def isneginf(x, y=None):
"""
Return True where x is -infinity, and False otherwise.
Parameters
----------
x : array_like
The input array.
y : array_like
A boolean array with the same shape as `x` to store the result.
Returns
-------
y : ndarray
A boolean array where y[i] = True only if x[i] = -Inf.
See Also
--------
isposinf, isfinite
Examples
--------
>>> np.isneginf([-np.inf, 0., np.inf])
array([ True, False, False], dtype=bool)
"""
if y is None:
x = nx.asarray(x)
y = nx.empty(x.shape, dtype=nx.bool_)
nx.logical_and(nx.isinf(x), nx.signbit(x), y)
return y
def isneginf(x, y=None):
"""
Return True where x is -infinity, and False otherwise.
Parameters
----------
x : array_like
The input array.
y : array_like
A boolean array with the same shape as `x` to store the result.
Returns
-------
y : ndarray
A boolean array where y[i] = True only if x[i] = -Inf.
See Also
--------
isposinf, isfinite
Examples
--------
>>> np.isneginf([-np.inf, 0., np.inf])
array([ True, False, False], dtype=bool)
"""
if y is None:
x = nx.asarray(x)
y = nx.empty(x.shape, dtype=nx.bool_)
nx.logical_and(nx.isinf(x), nx.signbit(x), y)
return y
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 isneginf(x, out=None):
"""
Test element-wise for negative infinity, return result as bool array.
Parameters
----------
x : array_like
The input array.
out : array_like, optional
A boolean array with the same shape and type as `x` to store the
result.
Returns
-------
out : ndarray
A boolean array with the same dimensions as the input.
If second argument is not supplied then a numpy boolean array is
returned with values True where the corresponding element of the
input is negative infinity and values False where the element of
the input is not negative infinity.
If a second argument is supplied the result is stored there. If the
type of that array is a numeric type the result is represented as
zeros and ones, if the type is boolean then as False and True. The
return value `out` is then a reference to that array.
See Also
--------
isinf, isposinf, isnan, isfinite
Notes
-----
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754).
Errors result if the second argument is also supplied when x is a scalar
input, or if first and second arguments have different shapes.
Examples
--------
>>> np.isneginf(np.NINF)
array(True, dtype=bool)
>>> np.isneginf(np.inf)
array(False, dtype=bool)
>>> np.isneginf(np.PINF)
array(False, dtype=bool)
>>> np.isneginf([-np.inf, 0., np.inf])
array([ True, False, False])
>>> x = np.array([-np.inf, 0., np.inf])
>>> y = np.array([2, 2, 2])
>>> np.isneginf(x, y)
array([1, 0, 0])
>>> y
array([1, 0, 0])
"""
return nx.logical_and(nx.isinf(x), nx.signbit(x), out)
def isneginf(x, out=None):
"""
Test element-wise for negative infinity, return result as bool array.
Parameters
----------
x : array_like
The input array.
out : array_like, optional
A boolean array with the same shape and type as `x` to store the
result.
Returns
-------
out : ndarray
A boolean array with the same dimensions as the input.
If second argument is not supplied then a numpy boolean array is
returned with values True where the corresponding element of the
input is negative infinity and values False where the element of
the input is not negative infinity.
If a second argument is supplied the result is stored there. If the
type of that array is a numeric type the result is represented as
zeros and ones, if the type is boolean then as False and True. The
return value `out` is then a reference to that array.
See Also
--------
isinf, isposinf, isnan, isfinite
Notes
-----
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754).
Errors result if the second argument is also supplied when x is a scalar
input, or if first and second arguments have different shapes.
Examples
--------
>>> np.isneginf(np.NINF)
array(True, dtype=bool)
>>> np.isneginf(np.inf)
array(False, dtype=bool)
>>> np.isneginf(np.PINF)
array(False, dtype=bool)
>>> np.isneginf([-np.inf, 0., np.inf])
array([ True, False, False], dtype=bool)
>>> x = np.array([-np.inf, 0., np.inf])
>>> y = np.array([2, 2, 2])
>>> np.isneginf(x, y)
array([1, 0, 0])
>>> y
array([1, 0, 0])
"""
return nx.logical_and(nx.isinf(x), nx.signbit(x), out)
def isneginf(x, y=None):
"""Return a boolean array y with y[i] True for x[i] = -Inf.
If y is an array, the result replaces the contents of y.
"""
if y is None:
x = asarray(x)
y = empty(x.shape, dtype=nx.bool_)
umath.logical_and(isinf(x), signbit(x), y)
return y
def isneginf(x, y=None):
"""Return a boolean array y with y[i] True for x[i] = -Inf.
If y is an array, the result replaces the contents of y.
"""
if y is None:
x = asarray(x)
y = empty(x.shape, dtype=nx.bool_)
umath.logical_and(isinf(x), signbit(x), y)
return y
def isneginf(x, out=None):
"""
Test element-wise for negative infinity, return result as bool array.
Parameters
----------
x : array_like
The input array.
out : array_like, optional
A location into which the result is stored. If provided, it must have a
shape that the input broadcasts to. If not provided or None, a
freshly-allocated boolean array is returned.
Returns
-------
out : ndarray
A boolean array with the same dimensions as the input.
If second argument is not supplied then a numpy boolean array is
returned with values True where the corresponding element of the
input is negative infinity and values False where the element of
the input is not negative infinity.
If a second argument is supplied the result is stored there. If the
type of that array is a numeric type the result is represented as
zeros and ones, if the type is boolean then as False and True. The
return value `out` is then a reference to that array.
See Also
--------
isinf, isposinf, isnan, isfinite
Notes
-----
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754).
Errors result if the second argument is also supplied when x is a scalar
input, if first and second arguments have different shapes, or if the
first argument has complex values.
Examples
--------
>>> np.isneginf(np.NINF)
True
>>> np.isneginf(np.inf)
False
>>> np.isneginf(np.PINF)
False
>>> np.isneginf([-np.inf, 0., np.inf])
array([ True, False, False])
>>> x = np.array([-np.inf, 0., np.inf])
>>> y = np.array([2, 2, 2])
>>> np.isneginf(x, y)
array([1, 0, 0])
>>> y
array([1, 0, 0])
"""
is_inf = nx.isinf(x)
try:
signbit = nx.signbit(x)
except TypeError as e:
dtype = nx.asanyarray(x).dtype
raise TypeError(f'This operation is not supported for {dtype} values '
'because it would be ambiguous.') from e
else:
return nx.logical_and(is_inf, signbit, out)
def isposinf(x, out=None):
"""
Test element-wise for positive infinity, return result as bool array.
Parameters
----------
x : array_like
The input array.
y : array_like, optional
A boolean array with the same shape as `x` to store the result.
Returns
-------
out : ndarray
A boolean array with the same dimensions as the input.
If second argument is not supplied then a boolean array is returned
with values True where the corresponding element of the input is
positive infinity and values False where the element of the input is
not positive infinity.
If a second argument is supplied the result is stored there. If the
type of that array is a numeric type the result is represented as zeros
and ones, if the type is boolean then as False and True.
The return value `out` is then a reference to that array.
See Also
--------
isinf, isneginf, isfinite, isnan
Notes
-----
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754).
Errors result if the second argument is also supplied when x is a scalar
input, if first and second arguments have different shapes, or if the
first argument has complex values
Examples
--------
>>> np.isposinf(np.PINF)
array(True, dtype=bool)
>>> np.isposinf(np.inf)
array(True, dtype=bool)
>>> np.isposinf(np.NINF)
array(False, dtype=bool)
>>> np.isposinf([-np.inf, 0., np.inf])
array([False, False, True])
>>> x = np.array([-np.inf, 0., np.inf])
>>> y = np.array([2, 2, 2])
>>> np.isposinf(x, y)
array([0, 0, 1])
>>> y
array([0, 0, 1])
"""
is_inf = nx.isinf(x)
try:
signbit = ~nx.signbit(x)
except TypeError:
raise TypeError('This operation is not supported for complex values '
'because it would be ambiguous.')
else:
return nx.logical_and(is_inf, signbit, out)
def isposinf(x, out=None):
"""
Test element-wise for positive infinity, return result as bool array.
Parameters
----------
x : array_like
The input array.
y : array_like, optional
A boolean array with the same shape as `x` to store the result.
Returns
-------
out : ndarray
A boolean array with the same dimensions as the input.
If second argument is not supplied then a boolean array is returned
with values True where the corresponding element of the input is
positive infinity and values False where the element of the input is
not positive infinity.
If a second argument is supplied the result is stored there. If the
type of that array is a numeric type the result is represented as zeros
and ones, if the type is boolean then as False and True.
The return value `out` is then a reference to that array.
See Also
--------
isinf, isneginf, isfinite, isnan
Notes
-----
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754).
Errors result if the second argument is also supplied when x is a scalar
input, if first and second arguments have different shapes, or if the
first argument has complex values
Examples
--------
>>> np.isposinf(np.PINF)
True
>>> np.isposinf(np.inf)
True
>>> np.isposinf(np.NINF)
False
>>> np.isposinf([-np.inf, 0., np.inf])
array([False, False, True])
>>> x = np.array([-np.inf, 0., np.inf])
>>> y = np.array([2, 2, 2])
>>> np.isposinf(x, y)
array([0, 0, 1])
>>> y
array([0, 0, 1])
"""
is_inf = nx.isinf(x)
try:
signbit = ~nx.signbit(x)
except TypeError:
raise TypeError('This operation is not supported for complex values '
'because it would be ambiguous.')
else:
return nx.logical_and(is_inf, signbit, out)
def isposinf(x, y=None):
"""
Shows which elements of the input are positive infinity.
Returns a numpy array resulting from an element-wise test for positive
infinity.
Parameters
----------
x : array_like
The input array.
y : array_like
A boolean array with the same shape as `x` to store the result.
Returns
-------
y : ndarray
A numpy boolean array with the same dimensions as the input.
If second argument is not supplied then a numpy boolean array is returned
with values True where the corresponding element of the input is positive
infinity and values False where the element of the input is not positive
infinity.
If second argument is supplied then an numpy integer array is returned
with values 1 where the corresponding element of the input is positive
positive infinity.
See Also
--------
isinf : Shows which elements are negative or positive infinity.
isneginf : Shows which elements are negative infinity.
isnan : Shows which elements are Not a Number (NaN).
isfinite: Shows which elements are not: Not a number, positive and
negative 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.
Also that positive infinity is not equivalent to negative infinity. But
infinity is equivalent to positive infinity.
Errors result if second argument is also supplied with scalar input or
if first and second arguments have different shapes.
Numpy's definitions for positive infinity (PINF) and negative infinity
(NINF) may be change in the future versions.
Examples
--------
>>> np.isposinf(np.PINF)
array(True, dtype=bool)
>>> np.isposinf(np.inf)
array(True, dtype=bool)
>>> np.isposinf(np.NINF)
array(False, dtype=bool)
>>> np.isposinf([-np.inf, 0., np.inf])
array([False, False, True], dtype=bool)
>>> x=np.array([-np.inf, 0., np.inf])
>>> y=np.array([2,2,2])
>>> np.isposinf(x,y)
array([1, 0, 0])
>>> y
array([1, 0, 0])
"""
if y is None:
x = nx.asarray(x)
y = nx.empty(x.shape, dtype=nx.bool_)
nx.logical_and(nx.isinf(x), ~nx.signbit(x), y)
return y
def isposinf(x, y=None):
"""
Shows which elements of the input are positive infinity.
Returns a numpy array resulting from an element-wise test for positive
infinity.
Parameters
----------
x : array_like
The input array.
y : array_like
A boolean array with the same shape as `x` to store the result.
Returns
-------
y : ndarray
A numpy boolean array with the same dimensions as the input.
If second argument is not supplied then a numpy boolean array is returned
with values True where the corresponding element of the input is positive
infinity and values False where the element of the input is not positive
infinity.
If second argument is supplied then an numpy integer array is returned
with values 1 where the corresponding element of the input is positive
positive infinity.
See Also
--------
isinf : Shows which elements are negative or positive infinity.
isneginf : Shows which elements are negative infinity.
isnan : Shows which elements are Not a Number (NaN).
isfinite: Shows which elements are not: Not a number, positive and
negative 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.
Also that positive infinity is not equivalent to negative infinity. But
infinity is equivalent to positive infinity.
Errors result if second argument is also supplied with scalar input or
if first and second arguments have different shapes.
Numpy's definitions for positive infinity (PINF) and negative infinity
(NINF) may be change in the future versions.
Examples
--------
>>> np.isposinf(np.PINF)
array(True, dtype=bool)
>>> np.isposinf(np.inf)
array(True, dtype=bool)
>>> np.isposinf(np.NINF)
array(False, dtype=bool)
>>> np.isposinf([-np.inf, 0., np.inf])
array([False, False, True], dtype=bool)
>>> x=np.array([-np.inf, 0., np.inf])
>>> y=np.array([2,2,2])
>>> np.isposinf(x,y)
array([1, 0, 0])
>>> y
array([1, 0, 0])
"""
if y is None:
x = nx.asarray(x)
y = nx.empty(x.shape, dtype=nx.bool_)
nx.logical_and(nx.isinf(x), ~nx.signbit(x), y)
return y
