ó 0™xTc@ sáddlmZddlmZddlmZddlmZddlmZddl m Z de fd„ƒYZ d e fd „ƒYZ d e fd „ƒYZd efd„ƒYZeZde fd„ƒYZd„ZdS(iÿÿÿÿ(tdivision(text(tweighting_schemes(tmiller(tflex(tadopt_init_argst least_squarescB seZd„Zd„ZRS(cC s&|dkst‚t|tƒƒdS(Ni(tAssertionErrorRtlocals(tselftcompute_scale_using_all_datatf_obstweightst r_free_flagst scale_factor((sH/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/xray/target_functors.pyt__init__ scC s¦|jƒj|jjƒƒs$t‚|jƒ|jjƒksEt‚tjd|jddd|jjƒd|j d|j jƒd|jƒdt |ƒd |j ƒS( NR tobs_typetFtobsR R tf_calctderivatives_depthR( t unit_cellt is_similar_toR Rt space_groupRttargets_least_squaresR tdataR R tintR(R Rtcompute_gradients((sH/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/xray/target_functors.pyt__call__s$!     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(RRRR(((sH/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/xray/target_functors.pyR s tunified_least_squares_residualcB sAeZdZedd„Zd„ZeZd„Zdd„Z RS(sB A least-square residual functor for refinement against F or F^2. cC s)|jƒp|jƒshtj|d|jƒd|jƒƒ|_|rX|jjƒqq|jjƒn ||_|jjƒs•|jjƒs•t ‚|j ƒjƒrÂt j |_ tjƒ}n-|j ƒjƒrït j|_ tjƒ}n|dkr|}n||_|j|j_d|_dS(s³ 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 RtsigmasN(tis_xray_amplitude_arraytis_xray_intensity_arrayRtarrayRR+t_obst#set_observation_type_xray_amplitudet#set_observation_type_xray_intensityRRRttargets_least_squares_residualt_ext_ls_residualRtamplitude_unit_weightingt,targets_least_squares_residual_for_intensitytintensity_quasi_unit_weightingR't _weightingtobservedt _scale_factor(R Rt is_amplitudet weightingtdefault_weighting((sH/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/xray/target_functors.pyRJs&(*     cC s|jS(s# The obs passed to the constructor (R/(R ((sH/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/xray/target_functors.pyR‡scC s|jS(s The weighting scheme (R7(R ((sH/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/xray/target_functors.pyR;sicC s|jƒj|jƒjƒƒs't‚|jƒ|jƒjƒksKt‚|jdkrfd|_n ||_||jƒ_|dkr“d}nt |jƒt j ƒsÃt |jƒt j ƒrî|jƒj ||ƒ|jƒj|_n|jƒj ƒ|jƒjdk rO|j|jƒjƒ|jƒj|jƒ||jƒ}n-|j|jƒjƒ|jƒ||jƒ}|jƒ|_|S(sá 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` iN(RRRRRR9R'R;t calculatedt isinstanceRtshelx_weightingtsimple_shelx_weightingtcomputeRR R3R(R Rtcompute_derivativesRtresult((sH/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/xray/target_functors.pyR“s6'$       N( RRt__doc__tTrueR'RRR R;R(((sH/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/xray/target_functors.pyR*Gs ;  t$least_squares_residual_for_amplitudecB s/eZdZejƒZed„Zd„ZRS(s3 A least-square residual functor for F refinement. cC s8|r|j}nd}tt|ƒj|t|ƒdS(N(tstatistical_weightingR'tsupertleast_squares_residualRRE(R R tuse_sigmas_as_weightsR;((sH/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/xray/target_functors.pyRÌs cC st|jtƒS(N(R>R7RG(R ((sH/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/xray/target_functors.pyRJÓs( RRRDRtpure_statistical_weightingRGtFalseRRJ(((sH/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/xray/target_functors.pyRFÇs  tintensity_correlationcB s;eZded„Zd„Zd„Zd„Zd„ZRS(cC sct|tƒdtƒ|jdks5|j s5t‚|jr_|jjƒj ƒj ƒ|_ndS(Nthide( RRREt_weightsR't_use_multiplicities_as_weightsRt_f_obstmultiplicitiesRt as_double(R R R tuse_multiplicities_as_weights((sH/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/xray/target_functors.pyRÛs cC s|jS(N(RQ(R ((sH/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/xray/target_functors.pyR âscC s|jS(N(RO(R ((sH/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/xray/target_functors.pyR åscC s|jS(N(RP(R ((sH/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/xray/target_functors.pyRTèscC sp|j|jƒƒst‚tjdddtj|jƒjƒƒd|jƒddd|jƒdt |ƒƒS(NRtIRR R RR( tis_similar_symmetryR RRttargets_correlationRtpow2RR R'R(R RRB((sH/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/xray/target_functors.pyRës   N( RRR'RLRR R RTR(((sH/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/xray/target_functors.pyRMÙs     cC sitd6td6td6S(NRIR*RM(RIR*RM(((sH/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/xray/target_functors.pytregistryõsN(t __future__Rt cctbx.xrayRRtcctbxRtcctbx.array_familyRtlibtbxRtobjectRRR*RFRIRMRY(((sH/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/xray/target_functors.pyts'€