Three dimensional plot of unequal arrays using matplotlib

Question

I have two numpy arrays representing x and ycoordinates in two dimensions. These (x, y) positions give coordinates of points in a 2D plane. Examples of the arrays x and y are given below.

x = np.array([1.0, 4.0, 3.0, 5.0])
y = np.array([1.5, 4.2, 3.5, 5.2])

In the above example, we have coordinates of four two dimensional (x,y) points [i.e.(1.0, 1.5), (4.0, 4.2), (3.0, 3.5), (5.0, 5.2)].

Starting from each of these four two dimensional (x, y) points, I have arrays which represent z coordinates. For this example, I will have four z arrays. Let's consider examples given below.

z1 = np.array([3.1, 3.6, 7.8, 9.2, 11.2, 11.7])
z2 = np.array([4.2, 4.8, 5.9, 10.2])
z3 = np.array([2.1, 2.5, 2.8, 3.5, 4.9])
z4 = np.array([2.0, 4.5, 5.7, 7.2, 7.8, 8.1, 9.2, 15.6])

The different z arrays (z1, z2, z3 and z4) are expected to be of unequal lengths.

I want to have a three dimensional plot of these z arrays (starting from the mentioned (x, y) positions ) using matplotlib. An example of the kind of plot that I am expecting can be found below. I tried to start with this:

Axes3D.scatter(xs=x, ys=y, zs=z1, zdir='z)

But, I get the following error:

TypeError: unbound method scatter() must be called with Axes3D instance as first argument (got nothing instead)

How can make the desired three dimensional plot for the simple example (x, y and z arrays) that I have given in this question (I am using python 2.7)?

keepAlive · Accepted Answer · 2018-11-06 00:45:26Z

3

What about doing

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np

x = np.array([1.0, 4.0, 3.0, 5.0])
y = np.array([1.5, 4.2, 3.5, 5.2])

z1 = np.array([3.1, 3.6, 7.8, 9.2, 11.2, 11.7])
z2 = np.array([4.2, 4.8, 5.9, 10.2])
z3 = np.array([2.1, 2.5, 2.8, 3.5, 4.9])
z4 = np.array([2.0, 4.5, 5.7, 7.2, 7.8, 8.1, 9.2, 15.6])

zs = [
    z1, z2, z3, z4
]

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

for i, x_i in enumerate(x):
    for z_ij in zs[i]:
        ax.scatter(xs=x_i, ys=y[i], zs=z_ij, zdir='z')
plt.show()

Note that we could have done the job without numpy, since we are actually only involving list-contained scalars.

edited Nov 6, 2018 at 0:45

answered Nov 6, 2018 at 0:06

keepAlive

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1 Comment

keepAlive Over a year ago

Any question @SiddTheKid ?

kuzand · Accepted Answer · 2018-11-06 08:03:45Z

Late to the game, but here's another way to do it without the for loops.

import matplotlib.pyplot as plt
import numpy as np

x = np.array([1.0, 4.0, 3.0, 5.0])
y = np.array([1.5, 4.2, 3.5, 5.2])

z1 = np.array([3.1, 3.6, 7.8, 9.2, 11.2, 11.7])
z2 = np.array([4.2, 4.8, 5.9, 10.2])
z3 = np.array([2.1, 2.5, 2.8, 3.5, 4.9])
z4 = np.array([2.0, 4.5, 5.7, 7.2, 7.8, 8.1, 9.2, 15.6])

zs = [z1, z2, z3, z4]
zi_len = [z.shape[0] for z in zs]  # [6, 4, 5, 8]

x_ = np.repeat(x, zi_len)  # [1.0, 1.0, 1.0, 1.0, 1.0,  1.0,  4.0, 4.0, 4.0, 4.0,  3.0, 3.0, 3.0, 3.0, 3.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0]
y_ = np.repeat(y, zi_len)  # [1.5, 1.5, 1.5, 1.5, 1.5,  1.5,  4.2, 4.2, 4.2, 4.2,  3.5, 3.5, 3.5, 3.5, 3.5, 5.2, 5.2, 5.2, 5.2, 5.2, 5.2, 5.2, 5.2]
z_ = np.concatenate(zs)    # [3.1, 3.6, 7.8, 9.2, 11.2, 11.7, 4.2, 4.8, 5.9, 10.2, 2.1, 2.5, 2.8, 3.5, 4.9, 2.0, 4.5, 5.7, 7.2, 7.8, 8.1, 9.2, 15.6]

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(xs=x_, ys=y_, zs=z_, zdir='z', c=z_)
plt.show()

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