[ { "path": ".circleci/circle_T1w.txt", "content": ".bids_db\n.bids_db/layout_index.sqlite\n.bidsignore\ndataset_description.json\ngroup_T1w.html\ngroup_T1w.tsv\nlogs\nlogs/config-18480913-163000_PhineasG-ageh-adhi-sacc-ident9b1ab0f.toml\nlogs/mriqc-18480913-163000_PhineasG-ageh-adhi-sacc-ident9b1ab0f.log\nsub-50137\nsub-50137/anat\nsub-50137/anat/sub-50137_T1w.json\nsub-50137/figures\nsub-50137/figures/sub-50137_desc-airmask_T1w.svg\nsub-50137/figures/sub-50137_desc-artifacts_T1w.svg\nsub-50137/figures/sub-50137_desc-background_T1w.svg\nsub-50137/figures/sub-50137_desc-brainmask_T1w.svg\nsub-50137/figures/sub-50137_desc-head_T1w.svg\nsub-50137/figures/sub-50137_desc-noisefit_T1w.svg\nsub-50137/figures/sub-50137_desc-norm_T1w.svg\nsub-50137/figures/sub-50137_desc-segmentation_T1w.svg\nsub-50137/figures/sub-50137_desc-zoomed_T1w.svg\nsub-50137_T1w.html\nsub-50152\nsub-50152/anat\nsub-50152/anat/sub-50152_T1w.json\nsub-50152/figures\nsub-50152/figures/sub-50152_desc-airmask_T1w.svg\nsub-50152/figures/sub-50152_desc-artifacts_T1w.svg\nsub-50152/figures/sub-50152_desc-background_T1w.svg\nsub-50152/figures/sub-50152_desc-brainmask_T1w.svg\nsub-50152/figures/sub-50152_desc-head_T1w.svg\nsub-50152/figures/sub-50152_desc-noisefit_T1w.svg\nsub-50152/figures/sub-50152_desc-norm_T1w.svg\nsub-50152/figures/sub-50152_desc-segmentation_T1w.svg\nsub-50152/figures/sub-50152_desc-zoomed_T1w.svg\nsub-50152_T1w.html\nsub-50785\nsub-50785/anat\nsub-50785/anat/sub-50785_T1w.json\nsub-50785/figures\nsub-50785/figures/sub-50785_desc-airmask_T1w.svg\nsub-50785/figures/sub-50785_desc-artifacts_T1w.svg\nsub-50785/figures/sub-50785_desc-background_T1w.svg\nsub-50785/figures/sub-50785_desc-brainmask_T1w.svg\nsub-50785/figures/sub-50785_desc-head_T1w.svg\nsub-50785/figures/sub-50785_desc-noisefit_T1w.svg\nsub-50785/figures/sub-50785_desc-norm_T1w.svg\nsub-50785/figures/sub-50785_desc-segmentation_T1w.svg\nsub-50785/figures/sub-50785_desc-zoomed_T1w.svg\nsub-50785_T1w.html\nsub-51187\nsub-51187/anat\nsub-51187/anat/sub-51187_T1w.json\nsub-51187/figures\nsub-51187/figures/sub-51187_desc-airmask_T1w.svg\nsub-51187/figures/sub-51187_desc-artifacts_T1w.svg\nsub-51187/figures/sub-51187_desc-background_T1w.svg\nsub-51187/figures/sub-51187_desc-brainmask_T1w.svg\nsub-51187/figures/sub-51187_desc-head_T1w.svg\nsub-51187/figures/sub-51187_desc-noisefit_T1w.svg\nsub-51187/figures/sub-51187_desc-norm_T1w.svg\nsub-51187/figures/sub-51187_desc-segmentation_T1w.svg\nsub-51187/figures/sub-51187_desc-zoomed_T1w.svg\nsub-51187_T1w.html\n/tmp/t1w/derivatives\n" }, { "path": ".circleci/circle_bold.txt", "content": ".bids_db\n.bids_db/layout_index.sqlite\n.bidsignore\ndataset_description.json\ngroup_bold.html\ngroup_bold.tsv\nlogs\nlogs/config-18480913-163000_PhineasG-ageh-adhi-sacc-ident9b1ab0f.toml\nlogs/mriqc-18480913-163000_PhineasG-ageh-adhi-sacc-ident9b1ab0f.log\nsub-ds205s03\nsub-ds205s03/figures\nsub-ds205s03/figures/sub-ds205s03_task-functionallocalizer_run-01_desc-background_bold.svg\nsub-ds205s03/figures/sub-ds205s03_task-functionallocalizer_run-01_desc-brainmask_bold.svg\nsub-ds205s03/figures/sub-ds205s03_task-functionallocalizer_run-01_desc-carpet_bold.svg\nsub-ds205s03/figures/sub-ds205s03_task-functionallocalizer_run-01_desc-mean_bold.svg\nsub-ds205s03/figures/sub-ds205s03_task-functionallocalizer_run-01_desc-norm_bold.svg\nsub-ds205s03/figures/sub-ds205s03_task-functionallocalizer_run-01_desc-stdev_bold.svg\nsub-ds205s03/figures/sub-ds205s03_task-functionallocalizer_run-01_desc-zoomed_bold.svg\nsub-ds205s03/figures/sub-ds205s03_task-view_run-01_desc-background_bold.svg\nsub-ds205s03/figures/sub-ds205s03_task-view_run-01_desc-brainmask_bold.svg\nsub-ds205s03/figures/sub-ds205s03_task-view_run-01_desc-carpet_bold.svg\nsub-ds205s03/figures/sub-ds205s03_task-view_run-01_desc-mean_bold.svg\nsub-ds205s03/figures/sub-ds205s03_task-view_run-01_desc-norm_bold.svg\nsub-ds205s03/figures/sub-ds205s03_task-view_run-01_desc-stdev_bold.svg\nsub-ds205s03/figures/sub-ds205s03_task-view_run-01_desc-zoomed_bold.svg\nsub-ds205s03/figures/sub-ds205s03_task-view_run-02_desc-background_bold.svg\nsub-ds205s03/figures/sub-ds205s03_task-view_run-02_desc-brainmask_bold.svg\nsub-ds205s03/figures/sub-ds205s03_task-view_run-02_desc-carpet_bold.svg\nsub-ds205s03/figures/sub-ds205s03_task-view_run-02_desc-mean_bold.svg\nsub-ds205s03/figures/sub-ds205s03_task-view_run-02_desc-norm_bold.svg\nsub-ds205s03/figures/sub-ds205s03_task-view_run-02_desc-stdev_bold.svg\nsub-ds205s03/figures/sub-ds205s03_task-view_run-02_desc-zoomed_bold.svg\nsub-ds205s03/func\nsub-ds205s03/func/sub-ds205s03_task-functionallocalizer_run-01_bold.json\nsub-ds205s03/func/sub-ds205s03_task-functionallocalizer_run-01_timeseries.json\nsub-ds205s03/func/sub-ds205s03_task-functionallocalizer_run-01_timeseries.tsv\nsub-ds205s03/func/sub-ds205s03_task-view_run-01_bold.json\nsub-ds205s03/func/sub-ds205s03_task-view_run-01_timeseries.json\nsub-ds205s03/func/sub-ds205s03_task-view_run-01_timeseries.tsv\nsub-ds205s03/func/sub-ds205s03_task-view_run-02_bold.json\nsub-ds205s03/func/sub-ds205s03_task-view_run-02_timeseries.json\nsub-ds205s03/func/sub-ds205s03_task-view_run-02_timeseries.tsv\nsub-ds205s03_task-functionallocalizer_run-01_bold.html\nsub-ds205s03_task-view_run-01_bold.html\nsub-ds205s03_task-view_run-02_bold.html\nsub-ds205s07\nsub-ds205s07/figures\nsub-ds205s07/figures/sub-ds205s07_task-functionallocalizer_run-01_desc-background_bold.svg\nsub-ds205s07/figures/sub-ds205s07_task-functionallocalizer_run-01_desc-brainmask_bold.svg\nsub-ds205s07/figures/sub-ds205s07_task-functionallocalizer_run-01_desc-carpet_bold.svg\nsub-ds205s07/figures/sub-ds205s07_task-functionallocalizer_run-01_desc-mean_bold.svg\nsub-ds205s07/figures/sub-ds205s07_task-functionallocalizer_run-01_desc-norm_bold.svg\nsub-ds205s07/figures/sub-ds205s07_task-functionallocalizer_run-01_desc-stdev_bold.svg\nsub-ds205s07/figures/sub-ds205s07_task-functionallocalizer_run-01_desc-zoomed_bold.svg\nsub-ds205s07/figures/sub-ds205s07_task-view_run-01_desc-background_bold.svg\nsub-ds205s07/figures/sub-ds205s07_task-view_run-01_desc-brainmask_bold.svg\nsub-ds205s07/figures/sub-ds205s07_task-view_run-01_desc-carpet_bold.svg\nsub-ds205s07/figures/sub-ds205s07_task-view_run-01_desc-mean_bold.svg\nsub-ds205s07/figures/sub-ds205s07_task-view_run-01_desc-norm_bold.svg\nsub-ds205s07/figures/sub-ds205s07_task-view_run-01_desc-stdev_bold.svg\nsub-ds205s07/figures/sub-ds205s07_task-view_run-01_desc-zoomed_bold.svg\nsub-ds205s07/figures/sub-ds205s07_task-view_run-02_desc-background_bold.svg\nsub-ds205s07/figures/sub-ds205s07_task-view_run-02_desc-brainmask_bold.svg\nsub-ds205s07/figures/sub-ds205s07_task-view_run-02_desc-carpet_bold.svg\nsub-ds205s07/figures/sub-ds205s07_task-view_run-02_desc-mean_bold.svg\nsub-ds205s07/figures/sub-ds205s07_task-view_run-02_desc-norm_bold.svg\nsub-ds205s07/figures/sub-ds205s07_task-view_run-02_desc-stdev_bold.svg\nsub-ds205s07/figures/sub-ds205s07_task-view_run-02_desc-zoomed_bold.svg\nsub-ds205s07/func\nsub-ds205s07/func/sub-ds205s07_task-functionallocalizer_run-01_bold.json\nsub-ds205s07/func/sub-ds205s07_task-functionallocalizer_run-01_timeseries.json\nsub-ds205s07/func/sub-ds205s07_task-functionallocalizer_run-01_timeseries.tsv\nsub-ds205s07/func/sub-ds205s07_task-view_run-01_bold.json\nsub-ds205s07/func/sub-ds205s07_task-view_run-01_timeseries.json\nsub-ds205s07/func/sub-ds205s07_task-view_run-01_timeseries.tsv\nsub-ds205s07/func/sub-ds205s07_task-view_run-02_bold.json\nsub-ds205s07/func/sub-ds205s07_task-view_run-02_timeseries.json\nsub-ds205s07/func/sub-ds205s07_task-view_run-02_timeseries.tsv\nsub-ds205s07_task-functionallocalizer_run-01_bold.html\nsub-ds205s07_task-view_run-01_bold.html\nsub-ds205s07_task-view_run-02_bold.html\nsub-ds205s09\nsub-ds205s09/figures\nsub-ds205s09/figures/sub-ds205s09_task-view_acq-LR_run-01_desc-background_bold.svg\nsub-ds205s09/figures/sub-ds205s09_task-view_acq-LR_run-01_desc-brainmask_bold.svg\nsub-ds205s09/figures/sub-ds205s09_task-view_acq-LR_run-01_desc-carpet_bold.svg\nsub-ds205s09/figures/sub-ds205s09_task-view_acq-LR_run-01_desc-mean_bold.svg\nsub-ds205s09/figures/sub-ds205s09_task-view_acq-LR_run-01_desc-norm_bold.svg\nsub-ds205s09/figures/sub-ds205s09_task-view_acq-LR_run-01_desc-stdev_bold.svg\nsub-ds205s09/figures/sub-ds205s09_task-view_acq-LR_run-01_desc-zoomed_bold.svg\nsub-ds205s09/figures/sub-ds205s09_task-view_acq-LR_run-02_desc-background_bold.svg\nsub-ds205s09/figures/sub-ds205s09_task-view_acq-LR_run-02_desc-brainmask_bold.svg\nsub-ds205s09/figures/sub-ds205s09_task-view_acq-LR_run-02_desc-carpet_bold.svg\nsub-ds205s09/figures/sub-ds205s09_task-view_acq-LR_run-02_desc-mean_bold.svg\nsub-ds205s09/figures/sub-ds205s09_task-view_acq-LR_run-02_desc-norm_bold.svg\nsub-ds205s09/figures/sub-ds205s09_task-view_acq-LR_run-02_desc-stdev_bold.svg\nsub-ds205s09/figures/sub-ds205s09_task-view_acq-LR_run-02_desc-zoomed_bold.svg\nsub-ds205s09/figures/sub-ds205s09_task-view_acq-RL_run-01_desc-background_bold.svg\nsub-ds205s09/figures/sub-ds205s09_task-view_acq-RL_run-01_desc-brainmask_bold.svg\nsub-ds205s09/figures/sub-ds205s09_task-view_acq-RL_run-01_desc-carpet_bold.svg\nsub-ds205s09/figures/sub-ds205s09_task-view_acq-RL_run-01_desc-mean_bold.svg\nsub-ds205s09/figures/sub-ds205s09_task-view_acq-RL_run-01_desc-norm_bold.svg\nsub-ds205s09/figures/sub-ds205s09_task-view_acq-RL_run-01_desc-stdev_bold.svg\nsub-ds205s09/figures/sub-ds205s09_task-view_acq-RL_run-01_desc-zoomed_bold.svg\nsub-ds205s09/func\nsub-ds205s09/func/sub-ds205s09_task-view_acq-LR_run-01_bold.json\nsub-ds205s09/func/sub-ds205s09_task-view_acq-LR_run-01_timeseries.json\nsub-ds205s09/func/sub-ds205s09_task-view_acq-LR_run-01_timeseries.tsv\nsub-ds205s09/func/sub-ds205s09_task-view_acq-LR_run-02_bold.json\nsub-ds205s09/func/sub-ds205s09_task-view_acq-LR_run-02_timeseries.json\nsub-ds205s09/func/sub-ds205s09_task-view_acq-LR_run-02_timeseries.tsv\nsub-ds205s09/func/sub-ds205s09_task-view_acq-RL_run-01_bold.json\nsub-ds205s09/func/sub-ds205s09_task-view_acq-RL_run-01_timeseries.json\nsub-ds205s09/func/sub-ds205s09_task-view_acq-RL_run-01_timeseries.tsv\nsub-ds205s09_task-view_acq-LR_run-01_bold.html\nsub-ds205s09_task-view_acq-LR_run-02_bold.html\nsub-ds205s09_task-view_acq-RL_run-01_bold.html\n/tmp/bold/derivatives\n" }, { "path": ".circleci/config.yml", "content": "version: 2.1\norbs:\n docker: circleci/docker@2.1.4\n\njobs:\n build:\n environment:\n - TZ: \"/usr/share/zoneinfo/America/Los_Angeles\"\n - MRIQC_API_TAG: 1.1.1\n - MRIQC_API_DOCKER_IMAGES: \"nginx:1.26.2 swaggerapi/swagger-ui:v5.17.14 mongo:6.0.15\n python:3.7-slim-bullseye\"\n - DOCKER_BUILDKIT: 1\n machine:\n # https://discuss.circleci.com/t/linux-machine-executor-images-2021-april-q2-update/39928\n # upgrade Docker version\n image: default\n docker_layer_caching: true\n working_directory: /tmp/src/mriqc\n steps:\n - checkout\n - persist_to_workspace:\n root: /tmp\n paths:\n - src/mriqc\n - run:\n name: Check whether build should be skipped\n command: |\n if [[ \"$( git log --format='format:%s' -n 1 $CIRCLE_SHA1 | grep -i -E '^docs?(\\(\\w+\\))?:' )\" != \"\" ]]; then\n echo \"Only docs build\"\n circleci step halt\n fi\n - restore_cache:\n keys:\n - build-v4-{{ .Branch }}-{{ .Revision }}\n - build-v4--{{ .Revision }}\n - build-v4-{{ .Branch }}-\n - build-v4-master-\n - build-v4-\n paths:\n - /tmp/docker\n - /tmp/images\n - docker/install-docker-credential-helper\n - run:\n name: Docker authentication\n command: |\n if [[ -n $DOCKER_PAT ]]; then\n echo \"$DOCKER_PAT\" | docker login -u $DOCKER_USER --password-stdin\n fi\n - run:\n name: Set up Docker registry\n command: |\n if [[ -f /tmp/images/registry.tar.gz ]]; then\n echo \"Loading saved registry image\"\n docker load < /tmp/images/registry.tar.gz\n else\n echo \"Pulling registry image from DockerHub\"\n docker pull registry:2\n mkdir -p /tmp/images\n docker save registry:2 | gzip > /tmp/images/registry.tar.gz\n fi\n docker run -d -p 5000:5000 --restart=always --name=registry \\\n -v /tmp/docker:/var/lib/registry registry:2\n - run:\n name: Pull images\n command: |\n set +e\n docker pull localhost:5000/miniconda\n success=$?\n set -e\n if [[ \"$success\" = \"0\" ]]; then\n echo \"Pulling from local registry\"\n docker tag localhost:5000/miniconda nipreps/miniconda:py39_2403.0\n docker pull localhost:5000/mriqc\n docker tag localhost:5000/mriqc nipreps/mriqc:latest\n docker tag localhost:5000/mriqc nipreps/mriqc\n else\n echo \"Pulling from Docker Hub\"\n docker pull nipreps/miniconda:py39_2403.0\n docker tag nipreps/miniconda:py39_2403.0 localhost:5000/miniconda\n docker push localhost:5000/miniconda\n docker pull nipreps/mriqc:latest\n fi\n\n - run:\n name: MRIQCWebAPI - Pull Docker images\n command: |\n webapi_images=($MRIQC_API_DOCKER_IMAGES)\n for image in ${webapi_images[@]}; do\n set +e\n docker pull localhost:5000/${image}\n success=$?\n set -e\n if [[ \"$success\" = \"0\" ]]; then\n docker tag localhost:5000/${image} ${image}\n else\n docker pull ${image}\n docker tag ${image} localhost:5000/${image}\n docker push localhost:5000/${image}\n fi\n done;\n\n - run:\n name: Prepare MRIQCWebAPI\n command: |\n set +e\n docker pull localhost:5000/dockereve-master-endpoints:latest\n success=$?\n set -e\n if [[ \"$success\" = 0 ]]; then\n docker tag localhost:5000/dockereve-master-endpoints:latest dockereve-master-endpoints:latest\n fi\n\n rm -rf /tmp/src/mriqcwebapi\n git clone https://github.com/nipreps/mriqcwebapi.git /tmp/src/mriqcwebapi\n cd /tmp/src/mriqcwebapi\n git checkout ${MRIQC_API_TAG}\n if [ \"${MRIQC_API_SECRET_TOKEN}\" != \"\" ]; then\n sed -i -E \"s//$MRIQC_API_SECRET_TOKEN/\" dockereve-master/.env\n grep -q -i $MRIQC_API_SECRET_TOKEN dockereve-master/.env\n fi\n sed -i -E \\\n -e 's#image: nginx:latest#image: nginx:1.26.2#' \\\n -e 's#image: swaggerapi/swagger-ui:latest#image: swaggerapi/swagger-ui:v5.17.14#' \\\n -e 's#image: mongo:latest#image: mongo:6.0.15#' \\\n dockereve-master/docker-compose.yml\n docker-compose -f /tmp/src/mriqcwebapi/dockereve-master/docker-compose.yml pull\n docker-compose -f /tmp/src/mriqcwebapi/dockereve-master/docker-compose.yml build\n\n docker tag dockereve-master-endpoints:latest localhost:5000/dockereve-master-endpoints:latest\n docker push localhost:5000/dockereve-master-endpoints:latest\n - run:\n name: Build Docker image\n no_output_timeout: 60m\n command: |\n pyenv local 3\n pip install hatch\n\n # Get version before making repo dirty\n THISVERSION=$( hatch version )\n\n # Inject MRIQC-WebAPI secret\n if [ \"${MRIQC_API_SECRET_TOKEN}\" != \"\" ]; then\n sed -i -E \"s//$MRIQC_API_SECRET_TOKEN/\" mriqc/config.py\n grep -q -i $MRIQC_API_SECRET_TOKEN mriqc/config.py\n fi\n\n if [[ ${THISVERSION:0:1} == \"0\" ]] ; then\n echo \"WARNING: latest git tag could not be found\"\n echo \"Please, make sure you fetch all tags from upstream with\"\n echo \"the command ``git fetch --tags --verbose`` and push\"\n echo \"them to your fork with ``git push origin --tags``\"\n fi\n\n echo \"Building version: $THISVERSION.\"\n\n # Build docker image\n e=1 && for i in {1..5}; do\n docker build \\\n --cache-from=nipreps/mriqc \\\n -t nipreps/mriqc:latest \\\n --build-arg BUILD_DATE=`date -u +\"%Y-%m-%dT%H:%M:%SZ\"` \\\n --build-arg VCS_REF=`git rev-parse --short HEAD` \\\n --build-arg VERSION=\"${CIRCLE_TAG:-$THISVERSION}\" . \\\n && e=0 && break || sleep 15\n done && [ \"$e\" -eq \"0\" ]\n\n TARGET_VERSION=\"${CIRCLE_TAG:-$THISVERSION}\"\n TARGET_VERSION=\"MRIQC v${TARGET_VERSION%+*}\"\n DOCKER_VERSION=$( docker run --rm nipreps/mriqc:latest --version )\n DOCKER_VERSION=\"${DOCKER_VERSION%+*}\"\n echo \"Target version: \\\"${TARGET_VERSION}\\\"\"\n echo \"Docker version: \\\"${DOCKER_VERSION}\\\"\"\n test \"${TARGET_VERSION}\" == \"$DOCKER_VERSION\"\n - run:\n name: Docker push to local registry\n no_output_timeout: 40m\n command: |\n docker tag nipreps/mriqc:latest localhost:5000/mriqc\n docker push localhost:5000/mriqc\n webapi_images=($MRIQC_API_DOCKER_IMAGES)\n for image in ${webapi_images[@]}; do\n docker tag ${image} localhost:5000/${image}\n docker push localhost:5000/${image}\n done\n - run:\n name: Docker registry garbage collection\n command: |\n docker exec -it registry /bin/registry garbage-collect --delete-untagged \\\n /etc/docker/registry/config.yml\n - save_cache:\n key: build-v4-{{ .Branch }}-{{ .Revision }}\n paths:\n - /tmp/docker\n - /tmp/images\n - persist_to_workspace:\n root: /tmp\n paths:\n - src/mriqc\n - src/mriqcwebapi\n\n get_data:\n machine:\n image: ubuntu-2204:2023.02.1\n environment:\n - TZ: \"/usr/share/zoneinfo/America/Los_Angeles\"\n - TEST_DATA_NAME: \"circle-tests\"\n - TEST_DATA_URL: \"https://files.osf.io/v1/resources/fvuh8/providers/osfstorage/5b\\\n 6c9950fed49e001a7885b6\"\n working_directory: /home/circleci/data\n steps:\n - checkout:\n path: /tmp/src/mriqc\n - run:\n name: Check whether build should be skipped\n command: |\n cd /tmp/src/mriqc\n if [[ \"$( git log --format='format:%s' -n 1 $CIRCLE_SHA1 | grep -i -E '^docs?(\\(\\w+\\))?:' )\" != \"\" ]]; then\n echo \"Only docs build\"\n circleci step halt\n fi\n - restore_cache:\n keys:\n - data-v2-{{ epoch }}\n - data-v2-\n - run:\n name: Get test data\n command: |\n mkdir -p /tmp/data\n if [[ ! -d /tmp/data/${TEST_DATA_NAME} ]]; then\n wget --retry-connrefused --waitretry=5 --read-timeout=20 --timeout=15 -t 0 -q \\\n -O ${TEST_DATA_NAME}.tar.gz \"${TEST_DATA_URL}\"\n tar xvzf ${TEST_DATA_NAME}.tar.gz -C /tmp/data/\n else\n echo \"Dataset ${TEST_DATA_NAME} was cached\"\n fi\n - run:\n name: Create Nipype config files\n command: |\n mkdir -p /tmp/t1w /tmp/bold\n printf \"[execution]\\nstop_on_first_crash = true\\n\" > /tmp/t1w/nipype.cfg\n echo \"poll_sleep_duration = 0.01\" >> /tmp/t1w/nipype.cfg\n echo \"hash_method = content\" >> /tmp/t1w/nipype.cfg\n cp /tmp/t1w/nipype.cfg /tmp/bold/nipype.cfg\n - save_cache:\n key: data-v2-{{ epoch }}\n paths:\n - /tmp/data\n - /tmp/t1w\n - /tmp/bold\n\n test_pytest:\n machine:\n image: ubuntu-2204:2023.02.1\n working_directory: /home/circleci/out/tests\n steps:\n - attach_workspace:\n at: /tmp\n - run:\n name: Check whether build should be skipped\n command: |\n cd /tmp/src/mriqc\n if [[ \"$( git log --format='format:%s' -n 1 $CIRCLE_SHA1 | grep -i -E '^docs?(\\(\\w+\\))?:' )\" != \"\" ]]; then\n echo \"Only docs build\"\n circleci step halt\n fi\n if [[ \"$( git log --format=oneline -n 1 $CIRCLE_SHA1 | grep -i -E '\\[only[ _]?(anat|func|smoke)\\]' )\" != \"\" ]]; then\n echo \"Only smoke-tests build\"\n circleci step halt\n fi\n - restore_cache:\n keys:\n - build-v4-{{ .Branch }}-{{ .Revision }}\n paths:\n - /tmp/docker\n - /tmp/images\n - docker/install-docker-credential-helper\n - run:\n name: Docker authentication\n command: |\n if [[ -n $DOCKER_PAT ]]; then\n echo \"$DOCKER_PAT\" | docker login -u $DOCKER_USER --password-stdin\n fi\n - run:\n name: Set-up a Docker registry\n command: |\n if [[ -f /tmp/images/registry.tar.gz ]]; then\n echo \"Loading saved registry image\"\n docker load < /tmp/images/registry.tar.gz\n else\n echo \"Pulling registry image from DockerHub\"\n docker pull registry:2\n fi\n docker run -d -p 5000:5000 --restart=always --name=registry \\\n -v /tmp/docker:/var/lib/registry registry:2\n - run:\n name: Pull images from local registry\n command: |\n docker pull localhost:5000/mriqc\n docker tag localhost:5000/mriqc nipreps/mriqc:latest\n - run:\n name: Generate _version.py\n command: |\n python3 -m pip install build\n\n pushd /tmp/src/mriqc\n python3 -m build\n popd\n - run:\n name: Run MRIQC tests\n no_output_timeout: 2h\n command: |\n docker run --rm -ti -v $PWD:/scratch --entrypoint=\"pytest\" \\\n -v /tmp/src:/src -w /src/mriqc \\\n nipreps/mriqc:latest mriqc \\\n --junitxml=/scratch/tests.xml \\\n --doctest-modules --ignore=mriqc/bin \\\n --ignore=mriqc/interfaces/transitional.py\n - store_test_results:\n path: /home/circleci/out/tests\n\n T1w:\n environment:\n - TZ: \"/usr/share/zoneinfo/America/Los_Angeles\"\n - TEST_DATA_NAME: \"circle-tests\"\n - MRIQC_API_DOCKER_IMAGES: \"nginx:1.26.2 swaggerapi/swagger-ui:v5.17.14 mongo:6.0.15\n python:3.7-slim-bullseye dockereve-master-endpoints:latest\"\n - MIGAS_OPTOUT: \"1\"\n machine:\n # https://discuss.circleci.com/t/linux-machine-executor-images-2021-april-q2-update/39928\n # upgrade Docker version\n image: ubuntu-2204:2023.02.1\n resource_class: large\n working_directory: /tmp/t1w\n steps:\n - attach_workspace:\n at: /tmp\n - run:\n name: Check whether build should be skipped\n command: |\n cd /tmp/src/mriqc\n if [[ \"$( git log --format='format:%s' -n 1 $CIRCLE_SHA1 | grep -i -E '^docs?(\\(\\w+\\))?:' )\" != \"\" ]]; then\n echo \"Only docs build\"\n circleci step halt\n fi\n if [[ \"$( git log --format=oneline -n 1 $CIRCLE_SHA1 | grep -i -E '\\[only[ _]?func\\]' )\" != \"\" ]]; then\n echo \"Only functional smoke-tests build\"\n circleci step halt\n fi\n - restore_cache:\n keys:\n - build-v4-{{ .Branch }}-{{ .Revision }}\n paths:\n - /tmp/docker\n - /tmp/images\n - run:\n name: Set-up a Docker registry\n command: |\n if [[ -f /tmp/images/registry.tar.gz ]]; then\n echo \"Loading saved registry image\"\n docker load < /tmp/images/registry.tar.gz\n else\n echo \"Pulling registry image from DockerHub\"\n docker pull registry:2\n fi\n docker run -d --restart=always --name=registry \\\n -e REGISTRY_HTTP_ADDR=0.0.0.0:5001 -p 5001:5001 \\\n -v /tmp/docker:/var/lib/registry registry:2\n - run:\n name: Pull images from local registry\n command: |\n docker pull localhost:5001/mriqc\n docker tag localhost:5001/mriqc nipreps/mriqc:latest\n\n webapi_images=($MRIQC_API_DOCKER_IMAGES)\n for image in ${webapi_images[@]}; do\n docker pull localhost:5001/${image}\n docker tag localhost:5001/${image} ${image}\n done;\n - run:\n name: Start MRIQC WebAPI endpoint\n command: |\n docker-compose -f /tmp/src/mriqcwebapi/dockereve-master/docker-compose.yml --verbose up -d\n background: true\n - restore_cache:\n keys:\n - data-v2-{{ epoch }}\n - data-v2-\n - restore_cache:\n keys:\n - t1w-v6-{{ .Branch }}\n - t1w-v6-master\n - t1w-v6-\n\n - run:\n name: Remove old, cached configs\n command: |\n rm -f /tmp/t1w/work/.mriqc.*.toml\n rm -f /tmp/t1w/work/.resources.*.tsv\n rm -f /tmp/t1w/work/.resources.*.png\n\n - run:\n name: Run participant-level on T1w images\n no_output_timeout: 2h\n command: |\n mkdir -p /tmp/t1w/work /tmp/t1w/derivatives\n # Run MRIQC\n docker run -u $( id -u ) --rm -ti \\\n -v /tmp/data/${TEST_DATA_NAME}:/data:ro \\\n -v /tmp/t1w:/scratch -w /scratch \\\n -e MRIQC_DEV=1 \\\n nipreps/mriqc:latest \\\n /data derivatives/ participant \\\n -vv --verbose-reports --profile -m T1w --dsname circletests \\\n --resource-monitor \\\n --n_procs 2 --ants-nthreads 1 --ants-float \\\n --webapi-url http://$( hostname -I | awk '{print $1}' )/api/v1 --upload-strict\n\n - run:\n name: Move temporary but relevant artifacts\n command: |\n mkdir /tmp/t1w/misc\n mv /tmp/t1w/work/.resources.*.tsv /tmp/t1w/misc\n mv /tmp/t1w/work/.resources.*.png /tmp/t1w/misc\n\n - store_artifacts:\n path: /tmp/t1w/misc\n\n - save_cache:\n key: t1w-v6-{{ .Branch }}\n paths:\n - /tmp/t1w/work\n\n - run:\n name: Run group-level on T1w images\n no_output_timeout: 2h\n command: |\n docker run -u $( id -u ) --rm -ti \\\n -v /tmp/data/${TEST_DATA_NAME}:/data:ro \\\n -v /tmp/t1w:/scratch -w /scratch \\\n -e MRIQC_DEV=1 \\\n nipreps/mriqc:latest \\\n /data derivatives/ group \\\n -m T1w -vv\n\n - store_artifacts:\n path: /tmp/t1w/derivatives\n\n - run:\n name: Checking presence of outputs\n command: |\n mkdir -p /tmp/t1w/test\n find /tmp/t1w/derivatives | sed s+/tmp/t1w/derivatives/++ | sort > /tmp/t1w/test/outputs.out\n diff /tmp/src/mriqc/.circleci/circle_T1w.txt /tmp/t1w/test/outputs.out\n exit $?\n\n - run:\n name: Clean-up work directory (just leave reports & commandlines)\n command: |\n find /tmp/t1w/work -type f -not -name \"report.rst\" -and -not -name \"command.txt\" -delete\n\n - store_artifacts:\n path: /tmp/t1w/work\n\n - run:\n name: Checking changes on IQMs\n command: |\n docker run --rm -ti -v $PWD:/scratch -w /scratch -v /tmp/src:/src \\\n --entrypoint=\"dfcheck\" nipreps/mriqc:latest \\\n -i /scratch/derivatives/group_T1w.tsv \\\n -r /src/mriqc/mriqc/data/testdata/group_T1w.tsv \\\n || true # ignore failure\n\n - run:\n name: WebAPI - Check records\n command: |\n docker run --rm -ti \\\n --entrypoint=\"/opt/conda/bin/mriqcwebapi_test\" \\\n nipreps/mriqc:latest \\\n T1w 4 \\\n --webapi-url http://$( hostname -I | awk '{print $1}' )/api/v1/T1w\n\n - store_artifacts:\n path: /tmp/t1w/test\n\n bold:\n environment:\n - TZ: \"/usr/share/zoneinfo/America/Los_Angeles\"\n - TEST_DATA_NAME: \"circle-tests\"\n - MRIQC_API_DOCKER_IMAGES: \"nginx:1.26.2 swaggerapi/swagger-ui:v5.17.14 mongo:6.0.15\n python:3.7-slim-bullseye dockereve-master-endpoints:latest\"\n - MIGAS_OPTOUT: \"1\"\n machine:\n # https://discuss.circleci.com/t/linux-machine-executor-images-2021-april-q2-update/39928\n # upgrade Docker version\n image: ubuntu-2204:2023.02.1\n working_directory: /tmp/bold\n steps:\n - attach_workspace:\n at: /tmp\n - run:\n name: Check whether build should be skipped\n command: |\n cd /tmp/src/mriqc\n if [[ \"$( git log --format='format:%s' -n 1 $CIRCLE_SHA1 | grep -i -E '^docs?(\\(\\w+\\))?:' )\" != \"\" ]]; then\n echo \"Only docs build\"\n circleci step halt\n fi\n if [[ \"$( git log --format=oneline -n 1 $CIRCLE_SHA1 | grep -i -E '\\[only[ _]?anat\\]' )\" != \"\" ]]; then\n echo \"Only anatomical smoke-tests build\"\n circleci step halt\n fi\n - restore_cache:\n keys:\n - build-v4-{{ .Branch }}-{{ .Revision }}\n paths:\n - /tmp/docker\n - /tmp/images\n - run:\n name: Set-up a Docker registry\n command: |\n if [[ -f /tmp/images/registry.tar.gz ]]; then\n echo \"Loading saved registry image\"\n docker load < /tmp/images/registry.tar.gz\n else\n echo \"Pulling registry image from DockerHub\"\n docker pull registry:2\n fi\n docker run -d --restart=always --name=registry \\\n -e REGISTRY_HTTP_ADDR=0.0.0.0:5001 -p 5001:5001 \\\n -v /tmp/docker:/var/lib/registry registry:2\n - run:\n name: Pull images from local registry\n command: |\n docker pull localhost:5001/mriqc\n docker tag localhost:5001/mriqc nipreps/mriqc:latest\n\n webapi_images=($MRIQC_API_DOCKER_IMAGES)\n for image in ${webapi_images[@]}; do\n docker pull localhost:5001/${image}\n docker tag localhost:5001/${image} ${image}\n done;\n - run:\n name: Start MRIQC WebAPI endpoint\n command: |\n docker-compose -f /tmp/src/mriqcwebapi/dockereve-master/docker-compose.yml --verbose up -d\n background: true\n\n - restore_cache:\n keys:\n - data-v2-{{ epoch }}\n - data-v2-\n - restore_cache:\n keys:\n - bold-v6-{{ .Branch }}\n - bold-v6-master\n - bold-v6-\n\n - run:\n name: Remove old, cached configs\n command: |\n rm -f /tmp/bold/work/.mriqc.*.toml\n rm -f /tmp/bold/work/.resources.*.tsv\n rm -f /tmp/bold/work/.resources.*.png\n\n - run:\n name: Run participant-level on BOLD images\n no_output_timeout: 2h\n command: |\n mkdir -p /tmp/bold/work /tmp/bold/derivatives\n # Run MRIQC\n docker run -u $( id -u ) --rm -ti -v /tmp/data/${TEST_DATA_NAME}:/data:ro \\\n -v $PWD:/scratch -w /scratch \\\n -e MRIQC_DEV=1 \\\n nipreps/mriqc:latest \\\n /data derivatives/ participant \\\n -vv --verbose-reports --profile -m bold --dsname circletests \\\n --n_procs 2 --ants-nthreads 1 --ants-float \\\n --resource-monitor --testing \\\n --webapi-url http://$( hostname -I | awk '{print $1}' )/api/v1 --upload-strict\n - run:\n name: Move temporary but relevant artifacts\n command: |\n mkdir /tmp/bold/misc\n mv /tmp/bold/work/.resources.*.tsv /tmp/bold/misc\n mv /tmp/bold/work/.resources.*.png /tmp/bold/misc\n\n - store_artifacts:\n path: /tmp/bold/misc\n\n - save_cache:\n key: bold-v6-{{ .Branch }}\n paths:\n - /tmp/bold/work\n\n - run:\n name: Run group-level on BOLD images\n no_output_timeout: 2h\n command: |\n docker run -u $( id -u ) --rm -ti -v /tmp/data/${TEST_DATA_NAME}:/data:ro \\\n -e MRIQC_DEV=1 \\\n -v $PWD:/scratch -w /scratch \\\n nipreps/mriqc:latest \\\n /data derivatives/ group \\\n -m bold -vv\n\n - store_artifacts:\n path: /tmp/bold/derivatives\n\n - run:\n name: Checking presence of outputs\n command: |\n mkdir -p /tmp/bold/test\n find /tmp/bold/derivatives | sed s+/tmp/bold/derivatives/++ | sort > /tmp/bold/test/outputs.out\n diff /tmp/src/mriqc/.circleci/circle_bold.txt /tmp/bold/test/outputs.out\n exit $?\n\n - run:\n name: Clean-up work directory (just leave reports & commandlines)\n command: |\n find /tmp/bold/work -type f -not -name \"report.rst\" -and -not -name \"command.txt\" -delete\n\n - store_artifacts:\n path: /tmp/bold/work\n\n - run:\n name: Checking changes on IQMs\n command: |\n docker run -u $( id -u ) --rm -ti -v /tmp/src:/src -v $PWD:/scratch -w /scratch \\\n --entrypoint=\"dfcheck\" nipreps/mriqc:latest \\\n -i /scratch/derivatives/group_bold.tsv \\\n -r /src/mriqc/mriqc/data/testdata/group_bold.tsv\n\n - run:\n name: WebAPI - Check records\n command: |\n docker run --rm -ti \\\n --entrypoint=\"/opt/conda/bin/mriqcwebapi_test\" \\\n nipreps/mriqc:latest \\\n bold 9 \\\n --webapi-url http://$( hostname -I | awk '{print $1}' )/api/v1/bold\n\n - store_artifacts:\n path: /tmp/bold/test\n # The resource_class feature allows configuring CPU and RAM resources for each job. Different resource classes are available for different executors. https://circleci.com/docs/2.0/configuration-reference/#resourceclass\n resource_class: large\n\n build_docs:\n docker:\n - image: cimg/python:3.10\n environment:\n - FSLOUTPUTTYPE: NIFTI\n steps:\n - checkout\n - run:\n name: Install Graphviz\n command: sudo apt update && sudo apt -y install graphviz\n - run:\n name: Install deps\n command: |\n pip install -U pip hatch docutils\n hatch version\n pip install .[docs]\n - run:\n name: Build MRIQC documentation\n no_output_timeout: 2h\n command: |\n make -C docs SPHINXOPTS=\"-W\" html | tee $PWD/builddocs.log\n cat $PWD/builddocs.log\n grep -qv \"ERROR\" $PWD/builddocs.log\n - store_artifacts:\n path: ./docs/_build/html\n\n test_package:\n docker:\n - image: cimg/python:3.9\n working_directory: /tmp/src/mriqc\n steps:\n - checkout\n - run:\n name: Start virtual environment\n command: |\n python -m venv /tmp/venv\n source /tmp/venv/bin/activate\n python -m pip install -U build hatch hatchling pip twine docutils\n - run:\n name: Build and check\n command: |\n source /tmp/venv/bin/activate\n python -m build -s -w\n python -m twine check dist/*\n - run:\n name: Validate version\n command: |\n source /tmp/venv/bin/activate\n THISVERSION=$( python3 -m hatch version | tail -n1 | xargs )\n python -m pip install dist/*.tar.gz\n mkdir empty\n cd empty\n INSTALLED=$( python -c 'import mriqc; print(mriqc.__version__)' )\n test \"${CIRCLE_TAG:-$THISVERSION}\" == \"$INSTALLED\"\n\n deploy_pypi:\n docker:\n - image: cimg/python:3.10\n working_directory: /tmp/src/mriqc\n steps:\n - checkout\n - run:\n name: Start virtual environment\n command: |\n python -m venv /tmp/venv\n source /tmp/venv/bin/activate\n python -m pip install -U hatch hatchling pip build twine docutils\n - run:\n name: Deploy to PyPi\n command: |\n source /tmp/venv/bin/activate\n\n # Set version on stone before editing the bundle\n export SETUPTOOLS_SCM_PRETEND_VERSION=$( python -m hatch version | tail -n1 | xargs )\n\n # Inject MRIQC-WebAPI secret\n if [ \"${MRIQC_API_SECRET_TOKEN}\" != \"\" ]; then\n sed -i -E \"s//$MRIQC_API_SECRET_TOKEN/\" mriqc/config.py\n grep -q -i $MRIQC_API_SECRET_TOKEN mriqc/config.py\n fi\n python -m build -s -w\n python -m twine check dist/*\n python -m twine upload dist/*\n\n deploy_docker:\n machine:\n image: default\n working_directory: /tmp/src/mriqc\n steps:\n - restore_cache:\n keys:\n - build-v4-{{ .Branch }}-{{ .Revision }}\n paths:\n - /tmp/docker\n - /tmp/images\n - docker/install-docker-credential-helper\n - run:\n name: Docker authentication\n command: |\n if [[ -n $DOCKER_PAT ]]; then\n echo \"$DOCKER_PAT\" | docker login -u $DOCKER_USER --password-stdin\n fi\n - run:\n name: Set-up a Docker registry\n command: |\n if [[ -f /tmp/images/registry.tar.gz ]]; then\n echo \"Loading saved registry image\"\n docker load < /tmp/images/registry.tar.gz\n else\n echo \"Pulling registry image from DockerHub\"\n docker pull registry:2\n fi\n docker run -d -p 5000:5000 --restart=always --name=registry \\\n -v /tmp/docker:/var/lib/registry registry:2\n - run:\n name: Pull images from local registry\n command: |\n docker pull localhost:5000/mriqc\n docker tag localhost:5000/mriqc nipreps/mriqc:latest\n\n - run:\n name: Deploy to Docker Hub\n no_output_timeout: 40m\n command: |\n # only tag & push latest if CIRCLE_TAG is set\n if [ -n \"${CIRCLE_TAG:-}\" ]; then\n docker push nipreps/mriqc:latest\n fi\n\n docker tag nipreps/mriqc nipreps/mriqc:\"${CIRCLE_TAG:=experimental}\"\n docker push nipreps/mriqc:\"$CIRCLE_TAG\"\n echo \"Pushed tag ${CIRCLE_TAG} to Docker Hub\"\n\nworkflows:\n version: 2\n build_test_deploy:\n jobs:\n - build_docs:\n filters:\n tags:\n only: /.*/\n\n - build:\n context:\n - nipreps-common\n filters:\n branches:\n ignore: /docs?\\/.*/\n tags:\n only: /.*/\n\n - get_data:\n filters:\n branches:\n ignore: /docs?\\/.*/\n tags:\n only: /.*/\n\n - test_package:\n context:\n - nipreps-common\n filters:\n branches:\n ignore: /docs?\\/.*/\n tags:\n only: /.*/\n\n - test_pytest:\n context:\n - nipreps-common\n requires:\n - build\n filters:\n branches:\n ignore: /docs?\\/.*/\n tags:\n only: /.*/\n\n - T1w:\n requires:\n - get_data\n - build\n filters:\n branches:\n ignore: /docs?\\/.*/\n tags:\n only: /.*/\n\n - bold:\n requires:\n - get_data\n - build\n filters:\n branches:\n ignore: /docs?\\/.*/\n tags:\n only: /.*/\n\n - deploy_docker:\n context:\n - nipreps-common\n requires:\n - build\n - test_pytest\n - test_package\n - build_docs\n - T1w\n - bold\n filters:\n branches:\n only:\n - master\n tags:\n only: /.*/\n\n - deploy_pypi:\n context:\n - nipreps-common\n requires:\n - deploy_docker\n filters:\n branches:\n ignore: /.*/\n tags:\n only: /.*/\n" }, { "path": ".codecov.yml", "content": "codecov:\n token: 507d37e9-5aac-4c1e-b28c-f2b4ef9d2edf\n branch: master\n bot: oesteban\n\ncoverage:\n precision: 2\n round: down\n range: \"70...100\"\n\n status:\n project:\n default:\n target: auto\n threshold: 5.0\n branches:\n - master\n\n patch:\n default:\n target: auto\n branches:\n - master\n\n changes:\n default:\n branches:\n - master\n\n ignore:\n - build/.*\n - agave/.*\n - .*/data/.*\n\ncomment:\n layout: \"header, diff, changes, sunburst, uncovered\"\n branches:\n - master\n behavior: default\n" }, { "path": ".dockerignore", "content": "# python cache\n.cache/\n__pycache__/**/*\n__pycache__\n*.pyc\n\n# python distribution\nbuild/**/*\nbuild\ndist/**/*\ndist\nmriqc.egg-info/**/*\nmriqc.egg-info\n.eggs/**/*\n.eggs\nsrc/**/*\nsrc/\nnotebooks/\n.maint/\n.maint/**/*\n\n# releasing\nMakefile\n\n###\n### git MUST NOT be ignored after moving to multi-staged builds\n###\n### with the multi-staged build, the strategy is to create a wheel\n### in a stage image. To create such a wheel, the git repo is necessary\n### for hatch-vcs to interpolate the version and initialize _version.py\n###\n# .github/\n# .gitignore\n# .gitattributes\n# .git/**/*\n# .git\n\n# Maintenance\n.circleci/\n.maint/\n.codecov.yml\n.coveragerc\n.pep8speaks.yml\n.pylintrc\n.readthedocs.yaml\n.travis.yml\n.zenodo.json\nCONTRIBUTING.md\ncodecov.yml\ndocs\ndocs/**/*\nlong_description.rst\nnotebooks\nnotebooks/**/*\nrequirements-dev.txt\nrequirements.txt\nvenv/\nvenv/**/*\n" }, { "path": ".git_archival.txt", "content": "node: $Format:%H$\nnode-date: $Format:%cI$\ndescribe-name: $Format:%(describe:tags=true,match=*[0-9]*)$\nref-names: $Format:%D$\n" }, { "path": ".gitattributes", "content": ".git_archival.txt export-subst\n" }, { "path": ".github/config.yml", "content": "# Comment to be posted to on pull requests merged by a first time user\nfirstPRMergeComment: >\n Thanks for opening this pull request and congratulations for taking it to the finish line!\n It looks like this is your first time contributing to *MRIQC*. :smile:\n \n We invite you to list yourself as an *MRIQC* contributor.\n To learn more about what that entails and how we credit our contributors,\n please check out the\n [contributing guidelines](https://www.nipreps.org/community/CONTRIBUTING/#recognizing-contributions).\n If your name is not already on the list, please insert it, in alphabetical order of (i) lastname and\n (ii) firstname, into the\n [``.maint/CONTRIBUTORS.md`` file](https://github.com/nipreps/mriqc/blob/master/.maint/CONTRIBUTORS.md).\n\n Of course, if you want to opt-out this time, there is no\n problem at all with adding your name later.\n You will be always welcome to add it in the future whenever\n you feel it should be listed.\n" }, { "path": ".github/dependabot.yml", "content": "---\nversion: 2\nupdates:\n - package-ecosystem: \"github-actions\"\n directory: \"/\"\n schedule:\n interval: \"weekly\"\n" }, { "path": ".github/workflows/pythonpackage.yml", "content": "# This workflow will install Python dependencies, run tests and lint with a variety of Python versions\n# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions\n\nname: Python package\n\non:\n push:\n branches: [ '*' ]\n tags: [ '*' ]\n pull_request:\n branches: [ master, 'maint/*' ]\n\nconcurrency:\n group: ${{ github.workflow }}-${{ github.ref }}\n cancel-in-progress: true\n\npermissions:\n contents: read\n\njobs:\n build:\n if: \"!startsWith(github.ref, 'refs/tags/') && !contains(github.event.head_commit.message, '[skip ci]')\"\n runs-on: ubuntu-latest\n strategy:\n matrix:\n python-version: ['3.9', '3.10', '3.11', '3.12', '3.13']\n\n steps:\n - uses: actions/checkout@v6\n with:\n fetch-depth: 0\n - name: Set up Python ${{ matrix.python-version }}\n uses: actions/setup-python@v6\n with:\n python-version: ${{ matrix.python-version }}\n - name: Build and check package\n run: |\n pipx run build\n pipx run twine check dist/mriqc-*\n - name: Interpolate version\n run: |\n # Interpolate version\n if [[ \"$GITHUB_REF\" == refs/tags/* ]]; then\n TAG=${GITHUB_REF##*/}\n fi\n THISVERSION=$( pipx run hatch version | tail -n1 | xargs )\n THISVERSION=${TAG:-$THISVERSION}\n echo \"Expected VERSION: \\\"${THISVERSION}\\\"\"\n echo \"THISVERSION=${THISVERSION}\" >> $GITHUB_ENV\n - name: Install in confined environment [pip]\n run: |\n python -m venv /tmp/pip\n source /tmp/pip/bin/activate\n python -m pip install .\n INSTALLED_VERSION=$(python -c 'import mriqc as qc; print(qc.__version__, end=\"\")')\n echo \"INSTALLED: \\\"${INSTALLED_VERSION}\\\"\"\n test \"${INSTALLED_VERSION}\" = \"${THISVERSION}\"\n rm -r /tmp/pip\n - name: Install in confined environment [sdist]\n run: |\n python -m venv /tmp/install_sdist\n source /tmp/install_sdist/bin/activate\n python -m pip install dist/mriqc*.tar.gz\n INSTALLED_VERSION=$(python -c 'import mriqc as qc; print(qc.__version__, end=\"\")')\n echo \"INSTALLED: \\\"${INSTALLED_VERSION}\\\"\"\n test \"${INSTALLED_VERSION}\" = \"${THISVERSION}\"\n rm -r /tmp/install_sdist\n - name: Install in confined environment [wheel]\n run: |\n python -m venv /tmp/install_wheel\n source /tmp/install_wheel/bin/activate\n python -m pip install dist/mriqc*.whl\n INSTALLED_VERSION=$(python -c 'import mriqc as qc; print(qc.__version__, end=\"\")')\n echo \"INSTALLED: \\\"${INSTALLED_VERSION}\\\"\"\n test \"${INSTALLED_VERSION}\" = \"${THISVERSION}\"\n rm -r /tmp/install_wheel\n\n ruff:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v6\n - run: |\n pipx run ruff check --output-format=github\n pipx run ruff format --diff\n\n codespell:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v6\n - uses: codespell-project/actions-codespell@v2\n" }, { "path": ".gitignore", "content": "# setuptools-scm\nmriqc/_version.py\n\n.DS_Store\n\n# IDE configuration\n.idea/\n.vscode\n\n# Documentation build\ndocs/build\nbuild/\n\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nenv/\nvenv/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\n*.egg-info/\n.installed.cfg\n*.egg\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*,cover\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff\n*.log\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Run files\nwork/" }, { "path": ".mailmap", "content": "Aaron Piccirilli \nAdam Huffman \nAdam Huffman verdurin\nAdam C. Raikes \nAdam C. Raikes araikes\nAdam C. Raikes Adam Raikes\nAriel Rokem \nAsier Erramuzpe \nAsier Erramuzpe erramuzpe\nBennet Fauber \nBennet Fauber justbennet\nCéline Provins \nCéline Provins cprovins\nCéline Provins celprov\nCéline Provins <77437752+celprov@users.noreply.github.com>\nChristopher J. Markiewicz \nChristopher J. Markiewicz \nChristopher J. Markiewicz \nConrad Ma \nConrad Ma Conrad \nConrad Ma 394822740 \nConrad Ma <394822740@qq.com>\nConrad Ma \ndependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>\nDimitri Papadopoulos <3234522+DimitriPapadopoulos@users.noreply.github.com>\nDylan Nielson \nDylan Nielson Dylan \nElodie Savary \nElodie Savary esavary\nJames D. Kent \nJames D. Kent \nJames D. Kent jdkent\nJames D. Kent Fred Mertz \nJohannes Achtzehn \nJohannes Achtzehn jAchtzehn\nJohn A. Lee \nJohn A. Lee john anthony lee\nJohn A. Lee leej3\nKrzysztof J. Gorgolewski \nKrzysztof J. Gorgolewski \nKrzysztof J. Gorgolewski \nKrzysztof J. Gorgolewski \nKrzysztof J. Gorgolewski \nKrzysztof J. Gorgolewski \nKrzysztof J. Gorgolewski \nMathias Goncalves \nMathias Goncalves mathiasg\nMathias Goncalves \nMathias Goncalves \nMcKenzie P. Hagen \nMcKenzie P. Hagen mckenziephagen\nMichael Dayan \nMichael G. Clark \nMichael G. Clark mgclark\nMichael G. Clark Michael Clark\nMichael Krause \nMichael Krause octomike\nNiPreps Bot \nNiPreps Bot nipreps-bot\nPablo Velasco \nPablo Velasco pvelasco\nPatrick Sadil \nPatrick Sadil psadil\nOscar Esteban \nOscar Esteban \nRoss Blair \nRoss Blair \nTeresa Gomez <46339554+teresamg@users.noreply.github.com>\nTeresa Gomez <46339554+teresamg@users.noreply.github.com> teresamg\nYibei Chen \nVictor Férat \nVictor Férat vferat\nWilliam Triplett \nZvi Baratz \nZvi Baratz ZviBaratz\nZvi Baratz zvi-quantivly\nZvi Baratz <133042121+zvi-quantivly@users.noreply.github.com>\nZvi Baratz \n" }, { "path": ".maint/CONTRIBUTORS.md", "content": "# CONTRIBUTORS\n\nThis document lists those who have made contributions to the Project.\nAs per the contributor guidelines, they should be included by default in any publications derived from the Project.\n\nIf you are new to the project, don't forget to add your name and affiliation to the list of contributors here! Our Welcome Bot will send an automated message reminding this to first-time contributors.\nBefore every release, unlisted contributors will be invited again to add their names to this file (just in case they missed the automated message from our Welcome Bot).\n\n| **Lastname** | **Name** | **Handle** | **ORCID** | **Affiliation** |\n| --- | --- | --- | --- | --- |\n| Achtzehn | Johannes | @jAchtzehn | 0000-0002-0657-6516 | Charité Berlin, Berlin, Germany |\n| Baratz | Zvi | @zvi-quantivly | 0000-0001-7159-1387 | Quantivly Inc., Somerville, MA, USA |\n| Beliy | Nikita | @nbeliy | | CRC ULiege, Liege, Belgium |\n| Birman | Daniel | @dbirman | 0000-0003-3748-6289 | Department of Psychology, Stanford University, CA, USA |\n| Blair | Ross W. | @rwblair | 0000-0003-3007-1056 | Department of Psychology, Stanford University, CA, USA |\n| Chen | Yibei | @yibeichan | 0000-0003-2882-0900 | McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, USA |\n| Clark | Michael G. | @mgclark | | National Institutes of Health, USA |\n| Dayan | Michael | @neurorepro | 0000-0002-2666-0969 | International Committee of the Red Cross - ICRC, Geneva, Switzerland |\n| Durnez | Joke | @jokedurnez | 0000-0001-9030-2202 | Department of Psychology, Stanford University, CA, USA |\n| Erramuzpe | Asier | @erramuzpe | 0000-0002-9402-2184 | Computational Neuroimaging Lab, BioCruces Health Research Institute |\n| Fauber | Bennet | @justbennet | | University of Michigan, Ann Arbor, USA |\n| Férat | Victor | @vferat | 0000-0003-1952-7657 | Department of Basic Neurosciences, Université de Genève, Geneva, Switzerland |\n| Garcia-Dias | Rafael | @garciadias | 0000-0001-9332-1580 | Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK |\n| Ghosh | Satrajit S. | @satra | 0000-0002-5312-6729 | McGovern Institute for Brain Research, MIT, MA, USA; and Department of Otolaryngology, Harvard Medical School, MA, USA |\n| Gomez | Teresa | @teresamg | | The University of Washington eScience Institute, WA, USA |\n| Goncalves | Mathias | @mgxd | 0000-0002-7252-7771 | Department of Psychology, Stanford University, CA, USA |\n| Halchenko | Yaroslav O. | @yarikoptic | 0000-0003-3456-2493 | Psychological and Brain Sciences Department, Dartmouth College, NH, USA |\n| Huffman | Adam | @verdurin | | Department of Physics, Imperial College London, London, UK |\n| Kay | Benjamin | @benkay86 | | Washington University School of Medicine, St.Louis, MO, USA |\n| Kent | James D. | @jdkent | 0000-0002-4892-2659 | Department of Psychology, University of Texas at Austin, TX, USA |\n| Krause | Michael | @octomike | | Max Planck Institute for Human Development, Berlin, Germany |\n| Lee | John A. | @leej3 | 0000-0001-5884-4247 | Quansight, Dublin, Ireland |\n| Legarreta Gorroño | Jon Haitz | @jhlegarreta | 0000-0002-9661-1396 | Brigham and Women's Hospital, Mass General Brigham, Harvard Medical School, MA, USA |\n| Nichols | Thomas | @nicholst | 0000-0002-4516-5103 | Oxford Big Data Institute, University of Oxford, Oxford, GB |\n| Nielson | Dylan | @Shotgunosine | 0000-0003-4613-6643 | Section on Clinical and Computational Psychiatry, National Institute of Mental Health, Bethesda, MD, USA |\n| Papadopoulos Orfanos | Dimitri | @DimitriPapadopoulos | 0000-0002-1242-8990 | NeuroSpin, CEA, Université Paris-Saclay, NeuroSpin, Gif-sur-Yvette, France |\n| Raikes | Adam C. | @araikes | | Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA |\n| Rokem | Ariel | @arokem | 0000-0003-0679-1985 | The University of Washington eScience Institute, WA, USA |\n| Sadil | Patrick | @psadil | 0000-0003-4141-1343 | Johns Hopkins Bloomberg School of Public Health, MD, USA |\n| Salo | Taylor | @tsalo | 0000-0001-9813-3167 | Department of Psychology, Florida International University, FL, USA |\n| Savary | Elodie | @esavary | 0000-0002-3896-6906 | Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland |\n| Tooley | Ursula A. | @utooley | 0000-0001-6377-3885 | Department of Neuroscience, University of Pennsylvania, PA, USA |\n| Triplett | William | @wtriplett | 0000-0002-9546-1306 | University of Florida: Gainesville, Florida, US |\n| Varada | Jan | @jvarada | | Functional MRI Facility, National Institute of Mental Health, Bethesda, MD, USA |\n| Velasco | Pablo | @pvelasco | 0000-0002-5749-6049 | Center for Brain Imaging, New York University, NY, USA |\n" }, { "path": ".maint/FORMER.md", "content": "# FORMER MEMBERS\n\nThis document lists former contributors or maintainers who want to disengage from the Project, and seek to be dismissed in communications or future papers.\nBy adding your name to this list you are giving up on all your responsibilities to the project.\nShould you desire to be considered back as a contributor or maintainer, please remove your name from this list and proceed as prescribed in the governance documents.\n\n| **Lastname** | **Name** | **Handle** |\n| --- | --- | --- |\n| Bot | NiPreps | @nipreps-bot |\n| dependabot[bot] | | @dependabot |\n| Ma | Conrad | @394822740 |\n| Piccirilli | Aaron | @apiccirilli |\n| Gorgolewski | Krzysztof J. | @chrisgorgo |\n" }, { "path": ".maint/MAINTAINERS.md", "content": "# Maintainers\n\nThis document lists the Maintainers of the project.\nMaintainers may be added once approved by the existing maintainers as described in the `../GOVERNANCE.md` document.\nBy adding your name to this list you are agreeing to abide by the governance documents and to abide by all of the Organization's polices, including the code of conduct, trademark policy, and antitrust policy.\nIf you are participating because of your affiliation with another organization (designated below), you represent that you have the authority to bind that organization to these policies.\n\n\n\n| **Lastname** | **Name** | **Handle** | **ORCID** | **Affiliation** |\n| --- | --- | --- | --- | --- |\n| Provins | Céline | @celprov | 0000-0002-1668-9629 | Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland |\n| Hagen | McKenzie P. | @mckenziephagen | 0000-0002-7454-8189 | Psychology Department, University of Washington, Seattle, WA, USA |\n| MacNicol | Eilidh | @eilidhmacnicol | 0000-0003-3715-7012 | Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK |\n| Esteban | Oscar | @oesteban | 0000-0001-8435-6191 | Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland |\n| Markiewicz | Christopher J. | @effigies | 0000-0002-6533-164X | Department of Psychology, Stanford University, CA, USA |\n" }, { "path": ".maint/PIs.md", "content": "# PRINCIPAL INVESTIGATORS\n\nThis documents the key personnel who oversees the development of the Project and secures funding.\nThe names in this file are designated by the Organization's TSC at the time of accepting the Project under the Organization.\nChanges to this file must be approved by the TSC.\nWhen a PI ceases serving as such, by default, their name will be placed in the `CONTRIBUTORS.md` file should it not be there already.\n\nBy having your name in this list you are agreeing to abide by the governance documents and to abide by all of the Organization's polices, including the code of conduct, trademark policy, and antitrust policy.\nIf you are participating because of your affiliation with another organization (designated below), you represent that you have the authority to bind that organization to these policies.\n\n| **Lastname** | **Name** | **Handle** | **ORCID** | **Affiliation** |\n| --- | --- | --- | --- | --- |\n| Esteban | Oscar | @oesteban | 0000-0001-8435-6191 | Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland |\n| Rokem | Ariel | @arokem | 0000-0003-0679-1985 | The University of Washington eScience Institute, WA, USA |\n| Poldrack | Russell A. | @poldrack | 0000-0001-6755-0259 | Department of Psychology, Stanford University, CA, USA |\n| Thomas | Adam G. | | 0000-0002-2850-1419 | Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD, USA |\n" }, { "path": ".maint/ROADMAP.md", "content": "# ROADMAP\n\nThis document states how the roadmap is built, discussed and monitored by the Maintainers, with the engagement of Contributors and PIs.\n\nFor example, if the GitHub Projects feature is used, this document will contain a link to the corresponding Project and document who is responsible of managing the project, contacting Contributors and Maintainers to monitor the progress of activities, organizing meetings and discussions around the activities, etc.\n\nThis document may also indicate how the *milestones* feature of the project is used and, as for Projects, who is responsible of what coordination actions, their frequence, etc.\n" }, { "path": ".maint/requirements.txt", "content": "click\nfuzzywuzzy\npython-Levenshtein" }, { "path": ".maint/update_authors.py", "content": "#!/usr/bin/env python3\n\"\"\"Update and sort the creators list of the zenodo record.\"\"\"\nimport json\nimport sys\nfrom pathlib import Path\n\nimport click\nfrom fuzzywuzzy import fuzz, process\n\n\ndef read_md_table(md_text):\n \"\"\"\n Extract the first table found in a markdown document as a Python dict.\n\n Examples\n --------\n >>> read_md_table('''\n ... # Some text\n ...\n ... More text\n ...\n ... | **Header1** | **Header2** |\n ... | --- | --- |\n ... | val1 | val2 |\n ... | | val4 |\n ...\n ... | **Header3** | **Header4** |\n ... | --- | --- |\n ... | val1 | val2 |\n ... | | val4 |\n ... ''')\n [{'header1': 'val1', 'header2': 'val2'}, {'header2': 'val4'}]\n\n \"\"\"\n prev = None\n keys = None\n retval = []\n for line in md_text.splitlines():\n if line.strip().startswith('| --- |'):\n keys = (\n k.replace('*', '').strip()\n for k in prev.split('|')\n )\n keys = [k.lower() for k in keys if k]\n continue\n elif not keys:\n prev = line\n continue\n\n if not line or not line.strip().startswith('|'):\n break\n\n values = [v.strip() or None for v in line.split('|')][1:-1]\n retval.append({k: v for k, v in zip(keys, values) if v})\n\n return retval\n\n\ndef sort_contributors(entries, git_lines, exclude=None, last=None):\n \"\"\"Return a list of author dictionaries, ordered by contribution.\"\"\"\n last = last or []\n sorted_authors = sorted(entries, key=lambda i: i['name'])\n\n first_last = [\n ' '.join(val['name'].split(',')[::-1]).strip() for val in sorted_authors\n ]\n first_last_excl = [\n ' '.join(val['name'].split(',')[::-1]).strip() for val in exclude or []\n ]\n\n unmatched = []\n author_matches = []\n for ele in git_lines:\n matches = process.extract(\n ele, first_last, scorer=fuzz.token_sort_ratio, limit=2\n )\n # matches is a list [('First match', % Match), ('Second match', % Match)]\n if matches[0][1] > 80:\n val = sorted_authors[first_last.index(matches[0][0])]\n else:\n # skip unmatched names\n if ele not in first_last_excl:\n unmatched.append(ele)\n continue\n\n if val not in author_matches:\n author_matches.append(val)\n\n names = {' '.join(val['name'].split(',')[::-1]).strip() for val in author_matches}\n for missing_name in first_last:\n if missing_name not in names:\n missing = sorted_authors[first_last.index(missing_name)]\n author_matches.append(missing)\n\n position_matches = []\n for i, item in enumerate(author_matches):\n pos = item.pop('position', None)\n if pos is not None:\n position_matches.append((i, int(pos)))\n\n for i, pos in position_matches:\n if pos < 0:\n pos += len(author_matches) + 1\n author_matches.insert(pos, author_matches.pop(i))\n\n return author_matches, unmatched\n\n\ndef get_git_lines(fname='line-contributors.txt'):\n \"\"\"Run git-line-summary.\"\"\"\n import shutil\n import subprocess as sp\n\n contrib_file = Path(fname)\n\n lines = []\n if contrib_file.exists():\n print('WARNING: Reusing existing line-contributors.txt file.', file=sys.stderr)\n lines = contrib_file.read_text().splitlines()\n\n git_line_summary_path = shutil.which('git-line-summary')\n if not git_line_summary_path:\n git_line_summary_path = 'git summary --dedup-by-email'.split(' ')\n else:\n git_line_summary_path = [git_line_summary_path]\n\n if not lines and git_line_summary_path:\n print('Running git-line-summary on repo')\n lines = sp.check_output(git_line_summary_path).decode().splitlines()\n lines = [line for line in lines if 'Not Committed Yet' not in line]\n contrib_file.write_text('\\n'.join(lines))\n\n if not lines:\n _msg = (\n ': git-line-summary not found, please install git-extras '\n * (git_line_summary_path is None)\n )\n raise RuntimeError(\n f'Could not find line-contributors from git repository{_msg}.'\n )\n return [' '.join(line.strip().split()[1:-1]) for line in lines if '%' in line]\n\n\ndef _namelast(inlist):\n retval = []\n for i in inlist:\n i['name'] = (f\"{i.pop('name', '')} {i.pop('lastname', '')}\").strip()\n if not i['name']:\n i['name'] = i.get('handle', '')\n retval.append(i)\n return retval\n\n\n@click.group()\ndef cli():\n \"\"\"Generate authorship boilerplates.\"\"\"\n pass\n\n\n@cli.command()\n@click.option('-z', '--zenodo-file', type=click.Path(exists=True), default='.zenodo.json')\n@click.option('-m', '--maintainers', type=click.Path(exists=True), default='.maint/MAINTAINERS.md')\n@click.option('-c', '--contributors', type=click.Path(exists=True),\n default='.maint/CONTRIBUTORS.md')\n@click.option('--pi', type=click.Path(exists=True), default='.maint/PIs.md')\n@click.option('-f', '--former-file', type=click.Path(exists=True), default='.maint/FORMER.md')\ndef zenodo(\n zenodo_file,\n maintainers,\n contributors,\n pi,\n former_file,\n):\n \"\"\"Generate a new Zenodo payload file.\"\"\"\n data = get_git_lines()\n\n zenodo = json.loads(Path(zenodo_file).read_text())\n\n former = _namelast(read_md_table(Path(former_file).read_text()))\n zen_creators, miss_creators = sort_contributors(\n _namelast(read_md_table(Path(maintainers).read_text())),\n data,\n exclude=former,\n )\n\n zen_contributors, miss_contributors = sort_contributors(\n _namelast(read_md_table(Path(contributors).read_text())),\n data,\n exclude=former\n )\n\n zen_pi = _namelast(reversed(read_md_table(Path(pi).read_text())))\n\n zenodo['creators'] = zen_creators\n zenodo['contributors'] = zen_contributors + [\n pi for pi in zen_pi if pi not in zen_contributors\n ]\n creator_names = {\n c['name'] for c in zenodo['creators']\n if c['name'] != ''\n }\n\n zenodo['contributors'] = [\n c for c in zenodo['contributors']\n if c['name'] not in creator_names\n ]\n\n misses = set(miss_creators).intersection(miss_contributors)\n if misses:\n print(\n \"Some people made commits, but are missing in .maint/ \"\n f\"files: {', '.join(misses)}\",\n file=sys.stderr,\n )\n\n # Remove position\n for creator in zenodo['creators']:\n creator.pop('position', None)\n creator.pop('handle', None)\n if 'affiliation' not in creator:\n creator['affiliation'] = 'Unknown affiliation'\n elif isinstance(creator['affiliation'], list):\n creator['affiliation'] = creator['affiliation'][0]\n\n for creator in zenodo['contributors']:\n creator.pop('handle', None)\n creator['type'] = 'Researcher'\n creator.pop('position', None)\n\n if 'affiliation' not in creator:\n creator['affiliation'] = 'Unknown affiliation'\n elif isinstance(creator['affiliation'], list):\n creator['affiliation'] = creator['affiliation'][0]\n\n Path(zenodo_file).write_text(\n f'{json.dumps(zenodo, indent=2)}\\n'\n )\n\n\n@cli.command()\n@click.option('-m', '--maintainers', type=click.Path(exists=True), default='.maint/MAINTAINERS.md')\n@click.option('-c', '--contributors', type=click.Path(exists=True),\n default='.maint/CONTRIBUTORS.md')\n@click.option('--pi', type=click.Path(exists=True), default='.maint/PIs.md')\n@click.option('-f', '--former-file', type=click.Path(exists=True), default='.maint/FORMER.md')\ndef publication(\n maintainers,\n contributors,\n pi,\n former_file,\n):\n \"\"\"Generate the list of authors and affiliations for papers.\"\"\"\n members = (\n _namelast(read_md_table(Path(maintainers).read_text()))\n + _namelast(read_md_table(Path(contributors).read_text()))\n )\n former_names = _namelast(read_md_table(Path(former_file).read_text()))\n\n hits, misses = sort_contributors(\n members,\n get_git_lines(),\n exclude=former_names,\n )\n\n pi_hits = _namelast(reversed(read_md_table(Path(pi).read_text())))\n pi_names = [pi['name'] for pi in pi_hits]\n hits = [\n hit for hit in hits\n if hit['name'] not in pi_names\n ] + pi_hits\n\n def _aslist(value):\n if isinstance(value, (list, tuple)):\n return value\n return [value]\n\n # Remove position\n affiliations = []\n for item in hits:\n item.pop('position', None)\n for a in _aslist(item.get('affiliation', 'Unaffiliated')):\n if a not in affiliations:\n affiliations.append(a)\n\n aff_indexes = [\n ', '.join(\n [\n '%d' % (affiliations.index(a) + 1)\n for a in _aslist(author.get('affiliation', 'Unaffiliated'))\n ]\n )\n for author in hits\n ]\n\n if misses:\n print(\n \"Some people made commits, but are missing in .maint/ \"\n f\"files: {', '.join(misses)}\",\n file=sys.stderr,\n )\n\n print('Authors (%d):' % len(hits))\n print(\n '{}.'.format('; '.join(\n [\n '{} \\\\ :sup:`{}`\\\\ '.format(i['name'], idx)\n for i, idx in zip(hits, aff_indexes)\n ]\n ))\n )\n\n print(\n '\\n\\nAffiliations:\\n{}'.format('\\n'.join(\n [f'{i + 1: >2}. {a}' for i, a in enumerate(affiliations)]\n ))\n )\n\n\nif __name__ == '__main__':\n \"\"\" Install entry-point \"\"\"\n cli()\n" }, { "path": ".maint/update_changes.sh", "content": "#!/bin/bash\n#\n# Collects the pull-requests since the latest release and\n# aranges them in the CHANGES.rst file.\n#\n# This is a script to be run before releasing a new version.\n#\n# Usage /bin/bash update_changes.sh 1.0.1\n#\n\n# Setting # $ help set\nset -u # Treat unset variables as an error when substituting.\nset -x # Print command traces before executing command.\n\n# Check whether the Upcoming release header is present\nhead -1 CHANGES.rst | grep -q Upcoming\nUPCOMING=$?\nif [[ \"$UPCOMING\" == \"0\" ]]; then\n head -n3 CHANGES.rst >> newchanges\nfi\n\n# Search for PRs since previous release\ngit show --pretty='format: * %b %s' HEAD | sed 's/Merge pull request \\#\\([^\\d]*\\)\\ from\\ .*/(\\#\\1)/' >> newchanges\n\n# Add back the Upcoming header if it was present\nif [[ \"$UPCOMING\" == \"0\" ]]; then\n tail -n+4 CHANGES.rst >> newchanges\nelse\n cat CHANGES.rst >> newchanges\nfi\n\n# Replace old CHANGES.rst with new file\nmv newchanges CHANGES.rst\n\n" }, { "path": ".readthedocs.yaml", "content": "# .readthedocs.yaml\n# Read the Docs configuration file\n# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details\n\n# Required\nversion: 2\n\n# Set the version of Python and other tools you might need\nbuild:\n os: ubuntu-20.04\n apt_packages:\n - graphviz\n tools:\n python: \"3.9\"\n\n# Build documentation in the docs/ directory with Sphinx\nsphinx:\n configuration: docs/source/conf.py\n\n# Install with pip install .[docs]\npython:\n install:\n - method: pip\n path: .\n extra_requirements:\n - docs\n" }, { "path": ".zenodo.json", "content": "{\n \"title\": \"MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites\",\n \"description\": \"

Automated Quality Control and visual reports for Quality Assessment of structural (T1w, T2w) and functional MRI of the brain http://mriqc.readthedocs.io.

\",\n \"creators\": [\n {\n \"orcid\": \"0000-0001-8435-6191\",\n \"affiliation\": \"Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland\",\n \"name\": \"Oscar Esteban\"\n },\n {\n \"orcid\": \"0000-0002-6533-164X\",\n \"affiliation\": \"Department of Psychology, Stanford University, CA, USA\",\n \"name\": \"Christopher J. Markiewicz\"\n },\n {\n \"orcid\": \"0000-0003-3715-7012\",\n \"affiliation\": \"Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK\",\n \"name\": \"Eilidh MacNicol\"\n },\n {\n \"orcid\": \"0000-0002-1668-9629\",\n \"affiliation\": \"Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland\",\n \"name\": \"C\\u00e9line Provins\"\n },\n {\n \"orcid\": \"0000-0002-7454-8189\",\n \"affiliation\": \"Psychology Department, University of Washington, Seattle, WA, USA\",\n \"name\": \"McKenzie P. Hagen\"\n }\n ],\n \"contributors\": [\n {\n \"orcid\": \"0000-0001-7159-1387\",\n \"affiliation\": \"Quantivly Inc., Somerville, MA, USA\",\n \"name\": \"Zvi Baratz\",\n \"type\": \"Researcher\"\n },\n {\n \"affiliation\": \"The University of Washington eScience Institute, WA, USA\",\n \"name\": \"Teresa Gomez\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0003-4613-6643\",\n \"affiliation\": \"Section on Clinical and Computational Psychiatry, National Institute of Mental Health, Bethesda, MD, USA\",\n \"name\": \"Dylan Nielson\",\n \"type\": \"Researcher\"\n },\n {\n \"affiliation\": \"Functional MRI Facility, National Institute of Mental Health, Bethesda, MD, USA\",\n \"name\": \"Jan Varada\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0003-3007-1056\",\n \"affiliation\": \"Department of Psychology, Stanford University, CA, USA\",\n \"name\": \"Ross W. Blair\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0003-0679-1985\",\n \"affiliation\": \"The University of Washington eScience Institute, WA, USA\",\n \"name\": \"Ariel Rokem\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0002-1242-8990\",\n \"affiliation\": \"NeuroSpin, CEA, Universit\\u00e9 Paris-Saclay, NeuroSpin, Gif-sur-Yvette, France\",\n \"name\": \"Dimitri Papadopoulos Orfanos\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0002-9546-1306\",\n \"affiliation\": \"University of Florida: Gainesville, Florida, US\",\n \"name\": \"William Triplett\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0002-7252-7771\",\n \"affiliation\": \"Department of Psychology, Stanford University, CA, USA\",\n \"name\": \"Mathias Goncalves\",\n \"type\": \"Researcher\"\n },\n {\n \"affiliation\": \"CRC ULiege, Liege, Belgium\",\n \"name\": \"Nikita Beliy\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0001-5884-4247\",\n \"affiliation\": \"Quansight, Dublin, Ireland\",\n \"name\": \"John A. Lee\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0002-9661-1396\",\n \"affiliation\": \"Brigham and Women's Hospital, Mass General Brigham, Harvard Medical School, MA, USA\",\n \"name\": \"Jon Haitz Legarreta Gorro\\u00f1o\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0003-4141-1343\",\n \"affiliation\": \"Johns Hopkins Bloomberg School of Public Health, MD, USA\",\n \"name\": \"Patrick Sadil\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0001-6377-3885\",\n \"affiliation\": \"Department of Neuroscience, University of Pennsylvania, PA, USA\",\n \"name\": \"Ursula A. Tooley\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0003-3456-2493\",\n \"affiliation\": \"Psychological and Brain Sciences Department, Dartmouth College, NH, USA\",\n \"name\": \"Yaroslav O. Halchenko\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0003-2882-0900\",\n \"affiliation\": \"McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, USA\",\n \"name\": \"Yibei Chen\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0002-4892-2659\",\n \"affiliation\": \"Department of Psychology, University of Texas at Austin, TX, USA\",\n \"name\": \"James D. Kent\",\n \"type\": \"Researcher\"\n },\n {\n \"affiliation\": \"University of Michigan, Ann Arbor, USA\",\n \"name\": \"Bennet Fauber\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0001-9813-3167\",\n \"affiliation\": \"Department of Psychology, Florida International University, FL, USA\",\n \"name\": \"Taylor Salo\",\n \"type\": \"Researcher\"\n },\n {\n \"affiliation\": \"Max Planck Institute for Human Development, Berlin, Germany\",\n \"name\": \"Michael Krause\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0002-5749-6049\",\n \"affiliation\": \"Center for Brain Imaging, New York University, NY, USA\",\n \"name\": \"Pablo Velasco\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0002-4516-5103\",\n \"affiliation\": \"Oxford Big Data Institute, University of Oxford, Oxford, GB\",\n \"name\": \"Thomas Nichols\",\n \"type\": \"Researcher\"\n },\n {\n \"affiliation\": \"Department of Physics, Imperial College London, London, UK\",\n \"name\": \"Adam Huffman\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0002-3896-6906\",\n \"affiliation\": \"Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland\",\n \"name\": \"Elodie Savary\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0002-0657-6516\",\n \"affiliation\": \"Charit\\u00e9 Berlin, Berlin, Germany\",\n \"name\": \"Johannes Achtzehn\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0001-9030-2202\",\n \"affiliation\": \"Department of Psychology, Stanford University, CA, USA\",\n \"name\": \"Joke Durnez\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0002-5312-6729\",\n \"affiliation\": \"McGovern Institute for Brain Research, MIT, MA, USA; and Department of Otolaryngology, Harvard Medical School, MA, USA\",\n \"name\": \"Satrajit S. Ghosh\",\n \"type\": \"Researcher\"\n },\n {\n \"affiliation\": \"Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA\",\n \"name\": \"Adam C. Raikes\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0002-9402-2184\",\n \"affiliation\": \"Computational Neuroimaging Lab, BioCruces Health Research Institute\",\n \"name\": \"Asier Erramuzpe\",\n \"type\": \"Researcher\"\n },\n {\n \"affiliation\": \"Washington University School of Medicine, St.Louis, MO, USA\",\n \"name\": \"Benjamin Kay\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0003-3748-6289\",\n \"affiliation\": \"Department of Psychology, Stanford University, CA, USA\",\n \"name\": \"Daniel Birman\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0002-2666-0969\",\n \"affiliation\": \"International Committee of the Red Cross - ICRC, Geneva, Switzerland\",\n \"name\": \"Michael Dayan\",\n \"type\": \"Researcher\"\n },\n {\n \"affiliation\": \"National Institutes of Health, USA\",\n \"name\": \"Michael G. Clark\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0001-9332-1580\",\n \"affiliation\": \"Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK\",\n \"name\": \"Rafael Garcia-Dias\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0002-2850-1419\",\n \"affiliation\": \"Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD, USA\",\n \"name\": \"Adam G. Thomas\",\n \"type\": \"Researcher\"\n },\n {\n \"orcid\": \"0000-0001-6755-0259\",\n \"affiliation\": \"Department of Psychology, Stanford University, CA, USA\",\n \"name\": \"Russell A. Poldrack\",\n \"type\": \"Researcher\"\n }\n ],\n \"keywords\": [\n \"neuroimaging\",\n \"workflow\",\n \"pipeline\",\n \"quality-control\",\n \"fMRI\",\n \"BIDS\"\n ],\n \"related_identifiers\": [\n {\n \"identifier\": \"http://mriqc.readthedocs.io\",\n \"relation\": \"documents\",\n \"scheme\": \"url\"\n },\n {\n \"identifier\": \"10.1371/journal.pone.0184661\",\n \"relation\": \"isPartOf\",\n \"scheme\": \"doi\"\n }\n ],\n \"license\": \"Apache-2.0\",\n \"upload_type\": \"software\"\n}\n" }, { "path": "AGENTS.md", "content": "# Agent Instructions for Codex\n\n## Operating Principles\n1. **Always plan first.** Provide a brief plan before any changes and do the hardest thinking during the planning phase.\n2. **Highlight critical points.** When proposing tasks, call out steps that could introduce side effects (e.g., migrations, API changes, backward-compatibility concerns).\n3. **Ask one question at a time.** If a decision is needed from a user, ask a single, clear question before proceeding.\n\n## Branch Naming\n- **Format:** `/`\n- **Examples:**\n - `fix/one-off-bug-gh134`\n - `enh/add-bids-validator`\n - `doc/update-readme`\n- If working from a tracked issue, include the issue number in the branch name (e.g., `fix/segfault-gh134`).\n\n## Commit Messages (Conventional Commits)\n- **Must strictly follow** Conventional Commits: https://www.conventionalcommits.org/en/v1.0.0/\n- Examples:\n - `fix(parser): handle empty token stream`\n - `docs(readme): add quickstart example`\n - `chore(ci): pin action versions`\n\n## Pull Request Titles & Descriptions\n- **PR Title format:** `TYPE: Short summary`\n - Type is **all caps**, from a conventional-commit-like set (e.g., FIX, ENH, MNT, DOC, CI, CHORE).\n - **No scope parenthetical** in PR titles.\n- **Examples:**\n - `FIX: Address one-off bug in traversing list`\n - `DOC: Complete docstring with missing arguments`\n- **PR Description should include:**\n - Summary of changes\n - Motivation / context\n - Testing performed (exact commands)\n\n## Linting & Pre-Change Validation\n- **Always run linting before proposing changes.**\n- This repository uses the GitHub Actions linting command:\n - `pipx run ruff format --diff`\n- If linting cannot run due to environment constraints, state the limitation and suggest a retry in a fully configured environment.\n" }, { "path": "CHANGES.rst", "content": "25.0.0 (TBD)\n============\nA new 25.x series.\n\nCHANGES\n-------\n * FIX: Decode bytes to produce a string (#1371)\n * FIX: Containers report wrong version (#1357)\n * FIX: Elapsed run time format (#1366)\n * FIX: Passing ``bytes`` into ``str.join()`` (#1356)\n * FIX: Optimize interface to minimize memory fingerprint (#1351)\n * ENH: Remove redundant *DIPY* function (#1369)\n * DOC: Add a HPC troubleshooting section to the documentation (#1349)\n * DOC: Provide a range of years ending with current for the copyright statement (#1359)\n * DOC: Fix missing code block start (#1368)\n * DOC: Reorganize documentation and redirect to *NiPreps* docs (#1367)\n\n24.0.2 (August 26, 2024)\n========================\nA patch release with bugfixes and enhancements.\n\nCHANGES\n-------\n\n* FIX: Pin latest *NiReports* release (24.0.2) addressing ``fMRIPlot`` issues by @oesteban (`#1342 `__)\n* FIX: Edge artifacts in first and last slices due to interpolation by @oesteban (`#1338 `__)\n* FIX: Normalize bids-filters' modality keys to be lowercase by @oesteban (`#1332 `__)\n* ENH: Add license NOTICE to start banner by @oesteban (`#1343 `__)\n* ENH: Enable writing crashfiles in compressed-pickle format by @oesteban (`#1339 `__)\n* ENH: Use ``orjson`` to serialize JSON, addressing *Numpy* serialization issues by @oesteban (`#1337 `__)\n* ENH: Handle WebAPI timeouts more gently by @oesteban (`#1336 `__)\n\n\n24.0.1 (August 20, 2024)\n========================\nA patch release with a large number of bugfixes (mostly focusing on memory issues), maintenance\nactivities, and metadata crawling before *Nipype* kicks in as a major optimization.\n\nWith thanks to @jhlegarreta for his first contribution in `#1293 __`.\n\n.. admonition:: Author list for papers based on *MRIQC* 24.0 series\n\n As described in the `Contributor Guidelines\n `__,\n anyone listed as a developer or contributor may write and submit manuscripts\n about *MRIQC*.\n To do so, please move the author(s) name(s) to the front of the following list:\n\n Christopher J. Markiewicz \\ :sup:`1`\\ ; Zvi Baratz \\ :sup:`2`\\ ; Eilidh MacNicol \\ :sup:`3`\\ ; Céline Provins \\ :sup:`4`\\ ; Teresa Gomez \\ :sup:`5`\\ ; Dylan Nielson \\ :sup:`6`\\ ; Ross W. Blair \\ :sup:`1`\\ ; Jan Varada \\ :sup:`7`\\ ; Dimitri Papadopoulos Orfanos \\ :sup:`8`\\ ; William Triplett \\ :sup:`9`\\ ; Mathias Goncalves \\ :sup:`1`\\ ; Nikita Beliy \\ :sup:`10`\\ ; John A. Lee \\ :sup:`11`\\ ; Yibei Chen \\ :sup:`12`\\ ; Ursula A. Tooley \\ :sup:`13`\\ ; Patrick Sadil \\ :sup:`14`\\ ; Yaroslav O. Halchenko \\ :sup:`15`\\ ; James D. Kent \\ :sup:`16`\\ ; Taylor Salo \\ :sup:`17`\\ ; Bennet Fauber \\ :sup:`18`\\ ; Thomas Nichols \\ :sup:`19`\\ ; Pablo Velasco \\ :sup:`20`\\ ; Michael Krause \\ :sup:`21`\\ ; Jon Haitz Legarreta Gorroño \\ :sup:`22`\\ ; Satrajit S. Ghosh \\ :sup:`23`\\ ; Joke Durnez \\ :sup:`1`\\ ; Johannes Achtzehn \\ :sup:`24`\\ ; Elodie Savary \\ :sup:`4`\\ ; Adam Huffman \\ :sup:`25`\\ ; Rafael Garcia-Dias \\ :sup:`26`\\ ; Michael G. Clark \\ :sup:`27`\\ ; Michael Dayan \\ :sup:`28`\\ ; McKenzie P. Hagen \\ :sup:`29`\\ ; Daniel Birman \\ :sup:`1`\\ ; Benjamin Kay \\ :sup:`30`\\ ; Asier Erramuzpe \\ :sup:`31`\\ ; Adam C. Raikes \\ :sup:`32`\\ ; Adam G. Thomas \\ :sup:`33`\\ ; Russell A. Poldrack \\ :sup:`1`\\ ; Ariel Rokem \\ :sup:`5`\\ ; Oscar Esteban \\ :sup:`4`\\ .\n\n Affiliations:\n\n 1. Department of Psychology, Stanford University, CA, USA\n 2. Quantivly Inc., Somerville, MA, USA\n 3. Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK\n 4. Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland\n 5. The University of Washington eScience Institute, WA, USA\n 6. Section on Clinical and Computational Psychiatry, National Institute of Mental Health, Bethesda, MD, USA\n 7. Functional MRI Facility, National Institute of Mental Health, Bethesda, MD, USA\n 8. NeuroSpin, CEA, Université Paris-Saclay, NeuroSpin, Gif-sur-Yvette, France\n 9. University of Florida: Gainesville, Florida, US\n 10. CRC ULiege, Liege, Belgium\n 11. Quansight, Dublin, Ireland\n 12. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, USA\n 13. Department of Neuroscience, University of Pennsylvania, PA, USA\n 14. Johns Hopkins Bloomberg School of Public Health, MD, USA\n 15. Psychological and Brain Sciences Department, Dartmouth College, NH, USA\n 16. Department of Psychology, University of Texas at Austin, TX, USA\n 17. Department of Psychology, Florida International University, FL, USA\n 18. University of Michigan, Ann Arbor, USA\n 19. Oxford Big Data Institute, University of Oxford, Oxford, GB\n 20. Center for Brain Imaging, New York University, NY, USA\n 21. Max Planck Institute for Human Development, Berlin, Germany\n 22. Brigham and Women's Hospital, Mass General Brigham, Harvard Medical School, MA, USA\n 23. McGovern Institute for Brain Research, MIT, MA, USA; and Department of Otolaryngology, Harvard Medical School, MA, USA\n 24. Charité Berlin, Berlin, Germany\n 25. Department of Physics, Imperial College London, London, UK\n 26. Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK\n 27. National Institutes of Health, USA\n 28. International Committee of the Red Cross - ICRC, Geneva, Switzerland\n 29. Psychology Department, University of Washington, Seattle, WA, USA\n 30. Washington University School of Medicine, St.Louis, MO, USA\n 31. Computational Neuroimaging Lab, BioCruces Health Research Institute\n 32. Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA\n 33. Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD, USA\n\nCHANGES\n-------\n\n* FIX: Multiecho fMRI crashing with 'unhashable type' errors by @oesteban in https://github.com/nipreps/mriqc/pull/1295\n* FIX: Set ``n_procs`` instead of ``num_threads`` on node ``apply_hmc`` by @oesteban in https://github.com/nipreps/mriqc/pull/1309\n* FIX: Address memory issues by limiting ``BigPlot``'s parallelization. by @oesteban in https://github.com/nipreps/mriqc/pull/1320\n* FIX: Address memory issues in the DWI pipeline by @oesteban in https://github.com/nipreps/mriqc/pull/1323\n* FIX: Limit IQMs' node number of processes and, therefore, memory by @oesteban in https://github.com/nipreps/mriqc/pull/1325\n* FIX: Resolve numeric overflow in drift estimation node by @oesteban in https://github.com/nipreps/mriqc/pull/1324\n* FIX: Revise bugfix #1324 by @oesteban in https://github.com/nipreps/mriqc/pull/1327\n* FIX: Remove unreachable code within DWI pipeline by @oesteban in https://github.com/nipreps/mriqc/pull/1328\n* ENH: Allow moving the cache folder with an environment variable by @oesteban in https://github.com/nipreps/mriqc/pull/1285\n* ENH: Flatten multi-echo lists in circumstances that they fail by @oesteban in https://github.com/nipreps/mriqc/pull/1286\n* ENH: Added type hints to config module by @zvi-quantivly in https://github.com/nipreps/mriqc/pull/1288\n* ENH: Add test for the CLI parser by @jhlegarreta in https://github.com/nipreps/mriqc/pull/1293\n* ENH: Add CLI entry point test by @jhlegarreta in https://github.com/nipreps/mriqc/pull/1294\n* ENH: Add a development Dockerfile for testing local changes to the repo. by @rwblair in https://github.com/nipreps/mriqc/pull/1299\n* ENH: Crawl dataset's metadata only once and before Nipype's workflow by @oesteban in https://github.com/nipreps/mriqc/pull/1317\n* ENH(dMRI): Deal gracefully with small CC masks by @oesteban in https://github.com/nipreps/mriqc/pull/1311\n* ENH: Leverage new spun-off apply interface by @oesteban in https://github.com/nipreps/mriqc/pull/1313\n* MAINT: Removed personal information from maintainers and updated in contributors by @zvi-quantivly in https://github.com/nipreps/mriqc/pull/1289\n* MAINT: Add JHLegarreta to the contributors list by @jhlegarreta in https://github.com/nipreps/mriqc/pull/1301\n* MAINT: Flexibilize pandas pinned version by @oesteban in https://github.com/nipreps/mriqc/pull/1310\n* MAINT: Remove *Pandas*'s ``FutureWarning`` by @oesteban in https://github.com/nipreps/mriqc/pull/1326\n* DOC: Add description of ``summary_fg`` to the documentation by @celprov in https://github.com/nipreps/mriqc/pull/1306\n* STY: Apply ruff/flake8-implicit-str-concat rule ISC001 by @DimitriPapadopoulos in https://github.com/nipreps/mriqc/pull/1296\n* STY: Format *Jupyter notebooks* by @oesteban in https://github.com/nipreps/mriqc/pull/1321\n\n**Full Changelog**: https://github.com/nipreps/mriqc/compare/24.0.0...24.0.1\n\n24.0.0 (April 17, 2024)\n=======================\nInitial major release of 2024, featuring the **extraction of IQMs from DWI data**\nfor the first time in *MRIQC*'s timeline.\n\nCHANGES\n-------\n\n* FIX: Bug in *toml* loader crashing with mixed arrays in config (#1281)\n* FIX: Remove *DataLad* as a node (#1278)\n* FIX: Calculation of trivial shells (#1276)\n* FIX: Finalized naming and connection of DWI IQMs (#1272)\n* FIX: Enable group reports for DWI (#1266)\n* FIX: Address issues that had broken the group reports (#1262)\n* FIX: Select filters if modalities are selected (#1261)\n* FIX: Make sure new logs and config file output are compatible with parallel processes (#1259)\n* FIX: Skip short BOLD runs that break outlier detection (#1120)\n* FIX: Revise config save/load and update inputs after dropping (#1245)\n* FIX: Drift should not be estimated when less than three low-b volumes present (#1242, #1243)\n* FIX: Handle ``NUMEXPR_MAX_THREADS`` like ``OMP_NUM_THREADS`` (#1241)\n* FIX: Exclude DWI runs with insufficient orientations or missing bvals (#1240)\n* FIX: Avert costly ``BIDSLayout.__repr__`` calls when saving config (#1239)\n* FIX: Duplicate node in anatomical workflow (#1234)\n* FIX: Typo in ``sorted(..., reverse=True)`` call (#1211)\n* ENH: Fail for non-formatted code (#1274)\n* ENH: Annotate nodes with ``n_procs`` to allow safe parallelization (#1277)\n* ENH: Mechanism to protect config's fields and write out config (#1258)\n* ENH: Improve documentation and logging of *SynthStrip*'s model (#1254)\n* ENH: Improve logging of runtime (#1253)\n* ENH: Expose a command-line option for minimum DWI volumes (#1249)\n* ENH: Improve error handling and logging (#1238)\n* ENH: Add *b*-vector angular deviations as IQMs (#1233)\n* ENH: Move from DTI to DKI with multishell data (#1230)\n* ENH: Noise floor estimated with PCA (``dwidenoise``) as an IQM (#1229)\n* ENH: Integrate PIESNO noise mask and sigma estimation (#1227)\n* ENH: Use MAD for robust estimation of sigma in the CC mask (#1228)\n* ENH: Add new IQM for DWI → NDC (#1226)\n* ENH: Add FA-based IQMs (nans percentage and degenerate percentage) (#1225)\n* ENH: Add computation of spiking voxels mask and percent IQMs (#1224)\n* ENH: Adds diffusion-related IQMs. (#1131)\n* ENH: Revise summary stats extraction and include controlled roundings (#1219)\n* DOC: Add changelog to documentation (#1217)\n* MAINT: Added ruff to development dependencies (#1271)\n* MAINT: Removed pre-commit from development dependencies (#1269)\n* MAINT: Clean up more ``FutureWarning`` issued by *Pandas* (#1257)\n* MAINT: Prevent pandas-originating deprecation warning (#1251)\n* MAINT: Move GitHub Actions and config files from *flake8* → *ruff* (#1212)\n* MAINT: Update contributor affiliation in ``CONTRIBUTORS.md`` (#1214)\n* STY: Reformat diffusion workflows module (#1279)\n* STY: Applied ruff formatting (#1273)\n\n23.1.1 (March 20, 2024)\n=======================\nA long-overdue hotfix release addressing many bugs generated during the development\nof the new dMRI workflows, and some relating to improvements of the handling of\nmulti-echo fMRI.\nThe release also include one year-worth of maintenance actions and a general code\ncleanup with *Ruff*.\n\nCHANGES\n-------\n\n* FIX: Missing connection to head-motion estimation node in DWI workflow by `@oesteban `__ in `#1207 `__\n* FIX: Revise porting to ``Loader`` by `@oesteban `__ in `#1201 `__\n* FIX: Revise the last two sloppy merges by `@oesteban `__ in `#1200 `__\n* FIX: Move from ``pkg_resources`` to ``niworkflows.data.Loader`` by `@oesteban `__ in `#1199 `__\n* FIX: DIPY not listed as a dependency by `@oesteban `__ in `#1197 `__\n* FIX: Include ``dwidenoise`` within Docker image by `@oesteban `__ in `#1196 `__\n* FIX: Copy name attribute of ``dataset_description.json`` from input dataset by `@celprov `__ in `#1187 `__\n* FIX: Remove FD as an ``iterfield`` in ``MapNode`` causing crash with ME-BOLD by `@celprov `__ in `#1179 `__\n* FIX: Incorrect plugin metadata passed to *Report Assembler* by `@oesteban `__ in `#1188 `__\n* FIX: Temporary fix of the missing ``\"dwi\"`` key by `@celprov `__ in `#1174 `__\n* FIX: Rearrange multi-echo report by `@celprov `__ in `#1164 `__\n* FIX: Typo in ``inputnode`` field in dMRI masking workflow by `@celprov `__ in `#1165 `__\n* FIX: Bug in group level workflow by `@celprov `__ in `#1148 `__\n* FIX: Bugs in DWI workflow by `@celprov `__ in `#1147 `__\n* FIX: Use simpler DWI reference workflow by `@yibeichan `__ in `#1145 `__\n* FIX: Drop deprecated *Networkx*'s API by `@celprov `__ in `#1137 `__\n* FIX: Replace ``np.float`` by ``np.float64`` by `@celprov `__ in `#1140 `__\n* ENH: Improved logging and optimize early checkpoint on subjects by `@oesteban `__ in `#1198 `__\n* ENH: Store confound timeseries data by `@psadil `__ in `#1166 `__\n* ENH: Large overhaul of the functional workflow w/focus on ME-EPI by `@oesteban `__ in `#1155 `__\n* ENH: Implement BIDS filters file and drop legacy BIDS querying by `@oesteban `__ in `#1154 `__\n* ENH: Swap background and zoomed-in visualizations in anatomical reports by `@oesteban `__ in `#1151 `__\n* MAINT: Test on *Python* 3.12 by `@DimitriPapadopoulos `__ in `#1156 `__\n* MAINT: Disable flaky T1w test on CircleCI by `@oesteban `__ in `#1202 `__\n* MAINT: Overhaul of the ``Dockerfile`` by `@oesteban `__ in `#1195 `__\n* MAINT: Revise package's extra dependencies by `@oesteban `__ in `#1194 `__\n* MAINT: Clean up some ``setuptools_scm`` remnants by `@oesteban `__ in `#1193 `__\n* MAINT: Load ``FMRISummary`` from *NiReports* rather than *NiWorkflows* by `@celprov `__ in `#1167 `__\n* MAINT: Update to latest *migas*' API by `@mgxd `__ in `#1160 `__\n* MAINT: Update bold to large resource class in ``config.yml`` by `@oesteban `__ in `#1158 `__\n* MAINT: Refresh cached intermediate results by `@oesteban `__ in `#1143 `__\n* MAINT: Simplify GitHub actions checks and update action versions by `@effigies `__ in `#1141 `__\n* MAINT: Python 3.11 is supported by `@DimitriPapadopoulos `__ in `#1123 `__\n* MAINT: Apply suggestions from pyupgrade by `@DimitriPapadopoulos `__ in `#1124 `__\n* DOC: Update *Sphinx* pinned version to 5 by `@oesteban `__ in `#1192 `__\n* DOC: http:// → https:// by `@DimitriPapadopoulos `__ in `#1126 `__\n* DOC: Add info on the *FreeSurfer* requirement for bare install to address #1034 by `@neurorepro `__ in `#1130 `__\n* STY: Add *Ruff* config and fix all warnings and errors by `@oesteban `__ in `#1203 `__\n* STY: Remove extraneous parentheses by `@DimitriPapadopoulos `__ in `#1186 `__\n* STY: Apply a few refurb suggestions by `@DimitriPapadopoulos `__ in `#1162 `__\n* STY: Fix typo found by codespell by `@DimitriPapadopoulos `__ in `#1161 `__\n\n23.1.0 (June 14, 2023)\n======================\nA new minor release featuring the new individual reports built with the new\n*NiReports* VRS (visual reports system). This means *MRIQC* now uses the same\npackage *fMRIPrep* uses for generating its reports. In addition to that,\nthis new release also features *Beta* support for diffusion MRI (dMRI). \n\nCHANGES\n-------\n\n* FIX: Better handling of BIDS cached indexation (`#1121 `__)\n* FIX: Make doctest of ``NumberOfShells`` more reliable (`#1122 `__)\n* FIX: Add protection for NaNs and INFs when calculating QI2 (`#1112 `__)\n* FIX: ``PlotMosaic`` expects lists, not tuples (`#1111 `__)\n* FIX: BIDS database directory handling (`#1110 `__)\n* FIX: Remove unused dipy import in the functional interfaces (`#1109 `__)\n* FIX: Refine the head mask after removal of FSL BET (`#1107 `__)\n* FIX: Inform *SynthStrip* about the desired intraop threads (`#1101 `__)\n* FIX: Test broken by #1098 (`#1100 `__)\n* FIX: Separate report bootstrap files (anat vs. func) (`#1098 `__)\n* FIX: Propagate logging level to subprocesses (`#1030 `__)\n* ENH: Incorporate new NiReports' DWI heatmaps (`#1119 `__)\n* ENH: More compact of shell-wise summary statistic maps (avg/std) (`#1116 `__)\n* ENH: Add a basic DTI fitting into the diffusion workflow (`#1115 `__)\n* ENH: MRIQC for DWI (`#1113 `__)\n* ENH: Culminate dropping FSL as a dependency (`#1108 `__)\n* ENH: Replace FSL FAST with ANTs Atropos for brain tissue segmentation (`#1099 `__)\n* ENH: Drop FSL MELODIC (without alternative) (`#1106 `__)\n* ENH: Drop FSL BET to estimate the \"outskin\" (head) mask (`#1105 `__)\n* ENH: Drop utilization of \"head\" mask from template (`#1104 `__)\n* ENH: Move templates' probsegs into individual at normalization (`#1103 `__)\n* ENH: Improving the resource monitor -- infer PID from process name (`#1049 `__) (`#1049 `__)\n* ENH: Refactor reports system to use *NiReports* and the general VRS (`#1085 `__)\n* MAINT: Move codespell configuration to ``pyproject.toml`` (`#1097 `__)\n* MAINT: Update deprecated ``nibabel.spatialimage.get_data()`` calls (`#1096 `__)\n\n.. admonition:: Author list for papers based on *MRIQC* 23.0 series\n\n As described in the `Contributor Guidelines\n `__,\n anyone listed as developer or contributor may write and submit manuscripts\n about *MRIQC*.\n To do so, please move the author(s) name(s) to the front of the following list:\n \n Zvi Baratz \\ :sup:`1`\\ ; Christopher J. Markiewicz \\ :sup:`2`\\ ; Eilidh MacNicol \\ :sup:`3`\\ ; Dylan Nielson \\ :sup:`4`\\ ; Jan Varada \\ :sup:`5`\\ ; Ross W. Blair \\ :sup:`2`\\ ; Céline Provins \\ :sup:`6`\\ ; William Triplett \\ :sup:`7`\\ ; Mathias Goncalves \\ :sup:`2`\\ ; Nikita Beliy \\ :sup:`8`\\ ; John A. Lee \\ :sup:`9`\\ ; Ursula A. Tooley \\ :sup:`10`\\ ; James D. Kent \\ :sup:`11`\\ ; Yaroslav O. Halchenko \\ :sup:`12`\\ ; Bennet Fauber \\ :sup:`13`\\ ; Taylor Salo \\ :sup:`14`\\ ; Michael Krause \\ :sup:`15`\\ ; Pablo Velasco \\ :sup:`16`\\ ; Thomas Nichols \\ :sup:`17`\\ ; Adam Huffman \\ :sup:`18`\\ ; Elodie Savary \\ :sup:`6`\\ ; Johannes Achtzehn \\ :sup:`19`\\ ; Joke Durnez \\ :sup:`2`\\ ; Satrajit S. Ghosh \\ :sup:`20`\\ ; Asier Erramuzpe \\ :sup:`21`\\ ; Benjamin Kay \\ :sup:`22`\\ ; Daniel Birman \\ :sup:`2`\\ ; McKenzie P. Hagen \\ :sup:`23`\\ ; Michael G. Clark \\ :sup:`24`\\ ; Patrick Sadil \\ :sup:`25`\\ ; Rafael Garcia-Dias \\ :sup:`26`\\ ; Adam G. Thomas \\ :sup:`27`\\ ; Russell A. Poldrack \\ :sup:`2`\\ ; Ariel Rokem \\ :sup:`28`\\ ; Oscar Esteban \\ :sup:`6`\\ .\n\n Affiliations:\n\n 1. Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel\n 2. Department of Psychology, Stanford University, CA, USA\n 3. Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK\n 4. Section on Clinical and Computational Psychiatry, National Institute of Mental Health, Bethesda, MD, USA\n 5. Functional MRI Facility, National Institute of Mental Health, Bethesda, MD, USA\n 6. Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland\n 7. University of Florida: Gainesville, Florida, US\n 8. CRC ULiege, Liege, Belgium\n 9. Quansight, Dublin, Ireland\n 10. Department of Neuroscience, University of Pennsylvania, PA, USA\n 11. Department of Psychology, University of Texas at Austin, TX, USA\n 12. Psychological and Brain Sciences Department, Dartmouth College, NH, USA\n 13. University of Michigan, Ann Arbor, USA\n 14. Department of Psychology, Florida International University, FL, USA\n 15. Max Planck Institute for Human Development, Berlin, Germany\n 16. Center for Brain Imaging, New York University, NY, USA\n 17. Oxford Big Data Institute, University of Oxford, Oxford, GB\n 18. Department of Physics, Imperial College London, London, UK\n 19. Charité Berlin, Berlin, Germany\n 20. McGovern Institute for Brain Research, MIT, MA, USA; and Department of Otolaryngology, Harvard Medical School, MA, USA\n 21. Computational Neuroimaging Lab, BioCruces Health Research Institute\n 22. Washington University School of Medicine, St.Louis, MO, USA\n 23. Psychology Department, University of Washington, Seattle, WA, USA\n 24. National Institutes of Health, USA\n 25. Johns Hopkins Bloomberg School of Public Health, MD, USA\n 26. Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK\n 27. Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD, USA\n 28. The University of Washington eScience Institute, WA, USA\n\n23.0.1 (March 24, 2023)\n=======================\nA hotfix release resolving a reggression introduced with the new optimized indexing.\n\n* FIX: Underspecified regex sets ``BIDSLayout`` to ignore data with sessions (`#1094 `__)\n* FIX: Input data has incompatible dimensionality (plotting ICA) (`#1082 `__)\n* ENH: Optimize metadata gathering reusing ``BIDSLayout`` db (`#1084 `__)\n* DOC : update anatomical example report in documentation (`#1088 `__)\n* MAINT: Drop old ``mriqc_plot`` script (`#1091 `__)\n\n23.0.0 (March 10, 2023)\n=======================\nThe new 23.0.x series include several prominent changes.\nVisualization has been migrated from *MRIQC* and *niworkflows* over to the new *NiReports* project.\nThis series include a major bugfix with **the optimization of the indexing** of the input BIDS folder,\nwhich was taking large times with sizeable datasets.\nTelemetry has also been incorporated with *migas*.\nThese new series also involve maintenance housekeeping, and includes some relevant bugfixes.\n\nNew contributors\n----------------\n\n* `@arokem `__ made their first contribution in `#1040 `__\n* `@yarikoptic `__ made their first contribution in `#1057 `__\n* `@esavary `__ made their first contribution in `#1047 `__\n\nCHANGES\n-------\n**Full Changelog**: https://github.com/nipreps/mriqc/compare/22.0.6...23.0.0\n\n* FIX: Send metadata extraction to workers (functional workflow) (`#1081 `__)\n* FIX: Plot coronal as main plain for mosaic of rodent images (`#1027 `__)\n* FIX: Address non-empty take from empty axes (anatomical IQMs) (`#1077 `__)\n* FIX: Uniformize building workflow message (anat vs. func) (`#1072 `__)\n* FIX: Move telemetry atexit into entrypoint func (`#1067 `__)\n* FIX: Preempt PyBIDS to spend time indexing non-BIDS folders (`#1050 `__)\n* FIX: Update T1w metrics (`#1063 `__)\n* FIX: Resource monitor would not ever start tracking (`#1051 `__)\n* ENH: Add DataLad getter to inputs of functional workflows (`#1071 `__)\n* ENH: Add migas telemetry (`#1036 `__)\n* ENH: Add codespell automation: config, action, and typos fixed (`#1057 `__)\n* MAINT: Update *NiReports* calls to upcoming interfaces API (`#1078 `__)\n* MAINT: Pacify codespell (`#1080 `__)\n* MAINT: Conclude porting of reportlets into *NiReports* (`#1068 `__)\n* MAINT: Migrate to hatchling (`#1070 `__)\n* MAINT: Pin PyBIDS 0.15.6 (culminating #1050) (`#1069 `__)\n* MAINT: Update niworkflows pin to support newer ANTs releases (`#1047 `__)\n* MAINT: Fix minor aspects of WebAPI deployment on CircleCI (`#1064 `__)\n* MAINT: Update CircleCI executor and use built-in docker-compose (`#1061 `__)\n* MAINT: Rotate CircleCI secrets and setup up org-level context (`#1046 `__)\n* DOC: Update documentation with the new carpet plot (`#1045 `__)\n* DOC: Complete the documentation of ``summary_stats()`` (`#1044 `__)\n* DOC: Fixes a couple of broken links to the *nipype* documentation (`#1040 `__)\n\n.. admonition:: Author list for papers based on *MRIQC* 23.0 series\n\n As described in the `Contributor Guidelines\n `__,\n anyone listed as developer or contributor may write and submit manuscripts\n about *MRIQC*.\n To do so, please move the author(s) name(s) to the front of the following list:\n\n Zvi Baratz \\ :sup:`1`\\ ; Christopher J. Markiewicz \\ :sup:`2`\\ ; Eilidh MacNicol \\ :sup:`3`\\ ; Dylan Nielson \\ :sup:`4`\\ ; Jan Varada \\ :sup:`5`\\ ; Ross W. Blair \\ :sup:`2`\\ ; Céline Provins \\ :sup:`6`\\ ; William Triplett \\ :sup:`7`\\ ; Mathias Goncalves \\ :sup:`2`\\ ; Nikita Beliy \\ :sup:`8`\\ ; John A. Lee \\ :sup:`9`\\ ; Ursula A. Tooley \\ :sup:`10`\\ ; James D. Kent \\ :sup:`11`\\ ; Yaroslav O. Halchenko \\ :sup:`12`\\ ; Bennet Fauber \\ :sup:`13`\\ ; Taylor Salo \\ :sup:`14`\\ ; Michael Krause \\ :sup:`15`\\ ; Pablo Velasco \\ :sup:`16`\\ ; Thomas Nichols \\ :sup:`17`\\ ; Adam Huffman \\ :sup:`18`\\ ; Johannes Achtzehn \\ :sup:`19`\\ ; Joke Durnez \\ :sup:`2`\\ ; Satrajit S. Ghosh \\ :sup:`20`\\ ; Asier Erramuzpe \\ :sup:`21`\\ ; Benjamin Kay \\ :sup:`22`\\ ; Daniel Birman \\ :sup:`2`\\ ; Elodie Savary \\ :sup:`23`\\ ; McKenzie P. Hagen \\ :sup:`24`\\ ; Michael G. Clark \\ :sup:`25`\\ ; Patrick Sadil \\ :sup:`26`\\ ; Rafael Garcia-Dias \\ :sup:`27`\\ ; Adam G. Thomas \\ :sup:`28`\\ ; Russell A. Poldrack \\ :sup:`2`\\ ; Ariel Rokem \\ :sup:`29`\\ ; Oscar Esteban \\ :sup:`30`\\ .\n\n Affiliations:\n\n 1. Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel\n 2. Department of Psychology, Stanford University, CA, USA\n 3. Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK\n 4. Section on Clinical and Computational Psychiatry, National Institute of Mental Health, Bethesda, MD, USA\n 5. Functional MRI Facility, National Institute of Mental Health, Bethesda, MD, USA\n 6. Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland\n 7. University of Florida: Gainesville, Florida, US\n 8. CRC ULiege, Liege, Belgium\n 9. Quansight, Dublin, Ireland\n 10. Department of Neuroscience, University of Pennsylvania, PA, USA\n 11. Department of Psychology, University of Texas at Austin, TX, USA\n 12. Psychological and Brain Sciences Department, Dartmouth College, NH, USA\n 13. University of Michigan, Ann Arbor, USA\n 14. Department of Psychology, Florida International University, FL, USA\n 15. Max Planck Institute for Human Development, Berlin, Germany\n 16. Center for Brain Imaging, New York University, NY, USA\n 17. Oxford Big Data Institute, University of Oxford, Oxford, GB\n 18. Department of Physics, Imperial College London, London, UK\n 19. Charité Berlin, Berlin, Germany\n 20. McGovern Institute for Brain Research, MIT, MA, USA; and Department of Otolaryngology, Harvard Medical School, MA, USA\n 21. Computational Neuroimaging Lab, BioCruces Health Research Institute\n 22. Washington University School of Medicine, St.Louis, MO, USA\n 23. Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland\n 24. Psychology Department, University of Washington, Seattle, WA, USA\n 25. National Institutes of Health, USA\n 26. Johns Hopkins Bloomberg School of Public Health, MD, USA\n 27. Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK\n 28. Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD, USA\n 29. The University of Washington eScience Institute, WA, USA\n 30. Department of Radiology, Lausanne University Hospital and University of Lausanne\n\n22.0.6 (August 24, 2022)\n========================\nA hotfix release partially rolling-back the previous fix #1025.\nThanks everyone for your patience with the excessively rushed release of 22.0.5.\n\n* FIX: Better fix to the multi-argument ``--participant-label`` issue (`#1026 `__)\n\n22.0.5 (August 24, 2022)\n========================\nA hotfix release addressing a problem with the argument parser.\n\n* FIX: Multiple valued ``--participant-label`` wrongly parsed (`#1025 `__)\n\n22.0.4 (August 23, 2022)\n========================\nA hotfix release to ensure smooth operation of datalad within Docker.\n\n* FIX: Major improvements to new datalad-based interface & perform within containers (`#1024 `__)\n* ENH: Bump Docker base to latest release (`#1022 `__)\n\n22.0.3 (August 19, 2022)\n========================\nA patch release containing a bugfix to the SynthStrip preprocessing.\n\n* FIX: SynthStrip preprocessing miscalculating new shape after reorientation (`#1021 `__)\n* ENH: Remove slice-timing correction (`#1019 `__)\n* ENH: Add a new ``DataladIdentityInterface`` (`#1020 `__)\n* ENH: Set rat-specific defaults for FD calculations (`#1005 `__)\n* ENH: New version of the rating widget (`#1012 `__)\n* DOC: Move readthedocs to use the config v2 file (YAML) (`#1018 `__)\n* MAINT: Fix statsmodels dependency, it is not optional (`#1017 `__)\n* MAINT: Several critical updates to CircleCI and Docker images (`#1016 `__)\n* MAINT: Update the T1w IQMs to the new reference after #997 (`#1014 `__)\n* MAINT: Fix failing tests as ``python setup.py`` is deprecated (`#1013 `__)\n\n22.0.2 (August 15, 2022)\n========================\nA patch release including the new ratings widget.\n\n* ENH: New version of the rating widget (`#1012 `__)\n* DOC: Move readthedocs to use the config v2 file (YAML) (`#1018 `__)\n* MAINT: Fix ``statsmodels`` dependency, it is not optional (`#1017 `__)\n* MAINT: Several critical updates to CircleCI and Docker images (`#1016 `__)\n* MAINT: Update the T1w IQMs to the new reference after #997 (`#1014 `__)\n* MAINT: Fix failing tests as ``python setup.py`` is deprecated (`#1013 `__)\n\n22.0.1 (May 3rd, 2022)\n======================\nA patch release addressing a new minor bug.\n\n* FIX: More lenient handling of skull-stripped datasets (`#997 `__)\n\n22.0.0 (May 3rd, 2022)\n======================\nFirst official release after migrating the repository into the *NiPreps*' organization.\nA major new feature is the rodent pipeline by Eilidh MacNicol (@eilidhmacnicol).\nA second major feature is the adoption of the updated carpet plots for BOLD fMRI,\ncontributed by Céline Provins (@celprov).\nVirtual memory allocation has been ten-fold cut down, and a complementary resource monitor instrumentation is now available with *MRIQC*.\nThis release updates the Docker image with up-to-date dependencies, updates\n*MRIQC*'s codebase to the latest *NiTransforms* and includes some minor bugfixes.\nThe code, modules and data related to the MRIQC classifier have been extracted into an\nisolated package called [*MRIQC-learn*](https://github.com/nipreps/mriqc-learn).\nFinally, this release also contains a major code style overhaul by Zvi Baratz.\n\nThe contributor/author crediting system has been adapted to the current draft of the\n*NiPreps Community* Governance documents.\n\nWith thanks to @ZviBaratz, @nbeliy, @octomike, @benkay86, @verdurin, @leej3, @utooley,\nand @jAchtzehn for their contributions.\n\n* FIX: Inconsistent API in anatomical CNR computation (`#995 `__)\n* FIX: Check sanity of input data before extracting IQMs (`#994 `__)\n* FIX: Plot segmentations after dropping off-diagonal (`#989 `__)\n* FIX: Replace all deprecated ``nibabel.get_data()`` in anatomical module (`#988 `__)\n* FIX: Resource profiler was broken with config file (`#981 `__)\n* FIX: preserve WM segments in rodents (`#979 `__)\n* FIX: Pin ``jinja2 < 3.1`` (`#978 `__)\n* FIX: Make toml config unique, works around #912 (`#960 `__)\n* FIX: Nipype multiproc plugin expects ``n_procs`` and not ``nprocs`` (`#961 `__)\n* FIX: Set TR when generating carpetplots (enables time for X axis) (`#971 `__)\n* FIX: ``template_resolution`` deprecation warning (`#941 `__)\n* FIX: Set entity ``datatype`` in ``BIDSLayout`` queries (`#942 `__)\n* FIX: T2w image of MNI template unavailable in Singularity (`#940 `__)\n* FIX: Release process -- Docker deployment not working + Python package lacks WebAPI token (`#938 `__)\n* FIX: Revise building documentation at RTD after migration (`#935 `__)\n* FIX: Final touch-ups in the maintenance of Docker image + CI (`#928 `__)\n* FIX: Update unit tests (`#927 `__)\n* FIX: Update dependencies and repair BOLD workflow accordingly (`#926 `__)\n* FIX: Update dependencies and repair T1w workflow accordingly (`#925 `__)\n* FIX: Set ``matplotlib`` on ``Agg`` output mode (`#892 `__)\n* ENH: Deprecate ``--start-idx`` / ``--stop-idx`` (`#993 `__)\n* ENH: Add SynthStrip base module (`#987 `__)\n* ENH: Improve building workflow message feedback (`#990 `__)\n* ENH: Add instrumentation to monitor resources (`#984 `__)\n* ENH: Standalone, lightweight version of MultiProc plugin (`#985 `__)\n* ENH: Revise plugin and workflow initialization (`#983 `__)\n* ENH: Base generalization of the pipeline for rodents (`#969 `__)\n* ENH: Update to new *NiWorkflows*' API, which adds the crown to the carpetplot (`#968 `__)\n* ENH: Optimize *PyBIDS*' layout initialization (`#939 `__)\n* ENH: Refactored long strings to a :mod:`mriqc.messages` module (`#901 `__)\n* ENH: Refactored :mod:`mriqc.interfaces.common` module (`#901 `__)\n* DOC: Improve documentation of ``--nprocs`` and ``--omp-nthreads`` (`#986 `__)\n* DOC: Add ``sbatch`` file example for SLURM execution (`#963 `__)\n* DOC: Various fixes to \"Running mriqc\" section (`#897 `__)\n* MAINT: Refactor ``Dockerfile`` using new miniconda image (`#974 `__)\n* MAINT: Outsource the classifier into nipreps/mriqc-learn (`#973 `__)\n* MAINT: Update ``CONTRIBUTORS.md`` (`#953 `__)\n* MAINT: Update contributor location (`#952 `__)\n* MAINT: Updates to ``CONTRIBUTORS.md`` file\n* MAINT: Revise Docker image settings & CircleCI (`#937 `__)\n* MAINT: Finalize transfer to ``nipreps`` organization (`#936 `__)\n* MAINT: Relicensing to Apache-2.0, for compliance with *NiPreps* and prior transfer to the org (`#930 `__)\n* MAINT: New Docker layer caching system of other *NiPreps* (`#929 `__)\n* MAINT: Code style overhaul (`#901 `__)\n* MAINT: Update ``Dockerfile`` and catch-up with *fMRIPrep*'s (`#924 `__)\n* STY: Run ``black`` at the top of the repo (`#932 `__)\n\n**Full Changelog**: https://github.com/nipreps/mriqc/compare/0.16.1...22.0.0\n\n.. admonition:: Author list for papers based on *MRIQC* 22.0.x\n\n As described in the `Contributor Guidelines\n `__,\n anyone listed as developer or contributor may write and submit manuscripts\n about *MRIQC*.\n To do so, please move the author(s) name(s) to the front of the following list:\n\n Zvi Baratz \\ :sup:`1`\\ ; Christopher J. Markiewicz \\ :sup:`2`\\ ; Eilidh MacNicol \\ :sup:`3`\\ ; Dylan Nielson \\ :sup:`4`\\ ; Jan Varada \\ :sup:`5`\\ ; Ross W. Blair \\ :sup:`2`\\ ; William Triplett \\ :sup:`6`\\ ; Nikita Beliy \\ :sup:`7`\\ ; Céline Provins \\ :sup:`8`\\ ; John A. Lee \\ :sup:`9`\\ ; Ursula A. Tooley \\ :sup:`10`\\ ; James D. Kent \\ :sup:`11`\\ ; Bennet Fauber \\ :sup:`12`\\ ; Taylor Salo \\ :sup:`13`\\ ; Mathias Goncalves \\ :sup:`2`\\ ; Michael Krause \\ :sup:`14`\\ ; Pablo Velasco \\ :sup:`15`\\ ; Thomas Nichols \\ :sup:`16`\\ ; Adam Huffman \\ :sup:`17`\\ ; Johannes Achtzehn \\ :sup:`18`\\ ; Joke Durnez \\ :sup:`2`\\ ; Satrajit S. Ghosh \\ :sup:`19`\\ ; Asier Erramuzpe \\ :sup:`20`\\ ; Benjamin Kay \\ :sup:`21`\\ ; Daniel Birman \\ :sup:`2`\\ ; Michael G. Clark \\ :sup:`22`\\ ; Rafael Garcia-Dias \\ :sup:`23`\\ ; Sean Marret \\ :sup:`5`\\ ; Adam G. Thomas \\ :sup:`24`\\ ; Russell A. Poldrack \\ :sup:`2`\\ ; Krzysztof J. Gorgolewski \\ :sup:`25`\\ ; Oscar Esteban \\ :sup:`26`\\ .\n\n Affiliations:\n\n 1. Sagol School of Neuroscience, Tel-Aviv University\n 2. Department of Psychology, Stanford University, CA, USA\n 3. Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK\n 4. Section on Clinical and Computational Psychiatry, National Institute of Mental Health, Bethesda, MD, USA\n 5. Functional MRI Facility, National Institute of Mental Health, Bethesda, MD, USA\n 6. University of Florida: Gainesville, Florida, US\n 7. CRC ULiege, Liege, Belgium\n 8. Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland\n 9. Quansight, Dublin, Ireland\n 10. Department of Neuroscience, University of Pennsylvania, PA, USA\n 11. Department of Psychology, University of Texas at Austin, TX, USA\n 12. University of Michigan, Ann Arbor, USA\n 13. Department of Psychology, Florida International University, FL, USA\n 14. Max Planck Institute for Human Development, Berlin, Germany\n 15. Center for Brain Imaging, New York University, NY, USA\n 16. Oxford Big Data Institute, University of Oxford, Oxford, GB\n 17. Department of Physics, Imperial College London, London, UK\n 18. Charité Berlin, Berlin, Germany\n 19. McGovern Institute for Brain Research, MIT, MA, USA; and Department of Otolaryngology, Harvard Medical School, MA, USA\n 20. Computational Neuroimaging Lab, BioCruces Health Research Institute\n 21. Washington University School of Medicine, St.Louis, MO, USA\n 22. National Institutes of Health, USA\n 23. Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK\n 24. Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD, USA\n 25. Google LLC\n 26. Department of Radiology, Lausanne University Hospital and University of Lausanne\n\nSeries 0.16.x\n=============\n0.16.1 (January 30, 2021)\n-------------------------\nBug-fix release in 0.16.x series.\n\nThis PR improves BIDS Derivatives compliance, fixes an issue with reading datasets with\nsubjects of the form ``sub-sXYZ``, and improves compatibility with more recent matplotlib.\n\n* FIX: Participant labels starting with ``[sub]`` cannot be used (`#890 `__)\n* FIX: Change deprecated ``normed`` to ``density`` in parameters to ``hist()`` (`#888 `__)\n* ENH: Write derivatives metadata (`#885 `__)\n* ENH: Add ``--pdb`` option to make debugging easier (`#884 `__)\n\n0.16.0 (January 5, 2021)\n------------------------\nNew feature release in 0.16.x series.\n\nThis version removes the FSL dependency from the fMRI workflow.\n\n* FIX: Skip version cache on read-only filesystems (`#862 `__)\n* FIX: Honor ``$OMP_NUM_THREADS`` environment variable (`#848 `__)\n* RF: Simplify comprehensions, using easy-to-read var names (`#875 `__)\n* RF: Free the fMRI workflow from FSL (`#842 `__)\n* CI: Fix up Circle builds (`#876 `__)\n* CI: Update machine images on Circle (`#874 `__)\n\nOlder (unsupported) series\n==========================\n0.15.3 (September 18, 2020)\n---------------------------\nA bugfix release to re-enable setting of ``--omp-nthreads/--ants-nthreads``.\n\n* FIX: ``omp_nthreads`` typo (`#846 `__)\n\n0.15.2 (April 6, 2020)\n----------------------\nA bugfix release containing mostly maintenance actions and documentation\nimprovements. This version drops Python 3.5.\nThe core of MRIQC has adopted the config-module pattern from fMRIPrep.\nWith thanks to A. Erramuzpe, @justbennet, U. Tooley, and A. Huffman\nfor contributions.\n\n* MAINT: revise style of all files (except for workflows) (`#839 `__)\n* MAINT: Clear the clutter of warnings (`#838 `__)\n* RF: Adopt config module pattern from *fMRIPrep* (`#837 `__)\n* MAINT: Clear the clutter of warnings (`#838 `__)\n* MAINT: Drop Python 3.5, simplify linting (`#833 `__)\n* MAINT: Update to latest Ubuntu Xenial tag (`#814 `__)\n* MAINT: Centralize all requirements and versions on ``setup.cfg`` (`#819 `__)\n* MAINT: Use recent Python image to build packages in CircleCI (`#808 `__)\n* DOC: Improve AQI (and other IQMs) and boxplot whiskers descriptions (`#816 `__)\n* DOC: Refactor how documentation is built on CircleCI (`#818 `__)\n* DOC: Corrected a couple of typos in ``--help`` text (`#809 `__)\n\n0.15.1 (July 26, 2019)\n----------------------\nA maintenance patch release updating PyBIDS.\n\n* FIX: ``FileNotFoundError`` when MELODIC (``--ica``) does not converge (`#800 `__) @oesteban\n* MAINT: Migrate MRIQC to a ``setup.cfg`` style of installation (`#799 `__) @oesteban\n* MAINT: Use PyBIDS 0.9.2+ via niworkflows PR (`#796 `__) @effigies\n\n0.15.0 (April 5, 2019)\n----------------------\nA long overdue update, pinning updated versions of\n`TemplateFlow `__ and\n`Niworkflows `__.\nWith thanks to @garciadias for contributions.\n\n* ENH: Revision of QI2 (`#606 `__) @oesteban\n* FIX: Set matplotlib backend early (`#759 `__) @oesteban\n* FIX: Niworkflows pin <0.5 (`#766 `__) @oesteban\n* DOC: Update BIDS validation link. (`#764 `__) @garciadias\n* DOC: Add data sharing agreement (`#765 `__) @oesteban\n* FIX: Catch uncaught exception in WebAPI upload. (`#774 `__) @rwblair\n* FIX/DOC: Append new line after dashes in ``mriqc_run`` help text (`#777 `__) @rwblair\n* ENH: Use TemplateFlow and niworkflows-0.8.x (`#782 `__) @oesteban\n* FIX: Correctly set WebAPI rating endpoint in BOLD reports. (`#785 `__) @oesteban\n* FIX: Correctly process values of rating widget (`#787 `__) @oesteban\n\n0.14.2 (August 20, 2018)\n------------------------\n\n* FIX: Preempt pandas resolving ``Path`` objects (`#746 `__) @oesteban\n* FIX: Codacy issues (`#745 `__) @oesteban\n\n0.14.1 (August 20, 2018)\n------------------------\n\n* FIX: Calculate relative path with sessions (`#742 `__) @oesteban\n* ENH: Add a toggle button to rating widget (`#743 `__) @oesteban\n\n0.14.0 (August 17, 2018)\n------------------------\n\n* ENH: New feedback widget (`#740 `__) @oesteban\n\n0.13.1 (August 16, 2018)\n------------------------\n\n* [ENH,FIX] Updates to individual reports, fix table after rating (`#739 `__) @oesteban\n\n0.13.0 (August 15, 2018)\n------------------------\n\n* MAINT: Overdue refactor (`#736 `__) @oesteban\n * FIX: Reorganize outputs (closes #396)\n * ENH: Memory usage - lessons learned with FMRIPREP (`#703 `__)\n * FIX: Cannot allocate memory (v 0.9.4) (closes #536)\n * FIX: Drop inoperative ``--report-dir`` flag (`#550 `__)\n * FIX: Drop misleading WARNING of the group-level execution (`#714 `__)\n * FIX: Expand usernames on input paths (`#721 `__)\n * MAINT: More robust naming of derivatives (related to #661)\n\n* FIX: Do not fail with spurious 4th dimension on T1w (`#738 `__) @oesteban\n* ENH: Move on to .tsv files (`#737 `__) @oesteban\n\n0.12.1 (August 13, 2018)\n------------------------\n\n* FIX: ``BIDSLayout`` queries (`#735 `__)\n\n\n0.12.0 (August 09, 2018)\n------------------------\n\n* FIX: Reduce tSNR memory requirements (`#712 `__)\n* DOC: Fix typos in IQM documentation (`#725 `__)\n* PIN: Update MRIQC WebAPI version (`#734 `__)\n* BUG: Fix missing library in singularity images (`#733 `__)\n* PIN: nipype 1.1.0, niworkflows (`#726 `__)\n\n0.11.0 (June 05, 2018)\n----------------------\n\n* RF: Resume external nipype dependency (`#715 `__)\n\n0.10.6 (May 29, 2018)\n---------------------\n\n* HOTFIX: Bug #659\n\n0.10.5 (May 28, 2018)\n---------------------\n\n* ENH: Report feedback (`#659 `__)\n\n0.10.4 (March 22, 2018)\n-----------------------\n\n* ENH: Various improvements to reports (`#708 `__)\n* MAINT: Style revision (`#704 `__)\n* PIN: pybids 0.5 (`#700 `__)\n* ENH: Increase FAST memory limits (`#702 `__)\n\n0.10.3 (February 26, 2018)\n--------------------------\n\n* ENH: Enable T2w metrics uploads (`#696 `__)\n* PIN: Updating niworkflows (`#698 `__)\n* DOC: Option ``-o`` is outdated for classifier (`#697 `__)\n\n0.10.2 (February 15, 2018)\n--------------------------\n\n* ENH: Add warning about mounting relative paths (`#690 `__)\n* FIX: Sanitize inputs (`#687 `__)\n* DOC: Fix documentation to use ``--version`` instead of ``-v`` (`#688 `__)\n\n0.10.1\n------\n\n* FIX: Fixed a bug in reading outputs of ``3dFWHMx`` (`#678 `__)\n\n0.9.10\n------\n\n* FIX: Updated AFNI to 17.3.03. Resolves errors regarding opening display by ``3dSkullStrip`` (`#669 `__)\n\n0.9.9\n-----\n\n* ENH: Update nipype to fix ``$DISPLAY`` problem of AFNI's ``3dSkullStrip``\n\n0.9.8\n-----\nWith thanks to Jan Varada (@jvarada) for the session/run filtering.\n\n* ENH: Report recall in cross-validation (requested by reviewer) (`#633 `__)\n* ENH: Hotfixes to 0.9.7 (`#635 `__)\n* FIX: Implement filters for session, run and task of BIDS input (`#612 `__)\n\n0.9.7\n-----\n\n* ENH: Clip outliers in FD and SPIKES group plots (`#593 `__)\n* ENH: Second revision of the classifier (`#555 `__):\n * Set matplotlib plugin to `agg` in docker image\n * Migrate scalings to sklearn pipelining system\n * Add Satra's feature selection for RFC (with thanks to S. Ghosh for his suggestion)\n * Make model selection compatible with sklearn `Pipeline`\n * Multiclass classification\n * Add feature selection filter based on Sites prediction (requires pinning to development sklearn-0.19)\n * Add `RobustLeavePGroupsOut`, replace `RobustGridSearchCV` with the standard `GridSearchCV` of sklearn.\n * Choice between `RepeatedStratifiedKFold` and `RobustLeavePGroupsOut` in `mriqc_clf`\n * Write cross-validation results to an `.npz` file.\n* ENH: First revision of the classifier (`#553 `__):\n * Add the possibility of changing the scorer function.\n * Unifize labels for raters in data tables (to `rater_1`)\n * Add the possibility of setting a custom decision threshold\n * Write the probabilities in the prediction file\n * Revised `mriqc_clf` processing flow\n * Revised labels file for ds030.\n * Add IQMs for ABIDE and DS030 calculated with MRIQC 0.9.6.\n* ANNOUNCEMENT: Dropped support for Python<-3.4\n* WARNING (`#596 `__):\n We have changed the default number of threads for ANTs. Using parallelism with ANTs\n causes numerical instability on the calculated measures. The most sensitive metrics to this\n problem are the kurtosis calculations on the intensities of regions and qi_2.\n\n0.9.6\n-----\n\n* ENH: Finished setting up `MRIQC Web API `_\n* ENH: Better error message when --participant_label is set (`#542 `__)\n* FIX: Allow --load-classifier option to be empty in mriqc_clf (`#544 `__)\n* FIX: Borked bias estimation derived from Conform (`#541 `__)\n* ENH: Test against web API 0.3.2 (`#540 `__)\n* ENH: Change the default Web API address (`#539 `__)\n* ENH: MRIQCWebAPI: hash fields that may have PI (`#538 `__)\n* ENH: Added token authorization to MRIQCWebAPI client (`#535 `__)\n* FIX: Do not mask and antsAffineInitializer twice (`#534 `__)\n* FIX: Datasets where air (hat) mask is empty (`#533 `__)\n* ENH: Integration testing for MRIQCWebAPI (`#520 `__)\n* ENH: Use AFNI to calculate gcor (`#531 `__)\n* ENH: Refactor derivatives (`#530 `__)\n* ENH: New bold-IQM: dummy_trs (non-stady state volumes) (`#524 `__)\n* FIX: Order of BIDS components in IQMs CSV table (`#525 `__)\n* ENH: Improved logging of mriqc_run (`#526 `__)\n\n0.9.5\n-----\n\n* ENH: Refactored structural metrics calculation (`#513 `__)\n* ENH: Calculate rotation mask (`#515 `__)\n* ENH: Intensity harmonization in the anatomical workflow (`#510 `__)\n* ENH: Set N4BiasFieldCorrection number of threads (`#506 `__)\n* ENH: Convert FWHM in pixel units (`#503 `__)\n* ENH: Add MRIQC client for feature crowdsourcing (`#464 `__)\n* DOC: Fix functional feature labels in documentation (docs_only) (`#507 `__)\n* FIX: New implementation for the rPVE feature (normalization, left-tail values) (`#505 `__)\n* ENH: Parse BIDS selectors (run, task, etc.), improve CLI (`#504 `__)\n\n\n0.9.4\n-----\n\n* ANNOUNCEMENT: Dropped Python 2 support\n* ENH: Use versioneer to handle versions (`#500 `__)\n* ENH: Speed up spatial normalization (`#495 `__)\n* ENH: Resampling of hat mask and TPMs with linear interp (`#498 `__)\n* TST: Build documentation in CircleCI (`#484 `__)\n* ENH: Use full-resolution T1w images from ABIDE (`#486 `__)\n* TST: Parallelize tests (`#493 `__)\n* TST: Binding /etc/localtime stopped working in docker 1.9.1 (`#492 `__)\n* TST: Downgrade docker to 1.9.1 in circle (build_only) (`#491 `__)\n* TST: Check for changes in intermediate nifti files (`#485 `__)\n* FIX: Erroneous flag --n_proc in CircleCI (`#490 `__)\n* ENH: Add build_only tag to circle builds (`#488 `__)\n* ENH: Update Dockerfile (`#482 `__)\n* FIX: Ignore --profile flag with Linear plugin (`#483 `__)\n* DOC: Deep revision of the documentation (`#479 `__)\n* ENH: Minor improvements: SpatialNormalization and segmentation (`#472 `__)\n* ENH: Fixed typo for neurodebian install via apt-get (`#478 `__)\n* ENH: Updating fs2gif script (`#465 `__)\n* ENH: RF: Use niworkflows.interface.SimpleInterface (`#468 `__)\n* ENH: Add reproducibility of metrics tracking (`#466 `__)\n\nRelease 0.9.3\n-------------\n\n* ENH: Reafactor of the Dockerfile to improve transparency, reduce size, and enable injecting code in Singularity (`#457 `__)\n* ENH: Make more the memory consumption estimates of each processing step more conservative to improve robustness (`#456 `__)\n* FIX: Minor documentation cleanups (`#461 `__)\n\nRelease 0.9.2\n-------------\n\n* ENH: Optional ICA reports for identifying spatiotemporal artifacts (`#412 `__)\n* ENH: Add --profile flag (`#435 `__)\n* ENH: Crashfiles are saved in plain text to improve portability (`#434 `__)\n* FIX: Fixes EPI mask erosion (`#442 `__)\n* ENH: Make FSL and AFNI motion correction more comparable by using the same scheme for defining the reference image (`#444 `__)\n* FIX: Temporarily disabling T1w quality classifier until it can be retrained on new measures (`#447 `__)\n\nRelease 0.9.1\n-------------\n\n* ENH: Add mriqc version and input image hash to IQMs json file (`#432 `__)\n* FIX: Affine and warp transforms are now applied in the correct order (`#431 `__)\n\nRelease 0.9.0-2\n---------------\n\n* ENH: Revise Docker paths (`#429 `__)\n* FIX: Greedy participant selection (`#426 `__)\n* FIX: Pin pybids to new version 0.1.0 (`#427 `__)\n* FIX: Amends sloppy PR #425 (`#428 `__)\n\nRelease 0.9.0-1\n---------------\n\n* FIX: BOLD reports clipped IQMs after spikes_num (`#425 `__)\n* FIX: Unicode error writing group reports (`#424 `__)\n* FIX: Respect Nifi header in fMRI conform node (`#415 `__)\n* DOC: Deep revision of documentation (#411, #416)\n* ENH: Added sphinx extension to plot workflow graphs (`#411 `__)\n* FIX: Removed repeated bias correction on anatomical workflows (`#410 `__)\n* FIX: Race condition in bold workflow when using shared workdir (`#409 `__)\n* FIX: Tests (#408, #407, #405)\n* FIX: Remove CDN for group level reports (`#406 `__)\n* FIX: Unused connection, matplotlib segfault (#403, #402)\n* ENH: Skip SpikeFFT detector by default (`#400 `__)\n* ENH: Use float32 (`#399 `__)\n* ENH: Spike finder performance improvoments (`#398 `__)\n* ENH: Basic T2w workflow (`#394 `__)\n* ENH: Re-enable 3dvolreg (`#390 `__)\n* ENH: Add T1w classifier (`#389 `__)\n\nRelease 0.9.0-0\n---------------\n\n* FIX: Remove non-repeatable step from pipeline (`#369 `__)\n* ENH: Improve group level command line, with more informative output when no IQMs are found for a modality (`#372 `__)\n* ENH: Make group reports self-contained (`#333 `__)\n* FIX: New mosaics, based on old ones (#361, #360, #334)\n* FIX: Require numpy>=1.12 to avoid casting problems (`#356 `__)\n* FIX: Add support for acq and rec tags of BIDS (`#346 `__)\n* DOC: Documentation updates (`#350 `__)\n* FIX: pybids compatibility \"No scans were found\" (#340, #347, #342)\n* ENH: Rewrite PYTHONPATH in docker/singularity images (`#345 `__)\n* ENH: Move metadata onto the bottom of the individual reports (`#332 `__)\n* ENH: Don't include MNI registration report unlesS --verbose-reports is used (`#362 `__)\n\n\nRelease 0.8.9\n-------------\n\n* ENH: Added registration svg panel to reports (`#297 `__)\n\n\nRelease 0.8.8\n-------------\n\n* FIX: Bug translating int16 to uint8 in conform image.\n* FIX: Error in ConformImage interface (`#297 `__)\n* ENH: Replace BBR by ANTs (#295, #296)\n* FIX: Singularity: user-environment leaking into container (`#293 `__)\n* ENH: Report failed cases in group report (`#291 `__)\n* FIX: Brighter anatomical --verbose-reports (`#290 `__)\n* FIX: X-flip in the mosaics (`#289 `__)\n* ENH: Show metadata in the individual report (`#288 `__)\n* ENH: Label in the cutoff threshold - fmriplot (`#287 `__)\n* ENH: PyBIDS (`#286 `__)\n* ENH: Simplify tests (`#284 `__)\n* FIX: MRIQC crashed generating csv files (`#283 `__)\n* FIX: Bug in setup.py (`#281 `__)\n* ENH: Makefile (`#280 `__)\n* FIX: Revision of IQMs (#266, #272, #279)\n* ENH: Deprecation of --nthreads, new flags (`#260 `__)\n* ENH: Improvements on plots rendering (#254, #257, #258, #267, #268, #269, #270)\n* ENH: FFT detection of spikes (#253, #272)\n* FIX: Labels and links of samples in group plots (`#249 `__)\n* ENH: Units in group plots (`#242 `__)\n* FIX: More reliable group level (`#238 `__)\n* ENH: Add --verbose-reports for fMRI (`#236 `__)\n* ENH: Migrate functional reports to html (`#232 `__)\n* ENH: Add 0.2 FD cutoff line (`#231 `__)\n* ENH: Add AFNI's outlier count to carpet plot confound charts (`#230 `__)\n\nRelease 0.8.7\n-------------\n\n* ENH: Anatomical Group reports in html (`#227 `__)\n* ENH: Add kurtosis to summary statistics (`#224 `__)\n* ENH: New report layout for fMRI, added carpetplot (`#198 `__)\n* ENH: Anatomical workflow refactor (`#219 `__).\n\nRelease 0.8.6\n-------------\n\n* [FIX, CRITICAL] Do not chmod in Docker internal scripts\n* FIX: Error creating derivatives folder\n* ENH: Moved MNI spatial normalization to NIworkflows, and made robust.\n* ENH: De-coupled participant and group (reports) levels\n* ENH: Use new FD and DVARs calculations from nipype (`#172 `__)\n* ENH: Started with python3 compatibility\n* ENH: Added new M2WM measure #158\n* FIX: QI2 is skipped if background intensity is not appropriate (`#147 `__)\n\nRelease 0.8.5\n-------------\n\n* FIX: Error inverting the T1w-to-MNI warping (`#146 `__)\n* FIX: TypeError computing DVARS (`#145 `__)\n* ENH: Plot figure of fitted background chi for QI2 (`#143 `__)\n* ENH: Move skull-stripping and reorient to NIworkflows (`#142 `__)\n* FIX: mriqc crashes if no anatomical scans are found (`#141 `__)\n* DOC: Added acknowledgments to CPAC team members (`#134 `__)\n* ENH: Use absolute imports (`#133 `__)\n* FIX: VisibleDeprecationWarning (`#132 `__)\n* ENH: Provide full FD/DVARS files (`#128 `__)\n* ENH: Use MCFLIRT to compute motion parameters. AFNI's 3dvolreg now is optional (`#121 `__)\n* FIX: BIDS trees with anatomical images with different acquisition tokens (`#116 `__)\n* FIX: BIDS trees with anatomical images with several runs (`#112 `__)\n* ENH: Options for ANTs normalization: reduced test times (`#124 `__),\n and updated options (`#115 `__)\n\nRelease 0.8.4\n-------------\n\n* ENH: PDF reports now use RST templates and jinja2 (`#109 `__)\n* FIX: Single-session-multiple-run anatomical files were not correctly located (`#112 `__)\n\nRelease 0.8.3\n-------------\n\n* DOC: Added examples of the PDF reports (`#107 `__)\n* FIX: Fixed problems with Python 3 when generating reports.\n\nRelease 0.8.2\n-------------\n\n* ENH: Python 3 compatibility (`#99 `__)\n* ENH: Add JSON settings file for ANTS (`#95 `__)\n* ENH: Generate reports automatically if mriqc is run without the -S flag (`#93 `__)\n* FIX: Revised implementation of QI2 measure (`#90 `__)\n* AGAVE: Fixed docker image for agave (`#89 `__)\n* FIX: Problem when generating the air mask with dipy installed (`#88 `__)\n* ENH: One-session-one-run execution mode (`#85 `__)\n* AGAVE: Added an agave app description generator (`#84 `__)\n\nRelease 0.3.0\n-------------\n\n* ENH: Updated CircleCI and Docker to use the version 2.1.0 of ANTs\n compiled by their developers.\n* ENH: New anatomical workflows to compute the air mask (`#56 `__)\n\nRelease 0.1.0\n-------------\n\n* FIX: #55\n* ENH: Added rotation of output csv files if they exist\n\nRelease 0.0.2\n-------------\n\n* ENH: Completed migration from QAP\n* ENH: Integration with ReadTheDocs\n* ENH: Submission to PyPi\n\nRelease 0.0.1\n-------------\n\n* Basic mriqc functionality\n" }, { "path": "Dockerfile", "content": "# MRIQC Docker Container Image distribution\n#\n# MIT License\n#\n# Copyright (c) 2021 The NiPreps Developers\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\n# Ubuntu 22.04 LTS - Jammy\nARG BASE_IMAGE=ubuntu:jammy-20240125\n\n#\n# Build wheel\n#\nFROM python:slim AS src\nRUN apt-get update && \\\n DEBIAN_FRONTEND=noninteractive \\\n apt-get install -y --no-install-recommends git\nARG VERSION\nENV SETUPTOOLS_SCM_PRETEND_VERSION=$VERSION\n\nRUN python -m pip install -U pip build\nCOPY . /src\nRUN python -m build /src\n\n# Utilities for downloading packages\nFROM ${BASE_IMAGE} as downloader\n# Bump the date to current to refresh curl/certificates/etc\nRUN echo \"2024.03.18\"\nRUN apt-get update && \\\n DEBIAN_FRONTEND=noninteractive \\\n apt-get install -y --no-install-recommends \\\n binutils \\\n bzip2 \\\n ca-certificates \\\n curl \\\n unzip && \\\n apt-get clean && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*\n\n\n# AFNI\nFROM downloader as afni\n# Bump the date to current to update AFNI\nRUN echo \"2024.03.18\"\nRUN mkdir -p /opt/afni-latest \\\n && curl -fsSL --retry 5 https://afni.nimh.nih.gov/pub/dist/tgz/linux_openmp_64.tgz \\\n | tar -xz -C /opt/afni-latest --strip-components 1 \\\n --exclude \"linux_openmp_64/*.gz\" \\\n --exclude \"linux_openmp_64/funstuff\" \\\n --exclude \"linux_openmp_64/shiny\" \\\n --exclude \"linux_openmp_64/afnipy\" \\\n --exclude \"linux_openmp_64/lib/RetroTS\" \\\n --exclude \"linux_openmp_64/lib_RetroTS\" \\\n --exclude \"linux_openmp_64/meica.libs\" \\\n # Keep only what we use\n && find /opt/afni-latest -type f -not \\( \\\n -name \"3dAutomask\" \\\n -or -name \"3dcalc\" \\\n -or -name \"3dFWHMx\" \\\n -or -name \"3dinfo\" \\\n -or -name \"3dmaskave\" \\\n -or -name \"3dSkullStrip\" \\\n -or -name \"3dTnorm\" \\\n -or -name \"3dToutcount\" \\\n -or -name \"3dTqual\" \\\n -or -name \"3dTshift\" \\\n -or -name \"3dTstat\" \\\n -or -name \"3dUnifize\" \\\n -or -name \"3dvolreg\" \\\n -or -name \"@compute_gcor\" \\\n -or -name \"afni\" \\\n \\) -delete\n\n# Use Ubuntu 20.04 LTS\nFROM nipreps/miniconda:py39_2403.0\n\nARG DEBIAN_FRONTEND=noninteractive\nENV LD_LIBRARY_PATH=\"/usr/lib/x86_64-linux-gnu:${CONDA_PATH}/lib\"\nENV CONDA_PATH=\"/opt/conda\"\n\n# Configure PPAs for libpng12 and libxp6\nRUN GNUPGHOME=/tmp gpg --keyserver hkps://keyserver.ubuntu.com --no-default-keyring --keyring /usr/share/keyrings/linuxuprising.gpg --recv 0xEA8CACC073C3DB2A \\\n && GNUPGHOME=/tmp gpg --keyserver hkps://keyserver.ubuntu.com --no-default-keyring --keyring /usr/share/keyrings/zeehio.gpg --recv 0xA1301338A3A48C4A \\\n && echo \"deb [signed-by=/usr/share/keyrings/linuxuprising.gpg] https://ppa.launchpadcontent.net/linuxuprising/libpng12/ubuntu jammy main\" > /etc/apt/sources.list.d/linuxuprising.list \\\n && echo \"deb [signed-by=/usr/share/keyrings/zeehio.gpg] https://ppa.launchpadcontent.net/zeehio/libxp/ubuntu jammy main\" > /etc/apt/sources.list.d/zeehio.list\n\n# Dependencies for AFNI; requires a discontinued multiarch-support package from bionic (18.04)\nRUN apt-get update -qq \\\n && apt-get install -y -q --no-install-recommends \\\n ed \\\n gsl-bin \\\n libglib2.0-0 \\\n libglu1-mesa-dev \\\n libglw1-mesa \\\n libgomp1 \\\n libjpeg62 \\\n libpng12-0 \\\n libxm4 \\\n libxp6 \\\n netpbm \\\n tcsh \\\n xfonts-base \\\n xvfb \\\n && curl -sSL --retry 5 -o /tmp/multiarch.deb http://archive.ubuntu.com/ubuntu/pool/main/g/glibc/multiarch-support_2.27-3ubuntu1.5_amd64.deb \\\n && dpkg -i /tmp/multiarch.deb \\\n && rm /tmp/multiarch.deb \\\n && apt-get install -f \\\n && apt-get clean && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* \\\n && gsl2_path=\"$(find / -name 'libgsl.so.19' || printf '')\" \\\n && if [ -n \"$gsl2_path\" ]; then \\\n ln -sfv \"$gsl2_path\" \"$(dirname $gsl2_path)/libgsl.so.0\"; \\\n fi \\\n && ldconfig\n\n# Install AFNI\nENV AFNI_DIR=\"/opt/afni\"\nCOPY --from=afni /opt/afni-latest ${AFNI_DIR}\nENV PATH=\"${AFNI_DIR}:$PATH\" \\\n AFNI_IMSAVE_WARNINGS=\"NO\" \\\n AFNI_MODELPATH=\"${AFNI_DIR}/models\" \\\n AFNI_TTATLAS_DATASET=\"${AFNI_DIR}/atlases\" \\\n AFNI_PLUGINPATH=\"${AFNI_DIR}/plugins\"\n\n# Install AFNI's dependencies\nRUN micromamba install -n base -c conda-forge \"ants=2.5\" \\\n && sync \\\n\t && micromamba clean -afy; sync \\\n\t && ldconfig\n\n# Unless otherwise specified each process should only use one thread - nipype\n# will handle parallelization\nENV MKL_NUM_THREADS=1 \\\n OMP_NUM_THREADS=1 \\\n NUMEXPR_MAX_THREADS=1\n\nCOPY --from=freesurfer/synthstrip@sha256:f19578e5f033f2c707fa66efc8b3e11440569facb46e904b45fd52f1a12beb8b /freesurfer/models/synthstrip.1.pt /opt/freesurfer/models/synthstrip.1.pt\n\nENV FREESURFER_HOME=/opt/freesurfer\n\nRUN apt update && apt install --no-install-recommends -y libtiff5 libpng16-16\n\nCOPY --from=mrtrix3/mrtrix3:3.0.4 /opt/mrtrix3/bin/dwidenoise /usr/local/bin\nCOPY --from=mrtrix3/mrtrix3:3.0.4 /opt/mrtrix3/lib/libmrtrix.so /usr/local/lib\n\n# Container Sentinel\nENV IS_DOCKER_8395080871=1\n\n# Create a shared $HOME directory\nRUN useradd -m -s /bin/bash -G users mriqc\nWORKDIR /home/mriqc\nENV HOME=\"/home/mriqc\"\n\n# Pacify datalad\nRUN git config --global user.name \"NiPreps - MRIQC\" \\\n && git config --global user.email \"nipreps@gmail.com\"\n\nRUN micromamba shell init -s bash\nENV PATH=\"${CONDA_PATH}/bin:$PATH\" \\\n CPATH=\"${CONDA_PATH}/include:$CPATH\" \\\n LD_LIBRARY_PATH=\"${CONDA_PATH}/lib:$LD_LIBRARY_PATH\"\n\n# Refresh linked libraries\nRUN echo \"${CONDA_PATH}/lib\" > /etc/ld.so.conf.d/conda.conf \\\n && ldconfig\n# Installing dev requirements (packages that are not in pypi)\nWORKDIR /src/\n# Precaching atlases\nRUN python -c \"from templateflow import api as tfapi; \\\n\t tfapi.get('MNI152NLin2009cAsym', resolution=[1, 2], suffix=['T1w', 'T2w'], desc=None); \\\n\t tfapi.get('MNI152NLin2009cAsym', resolution=[1, 2], suffix='mask',\\\n\t\t\t desc=['brain', 'head']); \\\n\t tfapi.get('MNI152NLin2009cAsym', resolution=1, suffix='dseg', desc='carpet'); \\\n\t tfapi.get('MNI152NLin2009cAsym', resolution=1, suffix='probseg',\\\n\t\t\t label=['CSF', 'GM', 'WM']);\\\n\t tfapi.get('MNI152NLin2009cAsym', resolution=[1, 2], suffix='boldref')\"\n\n# Installing MRIQC\nCOPY --from=src /src/dist/*.whl .\nRUN pip install --no-cache-dir $( ls *.whl )[container,rodents,test]\n\nRUN find $HOME -type d -exec chmod go=u {} + && \\\n find $HOME -type f -exec chmod go=u {} + && \\\n rm -rf $HOME/.npm $HOME/.conda $HOME/.empty\n\n# Best practices\nRUN ldconfig\n\n# Update version\nRUN export VERSION=$(python -m mriqc --version | awk '{print $NF}') \\\n && echo \"VERSION=$VERSION\" >> /etc/environment\n\nWORKDIR /tmp/\n\n# Run mriqc by default\nENTRYPOINT [\"/opt/conda/bin/mriqc\"]\nARG BUILD_DATE\nARG VCS_REF\nLABEL org.label-schema.build-date=$BUILD_DATE \\\n org.label-schema.name=\"MRIQC\" \\\n org.label-schema.description=\"MRIQC - Automated Quality Control and visual reports for Quality Assessment of structural (T1w, T2w) and functional MRI of the brain\" \\\n org.label-schema.url=\"https://mriqc.readthedocs.io\" \\\n org.label-schema.vcs-ref=$VCS_REF \\\n org.label-schema.vcs-url=\"https://github.com/nipreps/mriqc\" \\\n org.label-schema.version=$VERSION \\\n org.label-schema.schema-version=\"1.0\"\n" }, { "path": "Dockerfile_devel", "content": "FROM nipreps/mriqc:latest\nCOPY . /src/mriqc\nARG VERSION\nRUN export SETUPTOOLS_SCM_PRETEND_VERSION=$VERSION && pip install -e /src/mriqc[all]\n" }, { "path": "LICENSE", "content": " Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. Definitions.\n\n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n\n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n\n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. For the purposes of this definition,\n \"control\" means (i) the power, direct or indirect, to cause the\n direction or management of such entity, whether by contract or\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\n outstanding shares, or (iii) beneficial ownership of such entity.\n\n \"You\" (or \"Your\") shall mean an individual or Legal Entity\n exercising permissions granted by this License.\n\n \"Source\" form shall mean the preferred form for making modifications,\n including but not limited to software source code, documentation\n source, and configuration files.\n\n \"Object\" form shall mean any form resulting from mechanical\n transformation or translation of a Source form, including but\n not limited to compiled object code, generated documentation,\n and conversions to other media types.\n\n \"Work\" shall mean the work of authorship, whether in Source or\n Object form, made available under the License, as indicated by a\n copyright notice that is included in or attached to the work\n (an example is provided in the Appendix below).\n\n \"Derivative Works\" shall mean any work, whether in Source or Object\n form, that is based on (or derived from) the Work and for which the\n editorial revisions, annotations, elaborations, or other modifications\n represent, as a whole, an original work of authorship. For the purposes\n of this License, Derivative Works shall not include works that remain\n separable from, or merely link (or bind by name) to the interfaces of,\n the Work and Derivative Works thereof.\n\n \"Contribution\" shall mean any work of authorship, including\n the original version of the Work and any modifications or additions\n to that Work or Derivative Works thereof, that is intentionally\n submitted to Licensor for inclusion in the Work by the copyright owner\n or by an individual or Legal Entity authorized to submit on behalf of\n the copyright owner. For the purposes of this definition, \"submitted\"\n means any form of electronic, verbal, or written communication sent\n to the Licensor or its representatives, including but not limited to\n communication on electronic mailing lists, source code control systems,\n and issue tracking systems that are managed by, or on behalf of, the\n Licensor for the purpose of discussing and improving the Work, but\n excluding communication that is conspicuously marked or otherwise\n designated in writing by the copyright owner as \"Not a Contribution.\"\n\n \"Contributor\" shall mean Licensor and any individual or Legal Entity\n on behalf of whom a Contribution has been received by Licensor and\n subsequently incorporated within the Work.\n\n 2. Grant of Copyright License. Subject to the terms and conditions of\n this License, each Contributor hereby grants to You a perpetual,\n worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n copyright license to reproduce, prepare Derivative Works of,\n publicly display, publicly perform, sublicense, and distribute the\n Work and such Derivative Works in Source or Object form.\n\n 3. Grant of Patent License. Subject to the terms and conditions of\n this License, each Contributor hereby grants to You a perpetual,\n worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n (except as stated in this section) patent license to make, have made,\n use, offer to sell, sell, import, and otherwise transfer the Work,\n where such license applies only to those patent claims licensable\n by such Contributor that are necessarily infringed by their\n Contribution(s) alone or by combination of their Contribution(s)\n with the Work to which such Contribution(s) was submitted. If You\n institute patent litigation against any entity (including a\n cross-claim or counterclaim in a lawsuit) alleging that the Work\n or a Contribution incorporated within the Work constitutes direct\n or contributory patent infringement, then any patent licenses\n granted to You under this License for that Work shall terminate\n as of the date such litigation is filed.\n\n 4. Redistribution. You may reproduce and distribute copies of the\n Work or Derivative Works thereof in any medium, with or without\n modifications, and in Source or Object form, provided that You\n meet the following conditions:\n\n (a) You must give any other recipients of the Work or\n Derivative Works a copy of this License; and\n\n (b) You must cause any modified files to carry prominent notices\n stating that You changed the files; and\n\n (c) You must retain, in the Source form of any Derivative Works\n that You distribute, all copyright, patent, trademark, and\n attribution notices from the Source form of the Work,\n excluding those notices that do not pertain to any part of\n the Derivative Works; and\n\n (d) If the Work includes a \"NOTICE\" text file as part of its\n distribution, then any Derivative Works that You distribute must\n include a readable copy of the attribution notices contained\n within such NOTICE file, excluding those notices that do not\n pertain to any part of the Derivative Works, in at least one\n of the following places: within a NOTICE text file distributed\n as part of the Derivative Works; within the Source form or\n documentation, if provided along with the Derivative Works; or,\n within a display generated by the Derivative Works, if and\n wherever such third-party notices normally appear. The contents\n of the NOTICE file are for informational purposes only and\n do not modify the License. You may add Your own attribution\n notices within Derivative Works that You distribute, alongside\n or as an addendum to the NOTICE text from the Work, provided\n that such additional attribution notices cannot be construed\n as modifying the License.\n\n You may add Your own copyright statement to Your modifications and\n may provide additional or different license terms and conditions\n for use, reproduction, or distribution of Your modifications, or\n for any such Derivative Works as a whole, provided Your use,\n reproduction, and distribution of the Work otherwise complies with\n the conditions stated in this License.\n\n 5. Submission of Contributions. Unless You explicitly state otherwise,\n any Contribution intentionally submitted for inclusion in the Work\n by You to the Licensor shall be under the terms and conditions of\n this License, without any additional terms or conditions.\n Notwithstanding the above, nothing herein shall supersede or modify\n the terms of any separate license agreement you may have executed\n with Licensor regarding such Contributions.\n\n 6. Trademarks. This License does not grant permission to use the trade\n names, trademarks, service marks, or product names of the Licensor,\n except as required for reasonable and customary use in describing the\n origin of the Work and reproducing the content of the NOTICE file.\n\n 7. Disclaimer of Warranty. Unless required by applicable law or\n agreed to in writing, Licensor provides the Work (and each\n Contributor provides its Contributions) on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n implied, including, without limitation, any warranties or conditions\n of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n PARTICULAR PURPOSE. You are solely responsible for determining the\n appropriateness of using or redistributing the Work and assume any\n risks associated with Your exercise of permissions under this License.\n\n 8. Limitation of Liability. In no event and under no legal theory,\n whether in tort (including negligence), contract, or otherwise,\n unless required by applicable law (such as deliberate and grossly\n negligent acts) or agreed to in writing, shall any Contributor be\n liable to You for damages, including any direct, indirect, special,\n incidental, or consequential damages of any character arising as a\n result of this License or out of the use or inability to use the\n Work (including but not limited to damages for loss of goodwill,\n work stoppage, computer failure or malfunction, or any and all\n other commercial damages or losses), even if such Contributor\n has been advised of the possibility of such damages.\n\n 9. Accepting Warranty or Additional Liability. While redistributing\n the Work or Derivative Works thereof, You may choose to offer,\n and charge a fee for, acceptance of support, warranty, indemnity,\n or other liability obligations and/or rights consistent with this\n License. However, in accepting such obligations, You may act only\n on Your own behalf and on Your sole responsibility, not on behalf\n of any other Contributor, and only if You agree to indemnify,\n defend, and hold each Contributor harmless for any liability\n incurred by, or claims asserted against, such Contributor by reason\n of your accepting any such warranty or additional liability.\n\n END OF TERMS AND CONDITIONS\n\n APPENDIX: How to apply the Apache License to your work.\n\n To apply the Apache License to your work, attach the following\n boilerplate notice, with the fields enclosed by brackets \"[]\"\n replaced with your own identifying information. (Don't include\n the brackets!) The text should be enclosed in the appropriate\n comment syntax for the file format. We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n\n Copyright 2020 The NiPreps Developers\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n\n" }, { "path": "MANIFEST.in", "content": "#documentation\nrecursive-exclude .circleci/ *\nrecursive-exclude .github/ *\nrecursive-exclude .git/ *\nrecursive-exclude .maint/ *\nrecursive-exclude docker/ *\nrecursive-exclude docs/ *\nrecursive-exclude test/ *\n\nexclude .*\nexclude Dockerfile\nexclude Dockerfile_devel\nexclude Makefile\n\n#data\nrecursive-include mriqc/data *\n" }, { "path": "Makefile", "content": "# Basic actions\n\nTEST_PATH = ./\nMRIQC_VERSION = latest\n\n\n.PHONY: clean-pyc\nclean-pyc:\n\t\tfind . -name '__pycache__' -type d -exec rm -r {} +\n\t\tfind . -name '*.pyc' -exec rm --force {} +\n\t\tfind . -name '*.pyo' -exec rm --force {} +\n\t\tfind . -name '*~' -exec rm --force {} +\n\n.PHONY: clean-build\nclean-build:\n\t\trm --force --recursive build/\n\t\trm --force --recursive dist/\n\t\trm --force --recursive *.egg-info\n\t\trm --force --recursive src/\n\n.PHONY: tag\ntag:\n\t\tgit tag -a $(VERSION) -m \"Version ${VERSION}\"\n\t\tgit push origin $(VERSION)\n\t\tgit push upstream $(VERSION)\n\nlint:\n\t\tpylint ./mriqc/\n\n.PHONY: test\ntest: clean-pyc\n\t\tpy.test --ignore=src/ --verbose $(TEST_PATH)\n\ndist: clean-build clean-pyc\n\t\tpython setup.py sdist\n\n.PHONY: docker\ndocker:\n\t\tdocker build -t nipreps/mriqc:$(MRIQC_VERSION) \\\n\t\t\t--build-arg BUILD_DATE=`date -u +\"%Y-%m-%dT%H:%M:%SZ\"` \\\n\t\t\t--build-arg VCS_REF=`git rev-parse --short HEAD` \\\n\t\t\t--build-arg VERSION=`python setup.py --version` .\n\n.PHONY: release\nrelease: clean-build tag docker\n\t\tpython setup.py sdist\n\t\ttwine upload dist/*\n\nsingularity: docker\n\tmkdir -p build/singularity\n\tdocker run --privileged -ti --rm \\\n \t-v /var/run/docker.sock:/var/run/docker.sock \\\n \t-v $(shell pwd)/build/singularity:/output \\\n \tsingularityware/docker2singularity \\\n \tnipreps/mriqc:$(MRIQC_VERSION)\n\n" }, { "path": "README.rst", "content": "mriqc: image quality metrics for quality assessment of MRI\n==========================================================\n\n|DOI| |Zenodo| |Package| |Pythons| |DevStatus| |PackageBuild| |License| |Documentation| |CircleCI| |EOSS|\n\nMRIQC extracts no-reference IQMs (image quality metrics) from\nstructural (T1w and T2w), functional and diffusion MRI (magnetic resonance imaging)\ndata.\n\nMRIQC is an open-source project, developed under the following\nsoftware engineering principles:\n\n#. **Modularity and integrability**: MRIQC implements a\n `nipype `_ workflow to integrate modular\n sub-workflows that rely upon third party software toolboxes such as\n ANTs and AFNI.\n\n#. **Minimal preprocessing**: the MRIQC workflows should be as minimal\n as possible to estimate the IQMs on the original data or their minimally\n processed derivatives.\n\n#. **Interoperability and standards**: MRIQC follows the the `brain imaging data structure\n (BIDS) `_, and it adopts the `BIDS-App\n `_ standard.\n\n#. **Reliability and robustness**: the software undergoes frequent vetting sprints\n by testing its robustness against data variability (acquisition parameters,\n physiological differences, etc.) using images from `OpenfMRI `_.\n Its reliability is permanently checked and maintained with\n `CircleCI `_.\n\nCitation\n--------\n.. topic:: **When using MRIQC, please include the following citation:**\n\n Esteban O, Birman D, Schaer M, Koyejo OO, Poldrack RA, Gorgolewski KJ;\n *MRIQC: Advancing the Automatic Prediction of Image Quality in MRI from Unseen Sites*;\n PLOS ONE 12(9):e0184661; doi:`10.1371/journal.pone.0184661 `_.\n\nSupport and communication\n-------------------------\nThe documentation of this project is found here: https://mriqc.readthedocs.io/.\n\nUsers can get help using the `mriqc-users google group `_.\n\nAll bugs, concerns and enhancement requests for this software can be submitted here:\nhttps://github.com/nipreps/mriqc/issues.\n\nDevelopment\n-----------\nA local development build based on the latest docker build of MRIQC can be built with this command run from \nthe root of this repository::\n\n docker build -f Dockerfile_devel -t mriqc_devel .\n\nTo test changes the local source code will need to be mounted into the development container::\n\n docker run --rm -v .:/src/mriqc mriqc_devel\n\nNew Python dependencies can be added in ``pyproject.toml`` under ``dependencies``.\nAny time a dependency is changed or added there the docker image will need to be rebuilt\nusing the above ``docker build`` command.\n\nLicense information\n-------------------\n*MRIQC* adheres to the\n`general licensing guidelines `__\nof the *NiPreps framework*.\n\n*MRIQC* originally derives from, and hence is heavily influenced by, the\n`PCP Quality Assessment Protocol\n`__.\nPlease check the ``NOTICE`` file for further information.\n\nLicense\n~~~~~~~\nCopyright (c) 2021, the *NiPreps* Developers.\n\nAs of the 21.0.x pre-release and release series, *MRIQC* is\nlicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n`http://www.apache.org/licenses/LICENSE-2.0\n`__.\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\nAcknowledgements\n----------------\nThis work is steered and maintained by the `NiPreps Community `__.\nThe development of this resource was supported by\nthe Laura and John Arnold Foundation (RAP and KJG),\nthe NIBIB (R01EB020740, SSG; 1P41EB019936-01A1SSG, YOH),\nthe NIMH (RF1MH121867, RAP, OE; R24MH114705 and R24MH117179, RAP; 1RF1MH121885 SSG),\nNINDS (U01NS103780, RAP), and NSF (CRCNS 1912266, YOH).\nOE acknowledges financial support from the SNSF Ambizione project\n“*Uncovering the interplay of structure, function, and dynamics of\nbrain connectivity using MRI*” (grant number\n`PZ00P2_185872 `__).\n\n.. topic:: **Thanks**\n\n * The QAP developers (C. Craddock, S. Giavasis, D. Clark, Z. Shezhad, and J.\n Pellman) for the initial base of code which MRIQC was forked from.\n * W Triplett and CA Moodie for their initial contributions with bugfixes and documentation, and\n * J Varada for his contributions on the source code.\n\n\n.. |DOI| image:: https://img.shields.io/badge/doi-10.1371%2Fjournal.pone.0184661-blue.svg\n :target: https://doi.org/10.1371/journal.pone.0184661\n.. |Zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.2630889.svg\n :target: https://doi.org/10.5281/zenodo.2630889\n.. |Package| image:: https://img.shields.io/pypi/v/mriqc.svg\n :target: https://pypi.python.org/pypi/mriqc/\n.. |Pythons| image:: https://img.shields.io/pypi/pyversions/mriqc.svg\n :target: https://pypi.python.org/pypi/mriqc/\n.. |DevStatus| image:: https://img.shields.io/pypi/status/mriqc.svg\n :target: https://pypi.python.org/pypi/mriqc/\n.. |PackageBuild| image:: https://github.com/nipreps/mriqc/actions/workflows/pythonpackage.yml/badge.svg\n :target: https://github.com/nipreps/mriqc/actions/workflows/pythonpackage.yml\n.. |License| image:: https://img.shields.io/pypi/l/mriqc.svg\n :target: https://pypi.python.org/pypi/mriqc/\n.. |Documentation| image:: https://readthedocs.org/projects/mriqc/badge/?version=latest\n :target: http://mriqc.readthedocs.io/en/latest/?badge=latest\n.. |CircleCI| image:: https://circleci.com/gh/nipreps/mriqc/tree/master.svg?style=shield\n :target: https://circleci.com/gh/nipreps/mriqc/tree/master\n.. |EOSS| image:: https://chanzuckerberg.github.io/open-science/badges/CZI-EOSS.svg\n :target: https://czi.co/EOSS\n :alt: CZI's Essential Open Source Software for Science\n" }, { "path": "docker/files/neurodebian.gpg", "content": "-----BEGIN PGP PUBLIC KEY BLOCK-----\nVersion: GnuPG v1\n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\nUdSKVg9H+fQNyP5sbOjj4RDtdACfXHrRHa2+XjJP0dhpvJ8IfvYnQsU=\n=fAJZ\n-----END PGP PUBLIC KEY BLOCK-----\n" }, { "path": "docs/Makefile", "content": "# Makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line.\nSPHINXOPTS =\nSPHINXBUILD = sphinx-build\nPAPER =\nBUILDDIR = _build\nPYTHONPATH = $(PWD)\n\n# User-friendly check for sphinx-build\nifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)\n$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)\nendif\n\n# Internal variables.\nPAPEROPT_a4 = -D latex_paper_size=a4\nPAPEROPT_letter = -D latex_paper_size=letter\nALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source\n# the i18n builder cannot share the environment and doctrees with the others\nI18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source\n\n.PHONY: help\nhelp:\n\t@echo \"Please use \\`make ' where is one of\"\n\t@echo \" html to make standalone HTML files\"\n\t@echo \" dirhtml to make HTML files named index.html in directories\"\n\t@echo \" singlehtml to make a single large HTML file\"\n\t@echo \" pickle to make pickle files\"\n\t@echo \" json to make JSON files\"\n\t@echo \" htmlhelp to make HTML files and a HTML help project\"\n\t@echo \" qthelp to make HTML files and a qthelp project\"\n\t@echo \" applehelp to make an Apple Help Book\"\n\t@echo \" devhelp to make HTML files and a Devhelp project\"\n\t@echo \" epub to make an epub\"\n\t@echo \" latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter\"\n\t@echo \" latexpdf to make LaTeX files and run them through pdflatex\"\n\t@echo \" latexpdfja to make LaTeX files and run them through platex/dvipdfmx\"\n\t@echo \" text to make text files\"\n\t@echo \" man to make manual pages\"\n\t@echo \" texinfo to make Texinfo files\"\n\t@echo \" info to make Texinfo files and run them through makeinfo\"\n\t@echo \" gettext to make PO message catalogs\"\n\t@echo \" changes to make an overview of all changed/added/deprecated items\"\n\t@echo \" xml to make Docutils-native XML files\"\n\t@echo \" pseudoxml to make pseudoxml-XML files for display purposes\"\n\t@echo \" linkcheck to check all external links for integrity\"\n\t@echo \" doctest to run all doctests embedded in the documentation (if enabled)\"\n\t@echo \" coverage to run coverage check of the documentation (if enabled)\"\n\n.PHONY: clean\nclean:\n\trm -rf $(BUILDDIR)/*\n\n.PHONY: html\nhtml:\n\tPYTHONPATH=$(PYTHONPATH) $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html\n\t@echo\n\t@echo \"Build finished. The HTML pages are in $(BUILDDIR)/html.\"\n\n.PHONY: dirhtml\ndirhtml:\n\t$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml\n\t@echo\n\t@echo \"Build finished. The HTML pages are in $(BUILDDIR)/dirhtml.\"\n\n.PHONY: singlehtml\nsinglehtml:\n\t$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml\n\t@echo\n\t@echo \"Build finished. The HTML page is in $(BUILDDIR)/singlehtml.\"\n\n.PHONY: pickle\npickle:\n\t$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle\n\t@echo\n\t@echo \"Build finished; now you can process the pickle files.\"\n\n.PHONY: json\njson:\n\t$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json\n\t@echo\n\t@echo \"Build finished; now you can process the JSON files.\"\n\n.PHONY: htmlhelp\nhtmlhelp:\n\t$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp\n\t@echo\n\t@echo \"Build finished; now you can run HTML Help Workshop with the\" \\\n\t \".hhp project file in $(BUILDDIR)/htmlhelp.\"\n\n.PHONY: qthelp\nqthelp:\n\t$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp\n\t@echo\n\t@echo \"Build finished; now you can run \"qcollectiongenerator\" with the\" \\\n\t \".qhcp project file in $(BUILDDIR)/qthelp, like this:\"\n\t@echo \"# qcollectiongenerator $(BUILDDIR)/qthelp/mriqc.qhcp\"\n\t@echo \"To view the help file:\"\n\t@echo \"# assistant -collectionFile $(BUILDDIR)/qthelp/mriqc.qhc\"\n\n.PHONY: applehelp\napplehelp:\n\t$(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp\n\t@echo\n\t@echo \"Build finished. The help book is in $(BUILDDIR)/applehelp.\"\n\t@echo \"N.B. You won't be able to view it unless you put it in\" \\\n\t \"~/Library/Documentation/Help or install it in your application\" \\\n\t \"bundle.\"\n\n.PHONY: devhelp\ndevhelp:\n\t$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp\n\t@echo\n\t@echo \"Build finished.\"\n\t@echo \"To view the help file:\"\n\t@echo \"# mkdir -p $$HOME/.local/share/devhelp/mriqc\"\n\t@echo \"# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/mriqc\"\n\t@echo \"# devhelp\"\n\n.PHONY: epub\nepub:\n\t$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub\n\t@echo\n\t@echo \"Build finished. The epub file is in $(BUILDDIR)/epub.\"\n\n.PHONY: latex\nlatex:\n\t$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex\n\t@echo\n\t@echo \"Build finished; the LaTeX files are in $(BUILDDIR)/latex.\"\n\t@echo \"Run \\`make' in that directory to run these through (pdf)latex\" \\\n\t \"(use \\`make latexpdf' here to do that automatically).\"\n\n.PHONY: latexpdf\nlatexpdf:\n\t$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex\n\t@echo \"Running LaTeX files through pdflatex...\"\n\t$(MAKE) -C $(BUILDDIR)/latex all-pdf\n\t@echo \"pdflatex finished; the PDF files are in $(BUILDDIR)/latex.\"\n\n.PHONY: latexpdfja\nlatexpdfja:\n\t$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex\n\t@echo \"Running LaTeX files through platex and dvipdfmx...\"\n\t$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja\n\t@echo \"pdflatex finished; the PDF files are in $(BUILDDIR)/latex.\"\n\n.PHONY: text\ntext:\n\t$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text\n\t@echo\n\t@echo \"Build finished. The text files are in $(BUILDDIR)/text.\"\n\n.PHONY: man\nman:\n\t$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man\n\t@echo\n\t@echo \"Build finished. The manual pages are in $(BUILDDIR)/man.\"\n\n.PHONY: texinfo\ntexinfo:\n\t$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo\n\t@echo\n\t@echo \"Build finished. The Texinfo files are in $(BUILDDIR)/texinfo.\"\n\t@echo \"Run \\`make' in that directory to run these through makeinfo\" \\\n\t \"(use \\`make info' here to do that automatically).\"\n\n.PHONY: info\ninfo:\n\t$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo\n\t@echo \"Running Texinfo files through makeinfo...\"\n\tmake -C $(BUILDDIR)/texinfo info\n\t@echo \"makeinfo finished; the Info files are in $(BUILDDIR)/texinfo.\"\n\n.PHONY: gettext\ngettext:\n\t$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale\n\t@echo\n\t@echo \"Build finished. The message catalogs are in $(BUILDDIR)/locale.\"\n\n.PHONY: changes\nchanges:\n\t$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes\n\t@echo\n\t@echo \"The overview file is in $(BUILDDIR)/changes.\"\n\n.PHONY: linkcheck\nlinkcheck:\n\t$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck\n\t@echo\n\t@echo \"Link check complete; look for any errors in the above output \" \\\n\t \"or in $(BUILDDIR)/linkcheck/output.txt.\"\n\n.PHONY: doctest\ndoctest:\n\t$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest\n\t@echo \"Testing of doctests in the sources finished, look at the \" \\\n\t \"results in $(BUILDDIR)/doctest/output.txt.\"\n\n.PHONY: coverage\ncoverage:\n\t$(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage\n\t@echo \"Testing of coverage in the sources finished, look at the \" \\\n\t \"results in $(BUILDDIR)/coverage/python.txt.\"\n\n.PHONY: xml\nxml:\n\t$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml\n\t@echo\n\t@echo \"Build finished. The XML files are in $(BUILDDIR)/xml.\"\n\n.PHONY: pseudoxml\npseudoxml:\n\t$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml\n\t@echo\n\t@echo \"Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml.\"\n" }, { "path": "docs/notebooks/.gitignore", "content": ".ipynb_checkpoints/\nABIDE-BIDS/\ndata/\nexample_artifacts_dataset/\nout/\ntemp/\n*.svg\n" }, { "path": "docs/notebooks/MRIQC Web API.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Querying the MRIQC Web API\\n\",\n \"\\n\",\n \"This notebook shows how the web-API can be leveraged to analyze the image quality metrics (IQMs) that have been extracted with MRIQC\\n\",\n \"\\n\",\n \"This notebook is a derivative work of https://gist.github.com/chrisfilo/eccdb8b98f8e74d24a3395a49fbadf03\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import json\\n\",\n \"import multiprocessing as mp\\n\",\n \"import urllib.request\\n\",\n \"from json import load\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import pylab as plt\\n\",\n \"import seaborn as sns\\n\",\n \"from pandas.io.json import json_normalize\\n\",\n \"\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Preparation\\n\",\n \"\\n\",\n \"Let's define a function that will query the appropriate endpoint and a helper function to plot some distributions (at the bottom of this notebook).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def get_iqms(modality, versions=None, software='mriqc'):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Grab all iqms for the given modality and the list of versions\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" url_root = 'https://mriqc.nimh.nih.gov/api/v1/{modality}?{query}'\\n\",\n \" page = 1\\n\",\n \" dfs = []\\n\",\n \"\\n\",\n \" if versions is None:\\n\",\n \" versions = ['*']\\n\",\n \"\\n\",\n \" for version in versions:\\n\",\n \" while True:\\n\",\n \" query = []\\n\",\n \"\\n\",\n \" if software is not None:\\n\",\n \" query.append(f'\\\"provenance.software\\\":\\\"{software}\\\"')\\n\",\n \"\\n\",\n \" if version != '*':\\n\",\n \" query.append(f'\\\"provenance.version\\\":\\\"{version}\\\"')\\n\",\n \"\\n\",\n \" where = ','.join(query)\\n\",\n \" page_url = url_root.format(modality=modality, query=f'where={where}&page={page}')\\n\",\n \" with urllib.request.urlopen(page_url) as url:\\n\",\n \" data = json.loads(url.read().decode())\\n\",\n \" dfs.append(json_normalize(data['_items']))\\n\",\n \" if 'next' not in data['_links'].keys():\\n\",\n \" break\\n\",\n \" else:\\n\",\n \" page += 1\\n\",\n \"\\n\",\n \" # Compose a pandas dataframe\\n\",\n \" return pd.concat(dfs, ignore_index=True)\\n\",\n \"\\n\",\n \"\\n\",\n \"def plot_measure(data, xlabel=None, label=None, ax=None, min=None, max=None):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Distribution plot of a given measure\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" sns.distplot(data, ax=ax, label=label)\\n\",\n \"\\n\",\n \" if xlabel is not None:\\n\",\n \" ax.set_xlabel(xlabel)\\n\",\n \"\\n\",\n \" if min is None:\\n\",\n \" min = np.percentile(data, 0.5)\\n\",\n \"\\n\",\n \" if max is None:\\n\",\n \" max = np.percentile(data, 99.5)\\n\",\n \" ax.set_xlim((min, max))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Fetch IQMs\\n\",\n \"\\n\",\n \"Let's fetch IQMs for the two principal modalities of MRIQC, T1-weighted images and BOLD-fMRI. Filter out repeated images to obtain unique records.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# T1\\n\",\n \"df_t1w = get_iqms('T1w', software=None)\\n\",\n \"df_t1w_unique = df_t1w.drop_duplicates(subset=['provenance.md5sum'])\\n\",\n \"\\n\",\n \"# BOLD\\n\",\n \"df_bold = get_iqms('bold')\\n\",\n \"df_bold_unique = df_bold.drop_duplicates(subset=['provenance.md5sum'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Evolution of IQMs submission\\n\",\n \"\\n\",\n \"This code generates Figure 3A of the abstract\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAYAAAAEHCAYAAACncpHfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import datetime\\n\",\n \"\\n\",\n \"import matplotlib.dates as mdates\\n\",\n \"from dateutil import parser\\n\",\n \"\\n\",\n \"dates_t1w = [parser.parse(d) for d in df_t1w['_created'].values]\\n\",\n \"dates_t1w.sort()\\n\",\n \"\\n\",\n \"dates_t1w_u = [parser.parse(d) for d in df_t1w_unique['_created'].values]\\n\",\n \"dates_t1w_u.sort()\\n\",\n \"# mindate = dates_t1w[0]\\n\",\n \"ax = plt.subplot(111)\\n\",\n \"ax.plot(dates_t1w, list(range(1, len(dates_t1w) + 1)), label='total')\\n\",\n \"ax.plot(dates_t1w_u, list(range(1, len(dates_t1w_u) + 1)), label='unique')\\n\",\n \"ax.set_title('Number of T1w records in database')\\n\",\n \"ax.legend()\\n\",\n \"\\n\",\n \"plt.savefig('fig03a-0.svg', bbox_inches='tight', transparent=False, pad_inches=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAYAAAAEHCAYAAACncpHfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nEEKkKUkAQgiRpv4/92Ab946WMNQAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"dates_bold = [parser.parse(d) for d in df_bold['_created'].values]\\n\",\n \"dates_bold.sort()\\n\",\n \"dates_bold_u = [parser.parse(d) for d in df_bold_unique['_created'].values]\\n\",\n \"dates_bold_u.sort()\\n\",\n \"# mindate = dates_t1w[0]\\n\",\n \"ax = plt.subplot(111)\\n\",\n \"ax.plot(dates_bold, list(range(1, len(dates_bold) + 1)), label='total')\\n\",\n \"ax.plot(dates_bold_u, list(range(1, len(dates_bold_u) + 1)), label='unique')\\n\",\n \"ax.set_title('Number of BOLD records in database')\\n\",\n \"ax.legend()\\n\",\n \"plt.savefig('fig03a-1.svg', bbox_inches='tight', transparent=False, pad_inches=0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Playing with T1w IQMs\\n\",\n \"\\n\",\n \"Let's plot some of the IQMs for the T1w modality. First, let's check the names of the IQMs. These measures are explained in the documentation (http://mriqc.readthedocs.io/en/stable/iqms/t1w.html)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"cjv,cnr,efc,fber,fwhm_avg,fwhm_x,fwhm_y,fwhm_z,icvs_csf,icvs_gm,icvs_wm,inu_med,inu_range,qi_1,qi_2,rpve_csf,rpve_gm,rpve_wm,size_x,size_y,size_z,snr_csf,snr_gm,snr_total,snr_wm,snrd_csf,snrd_gm,snrd_total,snrd_wm,spacing_x,spacing_y,spacing_z,summary_bg_k,summary_bg_mad,summary_bg_mean,summary_bg_median,summary_bg_n,summary_bg_p05,summary_bg_p95,summary_bg_stdv,summary_csf_k,summary_csf_mad,summary_csf_mean,summary_csf_median,summary_csf_n,summary_csf_p05,summary_csf_p95,summary_csf_stdv,summary_gm_k,summary_gm_mad,summary_gm_mean,summary_gm_median,summary_gm_n,summary_gm_p05,summary_gm_p95,summary_gm_stdv,summary_wm_k,summary_wm_mad,summary_wm_mean,summary_wm_median,summary_wm_n,summary_wm_p05,summary_wm_p95,summary_wm_stdv,tpm_overlap_csf,tpm_overlap_gm,tpm_overlap_wm,wm2max\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\n\",\n \" ','.join(\\n\",\n \" [\\n\",\n \" line\\n\",\n \" for line in df_t1w.columns\\n\",\n \" if not line.startswith('_')\\n\",\n \" and not line.startswith('bids_meta')\\n\",\n \" and not line.startswith('provenance')\\n\",\n \" ]\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAABZYAAAFhCAYAAADjtv+1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f, ax = plt.subplots(1, 5, figsize=(25, 5))\\n\",\n \"plot_measure(df_t1w_unique.cjv, xlabel='Coefficient of joint variation (CJV)', ax=ax[0])\\n\",\n \"plot_measure(df_t1w_unique.cnr, xlabel='Contrast-to-noise ratio (CNR)', ax=ax[1])\\n\",\n \"plot_measure(\\n\",\n \" df_t1w_unique.snr_wm,\\n\",\n \" xlabel='Signal-to-noise ratio estimated on the white-matter (SNR)',\\n\",\n \" ax=ax[2],\\n\",\n \")\\n\",\n \"plot_measure(df_t1w_unique.fwhm_avg, xlabel='Smoothness (FWHM)', ax=ax[3])\\n\",\n \"plot_measure(df_t1w_unique.wm2max, xlabel='WM-to-max intensity ratio (WM2MAX)', ax=ax[4])\\n\",\n \"plt.suptitle('Distributions of some IQMs extracted from T1-weighted MRI')\\n\",\n \"plt.savefig('fig03b-0.svg', bbox_inches='tight', transparent=False, pad_inches=0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Playing with BOLD IQMs\\n\",\n \"\\n\",\n \"Let's plot some of the IQMs for the BOLD modality. First, let's check the names of the IQMs. These measures are explained in the documentation (http://mriqc.readthedocs.io/en/stable/iqms/bold.html)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"aor,aqi,dummy_trs,dvars_nstd,dvars_std,dvars_vstd,efc,fber,fd_mean,fd_num,fd_perc,fwhm_avg,fwhm_x,fwhm_y,fwhm_z,gcor,gsr_x,gsr_y,size_t,size_x,size_y,size_z,snr,spacing_tr,spacing_x,spacing_y,spacing_z,summary_bg_k,summary_bg_mad,summary_bg_mean,summary_bg_median,summary_bg_n,summary_bg_p05,summary_bg_p95,summary_bg_stdv,summary_fg_k,summary_fg_mad,summary_fg_mean,summary_fg_median,summary_fg_n,summary_fg_p05,summary_fg_p95,summary_fg_stdv,tsnr\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\n\",\n \" ','.join(\\n\",\n \" [\\n\",\n \" line\\n\",\n \" for line in df_bold.columns\\n\",\n \" if not line.startswith('_')\\n\",\n \" and not line.startswith('bids_meta')\\n\",\n \" and not line.startswith('provenance')\\n\",\n \" ]\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAABZYAAAFhCAYAAADjtv+1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nEARBEARBEARBEARBEAQ18f9JXQpScoARAQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f, ax = plt.subplots(1, 5, figsize=(25, 5))\\n\",\n \"plot_measure(\\n\",\n \" df_bold_unique[df_bold_unique.fd_mean < 10].fd_mean,\\n\",\n \" xlabel='Framewise Displacement (mm)',\\n\",\n \" max=2,\\n\",\n \" ax=ax[0],\\n\",\n \")\\n\",\n \"plot_measure(df_bold_unique[df_bold_unique.dvars_nstd < 100].dvars_nstd, xlabel='DVARS', ax=ax[1])\\n\",\n \"plot_measure(df_bold_unique.gcor, xlabel='Global correlation', ax=ax[2])\\n\",\n \"plot_measure(df_bold_unique.gsr_x, label='x-axis', ax=ax[3])\\n\",\n \"plot_measure(df_bold_unique.gsr_y, xlabel='Ghost-to-signal ratio (GSR)', label='y-axis', ax=ax[3])\\n\",\n \"ax[3].legend()\\n\",\n \"plot_measure(df_bold_unique.tsnr, xlabel='Temporal SNR (tSNR)', ax=ax[4])\\n\",\n \"plt.suptitle('Distributions of some IQMs extracted from BOLD fMRI')\\n\",\n \"plt.savefig(\\n\",\n \" 'fig03b-1.png', bbox_inches='tight', transparent=False, pad_inches=0, facecolor='white'\\n\",\n \")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/notebooks/Paper-v1.0.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"%load_ext autoreload\\n\",\n \"%autoreload 2\\n\",\n \"import os.path as op\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import seaborn as sns\\n\",\n \"\\n\",\n \"sns.set(style='whitegrid')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from mriqc.classifier import data as mcd\\n\",\n \"\\n\",\n \"abide, _ = mcd.read_dataset(x_path, y_path, rate_label='rater_1')\\n\",\n \"sites = sorted(set(abide.site.values.ravel()))\\n\",\n \"\\n\",\n \"fmt = r'{site} & \\\\pixmat{{{size[0]:d}$\\\\pm${sr[0]:d}}}{{{size[1]:d}$\\\\pm${sr[1]:d}}}{{{size[2]:d}$\\\\pm${sr[1]:d}}}'\\n\",\n \"fmt += r'& \\\\pixmat[mm]{{{sp[0]:.2f}$\\\\pm${spr[0]:.2f}}}{{{sp[1]:.2f}$\\\\pm${spr[1]:.2f}}}{{{sp[2]:.2f}$\\\\pm${spr[1]:.2f}}}'\\n\",\n \"\\n\",\n \"\\n\",\n \"for site in sites:\\n\",\n \" subabide = abide.loc[abide.site.str.contains(site)]\\n\",\n \"\\n\",\n \" medians = np.median(\\n\",\n \" subabide[['size_x', 'size_y', 'size_z', 'spacing_x', 'spacing_y', 'spacing_z']], axis=0\\n\",\n \" )\\n\",\n \"\\n\",\n \" mins = np.abs(\\n\",\n \" medians\\n\",\n \" - np.min(\\n\",\n \" subabide[['size_x', 'size_y', 'size_z', 'spacing_x', 'spacing_y', 'spacing_z']], axis=0\\n\",\n \" )\\n\",\n \" )\\n\",\n \"\\n\",\n \" maxs = np.abs(\\n\",\n \" medians\\n\",\n \" - np.max(\\n\",\n \" subabide[['size_x', 'size_y', 'size_z', 'spacing_x', 'spacing_y', 'spacing_z']], axis=0\\n\",\n \" )\\n\",\n \" )\\n\",\n \"\\n\",\n \" ranges = np.max(np.vstack((maxs, mins)), axis=0)\\n\",\n \"\\n\",\n \" print(\\n\",\n \" fmt.format(\\n\",\n \" site=site,\\n\",\n \" size=tuple(medians[:3].astype(int)),\\n\",\n \" sr=tuple(ranges[:3].astype(int)),\\n\",\n \" sp=tuple(medians[3:]),\\n\",\n \" spr=tuple(ranges[3:]),\\n\",\n \" )\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# data_path = '/home/oesteban/Google Drive/mriqc'\\n\",\n \"data_path = '/home/oesteban/tmp/mriqc-ml-tests-2/'\\n\",\n \"out_path = data_path\\n\",\n \"loso = pd.read_csv(op.join(data_path, 'cv_loso_inner.csv'), index_col=False)\\n\",\n \"kfold = pd.read_csv(op.join(data_path, 'cv_kfold_inner.csv'), index_col=False)\\n\",\n \"\\n\",\n \"kfold_outer = pd.read_csv(op.join(data_path, 'cv_kfold_outer.csv'), index_col=False)\\n\",\n \"loso_outer = pd.read_csv(op.join(data_path, 'cv_loso_outer.csv'), index_col=False)\\n\",\n \"\\n\",\n \"\\n\",\n \"def gen_newparams(dataframe):\\n\",\n \" thisdf = dataframe.copy()\\n\",\n \" thisdf['zscored_str'] = ['nzs'] * len(thisdf['zscored'])\\n\",\n \" thisdf.loc[thisdf.zscored == 1, 'zscored_str'] = 'zs'\\n\",\n \" thisdf['params'] = thisdf['clf'] + '-' + thisdf['zscored_str'] + ' ' + thisdf['params']\\n\",\n \" del thisdf['zscored_str']\\n\",\n \" return thisdf\\n\",\n \"\\n\",\n \"\\n\",\n \"loso = gen_newparams(loso)\\n\",\n \"kfold = gen_newparams(kfold)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"loso_models_list = list(set(loso.params.values.ravel().tolist()))\\n\",\n \"kfold_models_list = list(set(kfold.params.values.ravel().tolist()))\\n\",\n \"\\n\",\n \"best_param = {}\\n\",\n \"\\n\",\n \"spstr = ['LoSo', '10-fold']\\n\",\n \"best_models = {}\\n\",\n \"for i, split_cv in enumerate([loso, kfold]):\\n\",\n \" best_models[spstr[i]] = {}\\n\",\n \" splitcols = [col for col in split_cv.columns.ravel() if col.startswith('split0')]\\n\",\n \" for clf in [\\n\",\n \" 'svc_linear-nzs',\\n\",\n \" 'svc_rbf-nzs',\\n\",\n \" 'rfc-nzs',\\n\",\n \" 'svc_linear-zs',\\n\",\n \" 'svc_rbf-zs',\\n\",\n \" 'rfc-zs',\\n\",\n \" ]:\\n\",\n \" thismodeldf = split_cv.loc[split_cv.params.str.contains(clf)]\\n\",\n \" max_auc = thismodeldf.mean_auc.max()\\n\",\n \" best = thismodeldf.loc[thismodeldf.mean_auc >= max_auc]\\n\",\n \" best_list = best.params.values.ravel().tolist()\\n\",\n \"\\n\",\n \" if len(best_list) == 1:\\n\",\n \" best_models[spstr[i]][clf] = best_list[0]\\n\",\n \" else:\\n\",\n \" overall_means = [\\n\",\n \" thismodeldf.loc[thismodeldf.params.str.contains(pset), 'mean_auc'].mean()\\n\",\n \" for pset in best_list\\n\",\n \" ]\\n\",\n \" overall_max = np.max(overall_means)\\n\",\n \" if sum([val >= overall_max for val in overall_means]) == 1:\\n\",\n \" best_models[spstr[i]][clf] = best_list[np.argmax(overall_means)]\\n\",\n \" else:\\n\",\n \" best_models[spstr[i]][clf] = best_list[0]\\n\",\n \"\\n\",\n \"newdict = {'AUC': [], 'Classifier': [], 'Split scheme': []}\\n\",\n \"\\n\",\n \"modelnames = {\\n\",\n \" 'rfc-nzs': 'RFC-nzs',\\n\",\n \" 'rfc-zs': 'RFC-zs',\\n\",\n \" 'svc_linear-nzs': 'SVC_lin-nzs',\\n\",\n \" 'svc_linear-zs': 'SVC_lin-zs',\\n\",\n \" 'svc_rbf-nzs': 'SVC_rbf-nzs',\\n\",\n \" 'svc_rbf-zs': 'SVC_rbf-zs',\\n\",\n \"}\\n\",\n \"\\n\",\n \"for key, val in list(best_models['LoSo'].items()):\\n\",\n \" scores = loso.loc[loso.params.str.contains(val), 'mean_auc'].values.ravel().tolist()\\n\",\n \" nscores = len(scores)\\n\",\n \"\\n\",\n \" newdict['AUC'] += scores\\n\",\n \" newdict['Classifier'] += [modelnames[key]] * nscores\\n\",\n \" newdict['Split scheme'] += ['LoSo (16 folds)'] * nscores\\n\",\n \"\\n\",\n \"for key, val in list(best_models['10-fold'].items()):\\n\",\n \" scores = kfold.loc[kfold.params.str.contains(val), 'mean_auc'].values.ravel().tolist()\\n\",\n \" nscores = len(scores)\\n\",\n \"\\n\",\n \" newdict['AUC'] += scores\\n\",\n \" newdict['Classifier'] += [modelnames[key]] * nscores\\n\",\n \" newdict['Split scheme'] += ['10-fold'] * nscores\\n\",\n \"\\n\",\n \"newdf = pd.DataFrame(newdict).sort_values(by=['Split scheme', 'Classifier'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def plot_cv_outer(\\n\",\n \" data, score='auc', zscored=0, ax=None, ds030_score=None, split_type='LoSo', color='dodgerblue'\\n\",\n \"):\\n\",\n \" if ax is None:\\n\",\n \" ax = plt.gca()\\n\",\n \"\\n\",\n \" outer_score = data.loc[data[score].notnull(), [score, 'zscored']]\\n\",\n \" sns.distplot(\\n\",\n \" outer_score.loc[outer_score.zscored == zscored, score],\\n\",\n \" hist=True,\\n\",\n \" norm_hist=True,\\n\",\n \" ax=ax,\\n\",\n \" color=color,\\n\",\n \" label=split_type,\\n\",\n \" )\\n\",\n \" ax.set_xlim([0.4, 1.0])\\n\",\n \" ax.grid(False)\\n\",\n \" ax.set_yticklabels([])\\n\",\n \"\\n\",\n \" mean = outer_score.loc[outer_score.zscored == zscored, score].mean()\\n\",\n \" std = outer_score.loc[outer_score.zscored == zscored, score].std()\\n\",\n \"\\n\",\n \" mean_coord = draw_line(mean, ax=ax, color=color, lw=2.0, marker='o', extend=True)\\n\",\n \"\\n\",\n \" ymax = ax.get_ylim()[1]\\n\",\n \" draw_line(mean - std, ax=ax, color=color, extend=True)\\n\",\n \" draw_line(mean + std, ax=ax, color=color, extend=True)\\n\",\n \"\\n\",\n \" ax.annotate(\\n\",\n \" rf'$\\\\mu$={mean:0.3f}',\\n\",\n \" xy=(mean_coord[0], 0.75 * ymax),\\n\",\n \" xytext=(-35, 30),\\n\",\n \" textcoords='offset points',\\n\",\n \" va='center',\\n\",\n \" color='w',\\n\",\n \" size=14,\\n\",\n \" bbox={'boxstyle': 'round', 'fc': color, 'ec': 'none', 'color': 'none', 'lw': 0},\\n\",\n \" arrowprops={\\n\",\n \" 'arrowstyle': 'wedge,tail_width=0.8',\\n\",\n \" 'lw': 0,\\n\",\n \" 'patchA': None,\\n\",\n \" 'patchB': None,\\n\",\n \" 'fc': color,\\n\",\n \" 'ec': 'none',\\n\",\n \" 'relpos': (0.5, 0.5),\\n\",\n \" },\\n\",\n \" )\\n\",\n \" sigmay = 0.70 * ymax\\n\",\n \" ax.annotate(\\n\",\n \" s='',\\n\",\n \" xy=(mean - std, sigmay),\\n\",\n \" xytext=(mean + std, sigmay),\\n\",\n \" arrowprops={'arrowstyle': '<->'},\\n\",\n \" )\\n\",\n \" ax.annotate(\\n\",\n \" r'$2\\\\sigma$=%0.3f' % (2 * std),\\n\",\n \" xy=(mean_coord[0], 0.70 * ymax),\\n\",\n \" xytext=(-25, -12),\\n\",\n \" textcoords='offset points',\\n\",\n \" va='center',\\n\",\n \" color='k',\\n\",\n \" size=12,\\n\",\n \" bbox={\\n\",\n \" 'boxstyle': 'round',\\n\",\n \" 'fc': 'w',\\n\",\n \" 'ec': 'none',\\n\",\n \" 'color': 'none',\\n\",\n \" 'alpha': 0.7,\\n\",\n \" 'lw': 0,\\n\",\n \" },\\n\",\n \" )\\n\",\n \"\\n\",\n \" if ds030_score is not None:\\n\",\n \" ds030_coord = draw_line(ds030_score, ax=ax, color='k', marker='o')\\n\",\n \" ax.annotate(\\n\",\n \" 'DS030',\\n\",\n \" xy=ds030_coord,\\n\",\n \" xytext=(-100, 0),\\n\",\n \" textcoords='offset points',\\n\",\n \" va='center',\\n\",\n \" color='w',\\n\",\n \" size=16,\\n\",\n \" bbox={'boxstyle': 'round', 'fc': color, 'ec': 'none', 'color': 'none', 'lw': 0},\\n\",\n \" arrowprops={\\n\",\n \" 'arrowstyle': 'wedge,tail_width=0.8',\\n\",\n \" 'lw': 0,\\n\",\n \" 'patchA': None,\\n\",\n \" 'patchB': None,\\n\",\n \" 'fc': color,\\n\",\n \" 'ec': 'none',\\n\",\n \" 'relpos': (0.5, 0.5),\\n\",\n \" },\\n\",\n \" )\\n\",\n \"\\n\",\n \"\\n\",\n \"def draw_line(score, ax=None, color='k', marker=None, lw=0.7, extend=False):\\n\",\n \" if ax is None:\\n\",\n \" ax = plt.gca()\\n\",\n \"\\n\",\n \" if score > 1.0:\\n\",\n \" score = 1.0\\n\",\n \"\\n\",\n \" coords = [score, -1]\\n\",\n \" pdf_points = ax.lines[0].get_data()\\n\",\n \" coords[1] = np.interp([coords[0]], pdf_points[0], pdf_points[1])\\n\",\n \"\\n\",\n \" if extend:\\n\",\n \" ax.axvline(coords[0], ymin=coords[1] / ax.get_ylim()[1], ymax=0.75, color='gray', lw=0.7)\\n\",\n \"\\n\",\n \" ax.axvline(\\n\",\n \" coords[0],\\n\",\n \" ymin=coords[1] / ax.get_ylim()[1],\\n\",\n \" ymax=0,\\n\",\n \" color=color,\\n\",\n \" marker=marker,\\n\",\n \" markevery=2,\\n\",\n \" markeredgewidth=1.5,\\n\",\n \" markerfacecolor='w',\\n\",\n \" markeredgecolor=color,\\n\",\n \" lw=lw,\\n\",\n \" )\\n\",\n \"\\n\",\n \" return coords\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"sns.set(style='whitegrid')\\n\",\n \"\\n\",\n \"fig = plt.figure(figsize=(20, 8))\\n\",\n \"ax1 = plt.subplot2grid((2, 4), (0, 0), colspan=2, rowspan=2)\\n\",\n \"\\n\",\n \"sns.violinplot(\\n\",\n \" x='Classifier',\\n\",\n \" y='AUC',\\n\",\n \" hue='Split scheme',\\n\",\n \" data=newdf,\\n\",\n \" split=True,\\n\",\n \" palette=['dodgerblue', 'darkorange'],\\n\",\n \" ax=ax1,\\n\",\n \")\\n\",\n \"ax1.set_ylim([0.70, 1.0])\\n\",\n \"ax1.set_ylabel('AUC')\\n\",\n \"ax1.set_xlabel('Model')\\n\",\n \"ax1.set_title('Model selection - Inner loop of nested cross-validation')\\n\",\n \"\\n\",\n \"ax2 = plt.subplot2grid((2, 4), (0, 2))\\n\",\n \"plot_cv_outer(kfold_outer, zscored=0, score='auc', ax=ax2, ds030_score=0.695, split_type='10-fold')\\n\",\n \"ax2.set_xlabel('')\\n\",\n \"ax2.legend()\\n\",\n \"ax2.set_title('Evaluation - Outer loop of nested cross-validation')\\n\",\n \"ax2.title.set_position([1.1, 1.0])\\n\",\n \"\\n\",\n \"ax3 = plt.subplot2grid((2, 4), (1, 2))\\n\",\n \"plot_cv_outer(\\n\",\n \" loso_outer,\\n\",\n \" zscored=0,\\n\",\n \" score='auc',\\n\",\n \" ax=ax3,\\n\",\n \" ds030_score=0.695,\\n\",\n \" color='darkorange',\\n\",\n \" split_type='LoSo (17 folds)',\\n\",\n \")\\n\",\n \"ax3.legend()\\n\",\n \"ax3.set_xlabel('AUC')\\n\",\n \"\\n\",\n \"ax4 = plt.subplot2grid((2, 4), (0, 3))\\n\",\n \"plot_cv_outer(\\n\",\n \" kfold_outer, zscored=0, score='acc', ax=ax4, ds030_score=0.7283, split_type='10-fold'\\n\",\n \")\\n\",\n \"ax4.set_xlabel('')\\n\",\n \"ax4.legend()\\n\",\n \"\\n\",\n \"ax5 = plt.subplot2grid((2, 4), (1, 3))\\n\",\n \"plot_cv_outer(\\n\",\n \" loso_outer,\\n\",\n \" zscored=0,\\n\",\n \" score='acc',\\n\",\n \" ax=ax5,\\n\",\n \" ds030_score=0.7283,\\n\",\n \" color='darkorange',\\n\",\n \" split_type='LoSo (17 folds)',\\n\",\n \")\\n\",\n \"ax5.legend()\\n\",\n \"ax5.set_xlabel('Accuracy')\\n\",\n \"\\n\",\n \"\\n\",\n \"fig.savefig(op.join(out_path, 'crossvalidation.pdf'), bbox_inches='tight', pad_inches=0, dpi=300)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"zscoreddf = loso_outer.loc[loso_outer.zscored == 0, ['auc', 'acc', 'site']]\\n\",\n \"palette = sns.color_palette('cubehelix', len(set(zscoreddf.site)))\\n\",\n \"sns.pairplot(\\n\",\n \" zscoreddf.loc[zscoreddf.auc.notnull(), ['auc', 'acc', 'site']], hue='site', palette=palette\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"sites = sorted(set(loso_outer.site.ravel().tolist()))\\n\",\n \"palette = sns.color_palette('husl', len(sites))\\n\",\n \"fig = plt.figure()\\n\",\n \"for i, site in enumerate(sites):\\n\",\n \" sitedf = loso_outer.loc[loso_outer.site == site]\\n\",\n \" accdf = sitedf.loc[sitedf.zscored == 0]\\n\",\n \" sns.distplot(accdf.acc.values.ravel(), bins=20, kde=0, label=site, color=palette[i])\\n\",\n \"\\n\",\n \"fig.gca().legend()\\n\",\n \"fig.gca().set_xlim([0.5, 1.0])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/notebooks/Paper-v2.0.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Generation of tables and figures of MRIQC paper\\n\",\n \"\\n\",\n \"This notebook is associated to the paper:\\n\",\n \"\\n\",\n \"Esteban O, Birman D, Schaer M, Koyejo OO, Poldrack RA, Gorgolewski KJ; MRIQC: Predicting Quality in Manual MRI Assessment Protocols Using No-Reference Image Quality Measures; bioRxiv 111294; doi:[10.1101/111294](https://doi.org/10.1101/111294).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"%load_ext autoreload\\n\",\n \"%autoreload 2\\n\",\n \"import os.path as op\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"from mriqc.classifier.data import combine_datasets, read_dataset\\n\",\n \"from mriqc.viz import misc as mviz\\n\",\n \"from pkg_resources import resource_filename as pkgrf\\n\",\n \"\\n\",\n \"# Where the outputs should be saved\\n\",\n \"outputs_path = '../../mriqc-data/'\\n\",\n \"\\n\",\n \"# Path to ABIDE's BIDS structure\\n\",\n \"abide_path = '/home/oesteban/Data/ABIDE/'\\n\",\n \"# Path to DS030's BIDS structure\\n\",\n \"ds030_path = '/home/oesteban/Data/ds030/'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Read some data (from mriqc package)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"x_path = pkgrf('mriqc', 'data/csv/x_abide.csv')\\n\",\n \"y_path = pkgrf('mriqc', 'data/csv/y_abide.csv')\\n\",\n \"ds030_x_path = pkgrf('mriqc', 'data/csv/x_ds030.csv')\\n\",\n \"ds030_y_path = pkgrf('mriqc', 'data/csv/y_ds030.csv')\\n\",\n \"\\n\",\n \"rater_types = {'rater_1': float, 'rater_2': float, 'rater_3': float}\\n\",\n \"mdata = pd.read_csv(y_path, index_col=False, dtype=rater_types)\\n\",\n \"\\n\",\n \"sites = sorted(set(mdata.site.values.ravel().tolist()))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Figure 1: artifacts in MRI\\n\",\n \"Shows a couple of subpar datasets from the ABIDE dataset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"out_file = op.join(outputs_path, 'figures', 'fig01-artifacts.svg')\\n\",\n \"mviz.figure1(\\n\",\n \" op.join(abide_path, 'sub-50137', 'anat', 'sub-50137_T1w.nii.gz'),\\n\",\n \" op.join(abide_path, 'sub-50110', 'anat', 'sub-50110_T1w.nii.gz'),\\n\",\n \" out_file,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"out_file_pdf = out_file[:4] + '.pdf'\\n\",\n \"!rsvg-convert -f pdf -o $out_file_pdf $out_file\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Figure 2: batch effects\\n\",\n \"\\n\",\n \"This code was use to generate the second figure\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from mriqc.classifier.sklearn import preprocessing as mcsp\\n\",\n \"\\n\",\n \"# Concatenate ABIDE & DS030\\n\",\n \"fulldata = combine_datasets(\\n\",\n \" [\\n\",\n \" (x_path, y_path, 'ABIDE'),\\n\",\n \" (ds030_x_path, ds030_y_path, 'DS030'),\\n\",\n \" ]\\n\",\n \")\\n\",\n \"\\n\",\n \"# Names of all features\\n\",\n \"features = [\\n\",\n \" 'cjv',\\n\",\n \" 'cnr',\\n\",\n \" 'efc',\\n\",\n \" 'fber',\\n\",\n \" 'fwhm_avg',\\n\",\n \" 'fwhm_x',\\n\",\n \" 'fwhm_y',\\n\",\n \" 'fwhm_z',\\n\",\n \" 'icvs_csf',\\n\",\n \" 'icvs_gm',\\n\",\n \" 'icvs_wm',\\n\",\n \" 'inu_med',\\n\",\n \" 'inu_range',\\n\",\n \" 'qi_1',\\n\",\n \" 'qi_2',\\n\",\n \" 'rpve_csf',\\n\",\n \" 'rpve_gm',\\n\",\n \" 'rpve_wm',\\n\",\n \" 'size_x',\\n\",\n \" 'size_y',\\n\",\n \" 'size_z',\\n\",\n \" 'snr_csf',\\n\",\n \" 'snr_gm',\\n\",\n \" 'snr_total',\\n\",\n \" 'snr_wm',\\n\",\n \" 'snrd_csf',\\n\",\n \" 'snrd_gm',\\n\",\n \" 'snrd_total',\\n\",\n \" 'snrd_wm',\\n\",\n \" 'spacing_x',\\n\",\n \" 'spacing_y',\\n\",\n \" 'spacing_z',\\n\",\n \" 'summary_bg_k',\\n\",\n \" 'summary_bg_mad',\\n\",\n \" 'summary_bg_mean',\\n\",\n \" 'summary_bg_median',\\n\",\n \" 'summary_bg_n',\\n\",\n \" 'summary_bg_p05',\\n\",\n \" 'summary_bg_p95',\\n\",\n \" 'summary_bg_stdv',\\n\",\n \" 'summary_csf_k',\\n\",\n \" 'summary_csf_mad',\\n\",\n \" 'summary_csf_mean',\\n\",\n \" 'summary_csf_median',\\n\",\n \" 'summary_csf_n',\\n\",\n \" 'summary_csf_p05',\\n\",\n \" 'summary_csf_p95',\\n\",\n \" 'summary_csf_stdv',\\n\",\n \" 'summary_gm_k',\\n\",\n \" 'summary_gm_mad',\\n\",\n \" 'summary_gm_mean',\\n\",\n \" 'summary_gm_median',\\n\",\n \" 'summary_gm_n',\\n\",\n \" 'summary_gm_p05',\\n\",\n \" 'summary_gm_p95',\\n\",\n \" 'summary_gm_stdv',\\n\",\n \" 'summary_wm_k',\\n\",\n \" 'summary_wm_mad',\\n\",\n \" 'summary_wm_mean',\\n\",\n \" 'summary_wm_median',\\n\",\n \" 'summary_wm_n',\\n\",\n \" 'summary_wm_p05',\\n\",\n \" 'summary_wm_p95',\\n\",\n \" 'summary_wm_stdv',\\n\",\n \" 'tpm_overlap_csf',\\n\",\n \" 'tpm_overlap_gm',\\n\",\n \" 'tpm_overlap_wm',\\n\",\n \" 'wm2max',\\n\",\n \"]\\n\",\n \"\\n\",\n \"# Names of features that can be normalized\\n\",\n \"coi = [\\n\",\n \" 'cjv',\\n\",\n \" 'cnr',\\n\",\n \" 'efc',\\n\",\n \" 'fber',\\n\",\n \" 'fwhm_avg',\\n\",\n \" 'fwhm_x',\\n\",\n \" 'fwhm_y',\\n\",\n \" 'fwhm_z',\\n\",\n \" 'snr_csf',\\n\",\n \" 'snr_gm',\\n\",\n \" 'snr_total',\\n\",\n \" 'snr_wm',\\n\",\n \" 'snrd_csf',\\n\",\n \" 'snrd_gm',\\n\",\n \" 'snrd_total',\\n\",\n \" 'snrd_wm',\\n\",\n \" 'summary_csf_mad',\\n\",\n \" 'summary_csf_mean',\\n\",\n \" 'summary_csf_median',\\n\",\n \" 'summary_csf_p05',\\n\",\n \" 'summary_csf_p95',\\n\",\n \" 'summary_csf_stdv',\\n\",\n \" 'summary_gm_k',\\n\",\n \" 'summary_gm_mad',\\n\",\n \" 'summary_gm_mean',\\n\",\n \" 'summary_gm_median',\\n\",\n \" 'summary_gm_p05',\\n\",\n \" 'summary_gm_p95',\\n\",\n \" 'summary_gm_stdv',\\n\",\n \" 'summary_wm_k',\\n\",\n \" 'summary_wm_mad',\\n\",\n \" 'summary_wm_mean',\\n\",\n \" 'summary_wm_median',\\n\",\n \" 'summary_wm_p05',\\n\",\n \" 'summary_wm_p95',\\n\",\n \" 'summary_wm_stdv',\\n\",\n \"]\\n\",\n \"\\n\",\n \"# Plot batches\\n\",\n \"fig = mviz.plot_batches(\\n\",\n \" fulldata,\\n\",\n \" cols=list(reversed(coi)),\\n\",\n \" out_file=op.join(outputs_path, 'figures/fig02-batches-a.pdf'),\\n\",\n \")\\n\",\n \"\\n\",\n \"# Apply new site-wise scaler\\n\",\n \"scaler = mcsp.BatchRobustScaler(by='site', columns=coi)\\n\",\n \"scaled = scaler.fit_transform(fulldata)\\n\",\n \"fig = mviz.plot_batches(\\n\",\n \" scaled,\\n\",\n \" cols=coi,\\n\",\n \" site_labels='right',\\n\",\n \" out_file=op.join(outputs_path, 'figures/fig02-batches-b.pdf'),\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Figure 3: Inter-rater variability\\n\",\n \"\\n\",\n \"In this figure we evaluate the inter-observer agreement between both raters on the 100 data points overlapping of ABIDE. Also the Cohen's Kappa is computed.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.metrics import cohen_kappa_score\\n\",\n \"\\n\",\n \"overlap = mdata[np.all(~np.isnan(mdata[['rater_1', 'rater_2']]), axis=1)]\\n\",\n \"y1 = overlap.rater_1.values.ravel().tolist()\\n\",\n \"y2 = overlap.rater_2.values.ravel().tolist()\\n\",\n \"fig = mviz.inter_rater_variability(\\n\",\n \" y1, y2, out_file=op.join(outputs_path, 'figures', 'fig02-irv.pdf')\\n\",\n \")\\n\",\n \"\\n\",\n \"print(f\\\"Cohen's Kappa {cohen_kappa_score(y1, y2):f}\\\")\\n\",\n \"\\n\",\n \"y1 = overlap.rater_1.values.ravel()\\n\",\n \"y1[y1 == 0] = 1\\n\",\n \"\\n\",\n \"y2 = overlap.rater_2.values.ravel()\\n\",\n \"y2[y2 == 0] = 1\\n\",\n \"print(f\\\"Cohen's Kappa (binarized): {cohen_kappa_score(y1, y2):f}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Figure 5: Model selection\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"\\n\",\n \"rfc_acc = [\\n\",\n \" 0.842,\\n\",\n \" 0.815,\\n\",\n \" 0.648,\\n\",\n \" 0.609,\\n\",\n \" 0.789,\\n\",\n \" 0.761,\\n\",\n \" 0.893,\\n\",\n \" 0.833,\\n\",\n \" 0.842,\\n\",\n \" 0.767,\\n\",\n \" 0.806,\\n\",\n \" 0.850,\\n\",\n \" 0.878,\\n\",\n \" 0.798,\\n\",\n \" 0.559,\\n\",\n \" 0.881,\\n\",\n \" 0.375,\\n\",\n \"]\\n\",\n \"svc_lin_acc = [\\n\",\n \" 0.947,\\n\",\n \" 0.667,\\n\",\n \" 0.870,\\n\",\n \" 0.734,\\n\",\n \" 0.754,\\n\",\n \" 0.701,\\n\",\n \" 0.750,\\n\",\n \" 0.639,\\n\",\n \" 0.877,\\n\",\n \" 0.767,\\n\",\n \" 0.500,\\n\",\n \" 0.475,\\n\",\n \" 0.837,\\n\",\n \" 0.768,\\n\",\n \" 0.717,\\n\",\n \" 0.050,\\n\",\n \" 0.429,\\n\",\n \"]\\n\",\n \"svc_rbf_acc = [\\n\",\n \" 0.947,\\n\",\n \" 0.852,\\n\",\n \" 0.500,\\n\",\n \" 0.578,\\n\",\n \" 0.772,\\n\",\n \" 0.712,\\n\",\n \" 0.821,\\n\",\n \" 0.583,\\n\",\n \" 0.912,\\n\",\n \" 0.767,\\n\",\n \" 0.500,\\n\",\n \" 0.450,\\n\",\n \" 0.837,\\n\",\n \" 0.778,\\n\",\n \" 0.441,\\n\",\n \" 0.950,\\n\",\n \" 0.339,\\n\",\n \"]\\n\",\n \"\\n\",\n \"df = pd.DataFrame(\\n\",\n \" {\\n\",\n \" 'site': list(range(len(sites))) * 3,\\n\",\n \" 'accuracy': rfc_acc + svc_lin_acc + svc_rbf_acc,\\n\",\n \" 'Model': ['RFC'] * len(sites) + ['SVC_lin'] * len(sites) + ['SVC_rbf'] * len(sites),\\n\",\n \" }\\n\",\n \")\\n\",\n \"\\n\",\n \"\\n\",\n \"x = np.arange(len(sites))\\n\",\n \"data = list(zip(rfc_acc, svc_lin_acc, svc_rbf_acc))\\n\",\n \"\\n\",\n \"dim = len(data[0])\\n\",\n \"w = 0.81\\n\",\n \"dimw = w / dim\\n\",\n \"\\n\",\n \"colors = ['dodgerblue', 'orange', 'darkorange']\\n\",\n \"\\n\",\n \"allvals = [rfc_acc, svc_lin_acc, svc_rbf_acc]\\n\",\n \"\\n\",\n \"fig = plt.figure(figsize=(10, 3))\\n\",\n \"ax2 = plt.subplot2grid((1, 4), (0, 3))\\n\",\n \"plot = sns.violinplot(\\n\",\n \" data=df, x='Model', y='accuracy', ax=ax2, palette=colors, bw=0.1, linewidth=0.7\\n\",\n \")\\n\",\n \"for i in range(dim):\\n\",\n \" ax2.axhline(np.average(allvals[i]), ls='--', color=colors[i], lw=0.8)\\n\",\n \"# ax2.axhline(np.percentile(allvals[i], 50), ls='--', color=colors[i], lw=.8)\\n\",\n \"# sns.swarmplot(x=\\\"model\\\", y=\\\"accuracy\\\", data=df, color=\\\"w\\\", alpha=.5, ax=ax2);\\n\",\n \"ax2.yaxis.tick_right()\\n\",\n \"ax2.set_ylabel('')\\n\",\n \"ax2.set_xticklabels(ax2.get_xticklabels(), rotation=40)\\n\",\n \"ax2.set_ylim([0.0, 1.0])\\n\",\n \"\\n\",\n \"ax1 = plt.subplot2grid((1, 4), (0, 0), colspan=3)\\n\",\n \"for i in range(dim):\\n\",\n \" y = [d[i] for d in data]\\n\",\n \" b = ax1.bar(x + i * dimw, y, dimw, bottom=0.001, color=colors[i], alpha=0.6)\\n\",\n \" print(np.average(allvals[i]), np.std(allvals[i]))\\n\",\n \" ax1.axhline(np.average(allvals[i]), ls='--', color=colors[i], lw=0.8)\\n\",\n \"\\n\",\n \"\\n\",\n \"plt.xlim([-0.2, 16.75])\\n\",\n \"plt.grid(False)\\n\",\n \"_ = plt.xticks(np.arange(0, 17) + 0.33, sites, rotation='vertical')\\n\",\n \"ax1.set_ylim([0.0, 1.0])\\n\",\n \"ax1.set_ylabel('Accuracy (ACC)')\\n\",\n \"fig.savefig(op.join(outputs_path, 'figures/fig05-acc.pdf'), bbox_inches='tight', dpi=300)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"rfc_roc_auc = [\\n\",\n \" 0.597,\\n\",\n \" 0.380,\\n\",\n \" 0.857,\\n\",\n \" 0.610,\\n\",\n \" 0.698,\\n\",\n \" 0.692,\\n\",\n \" 0.963,\\n\",\n \" 0.898,\\n\",\n \" 0.772,\\n\",\n \" 0.596,\\n\",\n \" 0.873,\\n\",\n \" 0.729,\\n\",\n \" 0.784,\\n\",\n \" 0.860,\\n\",\n \" 0.751,\\n\",\n \" 0.900,\\n\",\n \" 0.489,\\n\",\n \"]\\n\",\n \"svc_lin_roc_auc = [\\n\",\n \" 0.583,\\n\",\n \" 0.304,\\n\",\n \" 0.943,\\n\",\n \" 0.668,\\n\",\n \" 0.691,\\n\",\n \" 0.754,\\n\",\n \" 1.000,\\n\",\n \" 0.778,\\n\",\n \" 0.847,\\n\",\n \" 0.590,\\n\",\n \" 0.857,\\n\",\n \" 0.604,\\n\",\n \" 0.604,\\n\",\n \" 0.838,\\n\",\n \" 0.447,\\n\",\n \" 0.650,\\n\",\n \" 0.501,\\n\",\n \"]\\n\",\n \"svc_rbf_roc_auc = [\\n\",\n \" 0.681,\\n\",\n \" 0.217,\\n\",\n \" 0.827,\\n\",\n \" 0.553,\\n\",\n \" 0.738,\\n\",\n \" 0.616,\\n\",\n \" 0.889,\\n\",\n \" 0.813,\\n\",\n \" 0.845,\\n\",\n \" 0.658,\\n\",\n \" 0.779,\\n\",\n \" 0.493,\\n\",\n \" 0.726,\\n\",\n \" 0.510,\\n\",\n \" 0.544,\\n\",\n \" 0.500,\\n\",\n \" 0.447,\\n\",\n \"]\\n\",\n \"\\n\",\n \"df = pd.DataFrame(\\n\",\n \" {\\n\",\n \" 'site': list(range(len(sites))) * 3,\\n\",\n \" 'auc': rfc_roc_auc + svc_lin_roc_auc + svc_rbf_roc_auc,\\n\",\n \" 'Model': ['RFC'] * len(sites) + ['SVC_lin'] * len(sites) + ['SVC_rbf'] * len(sites),\\n\",\n \" }\\n\",\n \")\\n\",\n \"\\n\",\n \"x = np.arange(len(sites))\\n\",\n \"data = list(zip(rfc_roc_auc, svc_lin_roc_auc, svc_rbf_roc_auc))\\n\",\n \"\\n\",\n \"dim = len(data[0])\\n\",\n \"w = 0.81\\n\",\n \"dimw = w / dim\\n\",\n \"\\n\",\n \"colors = ['dodgerblue', 'orange', 'darkorange']\\n\",\n \"\\n\",\n \"allvals = [rfc_roc_auc, svc_lin_roc_auc, svc_rbf_roc_auc]\\n\",\n \"\\n\",\n \"fig = plt.figure(figsize=(10, 3))\\n\",\n \"ax2 = plt.subplot2grid((1, 4), (0, 3))\\n\",\n \"plot = sns.violinplot(data=df, x='Model', y='auc', ax=ax2, palette=colors, bw=0.1, linewidth=0.7)\\n\",\n \"for i in range(dim):\\n\",\n \" ax2.axhline(np.average(allvals[i]), ls='--', color=colors[i], lw=0.8)\\n\",\n \"\\n\",\n \"ax2.yaxis.tick_right()\\n\",\n \"ax2.set_ylabel('')\\n\",\n \"ax2.set_xticklabels(ax2.get_xticklabels(), rotation=40)\\n\",\n \"ax2.set_ylim([0.0, 1.0])\\n\",\n \"\\n\",\n \"ax1 = plt.subplot2grid((1, 4), (0, 0), colspan=3)\\n\",\n \"for i in range(dim):\\n\",\n \" y = [d[i] for d in data]\\n\",\n \" b = ax1.bar(x + i * dimw, y, dimw, bottom=0.001, color=colors[i], alpha=0.6)\\n\",\n \" print(np.average(allvals[i]), np.std(allvals[i]))\\n\",\n \" ax1.axhline(np.average(allvals[i]), ls='--', color=colors[i], lw=0.8)\\n\",\n \"\\n\",\n \"\\n\",\n \"plt.xlim([-0.2, 16.75])\\n\",\n \"plt.grid(False)\\n\",\n \"_ = plt.xticks(np.arange(0, 17) + 0.33, sites, rotation='vertical')\\n\",\n \"ax1.set_ylim([0.0, 1.0])\\n\",\n \"ax1.set_ylabel('Area under the curve (AUC)')\\n\",\n \"fig.savefig(op.join(outputs_path, 'figures/fig05-auc.pdf'), bbox_inches='tight', dpi=300)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Evaluation on DS030\\n\",\n \"\\n\",\n \"This section deals with the results obtained on DS030.\\n\",\n \"\\n\",\n \"### Table 4: Confusion matrix\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.metrics import confusion_matrix\\n\",\n \"\\n\",\n \"pred_file = op.abspath(\\n\",\n \" op.join(\\n\",\n \" '..',\\n\",\n \" 'mriqc/data/csv',\\n\",\n \" 'mclf_run-20170724-191452_mod-rfc_ver-0.9.7-rc8_class-2_cv-loso_data-test_pred.csv',\\n\",\n \" )\\n\",\n \")\\n\",\n \"\\n\",\n \"pred_y = pd.read_csv(pred_file)\\n\",\n \"true_y = pd.read_csv(ds030_y_path)\\n\",\n \"true_y.rater_1 *= -1\\n\",\n \"true_y.rater_1[true_y.rater_1 < 0] = 0\\n\",\n \"print(\\n\",\n \" confusion_matrix(true_y.rater_1.tolist(), pred_y.pred_y.values.ravel().tolist(), labels=[0, 1])\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Figure 6A: Feature importances\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import seaborn as sns\\n\",\n \"from sklearn.externals.joblib import load as loadpkl\\n\",\n \"\\n\",\n \"sns.set_style('white')\\n\",\n \"\\n\",\n \"# Get the RFC\\n\",\n \"estimator = loadpkl(\\n\",\n \" pkgrf(\\n\",\n \" 'mriqc',\\n\",\n \" 'data/mclf_run-20170724-191452_mod-rfc_ver-0.9.7-rc8_class-2_cv-loso_data-train_estimator.pklz',\\n\",\n \" )\\n\",\n \")\\n\",\n \"forest = estimator.named_steps['rfc']\\n\",\n \"\\n\",\n \"# Features selected in cross-validation\\n\",\n \"features = [\\n\",\n \" 'cjv',\\n\",\n \" 'cnr',\\n\",\n \" 'efc',\\n\",\n \" 'fber',\\n\",\n \" 'fwhm_avg',\\n\",\n \" 'fwhm_x',\\n\",\n \" 'fwhm_y',\\n\",\n \" 'fwhm_z',\\n\",\n \" 'icvs_csf',\\n\",\n \" 'icvs_gm',\\n\",\n \" 'icvs_wm',\\n\",\n \" 'qi_1',\\n\",\n \" 'qi_2',\\n\",\n \" 'rpve_csf',\\n\",\n \" 'rpve_gm',\\n\",\n \" 'rpve_wm',\\n\",\n \" 'snr_csf',\\n\",\n \" 'snr_gm',\\n\",\n \" 'snr_total',\\n\",\n \" 'snr_wm',\\n\",\n \" 'snrd_csf',\\n\",\n \" 'snrd_gm',\\n\",\n \" 'snrd_total',\\n\",\n \" 'snrd_wm',\\n\",\n \" 'summary_bg_k',\\n\",\n \" 'summary_bg_stdv',\\n\",\n \" 'summary_csf_k',\\n\",\n \" 'summary_csf_mad',\\n\",\n \" 'summary_csf_mean',\\n\",\n \" 'summary_csf_median',\\n\",\n \" 'summary_csf_p05',\\n\",\n \" 'summary_csf_p95',\\n\",\n \" 'summary_csf_stdv',\\n\",\n \" 'summary_gm_k',\\n\",\n \" 'summary_gm_mad',\\n\",\n \" 'summary_gm_mean',\\n\",\n \" 'summary_gm_median',\\n\",\n \" 'summary_gm_p05',\\n\",\n \" 'summary_gm_p95',\\n\",\n \" 'summary_gm_stdv',\\n\",\n \" 'summary_wm_k',\\n\",\n \" 'summary_wm_mad',\\n\",\n \" 'summary_wm_mean',\\n\",\n \" 'summary_wm_median',\\n\",\n \" 'summary_wm_p05',\\n\",\n \" 'summary_wm_p95',\\n\",\n \" 'summary_wm_stdv',\\n\",\n \" 'tpm_overlap_csf',\\n\",\n \" 'tpm_overlap_gm',\\n\",\n \" 'tpm_overlap_wm',\\n\",\n \"]\\n\",\n \"\\n\",\n \"nft = len(features)\\n\",\n \"\\n\",\n \"forest = estimator.named_steps['rfc']\\n\",\n \"importances = np.median([tree.feature_importances_ for tree in forest.estimators_], axis=0)\\n\",\n \"# importances = np.median(, axis=0)\\n\",\n \"indices = np.argsort(importances)[::-1]\\n\",\n \"\\n\",\n \"df = {'Feature': [], 'Importance': []}\\n\",\n \"for tree in forest.estimators_:\\n\",\n \" for i in indices:\\n\",\n \" df['Feature'] += [features[i]]\\n\",\n \" df['Importance'] += [tree.feature_importances_[i]]\\n\",\n \"fig = plt.figure(figsize=(20, 6))\\n\",\n \"# plt.title(\\\"Feature importance plot\\\")\\n\",\n \"sns.boxplot(x='Feature', y='Importance', data=pd.DataFrame(df), linewidth=1, notch=True)\\n\",\n \"plt.xlabel(f'Features selected ({len(features)})')\\n\",\n \"# plt.bar(range(nft), importances[indices],\\n\",\n \"# color=\\\"r\\\", yerr=std[indices], align=\\\"center\\\")\\n\",\n \"plt.xticks(range(nft))\\n\",\n \"plt.gca().set_xticklabels([features[i] for i in indices], rotation=90)\\n\",\n \"plt.xlim([-1, nft])\\n\",\n \"plt.show()\\n\",\n \"fig.savefig(\\n\",\n \" op.join(outputs_path, 'figures', 'fig06-exp2-fi.pdf'),\\n\",\n \" bbox_inches='tight',\\n\",\n \" pad_inches=0,\\n\",\n \" dpi=300,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Figure 6B: Misclassified images of DS030\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"fn = [\\n\",\n \" '10225',\\n\",\n \" '10235',\\n\",\n \" '10316',\\n\",\n \" '10339',\\n\",\n \" '10365',\\n\",\n \" '10376',\\n\",\n \" '10429',\\n\",\n \" '10460',\\n\",\n \" '10506',\\n\",\n \" '10527',\\n\",\n \" '10530',\\n\",\n \" '10624',\\n\",\n \" '10696',\\n\",\n \" '10891',\\n\",\n \" '10948',\\n\",\n \" '10968',\\n\",\n \" '10977',\\n\",\n \" '11050',\\n\",\n \" '11052',\\n\",\n \" '11142',\\n\",\n \" '11143',\\n\",\n \" '11149',\\n\",\n \" '50004',\\n\",\n \" '50005',\\n\",\n \" '50008',\\n\",\n \" '50010',\\n\",\n \" '50016',\\n\",\n \" '50027',\\n\",\n \" '50029',\\n\",\n \" '50033',\\n\",\n \" '50034',\\n\",\n \" '50036',\\n\",\n \" '50043',\\n\",\n \" '50047',\\n\",\n \" '50049',\\n\",\n \" '50053',\\n\",\n \" '50054',\\n\",\n \" '50055',\\n\",\n \" '50085',\\n\",\n \" '60006',\\n\",\n \" '60010',\\n\",\n \" '60012',\\n\",\n \" '60014',\\n\",\n \" '60016',\\n\",\n \" '60021',\\n\",\n \" '60046',\\n\",\n \" '60052',\\n\",\n \" '60072',\\n\",\n \" '60073',\\n\",\n \" '60084',\\n\",\n \" '60087',\\n\",\n \" '70051',\\n\",\n \" '70060',\\n\",\n \" '70072',\\n\",\n \"]\\n\",\n \"fp = ['10280', '10455', '10523', '11112', '50020', '50048', '50052', '50061', '50073', '60077']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"fn_clear = [('10316', 98), ('10968', 122), ('11050', 110), ('11149', 111)]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"import nibabel as nb\\n\",\n \"from mriqc.viz.utils import plot_slice\\n\",\n \"\\n\",\n \"for im, z in fn_clear:\\n\",\n \" image_path = op.join(ds030_path, f'sub-{im}', 'anat', f'sub-{im}_T1w.nii.gz')\\n\",\n \" imdata = nb.load(image_path).get_data()\\n\",\n \"\\n\",\n \" fig, ax = plt.subplots()\\n\",\n \" plot_slice(imdata[..., z], annotate=True)\\n\",\n \" fig.savefig(\\n\",\n \" op.join(outputs_path, 'figures', f'fig-06_sub-{im}_slice-{z:03}.svg'),\\n\",\n \" dpi=300,\\n\",\n \" bbox_inches='tight',\\n\",\n \" )\\n\",\n \" plt.clf()\\n\",\n \" plt.close()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"fp_clear = [\\n\",\n \" ('10455', 140),\\n\",\n \" ('50073', 162),\\n\",\n \"]\\n\",\n \"for im, z in fp_clear:\\n\",\n \" image_path = op.join(ds030_path, f'sub-{im}', 'anat', f'sub-{im}_T1w.nii.gz')\\n\",\n \" imdata = nb.load(image_path).get_data()\\n\",\n \"\\n\",\n \" fig, ax = plt.subplots()\\n\",\n \" plot_slice(imdata[..., z], annotate=True)\\n\",\n \" fig.savefig(\\n\",\n \" op.join(outputs_path, 'figures', f'fig-06_sub-{im}_slice-{z:03}.svg'),\\n\",\n \" dpi=300,\\n\",\n \" bbox_inches='tight',\\n\",\n \" )\\n\",\n \" plt.clf()\\n\",\n \" plt.close()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "docs/notebooks/SpikesPlotter.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"%load_ext autoreload\\n\",\n \"%autoreload 2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import os.path as op\\n\",\n \"\\n\",\n \"wf_path = '/home/oesteban/tmp/mriqc-newcircle/work/workflow_enumerator/funcMRIQC/'\\n\",\n \"\\n\",\n \"in_fft = op.join(\\n\",\n \" wf_path,\\n\",\n \" 'ComputeIQMs/_in_file_..home..oesteban..Data..example_artifacts_dataset..sub-ben01'\\n\",\n \" '..func..sub-ben01_task-unknown_bold.nii.gz/SpikesFinderFFT/sub-ben01_task-unknown_bold_zsfft.nii.gz',\\n\",\n \")\\n\",\n \"\\n\",\n \"in_file = op.join(\\n\",\n \" wf_path,\\n\",\n \" '_in_file_..home..oesteban..Data..example_artifacts_dataset..sub-ben01..func..sub-ben01_'\\n\",\n \" 'task-unknown_bold.nii.gz/reorient_and_discard/sub-ben01_task-unknown_bold.nii.gz',\\n\",\n \")\\n\",\n \"in_spikes = op.join(\\n\",\n \" wf_path,\\n\",\n \" 'ComputeIQMs/_in_file_..home..oesteban..Data..example_artifacts_dataset..sub-ben01..func..'\\n\",\n \" 'sub-ben01_task-unknown_bold.nii.gz/SpikesFinderFFT/sub-ben01_task-unknown_bold_spikes.tsv',\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"in_fft = op.join(\\n\",\n \" wf_path,\\n\",\n \" 'ComputeIQMs/_in_file_..home..oesteban..Data..circle-tests..sub-ds205s09'\\n\",\n \" '..func..sub-ds205s09_task-view_acq-LR_run-02_bold.nii.gz/SpikesFinderFFT/sub-ds205s09_task-view_acq-LR_run-02_bold_zsfft.nii.gz',\\n\",\n \")\\n\",\n \"\\n\",\n \"in_file = op.join(\\n\",\n \" wf_path,\\n\",\n \" '_in_file_..home..oesteban..Data..circle-tests..sub-ds205s09..func..sub-ds205s09_'\\n\",\n \" 'task-view_acq-LR_run-02_bold.nii.gz/reorient_and_discard/sub-ds205s09_task-view_acq-LR_run-02_bold.nii.gz',\\n\",\n \")\\n\",\n \"in_spikes = op.join(\\n\",\n \" wf_path,\\n\",\n \" 'ComputeIQMs/_in_file_..home..oesteban..Data..circle-tests..sub-ds205s09'\\n\",\n \" '..func..sub-ds205s09_task-view_acq-LR_run-02_bold.nii.gz/SpikesFinderFFT/sub-ds205s09_task-view_acq-LR_run-02_bold_spikes.tsv',\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from mriqc.viz import utils as mvu\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"\\n\",\n \"spikes_list = [tuple(i) for i in np.atleast_2d(np.loadtxt(in_spikes, dtype=int)).tolist()]\\n\",\n \"spikes_list += [(5, 14)]\\n\",\n \"mvu.plot_spikes(in_file, in_fft, spikes_list)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"%load_ext autoreload\\n\",\n \"%autoreload 2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import os.path as op\\n\",\n \"\\n\",\n \"data_path = '/home/oesteban/Data/ABIDE/sub-50465/anat/sub-50465_T1w.nii.gz'\\n\",\n \"data_path = '/home/oesteban/tmp/mriqc-newcircle/work/workflow_enumerator/funcMRIQC/_in_file_..home..oesteban..Data..example_artifacts_dataset..sub-ben04..func..sub-ben04_task-unknown_bold.nii.gz/compute_tsnr/stdev.nii.gz'\\n\",\n \"# data_path = '/home/oesteban/Data/rewardBeastBIDS2/sub-119/func/sub-119_task-rest_sbref.nii.gz'\\n\",\n \"# data_path ='/home/oesteban/tmp/mriqc-newcircle/work/workflow_enumerator/funcMRIQC/_in_file_..home..oesteban..Data..circle-tests..sub-ds003s03..func..sub-ds003s03_task-rhymejudgment_bold.nii.gz/compute_tsnr/stdev.nii.gz'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from mriqc.viz import utils as mvu\\n\",\n \"\\n\",\n \"mvu.plot_mosaic(data_path, cmap='viridis')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "docs/notebooks/Supplemental Materials.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Generation of supplemental materials for the MRIQC paper\\n\",\n \"\\n\",\n \"This notebook is associated to the paper:\\n\",\n \"\\n\",\n \"Esteban O, Birman D, Schaer M, Koyejo OO, Poldrack RA, Gorgolewski KJ; MRIQC: Predicting Quality in Manual MRI Assessment Protocols Using No-Reference Image Quality Measures; bioRxiv 111294; doi:[10.1101/111294](https://doi.org/10.1101/111294).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"%load_ext autoreload\\n\",\n \"%autoreload 2\\n\",\n \"import os.path as op\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"from mriqc.viz import misc as mviz\\n\",\n \"from pkg_resources import resource_filename as pkgrf\\n\",\n \"\\n\",\n \"outputs_path = '../../mriqc-data/'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"x_path = pkgrf('mriqc', 'data/csv/x_abide.csv')\\n\",\n \"y_path = pkgrf('mriqc', 'data/csv/y_abide.csv')\\n\",\n \"ds030_x_path = pkgrf('mriqc', 'data/csv/x_ds030.csv')\\n\",\n \"ds030_y_path = pkgrf('mriqc', 'data/csv/y_ds030.csv')\\n\",\n \"\\n\",\n \"rater_types = {'rater_1': float, 'rater_2': float, 'rater_3': float}\\n\",\n \"mdata = pd.read_csv(y_path, index_col=False, dtype=rater_types)\\n\",\n \"\\n\",\n \"sites = sorted(set(mdata.site.values.ravel().tolist()))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"fig = mviz.raters_variability_plot(\\n\",\n \" mdata,\\n\",\n \" raters=['rater_1', 'rater_2', 'rater_3'],\\n\",\n \" rater_names=['Rater 1', 'Rater 2A', 'Rater 2B'],\\n\",\n \" out_file=op.join(outputs_path, 'figures', 'suppl-fig02.pdf'),\\n\",\n \" only_overlap=False,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.metrics import cohen_kappa_score\\n\",\n \"\\n\",\n \"overlap = mdata[np.all(~np.isnan(mdata[['rater_2', 'rater_3']]), axis=1)]\\n\",\n \"y1 = overlap.rater_2.values.ravel().tolist()\\n\",\n \"y2 = overlap.rater_3.values.ravel().tolist()\\n\",\n \"\\n\",\n \"fig = mviz.inter_rater_variability(\\n\",\n \" y1,\\n\",\n \" y2,\\n\",\n \" raters=['Protocol A', 'Protocol B'],\\n\",\n \" out_file=op.join(outputs_path, 'figures', 'suppl-intrarv.pdf'),\\n\",\n \")\\n\",\n \"\\n\",\n \"print(f\\\"Cohen's Kappa {cohen_kappa_score(y1, y2):f}\\\")\\n\",\n \"\\n\",\n \"y1 = overlap.rater_2.values.ravel()\\n\",\n \"y1[y1 == 0] = 1\\n\",\n \"\\n\",\n \"y2 = overlap.rater_3.values.ravel()\\n\",\n \"y2[y2 == 0] = 1\\n\",\n \"print(f\\\"Cohen's Kappa (binarized): {cohen_kappa_score(y1, y2):f}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"fig = mviz.plot_corrmat(x_path)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"fig = mviz.plot_histograms(x_path, y_path)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/notebooks/finding_spikes.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import nibabel\\n\",\n \"import nilearn\\n\",\n \"import nipy\\n\",\n \"import numpy as np\\n\",\n \"import pylab as plt\\n\",\n \"import seaborn as sns\\n\",\n \"from nilearn.image import mean_img, new_img_like\\n\",\n \"from nilearn.masking import compute_epi_mask\\n\",\n \"from nilearn.plotting import plot_anat, plot_epi, plot_roi\\n\",\n \"from nipy.labs.mask import compute_mask\\n\",\n \"\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"compute_mask?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"in_file = 'data/sub-ben01_task-unknown_bold.nii.gz'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"in_4d_nii = nibabel.load(in_file)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"mean_nii = mean_img(in_4d_nii)\\n\",\n \"plot_anat(mean_nii)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# mask_nii = compute_epi_mask(in_4d_nii)\\n\",\n \"\\n\",\n \"mask_data = compute_mask(mean_nii.get_data())\\n\",\n \"mask_nii = new_img_like(mean_nii, mask_data)\\n\",\n \"\\n\",\n \"plot_roi(mask_nii, mean_nii)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"a = np.where(mask_nii.get_data() != 0)\\n\",\n \"bbox = np.max(a[0]) - np.min(a[0]), np.max(a[1]) - np.min(a[1]), np.max(a[2]) - np.min(a[2])\\n\",\n \"print(bbox)\\n\",\n \"print(np.argmax(bbox))\\n\",\n \"longest_axis = np.argmax(bbox)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from scipy import ndimage\\n\",\n \"\\n\",\n \"# Input here is a binarized and intersected mask data from previous section\\n\",\n \"dil_mask = ndimage.binary_dilation(\\n\",\n \" mask_nii.get_data(), iterations=int(mask_nii.shape[longest_axis] / 8)\\n\",\n \")\\n\",\n \"\\n\",\n \"# Now, we visualize the same using `plot_roi` with data being converted to Nifti\\n\",\n \"# image. In all new image like, reference image is the same but second argument\\n\",\n \"# varies with data specific\\n\",\n \"dil_mask_nii = new_img_like(mask_nii, dil_mask.astype(np.int))\\n\",\n \"plot_roi(dil_mask_nii, mean_nii)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"dil_mask.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"rep = list(mask_nii.shape)\\n\",\n \"rep[longest_axis] = -1\\n\",\n \"new_mask_2d = dil_mask.max(axis=longest_axis).reshape(rep)\\n\",\n \"new_mask_2d.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"rep = [1, 1, 1]\\n\",\n \"rep[longest_axis] = mask_nii.shape[longest_axis]\\n\",\n \"new_mask_3d = np.logical_not(np.tile(new_mask_2d, rep))\\n\",\n \"# new_mask_3d =\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"new_mask_nii = new_img_like(mask_nii, new_mask_3d.astype(np.int))\\n\",\n \"plot_roi(new_mask_nii, mean_nii)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from scipy.stats.mstats import zscore\\n\",\n \"\\n\",\n \"data4d = in_4d_nii.get_data()\\n\",\n \"for slice_i in range(in_4d_nii.shape[2]):\\n\",\n \" slice_data = data4d[:, :, slice_i, :][new_mask_3d[:, :, slice_i]].mean(axis=0)\\n\",\n \" slice_data = zscore(slice_data)\\n\",\n \" plt.plot(slice_data)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"data4d[:, :, slice_i, :][new_mask_3d[:, :, slice_i]].shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"data4d.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"compute_mask?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"def plot_spikes(in_file, skip=0):\\n\",\n \" in_4d_nii = nibabel.load(in_file)\\n\",\n \" mean_nii = mean_img(in_4d_nii)\\n\",\n \" plot_anat(mean_nii)\\n\",\n \" mask_data = compute_mask(mean_nii.get_data())\\n\",\n \" mask_nii = new_img_like(mean_nii, mask_data)\\n\",\n \"\\n\",\n \" plot_roi(mask_nii, mean_nii)\\n\",\n \"\\n\",\n \" a = np.where(mask_nii.get_data() != 0)\\n\",\n \" bbox = np.max(a[0]) - np.min(a[0]), np.max(a[1]) - np.min(a[1]), np.max(a[2]) - np.min(a[2])\\n\",\n \" print(bbox)\\n\",\n \" print(np.argmax(bbox))\\n\",\n \" longest_axis = np.argmax(bbox)\\n\",\n \"\\n\",\n \" from scipy import ndimage\\n\",\n \"\\n\",\n \" # Input here is a binarized and intersected mask data from previous section\\n\",\n \" dil_mask = ndimage.binary_dilation(\\n\",\n \" mask_nii.get_data(), iterations=int(mask_nii.shape[longest_axis] / 8)\\n\",\n \" )\\n\",\n \"\\n\",\n \" # Now, we visualize the same using `plot_roi` with data being converted to Nifti\\n\",\n \" # image. In all new image like, reference image is the same but second argument\\n\",\n \" # varies with data specific\\n\",\n \" dil_mask_nii = new_img_like(mask_nii, dil_mask.astype(np.int))\\n\",\n \" plot_roi(dil_mask_nii, mean_nii)\\n\",\n \"\\n\",\n \" rep = list(mask_nii.shape)\\n\",\n \" rep[longest_axis] = -1\\n\",\n \" new_mask_2d = dil_mask.max(axis=longest_axis).reshape(rep)\\n\",\n \" new_mask_2d.shape\\n\",\n \"\\n\",\n \" rep = [1, 1, 1]\\n\",\n \" rep[longest_axis] = mask_nii.shape[longest_axis]\\n\",\n \" new_mask_3d = np.logical_not(np.tile(new_mask_2d, rep))\\n\",\n \"\\n\",\n \" new_mask_nii = new_img_like(mask_nii, new_mask_3d.astype(np.int))\\n\",\n \" plot_roi(new_mask_nii, mean_nii)\\n\",\n \"\\n\",\n \" from scipy.stats.mstats import zscore\\n\",\n \"\\n\",\n \" data4d = in_4d_nii.get_data()[:, :, :, skip:]\\n\",\n \" plt.figure()\\n\",\n \" for slice_i in range(in_4d_nii.shape[2]):\\n\",\n \" slice_data = data4d[:, :, slice_i, :][new_mask_3d[:, :, slice_i]].mean(axis=0)\\n\",\n \" # slice_data = zscore(slice_data)\\n\",\n \" plt.plot(slice_data)\\n\",\n \" plt.title(in_file)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"plot_spikes('D:/example_artifacts_dataset/sub-ben01/func/sub-ben01_task-unknown_bold.nii.gz')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from glob import glob\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"for file in glob('D:/*/sub-*/func/sub-*_task-*_bold.nii.gz'):\\n\",\n \" plot_spikes(file)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"plot_anat?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"glob('D:/*/sub-*/func/sub-*_task-*_bold.nii.gz')\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "docs/source/_static/bold-1subject-1task.html", "content": "\n \n \n \n\n \n
\n
\n

Start: 2017-03-24 07:49:23

Finish: 2017-03-24 08:31:04

Duration: 41.68 minutes

Nodes: 33

Cores: 10

\n
\n
\n

Estimated Resource

\n

Actual Resource

\n

Failed Node

\n
\n

07:49

07:59

08:09

08:19

08:29

08:39

Memory

Memory

Threads

Threads

\n
\n " }, { "path": "docs/source/_static/bold-1subject-8tasks.html", "content": "\n \n \n \n\n \n
\n
\n

Start: 2017-03-24 07:47:15

Finish: 2017-03-24 08:34:22

Duration: 47.11 minutes

Nodes: 264

Cores: 10

\n
\n
\n

Estimated Resource

\n

Actual Resource

\n

Failed Node

\n
\n

07:47

07:57

08:07

08:17

08:27

08:37

Memory

Memory

Threads

Threads

\n
\n " }, { "path": "docs/source/_static/example_funcreport.html", "content": "\n\n\n\n\n\nsub-03286_ses-NOT1ACH001_task-rest_bold :: MRIQC's BOLD fMRI report\n\n\n\n\n\n\n\n\n\n\n\n
\n

Basic echo-wise reports

\n
\n

Standard deviation of signal through time

The voxel-wise standard deviation of the signal (variability along time).

\n\n
\nGet figure file: sub-03286/figures/sub-03286_ses-NOT1ACH001_task-rest_desc-stdev_bold.svg\n\n
\n
\n

View of the background of the voxel-wise average of the BOLD timeseries

This panel shows a mosaic enhancing the background around the head. Artifacts usually unveil themselves in the air surrounding the head, where no signal sources are present.

\n\n
\nGet figure file: sub-03286/figures/sub-03286_ses-NOT1ACH001_task-rest_desc-background_bold.svg\n\n
\n
\n

Voxel-wise average of BOLD time-series, zoomed-in covering just the brain

This panel shows a mosaic of the brain. This mosaic is the most suitable to screen head-motion intensity inhomogeneities, global/local noise, signal leakage (for example, from the eyeballs and across the phase-encoding axis), etc.

\n\n
\nGet figure file: sub-03286/figures/sub-03286_ses-NOT1ACH001_task-rest_desc-zoomed_bold.svg\n\n
\n
\n

Carpetplot and nuisance signals

The so-called «carpetplot» may assist in assessing head-motion derived artifacts and respiation effects.

\n\n
\nGet figure file: sub-03286/figures/sub-03286_ses-NOT1ACH001_task-rest_desc-carpet_bold.svg\n\n
\n
\n
\n

Extended echo-wise reports

\n
\n

Voxel-wise average of BOLD time-series

The average signal calculated across the last axis (time).

\n\n
\nGet figure file: sub-03286/figures/sub-03286_ses-NOT1ACH001_task-rest_desc-mean_bold.svg\n\n
\n
\n
\n

About

\n
\n

Errors

No errors to report!

\n
\n
\n

Reproducibility and provenance information

Thanks for using MRIQC. The following information may assist in\nreconstructing the provenance of the corresponding derivatives.\n

\n\n\n
\n

\n \n

\n
\n
\n \n\n\n\n\n\n\n
Execution environmentdocker
Input filename/sub-03286/ses-NOT1ACH001/func/sub-03286_ses-NOT1ACH001_task-rest_bold.nii.gz
VersionsMRIQC24.1.0.dev0+gd5b13cb5.d20240826
VersionsNiPype1.8.6
VersionsTemplateFlow24.2.0
md5sum6462858509954133c8448a10b04757e5
\n\n
\n
\n
\n\n\n
\n

\n \n

\n
\n
\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
AcquisitionMatrixPE95
AcquisitionNumber8
AcquisitionTime10:15:56.720000
B0FieldSourcepepolar_fmap0
BodyPartExaminedBRAIN
CoilStringMULTI COIL
ConversionSoftwaredcm2niix
ConversionSoftwareVersionv1.0.20211006
DeviceSerialNumber17057
EchoTime0.039
EchoTrainLength95
EffectiveEchoSpacing0.000608401
EstimatedEffectiveEchoSpacing0.000608401
EstimatedTotalReadoutTime0.0577981
FlipAngle65
ImageOrientationPatientDICOM[0.998569, 0.00766079, 0.0529228, -0.00367802, 0.997181, -0.0749475]
ImageType['ORIGINAL', 'PRIMARY', 'M', 'FFE', 'M', 'FFE']
ImagingFrequency127.804
InPlanePhaseEncodingDirectionDICOMCOL
InstitutionNameSPMMRC Nottingham
InstitutionalDepartmentNameMRI
MRAcquisitionType2D
MTStateFalse
MagneticFieldStrength3
ManufacturerPhilips
ManufacturersModelNameAchieva
ModalityMR
ParallelAcquisitionTechniqueSENSE
PartialFourierDirectionPHASE
PartialFourierEnabledYES
PatientPositionHFS
PercentPhaseFOV100
PercentSampling100
PhaseEncodingAxisj
PhaseEncodingDirectionj
PhaseEncodingStepsNoPartialFourier95
PhilipsRescaleIntercept0
PhilipsRescaleSlope0.818071
PhilipsScaleSlope0.00153639
PixelBandwidth2328
ProcedureStepDescriptionH1408201447
ProtocolNameWIP rs-fmri_singleMB4_HF0.8
ReconMatrixPE96
RepetitionTime1.15
SAR0.0227829
ScanOptionsFS
ScanningSequenceGR
SequenceVariantSK
SeriesDescriptionrs-fmri_singleMB4_HF0.8
SeriesNumber801
SliceThickness2.4
SoftwareVersions5.3.0\\5.3.0.3
SpacingBetweenSlices2.4
StationNamePHILIPS-T6O3EHP
TaskNamerest
TotalReadoutTime0.0577981
UsePhilipsFloatNotDisplayScaling1
WaterFatShift25.3797
\n\n
\n
\n
\n\n\n
\n

\n \n

\n
\n
\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
aor0.0037614999999999997
aqi0.023297618900000004
dummy_trs0
dvarsnstd44.00571358506266
dvarsstd1.1092985154385975
dvarsvstd0.9788209838095238
efc0.4504
fber553307.625
fdmean0.2239716689042766
fdnum215
fdperc53.75
fwhmavg2.3961225784666915
fwhmx2.4589816467849612
fwhmy2.401594514446574
fwhmz2.32779157416854
gcor0.00260741
gsrx-0.018688196316361427
gsry0.006971913389861584
sizet400
sizex96
sizey96
sizez48
snr2.602224857820378
spacingtr1.1500004529953003
spacingx2.3958332538604736
spacingy2.3958332538604736
spacingz2.4000000953674316
summarybgk93.4153
summarybgmad959.2436
summarybgmean17223.3027
summarybgmedian647.0
summarybgn339609.0
summarybgp050.0
summarybgp9576174.0
summarybgstdv61176.0266
summaryfgk2.1816
summaryfgmad142208.0543
summaryfgmean500750.8674
summaryfgmedian481252.0
summaryfgn102759.0
summaryfgp05206803.0
summaryfgp95833312.0
summaryfgstdv184937.7685
tsnr25.0640248588752
\n\n
\n
\n
\n\n
\n
\n
\n\n
\n
\n Rating widget\n \n
\n
\n
\n
\n

\n \n

\n
\n
\n \n
    \n
  • Exclude
    • \n
  • Poor
    • \n
  • Acceptable
    • \n
  • Excellent
    • \n
\n
\n
\n
\n\n
\n

\n \n

\n
\n
\n
\n
\n \n \n
\n
\n \n \n
\n
\n \n \n
\n
\n \n \n
\n
\n \n \n
\n
\n \n \n
\n
\n \n \n
\n
\n \n \n
\n
\n \n \n
\n
\n \n \n
\n
\n \n \n
\n
\n \n \n
\n
\n \n \n
\n
\n \n \n
\n
\n
\n
\n
\n
\n

\n \n

\n
\n
\n
\n Comments\n \n
\n\n

Rater confidence

\n \n
    \n
  • Doubtful
    • \n
  • Confident
    • \n
\n
\n
\n
\n
\n
\nDownload\n\n
\n\n
\n\n\n\n" }, { "path": "docs/source/about.rst", "content": "Introduction\n************\n\n.. include:: ../../README.rst\n :start-line: 3\n" }, { "path": "docs/source/changes.rst", "content": "What's new\n**********\n\n.. include:: ../../CHANGES.rst\n" }, { "path": "docs/source/conf.py", "content": "\"\"\"\nmriqc documentation build configuration file, created by\nsphinx-quickstart on Thu Feb 25 09:31:58 2016.\n\nThis file is execfile()d with the current directory set to its\ncontaining dir.\n\nNote that not all possible configuration values are present in this\nautogenerated file.\n\nAll configuration values have a default; values that are commented out\nserve to show the default.\n\"\"\"\n\nimport os\n\nfrom packaging.version import Version\n\nfrom mriqc import __copyright__, __version__\n\n# Disable etelemetry during doc builds\nos.environ['NIPYPE_NO_ET'] = '1'\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n\n# -- General configuration ------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n 'sphinx.ext.autodoc',\n 'sphinx.ext.doctest',\n 'sphinx.ext.ifconfig',\n 'sphinx.ext.intersphinx',\n 'sphinx.ext.mathjax',\n 'sphinx.ext.napoleon',\n 'sphinx.ext.todo',\n 'sphinx.ext.viewcode',\n 'nipype.sphinxext.plot_workflow',\n 'sphinxarg.ext', # argparse extension\n # 'sphinx.ext.autosectionlabel',\n]\n\n# Mock modules in autodoc:\nautodoc_mock_imports = [\n 'dipy',\n 'matplotlib',\n 'nilearn',\n 'numpy',\n 'pandas',\n 'scipy',\n 'seaborn',\n 'sklearn',\n 'statsmodels',\n 'xgboost',\n]\n\nsuppress_warnings = ['image.nonlocal_uri']\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n# source_suffix = ['.rst', '.md']\nsource_suffix = '.rst'\n\n# The encoding of source files.\n# source_encoding = 'utf-8-sig'\n\n# The master toctree document.\nmaster_doc = 'index'\n\n# General information about the project.\nproject = 'mriqc'\nauthor = 'The NiPreps Developers'\ncopyright = __copyright__\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n# The full version, including alpha/beta/rc tags.\nrelease = __version__\n# The short X.Y version.\nversion = Version(__version__).public\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n#\n# This is also used if you do content translation via gettext catalogs.\n# Usually you set \"language\" from the command line for these cases.\nlanguage = 'en'\n\n# There are two options for replacing |today|: either, you set today to some\n# non-false value, then it is used:\n# today = ''\n# Else, today_fmt is used as the format for a strftime call.\n# today_fmt = '%B %d, %Y'\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\nexclude_patterns = []\n\n# The reST default role (used for this markup: `text`) to use for all\n# documents.\n# default_role = None\n\n# If true, '()' will be appended to :func: etc. cross-reference text.\n# add_function_parentheses = True\n\n# If true, the current module name will be prepended to all description\n# unit titles (such as .. function::).\n# add_module_names = True\n\n# If true, sectionauthor and moduleauthor directives will be shown in the\n# output. They are ignored by default.\n# show_authors = False\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = 'sphinx'\n\n# A list of ignored prefixes for module index sorting.\n# modindex_common_prefix = []\n\n# If true, keep warnings as \"system message\" paragraphs in the built documents.\n# keep_warnings = False\n\n# If true, `todo` and `todoList` produce output, else they produce nothing.\ntodo_include_todos = True\n\n\n# -- Options for HTML output ----------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\nhtml_theme = 'sphinx_rtd_theme'\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further. For a list of options available for each theme, see the\n# documentation.\n# html_theme_options = {}\n\n# Add any paths that contain custom themes here, relative to this directory.\n# html_theme_path = []\n\n# The name for this set of Sphinx documents. If None, it defaults to\n# \" v documentation\".\n# html_title = None\n\n# A shorter title for the navigation bar. Default is the same as html_title.\n# html_short_title = None\n\n# The name of an image file (relative to this directory) to place at the top\n# of the sidebar.\n# html_logo = None\n\n# The name of an image file (within the static path) to use as favicon of the\n# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32\n# pixels large.\n# html_favicon = None\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\n# Add any extra paths that contain custom files (such as robots.txt or\n# .htaccess) here, relative to this directory. These files are copied\n# directly to the root of the documentation.\n# html_extra_path = []\n\n# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,\n# using the given strftime format.\n# html_last_updated_fmt = '%b %d, %Y'\n\n# If true, SmartyPants will be used to convert quotes and dashes to\n# typographically correct entities.\n# html_use_smartypants = True\n\n# Custom sidebar templates, maps document names to template names.\n# html_sidebars = {}\n\n# Additional templates that should be rendered to pages, maps page names to\n# template names.\n# html_additional_pages = {}\n\n# If false, no module index is generated.\n# html_domain_indices = True\n\n# If false, no index is generated.\nhtml_use_index = True\n\n# If true, the index is split into individual pages for each letter.\n# html_split_index = False\n\n# If true, links to the reST sources are added to the pages.\n# html_show_sourcelink = True\n\n# If true, \"Created using Sphinx\" is shown in the HTML footer. Default is True.\n# html_show_sphinx = True\n\n# If true, \"(C) Copyright ...\" is shown in the HTML footer. Default is True.\n# html_show_copyright = True\n\n# If true, an OpenSearch description file will be output, and all pages will\n# contain a tag referring to it. The value of this option must be the\n# base URL from which the finished HTML is served.\n# html_use_opensearch = ''\n\n# This is the file name suffix for HTML files (e.g. \".xhtml\").\n# html_file_suffix = None\n\n# Language to be used for generating the HTML full-text search index.\n# Sphinx supports the following languages:\n# 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja'\n# 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr'\n# html_search_language = 'en'\n\n# A dictionary with options for the search language support, empty by default.\n# Now only 'ja' uses this config value\n# html_search_options = {'type': 'default'}\n\n# The name of a javascript file (relative to the configuration directory) that\n# implements a search results scorer. If empty, the default will be used.\n# html_search_scorer = 'scorer.js'\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = 'mriqcdoc'\n\n# -- Options for LaTeX output ---------------------------------------------\n\nlatex_elements = {\n # The paper size ('letterpaper' or 'a4paper').\n # 'papersize': 'letterpaper',\n # The font size ('10pt', '11pt' or '12pt').\n # 'pointsize': '10pt',\n # Additional stuff for the LaTeX preamble.\n # 'preamble': '',\n # Latex figure (float) alignment\n # 'figure_align': 'htbp',\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\n# author, documentclass [howto, manual, or own class]).\nlatex_documents = [\n (\n master_doc,\n 'mriqc.tex',\n 'mriqc Documentation',\n author,\n 'manual',\n ),\n]\n\n# The name of an image file (relative to this directory) to place at the top of\n# the title page.\n# latex_logo = None\n\n# For \"manual\" documents, if this is true, then toplevel headings are parts,\n# not chapters.\n# latex_use_parts = False\n\n# If true, show page references after internal links.\n# latex_show_pagerefs = False\n\n# If true, show URL addresses after external links.\n# latex_show_urls = False\n\n# Documents to append as an appendix to all manuals.\n# latex_appendices = []\n\n# If false, no module index is generated.\n# latex_domain_indices = True\n\n\n# -- Options for manual page output ---------------------------------------\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [(master_doc, 'mriqc', 'mriqc Documentation', [author], 1)]\n\n# If true, show URL addresses after external links.\n# man_show_urls = False\n\n\n# -- Options for Texinfo output -------------------------------------------\n\n# Grouping the document tree into Texinfo files. List of tuples\n# (source start file, target name, title, author,\n# dir menu entry, description, category)\ntexinfo_documents = [\n (\n master_doc,\n 'mriqc',\n 'mriqc Documentation',\n author,\n 'mriqc',\n 'One line description of project.',\n 'Miscellaneous',\n ),\n]\n\n# Documents to append as an appendix to all manuals.\n# texinfo_appendices = []\n\n# If false, no module index is generated.\n# texinfo_domain_indices = True\n\n# How to display URL addresses: 'footnote', 'no', or 'inline'.\n# texinfo_show_urls = 'footnote'\n\n# If true, do not generate a @detailmenu in the \"Top\" node's menu.\n# texinfo_no_detailmenu = False\n\n\n# Example configuration for intersphinx: refer to the Python standard library.\nintersphinx_mapping = {\n 'bids': ('https://bids-standard.github.io/pybids/', None),\n 'matplotlib': ('https://matplotlib.org/', None),\n 'nibabel': ('https://nipy.org/nibabel/', None),\n 'nipype': ('https://nipype.readthedocs.io/en/latest/', None),\n 'numpy': ('https://numpy.org/doc/stable/', None),\n 'pandas': ('https://pandas.pydata.org/pandas-docs/dev/', None),\n 'python': ('https://docs.python.org/3/', None),\n 'scipy': ('https://docs.scipy.org/doc/scipy/reference', None),\n}\n" }, { "path": "docs/source/dsa.rst", "content": "\n.. _dsa:\n\nData Sharing Agreement\n**********************\nFoundations\n-----------\n(Text in this section has been retrieved from https://www.ihris.org/toolkit/tools/data-sharing.html)\n\nData-sharing is an important way to increase the ability of researchers, scientists and policy-makers\nto analyze and translate data into meaningful reports and knowledge.\nSharing data discourages duplication of effort in data collection and encourages diverse thinking,\nas others are able to use the data to answer questions that the initial data collectors may not\nhave considered.\nSharing data also encourages accountability and transparency, enabling researchers to validate\none another's findings.\nFinally, data from multiple sources can often be combined to allow for comparisons that cross national\nand departmental lines.\n\nMRIQC DSA (Data Sharing Agreement)\n----------------------------------\nMRIQC extracts a vector of :abbr:`IQMs (image quality metrics)` from the input dataset.\nThese :abbr:`IQMs (image quality metrics)` are estimations of numerical properties of the\ndata array in the image, hence they are not usable in identifying data.\nIn other words, it is not possible to identify a natural person from this information.\nAdditionally, MRIQC collects metadata from the appropriate fields found in the BIDS\nstructure of the input dataset.\nAny information that could be used to identify the natural person the original data\nwere obtained from are stripped out (e.g. the subject identifier in the context of the\nstudy, date of acquisition, phenotypical information, etc.).\n\n**MRIQC does not share the original input dataset**.\nOnly an MD5 summary (*checksum*) of the input data is calculated and can be used to\ntrace back the original dataset which MRIQC extracted the information from.\nThis checksum cannot be used to regenerate the original data it was calculated from.\nTherefore, in the case that MRIQC was run on private data, the original images\nremain inaccessible to the public as no original data are shared.\n\nWithdrawing records and period of agreement\n...........................................\nIf you do not agree to these terms, please make use of the ``--no-sub`` (*do not submit*)\ncommand line flag with MRIQC.\nMRIQC will not collect any data when you OPT-OUT with this command line flag.\nIf you wish to withdraw :abbr:`IQMs (image quality metrics)` records from the database,\nplease send a request to the NiPreps Developers indicating the\nMD5 checksums of the datasets that are to be removed.\nIf any MD5 checksum matches any image publicly available or one or more associated\nquality rating records were found, then the corresponding record cannot be destroyed,\nwithdrawn or recalled.\nWithdrawing can be exercised within twelve months of the submission of the IQMs to\nthe Web-API.\nQuality ratings submitted via the \"Rating Widget\" of MRIQC reports or any other\nmeans cannot be withdrawn since it requires an active action from the submitter\nthat makes them aware that the data will be automatically shared publicly.\n\nThe MRIQC Web-API will keep collecting records until funding runs out.\nIn the case the service is shut down, existing records will be posted in a permanent,\nstatic storage service.\nThis agreement does not expire.\n\nIntended use of the data\n........................\nThe resource is particularly designed for researchers to share image quality metrics and\nannotations that can readily be reused in training human experts and machine learning\nalgorithms.\nThe ultimate goal of the MRIQC Web-API is to allow the development of fully automated\nquality control tools that outperform expert ratings in identifying degenerate images.\n\nConstraints on use of the data\n..............................\nData are shared under a CC0 (public domain) license.\n\nData confidentiality\n....................\nPlease use please make use of the ``--no-sub`` (*do not submit*) command line flag with MRIQC\nto ensure that your data remain confidential.\n\nFinancial costs of data-sharing\n...............................\nThe MRIQC Web-API infrastructure is funded by NIMH grants R24MH117179, and ZICMH002960.\n" }, { "path": "docs/source/index.rst", "content": "Welcome to *MRIQC*'s documentation!\n###################################\n\n.. include:: ../../README.rst\n :start-line: 3\n\n.. image :: _static/OHBM2017-poster.png\n\nContents\n--------\n\n.. toctree::\n :maxdepth: 3\n\n about\n install\n usage\n measures\n reports\n workflows\n dsa\n license\n changes\n\nIndices and tables\n------------------\n* :ref:`genindex`\n* :ref:`modindex`\n* :ref:`search`\n" }, { "path": "docs/source/install.rst", "content": "\nInstalling *MRIQC*\n******************\n*MRIQC* is a *NiPreps* (`www.nipreps.org `__)\n*BIDS App* [BIDSApps]_.\nAs such, *MRIQC* can be installed manually (*Bare-metal installation*,\nsee below) or containerized.\nFor containerized execution with *Docker* or *Singularity*, please\nfollow the documentation on the *NiPreps* site\n(`introduction `__).\n\n\"Bare-metal\" installation\n-------------------------\nIf, for some reason, you really need a custom installation,\n*MRIQC* can be installed as follows.\nFirst, please make sure you have the execution system dependencies\ninstalled (see below).\nSecond, the latest development version of MRIQC can be installed from\ngithub using ``pip`` on a Python 3 environment::\n\n python -m pip install -U mriqc\n\n\n.. warning::\n\n As of MRIQC 0.9.4, Python 2 is no longer supported.\n\n.. warning::\n\n MRIQC uses matplotlib to create graphics. By default, matplotlib is configured to\n plot through an interactive Tcl/tk interface, which requires a functional display to be available.\n In *head-less* settings (for example, when running under tmux),\n you may see an error::\n\n _tkinter.TclError: couldn't connect to display \"localhost:10.0\"\n\n There are two pathways to fix this issue.\n One is setting up a virtual display with a tool like XVfb.\n Alternatively, you can configure your matplotlib distribution to perform on\n head-less mode by default.\n That is achieved by uncommenting the ``backend : Agg`` line in the matplotlib's\n configuration file.\n The location of the configuration file can be retrieved with Python::\n\n >>> import matplotlib\n >>> print(matplotlib.matplotlib_fname())\n\n Alternatively, you can issue the following shell command-line to edit this setting::\n\n $ sed -i 's/\\(backend *: \\).*$/\\1Agg/g' $( python -c \"import matplotlib; print(matplotlib.matplotlib_fname())\" )\n\nExecution system dependencies\n.............................\nIf you are using a a `Neurodebian `_ Linux distribution,\ninstallation should be as easy as::\n\n sudo apt-get install afni ants\n sudo ln -sf /usr/lib/ants/N4BiasFieldCorrection /usr/local/bin/\n\nAfter installation, make sure that all the necessary binaries are added to the ``$PATH`` environment\nvariable, for the user that will run ``mriqc``.\n\nOtherwise, you can follow each software installation guide:\n`AFNI `_,\nand `ANTs `_.\n\n.. warning::\n\n Please note that *MRIQC* 22.0.0 and later requires Freesurfer's *SynthStrip* tool.\n\nPlease also install *FreeSurfer* (above 7.2) using `their guidelines `__.\nIf your *FreeSurfer* version is older than 7.2, then you can get *MRIQC*'s requirements with::\n\n wget https://github.com/freesurfer/freesurfer/blob/dev/mri_synthstrip/synthstrip.1.pt -P ${FREESURFER_HOME}/models/\n\n.. danger::\n\n You will get the following error if you do not install *SynthStrip*'s requirement::\n\n The 'model' trait of a _SynthStripInputSpec instance must be a pathlike object or string representing an existing file, but a value of '' was specified.`\n\n If the *SynthStrip* requirement was downloaded, please make sure your environment has defined the variable ``$FREESURFER_HOME`` and that it is pointing at the right directory::\n\n echo $FREESURFER_HOME\n\n If the ``$FREESURFER_HOME`` environment variable is defined, check whether the model file is available at the expected path::\n\n $ stat $FREESURFER_HOME/models/synthstrip.1.pt \n File: /opt/freesurfer/models/synthstrip.1.pt\n Size: 30851709 Blocks: 60264 IO Block: 4096 regular file\n Device: fd00h/64768d Inode: 12583379 Links: 1\n Access: (0644/-rw-r--r--) Uid: ( 0/ root) Gid: ( 0/ root)\n Access: 2024-04-09 11:13:29.091893354 +0200\n Modify: 2023-01-20 09:39:54.284056264 +0100\n Change: 2023-01-20 09:39:54.284056264 +0100\n Birth: 2023-01-20 09:39:54.224056213 +0100\n\n If *SynthStrip*'s model file is not present, the output will look like::\n\n $ stat $FREESURFER_HOME/models/synthstrip.1.pt\n stat: cannot statx '/opt/freesurfer/models/synthstrip.1.pt': No such file or directory\n" }, { "path": "docs/source/iqms/bold.rst", "content": ".. _iqms_bold:\n\nIQMs for functional images\n==========================\n.. automodule:: mriqc.qc.functional\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/iqms/dwi.rst", "content": ".. _iqms_dwi:\n\nIQMs for diffusion images\n=========================\n.. automodule:: mriqc.qc.diffusion\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/iqms/t1w.rst", "content": ".. _iqms_t1w:\n\nIQMs for structural images\n==========================\n.. automodule:: mriqc.qc.anatomical\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/license.rst", "content": "About the *NiPreps* framework licensing\n***************************************\nPlease check https://www.nipreps.org/community/licensing/ for detailed\ninformation on the criteria we use to license *MRIQC* and other\nprojects of the framework.\n\nLicense information\n-------------------\nCopyright (c) 2021, the *NiPreps* Developers.\n\nAs of the 21.0.x pre-release and release series, *MRIQC* is\nlicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\nhttp://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\nCopyright (c) 2015-2021, the *MRIQC* developers and the CRN.\nAll rights reserved.\n\n*MRIQC* 0.16.x series and earlier are\nlicensed under the BSD 3-clause license.\nYou may obtain a copy of the License at\nhttps://opensource.org/licenses/BSD-3-Clause\n\nAll trademarks referenced herein are property of their respective holders.\n\nNotice\n------\n .. include:: ../../NOTICE\n" }, { "path": "docs/source/measures.rst", "content": "\n.. _measures:\n\nImage Quality Metrics (IQMs)\n****************************\n\n.. automodule:: mriqc.qc\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/reports/bold.rst", "content": "\n.. _reports-bold:\n\nBOLD images\n===========\nOne individual report per input functional timeseries will be generated\nin the path ``/reports/sub-IDxxx_task-name_bold.html```.\nAn example report is given\n`here <../_static/example_funcreport.html>`_.\n\nThe individual report for the functional images is structured as follows:\n\n.. _reports-bold-summary:\n\nSummary\n-------\nThe first section summarizes some important information:\n\n * subject identifier, date and time of execution of\n ``mriqc``, software version;\n * workflow details and flags raised during execution; and\n * the extracted IQMs.\n\n.. _reports-bold-visual:\n\nVisual reports\n--------------\nThe section with visual reports contains:\n\n#. Mosaic view of the average BOLD signal.\n\n .. figure:: ../resources/reports-bold_mean.png\n :alt: mean epi mosaic\n\n#. Mosaic view of the temporal standard deviation.\n\n .. figure:: ../resources/reports-bold_sd.png\n :alt: sd of epi mosaic\n\n#. Summary plot, showing the slice-wise\n signal intensity at the extremes for the identification\n of spikes, the outliers metric, the DVARS and the\n :abbr:`FD (framewise displacement)`. Finally the\n so-called carpetplot [Power2016]_. \n The carpet plot rows correspond to voxelwise time series,\n and are separated into regions: cortical gray matter, deep \n gray matter, white matter and cerebrospinal fluid, cerebellum \n and the brain-edge or “crown” [Provins2022]_.\n The crown corresponds to the voxels located on a \n closed band around the brain [Patriat2015]_.\n\n .. figure:: ../resources/reports-bold_summary.png\n :alt: fMRI summary plot\n\n.. _reports-bold-verbose:\n\nVerbose reports\n---------------\nIf mriqc was run with the ``--verbose-reports`` flag, the\nfollowing plots will be appended:\n\n#. Mosaic view of the average BOLD signal, zoomed-in\n to the bounding box of brain activation.\n\n .. figure:: ../resources/reports-bold_mean_zoom.png\n :alt: zoomed mean epi mosaic\n\n#. Mosaic view of the average BOLD signal, with background\n enhancement.\n\n .. figure:: ../resources/reports-bold_mean_bg.png\n :alt: mean epi background mosaic\n\n#. One rows of axial views at different Z-axis points\n showing the calculated brain mask.\n\n .. figure:: ../resources/reports-bold_mask.png\n :alt: bold brainmasks\n\n#. Mosaic view with animation for assessment of the\n co-registration to MNI space (roll over the image\n to activate the animation).\n\n .. figure:: ../resources/reports-bold_mni.png\n :alt: bold-mni coregistration\n\n.. _reports-bold-metadata:\n\nMetadata\n--------\nIf some metadata was found in the BIDS structure, it is\nreported here.\n\n.. topic:: References\n\n .. [Patriat2015] Patriat, R., EK Molloy, RM Birn, T. Guitchev, and A. Popov. “Using Edge Voxel Information to \n Improve Motion Regression for Rs-FMRI Connectivity Studies.” Brain Connectivity 5, no. 9 (28 2015): 582–95. \n doi: `10.1089/brain.2014.0321 `__.\n\n .. [Power2016] Power JD, A simple but useful way to assess fMRI scan qualities.\n NeuroImage. 2016. doi: `10.1016/j.neuroimage.2016.08.009 `__.\n\n .. [Provins2022] Provins, Céline, Christopher J. Markiewicz, Rastko Ciric, Mathias Goncalves, César Caballero-Gaudes, \n Russell Poldrack, Patric Hagmann, and Oscar Esteban. “Quality Control and Nuisance Regression of FMRI, Looking out \n Where Signal Should Not Be Found.” OSF Preprints, January 21, 2022.\n doi: `10.31219/osf.io/hz52v `__.\n\n\n" }, { "path": "docs/source/reports/group.rst", "content": ".. _reports-group:\n\nGroup reports\n=============\nOnce a sample has been processed with the appropriate\n:ref:`workflow `, all the :abbr:`IQMs (image quality metrics)`\nwritten out in JSON files are collected in a\n:abbr:`CSV (comma separated values)` table, at the standard path\nof ``/.csv``. Therefore, for structural\nimages, the IQMs will be found in ``/T1w.csv``.\nCorrespondingly, ``/bold.csv`` is the designated path\nfor the functional IQM table.\n\nThe group report will process the ``/.csv``\ntable to generate one strip-plot per IQM:\n\n.. figure:: ../resources/reports-group_overview.png\n :alt: appearance of the group reports\n\nThe strip-plots are interactive, so each sample (the value of a specific\nIQM associated to an input image) can be clicked to open up the corresponding\nindividual report.\nThis is particularly useful when trying to identify subpar images that\nshow themselves as outliers in the general distribution of the IQM:\n\n.. figure:: ../resources/reports-group_outlier.png\n :alt: clicking on an outlier\n\nIn this example, the corresponding individual report for the selected\nsample will open up (`click here to see this example\nreport `_).\n\nThe boxplots shown are Tukey boxplots, with median and interquartile range shown.\nWhiskers extend to the farthest point that is within 1.5 * IQR of the upper (or lower) quartile.\n" }, { "path": "docs/source/reports/smri.rst", "content": ".. _reports-smri:\n\nT1 and T2 -weighed images\n=========================\nOne individual report per input structural volume will be generated\nin the path ``/reports/sub-IDxxx_T1w.html```.\nAn example report is given\n`here <../_static/example_anatreport.html>`_.\n\nThe individual report for the structural images is\nstructured as follows:\n\n.. _reports-smri-summary:\n\nSummary\n-------\nThe first section summarizes some important information:\n\n * subject identifier, date and time of execution of\n ``mriqc``, software version;\n * workflow details and flags raised during execution; and\n * the extracted IQMs.\n\n.. _reports-smri-visual:\n\nVisual reports\n--------------\nThe section with visual reports contains:\n\n#. Mosaic view, zoomed-in over the parenchyma of the brain.\n\n .. figure:: ../resources/reports-t1w_mosaic_zoom.png\n :alt: zoomed in mosaic\n\n#. Mosaic view with background noise enhancement.\n\n .. figure:: ../resources/reports-t1w_background.png\n :alt: t1 background\n\n.. _reports-smri-verbose:\n\nVerbose reports\n---------------\nIf mriqc was run with the ``--verbose-reports`` flag, the\nfollowing plots will be appended:\n\n#. Mosaic view with animation for assessment of the\n co-registration to MNI space (roll over the image\n to activate the animation).\n\n .. figure:: ../resources/reports-t1w_mni.svg\n :alt: t1-mni coregistration\n\n#. Three rows of axial views at different Z-axis points\n showing: the calculated brain mask, the segmentation\n done with FSL FAST and a saturated view of the background.\n\n .. figure:: ../resources/reports-t1w_masks1.png\n :alt: t1 verbose masks 1\n\n#. Two rows of coronal views, showing the object/background\n segmentation, and the air/no-air segmentation.\n\n .. figure:: ../resources/reports-t1w_masks2.png\n :alt: t1 verbose masks 2\n\n#. The :math:`\\chi^2` function fitting for the calculation\n of the QI2:\n\n .. figure:: ../resources/reports-t1w_qi2.png\n :alt: t1 fitting of QI2\n\n.. _reports-smri-metadata:\n\nMetadata\n--------\nIf some metadata was found in the BIDS structure, it is\nreported here." }, { "path": "docs/source/reports.rst", "content": "\n.. _reports:\n\nVisual Reports\n**************\nDemo: anatomical reports\n------------------------\n.. raw:: html\n\n \n\nDemo: functional reports\n------------------------\n\n.. raw:: html\n\n \n\nHow reports work\n----------------\n.. automodule:: mriqc.reports\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/resources/mriqc.sbatch", "content": "#!/bin/bash\n\n# NOTE: To use this script, edit lines 17, 26, 40, 55, 56, 58\n # labeled TODO with NOTES explaining how. \n\n# Set SBATCH Parameters: \n# ------------------------------------------\n# NOTE: These should work with Slurm HPC systems, \n # but these specific parameters have only been tested on \n # Stanford's Sherlock. Some parameters may need to be \n # adjusted for other HPCs, specifically --partition.\n#SBATCH --job-name mriqc\n\n# NOTE: Each HCP has different partitions for job submissions. \n # Check your HCPs documentation for what partitions are available\n # to you. \n#SBATCH --partition normal #TODO: update for your HPC\n\n# NOTE: The --array parameter allows multiple jobs to be launched at once, \n # and is generally recommended to efficiently run several hundred jobs \n # simultaneously This should be adjusted to the range for your dataset;\n # 1-n%j where n is the number of \n # participants and j is the maximum number of concurrent jobs. \n # In this example, there are 216 participants, and only 50 run\n # at a given time. \n#SBATCH --array=1-216%50 #TODO: adjust for your dataset\n\n# NOTE: These parameters request time and resources from the job\n # scheduler. These specific parameters should be sufficient for \n # most datasets. \n#SBATCH --time=1:00:00 #NOTE: likely longer than generally needed \n#SBATCH --ntasks 1\n#SBATCH --cpus-per-task=16\n#SBATCH --mem-per-cpu=4G\n\n# NOTE: These parameters set where log files will be written, and \n # where status emails will be sent. \n#SBATCH --output log/%x-%A-%a.out\n#SBATCH --error log/%x-%A-%a.err\n#SBATCH --mail-user=%u@stanford.edu #TODO: update to your email domain\n#SBATCH --mail-type=ALL \n\n## More information about these Slurm commands can be found at: \n # https://slurm.schedmd.com/sbatch.html\n# ------------------------------------------\n# Setup variables\n# ------------------------------------------\n# NOTE: These variables are paths to your data, and where you'd \n # like your output to be written. You should replace STUDY \n # with the directory one level below the directory where your data \n # is stored. \n # You should replace `ds0002785` with the name of your BIDS directory\nSTUDY=\"/scratch/users/mphagen/mriqc-protocol\" #TODO: replace with your path\nBIDS_DIR=\"${STUDY}/ds002785\" # TODO: replace with path to your dataset\n\n# NOTE: These variables set the \"Apptainer\" execution for your \n # MRIQC container\nMRIQC_VERSION=\"24.0.2\" #TODO: update if using a different version\nAPPTAINER_CMD=\"apptainer run -e mriqc_${MRIQC_VERSION}.sif\"\n\nOUTPUT_DIR=\"${BIDS_DIR}/derivatives/mriqc-${MRIQC_VERSION}\"\n\n# NOTE: The next two variables are used to extract participant IDs from \n # the mandatory participants.tsv. SLURM_ARRAY_TASK_ID is generated by the \n #--array parameter, and is determined by each job's order - i.e. the \n # first job has the SLURM_ARRAY_TASK_ID=1, the second SLURM_ARRAY_TASK_ID=2.\n # It is necessary to add 1 to the SLURM_ARRAY_TASK_ID because of the header\n # in participants.tsv. \nsubject_idx=$(( ${SLURM_ARRAY_TASK_ID} + 1 ))\n \n## NOTE: The first clause in this line selects a row in participants.tsv \n # using subject_idx. This is piped to grep to isolate the subject id. \n # This regex should work for most subject naming conventions, but \n # may need to be modified in some cases.\nsubject=$( sed -n ${subject_idx}p ${BIDS_DIR}/participants.tsv \\\n | grep -oP \"sub-[A-Za-z0-9_]*\" ) \n\necho Subject $subject\n\n# Define the Apptainer command that will be ran, with MRIQC CLI flags\ncmd=\"${APPTAINER_CMD} ${BIDS_DIR} ${OUTPUT_DIR} participant \\\n --participant-label $subject \\\n -w $PWD/work/ \\\n --omp-nthreads 10 --nprocs 12\" # For nodes with at least 32GB RAM\n\n# Print useful information to log files\necho Running task ${SLURM_ARRAY_TASK_ID}\necho Commandline: $cmd\n\n# Run the full command defined in cmd \neval $cmd\nexitcode=$?\n\n# Print useful information to log files\necho \"sub-$subject ${SLURM_ARRAY_TASK_ID} $exitcode\" \\\n >> ${SLURM_ARRAY_JOB_ID}.tsv\necho Finished tasks ${SLURM_ARRAY_TASK_ID} with exit code $exitcode\nexit $exitcode\n" }, { "path": "docs/source/resources/sbatch.sh", "content": "#!/bin/bash\n#\n#SBATCH -J mriqc\n#SBATCH --array=1-13\n#SBATCH --time=136:00:00\n#SBATCH -n 1\n#SBATCH --cpus-per-task=16\n#SBATCH --mem-per-cpu=4G\n# Outputs ----------------------------------\n#SBATCH -o log/%x-%A-%a.out\n#SBATCH -e log/%x-%A-%a.err\n#SBATCH --mail-user=%u@gmail.com\n#SBATCH --mail-type=ALL\n# ------------------------------------------\n\nunset PYTHONPATH\n\nBIDS_DIR=\"$STUDY/ds000030\"\nOUTPUT_DIR=\"${BIDS_DIR}/derivatives/mriqc-0.16.1\"\n\nSINGULARITY_CMD=\"singularity run -e $STUDY/mriqc-0.16.1.simg\"\n\nsubject=$( sed -n -E \\\n \"$((${SLURM_ARRAY_TASK_ID} + 1))s/sub (\\S*)\\>.*/\\1/gp\" \\\n ${BIDS_DIR}/participants.tsv )\n\ncmd=\"${SINGULARITY_CMD} ${BIDS_DIR} ${OUTPUT_DIR} participant \\\n --participant-label $subject \\\n -w $L_SCRATCH/work/ \\\n --omp-nthreads 8 --mem 10\"\necho Running task ${SLURM_ARRAY_TASK_ID}\necho Commandline: $cmd\neval $cmd\nexitcode=$?\necho \"sub-$subject ${SLURM_ARRAY_TASK_ID} $exitcode\" \\\n >> ${SLURM_ARRAY_JOB_ID}.tsv\necho Finished tasks ${SLURM_ARRAY_TASK_ID} with exit code $exitcode\nexit $exitcode\n" }, { "path": "docs/source/usage.rst", "content": "\n.. _running_mriqc:\n\nRunning *MRIQC*\n***************\n*MRIQC* is a `BIDS-App `_ [BIDSApps]_,\nand therefore it inherently understands the :abbr:`BIDS (brain\nimaging data structure)` standard [BIDS]_.\nBefore moving forward, please make sure to have read and understood\n*NiPreps*'s\n`introductory documentation `__).\n\nContainerized execution with *Docker* and *Singularity*/*Apptainer*\n-------------------------------------------------------------------\nFor containerized execution with *Docker* or *Singularity*/*Apptainer*, please\nfollow the documentation on the *NiPreps* site, which contains\ntip and troubleshooting guidelines for both\n`Docker `__, and\n`Singularity or Apptainer `__.\nIn addition to container-specific guidelines, the documentation\nalso includes specific\n`help for processing DataLad-managed datasets `__.\n\nThe rest of this documentation page applies to both *bare-metal*\nand containerized execution modes.\n\n*MRIQC* can fetch data in *DataLad* datasets\n--------------------------------------------\nAs of version 22.0.3, *MRIQC* bundles *DataLad*, enabling automatic\ndata fetching in *DataLad* datasets.\nEmploying this feature in containerized environments may lead to\nsomewhat obscure errors (see, for example,\n`nipreps/mriqc#1307 `__).\nIf you intend to use *DataLad* datasets, please read carefully\n*NiPreps*' `help for processing DataLad-managed datasets `__.\n\nAlternatively, this feature can be disabled by adding\n``--no-datalad-get`` to the command line.\nThis will separate *DataLad* management from *MRIQC*'s operation,\nwhich can be an effective way of debugging issues and averting\nerroneous conditions.\n\nTroubleshooting\n---------------\nIf you encounter problems, please check our\n`NiPreps Guidelines for Singularity or Apptainer `__.\nCommon tips and guidelines that used to be found within *MRIQC*'s\nor *fMRIPrep*'s documentation sites have been relocated in the\ngeneral *NiPreps* website.\n\nA *BIDS Apps* command line interface\n------------------------------------\n*MRIQC* follows the *BIDS Apps* standard command line interface::\n\n mriqc bids-root/ output-folder/ participant\n\nThat simple command runs *MRIQC* on all the *T1w* and *BOLD* images found\nunder the BIDS-compliant folder ``bids-root/``.\nThe last ``participant`` keyword indicates that the first level analysis\nis run. (i.e. extracting the :abbr:`IQMs (image quality metrics)` from the\nimages retrieved within ``bids-root/``).\nThe second level (``group``) is automatically run if no particular subject\nis provided for analysis.\n\n.. note::\n\n If the argument :code:`--participant-label` is not provided, then all\n subjects will be processed and the group level analysis will\n automatically be executed without need of running the command in item 3.\n\nTo specify one particular subject, the ``--participant-label`` argument\ncan be used::\n\n mriqc bids-root/ output-folder/ participant --participant-label S01 S02 S03\n\nThat command will run MRIQC only on the subjects indicated: only\n``bids-root/sub-S01``, ``bids-root/sub-S02``, and ``bids-root/sub-S03``\nwill be processed.\nIn this case, the ``group`` level will not be triggered automatically.\nWe generate the ``group`` level results (the group level report and the\nfeatures CSV table) with: ::\n\n mriqc bids-root/ output-folder/ group\n\nExamples of the generated visual reports are found\nin :ref:`The MRIQC Reports `.\n\n.. warning::\n\n MRIQC by default attempts to upload anonymized quality metrics to a publicly accessible\n web server (`mriqc.nimh.nih.gov `_). The uploaded data consists\n only of calculated quality metrics and scanning parameters. It removes all personal\n health information and participant identifiers. We try to collect this data to build normal\n distributions for improved outlier detection, but if you do not wish to participate you can\n disable the submission with the ``--no-sub`` flag.\n\n.. topic:: BIDS data organization\n\n The software automatically finds the data the input folder if it\n follows the :abbr:`BIDS (brain imaging data structure)` standard [BIDS]_.\n A fast and easy way to check that your dataset fulfills the\n :abbr:`BIDS (brain imaging data structure)` standard is\n the `BIDS validator `_.\n\n.. topic:: BIDS-App levels\n\n In the ``participant`` level, all individual images to be processed are run\n through the pipeline, and the :ref:`MRIQC measures ` are extracted and\n the individual reports (see :ref:`The MRIQC Reports `) generated.\n In the ``group`` level, the :abbr:`IQMs (image quality metrics)` extracted in\n first place are combined in a table and the group reports are generated.\n\nCommand line interface\n......................\n.. argparse::\n :ref: mriqc.cli.parser._build_parser\n :prog: mriqc\n :nodefault:\n :nodefaultconst:\n\nRunning *MRIQC* on HPC with *Singularity*/*Apptainer*\n-----------------------------------------------------\nWe have profiled cores and memory usages with the *resource profiler*\ntool of *Nipype*.\n\nAn *MRIQC* run of one subject (from the ABIDE) dataset, containing only one\nrun, one BOLD task (resting-state) yielded the following report:\n\n .. raw:: html\n\n \n\n Using the ``MultiProc`` plugin of nipype with ``nprocs=10``, the workflow\n nodes run across the available processors for 41.68 minutes.\n A memory peak of 8GB is reached by the end of the runtime, when the\n plotting nodes are fired up.\n\nWe also profiled MRIQC on a dataset with 8 tasks (one run per task),\non ds030 of OpenfMRI:\n\n .. raw:: html\n\n \n\n Again, we used ``n_procs=10``. The software run for roughly about the same\n time (47.11 min). Most of the run time, memory usage keeps around a\n maximum of 10GB. Since we saw a memory consumption of 1-2GB during the\n the 1-task example, a rule of thumb may be that each task takes around\n 1GB of memory.\n \n.. topic:: References\n\n .. [BIDS] `Brain Imaging Data Structure `_\n .. [BIDSApps] `BIDS-Apps: portable neuroimaging pipelines that understand BIDS\n datasets `_\n" }, { "path": "docs/source/workflows.rst", "content": "\n.. _workflows:\n\nWorkflows\n*********\n\n.. automodule:: mriqc.workflows\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "long_description.rst", "content": "MRIQC provides a series of image processing workflows\nto extract and compute a series of NR (no-reference), IQMs\n(image quality metrics) to be used in QAPs (quality\nassessment protocols) for MRI (magnetic\nresonance imaging)." }, { "path": "mriqc/__init__.py", "content": "# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n#\n# Copyright 2021 The NiPreps Developers \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# We support and encourage derived works from this project, please read\n# about our expectations at\n#\n# https://www.nipreps.org/community/licensing/\n#\n\"\"\"\nThe mriqc package provides a series of :abbr:`NR (no-reference)`,\n:abbr:`IQMs (image quality metrics)` to used in :abbr:`QAPs (quality\nassessment protocols)` for :abbr:`MRI (magnetic resonance imaging)`.\n\"\"\"\n\nfrom mriqc._version import __version__\n\n__copyright__ = 'Copyright 2016-2024, The NiPreps Developers'\n__download__ = f'https://github.com/nipreps/mriqc/archive/{__version__}.tar.gz'\n__all__ = ['__version__', '__copyright__', '__download__']\n" }, { "path": "mriqc/__main__.py", "content": "# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n#\n# Copyright 2022 The NiPreps Developers \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# We support and encourage derived works from this project, please read\n# about our expectations at\n#\n# https://www.nipreps.org/community/licensing/\n#\nfrom .cli.run import main\n\nif __name__ == '__main__':\n import sys\n\n from . import __name__ as module\n\n # `python -m ` typically displays the command as __main__.py\n if '__main__.py' in sys.argv[0]:\n sys.argv[0] = f'{sys.executable} -m {module}'\n main()\n" }, { "path": "mriqc/_warnings.py", "content": "# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n#\n# Copyright 2021 The NiPreps Developers \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# We support and encourage derived works from this project, please read\n# about our expectations at\n#\n# https://www.nipreps.org/community/licensing/\n#\n\"\"\"Manipulate Python warnings.\"\"\"\n\nimport logging\nimport sys\n\nfrom nipype import logging as nlogging\n\nlogging.addLevelName(26, 'BANNER') # Add a new level for banners\nlogging.addLevelName(25, 'IMPORTANT') # Add a new level between INFO and WARNING\nlogging.addLevelName(15, 'VERBOSE') # Add a new level between INFO and DEBUG\n\nLOGGER_FMT = (\n '%(asctime)s |{color} %(levelname)-8s {reset}|{color} %(name)-16s '\n '{reset}|{color} %(message)s{reset}'\n)\nDATE_FMT = '%Y-%m-%d %H:%M:%S'\nCONSOLE_COLORS = {\n logging.DEBUG: '\\x1b[38;20m',\n logging.INFO: '\\x1b[34;20m',\n 25: '\\x1b[33;20m',\n logging.WARNING: '\\x1b[93;20m',\n logging.ERROR: '\\x1b[31;20m',\n logging.CRITICAL: '\\x1b[31;1m',\n 'reset': '\\x1b[0m',\n}\n\n\nclass _LogFormatter(logging.Formatter):\n \"\"\"Customize the log format.\"\"\"\n\n _colored = True\n\n def __init__(self, datefmt=None, colored=True, **kwargs):\n self._colored = colored\n super().__init__(\n datefmt=datefmt or DATE_FMT,\n fmt=LOGGER_FMT.format(\n color=CONSOLE_COLORS['reset'] if colored else '',\n reset=CONSOLE_COLORS['reset'] if colored else '',\n ),\n )\n\n def format(self, record):\n reset = CONSOLE_COLORS['reset'] if self._colored else ''\n self._style._fmt = (\n '%(message)s'\n if record.levelno == 26\n else LOGGER_FMT.format(\n color=CONSOLE_COLORS.get(\n record.levelno,\n CONSOLE_COLORS['reset'],\n )\n if self._colored\n else '',\n reset=reset,\n )\n )\n return super().format(record)\n\n\nnlogging.getLogger('nipype')\n_wlog = logging.getLogger('py.warnings')\n_numexprlog = logging.getLogger('numexpr.utils')\n_dataladlog = logging.getLogger('datalad')\n\nfor logger_name in logging.root.manager.loggerDict:\n logging.getLogger(logger_name).handlers.clear()\n\n_root_logger = logging.getLogger()\n# _root_logger.handlers.clear()\n_handler = logging.StreamHandler(stream=sys.stdout)\n_handler.setFormatter(_LogFormatter())\n_root_logger.addHandler(_handler)\n\n_wlog.addHandler(logging.NullHandler())\n_numexprlog.addHandler(logging.NullHandler())\n_dataladlog.addHandler(logging.NullHandler())\n\n# def _warn(message, category=None, stacklevel=1, source=None):\n# \"\"\"Redefine the warning function.\"\"\"\n# if category is not None:\n# category = type(category).__name__\n# category = category.replace('type', 'WARNING')\n\n# logging.getLogger('py.warnings').debug(f\"{category or 'WARNING'}: {message}\")\n\n\n# def _showwarning(message, category, filename, lineno, file=None, line=None):\n# _warn(message, category=category)\n\n\n# warnings.warn = _warn\n# warnings.showwarning = _showwarning\n\n# warnings.filterwarnings(\"ignore\", category=FutureWarning)\n# warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n# warnings.filterwarnings(\"ignore\", category=ResourceWarning)\n# # cmp is not used by mriqc, so ignore nipype-generated warnings\n# warnings.filterwarnings(\"ignore\", \"cmp not installed\")\n# warnings.filterwarnings(\n# \"ignore\", \"This has not been fully tested. Please report any failures.\"\n# )\n# warnings.filterwarnings(\"ignore\", \"sklearn.externals.joblib is deprecated in 0.21\")\n# warnings.filterwarnings(\"ignore\", \"can't resolve package from __spec__ or __package__\")\n" }, { "path": "mriqc/bin/__init__.py", "content": "# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n#\n# Copyright 2021 The NiPreps Developers \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# We support and encourage derived works from this project, please read\n# about our expectations at\n#\n# https://www.nipreps.org/community/licensing/\n#\n\"\"\"\n``mriqc``'s executables.\n\"\"\"\n" }, { "path": "mriqc/bin/abide2bids.py", "content": "# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n#\n# Copyright 2021 The NiPreps Developers \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# We support and encourage derived works from this project, please read\n# about our expectations at\n#\n# https://www.nipreps.org/community/licensing/\n#\n\"\"\"ABIDE2BIDS download tool.\"\"\"\n\nfrom __future__ import annotations\n\nimport errno\nimport json\nimport os\nimport os.path as op\nimport shutil\nimport subprocess as sp\nimport tempfile\nfrom argparse import ArgumentParser, RawTextHelpFormatter\nfrom multiprocessing import Pool\n\nimport numpy as np\nfrom defusedxml import ElementTree as et\n\nfrom mriqc.bin import messages\n\n_curl_cmd = shutil.which('curl')\n_unzip_cmd = shutil.which('unzip')\n\n\ndef main():\n \"\"\"Entry point.\"\"\"\n parser = ArgumentParser(\n description='ABIDE2BIDS downloader.',\n formatter_class=RawTextHelpFormatter,\n )\n g_input = parser.add_argument_group('Inputs')\n g_input.add_argument('-i', '--input-abide-catalog', action='store', required=True)\n g_input.add_argument('-n', '--dataset-name', action='store', default='ABIDE Dataset')\n g_input.add_argument('-u', '--nitrc-user', action='store', default=os.getenv('NITRC_USER'))\n g_input.add_argument(\n '-p',\n '--nitrc-password',\n action='store',\n default=os.getenv('NITRC_PASSWORD'),\n )\n\n g_outputs = parser.add_argument_group('Outputs')\n g_outputs.add_argument('-o', '--output-dir', action='store', default='ABIDE-BIDS')\n\n opts = parser.parse_args()\n\n if opts.nitrc_user is None or opts.nitrc_password is None:\n raise RuntimeError('NITRC user and password are required')\n\n dataset_desc = {\n 'BIDSVersion': '1.0.0rc3',\n 'License': 'CC Attribution-NonCommercial-ShareAlike 3.0 Unported',\n 'Name': opts.dataset_name,\n }\n\n out_dir = op.abspath(opts.output_dir)\n try:\n os.makedirs(out_dir)\n except OSError as exc:\n if exc.errno != errno.EEXIST:\n raise exc\n\n with open(op.join(out_dir, 'dataset_description.json'), 'w') as dfile:\n json.dump(dataset_desc, dfile)\n\n catalog = et.parse(opts.input_abide_catalog).getroot()\n urls = [el.get('URI') for el in catalog.iter() if el.get('URI') is not None]\n\n pool = Pool()\n args_list = [(url, opts.nitrc_user, opts.nitrc_password, out_dir) for url in urls]\n res = pool.map(fetch, args_list)\n\n tsv_data = np.array([('subject_id', 'site_name')] + res)\n np.savetxt(\n op.join(out_dir, 'participants.tsv'),\n tsv_data,\n fmt='%s',\n delimiter='\\t',\n )\n\n\ndef fetch(args: (str, str, str, str)) -> (str, str):\n \"\"\"\n Downloads a subject and formats it into BIDS.\n\n Parameters\n ----------\n args : Tuple[str, str, str, str]\n URL, NITRC user, NITRC password, destination\n\n Returns\n -------\n Tuple[str, str]\n Subject ID, Site name\n \"\"\"\n out_dir = None\n if len(args) == 3:\n url, user, password = args\n else:\n url, user, password, out_dir = args\n\n tmpdir = tempfile.mkdtemp()\n if out_dir is None:\n out_dir = os.getcwd()\n else:\n out_dir = op.abspath(out_dir)\n\n pkg_id = [u[9:] for u in url.split('/') if u.startswith('NITRC_IR_')][0]\n sub_file = op.join(tmpdir, f'{pkg_id}.zip')\n\n cmd = [_curl_cmd, '-s', '-u', f'{user}:{password}', '-o', sub_file, url]\n sp.check_call(cmd)\n sp.check_call([_unzip_cmd, '-qq', '-d', tmpdir, '-u', sub_file])\n\n abide_root = op.join(tmpdir, 'ABIDE')\n files = []\n for root, path, fname in os.walk(abide_root):\n if fname and (fname[0].endswith('nii') or fname[0].endswith('nii.gz')):\n if path:\n root = op.join(root, path[0])\n files.append(op.join(root, fname[0]))\n\n index = len(abide_root) + 1\n site_name, sub_str = files[0][index:].split('/')[0].split('_')\n subject_id = 'sub-' + sub_str\n\n for i in files:\n ext = '.nii.gz'\n if i.endswith('.nii'):\n ext = '.nii'\n if 'mprage' in i:\n bids_dir = op.join(out_dir, subject_id, 'anat')\n try:\n os.makedirs(bids_dir)\n except OSError as exc:\n if exc.errno != errno.EEXIST:\n raise exc\n shutil.copy(i, op.join(bids_dir, subject_id + '_T1w' + ext))\n\n if 'rest' in i:\n bids_dir = op.join(out_dir, subject_id, 'func')\n try:\n os.makedirs(bids_dir)\n except OSError as exc:\n if exc.errno != errno.EEXIST:\n raise exc\n shutil.copy(i, op.join(bids_dir, subject_id + '_rest_bold' + ext))\n\n shutil.rmtree(tmpdir, ignore_errors=True, onerror=_myerror)\n\n success_message = messages.ABIDE_SUBJECT_FETCHED.format(\n subject_id=subject_id[4:], site_name=site_name\n )\n print(success_message)\n return subject_id[4:], site_name\n\n\ndef _myerror(message: str):\n \"\"\"\n Print warning in case an exception is raised for temporal files removal.\n\n Parameters\n ----------\n message : str\n `shutil.rmtree()` error message\n \"\"\"\n warning = messages.ABIDE_TEMPORAL_WARNING.format(message=message)\n print(warning)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "mriqc/bin/dfcheck.py", "content": "# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n#\n# Copyright 2021 The NiPreps Developers \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# We support and encourage derived works from this project, please read\n# about our expectations at\n#\n# https://www.nipreps.org/community/licensing/\n#\n\"\"\"\nCompares pandas dataframes by columns.\n\"\"\"\n\nimport sys\nfrom argparse import ArgumentParser, RawTextHelpFormatter\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nfrom mriqc.bin import messages\nfrom mriqc.utils.misc import BIDS_COMP\n\n\ndef read_iqms(feat_file):\n \"\"\"Read in a features table.\"\"\"\n feat_file = Path(feat_file)\n\n if feat_file.suffix == '.csv':\n x_df = pd.read_csv(feat_file, index_col=False, dtype=dict.fromkeys(BIDS_COMP, str))\n # Find present bids bits and sort by them\n bids_comps_present = list(set(x_df.columns) & set(BIDS_COMP))\n bids_comps_present = [bit for bit in BIDS_COMP if bit in bids_comps_present]\n x_df = x_df.sort_values(by=bids_comps_present)\n # Remove sub- prefix in subject_id\n x_df.subject_id = x_df.subject_id.str.lstrip('sub-')\n\n # Remove columns that are not IQMs\n feat_names = x_df._get_numeric_data().columns.tolist()\n for col in BIDS_COMP:\n try:\n feat_names.remove(col)\n except ValueError:\n pass\n else:\n bids_comps_present = ['subject_id']\n x_df = pd.read_csv(feat_file, index_col=False, sep='\\t', dtype={'bids_name': str})\n x_df = x_df.sort_values(by=['bids_name'])\n x_df['subject_id'] = x_df.bids_name.str.lstrip('sub-')\n x_df = x_df.drop(columns=['bids_name'])\n x_df.subject_id = ['_'.join(v.split('_')[:-1]) for v in x_df.subject_id.ravel()]\n feat_names = x_df._get_numeric_data().columns.tolist()\n\n for col in feat_names:\n if col.startswith(('size_', 'spacing_', 'Unnamed')):\n feat_names.remove(col)\n\n return x_df, feat_names, bids_comps_present\n\n\ndef main():\n \"\"\"Entry point.\"\"\"\n parser = ArgumentParser(\n description='Compare two pandas dataframes.',\n formatter_class=RawTextHelpFormatter,\n )\n g_input = parser.add_argument_group('Inputs')\n g_input.add_argument(\n '-i',\n '--input-csv',\n action='store',\n type=Path,\n required=True,\n help='input data frame',\n )\n g_input.add_argument(\n '-r',\n '--reference-csv',\n action='store',\n type=Path,\n required=True,\n help='reference dataframe',\n )\n g_input.add_argument(\n '--tolerance',\n type=float,\n default=1.0e-5,\n help='relative tolerance for comparison',\n )\n\n opts = parser.parse_args()\n\n ref_df, ref_names, ref_bids = read_iqms(opts.reference_csv)\n tst_df, tst_names, tst_bids = read_iqms(opts.input_csv)\n\n ref_df.set_index(ref_bids)\n tst_df.set_index(tst_bids)\n\n if sorted(ref_bids) != sorted(tst_bids):\n sys.exit(messages.DFCHECK_DIFFERENT_BITS)\n\n if sorted(ref_names) != sorted(tst_names):\n sys.exit(messages.DFCHECK_CSV_COLUMNS)\n\n ref_df = ref_df.sort_values(by=ref_bids)\n tst_df = tst_df.sort_values(by=tst_bids)\n\n if len(ref_df) != len(tst_df):\n different_length_message = messages.DFCHECK_DIFFERENT_LENGTH.format(\n len_input=len(ref_df), len_reference=len(tst_df)\n )\n print(different_length_message)\n tst_rows = tst_df[tst_bids]\n ref_rows = ref_df[ref_bids]\n\n print(tst_rows.shape, ref_rows.shape)\n\n tst_keep = np.sum(tst_rows.isin(ref_rows).values.ravel().tolist())\n print(tst_keep)\n\n diff = ~np.isclose(ref_df[ref_names].values, tst_df[tst_names].values, rtol=opts.tolerance)\n if np.any(diff):\n # ne_stacked = pd.DataFrame(data=diff, columns=ref_names).stack()\n # ne_stacked = np.isclose(ref_df[ref_names], tst_df[ref_names]).stack()\n # changed = ne_stacked[ne_stacked]\n # changed.set_index(ref_bids)\n difference_locations = np.where(diff)\n changed_from = ref_df[ref_names].values[difference_locations]\n changed_to = tst_df[ref_names].values[difference_locations]\n cols = [ref_names[v] for v in difference_locations[1]]\n bids_df = ref_df.loc[difference_locations[0], ref_bids].reset_index()\n chng_df = pd.DataFrame({'iqm': cols, 'from': changed_from, 'to': changed_to})\n table = pd.concat([bids_df, chng_df], axis=1)\n print(table[ref_bids + ['iqm', 'from', 'to']].to_string(index=False))\n\n corr = pd.DataFrame()\n corr['iqms'] = ref_names\n corr['cc'] = [\n float(\n np.corrcoef(\n ref_df[[var]].values.ravel(),\n tst_df[[var]].values.ravel(),\n rowvar=False,\n )[0, 1]\n )\n for var in ref_names\n ]\n\n if np.any(corr.cc < 0.95):\n iqms = corr[corr.cc < 0.95]\n iqms_message = messages.DFCHECK_IQMS_UNDER_095.format(iqms=iqms)\n print(iqms_message)\n sys.exit(messages.DFCHECK_CSV_CHANGED)\n else:\n print(messages.DFCHECK_IQMS_CORRELATED)\n\n sys.exit(0)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "mriqc/bin/fs2gif.py", "content": "# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n#\n# Copyright 2021 The NiPreps Developers \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# We support and encourage derived works from this project, please read\n# about our expectations at\n#\n# https://www.nipreps.org/community/licensing/\n#\n\"\"\"\nBatch export freesurfer results to animated gifs.\n\"\"\"\n\nimport os\nimport os.path as op\nimport subprocess as sp\nfrom argparse import ArgumentParser, RawTextHelpFormatter\nfrom errno import EEXIST\nfrom shutil import rmtree, which\nfrom tempfile import mkdtemp\n\nimport nibabel as nb\nimport numpy as np\nfrom skimage import exposure\n\n\ndef main():\n \"\"\"Entry point\"\"\"\n parser = ArgumentParser(\n description='Batch export freesurfer results to animated gifs.',\n formatter_class=RawTextHelpFormatter,\n )\n g_input = parser.add_argument_group('Inputs')\n g_input.add_argument('-s', '--subject-id', action='store')\n g_input.add_argument('-t', '--temp-dir', action='store')\n g_input.add_argument('--keep-temp', action='store_true', default=False)\n g_input.add_argument('--zoom', action='store_true', default=False)\n g_input.add_argument('--hist-eq', action='store_true', default=False)\n g_input.add_argument('--use-xvfb', action='store_true', default=False)\n\n g_outputs = parser.add_argument_group('Outputs')\n g_outputs.add_argument('-o', '--output-dir', action='store', default='fs2gif')\n\n opts = parser.parse_args()\n\n if opts.temp_dir is None:\n tmpdir = mkdtemp()\n else:\n tmpdir = op.abspath(opts.temp_dir)\n try:\n os.makedirs(tmpdir)\n except OSError as exc:\n if exc.errno != EEXIST:\n raise exc\n\n out_dir = op.abspath(opts.output_dir)\n try:\n os.makedirs(out_dir)\n except OSError as exc:\n if exc.errno != EEXIST:\n raise exc\n\n subjects_dir = os.getenv('SUBJECTS_DIR', op.abspath('subjects'))\n subject_list = [opts.subject_id]\n if opts.subject_id is None:\n subject_list = [\n op.basename(name)\n for name in os.listdir(subjects_dir)\n if op.isdir(os.path.join(subjects_dir, name))\n ]\n environ = os.environ.copy()\n environ['SUBJECTS_DIR'] = subjects_dir\n if opts.use_xvfb:\n environ['doublebufferflag'] = 1\n\n # tcl_file = pkgr.resource_filename('mriqc', 'data/fsexport.tcl')\n tcl_contents = \"\"\"\nSetOrientation 0\nSetCursor 0 128 128 128\nSetDisplayFlag 3 0\nSetDisplayFlag 22 1\nset i 0\n\"\"\"\n\n for subid in subject_list:\n sub_path = op.join(subjects_dir, subid)\n tmp_sub = op.join(tmpdir, subid)\n try:\n os.makedirs(tmp_sub)\n except OSError as exc:\n if exc.errno != EEXIST:\n raise exc\n\n data = nb.load(op.join(sub_path, 'mri', 'norm.mgz')).get_fdata()\n data[data > 0] = 1\n\n # Compute brain bounding box\n indexes = np.argwhere(data)\n bbox_min = indexes.min(0)\n bbox_max = indexes.max(0) + 1\n center = np.average([bbox_min, bbox_max], axis=0)\n\n if opts.hist_eq:\n ref_file = op.join(tmp_sub, f'{subid}.mgz')\n img = nb.load(op.join(sub_path, 'mri', 'norm.mgz'))\n data = exposure.equalize_adapthist(img.get_fdata(), clip_limit=0.03)\n nb.MGHImage(data, img.affine, img.header).to_filename(ref_file)\n\n if not opts.zoom:\n # Export tiffs for left hemisphere\n tcl_file = op.join(tmp_sub, f'{subid}.tcl')\n with open(tcl_file, 'w') as tclfp:\n tclfp.write(tcl_contents)\n tclfp.write(\n f'for {{ set slice {bbox_min[2]} }} {{ $slice < {bbox_max[2]} }} {{ incr slice }} {{'\n )\n tclfp.write(' SetSlice $slice\\n')\n tclfp.write(' RedrawScreen\\n')\n tclfp.write(f' SaveTIFF [format \"{tmp_sub}/{subid}-%03d.tif\" $i]\\n')\n tclfp.write(' incr i\\n')\n tclfp.write('}\\n')\n tclfp.write('QuitMedit\\n')\n cmd = [\n 'tkmedit',\n subid,\n 'T1.mgz',\n 'lh.pial',\n '-aux-surface',\n 'rh.pial',\n '-tcl',\n tcl_file,\n ]\n if opts.use_xvfb:\n cmd = _xvfb_run() + cmd\n\n print('Running tkmedit: {}'.format(' '.join(cmd)))\n sp.call(cmd, env=environ)\n # Convert to animated gif\n print('Stacking coronal slices')\n sp.call(\n [\n which('convert'),\n '-delay',\n '10',\n '-loop',\n '0',\n f'{tmp_sub}/{subid}-*.tif',\n f'{out_dir}/{subid}.gif',\n ]\n )\n\n else:\n # Export tiffs for left hemisphere\n tcl_file = op.join(tmp_sub, f'lh-{subid}.tcl')\n with open(tcl_file, 'w') as tclfp:\n tclfp.write(tcl_contents)\n tclfp.write('SetZoomLevel 2')\n tclfp.write(\n 'for {{ set slice {bbox_min[2]} }} {{ $slice < {bbox_max[2]} }} {{ incr slice }} {{'\n )\n tclfp.write(f' SetZoomCenter {center[0] + 30} {center[1] - 10} $slice\\n')\n tclfp.write(' SetSlice $slice\\n')\n tclfp.write(' RedrawScreen\\n')\n tclfp.write(f' SaveTIFF [format \"{tmp_sub}/{subid}-lh-%03d.tif\" $i]\\n')\n tclfp.write(' incr i\\n')\n tclfp.write('}\\n')\n tclfp.write('QuitMedit\\n')\n cmd = ['tkmedit', subid, 'norm.mgz', 'lh.white', '-tcl', tcl_file]\n if opts.use_xvfb:\n cmd = _xvfb_run() + cmd\n\n print('Running tkmedit: {}'.format(' '.join(cmd)))\n sp.call(cmd, env=environ)\n # Convert to animated gif\n print('Stacking coronal slices')\n\n # Export tiffs for right hemisphere\n tcl_file = op.join(tmp_sub, f'rh-{subid}.tcl')\n with open(tcl_file, 'w') as tclfp:\n tclfp.write(tcl_contents)\n tclfp.write('SetZoomLevel 2')\n tclfp.write(\n 'for {{ set slice {bbox_min[2]} }} {{ $slice < {bbox_max[2]} }} {{ incr slice }} {{'\n )\n tclfp.write(f' SetZoomCenter {center[0] - 30} {center[1] - 10} $slice\\n')\n tclfp.write(' SetSlice $slice\\n')\n tclfp.write(' RedrawScreen\\n')\n tclfp.write(f' SaveTIFF [format \"{tmp_sub}/{subid}-rh-%03d.tif\" $slice]\\n')\n tclfp.write(' incr i\\n')\n tclfp.write('}\\n')\n tclfp.write('QuitMedit\\n')\n cmd = ['tkmedit', subid, 'norm.mgz', 'rh.white', '-tcl', tcl_file]\n if opts.use_xvfb:\n cmd = _xvfb_run() + cmd\n\n print('Running tkmedit: {}'.format(' '.join(cmd)))\n sp.call(cmd, env=environ)\n # Convert to animated gif\n print('Stacking coronal slices')\n sp.call(\n [\n which('convert'),\n '-delay',\n '10',\n '-loop',\n '0',\n f'{tmp_sub}/{subid}-lh-*.tif',\n f'{out_dir}/{subid}-lh.gif',\n ]\n )\n sp.call(\n [\n which('convert'),\n '-delay',\n '10',\n '-loop',\n '0',\n f'{tmp_sub}/{subid}-rh-*.tif',\n f'{out_dir}/{subid}-rh.gif',\n ]\n )\n\n if not opts.keep_temp:\n rmtree(tmp_sub, ignore_errors=True, onerror=_myerror)\n\n\ndef _xvfb_run(wait=5, server_args='-screen 0, 1600x1200x24', logs=None):\n \"\"\"\n Wrap command with xvfb-run. Copied from:\n https://github.com/VUIIS/seam/blob/1dabd9ca5b1fc7d66ef7d41c34ea8d42d668a484/seam/util.py\n\n \"\"\"\n if logs is None:\n logs = op.join(mkdtemp(), 'fs2gif_xvfb')\n\n return [\n 'xvfb-run',\n '-a', # automatically get a free server number\n f'-f {logs}.out',\n f'-e {logs}.err',\n f'--wait={wait:d}',\n f'--server-args=\"{server_args}\"',\n ]\n\n\ndef _myerror(msg):\n print(f'WARNING: Error deleting temporal files: {msg}')\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "mriqc/bin/labeler.py", "content": "# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n#\n# Copyright 2021 The NiPreps Developers \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# We support and encourage derived works from this project, please read\n# about our expectations at\n#\n# https://www.nipreps.org/community/licensing/\n#\nimport csv\nimport os\nimport random\nimport sys\nimport webbrowser\n\nimport numpy as np\n\n\ndef num_rows(data):\n for j in range(1, 4):\n if len(data[j]) == 0:\n return j\n return 4\n\n\ndef main():\n \"\"\"read the input file\"\"\"\n print('Reading file sinfo.csv')\n csvfile = open('sinfo.csv', 'rb')\n csvreader = csv.reader(csvfile)\n file = list(csvreader)\n\n # display statistics\n finished = [0.0, 0.0, 0.0]\n hold = np.zeros((3, len(file) - 1))\n hold[:] = np.nan\n total = 601\n for i in range(1, len(file)):\n for j in range(1, 4):\n if len(file[i][j]) > 0:\n finished[j - 1] = finished[j - 1] + 1\n hold[j - 1, i - 1] = int(file[i][j])\n finished = np.divide(np.round(np.divide(finished, total) * 1000), 10)\n print(f'Completed: {\" \".join([f\"{f:g}%\" for f in finished])}')\n print(f'Total: {np.round(np.divide(np.sum(finished), 3))}%')\n input('Waiting: [enter]')\n\n # file[1:] are all the rows\n order = range(1, len(file))\n random.shuffle(order)\n # pick a random row\n for row in order:\n # check how many entries it has\n current = num_rows(file[row])\n if current <= 1:\n # if less than 1, run the row\n print('Check participant #' + file[row][0])\n fname = os.getcwd() + '/abide/' + file[row][0]\n if os.path.isfile(fname):\n webbrowser.open('file://' + fname)\n quality = input('Quality? [-1/0/1/e/c] ')\n if quality == 'e':\n break\n if quality == 'c':\n print('Current comment: ' + file[row][4])\n comment = input('Comment: ')\n if len(comment) > 0:\n file[row][4] = comment\n quality = input('Quality? [-1/0/1/e] ')\n if quality == 'e':\n break\n file[row][current] = quality\n else:\n print('File does not exist')\n\n print('Writing file sinfo.csv')\n outfile = open('sinfo.csv', 'wb')\n csvwriter = csv.writer(outfile)\n csvwriter.writerows(file)\n print('Ending')\n\n\nif __name__ == '__main__':\n main()\n sys.exit(0)\n" }, { "path": "mriqc/bin/messages.py", "content": "# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n#\n# Copyright 2021 The NiPreps Developers \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# We support and encourage derived works from this project, please read\n# about our expectations at\n#\n# https://www.nipreps.org/community/licensing/\n#\nABIDE_SUBJECT_FETCHED = 'Successfully processed subject {subject_id} from site {site_name}'\nABIDE_TEMPORAL_WARNING = 'WARNING: Error deleting temporal files: {message}'\nBIDS_LABEL_MISSING = 'Participant label(s) not found in the BIDS root directory: {label}'\nBIDS_GROUP_SIZE = 'Group size should be at least 0 (i.e. all participants assigned to same group).'\nCLF_CAPTURED_WARNING = 'Captured warning ({category}): {message}'\nCLF_CLASSIFIER_MISSING = 'No training samples were given, and the --load-classifier option {info}.'\nCLF_SAVED_RESULTS = 'Results saved as {path}.'\nCLF_TRAIN_LOAD_ERROR = 'Errors ({n_errors}) loading training set: {errors}.'\nCLF_WRONG_PARAMETER_COUNT = 'Wrong number of parameters.'\nDFCHECK_CSV_CHANGED = 'Output CSV file changed one or more values.'\nDFCHECK_CSV_COLUMNS = 'Output CSV file changed number of columns.'\nDFCHECK_DIFFERENT_BITS = 'Dataset has different BIDS bits w.r.t. reference.'\nDFCHECK_DIFFERENT_LENGTH = \"\"\"\\\nInput datasets have different lengths (input={len_input}, \\\nreference={len_reference}).\n\"\"\"\nDFCHECK_IQMS_CORRELATED = 'All IQMs show a Pearson correlation >= 0.95.'\nDFCHECK_IQMS_UNDER_095 = 'IQMs with Pearson correlation < 0.95:\\n{iqms}'\nHASH_REPORT = '{sha} {file_name}'\nPLOT_REPORT_VERSION = 'mriqc version:\\t{version}'\nPLOT_WORK_MISSING = 'Work directory of a previous MRIQC run was not found.'\nSUBJECT_WRANGLER_DESCRIPTION = \"\"\"\\\nBIDS-Apps participants wrangler tool\n------------------------------------\n\nThis command arranges the participant labels in groups for computation, and checks that the \\\nrequested participants have the corresponding folder in the bids_dir.\\\n\"\"\"\nWEBAPI_GET = 'Sending GET to {address}.'\nWEBAPI_REPORT = 'There are {n_records} records in database.'\n" }, { "path": "mriqc/bin/mriqcwebapi_test.py", "content": "# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n#\n# Copyright 2021 The NiPreps Developers \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# We support and encourage derived works from this project, please read\n# about our expectations at\n#\n# https://www.nipreps.org/community/licensing/\n#\nfrom mriqc.bin import messages\n\n\ndef get_parser():\n \"\"\"\n Build parser object.\n \"\"\"\n from argparse import ArgumentParser, RawTextHelpFormatter\n\n parser = ArgumentParser(\n description='MRIQCWebAPI: Check entries.', formatter_class=RawTextHelpFormatter\n )\n parser.add_argument(\n 'modality',\n action='store',\n choices=['T1w', 'bold'],\n help='number of expected items in the database',\n )\n parser.add_argument(\n 'expected',\n action='store',\n type=int,\n help='number of expected items in the database',\n )\n parser.add_argument(\n '--webapi-url',\n action='store',\n default='https://mriqc.nimh.nih.gov/api/v1/T1w',\n type=str,\n help='IP address where the MRIQC WebAPI is listening',\n )\n return parser\n\n\ndef main():\n \"\"\"Entry point.\"\"\"\n import logging\n\n from requests import get\n\n # Run parser\n MRIQC_LOG = logging.getLogger(__name__)\n opts = get_parser().parse_args()\n get_log_message = messages.WEBAPI_GET.format(address=opts.webapi_url)\n MRIQC_LOG.info(get_log_message)\n response = get(opts.webapi_url, timeout=5).json()\n n_records = response['_meta']['total']\n response_log_message = messages.WEBAPI_REPORT.format(n_records=n_records)\n MRIQC_LOG.info(response_log_message)\n if opts.expected != n_records:\n raise AssertionError\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "mriqc/bin/nib_hash.py", "content": "# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n#\n# Copyright 2021 The NiPreps Developers \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# We support and encourage derived works from this project, please read\n# about our expectations at\n#\n# https://www.nipreps.org/community/licensing/\n#\n\"\"\"\nExtracts the sha hash of the contents of a nifti file.\n\"\"\"\n\nfrom argparse import ArgumentParser, RawTextHelpFormatter\nfrom hashlib import sha1\n\nimport nibabel as nb\n\nfrom mriqc.bin import messages\n\n\ndef get_parser() -> ArgumentParser:\n \"\"\"\n A trivial parser.\n\n Returns\n -------\n ArgumentParser\n nib_hash execution parser\n \"\"\"\n\n parser = ArgumentParser(\n description='Compare two pandas dataframes.',\n formatter_class=RawTextHelpFormatter,\n )\n parser.add_argument('input_file', action='store', help='input nifti file')\n return parser\n\n\ndef get_hash(nii_file: str) -> str:\n \"\"\"\n Computes the sha1 hash for a given NIfTI format file.\n\n Parameters\n ----------\n nii_file : str\n Path to *nii* file\n\n Returns\n -------\n str\n SHA1 hash\n \"\"\"\n with nb.openers.ImageOpener(nii_file) as fobj:\n return sha1(fobj.read()).hexdigest()\n\n\ndef main():\n \"\"\"Entry point.\"\"\"\n file_name = get_parser().parse_args().input_file\n sha = get_hash(file_name)\n message = messages.HASH_REPORT.format(sha=sha, file_name=file_name)\n print(message)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "mriqc/bin/subject_wrangler.py", "content": "# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n#\n# Copyright 2021 The NiPreps Developers \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# We support and encourage derived works from this project, please read\n# about our expectations at\n#\n# https://www.nipreps.org/community/licensing/\n#\n\"\"\"BIDS-Apps subject wrangler.\"\"\"\n\nimport glob\nimport os.path as op\nfrom argparse import ArgumentParser, RawTextHelpFormatter\nfrom random import shuffle\nfrom textwrap import dedent\n\nfrom mriqc import __version__\nfrom mriqc.bin import messages\n\nCOMMAND = '{exec} {bids_dir} {out_dir} participant --participant_label {labels} {work_dir} {arguments} {logfile}' # noqa: E501\n\n\ndef main():\n \"\"\"Entry point.\"\"\"\n parser = ArgumentParser(\n formatter_class=RawTextHelpFormatter,\n description=dedent(messages.SUBJECT_WRANGLER_DESCRIPTION),\n )\n\n parser.add_argument(\n '-v',\n '--version',\n action='version',\n version=f'mriqc v{__version__}',\n )\n\n parser.add_argument(\n 'bids_dir',\n action='store',\n help='The directory with the input dataset formatted according to the BIDS standard.',\n )\n parser.add_argument(\n 'output_dir',\n action='store',\n help='The directory where the output files '\n 'should be stored. If you are running group level analysis '\n 'this folder should be prepopulated with the results of the'\n 'participant level analysis.',\n )\n parser.add_argument(\n '--participant_label',\n '--subject_list',\n '-S',\n action='store',\n help='The label(s) of the participant(s) that should be analyzed. '\n 'The label corresponds to sub- from the '\n 'BIDS spec (so it does not include \"sub-\"). If this parameter '\n 'is not provided all subjects should be analyzed. Multiple '\n 'participants can be specified with a space separated list.',\n nargs='*',\n )\n parser.add_argument(\n '--group-size',\n default=1,\n action='store',\n type=int,\n help='Parallelize participants in groups.',\n )\n parser.add_argument(\n '--no-randomize',\n default=False,\n action='store_true',\n help='Do not randomize participants list before grouping.',\n )\n parser.add_argument(\n '--log-groups',\n default=False,\n action='store_true',\n help='Append logging output.',\n )\n parser.add_argument(\n '--multiple-workdir',\n default=False,\n action='store_true',\n help='Split work directories by jobs.',\n )\n parser.add_argument(\n '--bids-app-name',\n default='mriqc',\n action='store',\n help='BIDS app to call.',\n )\n parser.add_argument('--args', default='', action='store', help='Append arguments.')\n\n opts = parser.parse_args()\n\n # Build settings dict\n bids_dir = op.abspath(opts.bids_dir)\n subject_dirs = glob.glob(op.join(bids_dir, 'sub-*'))\n all_subjects = sorted([op.basename(subj)[4:] for subj in subject_dirs])\n\n subject_list = opts.participant_label\n if subject_list is None or not subject_list:\n subject_list = all_subjects\n else:\n # remove sub- prefix, get unique\n for i, subj in enumerate(subject_list):\n subject_list[i] = subj[4:] if subj.startswith('sub-') else subj\n\n subject_list = sorted(set(subject_list))\n\n if list(set(subject_list) - set(all_subjects)):\n non_exist = list(set(subject_list) - set(all_subjects))\n missing_label_error = messages.BIDS_LABEL_MISSING.format(label=' '.join(non_exist))\n raise RuntimeError(missing_label_error)\n\n if not opts.no_randomize:\n shuffle(subject_list)\n\n gsize = opts.group_size\n\n if gsize < 0:\n raise RuntimeError(messages.BIDS_GROUP_SIZE)\n if gsize == 0:\n gsize = len(subject_list)\n\n j = i + gsize\n groups = [subject_list[i:j] for i in range(0, len(subject_list), gsize)]\n\n log_arg = '>> log/mriqc-{:04d}.log' if opts.log_groups else ''\n workdir_arg = ' -w work/sjob-{:04d}' if opts.multiple_workdir else ''\n for i, part_group in enumerate(groups):\n kwargs = {\n 'exec': opts.bids_app_name,\n 'bids_dir': bids_dir,\n 'out_dir': opts.output_dir,\n 'labels': ' '.join(part_group),\n 'work_dir': workdir_arg.format(i),\n 'arguments': opts.args,\n 'logfile': log_arg.format(i),\n }\n print(COMMAND.format(**kwargs))\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "mriqc/cli/__init__.py", "content": "" }, { "path": "mriqc/cli/parser.py", "content": "# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n#\n# Copyright 2021 The NiPreps Developers \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# We support and encourage derived works from this project, please read\n# about our expectations at\n#\n# https://www.nipreps.org/community/licensing/\n#\n\"\"\"Parser.\"\"\"\n\nimport re\n\nfrom mriqc import config\n\n\ndef _parse_participant_labels(value):\n \"\"\"\n Drop ``sub-`` prefix of participant labels.\n\n >>> _parse_participant_labels(\"s060\")\n ['s060']\n >>> _parse_participant_labels(\"sub-s060\")\n ['s060']\n >>> _parse_participant_labels(\"s060 sub-s050\")\n ['s050', 's060']\n >>> _parse_participant_labels(\"s060 sub-s060\")\n ['s060']\n >>> _parse_participant_labels(\"s060\\tsub-s060\")\n ['s060']\n\n \"\"\"\n return sorted(\n {re.sub(r'^sub-', '', item.strip()) for item in re.split(r'\\s+', f'{value}'.strip())}\n )\n\n\ndef _build_parser():\n \"\"\"Build parser object.\"\"\"\n import sys\n import warnings\n from argparse import Action, ArgumentDefaultsHelpFormatter, ArgumentParser\n from functools import partial\n from pathlib import Path\n\n from packaging.version import Version\n\n from .version import check_latest, is_flagged\n\n class DeprecateAction(Action):\n def __call__(self, parser, namespace, values, option_string=None):\n warnings.warn(\n f'Argument {option_string} is deprecated and is *ignored*.',\n stacklevel=2,\n )\n delattr(namespace, self.dest)\n\n class ParticipantLabelAction(Action):\n def __call__(self, parser, namespace, values, option_string=None):\n setattr(namespace, self.dest, _parse_participant_labels(' '.join(values)))\n\n def _path_exists(path, parser):\n \"\"\"Ensure a given path exists.\"\"\"\n if path is None or not Path(path).exists():\n raise parser.error(f'Path does not exist: <{path}>.')\n return Path(path).expanduser().absolute()\n\n def _min_one(value, parser):\n \"\"\"Ensure an argument is not lower than 1.\"\"\"\n value = int(value)\n if value < 1:\n raise parser.error(\"Argument can't be less than one.\")\n return value\n\n def _to_gb(value):\n scale = {'G': 1, 'T': 10**3, 'M': 1e-3, 'K': 1e-6, 'B': 1e-9}\n digits = ''.join([c for c in value if c.isdigit()])\n n_digits = len(digits)\n units = value[n_digits:] or 'G'\n return int(digits) * scale[units[0]]\n\n def _bids_filter(value):\n from json import loads\n\n if value and Path(value).exists():\n return loads(Path(value).read_text())\n\n verstr = f'MRIQC v{config.environment.version}'\n currentv = Version(config.environment.version)\n\n parser = ArgumentParser(\n description=f\"\"\"\\\nMRIQC {config.environment.version}\nAutomated Quality Control and visual reports for Quality Assessment of structural \\\n(T1w, T2w) and functional MRI of the brain.\n\n{config.DSA_MESSAGE}\"\"\",\n formatter_class=ArgumentDefaultsHelpFormatter,\n )\n PathExists = partial(_path_exists, parser=parser)\n PositiveInt = partial(_min_one, parser=parser)\n\n # Arguments as specified by BIDS-Apps\n # required, positional arguments\n # IMPORTANT: they must go directly with the parser object\n parser.add_argument(\n 'bids_dir',\n action='store',\n type=PathExists,\n help='The root folder of a BIDS valid dataset (sub-XXXXX folders should '\n 'be found at the top level in this folder).',\n )\n parser.add_argument(\n 'output_dir',\n action='store',\n type=Path,\n help='The directory where the output files '\n 'should be stored. If you are running group level analysis '\n 'this folder should be prepopulated with the results of the '\n 'participant level analysis.',\n )\n parser.add_argument(\n 'analysis_level',\n action='store',\n nargs='+',\n help='Level of the analysis that will be performed. '\n 'Multiple participant level analyses can be run independently '\n '(in parallel) using the same output_dir.',\n choices=['participant', 'group'],\n )\n\n # optional arguments\n parser.add_argument('--version', action='version', version=verstr)\n parser.add_argument(\n '-v',\n '--verbose',\n dest='verbose_count',\n action='count',\n default=0,\n help='Increases log verbosity for each occurrence, debug level is -vvv.',\n )\n\n # TODO: add 'mouse', 'macaque', and other populations once the pipeline is working\n parser.add_argument(\n '--species',\n action='store',\n type=str,\n default='human',\n choices=['human', 'rat'],\n help='Use appropriate template for population',\n )\n\n g_bids = parser.add_argument_group('Options for filtering BIDS queries')\n g_bids.add_argument(\n '--participant-label',\n '--participant_label',\n '--participant-labels',\n '--participant_labels',\n dest='participant_label',\n action=ParticipantLabelAction,\n nargs='+',\n help='A space delimited list of participant identifiers or a single '\n 'identifier (the sub- prefix can be removed).',\n )\n g_bids.add_argument(\n '--bids-filter-file',\n action='store',\n type=Path,\n metavar='PATH',\n help='a JSON file describing custom BIDS input filter using pybids '\n '{:{:,...},...} '\n '(https://github.com/bids-standard/pybids/blob/master/src/bids/layout/config/bids.json)',\n )\n g_bids.add_argument(\n '--session-id',\n action='store',\n nargs='*',\n type=str,\n help='Filter input dataset by session ID.',\n )\n g_bids.add_argument(\n '--run-id',\n action='store',\n type=int,\n nargs='*',\n help='DEPRECATED - This argument will be disabled. Use ``--bids-filter-file`` instead.',\n )\n g_bids.add_argument(\n '--task-id',\n action='store',\n nargs='*',\n type=str,\n help='Filter input dataset by task ID.',\n )\n g_bids.add_argument(\n '-m',\n '--modalities',\n action='store',\n choices=config.SUPPORTED_SUFFIXES,\n default=config.SUPPORTED_SUFFIXES,\n nargs='*',\n help='Filter input dataset by MRI type.',\n )\n g_bids.add_argument('--dsname', type=str, help='A dataset name.')\n g_bids.add_argument(\n '--bids-database-dir',\n metavar='PATH',\n help='Path to an existing PyBIDS database folder, for faster indexing '\n '(especially useful for large datasets).',\n )\n g_bids.add_argument(\n '--bids-database-wipe',\n action='store_true',\n default=False,\n help='Wipe out previously existing BIDS indexing caches, forcing re-indexing.',\n )\n g_bids.add_argument(\n '--no-datalad-get',\n action='store_false',\n dest='datalad_get',\n help='Disable attempting to get remote files in DataLad datasets.',\n )\n\n # General performance\n g_perfm = parser.add_argument_group('Options to handle performance')\n g_perfm.add_argument(\n '--nprocs',\n '--n_procs',\n '--n_cpus',\n '-n-cpus',\n action='store',\n type=PositiveInt,\n help=\"\"\"\\\nMaximum number of simultaneously running parallel processes executed by *MRIQC* \\\n(e.g., several instances of ANTs' registration). \\\nHowever, when ``--nprocs`` is greater or equal to the ``--omp-nthreads`` option, \\\nit also sets the maximum number of threads that simultaneously running processes \\\nmay aggregate (meaning, with ``--nprocs 16 --omp-nthreads 8`` a maximum of two \\\n8-CPU-threaded processes will be running at a given time). \\\nUnder this mode of operation, ``--nprocs`` sets the maximum number of processors \\\nthat can be assigned work within an *MRIQC* job, which includes all the processors \\\nused by currently running single- and multi-threaded processes. \\\nIf ``None``, the number of CPUs available will be automatically assigned (which may \\\nnot be what you want in, e.g., shared systems like a HPC cluster.\"\"\",\n )\n g_perfm.add_argument(\n '--omp-nthreads',\n '--ants-nthreads',\n action='store',\n type=PositiveInt,\n help=\"\"\"\\\nMaximum number of threads that multi-threaded processes executed by *MRIQC* \\\n(e.g., ANTs' registration) can use. \\\nIf ``None``, the number of CPUs available will be automatically assigned (which may \\\nnot be what you want in, e.g., shared systems like a HPC cluster.\"\"\",\n )\n g_perfm.add_argument(\n '--mem',\n '--mem_gb',\n '--mem-gb',\n dest='memory_gb',\n action='store',\n type=_to_gb,\n help='Upper bound memory limit for MRIQC processes.',\n )\n g_perfm.add_argument(\n '--testing',\n dest='debug',\n action='store_true',\n default=False,\n help='Use testing settings for a minimal footprint.',\n )\n g_perfm.add_argument(\n '-f',\n '--float32',\n action='store_true',\n default=True,\n help=\"Cast the input data to float32 if it's represented in higher precision \"\n '(saves space and improves performance).',\n )\n g_perfm.add_argument(\n '--pdb',\n dest='pdb',\n action='store_true',\n default=False,\n help='Open Python debugger (pdb) on exceptions.',\n )\n\n # Control instruments\n g_outputs = parser.add_argument_group('Instrumental options')\n g_outputs.add_argument(\n '-w',\n '--work-dir',\n action='store',\n type=Path,\n default=Path('work').absolute(),\n help='Path where intermediate results should be stored.',\n )\n g_outputs.add_argument('--verbose-reports', default=False, action='store_true')\n g_outputs.add_argument('--reports-only', default=False, action='store_true')\n g_outputs.add_argument(\n '--write-graph',\n action='store_true',\n default=False,\n help='Write workflow graph.',\n )\n g_outputs.add_argument(\n '--dry-run',\n action='store_true',\n default=False,\n help='Do not run the workflow.',\n )\n g_outputs.add_argument(\n '--resource-monitor',\n '--profile',\n dest='resource_monitor',\n action='store_true',\n default=False,\n help='Hook up the resource profiler callback to nipype.',\n )\n g_outputs.add_argument(\n '--use-plugin',\n action='store',\n default=None,\n type=Path,\n help='Nipype plugin configuration file.',\n )\n g_outputs.add_argument(\n '--crashfile-format',\n action='store',\n default='txt',\n choices=['txt', 'pklz'],\n type=str,\n help='Nipype crashfile format',\n )\n g_outputs.add_argument(\n '--no-sub',\n default=False,\n action='store_true',\n help=\"Turn off submission of anonymized quality metrics to MRIQC's metrics repository.\",\n )\n g_outputs.add_argument(\n '--email',\n action='store',\n default='',\n type=str,\n help='Email address to include with quality metric submission.',\n )\n\n g_outputs.add_argument(\n '--webapi-url',\n action='store',\n type=str,\n help='IP address where the MRIQC WebAPI is listening.',\n )\n g_outputs.add_argument(\n '--webapi-port',\n action='store',\n type=int,\n help='Port where the MRIQC WebAPI is listening.',\n )\n\n g_outputs.add_argument(\n '--upload-strict',\n action='store_true',\n default=False,\n help='Upload will fail if upload is strict.',\n )\n g_outputs.add_argument(\n '--notrack',\n action='store_true',\n help='Opt-out of sending tracking information of this run to the NiPreps developers. This'\n ' information helps to improve MRIQC and provides an indicator of real world usage '\n ' crucial for obtaining funding.',\n )\n\n # ANTs options\n g_ants = parser.add_argument_group('Specific settings for ANTs')\n g_ants.add_argument(\n '--ants-float',\n action='store_true',\n default=False,\n help='Use float number precision on ANTs computations.',\n )\n g_ants.add_argument(\n '--ants-settings',\n action='store',\n help='Path to JSON file with settings for ANTs.',\n )\n\n # Diffusion workflow settings\n g_dwi = parser.add_argument_group('Diffusion MRI workflow configuration')\n g_dwi.add_argument(\n '--min-dwi-length',\n action='store',\n default=config.workflow.min_len_dwi,\n dest='min_len_dwi',\n help='Drop DWI runs with fewer orientations than this threshold.',\n type=int,\n )\n\n # Functional workflow settings\n g_func = parser.add_argument_group('Functional MRI workflow configuration')\n g_func.add_argument(\n '--min-bold-length',\n action='store',\n default=config.workflow.min_len_bold,\n dest='min_len_bold',\n help='Drop BOLD runs with fewer time points than this threshold.',\n type=int,\n )\n g_func.add_argument(\n '--fft-spikes-detector',\n action='store_true',\n default=False,\n help='Turn on FFT based spike detector (slow).',\n )\n g_func.add_argument(\n '--fd_thres',\n action='store',\n default=0.2,\n type=float,\n help='Threshold on framewise displacement estimates to detect outliers.',\n )\n g_func.add_argument(\n '--deoblique',\n action='store_true',\n default=False,\n help='Deoblique the functional scans during head motion correction preprocessing.',\n )\n g_func.add_argument(\n '--despike',\n action='store_true',\n default=False,\n help='Despike the functional scans during head motion correction preprocessing.',\n )\n g_func.add_argument(\n '--start-idx',\n action=DeprecateAction,\n type=int,\n help='DEPRECATED Initial volume in functional timeseries that should be '\n 'considered for preprocessing.',\n )\n g_func.add_argument(\n '--stop-idx',\n action=DeprecateAction,\n type=int,\n help='DEPRECATED Final volume in functional timeseries that should be '\n 'considered for preprocessing.',\n )\n\n latest = check_latest()\n if latest is not None and currentv < latest:\n print(\n f\"\"\"\\\nYou are using MRIQC v{currentv}, and a newer version is available: {latest}.\"\"\",\n file=sys.stderr,\n )\n\n _blist = is_flagged()\n if _blist[0]:\n _reason = _blist[1] or 'unknown'\n print(\n f\"\"\"\\\nWARNING: This version of MRIQC ({config.environment.version}) has been FLAGGED\n(reason: {_reason}).\nThat means some severe flaw was found in it and we strongly \\\ndiscourage its usage.\"\"\",\n file=sys.stderr,\n )\n\n return parser\n\n\ndef parse_args(args=None, namespace=None):\n \"\"\"Parse args and run further checks on the command line.\"\"\"\n from json import loads\n from logging import DEBUG, FileHandler\n from pathlib import Path\n from pprint import pformat\n\n from niworkflows.utils.bids import DEFAULT_BIDS_QUERIES, collect_data\n\n from mriqc import __version__, data\n from mriqc._warnings import DATE_FMT, LOGGER_FMT, _LogFormatter\n from mriqc.messages import PARTICIPANT_START\n from mriqc.utils.misc import initialize_meta_and_data\n\n parser = _build_parser()\n opts = parser.parse_args(args, namespace)\n config.execution.log_level = int(max(25 - 5 * opts.verbose_count, DEBUG))\n\n config.loggers.init()\n\n _log_file = Path(opts.output_dir) / 'logs' / f'mriqc-{config.execution.run_uuid}.log'\n _log_file.parent.mkdir(exist_ok=True, parents=True)\n _handler = FileHandler(_log_file)\n _handler.setFormatter(\n _LogFormatter(\n fmt=LOGGER_FMT.format(color='', reset=''),\n datefmt=DATE_FMT,\n colored=False,\n )\n )\n config.loggers.default.addHandler(_handler)\n\n extra_messages = ['']\n\n if opts.bids_filter_file:\n extra_messages.insert(\n 0,\n f' * BIDS filters-file: {opts.bids_filter_file.absolute()}.',\n )\n\n notice_path = data.load.readable('NOTICE')\n config.loggers.cli.log(\n 26,\n PARTICIPANT_START.format(\n version=__version__,\n bids_dir=opts.bids_dir,\n output_dir=opts.output_dir,\n analysis_level=opts.analysis_level,\n notice='\\n '.join(\n ['NOTICE'] + notice_path.read_text().splitlines(keepends=False)[1:]\n ),\n extra_messages='\\n'.join(extra_messages),\n ),\n )\n config.from_dict(vars(opts))\n\n # Load base plugin_settings from file if --use-plugin\n if opts.use_plugin is not None:\n from yaml import safe_load as loadyml\n\n with open(opts.use_plugin) as f:\n plugin_settings = loadyml(f)\n _plugin = plugin_settings.get('plugin')\n if _plugin:\n config.nipype.plugin = _plugin\n config.nipype.plugin_args = plugin_settings.get('plugin_args', {})\n config.nipype.nprocs = config.nipype.plugin_args.get('nprocs', config.nipype.nprocs)\n\n # Load BIDS filters\n if opts.bids_filter_file:\n config.execution.bids_filters = {\n k.lower(): v for k, v in loads(opts.bids_filter_file.read_text()).items()\n }\n\n bids_dir = config.execution.bids_dir\n output_dir = config.execution.output_dir\n work_dir = config.execution.work_dir\n version = config.environment.version\n\n # Ensure input and output folders are not the same\n if output_dir == bids_dir:\n parser.error(\n 'The selected output folder is the same as the input BIDS folder. '\n f'Please modify the output path (suggestion: {bids_dir}).'\n / 'derivatives'\n / ('mriqc-{}'.format(version.split('+')[0]))\n )\n\n if bids_dir in work_dir.parents:\n parser.error(\n 'The selected working directory is a subdirectory of the input BIDS folder. '\n 'Please modify the output path.'\n )\n\n config.execution.bids_dir_datalad = (\n config.execution.datalad_get\n and (bids_dir / '.git').exists()\n and (bids_dir / '.datalad').exists()\n )\n\n # Setup directories\n config.execution.log_dir = output_dir / 'logs'\n # Check and create output and working directories\n config.execution.log_dir.mkdir(exist_ok=True, parents=True)\n output_dir.mkdir(exist_ok=True, parents=True)\n work_dir.mkdir(exist_ok=True, parents=True)\n\n # Force initialization of the BIDSLayout\n config.execution.init()\n\n participant_label = [\n d.name[4:] for d in config.execution.bids_dir.glob('sub-*') if d.is_dir() and d.exists()\n ]\n\n if config.execution.participant_label is not None:\n selected_label = set(config.execution.participant_label)\n if missing_subjects := selected_label - set(participant_label):\n parser.error(\n 'One or more participant labels were not found in the BIDS directory: '\n f'{\", \".join(missing_subjects)}.'\n )\n participant_label = selected_label\n\n config.execution.participant_label = sorted(participant_label)\n\n # Handle analysis_level\n analysis_level = set(config.workflow.analysis_level)\n if not config.execution.participant_label:\n analysis_level.add('group')\n config.workflow.analysis_level = list(analysis_level)\n\n # List of files to be run\n lc_modalities = [mod.lower() for mod in config.execution.modalities]\n bids_dataset, _ = collect_data(\n config.execution.layout,\n config.execution.participant_label,\n session_id=config.execution.session_id,\n task=config.execution.task_id,\n group_echos=True,\n bids_filters={mod: config.execution.bids_filters.get(mod, {}) for mod in lc_modalities},\n queries={mod: DEFAULT_BIDS_QUERIES[mod] for mod in lc_modalities},\n )\n\n # Drop empty queries\n bids_dataset = {mod: files for mod, files in bids_dataset.items() if files}\n config.workflow.inputs = bids_dataset\n\n # Check the query is not empty\n if not list(config.workflow.inputs.values()):\n ffile = (\n '(--bids-filter-file was not set)'\n if not opts.bids_filter_file\n else f\"(with '--bids-filter-file {opts.bids_filter_file}')\"\n )\n parser.error(\n f\"\"\"\\\nQuerying BIDS dataset at <{config.execution.bids_dir}> got an empty result.\nPlease, check out your currently set filters {ffile}:\n{pformat(config.execution.bids_filters, indent=2, width=99)}\"\"\"\n )\n\n # Check no DWI or others are sneaked into MRIQC\n unknown_mods = set(config.workflow.inputs.keys()) - {\n suffix.lower() for suffix in config.SUPPORTED_SUFFIXES\n }\n if unknown_mods:\n parser.error(\n f'MRIQC is unable to process the following modalities: {\", \".join(unknown_mods)}.'\n )\n\n initialize_meta_and_data()\n\n # set specifics for alternative populations\n if opts.species.lower() != 'human':\n config.workflow.species = opts.species\n # TODO: add other species once rats are working\n if opts.species.lower() == 'rat':\n config.workflow.template_id = 'Fischer344'\n # mean distance from the lateral edge to the center of the brain is\n # ~ PA:10 mm, LR:7.5 mm, and IS:5 mm (see DOI: 10.1089/089771503770802853)\n # roll movement is most likely to occur, so set to 7.5 mm\n config.workflow.fd_radius = 7.5\n # block uploads for the moment; can be reversed before wider release\n config.execution.no_sub = True\n" }, { "path": "mriqc/cli/run.py", "content": "# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n#\n# Copyright 2021 The NiPreps Developers \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# We support and encourage derived works from this project, please read\n# about our expectations at\n#\n# https://www.nipreps.org/community/licensing/\n#\n\"\"\"Definition of the command line interface's (CLI) entry point.\"\"\"\n\n\ndef format_elapsed_time(elapsed_timedelta):\n \"\"\"Format a timedelta instance as a %Hh %Mmin %Ss string.\"\"\"\n return (\n f'{elapsed_timedelta.days * 24 + elapsed_timedelta.seconds // 3600:02d}h '\n f'{(elapsed_timedelta.seconds % 3600) // 60:02d}min '\n f'{elapsed_timedelta.seconds % 60:02d}s'\n )\n\n\ndef main(argv=None):\n \"\"\"Entry point for MRIQC's CLI.\"\"\"\n import atexit\n import datetime\n import gc\n import os\n import sys\n import time\n from tempfile import mkstemp\n\n from mriqc import config, messages\n from mriqc.cli.parser import parse_args\n\n atexit.register(config.restore_env)\n\n config.settings.start_time = time.time()\n\n # Run parser\n parse_args(argv)\n\n if config.execution.pdb:\n from mriqc.utils.debug import setup_exceptionhook\n\n setup_exceptionhook()\n config.nipype.plugin = 'Linear'\n\n # CRITICAL Save the config to a file. This is necessary because the execution graph\n # is built as a separate process to keep the memory footprint low. The most\n # straightforward way to communicate with the child process is via the filesystem.\n # The config file name needs to be unique, otherwise multiple mriqc instances\n # will create write conflicts.\n config_file = config.to_filename()\n config.loggers.cli.info(f'MRIQC config file: {config_file}.')\n\n exitcode = 0\n # Set up participant level\n if 'participant' in config.workflow.analysis_level:\n _pool = None\n if config.nipype.plugin in ('MultiProc', 'LegacyMultiProc'):\n import multiprocessing as mp\n import multiprocessing.forkserver\n from concurrent.futures import ProcessPoolExecutor\n from contextlib import suppress\n\n os.environ['OMP_NUM_THREADS'] = '1'\n os.environ['NUMEXPR_MAX_THREADS'] = '1'\n\n with suppress(RuntimeError):\n mp.set_start_method('fork')\n gc.collect()\n\n _pool = ProcessPoolExecutor(\n max_workers=config.nipype.nprocs,\n initializer=config._process_initializer,\n initargs=(config_file,),\n )\n\n _resmon = None\n if config.execution.resource_monitor:\n from mriqc.instrumentation.resources import ResourceRecorder\n\n _resmon = ResourceRecorder(\n pid=os.getpid(),\n log_file=mkstemp(\n dir=config.execution.work_dir, prefix='.resources.', suffix='.tsv'\n )[1],\n )\n _resmon.start()\n\n if not config.execution.notrack:\n from ..utils.telemetry import setup_migas\n\n setup_migas()\n\n # CRITICAL Call build_workflow(config_file, retval) in a subprocess.\n # Because Python on Linux does not ever free virtual memory (VM), running the\n # workflow construction jailed within a process preempts excessive VM buildup.\n from multiprocessing import Manager, Process\n\n with Manager() as mgr:\n from .workflow import build_workflow\n\n retval = mgr.dict()\n p = Process(target=build_workflow, args=(str(config_file), retval))\n p.start()\n p.join()\n\n mriqc_wf = retval.get('workflow', None)\n exitcode = p.exitcode or retval.get('return_code', 0)\n\n # CRITICAL Load the config from the file. This is necessary because the ``build_workflow``\n # function executed constrained in a process may change the config (and thus the global\n # state of MRIQC).\n config.load(config_file)\n\n exitcode = exitcode or (mriqc_wf is None) * os.EX_SOFTWARE\n if exitcode != 0:\n sys.exit(exitcode)\n\n # Initialize nipype config\n config.nipype.init()\n # Make sure loggers are started\n config.loggers.init()\n\n if _resmon:\n config.loggers.cli.info(f'Started resource recording at {_resmon._logfile}.')\n\n # Resource management options\n if config.nipype.plugin in ('MultiProc', 'LegacyMultiProc') and (\n 1 < config.nipype.nprocs < config.nipype.omp_nthreads\n ):\n config.loggers.cli.warning(\n 'Per-process threads (--omp-nthreads=%d) exceed total '\n 'threads (--nthreads/--n_cpus=%d)',\n config.nipype.omp_nthreads,\n config.nipype.nprocs,\n )\n\n # Check synthstrip is properly installed\n if not config.environment.synthstrip_path:\n config.loggers.cli.warning(\n (\n 'Please make sure FreeSurfer is installed and the FREESURFER_HOME '\n 'environment variable is defined and pointing at the right directory.'\n )\n if config.environment.freesurfer_home is None\n else (\n f'FreeSurfer seems to be installed at {config.environment.freesurfer_home},'\n \" however SynthStrip's model is not found at the expected path.\"\n )\n )\n\n if mriqc_wf is None:\n sys.exit(os.EX_SOFTWARE)\n\n if mriqc_wf and config.execution.write_graph:\n mriqc_wf.write_graph(graph2use='colored', format='svg', simple_form=True)\n\n if not config.execution.dry_run and not config.execution.reports_only:\n # Warn about submitting measures BEFORE\n if not config.execution.no_sub:\n config.loggers.cli.warning(config.DSA_MESSAGE)\n\n # Clean up master process before running workflow, which may create forks\n gc.collect()\n # run MRIQC\n _plugin = config.nipype.get_plugin()\n if _pool:\n from mriqc.engine.plugin import MultiProcPlugin\n\n _plugin = {\n 'plugin': MultiProcPlugin(pool=_pool, plugin_args=config.nipype.plugin_args),\n }\n mriqc_wf.run(**_plugin)\n\n # Warn about submitting measures AFTER\n if not config.execution.no_sub:\n config.loggers.cli.warning(config.DSA_MESSAGE)\n\n if not config.execution.dry_run:\n from mriqc.reports.individual import generate_reports\n\n generate_reports()\n\n _subject_duration = (time.time() - config.settings.start_time) / sum(\n len(files) for files in config.workflow.inputs.values()\n )\n _subject_duration_td = datetime.timedelta(seconds=_subject_duration)\n time_strf = format_elapsed_time(_subject_duration_td)\n\n config.loggers.cli.log(\n 25,\n messages.PARTICIPANT_FINISHED.format(duration=time_strf),\n )\n\n if _resmon is not None:\n from mriqc.instrumentation.viz import plot\n\n _resmon.stop()\n plot(\n _resmon._logfile,\n param='mem_rss_mb',\n out_file=str(_resmon._logfile).replace('.tsv', '.rss.png'),\n )\n plot(\n _resmon._logfile,\n param='mem_vsm_mb',\n out_file=str(_resmon._logfile).replace('.tsv', '.vsm.png'),\n )\n\n # Set up group level\n if 'group' in config.workflow.analysis_level:\n from mriqc.reports.group import gen_html as group_html\n\n from ..utils.misc import generate_tsv # , generate_pred\n\n config.loggers.cli.log(26, messages.GROUP_START)\n\n # Generate reports\n mod_group_reports = []\n for mod in config.execution.modalities or config.SUPPORTED_SUFFIXES:\n output_dir = config.execution.output_dir\n dataframe, out_tsv = generate_tsv(output_dir, mod)\n # If there are no iqm.json files, nothing to do.\n if dataframe is None:\n continue\n\n tsv_message = messages.TSV_GENERATED.format(modality=mod, path=out_tsv)\n config.loggers.cli.info(tsv_message)\n\n # out_pred = generate_pred(derivatives_dir, settings['output_dir'], mod)\n # if out_pred is not None:\n # log.info('Predicted QA CSV table for the %s data generated (%s)',\n # mod, out_pred)\n\n out_html = output_dir / f'group_{mod}.html'\n group_html(\n out_tsv,\n mod,\n csv_failed=output_dir / f'group_variant-failed_{mod}.csv',\n out_file=out_html,\n )\n report_message = messages.GROUP_REPORT_GENERATED.format(modality=mod, path=out_html)\n config.loggers.cli.info(report_message)\n mod_group_reports.append(mod)\n\n if not mod_group_reports:\n raise Exception(messages.GROUP_NO_DATA)\n\n config.loggers.cli.info(messages.GROUP_FINISHED)\n\n from mriqc.utils.bids import write_bidsignore, write_derivative_description\n\n config.loggers.cli.info(messages.BIDS_META)\n write_derivative_description(config.execution.bids_dir, config.execution.output_dir)\n write_bidsignore(config.execution.output_dir)\n\n _run_duration = time.time() - config.settings.start_time\n _run_duration_td = datetime.timedelta(seconds=_run_duration)\n time_strf = format_elapsed_time(_run_duration_td)\n\n config.loggers.cli.log(\n 26,\n messages.RUN_FINISHED.format(duration=time_strf),\n )\n config.to_filename(\n config.execution.log_dir / f'config-{config.execution.run_uuid}.toml',\n store_inputs=False, # Inputs are not necessary anymore\n )\n sys.exit(exitcode)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "mriqc/cli/version.py", "content": "# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n#\n# Copyright 2021 The NiPreps Developers \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# We support and encourage derived works from this project, please read\n# about our expectations at\n#\n# https://www.nipreps.org/community/licensing/\n#\n\"\"\"Version CLI helpers.\"\"\"\n\nfrom contextlib import suppress\nfrom datetime import datetime, timezone\n\nimport requests\n\nfrom mriqc import __version__, config\n\nRELEASE_EXPIRY_DAYS = 14\nDATE_FMT = '%Y%m%d'\nUTC = timezone.utc\n\n\ndef check_latest():\n \"\"\"Determine whether this is the latest version.\"\"\"\n from packaging.version import InvalidVersion, Version\n\n latest = None\n date = None\n outdated = None\n cachefile = config.environment.cache_path / 'latest'\n try:\n cachefile.parent.mkdir(parents=True, exist_ok=True)\n except OSError:\n cachefile = None\n\n if cachefile and cachefile.exists():\n with suppress(Exception):\n latest, date = cachefile.read_text().split('|')\n\n if latest and date:\n try:\n latest = Version(latest)\n date = datetime.strptime(date, DATE_FMT).astimezone(UTC)\n except (InvalidVersion, ValueError):\n latest = None\n else:\n if abs((datetime.now(tz=UTC) - date).days) > RELEASE_EXPIRY_DAYS:\n outdated = True\n\n if latest is None or outdated is True:\n response = None\n\n with suppress(Exception):\n response = requests.get(url='https://pypi.org/pypi/mriqc/json', timeout=1.0)\n\n if response and response.status_code == 200:\n versions = [Version(rel) for rel in response.json()['releases'].keys()]\n versions = [rel for rel in versions if not rel.is_prerelease]\n if versions:\n latest = sorted(versions)[-1]\n else:\n latest = None\n\n if cachefile is not None and latest is not None:\n with suppress(Exception):\n cachefile.write_text('|'.join((f'{latest}', datetime.now(tz=UTC).strftime(DATE_FMT))))\n\n return latest\n\n\ndef is_flagged():\n \"\"\"Check whether current version is flagged.\"\"\"\n # https://raw.githubusercontent.com/nipreps/mriqc/master/.versions.json\n flagged = ()\n response = None\n with suppress(Exception):\n response = requests.get(\n url=\"\"\"\\\nhttps://raw.githubusercontent.com/nipreps/mriqc/master/.versions.json\"\"\",\n timeout=1.0,\n )\n\n if response and response.status_code == 200:\n flagged = response.json().get('flagged', {}) or {}\n\n if __version__ in flagged:\n return True, flagged[__version__]\n\n return False, None\n" }, { "path": "mriqc/cli/workflow.py", "content": "# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n#\n# Copyright 2021 The NiPreps Developers \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# We support and encourage derived works from this project, please read\n# about our expectations at\n#\n# https://www.nipreps.org/community/licensing/\n#\n\"\"\"\nThe workflow builder factory method.\n\nAll the checks and the construction of the workflow are done\ninside this function that has pickleable inputs and output\ndictionary (``retval``) to allow isolation using a\n``multiprocessing.Process`` that allows dmriprep to enforce\na hard-limited memory-scope.\n\"\"\"\n\n\ndef build_workflow(config_file, retval):\n \"\"\"Create the Nipype Workflow that supports the whole execution graph.\"\"\"\n import os\n\n from mriqc import config\n\n # We do not need OMP > 1 for workflow creation\n os.environ['OMP_NUM_THREADS'] = '1'\n os.environ['NUMEXPR_MAX_THREADS'] = '1'\n\n from mriqc.workflows.core import init_mriqc_wf\n\n config.load(config_file)\n # Initialize nipype config\n config.nipype.init()\n # Make sure loggers are started\n config.loggers.init()\n\n retval['return_code'] = 1\n retval['workflow'] = None\n\n config.loggers.cli.log(25, \"Building MRIQC's workflows...\")\n retval['workflow'] = init_mriqc_wf()\n retval['return_code'] = int(retval['workflow'] is None)\n config.loggers.cli.log(25, f'Workflow building finished (exit code {retval[\"return_code\"]}).')\n return retval\n" }, { "path": "mriqc/config.py", "content": "# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n#\n# Copyright 2021 The NiPreps Developers \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# We support and encourage derived works from this project, please read\n# about our expectations at\n#\n# https://www.nipreps.org/community/licensing/\n#\nr\"\"\"\nA Python module to maintain unique, run-wide *MRIQC* settings.\n\nThis module implements the memory structures to keep a consistent, singleton config.\nSettings are passed across processes via filesystem, and a copy of the settings for\neach run and subject is left under\n``/sub-/log//mriqc.toml``.\nSettings are stored using :abbr:`ToML (Tom's Markup Language)`.\nThe module has a :py:func:`~mriqc.config.to_filename` function to allow writing out\nthe settings to hard disk in *ToML* format, which looks like:\n\n.. literalinclude:: ../mriqc/data/config-example.toml\n :language: toml\n :name: mriqc.toml\n :caption: **Example file representation of MRIQC settings**.\n\nThis config file is used to pass the settings across processes,\nusing the :py:func:`~mriqc.config.load` function.\n\nConfiguration sections\n----------------------\n.. autoclass:: environment\n :members:\n.. autoclass:: execution\n :members:\n.. autoclass:: workflow\n :members:\n.. autoclass:: nipype\n :members:\n\nUsage\n-----\nA config file is used to pass settings and collect information as the execution\ngraph is built across processes.\n\n.. code-block:: Python\n\n from mriqc import config\n config_file = mktemp(dir=config.execution.work_dir, prefix='.mriqc.', suffix='.toml')\n config.to_filename(config_file)\n # Call build_workflow(config_file, retval) in a subprocess\n with Manager() as mgr:\n from .workflow import build_workflow\n retval = mgr.dict()\n p = Process(target=build_workflow, args=(str(config_file), retval))\n p.start()\n p.join()\n config.load(config_file)\n # Access configs from any code section as:\n value = config.section.setting\n\nLogging\n-------\n.. autoclass:: loggers\n :members:\n\nOther responsibilities\n----------------------\nThe :py:mod:`config` is responsible for other conveniency actions.\n\n * Switching Python's :obj:`multiprocessing` to *forkserver* mode.\n * Set up a filter for warnings as early as possible.\n * Automated I/O magic operations. Some conversions need to happen in the\n store/load processes (e.g., from/to :obj:`~pathlib.Path` \\<-\\> :obj:`str`,\n :py:class:`~bids.layout.BIDSLayout`, etc.)\n\n\"\"\"\n\nfrom __future__ import annotations\n\nimport os\nimport pickle\nimport sys\nfrom collections.abc import Iterable\nfrom contextlib import suppress\nfrom pathlib import Path\nfrom time import strftime\nfrom typing import TYPE_CHECKING, Any\nfrom uuid import uuid4\n\ntry:\n # This option is only available with Python 3.8\n from importlib.metadata import version as get_version\nexcept ImportError:\n from importlib_metadata import version as get_version\n\n# Ignore annoying warnings\nfrom mriqc._warnings import logging\n\nif TYPE_CHECKING:\n from re import Pattern\n\n from bids.layout import BIDSLayout\n\n__version__: str = get_version('mriqc')\n_pre_exec_env: dict[str, str] = dict(os.environ)\n\n# Reduce numpy's vms by limiting OMP_NUM_THREADS\n_default_omp_threads: int = int(os.getenv('OMP_NUM_THREADS', os.cpu_count()))\n\n# Disable NiPype etelemetry always\n_disable_et: bool = bool(os.getenv('NO_ET') is not None or os.getenv('NIPYPE_NO_ET') is not None)\nos.environ['NIPYPE_NO_ET'] = '1'\nos.environ['NO_ET'] = '1'\n\n_mriqc_dev: bool = os.getenv('MRIQC_DEV', '0').lower() in ('1', 'on', 'true', 'y', 'yes')\n\nif not hasattr(sys, '_is_pytest_session'):\n sys._is_pytest_session = False # Trick to avoid sklearn's FutureWarnings\n# Disable all warnings in main and children processes only on production versions\nif not any(\n (\n '+' in __version__,\n __version__.endswith('.dirty'),\n _mriqc_dev,\n )\n):\n os.environ['PYTHONWARNINGS'] = 'ignore'\n\n\nSUPPORTED_SUFFIXES: tuple[str, ...] = ('T1w', 'T2w', 'bold', 'dwi')\n\nDEFAULT_MEMORY_MIN_GB: float = 0.01\nDSA_MESSAGE: str = \"\"\"\\\nIMPORTANT: Anonymized quality metrics (IQMs) will be submitted to MRIQC's metrics \\\nrepository. \\\nSubmission of IQMs can be disabled using the ``--no-sub`` argument. \\\nPlease visit https://mriqc.readthedocs.io/en/latest/dsa.html to revise MRIQC's \\\nData Sharing Agreement.\"\"\"\n\n_exec_env: str = os.name\n_docker_ver: str | None = None\n# special variable set in the container\nif os.getenv('IS_DOCKER_8395080871'):\n _exec_env: str = 'singularity'\n _cgroup: Path = Path('/proc/1/cgroup')\n if _cgroup.exists() and 'docker' in _cgroup.read_text():\n _docker_ver = os.getenv('DOCKER_VERSION_8395080871')\n _exec_env = 'docker'\n del _cgroup\n\n_templateflow_home: Path = Path(\n os.getenv(\n 'TEMPLATEFLOW_HOME',\n os.path.join(os.getenv('HOME'), '.cache', 'templateflow'),\n )\n)\n\n_free_mem_at_start: int | None = None\nwith suppress(Exception):\n from psutil import virtual_memory\n\n _free_mem_at_start: int = round(virtual_memory().free / 1024**3, 1)\n\n_oc_limit: str = 'n/a'\n_oc_policy: str = 'n/a'\nwith suppress(Exception):\n # Memory policy may have a large effect on types of errors experienced\n _proc_oc_path: Path = Path('/proc/sys/vm/overcommit_memory')\n if _proc_oc_path.exists():\n _oc_policy: str = {'0': 'heuristic', '1': 'always', '2': 'never'}.get(\n _proc_oc_path.read_text().strip(), 'unknown'\n )\n if _oc_policy != 'never':\n _proc_oc_kbytes: Path = Path('/proc/sys/vm/overcommit_kbytes')\n if _proc_oc_kbytes.exists():\n _oc_limit: str = _proc_oc_kbytes.read_text().strip()\n if _oc_limit in ('0', 'n/a') and Path('/proc/sys/vm/overcommit_ratio').exists():\n _oc_limit: str = '{}%'.format(\n Path('/proc/sys/vm/overcommit_ratio').read_text().strip()\n )\n\n_memory_gb: float | None = None\n\nif 'linux' in sys.platform:\n with suppress(Exception):\n with open('/proc/meminfo') as f_in:\n _meminfo_lines: list[str] = f_in.readlines()\n _mem_total_line: str = [line for line in _meminfo_lines if 'MemTotal' in line][0]\n _mem_total: float = float(_mem_total_line.split()[1])\n _memory_gb = _mem_total / (1024.0**2)\nelif 'darwin' in sys.platform:\n from shutil import which\n from subprocess import check_output\n\n if _cmd := which('sysctl'):\n with suppress(Exception):\n _mem_str: str = check_output([_cmd, 'hw.memsize']).decode().strip().split(' ')[-1]\n _memory_gb: float = float(_mem_str) / (1024.0**3)\n\n# Check for FreeSurfer's SynthStrip model\n_fs_home: str | None = os.getenv('FREESURFER_HOME', None)\n_default_model_path: Path | None = (\n Path(_fs_home) / 'models' / 'synthstrip.1.pt' if _fs_home else None\n)\n\nif _fs_home and not _default_model_path.exists():\n _default_model_path = None\n\n# Override the unique ID if MRIQC_DEV is set\n_run_uuid: str = (\n '{}_{}'.format(strftime('%Y%m%d-%H%M%S'), uuid4())\n if not _mriqc_dev\n else '18480913-163000_PhineasG-ageh-adhi-sacc-ident9b1ab0f'\n)\n\n_mriqc_cache: Path = Path(\n os.getenv('MRIQC_CACHE_PATH', str(Path.home() / '.cache' / 'mriqc'))\n).absolute()\n\n\nclass _Config:\n \"\"\"An abstract class forbidding instantiation.\"\"\"\n\n _paths: tuple[str, ...] = ()\n _hidden: tuple[str, ...] = ()\n\n def __init__(self):\n \"\"\"Avert instantiation.\"\"\"\n raise RuntimeError('Configuration type is not instantiable.')\n\n @classmethod\n def load(cls, sections, init=True):\n \"\"\"Store settings from a dictionary.\"\"\"\n for k, v in sections.items():\n if v is None:\n continue\n if k in cls._paths:\n setattr(cls, k, Path(v).absolute())\n continue\n if hasattr(cls, k):\n setattr(cls, k, v)\n\n if init:\n try:\n cls.init()\n except AttributeError:\n pass\n\n @classmethod\n def get(cls) -> dict[str, Any]:\n \"\"\"Return defined settings.\"\"\"\n out: dict[str, str] = {}\n for k, v in cls.__dict__.items():\n if k.startswith('_') or v is None:\n continue\n if k in cls._hidden:\n continue\n if callable(getattr(cls, k)):\n continue\n if k in cls._paths:\n v = str(v)\n out[k] = v\n return out\n\n\nclass settings(_Config):\n \"\"\"Settings of this config module.\"\"\"\n\n file_path: Path | None = None\n \"\"\"Path to this configuration file.\"\"\"\n start_time: float | None = None\n \"\"\"A :obj:`~time.time` timestamp at the time the workflow is started.\"\"\"\n\n _paths: tuple[str, ...] = ('file_path',)\n\n\nclass environment(_Config):\n \"\"\"\n Read-only options regarding the platform and environment.\n\n Crawls runtime descriptive settings (e.g., default FreeSurfer license,\n execution environment, nipype and *MRIQC* versions, etc.).\n The ``environment`` section is not loaded in from file,\n only written out when settings are exported.\n This config section is useful when reporting issues,\n and these variables are tracked whenever the user does not\n opt-out using the ``--notrack`` argument.\n\n \"\"\"\n\n cache_path: Path = _mriqc_cache\n \"\"\"Path to the location of the cache directory.\"\"\"\n cpu_count: int = os.cpu_count()\n \"\"\"Number of available CPUs.\"\"\"\n exec_docker_version: str | None = _docker_ver\n \"\"\"Version of Docker Engine.\"\"\"\n exec_env: str = _exec_env\n \"\"\"A string representing the execution platform.\"\"\"\n free_mem: int | None = _free_mem_at_start\n \"\"\"Free memory at start.\"\"\"\n freesurfer_home: str | None = _fs_home\n \"\"\"Path to the *FreeSurfer* installation (from ``FREESURFER_HOME`` environment variable).\"\"\"\n overcommit_policy: str = _oc_policy\n \"\"\"Linux's kernel virtual memory overcommit policy.\"\"\"\n overcommit_limit: str = _oc_limit\n \"\"\"Linux's kernel virtual memory overcommit limits.\"\"\"\n nipype_version: str = get_version('nipype')\n \"\"\"Nipype's current version.\"\"\"\n synthstrip_path: Path | None = _default_model_path\n \"\"\"Path to *SynthStrip*'s model weights (requires *FreeSurfer*).\"\"\"\n templateflow_version: str = get_version('templateflow')\n \"\"\"The TemplateFlow client version installed.\"\"\"\n total_memory: float | None = _memory_gb\n \"\"\"Total memory available, in GB.\"\"\"\n version: str = __version__\n \"\"\"*MRIQC*'s version.\"\"\"\n _pre_mriqc: dict[str, str] = _pre_exec_env\n \"\"\"Environment variables before MRIQC's execution.\"\"\"\n\n\nclass nipype(_Config):\n \"\"\"Nipype settings.\"\"\"\n\n crashfile_format: str = 'txt'\n \"\"\"The file format for crashfiles, either text or pickle.\"\"\"\n get_linked_libs: bool = False\n \"\"\"Run NiPype's tool to enlist linked libraries for every interface.\"\"\"\n local_hash_check: bool = True\n \"\"\"Check if interface is cached locally before executing.\"\"\"\n memory_gb: float | str | None = None\n \"\"\"Estimation in GB of the RAM this workflow can allocate at any given time.\"\"\"\n nprocs: int = os.cpu_count()\n \"\"\"Number of processes (compute tasks) that can be run in parallel (multiprocessing only).\"\"\"\n omp_nthreads: int = _default_omp_threads\n \"\"\"Number of CPUs a single process can access for multithreaded execution.\"\"\"\n plugin: str = 'MultiProc'\n \"\"\"NiPype's execution plugin.\"\"\"\n plugin_args: dict[str, Any] = {\n 'maxtasksperchild': 1,\n 'raise_insufficient': False,\n }\n \"\"\"Settings for NiPype's execution plugin.\"\"\"\n remove_node_directories: bool = False\n \"\"\"Remove directories whose outputs have already been used up.\"\"\"\n resource_monitor: bool = False\n \"\"\"Enable resource monitor.\"\"\"\n stop_on_first_crash: bool = True\n \"\"\"Whether the workflow should stop or continue after the first error.\"\"\"\n\n @classmethod\n def get_plugin(cls) -> dict[str, Any]:\n \"\"\"Format a dictionary for Nipype consumption.\"\"\"\n out: dict[str, Any] = {\n 'plugin': cls.plugin,\n 'plugin_args': cls.plugin_args,\n }\n if cls.plugin in ('MultiProc', 'LegacyMultiProc'):\n out['plugin_args']['n_procs'] = int(cls.nprocs)\n if cls.memory_gb:\n out['plugin_args']['memory_gb'] = float(cls.memory_gb)\n return out\n\n @classmethod\n def init(cls) -> None:\n \"\"\"Set NiPype configurations.\"\"\"\n from nipype import config as ncfg\n\n # Nipype config (logs and execution)\n ncfg.update_config(\n {\n 'execution': {\n 'crashdump_dir': str(execution.log_dir),\n 'crashfile_format': cls.crashfile_format,\n 'get_linked_libs': cls.get_linked_libs,\n 'stop_on_first_crash': cls.stop_on_first_crash,\n }\n }\n )\n\n\nclass execution(_Config):\n \"\"\"Configure run-level settings.\"\"\"\n\n ants_float: bool = False\n \"\"\"Use float number precision for ANTs computations.\"\"\"\n bids_dir: str | os.PathLike | None = None\n \"\"\"An existing path to the dataset, which must be BIDS-compliant.\"\"\"\n bids_dir_datalad: bool = False\n \"\"\"Whether the input directory seems under DataLad's version control.\"\"\"\n bids_database_dir: str | os.PathLike | None = None\n \"\"\"Path to the directory containing SQLite database indices for the input BIDS dataset.\"\"\"\n bids_database_wipe: bool = False\n \"\"\"Wipe out previously existing BIDS indexing caches, forcing re-indexing.\"\"\"\n bids_description_hash: str | None = None\n \"\"\"Checksum (SHA256) of the ``dataset_description.json`` of the BIDS dataset.\"\"\"\n bids_filters: dict | None = None\n \"\"\"A dictionary describing custom BIDS input filter using PyBIDS.\"\"\"\n cwd: str = os.getcwd()\n \"\"\"Current working directory.\"\"\"\n datalad_get: bool = True\n \"\"\"\n If :obj:`~mriqc.config.execution.bids_dir` is a DataLad dataset, a ``datalad get`` command\n will be executed to ensure all remote files have been fetched.\n This behavior can be disabled with the command line option ``--no-datalad-get``.\n \"\"\"\n debug: bool = False\n \"\"\"Run in sloppy mode (meaning, suboptimal parameters that minimize run-time).\"\"\"\n dry_run: bool = False\n \"\"\"Just test, do not run.\"\"\"\n dsname: str = ''\n \"\"\"A dataset name used when generating files from the rating widget.\"\"\"\n echo_id: str | None = None\n \"\"\"Select a particular echo for multi-echo EPI datasets.\"\"\"\n float32: bool = True\n \"\"\"Cast the input data to float32 if it's represented with higher precision.\"\"\"\n layout: BIDSLayout | None = None\n \"\"\"A :py:class:`~bids.layout.BIDSLayout` object, see :py:func:`init`.\"\"\"\n log_dir: str | os.PathLike | None = None\n \"\"\"The path to a directory that contains execution logs.\"\"\"\n log_level: int | str = 25\n \"\"\"Output verbosity.\"\"\"\n modalities: Iterable[str] = None\n \"\"\"Filter input dataset by MRI type.\"\"\"\n no_sub: bool = False\n \"\"\"Turn off submission of anonymized quality metrics to Web API.\"\"\"\n notrack: bool = False\n \"\"\"Disable the sharing of usage information with developers.\"\"\"\n output_dir: str | os.PathLike | None = None\n \"\"\"Folder where derivatives will be stored.\"\"\"\n participant_label: Iterable[str] | None = None\n \"\"\"List of participant identifiers that are to be preprocessed.\"\"\"\n pdb: bool = False\n \"\"\"Drop into PDB when exceptions are encountered.\"\"\"\n reports_only: bool = False\n \"\"\"Only build the reports, based on the reportlets found in a cached working directory.\"\"\"\n resource_monitor: bool = False\n \"\"\"Enable resource monitor.\"\"\"\n run_id: str | None = None\n \"\"\"Filter input dataset by run identifier.\"\"\"\n run_uuid: str = _run_uuid\n \"\"\"Unique identifier of this particular run.\"\"\"\n session_id: str | None = None\n \"\"\"Filter input dataset by session identifier.\"\"\"\n task_id: str | None = None\n \"\"\"Select a particular task from all available in the dataset.\"\"\"\n templateflow_home: str | os.PathLike | None = _templateflow_home\n \"\"\"The root folder of the TemplateFlow client.\"\"\"\n upload_strict: bool = False\n \"\"\"Workflow will crash if upload is not successful.\"\"\"\n verbose_reports: bool = False\n \"\"\"Generate extended reports.\"\"\"\n webapi_token: str = ''\n \"\"\"Authorization token for the WebAPI service.\"\"\"\n webapi_url: str = 'https://mriqc.nimh.nih.gov:443/api/v1'\n \"\"\"IP address where the MRIQC WebAPI is listening.\"\"\"\n work_dir: Path = Path('work').absolute()\n \"\"\"Path to a working directory where intermediate results will be available.\"\"\"\n write_graph: bool = False\n \"\"\"Write out the computational graph corresponding to the planned preprocessing.\"\"\"\n\n _layout: BIDSLayout | None = None\n\n _paths: tuple[str, ...] = (\n 'anat_derivatives',\n 'bids_dir',\n 'bids_database_dir',\n 'fs_license_file',\n 'fs_subjects_dir',\n 'log_dir',\n 'output_dir',\n 'templateflow_home',\n 'work_dir',\n )\n\n _hidden: tuple[str, ...] = ('webapi_token',)\n\n @classmethod\n def init(cls) -> None:\n \"\"\"Create a new BIDS Layout accessible with :attr:`~execution.layout`.\"\"\"\n\n if cls.bids_filters is None:\n cls.bids_filters = {}\n\n # Process --run-id if the argument was provided\n if cls.run_id:\n for mod in cls.modalities:\n cls.bids_filters.setdefault(mod.lower(), {})['run'] = cls.run_id\n\n if cls._layout is None:\n import re\n\n from bids.layout import BIDSLayout\n from bids.layout.index import BIDSLayoutIndexer\n\n ignore_paths: list[Pattern] = [\n # Ignore folders at the top if they don't start with /sub-