Arange and Plotting (Numpy and Matplotlib Basics 1)

Learning Outcomes

  • use the arange function to create time vectors
  • create figures, plot data, add labels and legends, and save figures


  • function from numpy
  • create an array over a range
  • usage:
    • vect = arange(start, stop, step)
    • t = arange(0,1,0.01)
    • t = np.arange(0,1,0.01)

Sin, Cos, and Pi

  • I assume these numpy functions and variables are obvious
    • np.sin
    • np.cos
    • np.pi

Plotting Functions

From matplotlib.pyplot:

  • figure
  • clf
  • plot
  • xlabel
  • ylabel
  • legend
  • savefig


  • create or activate a figure:
    • figure(1)
  • activate if that figure number already exists; otherwise create a new figure
  • create as many figures as you want/need
  • the active figure is the one that gets drawn on by subsequent plot commands


  • clear the current figure
  • usage:
    • clf()
  • matplotlib turns “hold” on by default
  • subsequent runs of your code will draw onto the same figure
    • use clf sort of as a replacement for close all (Matlab)
    • you can use close('all'), but your code will run a little slower


  • plot(x,y)
  • actually plot y vs. x on the current axis
  • several different formats:
    • plot(x, y, 'r:')
    • plot(x, y, label='$y_1$')
      • makes legend creation very easy, but can only handle one x/y pair per plot command
    • plot(x1, y1, x2, y2)
    • plot(x1, y1, 'r-', x2, y2, 'g-.')
  • keep in mind that hold is on by default


  • xlabel("Time (sec.)")
  • add a string to the x-axis label

Scientific string note

  • matplotlib supports sub-scripts, super-scripts, and symbols using LaTeX syntax with dollar signs $:
    • $y_1$
    • $x^2$
    • $\\theta$
      • note that you have to escape the backslash


  • ylabel("$y_1(t)$")
  • add a string to the y-axis label


  • main usages:
    • legend([label1, label2])
    • legend([label1, label2], loc=2)
  • if you labeled all your plots:
    • legend()
    • legend(loc=2)


  • save the current figure to a file
  • optionally specify dpi if saving to png
  • savefig("fig1.png", dpi=300)
  • savefig("fig1.eps")
  • I mainly use png for websites and eps for documents
    • I convert eps to pdf using a shell script
  • direct pdf support may have improved over the years
  • from the help: “Most backends support png, pdf, ps, eps and svg”
    • I haven’t played with svg
  • png is easy to stick in a Word document, but pixelation usually leads to poor print quality

Plotting example

import matplotlib.pyplot as plt
import numpy as np

t = np.arange(0,1,0.01)
y = np.sin(2*np.pi*t)


plt.xlabel('Time (sec.)')