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%matplotlib inline
The aim of this course is to enable you to construct, modify and save matplotlib plots.
Matplotlib is a Python package used widely throughout the scientific Python community to produce high quality 2D publication graphics. It transparently supports a wide range of output formats including PNG (and other raster formats), PostScript/EPS, PDF and SVG and has interfaces for all of the major desktop GUI (Graphical User Interface) toolkits.
Matplotlib comes with a convenience sub-package called pyplot. For consistency with the wider maptlotlib community, this should always be imported as plt:
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import matplotlib.pyplot as plt
Learning Outcomes: by the end of this section, you will be able to understand the purpose of the matplotlib Figure object.
At the heart of every matplotlib plot is the "Figure". The Figure is the top level concept that can be drawn to one of the many output formats, or simply just to screen.
Let's create our first Figure using pyplot, and then show it:
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fig = plt.figure()
plt.show()
On its own, drawing the Figure is uninteresting and will result in an empty piece of paper (that's why we didn't see anything above).
Other visible elements are added to a Figure to make a plot. All visible items in Matplotlib are instances of the Artist class : The Figure and Axes are both types of Artist.
To start with we can draw an Axes artist in the Figure, to represent our data space. The most basic Axes is rectangular and has tick labels and tick marks. Multiple Axes artists can be placed on a Figure.
Let's go ahead and create a Figure with a single Axes, and show it using pyplot:
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ax = plt.axes()
plt.show()
Matplotlib's pyplot module makes the process of creating graphics easier by allowing us to skip some of the tedious object construction. For example, we did not need to manually create the Figure with plt.figure because it was implicit that we needed a Figure when we created the Axes.
Under the hood matplotlib still had to create a Figure; we just didn't need to capture it into a variable. We can access the created object with the "state" functions found in pyplot called gcf and gca.
Learning Outcomes: by the end of this section, you will be able to add data to a matplotlib axes and control its presentation.
As has already been mentioned, most of your time building a graphic in matplotlib will be spent on the Axes. Whilst the matplotlib documentation for the Axes is very detailed, it is also rather difficult to navigate (though this is an area of ongoing improvement).
As a result, it is often easier to find new plot types by looking at the pyplot module's documentation.
The first and most common Axes method is plot. Go ahead and look at the plot documentation from the following sources:
Plot can be used to draw one or more lines in axes data space:
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ax = plt.axes()
line1, = ax.plot([0, 1, 2, 1.5], [3, 1, 2, 4])
plt.show()
1. Manually set the x and y limits to $[0.5, 2]$ and $[1, 5]$ respectively.
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2. Define a margin such that there is 10% whitespace inside the axes around the drawn line (Hint: numbers to margins are normalised such that 0% is 0.0 and 100% is 1.0).
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3. Set a 10% margin on the Axes with the lower y limit set to 0. (Note: order is important here)
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If we want to create a plot in its simplest form, without any modifications to the Figure or Axes, we can leave out the creation of artist variables. Our simple line example then becomes:
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plt.plot([0, 1, 2, 1.5], [3, 1, 2, 4])
plt.show()
The simplicity of this example shows how visualisations can be produced quickly and easily with matplotlib, but it is worth remembering that for full control of the Figure and Axes artists we can mix the convenience of pyplot with the power of matplotlib's object oriented design.
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Learning Outcomes: by the end of this section, you will be able to understand how to produce a single figure containing multiple axes.
Subplot is matplotlib's description of adding multiple axes to a single Figure.
Matplotlib makes it relatively easy to add more than one Axes object to a Figure. The Figure.add_subplot() method, which is wrapped by the pyplot.subplot() function, adds an Axes in the grid position specified. To compute the position, we tell matplotlib the number of rows and columns (respectively) to divide the figure into, followed by the index of the axes to be created (1 based).
For example, to create an axes grid with two columns, the grid specification would be plt.subplot(1, 2, <plot_number>).
The left-hand Axes is plot number 1, created with subplot(1, 2, 1), and the right-hand one is number 2, subplot(1, 2, 2) :
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ax_left = plt.subplot(1, 2, 1)
plt.plot([2,1,3,4])
plt.title('left = #1')
ax_right = plt.subplot(1, 2, 2)
plt.plot([4,1,3,2])
plt.title('right = #2')
plt.show()
Likewise, for plots above + below one another we would use two rows and one column, as in subplot(2, 1, <plot_number>).
Now let's expand our grid to two rows and three columns, and place one set of axes on the top right (grid specification (2, 3, 3)) and another on the bottom left (grid specification (2, 3, 4))
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top_right_ax = plt.subplot(2, 3, 3, title='#3 = top-right')
bottom_left_ax = plt.subplot(2, 3, 4, title='#4 = bottom-left')
plt.show()
Exercise 3 continued: Copy the answer from the previous task (plotting $y=sin(x)$ and $y=cos(x)$) and add the appropriate plt.subplot calls to create a Figure with two rows of Axes, one showing $y=sin(x)$ and the other showing $y=cos(x)$.
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import numpy as np
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x = np.linspace(-180, 180, 60)
y = np.linspace(-90, 90, 30)
x2d, y2d = np.meshgrid(x, y)
data = np.cos(3 * np.deg2rad(x2d)) + np.sin(2 * np.deg2rad(y2d))
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plt.contourf(x, y, data)
plt.show()
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plt.imshow(data, extent=[-180, 180, -90, 90],
interpolation='nearest', origin='lower')
plt.show()
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plt.pcolormesh(x, y, data)
plt.show()
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plt.scatter(x2d, y2d, c=data, s=15)
plt.show()
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plt.bar(x, data.sum(axis=0), width=np.diff(x)[0])
plt.show()
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plt.plot(x, data.sum(axis=0), linestyle='--',
marker='d', markersize=10, color='red')
plt.show()
Matplotlib has convenience functions for the addition of plot elements such as titles, legends, colorbars and text based annotation.
The suptitle pyplot function allows us to set the title of a Figure, and the set_title method on an Axes allows us to set the title of an individual Axes. Additionally, an Axes has methods named set_xlabel and set_ylabel to label the respective x and y axes. Finally, we can add text, located by data coordinates, with the Axes text method:
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fig = plt.figure()
ax = plt.axes()
# Adjust the created axes so its topmost extent is 0.8 of the figure.
fig.subplots_adjust(top=0.8)
fig.suptitle('Figure title', fontsize=18, fontweight='bold')
ax.set_title('Axes title', fontsize=16)
ax.set_xlabel('The X axis')
ax.set_ylabel('The Y axis $y=f(x)$', fontsize=16)
ax.text(0.5, 0.5, 'Text centered at (0.5, 0.5)\nin data coordinates.',
horizontalalignment='center', fontsize=14)
plt.show()
The creation of a legend is as simple as adding a "label" to lines of interest. This can be done in the call to plt.plot and then followed up with a call to plt.legend:
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x = np.linspace(-3, 7, 200)
plt.plot(x, 0.5*x**3 - 3*x**2, linewidth=2,
label='$f(x)=0.5x^3-3x^2$')
plt.plot(x, 1.5*x**2 - 6*x, linewidth=2, linestyle='--',
label='Gradient of $f(x)$', )
plt.legend(loc='lower right')
plt.grid()
plt.show()
Colorbars are created with the plt.colorbar function:
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x = np.linspace(-180, 180, 60)
y = np.linspace(-90, 90, 30)
x2d, y2d = np.meshgrid(x, y)
data = np.cos(3 * np.deg2rad(x2d)) + np.sin(2 * np.deg2rad(y2d))
plt.contourf(x, y, data)
plt.colorbar(orientation='horizontal')
plt.show()
Matplotlib comes with powerful annotation capabilities, which are described in detail at http://matplotlib.org/users/annotations_intro.html.
The annotation's power can mean that the syntax is a little harder to read, which is demonstrated by one of the simplest examples of using annotate:
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x = np.linspace(-3, 7, 200)
plt.plot(x, 0.5*x**3 - 3*x**2, linewidth=2)
plt.annotate('Local minimum',
xy=(4, -18),
xytext=(-2, -40), fontsize=15,
arrowprops={'facecolor': 'black', 'headlength': 10})
plt.grid()
plt.show()
Learning Outcomes: by the end of this section, you will be able to save a plot to a file.
Matplotlib allows you to specify a "backend" to drive rendering the Figure. The backend includes the graphical user interface (GUI) library to use, and the most used backend (as it is normally the default one) is the "TkAgg" backend. When plt.show() is called, this backend pops up a Figure in a new TkInter window, which is rendered by the anti-grain graphics library (also known as "agg"). Generally, the most common reason to want to change backends is for automated Figure production on a headless server. In this situation, the "agg" backend can be used:
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
Note: The backend must be chosen before importing pyplot for the first time, unless the force keyword is added.
Non-interactive backends such as the "agg" backend will do nothing when plt.show() is called - this is because there is nowhere (no graphical display) specified for a Figure to be displayed.
To save a Figure programmatically the savefig function can be used from any backend:
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plt.plot(range(10))
plt.savefig('simple.svg')
For graphical backends, showing and subsequently closing the window of a Figure results in the Figure being cleared from the matplotlib system. This is not the case for plt.savefig, which typically should be called before plt.show.
It is also possible to manually close Figures without showing them by using the plt.close function. This could be called to remove the current Figure after saving it with plt.savefig on the occasion where not clearing the Figure might interfere with subsequent plots to be created.