grpc_start_server

Autogenerated DPF operator classes.

class ansys.dpf.core.operators.server.grpc_start_server.grpc_start_server(ip=None, port=None, starting_option=None, should_start_server=None, data_sources=None, dpf_context=None, config=None, server=None)

Starts a dpf’s grpc server (if local) or connect to one and keep it waiting for requests in a streams.

Parameters:

• ip (str, optional) – If no ip address is put, the local ip address is taken

• port (str or int, optional) – If no port is put, port 50052 is taken

• starting_option (int, optional) – Default is 1 that starts server in new thread. with 0, this thread will be waiting for grpc calls and will not be usable for anything else. with 2, it the server will be started in a new process.

• should_start_server (bool, optional) – If true, the server is assumed to be local and is started. if false, only a client (able to send grpc calls) will be started

• data_sources (DataSources, optional) – A data source with result key ‘grpc’ and file path ‘port:ip’ can be used instead of the input port and ip.

• dpf_context (str or int, optional) – This pin is associated with pin(2) = 2 (server started in a new process). user can enter the integer associated with a dpf context (1: standalone context - dpfcorestandalone.xml, 3: custom - dpfcustomdefined.xml) or a string with the path of the xml specifying the context.

Returns:

grpc_streams – Dpf streams handling the server, if the server is started in this thread, then nothing is added in output

Return type:

StreamsContainer, optional

Examples

>>> from ansys.dpf import core as dpf

>>> # Instantiate operator
>>> op = dpf.operators.server.grpc_start_server()

>>> # Make input connections
>>> my_ip = str()
>>> op.inputs.ip.connect(my_ip)
>>> my_port = str()
>>> op.inputs.port.connect(my_port)
>>> my_starting_option = int()
>>> op.inputs.starting_option.connect(my_starting_option)
>>> my_should_start_server = bool()
>>> op.inputs.should_start_server.connect(my_should_start_server)
>>> my_data_sources = dpf.DataSources()
>>> op.inputs.data_sources.connect(my_data_sources)
>>> my_dpf_context = str()
>>> op.inputs.dpf_context.connect(my_dpf_context)

>>> # Instantiate operator and connect inputs in one line
>>> op = dpf.operators.server.grpc_start_server(
...     ip=my_ip,
...     port=my_port,
...     starting_option=my_starting_option,
...     should_start_server=my_should_start_server,
...     data_sources=my_data_sources,
...     dpf_context=my_dpf_context,
... )

>>> # Get output data
>>> result_grpc_streams = op.outputs.grpc_streams()

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:

InputsGrpcStartServer

property outputs

Enables to get outputs of the operator by evaluating it

Returns:

outputs

Return type:

OutputsGrpcStartServer

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.server.grpc_start_server.InputsGrpcStartServer(op: ansys.dpf.core.dpf_operator.Operator)

Intermediate class used to connect user inputs to grpc_start_server operator.

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.server.grpc_start_server()
>>> my_ip = str()
>>> op.inputs.ip.connect(my_ip)
>>> my_port = str()
>>> op.inputs.port.connect(my_port)
>>> my_starting_option = int()
>>> op.inputs.starting_option.connect(my_starting_option)
>>> my_should_start_server = bool()
>>> op.inputs.should_start_server.connect(my_should_start_server)
>>> my_data_sources = dpf.DataSources()
>>> op.inputs.data_sources.connect(my_data_sources)
>>> my_dpf_context = str()
>>> op.inputs.dpf_context.connect(my_dpf_context)

property ip

Allows to connect ip input to the operator.

If no ip address is put, the local ip address is taken

Parameters:

my_ip (str) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.server.grpc_start_server()
>>> op.inputs.ip.connect(my_ip)
>>> # or
>>> op.inputs.ip(my_ip)

property port

Allows to connect port input to the operator.

If no port is put, port 50052 is taken

Parameters:

my_port (str or int) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.server.grpc_start_server()
>>> op.inputs.port.connect(my_port)
>>> # or
>>> op.inputs.port(my_port)

property starting_option

Allows to connect starting_option input to the operator.

Default is 1 that starts server in new thread. with 0, this thread will be waiting for grpc calls and will not be usable for anything else. with 2, it the server will be started in a new process.

Parameters:

my_starting_option (int) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.server.grpc_start_server()
>>> op.inputs.starting_option.connect(my_starting_option)
>>> # or
>>> op.inputs.starting_option(my_starting_option)

property should_start_server

Allows to connect should_start_server input to the operator.

If true, the server is assumed to be local and is started. if false, only a client (able to send grpc calls) will be started

Parameters:

my_should_start_server (bool) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.server.grpc_start_server()
>>> op.inputs.should_start_server.connect(my_should_start_server)
>>> # or
>>> op.inputs.should_start_server(my_should_start_server)

property data_sources

Allows to connect data_sources input to the operator.

A data source with result key ‘grpc’ and file path ‘port:ip’ can be used instead of the input port and ip.

Parameters:

my_data_sources (DataSources) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.server.grpc_start_server()
>>> op.inputs.data_sources.connect(my_data_sources)
>>> # or
>>> op.inputs.data_sources(my_data_sources)

property dpf_context

Allows to connect dpf_context input to the operator.

This pin is associated with pin(2) = 2 (server started in a new process). user can enter the integer associated with a dpf context (1: standalone context - dpfcorestandalone.xml, 3: custom - dpfcustomdefined.xml) or a string with the path of the xml specifying the context.

Parameters:

my_dpf_context (str or int) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.server.grpc_start_server()
>>> op.inputs.dpf_context.connect(my_dpf_context)
>>> # or
>>> op.inputs.dpf_context(my_dpf_context)

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.server.grpc_start_server.OutputsGrpcStartServer(op: ansys.dpf.core.dpf_operator.Operator)

Intermediate class used to get outputs from grpc_start_server operator.

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.server.grpc_start_server()
>>> # Connect inputs : op.inputs. ...
>>> result_grpc_streams = op.outputs.grpc_streams()

property grpc_streams

Allows to get grpc_streams output of the operator

Returns:

my_grpc_streams

Return type:

StreamsContainer

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.server.grpc_start_server()
>>> # Connect inputs : op.inputs. ...
>>> result_grpc_streams = op.outputs.grpc_streams()