def _transformBlock(self, block, currentds):
self._fe = {}
for ds in block.component_map(ContinuousSet).itervalues():
if currentds is None or currentds is ds.cname(True):
generate_finite_elements(ds, self._nfe[currentds])
if not ds.get_changed():
if len(ds) - 1 > self._nfe[currentds]:
print("***WARNING: More finite elements were found in ContinuousSet "\
"'%s' than the number of finite elements specified in apply. "\
"The larger number of finite elements will be used." %(ds.cname(True)))
self._nfe[ds.cname(True)] = len(ds) - 1
self._fe[ds.cname(True)] = sorted(ds)
# Adding discretization information to the differentialset object itself
# so that it can be accessed outside of the discretization object
disc_info = ds.get_discretization_info()
disc_info['nfe'] = self._nfe[ds.cname(True)]
disc_info['scheme'] = self._scheme_name + ' Difference'
# Maybe check to see if any of the ContinuousSets have been changed,
# if they haven't then the model components need not be updated
# or even iterated through
for c in block.component_map().itervalues():
update_contset_indexed_component(c)
for d in block.component_map(DerivativeVar).itervalues():
dsets = d.get_continuousset_list()
for i in set(dsets):
if currentds is None or i.cname(True) is currentds:
oldexpr = d.get_derivative_expression()
loc = d.get_state_var()._contset[i]
count = dsets.count(i)
if count >= 3:
raise DAE_Error(
"Error discretizing '%s' with respect to '%s'. Current implementation "\
"only allows for taking the first or second derivative with respect to "\
"a particular ContinuousSet" %(d.cname(True),i.cname(True)))
scheme = self._scheme[count - 1]
newexpr = create_partial_expression(
scheme, oldexpr, i, loc)
d.set_derivative_expression(newexpr)
# Reclassify DerivativeVar if all indexing ContinuousSets have been discretized
if d.is_fully_discretized():
add_discretization_equations(block, d)
block.reclassify_component_type(d, Var)
# Reclassify Integrals if all ContinuousSets have been discretized
if block_fully_discretized(block):
if block.contains_component(Integral):
for i in block.component_map(Integral).itervalues():
i.reconstruct()
block.reclassify_component_type(i, Expression)
# If a model contains integrals they are most likely to appear in the objective
# function which will need to be reconstructed after the model is discretized.
for k in block.component_map(Objective).itervalues():
k.reconstruct()
def _transformBlock(self, block, currentds):
self._fe = {}
for ds in block.component_map(ContinuousSet).itervalues():
if currentds is None or currentds == ds.cname(True):
generate_finite_elements(ds,self._nfe[currentds])
if not ds.get_changed():
if len(ds)-1 > self._nfe[currentds]:
print("***WARNING: More finite elements were found in ContinuousSet "\
"'%s' than the number of finite elements specified in apply. "\
"The larger number of finite elements will be used." %(ds.cname(True)))
self._nfe[ds.cname(True)]=len(ds)-1
self._fe[ds.cname(True)]=sorted(ds)
# Adding discretization information to the differentialset object itself
# so that it can be accessed outside of the discretization object
disc_info = ds.get_discretization_info()
disc_info['nfe']=self._nfe[ds.cname(True)]
disc_info['scheme']=self._scheme_name + ' Difference'
# Maybe check to see if any of the ContinuousSets have been changed,
# if they haven't then the model components need not be updated
# or even iterated through
for c in block.component_map().itervalues():
update_contset_indexed_component(c)
for d in block.component_map(DerivativeVar).itervalues():
dsets = d.get_continuousset_list()
for i in set(dsets):
if currentds is None or i.cname(True) == currentds:
oldexpr = d.get_derivative_expression()
loc = d.get_state_var()._contset[i]
count = dsets.count(i)
if count >= 3:
raise DAE_Error(
"Error discretizing '%s' with respect to '%s'. Current implementation "\
"only allows for taking the first or second derivative with respect to "\
"a particular ContinuousSet" %(d.cname(True),i.cname(True)))
scheme = self._scheme[count-1]
newexpr = create_partial_expression(scheme,oldexpr,i,loc)
d.set_derivative_expression(newexpr)
# Reclassify DerivativeVar if all indexing ContinuousSets have been discretized
if d.is_fully_discretized():
add_discretization_equations(block,d)
block.reclassify_component_type(d,Var)
# Reclassify Integrals if all ContinuousSets have been discretized
if block_fully_discretized(block):
if block.contains_component(Integral):
for i in block.component_map(Integral).itervalues():
i.reconstruct()
block.reclassify_component_type(i,Expression)
# If a model contains integrals they are most likely to appear in the objective
# function which will need to be reconstructed after the model is discretized.
for k in block.component_map(Objective).itervalues():
k.reconstruct()
def _transformBlock(self, block, currentds):
self._fe = {}
for ds in block.component_objects(ContinuousSet, descend_into=True):
if currentds is None or currentds == ds.name:
if 'scheme' in ds.get_discretization_info():
raise DAE_Error(
"Attempting to discretize ContinuousSet "
"'%s' after it has already been discretized. " %
ds.name)
generate_finite_elements(ds, self._nfe[currentds])
if not ds.get_changed():
if len(ds) - 1 > self._nfe[currentds]:
logger.warning(
"More finite elements were found in "
"ContinuousSet '%s' than the number of "
"finite elements specified in apply. The "
"larger number of finite elements will be "
"used." % ds.name)
self._nfe[ds.name] = len(ds) - 1
self._fe[ds.name] = list(ds)
generate_colloc_points(ds, self._tau[currentds])
# Adding discretization information to the continuousset
# object itself so that it can be accessed outside of the
# discretization object
disc_info = ds.get_discretization_info()
disc_info['nfe'] = self._nfe[ds.name]
disc_info['ncp'] = self._ncp[currentds]
disc_info['tau_points'] = self._tau[currentds]
disc_info['adot'] = self._adot[currentds]
disc_info['adotdot'] = self._adotdot[currentds]
disc_info['afinal'] = self._afinal[currentds]
disc_info['scheme'] = self._scheme_name
expand_components(block)
for d in block.component_objects(DerivativeVar, descend_into=True):
dsets = d.get_continuousset_list()
for i in ComponentSet(dsets):
if currentds is None or i.name == currentds:
oldexpr = d.get_derivative_expression()
loc = d.get_state_var()._contset[i]
count = dsets.count(i)
if count >= 3:
raise DAE_Error(
"Error discretizing '%s' with respect to '%s'. "
"Current implementation only allows for taking the"
" first or second derivative with respect to a "
"particular ContinuousSet" % (d.name, i.name))
scheme = self._scheme[count - 1]
newexpr = create_partial_expression(
scheme, oldexpr, i, loc)
d.set_derivative_expression(newexpr)
if self._scheme_name == 'LAGRANGE-LEGENDRE':
# Add continuity equations to DerivativeVar's parent
# block
add_continuity_equations(d.parent_block(), d, i, loc)
# Reclassify DerivativeVar if all indexing ContinuousSets have
# been discretized. Add discretization equations to the
# DerivativeVar's parent block.
if d.is_fully_discretized():
add_discretization_equations(d.parent_block(), d)
d.parent_block().reclassify_component_type(d, Var)
# Keep track of any reclassified DerivativeVar components so
# that the Simulator can easily identify them if the model
# is simulated after discretization
# TODO: Update the discretization transformations to use
# a Block to add things to the model and store discretization
# information. Using a list for now because the simulator
# does not yet support models containing active Blocks
reclassified_list = getattr(
block, '_pyomo_dae_reclassified_derivativevars', None)
if reclassified_list is None:
block._pyomo_dae_reclassified_derivativevars = list()
reclassified_list = \
block._pyomo_dae_reclassified_derivativevars
reclassified_list.append(d)
# Reclassify Integrals if all ContinuousSets have been discretized
if block_fully_discretized(block):
if block.contains_component(Integral):
for i in block.component_objects(Integral, descend_into=True):
i.parent_block().reclassify_component_type(i, Expression)
# TODO: The following reproduces the old behavior of
# "reconstruct()". We should come up with an
# implementation that does not rely on manipulating
# private attributes
i.clear()
i._constructed = False
i.construct()
# If a model contains integrals they are most likely to appear
# in the objective function which will need to be reconstructed
# after the model is discretized.
for k in block.component_objects(Objective, descend_into=True):
# TODO: check this, reconstruct might not work
# TODO: The following reproduces the old behavior of
# "reconstruct()". We should come up with an
# implementation that does not rely on manipulating
# private attributes
k.clear()
k._constructed = False
k.construct()
def _transformBlock(self, block, currentds):
self._fe = {}
for ds in block.component_objects(ContinuousSet, descend_into=True):
if currentds is None or currentds == ds.name:
if 'scheme' in ds.get_discretization_info():
raise DAE_Error("Attempting to discretize ContinuousSet "
"'%s' after it has already been discretized. "
% ds.name)
generate_finite_elements(ds, self._nfe[currentds])
if not ds.get_changed():
if len(ds) - 1 > self._nfe[currentds]:
logger.warn("More finite elements were found in "
"ContinuousSet '%s' than the number of "
"finite elements specified in apply. The "
"larger number of finite elements will be "
"used." % ds.name)
self._nfe[ds.name] = len(ds) - 1
self._fe[ds.name] = sorted(ds)
generate_colloc_points(ds, self._tau[currentds])
# Adding discretization information to the continuousset
# object itself so that it can be accessed outside of the
# discretization object
disc_info = ds.get_discretization_info()
disc_info['nfe'] = self._nfe[ds.name]
disc_info['ncp'] = self._ncp[currentds]
disc_info['tau_points'] = self._tau[currentds]
disc_info['adot'] = self._adot[currentds]
disc_info['adotdot'] = self._adotdot[currentds]
disc_info['afinal'] = self._afinal[currentds]
disc_info['scheme'] = self._scheme_name
expand_components(block)
for d in block.component_objects(DerivativeVar, descend_into=True):
dsets = d.get_continuousset_list()
for i in set(dsets):
if currentds is None or i.name == currentds:
oldexpr = d.get_derivative_expression()
loc = d.get_state_var()._contset[i]
count = dsets.count(i)
if count >= 3:
raise DAE_Error(
"Error discretizing '%s' with respect to '%s'. "
"Current implementation only allows for taking the"
" first or second derivative with respect to a "
"particular ContinuousSet" % (d.name, i.name))
scheme = self._scheme[count - 1]
newexpr = create_partial_expression(scheme, oldexpr, i,
loc)
d.set_derivative_expression(newexpr)
if self._scheme_name == 'LAGRANGE-LEGENDRE':
# Add continuity equations to DerivativeVar's parent
# block
add_continuity_equations(d.parent_block(), d, i, loc)
# Reclassify DerivativeVar if all indexing ContinuousSets have
# been discretized. Add discretization equations to the
# DerivativeVar's parent block.
if d.is_fully_discretized():
add_discretization_equations(d.parent_block(), d)
d.parent_block().reclassify_component_type(d, Var)
# Keep track of any reclassified DerivativeVar components so
# that the Simulator can easily identify them if the model
# is simulated after discretization
# TODO: Update the discretization transformations to use
# a Block to add things to the model and store discretization
# information. Using a list for now because the simulator
# does not yet support models containing active Blocks
reclassified_list = getattr(block,
'_pyomo_dae_reclassified_derivativevars',
None)
if reclassified_list is None:
block._pyomo_dae_reclassified_derivativevars = list()
reclassified_list = \
block._pyomo_dae_reclassified_derivativevars
reclassified_list.append(d)
# Reclassify Integrals if all ContinuousSets have been discretized
if block_fully_discretized(block):
if block.contains_component(Integral):
for i in block.component_objects(Integral, descend_into=True):
i.reconstruct()
i.parent_block().reclassify_component_type(i, Expression)
# If a model contains integrals they are most likely to appear
# in the objective function which will need to be reconstructed
# after the model is discretized.
for k in block.component_objects(Objective, descend_into=True):
# TODO: check this, reconstruct might not work
k.reconstruct()
def _transformBlock(self, block, currentds):
self._fe = {}
for ds in block.component_objects(ContinuousSet):
if currentds is None or currentds == ds.name or currentds is ds:
generate_finite_elements(ds, self._nfe[currentds])
if not ds.get_changed():
if len(ds) - 1 > self._nfe[currentds]:
print("***WARNING: More finite elements were found in "
"ContinuousSet '%s' than the number of finite "
"elements specified in apply. The larger number "
"of finite elements will be used." % ds.name)
self._nfe[ds.name] = len(ds) - 1
self._fe[ds.name] = sorted(ds)
# Adding discretization information to the differentialset
# object itself so that it can be accessed outside of the
# discretization object
disc_info = ds.get_discretization_info()
disc_info['nfe'] = self._nfe[ds.name]
disc_info['scheme'] = self._scheme_name + ' Difference'
# Maybe check to see if any of the ContinuousSets have been changed,
# if they haven't then the model components need not be updated
# or even iterated through
expand_components(block)
for d in block.component_objects(DerivativeVar, descend_into=True):
dsets = d.get_continuousset_list()
for i in set(dsets):
if currentds is None or i.name == currentds:
oldexpr = d.get_derivative_expression()
loc = d.get_state_var()._contset[i]
count = dsets.count(i)
if count >= 3:
raise DAE_Error(
"Error discretizing '%s' with respect to '%s'. "
"Current implementation only allows for taking the"
" first or second derivative with respect to "
"a particular ContinuousSet" % (d.name, i.name))
scheme = self._scheme[count - 1]
newexpr = create_partial_expression(
scheme, oldexpr, i, loc)
d.set_derivative_expression(newexpr)
# Reclassify DerivativeVar if all indexing ContinuousSets have
# been discretized. Add discretization equations to the
# DerivativeVar's parent block.
if d.is_fully_discretized():
add_discretization_equations(d.parent_block(), d)
d.parent_block().reclassify_component_type(d, Var)
# Reclassify Integrals if all ContinuousSets have been discretized
if block_fully_discretized(block):
if block.contains_component(Integral):
for i in block.component_objects(Integral, descend_into=True):
i.reconstruct()
i.parent_block().reclassify_component_type(i, Expression)
# If a model contains integrals they are most likely to
# appear in the objective function which will need to be
# reconstructed after the model is discretized.
for k in block.component_objects(Objective, descend_into=True):
# TODO: check this, reconstruct might not work
k.reconstruct()
def _transformBlock(self, block, currentds):
self._fe = {}
for ds in block.component_objects(ContinuousSet):
if currentds is None or currentds == ds.name or currentds is ds:
if 'scheme' in ds.get_discretization_info():
raise DAE_Error(
"Attempting to discretize ContinuousSet "
"'%s' after it has already been discretized. " %
ds.name)
generate_finite_elements(ds, self._nfe[currentds])
if not ds.get_changed():
if len(ds) - 1 > self._nfe[currentds]:
logger.warn("More finite elements were found in "
"ContinuousSet '%s' than the number of "
"finite elements specified in apply. The "
"larger number of finite elements will be "
"used." % ds.name)
self._nfe[ds.name] = len(ds) - 1
self._fe[ds.name] = sorted(ds)
# Adding discretization information to the ContinuousSet
# object itself so that it can be accessed outside of the
# discretization object
disc_info = ds.get_discretization_info()
disc_info['nfe'] = self._nfe[ds.name]
disc_info['scheme'] = self._scheme_name + ' Difference'
# Maybe check to see if any of the ContinuousSets have been changed,
# if they haven't then the model components need not be updated
# or even iterated through
expand_components(block)
for d in block.component_objects(DerivativeVar, descend_into=True):
dsets = d.get_continuousset_list()
for i in ComponentSet(dsets):
if currentds is None or i.name == currentds:
oldexpr = d.get_derivative_expression()
loc = d.get_state_var()._contset[i]
count = dsets.count(i)
if count >= 3:
raise DAE_Error(
"Error discretizing '%s' with respect to '%s'. "
"Current implementation only allows for taking the"
" first or second derivative with respect to "
"a particular ContinuousSet" % (d.name, i.name))
scheme = self._scheme[count - 1]
newexpr = create_partial_expression(
scheme, oldexpr, i, loc)
d.set_derivative_expression(newexpr)
# Reclassify DerivativeVar if all indexing ContinuousSets have
# been discretized. Add discretization equations to the
# DerivativeVar's parent block.
if d.is_fully_discretized():
add_discretization_equations(d.parent_block(), d)
d.parent_block().reclassify_component_type(d, Var)
# Keep track of any reclassified DerivativeVar components so
# that the Simulator can easily identify them if the model
# is simulated after discretization
# TODO: Update the discretization transformations to use
# a Block to add things to the model and store discretization
# information. Using a list for now because the simulator
# does not yet support models containing active Blocks
reclassified_list = getattr(
block, '_pyomo_dae_reclassified_derivativevars', None)
if reclassified_list is None:
block._pyomo_dae_reclassified_derivativevars = list()
reclassified_list = \
block._pyomo_dae_reclassified_derivativevars
reclassified_list.append(d)
# Reclassify Integrals if all ContinuousSets have been discretized
if block_fully_discretized(block):
if block.contains_component(Integral):
for i in block.component_objects(Integral, descend_into=True):
i.reconstruct()
i.parent_block().reclassify_component_type(i, Expression)
# If a model contains integrals they are most likely to
# appear in the objective function which will need to be
# reconstructed after the model is discretized.
for k in block.component_objects(Objective, descend_into=True):
# TODO: check this, reconstruct might not work
k.reconstruct()
def _transformBlock(self, block, currentds):
self._fe = {}
for ds in itervalues(block.component_map(ContinuousSet)):
if currentds is None or currentds == ds.name:
generate_finite_elements(ds, self._nfe[currentds])
if not ds.get_changed():
if len(ds) - 1 > self._nfe[currentds]:
print("***WARNING: More finite elements were found in differentialset "\
"'%s' than the number of finite elements specified in apply. "\
"The larger number of finite elements will be used." % (ds.name,))
self._nfe[ds.name] = len(ds) - 1
self._fe[ds.name] = sorted(ds)
generate_colloc_points(ds, self._tau[currentds])
# Adding discretization information to the differentialset object itself
# so that it can be accessed outside of the discretization object
disc_info = ds.get_discretization_info()
disc_info['nfe'] = self._nfe[ds.name]
disc_info['ncp'] = self._ncp[currentds]
disc_info['tau_points'] = self._tau[currentds]
disc_info['adot'] = self._adot[currentds]
disc_info['adotdot'] = self._adotdot[currentds]
disc_info['afinal'] = self._afinal[currentds]
disc_info['scheme'] = self._scheme_name
for c in itervalues(block.component_map()):
update_contset_indexed_component(c)
for d in itervalues(block.component_map(DerivativeVar)):
dsets = d.get_continuousset_list()
for i in set(dsets):
if currentds is None or i.name == currentds:
oldexpr = d.get_derivative_expression()
loc = d.get_state_var()._contset[i]
count = dsets.count(i)
if count >= 3:
raise DAE_Error(
"Error discretizing '%s' with respect to '%s'. Current implementation "\
"only allows for taking the first or second derivative with respect to "\
"a particular ContinuousSet" %s(d.name,i.name))
scheme = self._scheme[count - 1]
# print("%s %s" % (i.name, scheme.__name__))
newexpr = create_partial_expression(
scheme, oldexpr, i, loc)
d.set_derivative_expression(newexpr)
if self._scheme_name == 'LAGRANGE-LEGENDRE':
add_continuity_equations(block, d, i, loc)
# Reclassify DerivativeVar if all indexing ContinuousSets have been discretized
if d.is_fully_discretized():
add_discretization_equations(block, d)
block.reclassify_component_type(d, Var)
# Reclassify Integrals if all ContinuousSets have been discretized
if block_fully_discretized(block):
if block.contains_component(Integral):
for i in itervalues(block.component_map(Integral)):
i.reconstruct()
block.reclassify_component_type(i, Expression)
# If a model contains integrals they are most likely to appear in the objective
# function which will need to be reconstructed after the model is discretized.
for k in itervalues(block.component_map(Objective)):
k.reconstruct()
def _transformBlock(self, block, currentds):
self._fe = {}
for ds in itervalues(block.component_map(ContinuousSet)):
if currentds is None or currentds == ds.name:
generate_finite_elements(ds,self._nfe[currentds])
if not ds.get_changed():
if len(ds)-1 > self._nfe[currentds]:
print("***WARNING: More finite elements were found in differentialset "\
"'%s' than the number of finite elements specified in apply. "\
"The larger number of finite elements will be used." % (ds.name,))
self._nfe[ds.name]=len(ds)-1
self._fe[ds.name]=sorted(ds)
generate_colloc_points(ds,self._tau[currentds])
# Adding discretization information to the differentialset object itself
# so that it can be accessed outside of the discretization object
disc_info = ds.get_discretization_info()
disc_info['nfe']=self._nfe[ds.name]
disc_info['ncp']=self._ncp[currentds]
disc_info['tau_points']=self._tau[currentds]
disc_info['adot'] = self._adot[currentds]
disc_info['adotdot'] = self._adotdot[currentds]
disc_info['afinal'] = self._afinal[currentds]
disc_info['scheme'] = self._scheme_name
for c in itervalues(block.component_map()):
update_contset_indexed_component(c)
for d in itervalues(block.component_map(DerivativeVar)):
dsets = d.get_continuousset_list()
for i in set(dsets):
if currentds is None or i.name == currentds:
oldexpr = d.get_derivative_expression()
loc = d.get_state_var()._contset[i]
count = dsets.count(i)
if count >= 3:
raise DAE_Error(
"Error discretizing '%s' with respect to '%s'. Current implementation "\
"only allows for taking the first or second derivative with respect to "\
"a particular ContinuousSet" %(d.name,i.name))
scheme = self._scheme[count-1]
# print("%s %s" % (i.name, scheme.__name__))
newexpr = create_partial_expression(scheme,oldexpr,i,loc)
d.set_derivative_expression(newexpr)
if self._scheme_name == 'LAGRANGE-LEGENDRE':
add_continuity_equations(block,d,i,loc)
# Reclassify DerivativeVar if all indexing ContinuousSets have been discretized
if d.is_fully_discretized():
add_discretization_equations(block,d)
block.reclassify_component_type(d,Var)
# Reclassify Integrals if all ContinuousSets have been discretized
if block_fully_discretized(block):
if block.contains_component(Integral):
for i in itervalues(block.component_map(Integral)):
i.reconstruct()
block.reclassify_component_type(i,Expression)
# If a model contains integrals they are most likely to appear in the objective
# function which will need to be reconstructed after the model is discretized.
for k in itervalues(block.component_map(Objective)):
k.reconstruct()
