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Copy file name to clipboardExpand all lines: CONTRIBUTING.md
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where `{width}` and `{height}` are the values already present in the existing `width` and `height` XML attributes.
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- Load SVGs in HTML `object` elements with the `raw:: html` directive to preserve hyperlinks and scaling:
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- Embed SVGs with the `raw:: html` directive to preserve hyperlinks. Since the `svg-flowchart` container scales SVGs to the page width, add a link below to view them at their original scale:
Copy file name to clipboardExpand all lines: docs/_sources/developer/continuous_integration.rst
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Our continous integration flow relies on GitHub Actions, GitHub Container Registry, CircleCI and Docker Hub. GitHub Actions builds staging images based on changes to Dockerfiles and pushes those images to GitHub Container Registry. On each push, once staging images are built or determined to be up to date, GitHub Actions installs C-PAC into a development image for each variant (standard, lite, fMRIPrep-LTS, and ABCD-HCP). CircleCI runs tests in both Docker and Singularity for each of these images and generates a coverage report if all tests pass. On release, CircleCI also pushes production images to Docker Hub.
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Most C-PAC logging is handled by the built-in Python ``logging`` library. When logging, take care to choose an appropriate ``getLogger`` function or method. While :py:func:`CPAC.utils.monitoring.custom_logging.getLogger` and ``nipype.utils.logger.Logging.getLogger`` each fall back on ``logging.getLogger``,
C-PAC uses `FSL/FAST <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FAST>`_ to automatically segment brain images into white matter, gray matter, and CSF. This is done using probability maps that contain information about the likelihood that a given voxel will be of a particular tissue type. Users specify a probability threshold such that voxels meeting a minimum probability of being a particular tissue will be classified as such. This results in masks containing voxels of only a single tissue type.
Copy file name to clipboardExpand all lines: docs/_sources/user/compute_config.rst
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#. **Observed Usage:** The resource usage of each node depends on many factors, including the data, the pipeline configuration, and the system. To run many subjects with optimimzed resource estimation, first run a single subject with the desired number of cores and with a generous memory limit. Then, provide the ``callback.log`` generated from that initial run when you run the rest of the subjects.
#. **Callback log - [text]:** The path to a callback log file from a previous run, including any resource-management parameters that will be applied in this run, like ``n_cpus`` and ``num_ants_threads``. This file is used override memory estimates with previously observed memory usage. Can be overridden with the commandline flag ``--runtime_usage``.
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#. **Buffer - [percent]:** A percent of the previously observed memory usage that is to be added to the memory estimate. Default: 10. Can be overridden with the commandline flag ``--runtime_buffer``.
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