Use Python to create a mixed sinusoidal signal and determine fundamental frequencies with fft

from numpy.fft import fft

import numpy as np

import matplotlib.pyplot as plt

from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)

signalFrequency1 = 550

signalFrequency2 = 650

signalFrequency3 = 750

sampleFrequency = 20000

numberOfSamples = 2000

samplingInterval = 1 / sampleFrequency

endTime = numberOfSamples/sampleFrequency

timeAxis=np.arange(0, endTime, samplingInterval)

frequencyAxis = np.arange(0, sampleFrequency, 1/endTime)

signal1 = np.sin(2*np.pi*signalFrequency1*timeAxis)

signal1 = 10*signal1;

signal2 = np.sin(2*np.pi*signalFrequency2*timeAxis)

signal2 = 5*signal2;

signal3 = np.sin(2*np.pi*signalFrequency3*timeAxis)

signal3 = 2*signal3;

compositeSignal = signal1 + signal2 + signal3

transform = fft(compositeSignal)

fig1 = plt.figure(figsize=(15,4))

plt.plot(timeAxis, compositeSignal)

fig2, ax = plt.subplots(figsize=(15,3))

ax.bar(frequencyAxis[50:80], abs(transform)[50:80], width=2)

ax.xaxis.set_minor_locator(AutoMinorLocator())

ax.tick_params(which='both', width=2)

ax.tick_params(which='major', length=10)

ax.tick_params(which='minor', length=5, color='gray')

ax.xaxis.set_minor_locator(MultipleLocator(10))

ax.xaxis.set_major_locator(MultipleLocator(50))

plt.show()

plt.savefig('fft.png')