Initial code commit

modules/matplotlib/common.py

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+def common_input_types():
+    """
+    Defines the input types for a plot function.
+
+    Returns:
+        Dict[str, Any]: A dictionary specifying required input types.
+    """
+    return {
+        "required": {
+            "dataframe_json": ("STRING", {"multiscatter": True}),
+            "x_column_name": ("STRING", {"default": ""}),
+            "y_column_name": ("STRING", {"default": ""}),
+            "title": ("STRING", {"default": ""}),
+            "x_axis_label": ("STRING", {"default": ""}),
+            "y_axis_label": ("STRING", {"default": ""}),
+            "x_tick_as_int": ("BOOLEAN", {"default": False})  # To show 2002 instead of 2002.5 on x-axis
+        }
+    }

modules/matplotlib/mpl_bar.py

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+from typing import Any, Dict
+from matplotlib import pyplot as plt
+import pandas as pd
+from .common import common_input_types
+from .util import plot_post_steps
+
+class MPLBar:
+    """
+    MPLBar:
+        A class for generating bar charts from a pandas DataFrame.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the `bar_chart` function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return common_input_types()
+
+    RETURN_TYPES: tuple = ("IMAGE",)
+    FUNCTION: str = "bar_chart"
+    CATEGORY: str = "Data Analysis"
+
+    def bar_chart(self,
+                  dataframe_json: str,
+                  x_column_name: str,
+                  y_column_name: str,
+                  title: str,
+                  x_axis_label: str,
+                  y_axis_label: str,
+                  x_tick_as_int: bool) -> tuple:
+        """
+        Generates a bar chart from a pandas DataFrame.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+            x_column_name (str): The column name for the x-axis.
+            y_column_name (str): The column name for the y-axis.
+            title (str): The title of the plot.
+            x_axis_label (str): The label for the x-axis data.
+            y_axis_label (str): The label for the y-axis data.
+            x_tick_as_int (bool): If set, use integers for x ticks. For example,
+                                  if ticks show floating point numbers for years, e.g. 2002.5,
+                                  setting this flag will show integer values instead.
+        Returns:
+            tuple: A tuple containing the image tensor representation of the plot.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(dataframe_json)
+
+        # Create the plot
+        fig, ax = plt.subplots()
+        ax.bar(df[x_column_name], df[y_column_name])
+
+        return plot_post_steps(
+            fig,
+            ax,
+            plt,
+            title,
+            x_axis_label,
+            y_axis_label,
+            x_tick_as_int)

modules/matplotlib/mpl_line.py

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+from typing import Any, Dict
+from matplotlib import pyplot as plt
+import pandas as pd
+from .common import common_input_types
+from .util import plot_post_steps
+
+class MPLLine:
+    """
+    MPLPlotLine:
+        A class for generating line plots from a pandas DataFrame.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the `line_plot` function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return common_input_types()
+
+    RETURN_TYPES: tuple = ("IMAGE",)
+    FUNCTION: str = "line_plot"
+    CATEGORY: str = "Data Analysis"
+
+    def line_plot(self,
+                  dataframe_json: str,
+                  x_column_name: str,
+                  y_column_name: str,
+                  title: str,
+                  x_axis_label: str,
+                  y_axis_label: str,
+                  x_tick_as_int: bool) -> tuple:
+        """
+        Generates a line plot from a pandas DataFrame.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+            x_column_name (str): The column name for the x-axis.
+            y_column_name (str): The column name for the y-axis.
+            title (str): The title of the plot.
+            x_axis_label (str): The label for the x-axis data.
+            y_axis_label (str): The label for the y-axis data.
+            x_tick_as_int (bool): If set, use integers for x ticks. For example,
+                                  if ticks show floating point numbers for years, e.g. 2002.5,
+                                  setting this flag will show integer values instead.
+        Returns:
+            tuple: A tuple containing the image tensor representation of the plot.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(dataframe_json)
+
+        # Create the plot
+        fig, ax = plt.subplots()
+        ax.plot(df[x_column_name], df[y_column_name])
+
+        return plot_post_steps(
+            fig,
+            ax,
+            plt,
+            title,
+            x_axis_label,
+            y_axis_label,
+            x_tick_as_int)

modules/matplotlib/mpl_scatter.py

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+from typing import Any, Dict
+from matplotlib import pyplot as plt
+import pandas as pd
+from .common import common_input_types
+from .util import plot_post_steps
+
+class MPLScatter:
+    """
+    MPLPlotScatter:
+        A class for generating scatter plots from a pandas DataFrame.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the `scatter_plot` function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return common_input_types()
+
+    RETURN_TYPES: tuple = ("IMAGE",)
+    FUNCTION: str = "scatter_plot"
+    CATEGORY: str = "Data Analysis"
+
+    def scatter_plot(self,
+                  dataframe_json: str,
+                  x_column_name: str,
+                  y_column_name: str,
+                  title: str,
+                  x_axis_label: str,
+                  y_axis_label: str,
+                  x_tick_as_int: bool) -> tuple:
+        """
+        Generates a scatter plot from a pandas DataFrame.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+            x_column_name (str): The column name for the x-axis.
+            y_column_name (str): The column name for the y-axis.
+            title (str): The title of the plot.
+            x_axis_label (str): The label for the x-axis data.
+            y_axis_label (str): The label for the y-axis data.
+            x_tick_as_int (bool): If set, use integers for x ticks. For example,
+                                  if ticks show floating point numbers for years, e.g. 2002.5,
+                                  setting this flag will show integer values instead.
+        Returns:
+            tuple: A tuple containing the image tensor representation of the plot.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(dataframe_json)
+
+        # Create the plot
+        fig, ax = plt.subplots()
+        ax.plot(df[x_column_name], df[y_column_name])
+
+        return plot_post_steps(
+            fig,
+            ax,
+            plt,
+            title,
+            x_axis_label,
+            y_axis_label,
+            x_tick_as_int)

modules/matplotlib/util.py

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+import io
+from typing import Any, Dict
+import numpy as np
+import matplotlib.figure
+import matplotlib.axes
+from matplotlib import pyplot as plt
+import matplotlib.ticker as mticker
+import pandas as pd
+from PIL import Image
+import torch
+
+
+def plot_post_steps(
+    fig: matplotlib.figure.Figure,
+    ax: matplotlib.axes.Axes,
+    plt: plt,
+    title: str,
+    x_axis_label: str,
+    y_axis_label: str,
+    x_tick_as_int: bool
+) -> tuple:
+    r"""Plots a post-processed step plot using Matplotlib.
+
+    Args:
+        fig (matplotlib.figure.Figure): The figure object for the plot.
+        ax (matplotlib.axes.Axes): The axes object for the plot.
+        plt (module): The Matplotlib module.
+        title (str): The title of the plot.
+        x_axis_label (str): The label for the x-axis.
+        y_axis_label (str): The label for the y-axis.
+        x_tick_as_int (bool): If ``True``, use integers for x-axis ticks. 
+            For example, if ticks display floating point numbers (e.g., 2002.5), 
+            setting this flag forces integer values instead.
+
+    Returns:
+        tuple: A tuple containing the image tensor representation of the plot.
+    """
+    if x_tick_as_int:
+        ax.xaxis.set_major_locator(mticker.MaxNLocator(integer=True))
+
+    # Set title and labels
+    ax.set_title(title)
+    ax.set_xlabel(x_axis_label)
+    ax.set_ylabel(y_axis_label)
+
+    # Save the plot to a buffer
+    buf = io.BytesIO()
+    fig.savefig(buf, format='PNG')
+    buf.seek(0)
+    plt.close(fig)  # Close the figure to free memory
+
+    # Load image and convert to tensor
+    image = Image.open(buf).convert("RGB")
+    image_tensor = torch.from_numpy(np.array(image)).float() / 255.0  # Normalize to [0,1]
+    image_tensor = image_tensor.unsqueeze(0)
+    return (image_tensor,)