Commands

A .mod file ends with one or more commands that drive the solver.

simulate

simulate(T = 50, N = 250);

Solves the model on [0, T] discretised on N equal intervals (N + 1 grid points). The result is returned by the Python API (continuo.Model.simul()) as a Solution, or written to CSV by the CLI.

Arguments (all keyword):

T (required, float)

The simulation horizon. Positive.

N (required, int)

The number of grid intervals. Positive.

scheme (optional, string, default "crank_nicolson")

The discretisation scheme. Currently only crank_nicolson (implicit midpoint, second-order, A-stable) is implemented; other names parse but raise a SolveError at solve time.

A file may contain at most one simulate command. The Python API and the CLI both allow overriding T and N at the call site:

model.simul(horizon=100.0, intervals=500)

$ continuo model.mod -T 100 -N 500

steady

The steady command requests a diagnostic steady-state evaluation — it does not write a path, only reports the steady state at a specified time (or for a specified exogenous configuration).

steady;                          // SS at t = T (the terminal SS)
steady(t = 5);                   // SS at t = 5 under the active exogenous
steady(t = 0, e = {delta: 0.05});// SS at t = 0 with an explicit override

Arguments (all keyword, all optional):

t (float)

The time at which to evaluate the steady state. Defaults to the simulation horizon. Must be in [0, T].

e (mapping {varexo: value, …})

Exogenous override. Each key must be a declared varexo.

The Python API exposes the same calculation through continuo.Model.steady_state().

Note

The general steady_state(var, t=…, e={…}) callable inside model equations (the segment-aware terminal-SS reference described in the design spec) is not yet implemented. Inside initval, steady_state(var) and steady_state(var, e={…}) are honoured; see Initial conditions: initval and initial_guess.