Python Project Setup Guide

This guide explains how to set up a Python environment, configure VSCode, and measure time and space complexity for your project. It includes a decorator for automatic time and memory profiling for future use.

Table of Contents

• 1. Setting Up a Virtual Environment
• 2. Configuring VSCode with the Virtual Environment
• 3. Measuring Time and Space Complexity
  • Time Complexity
  • Space Complexity
• 4. Automatic Time and Space Profiling with a Decorator

1. Setting Up a Virtual Environment

Step 1: Install virtualenv

Install virtualenv if it's not already installed:

pip install virtualenv

Step 2: Create a Virtual Environment

Create a virtual environment for your project:

virtualenv venv

Replace venv with the desired name for your environment.

Step 3: Activate the Virtual Environment

Activate the environment:

• On Windows:

  .\venv\Scripts\activate

• On macOS/Linux:

  source venv/bin/activate

Step 4: Deactivate the Environment

When done, deactivate the environment:

deactivate

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2. Configuring VSCode with the Virtual Environment

Step 1: Open VSCode

Launch VSCode and open your project folder.

Step 2: Select the Python Interpreter

1. Press Ctrl + Shift + P to open the command palette.
2. Type Python: Select Interpreter and select the virtual environment you created (env_name).

This ensures VSCode uses the correct Python environment for running your code.

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3. Measuring Time and Space Complexity

Time Complexity

To measure the time complexity of a function, use Python’s time module:

import time

def example_function():
    start_time = time.time()
    # A dummy function to simulate processing
    return sum([i ** 2 for i in range(n)])
    end_time = time.time()
    print(f"Execution time: {end_time - start_time} seconds")

Space Complexity

To measure memory usage, use the memory-profiler library.

Step 1: Install memory-profiler

pip install memory-profiler

Step 2: Use the @profile Decorator

Apply the @profile decorator to your function:

from memory_profiler import profile

@profile
def example_function():
    # A dummy function to simulate processing
    return sum([i ** 2 for i in range(n)])

Step 3: Run the Profiler

Run the script using:

python -m memory_profiler your_script.py

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4. Automatic Time and Space Profiling with a Decorator

To automatically measure time and space complexity in your next project, use this custom decorator:

from memory_profiler import memory_usage
import time

def time_and_space_profiler(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()
        mem_before = memory_usage()[0]

        result = func(*args, **kwargs)

        mem_after = memory_usage()[0]
        end_time = time.time()

        print(f"Execution time: {end_time - start_time} seconds")
        print(f"Memory usage: {mem_after - mem_before} MiB")

        return result
    return wrapper

# Example usage:
@time_and_space_profiler
def example_function():
    # A dummy function to simulate processing
    return sum([i ** 2 for i in range(n)])

# Test the decorator
example_function(1000000)

This decorator can be applied to any function to automatically report time and memory usage when the function is run.

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Next Steps

In your upcoming projects, you will use the above setup and tools to work efficiently with Python and measure both time and space complexity of your code. Happy coding!