dddlmZddlZddlZddlZddlZddlmZmZm Z ddl m Z m Z m Z mZmZddlZddlmZmZmZddlmZddlmZmZddlmZdd lmZmZm Z dd l!m"Z" ddl#Z#n #e$$rdZ#YnwxYwe rdd l%m&Z&dd l'm(Z(e Z)ed Z*dZ+Gddee Z,Gdde,dZ-Gdde,dZ.Gddeee Z/Gdde/dZ0Gdde/dZ1dS)) annotationsN)HashableIteratorMapping) TYPE_CHECKINGAnyCallableGenericTypeVar)dtypesduck_array_opsutils)CoarsenArithmetic)OPTIONS_get_keep_attrs)is_duck_dask_array)CoarsenBoundaryOptions SideOptionsT_Xarray)either_dict_or_kwargs DataArrayDataset_TaReduce this object's data windows by applying `{name}` along its dimension. Parameters ---------- keep_attrs : bool, default: None If True, the attributes (``attrs``) will be copied from the original object to the new one. If False, the new object will be returned without attributes. If None uses the global default. **kwargs : dict Additional keyword arguments passed on to `{name}`. Returns ------- reduced : same type as caller New object with `{name}` applied along its rolling dimension. ceZdZdZdZdZ d;ddZe d>dZ d?d@dZ dZ e de _e d ejZe d!ejZe d"ejZe d#ejZe d$d%Ze d&d'Ze dde Ze d(dZe d)dZe d*dZdAd-Zd?dAd.Ze d/e_ dBdCd9Zd:ZdS)DRollingzA object that implements the moving window pattern. See Also -------- xarray.Dataset.groupby xarray.DataArray.groupby xarray.Dataset.rolling xarray.DataArray.rolling )objwindow min_periodscenterdim)rr r!r"NFrrwindowsMapping[Any, int]r int | Noner!bool | Mapping[Any, bool]returnNonecg|_g|_|D]N\}}|j||dkrt d|j|O||d|_||_||dkrt d|tj |jn||_ dS)ac Moving window object. Parameters ---------- obj : Dataset or DataArray Object to window. windows : mapping of hashable to int A mapping from the name of the dimension to create the rolling window along (e.g. `time`) to the size of the moving window. min_periods : int or None, default: None Minimum number of observations in window required to have a value (otherwise result is NA). The default, None, is equivalent to setting min_periods equal to the size of the window. center : bool or dict-like Hashable to bool, default: False Set the labels at the center of the window. If dict-like, set this property per rolling dimension. Returns ------- rolling : type of input argument rzwindow must be > 0FdefaultNz-min_periods must be greater than zero or None) r"ritemsappend ValueError_mapping_to_listr!rmathprodr )selfrr#r r!dws 3lib/python3.11/site-packages/xarray/core/rolling.py__init__zRolling.__init__As:$&!# MMOO " "DAq HOOA   Avv !5666 K  q ! ! ! !++FE+BB    "{a'7'7LMM M'2&9DIdk " " "{ strcdt|j|j|jD}d|jjd|S)z-provide a nice str repr of our rolling objectcNg|]"\}}}d|||rdnd#S)z {k}->{v}{c}z(center))kvc)format).0r<r4r>s r5 z$Rolling.__repr__..tsL   1a  11-DZZ" E E   r7{klass} [{attrs}],klassattrs)zipr"rr!r? __class____name__joinr2rFs r5__repr__zRolling.__repr__qse  txdkBB   #)).)%*   r7intcNtjfdjDS)Nc3<K|]}jj|VdSN)rsizesr@r3r2s r5 z"Rolling.__len__..}s,==q*======r7)r0r1r"r2s`r5__len__zRolling.__len__|s*y====DH======r7c*t|jSrP)lenr"rTs r5ndimz Rolling.ndims48}}r7namefillnarrolling_agg_funcCallable | NoneCallable[..., T_Xarray]crdntt|ttd|zddfd }||_t||_|S)zConstructs reduction methods built on a numpy reduction function (e.g. sum), a bottleneck reduction function (e.g. move_sum), or a Rolling reduction (_mean). Nmove_cT||}|jf|d|S)N) keep_attrsrZ)r_numpy_or_bottleneck_reduce)r2rakwargsarray_agg_funcbottleneck_move_funcrZr[s r5methodz&Rolling._reduce_method..methodsR--j99J343$ &    r7rYrP)getattrr bottleneckrI"_ROLLING_REDUCE_DOCSTRING_TEMPLATEr?__doc__)rYrZr[rfrdres `` @@r5_reduce_methodzRolling._reduce_methods  ;!NN$^T::N&z7T>4HH         ;BBBMM r7c |jdddi|tj|d|jjdz }|r|jj|_|S)NraFra)dtypecopy)sumr astypecountrrorF)r2rarcresults r5_meanz Rolling._meansj55U5f558M JJ%J ( (U9 9 9    *8>FL r7meanrgargmaxargminmaxminr1rrrstdvarmedianra bool | NonectrP)NotImplementedErrorr2ras r5_countszRolling._countss!###r7c||}||}||jk}||SNrn)rrr where)r2ra rolling_countenough_periodss r5rtz Rolling.countsI))*55  ;; &$*::"">222r7rtTarg_T | Mapping[Any, _T]r+ _T | None allow_defaultbool allow_allsamelist[_T]c(tjrJ|rfd|jDS|jD]}|vrtd|dfd|jDS|r g|jzS|jdkrgSt d|jd)Nc<g|]}|Srqget)r@r3rr+s r5rAz,Rolling._mapping_to_list..s'>>>7++>>>r7zArgument has no dimension key .c g|] }| Srqrq)r@r3rs r5rAz,Rolling._mapping_to_list..s---qCF---r7r|z"Mapping argument is necessary for z d-rolling.)r is_dict_liker"KeyErrorrXr.)r2rr+rrr3s `` r5r/zRolling._mapping_to_lists  c " " . ?>>>>>TX>>>>X J JC<<"#HA#H#H#HIII ----DH--- -  %549$ $ 9>>5LSdiSSSTTTr7c*|td}|SNTr*rrs r5rzRolling._get_keep_attrs  (666Jr7NF) rrr#r$r r%r!r&r'r(r'r8)r'rMrP)rYr8rZrr[r\r'r])rarr'r)NTT) rrr+rrrrrr'r) rI __module__ __qualname__rk __slots__ _attributesr6rLrUpropertyrXrlrvrjr?r NINFrxINFryrzr{r1rrrwr}r~rrrtr/rrqr7r5rr3sBI>>>XEI:7==6=JJEM ^Hfk 2 2F ^Hfj 1 1F . , ,C . + +C >&! $ $D . " "C >&$ . .D . % %C . % %C ^Hd + +F$$$$33333 7==7=KKEM """ UUUUU(r7rceZdZdZ d%d&fd Zd'dZddejdfd(dZddejdfd)dZ d*d+d!Z d,d"Z d#Z d$Z xZS)-DataArrayRolling) window_labelsNFrrr#r$r r%r!r&r'r(ct|||||j|jd|_dS)a! Moving window object for DataArray. You should use DataArray.rolling() method to construct this object instead of the class constructor. Parameters ---------- obj : DataArray Object to window. windows : mapping of hashable to int A mapping from the name of the dimension to create the rolling exponential window along (e.g. `time`) to the size of the moving window. min_periods : int, default: None Minimum number of observations in window required to have a value (otherwise result is NA). The default, None, is equivalent to setting min_periods equal to the size of the window. center : bool, default: False Set the labels at the center of the window. Returns ------- rolling : type of input argument See Also -------- xarray.DataArray.rolling xarray.DataArray.groupby xarray.Dataset.rolling xarray.Dataset.groupby )r r!rN)superr6rr"r)r2rr#r r!rHs r5r6zDataArrayRolling.__init__sDJ g;vNNN"Xdhqk2r7%Iterator[tuple[DataArray, DataArray]]c#,K|jdkrtd|jd}t|jd}|jdr|dzdznd}t j||jj ||z}||z }d|d||z <t|j ||D]k\}}}|j |t||i} | |g} | | |jk} || fVldS)Nr|z)__iter__ is only supported for 1d-rollingr)r")rXr.r"rMrr!nparangerrQrGriselslicertrr ) r2dim0window0offsetstopsstartslabelstartstoprcountss r5__iter__zDataArrayRolling.__iter__s+ 9q==HII Ix{dk!n%%'+{1~<'A+!##1 &$(."6"?@@%&!6!!""%d&8&%"H"H " " E5$X]]D%t*<*<#=>>F\\tf\--F\\&D,<"<==F&/ ! ! ! !  " "r7r| window_dim(Hashable | Mapping[Any, Hashable] | Nonestrideint | Mapping[Any, int] fill_valuerrarwindow_dim_kwargsrc 2|j|jf||||d|S)aG Convert this rolling object to xr.DataArray, where the window dimension is stacked as a new dimension Parameters ---------- window_dim : Hashable or dict-like to Hashable, optional A mapping from dimension name to the new window dimension names. stride : int or mapping of int, default: 1 Size of stride for the rolling window. fill_value : default: dtypes.NA Filling value to match the dimension size. keep_attrs : bool, default: None If True, the attributes (``attrs``) will be copied from the original object to the new one. If False, the new object will be returned without attributes. If None uses the global default. **window_dim_kwargs : Hashable, optional The keyword arguments form of ``window_dim`` {dim: new_name, ...}. Returns ------- DataArray that is a view of the original array. The returned array is not writeable. Examples -------- >>> da = xr.DataArray(np.arange(8).reshape(2, 4), dims=("a", "b")) >>> rolling = da.rolling(b=3) >>> rolling.construct("window_dim") array([[[nan, nan, 0.], [nan, 0., 1.], [ 0., 1., 2.], [ 1., 2., 3.]], [[nan, nan, 4.], [nan, 4., 5.], [ 4., 5., 6.], [ 5., 6., 7.]]]) Dimensions without coordinates: a, b, window_dim >>> rolling = da.rolling(b=3, center=True) >>> rolling.construct("window_dim") array([[[nan, 0., 1.], [ 0., 1., 2.], [ 1., 2., 3.], [ 2., 3., nan]], [[nan, 4., 5.], [ 4., 5., 6.], [ 5., 6., 7.], [ 6., 7., nan]]]) Dimensions without coordinates: a, b, window_dim )rrrra) _constructr)r2rrrrars r5 constructzDataArrayRolling.constructs@Dt H !!!       r7c :ddlm}||}|5tdkrt dfd|jD}||dd}||d} |j|j|j ||j | } |r|j ni} || |j t|z|j| |j } | d t#|j| DS) Nrrz/DataArrayRolling._construct..x'III1!.s1vv6IIIr7Frrr|r*)r)dimscoordsrFrYc8i|]\}}|tdd|SrPrr@r3ss r5rz/DataArrayRolling._construct..s*WWW1AuT433WWWr7)xarray.core.dataarrayrrrWr.r"r/variablerolling_windowrr!rFrtuplerrYrrG) r2rrrrrarr window_dimsstridesrrFrus ` r5rzDataArrayRolling._constructfsX 433333))*55  $%%** RJIIIIIIJ++ e5,  '''::,, Hdk; -  (/ R E+...:    {{WWDHg@V@VWWWXXXr7funcr rcc |}fdjD}|dtj}|tjurj|}nj}||||}|j|ft| |d|} d} | | j kS)aReduce the items in this group by applying `func` along some dimension(s). Parameters ---------- func : callable Function which can be called in the form `func(x, **kwargs)` to return the result of collapsing an np.ndarray over an the rolling dimension. keep_attrs : bool, default: None If True, the attributes (``attrs``) will be copied from the original object to the new one. If False, the new object will be returned without attributes. If None uses the global default. **kwargs : dict Additional keyword arguments passed on to `func`. Returns ------- reduced : DataArray Array with summarized data. Examples -------- >>> da = xr.DataArray(np.arange(8).reshape(2, 4), dims=("a", "b")) >>> rolling = da.rolling(b=3) >>> rolling.construct("window_dim") array([[[nan, nan, 0.], [nan, 0., 1.], [ 0., 1., 2.], [ 1., 2., 3.]], [[nan, nan, 4.], [nan, 4., 5.], [ 4., 5., 6.], [ 5., 6., 7.]]]) Dimensions without coordinates: a, b, window_dim >>> rolling.reduce(np.sum) array([[nan, nan, 3., 6.], [nan, nan, 15., 18.]]) Dimensions without coordinates: a, b >>> rolling = da.rolling(b=3, min_periods=1) >>> rolling.reduce(np.nansum) array([[ 0., 1., 3., 6.], [ 4., 9., 15., 18.]]) Dimensions without coordinates: a, b cVi|]%}|tjjjd|&S _rolling_dim_rget_temp_dimnamerrrRs r5rz+DataArrayRolling.reduce..E    u%dhm5HQ5H5HII   r7rZ)rar)r"raFrn)rr"popr NArrZrreducelistvaluesrrr ) r2rrarc rolling_dimrZrr#rurs ` r5rzDataArrayRolling.reducesn))*55     X   Hfi00  " "(//&))CC(C// "     ;--//00Z  KQ   //||Fd&66777r7cfdjD}j|dt jjDfdt jD|d|t| d|}|S) z1Number of non-nan entries in each rolling window.cVi|]%}|tjjjd|&SrrrRs r5rz,DataArrayRolling._counts..rr7rnci|]\}}|| Srqrq)r@r3r4s r5rz,DataArrayRolling._counts..s===$!QA===r7c2i|]\}}|j|Srqr!)r@ir3r2s r5rz,DataArrayRolling._counts..s%JJJda4;q>JJJr7rF)rra)r"skipnara) r"rnotnullrollingrGr enumeraterrrrr)r2rarrs` r5rzDataArrayRolling._countss    X    H    3 3 W==#dh "<"<===JJJJi6I6IJJJY{uY L L ST+,,..//*S U U  r7c ddlm}|j|jdkrd}n|j}|j|jd}|jj}|jdrt|j r`|j ddz dz}|j ddz dz} tdf|zt| | |jj |zfz} n:|j d dzdz}tdf|zt| dfz} | |jdd| fid}t|j rtd||j |j d||} |jdr| | } |r |jjni} || |jj| |jj S) Nrrr|rconstant)modezshould not be reachable)r min_countaxis)rFrY)rrr r get_axis_numr"rr!rdatarrshapepadAssertionErrorrFrrY) r2rrarcrrrpaddedshiftrvalidrrFs r5_bottleneck_reducez#DataArrayRolling._bottleneck_reduces333333   'D,<,A,AII(Ix$$TXa[11" ;q> M!&+.. G+a.1,-2+a.1,2t-&&48>$+?"?@@1+a.A-2t-vt1D1D0FFZZ!q5&k :ZLLF fk * *  !:;; ;T DKNidF ;q> #E]F",4"yDHMRRRRr7c Zd|vr(tjd|jdtd|d=tdr6|4t |jjs|jdkr|j |fd|i|S|r ||| | S||tj ur!tj |jjd }n.|tjur tj|jjd }|d d|d||j|fd|i|S)Nr"z7Reductions are applied along the rolling dimension(s) 'zA'. Passing the 'dim' kwarg to reduction operations has no effect.) stackleveluse_bottleneckr|rarnT) max_for_int) min_for_intrFrZ)warningswarnr"DeprecationWarningrrrrrXrrr rget_pos_infinityrorget_neg_infinity setdefaultr)r2rdrer[rarZrcs r5rbz,DataArrayRolling._numpy_or_bottleneck_reduces F?? M-H---#     u  $ % $0&tx}551 Q +4*$1;?E   W##DT5I5I*5U5UVVV V  ##0TRRR6;&&0TRRR   h . . .   h / / /t{>KKjKFKKKr7r) rrr#r$r r%r!r&r'r()r'r) rrrrrrrarrrr'r)rrrrrrrrrarrrr'rrPrr rarrcrr'r)rarr'r)rIrrrr6rr rrrrrrrb __classcell__rHs@r5rrs'"I #',1 (3(3(3(3(3(3(3T""""*@D*+ )"& I I I I I \@D*+ )"& &Y&Y&Y&Y&YR9=N8N8N8N8N8`,(S(S(ST)L)L)L)L)L)L)Lr7rrcfeZdZdZ d#d$fd ZdZ d%d&dZd'dZdZdde j dfd(d"Z xZ S))DatasetRolling)rollingsNFrrr#r$r r%r!r&r'r(ct|||tfdjDrt ji_jjD]{\}}gi}}tjD]3\}} | |j vr%| | j ||| <4|r(fd|D} t|| ||j|<|dS)a: Moving window object for Dataset. You should use Dataset.rolling() method to construct this object instead of the class constructor. Parameters ---------- obj : Dataset Object to window. windows : mapping of hashable to int A mapping from the name of the dimension to create the rolling exponential window along (e.g. `time`) to the size of the moving window. min_periods : int, default: None Minimum number of observations in window required to have a value (otherwise result is NA). The default, None, is equivalent to setting min_periods equal to the size of the window. center : bool or mapping of hashable to bool, default: False Set the labels at the center of the window. Returns ------- rolling : type of input argument See Also -------- xarray.Dataset.rolling xarray.DataArray.rolling xarray.Dataset.groupby xarray.DataArray.groupby c34K|]}|jjvVdSrPrrrRs r5rSz*DatasetRolling.__init__..ss,88!q %888888r7c"i|] }|| Srqrq)r@r3r#s r5rz+DatasetRolling.__init__..s111qQ 111r7N)rr6anyr"rrr data_varsr,rrr-r!r) r2rr#r r!keydarrr3r4rHs ` ` r5r6zDatasetRolling.__init__Ms#J g{F;;; 8888tx888 8 8 %48$$ $ x)//11 R RGCr&D!$(++ / /1<<KKNNN $ AF1I R1111D111%5b![&%Q%Q c" R Rr7c ddlm}||}i}|jjD]p\}t fd|jDr||j|fd|i|||<?|j| ||<|s i||_ q|r |jj ni}|||jj |S)Nrrc3*K|] }|jvVdSrPrr@r3rs r5rSz9DatasetRolling._dataset_implementation..s)22A1<222222r7rarrF) xarray.core.datasetrrrrr,rr"rrprFr) r2rrarcrreducedrrFrs @r5_dataset_implementationz&DatasetRolling._dataset_implementations//////))*55 x)//11 , ,GC222222222 ,#tDM#$6XX:XQWXX #x}1133 !,)+GCL&",4"wwtxeDDDDr7rr rarrcrrc ^|jtjtj|fd|i|S)aReduce the items in this group by applying `func` along some dimension(s). Parameters ---------- func : callable Function which can be called in the form `func(x, **kwargs)` to return the result of collapsing an np.ndarray over an the rolling dimension. keep_attrs : bool, default: None If True, the attributes (``attrs``) will be copied from the original object to the new one. If False, the new object will be returned without attributes. If None uses the global default. **kwargs : dict Additional keyword arguments passed on to `func`. Returns ------- reduced : DataArray Array with summarized data. )rra)r  functoolspartialrr)r2rrarcs r5rzDatasetRolling.reducesI0,t+  .5D A A A  !    r7cD|tj|Sr)r rrrs r5rzDatasetRolling._countss'++  $,   r7c b|jtjtj|||fd|i|S)N)rdrer[ra)r r"r#rrb)r2rdrer[rarcs r5rbz*DatasetRolling._numpy_or_bottleneck_reducesX,t+   <-%9!1       "     r7r|rrrrrrrc R ddlm}||}|5tdkrt dfd|jD}||dd||d i}|jj D]\} fd |jD} | rj fd t|jD} fd t|jD} |j | | || | ||<n ||<|s i||_|r |jjni} |||jj| dt#|jDS)a Convert this rolling object to xr.Dataset, where the window dimension is stacked as a new dimension Parameters ---------- window_dim : str or mapping, optional A mapping from dimension name to the new window dimension names. Just a string can be used for 1d-rolling. stride : int, optional size of stride for the rolling window. fill_value : Any, default: dtypes.NA Filling value to match the dimension size. **window_dim_kwargs : {dim: new_name, ...}, optional The keyword arguments form of ``window_dim``. Returns ------- Dataset with variables converted from rolling object. rrNrc<i|]}|t|Srqrrs r5rz,DatasetRolling.construct..rr7Frr|r*c&g|] }|jv |Srqrrs r5rAz,DatasetRolling.construct..s 888!1<.s,XXXDAq1PRPW<.s*TTT1qBG||a|||r7)rrrrarc8i|]\}}|tdd|SrPrrs r5rz,DatasetRolling.construct..s* H H HAQdD!$$ H H Hr7)rrrrWr.r"r/rrr,rrrrprFrrrG)r2rrrrarrdatasetrrwistrFrrrs ` @@@r5rzDatasetRolling.constructs: 0/////))*55  $%%** RJIIIIIIJ++ e5,  '''::x)//11 ( (GC8888tx888D )XXXXXIdh4G4GXXXTTTTT $(0C0CTTT#}S1;;!)) <    "wwyy  (%' "",4"wwtxeDDDII H HTXw1G1G H H H   r7r) rrr#r$r r%r!r&r'r(rPr )rarr'r) rrrrrrrarrrr'r) rIrrrr6r rrrbr rrr r s@r5rrJsI #',1 4R4R4R4R4R4R4RlEEE&9=     <       *@D*+ )"& F F F F F F F F F r7rrcJeZdZUdZdZdZded<ddZdZddZ dddZ dS)CoarsenzoA object that implements the coarsen. See Also -------- Dataset.coarsen DataArray.coarsen )rboundary coord_funcr#side trim_excess)r#r3r4rrr#r$r1rr3'SideOptions | Mapping[Any, SideOptions]r2-str | Callable | Mapping[Any, str | Callable]r'r(cz|_|_|_|_fd|D}|r%t d|djjjdtj sfdjj Djj D]}|vrtj |<_dS)a* Moving window object. Parameters ---------- obj : Dataset or DataArray Object to window. windows : mapping of hashable to int A mapping from the name of the dimension to create the rolling exponential window along (e.g. `time`) to the size of the moving window. boundary : {"exact", "trim", "pad"} If 'exact', a ValueError will be raised if dimension size is not a multiple of window size. If 'trim', the excess indexes are trimmed. If 'pad', NA will be padded. side : 'left' or 'right' or mapping from dimension to 'left' or 'right' coord_func : function (name) or mapping from coordinate name to function (name). Returns ------- coarsen c0g|]}|jjv|Srqr)r@r"r2s r5rAz$Coarsen.__init__..Js'QQQs48=8P8Ps8P8P8Pr7z Dimensions z not found in rci|]}|Srqrq)r@r3r2s r5rz$Coarsen.__init__..Ps???A!Z???r7N)rr#r3r1keysr.rHrIrrrrr rwr2)r2rr#r1r3r2 absent_dimsr>s` ` r5r6zCoarsen.__init__(s:    QQQQgllnnQQQ  YkYY48;M;VYYY !*-- @???????J 4 4A "" . 3 1 =Gr7c*|td}|Srrrs r5rzCoarsen._get_keep_attrsVrr7r8cfdjD}djjd|S)z-provide a nice str repr of our coarsen objectc\g|](}t|d|dt|)S)Nz->)rh)r@r<r2s r5rAz$Coarsen.__repr__.._sM   tQ%%1 & &GD!$$ & &111r7rBrCrD)rr?rHrIrJrKs` r5rLzCoarsen.__repr__\sa    %    #)).)%*   r7Nc ddlm}ddlm}t |dst dt dD}|rt d||td }ttj z }|rt d |tj tz }|rt d ||} tj |rj } nj } |r| jni| _| jD]\} t t!jfd t%jD} | jkrGfdD} | jj\}}|rjni}| ||f| | <| | <ttj jztj jz}| |}tj |rj |S|S)a Convert this Coarsen object to a DataArray or Dataset, where the coarsening dimension is split or reshaped to two new dimensions. Parameters ---------- window_dim: mapping A mapping from existing dimension name to new dimension names. The size of the second dimension will be the length of the coarsening window. keep_attrs: bool, optional Preserve attributes if True **window_dim_kwargs : {dim: new_name, ...} The keyword arguments form of ``window_dim``. Returns ------- Dataset or DataArray with reshaped dimensions Examples -------- >>> da = xr.DataArray(np.arange(24), dims="time") >>> da.coarsen(time=12).construct(time=("year", "month")) array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]]) Dimensions without coordinates: year, month See Also -------- DataArrayRolling.construct DatasetRolling.construct rrrzCoarsen.constructrc3pK|]1\}}t|dkst|t-|V2dS)rN)rW isinstancer8)r@winrs r5rSz$Coarsen.construct..sL  T4yyA~~D#!6!6~ ~~~  r7zTPlease provide exactly two dimension names for the following coarsening dimensions: NTr*zI'window_dim' must contain entries for all dimensions to coarsen. Missing z='window_dim' includes dimensions that will not be coarsened: c>g|]}||gSrqr)r@r"rs r5rAz%Coarsen.construct..s)!W!W!W*..se"<"i|]}|jv |j|Srq)rr#)r@r4r2r~s r5rz%Coarsen.construct..s(SSS!Q#(]]1dl1o]]]r7)rrrrrr.rr,rsetr#rAr_to_temp_datasetrF variables itertoolschainrrcoarsen_reshaper1r3r set_coords_from_temp_dataset)r2rrarrr bad_new_dims missing_dims extra_windowsreshapedrr reshaped_dimsr# reshaped_var_rFshould_be_coordsrur~s`` @r5rzCoarsen.constructhsR 433333//////* )+>   N   '--//      ugsuu   (666J:T\):)::  j\hjj DL))C OO;  _P]__ 799 dh * * (++--CC(C&08b ++-- $ $HC!!W!W!W!WSX!W!W!WXM((SSSSSzSSS"%"5"5gt}di"X"X a%/7 R!. e D #  OOc$(/.B.BBc HOG G  $$%566 dh * * 8..v66 6Mr7) rrr#r$r1rr3r5r2r6r'r(r)NN)r'r) rIrrrkrr__annotations__r6rrLrrqr7r5r0r0sI5KMMM,H,H,H,H\     hhhhhhhr7r0c>eZdZdZdZe ddd Z dddZd S)DataArrayCoarsenrqr;Frr include_skipnar numeric_onlyr'Callable[..., DataArray]c*i}|rd|d< d d fd }|S) r Return a wrapped function for injecting reduction methods. see ops.inject_reduce_methods Nrr2rWrarr'rcddlm}|}jjjjjj|fi|}i}jj D]u\}}|jj kr|||<tfd|j Dr5|jjjj|jj|fi|||<p|||<v||jj |jj S)Nrrc3*K|] }|jvVdSrP)r#rRs r5rSzHDataArrayCoarsen._reduce_method..wrapped_func..s*==1 ,======r7)rrrY)rrrrrcoarsenr#r1r3rr,rYrrr2) r2rarcrrrr>r=rs ` r5 wrapped_funcz5DataArrayCoarsen._reduce_method..wrapped_funcsK 8 7 7 7 7 7--j99J/dh'/ dDM49jLRGF--// & &1 %% 'F1II====af===== &$6AJ$6 L OA. M I& %% % %%q %&q 9dhmF r7rP)r2rWrarr'rrqclsrrXrYrcr`s ` r5rlzDataArrayCoarsen._reduce_methodsM"$  $#F8 ?C       <r7Nrarrc B||}||fd|i|S)aReduce the items in this group by applying `func` along some dimension(s). Parameters ---------- func : callable Function which can be called in the form `func(x, axis, **kwargs)` to return the result of collapsing an np.ndarray over the coarsening dimensions. It must be possible to provide the `axis` argument with a tuple of integers. keep_attrs : bool, default: None If True, the attributes (``attrs``) will be copied from the original object to the new one. If False, the new object will be returned without attributes. If None uses the global default. **kwargs : dict Additional keyword arguments passed on to `func`. Returns ------- reduced : DataArray Array with summarized data. Examples -------- >>> da = xr.DataArray(np.arange(8).reshape(2, 4), dims=("a", "b")) >>> coarsen = da.coarsen(b=2) >>> coarsen.reduce(np.sum) array([[ 1, 5], [ 9, 13]]) Dimensions without coordinates: a, b rarlr2rrarcr`s r5rzDataArrayCoarsen.reduces5F**400 |DBBZB6BBBr7FF)rr rXrrYrr'rZrP)rr rarr'rrIrrr_reduce_extra_args_docstring classmethodrlrrqr7r5rWrWskI#) PU))))[)X9=$C$C$C$C$C$C$Cr7rWc<eZdZdZdZe ddd ZdddZd S)DatasetCoarsenrqr;Frr rXrrYr'Callable[..., Dataset]c*i}|rd|d< d d fd }|S) r\Nrr2rkrarr'rcddlm}||}|r |jj}ni}i}|jjD]/\}}|jj|j |j |j fd|i|||<0i}|jj D]:\} } | jj|j |j | |j |j fd|i||| <;||||S)Nrrrar)rrrrrFrr,rr_r#r1r3rr2) r2rarcrrFrrrrr>r=rs r5r`z3DatasetCoarsen._reduce_method..wrapped_func<sE 4 3 3 3 3 3--j99J G8-3355  R2r{2LMI    *       F--//  1/AJ.LOA&MI  *   q 776??? ?r7rP)r2rkrarr'rrqras ` r5rlzDatasetCoarsen._reduce_method0sU"$  $#F8 =A$ @$ @$ @$ @$ @$ @$ @Lr7Nrc B||}||fd|i|S)anReduce the items in this group by applying `func` along some dimension(s). Parameters ---------- func : callable Function which can be called in the form `func(x, axis, **kwargs)` to return the result of collapsing an np.ndarray over the coarsening dimensions. It must be possible to provide the `axis` argument with a tuple of integers. keep_attrs : bool, default: None If True, the attributes (``attrs``) will be copied from the original object to the new one. If False, the new object will be returned without attributes. If None uses the global default. **kwargs : dict Additional keyword arguments passed on to `func`. Returns ------- reduced : Dataset Arrays with summarized data. rardres r5rzDatasetCoarsen.reduceds4.**400 |DBBZB6BBBr7rf)rr rXrrYrr'rlrP)rr r'rrgrqr7r5rkrk+sfI#) PU1111[1fCCCCCCCr7rk)2 __future__rr"rHr0rcollections.abcrrrtypingrrr r r numpyr xarray.corer r rxarray.core.arithmeticrxarray.core.optionsrrxarray.core.pycompatrxarray.core.typesrrrxarray.core.utilsrri ImportErrorrrrr RollingKeyrrjrrrr0rWrkrqr7r5r|s"""""" 7777777777AAAAAAAAAAAAAA555555555544444488888888333333KKKKKKKKKK333333JJJ//////++++++J B&"&eeeeegheeePlLlLlLlLlLw{+lLlLlL^ G G G G G WY'G G G T|||||!2|||~UCUCUCUCUCw{+UCUCUCpQCQCQCQCQCWY'QCQCQCQCQCs"A''A10A1