]h@^SrSSKrSSKrSSKrSSKrSSKrSSKrSSKrSSKrSSK J r SSK J r SSK Jr SSKJr SSKJrJrJrJrJrJrJrJrJrJr SSKrSSKJr SS K J!r!J"r"J#r#J$r$J%r% SS K&J'r'J(r(J)r)J*r*J+r+J,r,J-r-J.r. SS K/J0r0J1r1J2r2J3r3J4r4J5r5J6r6J7r7 SS K8J9r9J:r:J;r;JJ?r?J@r@ SSKAJBrC \\D\RS4rF\"S\2\3\55rG"SS5rHg) CmdStanModelN) OrderedDict)ThreadPoolExecutor)StringIO) cpu_count) AnyCallableDictIterableListLiteralMappingOptionalTypeVarUnion)tqdm)_CMDSTAN_REFRESH_CMDSTAN_SAMPLING_CMDSTAN_WARMUP_TMPDIR compilation) CmdStanArgsGenerateQuantitiesArgs LaplaceArgsMethod OptimizeArgsPathfinderArgs SamplerArgsVariationalArgs) CmdStanGQCmdStanLaplace CmdStanMCMC CmdStanMLECmdStanPathfinder CmdStanVBRunSetfrom_csv) cmdstan_pathcmdstan_version_before do_command get_loggerreturncode_msg) temp_initstemp_single_json)progressFitc@ \rSrSrSrSySS.S\\S\S\S \S \\ \\ 4S \\ \\ 4S \S \ \\ SS4SS4Sjjjr S\4Sjr\S\4Sj5r\S\4Sj5r\S\4Sj5rS\ \\44SjrS\ \\ 44SjrSzSS.S\S\ \\\\4S\S\SS4 Sjjjr\S\ \\ \\\444Sj5r\S\ \\ \\444Sj5r\S\4S j5rS\\4S!jrS{SS".S\S \\ \\ 4S \\ \\ 4S \S#\S$\SS4S%jjjrS|S'\ \\\ 4\\R>S4S(\\S)\ \\\ 4\ \\R>S4S*\S+\\S,\S-\\S.\\ S/\\ S0\\ S1\\ S2\\ S3\\ S4\\S5\\S6\S7\S8\S9\\S:\S;\\ S<\S\!4.S=jjr"S}SS>.S'\ \\\ 4\\R>S4S?\\S@\\SA\\S(\ \\#\S4SB\ \\#\S4S)\ \\\ 4\ \\#\\#\\\ 4S4SC\\SD\\SE\SF\\SG\\SH\ \\ \\ 4\#\\#\ \\ 4S4SI\ \ \#\ S4SJ\SK\\ SL\\SM\\SN\\SO\S*\S+\\SP\S,\SQ\S8\S9\\S:\S;\\ SR\\S\$4>SSjjjr%S~SST.S'\ \\\ 4\\R>S4SU\ \&\#\S4S(\\SV\S+\\S8\S9\\S:\S;\\ SW\ \$\#\S4S\'\&4SXjjjr(SSSY.S'\ \\\ 4\\R>S4S(\\S)\\ S*\S+\\SP\S,\S-\\S5\\SZ\\S[\\S\\\ SJ\S]\\S0\\ S^\\S_\\S7\S8\S9\\S:\S;\\ S`\\S\)40Sajjjr*SSSSSSSSSSSSSSSSSSSSSSS&SSSb.S'\ \\\ 4\\R>S4S.\\ S/\\ S0\\ S1\\ S2\\ S3\\ S4\\Sc\\Sd\\S_\\Se\\Sf\\Sg\Sh\S(\\S)\ \ \\ 4\ \\R>S4S*\S+\\S,\S8\S9\\S:\S;\\ Si\\S\+44Sjjjjr,SSSSk.Sl\ \ \\ 4\\R>4S'\ \\\ 4\\R>S4S<\S+\\S\-R\4 Smjjjr/SSSSSSSSS&SSSn. S'\ \\\ 4\\R>S4So\ \!\\R>S4S_\\S<\S(\\S*\S+\\S,\S8\S9\\S:\S;\\ Sp\\ \\ 4S\04Sqjjjr1SSr\2Ss\SQ\S8\St\\3\\/S4S;\\ SS4Sujjr4\5\6RnSB\#\Sv\S\\3\\/S44Swj55r8Sxr9g)rIaH The constructor method allows model instantiation given either the Stan program source file or the compiled executable, or both. This will compile the model if provided a Stan file and no executable, :param model_name: Model name, used for output file names. Optional, default is the base filename of the Stan program file. Deprecated: In version 2.0.0, model name cannot be specified and will always be taken from executable. :param stan_file: Path to Stan program file. :param exe_file: Path to compiled executable file. Optional, unless no Stan program file is specified. If both the program file and the compiled executable file are specified, the base filenames must match, (but different directory locations are allowed). :param force_compile: If ``True``, always compile, even if there is an existing executable file for this model. :param stanc_options: Options for stanc compiler, specified as a Python dictionary containing Stanc3 compiler option name, value pairs. Optional. :param cpp_options: Options for C++ compiler, specified as a Python dictionary containing C++ compiler option name, value pairs. Optional. :param user_header: A path to a header file to include during C++ compilation. Optional. :param compile: Whether or not to compile the model. Default is ``True``. If set to the string ``"force"``, it will always compile even if an existing executable is found. Deprecated: Use ``force_compile`` instead. The ability to instantiate a CmdStanModel without an executable will be removed in version 2.0.0. NF)compile model_name stan_fileexe_file force_compile stanc_options cpp_options user_headerr4forcereturnc  SUlSUlSUl[R"UUUS9UlUR R 5 SUlUcSnO[5RS5 U(aSnUb][5RS5 UR5(d[S RU55eUR5UlUcUc [S 5eGO [RR[RR!U55Ul[RR#UR5(d$[S RUR55e[RR%U5up['U 5S :dU R)S 5(d$[SRUR55eUR(d'[RR+U 5uUln [-URS5n U R/5n SSS5 SW ;aF[RR%UR5upUR R1U 5 [3SS5(dL[3SS5(a;UR55nSU;a$U=R['US5S:H-slUGbM[RR[RR!U55Ul[RR#UR5(d$[S RUR55e[RR%UR5un nUR(d([RR+U5uUln OUUR[RR+U5S:wa%[SRURU55e[8R:"5S:Xa&[=SS/SS9 [5R7S5 U(a9URc+URO[QU5RS5S:HSS$9 ggg!,(df  GN=f![a,nU(ae[5R7U5 SnAGNSnAff=f![>a [R@RCS5nUc,[RRE[G5SSSSS 5n[5R7S!U5 S"RE[I[JRL"U/[R@RCS#S5R%S"5-555[R@S#'GNqf=f)%a* Initialize object given constructor args. :param model_name: Deprecated. Model name, used for output file names. :param stan_file: Path to Stan program file. :param exe_file: Path to compiled executable file. :param force_compile: Whether or not to force recompilation if executable file already exists. :param stanc_options: Options for stanc compiler. :param cpp_options: Options for C++ compiler. :param user_header: A path to a header file to include during C++ compilation. :param compile: Deprecated. Whether or not to compile the model. Nr9r:r;FTzCmdStanModel(compile=...) is deprecated and will be removed in the next major version. The constructor will always ensure a model has a compiled executable. If you wish to force recompilation, use force_compile=True instead.r<zYCmdStanModel(model_name=...) is deprecated and will be removed in the next major version.z1Invalid value for argument model name, found "{}"zeMissing model file arguments, you must specify either Stan source or executable program file or both.zno such file {}z.stanzinvalid stan filename {}rz#include$ parametersrzZName mismatch between Stan file and compiled executable, expecting basename: {} found: {}.Windowsz where.exeztbb.dll)fd_outzTBB already found in load pathSTAN_TBBstanlib stan_mathtbbzAdding TBB (%s) to PATH;PATH)r< _internal)*_name _stan_file _exe_filerCompilerOptions_compiler_optionsvalidate _fixed_paramr+warningstrip ValueErrorformatospathrealpath expanduserexistssplitlenendswithsplitextopenreadadd_include_pathr)src_infodebugplatformsystemr* RuntimeErrorenvirongetjoinr(listrfromkeysr4strlower)selfr5r6r7r8r9r:r;r4_filenamefdprogramr] model_infoeexenamelibtbbs bC:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\cmdstanpy/model.py__init__CmdStanModel.__init__us4 !,!>$//22 !2!9!9$//!JKK''-- 2KA8}q (9(9'(B(B .55dooF:: " 0 0 :  Adoos+r''),W$''--8&&77=+1b117M288*!%J#z1))SL1I-Ja-OO)  WW--bgg.@.@.JKDN77>>$..11 !2!9!9$..!IJJt~~6JAw:: " 0 0 9  A::!1!1'!:1!==$&&,fTZZ&A ??  ) EK3DA" ""#CD t~~- LLs7|113w>$L O.7o,+"*L&&q))*.   3>WW\\${E5F ""#o779o>w<1$%,QZqz"  M ?   L  q !M s0A B43B/ "B4/B44 C#>CC#C#cURb[SS5(a0$[R"[ UR5UR 5$)z Run stanc with option '--info'. If stanc is older than 2.27 or if the stan file cannot be found, returns an empty dictionary. rCrD)r6r)rrhrrrUrs r}rhCmdStanModel.src_info5sB >> !%;Ar%B%BI##C$79O9OPPrT)backupoverwrite_file canonicalizemax_line_lengthrc [5RS5 URc [S5e[R "URUUUUUR S9 g)a Deprecated: Use :func:`cmdstanpy.format_stan_file()` instead. Run stanc's auto-formatter on the model code. Either saves directly back to the file or prints for inspection :param overwrite_file: If True, save the updated code to disk, rather than printing it. By default False :param canonicalize: Whether or not the compiler should 'canonicalize' the Stan model, removing things like deprecated syntax. Default is False. If True, all canonicalizations are run. If it is a list of strings, those options are passed to stanc (new in Stan 2.29) :param max_line_length: Set the wrapping point for the formatter. The default value is 78, which wraps most lines by the 80th character. :param backup: If True, create a stanfile.bak backup before writing to the file. Only disable this if you're sure you have other copies of the file or are using a version control system like Git. z|CmdStanModel.format() is deprecated and will be removed in the next major version. Use cmdstanpy.format_stan_file() instead.Nz"No Stan file found for this module)rrrrr9)r+rXr6rZrformat_stan_filer9)rtrrrrs r}r[CmdStanModel.formatAs[8   8 >> !AB B$$ NN)+%,,  rc.URR$)zOptions to stanc compilers.)rU_stanc_optionsrs r}r9CmdStanModel.stanc_optionsos%%444rc.URR$)zOptions to C++ compilers.)rU _cpp_optionsrs r}r:CmdStanModel.cpp_optionst%%222rc.URR$)z2The user header file if it exists, otherwise empty)rU _user_headerrs r}r;CmdStanModel.user_headeryrrc0UR(d [S5eSn[URS5nUR5nSSS5 U$!,(df  U$=f![a( [ 5R SUR5 U$f=f)z Return Stan program as a string.Please specify source fileNrBzCannot read file Stan file: %s)rRrlrerfIOErrorr+error)rtcoderws r}rCmdStanModel.code~s;< < doos+rwwy,  ,+    L  0$//   s.A#AA# A A# A##.BB)rPoverride_optionsrPcRU(d[5RS5 UR(d [S5eSnUcUcUbt[R "UUUS9nUR 5 XpR:wa?SnURcXplO)U(aXplOURRU5 [R"[UR5UURRURRURRS9Ulg)a  Deprecated: To compile a model, use the :class:`~cmdstanpy.CmdStanModel` constructor or :func:`cmdstanpy.compile_stan_file()`. Compile the given Stan program file. Translates the Stan code to C++, then calls the C++ compiler. By default, this function compares the timestamps on the source and executable files; if the executable is newer than the source file, it will not recompile the file, unless argument ``force`` is ``True`` or unless the compiler options have been changed. :param force: When ``True``, always compile, even if the executable file is newer than the source file. Used for Stan models which have ``#include`` directives in order to force recompilation when changes are made to the included files. :param stanc_options: Options for stanc compiler. :param cpp_options: Options for C++ compiler. :param user_header: A path to a header file to include during C++ compilation. :param override_options: When ``True``, override existing option. When ``False``, add/replace existing options. Default is ``False``. zCmdStanModel.compile() is deprecated and will be removed in the next major version. To compile a model, use the CmdStanModel() constructor or cmdstanpy.compile_stan_file().rNr@T)r<r9r:r;)r+rXrRrlrrTrVrUaddcompile_stan_filerrr6r9r:r;rS)rtr<r9r:r;rrPcompiler_optionss r}r4CmdStanModel.compilesF L O  ;< <  %&&*::+''    % % '#9#99))1-=*%-=***../?@$66  00>>..::..::  r %Y%m%d%H%M%Sdataseedinits output_dirsig_figs save_profile algorithm init_alphatol_obj tol_rel_objtol_grad tol_rel_grad tol_param history_sizeitersave_iterationsrequire_converged show_consolerefreshtime_fmttimeoutjacobianc[UUU U U U U UUUUS9 nU(a+[SSUR55(a [S5e[ U5n[ USS9n[ URURSUUUUUUUUS9 nS n[US US 9nURUUUUS 9 SSS5 SSS5 WR5 UR5(dpS RSRURS 55UR!55nSU;a!U(d[#5R%U5 O ['U5e[)U5nU$!,(df  N=f!,(df  N=f)a Run the specified CmdStan optimize algorithm to produce a penalized maximum likelihood estimate of the model parameters. This function validates the specified configuration, composes a call to the CmdStan ``optimize`` method and spawns one subprocess to run the optimizer and waits for it to run to completion. Unspecified arguments are not included in the call to CmdStan, i.e., those arguments will have CmdStan default values. The :class:`CmdStanMLE` object records the command, the return code, and the paths to the optimize method output CSV and console files. The output files are written either to a specified output directory or to a temporary directory which is deleted upon session exit. Output files are either written to a temporary directory or to the specified output directory. Output filenames correspond to the template '--' plus the file suffix which is either '.csv' for the CmdStan output or '.txt' for the console messages, e.g. 'bernoulli-201912081451-1.csv'. Output files written to the temporary directory contain an additional 8-character random string, e.g. 'bernoulli-201912081451-1-5nm6as7u.csv'. :param data: Values for all data variables in the model, specified either as a dictionary with entries matching the data variables, or as the path of a data file in JSON or Rdump format. :param seed: The seed for random number generator. Must be an integer between 0 and 2^32 - 1. If unspecified, :func:`numpy.random.default_rng` is used to generate a seed. :param inits: Specifies how the sampler initializes parameter values. Initialization is either uniform random on a range centered on 0, exactly 0, or a dictionary or file of initial values for some or all parameters in the model. The default initialization behavior will initialize all parameter values on range [-2, 2] on the *unconstrained* support. If the expected parameter values are too far from this range, this option may improve estimation. The following value types are allowed: * Single number, n > 0 - initialization range is [-n, n]. * 0 - all parameters are initialized to 0. * dictionary - pairs parameter name : initial value. * string - pathname to a JSON or Rdump data file. :param output_dir: Name of the directory to which CmdStan output files are written. If unspecified, output files will be written to a temporary directory which is deleted upon session exit. :param sig_figs: Numerical precision used for output CSV and text files. Must be an integer between 1 and 18. If unspecified, the default precision for the system file I/O is used; the usual value is 6. Introduced in CmdStan-2.25. :param save_profile: Whether or not to profile auto-diff operations in labelled blocks of code. If ``True``, CSV outputs are written to file '--profile-'. Introduced in CmdStan-2.26. :param algorithm: Algorithm to use. One of: 'BFGS', 'LBFGS', 'Newton' :param init_alpha: Line search step size for first iteration :param tol_obj: Convergence tolerance on changes in objective function value :param tol_rel_obj: Convergence tolerance on relative changes in objective function value :param tol_grad: Convergence tolerance on the norm of the gradient :param tol_rel_grad: Convergence tolerance on the relative norm of the gradient :param tol_param: Convergence tolerance on changes in parameter value :param history_size: Size of the history for LBFGS Hessian approximation. The value should be less than the dimensionality of the parameter space. 5-10 usually sufficient :param iter: Total number of iterations :param save_iterations: When ``True``, save intermediate approximations to the output CSV file. Default is ``False``. :param require_converged: Whether or not to raise an error if Stan reports that "The algorithm may not have converged". :param show_console: If ``True``, stream CmdStan messages sent to stdout and stderr to the console. Default is ``False``. :param refresh: Specify the number of iterations cmdstan will take between progress messages. Default value is 100. :param time_fmt: A format string passed to :meth:`~datetime.datetime.strftime` to decide the file names for output CSVs. Defaults to "%Y%m%d%H%M%S" :param timeout: Duration at which optimization times out in seconds. :param jacobian: Whether or not to use the Jacobian adjustment for constrained variables in optimization. By default this is false, meaning optimization yields the Maximum Likehood Estimate (MLE). Setting it to true yields the Maximum A Posteriori Estimate (MAP). :return: CmdStanMLE object ) rrrrrrrrrrrrC zQJacobian adjustment for optimization is only supported in CmdStan 2.32 and above.Fallow_multipleN chain_idsrrrrrr method_argsrrr/argschainsrrrz2Error during optimization! Command '{}' failed: {} zLine search failed)rr)rrZr.r-rrQrSr& _run_cmdstanraise_for_timeouts_check_retcodesr[ror get_err_msgsr+rXrlr#)rtrrrrrrrrrrrrrrrrrrrrrr optimize_args_data_initsrdummy_chain_idrunsetmsgmles r}optimizeCmdStanModel.optimizes|J%!#%%+   .q"dmmoFF-  d #uj %/  %!)) DNa(CF   )  %/ #0 !!#%%''FMMA')<)<)>C$s*3D $$S)"3''  G/ / # #s% E&AE#E& E# E&& E4)force_one_process_per_chainrparallel_chainsthreads_per_chainr iter_warmup iter_sampling save_warmupthin max_treedepthmetric step_size adapt_engaged adapt_deltaadapt_init_phaseadapt_metric_windowadapt_step_size fixed_paramsave_latent_dynamics show_progressrc, Uc URnUcSnUS:a[SRU55eUc[[ [ 5U5S5nOCX2:a[ 5RSUU5 UnO US:a[SRU55eUcSnUS:a[SRU55eUnUn Sn!UR5n"U"RS S 5R5n#US:XaSnUc![S S U"5(dU#S :Xa Sn!UU -n SnUSLaU[S S U"5(d)Sn!UU -n SnU#S :Xa[ 5RS5 O[ 5RSU5 [U 5[RS'Uc[!U5V$s/sHn$U$S-PM nn$O[#U[$5(a?US:a[SRU55e[!U5V$s/sHn$U$U-PM nn$OU!(d;['XfSS5H)un$n%U$U%S- :wdM[SRU55e [)U5U:Xd$[SRU[)U555eUH#n&U&S:dM [SRU&55e [+U!(aSOUUU U U U U UUUUUUUS9n'[-U5n([/XvSS9n)U!(a1[#U[05(aUV$s/sHn$U)SSSU$S3PM n*n$OU)n*[3UR4UR6UU(UU*UUUUU'US9 n+U(aSnO7U=(a [8R:"5n[ 5RS5 Sn,U(aGSn-Uc U-[<- n-OU-U- n-U c U-[>- n-OU-U - n-Uc[@nU-U-S -n-URCUU-S9n,[EU+UUUU!S9n.[GUS 9n/[!U.RH5H"n$U/RKURLU.U$UUU,US!9 M$ SSS5 U(aU,bU,"S"S#5 [NRP"S$S%9n0U0b^U0SS:aU[!U5HFn$[RRTRWS&U0S-5 [RRTRY5 MH [RRTRWS'5 [ 5RS(5 U.R[5 [ 5R]S)[_U.55 [aU.RbSS*5n1U1Re5n2[ 5R]S+U25 S,U2;a1[ 5R]S-5 SU'l3U'U.Rhl5SSS5 U.Rm5n3U.Ro5(d(S.U3S/[_U.53n4U(dU4S0- n4[qU45eU3(a*S1U33n4U(dU4S0- n4[ 5RU45 [sU.5n5SSS5 SSS5 W5$s sn$fs sn$fs sn$f!,(df  GN/=f!,(df  N=f!,(df  NJ=f!,(df  W5$=f)2a$ Run or more chains of the NUTS-HMC sampler to produce a set of draws from the posterior distribution of a model conditioned on some data. This function validates the specified configuration, composes a call to the CmdStan ``sample`` method and spawns one subprocess per chain to run the sampler and waits for all chains to run to completion. Unspecified arguments are not included in the call to CmdStan, i.e., those arguments will have CmdStan default values. For each chain, the :class:`CmdStanMCMC` object records the command, the return code, the sampler output file paths, and the corresponding console outputs, if any. The output files are written either to a specified output directory or to a temporary directory which is deleted upon session exit. Output files are either written to a temporary directory or to the specified output directory. Ouput filenames correspond to the template '--' plus the file suffix which is either '.csv' for the CmdStan output or '.txt' for the console messages, e.g. 'bernoulli-201912081451-1.csv'. Output files written to the temporary directory contain an additional 8-character random string, e.g. 'bernoulli-201912081451-1-5nm6as7u.csv'. :param data: Values for all data variables in the model, specified either as a dictionary with entries matching the data variables, or as the path of a data file in JSON or Rdump format. :param chains: Number of sampler chains, must be a positive integer. :param parallel_chains: Number of processes to run in parallel. Must be a positive integer. Defaults to :func:`multiprocessing.cpu_count`, i.e., it will only run as many chains in parallel as there are cores on the machine. Note that CmdStan 2.28 and higher can run all chains in parallel providing that the model was compiled with threading support. :param threads_per_chain: The number of threads to use in parallelized sections within an MCMC chain (e.g., when using the Stan functions ``reduce_sum()`` or ``map_rect()``). This will only have an effect if the model was compiled with threading support. For such models, CmdStan version 2.28 and higher will run all chains in parallel from within a single process. The total number of threads used will be ``parallel_chains * threads_per_chain``, where the default value for parallel_chains is the number of cpus, not chains. :param seed: The seed for random number generator. Must be an integer between 0 and 2^32 - 1. If unspecified, :func:`numpy.random.default_rng` is used to generate a seed which will be used for all chains. When the same seed is used across all chains, the chain-id is used to advance the RNG to avoid dependent samples. :param chain_ids: The offset for the random number generator, either an integer or a list of unique per-chain offsets. If unspecified, chain ids are numbered sequentially starting from 1. :param inits: Specifies how the sampler initializes parameter values. Initialization is either uniform random on a range centered on 0, exactly 0, or a dictionary or file of initial values for some or all parameters in the model. The default initialization behavior will initialize all parameter values on range [-2, 2] on the *unconstrained* support. If the expected parameter values are too far from this range, this option may improve adaptation. The following value types are allowed: * Single number n > 0 - initialization range is [-n, n]. * 0 - all parameters are initialized to 0. * dictionary - pairs parameter name : initial value. * string - pathname to a JSON or Rdump data file. * list of strings - per-chain pathname to data file. * list of dictionaries - per-chain initial values. :param iter_warmup: Number of warmup iterations for each chain. :param iter_sampling: Number of draws from the posterior for each chain. :param save_warmup: When ``True``, sampler saves warmup draws as part of the Stan CSV output file. :param thin: Period between recorded iterations. Default is 1, i.e., all iterations are recorded. :param max_treedepth: Maximum depth of trees evaluated by NUTS sampler per iteration. :param metric: Specification of the mass matrix, either as a vector consisting of the diagonal elements of the covariance matrix ('diag' or 'diag_e') or the full covariance matrix ('dense' or 'dense_e'). If the value of the metric argument is a string other than 'diag', 'diag_e', 'dense', or 'dense_e', it must be a valid filepath to a JSON or Rdump file which contains an entry 'inv_metric' whose value is either the diagonal vector or the full covariance matrix. If the value of the metric argument is a list of paths, its length must match the number of chains and all paths must be unique. If the value of the metric argument is a Python dict object, it must contain an entry 'inv_metric' which specifies either the diagnoal or dense matrix. If the value of the metric argument is a list of Python dicts, its length must match the number of chains and all dicts must containan entry 'inv_metric' and all 'inv_metric' entries must have the same shape. :param step_size: Initial step size for HMC sampler. The value is either a single number or a list of numbers which will be used as the global or per-chain initial step size, respectively. The length of the list of step sizes must match the number of chains. :param adapt_engaged: When ``True``, adapt step size and metric. :param adapt_delta: Adaptation target Metropolis acceptance rate. The default value is 0.8. Increasing this value, which must be strictly less than 1, causes adaptation to use smaller step sizes which improves the effective sample size, but may increase the time per iteration. :param adapt_init_phase: Iterations for initial phase of adaptation during which step size is adjusted so that the chain converges towards the typical set. :param adapt_metric_window: The second phase of adaptation tunes the metric and step size in a series of intervals. This parameter specifies the number of iterations used for the first tuning interval; window size increases for each subsequent interval. :param adapt_step_size: Number of iterations given over to adjusting the step size given the tuned metric during the final phase of adaptation. :param fixed_param: When ``True``, call CmdStan with argument ``algorithm=fixed_param`` which runs the sampler without updating the Markov Chain, thus the values of all parameters and transformed parameters are constant across all draws and only those values in the generated quantities block that are produced by RNG functions may change. This provides a way to use Stan programs to generate simulated data via the generated quantities block. Default value is ``False``. :param output_dir: Name of the directory to which CmdStan output files are written. If unspecified, output files will be written to a temporary directory which is deleted upon session exit. :param sig_figs: Numerical precision used for output CSV and text files. Must be an integer between 1 and 18. If unspecified, the default precision for the system file I/O is used; the usual value is 6. Introduced in CmdStan-2.25. :param save_latent_dynamics: Whether or not to output the position and momentum information for the model parameters (unconstrained). If ``True``, CSV outputs are written to an output file '--diagnostic-', e.g. 'bernoulli-201912081451-diagnostic-1.csv', see https://mc-stan.org/docs/cmdstan-guide/stan_csv.html, section "Diagnostic CSV output file" for details. :param save_profile: Whether or not to profile auto-diff operations in labelled blocks of code. If ``True``, CSV outputs are written to file '--profile-'. Introduced in CmdStan-2.26, see https://mc-stan.org/docs/cmdstan-guide/stan_csv.html, section "Profiling CSV output file" for details. :param show_progress: If ``True``, display progress bar to track progress for warmup and sampling iterations. Default is ``True``, unless package tqdm progress bar encounter errors. :param show_console: If ``True``, stream CmdStan messages sent to stdout and stderr to the console. Default is ``False``. :param refresh: Specify the number of iterations CmdStan will take between progress messages. Default value is 100. :param time_fmt: A format string passed to :meth:`~datetime.datetime.strftime` to decide the file names for output CSVs. Defaults to "%Y%m%d%H%M%S" :param force_one_process_per_chain: If ``True``, run multiple chains in distinct processes regardless of model ability to run parallel chains (CmdStan 2.28+ feature). If ``False``, always run multiple chains in one process (does not check that this is valid). If None (Default): Check that CmdStan version is >=2.28, and that model was compiled with STAN_THREADS=True, and utilize the parallel chain functionality if those conditions are met. :param timeout: Duration at which sampling times out in seconds. :return: CmdStanMCMC object Nr/z2Chains must be a positive integer value, found {}.zSRequested %u parallel_chains but only %u required, will run all chains in parallel.z>Argument parallel_chains must be a positive integer, found {}.z@Argument threads_per_chain must be a positive integer, found {}.T STAN_THREADSfalserCtrueFzStan program not compiled for threading, process will run chains sequentially. For multi-chain parallelization, recompile the model with argument "cpp_options={'STAN_THREADS':'TRUE'}.zInstalled version of CmdStan cannot multi-process chains, will run %d processes. Run "install_cmdstan" to upgrade to latest version.STAN_NUM_THREADSz4Chain_id must be a positive integer value, found {}.z8chain_ids must be sequential list of integers, found {}.zVChain_ids must correspond to number of chains specified {} chains, found {} chain_ids.rz8Chain_id must be a non-negative integer value, found {}.) num_chainsrrrrrrrrrrrrr)idruz.json rrrrrrrrrrzCmdStan start processing)rtotal)rrrrone_process_per_chain max_workers)ridxrr progress_hookrDone)P)fallbackrrzCmdStan done processing.z runset %srBzChain 1 console: %szrunning fixed_param samplerzDetected fixed param modelzError during sampling:  Command and output files: K Consider re-running with show_console=True if the above output is unclear!z!Non-fatal error during sampling: ):rWrZr[maxminrr+rrrnrsr)rXrrr\rmrange isinstanceintziprbrr.r-rprrQrSprogbarallow_show_progressrrr_wrap_sampler_progress_hookr&r num_procssubmitrshutilget_terminal_sizesysstdoutwriteflushrrirre stdout_filesrfr_argsrrrrlr")6rtrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrparallel_procs num_threadsr info_dict stan_threadsijchain_id sampler_argsrr cmdstan_initsrr  iter_totalrexecutor term_sizerw console_msgserrorsrmcmcs6 r}sampleCmdStanModel.samplesb  ++K >F A:DKK   "!#ik6":A>O  % L  3   %O q "F?3   $ !  q "F#45  )' $MMO  }}^W=CCE Q;*. ' ( /*1b)<<&$) !)K7KN &% /)!R;;(-%- ; !"7*L((D $$J  *-[)9 %&  (-f 6 1Q I6I)S))q=$%%+VI%65:&MBMqQ]M B , #I} =1A:",!--3VI->#!> 9~/$DDJF"C NE !*H!|())/)9!*#1qv#'#''#- 3+# "d #uj l/ $E4)@)@7@!7@!vcr{m1QCu-y! !'  ##%!%9)( D % - O'2M2M2O  !!"<=BFM &/1J+-J ("33J-/J?.G'72Q6 $ @ @'$!A! #!&; F$?8v//0AOO))%&3%1&3 '$1@!:fb)/5.F.F%/ (Yq\A-="6] ((y|);< ((*+   & !!"<=  % % ' L  |T&\ :f))!,c2b!wwy  ""#9<H0L@L&&'CD/3L,/;FLL, 3((*F))++.vh7226v,A$4C#3'':6(C#4C $$S)v&Dm/ #n O7CZ!j@?>32s/ / # #n sv'Y;Y Z"!Y3Y CY3,--' plus the file suffix which is either '.csv' for the CmdStan output or '.txt' for the console messages, e.g. 'bernoulli-201912081451-1.csv'. Output files written to the temporary directory contain an additional 8-character random string, e.g. 'bernoulli-201912081451-1-5nm6as7u.csv'. :param data: Values for all data variables in the model, specified either as a dictionary with entries matching the data variables, or as the path of a data file in JSON or Rdump format. :param previous_fit: Can be either a :class:`CmdStanMCMC`, :class:`CmdStanMLE`, or :class:`CmdStanVB` or a list of stan-csv files generated by fitting the model to the data using any Stan interface. :param seed: The seed for random number generator. Must be an integer between 0 and 2^32 - 1. If unspecified, :func:`numpy.random.default_rng` is used to generate a seed which will be used for all chains. *NOTE: Specifying the seed will guarantee the same result for multiple invocations of this method with the same inputs. However this will not reproduce results from the sample method given the same inputs because the RNG will be in a different state.* :param gq_output_dir: Name of the directory in which the CmdStan output files are saved. If unspecified, files will be written to a temporary directory which is deleted upon session exit. :param sig_figs: Numerical precision used for output CSV and text files. Must be an integer between 1 and 18. If unspecified, the default precision for the system file I/O is used; the usual value is 6. Introduced in CmdStan-2.25. :param show_console: If ``True``, stream CmdStan messages sent to stdout and stderr to the console. Default is ``False``. :param refresh: Specify the number of iterations CmdStan will take between progress messages. Default value is 100. :param time_fmt: A format string passed to :meth:`~datetime.datetime.strftime` to decide the file names for output CSVs. Defaults to "%Y%m%d%H%M%S" :param timeout: Duration at which generation times out in seconds. :return: CmdStanGQ object NzGCannot supply both 'previous_fit' and deprecated argument 'mcmc_sample'zKArgument name `mcmc_sample` is deprecated, please rename to `previous_fit`.r/z2Expecting list of Stan CSV files, found empty listzKInvalid sample from Stan CSV files, error: {} while processing files {}z zTPrevious fit must be either CmdStanPy fit object or list of paths to Stan CSV files.zdSample contains saved warmup draws which will be used to generate additional quantities of interest.rCzKMethod generate_quantities was not available for non-HMC until CmdStan 2.31z_MLE contains saved iterations which will be used to generate additional quantities of interest.) csv_files)rrrrrrr)rrrrrrz"Error during generate_quantities: rr)rr8)&rZr+rXrr"r#r%rr<rprbr'r[rrorr _save_warmupr)rl_save_iterationsrrVr.rrQrSr&rrrrrrrrrr )rtrr8rr9rrrrrr7 fit_object fit_csv_filesrzrrgenerate_quantities_argsrrrparallel_chains_availrr0r*r3r quantitiess r}generate_quantities CmdStanModel.generate_quantities}s8^  " 8 L ,  'L l[*i$H I I%J(//99M  d + +< 1$ H  , %m4 7  j+ . .&&F",,I&& $$E J / /%a,,"?FI** $$E &a,,"?FI#9#$   !))&1 d #u #(!4 D&F%.K !!#&;a&?"H!LO#@HvAOO))%1 ' $'A  % % '((*F9&B226v,A$4C#3''"&zJJS$Tq  44:FQ\!:5   @A@%$ #Ts> J*2AL0K0?A!L* K-44K((K-0 K> :L L)output_samples grad_samples elbo_sampleseta adapt_iter eval_elbodrawsrFcUb)Ub [S5e[5RS5 Un[UU U U U U UUUUS9 n[ U5n[ USS9n[ URURSUUUUUUUUUS9 nSn[US US 9nURUUUUS 9 SSS5 SSS5 WR5 URWn[R"S [R5n[!US 5n U R#5n!SSS5 [%[R&"UW!55S:a-U(a [)S5e[5RSS S5 UR+5(dURUn[!US 5n U R#5n!SSS5 [R"S[R5n[%[R&"UU!55S:aU cSn SR-U 5n"OSR-UR/55n"[)U"5e[1U5n#U#$!,(df  GN=f!,(df  GN=f!,(df  GNd=f!,(df  N=f)a Run CmdStan's variational inference algorithm to approximate the posterior distribution of the model conditioned on the data. This function validates the specified configuration, composes a call to the CmdStan ``variational`` method and spawns one subprocess to run the optimizer and waits for it to run to completion. Unspecified arguments are not included in the call to CmdStan, i.e., those arguments will have CmdStan default values. The :class:`CmdStanVB` object records the command, the return code, and the paths to the variational method output CSV and console files. The output files are written either to a specified output directory or to a temporary directory which is deleted upon session exit. Output files are either written to a temporary directory or to the specified output directory. Output filenames correspond to the template '--' plus the file suffix which is either '.csv' for the CmdStan output or '.txt' for the console messages, e.g. 'bernoulli-201912081451-1.csv'. Output files written to the temporary directory contain an additional 8-character random string, e.g. 'bernoulli-201912081451-1-5nm6as7u.csv'. :param data: Values for all data variables in the model, specified either as a dictionary with entries matching the data variables, or as the path of a data file in JSON or Rdump format. :param seed: The seed for random number generator. Must be an integer between 0 and 2^32 - 1. If unspecified, :func:`numpy.random.default_rng` is used to generate a seed which will be used for all chains. :param inits: Specifies how the sampler initializes parameter values. Initialization is uniform random on a range centered on 0 with default range of 2. Specifying a single number n > 0 changes the initialization range to [-n, n]. :param output_dir: Name of the directory to which CmdStan output files are written. If unspecified, output files will be written to a temporary directory which is deleted upon session exit. :param sig_figs: Numerical precision used for output CSV and text files. Must be an integer between 1 and 18. If unspecified, the default precision for the system file I/O is used; the usual value is 6. Introduced in CmdStan-2.25. :param save_latent_dynamics: Whether or not to save diagnostics. If ``True``, CSV outputs are written to output file '--diagnostic-', e.g. 'bernoulli-201912081451-diagnostic-1.csv'. :param save_profile: Whether or not to profile auto-diff operations in labelled blocks of code. If ``True``, CSV outputs are written to file '--profile-'. Introduced in CmdStan-2.26. :param algorithm: Algorithm to use. One of: 'meanfield', 'fullrank'. :param iter: Maximum number of ADVI iterations. :param grad_samples: Number of MC draws for computing the gradient. Default is 10. If problems arise, try doubling current value. :param elbo_samples: Number of MC draws for estimate of ELBO. :param eta: Step size scaling parameter. :param adapt_engaged: Whether eta adaptation is engaged. :param adapt_iter: Number of iterations for eta adaptation. :param tol_rel_obj: Relative tolerance parameter for convergence. :param eval_elbo: Number of iterations between ELBO evaluations. :param draws: Number of approximate posterior output draws to save. :param require_converged: Whether or not to raise an error if Stan reports that "The algorithm may not have converged". :param show_console: If ``True``, stream CmdStan messages sent to stdout and stderr to the console. Default is ``False``. :param refresh: Specify the number of iterations CmdStan will take between progress messages. Default value is 100. :param time_fmt: A format string passed to :meth:`~datetime.datetime.strftime` to decide the file names for output CSVs. Defaults to "%Y%m%d%H%M%S" :param timeout: Duration at which variational Bayesian inference times out in seconds. :return: CmdStanVB object NzCCannot supply both 'draws' and deprecated argument 'output_samples'zGArgument name `output_samples` is deprecated, please rename to `draws`.) rrrGrHrIrrJrrKrFFrrrr/rrz%The algorithm may not have converged.rBzsThe algorithm may not have converged. If you would like to inspect the output, re-call with require_converged=Falsez%s %sz4Proceeding because require_converged is set to Falsez/stan::variational::normal_meanfield::calc_grad: ztVariational algorithm gradient calculation failed. Double the value of argument "grad_samples", current value is {}.z&Error during variational inference: {})rZr+rXrr.r-rrQrSr&rrr$rer4Mrerfrbfindallrlrr[rr%)$rtrrrrrrrrrrGrHrIrrJrrKrLrrrrrrFvariational_argsrrrrrtranscript_filepat transcriptcontentsrvbs$ r} variationalCmdStanModel.variational>s~x  %  ' L %  #E*%%'!#   d #uj %/  %!%9), DNa(CF   )  )/ #4 !!#!--n=jjA244H /3 ':!(H( rzz#x( )A - "; L 7F  %%''$11.AOos+z%??,,**BBDDC2::c8,-1'#%L++16,+? ?EE'')s# # v  I/ / # #>( '",+s= IAII=I+I= I I I(+ I:= J )rrrrrrr num_pathsmax_lbfgs_itersrLnum_single_drawsnum_elbo_draws psis_resample calculate_lprrrrrrrrrr'rZr[r\r]r^r_r'cUR5n[SSU5(a [S5eU(aU(d[SSU5(a [S5eUbQUS:wa/URSS 5R 5S :wa [S 5e[ U5[ RS 'U S:XaU cU n U bX:wa [S 5e[UUUUUUUU U U U U UUS9n[U5n[U5n[URURSUUUUUUUUS9 nSn[USUS9n URU UUUS9 SSS5 SSS5 W R!5 U R#5(dISR%SR'U R)S55U R+55n![-U!5e[/U 5$!,(df  N=f!,(df  N=f)uc Run CmdStan's Pathfinder variational inference algorithm. :param data: Values for all data variables in the model, specified either as a dictionary with entries matching the data variables, or as the path of a data file in JSON or Rdump format. :param num_paths: Number of single-path Pathfinders to run. Default is 4, when the number of paths is 1 then no importance sampling is done. :param draws: Number of approximate draws to return. :param num_single_draws: Number of draws each single-pathfinder will draw. If ``num_paths`` is 1, only one of this and ``draws`` should be used. :param max_lbfgs_iters: Maximum number of L-BFGS iterations. :param num_elbo_draws: Number of Monte Carlo draws to evaluate ELBO. :param psis_resample: Whether or not to use Pareto Smoothed Importance Sampling on the result of the individual Pathfinders. If False, the result contains the draws from each path. :param calculate_lp: Whether or not to calculate the log probability for approximate draws. If False, this also implies that ``psis_resample`` will be set to False. :param seed: The seed for random number generator. Must be an integer between 0 and 2^32 - 1. If unspecified, :func:`numpy.random.default_rng` is used to generate a seed. :param inits: Specifies how the algorithm initializes parameter values. Initialization is either uniform random on a range centered on 0, exactly 0, or a dictionary or file of initial values for some or all parameters in the model. The default initialization behavior will initialize all parameter values on range [-2, 2] on the *unconstrained* support. If the expected parameter values are too far from this range, this option may improve adaptation. The following value types are allowed: * Single number n > 0 - initialization range is [-n, n]. * 0 - all parameters are initialized to 0. * dictionary - pairs parameter name : initial value. * string - pathname to a JSON or Rdump data file. * list of strings - per-path pathname to data file. * list of dictionaries - per-path initial values. :param init_alpha: For internal L-BFGS: Line search step size for first iteration :param tol_obj: For internal L-BFGS: Convergence tolerance on changes in objective function value :param tol_rel_obj: For internal L-BFGS: Convergence tolerance on relative changes in objective function value :param tol_grad: For internal L-BFGS: Convergence tolerance on the norm of the gradient :param tol_rel_grad: For internal L-BFGS: Convergence tolerance on the relative norm of the gradient :param tol_param: For internal L-BFGS: Convergence tolerance on changes in parameter value :param history_size: For internal L-BFGS: Size of the history for LBFGS Hessian approximation. The value should be less than the dimensionality of the parameter space. 5-10 is usually sufficient :param output_dir: Name of the directory to which CmdStan output files are written. If unspecified, output files will be written to a temporary directory which is deleted upon session exit. :param sig_figs: Numerical precision used for output CSV and text files. Must be an integer between 1 and 18. If unspecified, the default precision for the system file I/O is used; the usual value is 6. Introduced in CmdStan-2.25. :param save_profile: Whether or not to profile auto-diff operations in labelled blocks of code. If ``True``, CSV outputs are written to file '--profile-'. Introduced in CmdStan-2.26, see https://mc-stan.org/docs/cmdstan-guide/stan_csv.html, section "Profiling CSV output file" for details. :param show_console: If ``True``, stream CmdStan messages sent to stdout and stderr to the console. Default is ``False``. :param refresh: Specify the number of iterations CmdStan will take between progress messages. Default value is 100. :param time_fmt: A format string passed to :meth:`~datetime.datetime.strftime` to decide the file names for output CSVs. Defaults to "%Y%m%d%H%M%S" :param timeout: Duration at which Pathfinder times out in seconds. Defaults to None. :param num_threads: Number of threads to request for parallel execution. A number other than ``1`` requires the model to have been compiled with STAN_THREADS=True. :return: A :class:`CmdStanPathfinder` object References ---------- Zhang, L., Carpenter, B., Gelman, A., & Vehtari, A. (2022). Pathfinder: Parallel quasi-Newton variational inference. Journal of Machine Learning Research, 23(306), 1–49. Retrieved from http://jmlr.org/papers/v23/21-0889.html rC!zBMethod 'pathfinder' not available for CmdStan versions before 2.33"zcArguments 'psis_resample' and 'calculate_lp' are only available for CmdStan versions 2.34 and laterNr/rr?rzMModel must be compiled with 'STAN_THREADS=true' to use 'num_threads' argumentrzHCannot specify both 'draws' and 'num_single_draws' when 'num_paths' is 1)rrrrrrrnum_psis_drawsrZr[ num_drawsr]r^r_rrrrz0Error during Pathfinder! Command '{}' failed: {}r)rr)rZrnrsrrr\rmrr.r-rrQrSr&rrrr[rorrrlr$)"rtrrrrrrrrrZr[rLr\r]r^r_rrrrrrrrrr'rpathfinder_argsrrrrrrs" r} pathfinderCmdStanModel.pathfindersd==? !!R 2 2  \7M r88 8 @   "q LL4::<F ..1-=BJJ) * >'#(  %5%> - )!#%% +&)'% "d #uj.?6 %!)+ DNa(CF   )  !/@ #, !!#%%''DKKA')<)<)>Cs# # ((;/@.? # #s%, G8AG?G G G G')rrparamsc [SSUR55(a [S5e[U5n[U5n[ UR 5SSU3S[ U53/nUb USS U3/- n[R"UR[S 9n[RRUS 5n US S U 3/- nUbURS U35 [5R!S[ U55 ["R$"USSSS9n U R&(aP[5R)SU R*U R,5 [/S[ U R&5-5e[0R2"U SS9n U sSSS5 sSSS5 $!,(df  O=fSSS5 g!,(df  g=f)a Calculate the log probability and gradient at the given parameter values. .. note:: This function is **NOT** an efficient way to evaluate the log density of the model. It should be used for diagnostics ONLY. Please, do not use this for other purposes such as testing new sampling algorithms! :param params: Values for all parameters in the model, specified either as a dictionary with entries matching the parameter variables, or as the path of a data file in JSON or Rdump format. These should be given on the constrained (natural) scale. :param data: Values for all data variables in the model, specified either as a dictionary with entries matching the data variables, or as the path of a data file in JSON or Rdump format. :param jacobian: Whether or not to enable the Jacobian adjustment for constrained parameters. Defaults to ``True``. :param sig_figs: Numerical precision used for output CSV and text files. Must be an integer between 1 and 18. If unspecified, the default precision for the system file I/O is used; the usual value is 6. :return: A pandas.DataFrame containing columns "lp__" and additional columns for the gradient values. These gradients will be for the unconstrained parameters of the model. rCr;z@Method 'log_prob' not available for CmdStan versions before 2.31log_probzconstrained_params=z jacobian=Nrzfile=)prefixdirz output.csvoutputz sig_figs=zCmd: %sTF)capture_outputchecktextz.'log_prob' command failed! stdout:%s stderr:%sz*Method 'log_prob' failed with return code #)comment)r)rrZr.rrr7rtempfilemkdtemprrr\r]roappendr+ri subprocessrun returncoderr!stderrrlpdread_csv) rtrhrrrr_paramsrrrmprocrs r}rjCmdStanModel.log_probsN "!R 9 9 d #u.> / DMM"%gY/CM?+ C  %w00!))HJWW\\*l;F HfX./ /C# Yxj12 L  y#c( 3>>DDD ""FKKKK #@$//*+ [[5FI/ / # #/ / # # #s$ F;E F! F;! F/ +F;; G ) rrrrrrrrropt_argsmoderc [SSUR55(a [S5eU bUb [S5eUcDUUUUU U U U US. nURU =(d 05 SU -US'UR"SS U0UD6nO#[U[5(d [U5nOUnURR[R:wa [S 5eURRRR nUU:wa[S US U35e[#URR$SX45n['U5n[)UR*UR,SUUUUUUU S9 nSn[/USU S9nUR1UUU U S9 SSS5 WR35 [5UU5$![ an[ S 5UeSnAff=f!,(df  NH=f)a Run a Laplace approximation around the posterior mode. :param data: Values for all data variables in the model, specified either as a dictionary with entries matching the data variables, or as the path of a data file in JSON or Rdump format. :param mode: The mode around which to place the approximation, either * A :class:`CmdStanMLE` object * A path to a CSV file containing the output of an optimization run. * ``None`` - use default optimizer settings and/or any ``opt_args``. :param draws: Number of approximate draws to return. Defaults to 1000 :param jacobian: Whether or not to enable the Jacobian adjustment for constrained parameters. Defaults to ``True``. Note: This must match the argument used in the creation of ``mode``, if supplied. :param output_dir: Name of the directory to which CmdStan output files are written. If unspecified, output files will be written to a temporary directory which is deleted upon session exit. :param sig_figs: Numerical precision used for output CSV and text files. Must be an integer between 1 and 18. If unspecified, the default precision for the system file I/O is used; the usual value is 6. Introduced in CmdStan-2.25. :param save_profile: Whether or not to profile auto-diff operations in labelled blocks of code. If ``True``, CSV outputs are written to file '--profile-'. Introduced in CmdStan-2.26, see https://mc-stan.org/docs/cmdstan-guide/stan_csv.html, section "Profiling CSV output file" for details. :param show_console: If ``True``, stream CmdStan messages sent to stdout and stderr to the console. Default is ``False``. :param refresh: Specify the number of iterations CmdStan will take between progress messages. Default value is 100. :param time_fmt: A format string passed to :meth:`~datetime.datetime.strftime` to decide the file names for output CSVs. Defaults to "%Y%m%d%H%M%S" :param timeout: Duration at which Pathfinder times out in seconds. Defaults to None. :param opt_args: Dictionary of additional arguments which will be passed to :meth:`~CmdStanModel.optimize` :return: A :class:`CmdStanLaplace` object. rCrzFMethod 'laplace_sample' not available for CmdStan versions before 2.32Nz3Cannot specify both 'opt_args' and 'mode' arguments) rrrrrrrrrzopt-rrzXFailed to run optimizer on model. Consider supplying a mode or additional optimizer argsz6Mode must be a CmdStanMLE or a path to an optimize CSVzTJacobian argument to optimize and laplace must match! Laplace was run with jacobian=z%, but optimize was run with jacobian=r)rrrrrrrrr/rr)r)rrZupdater Exceptionrlrr#r'rmethodrOPTIMIZEr%rrrr<r.rrQrSr&rrr!)rtrrrLrrrrrrrrrrr cmdstan_moderz mode_jacobian laplace_argsrrrrs r}laplace_sampleCmdStanModel.laplace_sampleQs6P "!R 9 9   D$4E  <$$ , ,"$"( M  R 0(.(9M* % +/==,,#, D*--#D>LL    % % 8H     % % 1 1 : :  H $119 ;66C_F  #    ) )! ,e d #u %!)( DNa(CF   )  $* !!#fl33g "M :$ #s%/F3 AG3 G= G  G Grr r c f^^^[5RST5 [5RS[[RR S555 SnSnTR (a<SRTRT5nSRTRT5nTRT5n [5RSU 5 U(d[5RS U5 [TRTS 5n [R"U S [R[R [R"[RS S 9mU(a8SUUU4Sjjn [$R&"Xk5n S U lU R+5 OSn TR-5c~TR.b^TR.R15n U R3U 5 U R55n U(a[7UU 35 O Ub U"U T5 TR-5cM~TR95upTR:nTR=TU5 U (aU R?5 U(aAU R3U5 U(a)URAS5nUHn [7UU 35 M U RC5 U RC5 U(d[5RSU5 US:waA[IU5nSn[RJ"U5n[5RQSUUU5 gg![Da+nSR[U55n[GU5UeSnAff=f!W RC5 f=f![L[N4a N}f=f)a  Helper function which encapsulates call to CmdStan. Uses subprocess POpen object to run the process. Records stdout, stderr messages, and process returncode. 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