Numpy array operation to shift index

Question

I have a very specific situation: I have a long 1-D numpy array (arr). I am interested in those elements that are greater than a no. (n). So I am using: idx = np.argwhere(arr > n) and: val = arr[idx] to get the elements and their indices. Now the problem: I am adding an integer offset (ofs) to the indices (idx) and bringing back the overflowing indices to the front using: idx = (idx + ofs) % len(arr) (as if the original array (arr) is rolled and again argwhere used). If it is correct till here, what exactly should I use to get the updated val (the array that corresponds to the idx)? Thanks in advance.

Ex: Let arr=[2,5,8,4,9], n=4, so idx=[1,2,4] and val=[5,8,9]. Now let ofs=3, then idx=[4,5,7]%5=[4,0,2]. I expect val=[8,9,5].

Answer 1

I don't know if I understand the aim of this question correctly, but if we want to rearrange val with orders in idx, it can be done by np.argsort as:

mask_idx = np.where(arr > n)[0]                 # satisfied indices in arr, where elements are bigger than the specified value
val = arr[mask_idx]                             # satisfied corresponding values
mask_updated_idx = (mask_idx + ofs) % len(arr)  # --> [4 0 2]

idx_sorted = mask_updated_idx.argsort()         # --> [1 2 0]  indices rearranging order array
val = val[idx_sorted]                           # --> [8 9 5]