piel.visual.plot.position
=========================

.. py:module:: piel.visual.plot.position

.. autoapi-nested-parse::

   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
---------

.. autoapisummary::

   piel.visual.plot.position.create_axes_per_figure
   piel.visual.plot.position.list_to_separate_plots
   piel.visual.plot.position.list_to_overlayed_plots
   piel.visual.plot.position.create_plot_containers


Module Contents
---------------

.. py:function:: 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.


.. py:function:: 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.


.. py:function:: list_to_overlayed_plots(container_list: list, **kwargs) -> tuple

.. py:function:: create_plot_containers(container_list: list, axes_structure: piel.visual.types.AxesPlottingTypes = 'separate', **kwargs) -> tuple