import numpy as np
from scipy.optimize import minimize
from colour import *
from colour.recovery.jakob2019 import error_function
from matplotlib import pyplot as plt
from gsoc_common import plot_comparison


# This test checks if derivatives are calculated correctly by comparing them
# to finite differences.
if __name__ == "__main__":
    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

    target = np.array([72., -20., 61.])  # Some arbitrary Lab colour
    xs = np.linspace(-10, 10, 500)
    h = xs[1] - xs[0]

    # Vary one coefficient at a time
    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([0.0, 0, 0])
            c[c_index] = x

            error, derror_dc = error_function(
                c, target, shape, 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()