mapca - PyPI Package Compare versions

+232

.circleci/config.yml

		# Python CircleCI 2.1 configuration file
		#
		# Check https://circleci.com/docs/2.0/language-python/ for more details
		#
		version: 2.1
		orbs:
		codecov: codecov/codecov@1.0.5
		jobs:
		makeenv_38:
		docker:
		- image: continuumio/miniconda3
		working_directory: /tmp/src/mapca
		steps:
		- checkout
		- restore_cache:
		key: conda-py38-v1-{{ checksum "pyproject.toml" }}
		- run:
		name: Generate environment
		command: \|
		if [ ! -d /opt/conda/envs/mapca_py38 ]; then
		conda create -yq -n mapca_py38 python=3.8
		source activate mapca_py38
		pip install .[tests]
		fi
		- save_cache:
		key: conda-py38-v1-{{ checksum "pyproject.toml" }}
		paths:
		- /opt/conda/envs/mapca_py38

		unittest_39:
		docker:
		- image: continuumio/miniconda3
		working_directory: /tmp/src/mapca
		steps:
		- checkout
		- restore_cache:
		key: conda-py39-v1-{{ checksum "pyproject.toml" }}
		- run:
		name: Generate environment
		command: \|
		apt-get update
		apt-get install -yqq make
		if [ ! -d /opt/conda/envs/mapca_py39 ]; then
		conda create -yq -n mapca_py39 python=3.9
		source activate mapca_py39
		pip install .[tests]
		fi
		- run:
		name: Running unit tests
		command: \|
		source activate mapca_py39
		py.test --ignore mapca/tests/test_integration.py --cov-append --cov-report term-missing --cov=mapca mapca/
		mkdir /tmp/src/coverage
		mv /tmp/src/mapca/.coverage /tmp/src/coverage/.coverage.py39
		- save_cache:
		key: conda-py39-v1-{{ checksum "pyproject.toml" }}
		paths:
		- /opt/conda/envs/mapca_py39
		- persist_to_workspace:
		root: /tmp
		paths:
		- src/coverage/.coverage.py39

		unittest_310:
		docker:
		- image: continuumio/miniconda3
		working_directory: /tmp/src/mapca
		steps:
		- checkout
		- restore_cache:
		key: conda-py310-v1-{{ checksum "pyproject.toml" }}
		- run:
		name: Generate environment
		command: \|
		apt-get update
		apt-get install -yqq make
		if [ ! -d /opt/conda/envs/mapca_py310 ]; then
		conda create -yq -n mapca_py310 python=3.10
		source activate mapca_py310
		pip install .[tests]
		fi
		- run:
		name: Running unit tests
		command: \|
		source activate mapca_py310
		py.test --ignore mapca/tests/test_integration.py --cov-append --cov-report term-missing --cov=mapca mapca/
		mkdir /tmp/src/coverage
		mv /tmp/src/mapca/.coverage /tmp/src/coverage/.coverage.py310
		- save_cache:
		key: conda-py310-v1-{{ checksum "pyproject.toml" }}
		paths:
		- /opt/conda/envs/mapca_py310
		- persist_to_workspace:
		root: /tmp
		paths:
		- src/coverage/.coverage.py310

		unittest_38:
		docker:
		- image: continuumio/miniconda3
		working_directory: /tmp/src/mapca
		steps:
		- checkout
		- restore_cache:
		key: conda-py38-v1-{{ checksum "pyproject.toml" }}
		- run:
		name: Running unit tests
		command: \|
		apt-get update
		apt-get install -y make
		source activate mapca_py38 # depends on makeenv_38
		py.test --ignore mapca/tests/test_integration.py --cov-append --cov-report term-missing --cov=mapca mapca/
		mkdir /tmp/src/coverage
		mv /tmp/src/mapca/.coverage /tmp/src/coverage/.coverage.py38
		- persist_to_workspace:
		root: /tmp
		paths:
		- src/coverage/.coverage.py38

		integrationtest_38:
		docker:
		- image: continuumio/miniconda3
		working_directory: /tmp/src/mapca
		steps:
		- checkout
		- restore_cache:
		key: conda-py38-v1-{{ checksum "pyproject.toml" }}
		- run:
		name: Running unit tests
		command: \|
		apt-get update
		apt-get install -y make
		source activate mapca_py38 # depends on makeenv_38
		py.test --log-cli-level=INFO --cov-append --cov-report term-missing --cov=mapca -k test_integration mapca/tests/test_integration.py
		mkdir /tmp/src/coverage
		mv /tmp/src/mapca/.coverage /tmp/src/coverage/.coverage.py38
		- persist_to_workspace:
		root: /tmp
		paths:
		- src/coverage/.coverage.py38

		style_check:
		docker:
		- image: continuumio/miniconda3
		working_directory: /tmp/src/mapca
		steps:
		- checkout
		- restore_cache:
		key: conda-py38-v1-{{ checksum "pyproject.toml" }}
		- run:
		name: Style check
		command: \|
		apt-get update
		apt-get install -yqq make
		source activate mapca_py38 # depends on makeenv38
		flake8 mapca

		merge_coverage:
		working_directory: /tmp/src/mapca
		docker:
		- image: continuumio/miniconda3
		steps:
		- attach_workspace:
		at: /tmp
		- checkout
		- restore_cache:
		key: conda-py38-v1-{{ checksum "pyproject.toml" }}
		- run:
		name: Merge coverage files
		command: \|
		apt-get update
		apt-get install -yqq curl
		source activate mapca_py38 # depends on makeenv38
		cd /tmp/src/coverage/
		coverage combine
		coverage xml
		- store_artifacts:
		path: /tmp/src/coverage
		- codecov/upload:
		file: /tmp/src/coverage/coverage.xml

		deploy:
		docker:
		- image: continuumio/miniconda3
		steps:
		- checkout
		- run:
		name: init .pypirc
		command: \|
		echo -e "[pypi]" >> ~/.pypirc
		echo -e "username = $PYPI_USER" >> ~/.pypirc
		echo -e "password = $PYPI_PASSWORD" >> ~/.pypirc
		- run:
		name: create package
		command: \|
		python setup.py sdist
		python setup.py bdist_wheel
		- run:
		name: upload to pypi
		command: \|
		pip install twine
		twine upload dist/*

		workflows:
		version: 2.1
		build_test:
		jobs:
		- makeenv_38
		- unittest_39
		- unittest_310
		- unittest_38:
		requires:
		- makeenv_38
		- integrationtest_38:
		requires:
		- makeenv_38
		- style_check:
		requires:
		- makeenv_38
		- merge_coverage:
		requires:
		- unittest_38
		- unittest_39
		- unittest_310
		- deploy:
		requires:
		- merge_coverage
		filters:
		tags:
		only: /[0-9]+\.[0-9]+\.[0-9]+(?:rc)?(?:[0-9]+)?
		branches:
		ignore: /.*/

+20

.codecov.yml

		codecov:
		notify:
		require_ci_to_pass: yes

		coverage:
		status:
		project:
		default:
		# basic
		target: 0
		threshold: 0
		patch:
		default:
		target: 0
		threshold: 0

		ignore:
		- "mapca/tests/"
		- "mapca/_version.py"
		- "mapca/info.py"

+1

.gitattributes

mapca/_version.py export-subst

+7

.github/release-drafter.yml

		template: \|
		## Release Notes


		## Changes

		$CHANGES

+30

.github/workflows/python-publish.yml

		# This workflow will upload a Python Package using Twine when a release is created
		# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries

		name: Upload Python Package

		on:
		release:
		types: [created]

		jobs:
		deploy:
		runs-on: ubuntu-latest

		steps:
		- uses: actions/checkout@v2
		- name: Set up Python
		uses: actions/setup-python@v2
		with:
		python-version: "3.8"
		- name: Install dependencies
		run: \|
		python -m pip install --upgrade pip
		pip install setuptools wheel twine build
		- name: Build and publish
		env:
		TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
		TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
		run: \|
		python -m build --sdist --wheel --outdir dist/ .
		twine upload dist/*

+132

.gitignore

		# Byte-compiled / optimized / DLL files
		__pycache__/
		*.py[cod]
		*$py.class

		# C extensions
		*.so

		# Distribution / packaging
		.Python
		build/
		develop-eggs/
		dist/
		downloads/
		eggs/
		.eggs/
		lib/
		lib64/
		parts/
		sdist/
		var/
		wheels/
		pip-wheel-metadata/
		share/python-wheels/
		*.egg-info/
		.installed.cfg
		*.egg
		MANIFEST

		# PyInstaller
		# Usually these files are written by a python script from a template
		# before PyInstaller builds the exe, so as to inject date/other infos into it.
		*.manifest
		*.spec

		# Installer logs
		pip-log.txt
		pip-delete-this-directory.txt

		# Unit test / coverage reports
		htmlcov/
		.tox/
		.nox/
		.coverage
		.coverage.*
		.cache
		nosetests.xml
		coverage.xml
		*.cover
		*.py,cover
		.hypothesis/
		.pytest_cache/

		# Translations
		*.mo
		*.pot

		# Django stuff:
		*.log
		local_settings.py
		db.sqlite3
		db.sqlite3-journal

		# Flask stuff:
		instance/
		.webassets-cache

		# Scrapy stuff:
		.scrapy

		# Sphinx documentation
		docs/_build/

		# PyBuilder
		target/

		# Jupyter Notebook
		.ipynb_checkpoints

		# IPython
		profile_default/
		ipython_config.py

		# VSCode
		.vscode/

		# pyenv
		.python-version

		# pipenv
		# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
		# However, in case of collaboration, if having platform-specific dependencies or dependencies
		# having no cross-platform support, pipenv may install dependencies that don't work, or not
		# install all needed dependencies.
		#Pipfile.lock

		# PEP 582; used by e.g. github.com/David-OConnor/pyflow
		__pypackages__/

		# Celery stuff
		celerybeat-schedule
		celerybeat.pid

		# SageMath parsed files
		*.sage.py

		# Environments
		.env
		.venv
		env/
		venv/
		ENV/
		env.bak/
		venv.bak/

		# Spyder project settings
		.spyderproject
		.spyproject

		# Rope project settings
		.ropeproject

		# mkdocs documentation
		/site

		# mypy
		.mypy_cache/
		.dmypy.json
		dmypy.json

		# Pyre type checker
		.pyre/

+18

.pre-commit-config.yaml

		repos:
		- repo: https://github.com/pre-commit/pre-commit-hooks
		rev: v4.4.0
		hooks:
		- id: trailing-whitespace
		- id: end-of-file-fixer
		- id: check-yaml
		- id: check-added-large-files
		- repo: https://github.com/psf/black
		rev: 23.9.1
		hooks:
		- id: black
		files: ^mapca/
		- repo: https://github.com/pycqa/isort
		rev: 5.12.0
		hooks:
		- id: isort
		files: ^mapca/

+19

.readthedocs.yml

		# .readthedocs.yml
		# Read the Docs configuration file
		# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details

		# Required
		version: 2

		# Build documentation in the docs/ directory with Sphinx
		sphinx:
		configuration: docs/conf.py

		python:
		version: 3.7
		install:
		- method: pip
		path: .
		extra_requirements:
		- doc
		system_packages: true

+28

.zenodo.json

		{
		"creators": [
		{
		"name": "The ME-ICA Community"
		},
		{
		"name": "Ahmed, Zaki",
		"affiliation": "Mayo Clinic",
		"orcid": "0000-0001-5648-0590"
		},
		{
		"name": "Salo, Taylor",
		"affiliation": "Florida International University",
		"orcid": "0000-0001-9813-3167"
		},
		{
		"name": "Uruñuela, Eneko",
		"affiliation": "Basque Center on Cognition, Brain and Language",
		"orcid": "0000-0001-6849-9088"
		}
		],
		"keywords": [
		"neuroimaging",
		"fMRI"
		],
		"license": "GPL-2.0",
		"upload_type": "software"
		}

+69

CODE_OF_CONDUCT.md

		# Code of Conduct

		In the interest of fostering an open and welcoming environment we want
		participation in our project and our community to be a harassment-free
		experience for everyone.

		Although no list can hope to be all-encompassing, we explicitly honor diversity in age,
		body size, disability, ethnicity, gender identity and expression, level of experience, native language, education, socio-economic status, nationality, personal appearance, race, religion,
		or sexual identity and orientation.

		## Our Standards

		We aim to promote behavior that contributes to a positive and welcoming environment.
		Examples of such behavior include:

		* Using inclusive language
		* Being respectful of differing viewpoints and experiences
		* Showing empathy towards other community members

		We do not tolerate harassment or other, inappropriate behavior in our community.
		Examples of unacceptable behavior by participants include:

		* The use of sexualized language or imagery and unwelcome sexual attention or
		advances
		* Personal or political attacks on contributors, and insulting or derogatory
		comments on contributed code with the intent to undermine contributions
		* Public or private harassment

		## Our Responsibilities

		The maintainers are responsible for clarifying the standards
		of acceptable behavior and are expected to take appropriate and fair corrective
		action in response to any instances of unacceptable behavior.

		The maintainers have the right and responsibility to remove,
		edit, or reject comments, commits, code, issues, and other contributions
		that are not aligned to this Code of Conduct, or to ban temporarily or
		permanently any contributor for other behaviors that they deem inappropriate,
		threatening, offensive, or harmful.

		## Scope

		This Code of Conduct applies both within our online GitHub repository
		and in public spaces when an individual is representing the project or its community.
		Examples of representing a project or community include using an official project e-mail
		address, posting via an official social media account, or acting as an appointed
		representative at an online or offline event.

		## Enforcement

		Instances of abusive, harassing, or otherwise unacceptable behavior may be
		reported by contacting Eneko Urunuela at <e.urunuela@bcbl.eu>.
		Confidentiality will be respected in reporting.

		The enforcement officer can take any action they deem appropriate
		for the safety of the `mapca` community, including but not limited to:

		* facilitating a conversation between the two parties involved in the violation of the code of conduct
		* requesting a contributor apologize for their behaviour
		* asking a contributor or multiple contributors to enter a cooling off period that puts a
		time-limited pause on a particular discussion topic
		* asking a contributor to no longer participate in the development of `mapca`

		## Attribution

		This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
		available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html

		[homepage]: https://www.contributor-covenant.org

+339

LICENSE

		GNU GENERAL PUBLIC LICENSE
		Version 2, June 1991

		Copyright (C) 1989, 1991 Free Software Foundation, Inc.,
		51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
		Everyone is permitted to copy and distribute verbatim copies
		of this license document, but changing it is not allowed.

		Preamble

		The licenses for most software are designed to take away your
		freedom to share and change it. By contrast, the GNU General Public
		License is intended to guarantee your freedom to share and change free
		software--to make sure the software is free for all its users. This
		General Public License applies to most of the Free Software
		Foundation's software and to any other program whose authors commit to
		using it. (Some other Free Software Foundation software is covered by
		the GNU Lesser General Public License instead.) You can apply it to
		your programs, too.

		When we speak of free software, we are referring to freedom, not
		price. Our General Public Licenses are designed to make sure that you
		have the freedom to distribute copies of free software (and charge for
		this service if you wish), that you receive source code or can get it
		if you want it, that you can change the software or use pieces of it
		in new free programs; and that you know you can do these things.

		To protect your rights, we need to make restrictions that forbid
		anyone to deny you these rights or to ask you to surrender the rights.
		These restrictions translate to certain responsibilities for you if you
		distribute copies of the software, or if you modify it.

		For example, if you distribute copies of such a program, whether
		gratis or for a fee, you must give the recipients all the rights that
		you have. You must make sure that they, too, receive or can get the
		source code. And you must show them these terms so they know their
		rights.

		We protect your rights with two steps: (1) copyright the software, and
		(2) offer you this license which gives you legal permission to copy,
		distribute and/or modify the software.

		Also, for each author's protection and ours, we want to make certain
		that everyone understands that there is no warranty for this free
		software. If the software is modified by someone else and passed on, we
		want its recipients to know that what they have is not the original, so
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		Finally, any free program is threatened constantly by software
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		The precise terms and conditions for copying, distribution and
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		GNU GENERAL PUBLIC LICENSE
		TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION

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		You may charge a fee for the physical act of transferring a copy, and
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		Public License instead of this License.

+18

mapca/__about__.py

		# emacs: -- mode: python; py-indent-offset: 4; indent-tabs-mode: nil --
		# vi: set ft=python sts=4 ts=4 sw=4 et:
		"""Base module variables."""

		try:
		from mapca._version import __version__
		except ImportError:
		__version__ = "0+unknown"

		__packagename__ = "mapca"
		__copyright__ = "Copyright 2023, The ME-ICA Developers"
		__credits__ = (
		"Contributors: please check the ``.zenodo.json`` file at the top-level folder"
		"of the repository"
		)
		__url__ = "https://github.com/ME-ICA/mapca"

		DOWNLOAD_URL = f"https://github.com/ME-ICA/{__packagename__}/archive/{__version__}.tar.gz"

+3

-7

mapca/__init__.py

		@@ -6,9 +6,7 @@ """

		from ._version import get_versions
		from .due import Doi, due
		from .mapca import MovingAveragePCA, ma_pca
		import warnings

		__version__ = get_versions()["version"]
		from mapca.__about__ import __version__

		import warnings
		from .mapca import MovingAveragePCA, ma_pca

		@@ -23,3 +21,1 @@ # cmp is not used, so ignore nipype-generated warnings
		]

		del get_versions

+14

-19

mapca/_version.py

		@@ -0,21 +1,16 @@
		# file generated by setuptools_scm
		# don't change, don't track in version control
		TYPE_CHECKING = False
		if TYPE_CHECKING:
		from typing import Tuple, Union
		VERSION_TUPLE = Tuple[Union[int, str], ...]
		else:
		VERSION_TUPLE = object

		# This file was generated by 'versioneer.py' (0.18) from
		# revision-control system data, or from the parent directory name of an
		# unpacked source archive. Distribution tarballs contain a pre-generated copy
		# of this file.
		version: str
		__version__: str
		__version_tuple__: VERSION_TUPLE
		version_tuple: VERSION_TUPLE

		import json

		version_json = '''
		{
		"date": "2022-03-16T19:11:46+0100",
		"dirty": false,
		"error": null,
		"full-revisionid": "90ca567230dce39681ed5b826b438dd8391dbd8e",
		"version": "0.0.3"
		}
		''' # END VERSION_JSON


		def get_versions():
		return json.loads(version_json)
		__version__ = version = '0.0.4'
		__version_tuple__ = version_tuple = (0, 0, 4)

+85

-8

mapca/mapca.py

		@@ -131,3 +131,3 @@ """PCA based on Moving Average (stationary Gaussian) process.

		def _fit(self, img, mask):
		def _fit(self, img, mask, subsample_depth=None):
		LGR.info(
		@@ -213,4 +213,45 @@ "Performing dimensionality reduction based on GIFT "
		sub_iid_sp_median = int(np.round(np.median(sub_iid_sp)))

		# Will log the mean value to check if the differences in median within a dataset
		# represent very small changes in the mean. It seems like this is the closest
		# to a non-discrete value to store to compare across runs.
		sub_iid_sp_mean = np.round(np.mean(sub_iid_sp), 3)

		if np.floor(np.power(n_samples / n_timepoints, 1 / dim_n)) < sub_iid_sp_median:
		LGR.info(
		"Subsampling IID depth estimate too high. Subsampling depth will "
		"be defined by number of datapoints rather than IID estimates."
		)
		sub_iid_sp_median = int(np.floor(np.power(n_samples / n_timepoints, 1 / dim_n)))

		LGR.info("Estimated subsampling depth for effective i.i.d samples: %d" % sub_iid_sp_median)

		# Always save the calculated IID subsample value, but, if there is a user provide value,
		# assign that to sub_iid_sp_median and use that instead
		calculated_sub_iid_sp_median = sub_iid_sp_median
		if subsample_depth:
		if (
		(
		isinstance(subsample_depth, int)
		or (
		isinstance(subsample_depth, float)
		and subsample_depth == int(subsample_depth)
		)
		)
		and (1 <= subsample_depth)
		and ((n_samples / (subsample_depth**3)) >= 100)
		):
		sub_iid_sp_median = subsample_depth

		else:
		# The logic of the upper bound is subsample_depth^3 is the fraction of samples
		# that removed and it would be good to have at least 100 sampling remaining to
		# have a useful analysis. Given a masked volume is going to result in fewer
		# samples remaining in 3D space, this is likely a very liberal upper bound, but
		# probably good to at least include an upper bound.
		raise ValueError(
		"subsample_depth must be an integer > 1 and will retain >100 "
		"samples after subsampling. It is %d" % subsample_depth
		)

		N = np.round(n_samples / np.power(sub_iid_sp_median, dim_n))
		@@ -241,3 +282,3 @@

		LGR.info("Effective number of i.i.d. samples %d" % N)
		LGR.info("Effective number of i.i.d. samples %d from %d total voxels" % (N, n_samples))

		@@ -350,2 +391,10 @@ # Make eigen spectrum adjustment
		}
		self.subsampling_ = {
		"calculated_IID_subsample_depth": calculated_sub_iid_sp_median,
		"calculated_IID_subsample_mean": sub_iid_sp_mean,
		"IID_subsample_input": sub_iid_sp,
		"used_IID_subsample_depth": sub_iid_sp_median,
		"effective_num_IID_samples": N,
		"total_num_samples": n_samples,
		}

		@@ -372,3 +421,3 @@ # Assign attributes from model

		def fit(self, img, mask):
		def fit(self, img, mask, subsample_depth=None):
		"""Fit the model with X.
		@@ -382,2 +431,10 @@
		Mask to apply on ``img``.
		subsample_depth : int, optional
		Dimensionality reduction is calculated on a subset of voxels defined by
		this depth. 2 would mean using every other voxel in 3D space and 3 would
		mean every 3rd voxel. Default=None (estimated depth to make remaining
		voxels independent and identically distributed (IID)
		The subsampling value so that the voxels are assumed to be
		independent and identically distributed (IID).
		Default=None (use estimated value)

		@@ -389,6 +446,6 @@ Returns
		"""
		self._fit(img, mask)
		self._fit(img, mask, subsample_depth=subsample_depth)
		return self

		def fit_transform(self, img, mask):
		def fit_transform(self, img, mask, subsample_depth=None):
		"""Fit the model with X and apply the dimensionality reduction on X.
		@@ -402,2 +459,7 @@
		Mask to apply on ``img``.
		subsample_depth : int, optional
		Dimensionality reduction is calculated on a subset of voxels defined by
		this depth. 2 would mean using every other voxel in 3D space and 3 would
		mean every 3rd voxel. Default=None (estimated depth to make remaining
		voxels independent and identically distributed (IID)

		@@ -413,4 +475,14 @@ Returns
		which ignores explained variance.

		subsample_depth is always calculated automatically, but it should be consistent
		across a dataset with the same acquisition parameters, since spatial dependence
		should be similar. In practice, it sometimes gives a different value and causes
		problems. That is, for a dataset with 100 runs, it is 2 in most runs, but when
		it is 3, substantially fewer components are estimated and when it is 1, there is
		almost no dimensionality reduction. This has been added as an optional user provided
		parameter. If mapca seems to be having periodic mis-estimates, then this parameter
		should make it possible to set the IID subsample depth to be consistent across a
		dataset.
		"""
		self._fit(img, mask)
		self._fit(img, mask, subsample_depth=subsample_depth)
		return self.transform(img)
		@@ -483,3 +555,3 @@

		def ma_pca(img, mask, criterion="mdl", normalize=False):
		def ma_pca(img, mask, criterion="mdl", normalize=False, subsample_depth=None):
		"""Perform moving average-based PCA on imaging data.
		@@ -506,2 +578,7 @@
		Whether to normalize (zero mean and unit standard deviation) or not. Default is False.
		subsample_depth : int, optional
		Dimensionality reduction is calculated on a subset of voxels defined by
		this depth. 2 would mean using every other voxel in 3D space and 3 would
		mean every 3rd voxel. Default=None (estimated depth to make remaining
		voxels independent and identically distributed (IID)

		@@ -520,3 +597,3 @@ Returns
		pca = MovingAveragePCA(criterion=criterion, normalize=normalize)
		_ = pca.fit_transform(img, mask)
		_ = pca.fit_transform(img, mask, subsample_depth=subsample_depth)
		u = pca.u_
		@@ -523,0 +600,0 @@ s = pca.explained_variance_

+9

-15

mapca/tests/conftest.py

		@@ -28,3 +28,3 @@ import os
		"""
		url = 'https://osf.io/{}/download'.format(osf_id)
		url = "https://osf.io/{}/download".format(osf_id)
		full_path = os.path.join(path, filename)
		@@ -36,5 +36,5 @@ if not os.path.isfile(full_path):

		@pytest.fixture(scope='session')
		@pytest.fixture(scope="session")
		def testpath(tmp_path_factory):
		""" Test path that will be used to download all files """
		"""Test path that will be used to download all files"""
		return tmp_path_factory.getbasetemp()
		@@ -45,4 +45,3 @@
		def test_img(testpath):
		return fetch_file('jw43h', testpath,
		'data.nii.gz')
		return fetch_file("jw43h", testpath, "data.nii.gz")

		@@ -52,4 +51,3 @@
		def test_mask(testpath):
		return fetch_file('9u2m5', testpath,
		'mask.nii.gz')
		return fetch_file("9u2m5", testpath, "mask.nii.gz")

		@@ -59,4 +57,3 @@
		def test_ts(testpath):
		return fetch_file('gz2hb', testpath,
		'compt_ts.npy')
		return fetch_file("gz2hb", testpath, "comp_ts.npy")

		@@ -66,4 +63,3 @@
		def test_varex(testpath):
		return fetch_file('7xj5k', testpath,
		'varex.npy')
		return fetch_file("7xj5k", testpath, "varex.npy")

		@@ -73,4 +69,3 @@
		def test_varex_norm(testpath):
		return fetch_file('jrd9c', testpath,
		'varex_norm.npy')
		return fetch_file("jrd9c", testpath, "varex_norm.npy")

		@@ -80,3 +75,2 @@
		def test_weights(testpath):
		return fetch_file('t94m8', testpath,
		'voxel_comp_weights.npy')
		return fetch_file("t94m8", testpath, "voxel_comp_weights.npy")

+5

-5

mapca/tests/test_integration.py

		"""
		Integration test for mapca.
		"""
		import nibabel as nib
		import numpy as np
		import shutil
		from os.path import split

		import nibabel as nib
		import numpy as np

		from mapca.mapca import ma_pca


		def test_integration(test_img, test_mask, test_ts, test_varex,
		test_varex_norm, test_weights):

		def test_integration(test_img, test_mask, test_ts, test_varex, test_varex_norm, test_weights):
		"""Integration test for mapca."""
		test_path, _ = split(test_img)
		@@ -16,0 +16,0 @@

+9

-3

mapca/tests/test_mapca.py

		@@ -9,3 +9,3 @@ """

		from mapca.mapca import ma_pca, MovingAveragePCA
		from mapca.mapca import MovingAveragePCA, ma_pca

		@@ -18,3 +18,3 @@
		n_voxels = 20
		n_vox_total = n_voxels ** 3
		n_vox_total = n_voxels**3

		@@ -79,3 +79,3 @@ # Creates fake data to test with
		test_mask[5:-5, 5:-5, 5:-5] = 1
		test_mask_img = nib.nifti1.Nifti1Image(test_mask, xform)
		test_mask_img = nib.nifti1.Nifti1Image(test_mask, xform, dtype=np.int16)
		n_voxels_in_mask = np.sum(test_mask)
		@@ -101,1 +101,7 @@
		assert test_data_est.shape == test_img.shape

		# Testing setting inputting a pre-defined subsampling depth
		pca3 = MovingAveragePCA(criterion="mdl", normalize=False)
		pca3.fit(test_img, test_mask_img, subsample_depth=2)
		assert pca3.subsampling_["calculated_IID_subsample_depth"] == 1
		assert pca3.subsampling_["used_IID_subsample_depth"] == 2

+3

-4

mapca/tests/test_utils.py

		"""Unit tests for utils."""
		import numpy as np
		from scipy.signal.windows import parzen
		from pytest import raises
		from scipy.signal import detrend
		from scipy.signal.windows import parzen
		from scipy.stats import kurtosis

		from mapca.utils import (_autocorr, _eigensp_adj, _icatb_svd,
		_subsampling, ent_rate_sp)
		from mapca.utils import _autocorr, _eigensp_adj, _icatb_svd, _subsampling, ent_rate_sp

		@@ -110,3 +109,3 @@
		for i in range(test_data.shape[1]):
		data_norm = detrend(test_data[:, i], type='constant')
		data_norm = detrend(test_data[:, i], type="constant")
		data_norm /= np.std(data_norm)
		@@ -113,0 +112,0 @@ kurt_gift[i] = np.mean(data_norm**4) - 3

+383

-30

PKG-INFO

		Metadata-Version: 2.1
		Name: mapca
		Version: 0.0.3
		Summary: A Python implementation of the moving average principal components analysis methods from GIFT.
		Home-page: https://github.com/me-ica/mapca
		Version: 0.0.4
		Summary: Moving Average Principal Component Analysis for fMRI data
		Project-URL: Homepage, https://github.com/ME-ICA/mapca
		Project-URL: Documentation, https://www.mapca.readthedocs.io
		Author: mapca developers
		Author-email: e.urunuela@bcbl.eu
		Maintainer: Eneko Urunuela
		Maintainer-email: e.urunuela@bcbl.eu
		License: GPL-2.0
		Download-URL: https://github.com/ME-ICA/mapca/archive/0.0.3.tar.gz
		Description: # mapca
		A Python implementation of the moving average principal components analysis methods from GIFT
		License: GNU GENERAL PUBLIC LICENSE
		Version 2, June 1991

		[![Latest Version](https://img.shields.io/pypi/v/mapca.svg)](https://pypi.python.org/pypi/mapca/)
		[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mapca.svg)](https://pypi.python.org/pypi/mapca/)
		[![License](https://img.shields.io/badge/license-GPL--2.0-blue.svg)](https://opensource.org/licenses/GPL-2.0)
		[![CircleCI](https://circleci.com/gh/ME-ICA/mapca.svg?style=shield)](https://circleci.com/gh/ME-ICA/mapca)
		[![Codecov](https://codecov.io/gh/ME-ICA/mapca/branch/main/graph/badge.svg?token=GEKDT6R0B7)](https://codecov.io/gh/ME-ICA/mapca)
		[![Average time to resolve an issue](http://isitmaintained.com/badge/resolution/ME-ICA/mapca.svg)](http://isitmaintained.com/project/ME-ICA/mapca "Average time to resolve an issue")
		[![Percentage of issues still open](http://isitmaintained.com/badge/open/ME-ICA/mapca.svg)](http://isitmaintained.com/project/ME-ICA/mapca "Percentage of issues still open")
		[![Join the chat at https://gitter.im/ME-ICA/mapca](https://badges.gitter.im/ME-ICA/mapca.svg)](https://gitter.im/ME-ICA/mapca?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
		Copyright (C) 1989, 1991 Free Software Foundation, Inc.,
		51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
		Everyone is permitted to copy and distribute verbatim copies
		of this license document, but changing it is not allowed.

		## About
		Preamble

		`mapca` is a Python package that performs dimensionality reduction with principal component analysis (PCA) on functional magnetic resonance imaging (fMRI) data. It is a translation to Python of the dimensionality reduction technique used in the MATLAB-based [GIFT package](https://trendscenter.org/software/gift/) and introduced by Li et al. 2007[^1].
		The licenses for most software are designed to take away your
		freedom to share and change it. By contrast, the GNU General Public
		License is intended to guarantee your freedom to share and change free
		software--to make sure the software is free for all its users. This
		General Public License applies to most of the Free Software
		Foundation's software and to any other program whose authors commit to
		using it. (Some other Free Software Foundation software is covered by
		the GNU Lesser General Public License instead.) You can apply it to
		your programs, too.

		[^1]: Li, Y. O., Adali, T., & Calhoun, V. D. (2007). Estimating the number of independent components for functional magnetic resonance imaging data. Human Brain Mapping, 28(11), 1251–1266. https://doi.org/10.1002/hbm.20359
		When we speak of free software, we are referring to freedom, not
		price. Our General Public Licenses are designed to make sure that you
		have the freedom to distribute copies of free software (and charge for
		this service if you wish), that you receive source code or can get it
		if you want it, that you can change the software or use pieces of it
		in new free programs; and that you know you can do these things.

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		License-File: LICENSE
		Classifier: Development Status :: 4 - Beta
		Classifier: Intended Audience :: Science/Research
		Classifier: Topic :: Scientific/Engineering :: Information Analysis
		Classifier: License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)
		Classifier: Programming Language :: Python :: 3.5
		Classifier: Programming Language :: Python :: 3.6
		Classifier: Programming Language :: Python :: 3.7
		Classifier: Programming Language :: Python :: 3.8
		Classifier: Programming Language :: Python :: 3.9
		Requires-Python: >=3.5
		Description-Content-Type: text/markdown
		Classifier: Programming Language :: Python :: 3.10
		Classifier: Topic :: Scientific/Engineering :: Information Analysis
		Requires-Python: >=3.8
		Requires-Dist: nibabel>=2.5.1
		Requires-Dist: nilearn
		Requires-Dist: numpy>=1.16
		Requires-Dist: scikit-learn>=1.2
		Requires-Dist: scipy>=1.3.3
		Provides-Extra: all
		Requires-Dist: mapca[dev,doc,tests]; extra == 'all'
		Provides-Extra: dev
		Requires-Dist: pre-commit; extra == 'dev'
		Provides-Extra: doc
		Requires-Dist: sphinx-argparse; extra == 'doc'
		Requires-Dist: sphinx-copybutton; extra == 'doc'
		Requires-Dist: sphinx-rtd-theme>=1.2.2; extra == 'doc'
		Requires-Dist: sphinx>=6.2.1; extra == 'doc'
		Requires-Dist: sphinxcontrib-bibtex; extra == 'doc'
		Provides-Extra: tests
		Provides-Extra: duecredit
		Provides-Extra: all
		Requires-Dist: codecov; extra == 'tests'
		Requires-Dist: coverage; extra == 'tests'
		Requires-Dist: flake8-black; extra == 'tests'
		Requires-Dist: flake8-isort; extra == 'tests'
		Requires-Dist: flake8-pyproject; extra == 'tests'
		Requires-Dist: flake8>=3.7; extra == 'tests'
		Requires-Dist: pytest; extra == 'tests'
		Requires-Dist: pytest-cov>=4.0.0; extra == 'tests'
		Requires-Dist: requests; extra == 'tests'
		Description-Content-Type: text/markdown

		# mapca
		A Python implementation of the moving average principal components analysis methods from GIFT

		[![Latest Version](https://img.shields.io/pypi/v/mapca.svg)](https://pypi.python.org/pypi/mapca/)
		[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mapca.svg)](https://pypi.python.org/pypi/mapca/)
		[![License](https://img.shields.io/badge/license-GPL--2.0-blue.svg)](https://opensource.org/licenses/GPL-2.0)
		[![CircleCI](https://circleci.com/gh/ME-ICA/mapca.svg?style=shield)](https://circleci.com/gh/ME-ICA/mapca)
		[![Codecov](https://codecov.io/gh/ME-ICA/mapca/branch/main/graph/badge.svg?token=GEKDT6R0B7)](https://codecov.io/gh/ME-ICA/mapca)
		[![Average time to resolve an issue](http://isitmaintained.com/badge/resolution/ME-ICA/mapca.svg)](http://isitmaintained.com/project/ME-ICA/mapca "Average time to resolve an issue")
		[![Percentage of issues still open](http://isitmaintained.com/badge/open/ME-ICA/mapca.svg)](http://isitmaintained.com/project/ME-ICA/mapca "Percentage of issues still open")
		[![Join the chat at https://gitter.im/ME-ICA/mapca](https://badges.gitter.im/ME-ICA/mapca.svg)](https://gitter.im/ME-ICA/mapca?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

		## About

		`mapca` is a Python package that performs dimensionality reduction with principal component analysis (PCA) on functional magnetic resonance imaging (fMRI) data. It is a translation to Python of the dimensionality reduction technique used in the MATLAB-based [GIFT package](https://trendscenter.org/software/gift/) and introduced by Li et al. 2007[^1].

		[^1]: Li, Y. O., Adali, T., & Calhoun, V. D. (2007). Estimating the number of independent components for functional magnetic resonance imaging data. Human Brain Mapping, 28(11), 1251–1266. https://doi.org/10.1002/hbm.20359

+121

-6

pyproject.toml

		[build-system]
		requires = ["setuptools", "wheel"]
		requires = ["hatchling", "hatch-vcs"]
		build-backend = "hatchling.build"

		[project]
		name = "mapca"
		description = "Moving Average Principal Component Analysis for fMRI data"
		readme = "README.md"
		authors = [{name = "mapca developers"}]
		classifiers = [
		"Development Status :: 4 - Beta",
		"Intended Audience :: Science/Research",
		"Topic :: Scientific/Engineering :: Information Analysis",
		"License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)",
		"Programming Language :: Python :: 3.8",
		"Programming Language :: Python :: 3.9",
		"Programming Language :: Python :: 3.10",
		]
		license = {file = "LICENSE"}
		requires-python = ">=3.8"
		dependencies = [
		"nibabel>=2.5.1",
		"nilearn",
		"numpy>=1.16",
		"scikit-learn>=1.2",
		"scipy>=1.3.3",
		]
		dynamic = ["version"]

		[project.urls]
		Homepage = "https://github.com/ME-ICA/mapca"
		Documentation = "https://www.mapca.readthedocs.io"

		[project.optional-dependencies]
		dev = ["pre-commit"]
		doc = [
		"sphinx>=6.2.1",
		"sphinx_copybutton",
		"sphinx_rtd_theme>=1.2.2",
		"sphinx-argparse",
		"sphinxcontrib-bibtex",
		]
		tests = [
		"codecov",
		"coverage",
		"flake8>=3.7",
		"flake8-black",
		"flake8-isort",
		"flake8-pyproject",
		"pytest",
		"pytest-cov>=4.0.0",
		"requests",
		]

		# Aliases
		all = ["mapca[dev,doc,tests]"]

		#
		# Hatch configurations
		#

		[tool.hatch.metadata]
		allow-direct-references = true

		[tool.hatch.build.targets.sdist]
		exclude = [".git_archival.txt"] # No longer needed in sdist

		[tool.hatch.build.targets.wheel]
		packages = ["mapca"]
		exclude = [
		"mapca/tests/data", # Large test data directory
		]

		## The following two sections configure setuptools_scm in the hatch way

		[tool.hatch.version]
		source = "vcs"

		[tool.hatch.build.hooks.vcs]
		version-file = "mapca/_version.py"

		#
		# Developer tool configurations
		#

		[tool.black]
		@@ -9,2 +91,3 @@ line-length = 99
		exclude = '''

		(
		@@ -17,2 +100,3 @@ /(
		\| \.pytest_cache
		\| \.testing_data_cache
		\| _build
		@@ -22,8 +106,39 @@ \| build
		)/
		\| get_version.py
		\| versioneer.py
		\| mapca/info.py
		\| mapca/_version.py
		\| mapca/due.py
		)
		'''
		'''

		[tool.isort]
		profile = "black"
		multi_line_output = 3

		[tool.flake8]
		max-line-length = 99
		exclude = [
		"*build/",
		"mapca/_version.py",
		]
		ignore = ["E203", "E402", "W503"]
		per-file-ignores = [
		"*/__init__.py: F401",
		]
		docstring-convention = "numpy"

		[tool.coverage.run]
		branch = true
		omit = [
		"/tests/",
		"*/__init__.py",
		"*/conftest.py",
		"mapca/_version.py",
		]

		[tool.coverage.report]
		# Regexes for lines to exclude from consideration
		exclude_lines = [
		'raise NotImplementedError',
		'warnings\.warn',
		]

		[tool.pytest]
		log_cli = true

-1

mapca.egg-info/dependency_links.txt

mapca.egg-info/not-zip-safe

Sorry, the diff of this file is not supported yet

-46

mapca.egg-info/PKG-INFO

		Metadata-Version: 2.1
		Name: mapca
		Version: 0.0.3
		Summary: A Python implementation of the moving average principal components analysis methods from GIFT.
		Home-page: https://github.com/me-ica/mapca
		Author: mapca developers
		Author-email: e.urunuela@bcbl.eu
		Maintainer: Eneko Urunuela
		Maintainer-email: e.urunuela@bcbl.eu
		License: GPL-2.0
		Download-URL: https://github.com/ME-ICA/mapca/archive/0.0.3.tar.gz
		Description: # mapca
		A Python implementation of the moving average principal components analysis methods from GIFT

		[![Latest Version](https://img.shields.io/pypi/v/mapca.svg)](https://pypi.python.org/pypi/mapca/)
		[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mapca.svg)](https://pypi.python.org/pypi/mapca/)
		[![License](https://img.shields.io/badge/license-GPL--2.0-blue.svg)](https://opensource.org/licenses/GPL-2.0)
		[![CircleCI](https://circleci.com/gh/ME-ICA/mapca.svg?style=shield)](https://circleci.com/gh/ME-ICA/mapca)
		[![Codecov](https://codecov.io/gh/ME-ICA/mapca/branch/main/graph/badge.svg?token=GEKDT6R0B7)](https://codecov.io/gh/ME-ICA/mapca)
		[![Average time to resolve an issue](http://isitmaintained.com/badge/resolution/ME-ICA/mapca.svg)](http://isitmaintained.com/project/ME-ICA/mapca "Average time to resolve an issue")
		[![Percentage of issues still open](http://isitmaintained.com/badge/open/ME-ICA/mapca.svg)](http://isitmaintained.com/project/ME-ICA/mapca "Percentage of issues still open")
		[![Join the chat at https://gitter.im/ME-ICA/mapca](https://badges.gitter.im/ME-ICA/mapca.svg)](https://gitter.im/ME-ICA/mapca?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

		## About

		`mapca` is a Python package that performs dimensionality reduction with principal component analysis (PCA) on functional magnetic resonance imaging (fMRI) data. It is a translation to Python of the dimensionality reduction technique used in the MATLAB-based [GIFT package](https://trendscenter.org/software/gift/) and introduced by Li et al. 2007[^1].

		[^1]: Li, Y. O., Adali, T., & Calhoun, V. D. (2007). Estimating the number of independent components for functional magnetic resonance imaging data. Human Brain Mapping, 28(11), 1251–1266. https://doi.org/10.1002/hbm.20359

		Platform: UNKNOWN
		Classifier: Development Status :: 4 - Beta
		Classifier: Intended Audience :: Science/Research
		Classifier: Topic :: Scientific/Engineering :: Information Analysis
		Classifier: License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)
		Classifier: Programming Language :: Python :: 3.5
		Classifier: Programming Language :: Python :: 3.6
		Classifier: Programming Language :: Python :: 3.7
		Classifier: Programming Language :: Python :: 3.8
		Classifier: Programming Language :: Python :: 3.9
		Requires-Python: >=3.5
		Description-Content-Type: text/markdown
		Provides-Extra: dev
		Provides-Extra: doc
		Provides-Extra: tests
		Provides-Extra: duecredit
		Provides-Extra: all

-37

mapca.egg-info/requires.txt

		nibabel>=2.5.1
		nilearn
		numpy>=1.15
		scikit-learn>=0.22
		scipy>=1.3.3

		[all]
		requests
		duecredit
		sphinx-argparse
		sphinx_rtd_theme
		pytest-cov
		versioneer
		pytest
		flake8>=3.7
		codecov
		sphinx>=1.5.3
		coverage<5.0

		[dev]
		versioneer

		[doc]
		sphinx>=1.5.3
		sphinx_rtd_theme
		sphinx-argparse

		[duecredit]
		duecredit

		[tests]
		codecov
		coverage<5.0
		flake8>=3.7
		pytest
		pytest-cov
		requests

-23

mapca.egg-info/SOURCES.txt

		MANIFEST.in
		README.md
		pyproject.toml
		setup.cfg
		setup.py
		versioneer.py
		mapca/__init__.py
		mapca/_version.py
		mapca/due.py
		mapca/info.py
		mapca/mapca.py
		mapca/utils.py
		mapca.egg-info/PKG-INFO
		mapca.egg-info/SOURCES.txt
		mapca.egg-info/dependency_links.txt
		mapca.egg-info/not-zip-safe
		mapca.egg-info/requires.txt
		mapca.egg-info/top_level.txt
		mapca/tests/__init__.py
		mapca/tests/conftest.py
		mapca/tests/test_integration.py
		mapca/tests/test_mapca.py
		mapca/tests/test_utils.py

-1

mapca.egg-info/top_level.txt

mapca

-65

mapca/due.py

		"""
		Stub file for a guaranteed safe import of duecredit constructs: if duecredit
		is not available.
		To use it, place it into your project codebase to be imported, e.g. copy as
		cp stub.py /path/tomodule/module/due.py
		Note that it might be better to avoid naming it duecredit.py to avoid shadowing
		installed duecredit.
		Then use in your code as
		from .due import due, Doi, BibTeX
		See https://github.com/duecredit/duecredit/blob/master/README.md for examples.
		Origin: Originally a part of the duecredit
		Copyright: 2015-2016 DueCredit developers
		License: BSD-2
		"""

		__version__ = "0.0.5"


		class InactiveDueCreditCollector(object):
		"""Just a stub at the Collector which would not do anything"""

		def _donothing(self, args, *kwargs):
		"""Perform no good and no bad"""
		pass

		def dcite(self, args, *kwargs):
		"""If I could cite I would"""

		def nondecorating_decorator(func):
		return func

		return nondecorating_decorator

		cite = load = add = _donothing

		def __repr__(self):
		return self.__class__.__name__ + "()"


		def _donothing_func(args, *kwargs):
		"""Perform no good and no bad"""
		pass


		try:
		from duecredit import due, BibTeX, Doi, Url

		if "due" in locals() and not hasattr(due, "cite"):
		raise RuntimeError("Imported due lacks .cite. DueCredit is now disabled")
		except Exception as e:
		if type(e).__name__ != "ImportError":
		import logging

		logging.getLogger("duecredit").error("Failed to import duecredit due to %s" % str(e))
		# Initiate due stub
		due = InactiveDueCreditCollector()
		BibTeX = Doi = Url = _donothing_func

		# Emacs mode definitions
		# Local Variables:
		# mode: python
		# py-indent-offset: 4
		# tab-width: 4
		# indent-tabs-mode: nil
		# End:

-108

mapca/info.py

		# -- coding: utf-8 --
		# emacs: -- mode: python; py-indent-offset: 4; indent-tabs-mode: nil --
		# vi: set ft=python sts=4 ts=4 sw=4 et:
		"""
		Base module variables
		"""
		import importlib.util
		import json
		import os.path as op

		from pathlib import Path

		# Get version
		spec = importlib.util.spec_from_file_location(
		"_version", op.join(op.dirname(__file__), "mapca/_version.py")
		)
		_version = importlib.util.module_from_spec(spec)
		spec.loader.exec_module(_version)

		VERSION = _version.get_versions()["version"]
		del _version

		# Get list of authors from Zenodo file
		with open(op.join(op.dirname(__file__), ".zenodo.json"), "r") as fo:
		zenodo_info = json.load(fo)
		authors = [author["name"] for author in zenodo_info["creators"]]
		author_names = []
		for author in authors:
		if ", " in author:
		author_names.append(author.split(", ")[1] + " " + author.split(", ")[0])
		else:
		author_names.append(author)

		# Get package description from README
		# Since this file is executed from ../setup.py, the path to the README is determined by the
		# location of setup.py.
		readme_path = Path(__file__).parent.joinpath("README.md")
		longdesc = readme_path.open().read()

		# Fields
		AUTHOR = "mapca developers"
		COPYRIGHT = "Copyright 2020, mapca developers"
		CREDITS = author_names
		LICENSE = "GPL-2.0"
		MAINTAINER = "Eneko Urunuela"
		EMAIL = "e.urunuela@bcbl.eu"
		STATUS = "Prototype"
		URL = "https://github.com/me-ica/mapca"
		PACKAGENAME = "mapca"
		DESCRIPTION = (
		"A Python implementation of the moving average principal components analysis "
		"methods from GIFT."
		)
		LONGDESC = longdesc

		DOWNLOAD_URL = "https://github.com/ME-ICA/{name}/archive/{ver}.tar.gz".format(
		name=PACKAGENAME, ver=VERSION
		)

		REQUIRES = [
		"nibabel>=2.5.1",
		"nilearn",
		"numpy>=1.15",
		"scikit-learn>=0.22",
		"scipy>=1.3.3",
		]

		TESTS_REQUIRES = [
		"codecov",
		"coverage<5.0",
		"flake8>=3.7",
		"pytest",
		"pytest-cov",
		"requests",
		]

		EXTRA_REQUIRES = {
		"dev": ["versioneer"],
		"doc": [
		"sphinx>=1.5.3",
		"sphinx_rtd_theme",
		"sphinx-argparse",
		],
		"tests": TESTS_REQUIRES,
		"duecredit": ["duecredit"],
		}

		ENTRY_POINTS = {}

		# Enable a handle to install all extra dependencies at once
		EXTRA_REQUIRES["all"] = list(set([v for deps in EXTRA_REQUIRES.values() for v in deps]))

		# Supported Python versions using PEP 440 version specifiers
		# Should match the same set of Python versions as classifiers
		PYTHON_REQUIRES = ">=3.5"

		# Package classifiers
		CLASSIFIERS = [
		"Development Status :: 4 - Beta",
		"Intended Audience :: Science/Research",
		"Topic :: Scientific/Engineering :: Information Analysis",
		"License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)",
		"Programming Language :: Python :: 3.5",
		"Programming Language :: Python :: 3.6",
		"Programming Language :: Python :: 3.7",
		"Programming Language :: Python :: 3.8",
		"Programming Language :: Python :: 3.9",
		]

-22

setup.cfg

		[versioneer]
		vcs = git
		style = pep440
		versionfile_source = mapca/_version.py
		versionfile_build = mapca/_version.py
		tag_prefix =
		parentdir_prefix =

		[flake8]
		max-line-length = 99
		exclude = *build/
		ignore = E126,E203,E402,E722,W503,W504
		per-file-ignores =
		*/__init__.py:F401

		[tool:pytest]
		log_cli = true

		[egg_info]
		tag_build =
		tag_date = 0

-55

setup.py

		#!/usr/bin/env python
		# -- coding: utf-8 --
		""" mapca setup script """


		def main():
		""" Install entry-point """
		import os.path as op
		import versioneer
		from inspect import getfile, currentframe
		from io import open
		from setuptools import setup, find_packages

		ver_file = op.join("mapca", "info.py")
		with open(ver_file) as f:
		exec(f.read())
		vars = locals()

		root_dir = op.dirname(op.abspath(getfile(currentframe())))
		cmdclass = versioneer.get_cmdclass()

		pkg_data = {
		"mapca": [
		"tests/data/*",
		]
		}

		setup(
		name=vars["PACKAGENAME"],
		version=vars["VERSION"],
		description=vars["DESCRIPTION"],
		long_description=vars["LONGDESC"],
		long_description_content_type="text/markdown",
		author=vars["AUTHOR"],
		author_email=vars["EMAIL"],
		maintainer=vars["MAINTAINER"],
		maintainer_email=vars["EMAIL"],
		url=vars["URL"],
		license=vars["LICENSE"],
		classifiers=vars["CLASSIFIERS"],
		download_url=vars["DOWNLOAD_URL"],
		# Dependencies handling
		python_requires=vars["PYTHON_REQUIRES"],
		install_requires=vars["REQUIRES"],
		tests_require=vars["TESTS_REQUIRES"],
		extras_require=vars["EXTRA_REQUIRES"],
		entry_points=vars["ENTRY_POINTS"],
		packages=find_packages(exclude=("tests",)),
		zip_safe=False,
		cmdclass=cmdclass,
		)


		if __name__ == "__main__":
		main()

versioneer.py

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