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[doc] updated how to plot in gpy

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mzwiessele 2015-10-12 14:50:42 +01:00
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54
doc/source/tuto_plotting.rst
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@ -89,3 +89,57 @@ meanplot_1d, which we are for the 1d plot::
The full definition of the plotting then looks like this::
if len(free_dims)<=2:
if len(free_dims)==1:
# 1D plotting:
update_not_existing_kwargs(kwargs, pl().defaults.meanplot_1d) # @UndefinedVariable
plots = dict(covariance=[pl().plot(canvas, Xgrid[:, free_dims], K, label=label, **kwargs)])
else:
if projection == '2d':
update_not_existing_kwargs(kwargs, pl().defaults.meanplot_2d) # @UndefinedVariable
plots = dict(covariance=[pl().contour(canvas, xx[:, 0], yy[0, :],
K.reshape(resolution, resolution),
levels=levels, label=label, **kwargs)])
elif projection == '3d':
update_not_existing_kwargs(kwargs, pl().defaults.meanplot_3d) # @UndefinedVariable
plots = dict(covariance=[pl().surface(canvas, xx, yy,
K.reshape(resolution, resolution),
label=label,
**kwargs)])
return pl().add_to_canvas(canvas, plots)
else:
raise NotImplementedError("Cannot plot a kernel with more than two input dimensions")
Where we return whatever is returned by :py:func:`GPy.plotting.abstract_plotting_library.AbstractPlottingLibrary.add_to_canvas`,
so that the plotting library can choose what to do with the plot later, when we want to show it. In order
to show a plot, we can just call :py:func:`GPy.plotting.show` with the output of the plot above.
Now we want to add the plot to the :py:class:`GPy.kern.src.kern.Kern`. In order to do that, we inject the plotting function into the
class in the :py:mod:`GPy.plotting.__init__`, which will make sure that the on the fly change of the backend
works smoothly. Thus, in :py:mod:`GPy.plotting.__init__` we add the line::
from ..kern import Kern
Kern.plot_covariance = gpy_plot.kernel_plots.plot_covariance
And that's it. The plot can be shown in plotly by calling::
GPy.plotting.change_plotting_library('plotly')
k = GPy.kern.RBF(1) + GPy.kern.Matern32(1)
k.randomize()
fig = k.plot()
GPy.plotting.show(fig, <plot_library specific **kwargs>)
k = GPy.kern.RBF(2) + GPy.kern.Matern32(2)
k.randomize()
fig = k.plot()
GPy.plotting.show(fig, <plot_library specific **kwargs>)
k = GPy.kern.RBF(1) + GPy.kern.Matern32(2)
k.randomize()
fig = k.plot(projection='3d')
GPy.plotting.show(fig, <plot_library specific **kwargs>)
This explains the next thing. Changing the backend works *on-the-fly*. To show the above example in matplotlib, we just
exchange the first line by ``GPy.plotting.change_plotting_library('matplotlib')``.