PlotStream

PlotStream allows you to register your Python functions with a simple decorator, @plotstream_function, and automatically create dynamic, interactive dashboards to visualize their outputs. It supports a variety of data formats like DataFrames, arrays, and lists, and generates customizable charts with minimal effort.

Features

• Simple Function Registration: Use @plotstream_function to register functions effortlessly.
• Dynamic Dashboards: Automatically generate Streamlit dashboards with interactive controls for function inputs.
• Customizable Charts: Define chart types, axis mappings, colors, and more.
• Multi-format Support: Works seamlessly with Pandas DataFrames, Series, NumPy arrays, and Python lists.
• Minimal Code Overhead: Just decorate your functions and call run_plotstream_app().

Usage

1. Register your functions with the @plotstream_function decorator. Customize the parameters to control how the function is visualized.

   None of the parameters are required, but you can specify the following:

from plotstream import plotstream_function, run_plotstream_app

import pandas as pd
import numpy as np

@plotstream_function(
    name="Sine Wave",
    graph="Trigonometry Graph",
    chart_type="Line",
    color="#FF5733",
    x_col="x",
    y_col="y",
)
def sine_wave(a: float = 1.0, b: float = 2.0, n: int = 100) -> pd.DataFrame:
    """
    Generate a sine wave with given parameters.

    Args:
        a (float): Amplitude of the sine wave.
        b (float): Frequency of the sine wave.
        n (int): Number of points.

    Returns:
        pd.DataFrame: A DataFrame containing the sine wave (x, y).
    """
    x = np.linspace(0, 10, n)
    y = a * np.sin(b * x)
    return pd.DataFrame({"x": x, "y": y})

2. Run the PlotStream app to generate the dashboard.

run_plotstream_app()

The Streamlit dashboard will open in your browser, allowing you to:

• Select from registered functions.
• Adjust inputs using dynamically generated controls.
• View and interact with the resulting visualizations.

How It Works

1. Function Registration:

   Use @plotstream_function to register your function along with metadata like chart type, axis mappings, and colors. Metadata is stored in a global dictionary (FUNCTIONS) and serialized for reuse.

2. Data Processing:

   The process_data utility handles different data formats (DataFrames, arrays, lists) and extracts x/y data for visualization.

3. Streamlit App:

   The run_plotstream_app() function launches the Streamlit app, dynamically loading registered functions and their associated metadata.

Key Decorator Parameters

When using @plotstream_function, you can customize the visualization with the following parameters:

Future Improvements

• Add support for more chart types.
• Add support for more data formats (including multi graphs for multi columns DataFrames).
• Add support for more controls (e.g., sliders, dropdowns, etc.).
• Provide enhanced error handling and logging.
• Ignore unsupported input data types.
• Function naming in legend.

License

This project is licensed under the MIT License.