在python中将时间转换为Hz

Question

I have a dataset consists of timestamp(ms), x , y and z. I want to transform it to the frequency domain (Fourier). I used numpy.fft.fft(a, n=None, axis=-1, norm=None)[source]

my code is

import panda as pd
from scipy.fftpack import fft
import matplotlib.pyplot as plt

data=pd.read_csv('/home/Desktop/dataset.csv')
data = data.as_matrix()
xf=fft(data[:,3])
freq = numpy.fft.fftfreq(len(data), data[1,2] - data[0-1,2])
plt.plot(t,xf)

And I did the same for y and z. Is this way correct? I am not sure the way that I used for changing time to Hz is correct or not.

These is 10 rows of dataset:

Answer 1

From numpy.fft.fftfreq 's reference :

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.

Since your timestamps are in milliseconds, specifying the time spacing with data[1,2] - data[0,2] would give frequencies in units of cycles/milliseconds or kHz. To get frequencies in Hz, you should instead use:

freq = numpy.fft.fftfreq(len(data), 0.001*(data[1,2] - data[0,2]))

Then you should plot it using freq :

plt.plot(freq,xf)

_{As a side note for future readers, using data[1,2] - data[0,2] as the time step implicitly assumes that the data is evenly sampled in time (which seems to be the case here given the provided sample dataset).}