[SciPy-dev] Arbitrary n-dimensional rebin routine

[Angus McMorland]

Okay, my first reply is to myself, with all the bits I forgot to add.

Angus McMorland wrote:
>
[snip]

import numpy as n
import scipy.interpolate

def nvals(n, dims):
    '''Returns xvals-like volumes, indexing over any dimension n.
    Will probably crash and burn if n >= len(dims).'''
    evList = ''
    for i in range( len(dims) - 1):
        if i < n:
            evList = evList + '%d' % dims[i]
        else:
            evList = evList + '%d' % dims[i + 1]
        if i < len(dims) - 2:
            evList = evList + ', '
    evList = 'xvals( (%d,' % dims[n] + evList + ')' + ')'
    xs = eval( evList )

    evList = ''
    for i in range( len(dims) ):
        if i < n:
            ind = i + 1
        elif i == n:
            ind = 0
        else:
            ind = i
        evList = evList + '%d' % ind
        if i < len(dims) - 1:
            evList = evList + ', '
    evList = 'xs.transpose(' + evList + ')'
    return eval( evList )

> def congrid(a, nud, method='neighbour', centre=False, minusone=False):
>     '''Arbitrary resampling of source array to new dimension sizes.

[snip]

Also the docstring should have something about the first couple of params:

Usage:
 arg0: input array
 arg1: tuple of resulting dimensions

Example:
 rebinned = rebin.congrid( raw, (2,2,2), \
                           method=[neighbour|linear|cubic|spline], \
                           minusone=[True|False], \
                           centre=[True|False])

Let's try again.
-- 
Angus McMorland
email a.mcmorland at auckland.ac.nz
mobile +64-21-155-4906

PhD Student, Neurophysiology / Multiphoton & Confocal Imaging
Physiology, University of Auckland
phone +64-9-3737-599 x89707

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