Although matplotlib is mainly focused on plotting and mainly in two dimensions, there are different extensions that enable us to plot over geographical maps, to integrate more with Excel, and plot in 3D. These extensions are called toolkits in the matplotlib world. A toolkit is a collection of specific functions focused on one topic, such as plotting in 3D.
Popular toolkits are Basemap, GTK Tools, Excel Tools, Natgrid, AxesGrid, and mplot3d.
We will explore more about mplot3d in this recipe. Toolkit mpl_toolkits.mplot3d provides some basic 3D plotting. Plots supported are scatter, surf, line, and mesh plots. Although this is not the best 3D plotting library, it comes with matplotlib, and we are already familiar with the interface.
Basically, we still need to create a figure and add the desired axes to it. The difference is that we are now specifying a 3D projection for the figure and the axes we are adding are Axes3D.
Now, we can use almost the same functions for plotting. Of course, the difference is the argument, for we now have three axes, which we need to provide data for.
For example, the mpl_toolkits.mplot3d.Axes3D.plot function specifies the xs, ys, zs,and zdir arguments. All others are transferred directly to matplotlib.axes.Axes.plot. We will explain these specific arguments:
xs,ys: These are the coordinates for the X and Y axes.zs: This is the value(s) for the Z axis. There can be one value for all the points, or one for each point.zdir: This chooses what the Z-axis dimension (usually this iszs, but can also bexsorys) will be.Note
There is a method
rotate_axesin thempl_toolkits.mplot3d.art3dmodule that contains 3D artist code and functions to convert 2D artists into 3D, which can be added toAxes3Dto reorder coordinates so that the axes are rotated withzdiralong. The default value isz. Prepending the axis with a '-' does the inverse transform, sozdircan bex,-x,y,-y,z, or–z.
This is the code to demonstrate the concept explained here:
import random
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from mpl_toolkits.mplot3d import Axes3D
mpl.rcParams['font.size'] = 10
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for z in [2011, 2012, 2013, 2014]:
xs = xrange(1,13)
ys = 1000 * np.random.rand(12)
color = plt.cm.Set2(random.choice(xrange(plt.cm.Set2.N)))
ax.bar(xs, ys, zs=z, zdir='y', color=color, alpha=0.8)
ax.xaxis.set_major_locator(mpl.ticker.FixedLocator(xs))
ax.yaxis.set_major_locator(mpl.ticker.FixedLocator(ys))
ax.set_xlabel('Month')
ax.set_ylabel('Year')
ax.set_zlabel('Sales Net [usd]')
plt.show()This code produces the following figure:
We had to do the same prep work as in the 2D world. The difference here is that we needed to specify what "kind of backend". Then we generate random data for supposedly 4 years of sale (2011-2014).
We needed to specify Z values to be the same for the 3D axis.
We picked the color randomly from the color-map set, and then we associated each Z-order collection of xs, ys pairs that would be used to render the bar series.
The other plot from 2D matplotlib is available here—for example, scatter()—which has a similar interface to plot(), but with increased size of the point marker. We are also familiar with contour, contourf, and bar.
New types that are available only in 3D are wireframe, surface, and tri-surface plots.
For example, this code example plots a tri-surface plot of popular Pringle functions or, more mathematically, a hyperbolic paraboloid:
from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm import matplotlib.pyplot as plt import numpy as np n_angles = 36 n_radii = 8 # An array of radii # Does not include radius r=0, this is to eliminate duplicate points radii = np.linspace(0.125, 1.0, n_radii) # An array of angles angles = np.linspace(0, 2*np.pi, n_angles, endpoint=False) # Repeat all angles for each radius angles = np.repeat(angles[...,np.newaxis], n_radii, axis=1) # Convert polar (radii, angles) coords to cartesian (x, y) coords # (0, 0) is added here. There are no duplicate points in the (x, y) plane x = np.append(0, (radii*np.cos(angles)).flatten()) y = np.append(0, (radii*np.sin(angles)).flatten()) # Pringle surface z = np.sin(-x*y) fig = plt.figure() ax = fig.gca(projection='3d') ax.plot_trisurf(x, y, z, cmap=cm.jet, linewidth=0.2) plt.show()
The code will give the following output:
