5 Best Ways to Plot Points on the Surface of a Sphere in Python’s Matplotlib

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πŸ’‘ Problem Formulation: Visualizing data in three dimensions is a common challenge in computational fields. When plotting on a sphere’s surface, the input includes spherical coordinates or Cartesian coordinates, and the desired output is a graphical representation of those points on the sphere. This article guides you through different methods to achieve this using Python and Matplotlib.

Method 1: Using Scatter in 3D Plot

Matplotlib’s mplot3d toolkit extends the framework to include 3D plotting capabilities. We can plot points on a sphere’s surface using the Axes3D.scatter() function. This method requires the conversion of spherical coordinates to Cartesian coordinates, which are then passed to the scatter function to render the 3D plot.

Here’s an example:

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

# Define sphere parameters
r = 1
phi, theta = np.mgrid[0:2*np.pi:100j, 0:np.pi:50j]

# Convert to Cartesian coordinates
x = r * np.sin(theta) * np.cos(phi)
y = r * np.sin(theta) * np.sin(phi)
z = r * np.cos(theta)

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x, y, z)

plt.show()

The output is a 3D graph showing points distributed across the sphere’s surface.

This snippet first defines the sphere’s parameters and generates a grid of points in spherical coordinates (phi, theta). These are converted to Cartesian coordinates (x, y, z) and plotted using a 3D scatter plot utilising Matplotlib functions.

Method 2: Plotting with a Parametric Surface

The parametric surface method involves explicitly mapping spherical coordinates to their Cartesian counterparts and using Matplotlib’s plot_surface function. This method excels in creating a continuous surface instead of discrete points and offers the ability to customize transparency and colormap for improved aesthetics.

Here’s an example:

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

phi, theta = np.linspace(0, 2 * np.pi, 100), np.linspace(0, np.pi, 100)
phi, theta = np.meshgrid(phi, theta)
x = np.sin(theta) * np.cos(phi)
y = np.sin(theta) * np.sin(phi)
z = np.cos(theta)

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(x, y, z, color='b', alpha=0.5)

plt.show()

The output displays a semitransparent sphere with a bluish tint.

After setting up the meshgrid of spherical coordinates, this code converts them into Cartesian and plots the points using a surface plot. The sphere’s properties, such as color and transparency, are customizable.

Method 3: Wireframe Sphere

To visualize the lattice structure of a sphere, the wireframe method is most suited. It creates a 3D wireframe representation which emphasizes the globe-like structure without filling the surface. Matplotlib’s plot_wireframe is used to realize this visualization.

Here’s an example:

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

theta, phi = np.linspace(0, 2 * np.pi, 100), np.linspace(0, np.pi, 100)
theta, phi = np.meshgrid(theta, phi)
r = 1
x = r * np.sin(phi) * np.cos(theta)
y = r * np.sin(phi) * np.sin(theta)
z = r * np.cos(phi)

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_wireframe(x, y, z, color='r')

plt.show()

The resulting output is a 3D red wireframe sphere.

Similarly to Method 2, this wireframe sphere uses meshgrid coordinates in spherical form converted to Cartesian and plotted with plot_wireframe. The wireframe presents a skeletal view of the sphere.

Method 4: Custom Point Distribution on Sphere

For cases where specific distribution of points on a sphere is required, such as a Fibonacci lattice or uniformly distributed points, custom algorithms can be created to produce these patterns. Afterward, points are plotted using the scatter function.

Here’s an example:

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

def fibonacci_sphere(samples=1):

    points = []
    phi = np.pi * (3. - np.sqrt(5.))  # golden angle in radians
    
    for i in range(samples):
        y = 1 - (i / float(samples - 1)) * 2
        radius = np.sqrt(1 - y * y)
        
        theta = phi * i
        
        x = np.cos(theta) * radius
        z = np.sin(theta) * radius
        
        points.append((x, y, z))

    return points

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
points = fibonacci_sphere(200)
x, y, z = zip(*points)
ax.scatter(x, y, z)

plt.show()

The plot reveals points evenly distributed across the sphere’s surface.

Using the concept of the golden angle, this code snip generates a Fibonacci lattice of points on the sphere, which is theorized to be one of the most uniform distributions possible. These points are then plotted on a sphere in 3D.

Bonus One-Liner Method 5: Using the Quiver Function

The quiver function offers a quick one-liner for adding vector representations to the surface of a sphere. While not strictly plotting points, it provides a unique perspective on depicting directional data on a spherical surface.

Here’s an example:

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

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x, y, z = np.meshgrid(np.array([0]), np.array([0]), np.array([0]))
u, v, w = np.array([1]), np.array([1]), np.array([1])
ax.quiver(x, y, z, u, v, w)

plt.show()

An arrow originates from the center and touches the sphere’s surface, indicating direction.

This concise code uses quiver to add a single vector, visually striking for representing a specific point or direction on the sphere.

Summary/Discussion

  • Method 1: Scatter in 3D Plot. Ideal for plotting discrete data points. Each point can be styled individually, but can become cluttered with many points.
  • Method 2: Parametric Surface. Best for a continuous sphere surface. Provides a solid aesthetic but may obscure inner structures.
  • Method 3: Wireframe Sphere. Useful for a structural perspective. Does not represent surface continuity and can become visually complex.
  • Method 4: Custom Point Distribution. Suitable for specialized point distributions like Fibonacci lattices. May require additional code to generate the specific patterns.
  • Method 5: Using Quiver Function. Quick and effective for directional data. Represents vectors rather than points, offering different visual insights.