Move tests into the Colour fork.

4 files changed, 0 insertions, 106 deletions

diff --git a/gsoc_common.py b/gsoc_common.py
index 695edc6..c235375 100644
--- a/gsoc_common.py
+++ b/gsoc_common.py
@@ -3,13 +3,11 @@ from colour import *
 from colour.difference import delta_E_CIE1976
 from colour.colorimetry import *
 from colour.plotting import *
-from colour.models import RGB_COLOURSPACES
 from matplotlib import pyplot as plt
 
 
 D65_xy = ILLUMINANTS["CIE 1931 2 Degree Standard Observer"]["D65"]
 D65 = SpectralDistribution(ILLUMINANT_SDS["D65"])
-colourspace = RGB_COLOURSPACES["ProPhoto RGB"]
 
 # The same wavelength grid is used throughout
 wvl = SpectralShape(360, 830, 5)
diff --git a/test_colorchecker.py b/test_colorchecker.py
deleted file mode 100644
index 1503874..0000000
--- a/test_colorchecker.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import numpy as np
-from colour import *
-from colour.recovery import RGB_to_sd_Jakob2019
-from gsoc_common import *
-
-
-# This demo goes through SDs in a color checker
-if __name__ == "__main__":
-    for name, sd in COLOURCHECKER_SDS['ColorChecker N Ohta'].items():
-        XYZ = sd_to_XYZ(sd, illuminant=D65) / 100
-
-        RGB = XYZ_to_RGB(
-            XYZ,
-            D65_xy,
-            colourspace.whitepoint,
-            colourspace.XYZ_to_RGB_matrix,
-        )
-
-        recovered_sd, error = RGB_to_sd_Jakob2019(
-            RGB,
-            colourspace,
-            return_error=True
-        )
-
-        plot_comparison(sd, recovered_sd, name, error, D65)
diff --git a/test_diff.py b/test_diff.py
deleted file mode 100644
index 4429894..0000000
--- a/test_diff.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import numpy as np
-from scipy.optimize import minimize
-from colour import *
-from colour.recovery import error_function_Jakob2019
-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([50, -20, 30]) # 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([1.0, 1, 1])
-            c[c_index] = x
-
-            error, derror_dc = error_function_Jakob2019(
-                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()
diff --git a/test_interpolator.py b/test_interpolator.py
deleted file mode 100644
index ba476da..0000000
--- a/test_interpolator.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import numpy as np, struct
-from colour import *
-from colour.difference import delta_E_CIE1976
-from colour.models import eotf_inverse_sRGB
-from colour.recovery import Jakob2019Interpolator
-from gsoc_common import *
-
-
-# This script tests if the interpolator correctly handles multi-dimensional
-# inputs.
-if __name__ == "__main__":
-    interp = Jakob2019Interpolator()
-    interp.from_file("data/srgb.coeff")
-
-    RGBs = np.random.random((7, 6, 5, 4, 3))
-    ccs = interp.coefficients(RGBs)
-
-    RGB = eotf_inverse_sRGB(RGBs[0, 0, 0, 0, :])
-    XYZ = sRGB_to_XYZ(RGB)
-    Lab = XYZ_to_Lab(XYZ, D65_xy)
-    cc = ccs[0, 0, 0, 0, :]
-
-    matched_sd = model_sd(cc, primed=False)
-    matched_XYZ = sd_to_XYZ(matched_sd, illuminant=D65) / 100
-    matched_Lab = XYZ_to_Lab(matched_XYZ, D65_xy)
-    error = delta_E_CIE1976(Lab, matched_Lab)
-
-    plot_comparison(XYZ, matched_sd, "Model", error, D65)