Setup ci/cd linting

.flake8

@@ -0,0 +1,6 @@
+[flake8]
+max-line-length = 88
+extend-ignore = E203
+select = C,E,F,W,B,B950
+ignore = E203, E501, W503
+per-file-ignores = __init__.py:F401

.github/workflows/pre-commit.yml

@@ -0,0 +1,16 @@
+name: linting
+
+on:
+  push:
+    branches: "*"
+  pull_request:
+    branches: "*"
+
+jobs:
+  linting:
+    name: "pre-commit hooks"
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: actions/setup-python@v2
+      - uses: pre-commit/[email protected]

.pre-commit-config.yaml

@@ -0,0 +1,21 @@
+# https://pre-commit.com/
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.0.1
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+  # isort should run before black as black sometimes tweaks the isort output
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.12.0
+    hooks:
+      - id: isort
+  # https://github.com/python/black#version-control-integration
+  - repo: https://github.com/psf/black
+    rev: 22.3.0
+    hooks:
+      - id: black
+  - repo: https://github.com/PyCQA/flake8
+    rev: 6.1.0
+    hooks:
+      - id: flake8

README.md

@@ -1,5 +1,7 @@
 # weather-model-graphs
 
+[![linting](https://github.com/mllam/weather-model-graphs/actions/workflows/pre-commit.yml/badge.svg)](https://github.com/mllam/weather-model-graphs/actions/workflows/pre-commit.yml)
+
 `weather-model-graphs` is a package for creating, visualising and storing message-passing graphs for data-driven weather models.
 
 The package is designed to use `networkx.DiGraph` objects as the primary data structure for the graph representation right until the graph is to be stored on disk into a specific format.
@@ -29,7 +31,7 @@ pdm install --group pytorch
 
 # Usage
 
-The best way to understand how to use `weather-model-graphs` is to look at the [notebooks/constructing_the_graph.ipynb](notebooks/constructing_the_graph.ipynb) notebook, to have look at the tests in [tests/](tests/) or simply to read through the source code. 
+The best way to understand how to use `weather-model-graphs` is to look at the [notebooks/constructing_the_graph.ipynb](notebooks/constructing_the_graph.ipynb) notebook, to have look at the tests in [tests/](tests/) or simply to read through the source code.
 In addition you can read the [background and design](#background-and-design) section below to understand the design principles of `weather-model-graphs`.
 
 ## Example, Keisler 2021 flat graph architecture
@@ -101,9 +103,9 @@ The graph generation in `weather-model-graphs` is split into to the following st
     - **networkx** `.pickle` file: save `networkx.DiGraph` objects using `pickle` to disk (`weather_model_graphs.save.to_pickle(...)`)
 
     - [pytorch-geometric](https://github.com/pyg-team/pytorch_geometric) for [neural-lam](https://github.com/mllam/neural-lam): edges indexes and features are stored in separate `torch.Tensor` objects serialised to disk that can then be loaded into `torch_geometric.data.Data` objects (`weather_model_graphs.save.to_pyg(...)`
-    
+
 ### Diagram of the graph generation process:
-    
+
 Below visualises the graph generation process in `weather-model-graphs` for the example given above:
 
 ```mermaid
@@ -186,9 +188,9 @@ The code layout of `weather-model-graphs` is organise into submodules by the fun
 ```
 weather_model_graphs
     .create
-        .archetype: 
-            for creating specific archetype graph 
-            architectures (e.g. Keisler 2021, Lam et al 2023, 
+        .archetype:
+            for creating specific archetype graph
+            architectures (e.g. Keisler 2021, Lam et al 2023,
             Oscarsson et al 2023)
         .base
             general interface for creating graph architectures
@@ -198,7 +200,7 @@ weather_model_graphs
         .grid
             for creating the grid nodes
     .visualise
-        for plotting graphs, allowing for easy visualisation using any 
+        for plotting graphs, allowing for easy visualisation using any
         edge or node attribute for colouring
     .save
         for saving the graph to specific formats (e.g. pytorch-geometric)

notebooks/constructing_the_graph.ipynb

@@ -512,7 +512,9 @@
    ],
    "source": [
     "m2m_graph = graph_components.pop(\"m2m\")\n",
-    "m2m_graph_components = wmg.split_graph_by_edge_attribute(graph=m2m_graph, attribute=\"direction\")\n",
+    "m2m_graph_components = wmg.split_graph_by_edge_attribute(\n",
+    "    graph=m2m_graph, attribute=\"direction\"\n",
+    ")\n",
     "m2m_graph_components = {\n",
     "    f\"m2m_{name}\": graph for name, graph in m2m_graph_components.items()\n",
     "}\n",

pyproject.toml

@@ -28,6 +28,8 @@ visualisation = [
 requires = ["pdm-backend"]
 build-backend = "pdm.backend"
 
+[tool.isort]
+profile = "black"
 
 [tool.pdm]
 distribution = true

src/weather_model_graphs/__init__.py

@@ -1,2 +1,5 @@
 from . import create, visualise
-from .networkx_utils import split_graph_by_edge_attribute, replace_node_labels_with_unique_ids
+from .networkx_utils import (
+    replace_node_labels_with_unique_ids,
+    split_graph_by_edge_attribute,
+)

src/weather_model_graphs/create/__init__.py

@@ -1,2 +1,2 @@
+from . import archetype
 from .base import create_all_graph_components
-from . import archetype

src/weather_model_graphs/create/archetype.py

@@ -33,10 +33,13 @@ def create_keisler_graph(xy_grid, merge_components=True):
         g2m_connectivity="nearest_neighbours",
         g2m_connectivity_kwargs=dict(
             max_num_neighbours=4,
-        )
+        ),
     )
 
-def create_graphcast_graph(xy_grid, refinement_factor=3, max_num_levels=None, merge_components=True):
+
+def create_graphcast_graph(
+    xy_grid, refinement_factor=3, max_num_levels=None, merge_components=True
+):
     """
     Create a graph following the Lam et al (2023, https://arxiv.org/abs/2212.12794) GraphCast architecture.
 
@@ -46,7 +49,7 @@ def create_graphcast_graph(xy_grid, refinement_factor=3, max_num_levels=None, me
     to its nearest 4 grid points. The mesh to grid connectivity connects each grid point to the nearest mesh node.
 
     TODO: Verify that GraphCast does in fact use these g2m and m2g connectivities.
-    
+
     Parameters
     ----------
     xy_grid: np.ndarray
@@ -69,14 +72,17 @@ def create_graphcast_graph(xy_grid, refinement_factor=3, max_num_levels=None, me
         xy=xy_grid,
         merge_components=merge_components,
         m2m_connectivity="flat_multiscale",
-        m2m_connectivity_kwargs=dict(refinement_factor=refinement_factor, max_num_levels=max_num_levels),
+        m2m_connectivity_kwargs=dict(
+            refinement_factor=refinement_factor, max_num_levels=max_num_levels
+        ),
         m2g_connectivity="nearest_neighbour",
         g2m_connectivity="nearest_neighbours",
         g2m_connectivity_kwargs=dict(
             max_num_neighbours=4,
-        )
+        ),
     )
 
+
 def create_oscarsson_hierarchical_graph(xy_grid, merge_components=True):
     """
     Create a graph following Oscarsson et al (2023, https://arxiv.org/abs/2309.17370)
@@ -89,20 +95,20 @@ def create_oscarsson_hierarchical_graph(xy_grid, merge_components=True):
     edge connections each edge has a `direction` attribute (with value "up",
     "down", or "same"). In addition the `level` attribute indicates which two levels
     are connected for cross-level edges (e.g. "1>2" for edges between level 1 and 2).
-    
+
     The grid to mesh connectivity connects each mesh node to the four nearest
     grid points, and the mesh to grid connectivity connects each grid point to
     the nearest mesh node.
-    
+
     TODO: Is this the right connectivity for the g2m and m2g components?
-    
+
     Parameters
     ----------
     xy_grid: np.ndarray
         2D array of grid point positions.
     merge_components: bool
         Whether to merge the components of the graph.
-    
+
     Returns
     -------
     networkx.DiGraph or dict[networkx.DiGraph]
@@ -117,5 +123,5 @@ def create_oscarsson_hierarchical_graph(xy_grid, merge_components=True):
         g2m_connectivity="nearest_neighbours",
         g2m_connectivity_kwargs=dict(
             max_num_neighbours=4,
-        )
+        ),
     )