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R:RoRpRqRrRsRcRhRtR!RsResolution rangesN(obs)/N(possible)tcolumn_headerst table_rowsN(RRRRR8RRzR<RR,RRSR9tlibtbxt table_utilst simple_table(R.Rt data_labelRtreplace_none_withRoRRRR:RtrowRtlabels((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytas_simple_tables:       N(t__name__t __module__R#R7R;R<RERHRJRKRbRRzRRR,R(((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR!,sN       .  !t binned_datac B sGeZddZdeedeeddddd ZddZRS(cC s||_||_||_dS(N(R!RR(R.R!RR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR#s  RcR{c C sj|dkr|j}n|jjd|jd|d|d|d|d|d|d|d |d | d | S( NRRRoRpRqRrRRsRhR~R(RRR!RR( R.RRoRpRqRrRRsRhR~R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytshows   cC s:|dkr|j}n|jjd|jd|d|S(sE Export table rows for display elsewhere. (Used in Xtriage) RRRN(RRR!RR(R.RR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR8s    N(RRRR#R<RR(((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs  cC s5i}x(tt|D]}||||Z=e:d?Z>e:d@Z?e:dAZ@dBedCZAdBedDZBdEdFdGe:edHdIdJZCdKZDdEdFdGedkedHdIdLZEdEdFedHdIdMZFedNZGdOZHe:dPZIedQZJdRZKdldkdkdkdkdTe:dUZLeedkedVZMdkedmdkdkdkdkdkdWZNdXZOdkedkdkdkdkdkdYZPd:d:ed:d:dZZQd[d\d]d&d^ZRe:dkdkdkd_ZSd:d:dkd`daZTdbZUdcZVddZWdeZXdfZYe:dgZZdkdkdkdkdkdhZ[diZ\djZ]RS(ns Basic class for handling sets of Miller indices (h,k,l), including sorting and matching functions, symmetry handling, generation of R-free flags, and extraction of associated statistics. Does not actually contain data, but this can be added using the array(...) method. cC sM|dttfksttjj||||_||_d|_ dS(N( RR,R<RRR5t_copy_constructort_indicest_anomalous_flagt_binner(R.R1R$R&((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR#Rs   cC s8tjj|||j|_|j|_d|_dS(N(RR5RRRRR(R.tother((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRYs  cC s|jS(sz Return a reference to the internal array of indices. :returns: a cctbx.array_family.flex.miller_index array (R(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR$_scC s|jS(s6Indicate whether the set or array is anomalous or not.(R(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR&gscC s|jjS(s5Return the number of reflections in the set or array.(R$R'(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR'kscC std|d|jd|jS(sd Create a copy of the set, keeping references to the same crystal symmetry and indices. R1R$R&(R4RR(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytcopyos cC s|j}|dk r3tjd|j}n|jdkrNd}nt|j}tdtjd|d|d|j j d|j S(s Create a copy of the set, also copying the crystal symmetry and indices. :returns: a set object with all-new attributes. t parametersR1R2tspace_group_symbolR$R&N( R2RRRR%RSR4RR5R$t deep_copyR&(R.R2R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRys     cC s|tkr|}n|tkr0|j}n|tkrK|j}ntjj|d|d|}td|d|d|S(s Create a copy of the set, optionally changing the symmetry, indices, and/or anomalous flag (default = keep all unmodified). R2R%R1R$R&(RR$R&RR5tcustomized_copyR4(R.R1R$R&R2R%((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs    cC sy|dk r0|j|jjks0tn|dk r`|j|jjks`tntd|d|d|S(s Create an array object, given data and/or sigma arrays of identical dimensions to the indices array. :param data: a flex array (any format) or None :param sigmas: a flex array (any format, but almost always double) or None R0RtsigmasN(RR'RRR(R.RR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs  $ $cC sat|tjkst|jdk s3ttd|d|jj|d|jS(NR1R$R&( RRRRR$RR4RR&(R.R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs R{cC sr|dkrtj}n||dIt|jIJ||dI|jIJtjj|d|d||S(sMinimal Miller set summarysNumber of Miller indices:sAnomalous flag:R~RN( RR|R}RRR$R&RR5R(R.R~R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs   c C s|dk st|j}|r9|jj}n |j}tj}tj}tj}xMt|D]?\}} |j| d|j| d|j| dqvWd} tj tj || tj tj || tj tj || } } } |jj }d}d}t |d}x^t|D]P\}} |j | }|||||d| |d| |d| fIJqOWdS( sk Write out Miller indices as pseudo-waters for visualization. Note that this treats the indices as literal coordinates (times a scale factor), and the distances between points will not correspond to the distances in reciprocal space. See cctbx/miller/display.py and crys3d/hklview for an alternative (but less lightweight) approach. iiiids/CRYST1%9.3f%9.3f%9.3f%7.2f%7.2f%7.2f P1 sCHETATM%5d O HOH %5d %8.3f%8.3f%8.3f 1.00 1.00 O twN(RRR2t expand_to_p1R$R tintt enumerateR9RQtabstreciprocal_parameterstopentreciprocal_space_vector(R.t file_nameRtucR$thtktlti_mitmitscaletshtsktsltrptc1fmttfmttoftrsv((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytmiller_indices_as_pdb_files,      2$cC s:|dkrtj}n|jd|d||j}|jdk r|j}|jj}|j t }||dI|IJ|dkr|j d|}||dIJn|j jj t }||dI|IJn|j dk r|j}||d|IJ|jdk r|jjdkr|r|jd d |jd t } | j} | jddkst| jd dkst| jd } | jd } | dkr |d|d kr ||d | | IJn| | jd7} | dkrD||d| | IJn|jr|jsh|jr||d|jIJqqn|jdk r6|jr6|r6|j\} }||dI|jjIJ||dI|j|IJt|tr6|j r6||d|j!IJq6n|S(s1Display comprehensive Miller set or array summaryR~RsSystematic absences:it selections.Systematic absences not included in following:sCentric reflections:sResolution range: %.6g %.6gtn_binsit use_binningis&Completeness in resolution range: %.6gs&Completeness with d_max=infinity: %.6gs0Anomalous completeness in resolution range: %.6gsBijvoet pairs:sLone Bijvoet mates:sAnomalous signal: %.4fN("RR|R}RRR%tis_unique_set_under_symmetrytsys_absent_flagsRRR<tselectt centric_flagsR2tresolution_rangeR$R't setup_binnert completenessR!R;RRER&tis_xray_intensity_arraytis_xray_amplitude_arraytanomalous_completenesstmatch_bijvoet_matestpairst n_singlesRRt is_real_arraytanomalous_signal(R.R~Rt no_sys_absRRt n_sys_abst n_centrict d_max_mintcompleteness_d_max_d_minR!tn_obst n_completetasutmatches((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytshow_comprehensive_summarysd              ic C s|dkrtj}n|jd|x|jjD]}|jj|}|j|}|j\}}|j d|}|j t } |jj d|dt dt } d} || || || fIJ|jq;WdS(s6 Display the completeness in resolution bins. treflections_per_bintd_maxR:RoRrs%3d: %-17s %4.2f %6dN(RR|R}RR!RRRRRRR<RzR,tflush( R.RtoutR:tseltself_selRR(tcompltn_reftd_rangeR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytshow_completenesss cC sR|jtks*|jtks*t|jdtjj|d|jS(NR1R&(R&R,R<RRRR5treflection_intensity_symmetry(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR&s* cC sL|j}|r-|jd|j}n|jd|j|jS(s Generate a boolean Miller array flagging those reflections which are systematically absent under the current symmetry. R&R(t space_groupt(build_derived_reflection_intensity_groupR&Rt is_sys_absentR$(R.t integral_onlyteffective_group((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR,s   cC s"t||jj|jS(sG Generate a boolean Miller array flagging centric reflections. (RRt is_centricR$(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR8scC s+t||jj|j|jS(s Generate a Miller array (with integer data) indicating the multiplicity of each unique reflection. (If the set or array is already symmetry-unique, the multiplicity will be 1 for every reflection.) :returns: array object with flex.int data (RRt multiplicityR$R&(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytmultiplicities@scC s"t||jj|jS(N(RRtepsilonR$(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytepsilonsLscC s"t||jj|jS(N(RR2RdR$(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRdQscC s.t|tj|jj|jdS(Niig?(RR tpowR2RdR$(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt d_star_cubedUscC s"t||jj|jS(sW Generate a double Miller array containing the resolution d of each index. (RR2RcR$(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt d_spacingsYscC s"t||jj|jS(N(RR2RfR$(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytsin_theta_over_lambda_sqascC s(t||jj|j||S(s[ Generate a double Miller array containing the scattering angle of each index. (RR2RgR$(R.RhRi((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRgescC s"tj|jj|jS(s; High-resolution limit. :returns: Python float (Rtd_star_sq_as_dR2t max_d_star_sqR$(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR(mscC s|jj|jS(N(R2tmin_max_d_star_sqR$(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRtscC s,tg|jD]}tj|^qS(sO Low- and high-resolution limits. :returns: Python tuple of floats (RnRRR(R.Rd((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRwscC st|jS(N(t index_spanR$(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRscC s"|j}|j|jfS(s!Return the range of h,k,l indices(RtminRQ(R.tspan((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytmin_max_indicess cC s5|jj|}|dkr'd}n ||}|S(se Returns the element `ary` coresponding to the `miller_index` if `miller_index exists, otherwise returns None. :param miller_index: Miller index as a 3-tuple :type miller_index: tuple :param ary: any array (e.g. self.data(), self.sigmas()) :type ary: sequence (list, array, etc) :returns: type of contents of `ary`, or None N(Rt first_indexR(R.taryt miller_indextfirstRw((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytat_first_indexs    cC s|jj|S(sC Returns the first index of the item matching `miller_index`. If the `miller_index` is not found in `self`, then returns ``None``. :param miller_index: Miller index as a 3-tuple :type miller_index: tuple :returns: int, None -- index of first occurrence of `miller_index` or None (RR(R.R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs c C s|j\}}tt|d|d}tt|d|d}tt|d|d}d|||gkst|jjd \}}}d||d||d||fS(sR Returns the effective resolution limits along the reciprocal space axes. iiiig?N(RRQRRRR2R( R.tmin_mitmax_mitmax_htmax_ktmax_ltasttbsttcst((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytd_min_along_a_b_c_starsg{Gz?cC sd|jd|S(Nii(R((R.t tolerance((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt!minimum_wavelength_based_on_d_minscC s |jS(sSynonym for d_max_min().(R(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRsi2c C sX|jd|jjd|jd|d|d|d|d|d|d |d | S( s Given an isotropic or anisotropic displacement or B-factor, alculate resolution-dependent scale factors corresponding to the indices. (Note that to simply apply one of the input parameters to an existing Miller array, you can call array.apply_debye_waller_factors) :param u_iso: Isotropic displacement (in Angstroms) :param b_iso: Isotropic B-factor (8*pi^2*u_iso^2) :param u_cart: Anisotropic displacement tensor :param b_cart: Anisotropic B-factor :param u_star: Anisotropic displacement tensor in fractional space :param u_cif: Anisotropic displacement tensor, dimensionless basis :returns: cctbx.miller.array object RR6tu_isotb_isotu_carttb_carttu_ciftu_start exp_arg_limitttruncate_exp_arg(RR2tdebye_waller_factorsR$( R.R R RRRRRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs    cC s(|jdt\}}|jjS(s#Return the number of Bijvoet pairs.t#assert_is_unique_set_under_symmetry(RR,RR'(R.RR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytn_bijvoet_pairss cC std|d|jdtS(sg Return a copy of the set using the same indices but with the anomalous flag set to false. R1R$R&(R4R$R,(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytas_non_anomalous_sets cC s||gjddks!t|jjdkrBt}nh|dk rwd|j|jj|k}n3|dk r|j|k}n|jdk}td|d|jd|S(s Set the anomalous flag automatically depending on whether the data contain Bijvoet pairs (optionally given minimum cutoffs). :returns: a copy of the set with (maybe) a new anomalous flag iiR1R$R&N(RRRR$R'R,RR4(R.tmin_n_bijvoet_pairstmin_fraction_bijvoet_pairsR&((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytauto_anomalouss!     cC s4tjd|jjd|jd|jS(sG Determine whether the indices in the set are symmetry-unique. tspace_group_typeR&R6(R"RR%RR&R$(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs  cC s4tjd|jjd|jd|jS(NRR&R6(R"tunique_under_symmetry_selectionR%RR&R$(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs  cC s;|j}|j|jjkr.|S|j|S(N(RR'R$R(R.R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytunique_under_symmetrys cC s|jjj|jS(s Indicate whether the array is entirely contained within the reciprocal space asymmetric unit (ASU). Warning: this calls map_to_asu internally, so it is better to simply call map_to_asu without checking in many cases. (t map_to_asuR$tall_eq(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt is_in_asuscC se|jj}|j}|dkr3t}nt|jj||t|||jS(s Convert all indices to lie within the canonical asymmetric unit for the current space group (while preserving anomalous flag). Required for many downstream steps. N( R$RR&RR<RR%RR4(R.RR&((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs   gư>c C s+|jttfkst|jjdkr[td|d|jdtjS|d k rt d|d|jd|S|d k}|r|j }n|}|d k r|d|9}nt d|d|jd|d|}|r#|j |j j|dtk}n|Sd S( s] Generate the complete set of Miller indices expected for the current symmetry, excepting systematic absences. :param d_min_tolerance: tolerance factor for d_min (avoid precision errors) :param d_min: High-resolution limit (default = d_min of current set) :param d_max: Low-resolution limit (default = d_max of current set) iR1R&R$t max_indexiR(RN(R&R,R<RR$R'R4R RRR=R(RRRt fp_eps_double(R.RBR(RR!td_min_was_nonet d_min_exactRw((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRC,s4         &ic C s |dkst|sf|jd|d|}t|jjtd|jjd|S|jd k s~tg}xft|jj |jj D]=\}} | dkr|j |q|j ||| qWt d|jd|dd S( s Calculate the (fractional) completeness of the array relative to the theoretical complete set, either overall or in resolution bins. By default the current low-resolution limit will be used. :param d_min_tolerance: tolerance factor for d_min (avoid precision errors) :param d_max: Low-resolution limit (default = d_max of current set) :param multiplier: Factor to multiply the result by (usually 1 or 100) iRBRig?R!RRs%5.3fN( RRCRR$R'RQR!RtzipR;RER9R( R.RRBt return_failRt multiplierRCRtn_givenR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRPs  +  cC stj|jjtS(N(R tboolR$R'R<(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt all_selectioniscC sh|jdk st|dkr3|j}n|rC|}n|jj|}t|||S(s  Select a subset of reflections. :param selection: flex.bool or flex.size_t selection :param negate: select the inverse of the selection array :param anomalous_flag: anomalous flag for the new set :returns: a new set with a subset of indices N(R$RRR&RR4(R.RtnegateR&R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRls   cC s|j|jjS(sl Select only acentric reflections. :returns: A Miller set or array (depending on object type). (RRR(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR>|scC s|j|jjS(sk Select only centric reflections. :returns: A Miller set or array (depending on object type). (RRR(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR?scC s#|jd|jjd| S(NRR+(RRR(R.R+((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytremove_systematic_absencess cC s|j}|jj}|jds3ttj|}|dk rq|dkrq||d|kM}n|dk r|dkr||d|kM}n|S(se Obtain the selection (flex.bool array) corresponding to the specified resolution range. iiN(R*RdRtall_geRR tsqrtR(R.RR(RwRdtd_star((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytresolution_filter_selections icC s(|jd|jd|d|d|S(s Select a subset within the indicated resolution range. :param d_max: Low-resolution cutoff :param d_min: High-resolution cutoff :param negate: Select outside this range instead :returns: set or array depending on object type RRR(R+(RR0(R.RR(R+((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytresolution_filters cC sU|r|j|stn|j|jks<tt|j|jS(N(tis_similar_symmetryRR&t match_indicesR$(R.Rtassert_is_similar_symmetry((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR3scC s4|jd|d|j}|j|jdS(s Match the indices in the current set and another set, and return a set (or array) containing only those reflections present in both. Assumes that both sets are already in the asymmetric unit (ASU). RR4i(R3RRtcolumn(R.RR4R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt common_sets cC sn|jd|d|}|r4|j s4tn|j}|j|jd|j|jdgS(sn Like common_set(other), but returns a tuple containing matching copies of both sets (or arrays). RR4ii(R3t have_singlesRRRR5(R.RR4tassert_no_singlesRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt common_setss   cC s(|j|jd|d|jdS(s Match the indices in the current set and another set, and return a set (or array) containing reflections which are present only in the current set. Assumes that both sets are already in the asymmetric unit. RR4i(RR3tsingles(R.RR4((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytlone_setscC sF|jd|d|}|j|jd|j|jdgS(sq Like lone_set(other), but returns a tuple containing the reflections unique to each set (or array). RR4ii(R3RR:(R.RR4R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt lone_setss   cC s|jdtfkst|jdk s6t|jdk r~|j}t|jj|jd|}n|}t|jd|}||fS(s Group Bijvoet mates (or Friedel mates) together, returning an object that allows enumeration over matching pairs and/or singletons. RN( R&RR<RR$R%RRR(R.RRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs    t resolutionc C s|d kst|ttgks*t|dkratjd|jj|jd|S|dkrQ|jj}t |j j t||jj}t |j j t||j j }||k}tj|jjd}|j|jdt|j|j|}tjd|d|Stjdt|jj|jd|SdS( s Generate the selection array (flex.size_t object) to reorder the array by resolution or Miller index. :param by_value: sort type, must be "resolution" or "packed_indices" :param reverse: invert order :returns: flex.size_t object R=tpacked_indicest asu_indicesRtreverseig{Gz?N(R=R>s asu_indices(RR,R<R tsort_permutationR2RdR$RRR%RRRtdoubleR't set_selectedt iselectionRtpackt as_double( R.tby_valueR@R?tasu_indices_anomRtfriedel_mate_flagstoffsetR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRAs$   "   cC s|j|jd|d|S(ss Reorder reflections by resolution or Miller index. :param by_value: 'resolution' or 'packed_indices' RGR@(RRA(R.RGR@((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytsortsg?ig@itcnsc C s|d kst|r-|dks-tn|s[|jd|d|d|d|d |S|jd|d|d |d|d|d |Sd S( s Create an array of R-free flags for the current set, keeping anomalous pairs together. Requires that the set already be unique under symmetry, and generally assumes that the set is in the ASU. :param fraction: fraction of reflections to flag for the test set :param max_free: limit on size of test set, overrides fraction :param lattice_symmetry_max_delta: limit on lattice symmetry calculation :param use_lattice_symmetry: given the current symmetry, determine the highest possible lattice symmetry and generate flags for that symmetry, then expand to the current (lower) symmetry if necessary. This is almost always a good idea. :param use_dataman_shells: generate flags in thin resolution shells to avoid bias due to non-crystallographic symmetry. :param n_shells: number of resolution shells if use_dataman_shells=True :param format: convention of the resulting flags. 'cns' will return a boolean array (True = free), 'ccp4' will return an integer array from 0 to X (0 = free, X dependent on fraction), 'shelx' will return an integer array with values 1 (work) or -1 (free). :returns: a boolean or integer Miller array, depending on format. RLtccp4tshelxitfractiontmax_freetuse_dataman_shellstn_shellstformatt max_deltaN(RLRMRN(Rtgenerate_r_free_flags_basict)generate_r_free_flags_on_lattice_symmetry(R.RORPtlattice_symmetry_max_deltatuse_lattice_symmetryRQRRRS((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytgenerate_r_free_flagsscC s"tjd|jd|jS(s{Get crystal symmetry of the miller set :returns: a new crystal.symmetry object :rtype: cctbx.crystal.symmetry R2R%(RR5R2R%(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR1Ns  c C s|jj} | dkr-tdn| } |jrn|jd} | jj| j} n|jst|dk r|dko|dkstddn|dkst|t kstn|dk r@|dk stt | ||t | }|dks+t|t ks@tn|r| sSt|dkset|dkswt|dkstn|j } |j| } tj| jd|}tjd| jd |d t }| jjd |d tj| jjdj}|jj}|j}|jj}|jj}d}|s|r|d krtddntjd|d|d|}qOtj||d|}n~tj}t||d}x?t |D]1}tj!|}tj"|}|j#|qWtjt$|d|!}|j}|j%dtj"d |jj&|dt'dt'}|jd |dd}|j(}|jd tjd| jd | j)}|jj}|jr|j*}n|j| j+j}|j,|j}|jd|jd |j-}|j,|}|jj.|jst|S(s Generate R-free flags by converting to the highest possible lattice symmetry (regardless of intensity symmetry), creating flags, and expanding back to the original symmetry. This is a safeguard against reflections that are correlated due to twinning being split between the work and test sets. This method should usually not be called directly, but rather through set.generate_r_free_flags(...). is9An array of size zero is given for Free R flag generationig?s6R-free flags fraction must be greater than 0 and less s than 0.5.RTR2Rtassert_is_compatible_unit_cellR1RRMs5CCP4 convention not available when generating R-free sflags in resolution shells.tn_reflt fraction_freeRRSR$R@RN(/R$R'RR&RRRRRRR,Rtfloatt!change_of_basis_op_to_niggli_cellt change_basisRtgroupR2RR5RRR RBRtmerge_equivalentstaverage_bijvoet_matesRtassign_r_free_flags_by_shellstassign_random_r_free_flagstsize_tRRt random_doubleRAtextendtlistRRdR<RRtgenerate_bijvoet_matestinverseR6RR(R.RORPRTtreturn_integer_arrayt n_partitionsRQRRRSt n_originalt n_non_anoRtcb_op_to_nigglittmp_mat lattice_groupt lattice_xst tmp_flagstn_non_ano_lat_symR`Rwtn_timestiittmpR$((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRVXs           '                 !cC s|dko|dks+tddn|rP|dkrPtddn|dk r{|dkr{tdd n|jr|jd }|jd }|jd }|jd } |jd } |jj|j} ~n$|j st |j j} |dk rDt ||t d | }n|r|d kritddntjd| d|d|} ntjd| d|d|} |js|j } ne|j j|} | j|j j| | j|j j| | j| ks(t | jdtjd|jj| dtdt} |js}|jd| S~ |d krd}tj|j jd }nW|dkrd}tj|j jd }n$t}tj|j jt}|j}|| j}| | }|j|||j||j|ksdt |j|||j|d|j|kst ~~~|j| | ||!~ |j| | |~ |jd|S(s Generate R-free flags, without taking lattice symmetry into account (not recommended). Should not normally be called directly - use generate_r_free_flags(...) instead. ig?s6R-free flags fraction must be greater than 0 and less s than 0.5.is9You must use at least 5 resolution shells when assigning sR-free flags this way.s6The maximum number of free reflections must either be sNone, or a positive number.it+RMRMs5CCP4 convention not available when generating R-free sflags in resolution shells.R[R\RRSR$RR@RNiiN(RRR&Rtpairs_hemisphere_selectionR:RR'RRRR$RRQRRcRdRRgR RAR2RdR<RRR)R,RCR(R.RORPRQRRRSRtsel_pptsel_pmtsel_sptsel_smR`RwR$ttest_flag_valuet result_fullti_ppti_pp_sptr_pp((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRUs                  ! !  $(c C sttjd|jd}|r/|d9}n|dtj9}|jd|jjd|jd|d|S( NR'g?ihiRR6tphasesRi( R RfR'tmathtpiRRtnearest_valid_phasesR$(R.Rit random_phases((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt0random_phases_compatible_with_phase_restrictions!s  cC sXt|tr!tj|}ntj|dtjj||d|j |j S(s&Get a transformation of the miller set with a new basis specified by cb_op :param cb_op: object describing the desired transformation of the basis :type cb_op: string or sgtbx.change_of_basis_operator :returns: a new miller set with the new basis :rtype: cctbx.miller.set R1R$( RRSRtchange_of_basis_opR4RRR5R_tapplyR$(R.tcb_op((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR_*s  c C sy|jdk st|jdk s0t|jdk sHttd|jd|jd|jd|S(NRR&R$tbuild_iselection(R%RRR$R&texpand_to_p1_iselectionR(R.R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR8s   cC sS|jd|}td|jd|jd|j}|rO||jfS|S(sGet a transformation of the miller set to spacegroup P1 :returns: a new set of parameters (symmetry, miller indices, anomalous_flag) in spacegroup P1 :rtype: set(cctbx.crystal.symmetry, cctbx.miller.indices, boolean) RR1R$R&(RR4tcell_equivalent_p1R$R&RD(R.treturn_iselectiontproxyRw((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRBs   cC s7|jtksttj|dtjj|S(NR1(R&R,RR4RRR5tpatterson_symmetry(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRQs g@icC ss|d kr'|d kr'|j}ntjd|jd|d|d|d|d|jd|d|d | S( s Calculate real-space grid for FFT given array crystal symmetry, d_min, and desired resolution-dependent spacing. The actual grid dimensions will be adjusted to suit the needs of the FFT algorithm. R2R(tresolution_factortsteptsymmetry_flagsR%tmandatory_factorst max_primetassert_shannon_samplingN(RR(Rtcrystal_griddingR2R%(R.RR(t grid_stepRRRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRVs    c C sb|jdkr0|jdkr0|js6tt|tjrW|j}nt|tjst|tj st|t t fkstt|tjrBt j |j}|js*| st|j|jksttj|tj|jj|j}ql|sl|j}qln*|sW|j}nt j|j}|j|}|r|jjtj|jj}||9}n|d kr|j}n|rtj j!d|j"d|d|j#d|dt }n-tj j!d|d|j#d|dt }|j$} t%d|d | S( s Run FFT on a real-space map to calculate structure factors corresponding to the current set of Miller indices. :param map: flex.double map with 3D flex.grid accessor :param in_place_fft: perform the FFT in place instead of creating a copy of the map first :param use_scale: perform volume-scaling using current unit cell :param anomalous_flag: determines anomalous_flag for output array :param use_sg: use space-group symmetry :returns: array with same Miller indices and complex_double data iiRR&R6t complex_maptconjugate_flagR0RN(&t focus_size_1dtndt is_0_basedRRR RRFRBtcomplex_doubleR,R<R treal_to_complex_3dtfocust is_paddedtn_realRRtgridtm_realt set_focusRtcomplex_to_complex_3dtforwardR2tvolumeRtcoltproductRR&tstructure_factorstfrom_mapRR$RR( R.tmapt in_place_fftt use_scaleR&tuse_sgtfftRRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytstructure_factors_from_mapnsJ6*  3          c C sddlm} |dkr;| jjd|d|d|S| jjd|d|d|d|d |d |d |d | d|d|d |S(s Calculate structure factors for an :py:class:`cctbx.xray.structure` object corresponding to the current set of Miller indices. Can use either FFT or direct summation. :param xray_structure: :py:class:`cctbx.xray.structure` object :param algorithm: switch method to calculate structure factors - can be 'direct' or 'fft' :returns: array with same Miller indices and complex_double data i(txraytdirecttxray_structureR0t cos_sin_tabletgrid_resolution_factortquality_factortu_basetb_baset wing_cutofftexp_table_one_over_step_sizet algorithm(tcctbxRRtfrom_scatterers_directtfrom_scatterers( R.RRRRRRRRRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt!structure_factors_from_scattererss$    c C sddlm}tj}t}xF|jD]8\}}|j|jj|j |j|q2Wt j d|d|d|j d|j d|jd|jd |jj}t||S( s A miller.array whose data N(h) are the normalisations to convert between E's and F's: E(h) = F(h) / N(h) The argument wilson_plot shall feature attributes - wilson_intensity_scale_factor - wilson_b i(teltbxt form_factorsRtwilson_intensity_scale_factortwilson_bR$R2R(RRR RBtshared_gaussian_form_factorstitemsR9txray_scatteringtwk1995tfetchR"tamplitude_normalisationRRR$R2RtnormalisationsR( R.t asu_contentst wilson_plotRRt gaussianst chemical_typet multiplicyR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytamplitude_normalisationss        c C sR|jd|d|jd|d|d|d|d|d|d |d | jS( Nt f_obs_factortf_calcRRRRRRRR(tf_obs_minus_f_calcRR( R.RRtstructure_factor_algorithmRRRRRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt!f_obs_minus_xray_structure_f_calcs  cC s|s$|dks$|dks$t|sH|dksH|dksHt|r|dkrcd}n|dkrxd}ntt|j|d}||krtt|j|d}qn)|rtt|j|d}n|dkst|jdk st|jjdksG|dksGt|jdt|j||j|||j S(s` Create internal resolution binner object; required for many other methods to work. iiig?R/N( RRRRR$R2RR'RR/R!(R.RR(t auto_binningRRt n_per_bin((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs$$$    &#* %idg-C6?ic s|dkst||jjksF|d|jjkretj|jjtgS|jjj}dtj |}tj |d||d}|jj}|fd} | } t | |kr<t d|d| } ng} xt | D]\} } | t | dkr| | | d}}| j ||gqO| t | dkrO| | d| | }}| j ||gqOqOWg}xt | D]\} }|\}}| dkr?|||k}|||kM}nU| t | dkrx||k}||||kM}n||k}|||kM}|j |qW|t |d}|t |d}|jt|jtdkr|t |d }|j ||B|}ng}x{t |D]m\} }|jt|krt |ddkr|t |d|B|t |d|jd|dd|n|j|dd||jdt|j|S(Nig?g?iiiR/(RRRRdRRR'RQR R]R RARBtreserveR9RRR/R2( R.RR(Rt d_toleranceRdRR3tmR:R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytsetup_binner_counting_sortedms<    %   "   cC s|jS(sU Return a reference to the current resolution binner (or None if undefined). (R(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR!scC st|||_|jS(s| Use the resolution binning of the specified binner object (does not need to be from an identically sized set). (R!R(R.R/((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRscC s|jd|jS(sp Use the resolution binning of the specified set (does not need to be an identical set of indices). R/(RR!(R.R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytuse_binning_ofscC s4|jj|js!t|j|_|jS(sQ Use the exact binner of another set, which must have identical indices. (R$RRR(R.R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt use_binner_ofs! cC s d|_dS(N(RR(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt clear_binnerscC sm|r|j|stn|j|jks<ttd|d|jj|jd|jS(su Combine two Miller sets. Both must have the same anomalous flag, and similar symmetry is also assumed. R1R$R&(R2RR&R4Rt concatenateR$(R.RR4((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRsc C s|dk r9|dkstdddgj|}n|dk r|dkr]|dkscttd|jd|d|}n?d||gksttd|jd|d|d|}|j|S( NRRRR$t slice_axist slice_indext slice_startt slice_end(RRtindext simple_sliceR$t multi_sliceR(R.taxist axis_indexRRRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytslices       cC s7t|dkst|j|k}|j|S(sA Remove all reflections with the specified Miller index. i(RRRR$R(R.thkltsele((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt delete_indexscC sXtj|jjt}x*|jD]}||j|kM}q+W|j|S(s] Delete multiple reflections, as specified by the Miller indices of another set. (R R)R$R'R<R(R.RRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytdelete_indicessNgUUUUUU?gUUUUUU?(^RRt__doc__RR#RR$R&R'RRRRRRRR,RRRRRRRRRdRRRRgR(RRRRRRR R RRRRRRRRR RRCRR*RR>R?R,R0R1R<R3R6R9R;R<RRARKRYR1RVRURR_RRRRRRRRRRRRR!RRRRRRR R (((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR4Js4         4                         #            )  l Q    5    G  !        cC s|dk r`||gjddks-tt|t|jj||j|}nZt|t|j|jj||j|}|dk r|j d|}n|S(s Given crystal symmetry, anomalous flag, and resolution limits, create a complete set object. :param crystal_symmetry: cctbx.crystal.symmetry object :param anomalous_flag: Boolean, indicates whether to generate anomalous indices :param d_min: High-resolution limit (optional if max_index is specified) :param d_max: Low-resolution limit (optional) :param max_index: highest-resolution Miller index :returns: a set object iRN( RRRR4tindex_generatorR%Rtto_arrayR2R1(R1R&R(RR!Rw((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR=s( !      cC s_t|dkstt}x!|D]}|j|jq(W|djd|jS(NiR$(RRRtunion_of_indices_registrytupdateR$Rtas_array(t miller_setstuoitms((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt union_of_sets s   t array_infoc B szeZdZddddeedddd ZdZeeeeeeeeed ZdZ dZ dZ RS(sf Container for metadata associated with a Miller array, including labels read from a data file. c C st|tdS(N(Rtlocals( R.tsourcet source_typethistoryRtmergedtsystematic_absences_eliminatedtcrystal_symmetry_from_filettype_hints_from_fileRh((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR#s cC s8d|_|jj|t|ds4d|_ndS(NRh(RRt__dict__RthasattrRh(R.tstate((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt __setstate__%s c C s|tkr|j}n|tkr0|j}n|tkrH|j}n|tkr`|j}n|tkrx|j}n|tkr|j}n|tkr|j}n|tkr|j}n| tkr|j } nt d|d|d|d|d|d|d|d|d | S( sw Create a modified copy of the array_info object, keeping all attributes that are not explicitly modified. RRRRRRRRRh( RRRRRRRRRRhR( R.RRRRRRRRRh((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR+s8             cC s^g}|jdk r(|j|jn|jrA|jdn|jrZ|jdn|S(NRR(RRRgRR9R(R.tpart_2((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytas_string_part_2Qs  cC s/|j}t|dkr+dj|SdS(s A combined representation of the data labels extracted from the input file. This is generally how downstream programs will identify and select Miller arrays. it,N(R#RRRYR(R.R"((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt label_string[s  cC sg}|jdk r.|jt|jn(|jdk rV|jt|jn|j}t|dkr|jdj|nt|dkrdSdj|S(NiR$Rt:(RRR9RSRR#RRRY(R.RwR"((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt__str__es N( RRR RR,R#R!RRR#R%R'(((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs.    cC sN|dkrtdSy|jjdt|fSWntk rIdSXdS(Ns , size=%dtUnknown(RRSt __class__RRRt Exception(R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytraw_array_summaryrs   Rc B seZdZdddZdZdZdZdZdZ dZ dZ d Z d Z d Zdd d ZdZdZdZdZdZdZdZdZdZdZdZdZdZdZeeeeeeeeedd Ze dZ!eeeeedZ"dZ#d Z$d!Z%d"Z&dd d#Z'd$d%Z(d$dd&Z)d'd(dd)Z*d*d+d,Z+d-d.Z,d/d+d0Z-d1d2Z.d/d3Z/d1d4Z0dd5Z1e d6Z2de d7Z3e4d8Z5e4e4d9Z6e d:d;Z7d+ddd<d<d=Z8d>e4d?Z9d@Z:de4dAZ;ddBZ<e dCZ=dDZ>dEe4ddFZ?dGZ@dddHZAdIZBdJZCdddKZDdLZEdMZFdNZGe4d+dde dOZHdPZIe4dQZJe4e4e4dRZKe4d(ddSZLdTe d(dUZMe4dVZNe4dWZOe4ddXZPe4ddYZQe4ddZZRe4d[ZSe4d\ZTd]d^dd d_ZUd`ZVde4e4daZWe4ddbZXe4e4dcZYe4ddZZe4deZ[dddfZ\ddgZ]dddddde dhe4di Z^e4e4e4e4djZ_e4e4dkZ`e4e4e4dlZae4e4dmZbe4e4dnZce4e4e4doZddpdqZee4drZfdsZgdtZhd:duZid:dvZjddwZkdxZldyZme4dzZnd{Zod|Zpe4d}Zqd~de dZrdZsdZte4dd dZue4dZvdZwdZxdZydZzdZ{dZ|dZ}dZ~dZe4e dZdd ddZdZddddddde dd ZddZddZdddZddddddZdpdddddde dd Zddddde de4e4d Zde4dZdgdddZdddde4dZddddddddZdZdddZe ddddZddZddZdde4dZddddZeeZdZdZdde4dZddddZdZddde4dZddZdZddZdZdZe4d:e4dZe4dZdZdZdZdZe ddZddde e e ddZejd dZdddZddZRS(s Extension of the set class with addition of data and (optional) sigmas flex arrays, plus an optional array_info object and an optional flag for the observation type (amplitude, intensity, or reconstructed amplitude). cC s8tj||||_||_d|_d|_dS(N(R4Rt_datat_sigmasRt_infot_observation_type(R.R0RR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR#s    cC sDtj|||j|_|j|_|j|_|j|_dS(N(R4RR,R-R.R/(R.R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs    cC s ||_|S(N(R.(R.tinfo((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytset_infos cC s_ddlm}t|tr.|j}n|dksRt||jsRt||_|S(Ni(tobservation_types( t cctbx.xrayR2RRtobservation_typeRtanyRR/(R.R4R2((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytset_observation_types $ cC s#ddlm}|j|jS(s1 Flag the array as X-ray amplitudes (F). i(R2(R3R2R6t amplitude(R.R2((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt#set_observation_type_xray_amplitudescC s#ddlm}|j|jS(s2 Flag the array as X-ray intensities (I). i(R2(R3R2R6t intensity(R.R2((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt#set_observation_type_xray_intensityscC s|jS(N(R,(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRscC s|jS(N(R-(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRscC s@|dk r3|j|jjks3tn||_dS(N(RR'R$RR-(R.R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt set_sigmass 'cc sx|jdk rIx_t|j|j|jD] }|Vq7Wn+x(t|j|jD] }|VqeWdS(N(RRRR$R(R.titem((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt__iter__s + "cC s|jS(sB Return the associated info object, or None if undefined. (R.(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR0sR{cC swtj|d|d||jdk rst|jtrs|jj}|dk rs||d|IJqsn|S(NR~RsWavelength: %.4f(R4RR0RRRRh(R.R~RRh((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs ' cC s|jS(s Return the (experimental) data type, if defined. See the module cctbx.xray.observation_types for details. :returns: an object from cctbx.xray.observation_types (R/(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR4scC s|jdk st|jdk s0t|jj|jjksZt|jdk r|jj|jjkstntj|S(N(R$RRRR'RR4(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR's *-cC st|jtjS(N(RRR t std_string(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytis_string_arrayscC st|jtjS(N(RRR R)(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt is_bool_arrayscC sFt|jtjpEt|jtjpEt|jtjS(N(RRR RtlongRe(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytis_integer_arrayscC s.t|jtjp-t|jtjS(N(RRR R]RB(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRscC st|jtjS(N(RRR R(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytis_complex_arrayscC st|jtjS(N(RRR thendrickson_lattman(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytis_hendrickson_lattman_arrayscC s&ddlm}t|j|jS(Ni(R2(R3R2RR4R7(R.R2((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRscC s&ddlm}t|j|jS(Ni(R2(R3R2RR4treconstructed_amplitude(R.R2((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt%is_xray_reconstructed_amplitude_arrayscC s&ddlm}t|j|jS(Ni(R2(R3R2RR4R9(R.R2((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRscC s|jp|jS(N(RR(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytis_xray_data_arrayscC s=td|d|jd|jj|jj|S(sI Create a new array object using references to internal objects. R0RR(RRRR1R0R6(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs  cC sd}d}|jdk r3|jj}n|jdk rZ|jj}ntdtj|d|d|j|jj|S(sG Clone the array, making copies of all internal array objects. R0RRN( RRRRRR4R1R0R6(R.RcR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR s c C s|tkr|}n|tkr0|j}n|tkrK|j}n| tkrf|j} n| tkr|j} ntj|d|d|d|d|d|}td|d|d|j| j | S( NR1R$R&R2R%R0RR( RRRR0R4R4RRR6R1( R.R0RRR1R$R&R2R%R4R0((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs$       cC s|j|jgjddkr|jjd|jd|jd|jj|jd|jj|jS|jjd|jjd|jj|jSdS(NiRR4RR(RRRR4RRR(R.RR4((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR3s'  !c C s+tj|d|d|d|d|d|S(sA Return the basic cctbx.miller.set object for the array. R1R$R&R2R%(R4R(R.R1R$R&R2R%((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR4>s cC stj|ddS(s4 Create a copy of the array without sigmas. RN(RRR(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytdiscard_sigmasNscC s4|jsttj|dtj|jS(NR(RCRRRR tconjR(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt conjugateTscC s|jd|jjS(sz Create a copy of the array with the data converted to a flex.double type. Will fail for incompatible arrays. R(RRRF(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRFXsc C sCtdtj||dt|j|dt|j|S(NR0RR(RR4RR RR(R.R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR_scC s|dkrtj}n||d|jjI|jIJ||dI|jIJ||dIt|jIJ||dIt|j IJt j |d|d||S(NsMiller %s info:sObservation type:s Type of data:sType of sigmas:R~R( RR|R}R)RR0R4R+RRR4R(R.R~R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRes    ic C sm|jst|js$t|jdk s<t|j|jksZtt||j}|}|}|j}tj|j |j |j}|j tj |j }tj |dt | }|j |}|j |}|j |} |j |}|j} |jj } td| d|d|d| d|d| S(NR@R$tfo_sqtfc_sqtdelta_f_sq_over_sigmatfc_over_fc_maxR(RRRRR'Rtas_amplitude_arrayR RRRQRAR<RR$RR( R.t f_calc_sqt n_reflectionstfo2tfc2tfct delta_f_sqROtpermRNR$R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytdisagreeable_reflectionsos. )  c C s|dkrtj}n|j|d|}|dIJx~t|jjD]g}|d|j|I|d|jj||j j||j ||j ||j |fIJqMW|S(NRRsR h k l Fo^2 Fc^2 |Fo^2-Fc^2|/sig(F^2) Fc/max(Fc) d spacing(A)s %3i %3i %3is1 %9.2f %9.2f %9.2f %9.2f %9.2f( RR|R}RXtrangeRLR'R$RRMRNROR(R.RQRRRRwR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytshow_disagreeable_reflectionss    g{Gz?g@c C std|d|d|d|S(Nt miller_arrayt#unit_cell_relative_length_tolerancet"unit_cell_absolute_angle_tolerancetworking_point_group(t6crystal_symmetry_is_compatible_with_symmetry_from_file(R.R\R]R^((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR_s g?gư>cC sd}|jdk r|jjdkrt}|j|kjt}|jj}t|t|}||krt}qn|S(Ni(RRR'R<RR]R,(R.tcritical_ratioRRwt suspectedtalltratio((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytsigmas_are_sensibles*  gcC sH|jst|jdk s*t|js<t|j|jdk}|j|j}|j||k}tj|j|jd|j|j}tj|j|d}d|||jd||j|j}tjd|}|j d|d|j |j }|S(s Takes in an intensity array (including negatives) and spits out amplitudes. The basic assumption is that P(Itrue) \propto exp(-(Itrue-Iobs)**2/(2*s)) where Itrue>=0 (positivity constraint on error free amplitudes). For amplitudes, this results in P(Ftrue) \propto 2 Ftrue exp( -(Ftrue**2-Iobs)**2/(2s) ) A Gaussian approximation is fitted to the Mode of this distribution. An analytical solution exists and is implemented below. This method does not require any Wilson statistics assumptions. ig@g?g@RRN( RRRRRdRRR R.RR6RP(R.t i_sig_levelti_sigitdettf_saddlets_saddleRw((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytenforce_positive_amplitudess 7 txtal_3_7c C s8ddlm}|jdks4|js4ti|jd6|jd6}|j|}|dkrt dt |ddj g|j D]}t |^qn|j dkrt|||jj}n6||j|j |}t||j|j}|jr4|jn|S( s Given an intensity/F^2 array (or undefined observation type), return the equivalent amplitudes. Note that negative intensities will be discarded; for French-Wilson treatment, call the separate array.french_wilson() method. i(RRktcrystalssUnknown f_sq_as_f algorithm=%s s Possible choices are: s, N(RRR4RRRtarray_f_sq_as_f_xtal_3_7tarray_f_sq_as_f_crystalstgett RuntimeErrorRRYtkeysRRRR~tsigma_fR8( R.RR Rt converterst converterRRwtr((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt f_sq_as_fs $   F!  tsimplec C s|d kstddlm}|jd ksF|jsFt|jd kr|t||j|j j }n|dkr|j|j |j}t||j |j }nt j }t j }xpt|j |jD]S\}}|j|d|jdtd|ttdt||qWtd|d|d |}|jr|jn|S( sP Convert amplitudes (and associated sigmas, if present) to intensities. Rwtshelxli(Rig{Gz?R0RR(RwRxN(RRRR4RRRRtarray_f_as_f_sqRtf_sqt sigma_f_sqR RBR%R9RQRR:( R.RRRwRuRzts_sqR~R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt f_as_f_sqs"$$   (:  cC sr|jr@td|dtj|jd|jjS|jsRt|j rn|j d|S|S(s Convert the array to simple amplitudes if not already in that format. Only valid for complex (i.e. F,PHI), intensity, or amplitude arrays. R0RRR( RCRR RRRR8RRRRv(R.R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRPs . cC sT|jr"|jjd|S|js4t|jsP|jd|S|S(s Convert the array to intensities if not already in that format. Only valid for complex (F,PHI), amplitude, or intensity arrays. R(RCRPR}RRR(R.R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytas_intensity_array s   cC s|jj}|jj}|js<|jrdt|jj|j||nY|dkrt|jj|j|n(t|jj|j|||t t |||j||j j |S(s Convert all indices to lie within the canonical asymmetric unit for the current space group (while preserving anomalous flag). Required for many downstream steps. N(R$RRRCRERR%RR&RRR4RR6(R.RiRRc((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR s$       *cC s'|r|j|stn|jj|jjksHt|j|jksftt|j|jj}|jj|j|jst|j }|j }|dk r|j|}n|dk r|j|}nt d|d|d|j |S(NR0RR(R2RR$R'R&R3t permutationRRRRRRR6(R.RR4RRcR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt adopt_set1 s*!*    cC s|jj|jjks*t|jdk r{|jj|jjksft|dk s{tn|jd|d|j}t||jj r|j|jkst|j }n$|jj |jj|}|j |j d|jj |j d|jdkrNd}nt||jj r|j|jkst|j }n$|jj |jj|}|j |j d|jj |j d|jd|d|S(NRR4iiRR(RR'R$RRRR3RRR)RRCR5RR(R.Rtdata_substitutetsigmas_substituteR4RRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt matching_set? s0**  +  +c C s3|j|\}}|rAdtj|jjd}nt|jt|j}}d}|rtjjdtj |d|d|}dtj|jjd}|j tj | |j }n9tj ||} | dkrtj ||| }n|jd|j|S( Ng?g@itxtytziR(R9R RRRRtscitbxRtgaussian_fit_1d_analyticalR.taRtbtsumR( R.Rtresolution_dependentRtoRt scale_factorRutss_othertden((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR` s&%*#! cC s1||}}|r(|jd|}n|rt|jtjsLt|j|\}}|jd|}|j|\}} |j |}n|j |} |jj | j} |j j | j } d} |j dk r|j j | j } n|jd| d| d| S(NRt phase_sourceRR$R(RRRR RRR9tphase_transferR<RR;R$RRR(R.RRtreplace_phasesRRts_ctc_ots_ltc_ltoltd_newti_newt sigmas_new((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt complete_witho s  ic C s|j|jkst|jdks6t|jd|\}}|jd|\}}d}|rtjtj|j tj|j } | dkrtjtj|j tj|j | }qn|j |j |} | j |j || j |j |||j } | j |j | j |j t d|j d| d|j} | jd| S(NRiiR1R$R&R(R&RRRR9R<R RRRRgR$R4R1R( R.RRtscale_for_lonestf1_ctf2_ctf1_ltf2_ltscale_k1Rt result_datatresult_indicesR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytcombine s(1 ;  ic C sU|jd|d|d|}t|j|j}|j}|j} | dk rl| j} n|j} | dk r| j} n|jd} | j} | dkr|j}np|j |jj | }| dk r| j | j| |n| dk r9| j | j| |n|j d|d| d| S( NRBR(RiiR$RR( RCR3R$RRRRR:R'RRtresizeR( R.RBR(Rtnew_data_valuetnew_sigmas_valuetcsRRRcRRR`((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytcomplete_array s(          R=cC s|ttfkst|d krBtj|d|d|}n|dkrptjd|jd| }nr|dkrtjdtj|jd| }n;t|t rt d|ntjd|d| }|S( sH Generate the selection array (flex.size_t object) to reorder the array by resolution, Miller index, data values, or absolute data values. :param by_value: sort type, must be "resolution", "packed_indices", "data", or "abs" :param reverse: invert order :returns: flex.size_t object R=R>R?RGR@RRsUnknown: by_value=%s(s resolutionspacked_indicess asu_indices( R,R<RR4RAR RRRRSt ValueError(R.RGR@Rw((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRA s       " cC sO|j}|jr0tj|j}nttj|||jS(N(RRCR RRR4RR(R.R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR s   c C s|jd k st|jd k s0t|jd k sHt|jd k s`td }d }itd6td6j|jj j d }|d k r|d kst|j d kst|d|j d|jd|jd|j}|j}n|d k r|t tgks7t|j d ksOtt|jtjsmttd|j d|jd|jd|jd|}|j}nl|d kst|j}|jj|j}|j d k r|j j|j}n|j}td|jd|jd|jjd|d |j|}|r|d k st||fS|S( s- Generate the equivalent P1 dataset. RRDRR&R$RRiR1RN(R%RRR$R&Rtexpand_to_p1_complext expand_to_p1_hendrickson_lattmanRoR)RRRR,R<RR RBtexpand_to_p1_phasesRRRDR4RRR6( R.t phase_degRt p1_sigmasRDt expand_typetp1tp1_dataRw((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR s\                  c C st|tr!tj|}n|tks9|tkr{|jsKttd|d|j d|j d|}n|j rt d|d|j d|j }n|j s|js|jrtd|j|j d|j j}nB|jr>td|d|j d|j }n tdd}|jdk rt|jtjst|jj}ntdtd tjj||d|j d |jd|j d |j|j S( NRt indices_intdata_inRiR$Rs#Unsupported miller.array data type.R0R1R&R(!RRSRRR,R<RRtchange_basis_phases_doubleR$RRCtchange_basis_complex_doubleR@RBR RRREt change_basis_hendrickson_lattmanRpRRR RBRR4RR5R_R&R6R4(R.RRiRwt result_sigmas((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR_ sJ             cC s|jst|j|j|d|\}}|j|jksTt|j|j}}|tj|}tj|}tj tj |d}dt j dtj ||S(s The factor phi_{sym} quantifying whether complex structure factors are invariant under the given symmetry operator, as used in Superflip. Ref: J. Appl. Cryst. (2008). 41, 975-984 R4ii(RCRR9R_R'RR RJRRtargRRt mean_weighted(R.topR4R~top_ftcc_sftweightstdelta((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytsymmetry_agreement_factor/ scC s|jst|jj|js3t|j|jksQt|jrtd|d||j|jStd|d||jtj|jj d|SdS(NR0RR( RCRR$RR&RRR RR(R.RR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRA s! )g|=c C s|jdk stt|dr6|j}nt|jtjslt|jtjsltt|tjst|tjst|tjstt|tjr |dkrt }nt d|}|d|j d|j d|}nt|jtjr2|j}ntj |j}|j dk s_tt|tjrt d|dt|j |j |||d|jSt d|dt|j |j |||d|jS( s2Combines phases of phase_source with self's data if real (keeping the sign of self's data) or with self's amplitudes if complex. Centric reflections are forced to be compatible with the phase restrictions. phase_source can be a miller.array or a plain flex array. epsilon is only used when phase_source is a complex array. If both the real and the imaginary part of phase_source[i] < epsilon the phase is assumed to be 0. deg is only used if phase_source is an array of doubles. deg=True indicates that the phases are given in degrees, deg=False indicates phases are given in radians. phase_integrator_n_steps is only used if phase_source is an array of Hendrickson-Lattman coefficients. The centroid phases are determined on the fly using the given step size. Rtn_stepsRR6t hendrickson_lattman_coefficientsR0RN(RRRRRR RRBRDtphase_integratorRR$RRRR(R.RRRitphase_integrator_n_stepst integratorR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRO sN            cC s3dtjtj|jj}|j|S(Ni(RRR RfRR'R(R.R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytrandomize_phases s&c C s|jst|dks0|dks0t|dkrf|dkrTt}qftd|}ntd|d|d|jd|jd|jS(NRR0RRR6R(RERRRRRR$R(R.RR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytphase_integrals s     cC s@|jdk stt|tr`tj|j|jdksQt|j}ntj|j}t|tj st|tj stt|tj rtj|}n|}t j j d|d|}tj|j}tj|}|dksttj||}||dt jS(Nitphi1tphi2i(RRRRRR torderR$RRRBRRt phase_errorRRR(R.RRtp2teRtsum_wtsum_we((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytmean_weighted_phase_error s**cC s|jdk stt|tr`tj|j|jdksQt|j}ntj|j}t|tj st|tj stt|tj rtj|}n|}tj t j jd|d|}|dt jS(NiRRi(RRRRRR RR$RRRBtmeanRRRR(R.RRRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytmean_phase_error s**$c C s|jst|dkr?tj|jjt}nddl}|dkrl|j dd}nt j d|j d|d|d|d |}|j d|S( s* Add random error to reflections. iNii@BRRtamplitude_errortphase_error_degt random_seed(RCRRR R)R$R'R<trandomtrandintR"trandomize_amplitude_and_phaseRR(R.RRRRRtnew_data((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR s !     cC s|jdk st|j\}}|jd}|j|j}d}|jdk r~|j|j}ntt ||dt ||S(sm Returns an array object with DANO (i.e. F(+) - F(-)) as data, and optionally SIGDANO as sigmas. RxR&N( RRRRtmiller_indices_in_hemispheretminusRtadditive_sigmasRR4R,(R.RRRRcR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytanomalous_differences scC sp|dkst|jdk s*t|j\}}dj|}|jd|jj|dtS(NRxRMs+-RR&(RxRM( RRRRRRRR5R,(R.t plus_or_minusRRti_column((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pythemisphere_acentrics s cC sh|jdk st|j\}}tgdD]-}|jd|jj|dt^q4S(NiiRR&(ii( RRRRRnRRR5R,(R.RRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pythemispheres_acentrics s c C s|jr|jst|s|j}|rW|jd|d|d|}n|jj}|jtdkrdS|j |}|j } |j } t | j | dS|j d k stg} x~|j jD]j} |j j| } |j | }|j j| \}}| j|jd|d|d|qWtd|j d| d d S( s Return the percent of acenric reflections with both h,k,l and -h,-k,-l observed (only meaningful for amplitude and intensity arrays). By default this is calculated relative to the complete set. RR(RBig?trelative_to_complete_setR!RRs%5.3fN(R&RRRbRCRRRR,RR'RRR!RR8RRWR9RR(R.RRBRR(RRRtmerged_acentrict n_acentrict anom_diffstresultsR:Rt array_selt d_max_bint d_min_bin((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR s.     c C sD|js|S|j}|jdk r|j}|jj}|jt}|dkr}|j d|}n|j jjt}n|jdk r@|jr@|r@|j \}}|j j } |j|} | dkr@| d| |kr@|jj} | jj|}q@n|S(s Convert anomalous array into nonanomalous if the number of Bijvoet pairs is too small compared to the number of lone Bijvoet mates. iRg?N(R&RR%RRRRRR<RRRRR'Rtas_non_anomalous_arrayRaRR6( R.t thresholdRRRRRRRtn_pairst n_lone_matesR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt6convert_to_non_anomalous_if_ratio_pairs_lone_less_than s(      c C s| s|jdk st|sE|j|jdk}|jr[|j}n|j\}}|jj|jjkst|jjdkrdSt j t j |j|j}|dkstt j t j |jt j |j}|dks0tt j d||Sg}xK|jjD]7}|jj|} |j|j| jq^Wtd|jd|ddS(sGet the anomalous signal according to this formula: .. math:: \sqrt{\dfrac{<||F(+)|-|F(-)||^2>}{\frac{1}{2} (<|F(+)|>^2 + <|F(-)|>^2)}} :param use_binning: If 'True' the anomalous signal will be calculated for each bin of the data set individually :type use_binning: boolean :returns: the anomalous signal :rtype: float or cctbx.miller.binned_data iiR!RRs%7.4fN(R!RRRRRRvRR'R RRRR.R8RR9RR( R.Rtobstf_plustf_minust mean_sq_difft mean_sum_sqRR:R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR+ s(  *( cC s||gjtdks!t|jrtdd}|j|j|j|j}|r|j j}t j t j d|}|j|d}t j t j d|}|j|d}||}n|s|rt j|S|jd|}|Sq|jd k s/tg} xu|jjD]a} |jj| } |j| } d} | jdkrt j| } n| j| qHWtd|jd| dd Snd Sd S( sTGet phase entropy as measured in terms of an base-360 entropy (base-2 for centrics). An entropy of 0, indicates that the phase uncertainity is as low as possible An entropy of 1 however, indicates that the uncertainty is maximal: all phases are equally likely! :param return_binned_data: if 'True' you receive a binned object rather then a raw array :type return_binned_data: boolean :param exponentiate: whether or not to exponentiate the entropy. This will return a phase uncertainty in degrees (or the 'alphabet size') :type exponentiate: boolean iRihg@iRR!Rs%7.4fN(RR<RREt phase_entropytrelative_entropyRR$RRR RRRRCRRR!RR8RRR'R9R(R.t exponentiatetreturn_binned_datat return_meanRRwRtcentacentbinned_resultsR:Rtsel_dataR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRK s6! '  "cC s|jst| s1|jd k s1t|jd k sItt|}|sH|j|jdk}|jr|j }n|jj dkr|S|j \}}|jj |jj kstt j |j|j}t j|j|j|j|j}t j|dkj} |j| d}||} |jjt j|jdkjd}|j|} |jjt j|jdkjd}|j|} | |k| |k@| |k@jt} | j dkrA| | j S|Sng}xW|jjD]C}|jj|}|j|j|jd|d|qaWtd|jd|ddS( sFraction of reflections for which (:math:`\dfrac{|\Delta I|}{\sigma_{dI}}` > cutoff and :math:`min(\dfrac{I_{+}}{\sigma_{+}},\dfrac{I_{-}}{\sigma_{-}})` > cutoff iitcutoffR&R!RRs%7.4fN(R&RR!RRR]RRRR}R'RR tfabsR.R)RDRCRR<R8RR9t measurabilityR(R.RRR&Rti_plusti_minusttoptbottomtzerosRct i_plus_sigmat i_minus_sigmatmeasRR:R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR| s>  *3 33)R9cC s|jst|j|jdk}|dkrT|jrT|j}n'|dkr{|jr{|j}n|j\}}|jj |jj kst|j|jd}t j |j|j}|r^|j dk stt j |j|j} t j|j |j |j |j } t j| dkj} | j| d} | | } |j jt j|j dkjd} |j| } |j jt j|j dkjd} |j| }| |k| |k@||k@}|j|}|j|}n|j |j ks|t|dk}|j|}|j|}||S(NiR7R9ii(R&RRRRRvRR}RR'R RRRR.R)RDRC(R.tobs_typetmeasurable_onlyRRRRti_meantd_anoRRRRcRRRt non_zero_sele((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytbijvoet_ratios s@*  ' '  cC s| s|jdk st|s|jjdkrAdStj|jd}|dkrjdStj|j|Sg}xK|jj D]7}|jj |}|j |j |j qWtd|jd|ddS(s/()^2iiR!RRs%7.4fN(R!RRR$R'R RRtmean_sqR8RR9Rt second_momentR(R.Rt mean_data_sqRwR:R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR s  cC s| s|jdk std}|jrM|j}|j|n|}|s|jjdkrudStj |j }|dkrdS|Sg}xK|jj D]7}|jj |}|j |j|jqWtd|jd|ddS(siR!RRs%7.4fN(R!RRRR}RR$R'R RRR8RR9RRR(R.RRt mean_dataRwR:R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR s$    cC si| s|jdk st|s|jjdkr>|Sd}|jr|jrk|j}n|jr|}qn|dk r|j |j dk}|jjdkr|St j |j |j }|SdSng}xQ|jjD]=}|jj|}|j|j |jd|q Wtd|jd|ddS(s iR&R!RRs%7.4fN(R!RRR$R'RRR}RRRR RRR8RR9t i_over_sig_iR(R.RR&Rti_sig_iRwR:R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR s0     &cC s>| s|jdk st|s|jjdkr>|Sd}|jr|jrk|j}n|jr|}qn|dk rt j |j |j j j }|S|Sng}xQ|jjD]=}|jj|}|j|j|jd|qWtd|jd|ddS(s iR&R!RRs%7.4fN(R!RRR$R'RRR}RR RRRRFR8RR9Rt$mean_of_intensity_divided_by_epsilonR(R.RR&Rt weighted_meanRwR:R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR s.     cC s| s|jdk st|sm|jjdkr>|Stj|j|jjj }|Sg}xQ|jj D]=}|jj |}|j |j |jd|qWtd|jd|ddS(s iR&R!RRs%7.4fN(R!RRRR'R RRRRFR8RR9Rt(mean_of_squared_sigma_divided_by_epsilonR(R.RR&RRwR:R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR# scC sG|jr|}n"|j}|r7|j|n|jd|S(s7/()^2 (2.0 for untwinned, 1.5 for twinned data)R(RR}RR(R.RR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytsecond_moment_of_intensities8 s    cC s|js|}n"|j}|r7|j|n|jd|}|dkrYdS|sgd|Sg}xJ|jD]?}|dks|dkr|jdqw|jd|qwWtd|jd|ddS( s;()^2/ (0.785 for untwinned, 0.885 for twinned data)RiiR!RRs%7.4fN( RRvRRRRR9RR!(R.RRRRwtsm((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt wilson_ratioB s     i tusedc C sm|jst|dks$t||dIJ|dk rQ|jd|n|jdk sitg}g}d}xet|jd|D]G}|jj|} |jj| } | j t } | j | } |j | |j | |jj |} |dkrtdtt|jj }d|}||dIdt| I|d I|d Id IJd |}n||dI| I|| I|| Id d| td| | IJqW|jj t } |jj | } ||dIdt| dI|| I|| Id d| td| | IJ||fS(NRRbs.Number of work/free reflections by resolution:Rtrange_is%%%dsRLtworktfrees %frees%%%dds%5.1f%%gY@itoverall(Rsall(R@RRRR!tgetattrRRRRR<R'R9RzRQRRRSR$(R.Rt binner_rangeRRtn_workstn_freesRR:Rtflagstn_freetn_workRtwidth((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytshow_r_free_flags_infoS s> #   '  (#(cC s|jsttjdd}tjdd}|j}|j}d}xOt|D]A\}}|ra|d7}|j|d|j||qaqaWt d|d|S(Niitreflection_countstfree_fractions( R@RR ReRBRR'RR9R(R.trctffRcR`tcRR~((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytr_free_flags_accumulationy s   c C s:| s|jdk st|jdksO|jsO|jsOt|jdks|js|jst|jj|jjkst|s|jjdkrdS|r||}}n|j d|dt \}}t j |j}t j |j}|t krut j||}|dkr|t j|||9}qn|dk r||9}nt jt j ||t j|Sg}x]|jjD]I} |jj| } |j|j| j|j| ||qWtd|jd|ddS( sGet the R1 factor according to this formula .. math:: R1 = \dfrac{\sum{||F| - k|F'||}}{\sum{|F|}} where F is self.data() and F' is other.data() and k is the factor to put F' on the same scale as FiRR8R!RRs%7.4fN(R!RRR4RCRR$R'RR9R<R RRRR8RR9Rt r1_factorR( R.RRtassume_index_matchingRRR-RRR:R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR/ s6 *  !  'cC s|jdk st|dkr3|j}n|rC|}n|jj|}d}|jdk r|jj|}nd}|jdk r|jj|}ntt|||||j |S(s Select a sub-array. :param selection: flex.bool or flex.size_t selection :param negate: select the inverse of the selection array :param anomalous_flag: anomalous flag for the new set :returns: a new array with a subset of indices and data/sigmas N( R$RRR&RRRRR4R6(R.RR+R&RRcR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR s   cC s|rL|j}t|jjt|t|jj|}nt|j|}y |j|jdd|SWn=t k r}dt |krt dqt |nXdS(NiR+s<CCTBX_ASSERT(miller_indices_[1].size() == size_processed(1))s\cctbx.miller.array.select_indices(): This method can only be used reliably on a merged array( RRRRR<R3R$Rtpair_selectionRpRS(R.R$tmap_indices_to_asuR+tmatched_indicesR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytselect_indices s   cC se|jdk st|jdk s0ttj|j|j|k}|j||S(s Return a copy of the array filtered to remove reflections whose value is less than cutoff_factor*sigma (or the reverse, if negate=True). N(RRRRR RR(R.t cutoff_factorR+R$((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt sigma_filter s%cC sd}|j}|dk r|jdkrtj|dkrNd}q|jd}|sr|srtn|rtj|j|}qn|S(Nig(RRR'R Rtall_neRR(R.treturn_none_if_zero_sigmasRwRtsigmas_all_not_equal_zero((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytmin_f_over_sigma s  cC s||gjddks!t|jdk s9td}|dk rtjtj|j}|dkr|jS||}n|j|}|jdk r|j|}n|j d|d|j |j j |S(s2 Apply a scale factor to the data (and optionally sigmas). :param target_max: target maximum value for the scaled data - the scale factor will be determined automatically :param factor: explicit scale factor :returns: custumozed copy with scaled data and sigmas iiRRN( RRRRR RQRRRRR1R0R6(R.t target_maxtfactorRt current_maxRc((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt apply_scaling s !   c C s|dkr!|jj}n|jj|jsBt|j|sWt|jd||j|tj |jjd}x|j j D]}|j j |}|j |}|j |}d}tjtj|jtj|j} | dkrYtjtj|jtj|j| }n|j||qW|dkjt|jkst|jd|j|S(NRig?iR(RR$R'RRR2RRR RBR!RRRRRRRCRR<R( R.RRRR:Rtf1tf2tscale_R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt multiscale s$ ! 1 8'i2c C s|jd|d|d|d|d|d|d|d| j} |j| } |j} | d k r}|r}| | } n|jd | d | S( si Given an isotropic or anisotropic displacement or B-factor, apply resolution-dependent scale factors to the data (and optionally sigmas). :param u_iso: Isotropic displacement (in Angstroms) :param b_iso: Isotropic B-factor (8*pi^2*u_iso^2) :param u_cart: Anisotropic displacement tensor :param b_cart: Anisotropic B-factor :param u_star: Anisotropic displacement tensor in fractional space :param u_cif: Anisotropic displacement tensor, dimensionless basis :param apply_to_sigmas: Also scale sigma values (if present) :returns: cctbx.miller.array object with scaled data R R RRRRRRRRN(RRRRR( R.R R RRRRtapply_to_sigmasRRtdwsRcR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytapply_debye_waller_factors s      c C s|tks|tkst|s|jjdkr@dS| r| r|sgtj|jStj|j|jjj Sn|stj |j}n*tj |j|jjj }|rt j |S|S|jdk s tg}x]|jjD]I}|jj|}|j|j|jd|d|d|q"Wtd|jd|S(Nituse_multiplicitiestsquaredtrmsR!R(R,RRR'RR RRRRFRtmean_sq_weightedRR.R!R8RR9RR( R.RRFRGRHRwRR:R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR6 s6   cC s|jd|d|dtS(NRRFRG(RR<(R.RRF((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRW s cC s|jd|d|d|}|s2|j|S|jj}xG|jjD]3}|j|dk rW|j|c||9s>      !   cC s"|jd|jd|jS(NRR(RRR(R.R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytselect_sys_absentbscC s;|jdk st|jdk s0tt|t|kr_t||j|S|jdk swt|jdk stt|j|j}|jd}|j|j|j}d}|j dk r"|j dk r"|j |j |j }ntt ||||S(sOverload the '+' operator.iN( R$RRRRRR3tpaired_miller_indicestplusRRR4(R.RtmatchRRcR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt__add__fs$!cC so|jdk st|jdk s0t|j}||9}|j}|dk rk||9}n|S(N(R$RRRR(R.RRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt__imul__xs    cC sjt|trP|jj|js0t|jd|j|jS|j}||9}|S(NR(RRR$RRRRR(R.RRw((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt__mul__s !   cC so|jdk st|jdk s0t|j}||}|j}|dk rk||}n|S(N(R$RRRR(R.RRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt __itruediv__s    cC s|j}||}|S(s/ This requires from __future__ import division (R(R.RRw((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt __truediv__s  cC s>|j|g}t|jdks0t|jdS(sAExtract the value of the array for the specified reflection h,k,lii(R4RRRR(R.Rt hkl_array((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytvalue_at_indexscC s|j|j|S(s, Returns the value of data of the first index matching `miller_index`. If the `miller_index` is not found in `self`, then returns ``None``. :param miller_index: Miller index as a 3-tuple :type miller_index: tuple :returns: int, float, complex, None -- data value or None (RR,(R.R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytdata_at_first_indexs cC s(|jdk st|j|j|S(s0 Returns the value of sigmas of the first index matching `miller_index`. If the `miller_index` is not found in `self`, then returns ``None``. :param miller_index: Miller index as a 3-tuple :type miller_index: tuple :returns: int, float, complex, None -- sigmas value or None N(R-RRR(R.R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytsigma_at_first_indexs c C sY|jr|S|jj}|jj}|j|j| d}d}|jdk r|jj}|jr|jt j |j|q|j r|j|j|j q|j|j|n|j dk r+|j j}|j|j|nt dtd|d|dtd|d|S(sp If the array is not already anomalous, expand to generate anomalous pairs (without changing data). R0R1R$R&RRN(R&RRR$RRgRRRCR RJRERRR4R<(R.RR$RR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRis0  "  c C s|r|j|stn|js0t|jsBt| sa|jdk sat|}|jr|j r|j}n(|j r|jr|j}n|jd|d|\}}|stj |j |j S|j |g}x|jj D]z}|jj |}tj |j j||j j|}|js|jdq|j|jqWtd|jd|ddS(s Calculate correlation coefficient between two arrays (either globally or binned). :param other: another array of real numbers :param use_binning: calculate CC in resolution bins (default = calculate a single global value) :param assert_is_similar_symmetry: check that arrays have compatible crystal symmetry :returns: a Python float (if use_binning=False), or a binned_data object RR4R!RRs%6.3fN(R2RRR!RR&RiR9R tlinear_correlationRRR8RRtis_well_definedR9t coefficientR( R.RRR4tlhsRR:Rt correlation((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs2   cC s|dkrtj}n|jj|jjksBt|jr|dk rx8t|j|jD];\}}||t |I|It j |d|IJqvWn|j dkrxt|j|jD]&\}}||t |I|IJqWn|j j|jjks=txUt|j|j|j D]/\}}}||t |I|I|IJqbW|S(s"Listing of Miller indices and dataRiN( RR|R}RR'R$RRCR%RSR tabs_argR(R.R~RRiRRcR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt show_arrays *(6(!*4$cC stj|j}tj|j}|jj}|jj}tjtj|tj|}|dkrtj||tj|||SdS(Ni( R RRRRR.RKRtcosR(R.RRRRRR<((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytmap_correlation s% &iic C sr|d k r%td|d|d| Std|jd|d|d|d|d|d |d |d|d| Sd S( sa Calculate the FFT for the array, assuming the data are complex doubles. :param resolution_factor: when multiplied times the resolution limit, gives the approximate grid spacing of the map. :param d_min: High-resolution cutoff :param crystal_gridding: optional gridding to use (overrides automatic gridding) :param symmetry_flags: specifies how the grid should be constructed to handle symmetry :param f_000: Optional F(0,0,0) value (scalar added to entire map) :returns: an fft_map object Rtfourier_coefficientstf_000R(RRRRRRN(Rtfft_mapR( R.RR(RRRRRRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs    cC s|jst|}|jjjdkrE|j}n|js`|j}ntj d|j d|j d|}|dk r||S||j jSdS(s Calculates the exact map value at the specified fractional coordinate using direct Fourier summation. Relatively slow but avoids interpolation errors. iR6Rt site_fracN(RCRR%RtnumberRR&RiRtdirect_summation_at_pointR$RRR2R(R.Rtsigmat map_coeffsR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR?s      idcC st|jtjst|j}|j|}|jd|g}x|jj D]}|jj |}|jj d|dt dt }|j d|}|j\} } tj|j} |j| | | gqbWtj} tj} xt|jj|jD]_\}}xP|D]H}||dkrA||dkrA| j|d| j|PqAqAWq.Wt|| d t }t|| }|jd |S( NRR:RoRrRiiiR&R(RRR RBRRCR;RR!RRRzR,RRRR9RR%RR$R4RR(R.RRtlsRwR:RRtsselRR(t data_meant data_loneR$RcRRutlmstla((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytcomplete_with_bin_averageTs0   .    g?c C s|jst|jd|}|j|j}tjd|d|d||}|dk r}|jd|}n|j d|dt dt d t S( NRRt mean_scalet n_iterationsR(RRR&R( RCRRtapply_volume_scalingtreal_map_unpaddedRthoppe_gassman_modificationRRCRR<R,(R.RRRR(Rtmap_dataR[((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pythoppe_gassmann_modificationns    g@c C s|jst|jd|}|j|j}|j}~tj|j} tj |j} | | kr| } } nHt j |dtd|j } | j d|d|d|\} } t jd|d|d | d | }|dkr|j}n|jd|d td |jd t}|dkrR|}ndd}|d|d|}|dkr|}n|S(NRRiRtvol_cutoff_plus_percenttvol_cutoff_minus_percentRRtcutoffptcutoffmRR&RicS s*|jj|jjks*t|j|\}}td|jj}|jd||j|dtj|j jd}tj }tj }x|j j D]}|j j |} t|j| j} t|j| j} tj| | } tj| | } | dksT| dkrXdS|j| | |jtj|j| qW|jdkrtjjdtj|d|}dtj|j jd}|jtj| |j}n |d}|jd |j|S( NiRg?g@iiRRR(RR'RR9RRRR RRRBR!RRRRRRR9RRRRR.RRRR(R?R@Rtf1_tf2_RRtss_binR:Rtsel_f1tsel_f2R`RcRut k_isotropic((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs0* #  #$#! R?R@(RCRRtapply_sigma_scalingRRR Rtas_1dRQRt histogramR'tget_percentile_cutoffst denmod_simpleRRCRR<R&(R.RCRRRtscale_toRRRt value_mint value_maxRRthisttsfRRw((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytdouble_step_filtration~sD     $          c C sC|j} tj| jj} d| tj|} dtj| | tj| tj | d}|j d|d|d|d|d|d|d | d | }|j | j tj |j|}| jd |j|}t d |jd|d|d|d|d|d|d | d |j }|S(NiiRR(RRRRRRRRR(RCR R.RRRRtsinRRRRRRRRR(R.tradiustmean_solvent_densityRR(RRRRRRRCReRtsphere_reciprocalRttempt fourier_coeff((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytlocal_standard_deviation_maps8 4     c C s[|j} td| jd|d|d|d|d|d|d| d |d |d | S( s. Calculate an unphased Patterson map. RRR(RRRRtf_pattRt sharpeningtorigin_peak_removal(Rt patterson_mapR( R.RR(RRRRRRRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs   cC s0ddlm}|jd|d|d|dS(s8 Write reflections to a SHELX-format .hkl file. i(thklfR[t file_objecttnormalise_if_format_overflowN(t iotbx.shelxRt!miller_array_export_as_shelx_hklf(R.RRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytexport_as_shelx_hklfs  c C s<ddlm}||d|d|d|d|d|dS( s1 Write reflections to a CNS-format file. i(texport_as_cns_hklRRR0t array_namest r_free_flagsN(tiotbx.cns.miller_arrayR(R.RRR0RRtimplementation((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs  c C s?ddlm}|d|d|d|d|d|d|d S( s Write data in unmerged scalepack format. :param file_object: filehandle-like object :param file_name: output file to write :param batch_numbers: integer array indicating the batch (image) numbers corresponding to the indices (optional) :param spindle_flags: integer array indicating the position of the reflections on the detector (optional) i(twriterti_obsRRt batch_numberst spindle_flagst%scale_intensities_for_scalepack_mergeN(t'iotbx.scalepack.no_merge_original_indexR(R.RRRRRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytexport_as_scalepack_unmergeds Rtprojecttdatasetc C sddl} |dkr?|j} | dk r?| j}q?n|dkrTd}n| jj|d|d|d|d|d|d |d |d |S( s Generate an iotbx.mtz.dataset object for the array, which can be extended with additional arrays and eventually written to an MTZ file. iNg?tcolumn_root_labelt column_typestlabel_decoratorttitlet crystal_namet project_namet dataset_nameRh(t iotbx.mtzRR0Rhtmtztmiller_array_as_mtz_dataset( R.RRRRRRRRhtiotbxR0((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytas_mtz_dataset8s      cC s"ddl}|jj||jS(Ni(t iotbx.ciftciftmiller_arrays_as_cif_blockt cif_block(R.t array_typeR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt as_cif_blockVs tglobalcC sZ|dkrtj}nddl}|jjj}|jd|||<||IJdS(NiR(RR|R}RRtmodelR(R.RRt data_nameRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt as_cif_simple[s   c C sAddl}|jjd|d|d|d|d|d|dS( s$ Write phases to .phs file. iNR.Rtscale_amplitudesRt phases_degtfigures_of_merit(t iotbx.phasesRtmiller_array_as_phases_phs(R.RRRRRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt as_phases_phscs  cC s`|dkrMddlm}|j}|jdd|j||}ntj|||S(s Overriden version of set.amplitude_normalisation which computes the Wilson parameters from the array data if wilson_plot is None. i(t statisticsRiN(RRRRPRRR4R(R.RRRtf_obs((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRus   cC st|||S(N(tnormalised_amplitudes(R.RR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRsc C s|jdkrdS| s5|jdk s5t|rP|dksPtn|dk r|j|jjkstn|jst|j|jjkst|s|jjdkrdS|j}|jr|jj}ntj |j}|dk r|dks;t|tj |j|k}|j |}|j |}|dk r|j |}qn|dkrtj ||tj tj |Stj |||tj |tj |Sng}x~|jjD]j} |jj| }d} |dk rV|j |} n|j|j |j|j || qWtd|jd|ddS(s& The analytical expression for the least squares scale factor. K = sum(w * yo * yc) / sum(w * yc^2) If the optional cutoff_factor argument is provided, only the reflections whose magnitudes are greater than cutoff_factor * max(yo) will be included in the calculation. iiR!RRs%7.4fN(RRR!RR'RCRRiR RRQRRRR8RR9RR( R.RRR5RRtcalcRRR:t weights_sel((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRsH  '$     ' )c C seddl}ddlm}|jjd|d|d|d|jj}|dk r]||S|SdS(sg Class method for building an array from a CIF file (or filehandle). Depends on iotbx.cif. iN(tbuildersRt file_pathtdata_block_nametdata_structure_builder(RRRtcctbx_data_structures_from_ciftmiller_array_buildert miller_arraysR(tclsRRRRRtarrays((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytfrom_cifs    cC s|jst|jj}|jj|j|}d||tj|d|}|d7}t j|d}|S(s Extinction parameter x, where Fc is multiplied by: k[1 + 0.001 x Fc^2 wavelength^3 / sin(2theta)]^(-1/4) See SHELX-97 manual, page 7-7 for more information. Note: The scale factor, k, is not applied nor calculated by this function. The scale factor should be calculated and applied ***AFTER*** the application of the extinction corrections. gMbP?iigigп( RCRR~RR2t sin_two_thetaR$RRR (R.RRhRTt sin_2_thetat correction((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytshelxl_extinction_corrections " cC s,|j||}|jd|j|S(NR(RRR(R.RRhR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt"apply_shelxl_extinction_correctionsg?g?cC sy|jj|js!t|jrEtj|j}n|jj}|jj}tj|dkrdStj |}tj ||}|dkrdS|||9}t tj |tj |} | dkrdS|d| 9}|d| 9}d|d|} | |} d| } |} || || k|| || kB}|ru|||fS|S(s Preconditions (not checked explicitly): self is amplitude array, f_calc is complex array or amplitude array. iiN( R$RRRCR RRRRRRKRRQ(R.Rt offset_lowt offset_hightalso_return_x_and_yRRtsum_xxtsum_xyRtm_lowtb_lowtm_hightb_highRw((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt"f_obs_f_calc_fan_outlier_selections4 !  !    c C s|jdks0|js0|js0t|jsBt|jdk sZtddlm}d }|dk r|}n|dk rX|j |j j kst|dkpt |dk st|j|j |j |j|||}t dtd|d|j d|jd|j d |jj||_n3|j|j |j |j|}||_||_||_|S( Ni(t observationsiR0R1R$R&RR((R4RRRRRRR3RR'R$RRRRR4R&R6RLtref_twin_fractionstref_twin_components(R.t scale_indicesttwin_fractionsttwin_componentsRttw_cmpsRw((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytas_xray_observationss6   $%  $   cK sE|jstddlm}t|}||d<|j|S(s Perform French-Wilson treatment of X-ray intensities to estimate the "true" intensities, replacing very weak and/or negative values, and takes the square root to obtain amplitudes. :returns: an array of all-positive X-ray amplitudes i(t french_wilsonR[(RRRRtdicttfrench_wilson_scale(R.tkwdsR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR#s   cC sddl}|jj}|jj|}|j}tj|ttj|tj|}t j |j |} tj|}tj} x|j D]} tj| |} tj|t| } t j | j | } | j |}| | }d}| dkr~|dks2t ||}tdt|dkrad}ndt jt j|}n| j|qWxJtddD]9}| |k}d|jt|j|krPqqW|r|jd |S|jd |S( s Remove reflections corresponding to a cone shape in reciprocal space with the apex at the origin. Used to simulate incomplete data due to poor alignment of the crystal with the goniometer axis. iNig?gMbP?iigY@R(t scitbx.matrixR2tfractionalization_matrixRtsqrt transposeR RBRhRR.tdotR$RRRtacosR9RYRR<R'R(R.tfraction_percenttvertext axis_point_1t axis_point_2R+RtfmRt axis_lengthtopening_anglestpointtpoint_minus_vertextpoint_minus_vertex_lengtht numeratort denominatortopening_angle_deg_for_pointRctoaR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt remove_cone1s>  )      #cC s|jdkrdS|jdt}d\}}}xkt|j|j|j|jjD]6\}}}} |jj |} |ddkr|ddkr|||kr|dkr| |df}n|ddkr0|ddkr0|||kr0|dkr0| |df}n|ddkr|ddkr|||kr|dkr| |df}n|||gj ddkrkPqkqkW|||gj ddks|d|d|dgj ddkr|j \} } d} | | d| d}| | d| d}| | d| d}|j \}}}|dks~|ddkr||f}n|dks|ddkr||f}n|dks|ddkr||f}qn|||fS(NR@iiicS sFt||kr|St||kr,|St||krB|SdS(N(R(RR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pythelperks (NNN( RRRKR<R%R$RRR2RRRR (R.t sigma_cutofftsortedth_cuttk_cuttl_cutRR~RRcRRRR/thmtkmtlmtdhtdktdl((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt,ellipsoidal_resolutions_and_indices_by_sigmaZs<!%<<<* cC sL|jdkrdS|jdt}ggg}}}xt|j|j|j|jjD]\}}}}||} |ddkr|ddkr|j||d| fn|ddkr|ddkr|j||d| fn|ddkrp|ddkrp|j||d| fqpqpWdGHdGHxt t t |t t |t |D]} dd} yd || } Wnt k r| } nXyd || } Wn8t k r t | j dkr| } q | } nXyd || }Wnt k r6d }nX| G| G|GHqWdS( NR@iiis0a* b* c*sAd h F/sigma d k F/sigma d l F/sigmaRLis%7.3f %4d %8.3fR{(RRRKR<R%R$RRR9RRQRRR*tstrip(R.R1RRRRR~RRctdata_over_sigmaRtblancRRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt*show_mean_data_over_sigma_along_a_b_c_starxs@!%    !7      cC so|jd|\}}}|jj}t|d|d}t|d|d}t|d|d}|||gjddkr|jStj|j j t } |j } |j } xt|j D]u\} } |jj| }tj|d|d|d|d|d|d}|dkrt| | tn_trials(RRRRRKtcompute_cc_one_half(R.RRNR&t tmp_array((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt cc_one_halfs  c C s|jst|jdtj}|jd}|s'td|jd|jd|j dt }t d|d|jdt}|j stt d |d |j}|j}t d |d |j}|j}|jj|js t|jd |d t jS|jd |g} xs|jjD]_} |jj| } |j| } | jd kr| jdqP| j| jqPWtd|jd | ddS(s Calculate the correlation of the anomalous differences of two randomly assigned half-datasets (starting from unmerged data). R&R>tunmerged_indicest unmerged_datatunmerged_sigmastweightedR1R$R0RRRiR!Rs%6.3fN(RRRR<RRKtsplit_unmergedR$RRR,R4RRtdata_1Rtdata_2RRRRR!R8RRR'R9Rt"half_dataset_anomalous_correlationR( R.RRPtsplit_datasetstbase_settarray1tdano1tarray2tdano2RR:Rt bin_array((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRYsB          !cO s|j||S(s> Alias for array.half_dataset_anomalous_correlation() (RY(R.targsR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytcc_anomscC s|jst|jdtj}|jsK|jj}n|j}|j j d|}t j t j |j}t j t j |j}|dkst||S(sICalculate R_anom, which measures the agreement between Friedel mates. Unlike CC_anom and various other R-factors (such as R_pim, which it is usually compared to), this requires merged data. .. math:: R_{anom} = \dfrac{\sum_{hkl}{|I_{hkl} - I_{-h,-k,-l}|}}{\sum_{hkl}{\left \langle I_{hkl} \right \rangle}} R&Ri(RRRR<RRRaRRRbR6R RRR(R.RPR R R*R+((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytr_anoms   c C sa|dk r(d|ko#dkns.t|dk s@t|j}|jrg|j}n|jsyttj|dddd}|jj dkrt dntj |}|j |j}|j d |d t\}}d ||j||j}|d } |jd |d| j} |jr]| jS| S(s  Apply a twin law to the data, returning an array of the same original type. :params twin_law: a valid twin law expressed as h,k,l operations :params alpha: predicted twin fraction (0 to 0.5) :returns: a new array with synthetically twinned data gg?tr_denitt_deni is>The determinant of the provided twin law is not equal to unityRR4g?gY@RRN(RRRRR}RRtrt_mxRut determinantRRR_R9R,RRR:Rv( R.ttwin_lawtalphaRPRt new_arraytxatxbRt new_sigmasttwinned((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt twin_datas*.   "   cC s|dk r(d|ko#dkns.t|dk s@t|j}|jrg|j}n|jsyttj|dddd}|jj dkrt dntj |}|j |j}|j d |d t\}}|j|jkst|j}|j}|j}|j} d ||||dd |} d} tjdd |d ||dd |} |dk r| tjtj|d || d } n|jd| d| jd |} |jr| jS| S(s Detwin data using a known twin fraction, returning an array with the same original data type. :params twin_law: a valid twin law expressed as h,k,l operations :params alpha: predicted twin fraction (0 to 0.5) :returns: a new array with detwinned data gg?RdiRei is>The determinant of the provided twin law is not equal to unityRR4g?g@iRRN(RRRRR}RRRfRuRgRRR_R9R,R'RRRR.R RRR6Rv(R.RhRiRPRRjti_old1ti_old2ts_old1ts_old2RRmttmp_multt detwinned((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt detwin_data7s< .       "/ .   cC s|dkrtj}n|}|dIJ|dkrC|j}n|dkr^|j}n|dkry|j}nd|dkr|j}nIytj|}Wn3t k r}t dd|t |fnX|j j jr|dIJ|d I|jIJn |d IJ|d |j|jjfIJ|jjj}|d I|IJ|d kr|dkr|dIJ|dIJ|dIJn|rl|jd|j}tj|jj|} | } |j| } |j| } |dItj| jIJt| jd krc|dItj| jIJn| }n|jd|} |dIJtj j!| ddd|| |fS(s- Encapsulates a variety of reindexing operations, including handling for a variety of corner cases. :param change_of_basis: Python str for change-of-basis operator :param eliminate_invalid_indices: remove reflections with non-integral indices :returns: new Miller array sChange of basis:tto_reference_settingtto_primitive_settingtto_niggli_celltto_inverse_hands-The change-of-basis operator '%s' is invalid s(original error: %s)s< Change of basis operator in both h,k,l and x,y,z notation:s s- Change of basis operator in x,y,z notation:s %s [Inverse: %s]s Determinant:is@ **************************************************************s@ W A R N I N G: This change of basis operator changes the hand!R6s" Mean value for kept reflections:s% Mean value for invalid reflections:Rs) Crystal symmetry after change of basis:Rs R~N("RR|R}t'change_of_basis_op_to_reference_settingt'change_of_basis_op_to_primitive_settingR^t"change_of_basis_op_to_inverse_handRRRRRStc_invtttis_zerotas_hkltas_xyzRjR-RuRgt%apply_results_in_non_integral_indicesR$R R)R'RRRRRR_RR5R(R.tchange_of_basisteliminate_invalid_indicesRR[RRRcRttossRMt keep_arrayt toss_arraytprocessed_array((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytapply_change_of_basisdsb               gMbP?cC sx|dkrtj}n||gjddks9t|}|j} | j} |dIJ| jd|dd|j}|j} |dIJ| jd|dd|dkrt j |}n|j } | j | j kr$|r|dIJ|j }q$tjdd n| j} |jj} d | koYd knr|jrt jd }| j|}|jd | jd kr|dIJ|j|}|j} | j} qn|j j| s| }|j j|} |d|IJ|dt|jIJ|dt| jIJ|j}| j}t|||}||krtjdd|dtqntjd| d|}|jd|}|j dddt!d|\}}|r!|j" r!|j#j$}n|rT|j%}|j&}|j'd|}n|dIJ|jd|dd|S(s Encapsulates all operations required to convert the original data to a different symmetry (e.g. as suggested by Xtriage). This includes reindexing and adjusting the unit cell parameters if necessary, and expansion to P1 (for moving to lower symmetry) or merging equivalents. :param space_group_symbol: Python str for space group symbol (any format) :param space_group_info: Pre-defined sgtbx.space_group_info object :param volume_warning_threshold: Cutoff for relative change in unit cell volume beyond which a warning is issued. :param expand_to_p1_if_necessary: When moving to lower symmetry, expand the data to P1 first. :param remove_systematic_absences: eliminate reflections that are systematically absent in the new symmetry. :param merge_non_unique: merge reflections that are no longer symmetry- unique under the new symmetry. :param log: filehandle-like object :returns: Miller array in the new symmetry isCurrent symmetry:R~Rs sNiggli cell symmetry:s1Changing to lower symmetry, expanding to P1 firsts6This operation will result in incomplete data without ssymmetry expansion!iisa,-b,-cisReindexing with a,-b,-cs5Coercing unit cell into parameters compatible with %ss Old cell: %ss New cell: %ss3This operation will change the unit cell volume by smore than %.1f%%.idR2R%R1RRwRRRsNew Miller array:N((RR|R}RRR1RRt niggli_cellRR%R`tn_smxRtwarningstwarnR2RRtis_reference_settingRR_Rtis_compatible_unit_celltaverage_unit_cellRSRRt UserWarningRR5RRR<RRaRRR,R;(R.RR%tvolume_warning_thresholdtexpand_to_p1_if_necessaryR,tmerge_non_uniqueRR[tsymmtspace_group_oldtspace_group_newR2RRt unit_cell_newt unit_cell_oldt volume_startt volume_newtvolume_change_fractiontsymm_newtma_oldR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytchange_symmetrysz  !            (             cC st|jd|d|S(s For each possible space group sharing the same basic intensity symmetry, show a list of possible systematically absent reflections and corresponding I/sigmaI. Note that if the data have already been processed in a specific space group rather than the basic point group, for example P212121 instead of P222, all systematically absent reflections are likely to have been removed already. :returns: a systematic_absences_info object RR(tsystematic_absences_infoR(R.RR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt%show_all_possible_systematic_absencess g@cC sYddl}|jjds-tdnddlm}|jd|d|d|S( s Detect translational pseudosymmetry and twinning, using methods in Xtriage. Returns a mmtbx.scaling.twin_analyses.twin_law_interpretation object. (Requires mmtbx to be configured to be functional.) iNtmmtbxs)mmtbx is required for this functionality.(t twin_analysesR.R(R(tlibtbx.load_envtenvt has_modulet ImportErrort mmtbx.scalingRtanalyze_intensity_statistics(R.R(RRR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs  cC s|jd|jS(s Convenience method for identifying twinned data. Note that this is hugely inefficient if any other Xtriage analyses are planned, since it discards the other results. Requires mmtbx. R((Rt has_twinning(R.R(((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR sNgUUUUUU?gUUUUUU?gUUUUUU?(iii(iii(iii(RRR RR#RR1R6R8R:RRR;R=R0RR4R'R?R@RBRRCRERRGRRHRRRRR<RR4RIRKRFRRRXRZR_RdRjRvR}RPR~RRRR,RRRRRARRR_RRRRRRRRRRRRRRRRRRRRRRRRR(R.R/RR4R6R:R>RBRERRRRKRHRNRORTRVRWR`RbRgRhRiRRjRkRRaRRbRsRtRxRyRzR{R|R~RRRiRRRRRRRRRRRRRRRRRRRRRt classmethodRRR RRR.R;R?RCRIRMRQRYRbRcRoRvRRR|R}RRR(((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRys                                        4 '   ?        0( $      "  $                   $       %    <          .    #  (      (    / ? VR_cB s#eZddddZdZRS(g{Gz?g@c C s2||_t|_t|_|j}|dksB|jdkrFdS|jj}|dk r|j}|dk r|jd|d|d| rt |_qn|jj }|dk r.|dkr|j } | dk r| j }qn|dk r.|j } | |kr+t |_q+q.ndS(NRtrelative_length_tolerancetabsolute_angle_tolerance( R[R<tunit_cell_is_compatibletspace_group_is_compatibleR0RRR2t is_similar_toR,Rtbuild_derived_point_group( R.R[R\R]R^R0tucfRtsgftsgtpoint_group_from_file((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR#-s2                cC s6|j}g}d|t|jfg}|js|jd|jdt|jjjdt|jgn|js|jd|jdt|jjj dt|j gnt |dkr2t |d krd g}nd j d |d|dfd g|SdS(Ns %s: %ss unit cells Unit cell from file: s Working unit cell: s space groups Space group from file: s Working space group: iiscrystal symmetrys s$Working %s is not compatible with %ss from reflection file:( R[RSR0RR9RgRR2RR%RRRYR(R.tdata_descriptiontmatwhattmsg((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytformat_error_messageJs,      N(RRRR#R(((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR_+s RcB s5eZdZddZdZdZdZRS(s! E-values and related statistics cC s|jst|j||}|j|j}tdtd|jd|jjd|j |_ t j |}t j t j|d|_|dkjt|_dS(NR0R1R$Rii(RRRRRR4R1R$RR8t_arrayR RRRt_sum_e_sq_minus_1RR<t_n_e_greater_than_2(R.R[RRRRte_sq((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR#es   cC s|jS(N(R(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRsscC s|j|jjS(N(RRR'(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytmean_e_sq_minus_1vscC sd|j|jjS(NgY@(RRR'(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytpercent_e_sq_gt_2ysN(RRR RR#RRR(((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRbs    RacB seZdZddedZdZdZdZdZ dZ dZ d Z d Z d Zd Zd dddZRS(s Wrapper for merging redundant observations to obtain a symmetry-unique array. This also calculates some useful statistics resulting from the merging operation. Normally this would not be instantiated directly, but instead obtained by calling array.merge_equivalents(...). Rlc C s|dkstd|_d|_d|_|_|_|_d|_d|_ |j j j }it jd6t jd6t jd6t jd6j|d}|dk r|j}|jdd}ys|dkr||jj||j j|d |} n-||jj||j j|} WnAtk r} d t| krztt| d nnXd} ~t| d rt| j |_ qtnt|j tjrFtj|} | jdd}|jdk r|dkrMt j | jj||j j||jj|d |} ni|dkrt j!| jj||j j||jj|} | j"|_n td| j} n6t j#| jj||j j|} d} ~ | j$|_| j%|_| j&|_| j'|_| j(|_| j)|_n.tdt*|j t*|jft+dtd|d| jd|j,d| j d| j-||_.| j/|_0dS(s :param miller_array: a non-unique array of experimental data :param algorithm: merging method (options are "gaussian" or "shelx") RlRNR)RRRDRGR>Rms%merge_equivalents_exact: incompatibles! (mismatch between Friedel mates)tn_incompatible_flagsRns*Programming error (should be unreachable).sOcctbx.miller.merge_equivalents: unsupported array type: data: %s sigmas: %sR0R1R$R&RRN(sgaussiansshelx(sboolsint(1RRt _r_lineart _r_squaret_r_intt_r_merget_r_meast_r_pimt_inconsistent_equivalentsRRR)RR"tmerge_equivalents_exact_booltmerge_equivalents_exact_inttmerge_equivalents_complextmerge_equivalents_hlRoRRAR$RRpRSRRRR RBR4Rtmerge_equivalents_obstmerge_equivalents_shelxtinconsistent_equivalentstmerge_equivalents_realtr_lineartr_squaretr_inttr_mergetr_meastr_pimtreprRR&R6Rt redundanciest _redundancies( R.R[RRmRnt data_type_strt merge_typet asu_arrayRWt merge_extRRtasu_set((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR#s                       (  cC s|jS(s) Return the merged Miller array. (R(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRscC s|jjd|jS(sq Return an array representing the redundancy or multiplicity of each reflection in the merged array. R(RRR(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRscC s)|jdkrdS|jjd|jS(s7R-linear = sum(abs(data - mean(data))) / sum(abs(data))RN(RRRR(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRscC s)|jdkrdS|jjd|jS(s5R-square = sum((data - mean(data))**2) / sum(data**2)RN(RRRR(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRscC s|jS(N(R(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRscC s|jS(s Standard (but flawed) metric of dataset internal consistency. .. math:: R_{merge} = \dfrac{\sum_{hkl}{\sum_{i}{|I_{i}(hkl) - \left \langle I_{i}(hkl) \right \rangle|}}}{\sum_{hkl}{\sum_{i}{I_{i}(hkl)}}} (R(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRscC s|jS(so Alternate metric of dataset internal consistency. Explained in detail in Diederichs K & Karplus PA (1997) Nature Structural Biology 4:269-275. .. math:: R_{meas} = \dfrac{\sum_{hkl}{ {\left \{ N(hkl) / [N(hkl) - 1] \right \} }^{1/2} \times \sum_{i}{|I_{i}(hkl) - \left \langle I_{i}(hkl) \right \rangle|}}}{\sum_{hkl}{\sum_{i}{I_{i}(hkl)}}} (R(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRscC s|jS(sX Alternate metric of dataset internal consistency or quality. Explained in detail in Weiss MS (2001) J Appl Cryst 34:130-135. .. math:: R_{meas} = \dfrac{\sum_{hkl}{ {\left \{ 1 / [N(hkl) - 1] \right \} }^{1/2} \times \sum_{i}{|I_{i}(hkl) - \left \langle I_{i}(hkl) \right \rangle|}}}{\sum_{hkl}{\sum_{i}{I_{i}(hkl)}}} (R(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRscC s|jdkr|jSdS(Ni(RR(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRscC s2tj|jjtj|jjS(N(R RRRR(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytr_sigmasi R{cC s|dkrtj}n|jj}|jd||jdt}|jjdk}|j }|dk r|j |}|j ||jdt}n|j } | dk r| j |} | j || jdt} nddddg} |dkr$| j dn | j d| dkrM| j dn | j d | g} g| d D]} t| ^qn}x|jjD]}|jjd |d tg} |jj|}|jj |j}|jd kr| jddgn4| j d tj|| j d tj||j|dkrf| j dn| j d|j||dks|j|dkr| j dn| j d|j|| dks| j|dkr| j dn| j d| j|| j | gt|| D]!\}} t|t| ^q)}qW|dk rw|||j jIJn| dk r|||j jIJn|dk s| dk r||dIJntjtj|dd!d}dt|d |g|d}ddd|dddg} x=|| gD]/}|dkrW| j dq5| j dq5W|||t| jIJdt|}x,| D]$} |||t| jIJqWdS(NRRiR{tMintMaxtMeansR-linearsR-squareiR:Rrs%ds%.3fs%.4fs/In these sums single measurements are excluded.is%%%ds %%%ds %%%ds %%%dst RedundancyRLi isMean s(%%%ds %%%ds %%%ds %%%ds %%%ds %%%ds(RR|R}RRFRRR<RRRRRR9RRR!R8RzR,RR'RgR RRQR%R RRRntrstrip(R.RRRRtred_meanRRtr_l_meanRtr_s_meantfieldstlinestfieldt max_lengthsR:RRutmax_lenR`R((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs~            # 8  #   N(RRR RR<R#RRRRRRRRRRR(((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRa}s U       RcB seZdZddZdZedZedZdZ dZ dZ dZ d Z d Zded Zddd gd ZdddZRS(sN Container for an FFT from reciprocal space (complex double) into real space. Normally this is obtained by calling array.fft_map(...), not instantiated directly outside this module. If the input array is anomalous, the resulting map will be a flex.complex_double (with grid accessor), otherwise it will be a flex.double. cC stjj|||jttfks1t|jj|jsRt|j |j ksptt |j t j st|j|_|jstj|j}|j}n!tj|j}|j}tjjd|j d|jd|jd|j d|jdt j|dt}|dk r|jdd ksvtt||jds cC s)|jddtj|jjS(NRi(R>RRRR(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pytapply_fourier_scalingscC s |jdd|jjS(s/ Volume-scale the map values in place. Ri(R>R2R(R.((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRscC s]|j}|jdkr"|Sd|j}|jrMt|}n|jd|S(s. Sigma-scale the map values in place. iiR(RRR&RR>(R.RR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs  cC s.|jjd|d|jdtd|S(NRRRtverify_symmetry(ttagst peak_searchRR,(R.RR((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRsscctbx.miller.fft_mapcC sddlm}|jdt}|dkr7d}n|dkrt|j}|dkrtg|D]}|d^qk}qnt|dkst|j d|d|j d |j d |d |d |d t j |dS(sD Write the real component of the map to a CCP4-format file. i(tccp4_mapRiii RR2Rtgridding_firstt gridding_lastRRN(iii(iii(RRRR,RRnRRRRtwrite_ccp4_mapR2RR R>(R.RRRRRRR`((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt as_ccp4_maps     )  c C sddlm}|jdt}|d kr7d }n|d krt|j}|d krtg|D]}|d^qk}qn|jd|d|jd|d |d |d S(sD Write the real component of the map to a DSN6-format file. i(tdsn6RiiRR2RRRN(iii(iii( RRRR,RRnRtwrite_dsn6_mapR2(R.RRRRRR`((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyt as_dsn6_maps    ) N(RRR RR#R&R<RRRRR>RRRRRR(((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR[s"          cC s|jst|r7|jdt|j}n|j}|rh|jdt|j}nt|d|jt j |jj d}|dk r||}nt |||S(NRRi(tis_patterson_symmetryRRR<RbR}RVRRR RR'RR(RRRRRti_patt((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRs 1  ic C sg}|j|jdk}xt|D]}d}|dkr_ttjd}nd}}td|jd|jd|jd|}|j }|j }t j ||j }|j|q.Wt||S( s Implementation of array.cc_one_half, assuming that the reflections are already in the ASU. Because the implementation uses random numbers, the function has the option to calculate the mean over multiple trials. iii'RRRSRTtseedN(RRRYRRRRVR$RRWRXR RRR9R( tunmergedRNtcc_allRR RWRXRZtcc((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyROs"        RcB s,eZdZddZejddZRS(s Container for information about possible systematically absent reflections in the array, trying both the current space group and all intensity-equivalent groups (i.e. all possible screw axis combinations). This object would normally be instantiated directly from a Miller array, but is self-contained to enable saving as part of Xtriage results. :param obs: X-ray intensity (preferred) or amplitude array c C sR||_t|_|jdk s*t|jd}|jr]|j}t |_n|j sot|j s|j j }n|jr|j}n|j|_|jdk st|jj}|jjj}|jd|jj}g|_d|_d|_x|D]}|j|jj}|j|jj}t|} | |jkr| |_nt|dkrt||jkrt||_n|j|} |jj|j| fq3t|dkr.|jj|jtfq3|jj|jdfq3WdS(NR>R%i( t was_filteredR,tinput_amplitudesRRRRKRR}R<RRRaRR&RbR%t&reflection_intensity_equivalent_groupsR`RRR0RCt"space_group_symbols_and_selectionstn_possible_maxt n_found_maxRR$RDRRRR9( R.RRt all_groupst point_groupt complete_selR`t absent_selt all_possiblet n_possibletabsences((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR#3sF              R{c C sk|jdkr|dIJ|S|jr3|dIJnx1|jD]&\}}d}t|t|jkrsd}n|tkr||d||fIJq=|d kr||d||fIJq=||d||fIJxt|jD]s\}}|j |}|j |} d |} | dkrC||d | IJq||d | || fIJqWq=W|S( s For each possible space group, show a list of possible systematically absent reflections and corresponding I/sigmaI. isCNo systematic absences possible in any intensity-equivalent groups.sPlease note that the input data were amplitudes, which means that weaker reflections may have been modified by French-Wilson treatment or discarded altogether, and the original intensities will not be recovered. For best results, use intensities as input. R{s (input space group)s%%s%s: no systematic absences possibles%s%s: no absences founds%s%s:s(%4d, %4d, %4d)s %s: i/sigi = undefineds %s: i/sigi = %6.1fN( RRRRSR%R,RRR$RR( R.RRt group_infoRt group_noteti_hklRR9Rt indices_fmt((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyRZs6          N(RRR RR#R|R}R(((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyR)s  '(Ht __future__Rt cctbx.sgtbxRt boost.pythontboosttpythont import_extR"RRRRRRRtcctbx.array_familyR RR t scitbx.mathtlibtbx.math_utilsR RR tscitbx.python_utils.miscR Rtlibtbx.str_utilsRt libtbx.utilsRRRRRtlibtbx.table_utilst itertoolsRRRRRRttimeR|Rt!floating_point_epsilon_double_getR"tgenerate_r_free_params_strR R!tobjectRRR5R4RR=RRR+RR_RRaRRR,RROR(((sC/public2/ftp/pub/ccp4/6.5/ccp4-6.5/lib/py2/cctbx/miller/__init__.pyts             ,  ! ^ 7