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//opt/alt/python35/lib64/python3.5/site-packages/numpy/lib/type_check.py
"""Automatically adapted for numpy Sep 19, 2005 by convertcode.py """ from __future__ import division, absolute_import, print_function __all__ = ['iscomplexobj', 'isrealobj', 'imag', 'iscomplex', 'isreal', 'nan_to_num', 'real', 'real_if_close', 'typename', 'asfarray', 'mintypecode', 'asscalar', 'common_type'] import numpy.core.numeric as _nx from numpy.core.numeric import asarray, asanyarray, array, isnan, \ obj2sctype, zeros from .ufunclike import isneginf, isposinf _typecodes_by_elsize = 'GDFgdfQqLlIiHhBb?' 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. 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' """ typecodes = [(isinstance(t, str) and t) or asarray(t).dtype.char for t in typechars] intersection = [t for t in typecodes if t in typeset] if not intersection: return default if 'F' in intersection and 'd' in intersection: return 'D' l = [] for t in intersection: i = _typecodes_by_elsize.index(t) l.append((i, t)) l.sort() return l[0][1] 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.]) """ dtype = _nx.obj2sctype(dtype) if not issubclass(dtype, _nx.inexact): dtype = _nx.float_ return asarray(a, dtype=dtype) def real(val): """ Return the real part of the elements of the array. Parameters ---------- val : array_like Input array. Returns ------- out : ndarray Output array. 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]) """ return asanyarray(val).real def imag(val): """ Return the imaginary part of the elements of the array. Parameters ---------- val : array_like Input array. Returns ------- out : ndarray Output array. 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]) """ return asanyarray(val).imag 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 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`. 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) """ return imag(x) == 0 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) 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) #----------------------------------------------------------------------------- def _getmaxmin(t): from numpy.core import getlimits f = getlimits.finfo(t) return f.max, f.min def nan_to_num(x): """ 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. 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 negative 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]) """ x = _nx.array(x, subok=True) xtype = x.dtype.type if not issubclass(xtype, _nx.inexact): return x iscomplex = issubclass(xtype, _nx.complexfloating) isscalar = (x.ndim == 0) x = x[None] if isscalar else x dest = (x.real, x.imag) if iscomplex else (x,) maxf, minf = _getmaxmin(x.real.dtype) for d in dest: _nx.copyto(d, 0.0, where=isnan(d)) _nx.copyto(d, maxf, where=isposinf(d)) _nx.copyto(d, minf, where=isneginf(d)) return x[0] if isscalar else x #----------------------------------------------------------------------------- def real_if_close(a,tol=100): """ 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]) """ a = asanyarray(a) if not issubclass(a.dtype.type, _nx.complexfloating): return a if tol > 1: from numpy.core import getlimits f = getlimits.finfo(a.dtype.type) tol = f.eps * tol if _nx.allclose(a.imag, 0, atol=tol): a = a.real return a def asscalar(a): """ 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 """ return a.item() #----------------------------------------------------------------------------- _namefromtype = {'S1': 'character', '?': 'bool', 'b': 'signed char', 'B': 'unsigned char', 'h': 'short', 'H': 'unsigned short', 'i': 'integer', 'I': 'unsigned integer', 'l': 'long integer', 'L': 'unsigned long integer', 'q': 'long long integer', 'Q': 'unsigned long long integer', 'f': 'single precision', 'd': 'double precision', 'g': 'long precision', 'F': 'complex single precision', 'D': 'complex double precision', 'G': 'complex long double precision', 'S': 'string', 'U': 'unicode', 'V': 'void', 'O': 'object' } 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. 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 """ return _namefromtype[char] #----------------------------------------------------------------------------- #determine the "minimum common type" for a group of arrays. array_type = [[_nx.half, _nx.single, _nx.double, _nx.longdouble], [None, _nx.csingle, _nx.cdouble, _nx.clongdouble]] array_precision = {_nx.half: 0, _nx.single: 1, _nx.double: 2, _nx.longdouble: 3, _nx.csingle: 1, _nx.cdouble: 2, _nx.clongdouble: 3} 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 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'> """ is_complex = False precision = 0 for a in arrays: t = a.dtype.type if iscomplexobj(a): is_complex = True if issubclass(t, _nx.integer): p = 2 # array_precision[_nx.double] else: p = array_precision.get(t, None) if p is None: raise TypeError("can't get common type for non-numeric array") precision = max(precision, p) if is_complex: return array_type[1][precision] else: return array_type[0][precision]
