# Quick start¶

The fastest way to get started is to look at the examples/ directory, which has examples on how to compute transit/eclipse depths from planetary parameters, and on how to retrieve planetary parameters from transit/eclipse depths. This page is a short summary of the more detailed examples.

To compute transit depths, look at transit_depth_example.py, then go to TransitDepthCalculator for more info. In short:

from platon.transit_depth_calculator import TransitDepthCalculator
from platon.constants import M_jup, R_jup, R_sun

# All inputs and outputs for PLATON are in SI

Rs = 1.16 * R_sun
Mp = 0.73 * M_jup
Rp = 1.40 * R_jup
T = 1200

# The initializer loads all data files.  Create a TransitDepthCalculator
# object and hold on to it
calculator = TransitDepthCalculator(method="xsec") #"ktables" for correlated k

# compute_depths is fast once data files are loaded
wavelengths, depths, info_dict = calculator.compute_depths(Rs, Mp, Rp, T, logZ=0, CO_ratio=0.53, full_output=True)


You can adjust a variety of parameters, including the metallicity (Z) and C/O ratio. By default, logZ = 0 and C/O = 0.53. Any other value for logZ and C/O in the range -1 < logZ < 3 and 0.05 < C/O < 2 can also be used. full_output=True indicates you’d like extra information about the atmosphere, which is returned in info_dict. info_dict includes parameters like the temperatures, pressures, radii, abundances, and molecular weights of each atmospheric layer, and the line of sight optical depth (tau_los) through each layer.

You can also specify custom abundances, such as by providing the filename of one of the abundance files included in the package (from ExoTransmit). The custom abundance files specified by the user must be compatible with the ExoTransmit format:

calculator.compute_depths(Rs, Mp, Rp, T, logZ=None, CO_ratio=None,
custom_abundances=filename)


To retrieve atmospheric parameters, look at retrieve_multinest.py, retrieve_emcee.py, or retrieve_eclipses.py, then go to CombinedRetriever for more info. In short:

from platon.fit_info import FitInfo
from platon.combined_retriever import CombinedRetriever

retriever = CombinedRetriever()
fit_info = retriever.get_default_fit_info(Rs, Mp, Rp, T, logZ=0, T_star=6100)

# Decide what you want to fit for, and add those parameters to fit_info

# Fit for the stellar radius and planetary mass using Gaussian priors.  This
# is a way to account for the uncertainties in the published values

# Fit for other parameters using uniform priors

# Run nested sampling
result = retriever.run_multinest(
bins, depths, errors, #transit bins, depths, errors
None, None, None, #eclipse bins, depths, errors
fit_info, plot_best=True,
rad_method="xsec") #Change this to "ktables" for correlated k


Here, bins is a N x 2 array representing the start and end wavelengths of the bins, in metres; depths is a list of N transit depths; and errors is a list of N errors on those transit depths. plot_best=True indicates that the best fit solution should be plotted, along with the measured transit depths and their errors.

The example above retrieves the planetary radius (at a reference pressure of 100,000 Pa), the temperature of the isothermal atmosphere, and the metallicity. Other parameters you can retrieve for include the stellar radius, the planetary mass, C/O ratio, the cloudtop pressure, the scattering factor, the scattering slope, and the error multiple–which multiplies all errors by a constant. We recommend either fixing the stellar radius and planetary mass to the measured values, or setting Gaussian priors on them to account for measurement errors.

Once you get the result object, you should store the object, in addition to plotting the posterior distribution and the best fit:

with open("example_retrieval_result.pkl", "wb") as f:
pickle.dump(result, f)

result.plot_corner("my_corner.png")
result.plot_spectrum("my_best_fit") #leave off .png


If you prefer using MCMC instead of Nested Sampling in your retrieval, you can use the run_emcee method instead of the run_multinest method. Do note that Nested Sampling is recommended, as it is not trivial to deal with multi-modal posteriors or to check for convergence with emcee:

result = retriever.run_emcee(bins, depths, errors, fit_info)


For MCMC, the number of walkers and iterations/steps can also be specified. The result object returned by run_emcee is different from that returned by run_multinest, but still supports plot_corner and plot_spectrum.