migrate_to_h5dpf#

Autogenerated DPF operator classes.

class ansys.dpf.core.operators.result.migrate_to_h5dpf.migrate_to_h5dpf(dataset_size_compression_threshold=None, h5_native_compression=None, export_floats=None, filename=None, comma_separated_list_of_results=None, all_time_sets=None, streams_container=None, data_sources=None, compression_workflow=None, filtering_workflow=None, config=None, server=None)#

Read mesh properties from the results files contained in the streams or data sources and make those properties available through a mesh selection manager in output.

Parameters:

dataset_size_compression_threshold (int, optional) – Integer value that defines the minimum dataset size (in bytes) to use h5 native compression applicable for arrays of floats, doubles and integers.
h5_native_compression (int or DataTree, optional) – Integer value / datatree that defines the h5 native compression used for integer input {0: no compression (default); 1-9: gzip compression : 9 provides maximum compression but at the slowest speed.}for datatree input {type: none / gzip / zstd; level: gzip (1-9) / zstd (1-20); num_threads: zstd (>0)}
export_floats (bool, optional) – Converts double to float to reduce file size (default is true)
filename (str) – Filename of the migrated file
comma_separated_list_of_results (str, optional) – List of results (source operator names) separated by semicolons that will be stored. (example: u;s;epel). if empty, all available results will be converted.
all_time_sets (bool, optional) – Default is false
streams_container (StreamsContainer, optional) – Streams (result file container) (optional)
data_sources (DataSources, optional) – If the stream is null then we need to get the file path from the data sources
compression_workflow (Workflow or GenericDataContainer, optional) – Beta option: applies input compression workflow. user can input a genericdatacontainer that will map a compression workflow to a result name. example of map: {{ default: wf1}, {eul: wf2}, {eng_se: wf3}}
filtering_workflow (Workflow or GenericDataContainer, optional) – Applies input filtering workflow. user can input a genericdatacontainer of the format described for pin(6) that will map a filtering workflow to a result name.

Returns:

migrated_file

Return type:

DataSources

Examples

>>> from ansys.dpf import core as dpf

>>> # Instantiate operator
>>> op = dpf.operators.result.migrate_to_h5dpf()

>>> # Make input connections
>>> my_dataset_size_compression_threshold = int()
>>> op.inputs.dataset_size_compression_threshold.connect(my_dataset_size_compression_threshold)
>>> my_h5_native_compression = int()
>>> op.inputs.h5_native_compression.connect(my_h5_native_compression)
>>> my_export_floats = bool()
>>> op.inputs.export_floats.connect(my_export_floats)
>>> my_filename = str()
>>> op.inputs.filename.connect(my_filename)
>>> my_comma_separated_list_of_results = str()
>>> op.inputs.comma_separated_list_of_results.connect(my_comma_separated_list_of_results)
>>> my_all_time_sets = bool()
>>> op.inputs.all_time_sets.connect(my_all_time_sets)
>>> my_streams_container = dpf.StreamsContainer()
>>> op.inputs.streams_container.connect(my_streams_container)
>>> my_data_sources = dpf.DataSources()
>>> op.inputs.data_sources.connect(my_data_sources)
>>> my_compression_workflow = dpf.Workflow()
>>> op.inputs.compression_workflow.connect(my_compression_workflow)
>>> my_filtering_workflow = dpf.Workflow()
>>> op.inputs.filtering_workflow.connect(my_filtering_workflow)

>>> # Instantiate operator and connect inputs in one line
>>> op = dpf.operators.result.migrate_to_h5dpf(
...     dataset_size_compression_threshold=my_dataset_size_compression_threshold,
...     h5_native_compression=my_h5_native_compression,
...     export_floats=my_export_floats,
...     filename=my_filename,
...     comma_separated_list_of_results=my_comma_separated_list_of_results,
...     all_time_sets=my_all_time_sets,
...     streams_container=my_streams_container,
...     data_sources=my_data_sources,
...     compression_workflow=my_compression_workflow,
...     filtering_workflow=my_filtering_workflow,
... )

>>> # Get output data
>>> result_migrated_file = op.outputs.migrated_file()

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:: InputsMigrateToH5Dpf

property outputs#

Enables to get outputs of the operator by evaluating it

Returns:: outputs
Return type:: OutputsMigrateToH5Dpf

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.result.migrate_to_h5dpf.InputsMigrateToH5Dpf(op: ansys.dpf.core.dpf_operator.Operator)#

Intermediate class used to connect user inputs to migrate_to_h5dpf operator.

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.result.migrate_to_h5dpf()
>>> my_dataset_size_compression_threshold = int()
>>> op.inputs.dataset_size_compression_threshold.connect(my_dataset_size_compression_threshold)
>>> my_h5_native_compression = int()
>>> op.inputs.h5_native_compression.connect(my_h5_native_compression)
>>> my_export_floats = bool()
>>> op.inputs.export_floats.connect(my_export_floats)
>>> my_filename = str()
>>> op.inputs.filename.connect(my_filename)
>>> my_comma_separated_list_of_results = str()
>>> op.inputs.comma_separated_list_of_results.connect(my_comma_separated_list_of_results)
>>> my_all_time_sets = bool()
>>> op.inputs.all_time_sets.connect(my_all_time_sets)
>>> my_streams_container = dpf.StreamsContainer()
>>> op.inputs.streams_container.connect(my_streams_container)
>>> my_data_sources = dpf.DataSources()
>>> op.inputs.data_sources.connect(my_data_sources)
>>> my_compression_workflow = dpf.Workflow()
>>> op.inputs.compression_workflow.connect(my_compression_workflow)
>>> my_filtering_workflow = dpf.Workflow()
>>> op.inputs.filtering_workflow.connect(my_filtering_workflow)

property dataset_size_compression_threshold#

Allows to connect dataset_size_compression_threshold input to the operator.

Integer value that defines the minimum dataset size (in bytes) to use h5 native compression applicable for arrays of floats, doubles and integers.

Parameters:: my_dataset_size_compression_threshold (int) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.result.migrate_to_h5dpf()
>>> op.inputs.dataset_size_compression_threshold.connect(my_dataset_size_compression_threshold)
>>> # or
>>> op.inputs.dataset_size_compression_threshold(my_dataset_size_compression_threshold)

property h5_native_compression#

Allows to connect h5_native_compression input to the operator.

Integer value / datatree that defines the h5 native compression used for integer input {0: no compression (default); 1-9: gzip compression : 9 provides maximum compression but at the slowest speed.}for datatree input {type: none / gzip / zstd; level: gzip (1-9) / zstd (1-20); num_threads: zstd (>0)}

Parameters:: my_h5_native_compression (int or DataTree) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.result.migrate_to_h5dpf()
>>> op.inputs.h5_native_compression.connect(my_h5_native_compression)
>>> # or
>>> op.inputs.h5_native_compression(my_h5_native_compression)

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.result.migrate_to_h5dpf()
>>> op.inputs.export_floats.connect(my_export_floats)
>>> # or
>>> op.inputs.export_floats(my_export_floats)

property filename#

Allows to connect filename input to the operator.

Filename of the migrated file

Parameters:: my_filename (str) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.result.migrate_to_h5dpf()
>>> op.inputs.filename.connect(my_filename)
>>> # or
>>> op.inputs.filename(my_filename)

property comma_separated_list_of_results#

Allows to connect comma_separated_list_of_results input to the operator.

List of results (source operator names) separated by semicolons that will be stored. (example: u;s;epel). if empty, all available results will be converted.

Parameters:: my_comma_separated_list_of_results (str) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.result.migrate_to_h5dpf()
>>> op.inputs.comma_separated_list_of_results.connect(my_comma_separated_list_of_results)
>>> # or
>>> op.inputs.comma_separated_list_of_results(my_comma_separated_list_of_results)

property all_time_sets#

Allows to connect all_time_sets input to the operator.

Default is false

Parameters:: my_all_time_sets (bool) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.result.migrate_to_h5dpf()
>>> op.inputs.all_time_sets.connect(my_all_time_sets)
>>> # or
>>> op.inputs.all_time_sets(my_all_time_sets)

property streams_container#

Allows to connect streams_container input to the operator.

Streams (result file container) (optional)

Parameters:: my_streams_container (StreamsContainer) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.result.migrate_to_h5dpf()
>>> op.inputs.streams_container.connect(my_streams_container)
>>> # or
>>> op.inputs.streams_container(my_streams_container)

property data_sources#

Allows to connect data_sources input to the operator.

If the stream is null then we need to get the file path from the data sources

Parameters:: my_data_sources (DataSources) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.result.migrate_to_h5dpf()
>>> op.inputs.data_sources.connect(my_data_sources)
>>> # or
>>> op.inputs.data_sources(my_data_sources)

property compression_workflow#

Allows to connect compression_workflow input to the operator.

Beta option: applies input compression workflow. user can input a genericdatacontainer that will map a compression workflow to a result name. example of map: {{ default: wf1}, {eul: wf2}, {eng_se: wf3}}

Parameters:: my_compression_workflow (Workflow or GenericDataContainer) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.result.migrate_to_h5dpf()
>>> op.inputs.compression_workflow.connect(my_compression_workflow)
>>> # or
>>> op.inputs.compression_workflow(my_compression_workflow)

property filtering_workflow#

Allows to connect filtering_workflow input to the operator.

Applies input filtering workflow. user can input a genericdatacontainer of the format described for pin(6) that will map a filtering workflow to a result name.

Parameters:: my_filtering_workflow (Workflow or GenericDataContainer) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.result.migrate_to_h5dpf()
>>> op.inputs.filtering_workflow.connect(my_filtering_workflow)
>>> # or
>>> op.inputs.filtering_workflow(my_filtering_workflow)

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.result.migrate_to_h5dpf.OutputsMigrateToH5Dpf(op: ansys.dpf.core.dpf_operator.Operator)#

Intermediate class used to get outputs from migrate_to_h5dpf operator.

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.result.migrate_to_h5dpf()
>>> # Connect inputs : op.inputs. ...
>>> result_migrated_file = op.outputs.migrated_file()

property migrated_file#

Allows to get migrated_file output of the operator

Returns:: my_migrated_file
Return type:: DataSources

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.result.migrate_to_h5dpf()
>>> # Connect inputs : op.inputs. ...
>>> result_migrated_file = op.outputs.migrated_file()