def test_get_height_levels(coord_data, expected_pressure):
""" Tests height level data extracted successfully and pressure flag set correctly """
dimension_key = list(coord_data)[0]
if dimension_key == "realizations":
expected_height_levels = None
else:
expected_height_levels = coord_data[dimension_key]
height_levels, pressure = utilities.get_height_levels(coord_data=coord_data)
np.testing.assert_array_equal(expected_height_levels, height_levels)
assert expected_pressure == pressure
def process(
mandatory_attributes_json: cli.inputjson,
*,
name="air_pressure_at_sea_level",
units=None,
spatial_grid="latlon",
time_period: int = None,
json_input: cli.inputjson = None,
ensemble_members: int = 8,
grid_spacing: float = None,
domain_corner: cli.comma_separated_list_of_float = None,
npoints: int = 71,
):
""" Generate a cube with metadata only.
Args:
mandatory_attributes_json (Dict):
Specifies the values of the mandatory attributes, title, institution and
source.
name (Optional[str]):
Output variable name, or if creating a probability cube the name of the
underlying variable to which the probability field applies.
units (Optional[str]):
Output variable units, or if creating a probability cube the units of the
underlying variable / threshold.
spatial_grid (Optional[str]):
What type of x/y coordinate values to use. Permitted values are
"latlon" or "equalarea".
time_period (Optional[int]):
The period in minutes between the time bounds. This is used to calculate
the lower time bound. If unset the diagnostic will be instantaneous, i.e.
without time bounds.
json_input (Optional[Dict]):
Dictionary containing values for one or more of: "name", "units", "time",
"time_bounds", "frt", "spp__relative_to_threshold", "attributes"
(dictionary of additional metadata attributes) and "coords" (dictionary).
"coords" can contain "height_levels" (list of height/pressure level values),
and one of "realizations", "percentiles" or "thresholds" (list of dimension
values).
ensemble_members (Optional[int]):
Number of ensemble members. Default 8. Will not be used if "realizations",
"percentiles" or "thresholds" provided in json_input.
grid_spacing (Optional[float]):
Resolution of grid (metres or degrees).
domain_corner (Optional[Tuple[float, float]]):
Bottom left corner of grid domain (y,x) (degrees for latlon or metres for
equalarea).
npoints (Optional[int]):
Number of points along each of the y and x spatial axes.
Returns:
iris.cube.Cube:
Output of generate_metadata()
"""
# Set arguments to pass to generate_metadata function and remove json_input for
# processing contents before adding
generate_metadata_args = locals()
for key in ["mandatory_attributes_json", "json_input"]:
generate_metadata_args.pop(key, None)
from improver.synthetic_data.generate_metadata import generate_metadata
from improver.synthetic_data.utilities import (
get_height_levels,
get_leading_dimension,
)
from improver.utilities.temporal import cycletime_to_datetime
if json_input is not None:
# Get leading dimension and height/pressure data from json_input
if "coords" in json_input:
coord_data = json_input["coords"]
(
json_input["leading_dimension"],
json_input["cube_type"],
) = get_leading_dimension(coord_data)
json_input["height_levels"], json_input["pressure"] = get_height_levels(
coord_data
)
json_input.pop("coords", None)
# Convert str time, frt and time_bounds to datetime
if "time" in json_input:
json_input["time"] = cycletime_to_datetime(json_input["time"])
if "frt" in json_input:
json_input["frt"] = cycletime_to_datetime(json_input["frt"])
if "time_bounds" in json_input:
time_bounds = []
for tb in json_input["time_bounds"]:
time_bounds.append(cycletime_to_datetime(tb))
json_input["time_bounds"] = time_bounds
# Update generate_metadata_args with the json_input data
generate_metadata_args.update(json_input)
return generate_metadata(mandatory_attributes_json, **generate_metadata_args)
