repic.commands.run_ilp
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
Gurobi integer linear programming optimizer flag |
|
Gurobi integer linear programming optimizer flag |
|
module name (used by argparse subparser) |
|
argparse parse_args() object |
Functions
|
Adds argparse command line arguments for run_ilp.py |
|
Creates Matplotlib line plot of the expected number of particles per micrograph vs. clique weights |
|
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 |
Module Contents
- repic.commands.run_ilp.use_gurobi = False
Gurobi integer linear programming optimizer flag
- Type:
bool
- repic.commands.run_ilp.use_gurobi = True
Gurobi integer linear programming optimizer flag
- Type:
bool
- repic.commands.run_ilp.name = 'run_ilp'
module name (used by argparse subparser)
- Type:
str
- repic.commands.run_ilp.add_arguments(parser)
Adds argparse command line arguments for run_ilp.py
- Parameters:
parser (object) – argparse parse_args() object
- Returns:
None
- repic.commands.run_ilp.plot_particle_weights(args, weights, num_mrc, out_dir)
Creates Matplotlib line plot of the expected number of particles per micrograph vs. clique weights
- Parameters:
args (obj) – argparse command line argument object
weights (list) – list of consensus particle weights
num_mrc (int) – number of micrographs analyzed
out_dir (str) – dirpath to output directory
- Returns:
None
- repic.commands.run_ilp.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
- Parameters:
args (obj) – argparse command line argument object
- repic.commands.run_ilp.parser
argparse parse_args() object
- Type:
obj