a CCCfCC@sdZddlZddlmZmZddlmZmZgZ GdddeZ eD]"Z e j Z dd Zee e ee qDd d ZGd d d ZdS)zBase class for sparse matrice with a .data attribute subclasses must provide a _with_data() method that creates a new matrix with the same sparsity pattern as self but with a different data array N)_spbase _ufuncs_with_fixed_point_at_zero) isscalarlike validateaxisc@seZdZddZeddZejddZddZdd Zd&d d Z d dZ ddZ ddZ ddZ ddZd'ddZejje_d(ddZejje_ddZejje_dd Zejje_d)d"d#Zd$d%Zd!S)* _data_matrixcCst|dSN)r__init__selfr N/var/www/html/django/DPS/env/lib/python3.9/site-packages/scipy/sparse/_data.pyr sz_data_matrix.__init__cCs|jjSrdatadtyper r r r rsz_data_matrix.dtypecCs ||j_dSrr)r Znewtyper r r rscCst|dr||jS)Nsum_duplicates)hasattrrrr r r r _deduped_datas z_data_matrix._deduped_datacCs|t|Sr) _with_dataabsrr r r r __abs__$sz_data_matrix.__abs__rcCs|tj||dS)N)Zdecimals)rnpZaroundr)r ndigitsr r r __round__'sz_data_matrix.__round__cCs||jjSr)rrrealr r r r _real*sz_data_matrix._realcCs||jjSr)rrimagr r r r _imag-sz_data_matrix._imagcCs"|jjdkrtd||j S)Nbz0negating a boolean sparse array is not supported)rkindNotImplementedErrorrrr r r r __neg__0s z_data_matrix.__neg__cCs"t|r|j|9_|StSdSrrrNotImplementedr otherr r r __imul__6sz_data_matrix.__imul__cCs*t|r"d|}|j|9_|StSdS)Ng?r")r r%Zrecipr r r __itruediv__=s z_data_matrix.__itruediv__unsafeTcCsVt|}|j|krB|j|jj||dddd}|j|ddS|rN|S|SdS)NT)castingcopyr*F)rrrrastyperr*)r rr)r*matrixr r r r,Es  z_data_matrix.astypecCs8t|jtjr$|j|j|dS|r0|S|SdS)Nr+)rZ issubdtyperZcomplexfloatingrr conjugater*)r r*r r r r.Ts z_data_matrix.conjugatecCs|j|jddSNTr+)rrr*r r r r r*^sz_data_matrix.copycCst|Sr)r count_nonzerorr r r r r0csz_data_matrix.count_nonzeroNcCsDt|std|std|}|dur6||}|||S)a This function performs element-wise power. Parameters ---------- n : scalar n is a non-zero scalar (nonzero avoids dense ones creation) If zero power is desired, special case it to use `np.ones` dtype : If dtype is not specified, the current dtype will be preserved. Raises ------ NotImplementedError : if n is a zero scalar If zero power is desired, special case it to use `np.ones(A.shape, dtype=A.dtype)` zinput is not scalarzpzero power is not supported as it would densify the matrix. Use `np.ones(A.shape, dtype=A.dtype)` for this case.N)rr rr,r)r nrrr r r powerhs z_data_matrix.powercCs||j|Sr)rrr$r r r _mul_scalarsz_data_matrix._mul_scalar)r)r(T)T)N)__name__ __module__ __qualname__r propertyrsetterrrrrrr!r&r'r,r__doc__r.r*r0r2r3r r r r rs.         #rcs*fdd}dtdtd|_t|_|S)Ncs|}|j|ddSr/)rr)r resultopr r methods z_create_method..methodz Element-wise z. 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These are not implemented for dia_matrix, hence the separate class. c Cs |j|}|dkrtd|jd|}|j|d}|dkrD|n|}|||\}}t|j ||k} ||| d|| <|dk} t | |}t | |}|dkr|j |tj t ||d|ff|jd|fdS|j ||tj t ||dff|j|dfdSdS)Nr&zero-size array to reduction operationr)maxvalr)rshape)rJ ValueErrorZ_get_index_dtypetocsctocsrrZ _minor_reducerdiffindptrcompressZ_coo_containerzeroslenr) r axis min_or_maxNMZ idx_dtypematZ major_indexvaluenot_fullmaskr r r _min_or_max_axiss,      z_minmax_mixin._min_or_max_axiscCs|durtdt||jdkr6|dvr2tdd}|durd|jvrPtd|jd}|jdkrj|S|| }|jt |jkr|||}|S|dkr|d7}|dks|dkr| ||StddS)Nz0Sparse arrays do not support an 'out' parameter.rNrr@axis out of rangerrG) rKrndimrJrtypennzreducerZravelrprodr[)r rSoutrTzeromr r r _min_or_maxs,      z_minmax_mixin._min_or_maxcCs@|j|dkrtd|dkr&|d7}|jd}|dkrB|n|}|||j\}}tj |t d}t t |j \} | D]} |j | | d\} } |j| | } |j| | }|| }| |}|||s| | |kr|||| <qt||}||krt|||| <q||| <q|dkr6|dd}||S)Nrz8Cannot apply the operation along a zero-sized dimension.r^rIrr@)rJrKrr`rLrMrZ_swaprrQintZnonzerorNrOrindicesrEminZreshapeZ _ascontainer)r rSargmin_or_argmaxcomparererWZret_sizeZ line_sizeretZnz_linesipqrri extreme_index extreme_valueZzero_indr r r _arg_min_or_max_axiss2      z"_minmax_mixin._arg_min_or_max_axisc Cs:|durtdt||jdkr6|dvr2tdd}|durL||||Sd|jvr^td|jdkrldS|jd}|}| ||j }|j |}|jd} |||rt |j || t |j |St|j} | |jkrt |j || t |j |S|j | |j } t| | } ||kr6t| |S| S)Nz/Sparse types do not support an 'out' parameter.rr\r]rz.Cannot apply the operation to an empty matrix.r@)rKrr_rsrJrarr`ZtocoorrrhrowcolrrcrErj) r rSrdrkrlrerWrqrrZnum_colsizeZlinear_indicesZfirst_implicit_zero_indexr r r _arg_min_or_maxs:               z_minmax_mixin._arg_min_or_maxNcCs|||tjS)a7 Return the maximum of the array/matrix or maximum along an axis. This takes all elements into account, not just the non-zero ones. Parameters ---------- axis : {-2, -1, 0, 1, None} optional Axis along which the sum is computed. The default is to compute the maximum over all elements, returning a scalar (i.e., `axis` = `None`). out : None, optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used. Returns ------- amax : coo_matrix or scalar Maximum of `a`. If `axis` is None, the result is a scalar value. If `axis` is given, the result is a sparse.coo_matrix of dimension ``a.ndim - 1``. See Also -------- min : The minimum value of a sparse array/matrix along a given axis. numpy.matrix.max : NumPy's implementation of 'max' for matrices )rgrmaximumr rSrdr r r maxBsz_minmax_mixin.maxcCs|||tjS)a7 Return the minimum of the array/matrix or maximum along an axis. This takes all elements into account, not just the non-zero ones. Parameters ---------- axis : {-2, -1, 0, 1, None} optional Axis along which the sum is computed. The default is to compute the minimum over all elements, returning a scalar (i.e., `axis` = `None`). out : None, optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used. Returns ------- amin : coo_matrix or scalar Minimum of `a`. If `axis` is None, the result is a scalar value. If `axis` is given, the result is a sparse.coo_matrix of dimension ``a.ndim - 1``. See Also -------- max : The maximum value of a sparse array/matrix along a given axis. numpy.matrix.min : NumPy's implementation of 'min' for matrices )rgrminimumryr r r rjbsz_minmax_mixin.mincCs|||tjS)a Return the maximum of the array/matrix or maximum along an axis, ignoring any NaNs. This takes all elements into account, not just the non-zero ones. .. versionadded:: 1.11.0 Parameters ---------- axis : {-2, -1, 0, 1, None} optional Axis along which the maximum is computed. The default is to compute the maximum over all elements, returning a scalar (i.e., `axis` = `None`). out : None, optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used. Returns ------- amax : coo_matrix or scalar Maximum of `a`. If `axis` is None, the result is a scalar value. If `axis` is given, the result is a sparse.coo_matrix of dimension ``a.ndim - 1``. See Also -------- nanmin : The minimum value of a sparse array/matrix along a given axis, ignoring NaNs. max : The maximum value of a sparse array/matrix along a given axis, propagating NaNs. numpy.nanmax : NumPy's implementation of 'nanmax'. )rgrZfmaxryr r r nanmaxs$z_minmax_mixin.nanmaxcCs|||tjS)a Return the minimum of the array/matrix or minimum along an axis, ignoring any NaNs. This takes all elements into account, not just the non-zero ones. .. versionadded:: 1.11.0 Parameters ---------- axis : {-2, -1, 0, 1, None} optional Axis along which the minimum is computed. The default is to compute the minimum over all elements, returning a scalar (i.e., `axis` = `None`). out : None, optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used. Returns ------- amin : coo_matrix or scalar Minimum of `a`. If `axis` is None, the result is a scalar value. If `axis` is given, the result is a sparse.coo_matrix of dimension ``a.ndim - 1``. See Also -------- nanmax : The maximum value of a sparse array/matrix along a given axis, ignoring NaNs. min : The minimum value of a sparse array/matrix along a given axis, propagating NaNs. numpy.nanmin : NumPy's implementation of 'nanmin'. )rgrZfminryr r r nanmins$z_minmax_mixin.nanmincCs|||tjtjS)a.Return indices of maximum elements along an axis. Implicit zero elements are also taken into account. If there are several maximum values, the index of the first occurrence is returned. Parameters ---------- axis : {-2, -1, 0, 1, None}, optional Axis along which the argmax is computed. If None (default), index of the maximum element in the flatten data is returned. out : None, optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used. Returns ------- ind : numpy.matrix or int Indices of maximum elements. If matrix, its size along `axis` is 1. )rwrargmaxZgreaterryr r r r~sz_minmax_mixin.argmaxcCs|||tjtjS)a/Return indices of minimum elements along an axis. Implicit zero elements are also taken into account. If there are several minimum values, the index of the first occurrence is returned. Parameters ---------- axis : {-2, -1, 0, 1, None}, optional Axis along which the argmin is computed. If None (default), index of the minimum element in the flatten data is returned. out : None, optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used. Returns ------- ind : numpy.matrix or int Indices of minimum elements. If matrix, its size along `axis` is 1. )rwrargminlessryr r r rsz_minmax_mixin.argmin)NN)NN)NN)NN)NN)NN)r4r5r6r9r[rgrsrwrzrjr|r}r~rr r r r rFs$0 & & rF)r9numpyr_baserrZ_sputilsrr__all__rZnpfuncr4r>r?setattrrErFr r r r s}