c8b28432a637a780eed96547260722ff3dede57e,niftynet/engine/sampler_selective.py,,rand_choice_coordinates,#Any#Any#Any#Any#Any#Any#,350

Before Change

    // print(np.sum(candidates), list_indices)
    // print(len(candidates_indices), candidates_indices.shape)
    if mean_counts_size is not None:
        print("Probability weighting considered")
        proba = []
        for (c,  p) in zip(candidates.flatten(), mean_counts_size.flatten()):
            if c >= 1:
                proba.append(p)

After Change

        list_indices_fin = list_indices_fin[:n_samples]

    max_coords = np.zeros((n_samples, N_SPATIAL), dtype=np.int32)
    half_win = np.floor(np.asarray(win_sizes["image"]) / 2).astype(np.int)
    for (i_sample, ind) in enumerate(list_indices_fin):
        indices_to_add = candidates_indices[ind]
        max_coords[i_sample, :N_SPATIAL] = \
            indices_to_add[:N_SPATIAL] - half_win[:N_SPATIAL]

Instances