p7]cl@sG dZddlmZddlZddlmZddlmZm Z dZ ddd d Z d Z dd d Z dd d Zdd dZddddgZedkrC eddZejeZdZejdddddddddddd dd!g Zejddgd"d#gd$d%gd$d%gd$d%gd$d%gd$d%gd$d%gd$d%gd$d%gd"d#gddgg Zejed&efZee ed'ed(d eeed'ed(d egeeeD] Zejeeee!^qeedd'dd(d Zeed&d'dd(d Z eeee ee eee eeddd)feed'dd(d*e eed&e d&dd)feed'dd(d+e ede dd)feed'ed(d ed,ejed&efZ!eee!dd'dd(d+ee e!d'ed(d eee!d'ed(d e eeddddfee!d'dd(d*e eed&e d&dddfee!d'dd(d+e ede dddfee!d'ed(d e eedee!dd'dd(d*e eed&e d&d!ee!dd'dd(d+e ee d e e!d'ed(d dd-lm"Z"ee"j#j$e!ejdddgdd.d)ejdd/d0d1dd2d3d4d5d6d7d8d9d:d;d<d=d>d?d@dAdBdCdDdEdFdGdHdIdJdKdLdMdNdOdPdQdRdSdTdUdVdWdXdYdZd[d\d]d^d_d`dadbdcdddedfdgdhdidjdkdldmdndodpdqdrdsdtdudvdwdxdydzd{d|d}d~ddddddddddddddddddgdZ%e e%e ejddd ejd0d1dd2d3d4d5d6d7d8d9d:d;d<d=d>d?d@dAdBdCdDdEdFdGdHdIdJdKdLdMdNdOdPdQdRdSdTdUdVdWdXdYdZd[d\d]d^d_d`dadbdcdddedfdgdhdidjdkdldmdndodpdqdrdsdtdudvdwdxdydzd{d|d}d~dddddddddddddddddddddddddddgkZ&e e&e ejddd*ejddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddgdZ'e e'e ejddd+ndS(susing scipy signal and numpy correlate to calculate some time series statistics original developer notes see also scikits.timeseries (movstat is partially inspired by it) added 2009-08-29 timeseries moving stats are in c, autocorrelation similar to here I thought I saw moving stats somewhere in python, maybe not) TODO moving statistics - filters don't handle boundary conditions nicely (correctly ?) e.g. minimum order filter uses 0 for out of bounds value -> append and prepend with last resp. first value - enhance for nd arrays, with axis = 0 Note: Equivalence for 1D signals >>> np.all(signal.correlate(x,[1,1,1],'valid')==np.correlate(x,[1,1,1])) True >>> np.all(ndimage.filters.correlate(x,[1,1,1], origin = -1)[:-3+1]==np.correlate(x,[1,1,1])) True # multidimensional, but, it looks like it uses common filter across time series, no VAR ndimage.filters.correlate(np.vstack([x,x]),np.array([[1,1,1],[0,0,0]]), origin = 1) ndimage.filters.correlate(x,[1,1,1],origin = 1)) ndimage.filters.correlate(np.vstack([x,x]),np.array([[0.5,0.5,0.5],[0.5,0.5,0.5]]), origin = 1) >>> np.all(ndimage.filters.correlate(np.vstack([x,x]),np.array([[1,1,1],[0,0,0]]), origin = 1)[0]==ndimage.filters.correlate(x,[1,1,1],origin = 1)) True >>> np.all(ndimage.filters.correlate(np.vstack([x,x]),np.array([[0.5,0.5,0.5],[0.5,0.5,0.5]]), origin = 1)[0]==ndimage.filters.correlate(x,[1,1,1],origin = 1)) update 2009-09-06: cosmetic changes, rearrangements i(tprint_functionN(tsignal(tassert_array_equaltassert_array_almost_equalcCsm|}tj|dkr7|tj|df}ntjtj||d|tj||dfS(Niiii(tnptndimtshapetr_tones(txtktkadd((s>lib/python2.7/site-packages/statsmodels/sandbox/tsa/movstat.pyt expandarr4stmeditlaggedcCs|dkr|d}n9|dkr.d}n$|dkrL| dd}nttj|rj|}nQ|dkr|dd}n4|dkrd}n|d kr|d}ntt||}tj|tj|||||| !S( slmoving order statistics Parameters ---------- x : array time series data order : float or 'med', 'min', 'max' which order statistic to calculate windsize : int window size lag : 'lagged', 'centered', or 'leading' location of window relative to current position Returns ------- filtered array RitcentereditleadingiR tmintmax(t ValueErrorRtisfiniteR Rt order_filterR(R tordertwindsizetlagtleadtordtxext((s>lib/python2.7/site-packages/statsmodels/sandbox/tsa/movstat.pytmovorder;s$           cCsddlj}tjdd}t|dd}t||tjddd}t|dd}t||tt|dddd d |dtjd d tjd }tj|d}t|dd}|j |j ||d ||d |j dt|dddd }|j |j ||d ||d |j dt|dddd}|j |j ||d ||d |j ddS(sgraphical test for movorderiNii RRRRRiiis.-smoving max laggedsmoving max centeredRsmoving max leading( tmatplotlib.pylabtpylabRtarangeRRtlinspacetpitsintfiguretplotttitle(tpltR txottt((s>lib/python2.7/site-packages/statsmodels/sandbox/tsa/movstat.pytcheck_movorderis,  '     cCst|dd|d|S(smoving window mean Parameters ---------- x : array time series data windsize : int window size lag : 'lagged', 'centered', or 'leading' location of window relative to current position Returns ------- mk : array moving mean, with same shape as x Notes ----- for leading and lagging the data array x is extended by the closest value of the array it windowsizeR(t movmoment(R R*R((s>lib/python2.7/site-packages/statsmodels/sandbox/tsa/movstat.pytmovmeanscCsBt|dd|d|}t|dd|d|}|||S(sCmoving window variance Parameters ---------- x : array time series data windsize : int window size lag : 'lagged', 'centered', or 'leading' location of window relative to current position Returns ------- mk : array moving variance, with same shape as x iR*Ri(R+(R R*Rtm1tm2((s>lib/python2.7/site-packages/statsmodels/sandbox/tsa/movstat.pytmovvarsc Cs|}|dkrBd}t|dp(d d|dp9d }n|dkr| d}t|d|dpqd |d |dpd }n\|dkr| d}td|dd|pd d|d| dpd }nttj|t|}t||d}t||jdkrLtj |||d|St|j t|d d d fj t j |||d d d fd|d d fSd S( snon-central moment Parameters ---------- x : array time series data windsize : int window size lag : 'lagged', 'centered', or 'leading' location of window relative to current position Returns ------- mk : array k-th moving non-central moment, with same shape as x Notes ----- If data x is 2d, then moving moment is calculated for each column. RiiiRiRtfullN( tslicetNoneRRRtfloatR tprintRt correlateRR( R R R*RRRtsltavgkernR((s>lib/python2.7/site-packages/statsmodels/sandbox/tsa/movstat.pyR+s& *  7  ?  RR,R/R+t__main__s! checkin moving mean and variancei gig@g?g@g@g@g@g@g @ii g#q?g| q?gvWUU?gUUU@iR*RiRRs- checking moving moment for 2d (columns only)(tndimagetaxisg?g333333?g333333?g?g@gffffff@g @g@g@g@g@g!@g#@g%@g'@g)@g+@g-@g/@g0@g1@g2@g3@g4@g5@g6@g7@g8@g9@g:@g;@g<@g=@g>@g?@g@@@g@@g@A@gA@g@B@gB@g@C@gC@g@D@gD@g@E@gE@g@F@gF@g@G@gG@g@H@gH@g@I@gI@g@J@gJ@g@K@gK@g@L@gL@g@M@gM@g@N@gN@g@O@gO@g P@g`P@gP@gP@g Q@g`Q@gQ@gQ@g R@g`R@gR@gR@g S@g`S@gS@gS@g T@g`T@gT@gT@g U@g`U@gU@gU@g V@g`V@gV@gV@g W@g`W@gW@idgW@g X@g9X@g`X@gX@gX@g̬X@gX@gX@g@g?@g@@g@@gA@gA@gB@gB@gC@gC@gD@gD@gE@gE@gF@gF@gG@gG@gH@gH@gI@gI@gJ@gJ@gK@gK@gL@gL@gM@gM@gN@gN@gO@gO@gP@g@P@gP@gP@gQ@g@Q@gQ@gQ@gR@g@R@gR@gR@gS@g@S@gS@gS@gT@g@T@gT@gT@gU@g@U@gU@gU@gV@g@V@gV@gV@gW@g@W@gW@g⣋.W@g٧뢋W@gEX@gb\tEX@gw.hX@i ((t__doc__t __future__RtnumpyRtscipyRt numpy.testingRRR RR)R,R/R+t__all__t__name__R4tnobsRR twstarraytavetvatc_tave2dtrangetitvarR-R.tx2dR9tfilterst correlate1dtxgtxdtxc(((s>lib/python2.7/site-packages/statsmodels/sandbox/tsa/movstat.pyt+s  . 5S  (          :  #  )!5! 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