deleted empty lines

CM2S · Jul 26, 2024 · e701dea · e701dea
1 parent ccff038
commit e701dea
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diff --git a/piglot/objective.py b/piglot/objective.py
@@ -222,7 +222,6 @@ def scalarise(self, composition: Composition = None) -> float:
                 raise ValueError(
                     f"Invalid scalarisation '{self.scalarisation}'. Use 'mean' or 'stch'."
                 )
-
             if self.scalarisation == "stch":
                 # Sanitise the weights
                 weights = np.array(self.weights)
@@ -243,9 +242,7 @@ def scalarise(self, composition: Composition = None) -> float:
                 # Calculate the Tchebycheff function value
                 tch_values = (np.abs((norm_funcs - ideal_point) * costs) / u) * weights
                 return np.log(np.sum(np.exp(tch_values))) * u
-
             return np.mean(self.values)
-
         return composition.composition(self.values, self.params).item()
 
     def scalarise_mo(self, composition: Composition = None) -> List[float]:

diff --git a/piglot/objectives/design.py b/piglot/objectives/design.py
@@ -191,7 +191,6 @@ def __composition(
         # Sanitise scalarisation method
         if scalarisation not in ['mean', 'stch']:
             raise ValueError(f"Invalid scalarisation '{scalarisation}'. Use 'mean' or 'stch'.")
-
         return ResponseComposition(
             scalarise=scalarise,
             stochastic=stochastic,
@@ -275,7 +274,6 @@ def _objective(self, values: np.ndarray, concurrent: bool = False) -> ObjectiveR
             # Build statistical model for the target
             results.append(target.weight * np.mean(targets))
             variances.append(target.weight * np.var(targets) / len(targets))
-
         return ObjectiveResult(
             values,
             results,

diff --git a/piglot/utils/composition/responses.py b/piglot/utils/composition/responses.py
@@ -409,7 +409,6 @@ def composition_torch(self, inner: torch.Tensor, params: torch.Tensor) -> torch.
         # Sanitise the scalarisation method
         if self.scalarise and self.scalarisation not in ['mean', 'stch']:
             raise ValueError(f"Invalid scalarisation '{self.scalarisation}'. Use 'mean' or 'stch'.")
-
         # Split the inner responses
         responses = self.concat.split_torch(inner)
         # Unflatten each response
@@ -422,7 +421,6 @@ def composition_torch(self, inner: torch.Tensor, params: torch.Tensor) -> torch.
             reduction.reduce_torch(time, data, self._expand_params(time, params))
             for (time, data), reduction in zip(unflattened, self.reductions)
         ], dim=-1)
-
         # Mean scalarisation if requested
         if self.scalarise:
             # Smooth TCHebycheff scalarisation (STCH) if requested
@@ -450,5 +448,4 @@ def composition_torch(self, inner: torch.Tensor, params: torch.Tensor) -> torch.
             # Apply the weights
             objective = objective * torch.tensor(self.weights).to(inner.device)
             return torch.mean(objective, dim=-1)
-
         return objective * torch.tensor(self.weights).to(inner.device)