def test_load_dataset():
filename = "WAO-20magl_EUROPE_201306_small.nc"
dir_path = os.path.dirname(__file__)
test_data = "../data/emissions"
filepath = os.path.join(dir_path, test_data, filename)
ds = xr.load_dataset(filepath)
metadata = {"some": "metadata"}
d = Datasource()
d.add_data(metadata=metadata, data=ds, data_type="footprints")
d.save()
keys = d._data_keys["latest"]["keys"]
key = list(keys.values())[0]
bucket = get_local_bucket()
loaded_ds = Datasource.load_dataset(bucket=bucket, key=key)
assert loaded_ds.equals(ds)
def test_add_data(data):
d = Datasource()
metadata = data["ch4"]["metadata"]
ch4_data = data["ch4"]["data"]
assert ch4_data["ch4"][0] == pytest.approx(1959.55)
assert ch4_data["ch4_variability"][0] == pytest.approx(0.79)
assert ch4_data["ch4_number_of_observations"][0] == pytest.approx(26.0)
d.add_data(metadata=metadata, data=ch4_data, data_type="timeseries")
d.save()
bucket = get_local_bucket()
data_chunks = [
Datasource.load_dataset(bucket=bucket, key=k) for k in d.data_keys()
]
# Now read it out and make sure it's what we expect
combined = xr.concat(data_chunks, dim="time")
assert combined.equals(ch4_data)
expected_metadata = {
"site": "bsd",
"instrument": "picarro",
"sampling_period": "60",
"inlet": "248m",
"port": "9",
"type": "air",
"network": "decc",
"species": "ch4",
"scale": "wmo-x2004a",
"long_name": "bilsdale",
"data_owner": "simon o'doherty",
"data_owner_email": "*****@*****.**",
"inlet_height_magl": "248m",
"comment": "cavity ring-down measurements. output from gcwerks",
"source": "in situ measurements of air",
"conventions": "cf-1.6",
"calibration_scale": "wmo-x2004a",
"station_longitude": -1.15033,
"station_latitude": 54.35858,
"station_long_name": "bilsdale, uk",
"station_height_masl": 380.0,
"data_type": "timeseries",
}
assert d.metadata() == expected_metadata
def recombine_datasets(
keys: List[str],
sort: Optional[bool] = True,
attrs_to_check: Union[str, List[str], Dict[str, str], None] = None,
) -> Dataset:
"""Combines datasets stored separately in the object store
into a single dataset
Args:
keys: List of object store keys
sort: Sort the resulting Dataset by the time dimension. Default = True
attrs_to_check: Attributes to check for duplicates. If duplicates are present
a new data variable will be created containing the values from each dataset
If a dictionary is passed, the attribute(s) will be retained and the new value assigned.
If a list/string is passed, the attribute(s) will be removed.
Returns:
xarray.Dataset: Combined Dataset
"""
from xarray import concat as xr_concat
from openghg.store.base import Datasource
from openghg.objectstore import get_bucket
if not keys:
raise ValueError("No data keys passed.")
bucket = get_bucket()
data = [Datasource.load_dataset(bucket=bucket, key=k) for k in keys]
if attrs_to_check is None:
attrs_to_check = {"inlet": "multiple"}
# For specified attributes (e.g. "inlet")
# elevate duplicates to data variables within each Dataset
if attrs_to_check:
if isinstance(attrs_to_check, dict):
attributes = list(attrs_to_check.keys())
replace_values = list(attrs_to_check.values())
elif isinstance(attrs_to_check, str):
attributes = [attrs_to_check]
replace_values = [""]
else:
attributes = attrs_to_check
replace_values = [""] * len(attributes)
data = elevate_duplicate_attrs(ds_list=data, attributes=attributes)
# Concatenate datasets along time dimension
combined = xr_concat(data, dim="time")
# Replace/remove incorrect attributes
# - xr.concat will only take value from first dataset if duplicated
if attrs_to_check:
for attr, value in zip(attributes, replace_values):
if attr in combined: # Only update if attr was elevated to a data variable
if value:
combined.attrs[attr] = value
else:
combined.attrs.pop(attr)
if sort:
combined = combined.sortby("time")
# Check for duplicates?
# This is taken from https://stackoverflow.com/questions/51058379/drop-duplicate-times-in-xarray
# _, index = np.unique(combined['time'], return_index=True)
# combined = combined.isel(time=index)
return combined
