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Check the section C-API incompatibility at the Troubleshooting ImportError section at https://numpy.org/devdocs/user/troubleshooting-importerror.html#c-api-incompatibility for indications on how to solve this problem.FATAL: module compiled as unknown endianFATAL: module compiled as little endian, but detected different endianness at runtime../../../../../../usr/lib/python3/dist-packages/numpy/__init__.cython-30.pxdinit scipy.sparse.csgraph._matching_cython_3_1_6._common_types_metatype_cython_3_1_6.cython_function_or_method@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@x@@@@@@@@@@@@@p@h@@`@@@PX@P!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"!!!!!!!!!!!!#########%$##$##$$$##################%#%%%$##$####$##%""%"""""%@&""$""$$$""""""""""""""""%%@%"-%%%@&""$"""%$"%(*********+&**+&******************+&(******)*****************(*%&((zerosyxwarningswarnuint8tocsrtocsc__test__sum__spec__shape__set_name__scipy/sparse/csgraph/_matching.pyxscipy.sparse.csgraph._matchingscipy.sparse._sputilsscipy.sparsesafely_cast_index_arraysrowrange__qualname____pyx_vtable__popperm_typenumpy._core.umath failed to importnumpy._core.multiarray failed to importnumpynpno full matching exists__name__msg__module__min_weight_full_bipartite_matching (line 290)min_weight_full_bipartite_matching min_weight_full_bipartite_matching(biadjacency, maximize=False) Returns the minimum weight full matching of a bipartite graph. .. versionadded:: 1.6.0 Parameters ---------- biadjacency : sparse array or matrix Biadjacency matrix of the bipartite graph: A sparse array in CSR, CSC, or COO format whose rows represent one partition of the graph and whose columns represent the other partition. An edge between two vertices is indicated by the corresponding entry in the matrix, and the weight of the edge is given by the value of that entry. This should not be confused with the full adjacency matrix of the graph, as we only need the submatrix defining the bipartite structure. maximize : bool (default: False) Calculates a maximum weight matching if true. Returns ------- row_ind, col_ind : array An array of row indices and one of corresponding column indices giving the optimal matching. The total weight of the matching can be computed as ``graph[row_ind, col_ind].sum()``. The row indices will be sorted; in the case of a square matrix they will be equal to ``numpy.arange(graph.shape[0])``. Notes ----- Let :math:`G = ((U, V), E)` be a weighted bipartite graph with non-zero weights :math:`w : E \to \mathbb{R} \setminus \{0\}`. This function then produces a matching :math:`M \subseteq E` with cardinality .. math:: \lvert M \rvert = \min(\lvert U \rvert, \lvert V \rvert), which minimizes the sum of the weights of the edges included in the matching, :math:`\sum_{e \in M} w(e)`, or raises an error if no such matching exists. When :math:`\lvert U \rvert = \lvert V \rvert`, this is commonly referred to as a perfect matching; here, since we allow :math:`\lvert U \rvert` and :math:`\lvert V \rvert` to differ, we follow Karp [1]_ and refer to the matching as *full*. This function implements the LAPJVsp algorithm [2]_, short for "Linear assignment problem, Jonker--Volgenant, sparse". The problem it solves is equivalent to the rectangular linear assignment problem. [3]_ As such, this function can be used to solve the same problems as :func:`scipy.optimize.linear_sum_assignment`. That function may perform better when the input is dense, or for certain particular types of inputs, such as those for which the :math:`(i, j)`'th entry is the distance between two points in Euclidean space. If no full matching exists, this function raises a ``ValueError``. For determining the size of the largest matching in the graph, see :func:`maximum_bipartite_matching`. We require that weights are non-zero only to avoid issues with the handling of explicit zeros when converting between different sparse representations. Zero weights can be handled by adding a constant to all weights, so that the resulting matrix contains no zeros. If multiple valid solutions are possible, output may vary with SciPy and Python version. References ---------- .. [1] Richard Manning Karp: An algorithm to Solve the m x n Assignment Problem in Expected Time O(mn log n). Networks, 10(2):143-152, 1980. .. [2] Roy Jonker and Anton Volgenant: A Shortest Augmenting Path Algorithm for Dense and Sparse Linear Assignment Problems. Computing 38:325-340, 1987. .. [3] https://en.wikipedia.org/wiki/Assignment_problem Examples -------- >>> from scipy.sparse import csr_array >>> from scipy.sparse.csgraph import min_weight_full_bipartite_matching Let us first consider an example in which all weights are equal: >>> biadjacency = csr_array([[1, 1, 1], [1, 0, 0], [0, 1, 0]]) Here, all we get is a perfect matching of the graph: >>> print(min_weight_full_bipartite_matching(biadjacency)[1]) [2 0 1] That is, the first, second, and third rows are matched with the third, first, and second column respectively. Note that in this example, the 0 in the input matrix does *not* correspond to an edge with weight 0, but rather a pair of vertices not paired by an edge. Note also that in this case, the output matches the result of applying :func:`maximum_bipartite_matching`: >>> from scipy.sparse.csgraph import maximum_bipartite_matching >>> biadjacency = csr_array([[1, 1, 1], [1, 0, 0], [0, 1, 0]]) >>> print(maximum_bipartite_matching(biadjacency, perm_type='column')) [2 0 1] When multiple edges are available, the ones with lowest weights are preferred: >>> biadjacency = csr_array([[3, 3, 6], [4, 3, 5], [10, 1, 8]]) >>> row_ind, col_ind = min_weight_full_bipartite_matching(biadjacency) >>> print(col_ind) [0 2 1] The total weight in this case is :math:`3 + 5 + 1 = 9`: >>> print(biadjacency[row_ind, col_ind].sum()) 9 When the matrix is not square, i.e. when the two partitions have different cardinalities, the matching is as large as the smaller of the two partitions: >>> biadjacency = csr_array([[0, 1, 1], [0, 2, 3]]) >>> row_ind, col_ind = min_weight_full_bipartite_matching(biadjacency) >>> print(row_ind, col_ind) [0 1] [2 1] >>> biadjacency = csr_array([[0, 1], [3, 1], [1, 4]]) >>> row_ind, col_ind = min_weight_full_bipartite_matching(biadjacency) >>> print(row_ind, col_ind) [0 2] [1 0] When one or both of the partitions are empty, the matching is empty as well: >>> biadjacency = csr_array((2, 0)) >>> row_ind, col_ind = min_weight_full_bipartite_matching(biadjacency) >>> print(row_ind, col_ind) [] [] In general, we will always reach the same sum of weights as if we had used :func:`scipy.optimize.linear_sum_assignment` but note that for that one, missing edges are represented by a array entry of ``float('inf')``. Let us generate a random sparse array with integer entries between 1 and 10: >>> import numpy as np >>> from scipy.sparse import random_array >>> from scipy.optimize import linear_sum_assignment >>> sparse = random_array((10, 10), rng=42, density=.5, format='coo') * 10 >>> sparse.data = np.ceil(sparse.data) >>> dense = sparse.toarray() >>> dense = np.full(sparse.shape, np.inf) >>> dense[sparse.row, sparse.col] = sparse.data >>> sparse = sparse.tocsr() >>> row_ind, col_ind = linear_sum_assignment(dense) >>> print(dense[row_ind, col_ind].sum()) 25.0 >>> row_ind, col_ind = min_weight_full_bipartite_matching(sparse) >>> print(sparse[row_ind, col_ind].sum()) 25.0 minmaximum_bipartite_matching (line 18) maximum_bipartite_matching(graph, perm_type='row') Returns a matching of a bipartite graph whose cardinality is at least that of any given matching of the graph. Parameters ---------- graph : sparse array or matrix Input sparse in CSR format whose rows represent one partition of the graph and whose columns represent the other partition. An edge between two vertices is indicated by the corresponding entry in the matrix existing in its sparse representation. perm_type : str, {'row', 'column'} Which partition to return the matching in terms of: If ``'row'``, the function produces an array whose length is the number of columns in the input, and whose :math:`j`'th element is the row matched to the :math:`j`'th column. Conversely, if ``perm_type`` is ``'column'``, this returns the columns matched to each row. Returns ------- perm : ndarray A matching of the vertices in one of the two partitions. Unmatched vertices are represented by a ``-1`` in the result. Notes ----- This function implements the Hopcroft--Karp algorithm [1]_. Its time complexity is :math:`O(\lvert E \rvert \sqrt{\lvert V \rvert})`, and its space complexity is linear in the number of rows. In practice, this asymmetry between rows and columns means that it can be more efficient to transpose the input if it contains more columns than rows. By Konig's theorem, the cardinality of the matching is also the number of vertices appearing in a minimum vertex cover of the graph. Note that if the sparse representation contains explicit zeros, these are still counted as edges. The implementation was changed in SciPy 1.4.0 to allow matching of general bipartite graphs, where previous versions would assume that a perfect matching existed. As such, code written against 1.4.0 will not necessarily work on older versions. If multiple valid solutions are possible, output may vary with SciPy and Python version. References ---------- .. [1] John E. Hopcroft and Richard M. Karp. "An n^{5 / 2} Algorithm for Maximum Matchings in Bipartite Graphs" In: SIAM Journal of Computing 2.4 (1973), pp. 225--231. :doi:`10.1137/0202019` Examples -------- >>> from scipy.sparse import csr_array >>> from scipy.sparse.csgraph import maximum_bipartite_matching As a simple example, consider a bipartite graph in which the partitions contain 2 and 3 elements respectively. Suppose that one partition contains vertices labelled 0 and 1, and that the other partition contains vertices labelled A, B, and C. Suppose that there are edges connecting 0 and C, 1 and A, and 1 and B. This graph would then be represented by the following sparse array: >>> graph = csr_array([[0, 0, 1], [1, 1, 0]]) Here, the 1s could be anything, as long as they end up being stored as elements in the sparse array. We can now calculate maximum matchings as follows: >>> print(maximum_bipartite_matching(graph, perm_type='column')) [2 0] >>> print(maximum_bipartite_matching(graph, perm_type='row')) [ 1 -1 0] The first output tells us that 1 and 2 are matched with C and A respectively, and the second output tells us that A, B, and C are matched with 1, nothing, and 0 respectively. Note that explicit zeros are still converted to edges. This means that a different way to represent the above graph is by using the CSR structure directly as follows: >>> data = [0, 0, 0] >>> indices = [2, 0, 1] >>> indptr = [0, 1, 3] >>> graph = csr_array((data, indices, indptr)) >>> print(maximum_bipartite_matching(graph, perm_type='column')) [2 0] >>> print(maximum_bipartite_matching(graph, perm_type='row')) [ 1 -1 0] When one or both of the partitions are empty, the matching is empty as well: >>> graph = csr_array((2, 0)) >>> print(maximum_bipartite_matching(graph, perm_type='column')) [-1 -1] >>> print(maximum_bipartite_matching(graph, perm_type='row')) [] When the input array is square, and the graph is known to admit a perfect matching, i.e. a matching with the property that every vertex in the graph belongs to some edge in the matching, then one can view the output as the permutation of rows (or columns) turning the input array into one with the property that all diagonal elements are non-empty: >>> a = [[0, 1, 2, 0], [1, 0, 0, 1], [2, 0, 0, 3], [0, 1, 3, 0]] >>> graph = csr_array(a) >>> perm = maximum_bipartite_matching(graph, perm_type='row') >>> print(graph[perm].toarray()) [[1 0 0 1] [0 1 2 0] [0 1 3 0] [2 0 0 3]] maximum_bipartite_matchingmaximizemaxmatching__main__jissubdtypeissparseisposinf_is_coroutineint32_initializingindptrindicesiinfoigraph must be sparsegraph must be in CSC, CSR, or COO format.graphgot __func__formatfloat64explicit zero weights are removed before matchingexpected a matrix containing numerical entries, emptyeliminate_zerosdtypedoubledatacsr_arraycsrcsgraphcsccopycooconvert_pydata_sparse_to_scipycolumncline_in_traceback__class_getitem__bool_biadjacency_tbiadjacencybasyncio.coroutinesastypeasarrayargsortarangealla_?ValueErrorTypeErrorTImportErrorITYPEDTYPEBTYPE. min_weight_full_bipartite_matching(biadjacency, maximize=False) Returns the minimum weight full matching of a bipartite graph. .. versionadded:: 1.6.0 Parameters ---------- biadjacency : sparse array or matrix Biadjacency matrix of the bipartite graph: A sparse array in CSR, CSC, or COO format whose rows represent one partition of the graph and whose columns represent the other partition. An edge between two vertices is indicated by the corresponding entry in the matrix, and the weight of the edge is given by the value of that entry. This should not be confused with the full adjacency matrix of the graph, as we only need the submatrix defining the bipartite structure. maximize : bool (default: False) Calculates a maximum weight matching if true. Returns ------- row_ind, col_ind : array An array of row indices and one of corresponding column indices giving the optimal matching. The total weight of the matching can be computed as ``graph[row_ind, col_ind].sum()``. The row indices will be sorted; in the case of a square matrix they will be equal to ``numpy.arange(graph.shape[0])``. Notes ----- Let :math:`G = ((U, V), E)` be a weighted bipartite graph with non-zero weights :math:`w : E \to \mathbb{R} \setminus \{0\}`. This function then produces a matching :math:`M \subseteq E` with cardinality .. math:: \lvert M \rvert = \min(\lvert U \rvert, \lvert V \rvert), which minimizes the sum of the weights of the edges included in the matching, :math:`\sum_{e \in M} w(e)`, or raises an error if no such matching exists. When :math:`\lvert U \rvert = \lvert V \rvert`, this is commonly referred to as a perfect matching; here, since we allow :math:`\lvert U \rvert` and :math:`\lvert V \rvert` to differ, we follow Karp [1]_ and refer to the matching as *full*. This function implements the LAPJVsp algorithm [2]_, short for "Linear assignment problem, Jonker--Volgenant, sparse". The problem it solves is equivalent to the rectangular linear assignment problem. [3]_ As such, this function can be used to solve the same problems as :func:`scipy.optimize.linear_sum_assignment`. That function may perform better when the input is dense, or for certain particular types of inputs, such as those for which the :math:`(i, j)`'th entry is the distance between two points in Euclidean space. If no full matching exists, this function raises a ``ValueError``. For determining the size of the largest matching in the graph, see :func:`maximum_bipartite_matching`. We require that weights are non-zero only to avoid issues with the handling of explicit zeros when converting between different sparse representations. Zero weights can be handled by adding a constant to all weights, so that the resulting matrix contains no zeros. If multiple valid solutions are possible, output may vary with SciPy and Python version. References ---------- .. [1] Richard Manning Karp: An algorithm to Solve the m x n Assignment Problem in Expected Time O(mn log n). Networks, 10(2):143-152, 1980. .. [2] Roy Jonker and Anton Volgenant: A Shortest Augmenting Path Algorithm for Dense and Sparse Linear Assignment Problems. Computing 38:325-340, 1987. .. [3] https://en.wikipedia.org/wiki/Assignment_problem Examples -------- >>> from scipy.sparse import csr_array >>> from scipy.sparse.csgraph import min_weight_full_bipartite_matching Let us first consider an example in which all weights are equal: >>> biadjacency = csr_array([[1, 1, 1], [1, 0, 0], [0, 1, 0]]) Here, all we get is a perfect matching of the graph: >>> print(min_weight_full_bipartite_matching(biadjacency)[1]) [2 0 1] That is, the first, second, and third rows are matched with the third, first, and second column respectively. Note that in this example, the 0 in the input matrix does *not* correspond to an edge with weight 0, but rather a pair of vertices not paired by an edge. Note also that in this case, the output matches the result of applying :func:`maximum_bipartite_matching`: >>> from scipy.sparse.csgraph import maximum_bipartite_matching >>> biadjacency = csr_array([[1, 1, 1], [1, 0, 0], [0, 1, 0]]) >>> print(maximum_bipartite_matching(biadjacency, perm_type='column')) [2 0 1] When multiple edges are available, the ones with lowest weights are preferred: >>> biadjacency = csr_array([[3, 3, 6], [4, 3, 5], [10, 1, 8]]) >>> row_ind, col_ind = min_weight_full_bipartite_matching(biadjacency) >>> print(col_ind) [0 2 1] The total weight in this case is :math:`3 + 5 + 1 = 9`: >>> print(biadjacency[row_ind, col_ind].sum()) 9 When the matrix is not square, i.e. when the two partitions have different cardinalities, the matching is as large as the smaller of the two partitions: >>> biadjacency = csr_array([[0, 1, 1], [0, 2, 3]]) >>> row_ind, col_ind = min_weight_full_bipartite_matching(biadjacency) >>> print(row_ind, col_ind) [0 1] [2 1] >>> biadjacency = csr_array([[0, 1], [3, 1], [1, 4]]) >>> row_ind, col_ind = min_weight_full_bipartite_matching(biadjacency) >>> print(row_ind, col_ind) [0 2] [1 0] When one or both of the partitions are empty, the matching is empty as well: >>> biadjacency = csr_array((2, 0)) >>> row_ind, col_ind = min_weight_full_bipartite_matching(biadjacency) >>> print(row_ind, col_ind) [] [] In general, we will always reach the same sum of weights as if we had used :func:`scipy.optimize.linear_sum_assignment` but note that for that one, missing edges are represented by a array entry of ``float('inf')``. Let us generate a random sparse array with integer entries between 1 and 10: >>> import numpy as np >>> from scipy.sparse import random_array >>> from scipy.optimize import linear_sum_assignment >>> sparse = random_array((10, 10), rng=42, density=.5, format='coo') * 10 >>> sparse.data = np.ceil(sparse.data) >>> dense = sparse.toarray() >>> dense = np.full(sparse.shape, np.inf) >>> dense[sparse.row, sparse.col] = sparse.data >>> sparse = sparse.tocsr() >>> row_ind, col_ind = linear_sum_assignment(dense) >>> print(dense[row_ind, col_ind].sum()) 25.0 >>> row_ind, col_ind = min_weight_full_bipartite_matching(sparse) >>> print(sparse[row_ind, col_ind].sum()) 25.0 maximum_bipartite_matching(graph, perm_type='row') Returns a matching of a bipartite graph whose cardinality is at least that of any given matching of the graph. Parameters ---------- graph : sparse array or matrix Input sparse in CSR format whose rows represent one partition of the graph and whose columns represent the other partition. An edge between two vertices is indicated by the corresponding entry in the matrix existing in its sparse representation. perm_type : str, {'row', 'column'} Which partition to return the matching in terms of: If ``'row'``, the function produces an array whose length is the number of columns in the input, and whose :math:`j`'th element is the row matched to the :math:`j`'th column. Conversely, if ``perm_type`` is ``'column'``, this returns the columns matched to each row. Returns ------- perm : ndarray A matching of the vertices in one of the two partitions. Unmatched vertices are represented by a ``-1`` in the result. Notes ----- This function implements the Hopcroft--Karp algorithm [1]_. Its time complexity is :math:`O(\lvert E \rvert \sqrt{\lvert V \rvert})`, and its space complexity is linear in the number of rows. In practice, this asymmetry between rows and columns means that it can be more efficient to transpose the input if it contains more columns than rows. By Konig's theorem, the cardinality of the matching is also the number of vertices appearing in a minimum vertex cover of the graph. Note that if the sparse representation contains explicit zeros, these are still counted as edges. The implementation was changed in SciPy 1.4.0 to allow matching of general bipartite graphs, where previous versions would assume that a perfect matching existed. As such, code written against 1.4.0 will not necessarily work on older versions. If multiple valid solutions are possible, output may vary with SciPy and Python version. References ---------- .. [1] John E. Hopcroft and Richard M. Karp. "An n^{5 / 2} Algorithm for Maximum Matchings in Bipartite Graphs" In: SIAM Journal of Computing 2.4 (1973), pp. 225--231. :doi:`10.1137/0202019` Examples -------- >>> from scipy.sparse import csr_array >>> from scipy.sparse.csgraph import maximum_bipartite_matching As a simple example, consider a bipartite graph in which the partitions contain 2 and 3 elements respectively. Suppose that one partition contains vertices labelled 0 and 1, and that the other partition contains vertices labelled A, B, and C. Suppose that there are edges connecting 0 and C, 1 and A, and 1 and B. This graph would then be represented by the following sparse array: >>> graph = csr_array([[0, 0, 1], [1, 1, 0]]) Here, the 1s could be anything, as long as they end up being stored as elements in the sparse array. We can now calculate maximum matchings as follows: >>> print(maximum_bipartite_matching(graph, perm_type='column')) [2 0] >>> print(maximum_bipartite_matching(graph, perm_type='row')) [ 1 -1 0] The first output tells us that 1 and 2 are matched with C and A respectively, and the second output tells us that A, B, and C are matched with 1, nothing, and 0 respectively. Note that explicit zeros are still converted to edges. This means that a different way to represent the above graph is by using the CSR structure directly as follows: >>> data = [0, 0, 0] >>> indices = [2, 0, 1] >>> indptr = [0, 1, 3] >>> graph = csr_array((data, indices, indptr)) >>> print(maximum_bipartite_matching(graph, perm_type='column')) [2 0] >>> print(maximum_bipartite_matching(graph, perm_type='row')) [ 1 -1 0] When one or both of the partitions are empty, the matching is empty as well: >>> graph = csr_array((2, 0)) >>> print(maximum_bipartite_matching(graph, perm_type='column')) [-1 -1] >>> print(maximum_bipartite_matching(graph, perm_type='row')) [] When the input array is square, and the graph is known to admit a perfect matching, i.e. a matching with the property that every vertex in the graph belongs to some edge in the matching, then one can view the output as the permutation of rows (or columns) turning the input array into one with the property that all diagonal elements are non-empty: >>> a = [[0, 1, 2, 0], [1, 0, 0, 1], [2, 0, 0, 3], [0, 1, 3, 0]] >>> graph = csr_array(a) >>> perm = maximum_bipartite_matching(graph, perm_type='row') >>> print(graph[perm].toarray()) [[1 0 0 1] [0 1 2 0] [0 1 3 0] [2 0 0 3]] __pyx_fatalerror4AN0t81Aiq{(('iquBk+Zy we6"AjLA%[+WARqqaqt2T+QQauARyJaqt;a'"DA{(('kqZr7+VSTTYYZ Y.a}G4qxwkirG9A &k{!r1 1 =1 Mq$nA]!%2!%(2XQa 2T)4s'A *AQ Bhaxq Q -Q -Q #1"HAQ*AQa ;hc +V1$nA[%0%(2XQa 2T)4s'A *AQ Bhaxq 1 +1 +1 #11^&ap *!1t81AiquHHG7!iq Eqt5 Y.awgTt>)83a 2XQe:SaH@;[@(@=D͔pD \ ٘ y = \ h /X  ,//$Pdx0P` `<\@@0@,@T|8X0@$` @$ L @ `4 P| `  P p$ `\ $ `H  T P P P@0pprsD`tlzRx $8@FJ w?9*3$"DP\H0,t@rGAC PZ A ,&AC FM F $`(ES G J +%,+9xAH I$ J (@oEV G  B lHT`l), EC BED G J tr,(̩wC R AI H DX]QC H $x\{AC AZ A Z ysC t FysC t F(tEC A I L D  8 $ 4}EC @ H l $H}EC @ H l $pEC @ H l D $\EM F H t  ԰AF I| D ]AG T $ EX k H e K $,EU s C e K TвaZ H E$t@AQ u Y Z F PNC I H  D  C ,GAC DGD C  4Y` H @Y` H (`AC BEEKt (UwEC DFd WiC j ZiC Al <^iC q $|EC BDb H $@EC BDb H 4h:AC DEF F x H 0AC Mi F h H pAJ Ca ,ЌAC BHEEF $(OAC BEF} $P$iC BH G xAC  wFC m (!AC DL " 9EC Ak ,84@DAL h K a O W I W I 8x<EI MO F w I  K ,َ}AC BEEEH_ ,&GAE BEEQ# ( =AH BIH  @ tEF EM C d %(x AC BJ  bD 4YC GBA b  D M BA H 4 %uC A Fp H H 48 NF E B  FP p 5, -AC K E R , EC BEGD A , 0EI BEEI) A  5( k,< (vEF ~ G  D w Q 4l xEC BGD E  I , #EF DEED[ X $ UEH A K $ XUEH A K $$ UEH A K <L EC O I h H X H [ E z A ( EF P E Y C  EC H xbQ , uEC P2 K  b(4 EC Px $` cEC a G W $ cEC f B [ M GNU`~FDO{"type":"deb","os":"ubuntu","name":"scipy","version":"1.16.3-4build1","architecture":"amd64"}PvvK0IE0Ip& ``` `` ` `߇ ݇`ۇ`ׇ`Ї`ȇ````` `x`r```@`/`` ````` `݆`ֆ`І```1 @2 (`!` ` `*  `````~`p`` `P `@ `9`0 `  `` ``r q% q`W7 @W#`W. 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