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170 | def write(profiles, base_dir, verbose):
"""
Writing method for an instance of a (aprofiles.profiles.ProfilesData): class.
Args:
profiles (aprofiles.profiles.ProfilesData): Object to be written.
base_dir (str): Base path of the file should be written.
verbose (bool): Verbose mode.
"""
def _classify_scene(ds):
lowest_clouds = profiles._get_lowest_clouds()
scene = []
# got clouds classification here: https://www.metoffice.gov.uk/weather/learn-about/weather/types-of-weather/clouds
for i, lowest_cloud in enumerate(lowest_clouds):
if lowest_cloud <= 1981:
scene.append(3)
elif lowest_cloud > 1981 and lowest_cloud <= 6096:
scene.append(2)
elif lowest_cloud > 6096:
scene.append(1)
else:
scene.append(0)
# overwrite result based on foc
if ds.foc.data[i]:
scene[i] = 4
return scene
def _classify_retrieval_scene(ds):
lowest_clouds = profiles._get_lowest_clouds()
z_ref = profiles.data.z_ref.data
scene = []
for i, _ in enumerate(lowest_clouds):
if lowest_clouds[i] > z_ref[i]:
scene.append(1)
elif lowest_clouds[i] < z_ref[i]:
scene.append(3)
else:
scene.append(0)
# overwrite result based on foc
if ds.foc.data[i]:
scene[i] = 4
return scene
# get dataset from profilesdata
ds = profiles.data
# get date as string yyyy-mm-dd from first value of the time data
str_date = str(ds.time.values[0])[:10]
yyyy = str_date[:4]
mm = str_date[5:7]
dd = str_date[8:10]
filename = f"AP_{ds.wigos_station_id}-{ds.instrument_id}-{str_date}.nc"
path = os.path.join(base_dir, yyyy, mm, dd, filename)
if utils.file_exists(path) and verbose:
warnings.warn(f"{path} already exists and will be overwritten.")
else:
from pathlib import Path
Path(base_dir, yyyy, mm, dd).mkdir(parents=True, exist_ok=True)
# creates a copy od original dataset -> writes only necessary data
ds_towrite = copy.deepcopy(ds)
# for the mass concentration, we just need the mec.
mec = {}
for data_var in list(ds_towrite.data_vars):
if "mass_concentration:" in data_var:
mec[data_var.split(":")[1]] = ds[data_var].mec
ds_towrite = ds_towrite.drop(data_var)
# add mec as new dataarray
ds_towrite["mec"] = xr.DataArray(
data=list(mec.values()),
dims=["aer_type"],
coords=dict(
aer_type=list(mec.keys()),
),
attrs=dict(
long_name="Mass to Extinction Coefficient",
units="m2.g-1",
wavelength=ds.l0_wavelength.data,
),
)
# add attributes to aer_type
ds_towrite["aer_type"] = ds_towrite["aer_type"].assign_attrs(
{"long_name": "Aerosol type"}
)
# determines the scene classification for each profile
scene = _classify_scene(ds_towrite)
# add scene as new dataarray
ds_towrite["scene"] = ("time", scene)
ds_towrite["scene"] = ds_towrite["scene"].assign_attrs(
{
"long_name": "Scene classification",
"definition": "0: aer (aerosols only) / 1: high_cloud (base cloud above 6096 m) / 2: mid_cloud (base cloud between 1981 m and 6096 m) / 3: low-cloud (base cloud below 1981 m) - 4: foc (fog or condensation)",
}
)
# add scene for extinction profile: cloud_above, cloud_below
retrieval_scene = _classify_retrieval_scene(ds_towrite)
# add scene as new dataarray
ds_towrite["retrieval_scene"] = ("time", retrieval_scene)
ds_towrite["retrieval_scene"] = ds_towrite["retrieval_scene"].assign_attrs(
{
"long_name": "Retrieval scene classification",
"definition": "0: aer (aerosols only) / 1: cloud_above (cloud above reference altitude) / 3: cloud_below (cloud below reference point) / 4: foc (fog or condensation)",
}
)
# drop other variables
drop_variables = [
"start_time",
"vertical_visibility",
"cbh_uncertainties",
"uncertainties_att_backscatter_0",
]
for drop_var in drop_variables:
ds_towrite = ds_towrite.drop(drop_var)
# some variables have no dimension. Set it as attribute and drop the variable.
nodim_variables = ["l0_wavelength"]
for nodim_var in nodim_variables:
ds_towrite.attrs[nodim_var] = ds_towrite[nodim_var].data
ds_towrite = ds_towrite.drop(nodim_var)
# for convenience, add initial coordinates of station
coordinates_vars = ["station_altitude", "station_latitude", "station_longitude"]
for coordinate_var in coordinates_vars:
ds_towrite.attrs[f"{coordinate_var}_t0"] = ds_towrite[coordinate_var].data[0]
# add altitude direction
ds_towrite["altitude"] = ds_towrite["altitude"].assign_attrs({"positive": "up"})
# encoding with compression
encoding = {}
for varname, var in ds_towrite.variables.items():
if varname == "time":
continue
if var.dtype == np.int64:
encoding[varname] = {
"dtype": np.int32,
"zlib": True,
"chunksizes": var.shape,
}
if varname in ["extinction", "clouds"]:
encoding[varname] = {"zlib": True, "chunksizes": var.shape}
# convert also the quality_flag's variable flag_values attribute also to NC_INT instead of NC_INT64
ds_towrite["quality_flag"] = ds_towrite.quality_flag.assign_attrs(
{"flag_values": np.array([0, 1, 2], dtype=np.int32)}
)
# writes to netcdf
# ds_towrite.to_netcdf(path, mode="w", encoding=encoding)
ds_towrite.to_netcdf(path, mode="w")
|