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[readme] updated
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@ -65,8 +65,8 @@ deploy:
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password:
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secure: "vMEOlP7DQhFJ7hQAKtKC5hrJXFl5BkUt4nXdosWWiw//Kg8E+PPLg88XPI2gqIosir9wwgtbSBBbbwCxkM6uxRNMpoNR8Ixyv9fmSXp4rLl7bbBY768W7IRXKIBjpuEy2brQjoT+CwDDSzUkckHvuUjJDNRvUv8ab4P/qYO1LG4="
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on:
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tags: true
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#tags: true
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branch: plot_density
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server: https://testpypi.python.org/pypi
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#server: https://testpypi.python.org/pypi
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distributions: "bdist_wheel sdist"
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skip_cleanup: true
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@ -1 +1 @@
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__version__ = "0.8.8dev1"
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__version__ = "0.8.8dev0"
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37
README.md
37
README.md
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@ -9,21 +9,26 @@ The Gaussian processes framework in Python.
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* [Unit tests (Travis-CI)](https://travis-ci.org/SheffieldML/GPy)
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* [](http://opensource.org/licenses/BSD-3-Clause)
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#### Continuous integration
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## Continuous integration
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| | Travis-CI | Codecov |
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| ---: | :--: | :---: |
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| **master:** | [](https://travis-ci.org/SheffieldML/GPy) | [](http://codecov.io/github/SheffieldML/GPy?branch=master) |
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| **devel:** | [](https://travis-ci.org/SheffieldML/GPy) | [](http://codecov.io/github/SheffieldML/GPy?branch=devel) |
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| **master:** | [](https://travis-ci.org/SheffieldML/GPy) | [](http://codecov.io/github/SheffieldML/GPy?branch=master) |
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| **devel:** | [](https://travis-ci.org/SheffieldML/GPy) | [](http://codecov.io/github/SheffieldML/GPy?branch=devel) |
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### Supported Platforms:
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## Supported Platforms:
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[<img src="https://www.python.org/static/community_logos/python-logo-generic.svg" style="height: 40px;">](https://www.python.org/)
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[<img src="https://upload.wikimedia.org/wikipedia/commons/5/5f/Windows_logo_-_2012.svg" style="height: 40px;">](http://www.microsoft.com/en-gb/windows)
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[<img src="https://upload.wikimedia.org/wikipedia/commons/8/8e/OS_X-Logo.svg" style="height: 40px;">](http://www.apple.com/osx/)
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[<img src="https://upload.wikimedia.org/wikipedia/commons/3/35/Tux.svg" style="height: 40px;">](https://en.wikipedia.org/wiki/List_of_Linux_distributions)
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[<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)
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----
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Python 2.7, 3.3 and higher
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### Citation
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## Citation
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@Misc{gpy2014,
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author = {{The GPy authors}},
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@ -36,7 +41,7 @@ Python 2.7, 3.3 and higher
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We like to pronounce it 'g-pie'.
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### Getting started: installing with pip
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## Getting started: installing with pip
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We are now requiring the newest version (0.16) of
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[scipy](http://www.scipy.org/) and thus, we strongly recommend using
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@ -87,7 +92,7 @@ or using setuptools
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python setup.py test
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### Ubuntu hackers
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## Ubuntu hackers
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> Note: Right now the Ubuntu package index does not include scipy 0.16.0, and thus, cannot
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> be used for GPy. We hope this gets fixed soon.
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@ -102,20 +107,20 @@ clone this git repository and add it to your path:
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echo 'PYTHONPATH=$PYTHONPATH:~/SheffieldML' >> ~/.bashrc
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### Compiling documentation:
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## Compiling documentation:
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The documentation is stored in doc/ and is compiled with the Sphinx Python documentation generator, and is written in the reStructuredText format.
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The Sphinx documentation is available here: http://sphinx-doc.org/latest/contents.html
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##### Installing dependencies:
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**Installing dependencies:**
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To compile the documentation, first ensure that Sphinx is installed. On Debian-based systems, this can be achieved as follows:
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sudo apt-get install python-pip
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sudo pip install sphinx
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##### Compiling documentation:
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**Compiling documentation:**
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The documentation can be compiled as follows:
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@ -127,7 +132,6 @@ The HTML files are then stored in doc/build/html
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## Funding Acknowledgements
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Current support for the GPy software is coming through the following projects.
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* [EU FP7-HEALTH Project Ref 305626](http://radiant-project.eu) "RADIANT: Rapid Development and Distribution of Statistical Tools for High-Throughput Sequencing Data"
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@ -139,7 +143,8 @@ Current support for the GPy software is coming through the following projects.
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* [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"
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Previous support for the GPy software came from the following projects:
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* [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"
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* [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"
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* [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"
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* [Erasysbio](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/synergy/) "SYNERGY: Systems approach to gene regulation biology through nuclear receptors"
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- [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"
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- [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"
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- [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"
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- [Erasysbio](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/synergy/) "SYNERGY: Systems approach to gene regulation biology through nuclear receptors"
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91
doc/source/tuto_plotting.rst
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91
doc/source/tuto_plotting.rst
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********************
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Defining a new plotting function in GPy
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********************
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GPy has a wrapper for different plotting backends.
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There are some functions you can use for standard plotting.
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Anything going beyond the scope of the
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:py:class:`~GPy.plotting.abstract_plotting_library.AbstractPlottingLibrary` classes plot definitions
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should be considered carefully and maybe is a special case for your plotting library only.
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All plotting related code lives in :py:mod:`GPy.plotting` and beneath. No plotting related code needs to be
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anywhere else in GPy.
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As examples are always the easiest way to learn how to, we
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will implement an example of a plotting function, which plots the covariance of a kernel.
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Write your plotting function into a module under :py:mod:`GPy.plotting.gpy_plot` ``.<module_name>``
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using the plotting routines provided in :py:func:`GPy.plotting.plotting_library`.
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I like to ``from . import plotting_library as pl`` and the allways use ``pl().`` to access functionality of
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the plotting library.
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For the covariance plot we define the function in :py:mod:`GPy.plotting.kernel_plots`.
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The first thing is to define the function parameters *and write the documentation for them*!
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The first argument of the plotting function is always ``self`` for the class this plotting function
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will be attached to (we will get to attaching the function to a class that in detail later on)::
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def plot_covariance(kernel, x=None, label=None,
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plot_limits=None, visible_dims=None, resolution=None,
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projection=None, levels=20, **kwargs):
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"""
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Plot a kernel covariance w.r.t. another x.
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:param array-like x: the value to use for the other kernel argument (kernels are a function of two variables!)
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:param plot_limits: the range over which to plot the kernel
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:type plot_limits: Either (xmin, xmax) for 1D or (xmin, xmax, ymin, ymax) / ((xmin, xmax), (ymin, ymax)) for 2D
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:param array-like visible_dims: input dimensions (!) to use for x. Make sure to select 2 or less dimensions to plot.
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:resolution: the resolution of the lines used in plotting. for 2D this defines the grid for kernel evaluation.
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:param {2d|3d} projection: What projection shall we use to plot the kernel?
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:param int levels: for 2D projection, how many levels for the contour plot to use?
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:param kwargs: valid kwargs for your specific plotting library
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"""
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Having defined the outline of the function we can start implementing
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the real plotting.
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First, we will write the necessary logic behind getting the covariance function.
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This involves getting an Xgrid to plot with and the second x to compare the covariance to::
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from .plot_util import helper_for_plot_data
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X = np.ones((2, kernel.input_dim)) * [-4, 4]
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_, free_dims, Xgrid, xx, yy, _, _, resolution = helper_for_plot_data(kernel, X, plot_limits, visible_dims, None, resolution)
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from numbers import Number
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if x is None:
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x = np.zeros((1, kernel.input_dim))
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elif isinstance(x, Number):
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x = np.ones((1, kernel.input_dim))*x
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K = kernel.K(Xgrid, x)
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``free_dims`` holds the free dimensions after selecting
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from the visible_dims, ``Xgrid`` is the grid for the covariance,
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``xx, yy`` are the grid positions for 2D plotting and ``x`` is the
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``X2`` for the kernel and ``K`` holds the kernel covariance for
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all positions between ``Xgrid`` and ``x``.
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Then we need a canvas to plot on. Always push the keyword arguments
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of the specifig library through :py:func:`GPy.plotting.abstract_plotting_library.AbstractPlottingLibrary.new_canvas`::
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if projection == '3d':
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zlabel = "k(X, {!s})" % (np.asanyarray(x).tolist())
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xlabel = 'X[:,0]'
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ylabel = 'X[:,1]'
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else:
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xlabel = 'X'
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ylabel = "k(X, {!s})" % (np.asanyarray(x).tolist())
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canvas, kwargs = pl().new_canvas(projection=projection, xlabel=xlabel, ylabel=ylabel, zlabel=zlabel, **kwargs)
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Also very important is to use the defaults, which are defined for all plotting libraries implemented.
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This is done by updating the ``kwargs`` from the defaults. There is a helper function
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which takes care for existing keyword arguments. In this case we will just use the default for
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plotting a mean function for the covariance plot as well. If you want to define your own defaults
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add them to the defaults for each library and add it in here. See for example the defaults for
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matplotlib in :py:mod:`GPy.plotting.matplot_dep.defaults`. There is also the default for the
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meanplot_1d, which we are for the 1d plot::
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from .plot_util import update_not_existing_kwargs
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update_not_existing_kwargs(kwargs, pl().defaults.meanplot_1d) # @UndefinedVariable
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The full definition of the plotting then looks like this::
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[bumpversion]
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current_version = 0.8.8dev1
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current_version = 0.8.8dev0
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tag = True
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commit = True
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2
setup.py
2
setup.py
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import codecs
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def read(fname):
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with codecs.open(fname, 'r', 'utf-8') as f:
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with codecs.open(fname, 'r', 'latin') as f:
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return f.read()
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def read_to_rst(fname):
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