# ___________________________________________________________________________
#
# Pyomo: Python Optimization Modeling Objects
# Copyright (c) 2008-2022
# National Technology and Engineering Solutions of Sandia, LLC
# Under the terms of Contract DE-NA0003525 with National Technology and
# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
# rights in this software.
# This software is distributed under the 3-clause BSD License.
# ___________________________________________________________________________
"""Functionality for identifying variables that participate in expressions
"""
import enum
from pyomo.core.expr.visitor import identify_variables
from pyomo.repn import generate_standard_repn
from pyomo.common.backports import nullcontext
from pyomo.util.subsystems import TemporarySubsystemManager
from pyomo.contrib.incidence_analysis.config import IncidenceMethod, IncidenceConfig
#
# Handlers for different methods of generating the incidence graph
#
def _get_incident_via_identify_variables(expr, include_fixed):
# Note that identify_variables will not identify the same variable
# more than once.
return list(identify_variables(expr, include_fixed=include_fixed))
def _get_incident_via_standard_repn(expr, include_fixed, linear_only):
if include_fixed:
to_unfix = [
var for var in identify_variables(expr, include_fixed=True) if var.fixed
]
context = TemporarySubsystemManager(to_unfix=to_unfix)
else:
context = nullcontext()
with context:
repn = generate_standard_repn(expr, compute_values=False, quadratic=False)
# TODO: Check coefficients of linear variables.
# This is only necessary when handling mutable parameters and fixed
# variables as coefficients, which we're punting on for now. Variables
# with constant coefficients of zero don't appear here.
if linear_only:
return list(repn.linear_vars)
else:
# Combine linear and nonlinear variables and filter out duplicates. Note
# that quadratic=False, so we don't need to include repn.quadratic_vars.
variables = repn.linear_vars + repn.nonlinear_vars
unique_variables = []
id_set = set()
for var in variables:
v_id = id(var)
if v_id not in id_set:
id_set.add(v_id)
unique_variables.append(var)
return unique_variables
[docs]def get_incident_variables(expr, **kwds):
"""Get variables that participate in an expression
The exact variables returned depends on the method used to determine incidence.
For example, ``method=IncidenceMethod.identify_variables`` will return all
variables participating in the expression, while
``method=IncidenceMethod.standard_repn`` will return only the variables
identified by ``generate_standard_repn`` which ignores variables that only
appear multiplied by a constant factor of zero.
Keyword arguments must be valid options for ``IncidenceConfig``.
Parameters
----------
expr: ``NumericExpression``
Expression to search for variables
Returns
-------
list of VarData
List containing the variables that participate in the expression
Example
-------
.. doctest::
>>> import pyomo.environ as pyo
>>> from pyomo.contrib.incidence_analysis import get_incident_variables
>>> m = pyo.ConcreteModel()
>>> m.x = pyo.Var([1, 2, 3])
>>> expr = m.x[1] + 2*m.x[2] + 3*m.x[3]**2
>>> print([v.name for v in get_incident_variables(expr)])
['x[1]', 'x[2]', 'x[3]']
>>> print([v.name for v in get_incident_variables(expr, linear_only=True)])
['x[1]', 'x[2]']
"""
config = IncidenceConfig(kwds)
method = config.method
include_fixed = config.include_fixed
linear_only = config.linear_only
if linear_only and method is IncidenceMethod.identify_variables:
raise RuntimeError(
"linear_only=True is not supported when using identify_variables"
)
if method is IncidenceMethod.identify_variables:
return _get_incident_via_identify_variables(expr, include_fixed)
elif method is IncidenceMethod.standard_repn:
return _get_incident_via_standard_repn(expr, include_fixed, linear_only)
else:
raise ValueError(
f"Unrecognized value {method} for the method used to identify incident"
f" variables. Valid options are {IncidenceMethod.identify_variables}"
f" and {IncidenceMethod.standard_repn}."
)