Python Data.data[~msk]方法代码示例

# 需要导入模块: from pycmbs.data import Data [as 别名]
# 或者: from pycmbs.data.Data import data[~msk] [as 别名]
def get_T63_landseamask(shift_lon, mask_antarctica=True, area='land'):
    """
    get JSBACH T63 land sea mask
    the LS mask is read from the JSBACH init file

    area : str
        ['land','ocean']: When 'land', then the mask returned
        is True on land pixels, for ocean it is vice versa.
        In any other case, you get a valid field everywhere (globally)

    mask_antarctica : bool
        if True, then the mask is FALSE over Antarctica (<60S)
    """
    ls_file = get_data_pool_directory() \
        + 'variables/land/land_sea_mask/jsbach_T63_GR15_4tiles_1992.nc'
    ls_mask = Data(ls_file, 'slm', read=True, label='T63 land-sea mask',
                   lat_name='lat', lon_name='lon', shift_lon=shift_lon)
    if area == 'land':
        msk = ls_mask.data > 0.
    elif area == 'ocean':
        msk = ls_mask.data == 0.
    else:
        msk = np.ones(ls_mask.data.shape).astype('bool')

    ls_mask.data[~msk] = 0.
    ls_mask.data[msk] = 1.
    ls_mask.data = ls_mask.data.astype('bool')
    if mask_antarctica:
        ls_mask.data[ls_mask.lat < -60.] = False

    return ls_mask

# 需要导入模块: from pycmbs.data import Data [as 别名]
# 或者: from pycmbs.data.Data import data[~msk] [as 别名]
def get_generic_landseamask(shift_lon, mask_antarctica=True,
                            area='land', interpolation_method='remapnn',
                            target_grid='t63grid', force=False):
    """
    get generic land/sea mask. The routine uses the CDO command 'topo'
    to generate a 0.5 degree land/sea mask and remaps this
    using nearest neighbor
    to the target grid

    NOTE: using inconsistent land/sea masks between datasets can
    result in considerable biases. Note also that
    the application of l/s mask is dependent on the spatial resolution

    This routine implements a VERY simple approach, but assuming
    that all areas >0 m height are land and the rest is ocean.

    Parameters
    ----------
    shift_lon : bool
        specifies if longitudes shall be shifted
    interpolation_method : str
        specifies the interpolation method
        that shall be used for remapping the 0.5degree data
        to the target grid. This can be any of ['remapnn','remapcon',
        'remapbil']
    target_grid : str
        specifies target grid to interpolate to as
        similar to CDO remap functions. This can be either a string or
        a filename which includes valid geometry information
    force : bool
        force calculation (removes previous file) = slower

    area : str
        ['land','ocean']. When 'land', then the mask returned
        is True on land pixels, for ocean it is vice versa.
        in any other case, you get a valid field everywhere
        (globally)

    mask_antarctica : bool
        mask antarctica; if True, then the mask is
        FALSE over Antarctice (<60S)

    Returns
    -------
    returns a Data object
    """

    print ('WARNING: Automatic generation of land/sea mask. \
            Ensure that this is what you want!')

    cdo = Cdo()

    #/// construct output filename.
    #If a filename was given for the grid, replace path separators ///
    target_grid1 = target_grid.replace(os.sep, '_')
    outputfile = get_temporary_directory() + 'land_sea_fractions_' \
        + interpolation_method + '_' + target_grid1 + '.nc'

    print 'outfile: ', outputfile
    print 'cmd: ', '-remapnn,' + target_grid + ' -topo'

    #/// interpolate data to grid using CDO ///
    cdo.monmean(options='-f nc', output=outputfile,
                input='-remapnn,' + target_grid + ' -topo', force=force)

    #/// generate L/S mask from topography (land = height > 0.
    ls_mask = Data(outputfile, 'topo', read=True,
                   label='generic land-sea mask',
                   lat_name='lat', lon_name='lon',
                   shift_lon=shift_lon)
    print('Land/sea mask can be found on file: %s' % outputfile)

    if area == 'land':
        msk = ls_mask.data > 0.  # gives land
    elif area == 'ocean':
        msk = ls_mask.data <= 0.
    else:
        msk = np.ones(ls_mask.data.shape).astype('bool')
    ls_mask.data[~msk] = 0.
    ls_mask.data[msk] = 1.
    ls_mask.data = ls_mask.data.astype('bool')

    #/// mask Antarctica if desired ///
    if mask_antarctica:
        ls_mask.data[ls_mask.lat < -60.] = False
    return ls_mask