menobis generate

TL;DR

Use menobis generate to emit one seeded synthetic EdgeTable. Most commands fit constraints from the input network first; the microcanonical command samples directly from derived constraints. Data goes to stdout unless --output is set.

The Python API is the authoritative full model interface. CLI commands expose a convenience subset for the most common routes and may retain command names that do not mirror the model ontology exactly.

Commands

Command	Ensemble	Family	Constraint	Notes
strength-poisson	grand-canonical	ME	strength	default route
strength-multinomial	canonical	ME	strength	fixed total \(T\), needs --total-events
strength-edges-poisson	grand-canonical	ME	strength-edges	optional --target-edges
strength-degree-poisson	grand-canonical	ME	strength-degree
degree-events-poisson	grand-canonical	ME	degree-events	needs --total-events
strength-cost-poisson	grand-canonical	ME	strength-cost	needs --coordinates
custom-poisson	grand-canonical	ME	custom rates	needs --total-events, --ensemble
strength-degree-mcmc	microcanonical	ME	strength-degree	exact \((s,k)\); extras-first constructor + degree-fiber trace; no fit

Examples

menobis generate strength-poisson edges.csv --seed 42 -o sample.csv
menobis generate strength-multinomial edges.csv --total-events 1000 --json
menobis generate custom-poisson probabilities.csv --total-events 1000 --ensemble poisson
menobis generate strength-cost-poisson edges.csv --coordinates xy.csv --seed 7
menobis generate strength-edges-poisson edges.csv --target-edges 500
menobis generate strength-degree-poisson edges.csv --seed 99
menobis generate degree-events-poisson edges.csv --total-events 5000
menobis generate strength-degree-mcmc edges.csv --seed 99 --no-self-loops

Options

Option	Meaning
--output, -o	Write edge table
--json	Print JSON to stdout
--quiet	Suppress progress
--seed, -s	Random seed
--self-loops/--no-self-loops	Diagonal handling
--total-events	Total \(T\) (multinomial, custom, degree-events)
--ensemble	poisson or multinomial (custom only)
--target-edges	Target \(E\) (strength-edges)
--coordinates	Projected XY coordinate CSV (strength-cost)
--burn-in-sweeps, --sweeps-per-sample	MCMC settings (strength-degree-mcmc)

Microcanonical sampling

The CLI exposes the fixed-strength-degree microcanonical route (strength-degree-mcmc), which samples directly from derived constraints with no fitting step. Use the Python API (sample_model with ensemble=Ensemble.MICROCANONICAL) for the complete supported family × ensemble × constraint matrix — see Supported models and Microcanonical sampling.