def test_interpolation_tetrahedral(self):
"""
Tests :func:`colour.algebra.interpolation.\
table_interpolation_tetrahedral` definition.
"""
prng = np.random.RandomState(4)
V_xyz = random_triplet_generator(16, random_state=prng)
np.testing.assert_almost_equal(
table_interpolation_tetrahedral(V_xyz, LUT_TABLE),
np.array([
[1.08039215, -0.02840092, 0.55855303],
[0.52208945, 0.35297753, 0.13599555],
[1.14373467, -0.00422138, 0.13413290],
[0.71384967, 0.98420883, 0.57982724],
[0.76771576, 0.46280975, 0.55106736],
[0.20861663, 0.85077712, 0.57102264],
[0.90398698, 0.72351675, 0.41151955],
[0.03749453, 0.70226823, 0.52614254],
[0.29626758, 0.21645072, 0.47615873],
[0.46729624, 0.07494851, 0.68892548],
[0.85907681, 0.67744258, 0.84410486],
[0.24335535, 0.20896545, 0.21996717],
[0.79244027, 0.66930773, 0.39213595],
[1.08383608, 0.37985897, 0.49011919],
[0.14683649, 0.43624903, 0.58706947],
[0.61272658, 0.92799297, 0.29650424],
]))
def test_interpolation_tetrahedral(self):
"""
Tests :func:`colour.algebra.interpolation.\
table_interpolation_tetrahedral` definition.
"""
prng = np.random.RandomState(4)
V_xyz = random_triplet_generator(16, random_state=prng)
np.testing.assert_almost_equal(
table_interpolation_tetrahedral(V_xyz, LUT_TABLE),
np.array([
[1.08039215, -0.02840092, 0.55855303],
[0.52208945, 0.35297753, 0.13599555],
[1.14373467, -0.00422138, 0.13413290],
[0.71384967, 0.98420883, 0.57982724],
[0.76771576, 0.46280975, 0.55106736],
[0.20861663, 0.85077712, 0.57102264],
[0.90398698, 0.72351675, 0.41151955],
[0.03749453, 0.70226823, 0.52614254],
[0.29626758, 0.21645072, 0.47615873],
[0.46729624, 0.07494851, 0.68892548],
[0.85907681, 0.67744258, 0.84410486],
[0.24335535, 0.20896545, 0.21996717],
[0.79244027, 0.66930773, 0.39213595],
[1.08383608, 0.37985897, 0.49011919],
[0.14683649, 0.43624903, 0.58706947],
[0.61272658, 0.92799297, 0.29650424],
]))
def test_interpolation_trilinear(self):
"""
Tests :func:`colour.algebra.interpolation.\
table_interpolation_trilinear` definition.
"""
prng = np.random.RandomState(4)
V_xyz = random_triplet_generator(16, random_state=prng)
np.testing.assert_almost_equal(
table_interpolation_trilinear(V_xyz, LUT_TABLE),
np.array([
[1.07937594, -0.02773926, 0.55498254],
[0.53983424, 0.37099516, 0.13994561],
[1.13449122, -0.00305380, 0.13792909],
[0.73411897, 1.00141020, 0.59348239],
[0.74066176, 0.44679540, 0.55030394],
[0.20634750, 0.84797880, 0.55905579],
[0.92348649, 0.73112515, 0.42362820],
[0.03639248, 0.70357649, 0.52375041],
[0.29215488, 0.19697840, 0.44603879],
[0.47793470, 0.08696360, 0.70288463],
[0.88883354, 0.68680856, 0.87404642],
[0.21430977, 0.16796653, 0.19634247],
[0.82118989, 0.69239283, 0.39932389],
[1.06679072, 0.37974319, 0.49759377],
[0.17856230, 0.44755467, 0.62045271],
[0.59220355, 0.93136492, 0.30063692],
]))
def test_interpolation_trilinear(self):
"""
Tests :func:`colour.algebra.interpolation.\
table_interpolation_trilinear` definition.
"""
prng = np.random.RandomState(4)
V_xyz = random_triplet_generator(16, random_state=prng)
np.testing.assert_almost_equal(
table_interpolation_trilinear(V_xyz, LUT_TABLE),
np.array([
[1.07937594, -0.02773926, 0.55498254],
[0.53983424, 0.37099516, 0.13994561],
[1.13449122, -0.00305380, 0.13792909],
[0.73411897, 1.00141020, 0.59348239],
[0.74066176, 0.44679540, 0.55030394],
[0.20634750, 0.84797880, 0.55905579],
[0.92348649, 0.73112515, 0.42362820],
[0.03639248, 0.70357649, 0.52375041],
[0.29215488, 0.19697840, 0.44603879],
[0.47793470, 0.08696360, 0.70288463],
[0.88883354, 0.68680856, 0.87404642],
[0.21430977, 0.16796653, 0.19634247],
[0.82118989, 0.69239283, 0.39932389],
[1.06679072, 0.37974319, 0.49759377],
[0.17856230, 0.44755467, 0.62045271],
[0.59220355, 0.93136492, 0.30063692],
]))
def test_random_triplet_generator(self):
"""
Tests :func:`colour.algebra.random.random_triplet_generator`
definition.
Notes
-----
- The test is assuming that :func:`np.random.RandomState` definition
will return the same sequence no matter which *OS* or *Python*
version is used. There is however no formal promise about the
*prng* sequence reproducibility of either *Python* or *Numpy*
implementations, see :cite:`Laurent2012a`.
"""
prng = np.random.RandomState(4)
np.testing.assert_almost_equal(RANDOM_TRIPLETS,
random_triplet_generator(
10, random_state=prng),
decimal=7)
# TODO: Use "assertWarns" when dropping Python 2.7.
random_triplet_generator(5.5, random_state=prng)
def test_random_triplet_generator(self):
"""
Tests :func:`colour.algebra.random.random_triplet_generator`
definition.
Notes
-----
- The test is assuming that :func:`np.random.RandomState` definition
will return the same sequence no matter which *OS* or *Python*
version is used. There is however no formal promise about the
*prng* sequence reproducibility of either *Python* or *Numpy*
implementations, see :cite:`Laurent2012a`.
"""
prng = np.random.RandomState(4)
np.testing.assert_almost_equal(
RANDOM_TRIPLETS,
random_triplet_generator(10, random_state=prng),
decimal=7)
def test_random_triplet_generator(self):
"""
Tests :func:`colour.algebra.random.random_triplet_generator`
definition.
Notes
-----
The test is assuming that :func:`np.random.RandomState` definition will
return the same sequence no matter which *OS* or *Python* version is
used. There is however no formal promise about the *prng* sequence
reproducibility of either *Python or *Numpy* implementations: Laurent.
(2012). Reproducibility of python pseudo-random numbers across systems
and versions? Retrieved January 20, 2015, from
http://stackoverflow.com/questions/8786084/reproducibility-of-python-pseudo-random-numbers-across-systems-and-versions # noqa
"""
prng = np.random.RandomState(4)
np.testing.assert_almost_equal(
RANDOM_TRIPLETS,
np.array(list(random_triplet_generator(10, random_state=prng))),
decimal=7)
def test_random_triplet_generator(self):
"""
Tests :func:`colour.algebra.random.random_triplet_generator`
definition.
Notes
-----
The test is assuming that :func:`np.random.RandomState` definition will
return the same sequence no matter which *OS* or *Python* version is
used. There is however no formal promise about the *prng* sequence
reproducibility of either *Python* or *Numpy* implementations:
Laurent. (2012). Reproducibility of python pseudo-random numbers
across systems and versions? Retrieved January 20, 2015, from
http://stackoverflow.com/questions/8786084/\
reproducibility-of-python-pseudo-random-numbers-across-systems-and-versions
"""
prng = np.random.RandomState(4)
np.testing.assert_almost_equal(
RANDOM_TRIPLETS,
np.array(list(random_triplet_generator(10, random_state=prng))),
decimal=7)
def test_vertices_and_relative_coordinates(self):
"""
Tests :func:`colour.algebra.interpolation.\
vertices_and_relative_coordinates` definition.
"""
prng = np.random.RandomState(4)
V_xyz = random_triplet_generator(4, random_state=prng)
vertices, V_xyzr = vertices_and_relative_coordinates(V_xyz, LUT_TABLE)
np.testing.assert_almost_equal(
vertices,
np.array([
[
[0.58919500, 0.58919500, 0.13916400],
[0.33333300, 0.00000000, 0.33333300],
[0.83331100, 0.83331100, 0.83331100],
[0.79789400, -0.03541200, -0.03541200],
],
[
[0.59460100, 0.59460100, 0.36958600],
[0.39062300, 0.00000000, 0.78124600],
[0.83331100, 0.83331100, 1.24996300],
[0.75276700, -0.02847900, 0.36214400],
],
[
[0.66343200, 0.93018800, 0.12992000],
[0.41665500, 0.41665500, 0.41665500],
[0.70710200, 1.11043500, 0.70710200],
[0.63333300, 0.31666700, 0.00000000],
],
[
[0.68274900, 0.99108200, 0.37441600],
[0.41665500, 0.41665500, 0.83330800],
[0.51971400, 0.74472900, 0.74472900],
[0.73227800, 0.31562600, 0.31562600],
],
[
[0.89131800, 0.61982300, 0.07683300],
[0.75276700, -0.02847900, 0.36214400],
[1.06561000, 0.64895700, 0.64895700],
[1.19684100, -0.05311700, -0.05311700],
],
[
[0.95000000, 0.63333300, 0.31666700],
[0.66666700, 0.00000000, 0.66666700],
[1.00000000, 0.66666700, 1.00000000],
[1.16258800, -0.05037200, 0.35394800],
],
[
[0.88379200, 0.88379200, 0.20874600],
[0.73227800, 0.31562600, 0.31562600],
[0.89460600, 0.89460600, 0.66959000],
[1.03843900, 0.31089900, -0.05287000],
],
[
[0.88919900, 0.88919900, 0.43916800],
[0.66666700, 0.33333300, 0.66666700],
[1.24996600, 1.24996600, 1.24996600],
[1.13122500, 0.29792000, 0.29792000],
],
]))
np.testing.assert_almost_equal(
V_xyzr,
np.array([
[0.90108952, 0.09318647, 0.75894709],
[0.64169675, 0.64826849, 0.30437460],
[0.91805308, 0.92882336, 0.33814877],
[0.14444798, 0.01869077, 0.59305522],
]))
def test_vertices_and_relative_coordinates(self):
"""
Tests :func:`colour.algebra.interpolation.\
vertices_and_relative_coordinates` definition.
"""
prng = np.random.RandomState(4)
V_xyz = random_triplet_generator(4, random_state=prng)
vertices, V_xyzr = vertices_and_relative_coordinates(V_xyz, LUT_TABLE)
np.testing.assert_almost_equal(
vertices,
np.array([
[
[0.58919500, 0.58919500, 0.13916400],
[0.33333300, 0.00000000, 0.33333300],
[0.83331100, 0.83331100, 0.83331100],
[0.79789400, -0.03541200, -0.03541200],
],
[
[0.59460100, 0.59460100, 0.36958600],
[0.39062300, 0.00000000, 0.78124600],
[0.83331100, 0.83331100, 1.24996300],
[0.75276700, -0.02847900, 0.36214400],
],
[
[0.66343200, 0.93018800, 0.12992000],
[0.41665500, 0.41665500, 0.41665500],
[0.70710200, 1.11043500, 0.70710200],
[0.63333300, 0.31666700, 0.00000000],
],
[
[0.68274900, 0.99108200, 0.37441600],
[0.41665500, 0.41665500, 0.83330800],
[0.51971400, 0.74472900, 0.74472900],
[0.73227800, 0.31562600, 0.31562600],
],
[
[0.89131800, 0.61982300, 0.07683300],
[0.75276700, -0.02847900, 0.36214400],
[1.06561000, 0.64895700, 0.64895700],
[1.19684100, -0.05311700, -0.05311700],
],
[
[0.95000000, 0.63333300, 0.31666700],
[0.66666700, 0.00000000, 0.66666700],
[1.00000000, 0.66666700, 1.00000000],
[1.16258800, -0.05037200, 0.35394800],
],
[
[0.88379200, 0.88379200, 0.20874600],
[0.73227800, 0.31562600, 0.31562600],
[0.89460600, 0.89460600, 0.66959000],
[1.03843900, 0.31089900, -0.05287000],
],
[
[0.88919900, 0.88919900, 0.43916800],
[0.66666700, 0.33333300, 0.66666700],
[1.24996600, 1.24996600, 1.24996600],
[1.13122500, 0.29792000, 0.29792000],
],
]))
np.testing.assert_almost_equal(
V_xyzr,
np.array([
[0.90108952, 0.09318647, 0.75894709],
[0.64169675, 0.64826849, 0.30437460],
[0.91805308, 0.92882336, 0.33814877],
[0.14444798, 0.01869077, 0.59305522],
]))
