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,
stacklevel=2)
# 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.floating, nt.integer)):
x = um.multiply(x, x, out=x)
else:
x = um.multiply(x, um.conjugate(x), out=x).real
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 _mean(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
arr = asanyarray(a)
is_float16_result = False
rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where)
if rcount == 0 if where is True else umr_any(rcount == 0, axis=None):
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, where=where)
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 _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 _std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
arr = asanyarray(a)
is_float16_result = False
ret = _var(a,
axis=axis,
dtype=dtype,
out=out,
ddof=ddof,
keepdims=keepdims)
# 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
if isinstance(ret, mu.ndarray):
ret = um.sqrt(ret, out=ret)
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(um.sqrt(ret))
else:
ret = ret.dtype.type(um.sqrt(ret))
else:
ret = um.sqrt(ret)
return ret
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", RuntimeWarning,
stacklevel=2)
# 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.floating, nt.integer)):
x = um.multiply(x, x, out=x)
else:
x = um.multiply(x, um.conjugate(x), out=x).real
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 _var(a,
axis=None,
dtype=None,
out=None,
ddof=0,
keepdims=False,
*,
where=True):
arr = asanyarray(a)
rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where)
# Make this warning show up on top.
if ddof >= rcount if where is True else umr_any(ddof >= rcount, axis=None):
warnings.warn("Degrees of freedom <= 0 for slice",
RuntimeWarning,
stacklevel=2)
# 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, where=where)
# The shape of rcount has to match arrmean to not change the shape of out
# in broadcasting. Otherwise, it cannot be stored back to arrmean.
if rcount.ndim == 0:
# fast-path for default case when where is True
div = rcount
else:
# matching rcount to arrmean when where is specified as array
div = rcount.reshape(arrmean.shape)
if isinstance(arrmean, mu.ndarray):
arrmean = um.true_divide(arrmean,
div,
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.floating, nt.integer)):
x = um.multiply(x, x, out=x)
# Fast-paths for built-in complex types
elif x.dtype in _complex_to_float:
xv = x.view(dtype=(_complex_to_float[x.dtype], (2, )))
um.multiply(xv, xv, out=xv)
x = um.add(xv[..., 0], xv[..., 1], out=x.real).real
# Most general case; includes handling object arrays containing imaginary
# numbers and complex types with non-native byteorder
else:
x = um.multiply(x, um.conjugate(x), out=x).real
ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where)
# Compute degrees of freedom and make sure it is not negative.
rcount = um.maximum(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 _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,
stacklevel=2)
# 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.floating, nt.integer)):
x = um.multiply(x, x, out=x)
# Fast-paths for built-in complex types
elif x.dtype in _complex_to_float:
xv = x.view(dtype=(_complex_to_float[x.dtype], (2, )))
um.multiply(xv, xv, out=xv)
x = um.add(xv[..., 0], xv[..., 1], out=x.real).real
# Most general case; includes handling object arrays containing imaginary
# numbers and complex types with non-native byteorder
else:
x = um.multiply(x, um.conjugate(x), out=x).real
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
