network

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

pyphi.network.immutable(array)

Make a numpy array immutable.

class pyphi.network.Network(tpm, connectivity_matrix=None, node_labels=None, purview_cache=None)

A network of nodes.

Represents the network we’re analyzing and holds auxilary data about it.

Parameters:

tpm (np.ndarray) –

The transition probability matrix of the network.

The TPM can be provided in either state-by-node (either \(2\)-\(D\) or \(N\)-\(D\)) or state-by-state form. In either form, row indices must follow the LOLI convention (see discussion in the examples module.) In state-by-state form column indices must also follow LOLI convention.

If given in state-by-node form, the TPM can be either 2-dimensional, so that tpm[i] gives the probabilities of each node being on if the past state is encoded by \(i\) according to LOLI, or in \(N\)-\(D\) form, so that tpm[(0, 0, 1)] gives the probabilities of each node being on if the past state is \(\{N_0 = 0, N_1 = 0, N_2 = 1\}\).

The shape of the 2-dimensional form of a state-by-node TPM must be (S, N), and the shape of the \(N\)-\(D\) form of the TPM must be [2] * N + [N], where S is the number of states and \(N\) is the number of nodes in the network.

Keyword Arguments:
 
  • connectivity_matrix (np.ndarray) – A square binary adjacency matrix indicating the connections between nodes in the network. connectivity_matrix[i][j] == 1 means that node \(i\) is connected to node \(j\). If no connectivity matrix is given, every node is connected to every node (including itself).
  • node_labels (tuple[str]) – Human readable labels for each node in the network.

Example

In a 3-node network, a_network.tpm[(0, 0, 1)] gives the transition probabilities for each node at \(t_0\) given that state at \(t_{-1}\) was \(\{N_0 = 0, N_1 = 0, N_2 = 1\}\).

tpm

np.ndarray – The network’s transition probability matrix, in \(N\)-\(D\) form.

cm

np.ndarray – The network’s connectivity matrix.

A square binary adjacency matrix indicating the connections between nodes in the network.

connectivity_matrix

np.ndarray – Alias for Network.cm.

size

int – The number of nodes in the network.

num_states

int – The number of possible states of the network.

node_indices

tuple[int] – The indices of nodes in the network.

This is 0..network.size.

node_labels

tuple[str] – The labels of nodes in the network.

labels2indices(labels)

Convert a tuple of node labels to node indices.

indices2labels(indices)

Convert a tuple of node indices to node labels.

parse_node_indices(nodes)

Returns the nodes indices for nodes, where nodes is either already integer indices or node labels.

__eq__(other)

Return whether this network equals the other object.

Two networks are equal if they have the same TPM and CM.

to_json()
classmethod from_json(json)
pyphi.network.irreducible_purviews(cm, direction, mechanism, purviews)

Returns all purview which are irreducible for the mechanism.

Parameters:
  • cm (np.ndarray) – A \(N \times N\) connectivity matrix.
  • direction (Direction) – PAST or FUTURE.
  • purviews (list[tuple[int]]) – The purviews to check.
  • mechanism (tuple[int]) – The mechanism in question.
Returns:

list[tuple[int]]

All purviews in purviews which are not reducible

over mechanism.

Raises:

ValueError – If direction is invalid.

pyphi.network.from_json(filename)

Convert a JSON representation of a network to a PyPhi network.

Parameters:filename (str) – A path to a JSON file representing a network.
Returns:|Network| – The corresponding PyPhi network object.