plotting noise spectrum of the data

Question

i have a single column file(contain only one column) and a matrix file(contain 10 columns) of data which are noisy data and i want to plot the noise spectrum of both file using python.

sample data for single column file is attached here

-1.064599999999999921e-02
-1.146800000000000076e-02
-1.011899999999999952e-02
-7.400200000000000007e-03
-4.306500000000000432e-03
-1.644800000000000081e-03
1.936600000000000127e-04
1.239199999999999980e-03
1.759200000000000043e-03
2.019799999999999981e-03
2.148699999999999916e-03
2.153099999999999806e-03
2.008799999999999822e-03
1.700899999999999981e-03
1.181500000000000042e-03
3.194000000000000116e-04
-1.072000000000000036e-03
-3.133799999999999954e-03

and sample data for matrix file is attached here

-2.596100000000000057e-03 -1.856000000000000011e-03 -1.821400000000000102e-02 5.023599999999999594e-03 -1.064599999999999921e-02 -1.906300000000000008e-02 -6.370799999999999380e-05 5.814800000000000177e-03 -5.391800000000000412e-03 -1.311000000000000013e-02
1.636700000000000047e-03 -8.651600000000000176e-04 -2.490799999999999959e-02 1.645399999999999988e-02 -1.146800000000000076e-02 -4.609199999999999929e-03 6.475800000000000306e-03 1.265800000000000085e-02 1.855799999999999898e-03 -5.387499999999999928e-03
4.516499999999999682e-03 1.438899999999999901e-03 -2.911599999999999952e-02 2.590800000000000047e-02 -1.011899999999999952e-02 2.378800000000000012e-02 1.080200000000000084e-02 1.994299999999999892e-02 8.882299999999999224e-03 2.866500000000000124e-03
5.604699999999999786e-03 4.557799999999999872e-03 -2.870800000000000088e-02 2.832300000000000095e-02 -7.400200000000000007e-03 2.882099999999999940e-02 1.145799999999999944e-02 2.488800000000000040e-02 1.367299999999999939e-02 8.998799999999999508e-03
4.797400000000000275e-03 7.657399999999999970e-03 -2.582800000000000026e-02 2.288000000000000103e-02 -4.306500000000000432e-03 8.315499999999999975e-03 7.967600000000000030e-03 2.487999999999999934e-02 1.516600000000000066e-02 1.177899999999999954e-02
2.314300000000000038e-03 9.749700000000000033e-03 -2.252099999999999935e-02 1.762000000000000025e-02 -1.644800000000000081e-03 -1.257800000000000064e-02 1.220600000000000070e-03 1.866299999999999903e-02 1.377199999999999952e-02 1.163999999999999931e-02
-1.290700000000000094e-03 9.894599999999999923e-03 -1.928900000000000059e-02 1.360300000000000051e-02 1.936600000000000127e-04 -2.438999999999999849e-02 -6.739199999999999878e-03 6.961199999999999853e-03 1.086299999999999939e-02 1.015199999999999957e-02
-5.137400000000000300e-03 7.453800000000000009e-03 -1.615099999999999869e-02 1.018799999999999914e-02 1.239199999999999980e-03 -1.585699999999999957e-02 -1.349500000000000005e-02 -7.773600000000000301e-03 7.680499999999999827e-03 9.148399999999999241e-03
-8.159500000000000086e-03 2.403600000000000094e-03 -1.270400000000000001e-02 5.359000000000000048e-03 1.759200000000000043e-03 -9.746799999999999908e-03 -1.730999999999999900e-02 -2.229599999999999985e-02 4.641100000000000433e-03 9.871700000000000613e-03
-9.419600000000000195e-03 -4.305599999999999705e-03 -8.259700000000000028e-03 -3.140800000000000015e-03 2.019799999999999981e-03 -5.883300000000000161e-03 -1.772100000000000064e-02 -2.695099999999999926e-02 1.592399999999999892e-03 1.255299999999999992e-02
-8.469000000000000833e-03 -1.101399999999999949e-02 -2.205400000000000155e-03 -1.641199999999999951e-02 2.148699999999999916e-03 -3.635199999999999890e-03 -1.558000000000000010e-02 -1.839000000000000010e-02 -1.408900000000000039e-03 1.642899999999999916e-02
-5.529599999999999967e-03 -1.553999999999999999e-02 5.413199999999999956e-03 -4.248000000000000040e-03 2.153099999999999806e-03 -2.403199999999999868e-03 -1.255099999999999966e-02 -8.339100000000000332e-03 -3.665700000000000035e-03 2.009499999999999828e-02

i tried with https://www.earthinversion.com/techniques/visualizing-power-spectral-density-demo-obspy/ but for my ascii data set i could not do it.I hope experts may help me .Thanks in advance.

Answer 1

Maybe this can give you a start. Give your matrix of data in the file "x.data", this plots the raw data as 10 curves, then runs an FFT on each column and displays the FFT. The FFT isn't very interesting with only 12 points, but it will spark ideas.

There's still the problem of "how do you define noise"? The signals you presented do not seem to be very noisy. Unless you know what kind of signal you're expecting,an FFT might not do much good.

import numpy as np
import matplotlib.pyplot as plt
import scipy.fftpack

data = np.loadtxt("x.data")

for i in range(data.shape[1]):
    plt.plot( data[:,i] )
plt.show()

for i in range(data.shape[1]):
    f = scipy.fftpack.fft(data[:,i])
    plt.plot(np.abs(f))
plt.show()

Answer 2

Use numpy.loadtxt() to convert the data to a numpy array. Then you can apply the method described in the link you provided in order to obtain the spectra. E.g.:

import numpy as np 
data = np.loadtxt("file.txt")

The you plot the spectrum for that data. E.g.:

import matplotlib.pyplot as plt import scipy.fftpack 
yf = scipy.fftpack.fft(data)
fig, ax = plt.subplots()
ax.plot(np.abs(yf))
plt.show()