Documentation changes after Empenn Hackathon (#176)

* [BUG] inside unit_tests workflow

* [DOC] fix some broken links

* [DOC] adding template for pipeline testing

* [DOC] adding template for pipeline testing

* About implemented_pipelines

* Deal with test template

* [DOC] new readme for the doc

* Changes in README.md

* [DOC] slight changes to docs/README.md

* Add links to past events

* Changes in readme.md

* fMRI trail

* Adding trail description in contribution guide

* Separate trails in contribution guide

* [TEST] Solving pytest issues with template test

* Changing docker image in use

* FSL template correction

* [DOC] writing test files

* Codespell

* First step in writing documentation about NARPS

* [DOC] completing doc about narps

* [DOC] completing doc about narps

* [DOC] completing doc about narps

* [DATALAD] change results url

* [DOC] reference to the github project for reproduction mgmt

* [DOC] adding team id choices for narps open runner

* [DOC] list of available team ids in command tools documentation

* [DOC] configuration info inside INSTALL.md

* [DOC] configuration info inside INSTALL.md

* NARPS Exclusion comments

* Empenn hackathon names

* Data documentation (datalad get recursive

* Adjusting correlation thresholds inside testing configuration, after U26C results

* Update dataset size

* Freeze versions

INSTALL.md

@@ -43,15 +43,15 @@ datalad get data/original/ds001734/derivatives/fmriprep/sub-00[1-4] -J 12
 [Install Docker](https://docs.docker.com/engine/install/) then pull the nipype Docker image :
 
 ```bash
-docker pull nipype/nipype
+docker pull nipype/nipype:py38
 ```
 
 Once it's done you can check the image is available on your system :
 
 ```bash
 docker images
    REPOSITORY                 TAG       IMAGE ID        CREATED         SIZE
-   docker.io/nipype/nipype    latest    0f3c74d28406    9 months ago    22.7 GB
+   docker.io/nipype/nipype    py38      0f3c74d28406    9 months ago    22.7 GB
 ```
 
 > [!NOTE]  
@@ -63,10 +63,11 @@ Start a Docker container from the Docker image :
 
 ```bash
 # Replace PATH_TO_THE_REPOSITORY in the following command (e.g.: with /home/user/dev/narps_open_pipelines/)
-docker run -it -v PATH_TO_THE_REPOSITORY:/home/neuro/code/ nipype/nipype
+docker run -it -v PATH_TO_THE_REPOSITORY:/home/neuro/code/ nipype/nipype:py38
 ```
 
 Optionally edit the configuration file `narps_open/utils/configuration/default_config.toml` so that the referred paths match the ones inside the container. E.g.: if using the previous command line, the `directories` part of the configuration file should be :
+
 ```toml
 # default_config.toml
 # ...

README.md

@@ -53,7 +53,8 @@ This project is supported by Région Bretagne (Boost MIND) and by Inria (Explora
 This project is developed in the Empenn team by Boris Clénet, Elodie Germani, Jeremy Lefort-Besnard and Camille Maumet with contributions by Rémi Gau.
 
 In addition, this project was presented and received contributions during the following events:
- - [Brainhack Marseille 2023](https://brainhack-marseille.github.io/) (December 2023): 
+ - [Empenn team](https://team.inria.fr/empenn/) hackathon (February 2024): Mathieu Acher, Élise Bannier, Boris Clénet, Isabelle Corouge, Malo Gaubert, Élodie Germani,  Gauthier Le Bartz Lyan, Jérémy Lefort-Besnard, Camille Maumet, Youenn Merel, Alexandre Pron.
+ - [Brainhack Marseille 2023](https://brainhack-marseille.github.io/) (December 2023)
  - [ORIGAMI lab](https://neurodatascience.github.io/) hackathon (September 2023): 
  - [OHBM Brainhack 2023](https://ohbm.github.io/hackathon2023/) (July 2023): Arshitha Basavaraj, Boris Clénet, Rémi Gau, Élodie Germani, Yaroslav Halchenko, Camille Maumet, Paul Taylor.
  - [e-ReproNim FENS NENS Cluster Brainhack](https://repro.school/2023-e-repronim-brainhack/) (June 2023) : Liz Bushby, Boris Clénet, Michael Dayan, Aimee Westbrook.

docs/data.md

@@ -20,10 +20,15 @@ Tips for people using M1 MacBooks: `git-annex` is not yet available for M1 MacBo
 
 The `datalad install` command only downloaded the metadata associated with the dataset ; to download the actual files run the following command:
 
+> [! WARNING]
+> The following command lines will download **all** the data, which represents around :
+>  * 3 GB for `data/results/`
+>  * 880 GB for `data/original/`
+
 ```bash
 # To get all the data
 cd data/
-datalad get ./*
+datalad get --recursive ./*
 ```
 
 If you only want parts of the data, replace the `./*` by the paths to the desired files.

docs/environment.md

@@ -2,12 +2,12 @@
 
 ## The Docker container :whale:
 
-The NARPS Open Pipelines project is build upon several dependencies, such as [Nipype](https://nipype.readthedocs.io/en/latest/) but also the original software packages used by the pipelines (SPM, FSL, AFNI...). Therefore we recommend to use the [`nipype/nipype` Docker image](https://hub.docker.com/r/nipype/nipype/) that contains all the required software dependencies.
+The NARPS Open Pipelines project is build upon several dependencies, such as [Nipype](https://nipype.readthedocs.io/en/latest/) but also the original software packages used by the pipelines (SPM, FSL, AFNI...). Therefore we recommend to use the [`nipype/nipype:py38` Docker image](https://hub.docker.com/r/nipype/nipype/) that contains all the required software dependencies.
 
 The simplest way to start the container is by using the command below :
 
 ```bash
-docker run -it nipype/nipype
+docker run -it nipype/nipype:py38
 ```
 
 From this command line, you need to add volumes to be able to link with your local files (code repository).
@@ -16,7 +16,7 @@ From this command line, you need to add volumes to be able to link with your loc
 # Replace PATH_TO_THE_REPOSITORY in the following command (e.g.: with /home/user/dev/narps_open_pipelines/)
 docker run -it \
            -v PATH_TO_THE_REPOSITORY:/home/neuro/code/ \
-           nipype/nipype
+           nipype/nipype:py38
 ``` 
 
 ## Use Jupyter with the container
@@ -27,7 +27,7 @@ If you wish to use [Jupyter](https://jupyter.org/) to run the code, a port forwa
 docker run -it \
            -v PATH_TO_THE_REPOSITORY:/home/neuro/code/ \
            -p 8888:8888 \
-           nipype/nipype
+           nipype/nipype:py38
 ``` 
 
 Then, from inside the container :
@@ -81,7 +81,7 @@ To use SPM inside the container, use this command at the beginning of your scrip
 ```python
 from nipype.interfaces import spm
 
-matlab_cmd = '/opt/spm12-r7771/run_spm12.sh /opt/matlabmcr-2010a/v713/ script'
+matlab_cmd = '/opt/spm12-r7219/run_spm12.sh /opt/matlabmcr-2010a/v713/ script'
 
 spm.SPMCommand.set_mlab_paths(matlab_cmd=matlab_cmd, use_mcr=True)
 ```

narps_open/pipelines/__main__.py

@@ -0,0 +1,30 @@
+#!/usr/bin/python
+# coding: utf-8
+
+""" Provide a command-line interface for the package narps_open.pipelines """
+
+from argparse import ArgumentParser
+
+from narps_open.pipelines import get_implemented_pipelines
+
+def main():
+    """ Entry-point for the command line tool narps_open_pipeline """
+
+    # Parse arguments
+    parser = ArgumentParser(description='Get description of a NARPS pipeline.')
+    parser.add_argument('-v', '--verbose', action='store_true',
+        help='verbose mode')
+    arguments = parser.parse_args()
+
+    # Print header
+    print('NARPS Open Pipelines')
+
+    # Print general information about NARS Open Pipelines
+    print('A codebase reproducing the 70 pipelines of the NARPS study (Botvinik-Nezer et al., 2020) shared as an open resource for the community.')
+
+    # Print pipelines
+    implemented_pipelines = get_implemented_pipelines()
+    print(f'There are currently {len(implemented_pipelines)} implemented pipelines: {implemented_pipelines}')
+
+if __name__ == '__main__':
+    main()

narps_open/utils/configuration/testing_config.toml

@@ -23,4 +23,4 @@ neurovault_naming = true # true if results files are saved using the neurovault
 
 [testing.pipelines]
 nb_subjects_per_group = 4 # Compute first level analyses by subgroups of N subjects, to avoid lacking of disk and memory
-correlation_thresholds = [0.30, 0.70, 0.79, 0.85, 0.93] # Correlation between reproduced hypotheses files and results, respectively for [20, 40, 60, 80, 108] subjects.
+correlation_thresholds = [0.30, 0.70, 0.78, 0.85, 0.93] # Correlation between reproduced hypotheses files and results, respectively for [20, 40, 60, 80, 108] subjects.

setup.py

@@ -18,18 +18,18 @@
     'tomli>=2.0.1,<2.1',
     'networkx>=2.0,<3.0', # a workaround to nipype's bug (issue 3530)
     'nilearn>=0.10.0,<0.11',
-    'nipype',
-    'pandas'
+    'nipype>=1.8.6,<1.9',
+    'pandas>=1.5.2,<1.6'
 ]
 extras_require = {
     'tests': [
-        'pathvalidate',
-        'pylint',
-        'pytest',
-        'pytest-cov',
-        'pytest-helpers-namespace',
-        'pytest-mock',
-        'checksumdir'
+        'pathvalidate>=3.2.0,<3.3',
+        'pylint>=3.0.3,<3.1',
+        'pytest>=7.2.0,<7.3',
+        'pytest-cov>=2.10.1,<2.11',
+        'pytest-helpers-namespace>=2021.12.29,<2021.13',
+        'pytest-mock>=3.12.0,<3.13',
+        'checksumdir>=1.2.0,<1.3'
         ]
 }