NumPy SnippetBox

Smart, ultra-fast NumPy snippets for Python. Automatically detects your NumPy alias (np, numpy, or custom) and provides accurate, context-aware suggestions without slowing down VS Code.

⚡ Short & Punchy

Ultra-fast NumPy snippets for Python with automatic alias detection. Works instantly with np, numpy, or any custom alias.

🧠 Technical

A lightweight VS Code extension that uses AST-based analysis to detect NumPy imports and deliver real-time, alias-aware snippets with zero editor lag.

🚀 Marketing Style

Boost your Python productivity with smart NumPy snippets. Alias-aware, fast, and optimized for large files—no configuration required.

Documentation

NumPy SnippetBox is a high-performance VS Code extension for Python that delivers intelligent, alias-aware NumPy autocomplete and snippets. The extension automatically detects NumPy import aliases using AST parsing with a regex-based fallback, ensuring accuracy without impacting editor performance.

This document describes the internal architecture, detection logic, and performance considerations for developers maintaining or extending the extension.

Core Design Goals

• ⚡ Ultra-fast autocomplete and snippet suggestions

• 🎯 Accurate NumPy alias detection

• 🧠 Zero false positives from comments or strings

• 🚫 No performance degradation in VS Code

• 🧩 Scalable for large Python files and workspaces

import numpy as np
import numpy
import numpy as numpy_lib
from numpy import array
from numpy import zeros as z

Alias Detection Architecture

1. AST-Based Analysis (Primary Mechanism)

• Python source files are parsed into an Abstract Syntax Tree (AST).

• The extension traverses only relevant nodes:

• Import

• ImportFrom

• This approach allows the extension to:

• Reliably detect NumPy imports

• Extract alias names (np, numpy, custom aliases)

• Ignore comments, strings, and unrelated code

Why AST?

• AST parsing provides syntax-level accuracy and eliminates false positives common in text-based scanning.

2. Regex-Based Detection (Fallback)

• A lightweight, precompiled regex is used as a fallback mechanism.

• Regex execution is:

  • Scoped strictly to import lines

  • Triggered only during rapid typing or partial document states

• Ensures real-time responsiveness when AST parsing is temporarily deferred.

This hybrid model balances correctness and speed.

Snippet Resolution Logic

• Snippets are triggered only after a detected NumPy alias

• Suggestions are suppressed inside import statements

• Snippet prefixes are dynamically rewritten using the detected alias

Example:

np.z  →  np.zeros()

📦 Extension Information

• Extension Name: snippetbox-numpy

• Display Name:NumPy SnippetBox

• Extension ID: snippetbox-numpy

• Language Support: Python

• Platform: Visual Studio Code

🌐 Open Source

NumPy SnippetBox is fully open source and available under the MIT License. You are welcome to:

• Inspect the source code

• Contribute features or bug fixes

• Fork the project for personal use

• Submit issues or feature requests

GitHub Repository: https://github.com/vishvamramani/snippetbox-numpy

About the Developer

Vishvam Ramani is a software developer focused on building high-performance, developer-friendly VS Code extensions for Python and productivity tools. His work emphasizes:

• ⚡ Speed and real-time performance

• 🧠 Intelligent code analysis using AST and Regex

• 🧩 Scalability for large codebases

• 📦 Maintainability and clean architecture

Contributions & Support:

• Issues, feature requests, and pull requests are welcome.

• Codebase follows modular design for easy extensions and long-term maintenance.

Developed & Managed By: Vishvam Ramani

GitHub: Vishvam Ramani