def generate_four_moving_gaussians(x_min=-10,
x_max=10,
y_min=-10,
y_max=10,
size=200,
weights=None):
if weights is None:
weights = [1, 1, 1, 1]
int_gaussians = [None, None, None, None]
colorschemes = color_schemes.get_colorbrewer_schemes()
cov_matrix = [[5, 0], [0, 5]]
z_lists = []
z_sums = []
gaussians = []
for i in range(1, 6):
int_gaussians[0] = Gaussian(x_min=x_min,
x_max=x_max,
y_min=y_min,
y_max=y_max,
size=size,
means=[+i, +i],
cov_matrix=cov_matrix,
weight=weights[0])
int_gaussians[1] = Gaussian(x_min=x_min,
x_max=x_max,
y_min=y_min,
y_max=y_max,
size=size,
means=[-i, -i],
cov_matrix=cov_matrix,
weight=weights[1])
int_gaussians[2] = Gaussian(x_min=x_min,
x_max=x_max,
y_min=y_min,
y_max=y_max,
size=size,
means=[+i, -i],
cov_matrix=cov_matrix,
weight=weights[2])
int_gaussians[3] = Gaussian(x_min=x_min,
x_max=x_max,
y_min=y_min,
y_max=y_max,
size=size,
means=[-i, +i],
cov_matrix=cov_matrix,
weight=weights[3])
logger.info(" \n ".join(str(i) for i in int_gaussians))
z_list = helper.generate_distribution_grids(int_gaussians)
z_min, z_max, z_sum = helper.generate_weights(z_list)
z_lists.append(z_list)
z_sums.append(z_sum)
gaussians.append([copy.deepcopy(i) for i in int_gaussians])
return gaussians
def generate_reversed_moving_gaussian(x_min=-10,
x_max=10,
y_min=-10,
y_max=10,
size=200,
weight=0.5):
variance_x, variance_y = 5, 5
var_x, var_y = [2, 5, 10, 15, 20], [15, 20]
gaussians_2d = []
for i, mu_y in enumerate([5, 2, 0, -2, -5]):
for j, mu_x in enumerate([5, 2, 0, -2, -5]):
if mu_y < 0:
variance_y = var_y[i - 3]
variance_x = var_x[j]
else:
variance_x = 5
gaussians_2d.append(
Gaussian(means=[mu_x, mu_y],
cov_matrix=[[variance_x, 2], [2, variance_y]],
x_min=x_min,
x_max=x_max,
y_min=y_min,
y_max=y_max,
size=size,
weight=weight))
return gaussians_2d
def test_generate_crosses_colors(self):
dist_one_colors = [[0.77647059, 0.85882353, 0.9372549, 1.],
[0.12941176, 0.44313725, 0.70980392, 1.],
[.12941176, 0.44313725, 0.70980392, 1.],
[0.77647059, 0.85882353, 0.9372549, 1.]]
dist_two_colors = [[0.99607843, 0.92428677, 0.85887397, 1.],
[0.99215686, 0.77615063, 0.57159781, 1.],
[0.99215686, 0.77615063, 0.57159781, 1.],
[0.99607843, 0.92428677, 0.85887397, 1.]]
dist_1 = Gaussian(means=[1, 0],
cov_matrix=[[3, 2], [2, 3]],
x_min=self.x_min,
x_max=self.x_max,
y_min=self.y_min,
y_max=self.y_max,
size=self.size,
weight=self.weight)
dist_2 = Gaussian(means=[0, 1],
cov_matrix=[[6, 2], [2, 6]],
x_min=self.x_min,
x_max=self.x_max,
y_min=self.y_min,
y_max=self.y_max,
size=self.size,
weight=self.weight)
distributions = [dist_1, dist_2]
# unpleasant....
limits = helper.get_limits(distributions)
z_list = helper.generate_distribution_grids(distributions,
limits=limits)
z_min, z_max, z_sum = helper.generate_weights(z_list)
colorschemes = color_schemes.get_colorbrewer_schemes()
crosses = picture_cross.generate_crosses(distributions,
z_list,
z_min,
z_max,
colorschemes,
method="normal",
num_of_levels=1)
np.testing.assert_almost_equal(dist_one_colors, crosses[0][2])
np.testing.assert_almost_equal(dist_one_colors, crosses[0][3])
np.testing.assert_almost_equal(dist_two_colors, crosses[1][2])
np.testing.assert_almost_equal(dist_two_colors, crosses[1][3])
def test_generate_crosses_iso_lines(self):
dist_one_iso_lines = [0.25, 0.75, 0.75, 0.25]
dist_two_iso_lines = [
0.09889134000871382, 0.2966740200253193, 0.2966740200253193,
0.09889134000871382
]
dist_1 = Gaussian(means=[1, 0],
cov_matrix=[[3, 2], [2, 3]],
x_min=self.x_min,
x_max=self.x_max,
y_min=self.y_min,
y_max=self.y_max,
size=self.size,
weight=self.weight)
dist_2 = Gaussian(means=[0, 1],
cov_matrix=[[6, 2], [2, 6]],
x_min=self.x_min,
x_max=self.x_max,
y_min=self.y_min,
y_max=self.y_max,
size=self.size,
weight=self.weight)
distributions = [dist_1, dist_2]
# unpleasant....
limits = helper.get_limits(distributions)
z_list = helper.generate_distribution_grids(distributions,
limits=limits)
z_min, z_max, z_sum = helper.generate_weights(z_list)
colorschemes = color_schemes.get_colorbrewer_schemes()
crosses = picture_cross.generate_crosses(distributions,
z_list,
z_min,
z_max,
colorschemes,
method="normal",
num_of_levels=1)
np.testing.assert_almost_equal(dist_one_iso_lines, crosses[0][4])
np.testing.assert_almost_equal(dist_two_iso_lines, crosses[1][4])
def get_random_gaussian(x_min, x_max, y_min, y_max, variance_min, variance_max, size, scale_factor=1.):
"""
generates a random gaussian inbetween the given min and max values
:param x_min: minimal x-value for the x-expectation
:param x_max: maximal x-value for the x-expectation
:param y_min: minimal y-value for the y-expectation
:param y_max: maximal y-value for the y-expectation
:param variance_min: minimal variance of the gaussian
:param variance_max: maximal variance of the gaussian
:param size: shape of the 2D-gaussian (size*size)
:param scale_factor: scalar for x_min, x_max, y_min, y_max
:return: 2D-gaussian (size, size)
"""
mu_x_1 = random.randint(int(x_min * scale_factor), int(x_max * scale_factor))
mu_y_1 = random.randint(int(y_min * scale_factor), int(y_max * scale_factor))
cov = np.array([[random.randint(variance_min, variance_max), random.randint(variance_min, variance_max)],
[random.randint(variance_min, variance_max), random.randint(variance_min, variance_max)]])
cov = np.dot(cov, cov.transpose())
gau = Gaussian([mu_x_1, mu_y_1], np.array(cov), size=size)
logger.debug(f"Gaussian: {gau}")
return gau
def generate_default_gaussian(means=None, cov_matrix=None, *args, **kwargs):
if cov_matrix is None:
cov_matrix = [[5, 0], [0, 5]]
if means is None:
means = [0, 0]
return Gaussian(means=means, cov_matrix=cov_matrix, *args, **kwargs)
def test_generate_crosses_crosses(self):
dist_one_first_line = [((1., -4.), (5., 4.440892098500626e-16)),
((-0.181818, -2.818181), (3.818181, 1.181818)),
((-1., -2.), (3., 2.)),
((-1.818181, -1.181818), (2.181818, 2.818181)),
((-3., -4.440892098500626e-16), (1., 4.))]
dist_one_second_line = [((-1.4721359549995792, -6.47213595499958),
(-5.47213595499958, -2.472135954999579)),
((1.1162821280456323, -3.8837178719543686),
(-2.8837178719543686, 0.11628212804563232)),
((3.0000000000000004, -2.0000000000000004),
(-1.0000000000000004, 2.0000000000000004)),
((4.883717871954369, -0.11628212804563187),
(0.8837178719543681, 3.883717871954369)),
((7.47213595499958, 2.472135954999579),
(3.472135954999579, 6.47213595499958))]
dist_two_first_line = [
((0.0, -7.000000000000002), (8.000000000000002, 1.0)),
((-2.363636363636364, -4.636363636363638), (5.636363636363638,
3.363636363636364)),
((-4.000000000000001, -3.000000000000001), (4.000000000000001,
5.000000000000001)),
((-5.636363636363638, -1.3636363636363642), (2.363636363636364,
6.636363636363638)),
((-8.000000000000002, 1.0), (0.0, 9.000000000000002))
]
dist_two_second_line = [((-1.6568542494923806, -8.656854249492383),
(-9.656854249492383, -0.6568542494923806)),
((1.6172644221835126, -5.382735577816489),
(-6.382735577816489, 2.6172644221835126)),
((4.000000000000001, -3.000000000000001),
(-4.000000000000001, 5.000000000000001)),
((6.382735577816488, -0.6172644221835135),
(-1.6172644221835135, 7.382735577816488)),
((9.656854249492383, 2.6568542494923806),
(1.6568542494923806, 10.656854249492383))]
dist_1 = Gaussian(means=[1, 0],
cov_matrix=[[3, 2], [2, 3]],
x_min=self.x_min,
x_max=self.x_max,
y_min=self.y_min,
y_max=self.y_max,
size=self.size,
weight=self.weight)
dist_2 = Gaussian(means=[0, 1],
cov_matrix=[[6, 2], [2, 6]],
x_min=self.x_min,
x_max=self.x_max,
y_min=self.y_min,
y_max=self.y_max,
size=self.size,
weight=self.weight)
distributions = [dist_1, dist_2]
# unpleasant....
limits = helper.get_limits(distributions)
z_list = helper.generate_distribution_grids(distributions,
limits=limits)
z_min, z_max, z_sum = helper.generate_weights(z_list)
colorschemes = color_schemes.get_colorbrewer_schemes()
crosses = picture_cross.generate_crosses(distributions,
z_list,
z_min,
z_max,
colorschemes,
method="normal",
num_of_levels=1)
np.testing.assert_almost_equal(dist_one_first_line,
crosses[0][0],
decimal=5)
np.testing.assert_almost_equal(dist_one_second_line,
crosses[0][1],
decimal=5)
np.testing.assert_almost_equal(dist_two_first_line,
crosses[1][0],
decimal=5)
np.testing.assert_almost_equal(dist_two_second_line,
crosses[1][1],
decimal=5)