porcupy.utils.interactive_visualization
=======================================

.. py:module:: porcupy.utils.interactive_visualization

.. autoapi-nested-parse::

   Interactive visualization module for the Crested Porcupine Optimizer (CPO).

   This module provides interactive visualization tools and dashboard components
   for monitoring and analyzing the CPO algorithm's performance in real-time.



Classes
-------

.. autoapisummary::

   porcupy.utils.interactive_visualization.OptimizationDashboard
   porcupy.utils.interactive_visualization.ParameterTuningDashboard


Functions
---------

.. autoapisummary::

   porcupy.utils.interactive_visualization.create_interactive_optimization_plot


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

.. py:class:: OptimizationDashboard(objective_func: Callable, bounds: Tuple[numpy.ndarray, numpy.ndarray], dimensions: int = 2, update_interval: float = 0.5, figsize: Tuple[int, int] = (15, 10))

   Interactive dashboard for monitoring CPO optimization in real-time.

   This dashboard displays multiple visualizations simultaneously, including:
   - Convergence history
   - Population size history
   - Diversity metrics
   - Defense mechanism usage
   - Current porcupine positions

   The dashboard updates in real-time during optimization.


   .. py:attribute:: objective_func


   .. py:attribute:: bounds


   .. py:attribute:: dimensions
      :value: 2



   .. py:attribute:: update_interval
      :value: 0.5



   .. py:attribute:: figsize
      :value: (15, 10)



   .. py:attribute:: iterations
      :value: []



   .. py:attribute:: best_costs
      :value: []



   .. py:attribute:: pop_sizes
      :value: []



   .. py:attribute:: diversity_history
      :value: []



   .. py:attribute:: defense_counts


   .. py:attribute:: position_history
      :value: []



   .. py:attribute:: best_position_history
      :value: []



   .. py:attribute:: is_running
      :value: False



   .. py:attribute:: current_iteration
      :value: 0



   .. py:method:: update(iteration: int, best_cost: float, pop_size: int, positions: numpy.ndarray, best_position: numpy.ndarray, defense_types: List[str])

      Update the dashboard with new optimization data.

      :param iteration: Current iteration number.
      :type iteration: int
      :param best_cost: Current best cost value.
      :type best_cost: float
      :param pop_size: Current population size.
      :type pop_size: int
      :param positions: Current positions of the porcupines, shape (pop_size, dimensions).
      :type positions: ndarray
      :param best_position: Current global best position, shape (dimensions,).
      :type best_position: ndarray
      :param defense_types: List of defense mechanisms used by each porcupine.
      :type defense_types: list



   .. py:method:: start_monitoring()

      Start the dashboard monitoring thread.



   .. py:method:: stop_monitoring()

      Stop the dashboard monitoring thread.



   .. py:method:: save_dashboard(save_path: str, dpi: int = 300)

      Save the current dashboard state as an image.

      :param save_path: Path to save the dashboard image.
      :type save_path: str
      :param dpi: DPI for the saved image (default: 300).
      :type dpi: int, optional



   .. py:method:: close()

      Close the dashboard and clean up resources.



.. py:class:: ParameterTuningDashboard(parameter_name: str, parameter_range: List[float], result_metric: str = 'Best Cost', figsize: Tuple[int, int] = (12, 8))

   Interactive dashboard for parameter tuning and sensitivity analysis.

   This dashboard allows for real-time visualization of how different
   parameter values affect the performance of the CPO algorithm.


   .. py:attribute:: parameter_name


   .. py:attribute:: parameter_range


   .. py:attribute:: result_metric
      :value: 'Best Cost'



   .. py:attribute:: figsize
      :value: (12, 8)



   .. py:attribute:: results
      :value: []



   .. py:method:: update(parameter_value: float, result: float, convergence_history: Optional[List[float]] = None)

      Update the dashboard with new parameter tuning results.

      :param parameter_value: Value of the parameter being tested.
      :type parameter_value: float
      :param result: Result metric value (e.g., best cost).
      :type result: float
      :param convergence_history: Convergence history for this parameter value.
      :type convergence_history: list, optional



   .. py:method:: save_dashboard(save_path: str, dpi: int = 300)

      Save the current dashboard state as an image.

      :param save_path: Path to save the dashboard image.
      :type save_path: str
      :param dpi: DPI for the saved image (default: 300).
      :type dpi: int, optional



   .. py:method:: close()

      Close the dashboard and clean up resources.



.. py:function:: create_interactive_optimization_plot(objective_func: Callable, bounds: Tuple[numpy.ndarray, numpy.ndarray], initial_positions: numpy.ndarray, figsize: Tuple[int, int] = (10, 8))

   Create an interactive plot for exploring the optimization landscape.

   :param objective_func: The objective function to visualize.
   :type objective_func: callable
   :param bounds: A tuple (lb, ub) containing the lower and upper bounds.
   :type bounds: tuple
   :param initial_positions: Initial positions of the porcupines, shape (pop_size, 2).
   :type initial_positions: ndarray
   :param figsize: Figure size as (width, height) in inches (default: (10, 8)).
   :type figsize: tuple, optional

   :returns: A tuple containing (fig, ax, scatter) for further customization.
   :rtype: tuple