def __new__(cls, dtype):
        try:
            dtype = numeric.dtype(dtype)
        except TypeError:
            # In case a float instance was given
            dtype = numeric.dtype(type(dtype))

        obj = cls._finfo_cache.get(dtype,None)
        if obj is not None:
            return obj
        dtypes = [dtype]
        newdtype = numeric.obj2sctype(dtype)
        if newdtype is not dtype:
            dtypes.append(newdtype)
            dtype = newdtype
        if not issubclass(dtype, numeric.inexact):
            raise ValueError, "data type %r not inexact" % (dtype)
        obj = cls._finfo_cache.get(dtype,None)
        if obj is not None:
            return obj
        if not issubclass(dtype, numeric.floating):
            newdtype = _convert_to_float[dtype]
            if newdtype is not dtype:
                dtypes.append(newdtype)
                dtype = newdtype
        obj = cls._finfo_cache.get(dtype,None)
        if obj is not None:
            return obj
        obj = object.__new__(cls)._init(dtype)
        for dt in dtypes:
            cls._finfo_cache[dt] = obj
        return obj

 def __init__(self, int_type):
     try:
         self.dtype = numeric.dtype(int_type)
     except TypeError:
         self.dtype = numeric.dtype(type(int_type))
     self.kind = self.dtype.kind
     self.bits = self.dtype.itemsize * 8
     self.key = "%s%d" % (self.kind, self.bits)
     if not self.kind in 'iu':
         raise ValueError("Invalid integer data type.")

    def __new__(
        subtype,
        shape,
        dtype=None,
        buf=None,
        offset=0,
        strides=None,
        formats=None,
        names=None,
        titles=None,
        byteorder=None,
        aligned=False,
        order="C",
    ):

        if dtype is not None:
            descr = sb.dtype(dtype)
        else:
            descr = format_parser(formats, names, titles, aligned, byteorder)._descr

        if buf is None:
            self = ndarray.__new__(subtype, shape, (record, descr), order=order)
        else:
            self = ndarray.__new__(
                subtype, shape, (record, descr), buffer=buf, offset=offset, strides=strides, order=order
            )
        return self

    def _parseFormats(self, formats, aligned=0):
        """ Parse the field formats """

        if formats is None:
            raise ValueError, "Need formats argument"
        if isinstance(formats, list):
            if len(formats) < 2:
                formats.append('')
            formats = ','.join(formats)
        dtype = sb.dtype(formats, aligned)
        fields = dtype.fields
        if fields is None:
            dtype = sb.dtype([('f1', dtype)], aligned)
            fields = dtype.fields
        keys = dtype.names
        self._f_formats = [fields[key][0] for key in keys]
        self._offsets = [fields[key][1] for key in keys]
        self._nfields = len(keys)

    def _createdescr(self, byteorder):
        descr = sb.dtype(
            {"names": self._names, "formats": self._f_formats, "offsets": self._offsets, "titles": self._titles}
        )
        if byteorder is not None:
            byteorder = _byteorderconv[byteorder[0]]
            descr = descr.newbyteorder(byteorder)

        self._descr = descr

    def _createdescr(self, byteorder):
        descr = sb.dtype({'names':self._names,
                          'formats':self._f_formats,
                          'offsets':self._offsets,
                          'titles':self._titles})
        if (byteorder is not None):
            byteorder = _byteorderconv[byteorder[0]]
            descr = descr.newbyteorder(byteorder)

        self._descr = descr

 def view(self, obj):
     try:
         if issubclass(obj, ndarray):
             return ndarray.view(self, obj)
     except TypeError:
         pass
     dtype = sb.dtype(obj)
     if dtype.fields is None:
         return self.__array__().view(dtype)
     return ndarray.view(self, obj)

 def view(self, dtype=None, type=None):
     if dtype is None:
         return ndarray.view(self, type)
     elif type is None:
         try:
             if issubclass(dtype, ndarray):
                 return ndarray.view(self, dtype)
         except TypeError:
             pass
         dtype = sb.dtype(dtype)
         if dtype.fields is None:
             return self.__array__().view(dtype)
         return ndarray.view(self, dtype)
     else:
         return ndarray.view(self, dtype, type)

    def __new__(subtype, data, dtype=None, copy=True):
        if isinstance(data, matrix):
            dtype2 = data.dtype
            if (dtype is None):
                dtype = dtype2
            if (dtype2 == dtype) and (not copy):
                return data
            return data.astype(dtype)

        if isinstance(data, N.ndarray):
            if dtype is None:
                intype = data.dtype
            else:
                intype = N.dtype(dtype)
            new = data.view(subtype)
            if intype != data.dtype:
                return new.astype(intype)
            if copy: return new.copy()
            else: return new

        if isinstance(data, str):
            data = _convert_from_string(data)

        # now convert data to an array
        arr = N.array(data, dtype=dtype, copy=copy)
        ndim = arr.ndim
        shape = arr.shape
        if (ndim > 2):
            raise ValueError, "matrix must be 2-dimensional"
        elif ndim == 0:
            shape = (1,1)
        elif ndim == 1:
            shape = (1,shape[0])

        order = False
        if (ndim == 2) and arr.flags.fortran:
            order = True

        if not (order or arr.flags.contiguous):
            arr = arr.copy()

        ret = N.ndarray.__new__(subtype, shape, arr.dtype,
                                buffer=arr,
                                order=order)
        return ret

    def _init(self, dtype):
        self.dtype = numeric.dtype(dtype)
        if dtype is ntypes.double:
            itype = ntypes.int64
            fmt = '%24.16e'
            precname = 'double'
        elif dtype is ntypes.single:
            itype = ntypes.int32
            fmt = '%15.7e'
            precname = 'single'
        elif dtype is ntypes.longdouble:
            itype = ntypes.longlong
            fmt = '%s'
            precname = 'long double'
        elif dtype is ntypes.half:
            itype = ntypes.int16
            fmt = '%12.5e'
            precname = 'half'
        else:
            raise ValueError, repr(dtype)

        machar = MachAr(lambda v:array([v], dtype),
                        lambda v:_frz(v.astype(itype))[0],
                        lambda v:array(_frz(v)[0], dtype),
                        lambda v: fmt % array(_frz(v)[0], dtype),
                        'numpy %s precision floating point number' % precname)

        for word in ['precision', 'iexp',
                     'maxexp','minexp','negep',
                     'machep']:
            setattr(self,word,getattr(machar, word))
        for word in ['tiny','resolution','epsneg']:
            setattr(self,word,getattr(machar, word).flat[0])
        self.max = machar.huge.flat[0]
        self.min = -self.max
        self.eps = machar.eps.flat[0]
        self.nexp = machar.iexp
        self.nmant = machar.it
        self.machar = machar
        self._str_tiny = machar._str_xmin.strip()
        self._str_max = machar._str_xmax.strip()
        self._str_epsneg = machar._str_epsneg.strip()
        self._str_eps = machar._str_eps.strip()
        self._str_resolution = machar._str_resolution.strip()
        return self

    def _init(self, dtype):
        self.dtype = numeric.dtype(dtype)
        if dtype is ntypes.double:
            itype = ntypes.int64
            fmt = "%24.16e"
            precname = "double"
        elif dtype is ntypes.single:
            itype = ntypes.int32
            fmt = "%15.7e"
            precname = "single"
        elif dtype is ntypes.longdouble:
            itype = ntypes.longlong
            fmt = "%s"
            precname = "long double"
        elif dtype is ntypes.half:
            itype = ntypes.int16
            fmt = "%12.5e"
            precname = "half"
        else:
            raise ValueError(repr(dtype))

        machar = MachAr(
            lambda v: array([v], dtype),
            lambda v: _frz(v.astype(itype))[0],
            lambda v: array(_frz(v)[0], dtype),
            lambda v: fmt % array(_frz(v)[0], dtype),
            "numpy %s precision floating point number" % precname,
        )

        for word in ["precision", "iexp", "maxexp", "minexp", "negep", "machep"]:
            setattr(self, word, getattr(machar, word))
        for word in ["tiny", "resolution", "epsneg"]:
            setattr(self, word, getattr(machar, word).flat[0])
        self.max = machar.huge.flat[0]
        self.min = -self.max
        self.eps = machar.eps.flat[0]
        self.nexp = machar.iexp
        self.nmant = machar.it
        self.machar = machar
        self._str_tiny = machar._str_xmin.strip()
        self._str_max = machar._str_xmax.strip()
        self._str_epsneg = machar._str_epsneg.strip()
        self._str_eps = machar._str_eps.strip()
        self._str_resolution = machar._str_resolution.strip()
        return self

def fromstring(datastring, dtype=None, shape=None, offset=0, formats=None,
               names=None, titles=None, aligned=False, byteorder=None):
    """ create a (read-only) record array from binary data contained in
    a string"""


    if dtype is None and formats is None:
        raise ValueError, "Must have dtype= or formats="

    if dtype is not None:
        descr = sb.dtype(dtype)
    else:
        descr = format_parser(formats, names, titles, aligned, byteorder)._descr

    itemsize = descr.itemsize
    if (shape is None or shape == 0 or shape == -1):
        shape = (len(datastring) - offset) / itemsize

    _array = recarray(shape, descr, buf=datastring, offset=offset)
    return _array

def array(obj, dtype=None, shape=None, offset=0, strides=None, formats=None,
          names=None, titles=None, aligned=False, byteorder=None, copy=True):
    """Construct a record array from a wide-variety of objects.
    """

    if (isinstance(obj, (type(None), str)) or isfileobj(obj)) \
           and (formats is None) \
           and (dtype is None):
        raise ValueError("Must define formats (or dtype) if object is "\
                         "None, string, or an open file")

    kwds = {}
    if dtype is not None:
        dtype = sb.dtype(dtype)
    elif formats is not None:
        dtype = format_parser(formats, names, titles,
                              aligned, byteorder)._descr
    else:
        kwds = {'formats': formats,
                'names' : names,
                'titles' : titles,
                'aligned' : aligned,
                'byteorder' : byteorder
                }

    if obj is None:
        if shape is None:
            raise ValueError("Must define a shape if obj is None")
        return recarray(shape, dtype, buf=obj, offset=offset, strides=strides)

    elif isinstance(obj, bytes):
        return fromstring(obj, dtype, shape=shape, offset=offset, **kwds)

    elif isinstance(obj, (list, tuple)):
        if isinstance(obj[0], (tuple, list)):
            return fromrecords(obj, dtype=dtype, shape=shape, **kwds)
        else:
            return fromarrays(obj, dtype=dtype, shape=shape, **kwds)

    elif isinstance(obj, recarray):
        if dtype is not None and (obj.dtype != dtype):
            new = obj.view(dtype)
        else:
            new = obj
        if copy:
            new = new.copy()
        return new

    elif isfileobj(obj):
        return fromfile(obj, dtype=dtype, shape=shape, offset=offset)

    elif isinstance(obj, ndarray):
        if dtype is not None and (obj.dtype != dtype):
            new = obj.view(dtype)
        else:
            new = obj
        if copy:
            new = new.copy()
        res = new.view(recarray)
        if issubclass(res.dtype.type, nt.void):
            res.dtype = sb.dtype((record, res.dtype))
        return res

    else:
        interface = getattr(obj, "__array_interface__", None)
        if interface is None or not isinstance(interface, dict):
            raise ValueError("Unknown input type")
        obj = sb.array(obj)
        if dtype is not None and (obj.dtype != dtype):
            obj = obj.view(dtype)
        res = obj.view(recarray)
        if issubclass(res.dtype.type, nt.void):
            res.dtype = sb.dtype((record, res.dtype))
        return res

def fromfile(fd, dtype=None, shape=None, offset=0, formats=None,
             names=None, titles=None, aligned=False, byteorder=None):
    """Create an array from binary file data

    If file is a string then that file is opened, else it is assumed
    to be a file object.

    >>> from tempfile import TemporaryFile
    >>> a = np.empty(10,dtype='f8,i4,a5')
    >>> a[5] = (0.5,10,'abcde')
    >>>
    >>> fd=TemporaryFile()
    >>> a = a.newbyteorder('<')
    >>> a.tofile(fd)
    >>>
    >>> fd.seek(0)
    >>> r=np.core.records.fromfile(fd, formats='f8,i4,a5', shape=10,
    ... byteorder='<')
    >>> print r[5]
    (0.5, 10, 'abcde')
    >>> r.shape
    (10,)
    """

    if (shape is None or shape == 0):
        shape = (-1,)
    elif isinstance(shape, (int, long)):
        shape = (shape,)

    name = 0
    if isinstance(fd, str):
        name = 1
        fd = open(fd, 'rb')
    if (offset > 0):
        fd.seek(offset, 1)
    size = get_remaining_size(fd)

    if dtype is not None:
        descr = sb.dtype(dtype)
    else:
        descr = format_parser(formats, names, titles, aligned, byteorder)._descr

    itemsize = descr.itemsize

    shapeprod = sb.array(shape).prod()
    shapesize = shapeprod * itemsize
    if shapesize < 0:
        shape = list(shape)
        shape[ shape.index(-1) ] = size / -shapesize
        shape = tuple(shape)
        shapeprod = sb.array(shape).prod()

    nbytes = shapeprod * itemsize

    if nbytes > size:
        raise ValueError(
                "Not enough bytes left in file for specified shape and type")

    # create the array
    _array = recarray(shape, descr)
    nbytesread = fd.readinto(_array.data)
    if nbytesread != nbytes:
        raise IOError("Didn't read as many bytes as expected")
    if name:
        fd.close()

    return _array

def fromrecords(recList, dtype=None, shape=None, formats=None, names=None,
                titles=None, aligned=False, byteorder=None):
    """ create a recarray from a list of records in text form

        The data in the same field can be heterogeneous, they will be promoted
        to the highest data type.  This method is intended for creating
        smaller record arrays.  If used to create large array without formats
        defined

        r=fromrecords([(2,3.,'abc')]*100000)

        it can be slow.

        If formats is None, then this will auto-detect formats. Use list of
        tuples rather than list of lists for faster processing.

    >>> r=np.core.records.fromrecords([(456,'dbe',1.2),(2,'de',1.3)],
    ... names='col1,col2,col3')
    >>> print r[0]
    (456, 'dbe', 1.2)
    >>> r.col1
    array([456,   2])
    >>> r.col2
    chararray(['dbe', 'de'],
          dtype='|S3')
    >>> import cPickle
    >>> print cPickle.loads(cPickle.dumps(r))
    [(456, 'dbe', 1.2) (2, 'de', 1.3)]
    """

    nfields = len(recList[0])
    if formats is None and dtype is None:  # slower
        obj = sb.array(recList, dtype=object)
        arrlist = [sb.array(obj[..., i].tolist()) for i in xrange(nfields)]
        return fromarrays(arrlist, formats=formats, shape=shape, names=names,
                          titles=titles, aligned=aligned, byteorder=byteorder)

    if dtype is not None:
        descr = sb.dtype((record, dtype))
    else:
        descr = format_parser(formats, names, titles, aligned, byteorder)._descr

    try:
        retval = sb.array(recList, dtype=descr)
    except TypeError:  # list of lists instead of list of tuples
        if (shape is None or shape == 0):
            shape = len(recList)
        if isinstance(shape, (int, long)):
            shape = (shape,)
        if len(shape) > 1:
            raise ValueError, "Can only deal with 1-d array."
        _array = recarray(shape, descr)
        for k in xrange(_array.size):
            _array[k] = tuple(recList[k])
        return _array
    else:
        if shape is not None and retval.shape != shape:
            retval.shape = shape

    res = retval.view(recarray)

    return res

def fromarrays(arrayList, dtype=None, shape=None, formats=None,
               names=None, titles=None, aligned=False, byteorder=None):
    """ create a record array from a (flat) list of arrays

    >>> x1=np.array([1,2,3,4])
    >>> x2=np.array(['a','dd','xyz','12'])
    >>> x3=np.array([1.1,2,3,4])
    >>> r = np.core.records.fromarrays([x1,x2,x3],names='a,b,c')
    >>> print r[1]
    (2, 'dd', 2.0)
    >>> x1[1]=34
    >>> r.a
    array([1, 2, 3, 4])
    """

    arrayList = [sb.asarray(x) for x in arrayList]

    if shape is None or shape == 0:
        shape = arrayList[0].shape

    if isinstance(shape, int):
        shape = (shape,)

    if formats is None and dtype is None:
        # go through each object in the list to see if it is an ndarray
        # and determine the formats.
        formats = ''
        for obj in arrayList:
            if not isinstance(obj, ndarray):
                raise ValueError, "item in the array list must be an ndarray."
            formats += _typestr[obj.dtype.type]
            if issubclass(obj.dtype.type, nt.flexible):
                formats += `obj.itemsize`
            formats += ','
        formats = formats[:-1]

    if dtype is not None:
        descr = sb.dtype(dtype)
        _names = descr.names
    else:
        parsed = format_parser(formats, names, titles, aligned, byteorder)
        _names = parsed._names
        descr = parsed._descr

    # Determine shape from data-type.
    if len(descr) != len(arrayList):
        raise ValueError, "mismatch between the number of fields "\
              "and the number of arrays"

    d0 = descr[0].shape
    nn = len(d0)
    if nn > 0:
        shape = shape[:-nn]

    for k, obj in enumerate(arrayList):
        nn = len(descr[k].shape)
        testshape = obj.shape[:len(obj.shape) - nn]
        if testshape != shape:
            raise ValueError, "array-shape mismatch in array %d" % k

    _array = recarray(shape, descr)

    # populate the record array (makes a copy)
    for i in range(len(arrayList)):
        _array[_names[i]] = arrayList[i]

    return _array