def load_data(self, cell_list):
self.cell_info = cell_list
reload(hf)
for row in cell_list: # go through each cell
cur_exp = hf.load_analyzed_exp(row[0])
if np.size(cur_exp['spike_times']) >1:
self.spike_times.append( cur_exp['spike_times'][row[1]] ) # get spike times for given cell
else: # for single cell files, just append the single cell
self.spike_times.append( cur_exp['spike_times'] )
self.laser_times.append( cur_exp['laser_times'][row[2]:row[3]] ) # get laser times for range of trials
# initialize some variables for calculation later
self.num_cells = np.size(self.cell_info, 0)
self.mean_pre_isi = np.zeros(self.num_cells)
self.mean_first_spike = np.zeros(self.num_cells)
self.std_first_spike = np.zeros(self.num_cells)
self.first_firing_above_mean = np.zeros(self.num_cells)
self.inhib_latency = np.zeros(self.num_cells)
self.failure_rate = np.zeros(self.num_cells)
def main(bool_plot = True):
reload(hf)
# list of experiments to go through
exp_list = ['140819B', '140813B', '140812B', '140729B', '140722A', '140718A', '140710A', '140708B']
all_feed_rates = np.empty(0)
all_nonfeed_rates = np.empty(0)
for cur_exp in exp_list:
#$pdb.set_trace()
# load current experiment in list
cur_data = hf.load_analyzed_exp(cur_exp)
cur_spike_times = cur_data['spike_times']
feed_times, water_times = hf.load_feed_times(cur_exp)
# calculate average firing rate for experiment and add to list
feed_avg, nonfeed_avg = cfrf.main(cur_spike_times, feed_times)
all_feed_rates = np.append(all_feed_rates, feed_avg)
all_nonfeed_rates = np.append(all_nonfeed_rates, nonfeed_avg)
if bool_plot:
plot_nonfeed_vs_feed(all_nonfeed_rates, all_feed_rates)
return all_feed_rates, all_nonfeed_rates
