def plot_seq_effects(self):
for t,tag in enumerate(self.tags):
# Analyze each individual trial code (i.e. what was the previous trial?)
data_mean, data_sem = emergent.group_batch(self.data_settled[tag], ['prev_trial_code', 'inhibited', 'SS_presented'])
# Select those where a response was made and no stop signal was presented
idx = (data_mean['inhibited'] == 0.0) & (data_mean['SS_presented'] == 0.0) & ((data_mean['prev_trial_code'] != 1) )
#data_mean[idx]['prev_trial_code']
plt.errorbar([0,1,2], data_mean[idx]['minus_cycles']*self.ms, color=self.colors[t], yerr=data_sem[idx]['minus_cycles']*self.ms, label=tag, lw=3)
#plt.title('RTs depending on previous trial')
plt.xticks(np.arange(len(self.pt_code.values())), self.pt_code.values())
plt.ylabel('RTs (ms)')
plt.xlabel('Previous Trial Type')
plt.legend(loc=0)
plt.xlim((-.5, 2.5))
def plot_seq_effects(self):
for t,tag in enumerate(self.tags):
# Analyze each individual trial code (i.e. what was the previous trial?)
data_mean, data_sem = emergent.group_batch(self.data_settled[tag], ['prev_trial_code', 'inhibited', 'SS_presented'])
# Select those where a response was made and no stop signal was presented
idx = (data_mean['inhibited'] == 0.0) & (data_mean['SS_presented'] == 0.0) & ((data_mean['prev_trial_code'] != 1) )
#data_mean[idx]['prev_trial_code']
plt.errorbar([0,1,2], data_mean[idx]['minus_cycles']*self.ms, color=self.colors[t], yerr=data_sem[idx]['minus_cycles']*self.ms, label=tag, lw=3)
#plt.title('RTs depending on previous trial')
plt.xticks(np.arange(len(self.pt_code.values())), self.pt_code.values())
plt.ylabel('RTs (ms)')
plt.xlabel('Previous Trial Type')
plt.legend(loc=0)
plt.xlim((-.5, 2.5))
def analyze_act_stim_onset(self, name='SC', field='Thalam_unit_corr', tag='intact'):
start_cycle = 0
wind = (0,125)
#wind = (100,100)
# From emergent, SC threshold:
data_grp_mean, data_grp_sem = emergent.group_batch(self.data['trl'][tag], ['SS_presented'])
idx = data_grp_mean['SS_presented'] == 1
SSD = np.median(self.data_settled[tag]['SSD'])
ss_resp = self.extract_cycles(
tag,
((self.data['trl'][tag]['SS_presented'] == 1) &
(self.data['trl'][tag]['inhibited'] == 0) &
(self.data['trl'][tag]['epoch'] > 30) &
(self.data['trl'][tag]['SSD'] == SSD)),
field, cycle=start_cycle,
#center='SSD',
wind=wind)
ss_inhib = self.extract_cycles(
tag,
((self.data['trl'][tag]['SS_presented'] == 1) &
(self.data['trl'][tag]['inhibited'] == 1) &
(self.data['trl'][tag]['epoch'] > 30) &
(self.data['trl'][tag]['SSD'] == SSD)),
field, cycle=start_cycle,
#center='SSD',
wind=wind)
x=np.linspace(wind[0]+start_cycle,wind[1]+start_cycle,np.sum(wind)+1)*self.ms
#thr_cross = np.where(np.mean(thalam_ss_resp, axis=0) > thr)[0][0]
plt.plot(x, np.mean(ss_inhib, axis=0), label='canceled stop trials', color='.7', lw=3.)
plt.plot(x, np.mean(ss_resp, axis=0), label='non-canceled stop trials', color='k', lw=3.)
plt.axhline(y=self.SC_thr, color='k', lw=3., linestyle='-.')
#plt.axvline(x=thr_cross, color='k')
#plt.axvline(x=np.mean(self.SSD['intact'])+np.mean(self.SSRT['intact']), color='k')
#plt.axvline(x=np.mean(self.SSD['intact']), color='k')
plt.axvline(x=SSD*self.ms, color='k', lw=3.)
plt.axvline(x=(SSD + np.mean(self.SSRT[tag]))*self.ms, color='k', linestyle='--', lw=2.)
plt.ylim(0,1)
plt.xlabel('Time from Target Onset (ms)')
plt.ylabel('%s activity' % name)
#plt.title('SC activity during inhibited and not-inhibited stop trials: %s'%tag)
plt.legend(loc=0)
def analyze_act_stim_onset(self, name='SC', field='Thalam_unit_corr', tag='intact'):
start_cycle = 0
wind = (0,125)
#wind = (100,100)
# From emergent, SC threshold:
data_grp_mean, data_grp_sem = emergent.group_batch(self.data['trl'][tag], ['SS_presented'])
idx = data_grp_mean['SS_presented'] == 1
SSD = np.median(self.data_settled[tag]['SSD'])
ss_resp = self.extract_cycles(
tag,
((self.data['trl'][tag]['SS_presented'] == 1) &
(self.data['trl'][tag]['inhibited'] == 0) &
(self.data['trl'][tag]['epoch'] > 30) &
(self.data['trl'][tag]['SSD'] == SSD)),
field, cycle=start_cycle,
#center='SSD',
wind=wind)
ss_inhib = self.extract_cycles(
tag,
((self.data['trl'][tag]['SS_presented'] == 1) &
(self.data['trl'][tag]['inhibited'] == 1) &
(self.data['trl'][tag]['epoch'] > 30) &
(self.data['trl'][tag]['SSD'] == SSD)),
field, cycle=start_cycle,
#center='SSD',
wind=wind)
x=np.linspace(wind[0]+start_cycle,wind[1]+start_cycle,np.sum(wind)+1)*self.ms
#thr_cross = np.where(np.mean(thalam_ss_resp, axis=0) > thr)[0][0]
plt.plot(x, np.mean(ss_inhib, axis=0), label='canceled stop trials', color='.7', lw=3.)
plt.plot(x, np.mean(ss_resp, axis=0), label='non-canceled stop trials', color='k', lw=3.)
plt.axhline(y=self.SC_thr, color='k', lw=3., linestyle='-.')
#plt.axvline(x=thr_cross, color='k')
#plt.axvline(x=np.mean(self.SSD['intact'])+np.mean(self.SSRT['intact']), color='k')
#plt.axvline(x=np.mean(self.SSD['intact']), color='k')
plt.axvline(x=SSD*self.ms, color='k', lw=3.)
plt.axvline(x=(SSD + np.mean(self.SSRT[tag]))*self.ms, color='k', linestyle='--', lw=2.)
plt.ylim(0,1)
plt.xlabel('Time from Target Onset (ms)')
plt.ylabel('%s activity' % name)
#plt.title('SC activity during inhibited and not-inhibited stop trials: %s'%tag)
plt.legend(loc=0)
def analyze_SC_act_ind(self, SSDs=None, tag=None, plot_ind=False):
if tag is None:
tag = 'intact'
wind = (0,100)
start_cycle = 25
skip_epochs = 20
if SSDs is None:
SSDs = np.unique(self.data['trl']['intact']['SSD'])
for SSD in SSDs:
data_grp_mean, data_grp_sem = emergent.group_batch(self.data['trl'][tag], ['SS_presented'])
idx = data_grp_mean['SS_presented'] == 1
SSD_mean = data_grp_mean[idx]['SSD']
# Select responded and inhibited trials
resp = ((self.data['trl'][tag]['inhibited'] == 0) &
(self.data['trl'][tag]['SSD'] == SSD) &
(self.data['trl'][tag]['epoch'] > skip_epochs))
ss_resp = ((self.data['trl'][tag]['SS_presented'] == 1) &
(self.data['trl'][tag]['inhibited'] == 0) &
(self.data['trl'][tag]['SSD'] == SSD) &
(self.data['trl'][tag]['epoch'] > skip_epochs))
ss_inhib = ((self.data['trl'][tag]['SS_presented'] == 1) &
(self.data['trl'][tag]['inhibited'] == 1) &
(self.data['trl'][tag]['SSD'] == SSD) &
(self.data['trl'][tag]['epoch'] > skip_epochs))
# Calculate proportion of inhibited vs error trials
mean_ss_resp = (np.sum(ss_inhib)/np.sum(((self.data['trl'][tag]['SS_presented'] == 1) &
(self.data['trl'][tag]['SSD'] == SSD) &
(self.data['trl'][tag]['epoch'] > skip_epochs))))
print "Mean responded trials: %f" % mean_ss_resp
if mean_ss_resp == 0. or mean_ss_resp == 1.:
continue # No need to plot SSDs to which no or all responses where inhibited.
self.new_fig()
thalam_ss_resp = self.extract_cycles(tag, resp, 'Thalam_unit_corr', cycle=start_cycle, wind=wind)
thalam_ss_inhib = self.extract_cycles(tag, ss_inhib, 'Thalam_unit_corr', cycle=start_cycle, wind=wind)
x=np.linspace(wind[0]+start_cycle,wind[1]+start_cycle,np.sum(wind)+1)
#thr_cross = np.where(np.mean(thalam_ss_resp, axis=0) > thr)[0][0]
self.plot_filled(x, thalam_ss_inhib, label='SS_inhib', color='g')
self.plot_filled(x, thalam_ss_resp, label='SS_resp', color='r')
plt.axhline(y=self.SC_thr, color='k')
plt.axvline(x=SSD, color='k')
plt.axvline(x=SSD + np.mean(self.SSRT['intact']), color='k')
plt.xlabel('Stop-Signal')
plt.ylabel('Average SC activity')
plt.title('SC activity during inhibited and not-inhibited stop trials\n: %s, SSD: %i, mean response rate: %f'%(tag, SSD, mean_ss_resp))
plt.legend(loc=0)
if plot_ind:
self.new_fig()
self.plot_filled(x, thalam_ss_inhib, avg=False, label='SS_inhib', color='g')
self.plot_filled(x, thalam_ss_resp, avg=False, label='SS_resp', color='r')
plt.axhline(y=self.SC_thr, color='k')
plt.axvline(x=SSD, color='k')
plt.axvline(x=SSD + np.mean(self.SSRT['intact']), color='k')
plt.xlabel('Stop-Signal')
plt.ylabel('Average SC activity')
plt.title('SC activity during inhibited and not-inhibited stop trials\n: %s, SSD: %i, mean response rate: %f'%(tag, SSD, mean_ss_resp))
def analyze_SC_act_ind(self, SSDs=None, tag=None, plot_ind=False):
if tag is None:
tag = 'intact'
wind = (0,100)
start_cycle = 25
skip_epochs = 20
if SSDs is None:
SSDs = np.unique(self.data['trl']['intact']['SSD'])
for SSD in SSDs:
data_grp_mean, data_grp_sem = emergent.group_batch(self.data['trl'][tag], ['SS_presented'])
idx = data_grp_mean['SS_presented'] == 1
SSD_mean = data_grp_mean[idx]['SSD']
# Select responded and inhibited trials
resp = ((self.data['trl'][tag]['inhibited'] == 0) &
(self.data['trl'][tag]['SSD'] == SSD) &
(self.data['trl'][tag]['epoch'] > skip_epochs))
ss_resp = ((self.data['trl'][tag]['SS_presented'] == 1) &
(self.data['trl'][tag]['inhibited'] == 0) &
(self.data['trl'][tag]['SSD'] == SSD) &
(self.data['trl'][tag]['epoch'] > skip_epochs))
ss_inhib = ((self.data['trl'][tag]['SS_presented'] == 1) &
(self.data['trl'][tag]['inhibited'] == 1) &
(self.data['trl'][tag]['SSD'] == SSD) &
(self.data['trl'][tag]['epoch'] > skip_epochs))
# Calculate proportion of inhibited vs error trials
mean_ss_resp = (np.sum(ss_inhib)/np.sum(((self.data['trl'][tag]['SS_presented'] == 1) &
(self.data['trl'][tag]['SSD'] == SSD) &
(self.data['trl'][tag]['epoch'] > skip_epochs))))
print "Mean responded trials: %f" % mean_ss_resp
if mean_ss_resp == 0. or mean_ss_resp == 1.:
continue # No need to plot SSDs to which no or all responses where inhibited.
self.new_fig()
thalam_ss_resp = self.extract_cycles(tag, resp, 'Thalam_unit_corr', cycle=start_cycle, wind=wind)
thalam_ss_inhib = self.extract_cycles(tag, ss_inhib, 'Thalam_unit_corr', cycle=start_cycle, wind=wind)
x=np.linspace(wind[0]+start_cycle,wind[1]+start_cycle,np.sum(wind)+1)
#thr_cross = np.where(np.mean(thalam_ss_resp, axis=0) > thr)[0][0]
self.plot_filled(x, thalam_ss_inhib, label='SS_inhib', color='g')
self.plot_filled(x, thalam_ss_resp, label='SS_resp', color='r')
plt.axhline(y=self.SC_thr, color='k')
plt.axvline(x=SSD, color='k')
plt.axvline(x=SSD + np.mean(self.SSRT['intact']), color='k')
plt.xlabel('Stop-Signal')
plt.ylabel('Average SC activity')
plt.title('SC activity during inhibited and not-inhibited stop trials\n: %s, SSD: %i, mean response rate: %f'%(tag, SSD, mean_ss_resp))
plt.legend(loc=0)
if plot_ind:
self.new_fig()
self.plot_filled(x, thalam_ss_inhib, avg=False, label='SS_inhib', color='g')
self.plot_filled(x, thalam_ss_resp, avg=False, label='SS_resp', color='r')
plt.axhline(y=self.SC_thr, color='k')
plt.axvline(x=SSD, color='k')
plt.axvline(x=SSD + np.mean(self.SSRT['intact']), color='k')
plt.xlabel('Stop-Signal')
plt.ylabel('Average SC activity')
plt.title('SC activity during inhibited and not-inhibited stop trials\n: %s, SSD: %i, mean response rate: %f'%(tag, SSD, mean_ss_resp))
