import numpy as np, scipy.optimize as optimize
from colour import *
from colour.difference import *



# The same wavelength grid is used throughout
wvl = np.arange(360, 830, 1)
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):
	# FIXME: don't hardcode the wavelength grid; there should be a way
	#        of creating a SpectralDistribution from the function alone
	return SpectralDistribution(model(wvlp, 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)



# Map primed (dimensionless) coefficients to normal
# FIXME: is this even useful for anything?
def cc_from_primed(ccp):
	return np.array([
		ccp[0] / 220900,
		ccp[1] / 470 - 36/11045 * ccp[0],
		ccp[2] - 36/47 * ccp[1] + 1296/2209 * ccp[0]
	])


# This callback to scipy.optimize.basinhopping tells the solver to stop once
# the error is small enough. The threshold was chosen arbitrarily, as a small
# fraction of the JND (about 2.3 with this metric).
def cb_basinhopping(x, f, accept):
	return f < 0.1

# Finds the parameters for Jakob and Hanika's model
def jakob_hanika(target, ill_sd, ill_xy, ccp0=(0, 0, 0), verbose=True):
	# First, a conventional solver is called. For 'yellow green' this
	# actually fails: gets stuck at a local minimum that's far away
	# from the global one.
	# FIXME: better parameters, a better x0, a better method?
	# FIXME: stop iterating as soon as delta E is negligible
	opt = optimize.minimize(
		error_function, ccp0, (target, ill_sd, ill_xy),
		bounds=[[-300, 300]] * 3,
		options={"disp": verbose}
	)
	if verbose:
		print(opt)

	error = error_function(opt.x, target, ill_sd, ill_xy)
	if verbose:
		print("Delta E is %g" % error)

	# Basin hopping is far more likely to find the actual minimum we're
	# looking for, but it's extremely slow in comparison.
	if error > 0.1:
		if verbose:
			print("Error too large, trying global optimization")
		opt = optimize.basinhopping(
			lambda cc: error_function(cc, target, ill_sd, ill_xy),
			x0, disp=verbose, callback=cb_basinhopping
		)
		if verbose:
			print(opt)
		error = error_function(opt.x, target, ill_sd, ill_xy)
		if verbose:
			print("Global delta E is %g" % error)

	return opt.x, error