Add type hints to methods in custom_loggers.py (Fixes #454)

CONTRIBUTING.md

@@ -0,0 +1,115 @@
+# Contributing to Neural-LAM
+
+Thank you for your interest in contributing to Neural-LAM! 🎉
+
+We welcome contributions of all kinds, including bug fixes, documentation improvements, and new features.
+
+---
+
+## 🛠️ Getting Started
+
+### 1. Fork the repository
+Click the "Fork" button on the top right of the repository page.
+
+### 2. Clone your fork
+```bash
+git clone https://github.com/YOUR_USERNAME/neural-lam.git
+cd neural-lam
+3. Create a new branch
+git checkout -b your-branch-name
+⚙️ Setup the development environment
+
+We recommend installing in editable mode with development dependencies:
+
+pip install --group dev -e .
+
+Or using uv:
+
+uv pip install --group dev -e .
+✅ Before making a PR
+
+Run pre-commit checks:
+
+pre-commit run --all-files
+
+Make sure:
+
+Code is formatted correctly
+
+No linting errors
+
+Tests pass
+
+🚀 Making a Pull Request
+
+Commit your changes:
+
+git add .
+git commit -m "Your message"
+
+Push your branch:
+
+git push origin your-branch-name
+
+Open a Pull Request on GitHub
+
+💡 Guidelines
+
+Keep PRs small and focused
+
+Write clear commit messages
+
+Link related issues (e.g., Fixes #123)
+
+Be respectful and collaborative
+
+🙌 Need Help?
+
+Feel free to open an issue or ask questions in the Slack channel.
+
+Happy coding! 🚀
+
+
+---
+
+## 💾 Step 3: Commit
+
+```bash
+git add CONTRIBUTING.md
+git commit -m "Add CONTRIBUTING.md for new contributors (Fixes #406)"
+git push
+🚀 Step 4: Create PR
+Title:
+Add CONTRIBUTING.md to guide new contributors
+Description:
+Fixes #406
+
+Added a CONTRIBUTING.md file to guide new contributors on how to set up the project, run checks, and submit pull requests.
+
+This improves onboarding and developer experience.
+🔥 After this PR
+
+You will have:
+
+✅ Docs fix (#465)
+
+✅ Docs fix (#464)
+
+✅ New file contribution (#406)
+
+👉 This is VERY strong for GSoC
+
+🚀 Next level (after this)
+
+Then we move to:
+
+small bug fix OR
+
+type hints PR
+
+Say:
+👉 “done #406”
+or
+👉 “error while pushing”
+
+I’ll help instantly 👍

README.md

@@ -391,7 +391,7 @@ python -m neural_lam.datastore.npyfilesmeps.compute_standardization_stats <path-
 
 ### Graph creation
 
-Run `python -m neural_lam.create_mesh` with suitable options to generate the graph you want to use (see `python neural_lam.create_mesh --help` for a list of options).
+Run `python -m neural_lam.create_graph` with suitable options to generate the graph you want to use (see `python neural_lam.create_graph --help` for a list of options).
 The graphs used for the different models in the [paper](#graph-based-neural-weather-prediction-for-limited-area-modeling) can be created as:
 
 * **GC-LAM**: `python -m neural_lam.create_graph --config_path <neural-lam-config-path> --name multiscale`
@@ -530,13 +530,13 @@ The structure is shown with examples below:
 ```
 graphs
 ├── graph1                                  - Directory with a graph definition
-│   ├── m2m_edge_index.pt                   - Edges in mesh graph (neural_lam.create_mesh)
-│   ├── g2m_edge_index.pt                   - Edges from grid to mesh (neural_lam.create_mesh)
-│   ├── m2g_edge_index.pt                   - Edges from mesh to grid (neural_lam.create_mesh)
-│   ├── m2m_features.pt                     - Static features of mesh edges (neural_lam.create_mesh)
-│   ├── g2m_features.pt                     - Static features of grid to mesh edges (neural_lam.create_mesh)
-│   ├── m2g_features.pt                     - Static features of mesh to grid edges (neural_lam.create_mesh)
-│   └── mesh_features.pt                    - Static features of mesh nodes (neural_lam.create_mesh)
+│   ├── m2m_edge_index.pt                   - Edges in mesh graph (neural_lam.create_graph)
+│   ├── g2m_edge_index.pt                   - Edges from grid to mesh (neural_lam.create_graph)
+│   ├── m2g_edge_index.pt                   - Edges from mesh to grid (neural_lam.create_graph)
+│   ├── m2m_features.pt                     - Static features of mesh edges (neural_lam.create_graph)
+│   ├── g2m_features.pt                     - Static features of grid to mesh edges (neural_lam.create_graph)
+│   ├── m2g_features.pt                     - Static features of mesh to grid edges (neural_lam.create_graph)
+│   └── mesh_features.pt                    - Static features of mesh nodes (neural_lam.create_graph)
 ├── graph2
 ├── ...
 └── graphN
@@ -546,9 +546,9 @@ graphs
 To keep track of levels in the mesh graph, a list format is used for the files with mesh graph information.
 In particular, the files
 ```
-│   ├── m2m_edge_index.pt                   - Edges in mesh graph (neural_lam.create_mesh)
-│   ├── m2m_features.pt                     - Static features of mesh edges (neural_lam.create_mesh)
-│   ├── mesh_features.pt                    - Static features of mesh nodes (neural_lam.create_mesh)
+│   ├── m2m_edge_index.pt                   - Edges in mesh graph (neural_lam.create_graph)
+│   ├── m2m_features.pt                     - Static features of mesh edges (neural_lam.create_graph)
+│   ├── mesh_features.pt                    - Static features of mesh nodes (neural_lam.create_graph)
 ```
 all contain lists of length `L`, for a hierarchical mesh graph with `L` layers.
 For non-hierarchical graphs `L == 1` and these are all just singly-entry lists.
@@ -559,10 +559,10 @@ In addition, hierarchical mesh graphs (`L > 1`) feature a few additional files w
 ```
 ├── graph1
 │   ├── ...
-│   ├── mesh_down_edge_index.pt             - Downward edges in mesh graph (neural_lam.create_mesh)
-│   ├── mesh_up_edge_index.pt               - Upward edges in mesh graph (neural_lam.create_mesh)
-│   ├── mesh_down_features.pt               - Static features of downward mesh edges (neural_lam.create_mesh)
-│   ├── mesh_up_features.pt                 - Static features of upward mesh edges (neural_lam.create_mesh)
+│   ├── mesh_down_edge_index.pt             - Downward edges in mesh graph (neural_lam.create_graph)
+│   ├── mesh_up_edge_index.pt               - Upward edges in mesh graph (neural_lam.create_graph)
+│   ├── mesh_down_features.pt               - Static features of downward mesh edges (neural_lam.create_graph)
+│   ├── mesh_up_features.pt                 - Static features of upward mesh edges (neural_lam.create_graph)
 │   ├── ...
 ```
 These files have the same list format as the ones above, but each list has length `L-1` (as these edges describe connections between levels).

neural_lam/custom_loggers.py

@@ -1,5 +1,6 @@
 # Standard library
 import sys
+from typing import Any, List, Optional
 
 # Third-party
 import mlflow
@@ -15,7 +16,9 @@ class CustomMLFlowLogger(pl.loggers.MLFlowLogger):
     of version `2.0.3` at least.
     """
 
-    def __init__(self, experiment_name, tracking_uri, run_name):
+    def __init__(
+        self, experiment_name: str, tracking_uri: str, run_name: str
+    ) -> None:
         super().__init__(
             experiment_name=experiment_name, tracking_uri=tracking_uri
         )
@@ -25,7 +28,7 @@ def __init__(self, experiment_name, tracking_uri, run_name):
         mlflow.log_param("run_id", self.run_id)
 
     @property
-    def save_dir(self):
+    def save_dir(self) -> str:
         """
         Returns the directory where the MLFlow artifacts are saved.
         Used to define the path to save output when using the logger.
@@ -37,7 +40,9 @@ def save_dir(self):
         """
         return "mlruns"
 
-    def log_image(self, key, images, step=None):
+    def log_image(
+        self, key: str, images: List[Any], step: Optional[int] = None
+    ) -> None:
         """
         Log a matplotlib figure as an image to MLFlow