def asarray_chkfinite(a):
"""Like asarray, but check that no NaNs or Infs are present.
"""
a = asarray(a)
if (a.dtype.char in typecodes['AllFloat']) \
and (_nx.isnan(a).any() or _nx.isinf(a).any()):
raise ValueError, "array must not contain infs or NaNs"
return a
def asarray_chkfinite(a):
"""Like asarray, but check that no NaNs or Infs are present.
"""
a = asarray(a)
if (a.dtype.char in typecodes['AllFloat']) \
and (_nx.isnan(a).any() or _nx.isinf(a).any()):
raise ValueError, "array must not contain infs or NaNs"
return a
def nan_to_num(x):
"""
Replace nan with zero and inf with large numbers.
Parameters
----------
x : array_like
Input data.
Returns
-------
out : ndarray
Array with the same shape and dtype as `x`. Nan is replaced
by zero, and inf (-inf) is replaced by the largest (smallest)
floating point value that fits in the output dtype.
Examples
--------
>>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])
>>> np.nan_to_num(x)
array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000,
-1.28000000e+002, 1.28000000e+002])
"""
try:
t = x.dtype.type
except AttributeError:
t = obj2sctype(type(x))
if issubclass(t, _nx.complexfloating):
return nan_to_num(x.real) + 1j * nan_to_num(x.imag)
else:
try:
y = x.copy()
except AttributeError:
y = array(x)
if not issubclass(t, _nx.integer):
if not y.shape:
y = array([x])
scalar = True
else:
scalar = False
are_inf = isposinf(y)
are_neg_inf = isneginf(y)
are_nan = isnan(y)
maxf, minf = _getmaxmin(y.dtype.type)
y[are_nan] = 0
y[are_inf] = maxf
y[are_neg_inf] = minf
if scalar:
y = y[0]
return y
def nan_to_nanval(x, nanval=-1e5):
x = _nx.array(x, subok=True)
xtype = x.dtype.type
if not issubclass(xtype, _nx.inexact):
return x
iscomplex = issubclass(xtype, _nx.complexfloating)
isscalar = (x.ndim == 0)
x = x[None] if isscalar else x
dest = (x.real, x.imag) if iscomplex else (x, )
maxf, minf = getmaxmin(x.real.dtype)
for d in dest:
_nx.copyto(d, nanval, where=isnan(d))
_nx.copyto(d, maxf, where=isposinf(d))
_nx.copyto(d, minf, where=isneginf(d))
return x[0] if isscalar else x
def _nanvar(a, axis=None, dtype=None, out=None, ddof=0,
keepdims=False):
# Using array() instead of asanyarray() because the former always
# makes a copy, which is important due to the copyto() action later
arr = array(a, subok=True)
mask = isnan(arr)
# First compute the mean, saving 'rcount' for reuse later
if dtype is None and (issubdtype(arr.dtype, nt.integer) or
issubdtype(arr.dtype, nt.bool_)):
arrmean = um.add.reduce(arr, axis=axis, dtype='f8', keepdims=True)
else:
mu.copyto(arr, 0.0, where=mask)
arrmean = um.add.reduce(arr, axis=axis, dtype=dtype,
keepdims=True)
rcount = (~mask).sum(axis=axis, keepdims=True)
if isinstance(arrmean, mu.ndarray):
arrmean = um.true_divide(arrmean, rcount,
out=arrmean, casting='unsafe', subok=False)
else:
arrmean = arrmean / float(rcount)
# arr - arrmean
x = arr - arrmean
mu.copyto(x, 0.0, where=mask)
# (arr - arrmean) ** 2
if issubdtype(arr.dtype, nt.complex_):
x = um.multiply(x, um.conjugate(x), out=x).real
else:
x = um.multiply(x, x, out=x)
# add.reduce((arr - arrmean) ** 2, axis)
ret = um.add.reduce(x, axis=axis, dtype=dtype, out=out,
keepdims=keepdims)
# add.reduce((arr - arrmean) ** 2, axis) / (n - ddof)
if not keepdims and isinstance(rcount, mu.ndarray):
rcount = rcount.squeeze(axis=axis)
rcount -= ddof
if isinstance(ret, mu.ndarray):
ret = um.true_divide(ret, rcount,
out=ret, casting='unsafe', subok=False)
else:
ret = ret / float(rcount)
return ret
def _nanmean(a, axis=None, dtype=None, out=None, keepdims=False):
# Using array() instead of asanyarray() because the former always
# makes a copy, which is important due to the copyto() action later
arr = array(a, subok=True)
mask = isnan(arr)
# Cast bool, unsigned int, and int to float64
if dtype is None and (issubdtype(arr.dtype, nt.integer) or
issubdtype(arr.dtype, nt.bool_)):
ret = um.add.reduce(arr, axis=axis, dtype='f8',
out=out, keepdims=keepdims)
else:
mu.copyto(arr, 0.0, where=mask)
ret = um.add.reduce(arr, axis=axis, dtype=dtype,
out=out, keepdims=keepdims)
rcount = (~mask).sum(axis=axis)
if isinstance(ret, mu.ndarray):
ret = um.true_divide(ret, rcount,
out=ret, casting='unsafe', subok=False)
else:
ret = ret / float(rcount)
return ret
def nan_to_num(x):
"""
Returns a copy of replacing NaN's with 0 and Infs with large numbers
The following mappings are applied:
NaN -> 0
Inf -> limits.double_max
-Inf -> limits.double_min
"""
try:
t = x.dtype.type
except AttributeError:
t = obj2sctype(type(x))
if issubclass(t, _nx.complexfloating):
return nan_to_num(x.real) + 1j * nan_to_num(x.imag)
else:
try:
y = x.copy()
except AttributeError:
y = array(x)
if not issubclass(t, _nx.integer):
if not y.shape:
y = array([x])
scalar = True
else:
scalar = False
are_inf = isposinf(y)
are_neg_inf = isneginf(y)
are_nan = isnan(y)
maxf, minf = _getmaxmin(y.dtype.type)
y[are_nan] = 0
y[are_inf] = maxf
y[are_neg_inf] = minf
if scalar:
y = y[0]
return y
def nan_to_num(x):
"""
Returns a copy of replacing NaN's with 0 and Infs with large numbers
The following mappings are applied:
NaN -> 0
Inf -> limits.double_max
-Inf -> limits.double_min
"""
try:
t = x.dtype.type
except AttributeError:
t = obj2sctype(type(x))
if issubclass(t, _nx.complexfloating):
return nan_to_num(x.real) + 1j * nan_to_num(x.imag)
else:
try:
y = x.copy()
except AttributeError:
y = array(x)
if not issubclass(t, _nx.integer):
if not y.shape:
y = array([x])
scalar = True
else:
scalar = False
are_inf = isposinf(y)
are_neg_inf = isneginf(y)
are_nan = isnan(y)
maxf, minf = _getmaxmin(y.dtype.type)
y[are_nan] = 0
y[are_inf] = maxf
y[are_neg_inf] = minf
if scalar:
y = y[0]
return y
def nan_to_num(x):
"""
Replace nan with zero and inf with finite numbers.
Returns an array or scalar replacing Not a Number (NaN) with zero,
(positive) infinity with a very large number and negative infinity
with a very small (or negative) number.
Parameters
----------
x : array_like
Input data.
Returns
-------
out : ndarray, float
Array with the same shape as `x` and dtype of the element in `x` with
the greatest precision. NaN is replaced by zero, and infinity
(-infinity) is replaced by the largest (smallest or most negative)
floating point value that fits in the output dtype. All finite numbers
are upcast to the output dtype (default float64).
See Also
--------
isinf : Shows which elements are negative or negative infinity.
isneginf : Shows which elements are negative infinity.
isposinf : Shows which elements are positive infinity.
isnan : Shows which elements are Not a Number (NaN).
isfinite : Shows which elements are finite (not NaN, not infinity)
Notes
-----
Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Examples
--------
>>> np.set_printoptions(precision=8)
>>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])
>>> np.nan_to_num(x)
array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000,
-1.28000000e+002, 1.28000000e+002])
"""
try:
t = x.dtype.type
except AttributeError:
t = obj2sctype(type(x))
if issubclass(t, _nx.complexfloating):
return nan_to_num(x.real) + 1j * nan_to_num(x.imag)
else:
try:
y = x.copy()
except AttributeError:
y = array(x)
if not issubclass(t, _nx.integer):
if not y.shape:
y = array([x])
scalar = True
else:
scalar = False
are_inf = isposinf(y)
are_neg_inf = isneginf(y)
are_nan = isnan(y)
maxf, minf = _getmaxmin(y.dtype.type)
y[are_nan] = 0
y[are_inf] = maxf
y[are_neg_inf] = minf
if scalar:
y = y[0]
return y
def nan_to_num(x, copy=True):
"""
Replace NaN with zero and infinity with large finite numbers.
If `x` is inexact, NaN is replaced by zero, and infinity and -infinity
replaced by the respectively largest and most negative finite floating
point values representable by ``x.dtype``.
For complex dtypes, the above is applied to each of the real and
imaginary components of `x` separately.
If `x` is not inexact, then no replacements are made.
Parameters
----------
x : scalar or array_like
Input data.
copy : bool, optional
Whether to create a copy of `x` (True) or to replace values
in-place (False). The in-place operation only occurs if
casting to an array does not require a copy.
Default is True.
.. versionadded:: 1.13
Returns
-------
out : ndarray
`x`, with the non-finite values replaced. If `copy` is False, this may
be `x` itself.
See Also
--------
isinf : Shows which elements are positive or negative infinity.
isneginf : Shows which elements are negative infinity.
isposinf : Shows which elements are positive infinity.
isnan : Shows which elements are Not a Number (NaN).
isfinite : Shows which elements are finite (not NaN, not infinity)
Notes
-----
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Examples
--------
>>> np.nan_to_num(np.inf)
1.7976931348623157e+308
>>> np.nan_to_num(-np.inf)
-1.7976931348623157e+308
>>> np.nan_to_num(np.nan)
0.0
>>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])
>>> np.nan_to_num(x)
array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000,
-1.28000000e+002, 1.28000000e+002])
>>> y = np.array([complex(np.inf, np.nan), np.nan, complex(np.nan, np.inf)])
>>> np.nan_to_num(y)
array([ 1.79769313e+308 +0.00000000e+000j,
0.00000000e+000 +0.00000000e+000j,
0.00000000e+000 +1.79769313e+308j])
"""
x = _nx.array(x, subok=True, copy=copy)
xtype = x.dtype.type
isscalar = (x.ndim == 0)
if not issubclass(xtype, _nx.inexact):
return x[()] if isscalar else x
iscomplex = issubclass(xtype, _nx.complexfloating)
dest = (x.real, x.imag) if iscomplex else (x, )
maxf, minf = _getmaxmin(x.real.dtype)
for d in dest:
_nx.copyto(d, 0.0, where=isnan(d))
_nx.copyto(d, maxf, where=isposinf(d))
_nx.copyto(d, minf, where=isneginf(d))
return x[()] if isscalar else x
def nan_to_num(x, copy=True):
"""
Replace NaN with zero and infinity with large finite numbers.
If `x` is inexact, NaN is replaced by zero, and infinity and -infinity
replaced by the respectively largest and most negative finite floating
point values representable by ``x.dtype``.
For complex dtypes, the above is applied to each of the real and
imaginary components of `x` separately.
If `x` is not inexact, then no replacements are made.
Parameters
----------
x : scalar or array_like
Input data.
copy : bool, optional
Whether to create a copy of `x` (True) or to replace values
in-place (False). The in-place operation only occurs if
casting to an array does not require a copy.
Default is True.
.. versionadded:: 1.13
Returns
-------
out : ndarray
`x`, with the non-finite values replaced. If `copy` is False, this may
be `x` itself.
See Also
--------
isinf : Shows which elements are positive or negative infinity.
isneginf : Shows which elements are negative infinity.
isposinf : Shows which elements are positive infinity.
isnan : Shows which elements are Not a Number (NaN).
isfinite : Shows which elements are finite (not NaN, not infinity)
Notes
-----
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Examples
--------
>>> np.nan_to_num(np.inf)
1.7976931348623157e+308
>>> np.nan_to_num(-np.inf)
-1.7976931348623157e+308
>>> np.nan_to_num(np.nan)
0.0
>>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])
>>> np.nan_to_num(x)
array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000, # may vary
-1.28000000e+002, 1.28000000e+002])
>>> y = np.array([complex(np.inf, np.nan), np.nan, complex(np.nan, np.inf)])
array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000, # may vary
-1.28000000e+002, 1.28000000e+002])
>>> np.nan_to_num(y)
array([ 1.79769313e+308 +0.00000000e+000j, # may vary
0.00000000e+000 +0.00000000e+000j,
0.00000000e+000 +1.79769313e+308j])
"""
x = _nx.array(x, subok=True, copy=copy)
xtype = x.dtype.type
isscalar = (x.ndim == 0)
if not issubclass(xtype, _nx.inexact):
return x[()] if isscalar else x
iscomplex = issubclass(xtype, _nx.complexfloating)
dest = (x.real, x.imag) if iscomplex else (x,)
maxf, minf = _getmaxmin(x.real.dtype)
for d in dest:
_nx.copyto(d, 0.0, where=isnan(d))
_nx.copyto(d, maxf, where=isposinf(d))
_nx.copyto(d, minf, where=isneginf(d))
return x[()] if isscalar else x
def nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None):
"""
Replace NaN with zero and infinity with large finite numbers (default
behaviour) or with the numbers defined by the user using the `nan`,
`posinf` and/or `neginf` keywords.
If `x` is inexact, NaN is replaced by zero or by the user defined value in
`nan` keyword, infinity is replaced by the largest finite floating point
values representable by ``x.dtype`` or by the user defined value in
`posinf` keyword and -infinity is replaced by the most negative finite
floating point values representable by ``x.dtype`` or by the user defined
value in `neginf` keyword.
For complex dtypes, the above is applied to each of the real and
imaginary components of `x` separately.
If `x` is not inexact, then no replacements are made.
Parameters
----------
x : scalar or array_like
Input data.
copy : bool, optional
Whether to create a copy of `x` (True) or to replace values
in-place (False). The in-place operation only occurs if
casting to an array does not require a copy.
Default is True.
.. versionadded:: 1.13
nan : int, float, optional
Value to be used to fill NaN values. If no value is passed
then NaN values will be replaced with 0.0.
.. versionadded:: 1.17
posinf : int, float, optional
Value to be used to fill positive infinity values. If no value is
passed then positive infinity values will be replaced with a very
large number.
.. versionadded:: 1.17
neginf : int, float, optional
Value to be used to fill negative infinity values. If no value is
passed then negative infinity values will be replaced with a very
small (or negative) number.
.. versionadded:: 1.17
Returns
-------
out : ndarray
`x`, with the non-finite values replaced. If `copy` is False, this may
be `x` itself.
See Also
--------
isinf : Shows which elements are positive or negative infinity.
isneginf : Shows which elements are negative infinity.
isposinf : Shows which elements are positive infinity.
isnan : Shows which elements are Not a Number (NaN).
isfinite : Shows which elements are finite (not NaN, not infinity)
Notes
-----
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Examples
--------
>>> np.nan_to_num(np.inf)
1.7976931348623157e+308
>>> np.nan_to_num(-np.inf)
-1.7976931348623157e+308
>>> np.nan_to_num(np.nan)
0.0
>>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])
>>> np.nan_to_num(x)
array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000, # may vary
-1.28000000e+002, 1.28000000e+002])
>>> np.nan_to_num(x, nan=-9999, posinf=33333333, neginf=33333333)
array([ 3.3333333e+07, 3.3333333e+07, -9.9990000e+03,
-1.2800000e+02, 1.2800000e+02])
>>> y = np.array([complex(np.inf, np.nan), np.nan, complex(np.nan, np.inf)])
array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000, # may vary
-1.28000000e+002, 1.28000000e+002])
>>> np.nan_to_num(y)
array([ 1.79769313e+308 +0.00000000e+000j, # may vary
0.00000000e+000 +0.00000000e+000j,
0.00000000e+000 +1.79769313e+308j])
>>> np.nan_to_num(y, nan=111111, posinf=222222)
array([222222.+111111.j, 111111. +0.j, 111111.+222222.j])
"""
x = _nx.array(x, subok=True, copy=copy)
xtype = x.dtype.type
isscalar = (x.ndim == 0)
if not issubclass(xtype, _nx.inexact):
return x[()] if isscalar else x
iscomplex = issubclass(xtype, _nx.complexfloating)
dest = (x.real, x.imag) if iscomplex else (x, )
maxf, minf = _getmaxmin(x.real.dtype)
if posinf is not None:
maxf = posinf
if neginf is not None:
minf = neginf
for d in dest:
idx_nan = isnan(d)
idx_posinf = isposinf(d)
idx_neginf = isneginf(d)
_nx.copyto(d, nan, where=idx_nan)
_nx.copyto(d, maxf, where=idx_posinf)
_nx.copyto(d, minf, where=idx_neginf)
return x[()] if isscalar else x
def nan_to_num(x):
"""
Replace nan with zero and inf with finite numbers.
Returns an array or scalar replacing Not a Number (NaN) with zero,
(positive) infinity with a very large number and negative infinity
with a very small (or negative) number.
Parameters
----------
x : array_like
Input data.
Returns
-------
out : ndarray
New Array with the same shape as `x` and dtype of the element in
`x` with the greatest precision. If `x` is inexact, then NaN is
replaced by zero, and infinity (-infinity) is replaced by the
largest (smallest or most negative) floating point value that fits
in the output dtype. If `x` is not inexact, then a copy of `x` is
returned.
See Also
--------
isinf : Shows which elements are negative or negative infinity.
isneginf : Shows which elements are negative infinity.
isposinf : Shows which elements are positive infinity.
isnan : Shows which elements are Not a Number (NaN).
isfinite : Shows which elements are finite (not NaN, not infinity)
Notes
-----
Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Examples
--------
>>> np.set_printoptions(precision=8)
>>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])
>>> np.nan_to_num(x)
array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000,
-1.28000000e+002, 1.28000000e+002])
"""
x = _nx.array(x, subok=True)
xtype = x.dtype.type
if not issubclass(xtype, _nx.inexact):
return x
iscomplex = issubclass(xtype, _nx.complexfloating)
isscalar = (x.ndim == 0)
x = x[None] if isscalar else x
dest = (x.real, x.imag) if iscomplex else (x,)
maxf, minf = _getmaxmin(x.real.dtype)
for d in dest:
_nx.copyto(d, 0.0, where=isnan(d))
_nx.copyto(d, maxf, where=isposinf(d))
_nx.copyto(d, minf, where=isneginf(d))
return x[0] if isscalar else x
def nan_to_num(x):
"""
Replace nan with zero and inf with finite numbers.
Returns an array or scalar replacing Not a Number (NaN) with zero,
(positive) infinity with a very large number and negative infinity
with a very small (or negative) number.
Parameters
----------
x : array_like
Input data.
Returns
-------
out : ndarray, float
Array with the same shape as `x` and dtype of the element in `x` with
the greatest precision. NaN is replaced by zero, and infinity
(-infinity) is replaced by the largest (smallest or most negative)
floating point value that fits in the output dtype. All finite numbers
are upcast to the output dtype (default float64).
See Also
--------
isinf : Shows which elements are negative or negative infinity.
isneginf : Shows which elements are negative infinity.
isposinf : Shows which elements are positive infinity.
isnan : Shows which elements are Not a Number (NaN).
isfinite : Shows which elements are finite (not NaN, not infinity)
Notes
-----
Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Examples
--------
>>> np.set_printoptions(precision=8)
>>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])
>>> np.nan_to_num(x)
array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000,
-1.28000000e+002, 1.28000000e+002])
"""
try:
t = x.dtype.type
except AttributeError:
t = obj2sctype(type(x))
if issubclass(t, _nx.complexfloating):
return nan_to_num(x.real) + 1j * nan_to_num(x.imag)
else:
try:
y = x.copy()
except AttributeError:
y = array(x)
if not issubclass(t, _nx.integer):
if not y.shape:
y = array([x])
scalar = True
else:
scalar = False
are_inf = isposinf(y)
are_neg_inf = isneginf(y)
are_nan = isnan(y)
maxf, minf = _getmaxmin(y.dtype.type)
y[are_nan] = 0
y[are_inf] = maxf
y[are_neg_inf] = minf
if scalar:
y = y[0]
return y
def isclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False):
"""
Returns a boolean array where two arrays are element-wise equal within a
tolerance.
The tolerance values are positive, typically very small numbers. The
relative difference (`rtol` * abs(`b`)) and the absolute difference
`atol` are added together to compare against the absolute difference
between `a` and `b`.
Parameters
----------
a, b : array_like
Input arrays to compare.
rtol : float
The relative tolerance parameter (see Notes).
atol : float
The absolute tolerance parameter (see Notes).
equal_nan : bool
Whether to compare NaN's as equal. If True, NaN's in `a` will be
considered equal to NaN's in `b` in the output array.
Returns
-------
y : array_like
Returns a boolean array of where `a` and `b` are equal within the
given tolerance. If both `a` and `b` are scalars, returns a single
boolean value.
See Also
--------
allclose
Notes
-----
.. versionadded:: 1.7.0
For finite values, isclose uses the following equation to test whether
two floating point values are equivalent.
absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`))
The above equation is not symmetric in `a` and `b`, so that
`isclose(a, b)` might be different from `isclose(b, a)` in
some rare cases.
Examples
--------
>>> np.isclose([1e10,1e-7], [1.00001e10,1e-8])
array([True, False])
>>> np.isclose([1e10,1e-8], [1.00001e10,1e-9])
array([True, True])
>>> np.isclose([1e10,1e-8], [1.0001e10,1e-9])
array([False, True])
>>> np.isclose([1.0, np.nan], [1.0, np.nan])
array([True, False])
>>> np.isclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)
array([True, True])
"""
def within_tol(x, y, atol, rtol):
with errstate(invalid='ignore'):
result = less_equal(abs(x - y), atol + rtol * abs(y))
if isscalar(a) and isscalar(b):
result = bool(result)
return result
x = array(a, copy=False, subok=True, ndmin=1)
y = array(b, copy=False, subok=True, ndmin=1)
# Make sure y is an inexact type to avoid bad behavior on abs(MIN_INT).
# This will cause casting of x later. Also, make sure to allow subclasses
# (e.g., for numpy.ma).
dt = multiarray.result_type(y, 1.)
y = array(y, dtype=dt, copy=False, subok=True)
xfin = isfinite(x)
yfin = isfinite(y)
if all(xfin) and all(yfin):
return within_tol(x, y, atol, rtol)
else:
finite = xfin & yfin
cond = zeros_like(finite, subok=True)
# Because we're using boolean indexing, x & y must be the same shape.
# Ideally, we'd just do x, y = broadcast_arrays(x, y). It's in
# lib.stride_tricks, though, so we can't import it here.
x = x * ones_like(cond)
y = y * ones_like(cond)
# Avoid subtraction with infinite/nan values...
cond[finite] = within_tol(x[finite], y[finite], atol, rtol)
# Check for equality of infinite values...
cond[~finite] = (x[~finite] == y[~finite])
if equal_nan:
# Make NaN == NaN
both_nan = isnan(x) & isnan(y)
cond[both_nan] = both_nan[both_nan]
if isscalar(a) and isscalar(b):
return bool(cond)
else:
return cond
