skimpy.sampling.ga_flux_concentration_sampler
=============================================

.. py:module:: skimpy.sampling.ga_flux_concentration_sampler

.. autoapi-nested-parse::

   .. module:: skimpy
      :platform: Unix, Windows
      :synopsis: Simple Kinetic Models in Python

   .. moduleauthor:: SKiMPy team

   [---------]

   Copyright 2017 Laboratory of Computational Systems Biotechnology (LCSB),
   Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIE CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.



Attributes
----------

.. autoapisummary::

   skimpy.sampling.ga_flux_concentration_sampler.model_gen


Classes
-------

.. autoapisummary::

   skimpy.sampling.ga_flux_concentration_sampler.ItterableSeries
   skimpy.sampling.ga_flux_concentration_sampler.GaFluxConcentrationSampler


Functions
---------

.. autoapisummary::

   skimpy.sampling.ga_flux_concentration_sampler.convex_mating
   skimpy.sampling.ga_flux_concentration_sampler.sample_parameters


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

.. py:class:: ItterableSeries(this_series)

   .. py:attribute:: data


   .. py:method:: __iter__()


.. py:data:: model_gen

.. py:class:: GaFluxConcentrationSampler(parameters=None)

   Bases: :py:obj:`skimpy.sampling.flux_concentration_sampler.FluxConcentrationSampler`


   This sampler performs an optimizaion 


   .. py:class:: Parameters

      Bases: :py:obj:`tuple`


      Parameter type specified for the parameters sampling procedure
      :return:


      .. py:attribute:: n_samples


      .. py:attribute:: n_parameter_samples


      .. py:attribute:: max_generation


      .. py:attribute:: seed


      .. py:attribute:: mutation_probability


      .. py:attribute:: crossover_scaling


      .. py:attribute:: max_eigenvalue


      .. py:attribute:: min_eigenvalue


      .. py:attribute:: scaling_parameters



   .. py:method:: sample(tmodel, kmodel, simple_parameter_sampler, only_stable=True)

      :param compiled_model:
      :param flux_dict:
      :param concentration_dict:
      :param seed:
      :param max_generation:
      :param mutation_probability:
      :param eta:
      :return:



   .. py:method:: fitness(flux_concentration)

              
              



   .. py:method:: run_ea(toolbox, stats=None, verbose=False)


   .. py:method:: sample_tfa_model(n_samples)

      :param tmodel: pytfa.tmodel
      :param n_samples: integer
      :return: TODO pd.DataFrame indexed with reaction names and metabolite concentrations



   .. py:method:: mutate_ind(ind)


.. py:function:: convex_mating(ind1, ind2, eta=0.5)

.. py:function:: sample_parameters(kmodel, tmodel, individual, param_sampler, scaling_parameters, only_stable=True)

   Run sampling on first order model