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.
Returns:

concept –

The pair of maximally irreducible cause/effect

repertoires that constitute the concept specified by the given mechanism.
Return type:

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.
Returns:constellation –
A tuple of all the Concepts in the
constellation.
Return type:``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.
Returns:constellation –
A tuple of all the Concepts in the
constellation.
Return type:``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.
Returns:constellation –
A tuple of all the Concepts in the
constellation.
Return type:``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.
Returns:

distance –

The distance between the two concepts in

concept-space.
Return type:

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.
Returns:

distance –

The distance between the two constellations in

concept-space.
Return type:

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.
Returns: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.
Return type: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.