output_file += '_msdl'
np.savez(output_file, covariance=gsc.covariances_[..., i],
precision=gsc.precisions_[..., i], sparsity=sparsity,
roi_names=roi_names, roi_coords=roi_coords)
return gsc, roi_names, roi_coords
##############################################################################
preprocessing_folder = 'pipeline_1'
prefix = 'swr'
#preprocessing_folder = 'pipeline_2'
#prefix = 'resampled_wr'
msdl = False
dataset = load_dynacomp(preprocessing_folder=preprocessing_folder,
prefix=prefix)
for session_i in ['func1', 'func2']:
for i in range(len(dataset.subjects)):
print dataset.subjects[i], session_i
compute_pearson_connectivity(dataset.subjects[i],
dataset.group[i],
session=session_i,
preprocessing_folder=preprocessing_folder,
plot=True,
save=True,
save_file=True,
msdl=msdl)
compute_graph_lasso_covariance(dataset.subjects[i],
# -*- coding: utf-8 -*- """ Extract from each specific ROI Created on Fri Mar 27 16:39:01 2015 @author: [email protected] """ import os, time import numpy as np from loader import load_dynacomp, dict_to_list from nilearn.input_data import NiftiMapsMasker dataset = load_dynacomp(preprocessing_folder='pipeline_2', prefix='resampled_wr') # func1, func2 for idx, func in enumerate([dataset.func1, dataset.func2]): # all the subjects for i in range(len(dataset.subjects)): tic = time.clock() output_path, _ = os.path.split(func[i]) print dataset.subjects[i] maps_img = dict_to_list(dataset.rois[i]) # add mask, smoothing, filtering and detrending masker = NiftiMapsMasker(maps_img=maps_img, mask_img=dataset.mask, low_pass=.1, high_pass=.01, smoothing_fwhm=6.,
return best_w, best_acc
##############################################################################
# Load data
preprocs = []
preprocs.append({'preprocessing_folder': 'pipeline_2',
'prefix': 'resampled_wr'})
preprocs.append({'preprocessing_folder': 'pipeline_1',
'prefix': 'swr'})
for pr in preprocs:
preprocessing_folder = pr['preprocessing_folder']
prefix = pr['prefix']
dataset = load_dynacomp(preprocessing_folder, prefix)
for session in ['avg', 'func1', 'func2']:
for msdl in [False, True]:
print preprocessing_folder, prefix, session, msdl
# Roi names and coords
if msdl:
roi_names, roi_coords = load_msdl_names_and_coords()
msdl_str='msdl'
else:
roi_names, roi_coords = load_roi_names_and_coords(dataset.subjects[0])
msdl_str = ''
# Take only the lower diagonal values
ind = np.tril_indices(len(roi_names), k=-1)
# -*- coding: utf-8 -*- """ Created on Fri Mar 27 17:01:31 2015 @author: [email protected] """ from loader import load_dynacomp from nilearn.input_data import NiftiMasker, MultiNiftiMasker from nilearn.plotting import plot_roi CACHE_DIR = '.' dataset = load_dynacomp() def compute_all_subjects_mask(): """ Computes the mask of all the subjects and the sesssions """ masker = MultiNiftiMasker(mask_strategy='epi', memory=CACHE_DIR, memory_level=2, n_jobs=10, verbose=5) imgs = dataset.func1 + dataset.func2 masker.fit(imgs) masker.mask_img_.to_filename('all_subjects.nii.gz') plot_roi(masker.mask_img_)
import os
import numpy as np
import nibabel as nib
from loader import load_dynacomp, load_msdl_names_and_coords,\
load_dynacomp_fc, load_roi_names_and_coords,\
set_figure_base_dir
from nilearn.image import concat_imgs, mean_img
from nilearn.plotting import plot_roi, plot_stat_map, plot_img
from sklearn.decomposition import PCA
from sklearn.manifold import MDS, Isomap
import matplotlib.pyplot as plt
msdl = False
dataset = load_dynacomp()
roi_names, roi_coords = load_roi_names_and_coords(dataset.subjects[0])
if msdl:
roi_names, roi_coords = load_msdl_names_and_coords()
ind = np.tril_indices(len(roi_names), k=-1)
x = []
for subject_id in dataset.subjects:
c = load_dynacomp_fc(subject_id=subject_id,
session='func2',
metric='gl',
msdl=msdl,
preprocessing_folder='pipeline_2')
x.append(c[ind])
x = np.array(x)
# -*- coding: utf-8 -*- """ Extract from each specific ROI Created on Fri Mar 27 16:39:01 2015 @author: [email protected] """ import os, time import numpy as np from loader import load_dynacomp, dict_to_list from nilearn.input_data import NiftiMapsMasker dataset = load_dynacomp(preprocessing_folder='pipeline_2', prefix='resampled_wr') # func1, func2 for idx, func in enumerate([dataset.func1, dataset.func2]): # all the subjects for i in range(len(dataset.subjects)): tic = time.clock() output_path, _ = os.path.split(func[i]) print dataset.subjects[i] maps_img = dict_to_list(dataset.rois[i]) # add mask, smoothing, filtering and detrending masker = NiftiMapsMasker(maps_img=maps_img, mask_img=dataset.mask, low_pass=.1, high_pass=.01, smoothing_fwhm=6., t_r=1.05,
""" Created on Tue May 12 09:42:23 2015 @author: [email protected] """ import loader import numpy as np from nilearn.datasets import fetch_nyu_rest from nilearn.input_data import NiftiMapsMasker from sklearn.covariance import GraphLassoCV ############################################################################## # Dynacomp rs-fMRI ############################################################################## dyn_dataset = loader.load_dynacomp() roi_imgs = loader.dict_to_list(loader.load_dynacomp_rois()[0]) roi_names, roi_coords = loader.load_roi_names_and_coords( dyn_dataset.subjects[0]) ind = np.tril_indices(len(roi_names), k=-1) dyn_fc = [] for subject in dyn_dataset.subjects: dyn_fc.append( loader.load_dynacomp_fc(subject_id=subject, session='func1', metric='pc', msdl=False, preprocessing_folder='pipeline_1')[ind]) dyn_fc = np.asarray(dyn_fc)
Created on Tue May 12 09:42:23 2015 @author: [email protected] """ import loader import numpy as np from nilearn.datasets import fetch_nyu_rest from nilearn.input_data import NiftiMapsMasker from sklearn.covariance import GraphLassoCV ############################################################################## # Dynacomp rs-fMRI ############################################################################## dyn_dataset = loader.load_dynacomp() roi_imgs = loader.dict_to_list(loader.load_dynacomp_rois()[0]) roi_names, roi_coords = loader.load_roi_names_and_coords(dyn_dataset.subjects[0]) ind = np.tril_indices(len(roi_names), k=-1) dyn_fc = [] for subject in dyn_dataset.subjects: dyn_fc.append(loader.load_dynacomp_fc(subject_id=subject, session='func1', metric='pc', msdl=False, preprocessing_folder='pipeline_1')[ind]) dyn_fc = np.asarray(dyn_fc) ############################################################################## # NYU rs-fMRI ##############################################################################
