CODEWITHSUNDEEP
pythonpython-3.xsignal-processingmoving-average
Cesar Abascal
Cesar Abascal

Reputation: 69

How to implement a good moving average in Python


I'm doing some research and I'm implementing a moving average in Python based on this mathematical expression:

Moving Average for Peak Detection Where: n = sample and W1 = Window

I implemented like that:

def movingAverage(signal, window):

   sum = 0
   mAver = []
   k = int((window-1)/2)

   for i in np.arange(k, len(signal)-k):
       for ii in np.arange(i-k, i+k):
           sum = sum + signal[ii]
       #end-for
       mAver.append(sum / window)
       sum = 0
   #end-for

   zeros = [0]*k
   mAver = zeros + mAver + zeros

   return mAver

It work very well. But I'm trying to discover some way to implement the k variant to minimize the signal lost in beginning and in final (Now I'm using an list with zeros).


Can someone help me?

Upvotes: 1

Views: 1714

Answers (3)

fstop_22
fstop_22

Reputation: 1032

You could use a filter with taps of all 1

import scipy as sp
import scipy.signal as sig

h = sp.ones(10)/10
y = sig.lfilter(h, 1, x)

Upvotes: 1

Gideon Kogan
Gideon Kogan

Reputation: 763

At the time I have found the following code:

def moving_average(samples, wind_len=1000):
    wind_len = int(wind_len)
    cumsum_samples = np.cumsum(samples)
    cumsum_diff = cumsum_samples[wind_len:] - cumsum_samples[:-wind_len]
    samples_average = cumsum_diff / float(wind_len)
    return samples_average

def my_cumsum(samples):
    for ind in range(1, len(samples)):
       samples[ind] = samples[ind] + samples[ind - 1]

Upvotes: 0

Alexander
Alexander

Reputation: 109536

You could just use Pandas and specify center=True for your moving average.

import numpy as np
import pandas as pd

np.random.seed(0)

s = pd.Series(np.random.randn(7)).round(1)
moving_avg = s.rolling(window=3).mean(center=True)
>>> pd.concat([s, moving_avg.round(2)], axis=1).rename(columns={0: 'signal', 1: 'MA'})
   signal    MA
0     1.8   NaN
1     0.4  1.07  # 1.07 = (1.8 + 0.4 + 1.0) / 3
2     1.0  1.20  # 1.20 = (0.4 + 1.0 + 2.2) / 3
3     2.2  1.70
4     1.9  1.03
5    -1.0  0.63
6     1.0   NaN

Upvotes: 2

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