Initial phase of adding PyTorch support for data analysis. Added conversion nodes.

modules/numpy_wrapper/numpy_float_create.py

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+from typing import Any, Dict
+import numpy as np
+import ast
+
+
+class NumpyFloatCreate:
+    """
+    Creates a NumPy ndarray with 32-bit floating point precision 
+    using values entered in the text field.
+    
+    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": {
+                "data": ("STRING", {"multiline": True}),
+            }
+        }
+
+    RETURN_TYPES: tuple = ("NDARRAY",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Numpy wrapper"
+
+    def f(self, data: str) -> tuple:
+        """
+        Creates a Numpy ndarray using values entered in the text field.
+
+        Args:
+            data (str): String value in the text field.
+
+        Returns:
+            tuple: A tuple containing a the DataFrame.
+        """
+        # Convert string to Python list
+        list_data = ast.literal_eval(data)
+
+        # Convert list to NumPy array
+        array = np.array(list_data, dtype=np.float32)
+        return (array,)

modules/numpy_wrapper/numpy_int_create.py

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+from typing import Any, Dict
+import numpy as np
+import ast
+
+
+class NumpyIntCreate:
+    """
+    Creates a NumPy ndarray with 32-bit integer 
+    using values entered in the text field.
+    
+    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": {
+                "data": ("STRING", {"multiline": True}),
+            }
+        }
+
+    RETURN_TYPES: tuple = ("NDARRAY",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Numpy wrapper"
+
+    def f(self, data: str) -> tuple:
+        """
+        Creates a NumPy ndarray with 32-bit integer using values 
+        entered in the text field.
+
+        Args:
+            data (str): String value in the text field.
+
+        Returns:
+            tuple: A tuple containing the NumPy ndarray.
+        """
+        # Convert string to Python list
+        list_data = ast.literal_eval(data)
+
+        # Convert list to NumPy int32 array
+        array = np.array(list_data, dtype=np.int32)
+        return (array,)

modules/pandas_wrapper/pandas_create_from_numpy.py

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+from typing import Any, Dict
+import pandas as pd
+import numpy as np
+
+
+class PandasCreateFromNumpy:
+    """
+    Creates a pandas DataFrame from a NumPy ndarray.
+
+    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": ("NDARRAY", {}),
+            }
+        }
+
+    RETURN_TYPES: tuple = ("DATAFRAME",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, data: np.ndarray) -> tuple:
+        """
+        Creates a pandas DataFrame from a NumPy ndarray.
+
+        Args:
+            data (np.ndarray): A NumPy array.
+
+        Returns:
+            tuple: A tuple containing the DataFrame.
+        
+        Raises:
+            ValueError: If the array is not rank 1 or rank 2.
+        """
+        if len(data.shape) not in [1, 2]:
+            raise ValueError("Only rank 1 and rank 2 arrays are supported.")
+
+        return (pd.DataFrame(data),)
+

modules/pandas_wrapper/pandas_to_numpy.py

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+from typing import Any, Dict
+import pandas as pd
+
+class PandasToNumpy:
+    """
+    Converts a pandas DataFrame to Numpy ndarray.
+
+    category: Data type conversion
+    """
+    
+    @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", {}),
+            }
+        }
+
+    RETURN_TYPES: tuple = ("NDARRAY",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe: pd.DataFrame) -> tuple:
+        """
+        Converts a pandas DataFrame to a NumPy ndarray.
+
+        Args:
+            dataframe (DataFrame): The DataFrame.
+
+        Returns:
+            tuple: A tuple containing the NumPy representation of the DataFrame.
+        """
+        if not isinstance(dataframe, pd.DataFrame):
+            raise TypeError("PandasToNumpy expects pd.DataFrame.")
+        return (dataframe.to_numpy(),)

modules/pytorch_wrapper/pt_float_create.py

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+from typing import Any, Dict
+import torch
+import ast
+
+
+class PtFloatCreate:
+    """
+    Creates a PyTorch tensor with 32-bit floating point precision 
+    using values entered in the text field.
+    
+    category: PyTorch wrapper
+    """
+    
+    @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": {
+                "data": ("STRING", {"multiline": True}),
+            }
+        }
+
+    RETURN_TYPES: tuple = ("TENSOR",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "PyTorch wrapper"
+
+    def f(self, data: str) -> tuple:
+        """
+        Creates a PyTorch tensor using values entered in the text field.
+
+        Args:
+            data (str): String value in the text field.
+
+        Returns:
+            tuple: A tuple containing the tensor.
+        """
+        # Convert string to Python list
+        list_data = ast.literal_eval(data)
+
+        # Convert list to PyTorch tensor
+        tensor = torch.tensor(list_data, dtype=torch.float32)
+        return (tensor,)

modules/pytorch_wrapper/pt_from_numpy.py

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+from typing import Any, Dict, Tuple
+import numpy as np
+import torch
+
+
+class PtFromNumpy:
+    """
+    Converts a NumPy ndarray to a PyTorch tensor while preserving its data type.
+
+    category: PyTorch wrapper
+    """
+
+    @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", {}),
+            }
+        }
+
+    RETURN_TYPES: tuple = ("TENSOR",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "PyTorch wrapper"
+
+    def f(self, array: np.ndarray) -> Tuple[torch.Tensor]:
+        """
+        Converts a NumPy ndarray to a PyTorch tensor while preserving its data type.
+
+        Args:
+            array (np.ndarray): NumPy ndarray.
+
+        Returns:
+            tuple: A tuple containing the PyTorch tensor.
+        """
+        tens = torch.tensor(array, dtype=torch.from_numpy(array).dtype)
+        return (tens,)

modules/pytorch_wrapper/pt_int_create.py

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+from typing import Any, Dict
+import torch
+import ast
+
+
+class PtIntCreate:
+    """
+    Creates a PyTorch tensor with 32-bit integer 
+    using values entered in the text field.
+    
+    category: PyTorch wrapper
+    """
+    
+    @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": {
+                "data": ("STRING", {"multiline": True}),
+            }
+        }
+
+    RETURN_TYPES: tuple = ("TENSOR",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "PyTorch wrapper"
+
+    def f(self, data: str) -> tuple:
+        """
+        Creates a PyTorch tensor using values entered in the text field.
+
+        Args:
+            data (str): String value in the text field.
+
+        Returns:
+            tuple: A tuple containing the tensor.
+        """
+        # Convert string to Python list
+        list_data = ast.literal_eval(data)
+
+        # Convert list to PyTorch tensor
+        tensor = torch.tensor(list_data, dtype=torch.int32)
+        return (tensor,)

modules/pytorch_wrapper/pt_to_numpy.py

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+from typing import Any, Dict
+import torch
+
+
+class PtToNumpy:
+    """
+    Converts PyTorch tensor to NumPy ndarray.
+
+    category: PyTorch wrapper
+    """
+    
+    @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": {
+                "tens": ("TENSOR", {}),
+            }
+        }
+
+    RETURN_TYPES: tuple = ("NDARRAY",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "PyTorch wrapper"
+
+    def f(self, tens: torch.Tensor) -> tuple:
+        """
+        Converts PyTorch tensor to NumPy ndarray.
+
+        Args:
+            tens (torch.Tensor): PyTorch Tensor
+
+        Returns:
+            tuple: A tuple containing the ndarray.
+        """
+        array = tens.cpu().detach().numpy()
+        return (array,)