_id)
self.orivals = range(0, 180, 30)
self.sfvals = [0, 0.02, 0.04, 0.08, 0.16, 0.32]
self.phasevals = [0, 0.25, 0.5, 0.75]
self.sweep_events, self.mean_sweep_events, self.sweep_p_values, self.running_speed, self.response_events, self.response_trials = self.get_stimulus_response(
)
self.peak = self.get_peak()
self.save_data()
def __init__(self, session_id):
self.session_id = session_id
save_path_head = core.get_save_path()
self.save_path = os.path.join(save_path_head, 'LocallySparseNoise')
self.l0_events = core.get_L0_events(self.session_id)
#TODO: enable lsn, lsn4, lsn8
lsn_name = 'locally_sparse_noise'
self.stim_table, self.numbercells, self.specimen_ids = core.get_stim_table(
self.session_id, 'locally_sparse_noise')
self.LSN = core.get_stimulus_template('locally_sparse_noise')
self.stim_table_sp, _, _ = core.get_stim_table(self.session_id,
'spontaneous')
self.dxcm = core.get_running_speed(self.session_id)
self.sweep_events, self.mean_sweep_events, self.sweep_p_values, self.running_speed, self.response_events, self.response_trials = self.get_stimulus_response(
)
self.peak = self.get_peak()
self.save_data()
def Speed_Tuning(session_id, binsize=900):
save_path_head = core.get_save_path()
save_path = os.path.join(save_path_head, 'SpeedTuning')
l0_events = core.get_L0_events(session_id)
dxcm = core.get_running_speed(session_id)
_, numbercells, specimen_ids = core.get_stim_table(session_id,
'spontaneous')
# numbercells = l0_events.shape
numbins = 1 + len(dxcm[np.where(dxcm > 1)] / binsize)
#remove any NaNs from running and activity traces
dx_trim = dxcm[~np.isnan(dxcm)]
l0_trim = l0_events[:, ~np.isnan(dxcm)]
#rank sort traces by running speed
dx_sorted = dx_trim[np.argsort(dx_trim)]
events_sorted = l0_trim[:, np.argsort(dx_trim)]
#bin running and activity
binned_cells = np.zeros((numbercells, numbins, 2))
binned_dx = np.zeros((numbins, 2))
for i in range(numbins):
offset = np.were(dx_sorted > 1)[0][0]
if i == 0:
binned_dx[i, 0] = np.mean(dx_sorted[:offset])
binned_dx[i, 1] = np.std(dx_sorted[:offset]) / np.sqrt(offset)
binned_cells[:, i, 0] = np.mean(events_sorted[:, :offset], axis=1)
binned_cells[:, i, 1] = np.std(events_sorted[:, :offset],
axis=1) / np.sqrt(offset)
else:
start = offset + (i - 1) * binsize
binned_dx[i, 0] = np.mean(dx_sorted[start:start + binsize])
binned_dx[i, 1] = np.std(
dx_sorted[start:start + binsize]) / np.sqrt(binsize)
binned_cells[:, i,
0] = np.mean(events_sorted[:, start:start + binsize],
axis=1)
binned_cells[:, i,
1] = np.std(events_sorted[:, start:start + binsize],
axis=1) / np.sqrt(binsize)
#shuffled activity to get significance
binned_cells_shuffled = np.empty((numbercells, numbins, 2, 200))
for shuf in range(200):
events_shuffled = l0_trim[:,
np.random.permutation(np.size(l0_trim, 1))]
events_shuffled_sorted = events_shuffled[:, np.argsort(dx_trim)]
for i in range(numbins):
offset = np.were(dx_sorted > 1)[0][0]
if i == 0:
binned_cells_shuffled[:, i, 0, shuf] = np.mean(
events_shuffled_sorted[:, :offset], axis=1)
binned_cells_shuffled[:, i, 1,
shuf] = np.std(
events_shuffled_sorted[:, :offset],
axis=1) / np.sqrt(offset)
else:
start = offset + (i - 1) * binsize
binned_cells_shuffled[:, i, 0, shuf] = np.mean(
events_shuffled_sorted[:, start:start + binsize], axis=1)
binned_cells_shuffled[:, i, 1, shuf] = np.std(
events_shuffled_sorted[:, start:start + binsize],
axis=1) / np.sqrt(binsize)
shuffled_variance = binned_cells_shuffled[:, :, 0, :].std(axis=1)**2
variance_threshold = np.percentile(shuffled_variance, 99.9, axis=1)
response_variance = binned_cells[:, :, 0].std(axis=1)**2
peak = pd.DataFrame(columns=('cell_specimen_id', 'run_mod'),
index=range(numbercells))
peak.cell_specimen_id = specimen_ids
peak.run_mod = response_variance > variance_threshold
#save data
save_file = os.path.join(save_path,
str(session_id) + '_speed_tuning_events.h5')
store = pd.HDFStore(save_file)
store['peak'] = peak
store.close()
f = h5py.File(save_file, 'r+')
dset = f.create_dataset('binned_dx', data=binned_dx)
dset1 = f.create_dataset('binned_cells', data=binned_cells)
dset2 = f.create_dataset('binned_cells_shuffled',
data=binned_cells_shuffled)
f.close()