def test_center_initialized(self):
expected_c = EigenArray(3)
expected_c.get_matrix()[:] = [[1], [2], [3]]
cam = Camera(expected_c)
numpy.testing.assert_array_equal(
numpy.array([cam.center]).T, expected_c.get_matrix())
numpy.testing.assert_array_equal(cam.center, [1, 2, 3])
def test_mutability(self):
a = EigenArray(2, 3)
d = a.get_matrix() # The data pointer
for i in range(2):
for j in range(3):
d[i][j] = 0
numpy.testing.assert_array_equal(a.get_matrix(),
[[0, 0, 0], [0, 0, 0]])
d[:] = 1
numpy.testing.assert_array_equal(a.get_matrix(),
[[1, 1, 1], [1, 1, 1]])
d[1, 0] = 2
numpy.testing.assert_array_equal(a.get_matrix(),
[[1, 1, 1], [2, 1, 1]])
d[:, 2] = 3
numpy.testing.assert_array_equal(a.get_matrix(),
[[1, 1, 3], [2, 1, 3]])
d += 1
numpy.testing.assert_array_equal(a.get_matrix(),
[[2, 2, 4], [3, 2, 4]])
b = d * 0
numpy.testing.assert_array_equal(a.get_matrix(),
[[2, 2, 4], [3, 2, 4]])
numpy.testing.assert_array_equal(b, [[0, 0, 0], [0, 0, 0]])
def test_new_matrix(self):
a = EigenArray(2, 2, type='d')
m = a.get_matrix()
m[:] = 1.
c = Covariance.from_matrix(2, 'd', m)
m_out = c.to_matrix()
print('input matrix:\n', m)
print('output matrix:\n', m_out)
numpy.testing.assert_array_equal(m_out, m)
# Type casting should be handled
a = EigenArray(2, 2, type='f')
m = a.get_matrix()
m[:] = 1.
c = Covariance.from_matrix(2, 'd', m)
m_out = c.to_matrix()
print('input matrix:\n', m)
print('output matrix:\n', m_out)
numpy.testing.assert_array_equal(m_out, m)
# Any other numpy array of the correct shape should be acceptable
m = numpy.ndarray((2, 2))
m[:] = 3.
c = Covariance.from_matrix(2, 'f', init=m)
m_out = c.to_matrix()
print('input matrix:\n', m)
print('output matrix:\n', m_out)
numpy.testing.assert_array_equal(m_out, m)
# Diagonally congruent values should be averages when initializing with
# matrix
m = numpy.eye(3, dtype=numpy.double)
m[0, 2] = 2.
m_expected = m.copy()
m_expected[0, 2] = 1.
m_expected[2, 0] = 1.
c = Covariance.from_matrix(3, init=m)
m_out = c.to_matrix()
print('input matrix:\n', m)
print('output matrix:\n', m_out)
numpy.testing.assert_array_equal(m_out, m_expected)
def test_order_transform(self):
a = EigenArray(2, 3)
d = a.get_matrix() # The data pointer
# column-major 2x3 matrix [[ 1 2 3 ] (Eigen format)
# [ 4 5 6 ]]
d[0][0] = 1
d[0][1] = 2
d[0][2] = 3
d[1][0] = 4
d[1][1] = 5
d[1][2] = 6
numpy.testing.assert_array_equal(a.get_matrix(),
[[1., 2., 3.], [4., 5., 6.]])
ntools.assert_equal(a.get_matrix()[0, 0], 1)
ntools.assert_equal(a.get_matrix()[0, 1], 2)
ntools.assert_equal(a.get_matrix()[0, 2], 3)
ntools.assert_equal(a.get_matrix()[1, 0], 4)
ntools.assert_equal(a.get_matrix()[1, 1], 5)
ntools.assert_equal(a.get_matrix()[1, 2], 6)
def test_from_array(self):
# from list
expected_list = [[0.4, 0], [1, 1.123], [2.253, 4.768124]]
ea = EigenArray.from_array(expected_list)
em = ea.get_matrix()
numpy.testing.assert_array_equal(em, expected_list)
# from ndarray
expected_ndar = numpy.array(expected_list)
ea = EigenArray.from_array(expected_ndar)
em = ea.get_matrix()
numpy.testing.assert_array_equal(em, expected_ndar)
# from EigenArray, which should return the input object
ea = EigenArray(3, 2)
em = ea.get_matrix()
em[:] = expected_list
ea2 = EigenArray.from_array(em)
em2 = ea2.get_matrix()
numpy.testing.assert_array_equal(em2, em)