I am trying to plot 3d Surface plots using code from this site using matplotlib:
X,Y and Z are obtained as below:
from math import pi
from numpy import cos, meshgrid
alpha = 0.7
phi_ext = 2 * pi * 0.5
def flux_qubit_potential(phi_m, phi_p):
return 2 + alpha - 2 * cos(phi_p)*cos(phi_m) - alpha * cos(phi_ext - 2*phi_p)
phi_m = linspace(0, 2*pi, 100)
phi_p = linspace(0, 2*pi, 100)
X,Y = meshgrid(phi_p, phi_m)
Z = flux_qubit_potential(X, Y).T
And 3d plotting is done with following code:
from mpl_toolkits.mplot3d.axes3d import Axes3D
fig = plt.figure(figsize=(14,6))
# `ax` is a 3D-aware axis instance, because of the projection='3d' keyword argument to add_subplot
ax = fig.add_subplot(1, 2, 1, projection='3d')
p = ax.plot_surface(X, Y, Z, rstride=4, cstride=4, linewidth=0)
# surface_plot with color grading and color bar
ax = fig.add_subplot(1, 2, 2, projection='3d')
p = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=0, antialiased=False)
cb = fig.colorbar(p, shrink=0.5)
However, if I replace X,Y and Z by my x,y,z 3d data (sample give below), there is an error that Z has to be 2 dimensional. How can I plot with usual x,y,z values, as following:
x y z
0 12 0 0.1
1 13 1 0.8
2 14 3 1.0
3 16 4 1.2
4 18 4 0.7
This is because, in my understanding, to draw a surface you need to form a polygon mesh. To draw a 3d surface, you need to have small squares, for example, on the xy-plane and then have 1 corresponding z value for all the x-y points. The smaller the area of the square means finer mesh-grid and better resolution(smooth-looking surface.) Now if you have an arbitrary set of xyz points, how matplotlib can determine which surface to draw. That is why a mesh is required. You can of course plot 3d scatter or line plots with your data.