style: apply Black formatting to ensure consistent code style

Ran Black on the codebase to standardize formatting for this PR. No functional changes were made.

neso_solar_consumer/app.py

@@ -30,7 +30,11 @@ def get_forecast():
     """
     resource_id = "example_resource_id"  # Replace with the actual resource ID.
     limit = 100  # Number of records to fetch.
-    columns = ["DATE_GMT", "TIME_GMT", "EMBEDDED_SOLAR_FORECAST"]  # Relevant columns to extract.
+    columns = [
+        "DATE_GMT",
+        "TIME_GMT",
+        "EMBEDDED_SOLAR_FORECAST",
+    ]  # Relevant columns to extract.
     rename_columns = {
         "DATE_GMT": "start_utc",
         "TIME_GMT": "end_utc",

neso_solar_consumer/fetch_data.py

@@ -98,4 +98,4 @@ 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

@@ -2,15 +2,22 @@
 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 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")
+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:
+def format_to_forecast_sql(
+    data: pd.DataFrame, model_tag: str, model_version: str, session
+) -> list:
     logger.info("Starting format_to_forecast_sql process...")
 
     # Step 1: Retrieve model metadata
@@ -28,14 +35,19 @@ def format_to_forecast_sql(data: pd.DataFrame, model_tag: str, model_version: st
             continue
 
         try:
-            target_time = datetime.fromisoformat(row["start_utc"]).replace(tzinfo=timezone.utc)
+            target_time = datetime.fromisoformat(row["start_utc"]).replace(
+                tzinfo=timezone.utc
+            )
         except ValueError:
-            logger.warning(f"Invalid datetime format: {row['start_utc']}. Skipping row.")
+            logger.warning(
+                f"Invalid datetime format: {row['start_utc']}. Skipping row."
+            )
             continue
 
         forecast_value = ForecastValue(
             target_time=target_time,
-            expected_power_generation_megawatts=row["solar_forecast_kw"] / 1000,  # Convert to MW
+            expected_power_generation_megawatts=row["solar_forecast_kw"]
+            / 1000,  # Convert to MW
         ).to_orm()
         forecast_values.append(forecast_value)
 

tests/conftest.py

@@ -81,4 +81,4 @@ def test_config():
         "rename_columns": RENAME_COLUMNS,
         "model_name": MODEL_NAME,
         "model_version": MODEL_VERSION,
-    }
+    }

tests/test_fetch_data.py

@@ -25,6 +25,7 @@
     pytest tests/test_fetch_data.py -k "fetch_data"
 
 """
+
 from neso_solar_consumer.fetch_data import fetch_data, fetch_data_using_sql
 
 
@@ -48,8 +49,12 @@ def test_fetch_data_sql(test_config):
     """
     Test the fetch_data_using_sql function to ensure it fetches and processes data correctly via SQL.
     """
-    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"])
+    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(
         test_config["rename_columns"].values()
@@ -60,12 +65,18 @@ def test_data_consistency(test_config):
     """
     Validate that the data fetched by fetch_data and fetch_data_using_sql are consistent.
     """
-    sql_query = f'SELECT * FROM "{test_config["resource_id"]}" LIMIT {test_config["limit"]}'
+    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!"
+    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

@@ -26,11 +26,15 @@ def test_format_to_forecast_sql_real(db_session, test_config):
     forecast = forecasts[0]
 
     # Filter out invalid rows from the DataFrame (like your function would)
-    valid_data = data.drop_duplicates(subset=["start_utc", "end_utc", "solar_forecast_kw"])
+    valid_data = data.drop_duplicates(
+        subset=["start_utc", "end_utc", "solar_forecast_kw"]
+    )
     valid_data = valid_data[
-        valid_data["start_utc"].apply(lambda x: isinstance(x, str) and len(x) > 0)  # Valid datetime
+        valid_data["start_utc"].apply(
+            lambda x: isinstance(x, str) and len(x) > 0
+        )  # Valid datetime
     ]
 
-    assert len(forecast.forecast_values) == len(valid_data), (
-        f"Mismatch in ForecastValue entries! Expected {len(valid_data)} but got {len(forecast.forecast_values)}."
-    )
+    assert len(forecast.forecast_values) == len(
+        valid_data
+    ), f"Mismatch in ForecastValue entries! Expected {len(valid_data)} but got {len(forecast.forecast_values)}."