small refactoring

src/graphnet/models/components/embedding.py

@@ -11,22 +11,22 @@ class SinusoidalPosEmb(torch.nn.Module):
     digitization of the input data
     """
 
-    def __init__(self, dim: int = 16, m: int = 10000) -> None:
+    def __init__(self, dim: int = 16, n_freq: int = 10000) -> None:
         """Construct `SinusoidalPosEmb`.
 
         Args:
             dim: Embedding dimension.
-            m: Number of frequencies.
+            n_freq: Number of frequencies.
         """
         super().__init__()
         self.dim = dim
-        self.m = m
+        self.n_freq = n_freq
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """Apply learnable forward pass to the layer."""
         device = x.device
         half_dim = self.dim // 2
-        emb = torch.log(torch.tensor(self.m, device=device)) / half_dim
+        emb = torch.log(torch.tensor(self.n_freq, device=device)) / half_dim
         emb = torch.exp(torch.arange(half_dim, device=device) * (-emb))
         emb = x[..., None] * emb[None, ...]
         emb = torch.cat((emb.sin(), emb.cos()), dim=-1)

src/graphnet/models/rnn/node_rnn.py

@@ -19,7 +19,7 @@ class Node_RNN(GNN):
 
     The model takes as input the typical DOM data format and transforms it into
     a time series of DOM activations pr. DOM. before applying a RNN layer and
-    outputting the an RNN output for each DOM. This model is in it's current
+    outputting the an RNN output for each DOM. This model is in its current
     state not intended to be used as a standalone model. Furthermore, it needs
     to be used with a time-series dataset and a "cutter" (see
     NodeAsDOMTimeSeries), which is not standard in the graphnet framework.