p7]c @sddlmZmZmZmZmZddlmZddlZ ddl j j Z ddlZddlmZddlmZddlmZddlmZddlmZmZd ed d Zdd eed Zd ddZddedZ dddedZ!dZ"dZ#dZ$dZ%dZ&dZ'dZ(dZ)dZ*dZ+dZ,dZ-d Z.d!Z/d"Z0d#Z1d$Z2d%d&d'd(d)d*d+d,d-d.g Z3dS(/i(trangetlrangetlziptlongtPY3(trecarray_selectN(t DataFrame(toffsets(t to_offset(t ValueWarning(t_is_using_pandast _is_recarraytctskipcCs!|j}dddg}|dkr:|d }d}nl|dksR|dkr|d }|dkrx|dd !}nd}n%|d krd }ntd |t|}t|d p|}|s|r-|r|jj}tjj |}q<t |tj rtj|}q<|j }nt j|}t|} t jt jd| dd t j|d} t j| } |dkr| d d df} nd|kr|s|rd} |j| d} nDt jt j|dd} | dk}||ddk@}|} t j| r|dkr^dj|}t|q|dkr|d}| d d dd f} qqn|rdnd}|s|rtj| d|jd|} | |g}tj|d d |d}n(| |g}t j|d d |}|r|jdt}|jj}t|t|}|r||| |}n||| }tsg|D]}|d^q}g|D]}|d^q}g|D]}t|^q}tt ||}n|j!t j|}n|S(s Adds a trend and/or constant to an array. Parameters ---------- X : array-like Original array of data. trend : str {"c","t","ct","ctt"} "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 already exists in X. 'raise' will raise an error. 'add' will duplicate a constant. 'skip' will return the data without change. 'skip' is the default. Returns ------- y : array, recarray or DataFrame The original data with the additional trend columns. If x is a recarray or pandas Series or DataFrame, then the trend column names are 'const', 'trend' and 'trend_squared'. Notes ----- Returns columns as ["ctt","ct","c"] whenever applicable. There is currently no checking for an existing trend. See Also -------- statsmodels.tools.tools.add_constant tconstttrendt trend_squaredR iitctttitcttstrend %s not understoodtdtypeNcSs;y,tj|dko*tj|dkSWntSXdS(Ng(tnptptptanytFalse(ts((s7lib/python2.7/site-packages/statsmodels/tsa/tsatools.pyt safe_is_const\s,taxistraisesIx contains a constant. Adding a constant with trend='{0}' is not allowed.R itindextcolumns("tlowert ValueErrorR R tNoneRtdescrtpdRt from_recordst isinstancetSeriestcopyRt asanyarraytlentvandertarangetfloat64tfliplrtapplyRRtformatRtconcatt column_stackt to_recordsRRtbytestlisttziptastype(txRtprependt has_constantRt trendordert is_recarrayt is_pandasR"tnobsttrendarrRt col_consttptp0t col_is_consttnz_consttmsgtordert new_descrt extra_coltentrytnamestdtypestname((s7lib/python2.7/site-packages/statsmodels/tsa/tsatools.pyt add_trends&             /         %  "  icCs|jjr|jj}| rFtj|jdkrFtdnt|dkre|d}nt|tt fr|jj|}nt sg|D]'}t|t rt |n|^q}||j_t|t rt |}qn||}gt d|dD]}|dd|^q} t|d|dd} |tkrpt|j|d} nV|tkrt|d} n7|t|krd d l} | jd tn|} t|| } t|| }|r*|| kr| j| j|q*|j|j|n| rdt|| }tj||| | jd t}nmtjt||d t| ||jf|}x4t| D]&\}}| d d |f||>> import statsmodels.api as sm >>> data = sm.datasets.macrodata.load(as_pandas=False) >>> 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 is)col is None and the input array is not 1dit_sL(%i)tmaxlagttrimtBothiNs<insert > number of variables, inserting at the last positiontusemaskR("RRHRtsqueezetndimt IndexErrorR)R%tintRRtunicodeR3RtlagmattTrueR4RRtwarningstwarnR tpopRtnprft append_fieldstTtzerosRt enumerateR!tshapeRR1(R7tcoltlagstdroptinsertRHRJtcontemptit tmp_namestndlagstins_idxRXt first_namest last_namest_xt first_arrt first_colst last_cols((s7lib/python2.7/site-packages/statsmodels/tsa/tsatools.pytadd_lags)   1  2        (             icCs|jdkr-t|dkr-|j}n|jdkrKtdn|jd}|dkr}||jdd}nYtjtjt |d|d}tj j |j |}|tj ||}|jdkrt|dkr|j}n|S(s Detrend an array with a trend of given order along axis 0 or 1 Parameters ---------- x : array_like, 1d or 2d data, if 2d, then each row or column is independently detrended with the same trendorder, but independent trend estimates order : int specifies the polynomial order of the trend, zero is constant, one is linear trend, two is quadratic trend axis : int axis can be either 0, observations by rows, or 1, observations by columns Returns ------- detrended data series : ndarray The detrended series is the residual of the linear regression of the data on the trend of given order. iis0x.ndim > 2 is not implemented until it is needediRtN( RRRTR]tNotImplementedErrorR`tmeanRR*R+tfloattlinalgtpinvtdot(R7RDRR=tresidttrendstbeta((s7lib/python2.7/site-packages/statsmodels/tsa/tsatools.pytdetrends!   (! tforwardtexcCs t|do|}|dkr'dn|}|j}|rZ|dkrZtdntj|}d}|jdkr|dddf}n|j\}} |dkr| }n||krtd ntj||| |df} x]t dt |dD]B} || || ||| | || | || df>> 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.]]) 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. tnonetbackwardsDtrim cannot be 'none' or 'forward' when used on Series or DataFramesiiNR}tsepsmaxlag should be < nobsR|tbothstrim option not valids.L.RR(R~R(R}R(R~R|(RR(R~R(RR}(R R!RR RtasarrayRRR`R^RRTR)R%RRRJtstrtextendRtilocRc(R7RMRNtoriginalt use_pandasR<txatdropidxR=tnvartlmtktstartobststopobst x_columnsRaRtlagtlag_strRbtleads((s7lib/python2.7/site-packages/statsmodels/tsa/tsatools.pyRVHsTA    ! @      $2      c Css|dkr|}nt||}t|d}|jdkrs|rZtj|}q|dddf}n-|jdks|jdkrtdn|j\}} |r|rt|j dddf|d|ddd t } | j ddd|dfg} xut d| D]d} t|j dd| f|d|ddd t } | j | j dd||dfq%Wtj | d dS|rtj|}nt|dddf|d|ddddd|dfg} xct d| D]R} | j t|dd| f|d|dddd||dfqWtj| S( s Generate lagmatrix for 2d array, columns arranged by variables Parameters ---------- x : array_like, 2d 2d data, observation in rows and variables in columns maxlag0 : int for first variable all lags from zero to maxlag are included maxlagex : None or int max lag for all other variables all lags from zero to maxlag are included dropex : int (default is 0) exclude first dropex lags from other variables for all variables, except the first, lags from dropex to maxlagex are included trim : string * 'forward' : trim invalid observations in front * 'backward' : trim invalid initial observations * 'both' : trim invalid observations on both sides * 'none' : no trimming of observations use_pandas : bool, optional If true, returns a DataFrame when the input is a pandas Series or DataFrame. If false, return numpy ndarrays. Returns ------- lagmat : 2d array array with lagged observations, columns ordered by variable Notes ----- Inefficient implementation for unequal lags, implemented for convenience iNiis'Only supports 1 and 2-dimensional data.RNRtinRR(R!tmaxR RRR#RR R`RVRRWRtappendR0RR(R1( R7tmaxlag0tmaxlagextdropexRNRRMR<R=RRbtlagsliR((s7lib/python2.7/site-packages/statsmodels/tsa/tsatools.pyt lagmat2dss4$   %&%.HPcCs |jdS(NtF(travel(tmat((s7lib/python2.7/site-packages/statsmodels/tsa/tsatools.pytvecscCs|jjtt|S(N(R]ttaket _triu_indicesR)(R((s7lib/python2.7/site-packages/statsmodels/tsa/tsatools.pytvech scCs!tj|\}}|||S(N(Rt tril_indices(tntrowstcols((s7lib/python2.7/site-packages/statsmodels/tsa/tsatools.pyt _tril_indicesscCs!tj|\}}|||S(N(Rt triu_indices(RRR((s7lib/python2.7/site-packages/statsmodels/tsa/tsatools.pyRscCs!tj|\}}|||S(N(Rt diag_indices(RRR((s7lib/python2.7/site-packages/statsmodels/tsa/tsatools.pyt _diag_indicesscCsPttjt|}||t|ks7t|j||fddS(NRDR(RTRtsqrtR)tAssertionErrortreshape(tvR((s7lib/python2.7/site-packages/statsmodels/tsa/tsatools.pytunvecscCsddtjddt|}ttj|}tj||f}||tj|<||j}|tj|cd:<|S(Ng?iiii( RRR)RTtroundR^RR]R(RRtresult((s7lib/python2.7/site-packages/statsmodels/tsa/tsatools.pytunvech%s% cCsJtj||dd}tjg|D]}t|j^q(jS(s Create duplication matrix D_n which satisfies vec(S) = D_n vech(S) for symmetric matrix S Returns ------- D_n : ndarray ii(RteyetarrayRRR](RttmpR7((s7lib/python2.7/site-packages/statsmodels/tsa/tsatools.pytduplication_matrix4s cCs?ttjtj||f}tj|||dkS(s Create the elimination matrix L_n which satisfies vech(M) = L_n vec(M) for any matrix M Parameters ---------- Returns ------- i(RRttriltonesR(Rt vech_indices((s7lib/python2.7/site-packages/statsmodels/tsa/tsatools.pytelimination_matrixAs $cCsTtj||}tj||j||fdd}|j|jddS(s 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) RDRRi(RRR+RRR(tptqtKtindices((s7lib/python2.7/site-packages/statsmodels/tsa/tsatools.pytcommutation_matrixQs (c Cstj|d}tj|d}xltdt|D]U}||}x4t|D]&}||c||||d8>> 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.]) iii(R4R)RZt unintegrateRtcumsumtr_(R7tlevelstx0((s7lib/python2.7/site-packages/statsmodels/tsa/tsatools.pyRs  # cCst|tjs!t|}n|jj}|dksK|jdrOdS|dksj|jdrndS|d ks|jdrd S|d ks|jdrdS|dkrdS|dkrdS|dkrdStdj|dS(s- Convert a pandas frequency to a periodicity Parameters ---------- freq : str or offset Frequency to convert Returns ------- period : int Periodicity of freq Notes ----- Annual maps to 1, quarterly maps to 4, monthly to 12, weekly to 52. tAsA-sAS-itQsQ-sQS-itMsM-tMSi tWsW-i4tDitBitHisDfreq {} not understood. Please report if you think this is in error.N(sA-sAS-(sQ-sQS-(sM-R( R%Rt DateOffsetRt rule_codetuppert startswithR R/(tfreq((s7lib/python2.7/site-packages/statsmodels/tsa/tsatools.pytfreq_to_periods&    RVRRKRRRRRRR(4tstatsmodels.compat.pythonRRRRRtstatsmodels.compat.numpyRtnumpyRtnumpy.lib.recfunctionstlibt recfunctionsR[tpandasR#Rtpandas.tseriesRtpandas.tseries.frequenciesRtstatsmodels.tools.sm_exceptionsR tstatsmodels.tools.dataR R RRKR!RWRpR{RVRRRRRRRRRRRRRRRRRRt__all__(((s7lib/python2.7/site-packages/statsmodels/tsa/tsatools.pytsF(  *x  D               " )