scientific-cal 0.2.2

简介

这是一个用 Rust 编写的高性能科学计算信号处理库，旨在逐步重现 Python 中广泛使用的 scipy.signal 模块功能。
该库专注于滤波器设计、信号平滑、频域分析以及模态分解等，适用于嵌入式系统、实时处理、高性能计算等对性能和稳定性有较高要求的场景。

📦 模块功能覆盖情况（Rust 替代 SciPy.signal）

当前所有滤波器仅实现了低通 / 高通功能，带通与带阻支持将于后续版本添加。

📦 使用说明

1. 数字滤波器示例

use std::{f32::consts::PI, time::Instant};
// use scientific_cal::{bessel, butter_worth, FilterBand, FilterSelect, Sosfit};
use scientific_cal::{filters::{filter_factory, BesselCriterion, FilterBand, FilterType}, ScientificError};

#[test]
fn bessel_filter_test() -> Result<(), ScientificError>{
    // 参数输入
    let order = 4;
    let fs = 1000.0;
    let cutoff = 5.0;

    // 仿真数据
    let dt = 1.0 / fs as f32;
    let mut raw_data: Vec<f32> = (0..fs as i32)
        .map(|i| {
            let t = i as f32 * dt;
            (2.0 * PI * 2.0 * t).sin() + 0.5 * (2.0 * PI * 20.0 * t).sin()
        })
        .collect();

    // 定义低通滤波器
    let mut start = Instant::now();
    
    let ftype = FilterType::Bessel {  
        order: order,
        cutoff: vec![cutoff],
        band: FilterBand::LowPass,
        analog: false,
        criterion: BesselCriterion::Delay,
        fs: Some(fs),
    };

    let filter =  filter_factory::<f32>().create(ftype)?;

    println!("滤波初始化参数耗时: {:?}", start.elapsed());
    start = Instant::now();
    // 开始计算
    println!("滤波前：{:?}", &raw_data[0..10]);
    let _ = filter.apply_inplase(&mut raw_data)?;
    println!("滤波运行耗时: {:?}", start.elapsed());
    println!("滤波后：{:?}", &raw_data[0..10]);
    Ok(())
}

2. 平滑算法示例

use std::{f32::consts::PI, time::Instant};

use scientific_cal::{smooth::{sma, ema, rma, sma_inplase,}, ScientificError};


#[test]
fn move_ema_filter_test() -> Result<(), ScientificError>{
    // 参数输入
    let window_size = 5;

    let fs = 100000;
    // 仿真数据
    let dt = 1.0 / fs as f32;
    let mut raw_data: Vec<f32> = (0..fs as i32)
        .map(|i| {
            let t = i as f32 * dt;
            (2.0 * PI * 2.0 * t).sin() + 0.5 * (2.0 * PI * 20.0 * t).sin()
        })
        .collect();
    
    // 开始计算
    let start = Instant::now();
    let output = sma(&raw_data, window_size)?;  // ema rma用法一样
    // sma_inplase(&mut raw_data, window_size)?;        // ema  rma  没有实现 inplase
    println!("滤波运行耗时: {:?}", start.elapsed());
    println!("{:?}", &output[0..10]);
    Ok(())
}

3. 频谱分析示例

use std::{f32::consts::PI, time::Instant};
use scientific_cal::{spectrum::{fft, ifft}, ScientificError};


#[test]
fn fft_test() -> Result<(), ScientificError>{
    // 参数输入

    let n = 1024;
    // 仿真数据
    let raw_data: Vec<f32> = (0..n as i64)
        .map(|i| {
            (2.0 * PI * i as f32 / n as f32).sin()
        })
        .collect();
    
    // 开始计算
    let start = Instant::now();

    let mut fft_result = fft(&raw_data)?;
    println!("fft_result: {:?}", &fft_result[0..10]);
    // 幅值需要除以 N
    let n = raw_data.len();
    for f in fft_result.iter_mut(){
        *f = *f / (n as f32)
    }
    // 可以自定义滤波
    // 略
    // 反fft
    let ifft_result = ifft(&mut fft_result)?;
    println!("fft运行耗时: {:?}", start.elapsed());
    println!("ifft_result: {:?}", &ifft_result[0..10]);
    Ok(())
}

🚀 Quick Start

Add this to your Cargo.toml:

shell 安装

cargo add scientific-cal

or

cargo add scientific-cal --no-default-features --features filters

Cargo.toml 配置

# In your Cargo.toml

[dependencies]

scientific-cal = "0.2.0"

or

[dependencies]

scientific-cal = { version = "0.2.0", default-features = false, features = ["filters"] }