ph.SrSSKrSSKJr SSKr\R "\5rSr Sr Sr Sr \ r SrS rS rS rS r\rS rSrSrSr\ r\ r\r\r\r\r\r\r\r \r!\r"\r#g)z Conversion functions. See the documentation on PyPhi :ref:`tpm-conventions` for information on the different representations that these functions convert between. N)log2c\[[U5SSRU5SSS2S5$)zReverse the bits of the ``n``-bit decimal number ``i``. Examples: >>> reverse_bits(12, 7) 24 >>> reverse_bits(0, 1) 0 >>> reverse_bits(1, 2) 2 N)intbinzfillins G/home/james-whalen/.local/lib/python3.13/site-packages/pyphi/convert.py reverse_bitsrs/ s1vabz"4R4(! ,,c8U(a[SU55$S$)z*Convert nodes to a tuple of their indices.c38# UHoRv M g7fN)index.0r s r nodes2indices..%(%Q%tuplenodess r nodes2indicesr#,15(%( (9r9rc8U(a[SU55$S$)z)Convert nodes to a tuple of their states.c38# UHoRv M g7frstaters r rnodes2state..*rrrrrs r nodes2stater&(r rc[X5$)zJConvert between big-endian and little-endian for indices in ``range(n)``. )rr s r be2ler(-s  rcF[SRSU55S5$)aConvert a PyPhi state-tuple to a decimal index according to the big-endian convention. Args: state (tuple[int]): A state-tuple where the |ith| element of the tuple gives the state of the |ith| node. Returns: int: A decimal integer corresponding to a network state under the big-endian convention. Examples: >>> state2be_index((1, 0, 0, 0, 0)) 16 >>> state2be_index((1, 1, 1, 0, 0, 0, 0, 0)) 224 c3J# UHn[[U55v M g7frstrrrs r r!state2be_index..Is2Eqs3q6{{E!#rrjoinr#s r state2be_indexr27s $ rww2E22A 66rc R[SRSUSSS255S5$)aConvert a PyPhi state-tuple to a decimal index according to the little-endian convention. Args: state (tuple[int]): A state-tuple where the |ith| element of the tuple gives the state of the |ith| node. Returns: int: A decimal integer corresponding to a network state under the little-endian convention. Examples: >>> state2le_index((1, 0, 0, 0, 0)) 1 >>> state2le_index((1, 1, 1, 0, 0, 0, 0, 0)) 7 r*c3J# UHn[[U55v M g7frr,rs r r!state2le_index..^s8Kqs3q6{{Kr/Nrrr0r#s r state2le_indexr6Ls($ rww8E$B$K88! <>> number_of_nodes = 5 >>> le_index2state(1, number_of_nodes) (1, 0, 0, 0, 0) >>> number_of_nodes = 8 >>> le_index2state(7, number_of_nodes) (1, 1, 1, 0, 0, 0, 0, 0) c34># UH nTU- S-v M g7f)Nr)rr r s r r!le_index2state..ws>'=!!q&A'=s)rranger number_of_nodess` r le_index2stater>as, >u_'=> >>rc$[X5SSS2$)aoConvert a decimal integer to a PyPhi state tuple using the big-endian convention that the most-significant bits correspond to low-index nodes. The output is the reverse of |le_index2state()|. Args: i (int): A decimal integer corresponding to a network state under the big-endian convention. Returns: tuple[int]: A state-tuple where the |ith| element of the tuple gives the state of the |ith| node. Examples: >>> number_of_nodes = 5 >>> be_index2state(1, number_of_nodes) (0, 0, 0, 0, 1) >>> number_of_nodes = 8 >>> be_index2state(7, number_of_nodes) (0, 0, 0, 0, 0, 1, 1, 1) Nr)r>r<s r be_index2stater@zs, ! -dd 33rc[R"UR5nURSn[[ U55n[ U5HnU[ XC5SS24XSS24'M U$)aConvert a state-by-state TPM from big-endian to little-endian or vice versa. Args: tpm (np.ndarray): A state-by-state TPM. Returns: np.ndarray: The state-by-state TPM in the other indexing format. Example: >>> tpm = np.arange(16).reshape([4, 4]) >>> be2le_state_by_state(tpm) array([[ 0., 1., 2., 3.], [ 8., 9., 10., 11.], [ 4., 5., 6., 7.], [12., 13., 14., 15.]]) rN)npemptyshaperrr;r()tpmleNr r s r be2le_state_by_staterHs[$ #)) B ! A DG A 1XuQ{A~&a4 Irc[R"U5nURSnURS/U-U/-SS9R [ 5$)zReshape a state-by-node TPM to the multidimensional form. See documentation for the |Network| object for more information on TPM formats. rrForderrBarrayrDreshapeastypefloatrErGs r to_multidimensionalrSsJ ((3-C " A ;;sQw!}C; 0 7 7 >>rc[R"U5nURSnURSU-U/SS9R [ 5$)zReshape a state-by-node TPM to the 2-dimensional form. See :ref:`tpm-conventions` and documentation for the |Network| object for more information on TPM representations. rrrJrKrMrRs r to_2dimensionalrUsD ((3-C " A ;;1ay; , 3 3E ::rc [R"U5nURSn[[ U55n[R "S/U-U/-5n[ U5Vs0sHoD[XB5_M nn[R"[ U5VVs/sH$n[ U5Vs/sH oEUUPM snPM& snn5n[ U5Vs/sH o`Xv-PM nnUR5H<upI[ U5Vs/sHn[R"XU5PM snX9'M> U$s snfs snfs snnfs snfs snf)aPConvert a state-by-state TPM to a state-by-node TPM. .. danger:: Many nondeterministic state-by-state TPMs can be represented by a single a state-by-state TPM. However, the mapping can be made to be one-to-one if we assume the state-by-state TPM is conditionally independent, as this function does. **If the given TPM is not conditionally independent, the conditional dependencies will be silently lost.** .. note:: The indices of the rows and columns of the state-by-state TPM are assumed to follow the little-endian convention. The indices of the rows of the resulting state-by-node TPM also follow the little-endian convention. See the documentation on PyPhi the :ref:`tpm-conventions` more information. Args: tpm (list[list] or np.ndarray): A square state-by-state TPM with row and column indices following the little-endian convention. Returns: np.ndarray: A state-by-node TPM, with row indices following the little-endian convention. Example: >>> tpm = np.array([[0.5, 0.5, 0.0, 0.0], ... [0.0, 1.0, 0.0, 0.0], ... [0.0, 0.2, 0.0, 0.8], ... [0.0, 0.3, 0.7, 0.0]]) >>> state_by_state2state_by_node(tpm) array([[[0.5, 0. ], [1. , 0.8]], [[1. , 0. ], [0.3, 0.7]]]) rr) rBrNrDrrzerosr;r>itemssum) rESrGsbn_tpmr statesr node_onon_probabilitiesr$s r state_by_state2state_by_noder_sN ((3-C " A DG Ahha1# 'G/4Qx 8x!%%xF 8hh%(K(QuQx8x!1x8(KLG27(;(Qgj((;LLNCH(K(Q"&&!1!4Q!78(K # N99K; Ls*%D>E -E>E E%EE c [R"U5n[U5nURSnSU-n[R"X"45n[R "[R "US:US:55(d2[U5H!n[XA5n[X5nSX4U4'M# U$[U5Hn[XA5nXn[U5Hpn[R"[Xa5Vs/sHoPM sn5n [R"XyS:H5[R"SXyS:H- 5-X4U4'Mr M U$s snf)aConvert a state-by-node TPM to a state-by-state TPM. .. important:: A nondeterministic state-by-node TPM can have more than one representation as a state-by-state TPM. However, the mapping can be made to be one-to-one if we assume the TPMs to be conditionally independent. Therefore, **this function returns the corresponding conditionally independent state-by-state TPM.** .. note:: The indices of the rows of the state-by-node TPM are assumed to follow the little-endian convention, while the indices of the columns follow the big-endian convention. The indices of the rows and columns of the resulting state-by-state TPM both follow the big-endian convention. See the documentation on PyPhi :ref:`tpm-conventions` for more info. Args: tpm (list[list] or np.ndarray): A state-by-node TPM with row indices following the little-endian convention and column indices following the big-endian convention. Returns: np.ndarray: A state-by-state TPM, with both row and column indices following the big-endian convention. >>> tpm = np.array([[1, 1, 0], ... [0, 0, 1], ... [0, 1, 1], ... [1, 0, 0], ... [0, 0, 1], ... [1, 0, 0], ... [1, 1, 1], ... [1, 0, 1]]) >>> state_by_node2state_by_state(tpm) array([[0., 0., 0., 1., 0., 0., 0., 0.], [0., 0., 0., 0., 1., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 1., 0.], [0., 1., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 1., 0., 0., 0.], [0., 1., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 1.], [0., 0., 0., 0., 0., 1., 0., 0.]]) rrr9r) rBrNrSrDrWany logical_andr;r>r6prod) rErGrZsbs_tpmprevious_state_indexprevious_statecurrent_state_index marginal_tpmr current_states r state_by_node2state_by_staterjsQZ ((3-C c "C " A 1AhhvG 66"..q#'2 3 3$)!H ,,@DN"01D"E ABG*== > %-& N%*!H ,,@DN.L',Qx# " ./B FG F1Q FG!I GGL!);<=GGA a-? @@AB.AAB(0 %- N Hs= E )$__doc__loggingmathrnumpyrB getLogger__name__logrrr&r(le2ber2r6r>r@rHle2be_state_by_staterSrUr_rjb2ll2bl2sb2ss2ls2bb2l_sbsl2b_sbsto_mdto_2dsbn2sbssbs2sbnrrr rs ! -: :  7*=*?2424,?" ;9~K`     & &r