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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
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