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I have noisy data for which I want to calculate frequency and amplitude. The samples were collected every 1/100th sec. From trends, I believe frequency to be ~ 0.3
When I use numpy fft module, I end up getting very high frequency (36.32 /sec) which is clearly not correct. I tried to filter the data with pandas rolling_mean to remove the noise before fft, but that too didn't work.
import pandas as pd
from numpy import fft
import numpy as np
import matplotlib.pyplot as plt
Moisture_mean_x = pd.read_excel("signal.xlsx", header = None)
Moisture_mean_x = pd.rolling_mean(Moisture_mean_x, 10) # doesn't helps
Moisture_mean_x = Moisture_mean_x.dropna()
Moisture_mean_x = Moisture_mean_x -Moisture_mean_x.mean()
frate = 100. #/sec
Hn = fft.fft(Moisture_mean_x)
freqs = fft.fftfreq(len(Hn), 1/frate)
idx = np.argmax(np.abs(Hn))
freq_in_hertz = freqs[idx]
Can someone guide me how to fix this?
653
We can obtain the magnitude of frequency from a set of complex numbers obtained after performing FFT i.e Fast Fourier Transform in Python. The frequency can be obtained by calculating the magnitude of the complex number. So simple ab(x) on each of those complex numbers should return the frequency.
The output of the generic FFT normally used in programming is 0-22khz for a 44.1 sample and 0-24khz for a 48khz input.
Return the Discrete Fourier Transform sample frequencies. The returned float array f contains the frequency bin centers in cycles per unit of the sample spacing (with zero at the start). For instance, if the sample spacing is in seconds, then the frequency unit is cycles/second.
You are right there is something wrong. One needs to explictiy ask pandas for the zeroth column:
Hn = np.fft.fft(Moisture_mean_x[0])
Else something wrong happen, which you can see by the fact that the FFT result was not symetric, which should be the case for real input.
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