`fft`#

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)#

Bases: ansys.dpf.core.dpf_operator.Operator

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()

Overview#

Properties

`inputs`	Enables to connect inputs to the operator
`outputs`	Enables to get outputs of the operator by evaluating it

Static methods

default_config

Returns the default config of the operator.

Import detail#

from ansys.dpf.core.operators.mapping.fft import fft

Property detail#

property fft.inputs#

Enables to connect inputs to the operator

Returns:: inputs
Return type:: InputsFft

property fft.outputs#

Enables to get outputs of the operator by evaluating it

Returns:: outputs
Return type:: OutputsFft

Method detail#

static fft.default_config(server=None)#