meta_model_unstructured_comp.py¶
MetaModel provides basic meta modeling capability.

class
openmdao.components.meta_model_unstructured_comp.
MetaModelUnStructuredComp
(**kwargs)[source]¶ Bases:
openmdao.core.explicitcomponent.ExplicitComponent
Class that creates a reduced order model for outputs from inputs.
Each output may have its own surrogate model. Training inputs and outputs are automatically created with ‘train:’ prepended to the corresponding input/output name.
For a Float variable, the training data is an array of length m, where m is the number of training points.
Attributes
train
(bool) If True, training will occur on the next execution.

__init__
(**kwargs)[source]¶ Initialize all attributes.
 Parameters
 **kwargsdict of keyword arguments
Keyword arguments that will be mapped into the Component options.

add_constraint
(name, lower=None, upper=None, equals=None, ref=None, ref0=None, adder=None, scaler=None, units=None, indices=None, linear=False, parallel_deriv_color=None, vectorize_derivs=False, cache_linear_solution=False)¶ Add a constraint variable to this system.
 Parameters
 namestring
Name of the response variable in the system.
 lowerfloat or ndarray, optional
Lower boundary for the variable
 upperfloat or ndarray, optional
Upper boundary for the variable
 equalsfloat or ndarray, optional
Equality constraint value for the variable
 reffloat or ndarray, optional
Value of response variable that scales to 1.0 in the driver.
 ref0float or ndarray, optional
Value of response variable that scales to 0.0 in the driver.
 adderfloat or ndarray, optional
Value to add to the model value to get the scaled value for the driver. adder is first in precedence. adder and scaler are an alterantive to using ref and ref0.
 scalerfloat or ndarray, optional
value to multiply the model value to get the scaled value for the driver. scaler is second in precedence. adder and scaler are an alterantive to using ref and ref0.
 unitsstr, optional
Units to convert to before applying scaling.
 indicessequence of int, optional
If variable is an array, these indicate which entries are of interest for this particular response. These may be positive or negative integers.
 linearbool
Set to True if constraint is linear. Default is False.
 parallel_deriv_colorstring
If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
 vectorize_derivsbool
If True, vectorize derivative calculations.
 cache_linear_solutionbool
If True, store the linear solution vectors for this variable so they can be used to start the next linear solution with an initial guess equal to the solution from the previous linear solve.
Notes
The response can be scaled using ref and ref0. The argument
ref0
represents the physical value when the scaled value is 0. The argumentref
represents the physical value when the scaled value is 1. The arguments (lower
,upper
,equals
) can not be strings or variable names.

add_design_var
(name, lower=None, upper=None, ref=None, ref0=None, indices=None, adder=None, scaler=None, units=None, parallel_deriv_color=None, vectorize_derivs=False, cache_linear_solution=False)¶ Add a design variable to this system.
 Parameters
 namestring
Name of the design variable in the system.
 lowerfloat or ndarray, optional
Lower boundary for the param
 upperupper or ndarray, optional
Upper boundary for the param
 reffloat or ndarray, optional
Value of design var that scales to 1.0 in the driver.
 ref0float or ndarray, optional
Value of design var that scales to 0.0 in the driver.
 indicesiter of int, optional
If a param is an array, these indicate which entries are of interest for this particular design variable. These may be positive or negative integers.
 unitsstr, optional
Units to convert to before applying scaling.
 adderfloat or ndarray, optional
Value to add to the model value to get the scaled value for the driver. adder is first in precedence. adder and scaler are an alterantive to using ref and ref0.
 scalerfloat or ndarray, optional
value to multiply the model value to get the scaled value for the driver. scaler is second in precedence. adder and scaler are an alterantive to using ref and ref0.
 parallel_deriv_colorstring
If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
 vectorize_derivsbool
If True, vectorize derivative calculations.
 cache_linear_solutionbool
If True, store the linear solution vectors for this variable so they can be used to start the next linear solution with an initial guess equal to the solution from the previous linear solve.
Notes
The response can be scaled using ref and ref0. The argument
ref0
represents the physical value when the scaled value is 0. The argumentref
represents the physical value when the scaled value is 1.

add_discrete_input
(name, val, desc='', tags=None)¶ Add a discrete input variable to the component.
 Parameters
 namestr
name of the variable in this component’s namespace.
 vala picklable object
The initial value of the variable being added.
 descstr
description of the variable
 tagsstr or list of strs
User defined tags that can be used to filter what gets listed when calling list_inputs and list_outputs.
 Returns
 dict
metadata for added variable

add_discrete_output
(name, val, desc='', tags=None)¶ Add an output variable to the component.
 Parameters
 namestr
name of the variable in this component’s namespace.
 vala picklable object
The initial value of the variable being added.
 descstr
description of the variable.
 tagsstr or list of strs or set of strs
User defined tags that can be used to filter what gets listed when calling list_inputs and list_outputs.
 Returns
 dict
metadata for added variable

add_input
(name, val=1.0, training_data=None, **kwargs)[source]¶ Add an input to this component and a corresponding training input.
 Parameters
 namestring
Name of the input.
 valfloat or ndarray
Initial value for the input.
 training_datafloat or ndarray
training data for this variable. Optional, can be set by the problem later.
 **kwargsdict
Additional agruments for add_input.
 Returns
 dict
metadata for added variable

add_objective
(name, ref=None, ref0=None, index=None, units=None, adder=None, scaler=None, parallel_deriv_color=None, vectorize_derivs=False, cache_linear_solution=False)¶ Add a response variable to this system.
 Parameters
 namestring
Name of the response variable in the system.
 reffloat or ndarray, optional
Value of response variable that scales to 1.0 in the driver.
 ref0float or ndarray, optional
Value of response variable that scales to 0.0 in the driver.
 indexint, optional
If variable is an array, this indicates which entry is of interest for this particular response. This may be a positive or negative integer.
 unitsstr, optional
Units to convert to before applying scaling.
 adderfloat or ndarray, optional
Value to add to the model value to get the scaled value for the driver. adder is first in precedence. adder and scaler are an alterantive to using ref and ref0.
 scalerfloat or ndarray, optional
value to multiply the model value to get the scaled value for the driver. scaler is second in precedence. adder and scaler are an alterantive to using ref and ref0.
 parallel_deriv_colorstring
If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
 vectorize_derivsbool
If True, vectorize derivative calculations.
 cache_linear_solutionbool
If True, store the linear solution vectors for this variable so they can be used to start the next linear solution with an initial guess equal to the solution from the previous linear solve.
Notes
The objective can be scaled using scaler and adder, where
\[x_{scaled} = scaler(x + adder)\]or through the use of ref/ref0, which map to scaler and adder through the equations:
\[ \begin{align}\begin{aligned}0 = scaler(ref_0 + adder)\\1 = scaler(ref + adder)\end{aligned}\end{align} \]which results in:
\[ \begin{align}\begin{aligned}adder = ref_0\\scaler = \frac{1}{ref + adder}\end{aligned}\end{align} \]

add_output
(name, val=1.0, training_data=None, surrogate=None, **kwargs)[source]¶ Add an output to this component and a corresponding training output.
 Parameters
 namestring
Name of the variable output.
 valfloat or ndarray
Initial value for the output. While the value is overwritten during execution, it is useful for inferring size.
 training_datafloat or ndarray
Training data for this variable. Optional, can be set by the problem later.
 surrogate<SurrogateModel>, optional
Surrogate model to use for this output; if None, use default surrogate.
 **kwargsdict
Additional arguments for add_output.
 Returns
 dict
metadata for added variable

add_recorder
(recorder, recurse=False)¶ Add a recorder to the system.
 Parameters
 recorder<CaseRecorder>
A recorder instance.
 recurseboolean
Flag indicating if the recorder should be added to all the subsystems.

add_response
(name, type_, lower=None, upper=None, equals=None, ref=None, ref0=None, indices=None, index=None, units=None, adder=None, scaler=None, linear=False, parallel_deriv_color=None, vectorize_derivs=False, cache_linear_solution=False)¶ Add a response variable to this system.
The response can be scaled using ref and ref0. The argument
ref0
represents the physical value when the scaled value is 0. The argumentref
represents the physical value when the scaled value is 1. Parameters
 namestring
Name of the response variable in the system.
 type_string
The type of response. Supported values are ‘con’ and ‘obj’
 lowerfloat or ndarray, optional
Lower boundary for the variable
 upperupper or ndarray, optional
Upper boundary for the variable
 equalsequals or ndarray, optional
Equality constraint value for the variable
 reffloat or ndarray, optional
Value of response variable that scales to 1.0 in the driver.
 ref0upper or ndarray, optional
Value of response variable that scales to 0.0 in the driver.
 indicessequence of int, optional
If variable is an array, these indicate which entries are of interest for this particular response.
 indexint, optional
If variable is an array, this indicates which entry is of interest for this particular response.
 unitsstr, optional
Units to convert to before applying scaling.
 adderfloat or ndarray, optional
Value to add to the model value to get the scaled value for the driver. adder is first in precedence. adder and scaler are an alterantive to using ref and ref0.
 scalerfloat or ndarray, optional
value to multiply the model value to get the scaled value for the driver. scaler is second in precedence. adder and scaler are an alterantive to using ref and ref0.
 linearbool
Set to True if constraint is linear. Default is False.
 parallel_deriv_colorstring
If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
 vectorize_derivsbool
If True, vectorize derivative calculations.
 cache_linear_solutionbool
If True, store the linear solution vectors for this variable so they can be used to start the next linear solution with an initial guess equal to the solution from the previous linear solve.

check_config
(logger)[source]¶ Perform optional error checks.
 Parameters
 loggerobject
The object that manages logging output.

cleanup
()¶ Clean up resources prior to exit.

compute
(inputs, outputs)[source]¶ Predict outputs.
If the training flag is set, train the metamodel first.
 Parameters
 inputsVector
unscaled, dimensional input variables read via inputs[key]
 outputsVector
unscaled, dimensional output variables read via outputs[key]

compute_jacvec_product
(inputs, d_inputs, d_outputs, mode, discrete_inputs=None)¶ Compute jacvector product. The model is assumed to be in an unscaled state.
 If mode is:
‘fwd’: d_inputs > d_outputs
‘rev’: d_outputs > d_inputs
 Parameters
 inputsVector
unscaled, dimensional input variables read via inputs[key]
 d_inputsVector
see inputs; product must be computed only if var_name in d_inputs
 d_outputsVector
see outputs; product must be computed only if var_name in d_outputs
 modestr
either ‘fwd’ or ‘rev’
 discrete_inputsdict or None
If not None, dict containing discrete input values.

compute_partials
(inputs, partials)[source]¶ Compute subjacobian parts. The model is assumed to be in an unscaled state.
 Parameters
 inputsVector
unscaled, dimensional input variables read via inputs[key]
 partialsJacobian
subjac components written to partials[output_name, input_name]

convert2units
(name, val, units)¶ Convert the given value to the specified units.
 Parameters
 namestr
Name of the variable.
 valfloat or ndarray of float
The value of the variable.
 unitsstr
The units to convert to.
 Returns
 float or ndarray of float
The value converted to the specified units.

declare_coloring
(wrt='*', method='fd', form=None, step=None, per_instance=True, num_full_jacs=3, tol=1e25, orders=None, perturb_size=1e09, min_improve_pct=5.0, show_summary=True, show_sparsity=False)¶ Set options for deriv coloring of a set of wrt vars matching the given pattern(s).
 Parameters
 wrtstr or list of str
The name or names of the variables that derivatives are taken with respect to. This can contain input names, output names, or glob patterns.
 methodstr
Method used to compute derivative: “fd” for finite difference, “cs” for complex step.
 formstr
Finite difference form, can be “forward”, “central”, or “backward”. Leave undeclared to keep unchanged from previous or default value.
 stepfloat
Step size for finite difference. Leave undeclared to keep unchanged from previous or default value.
 per_instancebool
If True, a separate coloring will be generated for each instance of a given class. Otherwise, only one coloring for a given class will be generated and all instances of that class will use it.
 num_full_jacsint
Number of times to repeat partial jacobian computation when computing sparsity.
 tolfloat
Tolerance used to determine if an array entry is nonzero during sparsity determination.
 ordersint
Number of orders above and below the tolerance to check during the tolerance sweep.
 perturb_sizefloat
Size of input/output perturbation during generation of sparsity.
 min_improve_pctfloat
If coloring does not improve (decrease) the number of solves more than the given percentage, coloring will not be used.
 show_summarybool
If True, display summary information after generating coloring.
 show_sparsitybool
If True, display sparsity with coloring info after generating coloring.

declare_partials
(of, wrt, dependent=True, rows=None, cols=None, val=None, method='exact', step=None, form=None, step_calc=None)[source]¶ Declare information about this component’s subjacobians.
 Parameters
 ofstr or list of str
The name of the residual(s) that derivatives are being computed for. May also contain a glob pattern.
 wrtstr or list of str
The name of the variables that derivatives are taken with respect to. This can contain the name of any input or output variable. May also contain a glob pattern.
 dependentbool(True)
If False, specifies no dependence between the output(s) and the input(s). This is only necessary in the case of a sparse global jacobian, because if ‘dependent=False’ is not specified and declare_partials is not called for a given pair, then a dense matrix of zeros will be allocated in the sparse global jacobian for that pair. In the case of a dense global jacobian it doesn’t matter because the space for a dense subjac will always be allocated for every pair.
 rowsndarray of int or None
Row indices for each nonzero entry. For sparse subjacobians only.
 colsndarray of int or None
Column indices for each nonzero entry. For sparse subjacobians only.
 valfloat or ndarray of float or scipy.sparse
Value of subjacobian. If rows and cols are not None, this will contain the values found at each (row, col) location in the subjac.
 methodstr
The type of approximation that should be used. Valid options include: ‘fd’: Finite Difference, ‘cs’: Complex Step, ‘exact’: use the component defined analytic derivatives. Default is ‘exact’.
 stepfloat
Step size for approximation. Defaults to None, in which case the approximation method provides its default value.
 formstring
Form for finite difference, can be ‘forward’, ‘backward’, or ‘central’. Defaults to None, in which case the approximation method provides its default value.
 step_calcstring
Step type for finite difference, can be ‘abs’ for absolute’, or ‘rel’ for relative. Defaults to None, in which case the approximation method provides its default value.

get_approx_coloring_fname
()¶ Return the full pathname to a coloring file.
 Parameters
 systemSystem
The System having its coloring saved or loaded.
 Returns
 str
Full pathname of the coloring file.

get_constraints
(recurse=True)¶ Get the Constraint settings from this system.
Retrieve the constraint settings for the current system as a dict, keyed by variable name.
 Parameters
 recursebool, optional
If True, recurse through the subsystems and return the path of all constraints relative to the this system.
 Returns
 dict
The constraints defined in the current system.

get_design_vars
(recurse=True, get_sizes=True)¶ Get the DesignVariable settings from this system.
Retrieve all design variable settings from the system and, if recurse is True, all of its subsystems.
 Parameters
 recursebool
If True, recurse through the subsystems and return the path of all design vars relative to the this system.
 get_sizesbool, optional
If True, compute the size of each design variable.
 Returns
 dict
The design variables defined in the current system and, if recurse=True, its subsystems.

get_linear_vectors
(vec_name='linear')¶ Return the linear inputs, outputs, and residuals vectors.
 Parameters
 vec_namestr
Name of the linear righthandside vector. The default is ‘linear’.
 Returns
 (inputs, outputs, residuals)tuple of <Vector> instances
Yields the inputs, outputs, and residuals linear vectors for vec_name.

get_nonlinear_vectors
()¶ Return the inputs, outputs, and residuals vectors.
 Returns
 (inputs, outputs, residuals)tuple of <Vector> instances
Yields the inputs, outputs, and residuals nonlinear vectors.

get_objectives
(recurse=True)¶ Get the Objective settings from this system.
Retrieve all objectives settings from the system as a dict, keyed by variable name.
 Parameters
 recursebool, optional
If True, recurse through the subsystems and return the path of all objective relative to the this system.
 Returns
 dict
The objectives defined in the current system.

get_responses
(recurse=True, get_sizes=True)¶ Get the response variable settings from this system.
Retrieve all response variable settings from the system as a dict, keyed by variable name.
 Parameters
 recursebool, optional
If True, recurse through the subsystems and return the path of all responses relative to the this system.
 get_sizesbool, optional
If True, compute the size of each response.
 Returns
 dict
The responses defined in the current system and, if recurse=True, its subsystems.

is_active
()¶ Determine if the system is active on this rank.
 Returns
 bool
If running under MPI, returns True if this System has a valid communicator. Always returns True if not running under MPI.

property
linear_solver
¶ Get the linear solver for this system.

list_inputs
(values=True, prom_name=False, units=False, shape=False, global_shape=False, desc=False, hierarchical=True, print_arrays=False, tags=None, includes=None, excludes=None, all_procs=False, out_stream=<object object>)¶ Return and optionally log a list of input names and other optional information.
If the model is parallel, only the local variables are returned to the process. Also optionally logs the information to a user defined output stream. If the model is parallel, the rank 0 process logs information about all variables across all processes.
 Parameters
 valuesbool, optional
When True, display/return input values. Default is True.
 prom_namebool, optional
When True, display/return the promoted name of the variable. Default is False.
 unitsbool, optional
When True, display/return units. Default is False.
 shapebool, optional
When True, display/return the shape of the value. Default is False.
 global_shapebool, optional
When True, display/return the global shape of the value. Default is False.
 descbool, optional
When True, display/return description. Default is False.
 hierarchicalbool, optional
When True, human readable output shows variables in hierarchical format.
 print_arraysbool, optional
When False, in the columnar display, just display norm of any ndarrays with size > 1. The norm is surrounded by vertical bars to indicate that it is a norm. When True, also display full values of the ndarray below the row. Format is affected by the values set with numpy.set_printoptions Default is False.
 tagsstr or list of strs
User defined tags that can be used to filter what gets listed. Only inputs with the given tags will be listed. Default is None, which means there will be no filtering based on tags.
 includesNone or list_like
List of glob patterns for pathnames to include in the check. Default is None, which includes all components in the model.
 excludesNone or list_like
List of glob patterns for pathnames to exclude from the check. Default is None, which excludes nothing.
 all_procsbool, optional
When True, display output on all processors. Default is False.
 out_streamfilelike object
Where to send human readable output. Default is sys.stdout. Set to None to suppress.
 Returns
 list
list of input names and other optional information about those inputs

list_outputs
(explicit=True, implicit=True, values=True, prom_name=False, residuals=False, residuals_tol=None, units=False, shape=False, global_shape=False, bounds=False, scaling=False, desc=False, hierarchical=True, print_arrays=False, tags=None, includes=None, excludes=None, all_procs=False, out_stream=<object object>)¶ Return and optionally log a list of output names and other optional information.
If the model is parallel, only the local variables are returned to the process. Also optionally logs the information to a user defined output stream. If the model is parallel, the rank 0 process logs information about all variables across all processes.
 Parameters
 explicitbool, optional
include outputs from explicit components. Default is True.
 implicitbool, optional
include outputs from implicit components. Default is True.
 valuesbool, optional
When True, display/return output values. Default is True.
 prom_namebool, optional
When True, display/return the promoted name of the variable. Default is False.
 residualsbool, optional
When True, display/return residual values. Default is False.
 residuals_tolfloat, optional
If set, limits the output of list_outputs to only variables where the norm of the resids array is greater than the given ‘residuals_tol’. Default is None.
 unitsbool, optional
When True, display/return units. Default is False.
 shapebool, optional
When True, display/return the shape of the value. Default is False.
 global_shapebool, optional
When True, display/return the global shape of the value. Default is False.
 boundsbool, optional
When True, display/return bounds (lower and upper). Default is False.
 scalingbool, optional
When True, display/return scaling (ref, ref0, and res_ref). Default is False.
 descbool, optional
When True, display/return description. Default is False.
 hierarchicalbool, optional
When True, human readable output shows variables in hierarchical format.
 print_arraysbool, optional
When False, in the columnar display, just display norm of any ndarrays with size > 1. The norm is surrounded by vertical bars to indicate that it is a norm. When True, also display full values of the ndarray below the row. Format is affected by the values set with numpy.set_printoptions Default is False.
 tagsstr or list of strs
User defined tags that can be used to filter what gets listed. Only outputs with the given tags will be listed. Default is None, which means there will be no filtering based on tags.
 includesNone or list_like
List of glob patterns for pathnames to include in the check. Default is None, which includes all components in the model.
 excludesNone or list_like
List of glob patterns for pathnames to exclude from the check. Default is None, which excludes nothing.
 all_procsbool, optional
When True, display output on all processors. Default is False.
 out_streamfilelike
Where to send human readable output. Default is sys.stdout. Set to None to suppress.
 Returns
 list
list of output names and other optional information about those outputs

property
msginfo
¶ Our instance pathname, if available, or our class name. For use in error messages.
 Returns
 str
Either our instance pathname or class name.

property
nonlinear_solver
¶ Get the nonlinear solver for this system.

record_iteration
()¶ Record an iteration of the current System.

run_apply_linear
(vec_names, mode, scope_out=None, scope_in=None)¶ Compute jacvec product.
This calls _apply_linear, but with the model assumed to be in an unscaled state.
 Parameters
 vec_names[str, …]
list of names of the righthandside vectors.
 modestr
‘fwd’ or ‘rev’.
 scope_outset or None
Set of absolute output names in the scope of this matvec product. If None, all are in the scope.
 scope_inset or None
Set of absolute input names in the scope of this matvec product. If None, all are in the scope.

run_apply_nonlinear
()¶ Compute residuals.
This calls _apply_nonlinear, but with the model assumed to be in an unscaled state.

run_linearize
(sub_do_ln=True)¶ Compute jacobian / factorization.
This calls _linearize, but with the model assumed to be in an unscaled state.
 Parameters
 sub_do_lnboolean
Flag indicating if the children should call linearize on their linear solvers.

run_solve_linear
(vec_names, mode)¶ Apply inverse jac product.
This calls _solve_linear, but with the model assumed to be in an unscaled state.
 Parameters
 vec_names[str, …]
list of names of the righthandside vectors.
 modestr
‘fwd’ or ‘rev’.

run_solve_nonlinear
()¶ Compute outputs.
This calls _solve_nonlinear, but with the model assumed to be in an unscaled state.

set_check_partial_options
(wrt, method='fd', form=None, step=None, step_calc=None, directional=False)¶ Set options that will be used for checking partial derivatives.
 Parameters
 wrtstr or list of str
The name or names of the variables that derivatives are taken with respect to. This can contain the name of any input or output variable. May also contain a glob pattern.
 methodstr
Method for check: “fd” for finite difference, “cs” for complex step.
 formstr
Finite difference form for check, can be “forward”, “central”, or “backward”. Leave undeclared to keep unchanged from previous or default value.
 stepfloat
Step size for finite difference check. Leave undeclared to keep unchanged from previous or default value.
 step_calcstr
Type of step calculation for check, can be “abs” for absolute (default) or “rel” for relative. Leave undeclared to keep unchanged from previous or default value.
 directionalbool
Set to True to perform a single directional derivative for each vector variable in the pattern named in wrt.

set_initial_values
()¶ Set all input and output variables to their declared initial values.

set_solver_print
(level=2, depth=1e+99, type_='all')¶ Control printing for solvers and subsolvers in the model.
 Parameters
 levelint
iprint level. Set to 2 to print residuals each iteration; set to 1 to print just the iteration totals; set to 0 to disable all printing except for failures, and set to 1 to disable all printing including failures.
 depthint
How deep to recurse. For example, you can set this to 0 if you only want to print the top level linear and nonlinear solver messages. Default prints everything.
 type_str
Type of solver to set: ‘LN’ for linear, ‘NL’ for nonlinear, or ‘all’ for all.

setup
()¶ Declare inputs and outputs.
 Available attributes:
name pathname comm options

system_iter
(include_self=False, recurse=True, typ=None)¶ Yield a generator of local subsystems of this system.
 Parameters
 include_selfbool
If True, include this system in the iteration.
 recursebool
If True, iterate over the whole tree under this system.
 typtype
If not None, only yield Systems that match that are instances of the given type.

use_fixed_coloring
(coloring=<object object>, recurse=True)¶ Use a precomputed coloring for this System.
 Parameters
 coloringstr
A coloring filename. If no arg is passed, filename will be determined automatically.
 recursebool
If True, set fixed coloring in all subsystems that declare a coloring. Ignored if a specific coloring is passed in.
