def main():
print(dt.datetime.now().isoformat() + ' INFO: ' + 'Loading cell info...')
cell_info, id_adjustor = rc.loadCellInfo(csv_dir)
print(dt.datetime.now().isoformat() + ' INFO: ' + 'Loading stim info...')
stim_info = loadmat(os.path.join(mat_dir, 'experiment2stimInfo.mat'))
trials_info = rc.getStimTimesIds(stim_info)
signal_corr_frame = pd.DataFrame(columns=[
'region', 'first_cell_id', 'second_cell_id', 'signal_corr_coef',
'p_values', 'bin_width'
])
for region in rc.regions:
print(dt.datetime.now().isoformat() + ' INFO: ' +
'Making signal correlation frame for ' + region + '...')
cell_ids = cell_info[(cell_info.region == region)
& (cell_info.group == args.group)].index.values
spike_time_dict = rc.loadSpikeTimes(posterior_dir, frontal_dir,
cell_ids, id_adjustor)
responding_pairs = rc.getRespondingPairs(cell_ids,
trials_info,
spike_time_dict,
cell_info,
30,
is_strong=True)
for bin_width in rc.bin_widths:
signal_corr_frame = signal_corr_frame.append(
getBinWidthSignalCorrFrame(responding_pairs, trials_info,
spike_time_dict, cell_info,
bin_width, region))
csv_filename = os.path.join(csv_dir, 'signal_correlations_strong.csv')
signal_corr_frame.to_csv(csv_filename, index=False)
print(dt.datetime.now().isoformat() + ' INFO: ' + csv_filename + ' saved.')
def getAllWidthFrameForRegionStim(cell_info, stim_info, id_adjustor, region,
stim_id, group, wanted_num_pairs, is_weak,
bin_widths, threshold):
print(dt.datetime.now().isoformat() +
' INFO: Getting correlations for all widths for region = ' + region +
', stim ID = ' + str(stim_id) + '...')
cell_ids = cell_info[(cell_info.region == region)
& (cell_info.group == group)].index.values
print(dt.datetime.now().isoformat() + ' INFO: ' + 'Loading trial info...')
trials_info = rc.getStimTimesIds(stim_info, stim_id)
spike_time_dict = rc.loadSpikeTimes(posterior_dir, frontal_dir, cell_ids,
id_adjustor)
print(dt.datetime.now().isoformat() + ' INFO: ' +
'Getting pairs of responding cells...')
responding_pairs = rc.getRespondingPairs(cell_ids,
trials_info,
spike_time_dict,
cell_info,
wanted_num_pairs,
is_weak,
strong_threshold=threshold)
num_pairs = responding_pairs.shape[0]
responding_cells = np.unique(responding_pairs)
spike_time_dict = {k: spike_time_dict[k] for k in responding_cells}
return pd.concat([
getCorrFrameForWidth(bin_width, responding_pairs, trials_info,
spike_time_dict, cell_info, stim_id, region)
for bin_width in bin_widths
],
ignore_index=True)
def main():
print(dt.datetime.now().isoformat() + ' INFO: ' + 'Loading cell info...')
cell_info, id_adjustor = rc.loadCellInfo(csv_dir)
print(dt.datetime.now().isoformat() + ' INFO: ' + 'Loading stim info...')
stim_info = loadmat(os.path.join(mat_dir, 'experiment2stimInfo.mat'))
cell_ids = cell_info[cell_info.group==args.group].index.values
trials_info = rc.getStimTimesIds(stim_info)
print(dt.datetime.now().isoformat() + ' INFO: ' + 'Getting responding cells...')
spike_time_dict = rc.loadSpikeTimes(posterior_dir, frontal_dir, cell_ids, id_adjustor)
responding_cells = rc.getRespondingCells(cell_ids, trials_info, spike_time_dict, cell_info, num_cells=0, is_strong=args.is_strong, strong_threshold=args.threshold)
num_responding_cells = responding_cells.size
spike_time_dict = {k: spike_time_dict[k] for k in responding_cells}
all_rates = pd.concat([getFiringRatesForWidth(responding_cells, trials_info, spike_time_dict, cell_info, bin_width) for bin_width in rc.bin_widths], ignore_index=True)
all_rates.to_csv(os.path.join(csv_dir, args.filename))
print(dt.datetime.now().isoformat() + ' INFO: ' + 'Done.')
def main():
print(dt.datetime.now().isoformat() + ' INFO: ' + 'Loading cell info...')
cell_info, id_adjustor = rc.loadCellInfo(csv_dir)
print(dt.datetime.now().isoformat() + ' INFO: ' + 'Loading stim info...')
stim_info = loadmat(os.path.join(mat_dir, 'experiment2stimInfo.mat'))
trials_info = rc.getStimTimesIds(stim_info, args.stim_id)
num_trials = trials_info.shape[0]
delays = np.arange(-1000, 1001)
for region in ['motor_cortex', 'striatum', 'hippocampus', 'thalamus', 'v1']:
print(dt.datetime.now().isoformat() + ' INFO: ' + 'Starting region '+ region + '...')
cell_ids = cell_info[(cell_info.region==region)&(cell_info.group==args.group)].index.values
spike_time_dict = rc.loadSpikeTimes(posterior_dir, frontal_dir, cell_ids, id_adjustor)
responding_pairs = rc.getRespondingPairs(cell_ids, trials_info, spike_time_dict, cell_info, 5, is_strong=True)
responding_cells = np.unique(responding_pairs)
exp_frame = rc.getExperimentFrame(responding_cells, trials_info, spike_time_dict, cell_info, 0.001)
for pair in responding_pairs:
print(dt.datetime.now().isoformat() + ' INFO: ' + 'Processing ' + str(pair) + '...')
correlations, corr_std_errors = getCrossCorrelogramByPair(pair, exp_frame, num_trials, delays)
filepath = plotWithStdErrors(correlations, corr_std_errors, delays, "Correlation Coefficient (a.u.)", pair, region, args.stim_id)
print(dt.datetime.now().isoformat() + ' INFO: ' + 'Saved:' + filepath)
print(dt.datetime.now().isoformat() + ' INFO: ' + 'Done.')
def main():
print(dt.datetime.now().isoformat() + ' INFO: ' + 'Loading cell info...')
cell_info, id_adjustor = rc.loadCellInfo(csv_dir)
print(dt.datetime.now().isoformat() + ' INFO: ' + 'Selecting cells...')
cell_ids = rc.getRandomSelection(cell_info, args.number_of_cells,
args.group, args.probe, args.region)
spike_time_dict = rc.loadSpikeTimes(posterior_dir, frontal_dir, cell_ids,
id_adjustor)
print(dt.datetime.now().isoformat() + ' INFO: ' + 'Loading trial info...')
trials_info = rc.getStimTimesIds(stim_info, args.stim_id)
print(dt.datetime.now().isoformat() + ' INFO: ' +
'Creating experiment frame...')
exp_frame = rc.getExperimentFrame(cell_ids, trials_info, spike_time_dict,
cell_info, args.bin_length)
print(dt.datetime.now().isoformat() + ' INFO: ' +
'Measuring correlations...')
correlation_dict = {}
for stim_id in np.unique(stim_info['stimIDs'][0]).astype(int):
correlation_dict[stim_id] = getCorrelationMatrixForStim(
exp_frame, stim_id)
correlation_dict[-1] = getCorrelationMatrixForStim(exp_frame,
-1) # all stims at once
if args.plot_correlation_matrix: # show example correlation matrix for all stimuli
print(dt.datetime.now().isoformat() + ' INFO: ' +
'Plotting correlations...')
plotCorrMatrixForStim(correlation_dict[-1][0], cell_info,
exp_frame['cell_id'].unique())
if args.correlation_figure_filename == '':
plt.show(block=False)
showCellInfoTable(
cell_info,
exp_frame['cell_id'].unique()) # show cell info in table
plt.show(block=False)
else:
plt.savefig(
os.path.join(image_dir, 'pairwise_correlation_matrices',
args.correlation_figure_filename))
def getDataFrames(single_file, paired_file, bin_width):
firing_frame = pd.read_csv(os.path.join(csv_dir, args.single_file), usecols=lambda x: x != 'Unnamed: 0')
pairwise_frame = pd.read_csv(os.path.join(csv_dir, args.paired_file), usecols=lambda x: x != 'Unnamed: 0')
firing_frame = firing_frame[firing_frame.bin_width == bin_width]
pairwise_frame = pairwise_frame[pairwise_frame.bin_width == bin_width]
return firing_frame, pairwise_frame
pair = np.array(args.pair)
cell_info, id_adjustor = rc.loadCellInfo(csv_dir)
stim_info = loadmat(os.path.join(mat_dir, 'experiment2stimInfo.mat'))
firing_frame, pairwise_frame = getDataFrames(args.single_file, args.paired_file, args.bin_width)
region = firing_frame.loc[firing_frame.cell_id == pair[0]].region.unique()[0]
best_stim = rc.getBestStimFromRegion(pairwise_frame, region)
pairwise_region_frame = pairwise_frame[(pairwise_frame.region == region) & (pairwise_frame.stim_id == best_stim)]
stim_firing_frame = firing_frame.loc[firing_frame.stim_id == best_stim,:]
trials_info = rc.getStimTimesIds(stim_info, best_stim)
spike_time_dict = rc.loadSpikeTimes(posterior_dir, frontal_dir, pair, id_adjustor)
exp_frame = rc.getExperimentFrame(pair, trials_info, spike_time_dict, cell_info, 1.0)
corr = pairwise_region_frame.loc[(pairwise_region_frame.first_cell_id == pair[0]) & (pairwise_region_frame.second_cell_id == pair[1]), 'corr_coef'].iloc[0]
info = pairwise_region_frame.loc[(pairwise_region_frame.first_cell_id == pair[0]) & (pairwise_region_frame.second_cell_id == pair[1]), 'mutual_info_qe'].iloc[0]
first_firing_rate = stim_firing_frame.loc[firing_frame.cell_id == pair[0], 'firing_rate_mean'].iloc[0]
second_firing_rate = stim_firing_frame.loc[firing_frame.cell_id == pair[1], 'firing_rate_mean'].iloc[0]
geom_mean_firing_rate = np.sqrt(first_firing_rate * second_firing_rate)
first_responses = exp_frame.loc[exp_frame.cell_id == pair[0], 'num_spikes']
second_responses = exp_frame.loc[exp_frame.cell_id == pair[1], 'num_spikes']
plt.figure(figsize=(10,5))
plt.subplot(1,2,1)
plt.scatter(first_responses, second_responses)
plt.xlabel('Spike counts, cell ' + str(pair[0]), fontsize='large')
cax=cax,
cmap=cmap,
norm=norm,
boundaries=bounds,
ticks=np.array(bounds)[1:] - 0.5)
cb.set_ticklabels(
[c.replace('_', ' ').capitalize() for c in [''] + colours])
plt.tight_layout()
print(dt.datetime.now().isoformat() + ' INFO: ' + 'Loading cell info...')
cell_info, id_adjustor = rc.loadCellInfo(csv_dir)
print(dt.datetime.now().isoformat() + ' INFO: ' + 'Loading stim info...')
stim_info = loadmat(os.path.join(mat_dir, 'experiment2stimInfo.mat'))
print(dt.datetime.now().isoformat() + ' INFO: ' + 'Loading trials info...')
trials_info = rc.getStimTimesIds(stim_info, args.stim_id)
if args.numpy_file_prefix != '':
print(dt.datetime.now().isoformat() + ' INFO: ' +
'Loading data from disc...')
region_sorted_cell_ids = np.load(os.path.join(
npy_dir, args.numpy_file_prefix + '_responding_sorted_cell_ids.npy'),
allow_pickle=True)
pairs = np.load(os.path.join(
npy_dir, args.numpy_file_prefix + '_responding_pairs.npy'),
allow_pickle=True)
pairwise_measurements = pd.read_pickle(
os.path.join(npy_dir,
args.numpy_file_prefix + '_pairwise_measurements.pkl'))
info_matrix = np.load(os.path.join(
npy_dir, args.numpy_file_prefix + '_info_matrix.npy'),
allow_pickle=True)