# sum_dot_y, constregs=0 for 1st dot,
# label_tc normalised across trials, times and subjects
basefile = 'source_sequential_201707031206.h5'
r_name = 'dot_x'
file = os.path.join(ss.bem_dir, basefile[:-3] + '_slabs_%s.h5' % r_name)
second_level = pd.read_hdf(file, 'second_level_src')
options = pd.read_hdf(file, 'options')
testlabel = 'L_v23ab_ROI-lh'
testtimes = [120, 170, 320, 400]
testtheta = 0.5
N = 100
S = helpers.find_available_subjects(megdatadir=helpers.megdatadir).size
#%% create Stan model
folded_normal_stan = """
data {
int<lower = 0> S;
real y[S];
}
parameters {
real<lower=0> mu;
real<lower=0> sigma;
real<lower=0, upper=1> theta;
}
model {
#trial_info.support_correct = trial_info.loc[randind, 'support_correct'].values
#trial_info.support_correct_bin = trial_info.loc[randind, 'support_correct_bin'].values
# use "None" for all subjects; for a single subject (e.g. "[24]") plots will
# be shown
subjects = 3
if len(sys.argv) > 1:
if sys.argv[1] == 'None':
subjects = None
elif sys.argv[1][0] == '[':
subjects = list(map(int, sys.argv[1][1:-1].split(',')))
else:
subjects = [int(sys.argv[1])]
if subjects is None:
subjects = helpers.find_available_subjects(helpers.behavdatadir)
subjects = np.sort(np.atleast_1d(subjects))
S = subjects.size
subject_info = pd.DataFrame([], index=pd.Index(subjects, name='subject'))
for si, sub in enumerate(subjects):
respRT = helpers.load_subject_data(sub)
respRT_ext = pd.concat([respRT, trial_info], axis=1)
#%% drop trials in which the subject responded too fast
respRT_ext.drop(respRT_ext.index[respRT_ext.RT < 0.55], inplace=True)
#%% check whether subject responded correctly according to first 4 dots
respRT_ext['is_correct_4th'] = (
respRT_ext.correct_4th == respRT_ext.response).astype(int)
pars.add_param('bound', 0, 1, gaussprob(width=0.5, shift=0.5))
if 'bias' not in fix.keys():
pars.add_param('bias', 0, .2, gaussprob())
if 'ndtloc' not in fix.keys():
pars.add_param('ndtloc', -2, 1)
if 'ndtspread' not in fix.keys():
pars.add_param('ndtspread', np.log(0.2), 1, exponential())
if 'lapseprob' not in fix.keys():
pars.add_param('lapseprob', -1.65, 1, gaussprob()) # median approx at 0.05
if 'lapsetoprob' not in fix.keys():
pars.add_param('lapsetoprob', 0, 1, gaussprob())
prior = snl.pdfs.Gaussian(m=pars.mu, S=pars.cov)
#%%
subjects = helpers.find_available_subjects(
eegdir=helpers.eegdir if use_liks else None)
#subjects = [18]
options = dict(use_liks=use_liks,
censor_late=True,
exclude_to=False,
stats='hist')
resdir = os.path.join(helpers.resultsdir, 'snl', 'rotated_directions',
pd.datetime.now().strftime('%Y%m%d%H%M'))
os.mkdir(resdir)
for sub in subjects:
if options['stats'] == 'id':
data = helpers.load_subject(sub,
exclude_to=options['exclude_to'],
store['scalar_opt'] = pd.Series(
dict(exclude_to=exclude_to,
censor_late=censor_late,
B=B,
S=S,
PS=PS,
p_outlier=p_outlier,
bias=bias))
store['depends_on'] = pd.Series(depends_on)
#%% some helpers for data storage
is_easy = lambda s: re.match(r'wfpt\((\w+)\.\d\)', s).group(1) == 'True'
get_stim = lambda s: int(s[-2:-1])
#%% loop over subjects
for sub in helpers.find_available_subjects():
print("\ninferring for subject %2d ..." % sub)
data = helpers.load_subject(sub,
exclude_to=exclude_to,
censor_late=censor_late)
stim = data.response.copy().astype(int)
err = data.error.astype(bool)
stim[err] = -stim[err]
hddm_data = pd.DataFrame(
[
data.RT.values, (data.response + 1) / 2,
(stim + 1) / 2, sub * np.ones(data.shape[0]), data.easy.values
],
"""
Created on Fri Jan 20 18:45:48 2017
@author: bitzer
"""
import os
import numpy as np
import pandas as pd
import helpers
import posterior_model_measures
import scipy.stats
#%%
subjecti = pd.Index(helpers.find_available_subjects(behavdatadir=
helpers.behavdatadir), name='subject')
triali = pd.Index(np.arange(480) + 1, name='trial')
doti = pd.Index(np.arange(25)+1, name='dot')
dotpos = helpers.load_dots()
trial_info = helpers.get_5th_dot_infos(dotpos)
infresult_file = 'infres_collapse_201612121759.npz'
#%% trial
# correct
subinfo = helpers.load_subject_data(2)
trial = pd.DataFrame(subinfo['stimulus']).sort_index()
trial.rename(columns={'stimulus': 'correct'}, inplace=True)