Fix typos in code comments and docstrings

graphcast/autoregressive.py

@@ -75,7 +75,7 @@ def __init__(
         particular, no noise is added to the predictions that are fed back
         auto-regressively. Defaults to not adding noise.
       gradient_checkpointing: If True, gradient checkpointing will be
-        used at each step of the computation to save on memory. Roughtly this
+        used at each step of the computation to save on memory. Roughly this
         should make the backwards pass two times more expensive, and the time
         per step counting the forward pass, should only increase by about 50%.
         Note this parameter will be ignored with a warning if the scan sequence

graphcast/data_utils.py

@@ -83,7 +83,7 @@ def get_day_progress(
     longitude: 1D array of longitudes at which day progress is computed.
 
   Returns:
-    2D array of day progress values normalized to be in the [0, 1) inverval
+    2D array of day progress values normalized to be in the [0, 1) interval
       for each time point at each longitude.
   """
 

graphcast/graphcast.py

@@ -21,7 +21,7 @@
 Reference:
   https://arxiv.org/pdf/2202.07575.pdf
 
-It assumes data across time and level is stacked, and operates only operates in
+It assumes data across time and level is stacked, and only operates in
 a 2D mesh over latitudes and longitudes.
 """
 
@@ -278,7 +278,7 @@ def __init__(self, model_config: ModelConfig, task_config: TaskConfig):
 
     # Processor, which performs message passing on the multi-mesh.
     self._mesh_gnn = deep_typed_graph_net.DeepTypedGraphNet(
-        embed_nodes=False,  # Node features already embdded by previous layers.
+        embed_nodes=False,  # Node features already embedded by previous layers.
         embed_edges=True,  # Embed raw features of the multi-mesh edges.
         node_latent_size=dict(mesh_nodes=model_config.latent_size),
         edge_latent_size=dict(mesh=model_config.latent_size),
@@ -302,9 +302,9 @@ def __init__(self, model_config: ModelConfig, task_config: TaskConfig):
     # Decoder, which moves data from the mesh back into the grid with a single
     # message passing step.
     self._mesh2grid_gnn = deep_typed_graph_net.DeepTypedGraphNet(
-        # Require a specific node dimensionaly for the grid node outputs.
+        # Require a specific node dimensionality for the grid node outputs.
         node_output_size=dict(grid_nodes=num_outputs),
-        embed_nodes=False,  # Node features already embdded by previous layers.
+        embed_nodes=False,  # Node features already embedded by previous layers.
         embed_edges=True,  # Embed raw features of the mesh2grid edges.
         edge_latent_size=dict(mesh2grid=model_config.latent_size),
         node_latent_size=dict(
@@ -376,12 +376,12 @@ def __call__(self,
     # [num_mesh_nodes, batch, latent_size]
     updated_latent_mesh_nodes = self._run_mesh_gnn(latent_mesh_nodes)
 
-    # Transfer data frome the mesh to the grid.
+    # Transfer data from the mesh to the grid.
     # [num_grid_nodes, batch, output_size]
     output_grid_nodes = self._run_mesh2grid_gnn(
         updated_latent_mesh_nodes, latent_grid_nodes)
 
-    # Conver output flat vectors for the grid nodes to the format of the output.
+    # Convert output flat vectors for the grid nodes to the format of the output.
     # [num_grid_nodes, batch, output_size] ->
     # xarray (batch, one time step, lat, lon, level, multiple vars)
     return self._grid_node_outputs_to_prediction(

graphcast/model_utils.py

@@ -48,9 +48,9 @@ def get_graph_spatial_features(
     add_node_positions: Add unit norm absolute positions.
     add_node_latitude: Add a feature for latitude (cos(90 - lat))
         Note even if this is set to False, the model may be able to infer the
-        longitude from relative features, unless
+        latitude from relative features, unless
         `relative_latitude_local_coordinates` is also True, or if there is any
-        bias on the relative edge sizes for different longitudes.
+        bias on the relative edge sizes for different latitudes.
     add_node_longitude: Add features for longitude (cos(lon), sin(lon)).
         Note even if this is set to False, the model may be able to infer the
         longitude from relative features, unless
@@ -246,7 +246,7 @@ def get_relative_position_in_receiver_local_coordinates(
 
   The relative positions will be computed in a rotated space for a local
   coordinate system as defined by the receiver. The relative positions are
-  simply obtained by subtracting sender position minues receiver position in
+  simply obtained by subtracting sender position minus receiver position in
   that local coordinate system after the rotation in R^3.
 
   Args:
@@ -386,7 +386,7 @@ def get_rotation_matrices_to_local_coordinates(
     # We want to apply the polar rotation only, but we don't know the rotation
     # axis to apply a polar rotation. The simplest way to achieve this is to
     # first rotate all the way to longitude 0, then apply the polar rotation
-    # arond the y axis, and then rotate back to the original longitude.
+    # around the y axis, and then rotate back to the original longitude.
     return transform_.Rotation.from_euler(
         "zyz", np_.stack(
             [azimuthal_rotation, polar_rotation, -azimuthal_rotation]
@@ -562,7 +562,7 @@ def get_bipartite_relative_position_in_receiver_local_coordinates(
 
   The relative positions will be computed in a rotated space for a local
   coordinate system as defined by the receiver. The relative positions are
-  simply obtained by subtracting sender position minues receiver position in
+  simply obtained by subtracting sender position minus receiver position in
   that local coordinate system after the rotation in R^3.
 
   Args: