B dY@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_methodrh/work/yifan.wang/ringdown/master-ringdown-env/lib/python3.7/site-packages/astropy/modeling/optimizers.py__init__.szOptimization.__init__cCs|jS)zMaximum number of iterations)r )rrrrmaxiter4szOptimization.maxitercCs ||_dS)z Set maxiterN)r )rvalrrrr9scCs|jS)z=Step for the forward difference approximation of the Jacobian)r )rrrreps>szOptimization.epscCs ||_dS)z Set eps valueN)r )rrrrrrCscCs|jS)zRequested accuracy)r )rrrraccHszOptimization.acccCs ||_dS)z Set accuracyN)r )rrrrrrMscCs|jjd}|S)Nz()) __class____name__)rfmtrrr__repr__RszOptimization.__repr__cCs|jS)z Return the optimization method.)r)rrrrrVszOptimization.opt_methodcCs tddS)Nz'Subclasses should implement this method)NotImplementedError)rrrr__call__[szOptimization.__call__N)r __module__ __qualname____doc__supported_constraintsrpropertyrsetterrrrrabcabstractmethodrrrrrrs    ) 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.optimizer+superrfit_info)rr+)rrrrns   zSLSQP.__init__c sN|d|j|d<d|kr$|j|d<d|kr6|j|d<|dd}|dfdd jD}d d |D}t|}x<|D]4}|ddkrtd|d<|d dkr|td |d <q|Wtj|td }t j } t j } |j ||f|d ||| | d|\} } } }}| |j d<| |j d<||j d<||j d<|dkrDtdt| |j fS)a 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).0name)modelrr sz"SLSQP.__call__..cSsg|]}|js|js|jqSr)r)r*r&)r7parrrrr:s)ZdtypeT)args full_outputdispr&r'Zieqconsr,r-r.r/zLThe fit may be unsuccessful; check fit_info['message'] for more information.)popr r r Z param_namesnpZasarrayDEFAULT_BOUNDSfloatarrayr'r(rr2warningswarnr)robjfuncinitvalfargskwargsr?Zparsr&ir'r( fitparamsr,r-r.Zmessr)r9rrxs<                 zSLSQP.__call__)rrrrr rr __classcell__rr)rrr`s  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)r,r-r.num_function_calls)r0rNr1rr2)rZsimplex)rrrrs   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 t|jd dkrtdt||jfS)a 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 rrxtolr5NT)r=rPr?r>r,r-r.rOr<zLThe fit may be unsuccessful; Maximum number of function evaluations reached.zBThe fit may be unsuccessful; Maximum number of iterations reached.)r r r@rr2rErFr) rrGrHrIrJr?rLr,r-Zfuncallsr.rrrrs.          zSimplex.__call__)rrrrr rrrMrr)rrrs   )rrEr#numpyrAZastropy.utils.exceptionsr__all__rsqrtZfinforCrr r rBABCMetarrrrrrrs  EQ