xmip 0.7.2
Analysis ready CMIP6 data the easy way
License: Apache
Requires Python: >=3.8

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![BLM](BLM.png)

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](https://secure.actblue.com/donate/cmip6_preprocessing) to [Data for Black Lives](http://d4bl.org/) and [Black Lives Matter Action](https://blacklivesmatter.com/global-actions/).

I explicitly welcome suggestions regarding the wording of this statement and for additional organizations to support. Please raise an [issue](https://github.com/jbusecke/xmip/issues) for suggestions.



# xmip (formerly cmip6_preprocessing)

This package facilitates the cleaning, organization and interactive analysis of Model Intercomparison Projects (MIPs) within the [Pangeo](https://pangeo.io) 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](https://cmip6hack.github.io/#/) this package provides utility functions that play nicely with [intake-esm](https://github.com/NCAR/intake-esm).

We currently support the following functions

1. Preprocessing CMIP6 data (Please check out the [tutorial](docs/tutorial.ipynb) for some examples using the [pangeo cloud](ocean.pangeo.io)). 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](docs/regionmask.ipynb)

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](https://xgcm.readthedocs.io/en/latest/)

Check out this recent Earthcube [notebook](https://github.com/earthcube2020/ec20_busecke_etal) (cite via doi: [10.1002/essoar.10504241.1](https://www.essoar.org/doi/10.1002/essoar.10504241.1)) for a high level demo of `xmip` and [xgcm](https://github.com/xgcm/xgcm).


## Installation

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`