0^j~<ddlZddlZddlmZmZddlmZmZm Z ddl m cm Z dZdZdZdZd ZdZdZ dd Z dd Zd Zd ZdZy)N) _RichResult_call_callback_maybe_halt)array_namespacexp_sizexp_result_typec ~t|}|t|n|}|5g||j}t|j }||dd}t |d|d} |j g||} | d|| |d}}|D cgc]} |j| | }} |D cgc]} |j|| g|} } |dj} | dj|r]| |dfd }tj| } |D cgc]} |j| | }} |Dcgc]}|j|| }}d }|d |j}| Dcgc]}|j| k(}}t|s t||j| Dcgc]}|j c}| gz}|s|j#|d s td |D cgc]} |j| |d}} | Dcgc]}|j||d} }|D cgc]} |j%| d}} | Dcgc]}|j%|d} }|Dcgc]%}|j%|j|dd'}}||| || ||fScc} wcc} wcc} wcc}wcc}wcc}wcc} wcc}wcc} wcc}wcc}w)atInitialize 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`. kwargs : tuple, optional Additional keyword 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`. N)kwnamesfuncct|t|z }tt|||d}||g|d|i||S)N)lendictzip)xrrargskwargsnargskwarrayss s/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/scipy/_lib/_elementwise_iterative_method.pyrz_initialize..funcWsKIG ,ECef67H?D%L?H?? ?T)force_floatingxpdtyper)shapercVdtt|z z}|||g|i|S)N)r)r)rr rrrifshapes rrz_initialize..funcks3c&kCJ./A!.t.v. .rzpThe 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$)rrvaluestuplekeysrbroadcast_arraysasarrayr npbroadcast_shapes broadcast_tolowerall ValueError result_typerisdtypereshape)rxsrr complex_okpreserve_shapernxrxatxasrfsr argmessagef shapes_equalxfatr#s @r _initializer@sh RB!#" B (( ( &#* @ "Tb 9C "   )r )D )C3BxRSB,. /q"**Qc* " /B /.0 1"**T!^d^ $ 1B 1 qEKKE U[[F!&T /##FE213 4Abooa' 4 47;<U+<<DG!1'--/1BC.01AGGu$1L1 | !! 2>>b1QWW1SE9 ;D bjj?NOO8: ;1"**Qd* . ;B ;8: ;1"**Qd* . ;B ;)+ +1"**Q  +B +(* +1"**Q  +B +EI JcBJJrzz#Dz15 9 JD J RudB ..I 0 15< 22 < ; , + JsB>JJ J!JJJ!J&4J+J04J5*J:c | tdd}tj|}| j|}| Dcic]\}}|| j ||}}}| j || j |d<| j || jt| j|d<| j || j|d<| j || j|d<t|}||_ t||| || || }| t||| ||| || }t||rd }|j|krt!|r|s|r||}|jr|jd j"|j"k7rfg}|jD]N}|j"|j"z }|j$d |zz}|j'| j)||P||_ |j$}|r| j)||d z}||g|j}| j||}|r$| j)||}| j)||}|xj*|j"d k(rd n|j$dz c_|||||xjd z c_t||| || || }|!t||| ||| || }t||rd }n7t!|d k(rn(| ||j|krt!|r|s|rt-j.|j0ddj3|rt4nt6|_t||| ||| || Scc}}w)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_resultrrDrndimr appendr3rExpxatrCset _ECALLBACK _ECONVERR)workcallbackr maxiterrrr pre_func_evalpost_func_evalcheck_terminationpost_termination_checkcustomize_resultres_work_pairsrr6 cb_terminate n_elementsactiver"jres_dictrestemprr; n_new_dims new_shapex_shaper=s r_looprksIR z!"455L5!J YYz "FAOPA288Je844PHP((:RWW(=HYRZZ -ERXXVHXhhzh:HUOxx "((x;HV h CDI c>6 1>2GFtS.&%/E $Xt 4L ((W * $  991**aff4Dyy 8VVchh. IIZ7  BJJsI67 8DI''  1uu}.A  TYY  JJqJ &  1g&A 1g&A !&&A+Q1772;6 q!T" A #D#~v$5~rK  "4nfe#3^RID(48#  6?a  t$O ((W *R&&%a(,,nrrQr)any_update_activeitemsgetattrr) rXrfr`rcr]r6rstopproceedkeyvalr;s rrOrOs T "D vvd| tS.&$PRS c$iC66#c(+K8<rsj 9@@--        '+:>k/`@EMA`<98r