def _get_staircase(self):
pattern = self.paths['data'] + '%s_test.csv'
df = exp.get_behav_df(subjid=self.info['subjid'], pattern=pattern)
df = df[df.corr_resp == 'diff']
df_thres_all = []
df['thres'] = 0.
gr = PercLearn(info=self.info)
for subj in np.unique(df.subjid):
subjdf = df[df.subjid == subj]
for sid in subjdf.session.unique():
sdf = subjdf[subjdf.session == sid]
for pos in sdf.pos.unique():
posdf = sdf[sdf.pos == pos]
st_mod = _TestQuest(gr)
st_mod.get_blocks()
# get staircases from st_mod
exps = dict((st.name, st) for st in st_mod.blocks)
exps[pos].importData(
posdf.oridiff.astype(float),
np.array(posdf.corr_resp == posdf.subj_resp))
posdf.thres = exps[pos].mean()
df_thres_all.append(posdf)
df_thres = pandas.concat(df_thres_all)
return df_thres
def test(self):
"""Analysis of the test phase data
"""
pattern = self.paths['data'] + '%s_test.csv'
df = exp.get_behav_df(self.info['subjid'], pattern=pattern)
agg_acc = stats.accuracy(df, rows='session', cols='pos',
values='accuracy', yerr='subjid')
plt = plot.Plot()
plt.plot(agg_acc)
plt.show()
def test(self):
"""Analysis of the test phase data (for 2AFC task)
"""
pattern = self.paths['data'] + '%s_test.csv'
df = exp.get_behav_df(self.info['subjid'], pattern=pattern)
agg_acc = stats.accuracy(df,
rows='session',
cols='pos',
values='accuracy',
yerr='subjid')
plt = plot.Plot()
plt.plot(agg_acc)
plt.show()
def train_feedback(self, session):
pattern = self.paths['data'] + '%s_train.csv'
try:
df = exp.get_behav_df(self.info['subjid'], pattern=pattern)
except:
return 0
else:
#df = pandas.DataFrame(trial_list)
df = df[df.session == session]
df = self._train_acc(df)
cor = np.sum(df.accuracy == 'correct')
acc = float(cor) / (np.sum(df.accuracy == 'miss') + cor)
return acc
def train(self):
pattern = self.paths['data'] + '%s_train.csv'
df = exp.get_behav_df(self.info['subjid'], pattern=pattern)
dfs = []
for session in df.session.unique():
sdf = df[df.session == session]
dfs.append(self._train_acc(sdf))
df = pandas.concat(dfs)
agg_acc = stats.accuracy(df,
cols='session',
values='accuracy',
yerr='subjid',
incorrect='miss')
self.plot(agg_acc)
def _get_staircase(self):
pattern = self.paths['data'] + '%s.csv'
df = exp.get_behav_df(subjid=self.info['subjid'], pattern=pattern)
df_thres_all = []
df['thres'] = 0.
for subj in np.unique(df.subjid):
subjdf = df[df.subjid == subj]
for pos in subjdf.pos.unique():
posdf = subjdf[subjdf.pos == pos]
st_mod = Staircase()
st_mod.get_blocks()
# get staircases from st_mod
exps = dict((st.name, st) for st in st_mod.blocks)
exps[pos].importData(posdf.oridiff.astype(float),
np.array(posdf.corr_resp==posdf.subj_resp))
posdf.thres = exps[pos].mean()
df_thres_all.append(posdf)
df_thres = pandas.concat(df_thres_all)
return df_thres
