I am handling a set of data recorded by a 2D detector. Therefore, the data are represented by three arrays: x and y labelling the coordinate of a pixel and intensity storing the measured signal.

For example, a 6x6 grid will give a set of data:

xraw = np.array([0,1,2,3,4,5,0,1,2,3,4,5,...])
yraw = np.array([0,0,0,0,0,0,1,1,1,1,1,1,...])
intensity = np.array([i_00,i_01,i_02,i_03,i_04,i_05,i_10,i_11,...])

Due to various reasons, such as pixel defects, some of the data points are discarded in the raw data. Therefore, xraw, yraw, intensity have a size smaller than 36 (if that's a 6x6 grid), with, say, the point at (2,3) missing.

The intensity data needs further treatment by an element-wise multiplication with another array. This treatment array is from theoretical calculation and so it has a size of nxn (6x6 in this case). However, as some of the points in the true data are missing, the two arrays have different sizes.

I can use a loop to check for the missing points and eliminate the corresponding element in the treatment array. I wonder if there are some methods in numpy that take care of such operations. Thanks

CodePudding user response：

First, construct the indices of available and all possible pixel positions by

avail_ind = yraw * h   xraw
all_ind = np.arange(0, h * w)

where h and w is the image's height and width in pixels.

Then, find the indices of the missing pixels by

missing_ind = all_ind[~np.in1d(all_ind, avail_ind)]

Once having the missing indices, use np.delete to construct a copy of the treatment_array with elements at the indices removed, then simply multiply that with your intensity array.

result = intensity * np.delete(treatment_array, missing_ind)