def test_ipt(self, getvars):
S, spds, rfls, xyz, xyzw = getvars
labw = lx.xyz_to_ipt(xyzw)
lab = lx.xyz_to_ipt(xyz)
xyzw_ = lx.ipt_to_xyz(labw)
xyz_ = lx.ipt_to_xyz(lab)
assert np.isclose(xyz, xyz_).all()
assert np.isclose(xyzw, xyzw_).all()
def to_ipt(self, cieobs=_CIEOBS, xyzw=None, M=None):
"""
Convert XYZ tristimulus values to IPT color coordinates.
| I: Lightness axis, P, red-green axis, T: yellow-blue axis.
Args:
:xyzw:
| None or ndarray with xyz values of white point, optional
| None defaults to xyz of CIE D65 using the :cieobs: observer.
:cieobs:
| luxpy._CIEOBS, optional
| CMF set to use when calculating xyzw for rescaling Mxyz2lms
| (only when not None).
:M:
| None, optional
| None defaults to conversion matrix determined by :cieobs:
Returns:
:ipt:
| luxpy.LAB with .value field that is a ndarray
| with IPT color coordinates
Note:
:xyz: is assumed to be under D65 viewing conditions!!
| If necessary perform chromatic adaptation !!
"""
return LAB(value=xyz_to_ipt(self.value, cieobs=cieobs, xyzw=xyzw, M=M),
relative=self.relative,
dtype='ipt',
cieobs=cieobs,
xyzw=xyzw,
M=M)
def spd_to_mcri(SPD, D=0.9, E=None, Yb=20.0, out='Rm', wl=None):
"""
Calculates the MCRI or Memory Color Rendition Index, Rm
Args:
:SPD:
| ndarray with spectral data (can be multiple SPDs,
first axis are the wavelengths)
:D:
| 0.9, optional
| Degree of adaptation.
:E:
| None, optional
| Illuminance in lux
| (used to calculate La = (Yb/100)*(E/pi) to then calculate D
| following the 'cat02' model).
| If None: the degree is determined by :D:
| If (:E: is not None) & (:Yb: is None): :E: is assumed to contain
the adapting field luminance La (cd/m²).
:Yb:
| 20.0, optional
| Luminance factor of background. (used when calculating La from E)
| If None, E contains La (cd/m²).
:out:
| 'Rm' or str, optional
| Specifies requested output (e.g. 'Rm,Rmi,cct,duv')
:wl:
| None, optional
| Wavelengths (or [start, end, spacing]) to interpolate the SPDs to.
| None: default to no interpolation
Returns:
:returns:
| float or ndarray with MCRI Rm for :out: 'Rm'
| Other output is also possible by changing the :out: str value.
References:
1. `K.A.G. Smet, W.R. Ryckaert, M.R. Pointer, G. Deconinck, P. Hanselaer,(2012)
“A memory colour quality metric for white light sources,”
Energy Build., vol. 49, no. C, pp. 216–225.
<http://www.sciencedirect.com/science/article/pii/S0378778812000837>`_
"""
SPD = np2d(SPD)
if wl is not None:
SPD = spd(data=SPD, interpolation=_S_INTERP_TYPE, kind='np', wl=wl)
# unpack metric default values:
avg, catf, cieobs, cri_specific_pars, cspace, ref_type, rg_pars, sampleset, scale = [
_MCRI_DEFAULTS[x] for x in sorted(_MCRI_DEFAULTS.keys())
]
similarity_ai = cri_specific_pars['similarity_ai']
Mxyz2lms = cspace['Mxyz2lms']
scale_fcn = scale['fcn']
scale_factor = scale['cfactor']
sampleset = eval(sampleset)
# A. calculate xyz:
xyzti, xyztw = spd_to_xyz(SPD, cieobs=cieobs['xyz'], rfl=sampleset, out=2)
if 'cct' in out.split(','):
cct, duv = xyz_to_cct(xyztw,
cieobs=cieobs['cct'],
out='cct,duv',
mode='lut')
# B. perform chromatic adaptation to adopted whitepoint of ipt color space, i.e. D65:
if catf is not None:
Dtype_cat, F, Yb_cat, catmode_cat, cattype_cat, mcat_cat, xyzw_cat = [
catf[x] for x in sorted(catf.keys())
]
# calculate degree of adaptationn D:
if E is not None:
if Yb is not None:
La = (Yb / 100.0) * (E / np.pi)
else:
La = E
D = cat.get_degree_of_adaptation(Dtype=Dtype_cat, F=F, La=La)
else:
Dtype_cat = None # direct input of D
if (E is None) and (D is None):
D = 1.0 # set degree of adaptation to 1 !
if D > 1.0: D = 1.0
if D < 0.6: D = 0.6 # put a limit on the lowest D
# apply cat:
xyzti = cat.apply(xyzti,
cattype=cattype_cat,
catmode=catmode_cat,
xyzw1=xyztw,
xyzw0=None,
xyzw2=xyzw_cat,
D=D,
mcat=[mcat_cat],
Dtype=Dtype_cat)
xyztw = cat.apply(xyztw,
cattype=cattype_cat,
catmode=catmode_cat,
xyzw1=xyztw,
xyzw0=None,
xyzw2=xyzw_cat,
D=D,
mcat=[mcat_cat],
Dtype=Dtype_cat)
# C. convert xyz to ipt and split:
ipt = xyz_to_ipt(
xyzti, cieobs=cieobs['xyz'], M=Mxyz2lms
) #input matrix as published in Smet et al. 2012, Energy and Buildings
I, P, T = asplit(ipt)
# D. calculate specific (hue dependent) similarity indicators, Si:
if len(xyzti.shape) == 3:
ai = np.expand_dims(similarity_ai, axis=1)
else:
ai = similarity_ai
a1, a2, a3, a4, a5 = asplit(ai)
mahalanobis_d2 = (a3 * np.power((P - a1), 2.0) + a4 * np.power(
(T - a2), 2.0) + 2.0 * a5 * (P - a1) * (T - a2))
if (len(mahalanobis_d2.shape) == 3) & (mahalanobis_d2.shape[-1] == 1):
mahalanobis_d2 = mahalanobis_d2[:, :, 0].T
Si = np.exp(-0.5 * mahalanobis_d2)
# E. calculate general similarity indicator, Sa:
Sa = avg(Si, axis=0, keepdims=True)
# F. rescale similarity indicators (Si, Sa) with a 0-1 scale to memory color rendition indices (Rmi, Rm) with a 0 - 100 scale:
Rmi = scale_fcn(np.log(Si), scale_factor=scale_factor)
Rm = np2d(scale_fcn(np.log(Sa), scale_factor=scale_factor))
# G. calculate Rg (polyarea of test / polyarea of memory colours):
if 'Rg' in out.split(','):
I = I[
...,
None] #broadcast_shape(I, target_shape = None,expand_2d_to_3d = 0)
a1 = a1[:, None] * np.ones(
I.shape
) #broadcast_shape(a1, target_shape = None,expand_2d_to_3d = 0)
a2 = a2[:, None] * np.ones(
I.shape
) #broadcast_shape(a2, target_shape = None,expand_2d_to_3d = 0)
a12 = np.concatenate(
(a1, a2), axis=2
) #broadcast_shape(np.hstack((a1,a2)), target_shape = ipt.shape,expand_2d_to_3d = 0)
ipt_mc = np.concatenate((I, a12), axis=2)
nhbins, normalize_gamut, normalized_chroma_ref, start_hue = [
rg_pars[x] for x in sorted(rg_pars.keys())
]
Rg = jab_to_rg(ipt,
ipt_mc,
ordered_and_sliced=False,
nhbins=nhbins,
start_hue=start_hue,
normalize_gamut=normalize_gamut)
if (out != 'Rm'):
return eval(out)
else:
return Rm
labw = lx.xyz_to_Yuv(xyzw) lab = lx.xyz_to_Yuv(xyz) xyzw_ = lx.Yuv_to_xyz(labw) xyz_ = lx.Yuv_to_xyz(lab) labw = lx.xyz_to_lab(xyzw, xyzw=xyzw[:1, :]) lab = lx.xyz_to_lab(xyz, xyzw=xyzw[:1, :]) xyzw_ = lx.lab_to_xyz(labw, xyzw=xyzw[:1, :]) xyz_ = lx.lab_to_xyz(lab, xyzw=xyzw[:1, :]) labw = lx.xyz_to_luv(xyzw, xyzw=xyzw) lab = lx.xyz_to_luv(xyz, xyzw=xyzw) xyzw_ = lx.luv_to_xyz(labw, xyzw=xyzw) xyz_ = lx.luv_to_xyz(lab, xyzw=xyzw) labw = lx.xyz_to_ipt(xyzw) lab = lx.xyz_to_ipt(xyz) xyzw_ = lx.ipt_to_xyz(labw) xyz_ = lx.ipt_to_xyz(lab) labw = lx.xyz_to_wuv(xyzw, xyzw=xyzw) lab = lx.xyz_to_wuv(xyz, xyzw=xyzw) xyzw_ = lx.wuv_to_xyz(labw, xyzw=xyzw) xyz_ = lx.wuv_to_xyz(lab, xyzw=xyzw) labw = lx.xyz_to_Vrb_mb(xyzw) lab = lx.xyz_to_Vrb_mb(xyz) xyzw_ = lx.Vrb_mb_to_xyz(labw) xyz_ = lx.Vrb_mb_to_xyz(lab) labw = lx.xyz_to_Ydlep(xyzw)