5. Functions and modules¶

5.1. Functions¶

Functions are reusable pieces of programs. They allow you to give a name to a block of statements, allowing you to run that block using the specified name anywhere in your program and any number of times. This is known as calling the function. We have already used many built-in functions such as print(), len() and range().

The function concept is probably the most important building block of any non-trivial software (in any programming language), so we will explore various aspects of functions in this chapter.

Functions are defined using the def keyword. After this keyword comes an identifier name for the function, followed by a pair of parentheses which may enclose some names of variables, and by the final colon that ends the line. Next follows the block of statements that are part of this function. An example will show that this is actually very simple. Here we define a function called print_nuc_count() using the syntax as explained above. Run the example to see the output.

Example:

def print_nuc_count():
    dna = "AGCTTTTCATTCTGACTGCAACGGGCAATATGTCTCTGTGTGGATTAAAAAAAG"

    for n in "ACGT":
        print(n, dna.count(n))
    print()

print_nuc_count()
print_nuc_count()

A 16
C 9
G 12
T 17

A 16
C 9
G 12
T 17

Notice that we can call the same function twice which means we do not have to write the same code again.

5.1.1. Function Parameters¶

The function print_nuc_count as defined above takes no arguments. Therefore, there are no parameters declared in the parentheses in the function definition. Often, we want to supply some information to the function in the form of arguments, so that the function can do something utilising those values. In that case we specify parameters in the function definition. These are basically just variable names that define the input to the function so that we can pass in different values to it and get back corresponding results. The values of these variables are defined when we call the function and are already assigned values when the function runs. Parameters are specified within the pair of parentheses in the function definition, separated by commas. When we call the function, we supply the arguments in the same way.

The difference between arguments and parameters

Note the terminology used - the names given in the function definition are called parameters whereas the values you supply in the function call are called arguments.

The function we defined above is not very useful. We usually don’t want to count the number of nucleotides in the same sequence again and again. We will define a function that takes one argument, called seq, which is used to determine the number of nucleotides.

def print_nuc_count(seq):

    for n in "ACGT":
        print(n, seq.count(n))
    print()

print_nuc_count("ACCGGGTTT")

dna = "GACTGGATAGAGTAG"
print_nuc_count(dna)

A 1
C 2
G 3
T 3

A 5
C 1
G 6
T 3

How It Works

The first time we call the function print_nuc_count, we directly supply the sequence as arguments. In the second case, we call the function with a variable as argument. print_nuc_count(dna) causes the value of argument dna to be assigned to parameter seq. The print_nuc_count function works the same way in both cases.

5.1.2. Local Variables¶

When you declare variables inside a function definition, they are not related in any way to other variables with the same names used outside the function - i.e. variable names are local to the function. This is called the scope of the variable. All variables have the scope of the block they are declared in starting from the point of definition of the name.

x = 50

def func(x):
    print('x is', x)
    x = 2
    print('Changed local x to', x)

func(x)
print('x is still', x)

x is 50
Changed local x to 2
x is still 50

How It Works

The first time that we print the value of the name x with the first line in the function’s body, Python uses the value of the parameter declared in the main block, above the function definition.

Next, we assign the value 2 to x. The name x is local to our function. So, when we change the value of x in the function, the x defined in the main block remains unaffected.

With the last print statement, we display the value of x as defined in the main block, thereby confirming that it is actually unaffected by the local assignment within the previously called function.

5.1.3. Default Argument Values¶

For some functions, you may want to make some parameters optional and use default values in case the user does not want to provide values for them. This is done with the help of default argument values. You can specify default argument values for parameters by appending to the parameter name in the function definition the assignment operator (=) followed by the default value.

Note that the default argument value should be a constant.

def say(message, times=1):
    print(message * times)

say('Hello')
say('World', 5)

Hello
WorldWorldWorldWorldWorld

How It Works

The function named say is used to print a string as many times as specified. If we don’t supply a value, then by default, the string is printed just once. We achieve this by specifying a default argument value of 1 to the parameter times.

In the first usage of say, we supply only the string and it prints the string once. In the second usage of say, we supply both the string and an argument 5 stating that we want to say the string message 5 times.

Caution

Only those parameters which are at the end of the parameter list can be given default argument values i.e. you cannot have a parameter with a default argument value preceding a parameter without a default argument value in the function’s parameter list.

This is because the values are assigned to the parameters by position. For example, def func(a, b=5) is valid, but def func(a=5, b) is not valid.

5.1.4. Keyword Arguments¶

If you have some functions with many parameters and you want to specify only some of them, then you can give values for such parameters by naming them - this is called keyword arguments - we use the name (keyword) instead of the position (which we have been using all along) to specify the arguments to the function.

There are two advantages - one, using the function is easier since we do not need to worry about the order of the arguments. Two, we can give values to only those parameters to which we want to, provided that the other parameters have default argument values.

def func(a, b=5, c=10):
    print('a is', a, 'and b is', b, 'and c is', c)

func(3, 7)
func(25, c=24)
func(c=50, a=100)

a is 3 and b is 7 and c is 10
a is 25 and b is 5 and c is 24
a is 100 and b is 5 and c is 50

How It Works

The function named func has one parameter without a default argument value, followed by two parameters with default argument values.

In the first usage, func(3, 7), the parameter a gets the value 3, the parameter b gets the value 7 and c gets the default value of 10.

In the second usage func(25, c=24), the variable a gets the value of 25 due to the position of the argument. Then, the parameter c gets the value of 24 due to naming i.e. keyword arguments. The variable b gets the default value of 5.

In the third usage func(c=50, a=100), we use keyword arguments for all specified values. Notice that we are specifying the value for parameter c before that for a even though a is defined before c in the function definition.

5.1.5. The `return` statement¶

The return statement is used to return from a function i.e. break out of the function. We can optionally return a value from the function as well.

def maximum(x, y):
    if x > y:
        return x
    elif x == y:
        return 'The numbers are equal'
    else:
        return y

print(maximum(2, 3))

How It Works

The maximum function returns the maximum of the parameters, in this case the numbers supplied to the function. It uses a simple if..else statement to find the greater value and then returns that value.

Note that a return statement without a value is equivalent to return None. None is a special type in Python that represents nothingness. For example, it is used to indicate that a variable has no value if it has a value of None.

Every function implicitly contains a return None statement at the end unless you have written your own return statement. You can see this by running print(some_function()) where the function some_function does not use the return statement such as:

def some_function():
    pass

print(some_function())

None

The pass statement is used in Python to indicate an empty block of statements.

Note

There is a built-in function called max() that already implements the ‘find maximum’ functionality, so use this built-in function whenever possible.

5.2. DocStrings - documenting your functions¶

Python has a nifty feature called documentation strings, usually referred to by its shorter name docstrings. DocStrings are an important tool that you should make use of since it helps to document the program better and makes it easier to understand. We can even get the docstring back from, say a function, when the program is actually running!

# Please note the function below is not optimal, and would not be implemented like this.
# We would use a data type called 'dictionary', which is discussed in the next chapter.

def count_nuc(dna):
    """Returns the nucleotide content of a sequence.

    Accepts a string dna that should contain only "ACGT" characters."""
    # Initialize empty variables
    a = 0
    c = 0
    g = 0
    t = 0

    for nuc in dna.lower():
        if nuc == "a":
            a += 1
        elif nuc == "c":
            c += 1
        elif nuc == "g":
            g += 1
        elif nuc == "t":
            t += 1

    return a, c, g, t

print(count_nuc("GATTACA"))
print()
print("Help:")
print(count_nuc.__doc__)

(3, 1, 1, 2)

Help:
Returns the nucleotide content of a sequence.

    Accepts a string dna that should contain only "ACGT" characters.

How It Works

A string on the first logical line of a function is the docstring for that function. The convention followed for a docstring is a multi-line string where the first line starts with a capital letter and ends with a dot. Then the second line is blank followed by any detailed explanation starting from the third line. You are strongly advised to follow this convention for all your docstrings for all your non-trivial functions.

We can access the docstring of the count_nuc function using the __doc__ (notice the double underscores) attribute (name belonging to) of the function.

If you have used help() in Python, then you have already seen the usage of docstrings! What it does is just fetch the __doc__ attribute of that function and displays it in a neat manner for you. You can try it out on the function above - just include help(count_nuc) in your program. Remember to press the q key to exit help.

Automated tools can retrieve the documentation from your program in this manner. Therefore, we strongly recommend that you use docstrings for any non-trivial function that you write. The pydoc command that comes with your Python distribution works similarly to help() using docstrings.

Note

From now on, for this course, all your functions have to be documented using a docstring!

5.3. Exercises¶

Exercise 5.1

Write a function called median() that takes a list of numbers as an argument and returns the median as a float (see definition here).

Exercise 5.2

Write a function called sequence_type() that takes one argument, a string. The function should return a string with one of three values: "dna", "protein" or "other". This should depend on the input. If the input only contains A, C, T, G or N it should return "dna". If the input contains only valid one-letter IUPAC amino acid symbols (see below) it should return "protein". In all other cases it should return "other". The function should work, regardless of the input being upper-case, lower-case or a mix.

Note

Theoretically, a sequencing consisting of A, C, G and T can be an amino acid sequence consisting of Alanines, Cysteines, Glycines and Threonines. However, you can ignore this possibility for this exercise.

Amino Acid Code:  Three letter Code:  Amino Acid:
----------------  ------------------  -----------
A.................Ala.................Alanine
B.................Asx.................Aspartic acid or Asparagine
C.................Cys.................Cysteine
D.................Asp.................Aspartic Acid
E.................Glu.................Glutamic Acid
F.................Phe.................Phenylalanine
G.................Gly.................Glycine
H.................His.................Histidine
I.................Ile.................Isoleucine
K.................Lys.................Lysine
L.................Leu.................Leucine
M.................Met.................Methionine
N.................Asn.................Asparagine
P.................Pro.................Proline
Q.................Gln.................Glutamine
R.................Arg.................Arginine
S.................Ser.................Serine
T.................Thr.................Threonine
V.................Val.................Valine
W.................Trp.................Tryptophan
X.................Xaa.................Any amino acid
Y.................Tyr.................Tyrosine
Z.................Glx.................Glutamine or Glutamic acid

5.4. Modules¶

Python functions can be stored together in a module. A module, a collection of functions with a certain theme, can be imported by another program to make use of its functionality. This is how we can use the Python standard library as well. First, we will see how to use the standard library modules.

import math

print(math.sqrt(16))

4.0

How It Works

First, we import the math module using the import statement. Basically, this translates to us telling Python that we want to use this module. The math module contains mathematical functionality.

When Python executes the import math statement, it looks for the math module. In this case, it is one of the built-in modules, and hence Python knows where to find it.

If it was not a compiled module i.e. a module written in Python, then the Python interpreter will search for it in the directories listed in its sys.path variable. If the module is found, then the statements in the body of that module are run and the module is made available for you to use. Note that the initialization is done only the first time that we import a module.

The sqrt() function in the sys module is accessed using the dotted notation i.e. sys.sqrt(). It clearly indicates that this name is part of the sys module. Another advantage of this approach is that the name does not clash with any sqrt variable used in your program.

There are many more functions defined in the math module.

print(math.pow(2, 5))
print()

result = math.log(2.71828183)
print(result)
print(math.isclose(result, 1))

32.0

1.0000000005668856
True

All the functions in the module are described here.

5.4.1. The from … import statement¶

If you want to directly import the sqrt function into your program (to avoid typing the math.everytime for it), then you can use the from math import sqrt statement.

In general, the import statement is better since your program will avoid name clashes and will be more readable.

from math import sqrt
print("Square root of 16 is", sqrt(16))

Square root of 16 is 4.0

5.4.2. Multiple imports¶

You can import from many different modules.

import os
import random
from datetime import date
from math import sqrt

print(os.listdir("."))  # Show files in ".", the current directory
print("The current directory is: {}".format(os.getcwd()))
print()

n = 16
print("The square root of {} is {}".format(n, sqrt(n)))
print()

print("Today is {}".format(date.today()))
print()

print("Here you have a pseudo-random number:", random.random())
print("Or a random integer between 0 and 10:", random.randint(0, 10))

['lists.ipynb', 'control_flow.ipynb', 'dnase_motifs.ipynb', 'dictionaries.ipynb', 'visualization.ipynb', 'testfile.txt', 'first_exercises.ipynb', 'footprints.ipynb', 'debugging.ipynb', '01_introduction.md', 'loops.ipynb', 'img', 'functions_modules.ipynb', 'scripts.ipynb', 'extra_exercises.ipynb', 'pandas.ipynb', 'files.ipynb']
The current directory is: /home/runner/work/cfb_book/cfb_book/content

The square root of 16 is 4.0

Today is 2021-10-14

Here you have a pseudo-random number: 0.5385609881242942
Or a random integer between 0 and 10: 2

Note

In this book, we use the import statements in the cells where they’re needed. However, in general, imports are always put at the top of the file.

5.5. Exercises¶

Exercise 5.3

Write a function random_sequence() that generates a string with a random DNA sequence of length length. The length argument should have a default value of 100. All nucleotides should have an equal chance of occurring.

Exercise 5.4

Adapt your function random_sequence() and add an optional argument gc, which determines the total fraction of G + C in the sequence. This means that if you call random_sequence(length=100, gc=40) that 40 out of the 100 characters of the returned string should be a G or a C. The default GC% should be 50%. Note that there are multiple solutions possible. Hint: check the following functions from the random module:

randint()
random()
choice()
shuffle()

Exercise 5.5

Write a function list_fasta() that returns a list of all FASTA files in a directory. The first argument to list_fasta() should be the directory. For this exercise you can assume that FASTA files have a .fa, a .fsa or a .fasta extension.

NWI-BM066A Computation for Biologists

5. Functions and modules¶

5.1. Functions¶

5.1.1. Function Parameters¶

5.1.2. Local Variables¶

5.1.3. Default Argument Values¶

5.1.4. Keyword Arguments¶

5.1.5. The return statement¶

5.2. DocStrings - documenting your functions¶

5.3. Exercises¶

5.4. Modules¶

5.4.1. The from … import statement¶

5.4.2. Multiple imports¶

5.5. Exercises¶

5.1.5. The `return` statement¶