Adjusting ISeeCube to use the new FourierEncoder.

src/graphnet/models/transformer/iseecube.py

@@ -31,7 +31,7 @@ def __init__(
         max_rel_pos: int = 256,
         num_register_tokens: int = 3,
         scaled_emb: bool = False,
-        n_features: int = 6,
+        fourier_mapping: list = [0, 1, 2, 3, 4, 5],
     ):
         """Construct `ISeeCube`.
 
@@ -46,15 +46,17 @@ def __init__(
             max_rel_pos: Maximum relative position for relative position bias.
             num_register_tokens: The number of register tokens.
             scaled_emb: Whether to scale the sinusoidal positional embeddings.
-            n_features: The number of features in the input data.
+            fourier_mapping: Mapping of the data to [x,y,z,time,charge,
+                auxiliary] for the FourierEncoder. Use None for missing
+                features.
         """
         super().__init__(seq_length, hidden_dim)
         self.fourier_ext = FourierEncoder(
             seq_length=seq_length,
             mlp_dim=mlp_dim,
             output_dim=hidden_dim,
             scaled=scaled_emb,
-            n_features=n_features,
+            mapping=fourier_mapping,
         )
         self.pos_embedding = nn.Parameter(
             torch.empty(1, seq_length, hidden_dim).normal_(std=0.02),