FM / lib

/usr/lib64/python2.6/site-packages/numpy/lib/

/usr/lib64/python2.6/site-packages/numpy/lib

Name	Size	Mode	Actions
benchmarks/	-	0755	rm
tests/	-	0755	rm
arraysetops.py	13704	0644	edit dl rm
arraysetops.pyc	13380	0644	edit dl rm
arraysetops.pyo	13380	0644	edit dl rm
arrayterator.py	6437	0644	edit dl rm
arrayterator.pyc	7022	0644	edit dl rm
arrayterator.pyo	7022	0644	edit dl rm
financial.py	20872	0644	edit dl rm
financial.pyc	21546	0644	edit dl rm
financial.pyo	21546	0644	edit dl rm
format.py	19309	0644	edit dl rm
format.pyc	17279	0644	edit dl rm
format.pyo	17279	0644	edit dl rm
function_base.py	100524	0644	edit dl rm
function_base.pyc	99169	0644	edit dl rm
function_base.pyo	99169	0644	edit dl rm
getlimits.py	8790	0644	edit dl rm
getlimits.pyc	10053	0644	edit dl rm
getlimits.pyo	10053	0644	edit dl rm
index_tricks.py	26790	0644	edit dl rm
index_tricks.pyc	28008	0644	edit dl rm
index_tricks.pyo	28008	0644	edit dl rm
info.py	6240	0644	edit dl rm
info.pyc	6425	0644	edit dl rm
info.pyo	6425	0644	edit dl rm
io.py	53226	0644	edit dl rm
io.pyc	44102	0644	edit dl rm
io.pyo	44102	0644	edit dl rm
machar.py	10658	0644	edit dl rm
machar.pyc	8688	0644	edit dl rm
machar.pyo	8688	0644	edit dl rm
polynomial.py	35562	0644	edit dl rm
polynomial.pyc	38139	0644	edit dl rm
polynomial.pyo	38139	0644	edit dl rm
recfunctions.py	32038	0644	edit dl rm
recfunctions.pyc	29451	0644	edit dl rm
recfunctions.pyo	29451	0644	edit dl rm
scimath.py	13997	0644	edit dl rm
scimath.pyc	15867	0644	edit dl rm
scimath.pyo	15867	0644	edit dl rm
setup.py	588	0644	edit dl rm
setup.pyc	918	0644	edit dl rm
setup.pyo	918	0644	edit dl rm
setupscons.py	470	0644	edit dl rm
setupscons.pyc	824	0644	edit dl rm
setupscons.pyo	824	0644	edit dl rm
shape_base.py	24387	0644	edit dl rm
shape_base.pyc	25291	0644	edit dl rm
shape_base.pyo	25291	0644	edit dl rm
stride_tricks.py	3972	0644	edit dl rm
stride_tricks.pyc	3863	0644	edit dl rm
stride_tricks.pyo	3863	0644	edit dl rm
twodim_base.py	22946	0644	edit dl rm
twodim_base.pyc	25464	0644	edit dl rm
twodim_base.pyo	25464	0644	edit dl rm
type_check.py	17075	0644	edit dl rm
type_check.pyc	18392	0644	edit dl rm
type_check.pyo	18392	0644	edit dl rm
ufunclike.py	5452	0644	edit dl rm
ufunclike.pyc	6311	0644	edit dl rm
ufunclike.pyo	6311	0644	edit dl rm
user_array.py	7475	0644	edit dl rm
user_array.pyc	15727	0644	edit dl rm
user_array.pyo	15727	0644	edit dl rm
utils.py	34034	0644	edit dl rm
utils.pyc	30422	0644	edit dl rm
utils.pyo	30422	0644	edit dl rm
_compiled_base.so	19720	0755	edit dl rm
_datasource.py	20638	0644	edit dl rm
_datasource.pyc	20986	0644	edit dl rm
_datasource.pyo	20986	0644	edit dl rm
_iotools.py	27712	0644	edit dl rm
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_iotools.pyo	26916	0644	edit dl rm
__init__.py	936	0644	edit dl rm
__init__.pyc	1038	0644	edit dl rm
__init__.pyo	1038	0644	edit dl rm

Edit: /usr/lib64/python2.6/site-packages/numpy/lib/type_check.py (17075B)

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

__all__ = ['iscomplexobj','isrealobj','imag','iscomplex',
           'isreal','nan_to_num','real','real_if_close',
           'typename','asfarray','mintypecode','asscalar',
           'common_type', 'datetime_data']

import numpy.core.numeric as _nx
from numpy.core.numeric import asarray, asanyarray, array, isnan, \
                obj2sctype, zeros
from numpy.core.multiarray import METADATA_DTSTR
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 = [(type(t) is type('') 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):
    """
    Return True if x is a 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, `iscomplexobj` evaluates to True
    if the data type is complex.

Parameters
    ----------
    x : any
        The input can be of any type and shape.

Returns
    -------
    y : bool
        The return value, True if `x` is of a complex type.

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, float
        Array with the same shape as `x` and dtype of the element in `x`  with
        the greatest precision. 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. All finite numbers
        are upcast to the output dtype (default float64).

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
    --------
    >>> 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])

"""
    try:
        t = x.dtype.type
    except AttributeError:
        t = obj2sctype(type(x))
    if issubclass(t, _nx.complexfloating):
        return nan_to_num(x.real) + 1j * nan_to_num(x.imag)
    else:
        try:
            y = x.copy()
        except AttributeError:
            y = array(x)
    if not issubclass(t, _nx.integer):
        if not y.shape:
            y = array([x])
            scalar = True
        else:
            scalar = False
        are_inf = isposinf(y)
        are_neg_inf = isneginf(y)
        are_nan = isnan(y)
        maxf, minf = _getmaxmin(y.dtype.type)
        y[are_nan] = 0
        y[are_inf] = maxf
        y[are_neg_inf] = minf
        if scalar:
            y = y[0]
    return y

#-----------------------------------------------------------------------------

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 input data type is preserved.

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.single, _nx.double, _nx.longdouble],
              [_nx.csingle, _nx.cdouble, _nx.clongdouble]]
array_precision = {_nx.single : 0,
                   _nx.double : 1,
                   _nx.longdouble : 2,
                   _nx.csingle : 0,
                   _nx.cdouble : 1,
                   _nx.clongdouble : 2}
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 = 1
        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]

def datetime_data(dtype):
    """Return (unit, numerator, denominator, events) from a datetime dtype
    """
    try:
        import ctypes
    except ImportError:
        raise RuntimeError, "Cannot access date-time internals without ctypes installed"

if dtype.kind not in ['m','M']:
        raise ValueError, "Not a date-time dtype"
    
    obj = dtype.metadata[METADATA_DTSTR]
    class DATETIMEMETA(ctypes.Structure):
        _fields_ = [('base', ctypes.c_int),
                    ('num', ctypes.c_int),
                    ('den', ctypes.c_int),
                    ('events', ctypes.c_int)]

func = ctypes.pythonapi.PyCObject_AsVoidPtr
    func.argtypes = [ctypes.py_object]
    func.restype = ctypes.c_void_p

result = func(ctypes.py_object(obj))
    result = ctypes.cast(ctypes.c_void_p(result), ctypes.POINTER(DATETIMEMETA))

struct = result[0]
    base = struct.base

# FIXME: This needs to be kept consistent with enum in ndarrayobject.h
    from numpy.core.multiarray import DATETIMEUNITS
    obj = ctypes.py_object(DATETIMEUNITS)
    result = func(obj)
    _unitnum2name = ctypes.cast(ctypes.c_void_p(result), ctypes.POINTER(ctypes.c_char_p))

return (_unitnum2name[base], struct.num, struct.den, struct.events)

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