Skip to main content

Copulogram

This package is provides a new data visualisation tool to explore multivariate datasets developped with V.Chabridon.

A copulogram is an innovative plot as it decomposes a mutivariate dataset between the effects of the marginals and those of the dependence between features. To do so, it represents the marginals with univariate kernel density estimation plots or histograms (diagonal), and the dependence structure with scatter plots in the ranked space (upper triangle). On the bottom triangle the scatter plots are set in the physical space, gathering the effects of the marginals and the dependencies. Since the dependence structure is theoretically modeled by an underlying copula, this plot is called copulogram, generalizing the well-known ``correlogram'' to nonlinear dependencies. It gives a synthetic and empirical decomposition of the dataset.

Copulogram of a wind-wave dataset

Copulogram of wind-waves dataset

Installation

The following commands install the current version of the copulogram package.

~$ pip install copulogram

Example on iris dataset

Using the famous iris dataset, let us plot copulograms with different settings:

>>> import seaborn as sns
>>> import copulogram as cp

>>> data = sns.load_dataset('iris')
>>> copulogram = cp.Copulogram(data)
>>> copulogram.draw()
Copulogram of iris dataset
>>> copulogram.draw(alpha=0.8, hue='species', kde_on_marginals=False)
Copulogram of iris dataset
>>> copulogram.draw(hue='species', quantile_contour_levels=[0.2, 0.4, 0.6, 0.8])
Copulogram of iris dataset

References

  • Empirical Bernstein copula: Sancetta, A., & Satchell, S. (2004). The Bernstein Copula and Its Applications to Modeling and Approximations of Multivariate Distributions. Econometric Theory, 20(3), 535–562.

  • Nonparametric copula estimation: Nagler, T., Schellhase, C. & Czado, C. (2017). Nonparametric estimation of simplified vine copula models: comparison of methods. Dependence Modeling, 5(1), 99-120.

  • OpenTURNS: Baudin, M., Lebrun, R., Iooss, B., Popelin, A.L. (2017). OpenTURNS: An Industrial Software for Uncertainty Quantification in Simulation.

  • Wind-waves environmental dataset: The data was generated by a numerical model from ANEMOC (Digital Atlas of Ocean and Coastal Sea States, see http://anemoc.cetmef.developpement-durable.gouv.fr/)

Metadata

Release files for copulogram 0.0.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for copulogram 0.0.4
File Size Uploaded Provenance Actions
copulogram-0.0.4.tar.gz 19.2 kB Without provenance Details for copulogram-0.0.4.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for copulogram 0.0.4
File Interpreter ABI Platform Actions
copulogram-0.0.4-py3-none-any.whl Python 3 none any Details for copulogram-0.0.4-py3-none-any.whl

Total release size: 39.3 kB

Release files / copulogram-0.0.4.tar.gz

Download URL copulogram-0.0.4.tar.gz
Size 19.2 kB
Tags Source
SHA-256 checksum
How to use checksums
d826a2cf553d42cccb30988f9c1adb54ec3a7368651789178147db29f4e00a6b
BLAKE2b-256 checksum
How to use checksums
1d3c30fb0af7d86cd2cf628530c0bda39986f1feab4ccfca63abe0efeb4fae0c
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.11.4

Release files / copulogram-0.0.4-py3-none-any.whl

Download URL copulogram-0.0.4-py3-none-any.whl
Size 20.1 kB
Tags Python 3
SHA-256 checksum
How to use checksums
ed1f2888606ddcf18c7b0738520d0a3809865344711926efad241ec52860027d
BLAKE2b-256 checksum
How to use checksums
0cc38c1f91f607813afce8f7adbf900b404e8491b4d361ed81775f961846e579
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.11.4

Security for copulogram 0.0.4

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page