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"""Module for saving netcdf cubes with desired attribute types."""
import os
import warnings
from typing import Optional, Union
import cf_units
import iris
from iris.cube import Cube, CubeList
from improver.metadata.check_datatypes import check_mandatory_standards
[docs]def _order_cell_methods(cube: Cube) -> None:
"""
Sorts the cell methods on a cube such that if there are multiple methods
they are always written in a consistent order in the output cube. The
input cube is modified.
Args:
cube:
The cube on which the cell methods are to be sorted.
"""
cell_methods = tuple(sorted(cube.cell_methods))
cube.cell_methods = cell_methods
[docs]def save_netcdf(
cubelist: Union[Cube, CubeList],
filename: str,
compression_level: int = 1,
least_significant_digit: Optional[int] = None,
) -> None:
"""Save the input Cube or CubeList as a NetCDF file and check metadata
where required for integrity.
Uses the functionality provided by iris.fileformats.netcdf.save with
local_keys to record non-global attributes as data attributes rather than
global attributes.
Args:
cubelist:
Cube or list of cubes to be saved
filename:
Filename to save input cube(s)
compression_level:
1-9 to specify compression level, or 0 to not compress (default compress
with complevel 1)
least_significant_digit:
If specified will truncate the data to a precision given by
10**(-least_significant_digit), e.g. if least_significant_digit=2, then the data will
be quantized to a precision of 0.01 (10**(-2)). See
http://www.esrl.noaa.gov/psd/data/gridded/conventions/cdc_netcdf_standard.shtml
for details. When used with `compression level`, this will result in lossy
compression.
Raises:
warning if cubelist contains cubes of varying dimensions.
"""
if isinstance(cubelist, iris.cube.Cube):
cubelist = iris.cube.CubeList([cubelist])
elif not isinstance(cubelist, iris.cube.CubeList):
cubelist = iris.cube.CubeList(cubelist)
for cube in cubelist:
_order_cell_methods(cube)
_check_metadata(cube)
# iris.fileformats.netcdf.save will add a new "least_significant_digit"
# attribute, but will not update an existing attribute when saving with
# different precision. Therefore we remove the "least_significant_digit"
# attribute if present.
cube.attributes.pop("least_significant_digit", None)
# If all xy slices are the same shape, use this to determine
# the chunksize for the netCDF (eg. 1, 1, 970, 1042)
chunksizes = None
if len({cube.shape[:2] for cube in cubelist}) == 1:
cube = cubelist[0]
if cube.ndim >= 2:
xy_chunksizes = [cube.shape[-2], cube.shape[-1]]
chunksizes = tuple([1] * (cube.ndim - 2) + xy_chunksizes)
else:
msg = "Chunksize not set as cubelist " "contains cubes of varying dimensions"
warnings.warn(msg)
global_keys = [
"title",
"um_version",
"grid_id",
"source",
"Conventions",
"institution",
"history",
]
global_keys.extend([key for key in cube.attributes.keys() if "mosg__" in key])
local_keys = {
key
for cube in cubelist
for key in cube.attributes.keys()
if key not in global_keys
}
if compression_level not in range(10):
raise ValueError(
"Compression level must be an integer value between 0 and 9 (0 to disable compression)"
)
# save atomically by writing to a temporary file and then renaming
ftmp = str(filename) + ".tmp"
iris.fileformats.netcdf.save(
cubelist,
ftmp,
local_keys=local_keys,
complevel=compression_level,
shuffle=True,
zlib=compression_level > 0,
chunksizes=chunksizes,
least_significant_digit=least_significant_digit,
)
os.rename(ftmp, filename)