Online Julia to Janet Converter
Click to select or drop your input code file here.
You can also type the input code below.
How to use this tool?
This free online converter lets you convert code from Julia to Janet in a click of a button. To use this converter, take the following steps -
- Type or paste your Julia code in the input box.
- Click the convert button.
- The resulting Janet code from the conversion will be displayed in the output box.
Key differences between Julia and Janet
| Characteristic | Julia | Janet |
|---|---|---|
| Syntax | Syntax is similar to MATLAB and Python, designed for mathematical and scientific computing; uses familiar infix operators and array notation. | Lisp-like syntax with s-expressions; code is written in parentheses, which can be unfamiliar to those not used to Lisp dialects. |
| Paradigm | Multi-paradigm: supports procedural, functional, and object-oriented programming, with a focus on multiple dispatch. | Multi-paradigm: primarily functional and procedural, with strong support for metaprogramming and macros typical of Lisp languages. |
| Typing | Dynamically typed with optional type annotations; supports type inference and parametric types. | Dynamically typed; no static type system, types are checked at runtime. |
| Performance | High performance, close to C in many cases due to JIT compilation via LLVM. | Good performance for a scripting language, but generally slower than Julia; uses a custom VM and bytecode. |
| Libraries and frameworks | Rich ecosystem for scientific computing, data science, and machine learning; growing but smaller than Python or R. | Smaller standard library and ecosystem; focused on scripting, embedding, and extensibility rather than scientific computing. |
| Community and support | Active and growing community, especially in academia and scientific research; good documentation and forums. | Smaller, niche community; good documentation for its size, but less support and fewer resources than Julia. |
| Learning curve | Moderate; familiar to users of MATLAB, Python, or R, but advanced features (like multiple dispatch) may require adjustment. | Steeper for those unfamiliar with Lisp syntax; easier for those with Lisp experience, but less mainstream documentation. |
