How to Create Classes and Objects in Python

In this tutorial, we are going to learn about one of the core concepts of object-oriented programming: classes and objects. 

Understanding how to use classes and objects is essential for structuring robust and maintainable code in robotics, where you often model real-world entities like robots, sensors, and actuators.

Prerequisites

Getting Comfortable with Classes and Objects

Let’s start by defining what a class is. Think of a class like a recipe for making cookies. Just as a recipe tells us what ingredients we need (these are called variables or attributes in programming) and what steps to follow (these are called methods or functions in programming), a class defines both the data and the actions that our program can use. The recipe itself isn’t a cookie – it’s just instructions, just like a class is just code until we use it.

An object is when you actually make the cookies using your recipe – in programming terms, we call this “instantiating a class.” When you just write down a recipe (define a class), you’re not using any real ingredients or kitchen space yet (no computer memory is used). But when you start baking (create an object), that’s when you use real ingredients and take up real counter space (the computer allocates actual memory for your object). Each batch of cookies you make from the same recipe is like creating a new object from your class – they use the same instructions but are separate, physical things in your kitchen (separate spots in computer memory).

Let’s create a simple class called Robot. This class will have some basic attributes like name and color, and a method that allows the robot to introduce itself.

Create a program called robot_class.py inside the following folder: ~/Documents/python_tutorial.

Write this code:

class Robot:
    def __init__(self, name, color):
        self.name = name
        self.color = color

    def introduce_self(self):
        print(f"Hello, my name is {self.name} and my color is {self.color}.")

The __init__ method is a special method called a constructor. It is called when a new object is instantiated, and it’s used to initialize the attributes of the class.

Now, let’s create an object of the Robot class:

r1 = Robot("Addison", "red")
r1.introduce_self()

This creates an instance of Robot named r1 with the name “Addison” and the color “red”. We then call the introduce_self method to make the robot introduce itself.

You should see the output:

1-robot-class

This demonstrates how we’ve created a Robot object and used its method to perform an action.

Using classes and objects in robotics programming allows you to encapsulate behaviors and states associated with specific robotic components or subsystems, making your code more modular, reusable, and easier to manage. For example, each sensor or actuator can be modeled as an object with its methods for starting, stopping, and processing data.

Implementing a Basic Constructor

Let’s learn how to implement a basic constructor in Python, specifically within the context of robotics. 

Constructors are important for initializing robot objects with specific settings or parameters right when they’re created.

In Python, a constructor is defined using the __init__ method of a class. It is automatically invoked when a new object of that class is created.

The __init__ method can take parameters that define the initial state of the object. This is essential for robotics, where each robot might need specific configurations right from the start.

Let’s create a simple class called Robot. This class will have attributes such as name, type, and sensor_count, which we will set using the constructor.

Create a program called robot_constructor.py inside the following folder: ~/Documents/python_tutorial.

Write this code:

class Robot:
    def __init__(self, name, type, sensor_count):
        self.name = name
        self.type = type
        self.sensor_count = sensor_count
        print(f"Robot created: {self.name}, a {self.type} robot with {self.sensor_count} sensors.")

Here, the __init__ method initializes each new Robot instance with a name, type, and sensor count. These attributes allow us to differentiate between various robots, catering to different roles and functionalities in a robotics lab or factory.

Now, let’s create instances of our Robot class to see the constructor in action:

r1 = Robot("Atlas", "Humanoid", 5)
r2 = Robot("Spot", "Quadruped", 4)

This code creates two robots: “Atlas,” a humanoid robot with 5 sensors, and “Spot,” a quadruped robot with 4 sensors.

Now run this script.

2-robot-constructor

You should see output indicating that the robots have been successfully created with their respective attributes. This output verifies that our constructor is working as expected, initializing each robot with its specific characteristics.

Overriding a Function

Let’s explore the concept of overriding functions in Python, particularly in the context of object-oriented programming. This technique is essential in robotics programming when you need different implementations of the same method in parent and child classes.

Let me explain how overriding works with an example from robotics. Imagine you have a basic robot (parent class) that can move and dock to a charging station. When you want to create a special type of robot (child class) that does these actions differently, that’s where overriding comes in.

In programming, overriding lets you take a behavior that already exists in a parent class (like how the robot moves) and give it a new set of instructions in the child class (like making a cleaning robot move in a specific pattern). It’s like saying “ignore the original instructions and use these new ones instead.”

Before we dive into overriding, you need to understand inheritance, which is how we create new types of robots based on our basic robot design. Just like a cleaning robot inherits all the basic features of the basic robot, a child class inherits all the code from its parent class.

Let’s create a base class called Robot. This class will have a simple method that describes the robot’s operation.

Create a file named function_override.py inside the following folder: ~/Documents/python_tutorial, and write this code:

class Robot:
    def action(self):
        print("Performing a generic action")

Here, our Robot class has one method, action, which prints a generic message about the robot’s activity.

Now, let’s create a subclass (also known as “child class”) called FlyingRobot that inherits from Robot class and overrides the action method to provide more specific behavior:

class FlyingRobot(Robot):
    def action(self):
        print("Flying")

In the FlyingRobot subclass, we redefine the action method. When we call the action method on an instance of FlyingRobot, it will print “Flying”, which is specific to flying robots, instead of the generic action message.

Let’s see this in action. We’ll create instances of both Robot and FlyingRobot and call their action method:

generic_robot = Robot()
generic_robot.action()  # This calls the base class method

flying_robot = FlyingRobot()
flying_robot.action()  # This calls the overridden method in the subclass

Save this script, and run it.

You should see this output:

3-function-overrride

This output shows how the action method behaves differently depending on whether it’s called on an instance of the Robot or the FlyingRobot.

Overriding methods is particularly beneficial in robotics when you deal with different types of robots that might share some functionalities but also have unique behaviors. By using overriding, you can ensure that each robot type performs its tasks appropriately while still inheriting from a common base class.

That’s it for this tutorial on function overriding. Thanks, and I’ll see you in the next tutorial.

Keep building!

How to Handle Errors in Python

In this tutorial, we are going to learn about how to handle exceptions in Python. Exception handling is important because it helps your programs to gracefully handle unexpected situations, like errors during execution, without crashing. This is particularly important in robotics, where an unhandled error can cause a robot to stop functioning or behave unpredictably. 

Prerequisites

When Things Go Wrong: Handling Exceptions

In Python, errors detected during execution are called exceptions. These might occur, for example, when trying to read a file that doesn’t exist or attempt to divide a value by zero. Without proper handling, these exceptions will cause your program to stop and raise an error message.

Let’s set up an example where errors might occur. We’ll attempt to divide two numbers, where the divisor is zero.

Open your code editor and create a new file named exception_handling.py inside the following folder: ~/Documents/python_tutorial.

def divide(x, y):
    try:
        result = x / y
        print(f"The result is {result}")
    except ZeroDivisionError:
        print("You can't divide by zero!")

Here, the try block contains the code that might cause an exception. The except block tells Python what to do when a particular type of exception arises—in this case, a ZeroDivisionError. 

You can catch different types of exceptions by specifying them explicitly, or catch all exceptions by just using except: without specifying an error type.

Now, let’s test our function with different inputs:

divide(10, 2)  # This should work fine
divide(5, 0)   # This should raise an exception

Run the script.

You should see the output showing the result of the first division and a message “You can’t divide by zero!” for the second. This demonstrates that our program can handle the ZeroDivisionError gracefully without crashing.

1-exception-handling

Now let’s try an advanced example. Create a file called advanced_exception_handling.py, and type the following code:

def divide_with_multiple_exceptions(x, y):
    try:
        # Convert inputs to float to handle string inputs
        x = float(x)
        y = float(y)
        result = x / y
        print(f"The result is {result}")
    except ZeroDivisionError:
        print("You can't divide by zero!")
    except ValueError:
        print("Please enter valid numbers!")
    except Exception as e:
        print(f"An unexpected error occurred: {e}")

# Test cases
print("Test 1: Normal division")
divide_with_multiple_exceptions(10, 2)

print("\nTest 2: Division by zero")
divide_with_multiple_exceptions(5, 0)

print("\nTest 3: Invalid input")
divide_with_multiple_exceptions("abc", 2)

print("\nTest 4: Float division")
divide_with_multiple_exceptions(10.5, 2.5)

This is an expanded version that shows how to handle multiple types of exceptions and includes more test cases. It:

  • Handles ZeroDivisionError
  • Handles ValueError for invalid inputs
  • Includes a general Exception catch for unexpected errors

In robotics, exception handling is essential for maintaining robust and reliable control. For instance, if a sensor fails or provides invalid data, handling these exceptions can allow the robot to continue operating or switch to a safe state rather than failing outright.

2-advanced-exception-handling

That’s it for this tutorial on handling exceptions. Thanks, and I’ll see you in the next tutorial.

Keep building!

How to Write Functions in Python

In this tutorial, we are going to dive into the world of functions in Python, an essential skill in your journey toward becoming a robotics engineer. Functions allow us to encapsulate code into reusable blocks, making our programs more modular, maintainable, and testable—qualities crucial in robotics software development.

Prerequisites

Using Functions

Let’s start by defining what a function is in Python. A function is a set of instructions that performs a specific task; it can take inputs, execute code, and return an output. To create a function, we use the def keyword followed by the function name and parentheses.

Let’s start by opening your code editor and creating a new file named robot_functions.py inside the following folder: ~/Documents/python_tutorial.

Let’s define a function called greet. This function will take a name as an input and print a greeting message. Here’s how you do it:

def greet(name):
    print(f"Hello, {name}! Welcome to the robotics world.")

Here, name is a parameter of the function greet. The code inside the function will output a personalized greeting message when it is called.

Now let’s see this function in action. Below the function definition, call the function by passing a name:

greet("Addison")

After typing this, save your file, and run it.

You should see this output:

1-robot-functions

This example demonstrates how your function takes an input and processes it to return a result.

Understanding Lambda Functions

Let’s explore lambda functions in Python. Lambda functions are small anonymous functions defined with the lambda keyword. They are syntactically restricted to a single expression. This makes them very useful for simple functionalities that don’t require extensive formatting in robotics programming.

In robotics, lambda functions can be particularly useful for handling events or data transformations quickly without needing to create separate, named functions. This can help keep your code clean and efficient, which is vital in complex robotic systems where performance is a priority.

Let’s start by opening your code editor and creating a new file named lambda_functions.py inside the following folder: ~/Documents/python_tutorial.

Create a lambda function that multiplies two numbers. Here’s how you do it in your editor:

multiply = lambda x, y: x * y

This lambda function takes two arguments, x and y, and returns their product. The expression after the colon is what the function will return when called.

Now, let’s use this function by calling it with two numbers:

result = multiply(4, 5)
print(f"The product is {result}")

You should see the output:

2-lambda-functions

This demonstrates how our lambda function quickly processes inputs and provides outputs without the need for defining a conventional function.

Working with Maps

Let’s explore how to work with Python’s built-in map function. This is a tool that applies a function to every item in an iterable, such as a list or tuple, and returns a map object, which is an iterator. This can be particularly useful in robotics for transforming data efficiently.

Whether it’s sensor data or commands to actuators, using map can help you manage data transformations cleanly and effectively across various components of your robotic systems.

Let’s start by understanding the syntax of the map function. The basic structure is map(function, iterable), where function is the function that you want to apply to each item in the iterable.

Create a file called convert_temperatures.py inside the following folder: ~/Documents/python_tutorial.

First, we’ll define a simple function that we will use with map. Let’s create a function that converts temperatures from Celsius to Fahrenheit:

def celsius_to_fahrenheit(c):
    return (c * 9/5) + 32

This function takes a Celsius value, converts it to Fahrenheit, and returns the result. It’s a simple calculation but imagine needing to convert a list of temperatures—this is where map comes in handy.

Now, let’s use the map function to apply our celsius_to_fahrenheit function to a list of temperatures:

temperatures_celsius = [0, 20, 30, 40]
temperatures_fahrenheit = list(map(celsius_to_fahrenheit, temperatures_celsius))

Let’s print out the Fahrenheit temperatures:

print(temperatures_fahrenheit)

Now, save your script, and run it.

3-convert-temperatures

You should see the output as a list of temperatures in Fahrenheit. This demonstrates how the map function has applied our conversion function to each element in the list efficiently.

Creating Filters

Let’s learn how to use the filter function in Python. 

Filter is a built-in function that offers a convenient way to filter items from an iterable—like lists or tuples—based on a function that tests each item in the iterable to be true or not. This functionality is incredibly useful in robotics for extracting relevant data from a larger dataset, such as sensor readings or logs.

The syntax for the filter function is: 

filter(function, iterable)

Here, function is a function that tests each item in the iterable to determine if it should be included in the result.

Create a file called filter_even_numbers.py inside the following folder: ~/Documents/python_tutorial.

Let’s define a function that checks if a number is even. This function will return True if the number is even, which tells the filter to include it in the output:

def is_even(num):
    return num % 2 == 0

This function uses the modulo operator % to check if the number has no remainder when divided by 2, meaning it’s even.

Now, let’s apply this function to a list of numbers using filter:

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
even_numbers = list(filter(is_even, numbers))

Let’s print out the even numbers:

print(even_numbers)

Save your script, and run it.

You should see this output:

4-filter-even-numbers

This list contains all the even numbers from our original list, filtered using our is_even function.

In robotics, you might use filters to process sensor data, like filtering out readings that are beyond a certain threshold or that don’t meet certain conditions. This helps in focusing on relevant data and reducing noise in the system’s input, which is crucial for performance and accuracy.

Working with the Reduce Function

Now let’s explore the reduce function in Python, which is a fundamental tool for performing cumulative operations on a list or any iterable. 

Unlike map and filter, reduce doesn’t come built into the basic set of Python functions; it is part of the functools module, emphasizing its utility for more specialized tasks, such as aggregating data values in a sequence.

To use reduce, you first need to import it from the functools module. Let’s do that and explore how reduce works.

Create a file named reduce_example.py inside the following folder: ~/Documents/python_tutorial.

Type this code:

from functools import reduce

The reduce function applies a given function to the items of an iterable, cumulatively, to reduce the iterable to a single value. This function takes two arguments at a time and continues to combine them until only one remains.

Let’s define a simple operation to use with reduce: a function that multiplies two numbers. This will help us calculate the product of a list of numbers:

def multiply(x, y):
    return x * y

Now, let’s use reduce to multiply all the numbers in a list together:

numbers = [1, 2, 3, 4, 5]
product = reduce(multiply, numbers)

Let’s print out the result of multiplying all these numbers:

print(f"The product of the numbers is: {product}")

Run the script.

You should see the output:

5-reduce-function

This shows how reduce took our list of numbers and reduced them to a single value by multiplying them together sequentially.

  1. First iteration:
    • Takes first two numbers: 1 and 2
    • multiply(1, 2) = 2
    • Result so far: 2
  2. Second iteration:
    • Takes previous result (2) and next number (3)
    • multiply(2, 3) = 6
    • Result so far: 6
  3. Third iteration:
    • Takes previous result (6) and next number (4)
    • multiply(6, 4) = 24
    • Result so far: 24
  4. And so on…

In robotics, reduce can be useful for processing sequences of data to extract single cumulative values, like summing up sensor measurements to calculate an average or total over time, or combining a series of incremental movements into a total displacement. 

That’s it for this tutorial on the reduce function. 

Thanks, and I’ll see you in the next tutorial.

Keep building!