[Numpy-discussion] accuracy issues with numpy arrays?
Anne Archibald
peridot.faceted@gmail....
Tue Apr 29 17:10:26 CDT 2008
On 30/04/2008, eli bressert <bressert@gmail.com> wrote:
> I'm writing a quick script to import a fits (astronomy) image that has
> very low values for each pixel. Mostly on the order of 10^-9. I have
> written a python script that attempts to take low values and put them
> in integer format. I basically do this by taking the mean of the 1000
> lowest pixel values, excluding zeros, and dividing the rest of the
> image by that mean. Unfortunately, when I try this in practice, *all*
> of the values in the image are being treated as zeros. But, if I use a
> scipy.ndimage function, I get proper values. For example, I take the
> pixel that I know has the highest value and do
I think the bug is something else:
> import pyfits as p
> import scipy as s
> import scipy.ndimage as nd
> import numpy as n
>
> def flux2int(name):
> d = p.getdata(name)
> x,y = n.shape(d)
> l = x*y
> arr1 = n.array(d.reshape(x*y,1))
> temp = n.unique(arr1[0]) # This is where the bug starts. All values
> are treated as zeros. Hence only one value remains, zero.
Actually, since arr1 has shape (x*y, 1), arr1[0] has shape (1,), and
so it has only one entry:
In [82]: A = np.eye(3)
In [83]: A.reshape(9,1)
Out[83]:
array([[ 1.],
[ 0.],
[ 0.],
[ 0.],
[ 1.],
[ 0.],
[ 0.],
[ 0.],
[ 1.]])
In [84]: A.reshape(9,1)[0]
Out[84]: array([ 1.])
The python debugger is a good way to check this sort of thing out; if
you're using ipython, typing %pdb will start the debugger when an
exception is raised, at which point you can poke around in all your
local variables and evaluate expressions.
> arr1 = arr1.sort()
> arr1 = n.array(arr1)
> arr1 = n.array(arr1[s.where(arr1 >= temp)])
>
> val = n.mean(arr1[0:1000])
>
> d = d*(1.0/val)
> d = d.round()
> p.writeto(name[0,]+'fixed.fits',d,h)
Good luck,
Anne
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