Lab assignment #1: Python basics


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Lab assignment #1: Python basics
General Advice
• Work with a partner!
• The goals of this lab are
– to solve some physics and math problems numerically;
– to review basic programming concepts (such as loops, plotting,
etc.) using Python;
– to introduce the practice of writing “pseudocode” (or what we
call pseudocode in this class); and
– to learn how to time your code and test its performance.
• All our lab formats are similar in this course. We start by covering
the computational and the physics background we need to do the lab.
• After reading the background below, you might want to review some of
the computational physics material from PHY224 and/or PHY254 and
look over some of the review material in the text. In particular, ensure
that you take the time this week to learn the material in Chapters 2
& 3 as they will be expected knowledge for all the future labs. Useful
review material includes:
– Assigning variables: Sections 2.1, 2.2.1&2
– Mathematical operations: Section 2.2.4
– Loops: Sections 2.3 and 2.5
– Lists & Arrays: Section 2.4
– User defined functions: Section 2.6
– Making basic graphs: Section 3.1
• Specific instructions regarding what to hand out are written for each
question in bold red.
Not all questions require a submission: some are only here to help you.
When we do though, we are looking for “C3” solutions, i.e., solutions
that are Complete, Clear and Concise.
• An example of Clarity: make sure to label all of your plot axes and
include legends if you have more than one curve on a plot. Use fonts
that are large enough. For example, when integrated into your report,
the font size on your plots should visually be the same, or similar, as
the font size of the text in your report.
• Whether or not you are asked to hand in pseudocode, you need to
strategize and pseudocode before you start coding. Writing code
should be your last step, not your first step.
• Test your code as you go, not when it is finished. The easiest way to
test code is with print() statements. Print out values that you set
or calculate to make sure they are what you think they are.
• Practice modularity. It is the concept of breaking up your code into
pieces that as independent as possible form each other. That way,
if anything goes wrong, you can test each piece independently. One
way to practice modularity is to define external functions for repetitive
tasks. An external function is a piece of code that looks like this:
def MultiplyAngleByTwo(argument):
“””A header that explains the function
argument [float] is the angle in rad
res [float] is twice the argument”””
res = 2.*argument
return res
Place them in a separate file called e.g., and call and
use them in your answer files with:
import MyFunctions as myf # make sure file is in same folder
DescriptiveVariableName = 4.
myCalculationResult = myf.MultiplyAngleByTwo(DescriptiveVariableName)
Computational background
Numerical integration review. For a general first order system,
dt = F⃗ (⃗x). (1)
The simplest way to numerically integrate the system is by approximating
the derivative as:

⃗xi+1 − ⃗xi
. (2)
where the subscript i on ⃗xi refers to the time step. We can then rearrange
eqn. (1) to read
⃗xi+1 = ⃗xi + F⃗ (⃗xi)∆t. (3)
We can start with an initial ⃗x, pick a ∆t and implement equation (3) in
a loop to calculate the new value of ⃗x on each iteration. This is called
the “Euler” method, which you are expected to know from e.g. PHY224 or
It turns out that the Euler method is unstable. The “Euler-Cromer”
method, which updates the value of the derivative first, and then uses this
value to update ⃗x, is a small tweak that often leads to a stable result. For
example, when you update the position variables x and y, use the newly
updated velocities instead of the old velocities. So these lines should look
xi+1 = xi + vx,i+1∆t, (4a)
yi+1 = yi + vy,i+1∆t. (4b)
The updates for the velocities should remain as they are.
How to time the performance of your code. In Q3 we will explore
how to time numerical calculations using your computer’s built in stopwatch. I am going to present a crude way to do it, and there are better
methods to profile code. Nonetheless, it will be enough for our purposes.
Use time.time() as in the following example:
# import the “time” function from the “time” module
from time import time
start = time() # save start time
# run your calculation
# …
end = time() # save end time
diff = end-start # elapsed time (in seconds)
The time() function will not always return reliable results so it is worth
rerunning tests a couple of times to check for consistency.
Physics background
Newtonian orbits. The Newtonian gravitational force keeping a planet
in orbit can be approximated as
g = −
rˆ = −
(xxˆ + yyˆ), (5)
where MS is the mass of the Sun, Mp is the mass of the planet, r is the
distance between them, and ˆx, yˆ the unit vectors of the Cartesian coordinate
system. Using Newton’s Second law (F⃗ = m⃗a) and numerical integration,
we can solve for the velocity and position of a planet in orbit as a function
of time. (Note: we have assumed the planet is much less massive than the
Sun and hence that the Sun stays fixed at the centre of mass of the system).
Using eqn. (5) and Newton’s law, you can convince yourself that the
equations governing the motion of the planet can be written as a set of first
order equations, i.e., of the form of eqn. (1), in Cartesian coordinates as
dt = −
dt = −
dt = vx,
dt = vy.
General relativity orbits. The gravitational force law predicted by general relativity can be approximated as
g = −

1 +

(xxˆ + yyˆ), (7)
where α is a constant depending on the scenario. Notice this is just the
Newtonian formula plus a small correction term proportional to r
. Mercury is close enough to the Sun that the effects of this correction can be
observed. It results in a precession of Mercury’s elliptical orbit.
Useful constants. Because we are working on astronomical scales, it will
be easier to work in units larger than metres, seconds and kilograms. For
distances, we will use the AU (Astronomical Unit, approximately equal to
the distance between the Sun and the Earth), for mass, MS (solar mass)
and for time, the Earth year. Below are some constants you will need in
these units:
• MS = 2.0 × 1030 kg.
• 1 AU = 1.496 × 1011 m.
• G = 6.67 × 10−11 m3kg−1
−2 = 39.5 AU3 M−1
• α = 1.1 × 10−8 AU2
. For the code, use α = 0.01 AU2
• Mass of Jupiter: MJ = 10−3MS.
• Jupiter’s orbital radius: aJ = 5.2AU.
Note that the SciPy package includes some of these constants: https:// (see the test
file shipped with this lab).
The Three Body Problem. We can consider the sun and two planets
and see how the gravitational interactions affect the motions. This problem
cannot be solved analytically. If we assume one of the planets is much more
massive than the other, then we can just consider the gravitational influence
of the larger planet on the smaller one and neglect the effect of the smaller
object on the larger one (similar to how we treated the Sun in the 2 body
1. [40% of the lab] Modelling a planetary orbit
(a) Rearrange eqns. (6) to put in a format similar to eqn. (3) so that
they can be used for numerical integration.
Nothing to submit.
(b) As mentioned in the computational background section, you could
try and code this up, but the code would prove reluctant to give
you any satisfying answer. Instead, we will use the more stable
Euler-Cromer method from now on. Write a “pseudocode” for
a program that integrates your equations to calculate the position and velocity of the planet as a function of time under the
Newtonian gravity force. The output of your code should include
graphs of the components of velocity as a function of time and a
plot of the orbit (x vs. y) in space.
Submit your pseudocode and explanatory notes.
(c) Now write a real python code from your pseudocode. We will
assume the planet is Mercury. The initial conditions are:
x = 0.47 AU, y = 0.0 AU (8a)
vx = 0.0 AU/yr, vy = 8.17 AU/yr (8b)
Use a time step ∆t = 0.0001 yr and integrate for 1 year. Check
if angular momentum is conserved from the beginning to the end
of the orbit. You should see an elliptical orbit. Note: to correct
for the tendency of matplotlib to plot on uneven axes, you can
use one of the methods described here:
Other note: our unit of year here is actually the Earth year, so
your integration will cover several Mercury years.
Submit your code, plots, and explanatory notes.
(d) Now alter the gravitational force in your code to the general relativity form given in eqn. (7). The actual value of α for Mercury
is given in the physics background, but it will be too small for
our computational framework here. Instead, try α = 0.01 AU2
which will exaggerate the effect. Demonstrate Mercury’s orbital
precession by plotting several orbits in the x, y plane that show
the aphelion (furthest point) of the orbit moves around in time.
Submit your code (it can be the same file as in the previous question), plots, and explanatory notes.
2. [40% of the lab] The three body problem. Now lets add another
planet to our system. We will consider Earth as the small planet and
Jupiter as the large planet. The orbit of Earth will be determined
by the gravitational force of the Sun at the centre of the solar system
and the gravitational force of Jupiter at an orbital radius of 5.2 AU.
We will assume that Jupiter and the Sun are not affected by Earth’s
gravitational force.
(a) Write a pseudocode to add Jupiter to the system. Then alter
your code from Q1c (i.e., the Newtonian gravity code) to add
Jupiter and integrate the orbits for 10 (Earth) years. The figure
below might be of some help. Some steps you will need to take:
• Simulate Jupiter’s orbit,
• calculate the distance between Jupiter and Earth as a function of time,
• determine the net gravitational force on Earth due to both
the Sun and Jupiter.
Sun (not to scale)
FJ: Gravitational force on Earth due to Jupiter
FS: Gravitational force on Earth due to the Sun
(xJ,yJ): coordinates of Jupiter
(xE,yE): coordinates of Earth
Use the following initial conditions for Jupiter:
xJ = 5.2 AU, yJ = 0.0 AU, (9a)
vx,J = 0.0 AU/yr, vy,J = 2.63 AU/yr. (9b)
Use the following initial conditions for Earth:
xE = 1.0 AU, yE = 0.0 AU, (10a)
vE,x = 0.0 AU/yr, vE,y = 6.18 AU/yr. (10b)
Submit your pseudocode, your python code, and a plot
showing both Jupiter’s and Earth’s orbits.
(b) You should find that Jupiter does not have a big effect on Earth.
Let’s see what happens if Jupiter were more massive. Set Jupiter’s
mass to be 1000× its actual mass (i.e., if it had the same mass
as the Sun; note that our approximation of Jupiter not affecting
the Sun is now very wrong, but we will nonetheless retain it for
the sake of argument). Run your code again with the same initial
condition for Jupiter as in Q2a and plot the orbit in the x, y plane
for 3 years. What happens if you run the simulation any longer?
Submit the plot of the orbit in the x, y plane, and a written description of the long-term behaviour of the Earth.
(c) Next, return Jupiter to its normal mass and replace Earth with
an asteroid at an orbital distance of 3.3 AU. Plot the orbit in the
x, y plane for 20 (Earth) years. Here are the initial conditions for
the asteroid:
xa = 3.3 AU, ya = 0.0 AU (11a)
va,x = 0.0 AU/yr, va,y = 3.46 AU/yr (11b)
You should notice some perturbations in the asteroid’s orbit. This
is not only because the asteroid is closer to Jupiter, but also
because at this orbital distance, it is in a motion resonance with
Jupiter, which amplifies perturbations. Eventually, the asteroid
is likely to get kicked out of the system.
Submit the plot of the orbit in the x, y plane.
3. [20% of the lab] Timing Matrix multiplication
Read through Example 4.3 on pages 137-138 of the text, which show
you that to multiply two matrices of size O(N) on a side takes O(N3
operations. So multiplying matrices that are N = 1000 on a side takes
) operations (a “GigaFLOP”).
Create two constant matrices A and B using the numpy.ones function.
For example,
A = ones([N, N], float)*3.
will assign an N × N matrix to A with each entry equal to 3. Then
time how long it takes to multiply the matrices to form a matrix C,
using the code fragment in the textbook, for a range of N (from N = 2
to a few hundred). Print out and plot this time as a function of N and
as a function of N3
. Compare this time to the time it takes numpy
function to carry out the same operation. What do you
notice? See for more explanations.
Submit your code, and the written answers to the questions.


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