evhub/coconut
Coconut
Coconut 是一种编译为 Python 的函数式编程语言。它作为一个源到源编译器，将高级函数式语法转换为兼容的 Python 代码，以保持运行时兼容性。
该语言引入了一种用于模式匹配和解构复杂数据结构的逻辑系统。它提供了一种尾调用优化机制，以防止深度递归函数调用期间的栈溢出错误，并采用惰性求值引擎来推迟计算，直到明确需要结果为止。
该项目包括对代数数据类型、管道运算符和部分应用的支持。它还提供了一个通过跨多个 CPU 核心分发映射操作来进行并行数据处理的框架。
Features
- Functional-to-Python Transpilation - Functions as a source-to-source compiler that translates functional syntax into executable Python code.
- Algebraic Data Types - Allows the definition of algebraic data types to model complex values through tagged unions and variants.
- Lazy Evaluation - Implements a lazy evaluation engine to defer computation until results are explicitly required.
- Functional Programming Languages - Provides a full functional programming language that compiles to Python with integrated pattern matching and lazy evaluation.
- Python Functional Programming Libraries - Brings functional programming primitives like piping and partial application to the Python environment.
- Pattern Matching - Offers enhanced destructuring and pattern-matching logic for branching based on data structure shapes.
- Pattern Matching - Features a logic system for destructuring complex data structures using structural pattern matching.
- Functional Pipeline Composition - Provides pipeline operators to chain multiple functions together for sequential data flow.
- Source-to-Source Compilers - Translates high-level functional syntax into Python source code to maintain runtime compatibility.
- Tail-Call Optimizations - Implements a mechanism for tail call optimization to prevent stack overflow errors during deep recursive function calls.
- Functional Programming - Provides built-in syntactical support for lambdas, pipeline programming, and partial application.
- Stack-Safe Recursion - Optimizes recursive function calls to prevent stack overflow errors during deep recursion.
- Parallel Processing - Distributes mapping operations across multiple CPU cores to accelerate processing of large datasets.
- Multi-Core Workload Distribution - Provides a framework to split mapping operations across multiple CPU cores for parallel data processing.
- Parallel Processing - Improves computational performance by distributing mapping tasks across multiple CPU cores.
- Functional Programming - Adds functional programming features to Python syntax.
