mayhap 0.2.0

Where your functions may happen... or not. Embrace the uncertainty in your code execution!

Mayhap

Mayhap: Where your functions may happen... or not.
Embrace the uncertainty in your code execution!

---

Overview

Mayhap is a whimsical Python package that allows you to introduce controlled randomness into your function executions. By decorating your functions with @maybe, you can specify various probability distributions to determine whether a function should execute or not. It's perfect for simulations, testing, or just adding a bit of unpredictability to your code.

---

Features

• Multiple Distributions: Choose from uniform, weighted, normal, exponential, Bernoulli distributions, or define your own custom logic.
• Easy Integration: Simply decorate your functions with @maybe and specify the desired distribution and parameters.
• Flexible Probability Control: Tailor the execution probability to fit your specific needs.
• Optional Verbosity: Control whether skipped executions print a message with the verbose flag.

---

Installation

Install Mayhap using pip:

pip install mayhap

---

Usage

Here's how you can use Mayhap in your projects:

Uniform Distribution

Execute a function with a fixed probability.

from mayhap import maybe

@maybe(distribution='uniform', probability=0.7)
def greet(name):
    print(f"Hello, {name}!")

greet('Alice')  # Has a 70% chance to print the greeting.

Weighted Distribution

Assign different weights to execution outcomes.

@maybe(distribution='weighted', weights=[3, 1])
def farewell(name):
    print(f"Goodbye, {name}!")

farewell('Bob')  # 'Goodbye, Bob!' is three times more likely to print than not.

Normal Distribution

Execution probability follows a normal (Gaussian) distribution.

@maybe(distribution='normal', mean=0.5, stddev=0.1)
def announce(event):
    print(f"Announcing {event}!")

announce('the event')  # Execution probability is centered around 50%.

Exponential Distribution

Execution probability decreases exponentially over time.

@maybe(distribution='exponential', lambd=1.0)
def notify(user):
    print(f"Notification sent to {user}.")

notify('Charlie')  # Execution probability decreases over time.

Bernoulli Distribution

Execute based on a Bernoulli trial with probability p.

@maybe(distribution='bernoulli', p=0.3)
def alert():
    print("Alert triggered!")

alert()  # Has a 30% chance to trigger the alert.

Custom Probability Function

Define your own logic for execution probability.

def custom_logic():
    # Custom conditions for execution
    return some_external_condition_check()

@maybe(distribution='custom', custom_func=custom_logic)
def process():
    print("Processing data.")

process()  # Executes based on custom logic.

Controlling Verbosity

You can control whether skipped function calls print a message with the verbose flag.

@maybe(distribution='uniform', probability=0.5, verbose=False)
def silent():
    print("Might run... but won't explain if not.")

---

Contributing

Contributions are welcome! If you'd like to improve Mayhap or add new features, please fork the repository and submit a pull request. For major changes, open an issue first to discuss your ideas.

---

License

This project is licensed under the GPL-3.0-only License.
See the LICENSE file for more details.

---

Mayhap: Embrace the uncertainty in your code execution!