MNT: Add pre-commit

.github/workflows/mkdocs.yml

@@ -17,14 +17,14 @@ jobs:
       - uses: actions/setup-python@v5
         with:
           python-version: 3.x
-      - run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV 
+      - run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV
       - uses: actions/cache@v4
         with:
           key: mkdocs-material-${{ env.cache_id }}
           path: .cache
           restore-keys: |
             mkdocs-material-
-      - run: pip install mkdocs-material 
+      - run: pip install mkdocs-material
       - run: pip install "mkdocstrings[python]"
       - run: pip install mkdocs-jupyter
       - run: mkdocs gh-deploy --force

.pre-commit-config.yaml

@@ -0,0 +1,23 @@
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+repos:
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v3.2.0
+  hooks:
+  - id: trailing-whitespace
+  - id: end-of-file-fixer
+  - id: check-yaml
+  - id: check-added-large-files
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  rev: v0.9.3
+  hooks:
+  - id: ruff  # Run the linter
+  - id: ruff-format  # Run the formatter
+- repo: https://github.com/astral-sh/uv-pre-commit
+  rev: 0.5.25
+  hooks:
+  - id: uv-lock  # Keep uv.lock up to date with pyproject.toml
+- repo: https://github.com/crate-ci/typos
+  rev: v1.29.4
+  hooks:
+  - id: typos

docs/concepts/overview.md

@@ -16,4 +16,4 @@ Each time step, nodes interact randomly with the weights of edges in the adjacen
 
 The model uses two parameters, $\alpha$ and $\beta$, to govern the reinforcement dynamics. The $\alpha$ parameter tunes the amount by which nodes' attitudes are reinforced by acting as an exponent on the adjacency matrix. The sign of this parameter determines whether interaction with opposing attitude nodes strengthens (positive) or weakens (negative) the nodes' attitudes.
 
-The parameter $\beta$ governs the speed at which nodes change their attitudes. It is a constant which modulates the magnitude of attitude reinforcement. When $\beta$ is large, attitudes change rapidly, and as $\beta$ becomes smaller they change more slowly. The sign of this parameter governs whether the network tends to polarize or synchronize over time. If positive, as default, the reinforcemnt tends to polarize the network, if negative the reinforcement tends to synchronize the network.
+The parameter $\beta$ governs the speed at which nodes change their attitudes. It is a constant which modulates the magnitude of attitude reinforcement. When $\beta$ is large, attitudes change rapidly, and as $\beta$ becomes smaller they change more slowly. The sign of this parameter governs whether the network tends to polarize or synchronize over time. If positive, as default, the reinforcement tends to polarize the network, if negative the reinforcement tends to synchronize the network.

docs/javascripts/mathjax.js

@@ -10,10 +10,10 @@ window.MathJax = {
       processHtmlClass: "arithmatex"
     }
   };
-  
-  document$.subscribe(() => { 
+
+  document$.subscribe(() => {
     MathJax.startup.output.clearCache()
     MathJax.typesetClear()
     MathJax.texReset()
     MathJax.typesetPromise()
-  })
+  })

docs/user-guide/getting-started.md

@@ -25,7 +25,7 @@ With the model object instantiated as `nemodel`, you can use its attached method
 >>> nemodel.create_network(network_type="erdos_renyi", p=0.3)
 ```
 
-Simulations also depend on the individual attitudes of the in the basis network and the connections between them, which you can initalize like so:
+Simulations also depend on the individual attitudes of the in the basis network and the connections between them, which you can initialize like so:
 
 ```py
 >>> nemodel.initialize_connections()

pyproject.toml

@@ -17,6 +17,7 @@ dependencies = [
   "mkdocstrings[python]>=0.25.2",
   "rich>=13.9.4",
   "pandas>2.2.2",
+  "pre-commit>=4.1.0",
 ]
 
 [build-system]

src/netechos/core.py

@@ -143,10 +143,10 @@ def initialize_connections(
         ----------
         neighbor_weight : float
             The weight to assign to edges between nodes which have an
-            existing edge in the intial network.
+            existing edge in the initial network.
         non_neighbor_weight : float
             The weight to assign to edges between nodes which don't
-            have an existing edge in the intial network. If non-zero
+            have an existing edge in the initial network. If non-zero
             the network will technically become a complete network.
 
         Returns