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

Before Change

        list_indices_fin = np.random.choice(list_indices, n_samples,
                                            replace=False, p=proba)
    else:
        print("No probability weighting needed")
        list_indices_fin = list_indices
        np.random.shuffle(list_indices)
    for i in range(0, n_samples):
        indices_to_add = candidates_indices[list_indices_fin[i]]

After Change

    list_indices_fin = np.arange(len(candidates_indices))
    if mean_counts_size is not None:
        // Probability weighting considered
        proba = [p for (c, p)
                 in zip(candidates.flatten(), mean_counts_size.flatten())
                 if c >= 1]
        list_indices_fin = np.random.choice(
            list_indices_fin, n_samples, replace=False, p=proba)
    else:
        np.random.shuffle(list_indices_fin)
        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]

    // adjust max spatial coordinates based on each spatial window size
    all_coordinates = {}
    for mod in list(win_sizes):
        win_size = win_sizes[mod][:N_SPATIAL]
        half_win_diff = np.floor((max_spatial_win - win_size) / 2.0)

Instances