def plot_taskaxes(plot, yax, p_vc, basecolor):
abscohs = []
for choice, coh, context in p_vc:
abscohs.append(abs(coh))
abscohs = sorted(list(set(abscohs)))
#-------------------------------------------------------------------------------------
# Subtract mean
#-------------------------------------------------------------------------------------
p = p_vc.values()[0]
Xchoice = np.zeros_like(p[CHOICE])
Xmotion = np.zeros_like(p[MOTION])
Xcolour = np.zeros_like(p[COLOUR])
for p in p_vc.values():
Xchoice += p[CHOICE]
Xmotion += p[MOTION]
Xcolour += p[COLOUR]
mean_choice = Xchoice / len(p_vc)
mean_motion = Xmotion / len(p_vc)
mean_colour = Xcolour / len(p_vc)
for cond, p in p_vc.items():
p[CHOICE] -= mean_choice
p[MOTION] -= mean_motion
p[COLOUR] -= mean_colour
#-------------------------------------------------------------------------------------
xall = []
yall = []
for cond, p in p_vc.items():
idx = abscohs.index(abs(cond[1]))
if idx == 0:
color = apply_alpha(basecolor, 0.4)
elif idx == 1:
color = apply_alpha(basecolor, 0.7)
else:
color = apply_alpha(basecolor, 1)
if cond[1] > 0:
prop = dict(mfc=color, mec=color, ms=2.5, mew=0.5)
else:
prop = dict(mfc='w', mec=color, ms=2.5, mew=0.5)
plot.plot(p[CHOICE], p[yax], '-', color=color, lw=0.75)
plot.plot(p[CHOICE][::2], p[yax][::2], 'o', color=color, **prop)
xall.append(p[CHOICE])
yall.append(p[yax])
if yax == MOTION:
plot.ylabel('Motion')
elif yax == COLOUR:
plot.ylabel('Color')
return np.concatenate(xall), np.concatenate(yall)
def plot_taskaxes(plot, yax, p_vc, basecolor):
abscohs = []
for choice, coh, context in p_vc:
abscohs.append(abs(coh))
abscohs = sorted(list(set(abscohs)))
#-------------------------------------------------------------------------------------
# Subtract mean
#-------------------------------------------------------------------------------------
p = p_vc.values()[0]
Xchoice = np.zeros_like(p[CHOICE])
Xmotion = np.zeros_like(p[MOTION])
Xcolour = np.zeros_like(p[COLOUR])
for p in p_vc.values():
Xchoice += p[CHOICE]
Xmotion += p[MOTION]
Xcolour += p[COLOUR]
mean_choice = Xchoice/len(p_vc)
mean_motion = Xmotion/len(p_vc)
mean_colour = Xcolour/len(p_vc)
for cond, p in p_vc.items():
p[CHOICE] -= mean_choice
p[MOTION] -= mean_motion
p[COLOUR] -= mean_colour
#-------------------------------------------------------------------------------------
xall = []
yall = []
for cond, p in p_vc.items():
idx = abscohs.index(abs(cond[1]))
if idx == 0:
color = apply_alpha(basecolor, 0.4)
elif idx == 1:
color = apply_alpha(basecolor, 0.7)
else:
color = apply_alpha(basecolor, 1)
if cond[1] > 0:
prop = dict(mfc=color, mec=color, ms=2.5, mew=0.5)
else:
prop = dict(mfc='w', mec=color, ms=2.5, mew=0.5)
plot.plot(p[CHOICE], p[yax], '-', color=color, lw=0.75)
plot.plot(p[CHOICE][::2], p[yax][::2], 'o', color=color, **prop)
xall.append(p[CHOICE])
yall.append(p[yax])
if yax == MOTION:
plot.ylabel('Motion')
elif yax == COLOUR:
plot.ylabel('Color')
return np.concatenate(xall), np.concatenate(yall)
ms_empty = 2.5
mew_filled = 0.5
mew_empty = 0.5
y = 1.2
dx = 0.08
dy = 0.06
fontsize = 4.75
for context, plot, basecolor in zip(['Motion', 'Color'],
[plots['c1'], plots['c3']],
['k', Figure.colors('darkblue')]):
transform = plot.ax.transAxes
colors = [apply_alpha(basecolor, alpha) for alpha in [0.4, 0.7, 1]]
for i in xrange(3):
plot.plot(0.5+(i+0.5)*dx, y, 'o', mfc=colors[i], mec=colors[i],
ms=ms_filled, mew=mew_filled, transform=transform)
plot.plot(0.5-(i+0.5)*dx, y, 'o', mfc='none', mec=colors[i],
ms=ms_empty, mew=mew_empty, transform=transform)
# Strength label
plot.text(0.5, y+dy, 'Weak', ha='center', va='bottom', fontsize=fontsize,
color=colors[0], transform=transform)
plot.text(0.5+2.5*dx, y+dy, 'Strong', ha='center', va='bottom', fontsize=fontsize,
color=colors[-1], transform=transform)
plot.text(0.5-2.5*dx, y+dy, 'Strong', ha='center', va='bottom', fontsize=fontsize,
color=colors[-1], transform=transform)
if context == 'Motion':
def plot_unit(unit,
sortedfile,
plots,
t0=0,
tmin=-np.inf,
tmax=np.inf,
**kwargs):
# Load sorted trials
with open(sortedfile, 'rb') as f:
t, sorted_trials = pickle.load(f)
#-------------------------------------------------------------------------------------
# Labels
#-------------------------------------------------------------------------------------
# Unit no.
fontsize = kwargs.get('unit_fontsize', 7)
plots['choice'].text_upper_center('Unit ' + str(unit),
dy=0.07,
fontsize=fontsize)
# Sort-by
if kwargs.get('sortby_fontsize') is not None:
fontsize = kwargs['sortby_fontsize']
labels = {
'choice': 'choice',
'motion_choice': 'motion \& choice',
'colour_choice': 'color \& choice',
'context_choice': 'context \& choice'
}
for k, label in labels.items():
plots[k].ylabel(label)
#-------------------------------------------------------------------------------------
# Setup
#-------------------------------------------------------------------------------------
# Duration to plot
w, = np.where((tmin <= t) & (t <= tmax))
t = t - t0
# Linestyle
def get_linestyle(choice):
if choice == +1:
return '-'
return '--'
# Line width
lw = kwargs.get('lw', 1)
# For setting axis limits
yall = []
#-------------------------------------------------------------------------------------
# Choice
#-------------------------------------------------------------------------------------
plot = plots['choice']
condition_averaged = sorted_trials['choice']
for (choice, ), r in condition_averaged.items():
ls = get_linestyle(choice)
plot.plot(t[w], r[unit, w], ls, color=Figure.colors('red'), lw=lw)
yall.append(r[unit, w])
plot.xlim(t[w][0], t[w][-1])
plot.xticks([t[w][0], 0, t[w][-1]])
#-------------------------------------------------------------------------------------
# Motion & choice
#-------------------------------------------------------------------------------------
plot = plots['motion_choice']
condition_averaged = sorted_trials['motion_choice']
abscohs = []
for (choice, coh, context) in condition_averaged:
abscohs.append(abs(coh))
abscohs = sorted(list(set(abscohs)))
for (choice, coh, context), r in condition_averaged.items():
if context != 'm':
continue
ls = get_linestyle(choice)
idx = abscohs.index(abs(coh))
basecolor = 'k'
if idx == 0:
color = apply_alpha(basecolor, 0.4)
elif idx == 1:
color = apply_alpha(basecolor, 0.7)
else:
color = apply_alpha(basecolor, 1)
plot.plot(t[w], r[unit, w], ls, color=color, lw=lw)
yall.append(r[unit, w])
plot.xlim(t[w][0], t[w][-1])
plot.xticks([t[w][0], 0, t[w][-1]])
#-------------------------------------------------------------------------------------
# Colour & choice
#-------------------------------------------------------------------------------------
plot = plots['colour_choice']
condition_averaged = sorted_trials['colour_choice']
abscohs = []
for (choice, coh, context) in condition_averaged:
abscohs.append(abs(coh))
abscohs = sorted(list(set(abscohs)))
for (choice, coh, context), r in condition_averaged.items():
if context != 'c':
continue
ls = get_linestyle(choice)
idx = abscohs.index(abs(coh))
basecolor = Figure.colors('darkblue')
if idx == 0:
color = apply_alpha(basecolor, 0.4)
elif idx == 1:
color = apply_alpha(basecolor, 0.7)
else:
color = apply_alpha(basecolor, 1)
plot.plot(t[w], r[unit, w], ls, color=color, lw=lw)
yall.append(r[unit, w])
plot.xlim(t[w][0], t[w][-1])
plot.xticks([t[w][0], 0, t[w][-1]])
#-------------------------------------------------------------------------------------
# Context & choice
#-------------------------------------------------------------------------------------
plot = plots['context_choice']
condition_averaged = sorted_trials['context_choice']
for (choice, context), r in condition_averaged.items():
ls = get_linestyle(choice)
if context == 'm':
color = 'k'
else:
color = Figure.colors('darkblue')
plot.plot(t[w], r[unit, w], ls, color=color, lw=lw)
yall.append(r[unit, w])
plot.xlim(t[w][0], t[w][-1])
plot.xticks([t[w][0], 0, t[w][-1]])
return yall
ms_empty = 2.5
mew_filled = 0.5
mew_empty = 0.5
y = 1.2
dx = 0.08
dy = 0.06
fontsize = 4.75
for context, plot, basecolor in zip(['Motion', 'Color'],
[plots['c1'], plots['c3']],
['k', Figure.colors('darkblue')]):
transform = plot.ax.transAxes
colors = [apply_alpha(basecolor, alpha) for alpha in [0.4, 0.7, 1]]
for i in xrange(3):
plot.plot(0.5 + (i + 0.5) * dx,
y,
'o',
mfc=colors[i],
mec=colors[i],
ms=ms_filled,
mew=mew_filled,
transform=transform)
plot.plot(0.5 - (i + 0.5) * dx,
y,
'o',
mfc='none',
mec=colors[i],
ms=ms_empty,
def plot_unit(unit, sortedfile, plots, t0=0, tmin=-np.inf, tmax=np.inf, **kwargs):
# Load sorted trials
with open(sortedfile) as f:
t, sorted_trials = pickle.load(f)
#-------------------------------------------------------------------------------------
# Labels
#-------------------------------------------------------------------------------------
# Unit no.
fontsize = kwargs.get('unit_fontsize', 7)
plots['choice'].text_upper_center('Unit '+str(unit), dy=0.07, fontsize=fontsize)
# Sort-by
if kwargs.get('sortby_fontsize') is not None:
fontsize = kwargs['sortby_fontsize']
labels = {
'choice': 'choice',
'motion_choice': 'motion \& choice',
'colour_choice': 'color \& choice',
'context_choice': 'context \& choice'
}
for k , label in labels.items():
plots[k].ylabel(label)
#-------------------------------------------------------------------------------------
# Setup
#-------------------------------------------------------------------------------------
# Duration to plot
w, = np.where((tmin <= t) & (t <= tmax))
t = t - t0
# Linestyle
def get_linestyle(choice):
if choice == +1:
return '-'
return '--'
# Line width
lw = kwargs.get('lw', 1)
# For setting axis limits
yall = []
#-------------------------------------------------------------------------------------
# Choice
#-------------------------------------------------------------------------------------
plot = plots['choice']
condition_averaged = sorted_trials['choice']
for (choice,), r in condition_averaged.items():
ls = get_linestyle(choice)
plot.plot(t[w], r[unit,w], ls, color=Figure.colors('red'), lw=lw)
yall.append(r[unit,w])
plot.xlim(t[w][0], t[w][-1])
plot.xticks([t[w][0], 0, t[w][-1]])
#-------------------------------------------------------------------------------------
# Motion & choice
#-------------------------------------------------------------------------------------
plot = plots['motion_choice']
condition_averaged = sorted_trials['motion_choice']
abscohs = []
for (choice, coh, context) in condition_averaged:
abscohs.append(abs(coh))
abscohs = sorted(list(set(abscohs)))
for (choice, coh, context), r in condition_averaged.items():
if context != 'm':
continue
ls = get_linestyle(choice)
idx = abscohs.index(abs(coh))
basecolor = 'k'
if idx == 0:
color = apply_alpha(basecolor, 0.4)
elif idx == 1:
color = apply_alpha(basecolor, 0.7)
else:
color = apply_alpha(basecolor, 1)
plot.plot(t[w], r[unit,w], ls, color=color, lw=lw)
yall.append(r[unit,w])
plot.xlim(t[w][0], t[w][-1])
plot.xticks([t[w][0], 0, t[w][-1]])
#-------------------------------------------------------------------------------------
# Colour & choice
#-------------------------------------------------------------------------------------
plot = plots['colour_choice']
condition_averaged = sorted_trials['colour_choice']
abscohs = []
for (choice, coh, context) in condition_averaged:
abscohs.append(abs(coh))
abscohs = sorted(list(set(abscohs)))
for (choice, coh, context), r in condition_averaged.items():
if context != 'c':
continue
ls = get_linestyle(choice)
idx = abscohs.index(abs(coh))
basecolor = Figure.colors('darkblue')
if idx == 0:
color = apply_alpha(basecolor, 0.4)
elif idx == 1:
color = apply_alpha(basecolor, 0.7)
else:
color = apply_alpha(basecolor, 1)
plot.plot(t[w], r[unit,w], ls, color=color, lw=lw)
yall.append(r[unit,w])
plot.xlim(t[w][0], t[w][-1])
plot.xticks([t[w][0], 0, t[w][-1]])
#-------------------------------------------------------------------------------------
# Context & choice
#-------------------------------------------------------------------------------------
plot = plots['context_choice']
condition_averaged = sorted_trials['context_choice']
for (choice, context), r in condition_averaged.items():
ls = get_linestyle(choice)
if context == 'm':
color = 'k'
else:
color = Figure.colors('darkblue')
plot.plot(t[w], r[unit,w], ls, color=color, lw=lw)
yall.append(r[unit,w])
plot.xlim(t[w][0], t[w][-1])
plot.xticks([t[w][0], 0, t[w][-1]])
return yall
