I have some array, and a given index/array of indices. Using this index/indices, I have to update another array. These updates shouldn’t be in-place. I am able to do this for a single index, but unable to achieve the right result for array of indices. Here is an example:

I got the following two arrays for which I want to find all intersecting elements and the corresponding indexes.

I have a 3D array, say a. I want to create another array b which has 0s at least and second least absolute values in a. My approach is as follows

Here is a working Python snippet that get some 3D points (see pts array) and project them on a 2D image Npix x Npix keeping on the “j,i” pixel the mean of the third axis of all “pts” that are collected to that pixel. The code is probably clear.

I’m working with particle positions in different arrays, something like (three particles):

I saw a previous question about adding to an array and saw that both np.hstack and np.append can be used to do so. My problem is, I am working with nested arrays and I think that is causing a problem. I have an array such as the following:

I saw a previous question about adding to an array and saw that both np.hstack and np.append can be used to do so. My problem is, I am working with nested arrays and I think that is causing a problem. I have an array such as the following:

In the NumPy documentation, it is demonstrated that you can add a custom attribute by subclassing the ndarray:

I had an issue with a collision function returning very large values for distances

I have two sorted numerical arrays of unequal length and I’m looking for a way to match between elements in both arrays such that there’s a one-to-one match between (most) gt and pred elements. A match is only considered valid if it is below some threshold thrsh (so there could be unmatched gt elements), and we define a cost of a match as the difference between matched gt and pred elements.
From here we can define three types of matches: