*j\UddlmZddlZddlmZmZer ddlmZddlm Z ddl Z ddl m Z ddlmZej ddZej d Ze j&d e j(e j&d e j(e j&d e j(e j&d e j(e j&de j*e j&de j*e j&de j*e j&de j*e j&de j,e j&de j,e j&de j.e j&de j.i Zded<e j&d e j,e j&d e j,e j&d e j,e j&d e j,e j&de j,e j&de j,e j&de j,e j&de j,e j&de j,e j&de j,e j&de j,e j&de j,i Zded<e j&d e j6e j&d e j8e j&d e j:e j&d e j(e j&de j<e j&de j>e j&de j@e j&de j*e j&de jBe j&de j,e j&de jDe j&de j.i Z#ded< ddZ$y)) annotationsN) TYPE_CHECKINGAny)Callable)Scalar)import_optional_dependency)jit_user_functionctrddln tdt|j |||fd}|S)Nrnumbanopythonnogilparallelc4|dk(r|dddf}|jd}n|d}|jd} |g|}|ftjtj|jz}|dk(r|ddd}tj|}|dk(r:||d< j d|jdD]} ||g|||<|S||dddf< j d|jdD]}  |dd| fg||dd| f<|S)Nr)shapenp atleast_1dasarrayemptyprange) valuesaxisargs first_elemdim0res0 buf_shapebuffijnb_compat_funcr s =C:\FundamentalWorkbench\vendor\pandas\core\_numba\executor.py nb_looperz(generate_apply_looper..nb_loopers. 191J<<?DJ<<?Dj040GbmmBJJt,<=CCC 19!$B$Ixx " 19DG\\!V\\!_5(:T:Q6  DAJ\\!V\\!_5+F1a4L@4@QT 6 )rr rr jit)funcr rrr%r#r s @@r$generate_apply_looperr)sG*73&t,N YYYAB8 r&ctrddln td|r)j||| dfd }|Sj||| dfd }|S)Nrr r c tj|jd|f }i} j|jdD]A} || |||g|\}} |||<t | dkDs*tj | ||<C||fSNr)dtype)rrrrlenarray) rlabelsngroups min_periodsrresult na_positionsr!outputna_posr(r result_dtypes r$ column_looperz"make_looper..column_looperEsXXv||A8 MFL\\&,,q/2!%1I|VWk"LP"#q v;?&(hhv&6LO 3<' 'r&c2tj|jdt|f }i} j |jdD]A} || |||g|\}} |||<t| dkDs*tj | ||<C||fSr,)rrrr.rr/) rstartendr2rr3r4r!r5r6r(r r7s r$r8z"make_looper..column_looperZsXXv||AE ;<PFL\\&,,q/2!%1I|UC"GK"#q v;?&(hhv&6LO 3<' 'r&)r np.ndarrayr0r<r1intr2r=)rr<r:r<r;r<r2r=)rr rr')r(r7is_grouped_kernelr rrr8r s`` @r$ make_looperr?<s*73 HEH E ( ( ( (  ( F (V / HEH E  ( ( ( (  ( F  ($ r&int8int16int32int64uint8uint16uint32uint64float32float64 complex64 complex128zdict[np.dtype, Any]default_dtype_mappingfloat_dtype_mappingidentity_dtype_mappingc6 d dfd }|S)a7 Generate a Numba function that loops over the columns 2D object and applies a 1D numba kernel over each column. Parameters ---------- func : function aggregation function to be applied to each column dtype_mapping: dict or None If not None, maps a dtype to a result dtype. Otherwise, will fall back to default mapping. is_grouped_kernel: bool, default False Whether func operates using the group labels (True) or using starts/ends arrays If true, you also need to pass the number of groups to this function nopython : bool nopython to be passed into numba.jit nogil : bool nogil to be passed into numba.jit parallel : bool parallel to be passed into numba.jit Returns ------- Numba function c |j}t|}r|||||g|j\} } n|||||g|j\} } | jjdk(r6| jD]#} t | dkDs| j d} n| j D])\} } t | dkDstj| | | f<+| S)Nr!rrI) r-r?rkindr.astypeitemsrnan)rr:r;r0r1r2kwargsr7r8r3r4r6r! dtype_mappingr(r>rr rs r$looper_wrapperz2generate_shared_aggregator..looper_wrappers%V\\2 # , 18UH  #0$7=}}$ FL$1sK$28--/$ FL <<   # '--/v;?#]]95F0 &++-IAv6{Q$&FFq&y!. r&)NNNNr)r2r=)r(rVr>r rrrWs`````` r$generate_shared_aggregatorrYs2Z  $  $$L r&)TTF) r(zCallable[..., Scalar]rVzdict[np.dtype, np.dtype]r>boolr rZrrZrrZ)% __future__r functoolstypingrrcollections.abcrpandas._typingrnumpyrpandas.compat._optionalrpandas.core.util.numba_r cacher)r?r-rCrGrIrKrL__annotations__rMr@rArBrDrErFrHrJrNrYrXr&r$res" (%>5 $$N 44p BHHVbhh BHHWrxx BHHWrxx BHHWrxx BHHWryy BHHX  BHHX  BHHX  BHHY BHHY BHH[2== BHH\BMM .* & BHHVbjj BHHWrzz BHHWrzz BHHWrzz BHHWrzz BHHX  BHHX  BHHX  BHHY BHHY BHH[2:: BHH\BJJ ,(   BHHVbgg BHHWrxx BHHWrxx BHHWrxx BHHWrxx BHHX  BHHX  BHHX  BHHY BHHY BHH[2<< BHH\BMM /+  Q Q+QQ Q  Q  Qr&