Pywayne Dsp

Digital signal processing toolkit for filtering, peak detection, detrending, and curve similarity. Use when working with sensor data, signal preprocessing, f...

Security Scan

The skill is an instruction-only DSP documentation/usage guide that is internally consistent with its stated purpose and requests no credentials or installs.

Purpose & Capability

Name and description claim DSP utilities and the SKILL.md contains only DSP function docs and examples (filters, detrend, DTW, etc.). Nothing requested (no env, no binaries) conflicts with that purpose.

Instruction Scope

SKILL.md contains API examples and descriptions only. It does not instruct the agent to read arbitrary files, access unrelated environment variables, call external endpoints, or perform system-level operations.

Install Mechanism

No install specification and no code files — lowest-risk instruction-only skill. No downloads, package installs, or archive extraction are requested.

Credentials

The skill declares no required environment variables, credentials, or config paths. That is proportionate for a documentation/usage skill.

Persistence & Privilege

Defaults apply (not always:true). The skill does not request persistent system changes or elevated privileges.

Assessment

This SKILL.md is a documentation/integration guide for a Python package named pywayne.dsp and is internally consistent and low-risk: it neither requests credentials nor installs code. Two practical notes before enabling it: (1) provenance is unknown — there is no homepage or source link, so verify the package or its author if you plan to rely on an actual implementation; (2) because it is instruction-only, an agent following these examples would need the pywayne package available in the runtime Python environment — if it is not installed the imports will fail (and some platforms might attempt to auto-install packages). If you need a packaged implementation, prefer a skill that cites a well-known PyPI/GitHub release or provides an install spec.

数字信号处理工具集，提供滤波器、峰值检测、去趋势、曲线相似度等信号处理功能。

Quick Start

from pywayne.dsp import butter_bandpass_filter, peak_det, SignalDetrend

# Butterworth 低通滤波
filtered = butter_bandpass_filter(signal, order=3, lo=0.5, hi=40, fs=250)

# 峰值检测
peaks, valleys = peak_det(signal, delta=0.5)

# 信号去趋势
detrender = SignalDetrend(method='linear')
detrended = detrender(raw_signal)

Filtering - 滤波器

butter_bandpass_filter

巴特沃斯带通滤波器。

from pywayne.dsp import butter_bandpass_filter

# 带通滤波
filtered = butter_bandpass_filter(
    signal=raw_signal,
    order=4,
    lo=1,
    hi=50,
    fs=250,
    btype='bandpass'
)

参数说明：

参数	类型	说明
signal	array	输入信号
order	int	滤波器阶数
lo	float	下限截止频率 (Hz)
hi	float	上限截止频率 (Hz)
fs	float	采样频率，默认为 0（不归一化）
btype	str	滤波器类型：'lowpass', 'highpass', 'bandpass', 'bandstop'
realtime	bool	是否实时处理，默认 False

ButterworthFilter

纯 numpy 实现的巴特沃斯滤波器类，支持完整的 IIR 滤波功能。

from pywayne.dsp import ButterworthFilter

# 方式 1：通过参数设计
bf = ButterworthFilter.from_params(order=4, fs=200, btype='bandpass', cutoff=(1, 50))
y, zf = bf.lfilter(signal)

# 方式 2：通过系数构造
bf2 = ButterworthFilter.from_ba(b, a)
y, zf = bf2.lfilter(signal)

# 零相位滤波（前向-后向）
y, zf = bf.filtfilt(signal)

# 去趋势
detrended = ButterworthFilter.detrend(signal, method='linear')

参数设计方法：

ButterworthFilter.from_params(order, fs, btype, cutoff, cache_zi=True)
ButterworthFilter.from_ba(b, a, cache_zi=True)

参数说明：

参数	类型	说明
order	int	滤波器阶数
fs	float	采样频率 (Hz)
btype	str	'lowpass', 'highpass', 'bandpass', 'bandstop'
cutoff	float/Tuple	截止频率 (Hz)，带通为 (low, high) 元组
cache_zi	bool	是否预计算稳态初始条件

实例方法：

方法	说明
zi(self)	返回稳态初始条件数组
lfilter(self, x, zi=None)	零相位滤波，返回 (y, zf)
filtfilt(self, x, padtype='odd')	零相位滤波，可指定填充方式

Peak Detection - 峰值检测

peak_det

峰值检测函数，基于 MATLAB peakdet 转换。

from pywayne.dsp import peak_det

max_peaks, min_peaks = peak_det(signal, delta=0.5)

参数说明：

参数	类型	说明
v	array	输入信号
delta	float	检测阈值，控制检测灵敏度
x	array	可选的 x 轴数据，若未提供则使用下标

返回值：(maxima_indices, minima_indices) - 峰值和谷值的索引位置

find_extremum_in_sliding_window

在滑动窗口中查找极值。

from pywayne.dsp import find_extremum_in_sliding_window

extrema = find_extremum_in_sliding_window(data, k=50)

参数说明：

参数	类型	说明
data	list	输入数据列表
k	int	滑动窗口大小

返回值：[minima, maxima] - 含局部极值的列表

FindSlidingWindowExtremum

滑动窗口极值查找器类，用于实时数据流。

from pywayne.dsp import FindSlidingWindowExtremum

detector = FindSlidingWindowExtremum(win=100, find_max=True)

# 应用新值
for sample in stream:
    current_peak = detector.apply(sample)
    # 处理 current_peak

方法：

方法	说明
__init__(win, find_max)	初始化，指定窗口大小和查找类型（最大值或最小值）
apply(val)	更新窗口数据，返回当前极值

Detrending - 信号去趋势

SignalDetrend

信号去趋势处理器，支持多种去趋势算法。

from pywayne.dsp import SignalDetrend

# 去除线性趋势
detrender = SignalDetrend(method='linear')
detrended = detrender(signal)

# 去除均值趋势
detrender = SignalDetrend(method='mean')
detrended = detrender(signal)

# LOESS 去趋势
detrender = SignalDetrend(method='loess', span=0.3)
detrended = detrender(signal)

方法：

方法	说明
method	str
__call__(x)	应用去趋势算法处理输入信号

去趋势方法：

方法	说明
none	不处理，返回原信号
mean	去除均值
linear	去除线性趋势
poly	去除多项式趋势
loess	局部加权回归平滑
wavelet	小波变换去趋势
emd	EMD 去趋势
ceemdan	CEEMDAN 去趋势
median	中值滤波去趋势

Curve Similarity - 曲线相似度

CurveSimilarity

曲线相似度计算，支持动态时间规整（DTW）。

from pywayne.dsp import CurveSimilarity

cs = CurveSimilarity()
distance = cs.dtw(curve1, curve2, mode='global')

方法：

方法	说明
dtw(x, y, mode='global', *params)	计算两条曲线的 DTW 距离
mode	str

Other Tools - 其他工具

OneEuroFilter

一欧罗滤波器，用于平滑信号并减少延迟。

from pywayne.dsp import OneEuroFilter

# 初始化
euro_filter = OneEuroFilter(te=0.02, mincutoff=1.0, beta=0.007, dcutoff=1.0)

# 应用滤波
smooth_value = euro_filter.apply(new_measurement, te=0.02)

参数说明：

参数	类型	说明
te	float	采样时间（秒），自动推断默认值
mincutoff	float	最小截止频率
beta	float	调整速率参数
dcutoff	float	导数截止频率

WelfordStd

使用 Welford 算法进行在线标准差计算。

from pywayne.dsp import WelfordStd

std_calculator = WelfordStd(win=50)

for sample in data_stream:
    current_std = std_calculator.apply(sample)
    # 使用 current_std 进行判断

方法：

方法	说明
__init__(win)	初始化，指定窗口大小
apply(val)	更新标准差计算，返回当前窗口标准差

应用场景

场景	使用函数
心电图信号分析	butter_bandpass_filter, peak_det
传感器数据平滑	OneEuroFilter, ButterworthFilter
数据预处理	SignalDetrend
曲线相似度比较	CurveSimilarity.dtw
质量监控	WelfordStd