# models.concept¶

Objects that represent parts of cause-effect structures.

class pyphi.models.concept.Mip(phi, direction, mechanism, purview, partition, unpartitioned_repertoire, partitioned_repertoire, subsystem=None)

A minimum information partition for $$\varphi$$ calculation.

These can be compared with the built-in Python comparison operators (<, >, etc.). First, $$\varphi$$ values are compared. Then, if these are equal up to PRECISION, the size of the mechanism is compared (see the PICK_SMALLEST_PURVIEW option in config.)

phi

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

direction

DirectionPAST or FUTURE.

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

KPartition – 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.

subsystem

Subsystem – The Subsystem this MIP belongs to.

unorderable_unless_eq = ['direction']
order_by()
__bool__()

A Mip is True if it has $$\varphi > 0$$.

to_json()
class pyphi.models.concept.Mice(mip)

A maximally irreducible cause or effect.

These can be compared with the built-in Python comparison operators (<, >, etc.). First, $$\varphi$$ values are compared. Then, if these are equal up to PRECISION, the size of the mechanism is compared (see the PICK_SMALLEST_PURVIEW option in config.)

phi

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

direction

DirectionPAST or FUTURE.

mechanism

list[int] – The mechanism for which the MICE is evaluated.

purview

list[int] – The purview over which this mechanism’s $$\varphi$$ is maximal.

partition

KPartition – The partition that makes the least difference to the mechanism’s repertoire.

repertoire

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

partitioned_repertoire

np.ndarray – The partitioned repertoire of the mechanism over the purview.

mip

MIP – The minimum information partition for this mechanism.

unorderable_unless_eq = ['direction']
order_by()
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(mechanism=None, cause=None, effect=None, subsystem=None, time=None)

A the maximally irreducible cause and effect specified by a mechanism.

These can be compared with the built-in Python comparison operators (<, >, etc.). First, $$\varphi$$ values are compared. Then, if these are equal up to PRECISION, the size of the mechanism is compared (see the PICK_SMALLEST_PURVIEW option in config.)

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.

phi

float – The size of the concept.

This is the minimum of the $$\varphi$$ values of the concept’s core cause and core effect.

cause_purview

tuple[int] – The cause purview.

effect_purview

tuple[int] – The effect purview.

cause_repertoire

np.ndarray – The cause repertoire.

effect_repertoire

np.ndarray – The effect repertoire.

unorderable_unless_eq = ['subsystem']
order_by()
__bool__()

A concept is True if $$\varphi > 0$$.

eq_repertoires(other)

Return whether this concept has the same repertoires as another.

Warning

This only checks if the cause and effect repertoires are equal as arrays; mechanisms, purviews, or even the nodes that the mechanism and purview 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_effect_repertoire(new_purview=None)
expand_partitioned_cause_repertoire()
expand_partitioned_effect_repertoire()
to_json()

Return a JSON-serializable representation.

classmethod from_json(dct)
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 a tuple[concept]; this usage still works in all functions.

Normalize the order of concepts in the constellation.

static __new__(concepts=())

Normalize the order of concepts in the constellation.

to_json()
mechanisms

The mechanism of each concept.

phis

The $$\varphi$$ values of each concept.

labeled_mechanisms

The labeled mechanism of each concept.

classmethod from_json(json)
pyphi.models.concept.normalize_constellation(constellation)

Deterministically reorder the concepts in a constellation.

Parameters: constellation (Constellation) – The constellation in question. The constellation, ordered lexicographically by mechanism. Constellation