Simulation and Random Generation

simulate(network: technology_network) → dict[str, list[bool]]

simulate(network: aig_network) → dict[str, list[bool]]

simulate(network: xag_network) → dict[str, list[bool]]

simulate(network: mig_network) → dict[str, list[bool]]

simulate(layout: cartesian_gate_layout) → dict[str, list[bool]]

simulate(layout: shifted_cartesian_gate_layout) → dict[str, list[bool]]

simulate(layout: hexagonal_gate_layout) → dict[str, list[bool]]

simulate_outputs(network: technology_network) → list[tuple[str, list[bool]]]

simulate_outputs(network: aig_network) → list[tuple[str, list[bool]]]

simulate_outputs(network: xag_network) → list[tuple[str, list[bool]]]

simulate_outputs(network: mig_network) → list[tuple[str, list[bool]]]

simulate_outputs(layout: cartesian_gate_layout) → list[tuple[str, list[bool]]]

simulate_outputs(layout: shifted_cartesian_gate_layout) → list[tuple[str, list[bool]]]

simulate_outputs(layout: hexagonal_gate_layout) → list[tuple[str, list[bool]]]

Return (name, bits) pairs in output declaration order, preserving duplicate labels. Truth-table storage grows exponentially with the input count; fewer than 38 inputs is a representation bound, not a memory guarantee.

random_network(inputs: int, gates: int, seed: int, *, network_type: type[technology_network] = mnt.pyfiction.networks.technology_network) → technology_network

random_network(inputs: int, gates: int, seed: int, *, network_type: type[aig_network]) → aig_network

random_network(inputs: int, gates: int, seed: int, *, network_type: type[xag_network]) → xag_network

random_network(inputs: int, gates: int, seed: int, *, network_type: type[mig_network]) → mig_network

random_network(inputs: int, gates: int, seed: int, *, network_type: type[technology_network | aig_network | xag_network | mig_network]) → technology_network | aig_network | xag_network | mig_network

Overloaded function.

Overload 1

Generate directly in the selected network representation. network_type defaults to technology_network. Require at least two inputs and one gate; use the seed as the network name.

Overload 2

Overload 3

Overload 4

Overload 5

Random network generation accepts a keyword-only network_type class. The default is technology_network; aig_network, xag_network, and mig_network select their native representations. Pass the exact registered class; subclasses are not supported.

from mnt.pyfiction.networks import aig_network, random_network

random = random_network(3, 4, 42, network_type=aig_network)