<s> """<STR_LIT>""" <EOL> from nilearn import datasets <EOL> adhd_dataset = datasets . fetch_adhd ( n_subjects = <NUM_LIT:1> ) <EOL> print ( '<STR_LIT>' % <EOL> adhd_dataset . func [ <NUM_LIT:0> ] ) <EOL> dmn_coords = [ ( <NUM_LIT:0> , - <NUM_LIT> , <NUM_LIT> ) , ( - <NUM_LIT> , - <NUM_LIT> , <NUM_LIT:32> ) , ( <NUM_LIT> , - <NUM_LIT> , <NUM_LIT:32> ) , ( <NUM_LIT:0> , <NUM_LIT:50> , - <NUM_LIT:5> ) ] <EOL> labels = [ <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' <EOL> ] <EOL> from nilearn import input_data <EOL> masker = input_data . NiftiSpheresMasker ( <EOL> dmn_coords , radius = <NUM_LIT:8> , <EOL> detrend = True , standardize = True , <EOL> low_pass = <NUM_LIT:0.1> , high_pass = <NUM_LIT> , t_r = <NUM_LIT> , <EOL> memory = '<STR_LIT>' , memory_level = <NUM_LIT:1> , verbose = <NUM_LIT:2> ) <EOL> func_filename = adhd_dataset . func [ <NUM_LIT:0> ] <EOL> confound_filename = adhd_dataset . confounds [ <NUM_LIT:0> ] <EOL> time_series = masker . fit_transform ( func_filename , <EOL> confounds = [ confound_filename ] ) <EOL> import matplotlib . pyplot as plt <EOL> for time_serie , label in zip ( time_series . T , labels ) : <EOL> plt . plot ( time_serie , label = label ) <EOL> plt . title ( '<STR_LIT>' ) <EOL> plt . xlabel ( '<STR_LIT>' ) <EOL> plt . ylabel ( '<STR_LIT>' ) <EOL> plt . legend ( ) <EOL> plt . tight_layout ( ) <EOL> from sklearn . covariance import LedoitWolf <EOL> cve = LedoitWolf ( ) <EOL> cve . fit ( time_series ) <EOL> from nilearn import plotting <EOL> plotting . plot_connectome ( cve . precision_ , dmn_coords , <EOL> title = "<STR_LIT>" ) <EOL> plotting . show ( ) </s>
