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m Z d d l m Z m Z d Z d d d � Z e j d � Z d � Z d � Z d � Z d � Z d � Z d � Z d � Z e d � Z d! d" � Z d# � Z i d$ d% 6d& d' 6d( d) 6d* d+ 6d, d- 6d. d/ 6d0 d1 6d2 d3 6d4 d5 6d6 d7 6d8 d9 6d: d; 6d< d= 6d> d 6d? d@ 6dA dB 6dC dD 6dE dF 6dG dH 6dI dJ 6dK dL 6dM dN 6Z! dO � Z" e j# e j$ e j% e j& g d e j( e j) e j* g g Z+ i d e j# 6d e j$ 6dP e j% 6dQ e j& 6d e j( 6dP e j) 6dQ e j* 6Z, dR � Z- d S(S s@ Automatically adapted for numpy Sep 19, 2005 by convertcode.py
i ( t divisiont absolute_importt print_functiont iscomplexobjt isrealobjt imagt iscomplext isrealt
nan_to_numt realt
real_if_closet typenamet asfarrayt mintypecodet asscalart common_typeN( t asarrayt
asanyarrayt arrayt isnant zerosi ( t isneginft isposinfs GDFgdfQqLlIiHhBb?t GDFgdft dc C` s� g | D]- } t | t � r" | p1 t | � j j ^ q } g | D] } | | k rA | ^ qA } | si | Sd | k r� d | k r� d Sg } x0 | D]( } t j | � } | j | | f � q� W| j � | d d S( s�
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.
See Also
--------
dtype, sctype2char, maximum_sctype
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'
t FR t Di i ( t
isinstancet strR t dtypet chart _typecodes_by_elsizet indext appendt sort( t typecharst typesett defaultt tt typecodest intersectiont lt i( ( sG /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/type_check.pyR
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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.])
R ( t _nxt
obj2sctypet
issubclasst inexactt float_R ( t aR ( ( sG /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/type_check.pyR J s c C` s- y | j SWn t k
r( t | � j SXd S( s�
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.
See Also
--------
real_if_close, imag, angle
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
N( R t AttributeErrorR ( t val( ( sG /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/type_check.pyR k s #
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r( t | � j SXd S( s�
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.
See Also
--------
real, angle, real_if_close
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
N( R R1 R ( R2 ( ( sG /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/type_check.pyR � s
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S( se
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)
i (
R R- R t typeR+ t complexfloatingR R t shapet bool( t xt axt res( ( sG /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/type_check.pyR � s
c C` s t | � d k S( s>
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`.
See Also
--------
iscomplex
isrealobj : Return True if x is not a complex type.
Examples
--------
>>> np.isreal([1+1j, 1+0j, 4.5, 3, 2, 2j])
array([False, True, True, True, True, False], dtype=bool)
i ( R ( R7 ( ( sG /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/type_check.pyR � s c C` sL y | j } | j } Wn# t k
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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
( R R3 R1 R R- R+ R4 ( R7 R t type_( ( sG /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/type_check.pyR � s
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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
( R ( R7 ( ( sG /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/type_check.pyR # s c C` s/ d d l m } | j | � } | j | j f S( Ni ( t getlimits( t
numpy.coreR; t finfot maxt min( R& R; t f( ( sG /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/type_check.pyt
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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.
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
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 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.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])
t subokt copyi g t whereN( R+ R t TrueR R3 R- R. R4 t ndimt NoneR R RA t copytoR R R ( R7 RC t xtypeR t isscalart destt maxft minfR ( ( sG /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/type_check.pyR L s 5 !
id c C` s� t | � } t | j j t j � s( | S| d k ri d d l m } | j | j j � } | j | } n t j
t j | j � | k � r� | j
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If complex input returns a real array if complex parts are close to zero.
"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.
See Also
--------
real, imag, angle
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(np.float).eps' to print
out the machine epsilon for floats.
Examples
--------
>>> np.finfo(np.float).eps
2.2204460492503131e-16
>>> np.real_if_close([2.1 + 4e-14j], tol=1000)
array([ 2.1])
>>> np.real_if_close([2.1 + 4e-13j], tol=1000)
array([ 2.1 +4.00000000e-13j])
i i ( R; ( R R- R R3 R+ R4 R< R; R= t epst allt absoluteR R ( R0 t tolR; R@ ( ( sG /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/type_check.pyR
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Convert an array of size 1 to its scalar equivalent.
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
( t item( R0 ( ( sG /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/type_check.pyR � s t charactert S1R6 t ?s signed chart bs
unsigned chart Bt shortt hs unsigned shortt Ht integerR* s unsigned integert Is long integerR) s unsigned long integert Ls long long integert qs unsigned long long integert Qs single precisionR@ s double precisions long precisiont gs complex single precisionR s complex double precisionR s complex long double precisiont Gt stringt St unicodet Ut voidt Vt objectt Oc C` s t | S( sD
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.
See Also
--------
dtype, typecodes
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
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_namefromtype( R ( ( sG /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/type_check.pyR � s 1i i c G` s� t } d } x� | D]~ } | j j } t | � r: t } n t | t j � rU d } n- t j | d � } | d k r� t d � � n t | | � } q W| r� t
d | St
d | Sd S( s�
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 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.
See Also
--------
dtype, mintypecode
Examples
--------
>>> np.common_type(np.arange(2, dtype=np.float32))
<type 'numpy.float32'>
>>> np.common_type(np.arange(2, dtype=np.float32), np.arange(2))
<type 'numpy.float64'>
>>> np.common_type(np.arange(4), np.array([45, 6.j]), np.array([45.0]))
<type 'numpy.complex128'>
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