Set +x for __main__ entrypoints and remove unused shebangs

src/emhass/forecast.py

@@ -1,5 +1,3 @@
-#!/usr/bin/env python3
-
 import bz2
 import copy
 import json

src/emhass/machine_learning_forecaster.py

@@ -1,5 +1,3 @@
-#!/usr/bin/env python3
-
 import logging
 import time
 import warnings

src/emhass/optimization.py

@@ -1,5 +1,3 @@
-#!/usr/bin/env python3
-
 import bz2
 import copy
 import logging

src/emhass/retrieve_hass.py

@@ -1,5 +1,3 @@
-#!/usr/bin/env python3
-
 import copy
 import datetime
 import json

src/emhass/utils.py

@@ -1,4 +1,3 @@
-#!/usr/bin/env python3
 from __future__ import annotations
 
 import ast
@@ -590,16 +589,16 @@
                 if isinstance(forecast_input, dict):
                     forecast_data_df = pd.DataFrame.from_dict(forecast_input, orient="index").reset_index()
                     forecast_data_df.columns = ["time", "value"]
                     forecast_data_df['time'] = pd.to_datetime(forecast_data_df['time'], format='ISO8601', utc=True).dt.tz_convert(time_zone)
 
                     # align index with forecast_dates
                     forecast_data_df = (forecast_data_df
                         .resample(pd.to_timedelta(optimization_time_step, "minutes"), on='time')
                         .aggregate({'value': 'mean'})
                         .reindex(forecast_dates, method='nearest')
                     )
                     forecast_data_df['value'] = forecast_data_df['value'].ffill().bfill()
                     forecast_input = forecast_data_df['value'].tolist()
                 if isinstance(forecast_input, list) and len(forecast_input) >= len(forecast_dates):
                     params["passed_data"][forecast_key] = forecast_input
                     params["optim_conf"][forecast_methods[method]] = "list"