compute_time_scoping#
Autogenerated DPF operator classes.
- class ansys.dpf.core.operators.utility.compute_time_scoping.compute_time_scoping(time_freq_values=None, step=None, interpolation_type=None, time_freq_support=None, config=None, server=None)#
Computes the time frequency scoping (made of set IDs) necessary to interpolate on a list of time or frequencies.
- Parameters:
time_freq_values (float or Field or TimeFreqSupport) – List of frequencies or times needed. to specify load steps, put a field (and not a list) in input with a scoping located on “timefreq_steps”.
step (int, optional) –
interpolation_type (int, optional) – 1:ramped’ or 2:stepped’, default is ramped
time_freq_support (TimeFreqSupport) –
- Returns:
scoping (Scoping) – Time_scoping
field (Field) – Time_freq_values
Examples
>>> from ansys.dpf import core as dpf
>>> # Instantiate operator >>> op = dpf.operators.utility.compute_time_scoping()
>>> # Make input connections >>> my_time_freq_values = float() >>> op.inputs.time_freq_values.connect(my_time_freq_values) >>> my_step = int() >>> op.inputs.step.connect(my_step) >>> my_interpolation_type = int() >>> op.inputs.interpolation_type.connect(my_interpolation_type) >>> my_time_freq_support = dpf.TimeFreqSupport() >>> op.inputs.time_freq_support.connect(my_time_freq_support)
>>> # Instantiate operator and connect inputs in one line >>> op = dpf.operators.utility.compute_time_scoping( ... time_freq_values=my_time_freq_values, ... step=my_step, ... interpolation_type=my_interpolation_type, ... time_freq_support=my_time_freq_support, ... )
>>> # Get output data >>> result_scoping = op.outputs.scoping() >>> 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:
- property outputs#
Enables to get outputs of the operator by evaluating it
- Returns:
outputs
- Return type:
- 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:
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 isNone
. :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:
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 isNone
.
- 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:
- class ansys.dpf.core.operators.utility.compute_time_scoping.InputsComputeTimeScoping(op: ansys.dpf.core.dpf_operator.Operator)#
Intermediate class used to connect user inputs to compute_time_scoping operator.
Examples
>>> from ansys.dpf import core as dpf >>> op = dpf.operators.utility.compute_time_scoping() >>> my_time_freq_values = float() >>> op.inputs.time_freq_values.connect(my_time_freq_values) >>> my_step = int() >>> op.inputs.step.connect(my_step) >>> my_interpolation_type = int() >>> op.inputs.interpolation_type.connect(my_interpolation_type) >>> my_time_freq_support = dpf.TimeFreqSupport() >>> op.inputs.time_freq_support.connect(my_time_freq_support)
- property time_freq_values#
Allows to connect time_freq_values input to the operator.
List of frequencies or times needed. to specify load steps, put a field (and not a list) in input with a scoping located on “timefreq_steps”.
- Parameters:
my_time_freq_values (float or Field or TimeFreqSupport) –
Examples
>>> from ansys.dpf import core as dpf >>> op = dpf.operators.utility.compute_time_scoping() >>> op.inputs.time_freq_values.connect(my_time_freq_values) >>> # or >>> op.inputs.time_freq_values(my_time_freq_values)
- property step#
Allows to connect step input to the operator.
- Parameters:
my_step (int) –
Examples
>>> from ansys.dpf import core as dpf >>> op = dpf.operators.utility.compute_time_scoping() >>> op.inputs.step.connect(my_step) >>> # or >>> op.inputs.step(my_step)
- property interpolation_type#
Allows to connect interpolation_type input to the operator.
1:ramped’ or 2:stepped’, default is ramped
- Parameters:
my_interpolation_type (int) –
Examples
>>> from ansys.dpf import core as dpf >>> op = dpf.operators.utility.compute_time_scoping() >>> op.inputs.interpolation_type.connect(my_interpolation_type) >>> # or >>> op.inputs.interpolation_type(my_interpolation_type)
- property time_freq_support#
Allows to connect time_freq_support input to the operator.
- Parameters:
my_time_freq_support (TimeFreqSupport) –
Examples
>>> from ansys.dpf import core as dpf >>> op = dpf.operators.utility.compute_time_scoping() >>> op.inputs.time_freq_support.connect(my_time_freq_support) >>> # or >>> op.inputs.time_freq_support(my_time_freq_support)
- 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.utility.compute_time_scoping.OutputsComputeTimeScoping(op: ansys.dpf.core.dpf_operator.Operator)#
Intermediate class used to get outputs from compute_time_scoping operator.
Examples
>>> from ansys.dpf import core as dpf >>> op = dpf.operators.utility.compute_time_scoping() >>> # Connect inputs : op.inputs. ... >>> result_scoping = op.outputs.scoping() >>> result_field = op.outputs.field()
- property scoping#
Allows to get scoping output of the operator
- Returns:
my_scoping
- Return type:
Examples
>>> from ansys.dpf import core as dpf >>> op = dpf.operators.utility.compute_time_scoping() >>> # Connect inputs : op.inputs. ... >>> result_scoping = op.outputs.scoping()