[readme] updated

.travis.yml

@@ -65,8 +65,8 @@ deploy:
   password: 
     secure: "vMEOlP7DQhFJ7hQAKtKC5hrJXFl5BkUt4nXdosWWiw//Kg8E+PPLg88XPI2gqIosir9wwgtbSBBbbwCxkM6uxRNMpoNR8Ixyv9fmSXp4rLl7bbBY768W7IRXKIBjpuEy2brQjoT+CwDDSzUkckHvuUjJDNRvUv8ab4P/qYO1LG4="
   on:
-    tags: true
+    #tags: true
     branch: plot_density
-  server: https://testpypi.python.org/pypi
+  #server: https://testpypi.python.org/pypi
   distributions: "bdist_wheel sdist"
   skip_cleanup: true

GPy/__version__.py

@@ -1 +1 @@
-__version__ = "0.8.8dev1"
+__version__ = "0.8.8dev0"

README.md

@@ -9,21 +9,26 @@ The Gaussian processes framework in Python.
 * [Unit tests (Travis-CI)](https://travis-ci.org/SheffieldML/GPy)
 * [![licence](https://img.shields.io/badge/licence-BSD-blue.svg)](http://opensource.org/licenses/BSD-3-Clause) 
 
-#### Continuous integration
+## Continuous integration
+
 |      | Travis-CI | Codecov | 
-| ---: | :--: | :---: | 
+| ---: | :--: | :---: |
-| **master:** | [![master](https://travis-ci.org/SheffieldML/GPy.svg?branch=master)](https://travis-ci.org/SheffieldML/GPy) | [![codecov.io](http://codecov.io/github/SheffieldML/GPy/coverage.svg?branch=master)](http://codecov.io/github/SheffieldML/GPy?branch=master) | 
+| **master:** | [![master](https://travis-ci.org/SheffieldML/GPy.svg?branch=master)](https://travis-ci.org/SheffieldML/GPy) | [![codecov.io master](http://codecov.io/github/SheffieldML/GPy/coverage.svg?branch=master)](http://codecov.io/github/SheffieldML/GPy?branch=master) | 
-| **devel:**  | [![devel](https://travis-ci.org/SheffieldML/GPy.svg?branch=devel)](https://travis-ci.org/SheffieldML/GPy) | [![codecov.io](http://codecov.io/github/SheffieldML/GPy/coverage.svg?branch=devel)](http://codecov.io/github/SheffieldML/GPy?branch=devel) | 
+| **devel:**  | [![devel](https://travis-ci.org/SheffieldML/GPy.svg?branch=devel)](https://travis-ci.org/SheffieldML/GPy) | [![codecov.io devel](http://codecov.io/github/SheffieldML/GPy/coverage.svg?branch=devel)](http://codecov.io/github/SheffieldML/GPy?branch=devel) | 
 
-### Supported Platforms:
+## Supported Platforms:
+
+[<img src="https://www.python.org/static/community_logos/python-logo-generic.svg" style="height: 40px;">](https://www.python.org/)
+[<img src="https://upload.wikimedia.org/wikipedia/commons/5/5f/Windows_logo_-_2012.svg" style="height: 40px;">](http://www.microsoft.com/en-gb/windows)
+[<img src="https://upload.wikimedia.org/wikipedia/commons/8/8e/OS_X-Logo.svg" style="height: 40px;">](http://www.apple.com/osx/) 
+[<img src="https://upload.wikimedia.org/wikipedia/commons/3/35/Tux.svg" style="height: 40px;">](https://en.wikipedia.org/wiki/List_of_Linux_distributions)
 
-[<img src="https://www.python.org/static/community_logos/python-logo-generic.svg" height="40">](https://www.python.org/) [<img src="https://upload.wikimedia.org/wikipedia/commons/5/5f/Windows_logo_-_2012.svg" height="40">](http://www.microsoft.com/en-gb/windows) [<img src="https://upload.wikimedia.org/wikipedia/commons/8/8e/OS_X-Logo.svg" height="40">](http://www.apple.com/osx/) [<img src="https://upload.wikimedia.org/wikipedia/commons/3/35/Tux.svg" height="40">](https://en.wikipedia.org/wiki/List_of_Linux_distributions)
 ----
 
 Python 2.7, 3.3 and higher
 
 
-### Citation
+## Citation
 
     @Misc{gpy2014,
       author =   {{The GPy authors}},
@@ -36,7 +41,7 @@ Python 2.7, 3.3 and higher
 
 We like to pronounce it 'g-pie'.
 
-### Getting started: installing with pip 
+## Getting started: installing with pip 
 
 We are now requiring the newest version (0.16) of 
 [scipy](http://www.scipy.org/) and thus, we strongly recommend using 
@@ -87,7 +92,7 @@ or using setuptools
 
     python setup.py test
     
-### Ubuntu hackers
+## Ubuntu hackers
 
 > Note: Right now the Ubuntu package index does not include scipy 0.16.0, and thus, cannot
 > be used for GPy. We hope this gets fixed soon.
@@ -102,20 +107,20 @@ clone this git repository and add it to your path:
     echo 'PYTHONPATH=$PYTHONPATH:~/SheffieldML' >> ~/.bashrc
 
 
-### Compiling documentation:
+## Compiling documentation:
 
 The documentation is stored in doc/ and is compiled with the Sphinx Python documentation generator, and is written in the reStructuredText format.
 
 The Sphinx documentation is available here: http://sphinx-doc.org/latest/contents.html
 
-##### Installing dependencies:
+**Installing dependencies:**
 
 To compile the documentation, first ensure that Sphinx is installed. On Debian-based systems, this can be achieved as follows:
 
     sudo apt-get install python-pip
     sudo pip install sphinx
 
-##### Compiling documentation:
+**Compiling documentation:**
 
 The documentation can be compiled as follows:
 
@@ -127,7 +132,6 @@ The HTML files are then stored in doc/build/html
 
 ## Funding Acknowledgements
 
-
 Current support for the GPy software is coming through the following projects. 
 
 * [EU FP7-HEALTH Project Ref 305626](http://radiant-project.eu) "RADIANT: Rapid Development and Distribution of Statistical Tools for High-Throughput Sequencing Data"
@@ -139,7 +143,8 @@ Current support for the GPy software is coming through the following projects.
 *  [EU FP7-ICT Project Ref 612139](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/wysiwyd/) "WYSIWYD: What You Say is What You Did"
 
 Previous support for the GPy software came from the following projects:
-* [BBSRC Project No BB/K011197/1](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/recombinant/) "Linking recombinant gene sequence to protein product manufacturability using CHO cell genomic resources"
+
-* [EU FP7-KBBE Project Ref 289434](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/biopredyn/) "From Data to Models: New Bioinformatics Methods and Tools for Data-Driven Predictive Dynamic Modelling in Biotechnological Applications"
+- [BBSRC Project No BB/K011197/1](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/recombinant/) "Linking recombinant gene sequence to protein product manufacturability using CHO cell genomic resources"
-* [BBSRC Project No BB/H018123/2](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/iterative/) "An iterative pipeline of computational modelling and experimental design for uncovering gene regulatory networks in vertebrates"
+- [EU FP7-KBBE Project Ref 289434](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/biopredyn/) "From Data to Models: New Bioinformatics Methods and Tools for Data-Driven Predictive Dynamic Modelling in Biotechnological Applications"
-* [Erasysbio](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/synergy/) "SYNERGY: Systems approach to gene regulation biology through nuclear receptors"
+- [BBSRC Project No BB/H018123/2](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/iterative/) "An iterative pipeline of computational modelling and experimental design for uncovering gene regulatory networks in vertebrates"
+- [Erasysbio](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/synergy/) "SYNERGY: Systems approach to gene regulation biology through nuclear receptors"

doc/source/tuto_plotting.rst

@@ -0,0 +1,91 @@
+********************
+Defining a new plotting function in GPy
+********************
+
+GPy has a wrapper for different plotting backends.
+There are some functions you can use for standard plotting.
+Anything going beyond the scope of the
+:py:class:`~GPy.plotting.abstract_plotting_library.AbstractPlottingLibrary` classes plot definitions
+should be considered carefully and maybe is a special case for your plotting library only.
+
+All plotting related code lives in :py:mod:`GPy.plotting` and beneath. No plotting related code needs to be
+anywhere else in GPy.
+
+As examples are always the easiest way to learn how to, we 
+will implement an example of a plotting function, which plots the covariance of a kernel.
+
+Write your plotting function into a module under :py:mod:`GPy.plotting.gpy_plot` ``.<module_name>``
+using the plotting routines provided in :py:func:`GPy.plotting.plotting_library`.
+I like to ``from . import plotting_library as pl`` and the allways use ``pl().`` to access functionality of
+the plotting library.
+
+For the covariance plot we define the function in :py:mod:`GPy.plotting.kernel_plots`.
+
+The first thing is to define the function parameters *and write the documentation for them*!
+The first argument of the plotting function is always ``self`` for the class this plotting function
+will be attached to (we will get to attaching the function to a class that in detail later on)::
+
+ def plot_covariance(kernel, x=None, label=None,
+              plot_limits=None, visible_dims=None, resolution=None,
+              projection=None, levels=20, **kwargs):
+     """
+     Plot a kernel covariance w.r.t. another x.
+ 
+     :param array-like x: the value to use for the other kernel argument (kernels are a function of two variables!)
+     :param plot_limits: the range over which to plot the kernel
+     :type plot_limits: Either (xmin, xmax) for 1D or (xmin, xmax, ymin, ymax) / ((xmin, xmax), (ymin, ymax)) for 2D
+     :param array-like visible_dims: input dimensions (!) to use for x. Make sure to select 2 or less dimensions to plot.
+     :resolution: the resolution of the lines used in plotting. for 2D this defines the grid for kernel evaluation.
+     :param {2d|3d} projection: What projection shall we use to plot the kernel?
+     :param int levels: for 2D projection, how many levels for the contour plot to use?
+     :param kwargs:  valid kwargs for your specific plotting library
+     """
+
+Having defined the outline of the function we can start implementing
+the real plotting.
+
+First, we will write the necessary logic behind getting the covariance function.
+This involves getting an Xgrid to plot with and the second x to compare the covariance to::
+
+    from .plot_util import helper_for_plot_data
+    X = np.ones((2, kernel.input_dim)) * [-4, 4]
+    _, free_dims, Xgrid, xx, yy, _, _, resolution = helper_for_plot_data(kernel, X, plot_limits, visible_dims, None, resolution)
+    from numbers import Number
+    if x is None:
+        x = np.zeros((1, kernel.input_dim))
+    elif isinstance(x, Number):
+        x = np.ones((1, kernel.input_dim))*x
+    K = kernel.K(Xgrid, x)
+
+``free_dims`` holds the free dimensions after selecting
+from the visible_dims, ``Xgrid`` is the grid for the covariance,
+``xx, yy`` are the grid positions for 2D plotting and ``x`` is the
+``X2`` for the kernel and ``K`` holds the kernel covariance for
+all positions between ``Xgrid`` and ``x``.
+
+Then we need a canvas to plot on. Always push the keyword arguments
+of the specifig library through :py:func:`GPy.plotting.abstract_plotting_library.AbstractPlottingLibrary.new_canvas`::
+
+    if projection == '3d':
+        zlabel = "k(X, {!s})" % (np.asanyarray(x).tolist())
+        xlabel = 'X[:,0]'
+        ylabel = 'X[:,1]'
+    else:
+        xlabel = 'X'
+        ylabel = "k(X, {!s})" % (np.asanyarray(x).tolist())
+
+    canvas, kwargs = pl().new_canvas(projection=projection, xlabel=xlabel, ylabel=ylabel, zlabel=zlabel, **kwargs)
+
+Also very important is to use the defaults, which are defined for all plotting libraries implemented.
+This is done by updating the ``kwargs`` from the defaults. There is a helper function
+which takes care for existing keyword arguments. In this case we will just use the default for
+plotting a mean function for the covariance plot as well. If you want to define your own defaults
+add them to the defaults for each library and add it in here. See for example the defaults for
+matplotlib in :py:mod:`GPy.plotting.matplot_dep.defaults`. There is also the default for the
+meanplot_1d, which we are for the 1d plot::
+
+    from .plot_util import update_not_existing_kwargs
+    update_not_existing_kwargs(kwargs, pl().defaults.meanplot_1d)  # @UndefinedVariable
+
+The full definition of the plotting then looks like this::
+

setup.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.8.8dev1
+current_version = 0.8.8dev0
 tag = True
 commit = True
 

setup.py

@@ -42,7 +42,7 @@ import numpy as np
 import codecs
 
 def read(fname):
-    with codecs.open(fname, 'r', 'utf-8') as f:
+    with codecs.open(fname, 'r', 'latin') as f:
         return f.read()
         
 def read_to_rst(fname):