pynacolada 0.0.2

Process netcdf data.

Just imagine that your tons of terrabytes of data can be processed in (parametric) space or time (or combination) with only a few (readable!) lines of code waiting only maybe just one night. For this task, we would obviously fall back to tools like cdo or ncl. However, we are stuck to the data analysis options that these provide. That’s where pynacolada comes into play. We can now apply any arbitrary pre-defined or user-defined function analysis on a huge dataset, and with great performance (from the numpy/matlib library), and very simply.

The power of tools lies in their examples. Typical usage of the package, in particular ncmultifunc, looks like this:

 #!/usr/bin/env python
 # 2013-11-08: Below, you find  just a code snippet from the first tests.. Examples will be added very soon
#print fnout

 # Example 1: calculate the mean scalar wind speed of the first 10 layers
 import pynacolada as pcl
 from Scientific.IO import NetCDF
 import os
 import numpy as np
 import pylab as pl

 fnin = 'laf2009010100.nc'
 #print fnin
 # fobjin = open(fnin,'rb')
 fin = NetCDF.NetCDFFile(fnin,'r')
 fnout = 'laf2009010100_out.nc'
 os.system('rm '+fnout)
 #print fnout
 # fobjout = open(fnout,'wb+''rlat')
 fout = NetCDF.NetCDFFile(fnout,'w')
 # input data definitions
 datin =  [{'file': fin, \
            'varname': 'U', \
            'dsel': {'level' : range(30,40,1)}, \
            'daliases': { 'srlat':'rlat', 'srlon':'rlon' },\
           },\
           {'file': fin, \
            'varname':'V', \
            'dsel': {'level' : range(30,40,1)},
            'daliases': { 'srlat':'rlat', 'srlon':'rlon' },\
            }\
          ]
 # output data definitions
 datout = [{'file': fout, \
            'varname': 'u'}]
 # function definition:
 func = lambda U,V: np.array([np.mean(np.sqrt(U**2+V**2),axis=0 )])
 dnamsel = ['level',]
 pcl.pcl(func,dnamsel,datin,datout,appenddim=True)
 print 'output data written to:',fnout

A Section