:py:mod:`respy.solve`
=====================

.. py:module:: respy.solve

.. autoapi-nested-parse::

   Everything related to the solution of a structural model.

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


Functions
~~~~~~~~~

.. autoapisummary::

   respy.solve.get_solve_func
   respy.solve.solve
   respy.solve._create_param_specific_objects
   respy.solve._create_choice_rewards
   respy.solve._solve_with_backward_induction
   respy.solve._full_solution



.. py:function:: get_solve_func(params, options)

   
   Get the solve function.

   This function takes a model specification and returns the state space of the model
   along with components of the solution such as covariates, non-pecuniary rewards,
   wages, continuation values and expected value functions as attributes of the class.

   :Parameters:

       **params** : :obj:`pandas.DataFrame`
           DataFrame containing parameter series.

       **options** : :class:`python:dict`
           Dictionary containing model attributes which are not optimized.

   :Returns:

       **solve** : :func:`~respy.solve.solve`
           Function with partialed arguments.










   .. rubric:: Examples

   >>> import respy as rp
   >>> params, options = rp.get_example_model("robinson_crusoe_basic", with_data=False)
   >>> solve = rp.get_solve_func(params, options)
   >>> state_space = solve(params)



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.. py:function:: solve(params, options, state_space)

   
   Solve the model.
















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.. py:function:: _create_param_specific_objects(complex_, choice_set, optim_paras, options, dense_key_to_dense_covariates, transit_keys=None)

   
   Create param specific objects.

   This function creates objects that are not fixed for a given model.
   Depending on their size they are either kept in working memory such
   as wages or dumped on disk such as transition probabilities!
   In the medium run we could also allow for fixed params here by saving values
   on disk directly!
   For objects that we store on disk we will just return the prefix of the location.















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.. py:function:: _create_choice_rewards(states, choice_set, optim_paras)

   
   Create wage and non-pecuniary reward for each state and choice.
















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.. py:function:: _solve_with_backward_induction(state_space, optim_paras, options)

   
   Calculate utilities with backward induction.

   The expected value functions in one period are only computed by interpolation if:

   1. Interpolation is requested.
   2. If there are more states in the period than interpolation points.
   3. If there are at least two interpolation points per `dense_index`.

   :Parameters:

       **state_space** : :class:`~respy.state_space.StateSpace`
           State space of the model which is not solved yet.

       **optim_paras** : :class:`python:dict`
           Parsed model parameters affected by the optimization.

       **options** : :class:`python:dict`
           Optimization independent model options.

   :Returns:

       **state_space** : :class:`~respy.state_space.StateSpace`
           ..













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.. py:function:: _full_solution(wages, nonpecs, continuation_values, period_draws_emax_risk, optim_paras)

   
   Calculate the full solution of the model.

   In contrast to approximate solution, the Monte Carlo integration is done for each
   state and not only a subset of states.















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