*j*ddlmZddlZddlmZmZddlZddlm Z ddl m Z er ddl m Z ddlmZej d dZej dd Zej dd Zej d Zej dd Zy)) annotationsN) TYPE_CHECKINGAny)import_optional_dependency)jit_user_function)Callable)Scalarct|trddln tdj ||| dfd }|S)a Generate a numba jitted apply function specified by values from engine_kwargs. 1. jit the user's function 2. Return a rolling apply function with the jitted function inline Configurations specified in engine_kwargs apply to both the user's function _AND_ the rolling apply function. Parameters ---------- func : function function to be applied to each window and will be JITed 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 rNnumbanopythonnogilparallelcRtjt|} jt|D]j}||}||}|||} tjtj | } t| | z |k\r  | g|||<Xtj ||<l|S)N)npemptylenprangesumisnannan) valuesbeginendminimum_periodsargsresultistartstopwindow count_nanr numba_funcs ;C:\FundamentalWorkbench\vendor\pandas\core\window\numba_.py roll_applyz-generate_numba_apply_func..roll_apply:s#e*%c&k*A!HEq6DE$'Frxx/0I6{Y&/9&v55q FFq + ) r np.ndarrayrr'rr'rintrrreturnr'rrr rjit)funcr rrr%r r#s @@r$generate_numba_apply_funcr-s>#4(J*73 YYYA     B& r&c trddl n td j||| d fd }|S)a Generate a numba jitted ewm mean or sum function specified by values from engine_kwargs. Parameters ---------- 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 com : float adjust : bool ignore_na : bool deltas : tuple normalize : bool Returns ------- Numba function rNr r ctjt|}ddzz }d|z }rdn|}jt|D]:}||} ||} || | } tjt| } | d} t tj |  }||k\r| ntj | d<d}tdt| D]}| |}tj | }||z }tj | sX|ssXr||| |zdz zz}sdk(r d|z }n|| z} |r1r%| |k7r|| z||zz} r| ||zz } r||z }n d}n | |z } n|r|} ||k\r| ntj | |<| || | =|S)N?r)rrrrr(rrrange)rrrrralpha old_wt_factornew_wtrrr r! sub_resultweightednobsold_wtjcuris_observationadjustcomdeltas ignore_na normalizer s r$ewmz$generate_numba_ewm_func..ewmws#f+&sSy!e Ec%j)A!HEq6DE$'F#f+.JayH288H--.D(,(?HRVVJqMF1c&k*Qi%'XXc]!2&xx)%Y$#mveai!m7L&LLF#)cQh),v'4x'?H)(#+s?/5/@6Cr=r@r?rArBr s ````` @r$generate_numba_ewm_funcrEQssB*73 YYYA9999 9  99B9v Jr&ct|trddln tdj ||| dfd }|S)a Generate a numba jitted function to apply window calculations table-wise. Func will be passed an M window size x N number of columns array, and must return a 1 x N number of columns array. 1. jit the user's function 2. Return a rolling apply function with the jitted function inline Parameters ---------- func : function function to be applied to each window and will be JITed nopython : bool nopython to be passed into numba.jit nogil : bool nogil to be passed into numba.jit parallel : bool parallel to be passed into numba.jit Returns ------- Numba function rNr r ctjt||jdf}tj|j} j t|D]u}||}||} ||| } tj tj | d} t| | z |k\} | jr| g|||ddf<| ||ddf<wtj||tj}|S)Nr1r)axis) rrrshaperrranywherer)rrrrrrmin_periods_maskrrr r!r"nan_maskr r#s r$ roll_tablez-generate_numba_table_func..roll_tables3u:v||A7888FLL1c&k*A!HEq6DE$'Frxx/a8I6{Y./AH||~)&848q!t %- QT "+*FBFF; r&) rr'rr'rr'rr(rrr*)r,r rrrNr r#s @@r$generate_numba_table_funcrOsw>#4(J*73 YYYA   B* r&cptrddln tdjdddfd}|S)Nrr Tr ctj|jd}j|jdD]}|dd|f}|||<|S)Nr1)rrrIr)tablerr partitionnan_funcr s r$nan_agg_with_axiszBgenerate_manual_numpy_nan_agg_with_axis..nan_agg_with_axissV%++a.)ekk!n-Aad I +F1I. r&rD)rTrUr s` @r$'generate_manual_numpy_nan_agg_with_axisrVs<*73 YYD4Y89 r&c trddl n td j||| d fd }|S)a Generate a numba jitted ewm mean or sum function applied table wise specified by values from engine_kwargs. Parameters ---------- 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 com : float adjust : bool ignore_na : bool deltas : tuple normalize: bool Returns ------- Numba function rNr r c bddzz }d|z }rdn|}tj|jd}tj|j}|dj } tj | j tj} tj| |k\| tj|d<tdt|D]O} || } tj | } | | j tjz } jt| D]}tj | |s| |sr#r&||xx|| dz zzcc<sdk(rd||z }n || |z| |<| |s\rK| || |k7r*||| |z|| |zz| |<r| ||||zz | |<r||xx|z cc<d||<| |xx| |z cc<| |s| || |<tj| |k\| tj|| <R|S)Nr0r1r) ronesrIrcopyrastypeint64rKrr2rr)rrrrr3r4r5r9rr7r8rr;is_observationsr:r=r>r?r@rAr s r$ ewm_tablez0generate_numba_ewm_table_func..ewm_table/s(sSy!e Ea)&,,'!9>>#(##++BHH5HHT_4hGq q#f+&A)C!xx}nO O**2884 4D\\#c(+xx ,&q)$#1I&Q-)GGI#)cQh),vay*7(1+*EHQK*1-(#+A;#a(.q HQK(?&3q6/(Q%-QK(16>qkVAYQWEW6X #)$*1I$7I03F1I ( s1v 5 $Q'"%a&HQK9,<!8(BFFKF1IE'H r&rCrD) r rrr>r=r@r?rAr^r s ````` @r$generate_numba_ewm_table_funcr_ stB*73 YYYA3333 3  33B3j r&)r,zCallable[..., Scalar]r boolrr`rr`)r r`rr`rr`r>floatr=r`r@r`r?tuplerAr`)r,zCallable[..., np.ndarray]r r`rr`rr`) __future__r functoolstypingrrnumpyrpandas.compat._optionalrpandas.core.util.numba_rcollections.abcrpandas._typingr cacher-rErOrVr_r&r$rms" >5(% 8 88 8 88v aa aa  a  a  a aaaH : #:: : ::@ " [[ [[  [  [  [ [[[r&