Skip to main content

ibicus

pypi_release pypi_status PyPI license pypi_downloads docs PyPI pyversions made-with-python made-with-sphinx-doc Maintenance yes Ask Me Anything ! GitHub contributors

Ibicus provides a flexible and user-friendly toolkit for the bias correction of climate models and associated evaluation.

Ibicus implements a variety of methods for bias correction (8 currently) published in peer-reviewed literature, including ISIMIP (Lange 2019) and CDFt (Michelangeli et al. 2009) and provides a unified interface for their usage. The package enables the user to modify and refine their behavior with settings and parameters, and provides an evaluation framework to assess marginal, temporal, spatial, and multivariate properties of the bias corrected climate model.

Given future climate model data to debias (cm_future), climate model data during a reference period (cm_hist) and observational or reanalysis data during the same reference period (obs) running a debiaser is as easy as:

>>> from ibicus import CDFt
>>> debiaser = CDFt.from_variable("pr")
>>> debiased_cm_future = debiaser.apply(obs, cm_hist, cm_future)

Evaluating dry spell length can be as easy as:

>>> from ibicus.evaluate.metrics import dry_days
>>> spell_length = dry_days.calculate_spell_length(minimum_length: 4, obs = obs, raw = cm_future, ISIMIP = debiased_cm_future)

For more information on the usage have a look at our docs.

Install

Ibicus releases are available via PyPI. Just write:

pip install ibicus

For more information about installation and requirements see the install documentation in the docs.

Contact

If you have feedback on the package, suggestions for additions, questions you’d like to ask or would like to contribute, please contact us under ibicus.py@gmail.com. Similarly should you encounter bugs or issues using the package please open an issue. or write to us using the email adress above.

Metadata

Release files for ibicus 1.0.2

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

Source distribution (sdist)

Source distribution for ibicus 1.0.2
File Size Uploaded Provenance
ibicus-1.0.2.tar.gz 94.0 kB Without provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for ibicus 1.0.2
File Interpreter ABI Platform
ibicus-1.0.2-py3-none-any.whl Python 3 none any Details

Total release size: 216.4 kB

Release files / ibicus-1.0.2.tar.gz

Download URL ibicus-1.0.2.tar.gz
Size 94.0 kB
Tags Source
SHA-256 checksum
How to use checksums
0727c798012da53e19a99c7318fe99d01cb5b01515100ee76237a1ec55f95df4
BLAKE2b-256 checksum
How to use checksums
f0672fdf4093eb6acab6f34fe996a5eb89b9c7264ba4b1f82628d41576ae25a8
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.11.5

Release files / ibicus-1.0.2-py3-none-any.whl

Download URL ibicus-1.0.2-py3-none-any.whl
Size 122.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
bb637630972c49bc3421c1752472a03c0d25016e480b3b7c920192bda46d4c22
BLAKE2b-256 checksum
How to use checksums
ab2fed9b16c5da355c79bd0e4957028489ce644e32de93a44d19e79edfdd82bf
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.11.5

Security for ibicus 1.0.2

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