pySimBlocks 0.1.0

A block-diagram simulation environment with PySide6 editor

pySimBlocks

A deterministic block-diagram simulation framework for discrete-time modeling, co-simulation and research prototyping in Python.

pySimBlocks allows you to build, configure, and execute discrete-time systems using either:

• A pure Python API
• A graphical editor (PySide6)
• YAML project configuration
• Optional SOFA and hardware integration

Features

• Block-based modeling (Simulink-like)
• Deterministic discrete-time simulation engine
• PySide6 graphical editor
• YAML-based project serialization
• Exportable Python runner (run.py)
• Extensible block architecture

Installation

From GitHub

Install directly from GitHub using pip:

pip install git+https://github.com/AlessandriniAntoine/pySimBlocks

Locally

Clone the repository and install locally:

git clone https://github.com/AlessandriniAntoine/pySimBlocks.git
cd pySimBlocks
pip install .

Getting Started

Quick Example

The following example models a simple first-order low-pass filter, defined by the difference equation:

$$ y[k] = \alpha x[k] + (1-\alpha) y[k-1] $$

It can be implemented in pySimBlocks using the following code:

from pySimBlocks import Model, Simulator, SimulationConfig, PlotConfig
from pySimBlocks.blocks.operators import Gain, Sum, Delay
from pySimBlocks.blocks.sources import WhiteNoise
from pySimBlocks.project.plot_from_config import plot_from_config

# 1. Create the blocks
noise = WhiteNoise(name="noise", std=1.0)
delay = Delay(name="delay")
filtered = Sum("filtered", signs="++")
alpha_gain = Gain(name="alpha", gain=0.1)
complement = Gain(name="complement", gain=0.9)

# 2. Build the model
model = Model("Example")
for block in [noise, delay, filtered, alpha_gain, complement]:
    model.add_block(block)

model.connect("noise", "out", "alpha", "in")
model.connect("delay", "out", "complement", "in")
model.connect("alpha", "out", "filtered", "in1")
model.connect("complement", "out", "filtered", "in2")
model.connect("filtered", "out", "delay", "in")

# 3. Simulate the model
sim_cfg = SimulationConfig(dt=0.05, T=30.)
sim = Simulator(model, sim_cfg)
logs = sim.run(logging=["noise.outputs.out", "filtered.outputs.out"])

# 4. Plot the results
plot_cfg = PlotConfig([
    {"title": "Noisy signal vs Filtered",
     "signals": ["noise.outputs.out", "filtered.outputs.out"],},
    ])
plot_from_config(logs, plot_cfg)

The resulting plot should look like this:

See examples/quick_start/filter.py to run the example yourself.

Graphical Editor

The exact same model can be constructed visually using the graphical editor (as shown in the image above of this README).

To open the graphical editor, run:

pysimblocks gui examples/quick_start/gui

The quick-start GUI project is stored in a single examples/quick_start/gui/project.yaml file.

Learning Resources

Tutorials

Three step-by-step tutorials are available detailed in the guide:

	Tutorial	Description
1	Python API	Build a closed-loop PI control system in pure Python
2	GUI	Build the same system visually with the graphical editor
3	SOFA	Replace the plant with a SOFA physics simulation

Other Examples

A collection of basic and advanced examples is available in the examples directory, including:

• Control system demonstrations
• SOFA-based simulations (tested with SOFA v24.06 and later.)
• Hardware and real-time use cases
• Comparisons with external tools

See examples/README.md for an overview.

Information

License

pySimBlocks is licensed under LGPL-3.0-or-later.

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© 2026 Université de Lille & INRIA – Licensed under LGPL-3.0-or-later