#-----------------------------------------------------------------------------------------
x0 = 0.02
x1 = 0.27
x2 = 0.6
x3 = 0.64
y0 = 0.96
y1 = 0.5
plotlabels = {
'A': (x0, y0),
'B': (x1, y0),
'C': (x0, y1),
'D': (x2, y1)
}
fig.plotlabels(plotlabels, fontsize=paper.plotlabelsize)
#=========================================================================================
# Psychometric functions
#=========================================================================================
mante_plots = {
'm': plots['psy_m'],
'c': plots['psy_c']
}
mante.psychometric_function(trialsfile, mante_plots, ms=4.5)
plot = plots['psy_m']
plot.xlabel('Motion coherence (\%)')
plot.ylabel('Percent choice 1')
plots['context_choice'] = fig.add([x0, y1, w, h])
plots['colour_motion'] = fig.add([x1, y1, w, h])
plots['context_motion'] = fig.add([x0, y2, w, h])
plots['context_colour'] = fig.add([x1, y2, w, h])
#-----------------------------------------------------------------------------------------
x0 = 0.02
x1 = 0.27
x2 = 0.6
x3 = 0.64
y0 = 0.96
y1 = 0.5
plotlabels = {'A': (x0, y0), 'B': (x1, y0), 'C': (x0, y1), 'D': (x2, y1)}
fig.plotlabels(plotlabels, fontsize=paper.plotlabelsize)
#=========================================================================================
# Psychometric functions
#=========================================================================================
mante_plots = {'m': plots['psy_m'], 'c': plots['psy_c']}
mante.psychometric_function(trialsfile, mante_plots, ms=4.5)
plot = plots['psy_m']
plot.xlabel('Motion coherence (\%)')
plot.ylabel('Percent choice 1')
plot = plots['psy_c']
plot.xlabel('Color coherence (\%)')
plot.ylabel('Percent choice 1')
plots = {
'SL-f': fig.add([x0, y0, w, h]),
'SL-s': fig.add([x0, y0 - dy, w, h]),
'SL-e': fig.add([x0, y0 - 2 * dy, w, h], 'none'),
'SL-o': fig.add([x0, y0 - 3 * dy, w, h]),
'RL-f': fig.add([x0 + dx, y0, w, h]),
'RL-s': fig.add([x0 + dx, y0 - dy, w, h]),
'RL-e': fig.add([x0 + dx, y0 - 2 * dy, w, h], 'none'),
'RL-o': fig.add([x0 + dx, y0 - 3 * dy, w, h], 'none')
}
x0 = 0.01
x1 = x0 + dx
y0 = 0.95
plotlabels = {'A': (x0, y0), 'B': (x1, y0)}
fig.plotlabels(plotlabels, fontsize=12.5)
offset = 0.02
#-----------------------------------------------------------------------------------------
# Task structure
#-----------------------------------------------------------------------------------------
fixation = (0, 250)
stimulus = (250, 750)
decision = (750, 1000)
tmax = decision[-1]
names = ['Fixation', 'Stimulus', 'Decision']
epochs = [fixation, stimulus, decision]
'SL-e': fig.add([x0, y0-2*dy, w, h], 'none'),
'SL-o': fig.add([x0, y0-3*dy, w, h]),
'RL-f': fig.add([x0+dx, y0, w, h]),
'RL-s': fig.add([x0+dx, y0-dy, w, h]),
'RL-e': fig.add([x0+dx, y0-2*dy, w, h], 'none'),
'RL-o': fig.add([x0+dx, y0-3*dy, w, h], 'none')
}
x0 = 0.01
x1 = x0 + dx
y0 = 0.95
plotlabels = {
'A': (x0, y0),
'B': (x1, y0)
}
fig.plotlabels(plotlabels, fontsize=12.5)
offset = 0.02
#-----------------------------------------------------------------------------------------
# Task structure
#-----------------------------------------------------------------------------------------
fixation = (0, 250)
stimulus = (250, 750)
decision = (750, 1000)
tmax = decision[-1]
names = ['Fixation', 'Stimulus', 'Decision']
epochs = [fixation, stimulus, decision]
