arviz_plots.combine_plots

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arviz_plots.combine_plots#

arviz_plots.combine_plots(dt, plots, var_names=None, filter_vars=None, group='posterior', coords=None, sample_dims=None, expand='column', plot_names=None, backend=None, **pc_kwargs)[source]#

Arrange multiple batteries-included plots in a customizable column or row layout.

Parameters:
dtxarray.DataTree of dict of {strxarray.DataTree}

Input data. In case of dictionary input, the keys are taken to be model names. In such cases, a dimension “model” is generated and can be used to map to aesthetics.

Note that not all batteries included functions accept dictionary input, so it will only work when all plotting functions requested in plots are compatible with it.

plotslist of tuple of (callable, mapping)

List of all the plotting functions to be combined. Each element in this list is a tuple with two elements. The first is the function to be called, the second is a dictionary with any keyword arguments that should be used when calling that function.

var_namesstr or sequence of str, optional

One or more variables to be plotted. Prefix the variables by ~ when you want to exclude them from the plot.

filter_vars{None, “like”, “regex”}, default None

If None, interpret var_names as the real variables names. If “like”, interpret var_names as substrings of the real variables names. If “regex”, interpret var_names as regular expressions on the real variables names.

groupstr, default “posterior”

Group to be plotted.

coordsdict, optional
sample_dimsstr or sequence of hashable, optional

Dimensions to reduce unless mapped to an aesthetic. Defaults to rcParams["data.sample_dims"]

expand{“column”, “row”}, default “column”

How to combine the different plotting functions. If “column”, each plotting function will be added as a new column, if “row” it will be a new row instead.

plot_nameslist of str, optional

List of the same length as plots with the plot names to use as coordinate values in the returned PlotCollection.

backend{“matplotlib”, “bokeh”, “plotly”}, optional

Plotting backend to use. Defaults to rcParams["plot.backend"].

**pc_kwargs

Passed to arviz_plots.PlotCollection.grid

Returns:
PlotCollection

Examples

Customize the names of the plots in the returned PlotCollection

>>> import arviz_plots as azp
>>> azp.style.use("arviz-variat")
>>> from arviz_base import load_arviz_data
>>> rugby = load_arviz_data('rugby')
>>> pc = azp.combine_plots(
>>>     rugby,
>>>     plots=[
>>>         (azp.plot_ppc_pit, {}),
>>>         (azp.plot_ppc_rootogram, {}),
>>>     ],
>>>     group="posterior_predictive",
>>>     plot_names=["pit", "rootogram"],
>>> )
../../_images/arviz_plots-combine_plots-1.png

Now if we inspect the pc.viz attribute, we can see it has a column dimension with the requested coordinate values:

>>> pc.viz

Arrange three diagnostic plots (ESS evolution plot, rank plot and autocorrelation plot) in a custom column layout.

Custom diagnostic plots combination

Custom diagnostic plots combination
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