Create a plug-in package with multiple operators

This example shows how to create a plug-in package with multiple operators. The benefits of writing a package rather than simple scripts are:

• Componentization: You can split the code into several Python modules or files.

• Distribution: You can use standard Python tools to upload and download packages.

• Documentation: You can add README files, documentation, tests, and examples to the package.

For this example, the plug-in package contains two different operators:

• One that returns all scoping IDs having data higher than the average

• One that returns all scoping IDs having data lower than the average

Note

This example requires DPF 4.0 (Ansys 2022R2) or above. For more information, see Compatibility.

Create the plug-in package

Each operator implementation derives from the ansys.dpf.core.custom_operator.CustomOperatorBase class and a call to the ansys.dpf.core.custom_operator.record_operator() method, which records the operators of the plug-in package.

Download the average_filter_plugin plug-in package that has already been created for you.

from ansys.dpf.core import examples

plugin_folder = examples.download_average_filter_plugin()

Load the plug-in package

You use the function ansys.dpf.core.core.load_library() to load the plug-in package.

• The first argument is the path to the directory where the plug-in package is located.

• The second argument is py_<package>, where <package> is the name identifying the plug-in package.

• The third argument is the name of the function exposed in the __init__ file for the plug-in package that is used to record operators.

from ansys.dpf import core as dpf
from ansys.dpf.core import examples

# Python plugins are not supported in process.
dpf.start_local_server(config=dpf.AvailableServerConfigs.GrpcServer)

tmp = dpf.make_tmp_dir_server()
dpf.upload_files_in_folder(dpf.path_utilities.join(tmp, "average_filter_plugin"), plugin_folder)
dpf.load_library(
    dpf.path_utilities.join(tmp, "average_filter_plugin"),
    "py_average_filter",
    "load_operators",
)

'py_average_filter successfully loaded'

Instantiate the operator.

new_operator = dpf.Operator("ids_with_data_lower_than_average")

Connect a workflow

Connect a workflow that computes the norm of the displacement to the ids_with_data_lower_than_average operator. Methods of the ids_with_data_lower_than_average class are dynamically added because specifications for the operator are defined in the plug-in package.

Use the operator

ds = dpf.DataSources(dpf.upload_file_in_tmp_folder(examples.find_static_rst()))
displacement = dpf.operators.result.displacement(data_sources=ds)
norm = dpf.operators.math.norm(displacement)
new_operator.inputs.connect(norm)


new_scoping = new_operator.outputs.scoping()
print("scoping in was:", norm.outputs.field().scoping)
print("----------------------------------------------")
print("scoping out is:", new_scoping)

scoping in was: DPF  Scoping:
  with Nodal location and 81 entities

----------------------------------------------
scoping out is: DPF  Scoping:
  with Nodal location and 35 entities