Formatting and Saving Forecast Data with Tests

neso_solar_consumer/fetch_data.py

@@ -8,9 +8,7 @@
 import pandas as pd
 
 
-def fetch_data(
-    resource_id: str, limit: int, columns: list, rename_columns: dict
-) -> pd.DataFrame:
+def fetch_data(resource_id: str, limit: int, columns: list, rename_columns: dict) -> pd.DataFrame:
     """
     Fetch data from the NESO API and process it into a Pandas DataFrame.
 
@@ -55,9 +53,7 @@ def fetch_data(
         return pd.DataFrame()
 
 
-def fetch_data_using_sql(
-    sql_query: str, columns: list, rename_columns: dict
-) -> pd.DataFrame:
+def fetch_data_using_sql(sql_query: str, columns: list, rename_columns: dict) -> pd.DataFrame:
     """
     Fetch data from the NESO API using an SQL query, process it, and return specific columns with renamed headers.
 
@@ -98,4 +94,4 @@ def fetch_data_using_sql(
 
     except Exception as e:
         print(f"An error occurred: {e}")
-        return pd.DataFrame()
+        return pd.DataFrame()

neso_solar_consumer/format_forecast.py

@@ -0,0 +1,71 @@
+import logging
+from datetime import datetime, timezone
+import pandas as pd
+from nowcasting_datamodel.models import ForecastSQL, ForecastValue
+from nowcasting_datamodel.read.read import get_latest_input_data_last_updated, get_location
+from nowcasting_datamodel.read.read_models import get_model
+
+# Configure logging (set to INFO for production; use DEBUG during debugging)
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+logger = logging.getLogger(__name__)
+
+
+def format_to_forecast_sql(data: pd.DataFrame, model_tag: str, model_version: str, session) -> list:
+    """
+    Convert NESO solar forecast data into a single ForecastSQL object.
+
+    Args:
+        data (pd.DataFrame): Input DataFrame with forecast data.
+        model_tag (str): The model name/tag.
+        model_version (str): The model version.
+        session: SQLAlchemy session for database access.
+
+    Returns:
+        list: A list containing one ForecastSQL object.
+    """
+    logger.info("Starting format_to_forecast_sql process...")
+
+    # Step 1: Retrieve model metadata
+    model = get_model(name=model_tag, version=model_version, session=session)
+    logger.debug(f"Model Retrieved: {model}")
+
+    # Step 2: Fetch input data last updated timestamp
+    input_data_last_updated = get_latest_input_data_last_updated(session=session)
+    logger.debug(f"Input Data Last Updated: {input_data_last_updated}")
+
+    # Step 3: Fetch or create the location using get_location
+    location = get_location(session=session, gsp_id=0)
+    logger.debug(f"Location Retrieved or Created: {location}")
+
+    # Step 4: Process rows into ForecastValue objects
+    forecast_values = []
+    for _, row in data.iterrows():
+        if pd.isnull(row["start_utc"]) or pd.isnull(row["solar_forecast_kw"]):
+            logger.debug("Skipping row due to missing data")
+            continue
+
+        target_time = datetime.fromisoformat(row["start_utc"]).replace(tzinfo=timezone.utc)
+        forecast_value = ForecastValue(
+            target_time=target_time,
+            expected_power_generation_megawatts=row["solar_forecast_kw"],
+        ).to_orm()
+        forecast_values.append(forecast_value)
+        logger.debug(f"Forecast Value Created: {forecast_value}")
+
+    if not forecast_values:
+        logger.warning("No valid forecast values found in the data. Exiting.")
+        return []
+
+    # Step 5: Create a single ForecastSQL object
+    forecast = ForecastSQL(
+        model=model,
+        forecast_creation_time=datetime.now(tz=timezone.utc),
+        location=location,  # Directly using the location from get_location
+        input_data_last_updated=input_data_last_updated,
+        forecast_values=forecast_values,
+        historic=False,
+    )
+    logger.debug(f"ForecastSQL Object Created: {forecast}")
+
+    logger.info("ForecastSQL object successfully added to session and flushed.")
+    return [forecast]

neso_solar_consumer/save_forecasts.py

@@ -0,0 +1,18 @@
+# save_forecasts.py
+
+from nowcasting_datamodel.save.save import save
+
+
+def save_forecasts_to_db(forecasts: list, session):
+    """
+    Save a list of ForecastSQL objects to the database using the nowcasting_datamodel `save` function.
+
+    Parameters:
+        forecasts (list): The list of ForecastSQL objects to save.
+        session (Session): SQLAlchemy session for database access.
+    """
+    save(
+        forecasts=forecasts,
+        session=session,
+        save_to_last_seven_days=True,  # Save forecasts to the last seven days table
+    )

pyproject.toml

@@ -6,10 +6,10 @@ build-backend = "setuptools.build_meta"
 name = "neso_solar_consumer"
 version = "0.1"
 dependencies = [
-    "pandas"
+    "pandas","sqlalchemy","nowcasting_datamodel"
 ]
 
 [project.optional-dependencies]
 dev = [
-    "pytest","black","ruff"
-]
+    "pytest", "black", "ruff"
+]

tests/conftest.py

@@ -0,0 +1,72 @@
+import pytest
+from typing import Generator
+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker
+from nowcasting_datamodel.models.base import Base_Forecast
+from nowcasting_datamodel.models import MLModelSQL
+
+# Shared Test Configuration Constants
+TEST_DB_URL = "postgresql://postgres:12345@localhost/testdb"
+RESOURCE_ID = "db6c038f-98af-4570-ab60-24d71ebd0ae5"
+LIMIT = 5
+COLUMNS = ["DATE_GMT", "TIME_GMT", "EMBEDDED_SOLAR_FORECAST"]
+RENAME_COLUMNS = {
+    "DATE_GMT": "start_utc",
+    "TIME_GMT": "end_utc",
+    "EMBEDDED_SOLAR_FORECAST": "solar_forecast_kw",
+}
+MODEL_NAME = "real_data_model"
+MODEL_VERSION = "1.0"
+
+
+@pytest.fixture(scope="function")
+def db_session() -> Generator:
+    """
+    Fixture to set up and tear down a PostgreSQL database session for testing.
+
+    This fixture:
+    - Creates a fresh database schema before each test.
+    - Adds a dummy ML model for test purposes.
+    - Tears down the database session and cleans up resources after each test.
+
+    Returns:
+        Generator: A SQLAlchemy session object.
+    """
+    # Create database engine and tables
+    engine = create_engine(TEST_DB_URL)
+    Base_Forecast.metadata.drop_all(engine)  # Drop all tables to ensure a clean slate
+    Base_Forecast.metadata.create_all(engine)  # Recreate the tables
+
+    # Establish session
+    Session = sessionmaker(bind=engine)
+    session = Session()
+
+    # Insert a dummy model for testing
+    session.query(MLModelSQL).delete()  # Clean up any pre-existing data
+    model = MLModelSQL(name=MODEL_NAME, version=MODEL_VERSION)
+    session.add(model)
+    session.commit()
+
+    yield session  # Provide the session to the test
+
+    # Cleanup: close session and dispose of engine
+    session.close()
+    engine.dispose()
+
+
+@pytest.fixture(scope="session")
+def test_config():
+    """
+    Fixture to provide shared test configuration constants.
+
+    Returns:
+        dict: A dictionary of test configuration values.
+    """
+    return {
+        "resource_id": RESOURCE_ID,
+        "limit": LIMIT,
+        "columns": COLUMNS,
+        "rename_columns": RENAME_COLUMNS,
+        "model_name": MODEL_NAME,
+        "model_version": MODEL_VERSION,
+    }

tests/test_fetch_data.py

@@ -1,74 +1,44 @@
-"""
-Test Suite for `fetch_data` and `fetch_data_using_sql` Functions
-
-This script contains tests to validate the functionality and consistency of two data-fetching functions: 
-`fetch_data` (via API) and `fetch_data_using_sql` (via SQL query). It checks that the data returned 
-by both methods is correctly processed, contains the expected columns, and ensures the consistency 
-between the two methods.
-
-### How to Run the Tests:
-
-You can run the entire suite of tests in this file using `pytest` from the command line:
-
-    pytest tests/test_fetch_data.py
-
-To run a specific test, you can specify the function name:
-
-    pytest tests/test_fetch_data.py::test_fetch_data_api
-
-For verbose output, use the -v flag:
-
-    pytest tests/test_fetch_data.py -v
-
-To run tests matching a specific pattern, use the -k option:
-
-    pytest tests/test_fetch_data.py -k "fetch_data"
-
-"""
-
-import pytest
 from neso_solar_consumer.fetch_data import fetch_data, fetch_data_using_sql
 
 
-resource_id = "db6c038f-98af-4570-ab60-24d71ebd0ae5"
-limit = 5
-columns = ["DATE_GMT", "TIME_GMT", "EMBEDDED_SOLAR_FORECAST"]
-rename_columns = {
-    "DATE_GMT": "start_utc",
-    "TIME_GMT": "end_utc",
-    "EMBEDDED_SOLAR_FORECAST": "solar_forecast_kw",
-}
-sql_query = f'SELECT * from "{resource_id}" LIMIT {limit}'
-
-
-def test_fetch_data_api():
+def test_fetch_data_api(test_config):
     """
     Test the fetch_data function to ensure it fetches and processes data correctly via API.
     """
-    df_api = fetch_data(resource_id, limit, columns, rename_columns)
+    df_api = fetch_data(
+        test_config["resource_id"],
+        test_config["limit"],
+        test_config["columns"],
+        test_config["rename_columns"],
+    )
     assert not df_api.empty, "fetch_data returned an empty DataFrame!"
     assert set(df_api.columns) == set(
-        rename_columns.values()
+        test_config["rename_columns"].values()
     ), "Column names do not match after renaming!"
 
 
-def test_fetch_data_sql():
+def test_fetch_data_sql(test_config):
     """
     Test the fetch_data_using_sql function to ensure it fetches and processes data correctly via SQL.
     """
-    df_sql = fetch_data_using_sql(sql_query, columns, rename_columns)
+    sql_query = f'SELECT * FROM "{test_config["resource_id"]}" LIMIT {test_config["limit"]}'
+    df_sql = fetch_data_using_sql(sql_query, test_config["columns"], test_config["rename_columns"])
     assert not df_sql.empty, "fetch_data_using_sql returned an empty DataFrame!"
     assert set(df_sql.columns) == set(
-        rename_columns.values()
+        test_config["rename_columns"].values()
     ), "Column names do not match after renaming!"
 
 
-def test_data_consistency():
+def test_data_consistency(test_config):
     """
     Validate that the data fetched by fetch_data and fetch_data_using_sql are consistent.
     """
-    df_api = fetch_data(resource_id, limit, columns, rename_columns)
-    df_sql = fetch_data_using_sql(sql_query, columns, rename_columns)
-    assert df_api.equals(
-        df_sql
-    ), "Data from fetch_data and fetch_data_using_sql are inconsistent!"
+    sql_query = f'SELECT * FROM "{test_config["resource_id"]}" LIMIT {test_config["limit"]}'
+    df_api = fetch_data(
+        test_config["resource_id"],
+        test_config["limit"],
+        test_config["columns"],
+        test_config["rename_columns"],
+    )
+    df_sql = fetch_data_using_sql(sql_query, test_config["columns"], test_config["rename_columns"])
+    assert df_api.equals(df_sql), "Data from fetch_data and fetch_data_using_sql are inconsistent!"

tests/test_format_forecast.py

@@ -0,0 +1,27 @@
+from neso_solar_consumer.fetch_data import fetch_data
+from neso_solar_consumer.format_forecast import format_to_forecast_sql
+from nowcasting_datamodel.models import ForecastSQL, ForecastValue
+
+
+def test_format_to_forecast_sql_real(db_session, test_config):
+    """
+    Test `format_to_forecast_sql` with real data fetched from the NESO API.
+    """
+    # Step 1: Fetch mock data from the API
+    data = fetch_data(
+        test_config["resource_id"],
+        test_config["limit"],
+        test_config["columns"],
+        test_config["rename_columns"],
+    )
+    assert not data.empty, "fetch_data returned an empty DataFrame!"
+
+    # Step 2: Format the data into a ForecastSQL object
+    forecasts = format_to_forecast_sql(
+        data, test_config["model_name"], test_config["model_version"], db_session
+    )
+    assert len(forecasts) == 1, "More than one ForecastSQL object was created!"
+
+    # Step 3: Validate ForecastSQL content
+    forecast = forecasts[0]
+    assert len(forecast.forecast_values) == len(data), "Mismatch in ForecastValue entries!"

tests/test_save_forecasts.py

@@ -0,0 +1,51 @@
+"""
+Integration Test for Fetching, Formatting, and Saving Forecast Data
+
+This script validates the integration of fetching real data, formatting it into ForecastSQL objects,
+and saving it to the database.
+"""
+
+import pytest
+from nowcasting_datamodel.models import ForecastSQL
+from neso_solar_consumer.fetch_data import fetch_data
+from neso_solar_consumer.format_forecast import format_to_forecast_sql
+from neso_solar_consumer.save_forecasts import save_forecasts_to_db
+
+
+def test_save_real_forecasts(db_session, test_config):
+    """
+    Integration test: Fetch real data, format it into forecasts, and save to the database.
+
+    Steps:
+    1. Fetch real data from the NESO API.
+    2. Format the data into ForecastSQL objects.
+    3. Save the forecasts to the database.
+    4. Verify that the forecasts are correctly saved.
+    """
+    # Step 1: Fetch real data
+    df = fetch_data(
+        test_config["resource_id"],
+        test_config["limit"],
+        test_config["columns"],
+        test_config["rename_columns"],
+    )
+    assert not df.empty, "fetch_data returned an empty DataFrame!"
+
+    # Step 2: Format data into ForecastSQL objects
+    forecasts = format_to_forecast_sql(
+        df, test_config["model_name"], test_config["model_version"], db_session
+    )
+    assert forecasts, "No forecasts were generated from the fetched data!"
+
+    # Step 3: Save forecasts to the database
+    save_forecasts_to_db(forecasts, db_session)
+
+    # Step 4: Verify forecasts are saved in the database
+    saved_forecast = db_session.query(ForecastSQL).first()
+    assert saved_forecast is not None, "No forecast was saved to the database!"
+    assert saved_forecast.model.name == test_config["model_name"], "Model name does not match!"
+    assert len(saved_forecast.forecast_values) > 0, "No forecast values were saved!"
+
+    # Debugging Output (Optional)
+    print("Forecast successfully saved to the database.")
+    print(f"Number of forecast values: {len(saved_forecast.forecast_values)}")