├── .github └── workflows │ └── docbuild.yml ├── .gitignore ├── LICENSE.txt ├── MANIFEST.in ├── README.md ├── all-contributorsrc.txt ├── docs ├── Makefile ├── README.md ├── make.bat ├── requirements.txt └── source │ ├── _static │ └── vb_toolbox_logo.png │ ├── conf.py │ ├── index.rst │ ├── installation.rst │ ├── overview.rst │ └── usage.rst ├── pyproject.toml ├── requirements.txt └── vb_toolbox ├── __init__.py ├── io_util.py ├── math_util.py ├── multiprocess_util.py ├── vb_cli.py └── vol_pipe.py /.github/workflows/docbuild.yml: -------------------------------------------------------------------------------- 1 | name: Build and deploy docs 2 | 3 | on: 4 | push: 5 | branches: 6 | - master 7 | 8 | permissions: 9 | contents: read 10 | pages: write 11 | id-token: write 12 | 13 | concurrency: 14 | group: "pages" 15 | cancel-in-progress: false 16 | 17 | jobs: 18 | build: 19 | runs-on: ubuntu-latest 20 | steps: 21 | - name: Checkout 22 | uses: actions/checkout@v3 23 | 24 | - name: Set up Pages 25 | id: pages 26 | uses: actions/configure-pages@v3 27 | 28 | - name: Set up Python 29 | uses: actions/setup-python@v4 30 | with: 31 | python-version: '3.12' 32 | 33 | - name: Install dependencies 34 | run: | 35 | python -m pip install --upgrade pip 36 | pip install -r ./docs/requirements.txt 37 | 38 | - name: Build 39 | run: | 40 | cd docs 41 | make html 42 | 43 | - name: Upload artifact 44 | uses: actions/upload-pages-artifact@v2 45 | with: 46 | path: ./docs/_build/html 47 | 48 | deploy: 49 | environment: 50 | name: github-pages 51 | url: ${{ steps.deployment.outputs.page_url }} 52 | runs-on: ubuntu-latest 53 | needs: build 54 | steps: 55 | - name: Deploy to GitHub pages 56 | id: deployment 57 | uses: actions/deploy-pages@v2 -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | docs/_build 2 | docs/build 3 | .DS_Store 4 | vb_toolbox/__pycache__ -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | include vb_toolbox/assets/vb_gui_icon.png 2 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![All Contributors](https://img.shields.io/badge/all_contributors-8-orange.svg?style=flat-square)](#contributors-) 2 | [![DOI](https://zenodo.org/badge/224148416.svg)](https://zenodo.org/badge/latestdoi/224148416) [![PyPI version](https://badge.fury.io/py/vb-toolbox.svg)](https://badge.fury.io/py/vb-toolbox) 3 | 4 | 5 | # VBIndex 6 | Vogt-Bailey index [1-4] toolbox in Python 7 | 8 | ## Installation as a Python package 9 | 10 | Clone the repository: 11 | ```bash 12 | git clone https://github.com/VBIndex/py_vb_toolbox.git 13 | cd py_vb_toolbox 14 | ``` 15 | 16 | Proceed to install the toolbox via pip from the main project directory: 17 | 18 | ```bash 19 | pip install . 20 | ``` 21 | 22 | The installation will automatically install the dependencies specified in the requirements.txt file. In your terminal, check to see whether the VB toolbox has been properly installed by running: 23 | 24 | ```bash 25 | vb_tool --help 26 | ``` 27 | 28 | If you see output similar to the following (specifically showing vb_tool usage and the volumetric subcommand), the prerequisites have been properly installed. 29 | 30 | ```bash 31 | usage: vb_tool [-h] {volumetric} ... 32 | 33 | Calculate the Vogt-Bailey Index of a dataset. For more information, refer to 34 | [https://github.com/VBIndex/py_vb_toolbox](https://github.com/VBIndex/py_vb_toolbox). 35 | 36 | options: 37 | -h, --help show this help message and exit 38 | 39 | VB method: 40 | {volumetric} Different methods for computing the VB Index 41 | volumetric Computes the VB Index on volumetric data using a searchlight 42 | approach 43 | 44 | ``` 45 | 46 | ## Usage 47 | 48 | ### Volumetric VB Index 49 | 50 | This toolbox calculates the VB Index using a searchlight approach directly on volumetric data (NIfTI format). This method is referred to as "Volumetric analysis" and can be carried out with the following command: 51 | 52 | ```bash 53 | vb_tool volumetric --data input_data/data.nii.gz --output volumetric_output 54 | ``` 55 | 56 | This is the simplest way to run this analysis. It will output two files using the base name provided with --output and the normalization method used (e.g., volumetric_output.unnorm.vbi-vol.nii.gz and volumetric_output.unnorm.vbi-neigh.nii.gz). 57 | 58 | * The *.vbi-vol.nii.gz file stores the calculated VB Index (or ReHo value) for each voxel. 59 | * The *.vbi-neigh.nii.gz file stores the number of neighbours included in the searchlight calculation for each voxel. 60 | 61 | The analysis computes the index for every voxel within the NIfTI file's dimensions. This can take time. To speed this up and restrict the analysis to brain voxels, provide a volumetric brain mask: 62 | 63 | ```bash 64 | vb_tool volumetric --data input_data/data.nii.gz --volmask input_data/volumetric_mask.nii.gz --output volumetric_output 65 | ``` 66 | 67 | #### Volumetric approach with ReHo 68 | 69 | The VB Toolbox also supports analysing data with the Regional Homogeneity (ReHo) index [5]. The ReHo index measures the similarity between the Blood Oxygen Level Dependent (BOLD) signal of a voxel with respect to its immediate neighbors. To run ReHo for volumetric analysis: 70 | 71 | ```bash 72 | vb_tool volumetric --data input_data/data.nii.gz --volmask input_data/volumetric_mask.nii.gz --reho --output volumetric_reho_output 73 | ``` 74 | 75 | *(Note: Output filenames will reflect the ReHo analysis, e.g., volumetric_reho_output.unnorm.vbi-vol.nii.gz)* 76 | 77 | ## General Notes 78 | 79 | ### Note on the data file 80 | 81 | The volumetric analysis mode expects input data (--data) as a 4D NIfTI file (e.g., fMRI time series data in .nii or .nii.gz format). The optional mask (--volmask) should be a 3D NIfTI file coregistered with the data file, containing non-zero values for voxels to be included in the analysis. 82 | 83 | ### Notes on parallelism 84 | 85 | vb_tool uses multiprocessing to speed up computations. The number of parallel processes (threads) can be controlled using the -j or --jobs flag when running the volumetric command. For example: 86 | 87 | ```bash 88 | vb_tool volumetric --data ... --output ... --jobs 4 89 | ``` 90 | 91 | By default, the software will try to use all available CPU cores. Depending on your system and BLAS installation, adjusting the number of jobs might yield better performance. If unsure, keeping the default is usually a safe starting point. 92 | 93 | ## References 94 | 95 | [1] C. J. Bajada, L. Q. C. Campos, S. Caspers, R. Muscat, G. J. Parker, M. A. Lambon Ralph, ... and N. J. Trujillo-Barreto, (2020). “A tutorial and tool for exploring feature similarity gradients with MRI data,” NeuroImage, vol. 221, pp. 117140–117140, Jul. 2020, doi: https://doi.org/10.1016/j.neuroimage.2020.117140. 96 | 97 | [2] C. Farrugia, P. Galdi, Irati Arenzana Irazu, K. Scerri, and C. J. Bajada, “Local gradient analysis of human brain function using the Vogt-Bailey Index,” Brain structure & function, vol. 229, no. 2, pp. 497–512, Jan. 2024, doi: https://doi.org/10.1007/s00429-023-02751-7 98 | 99 | [3] K. G. Ciantar, C. Farrugia, P. Galdi, K. Scerri, T. Xu, and C. J. Bajada, “Geometric effects of volume-to-surface mapping of fMRI data,” Brain Structure and Function, vol. 227, no. 7, pp. 2457–2464, Jul. 2022, doi: https://doi.org/10.1007/s00429-022-02536-4. 100 | 101 | [4] K. Galea. A. A. Escudero, N. A. Montalto, N. Vella, R. E. Smith, C. Farrugia, P. Galdi, K. Scerri, L. Butler, and C. J. Bajada, “Testing the Vogt-Bailey Index using task-based fMRI across pulse sequence protocols,” bioRxiv, pp. 2025-02, 2025. 102 | 103 | [5] Y. Zang, T. Jiang, Y. Lu, Y. He, and L. Tian, “Regional homogeneity approach to fMRI data analysis,” NeuroImage, vol. 22, no. 1, pp. 394–400, May 2004, doi: https://doi.org/10.1016/j.neuroimage.2003.12.030. 104 | 105 | ## Contributors ✨ 106 | Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)): 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 |
Aitor-Alberdi
Aitor-Alberdi
💻 🚧		Keith George Ciantar
Keith George Ciantar
💻		NicoleEic
NicoleEic
💻		claudebajada
claudebajada
🐛 🤔 📆 💻		Lucas Campos
Lucas Campos
💻 🐛 🤔 🚧		paola-g
paola-g
💻		ChristineFarrugia
ChristineFarrugia
💻
jschewts
jschewts
💻		Kenneth Scerri
Kenneth Scerri
📆
127 | 128 | 129 | 130 | 131 | 132 | 133 | -------------------------------------------------------------------------------- /all-contributorsrc.txt: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "README.md" 4 | ], 5 | "imageSize": 100, 6 | "commit": false, 7 | "contributors": [ 8 | { 9 | "login": "KeithGeorgeCiantar", 10 | "name": "Keith George Ciantar", 11 | "avatar_url": "https://avatars1.githubusercontent.com/u/52758149?v=4", 12 | "profile": "https://github.com/KeithGeorgeCiantar", 13 | "contributions": [ 14 | "code" 15 | ] 16 | }, 17 | { 18 | "login": "NicoleEic", 19 | "name": "NicoleEic", 20 | "avatar_url": "https://avatars3.githubusercontent.com/u/25506847?v=4", 21 | "profile": "https://github.com/NicoleEic", 22 | "contributions": [ 23 | "code" 24 | ] 25 | }, 26 | { 27 | "login": "claudebajada", 28 | "name": "claudebajada", 29 | "avatar_url": "https://avatars3.githubusercontent.com/u/16142659?v=4", 30 | "profile": "http://claude.bajada.info", 31 | "contributions": [ 32 | "bug", 33 | "ideas", 34 | "projectManagement", 35 | "code" 36 | ] 37 | }, 38 | { 39 | "login": "LucasCampos", 40 | "name": "Lucas Campos", 41 | "avatar_url": "https://avatars1.githubusercontent.com/u/2735358?v=4", 42 | "profile": "https://github.com/LucasCampos", 43 | "contributions": [ 44 | "code", 45 | "bug", 46 | "ideas", 47 | "maintenance" 48 | ] 49 | }, 50 | { 51 | "login": "paola-g", 52 | "name": "paola-g", 53 | "avatar_url": "https://avatars.githubusercontent.com/u/7580862?v=4", 54 | "profile": "https://github.com/paola-g", 55 | "contributions": [ 56 | "code" 57 | ] 58 | }, 59 | { 60 | "login": "ChristineFarrugia", 61 | "name": "ChristineFarrugia", 62 | "avatar_url": "https://avatars.githubusercontent.com/u/83232978?v=4", 63 | "profile": "https://github.com/ChristineFarrugia", 64 | "contributions": [ 65 | "code" 66 | ] 67 | }, 68 | { 69 | "login": "jschewts", 70 | "name": "jschewts", 71 | "avatar_url": "https://avatars.githubusercontent.com/u/68106439?v=4", 72 | "profile": "https://github.com/jschewts", 73 | "contributions": [ 74 | "code" 75 | ] 76 | }, 77 | { 78 | "login": "kscerri", 79 | "name": "Kenneth Scerri", 80 | "avatar_url": "https://avatars.githubusercontent.com/u/153515?v=4", 81 | "profile": "http://www.kscerri.com/Personal/index.html", 82 | "contributions": [ 83 | "projectManagement" 84 | ] 85 | } 86 | ], 87 | "contributorsPerLine": 7, 88 | "projectName": "py_vb_toolbox", 89 | "projectOwner": "VBIndex", 90 | "repoType": "github", 91 | "repoHost": "https://github.com", 92 | "skipCi": true, 93 | "commitConvention": "angular" 94 | } 95 | -------------------------------------------------------------------------------- /docs/Makefile: -------------------------------------------------------------------------------- 1 | # Minimal makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line, and also 5 | # from the environment for the first two. 6 | SPHINXOPTS ?= 7 | SPHINXBUILD ?= sphinx-build 8 | SOURCEDIR = source 9 | BUILDDIR = _build 10 | 11 | # Put it first so that "make" without argument is like "make help". 12 | help: 13 | @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 14 | 15 | .PHONY: help Makefile 16 | 17 | # Catch-all target: route all unknown targets to Sphinx using the new 18 | # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). 19 | %: Makefile 20 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 21 | -------------------------------------------------------------------------------- /docs/README.md: -------------------------------------------------------------------------------- 1 | ## Documentation setup 2 | 3 | The documentation for this project is built with Sphinx. 4 | 5 | To be able to contribute to the docs, you need to: 6 | 7 | 1. Create and activate a virtual environment with a recent Python version 8 | 2. Clone the repository 9 | 4. Install the developer requirements with `pip`: 10 | 11 | ``` 12 | pip install -r docs/requirements.txt 13 | ``` 14 | 15 | After making changes to the docs, you can build the HTML files from the `docs` directory with: 16 | 17 | ``` 18 | make html 19 | ``` 20 | 21 | The resulting file at `docs/_build/html/index.html` can be opened in your local browser to view your changes. Once the build succeeds locally, create a pull request with your changes. -------------------------------------------------------------------------------- /docs/make.bat: -------------------------------------------------------------------------------- 1 | @ECHO OFF 2 | 3 | pushd %~dp0 4 | 5 | REM Command file for Sphinx documentation 6 | 7 | if "%SPHINXBUILD%" == "" ( 8 | set SPHINXBUILD=sphinx-build 9 | ) 10 | set SOURCEDIR=. 11 | set BUILDDIR=_build 12 | 13 | %SPHINXBUILD% >NUL 2>NUL 14 | if errorlevel 9009 ( 15 | echo. 16 | echo.The 'sphinx-build' command was not found. Make sure you have Sphinx 17 | echo.installed, then set the SPHINXBUILD environment variable to point 18 | echo.to the full path of the 'sphinx-build' executable. Alternatively you 19 | echo.may add the Sphinx directory to PATH. 20 | echo. 21 | echo.If you don't have Sphinx installed, grab it from 22 | echo.https://www.sphinx-doc.org/ 23 | exit /b 1 24 | ) 25 | 26 | if "%1" == "" goto help 27 | 28 | %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% 29 | goto end 30 | 31 | :help 32 | %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% 33 | 34 | :end 35 | popd 36 | -------------------------------------------------------------------------------- /docs/requirements.txt: -------------------------------------------------------------------------------- 1 | sphinx 2 | sphinx-book-theme -------------------------------------------------------------------------------- /docs/source/_static/vb_toolbox_logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VBIndex/py_vb_toolbox/2628fde661622e12938d19f5b62ee1f5a07878e6/docs/source/_static/vb_toolbox_logo.png -------------------------------------------------------------------------------- /docs/source/conf.py: -------------------------------------------------------------------------------- 1 | # Configuration file for the Sphinx documentation builder. 2 | # 3 | # For the full list of built-in configuration values, see the documentation: 4 | # https://www.sphinx-doc.org/en/master/usage/configuration.html 5 | 6 | # -- Project information ----------------------------------------------------- 7 | # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information 8 | 9 | project = 'Vogt-Bailey Index Toolbox' 10 | copyright = '2024, vbtoolbox developers' 11 | author = 'vbtoolbox developers' 12 | 13 | # -- General configuration --------------------------------------------------- 14 | # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration 15 | 16 | extensions = [] 17 | 18 | templates_path = ['_templates'] 19 | exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] 20 | 21 | 22 | 23 | # -- Options for HTML output ------------------------------------------------- 24 | # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output 25 | 26 | html_theme = 'sphinx_book_theme' 27 | html_static_path = ['_static'] 28 | html_theme_options = { 29 | "logo": { 30 | "text": "Vogt-Bailey Index Toolbox", 31 | "image_light": "_static/vb_toolbox_logo.png", 32 | "image_dark": "_static/vb_toolbox_logo.png", 33 | } 34 | } -------------------------------------------------------------------------------- /docs/source/index.rst: -------------------------------------------------------------------------------- 1 | .. Vogt-Bailey Index Toolbox documentation master file, created by 2 | sphinx-quickstart on Tue Dec 3 14:13:08 2024. 3 | You can adapt this file completely to your liking, but it should at least 4 | contain the root `toctree` directive. 5 | 6 | Vogt-Bailey Index Toolbox docs 7 | ============================== 8 | 9 | This documentation describes the `Vogt-Bailey Index Toolbox`_. 10 | 11 | .. _Vogt-Bailey Index Toolbox: https://github.com/VBIndex/py_vb_toolbox 12 | 13 | .. toctree:: 14 | :maxdepth: 2 15 | :caption: Contents: 16 | 17 | overview 18 | installation 19 | usage -------------------------------------------------------------------------------- /docs/source/installation.rst: -------------------------------------------------------------------------------- 1 | Installation 2 | ************ -------------------------------------------------------------------------------- /docs/source/overview.rst: -------------------------------------------------------------------------------- 1 | Overview 2 | ******** -------------------------------------------------------------------------------- /docs/source/usage.rst: -------------------------------------------------------------------------------- 1 | Usage 2 | ***** -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [build-system] 2 | requires = ["setuptools>=61.0", "wheel"] 3 | build-backend = "setuptools.build_meta" 4 | 5 | [project] 6 | name = "vb_tool" 7 | version = "2.3" 8 | authors = [ 9 | { name="Claude J. Bajada", email="claude.bajada@um.edu.mt" }, 10 | { name = "Lucas Campos"}, 11 | { name = "Christine Farrugia"}, 12 | { name = "Kenneth Scerri"}, 13 | { name = "Aitor Alberdi"}, 14 | { name = "Paola Galdi"}, 15 | { name = "Keith George Ciantar"}, 16 | { name = "Stephan Heunis"} 17 | ] 18 | description = "Vogt-Bailey Index toolbox in Python" 19 | readme = "README.md" 20 | requires-python = ">=3.7" 21 | license-files = ["LICENSE.txt"] 22 | keywords = ["Vogt-Bailey Index", "VB Index", "Neuroimaging", "fMRI", "Brain"] 23 | classifiers = [ 24 | "Programming Language :: Python :: 3", 25 | "Operating System :: POSIX :: Linux", 26 | "Operating System :: MacOS" 27 | ] 28 | 29 | dependencies = [ 30 | "dill==0.4.0", 31 | "multiprocess==0.70.18", 32 | "nibabel==5.3.2", 33 | "numpy==2.2.5", 34 | "packaging==25.0", 35 | "scipy==1.15.2", 36 | "textwrap3==0.9.2", 37 | "typing_extensions==4.13.2" 38 | ] 39 | 40 | [project.scripts] 41 | # CLI entry points: 42 | vb_tool = "vb_toolbox.vb_cli:main" 43 | 44 | [tool.setuptools] 45 | packages = ["vb_toolbox"] 46 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | dill==0.4.0 2 | multiprocess==0.70.18 3 | nibabel==5.3.2 4 | numpy==2.2.5 5 | packaging==25.0 6 | scipy==1.15.2 7 | textwrap3==0.9.2 8 | typing_extensions==4.13.2 9 | -------------------------------------------------------------------------------- /vb_toolbox/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VBIndex/py_vb_toolbox/2628fde661622e12938d19f5b62ee1f5a07878e6/vb_toolbox/__init__.py -------------------------------------------------------------------------------- /vb_toolbox/io_util.py: -------------------------------------------------------------------------------- 1 | import nibabel 2 | 3 | def save_nifti(result, affine, header, output_name, n_neigh=None): 4 | """ 5 | Saves the data from the volumetric analysis into 2 compressed files. 6 | 7 | Parameters 8 | ---------- 9 | result : numpy array of float32 (nRows, nCols, nSlices) 10 | Contains the processed results from process_vb_vol_results. 11 | n_neigh : numpy array of float32 (nRows, nCols, nSlices) 12 | Contains the amount of neighbours that every voxel has. 13 | affine : numpy array (M, M), optional 14 | Matrix used to compute spatial translations and rotations. 15 | header : Nifty header. 16 | This header contains crucial information about the structure and metadata 17 | associated with the image data stored in the file. 18 | output_name : string 19 | Name of the output file(s). 20 | 21 | Returns 22 | ------- 23 | None. 24 | 25 | """ 26 | 27 | img_new = nibabel.Nifti1Image(result, affine, header) 28 | nibabel.save(img_new, output_name + ".vbi-vol.nii.gz") 29 | if n_neigh is not None and n_neigh.any(): 30 | img_neigh = nibabel.Nifti1Image(n_neigh, affine, header) 31 | nibabel.save(img_neigh, output_name + ".vbi-neigh.nii.gz") -------------------------------------------------------------------------------- /vb_toolbox/math_util.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import scipy.linalg as spl 3 | import warnings 4 | from itertools import product 5 | 6 | class TimeSeriesTooShortError(Exception): 7 | """Raised when the time series in the input data have less than three elements""" 8 | pass 9 | 10 | #----get_neighborhood_vol----# 11 | 12 | # OFFSETS 13 | OFF27 = np.array(list(product((-1,0,1), repeat=3)), dtype=int) 14 | OFF7 = OFF27[np.sum(np.abs(OFF27), axis=1) <= 1] # centro + 6 caras 15 | 16 | def index_to_coords(i, shape): 17 | p0, q0, r0 = shape 18 | z = i % r0 19 | tmp = i // r0 20 | y = tmp % q0 21 | x = tmp // q0 22 | return np.array([x, y, z], dtype=int) 23 | 24 | def get_offsets(six_f): 25 | return OFF7 if six_f else OFF27 26 | 27 | def get_neighborhood_vol(data, i, six_f, mask=None): 28 | """ 29 | data: ndarray (X,Y,Z,N) 30 | i: flat index 31 | six_f: bool (True → 7-neighborhood; False → 27-neighborhood) 32 | mask: ndarray bool/0-1 or None 33 | """ 34 | shape_xyz = data.shape[:3] 35 | vox_coords = index_to_coords(i, shape_xyz) 36 | 37 | 38 | if mask is not None and not bool(mask[tuple(vox_coords)]): 39 | return np.nan, vox_coords 40 | 41 | offsets = get_offsets(six_f) 42 | neigh = vox_coords + offsets 43 | # Filtramos los que queden fuera del volumen 44 | in_bounds = np.all((neigh >= 0) & (neigh < shape_xyz), axis=1) 45 | neigh = neigh[in_bounds] 46 | 47 | if mask is not None: 48 | valid = mask[neigh[:,0], neigh[:,1], neigh[:,2]].astype(bool) 49 | neigh = neigh[valid] 50 | 51 | if neigh.size == 0: 52 | vals = np.empty((0, data.shape[-1])) 53 | else: 54 | vals = data[neigh[:,0], neigh[:,1], neigh[:,2], :] 55 | if mask is not None: 56 | non_zero = ~np.all(vals == 0, axis=1) 57 | vals = vals[non_zero] 58 | vals = np.atleast_2d(vals) 59 | 60 | return vals, vox_coords 61 | 62 | #----get_neighborhood_vol----# 63 | 64 | def create_affinity_matrix(neighborhood, eps=np.finfo(float).eps, verbose=False): 65 | """ 66 | Computes the affinity matrix of a given neighborhood 67 | 68 | Parameters 69 | ---------- 70 | neighborhood: (M, N) numpy array 71 | eps: float 72 | Small value which will replace negative numbers 73 | 74 | Returns 75 | ------- 76 | affinity: (M, M) numpy array 77 | Affinity matrix of the neighborhood 78 | """ 79 | # The following line is necessary for when neighborhood is an (M,) array (i.e. it is 1D), in 80 | # which case it is converted to an (M,1) array. 81 | neighborhood = np.atleast_2d(neighborhood) 82 | # Here, the affinity matrix should have n_neighbors x data_size shape 83 | if neighborhood.shape[1] < 3: 84 | raise TimeSeriesTooShortError("Time series have less than 3 entries. Your analysis will be compromised!\n") 85 | 86 | # Create a mean centered neighborhood 87 | neighborhood_mean = np.mean(neighborhood, axis=-1) 88 | neighborhood_mc = neighborhood - neighborhood_mean.reshape(-1, 1) 89 | neighborhood_mc[np.abs(neighborhood_mc) 1.0] = 1.0 104 | affinity[affinity < -1.0] = -1.0 105 | 106 | 107 | # "Linearlise" the affinity ensure positive correlations are between 0 to 1 108 | # what i am doing here is to change cosines to angles but to ensure that 109 | # this remains a similarity rather than dissimilarity I am treating the 110 | # values as the sine rather than cosine. I.e. identical signals will be 90 111 | # rather than 0. 90/90 == 1. 112 | A = np.arcsin(affinity)/np.pi*180.0 113 | A = A/90.0 114 | # Remove negative correlations 115 | A[A<0] = eps 116 | 117 | if verbose: 118 | print("affinity") 119 | print(affinity) 120 | print("A") 121 | print(A) 122 | 123 | return A 124 | 125 | def spectral_reorder(B, method='unnorm'): 126 | """Computes the spectral reorder of the matrix B 127 | 128 | Parameters 129 | ---------- 130 | B: (M, M) array_like 131 | Square matrix to be reordered 132 | method: string 133 | Method of reordering. Possibilities are 'geig', 'unnorm', 'rw' and 'sym' 134 | 135 | Returns 136 | ------- 137 | sorted_B: (M, M) numpy array 138 | Reordered matrix 139 | sort_idx: (M) numpy array 140 | Reordering mask applied to B 141 | eigenvalue: floating-point number 142 | Second-smallest eigenvalue 143 | eigenvector: (M) numpy array 144 | The Fiedler vector 145 | """ 146 | 147 | # Fix the input 148 | assert B.shape[0] == B.shape[1], "Matrix B must be square!" 149 | 150 | min_b = np.min(B) 151 | assert min_b >= -1.0, "This function only accepts matrices with a mininum negative value of -1" 152 | 153 | if min_b < 0: 154 | warnings.warn(""" 155 | The value 1 is being added to your similarity matrix to ensure positivity. 156 | This may cause issues with interpretation. Consider inputing a positive matrix""", 157 | warnings.UserWarning) 158 | C = B + 1 159 | else: 160 | C = B 161 | 162 | # Actual decomposition 163 | 164 | # create the laplacian matrix (Q). 165 | # For all non-diagonal elements, populate matrix Q with (minus) the corresponding 166 | # values of matrix C (i.e. Q[i,j] = -C[i,j]). 167 | # For each diagonal element Q[i,i], sum across the corresponding row of C (excluding the 168 | # diagonal element C[i,i]) and set Q[i,i] equal to that sum. 169 | 170 | triuC = np.triu(C,1) # Extract upper triangular elements; 171 | C = triuC + triuC.transpose() # Reconstruct a symmetric weighted adjacency matrix eliminating possible small errors in off-diagonal elements 172 | D = np.diag(np.sum(C, axis=-1)) # Compute the Degree matrix 173 | 174 | Q = D - C; # Compute un-normalised Laplacian 175 | 176 | method = method.lower() 177 | 178 | if method == 'geig': 179 | # Method using generalised spectral decomposition of the 180 | # un-normalised Laplacian (see Shi and Malik, 2000) 181 | 182 | vbi_value, eigenvector = get_fiedler_eigenpair(method, Q, D) 183 | 184 | elif method == 'unnorm': 185 | 186 | vbi_value, eigenvector = get_fiedler_eigenpair(method, Q) 187 | 188 | else: 189 | raise NameError("""Method '{}' not allowed. \n 190 | Please choose one of the following: 'sym', 'rw', 'geig', 'unnorm'.""".format(method)) 191 | 192 | v2 = eigenvector # Fiedler vector 193 | sort_idx = np.argsort(v2) # Find the reordering index 194 | sorted_B = B[sort_idx,:] # Reorder the original matrix 195 | sorted_B = sorted_B[:,sort_idx] # Reorder the original matrix 196 | 197 | return sorted_B, sort_idx, vbi_value, eigenvector 198 | 199 | def get_fiedler_eigenpair(method, Q, D=None, is_symmetric=True): 200 | 201 | """Solve the general eigenproblem to find the Fiedler vector and the corresponding eigenvalue. 202 | 203 | Solves the general eigenproblem Qx = lambda*Dx, and returns the Fiedler vector and associated eigenvalue. 204 | The former is unitary in the norm induced by the matrix D. If D is not set, the identity matrix is assumed. 205 | 206 | Parameters 207 | ---------- 208 | 209 | Q: (M, M) numpy array 210 | Main matrix 211 | D: (M, M) numpy array 212 | Matrix that accounts for node degree bias. If not set, this function will solve the 213 | standard eigenproblem (Qx = lambda*x) 214 | is_symmetric: boolean 215 | Indicates whether Q *and* D are symmetric. If set, the program will use a 216 | much faster, but less general, algorithm 217 | 218 | Returns 219 | ------- 220 | vbi_value: floating-point number 221 | The second smallest eigenvalue 222 | fiedler_vector: (M) numpy array 223 | The Fiedler vector 224 | """ 225 | if is_symmetric: 226 | 227 | eigenvalues, eigenvectors = spl.eigh(Q, D, check_finite=False) 228 | 229 | else: 230 | 231 | eigenvalues, eigenvectors = spl.eig(Q, D, check_finite=False) 232 | 233 | eigenvalues = np.array(eigenvalues, dtype=np.float32) 234 | eigenvectors = np.array(eigenvectors, dtype=np.float32) 235 | 236 | eigenvalues = np.real(eigenvalues) 237 | eigenvectors = np.real(eigenvectors) 238 | 239 | sort_eigen = np.argsort(eigenvalues) 240 | eigenvalues = eigenvalues[sort_eigen] 241 | 242 | dim = Q.shape[0] 243 | if method == 'unnorm': 244 | normalisation_factor = dim 245 | else: 246 | normalisation_factor = dim/(dim-1.) 247 | 248 | vbi_value = eigenvalues[1]/normalisation_factor 249 | vbi_value = vbi_value.astype(np.float32) 250 | 251 | fiedler_vector = eigenvectors[:, sort_eigen[1]] 252 | if D is None: 253 | D = np.identity(dim) 254 | n = np.matmul(fiedler_vector, np.matmul(D, fiedler_vector)) 255 | fiedler_vector = fiedler_vector/np.sqrt(n) 256 | 257 | return vbi_value, fiedler_vector 258 | 259 | def force_symmetric(M): 260 | """Force the symmetry of a given matrix. 261 | 262 | The symmetric version is computed by first replicating the diagonal and 263 | upper triangular components of the matrix, and mirroring the upper diagonal 264 | into the lower diagonal them into the lower triangular. 265 | 266 | Parameters 267 | ---------- 268 | M: (N, N) numpy array 269 | Matrix to be made symmetric 270 | 271 | Returns 272 | ------- 273 | M_sym: Symmetric version of M 274 | """ 275 | # One diag extracts the diagonal into an array, two 276 | # diags turns the array into a diagonal matrix. 277 | diag_M = np.diag(np.diag(M)) 278 | triu_M = np.triu(M, 1) 279 | 280 | return triu_M + diag_M + triu_M.transpose() -------------------------------------------------------------------------------- /vb_toolbox/multiprocess_util.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from multiprocessing import Pool, Value 3 | 4 | def init(a_counter, a_n): 5 | """ 6 | Store total number of vertices and counter of vertices computed 7 | 8 | Parameters 9 | ---------- 10 | a_counter : TYPE 11 | DESCRIPTION. 12 | a_n : TYPE 13 | DESCRIPTION. 14 | 15 | Returns 16 | ------- 17 | None. 18 | 19 | """ 20 | global counter 21 | global n 22 | counter = a_counter 23 | n = a_n 24 | 25 | def determine_items_and_cpus(n_cpus, data): 26 | """ 27 | Divides and parallelizes a job according to the number of CPUs available 28 | 29 | Parameters 30 | ---------- 31 | internal_loop_func : string 32 | The function that is going to run depending on the analysis to be done. 33 | surf_vertices : numpy array (M, 3) 34 | Vertices of the mesh. 35 | cluster_index : numpy array (M,) 36 | Indicates to which cluster each vertex belongs. 37 | n_cpus : integer 38 | How many CPU cores are available. 39 | data : Surface -> numpy array (M, N) 40 | Data to use to calculate the VB index. M must match the number of vertices in the mesh. 41 | Volumetric -> (nRows, nCols, nSlices, N) numpy array 42 | Volumetric data used to calculate the VB index. N is the number of maps. 43 | 44 | Returns 45 | ------- 46 | n_items : integer 47 | How many clusters are. 48 | n_cpus : integer 49 | How many CPU cores are going to be used. 50 | dn : integer 51 | How many elements will be processed for each CPU. 52 | """ 53 | 54 | 55 | n_items = data.shape[0]*data.shape[1]*data.shape[2] 56 | 57 | n_cpus = min(n_items, n_cpus) 58 | dn = max(n_items // n_cpus, 1) # Ensure at least one item per CPU 59 | return n_items, n_cpus, dn 60 | 61 | def initialize_multiprocessing(n_cpus, n_items): 62 | """ 63 | It initializes the multiprocessing threads. 64 | 65 | Parameters 66 | ---------- 67 | n_cpus : integer 68 | How many CPU cores are going to be used. 69 | n_items : integer 70 | How many clusters are. 71 | 72 | Returns 73 | ------- 74 | pool : Pool object. 75 | Object where pool information is stored. 76 | counter : Synchronized object. 77 | To synchronize pool processes. 78 | 79 | """ 80 | counter = Value('i', 0) 81 | pool = Pool(processes=n_cpus, initializer=init, initargs=(counter, n_items)) 82 | 83 | return pool, counter 84 | 85 | def run_multiprocessing(pool, internal_loop_func, n_items, dn, internal_loop_args): 86 | """ 87 | Initializes the specific function for each analysis and takes care of multiprocessing. 88 | 89 | Parameters 90 | ---------- 91 | pool : Pool object. 92 | Object where pool information is stored. 93 | internal_loop_func : string 94 | The function that is going to run depending on the analysis to be done. 95 | n_items : integer 96 | How many clusters are. 97 | dn : integer 98 | How many elements will be processed for each CPU. 99 | surf_vertices : numpy array (M, 3) 100 | Vertices of the mesh. 101 | surf_faces : numpy array (M, 3) 102 | Faces of the mesh. Used to find the neighborhood of a given vertex. 103 | data : Surface -> numpy array (M, N) 104 | Data to use to calculate the VB index. M must match the number of vertices in the mesh. 105 | Volumetric -> (nRows, nCols, nSlices, N) numpy array 106 | Volumetric data used to calculate the VB index. N is the number of maps. 107 | norm : string 108 | Method of reordering. Possibilities are 'geig', 'unnorm', 'rw' and 'sym'. 109 | residual_tolerance : float 110 | The target tolerance for the calculation of eigenpairs. 111 | max_num_iter : integer 112 | Number of iterations for eigenpair calculation. 113 | cluster_index : numpy array (M,) 114 | Array containing the cluster which each vertex belongs to. 115 | cort_index : numpy array (M,) 116 | Mask for detection of middle brain structures. 117 | affine : numpy array (M, M), optional 118 | Matrix used to compute spatial translations and rotations. 119 | k : integer 120 | Factor determining increase in density of input mesh. 121 | reho : Bool. 122 | Indicates that ReHo approach will be computed 123 | full_brain : Bool. 124 | Indicates that Full Brain analysis will be computed. 125 | brain_mask : numpy array of float32 (nRows, nCols, nSlices) 126 | Contains boolean values to indicate the area that belongs to the brain (value 1) 127 | and the area that belongs to the background(value 0). 128 | debug : boolean 129 | Outputs ribbon file for debugging. 130 | 131 | Returns 132 | ------- 133 | t.get(): numpy array. 134 | It gets the results of all the threads. 135 | 136 | """ 137 | 138 | def pool_callback(result): 139 | """ 140 | This function is used to handle pool errors 141 | 142 | Parameters 143 | ---------- 144 | result : pool object 145 | Error that occurred during the process. 146 | 147 | Returns 148 | ------- 149 | None. 150 | 151 | """ 152 | # Define error handling here 153 | print("Error occurred in pool execution:", result) 154 | # Terminate the pool in case of error 155 | pool.close() 156 | pool.terminate() 157 | 158 | threads = np.array([]) 159 | for i0 in range(0, n_items, dn): 160 | iN = min(i0 + dn, n_items) 161 | expanded_internal_loop_args = (i0, iN) + internal_loop_args 162 | threads = np.append(threads, (pool.apply_async(internal_loop_func, expanded_internal_loop_args, error_callback=pool_callback))) 163 | 164 | # Wait for all threads to complete 165 | pool.close() 166 | pool.join() 167 | 168 | res = np.empty((len(threads),), dtype=object) 169 | 170 | for r, t in enumerate(threads): 171 | res[r] = t.get() 172 | 173 | return res 174 | 175 | def cleanup(pool): 176 | """ 177 | This function is used to terminate de pool once it is no longer needed. 178 | 179 | Parameters 180 | ---------- 181 | pool : Pool object. 182 | Object where pool information is stored. 183 | 184 | Returns 185 | ------- 186 | None. 187 | 188 | """ 189 | pool.terminate() -------------------------------------------------------------------------------- /vb_toolbox/vb_cli.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # vim:fenc=utf-8 4 | # 5 | # Copyright © 2022 VB Index Team 6 | # 7 | # Created by The BOBLab 8 | # 9 | # Distributed under terms of the GNU license. 10 | 11 | from . import vol_pipe 12 | 13 | import argparse 14 | import multiprocessing 15 | import nibabel 16 | import numpy as np 17 | import textwrap as _textwrap 18 | import sys 19 | 20 | class MultilineFormatter(argparse.HelpFormatter): 21 | def _fill_text(self, text, width, indent): 22 | text = self._whitespace_matcher.sub(' ', text).strip() 23 | paragraphs = text.split('|n ') 24 | multiline_text = '' 25 | for paragraph in paragraphs: 26 | formatted_paragraph = _textwrap.fill(paragraph, width, initial_indent=indent, subsequent_indent=indent) + '\n\n' 27 | multiline_text = multiline_text + formatted_paragraph 28 | return multiline_text 29 | 30 | def create_parser(): 31 | authors = '''authors: 32 | |n The VB Index Team (See Contributors Section in the main README)''' 33 | copyright = '''copyright:|n 34 | This program is free software: you can redistribute it and/or modify 35 | it under the terms of the GNU General Public License as published by 36 | the Free Software Foundation, either version 3 of the License, or 37 | (at your option) any later version.|n 38 | This program is distributed in the hope that it will be useful, 39 | but WITHOUT ANY WARRANTY; without even the implied warranty of 40 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 41 | GNU General Public License for more details.|n 42 | You should have received a copy of the GNU General Public License 43 | along with this program. If not, see . 44 | ''' 45 | references = '''references:|n 46 | Bajada, C. J., Campos, L. Q. C., Caspers, S., Muscat, R., Parker, G. J., Ralph, M. A. L., ... & Trujillo-Barreto, N. J. (2020). A tutorial and tool for exploring feature similarity gradients with MRI data. NeuroImage, 221, 117140.|n 47 | Ciantar, K. G., Farrugia, C., Galdi, P., Scerri, K., Xu, T., & Bajada, C. J. (2022). Geometric effects of volume-to-surface mapping of fMRI data. Brain Structure and Function, 227(7), 2457-2464.|n 48 | Farrugia, C., Galdi, P., Irazu, I. A., Scerri, K., & Bajada, C. J. (2024). Local gradient analysis of human brain function using the Vogt-Bailey Index. Brain Structure and Function, 229(2), 497-512.|n 49 | Galea, K., Escudero, A. A., Montalto, N. A., Vella, N., Smith, R. E., Farrugia, C., ... & Bajada, C. J. (2025). Testing the Vogt-Bailey Index using task-based fMRI across pulse sequence protocols. bioRxiv, 2025-02. 50 | ''' 51 | 52 | parser = argparse.ArgumentParser(description='Calculate the Vogt-Bailey Index of a dataset. For more information, refer to https://github.com/VBIndex/py_vb_toolbox.', 53 | epilog=authors + " |n " + references + " |n " + copyright, 54 | formatter_class=MultilineFormatter) 55 | 56 | # Added subparsers for each of the different method for VB calculation 57 | subparsers = parser.add_subparsers(dest='method', 58 | title="VB method", 59 | required=True, 60 | help='Different methods for computing the VB Index') 61 | 62 | # Subparser for the volumetric analysis 63 | volumetric = subparsers.add_parser('volumetric', 64 | help='Computes the VB Index on volumetric data using a searchlight approach') 65 | 66 | # Adding custom arguments for the volumetric analysis 67 | volumetric.add_argument('-d', '--data', 68 | metavar='file', 69 | type=str, 70 | help="""NIfTI file containing the volumetric fMRI data.""", 71 | required=True) 72 | 73 | volumetric.add_argument('-o', '--output', 74 | metavar='file', 75 | type=str, 76 | help="""Base name for the output files.""", 77 | required=True) 78 | 79 | volumetric.add_argument('-vm', '--volmask', 80 | metavar='file', 81 | type=str, 82 | default=None, 83 | help="""NIfTI file containing the whole brain mask in volumetric space.""") 84 | 85 | volumetric.add_argument('-ln', '--little-neighbourhood', 86 | action='store_true', 87 | help="""Change the amount of voxels taken to create the neighbourhoods from 27 to 7.""") 88 | 89 | volumetric.add_argument('-rh', '--reho', 90 | action='store_true', 91 | help="""Calculate the KCC index for ReHo approach.""") 92 | 93 | volumetric.add_argument('-n', '--norm', 94 | action='store_true', 95 | help="""Laplacian normalization to be employed. If this flag is used the "geig" method is employed. Else, it defaults to "unnorm".""") 96 | 97 | volumetric.add_argument('-j', '--jobs', 98 | metavar='N', 99 | type=int, 100 | default=multiprocessing.cpu_count(), 101 | help="""Maximum number of jobs to be used. If absent, one job per CPU will be spawned.""") 102 | 103 | return parser, volumetric 104 | 105 | def main(): 106 | """ 107 | Main function where argument parser and main logic is handled. 108 | 109 | Returns 110 | ------- 111 | None. 112 | 113 | """ 114 | 115 | parser, volumetric = create_parser() 116 | 117 | # Show help if no arguments are given 118 | if len(sys.argv)==1: 119 | parser.print_help(sys.stderr) 120 | sys.exit(1) 121 | 122 | # Show help for the volumetric if user called volumetric but no additional arguments 123 | if len(sys.argv) == 2 and sys.argv[1] == "volumetric": 124 | print("hi") 125 | volumetric.print_help(sys.stderr) 126 | sys.exit(1) 127 | 128 | args = parser.parse_args() 129 | 130 | if args.method == 'volumetric': 131 | n_cpus = args.jobs 132 | nib = nibabel.load(args.data,mmap=False) 133 | data = np.array(nib.dataobj) 134 | affine = nib.affine 135 | header = nib.header 136 | 137 | if args.norm: 138 | L_norm = "geig" 139 | else: 140 | L_norm = "unnorm" 141 | 142 | if args.reho: 143 | print("Running ReHo approach with no surface mapping") 144 | else: 145 | print("Running searchlight analysis with no surface mapping") 146 | 147 | if args.volmask: 148 | brain_mask = nibabel.load(args.volmask,mmap=False) 149 | brain_mask = np.array(brain_mask.dataobj) 150 | else: 151 | brain_mask = None 152 | 153 | try: 154 | vol_pipe.main(n_cpus, data, affine, header, L_norm, brain_mask, output_name=args.output + "." + L_norm, six_f=args.little_neighbourhood, reho=args.reho) 155 | 156 | except Exception as error: 157 | sys.stderr.write(str(error)) 158 | sys.exit(2) 159 | quit() 160 | 161 | if __name__ == "__main__": 162 | main() -------------------------------------------------------------------------------- /vb_toolbox/vol_pipe.py: -------------------------------------------------------------------------------- 1 | from . import multiprocess_util as mu 2 | from . import math_util as ma 3 | from . import io_util as io 4 | 5 | import numpy as np 6 | import traceback 7 | from scipy.stats import rankdata 8 | 9 | def main(n_cpus, data, affine, header, norm, brain_mask, output_name, six_f, reho): 10 | 11 | # Determine n_items and n_cpus 12 | n_items, n_cpus, dn = mu.determine_items_and_cpus(n_cpus, data) 13 | 14 | # Initialize multiprocessing 15 | pool, counter = mu.initialize_multiprocessing(n_cpus, n_items) 16 | 17 | internal_loop_func = vol_internal_loop 18 | arguments = (data, norm, brain_mask, reho, six_f) 19 | 20 | # Run multiprocessing 21 | results = mu.run_multiprocessing(pool, internal_loop_func, n_items, dn, arguments) 22 | 23 | # Process and save results 24 | process_vb_vol_results(results, data, affine, header, output_name) 25 | 26 | def vol_internal_loop(i0, iN, data, norm, brain_mask, reho, six_f): 27 | 28 | """Computes the Vogt-Bailey index of voxels in a given range. 29 | 30 | Parameters 31 | ---------- 32 | i0: integer 33 | Index of first voxel to be analysed. 34 | iN: integer 35 | iN - 1 is the index of the last voxel to be analysed. 36 | data: numpy array (nRows, nCols, nSlices, N) 37 | Volumetric data used to calculate the VB index. N is the number of maps. 38 | norm: string 39 | Method of reordering. Possibilities are 'geig', 'unnorm', 'rw' and 'sym'. 40 | brain_mask: (nRows, nCols, nSlices) numpy array 41 | Whole brain mask. Used to eliminate voxels outside the brain from the analysis. 42 | residual_tolerance: float 43 | Residual tolerance (stopping criterion) for LOBPCG. Only relevant for the full brain algorithm. 44 | max_num_iter: integer 45 | Maximum number of iterations for LOBPCG. Only relevant for the full brain algorithm. 46 | reho : Bool. 47 | Indicates that ReHo approach will be computed 48 | debug: boolean 49 | Print the current progress of the system 50 | 51 | Returns 52 | ------- 53 | loc_result: numpy array 54 | Resulting VB index of the voxels in the given range. Will have size (iN-i0, 4). 55 | """ 56 | 57 | # Calculate how many vertices we will compute 58 | diff = iN - i0 59 | loc_result = np.zeros((diff,5)) 60 | 61 | for idx in range(diff): 62 | #Calculate the real index 63 | i = idx + i0 64 | neighborhood, vox_coords = ma.get_neighborhood_vol(data,i,six_f,brain_mask) 65 | if np.isnan(neighborhood).all(): 66 | 67 | loc_result[idx,0] = np.nan 68 | loc_result[idx,1:4] = vox_coords 69 | loc_result[idx,-1] = np.nan 70 | continue 71 | to_keep = np.where(np.std(neighborhood,axis=1)>1e-10) 72 | neighborhood = np.squeeze(neighborhood[to_keep,:]) 73 | neighborhood = np.atleast_2d(neighborhood) 74 | 75 | # Get neighborhood and its data 76 | try: 77 | 78 | if len(neighborhood) == 0: 79 | loc_result[idx,0] = np.nan 80 | loc_result[idx,1:4] = vox_coords 81 | loc_result[idx,-1] = 0 82 | continue 83 | 84 | affinity = ma.create_affinity_matrix(neighborhood) 85 | if reho: 86 | compute_vol_reho(neighborhood, i, idx, loc_result, affinity, vox_coords) 87 | else: 88 | compute_vol(neighborhood, i, idx, loc_result, affinity, vox_coords, norm) 89 | 90 | except ma.TimeSeriesTooShortError as error: 91 | raise error 92 | except Exception: 93 | traceback.print_exc() 94 | loc_result[idx,0] = np.nan 95 | loc_result[idx,1:4] = vox_coords 96 | loc_result[idx,-1] = np.nan 97 | 98 | return loc_result 99 | 100 | 101 | def compute_vol(neighborhood, i, idx, loc_result, affinity, vox_coords, norm): 102 | """ 103 | Computes the neighbourhood for every voxel in the volumetric analysis. 104 | 105 | Parameters 106 | ---------- 107 | neighborhood : numpy array of float32 (M, N) 108 | Computed neighbourhood for every voxel. 109 | i : integer 110 | Real index. 111 | idx : integer 112 | Number of iteration in the main for loop from vb_hybrid_internal_loop function. 113 | loc_result : numpy array of float32 (M, N) 114 | Stores all the computed eigenvalues. 115 | affinity : numpy array of float32 (M, M) 116 | The affinity matrix computed for the neighbourhood. 117 | vox_coords : numpy array of int64 (M,) 118 | The coords of the center voxel. 119 | residual_tolerance : float 120 | The target tolerance for the calculation of eigenpairs. 121 | max_num_iter : integer 122 | Number of iterations for eigenpair calculation. 123 | norm : string 124 | Method of reordering. Possibilities are 'geig', 'unnorm', 'rw' and 'sym'. 125 | 126 | Returns 127 | ------- 128 | loc_result : numpy array of float32 (M, N) 129 | Stores all the computed eigenvalues. 130 | 131 | """ 132 | assert affinity.shape[0] == len(neighborhood), 'affinity.shape[0] and len(neighborhood) do not match!' 133 | 134 | if affinity.shape[0] > 3: 135 | #Calculate the second smallest eigenvalue 136 | _, _, eigenvalue, _ = ma.spectral_reorder(affinity, norm) 137 | loc_result[idx,0] = eigenvalue 138 | loc_result[idx,1:4] = vox_coords 139 | loc_result[idx,-1] = affinity.shape[0] 140 | else: 141 | #print("Warning: too few neighbors ({})".format(no_of_voxels), "for vertex:",i) 142 | loc_result[idx,0] = np.nan 143 | loc_result[idx,1:4] = vox_coords 144 | loc_result[idx,-1] = affinity.shape[0] 145 | 146 | return loc_result 147 | 148 | def compute_vol_reho(neighborhood, i, idx, loc_result, affinity, vox_coords): 149 | """ 150 | Computes the neighbourhood for every voxel in the volumetric ReHo analysis. 151 | 152 | Parameters 153 | ---------- 154 | neighborhood : numpy array of float32 (M, N) 155 | Computed neighbourhood for every voxel. 156 | i : integer 157 | Real index. 158 | idx : integer 159 | Number of iteration in the main for loop from vb_hybrid_internal_loop function. 160 | loc_result : numpy array of float32 (M, N) 161 | Stores all the computed KCC values. 162 | affinity : numpy array of float32 (M, M) 163 | The affinity matrix computed for the neighbourhood. 164 | vox_coords : numpy array of int64 (M,) 165 | The coords of the center voxel. 166 | 167 | Returns 168 | ------- 169 | loc_result : numpy array of float32 (M, N) 170 | Stores all the computed KCC values. 171 | 172 | """ 173 | 174 | no_of_voxels = np.shape(neighborhood)[0] 175 | no_of_time_pts = np.shape(neighborhood)[1] 176 | ranked_neigh = np.ones((no_of_voxels,no_of_time_pts)) 177 | 178 | if no_of_voxels >= no_of_time_pts: 179 | print('neighborhood matrix must be transposed!') 180 | for s in range(no_of_voxels): 181 | ranked_neigh[s,:] = rankdata(neighborhood[s,:]) 182 | ranked_sums = np.sum(ranked_neigh,axis=0) 183 | Rbar = np.sum(ranked_sums)/no_of_time_pts 184 | R = np.sum((ranked_sums - Rbar)**2) 185 | KCC = 12.*R / ((no_of_voxels**2)*(no_of_time_pts**3 - no_of_time_pts)) 186 | 187 | if no_of_voxels > 3: 188 | loc_result[idx] = KCC 189 | loc_result[idx,1:4] = vox_coords 190 | loc_result[idx,-1] = affinity.shape[0] 191 | else: 192 | #print("Warning: too few neighbors ({})".format(no_of_voxels), "for vertex:",i) 193 | loc_result[idx] = np.nan 194 | loc_result[idx,1:4] = vox_coords 195 | loc_result[idx,-1] = affinity.shape[0] 196 | 197 | return loc_result 198 | 199 | def process_vb_vol_results(results, data, affine, header, output_name): 200 | """ 201 | This function processes the results gotten from the vb_vol_internal_loop. 202 | 203 | Parameters 204 | ---------- 205 | results : numpy object array of (M,) 206 | Numpy array that contains the computed vb index for every volumetric data. 207 | data : Volumetric -> (nRows, nCols, nSlices, N) numpy array 208 | Volumetric data used to calculate the VB index. N is the number of maps. 209 | affine : numpy array (M, M), optional 210 | Matrix used to compute spatial translations and rotations. The default is None. 211 | header : Nifty header. 212 | This header contains crucial information about the structure and metadata. 213 | output_name : string 214 | Name of the output file(s). 215 | debug : boolean, optional 216 | Outputs ribbon file for debugging. The default is False 217 | 218 | Returns 219 | ------- 220 | None. 221 | 222 | """ 223 | results_v2 = np.zeros((data.shape[0],data.shape[1],data.shape[2]), dtype=np.float32) 224 | n_neigh = np.zeros((data.shape[0],data.shape[1],data.shape[2]), dtype=np.float32) 225 | 226 | for i, res in enumerate(results): 227 | for r in results[i]: 228 | results_v2[int(r[1]),int(r[2]),int(r[3])] = r[0] 229 | n_neigh[int(r[1]),int(r[2]),int(r[3])] = r[4] 230 | 231 | # Save file 232 | if output_name is not None: 233 | io.save_nifti(result=results_v2, n_neigh=n_neigh, affine=affine, header=header, output_name=output_name) --------------------------------------------------------------------------------