easy and customized Processing huge amounts of gridded Climate Data.

Software package for easy and customized Processing huge amounts of gridded Climate Data.

Imagine that your tons of terrabytes of data can be processed in space or time (or combination) with only a few readable(!) lines of code. On the one hand, we would obviously fall back to tools like cdo or ncl for such tasks. However, we are stuck to the limited data analysis options that these provide. On the other hand, scientific software packages like Matlab, R and IDL (and also Python of course!) provide very customizable climate data analysis tools, yet they have severe memory restrictions due to the limited RAM in computers. They also don’t allow a quick moviewize visualization of such large data sets, simply because they cannot load all those ‘terrabytes’ into memory at once. The PynaColaDa-tool now provides the best of both worlds: it allows to perform any arbitrary pre-defined or user-defined custom function/analysis on a massively HUGE dataset very easily, with great performance (interfacing with the numpy library), and WITHOUT the memory restrictions! As it both directly ‘reads from’ and ‘writes to’ NetCDF format, it allows to easily scroll through your data analysis, i.e. with ncview.

This package also contains some convenient netcdf dataformat extraction (see ncgettypecode, ncgetdatetime ) and construction tools (see ncwritedatetime, nccopydimension, nccopyattrfile, nccopyattrvar, nccopyvariable,csv2netcdf)

The power of tools lies in their examples. Typical usage of the package, in particular pynacolada.pcd(), can be found below. Additional examples can be found under the ‘examples-directory’.:

import pynacolada as pcd import numpy as np import datetime as dt import sciproc as sp from time import sleep from Scientific.IO import NetCDF # purpose: A short tutorial of using pynacolada.pcd(). The example should work with COSMO-CLM data output. # author: Hendrik Wouters <hendrik.wouters@ees.kuleuven.be> print('example 1: calculate the mean temperature of entire vertical columns (per \ lat-lon grid cell)') fnin ='/media/URB_UIP_2/data/rcm/0.009_20020101_berlin/cclm/out01/lffd2002050100.nc' print('reading:',fnin) fin = NetCDF.NetCDFFile(fnin,'r') datin = [{'file': fin, \ 'varname': 'T', \ },\ ] fnout = '/media/URB_UIP_2/data/rcm/0.009_20020101_berlin/cclm/anatest0.nc' fout = NetCDF.NetCDFFile(fnout,'w') datout = [{'file': fout, \ 'varname': 'T'}] print(' the function definition: take the mean along the axis. Note that we need to \ preserve the "amount" of dimensions of the output data compared to the input\ data.') func = lambda x: np.array([np.mean(x)]) dnamsel = ['level',] pcd.pcd(func,dnamsel,datin,datout) fout.close() fin.close() print("previous example didn't go very fast! We try again with the 'example 1', in \ which we use the appenddim-option, and just generating exactly the same \ output! It results in only one call of the function, instead of 38025 times \ in a some python loop. Note that we need to specify the 'axis=0' in the \ function definition.") sleep(5) print("example 1: bis... calculate the mean temperature of entire vertical columns \ (per lat-lon grid cell)") fnin ='/media/URB_UIP_2/data/rcm/0.009_20020101_berlin/cclm/out01/lffd2002050100.nc' print('reading:',fnin) fin = NetCDF.NetCDFFile(fnin,'r') datin = [{'file': fin, \ 'varname': 'T', \ },\ ] fnout = '/media/URB_UIP_2/data/rcm/0.009_20020101_berlin/cclm/anatest1.nc' fout = NetCDF.NetCDFFile(fnout,'w') datout = [{'file': fout, \ 'varname': 'T'}] func = lambda x: np.array([np.mean(x,axis=0 )]) dnamsel = ['level',] # here is the appenddim option -> it results in only one call of the function, \ instead of 38025- times! pcd.pcd(func,dnamsel,datin,datout,appenddim=True) fout.close() fin.close() sleep(5) print("That was much faster isn't it?") print('example 2: calculate the mean temperature of the first 10 layers (per \ lat-lon grid cell)') fnin ='/media/URB_UIP_2/data/rcm/0.009_20020101_berlin/cclm/out01/lffd2002050100.nc' print('reading:',fnin) fin = NetCDF.NetCDFFile(fnin,'r') datin = [{'file': fin, \ 'varname': 'T', \ # we specify a 'subspace' using the 'dsel' option 'dsel': {'level' : range(30,40,1)}, \ },\ ] fnout = '/media/URB_UIP_2/data/rcm/0.009_20020101_berlin/cclm/anatest2.nc' fout = NetCDF.NetCDFFile(fnout,'w') datout = [{'file': fout, \ 'varname': 'T'}] func = lambda x: np.array([np.mean(x,axis=0 )]) dnamsel = ['level',] pcd.pcd(func,dnamsel,datin,datout,appenddim=True) fout.close() fin.close() print(' example 3: calculate the scalar horizontal mean wind speed of the first \ 10 layers ') fnin ='/media/URB_UIP_2/data/rcm/0.009_20020101_berlin/cclm/out01/lffd2002050100.nc' print('reading:',fnin) fin = NetCDF.NetCDFFile(fnin,'r') datin = [{'file': fin, \ 'varname': 'U', \ 'dsel': {'level' : range(30,40,1)}, \ # The U and V wind speed components are not exactly on the same grid so have other # (but similar) coordinates ! Therefore, we need to define dimension aliases (thus # preventing pcd from generating a huge dataset)! 'daliases': { 'srlat':'rlat', 'srlon':'rlon' },\ },\ # a second variable input definition {'file': fin, \ 'varname':'V', \ 'dsel': {'level' : range(30,40,1)}, # Same here as for the U component.... 'daliases': { 'srlat':'rlat', 'srlon':'rlon' },\ }\ ] fnout = '/media/URB_UIP_2/data/rcm/0.009_20020101_berlin/cclm/anatest3.nc' fout = NetCDF.NetCDFFile(fnout,'w') datout = [{'file': fout, \ 'varname': 'u'}] func = lambda U,V: np.array([np.mean(np.sqrt(U**2+V**2),axis=0 )]) dnamsel = ['level',] pcd.pcd(func,dnamsel,datin,datout,appenddim=True) # Note also that the calculation is not entirely correct because the U and V # locations are shifted by 1/2-gridcell! fout.close() fin.close() print("example 4: now we get to it's real power. Do the same as before, \ but for an hourly time series. ") # the timeseries: just a list of all the files in the right order dts = sp.dtrange(dt.datetime(2002,5,1),dt.datetime(2002,7,1),dt.timedelta(hours=1)) path = '/media/URB_UIP_2/data/rcm/0.009_20020101_berlin/cclm/out01/' dtfiles = [path+'lffd'+dt.datetime.strftime(e,"%Y%m%d%H")+'.nc' for e in dts] datin = [{'file': dtfiles, \ 'varname': 'U', \ 'dsel': {'level' : range(30,40,1)}, \ 'daliases': { 'srlat':'rlat', 'srlon':'rlon' },\ },\ {'file': dtfiles, \ 'varname':'V', \ 'dsel': {'level' : range(30,40,1)}, 'daliases': { 'srlat':'rlat', 'srlon':'rlon' },\ }\ ] fnout = '/media/URB_UIP_2/data/rcm/0.009_20020101_berlin/cclm/anatest4.nc' fout = NetCDF.NetCDFFile(fnout,'w') datout = [{'file': fout, \ 'varname': 'u'}] # we write the datetimes to the netcdf file (we could do it also afterwards # after the pcd.pcd()-call) pcd.ncwritedatetime(fout,dts,tunits='days', refdat=dt.datetime(2002,1,1)) # function definition: func = lambda U,V: np.array([np.mean(np.sqrt(U**2+V**2),axis=0 )]) dnamsel = ['level',] pcd.pcd(func,dnamsel,datin,datout,appenddim=True) fout.close(); print 'output data written to:',fnout print('and enjoy watching your processed data with ncview!')

## A Section

No manual Yet. Please see more examples under the ‘examples’-directory.

## Release History

## Download Files

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File Name & Checksum SHA256 Checksum Help | Version | File Type | Upload Date |
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pynacolada-0.2.28.tar.gz (452.4 kB) Copy SHA256 Checksum SHA256 | – | Source | Oct 2, 2014 |