address comments

Author: joehart2001

ml_peg/analysis/bulk_crystal/elasticity/analyse_elasticity.py

@@ -58,15 +58,17 @@ def _filter_results(df: pd.DataFrame, model_name: str) -> tuple[pd.DataFrame, in
     return valid, excluded
 
 
-def _collect_model_data() -> dict[str, dict[str, Any]]:
+@pytest.fixture
+def elasticity_stats() -> dict[str, dict[str, Any]]:
     """
-    Collect filtered results, MAEs, and exclusion counts per model.
+    Load and cache processed benchmark statistics per model.
 
     Returns
     -------
     dict[str, dict[str, Any]]
-        Mapping of model name to bulk/shear data and exclusion metadata.
+        Processed information per model (bulk, shear, exclusion counts).
     """
+    OUT_PATH.mkdir(parents=True, exist_ok=True)
     stats: dict[str, dict[str, Any]] = {}
     for model_name in MODELS:
         results_path = CALC_PATH / model_name / "moduli_results.csv"
@@ -85,21 +87,8 @@ def _collect_model_data() -> dict[str, dict[str, Any]]:
             },
             "excluded": excluded,
         }
-    return stats
-
 
-@pytest.fixture
-def elasticity_stats() -> dict[str, dict[str, Any]]:
-    """
-    Load and cache processed benchmark statistics per model.
-
-    Returns
-    -------
-    dict[str, dict[str, Any]]
-        Processed information per model (bulk, shear, exclusion counts).
-    """
-    OUT_PATH.mkdir(parents=True, exist_ok=True)
-    return _collect_model_data()
+    return stats
 
 
 @pytest.fixture
@@ -167,7 +156,7 @@ def bulk_density(elasticity_stats: dict[str, dict[str, Any]]) -> dict[str, dict]
     dict[str, dict]
         Mapping of model name to density-scatter data.
     """
-    return build_density_inputs(MODELS, elasticity_stats, "bulk", mae_fn=mae)
+    return build_density_inputs(MODELS, elasticity_stats, "bulk", metric_fn=mae)
 
 
 @pytest.fixture
@@ -191,7 +180,7 @@ def shear_density(elasticity_stats: dict[str, dict[str, Any]]) -> dict[str, dict
     dict[str, dict]
         Mapping of model name to density-scatter data.
     """
-    return build_density_inputs(MODELS, elasticity_stats, "shear", mae_fn=mae)
+    return build_density_inputs(MODELS, elasticity_stats, "shear", metric_fn=mae)
 
 
 @pytest.fixture

ml_peg/analysis/utils/utils.py

@@ -120,7 +120,7 @@ def build_density_inputs(
     model_stats: dict[str, dict[str, Any]],
     property_key: str,
     *,
-    mae_fn: Callable[[list, list], float] | None = None,
+    metric_fn: Callable[[list, list], float] | None = None,
 ) -> dict[str, dict[str, Any]]:
     """
     Prepare a model->data mapping for density scatter plots.
@@ -135,15 +135,15 @@ def build_density_inputs(
     property_key
         Key to extract from ``model_stats`` for each model (e.g. ``"bulk"`` or
         ``"shear"``).
-    mae_fn
-        Optional callable to compute MAE. Defaults to :func:`mae` when None.
+    metric_fn
+        Optional callable to compute metric. Defaults to :func:`mae` when None.
 
     Returns
     -------
     dict[str, dict[str, Any]]
         Mapping ready for ``plot_density_scatter``.
     """
-    mae_fn = mae if mae_fn is None else mae_fn
+    metric_fn = mae if metric_fn is None else metric_fn
     inputs: dict[str, dict[str, Any]] = {}
 
     for model_name in models:
@@ -156,7 +156,7 @@ def build_density_inputs(
         inputs[model_name] = {
             "ref": ref_vals,
             "pred": pred_vals,
-            "mae": mae_fn(ref_vals, pred_vals) if ref_vals else None,
+            "metric": metric_fn(ref_vals, pred_vals) if ref_vals else None,
             "meta": {"excluded": excluded} if excluded is not None else {},
         }
 

ml_peg/app/bulk_crystal/elasticity/app_elasticity.py

@@ -14,7 +14,9 @@
 
 MODELS = get_model_names(current_models)
 BENCHMARK_NAME = "Elasticity"
-DOCS_URL = "https://ddmms.github.io/ml-peg/user_guide/benchmarks/bulk.html#elasticity"
+DOCS_URL = (
+    "https://ddmms.github.io/ml-peg/user_guide/benchmarks/bulk_crystal.html#elasticity"
+)
 DATA_PATH = APP_ROOT / "data" / "bulk_crystal" / "elasticity"
 
 

ml_peg/app/utils/load.py

@@ -174,29 +174,25 @@ def _filter_density_figure_for_model(fig_dict: dict, model: str) -> dict:
         line.
     """
     data = fig_dict.get("data", [])
-    layout = deepcopy(fig_dict.get("layout", {}))
-    annotations_meta = (
-        layout.get("meta") if isinstance(layout.get("meta"), dict) else {}
-    )
+    layout = deepcopy(fig_dict.get("layout"))
+    annotations_meta = layout.get("meta")
 
     fig_data = []
     for trace in data:
         name = trace.get("name")
-        if name is None:
-            line_trace = deepcopy(trace)
-            line_trace["visible"] = True
-            line_trace["showlegend"] = False  # keep reference line, no legend
-            fig_data.append(line_trace)
-        elif name == model:
-            model_trace = deepcopy(trace)
-            model_trace["visible"] = True
-            model_trace["showlegend"] = False  # hide legend to avoid overlap
-            fig_data.append(model_trace)
-
-    # Pick the matching annotation when available; otherwise keep a simple fallback.
+        if name is None or name == model:
+            # y=x line or matching model trace
+            trace_copy = deepcopy(trace)
+            trace_copy["visible"] = True
+            trace_copy["showlegend"] = False
+            fig_data.append(trace_copy)
+
+    # Pick the matching annotation when available
+    stored_annotations = (
+        annotations_meta.get("annotations") if annotations_meta else None
+    )
+    model_order = annotations_meta.get("models") if annotations_meta else None
     chosen_annotation = None
-    stored_annotations = annotations_meta.get("annotations")
-    model_order = annotations_meta.get("models")
     if isinstance(stored_annotations, list) and isinstance(model_order, list):
         try:
             idx = model_order.index(model)
@@ -206,11 +202,6 @@ def _filter_density_figure_for_model(fig_dict: dict, model: str) -> dict:
             pass
     if chosen_annotation:
         layout["annotations"] = [chosen_annotation]
-    elif layout.get("annotations"):
-        fallback = deepcopy(layout["annotations"][0])
-        if isinstance(fallback, dict):
-            fallback["text"] = model
-            layout["annotations"] = [fallback]
 
     # Hide legend entirely to prevent overlap with the density colorbar.
     layout["showlegend"] = False

ml_peg/calcs/bulk_crystal/elasticity/calc_elasticity.py

@@ -79,7 +79,7 @@ def run_elasticity_benchmark(
         calc,
         model_name,
         n_jobs=n_jobs,
-        checkpoint_file=str(checkpoint_file) if checkpoint_file else None,
+        checkpoint_file=checkpoint_file if checkpoint_file else None,
         checkpoint_freq=100,
         delete_checkpoint_on_finish=False,
     )