Sacpy: Statistic Analysis for Climate data in Python

Sacpy (/Sæc’pai/) is an effecient Statistical Analysis tool (e.g. regression, EOF, MCA) for Climate and Meteorology data. Sacpy aims to provide a universal framework for climate data analysis.

It provides a toolkit for

• Linear Regression (univariate and multivariate)

• Empirical Orthogonal Function (EOF)

• Maximum Covariance Analysis (MCA or SVD)

• Convenient Plots for Cartopy, Xarray

• Convenient Tools for Statistic Analysis

• Power spectrum analysis (under testing)

For more details, please refer to the User Guide linked below.

Quick Installation

Use pip for Installation.

pip install sacpy

Why Choose Sacpy?

Fast!

For example, Sacpy is more than 60 times faster than the traditional regression analysis with Python. The following is the time spent performing the same task. Sacpy is fastest.

Turn to climate data customization!

Compatible with commonly used meteorological calculation libraries such as numpy and xarray.

Concise code

You can finish drawing a following figure with just seven lines of code. see examples of concise.

Documentation

• GitHub: https://github.com/ZiluM/sacpy

• Pypi: https://pypi.org/project/sacpy/

• Install: https://zilum.github.io/sacpy/installation.html

User Guide

• Installation
  • Quick Install
  • Install Sacpy in the Conda environment
• Quick Start
  • import useful module
  • Load Data
  • Linear Regression and Plot
• Linear Regression
  • Univariate Linear Regression
  • Multivariate Linear Regression
• Empirical Orthogonal Function
  • Parameter
  • Method
  • Example
• Maximum Covariance Analysis
  • Parameter
  • Method
  • Example
• Tools for xarray
  • Get Specific Months Data and annual mean
  • Get Anomaly Data
• Mean Value T Test
  • STMV Class
  • one_mean_test
  • two_mean_test
• Plot Function
  • get_levels
  • GeoAxesSubplot.scontourf
  • GeoAxesSubplot.scontour
  • GeoAxesSubplot.sig_plot
  • GeoAxesSubplot.spcolormesh
  • GeoAxesSubplot.squive
  • GeoAxesSubplot.draw_ticks
  • GeoAxesSubplot.init_map
  • xr.Dataarray.splot
  • Plot Example
• Useful Example
  • The impact of ENSO on the climate in the second year
  • IOD’s impact after removing the impact of ENSO
  • Regress SSTA on Nino3.4 Index

Contact us

• Author: Zilu Meng

• e-mail: zilumeng@uw.edu

Acknowledgements

Thanks for Dr.Feng Zhu’s (https://fzhu2e.github.io) help in this project!