distribution
¶
Functions for manipulating probability distributions.

pyphi.distribution.
normalize
(a)¶ Normalize a distribution.
Parameters: a (np.ndarray) – The array to normalize. Returns: a
normalized so that the sum of its entries is 1.Return type: np.ndarray

pyphi.distribution.
uniform_distribution
(number_of_nodes)¶ Return the uniform distribution for a set of binary nodes, indexed by state (so there is one dimension per node, the size of which is the number of possible states for that node).
Parameters: nodes (np.ndarray) – A set of indices of binary nodes. Returns: The uniform distribution over the set of nodes. Return type: np.ndarray

pyphi.distribution.
marginal_zero
(repertoire, node_index)¶ Return the marginal probability that the node is OFF.

pyphi.distribution.
marginal
(repertoire, node_index)¶ Get the marginal distribution for a node.

pyphi.distribution.
independent
(repertoire)¶ Check whether the repertoire is independent.

pyphi.distribution.
purview
(repertoire)¶ The purview of the repertoire.
Parameters: repertoire (np.ndarray) – A repertoire Returns: The purview that the repertoire was computed over. Return type: tuple[int]

pyphi.distribution.
purview_size
(repertoire)¶ Return the size of the purview of the repertoire.
Parameters: repertoire (np.ndarray) – A repertoire Returns: The size of purview that the repertoire was computed over. Return type: int

pyphi.distribution.
repertoire_shape
(purview, N)¶ Return the shape a repertoire.
Parameters:  purview (tuple[int]) – The purview over which the repertoire is computed.
 N (int) – The number of elements in the system.
Returns: The shape of the repertoire. Purview nodes have two dimensions and nonpurview nodes are collapsed to a unitary dimension.
Return type: list[int]
Example
>>> purview = (0, 2) >>> N = 3 >>> repertoire_shape(purview, N) [2, 1, 2]

pyphi.distribution.
flatten
(repertoire, big_endian=False)¶ Flatten a repertoire, removing empty dimensions.
By default, the flattened repertoire is returned in littleendian order.
Parameters: repertoire (np.ndarray or None) – A repertoire. Keyword Arguments: big_endian (boolean) – If True
, flatten the repertoire in bigendian order.Returns: The flattened repertoire. Return type: np.ndarray

pyphi.distribution.
max_entropy_distribution
(node_indices, number_of_nodes)¶ Return the maximum entropy distribution over a set of nodes.
This is different from the network’s uniform distribution because nodes outside
node_indices
are fixed and treated as if they have only 1 state.Parameters:  node_indices (tuple[int]) – The set of node indices over which to take the distribution.
 number_of_nodes (int) – The total number of nodes in the network.
Returns: The maximum entropy distribution over the set of nodes.
Return type: np.ndarray