def xyz_to_cct_HA(xyzw):
"""
Convert XYZ tristimulus values to correlated color temperature (CCT).
Args:
:xyzw:
| ndarray of tristimulus values
Returns:
:cct:
| ndarray of correlated color temperatures estimates
References:
1. `Hernández-Andrés, Javier; Lee, RL; Romero, J (September 20, 1999).
Calculating Correlated Color Temperatures Across the Entire Gamut
of Daylight and Skylight Chromaticities.
Applied Optics. 38 (27), 5703–5709. P
<https://www.osapublishing.org/ao/abstract.cfm?uri=ao-38-27-5703>`_
Notes:
According to paper small error from 3000 - 800 000 K, but a test with
Planckians showed errors up to 20% around 500 000 K;
e>0.05 for T>200 000, e>0.1 for T>300 000, ...
"""
if len(xyzw.shape)>2:
raise Exception('xyz_to_cct_HA(): Input xyzw.ndim must be <= 2 !')
out_of_range_code = np.nan
xe = [0.3366, 0.3356]
ye = [0.1735, 0.1691]
A0 = [-949.86315, 36284.48953]
A1 = [6253.80338, 0.00228]
t1 = [0.92159, 0.07861]
A2 = [28.70599, 5.4535*1e-36]
t2 = [0.20039, 0.01543]
A3 = [0.00004, 0.0]
t3 = [0.07125,1.0]
cct_ranges = np.array([[3000.0,50000.0],[50000.0,800000.0]])
Yxy = xyz_to_Yxy(xyzw)
CCT = np.ones((1,Yxy.shape[0]))*out_of_range_code
for i in range(2):
n = (Yxy[:,1]-xe[i])/(Yxy[:,2]-ye[i])
CCT_i = np2d(np.array(A0[i] + A1[i]*np.exp(np.divide(-n,t1[i])) + A2[i]*np.exp(np.divide(-n,t2[i])) + A3[i]*np.exp(np.divide(-n,t3[i]))))
p = (CCT_i >= (1.0-0.05*(i == 0))*cct_ranges[i][0]) & (CCT_i < (1.0+0.05*(i == 0))*cct_ranges[i][1])
CCT[p] = CCT_i[p]
p = (CCT_i < (1.0-0.05)*cct_ranges[0][0]) #smaller than smallest valid CCT value
CCT[p] = -1
if (np.isnan(CCT.sum()) == True) | (np.any(CCT == -1)):
print("Warning: xyz_to_cct_HA(): one or more CCTs out of range! --> (CCT < 3 kK, CCT >800 kK) coded as (-1, NaN) 's")
return CCT.T
def plotSL(cieobs =_CIEOBS, cspace = _CSPACE, DL = True, BBL = True, D65 = False,\
EEW = False, cctlabels = False, axh = None, show = True,\
cspace_pars = {}, formatstr = 'k-',\
diagram_colors = False, diagram_samples = 100, diagram_opacity = 1.0,\
diagram_lightness = 0.25,\
**kwargs):
"""
Plot spectrum locus for cieobs in cspace.
Args:
:DL:
| True or False, optional
| True plots Daylight Locus as well.
:BBL:
| True or False, optional
| True plots BlackBody Locus as well.
:D65:
| False or True, optional
| True plots D65 chromaticity as well.
:EEW:
| False or True, optional
| True plots Equi-Energy-White chromaticity as well.
:cctlabels:
| False or True, optional
| Add cct text labels at various points along the blackbody locus.
:axh:
| None or axes handle, optional
| Determines axes to plot data in.
| None: make new figure.
:show:
| True or False, optional
| Invoke matplotlib.pyplot.show() right after plotting
:cieobs:
| luxpy._CIEOBS or str, optional
| Determines CMF set to calculate spectrum locus or other.
:cspace:
| luxpy._CSPACE or str, optional
| Determines color space / chromaticity diagram to plot data in.
| Note that data is expected to be in specified :cspace:
:formatstr:
| 'k-' or str, optional
| Format str for plotting (see ?matplotlib.pyplot.plot)
:cspace_pars:
| {} or dict, optional
| Dict with parameters required by color space specified in :cspace:
| (for use with luxpy.colortf())
:diagram_colors:
| False, optional
| True: plot colored chromaticity diagram.
:diagram_samples:
| 256, optional
| Sampling resolution of color space.
:diagram_opacity:
| 1.0, optional
| Sets opacity of chromaticity diagram
:diagram_lightness:
| 0.25, optional
| Sets lightness of chromaticity diagram
:kwargs:
| additional keyword arguments for use with matplotlib.pyplot.
Returns:
:returns:
| None (:show: == True)
| or
| handle to current axes (:show: == False)
"""
SL = _CMF[cieobs]['bar'][1:4].T
SL = np.vstack((SL,SL[0]))
SL = 100.0*SL/SL[:,1,None]
SL = colortf(SL, tf = cspace, tfa0 = cspace_pars)
Y,x,y = asplit(SL)
showcopy = show
if np.any([DL,BBL,D65,EEW]):
show = False
if diagram_colors == True:
axh_ = plot_chromaticity_diagram_colors(axh = axh, show = diagram_colors, cspace = cspace, cieobs = cieobs,\
cspace_pars = cspace_pars,\
diagram_samples = diagram_samples,\
diagram_opacity = diagram_opacity,\
diagram_lightness = diagram_lightness,\
label_fontname = None, label_fontsize = None)
else:
axh_ = axh
axh_ = plot_color_data(x,y,axh = axh_, cieobs = cieobs, cspace = cspace, show = show, formatstr=formatstr, **kwargs)
if DL == True:
if 'label' in kwargs.keys(): # avoid label also being used for DL
kwargs.pop('label')
plotDL(ccts = None, cieobs = cieobs, cspace = cspace, axh = axh_, show = show, cspace_pars = cspace_pars, formatstr = 'k:', **kwargs)
if BBL == True:
if 'label' in kwargs.keys(): # avoid label also being used for BB
kwargs.pop('label')
plotBB(ccts = None, cieobs = cieobs, cspace = cspace, axh = axh_, show = show, cspace_pars = cspace_pars, cctlabels = cctlabels, formatstr = 'k-.', **kwargs)
if D65 == True:
YxyD65 = colortf(spd_to_xyz(_CIE_ILLUMINANTS['D65']), tf = cspace, tfa0 = cspace_pars)
plt.plot(YxyD65[...,1],YxyD65[...,2],'bo')
if EEW == True:
YxyEEW = colortf(spd_to_xyz(_CIE_ILLUMINANTS['E']), tf = cspace, tfa0 = cspace_pars)
plt.plot(YxyEEW[...,1],YxyEEW[...,2],'ko')
if showcopy == False:
return axh_
else:
plt.show()