scale#

Autogenerated DPF operator classes.

class ansys.dpf.core.operators.math.scale.scale(field=None, ponderation=None, boolean=None, algorithm=None, config=None, server=None)#

Scales a field by a constant factor. This factor can be a scalar or a vector, where each value of the vector represents a scaler per component. Number of the components are corresponding to the input field dimensionality

Parameters:

  • field (Field or FieldsContainer) – Field or fields container with only one field is expected

  • ponderation (float or Field) – Double/field/vector of doubles. when scoped on overall, same value(s) applied on all the data, when scoped elsewhere, corresponding values will be multiplied due to the scoping

  • boolean (bool, optional) – Default is false. if set to true, output of scale is made dimensionless

  • algorithm (int, optional) – Default is 0 use mkl. if set to 1, don’t

Returns:

field

Return type:

Field

Examples

>>> from ansys.dpf import core as dpf

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

>>> # Make input connections
>>> my_field = dpf.Field()
>>> op.inputs.field.connect(my_field)
>>> my_ponderation = float()
>>> op.inputs.ponderation.connect(my_ponderation)
>>> my_boolean = bool()
>>> op.inputs.boolean.connect(my_boolean)
>>> my_algorithm = int()
>>> op.inputs.algorithm.connect(my_algorithm)

>>> # Instantiate operator and connect inputs in one line
>>> op = dpf.operators.math.scale(
...     field=my_field,
...     ponderation=my_ponderation,
...     boolean=my_boolean,
...     algorithm=my_algorithm,
... )

>>> # Get output data
>>> result_field = op.outputs.field()
static default_config(server=None)#

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.DPFServer, optional) – Server with channel connected to the remote or local instance. When None, attempts to use the global server.

property inputs#

Enables to connect inputs to the operator

Returns:

inputs

Return type:

InputsScale

property outputs#

Enables to get outputs of the operator by evaluating it

Returns:

outputs

Return type:

OutputsScale

property 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
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 ]]...
connect_operator_as_input(pin, op)#

Connects an operator as an input on a pin. :type pin: :param pin: Number of the output pin. The default is 0. :type pin: int :type op: :param op: Requested type of the output. The default is None. :type op: ansys.dpf.core.dpf_operator.Operator

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()
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

static operator_specification(op_name, server=None)#

Documents an Operator with its description (what the Operator does), its inputs and outputs and some properties

property progress_bar: bool#

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

run()#

Evaluate this operator.

property specification#

Returns the Specification (or documentation) of this Operator

Return type:

Specification

class ansys.dpf.core.operators.math.scale.InputsScale(op: ansys.dpf.core.dpf_operator.Operator)#

Intermediate class used to connect user inputs to scale operator.

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.math.scale()
>>> my_field = dpf.Field()
>>> op.inputs.field.connect(my_field)
>>> my_ponderation = float()
>>> op.inputs.ponderation.connect(my_ponderation)
>>> my_boolean = bool()
>>> op.inputs.boolean.connect(my_boolean)
>>> my_algorithm = int()
>>> op.inputs.algorithm.connect(my_algorithm)
property field#

Allows to connect field input to the operator.

Field or fields container with only one field is expected

Parameters:

my_field (Field or FieldsContainer) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.math.scale()
>>> op.inputs.field.connect(my_field)
>>> # or
>>> op.inputs.field(my_field)
property ponderation#

Allows to connect ponderation input to the operator.

Double/field/vector of doubles. when scoped on overall, same value(s) applied on all the data, when scoped elsewhere, corresponding values will be multiplied due to the scoping

Parameters:

my_ponderation (float or Field) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.math.scale()
>>> op.inputs.ponderation.connect(my_ponderation)
>>> # or
>>> op.inputs.ponderation(my_ponderation)
property boolean#

Allows to connect boolean input to the operator.

Default is false. if set to true, output of scale is made dimensionless

Parameters:

my_boolean (bool) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.math.scale()
>>> op.inputs.boolean.connect(my_boolean)
>>> # or
>>> op.inputs.boolean(my_boolean)
property algorithm#

Allows to connect algorithm input to the operator.

Default is 0 use mkl. if set to 1, don’t

Parameters:

my_algorithm (int) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.math.scale()
>>> op.inputs.algorithm.connect(my_algorithm)
>>> # or
>>> op.inputs.algorithm(my_algorithm)
connect(inpt)#

Connect any input (an entity or an operator output) to any input pin of this operator. Searches for the input type corresponding to the output.

Parameters:

  • inpt (str, int, double, bool, list[int], list[float], Field, FieldsContainer, Scoping,) –

  • ScopingsContainer (E501) – Input of the operator.

  • MeshedRegion (E501) – Input of the operator.

  • MeshesContainer (E501) – Input of the operator.

  • DataSources (E501) – Input of the operator.

  • CyclicSupport (E501) – Input of the operator.

  • Outputs (E501) – Input of the operator.

  • noqa (os.PathLike #) – Input of the operator.

class ansys.dpf.core.operators.math.scale.OutputsScale(op: ansys.dpf.core.dpf_operator.Operator)#

Intermediate class used to get outputs from scale operator.

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.math.scale()
>>> # Connect inputs : op.inputs. ...
>>> result_field = op.outputs.field()
property field#

Allows to get field output of the operator

Returns:

my_field

Return type:

Field

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.math.scale()
>>> # Connect inputs : op.inputs. ...
>>> result_field = op.outputs.field()