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%matplotlib inline
# magic command that sets the backend of matplotlib to the 'inline' backend
import numpy as np
from scipy import sparse
import IPython.display as ipd
import matplotlib.pyplot as plt
Plot $y=x+1$, add labels to the axes and give your plot a title.
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plt.plot([1,2,3,4])
plt.xlabel('x')
plt.ylabel('y=x+1')
plt.axis([0, 4, 0, 4]);
plt.title('My first plot');
Create and plot an adjacency matrix for a graph with 10 nodes.
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row_ind = np.array([1, 3, 9, 7, 6, 9 ,2, 5, 8, 3, 7, 8])
col_ind = np.array([2, 5, 8, 3, 7, 8, 1, 3, 9, 7, 6, 9])
data = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
W = sparse.csr_matrix((data,(row_ind, col_ind)),shape=(10, 10)).toarray()
#np.nonzero(W-W.transpose())
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plt.spy(W,markersize=5);
plt.title('Adjacency Matrix W');
Find the degree of the nodes of the graph that you created in Example 1.2 and plot the degree histogram.
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d = W.sum(axis=0)
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n, bins, patches = plt.hist(d, 10, facecolor='g');
plt.xlabel('Degree')
plt.ylabel('Counts')
plt.title('Histogram of degrees')
plt.axis([0, 2, 0, 10])
plt.grid(True)
You can find a full list of all the functions provided by Pyplot here
So far we have created simple plots, like the ones you had to create for your first two assignments, using the Pyplot API. Pyplot provides a procedural interface similar to the one that you might have been used to from Matlab. However, the Object Oriented API of Matplotlib offers greater control and customization of the plots and therefore it is recommended to new Matplotlib users to learn how to use it.
Let's have a closer look at what happened when we called the pyplot.plot() before. Actually, two things happened in the background:
1) We created a figure instance describing the plot window and all of its properties.
2) We added an axes element within the figure. The axes element allows for the data to be arranged in x and y coordinates and provides a wide range of functions. The axes class will be your "best friend" for creating plots with the OO API and you can find out more about it here.
In order to fully understand Matplotlib you should have a look at this tutorial in order to learn about the underlying concepts of Artist, FigureCanvas and Renderer.
However, the basics in order to get you started are:
FigureCanvas is where your Figure will be drawn. When you use Pyplot to create a Figure instance, the FigureCanvas instance is created for you; you do not have to specify anything explicitly. In any other case you have to specify the FigureCanvas. This will also be the case when you want your Figure to appear in a Graphical User Interface (GUI).Figure will be the final "picture". A Figure may contain one or more plots. The Axes represents each individual plot.
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from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
# create a Figure instance
fig = Figure()
# Instanciate the FigureCanvas with the figure as argument
FigureCanvas(fig)
#print(type(fig.canvas))
ax = fig.add_subplot(111)
ax.plot([1, 2, 3],[1, 2, 3])
ax.set_title('My first title')
ax.grid(True)
ax.set_xlabel('x')
ax.set_ylabel('y=x')
fig.savefig('test')
Open the file test.png to see the plot that you created.
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ipd.Image(filename='test.png')
Now, change the title in your created plot as follows:
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ax.set_title('My new title')
fig.savefig('test')
Open again the file test.png. You will see that the title has been changed.
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ipd.Image(filename='test.png')
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%matplotlib notebook
#activate the nbagg backend, which enables interactivity within the Jupyter Notebook
A commonly used practice for production quality scripts is to use the matplotlib.pyplot just to create the figure and show calls, and use the OO API for everything else. There are two ways to do that:
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fig1 = plt.figure()
# Get the current axes instance on the current figure
ax1 = fig1.gca()
print(type(fig1.canvas))
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fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
print(type(fig2.canvas))
Now that you have created your figures you can plot on them using the Axes class. Here, as an example, we will plot a scatter plot in fig1 and a cool optical illusion created by Jake VanderPlas (the author of the Python Data Science Handbook) in fig2. If you are interested you can find more optical illusions created with Matplotlib by Jake VanderPlas here.
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#scatterplot
a = np.random.randint(1, 100, size=10)
b = np.random.randint(1, 100, size=10)
ax1.scatter(a, b, color ='red', edgecolors='black')
ax1.set_xscale("linear")
ax1.set_yscale("linear") #change to "log" and see what happens
ax1.set_xlim(0, 100)
ax1.set_ylim(0, 100)
ax1.set_title('My first scatterplot')
plt.show(fig1)
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#optical illusion by Jake VanderPlas
from matplotlib.collections import PatchCollection
squares = [plt.Rectangle((i + 0.2 * (j % 3), j), 1, 1)
for j in range(10) for i in range(0, 15, 2)]
ax2.add_collection(PatchCollection(squares, color='black'))
ax2.hlines(np.arange(11), 0, 16, color='gray')
ax2.axis('equal')
ax2.axis('off');
With axes.clear() you will clear the content of the figures fig1 and fig2.
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ax1.clear();
ax2.clear();
Now, you can spend some time to create you own plots with Matplotlib. Have fun!
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#your code here