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import pandas as pd

import deepchem as dc

import scipy.ndimage

import skimage

import skimage.util

class TestTransformers(unittest.TestCase):

    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

    dt = DataTransforms(self.d)

    height = 5

    width = 5

    shift = scipy.ndimage.shift(self.d, [height, width, 0])

    check_shift = dt.shift(width, height)

    assert np.allclose(shift, check_shift)

  def test_zoom(self):

    # Check zoom

    dt = DataTransforms(self.d)

    zx = 2

    zy = 2

    h, w = self.d.shape[0], self.d.shape[1]

    o_x = float(h) / 2 + 0.5

    o_y = float(w) / 2 + 0.5

    transform_matrix = np.array([[zx, 0, 0], [0, zy, 0], [0, 0, 1]])

    offset_matrix = np.array([[1, 0, o_x], [0, 1, o_y], [0, 0, 1]])

    reset_matrix = np.array([[1, 0, -o_x], [0, 1, -o_y], [0, 0, 1]])

    transform_matrix = np.dot(

        np.dot(offset_matrix, transform_matrix), reset_matrix)

    final_affine_matrix = np.linalg.inv(transform_matrix[:2, :2])

    final_offset = transform_matrix[:2, 2]

    x = np.rollaxis(self.d, 2, 0)

    channel_images = [

        scipy.ndimage.interpolation.affine_transform(

            x_channel,

            final_affine_matrix,

            final_offset,

            order=3,

            mode='constant') for x_channel in x

    ]

    x = np.stack(channel_images, axis=0)

    zoom = np.rollaxis(x, 0, 3)

    check_zoom = dt.zoom(zx, zy)

    assert np.allclose(zoom, check_zoom)

  def test_random_noise(self):

    # check random noise

    dt = DataTransforms(self.d)

    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.max(self.d) - np.min(self.d)) + np.min(self.d)

    check_random_noise = dt.random_noise(seed=seed)

    assert np.allclose(random_noise, check_random_noise)