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📁 ..
📄 __init__.py(2.53 KB)
📄 __init__.pyc(2.54 KB)
📄 __init__.pyo(2.54 KB)
📄 _dummy.so(4.06 KB)
📄 _internal.py(17.84 KB)
📄 _internal.pyc(16.34 KB)
📄 _internal.pyo(16.34 KB)
📄 _methods.py(4.21 KB)
📄 _methods.pyc(4.79 KB)
📄 _methods.pyo(4.79 KB)
📄 arrayprint.py(25 KB)
📄 arrayprint.pyc(23.4 KB)
📄 arrayprint.pyo(23.4 KB)
📄 cversions.py(413 B)
📄 cversions.pyc(730 B)
📄 cversions.pyo(730 B)
📄 defchararray.py(66.26 KB)
📄 defchararray.pyc(75.58 KB)
📄 defchararray.pyo(75.58 KB)
📄 fromnumeric.py(97.02 KB)
📄 fromnumeric.pyc(100.34 KB)
📄 fromnumeric.pyo(100.34 KB)
📄 function_base.py(6.73 KB)
📄 function_base.pyc(6.72 KB)
📄 function_base.pyo(6.72 KB)
📄 generate_numpy_api.py(7.24 KB)
📄 generate_numpy_api.pyc(6.93 KB)
📄 generate_numpy_api.pyo(6.93 KB)
📄 getlimits.py(9.49 KB)
📄 getlimits.pyc(10.83 KB)
📄 getlimits.pyo(10.83 KB)
📁 include
📄 info.py(4.58 KB)
📄 info.pyc(4.83 KB)
📄 info.pyo(4.83 KB)
📁 lib
📄 machar.py(10.54 KB)
📄 machar.pyc(8.66 KB)
📄 machar.pyo(8.66 KB)
📄 memmap.py(10.13 KB)
📄 memmap.pyc(10.18 KB)
📄 memmap.pyo(10.18 KB)
📄 multiarray.so(1.52 MB)
📄 multiarray_tests.so(48.04 KB)
📄 numeric.py(86.66 KB)
📄 numeric.pyc(89.99 KB)
📄 numeric.pyo(89.89 KB)
📄 numerictypes.py(28.11 KB)
📄 numerictypes.pyc(27.55 KB)
📄 numerictypes.pyo(27.51 KB)
📄 operand_flag_tests.so(7.26 KB)
📄 records.py(28.75 KB)
📄 records.pyc(24.87 KB)
📄 records.pyo(24.87 KB)
📄 setup.py(40.04 KB)
📄 setup.pyc(26.76 KB)
📄 setup.pyo(26.76 KB)
📄 setup_common.py(13.83 KB)
📄 setup_common.pyc(11.31 KB)
📄 setup_common.pyo(11.31 KB)
📄 shape_base.py(8.84 KB)
📄 shape_base.pyc(9.76 KB)
📄 shape_base.pyo(9.76 KB)
📄 struct_ufunc_test.so(7.3 KB)
📄 test_rational.so(40.23 KB)
📁 tests
📄 umath.so(705.3 KB)
📄 umath_tests.so(14.87 KB)

Editing: _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

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