#! /usr/bin/env python3
#
# Copyright 2018 California Institute of Technology
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# ISOFIT: Imaging Spectrometer Optimal FITting
# Author: Philip G. Brodrick, philip.brodrick@jpl.nasa.gov
from collections import OrderedDict
from typing import Dict, List, Type
from isofit.configs.base_config import BaseConfigSection
[docs]
class InversionConfig(BaseConfigSection):
"""
Inversion configuration.
"""
def __init__(self, sub_configdic: dict = None):
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self._windows_type = list()
"""List[List[float]]: inversion retrieval windows to operate over."""
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self._cressie_map_confidence_type = bool
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self.cressie_map_confidence = False
"""bool: N. Cressie [ASA 2018] suggests an alternate definition of S_hat for
more statistically-consistent posterior confidence estimation, this flag runs in this mode"""
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self._mcmc_type = McmcConfig
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self.mcmc = McmcConfig({})
"""MCMC parameters, only used if mode = mcmc."""
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self._integration_grid_type = OrderedDict
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self.integration_grid = OrderedDict({})
"""Grid of inversion points to execute if mode='grid'. Either fixed, or starting points, depending
on self.fixed_inversion_grid"""
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self._priors_in_initial_guess_type = bool
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self.priors_in_initial_guess = True
"""Boolean to inidicate the use of surface priors outside of the inversion windows during the
intial guess."""
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self._inversion_grid_as_preseed_type = bool
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self.inversion_grid_as_preseed = False
"""Parameter indicating whether to treat the inversion grid as:
(True) - a series of seeds for the optimization (variable by the optimization algorithm).
(False) - a set of fixed points (not variable by the optimization algorithm)
"""
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self._least_squares_params_type = LeastSquaresConfig
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self.least_squares_params = LeastSquaresConfig({})
"""
Least squares parameters for core inversion solve.
See https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.least_squares.html
for details.
"""
self.set_config_options(sub_configdic)
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def _check_config_validity(self) -> List[str]:
errors = list()
# TODO: add some checking to windows
if self.windows is None and self.mode != "simulation":
errors.append("windows is a required parameters inside of inversion")
else:
for subset in self.windows:
if isinstance(subset, List) is False:
errors.append(
"windows parameter must be a list of lists of wavelength ranges"
)
elif subset[0] > subset[1]:
errors.append(
"In inversion window subset {}, wavelength ranges must be in"
" order".format(subset)
)
return errors
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class McmcConfig(BaseConfigSection):
"""
MCMC inversion configuration.
"""
def __init__(self, sub_configdic: dict = None):
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self._iterations_type = int
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self.iterations = 10000
"""int: Number of MCMC iterations to run."""
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self._burnin_type = int
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self._regularizer_type = float
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self.regularizer = 1e-3
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self._proposal_scaling_type = float
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self.proposal_scaling = 0.01
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self._verbose_type = bool
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self._restart_every_type = int
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self.restart_every = 2000
self.set_config_options(sub_configdic)
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def _check_config_validity(self) -> List[str]:
errors = list()
# TODO: add flags for rile overright, and make sure files don't exist if not checked?
return errors
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class LeastSquaresConfig(BaseConfigSection):
"""
Least squares config parameters.
"""
def __init__(self, sub_configdic: dict = None):
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self._method_type = str
"""str: Optimzation method to use. Default 'trf'."""
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self._max_nfev_type = int
"""int: Maximum number of function evaluations before the termination.
If None (default), the value is chosen automatically. Default 20."""
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self._xtol_type = float
"""float: Tolerance for termination by the change of the independent variables.
Default is None, which disables termination from this criteria."""
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self._ftol_type = float
"""float: Tolerance for termination by the change of the cost function.
Default is 0.01"""
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self._gtol_type = float
"""float: Tolerance for termination by the norm of the gradient.
Default is None, which disables termination from this criteria."""
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self._tr_solver_type = str
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self.tr_solver = "lsmr"
"""str: Method for solving trust-region subproblems, relevant only for ‘trf’ and ‘dogbox’ methods.
Options are None, 'exact', or 'lsmr'"""
self.set_config_options(sub_configdic)
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def _check_config_validity(self) -> List[str]:
errors = list()
# TODO: add flags for rile overright, and make sure files don't exist if not checked?
if self.tr_solver is not None:
if self.tr_solver not in [None, "exact", "lsmr"]:
errors.append(
"Least squares tr_solver must be in [None, 'exact', 'lsmr']"
)
return errors