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fit(cls, *args, **kwargs)
dict()
isinstance(scheduler_options, dict)
isinstance(search_strategy, str)
dict()
copy.copy(scheduler_options)
searcher_for_hyperband_strategy.get(search_strategy)
dict()
isinstance(scheduler_options, dict)
copy.copy(scheduler_options)
isinstance(search_strategy, str)
dict()
update(scheduler_options['resource'])
copy()
scheduler_params.update(scheduler_options)
searcher_for_hyperband_strategy.get(scheduler_params['searcher'])
missing_options.append(option)
AssertionError(f'Missing required keys in scheduler_options: {missing_options}')
load_pheno_file(pheno_file)
os.path.isfile(pheno_file)
Exception(err)
open(os.path.abspath(pheno_file)
pd.read_csv(f)
load_group_participant_list(group_participant_list_file)
os.path.isfile(group_participant_list_file)
Exception(err)
open(group_participant_list_file,"r")
pd.read_csv(f)
Exception(err)
isinstance(pheno_file_dataframe, pd.DataFrame)
Exception(err)
isinstance(group_subs_dataframe, pd.DataFrame)
Exception(err)
isinstance(participant_id_label, str)
list(group_subs_dataframe.participant)
str(i)
list(group_subs_dataframe.session)
str(i)
list(group_subs_dataframe.series)
str(i)
range(0,len(subjects)
full_id.append(subject)
full_id.append(session)
full_id.append(scan)
int(subject)
full_id.append(session)
int(subject)
full_id.append(scan)
int(subject)
full_id.append(subject)
int(subject)
len(row)
str(full_id)
Exception(err)
len(row)
df_rows.append(row)
pd.concat(df_rows)
new_pheno_df.to_dict("records")
create_pheno_dict(pheno_file_rows, ev_selections, participant_id_label)
line.values()
line.keys()
CSV (such as ",,,,,")
len(line[key])
pheno_data_dict.keys()
ev_selections.keys()
append(key + str(line[key])
elif (key == subject_id_label)
or (key == "session")
append(line[key])
append(float(line[key])
elif (key == subject_id_label)
or (key == "session")
append(line[key])
append(float(line[key])
pheno_data_dict.keys()
ev_selections.keys()
float(val)
len(pheno_data_dict[key])
new_demeaned_evs.append(float(val)
ev_selections.keys()
np.array(pheno_data_dict[key])
get_measure_dict(param_file)
os.path.isfile(param_file)
Exception(err)
open(param_file,"r")
pd.read_csv(f, index_col=False)
list(motion_params["Subject"])
str(i)
list(motion_params["Scan"])
str(i)
list(motion_params[m])
float(i)
zip(part_ids, scan_ids, measure_vals)
get_custom_roi_info(roi_means_dict)
Exception(err_string)
len(roi_means_dict.values()
range(0,roi_num)
int(num+1)
roi_means_dict.keys()
ev_selections.keys()