:py:mod:`close_contacts_calculator`
===================================

.. py:module:: close_contacts_calculator


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

Classes
~~~~~~~

.. autoapisummary::

   close_contacts_calculator.CloseContactsCalculator




.. py:class:: CloseContactsCalculator(facility: citam.engine.facility.indoor_facility.Facility, contact_distance: float = 6.0)

   Bases: :py:obj:`citam.engine.calculators.calculator.Calculator`

   
   Helper class that provides a standard way to create an ABC using
   inheritance.
















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   .. py:method:: initialize(agents: OrderedDict[int, citam.engine.core.agent.Agent], workdir: Optional[Union[os.PathLike, str]] = None)


   .. py:method:: run(current_step: int, active_agents: List[citam.engine.core.agent.Agent]) -> None

      
      Iterate over agents, compute whether they fall within the contact
      distance or not and verify that they are indeed making contact (based
      on whether they are in the same space or not).

      :param agents: list of agents under consideration for contact
          statistics
      :type agents: List[Agent]
      :param contact_outfiles: Files to write contact data, one per floor,
              defaults to None
      :type contact_outfiles: List[TextIO], optional















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   .. py:method:: finalize(agents: List[citam.engine.core.agent.Agent], work_directory: Optional[Union[str, bytes, os.PathLike]] = None) -> None


   .. py:method:: identify_xy_proximity(positions_vector: numpy.ndarray) -> numpy.ndarray

      
      Compute pairwise distances, given a vector of xy positions, and
      return the indices of the ones that fall within the given contact
      distance.

      :param positions_vector: Array of current xy positions of all active
              agents
      :type positions_vector: np.ndarray
      :return: Array of indices of agents that are within the contact
          distance of each other
      :rtype: np.ndarray















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   .. py:method:: verify_and_add_contact(current_step: int, floor_number: int, agent1: citam.engine.core.agent.Agent, agent2: citam.engine.core.agent.Agent)

      
      Verify if agents are in nearby hallways before creating contact event.

      :param floor_number: [description]
      :type floor_number: int
      :param agent1: First agent
      :type agent1: Agent
      :param agent2: Second agent
      :type agent2: Agent















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   .. py:method:: add_contact_event(current_step: int, agent1: citam.engine.core.agent.Agent, agent2: citam.engine.core.agent.Agent) -> None

      
      Record contact event between agent1 and agent2.

      :param agent1: The first agent.
      :type agent1: Agent
      :param agent2: The second agent.
      :type agent2: Agent















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   .. py:method:: extract_contact_distribution_per_agent(agents: List[citam.engine.core.agent.Agent]) -> Tuple[List[str], List[int]]

      
      Compute and return total contacts per agent.

      :return: List of agent ids and their total contacts
      :rtype: Tuple[List[int], List[int]]















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   .. py:method:: save_to_files(agents: List[citam.engine.core.agent.Agent], work_directory: Union[str, bytes, os.PathLike])

      
      Write output files to the output directory

      :param work_directory: directory where all output files are to be
          saved.
      :type work_directory: str















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   .. py:method:: create_svg_heatmap(contacts_per_coord: Dict[Tuple[int, int], int], floor_directory: str) -> None

      
      Create and save a heatmap from coordinate contact data.

      :param contacts_per_coord: dictionary where each key is an (x,y )
              tuple and values are the number of contacts in that location.
      :type contacts_per_coord: Dict[Tuple[int, int], int]
      :param floor_directory: directory to save the heatmap file.
      :type floor_directory: str
      :raises FileNotFoundError: if the floor svg map file is not found.















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