Understanding Sum of Random Variables with Synchronized Timing in Python

Discover how to effectively sum random variable outputs from concurrent threads in Python using synchronized timing with the `sleep()` function.
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Understanding Sum of Random Variables with Synchronized Timing in Python

When working with multithreading in Python, you might encounter scenarios where two threads generate outputs that you want to sync up. A common problem arises when you want to sum two random numbers generated by two threads but only when both threads are printing at the same time. In this article, we will explore how to achieve that effectively.

The Challenge

The initial question is straightforward: how can you calculate the sum of random numbers from two separate outputs when they occur at the same time? In our example, we have two threads: one producing output every second and another every 1.5 seconds. The challenge is to sum the results when the print statements from both threads align.

Let's explore the code and see how we can solve this problem.

The Initial Code Structure

Here's the original code snippet that generates random numbers and prints them in two different threads:

[[See Video to Reveal this Text or Code Snippet]]

This setup executes two functions concurrently, but does not handle the requirement to sum their results based on timing.

A Step-by-Step Solution

To solve this, we need a few modifications:

Record the Random Values:
We'll keep track of the random values generated by both threads using global variables.

Track Time:
We'll check the time in seconds when each thread prints its output.

Sum When Timings Match:
If both threads print during the same second, we will calculate and print the sum of their random outputs.

Updated Code

After incorporating the above logic, the revised code looks like:

[[See Video to Reveal this Text or Code Snippet]]

Explanation of Key Changes

Global Variables: We declare randomA, randomB, timeA, and timeB as global to access them across our thread functions.

Timing Checks: Each thread checks if timeA is equal to timeB after printing their respective outputs. If they match, the sum is calculated and printed.

Concurrency Management: Using threading allows both functions to execute independently but still coordinate based on time, enabling the desired summation when outputs align.

Conclusion

By implementing a few simple modifications to the original code, we were able to solve the problem of summing randomly generated numbers from two threads when their print timing aligns. This method allows for effective synchronization between threads, showcasing how powerful Python's threading capabilities can be when properly managed.

Feel free to try this code yourself, and you'll see how easy it is to achieve time-sensitive calculations with multithreading in Python!