Juliet Mods

A collection of convenience functions for juliet.

It provides:

Classes for handling priors and instrument data
Plotting functions for RV, transits and long term photometry
Plotting functions for correlations (corner plots, GP alpha-vs-Prot, planet parameter correlations)
Automatic creation of LaTeX tables containing planet posteriors (including derived parameters like minimum mass and planet radius)

Documentation

The functions of the package have become very complex over the years. Given that I don't know how much it will be utilized by others, I refrain from providing a detailed documentation (would be just to time consuming). However, I tried to provide an comprehensive documentation of the code itself. If you have any questions, don't hesitate to approach me (either per DM or opening issues here on github.)

Usage

You can simply download the package and import it, if the download directory is in your Python path. I would reccoment to clone the repository to a local directory and install the package from source:

git clone https://github.com/JonasKemmer/JulietMods.git
cd JulietMods
python setup.py develop

In this way you can modify the package to your liking and synchronize quickly with the latest version from github using:

git pull

in the directory of the package.

If you want to install it using pip, you can use:

pip install git+https://github.com/JonasKemmer/JulietMods.git

Disclaimer:

The package was originally created for my own workflow, so there is no guarantee for correctness. I am happy for any bug reports, however it is unlikely that I will actively develop the package in the near future.

BUT the package is deliberately published with an open source licence. If you're an active juliet user, fork it, modify it to your liking and share it :)

Example file for an existing juliet fit:

import juliet
import juliet_mods as jm

julobj = juliet.load(input_folder='/path/to/juliet_out/')
results = julobj.fit(
        sampler='dynamic_dynesty',
        nthreads=5,
    )
jm.append_lnZ(results)
jm.plot_rv(results)
jm.plot_phased_rvs(results, show=True, saveformat='png')
jm.plot_parameter_correlation(results, 'P_vs_K', 'p1')

Example file of a joint RV and transit fit:

A full (real) example jupyter-notebook for a joint RV and transit fit can be found in the example folder, but it can basically look like:

import juliet
import juliet_mods as jm

priors = jm.PriorList.read_prior_file(f'./dummy_priors.priors')
data = jm.Data()

## RVs ##
data.add_rv('./RVinstr1.vels', 'RV1')
data.add_rv('./RVinstr2.vels', 'RV2')

## Photometry ##
data.add_photometry('./Photinstr1.lc', 'LC1')
data.add_photometry('./Photinstr2.lc', 'LC2')


julobj = juliet.load(priors=priors.priordict,
                     t_rv=data.t_rvs,
                     y_rv=data.rvs,
                     yerr_rv=data.rvs_err,
                     GP_regressors_rv=data.gp_reg_rvs,
                     t_lc=data.t_fluxes,
                     y_lc=data.fluxes,
                     yerr_lc=data.fluxes_err,
                     linear_regressors_lc=data.linear_regressors_lc,
                     GP_regressors_lc=data.gp_reg_lc,
                     out_folder=f'./dummyfit')
results = julobj.fit(
        sampler='dynamic_dynesty',
        nthreads=5,
    )
jm.append_lnZ(results)
jm.get_lc_residuals(results)
jm.get_rv_residuals(results)

jm.plot_rv_indv_panels(results, show=True)
jm.plot_phased_rvs(results, hide_uncertainty=False, show_binned=True, show=True)

jm.plot_lc_indv_panels(results, show=True)
for pnum in range(1, results.data.n_transiting_planets + 1):
    jm.plot_transit_overview(results,
                             minphase=-0.02,
                             maxphase=0.02,
                             binlength=0.001,
                             ncols=4,
                             pnum=pnum,
                             show=True,
                             nsamples=10000)

for pnum in range(1, results.data.n_rv_planets + 1):
    jm.plot_corner(results, [f'_p{pnum}', 'rho'],
                    mark_values=None,
                    show=False)


jm.plot_corner(results, 'GP')

jm.create_planet_posterior_table(results)
jm.create_planet_param_table(results,
                                R_star=(1, 0.2),
                                M_star=(1, 0.2),
                                L_star=(1, 0.2),
                                T_star=(6000, 100),
                                Kmag=(7.869, 0.02),
                                Jmag=(8.694, 0.024),
                                saveformat='LaTeX')

jm.plot_GPcorrelation(results, 'alpha_vs_Prot')
for instrument in np.unique(julobj.instruments_lc):
    jm.plot_corner(results, f'_{instrument}', mark_values=None, show=False)