11 files changed, 1974 insertions, 0 deletions

diff --git a/crl/__init__.py b/crl/__init__.py
new file mode 100644
index 0000000..bd51612
--- /dev/null
+++ b/crl/__init__.py
@@ -0,0 +1,3 @@
+from crl.color import *
+from crl.plots import chromaticity_diagram, plot_data
+from crl.cri import cri_Ra, cri_GAI, cri_FSI, cri_FSCI
diff --git a/crl/assets/diagram_Labstar.png b/crl/assets/diagram_Labstar.png
new file mode 100644
index 0000000..58e97a7
--- /dev/null
+++ b/crl/assets/diagram_Labstar.png
diff --git a/crl/assets/diagram_UVWstar.png b/crl/assets/diagram_UVWstar.png
new file mode 100644
index 0000000..de2629f
--- /dev/null
+++ b/crl/assets/diagram_UVWstar.png
diff --git a/crl/assets/diagram_uv.png b/crl/assets/diagram_uv.png
new file mode 100644
index 0000000..d43587e
--- /dev/null
+++ b/crl/assets/diagram_uv.png
diff --git a/crl/assets/diagram_uv76.png b/crl/assets/diagram_uv76.png
new file mode 100644
index 0000000..ec4f577
--- /dev/null
+++ b/crl/assets/diagram_uv76.png
diff --git a/crl/assets/diagram_xy.png b/crl/assets/diagram_xy.png
new file mode 100644
index 0000000..1f1a092
--- /dev/null
+++ b/crl/assets/diagram_xy.png
diff --git a/crl/color.py b/crl/color.py
new file mode 100644
index 0000000..d6240ff
--- /dev/null
+++ b/crl/color.py
@@ -0,0 +1,566 @@
+import numpy as np, scipy.interpolate, scipy.optimize
+import crl.tables as tables
+import crl.tables2 as tables2
+
+class Spectrum:
+	def __init__(self, X, Y):
+		self.X = X
+		self.Y = Y
+		self.Yi = scipy.interpolate.interp1d(self.X, self.Y, \
+		          "linear", fill_value=0, bounds_error=False)
+
+	def dot(A, B, Xmin=380, Xmax=780, Xstep=0.01):
+		if not isinstance(B, Spectrum):
+			raise TypeError("B is an instance of %s, not Spectrum" \
+			      % type(B))
+
+		if isinstance(A, PointSpectrum):
+			if isinstance(B, PointSpectrum):
+				raise TypeError("dotting two instances of PointSpectum isn't supported")
+				# ^ could be, but who cares?
+			return B.dot(A)
+
+		if isinstance(B, PointSpectrum):
+			return A.Yi(B.x_0) * B.S
+
+		X = np.arange(Xmin, Xmax, Xstep)
+		dX = X[1] - X[0]
+		return dX * np.sum(A.Yi(X) * B.Yi(X))
+
+	def normed(self, norm=100):
+		return NormedSpectrum(self, norm)
+
+	def __str__(self):
+		return "a tabulated Spectrum"
+
+class NormedSpectrum(Spectrum):
+	def __init__(self, spec, norm=100):
+		self.spec = spec
+
+		_, Y, _ = XYZ(self.spec)
+		self.factor = norm / Y
+
+	def Yi(self, wvl):
+		return self.factor * self.spec.Yi(wvl)
+
+	def __repr__(self):
+		return "NormedSpectrum(" + repr(self.spec) + ")"
+
+	def __str__(self):
+		return "normed  " + str(self.spec)
+
+class PointSpectrum(Spectrum):
+	# the "S" stands for "sum" (being the integral of the Dirac delta)
+	def __init__(self, x_0, S=1):
+		self.x_0 = x_0
+		self.S = S
+
+		# a crappy hack for easy plotting
+		self.X = np.array([380, x_0 - 0.001, x_0, x_0 + 0.001, 780])
+		self.Y = np.array([0, 0, S, 0, 0])
+
+	def __repr__(self):
+		return "PointSpectrum(x_0=%g, S=%g)" % (self.x_0, self.S)
+
+	def __str__(self):
+		return self.__repr__()
+
+class BlackBodySpectrum(Spectrum):
+	def __init__(self, T):
+		self.T = T
+
+	def Yi(self, wvl):
+		h = 6.62607015e-34 # kg m^2 / s
+		c = 299792458 # m / s
+		k_B = 1.3806485e-23 # kg m^2 / K s^2
+		return 2 * h * c ** 2 / ((wvl / 1e9) ** 5) \
+		       / (np.exp(h * c / (wvl / 1e9 * k_B * self.T)) - 1) \
+		       * 1e-9 # kg / (nm s^3)
+
+	def __repr__(self):
+		return "BlackBodySpectrum(T=%g)" % self.T
+
+	def __str__(self):
+		return repr(self)
+
+# Schanda, page 41
+class CIEDaylightSpectrum(Spectrum):
+	S0 = Spectrum(tables.D_eigenvectors[:, 0], tables.D_eigenvectors[:, 1])
+	S1 = Spectrum(tables.D_eigenvectors[:, 0], tables.D_eigenvectors[:, 2])
+	S2 = Spectrum(tables.D_eigenvectors[:, 0], tables.D_eigenvectors[:, 3])
+
+	def __init__(self, T):
+		if T < 4000 or T > 25000:
+			raise ValueError("D illuminants are not defined for CCTs outside the 4000 K-25000 K range")
+		elif T <= 7000:
+			x = -4.6070e9 / T**3 \
+			    + 2.9678e6 / T**2 \
+			    + 0.09911e3 / T \
+			    + 0.244063
+		else:
+			x = -2.0064e9 / T**3 \
+			    + 1.9018e6 / T**2 \
+			    + 0.0247483 / T \
+			    + 0.237040
+
+		y = -3 * x**2 + 2.870 * x - 0.275
+
+		self.M1 = (-1.3515 - 1.7703 * x + 5.9114 * y) \
+		          / (0.0241 + 0.2562 * x - 0.7341 * y)
+		self.M2 = (0.0300 - 31.4424 * x + 30.0717 * y) \
+		          / (0.0241 + 0.2562 * x - 0.7341 * y)
+
+		self.M1 = round(self.M1, 3)
+		self.M2 = round(self.M2, 3)
+
+	def Yi(self, wvl):
+		return self.S0.Yi(wvl) \
+		       + self.M1 * self.S1.Yi(wvl) \
+		       + self.M2 * self.S2.Yi(wvl)
+
+class CombinedSpectrum(Spectrum):
+	def __init__(self, A, B):
+		self.A = A
+		self.B = B
+
+	def Yi(self, X):
+		return self.A.Yi(X) * self.B.Yi(X)
+
+	def __repr__(self):
+		return "CombinedSpectrum(%s, %s)" % (repr(self.A), repr(self.B))
+
+def combine_spectra(A, B):
+	if isinstance(B, PointSpectrum):
+		A, B = B, A
+
+	if isinstance(A, PointSpectrum):
+		if isinstance(B, PointSpectrum):
+			# technically this would make sense but would be pretty useless
+			raise TypeError("can't combine two instances of PointSpectrum")
+
+		return PointSpectrum(A.x_0, A.S * B.Ef(A.x_0))
+
+	return CombinedSpectrum(A, B)
+
+#
+# Tables, standard observers, basic colors
+#
+
+class Illuminants:
+	# 0 l,nm
+	# 1 Standard Illuminant A
+	# 2 Standard Illuminant D65
+	# 3 Illuminant C
+	# 4 IlluminantD50
+	# 5 IlluminantD55
+	# 6 IlluminantD75
+	A = Spectrum(tables.illuminants[:, 0], tables.illuminants[:, 1])
+	D50 = Spectrum(tables.illuminants[:, 0], tables.illuminants[:, 4])
+	D55 = Spectrum(tables.illuminants[:, 0], tables.illuminants[:, 5])
+	D65 = Spectrum(tables.illuminants[:, 0], tables.illuminants[:, 2])
+	D75 = Spectrum(tables.illuminants[:, 0], tables.illuminants[:, 6])
+
+	_wvl = np.linspace(380, 780, 801)
+	_E = np.ones(_wvl.size)
+	E = Spectrum(_wvl, _E)
+
+	_E = np.zeros(_wvl.size)
+	_E[120] = 3 # 440 nm
+	_E[376] = 5 # 568 nm
+	RedmanTest1 = Spectrum(_wvl, _E) # 6500K, CRI -12
+
+class Observer:
+	def __init__(self, name, wvl, cmf_x, cmf_y, cmf_z):
+		self.name = name
+		self.cmf_x = Spectrum(wvl, cmf_x)
+		self.cmf_y = Spectrum(wvl, cmf_y)
+		self.cmf_z = Spectrum(wvl, cmf_z)
+
+	def __str__(self):
+		return self.name
+
+Observer_2deg = Observer("2-deg Standard Observer",
+                         *(tables.cmf[:, i] for i in range(4)))
+Observer_10deg = Observer("10-deg Standard Observer",
+                          *(tables.cmf_1964[:, i] for i in range(4)))
+
+class Color:
+	full_name = "Color"
+	observer = Observer_2deg
+
+	# Call either:
+	#   __init__(self, x, y, z)
+	#   __init__(self, color)
+	#   __init__(self, spectrum)
+	def __init__(self, *args, **kwargs):
+		observer = kwargs.get("observer", Observer_2deg)
+
+		if len(args) == 3:
+			self.array = np.array(args)
+		elif len(args) == 1:
+			if isinstance(args[0], Color):
+				self.array = args[0].to(self).array
+			elif isinstance(args[0], Spectrum):
+				self.array = self.from_spectrum(args[0], observer).array
+			else:
+				raise TypeError("argument is not a Color or a Spectrum")
+		else:
+			raise TypeError("bad number of arguments")
+
+	def __repr__(self):
+		return "%s(%g, %g, %g)" % (type(self).__name__, *self)
+
+	def __str__(self):
+		return "%s (%g, %g, %g), %s" % (self.full_name, *self,
+		                                self.observer)
+
+	@classmethod
+	def from_spectrum(cls, S, observer):
+		return cls.from_XYZ(XYZ.from_spectrum(S, observer))
+
+	def to(self, CS):
+		return CS.from_XYZ(self.to_XYZ())
+
+	@staticmethod
+	def from_XYZ(C):
+		raise NotImplementedError
+
+	def to_XYZ(C):
+		raise NotImplementedError
+
+	def __getitem__(self, index):
+		return self.array[index]
+
+	def __setitem__(self, index, value):
+		self.array[index] = value
+
+	def __iter__(self):
+		return self.array.__iter__()
+
+	def __add__(A, B):
+		return type(A)(*(A.array + B.array))
+
+class sRGB(Color):
+	full_name = "sRGB"
+	matrix = np.array([
+		[ 3.240479, -1.537150, -0.498353],
+		[-0.969256,  1.875992,  0.041556],
+		[ 0.055648, -0.204043,  1.057331]])
+	matrix_inverse = np.linalg.inv(matrix)
+
+	@classmethod
+	def from_XYZ(cls, C):
+		def gamma(x):
+			if x <= 0.0031308:
+				return 12.92 * x
+			return 1.055 * x ** (1 / 2.4) - 0.055
+
+		RGB = np.dot(cls.matrix, C.array)
+		return sRGB(*[gamma(x) for x in RGB])
+
+	def to_XYZ(self):
+		def gamma_inverse(x):
+			if x <= 0.040449936:
+				return x / 12.92;
+			return ((x + 0.055) / 1.055) ** 2.4
+
+		RGB = [gamma_inverse(x) for x in self]
+		return XYZ(*np.dot(self.matrix_inverse, RGB))
+
+class sRGB100(sRGB):
+	full_name = "sRGB (with Y=100 as white)"
+	matrix = 0.01 * sRGB.matrix
+	matrix_inverse = np.linalg.inv(matrix)
+
+#
+# CIE 1931
+#
+
+class XYZ(Color):
+	full_name = "CIE 1931 XYZ"
+
+	@staticmethod
+	def from_XYZ(C):
+		return C
+
+	def to_XYZ(self):
+		return self
+
+	@staticmethod
+	def from_spectrum(S, observer):
+		X = S.dot(observer.cmf_x)
+		Y = S.dot(observer.cmf_y)
+		Z = S.dot(observer.cmf_z)
+		return XYZ(X, Y, Z)
+
+	def normed(self, norm=100):
+		X, Y, Z = self
+		return XYZ(X * norm / Y, norm, Z * norm / Y)
+
+class xyY(Color):
+	full_name = "CIE 1931 xyY"
+
+	@staticmethod
+	def from_XYZ(C):
+		x = C[0] / sum(C)
+		y = C[1] / sum(C)
+		return xyY(x, y, C[1])
+
+	def to_XYZ(self):
+		x, y, Y = self
+		try:
+			X = Y / y * x
+			Z = Y / y * (1 - x - y)
+		except ZeroDivisionError:
+			X = 0
+			Z = 0
+		return XYZ(X, Y, Z)
+
+#
+# CIE 1960
+#
+
+class UVW(Color):
+	full_name = "CIE 1960 UCS (McAdam) UVW"
+
+	@staticmethod
+	def from_XYZ(C):
+		X, Y, Z = C
+		U = 2 * X / 3
+		V = Y
+		W = 1/2 * (-X + 3 * Y + Z)
+		return UVW(U, V, W)
+
+	def to_XYZ(self):
+		U, V, W = self
+		X = 3/2 * U
+		Y = V
+		Z = 3/2 * U - 3 * V + 2 * W
+		return XYZ(X, Y, Z)
+
+	def to_uvY(self):
+		U, V, W = self
+		u = U / sum(UVW)
+		v = V / sum(UVW)
+		return uvY(u, v, V)
+
+class uvY(Color):
+	full_name = "CIE 1960 uvY"
+
+	@staticmethod
+	def from_XYZ(C):
+		X, Y, Z = C
+		u = 4 * X / (X + 15 * Y + 3 * Z)
+		v = 6 * Y / (X + 15 * Y + 3 * Z)
+		return uvY(u, v, Y)
+
+	def to_UVW(self):
+		u, v, V = self
+		try:
+			U = V * u / v
+			W = -(V * v + V * u - V) / v
+		except ZeroDivisionError:
+			U = 0
+			W = 0
+		return UVW(U, V, W)
+
+	def to_XYZ(self):
+		return self.to_UVW().to_XYZ()
+
+#
+# CIE 1964
+#
+
+class UVWstar(Color):
+	full_name = "CIE 1964 U*V*W*"
+
+	@classmethod
+	def from_uvY(cls, C, CAT=True):
+		u, v, Y = C
+		un, vn, _ = cls.white_uvY if CAT else (0, 0, 0)
+
+		Wstar = 25 * Y ** (1/3) - 17
+		Ustar = 13 * Wstar * (u - un)
+		Vstar = 13 * Wstar * (v - vn)
+
+		return cls(Ustar, Vstar, Wstar)
+
+	@classmethod
+	def from_XYZ(cls, C):
+		return cls.from_uvY(C.to(uvY))
+
+	def to_uvY(self, CAT=True):
+		Ustar, Vstar, Wstar = self
+		un, vn, _ = self.white_uvY if CAT else (0, 0, 0)
+
+		u = Ustar / (13 * Wstar) + un
+		v = Vstar / (13 * Wstar) + vn
+		Y = (Wstar**3 + 51 * Wstar**2 + 867 * Wstar + 4913) / 15625
+
+		return uvY(u, v, Y)
+
+	def to_XYZ(self):
+		return self.to_uvY().to(XYZ)
+
+
+
+#
+# CIE 1976
+#
+
+class uvY76(Color):
+	full_name = "CIE 1976 u'v'Y"
+
+	@staticmethod
+	def from_XYZ(C):
+		X, Y, Z = C
+		u = 4 * X / (X + 15 * Y + 3 * Z)
+		v = 9 * Y / (X + 15 * Y + 3 * Z)
+		return uvY76(u, v, Y)
+
+	def to_XYZ(self):
+		u, v, Y = self
+		X = 9 * u / (4 * v) * Y
+		Z = (12 - 3 * u - 20 * v) / (4 * v) * Y
+		return XYZ(X, Y, Z)
+
+# The magic CIE 1976 function for mapping Y to L*
+def CIE1976_f(t):
+	if t > (6/29) ** 3:
+		return t ** (1/3)
+
+	return 841 * t / 108 + 4/29
+
+# The inverse of the above
+def CIE1976_f_inverse(t):
+	if t > 6/29:
+		return t ** 3
+
+	return (3132 * t - 432) / 24389
+
+class Luvstar(Color):
+	full_name = "CIE 1976 L*u*v*"
+
+	@classmethod
+	def from_uvY76(cls, C):
+		u, v, Y = C
+		un, vn, Yn = cls.white_uvY76
+
+		Lstar = 116 * CIE1976_f(Y / Yn) - 16
+		ustar = 13 * Lstar * (u - un)
+		vstar = 13 * Lstar * (v - vn)
+
+		return cls(Lstar, ustar, vstar)
+
+	@classmethod
+	def from_XYZ(cls, C):
+		return cls.from_uvY76(C.to(uvY76))
+
+	def to_uvY76(self):
+		Lstar, ustar, vstar = self
+		un, vn, Yn = self.white_uvY76
+
+		u = ustar / (13 * Lstar) + un
+		v = vstar / (13 * Lstar) + vn
+		Y = Yn * CIE1976_f_inverse((Lstar + 16) / 116)
+
+		return uvY76(u, v, Y)
+
+	def to_XYZ(self):
+		return self.to_uvY76().to_XYZ()
+
+class Labstar(Color):
+	full_name = "CIE 1976 L*a*b*"
+
+	@classmethod
+	def from_XYZ(cls, C):
+		X, Y, Z = C
+		Xn, Yn, Zn = cls.white
+
+		Lstar = 116 * CIE1976_f(Y / Yn) - 16
+		astar = 500 * (CIE1976_f(X / Xn) - CIE1976_f(Y / Yn))
+		bstar = 200 * (CIE1976_f(Y / Yn) - CIE1976_f(Z / Zn))
+
+		return cls(Lstar, astar, bstar)
+
+	def to_XYZ(self):
+		Lstar, astar, bstar = self
+		Xn, Yn, Zn = self.white
+
+		X = Xn * CIE1976_f_inverse(astar / 500 + (Lstar + 16) / 116)
+		Y = Yn * CIE1976_f_inverse((Lstar + 16) / 116)
+		Z = Zn * CIE1976_f_inverse(-bstar / 200 + (Lstar + 16) / 116)
+
+		return XYZ(X, Y, Z)
+
+#
+# Some useful predefined color spaces
+# 
+
+class UVWstarD65(UVWstar):
+	white_uvY = uvY(Illuminants.D65)
+
+class LuvstarD65(Luvstar):
+	white_uvY76 = uvY76(Illuminants.D65)
+
+class LabstarD65(Labstar):
+	white = XYZ(Illuminants.D65).normed()
+
+class UVstarNormed(Color):
+	full_name = "CIE 1964 U*V*W*, normed (plots only!)"
+
+	@staticmethod
+	def from_UVWstar(C):
+		if not issubclass(type(C), UVWstar):
+			raise TypeError(f"{type(C)} is not a subclass of UVWstar")
+
+		Ustar, Vstar, Wstar = C
+
+		Unorm = Ustar / Wstar
+		Vnorm = Vstar / Wstar
+
+		return UVstarNormed(Unorm, Vnorm, Wstar)
+
+	@staticmethod
+	def from_XYZ(C):
+		raise NotImplementedError("use the from_UVWstar method directly")
+
+	def to_UVWstar(self):
+		Unorm, Vnorm, Wstar = self
+
+		Ustar = Unorm * Wstar
+		Vstar = Vnorm * Wstar
+
+		return UVWstarD65(Ustar, Vstar, Wstar)
+
+	def to_XYZ(self):
+		return self.to_UVWstar().to_XYZ()
+
+class UVstarNormedD65(UVstarNormed):
+	@staticmethod
+	def from_XYZ(C):
+		return UVstarNormedD65.from_UVWstar(UVWstarD65(C))
+
+#
+# CCT
+#
+
+def cct(C):
+	u, v, _ = uvY(C)
+	uv = np.array([u, v])
+
+	uv_planck = scipy.interpolate.interp1d(
+		tables2.locus_uv[:, 0], tables2.locus_uv[:, 1:3], axis=0,
+		bounds_error=False, fill_value="extrapolate")
+
+	def delta(T):
+		return np.linalg.norm(uv_planck(T) - uv)
+
+	res = scipy.optimize.minimize_scalar(delta, bounds=[1000, 20000])
+	return res.x, res.fun
+
+def cct_McCamy(C):
+	x, y, _ = xyY(C)
+
+	n = (x - 0.3320) / (y - 0.1858)
+	return -449 * n**3 + 3525 * n**2 - 6823.3 * n + 5520.33
diff --git a/crl/cri.py b/crl/cri.py
new file mode 100644
index 0000000..af62b1f
--- /dev/null
+++ b/crl/cri.py
@@ -0,0 +1,117 @@
+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)
diff --git a/crl/data.py b/crl/data.py
new file mode 100644
index 0000000..69d69e2
--- /dev/null
+++ b/crl/data.py
@@ -0,0 +1,121 @@
+import re
+import numpy as np
+
+class LoadError(Exception):
+	pass
+
+
+
+
+def identify(contents):
+	if contents.startswith(b"SpectraSuite Data File"):
+		return "spectrasuite"
+	elif b"created by Plot Digitizer" in contents:
+		return "Plot Digitizer"
+	else:
+		return "unknown"
+
+def identify_csv(line):
+	line = line.strip().rstrip()
+
+	point = ","
+	delim = "\s+"
+
+	if "." in line:
+		point = "."
+		if "," in line:
+			delim = ","
+
+	num_cols = len(re.findall(delim, line)) + 1
+	if line[-1] == delim:
+		num_cols -= 1
+
+	re_int = "([\+-]?\d+)"
+	re_frac = "%s?(\d*)" % point
+	re_exp = "[eE]?([\+-]?\d*)"
+
+	regex = delim.join([re_int + re_frac + re_exp] * num_cols)
+
+	return num_cols, re.compile(regex)
+
+def parse_csv(lines):
+	num_cols, regex = identify_csv(lines[0])
+
+	data = np.empty((len(lines), num_cols), dtype="float")
+
+	for i, line in enumerate(lines):
+		if not len(line.strip()):
+			continue
+
+		rv = regex.match(line)
+		for j in range(num_cols):
+			str_int = rv.group(3 * j + 1)
+			str_frac = rv.group(3 * j + 2)
+			str_exp = rv.group(3 * j + 3)
+
+			fint = float(str_int)
+			frac = float(str_frac) * 10 ** (-len(str_frac))
+			if fint < 0:
+				frac = -frac
+
+			if str_exp != "":
+				number = (fint + frac) * 10 ** (int(str_exp))
+			else:
+				number = fint + frac
+
+			data[i, j] = number
+
+	return data
+
+re_spectrasuite_marker = re.compile("^>>>>>(.*)<<<<<")
+
+def parse_spectrasuite(lines):
+	data_start = None
+	data_end = None
+
+	for i, line in enumerate(lines):
+		rv = re_spectrasuite_marker.match(line)
+		if rv is not None:
+			marker = rv.group(1)
+
+			if marker == "Begin Processed Spectral Data":
+				data_start = i + 1
+			elif marker == "End Processed Spectral Data":
+				data_end = i
+
+	if data_start is None:
+		raise ValueError("Missing 'Begin Processed Spectral Data'")
+	if data_end is None:
+		raise ValueError("Missing 'End Processed Spectral Data'")
+
+	return parse_csv(lines[data_start:data_end])
+
+def parse_plot_digitizer(lines):
+	return parse_csv(lines[6:])
+
+def load(path):
+	with open(path, "rb") as fd:
+		contents = fd.read()
+
+	fmt = identify(contents)
+
+	try:
+		lines = [line.decode("ascii") for line in contents.split(b"\n")]
+	except UnicodeDecodeError:
+		raise LoadError("This non-ASCII data format isn't supported")
+
+	if fmt == "spectrasuite":
+		try:
+			return parse_spectrasuite(lines)
+		except Exception as exc:
+			raise LoadError("This SpectraSuite file couldn't be understood")
+	elif fmt == "Plot Digitizer":
+		try:
+			return parse_plot_digitizer(lines)
+		except Exception as exc:
+			raise LoadError("This Plot Digitizer file couldn't be understood")
+	else:
+		try:
+			return parse_csv(lines)
+		except Exception as exc:
+			raise LoadError("This data format isn't supported")
diff --git a/crl/plots.py b/crl/plots.py
new file mode 100644
index 0000000..81cde0c
--- /dev/null
+++ b/crl/plots.py
@@ -0,0 +1,162 @@
+import numpy as np, PIL, os
+from matplotlib import pyplot as plt, rcParams
+from crl.color import *
+
+def assets(path):
+	return os.path.join(os.path.dirname(__file__), "assets", path)
+
+plot_data = {
+	xyY: {
+		"diagram_path": assets("diagram_xy.png"),
+		"diagram_bounds": [0, 0.75, 0, 0.85],
+		"xlabel": "$x$",
+		"ylabel": "$y$"
+	},
+	uvY: {
+		"diagram_path": assets("diagram_uv.png"),
+		"diagram_bounds": [0, 0.7, 0, 0.4],
+		"xlabel": "$u$",
+		"ylabel": "$v$"
+	},
+	uvY76: {
+		"diagram_path": assets("diagram_uv76.png"),
+		"diagram_bounds": [0, 0.65, 0, 0.63],
+		"xlabel": "$u'$",
+		"ylabel": "$v'$"
+	},
+	UVstarNormedD65: {
+		"diagram_path": assets("diagram_UVWstar.png"),
+		"diagram_bounds": [-2.6, 5.6, -4, 1.1],
+		"xlabel": "$\\frac{U^*}{W^*}$",
+		"ylabel": "$\\frac{V^*}{W^*}$"
+	},
+	LabstarD65: {
+		"diagram_path": assets("diagram_Labstar.png"),
+		"diagram_bounds": [-50, 50, -50, 50],
+		"xlabel": "$a^*$",
+		"ylabel": "$b^*$"
+	}
+}
+
+def draw_isotherm(ax, CS, x, y, T, plot_space_scale, labels, label_scale):
+	u0, v0, _ = uvY(BlackBodySpectrum(T))
+	ut, vt, _ = uvY(BlackBodySpectrum(T + 100)) # t stands for 'tangent'
+	duv = np.array([ut - u0, vt - v0])
+	du, dv = np.matmul([[0, -1], [1, 0]], duv) # rotate 90 degrees
+
+	annotate = False
+	X, Y = [], []
+	for sign in [-1, 1]:
+		xp, yp, _ =  CS.from_XYZ(uvY(
+			u0 + sign * du,
+			v0 + sign * dv, 1).to_XYZ())
+
+		dxy = np.array([xp - x, yp - y])
+
+		if T % 1000 == 500:
+			size = 0.006
+		elif T <= 5000 or T == 10000:
+			size = 0.02
+			annotate = True
+		else:
+			size = 0.012
+
+		dxy *= plot_space_scale * size / np.linalg.norm(dxy)
+
+		X.append(x + dxy[0])
+		Y.append(y + dxy[1])
+
+	ax.plot(X, Y, "k-", linewidth=1)
+
+	if labels and annotate:
+		save = rcParams['font.size']
+		rcParams['font.size'] *= label_scale
+		ax.annotate("%d" % T, xy=(X[1], Y[1]),
+			    horizontalalignment="left",
+			    verticalalignment="top")
+		rcParams['font.size'] = save
+
+
+def chromaticity_diagram(CS, label_scale=1/1.5, ax=plt,
+                         pures=True, pures_labels=True,
+                         locus=True, isotherms=False,
+                         isotherm_labels = False, isotherm_label_scale=1/1.7):
+	if CS not in plot_data:
+		raise ValueError("color triangle plots for %s are not supported" % CS.full_name)
+
+	if CS == LabstarD65:
+		# These won't work in L*a*b*
+		pures = False
+		locus = False
+
+	if not hasattr(CS, "diagram_image"):
+		img = PIL.Image.open(plot_data[CS]["diagram_path"])
+		img.load()
+		CS.diagram_image = np.asarray(img, dtype="float") / 255
+
+	ax.imshow(CS.diagram_image, extent=plot_data[CS]["diagram_bounds"], \
+	          interpolation="bilinear")
+
+	save = rcParams['font.size']
+	rcParams['font.size'] *= label_scale
+
+	plot_space_scale = 10 if CS == UVstarNormed else 1
+
+	if pures:
+		wvls = np.linspace(380, 780, 300)
+		outline = np.array([[*CS(PointSpectrum(wvl))] for wvl in wvls])
+		ax.plot(outline[:, 0], outline[:, 1], 'k')
+
+		def tick(wvl, l, label=False):
+			x, y, _ = CS(PointSpectrum(wvl))
+			xe, ye, _ = CS(PointSpectrum(wvl + 5))
+			l *= plot_space_scale
+
+			dx = np.array([xe - x, ye - y])
+			dx *= l / np.linalg.norm(dx)
+			dx = np.matmul([[0, -1], [1, 0]], dx)
+		
+			ax.plot((x, x + dx[0]), (y, y + dx[1]), 'k-')
+		
+			if pures_labels and label:
+				ax.annotate("%d" % wvl, (x, y),
+					     xytext=np.array([x, y]) + dx * 2,
+					     horizontalalignment="center",
+					     verticalalignment="center")
+
+		L = [380, 460, 470, 480, 490, 500, 520, 540, 560, 580, 600, 620]
+
+		for wvl in range(400, 680, 5):
+			if wvl not in L:
+				if wvl % 10 == 0:
+					tick(wvl, 0.012)
+				else:
+					tick(wvl, 0.007)
+		for wvl in L:
+			tick(wvl, 0.02, True)
+
+	if locus:
+		x = np.zeros(50)
+		y = np.zeros(np.size(x))
+		for i in range(np.size(x)):
+			T = 1000 * 20 ** (i / (np.size(x) - 1))
+			x[i], y[i], _ = CS(BlackBodySpectrum(T))
+		ax.plot(x, y, "k-", linewidth=1)
+
+		if type(isotherms) == list:
+			Ts = isotherms
+		else:
+			Ts = range(2000, 10001, 500)
+		for T in Ts:
+			x, y, _ = CS(BlackBodySpectrum(T))
+
+			if isotherms:
+				draw_isotherm(ax, CS, x, y, T, plot_space_scale,
+				              isotherm_labels, isotherm_label_scale)
+			else:
+				ax.plot(x, y, "ko", markersize=2)
+
+	rcParams['font.size'] = save
+
+	ax.xlabel(plot_data[CS]["xlabel"])
+	ax.ylabel(plot_data[CS]["ylabel"])
diff --git a/crl/tables2.py b/crl/tables2.py
new file mode 100644
index 0000000..1f7a661
--- /dev/null
+++ b/crl/tables2.py
@@ -0,0 +1,1005 @@
+# TODO: autogenerate this file too
+import numpy as np
+
+locus_uv = np.array([
+	[1000,	0.447955,	0.35463,	3.93337e+06],
+	[1019.02,	0.443549,	0.355009,	6.10098e+06],
+	[1038.04,	0.439231,	0.355375,	9.31914e+06],
+	[1057.06,	0.435001,	0.355728,	1.40294e+07],
+	[1076.08,	0.430858,	0.356067,	2.08309e+07],
+	[1095.1,	0.4268,	0.356392,	3.05271e+07],
+	[1114.11,	0.422826,	0.356704,	4.41825e+07],
+	[1133.13,	0.418935,	0.357003,	6.31923e+07],
+	[1152.15,	0.415125,	0.357289,	8.93662e+07],
+	[1171.17,	0.411394,	0.357561,	1.25028e+08],
+	[1190.19,	0.407741,	0.357821,	1.73133e+08],
+	[1209.21,	0.404165,	0.358067,	2.37408e+08],
+	[1228.23,	0.400664,	0.358301,	3.22511e+08],
+	[1247.25,	0.397236,	0.358522,	4.34215e+08],
+	[1266.27,	0.39388,	0.358731,	5.79624e+08],
+	[1285.29,	0.390594,	0.358927,	7.67411e+08],
+	[1304.3,	0.387377,	0.35911,	1.00809e+09],
+	[1323.32,	0.384226,	0.359282,	1.31433e+09],
+	[1342.34,	0.381141,	0.359442,	1.70127e+09],
+	[1361.36,	0.37812,	0.35959,	2.18693e+09],
+	[1380.38,	0.375162,	0.359726,	2.79261e+09],
+	[1399.4,	0.372264,	0.359851,	3.54332e+09],
+	[1418.42,	0.369426,	0.359965,	4.46833e+09],
+	[1437.44,	0.366646,	0.360068,	5.60166e+09],
+	[1456.46,	0.363923,	0.36016,	6.98265e+09],
+	[1475.48,	0.361255,	0.360242,	8.65665e+09],
+	[1494.49,	0.358641,	0.360313,	1.06756e+10],
+	[1513.51,	0.35608,	0.360373,	1.30988e+10],
+	[1532.53,	0.35357,	0.360424,	1.59936e+10],
+	[1551.55,	0.35111,	0.360465,	1.94362e+10],
+	[1570.57,	0.3487,	0.360496,	2.35125e+10],
+	[1589.59,	0.346337,	0.360518,	2.83191e+10],
+	[1608.61,	0.344021,	0.360531,	3.39639e+10],
+	[1627.63,	0.34175,	0.360534,	4.05672e+10],
+	[1646.65,	0.339523,	0.360529,	4.82628e+10],
+	[1665.67,	0.33734,	0.360515,	5.71988e+10],
+	[1684.68,	0.335199,	0.360493,	6.75387e+10],
+	[1703.7,	0.3331,	0.360462,	7.94625e+10],
+	[1722.72,	0.331041,	0.360424,	9.31676e+10],
+	[1741.74,	0.329021,	0.360377,	1.0887e+11],
+	[1760.76,	0.327039,	0.360323,	1.26806e+11],
+	[1779.78,	0.325095,	0.360261,	1.47232e+11],
+	[1798.8,	0.323187,	0.360193,	1.70427e+11],
+	[1817.82,	0.321316,	0.360117,	1.96692e+11],
+	[1836.84,	0.319479,	0.360034,	2.26354e+11],
+	[1855.86,	0.317676,	0.359944,	2.59765e+11],
+	[1874.87,	0.315906,	0.359848,	2.97301e+11],
+	[1893.89,	0.314169,	0.359746,	3.3937e+11],
+	[1912.91,	0.312464,	0.359638,	3.86405e+11],
+	[1931.93,	0.31079,	0.359523,	4.38871e+11],
+	[1950.95,	0.309146,	0.359403,	4.97263e+11],
+	[1969.97,	0.307531,	0.359276,	5.62108e+11],
+	[1988.99,	0.305946,	0.359145,	6.33966e+11],
+	[2008.01,	0.304388,	0.359008,	7.13432e+11],
+	[2027.03,	0.302859,	0.358866,	8.01135e+11],
+	[2046.05,	0.301356,	0.358719,	8.9774e+11],
+	[2065.07,	0.29988,	0.358567,	1.00395e+12],
+	[2084.08,	0.29843,	0.35841,	1.1205e+12],
+	[2103.1,	0.297005,	0.358249,	1.24818e+12],
+	[2122.12,	0.295605,	0.358083,	1.38779e+12],
+	[2141.14,	0.294229,	0.357913,	1.5402e+12],
+	[2160.16,	0.292876,	0.357739,	1.7063e+12],
+	[2179.18,	0.291547,	0.357561,	1.88703e+12],
+	[2198.2,	0.29024,	0.357379,	2.08338e+12],
+	[2217.22,	0.288955,	0.357193,	2.29637e+12],
+	[2236.24,	0.287692,	0.357004,	2.52706e+12],
+	[2255.26,	0.28645,	0.356811,	2.77656e+12],
+	[2274.27,	0.285229,	0.356614,	3.04602e+12],
+	[2293.29,	0.284028,	0.356415,	3.33665e+12],
+	[2312.31,	0.282847,	0.356212,	3.64968e+12],
+	[2331.33,	0.281686,	0.356007,	3.9864e+12],
+	[2350.35,	0.280543,	0.355798,	4.34814e+12],
+	[2369.37,	0.279419,	0.355587,	4.73627e+12],
+	[2388.39,	0.278313,	0.355373,	5.15221e+12],
+	[2407.41,	0.277226,	0.355156,	5.59743e+12],
+	[2426.43,	0.276155,	0.354937,	6.07344e+12],
+	[2445.45,	0.275102,	0.354716,	6.58177e+12],
+	[2464.46,	0.274066,	0.354492,	7.12405e+12],
+	[2483.48,	0.273046,	0.354266,	7.70189e+12],
+	[2502.5,	0.272042,	0.354038,	8.317e+12],
+	[2521.52,	0.271054,	0.353809,	8.97109e+12],
+	[2540.54,	0.270081,	0.353577,	9.66594e+12],
+	[2559.56,	0.269124,	0.353343,	1.04034e+13],
+	[2578.58,	0.268181,	0.353108,	1.11852e+13],
+	[2597.6,	0.267253,	0.35287,	1.20134e+13],
+	[2616.62,	0.26634,	0.352632,	1.28898e+13],
+	[2635.64,	0.26544,	0.352392,	1.38165e+13],
+	[2654.65,	0.264554,	0.35215,	1.47954e+13],
+	[2673.67,	0.263681,	0.351907,	1.58286e+13],
+	[2692.69,	0.262822,	0.351663,	1.69183e+13],
+	[2711.71,	0.261976,	0.351417,	1.80665e+13],
+	[2730.73,	0.261142,	0.351171,	1.92754e+13],
+	[2749.75,	0.26032,	0.350923,	2.05472e+13],
+	[2768.77,	0.259511,	0.350674,	2.18842e+13],
+	[2787.79,	0.258714,	0.350425,	2.32886e+13],
+	[2806.81,	0.257929,	0.350174,	2.47628e+13],
+	[2825.83,	0.257155,	0.349923,	2.6309e+13],
+	[2844.84,	0.256392,	0.349671,	2.79298e+13],
+	[2863.86,	0.255641,	0.349418,	2.96273e+13],
+	[2882.88,	0.2549,	0.349165,	3.14042e+13],
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