def create_symbol_cube(self, cubes: Union[List[Cube], CubeList]) -> Cube:
"""
Create an empty weather symbol cube
Args:
cubes:
List of input cubes used to generate weather symbols
Returns:
A cube with suitable metadata to describe the weather symbols
that will fill it and data initiated with the value -1 to allow
any unset points to be readily identified.
"""
threshold_coord = find_threshold_coordinate(self.template_cube)
template_cube = next(self.template_cube.slices_over([threshold_coord
])).copy()
# remove coordinates and bounds that do not apply to weather symbols
template_cube.remove_coord(threshold_coord)
mandatory_attributes = generate_mandatory_attributes(cubes)
optional_attributes = weather_code_attributes()
if self.model_id_attr:
optional_attributes.update(
update_model_id_attr_attribute(cubes, self.model_id_attr))
symbols = create_new_diagnostic_cube(
"weather_code",
"1",
template_cube,
mandatory_attributes,
optional_attributes=optional_attributes,
data=np.ma.masked_all_like(template_cube.data).astype(np.int32),
)
return symbols
def _core_wxcube(time_points, num_grid_points):
"""
Set up a wxcube with unnamed spatial dimensions
Args:
time_points (numpy.ndarray):
Array of time points
num_grid_points (int):
Side length of square spatial grid
Returns:
iris.cube.Cube:
cube of weather codes set to 1
data shape (time_points, num_grid_points, num_grid_points)
"""
if time_points is None:
time_points = np.array([datetime_to_numdateval()])
data = np.ones((len(time_points), num_grid_points, num_grid_points))
cube = Cube(data,
long_name="weather_code",
units="1",
attributes=weather_code_attributes())
time_origin = "hours since 1970-01-01 00:00:00"
calendar = "gregorian"
tunit = Unit(time_origin, calendar)
cube.add_dim_coord(
DimCoord(time_points, standard_name="time", units=tunit), 0)
return cube
def create_symbol_cube(cubes):
"""
Create an empty weather symbol cube
Args:
cubes (list or iris.cube.CubeList):
List of input cubes used to generate weather symbols
Returns:
iris.cube.Cube:
A cube with suitable metadata to describe the weather symbols
that will fill it and data initiated with the value -1 to allow
any unset points to be readily identified.
"""
threshold_coord = find_threshold_coordinate(cubes[0])
template_cube = next(cubes[0].slices_over([threshold_coord])).copy()
# remove coordinates and bounds that do not apply to weather symbols
template_cube.remove_coord(threshold_coord)
for coord in template_cube.coords():
if coord.name() in ["forecast_period", "time"]:
coord.bounds = None
attributes = generate_mandatory_attributes(cubes)
symbols = create_new_diagnostic_cube(
"weather_code",
"1",
template_cube,
attributes,
optional_attributes=weather_code_attributes(),
data=np.ma.masked_all_like(template_cube.data).astype(np.int32),
)
return symbols
def create_symbol_cube(cubes):
"""
Create an empty weather symbol cube
Args:
cubes (list or iris.cube.CubeList):
List of input cubes used to generate weather symbols
Returns:
iris.cube.Cube:
A cube with suitable metadata to describe the weather symbols
that will fill it
"""
threshold_coord = find_threshold_coordinate(cubes[0])
template_cube = next(cubes[0].slices_over([threshold_coord])).copy()
# remove coordinates and bounds that do not apply to weather symbols
template_cube.remove_coord(threshold_coord)
for coord in template_cube.coords():
if coord.name() in ['forecast_period', 'time']:
coord.bounds = None
attributes = generate_mandatory_attributes(cubes)
symbols = create_new_diagnostic_cube(
"weather_code",
"1",
template_cube,
attributes,
optional_attributes=weather_code_attributes(),
dtype=np.int32)
return symbols
def test_metadata_saves(self):
"""Test that the metadata saves as NetCDF correctly."""
self.cube.attributes.update(weather_code_attributes())
save_netcdf(self.cube, self.nc_file)
result = load_cube(self.nc_file)
self.assertArrayEqual(result.attributes["weather_code"], self.wxcode)
self.assertEqual(result.attributes["weather_code_meaning"], self.wxmeaning)
def set_up_wxcube(
data=np.ones((16, 16), dtype=np.int8),
time=datetime.datetime(2018, 9, 12, 5, 43),
time_bounds=None,
frt=datetime.datetime(2018, 9, 12, 3),
lat_lon=False,
):
"""
Set up a wxcube for a particular time and location, which can cover
the terminator and test the "update_daynight" functionality
Args:
data (numpy array):
The weather codes with which to populate the array.
time (datetime.datetime):
Validity time for cube.
time_bounds ([datetime.datetime, datetime.datetime]):
Validity time bounds.
frt (datetime.datetime):
Forecast reference time for cube.
lat_lon (bool):
If True, returns a cube on a lat-lon grid.
If False, returns equal area.
Returns:
iris.cube.Cube:
cube of weather codes set to 1
data shape (time_points, 16, 16)
"""
kwargs = {
"name": "weather_code",
"units": 1,
"time": time,
"time_bounds": time_bounds,
"frt": frt,
"attributes": weather_code_attributes(),
"spatial_grid": "equalarea",
"domain_corner": (0, -30000),
}
if lat_lon:
kwargs.update({
"spatial_grid": "latlon",
"domain_corner": (49, -8),
"grid_spacing": 1
})
cube = set_up_variable_cube(data, **kwargs)
return cube
def wxcode_fixture():
"""Weather symbols cube (randomly sampled data in expected range)"""
data = np.random.randint(0, high=31, size=(3, 3))
attributes = {
"source": "IMPROVER",
"institution": "Met Office",
"title":
"IMPROVER Post-Processed Multi-Model Blend on 2 km Standard Grid",
"mosg__model_configuration": "uk_det uk_ens",
}
attributes.update(weather_code_attributes())
cube = set_up_variable_cube(
data,
name="weather_code",
units="1",
attributes=attributes,
spatial_grid="equalarea",
)
_update_blended_time_coords(cube)
return cube
def set_up_wxcube(time_points=None, lat_lon=False):
"""
Set up a wxcube for a particular time and location, which can cover
the terminator and test the "update_daynight" functionality
Args:
time_points (list of datetime.datetime):
List of time points as datetime instances
lat_lon (bool):
If True, returns a cube on a lat-lon grid.
If False, returns equal area.
Returns:
iris.cube.Cube:
cube of weather codes set to 1
data shape (time_points, 16, 16)
"""
kwargs = {
"name": "weather_code",
"units": 1,
"time": datetime.datetime(2018, 9, 12, 5, 43),
"frt": datetime.datetime(2018, 9, 12, 3),
"attributes": weather_code_attributes(),
"spatial_grid": "equalarea",
"domain_corner": (0, -30000),
}
if lat_lon:
kwargs.update({
"spatial_grid": "latlon",
"domain_corner": (49, -8),
"grid_spacing": 1
})
cube = set_up_variable_cube(np.ones((16, 16), dtype=np.float32), **kwargs)
if time_points is not None:
cube = add_coordinate(cube, time_points, "time", is_datetime=True)
return cube
def test_values(self):
"""Test attribute values are correctly set."""
result = weather_code_attributes()
self.assertArrayEqual(result["weather_code"], self.wxcode)
self.assertEqual(result["weather_code_meaning"], self.wxmeaning)
