artlib.common.visualization
===========================

.. py:module:: artlib.common.visualization

.. autoapi-nested-parse::

   Collection of visualization utilities.



Functions
---------

.. autoapisummary::

   artlib.common.visualization.plot_gaussian_contours_fading
   artlib.common.visualization.plot_gaussian_contours_covariance
   artlib.common.visualization.plot_weight_matrix_as_ellipse


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

.. py:function:: plot_gaussian_contours_fading(ax: matplotlib.axes.Axes, mean: numpy.ndarray, std_dev: numpy.ndarray, color: numpy.ndarray, max_std: int = 2, sigma_steps: float = 0.25, linewidth: int = 1)

   Plot concentric ellipses to represent the contours of a 2D Gaussian distribution
   with fading colors.

   :param ax: Matplotlib axis object to plot the ellipses.
   :type ax: matplotlib.axes.Axes
   :param mean: A numpy array representing the mean (μ) of the distribution.
   :type mean: np.ndarray
   :param std_dev: A numpy array representing the standard deviation (σ) of the distribution.
   :type std_dev: np.ndarray
   :param color: A 4D numpy array including RGB and alpha channels to specify the color and
                 initial opacity.
   :type color: np.ndarray
   :param max_std: Maximum number of standard deviations to draw contours to, by default 2.
   :type max_std: int, optional
   :param sigma_steps: Step size in standard deviations for each contour, by default 0.25.
   :type sigma_steps: float, optional
   :param linewidth: Width of the boundary line, by default 1.
   :type linewidth: int, optional


.. py:function:: plot_gaussian_contours_covariance(ax: matplotlib.axes.Axes, mean: numpy.ndarray, covariance: numpy.ndarray, color: numpy.ndarray, max_std: int = 2, sigma_steps: float = 0.25, linewidth: int = 1)

   Plot concentric ellipses to represent the contours of a 2D Gaussian distribution
   with fading colors. Accepts a covariance matrix to properly represent the
   distribution's orientation and shape.

   :param ax: Matplotlib axis object to plot the ellipses.
   :type ax: matplotlib.axes.Axes
   :param mean: A numpy array representing the mean (μ) of the distribution.
   :type mean: np.ndarray
   :param covariance: A 2x2 numpy array representing the covariance matrix of the distribution.
   :type covariance: np.ndarray
   :param color: A 4D numpy array including RGB and alpha channels to specify the color and
                 initial opacity.
   :type color: np.ndarray
   :param max_std: Maximum number of standard deviations to draw contours to, by default 2.
   :type max_std: int, optional
   :param sigma_steps: Step size in standard deviations for each contour, by default 0.25.
   :type sigma_steps: float, optional
   :param linewidth: Width of the boundary line, by default 1.
   :type linewidth: int, optional


.. py:function:: plot_weight_matrix_as_ellipse(ax: matplotlib.axes.Axes, s: float, W: numpy.ndarray, mean: numpy.ndarray, color: numpy.ndarray, linewidth: int = 1)

   Plot the transformation of a unit circle by the weight matrix W as an ellipse.

   :param ax: Matplotlib axis object to plot the ellipse.
   :type ax: matplotlib.axes.Axes
   :param s: Scalar to scale the weight matrix W.
   :type s: float
   :param W: 2x2 weight matrix.
   :type W: np.ndarray
   :param mean: The center point (x, y) of the ellipse.
   :type mean: np.ndarray
   :param color: Color of the ellipse.
   :type color: np.ndarray
   :param linewidth: Width of the boundary line, by default 1.
   :type linewidth: int, optional