def tcs_colorimetry_data(spd_t,
spd_r,
spds_tcs,
cmfs,
chromatic_adaptation=False):
"""
Returns the *test colour samples* colorimetry data.
Parameters
----------
spd_t : SpectralPowerDistribution
Test spectral power distribution.
spd_r : SpectralPowerDistribution
Reference spectral power distribution.
spds_tcs : dict
*Test colour samples* spectral power distributions.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
chromatic_adaptation : bool, optional
Perform chromatic adaptation.
Returns
-------
list
*Test colour samples* colorimetry data.
"""
XYZ_t = spectral_to_XYZ(spd_t, cmfs)
uv_t = np.ravel(UCS_to_uv(XYZ_to_UCS(XYZ_t)))
u_t, v_t = uv_t[0], uv_t[1]
XYZ_r = spectral_to_XYZ(spd_r, cmfs)
uv_r = np.ravel(UCS_to_uv(XYZ_to_UCS(XYZ_r)))
u_r, v_r = uv_r[0], uv_r[1]
tcs_data = []
for _key, value in sorted(TCS_INDEXES_TO_NAMES.items()):
spd_tcs = spds_tcs.get(value)
XYZ_tcs = spectral_to_XYZ(spd_tcs, cmfs, spd_t)
xyY_tcs = np.ravel(XYZ_to_xyY(XYZ_tcs))
uv_tcs = np.ravel(UCS_to_uv(XYZ_to_UCS(XYZ_tcs)))
u_tcs, v_tcs = uv_tcs[0], uv_tcs[1]
if chromatic_adaptation:
c = lambda x, y: (4 - x - 10 * y) / y
d = lambda x, y: (1.708 * y + 0.404 - 1.481 * x) / y
c_t, d_t = c(u_t, v_t), d(u_t, v_t)
c_r, d_r = (c(u_r, v_r),
d(u_r, v_r))
tcs_c, tcs_d = c(u_tcs, v_tcs), d(u_tcs, v_tcs)
u_tcs = ((10.872 + 0.404 * c_r / c_t * tcs_c - 4 *
d_r / d_t * tcs_d) /
(16.518 + 1.481 * c_r / c_t * tcs_c -
d_r / d_t * tcs_d))
v_tcs = (5.52 / (16.518 + 1.481 * c_r / c_t * tcs_c -
d_r / d_t * tcs_d))
W_tcs = 25 * xyY_tcs[-1] ** (1 / 3) - 17
U_tcs = 13 * W_tcs * (u_tcs - u_r)
V_tcs = 13 * W_tcs * (v_tcs - v_r)
tcs_data.append(
TCS_ColorimetryData(spd_tcs.name,
XYZ_tcs,
uv_tcs,
np.array([U_tcs, V_tcs, W_tcs])))
return tcs_data
def _tcs_colorimetry_data(test_spd,
reference_spd,
tsc_spds,
cmfs,
chromatic_adaptation=False):
"""
Returns the *test colour samples* colorimetry data.
Parameters
----------
test_spd : SpectralPowerDistribution
Test spectral power distribution.
reference_spd : SpectralPowerDistribution
Reference spectral power distribution.
tsc_spds : dict
Test colour samples.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
chromatic_adaptation : bool, optional
Perform chromatic adaptation.
Returns
-------
list
*Test colour samples* colorimetry data.
"""
test_XYZ = spectral_to_XYZ(test_spd, cmfs)
test_uv = np.ravel(UCS_to_uv(XYZ_to_UCS(test_XYZ)))
test_u, test_v = test_uv[0], test_uv[1]
reference_XYZ = spectral_to_XYZ(reference_spd, cmfs)
reference_uv = np.ravel(UCS_to_uv(XYZ_to_UCS(reference_XYZ)))
reference_u, reference_v = reference_uv[0], reference_uv[1]
tcs_data = []
for key, value in sorted(TCS_INDEXES_TO_NAMES.items()):
tcs_spd = tsc_spds.get(value)
tcs_XYZ = spectral_to_XYZ(tcs_spd, cmfs, test_spd)
tcs_xyY = np.ravel(XYZ_to_xyY(tcs_XYZ))
tcs_uv = np.ravel(UCS_to_uv(XYZ_to_UCS(tcs_XYZ)))
tcs_u, tcs_v = tcs_uv[0], tcs_uv[1]
if chromatic_adaptation:
c = lambda x, y: (4 - x - 10 * y) / y
d = lambda x, y: (1.708 * y + 0.404 - 1.481 * x) / y
test_c, test_d = c(test_u, test_v), d(test_u, test_v)
reference_c, reference_d = (c(reference_u, reference_v),
d(reference_u, reference_v))
tcs_c, tcs_d = c(tcs_u, tcs_v), d(tcs_u, tcs_v)
tcs_u = ((10.872 + 0.404 * reference_c / test_c * tcs_c - 4 *
reference_d / test_d * tcs_d) /
(16.518 + 1.481 * reference_c / test_c * tcs_c -
reference_d / test_d * tcs_d))
tcs_v = (5.52 / (16.518 + 1.481 * reference_c / test_c * tcs_c -
reference_d / test_d * tcs_d))
tcs_W = 25 * tcs_xyY[-1] ** (1 / 3) - 17
tcs_U = 13 * tcs_W * (tcs_u - reference_u)
tcs_V = 13 * tcs_W * (tcs_v - reference_v)
tcs_data.append(
TSC_COLORIMETRY_DATA_NXYZUVUVW(tcs_spd.name,
tcs_XYZ,
tcs_uv,
np.array([tcs_U, tcs_V, tcs_W])))
return tcs_data
def tcs_colorimetry_data(spd_t,
spd_r,
spds_tcs,
cmfs,
chromatic_adaptation=False):
"""
Returns the *test colour samples* colorimetry data.
Parameters
----------
spd_t : SpectralPowerDistribution
Test spectral power distribution.
spd_r : SpectralPowerDistribution
Reference spectral power distribution.
spds_tcs : dict
*Test colour samples* spectral power distributions.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
chromatic_adaptation : bool, optional
Perform chromatic adaptation.
Returns
-------
list
*Test colour samples* colorimetry data.
"""
XYZ_t = spectral_to_XYZ(spd_t, cmfs)
uv_t = UCS_to_uv(XYZ_to_UCS(XYZ_t))
u_t, v_t = uv_t[0], uv_t[1]
XYZ_r = spectral_to_XYZ(spd_r, cmfs)
uv_r = UCS_to_uv(XYZ_to_UCS(XYZ_r))
u_r, v_r = uv_r[0], uv_r[1]
tcs_data = []
for _key, value in sorted(TCS_INDEXES_TO_NAMES.items()):
spd_tcs = spds_tcs[value]
XYZ_tcs = spectral_to_XYZ(spd_tcs, cmfs, spd_t)
xyY_tcs = XYZ_to_xyY(XYZ_tcs)
uv_tcs = UCS_to_uv(XYZ_to_UCS(XYZ_tcs))
u_tcs, v_tcs = uv_tcs[0], uv_tcs[1]
if chromatic_adaptation:
def c(x, y):
"""
Computes the :math:`c` term.
"""
return (4 - x - 10 * y) / y
def d(x, y):
"""
Computes the :math:`d` term.
"""
return (1.708 * y + 0.404 - 1.481 * x) / y
c_t, d_t = c(u_t, v_t), d(u_t, v_t)
c_r, d_r = c(u_r, v_r), d(u_r, v_r)
tcs_c, tcs_d = c(u_tcs, v_tcs), d(u_tcs, v_tcs)
u_tcs = ((
10.872 + 0.404 * c_r / c_t * tcs_c - 4 * d_r / d_t * tcs_d) /
(16.518 + 1.481 * c_r / c_t * tcs_c - d_r / d_t * tcs_d))
v_tcs = (5.52 /
(16.518 + 1.481 * c_r / c_t * tcs_c - d_r / d_t * tcs_d))
W_tcs = 25 * xyY_tcs[-1] ** (1 / 3) - 17
U_tcs = 13 * W_tcs * (u_tcs - u_r)
V_tcs = 13 * W_tcs * (v_tcs - v_r)
tcs_data.append(
TCS_ColorimetryData(spd_tcs.name, XYZ_tcs, uv_tcs,
np.array([U_tcs, V_tcs, W_tcs])))
return tcs_data
def _tcs_colorimetry_data(test_spd,
reference_spd,
tsc_spds,
cmfs,
chromatic_adaptation=False):
"""
Returns the *test colour samples* colorimetry data.
Parameters
----------
test_spd : SpectralPowerDistribution
Test spectral power distribution.
reference_spd : SpectralPowerDistribution
Reference spectral power distribution.
tsc_spds : dict
Test colour samples.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
chromatic_adaptation : bool, optional
Perform chromatic adaptation.
Returns
-------
list
*Test colour samples* colorimetry data.
"""
test_XYZ = spectral_to_XYZ(test_spd, cmfs)
test_uv = np.ravel(UCS_to_uv(XYZ_to_UCS(test_XYZ)))
test_u, test_v = test_uv[0], test_uv[1]
reference_XYZ = spectral_to_XYZ(reference_spd, cmfs)
reference_uv = np.ravel(UCS_to_uv(XYZ_to_UCS(reference_XYZ)))
reference_u, reference_v = reference_uv[0], reference_uv[1]
tcs_data = []
for key, value in sorted(TCS_INDEXES_TO_NAMES.items()):
tcs_spd = tsc_spds.get(value)
tcs_XYZ = spectral_to_XYZ(tcs_spd, cmfs, test_spd)
tcs_xyY = np.ravel(XYZ_to_xyY(tcs_XYZ))
tcs_uv = np.ravel(UCS_to_uv(XYZ_to_UCS(tcs_XYZ)))
tcs_u, tcs_v = tcs_uv[0], tcs_uv[1]
if chromatic_adaptation:
c = lambda x, y: (4 - x - 10 * y) / y
d = lambda x, y: (1.708 * y + 0.404 - 1.481 * x) / y
test_c, test_d = c(test_u, test_v), d(test_u, test_v)
reference_c, reference_d = (c(reference_u, reference_v),
d(reference_u, reference_v))
tcs_c, tcs_d = c(tcs_u, tcs_v), d(tcs_u, tcs_v)
tcs_u = ((10.872 + 0.404 * reference_c / test_c * tcs_c -
4 * reference_d / test_d * tcs_d) /
(16.518 + 1.481 * reference_c / test_c * tcs_c -
reference_d / test_d * tcs_d))
tcs_v = (5.52 / (16.518 + 1.481 * reference_c / test_c * tcs_c -
reference_d / test_d * tcs_d))
tcs_W = 25 * tcs_xyY[-1]**(1 / 3) - 17
tcs_U = 13 * tcs_W * (tcs_u - reference_u)
tcs_V = 13 * tcs_W * (tcs_v - reference_v)
tcs_data.append(
TSC_COLORIMETRY_DATA_NXYZUVUVW(tcs_spd.name, tcs_XYZ, tcs_uv,
np.array([tcs_U, tcs_V, tcs_W])))
return tcs_data
