`models.concept`¶

class pyphi.models.concept.Mip¶

A minimum information partition for \(\varphi\) calculation.

MIPs may be compared with the built-in Python comparison operators (<, >, etc.). First, phi values are compared. Then, if these are equal up to constants.PRECISION, the size of the mechanism is compared (exclusion principle).

phi¶: float – This is the difference between the mechanism’s unpartitioned and partitioned repertoires.

direction¶: str – Either DIRECTIONS[PAST] or DIRECTIONS[FUTURE]. The temporal direction specifiying whether this MIP should be calculated with cause or effect repertoires.

mechanism¶: tuple(int – The mechanism over which to evaluate the MIP.

purview¶: tuple(int – The purview over which the unpartitioned repertoire differs the least from the partitioned repertoire.

partition¶: tuple(Part, Part – The partition that makes the least difference to the mechanism’s repertoire.

unpartitioned_repertoire¶: np.ndarray – The unpartitioned repertoire of the mechanism.

partitioned_repertoire¶: np.ndarray – The partitioned repertoire of the mechanism. This is the product of the repertoires of each part of the partition.

__bool__()¶

A Mip is truthy if it is not reducible.

(That is, if it has a significant amount of \(\varphi\).)

to_json()¶

class pyphi.models.concept.Mice(mip)¶

A maximally irreducible cause or effect (i.e., “core cause” or “core effect”).

MICEs may be compared with the built-in Python comparison operators (<, >, etc.). First, phi values are compared. Then, if these are equal up to constants.PRECISION, the size of the mechanism is compared (exclusion principle).

phi¶: float – The difference between the mechanism’s unpartitioned and partitioned repertoires.

direction¶: str – Either DIRECTIONS[PAST] or DIRECTIONS[FUTURE]. If DIRECTIONS[PAST] (DIRECTIONS[FUTURE]), this represents a maximally irreducible cause (effect).

mechanism¶: list(int) – The mechanism for which the MICE is evaluated.

purview¶: list(int) – The purview over which this mechanism’s \(\varphi\) is maximal.

repertoire¶: np.ndarray – The unpartitioned repertoire of the mechanism over the purview.

mip¶: Mip – The minimum information partition for this mechanism.

to_json()¶

damaged_by_cut(subsystem)¶

Return True if this Mice is affected by the subsystem’s cut.

The cut affects the Mice if it either splits the Mice‘s mechanism or splits the connections between the purview and mechanism.

class pyphi.models.concept.Concept(phi=None, mechanism=None, cause=None, effect=None, subsystem=None, normalized=False)¶

A star in concept-space.

The phi attribute is the \(\varphi^{\textrm{max}}\) value. cause and effect are the MICE objects for the past and future, respectively.

Concepts may be compared with the built-in Python comparison operators (<, >, etc.). First, phi values are compared. Then, if these are equal up to constants.PRECISION, the size of the mechanism is compared.

phi¶: float – The size of the concept. This is the minimum of the \(\varphi\) values of the concept’s core cause and core effect.

mechanism¶: tuple(int – The mechanism that the concept consists of.

cause¶: |Mice| – The Mice representing the core cause of this concept.

effect¶: |Mice| – The Mice representing the core effect of this concept.

subsystem¶: Subsystem – This concept’s parent subsystem.

time¶: float – The number of seconds it took to calculate.

location¶: tuple(np.ndarray) – The concept’s location in concept space. The two elements of the tuple are the cause and effect repertoires.

__bool__()¶

A concept is truthy if it is not reducible.

(That is, if it has a significant amount of \(\Phi\).)

eq_repertoires(other)¶: Return whether this concept has the same cause and effect repertoires as another.

Warning

This only checks if the cause and effect repertoires are equal as arrays; mechanisms, purviews, or even the nodes that node indices refer to, might be different.

emd_eq(other)¶: Return whether this concept is equal to another in the context of an EMD calculation.

expand_cause_repertoire(new_purview=None)¶: Expand a cause repertoire into a distribution over an entire network.

expand_effect_repertoire(new_purview=None)¶: Expand an effect repertoire into a distribution over an entire network.

expand_partitioned_cause_repertoire()¶: Expand a partitioned cause repertoire into a distribution over an entire network.

expand_partitioned_effect_repertoire()¶: Expand a partitioned effect repertoire into a distribution over an entire network.

to_json()¶

class pyphi.models.concept.Constellation¶

A constellation of concepts.

This is a wrapper around a tuple to provide a nice string representation and place to put constellation methods. Previously, constellations were represented as tuple(|Concept|); this usage still works in all functions.

to_json()¶

pyphi.models.concept.normalize_constellation(constellation)¶

Deterministically reorder the concepts in a constellation.

Parameters:	constellation (Constellation) – The constellation in question.

Returns

Constellation: The constellation, ordered lexicographically by: mechanism.

models.concept¶

`models.concept`¶