Matplotlib will be used for plotting throughout this chapter. It can be downloaded using pip or any Python package manager.

$ pip install matplotlib

In order to use Matplotlib in our program, we need to import it.

import matplotlib.pyplot as plt 

We use the as keyword to substitute for the longer part with plt. It will be assumed that you have this line for the rest of this chapter.

If you get an error related to this line, make sure your have installed Matplotlib correctly

The function plt.plot() allows us to plot straight line graphs quickly.

import matplotlib.pyplot as plt

x = np.array([1, 2, 3, 4, 5]); # Create a 1D numpy array
y = 2 * x # Use element-wise operations to generate f(x)
plt.plot(x, y) # Create a plot element on the current figure
plt.show() # Display the current figure in a window

Here, we are plotting \( f(x) = 2x \) between 1 and 5 and showing the result in a window. Note that plt.plot() uses an x list and y list to represent the locations on those axes. These lists can be normal Python lists or most other list-like objects you'll find in the wild.

We can include multiple plots in a single figure by calling multiple functions that add plots to the figure.

t = np.linspace(0, 2*np.pi, 100) # list representing time steps from 0 to 10 seconds
x = np.sin(t) # Position over time
v = np.cos(t) # Velocity over time

plt.plot(t, x)
plt.plot(t, v)
plt.show()

Documentaion for plt.plot() can be found online.

Plots can be manually saved through the window that pops up when using plt.show() or through Python code using plt.savefig().

plt.plot([1, 2, 3], [1, 2, 3]) # Add linear plot to figure
plt.savefig("name_of_image.png") # Save figure to current directory
plt.clf() # Clears your figure

The function plt.scatter() allows us to plot dots representing two different variables.

x = np.array([1, 2, 3])
y = np.array([2, 4, 6])
plt.scatter(x, y) # similar usage to plt.plot()
plt.scatter(x, y + 1, s=100) # s represents the size of dots plotted

Full documentation is available online.

The plt.errorbar() function is one of the most used in experimental labs. It allows use to produce a scatter-like plot but with lovely uncertainty markers.

# list showing time from 0 to 4 seconds
time = np.arange(0, 5) 

# numpy array of measured temprature data
temperature = np.array([82, 71, 63, 56, 50]) 

# Some uncertainty associated with the measurement
uncertainty = np.array([1.2, 1.0, 1.0, 0.8, 0.8]) 

# This will produce a plot with errorbars representing the uncertainty on the y axis
# The yerr parameter can either be a constant value
plt.errorbar(time, temperature, yerr=2.0)

# or yerr can be a list of the same length as the x and y parameters
plt.errorbar(time, temperature - 5, yerr=uncertainty)

# Including the linestyle parameter as "None" 
# will remove that annoying straight line between points
plt.errorbar(time, temperature - 10, yerr=uncertainty, linestyle="None")

Full documentation is available online.

1. Plot an exponential function \( f(x)= e^{2x} \) between -10 and 10 using np.exp()
2. In the same plot, include the derivative of the function over the same domain.
3. Save the figure and put it on the fridge.