I have to crop several images that have defects in them. I will inspect the images using ds9, and crop them with NumPy. Rather, I will not crop the images themselves, but use only part of them for growth curve analysis.

I'll have to update the galaxy center pixel coordinates, as the WCS information is taken from the original SDSS images. They would not have to change if the cropped part is below and right the original center.

I don't have a paper and a pencil at the moment, so I'm thinking out loud here. NumPy indexing goes like this, y first:

ds9, however, uses the standard mathematical notation: (x, y), where x goes left to right, and y goes up from the lower left corner:

This array had its (0, 0) and (10, 20) pixels set to 1:

I'll have to update the galaxy center pixel coordinates, as the WCS information is taken from the original SDSS images. They would not have to change if the cropped part is below and right the original center.

I don't have a paper and a pencil at the moment, so I'm thinking out loud here. NumPy indexing goes like this, y first:

+------------------> x | | | | V y

ds9, however, uses the standard mathematical notation: (x, y), where x goes left to right, and y goes up from the lower left corner:

y A | | | | + -------------------------> x

This array had its (0, 0) and (10, 20) pixels set to 1:

[[ 1. 0. 0. ..., 0. 0. 0.] [ 0. 0. 0. ..., 0. 0. 0.] [ 0. 0. 0. ..., 0. 0. 0.] ..., [ 0. 0. 0. ..., 0. 0. 0.] [ 0. 0. 0. ..., 0. 0. 0.] [ 0. 0. 0. ..., 0. 0. 0.]]And here is the corresponding ds9 view: tl;dr: if you get a pair of pixel coordinates from ds9 and want to use them in NumPy, you have to swap the coordinate numbers and flip the y-axis, i.e. subtract the ds9 y coordinate from the total image height (image.shape[0]).

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