U e{j@sdZddlZddlZddlZddlmZdddgZdZe e e j Z dZd ZGd ddejd ZGd ddeZGd ddeZdS)z> Optimization algorithms used in `~astropy.modeling.fitting`. N)AstropyUserWarning OptimizationSLSQPSimplexdgHz>)lJ)lJ)c@seZdZdZgZddZeddZejddZeddZ e jd dZ ed d Z e jd d Z d dZ eddZ e jddZdS)raE Base class for optimizers. Parameters ---------- opt_method : callable Implements optimization method Notes ----- The base Optimizer does not support any constraints by default; individual optimizers should explicitly set this list to the specific constraints it supports. cCs||_t|_t|_t|_dS)N) _opt_methodDEFAULT_MAXITER_maxiter DEFAULT_EPS_eps DEFAULT_ACC_acc)self opt_methodrZ/var/www/html/bwave/python/venv/lib/python3.8/site-packages/astropy/modeling/optimizers.py__init__0szOptimization.__init__cCs|jS)zMaximum number of iterationsr rrrrmaxiter6szOptimization.maxitercCs ||_dS)z Set maxiterNrrvalrrrr;scCs|jS)z=Step for the forward difference approximation of the Jacobianr rrrreps@szOptimization.epscCs ||_dS)z Set eps valueNrrrrrrEscCs|jS)zRequested accuracyr rrrraccJszOptimization.acccCs ||_dS)z Set accuracyNrrrrrrOscCs|jjd}|S)Nz()) __class____name__)rfmtrrr__repr__TszOptimization.__repr__cCs|jS)zReturn the optimization method.)rrrrrrXszOptimization.opt_methodcCs tddS)Nz'Subclasses should implement this method)NotImplementedErrorrrrr__call__]szOptimization.__call__N)r __module__ __qualname____doc__supported_constraintsrpropertyrsetterrrrrabcabstractmethodr!rrrrrs(       ) metaclasscs6eZdZdZdddddgZfddZd d ZZS) ra) Sequential Least Squares Programming optimization algorithm. The algorithm is described in [1]_. It supports tied and fixed parameters, as well as bounded constraints. Uses `scipy.optimize.fmin_slsqp`. References ---------- .. [1] http://www.netlib.org/toms/733 boundseqconsineqconsfixedtiedcs,ddlm}t|ddddd|_dS)Nr) fmin_slsqp)final_func_valnumiter exit_modemessage)scipy.optimizer0superrfit_info)rr0rrrrqs  zSLSQP.__init__c sJ|d|j|d<d|kr$|j|d<d|kr6|j|d<|dd}|dfdd jD}d d |D}t|}|D]4}|ddkrtd|d<|d dkrztd |d <qztj|td }t j } t j } |j ||f|d ||| | d|\} } } }}| |j d<| |j d<||j d<||j d<|dkr@tdt| |j fS) Run the solver. Parameters ---------- objfunc : callable objection function initval : iterable initial guess for the parameter values fargs : tuple other arguments to be passed to the statistic function kwargs : dict other keyword arguments to be passed to the solver riterepsilonr verblevelNrcsg|]}t|qSr)getattr).0namemodelrr sz"SLSQP.__call__..cSsg|]}|js|js|jqSr)r.r/r+)r>ZparrrrrBs)ZdtypeT)args full_outputdispr+r,Zieqconsr1r2r3r4zLThe fit may be unsuccessful; check fit_info['message'] for more information.)popr r r Z param_namesnpZasarrayDEFAULT_BOUNDSfloatarrayr,r-rr7warningswarnr)robjfuncinitvalfargskwargsrFZparsr+ir,r- fitparamsr1r2r3Zmessrr@rr!|sR               zSLSQP.__call__rr"r#r$r%rr! __classcell__rrr8rrbs  cs2eZdZdZdddgZfddZddZZS) ra= Neald-Mead (downhill simplex) algorithm. This algorithm [1]_ only uses function values, not derivatives. Uses `scipy.optimize.fmin`. References ---------- .. [1] Nelder, J.A. and Mead, R. (1965), "A simplex method for function minimization", The Computer Journal, 7, pp. 308-313 r+r.r/cs,ddlm}t|ddddd|_dS)Nr)fmin)r1r2r3num_function_calls)r5rVr6rr7)rZsimplexr8rrrs  zSimplex.__init__c Ksd|kr|j|d<d|kr.|d|_|dd|krJ|d|_|d|dd}|j||f||j|dd|\}}}} } ||jd<||jd <| |jd <| |jd <|jd d krtd tn|jd dkrtdt||jfS)r9rrxtolr<NT)rDrXrFrEr1r2r3rWrCzLThe fit may be unsuccessful; Maximum number of function evaluations reached.zBThe fit may be unsuccessful; Maximum number of iterations reached.)r r rGrr7rLrMr) rrNrOrPrQrFrSr1r2Zfuncallsr3rrrr!sF          zSimplex.__call__rTrrr8rrs  )r$r(rLnumpyrHZastropy.utils.exceptionsr__all__rsqrtZfinforJrr r rIABCMetarrrrrrrs  E\