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 : mzll1202@163.com

version: 0.0.6

## Why choose Sacpy?

### Quick!

For example, Sacpy is more than 60 times faster than the traditional regression analysis with Python (see **speed test**).

### Turn to climate data customization!

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

## Install

```
pip install sacpy
```

## Example

### example1

Calculate the correlation between SST and nino3.4 index

```
import numpy as np
import scapy as scp
import matplotlib.pyplot as plt
# 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
plt.contourf(linreg.corr)
# Significance test
plt.contourf(linreg.p_value,levels=[0, 0.05, 1],zorder=1,
hatches=['..', None],colors="None",)
# save
plt.savefig("./nino34.png")
```

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

### example2

multiple linear regression on Nino3.4 IODIdex and ssta pattern

```
import numpy as np
import scapy as scp
import matplotlib.pyplot as plt
# 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))
# calculate IODIdex
IODW = ssta.loc[:,-10:10,50:70].mean(axis=(1,2))
IODE = ssta.loc[:,-10:0,90:110].mean(axis=(1,2))
IODI = +IODW - IODE
# get x
X = np.vstack([np.array(Nino34),np.array(IODI)]).T
# multiple linear regression
MLR = scp.MultLinReg(X,np.array(ssta))
# plot IOD's effect
plt.contourf(MLR.slope[1])
# Significance test
plt.contourf(MLR.pv_i[1],levels=[0, 0.1, 1],zorder=1,
hatches=['..', None],colors="None",)
plt.savefig("../pic/MLR.png")
```

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

## Speed

As a comparison, we use the function **corr** function in the xarray library and **for-loop**. The time required to calculate the correlation coefficient between SSTA and nino3.4 for 50 times is shown in the figure below.

It can be seen that we are five times faster than xarray and 60 times faster than forloop.

Moreover, xarray will not return the **p value**. We can simply check the pvalue attribute of sacpy to get the p value.

## Acknowledgements

Thank Prof. Feng Zhu (NUIST,https://github.com/fzhu2e) for his guidance of this project

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