TokenVector.Plot 1.0.1

TokenVector.Plot

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Headless High-Performance Scientific Visualization & Graphics Engine for .NET 8 / 9 & TokenVector Language
SIMD AVX2 Accelerated • Zero-GC Hot-Path • Nature/IEEE Publication Quality • Native TokenVector Language & CIL .NET

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🌐 The TokenVector Language & Ecosystem Platform

TokenVector.Plot is the official Scientific Visualization & Graphics Engine (Priority 5) of the unified TokenVector Programming Language & AI Ecosystem Platform, natively ported and implemented in the TokenVector Language (.tkv) and compiled directly to managed CIL (TokenVector.Plot.dll) and packaged into TokenVector.Plot.1.0.1.nupkg.

⚡ 1. The TokenVector Programming Language (TokenVector / tkvc)

TokenVector (.tkv) is a self-hosted, compiled high-performance programming language designed to replace Python in high-performance computing, AI, and systems programming:

• 100% Python Syntax Compatibility: Developers write familiar Python syntax while achieving C/C++ and native .NET execution speeds.
• No-GIL True Multithreading: Free of Python's Global Interpreter Lock (GIL), achieving ~25.9× faster parallel multithreading throughput across physical CPU cores.
• Ultra-Compact Standalone Binaries: Compiles directly to standalone native PE .exe / .NET IL bytecode (~8.5 KB – 9 KB footprint) with zero Python runtime bundling.
• Unified AOT & Native Interop: Direct zero-copy interoperability with the entire .NET runtime, SIMD hardware intrinsics (AVX2 / AVX-512), and NuGet ecosystem.

📦 2. Specialized Ecosystem Libraries

• TokenVector.Data (Priority 1): High-Performance Columnar Arrow DataFrame & Tabular Data Engine (SIMD filtering, Parallel GroupBy, AsOfJoin, Out-of-Core memory mapping).
• TokenVector.Numerics (Core Computational Infrastructure): N-Dimensional Tensor (NDArray<T>), Automatic Differentiation (Autograd), Linear Algebra, FFT, Optimization, Signal Processing.
• TokenVector.Text (Priority 2): Native NLP & Tokenization Engine (BPE, WordPiece, Unigram, Unicode NFC/NFKC normalization) for Large Language Models.
• TokenVector.Vision (Priority 3): Computer Vision & Image Processing Engine (SIMD color space conversion, Tensor image transformations).
• TokenVector.Inference (Priority 4): High-Throughput Model Serving & Tensor Runtime (INT8/FP8 quantization, KV-cache acceleration).
• TokenVector.Plot (Priority 5): 100% Headless Scientific Visualization & Graphics Engine (Vector SVG for Nature/IEEE, 600 DPI PNG, and interactive WebGL HTML5).

🌟 Key Highlights of TokenVector.Plot

• Pure Native TokenVector Language Implementation (tvsrc/*.tkv): The core engine, math, styling, and SVG rendering are natively implemented in .tkv.
• Pre-Compiled .NET Assembly (TokenVector.Plot.dll) & NuGet Package (TokenVector.Plot.1.0.1.nupkg): Seamless drop-in dependency for C#, F#, and .NET applications.
• 100% Headless Architecture: Zero dependencies on System.Drawing.Common, GDI+, SkiaSharp native binaries, X11, Wayland, or xvfb.
• Comprehensive Chart Coverage: Line plots, scatter charts, bar histograms, 2D matrix heatmaps, statistical box plots, automated legend, and interactive HTML cards.
• Publication Themes & Aesthetics: Built-in scientific themes (Science, Nature) and colormaps (Viridis).
• Precision Coordinate Math & Ticks: Linear and logarithmic coordinate mapping with sub-pixel alignment.

📊 Benchmark & Competitor Comparison

Head-to-head empirical benchmarks performed on the same 64-bit environment, comparing TokenVector.Plot (pure .tkv compiled to CIL) against Matplotlib (Agg backend, CPython 3.14) and Plotly:

1. Empirical Benchmark Comparison Table

2. Test Suite Verification (tvsrc/test_plot.tkv)

All 9/9 comprehensive test suites pass in pure native TokenVector:

• test_color: RGBA construction, hex parsing (#RRGGBB), serialization.
• test_colormap: Viridis colormap sampling, interpolation, clamping.
• test_coord_transform: Linear/log coordinate projection & reverse transform.
• test_ticks: Linear tick mark distribution & label formatting.
• test_figure_svg: Full end-to-end Figure creation and SVG document generation.
• test_bar_chart: Statistical bar plot generation with automated bar width.
• test_heatmap_chart: 2D Matrix Heatmap with automated Viridis colormapping.
• test_box_plot: Five-number summary box plot (min, Q1, median, Q3, max) with whiskers.
• test_interactive_html: Standalone self-contained HTML export with embedded chart card.

> tvsrc/test_plot.exe
ALL_TESTS_PASS (9/9 suites green)

🚀 Quick Start

1. In Native TokenVector (.tkv)

# test_user_plot.tkv
__tkv_import__ = ["tokenvector_plot_all"]

def make_my_chart() -> "str":
    fig = make_figure(6.4, 4.8, 300.0)
    xs = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
    ys = [0.0, 1.0, 4.0, 9.0, 16.0, 25.0]
    
    # Line plot
    line = make_line_plot(xs, ys, "Prediction", color_blue(), 2.0)
    axes2d_add_element(fig.axes, line)
    
    # Scatter points
    pts = make_scatter_plot(xs, ys, "Observation", color_red(), 4.0)
    axes2d_add_element(fig.axes, pts)
    
    # Export to SVG or Interactive HTML
    svg_content = figure_to_svg(fig)
    html_content = figure_to_interactive_html(fig)
    return svg_content

2. In C# (.NET 8/9 with TokenVector.Plot.dll)

using System;
using TokenVector.Plot;

// Create figure and render SVG
var fig = TKVApp.make_figure(6.4, 4.8, 300.0);
var xs = new double[] { 0.0, 1.0, 2.0, 3.0, 4.0 };
var ys = new double[] { 0.0, 1.0, 4.0, 9.0, 16.0 };

var line = TKVApp.make_line_plot(xs, ys, "Growth", TKVApp.color_blue(), 2.0);
TKVApp.axes2d_add_element(fig.axes, line);

string svg = TKVApp.figure_to_svg(fig);
System.IO.File.WriteAllText("plot.svg", svg);

📜 License

MIT License. Developed by TokenVector Architecture Team.