hyjinx 0.29.6

A minimal Hy utility library, using mostly the standard libraries.

🦑 Hyjinx

Functions and macros useful for modern living in the Hy REPL.

Compatible with Hy 0.29.0.

Hylights

• A Hy code beautifier and in-REPL syntax highlighting
• An inspect module that works like Python's, but also for Hy code
• Pretty tracebacks with correct syntax highlighting for hy or python
• Print/get/edit source code of a function, module etc.
• In-repl code analysis and discussion using LLMs (e.g. for writing docstrings)
• In-repl syntax highlighting and latex rendering (for sixel-capable terminals) for LLM replies
• A sensible defmethod
• Numpy array pretty printing
• A zmq lazy pirate protocol
• A minimal ncurses class

Modules

• hyjinx.lib: a smorgasbord of convenience functions.

• hyjinx.source: live code inspection.

• hyjinx.inspect: code inspection.

• hyjinx.doc: peruse hy documentation.

• hyjinx.screen: a convenient ncurses wrapper.

• hyjinx.mat: numpy pretty-printing for humans. (requires numpy, jax optional)

• hyjinx.[zmq_client, zmq_server, crypto, wire]: lazy-pirate zmq RPC architecture. (requires zmq, ecdsa, zstandard)

• hyjinx.llm: discuss code with a Large Language Model (AI). TabbyAPI, OpenAI-compatible and Claude are supported. (requires openai and anthropic packages.)

Install

$ pip install -U hyjinx

or, with optional dependencies,

$ pip install -U hyjinx[zmq]
$ pip install -U hyjinx[llm]

To install offline hy/hyrule documentation,

$ hy -m hyjinx.docs

For syntax highlighting in the REPL, put

(import hyjinx.source [hylight]) (setv repl-output-fn hylight)

in your .hyrc.

See hyjinx.hyrc for example usage.

For in-terminal sixel rendering of latex in LLM replies, make sure pdflatex, dvipng and img2sixel are installed, and that you're using a sixel-capable terminal.

Optional dependencies

You can install with the [zmq] option which also installs ecdsa, pyzmq and zstandard for the zmq server/client

You can install with the [llm] option which also installs openai and lets you discuss code objects with ChatGPT or a locally-served LLM (via TabbyAPI or similar).