lint

india_forecast_app/adjuster.py

@@ -1,11 +1,12 @@
-from pvsite_datamodel.sqlmodels import ForecastValueSQL, GenerationSQL, ForecastSQL
-from sqlalchemy.sql import func
-from sqlalchemy import cast, INT, text
-
+""" Adjuster code, adjust forecast by last 7 days of ME"""
 from datetime import datetime, timedelta
-import pandas as pd
 from typing import Optional
 
+import pandas as pd
+from pvsite_datamodel.sqlmodels import ForecastSQL, ForecastValueSQL, GenerationSQL
+from sqlalchemy import INT, cast, text
+from sqlalchemy.sql import func
+
 # Wad to get all the forecast for the last 7 days made, at this time.
 # And find the ME for each forecast horizon
 
@@ -37,8 +38,9 @@ def get_me_values(
     """
     Get the ME values for the last 7 days for a given hour, for a given hour creation time
 
-    args:
-    hour: the hour of whent he forecast is created
+    Args:
+    hour: the hour of when the forecast is created
+    site_uuid: the site this is for
     start_datetime:: the start datetime to filter on.
     session: sqlalchemy session
 

india_forecast_app/app.py

@@ -13,15 +13,15 @@
 import pandas as pd
 import sentry_sdk
 from pvsite_datamodel import DatabaseConnection
-from pvsite_datamodel.read import get_pv_generation_by_sites, get_sites_by_country, get_site_by_uuid
+from pvsite_datamodel.read import get_pv_generation_by_sites, get_site_by_uuid, get_sites_by_country
 from pvsite_datamodel.sqlmodels import SiteAssetType, SiteSQL
 from pvsite_datamodel.write import insert_forecast_values
 from sqlalchemy.orm import Session
 
 import india_forecast_app
+from india_forecast_app.adjuster import get_me_values
 from india_forecast_app.models import PVNetModel, get_all_models
 from india_forecast_app.sentry import traces_sampler
-from india_forecast_app.adjuster import get_me_values
 
 log = logging.getLogger(__name__)
 version = india_forecast_app.__version__
@@ -216,6 +216,7 @@ def save_forecast(
             write_to_db: If true, forecast values are written to db, otherwise to stdout
             ml_model_name: Name of the ML model used for the forecast
             ml_model_version: Version of the ML model used for the forecast
+            use_adjuster: Make new model, adjusted by last 7 days of ME values
 
     Raises:
             IOError: An error if database save fails