def prepareBEH(self, project, part, factors, labels, project_param, to_filter):
'''
standard Behavior processing
'''
PP = PreProcessing(project = project, part = part, factor_headers = factors, factor_labels = labels)
PP.create_folder_structure()
PP.combine_single_subject_files(save = False)
PP.select_data(project_parameters = project_param, save = False)
PP.filter_data(to_filter = to_filter, filter_crit = ' and correct == 1', cnd_sel = False, save = True)
PP.exclude_outliers(criteria = dict(RT = 'RT_filter == True', correct = ''))
PP.save_data_file()
def prepareBEH(self, project, part, factors, labels, project_param): ''' standard Behavior processing ''' PP = PreProcessing(project = project, part = part, factor_headers = factors, factor_labels = labels) PP.create_folder_structure() PP.combine_single_subject_files(save = False) PP.select_data(project_parameters = project_param, save = False) #PP.filter_data(to_filter = to_filter, filter_crit = ' and correct == 1', cnd_sel = False, save = True) #PP.exclude_outliers(criteria = dict(dev_0 = '')) #PP.prep_JASP(agg_func = 'mean', voi = 'dev_0', data_filter = "", save = True) PP.save_data_file()
def prepareRep(self, project, part, factors, labels, project_param):
'''
standard Behavior processing
'''
PP = PreProcessing(project=project,
part=part,
factor_headers=factors,
factor_labels=labels)
PP.create_folder_structure()
PP.combine_single_subject_files(save=False)
PP.select_data(project_parameters=project_param, save=False)
PP.filter_data(to_filter=to_filter,
filter_crit=' and search_resp == 1',
cnd_sel=False,
save=True)
PP.exclude_outliers(criteria=dict(RT_search="RT_search_filter == True",
search_resp="",
memory_resp=""))
# create JASP output
pivot_data = PP.work_data.query("RT_search_filter == True")
pivot = pivot_data.pivot_table(values='RT_search',
index='subject_nr',
columns=PP.factor_headers,
aggfunc='mean')
# limit analysis to load 1
headers = [
'no-suppr_match', 'no-suppr_neutral', 'suppr_match',
'suppr_neutral', 'no'
]
X = np.zeros((pivot.shape[0], len(headers)))
X[:, 0] = np.stack((pivot['rel-match']['no'][1].values,
pivot['rel-mis']['no'][1].values)).mean(axis=0)
X[:, 1] = pivot['unrel']['no'][1].values
X[:, 2] = np.stack((pivot['rel-match']['yes'][1].values,
pivot['rel-mis']['yes'][1].values)).mean(axis=0)
X[:, 3] = pivot['unrel']['yes'][1].values
X[:, 4] = np.stack((pivot['no']['no'][1].values,
pivot['no']['yes'][1].values)).mean(axis=0)
np.savetxt(self.FolderTracker(['exp2', 'analysis'],
filename='RT_JASP.csv'),
X,
delimiter=",",
header=",".join(headers),
comments='')