bluemira.fuel_cycle.analysis
============================

.. py:module:: bluemira.fuel_cycle.analysis

.. autoapi-nested-parse::

   Fuel cycle analysis class for Monte Carlo statistics



Classes
-------

.. autoapisummary::

   bluemira.fuel_cycle.analysis.FuelCycleAnalysis


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

.. py:class:: FuelCycleAnalysis(fuel_cycle_model: bluemira.fuel_cycle.cycle.EUDEMOFuelCycleModel, **kwargs)

   Analysis class for compiling and analysing fuel cycle statistics.

   :param fuel_cycle_model: The model for the fuel cycle on a single timeline


   .. py:attribute:: build_tweaks
      :type:  ClassVar


   .. py:attribute:: model


   .. py:attribute:: m_T_req
      :value: []



   .. py:attribute:: t_d
      :value: []



   .. py:attribute:: m_dot_release
      :value: []



   .. py:method:: run_model(timelines: collections.abc.Iterable[dict[str, numpy.ndarray | int]])

      Run the tritium fuel cycle model for each timeline.

      :param timelines:
                        Timeline dict from LifeCycle object:
                            DEMO_t : np.array
                                Real time signal in seconds
                            DEMO_rt : np.array
                                Fusion time signal in years
                            DEMO_DT_rate : np.array
                                D-T fusion reaction rate signal (NOTE: P_fus is wrapped in
                                here, along with maintenance outages, etc.)
                            DEMO_DD_rate : np.array
                                D-D fusion reaction rate signal (NOTE: P_fus is wrapped in
                                here, along with maintenance outages, etc.)
                            bci : int
                                Blanket change index



   .. py:method:: get_startup_inventory(query: str = 'max') -> float

      Get the tritium start-up inventory.

      :param query: The type of statistical value to return
                    - [min, max, mean, median, 95th]

      :rtype: The tritium start-up inventory [kg]



   .. py:method:: get_doubling_time(query: str = 'max') -> float

      Get the reactor doubling time.

      :param query: The type of statistical value to return
                    - [min, max, mean, median, 95th]

      :rtype: The reactor doubling time [years]



   .. py:method:: _query(p: str, s: str) -> float


   .. py:method:: plot(figsize=(12, 6), bins=20, **kwargs)

      Plot the distributions of m_T_start and t_d.