[Neuroimaging] what's the problem??????
נתי שטרן
nsh531 at gmail.com
Thu Jul 14 00:18:34 EDT 2022
I want to swap indexes of dict
בתאריך יום ד׳, 13 ביולי 2022, 21:51, מאת David Welch <
david.m.welch at gmail.com>:
>
>> - Describe the research *you did* to try and understand the problem
>> *before* you asked the question.
>> - Describe the diagnostic steps *you took* to try and pin down the
>> problem yourself *before* you asked the question.
>>
>>
> On Wed, Jul 13, 2022 at 1:47 PM David Welch <david.m.welch at gmail.com>
> wrote:
>
>> From http://catb.org/~esr/faqs/smart-questions.html#beprecise:
>>
>>> Be precise and informative about your problem
>>>
>>> -
>>>
>>> Describe the symptoms of your problem or bug carefully and clearly.
>>> -
>>>
>>> Describe the environment in which it occurs (machine, OS,
>>> application, whatever). Provide your vendor's distribution and release
>>> level (e.g.: “Fedora Core 7”, “Slackware 9.1”, etc.).
>>> -
>>>
>>> Describe the research you did to try and understand the problem
>>> before you asked the question.
>>> -
>>>
>>> Describe the diagnostic steps you took to try and pin down the
>>> problem yourself before you asked the question.
>>> -
>>>
>>> Describe any possibly relevant recent changes in your computer or
>>> software configuration.
>>> -
>>>
>>> If at all possible, provide a way to *reproduce the problem in a
>>> controlled environment*.
>>>
>>> Do the best you can to anticipate the questions a hacker will ask, and
>>> answer them in advance in your request for help.
>>>
>>> Giving hackers the ability to reproduce the problem in a controlled
>>> environment is especially important if you are reporting something you
>>> think is a bug in code. When you do this, your odds of getting a useful
>>> answer and the speed with which you are likely to get that answer both
>>> improve tremendously.
>>>
>>> Simon Tatham has written an excellent essay entitled How to Report Bugs
>>> Effectively <http://www.chiark.greenend.org.uk/~sgtatham/bugs.html>. I
>>> strongly recommend that you read it.
>>>
>>>
>>
>> ---------- Forwarded message ---------
>> From: נתי שטרן <nsh531 at gmail.com>
>> Date: Wed, Jul 13, 2022 at 1:36 PM
>> Subject: Re: [Neuroimaging] what's the problem??????
>> To: Neuroimaging analysis in Python <neuroimaging at python.org>, <
>> python-list at python.org>
>>
>>
>> I want to set dict
>>
>> בתאריך יום ד׳, 13 ביולי 2022, 20:47, מאת נתי שטרן <nsh531 at 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')
>>>
>>>
>>>
>>> --
>>> <https://netanel.ml>
>>>
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