G.O.D Framework

Documentation: visualization.py

Dedicated to building insightful and interactive visualizations for data analytics within the G.O.D Framework.

Introduction

The visualization.py module is a key component of the G.O.D Framework, designed to create visual representations of data insights. It facilitates interactive plots, dashboards, and static reports to provide end-users with a deeper understanding of their data. With its integration of widely used visualization libraries, it delivers an intuitive experience for monitoring key metrics, trends, and patterns.

Purpose

The primary goals of the visualization.py module include:

Key Features

Logic and Implementation

Below is a simplified implementation of the visualization.py module demonstrating its core features: 


import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import pandas as pd


class Visualization:
    """
    A class for creating visualizations in the G.O.D Framework.
    """

    def __init__(self):
        self.theme = "default"

    def set_theme(self, theme_name="default"):
        """
        Configures the visualization theme.
        Args:
            theme_name (str): Name of the theme ("default", "darkgrid", "lightgrid").
        """
        self.theme = theme_name
        sns.set_theme(style=theme_name)

    def plot_time_series(self, data, x, y, title="Time Series Plot", save_path=None):
        """
        Creates a time series plot using matplotlib and seaborn.
        Args:
            data (pd.DataFrame): The dataframe containing the data.
            x (str): The column for the x-axis.
            y (str): The column for the y-axis.
            title (str): Title of the plot.
            save_path (str): Path to save the plot as an image (optional).
        """
        plt.figure(figsize=(10, 6))
        sns.lineplot(data=data, x=x, y=y)
        plt.title(title)
        plt.xlabel(x)
        plt.ylabel(y)
        if save_path:
            plt.savefig(save_path)
        plt.show()

    def create_interactive_plot(self, data, x, y, color=None, plot_type="scatter"):
        """
        Uses Plotly to create interactive plots.
        Args:
            data (pd.DataFrame): The dataframe containing the data.
            x (str): The column for the x-axis.
            y (str): The column for the y-axis.
            color (str): Column for color encoding (optional).
            plot_type (str): Type of plot ("scatter", "line").
        Returns:
            plotly.graph_objs.Figure: An interactive plot.
        """
        if plot_type == "scatter":
            fig = px.scatter(data, x=x, y=y, color=color, title="Interactive Scatter Plot")
        elif plot_type == "line":
            fig = px.line(data, x=x, y=y, color=color, title="Interactive Line Plot")
        else:
            raise ValueError("Unsupported plot_type. Choose 'scatter' or 'line'.")
        fig.show()
        return fig

The above example demonstrates key visualization functionalities, including:

Dependencies

Integration with the G.O.D Framework

This module interacts with several key components of the G.O.D Framework, enabling seamless data visualization:

Future Enhancements