def generate_ampl_repn(exp, idMap=None):
if idMap is None:
idMap = {}
degree = exp.polynomial_degree()
if (degree is None) or (degree > 1):
repn = _generate_ampl_repn(exp)
repn.compress()
elif degree == 0:
repn = AmplRepn()
repn._constant = value(exp)
# compress
repn._linear_vars = tuple()
repn._linear_terms_coef = tuple()
repn._nonlinear_vars = tuple()
else: # degree == 1
repn = AmplRepn()
# compress
repn._linear_vars = tuple()
repn._linear_terms_coef = tuple()
repn._nonlinear_vars = tuple()
coef, varmap = collect_linear_canonical_repn(exp, idMap=idMap)
if None in coef:
val = coef.pop(None)
if val:
repn._constant = val
# the six module is inefficient in terms of wrapping iterkeys and itervalues,
# in the context of Python 2.7. use the native dictionary methods where possible.
if using_py3 is False:
repn._linear_terms_coef = tuple(val for val in coef.itervalues() if val)
repn._linear_vars = tuple((varmap[var_hash] for var_hash,val in coef.iteritems() if val))
else:
repn._linear_terms_coef = tuple(val for val in coef.values() if val)
repn._linear_vars = tuple((varmap[var_hash] for var_hash,val in coef.items() if val))
return repn
def generate_ampl_repn(exp, idMap=None):
# We need to do this not at the global scope in case someone changed
# the mode after importing the environment.
_using_pyomo4_trees = expr_common.mode == expr_common.Mode.pyomo4_trees
if idMap is None:
idMap = {}
if exp is None:
return AmplRepn()
degree = exp.polynomial_degree()
if (degree is None) or (degree > 1):
repn = _generate_ampl_repn(exp)
repn.compress()
elif degree == 0:
repn = AmplRepn()
repn._constant = value(exp)
# compress
repn._linear_vars = tuple()
repn._linear_terms_coef = tuple()
repn._nonlinear_vars = tuple()
else: # degree == 1
repn = AmplRepn()
if _using_pyomo4_trees:
canonical_repn = generate_canonical_repn(exp, idMap=idMap)
# compress
repn._nonlinear_vars = tuple()
repn._constant = value(canonical_repn.constant)
repn._linear_vars = tuple(canonical_repn.variables)
repn._linear_terms_coef = tuple(
value(_v) for _v in canonical_repn.linear)
else:
# compress
repn._linear_vars = tuple()
repn._linear_terms_coef = tuple()
repn._nonlinear_vars = tuple()
coef, varmap = collect_linear_canonical_repn(exp, idMap=idMap)
if None in coef:
val = coef.pop(None)
if val:
repn._constant = val
# the six module is inefficient in terms of wrapping
# iterkeys and itervalues, in the context of Python
# 2.7. use the native dictionary methods where
# possible.
if using_py3 is False:
repn._linear_terms_coef = tuple(val
for val in coef.itervalues()
if val)
repn._linear_vars = tuple(
(varmap[var_hash] for var_hash, val in coef.iteritems()
if val))
else:
repn._linear_terms_coef = tuple(val for val in coef.values()
if val)
repn._linear_vars = tuple((varmap[var_hash]
for var_hash, val in coef.items()
if val))
return repn
def generate_ampl_repn(exp, idMap=None):
# We need to do this not at the global scope in case someone changed
# the mode after importing the environment.
_using_pyomo4_trees = expr_common.mode == expr_common.Mode.pyomo4_trees
_using_pyomo5_trees = expr_common.mode == expr_common.Mode.pyomo5_trees
if idMap is None:
idMap = {}
if _using_pyomo5_trees:
from pyomo.repn.standard_repn import generate_standard_repn
return generate_standard_repn(exp, quadratic=False)
if exp is None:
return AmplRepn()
degree = exp.polynomial_degree()
if (degree is None) or (degree > 1):
repn = _generate_ampl_repn(exp)
repn.compress()
elif degree == 0:
repn = AmplRepn()
repn._constant = value(exp)
# compress
repn._linear_vars = tuple()
repn._linear_terms_coef = tuple()
repn._nonlinear_vars = tuple()
else: # degree == 1
repn = AmplRepn()
if _using_pyomo4_trees:
canonical_repn = generate_canonical_repn(exp, idMap=idMap)
# compress
repn._nonlinear_vars = tuple()
repn._constant = value(canonical_repn.constant)
repn._linear_vars = tuple(canonical_repn.variables)
repn._linear_terms_coef = tuple(value(_v) for _v in canonical_repn.linear)
else:
# compress
repn._linear_vars = tuple()
repn._linear_terms_coef = tuple()
repn._nonlinear_vars = tuple()
coef, varmap = collect_linear_canonical_repn(exp, idMap=idMap)
if None in coef:
val = coef.pop(None)
if val:
repn._constant = val
# the six module is inefficient in terms of wrapping
# iterkeys and itervalues, in the context of Python
# 2.7. use the native dictionary methods where
# possible.
if using_py3 is False:
repn._linear_terms_coef = tuple(val for val in coef.itervalues() if val)
repn._linear_vars = tuple((varmap[var_hash]
for var_hash,val in coef.iteritems() if val))
else:
repn._linear_terms_coef = tuple(val for val in coef.values() if val)
repn._linear_vars = tuple((varmap[var_hash]
for var_hash,val in coef.items() if val))
return repn