Values being altered in numpy array

Question

So I have a 2D numpy array (256,256), containing values between 0 and 10, which is essentially an image. I need to remove the 0 values and set them to NaN so that I can plot the array using a specific library (APLpy). However whenever I try and change all of the 0 values, some of the other values get altered, in some cases to 100 times their original value (no idea why).

The code I'm using is:

for index, value in np.ndenumerate(tex_data):
    if value == 0:
        tex_data[index] = 'NaN'

where tex_data is the data array from which I need to remove the zeros. Unfortunately I can't just use a mask for the values I don't need, as APLpy wont except masked arrays as far as I can tell.

Is there anyway I can set the 0 values to NaN without changing the other values in the array?

Are you sure this is based on your for loop? I tried for myself and have not observed this effect — cadolphs, Jan 26 '17 at 19:36
Can you please provide some example code and data then? If we can't replicate, then we can't help O_O — cadolphs, Jan 26 '17 at 20:13
What's the `dtype` of this array? If it is a small int, trying to write a `NaN` string to it, or a float `np.nan` could produce problems (if it runs at all). — hpaulj, Jan 26 '17 at 20:38

wim · Answer 1 · 2017-01-26T19:38:15.837

5

Use fancy-indexing. Like this:

tex_data[tex_data==0] = np.nan

I don't know why your original code was failing. It looks correct to me, although terribly inefficient.

edited Jan 26 '17 at 19:38

answered Jan 26 '17 at 19:35

wim

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Note: `ndenumerate` does element-wise iteration. Index will be a 2D index, value will be that indices element. – cadolphs Jan 26 '17 at 19:36
Upvote for fancy indexing. The mantra with numpy is: Avoid for loops whenever you can! – cadolphs Jan 26 '17 at 19:37
Tried this and still produces the same problem? – Sam Billington Jan 26 '17 at 19:50
4

Then you will have to provide sufficient code and/or data to reproduce the problem. – wim Jan 26 '17 at 19:53

score 0 · Answer 2 · answered Jan 26 '17 at 20:51

0

Using float rules,

tex_data/tex_data*tex_data

make the job here also.

answered Jan 26 '17 at 20:51

B. M.

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Values being altered in numpy array

2 Answers2