{"id":26172476,"url":"https://github.com/pauljwright/sdoml","last_synced_at":"2025-09-09T08:46:38.816Z","repository":{"id":44373567,"uuid":"512308363","full_name":"PaulJWright/sdoml","owner":"PaulJWright","description":"package for the sdoml dataset","archived":false,"fork":false,"pushed_at":"2023-11-21T13:44:17.000Z","size":66,"stargazers_count":3,"open_issues_count":5,"forks_count":1,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-28T08:35:41.618Z","etag":null,"topics":["atmospheric-imaging-assembly","dataloader","deep-learning","heliophysics","machine-learning","solar-dynamics-observatory","solar-physics","sun"],"latest_commit_sha":null,"homepage":"https://sdoml.readthedocs.io/en/latest","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/PaulJWright.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":"licenses/LICENSE.rst","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2022-07-09T23:55:50.000Z","updated_at":"2023-11-21T13:44:31.000Z","dependencies_parsed_at":"2023-11-21T14:49:10.246Z","dependency_job_id":null,"html_url":"https://github.com/PaulJWright/sdoml","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PaulJWright%2Fsdoml","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PaulJWright%2Fsdoml/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PaulJWright%2Fsdoml/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PaulJWright%2Fsdoml/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/PaulJWright","download_url":"https://codeload.github.com/PaulJWright/sdoml/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248952361,"owners_count":21188434,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["atmospheric-imaging-assembly","dataloader","deep-learning","heliophysics","machine-learning","solar-dynamics-observatory","solar-physics","sun"],"created_at":"2025-03-11T19:56:39.565Z","updated_at":"2025-04-14T20:21:35.865Z","avatar_url":"https://github.com/PaulJWright.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"SDOML dataset\n---------------------\n\n.. image:: https://codecov.io/gh/PaulJWright/sdoml/branch/develop/graph/badge.svg?token=FUURJWMEEU\n    :target: https://codecov.io/gh/PaulJWright/sdoml\n\n.. image:: https://readthedocs.org/projects/sdoml/badge/?version=latest\n    :target: https://sdoml.readthedocs.io/en/latest/?badge=latest\n    :alt: Documentation Status\n\n.. image:: https://dl.circleci.com/status-badge/img/gh/PaulJWright/sdoml/tree/develop.svg?style=shield\n    :target: https://dl.circleci.com/status-badge/redirect/gh/PaulJWright/sdoml/tree/develop\n    :alt: CICD Status\n\n.. image:: https://mybinder.org/badge_logo.svg\n :target: https://mybinder.org/v2/gh/pauljwright/sdoml/develop?urlpath=https%3A%2F%2Fgithub.com%2FPaulJWright%2Fsdoml%2Ftree%2Fdevelop%2Fexamples%2Fdataset\n\nSDOML is an open-source package for working with the SDOML Dataset (`sdoml.org \u003chttps://sdoml.org\u003e`_).\n\n⚠️ **This package is in development. Currently, it is intended for internal use only. The syntax is subject to change, and the documentation is incomplete.** ⚠️\n\nInstallation\n------------\n\nIf you'd like to help develop the SDOML package, or just want to try out the package, you will need to install it from GitHub. The best way to do this is to create a new python virtual environment (either with pipenv or conda). Once you have that virtual environment:\n\n.. code:: bash\n\n  $ git clone https://https://github.com/PaulJWright/sdoml.git\n  $ cd sdoml\n  $ pip install -e .\n\n\nTo install the optional extras required for testing, this can be performed with pip as below (for ``bash``, and ``zsh``)\n\n.. code:: bash\n\n  $ pip install -e .[test]\n\n.. code:: zsh\n\n  ~ pip install -e '.[test]'\n\n.. If you would like to access and use the data stored on the Google Cloud Platform, you may need to install the Google Cloud Command Line Interface (`gcloud CLI \u003chttps://cloud.google.com/sdk/docs/install\u003e`_).\n.. After install, you may need to run the following commands:\n\n.. .. code:: bash\n\n..   gcloud init\n..   gcloud auth application-default login\n\n\npytest and coverage\n-------------------\n\n.. code:: python\n\n  coverage run -m pytest\n  coverage html\n\ncircleci\n--------\n\nYou can run circleci locally (https://circleci.com/docs/local-cli/), with brew, for example:\n\n.. code:: zsh\n\n  brew install circleci\n\nand validate the file with\n\n.. code:: zsh\n\n  circleci config validate\n\nLicense\n-------\n\nThis project is Copyright (c) Paul J. Wright and licensed under\nthe terms of the Apache Software License 2.0 license. This package is based upon\nthe `Openastronomy packaging guide \u003chttps://github.com/OpenAstronomy/packaging-guide\u003e`_\nwhich is licensed under the BSD 3-clause licence. See the licenses folder for\nmore information.\n\n\nContributing\n------------\n\nWe love contributions! sdoml is open source,\nbuilt on open source, and we'd love to have you hang out in our community.\n\n**Imposter syndrome disclaimer**: We want your help. No, really.\n\nThere may be a little voice inside your head that is telling you that you're not\nready to be an open source contributor; that your skills aren't nearly good\nenough to contribute. What could you possibly offer a project like this one?\n\nWe assure you - the little voice in your head is wrong. If you can write code at\nall, you can contribute code to open source. Contributing to open source\nprojects is a fantastic way to advance one's coding skills. Writing perfect code\nisn't the measure of a good developer (that would disqualify all of us!); it's\ntrying to create something, making mistakes, and learning from those\nmistakes. That's how we all improve, and we are happy to help others learn.\n\nBeing an open source contributor doesn't just mean writing code, either. You can\nhelp out by writing documentation, tests, or even giving feedback about the\nproject (and yes - that includes giving feedback about the contribution\nprocess). Some of these contributions may be the most valuable to the project as\na whole, because you're coming to the project with fresh eyes, so you can see\nthe errors and assumptions that seasoned contributors have glossed over.\n\nNote: This disclaimer was originally written by\n`Adrienne Lowe \u003chttps://github.com/adriennefriend\u003e`_ for a\n`PyCon talk \u003chttps://www.youtube.com/watch?v=6Uj746j9Heo\u003e`_, and was adapted by\nsdoml based on its use in the README file for the\n`MetPy project \u003chttps://github.com/Unidata/MetPy\u003e`_.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpauljwright%2Fsdoml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpauljwright%2Fsdoml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpauljwright%2Fsdoml/lists"}