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import numpy as np
import matplotlib.pyplot as plt
from numpy.fft import fft, fftfreq


data = np.loadtxt('table.txt')

x = data[:, 0]
y = data[:, 1]

'''
data sub-sample korsi cause sub-sample korar 
por actual shape er kono change hoyna, and it 
leads to faster result, also, frequency er max 
range ta plotting e kome jay so fft er plot 
dekhte shubidha hoy,
    (max freq range in plotting = sampling frequency / 2 ). 
(tui parle Discrete Fourier Transform er ektu thoery dekhis).
Per 100 ta theke 1ta kore data nisi.
'''
x = x[::100]
y = y[::100]

'''
remove avg of y from y to make it zero mean. 
0 frequency er component ta remove korsi.
'''
y = y - np.mean(y)

plt.plot(x, y)
plt.xlabel('t')
plt.ylabel('M_z')
plt.title('Data Plot')

fft_y = fft(y)
freq = fftfreq(len(y), x[1] - x[0]) 
half_length = int(len(freq)/2)

plt.figure()
plt.plot(freq[0:half_length], np.abs(fft_y[0:half_length]))# half length freq nisi because of symmetry

plt.xlabel('Frequency')
plt.ylabel('Amplitude')
plt.title('Fourier Transform')

plt.show()