python类masked()的实例源码

def dump(a, F):
    """
    Pickle a masked array to a file.

    This is a wrapper around ``cPickle.dump``.

    Parameters
    ----------
    a : MaskedArray
        The array to be pickled.
    F : str or file-like object
        The file to pickle `a` to. If a string, the full path to the file.

    """
    if not hasattr(F, 'readline'):
        F = open(F, 'w')
    return pickle.dump(a, F)

def loads(strg):
    """
    Load a pickle from the current string.

    The result of ``cPickle.loads(strg)`` is returned.

    Parameters
    ----------
    strg : str
        The string to load.

    See Also
    --------
    dumps : Return a string corresponding to the pickling of a masked array.

    """
    return pickle.loads(strg)

def get_masked_subclass(*arrays):
    """
    Return the youngest subclass of MaskedArray from a list of (masked) arrays.

    In case of siblings, the first listed takes over.

    """
    if len(arrays) == 1:
        arr = arrays[0]
        if isinstance(arr, MaskedArray):
            rcls = type(arr)
        else:
            rcls = MaskedArray
    else:
        arrcls = [type(a) for a in arrays]
        rcls = arrcls[0]
        if not issubclass(rcls, MaskedArray):
            rcls = MaskedArray
        for cls in arrcls[1:]:
            if issubclass(cls, rcls):
                rcls = cls
    # Don't return MaskedConstant as result: revert to MaskedArray
    if rcls.__name__ == 'MaskedConstant':
        return MaskedArray
    return rcls

def outer(self, a, b):
        """
        Return the function applied to the outer product of a and b.

        """
        (da, db) = (getdata(a), getdata(b))
        d = self.f.outer(da, db)
        ma = getmask(a)
        mb = getmask(b)
        if ma is nomask and mb is nomask:
            m = nomask
        else:
            ma = getmaskarray(a)
            mb = getmaskarray(b)
            m = umath.logical_or.outer(ma, mb)
        if (not m.ndim) and m:
            return masked
        if m is not nomask:
            np.copyto(d, da, where=m)
        if not d.shape:
            return d
        masked_d = d.view(get_masked_subclass(a, b))
        masked_d._mask = m
        masked_d._update_from(d)
        return masked_d

def __call__(self, *args, **params):
        methodname = self.__name__
        instance = self.obj
        # Fallback : if the instance has not been initialized, use the first
        # arg
        if instance is None:
            args = list(args)
            instance = args.pop(0)
        data = instance._data
        mask = instance._mask
        cls = type(instance)
        result = getattr(data, methodname)(*args, **params).view(cls)
        result._update_from(instance)
        if result.ndim:
            if not self._onmask:
                result.__setmask__(mask)
            elif mask is not nomask:
                result.__setmask__(getattr(mask, methodname)(*args, **params))
        else:
            if mask.ndim and (not mask.dtype.names and mask.all()):
                return masked
        return result

def unshare_mask(self):
        """
        Copy the mask and set the sharedmask flag to False.

        Whether the mask is shared between masked arrays can be seen from
        the `sharedmask` property. `unshare_mask` ensures the mask is not shared.
        A copy of the mask is only made if it was shared.

        See Also
        --------
        sharedmask

        """
        if self._sharedmask:
            self._mask = self._mask.copy()
            self._sharedmask = False
        return self

def common_fill_value(a, b):
    """
    Return the common filling value of two masked arrays, if any.

    If ``a.fill_value == b.fill_value``, return the fill value,
    otherwise return None.

    Parameters
    ----------
    a, b : MaskedArray
        The masked arrays for which to compare fill values.

    Returns
    -------
    fill_value : scalar or None
        The common fill value, or None.

    Examples
    --------
    >>> x = np.ma.array([0, 1.], fill_value=3)
    >>> y = np.ma.array([0, 1.], fill_value=3)
    >>> np.ma.common_fill_value(x, y)
    3.0

    """
    t1 = get_fill_value(a)
    t2 = get_fill_value(b)
    if t1 == t2:
        return t1
    return None

def __str__(self):
        return "Masked version of %s. [Invalid values are masked]" % str(self.f)

def reduce(self, target, axis=0, dtype=None):
        """
        Reduce `target` along the given `axis`.

        """
        tclass = get_masked_subclass(target)
        m = getmask(target)
        t = filled(target, self.filly)
        if t.shape == ():
            t = t.reshape(1)
            if m is not nomask:
                m = make_mask(m, copy=1)
                m.shape = (1,)

        if m is nomask:
            tr = self.f.reduce(t, axis)
            mr = nomask
        else:
            tr = self.f.reduce(t, axis, dtype=dtype or t.dtype)
            mr = umath.logical_and.reduce(m, axis)

        if not tr.shape:
            if mr:
                return masked
            else:
                return tr
        masked_tr = tr.view(tclass)
        masked_tr._mask = mr
        return masked_tr

def _check_mask_axis(mask, axis):
    "Check whether there are masked values along the given axis"
    if mask is not nomask:
        return mask.all(axis=axis)
    return nomask


###############################################################################
#                             Masking functions                               #
###############################################################################

def display(self):
        """
        Display the string to print for masked values.

        """
        return self._display

def set_display(self, s):
        """
        Set the string to print for masked values.

        """
        self._display = s

def __getitem__(self, indx):
        result = self.dataiter.__getitem__(indx).view(type(self.ma))
        if self.maskiter is not None:
            _mask = self.maskiter.__getitem__(indx)
            if isinstance(_mask, ndarray):
                # set shape to match that of data; this is needed for matrices
                _mask.shape = result.shape
                result._mask = _mask
            elif isinstance(_mask, np.void):
                return mvoid(result, mask=_mask, hardmask=self.ma._hardmask)
            elif _mask:  # Just a scalar, masked
                return masked
        return result

    # This won't work if ravel makes a copy

def __next__(self):
        """
        Return the next value, or raise StopIteration.

        Examples
        --------
        >>> x = np.ma.array([3, 2], mask=[0, 1])
        >>> fl = x.flat
        >>> fl.next()
        3
        >>> fl.next()
        masked_array(data = --,
                     mask = True,
               fill_value = 1e+20)
        >>> fl.next()
        Traceback (most recent call last):
          File "<stdin>", line 1, in <module>
          File "/home/ralf/python/numpy/numpy/ma/core.py", line 2243, in next
            d = self.dataiter.next()
        StopIteration

        """
        d = next(self.dataiter)
        if self.maskiter is not None:
            m = next(self.maskiter)
            if isinstance(m, np.void):
                return mvoid(d, mask=m, hardmask=self.ma._hardmask)
            elif m:  # Just a scalar, masked
                return masked
        return d

def __setslice__(self, i, j, value):
        """
        x.__setslice__(i, j, value) <==> x[i:j]=value

        Set the slice (i,j) of a to value. If value is masked, mask those
        locations.

        """
        self.__setitem__(slice(i, j), value)

def _set_recordmask(self):
        """
        Return the mask of the records.

        A record is masked when all the fields are masked.

        """
        raise NotImplementedError("Coming soon: setting the mask per records!")

def harden_mask(self):
        """
        Force the mask to hard.

        Whether the mask of a masked array is hard or soft is determined by
        its `hardmask` property. `harden_mask` sets `hardmask` to True.

        See Also
        --------
        hardmask

        """
        self._hardmask = True
        return self

def soften_mask(self):
        """
        Force the mask to soft.

        Whether the mask of a masked array is hard or soft is determined by
        its `hardmask` property. `soften_mask` sets `hardmask` to False.

        See Also
        --------
        hardmask

        """
        self._hardmask = False
        return self

def get_fill_value(self):
        """
        Return the filling value of the masked array.

        Returns
        -------
        fill_value : scalar
            The filling value.

        Examples
        --------
        >>> for dt in [np.int32, np.int64, np.float64, np.complex128]:
        ...     np.ma.array([0, 1], dtype=dt).get_fill_value()
        ...
        999999
        999999
        1e+20
        (1e+20+0j)

        >>> x = np.ma.array([0, 1.], fill_value=-np.inf)
        >>> x.get_fill_value()
        -inf

        """
        if self._fill_value is None:
            self._fill_value = _check_fill_value(None, self.dtype)
        return self._fill_value[()]

def compressed(self):
        """
        Return all the non-masked data as a 1-D array.

        Returns
        -------
        data : ndarray
            A new `ndarray` holding the non-masked data is returned.

        Notes
        -----
        The result is **not** a MaskedArray!

        Examples
        --------
        >>> x = np.ma.array(np.arange(5), mask=[0]*2 + [1]*3)
        >>> x.compressed()
        array([0, 1])
        >>> type(x.compressed())
        <type 'numpy.ndarray'>

        """
        data = ndarray.ravel(self._data)
        if self._mask is not nomask:
            data = data.compress(np.logical_not(ndarray.ravel(self._mask)))
        return data