znflow 0.1.0

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ZnFlow

The ZnFlow package provides a basic structure for building computational graphs based on functions or classes. It is designed as a lightweight abstraction layer to

• learn graph computing.
• build your own packages on top of it.

Installation

pip install znflow

Usage

Connecting Functions

With ZnFlow you can connect functions to each other by using the @nodify decorator. Inside the znflow.DiGraph the decorator will return a FunctionFuture object that can be used to connect the function to other nodes. The FunctionFuture object will also be used to retrieve the result of the function. Outside the znflow.DiGraph the function behaves as a normal function.

import znflow

@znflow.nodify
def compute_mean(x, y):
    return (x + y) / 2

print(compute_mean(2, 8))
# >>> 5

with znflow.DiGraph() as graph:
    mean = compute_mean(2, 8)

graph.run()
print(mean.result)
# >>> 5

with znflow.DiGraph() as graph:
    n1 = compute_mean(2, 8)
    n2 = compute_mean(13, 7)
    n3 = compute_mean(n1, n2)

graph.run()
print(n3.result)
# >>> 7.5

Connecting Classes

It is also possible to connect classes. They can be connected either directly or via class attributes. This is possible by returning znflow.Connections inside the znflow.DiGraph context manager. Outside the znflow.DiGraph the class behaves as a normal class.

In the following example we use a dataclass, but it works with all Python classes that inherit from znflow.Node.

import znflow
import dataclasses

@znflow.nodify
def compute_mean(x, y):
    return (x + y) / 2

@dataclasses.dataclass
class ComputeMean(znflow.Node):
    x: float
    y: float
    
    results: float = None
    
    def run(self):
        self.results = (self.x + self.y) / 2
        
with znflow.DiGraph() as graph:
    n1 = ComputeMean(2, 8)
    n2 = compute_mean(13, 7)
    # connecting classes and functions to a Node
    n3 = ComputeMean(n1.results, n2) 
    
graph.run()
print(n3.results)
# >>> 7.5