`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¶: Direction – PAST 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¶: Direction – PAST 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)¶

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.

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)¶: See expand_repertoire().

expand_effect_repertoire(new_purview=None)¶: See expand_repertoire().

expand_partitioned_cause_repertoire()¶: See expand_repertoire().

expand_partitioned_effect_repertoire()¶: See expand_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.
Returns:	The constellation, ordered lexicographically by mechanism.
Return type:	Constellation

models.concept¶

`models.concept`¶