Formatting #3
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Formatting #3
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Added necessary dependencies (excluding `pandas`) to support project functionality, testing, and CI workflows.
- Added `format_to_forecast_sql` in `format_forecast.py` to convert NESO solar forecast data into a `ForecastSQL` object. - Fetches model metadata, location, and input data last updated timestamp. - Processes rows to create `ForecastValue` objects and aggregates them into a single `ForecastSQL` object. - Logs key steps for improved traceability and debugging. - Added `test_format_to_forecast_sql_real` in `test_format_forecast.py` to validate the functionality of `format_to_forecast_sql`. - Fetches real data using `fetch_data` and ensures it is correctly formatted into a `ForecastSQL` object. - Performs validation to ensure the number of `ForecastValue` entries matches the filtered DataFrame. - Added `conftest.py` to provide shared test fixtures for the test suite: - `postgres_container` fixture spins up a PostgreSQL container for isolated testing using `testcontainers`. - `db_session` fixture sets up a clean database schema and a test ML model for each test function. - `test_config` fixture provides shared configuration constants for tests. These changes establish a robust framework for processing and testing solar forecast data effectively.
…tion - Replaced hardcoded variables (`resource_id`, `limit`, `columns`, `rename_columns`, `sql_query`) with `test_config` fixture for better reusability and maintainability. - Updated all test cases (`test_fetch_data_api`, `test_fetch_data_sql`, `test_data_consistency`) to use `test_config` for dynamic parameterization. - Ensured SQL queries and API calls use values from `test_config`. - Improved code readability and consistency across test cases.
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