This code provides experimental and simples tools for differents operations on climate data, mainly obtaining climatologies and anomalies values, in addition to others operations such as data extraction from continent, ocean or a shapefile.
pip install clima_anom
clone clima_anom and install in exists or new conda env.
- Clone repo and install
git clone https://github.com/mhacarthur/clima_anom.git
cd clima_anom
pip install .
- Python >= 3.5
- cartopy >= 0.18.0
- netcdf4 >= 1.5.7
- pyshp >= 2.1.3
- OS: Linux
- How to install dependencies
# cartopy
conda install -c conda-forge cartopy
# netcdf4
conda install netcdf4
# pyshp
pip install pyshp
The data use for examples is in directory data. For complete data see:
TRMM Precipitation L3 daily 0.25x0.25 V7
import os
import clima_anom as ca
data_dir = '..'+os.sep+'data'+os.sep+'3B42_199901_201212.nc'
data = ca.read_netcdf(data_dir,2)
lat = data['lat']
lon = data['lon']
pre = data['prec']
pre_dictionary = ca.data_dictionary(pre)
import clima_anom as ca
import matplotlib.pyplot as plt
cmap = plt.cm.Spectral_r
cmap_midle_white = ca.colorbar_middle_white(cmap,'middle')
import numpy as np
import clima_anom as ca
data_dir = '..'+os.sep+'data'+os.sep+'3B42_199901_201212_climatology.nc'
data = ca.read_netcdf(data_dir,2)
lat = data['lat']
lon = data['lon']
pre = data['pre']
pre_continent = ca.remove_continent_ocean(pre,lat,lon,'continent')
pre_ocen = ca.remove_continent_ocean(pre,lat,lon,'ocean')
import clima_anom as ca
import cartopy.io.shapereader as shpreader
file_shape = '..'+os.sep+'shapefile'+os.sep+''+os.sep+'Amazonas.shp'
amazonas = list(shpreader.Reader(file_shape).geometries())
data_dir = '..'+os.sep+'data'+os.sep+'3B42_199901_201212_climatology.nc'
data = ca.read_netcdf(data_dir,2)
lat = data['lat']
lon = data['lon']
pre = data['pre']
pre_amazonas = ca.extract_shapefile('..'+os.sep+'shapefile'+os.sep+'Amazonas.shp',pre,lat,lon,0)