Monte Carlo

Run Monte Carlo by mapping components to distributions and a per-trial analysis:

from cat.core.circuit import Circuit
from cat.core.components import Vdc, Resistor, Capacitor
from cat.core.net import GND
from cat.analysis import OP, monte_carlo, NormalPct

c = Circuit("rc")
V1, R1, C1 = Vdc("1", 5.0), Resistor("1", "1k"), Capacitor("1", "100n")
c.add(V1, R1, C1)
c.connect(V1.ports[0], R1.ports[0])
c.connect(R1.ports[1], C1.ports[0])
c.connect(V1.ports[1], GND)
c.connect(C1.ports[1], GND)

mc = monte_carlo(
    circuit=c,
    mapping={R1: NormalPct(0.05)},
    n=16,
    analysis_factory=lambda: OP(),
    seed=123,
)

df = mc.to_dataframe(param_prefix="param_")
print(df.head())

Tips: - Use workers for parallel execution: monte_carlo(..., workers=4). - Provide a metric function to compute scalar KPIs per trial, or set y=[...] and sample_at to extract trace values.

Progress bar / callback

Add a lightweight progress bar to stderr:

mc = monte_carlo(
    circuit=c,
    mapping={R1: NormalPct(0.01)},
    n=1000,
    analysis_factory=lambda: OP(),
    progress=True,   # prints: "MC: 317/1000 (31%)"
)

Or provide your own callback (done, total):

def cb(done: int, total: int) -> None:
    print(f"{done}/{total}", end="\r")

mc = monte_carlo(c, mapping={R1: NormalPct(0.01)}, n=1000,
                 analysis_factory=lambda: OP(), progress=cb)