numfu-lang


Namenumfu-lang JSON
Version 0.2.0 PyPI version JSON
download
home_pageNone
SummaryThe NumFu programming language
upload_time2025-09-07 12:47:49
maintainerNone
docs_urlNone
authorNone
requires_python>=3.10
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keywords programming language math functional interpreter numfu educational education programming language
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# NumFu Programming Language

**NumFu** is a pure, interpreted, functional programming language designed for readable & expressive code, extensibility, and ease of learning for beginners.

NumFu's simple syntax and semantics make it well-suited for educational applications, such as courses in functional programming and general programming introductions. At the same time, as its name suggests, NumFu is also ideal for exploring mathematical ideas and sketching algorithms, thanks to its native support for arbitrary-precision arithmetic.

## Features

- **Arbitrary Precision Arithmetic** - Reliable mathematical computing powered by Python's mpmath
- **First-Class Functions** - Automatic currying, partial application, and function composition
- **Expressive Syntax** - Infix operators, spread/rest operators, and lots of syntactic sugar
- **Tail Call Optimization** for efficient recursive algorithms without stack overflow
- **Interactive Development** - Friendly REPL and helpful error messages
- **Minimal Complexity** - Only four core types: `Number`, `Boolean`, `List`, and `String`
- **Python Integration** - Large & reliable standard library through NumFu's Python runtime
- **Extensible** - NumFu is written entirely in Python with the goal of being extensible and easy to understand.

## Quick Start

### Installation

#### From PyPI
```bash
pip install numfu-lang
```

#### From Source
```bash
git clone https://github.com/rphle/numfu
cd numfu
make install
```

### Hello NumFu!

Create `hello.nfu`:
```numfu
import sqrt from "math"

// Mathematical computing with arbitrary precision
let golden = {depth ->
  let recur =
    {d -> if d <= 0 then 1 else 1 + 1 / recur(d - 1)}
  in recur(depth)
} in golden(10) // β‰ˆ 1.618

// Function composition & piping
let add1 = {x -> x + 1},
    double = {x -> x * 2}
in 5 |> (add1 >> double) // 12

// Partial Application
{a, b, c -> a+b+c}(_, 5, _)
// {a,c -> a+5+c}

// Assertions
sqrt(49) ---> $ == 7

// Built-in testing with assertions
let square = {x -> x * x} in
  square(7) ---> $ == 49  // βœ“ passes
```

Run it:
```bash
numfu hello.nfu
```

### Interactive REPL

```bash
numfu repl
```

```
NumFu REPL. Type 'exit' or press Ctrl+D to exit.
>>> 2 + 3 * 4
14
>>> let square = {x -> x * x} in square(7)
49
>>> import max from "math"
>>> [1, 2, 3, 4, 5, 6, 7] |> filter(_, {x -> x%2 == 0}) |> max
6
```

## πŸ“– Documentation

- **[Language Guide](https://rphle.github.io/numfu/docs/)** - Complete language tutorial & reference
- **[Built-ins Reference](https://rphle.github.io/numfu/docs/reference/builtins)** - All built-in functions and operators
- **[CLI Reference](https://rphle.github.io/numfu/docs/reference/cli)** - Command-line interface guide

> [!NOTE]
> As a language interpreted in Python, which is itself an interpreted language, NumFu is not especially fast. Therefore, it is not recommended for performance-critical applications or large-scale projects. However, NumFu has not yet been thoroughly optimized so you can expect some performance improvements in the future.

## πŸ› οΈ Development

### Prerequisites

- Python β‰₯ 3.10

### Setup Development Environment

```bash
git clone https://github.com/rphle/numfu
cd numfu
make dev
```
The `make dev` command also installs Pyright and Ruff via Pip. To format code and check types, it is strongly recommended to run both `ruff check --fix` and `pyright` before committing.

## Building NumFu

```
make build
```

NumFu contains built-ins written in NumFu itself (src/numfu/stdlib/builtins.nfu).
`make build` first installs NumFu without the built-ins, then parses and serializes the file, and finally performs a full editable install. The script also builds NumFu and creates wheels.

### Building Documentation

```bash
cd docusaurus && npm i && cd .. # make sure to install dependencies

make serve  # local preview
make docs   # build to 'docs-build'
```

## Project Structure

```
numfu/
β”œβ”€β”€ src/numfu/
β”‚   β”œβ”€β”€ __init__.py         # Package exports
β”‚   β”œβ”€β”€ _version.py         # Version & metadata
β”‚   β”œβ”€β”€ classes.py          # Basic dataclasses
β”‚   β”œβ”€β”€ parser.py           # Lark-based parser & AST generator
β”‚   β”œβ”€β”€ interpreter.py      # Complete Interpreter
β”‚   β”œβ”€β”€ modules.py          # Import/export & module resolving
β”‚   β”œβ”€β”€ ast_types.py        # AST node definitions
β”‚   β”œβ”€β”€ builtins.py         # Built-in functions
β”‚   β”œβ”€β”€ cli.py              # Command-line interface
β”‚   β”œβ”€β”€ repl.py             # Interactive REPL
β”‚   β”œβ”€β”€ errors.py           # Error handling & display
β”‚   β”œβ”€β”€ typechecks.py       # Built-in type system
β”‚   β”œβ”€β”€ reconstruct.py      # Code reconstruction for printing
β”‚   β”œβ”€β”€ grammar/            # Lark grammar files
β”‚   └── stdlib/             # Standard library modules
β”œβ”€β”€ docs/                   # Language documentation
β”‚   β”œβ”€β”€ guide/              # User guides
β”‚   └── reference/          # Reference
β”œβ”€β”€ docusaurus/             # Docusaurus website
β”œβ”€β”€ tests/                  # Test files
β”œβ”€β”€ scripts/                # Build and utility scripts
└── pyproject.toml          # Configuration
```

## Testing

NumFu is tested with over 300 tests covering core features, edge cases, and real-world examples β€” including most snippets from the documentation. Tests are grouped by category and include handwritten cases as well as tests generated by LLMs (mostly Claude Sonnet 4).

Every test is self-validating using assertions and fails with an error if the output isn’t exactly as expected.

To run all tests from the `tests` folder:

```bash
make test
```

## Contributing

Found a bug or have an idea? [Open an issue](https://github.com/rphle/numfu/issues).

Want to contribute code?
- Check existing issues and [TODO.md](https://github.com/rphle/numfu/blob/main/TODO.md) for open tasks.
- Run all tests before committing.
- Please consider running `ruff check` and `pyright` to format code and check types before committing.
- Pull requests are welcome!


## License

This project is licensed under Apache License 2.0 - see the [LICENSE](https://github.com/rphle/numfu/blob/main/LICENSE) file for details.

            

Raw data

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    "keywords": "programming language, math, functional, interpreter, numfu, educational, education, programming, language",
    "author": null,
    "author_email": "Raphael <hello@alpharee.de>",
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    "description": "![](https://raw.githubusercontent.com/rphle/numfu/main/images/banner.png)\n\n![PyPI - Version](https://img.shields.io/pypi/v/numfu-lang)\n![PyPI - License](https://img.shields.io/pypi/l/numfu-lang)\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/numfu-lang)\n![PyPI Downloads](https://img.shields.io/pypi/dm/numfu-lang)\n[![Docs](https://img.shields.io/badge/docs-NumFu-brightgreen)](https://rphle.github.io/numfu/)\n![GitHub stars](https://img.shields.io/github/stars/rphle/numfu-lang?style=social)\n\n\n# NumFu Programming Language\n\n**NumFu** is a pure, interpreted, functional programming language designed for readable & expressive code, extensibility, and ease of learning for beginners.\n\nNumFu's simple syntax and semantics make it well-suited for educational applications, such as courses in functional programming and general programming introductions. At the same time, as its name suggests, NumFu is also ideal for exploring mathematical ideas and sketching algorithms, thanks to its native support for arbitrary-precision arithmetic.\n\n## Features\n\n- **Arbitrary Precision Arithmetic** - Reliable mathematical computing powered by Python's mpmath\n- **First-Class Functions** - Automatic currying, partial application, and function composition\n- **Expressive Syntax** - Infix operators, spread/rest operators, and lots of syntactic sugar\n- **Tail Call Optimization** for efficient recursive algorithms without stack overflow\n- **Interactive Development** - Friendly REPL and helpful error messages\n- **Minimal Complexity** - Only four core types: `Number`, `Boolean`, `List`, and `String`\n- **Python Integration** - Large & reliable standard library through NumFu's Python runtime\n- **Extensible** - NumFu is written entirely in Python with the goal of being extensible and easy to understand.\n\n## Quick Start\n\n### Installation\n\n#### From PyPI\n```bash\npip install numfu-lang\n```\n\n#### From Source\n```bash\ngit clone https://github.com/rphle/numfu\ncd numfu\nmake install\n```\n\n### Hello NumFu!\n\nCreate `hello.nfu`:\n```numfu\nimport sqrt from \"math\"\n\n// Mathematical computing with arbitrary precision\nlet golden = {depth ->\n  let recur =\n    {d -> if d <= 0 then 1 else 1 + 1 / recur(d - 1)}\n  in recur(depth)\n} in golden(10) // \u2248 1.618\n\n// Function composition & piping\nlet add1 = {x -> x + 1},\n    double = {x -> x * 2}\nin 5 |> (add1 >> double) // 12\n\n// Partial Application\n{a, b, c -> a+b+c}(_, 5, _)\n// {a,c -> a+5+c}\n\n// Assertions\nsqrt(49) ---> $ == 7\n\n// Built-in testing with assertions\nlet square = {x -> x * x} in\n  square(7) ---> $ == 49  // \u2713 passes\n```\n\nRun it:\n```bash\nnumfu hello.nfu\n```\n\n### Interactive REPL\n\n```bash\nnumfu repl\n```\n\n```\nNumFu REPL. Type 'exit' or press Ctrl+D to exit.\n>>> 2 + 3 * 4\n14\n>>> let square = {x -> x * x} in square(7)\n49\n>>> import max from \"math\"\n>>> [1, 2, 3, 4, 5, 6, 7] |> filter(_, {x -> x%2 == 0}) |> max\n6\n```\n\n## \ud83d\udcd6 Documentation\n\n- **[Language Guide](https://rphle.github.io/numfu/docs/)** - Complete language tutorial & reference\n- **[Built-ins Reference](https://rphle.github.io/numfu/docs/reference/builtins)** - All built-in functions and operators\n- **[CLI Reference](https://rphle.github.io/numfu/docs/reference/cli)** - Command-line interface guide\n\n> [!NOTE]\n> As a language interpreted in Python, which is itself an interpreted language, NumFu is not especially fast. Therefore, it is not recommended for performance-critical applications or large-scale projects. However, NumFu has not yet been thoroughly optimized so you can expect some performance improvements in the future.\n\n## \ud83d\udee0\ufe0f Development\n\n### Prerequisites\n\n- Python \u2265 3.10\n\n### Setup Development Environment\n\n```bash\ngit clone https://github.com/rphle/numfu\ncd numfu\nmake dev\n```\nThe `make dev` command also installs Pyright and Ruff via Pip. To format code and check types, it is strongly recommended to run both `ruff check --fix` and `pyright` before committing.\n\n## Building NumFu\n\n```\nmake build\n```\n\nNumFu contains built-ins written in NumFu itself (src/numfu/stdlib/builtins.nfu).\n`make build` first installs NumFu without the built-ins, then parses and serializes the file, and finally performs a full editable install. The script also builds NumFu and creates wheels.\n\n### Building Documentation\n\n```bash\ncd docusaurus && npm i && cd .. # make sure to install dependencies\n\nmake serve  # local preview\nmake docs   # build to 'docs-build'\n```\n\n## Project Structure\n\n```\nnumfu/\n\u251c\u2500\u2500 src/numfu/\n\u2502   \u251c\u2500\u2500 __init__.py         # Package exports\n\u2502   \u251c\u2500\u2500 _version.py         # Version & metadata\n\u2502   \u251c\u2500\u2500 classes.py          # Basic dataclasses\n\u2502   \u251c\u2500\u2500 parser.py           # Lark-based parser & AST generator\n\u2502   \u251c\u2500\u2500 interpreter.py      # Complete Interpreter\n\u2502   \u251c\u2500\u2500 modules.py          # Import/export & module resolving\n\u2502   \u251c\u2500\u2500 ast_types.py        # AST node definitions\n\u2502   \u251c\u2500\u2500 builtins.py         # Built-in functions\n\u2502   \u251c\u2500\u2500 cli.py              # Command-line interface\n\u2502   \u251c\u2500\u2500 repl.py             # Interactive REPL\n\u2502   \u251c\u2500\u2500 errors.py           # Error handling & display\n\u2502   \u251c\u2500\u2500 typechecks.py       # Built-in type system\n\u2502   \u251c\u2500\u2500 reconstruct.py      # Code reconstruction for printing\n\u2502   \u251c\u2500\u2500 grammar/            # Lark grammar files\n\u2502   \u2514\u2500\u2500 stdlib/             # Standard library modules\n\u251c\u2500\u2500 docs/                   # Language documentation\n\u2502   \u251c\u2500\u2500 guide/              # User guides\n\u2502   \u2514\u2500\u2500 reference/          # Reference\n\u251c\u2500\u2500 docusaurus/             # Docusaurus website\n\u251c\u2500\u2500 tests/                  # Test files\n\u251c\u2500\u2500 scripts/                # Build and utility scripts\n\u2514\u2500\u2500 pyproject.toml          # Configuration\n```\n\n## Testing\n\nNumFu is tested with over 300 tests covering core features, edge cases, and real-world examples \u2014 including most snippets from the documentation. Tests are grouped by category and include handwritten cases as well as tests generated by LLMs (mostly Claude Sonnet 4).\n\nEvery test is self-validating using assertions and fails with an error if the output isn\u2019t exactly as expected.\n\nTo run all tests from the `tests` folder:\n\n```bash\nmake test\n```\n\n## Contributing\n\nFound a bug or have an idea? [Open an issue](https://github.com/rphle/numfu/issues).\n\nWant to contribute code?\n- Check existing issues and [TODO.md](https://github.com/rphle/numfu/blob/main/TODO.md) for open tasks.\n- Run all tests before committing.\n- Please consider running `ruff check` and `pyright` to format code and check types before committing.\n- Pull requests are welcome!\n\n\n## License\n\nThis project is licensed under Apache License 2.0 - see the [LICENSE](https://github.com/rphle/numfu/blob/main/LICENSE) file for details.\n",
    "bugtrack_url": null,
    "license": "Apache License\n                                Version 2.0, January 2004\n                            http://www.apache.org/licenses/\n        \n        TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n        \n        1. Definitions.\n        \n            \"License\" shall mean the terms and conditions for use, reproduction,\n            and distribution as defined by Sections 1 through 9 of this document.\n        \n            \"Licensor\" shall mean the copyright owner or entity authorized by\n            the copyright owner that is granting the License.\n        \n            \"Legal Entity\" shall mean the union of the acting entity and all\n            other entities that control, are controlled by, or are under common\n            control with that entity. For the purposes of this definition,\n            \"control\" means (i) the power, direct or indirect, to cause the\n            direction or management of such entity, whether by contract or\n            otherwise, or (ii) ownership of fifty percent (50%) or more of the\n            outstanding shares, or (iii) beneficial ownership of such entity.\n        \n            \"You\" (or \"Your\") shall mean an individual or Legal Entity\n            exercising permissions granted by this License.\n        \n            \"Source\" form shall mean the preferred form for making modifications,\n            including but not limited to software source code, documentation\n            source, and configuration files.\n        \n            \"Object\" form shall mean any form resulting from mechanical\n            transformation or translation of a Source form, including but\n            not limited to compiled object code, generated documentation,\n            and conversions to other media types.\n        \n            \"Work\" shall mean the work of authorship, whether in Source or\n            Object form, made available under the License, as indicated by a\n            copyright notice that is included in or attached to the work\n            (an example is provided in the Appendix below).\n        \n            \"Derivative Works\" shall mean any work, whether in Source or Object\n            form, that is based on (or derived from) the Work and for which the\n            editorial revisions, annotations, elaborations, or other modifications\n            represent, as a whole, an original work of authorship. For the purposes\n            of this License, Derivative Works shall not include works that remain\n            separable from, or merely link (or bind by name) to the interfaces of,\n            the Work and Derivative Works thereof.\n        \n            \"Contribution\" shall mean any work of authorship, including\n            the original version of the Work and any modifications or additions\n            to that Work or Derivative Works thereof, that is intentionally\n            submitted to Licensor for inclusion in the Work by the copyright owner\n            or by an individual or Legal Entity authorized to submit on behalf of\n            the copyright owner. For the purposes of this definition, \"submitted\"\n            means any form of electronic, verbal, or written communication sent\n            to the Licensor or its representatives, including but not limited to\n            communication on electronic mailing lists, source code control systems,\n            and issue tracking systems that are managed by, or on behalf of, the\n            Licensor for the purpose of discussing and improving the Work, but\n            excluding communication that is conspicuously marked or otherwise\n            designated in writing by the copyright owner as \"Not a Contribution.\"\n        \n            \"Contributor\" shall mean Licensor and any individual or Legal Entity\n            on behalf of whom a Contribution has been received by Licensor and\n            subsequently incorporated within the Work.\n        \n        2. Grant of Copyright License. Subject to the terms and conditions of\n            this License, each Contributor hereby grants to You a perpetual,\n            worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n            copyright license to reproduce, prepare Derivative Works of,\n            publicly display, publicly perform, sublicense, and distribute the\n            Work and such Derivative Works in Source or Object form.\n        \n        3. Grant of Patent License. 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