o iP@sddlZddlZddlZddlZddlmZeZeddddZeddd d Z d d Z d dZ ddZ d$ddZ d$ddZd%ddZeddddZeddddZd&d d!Zd'd"d#ZdS)(N)_corezS 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_kernelcCs||jtjkrtjd|jtjtdd|tj}|dur6t |tj s(t d|j |j kr2t d| }| }||fS)z: Check a and weights have matching shapes, and ravel both z1Converting input from {} to {} for compatibility.) stacklevelNzweights must be a cupy.ndarrayz(weights should have the same shape as a.)dtypecupybool_warningswarnformatuint8RuntimeWarningastype isinstancendarray ValueErrorshaperavel)aweightsrW/home/ubuntu/veenaModal/venv/lib/python3.10/site-packages/cupy/_statistics/histogram.py_ravel_and_check_weights=s     rcCs|dur#|\}}||krtdt|rt|s"td||n(|jdkr-d}d}nt|}t|}t|rCt|sKtd||||krW|d}|d}||fS) z^ Determine the outer bin edges to use, from either the data or the range argument Nz/max must be larger than min in range parameter.z(supplied range of [{}, {}] is not finitergg?z,autodetected range of [{}, {}] is not finiteg?) rnumpyisfiniter sizefloatminmaxr)rrange first_edge last_edgerrr_get_outer_edgesRs8   r#cCs4d}d}t|tr tdt|tjst|dkrFt|tjr#|}nt|}|dd|ddkr:t dt|tjrEt|}n.t|dkrpzt |}Wn t y_t dw|dkrht dt ||\}}nt d |durt|||}t|tjrt|t}tj|||dd |d }|S) a5 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 implementedz1`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)rstrNotImplementedErrorrrrndimasarrayanyroperatorindex TypeErrorr# result_type issubdtypeintegerrlinspace)rbinsr n_equal_bins bin_edgesr!r"bin_typerrr_get_bin_edgesssL         r7 Fc Csl|jjdkr tdt|tjstdt||\}}t|||}|dur9tj |j dtj d}t |||j |nct |jtjpHt |jtj}|sOtd|jjdkrwtj |j dtjd}t|||j |j|jt|||j |j|jn%|jjdvrtj |j dtd}n tj |j dtjd}t|||j |||rtt|tj}||||fS||fS) a3Computes 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.ndarrayNr$rzJonly weights with dtype that can be cast to float or complex are supportedbui)rkindr(rrrrrr7zerosrint64_histogram_kernelcan_castfloat64 complex128_weighted_histogram_kernelrealimagintarraydiffsum) xr3r rdensityr5ysimple_weightsdbrrr histogramsD !     rOcsttjrjdkrddtjfj\}}n tjddj\}}t|t }|dg|dg}|dur?t |}zt |} | |krLt dWnt y[||g}Ynw|dured|}n t ||krot dt|D]} t|| dkr|| dkrt d | tdd| f|| \} } t || d} t| | | | <nks  zhistogramdd..) minlengthzInternal Shape Error)rrrr)newaxisrstackremptyrFr*lenrr._ranger r#r2r+rHtupleravel_multi_indexbincountprodreshaperrslicerI RuntimeError)rXr3r rrKnsamplesr)nbindedgesnbinsrUsminsmaxnumncounton_edgexyhistcoresrrrVr histogramdds 2             $      ruc Cszt|}Wn tyd}Ynw|dkr-|dkr-t|tjr)|}}||g}ntdt||g||||\} } | | d| dfS)aCompute the bi-dimensional histogram of two data samples. Args: x (cupy.ndarray): The first array of samples to be histogrammed. y (cupy.ndarray): The second array of samples to be histogrammed. 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) * The number of bins for all dimensions (nx=ny=bins). range (sequence, optional): A sequence of length two, 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 two None values. weights (cupy.ndarray): An array of values `w_i` weighing each sample `(x_i, y_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. edges0 (tuple of cupy.ndarray): A list of D arrays describing the bin edges for the first dimension. edges1 (tuple of cupy.ndarray): A list of D arrays describing the bin edges for the second dimension. .. warning:: This function may synchronize the device. .. seealso:: :func:`numpy.histogram2d` r$r[z%array-like bins not supported in CuPyr)r_r.rrrrru) rJrLr3r rrKnxedgesyedgesrrrWrrr histogram2ds+    ryzS xz raw U binzatomicAdd(&bin[x], U(1))cupy_bincount_kernelzS x, T wzatomicAdd(&bin[x], w) cupy_bincount_with_weight_kernelcCs|jdkr td|jdkrtd|jjdkrtd|dkr&td|dur4|j|jkr4td |durDt|}|dkrDtd tt |d}|durVt ||}|durjtj |ft j d }t |||Stj |ft jd }t||||S) a,Count number of occurrences of each value in array of non-negative ints. Args: x (cupy.ndarray): Input array. weights (cupy.ndarray): Weights array which has the same shape as ``x``. minlength (int): A minimum number of bins for the output array. Returns: cupy.ndarray: The result of binning the input array. The length of output is equal to ``max(cupy.max(x) + 1, minlength)``. .. warning:: This function may synchronize the device. .. seealso:: :func:`numpy.bincount` r$!object too deep for desired array+object of too small depth for desired arrayfzx must be int arrayrz3The first argument of bincount must be non-negativeNz0The weights and list don't have the same length.zminlength must be non-negativer:)r)rrr<r.r+rrFrrr=rintp_bincount_kernelrA_bincount_with_weight_kernel)rJrrZrbrrrrcs0       rccCsZ|jjdkr td|jdkrtd|jdkrtd|r dnd}tjj|||dd S) a~Finds 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` r9zx may not be complexr$r|r}leftrQNF) rr<r.r)rr_sortingsearch _searchsorted)rJr3rQrRrrrdigitizes    r)r8NNF)r8NNN)NN)F)r,r rrrr r`ElementwiseKernelr?rCrr#r7rOruryrrrcrrrrrsJ ! ; O ; 4