9jX\SSKJr SSKrSSKrSSKrSSKrSSKJr SSKJr SSK J r SSK J r SSK J r \ r\R"SS S S 5r\R"S S SS5rSrSrSrSSjrS SjrS!Sjr\R"SSSS5r\R"SSSS5rS"SjrS#Sjrg)$) annotationsN)_core) _accelerator)cub)common)runtimezS x, raw T bins, int32 n_binszraw U yaD if (x < bins[0] or bins[n_bins - 1] < x) { return; } int high = n_bins - 1; int low = 0; while (high - low > 1) { int mid = (high + low) / 2; if (bins[mid] <= x) { low = mid; } else { high = mid; } } atomicAdd(&y[low], U(1)); cupy_histogram_kernelz,S x, raw T bins, int32 n_bins, raw W weightszraw Y yaM if (x < bins[0] or bins[n_bins - 1] < x) { return; } int high = n_bins - 1; int low = 0; while (high - low > 1) { int mid = (high + low) / 2; if (bins[mid] <= x) { low = mid; } else { high = mid; } } atomicAdd(&y[low], (Y)weights[i]); cupy_weighted_histogram_kernelcUR[R:Xaa[R"SR UR[R 5[SS9 UR[R 5nUb_[U[R5(d [S5eURUR:wa [S5eUR5nUR5nX4$)z9Check a and weights have matching shapes, and ravel both z1Converting input from {} to {} for compatibility.) stacklevelzweights must be a cupy.ndarrayz(weights should have the same shape as a.)dtypecupybool_warningswarnformatuint8RuntimeWarningastype isinstancendarray ValueErrorshaperavel)aweightss T/home/wildlama/miniconda3/lib/python3.13/site-packages/cupy/_statistics/histogram.py_ravel_and_check_weightsrCs ww$** Ivaggtzz2$ 4 HHTZZ '4<<00=> > ==AGG #:< <--/  A :cUbeUup#X#:a [S5e[R"U5(a[R"U5(d[SRX#55eOURS:XaSnSnOpUR 5nUR 5n[R"U5(a[R"U5(d[SRX#55eX#:Xa US- nUS-nX#4$)zR Determine the outer bin edges to use, from either the data or the range argument z/max must be larger than min in range parameter.z(supplied range of [{}, {}] is not finiterz,autodetected range of [{}, {}] is not finiteg?)rnumpyisfinitersizeminmaxr)rrange first_edge last_edges r_get_outer_edgesr+Xs   %  !AC Cz**u~~i/H/H:AA+, ,0I 1  UUW EEG  j))dmmI.F.F>EE+, , #% O   r cSnSn[U[5(a [S5e[U[R5(d[ R "U5S:Xa[U[R5(aUnO[ R"U5nUSSUSS:R5(a [S5e[U[ R5(a[R"U5nO[[ R "U5S:Xa6[R"U5nUS:a [S5e[X5upVO [S 5eUbw[R"WWU5n[R"U[R 5(a[R"U["5n[R$"XVUS-S US 9nU$![a [S5ef=f) a Computes the bins used internally by `histogram`. Args: a (ndarray): Ravelled data array bins (int or ndarray): Forwarded argument from `histogram`. range (None or tuple): Forwarded argument from `histogram`. Returns: bin_edges (ndarray): Array of bin edges Nz+only integer and array bins are implementedr"z1`bins` must increase monotonically, when an arrayrz0`bins` must be an integer, a string, or an arrayz(`bins` must be positive, when an integerz `bins` must be 1d, when an arrayT)endpointr)rstrNotImplementedErrorrrr#ndimasarrayanyroperatorindex TypeErrorr+ result_type issubdtypeintegerfloatlinspace)rbinsr( n_equal_bins bin_edgesr)r*bin_types r_get_bin_edgesr@ysLI$! 9; ; D$,, ' '5::d+;q+@ dDLL ) )I d+I crNYqr] * / / 1 1CE E i / / Y/I D Q  D#>>$/L ! GH H 0 : I;<<##J 1= ??8T\\ 2 2''%8HMM [U[R 5(de[R "UR[R"5(a[$nOy[R&"URUR5nU[R(:Xa*[*R,"5(d[R.nUR1USS9nUR1US S9n URRS ;aU S ==S- ss'O;URRS :Xa!U S n [R2"XS-5U S '[4R6(aUR1[R8SS9n[:R<"XU 5n U cGMU n[4R6(aUR1[RSS9n GO [?XURU5 GO[R@"UR[RB5=(d/ [R@"UR[RD5n U (d [S 5eURRS:Xa[R"URS- [RDS9n[GXURURHURH5 [GXURURJURJ5 OURRS;a'[R"URS- [LS9nO0[R"URS- [RBS9n[GXURXF5 U(aP[RN"[RP"U5[RB5n Xm- URS5- U4$Xe4$)aComputes the histogram of a set of data. Args: x (cupy.ndarray): Input array. bins (int or cupy.ndarray): If ``bins`` is an int, it represents the number of bins. If ``bins`` is an :class:`~cupy.ndarray`, it represents a bin edges. range (2-tuple of float, optional): The lower and upper range of the bins. If not provided, range is simply ``(x.min(), x.max())``. Values outside the range are ignored. The first element of the range must be less than or equal to the second. `range` affects the automatic bin computation as well. While bin width is computed to be optimal based on the actual data within `range`, the bin count will fill the entire range including portions containing no data. density (bool, optional): If False, the default, returns the number of samples in each bin. If True, returns the probability *density* function at the bin, ``bin_count / sample_count / bin_volume``. weights (cupy.ndarray, optional): An array of weights, of the same shape as `x`. Each value in `x` only contributes its associated weight towards the bin count (instead of 1). Returns: tuple: ``(hist, bin_edges)`` where ``hist`` is a :class:`cupy.ndarray` storing the values of the histogram, and ``bin_edges`` is a :class:`cupy.ndarray` storing the bin edges. .. warning:: This function may synchronize the device. .. seealso:: :func:`numpy.histogram` czcomplex number is not supportedzx must be a cupy.ndarrayr"rF)copyTuir-fzJonly weights with dtype that can be cast to float or complex are supportedbui)*rkindr0rrrrrr@zerosr%int64rget_routine_acceleratorsACCELERATOR_CUBr#r8r9r:r7float16r_is_fp16_supportedfloat32r nextafterris_hipuint64r cub_histogram_histogram_kernelcan_castfloat64 complex128_weighted_histogram_kernelrealimagintarraydiffsum)xr<r(densityrr>y acceleratorr? acc_bin_edgelastoutsimple_weightsdbs r histogramrisoB ww||s!"CDD a & &344)!5JAq.I JJy~~) <'@@BK|;;;*,:1M")T\\::::##AGGU]];;$H$00!''JH EMM1 & 9 9 ; ;#(==6A(//t/D 77<<4' $)$WW\\S('+D'+~~d1H'EL$>>59A''l;;A>>%8AECH aINNA > MM'-- 6 : MM'-- 9 % ! ! ==   $ 9>>A-T__EA &inngllAFF D &inngllAFF D}}!!U*JJy~~1=JJy~~1F &inng : ZZ ),dll ;v** <r c^^[T[R5(a6TRS:XaTSS2[R4mTR upVO#[R "TSS9mTR upV[R"U[5nUS/-mUS/-nUb[R"U5n[U5n X:wa [S5eUcSU-nO[U5U:wa [S5e[U5GHZn [R"X5S:XadXS:a[S RU 55e[!TSS2U 4X*5up[XS-5n [R""XU 5TU 'O[R"X5S:Xar[X[R5(d [S 5eXTU 'TU SSTU SS:R%5(a[S RU 55eO[S RU 55e[TU 5S-Xz'[R&"TU 5X'GM] [)UU4S j[U555n[U5H$n TSS2U 4TU S:HnXU==S-ss'M& [R*"X5n[R,"UU[R."U5S9nUR1U5nUR3[45nU[7SS54-nUUnU(aMUR95n[U5H)n S/U-nXzS- UU 'UXR1U5- nM+ UU-n[%UR [R"U5S- :g5(a [;S5eUT4$![a Xa/-nGNf=f)a;Compute the multidimensional histogram of some data. Args: sample (cupy.ndarray): The data to be histogrammed. (N, D) or (D, N) array Note the unusual interpretation of sample when an array_like: * When an array, each row is a coordinate in a D-dimensional space - such as ``histogramdd(cupy.array([p1, p2, p3]))``. * When an array_like, each element is the list of values for single coordinate - such as ``histogramdd((X, Y, Z))``. The first form should be preferred. bins (int or tuple of int or cupy.ndarray): The bin specification: * A sequence of arrays describing the monotonically increasing bin edges along each dimension. * The number of bins for each dimension (nx, ny, ... =bins) * The number of bins for all dimensions (nx=ny=...=bins). range (sequence, optional): A sequence of length D, each an optional (lower, upper) tuple giving the outer bin edges to be used if the edges are not given explicitly in `bins`. An entry of None in the sequence results in the minimum and maximum values being used for the corresponding dimension. The default, None, is equivalent to passing a tuple of D None values. weights (cupy.ndarray): An array of values `w_i` weighing each sample `(x_i, y_i, z_i, ...)`. The values of the returned histogram are equal to the sum of the weights belonging to the samples falling into each bin. density (bool, optional): If False, the default, returns the number of samples in each bin. If True, returns the probability *density* function at the bin, ``bin_count / sample_count / bin_volume``. Returns: tuple: H (cupy.ndarray): The multidimensional histogram of sample x. See normed and weights for the different possible semantics. edges (list of cupy.ndarray): A list of D arrays describing the bin edges for each dimension. .. warning:: This function may synchronize the device. .. seealso:: :func:`numpy.histogramdd` r"Nr-)axiszFThe dimension of bins must be equal to the dimension of the sample x.)Nz0range argument must have one entry per dimensionrz,`bins[{}]` must be positive, when an integerzarray-like bins not supportedz:`bins[{}]` must be monotonically increasing, when an arrayz'`bins[{}]` must be a scalar or 1d arrayc3d># UH%n[R"TUTSS2U4SS9v M' g7f)Nright)side)r searchsorted).0iedgessamples r histogramdd..s5A %(F1a4Lw?s-0) minlengthzInternal Shape Error)rrrr1newaxisrstackr#emptyr\r2lenrr6_rangerr+r;r3r^tupleravel_multi_indexbincountprodreshaperr:slicer_ RuntimeError)rsr<r(rransamplesr1nbindedgesnbinsrqsminsmaxnumncounton_edgexyhistcoresrrrs` @r histogramddr$sd&$,,'' ;;! At||O,F$F, ;;tS !D D6ME TF]F,,w' D  =   }$ Ut KLLD\ 99TW  "w{ BII!L*&A,AJDdgk"C}}T5E!H YYtw 1 $dgt||44 !@AAwE!Ha" a ,1133 "F1I4 9@@C eAh-!#IIeAh' 16FD\A,%(2,. 'a     -B ==W 40@ AD << D ;;u D 5B > txx{ a D4# JJwejj 1'@@BKNNN#|'C'CC*,1C''d1f5;A H%C Q " H JJwemm 4$Q3 Hr c&URRS:Xa [S5eURS:a [ S5eURS:a [ S5eU(aSOSn[ R RRXUSS 5$) aFFinds the indices of the bins to which each value in input array belongs. .. note:: In order to avoid device synchronization, digitize does not raise an exception when the array is not monotonic Args: x (cupy.ndarray): Input array. bins (cupy.ndarray): Array of bins. It has to be 1-dimensional and monotonic increasing or decreasing. right (bool): Indicates whether the intervals include the right or the left bin edge. Returns: cupy.ndarray: Output array of indices, of same shape as ``x``. .. seealso:: :func:`numpy.digitize` rBzx may not be complexr"rrleftrmNF) rrIr6r1rr_sortingsearch _searchsorted)r`r<rmrns rdigitizerGsz, ww||s.// yy1}<== yy1}FGG6D ==   - -dtT5 IIr ) NFN)rNNF)rNNN)NN)F) __future__rr4rr#rr cupy._corer cupy.cudarrrr(r|ElementwiseKernelrUrYrr+r@rirrrrrrr rrs" # ++# ").#442 "%)&.*!B8vm`Zz8$v** ; %66 & ( < ~ Jr