Factum

What if every function remembered its own output, and knew when and where to acquire its own inputs?

Factum is a simple agent-inspired framework for computational DAG composition and execution.

Theory

Object Oriented Programming (OOP) was originally conceived, at least in part, as an abstraction of the actor model of distributed functional programming in a serial computing environment.

Factum represents a simple return to that vision. It's a functional paradigm facilitated by an object oriented implementation.

Core to the idea is the Fact object: a class that defines inputs, a function and an output. The inputs can change, but they're not meant to. Thus the Factum represents the function in a particular context: a fact - it always represents the same information.

It's basically a function that is responsible for gathering its own inputs and caching its output (so other function objects can efficiently gather their own inputs).

These Fact objects, taken together are wired up to require inputs from each other and generally form a directed acyclic graph, though there is no mechanism to enforce a DAG structure.

Use

from factum import Fact

def a_node():
    print('A running!')
    return 1

def b_node():
    print('B running!')
    return 2

def c_node(*args):
    print('C running!')
    return args[0] + args[1]

def d_node(self, **kwargs):
    return self._transformation(**kwargs)

def _transformation(self, cthing):
    print('D running!')
    return cthing + 1

# Fact(function, inputs, name)

a = Fact(a_node)
b = Fact(b_node, None, 'Beta')
c = Fact(c_node)
a > c
b > c
d = Fact(d_node, {'cthing': c})
d.add_method(_transformation)

d.run()
>>> A running!
>>> B running!
>>> C running!
>>> D running!
>>> 4

# cached
d.run()  
>>> 4

b.run(-1)
>>> B running!
>>> 2

# re-runs downstream of change
d.run()
>>> C running!
>>> D running!
>>> 4

d.visualize()