pyingraph 0.1.3

A Python-based framework for graphically integrating multiple Python algorithms

🐷 PyG: PyInGraph

A Python framework for creating computational graphs where each node represents an algorithm block. Perfect for integrating multiple algorithms into a visual, executable workflow.

Quick Start

Installation

From PyPI (recommended):

pip install pyingraph

From source:

cd PyInGraph
pip install -e .

Try the Examples

Run the included demo to see PyInGraph in action:

cd examples
python demo_simple_add.py

This demo loads a simple graph that adds two numbers using custom algorithm blocks.

Key Features

• Easy Workflow Integration: Algorithms, with inputs/outputs/internal-states, can be easily integrated via a graph structure. This graph-based approach, besides being code-efficient, enables a broad category of usages, such as system simulation, AI workflows, and network analysis (e.g., connectivity, condensation, etc.).
• Local & Remote Modules: Load algorithms from files or HTTP repositories
• Built-in Visualization: See your computational graph with NetworkX
• Parameter Management: Configure algorithm parameters via JSON

How It Works

1. Create Algorithm Blocks

Each algorithm is a Python class that inherits from BlockBase:

Block 1 of 2: mod_source_constant.py

from pyingraph import BlockBase

class ConstantSource(BlockBase):
    """
    A constant source block that outputs a user-specified constant value.
    """
    def __init__(self):
        super().__init__()
        self.attrNamesArr = ["value"]  # Parameter name for the constant value
        self.value = 0.0  # Default value
    
    def read_inputs(self, inputs: list) -> None:
        pass
    
    def compute_outputs(self, time: float = None) -> list:
        return [self.value]
    
    def reset(self) -> None:
        pass

Block 2 of 2: mod_sink_print.py

from pyingraph import BlockBase

class SinkPrint(BlockBase):
    """
    A print sink block that prints all inputs it receives.
    This is useful for debugging and monitoring data flow in the graph.
    """
    def __init__(self):
        super().__init__()
        self.attrNamesArr = []  # No parameters needed
    
    def read_inputs(self, inputs: list) -> None:
        self.inputs_received = inputs
    
    def compute_outputs(self, time: float = None) -> list:
        print(f"SinkPrint received: {self.inputs_received}")
        return []  # Sink blocks typically don't produce outputs
    
    def reset(self) -> None:
        self.inputs_received = None

2. Define Your Graph

Create a JSON file graph_example.json describing your computational graph:

{
    "nodes": [
        {
            "id": "node1",
            "name": "Constant Source 1",
            "folder_url": "",
            "folder_path": "./",
            "class_file": "mod_source_constant.py",
            "class_name": "ConstantSource",
            "parameters": {
                "value": 1.0
            }
        },
        {
            "id": "node2",
            "name": "Sink print",
            "folder_url": "",
            "folder_path": "./",
            "class_file": "mod_sink_print.py",
            "class_name": "SinkPrint",
            "parameters": {}
        }
    ],
    "edges": [
        {
            "source_node_id": "node1",
            "target_node_id": "node2",
            "source_port_idx": 0,
            "target_port_idx": 0,
            "properties": {}
        }
    ]
}

3. Load and Run

from pyingraph import GraphLoader

loader = GraphLoader("graph_example.json", flag_remote=False)
loader.load()
nx_graph = loader.get_nx_graph()
loader.visualize_graph(block=True)  # See your graph
loader.simple_traverse_graph()  # Execute the graph

[!IMPORTANT] Make sure all module files are in the current or child directories of the above loader script. The project-folder-based path should be specified in the folder_path field of the graph JSON file.

Examples Included

The examples/ folder contains ready-to-run demos:

• demo_simple_add.py: Simple demo of a basic graph that adds two numbers, using local modules and graph
• demo_control_system.py: Control system simulation example, using remote modules and graph
• graph_simple_demo.json: Graph definition for the simple demo demo_simple_add.py
• local_modules/: Sample algorithm blocks:
  • mod_source_constant.py: Constant value source
  • mod_summer.py: Addition operation
  • mod_sink_print.py: Output print as display

Remote Module Support

Load algorithm blocks from HTTP repositories by setting flag_remote=True:

loader = GraphLoader("http://example.com/graph.json", flag_remote=True)

By default, flag_remote=False

Getting Started

1. Install: pip install -e .
2. Run demo: cd examples && python demo_simple_add.py
3. Study examples: Check out the local_modules/ for sample blocks
4. Create your own: Inherit from BlockBase and define your algorithm
5. Build graphs: Write JSON descriptions connecting your blocks

Dependencies

• Python >= 3.7
• numpy, matplotlib, networkx
• httpimport, requests (for remote modules)

Support

Questions? Contact: [e-mail]

License

MIT License - see LICENSE file for details.