fft#

Autogenerated DPF operator classes.

class ansys.dpf.core.operators.mapping.fft.fft(field=None, scale_forward_transform=None, inplace=None, force_fft_points=None, cutoff_frequency=None, scale_right_amplitude=None, config=None, server=None)#

Computes the Fast Fourier Transform on each component of input Field or each field of input Fields Container (you can use transpose_fields_container to have relevant scoping). Fields are assumed with the same scoping, number of components and representing equally spaced data, ideally resampled to have a 2^n points (prepare_sampling_fft with time_freq_interpolation can help creating these fields). If Complex label is present, Complex to Complex FFT performed otherwise Real to Complex is performed (only half of the coefficient will be returned).

Parameters:

  • field (Field or FieldsContainer) – Field or fields container.

  • scale_forward_transform (float, optional) – Scale for forward transform, default is 2/field_num_elementary_data.

  • inplace (bool, optional) – True if inplace, default is false.

  • force_fft_points (int, optional) – Explicitely define number of fft points to either rescope or perform zero padding.

  • cutoff_frequency (float, optional) – Restrict output frequency up to this cutoff frequency

  • scale_right_amplitude (bool, optional) – If set to true (default is false), 2/field_num_entities scaling will be applied, to have right amplitude values.

Returns:

fields_container – Output complex fields container with labels matching input fields container. no supports binded, but prepare_sampling_fft provides it.

Return type:

FieldsContainer

Examples

>>> from ansys.dpf import core as dpf

>>> # Instantiate operator
>>> op = dpf.operators.mapping.fft()

>>> # Make input connections
>>> my_field = dpf.Field()
>>> op.inputs.field.connect(my_field)
>>> my_scale_forward_transform = float()
>>> op.inputs.scale_forward_transform.connect(my_scale_forward_transform)
>>> my_inplace = bool()
>>> op.inputs.inplace.connect(my_inplace)
>>> my_force_fft_points = int()
>>> op.inputs.force_fft_points.connect(my_force_fft_points)
>>> my_cutoff_frequency = float()
>>> op.inputs.cutoff_frequency.connect(my_cutoff_frequency)
>>> my_scale_right_amplitude = bool()
>>> op.inputs.scale_right_amplitude.connect(my_scale_right_amplitude)

>>> # Instantiate operator and connect inputs in one line
>>> op = dpf.operators.mapping.fft(
...     field=my_field,
...     scale_forward_transform=my_scale_forward_transform,
...     inplace=my_inplace,
...     force_fft_points=my_force_fft_points,
...     cutoff_frequency=my_cutoff_frequency,
...     scale_right_amplitude=my_scale_right_amplitude,
... )

>>> # Get output data
>>> result_fields_container = op.outputs.fields_container()
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:

InputsFft

property outputs#

Enables to get outputs of the operator by evaluating it

Returns:

outputs

Return type:

OutputsFft

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.mapping.fft.InputsFft(op: ansys.dpf.core.dpf_operator.Operator)#

Intermediate class used to connect user inputs to fft operator.

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.mapping.fft()
>>> my_field = dpf.Field()
>>> op.inputs.field.connect(my_field)
>>> my_scale_forward_transform = float()
>>> op.inputs.scale_forward_transform.connect(my_scale_forward_transform)
>>> my_inplace = bool()
>>> op.inputs.inplace.connect(my_inplace)
>>> my_force_fft_points = int()
>>> op.inputs.force_fft_points.connect(my_force_fft_points)
>>> my_cutoff_frequency = float()
>>> op.inputs.cutoff_frequency.connect(my_cutoff_frequency)
>>> my_scale_right_amplitude = bool()
>>> op.inputs.scale_right_amplitude.connect(my_scale_right_amplitude)
property field#

Allows to connect field input to the operator.

Field or fields container.

Parameters:

my_field (Field or FieldsContainer) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.mapping.fft()
>>> op.inputs.field.connect(my_field)
>>> # or
>>> op.inputs.field(my_field)
property scale_forward_transform#

Allows to connect scale_forward_transform input to the operator.

Scale for forward transform, default is 2/field_num_elementary_data.

Parameters:

my_scale_forward_transform (float) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.mapping.fft()
>>> op.inputs.scale_forward_transform.connect(my_scale_forward_transform)
>>> # or
>>> op.inputs.scale_forward_transform(my_scale_forward_transform)
property inplace#

Allows to connect inplace input to the operator.

True if inplace, default is false.

Parameters:

my_inplace (bool) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.mapping.fft()
>>> op.inputs.inplace.connect(my_inplace)
>>> # or
>>> op.inputs.inplace(my_inplace)
property force_fft_points#

Allows to connect force_fft_points input to the operator.

Explicitely define number of fft points to either rescope or perform zero padding.

Parameters:

my_force_fft_points (int) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.mapping.fft()
>>> op.inputs.force_fft_points.connect(my_force_fft_points)
>>> # or
>>> op.inputs.force_fft_points(my_force_fft_points)
property cutoff_frequency#

Allows to connect cutoff_frequency input to the operator.

Restrict output frequency up to this cutoff frequency

Parameters:

my_cutoff_frequency (float) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.mapping.fft()
>>> op.inputs.cutoff_frequency.connect(my_cutoff_frequency)
>>> # or
>>> op.inputs.cutoff_frequency(my_cutoff_frequency)
property scale_right_amplitude#

Allows to connect scale_right_amplitude input to the operator.

If set to true (default is false), 2/field_num_entities scaling will be applied, to have right amplitude values.

Parameters:

my_scale_right_amplitude (bool) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.mapping.fft()
>>> op.inputs.scale_right_amplitude.connect(my_scale_right_amplitude)
>>> # or
>>> op.inputs.scale_right_amplitude(my_scale_right_amplitude)
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.mapping.fft.OutputsFft(op: ansys.dpf.core.dpf_operator.Operator)#

Intermediate class used to get outputs from fft operator.

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.mapping.fft()
>>> # Connect inputs : op.inputs. ...
>>> result_fields_container = op.outputs.fields_container()
property fields_container#

Allows to get fields_container output of the operator

Returns:

my_fields_container

Return type:

FieldsContainer

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.mapping.fft()
>>> # Connect inputs : op.inputs. ...
>>> result_fields_container = op.outputs.fields_container()
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