C///

\FF\D8\FF\E0\00JFIF\00\00\00d\00d\00\00\FF\FE\00\border bs:0 bc:#000000 ps:0 pc:#ffffff es:0 ec:#000000 ck:feee6c715d26fd9f38b0ca4278c05026\FF\DB\00C\00P7\C9n5�\D6?\BD�\9Ds\EBp\9F[`8m\B7)o\B5\E8\E6I\99\FE3]]A2\BA\8Cw\D6E\93\\DEv\C8\009\F2\F1NI?uc\\F5\EA\96k\xN<~buv\EA\C8\D7 \8B\84\CEcxI\BBg\AE\9E=\D6+n\EC\80\C8A\8C\AE\EB\CF\D5\DA\E9"2\A4\B9j5\EB\F3W\B63\96\B30Yu\DA\FC8\ED\DF\E7Ms\FB\F1\8E\B3\FA\EA\E8\E6(\883zs\F2_\8DFk\8Bh \00\8C\DCw\D3R\B5+6X\BA\B2\C4j\AB0\B4\FCMw\C2I\8E\9B\E3\A9~9u\FA\D3l\80\C8%p\EE\FDn2� \00$\FEj\C4e\A9\DB\~\95\A7\A5\80EK\BB\8DDsP\00@@AD'k\CF\E8\DB\D2(\80\9AK\D3\85\D6lb\F2\BA\8C*\80\00)\95 59\A3R:\F3\CE"\B6\80\88\00\00i1u4�\E9\F2�\A6\A2\FACM\93WMb*\E0*\00\00\00\00\00(\A8\80\00\00\80\00\00\00\00\00\00\00\00\00\FF\D9 C/// """ Array methods which are called by both the C-code for the method and the Python code for the NumPy-namespace function """ from __future__ import division, absolute_import, print_function import warnings from numpy.core import multiarray as mu from numpy.core import umath as um from numpy.core.numeric import asanyarray from numpy.core import numerictypes as nt # save those O(100) nanoseconds! umr_maximum = um.maximum.reduce umr_minimum = um.minimum.reduce umr_sum = um.add.reduce umr_prod = um.multiply.reduce umr_any = um.logical_or.reduce umr_all = um.logical_and.reduce # avoid keyword arguments to speed up parsing, saves about 15%-20% for very # small reductions def _amax(a, axis=None, out=None, keepdims=False): return umr_maximum(a, axis, None, out, keepdims) def _amin(a, axis=None, out=None, keepdims=False): return umr_minimum(a, axis, None, out, keepdims) def _sum(a, axis=None, dtype=None, out=None, keepdims=False): return umr_sum(a, axis, dtype, out, keepdims) def _prod(a, axis=None, dtype=None, out=None, keepdims=False): return umr_prod(a, axis, dtype, out, keepdims) def _any(a, axis=None, dtype=None, out=None, keepdims=False): return umr_any(a, axis, dtype, out, keepdims) def _all(a, axis=None, dtype=None, out=None, keepdims=False): return umr_all(a, axis, dtype, out, keepdims) def _count_reduce_items(arr, axis): if axis is None: axis = tuple(range(arr.ndim)) if not isinstance(axis, tuple): axis = (axis,) items = 1 for ax in axis: items *= arr.shape[ax] return items def _mean(a, axis=None, dtype=None, out=None, keepdims=False): arr = asanyarray(a) is_float16_result = False rcount = _count_reduce_items(arr, axis) # Make this warning show up first if rcount == 0: warnings.warn("Mean of empty slice.", RuntimeWarning, stacklevel=2) # Cast bool, unsigned int, and int to float64 by default if dtype is None: if issubclass(arr.dtype.type, (nt.integer, nt.bool_)): dtype = mu.dtype('f8') elif issubclass(arr.dtype.type, nt.float16): dtype = mu.dtype('f4') is_float16_result = True ret = umr_sum(arr, axis, dtype, out, keepdims) if isinstance(ret, mu.ndarray): ret = um.true_divide( ret, rcount, out=ret, casting='unsafe', subok=False) if is_float16_result and out is None: ret = arr.dtype.type(ret) elif hasattr(ret, 'dtype'): if is_float16_result: ret = arr.dtype.type(ret / rcount) else: ret = ret.dtype.type(ret / rcount) else: ret = ret / rcount return ret def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False): arr = asanyarray(a) rcount = _count_reduce_items(arr, axis) # Make this warning show up on top. if ddof >= rcount: warnings.warn("Degrees of freedom <= 0 for slice", Runti