Skip to main content

Analysis ready CMIP6 data the easy way

Project description

Documentation Status Anaconda Cloud conda-forge Pypi Build Status Full Archive CI codecov License:MIT DOI

BLM

Science is not immune to racism. Academia is an elitist system with numerous gatekeepers that has mostly allowed a very limited spectrum of people to pursue a career. I believe we need to change that.

Open source development and reproducible science are a great way to democratize the means for scientific analysis. But you can't git clone software if you are being murdered by the police for being Black!

Free access to software and hollow diversity statements are hardly enough to crush the systemic and institutionalized racism in our society and academia.

If you are using this package, I ask you to go beyond just speaking out and donate here to Data for Black Lives and Black Lives Matter Action.

I explicitly welcome suggestions regarding the wording of this statement and for additional organizations to support. Please raise an issue for suggestions.

cmip6_preprocessing

Are you interested in CMIP6 data, but find that is is not quite analysis ready? Do you just want to run a simple (or complicated) analysis on various models and end up having to write logic for each seperate case, because various datasets still require fixes to names, coordinates, etc.? Then this package is for you.

Developed during the cmip6-hackathon this package provides utility functions that play nicely with intake-esm.

We currently support the following functions

  1. Preprocessing CMIP6 data (Please check out the tutorial for some examples using the pangeo cloud). The preprocessig includes: a. Fix inconsistent naming of dimensions and coordinates b. Fix inconsistent values,shape and dataset location of coordinates c. Homogenize longitude conventions d. Fix inconsistent units
  2. Creating large scale ocean basin masks for arbitrary model output

The following issues are under development:

  1. Reconstruct/find grid metrics
  2. Arrange different variables on their respective staggered grid, so they can work seamlessly with xgcm

Check out this recent Earthcube notebook (cite via doi: 10.1002/essoar.10504241.1) for a high level demo of cmip6_preprocessing and xgcm.

Installation

Install cmip6_preprocessing via pip:

pip install cmip6_preprocessing

or conda:

conda install -c conda-forge cmip6_preprocessing

To install the newest master from github you can use pip aswell:

pip install git+pip install git+https://github.com/jbusecke/cmip6_preprocessing.git

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for cmip6-preprocessing, version 0.3.2
Filename, size File type Python version Upload date Hashes
Filename, size cmip6_preprocessing-0.3.2-py3-none-any.whl (40.4 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size cmip6_preprocessing-0.3.2.tar.gz (7.1 MB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page