phpSrSSKrSSKJr SrSrSrSrS rS r S r S r SS jr \"0SS9S5r g)z7 Functions for manipulating probability distributions. N)cachec:UR5nUS:XaU$X- $)zNormalize a distribution. Args: a (np.ndarray): The array to normalize. Returns: np.ndarray: ``a`` normalized so that the sum of its entries is 1. r)sum)asum_as L/home/james-whalen/.local/lib/python3.13/site-packages/pyphi/distribution.py normalizer s# EEGE z 9cdSU-n[R"U5U- RS/U-5$)a@ 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). Args: nodes (np.ndarray): A set of indices of binary nodes. Returns: np.ndarray: The uniform distribution over the set of nodes. )nponesreshape)number_of_nodesnumber_of_statess r uniform_distributionrs<O+ GG$ %  %gqcO&;<=r ct[S5/UR-nSX!'U[U5R5$)z5Return the marginal probability that the node is OFF.Nr)slicendimtupler repertoire node_indexindexs r marginal_zeror2s6 4[MJOO +EE eEl # ' ' ))r ct^[U4Sj[UR555nURUSS9$)z)Get the marginal distribution for a node.c36># UHoT:wdM Uv M g7f)N).0irs r marginal..<sG3J!!3s  T)keepdims)rrangerrrs ` r marginalr&:s/ GU:??3G GE >>%$> //r c[UR5Vs/sHn[X5PM nnUSnUSSHnX4-nM [R"X5$s snf)z,Check whether the repertoire is independent.rrN)r%rr&r array_equal)rr! marginalsjointms r independentr,As_27 2HI2HQ*(2HII aLE qr]  >>* ,,JsAcTUcg[S[UR555$)zThe purview of the repertoire. Args: repertoire (np.ndarray): A repertoire Returns: tuple[int]: The purview that the repertoire was computed over. Nc3:# UHupUS:XdM Uv M g7f)r Nr)r r!dims r r"purview..]sI!>> purview = (0, 2) >>> N = 3 >>> repertoire_shape(purview, N) [2, 1, 2] r r)r%)r4Nr!s r repertoire_shaper:ls)&/4Ah 7hgA1 $h 77 7s$cZUcgU(aSOSnUR5RUS9$)aSFlatten a repertoire, removing empty dimensions. By default, the flattened repertoire is returned in little-endian order. Args: repertoire (np.ndarray or None): A repertoire. Keyword Args: big_endian (boolean): If ``True``, flatten the repertoire in big-endian order. Returns: np.ndarray: The flattened repertoire. NCF)order)squeezeravel)r big_endianr>s r flattenrBs5C3E     % %E % 22r )rmaxmemc\[R"[X55nX"R- $)aReturn 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. Args: 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: np.ndarray: The maximum entropy distribution over the set of nodes. )rrr:size) node_indicesr distributions r max_entropy_distributionrHs' 77+LJKL ++ ++r )F)__doc__numpyrrr rrr&r,r4r7r:rBrHrr r rKs[   =(*0 - J $8,30R,,r