(m, badval=UNSEEN, rtol=1e-5, atol=1e-8, copy=True)
return np.ma.masked_values(np.array(m), badval, rtol=rtol, atol=atol, copy=copy)
Return map as a masked array, with ``badval`` pixels masked.

    Parameters
    ----------
    m : a map (may be a sequence of maps)
    badval : float, optional
        The value of the pixel considered as bad (:const:`UNSEEN` by default)
    rtol : float, optional
        The relative tolerance
    atol : float, optional
        The absolute tolerance
    copy : bool, optional
        If ``True``, a copy of the input map is made.

    Returns
    -------
    a masked array with the same shape as the input map,
    masked where input map is close to badval.

    See Also
    --------
    mask_good, mask_bad, numpy.ma.masked_values

    Examples
    --------
    >>> import healpy as hp
    >>> m = np.arange(12.)
    >>> m[3] = hp.UNSEEN
    >>> hp.ma(m)
    masked_array(data = [0.0 1.0 2.0 -- 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0],
                 mask = [False False False  True False False False False False False False False],
           fill_value = -1.6375e+30)
    <BLANKLINE>
Return map as a masked array, with ``badval`` pixels masked.
Return map as a masked array with badval pixels masked .
def ma(m, badval=UNSEEN, rtol=1e-5, atol=1e-8, copy=True):
    """Return map as a masked array, with ``badval`` pixels masked.

    Parameters
    ----------
    m : a map (may be a sequence of maps)
    badval : float, optional
        The value of the pixel considered as bad (:const:`UNSEEN` by default)
    rtol : float, optional
        The relative tolerance
    atol : float, optional
        The absolute tolerance
    copy : bool, optional
        If ``True``, a copy of the input map is made.

    Returns
    -------
    a masked array with the same shape as the input map,
    masked where input map is close to badval.

    See Also
    --------
    mask_good, mask_bad, numpy.ma.masked_values

    Examples
    --------
    >>> import healpy as hp
    >>> m = np.arange(12.)
    >>> m[3] = hp.UNSEEN
    >>> hp.ma(m)
    masked_array(data = [0.0 1.0 2.0 -- 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0],
                 mask = [False False False  True False False False False False False False False],
           fill_value = -1.6375e+30)
    <BLANKLINE>
    """
    return np.ma.masked_values(np.array(m), badval, rtol=rtol, atol=atol, copy=copy)
def ma ( m , badval = UNSEEN , rtol = 1e-5 , atol = 1e-8 , copy = True ) : return np . ma . masked_values ( np . array ( m ) , badval , rtol = rtol , atol = atol , copy = copy )