4[g~~UddlZddlmZddlZddlZddlZddlZddlmZddl Z ddl Z ddl m Z m Z mZmZddlZddlmZmZmZeeed<eeed<eeed<ej6j9ej:d k\r dd lmZm Z m!Z!m"Z"n dd lmZm Z m!Z!e#Z"eed <eed <ej6j9ej:dk\rH ejH5ejJdde&ejNZ(ejRZ*dddnejXZ(ejZZ*e e.ej^fZ0e e1ejdej^fZ3e e4ed<ej6j9ej:dk\rdZ5nQej6j9ej:dkrdZ5n& ejldgjoddZ5erje e e0ejpjrejpjtfZ;ede ejpjrejpjtfZ< ddl=m9Z9d@dZ?dAdZ@e1ddfdZAd ZBd!e.d"e1fd#ZCd$ZD dBd%ZEdCd&ZFed'gd(ZGd)ZHGd*d+ZIGd,d-ZJ dDd.ZKedEd/ZLd0ZMdFd1ZNd2ZOdGd3ZPdHdd4d5ZQdCd6ZRd7ZSdd4d8ZTd9ZUd:ZVGd;dZZdId?Z[y#1swYxYw#e+$rejXZ(ejZZ*YwxYw#e#$rdZ5YFwxYw#e>$r GddZ9YwxYw)JN)contextmanager) namedtuple)OptionalUnion TYPE_CHECKINGTypeVar)array_namespaceis_numpysize AxisErrorComplexWarningVisibleDeprecationWarningz1.25.0)r r rDTypePromotionError)r r rnp_longnp_ulongz 2.0.0.dev0ignorez/.*In the future `np\.long` will be defined as.*copy_if_neededz2.0.0z1.28.0Fcopy GeneratorType)bound) Generatorc eZdZy)rN)__name__ __module__ __qualname__K/var/www/html/bid-api/venv/lib/python3.12/site-packages/scipy/_lib/_util.pyrrTs rrc tg|}||cxurn| | td|jg|}|jdgj}|j |d|d|ddc}|j|fd|D}|V|j|}|j |j|j} |jj|| } nT |j| fd |D} |j || } |jj| } | | <|| <| S) aReturn elements chosen from two possibilities depending on a condition Equivalent to ``f(*arrays) if cond else fillvalue`` performed elementwise. Parameters ---------- cond : array The condition (expressed as a boolean array). arrays : tuple of array Arguments to `f` (and `f2`). Must be broadcastable with `cond`. f : callable Where `cond` is True, output will be ``f(arr1[cond], arr2[cond], ...)`` fillvalue : object If provided, value with which to fill output array where `cond` is not True. f2 : callable If provided, output will be ``f2(arr1[cond], arr2[cond], ...)`` where `cond` is not True. Returns ------- out : array An array with elements from the output of `f` where `cond` is True and `fillvalue` (or elements from the output of `f2`) elsewhere. The returned array has data type determined by Type Promotion Rules with the output of `f` and `fillvalue` (or the output of `f2`). Notes ----- ``xp.where(cond, x, fillvalue)`` requires explicitly forming `x` even where `cond` is False. This function evaluates ``f(arr1[cond], arr2[cond], ...)`` onle where `cond` ``is True. Examples -------- >>> import numpy as np >>> a, b = np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8]) >>> def f(a, b): ... return a*b >>> _lazywhere(a > 2, (a, b), f, np.nan) array([ nan, nan, 21., 32.]) Nz1Exactly one of `fillvalue` or `f2` must be given.TrFrrc3(K|] }| ywNr.0arrconds r z_lazywhere..s73t9 fill_valuedtypec3(K|] }| ywr#r)r%r&nconds r r(z_lazywhere..s=fsE fr)r,) r ValueErrorbroadcast_arraysasarrayr,astype result_typefullshapeempty) r'arraysf fillvaluef2xpargs bool_dtypetemp1r,outtemp2r.s ` @r _lazywhererBXs<X  ' 'B iR^ 8MLMM 2  t -f -DTF#))J99T!Wju9=tABxLD& JJq778 9E zJJy) u{{IOO<ggdjjYegD 2=f=>?ue,hhtzzh/E CI Jrc tj|}tj|Dcgc]}|jjc}}tj tj |d||}t||D]g\} tj durtj |d\ }t fd|D} tj| || i|Scc}w)aZ Mimic `np.select(condlist, choicelist)`. Notice, it assumes that all `arrays` are of the same shape or can be broadcasted together. All functions in `choicelist` must accept array arguments in the order given in `arrays` and must return an array of the same shape as broadcasted `arrays`. Examples -------- >>> import numpy as np >>> x = np.arange(6) >>> np.select([x <3, x > 3], [x**2, x**3], default=0) array([ 0, 1, 4, 0, 64, 125]) >>> _lazyselect([x < 3, x > 3], [lambda x: x**2, lambda x: x**3], (x,)) array([ 0., 1., 4., 0., 64., 125.]) >>> a = -np.ones_like(x) >>> _lazyselect([x < 3, x > 3], ... [lambda x, a: x**2, lambda x, a: a * x**3], ... (x, a), default=np.nan) array([ 0., 1., 4., nan, -64., -125.]) rr*Fc3JK|]}tj|ywr#)npextractr$s r r(z_lazyselect..s=fsRZZc*fs #) rEr1 mintypecoder,charr5r6zipalltupleplace) condlist choicelistr8defaultatcoder@func_tempr's @r _lazyselectrUs8 & )F NN&9&QAGGLL&9 :E ''"((6!9%' GC*h/ d 66$%- %%dF1I6a=f== dD$K( 0 J:sC1Cctj|}| |j}t|ds|f}t j t j||jz}tj||zdztj}|jdd|z}|dk7r||z }||||zdzdd}tj||||}|jd|S)zAllocate a new ndarray with aligned memory. Primary use case for this currently is working around a f2py issue in NumPy 1.9.1, where dtype.alignment is such that np.zeros() does not necessarily create arrays aligned up to it. N__len__rdatar)order)rEr, alignmenthasattr functoolsreduceoperatormulitemsizer7uint8__array_interface__ndarrayfill)r6r,r[alignr bufoffsetrYs r _aligned_zerosrjs HHUOE } 5) $   HLL% 05>> AD ((4%? ?&2b"" G A  77bhhsm #>? ?}}QWWbjj1,A Hrc tj|}|||krt|d|d|S#t$rt|ddwxYw)a Validate a scalar integer. This function can be used to validate an argument to a function that expects the value to be an integer. It uses `operator.index` to validate the value (so, for example, k=2.0 results in a TypeError). Parameters ---------- k : int The value to be validated. name : str The name of the parameter. minimum : int, optional An optional lower bound. z must be an integer.Nz" must be an integer not less than )r`index TypeErrorr0)knameminimums r _validate_intrKst$A NN1 q7{D6"!!( +,15 6 H A4& 456D@As 1A  FullArgSpec)r=varargsvarkwdefaults kwonlyargskwonlydefaults annotationsc tj|}|jjDcgc]N}|jtj j tj jfvr |jP}}|jjDcgc]5}|jtj jk(r |j7}}|r|dnd}|jjDcgc]5}|jtj jk(r |j7}}|r|dnd}td|jjDxsd}|jjDcgc]5}|jtj jk(r |j7}}|jjDcic]X}|jtj jk(r/|j|jur|j|jZ}}|jjDcic]1}|j|jur|j|j3} }t!||||||xsd| Scc}wcc}wcc}wcc}wcc}wcc}w)asinspect.getfullargspec replacement using inspect.signature. If func is a bound method, do not list the 'self' parameter. Parameters ---------- func : callable A callable to inspect Returns ------- fullargspec : FullArgSpec(args, varargs, varkw, defaults, kwonlyargs, kwonlydefaults, annotations) NOTE: if the first argument of `func` is self, it is *not*, I repeat *not*, included in fullargspec.args. This is done for consistency between inspect.getargspec() under Python 2.x, and inspect.signature() under Python 3.x. rNc3K|]O}|jtjjk(r&|j|j ur|jQywr#)kindinspect ParameterPOSITIONAL_OR_KEYWORDrOr7)r%ps r r(z)getfullargspec_no_self..sD2a FFg''== = IIQWW $ 2sAA)r signature parametersvaluesrrrPOSITIONAL_ONLYrVAR_POSITIONAL VAR_KEYWORDrK KEYWORD_ONLYrOr7 annotationr) rRsigrr=rrrr kwdefaultsrs r getfullargspec_no_selfrys*   D !C--/ /1 66g''==''779 9 /   --//1 66W&&55 5 / $gajG--/ /1 66W&&22 2 /  E!H4E>>002   --//1 66W&&33 3 /.1^^-B-B-D,-DVVw00===))177*&&!))#-DJ,251F1F1H31HAll!''1661<<'1HK3 tWeXz!)T; 88;    ,3s%AJ3#:J8:J=:K+AK&6K ceZdZdZdZdZy)_FunctionWrapperz? Object to wrap user's function, allowing picklability c4||_|g|_y||_yr#r9r=)selfr9r=s r __init__z_FunctionWrapper.__init__s,B D rc<|j|g|jSr#r)rxs r __call__z_FunctionWrapper.__call__stvva$$))$$rN)rrr__doc__rrrrr rrs1%rrc<eZdZdZd dZdZdZdZdZdZ dZ y ) MapWrapperav Parallelisation wrapper for working with map-like callables, such as `multiprocessing.Pool.map`. Parameters ---------- pool : int or map-like callable If `pool` is an integer, then it specifies the number of threads to use for parallelization. If ``int(pool) == 1``, then no parallel processing is used and the map builtin is used. If ``pool == -1``, then the pool will utilize all available CPUs. If `pool` is a map-like callable that follows the same calling sequence as the built-in map function, then this callable is used for parallelization. cd|_t|_d|_t |r||_|j|_yddlm}t|dk(r/||_|jj|_d|_yt|dk(ryt|dkDr:|t||_|jj|_d|_ytd) NFr)PoolrZTr) processeszUNumber of workers specified must be -1, an int >= 1, or an object with a 'map' method) poolmap_mapfunc _own_poolcallablemultiprocessingrint RuntimeError)rrrs r rzMapWrapper.__init__s   D>DI IIDM ,4yB F $  !%TaTQ 3t95 $  !%"$,--rc|Sr#rrs r __enter__zMapWrapper.__enter__s rcR|jr|jjyyr#)rr terminaters r rzMapWrapper.terminates >> II   ! rcR|jr|jjyyr#)rrjoinrs r rzMapWrapper.joins >> IINN  rcR|jr|jjyyr#)rrclosers r rzMapWrapper.closes >> IIOO  rc|jr5|jj|jjyyr#)rrrr)rexc_type exc_value tracebacks r __exit__zMapWrapper.__exit__s, >> IIOO  II   ! rcb |j||S#t$r}td|d}~wwxYw)Nz;The map-like callable must be of the form f(func, iterable))rr)rrRiterablees r rzMapWrapper.__call__s= >==x0 0 >67<= > >s . ).Nr) rrrrrrrrrrrrrr rrs*-8"" >rrc.t|tr|j|||||S|$tjj j }|r3||j|dz||S||j||dz||S|j||||S)al Return random integers from low (inclusive) to high (exclusive), or if endpoint=True, low (inclusive) to high (inclusive). Replaces `RandomState.randint` (with endpoint=False) and `RandomState.random_integers` (with endpoint=True). Return random integers from the "discrete uniform" distribution of the specified dtype. If high is None (the default), then results are from 0 to low. Parameters ---------- gen : {None, np.random.RandomState, np.random.Generator} Random number generator. If None, then the np.random.RandomState singleton is used. low : int or array-like of ints Lowest (signed) integers to be drawn from the distribution (unless high=None, in which case this parameter is 0 and this value is used for high). high : int or array-like of ints If provided, one above the largest (signed) integer to be drawn from the distribution (see above for behavior if high=None). If array-like, must contain integer values. size : array-like of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned. dtype : {str, dtype}, optional Desired dtype of the result. All dtypes are determined by their name, i.e., 'int64', 'int', etc, so byteorder is not available and a specific precision may have different C types depending on the platform. The default value is 'int64'. endpoint : bool, optional If True, sample from the interval [low, high] instead of the default [low, high) Defaults to False. Returns ------- out: int or ndarray of ints size-shaped array of random integers from the appropriate distribution, or a single such random int if size not provided. )highr r,endpointr)r r,)rr r,)r~rintegersrEr{r|r}randint)genlowrr r,rs r rng_integersrsX#y!||CdU%-/ / ;))""((C |{{37U{CC{{3TAXD{NN{{3TE{BBrc#Ktjj|ffd tj_ dtj_y#tj_wxYww)z0Context with a fixed np.random.default_rng seed.c|Sr#rrorig_funs r z$_fixed_default_rng..Js htnrN)rEr{ default_rngrs @r _fixed_default_rngrFsGyy$$H(,._wrapped]sND#F#  FF75t <    s0)rcompileI)rRrrs` @r _rng_html_rewriterQs&jjFMG Orcjtj||}|r|tj||}|S)z argmin with a `keepdims` parameter. See https://github.com/numpy/numpy/issues/8710 If axis is not None, a.shape[axis] must be greater than 0. axis)rEargmin expand_dims)rPkeepdimsrrs r _argminrhs1 ))AD !CD$nnSt, Jrctttj||d}tj|||S)a Return the first non-nan value along the given axis. If a slice is all nan, nan is returned for that slice. The shape of the return value corresponds to ``keepdims=True``. Examples -------- >>> import numpy as np >>> nan = np.nan >>> a = np.array([[ 3., 3., nan, 3.], [ 1., nan, 2., 4.], [nan, nan, 9., -1.], [nan, 5., 4., 3.], [ 2., 2., 2., 2.], [nan, nan, nan, nan]]) >>> _first_nonnan(a, axis=0) array([[3., 3., 2., 3.]]) >>> _first_nonnan(a, axis=1) array([[ 3.], [ 1.], [ 9.], [ 5.], [ 2.], [nan]]) Trrr)rrEisnantake_along_axis)rPrrs r _first_nonnanr vs/8  $6A  a ..rcF|#|jdk(ry|j}d}nD|j}||dk(r0|d|d|zz||dzdz}tj|dt St ||}||k(tj|zj||S) a Determine if the values along an axis are all the same. nan values are ignored. `a` must be a numpy array. `axis` is assumed to be normalized; that is, 0 <= axis < a.ndim. For an axis of length 0, the result is True. That is, we adopt the convention that ``allsame([])`` is True. (There are no values in the input that are different.) `True` is returned for slices that are all nan--not because all the values are the same, but because this is equivalent to ``allsame([])``. Examples -------- >>> from numpy import nan, array >>> a = array([[ 3., 3., nan, 3.], ... [ 1., nan, 2., 4.], ... [nan, nan, 9., -1.], ... [nan, 5., 4., 3.], ... [ 2., 2., 2., 2.], ... [nan, nan, nan, nan]]) >>> _nan_allsame(a, axis=1, keepdims=True) array([[ True], [False], [False], [False], [ True], [ True]]) NrTrrr*rr) r ravelr6rEr5boolr rrJ)rPrrshpa0s r _nan_allsamersD | 66Q; GGIgg t9>et*tH},s4!89~=C7734t< < qt $B 1W # ( (dX ( FFr)r<c| t|}t| }|hd}||vrtdt|d|j |j dxs|j |j d}t |dk(rd}n|r!|j|j|}nt|rtj|j tr_d}|jD]I}tjt|tjs1tj|sGd}nnd}|r|d k(r td |r|r|d k(r d }t|||fS) N>omitraise propagateznan_policy must be one of .z real floatingzcomplex floatingrFTrzThe input contains nan valuesrz9`nan_policy='omit' is incompatible with non-NumPy arrays.)r r r0setisdtyper,xp_sizermaxrErobjectr typenumber) rP nan_policypoliciesr< not_numpyr contains_nanelmessages r _contains_nanr"s: z Q RL I1!5c(m_AFGGzz!''?3:**QWW&89 qzQ xxq * ""--8 '')B}}T"Xryy1bhhrl#    g-899\j&&8M!!  ##rcfd}|S)a Generate decorator for backward-compatible keyword renaming. Apply the decorator generated by `_rename_parameter` to functions with a recently renamed parameter to maintain backward-compatibility. After decoration, the function behaves as follows: If only the new parameter is passed into the function, behave as usual. If only the old parameter is passed into the function (as a keyword), raise a DeprecationWarning if `dep_version` is provided, and behave as usual otherwise. If both old and new parameters are passed into the function, raise a DeprecationWarning if `dep_version` is provided, and raise the appropriate TypeError (function got multiple values for argument). Parameters ---------- old_name : str Old name of parameter new_name : str New name of parameter dep_version : str, optional Version of SciPy in which old parameter was deprecated in the format 'X.Y.Z'. If supplied, the deprecation message will indicate that support for the old parameter will be removed in version 'X.Y+2.Z' Notes ----- Untested with functions that accept *args. Probably won't work as written. cJtjfd}|S)Nc ^|vrrjjd}tt|ddz|d<dj|}dddd|d }t j |t d|vrjd d }t||j|<|i|S) NrrrlzUse of keyword argument `z!` is deprecated and replaced by `z`. Support for `z` will be removed in SciPy ) stacklevelz2() got multiple values for argument now known as ``) splitstrrrwarningswarnDeprecationWarningrrpop)r=r end_versionr! dep_versionfunnew_nameold_names r wrapperz5_rename_parameter..decorator..wrapper s6!"-"3"3C"8K%([^)>FZH//7j9++6-q :GMM'+=!Lv%"%,,099A ! 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""K""K2<!rrScV|jd}d|z}d|zj|S)zQ Ensures that lines after the first are indented by the specified amount   )r(r)srpr(indents r _indenterrs/ GGDME UF 6M   &&rctj|rytj|rytj|rytj|dddS)zP Returns a string representation of a float with exactly ten characters z infz -infz nanrlF) precisionpad_leftunique)rEisposinfisneginfrformat_float_scientific)rs r _float_formatter_10rsE {{1~ Q ! % %a1q OOrct|trtttt |j |z}dj||Dcgc];\}}|j|dztt|||zdzd||dzz=c}}}|Stjd|z dddti5t|}d d d |Scc}}w#1swYSxYw) z Pretty printer for dictionaries `n` keeps track of the starting indentation; lines are indented by this much after a line break. `mplus` is additional left padding applied to keys rz: rlrL float_kind) linewidth edgeitems threshold formatterN)r~rrrlenr~ryrrjustrrzrE printoptionsrr))rvrpmplusrxmrvrs r rzrzs!T Caffh( )E 1 II#)!9.#,41awwqzD( AaCE1f!EqsKL#,. / H__r!tqB(46I'JLAAL H.L HsAC  C""C,)NN)r)TFFFFr#)NNint64F)l !E^1cuBn)FN)F)rN)rrN)\r contextlibrr^r`r*r collectionsrrrutypingrrrrnumpyrEscipy._lib._array_apir r r rr Exception__annotations__Warninglib NumpyVersion __version__numpy.exceptionsr r rrrcatch_warningsfilterwarnings FutureWarninglongrulongrrVint_uintrr IntNumberrtfloating DecimalNumberr rrn __array__r{rrSeedTyper numpy.random ImportErrorrBrUrjrorxrrrrrrrrrrrr rr"r6rArIrLrQrrSrrrzrrr rsx %" LL  ?W =(66r~~&(2 $ 66r~~&,6  $X $ $ & #H # #B  ggGxxH'ggGwwH #rzz/ " eR[["**45 66r~~&'1NVV(83N! T* i)<)< ii33456HO59L9L9;9N9N:O4PQM 3 DN%P!&S4;s;u;"<(,BG"'5 p RIJ 48n % %G>G>T8?=C@)). /@-G`#$$#$L4n /4*!$*!Z' P q' & ggGwwH*     sNK8!-K+K8;%L L*+K50K88LLL'&L'*L<;L<