# 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.)

phi

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

Type: float
direction
Type: Direction
mechanism

The mechanism that was analyzed.

Type: tuple[int]
purview

The purview over which the the mechanism was analyzed.

Type: tuple[int]
partition

The partition of the mechanism-purview pair that was analyzed.

Type: KPartition
repertoire

The repertoire of the mechanism over the purview.

Type: np.ndarray
partitioned_repertoire

The partitioned repertoire of the mechanism over the purview. This is the product of the repertoires of each part of the partition.

Type: np.ndarray
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.)

phi

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

Type: float
direction
Type: Direction
mechanism

The mechanism for which the MICE is evaluated.

Type: list[int]
purview

The purview over which this mechanism’s $$\varphi$$ is maximal.

Type: list[int]
mip

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

Type: KPartition
repertoire

The unpartitioned repertoire of the mechanism over the purview.

Type: np.ndarray
partitioned_repertoire

The partitioned repertoire of the mechanism over the purview.

Type: np.ndarray
ria

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.)

direction
Type: Direction
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.)

direction
Type: Direction
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.

effect

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

subsystem

This concept’s parent subsystem.

Type: Subsystem
time

The number of seconds it took to calculate.

Type: float
phi

The size of the concept.

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

Type: float
cause_purview

The cause purview.

Type: tuple[int]
effect_purview

The effect purview.

Type: tuple[int]
cause_repertoire

The cause repertoire.

Type: np.ndarray
effect_repertoire

The effect repertoire.

Type: np.ndarray
mechanism_state

The state of this mechanism.

Type: tuple(int)
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)
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)