def sure_stimulus_duration(trialsfile, plot, saved=None, **kwargs):
if saved is not None:
psure_by_duration_by_coh = saved
else:
trials, A, R, M, perf = utils.load(trialsfile)
# Sort
trials_by_cond = {}
for n, trial in enumerate(trials):
if not trial['wager']:
continue
cond = trial['coh']
if perf.choices[n] is not None:
trials_by_cond.setdefault(cond, {'durations': [], 'sures': []})
duration = np.ptp(trial['durations']['stimulus'])
trials_by_cond[cond]['durations'].append(duration)
trials_by_cond[cond]['sures'].append(perf.choices[n] == 'S')
# Number of bins
nbins = kwargs.get('nbins', 10)
# Average
psure_by_duration_by_coh = {}
for coh, v in trials_by_cond.items():
(xbins, ybins, xedges,
binsizes) = datatools.partition(v['durations'], v['sures'], nbins=nbins)
duration = [np.mean(xbin) for xbin in xbins]
psure = [utils.divide(np.sum(ybin > 0), len(ybin)) for ybin in ybins]
psure_by_duration_by_coh[coh] = (duration, psure)
#=====================================================================================
# Plot
#=====================================================================================
lineprop = {'lw': kwargs.get('lw', 1)}
dataprop = {'ms': kwargs.get('ms', 7),
'mew': kwargs.get('mew', 0)}
colors = kwargs.get('colors', kiani2009_colors)
cohs = sorted(psure_by_duration_by_coh)
for coh in cohs:
duration, psure = psure_by_duration_by_coh[coh]
plot.plot(duration, psure, color=colors[coh], label='{}\%'.format(coh), **lineprop)
plot.plot(duration, psure, 'o', mfc=colors[coh], **dataprop)
plot.xlim(100, 800)
plot.ylim(0, 1)
#=====================================================================================
return psure_by_duration_by_coh
def sure_stimulus_duration(trialsfile, plot, saved=None, **kwargs):
if saved is not None:
psure_by_duration_by_coh = saved
else:
trials, A, R, M, perf = utils.load(trialsfile)
# Sort
trials_by_cond = {}
for n, trial in enumerate(trials):
if not trial['wager']:
continue
cond = trial['coh']
if perf.choices[n] is not None:
trials_by_cond.setdefault(cond, {'durations': [], 'sures': []})
duration = np.ptp(trial['durations']['stimulus'])
trials_by_cond[cond]['durations'].append(duration)
trials_by_cond[cond]['sures'].append(perf.choices[n] == 'S')
# Number of bins
nbins = kwargs.get('nbins', 10)
# Average
psure_by_duration_by_coh = {}
for coh, v in trials_by_cond.items():
(xbins, ybins, xedges,
binsizes) = datatools.partition(v['durations'], v['sures'], nbins=nbins)
duration = [np.mean(xbin) for xbin in xbins]
psure = [utils.divide(np.sum(ybin > 0), len(ybin)) for ybin in ybins]
psure_by_duration_by_coh[coh] = (duration, psure)
#=====================================================================================
# Plot
#=====================================================================================
lineprop = {'lw': kwargs.get('lw', 1)}
dataprop = {'ms': kwargs.get('ms', 7),
'mew': kwargs.get('mew', 0)}
colors = kwargs.get('colors', kiani2009_colors)
cohs = sorted(psure_by_duration_by_coh)
for coh in cohs:
duration, psure = psure_by_duration_by_coh[coh]
plot.plot(duration, psure, color=colors[coh], label='{}\%'.format(coh), **lineprop)
plot.plot(duration, psure, 'o', mfc=colors[coh], **dataprop)
plot.xlim(100, 800)
plot.ylim(0, 1)
#=====================================================================================
return psure_by_duration_by_coh
def choice_pattern(trialsfile, offers, plot, **kwargs):
# Load trials
trials, A, R, M, perf = utils.load(trialsfile)
B_by_offer = {}
n_nondecision = 0
for n, trial in enumerate(trials):
if perf.choices[n] is None:
n_nondecision += 1
continue
juice_L, juice_R = trial['juice']
offer = trial['offer']
if perf.choices[n] == 'B':
B = 1
elif perf.choices[n] == 'A':
B = 0
else:
raise ValueError("invalid choice")
B_by_offer.setdefault(offer, []).append(B)
print("Non-decision trials: {}/{}".format(n_nondecision, len(trials)))
pB_by_offer = {}
for offer in B_by_offer:
Bs = B_by_offer[offer]
pB_by_offer[offer] = utils.divide(sum(Bs), len(Bs))
#print(offer, pB_by_offer[offer])
#-------------------------------------------------------------------------------------
# Plot
#-------------------------------------------------------------------------------------
if plot is None:
return
ms = kwargs.get('ms', 7)
rotation = kwargs.get('rotation', 60)
for i, offer in enumerate(offers):
plot.plot(i, 100 * pB_by_offer[offer], 'o', color='k', ms=ms)
plot.xticks(range(len(offers)))
plot.xticklabels(['{}B:{}A'.format(*offer) for offer in offers],
rotation=rotation)
plot.xlim(0, len(offers) - 1)
plot.ylim(0, 100)
def performance(trialsfile, plot, **kwargs):
# Load trials
trials, A, R, M, perf = utils.load(trialsfile)
correct_by_cond = {}
for n, trial in enumerate(trials):
if not perf.decisions[n]:
continue
gt_lt = trial['gt_lt']
fpair = trial['fpair']
if gt_lt == '>':
f1, f2 = fpair
else:
f2, f1 = fpair
cond = (f1, f2)
correct_by_cond.setdefault(cond, []).append(perf.corrects[n])
pcorrect_by_cond = {}
for c in correct_by_cond:
corrects = correct_by_cond[c]
pcorrect_by_cond[c] = utils.divide(sum(corrects), len(corrects))
#-------------------------------------------------------------------------------------
# Plot
#-------------------------------------------------------------------------------------
plot.equal()
lw = kwargs.get('lw', 1)
fontsize = kwargs.get('fontsize', 10)
_min, _max = kwargs.get('lims', (10 - 4, 34 + 4))
r = kwargs.get('r', 1.5)
for (f1, f2), pcorrect in pcorrect_by_cond.items():
plot.circle((f1, f2), r, ec='none', fc=smap.to_rgba(f1))
plot.text(f1,
f2,
'{}'.format(int(100 * pcorrect)),
color='w',
fontsize=fontsize,
ha='center',
va='center')
plot.xlim(_min, _max)
plot.ylim(_min, _max)
plot.plot([_min, _max], [_min, _max], color='k', lw=lw)
def choice_pattern(trialsfile, offers, plot, **kwargs):
# Load trials
trials, A, R, M, perf = utils.load(trialsfile)
B_by_offer = {}
n_nondecision = 0
for n, trial in enumerate(trials):
if perf.choices[n] is None:
n_nondecision += 1
continue
juice_L, juice_R = trial['juice']
offer = trial['offer']
if perf.choices[n] == 'B':
B = 1
elif perf.choices[n] == 'A':
B = 0
else:
raise ValueError("invalid choice")
B_by_offer.setdefault(offer, []).append(B)
print("Non-decision trials: {}/{}".format(n_nondecision, len(trials)))
pB_by_offer = {}
for offer in B_by_offer:
Bs = B_by_offer[offer]
pB_by_offer[offer] = utils.divide(sum(Bs), len(Bs))
#print(offer, pB_by_offer[offer])
#-------------------------------------------------------------------------------------
# Plot
#-------------------------------------------------------------------------------------
if plot is None:
return
ms = kwargs.get('ms', 7)
rotation = kwargs.get('rotation', 60)
for i, offer in enumerate(offers):
plot.plot(i, 100*pB_by_offer[offer], 'o', color='k', ms=ms)
plot.xticks(range(len(offers)))
plot.xticklabels(['{}B:{}A'.format(*offer) for offer in offers], rotation=rotation)
plot.xlim(0, len(offers)-1)
plot.ylim(0, 100)
def correct_stimulus_duration(trialsfile, plot, saved=None, **kwargs):
if saved is not None:
pcorrect_by_duration_by_coh, pcorrect_by_duration_by_coh_wager = saved
else:
trials, A, R, M, perf = utils.load(trialsfile)
# Sort
trials_by_cond = {}
trials_by_cond_wager = {}
for n, trial in enumerate(trials):
coh = trial['coh']
if coh == 0 or perf.choices[n] not in ['L', 'R']:
continue
cond = coh
duration = np.ptp(trial['durations']['stimulus'])
if trial['wager']:
trials_by_cond_wager.setdefault(cond, {'durations': [], 'corrects': []})
trials_by_cond_wager[cond]['durations'].append(duration)
trials_by_cond_wager[cond]['corrects'].append(perf.corrects[n])
else:
trials_by_cond.setdefault(cond, {'durations': [], 'corrects': []})
trials_by_cond[cond]['durations'].append(duration)
trials_by_cond[cond]['corrects'].append(perf.corrects[n])
# Number of bins
nbins = kwargs.get('nbins', 10)
# Average no-wager trials
pcorrect_by_duration_by_coh = {}
for coh, v in trials_by_cond.items():
(xbins, ybins, xedges,
binsizes) = datatools.partition(v['durations'], v['corrects'], nbins=nbins)
duration = [np.mean(xbin) for xbin in xbins]
pcorrect = [utils.divide(np.sum(ybin > 0), len(ybin)) for ybin in ybins]
pcorrect_by_duration_by_coh[coh] = (duration, pcorrect)
# Average wager trials
pcorrect_by_duration_by_coh_wager = {}
for coh, v in trials_by_cond_wager.items():
(xbins, ybins, xedges,
binsizes) = datatools.partition(v['durations'], v['corrects'], nbins=nbins)
duration = [np.mean(xbin) for xbin in xbins]
pcorrect = [utils.divide(np.sum(ybin > 0), len(ybin)) for ybin in ybins]
pcorrect_by_duration_by_coh_wager[coh] = (duration, pcorrect)
#=====================================================================================
# Plot
#=====================================================================================
lineprop = {'ls': '--',
'lw': kwargs.get('lw', 1),
'dashes': kwargs.get('dashes', [9, 4])}
dataprop = {'mew': kwargs.get('mew', 1)}
dataprop['ms'] = kwargs.get('ms', 6) + dataprop['mew']/2
lineprop_wager = {'lw': kwargs.get('lw', 1)}
dataprop_wager = {'ms': kwargs.get('ms', 7),
'mew': kwargs.get('mew', 0)}
colors = kwargs.get('colors', kiani2009_colors)
# No-wager trials
cohs = sorted(pcorrect_by_duration_by_coh)
for coh in cohs:
duration, pcorrect = pcorrect_by_duration_by_coh[coh]
plot.plot(duration, pcorrect, color=colors[coh], zorder=10, **lineprop)
plot.plot(duration, pcorrect, 'o', mfc='w', mec=colors[coh],
zorder=10, **dataprop)
# Wager trials
cohs = sorted(pcorrect_by_duration_by_coh_wager)
for coh in cohs:
duration, pcorrect = pcorrect_by_duration_by_coh_wager[coh]
plot.plot(duration, pcorrect, color=colors[coh], zorder=5, **lineprop_wager)
plot.plot(duration, pcorrect, 'o', mfc=colors[coh], mec=colors[coh],
zorder=5, **dataprop_wager)
plot.xlim(100, 800)
plot.ylim(0.5, 1)
#=====================================================================================
return pcorrect_by_duration_by_coh, pcorrect_by_duration_by_coh_wager
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, [])
high_by_freq[mod].setdefault(freq, [])
if perf.decisions[n]:
decision_by_freq[mod][freq].append(True)
if perf.choices[n] == 'H':
high = 1
else:
high = 0
high_by_freq[mod][freq].append(high)
else:
decision_by_freq[mod][freq].append(False)
freqs = {}
p_decision = {}
p_high = {}
for mod in decision_by_freq:
freqs[mod] = np.sort(high_by_freq[mod].keys())
p_decision[mod] = np.zeros(len(freqs[mod]))
p_high[mod] = np.zeros(len(freqs[mod]))
for i, freq in enumerate(freqs[mod]):
p_decision[mod][i] = sum(decision_by_freq[mod][freq]) / len(
decision_by_freq[mod][freq])
p_high[mod][i] = utils.divide(sum(high_by_freq[mod][freq]),
len(high_by_freq[mod][freq]))
#-------------------------------------------------------------------------------------
# Plot
#-------------------------------------------------------------------------------------
lw = kwargs.get('lw', 1.5)
ms = kwargs.get('ms', 6)
all_x = []
all_y = []
sigmas = {}
for mod in ['v', 'a', 'va']:
if mod == 'v':
label = 'Visual'
elif mod == 'a':
label = 'Auditory'
elif mod == 'va':
label = 'Multisensory'
else:
raise ValueError
x = freqs[mod]
y = p_high[mod]
# Fit psychometric curve
props = dict(lw=lw, color=colors[mod], label=label)
try:
popt, func = fittools.fit_psychometric(x, y)
sigmas[mod] = popt['sigma']
fit_x = np.linspace(min(x), max(x), 201)
fit_y = func(fit_x, **popt)
plot.plot(fit_x, 100 * fit_y, **props)
except RuntimeError:
print("Unable to fit, drawing a line through the points.")
plot.plot(x, 100 * y, **props)
plot.plot(x, 100 * y, 'o', ms=ms, mew=0, mfc=props['color'])
all_x.append(x)
# Is it optimal?
print("")
print(" Optimality test")
print(" ---------------")
print("")
for mod in ['v', 'a', 'va']:
print(" sigma_{:<2} = {:.6f}".format(mod, sigmas[mod]))
print(" 1/sigma_v**2 + 1/sigma_a**2 = {:.6f}".format(1 / sigmas['v']**2 +
1 / sigmas['a']**2))
print(" 1/sigma_va**2 = {:.6f}".format(1 / sigmas['va']**2))
plot.xlim(np.min(all_x), np.max(all_x))
plot.ylim(0, 100)
plot.yticks([0, 50, 100])
plot.xlabel('Frequency (events/sec)')
plot.ylabel('Percent high')
return [sigmas[k] for k in ['v', 'a', 'va']]
def correct_stimulus_duration(trialsfile, plot, saved=None, **kwargs):
if saved is not None:
pcorrect_by_duration_by_coh, pcorrect_by_duration_by_coh_wager = saved
else:
trials, A, R, M, perf = utils.load(trialsfile)
# Sort
trials_by_cond = {}
trials_by_cond_wager = {}
for n, trial in enumerate(trials):
coh = trial['coh']
if coh == 0 or perf.choices[n] not in ['L', 'R']:
continue
cond = coh
duration = np.ptp(trial['durations']['stimulus'])
if trial['wager']:
trials_by_cond_wager.setdefault(cond, {'durations': [], 'corrects': []})
trials_by_cond_wager[cond]['durations'].append(duration)
trials_by_cond_wager[cond]['corrects'].append(perf.corrects[n])
else:
trials_by_cond.setdefault(cond, {'durations': [], 'corrects': []})
trials_by_cond[cond]['durations'].append(duration)
trials_by_cond[cond]['corrects'].append(perf.corrects[n])
# Number of bins
nbins = kwargs.get('nbins', 10)
# Average no-wager trials
pcorrect_by_duration_by_coh = {}
for coh, v in trials_by_cond.items():
(xbins, ybins, xedges,
binsizes) = datatools.partition(v['durations'], v['corrects'], nbins=nbins)
duration = [np.mean(xbin) for xbin in xbins]
pcorrect = [utils.divide(np.sum(ybin > 0), len(ybin)) for ybin in ybins]
pcorrect_by_duration_by_coh[coh] = (duration, pcorrect)
# Average wager trials
pcorrect_by_duration_by_coh_wager = {}
for coh, v in trials_by_cond_wager.items():
(xbins, ybins, xedges,
binsizes) = datatools.partition(v['durations'], v['corrects'], nbins=nbins)
duration = [np.mean(xbin) for xbin in xbins]
pcorrect = [utils.divide(np.sum(ybin > 0), len(ybin)) for ybin in ybins]
pcorrect_by_duration_by_coh_wager[coh] = (duration, pcorrect)
#=====================================================================================
# Plot
#=====================================================================================
lineprop = {'ls': '--',
'lw': kwargs.get('lw', 1),
'dashes': kwargs.get('dashes', [9, 4])}
dataprop = {'mew': kwargs.get('mew', 1)}
dataprop['ms'] = kwargs.get('ms', 6) + dataprop['mew']/2
lineprop_wager = {'lw': kwargs.get('lw', 1)}
dataprop_wager = {'ms': kwargs.get('ms', 7),
'mew': kwargs.get('mew', 0)}
colors = kwargs.get('colors', kiani2009_colors)
# No-wager trials
cohs = sorted(pcorrect_by_duration_by_coh)
for coh in cohs:
duration, pcorrect = pcorrect_by_duration_by_coh[coh]
plot.plot(duration, pcorrect, color=colors[coh], zorder=10, **lineprop)
plot.plot(duration, pcorrect, 'o', mfc='w', mec=colors[coh],
zorder=10, **dataprop)
# Wager trials
cohs = sorted(pcorrect_by_duration_by_coh_wager)
for coh in cohs:
duration, pcorrect = pcorrect_by_duration_by_coh_wager[coh]
plot.plot(duration, pcorrect, color=colors[coh], zorder=5, **lineprop_wager)
plot.plot(duration, pcorrect, 'o', mfc=colors[coh], mec=colors[coh],
zorder=5, **dataprop_wager)
plot.xlim(100, 800)
plot.ylim(0.5, 1)
#=====================================================================================
return pcorrect_by_duration_by_coh, pcorrect_by_duration_by_coh_wager
def indifference_point(trialsfile, offers, plot=None, **kwargs):
# Load trials
trials, A, R, M, perf = utils.load(trialsfile)
B_by_offer = {}
n_nondecision = 0
for n, trial in enumerate(trials):
if perf.choices[n] is None:
n_nondecision += 1
continue
juice_L, juice_R = trial['juice']
offer = trial['offer']
if perf.choices[n] == 'B':
B = 1
elif perf.choices[n] == 'A':
B = 0
else:
raise ValueError("invalid choice")
B_by_offer.setdefault(offer, []).append(B)
print("Non-decision trials: {}/{}".format(n_nondecision, len(trials)))
pB_by_offer = {}
for offer in B_by_offer:
Bs = B_by_offer[offer]
pB_by_offer[offer] = utils.divide(sum(Bs), len(Bs))
#print(offer, pB_by_offer[offer])
X = []
Y = []
for i, offer in enumerate(offers):
B, A = offer
X.append((B - A) / (B + A))
Y.append(pB_by_offer[offer])
X = np.asarray(X)
Y = np.asarray(Y)
idx = np.argsort(X)
X = X[idx]
Y = Y[idx]
#-------------------------------------------------------------------------------------
# Fit
#-------------------------------------------------------------------------------------
try:
popt, func = fittools.fit_psychometric(X, Y)
mu = popt['mu']
idpt = (1 + mu) / (1 - mu)
print("Indifference point = {}".format(idpt))
fit_x = np.linspace(min(X), max(X), 201)
fit_y = func(fit_x, **popt)
fit = fit_x, fit_y
except RuntimeError:
print("Unable to fit, drawing a line through the points.")
mu = None
idpt = None
fit = X, Y
#-------------------------------------------------------------------------------------
# Plot
#-------------------------------------------------------------------------------------
if plot is None:
return idpt
lw = kwargs.get('lw', 1.5)
ms = kwargs.get('ms', 7)
rotation = kwargs.get('rotation', 60)
plot.plot(fit_x, 100 * fit_y, '-', color='k', lw=lw)
plot.plot(X, 100 * Y, 'o', color='k', ms=ms)
plot.hline(50, color='0.5', zorder=2)
if mu is not None:
plot.text_upper_left('1A = {:.1f}B'.format(idpt), fontsize=10)
plot.vline(mu, color='0.5', zorder=2)
#plot.xticks(range(len(offers)))
#plot.xticklabels(['{}B:{}A'.format(*offer) for offer in offers], rotation=rotation)
#plot.xlim(0, len(offers)-1)
plot.ylim(0, 100)
return idpt
#-------------------------------------------------------------------------------------
# Reward
#-------------------------------------------------------------------------------------
plot = fig['reward']
ntrials = []
rewards = []
pcorrects = []
for record in training_history:
ntrials.append(record['n_trials'])
rewards.append(record['mean_reward'])
perf = record['perf']
pcorrects.append(utils.divide(perf.n_correct, perf.n_decision))
ntrials = np.asarray(ntrials) / T
rewards = np.asarray(rewards)
pcorrects = np.asarray(pcorrects)
print(rewards)
w1 = list(np.where(rewards == -1)[0])
w2 = list(np.where(rewards > 0)[0])
try:
w1.append(w2[0])
except IndexError:
pass
dashes = [5, 3]
#plot.plot(ntrials[w1], rewards[w1], '--', color=color, lw=lw, dashes=dashes)
#-------------------------------------------------------------------------------------
# Reward
#-------------------------------------------------------------------------------------
plot = fig['reward']
ntrials = []
rewards = []
pcorrects = []
for record in training_history:
ntrials.append(record['n_trials'])
rewards.append(record['mean_reward'])
perf = record['perf']
pcorrects.append(utils.divide(perf.n_correct, perf.n_decision))
ntrials = np.asarray(ntrials)/T
rewards = np.asarray(rewards)
pcorrects = np.asarray(pcorrects)
print(rewards)
w1 = list(np.where(rewards == -1)[0])
w2 = list(np.where(rewards > 0)[0])
try:
w1.append(w2[0])
except IndexError:
pass
dashes = [5, 3]
#plot.plot(ntrials[w1], rewards[w1], '--', color=color, lw=lw, dashes=dashes)
def indifference_point(trialsfile, offers, plot=None, **kwargs):
# Load trials
trials, A, R, M, perf = utils.load(trialsfile)
B_by_offer = {}
n_nondecision = 0
for n, trial in enumerate(trials):
if perf.choices[n] is None:
n_nondecision += 1
continue
juice_L, juice_R = trial['juice']
offer = trial['offer']
if perf.choices[n] == 'B':
B = 1
elif perf.choices[n] == 'A':
B = 0
else:
raise ValueError("invalid choice")
B_by_offer.setdefault(offer, []).append(B)
print("Non-decision trials: {}/{}".format(n_nondecision, len(trials)))
pB_by_offer = {}
for offer in B_by_offer:
Bs = B_by_offer[offer]
pB_by_offer[offer] = utils.divide(sum(Bs), len(Bs))
#print(offer, pB_by_offer[offer])
X = []
Y = []
for i, offer in enumerate(offers):
B, A = offer
X.append((B - A)/(B + A))
Y.append(pB_by_offer[offer])
X = np.asarray(X)
Y = np.asarray(Y)
idx = np.argsort(X)
X = X[idx]
Y = Y[idx]
#-------------------------------------------------------------------------------------
# Fit
#-------------------------------------------------------------------------------------
try:
popt, func = fittools.fit_psychometric(X, Y)
mu = popt['mu']
idpt = (1+mu)/(1-mu)
print("Indifference point = {}".format(idpt))
fit_x = np.linspace(min(X), max(X), 201)
fit_y = func(fit_x, **popt)
fit = fit_x, fit_y
except RuntimeError:
print("Unable to fit, drawing a line through the points.")
mu = None
idpt = None
fit = X, Y
#-------------------------------------------------------------------------------------
# Plot
#-------------------------------------------------------------------------------------
if plot is None:
return idpt
lw = kwargs.get('lw', 1.5)
ms = kwargs.get('ms', 7)
rotation = kwargs.get('rotation', 60)
plot.plot(fit_x, 100*fit_y, '-', color='k', lw=lw)
plot.plot(X, 100*Y, 'o', color='k', ms=ms)
plot.hline(50, color='0.5', zorder=2)
if mu is not None:
plot.text_upper_left('1A = {:.1f}B'.format(idpt), fontsize=10)
plot.vline(mu, color='0.5', zorder=2)
#plot.xticks(range(len(offers)))
#plot.xticklabels(['{}B:{}A'.format(*offer) for offer in offers], rotation=rotation)
#plot.xlim(0, len(offers)-1)
plot.ylim(0, 100)
return idpt
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, [])
high_by_freq[mod].setdefault(freq, [])
if perf.decisions[n]:
decision_by_freq[mod][freq].append(True)
if perf.choices[n] == 'H':
high = 1
else:
high = 0
high_by_freq[mod][freq].append(high)
else:
decision_by_freq[mod][freq].append(False)
freqs = {}
p_decision = {}
p_high = {}
for mod in decision_by_freq:
freqs[mod] = np.sort(high_by_freq[mod].keys())
p_decision[mod] = np.zeros(len(freqs[mod]))
p_high[mod] = np.zeros(len(freqs[mod]))
for i, freq in enumerate(freqs[mod]):
p_decision[mod][i] = sum(decision_by_freq[mod][freq])/len(decision_by_freq[mod][freq])
p_high[mod][i] = utils.divide(sum(high_by_freq[mod][freq]), len(high_by_freq[mod][freq]))
#-------------------------------------------------------------------------------------
# Plot
#-------------------------------------------------------------------------------------
lw = kwargs.get('lw', 1.5)
ms = kwargs.get('ms', 6)
all_x = []
all_y = []
sigmas = {}
for mod in ['v', 'a', 'va']:
if mod == 'v':
label = 'Visual'
elif mod == 'a':
label = 'Auditory'
elif mod == 'va':
label = 'Multisensory'
else:
raise ValueError
x = freqs[mod]
y = p_high[mod]
# Fit psychometric curve
props = dict(lw=lw, color=colors[mod], label=label)
try:
popt, func = fittools.fit_psychometric(x, y)
sigmas[mod] = popt['sigma']
fit_x = np.linspace(min(x), max(x), 201)
fit_y = func(fit_x, **popt)
plot.plot(fit_x, 100*fit_y, **props)
except RuntimeError:
print("Unable to fit, drawing a line through the points.")
plot.plot(x, 100*y, **props)
plot.plot(x, 100*y, 'o', ms=ms, mew=0, mfc=props['color'])
all_x.append(x)
# Is it optimal?
print("")
print(" Optimality test")
print(" ---------------")
print("")
for mod in ['v', 'a', 'va']:
print(" sigma_{:<2} = {:.6f}".format(mod, sigmas[mod]))
print(" 1/sigma_v**2 + 1/sigma_a**2 = {:.6f}"
.format(1/sigmas['v']**2 + 1/sigmas['a']**2))
print(" 1/sigma_va**2 = {:.6f}".format(1/sigmas['va']**2))
plot.xlim(np.min(all_x), np.max(all_x))
plot.ylim(0, 100)
plot.yticks([0, 50, 100])
plot.xlabel('Frequency (events/sec)')
plot.ylabel('Percent high')
return [sigmas[k] for k in ['v', 'a', 'va']]