Maybe get rid of Cython

[amueller]

It would be really nice to get rid of the Cython code. The equivalent in numpy is

def query_integral_image(integral_image, size_x, size_y, random_state):
    x, y = integral_image.shape

    # calculate all areas
    i, j = np.ogrid[:x-size_x, :y-size_y]
    area = integral_image[i, j] + integral_image[i+size_x, j+size_y] - integral_image[i+size_x, j] - integral_image[i, j+size_y]
    zero_areas = np.argwhere(area == 0)

    if zero_areas.size == 0:
        # no room left
        return None

    # pick a location at random
    goal = random_state.choice(zero_areas.shape[0])
    return tuple(zero_areas[goal])

however, that seems to be much slower.
Not sure if there's a faster way to do it. cc @thomasjpfan

[thomasjpfan]

Here is the best I can do with pure NumPy:

def query_integral_image_np(integral_image, size_x, size_y, random_state):
    area = integral_image[:-size_x, :-size_y]
    area += integral_image[size_x:,size_y:]
    area -= integral_image[size_x:, :-size_y]
    area -= integral_image[:-size_x, size_y:]
    flat_area_zero = np.flatnonzero(area == 0)
    hits = flat_area_zero.shape[0]
    
    if hits == 0:
        # no room left
        return None

    goal = random_state.randint(0, hits)
    flat_idx = flat_area_zero[goal]
    
    w, h = integral_image.shape
    row = flat_idx // w
    col = flat_idx - row * w
    return row, col

The Cython code still is ~ 2.6 times faster than this.

[ccbogel]

@thomasjpfan I kept getting an IndexError:

flat_idx = flat_area_zero[goal]

IndexError: index 635 is out of bounds for axis 0 with size 600

[amueller]

@thomasjpfan hm if there's now a way to do version-independent binary wheels maybe that's the better way forward? Also clearly your numpy skills are better than GPT4 ;)
But maybe 2.6 times is fine also given the maintenance overhead?

[thomasjpfan]

@amueller I tried using ABI3 with word_cloud, but it will not work until the minimum support Python version is 3.11. query_integral_image uses the Buffer protocol, which became stable in 3.11: https://docs.python.org/3/c-api/buffer.html#buffer-structure

Although even with 3.11 as minimum version, Cython still uses non-stable CAPI calls. I suspect Cython's limited ABI support is not mature enough yet.

But maybe 2.6 times is fine also given the maintenance overhead?

I cannot tell without running benchmarks to see how much slower generate_from_frequencies gets.