class ArtsController():
"""A not so high level interface to ARTS."""
def __init__(self, verbosity=0, agenda_verbosity=0):
self.ws = Workspace(verbosity=verbosity, agenda_verbosity=agenda_verbosity)
self.retrieval_quantities = []
self._sensor = None
self._observations = []
def setup(self, atmosphere_dim=1, iy_unit='RJBT', ppath_lmax=-1, stokes_dim=1):
"""
Run boilerplate (includes, agendas) and set basic variables.
:param atmosphere_dim:
:param iy_unit:
:param ppath_lmax:
:param stokes_dim:
"""
boilerplate.include_general(self.ws)
boilerplate.copy_agendas(self.ws)
boilerplate.set_basics(self.ws, atmosphere_dim, iy_unit, ppath_lmax, stokes_dim)
# Deactivate some stuff (can be activated later)
self.ws.jacobianOff()
self.ws.cloudboxOff()
def checked_calc(self, negative_vmr_ok=False, bad_partition_functions_ok=False):
"""Run checked calculations."""
boilerplate.run_checks(self.ws, negative_vmr_ok, bad_partition_functions_ok)
def set_spectroscopy(self, abs_lines, abs_species, line_shape=None, abs_f_interp_order=3):
"""
Setup absorption species and spectroscopy data.
:param ws: The workspace.
:param abs_lines: Absoption lines.
:param abs_species: List of abs species tags.
:param line_shape: Line shape definition. Default: ['Voigt_Kuntz6', 'VVH', 750e9]
:param f_abs_interp_order: No effect for OnTheFly propmat. Default: 3
:type abs_lines: typhon.arts.catalogues.ArrayOfLineRecord
"""
boilerplate.setup_spectroscopy(self.ws, abs_lines, abs_species, line_shape)
self.ws.abs_f_interp_order = abs_f_interp_order # no effect for OnTheFly propmat
def set_spectroscopy_from_file(self, abs_lines_file, abs_species, format='Arts', line_shape=None, abs_f_interp_order=3):
"""
Setup absorption species and spectroscopy data from XML file.
:param ws: The workspace.
:param abs_lines_file: Path to an XML file.
:param abs_species: List of abs species tags.
:param format: One of 'Arts', 'Jpl', 'Hitran' (and others for which a WSM `abs_linesReadFrom...` exists)
:param line_shape: Line shape definition. Default: ['Voigt_Kuntz6', 'VVH', 750e9]
:param f_abs_interp_order: No effect for OnTheFly propmat. Default: 3
"""
ws = self.ws
if line_shape is None:
line_shape = ['Voigt_Kuntz6', 'VVH', 750e9]
ws.abs_speciesSet(abs_species)
ws.abs_lineshapeDefine(*line_shape)
#ws.ReadXML(ws.abs_lines, abs_lines_file)
read_fn = getattr(ws, 'abs_linesReadFrom' + format)
read_fn(filename=abs_lines_file, fmin=float(0), fmax=float(10e12))
ws.abs_lines_per_speciesCreateFromLines()
ws.abs_f_interp_order = abs_f_interp_order
def set_grids(self, f_grid, p_grid, lat_grid=None, lon_grid=None):
"""
Set the forward model grids. Basic checks are performed, depending on dimensionality of atmosphere.
:param f_grid:
:param p_grid:
:param lat_grid:
:param lon_grid:
"""
if lat_grid is None:
lat_grid = np.array([0])
if lon_grid is None:
lon_grid = np.array([0])
if not self.ws.atmosphere_dim.initialized:
raise Exception('atmosphere_dim must be initialized before assigning grids.')
if not _is_asc(f_grid):
raise ValueError('Values of f_grid must be strictly increasing.')
if not _is_desc(p_grid) or not np.all(p_grid > 0):
raise ValueError('Values of p_grid must be strictly decreasing and positive.')
if self.ws.atmosphere_dim.value == 1:
if lat_grid.size > 1 or lon_grid.size > 1:
raise ValueError('For 1D atmosphere, lat_grid and lon_grid shall be of length 1.')
elif self.ws.atmosphere_dim.value == 2:
if lon_grid is not None or len(lat_grid):
raise ValueError('For 2D atmosphere, lon_grid shall be empty.')
if lat_grid is None or len(lat_grid) == 0:
raise ValueError('For 2D atmosphere, lat_grid must be set.')
elif self.ws.atmosphere_dim.value == 3:
if lat_grid is None or len(lat_grid) < 2 or lon_grid is None or len(lon_grid) < 2:
raise ValueError('For 3D atmosphere, length of lat_grid and lon_grid must be >= 2.')
if max(abs(lon_grid)) > 360:
raise ValueError('Values of lon_grid must be in the range [-360,360].')
if max(abs(lat_grid)) > 90:
raise ValueError('Values of lat_grid must be in the range [-90,90].')
if lat_grid is not None and not _is_asc(lat_grid):
raise ValueError('Values of lat_grid must be strictly increasing.')
if lon_grid is not None and not _is_asc(lon_grid):
raise ValueError('Values of lon_grid must be strictly increasing.')
self.ws.f_grid = f_grid
self.ws.p_grid = p_grid
if self.atmosphere_dim > 1:
self.ws.lat_grid = lat_grid
self.ws.lon_grid = lon_grid
else:
self.ws.lat_grid = []
self.ws.lon_grid = []
self.ws.lat_true = lat_grid
self.ws.lon_true = lon_grid
self.set_surface(0)
def set_surface(self, altitude: float):
"""
Set surface altitude.
:param float altitude:
.. note:: Currently, only constant altitudes are supported.
"""
shape = max((1, 1), (self.n_lat, self.n_lat))
self.ws.z_surface = altitude * np.ones(shape)
def set_atmosphere(self, atmosphere, vmr_zeropadding=False):
"""
Set the atmospheric state.
:param atmosphere: Atmosphere with Temperature, Altitude and VMR Fields.
:type atmosphere: retrievals.arts.atmosphere.Atmosphere
:param vmr_zeropadding: Allow VMR zero padding wind fields are always zero
padded. Default: False.
.. note:: Currently only supports 1D atmospheres that is then expanded to a
multi-dimensional homogeneous atmosphere.
"""
vmr_zeropadding = 1 if vmr_zeropadding else 0
self.ws.t_field_raw = atmosphere.t_field
self.ws.z_field_raw = atmosphere.z_field
self.ws.vmr_field_raw = [atmosphere.vmr_field(mt) for mt in self.abs_species_maintags]
self.ws.nlte_field_raw = None
for c in ('u', 'v', 'w'):
try:
raw_field = atmosphere.wind_field(c)
field = p_interpolate(
self.p_grid, raw_field.grids[0], raw_field.data[:, 0, 0], fill=0
)
except KeyError:
field = np.zeros_like(self.p_grid)
field = np.tile(
field[:, np.newaxis, np.newaxis], (1, self.n_lat, self.n_lon)
)
field_name = 'wind_{}_field'.format(c)
setattr(self.ws, field_name, field)
if self.atmosphere_dim == 1:
self.ws.AtmFieldsCalc(vmr_zeropadding=vmr_zeropadding)
else:
self.ws.AtmFieldsCalcExpand1D(vmr_zeropadding=vmr_zeropadding)
def apply_hse(self, p_hse=100e2, z_hse_accuracy=0.5):
"""
Calculate z field from hydrostatic equilibrium. See :arts:method:`z_fieldFromHSE`.
:param p_hse: See :arts:variable:`p_hse`.
:param z_hse_accuracy: See :arts:variable:`z_hse_accuracy`.
"""
ws = self.ws
ws.p_hse = p_hse
ws.z_hse_accuracy = z_hse_accuracy
ws.atmfields_checkedCalc()
ws.z_fieldFromHSE()
def set_wind(self, wind_u=None, wind_v=None, wind_w=None):
"""
Set the wind fields to constant values.
"""
ws = self.ws
n_p, n_lat, n_lon = self.n_p, self.n_lat, self.n_lon
if wind_u is not None:
ws.Tensor3SetConstant(ws.wind_u_field, n_p, n_lat, n_lon, float(wind_u))
if wind_v is not None:
ws.Tensor3SetConstant(ws.wind_v_field, n_p, n_lat, n_lon, float(wind_v))
if wind_w is not None:
ws.Tensor3SetConstant(ws.wind_w_field, n_p, n_lat, n_lon, float(wind_w))
def _check_set_wind(self):
"""Set uninitialized wind fields to 0."""
ws = self.ws
if ws.wind_u_field.value.size == 0:
self.set_wind(wind_u=0)
if ws.wind_v_field.value.size == 0:
self.set_wind(wind_v=0)
if ws.wind_w_field.value.size == 0:
self.set_wind(wind_w=0)
def set_observations(self, observations):
"""
Set the geometry of the observations made.
:param observations:
:type observations: Iterable[retrievals.arts.interface.Observation]
"""
sensor_los = np.array([[obs.za, obs.aa] for obs in observations])
sensor_pos = np.array([[obs.alt, obs.lat, obs.lon] for obs in observations])
self.ws.sensor_los = sensor_los[:, :max(self.atmosphere_dim - 1, 1)]
self.ws.sensor_pos = sensor_pos[:, :self.atmosphere_dim]
self.ws.sensor_time = np.array([obs.time for obs in observations])
self._observations = observations
def set_y(self, ys):
"""
Set the observations.
:param ys: List with a spectrum for every observation.
:return:
"""
y = np.concatenate(ys)
self.ws.y = y
def set_sensor(self, sensor):
"""
Set the sensor.
:param sensor:
:type sensor: retrievals.arts.sensors.AbstractSensor
"""
self._sensor = sensor
sensor.apply(self.ws)
def y_calc(self, jacobian_do=False):
"""
Run the forward model.
:param jacobian_do: Not implemented yet.
:return: The measurements as list with length according to observations.
"""
if jacobian_do:
raise NotImplementedError('Jacobian not implemented yet.')
# self.ws.jacobian_do = 1
self.ws.yCalc()
return self.y
def define_retrieval(self, retrieval_quantities, y_vars):
"""
Define the retrieval quantities.
:param retrieval_quantities: Iterable of retrieval quantities `retrievals.arts.retrieval.RetrievalQuantity`.
:param y_vars: List or variance vectors according.
"""
ws = self.ws
if isinstance(y_vars, (list, tuple)):
y_vars = np.concatenate(y_vars)
if len(y_vars) != self.n_y:
raise ValueError('Variance vector y_vars must have same length as y.')
ws.retrievalDefInit()
# Retrieval quantities
self.retrieval_quantities = retrieval_quantities
for rq in retrieval_quantities:
rq.apply(ws)
# Se and its inverse
covmat_block = sparse.diags(y_vars, format='csr')
boilerplate.set_variable_by_xml(ws, ws.covmat_block, covmat_block)
ws.covmat_seAddBlock(block=ws.covmat_block)
covmat_block = sparse.diags(1/y_vars, format='csr')
boilerplate.set_variable_by_xml(ws, ws.covmat_block, covmat_block)
ws.covmat_seAddInverseBlock(block=ws.covmat_block)
ws.retrievalDefClose()
def oem(self, method='li', max_iter=10, stop_dx=0.01, lm_ga_settings=None, display_progress=True,
inversion_iterate_agenda=None):
"""
Run the optimal estimation. See Arts documentation for details.
:param method:
:param max_iter:
:param stop_dx:
:param lm_ga_settings: Default: [10, 2, 2, 100, 1, 99]
:param display_progress:
:param inversion_iterate_agenda: If set to None, a simple default agenda is used.
:return:
"""
ws = self.ws
if lm_ga_settings is None:
lm_ga_settings = [100.0, 2.0, 2.0, 10.0, 1.0, 1.0]
lm_ga_settings = np.array(lm_ga_settings)
# x, jacobian and yf must be initialised
ws.x = np.array([])
ws.yf = np.array([])
ws.jacobian = np.array([[]])
if inversion_iterate_agenda is None:
inversion_iterate_agenda = boilerplate.inversion_iterate_agenda
ws.Copy(ws.inversion_iterate_agenda, inversion_iterate_agenda)
ws.AgendaExecute(ws.sensor_response_agenda)
# a priori values
self._check_set_wind()
ws.xaStandard()
xa = ws.xa.value
for rq in self.retrieval_quantities:
rq.extract_apriori(xa)
# Run inversion
try:
ws.OEM(method=method, max_iter=max_iter, stop_dx=stop_dx, lm_ga_settings=lm_ga_settings,
display_progress=1 if display_progress else 0)
except Exception as e:
raise OemException(e)
if not self.oem_converged: # Just checks if dxdy is initialized
return False
ws.x2artsAtmAndSurf()
ws.x2artsSensor()
ws.avkCalc()
ws.covmat_ssCalc()
ws.covmat_soCalc()
ws.retrievalErrorsExtract()
x = ws.x.value
avk = ws.avk.value
eo = ws.retrieval_eo.value
es = ws.retrieval_ss.value
for rq in self.retrieval_quantities:
rq.extract_result(x, avk, eo, es)
return True
def get_level2_xarray(self):
ds = xr.merge([rq.to_xarray() for rq in self.retrieval_quantities])
# Spectra
f_backend = self._sensor.f_backend if self._sensor.f_backend is not None else self.f_grid
ds['f'] = ('f', f_backend)
ds['y'] = (('observation', 'f'), np.stack(self.y))
ds['yf'] = (('observation', 'f'), np.stack(self.yf))
ds['oem_diagnostics'] = ('oem_diagnostics_idx', self.oem_diagnostics)
y_baseline = self.y_baseline
if y_baseline is not None:
ds['y_baseline'] = (('observation', 'f'), np.stack(self.y_baseline))
# Observations
for f in Observation._fields:
values = np.array([getattr(obs, f) for obs in self._observations], dtype=np.float)
ds['obs_'+f] = ('observation', values)
# Global attributes
ds.attrs['arts_version'] = self.arts_version
ds.attrs['uuid'] = str(uuid.uuid4())
ds.attrs['date_created'] = datetime.datetime.utcnow().isoformat()
ds.attrs['nodename'] = os.uname().nodename
return ds
@property
def p_grid(self):
return self.ws.p_grid.value
@property
def lat_grid(self):
return self.ws.lat_grid.value
@property
def lon_grid(self):
return self.ws.lon_grid.value
@property
def f_grid(self):
return self.ws.f_grid.value
@property
def n_p(self):
return len(self.ws.p_grid.value)
@property
def n_lat(self):
if self.atmosphere_dim == 1:
return 1
return len(self.ws.lat_grid.value)
@property
def n_lon(self):
if self.atmosphere_dim == 1:
return 1
return len(self.ws.lat_grid.value)
@property
def n_y(self):
return len(self.ws.y.value)
@property
def y(self):
y = np.copy(self.ws.y.value)
return np.split(y, self.n_obs)
@property
def yf(self):
yf = np.copy(self.ws.yf.value)
return np.split(yf, self.n_obs)
@property
def y_baseline(self):
if self.ws.y_baseline.initialized:
bl = np.copy(self.ws.y_baseline.value)
if bl.size == 1 and bl[0] == 0:
return None
return np.split(bl, self.n_obs)
else:
return None
@property
def n_obs(self):
if self.ws.sensor_time.initialized:
return len(self.ws.sensor_time.value)
else:
return 0
@property
def abs_species_maintags(self):
abs_species = self.ws.abs_species.value
maintags = [st[0].split('-')[0] for st in abs_species]
return maintags
@property
def atmosphere_dim(self):
return self.ws.atmosphere_dim.value
@property
def oem_converged(self):
return self.ws.oem_diagnostics.value[0] == 0
@property
def oem_diagnostics(self):
return self.ws.oem_diagnostics.value
@property
def oem_errors(self):
return self.ws.oem_errors.value
@property
def arts_version(self):
cmd = os.path.join(os.environ['ARTS_BUILD_PATH'], 'src/arts')
output = os.popen(cmd + ' --version').read().splitlines()
return output[0]
# Atmosphere (A Priori)
# We create a pressure grid using the `PFromZSimple` function to create a grid of approximate pressure levels
# corresponding to altitudes in the range
# z = 0.0, 2000.0, ..., 94000.0
z_toa = 95e3
z_surf = 1e3
z_grid = np.arange(z_surf - 1e3, z_toa, 2e3)
ws.PFromZSimple(ws.p_grid, z_grid)
ws.lat_grid = np.arange(-40.0, 1.0, 40.0)
ws.lon_grid = np.arange(40.0, 61.0, 20.0)
ws.z_surface = z_surf * np.ones(
(np.asarray(ws.lat_grid).size, np.asarray(ws.lon_grid).size))
# For the a priori state we read data from the Fascod climatology that is part of the ARTS xml data.
ws.AtmRawRead(basename="planets/Earth/Fascod/tropical/tropical")
ws.AtmFieldsCalcExpand1D()
# Adding Wind
# Wind in ARTS is represented by the `wind_u_field` and `wind_v_field` WSVs, which hold the horizontal components
# of the wind at each grid point of the atmosphere model. For this example, a constant wind is assumed.
u_wind = 60.0
v_wind = -40.0
ws.wind_u_field = u_wind * np.ones(
(ws.p_grid.value.size, ws.lat_grid.value.size, ws.lon_grid.value.size))
ws.wind_v_field = v_wind * np.ones(
(ws.p_grid.value.size, ws.lat_grid.value.size, ws.lon_grid.value.size))
ws.wind_w_field = np.zeros((0, 0, 0))
# Frequency Grid and Sensor
# The frequency grid for the simulation consists of 119 grid points between 110.516 and 111.156 GHz. The frequencies
# are given by a degree-10 polynomial that has been obtained from a fit to the data from the original `qpack` example.
def test_wind_3d_demo():
ws = Workspace()
ws.execute_controlfile("general/general.arts")
ws.verbositySet(0, 0, 0, 0)
ws.execute_controlfile("general/agendas.arts")
ws.execute_controlfile("general/continua.arts")
ws.execute_controlfile("general/planet_earth.arts")
ws.Copy(ws.abs_xsec_agenda, ws.abs_xsec_agenda__noCIA)
ws.Copy(ws.ppath_agenda, ws.ppath_agenda__FollowSensorLosPath)
ws.Copy(ws.ppath_step_agenda, ws.ppath_step_agenda__GeometricPath)
ws.Copy(ws.iy_space_agenda, ws.iy_space_agenda__CosmicBackground)
ws.Copy(ws.iy_surface_agenda, ws.iy_surface_agenda__UseSurfaceRtprop)
ws.Copy(ws.iy_main_agenda, ws.iy_main_agenda__Emission)
ws.Copy(ws.propmat_clearsky_agenda, ws.propmat_clearsky_agenda__OnTheFly)
# General Settings
# For the wind retrievals, the forward model calculations are performed on a 3D atmosphere grid.
# Radiation is assumed to be unpolarized.
ws.atmosphere_dim = 3
ws.stokes_dim = 1
ws.iy_unit = "RJBT"
# Absorption
# We only consider absorption from ozone in this example. The lineshape data is available from
# the ARTS testdata available in `controlfiles/testdata`.
ws.abs_speciesSet(["O3", "H2O-PWR98"])
ws.abs_lineshapeDefine("Voigt_Kuntz6", "VVH", 750e9)
ws.ReadXML(ws.abs_lines, "testdata/ozone_line.xml")
ws.abs_lines_per_speciesCreateFromLines()
# Atmosphere (A Priori)
# We create a pressure grid using the `PFromZSimple` function to create a grid of approximate pressure levels
# corresponding to altitudes in the range
# z = 0.0, 2000.0, ..., 94000.0
z_toa = 95e3
z_surf = 1e3
z_grid = np.arange(z_surf - 1e3, z_toa, 2e3)
ws.PFromZSimple(ws.p_grid, z_grid)
ws.lat_grid = np.arange(-40.0, 1.0, 40.0)
ws.lon_grid = np.arange(40.0, 61.0, 20.0)
ws.z_surface = z_surf * np.ones(
(np.asarray(ws.lat_grid).size, np.asarray(ws.lon_grid).size))
# For the a priori state we read data from the Fascod climatology that is part of the ARTS xml data.
ws.AtmRawRead(basename="planets/Earth/Fascod/tropical/tropical")
ws.AtmFieldsCalcExpand1D()
# Adding Wind
# Wind in ARTS is represented by the `wind_u_field` and `wind_v_field` WSVs, which hold the horizontal components
# of the wind at each grid point of the atmosphere model. For this example, a constant wind is assumed.
u_wind = 60.0
v_wind = -40.0
ws.wind_u_field = u_wind * np.ones(
(ws.p_grid.value.size, ws.lat_grid.value.size, ws.lon_grid.value.size))
ws.wind_v_field = v_wind * np.ones(
(ws.p_grid.value.size, ws.lat_grid.value.size, ws.lon_grid.value.size))
ws.wind_w_field = np.zeros((0, 0, 0))
# Frequency Grid and Sensor
# The frequency grid for the simulation consists of 119 grid points between 110.516 and 111.156 GHz.
# The frequencies are given by a degree-10 polynomial that has been obtained from a fit to the data from
# the original `qpack` example. This is obscure but also kind of cool.
coeffs = np.array([
5.06312189e-08, -2.68851772e-05, 6.20655463e-03, -8.16344090e-01,
6.75337174e+01, -3.66786505e+03, 1.32578167e+05, -3.14514304e+06,
4.57491354e+07, 1.10516484e+11
])
ws.f_grid = np.poly1d(coeffs)(np.arange(119))
# For the sensor we assume a channel width and channel spacing of 50 kHz. We also call AntennaOff to compute
# only one pencilbeam along the line of sight of the sensor.
df = 50e3
f_backend = np.arange(ws.f_grid.value.min() + 2.0 * df,
ws.f_grid.value.max() - 2.0 * df, df)
ws.backend_channel_responseGaussian(np.array([df]), np.array([2.0]))
ws.AntennaOff()
ws.sensor_norm = 1
ws.sensor_time = np.zeros(1)
ws.sensor_responseInit()
# Sensor Position and Viewing Geometry
# 5 Measurements are performed, one straight up, and four with zenith angle 70∘70∘ in directions SW, NW, NE, SE.
# In ARTS the measurement directions are given by a two-column matrix, where the first column contains the zenith
# angle and the second column the azimuth angle.
ws.sensor_los = np.array([[
0.0,
0.0,
], [70.0, -135.0], [70.0, -45.0], [70.0, 45.0], [70.0, 135.0]])
ws.sensor_pos = np.array([[2000.0, -21.1, 55.6]] * 5)
# Reference Measurement
# Before we can calculate `y`, our setup needs to pass the following tests:
ws.abs_f_interp_order = 3
ws.propmat_clearsky_agenda_checkedCalc()
ws.sensor_checkedCalc()
ws.atmgeom_checkedCalc()
ws.atmfields_checkedCalc()
ws.abs_xsec_agenda_checkedCalc()
ws.jacobianOff()
ws.cloudboxOff()
ws.cloudbox_checkedCalc()
ws.yCalc()
y = np.copy(ws.y.value)
# Setting up the Retrieval
# In this example, we retrieve ozone and the horizontal and vertical components of the wind velocities.
# The state space covariance matrix in ARTS is represented by the **covmat_sa** WSV.
# It belongs to the CovarianceMatrix group, which is used to represent block diagonal matrices.
# For each retrieval quantity that is added to the retrieval, a corresponding block must be added to **covmat_sa**.
# This is usually done by the corresponding **retrievalAdd...** call, which looks for this block
# in the **covmat_block** WSV.
# In short the general workflow for adding a retrieval quantity is as follows:
# - Create the covariance matrix for the retrieval quantity either calling one of the **covmat...** WSV or
# by loading your own matrix
# - Write the matrix block into **covmat_block**
# - Call the **retrievalAdd...** method to add the retrieval quantity and the covariance matrix block
# to **covmat_sa**
lat_ret_grid = np.array([np.mean(ws.lat_grid)])
lon_ret_grid = np.array([np.mean(ws.lon_grid)])
n_p = ws.p_grid.value.size
ws.retrievalDefInit()
ws.covmat1D(
ws.covmat_block,
grid_1=z_grid,
sigma_1=0.1 * np.ones(n_p), # Relative uncertainty
cls_1=10e3 * np.ones(n_p), # 10km correlation length
fname="lin")
ws.retrievalAddAbsSpecies(species="O3",
unit="rel",
g1=ws.p_grid,
g2=lat_ret_grid,
g3=lon_ret_grid)
# Wind u-component
ws.covmat1D(
ws.covmat_block,
grid_1=z_grid[::2],
sigma_1=100.0 * np.ones(n_p // 2), # Relative uncertainty
cls_1=10e3 * np.ones(n_p // 2), # 10km correlation length
fname="lin")
ws.retrievalAddWind(g1=ws.p_grid.value[::2],
g2=np.array([np.mean(ws.lat_grid)]),
g3=np.array([np.mean(ws.lon_grid)]),
component="u")
# Wind v-component
ws.covmat1D(
ws.covmat_block,
grid_1=z_grid[::2],
sigma_1=100.0 * np.ones(n_p // 2), # Relative uncertainty
cls_1=10e3 * np.ones(n_p // 2), # 10km correlation length
fname="lin")
ws.retrievalAddWind(g1=ws.p_grid.value[::2],
g2=np.array([np.mean(ws.lat_grid)]),
g3=np.array([np.mean(ws.lon_grid)]),
component="v")
ws.retrievalDefClose()
ws.covmatDiagonal(ws.covmat_block,
ws.covmat_inv_block,
vars=0.0001 * np.ones(ws.y.value.shape))
ws.covmat_seSet(ws.covmat_block)
@arts_agenda
def inversion_iterate_agenda(ws):
ws.x2artsStandard()
ws.atmfields_checkedCalc()
ws.atmgeom_checkedCalc()
ws.yCalc()
ws.Print(ws.y)
ws.Print(ws.jacobian)
ws.VectorAddVector(ws.yf, ws.y, ws.y_baseline)
ws.IndexAdd(ws.inversion_iteration_counter,
ws.inversion_iteration_counter, 1)
ws.Copy(ws.inversion_iterate_agenda, inversion_iterate_agenda)
# A Priori State
# For the a priori state we assume zero wind in any direction. The a priori vector for the OEM is created by
# the `xaStandard` WSM, which computes $x_a$ from the current atmospheric state.
ws.wind_u_field.value[:] = 0.0
ws.wind_v_field.value[:] = 0.0
ws.xaStandard()
# The OEM Calculation
ws.x = np.zeros(0)
ws.jacobian = np.zeros((0, 0))
ws.y.value[:] = y
ws.OEM(method="lm",
max_iter=20,
display_progress=1,
lm_ga_settings=np.array([100.0, 2.0, 2.0, 10.0, 1.0, 1.0]))
ws.x2artsStandard()
z = ws.z_field.value[:, 0, 0].ravel()
wind_u = ws.wind_u_field.value[z > 40e3, 0, 0]
wind_v = ws.wind_v_field.value[z > 40e3, 0, 0]
assert np.allclose(wind_u, u_wind, atol=1)
assert np.allclose(wind_v, v_wind, atol=1)
