`mac`#

class ansys.dpf.core.operators.math.mac.mac(fields_containerA=None, fields_containerB=None, weights=None, config=None, server=None, ponderation=None)#

Bases: ansys.dpf.core.dpf_operator.Operator

Computes the Modal Assurance Criterion (MAC) matrix between two sets of mode shapes. For each pair of modes $\phi_i$ (from container A) and $\phi_j$ (from container B):

\[MAC_{ij} = \frac{|\phi_i^{\,*T} M \phi_j|^2}{(\phi_i^{\,*T} M \phi_i)(\phi_j^{\,*T} M \phi_j)}\]

where $^{*T}$ denotes the conjugate transpose and $M$ is the optional mass weighting matrix. When $M$ is omitted, the standard (unweighted) inner product is used.

For mixed real-complex input (one container real, one complex), only the real parts are used. Both containers must share the same number of components; this is not verified by the operator.

Inputs#

fields_containerA: FieldsContainer: Fields container $A$. Must have a time label and contain one field per mode shape.
fields_containerB: FieldsContainer: Fields container $B$. Must have a time label and contain one field per mode shape.
weights: Field, optional: Optional mass weighting field $M$. When omitted, the standard unweighted inner product is used.

Outputs#

field: Field: MAC matrix of size $N_A times N_B$, where $N_A$ and $N_B$ are the number of mode shapes in containers A and B respectively. Entries are stored row by row: entry at position $(i,j)$ is $MAC_{ij}$.

Examples

>>> from ansys.dpf import core as dpf

>>> # Instantiate operator
>>> op = dpf.operators.math.mac()

>>> # Make input connections
>>> my_fields_containerA = dpf.FieldsContainer()
>>> op.inputs.fields_containerA.connect(my_fields_containerA)
>>> my_fields_containerB = dpf.FieldsContainer()
>>> op.inputs.fields_containerB.connect(my_fields_containerB)
>>> my_weights = dpf.Field()
>>> op.inputs.weights.connect(my_weights)

>>> # Instantiate operator and connect inputs in one line
>>> op = dpf.operators.math.mac(
...     fields_containerA=my_fields_containerA,
...     fields_containerB=my_fields_containerB,
...     weights=my_weights,
... )

>>> # Get output data
>>> result_field = op.outputs.field()

Overview#

Methods

`connect`	Connect an input on the operator using a pin number.
`connect_operator_as_input`	Connect an operator as an input on a pin.
`get_output`	Retrieve the output of the operator on the pin number.
`run`	Evaluate this operator.
`eval`	Evaluate this operator.

Properties

`inputs`	Enables to connect inputs to the operator
`outputs`	Enables to get outputs of the operator by evaluating it
`progress_bar`	Enable or disable progress bar display when requesting the operator’s output.
`config`	Copy of the operator’s current configuration.
`id`	Retrieve the unique identifier of the operator.
`specification`	Returns the Specification (or documentation) of this Operator.
`changelog`	Return the changelog of this operator.
`version`	Return the current version of the operator.

Attributes

name

Static methods

`default_config`	Returns the default config of the operator.
`operator_specification`	Documents an Operator with its description (what the Operator does),its inputs and outputs and some properties.

Special methods

`__del__`	Delete this instance.
`__str__`	Describe the entity.
`__add__`	Add two fields or two fields containers.
`__sub__`	Subtract two fields or two fields containers.
`__pow__`	Raise each element of a field or a fields container to power 2.
`__mul__`	Multiply two fields or two fields containers.
`__truediv__`	Perform division with another operator or a scalar.

Import detail#

from ansys.dpf.core.operators.math.mac import mac

Property detail#

property mac.inputs: InputsMac#

Enables to connect inputs to the operator

Returns:: An instance of InputsMac.
Return type:: inputs

property mac.outputs: OutputsMac#

Enables to get outputs of the operator by evaluating it

Returns:: An instance of OutputsMac.
Return type:: outputs

property mac.progress_bar: bool#

Enable or disable progress bar display when requesting the operator’s output.

With this property, the user can choose to print a progress bar when the operator’s output is requested, default is False

property mac.config#

Copy of the operator’s current configuration.

You can modify the copy of the configuration and then use operator.config = new_config or instantiate an operator with the new configuration as a parameter.

For information on an operator’s options, see the documentation for that operator.

Returns:: Copy of the operator’s current configuration.
Return type:: ansys.dpf.core.config.Config

Examples

Modify the copy of an operator’s configuration and set it as current config of the operator.

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.math.add()
>>> config_add = op.config
>>> config_add.set_work_by_index_option(True)
>>> op.config = config_add

property mac.id: int#

Retrieve the unique identifier of the operator.

This property returns the unique ID associated with the operator. This property is lazily initialized.

Returns:: The unique identifier of the operator.
Return type:: int

Notes

Property available with server’s version starting at 10.0.

property mac.specification#

Returns the Specification (or documentation) of this Operator.

Return type:: Specification

property mac.changelog: ansys.dpf.core.changelog.Changelog#

Return the changelog of this operator.

Requires DPF 11.0 (2026 R1) or above.

Returns:: Changelog of the operator.
Return type:: changelog

property mac.version: packaging.version.Version#

Return the current version of the operator.

Requires DPF 11.0 (2026 R1) or above.

Attribute detail#

mac.name = None#

Method detail#

static mac.default_config(server: ansys.dpf.core.server_types.AnyServerType = None) → ansys.dpf.core.config.Config#

Returns the default config of the operator.

This config can then be changed to the user needs and be used to instantiate the operator. The Configuration allows to customize how the operation will be processed by the operator.

Parameters:: server – Server with channel connected to the remote or local instance. When None, attempts to use the global server.
Returns:: A new Config instance equivalent to the default config for this operator.
Return type:: config

mac.connect(pin, inpt, pin_out=0)#

Connect an input on the operator using a pin number.

Parameters:

pin (int) – Number of the input pin.
inpt (str, int, double, bool, list[int], list[float], Field, FieldsContainer, Scoping,)
ScopingsContainer – Operator, os.PathLike Object to connect to.
MeshedRegion – Operator, os.PathLike Object to connect to.
MeshesContainer – Operator, os.PathLike Object to connect to.
DataSources – Operator, os.PathLike Object to connect to.
CyclicSupport – Operator, os.PathLike Object to connect to.
dict – Operator, os.PathLike Object to connect to.
Outputs – Operator, os.PathLike Object to connect to.
pin_out (int, optional) – If the input is an operator, the output pin of the input operator. The default is 0.

Examples

Compute the minimum of displacement by chaining the "U" and "min_max_fc" operators.

>>> from ansys.dpf import core as dpf
>>> from ansys.dpf.core import examples
>>> data_src = dpf.DataSources(examples.find_multishells_rst())
>>> disp_op = dpf.operators.result.displacement()
>>> disp_op.inputs.data_sources(data_src)
>>> max_fc_op = dpf.operators.min_max.min_max_fc()
>>> max_fc_op.inputs.connect(disp_op.outputs)
>>> max_field = max_fc_op.outputs.field_max()
>>> max_field.data
DPFArray([[0.59428386, 0.00201751, 0.0006032 ]]...

mac.connect_operator_as_input(pin, op)#

Connect an operator as an input on a pin.

Parameters:

pin (int) – Number of the output pin. The default is 0.
op (ansys.dpf.core.dpf_operator.Operator) – Requested type of the output. The default is None.

mac.get_output(pin=0, output_type=None)#

Retrieve the output of the operator on the pin number.

To activate the progress bar for server version higher or equal to 3.0, use my_op.progress_bar=True

Parameters:

pin (int, optional) – Number of the output pin. The default is 0.
output_type (ansys.dpf.core.common.types, type, optional) – Requested type of the output. The default is None.

Returns:

Output of the operator.

Return type:

type

mac.__del__()#: Delete this instance.

mac.__str__()#

Describe the entity.

Returns:: Description of the entity.
Return type:: str

mac.run()#: Evaluate this operator.

mac.eval(pin=None)#

Evaluate this operator.

Parameters:: pin (int) – Number of the output pin. The default is None.
Returns:: output – Returns the first output of the operator by default and the output of a given pin when specified. Or, it only evaluates the operator without output.
Return type:: FieldsContainer, Field, MeshedRegion, Scoping

Examples

Use the eval method.

>>> from ansys.dpf import core as dpf
>>> import ansys.dpf.core.operators.math as math
>>> from ansys.dpf.core import examples
>>> data_src = dpf.DataSources(examples.find_multishells_rst())
>>> disp_op = dpf.operators.result.displacement()
>>> disp_op.inputs.data_sources(data_src)
>>> normfc = math.norm_fc(disp_op).eval()

mac.__add__(fields_b)#

Add two fields or two fields containers.

Returns:: add
Return type:: operators.math.add_fc

mac.__sub__(fields_b)#

Subtract two fields or two fields containers.

Returns:: minus
Return type:: operators.math.minus_fc

mac.__pow__(value)#: Raise each element of a field or a fields container to power 2.

mac.__mul__(value)#

Multiply two fields or two fields containers.

Returns:: mul
Return type:: operators.math.generalized_inner_product_fc

static mac.operator_specification(op_name, server=None)#: Documents an Operator with its description (what the Operator does),its inputs and outputs and some properties.

mac.__truediv__(inpt)#

Perform division with another operator or a scalar.

This method allows the use of the division operator (/) between an Operator instance and either another Operator or a scalar value (float).

mac#

Inputs#

Outputs#

Overview#

Import detail#

Property detail#

Attribute detail#

Method detail#

`mac`#