`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 non-purview 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 little-endian order.

Keyword Arguments:
Parameters:	repertoire (np.ndarray or None) – A repertoire.
	big_endian (boolean) – If `True`, flatten the repertoire in big-endian 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

distribution¶

`distribution`¶