===========
sciproc
===========


Sciproc in experimental stage provides tools to select, edit, convert scientific
(observed, model-generated) data. It needs Numpy. It's very experimental, as some functions aren't tested or only tested in
'idealised cases', so please be careful.
Currently selection from 1D data by coordinates or certain timestep and applying a function repeatedly on a multidimensional matrix is
implemented. However selecting, interpolating and editing procedures for
multidimensional data is planned in the near future.  You might want to use
it if you have have any observational data and you want to select a period,
make a selection with a certain timestep or make an interpolation. The aim
is to make a full python replacement which provide all operations from the
cdo and ncview (an ncdf package). It should be working with normal numpy data. However,
if you want to process netcdf-files, we recommend to use the ncdf-extra (still not available)
interface which directly uses this library and which also provides
command-line tools to process netcdf-files directly. Typical usage often
looks like this::

    #!/usr/bin/env python

    from numpy import *
    from sciproc import *

    # select data from a 1-D array:
    data = array([1.0,2.0,4.0,2.5])
    incoords = array([0.0,1.0,2.0,3.0])
    print(datatimeco(data,coords = incoords,outcoords = array([1.0,2.0]))

    a = array([[[1,3,2],[2,1,3],[4,1,3]],[[1,2,3],[4,1,2],[3,0,1]]])
    print('copy')
    print( multifunc(a,[False,False,True],lambda x: copyfunction(x)))
    print('take only elements 2 and 3 from third dimension')
    print(multifunc(a,[False,False,True],lambda x: secondandthirdelement(x)))
    print('take only elements 2 and 3 from second dimension')
    print(multifunc(a,[False,True,False],lambda x: secondandthirdelement(x)))
    print('reduce dimension')





A Section
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A Sub-Section
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