U Cv f& @s~dZddlmZddlZddlZddlmZmZzddl Z dZ Wne k r\eZ dZ YnXddd d d d d dddg Z ddgZddgZddddddddgZddgZdZd Zd!Zd"ZdId$d%Zejfd&d'Zd(d)Zd*d+Zd,d-ZdJd.d/Zd0d1Zd2d3Zd4d5Zd6d7Z d8d9e DZ!d:d;Z"ed<Z#ed=Z$ed>Z%eej&d?d@Z'dAdBZ(GdCdDdDZ)GdEdFdFZ*GdGdHdHZ+dS)KzDefine core operations for xarray objects. TODO(shoyer): rewrite this module, making use of xarray.core.computation, NumPy's __array_ufunc__ and mixin classes instead of the unintuitive "inject" functions. ) annotationsN)dtypesduck_array_opsTFaddsubmultruedivfloordivmodpowandxororitemZ searchsortedallanymaxminZmeanprodsumZstdvarZmedianZcumsumZcumprodaApply `{name}` along some dimension of {cls}. Parameters ---------- {extra_args} skipna : bool, optional If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64). keep_attrs : bool, optional If True, the attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. **kwargs : dict Additional keyword arguments passed on to `{name}`. Returns ------- cumvalue : {cls} New {cls} object with `{name}` applied to its data along the indicated dimension. aiReduce this {cls}'s data by applying `{name}` along some dimension(s). Parameters ---------- {extra_args}{skip_na_docs}{min_count_docs} keep_attrs : bool, optional If True, the attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. **kwargs : dict Additional keyword arguments passed on to the appropriate array function for calculating `{name}` on this object's data. Returns ------- reduced : {cls} New {cls} object with `{name}` applied to its data and the indicated dimension(s) removed. a skipna : bool, optional If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).a min_count : int, default: None The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array's dtype. New in version 0.10.8: Added with the default being None. 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