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Pydra interface for FastSurfer - A fast and accurate deep learning based neuroimaging pipeline

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Pydra task package for fastsurfer

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This package contains a Pydra interface for the fastsurfer toolkit.t.

Tests

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

the necessary dependencies to run the tests

   $ pip install -e .[test]

Then the tests, including doctests, 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/*

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

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.

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Pydra interface for FastSurfer - A fast and accurate deep learning based neuroimaging pipeline

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