import numpy as np, scipy.optimize as optimize
from colour import *
from colour.difference import *
from colour.plotting import *
from matplotlib import pyplot as plt



# Makes a comparison plot with SDs and swatches
def plot_comparison(target_sd, matched_sd, label, error):
	target_XYZ = sd_to_XYZ(target_sd)
	target_RGB = np.clip(XYZ_to_sRGB(target_XYZ / 100), 0, 1)
	target_swatch = ColourSwatch(label, target_RGB)
	matched_XYZ = sd_to_XYZ(matched_sd)
	matched_RGB = np.clip(XYZ_to_sRGB(matched_XYZ / 100), 0, 1)
	matched_swatch = ColourSwatch("Model", matched_RGB)
      
	axes = plt.subplot(2, 1, 1)
	plt.title(label)
	plot_multi_sds([target_sd, matched_sd], axes=axes, standalone=False)
      
	axes = plt.subplot(2, 1, 2)
	plt.title("ΔE = %g" % error)
	plot_multi_colour_swatches([target_swatch, matched_swatch], axes=axes)



# The same illuminant is used throughout
il = ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']

# The same wavelength grid is used throughout
wvl = np.arange(360, 830, 10)



# This is the model of spectral reflectivity described in the article.
def model(wvl, cc):
	# One thing they didn't mention in the text is that their polynomial
	# is nondimensionalized (mapped from 360--800 nm to 0--1).
	def remap(x):
		return (x - 360) / (830 - 360)

	x = cc[0] * remap(wvl) ** 2 + cc[1] * remap(wvl) + cc[2]
	return 1 / 2 + x / (2 * np.sqrt(1 + x ** 2))

# The goal is to minimize the color difference between a given distrbution
# and the one computed from the model above.
def error_function(cc, target):
	ev = model(wvl, cc)
	sd = SpectralDistribution(ev, wvl)
	Lab = XYZ_to_Lab(sd_to_XYZ(sd), il)
	return delta_E_CIE1976(target, Lab)



# 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

# This demo goes through SDs in a color checker
for name, sd in COLOURCHECKERS_SDS['ColorChecker N Ohta'].items():
	XYZ = sd_to_XYZ(sd)
	target = XYZ_to_Lab(XYZ, il)

	print("The target is '%s' with L=%g, a=%g, b=%g" % (name, *target))

	# 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 (instead of
	#        going).
	opt = optimize.minimize(
		error_function, (0, 0, 0), target, method="L-BFGS-B",
		options={"disp": True, "ftol": 1e-5}
	)
	print(opt)

	error = error_function(opt.x, target)
	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:
		print("Error too large, trying global optimization")
		opt = optimize.basinhopping(
			lambda cc: error_function(cc, target),
			(0, 0, 0), disp=True, callback=cb_basinhopping
		)
		print(opt)
		error = error_function(opt.x, target)
		print("Global delta E is %g" % error)

	matched_sd = SpectralDistribution(model(wvl, opt.x), wvl, name="Model")
	plot_comparison(sd, matched_sd, name, error)