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To visualize data in Python we will use the library Matplotlib. Matplotlib is a Python 2D plotting library with a variety of vizualisation tools. To see the full gallery of possibilities inluding tutorials, we highly recommend you to visit the offical Matplotlib page. In the following examples we will only cover some of the basic and most often used tools when visualizing data.


Info

If you have not already installed Matplotlib, visit the Matplotlib installation instructions. As NumPy is used a lot when working with Matplotlib, we also recommend checking it out.




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Table of Contents



Simple plot

Info

Visit this page for full documentation on simple plots using pyplot.


Code Block
languagepy
titleSimple plot code
collapsetrue
import numpy as np
import matplotlib.pyplot as plt

# Evenly sampled time from 0s to 10s at 200ms intervals
t = np.arange(0.0, 10.0, 0.2)

# Plotting t at x-axis and sin(t) at y-axis
plt.plot(t, np.sin(t))

# Naming the title and both axis
plt.title('Sinus function')
plt.ylabel('sin(t)')
plt.xlabel('t [s]')

# Need to call the show() function at the end to display my figure
plt.show()

Code Block
languagepy
titleMultiple plots in same figure
collapsetrue
import numpy as np
import matplotlib.pyplot as plt


# Evenly sampled time at 200ms intervals
t = np.arange(0.0, 5.0, 0.2)

# plot() can plot several lines in the same figure. To seperate the different lines 
# from eachother, we may change the line style and format strings.
# See the plot() documentation for a complete list of line styles and format strings.
# The following lines have red dashes, blue squares and green triangles
plt.plot(t, t, 'r--', label='Linear line')
plt.plot(t, t**2, color='blue', linestyle='none', marker='s', label='Second degree polynom')
plt.plot(t, t**3, 'g^', label='Third degree polynom')

# To describe our plot even more detailed we can draw the labels we previously gave our lines using legend.
plt.legend(loc='upper left')

# The function axis() sets the axis sizes, and takes the argument [xmin, xmax, ymin, ymax]
plt.axis([0, 5, 0, 100])

plt.title('Mulitple polynoms')
plt.show()


Info

More in-depth plot() documentation and legend() documentation.

Multiple figures and subplots

Info

A very good and more detailed guide on subplots and figures can be found here.


Code Block
languagepy
import matplotlib.pyplot as plt
import numpy as np

# Some example data to display
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)

A single plot

subplots() without arguments return a Figure and a single Axes. When dealing with multiple plots in the same figure, the different axes will seperate the different plots from eachother within the figure.

Code Block
languagepy
titleA single plot
collapsetrue
fig, ax = plt.subplots()
fig.suptitle('A single plot')
ax.plot(x, y)

Stacking subplots in one direction

The first two optional arguments of pyplot.subplots() define the number of rows and columns of the subplot grid.

When stacking in one direction only, the returned axs is a 1D numpy array containing the list of created Axes.

Code Block
languagepy
titleVertically stacked subplots
collapsetrue
fig, axs = plt.subplots(2)
fig.suptitle('Vertically stacked subplots')
axs[0].plot(x, y)
axs[1].plot(x, -y)

If you are creating just a few Axes, it's handy to unpack them immediately to dedicated variables for each Axes. That way, we can use ax1 instead of the more verbose axs[0].

Code Block
languagepy
titleVertically stacked subplots (alternative)
collapsetrue
fig, (ax1, ax2) = plt.subplots(2)
fig.suptitle('Vertically stacked subplots')
ax1.plot(x, y)
ax2.plot(x, -y)

To obtain side-by-side subplots, pass parameters 1, 2 for one row and two columns.

Code Block
languagepy
titleHorizontally stacked subplots
collapsetrue
fig, (ax1, ax2) = plt.subplots(1, 2)
fig.suptitle('Horizontally stacked subplots')
ax1.plot(x, y)
ax2.plot(x, -y)


Stacking subplots in two directions




Quiver plot

Info

More in-depth quiver documentation and functions.


Code Block
languagepy
titleSimple quiver plot
collapsetrue
import numpy as np
import matplotlib.pyplot as plt

# X and Y define the arrow locations
X, Y = np.meshgrid(np.arange(0, 2 * np.pi, .2), np.arange(0, 2 * np.pi, .2))

# U and V define the arrow directions, respectively in x- and y-direction
U = np.cos(X)
V = np.sin(Y)

# Call signature: quiver([X, Y], U, V, [C]), where C optionally sets the color
plt.quiver(X, Y, U, V)
plt.title('Simple quiver plot')
plt.show()

Note

The plot autoscaling does not take into account the arrows, so those on the boundaries may reach out of the picture. This is not an easy problem to solve in a perfectly general way. The recommended workaround is to manually set the Axes limits in such a case. An example showing autoscaling vs manually is shown below.


Code Block
languagepy
titleQuiver autoscaling vs manually set axes
collapsetrue
import numpy as np
import matplotlib.pyplot as plt

# X and Y define the arrow locations
# This setup gives us 10 arrows in width and height, as our interval is from -5 to 5 with step 1
X = np.arange(-5, 5, 1)
Y = np.arange(-5, 5, 1)

# U and V define the arrow directions, respectively in x- and y-direction
U, V = np.meshgrid(3*X, 3*Y)

plt.figure()

plt.subplot(121)
plt.quiver(X, Y, U, V)
plt.title('Only autoscaling')

plt.subplot(122)
plt.quiver(X, Y, U, V)
# Here we specify the axes. How much extra space you need depends on the arrow size and direction,
# and must therefore be adapted each time
plt.axis([-6.5, 5.5, -6.5, 5.5])
plt.title('Manually set axes')

plt.show()