M/Ph9dZddlZddlmZddlmZmZdZd(d Z d Z d)d Z d)d Z d)d Z gdZedkreddZejeZdZejgdZejddgddgddgddgddgddgddgddgddgddgddgddgg ZejedezfZee eedee eededeeeDe edddZe edddZeeeeeeeezz eeedz ddfe eddeeedze dzdzdfe eddeede dzdfe eededejedezfZee edddee eedee eedeeedz dddfe eddeeedze dzdzddfe eddeede dzddfe eedeeedz de edddeeedze dzdze edddeede dze eedddlmZeej eejgd d!z d"ejgd#Z!ee!e ejd$ddejgd%Z"ee"e ejd$ddejgd&Z#ee#e ejd$d'ddSdS)*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_equalc|}tj|dkr|tj|df}tjtj||dz|tj||dzfS)Nr)npndimshaper_ones)xkkadds _/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/sandbox/tsa/movstat.py expandarrr3sd D wqzzQbhqkk!n% 5qt#AbgdmmAbE&99 ::medlaggedcl|dkr|dz}n |dkrd}n|dkr | dzdz}nttj|r|}n+|dkr |dz dz }n|dkrd}n|d kr|dz }ntt||}t j|tj||||z ||z 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) ValueErrorr isfiniterr order_filterr)rorderwindsizelagleadordxexts rmovorderr&:s, h{    y!|Q {5  %!|Q % %l Q ! !D  tBGH$5$5c : :8D=8TX=IY;Y ZZrcddlm}tjdd}t |d}t ||tjddd}t |d}t ||t t |dd dd|ddtjdd tjzd }tj|dz}t |d}| | ||d ||d | dt |dd }| | ||d ||d | dt |dd }| | ||d ||d | ddS)zgraphical test for movorderrNr r)r r rr)r r"rz.-zmoving max laggedzmoving max centeredrzmoving max leading) matplotlib.pylabpylabr aranger&rlinspacepisinfigureplottitle)pltrxotts rcheck_movorderr6hs"""""" !BA !5 ! ! !Br1 "QrA !5 ! ! !Br1xJ???DaeLLL Qqwr " "B r QA !5 ! ! !BJJLLLHHR$r"T"""II!""" !5j 1 1 1BJJLLLHHR$r"T"""II#$$$ !5i 0 0 0BJJLLLHHR$r"T"""II"#####rc(t|d||S)amoving window mean Parameters ---------- x : ndarray time series data windsize : int window size lag : 'lagged', 'centered', or 'leading' location of window relative to current position Returns ------- mk : ndarray moving mean, with same shape as x Notes ----- for leading and lagging the data array x is extended by the closest value of the array r windowsizer" movmoment)rr9r"s rmovmeanr<s2 Qjc : : ::rc^t|d||}t|d||}|||zz S)aGmoving window variance Parameters ---------- x : ndarray time series data windsize : int window size lag : 'lagged', 'centered', or 'leading' location of window relative to current position Returns ------- mk : ndarray moving variance, with same shape as x rr8rr:)rr9r"m1m2s rmovvarr@s>( 1aJC 8 8 8B 1aJC 8 8 8B 2:rc|}|dkr d}t|dz pdd|dz zpd}nu|dkr.| dz}t|dz |dzzpd|dz |dzz pd}nA|dkr4| dz}td|dz zdz|zpdd|dz z|z dzpd}nttj|t |z }t ||dz }t ||jdkrtj||z|d |St |j t |dddfj tj||z|dddfd |ddfS) anon-central moment Parameters ---------- x : ndarray time series data windsize : int window size lag : 'lagged', 'centered', or 'leading' location of window relative to current position Returns ------- mk : ndarray k-th moving non-central moment, with same shape as x Notes ----- If data x is 2d, then moving moment is calculated for each column. rrrNrrrfull) slicerr rfloatrprintr correlater r) rrr9r"r!r#slavgkernr%s rr;r;s4H h HQJ'4XaZ)@D A A  y!| HQJ! +3txz]8Q;5N5VRV W W  y!| 1hqj>!#D(0DAxzN44G2H2J2Rd S Swz""5#4#44G Q # #D "III yA~~|D!GWf55b99 dj gaaafo#$$$a4&AA"QQQ$GGr)r&r<r@r;__main__z! checkin moving mean and variancer() gUUUUUU??g@@g@@@@ @gUUUUUU!@ rKg#q?g| q?gvWUU?gUUU@rr8c`g|]+}tjt|tz |,S)r varrws).0is r rY<s. 6 6 626!AbDF)   6 6 6rrrrz- checking moving moment for 2d (columns only))ndimage)rrrrM)axis)drKg?333333?333333?rL?@ffffff@ @@@@@!@#@%@'@)@+@-@/@0@1@2@3@4@5@6@7@8@9@:@;@<@=@>@?@@@@@@@A@A@@B@B@@C@C@@D@D@@E@E@@F@F@@G@G@@H@H@@I@I@@J@J@@K@K@@L@L@@M@M@@N@N@@O@O@ P@`P@P@P@ Q@`Q@Q@Q@ R@`R@R@R@ S@`S@S@S@ T@`T@T@T@ U@`U@U@U@ V@`V@V@V@ W@`W@W@d)kr\r]rLr^r_r`rarbrcrdrerfrgrhrirjrkrlrmrnrorprqrrrsrtrurvrwrxryrzr{r|r}r~rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrgW@g X@g9X@g`X@gX@gX@g̬X@gX@gX@)dg@g?@g@@g@@gA@gA@gB@gB@gC@gC@gD@gD@gE@gE@gF@gF@gG@gG@gH@gH@gI@gI@gJ@gJ@gK@gK@gL@gL@gM@gM@gN@gN@gO@gO@gP@g@P@gP@gP@gQ@g@Q@gQ@gQ@gR@g@R@gR@gR@gS@g@S@gS@gS@gT@g@T@gT@gT@gU@g@U@gU@gU@gV@g@V@gV@gV@gW@g@W@gW@g⣋.W@g٧뢋W@gEX@gb\tEX@gw.hX@ )rrr)rr)$__doc__numpyr scipyr numpy.testingrrrr&r6r<r@r;__all____name__rFnobsr,rrVarrayavevac_ave2dranger>r?x2drZfilters correlate1dxgxdxcrTrrrs**XGGGGGGGG;;;,[,[,[,[\$$$j;;;;609H9H9H9Hf 9 8 8 z E ./// D $A B "(  C R"-*-*-*-*-*-*-*-*-*-*-"- / 0 0B E#qu* E E''! 1 1 1222 E&&rx 0 0 0111 E 6 6eeBoo 6 6 6777 1aA8 4 4 4B 1aA8 4 4 4B E"III E"III E"r"u*bAqkF1 :::<<<bQsAvax!12F1 ;;;===b2#a%lF1:::<<<  E :;;; %1Q3-C E))Cqj 9 9 9::: E''#"( 3 3 3444 E&& 2 2 2333bAqqqkF31)<<<>>>bQsAvax!12F31*===???b2#a%lF328<<<>>>eBqDEElIc1 BBBDDDeBE2#q&(N3Ic1 CCCEEEeFbSUFmGCBH===??? E'/ % %c828GGG+<+