def test_retrieval_runs():
"""
"""
mwi = ICI(stokes_dimension = 1)
mwi.name = "mwi"
mwi.f_grid = np.array([19e9, 35e9, 89e9, 118e9])
mwi.sensor_line_of_sight = np.array([[135.0]])
mwi.sensor_position = np.array([[600e3]])
simulation = setup_retrieval_simulation(retrieval_type = "passive",
scattering = False)
simulation._sensors += [mwi]
def get_o2(self):
return 0.2091 * np.ones(21)
data_provider = DataProvider()
simulation.data_provider = data_provider
simulation.setup()
simulation.run()
data_provider.get_O2 = get_o2.__get__(data_provider)
# H2O a priori
h2o_a_priori = FixedAPriori("H2O", 0.5, Diagonal(0.01))
data_provider.add(h2o_a_priori)
# O2 a priori
o2_a_priori = FixedAPriori("O2", 1.0, Diagonal(0.001))
data_provider.add(o2_a_priori)
# observation error a priori
n = 0
for s in simulation.sensors:
n += s.f_grid.size * s.stokes_dimension
measurement_a_priori = IndependentMeasurementErrors(n, 1.0)
data_provider.add(measurement_a_priori)
h2o = simulation.atmosphere.absorbers[-1]
simulation.retrieval.add(h2o)
h2o.retrieval.unit = RelativeHumidity()
o2 = simulation.atmosphere.absorbers[0]
simulation.retrieval.add(o2)
o2.retrieval.unit = Relative(0.2091 * np.ones((21, 1, 1)))
y = np.copy(simulation.workspace.y)
simulation.retrieval.y = y
def remove_mwi_o2(retrieval_run):
retrieval_run.sensors = retrieval_run.sensors[:1]
retrieval_run.retrieval_quantities = retrieval_run.retrieval_quantities[:1]
simulation.retrieval.callbacks = [remove_mwi_o2, None]
simulation.setup()
simulation.run()
return simulation.workspace.x.value
def test_simulation_telsem():
atmosphere = Atmosphere1D(absorbers=[O2(), N2(), H2O()],
surface=Telsem("/home/simon/Downloads"))
mwi = MWI()
mwi.sensor_line_of_sight = np.array([[135.0]])
mwi.sensor_position = np.array([[600e3]])
data_provider = DataProvider()
data_provider.surface_temperature = 280.0 * np.ones((1, 1))
data_provider.latitude = 58.0
data_provider.longitude = 12.0
simulation = ArtsSimulation(atmosphere=atmosphere,
data_provider=data_provider,
sensors=[mwi])
simulation.setup()
simulation.run()
def test_simulation_scattering_combined(scattering_solver):
ice = ScatteringSpecies("ice", D14(-1.0, 2.0),
scattering_data = scattering_data,
scattering_meta_data = scattering_meta)
ice.psd.t_min = 0.0
ice.psd.t_max = 275.0
ici = ICI(channel_indices = [0, -1], stokes_dimension = 1)
ici.sensor_line_of_sight = np.array([[135.0]])
ici.sensor_position = np.array([[600e3]])
cs = CloudSat(stokes_dimension = 1)
cs.range_bins = np.linspace(0, 30e3, 31)
cs.sensor_line_of_sight = np.array([[135.0]])
cs.sensor_position = np.array([[600e3]])
atmosphere = Atmosphere1D(absorbers = [O2(), N2(), H2O()],
scatterers = [ice],
surface = Tessem())
simulation = ArtsSimulation(atmosphere = atmosphere,
data_provider = DataProvider(),
sensors = [ici, cs])
simulation.scattering_solver = scattering_solver()
simulation.setup()
simulation.run()
y = np.copy(simulation.workspace.y)
return y
def test_masks():
data_provider = DataProvider()
temperature = data_provider.get_temperature()
temperature_mask = TemperatureMask(lower_limit=230.0, upper_limit=280.0)
mask = temperature_mask(data_provider)
assert (np.all(temperature[mask] >= 230))
assert (np.all(temperature[mask] < 280))
tropopause_mask = TropopauseMask()
mask = tropopause_mask(data_provider)
assert (np.all(temperature[mask]) <= 225)
mask = And(temperature_mask, tropopause_mask)(data_provider)
assert (np.all(temperature[mask] >= 230))
assert (np.all(temperature[mask] < 280))
assert (np.all(temperature[mask]) <= 225)
def test_covariances():
data_provider = DataProvider()
z = data_provider.get_altitude()
diagonal = Diagonal(2)
diag = diagonal.get_covariance(data_provider)
assert (np.all(np.isclose(diag.diagonal(), 2.0 * np.ones(z.size))))
gauss = SpatialCorrelation(diagonal, 1000.0)
covmat = gauss.get_covariance(data_provider)
assert (np.all(np.isclose(covmat.diagonal(), 2.0 * np.ones(z.size))))
temperature_mask = TemperatureMask(lower_limit=230.0, upper_limit=280.0)
thik = Thikhonov(scaling=1.0, z_scaling=False, mask=temperature_mask)
precmat = thik.get_precision(data_provider)
mask = np.logical_not(temperature_mask(data_provider))
assert (np.all(precmat.diagonal()[mask] >= 1e12))
mask2 = np.logical_not(mask)[2:-2]
assert (np.all(precmat.diagonal()[2:-2][mask2] == 6))
def test_scattering_retrieval_active():
simulation = setup_retrieval_simulation(retrieval_type = "active",
scattering = True)
data_provider = DataProvider()
simulation.data_provider = data_provider
simulation.setup()
simulation.run()
y = np.copy(simulation.workspace.y)
ice = simulation.atmosphere.scatterers[0]
simulation.retrieval.add(ice.mass_density)
ice.mass_density.limit_low = 1e-6
simulation.retrieval.settings["max_iter"] = 5
ice.mass_density.transformation = Log10()
# Ice a priori
ice_a_priori = FixedAPriori("ice_mass_density", -6, Diagonal(2.0))
data_provider.add(ice_a_priori)
# Observation error a priori
n = simulation.sensors[0].range_bins.size - 1
measurement_a_priori = IndependentMeasurementErrors(n, 1.0)
data_provider.add(measurement_a_priori)
simulation.retrieval.y = y
simulation.setup()
simulation.run()
return simulation.workspace
def test_simulation_absorption_retrieval():
"""
This test runs a passive clearsky water vapor retrieval retrieving
relative humidity.
"""
simulation = setup_retrieval_simulation(retrieval_type = "passive",
scattering = False)
data_provider = DataProvider()
simulation.data_provider = data_provider
simulation.setup()
simulation.run()
# H2O a priori
h2o_a_priori = FixedAPriori("H2O", 0.5, Diagonal(0.01))
data_provider.add(h2o_a_priori)
n = simulation.sensors[0].f_grid.size * simulation.sensors[0].stokes_dimension
measurement_a_priori = IndependentMeasurementErrors(n, 1.0)
data_provider.add(measurement_a_priori)
h2o = simulation.atmosphere.absorbers[-1]
y = np.copy(simulation.workspace.y)
simulation.retrieval.add(h2o)
h2o.retrieval.unit = RelativeHumidity()
simulation.retrieval.y = y
simulation.setup()
simulation.run()
return simulation.workspace.x.value
def test_scattering_retrieval_passive():
simulation = setup_retrieval_simulation(retrieval_type = "passive",
scattering = True)
data_provider = DataProvider()
simulation.data_provider = data_provider
simulation.setup()
simulation.run()
y = np.copy(simulation.workspace.y)
ice = simulation.atmosphere.scatterers[0]
simulation.retrieval.add(ice.mass_density)
ice.mass_density.transformation = Log10()
ice.mass_density.limit_low = -10
simulation.retrieval.settings["max_iter"] = 1
ice_a_priori = FixedAPriori("ice_mass_density", -6, Diagonal(2.0))
data_provider.add(ice_a_priori)
n = simulation.sensors[0].f_grid.size * simulation.sensors[0].stokes_dimension
measurement_a_priori = IndependentMeasurementErrors(n, 1.0)
data_provider.add(measurement_a_priori)
simulation.retrieval.y = y
simulation.setup()
simulation.run()
return simulation.workspace
def test_scattering_combined_retrieval():
simulation = setup_retrieval_simulation(retrieval_type = "combined",
scattering = True)
data_provider = DataProvider()
simulation.data_provider = data_provider
simulation.sensors[1].stokes_dimension = 1
simulation.setup()
# Forward simulation
simulation.run()
y = np.copy(simulation.workspace.y)
ice = simulation.atmosphere.scatterers[0]
simulation.retrieval.add(ice.mass_density)
simulation.retrieval.add(ice.mass_weighted_diameter)
ice.mass_density.retrieval.limit_low = -10
ice.mass_density.transformation = Log10()
ice.mass_weighted_diameter.retrieval.limit_low = -6
ice.mass_weighted_diameter.transformation = Log10()
# Ice a prioris
ice_a_priori = FixedAPriori("ice_mass_density", -6, Diagonal(2.0))
data_provider.add(ice_a_priori)
ice_a_priori = FixedAPriori("ice_mass_weighted_diameter", -3, Diagonal(2.0))
data_provider.add(ice_a_priori)
# Observation errors
n = simulation.sensors[0].range_bins.size - 1
n += simulation.sensors[1].f_grid.size * simulation.sensors[1].stokes_dimension
measurement_a_priori = IndependentMeasurementErrors(n, 1.0)
data_provider.add(measurement_a_priori)
print(ice.mass_density.transformation)
simulation.retrieval.settings["max_iter"] = 1
simulation.retrieval.y = y
# Retrieval
simulation.workspace.x = []
simulation.run()
y_f = np.copy(simulation.workspace.yf.value)
x = np.copy(simulation.workspace.x.value)
return simulation
def test_data_provider_a_priori():
temperature_mask = TemperatureMask(lower_limit=230.0, upper_limit=280.0)
tropopause_mask = TropopauseMask()
covariance = Diagonal(2.0, And(temperature_mask, tropopause_mask))
data_provider = DataProvider()
data_provider.add(DataProviderAPriori("temperature", covariance))
t = data_provider.get_temperature()
assert (np.all(data_provider.get_temperature_xa() == t))
def test_simulation_combined():
surface_1 = Tessem()
surface_2 = Telsem("/home/simon/Downloads")
surface = CombinedSurface(surface_1, surface_2)
atmosphere = Atmosphere1D(absorbers=[O2(), N2(), H2O()], surface=surface)
mwi = MWI()
mwi.sensor_line_of_sight = np.array([[135.0]])
mwi.sensor_position = np.array([[600e3]])
data_provider = DataProvider()
data_provider.surface_temperature = 280.0 * np.ones((1, 1))
data_provider.surface_latitude = 58.0
data_provider.surface_longitude = 12.0
data_provider.surface_type = 0.0
simulation = ArtsSimulation(atmosphere=atmosphere,
data_provider=data_provider,
sensors=[mwi])
simulation.setup()
simulation.run()
y = np.copy(mwi.y)
atmosphere_r = Atmosphere1D(absorbers=[O2(), N2(), H2O()],
surface=surface_1)
simulation_r = ArtsSimulation(atmosphere=atmosphere_r,
data_provider=data_provider,
sensors=[mwi])
simulation_r.setup()
simulation_r.run()
y_r = np.copy(mwi.y)
assert (np.allclose(y, y_r))
data_provider.surface_type = 1.0
simulation.setup()
simulation.run()
y = np.copy(mwi.y)
atmosphere_r = Atmosphere1D(absorbers=[O2(), N2(), H2O()],
surface=surface_2)
simulation_r = ArtsSimulation(atmosphere=atmosphere_r,
data_provider=data_provider,
sensors=[mwi])
simulation_r.setup()
simulation_r.run()
y_r = np.copy(mwi.y)
assert (np.allclose(y, y_r))
def test_functional_a_priori():
temperature_mask = TemperatureMask(lower_limit=230.0, upper_limit=280.0)
tropopause_mask = TropopauseMask()
covariance = Diagonal(2.0, And(temperature_mask, tropopause_mask))
covariance_new = Diagonal(2.0 * np.ones(10))
data_provider = DataProvider()
t = data_provider.get_temperature()
f = lambda x: x**2
a_priori = FunctionalAPriori("temperature", "temperature", f, covariance)
data_provider.add(a_priori)
assert (np.all(np.isclose(t**2, data_provider.get_temperature_xa())))
def test_simulation_tessem():
atmosphere = Atmosphere1D(absorbers=[O2(), N2(), H2O()], surface=Tessem())
mwi = MWI()
mwi.sensor_line_of_sight = np.array([[135.0]])
mwi.sensor_position = np.array([[600e3]])
simulation = ArtsSimulation(atmosphere=atmosphere,
data_provider=DataProvider(),
sensors=[mwi])
simulation.setup()
simulation.run()
y1 = np.copy(mwi.y)
simulation.run()
y2 = np.copy(mwi.y)
assert (np.all(np.isclose(y1, y2)))
def test_simulation_absorption_jacobian():
atmosphere = Atmosphere1D(absorbers = [O2(), N2(), H2O()],
surface = Tessem())
o2, n2, h2o = atmosphere.absorbers
ici = ICI(channel_indices = [0, -1])
ici.sensor_line_of_sight = np.array([[135.0]])
ici.sensor_position = np.array([[600e3]])
simulation = ArtsSimulation(atmosphere = atmosphere,
data_provider = DataProvider(),
sensors = [ici])
simulation.jacobian.add(o2)
simulation.jacobian.add(n2)
simulation.jacobian.add(h2o)
simulation.setup()
simulation.run()
return np.copy(simulation.workspace.jacobian.value)
def test_simulation_multiview():
atmosphere = Atmosphere1D(absorbers = [O2(), N2(), H2O()],
surface = Tessem())
lines_of_sight = np.array([[135.0],
[180.0]])
positions = np.array([[600e3],
[600e3]])
ici = ICI(lines_of_sight=lines_of_sight,
positions=positions)
simulation = ArtsSimulation(atmosphere = atmosphere,
data_provider = DataProvider(),
sensors = [ici])
simulation.setup()
simulation.run()
y = np.copy(ici.y)
assert(y.shape[0] == 2)
def test_simulation_scattering(scattering_solver):
ice = ScatteringSpecies("ice", D14(-1.0, 2.0),
scattering_data = scattering_data,
scattering_meta_data = scattering_meta)
ice.psd.t_min = 0.0
ice.psd.t_max = 275.0
atmosphere = Atmosphere1D(absorbers = [O2(), N2(), H2O()],
scatterers = [ice],
surface = Tessem())
ici = ICI(stokes_dimension = 1, channel_indices = [1, -1])
ici.sensor_line_of_sight = np.array([[135.0]])
ici.sensor_position = np.array([[600e3]])
simulation = ArtsSimulation(atmosphere = atmosphere,
data_provider = DataProvider(),
sensors = [ici])
simulation.scattering_solver = scattering_solver()
simulation.setup()
simulation.run()
def test_simulation_scattering_jacobian():
ice = ScatteringSpecies("ice", D14(-1.0, 2.0),
scattering_data = scattering_data,
scattering_meta_data = scattering_meta)
ice.psd.t_min = 0.0
ice.psd.t_max = 275.0
atmosphere = Atmosphere1D(absorbers = [O2(), N2(), H2O()],
scatterers = [ice],
surface = Tessem())
ici = ICI(channel_indices = [1, -1])
ici.sensor_line_of_sight = np.array([[135.0]])
ici.sensor_position = np.array([[600e3]])
simulation = ArtsSimulation(atmosphere = atmosphere,
data_provider = DataProvider(),
sensors = [ici])
simulation.jacobian.add(ice.mass_density)
simulation.setup()
simulation.run()
print(simulation.workspace.particle_bulkprop_field.value)
return np.copy(simulation.workspace.jacobian.value)
def test_reduced_grid_a_priori():
tropopause_mask = TropopauseMask()
temperature_mask = TemperatureMask(lower_limit=230.0, upper_limit=280.0)
covariance = Diagonal(2.0, And(temperature_mask, tropopause_mask))
covariance_new = Diagonal(2.0 * np.ones(10))
data_provider = DataProvider()
t = data_provider.get_temperature()
a_priori = FixedAPriori("temperature", t, covariance)
a_priori = ReducedVerticalGrid(a_priori, np.logspace(3, 5, 10)[::-1])
data_provider.add(a_priori)
t_xa = data_provider.get_temperature_xa()
assert (t_xa.size == 10)
covmat = data_provider.get_temperature_covariance()
assert (covmat.shape == (10, 10))
data_provider = DataProvider()
a_priori = ReducedVerticalGrid(a_priori,
np.logspace(3, 5, 10)[::-1],
covariance=covariance_new)
data_provider.add(a_priori)
covmat = data_provider.get_temperature_covariance()
assert (np.all(covmat.diagonal() == 2.0))
data_provider = DataProvider(),
sensors = [cs])
simulation.setup()
simulation.run()
y = np.copy(cs.y)
assert(y.shape[0] == 2)
assert(y.shape[-1] == 2)
ice = ScatteringSpecies("ice", D14(-1.0, 2.0),
scattering_data = scattering_data,
scattering_meta_data = scattering_meta)
ice.psd.t_min = 0.0
ice.psd.t_max = 275.0
atmosphere = Atmosphere1D(absorbers = [O2(), N2(), H2O()],
scatterers = [],
surface = Tessem())
ici = ICI(stokes_dimension = 1, channel_indices = [1, -1])
ici.sensor_line_of_sight = np.array([[135.0]])
ici.sensor_position = np.array([[600e3]])
simulation = ArtsSimulation(atmosphere = atmosphere,
data_provider = DataProvider(),
sensors = [ici])
simulation.scattering_solver = RT4()
simulation.setup()
for i in range(100):
simulation.run()
