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A repaid Statistical Analysis tool for Climate or Meteorology data.

Project description

SACPY -- A Python Package for Statistical Analysis of Climate

Sacpy, a repaid Statistical Analysis tool for Climate or Meteorology data.

Author : Zilu Meng

e-mail :

Why choose Sacpy?


For example, Sacpy is more than 40 times faster than the traditional regression analysis with Python (see example 1).

Turn to climate data customization!

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


    pip install sacpy


Calculate the correlation between SST and nino3.4 index

import numpy as np
import scapy as scp
# load sst
sst = scp.load_sst()['sst']
# get ssta (method=1, Remove linear trend;method=0, Minus multi-year average)
ssta = scp.get_anom(sst,method=1)
# calculate Nino3.4
Nino34 = ssta.loc[:,-5:5,190:240].mean(axis=(1,2))
# regression
linreg = scp.LinReg(np.array(Nino34),np.array(ssta))
# plot
# Significance test
plt.contourf(linreg.p_value,levels=[0, 0.05, 1],zorder=1,
            hatches=['..', None],colors="None",)
# save

Result(For a detailed drawing process, see example):


As example, if we use conventional for-loop to finish it, it will take 40 times more time (see example).

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