B §d$ã@s°ddlmZmZmZddlmZddlmZddlmZddl m Z ddl m Z Gdd„de ƒZGd d „d e ƒZGd d „d e ƒZGd d„deƒZeZGdd„de ƒZdd„ZdS)é)Úabsolute_importÚdivisionÚprint_function)Úext)Úweighting_schemes)Úmiller)Úflex)Úadopt_init_argsc@seZdZdd„Zdd„ZdS)Ú least_squarescCs|dks t‚t|tƒƒdS)Nr)ÚAssertionErrorr Úlocals)ÚselfÚcompute_scale_using_all_dataÚf_obsÚweightsÚ r_free_flagsÚ scale_factor©rúy/mnt/filia/a/genomebrowser/www/genomebrowser/fleming/tools/molprobity/modules/cctbx_project/cctbx/xray/target_functors.pyÚ__init__ s zleast_squares.__init__c Csb| ¡ |j ¡¡st‚| ¡|j ¡ks.t‚tj|jd|j ¡|j |j  ¡| ¡t |ƒ|j dS)NÚF)rÚobs_typeÚobsrrÚf_calcÚderivatives_depthr) Ú unit_cellÚ is_similar_torr Ú space_grouprÚtargets_least_squaresrÚdatarrÚintr)r rÚcompute_gradientsrrrÚ__call__szleast_squares.__call__N)Ú__name__Ú __module__Ú __qualname__rr"rrrrr s r c@seZdZddd„Zdd„ZdS)Úmax_likeç@c Cst|tƒƒ| ¡ ¡|_dS)N)r r Ú centric_flagsr) r rrÚexperimental_phasesÚ alpha_betarÚepsilonsÚspacializationÚintegration_step_sizerrrr"s zmax_like.__init__c Cs.| ¡ |j ¡¡st‚| ¡|j ¡ks.t‚|jdkrà|jdkrˆtj|j  ¡|j   ¡|  ¡|j d  ¡|j d  ¡|j |j |j|d S|jdkrØtj|j  ¡|j   ¡|  ¡|j d  ¡|j d  ¡|j |j |j|d Sdsàt‚tj|j  ¡|j   ¡|j  ¡|  ¡|j d  ¡|j d  ¡|j |j|j|d S)NÚfré) rrrÚalphaÚbetarr+r(r!Úi) rrr)rr0r1r+r(r-r!)rrrr rr)r,rÚmlf_target_and_gradientsrrr*rr+r(Úmli_target_and_gradientsÚmlhl_target_and_gradientsr-)r rr!rrrr".sJ         zmax_like.__call__N)r')r#r$r%rr"rrrrr& s r&c@s8eZdZdZd dd„Zdd„ZeZdd „Zdd d „ZdS)Úunified_least_squares_residualzB A least-square residual functor for refinement against F or F^2. TNcCsÄ| ¡sF| ¡sFtj|| ¡| ¡d|_|r:|j ¡qL|j ¡n||_|j ¡sd|j ¡sdt ‚|  ¡ ¡r‚t j |_ t ¡}n|  ¡ ¡ržt j|_ t ¡}|dkrª|}||_|j|j_d|_dS)a³ Construct a least-square residuals .. |Sigma| unicode:: U+003A3 .. GREEK CAPITAL LETTER SIGMA |Sigma|:sub:`i` w[i] ( f_obs.data[i] - k abs(f_calc.data[i]) )^2 / |Sigma|:sub:`i` w[i] f_obs.data[i]^2 or |Sigma|:sub:`i` w[i] ( f_obs_square.data[i] - k abs(f_calc.data[i])^2 )^2 / |Sigma|:sub:`i` w[i] f_obs_square.data[i]^2 depending on which of f_obs and f_obs_square is not None. Note that - the sums are over the indices i of the reflections, - f_calc is to be passed to the __call__ method, - the weights w and the scale factor k are discussed below. :Parameters: obs : real miller.array the observed reflections, with F and sigma(F) (or F^2 and sigma(F^2)) respectively in obs.data() and obs.sigmas() is_amplitude : bool a flag to discriminate the type of data in obs if the latter does not spell out whether it contains amplitudes or intensities; the default means that amplitudes is the default when data type is unknown. weighting a weighting scheme for the data (c.f. cctbx.xray.weighting for common ones) or None, which means no weights )rÚsigmasN)Úis_xray_amplitude_arrayÚis_xray_intensity_arrayrÚarrayrr7Ú_obsÚ#set_observation_type_xray_amplitudeÚ#set_observation_type_xray_intensityr rrÚtargets_least_squares_residualÚ_ext_ls_residualrZamplitude_unit_weightingÚ,targets_least_squares_residual_for_intensityZintensity_quasi_unit_weightingÚ _weightingZobservedÚ _scale_factor)r rZ is_amplitudeÚ weightingZdefault_weightingrrrrXs&(       z'unified_least_squares_residual.__init__cCs|jS)z# The obs passed to the constructor )r;)r rrrr•sz"unified_least_squares_residual.obscCs|jS)z The weighting scheme )rA)r rrrrCsz(unified_least_squares_residual.weightingrcCs | ¡ | ¡ ¡¡st‚| ¡| ¡ ¡ks2t‚|jdkrDd|_n||_|| ¡_|dkr`d}t| ¡t j ƒs€t| ¡t j ƒrž| ¡  ||¡| ¡j |_n | ¡  ¡| ¡jdk rà| | ¡ ¡| ¡j| ¡||j¡}n| | ¡ ¡| ¡||j¡}|  ¡|_|S)aá Compute the least-squares residual value and perhaps its derivatives wrt to the calculated structure factor F_c of the i-th reflection :Parameters: f_calc : complex `cctbx.miller.array` f_calc.data()[i] constains F_c for the i-th reflection in f_obs() compute_derivatives : bool whether to compute the derivatives of the least square residual or not scale_factor : number the scale factor k if not null, otherwise k will be computed as a by-product of calling this method :return: An object holding the residual value, derivatives and scale factor :rtype: Boost.Python binding of :doxyclass:`cctbx::xray::targets::least_squares_residual` Nr)rrrr rrBrCZ calculatedÚ isinstancerZshelx_weightingZsimple_shelx_weightingÚcomputerrr?r)r rÚcompute_derivativesrÚresultrrrr"¡s6       z'unified_least_squares_residual.__call__)TN)r) r#r$r%Ú__doc__rrrrCr"rrrrr6Us ;r6cs2eZdZdZe ¡Zd‡fdd„ Zdd„Z‡Z S)Ú$least_squares_residual_for_amplitudez3 A least-square residual functor for F refinement. Fcs(|r |j}nd}tt|ƒ |d|¡dS)NT)Ústatistical_weightingÚsuperÚleast_squares_residualr)r rÚuse_sigmas_as_weightsrC)Ú __class__rrrÚsz-least_squares_residual_for_amplitude.__init__cCs t|jtƒS)N)rDrArJ)r rrrrMász:least_squares_residual_for_amplitude.use_sigmas_as_weights)F) r#r$r%rHrZpure_statistical_weightingrJrrMÚ __classcell__rr)rNrrIÕsrIc@s6eZdZd dd„Zdd„Zdd„Zd d „Zd d „ZdS)Úintensity_correlationNFcCsBt|tƒdd|jdks$|jr$t‚|jr>|j ¡ ¡ ¡|_dS)NT)Úhide) r r Ú_weightsÚ_use_multiplicities_as_weightsr Ú_f_obsÚmultiplicitiesrÚ as_double)r rrÚuse_multiplicities_as_weightsrrrrészintensity_correlation.__init__cCs|jS)N)rT)r rrrrðszintensity_correlation.f_obscCs|jS)N)rR)r rrrrószintensity_correlation.weightscCs|jS)N)rS)r rrrrWösz3intensity_correlation.use_multiplicities_as_weightscCsB| | ¡¡st‚tjdt | ¡ ¡¡| ¡d| ¡t |ƒdS)NÚI)rrrrrr) Úis_similar_symmetryrr rÚtargets_correlationrÚpow2rrr )r rrFrrrr"ùszintensity_correlation.__call__)NF)r#r$r%rrrrWr"rrrrrPçs  rPcCs tttdœS)N)rLr6rP)rLr6rPrrrrÚregistrysr\N)Ú __future__rrrÚ cctbx.xrayrrÚcctbxrÚcctbx.array_familyrÚlibtbxr Úobjectr r&r6rIrLrPr\rrrrÚs     5