f6584314b2
Add unit tests for connectors configuration, logger, workflows, and predictions batch - Implement tests for MLflow, OPC, and Postgres configuration builders to validate environment variable handling and default values. - Create tests for the logger to ensure default settings and handler configurations are correct. - Add comprehensive tests for the FormatAndExportPrediction and PredictionProcess workflows, covering various scenarios including path flags and activity execution. - Introduce tests for the PredictionsBatch workflow to verify the execution of local activities and child workflows. - Include a values.yaml file for Kubernetes deployment configuration, specifying image details, service account settings, environment variables, and resource limits.
106 lines
4.3 KiB
Python
106 lines
4.3 KiB
Python
from temporalio import activity, workflow
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with workflow.unsafe.imports_passed_through():
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from logging import Logger
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from sientia_do.notifications.handlers import NotificationHandler
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from sientia_do.notifications.models import NotificationLevel
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from laborious.activities.base import BaseActivity
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from laborious.utils.repository.opc_repository import OpcRepository
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from typing import Any
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import traceback
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from pandas import DataFrame
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class OPC(BaseActivity):
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def __init__(self, opc_servers: dict[str, dict[str, Any]],
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logger: Logger, notification_handler: NotificationHandler):
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self.logger = logger
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self.notification_handler = notification_handler
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self.opc_servers = opc_servers
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self.opc_repository = {}
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for name, server in opc_servers.items():
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self.opc_repository[name] = OpcRepository(
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name=name,
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url=server['url'],
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logger=self.logger,
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server_uri=server['server_uri'],
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cert_path=server['cert_path'],
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private_key_path=server['private_key_path'],
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server_cert_path=server['server_cert_path'],
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notification_handler=self.notification_handler,
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reconnection_interval=server['reconnection_interval'],
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)
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self.opc_repository[name].connect()
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BaseActivity.__init__(self, logger, notification_handler)
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def write_data(self, server: str, tag: str, data: Any,
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data_type: str, tag_type: str):
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try:
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self.opc_repository[server].write_data(
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tag, data, data_type)
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self.logger.debug(f"Wrote {tag_type} to {tag}")
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except Exception as e:
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trace = traceback.format_exc()
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self.notification_handler.build_and_send_notification(
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notification_id=f"WRITE_OPC_{tag_type.upper()}_ERROR",
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message=f"Error writing data to OPC server: {e}",
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block="write_opc_data",
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level=NotificationLevel.ERROR,
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attachment_content=trace
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)
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self.logger.error(trace)
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@activity.defn(name='write_opc_data')
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async def write_opc_data(self, input_data: dict[str, Any]):
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"""
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Write prediction and confidence data to OPC servers. The two writing
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operations are optional and independent of each other.
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Args:
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input_data (dict[str, Any]): The input data. Contains the following keys:
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- data (dict[str, Any]): The dataframe that contains the data to write
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to the OPC servers.
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- opc_output_config (dict[str, Any]): The OPC writing configuration.
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The keys are the OPC server names and the values contain:
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prediction_tags (dict[str, Any]): The tags to write to the OPC servers.
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confidence_tags (dict[str, Any]): The tags to write to the OPC servers.
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Returns:
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"""
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self.logger.debug("Writing data to OPC servers...")
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data = DataFrame(input_data['data'])
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opc_output_config = input_data['opc_output_config']
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self.logger.debug(data)
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for server, config in opc_output_config.items():
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if self.opc_repository.get(server) is None:
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self.logger.error(f"OPC server {server} not found")
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continue
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if 'prediction_tags' in config:
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for tag, tag_config in config['prediction_tags'].items():
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self.write_data(
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server=server,
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tag=tag,
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data=data.head(1)['prediction'].values[0],
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data_type=tag_config['data_type'],
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tag_type='prediction'
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)
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if 'confidence_tags' in config:
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for tag, tag_config in config['confidence_tags'].items():
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self.write_data(
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server=server,
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tag=tag,
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data=data.head(1)['prediction_confidence'].values[0],
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data_type=tag_config['data_type'],
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tag_type='confidence'
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)
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def shutdown(self):
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for opc in self.opc_repository.values():
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opc.disconnect()
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