python中的加權移動平均線

Question

我有基本隨機間隔采樣的數據。 我想用numpy（或其他python包）來計算加權移動平均線。 我有一個移動平均線的粗略實現，但我很難找到一個好的方法來進行加權移​​動平均線，因此朝向邊框中心的值的加權大於邊緣的值。

在這里，我生成一些樣本數據，然后采用移動平均線。 我怎樣才能最輕松地實現加權移動平均線？ 謝謝！

import numpy as np
import matplotlib.pyplot as plt

#first generate some datapoint for a randomly sampled noisy sinewave
x = np.random.random(1000)*10
noise = np.random.normal(scale=0.3,size=len(x))
y = np.sin(x) + noise

#plot the data
plt.plot(x,y,'ro',alpha=0.3,ms=4,label='data')
plt.xlabel('Time')
plt.ylabel('Intensity')

#define a moving average function
def moving_average(x,y,step_size=.1,bin_size=1):
    bin_centers  = np.arange(np.min(x),np.max(x)-0.5*step_size,step_size)+0.5*step_size
    bin_avg = np.zeros(len(bin_centers))

    for index in range(0,len(bin_centers)):
        bin_center = bin_centers[index]
        items_in_bin = y[(x>(bin_center-bin_size*0.5) ) & (x<(bin_center+bin_size*0.5))]
        bin_avg[index] = np.mean(items_in_bin)

    return bin_centers,bin_avg

#plot the moving average
bins, average = moving_average(x,y)
plt.plot(bins, average,label='moving average')

plt.show()

輸出：

使用crs17的建議在np.average函數中使用“weights =”，我得到了加權平均函數，它使用高斯函數來加權數據：

def weighted_moving_average(x,y,step_size=0.05,width=1):
    bin_centers  = np.arange(np.min(x),np.max(x)-0.5*step_size,step_size)+0.5*step_size
    bin_avg = np.zeros(len(bin_centers))

    #We're going to weight with a Gaussian function
    def gaussian(x,amp=1,mean=0,sigma=1):
        return amp*np.exp(-(x-mean)**2/(2*sigma**2))

    for index in range(0,len(bin_centers)):
        bin_center = bin_centers[index]
        weights = gaussian(x,mean=bin_center,sigma=width)
        bin_avg[index] = np.average(y,weights=weights)

    return (bin_centers,bin_avg)

結果看起來不錯：

Answer 1

您可以使用numpy.average來指定權重：

>>> bin_avg[index] = np.average(items_in_bin, weights=my_weights)

因此，要計算權重，您可以找到倉中每個數據點的x坐標，並計算它們到倉中心的距離。

Answer 2

這不會給出確切的解決方案，但它會讓您的生活更輕松，並且可能足夠好......首先，將您的樣品放在小容器中。 一旦您重新采樣數據為等間隔，您可以使用步幅技巧和np.average進行加權平均：

from numpy.lib.stride_tricks import as_strided

def moving_weighted_average(x, y, step_size=.1, steps_per_bin=10,
                            weights=None):
    # This ensures that all samples are within a bin
    number_of_bins = int(np.ceil(np.ptp(x) / step_size))
    bins = np.linspace(np.min(x), np.min(x) + step_size*number_of_bins,
                       num=number_of_bins+1)
    bins -= (bins[-1] - np.max(x)) / 2
    bin_centers = bins[:-steps_per_bin] + step_size*steps_per_bin/2

    counts, _ = np.histogram(x, bins=bins)
    vals, _ = np.histogram(x, bins=bins, weights=y)
    bin_avgs = vals / counts
    n = len(bin_avgs)
    windowed_bin_avgs = as_strided(bin_avgs,
                                   (n-steps_per_bin+1, steps_per_bin),
                                   bin_avgs.strides*2)

    weighted_average = np.average(windowed_bin_avgs, axis=1, weights=weights)

    return bin_centers, weighted_average

你現在可以這樣做：

#plot the moving average with triangular weights
weights = np.concatenate((np.arange(0, 5), np.arange(0, 5)[::-1]))
bins, average = moving_weighted_average(x, y, steps_per_bin=len(weights),
                                        weights=weights)
plt.plot(bins, average,label='moving average')

plt.show()