piel.visual.plot.position

We want to streamline the figure and axes generation based on a given list which contains the data to be plotted. Each component, as well, may require more than one plot or a given set of plots. So it makes sense to both generalize this as a creation of plots per a given set of parameters,

which may or not be linked to the number of data points in a given list. The other complexity is the given structure of the axes for a given figure.

There can be multiple elements per plot. There are overlaying plots and separate plots. Overlaying plots require sharing the same axes and separate plots require the same figure. So, we want to configure plotting measurement based on this. The question in this case, is of combining multiple figures, or just combining multiple axes. It sounds like creating the axes is the best way to implement this for a given figure.

Functions

create_axes_per_figure(→ tuple)	This function creates a figure and a set of axes in this figure according to the number of rows or columns defined.
list_to_separate_plots(→ tuple)	This function creates a list of plots that are separate from each other.
list_to_overlayed_plots(→ tuple)
create_plot_containers(→ tuple)

Module Contents

create_axes_per_figure(rows: int = 1, columns: int = 1, **kwargs) → tuple

This function creates a figure and a set of axes in this figure according to the number of rows or columns defined.

list_to_separate_plots(container_list: list, axes_per_element: int = 1, multi_axes_extension_direction: piel.visual.types.ExtensiblePlotsDirectionPerElement = 'x', **kwargs) → tuple

This function creates a list of plots that are separate from each other.

list_to_overlayed_plots(container_list: list, **kwargs) → tuple

create_plot_containers(container_list: list, axes_structure: piel.visual.types.AxesPlottingTypes = 'separate', **kwargs) → tuple