def __init__(self, sub_configdic: dict = None):
self._wavelength_file_type = str
self.wavelength_file = None
self._integrations_type = int
self.integrations = None
"""Number of integrations comprising the measurement.
Noise diminishes with the square root of this number. Applicable in concert with parametric_noise_file
or pushbroom_noise_file"""
self._unknowns_type = InstrumentUnknowns
self.unknowns: InstrumentUnknowns = None
self._fast_resample_type = bool
self.fast_resample = True
"""bool: Approximates a complete resampling by a convolution with a uniform FWHM."""
self._statevector_type = StateVectorConfig
self.statevector: StateVectorConfig = StateVectorConfig({})
self._SNR_type = float
self.SNR = None
"""float: We have several ways to define the instrument noise. The simplest model is based on a single uniform
SNR number that is signal-independnet and applied uniformly to all wavelengths"""
self._parametric_noise_file_type = str
self.parametric_noise_file = None
"""str: We have several ways to define the instrument noise.
The second option is a parametric, signal- and wavelength-
dependent noise function. This is given by a four-column
ASCII Text file. Rows represent, respectively, the reference
wavelength, and coefficients A, B, and C that define the
noise-equivalent radiance via NeDL = A * sqrt(B+L) + C
For the actual radiance L."""
self._pushbroom_noise_file_type = str
self.pushbroom_noise_file = None
"""str: We have several ways to define the instrument noise.
The third option is a full pushbroom noise model that
specifies noise columns and covariances independently for
each cross-track location via an ENVI-format binary data file."""
self._nedt_noise_file_type = str
self.nedt_noise_file = None
"""str: We have several ways to define the instrument noise. The last is NEDT noise"""
self.set_config_options(sub_configdic)
# If necessary, initialize some blank options
if self.statevector is None:
self.statevector = StateVectorConfig({})
def __init__(self, sub_configdic: dict = None):
self._statevector_type = StateVectorConfig
self.statevector: StateVectorConfig = StateVectorConfig({})
self._lut_grid_type = OrderedDict
self.lut_grid = None
self._unknowns_type = RadiativeTransferUnknownsConfig
self.unknowns: RadiativeTransferUnknownsConfig = None
self.set_config_options(sub_configdic)
# sort lut_grid
for key, value in self.lut_grid.items():
self.lut_grid[key] = sorted(self.lut_grid[key])
self.lut_grid = OrderedDict(
sorted(self.lut_grid.items(), key=lambda t: t[0]))
# Hold this parameter for after the config_options, as radiative_transfer_engines
# have a special (dynamic) load
self._radiative_transfer_engines_type = list()
self.radiative_transfer_engines = []
self._set_rt_config_options(
sub_configdic['radiative_transfer_engines'])
def __init__(self, sub_configdic: dict = None):
self._topography_model_type = bool
self.topography_model = False
"""
Flag to indicated whether to use atopographic-flux (topoflux)
implementation of the forward model. Only currently functional
with multipart MODTRAN
"""
self._statevector_type = StateVectorConfig
self.statevector: StateVectorConfig = StateVectorConfig({})
self._lut_grid_type = OrderedDict
self.lut_grid = None
self._unknowns_type = RadiativeTransferUnknownsConfig
self.unknowns: RadiativeTransferUnknownsConfig = None
self.set_config_options(sub_configdic)
# sort lut_grid
for key, value in self.lut_grid.items():
self.lut_grid[key] = sorted(self.lut_grid[key])
self.lut_grid = OrderedDict(sorted(self.lut_grid.items(), key=lambda t: t[0]))
# Hold this parameter for after the config_options, as radiative_transfer_engines
# have a special (dynamic) load
self._radiative_transfer_engines_type = list()
self.radiative_transfer_engines = []
self._interpolator_style_type = str
self.interpolator_style = 'nds-1'
"""str: Style of interpolation. Options are rd for scipy RegularGridInterpolator or nds-k
for ndsplines with k degrees"""
self._set_rt_config_options(sub_configdic['radiative_transfer_engines'])