DOC Use HTTPS links in docs

docs/source/_templates/sidebar.html

@@ -4,29 +4,29 @@ <h3>About</h3>
 C++) operates over numpy arrays for convenience.</p>
 
 <p>
-  <iframe src="http://ghbtns.com/github-btn.html?user=luispedro&repo=mahotas&type=watch&count=true&size=large"
+  <iframe src="https://ghbtns.com/github-btn.html?user=luispedro&repo=mahotas&type=watch&count=true&size=large"
     allowtransparency="true" frameborder="0" scrolling="0" width="200px" height="35px"></iframe>
 </p>
 
 <h3>Useful Links</h3>
 <ul>
-  <li><a href="http://pypi.python.org/pypi/mahotas">mahotas @ PyPI</a></li>
-  <li><a href="http://github.com/luispedro/mahotas">mahotas @ GitHub</a></li>
-  <li><a href="http://github.com/luispedro/mahotas/issues">Issue Tracker</a></li>
-  <li><a href="http://dx.doi.org/10.5334/jors.ac">mahotas publication</a></li>
+  <li><a href="https://pypi.python.org/pypi/mahotas">mahotas @ PyPI</a></li>
+  <li><a href="https://github.com/luispedro/mahotas">mahotas @ GitHub</a></li>
+  <li><a href="https://github.com/luispedro/mahotas/issues">Issue Tracker</a></li>
+  <li><a href="https://dx.doi.org/10.5334/jors.ac">mahotas publication</a></li>
 </ul>
 
 <h3>Stay Informed</h3>
 
-<p><iframe src="http://ghbtns.com/github-btn.html?user=luispedro&type=follow"
+<p><iframe src="https://ghbtns.com/github-btn.html?user=luispedro&type=follow"
   allowtransparency="true" frameborder="0" scrolling="0" width="165" height="20"></iframe></p>
 
 <h3>Cite</h3>
 
 <p>If you use mahotas in a publication, please cite:</p>
 
 <blockquote>
-    <a href="http://dx.doi.org/10.5334/jors.ac">Luis Pedro Coelho. <em>Mahotas:
+    <a href="https://dx.doi.org/10.5334/jors.ac">Luis Pedro Coelho. <em>Mahotas:
     Open source software for scriptable computer vision</em> in Journal of Open
     Research Software, vol 1, 2013. [DOI:10.5334/jors.ac]</a>
 </blockquote>

docs/source/classification.rst

@@ -15,7 +15,7 @@ Here is the simple algorithm:
 2. learn a classifier
 3. use that classifier on the unlabeled images
 
-In the code below I used `jug <http://luispedro.org/software/jug>`_ to give you
+In the code below I used `jug <https://luispedro.org/software/jug>`_ to give you
 the possibility of running it on multiple processors, but the code also works
 if you remove every line which mentions ``TaskGenerator``.
 
@@ -74,6 +74,6 @@ with other features in ``mahotas.features`` if you'd like (or try
 ``mahotas.surf``, but that gets more complicated).
 
 (This was motivated by `a question on Stackoverflow
-<http://stackoverflow.com/questions/5426482/using-pil-to-detect-a-scan-of-a-blank-page/5505754>`__).
+<https://stackoverflow.com/questions/5426482/using-pil-to-detect-a-scan-of-a-blank-page/5505754>`__).
 
 

docs/source/color.rst

@@ -52,7 +52,7 @@ Other Colour Spaces
 -------------------
 
 Mahotas can also convert to `XYZ space
-<http://en.wikipedia.org/wiki/CIE_1931_color_space>`__ and to the `Lab space
-<http://en.wikipedia.org/wiki/Lab_color_space>`__ with ``rgb2xyz`` and
+<https://en.wikipedia.org/wiki/CIE_1931_color_space>`__ and to the `Lab space
+<https://en.wikipedia.org/wiki/Lab_color_space>`__ with ``rgb2xyz`` and
 ``rgb2lab``, respectively.
 

docs/source/contributing.rst

@@ -47,7 +47,7 @@ the ``fast`` Make target to get the non-debug build::
 Using make will not change your environment. The ``DEBUG`` variable is set
 internally only.
 
-If you don't know about it, check out `ccache <http://ccache.samba.org/>`__
+If you don't know about it, check out `ccache <https://ccache.samba.org/>`__
 which is a great tool if you are developing in compiled languages (this is not
 specific to mahotas or even Python). It will allow you to quickly perform
 ``make clean; make debug`` and ``make clean; make fast`` so you never get your

docs/source/faq.rst

@@ -5,7 +5,7 @@ Frequently Asked Questions
 How do I install mahotas with anaconda?
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-If you are using `conda <http://anaconda.org/>`__, you can
+If you are using `conda <https://anaconda.org/>`__, you can
 install mahotas from `conda-forge
 <https://conda-forge.github.io/>`__ using the following
 commands::
@@ -19,7 +19,7 @@ Who uses mahotas?
 In June 2016, there were `34 papers
 <https://scholar.google.com/scholar?as_sdt=1,5&hl=en&sciodt=0,5&cites=18199654681754783804&scipsc=>`__
 citing the `mahotas paper
-<http://dx.doi.org/10.5334/jors.ac>`__
+<https://dx.doi.org/10.5334/jors.ac>`__
 
 Why did you not simply contribute to ``scipy.ndimage`` or ``scikits.image``?
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -76,7 +76,7 @@ If you use mahotas in a scientific publication, please cite:
 
     Coelho, L.P. 2013. Mahotas: Open source software for scriptable computer
     vision. Journal of Open Research Software 1(1), DOI:
-    http://dx.doi.org/10.5334/jors.ac
+    https://dx.doi.org/10.5334/jors.ac
 
 In BibTeX format::
 
@@ -87,7 +87,7 @@ In BibTeX format::
         year = {2013},
         volume = {1},
         doi = {10.5334/jors.ac},
-        url = {http://dx.doi.org/10.5334/jors.ac}
+        url = {https://dx.doi.org/10.5334/jors.ac}
     }
 
 This is accessible in Python using ``mahotas.citation()``.
@@ -109,5 +109,5 @@ as it uses other packages itself.  Thus, depending on what you had installed,
 the resulting images would be different.
 
 If you are running on Windows, you may wish to try `Christoph Gohlke's packages
-<http://www.lfd.uci.edu/~gohlke/pythonlibs/#mahotas>`__.
+<https://www.lfd.uci.edu/~gohlke/pythonlibs/#mahotas>`__.
 

docs/source/features.rst

@@ -42,7 +42,7 @@ They can be computed for 2-D or 3-D images and are available in the
 
 Only the first 13 features are implemented. The last (14th) feature is normally
 considered to be *unstable*, although it is not clear to me why this is.
-(`See this unanswered question on Cross-validated <http://stats.stackexchange.com/questions/9763/what-is-this-maximum-correlation-coefficient>`__).
+(`See this unanswered question on Cross-validated <https://stats.stackexchange.com/questions/9763/what-is-this-maximum-correlation-coefficient>`__).
 
 Local Binary Patterns
 ~~~~~~~~~~~~~~~~~~~~~
@@ -62,7 +62,7 @@ Threshold adjacency statistics (TAS) are a recent innovation too. In the
 original version, they have fixed parameters, but we have adapted them to
 *parameter-free* versions (see `Structured Literature Image Finder: Extracting
 Information from Text and Images in Biomedical Literature
-<http://dx.doi.org/10.1007/978-3-642-13131-8_4>`__ by Coelho
+<https://dx.doi.org/10.1007/978-3-642-13131-8_4>`__ by Coelho
 et al. for a reference). Mahotas supports both.
 
 Zernike Moments

docs/source/index.rst

@@ -6,7 +6,7 @@ Mahotas: Computer Vision in Python
 
         Coelho, L.P. 2013. Mahotas: Open source software for scriptable computer
         vision. Journal of Open Research Software 1(1):e3, DOI:
-        http://dx.doi.org/10.5334/jors.ac
+        https://dx.doi.org/10.5334/jors.ac
 
 Mahotas is a computer vision and image processing library for Python.
 
@@ -16,14 +16,14 @@ numpy arrays and with a very clean Python interface.
 Mahotas currently has over 100 functions for image processing and computer
 vision and it keeps growing. Some examples of mahotas functionality:
 
- - `watershed <http://mahotas.readthedocs.io/en/latest/api.html#mahotas.cwatershed>`__
+ - `watershed <https://mahotas.readthedocs.io/en/latest/api.html#mahotas.cwatershed>`__
  - convex points calculations.
- - `hit & miss. thinning <http://mahotas.readthedocs.io/en/latest/api.html#mahotas.hitmiss>`__
+ - `hit & miss. thinning <https://mahotas.readthedocs.io/en/latest/api.html#mahotas.hitmiss>`__
  - Zernike & Haralick, `local binary patterns
-   <http://mahotas.readthedocs.io/en/latest/lbp.html>`__, and TAS features.
- - `morphological processing <http://mahotas.readthedocs.io/en/latest/morphology.html>`__
- - `Speeded-Up Robust Features (SURF), a form of local features <http://mahotas.readthedocs.io/en/latest/surf.html>`__
- - `thresholding <http://mahotas.readthedocs.io/en/latest/thresholding.html>`__
+   <https://mahotas.readthedocs.io/en/latest/lbp.html>`__, and TAS features.
+ - `morphological processing <https://mahotas.readthedocs.io/en/latest/morphology.html>`__
+ - `Speeded-Up Robust Features (SURF), a form of local features <https://mahotas.readthedocs.io/en/latest/surf.html>`__
+ - `thresholding <https://mahotas.readthedocs.io/en/latest/thresholding.html>`__
  - convolution.
  - Sobel edge detection.
 
@@ -38,7 +38,7 @@ Bug reports with test cases typically get fixed in 24 hours.
 
 .. seealso::
 
-    `mahotas-imread <http://imread.readthedocs.io/en/latest/>`__ is side
+    `mahotas-imread <https://imread.readthedocs.io/en/latest/>`__ is side
     project which includes code to read/write images to files
 
 Examples

docs/source/install.rst

@@ -3,7 +3,7 @@ How To Install Mahotas
 
 .. image:: https://anaconda.org/conda-forge/mahotas/badges/installer/conda.svg   :target: https://conda.anaconda.org/conda-forge
 
-The simplest way to install mahotas is to use `conda <http://anaconda.org/>`__.
+The simplest way to install mahotas is to use `conda <https://anaconda.org/>`__.
 
 If you have conda installed, you can install mahotas using the following pair
 of commands::
@@ -26,7 +26,7 @@ You can get the released version using pip::
     pip install mahotas
 
 If you prefer, you can download the source from `PyPI
-<http://pypi.python.org/pypi/mahotas>`__ and run::
+<https://pypi.python.org/pypi/mahotas>`__ and run::
 
     python setup.py install
 
@@ -55,22 +55,22 @@ On Windows
 ~~~~~~~~~~
 
 On Windows, Christoph Gohlke does an excellent job maintaining `binary packages
-of mahotas <http://www.lfd.uci.edu/~gohlke/pythonlibs/>`__ (and several other
+of mahotas <https://www.lfd.uci.edu/~gohlke/pythonlibs/>`__ (and several other
 packages).
 
 
 WinPython
 ~~~~~~~~~
 
-`WinPython <http://winpython.sourceforge.net/>`__ ships with `mahotas as a
-standard package <http://sourceforge.net/p/winpython/wiki/PackageIndex_27/>`__
+`WinPython <https://winpython.sourceforge.net/>`__ ships with `mahotas as a
+standard package <https://sourceforge.net/p/winpython/wiki/PackageIndex_27/>`__
 
 Python(x, y)
 ~~~~~~~~~~~~
 
-If you use `Python(x, y) <http://python-xy.github.io/>`__, which is often a good
+If you use `Python(x, y) <https://python-xy.github.io/>`__, which is often a good
 solution, then you probably have it already as `mahotas is a standard plugin
-<http://python-xy.github.io>`__.
+<https://python-xy.github.io>`__.
 
 Enthought Canopy
 ~~~~~~~~~~~~~~~~
@@ -82,7 +82,7 @@ FreeBSD
 ~~~~~~~
 
 Mahotas is available for FreeBSD as `graphics/mahotas
-<http://www.freshports.org/graphics/mahotas>`__.
+<https://www.freshports.org/graphics/mahotas>`__.
 
 MacPorts
 ~~~~~~~~

docs/source/io.rst

@@ -16,7 +16,7 @@ It can use the following backends (it tries them in the following order):
     arrays. It supports PNG, JPEG, TIFF, WEBP, BMP, and a few TIFF-based
     microscopy formats (LSM and STK).
 
-2.  It also looks for `freeimage <http://freeimage.sourceforge.net/>`__.
+2.  It also looks for `freeimage <https://freeimage.sourceforge.net/>`__.
     Freeimage can read and write many formats. Unfortunately, it is harder to
     install and it is not as well-maintained as imread.
 
@@ -29,4 +29,4 @@ Thus, depending on what you had installed, the resulting images would be
 different.
 
 If you are running on Windows, you may wish to try `Christoph Gohlke's packages
-<http://www.lfd.uci.edu/~gohlke/pythonlibs/#mahotas>`__.
+<https://www.lfd.uci.edu/~gohlke/pythonlibs/#mahotas>`__.

docs/source/labeled.rst

@@ -68,7 +68,7 @@ Here is a slightly more complex example. The full code is in the `demos
 directory <https://github.com/luispedro/mahotas/tree/master/mahotas/demos>`__
 as ``nuclear.py``. We are going to use this image, a fluorescent microscopy
 image from a `nuclear segmentation benchmark
-<http://luispedro.org/projects/nuclear-segmentation>`__
+<https://luispedro.org/projects/nuclear-segmentation>`__
 
 This image is available as ``mahotas.demos.nuclear_image()``
 

docs/source/lbp.rst

@@ -8,7 +8,7 @@ diagram below:
 .. image:: LBP.svg
     :alt: Neighbourhood illustration
 
-(Image reference: `Wikipedia <http://en.wikipedia.org/wiki/Local_binary_patterns>`__)
+(Image reference: `Wikipedia <https://en.wikipedia.org/wiki/Local_binary_patterns>`__)
 
 The reference pixel is in red, at the centre. A number of points are defined at
 a distance ``r`` from it. These are the green points. As you go from left to

docs/source/mahotas-features.rst

@@ -26,7 +26,7 @@ You can obtain help on all the options by running ``mahotas-features.py
 --help``::
 
     If you use mahotas in a scientific publication, please cite
-        Coelho, LP (2013). http://dx.doi.org/10.5334/jors.ac
+        Coelho, LP (2013). https://dx.doi.org/10.5334/jors.ac
 
 
     usage: mahotas-features.py [-h] [--output OUTPUT] [--clobber]

docs/source/morphology.rst

@@ -38,8 +38,8 @@ After Oct 2013, you can get this image with mahotas as::
 Dilation & Erosion
 ------------------
 
-`Dilation <http://en.wikipedia.org/wiki/Dilation_(morphology)>`__ and `erosion
-<http://en.wikipedia.org/wiki/Erosion_(morphology)>`__ are two very basic
+`Dilation <https://en.wikipedia.org/wiki/Dilation_(morphology)>`__ and `erosion
+<https://en.wikipedia.org/wiki/Erosion_(morphology)>`__ are two very basic
 operators (mathematically, you only need one of them as you
 can define the erosion as dilation of the negative or vice-versa).
 

docs/source/polygon.rst

@@ -15,7 +15,7 @@ for many complex usage.
 
 The main purpose of these utilities is to aid debugging and visualisation. If
 you need to generate fancy graphs, look for packages such as `matplotlib
-<http://matplotlib.org>`__.
+<https://matplotlib.org>`__.
 
 Convex Hull
 -----------

docs/source/surfref.rst

@@ -4,7 +4,7 @@ Implementing SURF-ref With Mahotas
 
 This is a companion to the paper `Determining the subcellular location of new
 proteins from microscope images using local features
-<http://dx.doi.org/10.1093/bioinformatics/btt392>`__
+<https://dx.doi.org/10.1093/bioinformatics/btt392>`__
 by Coelho et al. (2013).
 
 ::

docs/source/tasks.rst

@@ -8,20 +8,20 @@ Here are a few ideas for improving mahotas.
 New Features
 ------------
 
-- `HOG <http://en.wikipedia.org/wiki/Histogram_of_oriented_gradients>`__
-- `BRISK <http://savvash.blogspot.pt/2011/08/brisk-binary-robust-invariant-scalable.html>`__
-- `Canny edge detection <http://en.wikipedia.org/wiki/Canny_edge_detector>`__
-- `Hough Transform <http://en.wikipedia.org/wiki/Hough_transform>`__
-- `bilateral filtering <http://en.wikipedia.org/wiki/Bilateral_filter>`__
-- `Non Local Filtering <http://en.wikipedia.org/wiki/Non-local_means>`__
-- `Wiener filtering <http://en.wikipedia.org/wiki/Wiener_filter>`__
+- `HOG <https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients>`__
+- `BRISK <https://savvash.blogspot.pt/2011/08/brisk-binary-robust-invariant-scalable.html>`__
+- `Canny edge detection <https://en.wikipedia.org/wiki/Canny_edge_detector>`__
+- `Hough Transform <https://en.wikipedia.org/wiki/Hough_transform>`__
+- `bilateral filtering <https://en.wikipedia.org/wiki/Bilateral_filter>`__
+- `Non Local Filtering <https://en.wikipedia.org/wiki/Non-local_means>`__
+- `Wiener filtering <https://en.wikipedia.org/wiki/Wiener_filter>`__
 
 Small Improvements
 ------------------
 
-- something like the ``overlay`` function from `pymorph <http://luispedro.org/software/pymorph>`__ (or even just copy it over and adapt it to mahotas style).
+- something like the ``overlay`` function from `pymorph <https://luispedro.org/software/pymorph>`__ (or even just copy it over and adapt it to mahotas style).
 - H-maxima transform (again, pymorph can provide a basis)
-- `entropy thresholding <http://en.wikipedia.org/wiki/Thresholding_(image_processing)>`__
+- `entropy thresholding <https://en.wikipedia.org/wiki/Thresholding_(image_processing)>`__
 
 Internals
 ---------

docs/source/wally.rst

@@ -3,7 +3,7 @@ Finding Wally
 =============
 
 This was originally an `answer on stackoverflow
-<http://stackoverflow.com/questions/8849869/how-do-i-find-wally-with-python>`__
+<https://stackoverflow.com/questions/8849869/how-do-i-find-wally-with-python>`__
 We can use it as a simple tutorial example.
 
 The problem is to find Wally (who goes by Waldo in the US) in the following