]he SrSSKJr SSKJrJrJrJrJrJ r J r J r J r J r JrJr SSKrSSKrSSKrSrSSKJr SSKJrJrJr SS KJr S S K J!r! S S K"J#r# S S K$J%r% S SK&J'r' S SK(J)r) \ "S\!\%\)5r*"SS\\*5r+g!\a SrNXf=f)zI Container for the result of running the generate quantities (GQ) method )Counter) AnyDictGenericHashableListMutableMappingNoReturnOptionalTupleTypeVarUnionoverloadNTF)Method)build_xarray_dataflatten_chains get_logger)scan_generic_csv) CmdStanMCMC)InferenceMetadata) CmdStanMLE)RunSet) CmdStanVBFitc \\rSrSrSrS\S\SS4SjrS\4Sjr S \S\ R4S jr S\ 4S jrS\\\44S jr\S\4S j5r\S\\4Sj5r\S\\S44Sj5r\S\4Sj5rSSSSS.S\S\S\S\S\ R4 SjjrS.S\\\\S4S\S\S\R>4Sjjr \!S.S\SS\\\\S4S\S\S\"4 Sjj5r#\!S.SSS\\\\S4S\S\SS4 S jj5r#S.S\\\\S4S\S\SS4S!jjr#S"\S#\S\ R4S$jr$S#\S\\\ R44S%jr%S/S&jr&S\S\\\44S'jr'S\S\ R4S(jr(S\\S\S\R>4S)jr)S0S*\*\SS4S+jjr+\S\\,\-\.44S,j5r/S-r0g)1 CmdStanGQ.zr Container for outputs from CmdStan generate_quantities run. Created by :meth:`CmdStanModel.generate_quantities`. runset previous_fitreturnNcUR[R:Xd$[SR UR55eXlX l[R"S5Ul UR5n[U5Ul g)zInitialize object.zJWrong runset method, expecting generate_quantities runset, found method {}N) methodrGENERATE_QUANTITIES ValueErrorformatrr nparray_draws_validate_csv_filesr _metadata)selfrr configs gC:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\cmdstanpy/stanfit/gq.py__init__CmdStanGQ.__init__4sl }} : ::""(&"7  !-"$((2, ))+*62c rSRURRURURRR R S/S95nSRUSRURR5SRURR55nU$)NzCmdStanGQ: model={} chains={}{}r)cmdz%{} csv_files: {} output_files: {}z ) r'rmodelchains_args method_argscomposejoin csv_files stdout_files)r-reprs r/__repr__CmdStanGQ.__repr__Gs077 KK   KK KK   ) ) 1 1! 1 <  =CC  KK -- . KK 00 1   r2attrcURS5(a[SU35eURU5$![an[UR6eSnAff=f)z)Synonymous with ``fit.stan_variable(attr)_zUnknown variable name N) startswithAttributeError stan_variabler&args)r-r@es r/ __getattr__CmdStanGQ.__getattr__TsY ??3   #9$!@A A *%%d+ + * !&&) ) *s7 AAAc:UR5 UR$N)_assemble_generated_quantities__dict__r-s r/ __getstate__CmdStanGQ.__getstate__^s ++-}}r2c x0n[UR5HnUS:Xa"[URRUS9nM+[URRUS9nUHMnUS;dM XX4:wdM[ SR URRUUXX455e M U$)z Checks that Stan CSV output files for all chains are consistent and returns dict containing config and column names. Raises exception when inconsistencies detected. r)path)id fitted_paramsdiagnostic_file metric_file profile_fileinitseedstart_datetimezFCmdStan config mismatch in Stan CSV file {}: arg {} is {}, expected {})ranger6rrr;r&r')r-dzeroidrestkeys r/r+CmdStanGQ._validate_csv_filesfst{{#AAv(..q1)..q1!C  "J%*4(88> $ 5 5a 8 # % % 9!$B r2c.URR$)zNumber of chains.)rr6rNs r/r6CmdStanGQ.chainss{{!!!r2c.URR$)z Chain ids.)r chain_idsrNs r/rdCmdStanGQ.chain_idss{{$$$r2.c4URRS$)z, Names of generated quantities of interest. column_names)r,cmdstan_configrNs r/rgCmdStanGQ.column_namess ~~,,^< 4,,j99)):: $$GD--y99 $$( &&33F&&F$+6?#;#A#A#C#CKD19#C  $&I  %%//99#>FFH ((4 --d3 Z!  .)!D  *a" $  !$++ij!Q.>"?@ @ 99IInia@KK j!Q   {{9:q!+,,?s  J-J-varsc8 Ub9[U[5(aU/nOUn[[R U55nU(a[UR [ 5(a5UR R(d[5RS5 O[UR [5(a5UR R(d[5RS5 O8[UR [5(a[5RS5 UR5 /SQ[UR5-n/n/nUGb.WGH&nXRR ;aMURR Un UR#URU R$U R&5 MjU(aXR RR ;aaUR RR Un UR#UR RU R$U R&5 MUS;aUR)U5 GM[+SR-U55e OUnUnUR/Xr5n UR1US9n U R2upn[4R6"[4R8"SU S-5U 5R;SX5R<n[4R>"[4R8"SU S-5U 5R;SX5R<n[4R8"SX-S-5R;SX5R<n[4R@"UUUU /SS 9n [BRD"[GU 5US 9nU(a-U(a&U(a[BRH"U UU/S S 9U$U $U(aUc[U RJ5n[URJ5n[MUU-5RO5VVs/sHunnUS:dMUPM nnn[BRH"U RQUS 9RSS S9U/SS 9$U(aUU$U$s snnf)a] Returns the generated quantities draws as a pandas DataFrame. Flattens all chains into single column. Container variables (array, vector, matrix) will span multiple columns, one column per element. E.g. variable 'matrix[2,2] foo' spans 4 columns: 'foo[1,1], ... foo[2,2]'. :param vars: optional list of variable names. :param inc_warmup: When ``True`` and the warmup draws are present in the output, i.e., the sampler was run with ``save_warmup=True``, then the warmup draws are included. Default value is ``False``. See Also -------- CmdStanGQ.draws CmdStanGQ.draws_xr CmdStanMCMC.draws_pd rrrsrt)chain__iter__draw__Unknown variable: {}rmrru)datar~r~)r~T)drop)*rwstrlistdictfromkeysr rrxrryrrzrrLrgr,r}r|rend_idxappendr&r'_previous_draws_pdrshaper(repeatarangereshapeTtile concatenatepd DataFramerconcatr~rr{r reset_index)r-rrmrp vars_list all_columnsgq_cols mcmc_varsvarinfoprevious_draws_pdrn_drawsn_chainsrB chains_coliter_coldraw_coldraws_pdrrrrrs r/rCmdStanGQ.draws_pd s2  $$$!F  T]]956I 4,,k::))66 $$> 4,,j99)):: $$GD--y99 $$( ++-5T=N=N8OO !   ..222>>33C8DNN))$..4<<H3*;*;*E*E*O*O#O,,66@@ED$$))66 NNT\\ ;;NN3'$%;%B%B3%GHH%!("GI 33IJ j 1${{1 IIbii8a<0' : WQ * Q  GGBIIa1-x 8 WQ * Q  IIa',1 2 WQ * Q   HhFQO<<&  )yy) )#   )( DL+334F(**+F$+6F?#;#A#A#C#CKD%19#C  99%**4*8DD$DO  G$ $s = RRr-)zCmdStanGQ[CmdStanMLE]zCmdStanGQ[CmdStanVB]cgrKr#r-rrmrps r/draws_xrCmdStanGQ.draws_xr r2zCmdStanGQ[CmdStanMCMC]z xr.DatasetcgrKr#rs r/rrrr2c [(d [S5e[UR[5(d [S5e/n/nUb[U[ 5(aU/nOUnUHnXpR R;dMU(aGXpRR R;a$URU5 URU5 Ml[SRU55e O[UR RR55nU(aTURR RR5H"nXv;dM Xt;dMURU5 M$ UHnURU5 M UR5 URRnURR R n U SSU SSU S3U SUS .n U(a;U S (a1XRR"- nURR"U S '0n UR$[&R("U5S .n UH4n[+U UR RUUR-US 95 M6 U(aNUHHn[+U URR RUURR-US 95 MJ [.R0"XU S9R3SSS5$)a Returns the generated quantities draws as a xarray Dataset. This method can only be called when the underlying fit was made through sampling, it cannot be used on MLE or VB outputs. :param vars: optional list of variable names. :param inc_warmup: When ``True`` and the warmup draws are present in the MCMC sample, then the warmup draws are included. Default value is ``False``. See Also -------- CmdStanGQ.draws CmdStanGQ.draws_pd CmdStanMCMC.draws_xr z>Package "xarray" is not installed, cannot produce draws array.zKMethod "draws_xr" is only available when original fit is done via Sampling.rstan_version_major.stan_version_minorstan_version_patchr5) stan_versionr5num_draws_sampling save_warmupnum_draws_warmup)chaindrawr)coordsattrsrr.)XARRAY_INSTALLED RuntimeErrorrwr rrr,r}rr&r'rkeysremoverLrrhrrdr(rrrxrDataset transpose) r-rrmrpmcmc_vars_listdup_varsrr num_draws sample_configrr coordinatess r/rrs0 P $++[995   $$$!F   nn666!00::DDD&--c2 ,()?)F)Fs)KLL!T^^55::<=I,,66@@EEGC+0I&--c2HC   S ! ++-%%88 ))33BB ,-ABC112311235#7+"+ 0  - 6 **;; ;I(,(9(9(J(JE$ %.0^^IIi(6 C ((- j 1  %!%%//99#>%%++z+B&zz$%@JJ VS  r2rkwargsc URRRR5nURRR5nX;d(X;d#[ SUS3SR X4-5-5eX;a1[ R"URR"U40UD65$UR5 URURSS5=(d URSS5S9upV[URUS5nURRURU5nU$) aQ Return a numpy.ndarray which contains the set of draws for the named Stan program variable. Flattens the chains, leaving the draws in chain order. The first array dimension, corresponds to number of draws in the sample. The remaining dimensions correspond to the shape of the Stan program variable. Underlyingly draws are in chain order, i.e., for a sample with N chains of M draws each, the first M array elements are from chain 1, the next M are from chain 2, and the last M elements are from chain N. * If the variable is a scalar variable, the return array has shape ( draws * chains, 1). * If the variable is a vector, the return array has shape ( draws * chains, len(vector)) * If the variable is a matrix, the return array has shape ( draws * chains, size(dim 1), size(dim 2) ) * If the variable is an array with N dimensions, the return array has shape ( draws * chains, size(dim 1), ..., size(dim N)) For example, if the Stan program variable ``theta`` is a 3x3 matrix, and the sample consists of 4 chains with 1000 post-warmup draws, this function will return a numpy.ndarray with shape (4000,3,3). This functionaltiy is also available via a shortcut using ``.`` - writing ``fit.a`` is a synonym for ``fit.stan_variable("a")`` :param var: variable name :param kwargs: Additional keyword arguments are passed to the underlying fit's ``stan_variable`` method if the variable is not a generated quantity. See Also -------- CmdStanGQ.stan_variables CmdStanMCMC.stan_variable CmdStanMLE.stan_variable CmdStanPathfinder.stan_variable CmdStanVB.stan_variable CmdStanLaplace.stan_variable zUnknown variable name: z Available variables are z, rmFrnrN)r r,r}rr&r:r( atleast_1drErLrgetrr*extract_reshape) r-rrmodel_var_names gq_var_namesdraw1rBrouts r/rECmdStanGQ.stan_variables.X++55??DDF~~//446 &#*=)#/++))O:;<   "==!!//>v>  ++-$$zz,63zz*E2% t{{5623..2237GGN r2c 40nURRRR5nURRR5nUHnUR"U40UD6X%'M UHnXT;dM UR"U40UD6X%'M! U$)a Return a dictionary mapping Stan program variables names to the corresponding numpy.ndarray containing the inferred values. :param kwargs: Additional keyword arguments are passed to the underlying fit's ``stan_variable`` method if the variable is not a generated quantity. See Also -------- CmdStanGQ.stan_variable CmdStanMCMC.stan_variables CmdStanMLE.stan_variables CmdStanPathfinder.stan_variables CmdStanVB.stan_variables CmdStanLaplace.stan_variables )r r,r}rrE)r-rresultsample_var_namesrnames r/stan_variablesCmdStanGQ.stan_variablesTs$,,66@@EEG~~//446  D--d=f=FL!$D'#11$A&A % r2c URRS:wagURSS9up[R"X R [ UR54[SS9n[UR 5Hhn[URRUS5nSU5n[R"U[RSS S S 9USS2USS24'SSS5 Mj X0lg!,(df  M=f) N)rTrF)dtypeorderrc3T# UHoRS5(aMUv M g7f)#N)rC).0lines r/ ;CmdStanGQ._assemble_generated_quantities..}sI"$OOC4H"s( (rr,)rndminskiprows delimiter)r*rrr(emptyr6lenrgfloatr[openrr;loadtxtndarray)r-rBr gq_samplerfdliness r/rL(CmdStanGQ._assemble_generated_quantitiesps ;;   $ ((D(9  "  S):):%; <! 4;;'Edkk++E2C8BI"I)+1qC* !UA+&98(  98s !:C.. C= cSnURn[U[5(aDURnUR(a$U(aXCR - nX$4$UR nX$4$[U[ 5(a<SnUR(a&[UR5nU(aUnX$4$US- nX$4$SnURRSnU(aUS- nX$4$)Nrr) r rwrrrxrrrzroptimized_iterations_npvariational_sampler)r-rmrp_fitr opt_iterss r/rCmdStanGQ._draws_starts!! e[ ) )00I!!!7!77I$!"22E z * *I%% = =>  )I&ME E0066q9IQ r2cURn[U[5(aURUS9$[U[5(aRU(a$UR (aUR SS2S4$[R"UR5SS2S4$U(a3[R"URUR/5SS2S4$URSS2S4$)z@ Extract the draws from self.previous_fit. Return is always 3-d rN) r rwrrrrzrr( atleast_2doptimized_params_npvstackvariational_params_npr)r-rmrs r/rCmdStanGQ._previous_drawss !! e[ ) );;*;5 5 z * *e4444QW====))g yy00%2J2JKT'++AtG4 4r2cVU(aUnO [SS5nURn[U[5(aUR U=(d SUS9$[U[ 5(a6U(a UR (aURU$URU$URU$)Nr) slicer rwrrrrzoptimized_iterations_pdoptimized_params_pdvariational_sample_pd)r-rrmselrs r/rCmdStanGQ._previous_draws_pds +/Cd#C!! e[ ) )>>$,$:>F F z * *e4444S990055..s3 3r2dirc:URRU5 g)a/ Move output CSV files to specified directory. If files were written to the temporary session directory, clean filename. E.g., save 'bernoulli-201912081451-1-5nm6as7u.csv' as 'bernoulli-201912081451-1.csv'. :param dir: directory path See Also -------- stanfit.RunSet.save_csvfiles cmdstanpy.from_csv N)r save_csvfiles)r-rs r/rCmdStanGQ.save_csvfiless !!#&r2cL[5RS5 UR$)Nz@Property `mcmc_sample` is deprecated, use `previous_fit` instead)rryr rNs r/ mcmc_sampleCmdStanGQ.mcmc_samples#  N    r2)r*r,r r)NFF)r!NrK)1__name__ __module__ __qualname____firstlineno____doc__rrr0rr>r(rrHrrOrrr+propertyintr6rrdr rgrrkboolrrrrrrr rrErrLrrrr rrrrr__static_attributes__r#r2r/rr.sn 333  3& # ** *d)T#s(^)V"""%49%%=eCHo== +!$# _-_- _-  _-  _- _-F-1  NDIsD()NN N  N`-1   CD CcD()       -1   & CcD()       -1  _ CcD()_ _  _  _ BCCCCJtS"**_0E8 & t c3h 65$52::5,4I4+/4 4('#'$'"!U; I#EF!!r2r),r collectionsrtypingrrrrrr r r r r rrnumpyr(pandasrxarrayrr ImportErrorcmdstanpy.cmdstan_argsrcmdstanpy.utilsrrrcmdstanpy.utils.stancsvrmcmcrrkrmlerrrvbrrrr#r2r/r/s       *II4' e[*i8p ! p !#sBB  B