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import numpy as np
import scipy.spatial
import crl.color as color
import crl.tables as tables
class CRIError(Exception):
pass
class CRIResult:
def __init__(self, method_name):
self.method_name = method_name
self.SCRIs = []
self.tcs = []
self.tcs_ref = []
def __str__(self):
ret = f"CRI result for {self.method_name}\n"
ret += f"\tXYZ = {self.XYZ}\n"
ret += f"\tCCT = {self.CCT} K (delta uv = {self.duv})\n"
SCRIs = ", ".join("%.1f" % SCRI for SCRI in self.SCRIs)
ret += f"\tSCRIs = [{SCRIs}]\n"
ret += f"\tCRI = {self.CRI}"
return ret
def CAT(sample, ref, test):
def c(u, v, _):
return (4 - u - 10 * v) / v
def d(u, v, _):
return (1.708 * v - 1.481 * u + 0.404) / v
cs, ds = c(*sample), d(*sample)
cr, dr = c(*ref), d(*ref)
ct, dt = c(*test), d(*test)
u = (10.872 + 0.404 * cr / ct * cs - 4 * dr / dt * ds) \
/ (16.518 + 1.481 * cr / ct * cs - dr / dt * ds)
v = 5.52 / (16.518 + 1.481 * cr / ct * cs - dr / dt * ds)
return color.uvY(u, v, sample[2])
def cri_Ra(S, observer=color.Observer_2deg, ignore_errors=False):
res = CRIResult("CRI Ra")
res.S = S.normed()
res.XYZ = color.XYZ(res.S, observer=observer)
res.CCT, res.duv = color.cct(res.XYZ)
if res.duv > 5e-2 and not ignore_errors:
raise CRIError(f"spectrum not white enough with delta uv = {res.duv}")
if res.CCT < 5000:
white = color.BlackBodySpectrum(res.CCT).normed()
else:
if res.CCT > 25000 and ignore_errors:
res.CCT = 25000
white = color.CIEDaylightSpectrum(res.CCT).normed()
res.XYZ_ref = color.XYZ(white, observer=observer)
class UVWstarRef(color.UVWstar):
white_uvY = color.uvY(white, observer=observer)
class UVWstar(color.UVWstar):
white_uvY = color.uvY(S, observer=observer)
for i in range(14):
tcs = color.Spectrum(tables.tcs_ra[:, 0], tables.tcs_ra[:, 1 + i])
lit = color.uvY(color.CombinedSpectrum(tcs, res.S))
lit = CAT(lit, UVWstarRef.white_uvY, UVWstar.white_uvY)
lit = UVWstarRef(lit)
lit_ref = UVWstarRef(color.CombinedSpectrum(tcs, white), observer=observer)
res.tcs.append(lit)
res.tcs_ref.append(lit_ref)
dE = np.linalg.norm(lit.array - lit_ref.array)
res.SCRIs.append(100 - 4.6 * dE)
res.CRI = np.mean(res.SCRIs[:8])
res.ECRI = np.mean(res.SCRIs)
return res
def cri_GAI(spec, observer=color.Observer_2deg):
points = []
for i in range(8):
tcs = color.Spectrum(color.tables.tcs_ra[:, 0], color.tables.tcs_ra[:, 1 + i])
u, v, _ = color.uvY76(color.CombinedSpectrum(tcs, spec), observer=observer)
points.append([u, v])
points = np.array(points)
hull = scipy.spatial.ConvexHull(points)
# The magic number is the hull.volume of the E illuminant
GAI = hull.volume * 100 / 0.007351716795637625
return GAI, hull.points[hull.vertices]
def cri_FSI(spec):
wvl = np.arange(380, 730, 1)
Y = spec.Yi(wvl)
dwvl = wvl[1] - wvl[0]
Y = spec.Yi(wvl)
Y /= np.sum(Y * dwvl)
CEE = (wvl - 380) / (730 - 380)
Is = []
for i, _ in enumerate(wvl):
C = np.cumsum(Y * dwvl)
D = (C - CEE) ** 2
Is.append(sum(D * dwvl))
Y = np.roll(Y, 1)
FSI = np.mean(Is)
return FSI
def cri_FSCI(spec):
return 100 - 5.1 * cri_FSI(spec)
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