`models.mechanism`¶

Mechanism-level objects.

class pyphi.models.mechanism.RepertoireIrreducibilityAnalysis(phi, direction, mechanism, purview, partition, repertoire, partitioned_repertoire, node_labels=None)¶

An analysis of the irreducibility (\(\varphi\)) of a mechanism over a purview, for a given partition, in one temporal direction.

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.)

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

property direction¶: Direction: CAUSE or EFFECT.

property mechanism¶: tuple[int]: The mechanism that was analyzed.

property purview¶: tuple[int]: The purview over which the the mechanism was analyzed.

property partition¶: KPartition: The partition of the mechanism-purview pair that was analyzed.

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

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

property node_labels¶: NodeLabels for this system.

unorderable_unless_eq = ['direction']¶

order_by()¶

Return a list of values to compare for ordering.

The first value in the list has the greatest priority; if the first objects are equal the second object is compared, etc.

__bool__()¶: A RepertoireIrreducibilityAnalysis is True if it has \(\varphi > 0\).

to_json()¶

class pyphi.models.mechanism.MaximallyIrreducibleCauseOrEffect(ria)¶

A maximally irreducible cause or effect (MICE).

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.)

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

property direction¶: Direction: CAUSE or EFFECT.

property mechanism¶: list[int]: The mechanism for which the MICE is evaluated.

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

property mip¶: KPartition: The partition that makes the least difference to the mechanism’s repertoire.

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

property partitioned_repertoire¶: np.ndarray: The partitioned repertoire of the mechanism over the purview.

property ria¶: RepertoireIrreducibilityAnalysis: The irreducibility analysis for this mechanism.

unorderable_unless_eq = ['direction']¶

order_by()¶

Return a list of values to compare for ordering.

The first value in the list has the greatest priority; if the first objects are equal the second object is compared, etc.

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.mechanism.MaximallyIrreducibleCause(ria)¶

A maximally irreducible cause (MIC).

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.)

property direction¶: Direction: CAUSE.

class pyphi.models.mechanism.MaximallyIrreducibleEffect(ria)¶

A maximally irreducible effect (MIE).

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.)

property direction¶: Direction: EFFECT.

class pyphi.models.mechanism.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¶

The mechanism that the concept consists of.

Type: tuple[int]

cause¶

The MaximallyIrreducibleCause representing the maximally-irreducible cause of this concept.

Type: MaximallyIrreducibleCause

effect¶

The MaximallyIrreducibleEffect representing the maximally-irreducible effect of this concept.

Type: MaximallyIrreducibleEffect

subsystem¶

This concept’s parent subsystem.

Type: Subsystem

time¶

The number of seconds it took to calculate.

Type: float

property phi¶

float: The size of the concept.

This is the minimum of the \(\varphi\) values of the concept’s MaximallyIrreducibleCause and MaximallyIrreducibleEffect.

property cause_purview¶: tuple[int]: The cause purview.

property effect_purview¶: tuple[int]: The effect purview.

property cause_repertoire¶: np.ndarray: The cause repertoire.

property effect_repertoire¶: np.ndarray: The effect repertoire.

property mechanism_state¶: tuple(int): The state of this mechanism.

unorderable_unless_eq = ['subsystem']¶

order_by()¶

Return a list of values to compare for ordering.

The first value in the list has the greatest priority; if the first objects are equal the second object is compared, etc.

__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)¶

models.mechanism¶

`models.mechanism`¶