def get_bounds(obj, input_keys, output_keys, J):
""" Returns a pair of dictionaries that contain the stop and end index
for each input and output in a pair of lists.
"""
ibounds = {}
nvar = 0
scope = getattr(obj, 'parent', None)
for key in input_keys:
# For parameter group, all should be equal so just get first.
if not isinstance(key, tuple):
key = [key]
val = obj.get(key[0])
width = flattened_size('.'.join((obj.name, key[0])), val,
scope=scope)
shape = getattr(val, 'shape', None)
for item in key:
ibounds[item] = (nvar, nvar+width, shape)
nvar += width
num_input = nvar
obounds = {}
nvar = 0
for key in output_keys:
val = obj.get(key)
width = flattened_size('.'.join((obj.name, key)), val)
shape = getattr(val, 'shape', None)
obounds[key] = (nvar, nvar+width, shape)
nvar += width
num_output = nvar
if num_input and num_output:
# Give the user an intelligible error if the size of J is wrong.
try:
J_output, J_input = J.shape
except ValueError as err:
exc_type, value, traceback = sys.exc_info()
msg = "Jacobian has the wrong dimensions. Expected 2D but got {}D."
msg = msg.format(J.ndim)
raise ValueError, ValueError(msg), traceback
if num_output != J_output or num_input != J_input:
msg = 'Jacobian is the wrong size. Expected ' + \
'(%dx%d) but got (%dx%d)' % (num_output, num_input,
J_output, J_input)
if ISystem.providedBy(obj):
raise RuntimeError(msg)
else:
obj.raise_exception(msg, RuntimeError)
return ibounds, obounds
def get_bounds(obj, input_keys, output_keys, J):
""" Returns a pair of dictionaries that contain the stop and end index
for each input and output in a pair of lists.
"""
ibounds = {}
nvar = 0
scope = getattr(obj, 'parent', None)
for key in input_keys:
# For parameter group, all should be equal so just get first.
if not isinstance(key, tuple):
key = [key]
val = obj.get(key[0])
width = flattened_size('.'.join((obj.name, key[0])), val, scope=scope)
shape = getattr(val, 'shape', None)
for item in key:
ibounds[item] = (nvar, nvar + width, shape)
nvar += width
num_input = nvar
obounds = {}
nvar = 0
for key in output_keys:
val = obj.get(key)
width = flattened_size('.'.join((obj.name, key)), val)
shape = getattr(val, 'shape', None)
obounds[key] = (nvar, nvar + width, shape)
nvar += width
num_output = nvar
if num_input and num_output:
# Give the user an intelligible error if the size of J is wrong.
try:
J_output, J_input = J.shape
except ValueError as err:
exc_type, value, traceback = sys.exc_info()
msg = "Jacobian has the wrong dimensions. Expected 2D but got {}D."
msg = msg.format(J.ndim)
raise ValueError, ValueError(msg), traceback
if num_output != J_output or num_input != J_input:
msg = 'Jacobian is the wrong size. Expected ' + \
'(%dx%d) but got (%dx%d)' % (num_output, num_input,
J_output, J_input)
if ISystem.providedBy(obj):
raise RuntimeError(msg)
else:
obj.raise_exception(msg, RuntimeError)
return ibounds, obounds
def applyJ(system, variables):
"""Multiply an input vector by the Jacobian. For an Explicit Component,
this automatically forms the "fake" residual, and calls into the
function hook "apply_deriv".
"""
J = system.J
obj = system.inner()
scope = system.scope
is_sys = ISystem.providedBy(obj)
arg = {}
for item in system.list_states():
collapsed = scope.name2collapsed.get(item)
if collapsed not in variables:
continue
key = item
if not is_sys:
key = item.partition('.')[-1]
parent = system
while True:
if item in parent.vec['du']:
arg[key] = parent.vec['du'][item]
break
parent = parent._parent_system
for item in system.list_inputs():
collapsed = scope.name2collapsed.get(item)
if collapsed not in variables:
continue
key = item
if not is_sys:
key = item.partition('.')[-1]
parent = system
while True:
parent = parent._parent_system
if item in parent.vec['dp']:
arg[key] = parent.vec['dp'][item]
break
result = {}
for item in system.list_outputs():
collapsed = scope.name2collapsed.get(item)
if collapsed not in variables:
continue
key = item
if not is_sys:
key = item.partition('.')[-1]
result[key] = system.rhs_vec[item]
for item in system.list_residuals():
key = item
if not is_sys:
key = item.partition('.')[-1]
result[key] = system.rhs_vec[item]
# Bail if this component is not connected in the graph
if len(arg) == 0 or len(result) == 0:
return
# Speedhack, don't call component's derivatives if incoming vector is zero.
nonzero = False
for key, value in arg.iteritems():
if any(value != 0):
nonzero = True
break
if nonzero is False:
#print 'applyJ', obj.name, arg, result
return
# If storage of the local Jacobian is a problem, the user can specify the
# 'apply_deriv' function instead of provideJ.
if J is None and hasattr(obj, 'apply_deriv'):
# TODO - We shouldn't need to calculate the size of the full arrays,
# so the cache shouldn't be needed. Cache is None for now.
shape_cache = {}
# The apply_deriv function expects the argument and result dicts for
# each input and output to have the same shape as the input/output.
resultkeys = sorted(result.keys())
for key in resultkeys:
pre_process_dicts(obj, key, result, shape_cache, scope, is_sys)
argkeys = arg.keys()
for key in sorted(argkeys):
pre_process_dicts(obj, key, arg, shape_cache, scope, is_sys)
obj.apply_deriv(arg, result)
# Result vector needs to be flattened.
for key in reversed(resultkeys):
post_process_dicts(key, result)
# Arg is still called afterwards, so flatten it back.
for key in argkeys:
value = arg[key]
if hasattr(value, 'flatten'):
arg[key] = value.flatten()
#print 'applyJ', obj.name, arg, result
return
if is_sys:
input_keys = system.list_inputs() + system.list_states()
output_keys = system.list_outputs() + system.list_residuals()
elif IAssembly.providedBy(obj):
input_keys = [item.partition('.')[-1] \
for item in system.list_inputs()]
output_keys = [item.partition('.')[-1] \
for item in system.list_outputs()]
else:
input_keys, output_keys = list_deriv_vars(obj)
#print 'J', input_keys, output_keys, J
# The Jacobian from provideJ is a 2D array containing the derivatives of
# the flattened output_keys with respect to the flattened input keys. We
# need to find the start and end index of each input and output.
if obj._provideJ_bounds is None:
obj._provideJ_bounds = get_bounds(obj, input_keys, output_keys, J)
ibounds, obounds = obj._provideJ_bounds
for okey in result:
odx = None
if okey in obounds:
o1, o2, osh = obounds[okey]
else:
basekey, _, odx = okey.partition('[')
try:
o1, o2, osh = obounds[basekey]
except KeyError:
if obj.missing_deriv_policy == 'error':
msg = "does not provide analytical derivatives" + \
" for %s" % okey
obj.raise_exception(msg, KeyError)
continue
tmp = result[okey]
used = set()
for ikey in arg:
idx = None
if ikey in ibounds:
i1, i2, ish = ibounds[ikey]
if (i1, i2) in used:
continue
used.add((i1, i2))
else:
basekey, _, idx = ikey.partition('[')
try:
i1, i2, ish = ibounds[basekey]
except KeyError:
if obj.missing_deriv_policy == 'error':
msg = "does not provide analytical derivatives" + \
" for %s" % ikey
obj.raise_exception(msg, KeyError)
continue
if (i1, i2, idx) in used or (i1, i2) in used:
continue
used.add((i1, i2, idx))
Jsub = reduce_jacobian(J, i1, i2, idx, ish, o1, o2, odx, osh)
#print ikey, okey, Jsub
tmp += Jsub.dot(arg[ikey])
def applyJ(system, variables):
"""Multiply an input vector by the Jacobian. For an Explicit Component,
this automatically forms the "fake" residual, and calls into the
function hook "apply_deriv".
"""
J = system.J
obj = system.inner()
scope = system.scope
is_sys = ISystem.providedBy(obj)
arg = {}
for item in system.list_states():
collapsed = scope.name2collapsed.get(item)
if collapsed not in variables:
continue
key = item
if not is_sys:
key = item.partition('.')[-1]
parent = system
while True:
if item in parent.vec['du']:
arg[key] = parent.vec['du'][item]
break
parent = parent._parent_system
for item in system.list_inputs():
collapsed = scope.name2collapsed.get(item)
if collapsed not in variables:
continue
key = item
if not is_sys:
key = item.partition('.')[-1]
parent = system
while True:
parent = parent._parent_system
if item in parent.vec['dp']:
arg[key] = parent.vec['dp'][item]
break
result = {}
for item in system.list_outputs():
collapsed = scope.name2collapsed.get(item)
if collapsed not in variables:
continue
key = item
if not is_sys:
key = item.partition('.')[-1]
result[key] = system.rhs_vec[item]
for item in system.list_residuals():
key = item
if not is_sys:
key = item.partition('.')[-1]
result[key] = system.rhs_vec[item]
# Bail if this component is not connected in the graph
if len(arg) == 0 or len(result) == 0:
return
# Speedhack, don't call component's derivatives if incoming vector is zero.
nonzero = False
for key, value in arg.iteritems():
if any(value != 0):
nonzero = True
break
if nonzero is False:
#print 'applyJ', obj.name, arg, result
return
# If storage of the local Jacobian is a problem, the user can specify the
# 'apply_deriv' function instead of provideJ.
if J is None and hasattr(obj, 'apply_deriv'):
# TODO - We shouldn't need to calculate the size of the full arrays,
# so the cache shouldn't be needed. Cache is None for now.
shape_cache = {}
# The apply_deriv function expects the argument and result dicts for
# each input and output to have the same shape as the input/output.
resultkeys = sorted(result.keys())
for key in resultkeys:
pre_process_dicts(obj, key, result, shape_cache, scope, is_sys)
argkeys = arg.keys()
for key in sorted(argkeys):
pre_process_dicts(obj, key, arg, shape_cache, scope, is_sys)
obj.apply_deriv(arg, result)
# Result vector needs to be flattened.
for key in reversed(resultkeys):
post_process_dicts(key, result)
# Arg is still called afterwards, so flatten it back.
for key in argkeys:
value = arg[key]
if hasattr(value, 'flatten'):
arg[key] = value.flatten()
#print 'applyJ', obj.name, arg, result
return
if is_sys:
input_keys = system.list_inputs() + system.list_states()
output_keys = system.list_outputs() + system.list_residuals()
elif IAssembly.providedBy(obj):
input_keys = [item.partition('.')[-1] \
for item in system.list_inputs()]
output_keys = [item.partition('.')[-1] \
for item in system.list_outputs()]
else:
input_keys, output_keys = list_deriv_vars(obj)
#print 'J', input_keys, output_keys, J
# The Jacobian from provideJ is a 2D array containing the derivatives of
# the flattened output_keys with respect to the flattened input keys. We
# need to find the start and end index of each input and output.
if obj._provideJ_bounds is None:
obj._provideJ_bounds = get_bounds(obj, input_keys, output_keys, J)
ibounds, obounds = obj._provideJ_bounds
for okey in result:
odx = None
if okey in obounds:
o1, o2, osh = obounds[okey]
else:
basekey, _, odx = okey.partition('[')
try:
o1, o2, osh = obounds[basekey]
except KeyError:
if obj.missing_deriv_policy == 'error':
msg = "does not provide analytical derivatives" + \
" for %s" % okey
obj.raise_exception(msg, KeyError)
continue
tmp = result[okey]
used = set()
for ikey in arg:
idx = None
if ikey in ibounds:
i1, i2, ish = ibounds[ikey]
if (i1, i2) in used:
continue
used.add((i1, i2))
else:
basekey, _, idx = ikey.partition('[')
try:
i1, i2, ish = ibounds[basekey]
except KeyError:
if obj.missing_deriv_policy == 'error':
msg = "does not provide analytical derivatives" + \
" for %s" % ikey
obj.raise_exception(msg, KeyError)
continue
if (i1, i2, idx) in used or (i1, i2) in used:
continue
used.add((i1, i2, idx))
Jsub = reduce_jacobian(J, i1, i2, idx, ish,
o1, o2, odx, osh)
#print ikey, okey, Jsub
tmp += Jsub.dot(arg[ikey])
