Gergő Jedlicska
70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
import typer
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from bg_specklepy.SpeckleServer.client import Client
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from bg_specklepy.Operations.columnOffsetEvaluation import ColumnOffsetEvaluation
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from specklepy.transports.memory import MemoryTransport
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from specklepy.transports.server import ServerTransport
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from specklepy.api import operations
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from pydantic import BaseModel, ConfigDict
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from stringcase import camelcase
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# Example Model can be found here: https://speckle.xyz/streams/ff47530e95
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class SpeckleProjectData(BaseModel):
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"""Values of the project / model that triggered the run of this function."""
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project_id: str
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model_id: str
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version_id: str
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speckle_server_url: str
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model_config = ConfigDict(alias_generator=camelcase, protected_namespaces=())
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class FunctionInputs(BaseModel):
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"""
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These are function author defined values, automate will make sure to supply them.
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"""
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tolerance: float
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echo_level: int
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scale_spheres: bool
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class Config:
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alias_generator = camelcase
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def main(speckle_project_data: str, function_inputs: str, speckle_token: str):
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project_data = SpeckleProjectData.model_validate_json(speckle_project_data)
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inputs = FunctionInputs.model_validate_json(function_inputs)
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stream_id = project_data.project_id
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commit_id = project_data.version_id
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speckle_server = project_data.speckle_server_url
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# Initiating appropriate server objects
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client_obj = Client(speckle_server, speckle_token)
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commit = client_obj.commit.get(stream_id, commit_id)
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memory_transport = MemoryTransport()
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server_transport = ServerTransport(stream_id, client_obj)
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commit_data = operations.receive(
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commit.referencedObject, server_transport, memory_transport
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)
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# Define and run analysis
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evaluation = ColumnOffsetEvaluation(
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client_obj=client_obj,
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stream_id=stream_id,
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commit_object=commit,
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commit_data=commit_data,
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tolerance=inputs.tolerance,
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echo_level=inputs.echo_level,
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scale_spheres=inputs.scale_spheres,
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)
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evaluation.run()
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if __name__ == "__main__":
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typer.run(main)
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