python，平滑二维图？ ，趋势线？

编程问答 2022-05-27

问题描述

enter image description here

第一张图是使用原始数据绘制的，第二张图是在应用超过 15（天）的移动平均值后绘制的

我可以不断增加移动平均线的窗口，但它有时会改变整体情况。

是否还有其他平滑线条的方法？

plt.plot(x,y)

def moving_avg(l,size):
    additional = int((size - 1) / 2)
    l2 = l[-additional:] + l + l[:additional]

    df = pd.DataFrame(l2,columns=["d"])

    result = (
        df.rolling(size,min_periods=size,center=True).mean()["d"].tolist()
    )
    result = result[additional:-additional]
    return result

x = range(1,365)

y = [0.25769641467531185,0.25769641467531185,0.15655577299412943,0.19569471624266177,0.2968353579238442,0.2981526713477847,0.31838079968402117,0.2792418564354889,0.21724015800283883,0.17810121475430643,0.1376449580818335,0.21855747142677937,0.25769641467531174,0.15655577299412934,0.1956947162426617,0.35883705635649416,0.2968353579238441,0.234833659491194,0.33597430117237637,0.3979759996050264,0.45997769803767646,0.4208387547891441,0.3816998115406118,0.5839810949029767,0.5448421516544443,0.6405573973141552,0.5899870764735639,0.48884643479238155,0.5279853780409139,0.32570409467854905,0.20770667938383622,0.19084990577030586,0.22998884901883823,0.2414202266108971,0.3425608682920795,0.44370150997326185,0.6291260197220963,0.6682649629706285,0.6176946421300374,0.4545523020162049,0.3534116603350225,0.25227101865384016,0.6231200381515088,0.6459827933356266,0.6068438500870943,0.6169579142552125,0.6675282350958037,0.19553857391695253,0.2966792155981349,0.23467751716548488,0.28524783800607606,0.3243867812546084,0.3142727170864902,0.3816998115406117,0.2805591698594293,0.2414202266108969,0.39313118913267053,0.4942718308138529,0.4639296383094982,0.36278899662831576,0.3965025438553766,0.49764318553655895,0.559644883969209,0.4585042422880266,0.47741505720032246,0.4509258415219175,0.38349874706779596,0.22035640695396347,0.15835470852131345,0.1974936517698458,0.19026932022118995,0.15655577299412912,0.11741682974559677,0.0985060148333009,0.13764495808183325,0.05936707158476854,0.03913894324853206,0.0492530074166503,0.08839195066518266,0.12753089391371503,0.1781012147543062,0.16798715058618796,0.12884820733765562,0.08970926408912326,0.07827788649706442,0.1794185281782468,0.24142022661089685,0.26670538703119245,0.20470368859854238,0.22998884901883798,0.33112949070002035,0.2691277922673703,0.25888617521346147,0.20831585437287034,0.14631415594022026,0.10717521269168791,0.1577455335322791,0.2906732340275474,0.358100328481669,0.5212426685955015,0.5603816118440337,0.5098112910034426,0.7120925743658073,0.651408189357098,0.6176946421300371,0.5785556988815047,0.4154133587676723,0.47741505720032235,0.3004189342582532,0.3257040946785488,0.39313118913267037,0.3449832735282571,0.3196981131079615,0.2590137280992521,0.30958404893984326,0.3715857473724933,0.33244680412396094,0.3438781817160198,0.31016463448895903,0.33039276282519553,0.28993650615272254,0.23936618531213139,0.1773644868794814,0.1659331092874225,0.09850601483330092,0.14570498095118606,0.3479862643135508,0.38712520756208313,0.4996972268353244,0.5388361700838568,0.43227013238120277,0.2502169773550745,0.21107803410654213,0.1767839013303656,0.21855747142677917,0.3816998115406116,0.42083875478914395,0.4599776980376763,0.49911664128620864,0.3979759996050262,0.2534893686319086,0.3154910670645586,0.27635212381602625,0.23721318056749394,0.17941852817824683,0.07827788649706446,0.03913894324853211,0.20228128336236453,0.3931311891326704,0.2691277922673704,0.3082667355159027,0.34740567876443507,0.3865446220129674,0.6508276038079822,0.4154133587676724,0.40998796274620086,0.3708490194976685,0.08187575755143311,0.09030414435819832,0.12944308760673068,0.13486848362820222,0.10115493640114144,0.11560359949845322,0.12644009682143703,0.1571506532632042,0.11801171001467185,0.19327231100648368,0.26912779226737044,0.2637023962458989,0.30284133949443126,0.2974159434729597,0.2859845658809008,0.18484392419971843,0.17135850530889415,0.1322195620603618,0.13764495808183336,0.1996466565144834,0.2299888490188381,0.20844340725866098,0.24758235050719332,0.19701202966660214,0.1970120296666021,0.2361509729151345,0.19569471624266155,0.19026932022118997,0.2155544806414856,0.17641553739295326,0.1372765941444209,0.14270199016589247,0.1565557729941292,0.19569471624266158,0.2348336594911939,0.15113037697265763,0.11199143372412527,0.16798715058618804,0.20712609383472042,0.24626503708325276,0.27397260273972623,0.11741682974559685,0.21855747142677923,0.21855747142677923]

如下图

sns.distplot(y,bins=100,color='k')

（好吧，这是一个不同的情节）

但它也非常循序渐进，并且以某种方式 seaborn 设法在其上绘制了平滑的线条..

它是如何做到的？

enter image description here

实际上，以下是我现在最好的

y_new = moving_avg(y,31)
import numpy as np
import matplotlib.pyplot as plt

from csaps import csaps

np.random.seed(1234)

xs = np.linspace(x[0],x[-1],52)

ys = csaps(x,y_new,xs,smooth=0.85)

plt.plot(x,y,'o',ys,'-')

enter image description here

解决方法

我在 uni 学到了以下技术，用于找到生物信号（ppg、ecg 等）中的趋势线，首先对信号应用移动均值，然后应用 Savitzky-Golay 平滑滤波器

代码如下，我使用了另一种移动平均技术，我从这里得到了更多： How to calculate rolling / moving average using NumPy / SciPy?

和 Scipy 的 Savitzky-Golay 过滤器： https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.savgol_filter.html

import numpy as np
from scipy.signal import savgol_filter

y = np.array(y)
x = range(1,365)

## from: https://stackoverflow.com/questions/14313510/how-to-calculate-rolling-moving-average-using-numpy-scipy
def moving_average(x,w):
    return np.convolve(x,np.ones(w),'valid') / w

y_ave = moving_average(y,10)  ## moving average
x_ave = np.arange(x[0],x[-1],x[-1]/y_ave.shape[0]) ## compensate for shorter signal 
y_savgol = savgol_filter(y_ave,99,3) ## Savitzky-Golay filtering 

fig,axs = plt.subplots(1,figsize=(30,15))
axs.plot(x,y)
axs.plot(x_ave,y_savgol)
print(y_savgol.shape)

您可以使用上面的文档来调整参数以达到您想要的结果，代码能够生成下图 - 根据您想要实现的目标，这可能过于平滑：

>

enter image description here

matplotlibnumpypythonscipyscipyseaborn