[AstroPy] got flux-conservative rebinning?
Thu Apr 19 15:42:49 CDT 2012
On 19 April 2012 18:50, Marshall Perrin <email@example.com> wrote:
> While we're fishing for useful algorithms, does anyone have a convenient
> implementation of flux-conservative rebinning? The image interpolations in
> scipy.ndimage are not conservative. Montage can do this, of course, but
> that seems like overkill (and adds a lot of interface complexity) if I just
> want to scale an image by 75% while conserving energy... Thanks in
> - Marshall
The pyast package (https://github.com/timj/starlink-pyast) can do
this. Specifically, see the rebin
(http://dsberry.github.com/starlink/node1.html#Rebin) and resample
(http://dsberry.github.com/starlink/node1.html#Resample) methods. For
# Create an object describing the mathematical transformation between
# of a value in the input array and its corresponding position in the
output array. Here,
# a simple mapping that zooms by a factor of 0.75 about the origin of
a 2D coordinate
# system. AST can do create many other forms of mapping, including compound
# mappings formed by combining simpler mappings.
zoommap = starlink.Ast.ZoomMap( 2, 0.75 )
# Create the 2D array to use as a test of the rebinning.
data_in = numpy.array( ....... )
# Paste the elements of this array into another array, using "zoommap" to
# describe the transformation from the position of each sample in the input
# array to its position in the output array. u/lbnd_in gives the
dimensions of the input
# array to be rebinned. Divide each input value up linearly between
# the neighbouring output values (other schemes are available too). Any input
# values equal to starlink.AST.BAD are ignored (useful for flagging
# The output array is created and returned as the method value, and
# given by lbnd_out/ubnd_out.
out = zoommap.rebin( 0.5, lbnd_in, ubnd_in, data_in, None, starlink.Ast.LINEAR,
starlink.Ast.USEBAD , 0.0, 100,
ubnd_out, lbnd, ubnd )
If an array holding the variance of each input value is available,
these methods can also calculate the variance of each output value.
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