historgram measurements

backend/execution.py

@@ -182,7 +182,7 @@ class ExecutionEngine:
     ) -> None:
         """Wire up broadcast callbacks on display node classes."""
         from backend.nodes.display import PreviewImage, PrintTable, View3D, ValueDisplay
-        from backend.nodes.analysis import CrossSection, LineCursors, TableMath
+        from backend.nodes.analysis import CrossSection, LineCursors, TableMath, Stats, HeightHistogram
         from backend.nodes.modify import CropResizeField
         from backend.nodes.mask import ThresholdMask, MaskMorphology, MaskInvert, MaskCombine
         from backend.nodes.io import SaveImage, LoadFile
@@ -196,6 +196,8 @@ class ExecutionEngine:
         PrintTable._broadcast_table_fn = on_table
         ValueDisplay._broadcast_value_fn = on_value
         TableMath._broadcast_value_fn = on_value
+        Stats._broadcast_value_fn = on_value
+        HeightHistogram._broadcast_overlay_fn = on_overlay
         CrossSection._broadcast_overlay_fn = on_overlay
         LineCursors._broadcast_overlay_fn = on_overlay
         CropResizeField._broadcast_overlay_fn = on_overlay
@@ -205,11 +207,11 @@ class ExecutionEngine:
     def _set_node_id_on_display(self, cls: type, node_id: str) -> None:
         """Inform display nodes of their current node_id for WS tagging."""
         from backend.nodes.display import PreviewImage, PrintTable, View3D, ValueDisplay
-        from backend.nodes.analysis import CrossSection, LineCursors, TableMath
+        from backend.nodes.analysis import CrossSection, LineCursors, TableMath, Stats, HeightHistogram
         from backend.nodes.modify import CropResizeField
         from backend.nodes.mask import ThresholdMask, MaskMorphology, MaskInvert, MaskCombine
         from backend.nodes.io import LoadFile, SaveImage
-        if cls in (PreviewImage, PrintTable, View3D, ValueDisplay, TableMath, CrossSection, LineCursors, CropResizeField,
+        if cls in (PreviewImage, PrintTable, View3D, ValueDisplay, TableMath, Stats, HeightHistogram, CrossSection, LineCursors, CropResizeField,
                    ThresholdMask, MaskMorphology, MaskInvert, MaskCombine,
                    LoadFile, SaveImage):
             cls._current_node_id = node_id

backend/nodes/analysis.py

@@ -71,26 +71,91 @@ class HeightHistogram:
                 "field": ("DATA_FIELD",),
                 "n_bins": ("INT", {"default": 256, "min": 10, "max": 1000, "step": 1}),
                 "y_scale": (["linear", "log"],),
+                "x1": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01, "hidden": True}),
+                "y1": ("FLOAT", {"default": 0.5,  "min": 0.0, "max": 1.0, "step": 0.01, "hidden": True}),
+                "x2": ("FLOAT", {"default": 0.75, "min": 0.0, "max": 1.0, "step": 0.01, "hidden": True}),
+                "y2": ("FLOAT", {"default": 0.5,  "min": 0.0, "max": 1.0, "step": 0.01, "hidden": True}),
             }
         }
 
-    RETURN_TYPES = ("LINE", "LINE")
+    RETURN_TYPES = ("TABLE",)
-    RETURN_NAMES = ("counts", "bin_centers")
+    RETURN_NAMES = ("measurements",)
     FUNCTION = "process"
     CATEGORY = "analysis"
     DESCRIPTION = (
         "Compute the height distribution histogram (DH). "
         "Use log scale to reveal small peaks next to a dominant background. "
+        "Outputs marker measurements while showing the histogram interactively in-node. "
         "Equivalent to gwy_data_field_dh."
     )
 
-    def process(self, field: DataField, n_bins: int, y_scale: str = "linear") -> tuple:
+    _broadcast_overlay_fn = None
-        counts, bin_edges = np.histogram(field.data.ravel(), bins=int(n_bins))
+    _current_node_id: str = ""
+
+    def process(
+        self,
+        field: DataField,
+        n_bins: int,
+        y_scale: str = "linear",
+        x1: float = 0.25,
+        y1: float = 0.5,
+        x2: float = 0.75,
+        y2: float = 0.5,
+    ) -> tuple:
+        raw_counts, bin_edges = np.histogram(field.data.ravel(), bins=int(n_bins))
         bin_centers = 0.5 * (bin_edges[:-1] + bin_edges[1:])
-        counts = counts.astype(np.float64)
+        counts = raw_counts.astype(np.float64)
         if y_scale == "log":
             counts = np.log10(1.0 + counts)
-        return (counts, bin_centers)
+
+        x1 = float(np.clip(x1, 0.0, 1.0))
+        x2 = float(np.clip(x2, 0.0, 0.0 + 1.0))
+
+        xmin = float(np.min(bin_centers)) if len(bin_centers) else 0.0
+        xmax = float(np.max(bin_centers)) if len(bin_centers) else 1.0
+
+        def x_frac_to_idx(frac):
+            if len(bin_centers) <= 1:
+                return 0
+            if xmax == xmin:
+                return 0
+            target_x = xmin + frac * (xmax - xmin)
+            return int(np.argmin(np.abs(bin_centers - target_x)))
+
+        idx_a = x_frac_to_idx(x1)
+        idx_b = x_frac_to_idx(x2)
+        xa = float(bin_centers[idx_a]) if len(bin_centers) else 0.0
+        xb = float(bin_centers[idx_b]) if len(bin_centers) else 0.0
+        ya = float(counts[idx_a]) if len(counts) else 0.0
+        yb = float(counts[idx_b]) if len(counts) else 0.0
+        count_unit = "count" if y_scale == "linear" else "log10(1+count)"
+
+        if HeightHistogram._broadcast_overlay_fn is not None:
+            HeightHistogram._broadcast_overlay_fn(
+                HeightHistogram._current_node_id,
+                {
+                    "kind": "line_plot",
+                    "section_title": "Histogram",
+                    "line": counts.tolist(),
+                    "x_axis": bin_centers.astype(np.float64).tolist(),
+                    "x1": float(np.clip(x1, 0.0, 1.0)),
+                    "x2": float(np.clip(x2, 0.0, 1.0)),
+                    "y1": float(y1),
+                    "y2": float(y2),
+                    "a_locked": False,
+                    "b_locked": False,
+                },
+            )
+
+        table = [
+            {"quantity": "A position", "value": xa, "unit": field.si_unit_z},
+            {"quantity": "A count",    "value": ya, "unit": count_unit},
+            {"quantity": "B position", "value": xb, "unit": field.si_unit_z},
+            {"quantity": "B count",    "value": yb, "unit": count_unit},
+            {"quantity": "delta X",    "value": xb - xa, "unit": field.si_unit_z},
+            {"quantity": "delta Y",    "value": yb - ya, "unit": count_unit},
+        ]
+        return (table,)
 
 
 # ---------------------------------------------------------------------------
@@ -164,6 +229,7 @@ class LineCursors:
                 LineCursors._current_node_id,
                 {
                     "kind": "line_plot",
+                    "section_title": "Line Cursors",
                     "line": y.tolist(),
                     "x_axis": x.tolist(),
                     "x1": x1,
@@ -582,6 +648,20 @@ TABLE_OPS: dict[str, Callable[[np.ndarray], float]] = {
     "count": lambda values: float(len(values)),
 }
 
+ARRAY_OPS: dict[str, Callable[[np.ndarray], float]] = {
+    "min": lambda values: float(np.min(values)),
+    "max": lambda values: float(np.max(values)),
+    "avg": lambda values: float(np.mean(values)),
+    "mean": lambda values: float(np.mean(values)),
+    "median": lambda values: float(np.median(values)),
+    "sum": lambda values: float(np.sum(values)),
+    "range": lambda values: float(np.max(values) - np.min(values)),
+    "std": lambda values: float(np.std(values)),
+    "variance": lambda values: float(np.var(values)),
+    "rms": lambda values: float(np.sqrt(np.mean(values * values))),
+    "count": lambda values: float(values.size),
+}
+
 
 @register_node(display_name="Table Math")
 class TableMath:
@@ -616,8 +696,8 @@ class TableMath:
         if not isinstance(table, list) or not table:
             raise ValueError("Table Math requires a non-empty TABLE input.")
 
-        column_name = self._resolve_column_name(table, column)
+        column_name = resolve_table_column_name(table, column)
-        values = self._extract_numeric_values(table, column_name)
+        values = extract_numeric_table_values(table, column_name)
         if not values:
             raise ValueError(f"Column '{column_name}' has no numeric values.")
 
@@ -630,35 +710,8 @@ class TableMath:
             TableMath._broadcast_value_fn(TableMath._current_node_id, result)
         return (result,)
 
-    def _resolve_column_name(self, table: list, column: str) -> str:
-        requested = str(column or "").strip()
-        if requested:
-            return requested
 
-        if self._extract_numeric_values(table, "value"):
+def extract_numeric_table_values(table: list, column: str) -> list[float]:
-            return "value"
-
-        numeric_columns = []
-        seen = set()
-        for row in table:
-            if not isinstance(row, dict):
-                continue
-            for key in row.keys():
-                if key in seen:
-                    continue
-                seen.add(key)
-                if self._extract_numeric_values(table, key):
-                    numeric_columns.append(key)
-
-        if len(numeric_columns) == 1:
-            return numeric_columns[0]
-        if not numeric_columns:
-            raise ValueError("Table Math could not find any numeric columns in the input table.")
-        raise ValueError(
-            "Table Math found multiple numeric columns; set the column name explicitly."
-        )
-
-    def _extract_numeric_values(self, table: list, column: str) -> list[float]:
     values = []
     for row in table:
         if not isinstance(row, dict) or column not in row:
@@ -673,3 +726,118 @@ class TableMath:
         if np.isfinite(numeric):
             values.append(numeric)
     return values
+
+
+def resolve_table_column_name(table: list, column: str) -> str:
+    requested = str(column or "").strip()
+    if requested:
+        return requested
+
+    if extract_numeric_table_values(table, "value"):
+        return "value"
+
+    numeric_columns = []
+    seen = set()
+    for row in table:
+        if not isinstance(row, dict):
+            continue
+        for key in row.keys():
+            if key in seen:
+                continue
+            seen.add(key)
+            if extract_numeric_table_values(table, key):
+                numeric_columns.append(key)
+
+    if len(numeric_columns) == 1:
+        return numeric_columns[0]
+    if not numeric_columns:
+        raise ValueError("Table Math could not find any numeric columns in the input table.")
+    raise ValueError(
+        "Table Math found multiple numeric columns; set the column name explicitly."
+    )
+
+
+@register_node(display_name="Stats")
+class Stats:
+    """Polymorphic scalar stats node for LINE, TABLE, DATA_FIELD, or IMAGE inputs."""
+
+    _broadcast_value_fn = None
+    _current_node_id: str = ""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "input": ("STATS_SOURCE",),
+                "column": ("STRING", {
+                    "default": "value",
+                    "choices_from_table_input": "input",
+                    "show_when_source_type": {
+                        "input": ["TABLE"],
+                    },
+                }),
+                "operation": ("STRING", {
+                    "default": "mean",
+                    "choices_by_source_type": {
+                        "LINE": list(LINE_OPS.keys()),
+                        "TABLE": list(TABLE_OPS.keys()),
+                        "DATA_FIELD": list(ARRAY_OPS.keys()),
+                        "IMAGE": list(ARRAY_OPS.keys()),
+                    },
+                    "source_type_input": "input",
+                }),
+            }
+        }
+
+    RETURN_TYPES = ("FLOAT",)
+    RETURN_NAMES = ("value",)
+    FUNCTION = "process"
+    CATEGORY = "analysis"
+    DESCRIPTION = (
+        "Compute a contextual scalar statistic from a LINE, TABLE, DATA_FIELD, or IMAGE. "
+        "The available operations adapt to the connected input type."
+    )
+
+    def process(self, input, operation: str, column: str = "value") -> tuple:
+        source_type, values = self._resolve_input_values(input, column)
+
+        if source_type == "TABLE":
+            ops = TABLE_OPS
+        elif source_type == "LINE":
+            ops = LINE_OPS
+        else:
+            ops = ARRAY_OPS
+
+        if operation not in ops:
+            raise ValueError(f"Operation '{operation}' is not valid for {source_type} input.")
+
+        op_entry = ops[operation]
+        fn = op_entry[0] if isinstance(op_entry, tuple) else op_entry
+        result = fn(values)
+        if Stats._broadcast_value_fn is not None:
+            Stats._broadcast_value_fn(Stats._current_node_id, result)
+        return (result,)
+
+    def _resolve_input_values(self, input_value, column: str) -> tuple[str, np.ndarray]:
+        if isinstance(input_value, DataField):
+            values = np.asarray(input_value.data, dtype=np.float64)
+            return ("DATA_FIELD", values.ravel())
+
+        if isinstance(input_value, list):
+            if not input_value:
+                raise ValueError("Stats requires a non-empty TABLE input.")
+            column_name = resolve_table_column_name(input_value, column)
+            values = extract_numeric_table_values(input_value, column_name)
+            if not values:
+                raise ValueError(f"Column '{column_name}' has no numeric values.")
+            return ("TABLE", np.asarray(values, dtype=np.float64))
+
+        if isinstance(input_value, np.ndarray):
+            values = np.asarray(input_value, dtype=np.float64)
+            if values.size == 0:
+                raise ValueError("Stats requires a non-empty input.")
+            if values.ndim == 1:
+                return ("LINE", values.ravel())
+            return ("IMAGE", values.ravel())
+
+        raise ValueError(f"Unsupported Stats input type: {type(input_value).__name__}")

frontend/src/App.jsx

@@ -18,7 +18,11 @@ import { serializeWorkflowState } from './workflowSerialization';
 
 // ── Constants ─────────────────────────────────────────────────────────
 
-const DATA_TYPES = new Set(['DATA_FIELD', 'IMAGE', 'LINE', 'TABLE', 'COORD']);
+const DATA_TYPES = new Set(['DATA_FIELD', 'IMAGE', 'LINE', 'TABLE', 'COORD', 'STATS_SOURCE']);
+
+const SOCKET_COMPATIBILITY = {
+  STATS_SOURCE: new Set(['DATA_FIELD', 'IMAGE', 'LINE', 'TABLE']),
+};
 
 const TYPE_COLORS = {
   DATA_FIELD: '#ff002f',
@@ -27,6 +31,7 @@ const TYPE_COLORS = {
   TABLE:      '#35e2fd',
   COORD:      '#e91ed1',
   FLOAT:      '#7dd3fc',
+  STATS_SOURCE:'#c084fc',
 };
 
 const NODE_TYPES = { custom: CustomNode };
@@ -45,6 +50,12 @@ function getOutputSlot(handleId) {
   return parseInt(handleId.split('::')[1], 10);
 }
 
+function socketTypesCompatible(sourceType, targetType) {
+  if (sourceType === targetType) return true;
+  const accepted = SOCKET_COMPATIBILITY[targetType];
+  return !!accepted?.has(sourceType);
+}
+
 async function waitForImageElement(img) {
   if (img.complete && img.naturalWidth > 0) return;
   if (typeof img.decode === 'function') {
@@ -220,11 +231,11 @@ function ContextMenu({ x, y, nodeDefs, onAdd, onClose, filterType, filterDirecti
           const allInputs = { ...req, ...opt };
           const hasMatch = Object.values(allInputs).some((spec) => {
             const [type] = Array.isArray(spec) ? spec : [spec];
-            return type === filterType;
+            return socketTypesCompatible(filterType, type);
           });
           if (!hasMatch) continue;
         } else {
-          if (!def.output.includes(filterType)) continue;
+          if (!def.output.some((type) => socketTypesCompatible(type, filterType))) continue;
         }
       }
       const cat = def.category || 'uncategorized';
@@ -474,7 +485,7 @@ function Flow() {
   const isValidConnection = useCallback((connection) => {
     const srcType = getHandleType(connection.sourceHandle);
     const tgtType = getHandleType(connection.targetHandle);
-    return srcType === tgtType;
+    return socketTypesCompatible(srcType, tgtType);
   }, []);
 
   const onConnect = useCallback((params) => {
@@ -667,10 +678,15 @@ function Flow() {
         const allInputs = { ...(def.input.required || {}), ...(def.input.optional || {}) };
         const inputName = Object.entries(allInputs).find(([, spec]) => {
           const [type] = Array.isArray(spec) ? spec : [spec];
-          return type === filterType;
+          return socketTypesCompatible(filterType, type);
         })?.[0];
         if (inputName) {
-          const targetHandle = `input::${inputName}::${filterType}`;
+          const targetType = (() => {
+            const spec = allInputs[inputName];
+            const [type] = Array.isArray(spec) ? spec : [spec];
+            return type;
+          })();
+          const targetHandle = `input::${inputName}::${targetType}`;
           const color = TYPE_COLORS[filterType] || '#999';
           setEdges((eds) => addEdge({
             source: contextMenu.pendingNodeId,
@@ -682,10 +698,11 @@ function Flow() {
         }
       } else {
         // Dragged from an input → connect from the first matching output on the new node
-        const outputIdx = def.output.indexOf(filterType);
+        const outputIdx = def.output.findIndex((type) => socketTypesCompatible(type, filterType));
         if (outputIdx !== -1) {
-          const sourceHandle = `output::${outputIdx}::${filterType}`;
+          const outputType = def.output[outputIdx];
-          const color = TYPE_COLORS[filterType] || '#999';
+          const sourceHandle = `output::${outputIdx}::${outputType}`;
+          const color = TYPE_COLORS[outputType] || '#999';
           setEdges((eds) => addEdge({
             source: newNodeId,
             sourceHandle,

frontend/src/CustomNode.jsx

@@ -8,7 +8,7 @@ const CropBoxOverlay = lazy(() => import('./CropBoxOverlay'));
 
 // ── Constants ─────────────────────────────────────────────────────────
 
-const DATA_TYPES = new Set(['DATA_FIELD', 'IMAGE', 'LINE', 'TABLE', 'COORD']);
+const DATA_TYPES = new Set(['DATA_FIELD', 'IMAGE', 'LINE', 'TABLE', 'COORD', 'STATS_SOURCE']);
 const SOCKET_WIDGET_TYPES = new Set(['FLOAT']);
 
 const TYPE_COLORS = {
@@ -18,6 +18,7 @@ const TYPE_COLORS = {
   TABLE:      '#fdd835',
   COORD:      '#e91e63',
   FLOAT:      '#7dd3fc',
+  STATS_SOURCE:'#c084fc',
 };
 
 const CAT_COLORS = {
@@ -205,6 +206,30 @@ function formatScalarValue(value) {
   return numeric.toFixed(abs >= 100 ? 2 : 4).replace(/\.?0+$/, '');
 }
 
+function getSourceTypeForInput(store, nodeId, inputName) {
+  const targetHandle = `input::${inputName}::`;
+  const edge = store.edges?.find((e) => e.target === nodeId && e.targetHandle?.startsWith(targetHandle));
+  if (!edge?.sourceHandle) return null;
+  const parts = edge.sourceHandle.split('::');
+  return parts[2] || null;
+}
+
+function getSourceNodeForInput(store, nodeId, inputName) {
+  const targetHandle = `input::${inputName}::`;
+  const edge = store.edges?.find((e) => e.target === nodeId && e.targetHandle?.startsWith(targetHandle));
+  if (!edge) return null;
+  return store.nodeLookup?.get(edge.source) || store.nodes?.find((n) => n.id === edge.source) || null;
+}
+
+function widgetVisibleForSourceType(widget, sourceType) {
+  const rules = widget?.opts?.show_when_source_type;
+  if (!rules || typeof rules !== 'object') return true;
+  const inputName = Object.keys(rules)[0];
+  const allowed = Array.isArray(rules[inputName]) ? rules[inputName] : [];
+  if (allowed.length === 0) return true;
+  return allowed.includes(sourceType);
+}
+
 function NodeTable({ rows }) {
   const columns = getTableColumns(rows);
   if (columns.length === 0) return null;
@@ -290,6 +315,20 @@ function CustomNode({ id, data }) {
     ),
   );
 
+  const connectedSourceTypes = useStore(
+    useCallback(
+      (s) => {
+        const sourceTypes = {};
+        const allInputs = { ...required, ...optional };
+        for (const name of Object.keys(allInputs)) {
+          sourceTypes[name] = getSourceTypeForInput(s, id, name);
+        }
+        return sourceTypes;
+      },
+      [id, required, optional],
+    ),
+  );
+
   for (const [name, spec] of Object.entries(optional)) {
     const [type, opts] = Array.isArray(spec) ? spec : [spec, {}];
     if (isProgressive && DATA_TYPES.has(type)) {
@@ -320,6 +359,13 @@ function CustomNode({ id, data }) {
 
   const catColor = CAT_COLORS[def.category] || '#333';
   const maxIORows = Math.max(dataInputs.length, outputs.length);
+  const hasInteractiveLineOverlay = data.overlay?.kind === 'line_plot' && hiddenWidgets.has('x1');
+  const overlayTitle = data.overlay?.section_title
+    || (data.overlay?.kind === 'crop_box'
+      ? 'Crop'
+      : data.overlay?.kind === 'line_plot'
+        ? 'Line Plot'
+        : 'Cross Section');
 
   return (
     <div className="custom-node">
@@ -380,7 +426,7 @@ function CustomNode({ id, data }) {
         )}
 
         {/* Widget rows */}
-        {widgets.map((w) => (
+        {widgets.filter((w) => widgetVisibleForSourceType(w, connectedSourceTypes?.[w.opts?.source_type_input || w.opts?.choices_from_table_input || Object.keys(w.opts?.show_when_source_type || {})[0]])).map((w) => (
           <div className={`widget-row${w.socketType ? ' widget-row-socket' : ''}`} key={w.name}>
             {w.socketType && (
               <Handle
@@ -425,7 +471,7 @@ function CustomNode({ id, data }) {
         )}
 
         {/* Collapsible preview image */}
-        {data.previewImage && (
+        {data.previewImage && !(hasInteractiveLineOverlay && typeof data.previewImage === 'object' && data.previewImage.kind === 'line_plot') && (
           <CollapsibleSection title="Preview" defaultOpen={true}>
             <PreviewBoundary
               resetKey={typeof data.previewImage === 'string' ? data.previewImage : JSON.stringify({
@@ -447,7 +493,7 @@ function CustomNode({ id, data }) {
 
         {/* Interactive cross-section overlay */}
         {data.overlay && hiddenWidgets.has('x1') && (
-          <CollapsibleSection title={data.overlay.kind === 'crop_box' ? 'Crop' : 'Cross Section'} defaultOpen={true}>
+          <CollapsibleSection title={overlayTitle} defaultOpen={true}>
             <Suspense fallback={<div className="node-preview" style={{color:'#64748b',padding:4}}>Loading...</div>}>
               {data.overlay.kind === 'line_plot' ? (
                 <LinePlotOverlay
@@ -504,21 +550,47 @@ function CustomNode({ id, data }) {
 function WidgetControl({ widget, nodeId, value, widgetValues, onChange, openFileBrowser }) {
   const { name, type, opts } = widget;
   const val = value ?? opts?.default ?? '';
+  const dynamicSourceType = useStore(
+    useCallback(
+      (s) => {
+        const inputName = opts?.source_type_input
+          || opts?.choices_from_table_input
+          || Object.keys(opts?.show_when_source_type || {})[0];
+        if (!inputName) return null;
+        return getSourceTypeForInput(s, nodeId, inputName);
+      },
+      [nodeId, opts],
+    ),
+  );
   const dynamicTableColumns = useStore(
     useCallback(
       (s) => {
         const tableInputName = opts?.choices_from_table_input;
         if (!tableInputName) return [];
-        const targetHandle = `input::${tableInputName}::TABLE`;
+        const sourceType = getSourceTypeForInput(s, nodeId, tableInputName);
-        const edge = s.edges?.find((e) => e.target === nodeId && e.targetHandle === targetHandle);
+        if (sourceType !== 'TABLE') return [];
-        if (!edge) return [];
+        const sourceNode = getSourceNodeForInput(s, nodeId, tableInputName);
-        const sourceNode = s.nodeLookup?.get(edge.source) || s.nodes?.find((n) => n.id === edge.source);
         const rows = sourceNode?.data?.tableRows;
         return Array.isArray(rows) ? getTableColumns(rows) : [];
       },
       [nodeId, opts?.choices_from_table_input],
     ),
   );
+  const dynamicTypeChoices = (() => {
+    const byType = opts?.choices_by_source_type;
+    if (!byType) return [];
+    if (dynamicSourceType) {
+      return Array.isArray(byType[dynamicSourceType]) ? byType[dynamicSourceType] : [];
+    }
+    const merged = [];
+    for (const choices of Object.values(byType)) {
+      if (!Array.isArray(choices)) continue;
+      for (const choice of choices) {
+        if (!merged.includes(choice)) merged.push(choice);
+      }
+    }
+    return merged;
+  })();
 
   useEffect(() => {
     if (!opts?.choices_from_table_input || dynamicTableColumns.length === 0) return;
@@ -528,6 +600,13 @@ function WidgetControl({ widget, nodeId, value, widgetValues, onChange, openFile
     if (preferred != null) onChange(nodeId, name, preferred);
   }, [dynamicTableColumns, name, nodeId, onChange, opts?.choices_from_table_input, val]);
 
+  useEffect(() => {
+    if (dynamicTypeChoices.length === 0) return;
+    const current = String(val ?? '');
+    if (dynamicTypeChoices.includes(current)) return;
+    onChange(nodeId, name, dynamicTypeChoices[0]);
+  }, [dynamicTypeChoices, name, nodeId, onChange, val]);
+
   // Combo / enum — type itself is the array of options
   if (Array.isArray(type)) {
     return (
@@ -546,6 +625,24 @@ function WidgetControl({ widget, nodeId, value, widgetValues, onChange, openFile
     );
   }
 
+  if (type === 'STRING' && dynamicTypeChoices.length > 0) {
+    const selected = dynamicTypeChoices.includes(String(val)) ? String(val) : dynamicTypeChoices[0];
+    return (
+      <>
+        <label>{name}</label>
+        <select
+          className="nodrag"
+          value={selected}
+          onChange={(e) => onChange(nodeId, name, e.target.value)}
+        >
+          {dynamicTypeChoices.map((choice) => (
+            <option key={choice} value={choice}>{choice}</option>
+          ))}
+        </select>
+      </>
+    );
+  }
+
   if (type === 'STRING' && opts?.choices_from_table_input && dynamicTableColumns.length > 0) {
     const selected = dynamicTableColumns.includes(String(val)) ? String(val) : dynamicTableColumns[0];
     return (

tests/test_nodes.py

@@ -481,17 +481,42 @@ def test_height_histogram():
     data = np.linspace(0, 1, 1000).reshape(25, 40)
     field = make_field(data=data)
 
-    counts, bin_centers = node.process(field, n_bins=10, y_scale="linear")
+    overlays = []
-    assert len(counts) == 10
+    HeightHistogram._broadcast_overlay_fn = lambda nid, data: overlays.append(data)
-    assert len(bin_centers) == 10
+    HeightHistogram._current_node_id = "test"
-    assert counts.dtype == np.float64
+
-    # Total counts should equal number of pixels
+    table, = node.process(
-    assert counts.sum() == 1000
+        field,
-    # For uniform data, each bin should have ~100 counts
+        n_bins=10,
-    assert np.std(counts) < 10, f"Histogram not flat enough: std={np.std(counts)}"
+        y_scale="linear",
-    # Bin centers should span the data range
+        x1=0.2,
-    assert bin_centers[0] > 0.0
+        y1=0.5,
-    assert bin_centers[-1] < 1.0
+        x2=0.8,
+        y2=0.5,
+    )
+    measurements = {row["quantity"]: row for row in table}
+    assert "A position" in measurements
+    assert "A count" in measurements
+    assert "B position" in measurements
+    assert "B count" in measurements
+    assert "delta X" in measurements
+    assert "delta Y" in measurements
+    assert measurements["A count"]["unit"] == "count"
+    assert measurements["B count"]["unit"] == "count"
+    assert measurements["B position"]["value"] > measurements["A position"]["value"]
+    assert len(overlays) == 1
+    assert overlays[0]["kind"] == "line_plot"
+    assert overlays[0]["section_title"] == "Histogram"
+    assert len(overlays[0]["line"]) == 10
+    assert len(overlays[0]["x_axis"]) == 10
+    assert np.isclose(overlays[0]["x1"], 0.2)
+    assert np.isclose(overlays[0]["x2"], 0.8)
+    assert np.isclose(
+        measurements["delta Y"]["value"],
+        measurements["B count"]["value"] - measurements["A count"]["value"],
+    )
+
+    HeightHistogram._broadcast_overlay_fn = None
     print("  PASS\n")
 
 
@@ -1380,6 +1405,49 @@ def test_table_math():
     print("  PASS\n")
 
 
+# =========================================================================
+# Analysis — Stats
+# =========================================================================
+
+def test_stats():
+    print("=== Test: Stats ===")
+    from backend.nodes.analysis import Stats
+
+    node = Stats()
+    captured = []
+    Stats._broadcast_value_fn = lambda node_id, value: captured.append((node_id, value))
+    Stats._current_node_id = "test"
+
+    line = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float64)
+    result, = node.process(line, operation="mean", column="value")
+    assert np.isclose(result, 2.5)
+    assert captured[-1] == ("test", result)
+
+    table = [
+        {"name": "a", "value": 3.0, "other": 10.0},
+        {"name": "b", "value": 7.0, "other": 20.0},
+    ]
+    result, = node.process(table, operation="max", column="value")
+    assert result == 7.0
+
+    field = make_field(data=np.array([[1.0, 5.0], [2.0, 4.0]], dtype=np.float64))
+    result, = node.process(field, operation="range", column="value")
+    assert result == 4.0
+
+    image = np.array([[0, 10], [20, 30]], dtype=np.uint8)
+    result, = node.process(image, operation="avg", column="value")
+    assert np.isclose(result, 15.0)
+
+    try:
+        node.process(table, operation="Rq", column="value")
+        raise AssertionError("Expected invalid TABLE operation to raise ValueError")
+    except ValueError:
+        pass
+
+    Stats._broadcast_value_fn = None
+    print("  PASS\n")
+
+
 # =========================================================================
 # Display — View3D
 # =========================================================================
@@ -1457,6 +1525,7 @@ if __name__ == "__main__":
     test_fft2d()
     test_line_math()
     test_table_math()
+    test_stats()
 
     # Mask
     test_threshold_mask()