In this chapter, we will look at how object-oriented programming (OOP) can be done using Python, how code can be arranged into modules, and how these modules can be used in scripts.
We will also have a quick look at how multithreading can be used within a Python script in order to perform multiple operations simultaneously within the program.
In this chapter, we will cover the following topics:
Object-oriented programming
Classes in Python
Threading
Object-oriented programming is a paradigm in which the program is structured around several objects rather than actions or functionality. The main difference is that in the procedural programming type that we have been using so far in the book, the focus is on the processing that is being carried out, whereas in object-oriented programming, the focus is on the data being processed.
To demonstrate this, we will write a simple module that provides an interface similar to a conventional calculator, that is, input in the form of a series of numerical values and operations.
The structure for this module is shown in the following Unified Modeling Language (UML) (www.uml.org) diagram:
Here each block represents a class, with the second square representing the member variables of the class and the bottom representing the functions.
An arrow indicates that a class inherits from the class it points to. This essentially means that the class inherits all of the parent class...
We will now look at how we create classes in Python in our calculator module.
First, we will create the Operation class. It is used to represent a single operation that can be performed by the Calculator class. Here, we are inheriting from the object class, which is the base class from which all the objects inherit in Python.
class Operation(object):
_operation = NoneThe __init__ function is the constructor of the class. It is called when a new instance of a class is created. Here, we will use it to validate the function that is provided by a parameter and store it in the class.
def __init__(self, name):
if name not in ["add", "subtract", "multiply", "divide"]:
raise ValueError("%s is not a valid operation" % (name))
self._operation = nameMethods in the Python classes are required to take a parameter typically named self. This parameter is the instance of the class that the function was called on, and it must be used when calling...
Multithreading allows an application to have multiple flows of control that are executed simultaneously. On the Raspberry Pi, this can be useful in applications that need to monitor the General Purpose Input and Output (GPIO) pins to react to the changes in switch and sensor states.
Multithreading is a large subject that can take a long time to cover completely, hence only a couple of the Python classes in the threading module will be covered here. However, the full documentation for this module is available at https://docs.python.org/2/library/threading.html.
First, we will import the required modules for the functionality that we will be using and will set up the default logger so that we can see the process ID when a log is written to it. You don't need to worry about this now, as we will be going over the logging framework in Python in a later chapter.
import logging
import threading
import time
logging.basicConfig(level=logging.INFO,
format="%(asctime)s...In this chapter, we looked at the object-oriented programming paradigm and how it can benefit applications, and the way that classes can be combined into modules and used in other scripts.
We also had a brief introduction to multithreading and the best practices for ensuring data validity between the threads that access shared data.
In the next chapter, we will have a look at the setuptools utility and see how it can be used to package the Python code for distribution to multiple systems, and how it can help manage dependencies on the other libraries.
© 2015 Packt Publishing Limited All Rights Reserved
