Update Python libraries

conda-environment.yml

@@ -8,19 +8,19 @@ dependencies:
   - python=3.12.9
   - _python_rc=1 # Needed on Mac since Python >= 3.12
   - ipykernel=7.2.0
-  - matplotlib=3.10.8
+  - matplotlib=3.10.9
   - numpy=2.2.5
   - pandas=2.3.3
-  - pip=26.0.1
+  - pip=26.1
   - setuptools=80.10.2 # opentsne uses sklearn.base uses joblib uses distutils missing in Python >= 12 (TODO use native openTSNE?) 
   - wordcloud=1.9.6
   - monotonic=1.6
-  - plotly=6.5.2
+  - plotly=6.6.0 # v6.7.0 in pyproject.toml not yet available in conda-forge
   - python-kaleido=1.2.0 # To render plotly plots. Static image export for web-based visualization libraries.
   - scikit-learn=1.8.0 # For anomaly detection HDBSCAN clustering, scores like AMI, and others 
   - seaborn=0.13.2 # To visualize clustering results
   - optuna=4.7.0
   - umap-learn=0.5.11 # to visualize node embeddings in 2D (UMAP dimensionality reduction)
   - shap=0.50.0
   - pip:
-      - neo4j==6.1.0
+      - neo4j==6.2.0

pyproject.toml

@@ -5,22 +5,22 @@ requires-python = ">=3.12,<3.14"
 dependencies = [
     # --- Jupyter (for notebook environments) ---
     "ipykernel==7.2.0",
-    "matplotlib==3.10.8",
+    "matplotlib==3.10.9",
     "numpy==2.2.5",
     "pandas==2.3.3",
-    "pip==26.0.1",
+    "pip==26.1",
     "setuptools==80.10.2", # opentsne uses sklearn.base uses joblib uses distutils missing in Python >= 12 (TODO use native openTSNE?)
     # --- Visualization ---
     "wordcloud==1.9.6",
     "monotonic==1.6",
-    "plotly[kaleido]==6.5.2",
+    "plotly[kaleido]==6.7.0",
     "seaborn==0.13.2", # To visualize clustering results
     # --- Machine Learning / Optimization ---
     "scikit-learn==1.8.0",
     "optuna==4.7.0",
     "umap-learn==0.5.11", # Dimensionality reduction to visualize node embeddings in 2D
     # --- Database connector ---
-    "neo4j==6.1.0",
+    "neo4j==6.2.0",
     # --- Native/scientific packages (may require compilation) ---
     # These are included but may cause install errors in pip/venv
     "shap==0.50.0", # For e.g. explaining anomaly detection results