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 \(N_0 = 0, N_1 = 0, N_2 = 1\).
property
tpm
¶ np.ndarray: The network’s transition probability matrix, in multidimensional form.

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

property
connectivity_matrix
¶ np.ndarray: Alias for
cm
.

property
causally_significant_nodes
¶

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

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

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

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

potential_purviews
(direction, mechanism)¶ All purviews which are not clearly reducible for mechanism.

__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.

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.