o 0Gf9@sdZddlZddlmZddlmZmZddZd/d d Z d d Z d0ddZ d0ddZ d0ddZ gdZedkreddZeeZdZegdZeddgddgddgddgddgddgddgddgddgddgddgddgg ZejedefZee eed dee eed ded d!eeeDe ed"dd dZe eddd dZeeeeeeeeeeed"ddfe edd#deeede dd"dfe edd$deede d"dfe eed ded%ejedefZee ed"dd$dee eed dee eed deeed"dddfe edd#deeede dd"ddfe edd$deede d"ddfe eed deeed"de ed"dd#deeede dd"e ed"dd$deede d"e eed ddd&lmZeejj eegd'd(dd)egd*Z!ee!e ed+dd egd,Z"ee"e ed+dd#egd-Z#ee#e ed+d.d$dSdS)1ausing 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)xkZkaddr?lib/python3.10/site-packages/statsmodels/sandbox/tsa/movstat.py expandarr3s,rmedlaggedcCs|dkr |d}n|dkrd}n|dkr| dd}ntt|r&|}n|dkr1|dd}n|dkr8d}n |d krA|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) ValueErrorrZisfiniterrZ order_filterr )r orderwindsizelagleadordxextrrrmovorder:s$    &rcCs6ddlm}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.pylabZpylabrarangerrZlinspaceZpiZsinZfigureZplottitle)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'rm1m2rrrmovvars r-c CsN|}|dkrd}t|dpdd|dpd}nF|dkr8| d}t|d|dp+d|d |dp5d}n&|dkr\| d}td|dd|pMdd|d| dpYd}ntt|t|}t||d}t||jdkrt|||d |St|j t|dddfj t |||dddfd |ddfS) 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. rrrNrrrZfull) slicerrr floatrprintr Z correlater r) r r r'rrrslZavgkernrrrrr)s&  . 6  *r))rr*r-r)__main__z! checkin moving mean and variancer ) gUUUUUU??g@@g@@@@ @gUUUUUU!@ r4g#q?g| q?gvWUU?gUUU@rr&cCs"g|] }tt|t|qSr)rvarr ws).0irrr <s"r@rrrz- checking moving moment for 2d (columns only))ndimage)rrrr6)Zaxis)dr4g?333333?333333?r5?@ffffff@ @@@@@!@#@%@'@)@+@-@/@0@1@2@3@4@5@6@7@8@9@:@;@<@=@>@?@@@@@@@A@A@@B@B@@C@C@@D@D@@E@E@@F@F@@G@G@@H@H@@I@I@@J@J@@K@K@@L@L@@M@M@@N@N@@O@O@ P@`P@P@P@ Q@`Q@Q@Q@ R@`R@R@R@ S@`S@S@S@ T@`T@T@T@ U@`U@U@U@ V@`V@V@V@ W@`W@W@d)krBrCr5rDrErFrGrHrIrJrKrLrMrNrOrPrQrRrSrTrUrVrWrXrYrZr[r\r]r^r_r`rarbrcrdrerfrgrhrirjrkrlrmrnrorprqrrrsrtrurvrwrxryrzr{r|r}r~rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrgW@g X@g9X@g`X@gX@gX@g̬X@gX@gX@)dg@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)$__doc__ZnumpyrZscipyrZ numpy.testingrrrrr%r*r-r)__all____name__r1Znobsr"r r=ZarrayZaveZvaZc_Zave2dranger+r,Zx2drAfiltersZ correlate1dZxgZxdZxcrrrrs,  . 5  S        "    $