driver.py¶
Base class for Driver.
-
class
openmdao.core.driver.
Driver
[source]¶ Bases:
object
Base class for drivers in OpenMDAO. Drivers can only be placed in a Problem, and every problem has a Driver. Driver is the simplest driver that runs (solves using solve_nonlinear) a problem once.
-
add_constraint
(name, lower=None, upper=None, equals=None, linear=False, jacs=None, indices=None, adder=0.0, scaler=1.0)[source]¶ Adds a constraint to this driver. For inequality constraints, lower or upper must be specified. For equality constraints, equals must be specified.
Args: name : string
Promoted pathname of the output that will serve as the quantity to constrain.
lower : float or ndarray, optional
Constrain the quantity to be greater than or equal to this value.
upper : float or ndarray, optional
Constrain the quantity to be less than or equal to this value.
equals : float or ndarray, optional
Constrain the quantity to be equal to this value.
linear : bool, optional
Set to True if this constraint is linear with respect to all design variables so that it can be calculated once and cached.
jacs : dict of functions, optional
Dictionary of user-defined functions that return the flattened Jacobian of this constraint with repsect to the design vars of this driver, as indicated by the dictionary keys. Default is None to let OpenMDAO calculate all derivatives. Note, this is currently unsupported
indices : iter of int, optional
If a constraint is an array, these indicate which entries are of interest for derivatives.
adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder is first in precedence.
scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler is second in precedence.
-
add_desvar
(name, lower=None, upper=None, low=None, high=None, indices=None, adder=0.0, scaler=1.0)[source]¶ Adds a design variable to this driver.
Args: name : string
Name of the design variable in the root system.
lower : float or ndarray, optional
Lower boundary for the param
upper : upper or ndarray, optional
Upper boundary for the param
indices : iter of int, optional
If a param is an array, these indicate which entries are of interest for derivatives.
adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder is first in precedence.
scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler is second in precedence.
-
add_objective
(name, indices=None, adder=0.0, scaler=1.0)[source]¶ Adds an objective to this driver.
Args: name : string
Promoted pathname of the output that will serve as the objective.
indices : iter of int, optional
If an objective is an array, these indicate which entries are of interest for derivatives.
adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder is first in precedence.
scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler is second in precedence.
-
add_param
(name, lower=None, upper=None, indices=None, adder=0.0, scaler=1.0)[source]¶ Deprecated. Use
add_desvar
instead.
-
add_recorder
(recorder)[source]¶ Adds a recorder to the driver.
Args: recorder : BaseRecorder
A recorder instance.
-
calc_gradient
(indep_list, unknown_list, mode='auto', return_format='array', sparsity=None)[source]¶ Returns the scaled gradient for the system that is contained in self.root, scaled by all scalers that were specified when the desvars and constraints were added.
Args: indep_list : list of strings
List of independent variable names that derivatives are to be calculated with respect to. All params must have a IndepVarComp.
unknown_list : list of strings
List of output or state names that derivatives are to be calculated for. All must be valid unknowns in OpenMDAO.
mode : string, optional
Deriviative direction, can be ‘fwd’, ‘rev’, ‘fd’, or ‘auto’. Default is ‘auto’, which uses mode specified on the linear solver in root.
return_format : string, optional
Format for the derivatives, can be ‘array’ or ‘dict’.
sparsity : dict, optional
Dictionary that gives the relevant design variables for each constraint. This option is only supported in the dict return format.
Returns: ndarray or dict
Jacobian of unknowns with respect to params.
-
desvars_of_interest
()[source]¶ Returns: list of tuples of str
The list of design vars, organized into tuples according to previously defined VOI groups.
-
generate_docstring
()[source]¶ Generates a numpy-style docstring for a user-created Driver class.
Returns: docstring : str
string that contains a basic numpy docstring.
-
get_constraint_metadata
()[source]¶ Returns a dict of constraint metadata.
Returns: dict
Keys are the constraint object names, and the values are the param values.
-
get_constraints
(ctype='all', lintype='all')[source]¶ Gets all constraints for this driver.
Args: ctype : string
Default is ‘all’. Optionally return just the inequality constraints with ‘ineq’ or the equality constraints with ‘eq’.
lintype : string
Default is ‘all’. Optionally return just the linear constraints with ‘linear’ or the nonlinear constraints with ‘nonlinear’.
Returns: dict
Key is the constraint name string, value is an ndarray with the values.
-
get_desvar_metadata
()[source]¶ Returns a dict of design variable metadata.
Returns: dict
Keys are the param object names, and the values are the param values.
-
get_desvars
()[source]¶ Returns a dict of possibly distributed design variables.
Returns: dict
Keys are the param object names, and the values are the param values.
-
get_objectives
(return_type='dict')[source]¶ Gets all objectives of this driver.
Args: return_type : string
Set to ‘dict’ to return a dictionary, or set to ‘array’ to return a flat ndarray.
Returns: dict (for return_type ‘dict’)
Key is the objective name string, value is an ndarray with the values.
ndarray (for return_type ‘array’)
Array containing all objective values in the order they were added.
-
get_req_procs
()[source]¶ Returns: tuple
A tuple of the form (min_procs, max_procs), indicating the min and max processors usable by this Driver.
-
outputs_of_interest
()[source]¶ Returns: list of tuples of str
The list of constraints and objectives, organized into tuples according to previously defined VOI groups.
-
parallel_derivs
(vnames)[source]¶ Specifies that the named variables of interest are to be grouped together so that their derivatives can be solved for concurrently.
Args: vnames : iter of str
The names of variables of interest that are to be grouped.
-
run
(problem)[source]¶ Runs the driver. This function should be overridden when inheriting.
Args: problem : Problem
Our parent Problem.
-