def test_basic(self): """Test that conform_metadata returns a cube with a suitable title attribute.""" result = conform_metadata(self.cube, self.cube_orig, self.coord) expected_attributes = {'title': 'IMPROVER Model Forecast'} self.assertIsInstance(result, iris.cube.Cube) self.assertDictEqual(result.attributes, expected_attributes)
def test_with_model_model_id_and_model_realization(self): """Test that a cube is dealt with correctly, if the cube contains a model, model_id and model_realization coordinate.""" coord = "model_id" result = conform_metadata(self.cube_model, self.cube_orig_model, coord) self.assertFalse(result.coords("model_id")) self.assertFalse(result.coords("model_realization"))
def test_scalar_coordinate_bound_removal(self): """Test that if a cube contains scalar coordinates, these coordinates do not have bounds.""" cube = self.cube cube.add_aux_coord( AuxCoord([10.], standard_name="height", units="m", bounds=np.array([5., 15.]))) result = conform_metadata( self.cube, self.cube_orig, self.coord, coords_for_bounds_removal=["height"]) self.assertFalse(result.coord("height").bounds)
def test_with_forecast_period(self): """Test that a cube is dealt with correctly, if the cube contains a forecast_reference_time and forecast_period coordinate.""" result = conform_metadata(self.cube, self.cube_orig, self.coord) self.assertEqual( result.coord("forecast_reference_time").points, np.max(self.cube_orig.coord("forecast_reference_time").points)) self.assertFalse(result.coord("forecast_reference_time").bounds) self.assertEqual( result.coord("forecast_period").points, np.min(self.cube_orig.coord("forecast_period").points)) self.assertFalse(result.coord("forecast_period").bounds)
def test_without_forecast_period(self): """Test that a cube is dealt with correctly, if the cube contains a forecast_reference_time coordinate but not a forecast_period.""" result = conform_metadata(self.cube_without_fp, self.cube_orig_without_fp, self.coord) fp_coord = self.cube_orig.coord("forecast_period").copy() fp_coord.convert_units("seconds") self.assertEqual( result.coord("forecast_reference_time").points, np.max(self.cube_orig.coord("forecast_reference_time").points)) self.assertFalse(result.coord("forecast_reference_time").bounds) self.assertEqual( result.coord("forecast_period").points, np.min(fp_coord.points)) self.assertFalse(result.coord("forecast_period").bounds)
def test_with_forecast_period_and_cycletime(self): """Test that a cube is dealt with correctly, if the cube contains a forecast_reference_time and forecast_period coordinate and a cycletime is specified.""" expected_forecast_reference_time = np.array([402294.]) expected_forecast_period = np.array([1.]) # 1 hour. result = conform_metadata( self.cube, self.cube_orig, self.coord, cycletime="20151123T0600Z") self.assertArrayAlmostEqual( result.coord("forecast_reference_time").points, expected_forecast_reference_time) self.assertFalse(result.coord("forecast_reference_time").bounds) self.assertEqual( result.coord("forecast_period").points, expected_forecast_period) self.assertFalse(result.coord("forecast_period").bounds)
def test_without_forecast_period_and_cycletime(self): """Test that a cube is dealt with correctly, if the cube contains a forecast_reference_time coordinate but not a forecast_period when a cycletime is specified. The same value for the forecast_period should be created compared to when the when the input cube has a forecast period coordinate.""" expected_forecast_reference_time = np.array([402294.]) expected_forecast_period = np.array([3600.]) result = conform_metadata( self.cube_without_fp, self.cube_orig_without_fp, self.coord, cycletime="20151123T0600Z") self.assertEqual( result.coord("forecast_reference_time").points, expected_forecast_reference_time) self.assertFalse(result.coord("forecast_reference_time").bounds) self.assertEqual( result.coord("forecast_period").points, expected_forecast_period) self.assertFalse(result.coord("forecast_period").bounds)
def test_forecast_coordinate_bounds_removal(self): """Test that if a cube has bounds on the forecast period and reference time, that these are removed""" self.cube_orig.coord("forecast_period").bounds = np.array( [[x - 0.5, x + 0.5] for x in self.cube_orig.coord("forecast_period").points]) self.cube_orig.coord("forecast_reference_time").bounds = np.array( [[x - 0.5, x + 0.5] for x in self.cube_orig.coord("forecast_reference_time").points]) self.cube.coord("forecast_period").bounds = np.array( [[x - 0.5, x + 0.5] for x in self.cube.coord("forecast_period").points]) self.cube.coord("forecast_reference_time").bounds = np.array( [[x - 0.5, x + 0.5] for x in self.cube.coord("forecast_reference_time").points]) result = conform_metadata(self.cube, self.cube_orig, "forecast_reference_time") self.assertIsNone(result.coord("forecast_reference_time").bounds) self.assertIsNone(result.coord("forecast_period").bounds)
def test_basic(self): """Test that conform_metadata returns a cube.""" result = conform_metadata(self.cube, self.cube_orig, self.coord) self.assertIsInstance(result, iris.cube.Cube)
def main(argv=None): """Load in arguments and ensure they are set correctly. Then load in the data to blend and calculate default weights using the method chosen before carrying out the blending.""" parser = ArgParser( description='Calculate the default weights to apply in weighted ' 'blending plugins using the ChooseDefaultWeightsLinear or ' 'ChooseDefaultWeightsNonLinear plugins. Then apply these ' 'weights to the dataset using the BasicWeightedAverage plugin.' ' Required for ChooseDefaultWeightsLinear: y0val and ynval.' ' Required for ChooseDefaultWeightsNonLinear: cval.' ' Required for ChooseWeightsLinear with dict: wts_dict.') parser.add_argument('--wts_calc_method', metavar='WEIGHTS_CALCULATION_METHOD', choices=['linear', 'nonlinear', 'dict'], default='linear', help='Method to use to calculate ' 'weights used in blending. "linear" (default): ' 'calculate linearly varying blending weights. ' '"nonlinear": calculate blending weights that decrease' ' exponentially with increasing blending coordinate. ' '"dict": calculate weights using a dictionary passed ' 'in as a command line argument.') parser.add_argument('coordinate', type=str, metavar='COORDINATE_TO_AVERAGE_OVER', help='The coordinate over which the blending ' 'will be applied.') parser.add_argument('--coordinate_unit', metavar='UNIT_STRING', default='hours since 1970-01-01 00:00:00', help='Units for blending coordinate. Default= ' 'hours since 1970-01-01 00:00:00') parser.add_argument('--calendar', metavar='CALENDAR', help='Calendar for time coordinate. Default=gregorian') parser.add_argument('--cycletime', metavar='CYCLETIME', type=str, help='The forecast reference time to be used after ' 'blending has been applied, in the format ' 'YYYYMMDDTHHMMZ. If not provided, the blended file ' 'will take the latest available forecast reference ' 'time from the input cubes supplied.') parser.add_argument('--model_id_attr', metavar='MODEL_ID_ATTR', type=str, default="mosg__model_configuration", help='The name of the netCDF file attribute to be ' 'used to identify the source model for ' 'multi-model blends. Default assumes Met Office ' 'model metadata. Must be present on all input ' 'files if blending over models.') parser.add_argument('--spatial_weights_from_mask', action='store_true', default=False, help='If set this option will result in the generation' ' of spatially varying weights based on the' ' masks of the data we are blending. The' ' one dimensional weights are first calculated ' ' using the chosen weights calculation method,' ' but the weights will then be adjusted spatially' ' based on where there is masked data in the data' ' we are blending. The spatial weights are' ' calculated using the' ' SpatiallyVaryingWeightsFromMask plugin.') parser.add_argument('weighting_mode', metavar='WEIGHTED_BLEND_MODE', choices=['weighted_mean', 'weighted_maximum'], help='The method used in the weighted blend. ' '"weighted_mean": calculate a normal weighted' ' mean across the coordinate. ' '"weighted_maximum": multiplies the values in the' ' coordinate by the weights, and then takes the' ' maximum.') parser.add_argument('input_filepaths', metavar='INPUT_FILES', nargs="+", help='Paths to input files to be blended.') parser.add_argument('output_filepath', metavar='OUTPUT_FILE', help='The output path for the processed NetCDF.') spatial = parser.add_argument_group( 'Spatial weights from mask options', 'Options for calculating the spatial weights using the ' 'SpatiallyVaryingWeightsFromMask plugin.') spatial.add_argument('--fuzzy_length', metavar='FUZZY_LENGTH', type=float, default=20000, help='When calculating spatially varying weights we' ' can smooth the weights so that areas close to' ' areas that are masked have lower weights than' ' those further away. This fuzzy length controls' ' the scale over which the weights are smoothed.' ' The fuzzy length is in terms of m, the' ' default is 20km. This distance is then' ' converted into a number of grid squares,' ' which does not have to be an integer. Assumes' ' the grid spacing is the same in the x and y' ' directions, and raises an error if this is not' ' true. See SpatiallyVaryingWeightsFromMask for' ' more detail.') linear = parser.add_argument_group( 'linear weights options', 'Options for the linear weights ' 'calculation in ' 'ChooseDefaultWeightsLinear') linear.add_argument('--y0val', metavar='LINEAR_STARTING_POINT', type=float, help='The relative value of the weighting start point ' '(lowest value of blend coord) for choosing default ' 'linear weights. This must be a positive float or 0.') linear.add_argument('--ynval', metavar='LINEAR_END_POINT', type=float, help='The relative value of the weighting ' 'end point (highest value of blend coord) for choosing' ' default linear weights. This must be a positive ' 'float or 0. Note that if blending over forecast ' 'reference time, ynval >= y0val would normally be ' 'expected (to give greater weight to the more recent ' 'forecast).') nonlinear = parser.add_argument_group( 'nonlinear weights options', 'Options for the non-linear ' 'weights calculation in ' 'ChooseDefaultWeightsNonLinear') nonlinear.add_argument('--cval', metavar='NON_LINEAR_FACTOR', type=float, help='Factor used to determine how skewed the ' 'non linear weights will be. ' 'A value of 1 implies equal weighting. If not ' 'set, a default value of cval=0.85 is set.') wts_dict = parser.add_argument_group( 'dict weights options', 'Options for linear weights to be ' 'calculated based on parameters ' 'read from a json file dict') wts_dict.add_argument('--wts_dict', metavar='WEIGHTS_DICTIONARY', help='Path to json file containing dictionary from ' 'which to calculate blending weights. Dictionary ' 'format is as specified in the improver.blending.' 'weights.ChooseWeightsLinear plugin.') wts_dict.add_argument('--weighting_coord', metavar='WEIGHTING_COORD', default='forecast_period', help='Name of ' 'coordinate over which linear weights should be ' 'scaled. This coordinate must be avilable in the ' 'weights dictionary.') args = parser.parse_args(args=argv) # if the linear weights method is called with non-linear args or vice # versa, exit with error if (args.wts_calc_method == "linear") and args.cval: parser.wrong_args_error('cval', 'linear') if ((args.wts_calc_method == "nonlinear") and np.any([args.y0val, args.ynval])): parser.wrong_args_error('y0val, ynval', 'non-linear') if (args.wts_calc_method == "dict") and not args.wts_dict: parser.error('Dictionary is required if --wts_calc_method="dict"') # set blending coordinate units if "time" in args.coordinate: coord_unit = Unit(args.coordinate_unit, args.calendar) elif args.coordinate_unit != 'hours since 1970-01-01 00:00:00.': coord_unit = args.coordinate_unit else: coord_unit = 'no_unit' # For blending across models, only blending across "model_id" is directly # supported. This is because the blending coordinate must be sortable, in # order to ensure that the data cube and the weights cube have coordinates # in the same order for blending. Whilst the model_configuration is # sortable itself, as it is associated with model_id, which is the # dimension coordinate, sorting the model_configuration coordinate can # result in the model_id coordinate becoming non-monotonic. As dimension # coordinates must be monotonic, this leads to the model_id coordinate # being demoted to an auxiliary coordinate. Therefore, for simplicity # model_id is used as the blending coordinate, instead of # model_configuration. # TODO: Support model_configuration as a blending coordinate directly. if args.coordinate == "model_configuration": blend_coord = "model_id" dict_coord = "model_configuration" else: blend_coord = args.coordinate dict_coord = args.coordinate # load cubes to be blended cubelist = load_cubelist(args.input_filepaths) # determine whether or not to equalise forecast periods for model # blending weights calculation weighting_coord = (args.weighting_coord if args.weighting_coord else "forecast_period") # prepare cubes for weighted blending merger = MergeCubesForWeightedBlending(blend_coord, weighting_coord=weighting_coord, model_id_attr=args.model_id_attr) cube = merger.process(cubelist, cycletime=args.cycletime) # if the coord for blending does not exist or has only one value, # update metadata only coord_names = [coord.name() for coord in cube.coords()] if (blend_coord not in coord_names) or (len( cube.coord(blend_coord).points) == 1): result = cube.copy() conform_metadata(result, cube, blend_coord, cycletime=args.cycletime) # raise a warning if this happened because the blend coordinate # doesn't exist if blend_coord not in coord_names: warnings.warn('Blend coordinate {} is not present on input ' 'data'.format(blend_coord)) # otherwise, calculate weights and blend across specified dimension else: weights = calculate_blending_weights( cube, blend_coord, args.wts_calc_method, wts_dict=args.wts_dict, weighting_coord=args.weighting_coord, coord_unit=coord_unit, y0val=args.y0val, ynval=args.ynval, cval=args.cval, dict_coord=dict_coord) if args.spatial_weights_from_mask: check_if_grid_is_equal_area(cube) grid_cells_x, _ = convert_distance_into_number_of_grid_cells( cube, args.fuzzy_length, int_grid_cells=False) SpatialWeightsPlugin = SpatiallyVaryingWeightsFromMask( grid_cells_x) weights = SpatialWeightsPlugin.process(cube, weights, blend_coord) # blend across specified dimension BlendingPlugin = WeightedBlendAcrossWholeDimension( blend_coord, args.weighting_mode, cycletime=args.cycletime) result = BlendingPlugin.process(cube, weights=weights) save_netcdf(result, args.output_filepath)
def process(self, cubelist, cycletime=None, model_id_attr=None, spatial_weights=False, fuzzy_length=20000): """ Merge a cubelist, calculate appropriate blend weights and compute the weighted mean. Returns a single cube collapsed over the dimension given by self.blend_coord. Args: cubelist (iris.cube.CubeList): List of cubes to be merged and blended Kwargs: cycletime (str): Forecast reference time to use for output cubes, in the format YYYYMMDDTHHMMZ. If not set, the latest of the input cube forecast reference times is used. model_id_attr (str): Name of the attribute by which to identify the source model and construct "model" coordinates for blending. spatial_weights (bool): If true, calculate spatial weights. fuzzy_length (float): Distance (in metres) over which to smooth spatial weights. Default is 20 km. """ # Prepare cubes for weighted blending, including creating model_id and # model_configuration coordinates for multi-model blending. The merged # cube has a monotonically ascending blend coordinate. Plugin raises an # error if blend_coord is not present on all input cubes. merger = MergeCubesForWeightedBlending( self.blend_coord, weighting_coord=self.weighting_coord, model_id_attr=model_id_attr) cube = merger.process(cubelist, cycletime=cycletime) # if blend_coord has only one value, or is not present (case where only # one model has been provided for a model blend) update metadata only coord_names = [coord.name() for coord in cube.coords()] if (self.blend_coord not in coord_names or len(cube.coord(self.blend_coord).points) == 1): result = cube.copy() conform_metadata(result, cube, self.blend_coord, cycletime=cycletime) # otherwise, calculate weights and blend across specified dimension else: # set up special treatment for model blending if "model" in self.blend_coord: self.blend_coord = "model_id" # calculate blend weights weights = self._calculate_blending_weights(cube) if spatial_weights: weights = self._update_spatial_weights(cube, weights, fuzzy_length) # blend across specified dimension BlendingPlugin = WeightedBlendAcrossWholeDimension( self.blend_coord, cycletime=cycletime) result = BlendingPlugin.process(cube, weights=weights) return result