Python multiarray.dtype函数代码示例

def _mean(a, axis=None, dtype=None, out=None, keepdims=False):
    arr = asanyarray(a)

    is_float16_result = False
    rcount = _count_reduce_items(arr, axis)
    # Make this warning show up first
    if rcount == 0:
        warnings.warn("Mean of empty slice.", RuntimeWarning, stacklevel=2)

    # Cast bool, unsigned int, and int to float64 by default
    if dtype is None:
        if issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
            dtype = mu.dtype('f8')
        elif issubclass(arr.dtype.type, nt.float16):
            dtype = mu.dtype('f4')
            is_float16_result = True

    ret = umr_sum(arr, axis, dtype, out, keepdims)
    if isinstance(ret, mu.ndarray):
        ret = um.true_divide(
                ret, rcount, out=ret, casting='unsafe', subok=False)
        if is_float16_result and out is None:
            ret = arr.dtype.type(ret)
    elif hasattr(ret, 'dtype'):
        if is_float16_result:
            ret = arr.dtype.type(ret / rcount)
        else:
            ret = ret.dtype.type(ret / rcount)
    else:
        ret = ret / rcount

    return ret

def issubdtype(arg1, arg2):
    """
    Returns True if first argument is a typecode lower/equal in type hierarchy.

    Parameters
    ----------
    arg1, arg2 : dtype_like
        dtype or string representing a typecode.

    Returns
    -------
    out : bool

    See Also
    --------
    issubsctype, issubclass_
    numpy.core.numerictypes : Overview of numpy type hierarchy.

    Examples
    --------
    >>> np.issubdtype('S1', np.string_)
    True
    >>> np.issubdtype(np.float64, np.float32)
    False

    """
    if not issubclass_(arg1, generic):
        arg1 = dtype(arg1).type
    if not issubclass_(arg2, generic):
        arg2_orig = arg2
        arg2 = dtype(arg2).type
        if not isinstance(arg2_orig, dtype):
            # weird deprecated behaviour, that tried to infer np.floating from
            # float, and similar less obvious things, such as np.generic from
            # basestring
            mro = arg2.mro()
            arg2 = mro[1] if len(mro) > 1 else mro[0]

            def type_repr(x):
                """ Helper to produce clear error messages """
                if not isinstance(x, type):
                    return repr(x)
                elif issubclass(x, generic):
                    return "np.{}".format(x.__name__)
                else:
                    return x.__name__

            # 1.14, 2017-08-01
            warnings.warn(
                "Conversion of the second argument of issubdtype from `{raw}` "
                "to `{abstract}` is deprecated. In future, it will be treated "
                "as `{concrete} == np.dtype({raw}).type`.".format(
                    raw=type_repr(arg2_orig),
                    abstract=type_repr(arg2),
                    concrete=type_repr(dtype(arg2_orig).type)
                ),
                FutureWarning, stacklevel=2
            )

    return issubclass(arg1, arg2)

def issubdtype(arg1, arg2):
    """
    Returns True if first argument is a typecode lower/equal in type hierarchy.

    Parameters
    ----------
    arg1, arg2 : dtype_like
        dtype or string representing a typecode.

    Returns
    -------
    out : bool

    See Also
    --------
    issubsctype, issubclass_
    numpy.core.numerictypes : Overview of numpy type hierarchy.

    Examples
    --------
    >>> np.issubdtype('S1', str)
    True
    >>> np.issubdtype(np.float64, np.float32)
    False

    """
    if issubclass_(arg2, generic):
        return issubclass(dtype(arg1).type, arg2)
    mro = dtype(arg2).type.mro()
    if len(mro) > 1:
        val = mro[1]
    else:
        val = mro[0]
    return issubclass(dtype(arg1).type, val)

def _add_trailing_padding(value, padding):
    """Inject the specified number of padding bytes at the end of a dtype"""
    from numpy.core.multiarray import dtype

    if value.fields is None:
        vfields = {'f0': (value, 0)}
    else:
        vfields = dict(value.fields)

    if value.names and value.names[-1] == '' and \
           value[''].char == 'V':
        # A trailing padding field is already present
        vfields[''] = ('V%d' % (vfields[''][0].itemsize + padding),
                       vfields[''][1])
        value = dtype(vfields)
    else:
        # Get a free name for the padding field
        j = 0
        while True:
            name = 'pad%d' % j
            if name not in vfields:
                vfields[name] = ('V%d' % padding, value.itemsize)
                break
            j += 1

        value = dtype(vfields)
        if '' not in vfields:
            # Strip out the name of the padding field
            names = list(value.names)
            names[-1] = ''
            value.names = tuple(names)
    return value

def bincount(x, weights=None, minlength=None):
    if minlength is None:
        minlength = 0
    else:
        if not isinstance(minlength, (int, long)):
            raise TypeError("an integer is required")
        if minlength <= 0:
            raise ValueError("minlength must be positive")

    x = array(x)
    len_output = minlength
    if len(x) > 0:
        if x.min() < 0:
            raise ValueError("x must not be negative")
        len_output = max(len_output, x.max() + 1)

    if x.dtype.kind not in 'ui':
        raise ValueError("x must be integer")

    if weights is None:
        output = zeros(len_output, dtype=dtype('int'))
        for elem in x:
            output[elem] += 1
    else:
        if len(weights) != len(x):
            raise ValueError("x and weights arrays must have the same size")
        output = zeros(len_output, dtype=dtype('float'))
        for i in range(len(x)):
            output[x[i]] += weights[i]
    return output

def find_common_type(array_types, scalar_types):
    """
    Determine common type following standard coercion rules

    Parameters
    ----------
    array_types : sequence
        A list of dtype convertible objects representing arrays
    scalar_types : sequence
        A list of dtype convertible objects representing scalars

    Returns
    -------
    datatype : dtype
        The common data-type which is the maximum of the array_types
        ignoring the scalar_types unless the maximum of the scalar_types
        is of a different kind.

        If the kinds is not understood, then None is returned.

    See Also
    --------
    dtype

    """
    array_types = [dtype(x) for x in array_types]
    scalar_types = [dtype(x) for x in scalar_types]

    if len(scalar_types) == 0:
        if len(array_types) == 0:
            return None
        else:
            return max(array_types)
    if len(array_types) == 0:
        return max(scalar_types)

    maxa = max(array_types)
    maxsc = max(scalar_types)

    try:
        index_a = _kind_list.index(maxa.kind)
        index_sc = _kind_list.index(maxsc.kind)
    except ValueError:
        return None

    if index_sc > index_a:
        return _find_common_coerce(maxsc,maxa)
    else:
        return maxa

def _usefields(adict, align):
    from .multiarray import dtype
    try:
        names = adict[-1]
    except KeyError:
        names = None
    if names is None:
        names, formats, offsets, titles = _makenames_list(adict, align)
    else:
        formats = []
        offsets = []
        titles = []
        for name in names:
            res = adict[name]
            formats.append(res[0])
            offsets.append(res[1])
            if (len(res) > 2):
                titles.append(res[2])
            else:
                titles.append(None)

    return dtype({"names" : names,
                  "formats" : formats,
                  "offsets" : offsets,
                  "titles" : titles}, align)

def _makenames_list(adict, align):
    from .multiarray import dtype
    allfields = []
    fnames = list(adict.keys())
    for fname in fnames:
        obj = adict[fname]
        n = len(obj)
        if not isinstance(obj, tuple) or n not in [2,3]:
            raise ValueError("entry not a 2- or 3- tuple")
        if (n > 2) and (obj[2] == fname):
            continue
        num = int(obj[1])
        if (num < 0):
            raise ValueError("invalid offset.")
        format = dtype(obj[0], align=align)
        if (format.itemsize == 0):
            raise ValueError("all itemsizes must be fixed.")
        if (n > 2):
            title = obj[2]
        else:
            title = None
        allfields.append((fname, format, num, title))
    # sort by offsets
    allfields.sort(key=lambda x: x[2])
    names = [x[0] for x in allfields]
    formats = [x[1] for x in allfields]
    offsets = [x[2] for x in allfields]
    titles = [x[3] for x in allfields]

    return names, formats, offsets, titles

def obj2sctype(rep, default=None):
    """
    Return the scalar dtype or NumPy equivalent of Python type of an object.

    Parameters
    ----------
    rep : any
        The object of which the type is returned.
    default : any, optional
        If given, this is returned for objects whose types can not be
        determined. If not given, None is returned for those objects.

    Returns
    -------
    dtype : dtype or Python type
        The data type of `rep`.

    See Also
    --------
    sctype2char, issctype, issubsctype, issubdtype, maximum_sctype

    Examples
    --------
    >>> np.obj2sctype(np.int32)
    <type 'numpy.int32'>
    >>> np.obj2sctype(np.array([1., 2.]))
    <type 'numpy.float64'>
    >>> np.obj2sctype(np.array([1.j]))
    <type 'numpy.complex128'>

    >>> np.obj2sctype(dict)
    <type 'numpy.object_'>
    >>> np.obj2sctype('string')
    <type 'numpy.string_'>

    >>> np.obj2sctype(1, default=list)
    <type 'list'>

    """
    try:
        if issubclass(rep, generic):
            return rep
    except TypeError:
        pass
    if isinstance(rep, dtype):
        return rep.type
    if isinstance(rep, type):
        return _python_type(rep)
    if isinstance(rep, ndarray):
        return rep.dtype.type
    try:
        res = dtype(rep)
    except:
        return default
    return res.type

def _check_field_overlap(new_fields, old_fields):
    """ Perform object memory overlap tests for two data-types (see
    _view_is_safe).

    This function checks that new fields only access memory contained in old
    fields, and that non-object fields are not interpreted as objects and vice
    versa.

    Parameters
    ----------
    new_fields : list of (data-type, int) pairs
        Flat list of (dtype, byte offset) pairs for the new data type, as
        returned by _get_all_field_offsets.
    old_fields: list of (data-type, int) pairs
        Flat list of (dtype, byte offset) pairs for the old data type, as
        returned by _get_all_field_offsets.

    Raises
    ------
    TypeError
        If the new fields are incompatible with the old fields

    """
    from .numerictypes import object_
    from .multiarray import dtype

    #first go byte by byte and check we do not access bytes not in old_fields
    new_bytes = set()
    for tp, off in new_fields:
        new_bytes.update(set(range(off, off+tp.itemsize)))
    old_bytes = set()
    for tp, off in old_fields:
        old_bytes.update(set(range(off, off+tp.itemsize)))
    if new_bytes.difference(old_bytes):
        raise TypeError("view would access data parent array doesn't own")

    #next check that we do not interpret non-Objects as Objects, and vv
    obj_offsets = [off for (tp, off) in old_fields if tp.type is object_]
    obj_size = dtype(object_).itemsize

    for fld_dtype, fld_offset in new_fields:
        if fld_dtype.type is object_:
            # check we do not create object views where
            # there are no objects.
            if fld_offset not in obj_offsets:
                raise TypeError("cannot view non-Object data as Object type")
        else:
            # next check we do not create non-object views
            # where there are already objects.
            # see validate_object_field_overlap for a similar computation.
            for obj_offset in obj_offsets:
                if (fld_offset < obj_offset + obj_size and
                        obj_offset < fld_offset + fld_dtype.itemsize):
                    raise TypeError("cannot view Object as non-Object type")

def _set_array_types():
    ibytes = [1, 2, 4, 8, 16, 32, 64]
    fbytes = [2, 4, 8, 10, 12, 16, 32, 64]
    for bytes in ibytes:
        bits = 8 * bytes
        _add_array_type("int", bits)
        _add_array_type("uint", bits)
    for bytes in fbytes:
        bits = 8 * bytes
        _add_array_type("float", bits)
        _add_array_type("complex", 2 * bits)
    _gi = dtype("p")
    if _gi.type not in sctypes["int"]:
        indx = 0
        sz = _gi.itemsize
        _lst = sctypes["int"]
        while indx < len(_lst) and sz >= _lst[indx](0).itemsize:
            indx += 1
        sctypes["int"].insert(indx, _gi.type)
        sctypes["uint"].insert(indx, dtype("P").type)

def _bits_of(obj):
    try:
        info = next(v for v in _concrete_typeinfo.values() if v.type is obj)
    except StopIteration:
        if obj in _abstract_types.values():
            raise ValueError("Cannot count the bits of an abstract type")

        # some third-party type - make a best-guess
        return dtype(obj).itemsize * 8
    else:
        return info.bits

def _set_array_types():
    ibytes = [1, 2, 4, 8, 16, 32, 64]
    fbytes = [2, 4, 8, 10, 12, 16, 32, 64]
    for bytes in ibytes:
        bits = 8*bytes
        _add_array_type('int', bits)
        _add_array_type('uint', bits)
    for bytes in fbytes:
        bits = 8*bytes
        _add_array_type('float', bits)
        _add_array_type('complex', 2*bits)
    _gi = dtype('p')
    if _gi.type not in sctypes['int']:
        indx = 0
        sz = _gi.itemsize
        _lst = sctypes['int']
        while (indx < len(_lst) and sz >= _lst[indx](0).itemsize):
            indx += 1
        sctypes['int'].insert(indx, _gi.type)
        sctypes['uint'].insert(indx, dtype('P').type)

def obj2sctype(rep, default=None):
    """
    Return the scalar dtype or NumPy equivalent of Python type of an object.

    Parameters
    ----------
    rep : any
        The object of which the type is returned.
    default : any, optional
        If given, this is returned for objects whose types can not be
        determined. If not given, None is returned for those objects.

    Returns
    -------
    dtype : dtype or Python type
        The data type of `rep`.

    See Also
    --------
    sctype2char, issctype, issubsctype, issubdtype, maximum_sctype

    Examples
    --------
    >>> np.obj2sctype(np.int32)
    <type 'numpy.int32'>
    >>> np.obj2sctype(np.array([1., 2.]))
    <type 'numpy.float64'>
    >>> np.obj2sctype(np.array([1.j]))
    <type 'numpy.complex128'>

    >>> np.obj2sctype(dict)
    <type 'numpy.object_'>
    >>> np.obj2sctype('string')
    <type 'numpy.string_'>

    >>> np.obj2sctype(1, default=list)
    <type 'list'>

    """
    # prevent abtract classes being upcast
    if isinstance(rep, type) and issubclass(rep, generic):
        return rep
    # extract dtype from arrays
    if isinstance(rep, ndarray):
        return rep.dtype.type
    # fall back on dtype to convert
    try:
        res = dtype(rep)
    except Exception:
        return default
    else:
        return res.type

def bitname(obj):
    """Return a bit-width name for a given type object"""
    bits = _bits_of(obj)
    char = dtype(obj).kind
    base = _kind_to_stem[char]

    if base == 'object':
        bits = 0

    if bits != 0:
        char = "%s%d" % (char, bits // 8)

    return base, bits, char