CheatsheetMatplotlib
Matplotlib is a Python data visualization library used to create high-quality static, animated, and interactive visualizations in Python. It is one of the most widely used libraries for data visualization in Python and provides a variety of tools for creating a wide range of visualizations such as line plots, scatter plots, bar plots, histograms, and more. Matplotlib allows users to create publication-quality visualizations with a minimal amount of code, and also provides a great deal of customization options for more advanced users. The library is highly extensible and can be integrated with other Python libraries, making it a powerful tool for data visualization and analysis.
Basic Usage
Import
import matplotlib.pyplot as plt
Simple Example
The following example shows how to plot a simple sine wave using numpy and matplotlib.
import numpy as np
import matplotlib.pyplot as plt
fs = 0.01 # sampling frequency
n = np.arange(100) # create and array with evenly spaced values within [0, 100) with step 1
t = n/fs
x = np.sin(2*np.pi*5*t)
plt.plot(t, x)
plt.xlabel('time [s]')
plt.show()
You can superpose multiple plots by calling plot several times.
import numpy as np
import matplotlib.pyplot as plt
fs = 0.01 # sampling frequency
n = np.arange(100) # create and array with evenly spaced values within [0, 100) with step 1
t = n/fs
x1 = np.sin(2*np.pi*5*t)
x2 = np.cos(2*np.pi*5*t)
plt.plot(t, x1, label="sin")
plt.plot(t, x2, label="cos")
plt.xlabel('time [s]')
plt.legend() # add a legend
plt.show()
Copy & Paste Plots
Analysis of LTI systems
Poles and Zeros Diagram
import matplotlib.pyplot as plt
from scipy.signal import lti
# create data
sys = lti([-0.5, 1], [1, 1, 1])
poles = sys.poles
zeros = sys.zeros
# create plot
plt.plot(poles.real, poles.imag, "x", label="poles")
plt.plot(zeros.real, zeros.imag, "o", label="zeros")
# update axes (name / type) / add legend
plt.xlabel("Real Part")
plt.ylabel("Imag Part")
plt.legend()
plt.show()
Time response
import matplotlib.pyplot as plt
from scipy.signal import lti
# create data
sys = lti([1], [1, 1])
t, s = sys.step()
# create plot
plt.plot(t, s)
# update axes (name / type) / add legend
plt.xlabel("time [s]")
plt.ylabel("response")
plt.show()
Bode Plot
import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import lti
# create data
sys = lti([1], [1, 1])
w,Hjw = sys.freqresp()
module = np.abs(Hjw)
phase = 180*np.angle(Hjw)/np.pi #convert radian to degree
# create plot
plt.subplot(2,1,1)
plt.loglog(w, module)
plt.ylabel("Magnitude")
plt.grid()
plt.subplot(2,1,2)
plt.semilogx(w, phase)
# update axes (name / type), grid
plt.ylabel("Phase [deg]")
plt.xlabel("w [rad/s]")
plt.grid()
plt.show()