# compute¶

Methods for computing concepts, constellations, and integrated information of subsystems.

pyphi.compute.concept(subsystem, mechanism)

Return the concept specified by a mechanism within a subsytem.

Parameters: subsystem (Subsytem) – The context in which the mechanism should be considered. mechanism (tuple(Node) – The candidate set of nodes. concept – The pair of maximally irreducible cause/effect repertoires that constitute the concept specified by the given mechanism. Concept

Note

The output can be persistently cached to avoid recomputation. This may be enabled in the configuration file—however, it is only available if the caching backend is a database (not the filesystem). See the documentation for the concept_caching and config modules.

pyphi.compute.sequential_constellation(subsystem, mechanism_indices_to_check=None)

Return the conceptual structure of this subsystem.

Parameters: subsystem (Subsystem) – The subsystem for which to determine the constellation. constellation – A tuple of all the Concepts in the constellation. tuple(Concept
pyphi.compute.parallel_constellation(subsystem, mechanism_indices_to_check=None)

Return the conceptual structure of this subsystem. Concepts are evaluated in parallel.

Parameters: subsystem (Subsystem) – The subsystem for which to determine the constellation. constellation – A tuple of all the Concepts in the constellation. tuple(Concept
pyphi.compute.constellation(subsystem, mechanism_indices_to_check=None)

Return the conceptual structure of this subsystem.

Parameters: subsystem (Subsystem) – The subsystem for which to determine the constellation. constellation – A tuple of all the Concepts in the constellation. tuple(Concept
pyphi.compute.concept_distance(c1, c2)

Return the distance between two concepts in concept-space.

Parameters: c1 (Mice) – The first concept. c2 (Mice) – The second concept. distance – The distance between the two concepts in concept-space. float
pyphi.compute.constellation_distance(C1, C2, subsystem)

Return the distance between two constellations in concept-space.

Parameters: C1 (tuple(Concept) – The first constellation. C2 (tuple(Concept) – The second constellation. null_concept (Concept) – The null concept of a candidate set, i.e the “origin” of the concept space in which the given constellations reside. distance – The distance between the two constellations in concept-space. float
pyphi.compute.conceptual_information(subsystem)

Return the conceptual information for a subsystem.

This is the distance from the subsystem’s constellation to the null concept.

pyphi.compute.big_mip(cache_key, subsystem)

Return the minimal information partition of a subsystem.

Parameters: subsystem (Subsystem) – The candidate set of nodes. big_mip – A nested structure containing all the data from the intermediate calculations. The top level contains the basic MIP information for the given subsystem. BigMip
pyphi.compute.big_phi(subsystem)

Return the $$\Phi$$ value of a subsystem.

pyphi.compute.subsystems(network)

Return a generator of all possible subsystems of a network.

pyphi.compute.all_complexes(network)

Return a generator for all complexes of the network, including reducible, zero-phi complexes (which are not, strictly speaking, complexes at all).

pyphi.compute.possible_complexes(network)

Return a generator of the subsystems of a network that could be a complex.

This is the just powerset of the nodes that have at least one input and output (nodes with no inputs or no outputs cannot be part of a main complex, because they do not have a causal link with the rest of the subsystem in the past or future, respectively).

pyphi.compute.complexes(network)

Return a generator for all irreducible complexes of the network.

pyphi.compute.main_complex(network)

Return the main complex of the network.

pyphi.compute.condensed(network)

Return the set of maximal non-overlapping complexes.