298 lines
11 KiB
Python
298 lines
11 KiB
Python
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"""
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Array methods which are called by both the C-code for the method
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and the Python code for the NumPy-namespace function
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"""
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import warnings
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from contextlib import nullcontext
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from numpy.core import multiarray as mu
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from numpy.core import umath as um
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from numpy.core.multiarray import asanyarray
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from numpy.core import numerictypes as nt
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from numpy.core import _exceptions
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from numpy.core._ufunc_config import _no_nep50_warning
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from numpy._globals import _NoValue
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from numpy.compat import pickle, os_fspath
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# save those O(100) nanoseconds!
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umr_maximum = um.maximum.reduce
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umr_minimum = um.minimum.reduce
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umr_sum = um.add.reduce
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umr_prod = um.multiply.reduce
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umr_any = um.logical_or.reduce
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umr_all = um.logical_and.reduce
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# Complex types to -> (2,)float view for fast-path computation in _var()
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_complex_to_float = {
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nt.dtype(nt.csingle) : nt.dtype(nt.single),
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nt.dtype(nt.cdouble) : nt.dtype(nt.double),
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}
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# Special case for windows: ensure double takes precedence
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if nt.dtype(nt.longdouble) != nt.dtype(nt.double):
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_complex_to_float.update({
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nt.dtype(nt.clongdouble) : nt.dtype(nt.longdouble),
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})
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# avoid keyword arguments to speed up parsing, saves about 15%-20% for very
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# small reductions
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def _amax(a, axis=None, out=None, keepdims=False,
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initial=_NoValue, where=True):
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return umr_maximum(a, axis, None, out, keepdims, initial, where)
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def _amin(a, axis=None, out=None, keepdims=False,
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initial=_NoValue, where=True):
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return umr_minimum(a, axis, None, out, keepdims, initial, where)
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def _sum(a, axis=None, dtype=None, out=None, keepdims=False,
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initial=_NoValue, where=True):
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return umr_sum(a, axis, dtype, out, keepdims, initial, where)
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def _prod(a, axis=None, dtype=None, out=None, keepdims=False,
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initial=_NoValue, where=True):
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return umr_prod(a, axis, dtype, out, keepdims, initial, where)
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def _any(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
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# Parsing keyword arguments is currently fairly slow, so avoid it for now
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if where is True:
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return umr_any(a, axis, dtype, out, keepdims)
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return umr_any(a, axis, dtype, out, keepdims, where=where)
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def _all(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
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# Parsing keyword arguments is currently fairly slow, so avoid it for now
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if where is True:
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return umr_all(a, axis, dtype, out, keepdims)
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return umr_all(a, axis, dtype, out, keepdims, where=where)
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def _count_reduce_items(arr, axis, keepdims=False, where=True):
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# fast-path for the default case
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if where is True:
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# no boolean mask given, calculate items according to axis
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if axis is None:
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axis = tuple(range(arr.ndim))
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elif not isinstance(axis, tuple):
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axis = (axis,)
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items = 1
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for ax in axis:
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items *= arr.shape[mu.normalize_axis_index(ax, arr.ndim)]
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items = nt.intp(items)
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else:
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# TODO: Optimize case when `where` is broadcast along a non-reduction
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# axis and full sum is more excessive than needed.
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# guarded to protect circular imports
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from numpy.lib.stride_tricks import broadcast_to
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# count True values in (potentially broadcasted) boolean mask
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items = umr_sum(broadcast_to(where, arr.shape), axis, nt.intp, None,
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keepdims)
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return items
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# Numpy 1.17.0, 2019-02-24
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# Various clip behavior deprecations, marked with _clip_dep as a prefix.
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def _clip_dep_is_scalar_nan(a):
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# guarded to protect circular imports
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from numpy.core.fromnumeric import ndim
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if ndim(a) != 0:
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return False
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try:
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return um.isnan(a)
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except TypeError:
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return False
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def _clip_dep_is_byte_swapped(a):
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if isinstance(a, mu.ndarray):
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return not a.dtype.isnative
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return False
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def _clip_dep_invoke_with_casting(ufunc, *args, out=None, casting=None, **kwargs):
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# normal path
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if casting is not None:
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return ufunc(*args, out=out, casting=casting, **kwargs)
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# try to deal with broken casting rules
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try:
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return ufunc(*args, out=out, **kwargs)
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except _exceptions._UFuncOutputCastingError as e:
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# Numpy 1.17.0, 2019-02-24
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warnings.warn(
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"Converting the output of clip from {!r} to {!r} is deprecated. "
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"Pass `casting=\"unsafe\"` explicitly to silence this warning, or "
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"correct the type of the variables.".format(e.from_, e.to),
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DeprecationWarning,
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stacklevel=2
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)
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return ufunc(*args, out=out, casting="unsafe", **kwargs)
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def _clip(a, min=None, max=None, out=None, *, casting=None, **kwargs):
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if min is None and max is None:
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raise ValueError("One of max or min must be given")
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# Numpy 1.17.0, 2019-02-24
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# This deprecation probably incurs a substantial slowdown for small arrays,
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# it will be good to get rid of it.
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if not _clip_dep_is_byte_swapped(a) and not _clip_dep_is_byte_swapped(out):
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using_deprecated_nan = False
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if _clip_dep_is_scalar_nan(min):
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min = -float('inf')
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using_deprecated_nan = True
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if _clip_dep_is_scalar_nan(max):
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max = float('inf')
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using_deprecated_nan = True
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if using_deprecated_nan:
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warnings.warn(
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"Passing `np.nan` to mean no clipping in np.clip has always "
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"been unreliable, and is now deprecated. "
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"In future, this will always return nan, like it already does "
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"when min or max are arrays that contain nan. "
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"To skip a bound, pass either None or an np.inf of an "
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"appropriate sign.",
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DeprecationWarning,
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stacklevel=2
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)
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if min is None:
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return _clip_dep_invoke_with_casting(
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um.minimum, a, max, out=out, casting=casting, **kwargs)
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elif max is None:
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return _clip_dep_invoke_with_casting(
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um.maximum, a, min, out=out, casting=casting, **kwargs)
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else:
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return _clip_dep_invoke_with_casting(
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um.clip, a, min, max, out=out, casting=casting, **kwargs)
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def _mean(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
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arr = asanyarray(a)
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is_float16_result = False
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rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where)
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if rcount == 0 if where is True else umr_any(rcount == 0, axis=None):
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warnings.warn("Mean of empty slice.", RuntimeWarning, stacklevel=2)
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# Cast bool, unsigned int, and int to float64 by default
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if dtype is None:
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if issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
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dtype = mu.dtype('f8')
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elif issubclass(arr.dtype.type, nt.float16):
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dtype = mu.dtype('f4')
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is_float16_result = True
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ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
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if isinstance(ret, mu.ndarray):
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with _no_nep50_warning():
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ret = um.true_divide(
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ret, rcount, out=ret, casting='unsafe', subok=False)
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if is_float16_result and out is None:
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ret = arr.dtype.type(ret)
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elif hasattr(ret, 'dtype'):
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if is_float16_result:
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ret = arr.dtype.type(ret / rcount)
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else:
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ret = ret.dtype.type(ret / rcount)
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else:
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ret = ret / rcount
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return ret
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def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *,
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where=True):
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arr = asanyarray(a)
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rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where)
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# Make this warning show up on top.
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if ddof >= rcount if where is True else umr_any(ddof >= rcount, axis=None):
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warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning,
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stacklevel=2)
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# Cast bool, unsigned int, and int to float64 by default
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if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
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dtype = mu.dtype('f8')
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# Compute the mean.
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# Note that if dtype is not of inexact type then arraymean will
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# not be either.
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arrmean = umr_sum(arr, axis, dtype, keepdims=True, where=where)
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# The shape of rcount has to match arrmean to not change the shape of out
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# in broadcasting. Otherwise, it cannot be stored back to arrmean.
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if rcount.ndim == 0:
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# fast-path for default case when where is True
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div = rcount
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else:
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# matching rcount to arrmean when where is specified as array
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div = rcount.reshape(arrmean.shape)
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if isinstance(arrmean, mu.ndarray):
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with _no_nep50_warning():
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arrmean = um.true_divide(arrmean, div, out=arrmean,
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casting='unsafe', subok=False)
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elif hasattr(arrmean, "dtype"):
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arrmean = arrmean.dtype.type(arrmean / rcount)
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else:
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arrmean = arrmean / rcount
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# Compute sum of squared deviations from mean
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# Note that x may not be inexact and that we need it to be an array,
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# not a scalar.
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x = asanyarray(arr - arrmean)
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if issubclass(arr.dtype.type, (nt.floating, nt.integer)):
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x = um.multiply(x, x, out=x)
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# Fast-paths for built-in complex types
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elif x.dtype in _complex_to_float:
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xv = x.view(dtype=(_complex_to_float[x.dtype], (2,)))
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um.multiply(xv, xv, out=xv)
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x = um.add(xv[..., 0], xv[..., 1], out=x.real).real
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# Most general case; includes handling object arrays containing imaginary
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# numbers and complex types with non-native byteorder
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else:
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x = um.multiply(x, um.conjugate(x), out=x).real
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ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where)
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# Compute degrees of freedom and make sure it is not negative.
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rcount = um.maximum(rcount - ddof, 0)
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# divide by degrees of freedom
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if isinstance(ret, mu.ndarray):
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with _no_nep50_warning():
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ret = um.true_divide(
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ret, rcount, out=ret, casting='unsafe', subok=False)
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elif hasattr(ret, 'dtype'):
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ret = ret.dtype.type(ret / rcount)
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else:
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ret = ret / rcount
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return ret
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def _std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *,
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where=True):
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ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
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keepdims=keepdims, where=where)
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if isinstance(ret, mu.ndarray):
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ret = um.sqrt(ret, out=ret)
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elif hasattr(ret, 'dtype'):
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ret = ret.dtype.type(um.sqrt(ret))
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else:
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ret = um.sqrt(ret)
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return ret
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def _ptp(a, axis=None, out=None, keepdims=False):
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return um.subtract(
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umr_maximum(a, axis, None, out, keepdims),
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umr_minimum(a, axis, None, None, keepdims),
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out
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)
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def _dump(self, file, protocol=2):
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if hasattr(file, 'write'):
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ctx = nullcontext(file)
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else:
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ctx = open(os_fspath(file), "wb")
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with ctx as f:
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pickle.dump(self, f, protocol=protocol)
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def _dumps(self, protocol=2):
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return pickle.dumps(self, protocol=protocol)
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