In this tutorial, we are going to learn how to work with files in Python. Handling files is a fundamental skill in programming, essential for tasks like logging data, saving configurations, and processing input in robotics applications.
Working with Files: Create, Write, Close, Append, and Read
Let’s cover how to create, write, close, append, and read files using Python’s built-in functions.
Creating and Writing to a File
To create and write to a file, you can use Python’s open function with the ‘w’ mode. This mode creates the file if it does not exist and overwrites it if it does.
Create a file called file_handling.py inside the following folder: ~/Documents/python_tutorial.
It’s important to close the file when you’re done with it. Closing the file ensures that all changes are saved and frees up system resources.
Appending to a File
If you want to add content to a file without overwriting the existing content, you use the ‘a’ mode with the open function.
file = open('example.txt', 'a')
file.write('\nAdding a second line.')
file.close()
Reading from a File
To read the contents of a file, you use the ‘r’ mode with open. There are several methods to read, such as read(), readline(), which reads one line at a time, and readlines(), which returns a list of lines.
While using open() and close(), it’s safer to handle files with a context manager using the with statement. This ensures that the file is properly closed after its suite finishes, even if an exception occurs.
with open('example.txt', 'r') as file:
content = file.read()
print(content)
You should see the content of example.txt printed, which now includes both the original line and the appended line.
In robotics, file handling is important for tasks such as storing sensor readings, saving configuration settings, or logging events for diagnostics. Efficient file management can help maintain the integrity and performance of robotic systems.
That’s it for this tutorial on working with files in Python. Thanks, and I’ll see you in the next tutorial.
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.
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:
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:
This code creates two robots: “Atlas,” a humanoid robot with 5 sensors, and “Spot,” a quadruped robot with 4 sensors.
Now run this script.
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:
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.
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.
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.
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.
That’s it for this tutorial on handling exceptions. Thanks, and I’ll see you in the next tutorial.
