Fix Flake8 warnings

Author: phumthep

src/pownet/core/__init__.py

@@ -1,4 +1,4 @@
-""" This is the core module. 
+""" This is the core module.
 """
 
 from .builder import ModelBuilder

src/pownet/modeling/constraint.py

@@ -1,12 +1,11 @@
 """constraint.py: Contains functions for constructing the objective function and constraints of the optimization model.
 Throughout this module, the number 24 is the number of hours in a day. This number is used to calculate the index of the next day in the optimization model."""
 
+from pownet.core.input import SystemInput
 import gurobipy as gp
 import networkx as nx
 import pandas as pd
 
-from pownet.core import SystemInput
-
 
 """Functions for thermal-unit constraints
 """

src/pownet/modeling/objfunc.py

@@ -1,7 +1,6 @@
 """model_formulation.py: Contains functions for constructing the objective function and constraints of the optimization model."""
 
-from pownet.core import SystemInput
-
+from pownet.core.input import SystemInput
 
 """ Functions for the objective function
 """

src/pownet/modeling/rounding_algo.py

@@ -166,7 +166,7 @@ def optimize_with_rounding(
 
     # If no integer solution is found after max_rounding_iter
     print(
-        f"\nPowNet: The rounding heuristic has terminated before finding an integer solution."
+        "\nPowNet: The rounding heuristic has terminated before finding an integer solution."
     )
     model.optimize()
     return model

src/pownet/reservoir/reservoir_operator.py

@@ -81,7 +81,7 @@ def get_hourly_hydropower(self) -> pd.DataFrame:
         return self.hourly_hydropower
 
     def export_hydropower_csv(self, timestep: str, output_folder: str) -> None:
-        csv_name = f"pownet_hydropower.csv"
+        csv_name = "pownet_hydropower.csv"
 
         # Check if the timestep is valid
         if timestep not in ["daily", "hourly"]:

src/pownet/stochastic/demand.py

@@ -131,7 +131,6 @@ def _get_synthetic(self, exog_data: pd.DataFrame, seed: int) -> pd.Series:
         synthetic_y = pd.Series()
         for month in self.months:
             # Models are fitted for each month
-            reg_model = self.monthly_reg_models[month]
             stl_result = self.monthly_stl_results[month]
             sarimax_model = self._monthly_models[month]
 
@@ -146,10 +145,12 @@ def _get_synthetic(self, exog_data: pd.DataFrame, seed: int) -> pd.Series:
                 reg_pred = self.monthly_reg_models[month].predict(exog=daily_exog)
                 reg_pred = pd.Series(reg_pred, index=daily_exog.index)
 
-                # Bootstrap the SARIMAX predictions within 75% confidence interval
+                # Bootstrap the SARIMAX predictions within 95% confidence interval
                 sarimax_pred = sarimax_model.predict()
                 sarimax_pred_ci = np.percentile(sarimax_pred, [2.5, 97.5])
-                sarimax_bootstrap = np.random.choice(sarimax_pred, size=len(daily_exog))
+                sarimax_bootstrap = np.random.choice(
+                    sarimax_pred_ci, size=len(daily_exog)
+                )
                 sarimax_bootstrap = pd.Series(sarimax_bootstrap, index=daily_exog.index)
 
                 # Recover electricity demand by adding predictions from the regression model,