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import numpy as np, scipy.optimize as optimize
from colour import *
from colour.difference import delta_E_CIE1976
from colour.colorimetry import *
from colour.plotting import *
from matplotlib import pyplot as plt
D65_xy = ILLUMINANTS["CIE 1931 2 Degree Standard Observer"]["D65"]
D65 = SpectralDistribution(ILLUMINANT_SDS["D65"])
# The same wavelength grid is used throughout
wvl = SpectralShape(360, 830, 5)
wvlp = (wvl.range() - 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.range()
return SpectralDistribution(model(grid, ccp), wvl.range(), name="Model")
# Makes a comparison plot with SDs and swatches
def plot_comparison(target, matched_sd, label, error, ill_sd, show=True):
if type(target) is SpectralDistribution:
target_XYZ = sd_to_XYZ(target, illuminant=ill_sd) / 100
else:
target_XYZ = target
target_RGB = np.clip(XYZ_to_sRGB(target_XYZ), 0, 1)
target_swatch = ColourSwatch(label, target_RGB)
matched_XYZ = sd_to_XYZ(matched_sd, illuminant=ill_sd) / 100
matched_RGB = np.clip(XYZ_to_sRGB(matched_XYZ), 0, 1)
matched_swatch = ColourSwatch("Model", matched_RGB)
axes = plt.subplot(2, 1, 1)
plt.title(label)
if type(target) is SpectralDistribution:
plot_multi_sds([target, matched_sd], axes=axes, standalone=False)
else:
plot_single_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],
standalone=show, axes=axes)
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