Introduction to Python for Computational Science, Hans Fangohr, Max Planck Institute for the Structure and Dynamics of Matter & University of Southampton

Laboratory 1: Getting started

Prerequisits: functions, import statements

Additional learning resources:

Socratica: Functions

Contents

Training preparations
Prerequisites
Worked example
- Python prompt notation
- Python and IPython console — what is the difference?
Laboratory 1 training
Submit your file
Assessment of lab work

Training preparations

Start the Spyder environment
Reset the name space using the %reset command.

This provides us a ”clean” IPython interpreter session (within Spyder). You should carry out these steps before every laboratory.

You now have an IPython interpreter available and are encouraged to carry out basic mathematical operations at the iPython prompt (In [?]:).

Prerequisites

You should know how to compute simple expressions such as

3 + 4
(9 + 3) / 6 and
11 * 12
Can you work out what 2 ** 2 and 2 ** 0.5 do?

You will need some of these expressions below, and you should be familiar with using the Python Shell to evaluate Python expressions.

You should know how to use a Python development environment. Assuming you use Spyder, you should know how to

execute Python code in Spyder and
call functions from the Python prompt.
For later laboratory sessions, you may also find it useful to know how to change and update function definitions and call the updated function from the Python prompt.

If necessary, go through the exercise First steps executing Python programs interactively which explains how to do this.

Worked example

Download training1.py and save as training1.py (the 1 shows that this file is part of laboratory 1).
Open the file with Spyder and try to understand its content.

Make sure the function definition average is known to Python (by pressing F5, for example) and then check that you can use the average function from the Python prompt:

In [ ]: average(10, 20)
Out[ ]: 15.0

In [ ]: average(10, 4)
Out[ ]: 7.0

In [ ]: help(average)
  Help on function average in module __main__:

  average(a, b)
     Given parameters a and b, compute and
     return the arithmetic mean of a and b.

Python prompt notation

Although we expect that you use IPython for these exercises, we will also provide examples of interacting with the Python prompt using the >>> prompt, as displayed in the normal Python console instead of the In [3] prompt displayed in the IPython console. The number in the IPython console, such as 3 in In [3], simply indicates the number of inputs in the current IPython session and has no other significance.

Same commands as above with the standard >>> prompt:

>>> average(10, 20)
15.0

>>> average(10, 4)
7.0

>>> help(average)
Help on function average in module __main__:

average(a, b)
       Given parameters a and b, compute and
       return the arithmetic mean of a and b.

Equivalent IPython interaction (where generally we replace the number with a space in our instructions here):

In [1]: average(10, 20)
Out[1]: 15.0

In [2]: average(10, 4)
Out[2]: 7.0

In [3]: help(average)
Help on function average in module __main__:

average(a, b)
       Given parameters a and b, compute and
       return the arithmetic mean of a and b.

Python and IPython console — what is the difference?

We also note that we will refer to the IPython console losely as the Python shell or Python console. In terms of the programming language, the IPython and Python shell can process the same command set. The IPython shell is more powerful in providing tools that help us, for example, to write and debug code efficiently.

Laboratory 1 training

Now, it is your turn. Please define the following functions in the file training1.py and make sure they behave as expected. You also should document them in a way similar to the average function.

A function distance(a, b) that returns the distance between numbers a and b.

Examples:

>>> distance(3, 4)
1
>>> distance(3, 1)
2

Examples (IPython):

In [ ]: distance(3, 4)
Out[ ]: 1

In [ ]: distance(3, 1)
Out[ ]: 2

A function geometric_mean(a, b) that returns the geometric mean of two numbers - i.e. the edge length a square would have to have so that its area equals that of a rectangle with sides a and b.

Examples:

>>> geometric_mean(2, 2)
2.0
>>> geometric_mean(2, 8)
4.0
>>> geometric_mean(2, 1)
1.4142135623730951

Examples (IPython):

In [ ]: geometric_mean(2, 2)
Out[ ]: 2.0

In [ ]: geometric_mean(2, 8)
Out[ ]: 4.0

In [ ]: geometric_mean(2, 1)
Out[ ]: 1.4142135623730951

A function pyramid_volume(A, h) that computes and returns the volume V of a pyramid with base area A and height h. You can use V=A*h/3.

Example:
```
>>> pyramid_volume(1, 2)
0.6666666666666666
```
Example (IPython):
```
In [ ]: pyramid_volume(1, 2)
Out[ ]: 0.6666666666666666
```

Submit your file

For every function try to check carefully that your implementation is correct. The tab Testing tips provides some suggestions in form of a short checklist.

Once you have gone through the checklist and think your functions are implemented correctly, please send an email to the coursework submission robot learn@mpsd.mpg.de with subject training 1 and attach the training1.py file.

You should soon (within a few minutes) get a a confirmation of the submission, and slightly later a report containing feedback through an automated analysis of your code. If the automated analysis says that some test failed, you can change your code and re-submit training1.py by emailing again to the same address. You can repeat this as often as you want.

We urge you to improve and re-submit your work until you pass all tests. Ask the demonstrators for advice to interpret the error messages you may have received in your feedback email if tests have failed.

Assessment of lab work

In future labs, there will be a training component and — what we call — an assessed component.

For the training, you can submit your work as often as you like and get feedback on each iteration. It is generally crucial preparation and practice for the assessed parts.

For the assessed part, you can submit your work only once and points will be awarded depending on how many functions you have implemented correctly.

We encourage you to use the ‘training’ part to familiarise yourself with the topic (and to be creative if you like), and then to use that knowledge to attempt to get the ‘assessed’ part of the laboratory completely right.

(Note: this course is not formally assessed - the automatic feedback is only for your information and to (hopefully) motivate you.)

Last updated: 2024-12-19 at 15:57

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