Added Pandas Load HTML, CDA Float Create, CDA Int Create, CDA String Create, CDA Text Create, CDA Show Float, CDA Show Int nodes.

modules/cda/cda_float_create.py

@@ -0,0 +1,40 @@
+from typing import Any, Dict
+
+
+class CDAFloatCreate:
+    """
+    CDA Float Create:
+    Creates a Python float using a value entered in the text field.
+
+    category: IO
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the `create` function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "data": ("STRING", {"multiline": False}),
+            }
+        }
+
+    RETURN_TYPES: tuple = ("FLOAT",)
+    FUNCTION: str = "create"
+    CATEGORY: str = "Data Analysis"
+
+    def create(self, data: str) -> tuple:
+        """
+        creating a Python float for the text entered in the text field.
+        
+        Args:
+            data (str): String value in the text field.
+
+        Returns:
+            tuple: A tuple containing the float converted from the text.
+        """
+        return (float(data),)

modules/cda/cda_int_create.py

@@ -0,0 +1,41 @@
+from typing import Any, Dict
+
+
+class CDAIntCreate:
+    """
+    CDA Int Create:
+    Creates a Python int using a value entered in the text field.
+
+    category: IO
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the `create` function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "data": ("STRING", {"multiline": False}),
+            }
+        }
+
+    RETURN_TYPES: tuple = ("INT",)
+    FUNCTION: str = "create"
+    CATEGORY: str = "Data Analysis"
+
+    def create(self, data: str) -> tuple:
+        """
+        creating a Python int for the text entered in the text field.
+        
+        Args:
+            data (str): String value in the text field.
+
+        Returns:
+            tuple: A tuple containing the int converted from the text.
+        """
+        return (int(data),)
+

modules/cda/cda_show_float.py

@@ -0,0 +1,62 @@
+"""
+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 sys
+
+
+class CDAShowFloat:
+    """
+    Displays a floating-point number as text.
+
+    category: Display data
+    """
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "float_scalar": ("FLOAT", {"default": 0.0, "min": sys.float_info.min, "max": sys.float_info.max})
+            },
+            "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, float_scalar, 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:
+                float_val = float_scalar[0]
+                text = [str(float_val)]  # 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  # [text]
+        else:
+            print("unique_id or extra_pnginfo is None.")
+
+        # "ui" value is the one that is shown on the node UI.
+        # Note that "ui" return value needs to a list of strings.
+        return {"ui": {"text": text},
+                "result": (text,)}

modules/cda/cda_show_int.py

@@ -0,0 +1,60 @@
+"""
+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.
+"""
+
+class CDAShowInt:
+    """
+    Displays a Pandas Int as text.
+
+    category: Display data
+    """
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "int_scalar": ("INT", {"default": 0, "min": -1, "max": 2**31})
+            },
+            "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, int_scalar, 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:
+                int_val = int_scalar[0]
+                text = [str(int_val)]  # 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  # [text]
+        else:
+            print("unique_id or extra_pnginfo is None.")
+
+        # "ui" value is the one that is shown on the node UI.
+        # Note that "ui" return value needs to a list of strings.
+        return {"ui": {"text": text},
+                "result": (text,)}

modules/cda/cda_string_create.py

@@ -0,0 +1,41 @@
+from typing import Any, Dict
+
+
+class CDAStringCreate:
+    """
+    CDA String Create:
+    Creates a Python String using a value entered in a single-line text field.
+
+    category: IO
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the `create` function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "data": ("STRING", {"multiline": False}),
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "create"
+    CATEGORY: str = "Data Analysis"
+
+    def create(self, data: str) -> tuple:
+        """
+        creating a Python String for the text entered in the text field.
+        
+        Args:
+            data (str): String value in the text field.
+
+        Returns:
+            tuple: A tuple containing the string converted from the text.
+        """
+        return (data,)
+

modules/cda/cda_text_create.py

@@ -0,0 +1,41 @@
+from typing import Any, Dict
+
+
+class CDATextCreate:
+    """
+    CDA Text Create:
+    Creates a Python String using a value entered in a multi-line text field.
+
+    category: IO
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the `create` function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "data": ("STRING", {"multiline": True}),
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "create"
+    CATEGORY: str = "Data Analysis"
+
+    def create(self, data: str) -> tuple:
+        """
+        creating a Python String for the text entered in the text field.
+        
+        Args:
+            data (str): String value in the text field.
+
+        Returns:
+            tuple: A tuple containing the string converted from the text.
+        """
+        return (data,)
+

modules/pandas_wrapper/pandas_load_csv.py

@@ -31,13 +31,13 @@ class PandasLoadCSV:
 
     def load_csv(self, file_path: str) -> tuple:
         """
-        Loads a CSV file into a pandas DataFrame and converts it to a JSON string.
+        Loads a CSV file into a pandas DataFrame.
 
         Args:
             file_path (str): The path to the CSV file.
 
         Returns:
-            tuple: A tuple containing a JSON string representation of the DataFrame.
+            tuple: A tuple containing a DataFrame.
         """
         # Read CSV file into DataFrame
         df = pd.read_csv(file_path)

modules/pandas_wrapper/pandas_load_csv_with_encoding.py

@@ -32,7 +32,7 @@ class PandasLoadCSVWithEncoding:
 
     def load_csv(self, file_path: str, encoding: str) -> tuple:
         """
-        Loads a CSV file into a pandas DataFrame and converts it to a JSON string. Allows specifying the file encoding to support formats other than UTF-8.
+        Loads a CSV file into a pandas DataFrame. Allows specifying the file encoding to support formats other than UTF-8.
 
         Refer to the below documents for the list of encodings:
         https://docs.python.org/3/library/codecs.html#standard-encodings
@@ -42,7 +42,7 @@ class PandasLoadCSVWithEncoding:
             encoding (str): Encoding of the file.
 
         Returns:
-            tuple: A tuple containing a JSON string representation of the DataFrame.
+            tuple: A tuple containing a DataFrame.
         """
         # Read CSV file into DataFrame
         df = pd.read_csv(file_path, encoding=encoding)

modules/pandas_wrapper/pandas_load_csv_with_index.py

@@ -33,14 +33,14 @@ class PandasLoadCSVWithIndex:
 
     def load_csv(self, file_path: str, index_col: int=0) -> tuple:
         """
-        Loads a CSV file into a pandas DataFrame and converts it to a JSON string.
+        Loads a CSV file into a pandas DataFrame.
         Input data is assume to have an index.
 
         Args:
             file_path (str): The path to the CSV file.
 
         Returns:
-            tuple: A tuple containing a JSON string representation of the DataFrame.
+            tuple: A tuple containing a DataFrame.
         """
         # Read CSV file into DataFrame
         df = pd.read_csv(file_path, index_col=index_col)

modules/pandas_wrapper/pandas_load_html.py

@@ -0,0 +1,68 @@
+from typing import Any, Dict
+import pandas as pd
+
+
+class PandasLoadHTML:
+    """
+    Pandas Load HTML:
+    Scans HTML files for tables and returns the number of DataFrames, with each table represented as a separate DataFrame and the list of those DataFrames.
+
+    category: IO
+    """
+    
+    @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": {
+                "file_path": ("STRING", {"multiline": False}),
+            }
+        }
+
+    # TODO: Reference doc generator script cannot parse programatically defined
+    #       list yet, so we need to list return types for now.
+    RETURN_TYPES: tuple = ("INT",
+                           "DATAFRAME", "DATAFRAME", "DATAFRAME","DATAFRAME",
+                           "DATAFRAME", "DATAFRAME", "DATAFRAME", "DATAFRAME",
+                           "DATAFRAME", "DATAFRAME")
+    RETURN_NAMES: tuple = ("Number of DataFrames",
+                           "DataFrame1", "DataFrame2", "DataFrame3", "DataFrame4",
+                           "DataFrame5", "DataFrame6", "DataFrame7", "DataFrame8",
+                           "DataFrame9", "DataFrame10")
+    assert(len(RETURN_NAMES) == len(RETURN_TYPES))
+
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, file_path: str) -> tuple:
+        """
+        Loads an HTML file into a pandas DataFrame.
+
+        Args:
+            file_path (str): The path to the HTML file. The path can be a URL.
+
+        Returns:
+            tuple: A tuple containing a DataFrame.
+        """
+        try:
+            dfs = pd.read_html(file_path, flavor="lxml")
+        except:
+            dfs = []
+            num_dfs = 0
+
+        if dfs is None:
+            num_dfs = 0
+            dfs =  []
+        else:
+            num_dfs = len(dfs)
+
+        df_slot_count = len(PandasLoadHTML.RETURN_TYPES) - 1
+        filler_count = df_slot_count - num_dfs
+        empty_dfs = [pd.DataFrame({})] * filler_count  # If filler = 0, this will still return []
+        dfs_out = dfs + empty_dfs
+        return tuple([num_dfs] + dfs_out)

modules/pandas_wrapper/pandas_load_json.py

@@ -31,13 +31,13 @@ class PandasLoadJSON:
 
     def load_json(self, file_path: str) -> tuple:
         """
-        Loads a JSON file into a pandas DataFrame and converts it to a JSON string.
+        Loads a JSON file into a pandas DataFrame.
 
         Args:
             file_path (str): The path to the JSON file.
 
         Returns:
-            tuple: A tuple containing a JSON string representation of the DataFrame.
+            tuple: A tuple containing a DataFrame.
         """
         # Read JSON file into DataFrame
         df = pd.read_json(file_path)