[hG! fSrSSKJr SSKJr SSKJr SSKJrJ r J r J r J r SSK rSSKJr "SS\5r\"S S 55rS \S \4S jrS\S \4SjrS\S \ \4SjrS\S \ \\44SjrSS.S\ \\4S\R0\R2S\S \ \\R0\44Sjjrg)z Classes and functions for reshaping Stan output. Especially with the addition of tuples, Stan writes flat arrays of data with a rich internal structure. ) dataclass)Enum)prod)AnyDictIterableListTupleNc \rSrSrSrSrSrSrg) VariableTypeN)__name__ __module__ __qualname____firstlineno__SCALARCOMPLEXTUPLE__static_attributes__raC:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\stanio/reshape.pyr r s FG Err cJ\rSrSr%Sr\\S'\\S'\\S'\\S4\S'\ \S'\ S\S 'SS \ S \ R4S jjr S \\4S jrS \4SjrS \4SjrSS\ R$S\S \ R$4SjjrSS\ R$S\ S \R*\4SjjrSrg)Variableaq This class represents a single output variable of a Stan model. It contains information about the name, dimensions, and type of the variable, as well as the indices of where that variable is located in the flattened output array Stan models write. Generally, this class should not be instantiated directly, but rather created by the :func:`parse_header()` function. name start_idxend_idx. dimensionstypecontentstopreturnc UR[R:Xa[[UR5VVs/sH"up#[ US-5UR SS94PM$ nnn[R "U5nO]UR[R:Xa[RnO.UR[R:Xa[RnU(aW$[R "WUR45$s snnf)NrF)r%) r#r r enumerater$strdtypenprfloat64r complex128r")selfr%iparameltsr*s rr*Variable.dtype2s 99 ** *!*$-- 8 8HAQUU[[U[34 8 HHTNE YY,-- -JJE YY,.. .MME L88UDOO45 5s)DcB[URUR5$N)ranger r!r.s rcolumnsVariable.columnsCsT^^T\\22rc,[UR5$r4)rr"r6s rnum_eltsVariable.num_eltsFsDOO$$rc4URUR- $r4)r!r r6s relt_sizeVariable.elt_sizeIs||dnn,,rsrcoffsetc N^ URU-nURU-nUR[R:Xa&USX424R "S/UR Q7SS06$UR[R:XaqUSX424R "SS/UR Q7SS06nUSS2SSS24SUSS2SSS24--nUR5R "S/UR Q7SS06$UR[R:XGa[R"[URSS5[UR 54[S9n[UR!55HnXpR#5-UR!5-nUR$V s/sHn U R'XU-S 9PM n n [U S RS 5Hm [)U 4S jU 55UT U4'M M UR "S/UR Q7SS06$gs sn f) N.orderFry?r)r*)r@rc3,># UH oTv M g7fr4r).0eltr/s r +Variable._extract_helper..bs'?$3A$s)r r!r#r rreshaper"rsqueezerr+emptyrshapeobjectr5r:r=r$_extract_helpertuple) r.r?r@startendretoutidxoffr0r1r/s @rrOVariable._extract_helperNs'llV# 99 ++ +sEI~&..rODOOO3O O YY,.. .c59n%--b!QdooQSQCa1f+SADqD\ 11C;;=((IdooISI I YY,,, , hhciin%tDOO'<=VCT]]_-MMO+t}}>"&!.))#ck)B!.tAw}}Q/0A"''?$'?"?C3K1 .;;r?DOO?3? ?- s#H"rNcURU5nU(dURUR55nURS:a0UR"/UR SSQUR Q7SS06nU$URSS9nU$)a  Given an array where the final dimension is the flattened output of a Stan model, (e.g. one row of a Stan CSV file), extract the variable and reshape it to the correct type and dimensions. This will most likely result in copies of the data being made if the variable is not a scalar. Parameters ---------- src : np.ndarray The array to extract from. Indicies besides the final dimension are preserved in the output. object : bool If True, the output of tuple types will be an object array, otherwise it will use custom dtypes to represent tuples. Returns ------- npt.NDArray[Any] The extracted variable, reshaped to the correct dimensions. If the variable is a tuple, this will be an object array, otherwise it will have a dtype of either float64 or complex128. rNrBrCrDr)axis)rOastyper*ndimrJrMr"rK)r.r?rNrTs rextract_reshapeVariable.extract_reshapees}8""3'**TZZ\*C 88a<++Ksyy"~KKsKC ++1+%C rrN)T)r)rrrr__doc__r)__annotations__intr r r boolr+r*rr7r:r=ndarrayrOnptNDArrayrr\rrrrrrs  IN Lc3h :666"3#3%#%-#- @2::@s@2::@.$2::$t$s{{SVGW$$rrtupr&c2SURSS5S-$)Ndummy_:rsplit)res r_munge_first_tuplerks ciiQ'* **rr0cNURS5SRS5S$)N.rrhri)r0s r_get_base_namerns& ;;s A  $ $S )! ,,rheadercVUR5S-nURS5n/nSn[US5n[S[ U5S- 5GHUnXn[XS-5nXt:wdMSU;a~URS5SSnSU;dSU;a[ R n USS nO[ Rn UR[UUUS-[[[U55U /S 95 OURS5SRS5SSnSR[R[[ XUS-555n UR[UUUS-[[[U55[ R"[%U 5S 95 US-nUnGMX U$) Nz,__dummy,rrrhrmz.realz.imagrB)rr r!r"r#r$)striprjrnr5lenr rrappendrrPmapr`joindictfromkeysrkr _from_header) roentriesparamsr rr/entry next_namedimsr# munged_headers rryrys \\^j (Fll3G FI '!* %D 1c'lQ& ' "7q5>2  %{{3'+e#w%'7'//D9D'..D !"+ !A#(S$#8!!#  {{3'*005ab9 #MM#&8'aRSe:T"UV!  !"+ !A#(S$#8)//!-m!<  AIDQ(T MrcX[U5Vs0sHoRU_M sn$s snf)a Given a comma-separated list of names of Stan outputs, like that from the header row of a CSV file, parse it into a dictionary of :class:`Variable` objects. Parameters ---------- header : str Comma separated list of Stan variables, including index information. For example, an ``array[2] real foo` would be represented as ``foo.1,foo.2``. Returns ------- Dict[str, Variable] A dictionary mapping the base name of each variable to a :class:`Variable`. )ryr)ror0s r parse_headerrs*$,8+? @+?%JJ +? @@ @s'TrN parameterssourcerNc ~UR5Vs0sHnURURXS9_M sn$s snf)a Given a dictionary of :class:`Variable` objects and a source array, extract the variables from the source array and reshape them to the correct dimensions. Parameters ---------- parameters : Dict[str, Variable] A dictionary of :class:`Variable` objects, like that returned by :func:`parse_header()`. source : npt.NDArray[np.float64] The array to extract from. object : bool If True, the output of tuple types will be an object array, otherwise it will use custom dtypes to represent tuples. Returns ------- Dict[str, npt.NDArray[Any]] A dictionary mapping the base name of each variable to the extracted and reshaped data. r)valuesrr\)rrrNr0s rstan_variablesrsI< &&( (E  E))&)@@(  s$:)r^ dataclassesrenumrmathrtypingrrrr r numpyr+ numpy.typingrcr rr)rkrnryrrdr,rarrrrrs "334  rr rj+C+C+-#-#-11h1hAAc8m!4A2 S(]# KK #    #s{{3  r