mc-dagprop 0.1.10

Fast, Monte Carlo DAG propagation simulator with user‑defined delay distributions

mc_dagprop

mc_dagprop is a fast, Monte Carlo–style propagation simulator for directed acyclic graphs (DAGs), written in C++
with Python bindings via pybind11. It allows you to model timing networks (timetables, precedence graphs, etc.)
and inject user-defined delay distributions on edges.

---

Features

• Lightweight & high-performance core in C++
• Simple Python API via poetry or pip
• Custom per-activity-type delay distributions:
  • Constant (linear scaling)
  • Exponential (with cutoff)
  • Gamma (shape & scale)
  • Easily extendable (Weibull, etc.)
• Single-run (run(seed)) and batch-run (run_many([seeds]))
• Returns a SimResult: realized times, per-edge delays, and causal predecessors

---

Installation

# with poetry
poetry add mc_dagprop

# or with pip
pip install mc_dagprop

---

Quickstart

from mc_dagprop import (
    EventTimestamp,
    SimEvent,
    SimActivity,
    SimContext,
    GenericDelayGenerator,
    Simulator,
)

# 1) Build your DAG timing context
events = [
    SimEvent("A", EventTimestamp(0.0, 5.0, 2.0)),
    SimEvent("B", EventTimestamp(10.0, 15.0, 12.0)),
]

activities = {
    (0, 1): SimActivity(minimal_duration=60.0, activity_type=1),
}

precedence = [
    (1, [(0, 0)]),
]

ctx = SimContext(
    events=events,
    activities=activities,
    precedence_list=precedence,
    max_delay=1800.0,
)

# 2) Configure delay generator
gen = GenericDelayGenerator()
gen.add_constant(activity_type=1, factor=1.5)
gen.add_exponential(activity_type=1, lambda_=2.0, max_scale=5.0)
gen.add_gamma(activity_type=1, shape=2.0, scale=0.5)

# 3) Simulate
sim = Simulator(ctx, gen)
result = sim.run(seed=42)
print(result.realized, result.delays, result.cause_event)

---

API Reference

EventTimestamp(earliest: float, latest: float, actual: float)

Holds the scheduling window and actual time for one event (node):

• earliest – earliest possible occurrence
• latest – latest allowed occurrence
• actual – scheduled (baseline) timestamp

SimEvent(node_id: str, timestamp: EventTimestamp)

Wraps a DAG node with its identifier and timing stamp:

• node_id – string key for the node
• timestamp – an EventTimestamp instance

SimActivity(duration: float, activity_type: int)

Represents an edge in the DAG:

• minimal_duration – minimal (base) duration
• activity_type – integer type id

SimContext(events, activities, precedence_list, max_delay)

Container for your DAG:

• events: List[SimEvent]
• activities: Dict[(src_idx, dst_idx), SimActivity]
• precedence_list: List[(target_idx, [(pred_idx, act_idx), …])]
• max_delay: overall cap on delay propagation for an event

GenericDelayGenerator

Configurable delay factory:

• .add_constant(activity_type, factor)
• .add_exponential(activity_type, lambda_, max_scale)
• .add_gamma(activity_type, shape, scale)
• .set_seed(seed)

Simulator(context: SimContext, generator: GenericDelayGenerator)

• .run(seed: int) → SimResult
• .run_many(seeds: Sequence[int]) → List[SimResult]

SimResult

• .realized: List[float] – event times after propagation
• .delays: List[float] – per-edge injected delays
• .cause_event: List[int] – which predecessor caused each event

---

Visualization Demo

# install plotly to run the demo
pip install plotly

# then from your project root
python -m mc_dagprop.utils.demo_distributions

Displays histograms of the realized times under Constant, Exponential, and Gamma delays.

---

Development

git clone https://github.com/WonJayne/mc_dagprop.git
cd mc_dagprop
poetry install
poetry run pytest

---

License

MIT — see LICENSE