fix: correct spelling errors across documentation and code

Dockerfile

@@ -35,7 +35,7 @@ RUN cuda=$(command -v nvcc > /dev/null && echo "true" || echo "false") \
 # Switch to bash shell
 SHELL ["/bin/bash", "-c"]
 
-# Set ${CONDA_ENV_NAME} to default virutal environment
+# Set ${CONDA_ENV_NAME} to default virtual environment
 RUN echo "source activate ${CONDA_ENV_NAME}" >> ~/.bashrc
 
 # Cp in the development directory and install

README.md

@@ -9,7 +9,7 @@ models have now been added, as well as general models for assimilation and forec
 
 The models implemented include:
 
-DeepMind's [Functional Generative Network (FGN)](https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/how-we-re-supporting-better-tropical-cyclone-prediction-with-ai/skillful-joint-probabilistic-weather-forecasting-from-marginals.pdf) for probablistic ensemble forecasting
+DeepMind's [Functional Generative Network (FGN)](https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/how-we-re-supporting-better-tropical-cyclone-prediction-with-ai/skillful-joint-probabilistic-weather-forecasting-from-marginals.pdf) for probabilistic ensemble forecasting
 
 DeepMind's [GenCast](https://www.nature.com/articles/s41586-024-08252-9) for graph diffusion-based forecasting
 

graph_weather/models/fgn/README.md

@@ -6,6 +6,6 @@ This is an unofficial implementation of the Functional Generative Network
 outlined in [Skillful joint probabilistic weather forecsting from marginals](https://arxiv.org/abs/2506.10772).
 
 This model is heavily based on GenCast, and is designed to make ensemble weather forecasts through a combination of
-mutliple trained models, and noise injected into the model parameters during inference.
+multiple trained models, and noise injected into the model parameters during inference.
 
 As it does not use diffusion, it is significantly faster to run than GenCast, while outperforming it on nearly all metrics.

graph_weather/models/gencast/graph/icosahedral_mesh.py

@@ -132,7 +132,7 @@ def get_icosahedron() -> TriangularMesh:
     #      /                                        \            |
     #  /                                                 \      YO-----> X
     # This results in:
-    #  (adjacent faceis now top plane)
+    #  (adjacent face is now top plane)
     #  ----------------------O\  (top arist)
     #                           \
     #                             \

graph_weather/models/gencast/graph/model_utils.py

@@ -173,7 +173,7 @@ def lat_lon_deg_to_spherical(
     node_lat: np.ndarray,
     node_lon: np.ndarray,
 ) -> Tuple[np.ndarray, np.ndarray]:
-    """Convert lat and lon to spherical coordiantes."""
+    """Convert lat and lon to spherical coordinates."""
     phi = np.deg2rad(node_lon)
     theta = np.deg2rad(90 - node_lat)
     return phi, theta
@@ -221,7 +221,7 @@ 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:
@@ -526,7 +526,7 @@ 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:
@@ -641,7 +641,7 @@ def dataset_to_stacked(
 ) -> xarray.DataArray:
     """Converts an xarray.Dataset to a single stacked array.
 
-    This takes each consistuent data_var, converts it into BHWC layout
+    This takes each constituent data_var, converts it into BHWC layout
     using `variable_to_stacked`, then concats them all along the channels axis.
 
     Args:

graph_weather/models/gencast/layers/modules.py

@@ -288,7 +288,7 @@ def __init__(
                 not be used inside TransformerConv.
             concat (bool): if true concatenate the outputs of each head, otherwise average them.
                 Defaults to True.
-            beta (bool): if true apply the beta weighting described in the paper. Defauls to True.
+            beta (bool): if true apply the beta weighting described in the paper. Defaults to True.
             activation_layer (torch.nn.Module, optional): activation function applied before
                 returning the output. If None skip the activation function. Defaults to nn.ReLU.
         """

graph_weather/models/gencast/layers/processor.py

@@ -85,7 +85,7 @@ def __init__(
                 activate_final=False,
             )
 
-        # Tranformers Blocks
+        # Transformers Blocks
         self.cond_transformers = torch.nn.ModuleList()
         if not sparse:
             for _ in range(num_blocks - 1):

graph_weather/models/gencast/utils/batching.py

@@ -35,7 +35,7 @@ def batch(senders, edge_index, edge_attr=None, batch_size=1):
 
 
 def hetero_batch(senders, receivers, edge_index, edge_attr=None, batch_size=1):
-    """Build big batched heterogenous graph.
+    """Build big batched heterogeneous graph.
 
     Returns nodes and edges of a big graph with batch_size disconnected copies of the original
     graph, with features shape [(b n) f].

graph_weather/models/gencast/utils/noise.py

@@ -17,7 +17,7 @@ def generate_isotropic_noise(num_lon: int, num_lat: int, num_samples=1, isotropi
     Args:
         num_lon (int): number of longitudes in the grid.
         num_lat (int): number of latitudes in the grid.
-        num_samples (int): number of indipendent samples. Defaults to 1.
+        num_samples (int): number of independent samples. Defaults to 1.
         isotropic (bool): if true generates isotropic noise, else flat noise. Defaults to True.
 
     Returns:

graph_weather/models/layers/assimilator_decoder.py

@@ -5,7 +5,7 @@
 The Decoder maps back to physical data defined on a latitude/longitude grid. The underlying graph is
 again bipartite, this time mapping icosahedron→lat/lon.
 The inputs to the Decoder come from the Processor, plus a skip connection back to the original
-state of the 78 atmospheric variables onthe latitude/longitude grid.
+state of the 78 atmospheric variables on the latitude/longitude grid.
 The output of the Decoder is the predicted 6-hour change in the 78 atmospheric variables,
 which is then added to the initial state to produce the new state. We found 6 hours to be a good
 balance between shorter time steps (simpler dynamics to model but more iterations required during
@@ -185,7 +185,7 @@ def forward(self, processor_features: torch.Tensor, batch_size: int) -> torch.Te
                 dim=1,
             )
 
-            # Readd nodes to match graph node number
+            # Re-add nodes to match graph node number
             features = einops.rearrange(processor_features, "(b n) f -> b n f", b=batch_size)
             features = torch.cat(
                 [features, einops.repeat(self.latlon_nodes, "n f -> b n f", b=batch_size)], dim=1

graph_weather/models/layers/assimilator_encoder.py

@@ -50,7 +50,7 @@ def __init__(
         use_checkpointing: bool = False,
     ):
         """
-        Encode the lat/lon data inot the isohedron graph
+        Encode the lat/lon data into the isohedron graph
 
         Args:
             resolution: H3 resolution level
@@ -208,7 +208,7 @@ def create_input_graph(self, features: torch.Tensor, lat_lons_heights: torch.Ten
             edge_targets.append(self.h3_mapping[lat_node])
         edge_index = torch.tensor([edge_sources, edge_targets], dtype=torch.long)
 
-        # Use homogenous graph to make it easier
+        # Use homogeneous graph to make it easier
         return Data(edge_index=edge_index, edge_attr=h3_distances)
 
     def create_latent_graph(self) -> Data:

graph_weather/models/layers/decoder.py

@@ -5,7 +5,7 @@
 The Decoder maps back to physical data defined on a latitude/longitude grid. The underlying graph is
 again bipartite, this time mapping icosahedron→lat/lon.
 The inputs to the Decoder come from the Processor, plus a skip connection back to the original
-state of the 78 atmospheric variables onthe latitude/longitude grid.
+state of the 78 atmospheric variables on the latitude/longitude grid.
 The output of the Decoder is the predicted 6-hour change in the 78 atmospheric variables,
 which is then added to the initial state to produce the new state. We found 6 hours to be a good
 balance between shorter time steps (simpler dynamics to model but more iterations required during

graph_weather/models/layers/encoder.py

@@ -52,7 +52,7 @@ def __init__(
         efficient_batching: bool = False,
     ):
         """
-        Encode the lat/lon data inot the isohedron graph
+        Encode the lat/lon data into the isohedron graph
 
         Args:
             lat_lons: List of (lat,lon) points
@@ -103,7 +103,7 @@ def __init__(
             edge_targets.append(self.h3_mapping[lat_node])
         edge_index = torch.tensor([edge_sources, edge_targets], dtype=torch.long)
 
-        # Use homogenous graph to make it easier
+        # Use homogeneous graph to make it easier
         self.graph = Data(edge_index=edge_index, edge_attr=self.h3_distances)
 
         self.latent_graph = self.create_latent_graph()

graph_weather/models/layers/graph_net_block.py

@@ -173,7 +173,7 @@ def forward(
 
         Args:
             x: Input nodes
-            edge_index: Edge indicies in COO format
+            edge_index: Edge indices in COO format
             edge_attr: Edge attributes
             u: Global attributes, ignored
             batch: Batch IDX, ignored
@@ -284,7 +284,7 @@ def forward(
 
         Args:
             x: Input nodes
-            edge_index: Edge indicies in COO format
+            edge_index: Edge indices in COO format
             edge_attr: Edge attributes
 
         Returns:

train/pl_graph_weather.py

@@ -422,7 +422,7 @@ def configure_optimizers(self):
 )
 def run(num_blocks, hidden, batch, gpus):
     """
-    Trainig process.
+    Training process.
 
     Args:
         num_blocks : Number of blocks.