def plot_weights(plot, W):
w = np.ravel(W)
w_exc = w[np.where(w > 0)]
w_inh = w[np.where(w < 0)]
plot.hist(w_exc, color=Figure.colors('blue'))
plot.hist(w_inh, color=Figure.colors('red'))
def plot_weights(plot, W):
w = np.ravel(W)
w_exc = w[np.where(w > 0)]
w_inh = w[np.where(w < 0)]
plot.hist(w_exc, color=Figure.colors('blue'))
plot.hist(w_inh, color=Figure.colors('red'))
def process_trial(plot, n):
if perf.choices[n] is None:
print("Trial {}: No decision.".format(n))
return
trial = trials[n]
time = trial['time']
u = U[:, n]
z = Z[:, n]
stimulus = np.asarray(trial['epochs']['stimulus'])
evidenceL = np.sum(u[stimulus - 1, inputs['LEFT']])
evidenceR = np.sum(u[stimulus - 1, inputs['RIGHT']])
decision = np.asarray(trial['epochs']['decision'])
t_choice = perf.t_choices[n]
idx = decision[np.where(decision <= t_choice)]
t0 = time[idx][0]
pL = z[idx, inputs['LEFT']]
pR = z[idx, inputs['RIGHT']]
S = pL + pR
if perf.choices[n] == 'R':
ls = '-'
#else:
# ls = '--'
plot.plot(time[idx] - t0,
pL / S,
ls,
color=Figure.colors('red'),
lw=0.5,
zorder=5)
if perf.choices[n] == 'R':
ls = '-'
#else:
# ls = '--'
plot.plot(time[idx] - t0,
pR / S,
ls,
color=Figure.colors('blue'),
lw=0.5,
zorder=5)
def plot_activity(plot, unit):
yall = [1]
min_trials = 20
# Pre-offer
epoch_by_cond = epochs_by_cond['preoffer']
color = '0.7'
if separate_by_choice:
for choice, marker in zip(['A', 'B'], ['d', 'o']):
x = []
y = []
for i, offer in enumerate(offers):
cond = (offer, choice)
if cond in n_by_cond and n_by_cond[cond] >= min_trials:
y_i = epoch_by_cond[cond][unit]
plot.plot(i,
y_i,
marker,
mfc=color,
mec=color,
ms=0.8 * ms,
mew=0.8 * mew,
zorder=10)
yall.append(y_i)
if i != 0 and i != len(offers) - 1:
x.append(i)
y.append(y_i)
plot.plot(x, y, '-', color=color, lw=0.8 * lw, zorder=5)
else:
x = []
y = []
for i, offer in enumerate(offers):
y_i = epoch_by_cond[offer][unit]
plot.plot(i,
y_i,
'o',
mfc=color,
mec=color,
ms=0.8 * ms,
mew=0.8 * mew,
zorder=10)
yall.append(y_i)
if i != 0 and i != len(offers) - 1:
x.append(i)
y.append(y_i)
plot.plot(x, y, '-', color=color, lw=0.8 * lw, zorder=5)
# Epoch
epoch_by_cond = epochs_by_cond[epoch]
if epoch == 'postoffer':
color = Figure.colors('darkblue')
elif epoch == 'latedelay':
color = Figure.colors('darkblue')
elif epoch == 'prechoice':
color = Figure.colors('darkblue')
else:
raise ValueError(epoch)
if separate_by_choice:
for choice, marker, color in zip(
['A', 'B'], ['d', 'o'],
[Figure.colors('red'),
Figure.colors('blue')]):
x = []
y = []
for i, offer in enumerate(offers):
cond = (offer, choice)
if cond in n_by_cond and n_by_cond[cond] >= min_trials:
y_i = epoch_by_cond[cond][unit]
yall.append(y_i)
plot.plot(i,
y_i,
marker,
mfc=color,
mec=color,
ms=ms,
mew=mew,
zorder=10)
if i != 0 and i != len(offers) - 1:
x.append(i)
y.append(y_i)
plot.plot(x, y, '-', color=color, lw=lw, zorder=5)
else:
x = []
y = []
for i, offer in enumerate(offers):
y_i = epoch_by_cond[offer][unit]
plot.plot(i,
y_i,
'o',
mfc=color,
mec=color,
ms=ms,
mew=mew,
zorder=10)
yall.append(y_i)
if i != 0 and i != len(offers) - 1:
x.append(i)
y.append(y_i)
plot.plot(x, y, '-', color=color, lw=lw, zorder=5)
plot.xticks(range(len(offers)))
plot.xticklabels(['{}B:{}A'.format(*offer) for offer in offers],
rotation=rotation)
plot.xlim(0, len(offers) - 1)
plot.lim('y', yall, lower=0)
return yall
mew=0)
#plot.plot(ntrials, 100*pcorrects, 'o', color=color, ms=6, mew=0)
plot = fig['reward']
plot.xlim(0, max([max(x) for x in xall]))
plot.ylim(-1, 1)
plot.xlabel(r'Number of trials ($\times$' + '{})'.format(T))
plot.ylabel('Reward per trial')
if len(num_trials) > 1:
mean = np.mean(num_trials)
sd = np.std(num_trials, ddof=1)
plot.text_lower_right(r'{:.1f} $\pm$ {:.1f} trials'.format(mean, sd),
dy=0.03,
fontsize=10,
color=Figure.colors('green'))
else:
mean = int(num_trials[0])
plot.text_lower_right('{} trials'.format(mean),
dy=0.03,
fontsize=10,
color=Figure.colors('green'))
plot = fig['correct']
plot.xlim(0, max([max(x) for x in xall]))
plot.ylim(40, 100)
plot.ylabel('Percent correct\n(decision trials)')
target_color = Figure.colors('red')
plot = fig['correct']
#plot.plot(ntrials[w1], 100*pcorrects[w1], '--', color=color, lw=lw)
plot.plot(ntrials[w2], 100*pcorrects[w2], color=color, lw=lw)
plot.plot(ntrials[w2][-1], 100*pcorrects[w2][-1], 'o', mfc=color, ms=4, mew=0)
#plot.plot(ntrials, 100*pcorrects, 'o', color=color, ms=6, mew=0)
plot = fig['reward']
plot.xlim(0, max([max(x) for x in xall]))
plot.ylim(-1, 1)
plot.xlabel(r'Number of trials ($\times$' + '{})'.format(T))
plot.ylabel('Reward per trial')
if len(num_trials) > 1:
mean = np.mean(num_trials)
sd = np.std(num_trials, ddof=1)
plot.text_lower_right(r'{:.1f} $\pm$ {:.1f} trials'.format(mean, sd), dy=0.03,
fontsize=10, color=Figure.colors('green'))
else:
mean = int(num_trials[0])
plot.text_lower_right('{} trials'.format(mean), dy=0.03,
fontsize=10, color=Figure.colors('green'))
plot = fig['correct']
plot.xlim(0, max([max(x) for x in xall]))
plot.ylim(40, 100)
plot.ylabel('Percent correct\n(decision trials)')
target_color = Figure.colors('red')
plot = fig['correct']
if modelname.startswith('rdm_fixed'):
target = 80
'stimulus': (600, 2000)
}
tmax = durations['stimulus'][-1]
time = np.linspace(0, tmax, 151)[1:]
#=========================================================================================
plot = fig['fixation']
plot.axis_off('bottom')
fixation = np.zeros_like(time)
for i, t in enumerate(time):
if t < durations['stimulus'][0]:
fixation[i] = 1
plot.plot(time, fixation, color=Figure.colors('magenta'), lw=lw)
plot.yticks([0, 1])
plot.yticklabels(['OFF', 'ON'], fontsize=5.5)
plot.xlim(0, tmax)
plot.ylim(0, 1)
plot.text(durations['stimulus'][0], 1.1, '\"Go\"', ha='left', va='bottom', fontsize=7)
#=========================================================================================
plot = fig['stimulus']
plot.axis_off('bottom')
# Stimulus
transform=plot.transAxes)
plot.text(-1.25,
0.5 - 0.13,
s2,
ha='center',
va='center',
fontsize=7,
color=color2,
transform=plot.transAxes)
custom_axislabel('mante-choice-0', 'choice', r'\textit{all trials}')
custom_axislabel('mante-motion-choice-0', 'motion \& choice',
r'\textbf{motion context}', 'k')
custom_axislabel('mante-color-choice-0', 'color \& choice',
r'\textbf{color context}', Figure.colors('darkblue'))
custom_axislabel('mante-context-choice-0', 'context \& choice',
r'\textit{all trials}')
#=========================================================================================
plot = fig['ms-behavior']
kwargs = dict(ms=4, lw=1)
multisensory_analysis.psychometric(multisensory_behavior, fig['ms-behavior'],
**kwargs)
# Legend
props = {
'prop': {
'size': 6
def sort(trialsfile, plots, units=None, network='p', **kwargs):
"""
Sort trials.
"""
# Load trials
data = utils.load(trialsfile)
trials, U, Z, Z_b, A, P, M, perf, r_p, r_v = data
# Which network?
if network == 'p':
r = r_p
else:
r = r_v
# Number of units
N = r.shape[-1]
# Same for every trial
time = trials[0]['time']
Ntime = len(time)
# Aligned time
time_a = np.concatenate((-time[1:][::-1], time))
Ntime_a = len(time_a)
#=====================================================================================
# Aligned to stimulus onset
#=====================================================================================
r_by_cond_stimulus = {}
n_r_by_cond_stimulus = {}
for n, trial in enumerate(trials):
if not perf.decisions[n]:
continue
if trial['mod'] == 'va':
continue
assert trial['mod'] == 'v' or trial['mod'] == 'a'
if not perf.corrects[n]:
continue
# Condition
mod = trial['mod']
choice = perf.choices[n]
cond = (mod, choice)
# Storage
r_by_cond_stimulus.setdefault(cond, np.zeros((Ntime_a, N)))
n_r_by_cond_stimulus.setdefault(cond, np.zeros((Ntime_a, N)))
# Firing rates
Mn = np.tile(M[:, n], (N, 1)).T
Rn = r[:, n] * Mn
# Align point
t0 = trial['epochs']['stimulus'][0] - 1
# Before
n_b = Rn[:t0].shape[0]
r_by_cond_stimulus[cond][Ntime - 1 - n_b:Ntime - 1] += Rn[:t0]
n_r_by_cond_stimulus[cond][Ntime - 1 - n_b:Ntime - 1] += Mn[:t0]
# After
n_a = Rn[t0:].shape[0]
r_by_cond_stimulus[cond][Ntime - 1:Ntime - 1 + n_a] += Rn[t0:]
n_r_by_cond_stimulus[cond][Ntime - 1:Ntime - 1 + n_a] += Mn[t0:]
for cond in r_by_cond_stimulus:
r_by_cond_stimulus[cond] = utils.div(r_by_cond_stimulus[cond],
n_r_by_cond_stimulus[cond])
#-------------------------------------------------------------------------------------
# Plot
#-------------------------------------------------------------------------------------
lw = kwargs.get('lw', 1.5)
dashes = kwargs.get('dashes', [3, 2])
vline_props = {'lw': kwargs.get('lw_vline', 0.5)}
if 'dashes_vline' in kwargs:
vline_props['linestyle'] = '--'
vline_props['dashes'] = dashes
colors_by_mod = {'v': Figure.colors('blue'), 'a': Figure.colors('green')}
linestyle_by_choice = {'L': '-', 'H': '--'}
lineprops = dict(lw=lw)
def plot_sorted(plot, unit, w, r_sorted):
t = time_a[w]
yall = [[1]]
for cond in [('v', 'H'), ('v', 'L'), ('a', 'H'), ('a', 'L')]:
mod, choice = cond
if mod == 'v':
label = 'Vis, '
elif mod == 'a':
label = 'Aud, '
else:
raise ValueError(mod)
if choice == 'H':
label += 'high'
elif choice == 'L':
label += 'low'
else:
raise ValueError(choice)
linestyle = linestyle_by_choice[choice]
if linestyle == '-':
lineprops = dict(linestyle=linestyle, lw=lw)
else:
lineprops = dict(linestyle=linestyle, lw=lw, dashes=dashes)
plot.plot(t,
r_sorted[cond][w, unit],
color=colors_by_mod[mod],
label=label,
**lineprops)
yall.append(r_sorted[cond][w, unit])
return t, yall
def on_stimulus(plot, unit):
w, = np.where((time_a >= -300) & (time_a <= 1000))
t, yall = plot_sorted(plot, unit, w, r_by_cond_stimulus)
plot.xlim(t[0], t[-1])
return yall
if units is not None:
for plot, unit in zip(plots, units):
on_stimulus(plot, unit)
else:
figspath, name = plots
for unit in xrange(N):
fig = Figure()
plot = fig.add()
#-----------------------------------------------------------------------------
yall = []
yall += on_stimulus(plot, unit)
plot.lim('y', yall, lower=0)
plot.vline(0)
plot.xlabel('Time (ms)')
plot.ylabel('Firing rate (a.u.)')
#-----------------------------------------------------------------------------
fig.save(path=figspath,
name=name + '_{}{:03d}'.format(network, unit))
fig.close()
fixation = np.zeros_like(trial_t)
w, = np.where((0 <= trial_t) & (trial_t <= durations['decision'][0]))
fixation[w] = 1
def rescale(y):
return y_fixation + 0.2 * y
fake_left_axis(rescale(0),
rescale(1),
thickness,
ticklabels=['OFF', 'ON'],
axislabel='Fixation cue')
plot.plot(trial_t, rescale(fixation), color=Figure.colors('darkgreen'), lw=lw)
#-----------------------------------------------------------------------------------------
# Plot stimulus
#-----------------------------------------------------------------------------------------
coh = 25.6
stimulus_L = np.zeros_like(trial_t)
stimulus_R = np.zeros_like(trial_t)
w, = np.where((durations['stimulus'][0] < trial_t)
& (trial_t <= durations['stimulus'][1]))
stimulus_L[w] = model.scale(-coh) + rng.normal(scale=0.15, size=len(w))
stimulus_R[w] = model.scale(+coh) + rng.normal(scale=0.15, size=len(w))
plot = fig['mante-{}-{}'.format(k, i)]
plot.ylim(lims[i][j])
plot.yticks(ticks[i][j])
def custom_axislabel(name, s1, s2, color2='k'):
plot = fig[name]
plot.text(-1.25, 0.5+0.13, s1, ha='center', va='center', fontsize=7,
transform=plot.transAxes)
plot.text(-1.25, 0.5-0.13, s2, ha='center', va='center', fontsize=7, color=color2,
transform=plot.transAxes)
custom_axislabel('mante-choice-0', 'choice', r'\textit{all trials}')
custom_axislabel('mante-motion-choice-0', 'motion \& choice', r'\textbf{motion context}',
'k')
custom_axislabel('mante-color-choice-0', 'color \& choice', r'\textbf{color context}',
Figure.colors('darkblue'))
custom_axislabel('mante-context-choice-0', 'context \& choice', r'\textit{all trials}')
#=========================================================================================
plot = fig['ms-behavior']
kwargs = dict(ms=4, lw=1)
multisensory_analysis.psychometric(multisensory_behavior, fig['ms-behavior'], **kwargs)
# Legend
props = {'prop': {'size': 6}, 'handlelength': 1,
'handletextpad': 0.9, 'labelspacing': 0.5}
plot.legend(bbox_to_anchor=(0.605, 1.15), **props)
plot.xlabel('Rate (events/s)')
ha='right', va='center', fontsize=4.5)
#-----------------------------------------------------------------------------------------
# Plot fixation
#-----------------------------------------------------------------------------------------
fixation = np.zeros_like(trial_t)
w, = np.where((0 <= trial_t) & (trial_t <= durations['decision'][0]))
fixation[w] = 1
def rescale(y):
return y_fixation + 0.2*y
fake_left_axis(rescale(0), rescale(1), thickness, ticklabels=['OFF', 'ON'],
axislabel='Fixation cue')
plot.plot(trial_t, rescale(fixation), color=Figure.colors('darkgreen'), lw=lw)
#-----------------------------------------------------------------------------------------
# Plot stimulus
#-----------------------------------------------------------------------------------------
coh = 25.6
stimulus_L = np.zeros_like(trial_t)
stimulus_R = np.zeros_like(trial_t)
w, = np.where((durations['stimulus'][0] < trial_t) & (trial_t <= durations['stimulus'][1]))
stimulus_L[w] = model.scale(-coh) + rng.normal(scale=0.15, size=len(w))
stimulus_R[w] = model.scale(+coh) + rng.normal(scale=0.15, size=len(w))
def rescale(y):
def sort(trialsfile, plots, units=None, network='p', **kwargs):
"""
Sort trials.
"""
# Load trials
data = utils.load(trialsfile)
trials, U, Z, Z_b, A, P, M, perf, r_p, r_v = data
# Which network?
if network == 'p':
r = r_p
else:
r = r_v
# Number of units
N = r.shape[-1]
# Same for every trial
time = trials[0]['time']
Ntime = len(time)
# Aligned time
time_a = np.concatenate((-time[1:][::-1], time))
Ntime_a = len(time_a)
#=====================================================================================
# Aligned to stimulus onset
#=====================================================================================
r_by_cond_stimulus = {}
n_r_by_cond_stimulus = {}
for n, trial in enumerate(trials):
if not perf.decisions[n]:
continue
if trial['mod'] == 'va':
continue
assert trial['mod'] == 'v' or trial['mod'] == 'a'
if not perf.corrects[n]:
continue
# Condition
mod = trial['mod']
choice = perf.choices[n]
cond = (mod, choice)
# Storage
r_by_cond_stimulus.setdefault(cond, np.zeros((Ntime_a, N)))
n_r_by_cond_stimulus.setdefault(cond, np.zeros((Ntime_a, N)))
# Firing rates
Mn = np.tile(M[:,n], (N,1)).T
Rn = r[:,n]*Mn
# Align point
t0 = trial['epochs']['stimulus'][0] - 1
# Before
n_b = Rn[:t0].shape[0]
r_by_cond_stimulus[cond][Ntime-1-n_b:Ntime-1] += Rn[:t0]
n_r_by_cond_stimulus[cond][Ntime-1-n_b:Ntime-1] += Mn[:t0]
# After
n_a = Rn[t0:].shape[0]
r_by_cond_stimulus[cond][Ntime-1:Ntime-1+n_a] += Rn[t0:]
n_r_by_cond_stimulus[cond][Ntime-1:Ntime-1+n_a] += Mn[t0:]
for cond in r_by_cond_stimulus:
r_by_cond_stimulus[cond] = utils.div(r_by_cond_stimulus[cond],
n_r_by_cond_stimulus[cond])
#-------------------------------------------------------------------------------------
# Plot
#-------------------------------------------------------------------------------------
lw = kwargs.get('lw', 1.5)
dashes = kwargs.get('dashes', [3, 2])
vline_props = {'lw': kwargs.get('lw_vline', 0.5)}
if 'dashes_vline' in kwargs:
vline_props['linestyle'] = '--'
vline_props['dashes'] = dashes
colors_by_mod = {
'v': Figure.colors('blue'),
'a': Figure.colors('green')
}
linestyle_by_choice = {
'L': '-',
'H': '--'
}
lineprops = dict(lw=lw)
def plot_sorted(plot, unit, w, r_sorted):
t = time_a[w]
yall = [[1]]
for cond in [('v', 'H'), ('v', 'L'), ('a', 'H'), ('a', 'L')]:
mod, choice = cond
if mod == 'v':
label = 'Vis, '
elif mod == 'a':
label = 'Aud, '
else:
raise ValueError(mod)
if choice == 'H':
label += 'high'
elif choice == 'L':
label += 'low'
else:
raise ValueError(choice)
linestyle = linestyle_by_choice[choice]
if linestyle == '-':
lineprops = dict(linestyle=linestyle, lw=lw)
else:
lineprops = dict(linestyle=linestyle, lw=lw, dashes=dashes)
plot.plot(t, r_sorted[cond][w,unit],
color=colors_by_mod[mod],
label=label,
**lineprops)
yall.append(r_sorted[cond][w,unit])
return t, yall
def on_stimulus(plot, unit):
w, = np.where((time_a >= -300) & (time_a <= 1000))
t, yall = plot_sorted(plot, unit, w, r_by_cond_stimulus)
plot.xlim(t[0], t[-1])
return yall
if units is not None:
for plot, unit in zip(plots, units):
on_stimulus(plot, unit)
else:
figspath, name = plots
for unit in xrange(N):
fig = Figure()
plot = fig.add()
#-----------------------------------------------------------------------------
yall = []
yall += on_stimulus(plot, unit)
plot.lim('y', yall, lower=0)
plot.vline(0)
plot.xlabel('Time (ms)')
plot.ylabel('Firing rate (a.u.)')
#-----------------------------------------------------------------------------
fig.save(path=figspath, name=name+'_{}{:03d}'.format(network, unit))
fig.close()
from __future__ import absolute_import, division
import os
import numpy as np
from pyrl import fittools, runtools, tasktools, utils
from pyrl.figtools import Figure
#/////////////////////////////////////////////////////////////////////////////////////////
colors = {
'v': Figure.colors('blue'),
'a': Figure.colors('green'),
'va': Figure.colors('orange')
}
#/////////////////////////////////////////////////////////////////////////////////////////
def psychometric(trialsfile, plot, **kwargs):
# Load trials
trials, A, R, M, perf = utils.load(trialsfile)
decision_by_freq = {}
high_by_freq = {}
for n, trial in enumerate(trials):
mod = trial['mod']
freq = trial['freq']
decision_by_freq.setdefault(mod, {})
high_by_freq.setdefault(mod, {})
decision_by_freq[mod].setdefault(freq, [])
'stimulus': (600, 2000)
}
tmax = durations['stimulus'][-1]
time = np.linspace(0, tmax, 151)[1:]
#=========================================================================================
plot = fig['fixation']
plot.axis_off('bottom')
fixation = np.zeros_like(time)
for i, t in enumerate(time):
if t < durations['stimulus'][0]:
fixation[i] = 1
plot.plot(time, fixation, color=Figure.colors('magenta'), lw=lw)
plot.yticks([0, 1])
plot.yticklabels(['OFF', 'ON'], fontsize=5.5)
plot.xlim(0, tmax)
plot.ylim(0, 1)
plot.text(durations['stimulus'][0], 1.1, '\"Go\"', ha='left', va='bottom', fontsize=7)
#=========================================================================================
plot = fig['stimulus']
plot.axis_off('bottom')
# Stimulus
from __future__ import absolute_import, division
import os
import numpy as np
from pyrl import fittools, runtools, tasktools, utils
from pyrl.figtools import Figure
#/////////////////////////////////////////////////////////////////////////////////////////
colors = {
'v': Figure.colors('blue'),
'a': Figure.colors('green'),
'va': Figure.colors('orange')
}
#/////////////////////////////////////////////////////////////////////////////////////////
def psychometric(trialsfile, plot, **kwargs):
# Load trials
trials, A, R, M, perf = utils.load(trialsfile)
decision_by_freq = {}
high_by_freq = {}
for n, trial in enumerate(trials):
mod = trial['mod']
freq = trial['freq']
decision_by_freq.setdefault(mod, {})
high_by_freq.setdefault(mod, {})
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'))
#=========================================================================================
plot = fig['choice-lower']
kwargs = {'ms': 4.5, 'lw': 1.25}
analysis.choice_pattern(trialsfile2_b, model2.offers, plot, **kwargs)
plot.yticks([0, 50, 100])
plot.xlabel('Offer (\#B : \#A)')
plot.ylabel('Percent choice B')
plot.text_upper_left('1A = {}B'.format(model2.A_to_B),
fontsize=7.5,
def plot_activity(plot, unit):
yall = [1]
min_trials = 20
# Pre-offer
epoch_by_cond = epochs_by_cond['preoffer']
color = '0.7'
if separate_by_choice:
for choice, marker in zip(['A', 'B'], ['d', 'o']):
x = []
y = []
for i, offer in enumerate(offers):
cond = (offer, choice)
if cond in n_by_cond and n_by_cond[cond] >= min_trials:
y_i = epoch_by_cond[cond][unit]
plot.plot(i, y_i, marker, mfc=color, mec=color, ms=0.8*ms,
mew=0.8*mew, zorder=10)
yall.append(y_i)
if i != 0 and i != len(offers)-1:
x.append(i)
y.append(y_i)
plot.plot(x, y, '-', color=color, lw=0.8*lw, zorder=5)
else:
x = []
y = []
for i, offer in enumerate(offers):
y_i = epoch_by_cond[offer][unit]
plot.plot(i, y_i, 'o', mfc=color, mec=color, ms=0.8*ms,
mew=0.8*mew, zorder=10)
yall.append(y_i)
if i != 0 and i != len(offers)-1:
x.append(i)
y.append(y_i)
plot.plot(x, y, '-', color=color, lw=0.8*lw, zorder=5)
# Epoch
epoch_by_cond = epochs_by_cond[epoch]
if epoch == 'postoffer':
color = Figure.colors('darkblue')
elif epoch == 'latedelay':
color = Figure.colors('darkblue')
elif epoch == 'prechoice':
color = Figure.colors('darkblue')
else:
raise ValueError(epoch)
if separate_by_choice:
for choice, marker, color in zip(['A', 'B'], ['d', 'o'], [Figure.colors('red'), Figure.colors('blue')]):
x = []
y = []
for i, offer in enumerate(offers):
cond = (offer, choice)
if cond in n_by_cond and n_by_cond[cond] >= min_trials:
y_i = epoch_by_cond[cond][unit]
yall.append(y_i)
plot.plot(i, y_i, marker, mfc=color, mec=color, ms=ms, mew=mew, zorder=10)
if i != 0 and i != len(offers)-1:
x.append(i)
y.append(y_i)
plot.plot(x, y, '-', color=color, lw=lw, zorder=5)
else:
x = []
y = []
for i, offer in enumerate(offers):
y_i = epoch_by_cond[offer][unit]
plot.plot(i, y_i, 'o', mfc=color, mec=color, ms=ms, mew=mew, zorder=10)
yall.append(y_i)
if i != 0 and i != len(offers)-1:
x.append(i)
y.append(y_i)
plot.plot(x, y, '-', color=color, lw=lw, zorder=5)
plot.xticks(range(len(offers)))
plot.xticklabels(['{}B:{}A'.format(*offer) for offer in offers],
rotation=rotation)
plot.xlim(0, len(offers)-1)
plot.lim('y', yall, lower=0)
return yall
'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'))
#=========================================================================================
plot = fig['choice-lower']
kwargs = {'ms': 4.5, 'lw': 1.25}
analysis.choice_pattern(trialsfile2_b, model2.offers, plot, **kwargs)
plot.yticks([0, 50, 100])
plot.xlabel('Offer (\#B : \#A)')
plot.ylabel('Percent choice B')
plot.text_upper_left('1A = {}B'.format(model2.A_to_B), fontsize=7.5,
color=Figure.colors('green'))
