prep_sampling_fft#

Autogenerated DPF operator classes.

class ansys.dpf.core.operators.mapping.prep_sampling_fft.prep_sampling_fft(time_freq_support=None, cutoff_frequency=None, number_sampling_point=None, config=None, server=None)#

Prepare time sampling optimum for FFT computation and expected frequencies in output.

Parameters:

  • time_freq_support (TimeFreqSupport) – Initial time domain timefreqsupport.

  • cutoff_frequency (float, optional) – Cutoff frequency. in this case, number of points is calculated computing (time_range * cutoff_freq * 2) and taking the next power of 2 (optimum for fft calculation).

  • number_sampling_point (int, optional) – For number of sampling point (calculation with cutoff_frequency is ignored).

Examples

>>> from ansys.dpf import core as dpf

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

>>> # Make input connections
>>> my_time_freq_support = dpf.TimeFreqSupport()
>>> op.inputs.time_freq_support.connect(my_time_freq_support)
>>> my_cutoff_frequency = float()
>>> op.inputs.cutoff_frequency.connect(my_cutoff_frequency)
>>> my_number_sampling_point = int()
>>> op.inputs.number_sampling_point.connect(my_number_sampling_point)

>>> # Instantiate operator and connect inputs in one line
>>> op = dpf.operators.mapping.prep_sampling_fft(
...     time_freq_support=my_time_freq_support,
...     cutoff_frequency=my_cutoff_frequency,
...     number_sampling_point=my_number_sampling_point,
... )

>>> # Get output data
>>> result_time_tfs_sampled = op.outputs.time_tfs_sampled()
>>> result_freq_tfs_fft = op.outputs.freq_tfs_fft()
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:

InputsPrepSamplingFft

property outputs#

Enables to get outputs of the operator by evaluating it

Returns:

outputs

Return type:

OutputsPrepSamplingFft

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.prep_sampling_fft.InputsPrepSamplingFft(op: ansys.dpf.core.dpf_operator.Operator)#

Intermediate class used to connect user inputs to prep_sampling_fft operator.

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.mapping.prep_sampling_fft()
>>> my_time_freq_support = dpf.TimeFreqSupport()
>>> op.inputs.time_freq_support.connect(my_time_freq_support)
>>> my_cutoff_frequency = float()
>>> op.inputs.cutoff_frequency.connect(my_cutoff_frequency)
>>> my_number_sampling_point = int()
>>> op.inputs.number_sampling_point.connect(my_number_sampling_point)
property time_freq_support#

Allows to connect time_freq_support input to the operator.

Initial time domain timefreqsupport.

Parameters:

my_time_freq_support (TimeFreqSupport) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.mapping.prep_sampling_fft()
>>> op.inputs.time_freq_support.connect(my_time_freq_support)
>>> # or
>>> op.inputs.time_freq_support(my_time_freq_support)
property cutoff_frequency#

Allows to connect cutoff_frequency input to the operator.

Cutoff frequency. in this case, number of points is calculated computing (time_range * cutoff_freq * 2) and taking the next power of 2 (optimum for fft calculation).

Parameters:

my_cutoff_frequency (float) –

Examples

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

Allows to connect number_sampling_point input to the operator.

For number of sampling point (calculation with cutoff_frequency is ignored).

Parameters:

my_number_sampling_point (int) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.mapping.prep_sampling_fft()
>>> op.inputs.number_sampling_point.connect(my_number_sampling_point)
>>> # or
>>> op.inputs.number_sampling_point(my_number_sampling_point)
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.prep_sampling_fft.OutputsPrepSamplingFft(op: ansys.dpf.core.dpf_operator.Operator)#

Intermediate class used to get outputs from prep_sampling_fft operator.

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.mapping.prep_sampling_fft()
>>> # Connect inputs : op.inputs. ...
>>> result_time_tfs_sampled = op.outputs.time_tfs_sampled()
>>> result_freq_tfs_fft = op.outputs.freq_tfs_fft()
property time_tfs_sampled#

Allows to get time_tfs_sampled output of the operator

Returns:

my_time_tfs_sampled

Return type:

TimeFreqSupport

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.mapping.prep_sampling_fft()
>>> # Connect inputs : op.inputs. ...
>>> result_time_tfs_sampled = op.outputs.time_tfs_sampled()
property freq_tfs_fft#

Allows to get freq_tfs_fft output of the operator

Returns:

my_freq_tfs_fft

Return type:

TimeFreqSupport

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.mapping.prep_sampling_fft()
>>> # Connect inputs : op.inputs. ...
>>> result_freq_tfs_fft = op.outputs.freq_tfs_fft()