repic.commands.run_ilp
======================

.. py:module:: repic.commands.run_ilp

.. autoapi-nested-parse::

   Apply integer linear programming (ILP) optimizer (either Gurobi or SciPy supported) to identify best subset of k-sized cliques (consensus particles) in a globally optimal manner



Attributes
----------

.. autoapisummary::

   repic.commands.run_ilp.use_gurobi
   repic.commands.run_ilp.use_gurobi
   repic.commands.run_ilp.name
   repic.commands.run_ilp.parser


Functions
---------

.. autoapisummary::

   repic.commands.run_ilp.add_arguments
   repic.commands.run_ilp.plot_particle_weights
   repic.commands.run_ilp.main


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

.. py:data:: use_gurobi
   :value: False


   Gurobi integer linear programming optimizer flag

   :type: bool

.. py:data:: use_gurobi
   :value: True


   Gurobi integer linear programming optimizer flag

   :type: bool

.. py:data:: name
   :value: 'run_ilp'


   module name (used by argparse subparser)

   :type: str

.. py:function:: add_arguments(parser)

   Adds argparse command line arguments for run_ilp.py

   :param parser: argparse parse_args() object
   :type parser: object

   :returns: None


.. py:function:: plot_particle_weights(args, weights, num_mrc, out_dir)

   Creates Matplotlib line plot of the expected number of particles per micrograph vs. clique weights

   :param args: argparse command line argument object
   :type args: obj
   :param weights: list of consensus particle weights
   :type weights: list
   :param num_mrc: number of micrographs analyzed
   :type num_mrc: int
   :param out_dir: dirpath to output directory
   :type out_dir: str

   :returns: None


.. py:function:: main(args)

   Applies integer linear programming optimizer to output of get_cliques.py (clique weights, constraint matrix, linear constraints, etc.) and identifies the globally optimal subset of cliques

   :param args: argparse command line argument object
   :type args: obj


.. py:data:: parser

   argparse parse_args() object

   :type: obj