Enhance OPC UA testing framework and documentation
- Added a new marker in pyproject.toml for tests using the in-process OPC UA server.
- Updated opc-communication.md to clarify E2E test scenarios involving the real OPC server and mock server.
- Introduced an in-process asyncua OPC UA server fixture in conftest.py for E2E tests.
- Created a new fixture for activities using the real OpcRepository connected to the in-process server.
- Updated scenarios.md to include instructions for running OPC real-server tests.
Enhance OPC UA communication and metrics tracking
- Updated README.md to include new OPC UA Communication section and detailed metrics for session and write diagnostics.
- Added new metrics in laborious/metrics.py for tracking OPC UA session states and write attempts.
- Refactored OPC activity in laborious/activities/opc.py to handle session errors and improve error reporting.
- Updated e2e tests to cover new scenarios for OPC session/channel errors and reconnect handling.
- Modified .gitignore to include relatorio files and mlruns directory.
- Added ipykernel to requirements-dev.txt for Jupyter notebook support.
Enhance E2E testing with MinIO support and update documentation
- Updated `requirements-dev.txt` to include MinIO support in testcontainers.
- Added a new fixture for MinIO container setup in `conftest.py` to facilitate E2E tests involving S3-compatible storage.
- Introduced a new test fixture for activities using a real MinIO container in `conftest.py`.
- Updated E2E test scenarios and documentation to reflect the integration of MinIO for offload uploads and clarified error handling in workflows.
- Refactored existing tests to improve clarity and maintainability.
Update dependencies and refactor input filter handling for consistency
- Updated sientia-dataops-library dependency version from 1.10.3 to 1.10.4 in requirements.txt.
- Refactored input filter handling in the Gates class to read policy and config keys in a case-insensitive manner.
- Updated test cases to ensure consistency in filter key naming conventions across various scenarios.
Implement MinIO Offload and Retention Features
- Added configuration options for MinIO retention hours and offload threshold in README.
- Introduced MinIO payload offloading for large DataFrame-derived payloads, storing them as parquet files.
- Updated activities to utilize MinIO for data loading and cleanup, including new methods for offloading and retention management.
- Refactored existing activities to integrate MinIO functionality, ensuring compatibility with previous workflows.
- Removed the legacy MinioRepository class, consolidating MinIO operations under a new manager structure.
- Updated requirements to use the latest version of the sientia-dataops-library.
Enhance end-to-end tests for PredictionsBatch workflow scenarios
- Introduced mock repositories for PI Web API and OPC operations to improve test coverage.
- Updated test scenarios to handle partial write errors for PI Web API and OPC.
- Refactored existing tests to assert correct behavior under various error conditions.
- Enhanced logging and error handling in API and OPC activities to provide clearer feedback on failures.
- Removed outdated integration test file to streamline test suite.
Enhance end-to-end tests for PredictionsBatch workflow scenarios
- Updated scenario descriptions and assertions for error handling in prediction exports.
- Introduced new test cases for handling exports with only OPC or PI Web API.
- Refactored existing tests to improve clarity and maintainability, including dynamic data insertion.
- Adjusted workflow input configurations to better reflect expected behaviors for various error scenarios.
Refactor end-to-end tests for PredictionsBatch workflow
- Updated the `assert_continue` function to accept dynamic prediction confidence and comments.
- Simplified test scenarios by introducing helper functions for data insertion.
- Enhanced test cases to verify behavior for CONTINUE, STOP, and REPEAT policies at transform gates.
- Improved clarity and maintainability of test structure.
SIENTIAPDE-1478
Enhance end-to-end tests for PredictionsBatch workflow
- Added new test scenarios for input and transform gates handling CONTINUE, STOP, and REPEAT policies.
- Implemented sample data insertion functions for testing various prediction outcomes.
- Updated existing tests to verify behavior under different input conditions and response validations.
- Refactored test structure for clarity and maintainability.
Update coverage source in pyproject.toml, add testcontainers for PostgreSQL in requirements-dev.txt, increment image tag and adjust probe delays in values.yaml, and refine condition checks in format_and_export_prediction.py and mlflow.py. Additionally, enhance test coverage in test_gates.py.
