def _train_acc(self, df):
resp_win = 5 # time window when a response is counted as correct
resp = df.subj_resp.copy()
resp[resp.isnull()] = 0
resp = np.array(resp)
resp[resp == ''] = 0
count = 0
for i in range(resp_win):
roll = np.roll(resp, -i)
roll[len(roll) - i:] = 0
count += roll
df['count'] = count
df['accuracy'] = ''
for idx, row in df.iterrows():
if row['count'] >= 1:
subj_resp = 1
else:
subj_resp = ''
if row['corr_resp'] == '' or pandas.isnull(row['corr_resp']):
corr_resp = ''
else:
corr_resp = row['corr_resp']
acc = exp.signal_det(corr_resp, subj_resp)
if acc == 'false alarm':
acc = ''
df.loc[idx, 'accuracy'] = acc
return df
def post_trial(self):
this_resp = self.all_keys.pop()
self.this_trial['subj_resp'] = self.computer.valid_responses[this_resp[0]]
acc = exp.signal_det(self.this_trial['corr_resp'], self.this_trial['subj_resp'])
self.this_trial['accuracy'] = acc
self.this_trial['rt'] = this_resp[1]
if acc == 'correct':
acc_int = 1
else:
acc_int = 0
self.staircase.addData(acc_int)
return self.this_trial