arviz_plots.PlotMatrix

class arviz_plots.PlotMatrix(data, facet_dims, aes=None, backend=None, figure_kwargs=None, **kwargs)

Low level base class for pairwise matrix arranges of plots.

Attributes:

vizxarray.DataTree

Information about the visual elements in the plot as a DataTree.

aesxarray.DataTree

Information about aesthetic mapping as a DataTree.

See also

arviz_plots.PlotCollection

Unidimensional facetting manager

__init__(data, facet_dims, aes=None, backend=None, figure_kwargs=None, **kwargs)

Initialize a PlotMatrix.

Parameters:

dataxarray.Dataset

Data for which to generate the requested matrix layout of plots.

facet_dimslist of hashable

List of dimensions to use for facetting. It also support the __variable__ indicator to facet across variables.

aesmapping of {strlist of hashable}, optional

Dictionary with aesthetics as keys and as values a list of the dimensions it should be mapped to. See generate_aes_dt for more details.

backendstr, optional

Plotting backend. It will be stored and passed down to the plotting functions when using methods like map.

**kwargsmapping, optional

Dictionary with aesthetics as keys and as values a list of the values that should be taken by that aesthetic.

Methods

__init__(data, facet_dims[, aes, backend, ...])	Initialize a PlotMatrix.
add_legend(dim[, aes, artist_kwargs, title, ...])	Add a legend for the given visual/aesthetic to the plot.
allocate_artist(fun_label, data, all_loop_dims)	Allocate an visual in the viz DataTree.
generate_aes_dt(aes[, data])	Generate the aesthetic mappings.
get_aes_as_dataset(aes_key)	Get the values of the provided aes_key for all variables as a Dataset.
get_aes_kwargs(aes, var_name, selection)	Get the aesthetic mappings for the given variable and selection as a dictionary.
get_target(var_name, selection[, ...])	Get the target that corresponds to the given variable and selection.
get_viz(artist_name[, var_name, sel])	Get element from .viz that corresponds to the provided subset.
grid(data[, cols, rows, backend, figure_kwargs])	Instatiate a PlotCollection and generate a plot grid iterating over rows and columns.
map(fun[, fun_label, data, coords, ...])	Apply the given plotting function along the diagonal with the corresponding aesthetics.
map_col(fun[, fun_label, index, data, ...])	Apply the given plotting function along the column with the corresponding aesthetics.
map_lower(*args, **kwargs)	Call map_triangle with triangle="lower".
map_row(fun[, fun_label, index, data, ...])	Apply the given plotting function along the row with the corresponding aesthetics.
map_triangle(fun[, fun_label, data, ...])	Apply the given plotting function to all plots with the corresponding aesthetics.
map_upper(*args, **kwargs)	Call map_triangle with triangle="upper".
rename_visuals([name_dict])	Rename visual data variables in the viz DataTree.
savefig(filename, **kwargs)	Call the backend function to save this figure.
set_fixed_var_attributes(index[, orientation])	Set fixed variable attributes for the current orientation according to given index.
show()	Call the backend function to show this figure.
store_in_artist_da(aux_artist, fun_label, ...)	Store visual object or array into its preallocated DataArray within viz.
update_aes([ignore_aes, coords])	Update list of aesthetics after indicating ignores and extra subsets.
update_aes_from_dataset(aes_key, dataset)	Update the values of aes_key with those in the provided Dataset.
wrap(data[, cols, col_wrap, backend, ...])	Instatiate a PlotCollection and generate a plot grid iterating over subsets and wrapping.

Attributes

aes	Information about aesthetic mapping as a DataTree.
aes_set	Return all aesthetics with a mapping defined as a set.
coords	Information about slicing operation to always be applied on the PlotCollection.
data	Dataset to be used as data for plotting.
facet_dims	Facetting dimensions.
viz	Information about the visual elements in the plot as a DataTree.