Creating 3D projections in Matplotlib is very simple. You need to import Axes3D, which registers the "3d" projection. Then create a subplot setting the projection to "3d". This returns an Axes3DSubplot object, which you can use to call plot_surface, giving x, y, and z coordinates, plus optional attributes.
Import Axes3D:
from mpl_toolkits.mplot3d import << your code goes here >>
Create the projection:
x = np.linspace(-5, 5, 50)
y = np.linspace(-5, 5, 50)
X, Y = np.meshgrid(x, y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)
Here, linspace returns evenly spaced numbers over a specified interval. meshgrid returns coordinate matrices from coordinate vectors.
Finally, we will create the figure, the subplot, and call the plot_surface with the coordinates we created earlier:
figure = plt.figure(1, figsize = (12, 4))
subplot3d = plt.subplot(111, projection='3d')
surface = subplot3d.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=matplotlib.cm.coolwarm, linewidth=0.1)
plt.show()
The figure module provides the top-level Artist , the Figure , which contains all the plot elements. The figsize marks the width and height of the plot.
Note - Having trouble with the assessment engine? Follow the steps listed here
Loading comments...