I want to set dict

בתאריך יום ד׳, 13 ביולי 2022, 20:47, מאת נתי שטרן ‏<nsh531@gmail.com>:

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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Nati,
This is a two-way process and requires you to be very clear on what is not working or what you are trying to do or help clear away having us try to understand lots of code that is not very related to the question.
Your code, as shown, makes an empty string repeatedly in a loop. 
a=dict()
I am guessing the code there works fine and does nothing useful. Well, what do you want in your dictionary? Most people create a dictionary outside the loop as an empty dictionary and then in the loop use one of many methods to add key:value pairs.
Earlier in the code you had another line:
a=labels.to_dict()

If the labels variable had a method like that, that is also a way.
So be specific about what LINE or region of code and what is wrong and what you already tried or error messages you got.
Avi


-----Original Message-----
From: נתי שטרן <nsh531@gmail.com>
To: Neuroimaging analysis in Python <neuroimaging@python.org>; python-list@python.org
Sent: Wed, Jul 13, 2022 2:35 pm
Subject: Re: what's the problem??????

I want to set dict

בתאריך יום ד׳, 13 ביולי 2022, 20:47, מאת נתי שטרן ‏<nsh531@gmail.com>:

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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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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