def test_get_ensemble(self):
EPSG = 32633
upper_left_x = 436100.0
upper_left_y = 7417800.0
nx = 74
ny = 94
dx = 1000.0
dy = 1000.0
# Period start
year = 2015
month = 7
day = 26
hour = 0
n_hours = 30
utc = api.Calendar() # No offset gives Utc
t0 = api.YMDhms(year, month, day, hour)
period = api.UtcPeriod(utc.time(t0), utc.time(t0) + api.deltahours(n_hours))
t_c = utc.time(t0) + api.deltahours(1)
base_dir = path.join(shyftdata_dir, "netcdf", "arome")
pattern = "fc*.nc"
bbox = ([upper_left_x, upper_left_x + nx*dx,
upper_left_x + nx*dx, upper_left_x],
[upper_left_y, upper_left_y,
upper_left_y - ny*dy, upper_left_y - ny*dy])
try:
repos = AromeDataRepository(EPSG, base_dir, filename=pattern, bounding_box=bbox)
data_names = ("temperature", "wind_speed", "relative_humidity")
ensemble = repos.get_forecast_ensemble(data_names, period, t_c, None)
self.assertTrue(isinstance(ensemble, list))
self.assertEqual(len(ensemble), 10)
except AromeDataRepositoryError as adre:
self.skipTest("(test inconclusive- missing arome-data {0})".format(adre))
def test_subsets(self):
EPSG, bbox = self.arome_epsg_bbox
# Period start
year = 2015
month = 8
day = 24
hour = 6
n_hours = 30
date_str = "{}{:02}{:02}_{:02}".format(year, month, day, hour)
utc = api.Calendar() # No offset gives Utc
t0 = api.YMDhms(year, month, day, hour)
period = api.UtcPeriod(utc.time(t0), utc.time(t0) + api.deltahours(n_hours))
base_dir = path.join(shyftdata_dir, "repository", "arome_data_repository")
filename = "arome_metcoop_red_default2_5km_{}.nc".format(date_str)
data_names = ("temperature", "wind_speed", "precipitation", "relative_humidity", "radiation")
allow_subset = False
reader = AromeDataRepository(EPSG, base_dir, filename=filename,
bounding_box=bbox, allow_subset=allow_subset)
with self.assertRaises(AromeDataRepositoryError) as context:
reader.get_timeseries(data_names, period, None)
self.assertEqual("Could not find all data fields", context.exception.args[0])
allow_subset = True
reader = AromeDataRepository(EPSG, base_dir, filename=filename,
bounding_box=bbox, allow_subset=allow_subset)
try:
sources = reader.get_timeseries(data_names, period, None)
except AromeDataRepositoryError as e:
self.fail("AromeDataRepository.get_timeseries(data_names, period, None) "
"raised AromeDataRepositoryError unexpectedly.")
self.assertEqual(len(sources), len(data_names) - 1)
def test_get_forecast(self):
# Period start
year = 2015
month = 8
day = 24
hour = 6
n_hours = 65
utc = api.Calendar() # No offset gives Utc
t0 = api.YMDhms(year, month, day, hour)
period = api.UtcPeriod(utc.time(t0), utc.time(t0) + api.deltahours(n_hours))
t_c1 = utc.time(t0) + api.deltahours(1)
t_c2 = utc.time(t0) + api.deltahours(7)
base_dir = path.join(shyftdata_dir, "repository", "arome_data_repository")
pattern = "arome_metcoop*default2_5km_*.nc"
EPSG, bbox = self.arome_epsg_bbox
repos = AromeDataRepository(EPSG, base_dir, filename=pattern, bounding_box=bbox)
data_names = ("temperature", "wind_speed", "precipitation", "relative_humidity")
tc1_sources = repos.get_forecast(data_names, period, t_c1, None)
tc2_sources = repos.get_forecast(data_names, period, t_c2, None)
self.assertTrue(len(tc1_sources) == len(tc2_sources))
self.assertTrue(set(tc1_sources) == set(data_names))
self.assertTrue(tc1_sources["temperature"][0].ts.size() == n_hours + 1)
tc1_precip = tc1_sources["precipitation"][0].ts
tc2_precip = tc2_sources["precipitation"][0].ts
self.assertEqual(tc1_precip.size(), n_hours)
self.assertTrue(tc1_precip.time(0) != tc2_precip.time(0))
def test_tiny_bbox(self):
EPSG, _ = self.arome_epsg_bbox
x0 = 436250.0 # lower left
y0 = 6823250.0 # lower right
nx = 1
ny = 1
dx = 5.0
dy = 5.0
bbox = ([x0, x0 + nx*dx, x0 + nx*dx, x0], [y0, y0, y0 + ny*dy, y0 + ny*dy])
print(bbox)
# Period start
year = 2015
month = 8
day = 24
hour = 6
n_hours = 30
date_str = "{}{:02}{:02}_{:02}".format(year, month, day, hour)
utc = api.Calendar() # No offset gives Utc
t0 = api.YMDhms(year, month, day, hour)
period = api.UtcPeriod(utc.time(t0), utc.time(t0) + api.deltahours(n_hours))
base_dir = path.join(shyftdata_dir, "repository", "arome_data_repository")
filename = "arome_metcoop_red_default2_5km_{}.nc".format(date_str)
reader = AromeDataRepository(EPSG, base_dir, filename=filename,
bounding_box=bbox, x_padding=0, y_padding=0)
data_names = ("temperature", "wind_speed", "precipitation", "relative_humidity")
try:
tss = reader.get_timeseries(data_names, period, None)
except AromeDataRepository as err:
self.fail("reader.get_timeseries raised AromeDataRepositoryError('{}') "
"unexpectedly.".format(err.args[0]))
def test_create_region_environment(self):
cal = api.Calendar()
time_axis = api.Timeaxis(cal.time(api.YMDhms(2015, 1, 1, 0, 0, 0)), api.deltahours(1), 240)
re = self.create_dummy_region_environment(time_axis, api.GeoPoint(1000, 1000, 100))
self.assertIsNotNone(re)
self.assertEqual(len(re.radiation), 1)
self.assertAlmostEqual(re.radiation[0].ts.value(0), 300.0)
def test_wrong_forecast(self):
with self.assertRaises(AromeDataRepositoryError) as context:
utc = api.Calendar() # No offset gives Utc
t0 = api.YMDhms(2015, 12, 25, 18)
period = api.UtcPeriod(utc.time(t0), utc.time(t0) + api.deltahours(30))
ar1 = AromeDataRepository(32632, shyftdata_dir, filename="plain_wrong_*.nc")
ar1.get_forecast(("temperature",), period, utc.time(t0), None)
self.assertTrue(all(x in context.exception.args[0] for x in
["No matches found for file_pattern = ", "and t_c = "]))
def test_get_dummy(self):
"""
#Simple regression test of dummy variables from `utils.dummy_var`
"""
EPSG, bbox, bpoly = self.senorge_epsg_bbox
# Period start
n_days = 5
t0 = api.YMDhms(1957, 1)
utc = api.Calendar() # No offset gives Utc
period = api.UtcPeriod(utc.time(t0),
utc.time(t0) + api.deltahours(n_days * 24))
base_dir = path.join(shyftdata_dir, "repository",
"senorge_data_repository")
f1 = "senorge_test.nc"
senorge1 = SeNorgeDataRepository(EPSG,
base_dir,
filename=f1,
allow_subset=True)
senorge1_data_names = ("radiation", "temperature", "wind_speed")
sources = senorge1.get_timeseries(senorge1_data_names,
period,
geo_location_criteria=bpoly)
self.assertTrue(set(sources) == set(senorge1_data_names))
r0 = sources["radiation"][0].ts
p0 = sources["temperature"][0].ts
self.assertTrue(p0.total_period().start <= period.start
and p0.total_period().end >= period.end)
self.assertTrue(r0.time(0), period.start)
t0 = api.YMDhms(1957, 1)
period = api.UtcPeriod(utc.time(t0),
utc.time(t0) + api.deltahours(n_days * 23))
sources = senorge1.get_timeseries(senorge1_data_names,
period,
geo_location_criteria=bpoly)
r0 = sources["radiation"][0].ts
p0 = sources["temperature"][0].ts
self.assertTrue(p0.total_period().start <= period.start
and p0.total_period().end >= period.end)
self.assertTrue(r0.time(0) == period.start)
self.assertTrue(r0.time_axis.time(1) - r0.time_axis.time(0) == 86400)
def test_read_forecast(self):
utc = api.Calendar()
ds = SmGTsRepository(PROD, FC_PROD)
nl = [
u'/LTMS-Abisko........-T0000A5P_EC00_ENS',
u'/LTMS-Abisko........-T0000A5P_EC00_E04',
u'/Vikf-Tistel........-T0017A3P_MAN',
u'/Vikf-Tistel........-T0000A5P_MAN'
] #[u'/ICC-test-v9.2']
t0 = utc.time(api.YMDhms(2015, 10, 1, 00, 00, 00))
t1 = utc.time(api.YMDhms(2015, 10, 10, 00, 00, 00))
p = api.UtcPeriod(t0, t1)
fclist = ds.read_forecast(nl, p)
self.assertIsNotNone(fclist)
fc1 = fclist[u'/LTMS-Abisko........-T0000A5P_EC00_E04']
fc1_v = [fc1.value(i) for i in range(fc1.size())]
# test times here, left for manual inspection here fc1_t=[utc.to_string(fc1.time(i)) for i in range(fc1.size())]
self.assertIsNotNone(fc1_v)
#values as read from preprod smg:
fc1_v_expected = [
0.00, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.08, 0.08, 0.08,
0.08, 0.08, 0.08, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.47, 0.47, 0.47, 0.47, 0.47,
0.47, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.12, 0.12, 0.12,
0.12, 0.12, 0.12, 0.20, 0.20, 0.20, 0.20, 0.20, 0.20, 0.14,
0.14, 0.14, 0.14, 0.14, 0.14, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.10,
0.10, 0.10, 0.10, 0.10, 0.10, 0.08, 0.08, 0.08, 0.08, 0.08,
0.08, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.23, 0.23, 0.23,
0.23, 0.23, 0.23, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.03,
0.03, 0.03, 0.03, 0.03, 0.03, 0.06, 0.06, 0.06, 0.06, 0.06,
0.06, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.13, 0.13, 0.13,
0.13, 0.13, 0.13, 0.10, 0.10, 0.10, 0.10
]
[
self.assertLess(
fabs(fc1_v_expected[i] - fc1_v[i]), 0.01,
"{}:{} !={}".format(i, fc1_v_expected[i], fc1_v[i]))
for i in range(len(fc1_v_expected))
]
def time_axis_from_dict(t_dict):
utc = api.Calendar()
sim_start = dt.datetime.strptime(t_dict['start_datetime'], "%Y-%m-%dT%H:%M:%S")
utc_start = utc.time(
api.YMDhms(sim_start.year, sim_start.month, sim_start.day, sim_start.hour, sim_start.minute, sim_start.second))
tstep = t_dict['run_time_step']
nstep = t_dict['number_of_steps']
time_axis = api.TimeAxisFixedDeltaT(utc_start, tstep, nstep)
return time_axis
def test_create_TargetSpecificationPts(self):
t = api.TargetSpecificationPts();
t.scale_factor = 1.0
t.calc_mode = api.NASH_SUTCLIFFE
t.calc_mode = api.KLING_GUPTA;
t.s_r = 1.0 # KGEs scale-factors
t.s_a = 2.0
t.s_b = 3.0
self.assertAlmostEqual(t.scale_factor, 1.0)
# create a ts with some points
cal = api.Calendar();
start = cal.time(api.YMDhms(2015, 1, 1, 0, 0, 0))
dt = api.deltahours(1)
tsf = api.TsFactory();
times = api.UtcTimeVector()
times.push_back(start + 1 * dt);
times.push_back(start + 3 * dt);
times.push_back(start + 4 * dt)
values = api.DoubleVector()
values.push_back(1.0)
values.push_back(3.0)
values.push_back(np.nan)
tsp = tsf.create_time_point_ts(api.UtcPeriod(start, start + 24 * dt), times, values)
# convert it from a time-point ts( as returned from current smgrepository) to a fixed interval with timeaxis, needed by calibration
tst = api.TsTransform()
tsa = tst.to_average(start, dt, 24, tsp)
# tsa2 = tst.to_average(start,dt,24,tsp,False)
# tsa_staircase = tst.to_average_staircase(start,dt,24,tsp,False) # nans infects the complete interval to nan
# tsa_staircase2 = tst.to_average_staircase(start,dt,24,tsp,True) # skip nans, nans are 0
# stuff it into the target spec.
# also show how to specify snow-calibration
cids = api.IntVector([0, 2, 3])
t2 = api.TargetSpecificationPts(tsa,cids, 0.7, api.KLING_GUPTA, 1.0, 1.0, 1.0, api.SNOW_COVERED_AREA)
t2.catchment_property = api.SNOW_WATER_EQUIVALENT
self.assertEqual(t2.catchment_property, api.SNOW_WATER_EQUIVALENT)
self.assertIsNotNone(t2.catchment_indexes)
for i in range(len(cids)):
self.assertEqual(cids[i],t2.catchment_indexes[i])
t.ts = tsa
#TODO: Does not work, list of objects are not yet convertible tv = api.TargetSpecificationVector([t, t2])
tv=api.TargetSpecificationVector()
tv.append(t)
tv.append(t2)
# now verify we got something ok
self.assertEqual(2, tv.size())
self.assertAlmostEqual(tv[0].ts.value(1), 1.5) # average value 0..1 ->0.5
self.assertAlmostEqual(tv[0].ts.value(2), 2.5) # average value 0..1 ->0.5
self.assertAlmostEqual(tv[0].ts.value(3), 3.0) # average value 0..1 ->0.5
# and that the target vector now have its own copy of ts
tsa.set(1, 3.0)
self.assertAlmostEqual(tv[0].ts.value(1), 1.5) # make sure the ts passed onto target spec, is a copy
self.assertAlmostEqual(tsa.value(1), 3.0) # and that we really did change the source
