Added Numpy Show, Numpy Squeeze, Pandas Feature Split To Numpy, Pt Show Size nodes.

modules/numpy_wrapper/numpy_show.py

@@ -0,0 +1,57 @@
+"""
+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.
+"""
+import numpy as np
+
+class NumpyShow:
+    """
+    Displays a Numpy ndarray as text.
+
+    category: Numpy
+    """
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "ndarray": ("NDARRAY", {}),
+            },
+            "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, ndarray, 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:
+                ind = ndarray[0]
+                text = [str(ind)]  # 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/numpy_wrapper/numpy_squeeze.py

@@ -0,0 +1,45 @@
+from typing import Any, Dict
+import numpy as np
+
+
+class NumpySqueeze:
+    """
+    Removes a dimension at the specified position in the input array if it is of size 1.
+    
+    Specifying a tuple of dimensions is not supported.
+    
+    category: Numpy
+    """
+    
+    @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": {
+                "array": ("NDARRAY", {}),
+                "dim": ("INT", {"default": -1, "min": -1, "max": 10})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("NDARRAY",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, array: np.ndarray, dim: int) -> tuple:
+        """
+        Removes a dimension at the specified position in the input array if it is of size 1.
+
+        Args:
+            array (np.ndarray): Input array.
+            dim (int): Dimension to remove if it is of size 1.
+
+        Returns:
+            tuple: A tuple containing the transformed NumPy array.
+        """
+        ar_out = np.squeeze(array, dim)
+        return (ar_out,)

modules/pandas_wrapper/pandas_feature_split_to_numpy.py

@@ -0,0 +1,71 @@
+from typing import Any, Dict
+import pandas as pd
+import numpy as np
+
+class PandasFeatureSplitToNumpy:
+    """
+    Splits a Pandas DataFrame into feature Numpy ndarray and label Numpy ndarray.
+    If output_1d_label is set, label will be output as a 1d array instead of a 2d array.
+
+    As shown in the below screenshot, this node can replace multiple nodes that are used to split Pandas DataFrame into two DataFrames, convert to NumPy, then squeeze the label data. 
+    ![Pandas Feature Split To Numpy](../images/pandas_feature_split.png)
+
+    category: Transformation
+    """
+    
+    @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": ("DATAFRAME", {}),
+                "label_integer_position": ("INT", {"default": -1, "min": -10, "max": 1024}),
+                "output_1d_label": ("BOOLEAN", {"default": True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("NDARRAY", "NDARRAY")
+    RETURN_NAMES: tuple = ("feature", "label")
+
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe: pd.DataFrame,
+        label_integer_position: int,
+        output_1d_label: bool) -> tuple:
+        """
+        Splits a Pandas DataFrame into feature Numpy ndarray and label Numpy ndarray.
+
+        Args:
+            dataframe (pd.DataFrame): The DataFrame to split.
+            label_integer_position (int): The column integer position of the label.
+            output_1d_label (bool): If output_1d_label is set, label will be output as a 1d array instead of a 2d array.
+
+        Returns:
+            tuple: A tuple containing the feature and label arrays.
+        """
+        num_columns = dataframe.shape[1]
+
+        if label_integer_position < 0:
+            label_integer_position += num_columns
+
+        # Validate label_integer_position
+        if label_integer_position >= num_columns:
+            raise ValueError(f"label_integer_position {label_integer_position} is out of range for dataframe with {num_columns} columns.")
+
+        # Extract features
+        np_left = dataframe.iloc[:, :label_integer_position].to_numpy()
+        np_right = dataframe.iloc[:, label_integer_position + 1:].to_numpy() if label_integer_position < num_columns - 1 else np.empty((len(dataframe), 0))
+        np_features = np.hstack([np_left, np_right]) if np_right.shape[1] > 0 else np_left
+
+        # Extract label
+        np_label = dataframe.iloc[:, label_integer_position].to_numpy()
+        if output_1d_label:
+            np_label = np_label.ravel()
+
+        return (np_features, np_label)

modules/pytorch_wrapper/pt_show_size.py

@@ -0,0 +1,60 @@
+"""
+Almost all of this code was taken from
+https://github.com/pythongosssss/ComfyUI-Custom-Scripts/blob/main/py/show_text.py
+
+See credit/credit.md for the full license.
+"""
+from typing import List
+import torch
+
+
+class PtShowSize:
+    """
+    Displays PyTorch Size object as a string.
+
+    category: Display data
+    """
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "size": ("PTSIZE", {}),
+            },
+            "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"
+
+    MAX_NUM_ELEMENTS_TO_DISPLAY = 100
+
+    def notify(self, size: torch.Size, unique_id=None, extra_pnginfo=None):
+        text = str(size[0])
+
+        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:
+                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],)}