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)#

Serialize the inputs in an hdf5 format.

Parameters

file_path (str or os.PathLike) – Output file path with .h5 extension
export_floats (bool) – Converts double to float to reduce file size (default is true)
export_flat_vectors (bool) – If true, vectors and matrices data are exported flat (x1,y1,z1,x2,y2,z2..) (default is false)
data1 – Only the data set explicitly to export is exported
data2 – 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 the global server.

property inputs#

Enables to connect inputs to the operator

Returns: inputs
Return type: InputsSerializeToHdf5

property outputs#

Enables to get outputs of the operator by evaluationg it

Returns: outputs
Return type: OutputsSerializeToHdf5

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 create an operator with the new configuration as a parameter.

Returns: Copy of the operator’s current configuration.
Return type: ansys.dpf.core.config.Config

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 of int, list of doubles,) –

Field, FieldsContainer, Scoping, ScopingsContainer, MeshedRegion,
MeshesContainer, DataSources, 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.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
array([[0.59428386, 0.00201751, 0.0006032 ]])

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.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, 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)#: Put the grpc spec message in self._spec

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.

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, Field, FieldsContainer, Scoping,) –

DataSources, MeshedRegion, ScopingsContainer, CyclicSupport,: …, Output, Outputs, Operator, 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. ...