jpt.plotting.helpers
====================

.. py:module:: jpt.plotting.helpers


Attributes
----------

.. autoapisummary::

   jpt.plotting.helpers.default_config


Functions
---------

.. autoapisummary::

   jpt.plotting.helpers.hex_to_rgb
   jpt.plotting.helpers.color_to_rgb
   jpt.plotting.helpers.pdf_grid_3d


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

.. py:data:: default_config

.. py:function:: hex_to_rgb(col) -> Tuple[int, Ellipsis]

   Parse hexadecimal string to extract color/alpha information

   :param col:     color string of one of the following forms
                   * #f0c (as short form of #ff00cc)
                   * #f0cf (as short form of #ff00ccff)
                   * #ff00cc
                   * #ff00ccff
   :return:        a tuple reprsenting either RGB or RGBA values


.. py:function:: color_to_rgb(color, opacity=0.6) -> Tuple[str, str]

   Extracts the color and alpha information of a given `color` string and reassembles it to an rgb and an rgba
   color.

   :param color:       the color to examine; accepts str of form:
                       * rgb(r,g,b) with r,g,b being int or float
                       * rgba(r,g,b,a) with r,g,b being int or float, a being float
                       * #f0c (as short form of #ff00cc) or #f0cf (as short form of #ff00ccff)
                       * #ff00cc
                       * #ff00ccff
   :param opacity:     the default opacity to assume if the given string contains only color information
   :return:            plotly.graph_objs.Figure


.. py:function:: pdf_grid_3d(jpt, variable1, variable2) -> Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]

   Create a mesh grid consistent of ``x``, ``y``, ``z`` coordinate values
   representing all distinct interval boundaries forming the joint probability
   density function a projection of ``variable1`` and ``variable2``
   represented the JPT passed the first argument.

   :param jpt:
   :param variable1:
   :param variable2:
   :return: