Merge pull request #565 from ahojnnes/doc-fix

DOC: Documentation fixes.

doc/examples/applications/plot_rank_filters.py

@@ -361,7 +361,7 @@ The example compares the local threshold with the global threshold
     Local thresholding is much slower than global one. There exists a function
     for global Otsu thresholding: `skimage.filter.threshold_otsu`.
 
-.. [1] http://en.wikipedia.org/wiki/Otsu's_method
+.. [4] http://en.wikipedia.org/wiki/Otsu's_method
 
 """
 

doc/examples/plot_swirl.py

@@ -42,9 +42,9 @@ mapping for the swirl transformation first computes, relative to a center
 
 .. math::
 
-    \theta = \arctan(y/x)
+    \\theta = \\arctan(y/x)
 
-    \rho = \sqrt{(x - x_0)^2 + (y - y_0)^2},
+    \\rho = \sqrt{(x - x_0)^2 + (y - y_0)^2},
 
 and then transforms them according to
 
@@ -56,12 +56,12 @@ and then transforms them according to
 
     s = \mathtt{strength}
 
-    \theta' = \phi + s \, e^{-\rho / r + \theta}
+    \\theta' = \phi + s \, e^{-\\rho / r + \\theta}
 
 where ``strength`` is a parameter for the amount of swirl, ``radius`` indicates
 the swirl extent in pixels, and ``rotation`` adds a rotation angle.  The
 transformation of ``radius`` into :math:`r` is to ensure that the
-transformation decays to :math:`\approx 1/1000^{\mathsf{th}}` within the
+transformation decays to :math:`\\approx 1/1000^{\mathsf{th}}` within the
 specified radius.
 
 """

doc/source/install.txt

@@ -6,8 +6,9 @@ Pre-built installation
 are kindly provided by Christoph Gohlke.
 
 The latest stable release is also included as part of the `Enthought Python
-Distribution (EPD) <http://enthought.com/products/epd.php>`__ and `Python(x,y)
-<http://code.google.com/p/pythonxy/wiki/Welcome>`__.
+Distribution (EPD) <http://enthought.com/products/epd.php>`__, `Python(x,y)
+<http://code.google.com/p/pythonxy/wiki/Welcome>`__ and
+`Anaconda <https://store.continuum.io/cshop/anaconda/>`__.
 
 On systems that support setuptools, the package can be installed from the
 `Python packaging index <http://pypi.python.org/pypi/scikit-image>`__ using
@@ -28,34 +29,26 @@ Installation from source
 
 Obtain the source from the git-repository at
 `http://github.com/scikit-image/scikit-image
-<http://github.com/scikit-image/scikit-image>`_.
-
-by running
-
-::
+<http://github.com/scikit-image/scikit-image>`_ by running::
 
   git clone http://github.com/scikit-image/scikit-image.git
 
-in a terminal (You will need to have git installed on your machine).
+in a terminal (you will need to have git installed on your machine).
 
 If you do not have git installed, you can also download a zipball from
 `https://github.com/scikit-image/scikit-image/zipball/master
 <https://github.com/scikit-image/scikit-image/zipball/master>`_.
 
-The SciKit can be installed globally using
-
-::
+The SciKit can be installed globally using::
 
   python setup.py install
 
-or locally using
-
-::
+or locally using::
 
   python setup.py install --prefix=${HOME}
 
 If you prefer, you can use it without installing, by simply adding
-this path to your PYTHONPATH variable and compiling extensions
+this path to your ``PYTHONPATH`` variable and compiling extensions
 in-place::
 
   python setup.py build_ext -i

doc/source/themes/scikit-image/static/css/custom.css

@@ -41,6 +41,12 @@ h6 {
     font-size: 13px;
     line-height: 15px;
 }
+blockquote {
+    border-left: 0;
+}
+dt {
+    font-weight: normal;
+}
 
 .logo {
     float: left;
@@ -222,3 +228,8 @@ p.admonition-title {
     width: 200px;
     text-align: center !important;
 }
+
+/* misc */
+div.math {
+    text-align: center;
+}

doc/source/user_guide/getting_help.txt

@@ -24,7 +24,7 @@ Contributing examples to the gallery can be done on github (see
 Search field
 ------------
 
-The ``quick search`` field located in the sidebar of the html
+The ``quick search`` field located in the navigation bar of the html
 documentation can be used to search for specific keywords (segmentation,
 rescaling, denoising, etc.).
 

skimage/_shared/utils.py

@@ -27,9 +27,9 @@ class deprecated(object):
 
         alt_msg = ''
         if self.alt_func is not None:
-            alt_msg = ' Use `%s` instead.' % self.alt_func
+            alt_msg = ' Use ``%s`` instead.' % self.alt_func
 
-        msg = 'Call to deprecated function `%s`.' % func.__name__
+        msg = 'Call to deprecated function ``%s``.' % func.__name__
         msg += alt_msg
 
         @functools.wraps(func)
@@ -48,6 +48,6 @@ class deprecated(object):
         if wrapped.__doc__ is None:
             wrapped.__doc__ = doc
         else:
-            wrapped.__doc__ = doc + '\n\n' + wrapped.__doc__
+            wrapped.__doc__ = doc + '\n\n    ' + wrapped.__doc__
 
         return wrapped

skimage/filter/__init__.py

@@ -9,6 +9,7 @@ from ._denoise_cy import denoise_bilateral, denoise_tv_bregman
 from ._rank_order import rank_order
 from ._gabor import gabor_kernel, gabor_filter
 from .thresholding import threshold_otsu, threshold_adaptive
+from . import rank
 
 
 __all__ = ['inverse',
@@ -36,4 +37,5 @@ __all__ = ['inverse',
            'gabor_kernel',
            'gabor_filter',
            'threshold_otsu',
-           'threshold_adaptive']
+           'threshold_adaptive',
+           'rank']

skimage/filter/thresholding.py

@@ -9,10 +9,10 @@ def threshold_adaptive(image, block_size, method='gaussian', offset=0,
                        mode='reflect', param=None):
     """Applies an adaptive threshold to an array.
 
-    Also known as local or dynamic thresholding where the threshold value is the
-    weighted mean for the local neighborhood of a pixel subtracted by a
-    constant. Alternatively the threshold can be determined dynamically by a
-    a given function using the 'generic' method.
+    Also known as local or dynamic thresholding where the threshold value is
+    the weighted mean for the local neighborhood of a pixel subtracted by a
+    constant. Alternatively the threshold can be determined dynamically by a a
+    given function using the 'generic' method.
 
     Parameters
     ----------
@@ -26,10 +26,10 @@ def threshold_adaptive(image, block_size, method='gaussian', offset=0,
         weighted mean image.
 
         * 'generic': use custom function (see `param` parameter)
-        * 'gaussian': apply gaussian filter (see `param` parameter for custom
+        * 'gaussian': apply gaussian filter (see `param` parameter for custom\
                       sigma value)
         * 'mean': apply arithmetic mean filter
-        * 'median' apply median rank filter
+        * 'median': apply median rank filter
 
         By default the 'gaussian' method is used.
     offset : float, optional
@@ -42,8 +42,8 @@ def threshold_adaptive(image, block_size, method='gaussian', offset=0,
     param : {int, function}, optional
         Either specify sigma for 'gaussian' method or function object for
         'generic' method. This functions takes the flat array of local
-        neighbourhood as a single argument and returns the calculated threshold
-        for the centre pixel.
+        neighbourhood as a single argument and returns the calculated
+        threshold for the centre pixel.
 
     Returns
     -------
@@ -52,8 +52,7 @@ def threshold_adaptive(image, block_size, method='gaussian', offset=0,
 
     References
     ----------
-    http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations
-        .html?highlight=threshold#adaptivethreshold
+    .. [1] http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html?highlight=threshold#adaptivethreshold
 
     Examples
     --------

skimage/measure/fit.py

@@ -559,7 +559,7 @@ def ransac(data, model_class, min_samples, residual_threshold,
     >>> ransac_model, inliers = ransac(data, EllipseModel, 5, 3, max_trials=50)
     >>> # ransac_model._params, inliers
 
-    Should give the correct result estimated without the faulty data:
+    Should give the correct result estimated without the faulty data::
 
         [ 20.12762373, 29.73563061, 4.81499637, 10.4743584, 0.05217117]
         [ 4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,