def dynamic_function_from_file(obj, filename):
"""
Load a dynamic function from a file and attach it to the obj. (A Network
or Trajectory.)
The filename must be <function_name>.py
"""
f = file(filename, 'r')
function_body = f.read()
f.close()
basename = os.path.basename(filename)
func = os.path.splitext(basename)[0]
file_type = os.path.splitext(basename)[1][1:]
if isinstance(obj, Trajectory_mod.Trajectory):
setattr(obj, '%s_functionBody' % func, function_body)
# We try to get the attribute 'namespace' for the object.
Network_mod._exec_dynamic_func(obj, func,
getattr(obj, 'namespace', {}))
elif isinstance(obj, Network_mod.Network):
if file_type == 'py':
obj._dynamic_funcs_python[func] = function_body
obj.exec_dynamic_functions(disable_c=True)
elif file_type == 'c':
obj.exec_dynamic_functions(curr_c_code=function_body)
def dynamic_function_from_file(obj, filename):
"""
Load a dynamic function from a file and attach it to the obj. (A Network
or Trajectory.)
The filename must be <function_name>.py
"""
f = file(filename, 'r')
function_body = f.read()
f.close()
basename = os.path.basename(filename)
func = os.path.splitext(basename)[0]
file_type = os.path.splitext(basename)[1][1:]
if isinstance(obj, Trajectory_mod.Trajectory):
setattr(obj, '%s_functionBody' % func, function_body)
# We try to get the attribute 'namespace' for the object.
Network_mod._exec_dynamic_func(obj, func, getattr(obj, 'namespace', {}))
elif isinstance(obj, Network_mod.Network):
if file_type == 'py':
obj._dynamic_funcs_python[func] = function_body
obj.exec_dynamic_functions(disable_c = True)
elif file_type == 'c':
obj.exec_dynamic_functions(curr_c_code = function_body)
def appendSensFromODEINT(self, timepoints, odeint_array, holds_dt=False):
if getattr(self, '_assignment', None) is None:
Network_mod._exec_dynamic_func(self,
'_assignment',
self.namespace,
bind=False)
if getattr(self, '_sens_assignment', None) is None:
Network_mod._exec_dynamic_func(self,
'_sens_assignment',
self.namespace,
bind=False)
numAdded = odeint_array.shape[0]
addedValues = scipy.zeros((numAdded, len(self.key_column)),
scipy.float_)
self.values = scipy.concatenate((self.values, addedValues))
self.timepoints = scipy.concatenate((self.timepoints, timepoints))
nDv = len(self.dynamicVarKeys)
nOv = len(self.optimizableVarKeys)
# fill in trajectory
for ii, dvId in enumerate(self.dynamicVarKeys):
self.values[-numAdded:, self.key_column.get(dvId)] = \
odeint_array[:, ii]
# ... and sensitivities
for ii, dvId in enumerate(self.dynamicVarKeys):
for jj, ovId in enumerate(self.optimizableVarKeys):
self.values[-numAdded:,
self.key_column.get((dvId, ovId))]\
= odeint_array[:, ii + (jj+1)*nDv]
self._assignment(self.values, self.timepoints, -numAdded, None)
self._sens_assignment(self.values, self.timepoints, -numAdded, None)
if holds_dt:
# fill in the time derivative of the trajectory
for ii, dvId in enumerate(self.dynamicVarKeys):
self.values[-numAdded:, self.key_column.get((dvId,'time'))] = \
odeint_array[:, ii + nDv*(nOv+1)]
# ... and of the sensitivities
for ii, dvId in enumerate(self.dynamicVarKeys):
for jj, ovId in enumerate(self.optimizableVarKeys):
self.values[-numAdded:,
self.key_column.get((dvId, ovId,'time'))]\
= odeint_array[:, ii + (jj+1)*nDv + nDv*(nOv+1)]
def appendSensFromODEINT(self, timepoints, odeint_array, holds_dt = False):
if getattr(self, '_assignment', None) is None:
Network_mod._exec_dynamic_func(self, '_assignment',
self.namespace, bind=False)
if getattr(self, '_sens_assignment', None) is None:
Network_mod._exec_dynamic_func(self, '_sens_assignment',
self.namespace, bind=False)
numAdded = odeint_array.shape[0]
addedValues = scipy.zeros((numAdded, len(self.key_column)),
scipy.float_)
self.values = scipy.concatenate((self.values, addedValues))
self.timepoints = scipy.concatenate((self.timepoints, timepoints))
nDv = len(self.dynamicVarKeys)
nOv = len(self.optimizableVarKeys)
# fill in trajectory
for ii, dvId in enumerate(self.dynamicVarKeys):
self.values[-numAdded:, self.key_column.get(dvId)] = \
odeint_array[:, ii]
# ... and sensitivities
for ii, dvId in enumerate(self.dynamicVarKeys):
for jj, ovId in enumerate(self.optimizableVarKeys):
self.values[-numAdded:,
self.key_column.get((dvId, ovId))]\
= odeint_array[:, ii + (jj+1)*nDv]
self._assignment(self.values, self.timepoints, -numAdded, None)
self._sens_assignment(self.values, self.timepoints, -numAdded, None)
if holds_dt :
# fill in the time derivative of the trajectory
for ii, dvId in enumerate(self.dynamicVarKeys):
self.values[-numAdded:, self.key_column.get((dvId,'time'))] = \
odeint_array[:, ii + nDv*(nOv+1)]
# ... and of the sensitivities
for ii, dvId in enumerate(self.dynamicVarKeys):
for jj, ovId in enumerate(self.optimizableVarKeys):
self.values[-numAdded:,
self.key_column.get((dvId, ovId,'time'))]\
= odeint_array[:, ii + (jj+1)*nDv + nDv*(nOv+1)]
def appendFromODEINT(self, timepoints, odeint_array, holds_dt = False):
if getattr(self, '_assignment', None) is None:
Network_mod._exec_dynamic_func(self, '_assignment',
self.namespace, bind=False)
numAdded = odeint_array.shape[0]
addedValues = scipy.zeros((numAdded, len(self.key_column)),
scipy.float_)
self.values = scipy.concatenate((self.values, addedValues))
self.timepoints = scipy.concatenate((self.timepoints, timepoints))
for ii, id in enumerate(self.dynamicVarKeys):
self.values[-numAdded:, self.key_column.get(id)] =\
odeint_array[:, ii]
self._assignment(self.values, self.timepoints, -numAdded, None)
if holds_dt :
for ii, id in enumerate(self.dynamicVarKeys) :
self.values[-numAdded:, self.key_column.get((id,'time'))] = \
odeint_array[:,ii+len(self.dynamicVarKeys)]
