CODE:
for nii in os.listdir("c:/users/administrator/desktop/nii"):
from nilearn import plotting
from nilearn import datasets
atlas = datasets.fetch_atlas_msdl()
# Loading atlas image stored in 'maps'
atlas_filename = "C:/Users/Administrator/Desktop/64/64/2mm/maps.nii.gz"
# Loading atlas data stored in 'labels'
labels = pd.read_csv(
"C:/Users/Administrator/Desktop/64/64/labels_64_dictionary.csv")
a=labels.to_dict()
b=a["Difumo_names"]
from nilearn.maskers import NiftiMapsMasker
masker = NiftiMapsMasker(maps_img=atlas_filename, standardize=True,
memory='nilearn_cache', verbose=5)
time_series = masker.fit_transform("c:/users/administrator/desktop/nii/"
+nii)
try:
from sklearn.covariance import GraphicalLassoCV
except ImportError:
# for Scitkit-Learn < v0.20.0
from sklearn.covariance import GraphLassoCV as GraphicalLassoCV
estimator = GraphicalLassoCV()
estimator.fit(time_series)
# Display the covariancec
aas={}
jsa=0
for i in estimator.covariance_:
r=list(a["Difumo_names"].values())[jsa]
jsa=jsa+1
a=dict()
for x in range(64):
g=list(a["Difumo_names"].values())[x]
print(aas)
t= nilearn.plotting.plot_img(estimator.covariance_, labels=list(a[
"Difumo_names"].values()),
figure=(9, 7), vmax=1, vmin=-1,
title='Covariance')# The covariance can be found at
estimator.covariance_
# The covariance can be found at estimator.covariance_
t2= nilearn.plotting.plot_matrix(estimator.covariance_, labels=list(a[
"Difumo_names"].values()),
figure=(9, 7), vmax=1, vmin=-1,
title='Covariance')
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