Update the tests again and remove some useless ones.

1 files changed, 33 insertions, 27 deletions

diff --git a/gsoc_common.py b/gsoc_common.py
index b73d81c..695edc6 100644
--- a/gsoc_common.py
+++ b/gsoc_common.py
@@ -3,47 +3,53 @@ 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)
 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))
+    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")
+    # 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)
+    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)