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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()