neuroHarmonize | Harmonization tools for multi-site neuroimaging analysis | Data Labeling library

by rpomponio Python Version: 2.4.5 License: MIT

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | neuroHarmonize Summary

neuroHarmonize is a Python library typically used in Artificial Intelligence, Data Labeling applications. neuroHarmonize has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install neuroHarmonize' or download it from GitHub, PyPI.

Harmonization tools for multi-site neuroimaging analysis. Implemented as a python package. Harmonization of MRI, sMRI, dMRI, fMRI variables with support for NIFTI images. Complements the work in Neuroimage by Pomponio et al. (2019).

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Support

neuroHarmonize has a low active ecosystem.

It has 45 star(s) with 22 fork(s). There are 5 watchers for this library.

There were 8 major release(s) in the last 12 months.

There are 9 open issues and 8 have been closed. On average issues are closed in 28 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of neuroHarmonize is 2.4.5

Quality

neuroHarmonize has 0 bugs and 0 code smells.

Security

neuroHarmonize has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

neuroHarmonize code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

neuroHarmonize is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

neuroHarmonize releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

It has 504 lines of code, 24 functions and 6 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed neuroHarmonize and discovered the below as its top functions. This is intended to give you an instant insight into neuroHarmonize implemented functionality, and help decide if they suit your requirements.

Perform harmonization on the data
R Standardize the model across features
Make a design matrix
Fits the LS model and returns the weighted priors
Applies standardization across features
Calculate the coefficients of the final data
Performs the iteration of the iteration
Calculates the parameters for a parameter distribution
Uses neuroglancer
Fit a model to a given design matrix
Estimates the variance of the features
Apply harmonization model to niftis
Applies a single sample model to the data
Apply harmonization to data

Get all kandi verified functions for this library.

neuroHarmonize Key Features

No Key Features are available at this moment for neuroHarmonize.

neuroHarmonize Examples and Code Snippets

No Code Snippets are available at this moment for neuroHarmonize.

Community Discussions

Trending Discussions on Data Labeling

How can I do this split process in Python?

Replacing a character with a space and dividing the string into two words in R

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

QUESTION

How can I do this split process in Python?

Asked 2021-Dec-30 at 14:06

I'm trying to make a data labeling in a table, and I need to do it in such a way that, in each row, the index is repeated, however, that in each column there is another Enum class.

What I've done so far is make this representation with the same enumerator class.

A solution using the column separately as a list would also be possible. But what would be the best way to resolve this?

...

ANSWER

Answered 2021-Dec-30 at 13:57

Instead of using Enum you can use a dict mapping. You can avoid loops if you flatten your dataframe:

Source https://stackoverflow.com/questions/70532286

QUESTION

Replacing a character with a space and dividing the string into two words in R

Asked 2020-Nov-18 at 07:32

I have a dataframe that contains a column that includes strings separeted with semi-colons and it is followed by a space. But unfortunately in some of the strings there is a semi-colon that is not followed by a space.

In this case, This is what i'd like to do: If there is a space after the semi-colon we do not need a change. However if there are letters before and after the semi-colon, we should change semi-colon with space

i have this:

...

ANSWER

Answered 2020-Nov-16 at 07:24

Try something like:

Source https://stackoverflow.com/questions/64853962

QUESTION

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

Asked 2020-Oct-28 at 20:31

Objective: Generate a down-sampled FileDataset using random sampling from a larger FileDataset to be used in a Data Labeling project.

Details: I have a large FileDataset containing millions of images. Each filename contains details about the 'section' it was taken from. A section may contain thousands of images. I want to randomly select a specific number of sections and all the images associated with those sections. Then register the sample as a new dataset.

Please note that the code below is not a direct copy and paste as there are elements such as filepaths and variables that have been renamed for confidentiality reasons.

...

ANSWER

Answered 2020-Oct-27 at 22:39

Is the data behind virtual network by any chance?

Source https://stackoverflow.com/questions/64546521

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

Vulnerabilities

No vulnerabilities reported

Install neuroHarmonize

You can install using 'pip install neuroHarmonize' or download it from GitHub, PyPI.
You can use neuroHarmonize 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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