def _save_cat(file_name, radar, lidar, model, obs, keep_uuid):
"""Creates a categorize netCDF4 file and saves all data into it."""
def _merge_source():
# Probably should include mwr and model source if existing
rootgrp.source = f"radar: {radar.source}\nlidar: {lidar.source}"
dims = {
'time': len(radar.time),
'height': len(radar.height),
'model_time': len(model.time),
'model_height': len(model.mean_height)
}
rootgrp = output.init_file(file_name, dims, obs, keep_uuid)
uuid = rootgrp.file_uuid
output.add_file_type(rootgrp, 'categorize')
output.copy_global(radar.dataset, rootgrp,
('year', 'month', 'day', 'location'))
rootgrp.title = f"Categorize file from {radar.location}"
# Needs to solve how to provide institution
# rootgrp.institution = f"Data processed at {config.INSTITUTE}"
rootgrp.references = 'https://doi.org/10.1175/BAMS-88-6-883'
output.merge_history(rootgrp, 'categorize', radar, lidar)
_merge_source()
rootgrp.close()
return uuid
def test_merge_history():
root = RootGrp()
file_type = 'dummy'
source1 = RootGrp()
source1.dataset.history = 'some history x'
source2 = RootGrp()
source2.dataset.history = 'some history y'
output.merge_history(root, file_type, source1, source2)
assert utils.is_timestamp(f"-{root.history[:19]}") is True
assert root.history[
19:] == ' - dummy file created\nsome history x\nsome history y'
def test_merge_history():
root = RootGrp()
file_type = "dummy"
source1 = RootGrp()
source1.dataset.history = "20:00 some history x"
source2 = RootGrp()
source2.dataset.history = "21:00 some history y"
output.merge_history(root, file_type, {"a": source1, "b": source2})
history = str(root.history)
assert utils.is_timestamp(f"-{history[:19]}") is True
assert (
history[19:] ==
" +00:00 - dummy file created\n21:00 some history y\n20:00 some history x"
)
def _save_cat(full_path: str, data_obs: dict, cloudnet_arrays: dict,
keep_uuid: bool, uuid: Union[str, None]) -> str:
"""Creates a categorize netCDF4 file and saves all data into it."""
dims = {
'time': len(data_obs['radar'].time),
'height': len(data_obs['radar'].height),
'model_time': len(data_obs['model'].time),
'model_height': len(data_obs['model'].mean_height)
}
file_type = 'categorize'
nc = output.init_file(full_path, dims, cloudnet_arrays, keep_uuid, uuid)
uuid = nc.file_uuid
output.add_file_type(nc, file_type)
output.copy_global(data_obs['radar'].dataset, nc,
('year', 'month', 'day', 'location'))
nc.title = f"{file_type.capitalize()} file from {data_obs['radar'].location}"
nc.source_file_uuids = output.get_source_uuids(*data_obs.values())
output.add_references(nc, file_type)
output.merge_history(nc, file_type, data_obs['radar'], data_obs['lidar'])
nc.close()
return uuid
def _save_cat(full_path: str, data_obs: dict, cloudnet_arrays: dict,
uuid: Union[str, None]) -> str:
"""Creates a categorize netCDF4 file and saves all data into it."""
dims = {
"time": len(data_obs["radar"].time),
"height": len(data_obs["radar"].height),
"model_time": len(data_obs["model"].time),
"model_height": len(data_obs["model"].mean_height),
}
file_type = "categorize"
nc = output.init_file(full_path, dims, cloudnet_arrays, uuid)
uuid_out = nc.file_uuid
nc.cloudnet_file_type = file_type
output.copy_global(data_obs["radar"].dataset, nc,
("year", "month", "day", "location"))
nc.title = f"Cloud categorization products from {data_obs['radar'].location}"
nc.source_file_uuids = output.get_source_uuids(*data_obs.values())
nc.references = output.get_references(file_type)
output.add_source_instruments(nc, data_obs)
output.merge_history(nc, file_type, data_obs)
nc.close()
return uuid_out