Initial Query

I want to vectorize a function frame = np.zeros((out_h, out_w, 3), dtype=np.uint8) for i, val1 in enumerate(rows): for j, val2 in enumerate(cols): for j, val3 in enumerate(ch): # Assuming you want to store the pair as tuples in the matrix frame[i, j, k] = image[val1, val2, val3] out.write(np.array(frame)) with a simple numpy function that does something like this what is it called

LLM Generation

Query

I want to vectorize a function frame = np.zeros((out_h, out_w, 3), dtype=np.uint8) for i, val1 in enumerate(rows): for j, val2 in enumerate(cols): for j, val3 in enumerate(ch): # Assuming you want to store the pair as tuples in the matrix frame[i, j, k] = image[val1, val2, val3] out.write(np.array(frame)) with a simple numpy function that does something like this what is it called

Output

To vectorize the nested loops efficiently, you can use **NumPy's advanced indexing** with `np.ix_` to generate grid indices. This replaces the explicit loops and directly extracts the desired elements from `image`: 

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frame = image[np.ix_(rows, cols, ch)]
out.write(frame)
**Explanation:** - **`np.ix_(rows, cols, ch)`** creates broadcastable index arrays for the dimensions of `rows`, `cols`, and `ch`. This constructs a grid of indices matching the shape `(out_h, out_w, 3)`. - **Advanced indexing** (`image[...]`) uses these indices to directly extract the elements from `image` into the desired `frame` structure. - This approach is **fast and concise**, leveraging NumPy’s optimized C backend instead of Python loops.

Substring Evaluation

Testing if the previous output contains the string ix_: True