rsnumpy 1.1.6

A Rust-powered NumPy-compatible array library.

rsnumpy

A Rust-powered NumPy-compatible array library.

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中文文档

1. 项目简介

rsnumpy 是一个由 Rust 驱动的高性能多维数组库，提供与 NumPy 兼容的 API。绝大部分计算逻辑（数组操作、数学函数、统计函数、线性代数、FFT、随机数、I/O、多项式等）均在 Rust 层实现，Python 层仅作为薄包装（thin wrapper），负责参数传递与结果包装。

优势：

• 性能：核心计算使用 Rust 实现，接近或超过 NumPy 的速度
• 类型安全：利用 Rust 的强类型系统避免运行时错误
• 内存安全：无数据竞争，无内存泄漏
• 完整 API：覆盖 NumPy 常用功能（数组操作、数学、统计、linalg、FFT、random、I/O、polynomial）

项目结构：

rsnumpy/
├── src/                       # Rust 源码
│   ├── lib.rs                 # 核心 ndarray 与通用函数
│   ├── indexing.rs            # 多维索引与切片
│   ├── fft.rs                 # 快速傅里叶变换
│   ├── linalg.rs              # 线性代数
│   └── random.rs              # 随机数生成（可复现并行采样）
├── python/rsnumpy/            # Python 薄包装
│   ├── __init__.py            # 主模块，整合所有 API
│   ├── array_methods.py       # ndarray 对象方法
│   ├── array_ops.py           # 数组操作函数
│   ├── _extra.py              # 补充 API（别名、nan 系列、集合运算、窗函数等）
│   ├── math_functions.py      # 数学函数
│   ├── statistics.py          # 统计函数
│   ├── char.py                # 字符串数组函数
│   ├── matlib.py              # 矩阵便捷构造
│   ├── io.py                  # 文件 I/O
│   ├── linalg/                # 线性代数子模块
│   ├── polynomial/            # 多项式子模块
│   └── random/                # 随机数子模块
├── Cargo.toml                 # Rust 依赖
├── pyproject.toml             # Python 构建配置
├── build_wheel.sh             # 构建脚本
└── README.md                  # 本文件

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2. 环境要求

工具	最低版本	说明
Python	≥ 3.10	推荐 3.10+
Rust	≥ 1.75	edition = "2024"
maturin	≥ 1.13, < 2.0	Rust ↔ Python 绑定
uv（可选）	最新	快速创建 venv 与安装

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3. 快速开始

3.1 克隆项目

git clone <your-repo-url> rsnumpy
cd rsnumpy

3.2 创建虚拟环境（推荐使用 uv）

# 使用 uv 创建 .venv
uv venv .venv --python 3.11
source .venv/bin/activate

# 安装 maturin 和 rsnumpy
uv pip install maturin

如果使用标准 venv：

python -m venv .venv
source .venv/bin/activate
pip install maturin

3.3 构建并安装

直接运行项目自带的构建脚本：

bash build_wheel.sh

脚本会自动：

1. 检测 .venv 中的 Python
2. 调用 maturin build --release
3. 生成 .whl 到 wheelhouse/
4. 使用 uv pip install --no-deps 安装到 .venv

构建完成后即可使用：

python -c "import rsnumpy; print(rsnumpy.__doc__)"

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4. 详细构建方式

4.1 使用构建脚本（推荐）

# 默认 release 模式，构建到 .venv
bash build_wheel.sh

# 指定 Python 解释器
bash build_wheel.sh --python .venv/bin/python

# 输出到指定目录
bash build_wheel.sh --out-dir build/

# Debug 模式（更快但更慢）
bash build_wheel.sh --debug

4.2 手动使用 maturin

# 开发模式：实时编辑，无需重新构建
maturin develop --release

# 仅构建 wheel
maturin build --release -o wheelhouse/

# 手动安装 wheel
uv pip install --no-deps wheelhouse/rsnumpy-*.whl

4.3 清理构建产物

rm -rf target/ wheelhouse/

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5. 使用示例

5.1 基本数组操作

import rsnumpy as np

# 创建数组
a = np.array([1.0, 2.0, 3.0])
b = np.zeros((2, 3))        # 2x3 全零
c = np.ones((2, 2))          # 2x2 全一
d = np.eye(3)                # 3x3 单位矩阵
e = np.arange(0, 10, 2)      # [0, 2, 4, 6, 8]
f = np.linspace(0, 1, 5)     # [0, 0.25, 0.5, 0.75, 1.0]

# 属性
print(a.shape)    # (3,)
print(a.ndim)     # 1
print(a.size)     # 3

5.2 数学函数

import rsnumpy as np

a = np.array([0.0, np.pi / 2, np.pi])
print(np.sin(a))         # [0.0, 1.0, 0.0]
print(np.cos(a))         # [1.0, 0.0, -1.0]
print(np.exp(a))         # 指数
print(np.sqrt(a))        # 平方根
print(np.log(a + 1))     # 自然对数

5.3 数组操作

import rsnumpy as np

a = np.array([[1, 2, 3], [4, 5, 6]])

# 变形
b = a.reshape((3, 2))
print(b.tolist())   # [[1, 2], [3, 4], [5, 6]]

# 转置
c = a.transpose()
print(c.tolist())   # [[1, 4], [2, 5], [3, 6]]

# 切片（多维 tuple 索引，由 Rust 实现）
print(a[0:2, 1:3].tolist())  # [[2, 3], [5, 6]]
print(a[:, 1].tolist())      # [[2], [5]]

# 拼接
d = np.concatenate([a, a], axis=0)
print(d.shape)   # (4, 3)

e = np.vstack([a, a])   # 垂直堆叠
f = np.hstack([a, a])   # 水平堆叠

5.4 统计函数

import rsnumpy as np

a = np.array([1.0, 2.0, 3.0, 4.0, 5.0])

print(np.sum(a))         # 15
print(np.mean(a))        # 3.0
print(np.std(a))         # 标准差
print(np.var(a))         # 方差
print(np.max(a))         # 5
print(np.argmin(a))      # 0

5.5 线性代数

import rsnumpy as np

A = np.array([[1.0, 2.0], [3.0, 4.0]])

print(np.linalg.det(A))           # -2.0
print(np.linalg.inv(A).tolist())  # 逆矩阵
print(np.linalg.norm(A))          # 范数
print(np.linalg.solve(A, [1, 1])) # 求解线性方程组

# 矩阵分解
U, S, V = np.linalg.svd(A)
Q, R = np.linalg.qr(A)

5.6 随机数

import rsnumpy as np

# 新 API
rng = np.random.default_rng(seed=42)
print(rng.random(5).tolist())   # 均匀分布
print(rng.normal(0, 1, 5).tolist())  # 正态分布
print(rng.integers(0, 10, 5).tolist())  # 整数

# 旧 API
np.random.seed(0)
print(np.random.rand(3).tolist())
print(np.random.randn(3).tolist())

可复现性：相同 seed 保证生成相同的结果。大数组采样在 Rust 层并行执行，采用固定分块 + splitmix64 派生各分块的独立种子，因此结果与线程数无关，在不同机器上同样可复现。同一个 Generator 每次调用都会推进内部状态，连续调用不会产生重复序列。

5.7 FFT

import rsnumpy as np

x = np.array([1.0, 0.0, 0.0, 0.0])
spectrum = np.fft.fft(x)
recovered = np.fft.ifft(spectrum)

# 实数输入
r_spectrum = np.fft.rfft(x)
recovered_r = np.fft.irfft(r_spectrum, n=4)

5.8 多项式

import rsnumpy as np

# 系数从高到低（NumPy 兼容）
p = np.polynomial.Poly([1, -3, 2])  # x^2 - 3x + 2
print(p(2))               # 0（在 x=2 处的值）
print(p.roots().tolist()) # [2.0, 1.0]

# 多项式运算
q = np.polynomial.Poly([1, -1])  # x - 1
print(p + q)              # Poly([1, -2, 1])
print(p * q)              # Poly([1, -4, 5, -2])

# 拟合
x = np.array([0, 1, 2, 3, 4])
y = np.array([1, 2, 5, 10, 17])  # y = x^2 + 1
coef = np.polynomial.polyfit(x, y, 2)

5.9 文件 I/O

import rsnumpy as np

# 保存 / 加载 .npy
a = np.array([1, 2, 3])
np.save('data.npy', a)
b = np.load('data.npy')

# 保存 / 加载文本
# fmt 参数控制输出格式，delimiter 参数指定分隔符
np.savetxt('data.txt', a, fmt='%d', delimiter=',')  # 保存为整数，逗号分隔
data = np.loadtxt('data.txt', delimiter=',', dtype=int)  # 加载为整数类型
print(data)  # [[0 0 1 1 2]
             #  [2 3 3 4 4]
             #  [5 5 6 6 7]
             #  [7 8 8 9 9]]

# 保存 / 加载 .npz（多个数组）
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
np.savez('multi.npz', a, b, c=a)
loaded = np.load_npz('multi.npz')
print(loaded['arr_0'].tolist())   # [1, 2, 3]
print(loaded['c'].tolist())       # [1, 2, 3]

# 从缓冲区创建
arr = np.frombuffer(bytes_data)

5.10 判断函数与常量

import rsnumpy as np

print(np.pi)           # 3.141592653589793
print(np.e)            # 2.718281828459045
print(np.inf)          # inf
print(np.nan)          # nan

a = np.array([1.0, np.nan, np.inf])
print(np.isnan(a))     # 逐元素判断
print(np.isinf(a))
print(np.isfinite(a))

5.11 日期时间与网格

import rsnumpy as np

# 日期时间标量（兼容 numpy.datetime64）
d = np.datetime64('2024-01-01')
delta = np.timedelta64(7, 'D')          # 7 天
print(d + delta)                         # numpy.datetime64('2024-01-08')
print(np.datetime64('2024-01-08') - d)   # numpy.timedelta64(7,'D')

# 用 datetime64/timedelta64 生成日期序列
days = np.arange(np.datetime64('2024-01-01'),
                 np.datetime64('2024-01-05'),
                 np.timedelta64(1, 'D'))

# 坐标网格（默认 'xy' 索引，与 NumPy 一致）
x = np.array([1, 2, 3])
y = np.array([10, 20])
X, Y = np.meshgrid(x, y)
print(X.tolist())   # [[1.0, 2.0, 3.0], [1.0, 2.0, 3.0]]
print(Y.tolist())   # [[10.0, 10.0, 10.0], [20.0, 20.0, 20.0]]

5.12 补充 API 覆盖（对齐 NumPy 2.5.1）

在核心 Rust 原语之上，_extra.py 补充了约 140 个 NumPy 兼容函数，全部对照 NumPy 2.5.1 逐函数验证通过。计算密集/逐元素部分复用现有 Rust 原语，纯变形与组合类则以薄 Python 包装实现。

import rsnumpy as np

# 三角/双曲别名（Array API 命名）
np.acos(1.0); np.asin(0.0); np.atan(1.0); np.atan2(1.0, 1.0)
np.radians(180.0); np.degrees(np.pi)

# 逐元素数学
np.hypot(3.0, 4.0)          # 5.0
np.maximum([1, 5], [3, 2])  # [3, 5]
np.fmax([np.nan, 2], [1, np.nan])  # 忽略 NaN
np.logaddexp(0.0, 0.0)      # 数值稳定 log(exp(a)+exp(b))
np.rint([0.5, 1.5, 2.5])    # 四舍六入五成双 -> [0, 2, 2]
np.gcd(12, 8); np.lcm(4, 6)
frac, whole = np.modf([1.5, 2.25])

# nan 系列归约
a = np.array([1.0, np.nan, 3.0])
np.nansum(a); np.nanmean(a); np.nanstd(a); np.nanmax(a)
np.nancumsum(a); np.nanargmax(a)
np.prod([1, 2, 3, 4])       # 24

# 集合运算
np.intersect1d([1, 2, 3], [2, 3, 4])  # [2, 3]
np.union1d([1, 2], [2, 3])            # [1, 2, 3]
np.setdiff1d([1, 2, 3], [2])          # [1, 3]
np.isin([1, 2, 5], [1, 5])            # [True, False, True]
uniq, counts = np.unique_counts([1, 1, 2, 3, 3, 3])

# 变形 / 组合
np.block([[np.eye(2), np.ones((2, 1))]])
np.pad([1, 2, 3], (1, 2), mode='reflect')
np.kron([1, 2], [1, 1])
np.tensordot(np.ones((2, 3)), np.ones((3, 4)), axes=1)

# 复数
z = np.array([1 + 2j, 3 - 1j])
np.real(z); np.imag(z); np.conjugate(z); np.angle(z)

# 信号 / 窗函数 / 插值
np.convolve([1, 2, 3], [1, 1])
np.interp(2.5, [1, 2, 3], [10, 20, 30])
np.hanning(8); np.hamming(8); np.blackman(8); np.bartlett(8)
np.vander([1, 2, 3], 3)
np.bincount([0, 1, 1, 2, 2, 2])

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6. 常见问题

Q1: ModuleNotFoundError: No module named 'rsnumpy'

A: 需要先构建并安装：bash build_wheel.sh

Q2: 编译报错 error: linker not found

A: 安装 Xcode Command Line Tools（macOS）：xcode-select --install

Q3: 编译报错 pyo3 版本冲突

A: 确保 Python ≥ 3.10，且 pip install --upgrade maturin pyo3

Q4: 性能是否优于 NumPy？

A: 多数逐元素与归约操作（sin/exp/sum/std/cumsum 等）在中大规模数组上快于 NumPy（约 1.3x–5x）；matmul/dot 在 macOS 上通过 Accelerate BLAS 与 NumPy 基本持平。小数组因跨语言调用开销可能略慢。详见「性能对比」。

Q5: 是否支持 GPU？

A: 当前版本仅支持 CPU。

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7. 开发提示

• 所有计算逻辑都应在 Rust 层实现，Python 层只保留 def f(x): return _core.f(x) 这样的薄包装
• 新增 API 时，先在 src/lib.rs 实现，再用 m.add_function(wrap_pyfunction!(name, m)?)? 注册
• 修改后需要重新运行 bash build_wheel.sh
• 提交前请运行测试脚本验证：.venv/bin/python test/run_test.py

8. 性能对比

以下为在 macOS (Apple Silicon) 上以 NumPy 为基准的实测结果。每个算子取 20 次运行的最优耗时（sort/matmul 取 5 次），输入为 arange 生成的 float64 数组。「相对 NumPy」= NumPy 耗时 ÷ rsnumpy 耗时，> 1 表示 rsnumpy 更快。

逐元素 / 归约（n = 1,000,000）

操作	rsnumpy	NumPy	相对 NumPy
add	0.113 ms	0.190 ms	1.7x
multiply	0.110 ms	0.186 ms	1.7x
divide	0.116 ms	0.188 ms	1.6x
sin	0.570 ms	2.969 ms	5.2x
exp	0.248 ms	1.349 ms	5.4x
sqrt	0.134 ms	0.225 ms	1.7x
sum	0.081 ms	0.103 ms	1.3x
mean	0.080 ms	0.104 ms	1.3x
std	0.271 ms	0.438 ms	1.6x
cumsum	1.030 ms	1.908 ms	1.9x
sort	0.741 ms	24.03 ms	32x
interp	0.227 ms	0.687 ms	3.0x
i0	4.459 ms	16.41 ms	3.7x

矩阵乘法（方阵，macOS 下经 Accelerate BLAS）

规模	rsnumpy	NumPy	相对 NumPy
128×128	0.013 ms	0.011 ms	0.9x
512×512	0.607 ms	0.605 ms	1.0x
1024×1024	4.375 ms	4.470 ms	1.0x

说明：小数组（约 n < 数万）因跨语言调用与线程调度开销，rsnumpy 可能略慢于 NumPy；随规模增大，Rust 层的并行与向量化优势显现。matmul 在 macOS 上分派到系统 Accelerate 框架，与 NumPy 基本持平。实际数字随机器与数据分布浮动。

8.1 性能架构

rsnumpy 的性能来自以下几项底层设计：

• 主动释放 GIL：计算密集算子（逐元素、归约、排序、cumsum、matmul 等）在进入 Rust 前通过 py.detach 释放 GIL，使纯计算与其它 Python 线程真正并行。
• 成本分级并行阈值：按算子的每元素成本设置不同的并行启用门槛——超越函数（sin/exp 等）阈值最低，访存密集型（add/mul/div）阈值最高——避免小数组因线程调度反而变慢。
• BLAS 后端：macOS 上 matmul/dot 经 ndarray 的 blas 特性分派到系统 Accelerate 框架；其它平台回退到多线程的纯 Rust matrixmultiply 内核。
• 连续内存与就地扫描：cumsum/cumprod 等在单份 C 序缓冲上就地前缀扫描并按连续块并行，减少分配与拷贝。
• 精简依赖：关闭 zip 等依赖中未使用的编解码器特性，配合 lto = "fat" 与符号裁剪，将扩展体积控制在约 5 MB。

9. CI/CD

项目使用 GitHub Actions 进行持续集成：

• 构建测试: 每次 push 自动构建并运行测试
• 代码质量: 使用 rust-clippy 检查代码风格
• 发布: 打 tag 时自动构建并发布到 PyPI

相关配置文件：

• .github/workflows/ci.yml - 主 CI 流程
• .github/workflows/rust-clippy.yml - Rust 代码质量检查
• .github/workflows/release.yml - 发布流程

10. 贡献指南

欢迎贡献代码！请遵循以下流程：

1. Fork 项目并创建新分支
2. 编写代码，确保遵循项目规范：
   • 所有计算逻辑在 Rust 层实现
   • Python 层只做参数传递
   • 添加适当的测试用例
3. 运行测试：.venv/bin/python test/run_test.py
4. 提交 PR，描述你的改动

---

English Documentation

1. Overview

rsnumpy is a high-performance multi-dimensional array library powered by Rust, providing a NumPy-compatible API. The vast majority of computation (array operations, math, statistics, linear algebra, FFT, random, I/O, polynomials, etc.) is implemented in Rust, while the Python layer is just a thin wrapper that handles argument passing and result wrapping.

Advantages:

• Performance: Core computations in Rust, comparable to or faster than NumPy
• Type safety: Strong typing from Rust eliminates runtime errors
• Memory safety: No data races, no memory leaks
• Comprehensive API: Covers all common NumPy functionality

Project layout:

rsnumpy/
├── src/                       # Rust source
│   ├── lib.rs                 # Core ndarray & general functions
│   ├── indexing.rs            # Multi-dimensional indexing & slicing
│   ├── fft.rs                 # Fast Fourier Transform
│   ├── linalg.rs              # Linear algebra
│   └── random.rs              # Random number generation (reproducible parallel sampling)
├── python/rsnumpy/            # Python thin wrappers
│   ├── __init__.py            # Main module, exports public API
│   ├── array_methods.py       # ndarray object methods
│   ├── array_ops.py           # Array manipulation functions
│   ├── _extra.py              # Supplementary API (aliases, nan-reductions, set ops, windows, ...)
│   ├── math_functions.py      # Math functions
│   ├── statistics.py          # Statistics functions
│   ├── char.py                # String array functions
│   ├── matlib.py              # Matrix construction helpers
│   ├── io.py                  # File I/O
│   ├── linalg/                # Linear algebra submodule
│   ├── polynomial/            # Polynomial submodule
│   └── random/                # Random submodule
├── Cargo.toml                 # Rust dependencies
├── pyproject.toml             # Python build config
├── build_wheel.sh             # Build script
└── README.md                  # This file

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2. Requirements

Tool	Minimum Version	Notes
Python	≥ 3.10	3.10+ recommended
Rust	≥ 1.75	edition = "2024"
maturin	≥ 1.13, < 2.0	Rust ↔ Python binding
uv (optional)	latest	Fast venv & package manager

---

3. Quick Start

3.1 Clone the repository

git clone <your-repo-url> rsnumpy
cd rsnumpy

3.2 Create a virtual environment (uv recommended)

# Create .venv with uv
uv venv .venv --python 3.11
source .venv/bin/activate

# Install maturin and numpy
uv pip install maturin numpy

Or with standard venv:

python -m venv .venv
source .venv/bin/activate
pip install maturin numpy

3.3 Build & install

Just run the build script:

bash build_wheel.sh

The script will:

1. Detect Python from .venv
2. Run maturin build --release
3. Generate .whl in wheelhouse/
4. Install into .venv with uv pip install --no-deps

After build, verify:

python -c "import rsnumpy; print(rsnumpy.__doc__)"

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4. Detailed Build Options

4.1 Using the build script (recommended)

# Default release mode, install to .venv
bash build_wheel.sh

# Specify Python interpreter
bash build_wheel.sh --python .venv/bin/python

# Output to a custom directory
bash build_wheel.sh --out-dir build/

# Debug mode (faster compile, slower runtime)
bash build_wheel.sh --debug

4.2 Manual maturin commands

# Develop mode: live editing, no rebuild needed
maturin develop --release

# Build a wheel only
maturin build --release -o wheelhouse/

# Install the wheel manually
uv pip install --no-deps wheelhouse/rsnumpy-*.whl

4.3 Clean build artifacts

rm -rf target/ wheelhouse/

---

5. Usage Examples

5.1 Basic array operations

import rsnumpy as np

# Array creation
a = np.array([1.0, 2.0, 3.0])
b = np.zeros((2, 3))        # 2x3 zeros
c = np.ones((2, 2))          # 2x2 ones
d = np.eye(3)                # 3x3 identity matrix
e = np.arange(0, 10, 2)      # [0, 2, 4, 6, 8]
f = np.linspace(0, 1, 5)     # [0, 0.25, 0.5, 0.75, 1.0]

# Attributes
print(a.shape)    # (3,)
print(a.ndim)     # 1
print(a.size)     # 3

5.2 Math functions

import rsnumpy as np

a = np.array([0.0, np.pi / 2, np.pi])
print(np.sin(a))         # [0.0, 1.0, 0.0]
print(np.cos(a))         # [1.0, 0.0, -1.0]
print(np.exp(a))         # exponential
print(np.sqrt(a))        # square root
print(np.log(a + 1))     # natural log

5.3 Array manipulation

import rsnumpy as np

a = np.array([[1, 2, 3], [4, 5, 6]])

# Reshape
b = a.reshape((3, 2))
print(b.tolist())   # [[1, 2], [3, 4], [5, 6]]

# Transpose
c = a.transpose()
print(c.tolist())   # [[1, 4], [2, 5], [3, 6]]

# Slicing (multi-dim tuple index, implemented in Rust)
print(a[0:2, 1:3].tolist())  # [[2, 3], [5, 6]]
print(a[:, 1].tolist())      # [[2], [5]]

# Concatenation
d = np.concatenate([a, a], axis=0)
print(d.shape)   # (4, 3)

e = np.vstack([a, a])   # vertical stack
f = np.hstack([a, a])   # horizontal stack

5.4 Statistics

import rsnumpy as np

a = np.array([1.0, 2.0, 3.0, 4.0, 5.0])

print(np.sum(a))         # 15
print(np.mean(a))        # 3.0
print(np.std(a))         # standard deviation
print(np.var(a))         # variance
print(np.max(a))         # 5
print(np.argmin(a))      # 0

5.5 Linear algebra

import rsnumpy as np

A = np.array([[1.0, 2.0], [3.0, 4.0]])

print(np.linalg.det(A))           # -2.0
print(np.linalg.inv(A).tolist())  # inverse matrix
print(np.linalg.norm(A))          # Frobenius norm
print(np.linalg.solve(A, [1, 1])) # solve linear system

# Decompositions
U, S, V = np.linalg.svd(A)
Q, R = np.linalg.qr(A)

5.6 Random numbers

import rsnumpy as np

# New API
rng = np.random.default_rng(seed=42)
print(rng.random(5).tolist())        # uniform
print(rng.normal(0, 1, 5).tolist())  # normal
print(rng.integers(0, 10, 5).tolist())  # integers

# Legacy API
np.random.seed(0)
print(np.random.rand(3).tolist())
print(np.random.randn(3).tolist())

Reproducibility: the same seed always produces the same result. Large-array sampling runs in parallel on the Rust side using fixed-size chunks with per-chunk seeds derived via splitmix64, so results are independent of the thread count and reproducible across machines. Each call on a Generator advances its internal state, so consecutive calls never repeat the same sequence.

5.7 FFT

import rsnumpy as np

x = np.array([1.0, 0.0, 0.0, 0.0])
spectrum = np.fft.fft(x)
recovered = np.fft.ifft(spectrum)

# Real input
r_spectrum = np.fft.rfft(x)
recovered_r = np.fft.irfft(r_spectrum, n=4)

5.8 Polynomials

import rsnumpy as np

# Coefficients from high to low (NumPy-compatible)
p = np.polynomial.Poly([1, -3, 2])  # x^2 - 3x + 2
print(p(2))               # 0 (value at x=2)
print(p.roots().tolist()) # [2.0, 1.0]

# Polynomial arithmetic
q = np.polynomial.Poly([1, -1])  # x - 1
print(p + q)              # Poly([1, -2, 1])
print(p * q)              # Poly([1, -4, 5, -2])

# Curve fitting
x = np.array([0, 1, 2, 3, 4])
y = np.array([1, 2, 5, 10, 17])  # y = x^2 + 1
coef = np.polynomial.polyfit(x, y, 2)

5.9 File I/O

import rsnumpy as np

# Save / load .npy
a = np.array([1, 2, 3])
np.save('data.npy', a)
b = np.load('data.npy')

# Save / load text
# fmt parameter controls output format, delimiter specifies separator
np.savetxt('data.txt', a, fmt='%d', delimiter=',')  # Save as integers, comma-separated
data = np.loadtxt('data.txt', delimiter=',', dtype=int)  # Load as integer type
print(data)  # [[0 0 1 1 2]
             #  [2 3 3 4 4]
             #  [5 5 6 6 7]
             #  [7 8 8 9 9]]

# Save / load .npz (multiple arrays)
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
np.savez('multi.npz', a, b, c=a)
loaded = np.load_npz('multi.npz')
print(loaded['arr_0'].tolist())   # [1, 2, 3]
print(loaded['c'].tolist())       # [1, 2, 3]

# From buffer
arr = np.frombuffer(bytes_data)

5.10 Constants & predicates

import rsnumpy as np

print(np.pi)           # 3.141592653589793
print(np.e)            # 2.718281828459045
print(np.inf)          # inf
print(np.nan)          # nan

a = np.array([1.0, np.nan, np.inf])
print(np.isnan(a))     # element-wise
print(np.isinf(a))
print(np.isfinite(a))

5.11 Datetime & meshgrid

import rsnumpy as np

# Datetime scalars (compatible with numpy.datetime64)
d = np.datetime64('2024-01-01')
delta = np.timedelta64(7, 'D')           # 7 days
print(d + delta)                          # numpy.datetime64('2024-01-08')
print(np.datetime64('2024-01-08') - d)    # numpy.timedelta64(7,'D')

# Generate a date range with datetime64/timedelta64
days = np.arange(np.datetime64('2024-01-01'),
                 np.datetime64('2024-01-05'),
                 np.timedelta64(1, 'D'))

# Coordinate grids (default 'xy' indexing, matching NumPy)
x = np.array([1, 2, 3])
y = np.array([10, 20])
X, Y = np.meshgrid(x, y)
print(X.tolist())   # [[1.0, 2.0, 3.0], [1.0, 2.0, 3.0]]
print(Y.tolist())   # [[10.0, 10.0, 10.0], [20.0, 20.0, 20.0]]

5.12 Supplementary API Coverage (aligned with NumPy 2.5.1)

On top of the core Rust primitives, _extra.py adds ~140 NumPy-compatible functions, each verified against NumPy 2.5.1. Compute-heavy / element-wise paths reuse existing Rust primitives, while pure reshaping and composition helpers are implemented as thin Python wrappers.

import rsnumpy as np

# Trig/hyperbolic aliases (Array API names)
np.acos(1.0); np.asin(0.0); np.atan(1.0); np.atan2(1.0, 1.0)
np.radians(180.0); np.degrees(np.pi)

# Element-wise math
np.hypot(3.0, 4.0)          # 5.0
np.maximum([1, 5], [3, 2])  # [3, 5]
np.fmax([np.nan, 2], [1, np.nan])  # NaN-ignoring
np.logaddexp(0.0, 0.0)      # numerically stable log(exp(a)+exp(b))
np.rint([0.5, 1.5, 2.5])    # round-half-to-even -> [0, 2, 2]
np.gcd(12, 8); np.lcm(4, 6)
frac, whole = np.modf([1.5, 2.25])

# nan-reductions
a = np.array([1.0, np.nan, 3.0])
np.nansum(a); np.nanmean(a); np.nanstd(a); np.nanmax(a)
np.nancumsum(a); np.nanargmax(a)
np.prod([1, 2, 3, 4])       # 24

# Set operations
np.intersect1d([1, 2, 3], [2, 3, 4])  # [2, 3]
np.union1d([1, 2], [2, 3])            # [1, 2, 3]
np.setdiff1d([1, 2, 3], [2])          # [1, 3]
np.isin([1, 2, 5], [1, 5])            # [True, False, True]
uniq, counts = np.unique_counts([1, 1, 2, 3, 3, 3])

# Reshaping / composition
np.block([[np.eye(2), np.ones((2, 1))]])
np.pad([1, 2, 3], (1, 2), mode='reflect')
np.kron([1, 2], [1, 1])
np.tensordot(np.ones((2, 3)), np.ones((3, 4)), axes=1)

# Complex
z = np.array([1 + 2j, 3 - 1j])
np.real(z); np.imag(z); np.conjugate(z); np.angle(z)

# Signal / windows / interpolation
np.convolve([1, 2, 3], [1, 1])
np.interp(2.5, [1, 2, 3], [10, 20, 30])
np.hanning(8); np.hamming(8); np.blackman(8); np.bartlett(8)
np.vander([1, 2, 3], 3)
np.bincount([0, 1, 1, 2, 2, 2])

---

6. FAQ

Q1: ModuleNotFoundError: No module named 'rsnumpy'

A: Build and install first: bash build_wheel.sh

Q2: Compilation error error: linker not found

A: Install Xcode Command Line Tools (macOS): xcode-select --install

Q3: Compilation error pyo3 version conflict

A: Ensure Python ≥ 3.10 and pip install --upgrade maturin pyo3

Q4: Is rsnumpy faster than NumPy?

A: For most element-wise and reduction operations (sin/exp/sum/std/cumsum, etc.) rsnumpy is faster than NumPy on medium-to-large arrays (roughly 1.3x–5x). matmul/dot on macOS dispatch to Accelerate BLAS and are on par with NumPy. Small arrays may be slightly slower due to cross-language call overhead. See "Performance Comparison" for numbers.

Q5: GPU support?

A: Not in the current version (CPU only).

---

7. Development Notes

• All computation must live in Rust; the Python layer should only contain thin wrappers like def f(x): return _core.f(x)
• When adding new APIs, implement them in src/lib.rs first, then register with m.add_function(wrap_pyfunction!(name, m)?)?
• After modification, run bash build_wheel.sh to rebuild
• Before committing, run .venv/bin/python test/run_test.py to verify functionality

8. Performance Comparison

Measured on macOS (Apple Silicon), using NumPy as the baseline. Each operator reports the best of 20 runs (sort/matmul best of 5); inputs are float64 arrays from arange. "vs NumPy" = NumPy time ÷ rsnumpy time, so > 1 means rsnumpy is faster.

Element-wise / reductions (n = 1,000,000)

Operation	rsnumpy	NumPy	vs NumPy
add	0.113 ms	0.190 ms	1.7x
multiply	0.110 ms	0.186 ms	1.7x
divide	0.116 ms	0.188 ms	1.6x
sin	0.570 ms	2.969 ms	5.2x
exp	0.248 ms	1.349 ms	5.4x
sqrt	0.134 ms	0.225 ms	1.7x
sum	0.081 ms	0.103 ms	1.3x
mean	0.080 ms	0.104 ms	1.3x
std	0.271 ms	0.438 ms	1.6x
cumsum	1.030 ms	1.908 ms	1.9x
sort	0.741 ms	24.03 ms	32x
interp	0.227 ms	0.687 ms	3.0x
i0	4.459 ms	16.41 ms	3.7x

Matrix multiply (square, via Accelerate BLAS on macOS)

Size	rsnumpy	NumPy	vs NumPy
128×128	0.013 ms	0.011 ms	0.9x
512×512	0.607 ms	0.605 ms	1.0x
1024×1024	4.375 ms	4.470 ms	1.0x

Note: for small arrays (roughly n < tens of thousands), cross-language call and thread-scheduling overhead can make rsnumpy slightly slower; the Rust-layer parallelism and vectorization pay off as sizes grow. matmul dispatches to the system Accelerate framework on macOS and is on par with NumPy. Absolute numbers vary with hardware and data distribution.

8.1 Performance Architecture

rsnumpy's performance comes from a few low-level design choices:

• Releasing the GIL: compute-heavy operators (element-wise, reductions, sort, cumsum, matmul, ...) release the GIL via py.detach before entering Rust, so pure computation runs truly in parallel with other Python threads.
• Cost-tiered parallel thresholds: the size at which parallelism kicks in varies by per-element cost — lowest for transcendental functions (sin/exp), highest for memory-bound ops (add/mul/div) — avoiding slowdowns from thread scheduling on small arrays.
• BLAS backend: on macOS, matmul/dot dispatch to the system Accelerate framework through ndarray's blas feature; other platforms fall back to the multi-threaded pure-Rust matrixmultiply kernel.
• Contiguous memory & in-place scans: cumsum/cumprod do an in-place prefix scan over a single C-order buffer, parallelized over contiguous blocks, minimizing allocation and copies.
• Lean dependencies: unused codec features (e.g. in zip) are disabled, and combined with lto = "fat" and symbol stripping the extension stays around 5 MB.

9. CI/CD

The project uses GitHub Actions for continuous integration:

• Build Testing: Auto-build and test on every push
• Code Quality: rust-clippy for code style checks
• Release: Auto-build and publish to PyPI when tagging

Related configuration files:

• .github/workflows/ci.yml - Main CI workflow
• .github/workflows/rust-clippy.yml - Rust code quality
• .github/workflows/release.yml - Release workflow

10. Contributing

Contributions are welcome! Please follow these steps:

1. Fork the project and create a new branch
2. Write code following project conventions:
   • All computation in Rust layer
   • Python layer only for argument passing
   • Add appropriate test cases
3. Run tests: .venv/bin/python test/run_test.py
4. Submit a PR describing your changes

---

License

MIT