# Joint RV & transit fits#

import exoplanet

exoplanet.utils.docs_setup()
print(f"exoplanet.__version__ = '{exoplanet.__version__}'")

exoplanet.__version__ = '0.5.4.dev23+g6747513'


In this tutorial, we will combine many of the previous tutorials to perform a fit of the K2-24 system using the K2 transit data and the RVs from Petigura et al. (2016). This is the same system that we fit in Radial velocity fitting and we’ll combine that model with the transit model from Transit fitting and the Gaussian Process noise model from Gaussian process models for stellar variability.

## Datasets and initializations#

To get started, let’s download the relevant datasets. First, the transit light curve from Everest:

import numpy as np
import matplotlib.pyplot as plt

from astropy.io import fits
from scipy.signal import savgol_filter

lc_url = "https://archive.stsci.edu/hlsps/everest/v2/c02/203700000/71098/hlsp_everest_k2_llc_203771098-c02_kepler_v2.0_lc.fits"
with fits.open(lc_url) as hdus:
lc = hdus.data

# Work out the exposure time
texp = lc_hdr["FRAMETIM"] * lc_hdr["NUM_FRM"]
texp /= 60.0 * 60.0 * 24.0

m = (
(np.arange(len(lc)) > 100)
& np.isfinite(lc["FLUX"])
& np.isfinite(lc["TIME"])
)
bad_bits = [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17]
qual = lc["QUALITY"]
m &= qual & 2 ** (b - 1) == 0

# Convert to parts per thousand
x = lc["TIME"][m]
y = lc["FLUX"][m]
mu = np.median(y)
y = (y / mu - 1) * 1e3

# Identify outliers
m = np.ones(len(y), dtype=bool)
for i in range(10):
y_prime = np.interp(x, x[m], y[m])
smooth = savgol_filter(y_prime, 101, polyorder=3)
resid = y - smooth
sigma = np.sqrt(np.mean(resid**2))
m0 = np.abs(resid) < 3 * sigma
if m.sum() == m0.sum():
m = m0
break
m = m0

m = resid < 3 * sigma

# Shift the data so that the K2 data start at t=0. This tends to make the fit
# better behaved since t0 covaries with period.
x_ref = np.min(x[m])
x -= x_ref

# Plot the data
plt.plot(x, y, "k", label="data")
plt.plot(x, smooth)
plt.plot(x[~m], y[~m], "xr", label="outliers")
plt.legend(fontsize=12)
plt.xlim(x.min(), x.max())
plt.xlabel("time")
plt.ylabel("flux")

# Make sure that the data type is consistent
x = np.ascontiguousarray(x[m], dtype=np.float64)
y = np.ascontiguousarray(y[m], dtype=np.float64)
smooth = np.ascontiguousarray(smooth[m], dtype=np.float64) import pandas as pd

# Don't forget to remove the time offset from above!
x_rv = np.array(data.t) - x_ref
y_rv = np.array(data.vel)
yerr_rv = np.array(data.errvel)

plt.errorbar(x_rv, y_rv, yerr=yerr_rv, fmt=".k")
plt.xlabel("time [days]") We can initialize the transit parameters using the box least squares periodogram from AstroPy. (Note: you’ll need AstroPy v3.1 or more recent to use this feature.) A full discussion of transit detection and vetting is beyond the scope of this tutorial so let’s assume that we know that there are two periodic transiting planets in this dataset.

from astropy.timeseries import BoxLeastSquares

m = np.zeros(len(x), dtype=bool)
period_grid = np.exp(np.linspace(np.log(5), np.log(50), 50000))
bls_results = []
periods = []
t0s = []
depths = []

# Compute the periodogram for each planet by iteratively masking out
# transits from the higher signal to noise planets. Here we're assuming
# that we know that there are exactly two planets.
for i in range(2):
bls = BoxLeastSquares(x[~m], y[~m] - smooth[~m])
bls_power = bls.power(period_grid, 0.1, oversample=20)
bls_results.append(bls_power)

# Save the highest peak as the planet candidate
index = np.argmax(bls_power.power)
periods.append(bls_power.period[index])
t0s.append(bls_power.transit_time[index])
depths.append(bls_power.depth[index])

# Mask the data points that are in transit for this candidate
m |= bls.transit_mask(x, periods[-1], 0.5, t0s[-1])


Let’s plot the initial transit estimates based on these periodograms:

fig, axes = plt.subplots(len(bls_results), 2, figsize=(15, 10))

for i in range(len(bls_results)):
# Plot the periodogram
ax = axes[i, 0]
ax.axvline(np.log10(periods[i]), color="C1", lw=5, alpha=0.8)
ax.plot(np.log10(bls_results[i].period), bls_results[i].power, "k")
ax.annotate(
"period = {0:.4f} d".format(periods[i]),
(0, 1),
xycoords="axes fraction",
xytext=(5, -5),
textcoords="offset points",
va="top",
ha="left",
fontsize=12,
)
ax.set_ylabel("bls power")
ax.set_yticks([])
ax.set_xlim(np.log10(period_grid.min()), np.log10(period_grid.max()))
if i < len(bls_results) - 1:
ax.set_xticklabels([])
else:
ax.set_xlabel("log10(period)")

# Plot the folded transit
ax = axes[i, 1]
p = periods[i]
x_fold = (x - t0s[i] + 0.5 * p) % p - 0.5 * p
m = np.abs(x_fold) < 0.4
ax.plot(x_fold[m], y[m] - smooth[m], ".k")

# Overplot the phase binned light curve
bins = np.linspace(-0.41, 0.41, 32)
denom, _ = np.histogram(x_fold, bins)
num, _ = np.histogram(x_fold, bins, weights=y - smooth)
denom[num == 0] = 1.0
ax.plot(0.5 * (bins[1:] + bins[:-1]), num / denom, color="C1")

ax.set_xlim(-0.4, 0.4)
ax.set_ylabel("relative flux [ppt]")
if i < len(bls_results) - 1:
ax.set_xticklabels([])
else:
ax.set_xlabel("time since transit") The discovery paper for K2-24 (Petigura et al. (2016)) includes the following estimates of the stellar mass and radius in Solar units:

M_star_petigura = 1.12, 0.05
R_star_petigura = 1.21, 0.11


Finally, using this stellar mass, we can also estimate the minimum masses of the planets given these transit parameters.

import exoplanet as xo
import astropy.units as u

msini = xo.estimate_minimum_mass(
periods, x_rv, y_rv, yerr_rv, t0s=t0s, m_star=M_star_petigura
)
msini = msini.to(u.M_earth)
print(msini)

[32.69897193 23.69934796] earthMass


## A joint transit and radial velocity model in PyMC3#

Now, let’s define our full model in PyMC3. There’s a lot going on here, but I’ve tried to comment it and most of it should be familiar from the other tutorials and case studies. In this case, I’ve put the model inside a model “factory” function because we’ll do some sigma clipping below.

import pymc3 as pm
import aesara_theano_fallback.tensor as tt

import pymc3_ext as pmx
from celerite2.theano import terms, GaussianProcess

# These arrays are used as the times/phases where the models are
# evaluated at higher resolution for plotting purposes
t_rv = np.linspace(x_rv.min() - 5, x_rv.max() + 5, 500)
phase_lc = np.linspace(-0.3, 0.3, 100)

with pm.Model() as model:
# Parameters for the stellar properties
mean_flux = pm.Normal("mean_flux", mu=0.0, sd=10.0)
star = xo.LimbDarkLightCurve(u_star)
BoundedNormal = pm.Bound(pm.Normal, lower=0, upper=3)
m_star = BoundedNormal(
"m_star", mu=M_star_petigura, sd=M_star_petigura
)
r_star = BoundedNormal(
"r_star", mu=R_star_petigura, sd=R_star_petigura
)

# Orbital parameters for the planets
t0 = pm.Normal("t0", mu=np.array(t0s), sd=1, shape=2)
log_m_pl = pm.Normal("log_m_pl", mu=np.log(msini.value), sd=1, shape=2)
log_period = pm.Normal("log_period", mu=np.log(periods), sd=1, shape=2)

# Fit in terms of transit depth (assuming b<1)
b = pm.Uniform("b", lower=0, upper=1, shape=2)
log_depth = pm.Normal(
"log_depth", mu=np.log(depths), sigma=2.0, shape=2
)
ror = pm.Deterministic(
"ror",
star.get_ror_from_approx_transit_depth(
1e-3 * tt.exp(log_depth), b
),
)
r_pl = pm.Deterministic("r_pl", ror * r_star)

m_pl = pm.Deterministic("m_pl", tt.exp(log_m_pl))
period = pm.Deterministic("period", tt.exp(log_period))

ecs = pmx.UnitDisk("ecs", shape=(2, 2), testval=0.01 * np.ones((2, 2)))
ecc = pm.Deterministic("ecc", tt.sum(ecs**2, axis=0))
omega = pm.Deterministic("omega", tt.arctan2(ecs, ecs))
xo.eccentricity.vaneylen19(
"ecc_prior", multi=True, shape=2, fixed=True, observed=ecc
)

# RV jitter & a quadratic RV trend
log_sigma_rv = pm.Normal(
"log_sigma_rv", mu=np.log(np.median(yerr_rv)), sd=5
)
trend = pm.Normal(
"trend", mu=0, sd=10.0 ** -np.arange(3)[::-1], shape=3
)

# Transit jitter & GP parameters
log_sigma_lc = pm.Normal(
)
log_rho_gp = pm.Normal("log_rho_gp", mu=0.0, sd=10)
log_sigma_gp = pm.Normal(
)

# Orbit models
orbit = xo.orbits.KeplerianOrbit(
r_star=r_star,
m_star=m_star,
period=period,
t0=t0,
b=b,
m_planet=xo.units.with_unit(m_pl, msini.unit),
ecc=ecc,
omega=omega,
)

# Compute the model light curve
light_curves = (
* 1e3
)
light_curve = pm.math.sum(light_curves, axis=-1) + mean_flux

# GP model for the light curve
kernel = terms.SHOTerm(
sigma=tt.exp(log_sigma_gp),
rho=tt.exp(log_rho_gp),
Q=1 / np.sqrt(2),
)
gp.marginal("transit_obs", observed=resid)

# And then include the RVs as in the RV tutorial
x_rv_ref = 0.5 * (x_rv.min() + x_rv.max())

def get_rv_model(t, name=""):
# First the RVs induced by the planets

# Define the background model
A = np.vander(t - x_rv_ref, 3)
bkg = pm.Deterministic("bkg" + name, tt.dot(A, trend))

# Sum over planets and add the background to get the full model
return pm.Deterministic(
"rv_model" + name, tt.sum(vrad, axis=-1) + bkg
)

# Define the model
rv_model = get_rv_model(x_rv)
get_rv_model(t_rv, name="_pred")

# The likelihood for the RVs
err = tt.sqrt(yerr_rv**2 + tt.exp(2 * log_sigma_rv))
pm.Normal("obs", mu=rv_model, sd=err, observed=y_rv)

# Compute and save the phased light curve models
pm.Deterministic(
"lc_pred",
1e3
* tt.stack(
[
star.get_light_curve(
orbit=orbit, r=r_pl, t=t0[n] + phase_lc, texp=texp
)[..., n]
for n in range(2)
],
axis=-1,
),
)

# Fit for the maximum a posteriori parameters, I've found that I can get
# a better solution by trying different combinations of parameters in turn
if start is None:
start = model.test_point
map_soln = pmx.optimize(start=start, vars=[trend])
map_soln = pmx.optimize(start=map_soln, vars=[log_sigma_lc])
map_soln = pmx.optimize(start=map_soln, vars=[log_depth, b])
map_soln = pmx.optimize(start=map_soln, vars=[log_period, t0])
map_soln = pmx.optimize(
start=map_soln, vars=[log_sigma_lc, log_sigma_gp]
)
map_soln = pmx.optimize(start=map_soln, vars=[log_rho_gp])
map_soln = pmx.optimize(start=map_soln)

extras = dict(
zip(
["light_curves", "gp_pred"],
pmx.eval_in_model([light_curves, gp.predict(resid)], map_soln),
)
)

return model, map_soln, extras

model0, map_soln0, extras0 = build_model()

optimizing logp for variables: [trend]

100.00% [11/11 00:00<00:00 logp = -8.619e+03]


message: Optimization terminated successfully.
logp: -8632.190067632055 -> -8618.998645065287

optimizing logp for variables: [log_sigma_lc]

100.00% [9/9 00:00<00:00 logp = 6.278e+02]


message: Optimization terminated successfully.
logp: -8618.998645065287 -> 627.8331752207268

optimizing logp for variables: [b, log_depth]

100.00% [24/24 00:00<00:00 logp = 7.050e+02]


message: Optimization terminated successfully.
logp: 627.8331752207268 -> 704.9947747873019

optimizing logp for variables: [t0, log_period]

100.00% [68/68 00:00<00:00 logp = 7.693e+02]


message: Desired error not necessarily achieved due to precision loss.
logp: 704.9947747873055 -> 769.3138431872046

optimizing logp for variables: [log_sigma_gp, log_sigma_lc]

100.00% [14/14 00:00<00:00 logp = 1.463e+03]


message: Optimization terminated successfully.
logp: 769.313843187203 -> 1462.738848734864

optimizing logp for variables: [log_rho_gp]

100.00% [62/62 00:00<00:00 logp = 1.835e+03]


message: Desired error not necessarily achieved due to precision loss.
logp: 1462.738848734864 -> 1835.4335852346771

optimizing logp for variables: [log_sigma_gp, log_rho_gp, log_sigma_lc, trend, log_sigma_rv, ecs, log_depth, b, log_period, log_m_pl, t0, r_star, m_star, u_star, mean_flux]

100.00% [320/320 00:01<00:00 logp = 4.737e+03]


message: Desired error not necessarily achieved due to precision loss.
logp: 1835.4335852346685 -> 4736.760682015959


Now let’s plot the map radial velocity model.

def plot_rv_curve(soln):
fig, axes = plt.subplots(2, 1, figsize=(10, 5), sharex=True)

ax = axes
ax.errorbar(x_rv, y_rv, yerr=yerr_rv, fmt=".k")
ax.plot(t_rv, soln["bkg_pred"], ":k", alpha=0.5)
ax.plot(t_rv, soln["rv_model_pred"], label="model")
ax.legend(fontsize=10)

ax = axes
err = np.sqrt(yerr_rv**2 + np.exp(2 * soln["log_sigma_rv"]))
ax.errorbar(x_rv, y_rv - soln["rv_model"], yerr=err, fmt=".k")
ax.axhline(0, color="k", lw=1)
ax.set_ylabel("residuals [m/s]")
ax.set_xlim(t_rv.min(), t_rv.max())
ax.set_xlabel("time [days]")

_ = plot_rv_curve(map_soln0) That looks pretty similar to what we got in Radial velocity fitting. Now let’s also plot the transit model.

def plot_light_curve(soln, extras, mask=None):

fig, axes = plt.subplots(3, 1, figsize=(10, 7), sharex=True)

ax = axes
gp_mod = extras["gp_pred"] + soln["mean_flux"]
ax.legend(fontsize=10)
ax.set_ylabel("relative flux [ppt]")

ax = axes
for i, l in enumerate("bc"):
mod = extras["light_curves"][:, i]
ax.legend(fontsize=10, loc=3)
ax.set_ylabel("de-trended flux [ppt]")

ax = axes
mod = gp_mod + np.sum(extras["light_curves"], axis=-1)
ax.axhline(0, color="#aaaaaa", lw=1)
ax.set_ylabel("residuals [ppt]")
ax.set_xlabel("time [days]")

return fig

_ = plot_light_curve(map_soln0, extras0) There are still a few outliers in the light curve and it can be useful to remove those before doing the full fit because both the GP and transit parameters can be sensitive to this.

## Sigma clipping#

To remove the outliers, we’ll look at the empirical RMS of the residuals away from the GP + transit model and remove anything that is more than a 7-sigma outlier.

mod = (
extras0["gp_pred"]
+ map_soln0["mean_flux"]
+ np.sum(extras0["light_curves"], axis=-1)
)
resid = y - mod
rms = np.sqrt(np.median(resid**2))
mask = np.abs(resid) < 7 * rms

plt.plot(x, resid, "k", label="data")
plt.axhline(0, color="#aaaaaa", lw=1)
plt.ylabel("residuals [ppt]")
plt.xlabel("time [days]")
plt.legend(fontsize=12, loc=4)
_ = plt.xlim(x.min(), x.max()) That looks better. Let’s re-build our model with this sigma-clipped dataset.

model, map_soln, extras = build_model(mask, map_soln0)

optimizing logp for variables: [trend]

100.00% [4/4 00:00<00:00 logp = 5.186e+03]


message: Optimization terminated successfully.
logp: 5186.334920178412 -> 5186.334920178412

optimizing logp for variables: [log_sigma_lc]

100.00% [8/8 00:00<00:00 logp = 5.268e+03]


message: Optimization terminated successfully.
logp: 5186.334920178412 -> 5268.249909844744

optimizing logp for variables: [b, log_depth]

100.00% [18/18 00:00<00:00 logp = 5.279e+03]


message: Optimization terminated successfully.
logp: 5268.249909844744 -> 5279.213407829388

optimizing logp for variables: [t0, log_period]

100.00% [79/79 00:00<00:00 logp = 5.281e+03]


message: Desired error not necessarily achieved due to precision loss.
logp: 5279.2134078293875 -> 5280.695847116778

optimizing logp for variables: [log_sigma_gp, log_sigma_lc]

100.00% [9/9 00:00<00:00 logp = 5.281e+03]


message: Optimization terminated successfully.
logp: 5280.695847116781 -> 5281.428981693997

optimizing logp for variables: [log_rho_gp]

100.00% [5/5 00:00<00:00 logp = 5.281e+03]


message: Optimization terminated successfully.
logp: 5281.428981693997 -> 5281.430243511078

optimizing logp for variables: [log_sigma_gp, log_rho_gp, log_sigma_lc, trend, log_sigma_rv, ecs, log_depth, b, log_period, log_m_pl, t0, r_star, m_star, u_star, mean_flux]

100.00% [170/170 00:00<00:00 logp = 5.283e+03]


message: Desired error not necessarily achieved due to precision loss.
logp: 5281.430243511079 -> 5282.934434028347 Great! Now we’re ready to sample.

## Sampling#

The sampling for this model is the same as for all the previous tutorials, but it takes a bit longer. This is partly because the model is more expensive to compute than the previous ones and partly because there are some non-affine degeneracies in the problem (for example between impact parameter, eccentricity, and radius/radius ratio). It might be worth thinking about reparameterizations (in terms of duration instead of eccentricity), but that’s beyond the scope of this tutorial. Besides, using more traditional MCMC methods, this would have taken a lot longer to get thousands of effective samples!

import multiprocessing

with model:
trace = pm.sample(
tune=1500,
draws=1000,
start=map_soln,
cores=2,
chains=2,
target_accept=0.95,
return_inferencedata=True,
random_seed=[203771098, 203775000],
mp_ctx=multiprocessing.get_context("fork"),
)

Auto-assigning NUTS sampler...

Initializing NUTS using adapt_full...

Multiprocess sampling (2 chains in 2 jobs)

NUTS: [log_sigma_gp, log_rho_gp, log_sigma_lc, trend, log_sigma_rv, ecs, log_depth, b, log_period, log_m_pl, t0, r_star, m_star, u_star, mean_flux]

100.00% [5000/5000 10:54<00:00 Sampling 2 chains, 0 divergences]
Sampling 2 chains for 1_500 tune and 1_000 draw iterations (3_000 + 2_000 draws total) took 658 seconds.

The number of effective samples is smaller than 25% for some parameters.


Let’s look at the convergence diagnostics for some of the key parameters:

import arviz as az

az.summary(
trace,
var_names=[
"period",
"r_pl",
"m_pl",
"ecc",
"omega",
"b",
"log_sigma_gp",
"log_rho_gp",
],
)

mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_bulk ess_tail r_hat
period 42.363 0.000 42.363 42.364 0.000 0.000 2944.0 1339.0 1.00
period 20.885 0.000 20.885 20.886 0.000 0.000 2919.0 1647.0 1.00
r_pl 0.078 0.004 0.070 0.086 0.000 0.000 423.0 835.0 1.00
r_pl 0.056 0.003 0.050 0.062 0.000 0.000 317.0 423.0 1.01
m_pl 27.342 5.751 16.786 38.139 0.116 0.082 2385.0 1335.0 1.00
m_pl 22.077 4.423 13.594 30.020 0.105 0.074 1837.0 1041.0 1.00
ecc 0.036 0.029 0.000 0.087 0.001 0.001 815.0 781.0 1.00
ecc 0.100 0.090 0.000 0.282 0.004 0.003 620.0 1102.0 1.00
omega 0.170 2.013 -3.067 2.922 0.077 0.054 944.0 1793.0 1.00
omega -0.370 1.054 -2.590 1.896 0.038 0.027 784.0 1299.0 1.00
b 0.590 0.045 0.509 0.662 0.002 0.001 574.0 755.0 1.00
b 0.540 0.101 0.339 0.712 0.007 0.005 222.0 162.0 1.01
log_sigma_gp 2.144 0.625 1.176 3.393 0.021 0.016 1246.0 793.0 1.00
log_rho_gp 4.543 0.423 3.822 5.298 0.014 0.010 1225.0 806.0 1.00

As you see, the effective number of samples for the impact parameters and eccentricites are lower than for the other parameters. This is because of the correlations that I mentioned above:

import corner

_ = corner.corner(trace, var_names=["b", "ecc", "r_pl"]) ## Phase plots#

Finally, we can make folded plots of the transits and the radial velocities and compare to the posterior model predictions. (Note: planets b and c in this tutorial are swapped compared to the labels from Petigura et al. (2016))

flat_samps = trace.posterior.stack(sample=("chain", "draw"))
gp_mod = extras["gp_pred"] + map_soln["mean_flux"]

for n, letter in enumerate("bc"):
plt.figure()

# Get the posterior median orbital parameters
p = np.median(flat_samps["period"][n])
t0 = np.median(flat_samps["t0"][n])

# Plot the folded data
x_fold = (x[mask] - t0 + 0.5 * p) % p - 0.5 * p
m = np.abs(x_fold) < 0.3
plt.plot(
x_fold[m], y[mask][m] - gp_mod[m], ".k", label="data", zorder=-1000
)

# Plot the folded model
pred = np.percentile(flat_samps["lc_pred"][:, n, :], [16, 50, 84], axis=-1)
plt.plot(phase_lc, pred, color="C1", label="model")
art = plt.fill_between(
phase_lc, pred, pred, color="C1", alpha=0.5, zorder=1000
)
art.set_edgecolor("none")

# Annotate the plot with the planet's period
txt = "period = {0:.4f} +/- {1:.4f} d".format(
np.mean(flat_samps["period"][n].values),
np.std(flat_samps["period"][n].values),
)
plt.annotate(
txt,
(0, 0),
xycoords="axes fraction",
xytext=(5, 5),
textcoords="offset points",
ha="left",
va="bottom",
fontsize=12,
)

plt.legend(fontsize=10, loc=4)
plt.xlabel("time since transit [days]")
plt.ylabel("de-trended flux")
plt.title("K2-24{0}".format(letter))
plt.xlim(-0.3, 0.3)  for n, letter in enumerate("bc"):
plt.figure()

# Get the posterior median orbital parameters
p = np.median(flat_samps["period"][n])
t0 = np.median(flat_samps["t0"][n])

# Compute the median of posterior estimate of the background RV
# and the contribution from the other planet. Then we can remove
# this from the data to plot just the planet we care about.
other = np.median(flat_samps["vrad"][:, (n + 1) % 2], axis=-1)
other += np.median(flat_samps["bkg"], axis=-1)

# Plot the folded data
x_fold = (x_rv - t0 + 0.5 * p) % p - 0.5 * p
plt.errorbar(x_fold, y_rv - other, yerr=yerr_rv, fmt=".k", label="data")

# Compute the posterior prediction for the folded RV model for this
# planet
t_fold = (t_rv - t0 + 0.5 * p) % p - 0.5 * p
inds = np.argsort(t_fold)
pred = np.percentile(
flat_samps["vrad_pred"][inds, n], [16, 50, 84], axis=-1
)
plt.plot(t_fold[inds], pred, color="C1", label="model")
art = plt.fill_between(
t_fold[inds], pred, pred, color="C1", alpha=0.3
)
art.set_edgecolor("none")

plt.legend(fontsize=10)
plt.xlim(-0.5 * p, 0.5 * p)
plt.xlabel("phase [days]")
plt.title("K2-24{0}".format(letter))  We can also compute the posterior constraints on the planet densities.

volume = 4 / 3 * np.pi * flat_samps["r_pl"].values ** 3
density = u.Quantity(
flat_samps["m_pl"].values / volume, unit=u.M_earth / u.R_sun**3
)
density = density.to(u.g / u.cm**3).value

bins = np.linspace(0, 1.1, 45)
for n, letter in enumerate("bc"):
plt.hist(
density[n],
bins,
histtype="step",
lw=2,
label="K2-24{0}".format(letter),
density=True,
)
plt.yticks([])
plt.legend(fontsize=12)
plt.xlim(bins, bins[-1])
plt.xlabel("density [g/cc]")
_ = plt.ylabel("posterior density") 