Use local colorconv rather than scikit-image

jenshnielsen · jenshnielsen · commit e038afb14ac5 · 2014-10-28T19:30:53.000Z
diff --git a/doc/users/plotting/colormaps/Lfunction.py b/doc/users/plotting/colormaps/Lfunction.py
@@ -6,7 +6,10 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-from skimage import color
+import colorconv as color
+#from skimage import color
+# we are using a local copy of colorconv from scikit-image to reduce dependencies. 
+# You should probably use the one from scikit-image in most cases. 
 import matplotlib as mpl
 from matplotlib import cm
 
diff --git a/doc/users/plotting/colormaps/grayscale.py b/doc/users/plotting/colormaps/grayscale.py
@@ -3,7 +3,10 @@
 Uses lightness L* as a proxy for grayscale value.
 '''
 
-from skimage import color
+import colorconv as color
+#from skimage import color
+# we are using a local copy of colorconv from scikit-image to reduce dependencies. 
+# You should probably use the one from scikit-image in most cases. 
 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib import cm
@@ -71,6 +74,7 @@ def plot_color_gradients(cmap_category, cmap_list):
     for ax in axes:
         ax[0].set_axis_off()
         ax[1].set_axis_off()
+    plt.show()
 
 
 for cmap_category, cmap_list in cmaps:
diff --git a/doc/users/plotting/colormaps/lightness.py b/doc/users/plotting/colormaps/lightness.py
@@ -2,7 +2,10 @@
 For each colormap, plot the lightness parameter L* from CIELAB colorspace along the y axis vs index through the colormap. Colormaps are examined in categories as in the original matplotlib gallery of colormaps.
 '''
 
-from skimage import color
+import colorconv as color
+#from skimage import color
+# we are using a local copy of colorconv from scikit-image to reduce dependencies. 
+# You should probably use the one from scikit-image in most cases. 
 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib import cm
@@ -126,4 +129,4 @@
     fig.text(-0.005, 0.55, 'Lightness $L^*$', fontsize=18, transform=fig.transFigure, rotation=90)
 
     fig.tight_layout(h_pad=0.05)
-    plt.show
+    plt.show()
