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.

This package facilitates the cleaning, organization and interactive analysis of Model Intercomparison Projects (MIPs) within the Pangeo software stack.

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 xmip and xgcm.

Install xmip via pip:

pip install xmip

or conda:

conda install -c conda-forge xmip

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

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