numpy.lib.type

module documentation

Automatically adapted for numpy Sep 19, 2005 by convertcode.py

Variable	`array_function_dispatch`	Undocumented
Variable	`array_precision`	Undocumented
Variable	`array_type`	Undocumented
Function	`_asfarray_dispatcher`	Undocumented
Function	`_asscalar_dispatcher`	Undocumented
Function	`_common_type_dispatcher`	Undocumented
Function	`_getmaxmin`	Undocumented
Function	`_imag_dispatcher`	Undocumented
Function	`_is_type_dispatcher`	Undocumented
Function	`_nan_to_num_dispatcher`	Undocumented
Function	`_real_dispatcher`	Undocumented
Function	`_real_if_close_dispatcher`	Undocumented
Function	`asfarray`	Return an array converted to a float type.
Function	`asscalar`	Convert an array of size 1 to its scalar equivalent.
Function	`common_type`	Return a scalar type which is common to the input arrays.
Function	`imag`	Return the imaginary part of the complex argument.
Function	`iscomplex`	Returns a bool array, where True if input element is complex.
Function	`iscomplexobj`	Check for a complex type or an array of complex numbers.
Function	`isreal`	Returns a bool array, where True if input element is real.
Function	`isrealobj`	Return True if x is a not complex type or an array of complex numbers.
Function	`mintypecode`	Return the character for the minimum-size type to which given types can be safely cast.
Function	`nan_to_num`	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.
Function	`real`	Return the real part of the complex argument.
Function	`real_if_close`	If input is complex with all imaginary parts close to zero, return real parts.
Function	`typename`	Return a description for the given data type code.
Variable	`_namefromtype`	Undocumented
Variable	`_typecodes_by_elsize`	Undocumented

array_function_dispatch =

Undocumented

array_precision =

Undocumented

array_type =

Undocumented

def _asfarray_dispatcher(a, dtype=None):

Undocumented

def _asscalar_dispatcher(a):

Undocumented

def _common_type_dispatcher(*arrays):

Undocumented

def _getmaxmin(t):

Undocumented

def _imag_dispatcher(val):

Undocumented

def _is_type_dispatcher(x):

Undocumented

def _nan_to_num_dispatcher(x, copy=None, nan=None, posinf=None, neginf=None):

Undocumented

def _real_dispatcher(val):

Undocumented

def _real_if_close_dispatcher(a, tol=None):

Undocumented

@array_function_dispatch(_asfarray_dispatcher)
def asfarray(a, dtype=_nx.float_):

Return an array converted to a float type.

Parameters

a : array_like: The input array.
dtype : str or dtype object, optional: Float type code to coerce input array a. If dtype is one of the 'int' dtypes, it is replaced with float64.

Returns

out : ndarray: The input a as a float ndarray.

Examples

>>> np.asfarray([2, 3])
array([2.,  3.])
>>> np.asfarray([2, 3], dtype='float')
array([2.,  3.])
>>> np.asfarray([2, 3], dtype='int8')
array([2.,  3.])

@array_function_dispatch(_asscalar_dispatcher)
def asscalar(a):

Convert an array of size 1 to its scalar equivalent.

Deprecated since version 1.16: Deprecated, use numpy.ndarray.item() instead.

Parameters

a : ndarray: Input array of size 1.

Returns

out : scalar: Scalar representation of a. The output data type is the same type returned by the input's item method.

Examples

>>> np.asscalar(np.array([24]))
24

@array_function_dispatch(_common_type_dispatcher)
def common_type(*arrays):

Return a scalar type which is common to the input arrays.

The return type will always be an inexact (i.e. floating point) scalar type, even if all the arrays are integer arrays. If one of the inputs is an integer array, the minimum precision type that is returned is a 64-bit floating point dtype.

All input arrays except int64 and uint64 can be safely cast to the returned dtype without loss of information.

Parameters

array1, array2, ... : ndarrays: Input arrays.

Returns

out : data type code: Data type code.

Examples

>>> np.common_type(np.arange(2, dtype=np.float32))
<class 'numpy.float32'>
>>> np.common_type(np.arange(2, dtype=np.float32), np.arange(2))
<class 'numpy.float64'>
>>> np.common_type(np.arange(4), np.array([45, 6.j]), np.array([45.0]))
<class 'numpy.complex128'>

@array_function_dispatch(_imag_dispatcher)
def imag(val):

Return the imaginary part of the complex argument.

Parameters

val : array_like: Input array.

Returns

out : ndarray or scalar: The imaginary component of the complex argument. If val is real, the type of val is used for the output. If val has complex elements, the returned type is float.

Examples

>>> a = np.array([1+2j, 3+4j, 5+6j])
>>> a.imag
array([2.,  4.,  6.])
>>> a.imag = np.array([8, 10, 12])
>>> a
array([1. +8.j,  3.+10.j,  5.+12.j])
>>> np.imag(1 + 1j)
1.0

@array_function_dispatch(_is_type_dispatcher)
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.

Examples

>>> np.iscomplex([1+1j, 1+0j, 4.5, 3, 2, 2j])
array([ True, False, False, False, False,  True])

@array_function_dispatch(_is_type_dispatcher)
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.

Examples

>>> np.iscomplexobj(1)
False
>>> np.iscomplexobj(1+0j)
True
>>> np.iscomplexobj([3, 1+0j, True])
True

@array_function_dispatch(_is_type_dispatcher)
def isreal(x):

Returns a bool array, where True if input element is real.

If element has complex type with zero complex part, the return value for that element is True.

Parameters

x : array_like: Input array.

Returns

out : ndarray, bool: Boolean array of same shape as x.

Notes

isreal may behave unexpectedly for string or object arrays (see examples)

Examples

>>> a = np.array([1+1j, 1+0j, 4.5, 3, 2, 2j], dtype=complex)
>>> np.isreal(a)
array([False,  True,  True,  True,  True, False])

The function does not work on string arrays.

>>> a = np.array([2j, "a"], dtype="U")
>>> np.isreal(a)  # Warns about non-elementwise comparison
False

Returns True for all elements in input array of dtype=object even if any of the elements is complex.

>>> a = np.array([1, "2", 3+4j], dtype=object)
>>> np.isreal(a)
array([ True,  True,  True])

isreal should not be used with object arrays

>>> a = np.array([1+2j, 2+1j], dtype=object)
>>> np.isreal(a)
array([ True,  True])

@array_function_dispatch(_is_type_dispatcher)
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.

Notes

The function is only meant for arrays with numerical values but it accepts all other objects. Since it assumes array input, the return value of other objects may be True.

>>> np.isrealobj('A string')
True
>>> np.isrealobj(False)
True
>>> np.isrealobj(None)
True

Examples

>>> np.isrealobj(1)
True
>>> np.isrealobj(1+0j)
False
>>> np.isrealobj([3, 1+0j, True])
False

@set_module('numpy')
def mintypecode(typechars, typeset='GDFgdf', default='d'):

Return the character for the minimum-size type to which given types can be safely cast.

The returned type character must represent the smallest size dtype such that an array of the returned type can handle the data from an array of all types in typechars (or if typechars is an array, then its dtype.char).

Parameters

typechars : list of str or array_like: If a list of strings, each string should represent a dtype. If array_like, the character representation of the array dtype is used.
typeset : str or list of str, optional: The set of characters that the returned character is chosen from. The default set is 'GDFgdf'.
default : str, optional: The default character, this is returned if none of the characters in typechars matches a character in typeset.

Returns

typechar : str: The character representing the minimum-size type that was found.

Examples

>>> np.mintypecode(['d', 'f', 'S'])
'd'
>>> x = np.array([1.1, 2-3.j])
>>> np.mintypecode(x)
'D'

>>> np.mintypecode('abceh', default='G')
'G'

@array_function_dispatch(_nan_to_num_dispatcher)
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.

New in version 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.

New in version 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.

New in version 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.

New in version 1.17.

Returns

out : ndarray: x, with the non-finite values replaced. If copy is False, this may be x itself.

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])

@array_function_dispatch(_real_dispatcher)
def real(val):

Return the real part of the complex argument.

Parameters

val : array_like: Input array.

Returns

out : ndarray or scalar: The real component of the complex argument. If val is real, the type of val is used for the output. If val has complex elements, the returned type is float.

Examples

>>> a = np.array([1+2j, 3+4j, 5+6j])
>>> a.real
array([1.,  3.,  5.])
>>> a.real = 9
>>> a
array([9.+2.j,  9.+4.j,  9.+6.j])
>>> a.real = np.array([9, 8, 7])
>>> a
array([9.+2.j,  8.+4.j,  7.+6.j])
>>> np.real(1 + 1j)
1.0

@array_function_dispatch(_real_if_close_dispatcher)
def real_if_close(a, tol=100):

If input is complex with all imaginary parts close to zero, return real parts.

"Close to zero" is defined as tol * (machine epsilon of the type for a).

Parameters

a : array_like: Input array.
tol : float: Tolerance in machine epsilons for the complex part of the elements in the array.

Returns

out : ndarray: If a is real, the type of a is used for the output. If a has complex elements, the returned type is float.

Notes

Machine epsilon varies from machine to machine and between data types but Python floats on most platforms have a machine epsilon equal to 2.2204460492503131e-16. You can use 'np.finfo(float).eps' to print out the machine epsilon for floats.

Examples

>>> np.finfo(float).eps
2.2204460492503131e-16 # may vary

>>> np.real_if_close([2.1 + 4e-14j, 5.2 + 3e-15j], tol=1000)
array([2.1, 5.2])
>>> np.real_if_close([2.1 + 4e-13j, 5.2 + 3e-15j], tol=1000)
array([2.1+4.e-13j, 5.2 + 3e-15j])

@set_module('numpy')
def typename(char):

Return a description for the given data type code.

Parameters

char : str: Data type code.

Returns

out : str: Description of the input data type code.

Examples

>>> typechars = ['S1', '?', 'B', 'D', 'G', 'F', 'I', 'H', 'L', 'O', 'Q',
...              'S', 'U', 'V', 'b', 'd', 'g', 'f', 'i', 'h', 'l', 'q']
>>> for typechar in typechars:
...     print(typechar, ' : ', np.typename(typechar))
...
S1  :  character
?  :  bool
B  :  unsigned char
D  :  complex double precision
G  :  complex long double precision
F  :  complex single precision
I  :  unsigned integer
H  :  unsigned short
L  :  unsigned long integer
O  :  object
Q  :  unsigned long long integer
S  :  string
U  :  unicode
V  :  void
b  :  signed char
d  :  double precision
g  :  long precision
f  :  single precision
i  :  integer
h  :  short
l  :  long integer
q  :  long long integer

_namefromtype: dict[str, str] =

Undocumented

_typecodes_by_elsize: str =

Undocumented

numpy.lib.type_check

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`numpy.lib.type_check`