Compact python primer for MATLAB users #

# matlab-esque things in python

# first always import numpy and matplotlib
import matplotlib.pyplot as plt
import numpy as np

# basic signal creation
# matlab, x = exp(1i*2*pi*(0:Nsamples)*f/fs); 
fs = 5000
f = 500
Nsamples = 50
x = np.exp(1j * 2 * np.pi * np.arange(Nsamples) * f / fs)

# basic plotting, much like matlab
plt.figure(1)
plt.subplot(2,1,1)
plt.plot(np.arange(Nsamples), np.real(x), marker='o', linestyle='dashed')
plt.title('real portion of signal x')
plt.grid()
plt.xlabel('sample')
plt.ylabel('magnitude')
plt.legend('real')

plt.subplot(2,1,2)
plt.plot(np.arange(Nsamples), np.imag(x), marker='x', linestyle='dashed')
plt.title('imag portion of signal x')
plt.xlabel('sample')
plt.ylabel('magnitude')
plt.legend('imag')
plt.grid()
plt.draw()

# 2nd figure
plt.figure(2)
plt.plot(np.real(x), np.imag(x), marker='x')
plt.title('complex samples')
plt.draw()

# fft example + figure
x_fft = np.fft.fftshift(20*np.log10(np.abs(np.fft.fft(x, Nsamples))))
# equivalent to [-.5:1/Nsamples:.5-1/Nsamples] in matlab
xaxis = np.arange(-0.5, 0.5, 1/Nsamples)

plt.figure(3)
plt.plot(xaxis, x_fft, marker='o', linestyle='dashed')
plt.title('real portion of signal x')
plt.grid()
plt.xlabel('sample')
plt.ylabel('magnitude')
plt.legend('real')
# put last to show all figures
plt.show()