a Df[@sddlmZddlZddlmZddlmZddlZddl m Z m Z m Z m Z ddl mZddlmZmZddlZddlmZmZdd lmZmZdd lmZz@dd lmZm Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)m*Z*m+Z+Wn2e,yd \ ZZ Z!Z"Z#Z$Z%Z&Z'Z(Z)Z*Z+Yn0zddl-Z-ddl.Z/Wne0y@dZ-Z/Yn0d dl1m2Z2m3Z3m4Z4m5Z5m6Z6m7Z7m8Z8m9Z9m:Z:m;Z;mZ>m?Z?m@Z@GdddeZAGdddeZBGddde2ZCGdddeCZDGdddeCZEGdddeEZFGdddeFZGGdddeGZHGdd d eEZIGd!d"d"e2ZJGd#d$d$eJZKGd%d&d&eKZLGd'd(d(eLZMGd)d*d*eMZNGd+d,d,eJZOGd-d.d.eKZPGd/d0d0eJZQGd1d2d2eJZRGd3d4d4eRZSGd5d6d6eRZTGd7d8d8eTZUGd9d:d:eRZVGd;d<dd>eRZXGd?d@d@eOZYGdAdBdBeJZZGdCdDdDeJZ[GdEdFdFeJZ\GdGdHdHeJZ]GdIdJdJe]Z^GdKdLdLe]Z_GdMdNdNeKZ`GdOdPdPe`ZaGdQdRdReaZbGdSdTdTeaZcGdUdVdVe`ZdGdWdXdXe`ZeGdYdZdZeJZfGd[d\d\eRZgGd]d^d^e2ZhGd_d`d`eKZiGdadbdbeiZjGdcddddeiZkGdedfdfe`ZlGdgdhdhelZmGdidjdjelZneoepdkdleqrDddgZsdS)m) annotationsN)Enum)Version)dshape isnumericRecordOption) coretypes)concatunique)AntialiasCombinationAntialiasStage2)isminus1isnull)cuda) cuda_atomic_nanmincuda_atomic_nanmax cuda_argscuda_row_min_in_placecuda_nanmax_n_in_place_4dcuda_nanmax_n_in_place_3dcuda_nanmin_n_in_place_4dcuda_nanmin_n_in_place_3dcuda_row_max_n_in_place_4dcuda_row_max_n_in_place_3dcuda_row_min_n_in_place_4dcuda_row_min_n_in_place_3dcuda_shift_and_insert) NNNNNNNNNNNNN)Exprngjitnansum_missingnanmax_in_placenansum_in_placerow_min_in_placenanmax_n_in_place_4dnanmax_n_in_place_3dnanmin_n_in_place_4dnanmin_n_in_place_3drow_max_n_in_place_4drow_max_n_in_place_3drow_min_n_in_place_4drow_min_n_in_place_3dshift_and_insertc@seZdZdZdZdS) SpecialColumna Internally datashader identifies the columns required by the user's Reductions and extracts them from the supplied source (e.g. DataFrame) to pass through the dynamically-generated append function in compiler.py and end up as arguments to the Reduction._append* functions. Each column is a string name or a SpecialColumn. A column of None is used in Reduction classes to denote that no column is required. rN)__name__ __module__ __qualname____doc__RowIndexr4r4b/nfs/NAS7/SABIOD/METHODE/ermites/ermites_venv/lib/python3.9/site-packages/datashader/reductions.pyr.-sr.c@seZdZdZdZdZdZdS) UsesCudaMutexaY Enum that encapsulates the need for a Reduction to use a CUDA mutex to operate correctly on a GPU. Possible values: No: the Reduction append_cuda function is atomic and no mutex is required. Local: Reduction append_cuda needs wrapping in a mutex. Global: the overall compiled append function needs wrapping in a mutex. rrN)r/r0r1r2NoLocalGlobalr4r4r4r5r69sr6c@s6eZdZdZddddZeddZedd Zd S) Preprocessz#Base clase for preprocessing steps.str | SpecialColumn | NonecolumncCs ||_dSNr=selfr>r4r4r5__init__IszPreprocess.__init__cCs|jfSr?r=rAr4r4r5inputsLszPreprocess.inputscCsdSr?r4rCr4r4r5nan_check_columnPszPreprocess.nan_check_columnN)r/r0r1r2rBpropertyrDrEr4r4r4r5r;Gs  r;c@seZdZdZddZdS)extractz=Extract a column from a dataframe as a numpy array of values.cCs||jtjurVd}t|tjr2|j|}|jd}n$t|dd}t|pF||d}t|}t rt|t j r|jtjurt j |||t jdS||jjjdkrt j}nd}tt jtdkr||jj|dSt ||jj|d S|jtjur$|r t j |||t jdSt j |||t jdSnTt|tjrl|r^t||jjt js^t ||jS||jjSn ||jjSdS) NZ_datashader_row_offsetZ_datashader_row_lengthattrsrdtypef22.02Zna_valuefillna)r>r.r3 isinstancexrZDatasetrHgetattrlencudf DataFramecparangenpint64rJkindnanr __version__to_cupyarray to_gpu_arraydataZndarrayasarrayvalues)rAdfr attr_name row_offsetZ row_lengthrHnullvalr4r4r5applyWs4      z extract.applyN)r/r0r1r2rgr4r4r4r5rGUsrGc@s4eZdZdZeddZddZddZdd Zd S) CategoryPreprocessz*Base class for categorizing preprocessors.cCs|jSz"Returns name of categorized columnr=rCr4r4r5 cat_column}szCategoryPreprocess.cat_columncCs tddS)z7Returns list of categories corresponding to input shapezcategories not implementedNNotImplementedErrorrA input_dshaper4r4r5 categoriesszCategoryPreprocess.categoriescCs tddS)zValidates input shapezvalidate not implementedNrkrA in_dshaper4r4r5validateszCategoryPreprocess.validatecCs tddS)z3Applies preprocessor to DataFrame and returns arrayzapply not implementedNrkrArcrr4r4r5rgszCategoryPreprocess.applyN) r/r0r1r2rFrjrorrrgr4r4r4r5rh{s  rhc@s(eZdZdZddZddZddZdS) category_codesal Extract just the category codes from a categorical column. 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Parameters ---------- column: column to use lower: lower bound of first bin upper: upper bound of last bin nbins: number of bins include_under: if True, values below bin 0 are assigned to category 0 include_over: if True, values above the last bin (nbins-1) are assigned to category nbin-1 TcsTt||d||_||t||_||_|r8dn||_|rJ|dn||_dSNrr)r~rBbin0floatbinsizenbins bin_underbin_over)rAr>lowerupperrZ include_underZ include_overrr4r5rBs zcategory_binning.__init__cst|j|j|j|jfSr?)r~rrrrrrCrr4r5rsz!category_binning._hashable_inputscCs|j|jvrtddS)Nrv)r>rwrxrpr4r4r5rrs zcategory_binning.validatecCstr\t|tjr\ttjtdkr8||jjtjd}nt ||jj dd}t |}n||j }t |}||j|j}d||<|t}|j||dk<|j|||jk<|j||<|S)NrLrMTrNr)rTrPrUrr\r>r]rVr[r^r_isnanZto_numpyrXrrZastypeintrrr)rArcrrbZ nan_valuesZ index_floatindexr4r4r5rgs    zcategory_binning.apply)TT) r/r0r1r2rBrrrrgrr4r4rr5rs  rcsPeZdZdZfddZeddZeddZdd Zd d Z d d Z Z S)category_valueszVExtract a category and a value column from a dataframe as (2,N) numpy array of values.cst|||_dSr?)r~rB categorizer)rArZ value_columnrr4r5rBs zcategory_values.__init__cCs|jj|jfSr?rr>rCr4r4r5rD szcategory_values.inputscCs|jjSrirrCr4r4r5rjszcategory_values.cat_columncCs|jjSr?)rrormr4r4r5roszcategory_values.categoriescCs |j|Sr?)rrrrpr4r4r5rrszcategory_values.validatecCs|j||}trt|tjrddl}|jtjkr8d}n||jj j dkrRt j }nd}| |}|jtjkrttj||}nr.r3rJrZrXr[rarGrr\r]rOstackrb)rArcrarrfbr4r4r5rgs&     zcategory_values.apply) r/r0r1r2rBrFrDrjrorrrgrr4r4rr5rs   rc@s eZdZdZd9ddddZeddZd d d d Zd dZddZ eddZ ddZ ddZ ddZ dd ddZddZddZd:d!d"Zd#d$Zd%d&Zd;d'd(Zd)d*Zed+d,Zed-d.Zed/d0Zed1d2Zed3d4Zed5d6Zed7d8ZdS)< Reductionz"Base class for per-bin reductions.Nr<r=cCs||_d|_dSr?)r>_nan_check_columnr@r4r4r5rB7szReduction.__init__cCs|jdurt|jSdSdSr?)rrGrCr4r4r5rE;s  zReduction.nan_check_columnr6returncCstjS)a1Return ``True`` if this Reduction needs to use a CUDA mutex to ensure that it is threadsafe across CUDA threads. 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Used by mean reduction. cCs ttjSr?)rryrrr4r4r5rsz%_count_ignore_antialiasing.out_dshaperrcCs$|rttjdfSttjdfSdSrr r rrrr4r4r5rsz-_count_ignore_antialiasing._antialias_stage_2cCs,t|s(|dkr(|||fd7<dSdSNgrrrrrr4r4r5rsz,_count_ignore_antialiasing._append_antialiascCs,t|s(|dkr(|||fd7<dSdSr!rrr4r4r5rsz?_count_ignore_antialiasing._append_antialias_not_self_intersectN) r/r0r1r2rrrr rrr4r4r4r5rs rcseZdZdZeffdd ZddZd0ddZed d Z ed d Z d dZ ddZ eddZ ddZddZeddZddddZddZdd Zd!dd"d#Zd$d%Zd&d'Zd(d)Zd1d*d+Zd,d-Zd.d/ZZS)2byazApply the provided reduction separately per category. Parameters ---------- cats: str or CategoryPreprocess instance Name of column to aggregate over, or a categorizer object that returns categories. Resulting aggregate has an outer dimension axis along the categories present. reduction : Reduction Per-category reduction function. cstt|tr||_nt|tr2t||_ntd|jj|_|jjf|_ t |dd}dur~|j |ddd7_ n|j t |ddf7_ ||_ |j durt |j|j |_n|j|_dS)NzEfirst argument must be a column name or a CategoryPreprocess instancecolumnsrr>)r~rBrPrhrstrrt TypeErrorr>r#rR reduction val_columnr preprocess)rArjr&r#rr4r5rBs       z by.__init__cCs tt|||j|jfSr?)hashrrrr&rCr4r4r5__hash__sz by.__hash__Fcstfddj|DS)Nc3s|]}tj|VqdSr?r"r).0tmprCr4r5 z"by._build_temps..)tupler&rrr4rCr5rszby._build_tempscCs |jdSrr#rCr4r4r5rjsz by.cat_columncCs |jdSNrr1rCr4r4r5r'sz by.val_columncCs|j||j|dSr?)r(rrr&rpr4r4r5rrs z by.validatecs8|j|}|j||||ttfdd|DS)Ncsg|] }|fqSr4r4)r,cZ red_shaper4r5 r/z!by.out_dshape..)rror&rrr)rArnrrrcatsr4r4r5rs z by.out_dshapecCs|jfSr?)r(rCr4r4r5rDsz by.inputscCsdSNTr4rCr4r4r5rszby.is_categoricalcCs |jSr?)r&rrCr4r4r5rsz by.is_wherecCs|jjSr?)r&rErCr4r4r5rEszby.nan_check_columnr6rcCs |jSr?)r&rrCr4r4r5rszby.uses_cuda_mutexcCs|j||Sr?)r&rrr4r4r5rszby.uses_row_indexcCs |jSr?)r&rrCr4r4r5rszby._antialias_requires_2_stagesrcCs2|j||}t|dj|dj|djddfS)NrT) combinationzero n_reductionr)r&rr r8r9r:rArrrr4r4r5rs  zby._antialias_stage_2cstjjfddS)Ncsj|f|Sr?)r&rrZn_catsrrAr4r5s z"by._build_create..)rSrurrArr4r<r5rs zby._build_createcs@j||}t|dkr*|dur*|Stfdd|DS)Nrrc3s|]}tj|VqdSr?r+)r,baserCr4r5r.r/z"by._build_bases..)r&rrSr0)rArrrr4rCr5rszby._build_basescCs|j|||||Sr?)r&rrr4r4r5rszby._build_appendcCs|j||||dSr7)r&rrr4r4r5rszby._build_combinecCs|j||Sr?)r&rrr4r4r5r szby._build_combine_tempscs&tjdfdd }|S)NFcsHt|}|djg7<|dj<j|fd|i|S)Ndimscoordsr)copydeepcopyrjr&rrr6rrAr4r5finalize&s z$by._build_finalize..finalize)F)rrrorArrEr4rDr5r#szby._build_finalize)F)F)r/r0r1r2rrBr*rrFrjr'rrrrDrrrErrrrrrrrrrrr4r4rr5r"s4       r"c@seZdZdZddZddddZeedd Zeed d Z eed d Z eeddZ eZ e Z dddZeddZeddZdS)anyzWhether any elements in ``column`` map to each bin. Parameters ---------- column : str, optional If provided, any elements in ``column`` that are ``NaN`` are skipped. cCs|rttjSttjSr?)rryrrrr4r4r5r8szany.out_dshaperrcCsttj|jfSr?r r MAXr[rr4r4r5r;szany._antialias_stage_2cCst|sd|||f<dSdS)NTrrrrr4r4r5r?s z any._appendcCs<t|s8t|||fs(||||fkr8||||f<dSdSrrrr4r4r5rGs   zany._append_antialiascCsd|||f<dS)NTrr4rr4r4r5rPs zany._append_no_fieldcCs4t|||fs ||||fkr0||||f<dSdSrrrr4r4r5rVs  zany._append_no_field_antialiasFcCs|r |jS|jSdSr?rrr4r4r5rbszany._build_combinecCs|jdddS)Nrrrrrr4r4r5rhsz any._combinecCs.|d}tdt|D]}t|||q|Sr)rrSr"rr4r4r5rlszany._combine_antialiasN)F)r/r0r1r2rrrr rrrrrrrrrr4r4r4r5rG0s,      rGc@sneZdZdZddZedddZeddZd d Z ee d d Z ee j d dddZeddZdS) _upsamplez+"Special internal class used for upsamplingcCstttjSr?rrryrrr4r4r5rvsz_upsample.out_dshapeFcKstj|dfi|Srrrr4r4r5rysz_upsample._finalizecCs t|jfSr?rrCr4r4r5rD}sz_upsample.inputscCs|jSr?)rr>r4r4r5rsz_upsample._build_createcCsdSr?r4rr4r4r5rsz_upsample._appendTr cCsdSr?r4rr4r4r5rsz_upsample._append_cudacCstj|ddSNrrrXZnanmaxrr4r4r5rsz_upsample._combineN)F)r/r0r1r2rrrrFrDrr rrrrrr4r4r4r5rJts     rJc@s&eZdZdZddZedddZdS) FloatingReductionzBBase classes for reductions that always have floating-point dtype.cCstttjSr?rKrr4r4r5rszFloatingReduction.out_dshapeFcKstj|dfi|Srrrr4r4r5rszFloatingReduction._finalizeN)F)r/r0r1r2rrrr4r4r4r5rNsrNc@szeZdZdZddddZddZeedd Zeed d Z eed d Z ee j ddddZ eddZdS) _sum_zerozSum of all elements in ``column``. Parameters ---------- column : str Name of the column to aggregate over. Column data type must be numeric. rrcCs$|rttjdfSttjdfSdSrr rr4r4r5rsz_sum_zero._antialias_stage_2cCs|jSr?rr>r4r4r5rsz_sum_zero._build_createcCs$t|s |||f|7<dSdSrrrr4r4r5rsz_sum_zero._appendcCs0|||}t|s,|||f|7<dSdSrrrrrrrrr r4r4r5rs  z_sum_zero._append_antialiascCs4||}t|s0||||fkr0||||f<dSdSrrrQr4r4r5rs  z._sum_zero._append_antialias_not_self_intersectTr cCs$t|s tj|||f|dSdSrrrr4r4r5rsz_sum_zero._append_cudacCs|jdddS)Nrrrrrr4r4r5rsz_sum_zero._combineN)r/r0r1r2rrrr rrrrrrrr4r4r4r5rOs"     rOcsBeZdZdZd fdd ZddZfdd Zfd d ZZS) !SelfIntersectingFloatingReductionz Base class for floating reductions for which self-intersecting geometry may or may not be desirable. Ignored if not using antialiasing. NTcst|||_dSr?rrrr4r5rBs z*SelfIntersectingFloatingReduction.__init__cCs|j Sr?rrCr4r4r5rsz>SelfIntersectingFloatingReduction._antialias_requires_2_stagescs@|r,|s,|rtdn|jdur&|jS|jSt|||||S)Nr)rlr>rrr~rrrr4r5rs  z/SelfIntersectingFloatingReduction._build_appendcst|jfSr?rrCrr4r5rsz2SelfIntersectingFloatingReduction._hashable_inputs)NTrr4r4rr5rRs  rRc@speZdZdZddddZddZeedd Zeed d Z eed d Z eddZ edddZ dS)raSum of all elements in ``column``. Elements of resulting aggregate are nan if they are not updated. Parameters ---------- column : str Name of the column to aggregate over. Column data type must be numeric. ``NaN`` values in the column are skipped. rrcCs(|rttj|jfSttj|jfSdSr?rrr4r4r5rszsum._antialias_stage_2cCs"|rt|jt|jfS|fSdSr?)rOr>rGrr4r4r5rszsum._build_basescCsBt|s>t|||fr&||||f<n|||f|7<dSdSrrrr4r4r5rs z sum._appendcCsN|||}t|sJt|||fr2||||f<n|||f|7<dSdSrrrQr4r4r5r s zsum._append_antialiascCsD||}t|s@t|||fs0||||fkr@||||f<dSdSrrrQr4r4r5r,s   z(sum._append_antialias_not_self_intersectcCs t|ddSrL)r!rr4r4r5r6sz sum._combineFcKsH|r.|\}}t||tj}tj|fi|Stj|dfi|SdSr)rXwherer[rQr)rrrsumsZanysrr4r4r5r:s z sum._finalizeN)F) r/r0r1r2rrrr rrrrrr4r4r4r5rs      rcsteZdZdZddddZfddZdd Zdd d Zee d dZ ee j ddddZ eddZZS)m2a`Sum of square differences from the mean of all elements in ``column``. Intermediate value for computing ``var`` and ``std``, not intended to be used on its own. Parameters ---------- column : str Name of the column to aggregate over. Column data type must be numeric. ``NaN`` values in the column are skipped. r6rcCstjSr?)r6r:rCr4r4r5rPszm2.uses_cuda_mutexcstt||||||Sr?)r~rUrrrr4r5rSszm2._build_appendcCs|jSr?rPr>r4r4r5rVszm2._build_createFcCst|jt|jfSr?)rOr>rrr4r4r5rYszm2._build_tempscCs\t|sX|dkrXt||}t|||d}|||f||||7<dSdSNrrrrrXrrrrUrrru1ur4r4r5r]s z m2._appendTr cCs\t|sX|dkrXt||}t|||d}|||f||||7<dSdSrVrWrXr4r4r5rks zm2._append_cudacCsntjdddLtj|dd|jdd}tj|||||dddWdS1s`0YdS)Nignoredivideinvalidrrr7)rXerrstateZnansumr)ZMsrTnsmur4r4r5rxsz m2._combine)F)r/r0r1r2rrrrrr rrrrrrr4r4rr5rUDs      rUc@sjeZdZdZddZddddZeedd Zeed d Z ee j d d ddZ eddZ dS)minzMinimum value of all elements in ``column``. Parameters ---------- column : str Name of the column to aggregate over. Column data type must be numeric. ``NaN`` values in the column are skipped. cCsdSr7r4rCr4r4r5rsz min._antialias_requires_2_stagesrrcCsttj|jfSr?r r MINr[rr4r4r5rszmin._antialias_stage_2cCs<t|s8t|||fs(|||f|kr8||||f<dSdSrrrr4r4r5rs( z min._appendcCsD||}t|s@t|||fs0||||fkr@||||f<dSdSrrrQr4r4r5rs ( zmin._append_antialiasTr cCs0t|s,t|||f|}t|s(||kr,dSdSr)rrrrrrr r4r4r5rs zmin._append_cudacCstj|ddSrL)rXZnanminrr4r4r5rsz min._combineN)r/r0r1r2rrrr rrrrrrr4r4r4r5rbs    rbc@szeZdZdZddddZeeddZeedd Zee j d d d d Z ee j d d ddZ eddZ dS)maxzMaximum value of all elements in ``column``. Parameters ---------- column : str Name of the column to aggregate over. Column data type must be numeric. ``NaN`` values in the column are skipped. rrcCsttj|jfSr?rHrr4r4r5rszmax._antialias_stage_2cCs<t|s8t|||fs(|||f|kr8||||f<dSdSrrrr4r4r5rs( z max._appendcCsD||}t|s@t|||fs0||||fkr@||||f<dSdSrrrQr4r4r5rs ( zmax._append_antialiasTr cCs8||}t|s4t|||f|}t|s0||kr4dSdSrr r r4r4r5rs zmax._append_antialias_cudacCs0t|s,t|||f|}t|s(||kr,dSdSrr rer4r4r5rs zmax._append_cudacCstj|ddSrLrMrr4r4r5rsz max._combineN)r/r0r1r2rrr rrrrrrrr4r4r4r5rfs       rfcs eZdZdZfddZZS) count_cata]Count of all elements in ``column``, grouped by category. Alias for `by(...,count())`, for backwards compatibility. Parameters ---------- column : str Name of the column to aggregate over. Column data type must be categorical. Resulting aggregate has a outer dimension axis along the categories present. cstt||tdSr?)r~rgrBrr@rr4r5rBszcount_cat.__init__)r/r0r1r2rBrr4r4rr5rgs rgc@s&eZdZdZddZedddZdS) meanzMean of all elements in ``column``. Parameters ---------- column : str Name of the column to aggregate over. Column data type must be numeric. ``NaN`` values in the column are skipped. cCst|jt|jfSr?)rOr>rrr4r4r5rszmean._build_basesFcKs`|\}}tjddd(t|dk||tj}Wdn1sD0Ytj|fi|SNr[r\rrXr_rSr[rQr)rrrrTcountsrr4r4r5rs6zmean._finalizeN)Fr/r0r1r2rrrr4r4r4r5rhsrhc@s&eZdZdZddZedddZdS) varzVariance of all elements in ``column``. Parameters ---------- column : str Name of the column to aggregate over. Column data type must be numeric. ``NaN`` values in the column are skipped. cCst|jt|jt|jfSr?rOr>rrUrr4r4r5rszvar._build_basesFcKsb|\}}}tjddd(t|dk||tj}Wdn1sF0Ytj|fi|SrirjrrrrTrkZm2srr4r4r5rs 6z var._finalizeN)Frlr4r4r4r5rm srmc@s&eZdZdZddZedddZdS) stdzStandard Deviation of all elements in ``column``. Parameters ---------- column : str Name of the column to aggregate over. Column data type must be numeric. ``NaN`` values in the column are skipped. cCst|jt|jt|jfSr?rnrr4r4r5r&szstd._build_basesFcKsh|\}}}tjddd.t|dkt||tj}Wdn1sL0Ytj|fi|Sri)rXr_rSsqrtr[rQrror4r4r5r)s <z std._finalizeN)Frlr4r4r4r5rpsrpcsZeZdZdZddZddZddZfdd Zed d Z d d Z edddZ Z S)_first_or_lastz6Abstract base class of first and last reductions. cCs ttjSr?rryrrr4r4r5r4sz_first_or_last.out_dshapecCs|p|Sr?r4rr4r4r5r7sz_first_or_last.uses_row_indexcCsdSr7r4rCr4r4r5r:sz+_first_or_last._antialias_requires_2_stagescsN|||r<|}t||jd}|j|_||||fSt||SdSNselector lookup_columnr_create_row_index_selectorrSr>rrr~rArrrow_index_selectorwrapperrr4r5r=s  z_first_or_last._build_basescCst|dkrtd|dSNrz6_combine should never be called with more than one aggrrS RuntimeErrorrr4r4r5rGs z_first_or_last._combinecCsdSr?r4rCr4r4r5ryOsz)_first_or_last._create_row_index_selectorFcKstj|dfi|SNrrrr4r4r5rRsz_first_or_last._finalize)F) r/r0r1r2rrrrrrryrrr4r4rr5rr1s  rrc@sFeZdZdZddddZeeddZeedd Zd d Z d S) firstaFirst value encountered in ``column``. Useful for categorical data where an actual value must always be returned, not an average or other numerical calculation. Currently only supported for rasters, externally to this class. Parameters ---------- column : str Name of the column to aggregate over. If the data type is floating point, ``NaN`` values in the column are skipped. rrcCsttj|jfSr?r r ZFIRSTr[rr4r4r5rhszfirst._antialias_stage_2cCs,t|s(t|||fr(||||f<dSdSrrrr4r4r5rks z first._appendcCsD||}t|s@t|||fs0||||fkr@||||f<dSdSrrrQr4r4r5rss ( zfirst._append_antialiascCstSr?)_min_row_indexrCr4r4r5ry|sz first._create_row_index_selectorN r/r0r1r2rrr rrryr4r4r4r5rZs   rc@sFeZdZdZddddZeeddZeedd Zd d Z d S) lastaLast value encountered in ``column``. Useful for categorical data where an actual value must always be returned, not an average or other numerical calculation. Currently only supported for rasters, externally to this class. Parameters ---------- column : str Name of the column to aggregate over. If the data type is floating point, ``NaN`` values in the column are skipped. rrcCsttj|jfSr?r r ZLASTr[rr4r4r5rszlast._antialias_stage_2cCst|s||||f<dSdSrrrr4r4r5rs z last._appendcCsD||}t|s@t|||fs0||||fkr@||||f<dSdSrrrQr4r4r5rs ( zlast._append_antialiascCstSr?)_max_row_indexrCr4r4r5ryszlast._create_row_index_selectorNrr4r4r4r5rs   rcsNeZdZdfdd ZddZddZfd d Zd d Zfd dZZ S)FloatingNReductionNrcs"t||dkr|nd|_dSr2)r~rBn)rAr>rrr4r5rBs zFloatingNReduction.__init__cCs ttjSr?rsrr4r4r5rszFloatingNReduction.out_dshapecKs<d}t|j}t|}|d|g7<||d|<|S)Nrr@rA)rXrWrrBrC)rArZn_namen_valuesr4r4r5_add_finalize_kwargss    z'FloatingNReduction._add_finalize_kwargscsfddS)Ncstt|jf|Sr?)r~rrrrrrrAr4r5r=s  z2FloatingNReduction._build_create..r4r>rrrAr5rsz FloatingNReduction._build_createcsdfdd }|S)NFcs&jfi|}j|fd|i|S)Nr)rrrrCr4r5rEsz4FloatingNReduction._build_finalize..finalize)Fr4rFr4rCr5rsz"FloatingNReduction._build_finalizecst|jfSr?)r~rrrCrr4r5rsz#FloatingNReduction._hashable_inputs)Nr) r/r0r1rBrrrrrrr4r4rr5rs  rcsReZdZdZddZddZfddZedd Zd d Z edd dZ Z S)_first_n_or_last_nz:Abstract base class of first_n and last_n reductions. cCs|p|Sr?r4rr4r4r5rsz!_first_n_or_last_n.uses_row_indexcCsdSr7r4rCr4r4r5rsz/_first_n_or_last_n._antialias_requires_2_stagescsN|||r<|}t||jd}|j|_||||fSt||SdSrtrxrzrr4r5rs  z_first_n_or_last_n._build_basescCst|dkrtd|dSr}r~rr4r4r5rs z_first_n_or_last_n._combinecCsdSr?r4rCr4r4r5rysz-_first_n_or_last_n._create_row_index_selectorFcKstj|dfi|Srrrr4r4r5rsz_first_n_or_last_n._finalize)F) r/r0r1r2rrrrrryrrr4r4rr5rs  rc@sBeZdZddddZeeddZeeddZd d Zd S) first_nrrcCsttj|jddfSNTr:rrr4r4r5rszfirst_n._antialias_stage_2cCsft|sb|jd}t||||dfs,dSt|D],}t||||fr4|||||f<|Sq4dSNr7rrrrrrrrrrrr4r4r5rs   zfirst_n._appendc Csn||}t|sj|jd}t||||dfs4dSt|D],}t||||fr<|||||f<|Sq>> canvas.line(df, 'x', 'y', agg=ds.where(ds.max("value"), "other")) # doctest: +SKIP This returns the values of the "other" column that correspond to the maximum of the "value" column in each bin. Parameters ---------- selector: Reduction Reduction used to select the values of the ``lookup_column`` which are returned by this ``where`` reduction. lookup_column : str | None Column containing values that are returned from this ``where`` reduction, or ``None`` to return row indexes instead. 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