-i:SSKrSSKrSSKJrJr SSKJrJrJ r SSK J s J r SrSrSrSrS rSrSrSS jrSS jrS rS rSrg)N) _RichResult_call_callback_maybe_halt)array_namespacexp_sizexp_result_typec F^[U5nUc[U6OUn[USUS.6nUR"/UQUQ76nUSUXSp!UV s/sHoR XS9PM nn UV s/sHoR U"U /UQ765PM n n USR n U SR mU(aaXS.U4Sjjn[ R"TU 5n UV s/sHoRX5PM nn UV s/sHoRX5PM nn Sn UbS U R53n U Vs/sHoR U :HPM nn[U5(d [U 5eUR"U Vs/sHoRPM snU/-6nU(d"URUS 5(d [S 5eUV s/sHoR U USS 9PM nn U Vs/sHoR UUSS 9PM n nUV s/sHoRU S 5PM nn U Vs/sHoRUS 5PM n nUV s/sH"oRUR U SS9S 5PM$ nn XXU UU4$s sn fs sn fs sn fs sn fs snfs snfs sn fs snfs sn fs snfs sn f)avInitialize abscissa, function, and args arrays for elementwise function Parameters ---------- func : callable An elementwise function with signature func(x: ndarray, *args) -> ndarray where each element of ``x`` is a finite real and ``args`` is a tuple, which may contain an arbitrary number of arrays that are broadcastable with ``x``. xs : tuple of arrays Finite real abscissa arrays. Must be broadcastable. args : tuple, optional Additional positional arguments to be passed to `func`. preserve_shape : bool, default:False When ``preserve_shape=False`` (default), `func` may be passed arguments of any shape; `_scalar_optimization_loop` is permitted to reshape and compress arguments at will. When ``preserve_shape=False``, arguments passed to `func` must have shape `shape` or ``shape + (n,)``, where ``n`` is any integer. xp : namespace Namespace of array arguments in `xs`. Returns ------- xs, fs, args : tuple of arrays Broadcasted, writeable, 1D abscissa and function value arrays (or NumPy floats, if appropriate). The dtypes of the `xs` and `fs` are `xfat`; the dtype of the `args` are unchanged. shape : tuple of ints Original shape of broadcasted arrays. xfat : NumPy dtype Result dtype of abscissae, function values, and args determined using `np.result_type`, except integer types are promoted to `np.float64`. Raises ------ ValueError If the result dtype is not that of a real scalar Notes ----- Useful for initializing the input of SciPy functions that accept an elementwise callable, abscissae, and arguments; e.g. `scipy.optimize._chandrupatla`. NT)force_floatingxpdtyper)shapefunccT>S[T5[U5- -nU"X/UQ70UD6$)N)r)len)xrrargskwargsifshapes [/var/www/html/venv/lib/python3.13/site-packages/scipy/_lib/_elementwise_iterative_method.pyr_initialize..func`s1c&kCJ./A.t.v. .zpThe shape of the array returned by `func` must be the same as the broadcasted shape of `x` and all other `args`.zWhen `preserve_shape=False`, z real floatingz3Abscissae and function output must be real numbers.)rcopyr )r)rrrbroadcast_arraysasarrayrnpbroadcast_shapes broadcast_tolowerall ValueError result_typerisdtypereshape)rxsr complex_okpreserve_shapernxxatxasrfsrargmessagef shapes_equalxfatrs @r _initializer8sb RB!#" B "Tb 9C   )r )D )C3BxS,. /Bq**Q* "BB /.0 1b**T!^d^ $bB 1 qEKKE U[[F!& / /##FE213 4Aooa' 47;>b1bWWb1SE9 ;D bjj??NOO8: ;1**Qd* .B ;8: ;1**Qd* .B ;)+ +1**Q B +(* +1**Q B +EI JTcJJrzz#Dz15 9TD J RudB ..I 0 15< 22 < ; , + JsBI,$!I1I61I;)J.J8J J8JJ:)Jc `U c [S5eSn[R"U5nU RU5nU VVs0sHunnUU R UUS9_M nnnU R UU R S9US'U R UU R[5U RS9US'U R UU RS9US'U R UU RS9US'[U5nXPl [UUU UXU 5nUb$[UUU UUXU 5n[UU5(aS nURU:Ga[!U5(GaU(GdU(GaU"U5nUR(aURS R"UR":wag/nURHOnUR"UR"- nUR$S U--nUR'U R)UU55 MQ XPl UR$nU(aU R)UUS -5nU"U/URQ76nU RUUS9nU(a$U R)UU5nU R)UU5nU=R*UR"S :XaS OUR$S- slU"UUU5 U=RS - sl[UUU UXU 5nUb%[UUU UUXU 5n[UU5(aS nOI[!U5S :XaO9U "U5 URU:a![!U5(aU(d U(aGM[,R."UR05SSR3U(a[4O[65Ul[UUU UUXU 5$s snnf)a Main loop of a vectorized scalar optimization algorithm Parameters ---------- work : _RichResult All variables that need to be retained between iterations. Must contain attributes `nit`, `nfev`, and `success`. All arrays are subject to being "compressed" if `preserve_shape is False`; nest arrays that should not be compressed inside another object (e.g. `dict` or `_RichResult`). callback : callable User-specified callback function shape : tuple of ints The shape of all output arrays maxiter : Maximum number of iterations of the algorithm func : callable The user-specified callable that is being optimized or solved args : tuple Additional positional arguments to be passed to `func`. dtype : NumPy dtype The common dtype of all abscissae and function values pre_func_eval : callable A function that accepts `work` and returns `x`, the active elements of `x` at which `func` will be evaluated. May modify attributes of `work` with any algorithmic steps that need to happen at the beginning of an iteration, before `func` is evaluated, post_func_eval : callable A function that accepts `x`, `func(x)`, and `work`. May modify attributes of `work` with any algorithmic steps that need to happen in the middle of an iteration, after `func` is evaluated but before the termination check. check_termination : callable A function that accepts `work` and returns `stop`, a boolean array indicating which of the active elements have met a termination condition. post_termination_check : callable A function that accepts `work`. May modify `work` with any algorithmic steps that need to happen after the termination check and before the end of the iteration. customize_result : callable A function that accepts `res` and `shape` and returns `shape`. May modify `res` (in-place) according to preferences (e.g. rearrange elements between attributes) and modify `shape` if needed. res_work_pairs : list of (str, str) Identifies correspondence between attributes of `res` and attributes of `work`; i.e., attributes of active elements of `work` will be copied to the appropriate indices of `res` when appropriate. The order determines the order in which _RichResult attributes will be pretty-printed. preserve_shape : bool, default: False Whether to compress the attributes of `work` (to avoid unnecessary computation on elements that have already converged). Returns ------- res : _RichResult The final result object Notes ----- Besides providing structure, this framework provides several important services for a vectorized optimization algorithm. - It handles common tasks involving iteration count, function evaluation count, a user-specified callback, and associated termination conditions. - It compresses the attributes of `work` to eliminate unnecessary computation on elements that have already converged. NzMust provide xp.FrsuccessstatusnitnfevTr)rr rr )NotImplementedErrormathprodarangezerosboolfullr" _EINPROGRESSint32rr_check_termination_prepare_resultrr<rndimrappendr+r=xpxatr;set _ECALLBACK _ECONVERR)workcallbackrmaxiterrrr pre_func_evalpost_func_evalcheck_terminationpost_termination_checkcustomize_resultres_work_pairsrr. cb_terminate n_elementsactiverjres_dictrestemprr3 n_new_dims new_shapex_shaper5s r_looprcsCR z!"455L5!J YYz "FAOPA288Je844HP((:RWW(=HYRZZ -ERXXVHXhhzh:HUOxx "((x;HV h CI c>6 12GFtS.&%/E $Xt 4 4L ((W * $  991**aff4DyyVVchh. IIZ7  BJJsI67!I''  1uu}.A  TYY  JJqJ &  1g&A 1g&A !&&A+Q1772;6 q!T" A #D#~v$5rK  "4nfe#3RID(488#  6?a  t$O ((W **R&&%a(,,nrrIr)any_update_activeitemsgetattrr) rPr^rXr[rUr.rstopproceedkeyvalr3s rrGrGs T "D vvd|| t.$PRS ciC66#(+K8<rsd 9@@((        `/J@EMA`<98r