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https://github.com/SheffieldML/GPy.git
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[added testing and plotting] restructuring the plotting library
This commit is contained in:
mzwiessele
parent
b9bfd0fc6d
commit
c7d50ee83b
15 changed files with 509 additions and 0 deletions
29
GPy/plotting/gpy_plot/__init__.py
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29
GPy/plotting/gpy_plot/__init__.py
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def update_not_existing_kwargs(to_update, update_from):
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return to_update.update({k:v for k,v in update_from.items() if k not in to_update})
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#===============================================================================
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# Implement library specific defaults in the specific plotting librarys defaults.py file.
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# The following lines ensure, that an empty kwarg gets returned, when accessing a not
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# existing default
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from .. import plotting_library as pl
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from collections import defaultdict
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class defaultdict(defaultdict):
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def __getattr__(self, *args, **kwargs):
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return defaultdict.__getitem__(self, *args, **kwargs)
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defaults = defaultdict(dict, **pl.defaults.__dict__)
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pl.defaults = defaults
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#===============================================================================
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#===============================================================================
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# Make sure that the necessary files and functions are
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# defined in the plotting library:
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assert hasattr(pl, 'get_new_canvas'), "Please implement a function to get a new canvas for the specific library in plotting_library.get_new_canvas(**kwargs)"
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assert hasattr(pl, 'plot'), "Please implement a function to plot a simple line"
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assert hasattr(pl, 'scatter'), "Please implement a function to plot a simple scatterplot"
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#assert hasattr(pl, 'xerrorbar'), "Please implement a function to plot an errorbar along the xaxis"
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#assert hasattr(pl, 'xerrorbar'), "Please implement a function to plot an errorbar along the yaxis"
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#assert hasattr(pl, 'fill'), "Please implement a function to fill a section between points"
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#assert hasattr(pl, 'imshow'), "Please implement a function to plot an image in the given boundaries"
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#===============================================================================
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from . import data_plots, gp_plots
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104
GPy/plotting/gpy_plot/data_plots.py
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104
GPy/plotting/gpy_plot/data_plots.py
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#===============================================================================
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# Copyright (c) 2012-2015, GPy authors (see AUTHORS.txt).
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# * Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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#
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# * Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# * Neither the name of GPy nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#===============================================================================
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from . import pl
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from . import update_not_existing_kwargs
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from . import defaults
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from functools import wraps
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import numpy as np
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def _plot_data(self, canvas, which_data_rows='all',
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which_data_ycols='all', visible_dims=None,
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error_kwargs=None, **plot_kwargs):
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"""
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Plot the training data
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- For higher dimensions than two, use fixed_inputs to plot the data points with some of the inputs fixed.
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Can plot only part of the data
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using which_data_rows and which_data_ycols.
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:param which_data_rows: which of the training data to plot (default all)
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:type which_data_rows: 'all' or a slice object to slice self.X, self.Y
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:param which_data_ycols: when the data has several columns (independant outputs), only plot these
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:type which_data_rows: 'all' or a list of integers
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:param visible_dims: an array specifying the input dimensions to plot (maximum two)
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:type visible_dims: a numpy array
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:param dict error_kwargs: kwargs for the error plot for the plotting library you are using
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:param kwargs plot_kwargs: kwargs for the data plot for the plotting library you are using
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"""
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#deal with optional arguments
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if which_data_rows == 'all':
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which_data_rows = slice(None)
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if which_data_ycols == 'all':
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which_data_ycols = np.arange(self.output_dim)
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if error_kwargs is None:
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error_kwargs = {}
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if hasattr(self, 'has_uncertain_inputs') and self.has_uncertain_inputs():
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X = self.X.mean
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X_variance = self.X.variance
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else:
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X = self.X
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X_variance = None
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Y = self.Y
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#work out what the inputs are for plotting (1D or 2D)
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if visible_dims is None:
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visible_dims = np.arange(self.input_dim)
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assert visible_dims.size <= 2, "Visible inputs cannot be larger than two"
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free_dims = visible_dims
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#one dimensional plotting
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if len(free_dims) == 1:
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for d in which_data_ycols:
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update_not_existing_kwargs(plot_kwargs, defaults.data_1d)
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canvas.append(pl.scatter(canvas, X[which_data_rows, free_dims], Y[which_data_rows, d], **plot_kwargs))
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if X_variance is not None:
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update_not_existing_kwargs(error_kwargs, defaults.xerrorbar)
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canvas.append(pl.xerrorbar(canvas, X[which_data_rows, free_dims].flatten(), Y[which_data_rows, d].flatten(),
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2 * np.sqrt(X_variance[which_data_rows, free_dims].flatten()),
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**error_kwargs))
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#2D plotting
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elif len(free_dims) == 2:
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for d in which_data_ycols:
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update_not_existing_kwargs(plot_kwargs, defaults.data_2d)
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canvas = pl.scatter(canvas, X[which_data_rows, free_dims[0]], X[which_data_rows, free_dims[1]],
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c=Y[which_data_rows, d], vmin=Y.min(), vmax=Y.max(), **plot_kwargs)
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else:
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raise NotImplementedError("Cannot plot in more then two dimensions")
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return canvas
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@wraps(_plot_data)
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def plot_data(self, which_data_rows='all',
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which_data_ycols='all', visible_dims=None,
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error_kwargs=None, **plot_kwargs):
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canvas, kwargs = pl.get_new_canvas(plot_kwargs)
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_plot_data(self, canvas, which_data_rows, which_data_ycols, visible_dims, error_kwargs, **kwargs)
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return pl.show_canvas(canvas)
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105
GPy/plotting/gpy_plot/gp_plots.py
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105
GPy/plotting/gpy_plot/gp_plots.py
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#===============================================================================
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# Copyright (c) 2012-2015, GPy authors (see AUTHORS.txt).
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# * Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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#
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# * Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# * Neither the name of GPy nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#===============================================================================
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from . import pl
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from . import update_not_existing_kwargs, defaults
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from .util import x_frame1D
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from scipy import sparse
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import numpy as np
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def plot_mean(self, plot_limits=None, fixed_inputs=[],
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resolution=None, plot_raw=False,
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Y_metadata=None, apply_link=False,
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plot_uncertain_inputs=True, predict_kw=None,
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**kwargs):
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"""
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Plot the mean of a GP.
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:param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
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:type plot_limits: np.array
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:param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
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:type fixed_inputs: a list of tuples
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:param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
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:type levels: int
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"""
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if hasattr(self, 'has_uncertain_inputs') and self.has_uncertain_inputs():
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X = self.X.mean
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X_variance = self.X.variance
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else:
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X = self.X
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Y = self.Y
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if sparse.issparse(Y): Y = Y.todense().view(np.ndarray)
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if predict_kw is None:
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predict_kw = {}
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#work out what the inputs are for plotting (1D or 2D)
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fixed_dims = np.array([i for i,v in fixed_inputs])
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free_dims = np.setdiff1d(np.arange(self.input_dim),fixed_dims)
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#define the frame on which to plot
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Xnew, xmin, xmax = x_frame1D(X[:,free_dims], plot_limits=plot_limits, resolution=resolution or 200)
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Xgrid = np.empty((Xnew.shape[0],self.input_dim))
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Xgrid[:,free_dims] = Xnew
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for i,v in fixed_inputs:
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Xgrid[:,i] = v
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if plot_raw:
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mu = self._raw_predict(Xgrid)[0]
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update_not_existing_kwargs(kwargs, defaults.meanplot)
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return pl.plot(Xgrid, mu, **kwargs)
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def gpplot(x, mu, lower, upper, edgecol='#3300FF', fillcol='#33CCFF', ax=None, fignum=None, **kwargs):
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_, axes = ax_default(fignum, ax)
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mu = mu.flatten()
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x = x.flatten()
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lower = lower.flatten()
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upper = upper.flatten()
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plots = []
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#here's the mean
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plots.append(meanplot(x, mu, edgecol, axes))
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#here's the box
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kwargs['linewidth']=0.5
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if not 'alpha' in kwargs.keys():
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kwargs['alpha'] = 0.3
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plots.append(axes.fill(np.hstack((x,x[::-1])),np.hstack((upper,lower[::-1])),color=fillcol,**kwargs))
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#this is the edge:
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plots.append(meanplot(x, upper,color=edgecol, linewidth=0.2, ax=axes))
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plots.append(meanplot(x, lower,color=edgecol, linewidth=0.2, ax=axes))
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return plots
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29
GPy/plotting/gpy_plot/plot_util.py
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29
GPy/plotting/gpy_plot/plot_util.py
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#===============================================================================
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# Copyright (c) 2012-2015, GPy authors (see AUTHORS.txt).
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
|
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# modification, are permitted provided that the following conditions are met:
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#
|
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# * Redistributions of source code must retain the above copyright notice, this
|
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# list of conditions and the following disclaimer.
|
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#
|
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# * Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# * Neither the name of GPy nor the names of its
|
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# contributors may be used to endorse or promote products derived from
|
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# this software without specific prior written permission.
|
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#
|
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#===============================================================================
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68
GPy/plotting/gpy_plot/util.py
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68
GPy/plotting/gpy_plot/util.py
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#===============================================================================
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# Copyright (c) 2012-2015 GPy Authors
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
|
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# modification, are permitted provided that the following conditions are met:
|
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#
|
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# * Redistributions of source code must retain the above copyright notice, this
|
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# list of conditions and the following disclaimer.
|
||||
#
|
||||
# * Redistributions in binary form must reproduce the above copyright notice,
|
||||
# this list of conditions and the following disclaimer in the documentation
|
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# and/or other materials provided with the distribution.
|
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#
|
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# * Neither the name of GPy nor the names of its
|
||||
# contributors may be used to endorse or promote products derived from
|
||||
# this software without specific prior written permission.
|
||||
#
|
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#===============================================================================
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import numpy as np
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def x_frame1D(X,plot_limits=None,resolution=None):
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"""
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Internal helper function for making plots, returns a set of input values to plot as well as lower and upper limits
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"""
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assert X.shape[1] ==1, "x_frame1D is defined for one-dimensional inputs"
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if plot_limits is None:
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from ...core.parameterization.variational import VariationalPosterior
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if isinstance(X, VariationalPosterior):
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xmin,xmax = X.mean.min(0),X.mean.max(0)
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else:
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xmin,xmax = X.min(0),X.max(0)
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xmin, xmax = xmin-0.2*(xmax-xmin), xmax+0.2*(xmax-xmin)
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elif len(plot_limits)==2:
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xmin, xmax = plot_limits
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else:
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raise ValueError("Bad limits for plotting")
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Xnew = np.linspace(xmin,xmax,resolution or 200)[:,None]
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return Xnew, xmin, xmax
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def x_frame2D(X,plot_limits=None,resolution=None):
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"""
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Internal helper function for making plots, returns a set of input values to plot as well as lower and upper limits
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"""
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assert X.shape[1] ==2, "x_frame2D is defined for two-dimensional inputs"
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if plot_limits is None:
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xmin,xmax = X.min(0),X.max(0)
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xmin, xmax = xmin-0.2*(xmax-xmin), xmax+0.2*(xmax-xmin)
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elif len(plot_limits)==2:
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xmin, xmax = plot_limits
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else:
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raise ValueError("Bad limits for plotting")
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resolution = resolution or 50
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xx,yy = np.mgrid[xmin[0]:xmax[0]:1j*resolution,xmin[1]:xmax[1]:1j*resolution]
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Xnew = np.vstack((xx.flatten(),yy.flatten())).T
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return Xnew, xx, yy, xmin, xmax
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50
GPy/plotting/matplot_dep/defaults.py
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50
GPy/plotting/matplot_dep/defaults.py
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#===============================================================================
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# Copyright (c) 2015, Max Zwiessele
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# All rights reserved.
|
||||
#
|
||||
# Redistribution and use in source and binary forms, with or without
|
||||
# modification, are permitted provided that the following conditions are met:
|
||||
#
|
||||
# * Redistributions of source code must retain the above copyright notice, this
|
||||
# list of conditions and the following disclaimer.
|
||||
#
|
||||
# * Redistributions in binary form must reproduce the above copyright notice,
|
||||
# this list of conditions and the following disclaimer in the documentation
|
||||
# and/or other materials provided with the distribution.
|
||||
#
|
||||
# * Neither the name of GPy nor the names of its
|
||||
# contributors may be used to endorse or promote products derived from
|
||||
# this software without specific prior written permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
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#===============================================================================
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'''
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This file is for defaults for the gpy plot, specific to the plotting library.
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Create a kwargs dictionary with the right name for the plotting function
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you are implementing. If you do not provide defaults, the default behaviour of
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the plotting library will be used.
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In the code, always ise plotting.gpy_plots.defaults to get the defaults, as
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it gives back an empty default, when defaults are not defined.
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'''
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from matplotlib import cm
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# Data:
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data_1d = dict(lw=1.5, marker='x', edgecolor='k')
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data_2d = dict(s=35, edgecolors='none', linewidth=0., cmap=cm.get_cmap('hot'))
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xerrorbar = dict(ecolor='k', fmt='none', elinewidth=.5, alpha=.5)
|
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yerrorbar = dict(ecolor='darkred', fmt='none', elinewidth=.5, alpha=.5)
|
||||
|
||||
# GP plots
|
||||
meanplot = dict(color='#3300FF', linewidth=2)
|
||||
Loading…
Add table
Add a link
Reference in a new issue