U Kv f9m@sVdZddlZddlmZddlmZmZddZdd d Z d d Z dddZ dddZ dddZ d dddgZedkrReddZeeZdZedddddddddd d!d"g Zeddgd#d$gd%d&gd%d&gd%d&gd%d&gd%d&gd%d&gd%d&gd%d&gd#d$gddgg Zejed'efZee eed d(ee eed d(ed)d*eeeDe ed+dd d(Ze ed'dd d(Zeeeeeeeeeeed+ddfe edd,d(eeed'e d'd+dfe edd-d(eede d+dfe eed d(ed.ejed'efZee ed+dd-d(ee eed d(ee eed d(eeed+dddfe edd,d(eeed'e d'd+ddfe edd-d(eede d+ddfe eed d(eeed+de ed+dd,d(eeed'e d'd+e ed+dd-d(eede d+e eed d(dd/lmZeejj eed+d+d+gddd0edd1d2d3dd4d5d6d7d8d9d:d;dd?d@dAdBdCdDdEdFdGdHdIdJdKdLdMdNdOdPdQdRdSdTdUdVdWdXdYdZd[d\d]d^d_d`dadbdcdddedfdgdhdidjdkdldmdndodpdqdrdsdtdudvdwdxdydzd{d|d}d~ddddddddddddddddddddgdZ!ee!e eddd ed2d3dd4d5d6d7d8d9d:d;dd?d@dAdBdCdDdEdFdGdHdIdJdKdLdMdNdOdPdQdRdSdTdUdVdWdXdYdZd[d\d]d^d_d`dadbdcdddedfdgdhdidjdkdldmdndodpdqdrdsdtdudvdwdxdydzd{d|d}d~dddddddddddddddddddddddddddddgkZ"ee"e eddd,eddddddddd dddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddgdZ#ee#e eddd-dS(ausing 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 do not 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 N)signal)assert_array_equalassert_array_almost_equalcCsP|}t|dkr$|t|df}tjt||d|t||dfS)Nr)npndimshapeZr_ones)xkZkaddrC/tmp/pip-unpacked-wheel-2v6byqio/statsmodels/sandbox/tsa/movstat.py expandarr3srmedlaggedcCs|dkr|d}n*|dkr d}n|dkr8| dd}ntt|rL|}n:|dkrb|dd}n$|dkrpd}n|d kr|d}ntt||}t|t|||||| S) anmoving order statistics Parameters ---------- x : ndarray 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 rrcenteredrleadingrrminmax) ValueErrorrisfiniterrZ order_filterr )r orderwindsizelagleadordxextrrrmovorder:s$    r cCs6ddlm}tdd}t|dd}t||tddd}t|dd}t||tt|dd d dd|ddtdd tjd }t|d}t|dd}| | ||d ||d | dt|dd d }| | ||d ||d | dt|ddd }| | ||d ||d | ddS)zgraphical test for movorderrNr r)rrrr)rrrz.-zmoving max laggedzmoving max centeredrzmoving max leading) Zmatplotlib.pylabZpylabraranger rZlinspacepisinfigureZplottitle)Zpltr Zxottrrrcheck_movorderhs,      $   r)cCst|d||dS)amoving window mean Parameters ---------- x : ndarray time series data windsize : int window size lag : 'lagged', 'centered', or 'leading' location of window relative to current position Returns ------- mk : ndarray 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 r windowsizer movmoment)r r+rrrrmovmeansr.cCs,t|d||d}t|d||d}|||S)aGmoving window variance Parameters ---------- x : ndarray time series data windsize : int window size lag : 'lagged', 'centered', or 'leading' location of window relative to current position Returns ------- mk : ndarray moving variance, with same shape as x rr*rr,)r r+rm1m2rrrmovvarsr1c CsT|}|dkr0d}t|dpdd|dp*d}n|dkrp| d}t|d|dpVd|d |dpjd}nL|dkr| d}td|dd|pdd|d| dpd}ntt|t|}t||d}t||jdkrt|||d |St|j t|dddfj t |||dddfd |ddfSdS) anon-central moment Parameters ---------- x : ndarray time series data windsize : int window size lag : 'lagged', 'centered', or 'leading' location of window relative to current position Returns ------- mk : ndarray 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. rrrNrrrfull) slicerrr floatrprintr Z correlater r) r r r+rrrslZavgkernrrrrr-s&  . 6  r-__main__z! checkin moving mean and variancer!ggUUUUUU?g?g@g@g@g@g@g@g @gUUUUUU!@ g#q?g| q?gvWUU?gUUU@rr*cCs"g|]}tt|t|qSr)rvarr ws).0irrr <sr>rrrz- checking moving moment for 2d (columns only))ndimage)Zaxisg?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@dgW@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@ )rrr)rr)rr)rr)$__doc__ZnumpyrZscipyrZ numpy.testingrrrr r)r.r1r-__all____name__r6Znobsr#r r;arrayZavevaZc_Zave2dranger/r0Zx2dr?filtersZ correlate1dZxgZxdZxcrrrrs,  .5   S         "    &