Minor fixes and improvements: PEP8, appearance, etc.

Removed a clumsy workaround (about paths)

Fixed links to image files to fix sphinx warning.

Removed non-ascii character

Another non ascii character

And another non-ascii character... (to be squashed later on)

Corrected some typos in the docstrings of sphinx-gallery files

These corrections have also been submitted as a patch to the original
sphinx-gallery project (#121)

Corrected the appearance of two examples of the gallery

Tweaked CSS for larger images

Added sphinx-gallery's license and a README.txt about the origin of
this directory.

Edited gabor_from_astronaut example for nicer popup

PEP 8 + minor fixes

Removed commented lines of code

doc/examples/color_exposure/plot_equalize.py

@@ -72,12 +72,12 @@ img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)
 
 # Display results
 fig = plt.figure(figsize=(8, 5))
-axes = np.zeros((2,4), dtype=np.object)
-axes[0,0] = fig.add_subplot(2, 4, 1)
-for i in range(1,4):
-    axes[0,i] = fig.add_subplot(2, 4, 1+i, sharex=axes[0,0], sharey=axes[0,0])
-for i in range(0,4):
-    axes[1,i] = fig.add_subplot(2, 4, 5+i)
+axes = np.zeros((2, 4), dtype=np.object)
+axes[0, 0] = fig.add_subplot(2, 4, 1)
+for i in range(1, 4):
+    axes[0, i] = fig.add_subplot(2, 4, 1+i, sharex=axes[0,0], sharey=axes[0,0])
+for i in range(0, 4):
+    axes[1, i] = fig.add_subplot(2, 4, 5+i)
 
 ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])
 ax_img.set_title('Low contrast image')

doc/examples/color_exposure/plot_ihc_color_separation.py

@@ -60,7 +60,6 @@ h = rescale_intensity(ihc_hed[:, :, 0], out_range=(0, 1))
 d = rescale_intensity(ihc_hed[:, :, 2], out_range=(0, 1))
 zdh = np.dstack((np.zeros_like(h), d, h))
 
-#fig, ax = plt.subplots()
 fig = plt.figure()
 ax = plt.subplot(1, 1, 1, sharex=ax0, sharey=ax0, adjustable='box-forced')
 ax.imshow(zdh)

doc/examples/features_detection/plot_gabors_from_astronaut.py

@@ -3,7 +3,7 @@
 Gabors / Primary Visual Cortex "Simple Cells" from an Image
 ============================================================
 
-How to build a (bio-plausible) "sparse" dictionary (or 'codebook', or
+How to build a (bio-plausible) *sparse* dictionary (or 'codebook', or
 'filterbank') for e.g. image classification without any fancy math and
 with just standard python scientific libraries?
 

doc/examples/features_detection/plot_holes_and_peaks.py

@@ -3,11 +3,10 @@
 Filling holes and finding peaks
 ===============================
 
-In this example, we fill holes (i.e. isolated, dark spots) in an image using
-morphological reconstruction by erosion. Erosion expands the minimal values of
-the seed image until it encounters a mask image. Thus, the seed image and mask
-image represent the maximum and minimum possible values of the reconstructed
-image.
+We fill holes (i.e. isolated, dark spots) in an image using morphological
+reconstruction by erosion. Erosion expands the minimal values of the seed image
+until it encounters a mask image. Thus, the seed image and mask image represent
+the maximum and minimum possible values of the reconstructed image.
 
 We start with an image containing both peaks and holes:
 
@@ -21,14 +20,6 @@ image = data.moon()
 # Rescale image intensity so that we can see dim features.
 image = rescale_intensity(image, in_range=(50, 200))
 
-fig,ax = plt.subplots(2, 2, figsize=(5, 4), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
-ax = ax.ravel()
-
-
-ax[0].imshow(image)
-ax[0].set_title('Original image')
-ax[0].axis('off')
-
 ######################################################################
 # Now we need to create the seed image, where the minima represent the
 # starting points for erosion.  To fill holes, we initialize the seed image
@@ -46,21 +37,12 @@ mask = image
 
 filled = reconstruction(seed, mask, method='erosion')
 
-ax[1].imshow(filled)
-ax[1].set_title('after filling holes')
-ax[1].axis('off')
-
 ######################################################################
 # As shown above, eroding inward from the edges removes holes, since (by
 # definition) holes are surrounded by pixels of brighter value. Finally, we
 # can isolate the dark regions by subtracting the reconstructed image from
 # the original image.
-
-ax[2].imshow(image-filled)
-ax[2].set_title('holes')
-ax[2].axis('off')
-
-######################################################################
+#
 # Alternatively, we can find bright spots in an image using morphological
 # reconstruction by dilation. Dilation is the inverse of erosion and expands
 # the *maximal* values of the seed image until it encounters a mask image.
@@ -72,7 +54,23 @@ seed = np.copy(image)
 seed[1:-1, 1:-1] = image.min()
 rec = reconstruction(seed, mask, method='dilation')
 
-ax[3].imshow(image-rec)
+fig, ax = plt.subplots(2, 2, figsize=(5, 4), sharex=True, sharey=True,
+                       subplot_kw={'adjustable': 'box-forced'})
+ax = ax.ravel()
+
+ax[0].imshow(image, cmap='gray')
+ax[0].set_title('Original image')
+ax[0].axis('off')
+
+ax[1].imshow(filled, cmap='gray')
+ax[1].set_title('after filling holes')
+ax[1].axis('off')
+
+ax[2].imshow(image-filled, cmap='gray')
+ax[2].set_title('holes')
+ax[2].axis('off')
+
+ax[3].imshow(image-rec, cmap='gray')
 ax[3].set_title('peaks')
 ax[3].axis('off')
 plt.show()

doc/examples/xx_applications/plot_morphology.py

@@ -12,19 +12,12 @@ neighborhood around a pixel.
 In this document we outline the following basic morphological operations:
 
 1. Erosion
-
 2. Dilation
-
 3. Opening
-
 4. Closing
-
 5. White Tophat
-
 6. Black Tophat
-
 7. Skeletonize
-
 8. Convex Hull
 
 
@@ -46,6 +39,7 @@ ax.imshow(orig_phantom, cmap=plt.cm.gray)
 ######################################################################
 # Let's also define a convenience function for plotting comparisons:
 
+
 def plot_comparison(original, filtered, filter_name):
 
     fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4), sharex=True,
@@ -188,9 +182,9 @@ plot_comparison(phantom, b_tophat, 'black tophat')
 #
 # 1. Erosion <-> Dilation
 #
-# 2. Opening <-> Closing
+# 2. Opening <-> Closing
 #
-# 3. White tophat <-> Black tophat
+# 3. White tophat <-> Black tophat
 #
 #Skeletonize
 #===========

doc/examples/xx_applications/plot_rank_filters.py

@@ -467,7 +467,7 @@ for ax in ax.ravel():
 ######################################################################
 #
 # Feature extraction
-# ===================
+# ===================
 #
 # Local histograms can be exploited to compute local entropy, which is
 # related to the local image complexity. Entropy is computed using base 2