One amazing feature of Jupyter Notebooks is the magic command %matplotlib notebook which, if ran at the top of your notebook, draws all your plots in an interactive window. On the other hand, they are more complicated since we are so used to 2D plots. In some ways 3D plots are more natural for us to work with since we live in a 3D world. All the functions you know and love such as ax.plot() and ax.scatter() accept the same keyword arguments but they now also accept three positional arguments – X, Y and Z. Then you need to pass projection='3d' which tells matplotlib it is a 3D plot.įrom now on everything is (almost) the same as 2D plotting. If you just want a single Axes, pass 111 to indicate it’s 1 row, 1 column and you are selecting the 1st one. You set up your Figure in the standard way fig = plt.figure()Īnd add a subplots to that figure using the standard fig.add_subplot() method. This imports a 3D Axes object on which a) you can plot 3D data and b) you will make all your plot calls with respect to. If you are not comfortable with Figure and Axes plotting notation, check out this article to help you.īesides the standard import matplotlib.pyplot as plt, you must also from mpl_toolkits.mplot3d import axes3d. If you are used to plotting with Figure and Axes notation, making 3D plots in matplotlib is almost identical to creating 2D ones. ![]() Just execute the code and look at the generated “plot.png” file: Try it yourself with our interactive Python shell. # Plot using Axes notation and standard function callsĪwesome! You’ve just created your first 3D plot! Don’t worry if that was a bit fast, let’s dive into a more detailed example. Plot it using Axes notation and standard function callsĪx = fig.add_subplot(111, projection='3d').Initialize your Figure and Axes3D objects.Next, we call the legend() function to display the legend.In addition to import matplotlib.pyplot as plt and calling plt.show(), to create a 3D plot in matplotlib, you need to: Here, we want to show the difference in sizes between points that represent low, average, and high reliability. The code above is creating three additional plots so that, when the legends are created, it identifies three unique labels. savefig ( 'scatter3d.png', dpi = 300, bbox_inches = 'tight' ) plt. ![]() legend ( loc = 'upper right', bbox_to_anchor = ( 1, 0.96 ), scatterpoints = 1, ncol = 1, fontsize = 15 ) plt. max () * 200, label = 'High reliability' ) ax3d. mean () * 200, label = 'Avg reliability' ) ax3d. min () * 200, label = 'Low reliability' ) ax3d. Take a look at this page on the Lab Manual for more details on color bars, and how to make them. Once we create the plot itself, we will make the colorbar to show how the utility’s infrastructure NPC regret, NPC_regret, varies with the three values on the axes. Feel free to experiment with different scaling factors! Points of these sizes will be nearly invisible on the plot we need scale them up so they are visible on the 3D plot. This is scaling the REL values up by a factor of 200, since these values (as they are) are between the values of 0-1. The last line of code here is important: by varying the values of the azim parameter, you will be able to view the 3D plot from different angles. view_init ( elev = 30, azim = 45 ) # Set elevation and azimuth angles set_zlabel ( 'Infrastructure NPC \n (preferred) $\longrightarrow$' ) ax3d. ![]() set_ylabel ( 'Transfer trigger \n $\longleftarrow$ (increased use)', labelpad = 10 ) ax3d. set_xlabel ( 'Infrastructure trigger \n (increased use) $\longrightarrow$', labelpad = 10 ) ax3d. ![]() S = REL * 200, alpha = 0.8 ) # Set labels for the three axes ax3d. scatter ( inf_trigger, tt_trigger, INPC, c = normalize_regret ( INPC_regret ), cmap = 'viridis_r', \
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