nilearn | Machine learning for NeuroImaging in Python | Machine Learning library

 by   nilearn Python Version: 0.10.1 License: Non-SPDX

kandi X-RAY | nilearn Summary

kandi X-RAY | nilearn Summary

nilearn is a Python library typically used in Artificial Intelligence, Machine Learning applications. nilearn has no bugs, it has no vulnerabilities and it has high support. However nilearn build file is not available and it has a Non-SPDX License. You can download it from GitHub.

Machine learning for NeuroImaging in Python
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            kandi-support Support

              nilearn has a highly active ecosystem.
              It has 1002 star(s) with 525 fork(s). There are 73 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 262 open issues and 1515 have been closed. On average issues are closed in 125 days. There are 30 open pull requests and 0 closed requests.
              OutlinedDot
              It has a negative sentiment in the developer community.
              The latest version of nilearn is 0.10.1

            kandi-Quality Quality

              nilearn has 0 bugs and 0 code smells.

            kandi-Security Security

              nilearn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              nilearn code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              nilearn has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              nilearn releases are available to install and integrate.
              nilearn has no build file. You will be need to create the build yourself to build the component from source.
              It has 48170 lines of code, 2320 functions and 383 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed nilearn and discovered the below as its top functions. This is intended to give you an instant insight into nilearn implemented functionality, and help decide if they suit your requirements.
            • Perform permutation on the given variables
            • Calculate a tfce
            • Normalize a 2D matrix
            • Flattens a list of 2d arrays
            • Fetch localizer contrasts
            • Fetch a file
            • Display a title
            • Send request
            • Resample an image
            • Fits a nonparametric model
            • Permute the permuted objective function
            • Perform permutations on a given chunk
            • Compute first - level first - level first - level first - level image
            • Plot a 2D image
            • Save a model to BIDS
            • Load confounds per image
            • Makes a GMM summary plot
            • Get a table of cluster information
            • Fetch aurovault
            • Fetch Oasis VBM dataset
            • Compute the score for a given model
            • Fetch the ABRA PCP
            • Generate a table of cluster IDs
            • Plot an ANTsImage using matplotlib
            • Load confounds strategy
            • Compute the covariance matrix
            • Make the first - level design matrix for each frame
            • Clean image data
            Get all kandi verified functions for this library.

            nilearn Key Features

            No Key Features are available at this moment for nilearn.

            nilearn Examples and Code Snippets

            No Code Snippets are available at this moment for nilearn.

            Community Discussions

            QUESTION

            Can't initialize object of Detector class from py-feat
            Asked 2022-Mar-19 at 20:41

            I try to detecting FEX from videos according to this instruction: https://py-feat.org/content/detector.html#detecting-fex-from-videos

            But I can't initialize object of Detector class. Code that I use:

            ...

            ANSWER

            Answered 2022-Mar-19 at 20:41

            It looks like one of your files was corrupted.

            You can try to solve the problem by opening the directory C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\ and deleting the file ResMaskNet_Z_resmasking_dropout1_rot30.pth.

            Then run again the code and it should redownload the deleted file.

            The warning in the first two lines is just a warning, it's saying that some of the code in the library nilearn is deprecated. Most of the times you would just ignore this line, this will be probably fixed by the coders of nilearn in a future patch.

            Source https://stackoverflow.com/questions/71541634

            QUESTION

            Removing/Hiding empty subplots in matplotlib, when plotting a flexible grid
            Asked 2020-Oct-13 at 10:24

            I am using nilearn to plot a range of cuts along a axis through a 3D image file of a brain. My goal is to make a flexible function, in which I can change the number of rows, cols and coordinate range of the cuts as I please. The reason for this, is that I can generate a .png file and use it for visualization, e.g. directly in a paper.

            So basically I use a nested loop, to generate a grid of matplotlib subplots and fill them with the brain-images. Those come from one line of code from a nilearn-function, so that's not the issue.

            My question is: Is there a way to detect and hide/delete empty subplots? The problem is, that now the empty subplots come wit an error message, which seems to mess with the .png file it returns. Also the empty subplots are shown in the .png file, which should not be the case.

            Here is my code:

            ...

            ANSWER

            Answered 2020-Oct-13 at 10:24

            I assume you know the number of images N that you want to plot.

            This is how I would write it:

            Source https://stackoverflow.com/questions/64331987

            QUESTION

            Using nibabel to save nifti in "SPM" style
            Asked 2020-Sep-04 at 08:04

            I have used python to analyse some fMRI data and would now like to save my results as niftis that I can then use in an SPM analysis.

            My data scores is an array of float64 of shape (97, 115, 97). I have used the following code to save it:

            ...

            ANSWER

            Answered 2020-Sep-04 at 08:04

            Okay in case anyone else is trying to do this at some point, I have found a way to do this by using the following code:

            Source https://stackoverflow.com/questions/63721140

            QUESTION

            placing existing matplotlib figures into subplots
            Asked 2020-May-18 at 06:59

            I would like some advise about how to arrange matplotlib.figure.Figure objects

            I make an object of type 'matplotlib.figure.Figure' using the following function (https://nilearn.github.io/modules/generated/nilearn.plotting.plot_surf_roi.html) :

            ...

            ANSWER

            Answered 2020-May-18 at 06:59

            QUESTION

            Plot PCA components derived from sklearn.decomposition.PCA separately in three dimensional space
            Asked 2020-Apr-06 at 20:16

            For my project, I work with three dimensional MRI data, where the fourth dimension represents different subjects (I use the package nilearn for this). I am using sklearn.decomposition.PCA to extract a given number of principal components from my data. Now I would like to plot the components separately on a brain image, that is, I would like to show a brain image with my extracted components (in this case, 2) in different colors.

            Here’s an example code using the OASIS dataset, which can be downloaded via the nilearn API:

            1. masking using nilearn.input_data.NiftiMasker, which converts my 4 dimensional data into a 2 dimesional array (n_subjects x n_voxels).
            2. standardizing the data matrix using StandardScaler
            3. running the PCA using sklearn.decomposition.PCA:
            ...

            ANSWER

            Answered 2020-Apr-06 at 20:16

            To get data back in to image format, you will need to do a NiftiMasker.inverse_transform(). To do so it is required that you preserve the dimensions in voxel space.

            So, the way the pipeline is working now, you are using dimensionality reduction on voxel space. Just in case you wanted to reduce dimensionality in subject space, you would change the following:

            Source https://stackoverflow.com/questions/61061896

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install nilearn

            You can download it from GitHub.
            You can use nilearn like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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