def test_surface_variable(self):
variable = "air_pressure_at_sea_level"
times = [dt.datetime(2019, 1, 1), dt.datetime(2019, 1, 1, 12)]
longitudes = [0, 1, 2]
latitudes = [0, 1, 2]
values = np.arange(2 * 3 * 3).reshape(2, 3, 3)
with netCDF4.Dataset(self.path, "w") as dataset:
variable_surface(dataset, variable, times, longitudes, latitudes,
values)
lon = 0
lat = 1
loader = series.SeriesLoader([self.path])
result = loader.series_file(self.path, variable, lon, lat)
expect = {"x": times, "y": values[:, 1, 0]}
npt.assert_array_equal(expect["x"], result["x"])
npt.assert_array_equal(expect["y"], result["y"])
def test_3d_variable_scalar_time(tmpdir):
path = str(tmpdir / "file.nc")
variable = "relative_humidity"
time = dt.datetime(2019, 1, 1)
pressures = [1000.001, 500, 250]
longitudes = [0, 1]
latitudes = [0, 1]
values = np.arange(3 * 2 * 2).reshape(3, 2, 2)
with netCDF4.Dataset(path, "w") as dataset:
variable_3d_scalar_time(dataset, variable, time, pressures, longitudes,
latitudes, values)
lon, lat = 0.1, 0.1
loader = series.SeriesLoader([path])
result = loader._load_netcdf4(path, variable, lon, lat, pressure=500)
expect = {"x": [time], "y": [values[1, 0, 0]]}
npt.assert_array_equal(expect["x"], result["x"])
npt.assert_array_equal(expect["y"], result["y"])
def test_series_given_missing_variable_returns_empty(self):
pressure = 500
lon = 1
lat = 1
p0, p1 = 1000, 500
t0 = dt.datetime(2019, 1, 1)
t1 = dt.datetime(2019, 1, 1, 3)
longitudes = [0, 1]
latitudes = [0, 1]
pressures = [p0, p1, p0, p1]
times = [t0, t0, t1, t1]
values = np.arange(4 * 2 * 2).reshape(4, 2, 2)
with netCDF4.Dataset(self.path, "w") as dataset:
variable_dim0(dataset, pressures, times, longitudes, latitudes,
values)
loader = series.SeriesLoader([self.path])
variable = "not_in_file"
result = loader.series_file(self.path, variable, lon, lat, pressure)
expect = {"x": [], "y": []}
npt.assert_array_equal(expect["x"], result["x"])
npt.assert_array_equal(expect["y"], result["y"])
def test_4d_variable(tmpdir):
path = str(tmpdir / "file.nc")
variable = "wet_bulb_potential_temperature"
times = [
dt.datetime(2019, 1, 1),
dt.datetime(2019, 1, 1, 6),
dt.datetime(2019, 1, 1, 12)
]
pressures = [1000.001, 500, 250]
longitudes = [0, 1, 2]
latitudes = [0, 1, 2]
values = np.arange(3 * 3 * 3 * 3).reshape(3, 3, 3, 3)
with netCDF4.Dataset(path, "w") as dataset:
variable_4d(dataset, variable, times, pressures, longitudes, latitudes,
values)
lon, lat = 0.1, 0.1
loader = series.SeriesLoader([path])
result = loader._load_netcdf4(path, variable, lon, lat, pressure=500)
expect = {"x": times, "y": values[:, 1, 0, 0]}
npt.assert_array_equal(expect["x"], result["x"])
npt.assert_array_equal(expect["y"], result["y"])
def test_series_given_dim0_variable(self):
variable = "relative_humidity"
pressure = 500
lon = 1
lat = 1
p0, p1 = 1000, 500
t0 = dt.datetime(2019, 1, 1)
t1 = dt.datetime(2019, 1, 1, 3)
longitudes = [0, 1]
latitudes = [0, 1]
pressures = [p0, p1, p0, p1]
times = [t0, t0, t1, t1]
values = np.arange(4 * 2 * 2).reshape(4, 2, 2)
with netCDF4.Dataset(self.path, "w") as dataset:
variable_dim0(dataset, pressures, times, longitudes, latitudes,
values)
loader = series.SeriesLoader([self.path])
result = loader.series_file(self.path, variable, lon, lat, pressure)
i, j = 1, 1
expect = {"x": [t0, t1], "y": [values[1, j, i], values[3, j, i]]}
npt.assert_array_equal(expect["x"], result["x"])
npt.assert_array_equal(expect["y"], result["y"])
