1 files changed, 0 insertions, 93 deletions
diff --git a/jakob_hanika.py b/jakob_hanika.py
deleted file mode 100644
index 22b87fc..0000000
--- a/jakob_hanika.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import numpy as np, scipy.optimize as optimize
-from colour import *
-from colour.difference import delta_E_CIE1976
-from colour.colorimetry import *
-
-
-
-# The same wavelength grid is used throughout
-wvl = np.arange(360, 830, 5)
-wvlp = (wvl - 360) / (830 - 360)
-
-# This is the model of spectral reflectivity described in the article.
-def model(wvlp, ccp):
-	yy = ccp[0] * wvlp**2 + ccp[1] * wvlp + ccp[2]
-	return 1 / 2 + yy / (2 * np.sqrt(1 + yy ** 2))
-
-# Create a SpectralDistribution using given coefficients
-def model_sd(ccp, primed=True):
-	# FIXME: don't hardcode the wavelength grid; there should be a way
-	#        of creating a SpectralDistribution from the function alone
-	grid = wvlp if primed else wvl
-	return SpectralDistribution(model(grid, ccp), wvl, name="Model")
-
-# The goal is to minimize the color difference between a given distrbution
-# and the one computed from the model above.
-def error_function(ccp, target, ill_sd, ill_xy):
-	# One thing they didn't mention in the text is that their polynomial
-	# is nondimensionalized (mapped from 360--800 nm to 0--1).
-	sd = model_sd(ccp)
-	Lab = XYZ_to_Lab(sd_to_XYZ(sd, illuminant=ill_sd) / 100, ill_xy)
-	return delta_E_CIE1976(target, Lab)
-
-
-
-# Finds the parameters for Jakob and Hanika's model
-def jakob_hanika(target_XYZ, ill_sd, ill_xy, ccp0=(0, 0, 0), verbose=True, try_hard=True):
-	def do_optimize(XYZ, ccp0):
-		Lab = XYZ_to_Lab(XYZ, ill_xy)
-		opt = optimize.minimize(
-			error_function, ccp0, (Lab, ill_sd, ill_xy),
-			method="Nelder-Mead", options={"disp": verbose}
-		)
-		if verbose:
-			print(opt)
-		return opt
-
-	# A special case that's hard to solve numerically
-	if np.allclose(target_XYZ, [0, 0, 0]):
-		return np.array([0, 0, -1e+9]), 0 # FIXME: dtype?
-
-	if verbose:
-		print("Trying the target directly, XYZ=%s" % target_XYZ)
-	opt = do_optimize(target_XYZ, ccp0)
-	if opt.fun < 0.1 or not try_hard:
-		return opt.x, opt.fun
-
-	good_XYZ = (1/3, 1/3, 1/3)
-	good_ccp = (2.1276356, -1.07293026, -0.29583292) # FIXME: valid only for D65
-
-	divisions = 3
-	while divisions < 10:
-		if verbose:
-			print("Trying with %d divisions" % divisions)
-
-		keep_divisions = False
-		ref_XYZ = good_XYZ
-		ref_ccp = good_ccp
-
-		ccp0 = ref_ccp
-		for i in range(1, divisions):
-			intermediate_XYZ = ref_XYZ + (target_XYZ - ref_XYZ) * i / (divisions - 1)
-			if verbose:
-				print("Intermediate step %d/%d, XYZ=%s with ccp0=%s" %
-				      (i + 1, divisions, intermediate_XYZ, ccp0))
-			opt = do_optimize(intermediate_XYZ, ccp0)
-			if opt.fun > 0.1:
-				if verbose:
-					print("WARNING: intermediate optimization failed")
-				break
-			else:
-				good_XYZ = intermediate_XYZ
-				good_ccp = opt.x
-				keep_divisions = True
-
-			ccp0 = opt.x
-		else:
-			return opt.x, opt.fun
-
-		if not keep_divisions:
-			divisions += 2
-
-	raise Exception("optimization failed for target_XYZ=%s, ccp0=%s" \
-	                % (target_XYZ, ccp0))