Orion Constellation < Visualize Data with Python

I completed the “Orion Constellation” Graphique Cumulative Project in the "Visualize Data with Python course.
Comments, commits, approvals, welcome !

Project: Visualizing the Orion Constellation

In this project you are Dr. Jillian Bellovary, a real-life astronomer for the Hayden Planetarium at the American Museum of Natural History. As an astronomer, part of your job is to study the stars. You’ve recently become interested in the constellation Orion, a collection of stars that appear in our night sky and form the shape of Orion, a warrior God from ancient Greek mythology.

As a researcher on the Hayden Planetarium team, you are in charge of visualizing the Orion constellation in 3D using the Matplotlib function .scatter() . To learn more about the .scatter() you can see the Matplotlib documentation here.

You will create a rotate-able visualization of the position of the Orion’s stars and get a better sense of their actual positions. To achieve this, you will be mapping real data from outer space that maps the position of the stars in the sky

The goal of the project is to understand spatial perspective. Once you visualize Orion in both 2D and 3D, you will be able to see the difference in the constellation shape humans see from earth versus the actual position of the stars that make up this constellation.

1. Set-Up

The following set-up is new and specific to the project. It is very similar to the way you have imported Matplotlib in previous lessons.

  • Add %matplotlib notebook in the cell below. This is a new statement that you may not have seen before. It will allow you to be able to rotate your visualization in this jupyter notebook.

  • We will be using a subset of Matplotlib: matplotlib.pyplot . Import the subset as you have been importing it in previous lessons: from matplotlib import pyplot as plt

  • In order to see our 3D visualization, we also need to add this new line after we import Matplotlib: from mpl_toolkits.mplot3d import Axes3D

In [22]:

%matplotlib notebook
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

2. Get familiar with real data

Astronomers describe a star’s position in the sky by using a pair of angles: declination and right ascension. Declination is similar to longitude, but it is projected on the celestian fear. Right ascension is known as the “hour angle” because it accounts for time of day and earth’s rotaiton. Both angles are relative to the celestial equator. You can learn more about star position here.

The x , y , and z lists below are composed of the x, y, z coordinates for each star in the collection of stars that make up the Orion constellation as documented in a paper by Nottingham Trent Univesity on “The Orion constellation as an installation” found here.

Spend some time looking at x , y , and z , does each fall within a range?

In [28]:


x = [-0.41, 0.57, 0.07, 0.00, -0.29, -0.32,-0.50,-0.23, -0.23]
y = [4.12, 7.71, 2.36, 9.10, 13.35, 8.13, 7.19, 13.25,13.43]
z = [2.06, 0.84, 1.56, 2.07, 2.36, 1.72, 0.66, 1.25,1.38]

3. Create a 2D Visualization

Before we visualize the stars in 3D, let’s get a sense of what they look like in 2D.

Create a figure for the 2d plot and save it to a variable name fig . (hint: plt.figure() )

Add your subplot .add_subplot() as the single subplot, with 1,1,1 .(hint: add_subplot(1,1,1) )

Use the scatter function to visualize your x and y coordinates. (hint: .scatter(x,y) )

Render your visualization. (hint: plt.show() )

Does the 2D visualization look like the Orion constellation we see in the night sky? Do you recognize its shape in 2D? There is a curve to the sky, and this is a flat visualization, but we will visualize it in 3D in the next step to get a better sense of the actual star positions.

In [37]:

fig = plt.figure()
plt.scatter(x, y,
marker='*', color='orange',
label="Orion's stars")
plt.xlabel("X coordinates")
plt.ylabel('Y coordinates')
plt.title("2D Orion Constellation")


4. Create a 3D Visualization

Create a figure for the 3D plot and save it to a variable name fig_3d . (hint: plt.figure() )

Since this will be a 3D projection, we want to make to tell Matplotlib this will be a 3D plot.

To add a 3D projection, you must include a the projection argument. It would look like this:


Add your subplot with .add_subplot() as the single subplot 1,1,1 and specify your projection as 3d :

fig_3d.add_subplot(1,1,1,projection="3d") )

Since this visualization will be in 3D, we will need our third dimension. In this case, our z coordinate.

Create a new variable constellation3d and call the scatter function with your x , y and z coordinates.

Include z just as you have been including the other two axes. (hint: .scatter(x,y,z) )

Render your visualization. (hint plt.show() .)

In [44]:

fig_3d = plt.figure()
constellation3d = fig_3d.add_subplot(1,1,1, projection="3d")
marker = '*', color = 'orange',
label = "Orion's stars")
plt.xlabel('X coordinates')
plt.ylabel('Y coordinates')
plt.title('3D Orion Constellation')


1 Like

Hi elfo2285,

Well done! All the code looks good, and below is my feedback based on the rubric for this project. I like the choice of * for the markers and the use of titles/labels/legends. My only recommendation would be to add a label to the Z-axis as well in the 3D graph. Unfortunately, plt.zlabel(' Z coordinate') does not exist, so you would have to do something else to label the Z-axis like: constellation3d.set_zlabel('Z coordinate')

Matplotlib and CSV - 4 / 4
Implementation of 2D visualization - 4 / 4
Implementation of 3D visualization - 4 / 4


Hi Thiago,

Thanks a lot for your review!

Best regards,