Remove unnecessary lines

src/progpy/mixture_of_experts.py

@@ -173,7 +173,7 @@ def best_model(self, x, _excepting=[]):
             tuple[str, PrognosticsModel]: The name and model for the model with the highest score. If two models are tied, the first one will be returned in the order they were passed to the constructor.
         """
         # Identify best model
-        best_value = -1
+        best_value: float = -1
         for i, (key, _) in enumerate(self.parameters['models']):
             if key in _excepting:
                 continue # Skip excepting
@@ -286,7 +286,3 @@ def performance_metrics(self, x):
                 performance_metrics_seen |= new_performance_metrics
 
         return pm
-
-        name, m = self.best_model(x)
-        x_i = m.StateContainer({key: x[name + '.' + key] for key in m.states})
-        return m.performance_metrics(x_i)

src/progpy/models/aircraft_model/vehicles/control/allocation_functions.py

@@ -3,6 +3,8 @@
 
 import numpy as np
 
+SQRT2_2: float = np.sqrt(2.0) / 2.0
+
 def rotorcraft_cam(n, length, b, d, constrained=False):
     """
     Generate control allocation matrix (CAM) to transform rotor's angular velocity into thrust and torques around three main body axes.
@@ -28,7 +30,7 @@ def rotorcraft_cam(n, length, b, d, constrained=False):
     optional = {}
     if n == 8 and not constrained:
         l_b = length * b
-        l_b_sq2o2 = l_b * np.sqrt(2.0) / 2.0
+        l_b_sq2o2 = l_b * SQRT2_2
         # This CAM is assuming there's no rotor pointing towards the drone forward direction (x-axis)
         # See Dmitry Luchinsky's report for details (TO BE VERIFIED)
         Gamma = np.array([[b, b, b, b, b, b, b, b],