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 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 value_stimulus_duration(trialsfile, plot, saved=None, **kwargs):
if saved is not None:
value_by_duration_by_coh, value_by_duration_by_coh_wager = saved
else:
# Load trials
trials, U, Z, Z_b, A, P, M, perf, r_p, r_v = utils.load(trialsfile)
# Time
time = trials[0]['time']
# 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'])
delay_start, _ = trial['durations']['delay']
before_sure, = np.where((delay_start <= time) & (time < delay_start+500))
value = np.mean(Z_b[before_sure,n])
if trial['wager']:
trials_by_cond_wager.setdefault(cond, {'durations': [], 'values': []})
trials_by_cond_wager[cond]['durations'].append(duration)
trials_by_cond_wager[cond]['values'].append(value)
else:
trials_by_cond.setdefault(cond, {'durations': [], 'values': []})
trials_by_cond[cond]['durations'].append(duration)
trials_by_cond[cond]['values'].append(value)
# Number of bins
nbins = kwargs.get('nbins', 10)
# Average no-wager trials
value_by_duration_by_coh = {}
for coh, v in trials_by_cond.items():
(xbins, ybins, xedges,
binsizes) = datatools.partition(v['durations'], v['values'], nbins=nbins)
duration = [np.mean(xbin) for xbin in xbins]
value = [np.mean(ybin) for ybin in ybins]
value_by_duration_by_coh[coh] = (duration, value)
# Average wager trials
value_by_duration_by_coh_wager = {}
for coh, v in trials_by_cond_wager.items():
(xbins, ybins, xedges,
binsizes) = datatools.partition(v['durations'], v['values'], nbins=nbins)
duration = [np.mean(xbin) for xbin in xbins]
value = [np.mean(ybin) for ybin in ybins]
value_by_duration_by_coh_wager[coh] = (duration, value)
#=====================================================================================
# 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(value_by_duration_by_coh)
for coh in cohs:
duration, value = value_by_duration_by_coh[coh]
plot.plot(duration, value, color=colors[coh], zorder=10, **lineprop)
plot.plot(duration, value, 'o', mfc='w', mec=colors[coh],
zorder=10, **dataprop)
# Wager trials
cohs = sorted(value_by_duration_by_coh_wager)
for coh in cohs:
duration, value = value_by_duration_by_coh_wager[coh]
plot.plot(duration, value, color=colors[coh], zorder=5, **lineprop_wager)
plot.plot(duration, value, 'o', mfc=colors[coh], mec=colors[coh],
zorder=5, **dataprop_wager)
plot.xlim(100, 800)
#plot.ylim(0.5, 1)
#=====================================================================================
return value_by_duration_by_coh, value_by_duration_by_coh_wager
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 value_stimulus_duration(trialsfile, plot, saved=None, **kwargs):
if saved is not None:
value_by_duration_by_coh, value_by_duration_by_coh_wager = saved
else:
# Load trials
trials, U, Z, Z_b, A, P, M, perf, r_p, r_v = utils.load(trialsfile)
# Time
time = trials[0]['time']
# 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'])
delay_start, _ = trial['durations']['delay']
before_sure, = np.where((delay_start <= time) & (time < delay_start+500))
value = np.mean(Z_b[before_sure,n])
if trial['wager']:
trials_by_cond_wager.setdefault(cond, {'durations': [], 'values': []})
trials_by_cond_wager[cond]['durations'].append(duration)
trials_by_cond_wager[cond]['values'].append(value)
else:
trials_by_cond.setdefault(cond, {'durations': [], 'values': []})
trials_by_cond[cond]['durations'].append(duration)
trials_by_cond[cond]['values'].append(value)
# Number of bins
nbins = kwargs.get('nbins', 10)
# Average no-wager trials
value_by_duration_by_coh = {}
for coh, v in trials_by_cond.items():
(xbins, ybins, xedges,
binsizes) = datatools.partition(v['durations'], v['values'], nbins=nbins)
duration = [np.mean(xbin) for xbin in xbins]
value = [np.mean(ybin) for ybin in ybins]
value_by_duration_by_coh[coh] = (duration, value)
# Average wager trials
value_by_duration_by_coh_wager = {}
for coh, v in trials_by_cond_wager.items():
(xbins, ybins, xedges,
binsizes) = datatools.partition(v['durations'], v['values'], nbins=nbins)
duration = [np.mean(xbin) for xbin in xbins]
value = [np.mean(ybin) for ybin in ybins]
value_by_duration_by_coh_wager[coh] = (duration, value)
#=====================================================================================
# 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(value_by_duration_by_coh)
for coh in cohs:
duration, value = value_by_duration_by_coh[coh]
plot.plot(duration, value, color=colors[coh], zorder=10, **lineprop)
plot.plot(duration, value, 'o', mfc='w', mec=colors[coh],
zorder=10, **dataprop)
# Wager trials
cohs = sorted(value_by_duration_by_coh_wager)
for coh in cohs:
duration, value = value_by_duration_by_coh_wager[coh]
plot.plot(duration, value, color=colors[coh], zorder=5, **lineprop_wager)
plot.plot(duration, value, 'o', mfc=colors[coh], mec=colors[coh],
zorder=5, **dataprop_wager)
plot.xlim(100, 800)
#plot.ylim(0.5, 1)
#=====================================================================================
return value_by_duration_by_coh, value_by_duration_by_coh_wager