Fix some sphinx warnings and add to build

Fix some sphinx warnings

Add documentation build to test

Add documentation build to test

Remove change in numpydoc

Remove change in apigen

Add makefile target for html and add to travis script

Add a makefile target for html and add to travis script

Fix more sphinx warnings

skimage/draw/draw.py

@@ -95,6 +95,7 @@ def circle(cy, cx, radius, shape=None):
         Pixel coordinates of circle.
         May be used to directly index into an array, e.g.
         ``img[rr, cc] = 1``.
+
     Notes
     -----
         This function is a wrapper for skimage.draw.ellipse()

skimage/filters/thresholding.py

@@ -200,8 +200,10 @@ def threshold_isodata(image, nbins=256, return_all=False):
 
     Histogram-based threshold, known as Ridler-Calvard method or inter-means.
     Threshold values returned satisfy the following equality:
-    threshold = (image[image <= threshold].mean() +
-                 image[image > threshold].mean()) / 2.0
+
+    ``threshold = (image[image <= threshold].mean() +``
+                 ``image[image > threshold].mean()) / 2.0``
+
     That is, returned thresholds are intensities that separate the image into
     two groups of pixels, where the threshold intensity is midway between the
     mean intensities of these groups.

skimage/future/graph/rag.py

@@ -220,7 +220,7 @@ def rag_mean_color(image, labels, connectivity=2, mode='distance',
     labels : ndarray, shape(M, N, [..., P,])
         The labelled image. This should have one dimension less than
         `image`. If `image` has dimensions `(M, N, 3)` `labels` should have
-         dimensions `(M, N)`.
+        dimensions `(M, N)`.
     connectivity : int, optional
         Pixels with a squared distance less than `connectivity` from each other
         are considered adjacent. It can range from 1 to `labels.ndim`. Its

skimage/measure/_ccomp.pyx

@@ -368,7 +368,7 @@ def label(input, neighbors=None, background=None, return_num=False,
     Two pixels are connected when they are neighbors and have the same value.
     In 2D, they can be neighbors either in a 1- or 2-connected sense.
     The value refers to the maximum number of orthogonal hops to consider a
-    pixel/voxel a neighbor.
+    pixel/voxel a neighbor::
 
       1-connectivity      2-connectivity     diagonal connection close-up
 

skimage/segmentation/boundaries.py

@@ -65,15 +65,15 @@ def find_boundaries(label_img, connectivity=1, mode='thick', background=0):
         How to mark the boundaries:
 
         - thick: any pixel not completely surrounded by pixels of the
-        same label (defined by `connectivity`) is marked as a boundary.
-        This results in boundaries that are 2 pixels thick.
+          same label (defined by `connectivity`) is marked as a boundary.
+          This results in boundaries that are 2 pixels thick.
         - inner: outline the pixels *just inside* of objects, leaving
-        background pixels untouched.
+          background pixels untouched.
         - outer: outline pixels in the background around object
-        boundaries. When two objects touch, their boundary is also
-        marked.
+          boundaries. When two objects touch, their boundary is also
+          marked.
         - subpixel: return a doubled image, with pixels *between* the
-        original pixels marked as boundary where appropriate.
+          original pixels marked as boundary where appropriate.
     background: int, optional
         For modes 'inner' and 'outer', a definition of a background
         label is required. See `mode` for descriptions of these two.
@@ -197,7 +197,7 @@ def mark_boundaries(image, label_img, color=(1, 1, 0),
 
     See Also
     --------
-    ``find_boundaries``.
+    find_boundaries
     """
     marked = img_as_float(image, force_copy=True)
     if marked.ndim == 2:

skimage/util/noise.py

@@ -16,15 +16,15 @@ def random_noise(image, mode='gaussian', seed=None, clip=True, **kwargs):
     mode : str
         One of the following strings, selecting the type of noise to add:
 
-        'gaussian'  Gaussian-distributed additive noise.
-        'localvar'  Gaussian-distributed additive noise, with specified
-                    local variance at each point of `image`
-        'poisson'   Poisson-distributed noise generated from the data.
-        'salt'      Replaces random pixels with 1.
-        'pepper'    Replaces random pixels with 0.
-        's&p'       Replaces random pixels with 0 or 1.
-        'speckle'   Multiplicative noise using out = image + n*image, where
-                    n is uniform noise with specified mean & variance.
+        - 'gaussian'  Gaussian-distributed additive noise.
+        - 'localvar'  Gaussian-distributed additive noise, with specified
+                      local variance at each point of `image`
+        - 'poisson'   Poisson-distributed noise generated from the data.
+        - 'salt'      Replaces random pixels with 1.
+        - 'pepper'    Replaces random pixels with 0.
+        - 's&p'       Replaces random pixels with 0 or 1.
+        - 'speckle'   Multiplicative noise using out = image + n*image, where
+                      n is uniform noise with specified mean & variance.
     seed : int
         If provided, this will set the random seed before generating noise,
         for valid pseudo-random comparisons.