pygraph-sp 2026.3

A pure Rust graph executor supporting implicit node connections, branching, and config sweeps

graph-sp

graph-sp is a pure Rust grid/node graph executor and optimizer. The project focuses on representing directed dataflow graphs, computing port mappings by graph inspection, and executing nodes efficiently in-process with parallel CPU execution.

Core Features

• Implicit Node Connections: Nodes automatically connect based on execution order
• Parallel Branching: Create fan-out execution paths with .branch()
• Configuration Variants: Use .variant() to create parameter sweeps
• DAG Analysis: Automatic inspection and optimization of execution paths
• Mermaid Visualization: Generate diagrams with .to_mermaid()
• In-process Execution: Parallel execution using rayon

Installation

Rust

Add to your Cargo.toml:

[dependencies]
graph-sp = "0.1.0"

# Optional: For radar signal processing examples with ndarray and FFT support
[features]
radar_examples = ["graph-sp/radar_examples"]

For radar signal processing with ndarray and complex number support, enable the radar_examples feature.

Python

The library can also be used from Python via PyO3 bindings:

pip install pygraph-sp

Or build from source:

pip install maturin
maturin build --release --features python
pip install target/wheels/pygraph_sp-*.whl

Quick Start

Rust

Basic Sequential Pipeline

use graph_sp::{Graph, GraphData};
use std::collections::HashMap;

fn data_source(_: &HashMap<String, GraphData>, _: &HashMap<String, GraphData>) -> HashMap<String, GraphData> {
    let mut result = HashMap::new();
    result.insert("value".to_string(), GraphData::int(42));
    result
}

fn multiply(inputs: &HashMap<String, GraphData>, _: &HashMap<String, GraphData>) -> HashMap<String, GraphData> {
    let mut result = HashMap::new();
    if let Some(val) = inputs.get("x").and_then(|d| d.as_int()) {
        result.insert("doubled".to_string(), GraphData::int(val * 2));
    }
    result
}

fn main() {
    let mut graph = Graph::new();
    
    // Add source node
    graph.add(data_source, Some("DataSource"), None, Some(vec![("value", "data")]));
    
    // Add processing node
    graph.add(multiply, Some("Multiply"), Some(vec![("data", "x")]), Some(vec![("doubled", "result")]));
    
    let dag = graph.build();
    let context = dag.execute(false, None);
    
    println!("Result: {}", context.get("result").unwrap().to_string_repr());
}

Python

Basic Sequential Pipeline

import graph_sp

def data_source(inputs, variant_params):
    return {"value": "42"}

def multiply(inputs, variant_params):
    val = int(inputs.get("x", "0"))
    return {"doubled": str(val * 2)}

# Create graph
graph = graph_sp.PyGraph()

# Add source node
graph.add(
    function=data_source,
    label="DataSource",
    inputs=None,
    outputs=[("value", "data")]
)

# Add processing node
graph.add(
    function=multiply,
    label="Multiply",
    inputs=[("data", "x")],
    outputs=[("doubled", "result")]
)

# Build and execute
dag = graph.build()
context = dag.execute()

print(f"Result: {context['result']}")

Mermaid visualization output:

graph TD
    0["DataSource"]
    1["Multiply"]
    0 -->|data → x| 1

Parallel Branching (Fan-Out)

let mut graph = Graph::new();

// Source node
graph.add(source_fn, Some("Source"), None, Some(vec![("data", "data")]));

// Create parallel branches
graph.branch();
graph.add(stats_fn, Some("Statistics"), Some(vec![("data", "input")]), Some(vec![("mean", "stats")]));

graph.branch();
graph.add(model_fn, Some("MLModel"), Some(vec![("data", "input")]), Some(vec![("prediction", "model")]));

graph.branch();
graph.add(viz_fn, Some("Visualization"), Some(vec![("data", "input")]), Some(vec![("plot", "viz")]));

let dag = graph.build();

Mermaid visualization output:

graph TD
    0["Source"]
    1["Statistics"]
    2["MLModel"]
    3["Visualization"]
    0 -->|data → input| 1
    0 -->|data → input| 2
    0 -->|data → input| 3
    style 1 fill:#e1f5ff
    style 2 fill:#e1f5ff
    style 3 fill:#e1f5ff

DAG Statistics:

• Nodes: 4
• Depth: 2 levels
• Max Parallelism: 3 nodes (all branches execute in parallel)

Parameter Sweep with Variants

use graph_sp::{Graph, Linspace};

let mut graph = Graph::new();

// Source node
graph.add(source_fn, Some("DataSource"), None, Some(vec![("value", "data")]));

// Create variants for different learning rates
let learning_rates = vec![0.001, 0.01, 0.1, 1.0];
graph.variant("learning_rate", learning_rates);
graph.add(scale_fn, Some("ScaleLR"), Some(vec![("data", "input")]), Some(vec![("scaled", "output")]));

let dag = graph.build();

Mermaid visualization output:

graph TD
    0["DataSource"]
    1["ScaleLR (v0)"]
    2["ScaleLR (v1)"]
    3["ScaleLR (v2)"]
    4["ScaleLR (v3)"]
    0 -->|data → input| 1
    0 -->|data → input| 2
    0 -->|data → input| 3
    0 -->|data → input| 4
    style 1 fill:#e1f5ff
    style 2 fill:#e1f5ff
    style 3 fill:#e1f5ff
    style 4 fill:#e1f5ff
    style 1 fill:#ffe1e1
    style 2 fill:#e1ffe1
    style 3 fill:#ffe1ff
    style 4 fill:#ffffe1

DAG Statistics:

• Nodes: 5
• Depth: 2 levels
• Max Parallelism: 4 nodes
• Variants: 4 (all execute in parallel)

Radar Signal Processing Example

This example demonstrates a complete radar signal processing pipeline using GraphData with ndarray arrays and complex numbers. The pipeline implements:

1. LFM Pulse Generation - Creates a Linear Frequency Modulation chirp signal
2. Pulse Stacking - Accumulates multiple pulses with Doppler shifts
3. Range Compression - FFT-based matched filtering
4. Doppler Compression - Creates Range-Doppler map

Rust Implementation

use graph_sp::{Graph, GraphData};
use ndarray::Array1;
use num_complex::Complex;
use std::collections::HashMap;

// LFM pulse generator node
fn lfm_generator(_inputs: &HashMap<String, GraphData>, params: &HashMap<String, GraphData>) 
    -> HashMap<String, GraphData> {
    let num_samples = params.get("num_samples")
        .and_then(|d| d.as_int())
        .unwrap_or(256) as usize;
    
    let bandwidth = params.get("bandwidth")
        .and_then(|d| d.as_float())
        .unwrap_or(100e6); // 100 MHz
    
    let pulse_width = params.get("pulse_width")
        .and_then(|d| d.as_float())
        .unwrap_or(1e-6); // 1 microsecond
    
    // Generate LFM chirp signal
    let sample_rate = 100e6;
    let chirp_rate = bandwidth / pulse_width;
    let mut signal = Array1::<Complex<f64>>::zeros(num_samples);
    
    // ... signal generation code ...
    
    let mut output = HashMap::new();
    output.insert("pulse".to_string(), GraphData::complex_array(signal));
    output.insert("num_samples".to_string(), GraphData::int(num_samples as i64));
    output
}

// Stack pulses node
fn stack_pulses(inputs: &HashMap<String, GraphData>, params: &HashMap<String, GraphData>) 
    -> HashMap<String, GraphData> {
    let num_pulses = params.get("num_pulses")
        .and_then(|d| d.as_int())
        .unwrap_or(128) as usize;
    
    // Get input pulse as ComplexArray
    let pulse = inputs.get("pulse")
        .and_then(|d| d.as_complex_array())
        .unwrap().clone();
    
    // Stack with Doppler shifts
    // ... stacking logic ...
    
    let mut output = HashMap::new();
    output.insert("stacked".to_string(), GraphData::complex_array(stacked_data));
    output.insert("num_pulses".to_string(), GraphData::int(num_pulses as i64));
    output
}

fn main() {
    let mut graph = Graph::new();
    
    // Add LFM generator
    graph.add(
        lfm_generator,
        Some("LFMGenerator"),
        None,
        Some(vec![("pulse", "lfm_pulse"), ("num_samples", "num_samples")])
    );
    
    // Add pulse stacking
    graph.add(
        stack_pulses,
        Some("StackPulses"),
        Some(vec![("lfm_pulse", "pulse")]),
        Some(vec![("stacked", "stacked_data"), ("num_pulses", "num_pulses")])
    );
    
    // Add range compression
    graph.add(
        range_compress,
        Some("RangeCompress"),
        Some(vec![("stacked_data", "data"), ("lfm_pulse", "reference")]),
        Some(vec![("compressed", "compressed_data")])
    );
    
    // Add Doppler compression
    graph.add(
        doppler_compress,
        Some("DopplerCompress"),
        Some(vec![
            ("compressed_data", "data"),
            ("num_pulses", "num_pulses"),
            ("num_samples", "num_samples")
        ]),
        Some(vec![
            ("range_doppler", "range_doppler_map"),
            ("peak_value", "peak"),
            ("peak_doppler_bin", "peak_doppler"),
            ("peak_range_bin", "peak_range")
        ])
    );
    
    let dag = graph.build();
    let context = dag.execute(false, None);
    
    // Display results
    if let Some(peak) = context.get("peak").and_then(|d| d.as_float()) {
        println!("Peak magnitude: {:.2}", peak);
    }
    if let Some(doppler) = context.get("peak_doppler").and_then(|d| d.as_int()) {
        println!("Peak Doppler bin: {}", doppler);
    }
    if let Some(range) = context.get("peak_range").and_then(|d| d.as_int()) {
        println!("Peak Range bin: {}", range);
    }
}

Run the example:

cargo run --example radar_demo --features radar_examples

Mermaid visualization output:

graph TD
    0["LFMGenerator"]
    1["StackPulses"]
    2["RangeCompress"]
    3["DopplerCompress"]
    0 -->|lfm_pulse → pulse| 1
    1 -->|stacked_data → data| 2
    2 -->|compressed_data → data| 3

DAG Statistics:

• Nodes: 4
• Depth: 4 levels
• Max Parallelism: 1 node

Execution Output:

LFMGenerator: Generated 256 sample LFM pulse
StackPulses: Stacked 128 pulses with Doppler shifts
RangeCompress: Performed matched filtering on 32768 samples
DopplerCompress: Created Range-Doppler map of shape (128, 256)
  Peak at Doppler bin 13, Range bin 255
  Magnitude: 11974.31

Peak magnitude: 11974.31
Peak Doppler bin: 13
Peak Range bin: 255

Python Implementation

import graph_sp
import numpy as np

def lfm_generator(inputs, variant_params):
    """Generate LFM pulse with rectangular envelope."""
    num_samples = 256
    bandwidth = 100e6  # 100 MHz
    pulse_width = 1e-6  # 1 microsecond
    sample_rate = 100e6
    
    # Generate LFM chirp
    chirp_rate = bandwidth / pulse_width
    signal = np.zeros(num_samples, dtype=complex)
    
    # ... signal generation code ...
    
    # Return numpy array directly (no conversion needed)
    return {
        "pulse": signal,  # Can pass numpy arrays directly
        "num_samples": num_samples
    }

def stack_pulses(inputs, variant_params):
    """Stack multiple pulses with Doppler shifts."""
    num_pulses = 128
    
    # Get pulse data directly as complex array (implicit handling)
    pulse_data = inputs.get("pulse", [])
    pulse = np.array(pulse_data, dtype=complex)
    
    # Stack with Doppler shifts
    # ... stacking logic ...
    
    # Return numpy array directly (no conversion needed)
    return {
        "stacked": stacked,  # Can pass numpy arrays directly
        "num_pulses": num_pulses
    }

# Create graph
graph = graph_sp.PyGraph()

# Add nodes
graph.add(
    function=lfm_generator,
    label="LFMGenerator",
    inputs=None,
    outputs=[("pulse", "lfm_pulse"), ("num_samples", "num_samples")]
)

graph.add(
    function=stack_pulses,
    label="StackPulses",
    inputs=[("lfm_pulse", "pulse")],
    outputs=[("stacked", "stacked_data"), ("num_pulses", "num_pulses")]
)

graph.add(
    function=range_compress,
    label="RangeCompress",
    inputs=[("stacked_data", "data"), ("lfm_pulse", "reference")],
    outputs=[("compressed", "compressed_data")]
)

graph.add(
    function=doppler_compress,
    label="DopplerCompress",
    inputs=[
        ("compressed_data", "data"),
        ("num_pulses", "num_pulses"),
        ("num_samples", "num_samples")
    ],
    outputs=[
        ("range_doppler", "range_doppler_map"),
        ("peak_value", "peak"),
        ("peak_doppler_bin", "peak_doppler"),
        ("peak_range_bin", "peak_range")
    ]
)

# Build and execute
dag = graph.build()
context = dag.execute()

print(f"Peak magnitude: {context['peak']}")
print(f"Peak Doppler bin: {context['peak_doppler']}")
print(f"Peak Range bin: {context['peak_range']}")

Run the example:

python examples/python_radar_demo.py

Key Features Demonstrated

• Native Type Support: Uses GraphData::complex_array() for signal data, GraphData::int() for metadata
• No String Conversions: Numeric data stays in native format (i64, f64, Complex)
• Implicit Complex Number Handling: Python complex numbers (numpy.complex128, built-in complex) are automatically converted to/from GraphData::Complex without manual real/imag splitting
• Direct Numpy Array Support: Pass numpy ndarrays directly without .tolist() conversion - automatic detection and conversion
• Type Safety: Accessor methods (.as_complex_array(), .as_int(), .as_float()) provide safe type extraction
• Complex Signal Processing: Full FFT-based radar processing with ndarray integration

Adding Plotting Nodes

Plotting and visualization functions can be added as terminal nodes that take input but produce no output:

fn plot_range_doppler(inputs: &HashMap<String, GraphData>, _params: &HashMap<String, GraphData>) 
    -> HashMap<String, GraphData> {
    // Extract data for plotting
    if let Some(map) = inputs.get("range_doppler").and_then(|d| d.as_complex_array()) {
        // Generate plot (save to file, display, etc.)
        println!("Generating Range-Doppler map plot...");
        // ... plotting code using matplotlib, plotters, etc. ...
    }
    
    // No outputs - this is a terminal/visualization node
    HashMap::new()
}

// Add to graph
graph.add(
    plot_range_doppler,
    Some("PlotRangeDoppler"),
    Some(vec![("range_doppler_map", "range_doppler")]),
    None  // No outputs for visualization nodes
);

This pattern allows visualization and logging nodes to be integrated into the pipeline without affecting data flow.

API Overview

Rust API

Graph Construction

• Graph::new() - Create a new graph
• graph.add(fn, name, inputs, outputs) - Add a node
  • fn: Node function with signature fn(&HashMap<String, GraphData>, &HashMap<String, GraphData>) -> HashMap<String, GraphData>
  • name: Optional node name
  • inputs: Optional vector of (broadcast_var, impl_var) tuples for input mappings
  • outputs: Optional vector of (impl_var, broadcast_var) tuples for output mappings
• graph.branch() - Create a new parallel branch
• graph.variant(param_name, values) - Create parameter sweep variants
• graph.build() - Build the DAG

DAG Operations

• dag.execute() - Execute the graph and return execution context
• dag.stats() - Get DAG statistics (nodes, depth, parallelism, branches, variants)
• dag.to_mermaid() - Generate Mermaid diagram representation

Python API

The Python bindings provide a similar API with proper GIL handling:

Graph Construction

• PyGraph() - Create a new graph
• graph.add(function, label, inputs, outputs) - Add a node
  • function: Python callable with signature fn(inputs: dict, variant_params: dict) -> dict
  • label: Optional node name (str)
  • inputs: Optional list of (broadcast_var, impl_var) tuples or dict
  • outputs: Optional list of (impl_var, broadcast_var) tuples or dict
• graph.branch(subgraph) - Create a new parallel branch with a subgraph
• graph.build() - Build the DAG and return a PyDag

DAG Operations

• dag.execute() - Execute the graph and return execution context (dict)
• dag.execute_parallel() - Execute with parallel execution where possible (dict)
• dag.to_mermaid() - Generate Mermaid diagram representation (str)

GIL Handling

The Python bindings are designed with proper GIL handling:

• GIL Release: The Rust executor runs without holding the GIL, allowing true parallelism
• GIL Acquisition: Python callables used as node functions acquire the GIL only during their execution
• Thread Safety: The bindings use pyo3::prepare_freethreaded_python() (via auto-initialize) for multi-threaded safety

This means that while Python functions execute sequentially (due to the GIL), the Rust graph traversal and coordination happens in parallel without GIL contention.

Development

Rust Development

Prerequisites:

• Rust (stable toolchain) installed: https://www.rust-lang.org/tools/install

Build and run tests:

cargo build --release
cargo test

Run examples:

cargo run --example comprehensive_demo
cargo run --example parallel_execution_demo
cargo run --example variant_demo_full
cargo run --example radar_demo --features radar_examples

Python Development

Prerequisites:

• Python 3.8+ installed
• Rust toolchain installed

Build Python bindings:

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install maturin
pip install maturin==1.2.0

# Build and install in development mode
maturin develop --release --features python

# Run Python example
python examples/python_demo.py

Build wheel for distribution:

maturin build --release --features python
# Wheel will be in target/wheels/

Publishing

This repository is configured with GitHub Actions workflows to automatically publish to crates.io and PyPI when a release tag is pushed.

Required Repository Secrets

To enable automatic publishing, the repository owner must configure the following secrets in GitHub Settings → Secrets and variables → Actions:

• CRATES_IO_TOKEN: Your crates.io API token (obtain from https://crates.io/me)
• PYPI_API_TOKEN: Your PyPI API token (obtain from https://pypi.org/manage/account/token/)

Publishing Process

The publish workflow (.github/workflows/publish.yml) will automatically run when:

1. A tag matching v* is pushed (e.g., v0.1.0, v1.0.0)
2. The workflow is manually triggered via workflow_dispatch

Creating a release:

# Ensure version numbers in Cargo.toml and pyproject.toml are correct
git tag -a v0.1.0 -m "Release v0.1.0"
git push origin v0.1.0

The workflow will:

1. Build Python wheels for Python 3.8-3.11 on Linux, macOS, and Windows
2. Upload wheel artifacts to the GitHub Actions run (always, even without secrets)
3. Publish to PyPI (only if PYPI_API_TOKEN is set) - prebuilt wheels mean end users do not need Rust
4. Publish to crates.io (only if CRATES_IO_TOKEN is set)

Important notes:

• Installing from PyPI with pip install pygraph-sp will not require Rust on the target machine because prebuilt platform-specific wheels are published
• Both crates.io and PyPI will reject duplicate version numbers - update versions before tagging
• The workflow will continue even if tokens are not set, allowing you to download artifacts for manual publishing
• For local testing, you can build wheels with maturin build --release --features python

Manual Publishing

If you prefer to publish manually or need to publish from a local machine:

To crates.io:

cargo publish --token YOUR_CRATES_IO_TOKEN

To PyPI:

# Install maturin
pip install maturin==1.2.0

# Build and publish wheels
maturin publish --username __token__ --password YOUR_PYPI_API_TOKEN --features python