A block-based time-domain system simulation framework in Python
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--- PathSim lets you model and simulate complex dynamical systems using an intuitive block diagram approach. Connect sources, integrators, functions, and scopes to build continuous-time, discrete-time, or hybrid systems. Minimal dependencies: just `numpy`, `scipy`, and `matplotlib`. ## Features - **Hot-swappable** — modify blocks and solvers during simulation - **Stiff solvers** — implicit methods (BDF, ESDIRK) for challenging systems - **Event handling** — zero-crossing detection for hybrid systems - **Hierarchical** — nest subsystems for modular designs - **Extensible** — subclass `Block` to create custom components ## Install ```bash pip install pathsim ``` or with conda: ```bash conda install conda-forge::pathsim ``` ## Quick Example ```python from pathsim import Simulation, Connection from pathsim.blocks import Integrator, Amplifier, Adder, Scope # Damped harmonic oscillator: x'' + 0.5x' + 2x = 0 int_v = Integrator(5) # velocity, v0=5 int_x = Integrator(2) # position, x0=2 amp_c = Amplifier(-0.5) # damping amp_k = Amplifier(-2) # spring add = Adder() scp = Scope() sim = Simulation( blocks=[int_v, int_x, amp_c, amp_k, add, scp], connections=[ Connection(int_v, int_x, amp_c), Connection(int_x, amp_k, scp), Connection(amp_c, add), Connection(amp_k, add[1]), Connection(add, int_v), ], dt=0.05 ) sim.run(30) scp.plot() ``` ## PathView [PathView](https://view.pathsim.org) is the graphical editor for PathSim — design systems visually and export to Python. ## Learn More - [Documentation](https://docs.pathsim.org) — tutorials, examples, and API reference - [Homepage](https://pathsim.org) — overview and getting started - [Contributing](https://docs.pathsim.org/pathsim/latest/contributing) — how to contribute ## Citation If you use PathSim in research, please cite: ```bibtex @article{Rother2025, author = {Rother, Milan}, title = {PathSim - A System Simulation Framework}, journal = {Journal of Open Source Software}, year = {2025}, volume = {10}, number = {109}, pages = {8158}, doi = {10.21105/joss.08158} } ``` ## License MIT ================================================ FILE: conftest.py ================================================ def pytest_addoption(parser): parser.addoption( "--run-all", action="store_true", default=False, help="Run all tests including slow eval tests.", ) def pytest_configure(config): if config.getoption("--run-all"): # Override the default marker filter config.option.markexpr = "" ================================================ FILE: docs/.readthedocs.yaml ================================================ # Read the Docs configuration file # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details # Required version: 2 # Set the OS, Python version, and other tools you might need build: os: ubuntu-24.04 tools: python: "3.13" # Build documentation in the "docs/source/" directory with Sphinx sphinx: configuration: docs/source/conf.py # Optionally, but recommended, # declare the Python requirements required to build your documentation # See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html python: install: - requirements: docs/requirements.txt - method: pip path: . ================================================ FILE: docs/Makefile ================================================ # Minimal makefile for Sphinx documentation # # You can set these variables from the command line, and also # from the environment for the first two. SPHINXOPTS ?= SPHINXBUILD ?= sphinx-build SOURCEDIR = docs BUILDDIR = build # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) .PHONY: help Makefile # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) ================================================ FILE: docs/requirements.txt ================================================ numpy>=1.15,<2 matplotlib>=3.1 requests>=2.28.0 scipy>=1.2 sphinx furo myst-parser sphinx-autoapi sphinx-copybutton sphinx-design nbsphinx jupyter ipykernel pandoc fmpy scikit-rf ================================================ FILE: docs/source/_ext/github_issues.py ================================================ import requests import os from datetime import datetime def fetch_github_issues(app, config): """Fetch GitHub issues and generate RST file""" # Configure these repo = "milanofthe/pathsim" token = os.environ.get('GITHUB_TOKEN') # API request url = f"https://api.github.com/repos/{repo}/issues" headers = {'Authorization': f'token {token}'} if token else {} params = { 'state': 'all', 'sort': 'created', 'direction': 'desc', 'per_page': 100 } try: response = requests.get(url, headers=headers, params=params) response.raise_for_status() issues = response.json() # Filter out pull requests issues = [i for i in issues if 'pull_request' not in i] # Generate RST file output_path = os.path.join(app.srcdir, 'roadmap_generated.rst') with open(output_path, 'w', encoding='utf-8') as f: f.write(".. raw:: html\n\n") f.write("Last updated: {datetime.now().strftime('%Y-%m-%d %H:%M UTC')}
\n\n") # Group by state open_issues = [i for i in issues if i['state'] == 'open'] if open_issues: for issue in open_issues: # Write only 'roadmap' labeled issues if any(label['name'] == 'roadmap' for label in issue.get('labels', [])): write_issue_html(f, issue) else: f.write("No roadmap items found.
\n") f.write("⚠️ Could not fetch issues from GitHub
\n") f.write("{body}
\n") f.write("📢 Redirecting to the new documentation site...
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A scalable block-based time-domain system simulation framework in Python with hierarchical modeling and event handling!
Last updated: 2025-11-20 23:01 UTC
The `Simulation` class is big (1.8k LOC) and has many responsibilities:
- managing all the blocks, connections and events and their states
- serialization and deserialization
- linearization and delinearization
- timestepping including system evaluation, event handling, ...
- 4 different timestepping strategies and 1 steady state solve method
This can probably be broken down to be more modular a...
Some runge kutta solvers such as `RKBS32` and `RKDP54` have the first-same-as-last (FSAL) property, where the last slope of the previous stage is the first slope of the next one. This saves one function evaluation per successful step and therefore increases solver efficiency.
Currently this is not implemented in pathsim, but it would give some additional 5% to 20% speedup for some solvers.
- implement real-time mode for the simulation with a tick-rate
- warnings when realtime is not reached, reducing tick-rate
You can never have enough examples.
Here is a list of ideas for example systems that want to be implemented and documented:
- Kalman Filter
- Chemical process
- DC motor control
- Inverted pendulum with controler
- Battery charging
- Phase-locked loop
Currently, PathSim is not explicitly typed. Adding type hints makes sense.
Saving simulation state as checkpoint and loading simulation state from checkpoints.
Sometimes in big complex systems, individual components have wildly different timescales for their physics. In some cases it makes sense to approximate components with very fast dynamics as being in a steady state at each timestep, such that the component becomes purely algebraic.
To achieve this, the time derivative of the block ode
```math
\dot{x} = f(x, u, t)
```
will be forced to zero (tr...
PathSim already supports adding and activating/deactivating blocks and connections at simulation runtime. For example through events. Whats missing is the capability to cleanly replace and remove blocks in a similar fashion.
**What this will enable:**
Imagine you are running a big system simulation with many (maybe hundreds) of blocks that might be small or large individual models themself. Some ...
PathSim has a decentralized architecture for the blocks which lends itself to parallelism and asynchronizity. Expensive blocks should compute asynchronously and not make the other blocks wait. With free-threading from Python 3.13, parallelization of the block updates is possible and has been verified with multiprocessing (slow but validation of the concept) for an earlier build.
Near linear scali...
Implement a `copy` method for the blocks, the `Subsystem` class, and the `Simulation`.
This should enable convenient copying of standard blocks for defining a system.
Implementing implicit-explicit ode solvers.
Some blocks in large systems may exhibit local stiffness while the coupling to external blocks is non-stiff. In these cases it would be nice to use more stable implicit ode solvers for these blocks while using explicit solvers for the other, non-stiff blocks.
The global solver would remain explicit, while locally, blocks can be flagged as stiff and t...
Using exponential integrators is a way to eliminate stiffness from linear dynamical systems. Many pathsim blocks are pure linear odes such as the `StateSpace` blocks and its derivates, as well as the `Differentiator` and the `PID`.
They are more or less of the following form:
```math
\dot{\vec{x}} = \mathbf{A} \vec{x} + \mathbf{B} \vec{u}
```
Stiffness occurs when the eigenvalues of A are on ...