pg_wind_rotation.py

class openaerostruct.aerodynamics.pg_wind_rotation.RotateFromWindFrame(**kwargs)[source]

Bases: ExplicitComponent

Rotate the aerodynamic sectional and nodal force vectors from the wind to the standard aerodynamic frame. This is the reverse operation of the RotateToWindFrame component.

This transformation is given by the following rotation matrix:

F_x_aero |   | cosb*cosa, sinb*cosa, -sina |   | F_x_wind |
F_y_aero | = | -sinb,          cosb,     0 | . | F_y_wind |
F_z_aero |   | cosb*sina, sinb*sina,  cosa |   | F_z_wind |

Where “a” is the angle of attack and “b” is the sideslip angle.

Parameters:

sec_forces_w_frame[nx-1, ny-1, 3]numpy array: Force vectors on each panel (lattice) in wind frame.
alphafloat: Angle of attack in degrees.
betafloat: Sideslip angle in degrees.

Returns:

sec_forces[nx-1, ny-1, 3]numpy array: Force vectors on each panel (lattice) in aero frame.

Store some bound methods so we can detect runtime overrides.

abs_meta_iter(iotype, local=True, cont=True, discrete=False)

Iterate over absolute variable names and their metadata for this System.

By setting appropriate values for ‘cont’ and ‘discrete’, yielded variable names can be continuous only, discrete only, or both.

Parameters:

iotypestr: Either ‘input’ or ‘output’.
localbool: If True, include only names of local variables. Default is True.
contbool: If True, include names of continuous variables. Default is True.
discretebool: If True, include names of discrete variables. Default is False.

Yields:

str, dict

abs_name_iter(iotype, local=True, cont=True, discrete=False)

Iterate over absolute variable names for this System.

By setting appropriate values for ‘cont’ and ‘discrete’, yielded variable names can be continuous only, discrete only, or both.

Parameters:

iotypestr: Either ‘input’ or ‘output’.
localbool: If True, include only names of local variables. Default is True.
contbool: If True, include names of continuous variables. Default is True.
discretebool: If True, include names of discrete variables. Default is False.

Yields:

str

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.

best_partial_deriv_direction()

Return the best direction for partial deriv calculations based on input and output sizes.

Returns:

str: The best direction for derivative calculations, ‘fwd’ or ‘rev’.

check_subjac_sparsity(): Check the declared sparsity of the sub-jacobians vs. the computed sparsity.

property checking

Return True if check_partials or check_totals is executing.

Returns:

bool: True if we’re running within check_partials or check_totals.

property comm

Return the MPI communicator object for the system.

Returns:

MPI.Comm: MPI communicator object.

comm_info_iter()

Yield comm size for this system and all subsystems.

Yields:

tuple: A tuple of the form (abs_name, comm_size).

compute_sparsity()

Compute the sparsity of the partial jacobian.

Returns:

coo_matrix: The sparsity matrix.
dict: Metadata about the sparsity computation.

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.

convert_from_units(name, val, units)

Convert the given value from the specified units to those of the named variable.

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.

convert_units(name, val, units_from, units_to)

Wrap the utility convert_units and give a good error message.

Parameters:

namestr: Name of the variable.
valfloat or ndarray of float: The value of the variable.
units_fromstr: The units to convert from.
units_tostr: 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=1e-25, orders=None, perturb_size=1e-09, 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.

dist_size_iter(io, top_comm)

Yield names and distributed ranges of all local and remote variables in this system.

Parameters:

iostr: Either ‘input’ or ‘output’.
top_commMPI.Comm or None: The top-level MPI communicator.

Yields:

tuple: A tuple of the form ((abs_name, rank), start, end).

get_coloring_fname(mode)

Return the full pathname to a coloring file.

Parameters:

modestr: The type of coloring file desired. Must be either ‘input’ or ‘output’.

Returns:

pathlib.Path: Full pathname of the coloring file.

get_io_metadata(iotypes=('input', 'output'), metadata_keys=None, includes=None, excludes=None, is_indep_var=None, is_design_var=None, tags=None, get_remote=False, rank=None, return_rel_names=True)

Retrieve metadata for a filtered list of variables.

Parameters:

iotypesstr or iter of str: Will contain either ‘input’, ‘output’, or both. Defaults to both.
metadata_keysiter of str or None: Names of metadata entries to be retrieved or None, meaning retrieve all available ‘allprocs’ metadata. If ‘val’ or ‘src_indices’ are required, their keys must be provided explicitly since they are not found in the ‘allprocs’ metadata and must be retrieved from local metadata located in each process.
includesstr, iter of str or None: Collection of glob patterns for pathnames of variables to include. Default is None, which includes all variables.
excludesstr, iter of str or None: Collection of glob patterns for pathnames of variables to exclude. Default is None.
is_indep_varbool or None: If None (the default), do no additional filtering of the inputs. If True, list only inputs connected to an output tagged openmdao:indep_var. If False, list only inputs _not_ connected to outputs tagged openmdao:indep_var.
is_design_varbool or None: If None (the default), do no additional filtering of the inputs. If True, list only inputs connected to outputs that are driver design variables. If False, list only inputs _not_ connected to outputs that are driver design variables.
tagsstr or iter 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.
get_remotebool: If True, retrieve variables from other MPI processes as well.
rankint or None: If None, and get_remote is True, retrieve values from all MPI process to all other MPI processes. Otherwise, if get_remote is True, retrieve values from all MPI processes only to the specified rank.
return_rel_namesbool: If True, the names returned will be relative to the scope of this System. Otherwise they will be absolute names.

Returns:

dict: A dict of metadata keyed on name, where name is either absolute or relative based on the value of the return_rel_names arg, and metadata is a dict containing entries based on the value of the metadata_keys arg. Every metadata dict will always contain two entries, ‘prom_name’ and ‘discrete’, to indicate a given variable’s promoted name and whether or not it is discrete.

get_outputs_dir(*subdirs, mkdir=True)

Get the path under which all output files of this system are to be placed.

Parameters:

*subdirsstr: Subdirectories nested under the relevant problem output directory. To create {prob_output_dir}/a/b one would pass system.get_outputs_dir(‘a’, ‘b’).
mkdirbool: If True, attempt to create this directory if it does not exist.

Returns:

pathlib.Path: The path of the outputs directory for the problem.

get_promotions(inprom=None, outprom=None)

Return all promotions for the given promoted variable(s).

In other words, how and where did promotions occur to convert absolute variable names into the given promoted name(s) at the current System level.

Parameters:

inpromstr or None: The promoted input variable name.
outpromstr or None: The promoted output variable name.

Returns:

dict: Dictionary keyed on system pathname containing input and/or output promotion lists for each System where promotions occurred to produce the given promoted variable(s).

get_reports_dir()

Get the path to the directory where the report files should go.

If it doesn’t exist, it will be created.

Returns:

str: The path to the directory where reports should be written.

get_self_statics()

Override this in derived classes if compute_primal references static values.

Do NOT include self._discrete_inputs in the returned tuple. Include things like self.options[‘opt_name’], etc., that are used in compute_primal but are assumed to be constant during derivative computation.

Return value MUST be a tuple. Don’t forget the trailing comma if tuple has only one item. Return value MUST be hashable.

The order of these values doesn’t matter. They are only checked (by computing their hash) to see if they have changed since the last time compute_primal was jitted, and if so, compute_primal will be re-jitted.

Returns:

tuple: Tuple containing all static values required by compute_primal.

get_source(name)

Return the source variable connected to the given named variable.

The name can be a promoted name or an absolute name. If the given variable is an input, the absolute name of the connected source will be returned. If the given variable itself is a source, its own absolute name will be returned.

Parameters:

namestr: Absolute or promoted name of the variable.

Returns:

str: The absolute name of the source variable.

get_val(name, units=None, indices=None, get_remote=False, rank=None, vec_name='nonlinear', kind=None, flat=False, from_src=True)

Get an output/input/residual variable.

Function is used if you want to specify display units.

Parameters:

namestr: Promoted or relative variable name in the root system’s namespace.
unitsstr, optional: Units to convert to before return.
indicesint or list of ints or tuple of ints or int ndarray or Iterable or None, optional: Indices or slice to return.
get_remotebool or None: If True, retrieve the value even if it is on a remote process. Note that if the variable is remote on ANY process, this function must be called on EVERY process in the Problem’s MPI communicator. If False, only retrieve the value if it is on the current process, or only the part of the value that’s on the current process for a distributed variable. If None and the variable is remote or distributed, a RuntimeError will be raised.
rankint or None: If not None, only gather the value to this rank.
vec_namestr: Name of the vector to use. Defaults to ‘nonlinear’.
kindstr or None: Kind of variable (‘input’, ‘output’, or ‘residual’). If None, returned value will be either an input or output.
flatbool: If True, return the flattened version of the value.
from_srcbool: If True, retrieve value of an input variable from its connected source.

Returns:

object: The value of the requested output/input variable.

get_var_dup_info(name, io)

Return information about how the given variable is duplicated across MPI processes.

Parameters:

namestr: Name of the variable.
iostr: Either ‘input’ or ‘output’.

Returns:

tuple: A tuple of the form (is_duplicated, num_zeros, is_distributed).

get_var_sizes(name, io)

Return the sizes of the given variable on all procs.

Parameters:

namestr: Name of the variable.
iostr: Either ‘input’ or ‘output’.

Returns:

ndarray: Array of sizes of the variable on all procs.

is_explicit()

Return True if this is an explicit component.

Returns:

bool: True if this is an explicit component.

list_vars(val=True, prom_name=True, residuals=False, residuals_tol=None, units=False, shape=False, global_shape=False, bounds=False, scaling=False, desc=False, print_arrays=False, tags=None, print_tags=False, includes=None, excludes=None, is_indep_var=None, is_design_var=None, all_procs=False, list_autoivcs=False, out_stream=DEFAULT_OUT_STREAM, print_min=False, print_max=False, return_format='list')

Write a list of inputs and outputs sorted by component in execution order.

Parameters:

valbool, optional: When True, display output values. Default is True.
prom_namebool, optional: When True, display the promoted name of the variable. Default is True.
residualsbool, optional: When True, display 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 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.
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.
print_tagsbool: When true, display tags in the columnar display.
includesNone, str, or iter of str: Collection of glob patterns for pathnames of variables to include. Default is None, which includes all output variables.
excludesNone, str, or iter of str: Collection of glob patterns for pathnames of variables to exclude. Default is None.
is_indep_varbool or None: If None (the default), do no additional filtering of the inputs. If True, list only outputs tagged openmdao:indep_var. If False, list only outputs that are _not_ tagged openmdao:indep_var.
is_design_varbool or None: If None (the default), do no additional filtering of the inputs. If True, list only inputs connected to outputs that are driver design variables. If False, list only inputs _not_ connected to outputs that are driver design variables.
all_procsbool, optional: When True, display output on all processors. Default is False.
list_autoivcsbool: If True, include auto_ivc outputs in the listing. Defaults to False.
out_streamfile-like: Where to send human readable output. Default is sys.stdout. Set to None to suppress.
print_minbool: When true, if the output value is an array, print its smallest value.
print_maxbool: When true, if the output value is an array, print its largest value.
return_formatstr: Indicates the desired format of the return value. Can have value of ‘list’ or ‘dict’. If ‘list’, the return value is a list of (name, metadata) tuples. if ‘dict’, the return value is a dictionary mapping {name: metadata}.

Returns:

list of (name, metadata) or dict of {name: metadata}: List or dict of output names and other optional information about those outputs.

load_case(case)

Pull all input and output variables from a Case into this System.

Override this method if the System requires special handling when loading a case.

Parameters:

caseCase or dict: A Case from a CaseReader, or a dictionary with key ‘inputs’ mapped to the output of problem.model.list_inputs and key ‘outputs’ mapped to the output of prob.model.list_outputs. Both list_inputs and list_outputs should be called with prom_name=True and return_format=’dict’.

load_model_options()

Load the relevant model options from Problem._metadata[‘model_options’].

This method examines each path filter and corresponding options in self._problem_meta[‘model_options’]. If this System’s pathname matches the given path filter, it will assume the value for each given option which it possesses.

local_range_iter(io)

Yield names and local ranges of all local variables in this system.

Parameters:

iostr: Either ‘input’ or ‘output’.

Yields:

tuple: A tuple of the form (abs_name, start, end).

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.

set_constraint_options(name, ref=UNDEFINED, ref0=UNDEFINED, equals=UNDEFINED, lower=UNDEFINED, upper=UNDEFINED, adder=UNDEFINED, scaler=UNDEFINED, alias=UNDEFINED)

Set options for constraints in the model.

Can be used to set options that were set using add_constraint.

Parameters:

namestr: Name of the response variable in the system, or alias if given.
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.
equalsfloat or ndarray, optional: Equality constraint value for the variable.
lowerfloat or ndarray, optional: Lower boundary for the variable.
upperfloat or ndarray, optional: Upper boundary for the variable.
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.
aliasstr, optional: Alias for this response. Necessary when adding multiple constraints on different indices or slices of a single variable.

set_design_var_options(name, lower=UNDEFINED, upper=UNDEFINED, scaler=UNDEFINED, adder=UNDEFINED, ref=UNDEFINED, ref0=UNDEFINED)

Set options for design vars in the model.

Can be used to set the options outside of setting them when calling add_design_var

Parameters:

namestr: Name of the variable in this system’s namespace.
lowerfloat or ndarray, optional: Lower boundary for the input.
upperupper or ndarray, optional: Upper boundary for the input.
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.
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.
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.

set_objective_options(name, ref=UNDEFINED, ref0=UNDEFINED, adder=UNDEFINED, scaler=UNDEFINED, alias=UNDEFINED)

Set options for objectives in the model.

Can be used to set options after they have been set by add_objective.

Parameters:

namestr: Name of the response variable in the system, or alias if given.
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.
aliasstr: Alias for this response. Used to disambiguate variable names when adding multiple objectives on different indices or slices of a single variable. Deprecated.

set_output_solver_options(name, lower=UNDEFINED, upper=UNDEFINED, ref=UNDEFINED, ref0=UNDEFINED, res_ref=UNDEFINED)

Set solver output options.

Allows the user to set output solver options after the output has been defined and metadata set using the add_ouput method.

Parameters:

namestr: Name of the variable in this system’s namespace.
lowerfloat or list or tuple or ndarray or None: Lower bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no lower bound. Default is None.
upperfloat or list or tuple or ndarray or None: Upper bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no upper bound. Default is None.
reffloat: Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 1. Default is 1.
ref0float: Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 0. Default is 0.
res_reffloat: Scaling parameter. The value in the user-defined res_units of this output’s residual when the scaled value is 1. Default is None, which means residual scaling matches output scaling.

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.

set_val(name, val, units=None, indices=None)

Set an input or output variable.

Parameters:

namestr: Promoted or relative variable name in the system’s namespace.
valobject: Value to assign to this variable.
unitsstr, optional: Units of the value.
indicesint or list of ints or tuple of ints or int ndarray or Iterable or None, optional: Indices or slice to set.

setup_partials()

Declare partials.

This is meant to be overridden by component classes. All partials should be declared here since this is called after all size/shape information is known for all variables.

property under_approx

Return True if under complex step or finite difference.

Returns:

bool: True if under CS or FD.

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.

class openaerostruct.aerodynamics.pg_wind_rotation.RotateToWindFrame(**kwargs)[source]

Bases: ExplicitComponent

Rotate the VLM Geometry from the standard aerodynamic to the wind frame. In the wind frame the freestream will be along the x-axis.

This transformation is given by the following rotation matrix:

x_wind |   | cosb*cosa, -sinb, cosb*sina |   | x_aero |
y_wind | = | sinb*cosa,  cosb, sinb*sina | . | y_aero |
z_wind |   | -sina,         0,      cosa |   | z_aero |

Where “a” is the angle of attack and “b” is the sideslip angle.

Parameters:

def_mesh[nx, ny, 3]numpy array: Array defining the nodal coordinates of the lifting surface in aero frame.
bound_vecs[num_eval_points, 3]numpy array: The vectors representing the bound vortices for each panel in the problem. This array contains points for all lifting surfaces in the problem.
coll_pts[num_eval_points, 3]numpy array: The xyz coordinates of the collocation points used in the VLM analysis. This array contains points for all lifting surfaces in the problem.
force_pts[num_eval_points, 3]numpy array: The xyz coordinates of the force points used in the VLM analysis. We evaluate the velocity of the air at these points to get the sectional forces acting on the panel. This includes both the freestream and the induced velocity acting at these points. This array contains points for all lifting surfaces in the problem.
normals[nx-1, ny-1, 3]numpy array: The normal vector for each panel in aero frame, computed as the cross of the two diagonals from the mesh points.
rotational_velocities[num_eval_points, 3]numpy array: The rotated freestream velocities at each evaluation point for all lifting surfaces. This array contains points for all lifting surfaces in the problem.
alphafloat: Angle of attack in degrees.
betafloat: Sideslip angle in degrees.

Returns:

def_mesh_w_frame[nx, ny, 3]numpy array: Array defining the nodal coordinates of the lifting surface in wind frame.
bound_vecs_w_frame[num_eval_points, 3]numpy array: Bound points for the horseshoe vortices in wind frame.
coll_pts_w_frame[num_eval_points, 3]numpy array: Collocation points on the 3/4 chord line where the flow tangency condition is satisfed in wind frame.
force_pts_w_frame[num_eval_points, 3]numpy array: Force pts in wind frame.
normals_w_frame[nx-1, ny-1, 3]numpy array: The normal vector for each panel in wind frame.
rotational_velocities_w_frame[num_eval_points, 3]numpy array: Velocity component at collecation points due to rotational velocity in wind frame.

Store some bound methods so we can detect runtime overrides.

abs_meta_iter(iotype, local=True, cont=True, discrete=False)

Iterate over absolute variable names and their metadata for this System.

By setting appropriate values for ‘cont’ and ‘discrete’, yielded variable names can be continuous only, discrete only, or both.

Parameters:

iotypestr: Either ‘input’ or ‘output’.
localbool: If True, include only names of local variables. Default is True.
contbool: If True, include names of continuous variables. Default is True.
discretebool: If True, include names of discrete variables. Default is False.

Yields:

str, dict

abs_name_iter(iotype, local=True, cont=True, discrete=False)

Iterate over absolute variable names for this System.

By setting appropriate values for ‘cont’ and ‘discrete’, yielded variable names can be continuous only, discrete only, or both.

Parameters:

iotypestr: Either ‘input’ or ‘output’.
localbool: If True, include only names of local variables. Default is True.
contbool: If True, include names of continuous variables. Default is True.
discretebool: If True, include names of discrete variables. Default is False.

Yields:

str

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.

best_partial_deriv_direction()

Return the best direction for partial deriv calculations based on input and output sizes.

Returns:

str: The best direction for derivative calculations, ‘fwd’ or ‘rev’.

check_subjac_sparsity(): Check the declared sparsity of the sub-jacobians vs. the computed sparsity.

property checking

Return True if check_partials or check_totals is executing.

Returns:

bool: True if we’re running within check_partials or check_totals.

property comm

Return the MPI communicator object for the system.

Returns:

MPI.Comm: MPI communicator object.

comm_info_iter()

Yield comm size for this system and all subsystems.

Yields:

tuple: A tuple of the form (abs_name, comm_size).

compute_sparsity()

Compute the sparsity of the partial jacobian.

Returns:

coo_matrix: The sparsity matrix.
dict: Metadata about the sparsity computation.

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.

convert_from_units(name, val, units)

Convert the given value from the specified units to those of the named variable.

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.

convert_units(name, val, units_from, units_to)

Wrap the utility convert_units and give a good error message.

Parameters:

namestr: Name of the variable.
valfloat or ndarray of float: The value of the variable.
units_fromstr: The units to convert from.
units_tostr: 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=1e-25, orders=None, perturb_size=1e-09, 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.

dist_size_iter(io, top_comm)

Yield names and distributed ranges of all local and remote variables in this system.

Parameters:

iostr: Either ‘input’ or ‘output’.
top_commMPI.Comm or None: The top-level MPI communicator.

Yields:

tuple: A tuple of the form ((abs_name, rank), start, end).

get_coloring_fname(mode)

Return the full pathname to a coloring file.

Parameters:

modestr: The type of coloring file desired. Must be either ‘input’ or ‘output’.

Returns:

pathlib.Path: Full pathname of the coloring file.

get_io_metadata(iotypes=('input', 'output'), metadata_keys=None, includes=None, excludes=None, is_indep_var=None, is_design_var=None, tags=None, get_remote=False, rank=None, return_rel_names=True)

Retrieve metadata for a filtered list of variables.

Parameters:

iotypesstr or iter of str: Will contain either ‘input’, ‘output’, or both. Defaults to both.
metadata_keysiter of str or None: Names of metadata entries to be retrieved or None, meaning retrieve all available ‘allprocs’ metadata. If ‘val’ or ‘src_indices’ are required, their keys must be provided explicitly since they are not found in the ‘allprocs’ metadata and must be retrieved from local metadata located in each process.
includesstr, iter of str or None: Collection of glob patterns for pathnames of variables to include. Default is None, which includes all variables.
excludesstr, iter of str or None: Collection of glob patterns for pathnames of variables to exclude. Default is None.
is_indep_varbool or None: If None (the default), do no additional filtering of the inputs. If True, list only inputs connected to an output tagged openmdao:indep_var. If False, list only inputs _not_ connected to outputs tagged openmdao:indep_var.
is_design_varbool or None: If None (the default), do no additional filtering of the inputs. If True, list only inputs connected to outputs that are driver design variables. If False, list only inputs _not_ connected to outputs that are driver design variables.
tagsstr or iter 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.
get_remotebool: If True, retrieve variables from other MPI processes as well.
rankint or None: If None, and get_remote is True, retrieve values from all MPI process to all other MPI processes. Otherwise, if get_remote is True, retrieve values from all MPI processes only to the specified rank.
return_rel_namesbool: If True, the names returned will be relative to the scope of this System. Otherwise they will be absolute names.

Returns:

dict: A dict of metadata keyed on name, where name is either absolute or relative based on the value of the return_rel_names arg, and metadata is a dict containing entries based on the value of the metadata_keys arg. Every metadata dict will always contain two entries, ‘prom_name’ and ‘discrete’, to indicate a given variable’s promoted name and whether or not it is discrete.

get_outputs_dir(*subdirs, mkdir=True)

Get the path under which all output files of this system are to be placed.

Parameters:

*subdirsstr: Subdirectories nested under the relevant problem output directory. To create {prob_output_dir}/a/b one would pass system.get_outputs_dir(‘a’, ‘b’).
mkdirbool: If True, attempt to create this directory if it does not exist.

Returns:

pathlib.Path: The path of the outputs directory for the problem.

get_promotions(inprom=None, outprom=None)

Return all promotions for the given promoted variable(s).

In other words, how and where did promotions occur to convert absolute variable names into the given promoted name(s) at the current System level.

Parameters:

inpromstr or None: The promoted input variable name.
outpromstr or None: The promoted output variable name.

Returns:

dict: Dictionary keyed on system pathname containing input and/or output promotion lists for each System where promotions occurred to produce the given promoted variable(s).

get_reports_dir()

Get the path to the directory where the report files should go.

If it doesn’t exist, it will be created.

Returns:

str: The path to the directory where reports should be written.

get_self_statics()

Override this in derived classes if compute_primal references static values.

Do NOT include self._discrete_inputs in the returned tuple. Include things like self.options[‘opt_name’], etc., that are used in compute_primal but are assumed to be constant during derivative computation.

Return value MUST be a tuple. Don’t forget the trailing comma if tuple has only one item. Return value MUST be hashable.

The order of these values doesn’t matter. They are only checked (by computing their hash) to see if they have changed since the last time compute_primal was jitted, and if so, compute_primal will be re-jitted.

Returns:

tuple: Tuple containing all static values required by compute_primal.

get_source(name)

Return the source variable connected to the given named variable.

The name can be a promoted name or an absolute name. If the given variable is an input, the absolute name of the connected source will be returned. If the given variable itself is a source, its own absolute name will be returned.

Parameters:

namestr: Absolute or promoted name of the variable.

Returns:

str: The absolute name of the source variable.

get_val(name, units=None, indices=None, get_remote=False, rank=None, vec_name='nonlinear', kind=None, flat=False, from_src=True)

Get an output/input/residual variable.

Function is used if you want to specify display units.

Parameters:

namestr: Promoted or relative variable name in the root system’s namespace.
unitsstr, optional: Units to convert to before return.
indicesint or list of ints or tuple of ints or int ndarray or Iterable or None, optional: Indices or slice to return.
get_remotebool or None: If True, retrieve the value even if it is on a remote process. Note that if the variable is remote on ANY process, this function must be called on EVERY process in the Problem’s MPI communicator. If False, only retrieve the value if it is on the current process, or only the part of the value that’s on the current process for a distributed variable. If None and the variable is remote or distributed, a RuntimeError will be raised.
rankint or None: If not None, only gather the value to this rank.
vec_namestr: Name of the vector to use. Defaults to ‘nonlinear’.
kindstr or None: Kind of variable (‘input’, ‘output’, or ‘residual’). If None, returned value will be either an input or output.
flatbool: If True, return the flattened version of the value.
from_srcbool: If True, retrieve value of an input variable from its connected source.

Returns:

object: The value of the requested output/input variable.

get_var_dup_info(name, io)

Return information about how the given variable is duplicated across MPI processes.

Parameters:

namestr: Name of the variable.
iostr: Either ‘input’ or ‘output’.

Returns:

tuple: A tuple of the form (is_duplicated, num_zeros, is_distributed).

get_var_sizes(name, io)

Return the sizes of the given variable on all procs.

Parameters:

namestr: Name of the variable.
iostr: Either ‘input’ or ‘output’.

Returns:

ndarray: Array of sizes of the variable on all procs.

is_explicit()

Return True if this is an explicit component.

Returns:

bool: True if this is an explicit component.

list_vars(val=True, prom_name=True, residuals=False, residuals_tol=None, units=False, shape=False, global_shape=False, bounds=False, scaling=False, desc=False, print_arrays=False, tags=None, print_tags=False, includes=None, excludes=None, is_indep_var=None, is_design_var=None, all_procs=False, list_autoivcs=False, out_stream=DEFAULT_OUT_STREAM, print_min=False, print_max=False, return_format='list')

Write a list of inputs and outputs sorted by component in execution order.

Parameters:

valbool, optional: When True, display output values. Default is True.
prom_namebool, optional: When True, display the promoted name of the variable. Default is True.
residualsbool, optional: When True, display 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 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.
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.
print_tagsbool: When true, display tags in the columnar display.
includesNone, str, or iter of str: Collection of glob patterns for pathnames of variables to include. Default is None, which includes all output variables.
excludesNone, str, or iter of str: Collection of glob patterns for pathnames of variables to exclude. Default is None.
is_indep_varbool or None: If None (the default), do no additional filtering of the inputs. If True, list only outputs tagged openmdao:indep_var. If False, list only outputs that are _not_ tagged openmdao:indep_var.
is_design_varbool or None: If None (the default), do no additional filtering of the inputs. If True, list only inputs connected to outputs that are driver design variables. If False, list only inputs _not_ connected to outputs that are driver design variables.
all_procsbool, optional: When True, display output on all processors. Default is False.
list_autoivcsbool: If True, include auto_ivc outputs in the listing. Defaults to False.
out_streamfile-like: Where to send human readable output. Default is sys.stdout. Set to None to suppress.
print_minbool: When true, if the output value is an array, print its smallest value.
print_maxbool: When true, if the output value is an array, print its largest value.
return_formatstr: Indicates the desired format of the return value. Can have value of ‘list’ or ‘dict’. If ‘list’, the return value is a list of (name, metadata) tuples. if ‘dict’, the return value is a dictionary mapping {name: metadata}.

Returns:

list of (name, metadata) or dict of {name: metadata}: List or dict of output names and other optional information about those outputs.

load_case(case)

Pull all input and output variables from a Case into this System.

Override this method if the System requires special handling when loading a case.

Parameters:

caseCase or dict: A Case from a CaseReader, or a dictionary with key ‘inputs’ mapped to the output of problem.model.list_inputs and key ‘outputs’ mapped to the output of prob.model.list_outputs. Both list_inputs and list_outputs should be called with prom_name=True and return_format=’dict’.

load_model_options()

Load the relevant model options from Problem._metadata[‘model_options’].

This method examines each path filter and corresponding options in self._problem_meta[‘model_options’]. If this System’s pathname matches the given path filter, it will assume the value for each given option which it possesses.

local_range_iter(io)

Yield names and local ranges of all local variables in this system.

Parameters:

iostr: Either ‘input’ or ‘output’.

Yields:

tuple: A tuple of the form (abs_name, start, end).

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.

set_constraint_options(name, ref=UNDEFINED, ref0=UNDEFINED, equals=UNDEFINED, lower=UNDEFINED, upper=UNDEFINED, adder=UNDEFINED, scaler=UNDEFINED, alias=UNDEFINED)

Set options for constraints in the model.

Can be used to set options that were set using add_constraint.

Parameters:

namestr: Name of the response variable in the system, or alias if given.
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.
equalsfloat or ndarray, optional: Equality constraint value for the variable.
lowerfloat or ndarray, optional: Lower boundary for the variable.
upperfloat or ndarray, optional: Upper boundary for the variable.
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.
aliasstr, optional: Alias for this response. Necessary when adding multiple constraints on different indices or slices of a single variable.

set_design_var_options(name, lower=UNDEFINED, upper=UNDEFINED, scaler=UNDEFINED, adder=UNDEFINED, ref=UNDEFINED, ref0=UNDEFINED)

Set options for design vars in the model.

Can be used to set the options outside of setting them when calling add_design_var

Parameters:

namestr: Name of the variable in this system’s namespace.
lowerfloat or ndarray, optional: Lower boundary for the input.
upperupper or ndarray, optional: Upper boundary for the input.
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.
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.
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.

set_objective_options(name, ref=UNDEFINED, ref0=UNDEFINED, adder=UNDEFINED, scaler=UNDEFINED, alias=UNDEFINED)

Set options for objectives in the model.

Can be used to set options after they have been set by add_objective.

Parameters:

namestr: Name of the response variable in the system, or alias if given.
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.
aliasstr: Alias for this response. Used to disambiguate variable names when adding multiple objectives on different indices or slices of a single variable. Deprecated.

set_output_solver_options(name, lower=UNDEFINED, upper=UNDEFINED, ref=UNDEFINED, ref0=UNDEFINED, res_ref=UNDEFINED)

Set solver output options.

Allows the user to set output solver options after the output has been defined and metadata set using the add_ouput method.

Parameters:

namestr: Name of the variable in this system’s namespace.
lowerfloat or list or tuple or ndarray or None: Lower bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no lower bound. Default is None.
upperfloat or list or tuple or ndarray or None: Upper bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no upper bound. Default is None.
reffloat: Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 1. Default is 1.
ref0float: Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 0. Default is 0.
res_reffloat: Scaling parameter. The value in the user-defined res_units of this output’s residual when the scaled value is 1. Default is None, which means residual scaling matches output scaling.

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.

set_val(name, val, units=None, indices=None)

Set an input or output variable.

Parameters:

namestr: Promoted or relative variable name in the system’s namespace.
valobject: Value to assign to this variable.
unitsstr, optional: Units of the value.
indicesint or list of ints or tuple of ints or int ndarray or Iterable or None, optional: Indices or slice to set.

setup_partials()

Declare partials.

This is meant to be overridden by component classes. All partials should be declared here since this is called after all size/shape information is known for all variables.

property under_approx

Return True if under complex step or finite difference.

Returns:

bool: True if under CS or FD.

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.