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import numpy as np
from scipy.optimize import minimize
from colour import *
from colour.colorimetry import STANDARD_OBSERVER_CMFS, ILLUMINANT_SDS
from colour.models import eotf_inverse_sRGB, sRGB_to_XYZ
from matplotlib import pyplot as plt
from gsoc_common import plot_comparison, error_function, model_sd, D65_xy
shape = SpectralShape(360, 830, 1)
cmfs = STANDARD_OBSERVER_CMFS["CIE 1931 2 Degree Standard Observer"].align(shape)
illuminant = SpectralDistribution(ILLUMINANT_SDS["D65"]).align(shape)
illuminant_XYZ = sd_to_XYZ(illuminant) / 100
wvl = np.linspace(0, 1, len(shape.range()))
target = np.array([50, -20, 30]) # Some arbitrary Lab coordinates
xs = np.linspace(-10, 10, 500)
h = xs[1] - xs[0]
# This test checks if derivatives are calculated correctly by comparing them
# to finite differences.
for c_index in range(3):
errors = np.empty(len(xs))
derrors = np.empty(len(xs))
for i, x in enumerate(xs):
c = np.array([1.0, 1, 1])
c[c_index] = x
error, derror_dc = error_function(
c, target, wvl, cmfs, illuminant, illuminant_XYZ
)
errors[i] = error
derrors[i] = derror_dc[c_index]
plt.subplot(2, 3, 1 + c_index)
plt.xlabel("c%d" % c_index)
plt.ylabel("ΔE")
plt.plot(xs, errors)
plt.subplot(2, 3, 4 + c_index)
plt.xlabel("c%d" % c_index)
plt.ylabel("dΔE/dc%d" % c_index)
plt.plot(xs, derrors, "k-")
plt.plot(xs[:-1] + h / 2, np.diff(errors) / h, "r:")
plt.show()
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