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: 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 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 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[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
) – 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 \(N_0 = 0, N_1 = 0, N_2 = 1\).- property tpm¶
The network’s transition probability matrix, in multidimensional form.
- Type
np.ndarray
- property cm¶
The network’s connectivity matrix.
A square binary adjacency matrix indicating the connections between nodes in the network.
- Type
np.ndarray
- property connectivity_matrix¶
Alias for
cm
.- Type
np.ndarray
- property causally_significant_nodes¶
- property size¶
The number of nodes in the network.
- Type
int
- property num_states¶
The number of possible states of the network.
- Type
int
- property node_indices¶
The indices of nodes in the network.
This is equivalent to
tuple(range(network.size))
.- Type
tuple[int]
- property 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.
- __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.
- 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.