U #cM@s2dZddlZddlZddddddd d d d d dg ZddlmmZddlmZm Z m Z m Z ddl m Z ddlmZddlmZmZejejddZdZe ddQdd ZdRddZeeejfdd ZddZeedd Zd d!Zeed"dZd#d$Zeed%dZeed&dZ eed'dZ!eed(dZ"d)d*Z#dSd+d,Z$ee$dTd/dZ%dUd0d1Z&ee&dVd3d Z'd4d5d6d7d8d9d:d;dd?d@dAdBdCdDdEdFdGdHdIdJZ(e ddKd Z)ej*ej+ej,ej-gdej.ej/ej0ggZ1ej*dej+dej,dLej-dMej.dej/dLej0dMiZ2dNdOZ3ee3dPdZ4dS)Wz@Automatically adapted for numpy Sep 19, 2005 by convertcode.py N iscomplexobj isrealobjimag iscomplexisreal nan_to_numreal real_if_closetypenameasfarray mintypecode common_type)asarray asanyarrayisnanzeros) set_module) overrides)isneginfisposinfZnumpy)modulezGDFgdfQqLlIiHhBb?GDFgdfdcsNdd|D}tfdd|D}|s,|Sd|kr@d|kr@dSt|tjdS)a Return the character for the minimum-size type to which given types can be safely cast. The returned type character must represent the smallest size dtype such that an array of the returned type can handle the data from an array of all types in `typechars` (or if `typechars` is an array, then its dtype.char). Parameters ---------- typechars : list of str or array_like If a list of strings, each string should represent a dtype. If array_like, the character representation of the array dtype is used. typeset : str or list of str, optional The set of characters that the returned character is chosen from. The default set is 'GDFgdf'. default : str, optional The default character, this is returned if none of the characters in `typechars` matches a character in `typeset`. Returns ------- typechar : str The character representing the minimum-size type that was found. See Also -------- dtype, sctype2char, maximum_sctype Examples -------- >>> np.mintypecode(['d', 'f', 'S']) 'd' >>> x = np.array([1.1, 2-3.j]) >>> np.mintypecode(x) 'D' >>> np.mintypecode('abceh', default='G') 'G' css(|] }t|tr|pt|jjVqdSN) isinstancestrrdtypechar.0tr"8/tmp/pip-unpacked-wheel-b2rbor69/numpy/lib/type_check.py Fszmintypecode..c3s|]}|kr|VqdSrr"rtypesetr"r#r$HsFrD)key)setmin_typecodes_by_elsizeindex)Z typecharsr&defaultZ typecodes intersectionr"r%r#r s,cCs|fSrr"arr"r"r#_asfarray_dispatcherPsr2cCs t|tjstj}t||dS)aJ 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)_nxZ issubdtypeinexactfloat_rr0r"r"r#r TscCs|fSrr"valr"r"r#_real_dispatcherusr8cCs,z|jWStk r&t|jYSXdS)a 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)rAttributeErrorrr6r"r"r#rys$cCs|fSrr"r6r"r"r#_imag_dispatchersr:cCs,z|jWStk r&t|jYSXdS)a 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)rr9rr6r"r"r#rs!cCs|fSrr"xr"r"r#_is_type_dispatchersr=cCs6t|}t|jjtjr"|jdkSt|jt }|dS)aY 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]) rr") r issubclassrtyper3complexfloatingrrshapebool)r<Zaxresr"r"r#rs   cCs t|dkS)a 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) See Also -------- iscomplex isrealobj : Return True if x is not a complex type. 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]) r)rr;r"r"r#rs5cCs>z|j}|j}Wn tk r0t|jj}YnXt|tjS)a 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 )rr?r9rr>r3r@)r<rtype_r"r"r#r/s ! cCs t| S)a 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 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 )rr;r"r"r#rXs.cCs"ddlm}||}|j|jfS)Nr getlimits) numpy.corerFfinfomaxr+)r!rFfr"r"r# _getmaxmins  rKcCs|fSrr")r<copynanposinfneginfr"r"r#_nan_to_num_dispatchersrPTcCstj|d|d}|jj}|jdk}t|tjs>|r:|dS|St|tj}|rZ|j|j fn|f}t |jj\} } |dk r||} |dk r|} |D]L} t | } t | } t | }tj| || dtj| | | dtj| | |dq|r|dS|S)a 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]) T)ZsubokrLrr"N)where)r3arrayrr?ndimr>r4r@rrrKrrrZcopyto)r<rLrMrNrOZxtypeZisscalarrdestZmaxfZminfrZidx_nanZ idx_posinfZ idx_neginfr"r"r#rs(^   cCs|fSrr")r1tolr"r"r#_real_if_close_dispatcher srWdcCsht|}t|jjtjs|S|dkrHddlm}||jj}|j |}t t |j |krd|j }|S)a} 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. 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(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]) rrrE)rr>rr?r3r@rGrFrHZepsallabsoluterr)r1rVrFrJr"r"r#r s-   characterrBz signed charz unsigned charshortzunsigned shortintegerzunsigned integerz long integerzunsigned long integerzlong long integerzunsigned long long integerzsingle precisionzdouble precisionzlong precisionzcomplex single precisionzcomplex double precisionzcomplex long double precisionstringunicodevoidobject)ZS1?bBhHiIlLqQrJrgr'r(GSUVOcCst|S)aD 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 ) _namefromtype)rr"r"r#r ds2cGs|Srr")arraysr"r"r#_common_type_dispatchersrwcGs~d}d}|D]P}|jj}t|r$d}t|tjr6d}nt|d}|dkrRtdt ||}q |rnt d|St d|SdS)a 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. See Also -------- dtype, mintypecode Examples -------- >>> np.common_type(np.arange(2, dtype=np.float32)) >>> np.common_type(np.arange(2, dtype=np.float32), np.arange(2)) >>> np.common_type(np.arange(4), np.array([45, 6.j]), np.array([45.0])) FrTrtNz+can't get common type for non-numeric arrayr) rr?rr>r3r]array_precisionget TypeErrorrI array_type)rvZ is_complexZ precisionr1r!pr"r"r#r s%    )rr)N)NNNN)TrQNN)N)rX)5__doc__ functoolswarnings__all__Znumpy.core.numericcorenumericr3rrrrZnumpy.core.overridesrrGrZ ufunclikerrpartialZarray_function_dispatchr,r r2r5r r8rr:rr=rrrrrKrPrrWr rsr ZhalfZsingledoubleZ longdoubleZcsingleZcdoubleZ clongdoubler{rxrwr r"r"r"r#s   5   ) & $ 7 ( 1  x  : 6