diff --git a/.travis.yml b/.travis.yml
index 5cbe7c00..35d31f2b 100644
--- a/.travis.yml
+++ b/.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
diff --git a/GPy/__version__.py b/GPy/__version__.py
index 64b3f18e..21311fc4 100644
--- a/GPy/__version__.py
+++ b/GPy/__version__.py
@@ -1 +1 @@
-__version__ = "0.8.8dev1"
+__version__ = "0.8.8dev0"
diff --git a/README.md b/README.md
index f8ef8961..15c405c4 100644
--- a/README.md
+++ b/README.md
@@ -9,21 +9,26 @@ The Gaussian processes framework in Python.
* [Unit tests (Travis-CI)](https://travis-ci.org/SheffieldML/GPy)
* [](http://opensource.org/licenses/BSD-3-Clause)
-#### Continuous integration
+## Continuous integration
+
| | Travis-CI | Codecov |
-| ---: | :--: | :---: |
-| **master:** | [](https://travis-ci.org/SheffieldML/GPy) | [](http://codecov.io/github/SheffieldML/GPy?branch=master) |
-| **devel:** | [](https://travis-ci.org/SheffieldML/GPy) | [](http://codecov.io/github/SheffieldML/GPy?branch=devel) |
+| ---: | :--: | :---: |
+| **master:** | [](https://travis-ci.org/SheffieldML/GPy) | [](http://codecov.io/github/SheffieldML/GPy?branch=master) |
+| **devel:** | [](https://travis-ci.org/SheffieldML/GPy) | [](http://codecov.io/github/SheffieldML/GPy?branch=devel) |
-### Supported Platforms:
+## Supported Platforms:
+
+[
](https://www.python.org/)
+[
](http://www.microsoft.com/en-gb/windows)
+[
](http://www.apple.com/osx/)
+[
](https://en.wikipedia.org/wiki/List_of_Linux_distributions)
-[](https://www.python.org/) [](http://www.microsoft.com/en-gb/windows) [](http://www.apple.com/osx/) [](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/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"
+
+- [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/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"
diff --git a/doc/source/tuto_plotting.rst b/doc/source/tuto_plotting.rst
new file mode 100644
index 00000000..adf00b66
--- /dev/null
+++ b/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` ``.``
+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::
+
diff --git a/setup.cfg b/setup.cfg
index 59497875..d79d8bbd 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,5 +1,5 @@
[bumpversion]
-current_version = 0.8.8dev1
+current_version = 0.8.8dev0
tag = True
commit = True
diff --git a/setup.py b/setup.py
index dfab2663..ed7e8296 100644
--- a/setup.py
+++ b/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):