PyPhi¶
PyPhi is a Python library for computing integrated information.
If you use this software in your research, please cite the paper:
Mayner WGP, Marshall W, Albantakis L, Findlay G, Marchman R, Tononi G. (2018) PyPhi: A toolbox for integrated information theory. PLOS Computational Biology 14(7): e1006343. https://doi.org/10.1371/journal.pcbi.1006343
To report issues, use the issue tracker on the GitHub repository. Bug reports and pull requests are welcome.
For general discussion, you are welcome to join the pyphiusers group.
Installation¶
To install the latest stable release, run
pip install pyphi
To install the latest development version, which is a work in progress and may have bugs, run
pip install "git+https://github.com/wmayner/pyphi@develop#egg=pyphi"
Tip
For detailed instructions on how to install PyPhi on macOS, see the Detailed installation guide for macOS.
Note
Windows users: PyPhi is only supported on Linux and macOS operating
systems. However, you can run it on Windows by using the Anaconda Python distribution and installing
PyPhi with conda: conda install c
wmayner pyphi
Installation¶
To install the latest stable release, run
pip install pyphi
To install the latest development version, which is a work in progress and may have bugs, run
pip install "git+https://github.com/wmayner/pyphi@develop#egg=pyphi"
Tip
For detailed instructions on how to install PyPhi on macOS, see the Detailed installation guide for macOS.
Note
Windows users: PyPhi is only supported on Linux and macOS operating
systems. However, you can run it on Windows by using the Anaconda Python distribution and installing
PyPhi with conda: conda install c
wmayner pyphi
Getting started¶
To explore the following examples, install IPython by running pip install ipython
on the
command line. Then run it with the command ipython
.
Lines of code beginning with >>>
and ...
can be pasted directly into
IPython.
Basic Usage¶
Let’s make a simple 3node network and compute its \(\Phi\).
To make a network, we need a TPM and (optionally) a connectivity matrix. The
TPM can be in more than one form; see the documentation for Network
. Here
we’ll use the 2dimensional statebynode form.
>>> import pyphi
>>> import numpy as np
>>> tpm = np.array([
... [0, 0, 0],
... [0, 0, 1],
... [1, 0, 1],
... [1, 0, 0],
... [1, 1, 0],
... [1, 1, 1],
... [1, 1, 1],
... [1, 1, 0]
... ])
The connectivity matrix is a square matrix such that the \((i,j)^{\textrm{th}}\) entry is 1 if there is a connection from node \(i\) to node \(j\), and 0 otherwise.
>>> cm = np.array([
... [0, 0, 1],
... [1, 0, 1],
... [1, 1, 0]
... ])
We’ll also make labels for the network nodes so that PyPhi’s output is easier to read.
>>> labels = ('A', 'B', 'C')
Now we construct the network itself with the arguments we just created:
>>> network = pyphi.Network(tpm, cm=cm, node_labels=labels)
The next step is to define a subsystem for which we want to evaluate \(\Phi\). To make a subsystem, we need the network that it belongs to, the state of that network, and the indices of the subset of nodes which should be included.
The state should be an \(n\)tuple, where \(n\) is the number of nodes in the network, and where the \(i^{\textrm{th}}\) element is the state of the \(i^{\textrm{th}}\) node in the network.
>>> state = (1, 0, 0)
In this case, we want the \(\Phi\) of the entire network, so we simply include every node in the network in our subsystem:
>>> node_indices = (0, 1, 2)
>>> subsystem = pyphi.Subsystem(network, state, node_indices)
Tip
If you do not explicitly provide node indices to a Subsystem
the system
will, by default, cover the entire network. For example, the following is
equivalent to the above definition of subsystem
:
>>> subsystem = pyphi.Subsystem(network, state)
Tip
Node labels can be used instead of indices when constructing a Subsystem
:
>>> pyphi.Subsystem(network, state, ('B', 'C'))
Subsystem(B, C)
Now we use the phi()
function to compute the \(\Phi\) of our
subsystem:
>>> pyphi.compute.phi(subsystem)
2.3125
If we want to take a deeper look at the integratedinformationtheoretic
properties of our network, we can access all the intermediate quantities and
structures that are calculated in the course of arriving at a final \(\Phi\)
value by using sia()
. This returns a nested object,
SystemIrreducibilityAnalysis
, that contains data about the subsystem’s
causeeffect structure, cause and effect repertoires, etc.
>>> sia = pyphi.compute.sia(subsystem)
For instance, we can see that this network has 4 concepts:
>>> len(sia.ces)
4
See the documentation for SystemIrreducibilityAnalysis
and Concept
for more
information on these objects.
Tip
The network and subsystem discussed here are returned by the
pyphi.examples.basic_network()
and
pyphi.examples.basic_subsystem()
functions.
IIT 3.0 Paper (2014)¶
This section is meant to serve as a companion to the paper From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0 by Oizumi, Albantakis, and Tononi, and as a demonstration of how to use PyPhi. Readers are encouraged to follow along and analyze the systems shown in the figures, in order to become more familiar with both the theory and the software.
Install IPython by running pip install
ipython
on the command line. Then run it with the command ipython
.
Lines of code beginning with >>>
and ...
can be pasted directly into
IPython.
We begin by importing PyPhi and NumPy:
>>> import pyphi
>>> import numpy as np
Figure 1¶
Existence: Mechanisms in a state having causal power.
For the first figure, we’ll demonstrate how to set up a network and a candidate set. In PyPhi, networks are built by specifying a transition probability matrix and (optionally) a connectivity matrix. (If no connectivity matrix is given, full connectivity is assumed.) So, to set up the system shown in Figure 1, we’ll start by defining its TPM.
Note
The TPM in the figure is given in statebystate form; there is a row and a column for each state. However, in PyPhi, we use a more compact representation: statebynode form, in which there is a row for each state, but a column for each node. The \((i,j)^{\textrm{th}}\) entry gives the probability that the \(j^{\textrm{th}}\) node is ON in the \(i^{\textrm{th}}\) state. For more information on how TPMs are represented in PyPhi, see Transition probability matrix conventions.
In the figure, the TPM is shown only for the candidate set. We’ll define the entire network’s TPM. Also, nodes \(D\), \(E\) and \(F\) are not assigned mechanisms; for the purposes of this example we will assume they are OR gates. With that assumption, we get the following TPM (before copying and pasting, see note below):
>>> tpm = np.array([
... [0, 0, 0, 0, 0, 0],
... [0, 0, 1, 0, 0, 0],
... [1, 0, 1, 0, 1, 0],
... [1, 0, 0, 0, 1, 0],
... [1, 0, 0, 0, 0, 0],
... [1, 1, 1, 0, 0, 0],
... [1, 0, 1, 0, 1, 0],
... [1, 1, 0, 0, 1, 0],
... [1, 0, 0, 0, 0, 0],
... [1, 0, 1, 0, 0, 0],
... [1, 0, 1, 0, 1, 0],
... [1, 0, 0, 0, 1, 0],
... [1, 0, 0, 0, 0, 0],
... [1, 1, 1, 0, 0, 0],
... [1, 0, 1, 0, 1, 0],
... [1, 1, 0, 0, 1, 0],
... [0, 0, 0, 0, 0, 0],
... [0, 0, 1, 0, 0, 0],
... [1, 0, 1, 0, 1, 0],
... [1, 0, 0, 0, 1, 0],
... [1, 0, 0, 0, 0, 0],
... [1, 1, 1, 0, 0, 0],
... [1, 0, 1, 0, 1, 0],
... [1, 1, 0, 0, 1, 0],
... [1, 0, 0, 0, 0, 0],
... [1, 0, 1, 0, 0, 0],
... [1, 0, 1, 0, 1, 0],
... [1, 0, 0, 0, 1, 0],
... [1, 0, 0, 0, 0, 0],
... [1, 1, 1, 0, 0, 0],
... [1, 0, 1, 0, 1, 0],
... [1, 1, 0, 0, 1, 0],
... [0, 0, 0, 0, 0, 0],
... [0, 0, 1, 0, 0, 0],
... [1, 0, 1, 0, 1, 0],
... [1, 0, 0, 0, 1, 0],
... [1, 0, 0, 0, 0, 0],
... [1, 1, 1, 0, 0, 0],
... [1, 0, 1, 0, 1, 0],
... [1, 1, 0, 0, 1, 0],
... [1, 0, 0, 0, 0, 0],
... [1, 0, 1, 0, 0, 0],
... [1, 0, 1, 0, 1, 0],
... [1, 0, 0, 0, 1, 0],
... [1, 0, 0, 0, 0, 0],
... [1, 1, 1, 0, 0, 0],
... [1, 0, 1, 0, 1, 0],
... [1, 1, 0, 0, 1, 0],
... [0, 0, 0, 0, 0, 0],
... [0, 0, 1, 0, 0, 0],
... [1, 0, 1, 0, 1, 0],
... [1, 0, 0, 0, 1, 0],
... [1, 0, 0, 0, 0, 0],
... [1, 1, 1, 0, 0, 0],
... [1, 0, 1, 0, 1, 0],
... [1, 1, 0, 0, 1, 0],
... [1, 0, 0, 0, 0, 0],
... [1, 0, 1, 0, 0, 0],
... [1, 0, 1, 0, 1, 0],
... [1, 0, 0, 0, 1, 0],
... [1, 0, 0, 0, 0, 0],
... [1, 1, 1, 0, 0, 0],
... [1, 0, 1, 0, 1, 0],
... [1, 1, 0, 0, 1, 0]
... ])
Note
This network is already built for you; you can get it from the examples
module with network = pyphi.examples.fig0a()
. The TPM can then be
accessed with network.tpm
.
Next we’ll define the connectivity matrix. In PyPhi, the \((i,j)^{\textrm{th}}\) entry in a connectivity matrix indicates whether node \(i\) is connected to node \(j\). Thus, this network’s connectivity matrix is
>>> cm = np.array([
... [0, 1, 1, 0, 0, 0],
... [1, 0, 1, 0, 1, 0],
... [1, 1, 0, 0, 0, 0],
... [1, 0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0, 0]
... ])
Now we can pass the TPM and connectivity matrix as arguments to the network constructor:
>>> network = pyphi.Network(tpm, cm=cm)
Now the network shown in the figure is stored in a variable called network
.
You can find more information about the network object we just created by
running help(network)
or by consulting the documentation for Network
.
The next step is to define the candidate set shown in the figure, consisting of
nodes \(A\), \(B\) and \(C\). In PyPhi, a candidate set for \(\Phi\) evaluation is
represented by the Subsystem
class. Subsystems are built by giving the
network it is a part of, the state of the network, and indices of the nodes to
be included in the subsystem. So, we define our candidate set like so:
>>> state = (1, 0, 0, 0, 1, 0)
>>> ABC = pyphi.Subsystem(network, state, [0, 1, 2])
For more information on the subsystem object, see the documentation for
Subsystem
.
That covers the basic workflow with PyPhi and introduces the two types of objects we use to represent and analyze networks. First you define the network of interest with a TPM and connectivity matrix; then you define a candidate set you want to analyze.
Figure 3¶
Information requires selectivity.
(A)¶
We’ll start by setting up the subsytem depicted in the figure and labeling the nodes. In this case, the subsystem is just the entire network.
>>> network = pyphi.examples.fig3a()
>>> state = (1, 0, 0, 0)
>>> subsystem = pyphi.Subsystem(network, state)
>>> A, B, C, D = subsystem.node_indices
Since the connections are noisy, we see that \(A = 1\) is unselective; all previous states are equally likely:
>>> subsystem.cause_repertoire((A,), (B, C, D))
array([[[[0.125, 0.125],
[0.125, 0.125]],
<BLANKLINE>
[[0.125, 0.125],
[0.125, 0.125]]]])
And this gives us zero cause information:
>>> subsystem.cause_info((A,), (B, C, D))
0.0
(B)¶
The same as (A) but without noisy connections:
>>> network = pyphi.examples.fig3b()
>>> subsystem = pyphi.Subsystem(network, state)
>>> A, B, C, D = subsystem.node_indices
Now, \(A\)’s cause repertoire is maximally selective.
>>> cr = subsystem.cause_repertoire((A,), (B, C, D))
>>> cr
array([[[[0., 0.],
[0., 0.]],
<BLANKLINE>
[[0., 0.],
[0., 1.]]]])
Since the cause repertoire is over the purview \(BCD\), the first dimension (which corresponds to \(A\)’s states) is a singleton. We can squeeze out \(A\)’s singleton dimension with
>>> cr = cr.squeeze()
and now we can see that the probability of \(B\), \(C\), and \(D\) having been all ON is 1:
>>> cr[(1, 1, 1)]
1.0
Now the cause information specified by \(A = 1\) is \(1.5\):
>>> subsystem.cause_info((A,), (B, C, D))
1.5
(C)¶
The same as (B) but with \(A = 0\):
>>> state = (0, 0, 0, 0)
>>> subsystem = pyphi.Subsystem(network, state)
>>> A, B, C, D = subsystem.node_indices
And here the cause repertoire is minimally selective, only ruling out the state where \(B\), \(C\), and \(D\) were all ON:
>>> subsystem.cause_repertoire((A,), (B, C, D))
array([[[[0.14285714, 0.14285714],
[0.14285714, 0.14285714]],
<BLANKLINE>
[[0.14285714, 0.14285714],
[0.14285714, 0. ]]]])
And so we have less cause information:
>>> subsystem.cause_info((A,), (B, C, D))
0.214284
Figure 4¶
Information: “Differences that make a difference to a system from its own intrinsic perspective.”
First we’ll get the network from the examples
module, set up a subsystem, and
label the nodes, as usual:
>>> network = pyphi.examples.fig4()
>>> state = (1, 0, 0)
>>> subsystem = pyphi.Subsystem(network, state)
>>> A, B, C = subsystem.node_indices
Then we’ll compute the cause and effect repertoires of mechanism \(A\) over purview \(ABC\):
>>> subsystem.cause_repertoire((A,), (A, B, C))
array([[[0. , 0.16666667],
[0.16666667, 0.16666667]],
<BLANKLINE>
[[0. , 0.16666667],
[0.16666667, 0.16666667]]])
>>> subsystem.effect_repertoire((A,), (A, B, C))
array([[[0.0625, 0.0625],
[0.0625, 0.0625]],
<BLANKLINE>
[[0.1875, 0.1875],
[0.1875, 0.1875]]])
And the unconstrained repertoires over the same (these functions don’t take a mechanism; they only take a purview):
>>> subsystem.unconstrained_cause_repertoire((A, B, C))
array([[[0.125, 0.125],
[0.125, 0.125]],
<BLANKLINE>
[[0.125, 0.125],
[0.125, 0.125]]])
>>> subsystem.unconstrained_effect_repertoire((A, B, C))
array([[[0.09375, 0.09375],
[0.03125, 0.03125]],
<BLANKLINE>
[[0.28125, 0.28125],
[0.09375, 0.09375]]])
The Earth Mover’s distance between them gives the cause and effect information:
>>> subsystem.cause_info((A,), (A, B, C))
0.333332
>>> subsystem.effect_info((A,), (A, B, C))
0.25
And the minimum of those gives the causeeffect information:
>>> subsystem.cause_effect_info((A,), (A, B, C))
0.25
Figure 5¶
A mechanism generates information only if it has both selective causes and selective effects within the system.
(A)¶
>>> network = pyphi.examples.fig5a()
>>> state = (1, 1, 1)
>>> subsystem = pyphi.Subsystem(network, state)
>>> A, B, C = subsystem.node_indices
\(A\) has inputs, so its cause repertoire is selective and it has cause information:
>>> subsystem.cause_repertoire((A,), (A, B, C))
array([[[0. , 0. ],
[0. , 0.5]],
<BLANKLINE>
[[0. , 0. ],
[0. , 0.5]]])
>>> subsystem.cause_info((A,), (A, B, C))
1.0
But because it has no outputs, its effect repertoire no different from the unconstrained effect repertoire, so it has no effect information:
>>> np.array_equal(subsystem.effect_repertoire((A,), (A, B, C)),
... subsystem.unconstrained_effect_repertoire((A, B, C)))
True
>>> subsystem.effect_info((A,), (A, B, C))
0.0
And thus its cause effect information is zero.
>>> subsystem.cause_effect_info((A,), (A, B, C))
0.0
(B)¶
>>> network = pyphi.examples.fig5b()
>>> state = (1, 0, 0)
>>> subsystem = pyphi.Subsystem(network, state)
>>> A, B, C = subsystem.node_indices
Symmetrically, \(A\) now has outputs, so its effect repertoire is selective and it has effect information:
>>> subsystem.effect_repertoire((A,), (A, B, C))
array([[[0., 0.],
[0., 0.]],
<BLANKLINE>
[[0., 0.],
[0., 1.]]])
>>> subsystem.effect_info((A,), (A, B, C))
0.5
But because it now has no inputs, its cause repertoire is no different from the unconstrained effect repertoire, so it has no cause information:
>>> np.array_equal(subsystem.cause_repertoire((A,), (A, B, C)),
... subsystem.unconstrained_cause_repertoire((A, B, C)))
True
>>> subsystem.cause_info((A,), (A, B, C))
0.0
And its cause effect information is again zero.
>>> subsystem.cause_effect_info((A,), (A, B, C))
0.0
Figure 6¶
Integrated information: The information generated by the whole that is irreducible to the information generated by its parts.
>>> network = pyphi.examples.fig6()
>>> state = (1, 0, 0)
>>> subsystem = pyphi.Subsystem(network, state)
>>> ABC = subsystem.node_indices
Here we demonstrate the functions that find the minimum information partition a mechanism over a purview:
>>> mip_c = subsystem.cause_mip(ABC, ABC)
>>> mip_e = subsystem.effect_mip(ABC, ABC)
These objects contain the \(\varphi^{\textrm{MIP}}_{\textrm{cause}}\) and
\(\varphi^{\textrm{MIP}}_{\textrm{effect}}\) values in their respective
phi
attributes, and the minimal partitions in their partition
attributes:
>>> mip_c.phi
0.499999
>>> mip_c.partition # doctest: +NORMALIZE_WHITESPACE
A B,C
─── ✕ ─────
∅ A,B,C
>>> mip_e.phi
0.25
>>> mip_e.partition # doctest: +NORMALIZE_WHITESPACE
∅ A,B,C
─── ✕ ─────
B A,C
For more information on these objects, see the documentation for the
RepertoireIrreducibilityAnalysis
class, or use help(mip_c)
.
Note that the minimal partition found for the cause is
rather than the one shown in the figure. However, both partitions result in a difference of \(0.5\) between the unpartitioned and partitioned cause repertoires. So we see that in small networks like this, there can be multiple choices of partition that yield the same, minimal \(\varphi^{\textrm{MIP}}\). In these cases, which partition the software chooses is left undefined.
Figure 7¶
A mechanism generates integrated information only if it has both integrated causes and integrated effects.
It is left as an exercise for the reader to use the subsystem methods
cause_mip
and effect_mip
, introduced in the previous section, to
demonstrate the points made in Figure 7.
To avoid building TPMs and connectivity matrices by hand, you can use the graphical user interface for PyPhi available online at http://integratedinformationtheory.org/calculate.html. You can build the networks shown in the figure there, and then use the Export button to obtain a JSON file representing the network. You can then import the file into Python like so:
network = pyphi.network.from_json('path/to/network.json')
Figure 8¶
The maximally integrated cause repertoire over the power set of purviews is the “core cause” specified by a mechanism.
>>> network = pyphi.examples.fig8()
>>> state = (1, 0, 0)
>>> subsystem = pyphi.Subsystem(network, state)
>>> A, B, C = subsystem.node_indices
In PyPhi, the “core cause” is called the maximallyirreducible cause (MIC).
To find the MIC of a mechanism over all purviews, use the mic()
method:
>>> mic = subsystem.mic((B, C))
>>> mic.phi
0.333334
Similarly, the mie()
method returns the “core effect” or
maximallyirreducible effect (MIE).
For a detailed description of the MIC and MIE objects returned by these
methods, see the documentation for MaximallyIrreducibleCause
or use help(subsystem.mic)
and
help(subsystem.mie)
.
Figure 9¶
A mechanism that specifies a maximally irreducible causeeffect repertoire.
This figure and the next few use the same network as in Figure 8, so we don’t
need to reassign the network
and subsystem
variables.
Together, the MIC and MIE of a mechanism specify a concept. In PyPhi, this is
represented by the Concept
object. Concepts are computed using the
concept()
method of a subsystem:
>>> concept_A = subsystem.concept((A,))
>>> concept_A.phi
0.166667
As usual, please consult the documentation or use help(concept_A)
for a
detailed description of the Concept
object.
Figure 10¶
Information: A conceptual structure C (constellation of concepts) is the set of all concepts generated by a set of elements in a state.
For functions of entire subsystems rather than mechanisms within them, we use
the compute
module. In this figure, we see the constellation of concepts of
the powerset of \(ABC\)’s mechanisms. A constellation of concepts is
represented in PyPhi by a CauseEffectStructure
. We can compute the
causeeffect structure of the subsystem like so:
>>> ces = pyphi.compute.ces(subsystem)
And verify that the \(\varphi\) values match:
>>> ces.labeled_mechanisms
(['A'], ['B'], ['C'], ['A', 'B'], ['B', 'C'], ['A', 'B', 'C'])
>>> ces.phis
[0.166667, 0.166667, 0.25, 0.25, 0.333334, 0.499999]
The null concept (the small black cross shown in conceptspace) is available as an attribute of the subsystem:
>>> subsystem.null_concept.phi
0.0
Figure 11¶
Assessing the conceptual information CI of a conceptual structure (constellation of concepts).
Conceptual information can be computed using the function named, as you might
expect, conceptual_info()
:
>>> pyphi.compute.conceptual_info(subsystem)
2.111109
Figure 12¶
Assessing the integrated conceptual information Φ of a constellation C.
To calculate \(\Phi^{\textrm{MIP}}\) for a candidate set, we use the
function sia()
:
>>> sia = pyphi.compute.sia(subsystem)
The returned value is a large object containing the \(\Phi^{\textrm{MIP}}\)
value, the minimal cut, the causeeffect structure of the whole set and that of
the partitioned set \(C_{\rightarrow}^{\textrm{MIP}}\), the total
calculation time, the calculation time for just the unpartitioned causeeffect
structure, a reference to the subsystem that was analyzed, and a reference to
the subsystem with the minimal unidirectional cut applied. For details see the
documentation for SystemIrreducibilityAnalysis
or use help(sia)
.
We can verify that the \(\Phi^{\textrm{MIP}}\) value and minimal cut are as shown in the figure:
>>> sia.phi
1.916665
>>> sia.cut
Cut [A, B] ━━/ /━━➤ [C]
Note
This Cut
represents removing any connections from the nodes with
indices 0
and 1
to the node with index 2
.
Figure 13¶
A set of elements generates integrated conceptual information Φ only if each subset has both causes and effects in the rest of the set.
It is left as an exercise for the reader to demonstrate that of the networks shown, only (B) has \(\Phi > 0\).
Figure 14¶
A complex: A local maximum of integrated conceptual information Φ.
>>> network = pyphi.examples.fig14()
>>> state = (1, 0, 0, 0, 1, 0)
To find the subsystem within a network that is the major complex, we use the
function of that name, which returns a SystemIrreducibilityAnalysis
object:
>>> major_complex = pyphi.compute.major_complex(network, state)
And we see that the nodes in the complex are indeed \(A\), \(B\), and \(C\):
>>> major_complex.subsystem.nodes
(A, B, C)
Figure 15¶
A quale: The maximally irreducible conceptual structure (MICS) generated by a complex.
You can use the visual interface at http://integratedinformationtheory.org/calculate.html to view a conceptual structure structure in a 3D projection of qualia space. The network in the figure is already built for you; click the Load Example button and select “IIT 3.0 Paper, Figure 1” (this network is the same as the candidate set in Figure 1).
Figure 16¶
A system can condense into a major complex and minor complexes that may or may not interact with it.
For this figure, we omit nodes \(H\), \(I\), \(J\), \(K\) and \(L\), since the TPM of the full 12node network is very large, and the point can be illustrated without them.
>>> network = pyphi.examples.fig16()
>>> state = (1, 0, 0, 1, 1, 1, 0)
To find the maximal set of nonoverlapping complexes that a network condenses
into, use condensed()
:
>>> condensed = pyphi.compute.condensed(network, state)
We find that there are two complexes: the major complex \(ABC\) with \(\Phi \approx 1.92\), and a minor complex \(FG\) with \(\Phi \approx 0.069\) (note that there is typo in the figure: \(FG\)’s \(\Phi\) value should be \(0.069\)). Furthermore, the program has been updated to only consider background conditions of current states, not previous states; as a result the minor complex \(DE\) shown in the paper no longer exists.
>>> len(condensed)
2
>>> ABC, FG = condensed
>>> (ABC.subsystem.nodes, ABC.phi)
((A, B, C), 1.916665)
>>> (FG.subsystem.nodes, FG.phi)
((F, G), 0.069445)
There are several other functions available for working with complexes; see the
documentation for subsystems()
, all_complexes()
,
possible_complexes()
, and complexes()
.
Conditional Independence¶
Conditional independence is the property of a TPM that each node’s state at time \(t+1\) must be independent of the state of the others, given the state of the network at time \(t\):
This example explores the assumption of conditional independence, and the behaviour of the program when it is not satisfied.
Every statebynode TPM corresponds to a unique statebystate TPM which
satisfies the conditional independence property (see Transition probability matrix conventions for
a discussion of the different TPM forms). If a statebynode TPM is given as
input for a Network
, PyPhi assumes that it is from a system with the
corresponding conditionally independent statebystate TPM.
When a statebystate TPM is given as input for a Network
, the statebystate
TPM is first converted to a statebynode TPM. PyPhi then assumes that the
system corresponds to the unique conditionally independent representation of
the statebynode TPM.
Note
Every deterministic statebystate TPM satisfies the conditional independence property.
Consider a system of two binary nodes (\(A\) and \(B\)) which do not change if they have the same value, but flip with probability 50% if they have different values.
We’ll load the statebystate TPM for such a system from the examples
module:
>>> import pyphi
>>> tpm = pyphi.examples.cond_depend_tpm()
>>> print(tpm)
[[1. 0. 0. 0. ]
[0. 0.5 0.5 0. ]
[0. 0.5 0.5 0. ]
[0. 0. 0. 1. ]]
This system does not satisfy the conditional independence property; given a
previous state of (1, 0)
, the current state of node \(A\) depends on whether
or not \(B\) has flipped.
If a conditionally dependent TPM is used to create a Network
, PyPhi will
raise an error:
>>> network = pyphi.Network(tpm)
Traceback (most recent call last):
...
pyphi.exceptions.ConditionallyDependentError: TPM is not conditionally independent.
See the conditional independence example in the documentation for more info.
To see the conditionally independent TPM that corresponds to the conditionally dependent TPM, convert it to statebynode form and then back to statebystate form:
>>> sbn_tpm = pyphi.convert.state_by_state2state_by_node(tpm)
>>> print(sbn_tpm)
[[[0. 0. ]
[0.5 0.5]]
<BLANKLINE>
[[0.5 0.5]
[1. 1. ]]]
>>> sbs_tpm = pyphi.convert.state_by_node2state_by_state(sbn_tpm)
>>> print(sbs_tpm)
[[1. 0. 0. 0. ]
[0.25 0.25 0.25 0.25]
[0.25 0.25 0.25 0.25]
[0. 0. 0. 1. ]]
A system which does not satisfy the conditional independence property exhibits “instantaneous causality.” In such situations, there must be additional exogenous variable(s) which explain the dependence.
Now consider the above example, but with the addition of a third node (\(C\)) which is equally likely to be ON or OFF, and such that when nodes \(A\) and \(B\) are in different states, they will flip when \(C\) is ON, but stay the same when \(C\) is OFF.
>>> tpm2 = pyphi.examples.cond_independ_tpm()
>>> print(tpm2)
[[0.5 0. 0. 0. 0.5 0. 0. 0. ]
[0. 0.5 0. 0. 0. 0.5 0. 0. ]
[0. 0. 0.5 0. 0. 0. 0.5 0. ]
[0. 0. 0. 0.5 0. 0. 0. 0.5]
[0.5 0. 0. 0. 0.5 0. 0. 0. ]
[0. 0. 0.5 0. 0. 0. 0.5 0. ]
[0. 0.5 0. 0. 0. 0.5 0. 0. ]
[0. 0. 0. 0.5 0. 0. 0. 0.5]]
The resulting statebystate TPM now satisfies the conditional independence property.
>>> sbn_tpm2 = pyphi.convert.state_by_state2state_by_node(tpm2)
>>> print(sbn_tpm2)
[[[[0. 0. 0.5]
[0. 0. 0.5]]
<BLANKLINE>
[[0. 1. 0.5]
[1. 0. 0.5]]]
<BLANKLINE>
<BLANKLINE>
[[[1. 0. 0.5]
[0. 1. 0.5]]
<BLANKLINE>
[[1. 1. 0.5]
[1. 1. 0.5]]]]
The node indices are 0
and 1
for \(A\) and \(B\), and 2
for \(C\):
>>> AB = [0, 1]
>>> C = [2]
From here, if we marginalize out the node \(C\);
>>> tpm2_marginalizeC = pyphi.tpm.marginalize_out(C, sbn_tpm2)
And then restrict the purview to only nodes \(A\) and \(B\);
>>> import numpy as np
>>> tpm2_purviewAB = np.squeeze(tpm2_marginalizeC[:,:,:,AB])
We get back the original statebynode TPM from the system with just \(A\) and \(B\).
>>> np.all(tpm2_purviewAB == sbn_tpm)
True
XOR Network¶
This example describes a system of three fully connected XOR nodes, \(A\), \(B\) and \(C\) (no selfconnections).
First let’s create the XOR network:
>>> import pyphi
>>> network = pyphi.examples.xor_network()
We’ll consider the state with all nodes OFF.
>>> state = (0, 0, 0)
According to IIT, existence is a holistic notion; the whole is more important than its parts. The first step is to confirm the existence of the whole, by finding the major complex of the network:
>>> major_complex = pyphi.compute.major_complex(network, state)
The major complex exists (\(\Phi > 0\)),
>>> major_complex.phi
1.874999
and it consists of the entire network:
>>> major_complex.subsystem
Subsystem(A, B, C)
Knowing what exists at the system level, we can now investigate the existence of concepts within the complex.
>>> ces = major_complex.ces
>>> len(ces)
3
>>> ces.labeled_mechanisms
(['A', 'B'], ['A', 'C'], ['B', 'C'])
There are three concepts in the causeeffect structure. They are all the possible second order mechanisms: \(AB\), \(AC\) and \(BC\).
Focusing on the concept specified by mechanism \(AB\), we investigate existence, and the irreducible cause and effect. Based on the symmetry of the network, the results will be similar for the other second order mechanisms.
>>> concept = ces[0]
>>> concept.mechanism
(0, 1)
>>> concept.phi
0.5
The concept has \(\varphi = \frac{1}{2}\).
>>> concept.cause.purview
(0, 1, 2)
>>> concept.cause.repertoire
array([[[0.5, 0. ],
[0. , 0. ]],
<BLANKLINE>
[[0. , 0. ],
[0. , 0.5]]])
So we see that the cause purview of this mechanism is the whole system \(ABC\),
and that the repertoire shows a \(0.5\) of probability the previous state
being (0, 0, 0)
and the same for (1, 1, 1)
:
>>> concept.cause.repertoire[(0, 0, 0)]
0.5
>>> concept.cause.repertoire[(1, 1, 1)]
0.5
This tells us that knowing both \(A\) and \(B\) are currently OFF means that the previous state of the system was either all OFF or all ON with equal probability.
For any reduced purview, we would still have the same information about the elements in the purview (either all ON or all OFF), but we would lose the information about the elements outside the purview.
>>> concept.effect.purview
(2,)
>>> concept.effect.repertoire
array([[[1., 0.]]])
The effect purview of this concept is the node \(C\). The mechanism \(AB\) is able to completely specify the next state of \(C\). Since both nodes are OFF, the next state of \(C\) will be OFF.
The mechanism \(AB\) does not provide any information about the next state of either \(A\) or \(B\), because the relationship depends on the value of \(C\). That is, the next state of \(A\) (or \(B\)) may be either ON or OFF, depending on the value of \(C\). Any purview larger than \(C\) would be reducible by pruning away the additional elements.
Major Complex: \(ABC\) with \(\Phi = 1.875\)  

Mechanism  \(\varphi\)  Cause Purview  Effect Purview 
\(AB\)  0.5  \(ABC\)  \(C\) 
\(AC\)  0.5  \(ABC\)  \(B\) 
\(BC\)  0.5  \(ABC\)  \(A\) 
An analysis of the intrinsic existence of this system reveals that the major complex of the system is the entire network of XOR nodes. Furthermore, the concepts which exist within the complex are those specified by the secondorder mechanisms \(AB\), \(AC\), and \(BC\).
To understand the notion of intrinsic existence, in addition to determining what exists for the system, it is useful to consider also what does not exist.
Specifically, it may be surprising that none of the first order mechanisms \(A\), \(B\) or \(C\) exist. This physical system of XOR gates is sitting on the table in front of me; I can touch the individual elements of the system, so how can it be that they do not exist?
That sort of existence is what we term extrinsic existence. The XOR gates exist for me as an observer, external to the system. I am able to manipulate them, and observe their causes and effects, but the question that matters for intrinsic existence is, do they have irreducible causes and effects within the system? There are two reasons a mechanism may have no irreducible causeeffect power: either the causeeffect power is completely reducible, or there was no causeeffect power to begin with. In the case of elementary mechanisms, it must be the latter.
To see this, again due to symmetry of the system, we will focus only on the mechanism \(A\).
>>> subsystem = pyphi.examples.xor_subsystem()
>>> A = (0,)
>>> ABC = (0, 1, 2)
In order to exist, a mechanism must have irreducible cause and effect power within the system.
>>> subsystem.cause_info(A, ABC)
0.5
>>> subsystem.effect_info(A, ABC)
0.0
The mechanism has no effect power over the entire subsystem, so it cannot have effect power over any purview within the subsystem. Furthermore, if a mechanism has no effect power, it certainly has no irreducible effect power. The firstorder mechanisms of this system do not exist intrinsically, because they have no effect power (having causal power is not enough).
To see why this is true, consider the effect of \(A\). There is no selfloop, so \(A\) can have no effect on itself. Without knowing the current state of \(A\), in the next state \(B\) could be either ON or OFF. If we know that the current state of \(A\) is ON, then \(B\) could still be either ON or OFF, depending on the state of \(C\). Thus, on its own, the current state of \(A\) does not provide any information about the next state of \(B\). A similar result holds for the effect of \(A\) on \(C\). Since \(A\) has no effect power over any element of the system, it does not exist from the intrinsic perspective.
To complete the discussion, we can also investigate the potential third order mechanism \(ABC\). Consider the cause information over the purview \(ABC\):
>>> subsystem.cause_info(ABC, ABC)
0.749999
Since the mechanism has nonzero cause information, it has causal power over the system—but is it irreducible?
>>> mip = subsystem.cause_mip(ABC, ABC)
>>> mip.phi
0.0
>>> mip.partition # doctest: +NORMALIZE_WHITESPACE
A B,C
─── ✕ ─────
∅ A,B,C
The mechanism has \(ci = 0.75\), but it is completely reducible (\(\varphi = 0\)) to the partition
This result can be understood as follows: knowing that \(B\) and \(C\) are OFF in the current state is sufficient to know that \(A\), \(B\), and \(C\) were all OFF in the previous state; there is no additional information gained by knowing that \(A\) is currently OFF.
Similarly for any other potential purview, the current state of \(B\) and \(C\)
being (0, 0)
is always enough to fully specify the previous state, so the
mechanism is reducible for all possible purviews, and hence does not exist.
Emergence (coarsegraining and blackboxing)¶
Coarsegraining¶
We’ll use the macro
module to explore alternate spatial scales of a network.
The network under consideration is a 4node nondeterministic network,
available from the examples
module.
>>> import pyphi
>>> network = pyphi.examples.macro_network()
The connectivity matrix is alltoall:
>>> network.cm
array([[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]])
We’ll set the state so that nodes are OFF.
>>> state = (0, 0, 0, 0)
At the “micro” spatial scale, we can compute the major complex, and determine the \(\Phi\) value:
>>> major_complex = pyphi.compute.major_complex(network, state)
>>> major_complex.phi
0.113889
The question is whether there are other spatial scales which have greater values of \(\Phi\). This is accomplished by considering all possible coarsegraining of microelements to form macroelements. A coarsegraining of nodes is any partition of the elements of the micro system. First we’ll get a list of all possible coarsegrainings:
>>> grains = list(pyphi.macro.all_coarse_grains(network.node_indices))
We start by considering the first coarse grain:
>>> coarse_grain = grains[0]
Each CoarseGrain
has two attributes: the partition
of states into macro
elements, and the grouping
of microstates into macrostates. Let’s first
look at the partition:
>>> coarse_grain.partition
((0, 1, 2), (3,))
There are two macroelements in this partition: one consists of microelements
(0, 1, 2)
and the other is simply microelement 3
.
We must then determine the relationship between microelements and macroelements. When coarsegraining the system we assume that the resulting macroelements do not differentiate the different microelements. Thus any correspondence between states must be stated solely in terms of the number of microelements which are ON, and not depend on which microelements are ON.
For example, consider the macroelement (0, 1, 2)
. We may say that the
macroelement is ON if at least one microelement is ON, or if all
microelements are ON; however, we may not say that the macroelement is ON if
microelement 1
is ON, because this relationship involves identifying
specific microelements.
The grouping
attribute of the CoarseGrain
describes how the state of
microelements describes the state of macroelements:
>>> grouping = coarse_grain.grouping
>>> grouping
(((0, 1, 2), (3,)), ((0,), (1,)))
The grouping consists of two lists, one for each macroelement:
>>> grouping[0]
((0, 1, 2), (3,))
For the first macroelement, this grouping means that the element will be OFF if zero, one or two of its microelements are ON, and will be ON if all three microelements are ON.
>>> grouping[1]
((0,), (1,))
For the second macroelement, the grouping means that the element will be OFF if its microelement is OFF, and ON if its microelement is ON.
One we have selected a partition and grouping for analysis, we can create a mapping between microstates and macrostates:
>>> mapping = coarse_grain.make_mapping()
>>> mapping
array([0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 3])
The interpretation of the mapping uses the littleendian convention of indexing (see Littleendian convention).
>>> mapping[7]
1
This says that microstate 7 corresponds to macrostate 1:
>>> pyphi.convert.le_index2state(7, 4)
(1, 1, 1, 0)
>>> pyphi.convert.le_index2state(1, 2)
(1, 0)
In microstate 7, all three elements corresponding to the first macroelement are ON, so that macroelement is ON. The microelement corresponding to the second macroelement is OFF, so that macroelement is OFF.
The CoarseGrain
object uses the mapping internally to create a statebystate
TPM for the macrosystem corresponding to the selected partition and grouping
>>> coarse_grain.macro_tpm(network.tpm)
Traceback (most recent call last):
...
pyphi.exceptions.ConditionallyDependentError...
However, this macroTPM does not satisfy the conditional independence
assumption, so this particular partition and grouping combination is not a
valid coarsegraining of the system. Constructing a MacroSubsystem
with this
coarsegraining will also raise a ConditionallyDependentError
.
Let’s consider a different coarsegraining instead.
>>> coarse_grain = grains[14]
>>> coarse_grain.partition
((0, 1), (2, 3))
>>> coarse_grain.grouping
(((0, 1), (2,)), ((0, 1), (2,)))
>>> mapping = coarse_grain.make_mapping()
>>> mapping
array([0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 2, 2, 2, 3])
>>> coarse_grain.macro_tpm(network.tpm)
array([[[0.09, 0.09],
[1. , 0.09]],
<BLANKLINE>
[[0.09, 1. ],
[1. , 1. ]]])
We can now construct a MacroSubsystem
using this coarsegraining:
>>> macro_subsystem = pyphi.macro.MacroSubsystem(
... network, state, coarse_grain=coarse_grain)
>>> macro_subsystem
MacroSubsystem((m0, m1))
We can then consider the integrated information of this macronetwork and compare it to the micronetwork.
>>> macro_sia = pyphi.compute.sia(macro_subsystem)
>>> macro_sia.phi
0.597212
The integrated information of the macro subsystem (\(\Phi = 0.597212\)) is greater than the integrated information of the micro system (\(\Phi = 0.113889\)). We can conclude that a macroscale is appropriate for this system, but to determine which one, we must check all possible partitions and all possible groupings to find the maximum of integrated information across all scales.
>>> M = pyphi.macro.emergence(network, state)
>>> M.emergence
0.483323
>>> M.system
(0, 1, 2, 3)
>>> M.coarse_grain.partition
((0, 1), (2, 3))
>>> M.coarse_grain.grouping
(((0, 1), (2,)), ((0, 1), (2,)))
The analysis determines the partition and grouping which results in the maximum value of integrated information, as well as the emergence (increase in \(\Phi\)) from the microscale to the macroscale.
Blackboxing¶
The macro
module also provides tools for studying the emergence of systems
using blackboxing.
>>> import pyphi
>>> network = pyphi.examples.blackbox_network()
We consider the state where all nodes are OFF:
>>> state = (0, 0, 0, 0, 0, 0)
The system has minimal \(\Phi\) without blackboxing:
>>> subsys = pyphi.Subsystem(network, state)
>>> pyphi.compute.phi(subsys)
0.215278
We will consider the blackbox system consisting of two blackbox elements, \(ABC\) and \(DEF\), where \(C\) and \(F\) are output elements and \(AB\) and \(DE\) are hidden within their respective blackboxes.
Blackboxing is done with a Blackbox
object. As with CoarseGrain
, we pass it
a partition of microelements:
>>> partition = ((0, 1, 2), (3, 4, 5))
>>> output_indices = (2, 5)
>>> blackbox = pyphi.macro.Blackbox(partition, output_indices)
Blackboxes have a few convenient attributes and methods. The hidden_indices
attribute returns the elements which are hidden within blackboxes:
>>> blackbox.hidden_indices
(0, 1, 3, 4)
The micro_indices
attribute lists all the microelements in the box:
>>> blackbox.micro_indices
(0, 1, 2, 3, 4, 5)
The macro_indices
attribute generates a set of indices which index the
blackbox macroelements. Since there are two blackboxes in our example, and
each has one output element, there are two macroindices:
>>> blackbox.macro_indices
(0, 1)
The macro_state
method converts a state of the micro elements to the state
of the macroelements. The macrostate of a blackbox system is simply the state
of the system’s output elements:
>>> micro_state = (0, 0, 0, 0, 0, 1)
>>> blackbox.macro_state(micro_state)
(0, 1)
Let us also define a time scale over which to perform our analysis:
>>> time_scale = 2
As in the coarsegraining example, the blackbox and time scale are passed to
MacroSubsystem
:
>>> macro_subsystem = pyphi.macro.MacroSubsystem(network, state,
... blackbox=blackbox,
... time_scale=time_scale)
We can now compute \(\Phi\) for this macro system:
>>> pyphi.compute.phi(macro_subsystem)
0.638888
We find that the macro subsystem has greater integrated information (\(\Phi = 0.638888\)) than the micro system (\(\Phi = 0.215278\))—the system demonstrates emergence.
Actual Causation¶
This section demonstrates how to use PyPhi to evaluate actual causation as described in
>>> import pyphi
>>> from pyphi import actual, config, Direction
Configuration¶
Before we begin we need to set some configuration values. The correct way of
partitioning for actual causation is using the 'ALL'
partitions setting;
'TRI'
partitions are a reasonable approximation. In case of ties the
smaller purview should be chosen. IIT 3.0 style bipartitions will give
incorrect results.
>>> config.PARTITION_TYPE = 'TRI'
>>> config.PICK_SMALLEST_PURVIEW = True
When calculating a causal account of the transition between a set of elements \(X\) at time \(t1\) and a set of elements \(Y\) at time \(t\), with \(X\) and \(Y\) being subsets of the same system, the transition should be valid according to the system’s TPM. However, the state of \(X\) at \(t1\) does not necessarily need to have a valid previous state so we can disable state validation:
>>> config.VALIDATE_SUBSYSTEM_STATES = False
Computation¶
We will look at how to perform computations over the basic ORAND network introduced in Figure 1 of the paper.
>>> network = pyphi.examples.actual_causation()
This is a standard PyPhi Network
so we can look at its TPM:
>>> pyphi.convert.state_by_node2state_by_state(network.tpm)
array([[1., 0., 0., 0.],
[0., 1., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 0., 1.]])
The OR
gate is element 0
, and the AND
gate is element 1
in the
network.
>>> OR = 0
>>> AND = 1
We want to observe both elements at \(t1\) and \(t\), with OR
ON and AND
OFF in both observations:
>>> X = Y = (OR, AND)
>>> X_state = Y_state = (1, 0)
The Transition
object is the core of all actual causation calculations. To
instantiate a Transition
, we pass it a Network
, the state of the network at
\(t1\) and \(t\), and elements of interest at \(t1\) and \(t\). Note that PyPhi
requires the state to be the state of the entire network, not just the state of
the nodes in the transition.
>>> transition = actual.Transition(network, X_state, Y_state, X, Y)
Cause and effect repertoires can be obtained for the transition. For example, as shown on the right side of Figure 2B, we can compute the effect repertoire to see how \(X_{t1} = \{OR = 1\}\) constrains the probability distribution of the purview \(Y_t = \{OR, AND\}\):
>>> transition.effect_repertoire((OR,), (OR, AND))
array([[0. , 0. ],
[0.5, 0.5]])
Similarly, as in Figure 2C, we can compute the cause repertoire of \(Y_t = \{OR, AND = 10\}\) to see how it constrains the purview \(X_{t1} = \{OR\}\):
>>> transition.cause_repertoire((OR, AND), (OR,))
array([[0.5],
[0.5]])
Note
In all Transition
methods the constraining occurence is passed as
the mechanism
argument and the constrained occurence is the purview
argument. This mirrors the terminology introduced in the IIT code.
Transition
also provides methods for computing cause and effect
ratios. For example, the effect ratio of \(X_{t1} = \{OR = 1\}\) constraining
\(Y_t = \{OR\}\) (as shown in Figure 3A) is computed as follows:
>>> transition.effect_ratio((OR,), (OR,))
0.415037
The effect ratio of \(X_{t1} = \{OR = 1\}\) constraining \(Y_t = \{AND\}\) is negative:
>>> transition.effect_ratio((OR,), (AND,))
0.584963
And the cause ratio of \(Y_t = \{OR = 1\}\) constraining \(X_{t1} = \{OR, AND\}\) (Figure 3B) is:
>>> transition.cause_ratio((OR,), (OR, AND))
0.415037
We can evaluate \(\alpha\) for a particular pair of occurences, as in Figure 3C.
For example, to find the irreducible effect ratio of \(\{OR, AND\} \rightarrow \{OR, AND\}\),
we use the find_mip
method:
>>> link = transition.find_mip(Direction.EFFECT, (OR, AND), (OR, AND))
This returns a AcRepertoireIrreducibilityAnalysis
object, with a number of
useful properties. This particular MIP is reducible, as we can see by checking
the value of \(\alpha\):
>>> link.alpha
0.0
The partition
property shows the minimum information partition that
reduces the occurence and candidate effect:
>>> link.partition # doctest: +NORMALIZE_WHITESPACE
∅ OR AND
─── ✕ ─── ✕ ───
∅ OR AND
Let’s look at the MIP for the irreducible occurence \(Y_t = \{OR, AND\}\) constraining \(X_{t1} = \{OR, AND\}\) (Figure 3D). This candidate causal link has positive \(\alpha\):
>>> link = transition.find_mip(Direction.CAUSE, (OR, AND), (OR, AND))
>>> link.alpha
0.169925
To find the actual cause or actual effect of a particular occurence, use the
find_actual_cause
or find_actual_effect
methods:
>>> transition.find_actual_cause((OR, AND))
CausalLink
α = 0.1699 [OR, AND] ◀━━ [OR, AND]
Accounts¶
The complete causal account of our transition can be computed with the
account
function:
>>> account = actual.account(transition)
>>> print(account) # doctest: +NORMALIZE_WHITESPACE
<BLANKLINE>
Account (5 causal links)
***********************************
Irreducible effects
α = 0.415 [OR] ━━▶ [OR]
α = 0.415 [AND] ━━▶ [AND]
Irreducible causes
α = 0.415 [OR] ◀━━ [OR]
α = 0.415 [AND] ◀━━ [AND]
α = 0.1699 [OR, AND] ◀━━ [OR, AND]
We see that this function produces the causal links shown in Figure 4. The
Account
object is a subclass of tuple
, and can manipulated the same:
>>> len(account)
5
Irreducible Accounts¶
The irreducibility of the causal account of our transition of interest can be evaluated using the following function:
>>> sia = actual.sia(transition)
>>> sia.alpha
0.169925
As shown in Figure 4, the second order occurence \(Y_t = \{OR, AND = 10\}\) is destroyed by the MIP:
>>> sia.partitioned_account # doctest: +NORMALIZE_WHITESPACE
<BLANKLINE>
Account (4 causal links)
**************************
Irreducible effects
α = 0.415 [OR] ━━▶ [OR]
α = 0.415 [AND] ━━▶ [AND]
Irreducible causes
α = 0.415 [OR] ◀━━ [OR]
α = 0.415 [AND] ◀━━ [AND]
The partition of the MIP is available in the cut
property:
>>> sia.cut # doctest: +NORMALIZE_WHITESPACE
KCut CAUSE
∅ OR AND
─── ✕ ─── ✕ ───
∅ OR AND
To find all irreducible accounts within the transition of interest, use
nexus
:
>>> all_accounts = actual.nexus(network, X_state, Y_state)
This computes \(\mathcal{A}\) for all permutations of of elements in \(X_{t1}\) and
\(Y_t\) and returns a tuple
of all AcSystemIrreducibilityAnalysis
objects
with \(\mathcal{A} > 0\):
>>> for n in all_accounts:
... print(n.transition, n.alpha)
Transition([OR] ━━▶ [OR]) 2.0
Transition([AND] ━━▶ [AND]) 2.0
Transition([OR, AND] ━━▶ [OR, AND]) 0.169925
The causal_nexus
function computes the maximally irreducible account for
the transition of interest:
>>> cn = actual.causal_nexus(network, X_state, Y_state)
>>> cn.alpha
2.0
>>> cn.transition
Transition([OR] ━━▶ [OR])
Disjunction of conjunctions¶
If you are interested in exploring further, the disjunction of conjunctions network from Figure 7 is provided as well:
>>> network = pyphi.examples.disjunction_conjunction_network()
>>> cn = actual.causal_nexus(network, (1, 0, 1, 0), (0, 0, 0, 1))
The only irreducible transition is from \(X_{t1} = C\) to \(Y_t = D\), with \(\mathcal{A}\) of 2.0:
>>> cn.transition
Transition([C] ━━▶ [D])
>>> cn.alpha
2.0
Residue¶
This example describes a system containing two AND gates, \(A\) and \(B\), with a single overlapping input node.
First let’s create the subsystem corresponding to the residue network, with all nodes OFF in the current and previous states.
>>> import pyphi
>>> subsystem = pyphi.examples.residue_subsystem()
Next, we can define the mechanisms of interest. Mechanisms and purviews are represented by tuples of node indices in the network:
>>> A = (0,)
>>> B = (1,)
>>> AB = (0, 1)
And the possible cause purviews that we’re interested in:
>>> CD = (2, 3)
>>> DE = (3, 4)
>>> CDE = (2, 3, 4)
We can then evaluate the cause information for each of the mechanisms over the cause purview \(CDE\).
>>> subsystem.cause_info(A, CDE)
0.333332
>>> subsystem.cause_info(B, CDE)
0.333332
>>> subsystem.cause_info(AB, CDE)
0.5
The composite mechanism \(AB\) has greater cause information than either of the individual mechanisms. This contradicts the idea that \(AB\) should exist minimally in this system.
Instead, we can quantify existence as the irreducible cause information of a
mechanism. The MIP of a mechanism is the partition of mechanism and purview
which makes the least difference to the cause repertoire (see the documentation
for the RepertoireIrreducibilityAnalysis
object). The irreducible cause
information is the distance between the unpartitioned and partitioned
repertoires.
To analyze the irreducibility of the mechanism \(AB\) on the cause side:
>>> mip_AB = subsystem.cause_mip(AB, CDE)
We can then determine what the specific partition is.
>>> mip_AB.partition # doctest: +NORMALIZE_WHITESPACE
∅ A,B
─── ✕ ───
C D,E
The indices (0, 1, 2, 3, 4)
correspond to nodes \(A, B, C, D, E\)
respectively. Thus the MIP is \(\frac{AB}{DE} \times \frac{\varnothing}{C}\), where \(\varnothing\)
denotes the empty mechanism.
The partitioned repertoire of the MIP can also be retrieved:
>>> mip_AB.partitioned_repertoire
array([[[[[0.2, 0.2],
[0.1, 0. ]],
<BLANKLINE>
[[0.2, 0.2],
[0.1, 0. ]]]]])
And we can then calculate the irreducible cause information as the difference between partitioned and unpartitioned repertoires.
>>> mip_AB.phi
0.1
One counterintuitive result that merits discussion is that since irreducible cause information is what defines existence, we must also evaluate the irreducible cause information of the mechanisms \(A\) and \(B\).
The mechanism \(A\) over the purview \(CDE\) is completely reducible to \(\frac{A}{CD} \times \frac{\varnothing}{E}\) because \(E\) has no effect on \(A\), so it has zero \(\varphi\).
>>> subsystem.cause_mip(A, CDE).phi
0.0
>>> subsystem.cause_mip(A, CDE).partition # doctest: +NORMALIZE_WHITESPACE
∅ A
─── ✕ ───
E C,D
Instead, we should evaluate \(A\) over the purview \(CD\).
>>> mip_A = subsystem.cause_mip(A, CD)
In this case, there is a welldefined MIP
>>> mip_A.partition # doctest: +NORMALIZE_WHITESPACE
∅ A
─── ✕ ───
C D
which is \(\frac{\varnothing}{C} \times \frac{A}{D}\). It has partitioned repertoire
>>> mip_A.partitioned_repertoire
array([[[[[0.33333333],
[0.16666667]],
<BLANKLINE>
[[0.33333333],
[0.16666667]]]]])
and irreducible cause information
>>> mip_A.phi
0.166667
A similar result holds for \(B\). Thus the mechanisms \(A\) and \(B\) exist at levels of \(\varphi = \frac{1}{6}\), while the higherorder mechanism \(AB\) exists only as the residual of causes, at a level of \(\varphi = \frac{1}{10}\).
Magic Cuts¶
This example explores a system of three fully connected elements \(A\), \(B\) and \(C\), which follow the logic of the Rule 110 cellular automaton. The point of this example is to highlight an unexpected behaviour of system cuts: that the minimum information partition of a system can result in new concepts being created.
First let’s create the the Rule 110 network, with all nodes OFF in the current state.
>>> import pyphi
>>> network = pyphi.examples.rule110_network()
>>> state = (0, 0, 0)
Next, we want to identify the spatial scale and major complex of the network:
>>> macro = pyphi.macro.emergence(network, state)
>>> print(macro.emergence)
1.112671
Since the emergence value is negative, there is no macro scale which has greater integrated information than the original micro scale. We can now analyze the micro scale to determine the major complex of the system:
>>> major_complex = pyphi.compute.major_complex(network, state)
>>> major_complex.subsystem
Subsystem(A, B, C)
>>> print(major_complex.phi)
1.35708
The major complex of the system contains all three nodes of the system, and it has integrated information \(\Phi = 1.35708\). Now that we have identified the major complex of the system, we can explore its causeeffect structure and the effect of the MIP.
>>> ces = major_complex.ces
There two equivalent cuts for this system; for concreteness we sever all connections from elements \(A\) and \(B\) to \(C\).
>>> cut = pyphi.models.Cut(from_nodes=(0, 1), to_nodes=(2,))
>>> cut_subsystem = pyphi.Subsystem(network, state, cut=cut)
>>> cut_ces = pyphi.compute.ces(cut_subsystem)
Let’s investigate the concepts in the unpartitioned causeeffect structure,
>>> ces.labeled_mechanisms
(['A'], ['B'], ['C'], ['A', 'B'], ['A', 'C'], ['B', 'C'])
>>> ces.phis
[0.125, 0.125, 0.125, 0.499999, 0.499999, 0.499999]
>>> sum(ces.phis)
1.8749970000000002
and also the concepts of the partitioned causeeffect structure.
>>> cut_ces.labeled_mechanisms
(['A'], ['B'], ['C'], ['A', 'B'], ['B', 'C'], ['A', 'B', 'C'])
>>> cut_ces.phis
[0.125, 0.125, 0.125, 0.499999, 0.266666, 0.333333]
>>> sum(_)
1.4749980000000003
The unpartitioned causeeffect structure includes all possible first and second order concepts, but there is no third order concept. After applying the cut and severing the connections from \(A\) and \(B\) to \(C\), the third order concept \(ABC\) is created and the second order concept \(AC\) is destroyed. The overall amount of \(\varphi\) in the system decreases from \(1.875\) to \(1.475\).
Let’s explore the concept which was created to determine why it does not exist in the unpartitioned causeeffect structure and what changed in the partitioned causeeffect structure.
>>> subsystem = major_complex.subsystem
>>> ABC = subsystem.node_indices
>>> subsystem.cause_info(ABC, ABC)
0.749999
>>> subsystem.effect_info(ABC, ABC)
1.875
The mechanism does have cause and effect power over the system. But, since it doesn’t specify a concept, it must be that this power is reducible:
>>> mic = subsystem.mic(ABC)
>>> mic.phi
0.0
>>> mie = subsystem.mie(ABC)
>>> mie.phi
0.625
The reason ABC does not exist as a concept is that its cause is reducible. Looking at the TPM of the system, there are no possible states where two elements are OFF. This means that knowing two elements are OFF is enough to know that the third element must also be OFF, and thus the third element can always be cut from the concept without a loss of information. This will be true for any purview, so the cause information is reducible.
>>> BC = (1, 2)
>>> A = (0,)
>>> repertoire = subsystem.cause_repertoire(ABC, ABC)
>>> cut_repertoire = (subsystem.cause_repertoire(BC, ABC) *
... subsystem.cause_repertoire(A, ()))
>>> pyphi.distance.hamming_emd(repertoire, cut_repertoire)
0.0
Next, let’s look at the cut subsystem to understand how the new concept comes into existence.
>>> ABC = (0, 1, 2)
>>> C = (2,)
>>> AB = (0, 1)
The cut applied to the subsystem severs the connections going to \(C\) from
either \(A\) or \(B\). In this circumstance, knowing the state of \(A\) or \(B\) does
not tell us anything about the state of \(C\); only the previous state of \(C\) can
tell us about the next state of \(C\). C_node.tpm_on
gives us the probability
of \(C\) being ON in the next state, while C_node.tpm_off
would give us the
probability of \(C\) being OFF.
>>> C_node = cut_subsystem.indices2nodes(C)[0]
>>> C_node.tpm_on.flatten()
array([0.5 , 0.75])
This states that \(C\) has a 50% chance of being ON in the next state if it currently OFF, but a 75% chance of being ON in the next state if it is currently ON. Thus, unlike the unpartitioned case, knowing the current state of \(C\) gives us additional information over and above knowing the state of \(A\) or \(B\).
>>> repertoire = cut_subsystem.cause_repertoire(ABC, ABC)
>>> cut_repertoire = (cut_subsystem.cause_repertoire(AB, ABC) *
... cut_subsystem.cause_repertoire(C, ()))
>>> print(pyphi.distance.hamming_emd(repertoire, cut_repertoire))
0.500001
With this partition, the integrated information is \(\varphi = 0.5\), but we must check all possible partitions to find the maximallyirreducible cause:
>>> mic = cut_subsystem.mic(ABC)
>>> mic.purview
(0, 1, 2)
>>> mic.phi
0.333333
It turns out that the MIP of the maximallyirreducible cause is
and the integrated information of mechanism \(ABC\) is \(\varphi = 1/3\).
Note that in order for a new concept to be created by a cut, there must be a withinmechanism connection severed by the cut.
In the previous example, the MIP created a new concept, but the amount of \(\varphi\) in the causeeffect structure still decreased. This is not always the case. Next we will look at an example of system whoes MIP increases the amount of \(\varphi\). This example is based on a fivenode network that implements the logic of the Rule 154 cellular automaton. Let’s first load the network:
>>> network = pyphi.examples.rule154_network()
>>> state = (1, 0, 0, 0, 0)
For this example, it is the subsystem consisting of \(A\), \(B\), and \(E\) that we explore. This is not the major complex of the system, but it serves as a proof of principle regardless.
>>> subsystem = pyphi.Subsystem(network, state, (0, 1, 4))
Calculating the MIP of the system,
>>> sia = pyphi.compute.sia(subsystem)
>>> sia.phi
0.217829
>>> sia.cut
Cut [A, E] ━━/ /━━➤ [B]
we see that this subsystem has a \(\Phi\) value of 0.15533, and the MIP cuts the connections from \(AE\) to \(B\). Investigating the concepts in both the partitioned and unpartitioned causeeffect structures,
>>> sia.ces.labeled_mechanisms
(['A'], ['B'], ['A', 'B'])
>>> sia.ces.phis
[0.25, 0.166667, 0.178572]
>>> print(sum(_))
0.5952390000000001
We see that the unpartitioned causeeffect structure has mechanisms \(A\), \(B\) and \(AB\) with \(\sum\varphi = 0.595239\).
>>> sia.partitioned_ces.labeled_mechanisms
(['A'], ['B'], ['A', 'B'])
>>> sia.partitioned_ces.phis
[0.25, 0.166667, 0.214286]
>>> print(sum(_))
0.630953
The partitioned causeeffect structure has mechanisms \(A\), \(B\) and \(AB\) but with \(\sum\varphi = 0.630953\). There are the same number of concepts in both causeeffect structures, over the same mechanisms; however, the partitioned causeeffect structure has a greater \(\varphi\) value for the concept \(AB\), resulting in an overall greater \(\sum\varphi\) for the partitioned causeeffect structure.
Although situations described above are rare, they do occur, so one must be careful when analyzing the integrated information of physical systems not to dismiss the possibility of partitions creating new concepts or increasing the amount of \(\varphi\); otherwise, an incorrect major complex may be identified.
Detailed installation guide for macOS¶
This is a stepbystep guide intended for those unfamiliar with Python or the commandline (a.k.a. the “shell”).
A shell can be opened by opening a new tab in the Terminal app (located in
Utilities). Text that is formatted like code
is meant to be copied and
pasted into the terminal (hit the Enter key to run the command).
The fist step is to install the versions of Python that we need. The most convenient way of doing this is to use the OS X package manager Homebrew. Install Homebrew by running this command:
/usr/bin/ruby e "$(curl fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
Now you should have access to the brew
command. First, we need to install
Python 2 and 3. Using these socalled “brewed” Python versions, rather than the
version of Python that comes with your computer, will protect your computer’s
Python version from unwanted changes that could interfere with other
applications.
brew install python python3
Then we need to ensure that the terminal “knows about” the newlyinstalled Python versions:
brew link overwrite python
brew link overwrite python3
Now that we’re using our shiny new Python versions, it is highly recommended to set up a virtual environment in which to install PyPhi. Virtual environments allow different projects to isolate their dependencies from one another, so that they don’t interact in unexpected ways. Please see this guide for more information.
To do this, you must install virtualenvwrapper
, a tool for manipulating
virtual environments. This tool
is available on PyPI, the Python package
index, and can be installed with pip
, the commandline utility for
installing and managing Python packages (pip
was installed automatically
with the brewed Python):
pip install virtualenvwrapper
Now we need to edit your shell startup file. This is a file that runs
automatically every time you open a new shell (a new window or tab in the
Terminal app). This file should be in your home directory, though it will be
invisible in the Finder because the filename is preceded by a period. On most
Macs it is called .bash_profile
. You can open this in a text editor by
running this command:
open a TextEdit ~/.bash_profile
If you get an error that says the file doesn’t exist, then run touch
~/.bash_profile
first to create it.
Now, you’ll add three lines to the shell startup file. These lines will set the
location where the virtual environments will live, the location of your
development project directories, and the location of the script installed with
this package, respectively. Note: The location of the script can be found
by running which virtualenvwrapper.sh
.
The filepath after the equals sign on the second line will different for everyone, but here is an example:
export WORKON_HOME=$HOME/.virtualenvs
export PROJECT_HOME=$HOME/dev
source /usr/local/bin/virtualenvwrapper.sh
After editing the startup file and saving it, open a new terminal shell by
opening a new tab or window (or just reload the startup file by running
source ~/.bash_profile
).
Now that virtualenvwrapper
is fully installed, use it to create a Python 3
virtual environment, like so:
mkvirtualenv p `which python3` <name_of_your_project>
The option p `which python3`
ensures that when the virtual environment is
activated, the commands python
and pip
will refer to their Python 3
counterparts.
The virtual environment should have been activated automatically after creating
it. Virtual environments can be manually activated with workon
<name_of_your_project>
, and deactivated with deactivate
.
Important: Remember to activate the virtual environment with the workon
command every time you begin working on your project. Also, note that the
currently active virtual environment is not associated with any particular
folder; it is associated with a terminal shell. In other words, each time you
open a new Terminal tab or terminal window, you need to run workon
<name_of_your_project
(with some extra setup, this can be done automatically;
see here).
When a virtual environment is active, your commandline prompt will be
prepended with the name of the virtual environment in parentheses.
Once you’ve checked that the new virtual environment is active, you’re finally ready to install PyPhi into it (note that this may take a few minutes):
pip install pyphi
Congratulations, you’ve just installed PyPhi!
To play around with the software, ensure that you’ve activated the virtual
environment with workon <name_of_your_project>
. Then run python
to
start a Python 3 interpreter. Then, in the interpreter’s commandline (which is
preceded by the >>>
prompt), run
import pyphi
Optionally, you can also install IPython with pip
install ipython
to get a more useful Python interpreter that offers things
like tabcompletion. Once you’ve installed it, you can start the IPython
interpreter with the command ipython
.
Next, please see the documentation for some examples of how to use PyPhi and information on how to configure it.
Transition probability matrix conventions¶
A Network
can be created with a transition probability matrix (TPM) in any of
the three forms described below. However, in PyPhi the canonical TPM
representation is multidimensional statebynode form. The TPM will be
converted to this form when the Network
is built.
Tip
Functions for converting TPMs from one form to another are available in the
convert
module.
Statebynode form¶
A TPM in statebynode form is a matrix where the entry \((i,j)\) gives the probability that the \(j^{\textrm{th}}\) node will be ON at time \(t+1\) if the system is in the \(i^{\textrm{th}}\) state at time \(t\).
Multidimensional statebynode form¶
A TPM in multidimensional statebynode form is a statebynode form that has been reshaped so that it has \(n+1\) dimensions instead of two. The first \(n\) dimensions correspond to each of the \(n\) nodes at time \(t\), while the last dimension corresponds to the probabilities of each node being ON at \(t+1\).
With this form, we can take advantage of NumPy array indexing and use a network state as an index directly:
>>> from pyphi.examples import basic_noisy_selfloop_network
>>> tpm = basic_noisy_selfloop_network().tpm
>>> state = (0, 0, 1) # A network state is a binary tuple
>>> tpm[state]
array([0.919, 0.91 , 0.756])
This tells us that if the current state is \(N_0 = 0, N_1 = 0, N_2 = 1\), then the for the next state, \(\Pr(N_0 = 1) = 0.919\), \(\Pr(N_1 = 1) = 0.91\) and \(\Pr(N_2 = 1) = 0.756\).
Important
The multidimensional statebynode form is used throughout PyPhi,
regardless of the form that was used to create the Network
.
Statebystate form¶
A TPM in statebystate form is a matrix where the entry \((i,j)\) gives the probability that the state at time \(t+1\) will be \(j\) if the state at time \(t\) is labeled by \(i\).
Warning
When converting a statebystate TPM to one of the other forms, information may be lost!
This is because the space of possible statebystate TPMs is larger than the space of statebynode TPMs (so the conversion cannot be injective). However, if we restrict the statebystate TPMs to only those that satisfy the conditional independence property, then the mapping becomes bijective.
See Conditional Independence for a more detailed discussion.
Littleendian convention¶
Even after choosing one of the above representations, there are several ways to write down the TPM.
With both statebystate and statebynode TPMs, one is confronted with a choice about which rows correspond to which states. In statebystate TPMs, this choice must also be made for the columns.
There are two possible choices for the rows. Either the first node changes state every other row:
State at \(t\) \(\Pr(N = ON)\) at \(t+1\) A, B A B (0, 0) 0.1 0.2 (1, 0) 0.3 0.4 (0, 1) 0.5 0.6 (1, 1) 0.7 0.8
Or the last node does:
State at \(t\) \(\Pr(N = ON)\) at \(t+1\) A, B A B (0, 0) 0.1 0.2 (0, 1) 0.5 0.6 (1, 0) 0.3 0.4 (1, 1) 0.7 0.8
Note that the index \(i\) of a row in a TPM encodes a network state: convert the index to binary, and each bit gives the state of a node. The question is, which node?
Throughout PyPhi, we always choose the first convention—the state of the first node (the one with the lowest index) varies the fastest. So, the leastsignficant bit—the one’s place—gives the state of the lowestindex node.
This is analogous to the littleendian convention in organizing computer memory. The other convention, where the highestindex node varies the fastest, is analogous to the bigendian convention (see Endianness).
The rationale for this choice of convention is that the littleendian mapping is stable under changes in the number of nodes, in the sense that the same bit always corresponds to the same node index. The bigendian mapping does not have this property.
Tip
Functions to convert states to indices and vice versa, according to either
the littleendian or bigendian convention, are available in the convert
module.
Note
This applies to only situations where decimal indices are encoding states. Whenever a network state is represented as a list or tuple, we use the only sensible convention: the \(i^{\textrm{th}}\) element gives the state of the \(i^{\textrm{th}}\) node.
Connectivity matrix conventions¶
Throughout PyPhi, if \(CM\) is a connectivity matrix, then \([CM]_{i,j} = 1\) means that there is a directed edge \((i,j)\) from node \(i\) to node \(j\), and \([CM]_{i,j} = 0\) means there is no edge from \(i\) to \(j\).
For example, this network of four nodes
has the following connectivity matrix:
>>> cm = [[0, 0, 1, 0],
... [1, 0, 1, 0],
... [0, 1, 0, 1],
... [0, 0, 0, 1]]
Loading a configuration¶
Various aspects of PyPhi’s behavior can be configured.
When PyPhi is imported, it checks for a YAML file named pyphi_config.yml
in
the current directory and automatically loads it if it exists; otherwise the
default configuration is used.
The various settings are listed here with their defaults.
>>> import pyphi
>>> defaults = pyphi.config.defaults()
Print the config
object to see the current settings:
>>> print(pyphi.config) # doctest: +SKIP
{ 'ASSUME_CUTS_CANNOT_CREATE_NEW_CONCEPTS': False,
'CACHE_SIAS': False,
'CACHE_POTENTIAL_PURVIEWS': True,
'CACHING_BACKEND': 'fs',
...
Setting can be changed on the fly by assigning them a new value:
>>> pyphi.config.PROGRESS_BARS = False
It is also possible to manually load a configuration file:
>>> pyphi.config.load_file('pyphi_config.yml')
Or load a dictionary of configuration values:
>>> pyphi.config.load_dict({'PRECISION': 1})
Approximations and theoretical options¶
These settings control the algorithms PyPhi uses.
Parallelization and system resources¶
These settings control how much processing power and memory is available for PyPhi to use. The default values may not be appropriate for your usecase or machine, so please check these settings before running anything. Otherwise, there is a risk that simulations might crash (potentially after running for a long time!), resulting in data loss.
MAXIMUM_CACHE_MEMORY_PERCENTAGE
Important
Only one of
PARALLEL_CONCEPT_EVALUATION
,PARALLEL_CUT_EVALUATION
, andPARALLEL_COMPLEX_EVALUATION
can be set toTrue
at a time.For most networks,
PARALLEL_CUT_EVALUATION
is the most efficient. This is because the algorithm is exponential time in the number of nodes, so the most of the time is spent on the largest subsystem.You should only parallelize concept evaluation if you are just computing a
CauseEffectStructure
.
Memoization and caching¶
PyPhi provides a number of ways to cache intermediate results.
Logging¶
These settings control how PyPhi handles log messages. Logs can be written to standard output, a file, both, or none. If these simple default controls are not flexible enough for you, you can override the entire logging configuration. See the documentation on Python’s logger for more information.
The config
API¶

class
pyphi.conf.
Option
(default, values=None, on_change=None, doc=None)¶ A descriptor implementing PyPhi configuration options.
Parameters: default – The default value of this
Option
.Keyword Arguments:  values (list) – Allowed values for this option. A
ValueError
will be raised ifvalues
is notNone
and the option is set to be a value not in the list.  on_change (function) – Optional callback that is called when the value
of the option is changed. The
Config
instance is passed as the only argument to the callback.  doc (str) – Optional docstring for the option.
 values (list) – Allowed values for this option. A

class
pyphi.conf.
ConfigMeta
(cls_name, bases, namespace)¶ Metaclass for
Config
.Responsible for setting the name of each
Option
when a subclass ofConfig
is created; becauseOption
objects are defined on the class, not the instance, their name should only be set once.Python 3.6 handles this exact need with the special descriptor method
__set_name__
(see PEP 487). We should use that once we drop support for 3.4 & 3.5.

class
pyphi.conf.
Config
¶ Base configuration object.
See
PyphiConfig
for usage.
classmethod
options
()¶ Return a dictionary the
Option
objects for this config

defaults
()¶ Return the default values of this configuration.

load_dict
(dct)¶ Load a dictionary of configuration values.

load_file
(filename)¶ Load config from a YAML file.

snapshot
()¶ Return a snapshot of the current values of this configuration.

override
(**new_values)¶ Decorator and context manager to override configuration values.
The initial configuration values are reset after the decorated function returns or the context manager completes it block, even if the function or block raises an exception. This is intended to be used by tests which require specific configuration values.
Example
>>> from pyphi import config >>> @config.override(PRECISION=20000) ... def test_something(): ... assert config.PRECISION == 20000 ... >>> test_something() >>> with config.override(PRECISION=100): ... assert config.PRECISION == 100 ...

classmethod

pyphi.conf.
configure_logging
(conf)¶ Reconfigure PyPhi logging based on the current configuration.

class
pyphi.conf.
PyphiConfig
¶ pyphi.config
is an instance of this class.
ASSUME_CUTS_CANNOT_CREATE_NEW_CONCEPTS
¶ default=False
In certain cases, making a cut can actually cause a previously reducible concept to become a proper, irreducible concept. Assuming this can never happen can increase performance significantly, however the obtained results are not strictly accurate.

CUT_ONE_APPROXIMATION
¶ default=False
When determining the MIP for \(\Phi\), this restricts the set of system cuts that are considered to only those that cut the inputs or outputs of a single node. This restricted set of cuts scales linearly with the size of the system; the full set of all possible bipartitions scales exponentially. This approximation is more likely to give theoretically accurate results with modular, sparselyconnected, or homogeneous networks.

MEASURE
¶ default='EMD'
The measure to use when computing distances between repertoires and concepts. A full list of currently installed measures is available by calling
print(pyphi.distance.measures.all())
. Note that some measures cannot be used for calculating \(\Phi\) because they are asymmetric.Custom measures can be added using the
pyphi.distance.measures.register
decorator. For example:from pyphi.distance import measures @measures.register('ALWAYS_ZERO') def always_zero(a, b): return 0
This measure can then be used by setting
config.MEASURE = 'ALWAYS_ZERO'
.If the measure is asymmetric you should register it using the
asymmetric
keyword argument. Seedistance
for examples.

PARALLEL_CONCEPT_EVALUATION
¶ default=False
Controls whether concepts are evaluated in parallel when computing causeeffect structures.

PARALLEL_CUT_EVALUATION
¶ default=True
Controls whether system cuts are evaluated in parallel, which is faster but requires more memory. If cuts are evaluated sequentially, only two
SystemIrreducibilityAnalysis
instances need to be in memory at once.

PARALLEL_COMPLEX_EVALUATION
¶ default=False
Controls whether systems are evaluated in parallel when computing complexes.

NUMBER_OF_CORES
¶ default=1
Controls the number of CPU cores used to evaluate unidirectional cuts. Negative numbers count backwards from the total number of available cores, with
1
meaning ‘use all available cores.’

MAXIMUM_CACHE_MEMORY_PERCENTAGE
¶ default=50
PyPhi employs several inmemory caches to speed up computation. However, these can quickly use a lot of memory for large networks or large numbers of them; to avoid thrashing, this setting limits the percentage of a system’s RAM that the caches can collectively use.

CACHE_SIAS
¶ default=False
PyPhi is equipped with a transparent caching system for
SystemIrreducibilityAnalysis
objects which stores them as they are computed to avoid having to recompute them later. This makes it easy to play around interactively with the program, or to accumulate results with minimal effort. For larger projects, however, it is recommended that you manage the results explicitly, rather than relying on the cache. For this reason it is disabled by default.

CACHE_REPERTOIRES
¶ default=True
PyPhi caches cause and effect repertoires. This greatly improves speed, but can consume a significant amount of memory. If you are experiencing memory issues, try disabling this.

CACHE_POTENTIAL_PURVIEWS
¶ default=True
Controls whether the potential purviews of mechanisms of a network are cached. Caching speeds up computations by not recomputing expensive reducibility checks, but uses additional memory.

CLEAR_SUBSYSTEM_CACHES_AFTER_COMPUTING_SIA
¶ default=False
Controls whether a
Subsystem
’s repertoire and MICE caches are cleared withclear_caches()
after computing theSystemIrreducibilityAnalysis
. If you don’t need to do any more computations after runningsia()
, then enabling this may help conserve memory.

CACHING_BACKEND
¶ default='fs'
Controls whether precomputed results are stored and read from a local filesystembased cache in the current directory or from a database. Set this to
'fs'
for the filesystem,'db'
for the database.

FS_CACHE_VERBOSITY
¶ default=0
Controls how much caching information is printed if the filesystem cache is used. Takes a value between
0
and11
.

FS_CACHE_DIRECTORY
¶ default='__pyphi_cache__'
If the filesystem is used for caching, the cache will be stored in this directory. This directory can be copied and moved around if you want to reuse results e.g. on a another computer, but it must be in the same directory from which Python is being run.

MONGODB_CONFIG
¶ ‘cache’, ‘port’: 27017, ‘host’: ‘localhost’}``
Set the configuration for the MongoDB database backend (only has an effect if
CACHING_BACKEND
is'db'
).Type: ``default={‘database_name’ Type: ‘pyphi’, ‘collection_name’

REDIS_CACHE
¶ default=False
Specifies whether to use Redis to cache
MaximallyIrreducibleCauseOrEffect
.

REDIS_CONFIG
¶ 6379, ‘host’: ‘localhost’, ‘test_db’: 1}``
Configure the Redis database backend. These are the defaults in the provided
redis.conf
file.Type: ``default={‘db’ Type: 0, ‘port’

LOG_FILE
¶ default='pyphi.log'
,on_change=configure_logging
Controls the name of the log file.

LOG_FILE_LEVEL
¶ default='INFO'
,on_change=configure_logging
Controls the level of log messages written to the log file. This setting has the same possible values as
LOG_STDOUT_LEVEL
.

LOG_STDOUT_LEVEL
¶ default='WARNING'
,on_change=configure_logging
Controls the level of log messages written to standard output. Can be one of
'DEBUG'
,'INFO'
,'WARNING'
,'ERROR'
,'CRITICAL'
, orNone
.'DEBUG'
is the least restrictive level and will show the most log messages.'CRITICAL'
is the most restrictive level and will only display information about fatal errors. If set toNone
, logging to standard output will be disabled entirely.

PROGRESS_BARS
¶ default=True
Controls whether to show progress bars on the console.
Tip
If you are iterating over many systems rather than doing one longrunning calculation, consider disabling this for speed.

PRECISION
¶ default=6
If
MEASURE
isEMD
, then the Earth Mover’s Distance is calculated with an external C++ library that a numerical optimizer to find a good approximation. Consequently, systems with analytically zero \(\Phi\) will sometimes be numerically found to have a small but nonzero amount. This setting controls the number of decimal places to which PyPhi will consider EMD calculations accurate. Values of \(\Phi\) lower than10ePRECISION
will be considered insignificant and treated as zero. The default value is about as accurate as the EMD computations get.

VALIDATE_SUBSYSTEM_STATES
¶ default=True
Controls whether PyPhi checks if the subsystems’s state is possible (reachable with nonzero probability from some previous state), given the subsystem’s TPM (which is conditioned on background conditions). If this is turned off, then calculated \(\Phi\) values may not be valid, since they may be associated with a subsystem that could never be in the given state.

VALIDATE_CONDITIONAL_INDEPENDENCE
¶ default=True
Controls whether PyPhi checks if a system’s TPM is conditionally independent.

SINGLE_MICRO_NODES_WITH_SELFLOOPS_HAVE_PHI
¶ default=False
If set to
True
, the \(\Phi\) value of single micronode subsystems is the difference between their unpartitionedCauseEffectStructure
(a single concept) and the null concept. If set to False, their \(\Phi\) is defined to be zero. Single macronode subsystems may always be cut, regardless of circumstances.

REPR_VERBOSITY
¶ default=2
,values=[0, 1, 2]
Controls the verbosity of
__repr__
methods on PyPhi objects. Can be set to0
,1
, or2
. If set to1
, callingrepr
on PyPhi objects will return prettyformatted and legible strings, excluding repertoires. If set to2
,repr
calls also include repertoires.Although this breaks the convention that
__repr__
methods should return a representation which can reconstruct the object, readable representations are convenient since the Python REPL callsrepr
to represent all objects in the shell and PyPhi is often used interactively with the REPL. If set to0
,repr
returns more traditional object representations.

PRINT_FRACTIONS
¶ default=True
Controls whether numbers in a
repr
are printed as fractions. Numbers are still printed as decimals if the fraction’s denominator would be large. This only has an effect ifREPR_VERBOSITY > 0
.

PARTITION_TYPE
¶ default='BI'
Controls the type of partition used for \(\varphi\) computations.
If set to
'BI'
, partitions will have two parts.If set to
'TRI'
, partitions will have three parts. In addition, computations will only consider partitions that strictly partition the mechanism. That is, for the mechanism(A, B)
and purview(B, C, D)
the partition:A,B ∅ ─── ✕ ─── B C,D
is not considered, but:
A B ─── ✕ ─── B C,D
is. The following is also valid:
A,B ∅ ─── ✕ ───── ∅ B,C,D
In addition, this setting introduces “wedge” tripartitions of the form:
A B ∅ ─── ✕ ─── ✕ ─── B C D
where the mechanism in the third part is always empty.
Finally, if set to
'ALL'
, all possible partitions will be tested.You can experiment with custom partitioning strategies using the
pyphi.partition.partition_types.register
decorator. For example:from pyphi.models import KPartition, Part from pyphi.partition import partition_types @partition_types.register('SINGLE_NODE') def single_node_partitions(mechanism, purview, node_labels=None): for element in mechanism: element = tuple([element]) others = tuple(sorted(set(mechanism)  set(element))) part1 = Part(mechanism=element, purview=()) part2 = Part(mechanism=others, purview=purview) yield KPartition(part1, part2, node_labels=node_labels)
This generates the set of partitions that cut connections between a single mechanism element and the entire purview. The mechanism and purview of each
Part
remain undivided  only connections between parts are severed.You can use this new partititioning scheme by setting
config.PARTITION_TYPE = 'SINGLE_NODE'
.See
partition
for more examples.

PICK_SMALLEST_PURVIEW
¶ default=False
When computing a
MaximallyIrreducibleCause
orMaximallyIrreducibleEffect
, it is possible for several MIPs to have the same \(\varphi\) value. If this setting is set toTrue
the MIP with the smallest purview is chosen; otherwise, the one with largest purview is chosen.

USE_SMALL_PHI_DIFFERENCE_FOR_CES_DISTANCE
¶ default=False
If set to
True
, the distance between causeeffect structures (when computing aSystemIrreducibilityAnalysis
) is calculated using the difference between the sum of \(\varphi\) in the causeeffect structures instead of the extended EMD.

SYSTEM_CUTS
¶ default='3.0_STYLE'
,values=['3.0_STYLE', 'CONCEPT_STYLE']
If set to
'3.0_STYLE'
, then traditional IIT 3.0 cuts will be used when computing \(\Phi\). If set to'CONCEPT_STYLE'
, then experimental conceptstyle system cuts will be used instead.

log
()¶ Log current settings.

actual
¶
Methods for computing actual causation of subsystems and mechanisms.

pyphi.actual.
log2
(x)¶ Rounded version of
log2
.

class
pyphi.actual.
Transition
(network, before_state, after_state, cause_indices, effect_indices, cut=None, noise_background=False)¶ A state transition between two sets of nodes in a network.
A
Transition
is implemented with twoSubsystem
objects: one representing the system at time \(t1\) used to compute effect coefficients, and another representing the system at time \(t\) which is used to compute cause coefficients. These subsystems are accessed with theeffect_system
andcause_system
attributes, and are mapped to the causal directions via thesystem
attribute.Parameters:  network (Network) – The network the subsystem belongs to.
 before_state (tuple[int]) – The state of the network at time \(t1\).
 after_state (tuple[int]) – The state of the network at time \(t\).
 cause_indices (tuple[int] or tuple[str]) – Indices of nodes in the cause system. (TODO: clarify)
 effect_indices (tuple[int] or tuple[str]) – Indices of nodes in the effect system. (TODO: clarify)
Keyword Arguments: noise_background (bool) – If
True
, background conditions are noised instead of frozen.
node_indices
¶ The indices of the nodes in the system.
Type: tuple[int]

before_state
¶ The state of the network at time \(t1\).
Type: tuple[int]

after_state
¶ The state of the network at time \(t\).
Type: tuple[int]

effect_system
¶ The system in
before_state
used to compute effect repertoires and coefficients.Type: Subsystem

cause_system
¶ The system in
after_state
used to compute cause repertoires and coefficients.Type: Subsystem

cause_system
Type: Subsystem

system
¶ A dictionary mapping causal directions to the system used to compute repertoires in that direction.
Type: dict
Note
During initialization, both the cause and effect systems are conditioned on
before_state
as the background state. After conditioning theeffect_system
is then properly reset toafter_state
.
node_labels
¶

to_json
()¶ Return a JSONserializable representation.

apply_cut
(cut)¶ Return a cut version of this transition.

cause_repertoire
(mechanism, purview)¶ Return the cause repertoire.

effect_repertoire
(mechanism, purview)¶ Return the effect repertoire.

unconstrained_cause_repertoire
(purview)¶ Return the unconstrained cause repertoire of the occurence.

unconstrained_effect_repertoire
(purview)¶ Return the unconstrained effect repertoire of the occurence.

repertoire
(direction, mechanism, purview)¶ Return the cause or effect repertoire function based on a direction.
Parameters: direction (str) – The temporal direction, specifiying the cause or effect repertoire.

state_probability
(direction, repertoire, purview)¶ Compute the probability of the purview in its current state given the repertoire.
Collapses the dimensions of the repertoire that correspond to the purview nodes onto their state. All other dimension are already singular and thus receive 0 as the conditioning index.
Returns: A single probabilty. Return type: float

probability
(direction, mechanism, purview)¶ Probability that the purview is in it’s current state given the state of the mechanism.

unconstrained_probability
(direction, purview)¶ Unconstrained probability of the purview.

purview_state
(direction)¶ The state of the purview when we are computing coefficients in
direction
.For example, if we are computing the cause coefficient of a mechanism in
after_state
, the direction is``CAUSE`` and thepurview_state
isbefore_state
.

mechanism_state
(direction)¶ The state of the mechanism when computing coefficients in
direction
.

mechanism_indices
(direction)¶ The indices of nodes in the mechanism system.

purview_indices
(direction)¶ The indices of nodes in the purview system.

cause_ratio
(mechanism, purview)¶ The cause ratio of the
purview
givenmechanism
.

effect_ratio
(mechanism, purview)¶ The effect ratio of the
purview
givenmechanism
.

partitioned_repertoire
(direction, partition)¶ Compute the repertoire over the partition in the given direction.

partitioned_probability
(direction, partition)¶ Compute the probability of the mechanism over the purview in the partition.

find_mip
(direction, mechanism, purview, allow_neg=False)¶ Find the ratio minimum information partition for a mechanism over a purview.
Parameters: Keyword Arguments: allow_neg (boolean) – If true,
alpha
is allowed to be negative. Otherwise, negative values ofalpha
will be treated as if they were 0.Returns: The irreducibility analysis for the mechanism.
Return type:

potential_purviews
(direction, mechanism, purviews=False)¶ Return all purviews that could belong to the
MaximallyIrreducibleCause
/MaximallyIrreducibleEffect
.Filters out triviallyreducible purviews.
Parameters: Keyword Arguments: purviews (tuple[int]) – Optional subset of purviews of interest.

find_causal_link
(direction, mechanism, purviews=False, allow_neg=False)¶ Return the maximally irreducible cause or effect ratio for a mechanism.
Parameters:  direction (str) – The temporal direction, specifying cause or effect.
 mechanism (tuple[int]) – The mechanism to be tested for irreducibility.
Keyword Arguments: purviews (tuple[int]) – Optionally restrict the possible purviews to a subset of the subsystem. This may be useful for _e.g._ finding only concepts that are “about” a certain subset of nodes.
Returns: The maximallyirreducible actual cause or effect.
Return type:

find_actual_cause
(mechanism, purviews=False)¶ Return the actual cause of a mechanism.

find_actual_effect
(mechanism, purviews=False)¶ Return the actual effect of a mechanism.

find_mice
(*args, **kwargs)¶ Backwardscompatible alias for
find_causal_link()
.

pyphi.actual.
directed_account
(transition, direction, mechanisms=False, purviews=False, allow_neg=False)¶ Return the set of all
CausalLink
of the specified direction.

pyphi.actual.
account
(transition, direction=<Direction.BIDIRECTIONAL: 2>)¶ Return the set of all causal links for a
Transition
.Parameters: transition (Transition) – The transition of interest. Keyword Arguments: direction (Direction) – By default the account contains actual causes and actual effects.

pyphi.actual.
account_distance
(A1, A2)¶ Return the distance between two accounts. Here that is just the difference in sum(alpha)
Parameters: Returns: The distance between the two accounts.
Return type: float

pyphi.actual.
sia
(transition, direction=<Direction.BIDIRECTIONAL: 2>)¶ Return the minimal information partition of a transition in a specific direction.
Parameters: transition (Transition) – The candidate system. Returns: A nested structure containing all the data from the intermediate calculations. The top level contains the basic irreducibility information for the given subsystem. Return type: AcSystemIrreducibilityAnalysis

class
pyphi.actual.
ComputeACSystemIrreducibility
(iterable, *context)¶ Computation engine for AC SIAs.

description
= 'Evaluating AC cuts'¶

empty_result
(transition, direction, unpartitioned_account)¶ Return the default result with which to begin the computation.

static
compute
(cut, transition, direction, unpartitioned_account)¶ Map over a single object from
self.iterable
.

process_result
(new_sia, min_sia)¶ Reduce handler.
Every time a new result is generated by
compute
, this method is called with the result and the previous (accumulated) result. This method compares or collates these two values, returning the new result.Setting
self.done
toTrue
in this method will abort the remainder of the computation, returning this final result.


pyphi.actual.
transitions
(network, before_state, after_state)¶ Return a generator of all possible transitions of a network.

pyphi.actual.
nexus
(network, before_state, after_state, direction=<Direction.BIDIRECTIONAL: 2>)¶ Return a tuple of all irreducible nexus of the network.

pyphi.actual.
causal_nexus
(network, before_state, after_state, direction=<Direction.BIDIRECTIONAL: 2>)¶ Return the causal nexus of the network.

pyphi.actual.
nice_true_ces
(tc)¶ Format a true
CauseEffectStructure
.

pyphi.actual.
events
(network, previous_state, current_state, next_state, nodes, mechanisms=False)¶ Find all events (mechanisms with actual causes and actual effects).

pyphi.actual.
true_ces
(subsystem, previous_state, next_state)¶ Set of all sets of elements that have true causes and true effects.
Note
Since the true
CauseEffectStructure
is always about the full system, the background conditions don’t matter and the subsystem should be conditioned on the current state.

pyphi.actual.
true_events
(network, previous_state, current_state, next_state, indices=None, major_complex=None)¶ Return all mechanisms that have true causes and true effects within the complex.
Parameters:  network (Network) – The network to analyze.
 previous_state (tuple[int]) – The state of the network at
t  1
.  current_state (tuple[int]) – The state of the network at
t
.  next_state (tuple[int]) – The state of the network at
t + 1
.
Keyword Arguments:  indices (tuple[int]) – The indices of the major complex.
 major_complex (AcSystemIrreducibilityAnalysis) – The major complex. If
major_complex
is given thenindices
is ignored.
Returns: List of true events in the major complex.
Return type: tuple[Event]

pyphi.actual.
extrinsic_events
(network, previous_state, current_state, next_state, indices=None, major_complex=None)¶ Set of all mechanisms that are in the major complex but which have true causes and effects within the entire network.
Parameters:  network (Network) – The network to analyze.
 previous_state (tuple[int]) – The state of the network at
t  1
.  current_state (tuple[int]) – The state of the network at
t
.  next_state (tuple[int]) – The state of the network at
t + 1
.
Keyword Arguments:  indices (tuple[int]) – The indices of the major complex.
 major_complex (AcSystemIrreducibilityAnalysis) – The major complex. If
major_complex
is given thenindices
is ignored.
Returns: List of extrinsic events in the major complex.
Return type: tuple(actions)
cache
¶
Memoization and caching utilities.

pyphi.cache.
memory_full
()¶ Check if the memory is too full for further caching.

pyphi.cache.
cache
(cache={}, maxmem=50, typed=False)¶ Memorylimited cache decorator.
maxmem
is a float between 0 and 100, inclusive, specifying the maximum percentage of physical memory that the cache can use.If
typed
isTrue
, arguments of different types will be cached separately. For example, f(3.0) and f(3) will be treated as distinct calls with distinct results.Arguments to the cached function must be hashable.
View the cache statistics named tuple (hits, misses, currsize) with f.cache_info(). Clear the cache and statistics with f.cache_clear(). Access the underlying function with f.__wrapped__.

class
pyphi.cache.
DictCache
¶ A generic dictionarybased cache.
Intended to be used as an objectlevel cache of method results.

clear
()¶

size
()¶ Number of items in cache

info
()¶ Return info about cache hits, misses, and size

get
(key)¶ Get a value out of the cache.
Returns None if the key is not in the cache. Updates cache statistics.

set
(key, value)¶ Set a value in the cache

key
(*args, _prefix=None, **kwargs)¶ Get the cache key for the given function args.
 Kwargs:
 prefix: A constant to prefix to the key.


pyphi.cache.
redis_init
(db)¶

pyphi.cache.
redis_available
()¶ Check if the Redis server is connected.

class
pyphi.cache.
RedisCache
¶ 
clear
()¶ Flush the cache.

static
size
()¶ Size of the Redis cache.
Note
This is the size of the entire Redis database.

info
()¶ Return cache information.
Note
This is not the cache info for the entire Redis key space.

get
(key)¶ Get a value from the cache.
Returns None if the key is not in the cache.

set
(key, value)¶ Set a value in the cache.

key
()¶ Delegate to subclasses.


pyphi.cache.
validate_parent_cache
(parent_cache)¶

class
pyphi.cache.
RedisMICECache
(subsystem, parent_cache=None)¶ A Redisbacked cache for
find_mice()
.See
MICECache
for more info.
get
(key)¶ Get a value from the cache.
If the
MaximallyIrreducibleCauseOrEffect
cannot be found in this cache, try and find it in the parent cache.

set
(key, value)¶ Only need to set if the subsystem is uncut.
Caches are only inherited from uncut subsystems.

key
(direction, mechanism, purviews=False, _prefix=None)¶ Cache key. This is the call signature of
find_mice()
.


class
pyphi.cache.
DictMICECache
(subsystem, parent_cache=None)¶ A subsystemlocal cache for
MaximallyIrreducibleCauseOrEffect
objects.See
MICECache
for more info.
set
(key, mice)¶ Set a value in the cache.
 Only cache if:
 The subsystem is uncut (caches are only inherited from uncut subsystems so there is no reason to cache on cut subsystems.)
 \(\varphi\) > 0. Ideally we would cache all mice, but the size of the cache grows way too large, making parallel computations incredibly inefficient because the caches have to be passed between process. This will be changed once global caches are implemented.
 Memory is not too full.

key
(direction, mechanism, purviews=False, _prefix=None)¶ Cache key. This is the call signature of
find_mice()
.


pyphi.cache.
MICECache
(subsystem, parent_cache=None)¶ Construct a
MaximallyIrreducibleCauseOrEffect
cache.Uses either a Redisbacked cache or a local dict cache on the object.
Parameters: subsystem (Subsystem) – The subsystem that this is a cache for.  Kwargs:
 parent_cache (MICECache): The cache generated by the uncut
 version of
subsystem
. Any cachedMaximallyIrreducibleCauseOrEffect
which are unaffected by the cut are reused in this cache. If None, the cache is initialized empty.

class
pyphi.cache.
PurviewCache
¶ A networklevel cache for possible purviews.

set
(key, value)¶ Only set if purview caching is enabled


pyphi.cache.
method
(cache_name, key_prefix=None)¶ Caching decorator for objectlevel method caches.
Cache key generation is delegated to the cache.
Parameters:  cache_name (str) – The name of the (alreadyinstantiated) cache on the decorated object which should be used to store results of this method.
 *key_prefix – A constant to use as part of the cache key in addition to the method arguments.
compute
¶
See pyphi.compute.subsystem
, pyphi.compute.network
, pyphi.compute.distance
, and
pyphi.compute.parallel
for documentation.

pyphi.compute.
all_complexes
¶ Alias for
pyphi.compute.network.all_complexes()
.

pyphi.compute.
ces
¶ Alias for
pyphi.compute.subsystem.ces()
.

pyphi.compute.
ces_distance
¶ Alias for
pyphi.compute.distance.ces_distance()
.

pyphi.compute.
complexes
¶ Alias for
pyphi.compute.network.complexes()
.

pyphi.compute.
concept_distance
¶ Alias for
pyphi.compute.distance.concept_distance()
.

pyphi.compute.
conceptual_info
¶ Alias for
pyphi.compute.subsystem.conceptual_info()
.

pyphi.compute.
condensed
¶ Alias for
pyphi.compute.network.condensed()
.

pyphi.compute.
evaluate_cut
¶ Alias for
pyphi.compute.subsystem.evaluate_cut()
.

pyphi.compute.
major_complex
¶ Alias for
pyphi.compute.network.major_complex()
.

pyphi.compute.
phi
¶ Alias for
pyphi.compute.subsystem.phi()
.

pyphi.compute.
possible_complexes
¶ Alias for
pyphi.compute.network.possible_complexes()
.

pyphi.compute.
sia
¶ Alias for
pyphi.compute.subsystem.sia()
.

pyphi.compute.
subsystems
¶ Alias for
pyphi.compute.network.subsystems()
.
compute.distance
¶
Functions for computing distances between various PyPhi objects.

pyphi.compute.distance.
concept_distance
(c1, c2)¶ Return the distance between two concepts in concept space.
Parameters: Returns: The distance between the two concepts in concept space.
Return type: float

pyphi.compute.distance.
ces_distance
(C1, C2)¶ Return the distance between two causeeffect structures.
Parameters:  C1 (CauseEffectStructure) – The first
CauseEffectStructure
.  C2 (CauseEffectStructure) – The second
CauseEffectStructure
.
Returns: The distance between the two causeeffect structures in concept space.
Return type: float
 C1 (CauseEffectStructure) – The first

pyphi.compute.distance.
small_phi_ces_distance
(C1, C2)¶ Return the difference in \(\varphi\) between
CauseEffectStructure
.
compute.network
¶
Functions for computing networklevel properties.

pyphi.compute.network.
subsystems
(network, state)¶ Return a generator of all possible subsystems of a network.
Note
Does not return subsystems that are in an impossible state (after conditioning the subsystem TPM on the state of the other nodes).
Parameters: Yields: Subsystem – A
Subsystem
for each subset of nodes in the network, excluding subsystems that would be in an impossible state.

pyphi.compute.network.
possible_complexes
(network, state)¶ Return a generator of subsystems of a network that could be a complex.
This is the just powerset of the nodes that have at least one input and output (nodes with no inputs or no outputs cannot be part of a main complex, because they do not have a causal link with the rest of the subsystem in the previous or next timestep, respectively).
Note
Does not return subsystems that are in an impossible state (after conditioning the subsystem TPM on the state of the other nodes).
Parameters: Yields: Subsystem – The next subsystem that could be a complex.

class
pyphi.compute.network.
FindAllComplexes
(iterable, *context)¶ Computation engine for finding all complexes.

description
= 'Finding complexes'¶

empty_result
()¶ Return the default result with which to begin the computation.

static
compute
(subsystem)¶ Map over a single object from
self.iterable
.

process_result
(new_sia, sias)¶ Reduce handler.
Every time a new result is generated by
compute
, this method is called with the result and the previous (accumulated) result. This method compares or collates these two values, returning the new result.Setting
self.done
toTrue
in this method will abort the remainder of the computation, returning this final result.


pyphi.compute.network.
all_complexes
(network, state)¶ Return a generator for all complexes of the network.
Note
Includes reducible, zero\(\Phi\) complexes (which are not, strictly speaking, complexes at all).
Parameters: Yields: SystemIrreducibilityAnalysis – A
SystemIrreducibilityAnalysis
for eachSubsystem
of theNetwork
.

class
pyphi.compute.network.
FindIrreducibleComplexes
(iterable, *context)¶ Computation engine for finding irreducible complexes of a network.

process_result
(new_sia, sias)¶ Reduce handler.
Every time a new result is generated by
compute
, this method is called with the result and the previous (accumulated) result. This method compares or collates these two values, returning the new result.Setting
self.done
toTrue
in this method will abort the remainder of the computation, returning this final result.


pyphi.compute.network.
complexes
(network, state)¶ Return all irreducible complexes of the network.
Parameters: Yields: SystemIrreducibilityAnalysis – A
SystemIrreducibilityAnalysis
for eachSubsystem
of theNetwork
, excluding those with \(\Phi = 0\).

pyphi.compute.network.
major_complex
(network, state)¶ Return the major complex of the network.
Parameters: Returns: The
SystemIrreducibilityAnalysis
for theSubsystem
with maximal \(\Phi\).Return type:

pyphi.compute.network.
condensed
(network, state)¶ Return a list of maximal nonoverlapping complexes.
Parameters: Returns: A list of
SystemIrreducibilityAnalysis
for nonoverlapping complexes with maximal \(\Phi\) values.Return type:
compute.parallel
¶
Utilities for parallel computation.

pyphi.compute.parallel.
get_num_processes
()¶ Return the number of processes to use in parallel.

class
pyphi.compute.parallel.
ExceptionWrapper
(exception)¶ A picklable wrapper suitable for passing exception tracebacks through instances of
multiprocessing.Queue
.Parameters: exception (Exception) – The exception to wrap. 
reraise
()¶ Reraise the exception.


class
pyphi.compute.parallel.
MapReduce
(iterable, *context)¶ An engine for doing heavy computations over an iterable.
This is similar to
multiprocessing.Pool
, but allows computations to shortcircuit, and supports both parallel and sequential computations.Parameters:  iterable (Iterable) – A collection of objects to perform a computation over.
 *context – Any additional data necessary to complete the computation.
Any subclass of
MapReduce
must implement three methods: ``empty_result``,  ``compute``, (map), and  ``process_result`` (reduce).
The engine includes a builtin
tqdm
progress bar; this can be disabled by settingpyphi.config.PROGRESS_BARS
toFalse
.Parallel operations start a daemon thread which handles log messages sent from worker processes.
Subprocesses spawned by
MapReduce
cannot spawn more subprocesses; be aware of this when composing nested computations. This is not an issue in practice because it is typically most efficient to only parallelize the top level computation.
description
= ''¶

empty_result
(*context)¶ Return the default result with which to begin the computation.

static
compute
(obj, *context)¶ Map over a single object from
self.iterable
.

process_result
(new_result, old_result)¶ Reduce handler.
Every time a new result is generated by
compute
, this method is called with the result and the previous (accumulated) result. This method compares or collates these two values, returning the new result.Setting
self.done
toTrue
in this method will abort the remainder of the computation, returning this final result.

init_progress_bar
()¶ Initialize and return a progress bar.

static
worker
(compute, task_queue, result_queue, log_queue, complete, *context)¶ A worker process, run by
multiprocessing.Process
.

start_parallel
()¶ Initialize all queues and start the worker processes and the log thread.

initialize_tasks
()¶ Load the input queue to capacity.
Overfilling causes a deadlock when queue.put blocks when full, so further tasks are enqueued as results are returned.

maybe_put_task
()¶ Enqueue the next task, if there are any waiting.

run_parallel
()¶ Perform the computation in parallel, reading results from the output queue and passing them to
process_result
.

finish_parallel
()¶ Orderly shutdown of workers.

run_sequential
()¶ Perform the computation sequentially, only holding two computed objects in memory at a time.

run
(parallel=True)¶ Perform the computation.
Keyword Arguments: parallel (boolean) – If True, run the computation in parallel. Otherwise, operate sequentially.

class
pyphi.compute.parallel.
LogThread
(q)¶ Thread which handles log records sent from
MapReduce
processes.It listens to an instance of
multiprocessing.Queue
, rewriting log messages to the PyPhi log handler.
run
()¶ Method representing the thread’s activity.
You may override this method in a subclass. The standard run() method invokes the callable object passed to the object’s constructor as the target argument, if any, with sequential and keyword arguments taken from the args and kwargs arguments, respectively.


pyphi.compute.parallel.
configure_worker_logging
(queue)¶ Configure a worker process to log all messages to
queue
.
compute.subsystem
¶
Functions for computing subsystemlevel properties.

class
pyphi.compute.subsystem.
ComputeCauseEffectStructure
(iterable, *context)¶ Engine for computing a
CauseEffectStructure
.
description
= 'Computing concepts'¶

subsystem
¶

empty_result
(*args)¶ Return the default result with which to begin the computation.

static
compute
(mechanism, subsystem, purviews, cause_purviews, effect_purviews)¶ Compute a
Concept
for a mechanism, in thisSubsystem
with the provided purviews.

process_result
(new_concept, concepts)¶ Save all concepts with nonzero \(\varphi\) to the
CauseEffectStructure
.


pyphi.compute.subsystem.
ces
(subsystem, mechanisms=False, purviews=False, cause_purviews=False, effect_purviews=False, parallel=False)¶ Return the conceptual structure of this subsystem, optionally restricted to concepts with the mechanisms and purviews given in keyword arguments.
If you don’t need the full
CauseEffectStructure
, restricting the possible mechanisms and purviews can make this function much faster.Parameters: subsystem (Subsystem) – The subsystem for which to determine the
CauseEffectStructure
.Keyword Arguments:  mechanisms (tuple[tuple[int]]) – Restrict possible mechanisms to those in this list.
 purviews (tuple[tuple[int]]) – Same as in
concept()
.  cause_purviews (tuple[tuple[int]]) – Same as in
concept()
.  effect_purviews (tuple[tuple[int]]) – Same as in
concept()
.  parallel (bool) – Whether to compute concepts in parallel. If
True
, overridesconfig.PARALLEL_CONCEPT_EVALUATION
.
Returns: A tuple of every
Concept
in the causeeffect structure.Return type:

pyphi.compute.subsystem.
conceptual_info
(subsystem)¶ Return the conceptual information for a
Subsystem
.This is the distance from the subsystem’s
CauseEffectStructure
to the null concept.

pyphi.compute.subsystem.
evaluate_cut
(uncut_subsystem, cut, unpartitioned_ces)¶ Compute the system irreducibility for a given cut.
Parameters:  uncut_subsystem (Subsystem) – The subsystem without the cut applied.
 cut (Cut) – The cut to evaluate.
 unpartitioned_ces (CauseEffectStructure) – The causeeffect structure of the uncut subsystem.
Returns: The
SystemIrreducibilityAnalysis
for that cut.Return type:

class
pyphi.compute.subsystem.
ComputeSystemIrreducibility
(iterable, *context)¶ Computation engine for systemlevel irreducibility.

description
= 'Evaluating Φ cuts'¶

empty_result
(subsystem, unpartitioned_ces)¶ Begin with a
SystemIrreducibilityAnalysis
with infinite \(\Phi\); all actual SIAs will have less.

static
compute
(cut, subsystem, unpartitioned_ces)¶ Evaluate a cut.

process_result
(new_sia, min_sia)¶ Check if the new SIA has smaller \(\Phi\) than the standing result.


pyphi.compute.subsystem.
sia_bipartitions
(nodes, node_labels=None)¶ Return all \(\Phi\) cuts for the given nodes.
This value changes based on
config.CUT_ONE_APPROXIMATION
.Parameters: nodes (tuple[int]) – The node indices to partition. Returns: All unidirectional partitions. Return type: list[Cut]

pyphi.compute.subsystem.
sia
(cache_key, subsystem)¶ Return the minimal information partition of a subsystem.
Parameters: subsystem (Subsystem) – The candidate set of nodes. Returns: A nested structure containing all the data from the intermediate calculations. The top level contains the basic irreducibility information for the given subsystem. Return type: SystemIrreducibilityAnalysis

pyphi.compute.subsystem.
phi
(subsystem)¶ Return the \(\Phi\) value of a subsystem.

class
pyphi.compute.subsystem.
ConceptStyleSystem
(subsystem, direction, cut=None)¶ A functional replacement for
Subsystem
implementing conceptstyle system cuts.
apply_cut
(cut)¶

__getattr__
(name)¶ Pass attribute access through to the basic subsystem.

cause_system
¶

effect_system
¶

concept
(mechanism, purviews=False, cause_purviews=False, effect_purviews=False)¶ Compute a concept, using the appropriate system for each side of the cut.


pyphi.compute.subsystem.
concept_cuts
(direction, node_indices, node_labels=None)¶ Generator over all conceptsyle cuts for these nodes.

pyphi.compute.subsystem.
directional_sia
(subsystem, direction, unpartitioned_ces=None)¶ Calculate a conceptstyle SystemIrreducibilityAnalysisCause or SystemIrreducibilityAnalysisEffect.

class
pyphi.compute.subsystem.
SystemIrreducibilityAnalysisConceptStyle
(sia_cause, sia_effect)¶ Represents a
SystemIrreducibilityAnalysis
computed using conceptstyle system cuts.
min_sia
¶

__getattr__
(name)¶ Pass attribute access through to the minimal SIA.

unorderable_unless_eq
= ['network']¶

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.


pyphi.compute.subsystem.
sia_concept_style
(subsystem)¶ Compute a conceptstyle SystemIrreducibilityAnalysis
conf
¶
Loading a configuration¶
Various aspects of PyPhi’s behavior can be configured.
When PyPhi is imported, it checks for a YAML file named pyphi_config.yml
in
the current directory and automatically loads it if it exists; otherwise the
default configuration is used.
The various settings are listed here with their defaults.
>>> import pyphi
>>> defaults = pyphi.config.defaults()
Print the config
object to see the current settings:
>>> print(pyphi.config) # doctest: +SKIP
{ 'ASSUME_CUTS_CANNOT_CREATE_NEW_CONCEPTS': False,
'CACHE_SIAS': False,
'CACHE_POTENTIAL_PURVIEWS': True,
'CACHING_BACKEND': 'fs',
...
Setting can be changed on the fly by assigning them a new value:
>>> pyphi.config.PROGRESS_BARS = False
It is also possible to manually load a configuration file:
>>> pyphi.config.load_file('pyphi_config.yml')
Or load a dictionary of configuration values:
>>> pyphi.config.load_dict({'PRECISION': 1})
Approximations and theoretical options¶
These settings control the algorithms PyPhi uses.
Parallelization and system resources¶
These settings control how much processing power and memory is available for PyPhi to use. The default values may not be appropriate for your usecase or machine, so please check these settings before running anything. Otherwise, there is a risk that simulations might crash (potentially after running for a long time!), resulting in data loss.
MAXIMUM_CACHE_MEMORY_PERCENTAGE
Important
Only one of
PARALLEL_CONCEPT_EVALUATION
,PARALLEL_CUT_EVALUATION
, andPARALLEL_COMPLEX_EVALUATION
can be set toTrue
at a time.For most networks,
PARALLEL_CUT_EVALUATION
is the most efficient. This is because the algorithm is exponential time in the number of nodes, so the most of the time is spent on the largest subsystem.You should only parallelize concept evaluation if you are just computing a
CauseEffectStructure
.
Memoization and caching¶
PyPhi provides a number of ways to cache intermediate results.
Logging¶
These settings control how PyPhi handles log messages. Logs can be written to standard output, a file, both, or none. If these simple default controls are not flexible enough for you, you can override the entire logging configuration. See the documentation on Python’s logger for more information.
The config
API¶

class
pyphi.conf.
Option
(default, values=None, on_change=None, doc=None) A descriptor implementing PyPhi configuration options.
Parameters: default – The default value of this
Option
.Keyword Arguments:  values (list) – Allowed values for this option. A
ValueError
will be raised ifvalues
is notNone
and the option is set to be a value not in the list.  on_change (function) – Optional callback that is called when the value
of the option is changed. The
Config
instance is passed as the only argument to the callback.  doc (str) – Optional docstring for the option.
 values (list) – Allowed values for this option. A

class
pyphi.conf.
ConfigMeta
(cls_name, bases, namespace) Metaclass for
Config
.Responsible for setting the name of each
Option
when a subclass ofConfig
is created; becauseOption
objects are defined on the class, not the instance, their name should only be set once.Python 3.6 handles this exact need with the special descriptor method
__set_name__
(see PEP 487). We should use that once we drop support for 3.4 & 3.5.

class
pyphi.conf.
Config
Base configuration object.
See
PyphiConfig
for usage.
classmethod
options
() Return a dictionary the
Option
objects for this config

defaults
() Return the default values of this configuration.

load_dict
(dct) Load a dictionary of configuration values.

load_file
(filename) Load config from a YAML file.

snapshot
() Return a snapshot of the current values of this configuration.

override
(**new_values) Decorator and context manager to override configuration values.
The initial configuration values are reset after the decorated function returns or the context manager completes it block, even if the function or block raises an exception. This is intended to be used by tests which require specific configuration values.
Example
>>> from pyphi import config >>> @config.override(PRECISION=20000) ... def test_something(): ... assert config.PRECISION == 20000 ... >>> test_something() >>> with config.override(PRECISION=100): ... assert config.PRECISION == 100 ...

classmethod

pyphi.conf.
configure_logging
(conf) Reconfigure PyPhi logging based on the current configuration.

class
pyphi.conf.
PyphiConfig
pyphi.config
is an instance of this class.
ASSUME_CUTS_CANNOT_CREATE_NEW_CONCEPTS
default=False
In certain cases, making a cut can actually cause a previously reducible concept to become a proper, irreducible concept. Assuming this can never happen can increase performance significantly, however the obtained results are not strictly accurate.

CUT_ONE_APPROXIMATION
default=False
When determining the MIP for \(\Phi\), this restricts the set of system cuts that are considered to only those that cut the inputs or outputs of a single node. This restricted set of cuts scales linearly with the size of the system; the full set of all possible bipartitions scales exponentially. This approximation is more likely to give theoretically accurate results with modular, sparselyconnected, or homogeneous networks.

MEASURE
default='EMD'
The measure to use when computing distances between repertoires and concepts. A full list of currently installed measures is available by calling
print(pyphi.distance.measures.all())
. Note that some measures cannot be used for calculating \(\Phi\) because they are asymmetric.Custom measures can be added using the
pyphi.distance.measures.register
decorator. For example:from pyphi.distance import measures @measures.register('ALWAYS_ZERO') def always_zero(a, b): return 0
This measure can then be used by setting
config.MEASURE = 'ALWAYS_ZERO'
.If the measure is asymmetric you should register it using the
asymmetric
keyword argument. Seedistance
for examples.

PARALLEL_CONCEPT_EVALUATION
default=False
Controls whether concepts are evaluated in parallel when computing causeeffect structures.

PARALLEL_CUT_EVALUATION
default=True
Controls whether system cuts are evaluated in parallel, which is faster but requires more memory. If cuts are evaluated sequentially, only two
SystemIrreducibilityAnalysis
instances need to be in memory at once.

PARALLEL_COMPLEX_EVALUATION
default=False
Controls whether systems are evaluated in parallel when computing complexes.

NUMBER_OF_CORES
default=1
Controls the number of CPU cores used to evaluate unidirectional cuts. Negative numbers count backwards from the total number of available cores, with
1
meaning ‘use all available cores.’

MAXIMUM_CACHE_MEMORY_PERCENTAGE
default=50
PyPhi employs several inmemory caches to speed up computation. However, these can quickly use a lot of memory for large networks or large numbers of them; to avoid thrashing, this setting limits the percentage of a system’s RAM that the caches can collectively use.

CACHE_SIAS
default=False
PyPhi is equipped with a transparent caching system for
SystemIrreducibilityAnalysis
objects which stores them as they are computed to avoid having to recompute them later. This makes it easy to play around interactively with the program, or to accumulate results with minimal effort. For larger projects, however, it is recommended that you manage the results explicitly, rather than relying on the cache. For this reason it is disabled by default.

CACHE_REPERTOIRES
default=True
PyPhi caches cause and effect repertoires. This greatly improves speed, but can consume a significant amount of memory. If you are experiencing memory issues, try disabling this.

CACHE_POTENTIAL_PURVIEWS
default=True
Controls whether the potential purviews of mechanisms of a network are cached. Caching speeds up computations by not recomputing expensive reducibility checks, but uses additional memory.

CLEAR_SUBSYSTEM_CACHES_AFTER_COMPUTING_SIA
default=False
Controls whether a
Subsystem
’s repertoire and MICE caches are cleared withclear_caches()
after computing theSystemIrreducibilityAnalysis
. If you don’t need to do any more computations after runningsia()
, then enabling this may help conserve memory.

CACHING_BACKEND
default='fs'
Controls whether precomputed results are stored and read from a local filesystembased cache in the current directory or from a database. Set this to
'fs'
for the filesystem,'db'
for the database.

FS_CACHE_VERBOSITY
default=0
Controls how much caching information is printed if the filesystem cache is used. Takes a value between
0
and11
.

FS_CACHE_DIRECTORY
default='__pyphi_cache__'
If the filesystem is used for caching, the cache will be stored in this directory. This directory can be copied and moved around if you want to reuse results e.g. on a another computer, but it must be in the same directory from which Python is being run.

MONGODB_CONFIG
‘cache’, ‘port’: 27017, ‘host’: ‘localhost’}``
Set the configuration for the MongoDB database backend (only has an effect if
CACHING_BACKEND
is'db'
).Type: ``default={‘database_name’ Type: ‘pyphi’, ‘collection_name’

REDIS_CACHE
default=False
Specifies whether to use Redis to cache
MaximallyIrreducibleCauseOrEffect
.

REDIS_CONFIG
6379, ‘host’: ‘localhost’, ‘test_db’: 1}``
Configure the Redis database backend. These are the defaults in the provided
redis.conf
file.Type: ``default={‘db’ Type: 0, ‘port’

LOG_FILE
default='pyphi.log'
,on_change=configure_logging
Controls the name of the log file.

LOG_FILE_LEVEL
default='INFO'
,on_change=configure_logging
Controls the level of log messages written to the log file. This setting has the same possible values as
LOG_STDOUT_LEVEL
.

LOG_STDOUT_LEVEL
default='WARNING'
,on_change=configure_logging
Controls the level of log messages written to standard output. Can be one of
'DEBUG'
,'INFO'
,'WARNING'
,'ERROR'
,'CRITICAL'
, orNone
.'DEBUG'
is the least restrictive level and will show the most log messages.'CRITICAL'
is the most restrictive level and will only display information about fatal errors. If set toNone
, logging to standard output will be disabled entirely.

PROGRESS_BARS
default=True
Controls whether to show progress bars on the console.
Tip
If you are iterating over many systems rather than doing one longrunning calculation, consider disabling this for speed.

PRECISION
default=6
If
MEASURE
isEMD
, then the Earth Mover’s Distance is calculated with an external C++ library that a numerical optimizer to find a good approximation. Consequently, systems with analytically zero \(\Phi\) will sometimes be numerically found to have a small but nonzero amount. This setting controls the number of decimal places to which PyPhi will consider EMD calculations accurate. Values of \(\Phi\) lower than10ePRECISION
will be considered insignificant and treated as zero. The default value is about as accurate as the EMD computations get.

VALIDATE_SUBSYSTEM_STATES
default=True
Controls whether PyPhi checks if the subsystems’s state is possible (reachable with nonzero probability from some previous state), given the subsystem’s TPM (which is conditioned on background conditions). If this is turned off, then calculated \(\Phi\) values may not be valid, since they may be associated with a subsystem that could never be in the given state.

VALIDATE_CONDITIONAL_INDEPENDENCE
default=True
Controls whether PyPhi checks if a system’s TPM is conditionally independent.

SINGLE_MICRO_NODES_WITH_SELFLOOPS_HAVE_PHI
default=False
If set to
True
, the \(\Phi\) value of single micronode subsystems is the difference between their unpartitionedCauseEffectStructure
(a single concept) and the null concept. If set to False, their \(\Phi\) is defined to be zero. Single macronode subsystems may always be cut, regardless of circumstances.

REPR_VERBOSITY
default=2
,values=[0, 1, 2]
Controls the verbosity of
__repr__
methods on PyPhi objects. Can be set to0
,1
, or2
. If set to1
, callingrepr
on PyPhi objects will return prettyformatted and legible strings, excluding repertoires. If set to2
,repr
calls also include repertoires.Although this breaks the convention that
__repr__
methods should return a representation which can reconstruct the object, readable representations are convenient since the Python REPL callsrepr
to represent all objects in the shell and PyPhi is often used interactively with the REPL. If set to0
,repr
returns more traditional object representations.

PRINT_FRACTIONS
default=True
Controls whether numbers in a
repr
are printed as fractions. Numbers are still printed as decimals if the fraction’s denominator would be large. This only has an effect ifREPR_VERBOSITY > 0
.

PARTITION_TYPE
default='BI'
Controls the type of partition used for \(\varphi\) computations.
If set to
'BI'
, partitions will have two parts.If set to
'TRI'
, partitions will have three parts. In addition, computations will only consider partitions that strictly partition the mechanism. That is, for the mechanism(A, B)
and purview(B, C, D)
the partition:A,B ∅ ─── ✕ ─── B C,D
is not considered, but:
A B ─── ✕ ─── B C,D
is. The following is also valid:
A,B ∅ ─── ✕ ───── ∅ B,C,D
In addition, this setting introduces “wedge” tripartitions of the form:
A B ∅ ─── ✕ ─── ✕ ─── B C D
where the mechanism in the third part is always empty.
Finally, if set to
'ALL'
, all possible partitions will be tested.You can experiment with custom partitioning strategies using the
pyphi.partition.partition_types.register
decorator. For example:from pyphi.models import KPartition, Part from pyphi.partition import partition_types @partition_types.register('SINGLE_NODE') def single_node_partitions(mechanism, purview, node_labels=None): for element in mechanism: element = tuple([element]) others = tuple(sorted(set(mechanism)  set(element))) part1 = Part(mechanism=element, purview=()) part2 = Part(mechanism=others, purview=purview) yield KPartition(part1, part2, node_labels=node_labels)
This generates the set of partitions that cut connections between a single mechanism element and the entire purview. The mechanism and purview of each
Part
remain undivided  only connections between parts are severed.You can use this new partititioning scheme by setting
config.PARTITION_TYPE = 'SINGLE_NODE'
.See
partition
for more examples.

PICK_SMALLEST_PURVIEW
default=False
When computing a
MaximallyIrreducibleCause
orMaximallyIrreducibleEffect
, it is possible for several MIPs to have the same \(\varphi\) value. If this setting is set toTrue
the MIP with the smallest purview is chosen; otherwise, the one with largest purview is chosen.

USE_SMALL_PHI_DIFFERENCE_FOR_CES_DISTANCE
default=False
If set to
True
, the distance between causeeffect structures (when computing aSystemIrreducibilityAnalysis
) is calculated using the difference between the sum of \(\varphi\) in the causeeffect structures instead of the extended EMD.

SYSTEM_CUTS
default='3.0_STYLE'
,values=['3.0_STYLE', 'CONCEPT_STYLE']
If set to
'3.0_STYLE'
, then traditional IIT 3.0 cuts will be used when computing \(\Phi\). If set to'CONCEPT_STYLE'
, then experimental conceptstyle system cuts will be used instead.

log
() Log current settings.

connectivity
¶
Functions for determining network connectivity properties.

pyphi.connectivity.
apply_boundary_conditions_to_cm
(external_indices, cm)¶ Remove connections to or from external nodes.

pyphi.connectivity.
get_inputs_from_cm
(index, cm)¶ Return indices of inputs to the node with the given index.

pyphi.connectivity.
get_outputs_from_cm
(index, cm)¶ Return indices of the outputs of node with the given index.

pyphi.connectivity.
causally_significant_nodes
(cm)¶ Return indices of nodes that have both inputs and outputs.

pyphi.connectivity.
relevant_connections
(n, _from, to)¶ Construct a connectivity matrix.
Parameters:  n (int) – The dimensions of the matrix
 _from (tuple[int]) – Nodes with outgoing connections to
to
 to (tuple[int]) – Nodes with incoming connections from
_from
Returns: An \(N \times N\) connectivity matrix with the \((i,j)^{\textrm{th}}\) entry is
1
if \(i\) is in_from
and \(j\) is into
, and 0 otherwise.Return type: np.ndarray

pyphi.connectivity.
block_cm
(cm)¶ Return whether
cm
can be arranged as a block connectivity matrix.If so, the corresponding mechanism/purview is trivially reducible. Technically, only square matrices are “block diagonal”, but the notion of connectivity carries over.
We test for block connectivity by trying to grow a block of nodes such that:
 ‘source’ nodes only input to nodes in the block
 ‘sink’ nodes only receive inputs from source nodes in the block
For example, the following connectivity matrix represents connections from
nodes1 = A, B, C
tonodes2 = D, E, F, G
(without loss of generality, note thatnodes1
andnodes2
may share elements):D E F G A [1, 1, 0, 0] B [1, 1, 0, 0] C [0, 0, 1, 1]
Since nodes \(AB\) only connect to nodes \(DE\), and node \(C\) only connects to nodes \(FG\), the subgraph is reducible, because the cut
A,B C ─── ✕ ─── D,E F,G
does not change the structure of the graph.

pyphi.connectivity.
block_reducible
(cm, nodes1, nodes2)¶ Return whether connections from
nodes1
tonodes2
are reducible.Parameters:  cm (np.ndarray) – The network’s connectivity matrix.
 nodes1 (tuple[int]) – Source nodes
 nodes2 (tuple[int]) – Sink nodes

pyphi.connectivity.
is_strong
(cm, nodes=None)¶ Return whether the connectivity matrix is strongly connected.
Remember that a singleton graph is strongly connected.
Parameters: cm (np.ndarray) – A square connectivity matrix. Keyword Arguments: nodes (tuple[int]) – A subset of nodes to consider.

pyphi.connectivity.
is_weak
(cm, nodes=None)¶ Return whether the connectivity matrix is weakly connected.
Parameters: cm (np.ndarray) – A square connectivity matrix. Keyword Arguments: nodes (tuple[int]) – A subset of nodes to consider.

pyphi.connectivity.
is_full
(cm, nodes1, nodes2)¶ Test connectivity of one set of nodes to another.
Parameters:  cm (
np.ndarrray
) – The connectivity matrix  nodes1 (tuple[int]) – The nodes whose outputs to
nodes2
will be tested.  nodes2 (tuple[int]) – The nodes whose inputs from
nodes1
will be tested.
Returns: True
if all elements innodes1
output to some element innodes2
and all elements innodes2
have an input from some element innodes1
, or if either set of nodes is empty;False
otherwise.Return type: bool
 cm (
constants
¶
Packagewide constants.

pyphi.constants.
EPSILON
= 1e06¶ The threshold below which we consider differences in phi values to be zero.

pyphi.constants.
FILESYSTEM
= 'fs'¶ Label for the filesystem cache backend.

pyphi.constants.
DATABASE
= 'db'¶ Label for the MongoDB cache backend.

pyphi.constants.
PICKLE_PROTOCOL
= 4¶ The protocol used for pickling objects.

pyphi.constants.
joblib_memory
= Memory(location=__pyphi_cache__/joblib)¶ The joblib
Memory
object for persistent caching without a database.

pyphi.constants.
OFF
= (0,)¶ Node states
convert
¶
Conversion functions.
See the documentation on PyPhi Transition probability matrix conventions for information on the different representations that these functions convert between.

pyphi.convert.
reverse_bits
(i, n)¶ Reverse the bits of the
n
bit decimal numberi
.Examples
>>> reverse_bits(12, 7) 24 >>> reverse_bits(0, 1) 0 >>> reverse_bits(1, 2) 2

pyphi.convert.
nodes2indices
(nodes)¶ Convert nodes to a tuple of their indices.

pyphi.convert.
nodes2state
(nodes)¶ Convert nodes to a tuple of their states.

pyphi.convert.
be2le
(i, n)¶ Convert between bigendian and littleendian for indices in
range(n)
.

pyphi.convert.
le2be
(i, n)¶ Convert between bigendian and littleendian for indices in
range(n)
.

pyphi.convert.
state2be_index
(state)¶ Convert a PyPhi statetuple to a decimal index according to the bigendian convention.
Parameters: state (tuple[int]) – A statetuple where the \(i^{\textrm{th}}\) element of the tuple gives the state of the \(i^{\textrm{th}}\) node. Returns: A decimal integer corresponding to a network state under the bigendian convention. Return type: int Examples
>>> state2be_index((1, 0, 0, 0, 0)) 16 >>> state2be_index((1, 1, 1, 0, 0, 0, 0, 0)) 224

pyphi.convert.
state2le_index
(state)¶ Convert a PyPhi statetuple to a decimal index according to the littleendian convention.
Parameters: state (tuple[int]) – A statetuple where the \(i^{\textrm{th}}\) element of the tuple gives the state of the \(i^{\textrm{th}}\) node. Returns: A decimal integer corresponding to a network state under the littleendian convention. Return type: int Examples
>>> state2le_index((1, 0, 0, 0, 0)) 1 >>> state2le_index((1, 1, 1, 0, 0, 0, 0, 0)) 7

pyphi.convert.
le_index2state
(i, number_of_nodes)¶ Convert a decimal integer to a PyPhi state tuple with the littleendian convention.
The output is the reverse of
be_index2state()
.Parameters: i (int) – A decimal integer corresponding to a network state under the littleendian convention. Returns: A statetuple where the \(i^{\textrm{th}}\) element of the tuple gives the state of the \(i^{\textrm{th}}\) node. Return type: tuple[int] Examples
>>> number_of_nodes = 5 >>> le_index2state(1, number_of_nodes) (1, 0, 0, 0, 0) >>> number_of_nodes = 8 >>> le_index2state(7, number_of_nodes) (1, 1, 1, 0, 0, 0, 0, 0)

pyphi.convert.
be_index2state
(i, number_of_nodes)¶ Convert a decimal integer to a PyPhi state tuple using the bigendian convention that the mostsignificant bits correspond to lowindex nodes.
The output is the reverse of
le_index2state()
.Parameters: i (int) – A decimal integer corresponding to a network state under the bigendian convention. Returns: A statetuple where the \(i^{\textrm{th}}\) element of the tuple gives the state of the \(i^{\textrm{th}}\) node. Return type: tuple[int] Examples
>>> number_of_nodes = 5 >>> be_index2state(1, number_of_nodes) (0, 0, 0, 0, 1) >>> number_of_nodes = 8 >>> be_index2state(7, number_of_nodes) (0, 0, 0, 0, 0, 1, 1, 1)

pyphi.convert.
be2le_state_by_state
(tpm)¶ Convert a statebystate TPM from bigendian to littleendian or vice versa.
Parameters: tpm (np.ndarray) – A statebystate TPM. Returns: The statebystate TPM in the other indexing format. Return type: np.ndarray Example
>>> tpm = np.arange(16).reshape([4, 4]) >>> be2le_state_by_state(tpm) array([[ 0., 1., 2., 3.], [ 8., 9., 10., 11.], [ 4., 5., 6., 7.], [12., 13., 14., 15.]])

pyphi.convert.
le2be_state_by_state
(tpm)¶ Convert a statebystate TPM from bigendian to littleendian or vice versa.
Parameters: tpm (np.ndarray) – A statebystate TPM. Returns: The statebystate TPM in the other indexing format. Return type: np.ndarray Example
>>> tpm = np.arange(16).reshape([4, 4]) >>> be2le_state_by_state(tpm) array([[ 0., 1., 2., 3.], [ 8., 9., 10., 11.], [ 4., 5., 6., 7.], [12., 13., 14., 15.]])

pyphi.convert.
to_multidimensional
(tpm)¶ Reshape a statebynode TPM to the multidimensional form.
See documentation for the
Network
object for more information on TPM formats.

pyphi.convert.
to_2dimensional
(tpm)¶ Reshape a statebynode TPM to the 2dimensional form.
See Transition probability matrix conventions and documentation for the
Network
object for more information on TPM representations.

pyphi.convert.
state_by_state2state_by_node
(tpm)¶ Convert a statebystate TPM to a statebynode TPM.
Danger
Many nondeterministic statebystate TPMs can be represented by a single a statebystate TPM. However, the mapping can be made to be onetoone if we assume the statebystate TPM is conditionally independent, as this function does. If the given TPM is not conditionally independent, the conditional dependencies will be silently lost.
Note
The indices of the rows and columns of the statebystate TPM are assumed to follow the littleendian convention. The indices of the rows of the resulting statebynode TPM also follow the littleendian convention. See the documentation on PyPhi the Transition probability matrix conventions more information.
Parameters: tpm (list[list] or np.ndarray) – A square statebystate TPM with row and column indices following the littleendian convention. Returns: A statebynode TPM, with row indices following the littleendian convention. Return type: np.ndarray Example
>>> tpm = np.array([[0.5, 0.5, 0.0, 0.0], ... [0.0, 1.0, 0.0, 0.0], ... [0.0, 0.2, 0.0, 0.8], ... [0.0, 0.3, 0.7, 0.0]]) >>> state_by_state2state_by_node(tpm) array([[[0.5, 0. ], [1. , 0.8]], <BLANKLINE> [[1. , 0. ], [0.3, 0.7]]])

pyphi.convert.
state_by_node2state_by_state
(tpm)¶ Convert a statebynode TPM to a statebystate TPM.
Important
A nondeterministic statebynode TPM can have more than one representation as a statebystate TPM. However, the mapping can be made to be onetoone if we assume the TPMs to be conditionally independent. Therefore, this function returns the corresponding conditionally independent statebystate TPM.
Note
The indices of the rows of the statebynode TPM are assumed to follow the littleendian convention, while the indices of the columns follow the bigendian convention. The indices of the rows and columns of the resulting statebystate TPM both follow the bigendian convention. See the documentation on PyPhi Transition probability matrix conventions for more info.
Parameters: tpm (list[list] or np.ndarray) – A statebynode TPM with row indices following the littleendian convention and column indices following the bigendian convention. Returns: A statebystate TPM, with both row and column indices following the bigendian convention. Return type: np.ndarray >>> tpm = np.array([[1, 1, 0], ... [0, 0, 1], ... [0, 1, 1], ... [1, 0, 0], ... [0, 0, 1], ... [1, 0, 0], ... [1, 1, 1], ... [1, 0, 1]]) >>> state_by_node2state_by_state(tpm) array([[0., 0., 0., 1., 0., 0., 0., 0.], [0., 0., 0., 0., 1., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 1., 0.], [0., 1., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 1., 0., 0., 0.], [0., 1., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 1.], [0., 0., 0., 0., 0., 1., 0., 0.]])

pyphi.convert.
b2l
(i, n)¶ Convert between bigendian and littleendian for indices in
range(n)
.

pyphi.convert.
l2b
(i, n)¶ Convert between bigendian and littleendian for indices in
range(n)
.

pyphi.convert.
l2s
(i, number_of_nodes)¶ Convert a decimal integer to a PyPhi state tuple with the littleendian convention.
The output is the reverse of
be_index2state()
.Parameters: i (int) – A decimal integer corresponding to a network state under the littleendian convention. Returns: A statetuple where the \(i^{\textrm{th}}\) element of the tuple gives the state of the \(i^{\textrm{th}}\) node. Return type: tuple[int] Examples
>>> number_of_nodes = 5 >>> le_index2state(1, number_of_nodes) (1, 0, 0, 0, 0) >>> number_of_nodes = 8 >>> le_index2state(7, number_of_nodes) (1, 1, 1, 0, 0, 0, 0, 0)

pyphi.convert.
b2s
(i, number_of_nodes)¶ Convert a decimal integer to a PyPhi state tuple using the bigendian convention that the mostsignificant bits correspond to lowindex nodes.
The output is the reverse of
le_index2state()
.Parameters: i (int) – A decimal integer corresponding to a network state under the bigendian convention. Returns: A statetuple where the \(i^{\textrm{th}}\) element of the tuple gives the state of the \(i^{\textrm{th}}\) node. Return type: tuple[int] Examples
>>> number_of_nodes = 5 >>> be_index2state(1, number_of_nodes) (0, 0, 0, 0, 1) >>> number_of_nodes = 8 >>> be_index2state(7, number_of_nodes) (0, 0, 0, 0, 0, 1, 1, 1)

pyphi.convert.
s2l
(state)¶ Convert a PyPhi statetuple to a decimal index according to the littleendian convention.
Parameters: state (tuple[int]) – A statetuple where the \(i^{\textrm{th}}\) element of the tuple gives the state of the \(i^{\textrm{th}}\) node. Returns: A decimal integer corresponding to a network state under the littleendian convention. Return type: int Examples
>>> state2le_index((1, 0, 0, 0, 0)) 1 >>> state2le_index((1, 1, 1, 0, 0, 0, 0, 0)) 7

pyphi.convert.
s2b
(state)¶ Convert a PyPhi statetuple to a decimal index according to the bigendian convention.
Parameters: state (tuple[int]) – A statetuple where the \(i^{\textrm{th}}\) element of the tuple gives the state of the \(i^{\textrm{th}}\) node. Returns: A decimal integer corresponding to a network state under the bigendian convention. Return type: int Examples
>>> state2be_index((1, 0, 0, 0, 0)) 16 >>> state2be_index((1, 1, 1, 0, 0, 0, 0, 0)) 224

pyphi.convert.
b2l_sbs
(tpm)¶ Convert a statebystate TPM from bigendian to littleendian or vice versa.
Parameters: tpm (np.ndarray) – A statebystate TPM. Returns: The statebystate TPM in the other indexing format. Return type: np.ndarray Example
>>> tpm = np.arange(16).reshape([4, 4]) >>> be2le_state_by_state(tpm) array([[ 0., 1., 2., 3.], [ 8., 9., 10., 11.], [ 4., 5., 6., 7.], [12., 13., 14., 15.]])

pyphi.convert.
l2b_sbs
(tpm)¶ Convert a statebystate TPM from bigendian to littleendian or vice versa.
Parameters: tpm (np.ndarray) – A statebystate TPM. Returns: The statebystate TPM in the other indexing format. Return type: np.ndarray Example
>>> tpm = np.arange(16).reshape([4, 4]) >>> be2le_state_by_state(tpm) array([[ 0., 1., 2., 3.], [ 8., 9., 10., 11.], [ 4., 5., 6., 7.], [12., 13., 14., 15.]])

pyphi.convert.
to_md
(tpm)¶ Reshape a statebynode TPM to the multidimensional form.
See documentation for the
Network
object for more information on TPM formats.

pyphi.convert.
to_2d
(tpm)¶ Reshape a statebynode TPM to the 2dimensional form.
See Transition probability matrix conventions and documentation for the
Network
object for more information on TPM representations.

pyphi.convert.
sbn2sbs
(tpm)¶ Convert a statebynode TPM to a statebystate TPM.
Important
A nondeterministic statebynode TPM can have more than one representation as a statebystate TPM. However, the mapping can be made to be onetoone if we assume the TPMs to be conditionally independent. Therefore, this function returns the corresponding conditionally independent statebystate TPM.
Note
The indices of the rows of the statebynode TPM are assumed to follow the littleendian convention, while the indices of the columns follow the bigendian convention. The indices of the rows and columns of the resulting statebystate TPM both follow the bigendian convention. See the documentation on PyPhi Transition probability matrix conventions for more info.
Parameters: tpm (list[list] or np.ndarray) – A statebynode TPM with row indices following the littleendian convention and column indices following the bigendian convention. Returns: A statebystate TPM, with both row and column indices following the bigendian convention. Return type: np.ndarray >>> tpm = np.array([[1, 1, 0], ... [0, 0, 1], ... [0, 1, 1], ... [1, 0, 0], ... [0, 0, 1], ... [1, 0, 0], ... [1, 1, 1], ... [1, 0, 1]]) >>> state_by_node2state_by_state(tpm) array([[0., 0., 0., 1., 0., 0., 0., 0.], [0., 0., 0., 0., 1., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 1., 0.], [0., 1., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 1., 0., 0., 0.], [0., 1., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 1.], [0., 0., 0., 0., 0., 1., 0., 0.]])

pyphi.convert.
sbs2sbn
(tpm)¶ Convert a statebystate TPM to a statebynode TPM.
Danger
Many nondeterministic statebystate TPMs can be represented by a single a statebystate TPM. However, the mapping can be made to be onetoone if we assume the statebystate TPM is conditionally independent, as this function does. If the given TPM is not conditionally independent, the conditional dependencies will be silently lost.
Note
The indices of the rows and columns of the statebystate TPM are assumed to follow the littleendian convention. The indices of the rows of the resulting statebynode TPM also follow the littleendian convention. See the documentation on PyPhi the Transition probability matrix conventions more information.
Parameters: tpm (list[list] or np.ndarray) – A square statebystate TPM with row and column indices following the littleendian convention. Returns: A statebynode TPM, with row indices following the littleendian convention. Return type: np.ndarray Example
>>> tpm = np.array([[0.5, 0.5, 0.0, 0.0], ... [0.0, 1.0, 0.0, 0.0], ... [0.0, 0.2, 0.0, 0.8], ... [0.0, 0.3, 0.7, 0.0]]) >>> state_by_state2state_by_node(tpm) array([[[0.5, 0. ], [1. , 0.8]], <BLANKLINE> [[1. , 0. ], [0.3, 0.7]]])
direction
¶
Causal directions.

class
pyphi.direction.
Direction
¶ Constant that parametrizes cause and effect methods.
Accessed using
Direction.CAUSE
andDirection.EFFECT
, etc.
CAUSE
= 0¶

EFFECT
= 1¶

BIDIRECTIONAL
= 2¶

to_json
()¶

from_json
= <bound method Direction.from_json of <enum 'Direction'>>¶

order
(mechanism, purview)¶ Order the mechanism and purview in time.
If the direction is
CAUSE
, then the purview is at \(t1\) and the mechanism is at time \(t\). If the direction isEFFECT
, then the mechanism is at time \(t\) and the purview is at \(t+1\).

distance
¶
Functions for measuring distances.

class
pyphi.distance.
MeasureRegistry
¶ Storage for measures registered with PyPhi.
Users can define custom measures:
Examples
>>> @measures.register('ALWAYS_ZERO') # doctest: +SKIP ... def always_zero(a, b): ... return 0
And use them by setting
config.MEASURE = 'ALWAYS_ZERO'
.
desc
= 'measures'¶

register
(name, asymmetric=False)¶ Decorator for registering a measure with PyPhi.
Parameters: name (string) – The name of the measure. Keyword Arguments: asymmetric (boolean) – True
if the measure is asymmetric.

asymmetric
()¶ Return a list of asymmetric measures.


class
pyphi.distance.
np_suppress
¶ Decorator to suppress NumPy warnings about dividebyzero and multiplication of
NaN
.Note
This should only be used in cases where you are sure that these warnings are not indicative of deeper issues in your code.

pyphi.distance.
hamming_emd
(d1, d2)¶ Return the Earth Mover’s Distance between two distributions (indexed by state, one dimension per node) using the Hamming distance between states as the transportation cost function.
Singleton dimensions are sqeezed out.

pyphi.distance.
effect_emd
(d1, d2)¶ Compute the EMD between two effect repertoires.
Because the nodes are independent, the EMD between effect repertoires is equal to the sum of the EMDs between the marginal distributions of each node, and the EMD between marginal distribution for a node is the absolute difference in the probabilities that the node is OFF.
Parameters:  d1 (np.ndarray) – The first repertoire.
 d2 (np.ndarray) – The second repertoire.
Returns: The EMD between
d1
andd2
.Return type: float

pyphi.distance.
l1
(d1, d2)¶ Return the L1 distance between two distributions.
Parameters:  d1 (np.ndarray) – The first distribution.
 d2 (np.ndarray) – The second distribution.
Returns: The sum of absolute differences of
d1
andd2
.Return type: float

pyphi.distance.
kld
(d1, d2)¶ Return the KullbackLeibler Divergence (KLD) between two distributions.
Parameters:  d1 (np.ndarray) – The first distribution.
 d2 (np.ndarray) – The second distribution.
Returns: The KLD of
d1
fromd2
.Return type: float

pyphi.distance.
entropy_difference
(d1, d2)¶ Return the difference in entropy between two distributions.

pyphi.distance.
psq2
(d1, d2)¶ Compute the PSQ2 measure.
Parameters:  d1 (np.ndarray) – The first distribution.
 d2 (np.ndarray) – The second distribution.

pyphi.distance.
mp2q
(p, q)¶ Compute the MP2Q measure.
Parameters:  p (np.ndarray) – The unpartitioned repertoire
 q (np.ndarray) – The partitioned repertoire

pyphi.distance.
klm
(p, q)¶ Compute the KLM divergence.

pyphi.distance.
directional_emd
(direction, d1, d2)¶ Compute the EMD between two repertoires for a given direction.
The full EMD computation is used for cause repertoires. A fast analytic solution is used for effect repertoires.
Parameters: Returns: The EMD between
d1
andd2
, rounded toPRECISION
.Return type: float
Raises: ValueError – If
direction
is invalid.

pyphi.distance.
repertoire_distance
(direction, r1, r2)¶ Compute the distance between two repertoires for the given direction.
Parameters: Returns: The distance between
d1
andd2
, rounded toPRECISION
.Return type: float

pyphi.distance.
system_repertoire_distance
(r1, r2)¶ Compute the distance between two repertoires of a system.
Parameters:  r1 (np.ndarray) – The first repertoire.
 r2 (np.ndarray) – The second repertoire.
Returns: The distance between
r1
andr2
.Return type: float
distribution
¶
Functions for manipulating probability distributions.

pyphi.distribution.
normalize
(a)¶ Normalize a distribution.
Parameters: a (np.ndarray) – The array to normalize. Returns: a
normalized so that the sum of its entries is 1.Return type: np.ndarray

pyphi.distribution.
uniform_distribution
(number_of_nodes)¶ Return the uniform distribution for a set of binary nodes, indexed by state (so there is one dimension per node, the size of which is the number of possible states for that node).
Parameters: nodes (np.ndarray) – A set of indices of binary nodes. Returns: The uniform distribution over the set of nodes. Return type: np.ndarray

pyphi.distribution.
marginal_zero
(repertoire, node_index)¶ Return the marginal probability that the node is OFF.

pyphi.distribution.
marginal
(repertoire, node_index)¶ Get the marginal distribution for a node.

pyphi.distribution.
independent
(repertoire)¶ Check whether the repertoire is independent.

pyphi.distribution.
purview
(repertoire)¶ The purview of the repertoire.
Parameters: repertoire (np.ndarray) – A repertoire Returns: The purview that the repertoire was computed over. Return type: tuple[int]

pyphi.distribution.
purview_size
(repertoire)¶ Return the size of the purview of the repertoire.
Parameters: repertoire (np.ndarray) – A repertoire Returns: The size of purview that the repertoire was computed over. Return type: int

pyphi.distribution.
repertoire_shape
(purview, N)¶ Return the shape a repertoire.
Parameters:  purview (tuple[int]) – The purview over which the repertoire is computed.
 N (int) – The number of elements in the system.
Returns: The shape of the repertoire. Purview nodes have two dimensions and nonpurview nodes are collapsed to a unitary dimension.
Return type: list[int]
Example
>>> purview = (0, 2) >>> N = 3 >>> repertoire_shape(purview, N) [2, 1, 2]

pyphi.distribution.
flatten
(repertoire, big_endian=False)¶ Flatten a repertoire, removing empty dimensions.
By default, the flattened repertoire is returned in littleendian order.
Parameters: repertoire (np.ndarray or None) – A repertoire. Keyword Arguments: big_endian (boolean) – If True
, flatten the repertoire in bigendian order.Returns: The flattened repertoire. Return type: np.ndarray

pyphi.distribution.
max_entropy_distribution
(node_indices, number_of_nodes)¶ Return the maximum entropy distribution over a set of nodes.
This is different from the network’s uniform distribution because nodes outside
node_indices
are fixed and treated as if they have only 1 state.Parameters:  node_indices (tuple[int]) – The set of node indices over which to take the distribution.
 number_of_nodes (int) – The total number of nodes in the network.
Returns: The maximum entropy distribution over the set of nodes.
Return type: np.ndarray
examples
¶
Example networks and subsystems to go along with the documentation.

pyphi.examples.
basic_network
(cm=False)¶ A 3node network of logic gates.
Diagram:
+~~~~~~~~+ +~~~~> A <~~~~+   (OR) +~~~+   +~~~~~~~~+       v  +~+~~~~~~+ +~~~~~+~+  B <~~~~~~+ C   (COPY) +~~~~~~> (XOR)  +~~~~~~~~+ +~~~~~~~+
TPM:
Previous state Current state A, B, C A, B, C 0, 0, 0 0, 0, 0 1, 0, 0 0, 0, 1 0, 1, 0 1, 0, 1 1, 1, 0 1, 0, 0 0, 0, 1 1, 1, 0 1, 0, 1 1, 1, 1 0, 1, 1 1, 1, 1 1, 1, 1 1, 1, 0 Connectivity matrix:
. A B C A 0 0 1 B 1 0 1 C 1 1 0 Note
\([CM]_{i,j} = 1\) means that there is a directed edge \((i,j)\) from node \(i\) to node \(j\) and \([CM]_{i,j} = 0\) means there is no edge from \(i\) to \(j\).

pyphi.examples.
basic_state
()¶ The state of nodes in
basic_network()
.

pyphi.examples.
basic_subsystem
()¶ A subsystem containing all the nodes of the
basic_network()
.

pyphi.examples.
basic_noisy_selfloop_network
()¶ Based on the basic_network, but with added selfloops and noisy edges.
Nodes perform deterministic functions of their inputs, but those inputs may be flipped (i.e. what should be a 0 becomes a 1, and vice versa) with probability epsilon (eps = 0.1 here).
Diagram:
+~~+  v +~~~~~~~~+ +~~~~> A <~~~~+   (OR) +~~~+   +~~~~~~~~+       v  +~+~~~~~~+ +~~~~~+~+  B <~~~~~~+ C  +> (COPY) +~~~~~~> (XOR) <+  +~~~~~~~~+ +~~~~~~~+      +~~~+ +~~~+

pyphi.examples.
basic_noisy_selfloop_subsystem
()¶ A subsystem containing all the nodes of the
basic_noisy_selfloop_network()
.

pyphi.examples.
residue_network
()¶ The network for the residue example.
Current and previous state are all nodes OFF.
Diagram:
+~~~~~~~+ +~~~~~~~+  A   B  +~~> (AND)   (AND) <~~+  +~~~~~~~+ +~~~~~~~+   ^ ^       +~~~~~+ +~~~~~+      +~~~+~~~+ +~+~~~+~+ +~~~+~~~+  C   D   E        +~~~~~~~+ +~~~~~~~+ +~~~~~~~+
Connectivity matrix:
. A B C D E A 0 0 0 0 0 B 0 0 0 0 0 C 1 0 0 0 0 D 1 1 0 0 0 E 0 1 0 0 0

pyphi.examples.
residue_subsystem
()¶ The subsystem containing all the nodes of the
residue_network()
.

pyphi.examples.
xor_network
()¶ A fully connected system of three XOR gates. In the state
(0, 0, 0)
, none of the elementary mechanisms exist.Diagram:
+~~~~~~~+ +~~~~~~~+  A +<~~~~~~+ B   (XOR) +~~~~~~> (XOR)  +~+~~~~~+ +~~~~~+~+  ^ ^    +~~~~~~~+    +~~~+ C +~~~+  +~~~~> (XOR) +<~~~~+ +~~~~~~~+
Connectivity matrix:
. A B C A 0 1 1 B 1 0 1 C 1 1 0

pyphi.examples.
xor_subsystem
()¶ The subsystem containing all the nodes of the
xor_network()
.

pyphi.examples.
cond_depend_tpm
()¶ A system of two general logic gates A and B such if they are in the same state they stay the same, but if they are in different states, they flip with probability 50%.
Diagram:
+~~~~~+ +~~~~~+  A <~~~~~~~~+ B   +~~~~~~~~>  +~~~~~+ +~~~~~+
TPM:
(0, 0) (1, 0) (0, 1) (1, 1) (0, 0) 1.0 0.0 0.0 0.0 (1, 0) 0.0 0.5 0.5 0.0 (0, 1) 0.0 0.5 0.5 0.0 (1, 1) 0.0 0.0 0.0 1.0 Connectivity matrix:
. A B A 0 1 B 1 0

pyphi.examples.
cond_independ_tpm
()¶ A system of three general logic gates A, B and C such that: if A and B are in the same state then they stay the same; if they are in different states, they flip if C is ON and stay the same if C is OFF; and C is ON 50% of the time, independent of the previous state.
Diagram:
+~~~~~+ +~~~~~+  A +~~~~~~~~> B   <~~~~~~~~+  +~+~~~+ +~~~+~+  ^ ^    +~~~~~+    ~~~~+ C +~~~+  +~~~~> <~~~~+ +~~~~~+
TPM:
(0, 0, 0) (1, 0, 0) (0, 1, 0) (1, 1, 0) (0, 0, 1) (1, 0, 1) (0, 1, 1) (1, 1, 1) (0, 0, 0) 0.5 0.0 0.0 0.0 0.5 0.0 0.0 0.0 (1, 0, 0) 0.0 0.5 0.0 0.0 0.0 0.5 0.0 0.0 (0, 1, 0) 0.0 0.0 0.5 0.0 0.0 0.0 0.5 0.0 (1, 1, 0) 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.5 (0, 0, 1) 0.5 0.0 0.0 0.0 0.5 0.0 0.0 0.0 (1, 0, 1) 0.0 0.0 0.5 0.0 0.0 0.0 0.5 0.0 (0, 1, 1) 0.0 0.5 0.0 0.0 0.0 0.5 0.0 0.0 (1, 1, 1) 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.5 Connectivity matrix:
. A B C A 0 1 0 B 1 0 0 C 1 1 0

pyphi.examples.
propagation_delay_network
()¶ A version of the primary example from the IIT 3.0 paper with deterministic COPY gates on each connection. These copy gates essentially function as propagation delays on the signal between OR, AND and XOR gates from the original system.
The current and previous states of the network are also selected to mimic the corresponding states from the IIT 3.0 paper.
Diagram:
++ ++ C (COPY) +<+ v ++  +++ +++   ++    A (OR) +>+ B (COPY) +>+ D (XOR)    ++   ++++ ++++  ^ ^        ++ ++    ++ H (COPY) +<+ +>+ F (COPY) ++   ++   ++       ++++   ++   ++  +>+ I (COPY) +> G (AND) <+ E (COPY) +<+ ++   ++ ++
Connectivity matrix:
. A B C D E F G H I A 0 1 0 0 0 0 0 0 1 B 0 0 0 1 0 0 0 0 0 C 1 0 0 0 0 0 0 0 0 D 0 0 1 0 1 0 0 0 0 E 0 0 0 0 0 0 1 0 0 F 0 0 0 1 0 0 0 0 0 G 0 0 0 0 0 1 0 1 0 H 1 0 0 0 0 0 0 0 0 I 0 0 0 0 0 0 1 0 0 States:
In the IIT 3.0 paper example, the previous state of the system has only the XOR gate ON. For the propagation delay network, this corresponds to a state of
(0, 0, 0, 1, 0, 0, 0, 0, 0)
.The current state of the IIT 3.0 example has only the OR gate ON. By advancing the propagation delay system two time steps, the current state
(1, 0, 0, 0, 0, 0, 0, 0, 0)
is achieved, with corresponding previous state(0, 0, 1, 0, 1, 0, 0, 0, 0)
.

pyphi.examples.
macro_network
()¶ A network of micro elements which has greater integrated information after coarse graining to a macro scale.

pyphi.examples.
macro_subsystem
()¶ A subsystem containing all the nodes of
macro_network()
.

pyphi.examples.
blackbox_network
()¶ A micronetwork to demonstrate blackboxing.
Diagram:
++ +>+ A (COPY) + <+  ++   ++   ++ B (COPY) + <+  v v ++   ++++ ++++      C (AND)   F (AND)      ++++ ++++   ^ ^   ++    +> + D (COPY) ++   ++   ++  +> + E (COPY) ++ ++
Connectivity Matrix:
. A B C D E F A 0 0 1 0 0 0 B 0 0 1 0 0 0 C 0 0 0 1 1 0 D 0 0 0 0 0 1 E 0 0 0 0 0 1 F 1 1 0 0 0 0 In the documentation example, the state is (0, 0, 0, 0, 0, 0).

pyphi.examples.
rule110_network
()¶ A network of three elements which follows the logic of the Rule 110 cellular automaton with current and previous state (0, 0, 0).

pyphi.examples.
rule154_network
()¶ A network of three elements which follows the logic of the Rule 154 cellular automaton.

pyphi.examples.
fig1a
()¶ The network shown in Figure 1A of the 2014 IIT 3.0 paper.

pyphi.examples.
fig3a
()¶ The network shown in Figure 3A of the 2014 IIT 3.0 paper.

pyphi.examples.
fig3b
()¶ The network shown in Figure 3B of the 2014 IIT 3.0 paper.

pyphi.examples.
fig4
()¶ The network shown in Figure 4 of the 2014 IIT 3.0 paper.
Diagram:
+~~~~~~~+ +~~~~> A <~~~~+  +~~~+ (OR) +~~~+    +~~~~~~~+        v v  +~+~~~~~+ +~~~~~+~+  B <~~~~~~+ C   (AND) +~~~~~~> (XOR)  +~~~~~~~+ +~~~~~~~+

pyphi.examples.
fig5a
()¶ The network shown in Figure 5A of the 2014 IIT 3.0 paper.
Diagram:
+~~~~~~~+ +~~~~> A <~~~~+   (AND)    +~~~~~~~+    +~~+~~~~~+ +~~~~~+~~+  B <~~~~~~+ C   (COPY) +~~~~~~> (COPY)  +~~~~~~~~+ +~~~~~~~~+

pyphi.examples.
fig5b
()¶ The network shown in Figure 5B of the 2014 IIT 3.0 paper.
Diagram:
+~~~~~~~+ +~~~~+ A +~~~~+   (AND)    +~~~~~~~+  v v +~~~~~~~~+ +~~~~~~~~+  B <~~~~~~+ C   (COPY) +~~~~~~> (COPY)  +~~~~~~~~+ +~~~~~~~~+

pyphi.examples.
fig6
()¶ The network shown in Figure 4 of the 2014 IIT 3.0 paper.
Diagram:
+~~~~~~~+ +~~~~> A <~~~~+  +~~~+ (OR) +~~~+    +~~~~~~~+        v v  +~+~~~~~+ +~~~~~+~+  B <~~~~~~+ C   (AND) +~~~~~~> (XOR)  +~~~~~~~+ +~~~~~~~+

pyphi.examples.
fig8
()¶ The network shown in Figure 4 of the 2014 IIT 3.0 paper.
Diagram:
+~~~~~~~+ +~~~~> A <~~~~+  +~~~+ (OR) +~~~+    +~~~~~~~+        v v  +~+~~~~~+ +~~~~~+~+  B <~~~~~~+ C   (AND) +~~~~~~> (XOR)  +~~~~~~~+ +~~~~~~~+

pyphi.examples.
fig9
()¶ The network shown in Figure 4 of the 2014 IIT 3.0 paper.
Diagram:
+~~~~~~~+ +~~~~> A <~~~~+  +~~~+ (OR) +~~~+    +~~~~~~~+        v v  +~+~~~~~+ +~~~~~+~+  B <~~~~~~+ C   (AND) +~~~~~~> (XOR)  +~~~~~~~+ +~~~~~~~+

pyphi.examples.
fig10
()¶ The network shown in Figure 4 of the 2014 IIT 3.0 paper.
Diagram:
+~~~~~~~+ +~~~~> A <~~~~+  +~~~+ (OR) +~~~+    +~~~~~~~+        v v  +~+~~~~~+ +~~~~~+~+  B <~~~~~~+ C   (AND) +~~~~~~> (XOR)  +~~~~~~~+ +~~~~~~~+

pyphi.examples.
fig14
()¶ The network shown in Figure 1A of the 2014 IIT 3.0 paper.

pyphi.examples.
fig16
()¶ The network shown in Figure 5B of the 2014 IIT 3.0 paper.

pyphi.examples.
actual_causation
()¶ The actual causation example network, consisting of an
OR
andAND
gate with selfloops.

pyphi.examples.
disjunction_conjunction_network
()¶ The disjunctionconjunction example from Actual Causation Figure 7.
A network of four elements, one output
D
with three inputsA B C
. The output turns ON ifA
ANDB
are ON or ifC
is ON.

pyphi.examples.
prevention
()¶ The
Transition
for the prevention example from Actual Causation Figure 5D.
exceptions
¶
PyPhi exceptions.

exception
pyphi.exceptions.
StateUnreachableError
(state)¶ The current state cannot be reached from any previous state.

exception
pyphi.exceptions.
ConditionallyDependentError
¶ The TPM is conditionally dependent.

exception
pyphi.exceptions.
JSONVersionError
¶ JSON was serialized with a different version of PyPhi.

exception
pyphi.exceptions.
WrongDirectionError
¶ The wrong direction was provided.
jsonify
¶
PyPhi and NumPyaware JSON serialization.
To be properly serialized and deserialized, PyPhi objects must implement a
to_json
method which returns a dictionary of attribute names and attribute
values. These attributes should be the names of arguments passed to the object
constructor. If the constructor takes additional, fewer, or different
arguments, the object needs to implement a custom classmethod
called
from_json
that takes a Python dictionary as an argument and returns a PyPhi
object. For example:
class Phi:
def __init__(self, phi):
self.phi = phi
def to_json(self):
return {'phi': self.phi, 'twice_phi': 2 * self.phi}
@classmethod
def from_json(cls, json):
return Phi(json['phi'])
The object must also be added to jsonify._loadable_models
.
The JSON encoder adds the name of the object and the current PyPhi version to the JSON stream. The JSON decoder uses this metadata to recursively deserialize the stream to a nested PyPhi object structure. The decoder will raise an exception if current PyPhi version doesn’t match the version in the JSON data.

pyphi.jsonify.
jsonify
(obj)¶ Return a JSONencodable representation of an object, recursively using any available
to_json
methods, converting NumPy arrays and datatypes to native lists and types along the way.

class
pyphi.jsonify.
PyPhiJSONEncoder
(skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)¶ JSONEncoder that allows serializing PyPhi objects with
jsonify
.Constructor for JSONEncoder, with sensible defaults.
If skipkeys is false, then it is a TypeError to attempt encoding of keys that are not str, int, float or None. If skipkeys is True, such items are simply skipped.
If ensure_ascii is true, the output is guaranteed to be str objects with all incoming nonASCII characters escaped. If ensure_ascii is false, the output can contain nonASCII characters.
If check_circular is true, then lists, dicts, and custom encoded objects will be checked for circular references during encoding to prevent an infinite recursion (which would cause an OverflowError). Otherwise, no such check takes place.
If allow_nan is true, then NaN, Infinity, and Infinity will be encoded as such. This behavior is not JSON specification compliant, but is consistent with most JavaScript based encoders and decoders. Otherwise, it will be a ValueError to encode such floats.
If sort_keys is true, then the output of dictionaries will be sorted by key; this is useful for regression tests to ensure that JSON serializations can be compared on a daytoday basis.
If indent is a nonnegative integer, then JSON array elements and object members will be prettyprinted with that indent level. An indent level of 0 will only insert newlines. None is the most compact representation.
If specified, separators should be an (item_separator, key_separator) tuple. The default is (‘, ‘, ‘: ‘) if indent is
None
and (‘,’, ‘: ‘) otherwise. To get the most compact JSON representation, you should specify (‘,’, ‘:’) to eliminate whitespace.If specified, default is a function that gets called for objects that can’t otherwise be serialized. It should return a JSON encodable version of the object or raise a
TypeError
.
encode
(obj)¶ Encode the output of
jsonify
with the default encoder.

iterencode
(obj, **kwargs)¶ Analog to encode used by json.dump.


pyphi.jsonify.
dumps
(obj, **user_kwargs)¶ Serialize
obj
as JSONformatted stream.

pyphi.jsonify.
dump
(obj, fp, **user_kwargs)¶ Serialize
obj
as a JSONformatted stream and write tofp
(a.write()
supporting filelike object.

class
pyphi.jsonify.
PyPhiJSONDecoder
(*args, **kwargs)¶ Extension of the default encoder which automatically deserializes PyPhi JSON to the appropriate model classes.

pyphi.jsonify.
loads
(string)¶ Deserialize a JSON string to a Python object.

pyphi.jsonify.
load
(fp)¶ Deserialize a JSON stream to a Python object.
macro
¶
Methods for coarsegraining systems to different levels of spatial analysis.

pyphi.macro.
reindex
(indices)¶ Generate a new set of node indices, the size of indices.

pyphi.macro.
rebuild_system_tpm
(node_tpms)¶ Reconstruct the network TPM from a collection of node TPMs.

pyphi.macro.
remove_singleton_dimensions
(tpm)¶ Remove singleton dimensions from the TPM.
Singleton dimensions are created by conditioning on a set of elements. This removes those elements from the TPM, leaving a TPM that only describes the nonconditioned elements.
Note that indices used in the original TPM must be reindexed for the smaller TPM.

pyphi.macro.
run_tpm
(system, steps, blackbox)¶ Iterate the TPM for the given number of timesteps.
Returns: tpm * (noise_tpm^(t1)) Return type: np.ndarray

class
pyphi.macro.
SystemAttrs
¶ An immutable container that holds all the attributes of a subsystem.
Versions of this object are passed down the steps of the microtomacro pipeline.
Create new instance of SystemAttrs(tpm, cm, node_indices, state)

node_labels
¶ Return the labels for macro nodes.

nodes
¶

static
pack
(system)¶

apply
(system)¶


class
pyphi.macro.
MacroSubsystem
(network, state, nodes=None, cut=None, mice_cache=None, time_scale=1, blackbox=None, coarse_grain=None)¶ A subclass of
Subsystem
implementing macro computations.This subsystem performs blackboxing and coarsegraining of elements.
Unlike
Subsystem
, whose TPM has dimensionality equal to that of the subsystem’s network and represents nodes external to the system using singleton dimensions,MacroSubsystem
squeezes the TPM to remove these singletons. As a result, the node indices of the system are also squeezed to0..n
so they properly index the TPM, and the statetuple is reduced to the size of the system.After each macro update (temporal blackboxing, spatial blackboxing, and spatial coarsegraining) the TPM, CM, nodes, and state are updated so that they correctly represent the updated system.

cut_indices
¶ The indices of this system to be cut for \(\Phi\) computations.
For macro computations the cut is applied to the underlying microsystem.

cut_mechanisms
¶ The mechanisms of this system that are currently cut.
Note that although
cut_indices
returns micro indices, this returns macro mechanisms.Yields: tuple[int]

cut_node_labels
¶ Labels for the nodes that can be cut.
These are the labels of the micro elements.

apply_cut
(cut)¶ Return a cut version of this
MacroSubsystem
.Parameters: cut (Cut) – The cut to apply to this MacroSubsystem
.Returns: The cut version of this MacroSubsystem
.Return type: MacroSubsystem

potential_purviews
(direction, mechanism, purviews=False)¶ Override Subsystem implementation using Networklevel indices.

macro2micro
(macro_indices)¶ Return all micro indices which compose the elements specified by
macro_indices
.

macro2blackbox_outputs
(macro_indices)¶ Given a set of macro elements, return the blackbox output elements which compose these elements.


class
pyphi.macro.
CoarseGrain
¶ Represents a coarse graining of a collection of nodes.

partition
¶ The partition of microelements into macroelements.
Type: tuple[tuple]

grouping
¶ The grouping of microstates into macrostates.
Type: tuple[tuple[tuple]]
Create new instance of CoarseGrain(partition, grouping)

micro_indices
¶ Indices of micro elements represented in this coarsegraining.

macro_indices
¶ Indices of macro elements of this coarsegraining.

reindex
()¶ Reindex this coarse graining to use squeezed indices.
The output grouping is translated to use indices
0..n
, wheren
is the number of micro indices in the coarsegraining. Reindexing does not effect the state grouping, which is already indexindependent.Returns: A new CoarseGrain
object, indexed from0..n
.Return type: CoarseGrain Example
>>> partition = ((1, 2),) >>> grouping = (((0,), (1, 2)),) >>> coarse_grain = CoarseGrain(partition, grouping) >>> coarse_grain.reindex() CoarseGrain(partition=((0, 1),), grouping=(((0,), (1, 2)),))

macro_state
(micro_state)¶ Translate a micro state to a macro state
Parameters: micro_state (tuple[int]) – The state of the micro nodes in this coarsegraining. Returns: The state of the macro system, translated as specified by this coarsegraining. Return type: tuple[int] Example
>>> coarse_grain = CoarseGrain(((1, 2),), (((0,), (1, 2)),)) >>> coarse_grain.macro_state((0, 0)) (0,) >>> coarse_grain.macro_state((1, 0)) (1,) >>> coarse_grain.macro_state((1, 1)) (1,)

make_mapping
()¶ Return a mapping from microstate to the macrostates based on the partition and state grouping of this coarsegrain.
Returns: A mapping from microstates to macrostates. The \(i^{\textrm{th}}\) entry in the mapping is the macrostate corresponding to the \(i^{\textrm{th}}\) microstate. Return type: (nd.ndarray)

macro_tpm_sbs
(state_by_state_micro_tpm)¶ Create a statebystate coarsegrained macro TPM.
Parameters: micro_tpm (nd.array) – The statebystate TPM of the microsystem. Returns: The statebystate TPM of the macrosystem. Return type: np.ndarray

macro_tpm
(micro_tpm, check_independence=True)¶ Create a coarsegrained macro TPM.
Parameters:  micro_tpm (nd.array) – The TPM of the microsystem.
 check_independence (bool) – Whether to check that the macro TPM is conditionally independent.
Raises: ConditionallyDependentError – If
check_independence
isTrue
and the macro TPM is not conditionally independent.Returns: The statebynode TPM of the macrosystem.
Return type: np.ndarray


class
pyphi.macro.
Blackbox
¶ Class representing a blackboxing of a system.

partition
¶ The partition of nodes into boxes.
Type: tuple[tuple[int]]

output_indices
¶ Outputs of the blackboxes.
Type: tuple[int]
Create new instance of Blackbox(partition, output_indices)
All elements hidden inside the blackboxes.

micro_indices
¶ Indices of microelements in this blackboxing.

macro_indices
¶ Fresh indices of macroelements of the blackboxing.

outputs_of
(partition_index)¶ The outputs of the partition at
partition_index
.Note that this returns a tuple of element indices, since coarse grained blackboxes may have multiple outputs.

reindex
()¶ Squeeze the indices of this blackboxing to
0..n
.Returns: a new, reindexed Blackbox
.Return type: Blackbox Example
>>> partition = ((3,), (2, 4)) >>> output_indices = (2, 3) >>> blackbox = Blackbox(partition, output_indices) >>> blackbox.reindex() Blackbox(partition=((1,), (0, 2)), output_indices=(0, 1))

macro_state
(micro_state)¶ Compute the macrostate of this blackbox.
This is just the state of the blackbox’s output indices.
Parameters: micro_state (tuple[int]) – The state of the microelements in the blackbox. Returns: The state of the output indices. Return type: tuple[int]

in_same_box
(a, b)¶ Return
True
if nodesa
andb`
are in the same box.
Return True if
a
is hidden in a different box thanb
.


pyphi.macro.
all_partitions
(indices)¶ Return a list of all possible coarse grains of a network.
Parameters: indices (tuple[int]) – The micro indices to partition. Yields: tuple[tuple] – A possible partition. Each element of the tuple is a tuple of microelements which correspond to macroelements.

pyphi.macro.
all_groupings
(partition)¶ Return all possible groupings of states for a particular coarse graining (partition) of a network.
Parameters: partition (tuple[tuple]) – A partition of microelements into macro elements. Yields: tuple[tuple[tuple]] – A grouping of microstates into macro states of system. TODO: document exactly how to interpret the grouping.

pyphi.macro.
all_coarse_grains
(indices)¶ Generator over all possible
CoarseGrain
of these indices.Parameters: indices (tuple[int]) – Node indices to coarse grain. Yields: CoarseGrain – The next CoarseGrain
forindices
.

pyphi.macro.
all_coarse_grains_for_blackbox
(blackbox)¶ Generator over all
CoarseGrain
for the given blackbox.If a box has multiple outputs, those outputs are partitioned into the same coarsegrain macroelement.

pyphi.macro.
all_blackboxes
(indices)¶ Generator over all possible blackboxings of these indices.
Parameters: indices (tuple[int]) – Nodes to blackbox. Yields: Blackbox – The next Blackbox
ofindices
.

class
pyphi.macro.
MacroNetwork
(network, system, macro_phi, micro_phi, coarse_grain, time_scale=1, blackbox=None)¶ A coarsegrained network of nodes.
See the Emergence (coarsegraining and blackboxing) example in the documentation for more information.

phi
¶ The \(\Phi\) of the network’s major complex.
Type: float

micro_phi
¶ The \(\Phi\) of the major complex of the corresponding microsystem.
Type: float

coarse_grain
¶ The coarsegraining of microelements into macroelements.
Type: CoarseGrain

time_scale
¶ The time scale the macronetwork run over.
Type: int

emergence
¶ The difference between the \(\Phi\) of the macro and the microsystem.
Type: float

emergence
Difference between the \(\Phi\) of the macro and micro systems


pyphi.macro.
coarse_graining
(network, state, internal_indices)¶ Find the maximal coarsegraining of a microsystem.
Parameters:  network (Network) – The network in question.
 state (tuple[int]) – The state of the network.
 internal_indices (tuple[int]) – Nodes in the microsystem.
Returns: The phivalue of the maximal
CoarseGrain
.Return type: tuple[int, CoarseGrain]

pyphi.macro.
all_macro_systems
(network, state, do_blackbox=False, do_coarse_grain=False, time_scales=None)¶ Generator over all possible macrosystems for the network.

pyphi.macro.
emergence
(network, state, do_blackbox=False, do_coarse_grain=True, time_scales=None)¶ Check for the emergence of a microsystem into a macrosystem.
Checks all possible blackboxings and coarsegrainings of a system to find the spatial scale with maximum integrated information.
Use the
do_blackbox
anddo_coarse_grain
args to specifiy whether to use blackboxing, coarsegraining, or both. The default is to just coarsegrain the system.Parameters:  network (Network) – The network of the microsystem under investigation.
 state (tuple[int]) – The state of the network.
 do_blackbox (bool) – Set to
True
to enable blackboxing. Defaults toFalse
.  do_coarse_grain (bool) – Set to
True
to enable coarsegraining. Defaults toTrue
.  time_scales (list[int]) – List of all time steps over which to check for emergence.
Returns: The maximal macrosystem generated from the microsystem.
Return type:

pyphi.macro.
phi_by_grain
(network, state)¶

pyphi.macro.
effective_info
(network)¶ Return the effective information of the given network.
Note
For details, see:
Hoel, Erik P., Larissa Albantakis, and Giulio Tononi. “Quantifying causal emergence shows that macro can beat micro.” Proceedings of the National Academy of Sciences 110.49 (2013): 1979019795.
Available online: doi: 10.1073/pnas.1314922110.
models
¶
See pyphi.models.subsystem
, pyphi.models.mechanism
, and pyphi.models.cuts
for
documentation.

pyphi.models.
Account
¶ Alias for
pyphi.models.actual_causation.Account
.

pyphi.models.
AcRepertoireIrreducibilityAnalysis
¶ Alias for
pyphi.models.actual_causation.AcRepertoireIrreducibilityAnalysis
.

pyphi.models.
AcSystemIrreducibilityAnalysis
¶ Alias for
pyphi.models.actual_causation.AcSystemIrreducibilityAnalysis
.

pyphi.models.
ActualCut
¶ Alias for
pyphi.models.cuts.ActualCut
.

pyphi.models.
Bipartition
¶ Alias for
pyphi.models.cuts.Bipartition
.

pyphi.models.
CausalLink
¶ Alias for
pyphi.models.actual_causation.CausalLink
.

pyphi.models.
CauseEffectStructure
¶ Alias for
pyphi.models.subsystem.CauseEffectStructure
.

pyphi.models.
Concept
¶ Alias for
pyphi.models.mechanism.Concept
.

pyphi.models.
Cut
¶ Alias for
pyphi.models.cuts.Cut
.

pyphi.models.
DirectedAccount
¶

pyphi.models.
MaximallyIrreducibleCause
¶

pyphi.models.
MaximallyIrreducibleEffect
¶ Alias for
pyphi.models.mechanism.MaximallyIrreducibleEffect
.

pyphi.models.
MaximallyIrreducibleCauseOrEffect
¶ Alias for
pyphi.models.mechanism.MaximallyIrreducibleCauseOrEffect
.

pyphi.models.
Part
¶ Alias for
pyphi.models.cuts.Part
.

pyphi.models.
RepertoireIrreducibilityAnalysis
¶ Alias for
pyphi.models.mechanism.RepertoireIrreducibilityAnalysis
.

pyphi.models.
SystemIrreducibilityAnalysis
¶ Alias for
pyphi.models.subsystem.SystemIrreducibilityAnalysis
.
models.actual_causation
¶
Objects that represent structures used in actual causation.

pyphi.models.actual_causation.
greater_than_zero
(alpha)¶ Return
True
if alpha is greater than zero, accounting for numerical errors.

class
pyphi.models.actual_causation.
AcRepertoireIrreducibilityAnalysis
(alpha, state, direction, mechanism, purview, partition, probability, partitioned_probability, node_labels=None)¶ A minimum information partition for ac_coef calculation.
These can be compared with the builtin Python comparison operators (
<
,>
, etc.). First, \(\alpha\) values are compared. Then, if these are equal up toPRECISION
, the size of the mechanism is compared.
alpha
¶ This is the difference between the mechanism’s unpartitioned and partitioned actual probability.
Type: float

state
¶ state of system in specified direction (cause, effect)
Type: tuple[int]

direction
¶ The temporal direction specifiying whether this analysis should be calculated with cause or effect repertoires.
Type: str

mechanism
¶ The mechanism to analyze.
Type: tuple[int]

purview
¶ The purview over which the unpartitioned actual probability differs the least from the actual probability of the partition.
Type: tuple[int]

partition
¶ The partition that makes the least difference to the mechanism’s repertoire.
Type: tuple[Part, Part]

probability
¶ The probability of the state in the previous/next timestep.
Type: float

partitioned_probability
¶ The probability of the state in the partitioned repertoire.
Type: float

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__
()¶ An
AcRepertoireIrreducibilityAnalysis
isTrue
if it has \(\alpha > 0\).

phi
¶ Alias for \(\alpha\) for PyPhi utility functions.

to_json
()¶ Return a JSONserializable representation.


class
pyphi.models.actual_causation.
CausalLink
(ria)¶ A maximally irreducible actual cause or effect.
These can be compared with the builtin Python comparison operators (
<
,>
, etc.). First, \(\alpha\) values are compared. Then, if these are equal up toPRECISION
, the size of the mechanism is compared.
alpha
¶ The difference between the mechanism’s unpartitioned and partitioned actual probabilities.
Type: float

phi
¶ Alias for \(\alpha\) for PyPhi utility functions.

mechanism
¶ The mechanism for which the action is evaluated.
Type: list[int]

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

ria
¶ The irreducibility analysis for this mechanism.
Type: AcRepertoireIrreducibilityAnalysis

node_labels
¶

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__
()¶ An
CausalLink
isTrue
if \(\alpha > 0\).

to_json
()¶ Return a JSONserializable representation.


class
pyphi.models.actual_causation.
Event
¶ A mechanism which has both an actual cause and an actual effect.

actual_cause
¶ The actual cause of the mechanism.
Type: CausalLink

actual_effect
¶ The actual effect of the mechanism.
Type: CausalLink
Create new instance of Event(actual_cause, actual_effect)

mechanism
¶ The mechanism of the event.


class
pyphi.models.actual_causation.
Account
(causal_links)¶ The set of
CausalLink
with \(\alpha > 0\). This includes both actual causes and actual effects.
to_json
()¶

classmethod
from_json
(dct)¶


class
pyphi.models.actual_causation.
DirectedAccount
(causal_links)¶ The set of
CausalLink
with \(\alpha > 0\) for one direction of a transition.

class
pyphi.models.actual_causation.
AcSystemIrreducibilityAnalysis
(alpha=None, direction=None, account=None, partitioned_account=None, transition=None, cut=None)¶ An analysis of transitionlevel irreducibility (\(\mathcal{A}\)).
Contains the \(\mathcal{A}\) value of the
Transition
, the causal account, and all the intermediate results obtained in the course of computing them.
alpha
¶ The \(\mathcal{A}\) value for the transition when taken against this analysis, i.e. the difference between the unpartitioned account and this analysis’s partitioned account.
Type: float

transition
¶ The transition this analysis was calculated for.
Type: Transition

before_state
¶ Return the actual previous state of the
Transition
.

after_state
¶ Return the actual current state of the
Transition
.

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__
()¶ An
AcSystemIrreducibilityAnalysis
isTrue
if it has \(\mathcal{A} > 0\).

to_json
()¶

models.cuts
¶
Objects that represent partitions of sets of nodes.

class
pyphi.models.cuts.
NullCut
(indices, node_labels=None)¶ The cut that does nothing.

is_null
¶ This is the only cut where
is_null == True
.

indices
¶ Indices of the cut.

cut_matrix
(n)¶ Return a matrix of zeros.

to_json
()¶


class
pyphi.models.cuts.
Cut
(from_nodes, to_nodes, node_labels=None)¶ Represents a unidirectional cut.

from_nodes
¶ Connections from this group of nodes to those in
to_nodes
are from_nodes.Type: tuple[int]

to_nodes
¶ Connections to this group of nodes from those in
from_nodes
are from_nodes.Type: tuple[int]

from_nodes

to_nodes

node_labels
¶

indices
¶ Indices of this cut.

cut_matrix
(n)¶ Compute the cut matrix for this cut.
The cut matrix is a square matrix which represents connections severed by the cut.
Parameters: n (int) – The size of the network. Example
>>> cut = Cut((1,), (2,)) >>> cut.cut_matrix(3) array([[0., 0., 0.], [0., 0., 1.], [0., 0., 0.]])

to_json
()¶ Return a JSONserializable representation.


class
pyphi.models.cuts.
KCut
(direction, partition, node_labels=None)¶ A cut that severs all connections between parts of a Kpartition.

indices
¶ Indices of this cut.

cut_matrix
(n)¶ The matrix of connections that are severed by this cut.

to_json
()¶


class
pyphi.models.cuts.
ActualCut
(direction, partition, node_labels=None)¶ Represents an cut for a
Transition
.
indices
¶ Indices of this cut.


class
pyphi.models.cuts.
Part
¶ Represents one part of a
Bipartition
.
mechanism
¶ The nodes in the mechanism for this part.
Type: tuple[int]

purview
¶ The nodes in the mechanism for this part.
Type: tuple[int]
Example
When calculating \(\varphi\) of a 3node subsystem, we partition the system in the following way:
mechanism: A,C B ─── ✕ ─── purview: B A,C
This class represents one term in the above product.
Create new instance of Part(mechanism, purview)

to_json
()¶ Return a JSONserializable representation.


class
pyphi.models.cuts.
KPartition
(*parts, node_labels=None)¶ A partition with an arbitrary number of parts.

parts
¶

node_labels
¶

mechanism
¶ The nodes of the mechanism in the partition.
Type: tuple[int]

purview
¶ The nodes of the purview in the partition.
Type: tuple[int]

normalize
()¶ Normalize the order of parts in the partition.

to_json
()¶

classmethod
from_json
(dct)¶

models.mechanism
¶
Mechanismlevel 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 builtin Python comparison operators (
<
,>
, etc.). First, \(\varphi\) values are compared. Then, if these are equal up toPRECISION
, the size of the mechanism is compared (see thePICK_SMALLEST_PURVIEW
option inconfig
.)
phi
¶ This is the difference between the mechanism’s unpartitioned and partitioned repertoires.
Type: float

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 mechanismpurview 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
isTrue
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 builtin Python comparison operators (
<
,>
, etc.). First, \(\varphi\) values are compared. Then, if these are equal up toPRECISION
, the size of the mechanism is compared (see thePICK_SMALLEST_PURVIEW
option inconfig
.)
phi
¶ The difference between the mechanism’s unpartitioned and partitioned repertoires.
Type: float

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.
Type: RepertoireIrreducibilityAnalysis

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 builtin Python comparison operators (
<
,>
, etc.). First, \(\varphi\) values are compared. Then, if these are equal up toPRECISION
, the size of the mechanism is compared (see thePICK_SMALLEST_PURVIEW
option inconfig
.)

class
pyphi.models.mechanism.
MaximallyIrreducibleEffect
(ria)¶ A maximally irreducible effect (MIE).
These can be compared with the builtin Python comparison operators (
<
,>
, etc.). First, \(\varphi\) values are compared. Then, if these are equal up toPRECISION
, the size of the mechanism is compared (see thePICK_SMALLEST_PURVIEW
option inconfig
.)

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 builtin Python comparison operators (
<
,>
, etc.). First, \(\varphi\) values are compared. Then, if these are equal up toPRECISION
, the size of the mechanism is compared.
mechanism
¶ The mechanism that the concept consists of.
Type: tuple[int]

cause
¶ The
MaximallyIrreducibleCause
representing the maximallyirreducible cause of this concept.Type: MaximallyIrreducibleCause

effect
¶ The
MaximallyIrreducibleEffect
representing the maximallyirreducible effect of this concept.Type: MaximallyIrreducibleEffect

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
andMaximallyIrreducibleEffect
.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)¶ 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 JSONserializable representation.

classmethod
from_json
(dct)¶

models.subsystem
¶
Subsystemlevel objects.

class
pyphi.models.subsystem.
CauseEffectStructure
(concepts=(), subsystem=None, time=None)¶ A collection of concepts.

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
()¶

mechanisms
¶ The mechanism of each concept.

phis
¶ The \(\varphi\) values of each concept.

labeled_mechanisms
¶ The labeled mechanism of each concept.


class
pyphi.models.subsystem.
SystemIrreducibilityAnalysis
(phi=None, ces=None, partitioned_ces=None, subsystem=None, cut_subsystem=None, time=None)¶ An analysis of system irreducibility (\(\Phi\)).
Contains the \(\Phi\) value of the
Subsystem
, the causeeffect structure, and all the intermediate results obtained in the course of computing them.These can be compared with the builtin Python comparison operators (
<
,>
, etc.). First, \(\Phi\) values are compared. Then, if these are equal up toPRECISION
, the one with the larger subsystem is greater.
phi
¶ The \(\Phi\) value for the subsystem when taken against this analysis, i.e. the difference between the causeeffect structure and the partitioned causeeffect structure for this analysis.
Type: float

ces
¶ The causeeffect structure of the whole subsystem.
Type: CauseEffectStructure

partitioned_ces
¶ The causeeffect structure when the subsystem is cut.
Type: CauseEffectStructure

time
¶ The number of seconds it took to calculate.
Type: float

print
(ces=True)¶ Print this
SystemIrreducibilityAnalysis
, optionally without causeeffect structures.

small_phi_time
¶ The number of seconds it took to calculate the CES.

cut
¶ The unidirectional cut that makes the least difference to the subsystem.

network
¶ The network the subsystem belongs to.

unorderable_unless_eq
= ['network']¶

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
SystemIrreducibilityAnalysis
isTrue
if it has \(\Phi > 0\).

to_json
()¶ Return a JSONserializable representation.

classmethod
from_json
(dct)¶

network
¶
Represents the network of interest. This is the primary object of PyPhi and the context of all \(\varphi\) and \(\Phi\) computation.

class
pyphi.network.
Network
(tpm, cm=None, node_labels=None, purview_cache=None)¶ A network of nodes.
Represents the network under analysis and holds auxilary data about it.
Parameters: tpm (np.ndarray) –
The transition probability matrix of the network.
The TPM can be provided in any of three forms: statebystate, statebynode, or multidimensional statebynode form. In the statebynode forms, row indices must follow the littleendian convention (see Littleendian convention). In statebystate form, column indices must also follow the littleendian convention.
If the TPM is given in statebynode form, it can be either 2dimensional, so that
tpm[i]
gives the probabilities of each node being ON if the previous state is encoded by \(i\) according to the littleendian convention, or in multidimensional form, so thattpm[(0, 0, 1)]
gives the probabilities of each node being ON if the previous state is \(N_0 = 0, N_1 = 0, N_2 = 1\).The shape of the 2dimensional form of a statebynode TPM must be
(s, n)
, and the shape of the multidimensional form of the TPM must be[2] * n + [n]
, wheres
is the number of states andn
is the number of nodes in the network.Keyword Arguments:  cm (np.ndarray) – A square binary adjacency matrix indicating the
connections between nodes in the network.
cm[i][j] == 1
means that node \(i\) is connected to node \(j\) (see Connectivity matrix conventions). If no connectivity matrix is given, PyPhi assumes that every node is connected to every node (including itself).  node_labels (tuple[str] or
NodeLabels
) – Humanreadable labels for each node in the network.
Example
In a 3node network,
the_network.tpm[(0, 0, 1)]
gives the transition probabilities for each node at \(t\) given that state at \(t1\) was \(N_0 = 0, N_1 = 0, N_2 = 1\).
tpm
¶ The network’s transition probability matrix, in multidimensional form.
Type: np.ndarray

cm
¶ The network’s connectivity matrix.
A square binary adjacency matrix indicating the connections between nodes in the network.
Type: np.ndarray

connectivity_matrix
¶ Alias for
cm
.Type: np.ndarray

causally_significant_nodes
¶

size
¶ The number of nodes in the network.
Type: int

num_states
¶ The number of possible states of the network.
Type: int

node_indices
¶ The indices of nodes in the network.
This is equivalent to
tuple(range(network.size))
.Type: tuple[int]

node_labels
¶ The labels of nodes in the network.
Type: tuple[str]

potential_purviews
(direction, mechanism)¶ All purviews which are not clearly reducible for mechanism.
Parameters: Returns: All purviews which are irreducible over
mechanism
.Return type: list[tuple[int]]

__len__
()¶ int: The number of nodes in the network.

__eq__
(other)¶ Return whether this network equals the other object.
Networks are equal if they have the same TPM and CM.

to_json
()¶ Return a JSONserializable representation.
 cm (np.ndarray) – A square binary adjacency matrix indicating the
connections between nodes in the network.

pyphi.network.
irreducible_purviews
(cm, direction, mechanism, purviews)¶ Return all purviews which are irreducible for the mechanism.
Parameters: Returns: All purviews in
purviews
which are not reducible overmechanism
.Return type: list[tuple[int]]
Raises: ValueError – If
direction
is invalid.
node
¶
Represents a node in a network. Each node has a unique index, its position in the network’s list of nodes.

class
pyphi.node.
Node
(tpm, cm, index, state, node_labels)¶ A node in a subsystem.
Parameters:  tpm (np.ndarray) – The TPM of the subsystem.
 cm (np.ndarray) – The CM of the subsystem.
 index (int) – The node’s index in the network.
 state (int) – The state of this node.
 node_labels (
NodeLabels
) – Labels for these nodes.

tpm
¶ The node TPM is an array with shape
(2,)*(n + 1)
, wheren
is the size of theNetwork
. The firstn
dimensions correspond to each node in the system. Dimensions corresponding to nodes that provide input to this node are of size 2, while those that do not correspond to inputs are of size 1, so that the TPM has \(2^m \times 2\) elements where \(m\) is the number of inputs. The last dimension corresponds to the state of the node in the next timestep, so thatnode.tpm[..., 0]
gives probabilities that the node will be ‘OFF’ andnode.tpm[..., 1]
gives probabilities that the node will be ‘ON’.Type: np.ndarray

tpm_off
¶ The TPM of this node containing only the ‘OFF’ probabilities.

tpm_on
¶ The TPM of this node containing only the ‘ON’ probabilities.

inputs
¶ The set of nodes with connections to this node.

outputs
¶ The set of nodes this node has connections to.

label
¶ The textual label for this node.

__eq__
(other)¶ Return whether this node equals the other object.
Two nodes are equal if they belong to the same subsystem and have the same index (their TPMs must be the same in that case, so this method doesn’t need to check TPM equality).
Labels are for display only, so two equal nodes may have different labels.

to_json
()¶ Return a JSONserializable representation.

pyphi.node.
generate_nodes
(tpm, cm, network_state, indices, node_labels=None)¶ Generate
Node
objects for a subsystem.Parameters:  tpm (np.ndarray) – The system’s TPM
 cm (np.ndarray) – The corresponding CM.
 network_state (tuple) – The state of the network.
 indices (tuple[int]) – Indices to generate nodes for.
Keyword Arguments: node_labels (
NodeLabels
) – Textual labels for each node.Returns: The nodes of the system.
Return type: tuple[Node]

pyphi.node.
expand_node_tpm
(tpm)¶ Broadcast a node TPM over the full network.
This is different from broadcasting the TPM of a full system since the last dimension (containing the state of the node) contains only the probability of this node being on, rather than the probabilities for each node.
partition
¶
Functions for generating partitions.

pyphi.partition.
partitions
(collection)¶ Generate all set partitions of a collection.
Example
>>> list(partitions(range(3))) # doctest: +NORMALIZE_WHITESPACE [[[0, 1, 2]], [[0], [1, 2]], [[0, 1], [2]], [[1], [0, 2]], [[0], [1], [2]]]

pyphi.partition.
bipartition_indices
(N)¶ Return indices for undirected bipartitions of a sequence.
Parameters: N (int) – The length of the sequence. Returns: A list of tuples containing the indices for each of the two parts. Return type: list Example
>>> N = 3 >>> bipartition_indices(N) [((), (0, 1, 2)), ((0,), (1, 2)), ((1,), (0, 2)), ((0, 1), (2,))]

pyphi.partition.
bipartition
(seq)¶ Return a list of bipartitions for a sequence.
Parameters: a (Iterable) – The sequence to partition. Returns: A list of tuples containing each of the two partitions. Return type: list[tuple[tuple]] Example
>>> bipartition((1,2,3)) [((), (1, 2, 3)), ((1,), (2, 3)), ((2,), (1, 3)), ((1, 2), (3,))]

pyphi.partition.
directed_bipartition_indices
(N)¶ Return indices for directed bipartitions of a sequence.
Parameters: N (int) – The length of the sequence. Returns: A list of tuples containing the indices for each of the two parts. Return type: list Example
>>> N = 3 >>> directed_bipartition_indices(N) # doctest: +NORMALIZE_WHITESPACE [((), (0, 1, 2)), ((0,), (1, 2)), ((1,), (0, 2)), ((0, 1), (2,)), ((2,), (0, 1)), ((0, 2), (1,)), ((1, 2), (0,)), ((0, 1, 2), ())]

pyphi.partition.
directed_bipartition
(seq, nontrivial=False)¶ Return a list of directed bipartitions for a sequence.
Parameters: seq (Iterable) – The sequence to partition. Returns: A list of tuples containing each of the two parts. Return type: list[tuple[tuple]] Example
>>> directed_bipartition((1, 2, 3)) # doctest: +NORMALIZE_WHITESPACE [((), (1, 2, 3)), ((1,), (2, 3)), ((2,), (1, 3)), ((1, 2), (3,)), ((3,), (1, 2)), ((1, 3), (2,)), ((2, 3), (1,)), ((1, 2, 3), ())]

pyphi.partition.
bipartition_of_one
(seq)¶ Generate bipartitions where one part is of length 1.

pyphi.partition.
reverse_elements
(seq)¶ Reverse the elements of a sequence.

pyphi.partition.
directed_bipartition_of_one
(seq)¶ Generate directed bipartitions where one part is of length 1.
Parameters: seq (Iterable) – The sequence to partition. Returns: A list of tuples containing each of the two partitions. Return type: list[tuple[tuple]] Example
>>> partitions = directed_bipartition_of_one((1, 2, 3)) >>> list(partitions) # doctest: +NORMALIZE_WHITESPACE [((1,), (2, 3)), ((2,), (1, 3)), ((3,), (1, 2)), ((2, 3), (1,)), ((1, 3), (2,)), ((1, 2), (3,))]

pyphi.partition.
directed_tripartition_indices
(N)¶ Return indices for directed tripartitions of a sequence.
Parameters: N (int) – The length of the sequence. Returns: A list of tuples containing the indices for each partition. Return type: list[tuple] Example
>>> N = 1 >>> directed_tripartition_indices(N) [((0,), (), ()), ((), (0,), ()), ((), (), (0,))]

pyphi.partition.
directed_tripartition
(seq)¶ Generator over all directed tripartitions of a sequence.
Parameters: seq (Iterable) – a sequence. Yields: tuple[tuple] – A tripartition of seq
.Example
>>> seq = (2, 5) >>> list(directed_tripartition(seq)) # doctest: +NORMALIZE_WHITESPACE [((2, 5), (), ()), ((2,), (5,), ()), ((2,), (), (5,)), ((5,), (2,), ()), ((), (2, 5), ()), ((), (2,), (5,)), ((5,), (), (2,)), ((), (5,), (2,)), ((), (), (2, 5))]

pyphi.partition.
k_partitions
(collection, k)¶ Generate all
k
partitions of a collection.Example
>>> list(k_partitions(range(3), 2)) [[[0, 1], [2]], [[0], [1, 2]], [[0, 2], [1]]]

class
pyphi.partition.
PartitionRegistry
¶ Storage for partition schemes registered with PyPhi.
Users can define custom partitions:
Examples
>>> @partition_types.register('NONE') # doctest: +SKIP ... def no_partitions(mechanism, purview): ... return []
And use them by setting
config.PARTITION_TYPE = 'NONE'

desc
= 'partitions'¶


pyphi.partition.
mip_partitions
(mechanism, purview, node_labels=None)¶ Return a generator over all mechanismpurview partitions, based on the current configuration.

pyphi.partition.
mip_bipartitions
(mechanism, purview, node_labels=None)¶ Return an generator of all \(\varphi\) bipartitions of a mechanism over a purview.
Excludes all bipartitions where one half is entirely empty, e.g:
A ∅ ─── ✕ ─── B ∅
is not valid, but
A ∅ ─── ✕ ─── ∅ B
is.
Parameters:  mechanism (tuple[int]) – The mechanism to partition
 purview (tuple[int]) – The purview to partition
Yields: Bipartition –
Where each bipartition is:
bipart[0].mechanism bipart[1].mechanism ─────────────────── ✕ ─────────────────── bipart[0].purview bipart[1].purview
Example
>>> mechanism = (0,) >>> purview = (2, 3) >>> for partition in mip_bipartitions(mechanism, purview): ... print(partition, '\n') # doctest: +NORMALIZE_WHITESPACE ∅ 0 ─── ✕ ─── 2 3 <BLANKLINE> ∅ 0 ─── ✕ ─── 3 2 <BLANKLINE> ∅ 0 ─── ✕ ─── 2,3 ∅

pyphi.partition.
wedge_partitions
(mechanism, purview, node_labels=None)¶ Return an iterator over all wedge partitions.
These are partitions which strictly split the mechanism and allow a subset of the purview to be split into a third partition, e.g.:
A B ∅ ─── ✕ ─── ✕ ─── B C D
See
PARTITION_TYPE
inconfig
for more information.Parameters:  mechanism (tuple[int]) – A mechanism.
 purview (tuple[int]) – A purview.
Yields: Tripartition – all unique tripartitions of this mechanism and purview.

pyphi.partition.
all_partitions
(mechanism, purview, node_labels=None)¶ Return all possible partitions of a mechanism and purview.
Partitions can consist of any number of parts.
Parameters:  mechanism (tuple[int]) – A mechanism.
 purview (tuple[int]) – A purview.
Yields: KPartition – A partition of this mechanism and purview into
k
parts.
subsystem
¶
Represents a candidate system for \(\varphi\) and \(\Phi\) evaluation.

class
pyphi.subsystem.
Subsystem
(network, state, nodes=None, cut=None, mice_cache=None, repertoire_cache=None, single_node_repertoire_cache=None, _external_indices=None)¶ A set of nodes in a network.
Parameters:  network (Network) – The network the subsystem belongs to.
 state (tuple[int]) – The state of the network.
Keyword Arguments: 
tpm
¶ The TPM conditioned on the state of the external nodes.
Type: np.ndarray

cm
¶ The connectivity matrix after applying the cut.
Type: np.ndarray

state
¶ The state of the network.
Type: tuple[int]

node_indices
¶ The indices of the nodes in the subsystem.
Type: tuple[int]

proper_state
¶ The state of the subsystem.
proper_state[i]
gives the state of the \(i^{\textrm{th}}\) node in the subsystem. Note that this is not the state ofnodes[i]
.Type: tuple[int]

size
¶ The number of nodes in the subsystem.
Type: int

is_cut
¶ True
if this Subsystem has a cut applied to it.Type: bool

cut_indices
¶ The nodes of this subsystem to cut for \(\Phi\) computations.
This was added to support
MacroSubsystem
, which cuts indices other thannode_indices
.Yields: tuple[int] Type: tuple[int]

cut_mechanisms
¶ The mechanisms that are cut in this system.
Type: list[tuple[int]]

cut_node_labels
¶ Labels for the nodes of this system that will be cut.
Type: NodeLabels

tpm_size
¶ The number of nodes in the TPM.
Type: int

cache_info
()¶ Report repertoire cache statistics.

clear_caches
()¶ Clear the mice and repertoire caches.

__bool__
()¶ Return
False
if the Subsystem has no nodes,True
otherwise.

__eq__
(other)¶ Return whether this Subsystem is equal to the other object.
Two Subsystems are equal if their sets of nodes, networks, and cuts are equal.

__lt__
(other)¶ Return whether this subsystem has fewer nodes than the other.

__gt__
(other)¶ Return whether this subsystem has more nodes than the other.

__len__
()¶ Return the number of nodes in this Subsystem.

to_json
()¶ Return a JSONserializable representation.

apply_cut
(cut)¶ Return a cut version of this
Subsystem
.Parameters: cut (Cut) – The cut to apply to this Subsystem
.Returns: The cut subsystem. Return type: Subsystem

indices2nodes
(indices)¶ Return
Node
for these indices.Parameters: indices (tuple[int]) – The indices in question. Returns: The Node
objects corresponding to these indices.Return type: tuple[Node] Raises: ValueError – If requested indices are not in the subsystem.

cause_repertoire
(mechanism, purview)¶ Return the cause repertoire of a mechanism over a purview.
Parameters:  mechanism (tuple[int]) – The mechanism for which to calculate the cause repertoire.
 purview (tuple[int]) – The purview over which to calculate the cause repertoire.
Returns: The cause repertoire of the mechanism over the purview.
Return type: np.ndarray
Note
The returned repertoire is a distribution over purview node states, not the states of the whole network.

effect_repertoire
(mechanism, purview)¶ Return the effect repertoire of a mechanism over a purview.
Parameters:  mechanism (tuple[int]) – The mechanism for which to calculate the effect repertoire.
 purview (tuple[int]) – The purview over which to calculate the effect repertoire.
Returns: The effect repertoire of the mechanism over the purview.
Return type: np.ndarray
Note
The returned repertoire is a distribution over purview node states, not the states of the whole network.

repertoire
(direction, mechanism, purview)¶ Return the cause or effect repertoire based on a direction.
Parameters: Returns: The cause or effect repertoire of the mechanism over the purview.
Return type: np.ndarray
Raises: ValueError – If
direction
is invalid.

unconstrained_repertoire
(direction, purview)¶ Return the unconstrained cause/effect repertoire over a purview.

unconstrained_cause_repertoire
(purview)¶ Return the unconstrained cause repertoire for a purview.
This is just the cause repertoire in the absence of any mechanism.

unconstrained_effect_repertoire
(purview)¶ Return the unconstrained effect repertoire for a purview.
This is just the effect repertoire in the absence of any mechanism.

partitioned_repertoire
(direction, partition)¶ Compute the repertoire of a partitioned mechanism and purview.

expand_repertoire
(direction, repertoire, new_purview=None)¶ Distribute an effect repertoire over a larger purview.
Parameters: Keyword Arguments: new_purview (tuple[int]) – The new purview to expand the repertoire over. If
None
(the default), the new purview is the entire network.Returns: A distribution over the new purview, where probability is spread out over the new nodes.
Return type: np.ndarray
Raises: ValueError – If the expanded purview doesn’t contain the original purview.

expand_cause_repertoire
(repertoire, new_purview=None)¶ Alias for
expand_repertoire()
withdirection
set toCAUSE
.

expand_effect_repertoire
(repertoire, new_purview=None)¶ Alias for
expand_repertoire()
withdirection
set toEFFECT
.

cause_info
(mechanism, purview)¶ Return the cause information for a mechanism over a purview.

effect_info
(mechanism, purview)¶ Return the effect information for a mechanism over a purview.

cause_effect_info
(mechanism, purview)¶ Return the causeeffect information for a mechanism over a purview.
This is the minimum of the cause and effect information.

evaluate_partition
(direction, mechanism, purview, partition, repertoire=None)¶ Return the \(\varphi\) of a mechanism over a purview for the given partition.
Parameters:  direction (Direction) –
CAUSE
orEFFECT
.  mechanism (tuple[int]) – The nodes in the mechanism.
 purview (tuple[int]) – The nodes in the purview.
 partition (Bipartition) – The partition to evaluate.
Keyword Arguments: repertoire (np.array) – The unpartitioned repertoire. If not supplied, it will be computed.
Returns: The distance between the unpartitioned and partitioned repertoires, and the partitioned repertoire.
Return type: tuple[int, np.ndarray]
 direction (Direction) –

find_mip
(direction, mechanism, purview)¶ Return the minimum information partition for a mechanism over a purview.
Parameters: Returns: The irreducibility analysis for the mininuminformation partition in one temporal direction.
Return type:

cause_mip
(mechanism, purview)¶ Return the irreducibility analysis for the cause MIP.
Alias for
find_mip()
withdirection
set toCAUSE
.

effect_mip
(mechanism, purview)¶ Return the irreducibility analysis for the effect MIP.
Alias for
find_mip()
withdirection
set toEFFECT
.

phi_cause_mip
(mechanism, purview)¶ Return the \(\varphi\) of the cause MIP.
This is the distance between the unpartitioned cause repertoire and the MIP cause repertoire.

phi_effect_mip
(mechanism, purview)¶ Return the \(\varphi\) of the effect MIP.
This is the distance between the unpartitioned effect repertoire and the MIP cause repertoire.

phi
(mechanism, purview)¶ Return the \(\varphi\) of a mechanism over a purview.

potential_purviews
(direction, mechanism, purviews=False)¶ Return all purviews that could belong to the
MaximallyIrreducibleCause
/MaximallyIrreducibleEffect
.Filters out triviallyreducible purviews.
Parameters: Keyword Arguments: purviews (tuple[int]) – Optional subset of purviews of interest.

find_mice
(direction, mechanism, purviews=False)¶ Return the
MaximallyIrreducibleCause
orMaximallyIrreducibleEffect
for a mechanism.Parameters: Keyword Arguments: purviews (tuple[int]) – Optionally restrict the possible purviews to a subset of the subsystem. This may be useful for _e.g._ finding only concepts that are “about” a certain subset of nodes.
Returns: The
MaximallyIrreducibleCause
orMaximallyIrreducibleEffect
.Return type:

mic
(mechanism, purviews=False)¶ Return the mechanism’s maximallyirreducible cause (
MaximallyIrreducibleCause
).Alias for
find_mice()
withdirection
set toCAUSE
.

mie
(mechanism, purviews=False)¶ Return the mechanism’s maximallyirreducible effect (
MaximallyIrreducibleEffect
).Alias for
find_mice()
withdirection
set toEFFECT
.

phi_max
(mechanism)¶ Return the \(\varphi^{\textrm{max}}\) of a mechanism.
This is the maximum of \(\varphi\) taken over all possible purviews.

null_concept
¶ Return the null concept of this subsystem.
The null concept is a point in concept space identified with the unconstrained cause and effect repertoire of this subsystem.

concept
(mechanism, purviews=False, cause_purviews=False, effect_purviews=False)¶ Return the concept specified by a mechanism within this subsytem.
Parameters: mechanism (tuple[int]) – The candidate set of nodes.
Keyword Arguments:  purviews (tuple[tuple[int]]) – Restrict the possible purviews to those in this list.
 cause_purviews (tuple[tuple[int]]) – Restrict the possible cause
purviews to those in this list. Takes precedence over
purviews
.  effect_purviews (tuple[tuple[int]]) – Restrict the possible effect
purviews to those in this list. Takes precedence over
purviews
.
Returns: The pair of maximally irreducible cause/effect repertoires that constitute the concept specified by the given mechanism.
Return type:
timescale
¶
Functions for converting the timescale of a TPM.

pyphi.timescale.
sparse
(matrix, threshold=0.1)¶

pyphi.timescale.
sparse_time
(tpm, time_scale)¶

pyphi.timescale.
dense_time
(tpm, time_scale)¶

pyphi.timescale.
run_tpm
(tpm, time_scale)¶ Iterate a TPM by the specified number of time steps.
Parameters:  tpm (np.ndarray) – A statebynode tpm.
 time_scale (int) – The number of steps to run the tpm.
Returns: np.ndarray

pyphi.timescale.
run_cm
(cm, time_scale)¶ Iterate a connectivity matrix the specified number of steps.
Parameters:  cm (np.ndarray) – A connectivity matrix.
 time_scale (int) – The number of steps to run.
Returns: The connectivity matrix at the new timescale.
Return type: np.ndarray
tpm
¶
Functions for manipulating transition probability matrices.

pyphi.tpm.
tpm_indices
(tpm)¶ Return the indices of nodes in the TPM.

pyphi.tpm.
is_state_by_state
(tpm)¶ Return
True
iftpm
is in statebystate form, otherwiseFalse
.

pyphi.tpm.
condition_tpm
(tpm, fixed_nodes, state)¶ Return a TPM conditioned on the given fixed node indices, whose states are fixed according to the given statetuple.
The dimensions of the new TPM that correspond to the fixed nodes are collapsed onto their state, making those dimensions singletons suitable for broadcasting. The number of dimensions of the conditioned TPM will be the same as the unconditioned TPM.

pyphi.tpm.
expand_tpm
(tpm)¶ Broadcast a statebynode TPM so that singleton dimensions are expanded over the full network.

pyphi.tpm.
marginalize_out
(node_indices, tpm)¶ Marginalize out nodes from a TPM.
Parameters:  node_indices (list[int]) – The indices of nodes to be marginalized out.
 tpm (np.ndarray) – The TPM to marginalize the node out of.
Returns: A TPM with the same number of dimensions, with the nodes marginalized out.
Return type: np.ndarray

pyphi.tpm.
infer_edge
(tpm, a, b, contexts)¶ Infer the presence or absence of an edge from node A to node B.
Let \(S\) be the set of all nodes in a network. Let \(A' = S  \{A\}\). We call the state of \(A'\) the context \(C\) of \(A\). There is an edge from \(A\) to \(B\) if there exists any context \(C(A)\) such that \(\Pr(B \mid C(A), A = 0) \neq \Pr(B \mid C(A), A = 1)\).
Parameters:  tpm (np.ndarray) – The TPM in statebynode, multidimensional form.
 a (int) – The index of the putative source node.
 b (int) – The index of the putative sink node.
Returns: True
if the edge \(A \rightarrow B\) exists,False
otherwise.Return type: bool

pyphi.tpm.
infer_cm
(tpm)¶ Infer the connectivity matrix associated with a statebynode TPM in multidimensional form.
utils
¶
Functions used by more than one PyPhi module or class, or that might be of external use.

pyphi.utils.
state_of
(nodes, network_state)¶ Return the statetuple of the given nodes.

pyphi.utils.
all_states
(n, big_endian=False)¶ Return all binary states for a system.
Parameters:  n (int) – The number of elements in the system.
 big_endian (bool) – Whether to return the states in bigendian order instead of littleendian order.
Yields: tuple[int] – The next state of an
n
element system, in littleendian order unlessbig_endian
isTrue
.

pyphi.utils.
np_immutable
(a)¶ Make a NumPy array immutable.

pyphi.utils.
np_hash
(a)¶ Return a hash of a NumPy array.

class
pyphi.utils.
np_hashable
(array)¶ A hashable wrapper around a NumPy array.

pyphi.utils.
eq
(x, y)¶ Compare two values up to
PRECISION
.

pyphi.utils.
combs
(a, r)¶ NumPy implementation of
itertools.combinations
.Return successive
r
length combinations of elements in the arraya
.Parameters:  a (np.ndarray) – The array from which to get combinations.
 r (int) – The length of the combinations.
Returns: An array of combinations.
Return type: np.ndarray

pyphi.utils.
comb_indices
(n, k)¶ n
dimensional version of itertools.combinations.Parameters:  a (np.ndarray) – The array from which to get combinations.
 k (int) – The desired length of the combinations.
Returns: Indices that give the
k
combinations ofn
elements.Return type: np.ndarray
Example
>>> n, k = 3, 2 >>> data = np.arange(6).reshape(2, 3) >>> data[:, comb_indices(n, k)] array([[[0, 1], [0, 2], [1, 2]], <BLANKLINE> [[3, 4], [3, 5], [4, 5]]])

pyphi.utils.
powerset
(iterable, nonempty=False, reverse=False)¶ Generate the power set of an iterable.
Parameters: iterable (Iterable) – The iterable from which to generate the power set.
Keyword Arguments:  nonempty (boolean) – If True, don’t include the empty set.
 reverse (boolean) – If True, reverse the order of the powerset.
Returns: An iterator over the power set.
Return type: Iterable
Example
>>> ps = powerset(np.arange(2)) >>> list(ps) [(), (0,), (1,), (0, 1)] >>> ps = powerset(np.arange(2), nonempty=True) >>> list(ps) [(0,), (1,), (0, 1)] >>> ps = powerset(np.arange(2), nonempty=True, reverse=True) >>> list(ps) [(1, 0), (1,), (0,)]

pyphi.utils.
load_data
(directory, num)¶ Load numpy data from the data directory.
The files should stored in
../data/<dir>
and named0.npy, 1.npy, ... <num  1>.npy
.Returns: A list of loaded data, such that list[i]
contains the the contents ofi.npy
.Return type: list

pyphi.utils.
time_annotated
(func, *args, **kwargs)¶ Annotate the decorated function or method with the total execution time.
The result is annotated with a time attribute.
validate
¶
Methods for validating arguments.

pyphi.validate.
direction
(direction, allow_bi=False)¶ Validate that the given direction is one of the allowed constants.
If
allow_bi
isTrue
thenDirection.BIDIRECTIONAL
is acceptable.

pyphi.validate.
tpm
(tpm, check_independence=True)¶ Validate a TPM.
The TPM can be in
 2dimensional statebystate form,
 2dimensional statebynode form, or
 multidimensional statebynode form.

pyphi.validate.
conditionally_independent
(tpm)¶ Validate that the TPM is conditionally independent.

pyphi.validate.
connectivity_matrix
(cm)¶ Validate the given connectivity matrix.

pyphi.validate.
node_labels
(node_labels, node_indices)¶ Validate that there is a label for each node.

pyphi.validate.
node_states
(state)¶ Check that the state contains only zeros and ones.

pyphi.validate.
state_length
(state, size)¶ Check that the state is the given size.

pyphi.validate.
state_reachable
(subsystem)¶ Return whether a state can be reached according to the network’s TPM.

pyphi.validate.
cut
(cut, node_indices)¶ Check that the cut is for only the given nodes.

pyphi.validate.
time_scale
(time_scale)¶ Validate a macro temporal time scale.

pyphi.validate.
partition
(partition)¶ Validate a partition  used by blackboxes and coarse grains.

pyphi.validate.
coarse_grain
(coarse_grain)¶ Validate a macro coarsegraining.

pyphi.validate.
blackbox
(blackbox)¶ Validate a macro blackboxing.

pyphi.validate.
blackbox_and_coarse_grain
(blackbox, coarse_grain)¶ Validate that a coarsegraining properly combines the outputs of a blackboxing.
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