Added Pandas Count, Max, Mean, Median, Min, Mode, Std, Sum, Var, Show Series nodes.

__init__.py

@@ -4,6 +4,7 @@ from .modules.matplotlib.mpl_bar import MPLBar
 from .modules.matplotlib.mpl_line import MPLLine
 from .modules.matplotlib.mpl_scatter import MPLScatter
 from .modules.pandas_wrapper.pandas_columns import PandasColumns
+from .modules.pandas_wrapper.pandas_count import PandasCount
 from .modules.pandas_wrapper.pandas_create import PandasCreate
 from .modules.pandas_wrapper.pandas_create_with_index import PandasCreateWithIndex
 from .modules.pandas_wrapper.pandas_dropna import PandasDropNA
@@ -20,16 +21,26 @@ from .modules.pandas_wrapper.pandas_load_csv_with_index import PandasLoadCSVWith
 from .modules.pandas_wrapper.pandas_load_json import PandasLoadJSON
 from .modules.pandas_wrapper.pandas_loc_cell_str import PandasLocCellStr
 from .modules.pandas_wrapper.pandas_loc_row_series import PandasLocRowSeries
+from .modules.pandas_wrapper.pandas_max import PandasMax
+from .modules.pandas_wrapper.pandas_mean import PandasMean
+from .modules.pandas_wrapper.pandas_median import PandasMedian
+from .modules.pandas_wrapper.pandas_min import PandasMin
+from .modules.pandas_wrapper.pandas_mode import PandasMode
 from .modules.pandas_wrapper.pandas_save_csv import PandasSaveCSV
 from .modules.pandas_wrapper.pandas_save_json import PandasSaveJSON
 from .modules.pandas_wrapper.pandas_select_columns import PandasSelectColumns
 from .modules.pandas_wrapper.pandas_select_rows import PandasSelectRows
 from .modules.pandas_wrapper.pandas_series_to_string import PandasSeriesToString
 from .modules.pandas_wrapper.pandas_show_dataframe import PandasShowDataFrame
+from .modules.pandas_wrapper.pandas_show_series import PandasShowSeries
 from .modules.pandas_wrapper.pandas_show_text import PandasShowText
 from .modules.pandas_wrapper.pandas_sort import PandasSort
+from .modules.pandas_wrapper.pandas_std import PandasStd
+from .modules.pandas_wrapper.pandas_sum import PandasSum
 from .modules.pandas_wrapper.pandas_summary import PandasSummary
 from .modules.pandas_wrapper.pandas_to_string import PandasToString
+from .modules.pandas_wrapper.pandas_var import PandasVar
+
 
 T = TypeVar("T")
 
@@ -39,6 +50,7 @@ NODE_CLASS_MAPPINGS: Dict[str, Type[T]] = {
     "MPLLine": MPLLine,
     "MPLScatter": MPLScatter,
     "PandasColumns": PandasColumns,
+    "PandasCount": PandasCount,
     "PandasCreate": PandasCreate,
     "PandasCreateWithIndex": PandasCreateWithIndex,
     "PandasDropNA": PandasDropNA,
@@ -55,16 +67,25 @@ NODE_CLASS_MAPPINGS: Dict[str, Type[T]] = {
     "PandasLoadJSON": PandasLoadJSON,
     "PandasLocCellStr": PandasLocCellStr,
     "PandasLocRowSeries": PandasLocRowSeries,
+    "PandasMax": PandasMax,
+    "PandasMean": PandasMean,
+    "PandasMedian": PandasMedian,
+    "PandasMin": PandasMin,
+    "PandasMode": PandasMode,
     "PandasSaveCSV": PandasSaveCSV,
     "PandasSaveJSON": PandasSaveJSON,
     "PandasSelectColumns": PandasSelectColumns,
     "PandasSelectRows": PandasSelectRows,
     "PandasSeriesToString": PandasSeriesToString,
     "PandasShowDataFrame": PandasShowDataFrame,
+    "PandasShowSeries": PandasShowSeries,
     "PandasShowText": PandasShowText,
     "PandasSort": PandasSort,
+    "PandasStd": PandasStd,
+    "PandasSum": PandasSum,
     "PandasSummary": PandasSummary,
     "PandasToString": PandasToString,
+    "PandasVar": PandasVar
 }
 """
 NODE_CLASS_MAPPINGS (Dict[str, Type[T]]):
@@ -77,6 +98,7 @@ NODE_DISPLAY_NAME_MAPPINGS: Dict[str, str] = {
     "MPLLine": "MPL Line Plot",
     "MPLScatter": "MPL Scatter Plot",
     "PandasColumns": "Pandas Columns",
+    "PandasCount": "Pandas Count",
     "PandasCreate": "Pandas Create",
     "PandasCreateWithIndex": "Pandas Create With Index",
     "PandasDropNA": "Pandas Drop NA",
@@ -93,16 +115,25 @@ NODE_DISPLAY_NAME_MAPPINGS: Dict[str, str] = {
     "PandasLoadJSON": "Pandas Load JSON",
     "PandasLocCellStr": "Pandas Loc Cell Str",
     "PandasLocRowSeries": "Pandas Loc Row Series",
+    "PandasMax": "Pandas Max",
+    "PandasMean": "Pandas Mean",
+    "PandasMedian": "Pandas Median",
+    "PandasMin": "Pandas Min",
+    "PandasMode": "Pandas Mode",
     "PandasSaveCSV": "Pandas Save CSV",
     "PandasSaveJSON": "Pandas Save JSON",
     "PandasSelectColumns": "Pandas Select Columns",
     "PandasSelectRows": "Pandas Select Rows",
     "PandasSeriesToString": "Pandas Series To String",
     "PandasShowDataFrame": "Pandas Show DataFrame",
+    "PandasShowSeries": "Pandas Show Series",
     "PandasShowText": "Pandas Show Text",
     "PandasSort": "Pandas Sort",
+    "PandasStd": "Pandas Std",
+    "PandasSum": "Pandas Sum",
     "PandasSummary": "Pandas Summary",
     "PandasToString": "Pandas To String",
+    "PandasVar": "Pandas Var",
 }
 """
 NODE_DISPLAY_NAME_MAPPINGS (Dict[str, str]):

docs/ug.md

@@ -49,6 +49,7 @@ You can right-click, select **Add Node**, go to **Data Analysis**, and look for
 | Node Name               | Functionality                        |
 |-------------------------|-------------------------------------|
 | **Pandas Show DataFrame** | Display DataFrame contents        |
+| **Pandas Show Series** | Display Series contents        |
 | **Pandas Show Text**    | Display Text        |
 | **Pandas Summary**      | Extract DataFrame statistics          |
 
@@ -58,6 +59,19 @@ You can right-click, select **Add Node**, go to **Data Analysis**, and look for
 | **Pandas Is NA**  | Check DataFrame for missing values      |
 | **Pandas Drop NA**  | Drop missing values from DataFrame    |
 
+**Summary statistics**
+| Node Name               | Functionality                        |
+|-------------------------|-------------------------------------|
+| **Pandas Count**  | Number of rows     |
+| **Pandas Max**  | Maximum value of row values     |
+| **Pandas Mean**  | Mean of row values     |
+| **Pandas Median**  | Median of row values     |
+| **Pandas Min**  | Minimum value of row values     |
+| **Pandas Mode**  | Mode of row values     |
+| **Pandas Std**  | Standard deviation of row values     |
+| **Pandas Sum**  | Sum of row values     |
+| **Pandas Var**  | Variance of row values     |
+
 **Converting to displayable string**
 | Node Name               | Functionality                        |
 |-------------------------|-------------------------------------|
@@ -192,7 +206,10 @@ https://github.com/pythongosssss/ComfyUI-Custom-Scripts/blob/main/py/show_text.p
 This node has been copied to display text without requiring the user to install the above custom nodes package. However, if you already have ComfyUI-Custom-Scripts, you can also use the Show Text node from the package.
 
 ## Displaying DataFrame contents
-Use **Pandas Show DataFrame** node. When the number of rows is large, Pandas automatically hides middle rows during string conversion, so the display size should be always manageable.
+Use **Pandas Show DataFrame** node. When the number of rows is large, This node automatically hides middle rows during string conversion, so the display size should be always manageable.
+
+## Displaying Series contents
+Use **Pandas Show Series** node.
 
 ## Extracting DataFrame statistics
 Use **Pandas Summary** to extract statistics then connect the output to **Pandas Show Text** for display.
@@ -203,6 +220,31 @@ Use **Pandas Sort**.
 
 ![Pandas Sort](images/sort.jpg)
 
+## Summary statistics
+You can compute summary statistics for each row in a DataFrame using below nodes:
+* **Pandas Count** to get the number of rows.
+* **Pandas Min** to get the minimum row value.
+* **Pandas Max** to get the maximum value of row values.
+* **Pandas Mean** to get the mean of row values.
+* **Pandas Median** to get the median of row values.
+* **Pandas Mode** to get the mode of row values.
+* **Pandas Sum** to get the sum of row values.
+* **Pandas Std** to get the standard deviation of row values.
+* **Pandas Var** to get the variance of row values.
+
+All nodes return a Series except for Pandas Mode, which returns a DataFrame. This is necessary because multiple values may occur with the highest frequency. For example:
+```
+apple
+apple
+apple
+banana
+donut
+coconut
+coconut
+coconut
+```
+In this case, both apple and coconut are the mode.
+
 ## Convert DataFrame to a string
 Use **Pandas To String**. This node is to convert the DataFrame to a displayable string, and this truncates the data when the data is large.  This can be used to display the data content in a text display node (e.g. Show Text node). However, you can use Show DataFrame nodes to display the DataFrame content, so you do not normally need to use this.
 

modules/pandas_wrapper/pandas_count.py

@@ -0,0 +1,42 @@
+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+from .utils import series_to_jsons
+
+class PandasCount:
+    """
+    PandasCount:
+        A class for computing the count of a pandas DataFrame.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "dataframe_json": ("STRING", {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe_json: str) -> tuple:
+        """
+        Returns a Series with count for each column.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+
+        Returns:
+            tuple: A tuple containing a JSON string for the Series.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(StringIO(dataframe_json))
+        return (series_to_jsons(df.count()),)

modules/pandas_wrapper/pandas_max.py

@@ -0,0 +1,42 @@
+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+from .utils import series_to_jsons
+
+class PandasMax:
+    """
+    PandasMin:
+        A class for computing the max of a pandas DataFrame.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "dataframe_json": ("STRING", {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe_json: str) -> tuple:
+        """
+        Returns a Series with max for each column.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+
+        Returns:
+            tuple: A tuple containing a JSON string for the Series.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(StringIO(dataframe_json))
+        return (series_to_jsons(df.max()),)

modules/pandas_wrapper/pandas_mean.py

@@ -0,0 +1,42 @@
+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+from .utils import series_to_jsons
+
+class PandasMean:
+    """
+    PandasMin:
+        A class for computing the mean of a pandas DataFrame.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "dataframe_json": ("STRING", {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe_json: str) -> tuple:
+        """
+        Returns a Series with mean for each column.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+
+        Returns:
+            tuple: A tuple containing a JSON string for the Series.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(StringIO(dataframe_json))
+        return (series_to_jsons(df.mean()),)

modules/pandas_wrapper/pandas_median.py

@@ -0,0 +1,42 @@
+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+from .utils import series_to_jsons
+
+class PandasMedian:
+    """
+    PandasMin:
+        A class for computing the median of a pandas DataFrame.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "dataframe_json": ("STRING", {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe_json: str) -> tuple:
+        """
+        Returns a Series with median for each column.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+
+        Returns:
+            tuple: A tuple containing a JSON string for the Series.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(StringIO(dataframe_json))
+        return (series_to_jsons(df.median()),)

modules/pandas_wrapper/pandas_min.py

@@ -0,0 +1,42 @@
+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+from .utils import series_to_jsons
+
+class PandasMin:
+    """
+    PandasMin:
+        A class for computing the min of a pandas DataFrame.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "dataframe_json": ("STRING", {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe_json: str) -> tuple:
+        """
+        Returns a Series with min for each column.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+
+        Returns:
+            tuple: A tuple containing a JSON string for the Series.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(StringIO(dataframe_json))
+        return (series_to_jsons(df.min()),)

modules/pandas_wrapper/pandas_mode.py

@@ -0,0 +1,41 @@
+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+
+class PandasMode:
+    """
+    PandasMode:
+        A class for computing the mode of a pandas DataFrame.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "dataframe_json": ("STRING", {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe_json: str) -> tuple:
+        """
+        Returns a DataFrame with mode for each column.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+
+        Returns:
+            tuple: A tuple containing a JSON string for the DataFrame.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(StringIO(dataframe_json))
+        return (df.mode().to_json(),)

modules/pandas_wrapper/pandas_show_series.py

@@ -0,0 +1,56 @@
+"""
+This code is based on
+https://github.com/pythongosssss/ComfyUI-Custom-Scripts/blob/main/py/show_text.py
+
+See credit/credit.md for the full license.
+"""
+from io import StringIO
+import pandas as pd
+from .utils import series_to_jsons
+from .utils import jsons_to_series
+
+class PandasShowSeries:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "series_json": ("STRING", {"forceInput": True}),
+            },
+            "hidden": {
+                "unique_id": "UNIQUE_ID",
+                "extra_pnginfo": "EXTRA_PNGINFO",
+            },
+        }
+
+    INPUT_IS_LIST = True
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "notify"
+    OUTPUT_NODE = True
+    OUTPUT_IS_LIST = (True,)
+
+    CATEGORY = "Data Analysis"
+
+    def notify(self, series_json, unique_id=None, extra_pnginfo=None):
+        text = None
+        if unique_id is not None and extra_pnginfo is not None:
+            if not isinstance(extra_pnginfo, list):
+                print("Error: extra_pnginfo is not a list")
+            elif (
+                not isinstance(extra_pnginfo[0], dict)
+                or "workflow" not in extra_pnginfo[0]
+            ):
+                print("Error: extra_pnginfo[0] is not a dict or missing 'workflow' key")
+            else:
+                # Deserialize JSON string to DataFrame
+                series = jsons_to_series(series_json[0])
+                text = [series_to_jsons(series)]  # wrap in list
+                workflow = extra_pnginfo[0]["workflow"]
+                node = next(
+                    (x for x in workflow["nodes"] if str(x["id"]) == str(unique_id[0])),
+                    None,
+                )
+                if node:
+                    node["widgets_values"] = [text]
+        else:
+            print("unique_id or extra_pnginfo is None.")
+        return {"ui": {"text": text}, "result": (text,)}

modules/pandas_wrapper/pandas_std.py

@@ -0,0 +1,42 @@
+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+from .utils import series_to_jsons
+
+class PandasStd:
+    """
+    PandasStd:
+        A class for computing the standard deviation of a pandas DataFrame.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "dataframe_json": ("STRING", {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe_json: str) -> tuple:
+        """
+        Returns a Series with standard deviation for each column.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+
+        Returns:
+            tuple: A tuple containing a JSON string for the Series.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(StringIO(dataframe_json))
+        return (series_to_jsons(df.std()),)

modules/pandas_wrapper/pandas_sum.py

@@ -0,0 +1,42 @@
+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+from .utils import series_to_jsons
+
+class PandasSum:
+    """
+    PandasSum:
+        A class for computing the sum of a pandas DataFrame.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "dataframe_json": ("STRING", {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe_json: str) -> tuple:
+        """
+        Returns a Series with sum for each column.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+
+        Returns:
+            tuple: A tuple containing a JSON string for the Series.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(StringIO(dataframe_json))
+        return (series_to_jsons(df.sum()),)

modules/pandas_wrapper/pandas_var.py

@@ -0,0 +1,42 @@
+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+from .utils import series_to_jsons
+
+class PandasVar:
+    """
+    PandasVar:
+        A class for computing the variance of a pandas DataFrame.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "dataframe_json": ("STRING", {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe_json: str) -> tuple:
+        """
+        Returns a Series with variance for each column.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+
+        Returns:
+            tuple: A tuple containing a JSON string for the Series.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(StringIO(dataframe_json))
+        return (series_to_jsons(df.var()),)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "data-analysis"
 description = "Data analysis custom modules for ComfyUI - Use Pandas & Matplotlib within ComfyUI"
-version = "1.0.18"
+version = "1.0.19"
 license = {file = "LICENSE"}
 
 [project.urls]

web/js/ShowSeries.js

@@ -0,0 +1,64 @@
+/*
+This code is based on
+https://github.com/pythongosssss/ComfyUI-Custom-Scripts/blob/main/web/js/showText.js
+
+See credit/credit.md for the full license.
+*/
+
+import { app } from "../../../scripts/app.js";
+import { ComfyWidgets } from "../../../scripts/widgets.js";
+
+// Displays input text on a node
+app.registerExtension({
+	name: "HowToSD.ShowSeries",
+	async beforeRegisterNodeDef(nodeType, nodeData, app) {
+		if (nodeData.name === "PandasShowSeries") {  // This needs to match NODE_CLASS_MAPPINGS in Python
+			function populate(text) {
+				if (this.widgets) {
+					for (let i = 1; i < this.widgets.length; i++) {
+						this.widgets[i].onRemove?.();
+					}
+					this.widgets.length = 1;
+				}
+
+				const v = [...text];
+				if (!v[0]) {
+					v.shift();
+				}
+				for (const list of v) {
+					const w = ComfyWidgets["STRING"](this, "text2", ["STRING", { multiline: true }], app).widget;
+					w.inputEl.readOnly = true;
+					w.inputEl.style.opacity = 0.6;
+					w.value = list;
+				}
+
+				requestAnimationFrame(() => {
+					const sz = this.computeSize();
+					if (sz[0] < this.size[0]) {
+						sz[0] = this.size[0];
+					}
+					if (sz[1] < this.size[1]) {
+						sz[1] = this.size[1];
+					}
+					this.onResize?.(sz);
+					app.graph.setDirtyCanvas(true, false);
+				});
+			}
+
+			// When the node is executed we will be sent the input text, display this in the widget
+			const onExecuted = nodeType.prototype.onExecuted;
+			nodeType.prototype.onExecuted = function (message) {
+				onExecuted?.apply(this, arguments);
+				populate.call(this, message.text);
+			};
+
+			const onConfigure = nodeType.prototype.onConfigure;
+			nodeType.prototype.onConfigure = function () {
+				onConfigure?.apply(this, arguments);
+				if (this.widgets_values?.length) {
+					populate.call(this, this.widgets_values.slice(+this.widgets_values.length > 1));
+				}
+			};
+		}
+	},
+});