AEDT example

[Samuelopez-ansys]

📝 Description of the example

I think an AEDT example would be great to add to PyOptislang documentation. I have the attached script to create a workflow with AEDT and pyAEDT. It was implemented using client/server mode of oSL, maybe now with optiSLang is easier to implement.

📁 Files needed for running the example

Example.zip

💻 Which operating system are you using?

Windows

📀 Which ANSYS version are you using?

2023R1

🐍 Which Python version are you using?

3.7

📦 Installed packages

pyaedt

[jmaravin]

Hi Samuel, thanks very much for this.. can you do an optimization in Optislang of an AEDT project with this script? Apologies as I am not an expert in Python and there´s quite a few commands on your code which I dont understand

[Samuelopez-ansys]

Hi @jmaravin ,

Yes, with this script the optiSLang workflow is automatically created, but it uses legacy optiSLang API, what I asked here is to reproduce the same script but using pyoptislang.

The workflow generated contains an AEDT component, and a python component which executes a PyAEDT script to get the results, this PyAEDT script could do any pre,post processing task.

[jmaravin]

Hi Samuel,
thanks very much for your response.. really appreciate it..
Apologies again as I didn´t fully understand your response. What does "it uses legacy optiSlang API"? What I would like to do is launch and run an optislang optimization in python and control in python all the inputs and outputs without the need of the GUI. Is this possible with the script you sent?
Thanks again for your help and apologies again.

Regards,
Jaime

[Samuelopez-ansys]

Hi,

Before pyoptislang, the only way to control optiSLang was through its API, but with pyoptislang, this should be much easier. That was my request in this discussion, to convert this legacy script to pyoptislang.

Please, reach an Ansys OptiSLang engineer, if you need further help because I am not an optiSLang expert.

Regards,
Samuel

[jmaravin]

Thanks very much for the clarification Samuel. Let´s see if an expert here can help us.

Jaime

[PanekOndrej]

Hi,

it's definitely possible to recreate this using PyOptiSLang, however there are some commands that are not yet fully implemented. Server API would have to be used to recreate this, which would lead to similar code (https://optislang.docs.pyansys.com/version/stable/user_guide/osl_server_usage.html) and therefore I would add this example later.

Regards,
Ondrej

[jmaravin]

Hi Ondrej, thanks very much for your response.. do you think you can do it? If so let me know and I could try to help you. I am quite interested in this..

Regards,
Jaime

[PanekOndrej]

I just quickly rewrote the part with generating workflow to use ansys.optislang.core, hope it helps.

from ansys.optislang.core import Optislang
import ansys.optislang.core.server_commands as commands
from ansys.optislang.core.nodes import DesignFlow
import json
from pathlib import Path

# inputs
optislang_project_path = Path(r"")
python_path = Path(r"")
input_folder_path = Path(r"")
script_name = ""
aedt_name = None

# start optiSLang, open optislang_project_path (or create if doesn't exist) and get server
osl = Optislang(project_path=optislang_project_path)
server = osl.get_osl_server()

# create nodes
sensitivity_uid = server.send_command(
    commands.create_node(
        type_="Sensitivity",
        name="SYSTEM",
    )
)[0][
    "result_data"
]["actor_uid"]

# `create_node`` command doesn't take `integration_type` arg in current release
# so it has to be modified manually
cmd = commands.create_node(
    type_="JSON_input",
    name="INPUT",
    parent_uid=sensitivity_uid,
    design_flow=DesignFlow.RECEIVE.name,
)
cmd_dict = json.loads(cmd)
cmd_dict["projects"][0]["commands"][0]["args"][
    "integration_type"
] = "python_based_integration_plugin"
input_json_uid = server.send_command(json.dumps(cmd_dict))[0]["result_data"]["actor_uid"]

cmd = commands.create_node(
    type_="JSON_output",
    name="OUTPUT",
    parent_uid=sensitivity_uid,
    design_flow=DesignFlow.SEND.name,
)
cmd_dict = json.loads(cmd)
cmd_dict["projects"][0]["commands"][0]["args"][
    "integration_type"
] = "python_based_integration_plugin"
output_json_uid = server.send_command(json.dumps(cmd_dict))[0]["result_data"]["actor_uid"]


batch_uid = server.send_command(
    commands.create_node(
        type_="BatchScript",
        parent_uid=sensitivity_uid,
        name="BATCH",
    )
)[0]["result_data"]["actor_uid"]

postprocessing_uid = server.send_command(
    commands.create_node(
        type_="Postprocessing",
        name="POSTPROCESSING",
    )
)[0]["result_data"]["actor_uid"]

server.send_command(commands.connect_nodes(input_json_uid, "ODesign", batch_uid, "IDesign"))
server.send_command(commands.connect_nodes(batch_uid, "ODesign", output_json_uid, "IDesign"))
server.send_command(
    commands.connect_nodes(sensitivity_uid, "OMDBPath", postprocessing_uid, "IMDBPath")
)

# Input JSON config
# Add path of the input file to the input node
base_path = server.get_project_location().parent
input_json = base_path / "input_data" / "parameter.json"

json_input_path = {}
json_input_path["base_path"] = ""
# json_input_path['base_path_mode'] = "ABSOLUTE_PATH"
json_input_path["base_path_mode"] = "WORKING_DIR_RELATIVE"
json_input_path["split_path"] = {"head": "", "tail": str(input_json)}

server.send_command(
    commands.set_actor_property(
        actor_uid=input_json_uid, name="Path", value={"path": json_input_path}
    )
)

# setup batch node
script_file = input_folder_path / script_name
if aedt_name:
    aedt_file = input_folder_path / aedt_name

content = "set PYTHON=" + str(python_path) + "\n"
content = content + "set REF_DIR=" + str(server.get_working_dir()) + "\n"
content = content + "\n"
content = content + "copy " + str(script_file) + "\n"
if aedt_name:
    content = content + "copy " + str(aedt_file) + "\n"
content = content + "\n"
content = content + "%PYTHON% " + script_name

server.send_command(commands.register_locations_as_parameter(input_json_uid))
server.send_command(commands.set_actor_property(actor_uid=batch_uid, name="Content", value=content))

# Add path of the output file to the output node
output_json_file = base_path / "input_data" / "out.json"
json_output_path = {}
json_output_path["base_path"] = ""
json_output_path["base_path_mode"] = "WORKING_DIR_RELATIVE"
json_output_path["split_path"] = {"head": "", "tail": str(output_json_file)}

server.send_command(
    commands.set_actor_property(
        actor_uid=output_json_uid, name="Path", value={"path": json_output_path}
    )
)
osl.save()
osl.dispose()

[rfahlberg]

@PanekOndrej It would be nice to re-create this example using the new "Workflow Generation" API and add it to the workflow generation examples.

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