Refactored Code with SciML Style Guide

.JuliaFormatter.toml

@@ -0,0 +1,2 @@
+style = "sciml"
+separate_kwargs_with_semicolon = true

.github/dependabot.yml

@@ -0,0 +1,9 @@
+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "monthly"
+    labels:
+      - "dependencies"
+      - "github-actions"

.github/workflows/Formatter.yml

@@ -0,0 +1,36 @@
+name: Formatter
+
+on:
+  pull_request:
+  push:
+    branches:
+      - 'main'
+    tags: '*'
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - name: "Check out repository"
+        uses: actions/checkout@v4
+
+      - name: "Set up Julia"
+        uses: julia-actions/setup-julia@v1
+        with:
+          version: '1.10'
+          arch: x64
+
+      - name: Install JuliaFormatter and format
+        run: julia  -e 'using Pkg; Pkg.add(PackageSpec(name="JuliaFormatter")); using JuliaFormatter; format(".", verbose=true)'
+
+      - name: Format check
+        run: |
+          julia -e '
+          out = Cmd(`git diff`) |> read |> String
+          if out == ""
+              exit(0)
+          else
+              @error "Some files are not formatted! Please use the SciMLStyle to format your files!"
+              write(stdout, out)
+              exit(1)
+          end'

README.md

@@ -2,6 +2,7 @@
 
 [![Docs](https://img.shields.io/badge/docs-dev-blue.svg)](https://una-auxme.github.io/MeshGraphNets.jl/dev)
 [![Aqua QA](https://raw.githubusercontent.com/JuliaTesting/Aqua.jl/master/badge.svg)](https://github.com/JuliaTesting/Aqua.jl)
+[![SciML Code Style](https://img.shields.io/static/v1?label=code%20style&message=SciML&color=9558b2&labelColor=389826)](https://github.com/SciML/SciMLStyle)
 
 [*GraphNetCore.jl*](https://github.com/una-auxme/GraphNetCore.jl) is a software package for the Julia programming language that provides an the core functionality of the [*MeshGraphNets.jl*](https://github.com/una-auxme/MeshGraphNets.jl) package. Some parts are based on the implementation of the [MeshGraphNets](https://arxiv.org/abs/2010.03409) framework by [Google DeepMind](https://deepmind.google/) for simulating mesh-based physical systems via graph neural networks:
 

src/GraphNetCore.jl

@@ -19,7 +19,8 @@ export FeatureGraph
 # graph_network.jl
 export GraphNetwork
 # normaliser.jl
-export NormaliserOffline, NormaliserOfflineMinMax, NormaliserOfflineMeanStd, NormaliserOnline
+export NormaliserOffline, NormaliserOfflineMinMax, NormaliserOfflineMeanStd,
+       NormaliserOnline
 
 # graph_network.jl
 export build_model, step!, save!, load

src/feature_graph.jl

@@ -1,78 +1,80 @@
-#
-# Copyright (c) 2023 Julian Trommer
-# Licensed under the MIT license. See LICENSE file in the project root for details.
-#
-
-"""
-    FeatureGraph(nf, ef, senders, receivers)
-
-Data structure that is used as an input for the [`GraphNetwork`](@ref).
-
-## Arguments
-- `nf`: Node features of the graph.
-- `ef`: edge features of the graph.
-- `senders`: List of nodes in the mesh where graph edges start.
-- `receivers`: List of nodes in the mesh where graph edges end.
-"""
-mutable struct FeatureGraph
-    nf
-    ef
-    senders
-    receivers
-end
-
-"""
-    update_features!(g; nf, ef)
-
-Updates the node and edge features of the given [`FeatureGraph`](@ref).
-
-## Arguments
-- `g`: [`FeatureGraph`](@ref) that should be updated.
-
-## Keyword Arguments
-- `nf`: Updated node features.
-- `ef`: Updated edge features.
-
-## Returns
-- Updated graph as a [`FeatureGraph`](@ref) struct.
-"""
-function update_features!(g::FeatureGraph; nf, ef)
-    g.nf = nf
-    g.ef = ef
-    return g
-end
-
-"""
-    aggregate_edge_features(graph)
-
-Aggregates the edge features based on the senders and receivers of the given [`FeatureGraph`](@ref).
-
-## Arguments
-- `graph`: [`FeatureGraph`](@ref) which node and edge features are used.
-
-## Returns
-- Two-dimensional array with the
-    - 1. dimension containing the concatenated features as new edge features and the
-    - 2. dimension representing the individual edges.
-"""
-@inline function aggregate_edge_features(graph::FeatureGraph)
-    return vcat(graph.nf[:, graph.senders], graph.nf[:, graph.receivers], graph.ef)
-end
-
-"""
-    aggregate_node_features(graph, updated_edge_features)
-
-Aggregates the node features based on the given [`FeatureGraph`](@ref) and updated edge features.
-
-## Arguments
-- `graph`: [`FeatureGraph`](@ref) which node features are used.
-- `updated_edge_features`: New edge features that were calculated in a previous step.
-
-## Returns
-- Two dimensional array with the
-    - 1. dimension containing the concatenated features as new node features and the
-    - 2. dimension representing the individual nodes.
-"""
-@inline function aggregate_node_features(graph::FeatureGraph, updated_edge_features)
-    return vcat(graph.nf, NNlib.scatter(+, updated_edge_features, graph.receivers, dstsize = size(graph.nf)))
-end
+#
+# Copyright (c) 2023 Julian Trommer
+# Licensed under the MIT license. See LICENSE file in the project root for details.
+#
+
+"""
+    FeatureGraph(nf, ef, senders, receivers)
+
+Data structure that is used as an input for the [`GraphNetwork`](@ref).
+
+## Arguments
+- `nf`: Node features of the graph.
+- `ef`: edge features of the graph.
+- `senders`: List of nodes in the mesh where graph edges start.
+- `receivers`: List of nodes in the mesh where graph edges end.
+"""
+mutable struct FeatureGraph{F <: AbstractArray, T <: AbstractArray}
+    nf::F
+    ef::F
+    senders::T
+    receivers::T
+end
+
+"""
+    update_features!(g; nf, ef)
+
+Updates the node and edge features of the given [`FeatureGraph`](@ref).
+
+## Arguments
+- `g`: [`FeatureGraph`](@ref) that should be updated.
+
+## Keyword Arguments
+- `nf`: Updated node features.
+- `ef`: Updated edge features.
+
+## Returns
+- Updated graph as a [`FeatureGraph`](@ref) struct.
+"""
+function update_features!(g::FeatureGraph; nf, ef)
+    g.nf = nf
+    g.ef = ef
+
+    return g
+end
+
+"""
+    aggregate_edge_features(graph)
+
+Aggregates the edge features based on the senders and receivers of the given [`FeatureGraph`](@ref).
+
+## Arguments
+- `graph`: [`FeatureGraph`](@ref) which node and edge features are used.
+
+## Returns
+- Two-dimensional array with the
+    - 1. dimension containing the concatenated features as new edge features and the
+    - 2. dimension representing the individual edges.
+"""
+@inline function aggregate_edge_features(graph::FeatureGraph)
+    return vcat(graph.nf[:, graph.senders], graph.nf[:, graph.receivers], graph.ef)
+end
+
+"""
+    aggregate_node_features(graph, updated_edge_features)
+
+Aggregates the node features based on the given [`FeatureGraph`](@ref) and updated edge features.
+
+## Arguments
+- `graph`: [`FeatureGraph`](@ref) which node features are used.
+- `updated_edge_features`: New edge features that were calculated in a previous step.
+
+## Returns
+- Two dimensional array with the
+    - 1. dimension containing the concatenated features as new node features and the
+    - 2. dimension representing the individual nodes.
+"""
+@inline function aggregate_node_features(graph::FeatureGraph, updated_edge_features)
+    return vcat(graph.nf,
+        NNlib.scatter(+, updated_edge_features, graph.receivers; dstsize = size(graph.nf)))
+end