SRSG UNIVERSE-HPC Training

Break programs down into functions to make them easier to understand

Human beings can only keep a few items in working memory at a time.
Understand larger/more complicated ideas by understanding and combining pieces.
- Components in a machine.
- Lemmas when proving theorems.
Functions serve the same purpose in programs.
- Encapsulate complexity so that we can treat it as a single "thing".
Also enables re-use.
- Write one time, use many times.

Define a function using `def` with a name, parameters, and a block of code

Begin the definition of a new function with def.
Followed by the name of the function.
- Must obey the same rules as variable names.
Then parameters in parentheses.
- Empty parentheses if the function doesn't take any inputs.
- We will discuss this in detail in a moment.
Then a colon.
Then an indented block of code.

def print_greeting():
    print('Hello!')

Defining a function does not run it

Defining a function does not run it.
- Like assigning a value to a variable.
Must call the function to execute the code it contains.

print_greeting()

Hello!

Arguments in call are matched to parameters in definition

Functions are most useful when they can operate on different data.
Specify parameters when defining a function.
- These become variables when the function is executed.
- Are assigned the arguments in the call (i.e., the values passed to the function).
- If you don't name the arguments when using them in the call, the arguments will be matched to parameters in the order the parameters are defined in the function.

def print_date(year, month, day):
    joined = str(year) + '/' + str(month) + '/' + str(day)
    print(joined)

print_date(1871, 3, 19)

1871/3/19

Or, we can name the arguments when we call the function, which allows us to specify them in any order:

print_date(month=3, day=19, year=1871)

1871/3/19

Via Twitter: () contains the ingredients for the function while the body contains the recipe.

Functions may return a result to their caller using `return`

Use return ... to give a value back to the caller.
May occur anywhere in the function.
But functions are easier to understand if return occurs:
- At the start to handle special cases.
- At the very end, with a final result.

def average(values):
    if len(values) == 0:
        return None
    return sum(values) / len(values)

a = average([1, 3, 4])
print('average of actual values:', a)

average of actual values: 2.6666666666666665

print('average of empty list:', average([]))

average of empty list: None

Remember: [every function returns something]({{ page.root }}/04-built-in/).
A function that doesn't explicitly return a value automatically returns None.

result = print_date(1871, 3, 19)
print('result of call is:', result)

1871/3/19
result of call is: None

Identifying Syntax Errors

Read the code below and try to identify what the errors are without running it.
Run the code and read the error message. Is it a SyntaxError or an IndentationError?
Fix the error.
Repeat steps 2 and 3 until you have fixed all the errors.

def another_function
  print("Syntax errors are annoying.")
   print("But at least python tells us about them!")
  print("So they are usually not too hard to fix.")

Definition and Use

What does the following program print?

def report(pressure):
    print('pressure is', pressure)

print('calling', report, 22.5)

Order of Operations

What's wrong in this example?

result = print_time(11, 37, 59)

def print_time(hour, minute, second):
    time_string = str(hour) + ':' + str(minute) + ':' + str(second)
    print(time_string)

After fixing the problem above, explain why running this example code:

result = print_time(11, 37, 59)
print('result of call is:', result)

gives this output:

11:37:59
result of call is: None

Why is the result of the call None?

The problem with the example is that the function print_time() is defined after the call to the function is made. Python doesn't know how to resolve the name print_time since it hasn't been defined yet and will raise a NameError e.g., NameError: name 'print_time' is not defined
The first line of output 11:37:59 is printed by the first line of code, result = print_time(11, 37, 59) that binds the value returned by invoking print_time to the variable result. The second line is from the second print call to print the contents of the result variable.
print_time() does not explicitly return a value, so it automatically returns None.

Encapsulation

Fill in the blanks to create a function that takes a single filename as an argument, loads the data in the file named by the argument, and returns the minimum value in that data.

import pandas as pd

def min_in_data(____):
    data = ____
    return ____

Find the First

Fill in the blanks to create a function that takes a list of numbers as an argument and returns the first negative value in the list. What does your function do if the list is empty?

def first_negative(values):
    for v in ____:
        if ____:
            return ____

Calling by Name

Earlier we saw this function:

def print_date(year, month, day):
    joined = str(year) + '/' + str(month) + '/' + str(day)
    print(joined)

We saw that we can call the function using named arguments, like this:

print_date(day=1, month=2, year=2003)

What does print_date(day=1, month=2, year=2003) print?
When have you seen a function call like this before?
When and why is it useful to call functions this way?

2003/2/1
We saw examples of using named arguments when working with the pandas library. For example, when reading in a dataset using data = pd.read_csv('data/gapminder_gdp_europe.csv', index_col='country'), the last argument index_col is a named argument.
Using named arguments can make code more readable since one can see from the function call what name the different arguments have inside the function. It can also reduce the chances of passing arguments in the wrong order, since by using named arguments the order doesn't matter.

Encapsulation of an If/Print Block

The code below will run on a label-printer for chicken eggs. A digital scale will report a chicken egg mass (in grams) to the computer and then the computer will print a label.

Please re-write the code so that the if-block is encapsulated in a function.

import random
for i in range(10):

    # simulating the mass of a chicken egg
    # the (random) mass will be 70 +/- 20 grams
    mass = 70 + 20.0 * (2.0 * random.random() - 1.0)

    print(mass)
    # egg sizing machinery prints a label
    if mass >= 85:
        print("jumbo")
    elif mass >= 70:
        print("large")
    elif mass < 70 and mass >= 55:
        print("medium")
    else:
        print("small")

The simplified program follows. What function definition will make it functional?

# revised version
import random
for i in range(10):

    # simulating the mass of a chicken egg
    # the (random) mass will be 70 +/- 20 grams
    mass = 70 + 20.0 * (2.0 * random.random() - 1.0)

    print(mass, print_egg_label(mass))

Create a function definition for print_egg_label() that will work with the revised program above. Note, the function's return value will be significant. Sample output might be 71.23 large.
A dirty egg might have a mass of more than 90 grams, and a spoiled or broken egg will probably have a mass that's less than 50 grams. Modify your print_egg_label() function to account for these error conditions. Sample output could be 25 too light, probably spoiled.

Encapsulating Data Analysis

Assume that the following code has been executed:

import pandas as pd

df = pd.read_csv('data/gapminder_gdp_asia.csv', index_col=0)
japan = df.loc['Japan']

Complete the statements below to obtain the average GDP for Japan across the years reported for the 1980s.

year = 1983
gdp_decade = 'gdpPercap_' + str(year // ____)
avg = (japan.loc[gdp_decade + ___] + japan.loc[gdp_decade + ___]) / 2

Abstract the code above into a single function.

def avg_gdp_in_decade(country, continent, year):
    df = pd.read_csv('data/gapminder_gdp_'+___+'.csv',delimiter=',',index_col=0)
    ____
    ____
    ____
    return avg

How would you generalize this function if you did not know beforehand which specific years occurred as columns in the data? For instance, what if we also had data from years ending in 1 and 9 for each decade? (Hint: use the columns to filter out the ones that correspond to the decade, instead of enumerating them in the code.)

The average GDP for Japan across the years reported for the 1980s is computed with:

year = 1983
gdp_decade = 'gdpPercap_' + str(year // 10)
avg = (japan.loc[gdp_decade + '2'] + japan.loc[gdp_decade + '7']) / 2

That code as a function is:

def avg_gdp_in_decade(country, continent, year):
    df = pd.read_csv('data/gapminder_gdp_' + continent + '.csv', index_col=0)
    c = df.loc[country]
    gdp_decade = 'gdpPercap_' + str(year // 10)
    avg = (c.loc[gdp_decade + '2'] + c.loc[gdp_decade + '7'])/2
    return avg

To obtain the average for the relevant years, we need to loop over them:

def avg_gdp_in_decade(country, continent, year):
    df = pd.read_csv('data/gapminder_gdp_' + continent + '.csv', index_col=0)
    c = df.loc[country]
    gdp_decade = 'gdpPercap_' + str(year // 10)
    total = 0.0
    num_years = 0
    for yr_header in c.index: # c's index contains reported years
        if yr_header.startswith(gdp_decade):
            total = total + c.loc[yr_header]
            num_years = num_years + 1
    return total/num_years

The function can now be called by:

avg_gdp_in_decade('Japan','asia',1983)

20880.023800000003

Simulating a dynamical system

In mathematics, a dynamical system is a system in which a function describes the time dependence of a point in a geometrical space. A canonical example of a dynamical system is the logistic map, a growth model that computes a new population density (between 0 and 1) based on the current density. In the model, time takes discrete values 0, 1, 2, ...

Define a function called logistic_map that takes two inputs: x, representing the current population (at time t), and a parameter r = 1. This function should return a value representing the state of the system (population) at time t + 1, using the mapping function:

f(t+1) = r * f(t) * [1 - f(t)]
Using a for or while loop, iterate the logistic_map function defined in part 1, starting from an initial population of 0.5, for a period of time t_final = 10. Store the intermediate results in a list so that after the loop terminates you have accumulated a sequence of values representing the state of the logistic map at times t = [0,1,...,t_final]. Print this list to see the evolution of the population.
Encapsulate the logic of your loop into a function called iterate that takes the initial population as its first input, the parameter t_final as its second input and the parameter r as its third input. The function should return the list of values representing the state of the logistic map at times t = [0,1,...,t_final]. Run this function for periods t_final = 100 and 1000 and print some of the values. Is the population trending toward a steady state?

def logistic_map(x, r):
    return r * x * (1 - x)

initial_population = 0.5
t_final = 10
r = 1.0
population = [initial_population]
for t in range(1, t_final):
    population.append( logistic_map(population[t-1], r) )

def iterate(initial_population, t_final, r):
    population = [initial_population]
    for t in range(1, t_final):
        population.append( logistic_map(population[t-1], r) )
    return population

for period in (10, 100, 1000):
    population = iterate(0.5, period, 1)
    print(population[-1])

0.07508929631879595
0.009485759503982033
0.0009923756709128578

The population seems to be approaching zero.

Writing Functions