def _sample_distant_same()

in data_preprocessing/sample_quadruplets/sample_for_counties.py [0:0]

• 25 lines of code
• 17 McCabe index (conditional complexity)

def _sample_distant_same(img, a_lat, a_lon, neighborhood_radius, distant_radius, tile_radius, timestep, size_even):
    if neighborhood_radius is None:
        return _sample_distant_diff(img, tile_radius, timestep, size_even)

    _, _, img_h, img_w = img.shape
    while True:
        d_lat, d_lon = a_lat, a_lon

        if distant_radius is None:
            while (d_lat >= a_lat - neighborhood_radius) and (d_lat <= a_lat + neighborhood_radius):
                d_lat = np.random.randint(tile_radius, img_h - tile_radius)
            while (d_lon >= a_lon - neighborhood_radius) and (d_lon <= a_lon + neighborhood_radius):
                d_lon = np.random.randint(tile_radius, img_w - tile_radius)
        else:
            while ((d_lat >= a_lat - neighborhood_radius) and (d_lat <= a_lat + neighborhood_radius)) \
                    or d_lat >= a_lat + distant_radius \
                    or d_lat <= a_lat - distant_radius:
                d_lat = np.random.randint(tile_radius, img_h - tile_radius)
            while ((d_lon >= a_lon - neighborhood_radius) and (d_lon <= a_lon + neighborhood_radius))\
                    or d_lon >= a_lon + distant_radius \
                    or d_lon <= a_lon - distant_radius:
                d_lon = np.random.randint(tile_radius, img_w - tile_radius)
        lat0, lat1, lon0, lon1 = _get_lat_lon_range(d_lat, d_lon, tile_radius, size_even)
        tile = img[:, timestep, lat0:lat1 + 1, lon0:lon1 + 1]
        if ma.count_masked(tile) == 0:
            break

    return tile, d_lat, d_lon