===========
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 an addition to the cdo climate data operators with
python power (see also ncdfextra). It should be working with normal numpy data. However, if you want to
process netcdf-files, we recommend to use the ncdfextra interface which acutally uses sciproc. 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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