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File Content: `_methods.py`

"""
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)

    rcount = _count_reduce_items(arr, axis)
    # Make this warning show up first
    if rcount == 0:
        warnings.warn("Mean of empty slice.", RuntimeWarning)

    # Cast bool, unsigned int, and int to float64 by default
    if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
        dtype = mu.dtype('f8')

    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)
    elif hasattr(ret, 'dtype'):
        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", RuntimeWarning)

    # Cast bool, unsigned int, and int to float64 by default
    if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
        dtype = mu.dtype('f8')

    # Compute the mean.
    # Note that if dtype is not of inexact type then arraymean will
    # not be either.
    arrmean = umr_sum(arr, axis, dtype, keepdims=True)
    if isinstance(arrmean, mu.ndarray):
        arrmean = um.true_divide(
                arrmean, rcount, out=arrmean, casting='unsafe', subok=False)
    else:
        arrmean = arrmean.dtype.type(arrmean / rcount)

    # Compute sum of squared deviations from mean
    # Note that x may not be inexact and that we need it to be an array,
    # not a scalar.
    x = asanyarray(arr - arrmean)
    if issubclass(arr.dtype.type, nt.complexfloating):
        x = um.multiply(x, um.conjugate(x), out=x).real
    else:
        x = um.multiply(x, x, out=x)
    ret = umr_sum(x, axis, dtype, out, keepdims)

    # Compute degrees of freedom and make sure it is not negative.
    rcount = max([rcount - ddof, 0])

    # divide by degrees of freedom
    if isinstance(ret, mu.ndarray):
        ret = um.true_divide(
                ret, rcount, out=ret, casting='unsafe', subok=False)
    elif hasattr(ret, 'dtype'):
        ret = ret.dtype.type(ret / rcount)
    else:
        ret = ret / rcount

    return ret

def _std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
    ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
               keepdims=keepdims)

    if isinstance(ret, mu.ndarray):
        ret = um.sqrt(ret, out=ret)
    elif hasattr(ret, 'dtype'):
        ret = ret.dtype.type(um.sqrt(ret))
    else:
        ret = um.sqrt(ret)

    return ret

Name	Size	Permission
__pycache__	---	0755
include	---	0755
lib	---	0755
tests	---	0755
__init__.py	2586 bytes	0644
_dummy.cpython-35m-x86_64-linux-gnu.so	7152 bytes	0755
_internal.py	18264 bytes	0644
_methods.py	4308 bytes	0644
arrayprint.py	25597 bytes	0644
cversions.py	413 bytes	0644
defchararray.py	67850 bytes	0644
fromnumeric.py	99350 bytes	0644
function_base.py	6891 bytes	0644
generate_numpy_api.py	7417 bytes	0644
getlimits.py	9714 bytes	0644
info.py	4690 bytes	0644
machar.py	10790 bytes	0644
memmap.py	10378 bytes	0644
multiarray.cpython-35m-x86_64-linux-gnu.so	1612632 bytes	0755
multiarray_tests.cpython-35m-x86_64-linux-gnu.so	50200 bytes	0755
numeric.py	88743 bytes	0644
numerictypes.py	28784 bytes	0644
operand_flag_tests.cpython-35m-x86_64-linux-gnu.so	11384 bytes	0755
records.py	29435 bytes	0644
setup.py	40998 bytes	0644
setup_common.py	14166 bytes	0644
shape_base.py	9051 bytes	0644
struct_ufunc_test.cpython-35m-x86_64-linux-gnu.so	11344 bytes	0755
test_rational.cpython-35m-x86_64-linux-gnu.so	45800 bytes	0755
umath.cpython-35m-x86_64-linux-gnu.so	797320 bytes	0755
umath_tests.cpython-35m-x86_64-linux-gnu.so	19976 bytes	0755

File Content: _methods.py

File Content: `_methods.py`