def test_n_dimensional_log_encoding_CanonLog(self):
"""
Test :func:`colour.models.rgb.transfer_functions.canon_log.\
log_encoding_CanonLog` definition n-dimensional arrays support.
"""
x = 0.18
clog = log_encoding_CanonLog(x)
x = np.tile(x, 6)
clog = np.tile(clog, 6)
np.testing.assert_almost_equal(log_encoding_CanonLog(x),
clog,
decimal=7)
x = np.reshape(x, (2, 3))
clog = np.reshape(clog, (2, 3))
np.testing.assert_almost_equal(log_encoding_CanonLog(x),
clog,
decimal=7)
x = np.reshape(x, (2, 3, 1))
clog = np.reshape(clog, (2, 3, 1))
np.testing.assert_almost_equal(log_encoding_CanonLog(x),
clog,
decimal=7)
def test_n_dimensional_log_encoding_CanonLog(self):
"""
Tests :func:`colour.models.rgb.transfer_functions.canon_log.\
log_encoding_CanonLog` definition n-dimensional arrays support.
"""
L = 0.18
V = 0.312012855550395
np.testing.assert_almost_equal(
log_encoding_CanonLog(L),
V,
decimal=7)
L = np.tile(L, 6)
V = np.tile(V, 6)
np.testing.assert_almost_equal(
log_encoding_CanonLog(L),
V,
decimal=7)
L = np.reshape(L, (2, 3))
V = np.reshape(V, (2, 3))
np.testing.assert_almost_equal(
log_encoding_CanonLog(L),
V,
decimal=7)
L = np.reshape(L, (2, 3, 1))
V = np.reshape(V, (2, 3, 1))
np.testing.assert_almost_equal(
log_encoding_CanonLog(L),
V,
decimal=7)
def test_nan_log_encoding_CanonLog(self):
"""
Tests :func:`colour.models.rgb.transfer_functions.canon_log.\
log_encoding_CanonLog` definition nan support.
"""
log_encoding_CanonLog(
np.array([-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan]))
def test_nan_log_encoding_CanonLog(self):
"""
Test :func:`colour.models.rgb.transfer_functions.canon_log.\
log_encoding_CanonLog` definition nan support.
"""
log_encoding_CanonLog(
np.array([-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan]))
def test_domain_range_scale_log_encoding_CanonLog(self):
"""
Test :func:`colour.models.rgb.transfer_functions.canon_log.\
log_encoding_CanonLog` definition domain and range scale support.
"""
x = 0.18
clog = log_encoding_CanonLog(x)
d_r = (("reference", 1), ("1", 1), ("100", 100))
for scale, factor in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(log_encoding_CanonLog(x *
factor),
clog * factor,
decimal=7)
def test_domain_range_scale_log_encoding_CanonLog(self):
"""
Tests :func:`colour.models.rgb.transfer_functions.canon_log.\
log_encoding_CanonLog` definition domain and range scale support.
"""
x = 0.18
clog = log_encoding_CanonLog(x)
d_r = (('reference', 1), (1, 1), (100, 100))
for scale, factor in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(
log_encoding_CanonLog(x * factor),
clog * factor,
decimal=7)
def test_log_encoding_CanonLog(self):
"""
Tests :func:`colour.models.rgb.transfer_functions.canon_log.\
log_encoding_CanonLog` definition.
"""
self.assertAlmostEqual(log_encoding_CanonLog(0.0),
0.073059700000000,
places=7)
self.assertAlmostEqual(log_encoding_CanonLog(0.18),
0.312012855550395,
places=7)
self.assertAlmostEqual(log_encoding_CanonLog(1.0),
0.627408304537653,
places=7)
def test_log_encoding_CanonLog(self):
"""
Test :func:`colour.models.rgb.transfer_functions.canon_log.\
log_encoding_CanonLog` definition.
"""
self.assertAlmostEqual(log_encoding_CanonLog(-0.1),
-0.023560122781997,
places=7)
self.assertAlmostEqual(log_encoding_CanonLog(0.0),
0.125122480156403,
places=7)
self.assertAlmostEqual(log_encoding_CanonLog(0.18),
0.343389651726069,
places=7)
self.assertAlmostEqual(log_encoding_CanonLog(0.18, 12),
0.343138084215647,
places=7)
self.assertAlmostEqual(log_encoding_CanonLog(0.18, 10, False),
0.327953896935809,
places=7)
self.assertAlmostEqual(
log_encoding_CanonLog(0.18, 10, False, False),
0.312012855550395,
places=7,
)
self.assertAlmostEqual(log_encoding_CanonLog(1.0),
0.618775485598649,
places=7)
def test_log_encoding_CanonLog(self):
"""
Tests :func:`colour.models.rgb.transfer_functions.canon_log.\
log_encoding_CanonLog` definition.
"""
self.assertAlmostEqual(
log_encoding_CanonLog(-0.1), -0.023560122781997, places=7)
self.assertAlmostEqual(
log_encoding_CanonLog(0.0), 0.125122480156403, places=7)
self.assertAlmostEqual(
log_encoding_CanonLog(0.18), 0.343389651726069, places=7)
self.assertAlmostEqual(
log_encoding_CanonLog(0.18, 12), 0.343138084215647, places=7)
self.assertAlmostEqual(
log_encoding_CanonLog(0.18, 10, False),
0.327953896935809,
places=7)
self.assertAlmostEqual(
log_encoding_CanonLog(0.18, 10, False, False),
0.312012855550395,
places=7)
self.assertAlmostEqual(
log_encoding_CanonLog(1.0), 0.618775485598649, places=7)
def test_log_encoding_CanonLog(self):
"""
Tests :func:`colour.models.rgb.transfer_functions.canon_log.\
log_encoding_CanonLog` definition.
"""
self.assertAlmostEqual(
log_encoding_CanonLog(0.0),
0.073059700000000,
places=7)
self.assertAlmostEqual(
log_encoding_CanonLog(0.18),
0.312012855550395,
places=7)
self.assertAlmostEqual(
log_encoding_CanonLog(1.0),
0.627408304537653,
places=7)
def test_n_dimensional_log_encoding_CanonLog(self):
"""
Tests :func:`colour.models.rgb.transfer_functions.canon_log.\
log_encoding_CanonLog` definition n-dimensional arrays support.
"""
L = 0.18
V = 0.312012855550395
np.testing.assert_almost_equal(log_encoding_CanonLog(L), V, decimal=7)
L = np.tile(L, 6)
V = np.tile(V, 6)
np.testing.assert_almost_equal(log_encoding_CanonLog(L), V, decimal=7)
L = np.reshape(L, (2, 3))
V = np.reshape(V, (2, 3))
np.testing.assert_almost_equal(log_encoding_CanonLog(L), V, decimal=7)
L = np.reshape(L, (2, 3, 1))
V = np.reshape(V, (2, 3, 1))
np.testing.assert_almost_equal(log_encoding_CanonLog(L), V, decimal=7)
def test_n_dimensional_log_encoding_CanonLog(self):
"""
Tests :func:`colour.models.rgb.transfer_functions.canon_log.\
log_encoding_CanonLog` definition n-dimensional arrays support.
"""
x = 0.18
clog = log_encoding_CanonLog(x)
x = np.tile(x, 6)
clog = np.tile(clog, 6)
np.testing.assert_almost_equal(
log_encoding_CanonLog(x), clog, decimal=7)
x = np.reshape(x, (2, 3))
clog = np.reshape(clog, (2, 3))
np.testing.assert_almost_equal(
log_encoding_CanonLog(x), clog, decimal=7)
x = np.reshape(x, (2, 3, 1))
clog = np.reshape(clog, (2, 3, 1))
np.testing.assert_almost_equal(
log_encoding_CanonLog(x), clog, decimal=7)
