Pydra task package for afni

This package contains a collection of Pydra task interfaces for the afni toolkit. The basis of this collection has been formed by the semi-automatic conversion of existing Nipype interfaces to Pydra using the Nipype2Pydra tool

Automatically-generated vs manually-curated tasks

Automatically generated tasks can be found in the pydra.tasks.afni.auto package. These packages should be treated with extreme caution as they likely do not pass testing. Generated tasks that have been edited and pass testing are imported into one or more of the pydra.tasks.afni.v* packages, corresponding to the version of the afni toolkit they are designed for.

Tests

This package comes with a battery of automatically generated test modules. To install the necessary dependencies to run the tests

$ pip install -e .[test]

Then the tests, including doctests <https://docs.python.org/3/library/doctest.html>`__, can be launched using

$ pytest --doctest-modules pydra/tasks/*

By default, the tests are set to time-out after 10s, after which the underlying tool is assumed to have passed the validation/initialisation phase and we assume that it will run to completion. To disable this and run the test(s) through to completion run

$ pytest --doctest-modules --timeout-pass 0 pydra/tasks/*

Continuous integration

This template uses GitHub Actions <https://docs.github.com/en/actions/>`__ to run tests and deploy packages to PYPI. New packages are built and uploaded when releases are created on GitHub, or new releases of Nipype or the Nipype2Pydra conversion tool are released. Releases triggered by updates to Nipype or Nipype2Pydra are signified by the postN suffix where N = <nipype-version><nipype2pydra-version> with the '.'s stripped, e.g. v0.2.3post185010 corresponds to the v0.2.3 tag of this repository with auto-generated packages from Nipype 1.8.5 using Nipype2Pydra 0.1.0.

Contributing to this package

Developer installation

Install repo in developer mode from the source directory and install pre-commit to ensure consistent code-style and quality.

$ pip install -e .[test,dev]
$ pre-commit install

Next install the requirements for running the auto-conversion script and generate the Pydra task interfaces from their Nipype counterparts

$ pip install -r nipype-auto-conv/requirements.txt

The run the conversion script to convert Nipype interfaces to Pydra

$ nipype-auto-conv/generate

## Methodology

The development of this package is expected to have two phases

1. Where the corresponding Nipype interfaces are considered to be the ground truth, and the Pydra tasks are generated from them
2. When the Pydra tasks are considered be mature and they are edited by hand

Different tasks will probably mature at different times so there will probably be an intermediate phase between 1 and 2.

Auto-conversion phase

The auto-converted Pydra tasks are generated from their corresponding Nipype interface in combination with "conversion hints" contained in YAML specs located in nipype-auto-conv/specs/. The self-documented conversion specs are to be edited by hand in order to assist the auto-converter produce valid pydra tasks. After editing one or more conversion specs the pydra.tasks.afni.auto package should be regenerated by running

$ nipype-auto-conv/generate

The tests should be run on the auto-generated tasks to see if they are valid

$ pytest --doctest-modules pydra/tasks/afni/auto/tests/test_<the-name-of-the-task-you-edited>.py

If the test passes you should then edit the pydra/tasks/afni/v<tool-version>/__init__.py file to import the now valid task interface to signify that it has been validated and is ready for use, e.g.

Typing and sample test data

The automatically generated tests will attempt to provided the task instance to be tested with sensible default values based on the type of the field and any constraints it has on it. However, these will often need to be manually overridden after consulting the underlying tool's documentation.

For file-based data, automatically generated file-system objects will be created for selected format types, e.g. Nifti, Dicom. Therefore, it is important to specify the format of the file using the "mime-like" string corresponding to a fileformats class in the inputs > types and outputs > types dicts of the YAML spec.

If the required file-type is not found implemented within fileformats, please see the fileformats docs for instructions on how to define new fileformat types, and see fileformats-medimage-extras for an example on how to implement methods to generate sample data for them.