I need to reduce my noise like behavior in my data. I tried one of the method called Savitzky-Golay Filter . On the other hand, I need to find fastest method, because the filtering algorithm will be in the most running script in my code.
I am not familiar with the signal processing methods. Can you suggest faster methods and usage of them briefly?
I do not need complex structure like low-pass, high-pass etc (I know there are thousands of them). As fast as possible smoothening method is what I want to use.
Here my test script:
import numpy as np
import matplotlib.pyplot as plt
noisyData=np.array([
2.77741650e+43, 1.30016392e+42, 8.05792443e+42, 1.74277713e+43,
2.33814198e+43, 6.75553976e+42, 2.56642073e+43, 4.71467220e+43,
4.25047666e+43, 3.07095152e+43, 7.30694187e+43, 7.54411548e+43,
1.29555422e+43, 8.09272000e+42, 9.18193162e+43, 2.25447063e+44,
3.43044832e+41, 7.02901256e+43, 2.54438379e+43, 8.72303015e+43,
7.80333557e+42, 7.55039871e+43, 7.70164773e+43, 4.38740319e+43,
8.43139041e+43, 6.12168640e+43, 5.64352020e+43, 3.63824769e+42,
2.35296604e+43, 4.66272666e+43, 5.03660902e+44, 1.65071897e+44,
2.81055925e+44, 1.46401444e+44, 5.44407940e+43, 4.50672710e+43,
1.60833084e+44, 1.68038069e+44, 1.08588606e+44, 7.00867980e+43])
xAxis=np.arange(len(noisyData))
# ------------- Savitzky-Golay Filter ---------------------
windowLength = len(xAxis) - 5
polyOrder = 6
from scipy.signal import savgol_filter
# Function
def set_SavgolFilter(noisyData,windowLength,polyOrder):
return savgol_filter(noisyData, windowLength, polyOrder)
plt.plot(xAxis,noisyData,alpha=0.5)
plt.plot(xAxis,set_SavgolFilter(noisyData,windowLength,polyOrder))
# ------------- Time Comparison ----------------------
import time
start_time = time.time()
for i in range(50):
savgolfilter1 = set_SavgolFilter(noisyData,windowLength,polyOrder)
print(" %s seconds " % (time.time() - start_time))
# === OTHER METHODS WILL BE HERE
Unless you really need polynomial-based smoothing, Savitzky-Golay does not have any particular advantages. It's basically a bad lowpass filter. For more details, see https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5888646
Using a basic Butterworth lowpass filter instead:
from scipy.signal import butter, filtfilt
b, a = butter(5, .2)
datafilt = filtfilt(b, a, noisyData)
The filtfilt call seems to be several times faster than savgol_filter. How much faster do you need? Using lfilter from scipy is at least 10 times faster, but the result will be delayed with respect to the input signal.