:mod:`geckopy.integration`
==========================

.. py:module:: geckopy.integration


Submodules
----------
.. toctree::
   :titlesonly:
   :maxdepth: 1

   pytfa/index.rst
   relaxation/index.rst


Package Contents
----------------


Functions
~~~~~~~~~

.. autoapisummary::

   geckopy.integration.relax_thermo_concentrations_proteins
   geckopy.integration.relax_thermo_proteins


.. function:: relax_thermo_concentrations_proteins(model: M, prot_candidates: Set[str], objective_rule: geckopy.experimental.relaxation.Objective_rule = Objective_rule.MIN_ELASTIC_SUM_OBJECTIVE, metabolites_to_ignore: Optional[Set[str]] = None) -> Set

   Get one IIS of protein concentrations and the log concentration constraints.

   __Inplace__ operation. The log concentrations refer to metabolomics
   constraints in the MILP as formulated by pytfa. This combines both
   `pytfa.optim.relax_lc` and
   :py:func:`geckopy.experimental.relaxation.get_upper_relaxation` and avoids
   copying the model since that fails for a `pytfa.ThermoModel` with proteins.

   :param original_model:
   :type original_model: cobra.Model
   :param elastic_candidates:
   :type elastic_candidates: list[str]
   :param objective_rule: The IIS is selected by minimizing an objective as defined in
                          :class:`Objective_rule`.
   :type objective_rule: Objective_rule
   :param metabolites_to_ignore: Metabolites whose LogConcentration will not be used in the relaxation
                                 of the problem. Proteins will be automatically added to this set.
   :type metabolites_to_ignore: Optional[Set[str]]

   :returns: * **iis** (*Set*) -- variables that relax the model to make it feashible.
             * **status** (*str*)


.. function:: relax_thermo_proteins(model: M, prot_candidates: Set[str], objective_rule: geckopy.experimental.relaxation.Objective_rule = Objective_rule.MIN_ELASTIC_SUM_OBJECTIVE) -> Set

   Get one IIS of protein concentrations and the :math:`\Delta G_r` constraints.

   __Inplace__ operation. This combines both `pytfa.optim.relax_dgo` and
   :py:func:`geckopy.experimental.relaxation.get_upper_relaxation` and avoids
   copying the model since that fails for a `pytfa.ThermoModel` with proteins.

   :param original_model:
   :type original_model: cobra.Model
   :param elastic_candidates:
   :type elastic_candidates: list[str]
   :param objective_rule: The IIS is selected by minimizing an objective as defined in
                          :class:`Objective_rule`.
   :type objective_rule: Objective_rule

   :returns: * **iis** (*Set*) -- variables that relax the model to make it feashible.
             * **status** (*str*)