Reactive Dataflow Graphs
Project description
Reactive-Dataflow
Reactive Processing Graphs for Python.
Getting Started
Installation
pip install reactivedataflow
The key dependencies for this project include rx
and networkx
. These are outlined in the pyproject.toml
dependencies section.
Usage
import reactivex as rx
from reactivedataflow import (
GraphAssembler,
GraphModel,
VerbNodeModel,
InputNodeModel,
InputModel,
verb,
InputPort,
ConfigPort
)
#
# Define a processing verb
#
@verb(
name="print",
ports=[
InputPort(name="values", required=True, parameter="val"),
ConfigPort(name="prefix", required=False, parameter="prefix"),
]
)
def print_verb(val, prefix=""):
return f"{prefix}{val}"
#
# Define a simple graph
#
assembler = GraphAssembler()
assembler.load(
GraphModel(
inputs=[
# This is an input stream of values we'll define on build
InputNodeModel(id="input_values")
],
nodes=[
# Here we define the processing nodes
VerbNodeModel(
id="verb1",
verb="print",
config={"prefix": "emitted: "},
input={
"values": InputModel(node="input_values")
}
),
],
),
)
#
# Build the graph and bind input streams
#
graph = assembler.build(
inputs={
"input_values": rx.of([1, 2, 3])
}
)
#
# Watch graph outputs
#
graph.output("verb1").subscribe(print)
# Output:
# emitted: 1
# emitted: 2
# emitted: 3
Developing
This project uses poetry
for dependency management. You should have a recent Python version (e.g. 3.10+) and Poetry 1.8+ installed on your system.
# Install dependencies
poetry install
# Run tests
poetry run poe test
# Run static checks
poetry run poe check
Project details
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