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    w = 150

    scale = scipy.misc.imresize(self.d, (h, w))

    check_scale = dt.scale(h, w)

    np.allclose(scale, check_scale)

    assert np.allclose(scale, check_scale)

  def test_shift(self):

    # Check shift

    check_zoom = dt.zoom(zx, zy)

    assert np.allclose(zoom, check_zoom)

  def test_random_noise(self):

    # check random noise

  def test_gaussian_noise(self):

    # check gaussian noise

    dt = DataTransforms(self.d)

    seed = 0

    np.random.seed(0)

    x = (self.d - np.min(self.d)) / (np.max(self.d) - np.min(self.d))

    x = skimage.util.random_noise(x, seed=seed)

    random_noise = x + np.random.normal(loc=0, scale=0.1, size=self.d.shape)

    random_noise[random_noise > 1] = 1

    random_noise = random_noise * (np.max(self.d) - np.min(self.d)) + np.min(

        self.d)

    np.random.seed(0)

    check_random_noise = dt.gaussian_noise()

    assert np.allclose(random_noise, check_random_noise)

  def test_salt_pepper_noise(self):

    # check salt and pepper noise

    dt = DataTransforms(self.d)

    np.random.seed(0)

    prob = 0.05

    x = (self.d - np.min(self.d)) / (np.max(self.d) - np.min(self.d))

    noise = np.random.random(size=self.d.shape)

    x[noise < (prob / 2)] = 0

    x[noise > (1 - prob / 2)] = 1

    random_noise = x * (np.max(self.d) - np.min(self.d)) + np.min(self.d)

    check_random_noise = dt.random_noise(seed=seed)

    np.random.seed(0)

    check_random_noise = dt.salt_pepper_noise(prob)

    assert np.allclose(random_noise, check_random_noise)