serialize_to_hdf5#
Autogenerated DPF operator classes.
- class ansys.dpf.core.operators.serialization.serialize_to_hdf5.serialize_to_hdf5(file_path=None, export_floats=None, export_flat_vectors=None, data1=None, data2=None, config=None, server=None)#
This operator is deprecated: use ‘hdf5::h5dpf::make_result_file’ instead. Serialize the inputs in an hdf5 format.
- Parameters:
file_path (str) – Output file path with .h5 extension
export_floats (bool, optional) – Converts double to float to reduce file size (default is true)
export_flat_vectors (bool, optional) – If true, vectors and matrices data are exported flat (x1,y1,z1,x2,y2,z2..) (default is false)
data1 (default:
None
) – Only the data set explicitly to export is exporteddata2 (default:
None
) – Only the data set explicitly to export is exported
Examples
>>> from ansys.dpf import core as dpf
>>> # Instantiate operator >>> op = dpf.operators.serialization.serialize_to_hdf5()
>>> # Make input connections >>> my_file_path = str() >>> op.inputs.file_path.connect(my_file_path) >>> my_export_floats = bool() >>> op.inputs.export_floats.connect(my_export_floats) >>> my_export_flat_vectors = bool() >>> op.inputs.export_flat_vectors.connect(my_export_flat_vectors) >>> my_data1 = dpf.() >>> op.inputs.data1.connect(my_data1) >>> my_data2 = dpf.() >>> op.inputs.data2.connect(my_data2)
>>> # Instantiate operator and connect inputs in one line >>> op = dpf.operators.serialization.serialize_to_hdf5( ... file_path=my_file_path, ... export_floats=my_export_floats, ... export_flat_vectors=my_export_flat_vectors, ... data1=my_data1, ... data2=my_data2, ... )
- 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.serialization.serialize_to_hdf5.InputsSerializeToHdf5(op: ansys.dpf.core.dpf_operator.Operator)#
Intermediate class used to connect user inputs to serialize_to_hdf5 operator.
Examples
>>> from ansys.dpf import core as dpf >>> op = dpf.operators.serialization.serialize_to_hdf5() >>> my_file_path = str() >>> op.inputs.file_path.connect(my_file_path) >>> my_export_floats = bool() >>> op.inputs.export_floats.connect(my_export_floats) >>> my_export_flat_vectors = bool() >>> op.inputs.export_flat_vectors.connect(my_export_flat_vectors) >>> my_data1 = dpf.() >>> op.inputs.data1.connect(my_data1) >>> my_data2 = dpf.() >>> op.inputs.data2.connect(my_data2)
- property file_path#
Allows to connect file_path input to the operator.
Output file path with .h5 extension
- Parameters:
my_file_path (str) –
Examples
>>> from ansys.dpf import core as dpf >>> op = dpf.operators.serialization.serialize_to_hdf5() >>> op.inputs.file_path.connect(my_file_path) >>> # or >>> op.inputs.file_path(my_file_path)
- property export_floats#
Allows to connect export_floats input to the operator.
Converts double to float to reduce file size (default is true)
- Parameters:
my_export_floats (bool) –
Examples
>>> from ansys.dpf import core as dpf >>> op = dpf.operators.serialization.serialize_to_hdf5() >>> op.inputs.export_floats.connect(my_export_floats) >>> # or >>> op.inputs.export_floats(my_export_floats)
- property export_flat_vectors#
Allows to connect export_flat_vectors input to the operator.
If true, vectors and matrices data are exported flat (x1,y1,z1,x2,y2,z2..) (default is false)
- Parameters:
my_export_flat_vectors (bool) –
Examples
>>> from ansys.dpf import core as dpf >>> op = dpf.operators.serialization.serialize_to_hdf5() >>> op.inputs.export_flat_vectors.connect(my_export_flat_vectors) >>> # or >>> op.inputs.export_flat_vectors(my_export_flat_vectors)
- property data1#
Allows to connect data1 input to the operator.
Only the data set explicitly to export is exported
- Parameters:
my_data1 –
Examples
>>> from ansys.dpf import core as dpf >>> op = dpf.operators.serialization.serialize_to_hdf5() >>> op.inputs.data1.connect(my_data1) >>> # or >>> op.inputs.data1(my_data1)
- property data2#
Allows to connect data2 input to the operator.
Only the data set explicitly to export is exported
- Parameters:
my_data2 –
Examples
>>> from ansys.dpf import core as dpf >>> op = dpf.operators.serialization.serialize_to_hdf5() >>> op.inputs.data2.connect(my_data2) >>> # or >>> op.inputs.data2(my_data2)
- 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.serialization.serialize_to_hdf5.OutputsSerializeToHdf5(op: ansys.dpf.core.dpf_operator.Operator)#
Intermediate class used to get outputs from serialize_to_hdf5 operator.
Examples
>>> from ansys.dpf import core as dpf >>> op = dpf.operators.serialization.serialize_to_hdf5() >>> # Connect inputs : op.inputs. ...