jPlot 📈

jPlot is a high-performance, lightweight Java 2D plotting engine specifically designed for scientific visualization, data analysis, and professional reporting. It features a custom rendering pipeline optimized for pixel-perfect accuracy and high-resolution output.

Technical Highlights

• SDF-Based Antialiasing: Unlike standard rendering, jPlot uses Signed Distance Fields to calculate sub-pixel coverage, ensuring razor-sharp curves and eliminating artifacts at line intersections.
• Professional Alpha Compositing: Implements true alpha blending and max-splatting algorithms, allowing for clean visualization of dense, overlapping datasets without color "muddiness."
• Headless 4K Rendering: Designed to operate without a GUI, enabling the generation of massive ultra-high-definition (3840x2160+) charts directly to disk.
• First-Class Function Plotting: Support for native Java Lambda expressions to render mathematical functions with infinite precision.
• Facade Pattern Architecture: Strict separation of mathematical modeling, rendering, and API presentation for a zero-configuration developer experience.

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Visual Gallery

🌡️ Heatmap Density Analysis

Multi-class distribution rendering with alpha-blended Gaussian splatting.

📊 Statistical Histograms

Auto-binning and alpha-blended distributions for continuous variables.

📍 Discrete Time Signals (Stem Plot)

SDF-rendered stems and programmable markers for DSP applications.

📉 Categorical Time-Series (Polyline & Step Plots)

Custom axis formatting and orthogonal step plotting for indexed and temporal data.

🏢 Grouped & Stacked Bars

Financial reporting with automatic spatial grouping and cumulative limit scaling.

🔭 Scientific & Mathematical Functions

Advanced rendering of trigonometric and polynomial functions using smooth-step interpolation.

🌌 High-Density Data (Bubble Charts)

Visualization of massive datasets using custom alpha-blending to highlight clusters.

🍰 Sector Distribution (Pie Chart)

Clean pie charts with precise radial SDF antialiasing for professional reports.

📏 Linear Classification & Geometry

Intersection of functional lines with scattered datasets, demonstrating spatial classification.

📑 Horizontal Comparisons

Optimized layout for categorical rankings and comparative horizontal analysis.

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🖼️ Multi-Chart Dashboards & Composition

The MultiPlotter engine allows for the orchestration of multiple independent charts into a unified grid-based dashboard.

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Quick Start

1. Basic Line Chart

import ui.Plotter;
import java.awt.Color;

public class ChartApp {
    public static void main(String[] args) {
        Plotter p = new Plotter("Performance Metrics", Plotter.LINE_PLOT, "Time", "Value");
        p.create(new Color(41, 128, 185), "name", "Sensor A", "type", "LINE", "style", "SOLID");
        p.add("Sensor A", 1.0, 10.5);
        p.add("Sensor A", 2.0, 15.2);
        p.img(3840, 2160, "output.png");
    }
}

2. Functional Plotting

import ui.Plotter;
import java.awt.Color;

public class MathAnalysis {
    public static void main(String[] args) {
        Plotter p = new Plotter("Waveform", Plotter.LINE_PLOT, "X", "Y");
        p.create(Color.RED, "name", "Sine Wave", "type", "FUNCTION");
        p.add("Sine Wave", 0.0, 10.0, 1000, x -> Math.sin(x));
        p.img(1920, 1080, "function.png");
    }
}

3. Categorical Polyline

import ui.Plotter;
import java.awt.Color;

public class CategoryApp {
    public static void main(String[] args) {
        Plotter p = new Plotter("Weekly Index", Plotter.POLYLINE_PLOT, "Days", "Value");
        p.create(new Color(0, 85, 140), "name", "Metric", "marker", "CIRCLE");

        p.add("Metric", "Mon", 100.2);
        p.add("Metric", "Tue", 108.3);
        p.add("Metric", "Wed", 105.0);

        p.plot();
    }
}

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Installation

Currently, jPlot can be integrated by including the src files into your project. No external dependencies are required.

License

This project is licensed under the MIT License - see the LICENSE file for details.