MAINT Add pytest.mark.xfail_browsers and use it to reduce boilerplate. Use a decorator to indicate which browsers we want to xfail test. This works for `run_in_pyodide` as well as normal tests.
# from: http://continuum.io/blog/numba_performance
# setup: N = 10 ; import numpy as np ; image = np.random.rand(N, N, 3) ; state = np.zeros((N, N, 2)) ; state_next = np.empty_like(state) ; state[0, 0, 0] = state[0, 0, 1] = 1
# run: growcut(image, state, state_next, 2)

# pythran export growcut(float[][][], float[][][], float[][][], int)
import math


def window_floor(idx, radius):
    if radius > idx:
        return 0
    else:
        return idx - radius


def window_ceil(idx, ceil, radius):
    if idx + radius > ceil:
        return ceil
    else:
        return idx + radius


def growcut(image, state, state_next, window_radius):
    changes = 0
    sqrt_3 = math.sqrt(3.0)

    height = image.shape[0]
    width = image.shape[1]

    for j in range(width):
        for i in range(height):

            winning_colony = state[i, j, 0]
            defense_strength = state[i, j, 1]

            for jj in range(
                window_floor(j, window_radius), window_ceil(j + 1, width, window_radius)
            ):
                for ii in range(
                    window_floor(i, window_radius),
                    window_ceil(i + 1, height, window_radius),
                ):
                    if ii != i and jj != j:
                        d = image[i, j, 0] - image[ii, jj, 0]
                        s = d * d
                        for k in range(1, 3):
                            d = image[i, j, k] - image[ii, jj, k]
                            s += d * d
                        gval = 1.0 - math.sqrt(s) / sqrt_3

                        attack_strength = gval * state[ii, jj, 1]

                        if attack_strength > defense_strength:
                            defense_strength = attack_strength
                            winning_colony = state[ii, jj, 0]
                            changes += 1

            state_next[i, j, 0] = winning_colony
            state_next[i, j, 1] = defense_strength

    return changes

# from: http://continuum.io/blog/numba_performance
# setup: N = 10 ; import numpy as np ; image = np.random.rand(N, N, 3) ; state = np.zeros((N, N, 2)) ; state_next = np.empty_like(state) ; state[0, 0, 0] = state[0, 0, 1] = 1
# run: growcut(image, state, state_next, 2)

# pythran export growcut(float[][][], float[][][], float[][][], int)
import math


def window_floor(idx, radius):
    if radius > idx:
        return 0
    else:
        return idx - radius


def window_ceil(idx, ceil, radius):
    if idx + radius > ceil:
        return ceil
    else:
        return idx + radius


def growcut(image, state, state_next, window_radius):
    changes = 0
    sqrt_3 = math.sqrt(3.0)

    height = image.shape[0]
    width = image.shape[1]

    for j in range(width):
        for i in range(height):

            winning_colony = state[i, j, 0]
            defense_strength = state[i, j, 1]

            for jj in range(
                window_floor(j, window_radius), window_ceil(j + 1, width, window_radius)
            ):
                for ii in range(
                    window_floor(i, window_radius),
                    window_ceil(i + 1, height, window_radius),
                ):
                    if ii != i and jj != j:
                        d = image[i, j, 0] - image[ii, jj, 0]
                        s = d * d
                        for k in range(1, 3):
                            d = image[i, j, k] - image[ii, jj, k]
                            s += d * d
                        gval = 1.0 - math.sqrt(s) / sqrt_3

                        attack_strength = gval * state[ii, jj, 1]

                        if attack_strength > defense_strength:
                            defense_strength = attack_strength
                            winning_colony = state[ii, jj, 0]
                            changes += 1

            state_next[i, j, 0] = winning_colony
            state_next[i, j, 1] = defense_strength

    return changes
