Object Oriented Programming

Overview

Teaching: 30 min
Exercises: 20 min

Questions

How can we use code to describe the structure of data?

How should the relationships between structures be described?

Objectives

Describe the core concepts that define the object oriented paradigm

Use classes to encapsulate data within a more complex program

Structure concepts within a program in terms of sets of behaviour

Identify different types of relationship between concepts within a program

Structure data within a program using these relationships

Introduction

Object oriented programming is a programming paradigm based on the concept of objects, which are data structures that contain (encapsulate) data and code. Data is encapsulated in the form of fields (attributes) of objects, while code is encapsulated in the form of procedures (methods) that manipulate objects’ attributes and define “behaviour” of objects. So, in object oriented programming, we first think about the data and the things that we’re modelling - and represent these by objects - rather than define the logic of the program, and code becomes a series of interactions between objects.

Structuring Data

One of the main difficulties we encounter when building more complex software is how to structure our data. So far, we’ve been processing data from a single source and with a simple tabular structure, but it would be useful to be able to combine data from a range of different sources and with more data than just an array of numbers.

data = pd.DataFrame([[1., 2., 3.], [4., 5., 6.]],
                     index=['FP35','FP56'])

Using this data structure has the advantage of being able to use Pandas and NumPy operations to process the data, and Matplotlib to plot it, but often we need to have more structure than this. For example, the measurement data we are interested in has a hierarchy of situational information: each data set is recorded by a particular instrument, in a particular measurement site, in a particular catchment area. This structure can be captured using Pandas MultiIndexes, for example:

location_measurement = [
    ("FP", "FP35", "Rainfall"),
    ("FP", "FP56", "River Level"),
    ("PL", "PL23", "River Level"),
    ("PL", "PL23", "Water pH")
]
index_names = ["Catchment", "Site", "Measurement"]
index = pd.MultiIndex.from_tuples(location_measurement,names=index_names)

data = [
    [0., 2., 1.],
    [30., 29., 34.],
    [34., 32., 33.],
    [7.8, 8., 7.9]
]

pd.DataFrame(data,index=index)

                               0     1     2
Catchment Site Measurement                  
FP        FP35 Rainfall      0.0   2.0   1.0
          FP56 River Level  30.0  29.0  34.0
PL        PL23 River Level  34.0  32.0  33.0
               Water pH      7.8   8.0   7.9

However, we may need to attach more information about the sites and store this alongside our measurement data. Or we might want to store the data from different sites or instruments at different frequencies. These requirements are more difficult to accomodate within a Pandas DataFrame, and would require the use of extra data structures, or lead to messy data frames.

Instead, we can do this using the Python data structures we’re already familiar with, dictionaries and lists. For instance, we could attach an identifier to the measurements from each site:

measurement_data = [
    {
        'site': 'FP35',
        'measurement': 'Rainfall'
        'data': [0.0, 2.0, 1.0],
    },
    {
        'site': 'FP56',
        'measurement': 'River level'
        'data': [30.0, 29.0, 34.0],
    },
]

Exercise: Structuring Data

Write a function, called attach_sites, which can be used to attach IDs to our measurement dataset. When used as below, it should produce the expected output.

If you’re not sure where to begin, think about ways you might be able to effectively loop over two collections at once. Also, don’t worry too much about the data type of the data value, it can be a Python list, a NumPy array, or a Pandas DataFrame - any is fine.
data = np.array([[34., 32., 33.],
                 [7.8, 8.0, 7.9]])

output = attach_information(data, ['PL23', 'PL23'], ['River Level', 'pH'])
print(output)
[
    {
        'site': 'PL23',
        'measurement': 'River Level',
        'data': [34., 32., 33.],
    },
    {
        'site': 'PL23',
        'measurement': 'pH',
        'data': [7.8, 8.0, 7.9],
    },
]
Solution

One possible solution, perhaps the most obvious, is to use the range function to index into all three lists at the same location:
def attach_information(data, sites, measurements):
    """Create datastructure containing data from a range of sites
       and instruments."""
    output = []

    for i in range(len(data)):
        output.append({'site': sites[i],
                       'measurement': measurements[i],
                       'data': data[i]})

    return output
However, this solution has a potential problem that can occur sometimes, depending on the input. What might go wrong with this solution? How could we fix it?
A Better Solution

What would happen if the data, measurements, and/or sites inputs were different lengths?

If sites or measurements is longer, we’ll loop through, until we run out of rows in the data input, at which point we’ll stop processing the last few names. If data is longer, we’ll loop through, but at some point we’ll run out of sites or measurements - but this time we try to access part of the list that doesn’t exist, so we’ll get an exception.

A better solution would be to use the zip function, which allows us to iterate over multiple iterables without needing an index variable. The zip function also limits the iteration to whichever of the iterables is smaller, so we won’t raise an exception here, but this might not quite be the behaviour we want, so we’ll also explicitly assert that the inputs should be the same length. Checking that our inputs are valid in this way is an example of a precondition, which we introduced conceptually in an earlier episode.

If you’ve not previously come across the zip function, read this section of the Python documentation.
def attach_names(data, sites, measurements):
    """Create datastructure containing measurement data from a range of sites."""
    assert len(data) == len(sites)
    assert len(data) == len(measurements)
    output = []

    for data_row, measurement, site in zip(data, measurements, sites):
        output.append({'site': site,
                       'measurement': measurement,
                       'data': data_row})

    return output

Classes in Python

Using nested dictionaries and lists should work for some of the simpler cases where we need to handle structured data, but they get quite difficult to manage once the structure becomes a bit more complex. For this reason, in the object oriented paradigm, we use classes to help with managing this data and the operations we would want to perform on it. A class is a template (blueprint) for a structured piece of data, so when we create some data using a class, we can be certain that it has the same structure each time.

With our list of dictionaries we had in the example above, we have no real guarantee that each dictionary has the same structure, e.g. the same keys (site and data) unless we check it manually. With a class, if an object is an instance of that class (i.e. it was made using that template) we know it will have the structure defined by that class. Different programming languages make slightly different guarantees about how strictly the structure will match, but in object oriented programming this is one of the core ideas - all objects derived from the same class must follow the same behaviour.

You may not have realised, but you should already be familiar with some of the classes that come bundled as part of Python, for example:

my_list = [1, 2, 3]
my_dict = {1: '1', 2: '2', 3: '3'}
my_set = {1, 2, 3}

print(type(my_list))
print(type(my_dict))
print(type(my_set))

<class 'list'>
<class 'dict'>
<class 'set'>

Lists, dictionaries and sets are a slightly special type of class, but they behave in much the same way as a class we might define ourselves:

They each hold some data (attributes or state).
They also provide some methods describing the behaviours of the data - what can the data do and what can we do to the data?

The behaviours we may have seen previously include:

Lists can be appended to
Lists can be indexed
Lists can be sliced
Key-value pairs can be added to dictionaries
The value at a key can be looked up in a dictionary
The union of two sets can be found (the set of values present in any of the sets)
The intersection of two sets can be found (the set of values present in all of the sets)

Encapsulating Data

Let’s start with a minimal example of a class representing a measurement site.

# file: catchment/models.py

class Site:
    def __init__(self, name):
        self.name = name
        self.measurements = {}

from catchment.models import Site

FP35 = Site('FP35')
print(FP35.name)

FP35

Here we’ve defined a class with one method: __init__. This method is the initialiser method, which is responsible for setting up the initial values and structure of the data inside a new instance of the class - this is very similar to constructors in other languages, so the term is often used in Python too. The __init__ method is called every time we create a new instance of the class, as in Site('FP35'). The argument self refers to the instance on which we are calling the method and gets filled in automatically by Python - we don’t need to provide a value for this when we call the method.

Data encapsulated within our Site class includes the name of the site and a dictionary of measurement datasets. In the initialiser method, we set a site’s name to the value provided, and create a dictionary of measurement datasets for the site (initially empty). Such data is also referred to as the attributes of a class and holds the current state of an instance of the class. Attributes are typically hidden (encapsulated) internal object details ensuring that access to data is protected from unintended changes. They are manipulated internally by the class, which, in addition, can expose certain functionality as public behavior of the class to allow other objects to interact with this class’ instances.

Encapsulating Behaviour

In addition to representing a piece of structured data (e.g. a site which has a name and a dictionary of measurement data), a class can also provide a set of functions, or methods, which describe the behaviours of the data encapsulated in the instances of that class. To define the behaviour of a class we add functions which operate on the data the class contains. These functions are the member functions or methods.

Methods on classes are the same as normal functions, except that they live inside a class and have an extra first parameter self. Using the name self is not strictly necessary, but is a very strong convention - it is extremely rare to see any other name chosen. When we call a method on an object, the value of self is automatically set to this object - hence the name. As we saw with the __init__ method previously, we do not need to explicitly provide a value for the self argument, this is done for us by Python.

Let’s add another method on our Site class that adds a new measurement dataset to a Site instance.

# file: catchment/models.py

class Site:
    """A measurement site in the study."""
    def __init__(self, name):
        self.name = name
        self.measurements = {}

    def add_measurement(self, measurement_id, data):
        if measurement_id in self.measurements.keys():
            self.measurements[measurement_id] = \
                    pd.concat([self.measurements[measurement_id], data])
        
        else:
            self.measurements[measurement_id] = data
            self.measurements[measurement_id].name = measurement_id

from catchment.models import Site
import pandas as pd
import datetime

FP35 = Site('FP35')
print(FP35)

rainfall_data = pd.Series(
    [0.0,2.0,1.0],
    index=[
        datetime.date(2000,1,1),
        datetime.date(2000,1,2),
        datetime.date(2000,1,3)
        ]
    )

FP35.add_measurement('Rainfall',rainfall_data)

print(FP35.measurements.keys())
print(FP35.measurements['Rainfall'])

<__main__.Site object at 0x7fada93d0820>
dict_keys(['Rainfall'])
2000-01-01  0.0
2000-01-02  2.0
2000-01-03  1.0
Name: Rainfall, dtype: float64

Note here that we have created a pandas.Series object, rather than a pandas.DataFrame object, to contain our measurement data, and that we are setting the name of each series to match the measurement_id. DataFrames can be considered to be a collection of series, each containing separate data. Our Site object replaces the dataframe for this purpose, later we will show you how to combine the series objects into dataframes again, using the series name that we set here.

Class and Static Methods

Sometimes, the function we’re writing doesn’t need access to any data belonging to a particular object. For these situations, we can instead use a class method or a static method. Class methods have access to the class that they’re a part of, and can access data on that class - but do not belong to a specific instance of that class, whereas static methods have access to neither the class nor its instances.

By convention, class methods use cls as their first argument instead of self - this is how we access the class and its data, just like self allows us to access the instance and its data. Static methods have neither self nor cls so the arguments look like a typical free function. These are the only common exceptions to using self for a method’s first argument.

Both of these method types are created using decorators - for more information see the classmethod and staticmethod decorator sections of the Python documentation.

Dunder Methods

Why is the __init__ method not called init? There are a few special method names that we can use which Python will use to provide a few common behaviours, each of which begins and ends with a double-underscore, hence the name dunder method.

When writing your own Python classes, you’ll almost always want to write an __init__ method, but there are a few other common ones you might need sometimes. You may have noticed in the code above that the method print(FP35) returned <__main__.Site object at 0x7fada93d0820>, which is the string represenation of the FP35 object. We may want the print statement to display the object’s name instead. We can achieve this by overriding the __str__ method of our class.

# file: catchment/models.py

class Site:
    """A measurement site in the study."""
    def __init__(self, name):
        self.name = name
        self.measurements = {}

    def add_measurement(self, measurement_id, data):
        if measurement_id in self.measurements.keys():
            self.measurements[measurement_id] = \
                    pd.concat([self.measurements[measurement_id], data])
        
        else:
            self.measurements[measurement_id] = data
            self.measurements[measurement_id].name = measurement_id

    def __str__(self):
        return self.name

from catchment.models import Site

FP35 = Site('FP35')
print(FP35)

FP35

These dunder methods are not usually called directly, but rather provide the implementation of some functionality we can use - we didn’t call FP35.__str__(), but it was called for us when we did print(FP35). Some we see quite commonly are:

__str__ - converts an object into its string representation, used when you call str(object) or print(object)
__getitem__ - Accesses an object by key, this is how list[x] and dict[x] are implemented
__len__ - gets the length of an object when we use len(object) - usually the number of items it contains

There are many more described in the Python documentation, but it’s also worth experimenting with built in Python objects to see which methods provide which behaviour. For a more complete list of these special methods, see the Special Method Names section of the Python documentation.

Exercise: A Basic Class

Implement a class to represent a book. Your class should:

Have a title

Have an author

When printed using print(book), show text in the format “title by author”
book = Book('A Book', 'Me')

print(book)
A Book by Me
Solution
class Book:
    def __init__(self, title, author):
        self.title = title
        self.author = author

    def __str__(self):
        return self.title + ' by ' + self.author

Properties

The final special type of method we will introduce is a property. Properties are methods which behave like data - when we want to access them, we do not need to use brackets to call the method manually.

For example, we will add a method which will return the last data point in each measurement series, combined into a single dataframe:

# file: catchment/models.py

class Site:
    ...

    @property
    def last_measurements(self):
        return pd.concat(
            [self.measurements[key].series[-1:] for key in self.measurements.keys()],
            axis=1).sort_index()

from catchment.models import Site
import pandas as pd
import datetime

PL23 = Site('PL23')

riverlevel_data = pd.Series(
    [34.0,32.0,33.0,31.0],
    index=[
        datetime.date(2000,1,1),
        datetime.date(2000,1,2),
        datetime.date(2000,1,3),
        datetime.date(2000,1,4),
        ]
    )

waterph_data = pd.Series(
    [7.8,8.0,7.9],
    index=[
        datetime.date(2000,1,1),
        datetime.date(2000,1,2),
        datetime.date(2000,1,3)
        ]
    )

PL23.add_measurement('River Level', riverlevel_data)
PL23.add_measurement('Water pH', waterph_data)

lastdata = PL23.last_measurements
print(lastdata)

            River Level  Water pH
2000-01-03          NaN       7.9
2000-01-04         31.0       NaN

You may recognise the @ syntax from episodes on parameterising unit tests and functional programming - property is another example of a decorator. In this case the property decorator is taking the last_measurements function and modifying its behaviour, so it can be accessed as if it were a normal attribute. It is also possible to make your own decorators, but we won’t cover it here.

Relationships Between Classes

We now have a language construct for grouping data and behaviour related to a single conceptual object. The next step we need to take is to describe the relationships between the concepts in our code.

There are two fundamental types of relationship between objects which we need to be able to describe:

Ownership - x has a y - this is composition
Identity - x is a y - this is inheritance

Composition

You should hopefully have come across the term composition already - in the novice Software Carpentry, we use composition of functions to reduce code duplication. That time, we used a function which converted temperatures in Celsius to Kelvin as a component of another function which converted temperatures in Fahrenheit to Kelvin.

In the same way, in object oriented programming, we can make things components of other things.

We often use composition where we can say ‘x has a y’ - for example in our catchment study project, we might want to say that a catchment area has measurement sites or that a measurement site has a collection of measurement sets.

In the case of our example, we have said any given measurement site has a collection of measurement sets, so we’re already using composition here. We’re currently implementing the collection of measurement sets as a dictionary with a known set of keys though, so maybe we should make a MeasurementSeries class as well. This class will contain the Pandas Series it replaces, but enable us to now associate extra information and methods with that dataset.

# file: catchment/models.py

class MeasurementSeries:
    def __init__(self, series, name, units):
        self.series = series
        self.name = name
        self.units = units
        self.series.name = self.name
    
    def add_measurement(self, data):
        self.series = pd.concat([self.series,data])
        self.series.name = self.name
    
    def __str__(self):
        if self.units:
            return f"{self.name} ({self.units})"
        else:
            return self.name


class Site:
    def __init__(self,name):
        self.name = name
        self.measurements = {}
    
    def add_measurement(self, measurement_id, data, units=None):    
        if measurement_id in self.measurements.keys():
            self.measurements[measurement_id].add_measurement(data)
        
        else:
            self.measurements[measurement_id] = MeasurementSeries(data, measurement_id, units)
    
    @property
    def last_measurements(self):
        return pd.concat(
            [self.measurements[key].series[-1:] for key in self.measurements.keys()],
            axis=1).sort_index()
    
    def __str__(self):
        return self.name

from catchment.models import Site
import pandas as pd

PL23 = Site('PL23')

riverlevel_data = pd.Series(
    [34.0,32.0,33.0,31.0],
    index=[
        datetime.date(2000,1,1),
        datetime.date(2000,1,2),
        datetime.date(2000,1,3),
        datetime.date(2000,1,4),
        ]
    )

waterph_data = pd.Series(
    [7.8,8.0,7.9],
    index=[
        datetime.date(2000,1,1),
        datetime.date(2000,1,2),
        datetime.date(2000,1,3)
        ]
    )

PL23.add_measurement('River Level', riverlevel_data, 'mm')
PL23.add_measurement('Water pH', waterph_data)


print(PL23.measurements['River Level'])
print(PL23.measurements['Water pH'])

lastdata = PL23.last_measurements
print(lastdata)

River Level (mm)
Water pH
            River Level  Water pH
2000-01-03          NaN       7.9
2000-01-04         31.0       NaN

Note that, within the Site class, we now access the measurement series by adding .series to the end of the self.measurements[measurement_id] object.

Note also how we used units=None in the parameter list of the add_measurement method, enabling us to still initialise the MeasurementSet class even if the end user doesn’t supply the measurement unit information. This is one of the common ways to handle an optional argument in Python, so we’ll see this pattern quite a lot in real projects.

Now we’re using a composition of two custom classes to describe the relationship between two types of entity in the system that we’re modelling.

Inheritance

The other type of relationship used in object oriented programming is inheritance. Inheritance is about data and behaviour shared by classes, because they have some shared identity - ‘x is a y’. If class X inherits from (is a) class Y, we say that Y is the superclass or parent class of X, or X is a subclass of Y.

If we want to extend the previous example to also manage locations which aren’t measurement sites we can add another class Location. But Location will share some data and behaviour with Site - in this case both have a name and show that name when you print them. Since we expect all sites to be locations, it makes sense to implement the behaviour in Location and then reuse it in Site.

To write our class in Python, we used the class keyword, the name of the class, and then a block of the functions that belong to it. If the class inherits from another class, we include the parent class name in brackets.

# file: catchment/models.py

class MeasurementSeries:
    def __init__(self, series, name, units):
        self.series = series
        self.name = name
        self.units = units
        self.series.name = self.name
    
    def add_measurement(self, data):
        self.series = pd.concat([self.series,data])
        self.series.name = self.name
    
    def __str__(self):
        if self.units:
            return f"{self.name} ({self.units})"
        else:
            return self.name

class Location:
    def __init__(self, name):
        self.name = name

    def __str__(self):
        return self.name

class Site(Location):
    def __init__(self,name):
        super().__init__(name)
        self.measurements = {}
    
    def add_measurement(self, measurement_id, data, units=None):    
        if measurement_id in self.measurements.keys():
            self.measurements[measurement_id].add_measurement(data)
    
        else:
            self.measurements[measurement_id] = MeasurementSeries(data, measurement_id, units)
    
    @property
    def last_measurements(self):
        return pd.concat(
            [self.measurements[key].series[-1:] for key in self.measurements.keys()],
            axis=1).sort_index()

from catchment.models import Site
import pandas as pd
import datetime

FP23 = Site('FP23')

print(FP23)

riverlevel_data = pd.Series(
    [34.0,32.0,33.0,31.0],
    index=[
        datetime.date(2000,1,1),
        datetime.date(2000,1,2),
        datetime.date(2000,1,3),
        datetime.date(2000,1,4),
        ]
    )

FP23.add_measurement('River Level',riverlevel_data,'mm')

print(FP23.measurements['River Level'].series)

PL12 = Location('PL12')
print(PL12)

PL12.add_measurement('River Level',riverlevel_data,'mm')

FP23
2000-01-01  34.0
2000-01-02  32.0
2000-01-03  33.0
2000-01-04  31.0
name: River Level, dtype: float 64 
PL12
...
AttributeError: 'Location' object has no attribute 'add_measurement'

As expected, an error is thrown because we cannot add measurement data to PL12, which is a Location but not a Site.

We see in the example above that to say that a class inherits from another, we put the parent class (or superclass) in brackets after the name of the subclass.

There’s something else we need to add as well - Python doesn’t automatically call the __init__ method on the parent class if we provide a new __init__ for our subclass, so we’ll need to call it ourselves. This makes sure that everything that needs to be initialised on the parent class has been, before we need to use it. If we don’t define a new __init__ method for our subclass, Python will look for one on the parent class and use it automatically. This is true of all methods - if we call a method which doesn’t exist directly on our class, Python will search for it among the parent classes. The order in which it does this search is known as the method resolution order - a little more on this in the Multiple Inheritance callout below.

The line super().__init__(name) gets the parent class, then calls the __init__ method, providing the name variable that Location.__init__ requires. This is quite a common pattern, particularly for __init__ methods, where we need to make sure an object is initialised as a valid X, before we can initialise it as a valid Y - e.g. a valid Location must have a name, before we can properly initialise a Site model with the corresponding measurement data.

Composition vs Inheritance

When deciding how to implement a model of a particular system, you often have a choice of either composition or inheritance, where there is no obviously correct choice. For example, it’s not obvious whether a photocopier is a printer and is a scanner, or has a printer and has a scanner.
class Machine:
    pass

class Printer(Machine):
    pass

class Scanner(Machine):
    pass

class Copier(Printer, Scanner):
    # Copier `is a` Printer and `is a` Scanner
    pass
class Machine:
    pass

class Printer(Machine):
    pass

class Scanner(Machine):
    pass

class Copier(Machine):
    def __init__(self):
        # Copier `has a` Printer and `has a` Scanner
        self.printer = Printer()
        self.scanner = Scanner()
Both of these would be perfectly valid models and would work for most purposes. However, unless there’s something about how you need to use the model which would benefit from using a model based on inheritance, it’s usually recommended to opt for composition over inheritance. This is a common design principle in the object oriented paradigm and is worth remembering, as it’s very common for people to overuse inheritance once they’ve been introduced to it.

For much more detail on this see the Python Design Patterns guide.

Multiple Inheritance

Multiple Inheritance is when a class inherits from more than one direct parent class. It exists in Python, but is often not present in other Object Oriented languages. Although this might seem useful, like in our inheritance-based model of the photocopier above, it’s best to avoid it unless you’re sure it’s the right thing to do, due to the complexity of the inheritance heirarchy. Often using multiple inheritance is a sign you should instead be using composition - again like the photocopier model above.

Exercise: A Model Site

Let’s use what we have learnt in this episode and combine it with what we have learnt on software requirements to formulate and implement a few new solution requirements to extend the model layer of our measurement campaign system.

Let’s can start with extending the system such that there must be a Catchment class to hold the data representing a single catchment, which:

must have a name attribute
must have a dictionary of sites that are within this catchment area.

In addition to these, try to think of an extra feature you could add to the models which would be useful for managing a dataset like this - imagine we’re running a field measurement campaign, what else might we want to know? Try using Test Driven Development for any features you add: write the tests first, then add the feature. The tests have been started for you in tests/test_sites.py, but you will probably want to add some more.

Once you’ve finished the initial implementation, do you have much duplicated code? Is there anywhere you could make better use of composition or inheritance to improve your implementation?

For any extra features you’ve added, explain them and how you implemented them to your neighbour. Would they have implemented that feature in the same way?

Solution

One example solution is shown below. You may start by writing some tests (that will initially fail), and then develop the code to satisfy the new requirements and pass the tests.

# file: tests/test_sites.py   
"""Tests for the Site model."""    

def test_create_site():
    """Check a site is created correctly given a name."""
    from catchment.models import Site
    name = 'PL23'
    p = Site(name=name)
    assert p.name == name

def test_create_catchment():
    """Check a catchment is created correctly given a name."""
    from catchment.models import Catchment
    name = 'Spain'
    catchment = Catchment(name=name)
    assert catchment.name == name

def test_catchment_is_location():
    """Check if a catchment is a location."""
    from catchment.models import Catchment, Location
    catchment = Catchment("Spain")
    assert isinstance(catchment, Location)

def test_site_is_location():
    """Check if a site is a location."""
    from catchment.models import Site, Location
    PL23 = Site("PL23")
    assert isinstance(PL23, Location)

def test_sites_added_correctly():
    """Check sites are being added correctly by a catchment. """
    from catchment.models import Catchment, Site
    catchment = Catchment("Spain")
    PL23 = Site("PL23")
    catchment.add_site(PL23)
    assert catchment.sites is not None
    assert len(catchment.sites) == 1

def test_no_duplicate_sites():
    """Check adding the same site to the same catchment twice does not result in duplicates. """
    from catchment.models import Catchment, Site
    catchment = Catchment("Sheila Wheels")
    PL23 = Site("PL23")
    catchment.add_site(PL23)
    catchment.add_site(PL23)
    assert len(catchment.sites) == 1   
...

# file: catchment/models.py
...
class Location:
    """A Location."""
    def __init__(self, name):
        self.name = name

    def __str__(self):
        return self.name

class Site(Location):
    """A measurement site in the study."""
    def __init__(self, name):
        super().__init__(name)
        self.measurements = {}

   def add_measurement(self, measurement_id, data, units=None):    
       if measurement_id in self.measurements.keys():
           self.measurements[measurement_id].add_measurement(data)
    
       else:
            self.measurements[measurement_id] = MeasurementSeries(data, measurement_id, units)
   
   @property
   def last_measurements(self):
       return pd.concat(
           [self.measurements[key].series[-1:] for key in self.measurements.keys()],
           axis=1).sort_index()


class Catchment(Location):
    """A catchment area in the study."""
    def __init__(self, name):
        super().__init__(name)
        self.sites = {}


    def add_site(self, new_site):
        # Basic check to see if the site has already been added to the catchment area 
        for site in self.sites:
            if site == new_site:
                print(f'{new_site} has already been added to site list')
                return

        self.sites[new_site.name] = Site(new_site)
...

Geospatial Data

Once we have objects for both Sites and Catchments we can make use of the Geopandas library and geospatial data for each Site and Catchment to check the relationships between these. This is covered in the extra episode on Geospatial data with Geopandas.

Key Points

Object oriented programming is a programming paradigm based on the concept of classes, which encapsulate data and code.

Classes allow us to organise data into distinct concepts.

By breaking down our data into classes, we can reason about the behaviour of parts of our data.

Relationships between concepts can be described using inheritance (is a) and composition (has a).

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Intermediate Research Software Development

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Object Oriented Programming

Overview

Introduction

Structuring Data

Exercise: Structuring Data

Solution

A Better Solution

Classes in Python

Encapsulating Data

Encapsulating Behaviour

Class and Static Methods

Dunder Methods

Exercise: A Basic Class

Solution

Properties

Relationships Between Classes

Composition

Inheritance

Composition vs Inheritance

Multiple Inheritance

Exercise: A Model Site

Solution

Geospatial Data

Key Points

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