
Laboratory 1: Getting started
Prerequisites: functions, import statements
 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
[?]:).
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.
Download lab1.py and save as lab1.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.
Although we expect that you use IPython for these exercises, we will
initially also provide examples for the interaction with the Python
prompt with >>> as displayed by the normal Python console instead
of In [3]: as displayed by the IPython console: the number 3 here
is meaningless as it only counts the number of inputs is that
particular session. We write the example above therefore as:
>>> 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:
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.
 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 only more powerful in providing tools that help us
to write and debug code efficiently.
Now, it is your turn. Please define the following functions in the file lab1.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 of a
pyramid with base area A and height h.
Example:
>>> pyramid_volume(1, 2)
0.6666666666666666
Example (IPython):
In [ ]: pyramid_volume(1, 2)
Out[ ]: 0.6666666666666666
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 feeg1001@soton.ac.uk with subject lab 1 and attach the lab1.py file.
You should soon 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
resubmit lab1.py by emailing again to the same address.
You can repeat this as often as you want.
We urge you to improve and resubmit 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.
While the work in this laboratory is not assessed, it is a crucial preparation
for the assessment of later laboratory sessions and the class room tests.
Last updated: 20190106 at 17:34
