def resample_timeseries():
paths = Paths()
io = ISMN_Interface(paths.ismn / 'downloaded' / 'CONUS_20100101_20190101')
# get all stations / sensors for each grid cell.
lut = pd.read_csv(paths.ismn / 'station_list.csv', index_col=0)
lut = lut.groupby('ease2_gpi').apply(
lambda x: '-'.join([i for i in x.index]))
dir_out = paths.ismn / 'timeseries'
for cnt, (gpi, indices) in enumerate(lut.iteritems()):
print('%i / %i' % (cnt, len(lut)))
fname = dir_out / ('%i.csv' % gpi)
idx = indices.split('-')
# Only one station within grid cell
if len(idx) == 1:
try:
ts = io.read_ts(int(idx[0]))
ts = ts[ts['soil moisture_flag'] == 'G']['soil moisture']
ts.tz_convert(None).to_csv(fname, float_format='%.4f')
except:
print('Corrupt file: ' + io.metadata[int(idx[0])]['filename'])
# Multiple stations within grid cell
else:
df = []
for i in idx:
try:
ts = io.read_ts(int(i))
df += [
ts[ts['soil moisture_flag'] == 'G']['soil moisture']
]
except:
print('Corrupt file: ' + io.metadata[int(i)]['filename'])
if len(df) == 0:
continue
df = pd.concat(df, axis=1)
df.columns = np.arange(len(df.columns))
# match temporal mean and standard deviation to those of the station with the maximum temporal coverage
n = np.array([len(df[i].dropna()) for i in df])
ref = np.where(n == n.max())[0][0]
for col in df:
if col != ref:
df[col] = (df[col] - df[col].mean()) / df[col].std(
) * df[ref].std() + df[ref].mean()
# Average measurements of all stations
df.mean(axis='columns').tz_convert(None).to_csv(
fname, float_format='%.4f')
def test_timezone_adapter(self):
c3s_data_folder = path.join(
Dataset.objects.get(short_name='C3S').storage_path,
'C3S_V201706/TCDR/063_images_to_ts/combined-daily')
c3s_reader = c3s_read(c3s_data_folder)
timezone_reader = TimezoneAdapter(c3s_reader)
orig_data = c3s_reader.read_ts(-155.42, 19.78)
data = timezone_reader.read_ts(-155.42, 19.78)
self.assertTrue(
np.array_equal(orig_data.index.values, data.index.values))
self.assertTrue(not hasattr(data.index, 'tz') or data.index.tz is None)
orig_data = c3s_reader.read(-155.42, 19.78)
data = timezone_reader.read(-155.42, 19.78)
self.assertTrue(
np.array_equal(orig_data.index.values, data.index.values))
self.assertTrue((not hasattr(data.index, 'tz'))
or (data.index.tz is None))
ismn_data_folder = path.join(
Dataset.objects.get(short_name='ISMN').storage_path,
'ISMN_V20191211')
ismn_reader = ISMN_Interface(ismn_data_folder)
timezone_reader2 = TimezoneAdapter(ismn_reader)
orig_data = ismn_reader.read_ts(0)
data = timezone_reader2.read_ts(0)
self.assertTrue(
np.array_equal(orig_data.index.values, data.index.values))
self.assertTrue((not hasattr(data.index, 'tz'))
or (data.index.tz is None))
max_depth=0.1)
for idx in ids:
metadata = ismn_reader.metadata[idx]
jobs.append((idx, metadata['longitude'], metadata['latitude']))
print("Jobs (gpi, lon, lat):")
print(jobs)
# For this small test dataset it is only one job
#
# It is important here that the ISMN reader has a read_ts function that works by just using the `dataset_id`. In this
# way the validation framework can go through the jobs and read the correct time series.
# In[6]:
data = ismn_reader.read_ts(ids[0])
print('ISMN data example:')
print(data.head())
# ## Initialize the Validation class
#
# The Validation class is the heart of the validation framwork. It contains the information about which datasets to
# read using which arguments or keywords and if they are spatially compatible. It also contains the settings about
# which metric calculators to use and how to perform the scaling into the reference data space. It is initialized in
# the following way:
# In[7]:
datasets = {
'ISMN': {
'class': ismn_reader,
def resample_ismn():
"""
This resamples ISMN data onto the EASE2 grid and stores data for each grid cell into .csv files.
If single grid cells contain multiple stations, they are averaged.
A grid look-up table needs to be created first (method: ancillary.grid.create_lut).
"""
paths = Paths()
io = ISMN_Interface(paths.ismn_raw)
# get all stations / sensors for each grid cell.
lut = pd.read_csv(paths.ismn / 'station_list.csv',index_col=0)
lut = lut.groupby('ease2_gpi').apply(lambda x: '-'.join([i for i in x.index]))
dir_out = paths.ismn / 'timeseries'
if not dir_out.exists():
dir_out.mkdir()
for cnt, (gpi, indices) in enumerate(lut.iteritems()):
print('%i / %i' % (cnt, len(lut)))
fname = dir_out / ('%i.csv' % gpi)
idx = indices.split('-')
# Only one station within grid cell
if len(idx) == 1:
try:
ts = io.read_ts(int(idx[0]))
ts = ts[ts['soil moisture_flag'] == 'G']['soil moisture'] # Get only "good" data based on ISMN QC
ts.tz_convert(None).to_csv(fname, float_format='%.4f')
except:
print('Corrupt file: ' + io.metadata[int(idx[0])]['filename'])
# Multiple stations within grid cell
else:
df = []
for i in idx:
try:
ts = io.read_ts(int(i))
df += [ts[ts['soil moisture_flag'] == 'G']['soil moisture']] # Get only "good" data based on ISMN QC
except:
print('Corrupt file: ' + io.metadata[int(i)]['filename'])
if len(df) == 0:
continue
df = pd.concat(df, axis=1)
df.columns = np.arange(len(df.columns))
# match temporal mean and standard deviation to those of the station with the maximum temporal coverage
n = np.array([len(df[i].dropna()) for i in df])
ref = np.where(n==n.max())[0][0]
for col in df:
if col != ref:
df[col] = (df[col] - df[col].mean())/df[col].std() * df[ref].std() + df[ref].mean()
# Average measurements of all stations
df.mean(axis='columns').tz_convert(None).to_csv(fname, float_format='%.4f')
class Test_ISMN_Interface_CeopUnzipped(unittest.TestCase):
@classmethod
def setUpClass(cls):
super(Test_ISMN_Interface_CeopUnzipped, cls).setUpClass()
testdata = os.path.join(testdata_root,
"Data_seperate_files_20170810_20180809")
metadata_path = os.path.join(testdata, "python_metadata")
cleanup(metadata_path)
ds = ISMN_Interface(testdata, network=[], parallel=True)
assert ds.networks == OrderedDict()
cls.testdata = testdata
def setUp(self) -> None:
self.ds = ISMN_Interface(self.testdata, network=["COSMOS"])
def tearDown(self) -> None:
self.ds.close_files()
logging.shutdown()
def test_list(self):
with pytest.deprecated_call():
assert len(self.ds.list_networks()) == 1
assert len(self.ds.list_stations()) == len(
self.ds.list_stations("COSMOS")) == 2
assert len(self.ds.list_sensors()) == 2
assert len(self.ds.list_sensors(station="Barrow-ARM")) == 1
def test_network_for_station(self):
assert self.ds.network_for_station("Barrow-ARM") == "COSMOS"
assert self.ds.network_for_station("ARM-1") == "COSMOS"
def test_stations_that_measure(self):
for s in self.ds.stations_that_measure("soil_moisture"):
assert s.name in ["ARM-1", "Barrow-ARM"]
for s in self.ds.stations_that_measure("nonexisting"):
raise AssertionError("Found var that doesnt exist")
def test_get_dataset_ids(self):
ids = self.ds.get_dataset_ids("soil_moisture",
max_depth=100,
groupby="network")
assert list(ids.keys()) == ["COSMOS"]
assert ids["COSMOS"] == [0, 1]
ids = self.ds.get_dataset_ids("soil_moisture", max_depth=0.19)
assert ids == [0]
ids = self.ds.get_dataset_ids(
["soil_moisture"],
max_depth=99,
filter_meta_dict={
"lc_2010": 210,
"network": "COSMOS",
"station": "Barrow-ARM",
},
)
assert ids == [1]
ids = self.ds.get_dataset_ids("novar")
assert len(ids) == 0
ids = self.ds.get_dataset_ids(["soil_moisture", "shouldhavenoeffect"],
0.0, 0.19) # should get 1
assert len(ids) == 1
ids = self.ds.get_dataset_ids("soil_moisture", 0.0,
1.0) # should get 2
assert len(ids) == 2
ids = self.ds.get_dataset_ids("soil_moisture",
0.0,
1.0,
filter_meta_dict={"lc_2010":
210}) # should get 1
assert len(ids) == 1
ids = self.ds.get_dataset_ids("nonexisting") # should get 0
assert len(ids) == 0
def test_read_ts(self):
data1 = self.ds.read(0)
assert not data1.empty
data2, meta = self.ds.read_ts(1, return_meta=True)
assert not data2.empty
def test_read_metadata(self):
data2, meta = self.ds.read_ts(1, return_meta=True)
assert all(meta == self.ds.read_metadata(1, format="pandas"))
assert self.ds.read_metadata(1, format="dict") is not None
assert self.ds.read_metadata([1], format="obj") is not None
assert not self.ds.metadata.empty
assert self.ds.metadata.loc[1]['station']['val'] \
== self.ds.read_metadata([0,1]).loc[1, ('station', 'val')]
def test_find_nearest_station(self):
should_lon, should_lat = -156.62870, 71.32980
station = self.ds.find_nearest_station(should_lon, should_lat)
assert station.lon == should_lon
assert station.lat == should_lat
def test_plot_station_locations(self):
with TemporaryDirectory() as out_dir:
outpath = os.path.join(out_dir, "plot.png")
self.ds.plot_station_locations(["soil_moisture", 'precipitation'],
markersize=5,
filename=outpath)
assert len(os.listdir(out_dir)) == 1
def test_get_min_max_obs_timestamps(self):
tmin, tmax = self.ds.get_min_max_obs_timestamps("soil_moisture",
max_depth=0.19)
assert tmin == datetime(2017, 8, 10, 0)
assert tmax == datetime(2018, 8, 9, 23)
def test_get_min_max_obs_timestamps_for_station(self):
station = self.ds.collection.networks["COSMOS"].stations["ARM-1"]
tmin, tmax = station.get_min_max_obs_timestamp("soil_moisture", 0,
0.19)
assert tmin == datetime(2017, 8, 10, 0)
assert tmax == datetime(2018, 8, 9, 23)
def test_get_static_var_val(self):
vals = self.ds.get_static_var_vals("soil_moisture", max_depth=0.19)
assert vals == {130: "Grassland"}
vals = self.ds.get_landcover_types("soil_moisture", max_depth=100)
assert len(vals) == 2
assert vals[130] == "Grassland"
assert vals[210] == "Water"
self.ds.print_landcover_dict()
vals = self.ds.get_climate_types("soil_moisture",
max_depth=100,
climate="climate_KG")
assert len(vals) == 2
assert vals["ET"] == "Polar Tundra"
assert vals["Cfa"] == "Temperate Without Dry Season, Hot Summer"
self.ds.print_climate_dict()
def test_get_var(self):
vars = self.ds.get_variables()
assert vars == ["soil_moisture"]
def test_get_sensors(self):
i = 0
for nw, station in self.ds.collection.iter_stations(
filter_meta_dict={"network": "COSMOS"}):
for se in station.iter_sensors():
data = se.read_data()
# check if the networks is COSMOS or station in [ARM, Barrow-ARM]
assert not data.empty
# check something for that one station
i += 1
assert i == 2
i = 0
for se in self.ds.networks["COSMOS"].stations[
"Barrow-ARM"].iter_sensors():
data = se.read_data()
assert not data.empty
# check something for that one station
i += 1
assert i == 1
i = 0
for net, stat, sens in self.ds.collection.iter_sensors(
depth=Depth(0, 1),
filter_meta_dict={"station": ["Barrow-ARM", "ARM-1"]},
):
data = sens.read_data()
assert not data.empty
i += 1
assert i == 2
for nw, station in self.ds.collection.iter_stations():
for se in station.iter_sensors(variable="nonexisting"):
raise ValueError("Found sensor, although none should exist")
def test_get_nearest_station(self):
should_lon, should_lat = -156.62870, 71.32980
station, dist = self.ds.collection.get_nearest_station(
should_lon, should_lat)
assert dist == 0
assert station.lon == should_lon
assert station.lat == should_lat
gpi, dist = self.ds.collection.grid.find_nearest_gpi(
int(should_lon), int(should_lat))
assert dist != 0
for net in self.ds.collection.iter_networks():
if station.name in net.stations.keys():
assert net.stations[station.name].lon == should_lon
assert net.stations[station.name].lat == should_lat
station, dist = self.ds.find_nearest_station(0,
0,
return_distance=True,
max_dist=100)
assert station == dist == None
def test_citation(self):
with TemporaryDirectory() as out_dir:
out_file = os.path.join(out_dir, 'citation.txt')
refs = self.ds.collection.export_citations(out_file=out_file)
assert all([
net in refs.keys()
for net in list(self.ds.collection.networks.keys())
])
assert os.path.exists(out_file)
with open(out_file, mode='r') as f:
lines = f.readlines()
assert len(lines) > 0
class Test_ISMN_Interface_CeopUnzipped(unittest.TestCase):
@classmethod
def setUpClass(cls):
super(Test_ISMN_Interface_CeopUnzipped, cls).setUpClass()
testdata = os.path.join(testdata_root,
'Data_seperate_files_20170810_20180809')
metadata_path = os.path.join(testdata, 'python_metadata')
cleanup(metadata_path)
ds = ISMN_Interface(testdata, network=[])
assert ds.networks == OrderedDict()
cls.testdata = testdata
def setUp(self) -> None:
self.ds = ISMN_Interface(self.testdata, network=['COSMOS'])
def tearDown(self) -> None:
self.ds.close_files()
logging.shutdown()
def test_list(self):
assert len(self.ds.list_networks()) == 1
assert len(self.ds.list_stations()) == len(self.ds.list_stations('COSMOS')) == 2
assert len(self.ds.list_sensors()) == 2
assert len(self.ds.list_sensors(station='Barrow-ARM')) == 1
def test_network_for_station(self):
assert self.ds.network_for_station('Barrow-ARM') == 'COSMOS'
assert self.ds.network_for_station('ARM-1') == 'COSMOS'
def test_stations_that_measure(self):
for s in self.ds.stations_that_measure('soil_moisture'):
assert s.name in ['ARM-1', 'Barrow-ARM']
for s in self.ds.stations_that_measure('nonexisting'):
raise AssertionError("Found var that doesnt exist")
def test_get_dataset_ids(self):
ids = self.ds.get_dataset_ids('soil_moisture', max_depth=100, groupby='network')
assert list(ids.keys()) == ['COSMOS']
assert ids['COSMOS'] == [0,1]
ids = self.ds.get_dataset_ids('soil_moisture', max_depth=0.19)
assert ids == [0]
ids = self.ds.get_dataset_ids('soil_moisture', max_depth=99,
filter_meta_dict={'lc_2010': 210,
'network': 'COSMOS',
'station': 'Barrow-ARM'})
assert ids == [1]
ids = self.ds.get_dataset_ids('novar')
assert len(ids) == 0
ids = self.ds.get_dataset_ids('soil_moisture', 0., 0.19) # should get 1
assert len(ids) == 1
ids = self.ds.get_dataset_ids('soil_moisture', 0., 1.) # should get 2
assert len(ids) == 2
ids = self.ds.get_dataset_ids('soil_moisture', 0., 1.,
filter_meta_dict={'lc_2010': 210}) # should get 1
assert len(ids) == 1
ids = self.ds.get_dataset_ids('nonexisting') # should get 0
assert len(ids) == 0
def test_read_ts(self):
data1 = self.ds.read(0)
assert not data1.empty
data2 = self.ds.read_ts(1)
assert not data2.empty
assert len(data1.index) != len(data2.index) # make sure they are not same
def test_find_nearest_station(self):
should_lon, should_lat = -156.62870, 71.32980
station = self.ds.find_nearest_station(should_lon, should_lat)
assert station.lon == should_lon
assert station.lat == should_lat
def test_plot_station_locations(self):
with TemporaryDirectory() as out_dir:
outpath = os.path.join(out_dir, 'plot.png')
self.ds.plot_station_locations('soil_moisture', markersize=5,
filename=outpath)
assert len(os.listdir(out_dir)) == 1
def test_get_min_max_obs_timestamps(self):
tmin, tmax = self.ds.get_min_max_obs_timestamps('soil_moisture', max_depth=0.19)
assert tmin == datetime(2017, 8, 10, 0)
assert tmax == datetime(2018, 8, 9, 23)
def test_get_min_max_obs_timestamps_for_station(self):
station = self.ds.collection.networks['COSMOS'].stations['ARM-1']
tmin, tmax = station.get_min_max_obs_timestamp('soil_moisture', 0, 0.19)
assert tmin == datetime(2017, 8, 10, 0)
assert tmax == datetime(2018, 8, 9, 23)
def test_get_static_var_val(self):
vals = self.ds.get_static_var_vals('soil_moisture', max_depth=0.19)
assert vals == {130: 'Grassland'}
vals = self.ds.get_landcover_types('soil_moisture', max_depth=100)
assert len(vals) == 2
assert vals[130] == 'Grassland'
assert vals[210] == 'Water'
self.ds.print_landcover_dict()
vals = self.ds.get_climate_types('soil_moisture', max_depth=100,
climate='climate_KG')
assert len(vals) == 2
assert vals['ET'] == 'Polar Tundra'
assert vals['Cfa'] == 'Temperate Without Dry Season, Hot Summer'
self.ds.print_climate_dict()
def test_get_var(self):
vars = self.ds.get_variables()
assert vars == ['soil_moisture']
def test_get_sensors(self):
i = 0
for nw, station in self.ds.collection.iter_stations(
filter_meta_dict={'network': 'COSMOS'}):
for se in station.iter_sensors():
data = se.read_data()
# check if the networks is COSMOS or station in [ARM, Barrow-ARM]
assert not data.empty
# check something for that one station
i += 1
assert i == 2
i = 0
for se in self.ds.networks['COSMOS'].stations['Barrow-ARM'].iter_sensors():
data = se.read_data()
assert not data.empty
# check something for that one station
i += 1
assert i == 1
i = 0
for net, stat, sens in self.ds.collection.iter_sensors(
depth=Depth(0,1),
filter_meta_dict={'station': ['Barrow-ARM', 'ARM-1']}):
data = sens.read_data()
assert not data.empty
i +=1
assert i == 2
for nw, station in self.ds.collection.iter_stations():
for se in station.iter_sensors(variable='nonexisting'):
raise ValueError("Found sensor, although none should exist")
def test_get_nearest_station(self):
should_lon, should_lat = -156.62870, 71.32980
station, dist = self.ds.collection.get_nearest_station(should_lon, should_lat)
assert dist == 0
assert station.lon == should_lon
assert station.lat == should_lat
gpi, dist = self.ds.collection.grid.find_nearest_gpi(int(should_lon),int(should_lat))
assert dist != 0
for net in self.ds.collection.iter_networks():
if station.name in net.stations.keys():
assert net.stations[station.name].lon == should_lon
assert net.stations[station.name].lat == should_lat
station, dist = self.ds.find_nearest_station(0, 0, return_distance=True,
max_dist=100)
assert station == dist == None
metadata = ismn_reader.metadata[idx]
jobs.append((idx, metadata['longitude'], metadata['latitude']))
print("Jobs (gpi, lon, lat):")
print(jobs)
# For this small test dataset it is only one job
#
# It is important here that the ISMN reader has a read_ts function that works by just using the `dataset_id`. In this
# way the validation framework can go through the jobs and read the correct time series.
# In[6]:
data = ismn_reader.read_ts(ids[0])
print('ISMN data example:')
print(data.head())
# ## Initialize the Validation class
#
# The Validation class is the heart of the validation framwork. It contains the information about which datasets to
# read using which arguments or keywords and if they are spatially compatible. It also contains the settings about
# which metric calculators to use and how to perform the scaling into the reference data space. It is initialized in
# the following way:
# In[7]:
datasets = {
'ISMN': {
