skimpy.sampling.simple_resampler
================================

.. py:module:: skimpy.sampling.simple_resampler

.. 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.



Classes
-------

.. autoapisummary::

   skimpy.sampling.simple_resampler.SimpleResampler


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

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

   Bases: :py:obj:`skimpy.sampling.SimpleParameterSampler`


   A parameter sampler that tries to resample parameters that are not included
   in the given fixed_parameter_population.

   The maximum number of trials to get a stable model is implemented
   differently than `SimpleParameterSampler`. In `SimpleParameterSampler` the
   maximum number of trials is the maximum # of sampling attemps to get a
   stable model when `.sample()` is called. Here, maximum number of trials is
   defined per parameter vector in `fixed_parameter_population`. If a certain
   parameter vector exceeds the maximum # of trials to get a stable model, the
   method moves to the next parameter vector in `fixed_parameter_population`

   Used for performing Global Sensitivity Analysis


   .. py:method:: sample(compiled_model, flux_dict, concentration_dict, parameters_to_resample, fixed_parameter_population, min_max_eigenvalues=False, seed=321, bounds_sample=(0, 1))

      :return: