fig.add('choice-lower', [x0, y0, w, h])
fig.add('choice-upper', [fig[-1].x, fig[-1].top + DY, w, h])
fig.add('activity-1',
[fig['choice-upper'].right + DX, fig['choice-upper'].y, w, h])
fig.add('activity-2', [fig[-1].right + dx, fig[-1].y, w, h])
fig.add('activity-3', [fig[-1].right + dx, fig[-1].y, w, h])
fig.add('activity-4',
[fig['choice-lower'].right + DX, fig['choice-lower'].y, w, h])
fig.add('activity-5', [fig[-1].right + dx, fig[-1].y, w, h])
fig.add('activity-6', [fig[-1].right + dx, fig[-1].y, w, h])
plotlabels = {'A': (0.01, 0.935), 'B': (0.28, 0.935)}
fig.plotlabels(plotlabels)
#=========================================================================================
plot = fig['choice-upper']
kwargs = {'ms': 4.5, 'lw': 1.25}
analysis.choice_pattern(trialsfile_b, model.offers, plot, **kwargs)
plot.yticks([0, 50, 100])
plot.text_upper_left('1A = {}B'.format(model.A_to_B),
fontsize=7.5,
color=Figure.colors('green'))
#=========================================================================================
fig.add('task', [x0, y0, w_task, h_task]),
fig.add('sure-stimulus-duration', [x0, y1, w_behavior, h_behavior]),
fig.add('correct-stimulus-duration',
[fig[-1].right + DX, y1, w_behavior, h_behavior]),
fig.add('noTs-stimulus', [x0, y2, w_fr, h_fr]),
fig.add('noTs-choice', [fig[-1].right + dx, y2, 5 / 8 * w_fr, h_fr]),
fig.add('Ts-stimulus', [fig[-1].right + 1.1 * DX, y2, w_fr, h_fr]),
fig.add('Ts-sure', [fig[-1].right + dx, y2, w_fr, h_fr]),
fig.add('Ts-choice', [fig[-1].right + dx, y2, 5 / 8 * w_fr, h_fr])
pl_x0 = 0.025
pl_y0 = 0.945
pl_y1 = 0.595
pl_y2 = 0.28
plotlabels = {'A': (pl_x0, pl_y0), 'B': (pl_x0, pl_y1), 'C': (pl_x0, pl_y2)}
fig.plotlabels(plotlabels, fontsize=9)
#=========================================================================================
# Task
#=========================================================================================
rng = np.random.RandomState(1)
plot = fig['task']
plot.axis_off('left')
plot.axis_off('bottom')
ms = 2.5
dx_circ = 0.14
dy_above = +0.08
dy_below = -0.1
fig.add('romo-1', [fig['romo-0'].right+dx, fig['romo-behavior'].y,
w_activity, h_activity])
fig.add('romo-2', [fig['romo-1'].right+dx, fig['romo-behavior'].y,
w_activity, h_activity])
#=========================================================================================
# Annotations
#=========================================================================================
pl_x0 = 0.02
plotlabels = {
'A': (pl_x0, 0.96),
'B': (pl_x0, 0.52),
'C': (pl_x0, 0.25)
}
fig.plotlabels(plotlabels, fontsize=12)
plot = fig['mante-m']
plot.text_upper_center(r'\textbf{Behavior}', fontsize=9.5, dy=0.14)
plot.text_upper_center(r'\textbf{Neural activity}', fontsize=9.5, dx=2.5, dy=0.14)
#=========================================================================================
kwargs = dict(ms=5, lw=1)
mante_analysis.psychometric(mante_behavior, {'m': fig['mante-m'], 'c': fig['mante-c']},
**kwargs)
plot = fig['mante-m']
plot.xlabel('Percent motion coherence')
plot.ylabel('Percent choice R')
fig.add('noTs-stimulus', [x0, y2, w_fr, h_fr]),
fig.add('noTs-choice', [fig[-1].right+dx, y2, 5/8*w_fr, h_fr]),
fig.add('Ts-stimulus', [fig[-1].right+1.1*DX, y2, w_fr, h_fr]),
fig.add('Ts-sure', [fig[-1].right+dx, y2, w_fr, h_fr]),
fig.add('Ts-choice', [fig[-1].right+dx, y2, 5/8*w_fr, h_fr])
pl_x0 = 0.025
pl_y0 = 0.945
pl_y1 = 0.595
pl_y2 = 0.28
plotlabels = {
'A': (pl_x0, pl_y0),
'B': (pl_x0, pl_y1),
'C': (pl_x0, pl_y2)
}
fig.plotlabels(plotlabels, fontsize=9)
#=========================================================================================
# Task
#=========================================================================================
rng = np.random.RandomState(1)
plot = fig['task']
plot.axis_off('left')
plot.axis_off('bottom')
ms = 2.5
dx_circ = 0.14
dy_above = +0.08
dy_below = -0.1
h_activity
])
fig.add(
'romo-1',
[fig['romo-0'].right + dx, fig['romo-behavior'].y, w_activity, h_activity])
fig.add(
'romo-2',
[fig['romo-1'].right + dx, fig['romo-behavior'].y, w_activity, h_activity])
#=========================================================================================
# Annotations
#=========================================================================================
pl_x0 = 0.02
plotlabels = {'A': (pl_x0, 0.96), 'B': (pl_x0, 0.52), 'C': (pl_x0, 0.25)}
fig.plotlabels(plotlabels, fontsize=12)
plot = fig['mante-m']
plot.text_upper_center(r'\textbf{Behavior}', fontsize=9.5, dy=0.14)
plot.text_upper_center(r'\textbf{Neural activity}',
fontsize=9.5,
dx=2.5,
dy=0.14)
#=========================================================================================
kwargs = dict(ms=5, lw=1)
mante_analysis.psychometric(mante_behavior, {
'm': fig['mante-m'],
'c': fig['mante-c']
}, **kwargs)
fig.add('choice-lower', [x0, y0, w, h])
fig.add('choice-upper', [fig[-1].x, fig[-1].top+DY, w, h])
fig.add('activity-1', [fig['choice-upper'].right+DX, fig['choice-upper'].y, w, h])
fig.add('activity-2', [fig[-1].right+dx, fig[-1].y, w, h])
fig.add('activity-3', [fig[-1].right+dx, fig[-1].y, w, h])
fig.add('activity-4', [fig['choice-lower'].right+DX, fig['choice-lower'].y, w, h])
fig.add('activity-5', [fig[-1].right+dx, fig[-1].y, w, h])
fig.add('activity-6', [fig[-1].right+dx, fig[-1].y, w, h])
plotlabels = {
'A': (0.01, 0.935),
'B': (0.28, 0.935)
}
fig.plotlabels(plotlabels)
#=========================================================================================
plot = fig['choice-upper']
kwargs = {'ms': 4.5, 'lw': 1.25}
analysis.choice_pattern(trialsfile_b, model.offers, plot, **kwargs)
plot.yticks([0, 50, 100])
plot.text_upper_left('1A = {}B'.format(model.A_to_B), fontsize=7.5,
color=Figure.colors('green'))
#=========================================================================================
