🎨 Format Python code with psf/black

pina/model/layers/embedding.py

@@ -158,18 +158,14 @@ class PeriodicBoundaryEmbedding(torch.nn.Module):
         return self._period
 
 
-
 class FourierFeatureEmbedding(torch.nn.Module):
-    def __init__(self,
+    def __init__(self, input_dimension, output_dimension, sigma):
-                 input_dimension,
-                 output_dimension,
-                 sigma):
         r"""
         Fourier Feature Embedding class for encoding input features
         using random Fourier features.This class applies a Fourier
         transformation to the input features,
         which can help in learning high-frequency variations in data.
-        If multiple sigma are provided, the class 
+        If multiple sigma are provided, the class
         supports multiscale feature embedding, creating embeddings for
         each scale specified by the sigma.
 
@@ -224,18 +220,22 @@ class FourierFeatureEmbedding(torch.nn.Module):
         check_consistency(output_dimension, int)
         check_consistency(input_dimension, int)
         if output_dimension % 2:
-            raise RuntimeError('Expected output_dimension to be a even number, '
+            raise RuntimeError(
-                               f'got {output_dimension}.')
+                "Expected output_dimension to be a even number, "
+                f"got {output_dimension}."
+            )
 
         # assign sigma
         self._sigma = sigma
 
         # create non-trainable matrices
-        self._matrix = torch.rand(
+        self._matrix = (
-                        size = (input_dimension,
+            torch.rand(
-                                output_dimension // 2),
+                size=(input_dimension, output_dimension // 2),
-                        requires_grad = False
+                requires_grad=False,
-                                ) * self.sigma
+            )
+            * self.sigma
+        )
 
     def forward(self, x):
         """
@@ -248,12 +248,14 @@ class FourierFeatureEmbedding(torch.nn.Module):
         # compute random matrix multiplication
         out = torch.mm(x, self._matrix)
         # return embedding
-        return torch.cat([torch.cos(2*torch.pi*out), torch.sin(2*torch.pi*out)], dim=-1)
+        return torch.cat(
-    
+            [torch.cos(2 * torch.pi * out), torch.sin(2 * torch.pi * out)],
+            dim=-1,
+        )
 
     @property
     def sigma(self):
         """
         Returning the variance of the sampled matrix for Fourier Embedding.
         """
-        return self._sigma
+        return self._sigma

pina/plotter.py

@@ -246,7 +246,7 @@ class Plotter:
         plt.tight_layout()
         if title is not None:
             plt.title(title)
-            
+
         if filename:
             plt.savefig(filename)
             plt.close()