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Plot hierarchy
Ari Hartikainen edited this page Nov 24, 2019
·
8 revisions
NOTE: This currently WIP
Function structure:
function
= one axis <-> one function
{function1, function2}
= one axis <-> select one function
(function1, function2)
= one axis <-> multiple functions
function_name <- function_combine[{function_select1,function_select2},function_name1,(function_same_ax1, function_same_axis2)]
ArviZ has hierarchical plot-function structure.
The first level functions operates against one axis with one or more variables. Each function then calls backend function to do actual plotting.
These functions include:
plot_atomic_kde
plot_atomic_hist
plot_atomic_dist <- {plot_atomic_kde, plot_atomic_hist}
plot_atomic_line
plot_atomic_box
plot_atomic_rug
plot_atomic_text
plot_atomic_scatter
plot_atomic_autocorr
plot_atomic_rank
The common input and default values for these functions are
*arr: one or two {ndarray, str}
source: {xarray.Dataset, pandas.DataFrame,bokeh.ColumnDataSource}; optional
ax: {matplotlib.axis, bokeh.figure}; optional
labels: one or two {str, bokeh.Text}
line_kwargs: {dict}; optional
line properties
fill_kwargs: {dict}; optional
area properties
special_kwargs: {dict}; optional
special plot specific properties
backend: {"matplotlib","bokeh"}
Atomic plot returns the axis.
plot_hpd
plot_regression
plot_pair
plot_parallel
plot_ess
plot_density <- {plot_atomic_kde, plot_atomic_hist}
plot_rank
plot_ppc
plot_joint
plot_autocorr <- plot_grid[plot_atomic_autocorr]
plot_trace <- plot_col[plot_row[plot_atomic_dist,{plot_atomic_line,plot_atomic_rank}]]
plot_forest <- plot_grid[plot_grid[{plot_atomic_box,plot_atomic_kde,plot_atomic_violin},plot_atomic_box,plot_atomic_box]]
plot_compare
plot_elpd <- plot_atomic_box
plot_energy <- plot_atomic_kde
plot_khat
plot_loopit
plot_posterior <- plot_grid[(plot_atomic_dist,plotatomic_text)]
plot_dashboard
plot_grid <- plot_row + plot_col
plot_row
plot_col