Signal spectrum using NumPy

SIGNAL = [2014, 2025, 2033, 2046, 2049, ...]

import numpy as np

from matplotlib import pyplot as plt

if __name__ == '__main__':

    dt = 0.0005
    t = np.arange(0, len(SIGNAL) * dt, dt)
    plt.plot(t, SIGNAL)
    plt.xlabel('time(seconds)')
    plt.ylabel('signal')
    plt.show()

min_val = max_val = SIGNAL[0]
for i in SIGNAL:
    if i < min_val:
        min_val = i
    if i > max_val:
        max_val = i
mid_val = 1. * (max_val + min_val) / 2
for i in range(10):
    s = 0
    for i in SIGNAL:
        s += i - mid_val
    if s > 0:
        min_val = mid_val
        mid_val = (max_val + mid_val) / 2
    elif s < 0:
        max_val = mid_val
        mid_val = (min_val + mid_val) / 2
    else:
        break
SIGNAL = [i - mid_val for i in SIGNAL]

dt = 0.0005
t = np.arange(0, len(SIGNAL) * dt, dt)
plt.plot(t, SIGNAL)
plt.xlabel('time(seconds)')
plt.ylabel('signal')
plt.show()

n = len(SIGNAL)
y = np.fft.fft(SIGNAL) / n # normalize
frq = np.fft.fftfreq(n=n, d=dt)
# only positive frequencies
y = y[:n/2]
frq = frq[:n/2]

plt.bar(frq, abs(y))
plt.xlabel('Freq (Hz)')
plt.ylabel('|Y(freq)|')
plt.show()