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Merge pull request #1230 from poldracklab/oesteban-patch-1
[DOC] Updating long description
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‎.circleci/config.yml

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keys:
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- regression-v0-{{ epoch }}
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- regression-v0-
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- run:
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name: Check PyPi preconditions
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command: |
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pip install "setuptools>=27.0" cython numpy twine future docutils
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python setup.py check -r -s
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python setup.py sdist
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- run:
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name: Load Docker image layer cache
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no_output_timeout: 30m
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- run:
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name: Deploy to PyPi
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command: |
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pip install "setuptools>=27.0" cython numpy twine future
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pip install "setuptools>=27.0" cython numpy twine future docutils
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echo "${CIRCLE_TAG}" > fmriprep/VERSION
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python setup.py check -r -s
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python setup.py sdist
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twine upload dist/*
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cd wrapper && python setup.py sdist

‎fmriprep/__about__.py

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# -*- coding: utf-8 -*-
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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
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# vi: set ft=python sts=4 ts=4 sw=4 et:
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"""
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Base module variables
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"""
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from ._version import get_versions
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__version__ = get_versions()['version']
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del get_versions
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__author__ = 'The CRN developers'
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__copyright__ = 'Copyright 2018, Center for Reproducible Neuroscience, Stanford University'
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__credits__ = ['Craig Moodie', 'Ross Blair', 'Oscar Esteban', 'Chris Gorgolewski',
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'Shoshana Berleant', 'Christopher J. Markiewicz', 'Russell A. Poldrack']
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__license__ = '3-clause BSD'
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__maintainer__ = 'Ross Blair'
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__email__ = '[email protected]'
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__status__ = 'Prototype'
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__url__ = 'https://github.com/poldracklab/fmriprep'
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__packagename__ = 'fmriprep'
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__description__ = ("FMRIprep is a functional magnetic resonance image pre-processing pipeline "
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"that is designed to provide an easily accessible, state-of-the-art interface "
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"that is robust to differences in scan acquisition protocols and that requires "
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"minimal user input, while providing easily interpretable and comprehensive "
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"error and output reporting.")
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__longdesc__ = """\
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Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize
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data before statistical analysis.
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Generally, researchers create ad hoc preprocessing workflows for each new dataset,
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building upon a large inventory of tools available for each step.
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The complexity of these workflows has snowballed with rapid advances in MR data
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acquisition and image processing techniques.
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FMRIPrep is an analysis-agnostic tool that addresses the challenge of robust and
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reproducible preprocessing for task-based and resting fMRI data.
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FMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of
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virtually any dataset, ensuring high-quality preprocessing with no manual intervention,
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while providing easily interpretable and comprehensive error and output reporting.
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It performs basic preprocessing steps (coregistration, normalization, unwarping, noise
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component extraction, segmentation, skullstripping etc.) providing outputs that can be
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easily submitted to a variety of group level analyses, including task-based or resting-state
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fMRI, graph theory measures, surface or volume-based statistics, etc.
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The workflow is based on `Nipype <https://nipype.readthedocs.io>`_ and encompases a large
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set of tools from well-known neuroimaging packages, including
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`FSL <https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/>`_,
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`ANTs <https://stnava.github.io/ANTs/>`_,
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`FreeSurfer <https://surfer.nmr.mgh.harvard.edu/>`_,
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`AFNI <https://afni.nimh.nih.gov/>`_,
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and `Nilearn <https://nilearn.github.io/>`_.
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This pipeline was designed to provide the best software implementation for each state of
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preprocessing, and will be updated as newer and better neuroimaging software becomes
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available.
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This tool allows you to easily do the following:
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* Take fMRI data from *unprocessed* (only reconstructed) to ready for analysis.
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* Implement tools from different software packages.
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* Achieve optimal data processing quality by using the best tools available.
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* Generate preprocessing-assessment reports, with which the user can easily identify problems.
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* Receive verbose output concerning the stage of preprocessing for each subject, including
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meaningful errors.
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* Automate and parallelize processing steps, which provides a significant speed-up from
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typical linear, manual processing.
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FMRIPrep has the potential to transform fMRI research by equipping
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neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow
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which can help ensure the validity of inference and the interpretability of their results.
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[Pre-print doi:`10.1101/306951 <https://doi.org/10.1101/306951>`_]
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[Documentation `fmriprep.org <https://fmriprep.readthedocs.io>`_]
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[Software doi:`10.5281/zenodo.852659 <https://doi.org/10.5281/zenodo.852659>`_]
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[Support `neurostars.org <https://neurostars.org/tags/fmriprep>`_]
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"""
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DOWNLOAD_URL = (
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'https://github.com/poldracklab/{name}/archive/{ver}.tar.gz'.format(
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name=__packagename__, ver=__version__))
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SETUP_REQUIRES = [
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'setuptools>=18.0',
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'numpy',
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'cython',
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]
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REQUIRES = [
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'numpy',
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'lockfile',
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'future',
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'scikit-learn',
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'matplotlib>=2.2.0',
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'nilearn',
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'sklearn',
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'nibabel>=2.2.1',
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'pandas',
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'grabbit',
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'pybids>=0.6.3',
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'nitime',
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'nipype>=1.1.1',
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'niworkflows>=0.4.2',
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'statsmodels',
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'seaborn',
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'indexed_gzip>=0.8.2',
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'scikit-image',
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'versioneer',
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]
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LINKS_REQUIRES = [
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]
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TESTS_REQUIRES = [
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"mock",
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"codecov",
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"pytest",
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]
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EXTRA_REQUIRES = {
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'doc': [
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'sphinx>=1.5.3',
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'sphinx_rtd_theme',
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'sphinx-argparse',
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'pydotplus',
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'pydot>=1.2.3',
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'packaging',
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'nbsphinx',
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],
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'tests': TESTS_REQUIRES,
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'duecredit': ['duecredit'],
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'datalad': ['datalad'],
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'resmon': ['psutil>=5.4.0'],
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'sentry': ['raven'],
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}
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EXTRA_REQUIRES['docs'] = EXTRA_REQUIRES['doc']
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# Enable a handle to install all extra dependencies at once
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EXTRA_REQUIRES['all'] = list(set([
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v for deps in EXTRA_REQUIRES.values() for v in deps]))
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CLASSIFIERS = [
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'Development Status :: 3 - Alpha',
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'Intended Audience :: Science/Research',
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'Topic :: Scientific/Engineering :: Image Recognition',
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'License :: OSI Approved :: BSD License',
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'Programming Language :: Python :: 3.5',
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'Programming Language :: Python :: 3.6',
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'Programming Language :: Python :: 3.7',
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]

‎fmriprep/__init__.py

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as well as for open-source software distribution.
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"""
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from .info import (
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from .__about__ import ( # noqa
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__version__,
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__author__,
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__copyright__,

‎fmriprep/cli/run.py

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def get_parser():
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"""Build parser object"""
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from ..info import __version__
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from ..__about__ import __version__
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verstr = 'fmriprep v{}'.format(__version__)
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from nipype import logging as nlogging
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from multiprocessing import set_start_method, Process, Manager
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from ..viz.reports import generate_reports
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from ..info import __version__
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from ..__about__ import __version__
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set_start_method('forkserver')
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warnings.showwarning = _warn_redirect
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from pkg_resources import resource_filename as pkgrf
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from nipype import logging, config as ncfg
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from ..info import __version__
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from ..__about__ import __version__
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from ..workflows.base import init_fmriprep_wf
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from ..utils.bids import collect_participants
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from ..viz.reports import generate_reports

‎fmriprep/info.py

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‎fmriprep/workflows/base.py

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)
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from ..utils.bids import collect_data
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from ..utils.misc import fix_multi_T1w_source_name
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from ..info import __version__
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from ..__about__ import __version__
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from .anatomical import init_anat_preproc_wf
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from .bold import init_func_preproc_wf

‎get_version.py

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def main():
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sys.path.insert(0, op.abspath('.'))
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from fmriprep.info import __version__
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from fmriprep.__about__ import __version__
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print(__version__)
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‎setup.py

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from setuptools import setup, find_packages
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from setuptools.extension import Extension
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from numpy import get_include
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from fmriprep.info import (
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from fmriprep.__about__ import (
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__packagename__,
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__version__,
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__author__,

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