import os
import numpy as np
import matplotlib.pyplot as plt
import scipy.interpolate
import emcee
import copy
from .transit_depth_calculator import TransitDepthCalculator
from .fit_info import FitInfo
from .constants import METRES_TO_UM
from ._params import _UniformParam
from .errors import AtmosphereError
from ._output_writer import write_param_estimates_file
from .combined_retriever import CombinedRetriever
[docs]class Retriever:
[docs] def __init__(self):
raise RuntimeError("To avoid confusion, use CombinedRetriever instead")
self.combined_retriever = CombinedRetriever()
[docs] def run_emcee(self, wavelength_bins, depths, errors, fit_info, nwalkers=50,
nsteps=1000, include_condensation=True, rad_method="xsec",
plot_best=False):
'''Runs affine-invariant MCMC to retrieve atmospheric parameters.
Parameters
----------
wavelength_bins : array_like, shape (N,2)
Wavelength bins, where wavelength_bins[i][0] is the start
wavelength and wavelength_bins[i][1] is the end wavelength for
bin i.
depths : array_like, length N
Measured transit depths for the specified wavelength bins
errors : array_like, length N
Errors on the aforementioned transit depths
fit_info : :class:`.FitInfo` object
Tells the method what parameters to
freely vary, and in what range those parameters can vary. Also
sets default values for the fixed parameters.
nwalkers : int, optional
Number of walkers to use
nsteps : int, optional
Number of steps that the walkers should walk for
include_condensation : bool, optional
When determining atmospheric abundances, whether to include
condensation.
plot_best : bool, optional
If True, plots the best fit model with the data
Returns
-------
result : EnsembleSampler object
This returns emcee's EnsembleSampler object. The most useful
attributes in this item are result.chain, which is a (W x S X P)
array where W is the number of walkers, S is the number of steps,
and P is the number of parameters; and result.lnprobability, a
(W x S) array of log probabilities. For your convenience, this
object also contains result.flatchain, which is a (WS x P) array
where WS = W x S is the number of samples; and
result.flatlnprobability, an array of length WS
'''
return self.combined_retriever.run_emcee(
wavelength_bins, depths, errors, None, None, None,
fit_info, nwalkers, nsteps, include_condensation, rad_method,
plot_best)
[docs] def run_multinest(self, wavelength_bins, depths, errors, fit_info,
include_condensation=True, rad_method="xsec",
plot_best=False,
maxiter=None, maxcall=None, nlive=100,
**dynesty_kwargs):
'''Runs nested sampling to retrieve atmospheric parameters.
Parameters
----------
wavelength_bins : array_like, shape (N,2)
Wavelength bins, where wavelength_bins[i][0] is the start
wavelength and wavelength_bins[i][1] is the end wavelength for
bin i.
depths : array_like, length N
Measured transit depths for the specified wavelength bins
errors : array_like, length N
Errors on the aforementioned transit depths
fit_info : :class:`.FitInfo` object
Tells us what parameters to
freely vary, and in what range those parameters can vary. Also
sets default values for the fixed parameters.
include_condensation : bool, optional
When determining atmospheric abundances, whether to include
condensation.
plot_best : bool, optional
If True, plots the best fit model with the data
nlive : int
Number of live points to use for nested sampling
**dynesty_kwargs : keyword arguments to pass to dynesty's NestedSampler
Returns
-------
result : Result object
This returns 'results' of the NestedSampler object. It is
dictionary-like and has many useful items. For example,
result.samples (or alternatively, result["samples"]) are the
parameter values of each sample, result.weights contains the
weights, and result.logl contains the log likelihoods. result.logz
is the natural logarithm of the evidence.
'''
return self.combined_retriever.run_multinest(
wavelength_bins, depths, errors, None, None, None, fit_info,
include_condensation, rad_method, plot_best, maxiter, maxcall,
nlive=nlive, **dynesty_kwargs)
[docs] @staticmethod
def get_default_fit_info(Rs, Mp, Rp, T, logZ=0, CO_ratio=0.53,
log_cloudtop_P=np.inf, log_scatt_factor=0,
scatt_slope=4, error_multiple=1, T_star=None,
T_spot=None, spot_cov_frac=None,frac_scale_height=1,
log_number_density=-np.inf, log_part_size =-6,
n=None, log_k=-np.inf, log_P_quench=-99,
part_size_std = 0.5, wfc3_offset_transit=0):
'''Get a :class:`.FitInfo` object filled with best guess values. A few
parameters are required, but others can be set to default values if you
do not want to specify them. All parameters are in SI.
For information on the parameters, see the documentation for
:func:`~platon.transit_depth_calculator.TransitDepthCalculator.compute_depths`
Returns
-------
fit_info : :class:`.FitInfo` object
This object is used to indicate which parameters to fit for, which
to fix, and what values all parameters should take.'''
raise RuntimeError("To avoid confusion, use CombinedRetriever instead")
fit_info = FitInfo(locals().copy())
return fit_info