reaktiv 0.3.0

Reactive signals for Python with async support

reaktiv

Reactive Signals for Python with first-class async support, inspired by Angular's reactivity model.

Why reaktiv?

If you've worked with modern frontend frameworks like React, Vue, or Angular, you're familiar with the power of reactive state management. The idea is simple but transformative: when data changes, everything that depends on it updates automatically. This is the magic behind dynamic UIs and real-time systems.

But why should Python miss out on the benefits of reactivity? reaktiv brings these reactive programming advantages to your Python projects:

• Automatic state propagation: Change a value once, and all dependent computations update automatically.
• Efficient updates: Only the necessary parts are recomputed.
• Async-friendly: Seamlessly integrates with Python's asyncio for managing real-time data flows.
• Zero external dependencies: Lightweight and easy to incorporate into any project.
• Type-safe: Fully annotated for clarity and maintainability.

Real-World Use Cases

Reactive programming isn't just a frontend paradigm. In Python, it can simplify complex backend scenarios such as:

• Real-time data streams: Stock prices, sensor readings, or live updates.
• User session management: Keep track of state changes without heavy manual subscription management.
• Complex API workflows: Automatically cascade changes across related computations.

By combining these features, reaktiv provides a robust foundation for building reactive, real-time systems - whether for data streaming, live monitoring, or even Python-powered UI frameworks.

How it Works

reaktiv provides three core primitives:

1. Signals: Store a value and notify dependents when it changes.
2. Computed Signals: Derive values that automatically update when dependencies change.
3. Effects: Run side effects when signals or computed signals change.

Core Concepts

graph LR
    A[Signal] -->|Value| B[Computed Signal]
    A -->|Change| C[Effect]
    B -->|Value| C
    B -->|Change| C
    C -->|Update| D[External System]
    
    classDef signal fill:#4CAF50,color:white;
    classDef computed fill:#2196F3,color:white;
    classDef effect fill:#FF9800,color:white;
    
    class A,B signal;
    class B computed;
    class C effect;

Installation

pip install reaktiv
# or with uv
uv pip install reaktiv

Quick Start

Basic Reactivity

import asyncio
from reaktiv import Signal, Effect

async def main():
    name = Signal("Alice")

    async def greet():
        print(f"Hello, {name.get()}!")

    # Create and schedule effect
    # IMPORTANT: Assign the Effect to a variable to ensure it is not garbage collected.
    greeter = Effect(greet)
    greeter.schedule()

    name.set("Bob")  # Prints: "Hello, Bob!"
    await asyncio.sleep(0)  # Process effects

asyncio.run(main())

Computed Values

from reaktiv import Signal, ComputeSignal

# Synchronous context example
price = Signal(100)
tax_rate = Signal(0.2)
total = ComputeSignal(lambda: price.get() * (1 + tax_rate.get()))

print(total.get())  # 120.0
tax_rate.set(0.25)
print(total.get())  # 125.0

Async Effects

import asyncio
from reaktiv import Signal, Effect

async def main():
    counter = Signal(0)

    async def print_counter():
        print(f"Counter value is: {counter.get()}")

    # IMPORTANT: Assign the Effect to a variable to prevent it from being garbage collected.
    counter_effect = Effect(print_counter)
    counter_effect.schedule()

    for i in range(1, 4):
        await asyncio.sleep(1)  # Simulate an asynchronous operation or delay.
        counter.set(i)

    # Wait a bit to allow the last effect to process.
    await asyncio.sleep(1)

asyncio.run(main())

Real-Time Example: Polling System

import asyncio
from reaktiv import Signal, ComputeSignal, Effect

async def main():
    candidate_a = Signal(100)
    candidate_b = Signal(100)
    
    total_votes = ComputeSignal(lambda: candidate_a.get() + candidate_b.get())
    percent_a = ComputeSignal(lambda: (candidate_a.get() / total_votes.get()) * 100)
    percent_b = ComputeSignal(lambda: (candidate_b.get() / total_votes.get()) * 100)

    async def display_results():
        print(f"Total: {total_votes.get()} | A: {candidate_a.get()} ({percent_a.get():.1f}%) | B: {candidate_b.get()} ({percent_b.get():.1f}%)")

    async def check_dominance():
        if percent_a.get() > 60:
            print("Alert: Candidate A is dominating!")
        elif percent_b.get() > 60:
            print("Alert: Candidate B is dominating!")

    # Assign effects to variables to ensure they are retained
    display_effect = Effect(display_results)
    alert_effect = Effect(check_dominance)
    
    display_effect.schedule()
    alert_effect.schedule()

    for _ in range(3):
        await asyncio.sleep(1)
        candidate_a.set(candidate_a.get() + 40)
        candidate_b.set(candidate_b.get() + 10)
    
    await asyncio.sleep(1)

asyncio.run(main())

Sample Output:

Total: 200 | A: 100 (50.0%) | B: 100 (50.0%)
Total: 250 | A: 140 (56.0%) | B: 110 (44.0%)
Total: 300 | A: 180 (60.0%) | B: 120 (40.0%)
Total: 350 | A: 220 (62.9%) | B: 130 (37.1%)
Alert: Candidate A is dominating!