# Modifying Priors¶

by Sarah Blunt (2018)

Most often, you will use the Driver class to interact with orbitize. This class automatically reads your input file, creates all of the orbitize objects you need to run an orbit fit, and allows you to run the orbit fit. See the introductory OFTI and MCMC tutorials for examples of working with this class.

However, sometimes you will want to work with the underlying methods directly. Doing this gives you control over the functionality Driver executes automatically, and allows you more flexibility.

Modifying priors is an example of something you might want to use the underlying API for. This tutorial walks you through how to do that.

Goals of this tutorial: - Learn to modify priors in orbitize - Learn about the structure of the orbitize code base

:

from matplotlib import pyplot as plt
import orbitize
from orbitize import read_input, system, priors, sampler

WARNING: KEPLER: Unable to import C-based Kepler's equation solver. Falling back to the slower NumPy implementation.


First, let’s read in our data table. This is accomplished with orbitize.read_input:

:

data_table = read_input.read_file('{}/GJ504.csv'.format(orbitize.DATADIR))

print(data_table)

    epoch      object quant1 quant1_err quant2 quant2_err quant_type
-------------- ------ ------ ---------- ------ ---------- ----------
55645.95      1 2479.0       16.0 327.94       0.39      seppa
55702.89      1 2483.0        8.0 327.45       0.19      seppa
55785.015      1 2481.0       33.0 326.84       0.94      seppa
55787.935      1 2448.0       24.0 325.82       0.66      seppa
55985.19400184      1 2483.0       15.0 326.46       0.36      seppa
56029.11400323      1 2487.0        8.0 326.54       0.18      seppa
56072.30200459      1 2499.0       26.0 326.14       0.61      seppa


## Initialize System Object¶

Next, we initialize an orbitize.system.System object. This object stores information about the system you’re fitting, such as your data, the total mass, and the parallax.

:

# number of secondary bodies in system
num_planets = 1

# total mass & error [msol]
system_mass = 1.22
mass_err = 0.08

# parallax & error[mas]
plx = 56.95
plx_err = 0

sys = system.System(
num_planets, data_table, system_mass,
plx, mass_err=mass_err, plx_err=plx_err
)


The System object has a few handy attributes to help you keep track of your fitting parameters. System.labels is a list of the names of your fit parameters, and System.sys_priors is a list of the priors on each parameter. Notice that the “prior” on parallax (plx) is just a float. That’s because we fixed this parameter at the printed value by specifying that plx_err=0.

Finally, System.param_idx is a dictionary that maps the parameter names from System.labels to their indices in System.sys_priors.

:

print(sys.labels)
print(sys.sys_priors)
print(sys.param_idx)

# alias for convenience
lab = sys.param_idx

['sma1', 'ecc1', 'inc1', 'aop1', 'pan1', 'tau1', 'plx', 'mtot']
[Log Uniform, Uniform, Sine, Uniform, Uniform, Uniform, 56.95, Gaussian]
{'sma1': 0, 'ecc1': 1, 'inc1': 2, 'aop1': 3, 'pan1': 4, 'tau1': 5, 'plx': 6, 'mtot': 7}


## Explore & Modify Priors¶

Priors in orbitize are Python objects. You can view an exhaustive list here. Let’s print out the attributes of some of our priors:

:

print(vars(sys.sys_priors[lab['ecc1']]))
print(vars(sys.sys_priors[lab['sma1']]))

{'minval': 0.0, 'maxval': 1.0}
{'minval': 0.001, 'maxval': 10000000.0, 'logmin': -6.907755278982137, 'logmax': 16.11809565095832}


Now that we understand how priors are represented and where they are stored, we can modify them! Here’s an example of changing the prior on eccentricity from the current uniform prior to a Gaussian prior:

:

mu = 0.2
sigma = 0.05

sys.sys_priors[lab['ecc1']] = priors.GaussianPrior(mu, sigma)

print(sys.labels)
print(sys.sys_priors)
print(vars(sys.sys_priors[lab['ecc1']]))

['sma1', 'ecc1', 'inc1', 'aop1', 'pan1', 'tau1', 'plx', 'mtot']
[Log Uniform, Gaussian, Sine, Uniform, Uniform, Uniform, 56.95, Gaussian]
{'mu': 0.2, 'sigma': 0.05, 'no_negatives': True}


## Run OFTI¶

All right! We’re in business. To finish up, I’ll demonstrate how to run an orbit fit with our modified System object, first with OFTI, then with MCMC.

:

ofti_sampler = sampler.OFTI(sys)

# number of orbits to accept
n_orbs = 500

_ = ofti_sampler.run_sampler(n_orbs)

accepted_eccentricities = ofti_sampler.results.post[:, lab['ecc1']]
plt.hist(accepted_eccentricities)
plt.xlabel('ecc'); plt.ylabel('number of orbits')

500/500 orbits found

:

Text(0, 0.5, 'number of orbits') ## Run MCMC¶

:

# number of temperatures & walkers for MCMC
num_temps = 3
num_walkers = 50

# number of steps to take
n_orbs = 500

mcmc_sampler = sampler.MCMC(sys, num_temps, num_walkers)

# number of orbits to accept
n_orbs = 500

_ = mcmc_sampler.run_sampler(n_orbs)

accepted_eccentricities = mcmc_sampler.results.post[:, lab['ecc1']]
plt.hist(accepted_eccentricities)
plt.xlabel('ecc'); plt.ylabel('number of orbits')

Burn in complete

/Users/bluez3303/miniconda3/envs/python3.6/lib/python3.6/site-packages/orbitize/priors.py:163: RuntimeWarning: invalid value encountered in log
lnprob = -np.log((element_array*normalizer))
/Users/bluez3303/miniconda3/envs/python3.6/lib/python3.6/site-packages/orbitize/priors.py:269: RuntimeWarning: invalid value encountered in log
lnprob = np.log(np.sin(element_array)/normalization)

10/10 steps completed
Run complete

:

Text(0, 0.5, 'number of orbits') 