Welcome to REAME pyClimat 👋

pyClimat is a python package for analysising GCM model output and visualization. The package is written in a function based (which would be pivoted to OPP style in future development). The analysis module features climate variable extraction and estimation of statistical long-term means and difference. Statistical tools like PCA or EOF analysis are included for specific estimates and many other classical methods like testing, OLS estiates, etc.

installation

The easy way to install is with pip install pyClimat (but would require some dependencies) The following packages must be installed in your environment : cartopy and xarray. If you failed to compile this in your environment, kindy raise an issue on that so I rebuild the distro for all systems if is not working. The stable verison should work on UNIX platforms for now

Alternatively, the package can be installed in editing mode with -e flag in edit mode

Documentation

The docs folder contains all the distribution files for the documentation compilation:

cd docs | make html to update the doc files

The direct stable link would be shared shortly (when a stable version of the package is released)

Examples

This package was adopted for all the visualization in the research study by Boateng et. al 2022 ( Impacts of surface uplift on regional climate, which is currently under review for publication in the Climate of the Past journal). The scripts specifically for that study are compiled in the Alps folder.

Gallery

• Andes precipitation for PreIndustrial time: can be adapted from the this script

• Oxygen isotope in precipitatiom comparsion to GNIP data

• NAO pattern expressed as the loading covariance of the EOF and the pressure anomalies field using rotated EOF analysis

Package structure

• pyClimat.data loads the climate model output (mostly the long-term means), however, xarray.open_dataset can be used to read data and use the other utilities
• pyClimat.analysis is used for data analysis such as means, anomalies, EOF analysis and extraction of some section of the data
• pyClimat.plot features many plotting functionalities such as spatial maps, profiles and scatter points (check the examples or the associated research papers for more hints)
• pyClima.stats consist of most of the statistical tools used in climate analysis. Eg. EOF or PCA analysis, correlation estimate using spearman or pearson, normality testing, and the various significant statistical testing tools

To do

Structure the modules in classes for easy scripting

Update the documentation for all the functions

Add more ploting styles and examples

Extend the functions to use **Basemap** and **pyGMT**

Host the documentation on readme.doc

⚡ Fun fact Happy coding and contact me if you have issues with the package

(c) Boateng Daniel