# 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: Keyword Arguments: tpm (np.ndarray) – The transition probability matrix of the network. The TPM can be provided in any of three forms: state-by-state, state-by-node, or multidimensional state-by-node form. In the state-by-node forms, row indices must follow the little-endian convention (see Little-endian convention). In state-by-state form, column indices must also follow the little-endian convention. If the TPM is 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 previous state is encoded by $$i$$ according to the little-endian convention, or in multidimensional form, so that tpm[(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 2-dimensional form of a state-by-node TPM must be (s, n), and the shape of the multidimensional form of the TPM must be  * n + [n], where s is the number of states and n is the number of nodes in the network. 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) – Human-readable labels for each node in the network.

Example

In a 3-node network, the_network.tpm[(0, 0, 1)] gives the transition probabilities for each node at $$t$$ given that state at $$t-1$$ 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: direction (Direction) – CAUSE or EFFECT. mechanism (tuple[int]) – The mechanism which all purviews are checked for reducibility over. All purviews which are irreducible over mechanism. 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 JSON-serializable representation.

classmethod from_json(json_dict)

Return a Network object from a JSON dictionary representation.

pyphi.network.irreducible_purviews(cm, direction, mechanism, purviews)

Return all purviews which are irreducible for the mechanism.

Parameters: cm (np.ndarray) – An $$N \times N$$ connectivity matrix. direction (Direction) – CAUSE or EFFECT. purviews (list[tuple[int]]) – The purviews to check. mechanism (tuple[int]) – The mechanism in question. All purviews in purviews which are not reducible over mechanism. list[tuple[int]] ValueError – If direction is invalid.
pyphi.network.from_json(filename)

Convert a JSON network to a PyPhi network.

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