ran black

src/watertap_contrib/reflo/solar_models/surrogate/pv/pv_surrogate.py

@@ -62,7 +62,7 @@ def build(self):
             units=pyunits.kWh,
             doc="annual energy produced by the plant in kWh",
         )
-        
+
         self.land_req = Var(
             initialize=7e7,
             units=pyunits.acre,
@@ -77,12 +77,13 @@ def build(self):
 
         self.electricity_constraint = Constraint(
             expr=self.annual_energy
-            == -1 * self.electricity
+            == -1
+            * self.electricity
             * pyunits.convert(1 * pyunits.year, to_units=pyunits.hour)
         )
 
     def load_surrogate(self):
-        print('Loading surrogate file...')
+        print("Loading surrogate file...")
         self.surrogate_file = os.path.join(
             os.path.dirname(__file__), "pv_surrogate.json"
         )
@@ -107,21 +108,21 @@ def get_training_validation(self):
         self.dataset_filename = os.path.join(
             os.path.dirname(__file__), "data/dataset.pkl"
         )
-        print('Loading Training Data...\n')
+        print("Loading Training Data...\n")
         time_start = time.process_time()
         pkl_data = pd.read_pickle(self.dataset_filename)
-        data = pkl_data.sample(n=int(len(pkl_data))) #FIX default this to 100% of data
+        data = pkl_data.sample(n=int(len(pkl_data)))  # FIX default this to 100% of data
         self.data_training, self.data_validation = split_training_validation(
             data, self.training_fraction, seed=len(data)
         )
         time_stop = time.process_time()
         print("Data Loading Time:", time_stop - time_start, "\n")
-    
+
     def create_surrogate(
-            self,
-            save=False,
-        ):
-        self.sample_fraction = 0.1 # fraction of the generated data to train with. More flexible than n_samples.
+        self,
+        save=False,
+    ):
+        self.sample_fraction = 0.1  # fraction of the generated data to train with. More flexible than n_samples.
         self.training_fraction = 0.8
 
         self.get_training_validation()
@@ -156,13 +157,16 @@ def create_surrogate(
         # Create callable surrogate object
         xmin, xmax = [self.design_size.bounds[0]], [self.design_size.bounds[1]]
         input_bounds = {
-            self.input_labels[i]: (xmin[i], xmax[i]) for i in range(len(self.input_labels))
+            self.input_labels[i]: (xmin[i], xmax[i])
+            for i in range(len(self.input_labels))
         }
-        rbf_surr = PysmoSurrogate(rbf_train, self.input_labels, self.output_labels, input_bounds)
-
+        rbf_surr = PysmoSurrogate(
+            rbf_train, self.input_labels, self.output_labels, input_bounds
+        )
+
         # Save model to JSON
         if (self.surrogate_file is not None) and (save is True):
-            print(f'Writing surrogate model to {self.surrogate_file}')
+            print(f"Writing surrogate model to {self.surrogate_file}")
             model = rbf_surr.save_to_file(self.surrogate_file, overwrite=True)
 
         # Revert back to standard output
@@ -172,7 +176,7 @@ def create_surrogate(
         print("Model Training Time:", time_stop - time_start, "\n")
 
         return rbf_surr
-    
+
 
 if __name__ == "__main__":
     m = ConcreteModel()
@@ -185,4 +189,4 @@ def create_surrogate(
     results = m.fs.pv.surrogate.evaluate_surrogate(
         m.fs.pv.data_validation[m.fs.pv.input_labels]
     )
-    print(results)
+    print(results)