Python numpy.core.numeric.complexfloating() Examples
The following are 30
code examples of numpy.core.numeric.complexfloating().
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Example #1
| Source File: type_check.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def iscomplex(x):
"""
Returns a bool array, where True if input element is complex.
What is tested is whether the input has a non-zero imaginary part, not if
the input type is complex.
Parameters
----------
x : array_like
Input array.
Returns
-------
out : ndarray of bools
Output array.
See Also
--------
isreal
iscomplexobj : Return True if x is a complex type or an array of complex
numbers.
Examples
--------
>>> np.iscomplex([1+1j, 1+0j, 4.5, 3, 2, 2j])
array([ True, False, False, False, False, True])
"""
ax = asanyarray(x)
if issubclass(ax.dtype.type, _nx.complexfloating):
return ax.imag != 0
res = zeros(ax.shape, bool)
return res[()] # convert to scalar if needed
Example #2
| Source File: defmatrix.py From lambda-packs with MIT License | 5 votes |
|
def getH(self):
"""
Returns the (complex) conjugate transpose of `self`.
Equivalent to ``np.transpose(self)`` if `self` is real-valued.
Parameters
----------
None
Returns
-------
ret : matrix object
complex conjugate transpose of `self`
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4)))
>>> z = x - 1j*x; z
matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j],
[ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j],
[ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]])
>>> z.getH()
matrix([[ 0. +0.j, 4. +4.j, 8. +8.j],
[ 1. +1.j, 5. +5.j, 9. +9.j],
[ 2. +2.j, 6. +6.j, 10.+10.j],
[ 3. +3.j, 7. +7.j, 11.+11.j]])
"""
if issubclass(self.dtype.type, N.complexfloating):
return self.transpose().conjugate()
else:
return self.transpose()
Example #3
| Source File: defmatrix.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def getH(self):
"""
Returns the (complex) conjugate transpose of `self`.
Equivalent to ``np.transpose(self)`` if `self` is real-valued.
Parameters
----------
None
Returns
-------
ret : matrix object
complex conjugate transpose of `self`
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4)))
>>> z = x - 1j*x; z
matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j],
[ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j],
[ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]])
>>> z.getH()
matrix([[ 0. +0.j, 4. +4.j, 8. +8.j],
[ 1. +1.j, 5. +5.j, 9. +9.j],
[ 2. +2.j, 6. +6.j, 10.+10.j],
[ 3. +3.j, 7. +7.j, 11.+11.j]])
"""
if issubclass(self.dtype.type, N.complexfloating):
return self.transpose().conjugate()
else:
return self.transpose()
Example #4
| Source File: type_check.py From Computable with MIT License | 5 votes |
|
def isrealobj(x):
"""
Return True if x is a not complex type or an array of complex numbers.
The type of the input is checked, not the value. So even if the input
has an imaginary part equal to zero, `isrealobj` evaluates to False
if the data type is complex.
Parameters
----------
x : any
The input can be of any type and shape.
Returns
-------
y : bool
The return value, False if `x` is of a complex type.
See Also
--------
iscomplexobj, isreal
Examples
--------
>>> np.isrealobj(1)
True
>>> np.isrealobj(1+0j)
False
>>> np.isrealobj([3, 1+0j, True])
False
"""
return not issubclass( asarray(x).dtype.type, _nx.complexfloating)
#-----------------------------------------------------------------------------
Example #5
| Source File: function_base.py From Computable with MIT License | 5 votes |
|
def sort_complex(a):
"""
Sort a complex array using the real part first, then the imaginary part.
Parameters
----------
a : array_like
Input array
Returns
-------
out : complex ndarray
Always returns a sorted complex array.
Examples
--------
>>> np.sort_complex([5, 3, 6, 2, 1])
array([ 1.+0.j, 2.+0.j, 3.+0.j, 5.+0.j, 6.+0.j])
>>> np.sort_complex([1 + 2j, 2 - 1j, 3 - 2j, 3 - 3j, 3 + 5j])
array([ 1.+2.j, 2.-1.j, 3.-3.j, 3.-2.j, 3.+5.j])
"""
b = array(a, copy=True)
b.sort()
if not issubclass(b.dtype.type, _nx.complexfloating):
if b.dtype.char in 'bhBH':
return b.astype('F')
elif b.dtype.char == 'g':
return b.astype('G')
else:
return b.astype('D')
else:
return b
Example #6
| Source File: defmatrix.py From Mastering-Elasticsearch-7.0 with MIT License | 5 votes |
|
def getH(self):
"""
Returns the (complex) conjugate transpose of `self`.
Equivalent to ``np.transpose(self)`` if `self` is real-valued.
Parameters
----------
None
Returns
-------
ret : matrix object
complex conjugate transpose of `self`
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4)))
>>> z = x - 1j*x; z
matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j],
[ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j],
[ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]])
>>> z.getH()
matrix([[ 0. +0.j, 4. +4.j, 8. +8.j],
[ 1. +1.j, 5. +5.j, 9. +9.j],
[ 2. +2.j, 6. +6.j, 10.+10.j],
[ 3. +3.j, 7. +7.j, 11.+11.j]])
"""
if issubclass(self.dtype.type, N.complexfloating):
return self.transpose().conjugate()
else:
return self.transpose()
Example #7
| Source File: type_check.py From Mastering-Elasticsearch-7.0 with MIT License | 5 votes |
|
def iscomplex(x):
"""
Returns a bool array, where True if input element is complex.
What is tested is whether the input has a non-zero imaginary part, not if
the input type is complex.
Parameters
----------
x : array_like
Input array.
Returns
-------
out : ndarray of bools
Output array.
See Also
--------
isreal
iscomplexobj : Return True if x is a complex type or an array of complex
numbers.
Examples
--------
>>> np.iscomplex([1+1j, 1+0j, 4.5, 3, 2, 2j])
array([ True, False, False, False, False, True])
"""
ax = asanyarray(x)
if issubclass(ax.dtype.type, _nx.complexfloating):
return ax.imag != 0
res = zeros(ax.shape, bool)
return res[()] # convert to scalar if needed
Example #8
| Source File: type_check.py From Mastering-Elasticsearch-7.0 with MIT License | 5 votes |
|
def iscomplexobj(x):
"""
Check for a complex type or an array of complex numbers.
The type of the input is checked, not the value. Even if the input
has an imaginary part equal to zero, `iscomplexobj` evaluates to True.
Parameters
----------
x : any
The input can be of any type and shape.
Returns
-------
iscomplexobj : bool
The return value, True if `x` is of a complex type or has at least
one complex element.
See Also
--------
isrealobj, iscomplex
Examples
--------
>>> np.iscomplexobj(1)
False
>>> np.iscomplexobj(1+0j)
True
>>> np.iscomplexobj([3, 1+0j, True])
True
"""
try:
dtype = x.dtype
type_ = dtype.type
except AttributeError:
type_ = asarray(x).dtype.type
return issubclass(type_, _nx.complexfloating)
Example #9
| Source File: type_check.py From ImageFusion with MIT License | 5 votes |
|
def isrealobj(x):
"""
Return True if x is a not complex type or an array of complex numbers.
The type of the input is checked, not the value. So even if the input
has an imaginary part equal to zero, `isrealobj` evaluates to False
if the data type is complex.
Parameters
----------
x : any
The input can be of any type and shape.
Returns
-------
y : bool
The return value, False if `x` is of a complex type.
See Also
--------
iscomplexobj, isreal
Examples
--------
>>> np.isrealobj(1)
True
>>> np.isrealobj(1+0j)
False
>>> np.isrealobj([3, 1+0j, True])
False
"""
return not issubclass(asarray(x).dtype.type, _nx.complexfloating)
#-----------------------------------------------------------------------------
Example #10
| Source File: defmatrix.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def getH(self):
"""
Returns the (complex) conjugate transpose of `self`.
Equivalent to ``np.transpose(self)`` if `self` is real-valued.
Parameters
----------
None
Returns
-------
ret : matrix object
complex conjugate transpose of `self`
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4)))
>>> z = x - 1j*x; z
matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j],
[ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j],
[ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]])
>>> z.getH()
matrix([[ 0. +0.j, 4. +4.j, 8. +8.j],
[ 1. +1.j, 5. +5.j, 9. +9.j],
[ 2. +2.j, 6. +6.j, 10.+10.j],
[ 3. +3.j, 7. +7.j, 11.+11.j]])
"""
if issubclass(self.dtype.type, N.complexfloating):
return self.transpose().conjugate()
else:
return self.transpose()
Example #11
| Source File: type_check.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def iscomplex(x):
"""
Returns a bool array, where True if input element is complex.
What is tested is whether the input has a non-zero imaginary part, not if
the input type is complex.
Parameters
----------
x : array_like
Input array.
Returns
-------
out : ndarray of bools
Output array.
See Also
--------
isreal
iscomplexobj : Return True if x is a complex type or an array of complex
numbers.
Examples
--------
>>> np.iscomplex([1+1j, 1+0j, 4.5, 3, 2, 2j])
array([ True, False, False, False, False, True])
"""
ax = asanyarray(x)
if issubclass(ax.dtype.type, _nx.complexfloating):
return ax.imag != 0
res = zeros(ax.shape, bool)
return +res # convert to array-scalar if needed
Example #12
| Source File: type_check.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def iscomplexobj(x):
"""
Check for a complex type or an array of complex numbers.
The type of the input is checked, not the value. Even if the input
has an imaginary part equal to zero, `iscomplexobj` evaluates to True.
Parameters
----------
x : any
The input can be of any type and shape.
Returns
-------
iscomplexobj : bool
The return value, True if `x` is of a complex type or has at least
one complex element.
See Also
--------
isrealobj, iscomplex
Examples
--------
>>> np.iscomplexobj(1)
False
>>> np.iscomplexobj(1+0j)
True
>>> np.iscomplexobj([3, 1+0j, True])
True
"""
try:
dtype = x.dtype
type_ = dtype.type
except AttributeError:
type_ = asarray(x).dtype.type
return issubclass(type_, _nx.complexfloating)
Example #13
| Source File: type_check.py From ImageFusion with MIT License | 5 votes |
|
def iscomplexobj(x):
"""
Check for a complex type or an array of complex numbers.
The type of the input is checked, not the value. Even if the input
has an imaginary part equal to zero, `iscomplexobj` evaluates to True.
Parameters
----------
x : any
The input can be of any type and shape.
Returns
-------
iscomplexobj : bool
The return value, True if `x` is of a complex type or has at least
one complex element.
See Also
--------
isrealobj, iscomplex
Examples
--------
>>> np.iscomplexobj(1)
False
>>> np.iscomplexobj(1+0j)
True
>>> np.iscomplexobj([3, 1+0j, True])
True
"""
return issubclass(asarray(x).dtype.type, _nx.complexfloating)
Example #14
| Source File: type_check.py From ImageFusion with MIT License | 5 votes |
|
def iscomplex(x):
"""
Returns a bool array, where True if input element is complex.
What is tested is whether the input has a non-zero imaginary part, not if
the input type is complex.
Parameters
----------
x : array_like
Input array.
Returns
-------
out : ndarray of bools
Output array.
See Also
--------
isreal
iscomplexobj : Return True if x is a complex type or an array of complex
numbers.
Examples
--------
>>> np.iscomplex([1+1j, 1+0j, 4.5, 3, 2, 2j])
array([ True, False, False, False, False, True], dtype=bool)
"""
ax = asanyarray(x)
if issubclass(ax.dtype.type, _nx.complexfloating):
return ax.imag != 0
res = zeros(ax.shape, bool)
return +res # convet to array-scalar if needed
Example #15
| Source File: defmatrix.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def getH(self):
"""
Returns the (complex) conjugate transpose of `self`.
Equivalent to ``np.transpose(self)`` if `self` is real-valued.
Parameters
----------
None
Returns
-------
ret : matrix object
complex conjugate transpose of `self`
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4)))
>>> z = x - 1j*x; z
matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j],
[ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j],
[ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]])
>>> z.getH()
matrix([[ 0. +0.j, 4. +4.j, 8. +8.j],
[ 1. +1.j, 5. +5.j, 9. +9.j],
[ 2. +2.j, 6. +6.j, 10.+10.j],
[ 3. +3.j, 7. +7.j, 11.+11.j]])
"""
if issubclass(self.dtype.type, N.complexfloating):
return self.transpose().conjugate()
else:
return self.transpose()
Example #16
| Source File: function_base.py From ImageFusion with MIT License | 5 votes |
|
def sort_complex(a):
"""
Sort a complex array using the real part first, then the imaginary part.
Parameters
----------
a : array_like
Input array
Returns
-------
out : complex ndarray
Always returns a sorted complex array.
Examples
--------
>>> np.sort_complex([5, 3, 6, 2, 1])
array([ 1.+0.j, 2.+0.j, 3.+0.j, 5.+0.j, 6.+0.j])
>>> np.sort_complex([1 + 2j, 2 - 1j, 3 - 2j, 3 - 3j, 3 + 5j])
array([ 1.+2.j, 2.-1.j, 3.-3.j, 3.-2.j, 3.+5.j])
"""
b = array(a, copy=True)
b.sort()
if not issubclass(b.dtype.type, _nx.complexfloating):
if b.dtype.char in 'bhBH':
return b.astype('F')
elif b.dtype.char == 'g':
return b.astype('G')
else:
return b.astype('D')
else:
return b
Example #17
| Source File: type_check.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def iscomplexobj(x):
"""
Check for a complex type or an array of complex numbers.
The type of the input is checked, not the value. Even if the input
has an imaginary part equal to zero, `iscomplexobj` evaluates to True.
Parameters
----------
x : any
The input can be of any type and shape.
Returns
-------
iscomplexobj : bool
The return value, True if `x` is of a complex type or has at least
one complex element.
See Also
--------
isrealobj, iscomplex
Examples
--------
>>> np.iscomplexobj(1)
False
>>> np.iscomplexobj(1+0j)
True
>>> np.iscomplexobj([3, 1+0j, True])
True
"""
try:
dtype = x.dtype
type_ = dtype.type
except AttributeError:
type_ = asarray(x).dtype.type
return issubclass(type_, _nx.complexfloating)
Example #18
| Source File: defmatrix.py From ImageFusion with MIT License | 5 votes |
|
def getH(self):
"""
Returns the (complex) conjugate transpose of `self`.
Equivalent to ``np.transpose(self)`` if `self` is real-valued.
Parameters
----------
None
Returns
-------
ret : matrix object
complex conjugate transpose of `self`
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4)))
>>> z = x - 1j*x; z
matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j],
[ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j],
[ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]])
>>> z.getH()
matrix([[ 0. +0.j, 4. +4.j, 8. +8.j],
[ 1. +1.j, 5. +5.j, 9. +9.j],
[ 2. +2.j, 6. +6.j, 10.+10.j],
[ 3. +3.j, 7. +7.j, 11.+11.j]])
"""
if issubclass(self.dtype.type, N.complexfloating):
return self.transpose().conjugate()
else:
return self.transpose()
Example #19
| Source File: defmatrix.py From Fluid-Designer with GNU General Public License v3.0 | 5 votes |
|
def getH(self):
"""
Returns the (complex) conjugate transpose of `self`.
Equivalent to ``np.transpose(self)`` if `self` is real-valued.
Parameters
----------
None
Returns
-------
ret : matrix object
complex conjugate transpose of `self`
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4)))
>>> z = x - 1j*x; z
matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j],
[ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j],
[ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]])
>>> z.getH()
matrix([[ 0. +0.j, 4. +4.j, 8. +8.j],
[ 1. +1.j, 5. +5.j, 9. +9.j],
[ 2. +2.j, 6. +6.j, 10.+10.j],
[ 3. +3.j, 7. +7.j, 11.+11.j]])
"""
if issubclass(self.dtype.type, N.complexfloating):
return self.transpose().conjugate()
else:
return self.transpose()
Example #20
| Source File: type_check.py From Fluid-Designer with GNU General Public License v3.0 | 5 votes |
|
def iscomplex(x):
"""
Returns a bool array, where True if input element is complex.
What is tested is whether the input has a non-zero imaginary part, not if
the input type is complex.
Parameters
----------
x : array_like
Input array.
Returns
-------
out : ndarray of bools
Output array.
See Also
--------
isreal
iscomplexobj : Return True if x is a complex type or an array of complex
numbers.
Examples
--------
>>> np.iscomplex([1+1j, 1+0j, 4.5, 3, 2, 2j])
array([ True, False, False, False, False, True], dtype=bool)
"""
ax = asanyarray(x)
if issubclass(ax.dtype.type, _nx.complexfloating):
return ax.imag != 0
res = zeros(ax.shape, bool)
return +res # convet to array-scalar if needed
Example #21
| Source File: type_check.py From Fluid-Designer with GNU General Public License v3.0 | 5 votes |
|
def iscomplexobj(x):
"""
Check for a complex type or an array of complex numbers.
The type of the input is checked, not the value. Even if the input
has an imaginary part equal to zero, `iscomplexobj` evaluates to True.
Parameters
----------
x : any
The input can be of any type and shape.
Returns
-------
iscomplexobj : bool
The return value, True if `x` is of a complex type or has at least
one complex element.
See Also
--------
isrealobj, iscomplex
Examples
--------
>>> np.iscomplexobj(1)
False
>>> np.iscomplexobj(1+0j)
True
>>> np.iscomplexobj([3, 1+0j, True])
True
"""
return issubclass(asarray(x).dtype.type, _nx.complexfloating)
Example #22
| Source File: type_check.py From Fluid-Designer with GNU General Public License v3.0 | 5 votes |
|
def isrealobj(x):
"""
Return True if x is a not complex type or an array of complex numbers.
The type of the input is checked, not the value. So even if the input
has an imaginary part equal to zero, `isrealobj` evaluates to False
if the data type is complex.
Parameters
----------
x : any
The input can be of any type and shape.
Returns
-------
y : bool
The return value, False if `x` is of a complex type.
See Also
--------
iscomplexobj, isreal
Examples
--------
>>> np.isrealobj(1)
True
>>> np.isrealobj(1+0j)
False
>>> np.isrealobj([3, 1+0j, True])
False
"""
return not issubclass(asarray(x).dtype.type, _nx.complexfloating)
#-----------------------------------------------------------------------------
Example #23
| Source File: function_base.py From Fluid-Designer with GNU General Public License v3.0 | 5 votes |
|
def sort_complex(a):
"""
Sort a complex array using the real part first, then the imaginary part.
Parameters
----------
a : array_like
Input array
Returns
-------
out : complex ndarray
Always returns a sorted complex array.
Examples
--------
>>> np.sort_complex([5, 3, 6, 2, 1])
array([ 1.+0.j, 2.+0.j, 3.+0.j, 5.+0.j, 6.+0.j])
>>> np.sort_complex([1 + 2j, 2 - 1j, 3 - 2j, 3 - 3j, 3 + 5j])
array([ 1.+2.j, 2.-1.j, 3.-3.j, 3.-2.j, 3.+5.j])
"""
b = array(a, copy=True)
b.sort()
if not issubclass(b.dtype.type, _nx.complexfloating):
if b.dtype.char in 'bhBH':
return b.astype('F')
elif b.dtype.char == 'g':
return b.astype('G')
else:
return b.astype('D')
else:
return b
Example #24
| Source File: defmatrix.py From pySINDy with MIT License | 5 votes |
|
def getH(self):
"""
Returns the (complex) conjugate transpose of `self`.
Equivalent to ``np.transpose(self)`` if `self` is real-valued.
Parameters
----------
None
Returns
-------
ret : matrix object
complex conjugate transpose of `self`
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4)))
>>> z = x - 1j*x; z
matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j],
[ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j],
[ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]])
>>> z.getH()
matrix([[ 0. +0.j, 4. +4.j, 8. +8.j],
[ 1. +1.j, 5. +5.j, 9. +9.j],
[ 2. +2.j, 6. +6.j, 10.+10.j],
[ 3. +3.j, 7. +7.j, 11.+11.j]])
"""
if issubclass(self.dtype.type, N.complexfloating):
return self.transpose().conjugate()
else:
return self.transpose()
Example #25
| Source File: type_check.py From pySINDy with MIT License | 5 votes |
|
def iscomplex(x):
"""
Returns a bool array, where True if input element is complex.
What is tested is whether the input has a non-zero imaginary part, not if
the input type is complex.
Parameters
----------
x : array_like
Input array.
Returns
-------
out : ndarray of bools
Output array.
See Also
--------
isreal
iscomplexobj : Return True if x is a complex type or an array of complex
numbers.
Examples
--------
>>> np.iscomplex([1+1j, 1+0j, 4.5, 3, 2, 2j])
array([ True, False, False, False, False, True])
"""
ax = asanyarray(x)
if issubclass(ax.dtype.type, _nx.complexfloating):
return ax.imag != 0
res = zeros(ax.shape, bool)
return +res # convert to array-scalar if needed
Example #26
| Source File: type_check.py From pySINDy with MIT License | 5 votes |
|
def iscomplexobj(x):
"""
Check for a complex type or an array of complex numbers.
The type of the input is checked, not the value. Even if the input
has an imaginary part equal to zero, `iscomplexobj` evaluates to True.
Parameters
----------
x : any
The input can be of any type and shape.
Returns
-------
iscomplexobj : bool
The return value, True if `x` is of a complex type or has at least
one complex element.
See Also
--------
isrealobj, iscomplex
Examples
--------
>>> np.iscomplexobj(1)
False
>>> np.iscomplexobj(1+0j)
True
>>> np.iscomplexobj([3, 1+0j, True])
True
"""
try:
dtype = x.dtype
type_ = dtype.type
except AttributeError:
type_ = asarray(x).dtype.type
return issubclass(type_, _nx.complexfloating)
Example #27
| Source File: type_check.py From mxnet-lambda with Apache License 2.0 | 5 votes |
|
def iscomplexobj(x):
"""
Check for a complex type or an array of complex numbers.
The type of the input is checked, not the value. Even if the input
has an imaginary part equal to zero, `iscomplexobj` evaluates to True.
Parameters
----------
x : any
The input can be of any type and shape.
Returns
-------
iscomplexobj : bool
The return value, True if `x` is of a complex type or has at least
one complex element.
See Also
--------
isrealobj, iscomplex
Examples
--------
>>> np.iscomplexobj(1)
False
>>> np.iscomplexobj(1+0j)
True
>>> np.iscomplexobj([3, 1+0j, True])
True
"""
try:
dtype = x.dtype
type_ = dtype.type
except AttributeError:
type_ = asarray(x).dtype.type
return issubclass(type_, _nx.complexfloating)
Example #28
| Source File: type_check.py From mxnet-lambda with Apache License 2.0 | 5 votes |
|
def iscomplex(x):
"""
Returns a bool array, where True if input element is complex.
What is tested is whether the input has a non-zero imaginary part, not if
the input type is complex.
Parameters
----------
x : array_like
Input array.
Returns
-------
out : ndarray of bools
Output array.
See Also
--------
isreal
iscomplexobj : Return True if x is a complex type or an array of complex
numbers.
Examples
--------
>>> np.iscomplex([1+1j, 1+0j, 4.5, 3, 2, 2j])
array([ True, False, False, False, False, True], dtype=bool)
"""
ax = asanyarray(x)
if issubclass(ax.dtype.type, _nx.complexfloating):
return ax.imag != 0
res = zeros(ax.shape, bool)
return +res # convet to array-scalar if needed
Example #29
| Source File: defmatrix.py From mxnet-lambda with Apache License 2.0 | 5 votes |
|
def getH(self):
"""
Returns the (complex) conjugate transpose of `self`.
Equivalent to ``np.transpose(self)`` if `self` is real-valued.
Parameters
----------
None
Returns
-------
ret : matrix object
complex conjugate transpose of `self`
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4)))
>>> z = x - 1j*x; z
matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j],
[ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j],
[ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]])
>>> z.getH()
matrix([[ 0. +0.j, 4. +4.j, 8. +8.j],
[ 1. +1.j, 5. +5.j, 9. +9.j],
[ 2. +2.j, 6. +6.j, 10.+10.j],
[ 3. +3.j, 7. +7.j, 11.+11.j]])
"""
if issubclass(self.dtype.type, N.complexfloating):
return self.transpose().conjugate()
else:
return self.transpose()
Example #30
| Source File: utils.py From sem with GNU General Public License v2.0 | 5 votes |
|
def stdout_automatic_parser(result):
"""
Try and automatically convert strings formatted as tables into a matrix.
Under the hood, this function essentially applies the genfromtxt function
to the stdout.
Args:
result (dict): the result to parse.
"""
np.seterr(all='raise')
parsed = {}
# By default, if dtype is None, the order Numpy tries to convert a string
# to a value is: bool, int, float. We don't like this, since it would give
# us a mixture of integers and doubles in the output, if any integers
# existed in the data. So, we modify the StringMapper's default mapper to
# skip the int check and directly convert numbers to floats.
oldmapper = np.lib._iotools.StringConverter._mapper
np.lib._iotools.StringConverter._mapper = [(nx.bool_, np.lib._iotools.str2bool, False),
(nx.floating, float, nx.nan),
(nx.complexfloating, complex, nx.nan + 0j),
(nx.longdouble, nx.longdouble, nx.nan)]
file_contents = result['output']['stdout']
with warnings.catch_warnings():
warnings.simplefilter("ignore")
parsed = np.genfromtxt(io.StringIO(file_contents))
# Here we restore the original mapper, so no side-effects remain.
np.lib._iotools.StringConverter._mapper = oldmapper
return parsed
