pyphi.models#
See pyphi.models.sia, pyphi.models.mice, and
pyphi.models.partitions for documentation.
- ivar Account:
Alias for
pyphi.models.actual_causation.Account.- ivar AcRepertoireIrreducibilityAnalysis:
Alias for
pyphi.models.actual_causation.AcRepertoireIrreducibilityAnalysis.- ivar AcSystemIrreducibilityAnalysis:
Alias for
pyphi.models.actual_causation.AcSystemIrreducibilityAnalysis.- ivar DirectedJointPartition:
- ivar JointBipartition:
Alias for
pyphi.models.partitions.JointBipartition.- ivar CausalLink:
Alias for
pyphi.models.actual_causation.CausalLink.- ivar CauseEffectStructure:
Alias for
pyphi.models.ces.CauseEffectStructure— the distinctions-plus-relations object specified by any candidate system (Albantakis et al. 2023). When the candidate is a complex, this is what the IIT 4.0 paper calls a Φ-structure.- ivar Distinctions:
Alias for
pyphi.models.distinctions.Distinctions— the bag of distinctions, no relations.- ivar Concept:
Alias for
pyphi.models.distinction.Distinction— IIT 3.0 paper terminology for the same object.- ivar Distinction:
Alias for
pyphi.models.distinction.Distinction.- ivar DirectedAccount:
- ivar MaximallyIrreducibleCause:
Alias for
pyphi.models.mice.MaximallyIrreducibleCause.- ivar MaximallyIrreducibleEffect:
Alias for
pyphi.models.mice.MaximallyIrreducibleEffect.- ivar MaximallyIrreducibleCauseOrEffect:
Alias for
pyphi.models.mice.MaximallyIrreducibleCauseOrEffect.- ivar Part:
Alias for
pyphi.models.partitions.Part.- ivar RepertoireIrreducibilityAnalysis:
Alias for
pyphi.models.ria.RepertoireIrreducibilityAnalysis.- ivar IIT3SystemIrreducibilityAnalysis:
Alias for
pyphi.models.sia.IIT3SystemIrreducibilityAnalysis— the IIT 3.0 result type, used to reproduce published results. The system irreducibility analysis of IIT isSystemIrreducibilityAnalysisinpyphi.formalism.iit4.
Submodules
Objects that represent structures used in actual causation. |
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Cause-effect structure: distinctions + relations (Albantakis et al. 2023). |
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Utilities for comparing phi-objects. |
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The |
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Structured deltas between two results ( |
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Distinction: the maximally irreducible cause and effect specified by a mechanism (Albantakis et al. 2023). |
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Typed explanations of why a result came out as it did ( |
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Helper functions for formatting pretty representations of PyPhi models. |
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Maximally irreducible cause/effect (MICE) wrapper objects. |
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Utilities for working with Pandas data structures. |
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Partition and edge-cut value types. |
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Protocols declaring the cross-formalism surface of analysis results. |
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Mechanism-level repertoire irreducibility analysis (RIA). |
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IIT 3.0 |
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Specified states and the per-unit |