fastkpcv 0.1.1

High-performance kernelized parallel coordinates visualization tool in Python

fastkpcv

fastkpcv is a lightweight Python wrapper for fast density computation for Kernelized Parallel Coordinates Visualization (KPCV).

This package provides an efficient way to compute density results through a simple Python interface.
The current version focuses on fast density computation and outputs the computed results as a JSON file.

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✨ Features

• 🚀 High-performance backend implemented in Java
• 🐍 Simple Python interface (kpcv.run(...))
• 📦 Bundled .jar — no need to manage Java files manually
• 🔧 Minimal user inputs
• ⚡ Fast density computation for large-scale and high-resolution settings
• 📄 Outputs density computation results as a JSON file

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📦 Installation

Install from PyPI:

pip install fastkpcv

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⚠️ Requirements

• Python >= 3.9
• Java (JRE or JDK) must be installed and available in your system PATH

Verify Java installation:

java -version

JDK version 1.8 is suggested.

Quick Example

import fastkpcv as kpcv

# 1. Specify the input dataset path and output path
data_path = "dataset/diabetes.xlsx"
out_path = "output/diabetes.json"

# 2. Run fastkpcv with the desired resolution and number of rows
kpcv.run(
    datapath=data_path,
    nrows=768,
    x_res=1920,
    y_res=1080,
    outpath=out_path
)

# 3. The visualization result will be written to the output path
print(f"Result saved to: {out_path}")

Notes

• The current release is designed for fast density computation.
• Visualization functionality will be integrated in future versions.