def psychometric_function(trialsfile, plot=None, threshold=False, **kwargs):
"""
Compute and plot the sychometric function.
"""
# Load trials
trials, ntrials = load_trials(trialsfile)
#-------------------------------------------------------------------------------------
# Compute psychometric function
#-------------------------------------------------------------------------------------
choice_by_coh = {}
ntot = 0
ncorrect = 0
for trial in trials:
info = trial['info']
# Signed coherence
coh = info['in_out'] * info['coh']
# Choice
choice = get_choice(trial, threshold)
if choice is None:
continue
if coh != 0:
ntot += 1
if isinstance(choice, tuple):
choice, _ = choice
choice_by_coh.setdefault(coh, []).append(choice)
# Correct
if coh != 0 and choice == info['choice']:
ncorrect += 1
# Report overall performance
pcorrect = 100 * ncorrect / ntot
print("[ {}.psychometric_function ] {}/{} = {:.2f}% correct.".format(
THIS, ncorrect, ntot, pcorrect))
cohs = np.sort(np.array(choice_by_coh.keys()))
p0 = np.zeros(len(cohs))
for i, coh in enumerate(cohs):
choices = np.array(choice_by_coh[coh])
p0[i] = 1 - np.sum(choices) / len(choices)
scaled_cohs = SCALE * cohs
#-------------------------------------------------------------------------------------
# Plot
#-------------------------------------------------------------------------------------
if plot is not None:
# Data
prop = {
'ms': kwargs.get('ms', 6),
'mfc': kwargs.get('mfc', '0.2'),
'mew': kwargs.get('mew', 0),
'zorder': 10
}
plot.plot(scaled_cohs, 100 * p0, 'o', **prop)
# Fit
prop = {
'color': kwargs.get('mfc', '0.2'),
'lw': kwargs.get('lw', 1),
'zorder': 5
}
try:
popt, func = fittools.fit_psychometric(scaled_cohs, p0)
fit_cohs = np.linspace(min(scaled_cohs), max(scaled_cohs), 201)
fit_p0 = func(fit_cohs, **popt)
plot.plot(fit_cohs, 100 * fit_p0, **prop)
except RuntimeError:
print("[ {}.psychometric_function ]".format(THIS) +
" Unable to fit, drawing a line through the points.")
plot.plot(scaled_cohs, 100 * p0, **prop)
plot.ylim(0, 100)
plot.yticks([0, 25, 50, 75, 100])
plot.lim('x', scaled_cohs)
def psychometric_function(trialsfile, plots=None, **kwargs):
"""
Psychometric function.
"""
# Load trials
trials, ntrials = load_trials(trialsfile)
#-------------------------------------------------------------------------------------
# Compute psychometric function
#-------------------------------------------------------------------------------------
results = {cond: {} for cond in ['mm', 'mc', 'cm', 'cc']}
ncorrect = 0
for trial in trials:
info = trial['info']
coh_m = info['left_right_m'] * info['coh_m']
coh_c = info['left_right_c'] * info['coh_c']
choice = get_choice(trial)
if choice == info['choice']:
ncorrect += 1
if info['context'] == 'm':
results['mm'].setdefault(coh_m, []).append(choice)
results['mc'].setdefault(coh_c, []).append(choice)
else:
results['cm'].setdefault(coh_m, []).append(choice)
results['cc'].setdefault(coh_c, []).append(choice)
print("[ {}.psychometric_function ] {:.2f}% correct.".format(
THIS, 100 * ncorrect / ntrials))
for cond in results:
choice_by_coh = results[cond]
cohs = np.sort(np.array(choice_by_coh.keys()))
p0 = np.zeros(len(cohs))
for i, coh in enumerate(cohs):
choices = np.array(choice_by_coh[coh])
p0[i] = 1 - np.sum(choices) / len(choices)
scaled_cohs = SCALE * cohs
results[cond] = (scaled_cohs, p0)
#-------------------------------------------------------------------------------------
# Plot
#-------------------------------------------------------------------------------------
if plots is not None:
ms = kwargs.get('ms', 5)
color_m = '0.2'
color_c = Figure.colors('darkblue')
for cond, result in results.items():
# Context
if cond[0] == 'm':
color = color_m
label = 'Motion context'
else:
color = color_c
label = 'Color context'
# Stimulus
if cond[1] == 'm':
plot = plots['m']
else:
plot = plots['c']
# Result
scaled_cohs, p0 = result
# Data points
plot.plot(scaled_cohs,
100 * p0,
'o',
ms=ms,
mew=0,
mfc=color,
zorder=10)
# Fit
try:
popt, func = fittools.fit_psychometric(scaled_cohs, p0)
fit_cohs = np.linspace(min(scaled_cohs), max(scaled_cohs), 201)
fit_p0 = func(fit_cohs, **popt)
plot.plot(fit_cohs,
100 * fit_p0,
color=color,
lw=1,
zorder=5,
label=label)
except RuntimeError:
print("[ {}.psychometric_function ]".format(THIS) +
" Unable to fit, drawing a line through the points.")
plot.plot(scaled_cohs,
100 * p0,
color=color,
lw=1,
zorder=5,
label=label)
plot.lim('x', scaled_cohs)
plot.ylim(0, 100)
#-------------------------------------------------------------------------------------
return results
def psychometric_function(trialsfile, plot=None, **kwargs):
"""
Compute and plot the sychometric function.
"""
# Load trials
with open(trialsfile) as f:
trials = pickle.load(f)
ntrials = len(trials)
# Get modalities
mods = list(set([trial['info']['modality'] for trial in trials]))
#-------------------------------------------------------------------------------------
# Compute psychometric function
#-------------------------------------------------------------------------------------
results = {mod: {} for mod in mods}
ncorrect_by_mod = {mod: 0 for mod in mods}
ntrials_by_mod = {mod: 0 for mod in mods}
for trial in trials:
# Condition
info = trial['info']
mod = info['modality']
freq = info['freq']
ntrials_by_mod[mod] += 1
# Choice
choice = get_choice(trial)
results[mod].setdefault(freq, []).append(choice)
# Correct
if choice == info['choice']:
ncorrect_by_mod[mod] += 1
# Report overall performance
pcorrect_by_mod = {mod: 100*ncorrect_by_mod[mod]/ntrials_by_mod[mod] for mod in mods}
print("[ {}.psychometric_function ]".format(THIS))
print(" v {:.2f}% correct.".format(pcorrect_by_mod['v']))
print(" a {:.2f}% correct.".format(pcorrect_by_mod['a']))
print(" va {:.2f}% correct.".format(pcorrect_by_mod['va']))
# Psychometric function
for mod in mods:
choice_by_freq = results[mod]
freqs = np.sort(np.array(choice_by_freq.keys()))
p0 = np.zeros(len(freqs))
for i, freq in enumerate(freqs):
choices = np.array(choice_by_freq[freq])
p0[i] = 1 - np.sum(choices)/len(choices)
results[mod] = (freqs, p0)
#-------------------------------------------------------------------------------------
# Plot
#-------------------------------------------------------------------------------------
if plot is not None:
sigmas = {}
for mod in ['v', 'a', 'va']:
freqs, p0 = results[mod]
# Data
prop = {'ms': kwargs.get('ms', 6),
'mfc': kwargs.get('mfc', colors[mod]),
'mew': kwargs.get('mew', 0),
'zorder': 10}
plot.plot(freqs, 100*p0, 'o', **prop)
if mod == 'v':
label = 'Visual'
elif mod == 'a':
label = 'Auditory'
elif mod == 'va':
label = 'Multisensory'
else:
raise ValueError(" [ {}.psychometric_function ] Unknown modality.".
format(THIS))
# Fit
prop = {'color': kwargs.get('mfc', colors[mod]),
'lw': kwargs.get('lw', 1),
'label': label,
'zorder': 5}
try:
popt, func = fittools.fit_psychometric(freqs, p0)
fit_freqs = np.linspace(min(freqs), max(freqs), 201)
fit_p0 = func(fit_freqs, **popt)
plot.plot(fit_freqs, 100*fit_p0, **prop)
sigmas[mod] = popt['sigma']
except RuntimeError:
print("[ {}.psychometric_function ]".format(THIS)
+ " Unable to fit, drawing a line through the points.")
plot.plot(freqs, 100*p0, **prop)
plot.ylim(0, 100)
plot.yticks([0, 25, 50, 75, 100])
plot.lim('x', freqs)
# Is it optimal?
print("")
print(" Optimality test")
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))
def psychometric_function(trialsfile, plot=None, **kwargs):
"""
Compute and plot the sychometric function.
"""
# Load trials
with open(trialsfile) as f:
trials = pickle.load(f)
ntrials = len(trials)
# Get modalities
mods = list(set([trial['info']['modality'] for trial in trials]))
#-------------------------------------------------------------------------------------
# Compute psychometric function
#-------------------------------------------------------------------------------------
results = {mod: {} for mod in mods}
ncorrect_by_mod = {mod: 0 for mod in mods}
ntrials_by_mod = {mod: 0 for mod in mods}
for trial in trials:
# Condition
info = trial['info']
mod = info['modality']
freq = info['freq']
ntrials_by_mod[mod] += 1
# Choice
choice = get_choice(trial)
results[mod].setdefault(freq, []).append(choice)
# Correct
if choice == info['choice']:
ncorrect_by_mod[mod] += 1
# Report overall performance
pcorrect_by_mod = {
mod: 100 * ncorrect_by_mod[mod] / ntrials_by_mod[mod]
for mod in mods
}
print("[ {}.psychometric_function ]".format(THIS))
print(" v {:.2f}% correct.".format(pcorrect_by_mod['v']))
print(" a {:.2f}% correct.".format(pcorrect_by_mod['a']))
print(" va {:.2f}% correct.".format(pcorrect_by_mod['va']))
# Psychometric function
for mod in mods:
choice_by_freq = results[mod]
freqs = np.sort(np.array(choice_by_freq.keys()))
p0 = np.zeros(len(freqs))
for i, freq in enumerate(freqs):
choices = np.array(choice_by_freq[freq])
p0[i] = 1 - np.sum(choices) / len(choices)
results[mod] = (freqs, p0)
#-------------------------------------------------------------------------------------
# Plot
#-------------------------------------------------------------------------------------
if plot is not None:
sigmas = {}
for mod in ['v', 'a', 'va']:
freqs, p0 = results[mod]
# Data
prop = {
'ms': kwargs.get('ms', 6),
'mfc': kwargs.get('mfc', colors[mod]),
'mew': kwargs.get('mew', 0),
'zorder': 10
}
plot.plot(freqs, 100 * p0, 'o', **prop)
if mod == 'v':
label = 'Visual'
elif mod == 'a':
label = 'Auditory'
elif mod == 'va':
label = 'Multisensory'
else:
raise ValueError(
" [ {}.psychometric_function ] Unknown modality.".format(
THIS))
# Fit
prop = {
'color': kwargs.get('mfc', colors[mod]),
'lw': kwargs.get('lw', 1),
'label': label,
'zorder': 5
}
try:
popt, func = fittools.fit_psychometric(freqs, p0)
fit_freqs = np.linspace(min(freqs), max(freqs), 201)
fit_p0 = func(fit_freqs, **popt)
plot.plot(fit_freqs, 100 * fit_p0, **prop)
sigmas[mod] = popt['sigma']
except RuntimeError:
print("[ {}.psychometric_function ]".format(THIS) +
" Unable to fit, drawing a line through the points.")
plot.plot(freqs, 100 * p0, **prop)
plot.ylim(0, 100)
plot.yticks([0, 25, 50, 75, 100])
plot.lim('x', freqs)
# Is it optimal?
print("")
print(" Optimality test")
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))
def psychometric_function(trialsfile, plot=None, threshold=False, **kwargs):
"""
Compute and plot the sychometric function.
"""
# Load trials
trials, ntrials = load_trials(trialsfile)
#-------------------------------------------------------------------------------------
# Compute psychometric function
#-------------------------------------------------------------------------------------
choice_by_coh = {}
ntot = 0
ncorrect = 0
for trial in trials:
info = trial['info']
# Signed coherence
coh = info['in_out']*info['coh']
# Choice
choice = get_choice(trial, threshold)
if choice is None:
continue
if coh != 0:
ntot += 1
if isinstance(choice, tuple):
choice, _ = choice
choice_by_coh.setdefault(coh, []).append(choice)
# Correct
if coh != 0 and choice == info['choice']:
ncorrect += 1
# Report overall performance
pcorrect = 100*ncorrect/ntot
print("[ {}.psychometric_function ] {}/{} = {:.2f}% correct."
.format(THIS, ncorrect, ntot, pcorrect))
cohs = np.sort(np.array(choice_by_coh.keys()))
p0 = np.zeros(len(cohs))
for i, coh in enumerate(cohs):
choices = np.array(choice_by_coh[coh])
p0[i] = 1 - np.sum(choices)/len(choices)
scaled_cohs = SCALE*cohs
#-------------------------------------------------------------------------------------
# Plot
#-------------------------------------------------------------------------------------
if plot is not None:
# Data
prop = {'ms': kwargs.get('ms', 6),
'mfc': kwargs.get('mfc', '0.2'),
'mew': kwargs.get('mew', 0),
'zorder': 10}
plot.plot(scaled_cohs, 100*p0, 'o', **prop)
# Fit
prop = {'color': kwargs.get('mfc', '0.2'),
'lw': kwargs.get('lw', 1),
'zorder': 5}
try:
popt, func = fittools.fit_psychometric(scaled_cohs, p0)
fit_cohs = np.linspace(min(scaled_cohs), max(scaled_cohs), 201)
fit_p0 = func(fit_cohs, **popt)
plot.plot(fit_cohs, 100*fit_p0, **prop)
except RuntimeError:
print("[ {}.psychometric_function ]".format(THIS)
+ " Unable to fit, drawing a line through the points.")
plot.plot(scaled_cohs, 100*p0, **prop)
plot.ylim(0, 100)
plot.yticks([0, 25, 50, 75, 100])
plot.lim('x', scaled_cohs)
def psychometric_function(trialsfile, plots=None, **kwargs):
"""
Psychometric function.
"""
# Load trials
trials, ntrials = load_trials(trialsfile)
#-------------------------------------------------------------------------------------
# Compute psychometric function
#-------------------------------------------------------------------------------------
results = {cond: {} for cond in ['mm', 'mc', 'cm', 'cc']}
ncorrect = 0
for trial in trials:
info = trial['info']
coh_m = info['left_right_m']*info['coh_m']
coh_c = info['left_right_c']*info['coh_c']
choice = get_choice(trial)
if choice == info['choice']:
ncorrect += 1
if info['context'] == 'm':
results['mm'].setdefault(coh_m, []).append(choice)
results['mc'].setdefault(coh_c, []).append(choice)
else:
results['cm'].setdefault(coh_m, []).append(choice)
results['cc'].setdefault(coh_c, []).append(choice)
print("[ {}.psychometric_function ] {:.2f}% correct."
.format(THIS, 100*ncorrect/ntrials))
for cond in results:
choice_by_coh = results[cond]
cohs = np.sort(np.array(choice_by_coh.keys()))
p0 = np.zeros(len(cohs))
for i, coh in enumerate(cohs):
choices = np.array(choice_by_coh[coh])
p0[i] = 1 - np.sum(choices)/len(choices)
scaled_cohs = SCALE*cohs
results[cond] = (scaled_cohs, p0)
#-------------------------------------------------------------------------------------
# Plot
#-------------------------------------------------------------------------------------
if plots is not None:
ms = kwargs.get('ms', 5)
color_m = '0.2'
color_c = Figure.colors('darkblue')
for cond, result in results.items():
# Context
if cond[0] == 'm':
color = color_m
label = 'Motion context'
else:
color = color_c
label = 'Color context'
# Stimulus
if cond[1] == 'm':
plot = plots['m']
else:
plot = plots['c']
# Result
scaled_cohs, p0 = result
# Data points
plot.plot(scaled_cohs, 100*p0, 'o', ms=ms, mew=0, mfc=color, zorder=10)
# Fit
try:
popt, func = fittools.fit_psychometric(scaled_cohs, p0)
fit_cohs = np.linspace(min(scaled_cohs), max(scaled_cohs), 201)
fit_p0 = func(fit_cohs, **popt)
plot.plot(fit_cohs, 100*fit_p0, color=color, lw=1, zorder=5, label=label)
except RuntimeError:
print("[ {}.psychometric_function ]".format(THIS)
+ " Unable to fit, drawing a line through the points.")
plot.plot(scaled_cohs, 100*p0, color=color, lw=1, zorder=5, label=label)
plot.lim('x', scaled_cohs)
plot.ylim(0, 100)
#-------------------------------------------------------------------------------------
return results
