&PhdZddlmZddlZddlZddlmZmZmZddl Z ddl m Z ddl mZmZerddlmZd dZej& d d Zej& d d Zy)z6Common utilities for Numba operations with groupby ops) annotationsN) TYPE_CHECKINGAnyCallable)import_optional_dependency)NumbaUtilErrorjit_user_function)Scalarc"t|s tdttj|j j }ddg}t|}t||ks|d||k7rtd|d|jd|y)a^ Validate user defined function for ops when using Numba with groupby ops. The first signature arguments should include: def f(values, index, ...): ... Parameters ---------- func : function, default False user defined function Returns ------- None Raises ------ NumbaUtilError z5Numba engine can only be used with a single function.valuesindexNz The first z arguments to z must be ) callableNotImplementedErrorlistinspect signature parameterskeyslenr__name__)func udf_signature expected_argsmin_number_argss eC:\Users\daisl\Desktop\realtime-object-detection\venv\Lib\site-packages\pandas/core/groupby/numba_.py validate_udfrs, D>! C  **40;;@@BCMw'M-(O M_, )/ *m ;) io    <ct|trddln tdj ||| dfd }|S)a Generate a numba jitted agg function specified by values from engine_kwargs. 1. jit the user's function 2. Return a groupby agg function with the jitted function inline Configurations specified in engine_kwargs apply to both the user's function _AND_ the groupby evaluation loop. Parameters ---------- func : function function to be applied to each group 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 rNnumbanopythonnogilparallelc(t|t|k(sJt|}tj||f} j|D]A}|||||} j|D] } |||||| f}  | | g|||| f<"C|SNrnpemptypranger r beginend num_columnsargs num_groupsresulti group_indexjgroupr numba_funcs r group_aggz*generate_numba_agg_func..group_agges5zSX%%%Z :{34j)Aa3q62K\\+.uQx#a&0!34)%DtDq!t /*  rr np.ndarrayr r8r+r8r,r8r-intr.rreturnr8r rrrjit)rr!r"r#r6rr5s @@rgenerate_numba_agg_funcr=@s>#4(J*73 YYYA      B& rct|trddln tdj ||| dfd }|S)a Generate a numba jitted transform function specified by values from engine_kwargs. 1. jit the user's function 2. Return a groupby transform function with the jitted function inline Configurations specified in engine_kwargs apply to both the user's function _AND_ the groupby evaluation loop. 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 rNrr cJt|t|k(sJt|}tjt||f} j|D]I}|||||} j|D](} |||||| f}  | | g||||||| f<*K|Sr%r&r*s rgroup_transformz6generate_numba_transform_func..group_transforms5zSX%%%Z 3v; 45j)Aa3q62K\\+.uQx#a&0!34/9%/Tt/TuQx#a&(!+,/*  rr7r;)rr!r"r#r@rr5s @@rgenerate_numba_transform_funcrA|s>#4(J*73 YYYA      B& r)rrr:None) rzCallable[..., Scalar]r!boolr"rCr#rCr:PCallable[[np.ndarray, np.ndarray, np.ndarray, np.ndarray, int, Any], np.ndarray]) rzCallable[..., np.ndarray]r!rCr"rCr#rCr:rD)__doc__ __future__r functoolsrtypingrrrnumpyr'pandas.compat._optionalrpandas.core.util.numba_rr pandas._typingr rcacher=rArrrOs<" > %$ N 8 88 8 8 V 88v 8 #88 8 8 V 88r