def reaction_times_first_step(sessions):
median_reaction_times = np.zeros([len(sessions),4])
all_reaction_times = []
for i,session in enumerate(sessions):
event_times = ut.get_event_times(session.time_stamps, session.event_codes, session.IDs)
ITI_start_times = event_times['ITI_start']
center_poke_times = sorted(np.hstack((event_times['high_poke'], event_times['low_poke'])))
reaction_times = 1000 * _latencies(ITI_start_times, center_poke_times)[1:-1]
all_reaction_times.append(reaction_times)
transitions = (session.blocks['trial_trans_state'] == session.CTSO['transitions'])[:len(reaction_times)] # Transitions common/rare.
outcomes = session.CTSO['outcomes'][:len(reaction_times)].astype(bool)
median_reaction_times[i, 0] = np.median(reaction_times[ transitions & outcomes]) # Common transition, rewarded.
median_reaction_times[i, 1] = np.median(reaction_times[~transitions & outcomes]) # Rare transition, rewarded.
median_reaction_times[i, 2] = np.median(reaction_times[ transitions & ~outcomes]) # Common transition, non-rewarded.
median_reaction_times[i, 3] = np.median(reaction_times[~transitions & ~outcomes]) # Rare transition, non-rewarded.
mean_RTs = np.mean(median_reaction_times,0)
SEM_RTs = np.sqrt(np.var(median_reaction_times,0)/len(sessions))
p.figure(1)
p.clf()
p.title('First step reaction times')
p.bar([1,2,3,4], mean_RTs, yerr = SEM_RTs)
p.ylim(min(mean_RTs) * 0.8, max(mean_RTs) * 1.1)
p.xticks([1.4, 2.4, 3.4, 4.4], ['Com. Rew.', 'Rare Rew.', 'Com. Non.', 'Rare. Non.'])
p.xlim(0.8,5)
p.ylabel('Reaction time (ms)')
all_reaction_times = np.hstack(all_reaction_times)
bin_edges = np.arange(0,3001)
rt_hist = np.histogram(all_reaction_times, bin_edges)[0]
cum_rt_hist = np.cumsum(rt_hist) / float(len(all_reaction_times))
p.figure(2)
p.clf()
p.plot(bin_edges[:-1],cum_rt_hist)
p.ylim(0,1)
p.xlabel('Time from ITI start (ms)')
p.ylabel('Cumumative fraction of first central pokes.')
def reaction_times_second_step(sessions, fig_no = 1):
'Reaction times for second step pokes as function of common / rare transition.'
sec_step_IDs = ut.get_IDs(sessions[0].IDs, ['right_active', 'left_active'])
median_RTs_common = np.zeros(len(sessions))
median_RTs_rare = np.zeros(len(sessions))
for i,session in enumerate(sessions):
event_times = ut.get_event_times(session.time_stamps, session.event_codes, session.IDs)
left_active_times = event_times['left_active']
right_active_times = event_times['right_active']
left_reaction_times = _latencies(left_active_times, event_times['left_poke'])
right_reaction_times = _latencies(right_active_times, event_times['right_poke'])
ordered_reaction_times = np.hstack((left_reaction_times,right_reaction_times))\
[np.argsort(np.hstack((left_active_times,right_active_times)))]
transitions = session.blocks['trial_trans_state'] == session.CTSO['transitions'] # common vs rare.
median_RTs_common[i] = np.median(ordered_reaction_times[ transitions])
median_RTs_rare[i] = np.median(ordered_reaction_times[~transitions])
mean_RT_common = 1000 * np.mean(median_RTs_common)
mean_RT_rare = 1000 * np.mean(median_RTs_rare)
SEM_RT_common = 1000 * np.sqrt(np.var(median_RTs_common/len(sessions)))
SEM_RT_rare = 1000 * np.sqrt(np.var(median_RTs_rare /len(sessions)))
p.figure(fig_no)
p.bar([1,2],[mean_RT_common, mean_RT_rare], yerr = [SEM_RT_common,SEM_RT_rare])
p.xlim(0.8,3)
p.ylim(mean_RT_common * 0.8, mean_RT_rare * 1.1)
p.xticks([1.4, 2.4], ['Common', 'Rare'])
p.title('Second step reaction times')
p.ylabel('Reaction time (ms)')
print('Paired t-test P value: {}'.format(ttest_rel(median_RTs_common, median_RTs_rare)[1]))
