def plotDL(ccts = None, cieobs =_CIEOBS, cspace = _CSPACE, axh = None, \
show = True, force_daylight_below4000K = False, cspace_pars = {}, \
formatstr = 'k-', **kwargs):
"""
Plot daylight locus.
Args:
:ccts:
| None or list[float], optional
| None defaults to [4000 K to 1e19 K] in 100 steps on a log10 scale.
:force_daylight_below4000K:
| False or True, optional
| CIE daylight phases are not defined below 4000 K.
| If True plot anyway.
: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())
:kwargs:
| additional keyword arguments for use with matplotlib.pyplot.
Returns:
:returns:
| None (:show: == True)
| or
| handle to current axes (:show: == False)
"""
if ccts is None:
ccts = 10**np.linspace(np.log10(4000.0),np.log10(10.0**19.0),100.0)
xD,yD = daylightlocus(ccts, force_daylight_below4000K = force_daylight_below4000K)
Y = 100*np.ones(xD.shape)
DL = Yxy_to_xyz(np.vstack((Y, xD,yD)).T)
DL = colortf(DL, tf = cspace, tfa0 = cspace_pars)
Y,x,y = asplit(DL)
axh = plot_color_data(x,y,axh = axh, cieobs = cieobs, cspace = cspace, show=show, formatstr=formatstr, **kwargs)
if show == False:
return axh
def xyz_to_cct_search(xyzw,
cieobs=_CIEOBS,
out='cct',
wl=None,
accuracy=0.1,
upper_cct_max=10.0**20,
approx_cct_temp=True):
"""
Convert XYZ tristimulus values to correlated color temperature (CCT) and
Duv(distance above (> 0) or below ( < 0) the Planckian locus) by a
brute-force search.
| The algorithm uses an approximate cct_temp (HA approx., see xyz_to_cct_HA)
as starting point or uses the middle of the allowed cct-range
(1e2 K - 1e20 K, higher causes overflow) on a log-scale, then constructs
a 4-step section of the blackbody (Planckian) locus on which to find the
minimum distance to the 1960 uv chromaticity of the test source.
Args:
:xyzw:
| ndarray of tristimulus values
:cieobs:
| luxpy._CIEOBS, optional
| CMF set used to calculated xyzw.
:out:
| 'cct' (or 1), optional
| Determines what to return.
| Other options: 'duv' (or -1), 'cct,duv'(or 2), "[cct,duv]" (or -2)
:wl:
| None, optional
| Wavelengths used when calculating Planckian radiators.
:accuracy:
| float, optional
| Stop brute-force search when cct :accuracy: is reached.
:upper_cct_max:
| 10.0**20, optional
| Limit brute-force search to this cct.
:approx_cct_temp:
| True, optional
| If True: use xyz_to_cct_HA() to get a first estimate of cct to
speed up search.
Returns:
:returns:
| ndarray with:
| cct: out == 'cct' (or 1)
| duv: out == 'duv' (or -1)
| cct, duv: out == 'cct,duv' (or 2)
| [cct,duv]: out == "[cct,duv]" (or -2)
Notes:
This program is more accurate, but slower than xyz_to_cct_ohno!
Note that cct must be between 1e3 K - 1e20 K
(very large cct take a long time!!!)
"""
xyzw = np2d(xyzw)
if len(xyzw.shape) > 2:
raise Exception('xyz_to_cct_search(): Input xyzw.shape must be <= 2 !')
# get 1960 u,v of test source:
Yuvt = xyz_to_Yuv(np.squeeze(
xyzw)) # remove possible 1-dim + convert xyzw to CIE 1976 u',v'
#axis_of_v3t = len(Yuvt.shape)-1 # axis containing color components
ut = Yuvt[:, 1, None] #.take([1],axis = axis_of_v3t) # get CIE 1960 u
vt = (2 / 3) * Yuvt[:, 2,
None] #.take([2],axis = axis_of_v3t) # get CIE 1960 v
# Initialize arrays:
ccts = np.ones((xyzw.shape[0], 1)) * np.nan
duvs = ccts.copy()
#calculate preliminary solution(s):
if (approx_cct_temp == True):
ccts_est = xyz_to_cct_HA(xyzw)
procent_estimates = np.array([[3000.0, 100000.0, 0.05],
[100000.0, 200000.0, 0.1],
[200000.0, 300000.0, 0.25],
[300000.0, 400000.0, 0.4],
[400000.0, 600000.0, 0.4],
[600000.0, 800000.0, 0.4],
[800000.0, np.inf, 0.25]])
else:
upper_cct = np.array(upper_cct_max)
lower_cct = np.array(10.0**2)
cct_scale_fun = lambda x: np.log10(x)
cct_scale_ifun = lambda x: np.power(10.0, x)
dT = (cct_scale_fun(upper_cct) - cct_scale_fun(lower_cct)) / 2
ccttemp = np.array([cct_scale_ifun(cct_scale_fun(lower_cct) + dT)])
ccts_est = np2d(ccttemp * np.ones((xyzw.shape[0], 1)))
dT_approx_cct_False = dT.copy()
# Loop through all ccts:
for i in range(xyzw.shape[0]):
#initialize CCT search parameters:
cct = np.nan
duv = np.nan
ccttemp = ccts_est[i].copy()
# Take care of (-1, NaN)'s from xyz_to_cct_HA signifying (CCT < lower, CCT > upper) bounds:
approx_cct_temp_temp = approx_cct_temp
if (approx_cct_temp == True):
cct_scale_fun = lambda x: x
cct_scale_ifun = lambda x: x
if (ccttemp != -1) & (
np.isnan(ccttemp) == False
): # within validity range of CCT estimator-function
for ii in range(procent_estimates.shape[0]):
if (ccttemp >=
(1.0 - 0.05 *
(ii == 0)) * procent_estimates[ii, 0]) & (
ccttemp < (1.0 + 0.05 *
(ii == 0)) * procent_estimates[ii, 1]):
procent_estimate = procent_estimates[ii, 2]
break
dT = np.multiply(
ccttemp, procent_estimate
) # determines range around CCTtemp (25% around estimate) or 100 K
elif (ccttemp == -1) & (np.isnan(ccttemp) == False):
ccttemp = np.array([procent_estimates[0, 0] / 2])
procent_estimate = 1 # cover 0 K to min_CCT of estimator
dT = np.multiply(ccttemp, procent_estimate)
elif (np.isnan(ccttemp) == True):
upper_cct = np.array(upper_cct_max)
lower_cct = np.array(10.0**2)
cct_scale_fun = lambda x: np.log10(x)
cct_scale_ifun = lambda x: np.power(10.0, x)
dT = (cct_scale_fun(upper_cct) - cct_scale_fun(lower_cct)) / 2
ccttemp = np.array(
[cct_scale_ifun(cct_scale_fun(lower_cct) + dT)])
approx_cct_temp = False
else:
dT = dT_approx_cct_False
nsteps = 3
signduv = 1.0
ccttemp = ccttemp[0]
delta_cct = dT
while ((delta_cct > accuracy)): # keep converging on CCT
#generate range of ccts:
ccts_i = cct_scale_ifun(
np.linspace(
cct_scale_fun(ccttemp) - dT,
cct_scale_fun(ccttemp) + dT, nsteps + 1))
ccts_i[ccts_i < 100.0] = 100.0 # avoid nan's in calculation
# Generate BB:
BB = cri_ref(ccts_i, wl3=wl, ref_type=['BB'], cieobs=cieobs)
# Calculate xyz:
xyz = spd_to_xyz(BB, cieobs=cieobs)
# Convert to CIE 1960 u,v:
Yuv = xyz_to_Yuv(np.squeeze(
xyz)) # remove possible 1-dim + convert xyz to CIE 1976 u',v'
#axis_of_v3 = len(Yuv.shape)-1 # axis containing color components
u = Yuv[:, 1, None] # get CIE 1960 u
v = (2.0 / 3.0) * Yuv[:, 2, None] # get CIE 1960 v
# Calculate distance between list of uv's and uv of test source:
dc = ((ut[i] - u)**2 + (vt[i] - v)**2)**0.5
if np.isnan(dc.min()) == False:
#eps = _EPS
q = dc.argmin()
if np.size(
q
) > 1: #to minimize calculation time: only calculate median when necessary
cct = np.median(ccts[q])
duv = np.median(dc[q])
q = np.median(q)
q = int(q) #must be able to serve as index
else:
cct = ccts_i[q]
duv = dc[q]
if (q == 0):
ccttemp = cct_scale_ifun(
np.array(cct_scale_fun([cct])) + 2 * dT / nsteps)
#dT = 2.0*dT/nsteps
continue # look in higher section of planckian locus
if (q == np.size(ccts_i)):
ccttemp = cct_scale_ifun(
np.array(cct_scale_fun([cct])) - 2 * dT / nsteps)
#dT = 2.0*dT/nsteps
continue # look in lower section of planckian locus
if (q > 0) & (q < np.size(ccts_i) - 1):
dT = 2 * dT / nsteps
# get Duv sign:
d_p1m1 = ((u[q + 1] - u[q - 1])**2.0 +
(v[q + 1] - v[q - 1])**2.0)**0.5
x = (dc[q - 1]**2.0 - dc[q + 1]**2.0 +
d_p1m1**2.0) / 2.0 * d_p1m1
vBB = v[q - 1] + ((v[q + 1] - v[q - 1]) * (x / d_p1m1))
signduv = np.sign(vt[i] - vBB)
#calculate difference with previous intermediate solution:
delta_cct = abs(cct - ccttemp)
ccttemp = np.array(cct) #%set new intermediate CCT
approx_cct_temp = approx_cct_temp_temp
else:
ccttemp = np.nan
cct = np.nan
duv = np.nan
duvs[i] = signduv * abs(duv)
ccts[i] = cct
# Regulate output:
if (out == 'cct') | (out == 1):
return np2d(ccts)
elif (out == 'duv') | (out == -1):
return np2d(duvs)
elif (out == 'cct,duv') | (out == 2):
return np2d(ccts), np2d(duvs)
elif (out == "[cct,duv]") | (out == -2):
return np.vstack((ccts, duvs)).T
# load TM30 spd data base:
_IESTM30 = {
'S': {
'data': getdata(_S_PATH + 'IESTM30_Sspds.dat', kind='np').transpose()
}
}
_IESTM30['S']['info'] = getdata(_S_PATH + 'IESTM30_Sinfo.txt',
kind='np',
header='infer',
verbosity=False)
_IESTM30_S = _IESTM30['S']
#------------------------------------------------------------------------------
# Illuminant library: set some typical CIE illuminants:
E = np.array([np.linspace(380, 780, 401), np.ones(401)])
D65 = np.array(
[[
380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393,
394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407,
408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421,
422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435,
436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449,
450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463,
464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477,
478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491,
492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505,
506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519,
520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533,
534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547,
548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561,
def plot_chromaticity_diagram_colors(diagram_samples = 256, diagram_opacity = 1.0, diagram_lightness = 0.25,\
cieobs = _CIEOBS, cspace = 'Yxy', cspace_pars = {},\
show = True, axh = None,\
show_grid = True, label_fontname = 'Times New Roman', label_fontsize = 12,\
**kwargs):
"""
Plot the chromaticity diagram colors.
Args:
: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
: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:
:cspace_pars:
| {} or dict, optional
| Dict with parameters required by color space specified in :cspace:
| (for use with luxpy.colortf())
:show_grid:
| True, optional
| Show grid (True) or not (False)
:label_fontname:
| 'Times New Roman', optional
| Sets font type of axis labels.
:label_fontsize:
| 12, optional
| Sets font size of axis labels.
:kwargs:
| additional keyword arguments for use with matplotlib.pyplot.
Returns:
"""
offset = _EPS
ii, jj = np.meshgrid(np.linspace(offset, 1 + offset, diagram_samples), np.linspace(1+offset, offset, diagram_samples))
ij = np.dstack((ii, jj))
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)
SL = np.vstack((x,y)).T
ij2D = ij.reshape((diagram_samples**2,2))
ij2D = np.hstack((diagram_lightness*100*np.ones((ij2D.shape[0],1)), ij2D))
xyz = colortf(ij2D, tf = cspace + '>xyz', tfa0 = cspace_pars)
xyz[xyz < 0] = 0
xyz[np.isinf(xyz.sum(axis=1)),:] = np.nan
xyz[np.isnan(xyz.sum(axis=1)),:] = offset
srgb = xyz_to_srgb(xyz)
srgb = srgb/srgb.max()
srgb = srgb.reshape((diagram_samples,diagram_samples,3))
if show == True:
if axh is None:
fig = plt.figure()
axh = fig.add_subplot(111)
polygon = Polygon(SL, facecolor='none', edgecolor='none')
axh.add_patch(polygon)
image = axh.imshow(
srgb,
interpolation='bilinear',
extent = (0.0, 1, -0.05, 1),
clip_path=None,
alpha=diagram_opacity)
image.set_clip_path(polygon)
plt.plot(x,y, color = 'darkgray')
if cspace == 'Yxy':
plt.xlim([0,1])
plt.ylim([0,1])
elif cspace == 'Yuv':
plt.xlim([0,0.6])
plt.ylim([0,0.6])
if (cspace is not None):
xlabel = _CSPACE_AXES[cspace][1]
ylabel = _CSPACE_AXES[cspace][2]
if (label_fontname is not None) & (label_fontsize is not None):
plt.xlabel(xlabel, fontname = label_fontname, fontsize = label_fontsize)
plt.ylabel(ylabel, fontname = label_fontname, fontsize = label_fontsize)
if show_grid == True:
plt.grid()
#plt.show()
return axh
else:
return None
def plotellipse(v, cspace_in = 'Yxy', cspace_out = None, nsamples = 100, \
show = True, axh = None, \
line_color = 'darkgray', line_style = ':', line_width = 1, line_marker = '', line_markersize = 4,\
plot_center = False, center_marker = 'o', center_color = 'darkgray', center_markersize = 4,\
show_grid = True, label_fontname = 'Times New Roman', label_fontsize = 12,\
out = None):
"""
Plot ellipse(s) given in v-format [Rmax,Rmin,xc,yc,theta].
Args:
:v:
| (Nx5) ndarray
| ellipse parameters [Rmax,Rmin,xc,yc,theta]
:cspace_in:
| 'Yxy', optional
| Color space of v.
| If None: no color space assumed. Axis labels assumed ('x','y').
:cspace_out:
| None, optional
| Color space to plot ellipse(s) in.
| If None: plot in cspace_in.
:nsamples:
| 100 or int, optional
| Number of points (samples) in ellipse boundary
:show:
| True or boolean, optional
| Plot ellipse(s) (True) or not (False)
:axh:
| None, optional
| Ax-handle to plot ellipse(s) in.
| If None: create new figure with axes.
:line_color:
| 'darkgray', optional
| Color to plot ellipse(s) in.
:line_style:
| ':', optional
| Linestyle of ellipse(s).
:line_width':
| 1, optional
| Width of ellipse boundary line.
:line_marker:
| 'none', optional
| Marker for ellipse boundary.
:line_markersize:
| 4, optional
| Size of markers in ellipse boundary.
:plot_center:
| False, optional
| Plot center of ellipse: yes (True) or no (False)
:center_color:
| 'darkgray', optional
| Color to plot ellipse center in.
:center_marker:
| 'o', optional
| Marker for ellipse center.
:center_markersize:
| 4, optional
| Size of marker of ellipse center.
:show_grid:
| True, optional
| Show grid (True) or not (False)
:label_fontname:
| 'Times New Roman', optional
| Sets font type of axis labels.
:label_fontsize:
| 12, optional
| Sets font size of axis labels.
:out:
| None, optional
| Output of function
| If None: returns None. Can be used to output axh of newly created
| figure axes or to return Yxys an ndarray with coordinates of
| ellipse boundaries in cspace_out (shape = (nsamples,3,N))
Returns:
:returns: None, or whatever set by :out:.
"""
Yxys = np.zeros((nsamples,3,v.shape[0]))
ellipse_vs = np.zeros((v.shape[0],5))
for i,vi in enumerate(v):
# Set sample density of ellipse boundary:
t = np.linspace(0, 2*np.pi, nsamples)
a = vi[0] # major axis
b = vi[1] # minor axis
xyc = vi[2:4,None] # center
theta = vi[-1] # rotation angle
# define rotation matrix:
R = np.hstack(( np.vstack((np.cos(theta), np.sin(theta))), np.vstack((-np.sin(theta), np.cos(theta)))))
# Calculate ellipses:
Yxyc = np.vstack((1, xyc)).T
Yxy = np.vstack((np.ones((1,nsamples)), xyc + np.dot(R, np.vstack((a*np.cos(t), b*np.sin(t))) ))).T
Yxys[:,:,i] = Yxy
# Convert to requested color space:
if (cspace_out is not None) & (cspace_in is not None):
Yxy = colortf(Yxy, cspace_in + '>' + cspace_out)
Yxyc = colortf(Yxyc, cspace_in + '>' + cspace_out)
Yxys[:,:,i] = Yxy
# get ellipse parameters in requested color space:
ellipse_vs[i,:] = math.fit_ellipse(Yxy[:,1:])
#de = np.sqrt((Yxy[:,1]-Yxyc[:,1])**2 + (Yxy[:,2]-Yxyc[:,2])**2)
#ellipse_vs[i,:] = np.hstack((de.max(),de.min(),Yxyc[:,1],Yxyc[:,2],np.nan)) # nan because orientation is xy, but request is some other color space. Change later to actual angle when fitellipse() has been implemented
# plot ellipses:
if show == True:
if (axh is None) & (i == 0):
fig = plt.figure()
axh = fig.add_subplot(111)
if (cspace_in is None):
xlabel = 'x'
ylabel = 'y'
else:
xlabel = _CSPACE_AXES[cspace_in][1]
ylabel = _CSPACE_AXES[cspace_in][2]
if (cspace_out is not None):
xlabel = _CSPACE_AXES[cspace_out][1]
ylabel = _CSPACE_AXES[cspace_out][2]
if plot_center == True:
plt.plot(Yxyc[:,1],Yxyc[:,2],color = center_color, linestyle = 'none', marker = center_marker, markersize = center_markersize)
plt.plot(Yxy[:,1],Yxy[:,2],color = line_color, linestyle = line_style, linewidth = line_width, marker = line_marker, markersize = line_markersize)
plt.xlabel(xlabel, fontname = label_fontname, fontsize = label_fontsize)
plt.ylabel(ylabel, fontname = label_fontname, fontsize = label_fontsize)
if show_grid == True:
plt.grid()
#plt.show()
Yxys = np.transpose(Yxys,axes=(0,2,1))
if out is not None:
return eval(out)
else:
return None
def plotBB(ccts = None, cieobs =_CIEOBS, cspace = _CSPACE, axh = None, cctlabels = True, show = True, cspace_pars = {}, formatstr = 'k-', **kwargs):
"""
Plot blackbody locus.
Args:
:ccts:
| None or list[float], optional
| None defaults to [1000 to 1e19 K].
| Range:
| [1000,1500,2000,2500,3000,3500,4000,5000,6000,8000,10000]
| + [15000 K to 1e19 K] in 100 steps on a log10 scale
:cctlabels:
| True or False, 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())
:kwargs:
| additional keyword arguments for use with matplotlib.pyplot.
Returns:
:returns:
| None (:show: == True)
| or
| handle to current axes (:show: == False)
"""
if ccts is None:
ccts1 = np.array([1000.0,1500.0,2000.0,2500.0,3000.0,3500.0,4000.0,5000.0,6000.0,8000.0,10000.0])
ccts2 = 10**np.linspace(np.log10(15000.0),np.log10(10.0**19.0),100.0)
ccts = np.hstack((ccts1,ccts2))
else:
ccts1 = None
BB = cri_ref(ccts,ref_type='BB')
xyz = spd_to_xyz(BB,cieobs = cieobs)
Yxy = colortf(xyz, tf = cspace, tfa0 = cspace_pars)
Y,x,y = asplit(Yxy)
axh = plot_color_data(x,y,axh = axh, cieobs = cieobs, cspace = cspace, show=show, formatstr=formatstr, **kwargs)
if (cctlabels == True) & (ccts1 is not None):
for i in range(ccts1.shape[0]):
if ccts1[i]>= 3000.0:
if i%2 == 0.0:
plt.plot(x[i],y[i],'k+', color = '0.5')
plt.text(x[i]*1.05,y[i]*0.95,'{:1.0f}K'.format(ccts1[i]), color = '0.5')
plt.plot(x[-1],y[-1],'k+', color = '0.5')
plt.text(x[-1]*1.05,y[-1]*0.95,'{:1.0e}K'.format(ccts[-1]), color = '0.5')
if show == False:
return axh
# load TM30 spd data base:
_IESTM3015 = {'S': {'data': getdata(_S_PATH + 'IESTM30_15_Sspds.dat',kind='np').transpose()}}
_IESTM3015['S']['info'] = getdata(_S_PATH + 'IESTM30_15_Sinfo.txt',kind='np',header='infer',verbosity = False)
_IESTM3015_S = _IESTM3015['S']
_IESTM3018 = {'S': {'data': getdata(_S_PATH + 'IESTM30_15_Sspds.dat',kind='np').transpose()}}
_IESTM3018['S']['info'] = getdata(_S_PATH + 'IESTM30_15_Sinfo.txt',kind='np',header='infer',verbosity = False)
_IESTM3018_S = _IESTM3018['S']
#------------------------------------------------------------------------------
# Illuminant library: set some typical CIE illuminants:
E = np.array([np.linspace(360,830,471),np.ones(471)])
_CIE_E = E.copy()
#D65 = np.array([[380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780],
# [49.98,50.33,50.84,51.42,51.94,52.31,52.47,52.58,52.84,53.46,54.65,56.56,59.08,62.05,65.31,68.7,72.06,75.26,78.19,80.73,82.75,84.2,85.18,85.89,86.47,87.12,87.95,88.91,89.89,90.78,91.49,91.92,92.15,92.26,92.33,92.46,92.69,92.99,93.26,93.44,93.43,93.18,92.7,92,91.11,90.06,88.89,87.78,86.91,86.48,86.68,87.64,89.23,91.24,93.49,95.77,97.93,99.92,101.7,103.4,104.9,106.2,107.4,108.5,109.7,110.9,112.3,113.7,115,116.2,117,117.5,117.6,117.6,117.5,117.4,117.4,117.6,117.7,117.8,117.8,117.7,117.4,117.1,116.7,116.3,115.9,115.6,115.3,115,114.9,114.8,114.8,115,115.2,115.4,115.7,115.9,116.1,116.1,115.9,115.5,114.9,114.1,113.3,112.4,111.5,110.6,109.8,109.2,108.8,108.6,108.6,108.7,108.9,109.1,109.2,109.3,109.4,109.4,109.4,109.2,109.1,108.9,108.7,108.6,108.4,108.3,108.1,108,107.8,107.6,107.3,107,106.7,106.3,105.9,105.5,105.1,104.9,104.8,104.9,105.1,105.4,105.8,106.2,106.7,107.1,107.4,107.6,107.7,107.6,107.3,106.9,106.5,106,105.6,105.2,104.9,104.6,104.4,104.3,104.2,104.2,104.2,104.2,104.3,104.3,104.3,104.2,104,103.8,103.4,103,102.5,102,101.6,101.2,100.8,100.4,100,99.64,99.29,98.93,98.56,98.17,97.76,97.34,96.95,96.61,96.33,96.15,96.04,96,96.01,96.06,96.13,96.19,96.18,96.06,95.79,95.33,94.71,93.96,93.13,92.24,91.33,90.47,89.7,89.08,88.69,88.54,88.59,88.79,89.06,89.35,89.58,89.76,89.89,89.97,90.01,90,89.96,89.9,89.85,89.8,89.78,89.76,89.74,89.69,89.6,89.45,89.27,89.06,88.85,88.65,88.48,88.32,88.16,87.96,87.7,87.37,86.97,86.52,86.02,85.49,84.95,84.43,83.95,83.56,83.29,83.15,83.14,83.21,83.34,83.49,83.64,83.76,83.82,83.81,83.7,83.48,83.16,82.77,82.33,81.86,81.4,80.95,80.56,80.24,80.03,79.93,79.92,79.98,80.06,80.12,80.15,80.15,80.15,80.16,80.21,80.32,80.48,80.69,80.95,81.25,81.58,81.9,82.15,82.3,82.28,82.06,81.7,81.23,80.74,80.28,79.89,79.54,79.19,78.79,78.28,77.64,76.88,76,75.04,74,72.93,71.88,70.95,70.2,69.72,69.56,69.67,69.95,70.31,70.67,70.95,71.16,71.33,71.47,71.61,71.77,71.96,72.22,72.55,72.98,73.5,74.02,74.41,74.56,74.35,73.69,72.64,71.28,69.7,67.98,66.22,64.56,63.14,62.1,61.6,61.73,62.38,63.37,64.55,65.74,66.83,67.77,68.59,69.29,69.89,70.39,70.84,71.31,71.84,72.49,73.27,74.08,74.75,75.14,75.09,74.5,73.47,72.17,70.74,69.34,68.09,66.95,65.87,64.77,63.59,62.27,60.77,59.08,57.17,55.01,52.63,50.28,48.27,46.88,46.42,47.09,48.72,51.03,53.75,56.61,59.36,61.86,64.01,65.69,66.81,67.28,67.21,66.73,65.98,65.09,64.21,63.46,62.98,62.91,63.38]])
D65 = getdata(_S_PATH + 'D65.dat',kind='np').T
_CIE_D65 = D65.copy()
#A = np.array([[380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780],
# [9.795,10.01,10.23,10.45,10.67,10.9,11.13,11.36,11.6,11.84,12.09,12.33,12.58,12.84,13.09,13.35,13.62,13.89,14.16,14.43,14.71,14.99,15.27,15.56,15.85,16.15,16.45,16.75,17.05,17.36,17.68,17.99,18.31,18.63,18.96,19.29,19.62,19.96,20.3,20.65,21,21.35,21.7,22.06,22.42,22.79,23.16,23.53,23.91,24.29,24.67,25.06,25.45,25.84,26.24,26.64,27.05,27.46,27.87,28.28,28.7,29.13,29.55,29.98,30.41,30.85,31.29,31.73,32.18,32.63,33.09,33.54,34,34.47,34.94,35.41,35.88,36.36,36.84,37.32,37.81,38.3,38.8,39.3,39.8,40.3,40.81,41.32,41.83,42.35,42.87,43.39,43.92,44.45,44.98,45.52,46.06,46.6,47.14,47.69,48.24,48.8,49.35,49.91,50.48,51.04,51.61,52.18,52.76,53.33,53.91,54.5,55.08,55.67,56.26,56.85,57.45,58.05,58.65,59.26,59.86,60.47,61.08,61.7,62.31,62.93,63.55,64.18,64.8,65.43,66.06,66.7,67.33,67.97,68.61,69.25,69.9,70.54,71.19,71.84,72.5,73.15,73.81,74.47,75.13,75.79,76.45,77.12,77.79,78.46,79.13,79.81,80.48,81.16,81.84,82.52,83.2,83.89,84.57,85.26,85.95,86.64,87.33,88.02,88.72,89.41,90.11,90.81,91.51,92.21,92.91,93.62,94.32,95.03,95.73,96.44,97.15,97.86,98.57,99.29,100,100.7,101.4,102.2,102.9,103.6,104.3,105,105.7,106.5,107.2,107.9,108.6,109.3,110.1,110.8,111.5,112.3,113,113.7,114.4,115.2,115.9,116.6,117.3,118.1,118.8,119.5,120.3,121,121.7,122.5,123.2,123.9,124.7,125.4,126.1,126.8,127.6,128.3,129,129.8,130.5,131.2,132,132.7,133.4,134.2,134.9,135.6,136.3,137.1,137.8,138.5,139.3,140,140.7,141.4,142.2,142.9,143.6,144.3,145.1,145.8,146.5,147.2,148,148.7,149.4,150.1,150.8,151.6,152.3,153,153.7,154.4,155.1,155.8,156.6,157.3,158,158.7,159.4,160.1,160.8,161.5,162.2,162.9,163.6,164.3,165,165.7,166.4,167.1,167.8,168.5,169.2,169.9,170.6,171.3,172,172.7,173.3,174,174.7,175.4,176.1,176.7,177.4,178.1,178.8,179.4,180.1,180.8,181.4,182.1,182.8,183.4,184.1,184.8,185.4,186.1,186.7,187.4,188.1,188.7,189.3,190,190.6,191.3,191.9,192.6,193.2,193.8,194.5,195.1,195.8,196.4,197,197.6,198.3,198.9,199.5,200.1,200.7,201.4,202,202.6,203.2,203.8,204.4,205,205.6,206.2,206.8,207.4,208,208.6,209.2,209.8,210.4,210.9,211.5,212.1,212.7,213.3,213.8,214.4,215,215.6,216.1,216.7,217.3,217.8,218.4,218.9,219.5,220,220.6,221.1,221.7,222.2,222.8,223.3,223.8,224.4,224.9,225.4,225.9,226.5,227,227.5,228,228.6,229.1,229.6,230.1,230.6,231.1,231.6,232.1,232.6,233.1,233.6,234.1,234.6,235.1,235.6,236.1,236.5,237,237.5,238,238.4,238.9,239.4,239.8,240.3,240.8,241.2,241.7]])
A = getdata(_S_PATH + 'A.dat',kind='np').T
_CIE_A = A.copy()
C = np.array([[360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830],
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_CIE_C = C.copy()
F4 = np.array([[380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780],
[0.57,0.596,0.622,0.648,0.674,0.7,0.734,0.768,0.802,0.836,0.87,0.892,0.914,0.936,0.958,0.98,1.186,1.392,1.598,1.804,2.01,4.358,6.706,9.054,11.4,13.75,11.39,9.03,6.67,4.31,1.95,1.878,1.806,1.734,1.662,1.59,1.624,1.658,1.692,1.726,1.76,1.794,1.828,1.862,1.896,1.93,1.964,1.998,2.032,2.066,2.1,7.736,13.37,19.01,24.64,30.28,25.83,21.38,16.93,12.48,8.03,6.934,5.838,4.742,3.646,2.55,2.58,2.61,2.64,2.67,2.7,2.724,2.748,2.772,2.796,2.82,2.838,2.856,2.874,2.892,2.91,2.926,2.942,2.958,2.974,2.99,3,3.01,3.02,3.03,3.04,3.048,3.056,3.064,3.072,3.08,3.082,3.084,3.086,3.088,3.09,3.09,3.09,3.09,3.09,3.09,3.1,3.11,3.12,3.13,3.14,3.124,3.108,3.092,3.076,3.06,3.048,3.036,3.024,3.012,3,2.996,2.992,2.988,2.984,2.98,2.986,2.992,2.998,3.004,3.01,3.036,3.062,3.088,3.114,3.14,3.194,3.248,3.302,3.356,3.41,3.508,3.606,3.704,3.802,3.9,4.058,4.216,4.374,4.532,4.69,4.914,5.138,5.362,5.586,5.81,6.112,6.414,6.716,7.018,7.32,10.37,13.43,16.48,19.54,22.59,21.09,19.6,18.1,16.61,15.11,14.86,14.62,14.37,14.13,13.88,14.37,14.86,15.35,15.84,16.33,16.8,17.27,17.74,18.21,18.68,19.07,19.46,19.86,20.25,20.64,21.37,22.1,22.82,23.55,24.28,24.68,25.07,25.47,25.86,26.26,25.66,25.07,24.47,23.88,23.28,23.21,23.14,23.08,23.01,22.94,22.78,22.62,22.46,22.3,22.14,21.89,21.65,21.4,21.16,20.91,20.61,20.32,20.02,19.73,19.43,19.09,18.75,18.42,18.08,17.74,17.39,17.04,16.7,16.35,16,15.68,15.37,15.05,14.74,14.42,14.05,13.68,13.3,12.93,12.56,12.23,11.91,11.58,11.26,10.93,10.65,10.37,10.08,9.802,9.52,9.252,8.984,8.716,8.448,8.18,7.946,7.712,7.478,7.244,7.01,6.808,6.606,6.404,6.202,6,5.822,5.644,5.466,5.288,5.11,4.96,4.81,4.66,4.51,4.36,4.226,4.092,3.958,3.824,3.69,3.578,3.466,3.354,3.242,3.13,3.032,2.934,2.836,2.738,2.64,2.56,2.48,2.4,2.32,2.24,2.174,2.108,2.042,1.976,1.91,1.868,1.826,1.784,1.742,1.7,1.638,1.576,1.514,1.452,1.39,1.348,1.306,1.264,1.222,1.18,1.15,1.12,1.09,1.06,1.03,1,0.97,0.94,0.91,0.88,0.852,0.824,0.796,0.768,0.74,0.72,0.7,0.68,0.66,0.64,0.62,0.6,0.58,0.56,0.54,0.53,0.52,0.51,0.5,0.49,0.484,0.478,0.472,0.466,0.46,0.452,0.444,0.436,0.428,0.42,0.41,0.4,0.39,0.38,0.37,0.37,0.37,0.37,0.37,0.37,0.362,0.354,0.346,0.338,0.33,0.334,0.338,0.342,0.346,0.35,0.352,0.354,0.356,0.358,0.36,0.35,0.34,0.33,0.32,0.31,0.3,0.29,0.28,0.27,0.26,0.246,0.232,0.218,0.204,0.19]])
_CIE_F4 = F4.copy()
