Python

 

 

 

Plot3D

 

Like 2D plot, in 3D as well you would have most of functions that you can get from Matlab.

 

 

 

 

Draw a line between two points

 

This show how to draw a single line from (-1,-2,-3) to (1,2,3)

 

Filename : Line3D.py

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import mpl_toolkits.mplot3d.axes3d as axes3d

 

fig = plt.figure()

 

ax = fig.add_subplot(1,1,1, projection='3d')

 

ax.plot([-1,1],[-2,2],[-3,3],'r--')

 

ax.set_xlim3d(-5, 5)

ax.set_ylim3d(-5, 5)

ax.set_zlim3d(-5, 5)

 

plt.show()

 
Result :

 

 

 

Drawing lines connecting multiple points

 

This show how to draw the line traces connecting multiple points

 

Filename : Line3D.py

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import mpl_toolkits.mplot3d.axes3d as axes3d

 

fig = plt.figure()

 

ax = fig.add_subplot(1,1,1, projection='3d')

 

xList = [0,1,2,3,4]

yList = [-1,1,-1,1,-1]

zList = [2,-2,2,-2,2]

 

ax.plot(xList, yList, zList,'b')

 

ax.set_xlim3d(-5, 5)

ax.set_ylim3d(-5, 5)

ax.set_zlim3d(-5, 5)

 

plt.show()

 
Result :

 

 

 

3D Parametric Plot

 

This show how to draw 3d Parameteric Plot

 

Filename : Line3D.py

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import mpl_toolkits.mplot3d.axes3d as axes3d

 

fig = plt.figure()

 

ax = fig.add_subplot(1,1,1, projection='3d')

 

t = np.linspace(0, 4 * np.pi, 100)

x = t

y = np.cos(t)

z = np.sin(t)

 

ax.plot(x, y, z,'b')

 

ax.set_xlim3d(0, t.max())

ax.set_ylim3d(-2, 2)

ax.set_zlim3d(-2, 2)

 

plt.show()

 
Result :

 

 

 

Surface Plot

 

This show how to draw 3d Parameteric Plot

 

Filename : plot3d_surce_01.py

import numpy as np

import matplotlib.pyplot as plt

 

# generate some sample data

X, Y = np.meshgrid(np.linspace(-10, 10, 100), np.linspace(-10, 10, 100))

Z = np.sin(np.sqrt(X**2 + Y**2))

 

# create the plot

fig = plt.figure()

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

ax.plot_surface(X, Y, Z, cmap='viridis')

 

# add labels and show the plot

ax.set_xlabel('X')

ax.set_ylabel('Y')

ax.set_zlabel('Z')

plt.show()

 
Result :

 

 

 

Contour Plot

 

This show how to draw 3d Contour Plot

 

Filename : plot3d_contour_01.py

import numpy as np

import matplotlib.pyplot as plt

 

# generate some sample data

X, Y = np.meshgrid(np.linspace(-10, 10, 100), np.linspace(-10, 10, 100))

Z = np.sin(np.sqrt(X**2 + Y**2))

 

# create the plot

fig, ax = plt.subplots()

 

# plot filled contours

c = ax.contourf(X, Y, Z)

 

# add colorbar and labels

cb = fig.colorbar(c)

ax.set_xlabel('X')

ax.set_ylabel('Y')

 

plt.show()

 
Result :

 

 

Removing Surface Color

 

This show how to remove the surface color. Use contour() function instead of contourf().

 

Filename : plot3d_contour_01.py

import numpy as np

import matplotlib.pyplot as plt

 

# generate some sample data

X, Y = np.meshgrid(np.linspace(-10, 10, 100), np.linspace(-10, 10, 100))

Z = np.sin(np.sqrt(X**2 + Y**2))

 

# create the plot

fig, ax = plt.subplots()

 

# plot contours with single color or grayscale colormap

plt.contour(X, Y, Z, cmap='jet')

plt.colorbar()

 

# add labels

ax.set_xlabel('X')

ax.set_ylabel('Y')

 

plt.show()
Result :

 

 

Changing Contour Level

 

Filename : plot3d_contour_01.py

import numpy as np

import matplotlib.pyplot as plt

 

# generate some sample data

X, Y = np.meshgrid(np.linspace(-10, 10, 100), np.linspace(-10, 10, 100))

Z = np.sin(np.sqrt(X**2 + Y**2))

 

# create the plot

fig, ax = plt.subplots()

 

# plot contours with single color or grayscale colormap

levels = [-1.0, -0.5, 0, 0.5, 1.0]

plt.contour(X, Y, Z, levels=levels)

plt.colorbar()

 

# add labels

ax.set_xlabel('X')

ax.set_ylabel('Y')

 

plt.show()
Result :

 

 

Printing Contour Level

 

Filename : plot3d_contour_01.py

import numpy as np

import matplotlib.pyplot as plt

 

# generate some sample data

X, Y = np.meshgrid(np.linspace(-10, 10, 100), np.linspace(-10, 10, 100))

Z = np.sin(np.sqrt(X**2 + Y**2))

 

# create the plot

fig, ax = plt.subplots()

 

# plot contours with single color or grayscale colormap

levels = [-1.0, -0.5, 0, 0.5, 1.0]

contours = plt.contour(X, Y, Z, levels=levels)

plt.clabel(contours, inline=True, fontsize=8, fmt='%1.1f')

plt.colorbar()

 

# add labels

ax.set_xlabel('X')

ax.set_ylabel('Y')

 

plt.show()
Result :

 

 

 

Ticks, Tick Mark, Axis Label

 

This show how to format tick mark,tick label,axis label

 

Filename : Line3D.py

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import mpl_toolkits.mplot3d.axes3d as axes3d

 

fig = plt.figure()

 

ax = fig.add_subplot(1,1,1, projection='3d')

 

t = np.linspace(0, 4 * np.pi, 100)

x = t

y = np.cos(t)

z = np.sin(t)

 

ax.plot(x, y, z,'b')

 

ax.set_xlim3d(0, t.max())

ax.set_ylim3d(-2, 2)

ax.set_zlim3d(-2, 2)

 

ax.set_xlabel('x', fontsize=15, rotation=0)

ax.set_ylabel('y', fontsize=15, rotation=0)

ax.set_zlabel('z', fontsize=15, rotation=90)

 

ax.xaxis.set_ticks([0,np.pi,2*np.pi,3*np.pi,4*np.pi])

ax.set_xticklabels([0,'$\pi$','2$\pi$','3$\pi$','4$\pi$'])

 

ax.yaxis.set_ticks([-2,-1,0,1,2])

ax.zaxis.set_ticks([-2,-1,0,1,2])

 

plt.show()

 
Result :

 

 

 

Turn Off Axis, Axis Labels, Grid

 

Filename : Line3D.py

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import mpl_toolkits.mplot3d.axes3d as axes3d

 

fig = plt.figure()

 

ax = fig.add_subplot(1,1,1, projection='3d')

 

t = np.linspace(0, 4 * np.pi, 100)

x = t

y = np.cos(t)

z = np.sin(t)

 

ax.plot(x, y, z,'b')

 

ax.set_xlim3d(0, t.max())

ax.set_ylim3d(-2, 2)

ax.set_zlim3d(-2, 2)

 

ax.set_xlabel('x', fontsize=15, rotation=0)

ax.set_ylabel('y', fontsize=15, rotation=0)

ax.set_zlabel('z', fontsize=15, rotation=90)

 

ax.xaxis.set_ticks([0,np.pi,2*np.pi,3*np.pi,4*np.pi])

ax.set_xticklabels([0,'$\pi$','2$\pi$','3$\pi$','4$\pi$'])

 

ax.yaxis.set_ticks([-2,-1,0,1,2])

ax.zaxis.set_ticks([-2,-1,0,1,2])

ax.grid(False)

ax.axis('off')

ax.set_xticks([])

ax.set_yticks([])

ax.set_zticks([])

 

plt.show()

 
Result :

 

 

 

Color map

 

For most of 3D surface plot, you may not need to worry much about the colormap because the default color map would be good enough unless you have any specific preferance of your own. However for the 3D surface plot in spherical coordinate you may want to apply different colormap. In default colormap in 3d surface plot, the surface color changes as the value in Z axis varies, but in spherical plot it would be more meaning to change color of the surface as the value in Radial axis varies. This is an example of showing the color variation along with radial axis (not along with Z axis).

 

 

cmap in Spherical Coordinate

 

Filename : plot3d_spherical_color_01.py

import numpy as np

import matplotlib.pyplot as plt

import matplotlib.cm as cm

from mpl_toolkits.mplot3d import Axes3D

 

# Generate spherical coordinates

phi = np.linspace(0, np.pi, 100)

theta = np.linspace(0, 2 * np.pi, 100)

PHI, THETA = np.meshgrid(phi, theta)

R = (np.sin(PHI) ** 2) * np.cos(2 * THETA)

 

 

# Convert spherical coordinates to cartesian coordinates

X = R * np.sin(PHI) * np.cos(THETA)

Y = R * np.sin(PHI) * np.sin(THETA)

Z = R * np.cos(PHI)

 

# Normalize r for color encoding

normalized_r = np.abs(R)

normalized_r = (normalized_r - normalized_r.min()) / (normalized_r.max() - normalized_r.min())

 

# Define a colormap that maps normalized r to red or blue

cmap = cm.get_cmap('RdBu_r')

colors = cmap(normalized_r)

 

# Plot the surface

fig = plt.figure()

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

ax.plot_surface(X, Y, Z, facecolors=colors, linewidth=0)

ax.set_xlabel(r'$X$')

ax.set_ylabel(r'$Y$')

ax.set_zlabel(r'$Z$')

plt.show()

 
Result :

 

 

cmap in Cartesian Coordinate

 

If you apply the color map to cartesian surface plot, it become a little bit simple as below.

 

Filename : plot3d_surce_01_cmap.py

import numpy as np

import matplotlib.pyplot as plt

 

# generate some sample data

X, Y = np.meshgrid(np.linspace(-10, 10, 100), np.linspace(-10, 10, 100))

Z = np.sin(np.sqrt(X**2 + Y**2))

 

# create the plot

fig = plt.figure()

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

 

# create custom colormap

cmap = plt.cm.get_cmap('seismic')

 

# plot surface using custom colormap

ax.plot_surface(X, Y, Z, cmap=cmap)

 

# add labels and show the plot

ax.set_xlabel('X')

ax.set_ylabel('Y')

ax.set_zlabel('Z')

plt.show()

 
Result :

 

 

Setting Transparency

 

If you can change transparency of the surface color as shown below.

 

Filename : plot3d_surce_01_cmap.py

import numpy as np

import matplotlib.pyplot as plt

 

# generate some sample data

X, Y = np.meshgrid(np.linspace(-10, 10, 100), np.linspace(-10, 10, 100))

Z = np.sin(np.sqrt(X**2 + Y**2))

 

# create the plot

fig = plt.figure()

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

 

# plot surface with transparency

ax.plot_surface(X, Y, Z, cmap='viridis', alpha=0.5)

 

# add labels and show the plot

ax.set_xlabel('X')

ax.set_ylabel('Y')

ax.set_zlabel('Z')

plt.show()

 

 
Result :

 

 

 

Reference :

 

[1] Matplotlib tutorial (Nicolas P. Rougier)