Update branch with main

.github/workflows/documentation.yml

@@ -0,0 +1,27 @@
+name: documentation
+
+on: [push, pull_request, workflow_dispatch]
+
+permissions:
+  contents: write
+
+jobs:
+  docs:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+      - name: Install dependencies
+        run: |
+          pip install obsidian-apo[docs]
+      - name: Sphinx build
+        run: |
+          sphinx-build docs _build
+      - name: Deploy to GitHub Pages
+        uses: peaceiris/actions-gh-pages@v3
+        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/main' }}
+        with:
+          publish_branch: gh-pages
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          publish_dir: _build/
+          force_orphan: true

CHANGELOG.md

@@ -7,6 +7,7 @@
 ### Modified
 - Renamed aq_func hyperparmeter "Xi_f" to "inflate"
 - Moved default aq_func choices for single/multi into aq_defaults of acquisition.config
+- Fixed and improved campaign analysis methods
 
 ## [0.7.11]
 ### Added

obsidian/plotting/plotly.py

@@ -299,13 +299,16 @@ def surface_plot(optimizer: Optimizer,
     if plot_data:
         # Use a marker size which is larger if closer to the "other" values of the surface
         # Smaller markers mean there are other variables pulling it away from the surface plot
-        X_t_train_ex = optimizer.X_t_train.copy().drop(columns=[X0_name, X1_name]).values
-        X_t_test_ex = optimizer.X_space.encode(X_test.copy().drop(columns=[X0_name, X1_name]).drop_duplicates()).values
-
-        if X_t_train_ex.shape[0] > 1:
-            X_t_dist = np.linalg.norm(X_t_train_ex-X_t_test_ex, ord=2, axis=1)
-            X_t_dist_scaled = (X_t_dist - X_t_dist.min())/(X_t_dist.max()-X_t_dist.min())
-            dist_scaled = 15-10*X_t_dist_scaled
+        if len(optimizer.X_space) != 2:
+            X_t_train_ex = optimizer.X_t_train.copy().drop(columns=[X0_name, X1_name]).values
+            X_t_test_ex = optimizer.X_space.encode(X_test.copy().drop(columns=[X0_name, X1_name]).drop_duplicates()).values
+
+            if X_t_train_ex.shape[0] > 1:
+                X_t_dist = np.linalg.norm(X_t_train_ex-X_t_test_ex, ord=2, axis=1)
+                X_t_dist_scaled = (X_t_dist - X_t_dist.min())/(X_t_dist.max()-X_t_dist.min())
+                dist_scaled = 15-10*X_t_dist_scaled
+            else:
+                dist_scaled = 10
         else:
             dist_scaled = 10