@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
import numpy as np
from scipy import stats, linalg
from sklearn.covariance import GraphLassoCV, LedoitWolf, OAS, \
ShrunkCovariance
from sklearn.datasets.base import Bunch
from sklearn.base import BaseEstimator, TransformerMixin
from nilearn.input_data import NiftiLabelsMasker, NiftiMapsMasker
import nibabel as nib
from joblib import Parallel, delayed
from nilearn.datasets import fetch_msdl_atlas
from fetch_data import set_cache_base_dir
from embedding import CovEmbedding, vec_to_sym
CACHE_DIR = set_cache_base_dir()
def atlas_rois_to_coords(atlas_name, rois):
"""Returns coords of atlas ROIs
"""
atlas = fetch_atlas(atlas_name)
affine = nib.load(atlas).get_affine()
data = nib.load(atlas).get_data()
centroids = []
if len(data.shape) == 4:
for i in range(data.shape[-1]):
voxels = np.where(data[..., i] > 0)
centroid = np.mean(voxels, axis=1)
dvoxels = data[..., i]