{"id":13477338,"url":"https://github.com/SebastianBocquet/pygtc","last_synced_at":"2025-03-27T05:31:27.011Z","repository":{"id":41490354,"uuid":"63821488","full_name":"SebastianBocquet/pygtc","owner":"SebastianBocquet","description":"Make a sweet giant triangle confusogram (GTC) plot","archived":false,"fork":false,"pushed_at":"2023-10-12T06:29:32.000Z","size":27362,"stargazers_count":34,"open_issues_count":6,"forks_count":9,"subscribers_count":6,"default_branch":"master","last_synced_at":"2025-03-06T08:11:43.514Z","etag":null,"topics":["bayesian-data-analysis","data-visualization","mcmc"],"latest_commit_sha":null,"homepage":"http://pygtc.readthedocs.io/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/SebastianBocquet.png","metadata":{"files":{"readme":"README.rst","changelog":"CHANGELOG.rst","contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2016-07-20T23:27:33.000Z","updated_at":"2024-10-18T12:15:48.000Z","dependencies_parsed_at":"2024-01-13T19:18:45.564Z","dependency_job_id":"0ea937da-0e5f-4ea4-a3bd-abaeebd1dcef","html_url":"https://github.com/SebastianBocquet/pygtc","commit_stats":{"total_commits":334,"total_committers":4,"mean_commits":83.5,"dds":0.4251497005988024,"last_synced_commit":"c8ad284457f818b55ce87307540ab3a7faa52773"},"previous_names":[],"tags_count":11,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SebastianBocquet%2Fpygtc","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SebastianBocquet%2Fpygtc/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SebastianBocquet%2Fpygtc/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SebastianBocquet%2Fpygtc/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SebastianBocquet","download_url":"https://codeload.github.com/SebastianBocquet/pygtc/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245791492,"owners_count":20672665,"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":["bayesian-data-analysis","data-visualization","mcmc"],"created_at":"2024-07-31T16:01:41.291Z","updated_at":"2025-03-27T05:31:25.263Z","avatar_url":"https://github.com/SebastianBocquet.png","language":"Jupyter Notebook","funding_links":[],"categories":["Jupyter Notebook"],"sub_categories":[],"readme":"pygtc.py\n=========\n\n**What is a Giant Triangle Confusogram?**\n\nA Giant-Triangle-Confusogram (GTC, aka triangle plot) is a way of\ndisplaying the results of a Monte-Carlo Markov Chain (MCMC) sampling or similar\nanalysis. (For a discussion of MCMC analysis, see the excellent ``emcee``\npackage.) The recovered parameter constraints are displayed on a grid in which\nthe diagonal shows the one-dimensional posteriors (and, optionally, priors) and\nthe lower-left triangle shows the pairwise projections. You might want to look\nat a plot like this if you are fitting a model to data and want to see the\nparameter covariances along with the priors.\n\nHere's an example of a GTC with some random data and arbitrary labels::\n\n  pygtc.plotGTC(chains=[samples1,samples2],\n                paramNames=names,\n                chainLabels=chainLabels,\n                truths=truths,\n                truthLabels=truthLabels,\n                priors=priors,\n                paramRanges=paramRanges,\n                figureSize='MNRAS_page')\n\n.. image:: https://raw.githubusercontent.com/SebastianBocquet/pygtc/master/docs/_static/demo_files/demo_9_1.png\n\n**But doesn't this already exist in corner.py, distUtils, etc...?**\n\nAlthough several other packages exists to make such a plot, we were unsatisfied\nwith the amount of extra work required to massage the result into something we\nwere happy to publish. With ``pygtc``, we hope to take that extra legwork out of\nthe equation by providing a package that gives a figure that is publication\nready on the first try! You should try all the packages and use the one you like\nmost; for us, that is ``pygtc``!\n\nInstallation\n------------\nFor a quick start, you can install with either ``pip`` or ``conda``. Either will install the required\ndependencies for you (``packaging``, ``numpy``, and ``matplotlib``)::\n\n  $ pip install pygtc\n\nor, if you use ``conda``::\n\n  $ conda install pygtc -c conda-forge\n\nFor more installation details, see the `documentation \u003chttp://pygtc.readthedocs.io/\u003e`_.\n\nDocumentation\n-------------\nDocumentation is hosted at `ReadTheDocs \u003chttp://pygtc.readthedocs.io/\u003e`_. Find\nan exhaustive set of examples there!\n\nCitation\n--------\nIf you use pygtc to generate plots for a publication, please cite as::\n\n  @article{Bocquet2016,\n    doi = {10.21105/joss.00046},\n    url = {http://dx.doi.org/10.21105/joss.00046},\n    year  = {2016},\n    month = {oct},\n    publisher = {The Open Journal},\n    volume = {1},\n    number = {6},\n    author = {Sebastian Bocquet and Faustin W. Carter},\n    title = {pygtc: beautiful parameter covariance plots (aka. Giant Triangle Confusograms)},\n    journal = {The Journal of Open Source Software}\n  }\n\n\nCopyright 2016, Sebastian Bocquet and Faustin W. Carter\n\n.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.159091.svg\n   :target: https://doi.org/10.5281/zenodo.159091\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FSebastianBocquet%2Fpygtc","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FSebastianBocquet%2Fpygtc","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FSebastianBocquet%2Fpygtc/lists"}