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, connectivity_matrix=None, node_labels=None, perturb_vector=None, purview_cache=None)

A network of nodes.

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

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 \(\{N_0 = 0, N_1 = 0, N_2 = 1\}\).

Parameters:

tpm (np.ndarray) – See the corresponding attribute.
Keyword Arguments:
 
  • connectivity_matrix (array or sequence) – 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.
tpm

np.ndarray – The network’s transition probability matrix. It 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), and in state-by-state form, so must column indices. If given in state-by-node form, it 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.

connectivity_matrix

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

size

int – The number of nodes in the network.

num_states

int – The number of possible states of the network.

size
num_states
node_indices
node_labels
tpm
connectivity_matrix
perturb_vector
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, connectivity matrix, and perturbation vector.

to_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 (str) – DIRECTIONS[PAST] or DIRECTIONS[FUTURE].
  • purviews (list(tuple(int) – The purviews to check.
  • mechanism (tuple(int) – The mechanism in question.
Returns:

list –

All purviews in purviews which are not reducible

over mechanism.
Return type:

tuple(int
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.
Return type:Network