>h]fSSKJr SSKJr SSKrSSKrSSKrSSKJ r SSK J r SSK J r SSKJr SSKJrJr SS KJr SS KJr SS KJrJrJrJr /S QrS#S jrS$SjrS%SjrS&S'Sjjr S(Sjr!Sr"Sr#Sr$Sr%Sr&Sr'Sr(Sr)Sr*Sr+Sr,Sr-Sr.Sr/S r0S!r1S)S"jr2g)*) annotations)lrangeN) DataFrame)offsets) to_offset)Literal) _is_recarray_is_using_pandas) ValueWarning)NDArray) array_like bool_likeint_like string_like) lagmat lagmat2ds add_trendduplication_matrixelimination_matrixcommutation_matrixvecvechunvecunvechfreq_to_periodcL[US5n[USSS9n[USSS9n/SQnUS:XaUR5$US :XaUS S nS nO'US :XdUS:XaUS SnUS:XaUS SnS nOUS:XaSn[U5(aS SKJn [ U5e[US 5nU(aG[U[R5(a[R"U5nO'UR5nO[R"U5n[U5n[R"[R "S US -[R"S9WS -5n [R$"U 5n US:Xa U S S 2S 4n S U;Ga6U(aSn UR'U S 5n O:[R("[R"U5S S9n U S :Hn XS S :g-nUn [R*"U 5(aUS:XaUR,S :XaSnO[R "UR.S 5U n[U[R5(a UR0nSR3UVs/sHn[5U5PM sn5nSUS3nUSUS3n[7U5eUS:XaUS S nU S S 2S S 24n U(aS OSnU(a?[R"XR8US9n X/n[R:"US S U2S S9nU$X/n[R<"US S U25nU$s snf)aT Add a trend and/or constant to an array. Parameters ---------- x : array_like Original array of data. trend : str {'n', 'c', 't', 'ct', 'ctt'} The trend to add. * 'n' add no trend. * 'c' add constant only. * 't' add trend only. * 'ct' add constant and linear trend. * 'ctt' add constant and linear and quadratic trend. prepend : bool If True, prepends the new data to the columns of X. has_constant : str {'raise', 'add', 'skip'} Controls what happens when trend is 'c' and a constant column already exists in x. 'raise' will raise an error. 'add' will add a column of 1s. 'skip' will return the data without change. 'skip' is the default. Returns ------- array_like The original data with the additional trend columns. If x is a pandas Series or DataFrame, then the trend column names are 'const', 'trend' and 'trend_squared'. See Also -------- statsmodels.tools.tools.add_constant Add a constant column to an array. Notes ----- Returns columns as ['ctt','ct','c'] whenever applicable. There is currently no checking for an existing trend. prependtrend)nctctcttoptions has_constant)raiseaddskip)constr trend_squaredrr Nrr"r!r#)recarray_exception)dtypec[R"U5S:H=(a [R"US:g5$! g=f)NgF)npptpany)ss kC:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\statsmodels/tsa/tsatools.py safe_is_const add_trend..safe_is_const~s6!66!9+@qCx0@@! s8;?axisr'zx is constant.z, z3x contains one or more constant columns. Column(s) z are constant.z Adding a constant with trend='z' is not allowed.r)indexcolumns)rrcopyr statsmodels.tools.sm_exceptionsr.NotImplementedErrorr isinstancepdSeriesrr1 asanyarraylenvanderarangefloat64fliplrapplyr2r3ndimshaper=joinstr ValueErrorr<concat column_stack)xrrr&r= trendorderr. is_pandasnobstrendarrr6 col_constptp0 col_is_constnz_constbase_errr const_colsmsgorders r5rr'sP+G w0L MEn.FL 2G |vvx #"1+ $%3,"1+ C<alG % AF!"455 D)I a # # QAA MM!  q6Dyy !TAXRZZ0*q.Hyy"H |AqD> e|  !  q1I66"--*3D19L#tqy1H I 66)  w&66Q;/H ii 3I>G!!R\\22"#))!%G+DGqCFG+D!EJM%,n6" "A%HYZ o%'!!"+#AqrE?AbE<<I M IIa%jq ) HM OOAggJ ' H),Es$L!c[US5n[US5n[USSS9nUcSnUS:aURSU-nURS:Xa USS2S4nUSS2U4nUS LaUS-nOnUS LaURSnOYUS:aURSU-S-nX@RS:a*URSn[ R "S [5 Un[XRS S 9n[U5n[X`RS5n U(aFX;a!URURU55 O U RU RU55 [R"XS2U4XpUS2U 445$)a Returns an array with lags included given an array. Parameters ---------- x : array_like An array or NumPy ndarray subclass. Can be either a 1d or 2d array with observations in columns. col : int or None `col` can be an int of the zero-based column index. If it's a 1d array `col` can be None. lags : int The number of lags desired. drop : bool Whether to keep the contemporaneous variable for the data. insert : bool or int If True, inserts the lagged values after `col`. If False, appends the data. If int inserts the lags at int. Returns ------- array : ndarray Array with lags Examples -------- >>> import statsmodels.api as sm >>> data = sm.datasets.macrodata.load() >>> data = data.data[['year','quarter','realgdp','cpi']] >>> data = sm.tsa.add_lag(data, 'realgdp', lags=2) Notes ----- Trims the array both forward and backward, so that the array returned so that the length of the returned array is len(`X`) - lags. The lags are returned in increasing order, ie., t-1,t-2,...,t-lags lagsdroprRr-)rKNrr,TFz number of variables, inserting at the last positionBoth)trim)rrr rLrKwarningswarnr rrpopr<r1rQ) rRcolr`rainsertcontempins_idxndlags first_cols last_colss r5add_lagrnssN D& !D T6 "D1c"A { Qwggaj3vv{ agJ3iG ~' 5''!* A:WWQZ&(1,F GGAJ WWQZF MM!   G /FJw +I   NN:++C0 1 MM)//#. / ??AeZ/0&DE9 2 is not implemented until it is neededrr8)N)rrKintTr@rLmeanr1rFrGfloatlinalgpinvdot)rRr^r9rUresidtrendsbetas r5detrendr|s, UG $E D& !Dvv{s4yA~ CC !! >   771:D zFFFN"299U4[1UQY?yy~~f%))!,BFF6((vv{s4yA~ Lroc N[US5n[US5n[USSSS9n[USSS 9nUn[US S S S 9n[ US 5=(a UnUcSOUnUR 5nU(aUS;a [ S5eSnURupUS;aU nX:a [ S5e[R"X-XS--45n [S[US-55H"n UU X- X-U - 2XU - -XU - S--24'M$ US;aSn OUS;aUn O [ S5eUS;a [U 5n OUn U(Ga2Un[U[5(aVURVs/sHn[!U5PM nn[[#U55URS:wa [ S5eO[!UR$5/nUVs/sHn[!U5PM nn[U5HBn[!US-5nUR'UVs/sHn[!U5S-U-PM sn5 MD [U S U UR(US9n U R*U S nUS;aUUnUR-USS9nOXU 2US 24nUS:Xa XU 2S U24nUS:XaUW4$U$s snfs snfs snf)aH Create 2d array of lags. Parameters ---------- x : array_like Data; if 2d, observation in rows and variables in columns. maxlag : int All lags from zero to maxlag are included. trim : {'forward', 'backward', 'both', 'none', None} The trimming method to use. * 'forward' : trim invalid observations in front. * 'backward' : trim invalid initial observations. * 'both' : trim invalid observations on both sides. * 'none', None : no trimming of observations. original : {'ex','sep','in'} How the original is treated. * 'ex' : drops the original array returning only the lagged values. * 'in' : returns the original array and the lagged values as a single array. * 'sep' : returns a tuple (original array, lagged values). The original array is truncated to have the same number of rows as the returned lagmat. use_pandas : bool If true, returns a DataFrame when the input is a pandas Series or DataFrame. If false, return numpy ndarrays. Returns ------- lagmat : ndarray The array with lagged observations. y : ndarray, optional Only returned if original == 'sep'. Notes ----- When using a pandas DataFrame or Series with use_pandas=True, trim can only be 'forward' or 'both' since it is not possible to consistently extend index values. Examples -------- >>> from statsmodels.tsa.tsatools import lagmat >>> import numpy as np >>> X = np.arange(1,7).reshape(-1,2) >>> lagmat(X, maxlag=2, trim="forward", original='in') array([[ 1., 2., 0., 0., 0., 0.], [ 3., 4., 1., 2., 0., 0.], [ 5., 6., 3., 4., 1., 2.]]) >>> lagmat(X, maxlag=2, trim="backward", original='in') array([[ 5., 6., 3., 4., 1., 2.], [ 0., 0., 5., 6., 3., 4.], [ 0., 0., 0., 0., 5., 6.]]) >>> lagmat(X, maxlag=2, trim="both", original='in') array([[ 5., 6., 3., 4., 1., 2.]]) >>> lagmat(X, maxlag=2, trim="none", original='in') array([[ 1., 2., 0., 0., 0., 0.], [ 3., 4., 1., 2., 0., 0.], [ 5., 6., 3., 4., 1., 2.], [ 0., 0., 5., 6., 3., 4.], [ 0., 0., 0., 0., 5., 6.]]) maxlag use_pandasrcTforwardbackwardbothnoneoptionalr%original)exsepinr$rRr-N)rKr/r)rrzEtrim cannot be 'none' or 'backward' when used on Series or DataFramesr)rrzmaxlag should be < nobsr,)rr)rrztrim option not validzSColumns names must be distinct after conversion to string (if not already strings).z.L.r;)rrr8r)rrrr r lowerrOrLr1zerosrangerrrErArr=rNsetnameextendr<ilocra)rRr~rcrrorigrTdropidxrUnvarlmkstartobsstopobsr x_columnsrgr=laglag_strr`leadss r5rr)sRfh 'F:|4J  7  D 8Z9LMH D1c.A t,;I\6tD ::"aggaj0 01 QVV I'01y3s8y1=C#'lG NN)L)3CHu,w6)L M!r(7|177G Dwwxy! } $OE99YQ9/D7"GH,- u ''12E5U{ 342Ms%JJ J" c [US5n[USSS9n[USSSS9nUcUn[X5n[US5nURS :Xa(U(a[ R "U5nO5USS2S4nO+URS :XdURS :a [S 5eURupU(aU(a[URSS2S 4XdS SS9n U RSS2SUS -24/n [S U 5HFn [URSS2U 4XdS SS9n U RU RSS2X2S -245 MH [ R"U S S9$U(a[R"U5n[USS2S 4XdS S9SS2SUS -24/n [S U 5H/n U R[USS2U 4XdS S9SS2X2S -245 M1 [R "U 5$)a1 Generate lagmatrix for 2d array, columns arranged by variables. Parameters ---------- x : array_like Data, 2d. Observations in rows and variables in columns. maxlag0 : int The first variable all lags from zero to maxlag are included. maxlagex : {None, int} The max lag for all other variables all lags from zero to maxlag are included. dropex : int Exclude first dropex lags from other variables. For all variables, except the first, lags from dropex to maxlagex are included. trim : str The trimming method to use. * 'forward' : trim invalid observations in front. * 'backward' : trim invalid initial observations. * 'both' : trim invalid observations on both sides. * 'none' : no trimming of observations. use_pandas : bool If true, returns a DataFrame when the input is a pandas Series or DataFrame. If false, return numpy ndarrays. Returns ------- ndarray The array with lagged observations, columns ordered by variable. Notes ----- Inefficient implementation for unequal lags, implemented for convenience. maxlag0maxlagexT)rrcrrNr,rr-z'Only supports 1 and 2-dimensional data.r)rcrrr8)rcr)rrmaxr rKrBrrOrLrrrappendrPr1rDrQ) rRrrdropexrcrr~rTrUrr`lagslirs r5rrsLw *G*t  yya((  MM!  qAwD9!]w{]:JKF1d^ 1QT7F =6qL((   ??6 ""roc$URS5$)NF)ravelmats r5rrs 99S>roc\URR[[U555$N)rstake _triu_indicesrErs r5rrs 55::mCH- ..roc@[R"U5upX-U-$r)r1 tril_indicesrrowscolss r5 _tril_indicesr##JD 8d?roc@[R"U5upX-U-$r)r1 triu_indicesrs r5rr(rroc@[R"U5upX-U-$r)r1 diag_indicesrs r5 _diag_indicesr-rroc[[R"[U555nX-[U5:XdeUR X4SS9$)Nrr^)rrr1sqrtrEreshape)vrs r5rr2s? BGGCFOA 5CF?? 99aV39 ''roc VSS[R"SS[U5--5--n[[R"U55n[R "X45nX[R "U5'X"R-nU[R"U5==S-ss'U$)Ng?r:r,r-) r1rrErrroundrrrsr)rrresults r5rr8s "rwwq1s1v:~.. /D rxx~ D XXtl #F$%2??4 ! hh F 2??4 !Q&! Mroc[US5n[R"XS--S-5n[R"UVs/sHn[ U5R 5PM sn5R $s snf)zz Create duplication matrix D_n which satisfies vec(S) = D_n vech(S) for symmetric matrix S Returns ------- D_n : ndarray rr,r-)rr1eyearrayrrrs)rtmprRs r5rrGs[ CA &&!e! "C 8841VAY__&4 5 7 774s#A3c[US5n[[R"[R"X4555n[R "X-5US:g$)z Create the elimination matrix L_n which satisfies vech(M) = L_n vec(M) for any matrix M Parameters ---------- Returns ------- rr)rrr1trilonesr)r vech_indicess r5rrUsG CArwwrwwv/0L 66!%=* ++roc[US5n[US5n[R"X-5n[R"X-5R X4SS9nUR UR 5SS9$)z Create the commutation matrix K_{p,q} satisfying vec(A') = K_{p,q} vec(A) Parameters ---------- p : int q : int Returns ------- K : ndarray (pq x pq) pqrrrr8)rr1rrGrrr)rrKindicess r5rresf CACA qu Aii&&vS&9G 66'--/6 **roc  [R"US- 5n[R"US- 5n[S[U55H7nXn[U5HnX%==XAX5- S- --ss'M USUUSU&M9 U$)z Transforms params to induce stationarity/invertability. Parameters ---------- params : array_like The AR coefficients Reference --------- Jones(1980) r-r,N)r1tanhrrE)params newparamsrjakiters r5_ar_transparamsrzs #I ''&1* C 1c&k " L1XE J! A 66 6JBQ "1 # roc,UR5nUR5n[[U5S- SS5H=nXn[U5HnXX0X$- S- --SUS-- - X'M! USUUSU&M? S[R"U5-nU$)zx Inverse of the Jones reparameterization Parameters ---------- params : array_like The transformed AR coefficients r,rr:r-N)r>rrEr1arctanh)rrrrr invarcoefss r5_ar_invtransparamsrs[[]F ++-C 3v;?Ar * I1XE -!QY].C*CCAF CJ!Wr +RZZ''J roc S[R"U*5- S[R"U*5-- R5nS[R"U*5- S[R"U*5-- R5n[S[ U55H7nXn[U5HnX%==XAX5- S- -- ss'M USUUSU&M9 U$)z Transforms params to induce stationarity/invertability. Parameters ---------- params : ndarray The ma coeffecients of an (AR)MA model. Reference --------- Jones(1980) r,N)r1expr>rrE)rrrrbrs r5_ma_transparamsrsbfffWo%!bfffWo*=>DDFI w Aw$7 8 > > @C1c&k " L1XE J! A 66 6JBQ "1 # rocUR5n[[U5S- SS5H=nXn[U5HnXX0X$- S- -- SUS-- - X'M! USUUSU&M? [R"SU- SU-- 5*nU$)zu Inverse of the Jones reparameterization Parameters ---------- params : ndarray The transformed MA coefficients r,rr:r-N)r>rrEr1log)macoefsrrrr invmacoefss r5_ma_invtransparamsrs ,,.C 3w>> x = np.array([1, 3, 9., 19, 8.]) >>> levels = unintegrate_levels(x, 2) >>> levels array([ 1., 2.]) >>> unintegrate(np.diff(x, 2), levels) array([ 1., 3., 9., 19., 8.]) Nr,r:r)listrErf unintegrater1cumsumr_)rRlevelsx0s r5rrsk4&\!_F 6{Q ZZ^299RUU25\2F;; B 99RUU25\ ""roc[U[R5(d [U5n[U[R5(deURR 5nSnUS;dUR U5(agUS:XdUR S5(agUS:XdUR S5(ag US :XdUR S 5(ag US :XagUS:XagUS:Xag[SRU55e)z Convert a pandas frequency to a periodicity Parameters ---------- freq : str or offset Frequency to convert Returns ------- int Periodicity of freq Notes ----- Annual maps to 1, quarterly maps to 4, monthly to 12, weekly to 52. )zA-zAS-zY-zYS-zYE-)AYr,Q)zQ-QSQEM)zM-MSME WzW-4DBHzDfreq {} not understood. Please report if you think this is in error.) rAr DateOffsetr rule_codeupper startswithrOformat)freq yearly_freqss r5rrs$ dG.. / / dG.. / // / >>   !D4L zT__\:: (:;; (:;; --    &&,fTl  ro)r Fr))Nr,FT)r,r)rrF) r~rrrcz.Literal['forward', 'backward', 'both', 'none']rzLiteral['ex', 'sep', 'in']rboolreturnzKNDArray | DataFrame | tuple[NDArray, NDArray] | tuple[DataFrame, DataFrame])NrrF)r zstr | offsets.DateOffsetr rr)3 __future__rstatsmodels.compat.pythonrrdnumpyr1pandasrBrpandas.tseriesrpandas.tseries.frequenciesrtypingrstatsmodels.tools.datar r r?r statsmodels.tools.typingr statsmodels.tools.validationr rrr__all__rrnr|rrrrrrrrrrrrrrrrrrrror5rs","0A8, @ FMP`,bAJ04! TT?T0T T [ TpEJT#n/   (  8 , +*.,2*G4#D* ro