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pygtc

pygtc: beautiful parameter covariance plots (aka. Giant Triangle Confusograms) - Published in JOSS (2016)

https://github.com/sebastianbocquet/pygtc

Science Score: 95.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 8 DOI reference(s) in README and JOSS metadata
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ✓
    Committers with academic emails
    1 of 5 committers (20.0%) from academic institutions
  • ○
    Institutional organization owner
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    JOSS paper metadata
    Published in Journal of Open Source Software

Keywords

bayesian-data-analysis data-visualization mcmc

Scientific Fields

Engineering Computer Science - 40% confidence
Last synced: 6 months ago · JSON representation

Repository

Make a sweet giant triangle confusogram (GTC) plot

Basic Info
  • Host: GitHub
  • Owner: SebastianBocquet
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage: http://pygtc.readthedocs.io/
  • Size: 26.1 MB
Statistics
  • Stars: 34
  • Watchers: 5
  • Forks: 9
  • Open Issues: 6
  • Releases: 9
Topics
bayesian-data-analysis data-visualization mcmc
Created over 9 years ago · Last pushed over 2 years ago
Metadata Files
Readme Changelog License

README.rst 

pygtc.py
=========

**What is a Giant Triangle Confusogram?**

A Giant-Triangle-Confusogram (GTC, aka triangle plot) is a way of
displaying the results of a Monte-Carlo Markov Chain (MCMC) sampling or similar
analysis. (For a discussion of MCMC analysis, see the excellent ``emcee``
package.) The recovered parameter constraints are displayed on a grid in which
the diagonal shows the one-dimensional posteriors (and, optionally, priors) and
the lower-left triangle shows the pairwise projections. You might want to look
at a plot like this if you are fitting a model to data and want to see the
parameter covariances along with the priors.

Here's an example of a GTC with some random data and arbitrary labels::

  pygtc.plotGTC(chains=[samples1,samples2],
                paramNames=names,
                chainLabels=chainLabels,
                truths=truths,
                truthLabels=truthLabels,
                priors=priors,
                paramRanges=paramRanges,
                figureSize='MNRAS_page')

.. image:: https://raw.githubusercontent.com/SebastianBocquet/pygtc/master/docs/_static/demo_files/demo_9_1.png

**But doesn't this already exist in corner.py, distUtils, etc...?**

Although several other packages exists to make such a plot, we were unsatisfied
with the amount of extra work required to massage the result into something we
were happy to publish. With ``pygtc``, we hope to take that extra legwork out of
the equation by providing a package that gives a figure that is publication
ready on the first try! You should try all the packages and use the one you like
most; for us, that is ``pygtc``!

Installation
------------
For a quick start, you can install with either ``pip`` or ``conda``. Either will install the required
dependencies for you (``packaging``, ``numpy``, and ``matplotlib``)::

  $ pip install pygtc

or, if you use ``conda``::

  $ conda install pygtc -c conda-forge

For more installation details, see the `documentation `_.

Documentation
-------------
Documentation is hosted at `ReadTheDocs `_. Find
an exhaustive set of examples there!

Citation
--------
If you use pygtc to generate plots for a publication, please cite as::

  @article{Bocquet2016,
    doi = {10.21105/joss.00046},
    url = {http://dx.doi.org/10.21105/joss.00046},
    year  = {2016},
    month = {oct},
    publisher = {The Open Journal},
    volume = {1},
    number = {6},
    author = {Sebastian Bocquet and Faustin W. Carter},
    title = {pygtc: beautiful parameter covariance plots (aka. Giant Triangle Confusograms)},
    journal = {The Journal of Open Source Software}
  }


Copyright 2016, Sebastian Bocquet and Faustin W. Carter

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.159091.svg
   :target: https://doi.org/10.5281/zenodo.159091

Owner

  • Name: Sebastian Bocquet
  • Login: SebastianBocquet
  • Kind: user
  • Location: Munich
  • Company: Ludwig-Maximilians-Universität München (LMU Munich)

Astrophysicist at LMU Munich

JOSS Publication

pygtc: beautiful parameter covariance plots (aka. Giant Triangle Confusograms)
Published
October 08, 2016
DOI
Volume 1, Issue 6, Page 46
Authors
Sebastian Bocquet ORCID
Argonne National Laboratory
Faustin W. Carter ORCID
Argonne National Laboratory
Editor
Arfon Smith ORCID
Tags
data analysis visualization
View PDF Review Thread Software Archive

GitHub Events

Total
  • Watch event: 2
Last Year
  • Watch event: 2

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 341
  • Total Committers: 5
  • Avg Commits per committer: 68.2
  • Development Distribution Score (DDS): 0.422
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Faustin Carter f****r@g****m 197
Sebastian Bocquet s****t@g****m 100
Sebastian Bocquet s****t@a****v 41
samueldmcdermott s****t@g****m 2
Jonathan Fraine e****r 1
Committer Domains (Top 20 + Academic)
anl.gov: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 19
  • Total pull requests: 16
  • Average time to close issues: 7 months
  • Average time to close pull requests: 3 months
  • Total issue authors: 12
  • Total pull request authors: 5
  • Average comments per issue: 1.63
  • Average comments per pull request: 1.94
  • Merged pull requests: 15
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • FaustinCarter (7)
  • abmantz (2)
  • IhCosmo (1)
  • Joefva (1)
  • flo1984 (1)
  • bikramATastro (1)
  • samueldmcdermott (1)
  • nesar (1)
  • exowanderer (1)
  • ebachelet (1)
  • joergdietrich (1)
  • SebastianBocquet (1)
Pull Request Authors
  • SebastianBocquet (7)
  • FaustinCarter (6)
  • samueldmcdermott (1)
  • alulujasmine (1)
  • exowanderer (1)
Top Labels
Issue Labels
enhancement (3)
Pull Request Labels

Packages

  • Total packages: 2
  • Total downloads:
    • pypi 345 last-month
  • Total dependent packages: 1
    (may contain duplicates)
  • Total dependent repositories: 5
    (may contain duplicates)
  • Total versions: 14
  • Total maintainers: 1
pypi.org: pygtc

Make an awesome giant triangle confusogram (gtc)!

  • Versions: 12
  • Dependent Packages: 1
  • Dependent Repositories: 5
  • Downloads: 345 Last month
Rankings
Dependent packages count: 4.7%
Dependent repos count: 6.7%
Average: 9.3%
Downloads: 11.0%
Stargazers count: 11.4%
Forks count: 12.6%
Maintainers (1)
faustin315
Last synced: 6 months ago
conda-forge.org: pygtc
  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent repos count: 34.0%
Stargazers count: 42.0%
Average: 43.3%
Forks count: 46.0%
Dependent packages count: 51.2%
Last synced: 6 months ago

Dependencies

requirements.txt pypi
  • matplotlib >=1.5.3
  • numpy >=1.5
  • packaging *
setup.py pypi
  • packaging *
.github/workflows/pythonpackage.yml actions
  • actions/checkout v2 composite
  • conda-incubator/setup-miniconda v2 composite