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)