"""
pyStrut_problem
"""
# =============================================================================
# Imports
# =============================================================================
from ..utils import Error, SolverHistory
[docs]
class StructProblem:
"""
The main purpose of this class is to represent all relevant
information for a structural analysis. This will include
information defining the loading condition as well as various
other pieces of information.
Parameters
----------
name : str
Name of this structural problem
loadFile : str
Filename of the (static) external load file. Should be
generated from either ADflow or Tripan.
Examples
--------
>>> sp = StructProblem('lc0', loadFile='loads.txt')
"""
def __init__(self, name, loadFile=None, loadFactor=None, evalFuncs=None):
# Always have to have the name
self.name = name
self.loadFile = loadFile
# Set defaults for loadFactor and evalFuncs if not supplied
if loadFactor is None:
self.loadFactor = 1.0
else:
self.loadFactor = loadFactor
if evalFuncs is None:
self.evalFuncs = set()
else:
self.evalFuncs = set(evalFuncs)
# we cast the set to a sorted list, so that each proc can loop over in the same order
self.evalFuncs = sorted(self.evalFuncs)
# When a solver calls its evalFunctions() it must write the
# unique name it gives to funcNames.
self.funcNames = {}
self.possibleFunctions = set()
# Storage of DVs (non as of yet)
self.DVs = {}
self.DVNames = {}
# Solver History
self.history = SolverHistory()
[docs]
def addDV(self, key, value=None, lower=None, upper=None, scale=1.0, name=None):
"""
No design variable functions yet.
Parameters
----------
key : str
Name of variable to add. See above for possible ones
value : float. Default is None
Initial value for variable. If not given, current value
of the attribute will be used.
lower : float. Default is None
Optimization lower bound. Default is unbonded.
upper : float. Default is None
Optimization upper bound. Default is unbounded.
scale : float. Default is 1.0
Set scaling parameter for the optimization to use.
name : str. Default is None
Overwrite the default auto-generated name of this variable.
"""
# First check if we are allowed to add the DV:
if key not in self.possibleDVs:
raise Error(f"The DV '{key}' could not be added. The list of possible DVs are: {self.possibleDVs!r}.")
if name is None:
dvName = f"{key}_{self.name}"
else:
dvName = name
if value is None:
raise Error("Value must be given for keyword 'value'.")
self.DVs[dvName] = structDV(key, value, lower, upper, scale, offset) # noqa
self.DVNames[key] = dvName
[docs]
def setDesignVars(self, x):
"""
Set the variables in the x-dict for this object.
Parameters
----------
x : dict
Dictionary of variables which may or may not contain the
design variable names this object needs
"""
for key in self.DVNames:
dvName = self.DVNames[key]
if dvName in x:
setattr(self, key, x[dvName] + self.DVs[dvName].offset)
[docs]
def addVariablesPyOpt(self, optProb):
"""
Add the current set of variables to the optProb object.
Parameters
----------
optProb : pyOpt_optimization class
Optimization problem definition to which variables are added
"""
for key in self.DVs:
dv = self.DVs[key]
optProb.addVar(key, "c", value=dv.value, lower=dv.lower, upper=dv.upper, scale=dv.scale)
def __getitem__(self, key):
return self.funcNames[key]
[docs]
def evalFunctions(self, funcs, evalFuncs, ignoreMissing=False):
"""
No current functions
Parameters
----------
funcs : dict
Dictionary into which the functions are save
evalFuncs : iterable object containing strings
The functions that the user wants evaluated
"""
if set(evalFuncs) <= self.possibleFunctions:
# All the functions are ok:
for f in evalFuncs:
# Save the key into funcNames
key = f"{self.name}_{f}"
self.funcNames[f] = key
funcs[key] = getattr(self, f)
else:
if not ignoreMissing:
raise Error(
"One of the functions in 'evalFuncs' was not "
"valid. The valid list of functions is: %s." % (repr(self.possibleFunctions))
)
[docs]
def evalFunctionsSens(self, funcsSens, evalFuncs, ignoreMissing=False):
"""
Evaluate the sensitivity of the desired functions
Parameters
----------
funcsSens : dict
Dictionary into which the function sensitivities are saved
evalFuncs : iterable object containing strings
The functions that the user wants evaluated
"""
# Make sure all the functions have been evaluated.
tmp = {}
self.evalFunctions(tmp, evalFuncs, ignoreMissing)
# Check that all functions are ok:
if set(evalFuncs) <= self.possibleFunctions:
for f in evalFuncs:
funcsSens[self.funcNames[f]] = self._getDVSens(f)
else:
if not ignoreMissing:
raise Error(
"One of the functions in 'evalFunctionsSens' was "
"not valid. The valid list of functions is: %s." % (repr(self.possibleFunctions))
)
class structDV:
"""
A container storing information regarding an 'structral problem' variable.
"""
def __init__(self, key, value, lower, upper, scale, offset):
self.key = key
self.value = value
self.lower = lower
self.upper = upper
self.scale = scale
self.offset = offset