@author: [email protected] """ import os, glob import numpy as np import matplotlib.pyplot as plt from nilearn.plotting import plot_stat_map, plot_roi from nilearn.image import index_img from fetch_data import fetch_adni_masks, array_to_niis, array_to_nii from scipy.ndimage import label from matplotlib import cm import nibabel as nib from fetch_data import set_cache_base_dir from joblib import Parallel, delayed base_dir = os.path.join(set_cache_base_dir(), 'decomposition') mask = fetch_adni_masks()['mask_petmr'] mask_shape = nib.load(mask).shape mask_affine = nib.load(mask).get_affine() np_files = os.listdir(base_dir) def extract_region_i(maps, i): """ Extract ROIs and plot """ m = maps[i, ...] th_value = np.percentile(m, 100. - (100. / 42.)) data = np.absolute(array_to_nii(m, mask).get_data()) data[data <= th_value] = 0 data[data > th_value] = 1 data_lab = label(data)[0]
""" import os, sys import numpy as np from fetch_data import fetch_adni_baseline_rs_fmri, fetch_adni_masks, \ fetch_adni_longitudinal_rs_fmri_DARTEL, set_cache_base_dir from base_connectivity_classifier import ConnectivityClassifier #sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', 0) #sys.stderr = os.fdopen(sys.stderr.fileno(), 'w', 0) CACHE_DIR = set_cache_base_dir() #dataset = fetch_adni_baseline_rs_fmri() dataset = fetch_adni_longitudinal_rs_fmri_DARTEL() mask = fetch_adni_masks()['mask_fmri'] all_groups = [['AD', 'MCI'], ['AD', 'Normal'], ['MCI', 'Normal']] atlas_names = [ 'msdl', 'canica141', 'canica', 'mayo', 'harvard_oxford', 'juelich', 'tvmsdl' ] classifier_names = ['ridge', 'svc_l1', 'svc_l2', 'logreg_l1', 'logreg_l2'] conn_names = ['corr', 'correlation', 'tangent', 'gl', 'lw', 'oas', 'scov'] ### atlas_names = ['mayo'] #, 'msdl', 'canica'] classifier_names = ['svc_l2', 'logreg_l2']