code stringlengths 3 6.57k |
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formula.replace(EV_name, 'C(' + EV_name + ') |
str(cat_ev_value) |
replace(str(grouping_var) |
groupvar_levels.append(cat_ev_level) |
append(idx) |
pheno_data_dict.keys() |
file (not Patsy format) |
if (key in formula) |
and (key != grouping_var) |
str(grouping_var) |
str(level) |
append(new_key) |
append(val) |
append(0) |
remove(key) |
formula.replace(key, new_key_string) |
pheno_data_dict.update(split_EVs) |
C(<name>, Sum) |
ev_selections.keys() |
formula.replace(EV_name, 'C(' + EV_name + ') |
check_multicollinearity(matrix) |
np.linalg.svd(matrix) |
np.max(s) |
np.min(s) |
np.linalg.matrix_rank(matrix) |
float(max_singular) |
float(min_singular) |
len(design_matrix.shape) |
range(0, dimy) |
os.path.join(output_dir, filename) |
os.path.exists(output_dir) |
os.makedirs(output_dir) |
open(out_file, 'wt') |
np.savetxt(f, design_matrix, fmt='%1.5e', delimiter='\t') |
len(design_matrix.shape) |
np.ones(dimx) |
not (grouping_var_id_dict == None) |
sorted(grouping_var_id_dict.keys() |
os.path.join(output_dir, filename) |
os.path.exists(output_dir) |
os.makedirs(output_dir) |
open(out_file, "wt") |
np.savetxt(f, design_matrix_ones, fmt='%d', delimiter='\t') |
os.getcwd() |
rows (participants) |
load_pheno_file(pheno_file) |
load_group_participant_list(sub_list) |
len(participant_list_df) |
append("session") |
append("series") |
new_regressor_dict.keys() |
if (measure in formula) |
if ("session" in pheno_row_dict.keys() |
pheno_row_dict.keys() |
pheno_row_dict.keys() |
pheno_row_dict.keys() |
append(measure) |
Exception(err) |
participant (the keys are the participant IDs) |
get_custom_roi_info(roi_means_dict) |
roi_dict_dict.keys() |
if ("session" in pheno_row_dict.keys() |
pheno_row_dict.keys() |
pheno_row_dict.keys() |
pheno_row_dict.keys() |
append(roi_column) |
formula.replace("Custom_ROI_Mean",add_formula_string) |
EVs (non-categorical) |
separately (if enabled) |
ev_selections.keys() |
formula.replace(EV_name, 'C(' + EV_name + ') |
patsy.dmatrix(formula, pheno_data_dict, NA_action='raise') |
columns (regressors) |
pheno_data_dict.keys() |
check_multicollinearity(np.array(dmatrix) |
np.array(dmatrix, dtype=np.float16) |
len(column_names) |
len(column_names) |
Exception(err) |
C(adhd, Sum) |
column_string.replace(string_for_removal, '') |
column_string.replace(']', '') |
depatsified_EV_names.append(column_string) |
positive(dmat, a, coding, group_sep, grouping_var) |
np.zeros(dmat.shape[1]) |
a.split("__") |
len(ev_desc) |
a.split('[') |
ev.startswith(term) |
len(a.split(grouping_var) |
a.split(".") |
a.split('[') |
ev.startswith(term) |
a.split('[') |
ev.startswith(term) |
greater_than(dmat, a, b, coding, group_sep, grouping_var) |
positive(dmat, a, coding, group_sep, grouping_var) |
positive(dmat, b, coding, group_sep, grouping_var) |
negative(dmat, a, coding, group_sep, grouping_var) |
positive(dmat, a, coding, group_sep, grouping_var) |
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