def test_locator_given_time_pressure_format(self):
pattern = self.path
reference_time = dt.datetime(2019, 1, 1)
times = [dt.datetime(2019, 1, 2), dt.datetime(2019, 1, 2, 3)]
pressures = [1000, 950, 850]
with netCDF4.Dataset(self.path, "w") as dataset:
um = tutorial.UM(dataset)
dataset.createDimension("longitude", 1)
dataset.createDimension("latitude", 1)
var = um.times("time", length=len(times))
var[:] = netCDF4.date2num(times, units=var.units)
um.forecast_reference_time(reference_time)
var = um.pressures("pressure", length=len(pressures))
var[:] = pressures
dims = ("time", "pressure", "longitude", "latitude")
coordinates = "forecast_period_1 forecast_reference_time"
var = um.relative_humidity(dims, coordinates=coordinates)
var[:] = 100.
variable = "relative_humidity"
initial_time = reference_time
valid_time = times[1]
pressure = pressures[2]
locator = unified_model.Locator([self.path])
_, result = locator.locate(pattern, variable, initial_time, valid_time,
pressure)
expect = (1, 2)
self.assertEqual(expect, result)
def test_locator_given_dim0_format(self):
pattern = self.path
times = [dt.datetime(2019, 1, 1), dt.datetime(2019, 1, 2)]
with netCDF4.Dataset(self.path, "w") as dataset:
um = tutorial.UM(dataset)
dataset.createDimension("longitude", 1)
dataset.createDimension("latitude", 1)
var = um.times("time", length=len(times), dim_name="dim0")
var[:] = netCDF4.date2num(times, units=var.units)
um.forecast_reference_time(times[0])
var = um.pressures("pressure", length=len(times), dim_name="dim0")
var[:] = 1000.
dims = ("dim0", "longitude", "latitude")
coordinates = "forecast_period_1 forecast_reference_time pressure time"
var = um.relative_humidity(dims, coordinates=coordinates)
var[:] = 100.
variable = "relative_humidity"
initial_time = dt.datetime(2019, 1, 1)
valid_time = dt.datetime(2019, 1, 2)
locator = unified_model.Locator([self.path])
_, result = locator.locate(pattern,
variable,
initial_time,
valid_time,
pressure=1000.0001)
expect = (1, )
self.assertEqual(expect, result)
def from_files(cls, label, pattern, files, file_type):
"""Builds a loader from list of files and a file type"""
locator = None # RDT, EIDA50 etc. have built-in locators
if file_type == 'unified_model':
locator = unified_model.Locator(files)
return cls.file_loader(file_type,
pattern,
label=label,
locator=locator)
def test_locator_given_empty_file_raises_exception(self):
pattern = self.path
with netCDF4.Dataset(self.path, "w") as dataset:
pass
variable = "relative_humidity"
initial_time = dt.datetime(2019, 1, 1)
valid_time = dt.datetime(2019, 1, 1)
locator = unified_model.Locator([self.path])
with self.assertRaises(SearchFail):
locator.locate(pattern, variable, initial_time, valid_time)
def test_locate_given_one_thousand_files(self):
N = 10**4
k = np.random.randint(N)
start = dt.datetime(2019, 1, 10)
times = [start + dt.timedelta(hours=i) for i in range(N)]
paths = [
"test-locate-{:%Y%m%dT%H%MZ}.nc".format(time) for time in times
]
locator = unified_model.Locator(paths)
result = locator.find_paths(times[k])
expect = [paths[k]]
self.assertEqual(expect, result)
