src.gridmind.utils.vis_util
===========================

.. py:module:: src.gridmind.utils.vis_util


Attributes
----------

.. autoapisummary::

   src.gridmind.utils.vis_util.feature1


Classes
-------

.. autoapisummary::

   src.gridmind.utils.vis_util.VideoUtil


Functions
---------

.. autoapisummary::

   src.gridmind.utils.vis_util.print_state_action_values
   src.gridmind.utils.vis_util.plot_state_values
   src.gridmind.utils.vis_util.print_value_table


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

.. py:function:: print_state_action_values(q_table: Dict[Hashable, numpy.ndarray], filename: str = None)

.. py:function:: plot_state_values(states, true_values, estimated_values)

   Plots the true values and estimated values for each state.

   :param states: List of state names (e.g., ['A', 'B', 'C', ...])
   :param true_values: List of true values corresponding to the states
   :param estimated_values: List of lists containing estimated values for each state over iterations


.. py:function:: print_value_table(feature1, feature2, state_values, feature1_name='Feature1', feature2_name='Feature2', filename: str = None, append: bool = False)

.. py:class:: VideoUtil

   Utility class for video loading and processing operations.


   .. py:method:: load_video_as_tensor(video_save_path: str, logger: Optional[logging.Logger] = None)
      :staticmethod:


      Load video file(s) and convert to tensor format for TensorBoard.

      :param video_save_path: Base path for the video files (without extension)
      :param logger: Optional logger for logging messages

      :returns:

                Video tensor in format (N, T, C, H, W) where:
                    N = batch size (1)
                    T = number of frames
                    C = channels (3 for RGB)
                    H = height
                    W = width
                Returns None if video cannot be loaded.
      :rtype: torch.Tensor



.. py:data:: feature1
   :value: [0, 0, 1, 1]