Direct communication using the optiSLang server API

PyOptiSLang is intended to provide a Pythonic API on top of Ansys optiSLang. However, not each and every capability available through the Ansys optiSLang server API is already exposed via explicit PyOptiSLang API capability.

The TcpOslServer wrapper class can be used for raw communication with optiSLang, to overcome this limitation. It provides explicit methods for accessing specific optiSLang API endpoints. Additionally, the generic TcpOslServer.send_command method can be used in conjunction with the convenience functions from the server_queries and server_commands modules.

Note

Please note, that direct communication with Ansys optiSLang server API is discouraged for productive usage, as this API and underlying technology is subject to change. Please prefer using explicit PyOptiSLang API capability wherever possible.

You can either directly create an instance of TcpOslServer class to connect to an already running instance of optiSLang or just use the Optislang.osl_server property to obtain a handle to the TcpOslServer used by the Optislang instance internally. Subsequently, the TcpOslServer class methods can be used to access the optiSLang server:

from ansys.optislang.core import Optislang
from ansys.optislang.core.project_parametric import Design
from ansys.optislang.core import examples
from pathlib import Path

# open project with defined parameters
parametric_project = examples.get_files("calculator_with_params")[1][0]
osl = Optislang(project_path=parametric_project)

# Query basic server/project info.
print(f"Basic project info: {osl.osl_server.get_basic_project_info()}")
print(f"Server info: {osl.osl_server.get_server_info()}")
print(f"Full project tree: {osl.osl_server.get_full_project_tree()}")

For any optiSLang server API capability not yet directly exposed in TcpOslServer class, the generic TcpOslServer.send_command method can be used. It takes a generic request string, sends the request to optiSLang server and returns the corresponding response. As a convenience, the functions from the server_queries and server_commands modules can be used to generate the request strings:

from ansys.optislang.core.tcp import server_commands as commands
from ansys.optislang.core.tcp import server_queries as queries
from ansys.optislang.core.project_parametric import Parameter

# Use raw osl server communication to modify the first parameter
# on project root level.

# Get the first parameter on project root level
root_system_uid = osl.project.root_system.uid
root_system_properties = osl.osl_server.send_command(
    queries.actor_properties(uid=root_system_uid)
)
root_system_pm_raw = root_system_properties["properties"]["ParameterManager"]

first_parameter = Parameter.from_dict(root_system_pm_raw["parameter_container"][0])

# Print out the reference value
print(
    f'Parameter "{first_parameter.name}" reference value: {first_parameter.reference_value}'
)

# Modify the reference value
first_parameter.reference_value = 15.0

# Adapt the parameter manager to the changes and
# send the modified parameter manager back to optiSLang
root_system_pm_raw["parameter_container"][0] = first_parameter.to_dict()

server_response = osl.osl_server.send_command(
    commands.set_actor_property(
        actor_uid=root_system_uid, name="ParameterManager", value=root_system_pm_raw
    )
)

print(f'Modifying parameter reference value: {server_response[0]["status"]}')

# Get and print the (now modified) first parameter on project root level
root_system_properties = osl.osl_server.send_command(
    queries.actor_properties(uid=root_system_uid)
)
root_system_pm_raw = root_system_properties["properties"]["ParameterManager"]

modified_first_parameter = Parameter.from_dict(
    root_system_pm_raw["parameter_container"][0]
)

print(
    f'Modified parameter "{modified_first_parameter.name}" reference value: {modified_first_parameter.reference_value}'
)