Python numeric.dtype方法代碼示例

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import dtype [as 別名]
def has_nested_fields(ndtype):
    """
    Returns whether one or several fields of a dtype are nested.

    Parameters
    ----------
    ndtype : dtype
        Data-type of a structured array.

    Raises
    ------
    AttributeError
        If `ndtype` does not have a `names` attribute.

    Examples
    --------
    >>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float)])
    >>> np.lib._iotools.has_nested_fields(dt)
    False

    """
    for name in ndtype.names or ():
        if ndtype[name].names:
            return True
    return False 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import dtype [as 別名]
def _strict_call(self, value):
        try:

            # We check if we can convert the value using the current function
            new_value = self.func(value)

            # In addition to having to check whether func can convert the
            # value, we also have to make sure that we don't get overflow
            # errors for integers.
            if self.func is int:
                try:
                    np.array(value, dtype=self.type)
                except OverflowError:
                    raise ValueError

            # We're still here so we can now return the new value
            return new_value

        except ValueError:
            if value.strip() in self.missing_values:
                if not self._status:
                    self._checked = False
                return self.default
            raise ValueError("Cannot convert string '%s'" % value)
    # 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import dtype [as 別名]
def test_flatten_dtype(self):
        "Testing flatten_dtype"
        # Standard dtype
        dt = np.dtype([("a", "f8"), ("b", "f8")])
        dt_flat = flatten_dtype(dt)
        assert_equal(dt_flat, [float, float])
        # Recursive dtype
        dt = np.dtype([("a", [("aa", '|S1'), ("ab", '|S2')]), ("b", int)])
        dt_flat = flatten_dtype(dt)
        assert_equal(dt_flat, [np.dtype('|S1'), np.dtype('|S2'), int])
        # dtype with shaped fields
        dt = np.dtype([("a", (float, 2)), ("b", (int, 3))])
        dt_flat = flatten_dtype(dt)
        assert_equal(dt_flat, [float, int])
        dt_flat = flatten_dtype(dt, True)
        assert_equal(dt_flat, [float] * 2 + [int] * 3)
        # dtype w/ titles
        dt = np.dtype([(("a", "A"), "f8"), (("b", "B"), "f8")])
        dt_flat = flatten_dtype(dt)
        assert_equal(dt_flat, [float, float]) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import dtype [as 別名]
def _vectorize_call(self, func, args):
        """Vectorized call to `func` over positional `args`."""
        if self.signature is not None:
            res = self._vectorize_call_with_signature(func, args)
        elif not args:
            res = func()
        else:
            ufunc, otypes = self._get_ufunc_and_otypes(func=func, args=args)

            # Convert args to object arrays first
            inputs = [array(a, copy=False, subok=True, dtype=object)
                      for a in args]

            outputs = ufunc(*inputs)

            if ufunc.nout == 1:
                res = array(outputs, copy=False, subok=True, dtype=otypes[0])
            else:
                res = tuple([array(x, copy=False, subok=True, dtype=t)
                             for x, t in zip(outputs, otypes)])
        return res 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import dtype [as 別名]
def asmatrix(data, dtype=None):
    """
    Interpret the input as a matrix.

    Unlike `matrix`, `asmatrix` does not make a copy if the input is already
    a matrix or an ndarray.  Equivalent to ``matrix(data, copy=False)``.

    Parameters
    ----------
    data : array_like
        Input data.
    dtype : data-type
       Data-type of the output matrix.

    Returns
    -------
    mat : matrix
        `data` interpreted as a matrix.

    Examples
    --------
    >>> x = np.array([[1, 2], [3, 4]])

    >>> m = np.asmatrix(x)

    >>> x[0,0] = 5

    >>> m
    matrix([[5, 2],
            [3, 4]])

    """
    return matrix(data, dtype=dtype, copy=False) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import dtype [as 別名]
def sum(self, axis=None, dtype=None, out=None):
        """
        Returns the sum of the matrix elements, along the given axis.

        Refer to `numpy.sum` for full documentation.

        See Also
        --------
        numpy.sum

        Notes
        -----
        This is the same as `ndarray.sum`, except that where an `ndarray` would
        be returned, a `matrix` object is returned instead.

        Examples
        --------
        >>> x = np.matrix([[1, 2], [4, 3]])
        >>> x.sum()
        10
        >>> x.sum(axis=1)
        matrix([[3],
                [7]])
        >>> x.sum(axis=1, dtype='float')
        matrix([[ 3.],
                [ 7.]])
        >>> out = np.zeros((1, 2), dtype='float')
        >>> x.sum(axis=1, dtype='float', out=out)
        matrix([[ 3.],
                [ 7.]])

        """
        return N.ndarray.sum(self, axis, dtype, out, keepdims=True)._collapse(axis)


    # To update docstring from array to matrix... 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import dtype [as 別名]
def mean(self, axis=None, dtype=None, out=None):
        """
        Returns the average of the matrix elements along the given axis.

        Refer to `numpy.mean` for full documentation.

        See Also
        --------
        numpy.mean

        Notes
        -----
        Same as `ndarray.mean` except that, where that returns an `ndarray`,
        this returns a `matrix` object.

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3, 4)))
        >>> x
        matrix([[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]])
        >>> x.mean()
        5.5
        >>> x.mean(0)
        matrix([[ 4.,  5.,  6.,  7.]])
        >>> x.mean(1)
        matrix([[ 1.5],
                [ 5.5],
                [ 9.5]])

        """
        return N.ndarray.mean(self, axis, dtype, out, keepdims=True)._collapse(axis) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import dtype [as 別名]
def std(self, axis=None, dtype=None, out=None, ddof=0):
        """
        Return the standard deviation of the array elements along the given axis.

        Refer to `numpy.std` for full documentation.

        See Also
        --------
        numpy.std

        Notes
        -----
        This is the same as `ndarray.std`, except that where an `ndarray` would
        be returned, a `matrix` object is returned instead.

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3, 4)))
        >>> x
        matrix([[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]])
        >>> x.std()
        3.4520525295346629
        >>> x.std(0)
        matrix([[ 3.26598632,  3.26598632,  3.26598632,  3.26598632]])
        >>> x.std(1)
        matrix([[ 1.11803399],
                [ 1.11803399],
                [ 1.11803399]])

        """
        return N.ndarray.std(self, axis, dtype, out, ddof, keepdims=True)._collapse(axis) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import dtype [as 別名]
def prod(self, axis=None, dtype=None, out=None):
        """
        Return the product of the array elements over the given axis.

        Refer to `prod` for full documentation.

        See Also
        --------
        prod, ndarray.prod

        Notes
        -----
        Same as `ndarray.prod`, except, where that returns an `ndarray`, this
        returns a `matrix` object instead.

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
        matrix([[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]])
        >>> x.prod()
        0
        >>> x.prod(0)
        matrix([[  0,  45, 120, 231]])
        >>> x.prod(1)
        matrix([[   0],
                [ 840],
                [7920]])

        """
        return N.ndarray.prod(self, axis, dtype, out, keepdims=True)._collapse(axis) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import dtype [as 別名]
def getH(self):
        """
        Returns the (complex) conjugate transpose of `self`.

        Equivalent to ``np.transpose(self)`` if `self` is real-valued.

        Parameters
        ----------
        None

        Returns
        -------
        ret : matrix object
            complex conjugate transpose of `self`

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3,4)))
        >>> z = x - 1j*x; z
        matrix([[  0. +0.j,   1. -1.j,   2. -2.j,   3. -3.j],
                [  4. -4.j,   5. -5.j,   6. -6.j,   7. -7.j],
                [  8. -8.j,   9. -9.j,  10.-10.j,  11.-11.j]])
        >>> z.getH()
        matrix([[  0. +0.j,   4. +4.j,   8. +8.j],
                [  1. +1.j,   5. +5.j,   9. +9.j],
                [  2. +2.j,   6. +6.j,  10.+10.j],
                [  3. +3.j,   7. +7.j,  11.+11.j]])

        """
        if issubclass(self.dtype.type, N.complexfloating):
            return self.transpose().conjugate()
        else:
            return self.transpose() 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import dtype [as 別名]
def flatten_dtype(ndtype, flatten_base=False):
    """
    Unpack a structured data-type by collapsing nested fields and/or fields
    with a shape.

    Note that the field names are lost.

    Parameters
    ----------
    ndtype : dtype
        The datatype to collapse
    flatten_base : bool, optional
       If True, transform a field with a shape into several fields. Default is
       False.

    Examples
    --------
    >>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float),
    ...                ('block', int, (2, 3))])
    >>> np.lib._iotools.flatten_dtype(dt)
    [dtype('|S4'), dtype('float64'), dtype('float64'), dtype('int32')]
    >>> np.lib._iotools.flatten_dtype(dt, flatten_base=True)
    [dtype('|S4'), dtype('float64'), dtype('float64'), dtype('int32'),
     dtype('int32'), dtype('int32'), dtype('int32'), dtype('int32'),
     dtype('int32')]

    """
    names = ndtype.names
    if names is None:
        if flatten_base:
            return [ndtype.base] * int(np.prod(ndtype.shape))
        return [ndtype.base]
    else:
        types = []
        for field in names:
            info = ndtype.fields[field]
            flat_dt = flatten_dtype(info[0], flatten_base)
            types.extend(flat_dt)
        return types 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import dtype [as 別名]
def _getdtype(cls, val):
        """Returns the dtype of the input variable."""
        return np.array(val).dtype
    # 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import dtype [as 別名]
def _dtypeortype(cls, dtype):
        """Returns dtype for datetime64 and type of dtype otherwise."""
        if dtype.type == np.datetime64:
            return dtype
        return dtype.type
    # 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import dtype [as 別名]
def upgrade_mapper(cls, func, default=None):
        """
    Upgrade the mapper of a StringConverter by adding a new function and
    its corresponding default.

    The input function (or sequence of functions) and its associated
    default value (if any) is inserted in penultimate position of the
    mapper.  The corresponding type is estimated from the dtype of the
    default value.

    Parameters
    ----------
    func : var
        Function, or sequence of functions

    Examples
    --------
    >>> import dateutil.parser
    >>> import datetime
    >>> dateparser = datetustil.parser.parse
    >>> defaultdate = datetime.date(2000, 1, 1)
    >>> StringConverter.upgrade_mapper(dateparser, default=defaultdate)
        """
        # Func is a single functions
        if hasattr(func, '__call__'):
            cls._mapper.insert(-1, (cls._getsubdtype(default), func, default))
            return
        elif hasattr(func, '__iter__'):
            if isinstance(func[0], (tuple, list)):
                for _ in func:
                    cls._mapper.insert(-1, _)
                return
            if default is None:
                default = [None] * len(func)
            else:
                default = list(default)
                default.append([None] * (len(func) - len(default)))
            for (fct, dft) in zip(func, default):
                cls._mapper.insert(-1, (cls._getsubdtype(dft), fct, dft))
    # 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import dtype [as 別名]
def test_upgrade(self):
        "Tests the upgrade method."

        converter = StringConverter()
        assert_equal(converter._status, 0)

        # test int
        assert_equal(converter.upgrade('0'), 0)
        assert_equal(converter._status, 1)

        # On systems where long defaults to 32-bit, the statuses will be
        # offset by one, so we check for this here.
        import numpy.core.numeric as nx
        status_offset = int(nx.dtype(nx.int_).itemsize < nx.dtype(nx.int64).itemsize)

        # test int > 2**32
        assert_equal(converter.upgrade('17179869184'), 17179869184)
        assert_equal(converter._status, 1 + status_offset)

        # test float
        assert_allclose(converter.upgrade('0.'), 0.0)
        assert_equal(converter._status, 2 + status_offset)

        # test complex
        assert_equal(converter.upgrade('0j'), complex('0j'))
        assert_equal(converter._status, 3 + status_offset)

        # test str
        # note that the longdouble type has been skipped, so the
        # _status increases by 2. Everything should succeed with
        # unicode conversion (5).
        for s in ['a', u'a', b'a']:
            res = converter.upgrade(s)
            assert_(type(res) is unicode)
            assert_equal(res, u'a')
            assert_equal(converter._status, 5 + status_offset)