def test_combining_overlapping_dateranges():
d = Datasource()
daterange_1 = "2001-01-01-00:00:00_2001-03-01-00:00:00"
daterange_2 = "2001-02-01-00:00:00_2001-06-01-00:00:00"
dateranges = [daterange_1, daterange_2]
combined = d.combine_dateranges(dateranges=dateranges)
assert combined == ['2001-01-01-00:00:00+00:00_2001-06-01-00:00:00+00:00']
daterange_1 = "2001-01-01-00:00:00_2001-03-01-00:00:00"
daterange_2 = "2001-02-01-00:00:00_2001-06-01-00:00:00"
daterange_3 = "2001-05-01-00:00:00_2001-08-01-00:00:00"
daterange_4 = "2004-05-01-00:00:00_2004-08-01-00:00:00"
daterange_5 = "2004-04-01-00:00:00_2004-09-01-00:00:00"
daterange_6 = "2007-04-01-00:00:00_2007-09-01-00:00:00"
dateranges = [daterange_1, daterange_2, daterange_3, daterange_4, daterange_5, daterange_6]
combined = d.combine_dateranges(dateranges=dateranges)
assert combined == ['2001-01-01-00:00:00+00:00_2001-08-01-00:00:00+00:00',
'2004-04-01-00:00:00+00:00_2004-09-01-00:00:00+00:00',
'2007-04-01-00:00:00+00:00_2007-09-01-00:00:00+00:00']
def test_set_incorrect_rank_raises():
d = Datasource()
daterange = "2027-08-01-00:00:00_2027-12-01-00:00:00"
with pytest.raises(TypeError):
d.set_rank(rank=42, daterange=daterange)
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 = xarray.load_dataset(filepath)
metadata = {"some": "metadata"}
d = Datasource("dataset_test")
d.add_data(metadata=metadata, data=ds, data_type="footprint")
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_exists():
d = Datasource(name="testing")
d.save()
exists = Datasource.exists(datasource_id=d.uuid())
assert exists == True
def test_save_footprint():
bucket = get_local_bucket(empty=True)
metadata = {"test": "testing123"}
dir_path = os.path.dirname(__file__)
test_data = "../data/emissions"
filename = "WAO-20magl_EUROPE_201306_downsampled.nc"
filepath = os.path.join(dir_path, test_data, filename)
data = xarray.open_dataset(filepath)
datasource = Datasource(name="test_name")
datasource.add_data(metadata=metadata, data=data, data_type="footprint")
datasource.save()
prefix = f"{Datasource._datasource_root}/uuid/{datasource._uuid}"
objs = get_object_names(bucket, prefix)
datasource_2 = Datasource.load(bucket=bucket, key=objs[0])
date_key = "2013-06-02-00:00:00+00:00_2013-06-30-00:00:00+00:00"
data = datasource_2._data[date_key]
assert float(data.pressure[0].values) == pytest.approx(1023.971)
assert float(data.pressure[2].values) == pytest.approx(1009.940)
assert float(data.pressure[-1].values) == pytest.approx(1021.303)
def test_add_data(data):
d = Datasource(name="test")
metadata = data["ch4"]["metadata"]
ch4_data = data["ch4"]["data"]
assert ch4_data["ch4"][0] == pytest.approx(1960.24)
assert ch4_data["ch4 stdev"][0] == pytest.approx(0.236)
assert ch4_data["ch4 n_meas"][0] == pytest.approx(26.0)
d.add_data(metadata=metadata, data=ch4_data)
date_key = "2014-01-30-10:52:30+00:00_2014-01-30-14:20:30+00:00"
assert d._data[date_key]["ch4"].equals(ch4_data["ch4"])
assert d._data[date_key]["ch4 stdev"].equals(ch4_data["ch4 stdev"])
assert d._data[date_key]["ch4 n_meas"].equals(ch4_data["ch4 n_meas"])
datasource_metadata = d.metadata()
assert datasource_metadata["data_type"] == "timeseries"
assert datasource_metadata["inlet"] == "248m"
assert datasource_metadata["instrument"] == "picarro"
assert datasource_metadata["port"] == "8"
assert datasource_metadata["site"] == "bsd"
assert datasource_metadata["species"] == "ch4"
def assign_data(gas_data, lookup_results, overwrite):
""" Create or get an existing Datasource for each gas in the file
Args:
gas_data (dict): Dictionary containing data and metadata for species
Returns:
dict: Dictionary of UUIDs of Datasources data has been assigned to keyed by species name
"""
from HUGS.Modules import Datasource
uuids = {}
# Add in copying of attributes, or add attributes to the metadata at an earlier state.
for species in gas_data:
metadata = gas_data[species]["metadata"]
data = gas_data[species]["data"]
name = lookup_results[species]["name"]
uuid = lookup_results[species]["uuid"]
# If we have a UUID for this Datasource load the existing object
# from the object store
if uuid:
datasource = Datasource.load(uuid=uuid)
else:
datasource = Datasource(name=name)
# Add the dataframe to the datasource
datasource.add_data(metadata=metadata, data=data, overwrite=overwrite)
# Save Datasource to object store
datasource.save()
uuids[name] = datasource.uuid()
return uuids
def test_combining_single_dateranges_returns():
d = Datasource()
daterange = "2027-08-01-00:00:00_2027-12-01-00:00:00"
combined = d.combine_dateranges(dateranges=[daterange])
assert combined[0] == daterange
def test_incorrect_datatype_raises(data):
d = Datasource(name="testing_123")
metadata = data["ch4"]["metadata"]
ch4_data = data["ch4"]["data"]
with pytest.raises(TypeError):
d.add_data(metadata=metadata, data=ch4_data, data_type="CRDS")
def test_set_rank():
d = Datasource()
daterange = "2027-08-01-00:00:00_2027-12-01-00:00:00"
d.set_rank(rank=1, daterange=daterange)
assert d._rank[1] == ['2027-08-01-00:00:00_2027-12-01-00:00:00']
def test_combining_no_overlap():
d = Datasource()
daterange_1 = "2001-01-01-00:00:00_2001-03-01-00:00:00"
daterange_2 = "2011-02-01-00:00:00_2011-06-01-00:00:00"
dateranges = [daterange_1, daterange_2]
combined = d.combine_dateranges(dateranges=dateranges)
assert combined == ['2001-01-01-00:00:00+00:00_2001-03-01-00:00:00+00:00', '2011-02-01-00:00:00+00:00_2011-06-01-00:00:00+00:00']
def test_search_metadata():
d = Datasource(name="test_search")
d._metadata = {"unladen": "swallow", "spam": "eggs"}
assert d.search_metadata("swallow") == True
assert d.search_metadata("eggs") == True
assert d.search_metadata("eggs") == True
assert d.search_metadata("Swallow") == True
assert d.search_metadata("beans") == False
assert d.search_metadata("flamingo") == False
def test_save(mock_uuid2):
bucket = get_local_bucket()
datasource = Datasource(name="test_name")
datasource.add_metadata(key="data_type", value="timeseries")
datasource.save(bucket)
prefix = f"{Datasource._datasource_root}/uuid/{datasource._uuid}"
objs = get_object_names(bucket, prefix)
assert objs[0].split("/")[-1] == mocked_uuid2
def test_search_metadata_find_all():
d = Datasource(name="test_search")
d._metadata = {"inlet": "100m", "instrument": "violin", "car": "toyota"}
result = d.search_metadata(search_terms=["100m", "violin", "toyota"], find_all=True)
assert result is True
result = d.search_metadata(search_terms=["100m", "violin", "toyota", "swallow"], find_all=True)
assert result is False
def test_split_daterange_str():
d = Datasource()
start_true = pd.Timestamp("2001-01-01-00:00:00", tz="UTC")
end_true = pd.Timestamp("2001-03-01-00:00:00", tz="UTC")
daterange_1 = "2001-01-01-00:00:00_2001-03-01-00:00:00"
start, end = d.split_datrange_str(daterange_str=daterange_1)
assert start_true == start
assert end_true == end
def test_setting_overlapping_dateranges():
d = Datasource()
daterange = "2027-08-01-00:00:00_2027-12-01-00:00:00"
d.set_rank(rank=1, daterange=daterange)
assert d._rank[1] == ['2027-08-01-00:00:00_2027-12-01-00:00:00']
daterange_two = "2027-11-01-00:00:00_2028-06-01-00:00:00"
d.set_rank(rank=1, daterange=daterange_two)
assert d._rank[1] == ['2027-08-01-00:00:00+00:00_2028-06-01-00:00:00+00:00']
def test_get_dataframe_daterange():
n_days = 100
epoch = datetime.datetime(1970, 1, 1, 1, 1)
random_data = pd.DataFrame(
data=np.random.randint(0, 100, size=(100, 4)),
index=pd.date_range(epoch, epoch + datetime.timedelta(n_days - 1), freq="D"),
columns=list("ABCD"),
)
d = Datasource(name="test")
start, end = d.get_dataframe_daterange(random_data)
assert start == pd.Timestamp("1970-01-01 01:01:00+0000")
assert end == pd.Timestamp("1970-04-10 01:01:00+0000")
def test_versioning(data):
# Take head of data
# Then add the full data, check versioning works correctly
metadata = {"foo": "bar"}
d = Datasource(name="foo")
# Fix the UUID for the tests
d._uuid = "4b91f73e-3d57-47e4-aa13-cb28c35d3b3d"
ch4_data = data["ch4"]["data"]
v1 = ch4_data.head(20)
v2 = ch4_data.head(30)
v3 = ch4_data.head(40)
d.add_data(metadata=metadata, data=v1)
d.save()
d.add_data(metadata=metadata, data=v2)
d.save()
d.add_data(metadata=metadata, data=v3)
d.save()
keys = d.versions()
assert (
keys["v1"]["keys"]["2014-01-30-10:52:30+00:00_2014-01-30-12:20:30+00:00"]
== "data/uuid/4b91f73e-3d57-47e4-aa13-cb28c35d3b3d/v1/2014-01-30-10:52:30+00:00_2014-01-30-12:20:30+00:00"
)
assert list(keys["v2"]["keys"].values()) == [
"data/uuid/4b91f73e-3d57-47e4-aa13-cb28c35d3b3d/v2/2014-01-30-10:52:30+00:00_2014-01-30-13:12:30+00:00"
]
assert list(keys["v3"]["keys"].values()) == [
"data/uuid/4b91f73e-3d57-47e4-aa13-cb28c35d3b3d/v3/2014-01-30-10:52:30+00:00_2014-01-30-13:22:30+00:00"
]
assert keys["v3"]["keys"] == keys["latest"]["keys"]
def test_in_daterange(data):
metadata = data["ch4"]["metadata"]
data = data["ch4"]["data"]
d = Datasource()
d.add_data(metadata=metadata, data=data)
d.save()
start = pd.Timestamp("2014-1-1")
end = pd.Timestamp("2014-2-1")
daterange = create_daterange_str(start=start, end=end)
d._data_keys["latest"]["2014-01-30-10:52:30+00:00_2014-01-30-14:20:30+00:00"] = ['data/uuid/ace2bb89-7618-4104-9404-a329c2bcd318/v1/2014-01-30-10:52:30+00:00_2014-01-30-14:20:30+00:00']
d._data_keys["latest"]["2015-01-30-10:52:30+00:00_2016-01-30-14:20:30+00:00"] = ['data/uuid/ace2bb89-7618-4104-9404-a329c2bcd318/v1/2015-01-30-10:52:30+00:00_2016-01-30-14:20:30+00:00']
d._data_keys["latest"]["2016-01-31-10:52:30+00:00_2017-01-30-14:20:30+00:00"] = ['data/uuid/ace2bb89-7618-4104-9404-a329c2bcd318/v1/2016-01-31-10:52:30+00:00_2017-01-30-14:20:30+00:00']
keys = d.in_daterange(daterange=daterange)
assert keys[0].split("/")[-1] == '2014-01-30-10:52:30+00:00_2014-01-30-14:20:30+00:00'
def test_update_daterange_replacement(data):
metadata = {"foo": "bar"}
d = Datasource(name="foo")
ch4_data = data["ch4"]["data"]
d.add_data(metadata=metadata, data=ch4_data)
assert d._start_datetime == pd.Timestamp("2014-01-30 10:52:30+00:00")
assert d._end_datetime == pd.Timestamp("2014-01-30 14:20:30+00:00")
ch4_short = ch4_data.head(40)
d._data = None
d.add_data(metadata=metadata, data=ch4_short, overwrite=True)
assert d._start_datetime == pd.Timestamp("2014-01-30 10:52:30+00:00")
assert d._end_datetime == pd.Timestamp("2014-01-30 13:22:30+00:00")
def test_to_data(data):
d = Datasource(name="testing_123")
metadata = data["ch4"]["metadata"]
ch4_data = data["ch4"]["data"]
assert ch4_data["ch4"][0] == pytest.approx(1960.24)
assert ch4_data["ch4 stdev"][0] == pytest.approx(0.236)
assert ch4_data["ch4 n_meas"][0] == pytest.approx(26.0)
d.add_data(metadata=metadata, data=ch4_data, data_type="timeseries")
obj_data = d.to_data()
metadata = obj_data["metadata"]
assert obj_data["name"] == "testing_123"
assert metadata["site"] == "bsd"
assert metadata["instrument"] == "picarro"
assert metadata["time_resolution"] == "1_minute"
assert metadata["inlet"] == "248m"
assert obj_data["data_type"] == "timeseries"
assert len(obj_data["data_keys"]) == 0
def test_from_data(data):
d = Datasource(name="testing_123")
metadata = data["ch4"]["metadata"]
ch4_data = data["ch4"]["data"]
d.add_data(metadata=metadata, data=ch4_data, data_type="timeseries")
obj_data = d.to_data()
bucket = get_local_bucket()
# Create a new object with the data from d
d_2 = Datasource.from_data(bucket=bucket, data=obj_data, shallow=False)
metadata = d_2.metadata()
assert metadata["site"] == "bsd"
assert metadata["instrument"] == "picarro"
assert metadata["time_resolution"] == "1_minute"
assert metadata["inlet"] == "248m"
assert d_2.to_data() == d.to_data()
def assign_data(self,
lookup_results,
source_name,
data,
metadata,
overwrite=False):
""" Assign data to a new or existing Datasource
Args:
lookup_results (dict): Results of Datasource lookup
source_name (str): Name of data source
data (xarray.Dataset): Data
metadata (dict): Dictionary of metadata
overwrite (bool, default=False): Should exisiting data be overwritten
Returns:
list: List of Datasource UUIDs
"""
from HUGS.Modules import Datasource
uuids = {}
for key in lookup_results:
uuid = lookup_results[key]["uuid"]
name = metadata["name"]
if uuid:
datasource = Datasource.load(uuid=uuid)
else:
datasource = Datasource(name=name)
datasource.add_data(metadata=metadata,
data=data,
data_type="footprint")
datasource.save()
uuids[name] = datasource.uuid()
return uuids
def datasource():
return Datasource(name="test_name",)
