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radCAD

A cadCAD-compatible Python framework for modelling and simulating dynamical systems.

A dynamical system is one whose state evolves over time according to a fixed set of rules that depend on its current state and, optionally, external inputs. With radCAD you describe a system as a set of state transitions (encoding differential equations, agent behaviour, or other logic), then run parameter sweeps, Monte Carlo runs, and A/B tests over it in parallel across multiple processing backends.

import pandas as pd
from radcad import Model, Simulation

initial_state = {"x": 1}
params = {"growth": [1, 2]}

def policy(params, substep, state_history, previous_state):
    return {"dx": params["growth"]}

def update_x(params, substep, state_history, previous_state, policy_input):
    return "x", previous_state["x"] + policy_input["dx"]

state_update_blocks = [{
    "policies": {"growth_policy": policy},
    "variables": {"x": update_x},
}]

model = Model(initial_state=initial_state, state_update_blocks=state_update_blocks, params=params)
simulation = Simulation(model=model, timesteps=10, runs=1)
df = pd.DataFrame(simulation.run())

Where should I go?

  • Tutorials

    Learning-oriented. New to radCAD? Start here. Install the framework and build your first working model step by step.

  • How-to guides

    Task-oriented. How to get a specific job done: parameter sweeps, Monte Carlo runs, A/B tests, backends, performance, hooks, and more.

  • Reference

    Information-oriented. The full API, the model data structures, every Engine setting, and a glossary of terms.

  • Explanation

    Understanding-oriented. The concepts behind radCAD: dynamical systems, the architecture, the deepcopy performance trade-off, and how radCAD relates to cadCAD.

Why radCAD?

  • Simple API: describe a model, wrap it in a simulation, run it.
  • Fast: optimised for large parameter sweeps and Monte Carlo runs.
  • cadCAD compatible: standard functions, data structures, and simulation results, plus a compatibility mode for existing cadCAD models.
  • Extensible: hooks, iterable models, and typed dataclass parameters.
  • Robust exception handling: failed runs return partial results and tracebacks, so one error won't discard a long-running simulation.
  • Distributed execution: parallel processing across multiple backends, including remote Ray clusters.
  • Tested: against Python 3.10–3.12 on Linux, macOS, and Windows.

Open-source models using radCAD

Getting help