你也可以用这个:

def smooth(scalars, weight = 0.8):  # Weight between 0 and 1
        return [scalars[i] * weight + (1 - weight) * scalars[i+1] for i in range(len(scalars)) if i < len(scalars)-1]

使用移动平均线，一种快速的方法(也适用于非双射函数)是

def smoothen(x, winsize=5):
    return np.array(pd.Series(x).rolling(winsize).mean())[winsize-1:]

此代码基于https://towardsdatascience.com/data-smoothing-for-data-science-visualization-the-goldilocks-trio-part-1-867765050615。文中还讨论了更先进的解决方案。

如果您正在绘制时间序列图，并且如果您已经使用mtplotlib来绘制图形，那么请使用 中值方法平滑图

smotDeriv = timeseries.rolling(window=20, min_periods=5, center=True).median()

时间序列是您传递的数据集，您可以更改窗口大小以更平滑。

如果你对周期信号的“平滑”版本感兴趣(就像你的例子)，那么FFT是正确的方法。进行傅里叶变换并减去低贡献频率:

import numpy as np
import scipy.fftpack

N = 100
x = np.linspace(0,2*np.pi,N)
y = np.sin(x) + np.random.random(N) * 0.2

w = scipy.fftpack.rfft(y)
f = scipy.fftpack.rfftfreq(N, x[1]-x[0])
spectrum = w**2

cutoff_idx = spectrum < (spectrum.max()/5)
w2 = w.copy()
w2[cutoff_idx] = 0

y2 = scipy.fftpack.irfft(w2)

即使你的信号不是完全周期性的，这也能很好地去除白噪声。有许多类型的过滤器可以使用(高通，低通，等等…)，合适的一个取决于你正在寻找什么。

从SciPy Cookbook中清晰地定义了1D信号的平滑，向您展示了它是如何工作的。

快捷方式:

import numpy

def smooth(x,window_len=11,window='hanning'):
    """smooth the data using a window with requested size.

    This method is based on the convolution of a scaled window with the signal.
    The signal is prepared by introducing reflected copies of the signal 
    (with the window size) in both ends so that transient parts are minimized
    in the begining and end part of the output signal.

    input:
        x: the input signal 
        window_len: the dimension of the smoothing window; should be an odd integer
        window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'
            flat window will produce a moving average smoothing.

    output:
        the smoothed signal

    example:

    t=linspace(-2,2,0.1)
    x=sin(t)+randn(len(t))*0.1
    y=smooth(x)

    see also: 

    numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve
    scipy.signal.lfilter

    TODO: the window parameter could be the window itself if an array instead of a string
    NOTE: length(output) != length(input), to correct this: return y[(window_len/2-1):-(window_len/2)] instead of just y.
    """

    if x.ndim != 1:
        raise ValueError, "smooth only accepts 1 dimension arrays."

    if x.size < window_len:
        raise ValueError, "Input vector needs to be bigger than window size."


    if window_len<3:
        return x


    if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:
        raise ValueError, "Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'"


    s=numpy.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]]
    #print(len(s))
    if window == 'flat': #moving average
        w=numpy.ones(window_len,'d')
    else:
        w=eval('numpy.'+window+'(window_len)')

    y=numpy.convolve(w/w.sum(),s,mode='valid')
    return y




from numpy import *
from pylab import *

def smooth_demo():

    t=linspace(-4,4,100)
    x=sin(t)
    xn=x+randn(len(t))*0.1
    y=smooth(x)

    ws=31

    subplot(211)
    plot(ones(ws))

    windows=['flat', 'hanning', 'hamming', 'bartlett', 'blackman']

    hold(True)
    for w in windows[1:]:
        eval('plot('+w+'(ws) )')

    axis([0,30,0,1.1])

    legend(windows)
    title("The smoothing windows")
    subplot(212)
    plot(x)
    plot(xn)
    for w in windows:
        plot(smooth(xn,10,w))
    l=['original signal', 'signal with noise']
    l.extend(windows)

    legend(l)
    title("Smoothing a noisy signal")
    show()


if __name__=='__main__':
    smooth_demo()

编辑:看看这个答案。使用np。Cumsum比np.卷积快得多

我使用了一种快速而肮脏的方法来平滑数据，基于移动平均盒(通过卷积):

x = np.linspace(0,2*np.pi,100)
y = np.sin(x) + np.random.random(100) * 0.8

def smooth(y, box_pts):
    box = np.ones(box_pts)/box_pts
    y_smooth = np.convolve(y, box, mode='same')
    return y_smooth

plot(x, y,'o')
plot(x, smooth(y,3), 'r-', lw=2)
plot(x, smooth(y,19), 'g-', lw=2)