:py:mod:`respy.parallelization`
===============================

.. py:module:: respy.parallelization

.. autoapi-nested-parse::

   This module contains the code to control parallel execution.

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


Functions
~~~~~~~~~

.. autoapisummary::

   respy.parallelization.parallelize_across_dense_dimensions
   respy.parallelization.split_and_combine_df
   respy.parallelization._infer_dense_keys_from_arguments
   respy.parallelization._is_dictionary_with_integer_keys
   respy.parallelization._broadcast_arguments
   respy.parallelization._is_dense_dictionary_argument
   respy.parallelization._split_dataframe



.. py:function:: parallelize_across_dense_dimensions(func=None, *, n_jobs=1)

   
   Parallelizes decorated function across dense state space dimensions.

   Parallelization is only possible if the decorated function has no side-effects to
   other dense dimensions. This might be true for different levels. E.g.
   :meth:`respy.solve._create_choice_rewards` can be directly
   parallelized. :func:`respy.solve._solve_with_backward_induction` cannot be
   directly parallelized because the continuation values for one dense dimension will
   become important for others if we implement exogenous processes. Thus, parallelize
   across periods.

   If applied to a function, the decorator recognizes if the model or state space
   contains dense dimensions likes types or observables. Then, it splits the operation
   across dense dimensions by patching the attribute access such that each sub state
   space can only access its attributes.

   The decorator can be applied to functions without trailing parentheses. At the same
   time, the `*` prohibits to use the decorator with positional arguments.















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.. py:function:: split_and_combine_df(func)

   
   Split the data across dense indices, run a function, and combine again.
















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.. py:function:: _infer_dense_keys_from_arguments(args, kwargs)

   
   Infer the dense indices from the arguments.

   Dense indices can be found in dictionaries with only integer keys.

   This function uses the intersection of all dense indices from the arguments. Since
   the simulated data or data for the likelihood might not comprise all dense
   dimensions, we might need to discard some indices.















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.. py:function:: _is_dictionary_with_integer_keys(candidate)

   
   Infer whether the argument is a dictionary with integer keys.
















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.. py:function:: _broadcast_arguments(args, kwargs, dense_keys)

   
   Broadcast arguments to dense state space dimensions.
















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.. py:function:: _is_dense_dictionary_argument(argument, dense_keys)

   
   Check whether all keys of the dictionary argument are also dense indices.

   We cannot check whether all dense indices are in the argument because `splitted_df`
   in :func:`split_and_combine_df` may not cover all dense combinations.















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.. py:function:: _split_dataframe(df)

   
   Split a DataFrame by creating groups of the same values for the dense dims.
















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