def get_frac_dev(self, logZ, CO_ratio, custom_abundances):
Rp = 7.14e7
Mp = 2.0e27
Rs = 7e8
T = 1200
depth_calculator = TransitDepthCalculator()
wavelengths, transit_depths = depth_calculator.compute_depths(
Rs, Mp, Rp, T, logZ=logZ, CO_ratio=CO_ratio,
custom_abundances=custom_abundances, cloudtop_pressure=1e4)
# This ExoTransmit run is done without SH, since it's not present in
# GGchem
ref_wavelengths, ref_depths = np.loadtxt("tests/testing_data/hot_jupiter_spectra.dat", unpack=True, skiprows=2)
ref_depths /= 100
frac_dev = np.abs(ref_depths - transit_depths) / ref_depths
'''plt.plot(wavelengths, transit_depths, label="platon")
plt.plot(ref_wavelengths, ref_depths, label="ExoTransmit")
plt.legend()
plt.figure()
plt.plot(np.log10(frac_dev))
plt.show()'''
return frac_dev
def test_power_law_haze(self):
Rs = R_sun
Mp = M_jup
Rp = R_jup
T = 1200
abundances = AbundanceGetter().get(0, 0.53)
for key in abundances:
abundances[key] *= 0
abundances["H2"] += 1
depth_calculator = TransitDepthCalculator()
wavelengths, transit_depths, info_dict = depth_calculator.compute_depths(
Rs, Mp, Rp, T, logZ=None, CO_ratio=None, cloudtop_pressure=np.inf,
custom_abundances = abundances,
add_gas_absorption=False, add_collisional_absorption=False, full_output=True)
g = G * Mp / Rp**2
H = k_B * T / (2 * AMU * g)
gamma = 0.57721
polarizability = 0.8059e-30
sigma = 128. * np.pi**5/3 * polarizability**2 / depth_calculator.atm.lambda_grid**4
kappa = sigma / (2 * AMU)
P_surface = 1e8
R_surface = info_dict["radii"][-1]
tau_surface = P_surface/g * np.sqrt(2*np.pi*R_surface/H) * kappa
analytic_R = R_surface + H*(gamma + np.log(tau_surface) + scipy.special.expn(1, tau_surface))
analytic_depths = analytic_R**2 / Rs**2
ratios = analytic_depths / transit_depths
relative_diffs = np.abs(ratios - 1)
self.assertTrue(np.all(relative_diffs < 0.001))
def test_unbound_atmosphere(self):
Rp = 6.378e6
Mp = 5.97e20 # Note how low this is--10^-4 Earth masses!
Rs = 6.97e8
T = 300
depth_calculator = TransitDepthCalculator()
with self.assertRaises(AtmosphereError):
wavelengths, transit_depths = depth_calculator.compute_depths(
Rs, Mp, Rp, T, logZ=0.2, CO_ratio=1.1, T_star=6100)
def test_k_coeffs_binned(self):
wavelengths = np.exp(np.arange(np.log(0.31e-6), np.log(29e-6), 1./20))
wavelength_bins = np.array([wavelengths[0:-1], wavelengths[1:]]).T
xsec_calc = TransitDepthCalculator(method="xsec")
xsec_calc.change_wavelength_bins(wavelength_bins)
ktab_calc = TransitDepthCalculator(method="ktables")
ktab_calc.change_wavelength_bins(wavelength_bins)
wavelengths, xsec_depths = xsec_calc.compute_depths(R_sun, M_jup, R_jup, 300, logZ=1, CO_ratio=1.5)
wavelengths, ktab_depths = ktab_calc.compute_depths(R_sun, M_jup, R_jup, 300, logZ=1, CO_ratio=1.5)
diffs = np.abs(ktab_depths - xsec_depths)
'''plt.semilogx(wavelengths, xsec_depths)
plt.semilogx(wavelengths, ktab_depths)
plt.figure()
plt.semilogx(wavelengths, 1e6 * diffs)
plt.show()'''
self.assertTrue(np.median(diffs) < 10e-6)
self.assertTrue(np.percentile(diffs, 95) < 20e-6)
self.assertTrue(np.max(diffs) < 30e-6)
def test_k_coeffs_unbinned(self):
xsec_calc = TransitDepthCalculator(method="xsec")
ktab_calc = TransitDepthCalculator(method="ktables")
xsec_wavelengths, xsec_depths = xsec_calc.compute_depths(R_sun, M_jup, R_jup, 1000)
#Smooth from R=1000 to R=100 to match ktables
N = 10
smoothed_xsec_wavelengths = uniform_filter(xsec_wavelengths, N)[::N]
smoothed_xsec_depths = uniform_filter(xsec_depths, N)[::N]
ktab_wavelengths, ktab_depths = ktab_calc.compute_depths(R_sun, M_jup, R_jup, 1000)
diffs = np.abs(ktab_depths - smoothed_xsec_depths[:-1])
self.assertTrue(np.median(diffs) < 20e-6)
self.assertTrue(np.percentile(diffs, 95) < 50e-6)
self.assertTrue(np.max(diffs) < 150e-6)
def test_bin_wavelengths(self):
Rp = 7.14e7
Mp = 7.49e26
Rs = 7e8
T = 1200
depth_calculator = TransitDepthCalculator()
bins = np.array([[0.4,0.6], [1,1.1], [1.2,1.4], [3.2,4], [5,6]])
bins *= 1e-6
depth_calculator.change_wavelength_bins(bins)
wavelengths, transit_depths = depth_calculator.compute_depths(
Rs, Mp, Rp, T, logZ=0.2, CO_ratio=1.1, T_star=6100)
self.assertEqual(len(wavelengths), len(bins))
self.assertEqual(len(transit_depths), len(bins))
wavelengths, transit_depths = depth_calculator.compute_depths(
Rs, Mp, Rp, T, logZ=0.2, CO_ratio=1.1, T_star=12000)
self.assertEqual(len(wavelengths), len(bins))
self.assertEqual(len(transit_depths), len(bins))
def __init__(self, *args, **kwargs):
super(TestMieAbsorption, self).__init__(*args, **kwargs)
# We're storing this object to take advantage of its cache, not
# for speed, but to test the cache
self.calc = TransitDepthCalculator()
def test_bounds_checking(self):
Rp = 7.14e7
Mp = 7.49e26
Rs = 7e8
T = 1200
logZ = 0
CO_ratio = 1.1
calculator = TransitDepthCalculator()
with self.assertRaises(AtmosphereError):
calculator.compute_depths(Rs, Mp, Rp, 199, logZ=logZ, CO_ratio=CO_ratio)
with self.assertRaises(AtmosphereError):
calculator.compute_depths(Rs, Mp, Rp, 3001, logZ=logZ, CO_ratio=CO_ratio)
with self.assertRaises(ValueError):
calculator.compute_depths(Rs, Mp, Rp, T, logZ=-1.1, CO_ratio=CO_ratio)
with self.assertRaises(ValueError):
calculator.compute_depths(Rs, Mp, Rp, T, logZ=3.1, CO_ratio=CO_ratio)
with self.assertRaises(ValueError):
calculator.compute_depths(Rs, Mp, Rp, T, logZ=logZ, CO_ratio=0.01)
with self.assertRaises(ValueError):
calculator.compute_depths(Rs, Mp, Rp, T, logZ=logZ, CO_ratio=11)
with self.assertRaises(ValueError):
calculator.compute_depths(Rs, Mp, Rp, T, logZ=logZ, CO_ratio=CO_ratio, cloudtop_pressure=1e-4)
with self.assertRaises(ValueError):
calculator.compute_depths(Rs, Mp, Rp, T, logZ=logZ, CO_ratio=CO_ratio, cloudtop_pressure=1.1e8)
# Infinity should be fine
calculator.compute_depths(Rs, Mp, Rp, T, logZ=logZ, CO_ratio=CO_ratio, cloudtop_pressure=np.inf)
def __init__(self):
'''
The SpectrumGenerator generates spectra at different resolutions and
with noises
'''
self.calculator = TransitDepthCalculator()
return np.array(wavelength_bins)
def spitzer_bins():
wave_bins = []
wave_bins.append([3.2, 4.0])
wave_bins.append([4.0, 5.0])
return 1e-6 * np.array(wave_bins)
bins = np.concatenate([stis_bins(), wfc3_bins(), spitzer_bins()])
R_guess = 1.4 * R_jup
T_guess = 1200
depth_calculator = TransitDepthCalculator()
depth_calculator.change_wavelength_bins(bins)
wavelengths, depths = depth_calculator.compute_depths(1.19 * R_sun,
0.73 * M_jup,
R_guess,
T_guess,
T_star=6091)
# Uncomment the code below to print
#print("#Wavelength(um) Depth")
#for i in range(len(wavelengths)):
# print(wavelengths[i], depths[i])
# Uncomment the code below to plot
import matplotlib.pyplot as plt
import corner
from platon.fit_info import FitInfo
from platon.transit_depth_calculator import TransitDepthCalculator
from platon.retriever import Retriever
Rs = 7e8
g = 9.8
Rp = 7.14e7
logZ = 0
CO = 0.53
log_cloudtop_P = 3
temperature = 1200
depth_calculator = TransitDepthCalculator(Rs, g)
wavelength_bins = []
stis_wavelengths = np.linspace(0.4e-6, 0.7e-6, 30)
for i in range(len(stis_wavelengths) - 1):
wavelength_bins.append([stis_wavelengths[i], stis_wavelengths[i + 1]])
wfc_wavelengths = np.linspace(1.1e-6, 1.7e-6, 30)
for i in range(len(wfc_wavelengths) - 1):
wavelength_bins.append([wfc_wavelengths[i], wfc_wavelengths[i + 1]])
wavelength_bins.append([3.2e-6, 4e-6])
wavelength_bins.append([4e-6, 5e-6])
depth_calculator.change_wavelength_bins(wavelength_bins)
wavelengths, transit_depths = depth_calculator.compute_depths(
def run_multinest(self,
transit_bins,
transit_depths,
transit_errors,
eclipse_bins,
eclipse_depths,
eclipse_errors,
fit_info,
include_condensation=True,
plot_best=False,
**nestle_kwargs):
'''Runs nested sampling to retrieve atmospheric parameters.
Parameters
----------
transit_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.
transit_depths : array_like, length N
Measured transit depths for the specified wavelength bins
transit_errors : array_like, length N
Errors on the aforementioned transit depths
eclipse_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.
eclipse_depths : array_like, length N
Measured eclipse depths for the specified wavelength bins
eclipse_errors : array_like, length N
Errors on the aforementioned eclipse 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
**nestle_kwargs : keyword arguments to pass to nestle's sample method
Returns
-------
result : Result object
This returns the object returned by nestle.sample, slightly
modified. The object 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, result.logl contains the ln likelihoods, and result.logp
contains the ln posteriors (this is added by PLATON). result.logz
is the natural logarithm of the evidence.
'''
transit_calc = TransitDepthCalculator(
include_condensation=include_condensation)
transit_calc.change_wavelength_bins(transit_bins)
eclipse_calc = EclipseDepthCalculator()
eclipse_calc.change_wavelength_bins(eclipse_bins)
self._validate_params(fit_info, transit_calc)
def transform_prior(cube):
new_cube = np.zeros(len(cube))
for i in range(len(cube)):
new_cube[i] = fit_info._from_unit_interval(i, cube[i])
return new_cube
def multinest_ln_like(cube):
return self._ln_like(cube, transit_calc, eclipse_calc, fit_info,
transit_depths, transit_errors,
eclipse_depths, eclipse_errors)
def callback(callback_info):
print("Iteration {}: {}".format(callback_info["it"],
self.pretty_print(fit_info)))
result = nestle.sample(multinest_ln_like,
transform_prior,
fit_info._get_num_fit_params(),
callback=callback,
method='multi',
**nestle_kwargs)
result.logp = result.logl + np.array(
[fit_info._ln_prior(params) for params in result.samples])
best_params_arr = result.samples[np.argmax(result.logp)]
write_param_estimates_file(
nestle.resample_equal(result.samples, result.weights),
best_params_arr, np.max(result.logp), fit_info.fit_param_names)
if plot_best:
self._ln_prob(best_params_arr,
transit_calc,
eclipse_calc,
fit_info,
transit_depths,
transit_errors,
eclipse_depths,
eclipse_errors,
plot=True)
return result
def run_emcee(self,
transit_bins,
transit_depths,
transit_errors,
eclipse_bins,
eclipse_depths,
eclipse_errors,
fit_info,
nwalkers=50,
nsteps=1000,
include_condensation=True,
plot_best=False):
'''Runs affine-invariant MCMC to retrieve atmospheric parameters.
Parameters
----------
transit_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.
transit_depths : array_like, length N
Measured transit depths for the specified wavelength bins
transit_errors : array_like, length N
Errors on the aforementioned transit depths
eclipse_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.
eclipse_depths : array_like, length N
Measured eclipse depths for the specified wavelength bins
eclipse_errors : array_like, length N
Errors on the aforementioned eclipse 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
'''
initial_positions = fit_info._generate_rand_param_arrays(nwalkers)
transit_calc = TransitDepthCalculator(
include_condensation=include_condensation)
transit_calc.change_wavelength_bins(transit_bins)
eclipse_calc = EclipseDepthCalculator()
eclipse_calc.change_wavelength_bins(eclipse_bins)
self._validate_params(fit_info, transit_calc)
sampler = emcee.EnsembleSampler(
nwalkers,
fit_info._get_num_fit_params(),
self._ln_prob,
args=(transit_calc, eclipse_calc, fit_info, transit_depths,
transit_errors, eclipse_depths, eclipse_errors))
for i, result in enumerate(
sampler.sample(initial_positions, iterations=nsteps)):
if (i + 1) % 10 == 0:
print("Step {}: {}".format(i + 1, self.pretty_print(fit_info)))
best_params_arr = sampler.flatchain[np.argmax(
sampler.flatlnprobability)]
write_param_estimates_file(sampler.flatchain, best_params_arr,
np.max(sampler.flatlnprobability),
fit_info.fit_param_names)
if plot_best:
self._ln_prob(best_params_arr,
transit_calc,
eclipse_calc,
fit_info,
transit_depths,
transit_errors,
eclipse_depths,
eclipse_errors,
plot=True)
return sampler
from platon.constants import M_jup, R_sun, R_jup, M_earth, R_earth
from platon.visualizer import Visualizer
# All quantities in SI
Rs = 0.947 * R_sun
Mp = 8.145 * M_earth
Rp = 1.7823 * R_earth
T = 1970
logZ = 1.09
CO_ratio = 1.57
log_cloudtop_P = 4.875
#create a TransitDepthCalculator object and compute wavelength dependent transit depths
depth_calculator = TransitDepthCalculator()
wavelengths, transit_depths, info = depth_calculator.compute_depths(
Rs,
Mp,
Rp,
T,
T_star=5196,
logZ=logZ,
CO_ratio=CO_ratio,
cloudtop_pressure=10.0**log_cloudtop_P,
full_output=True)
color_bins = 1e-6 * np.array([
[4, 5],
[3.2, 4],
[1.1, 1.7],
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from platon.transit_depth_calculator import TransitDepthCalculator
from platon.constants import M_jup, R_sun, R_jup
# All quantities in SI
Rs = 1.16 * R_sun #Radius of star
Mp = 0.73 * M_jup #Mass of planet
Rp = 1.40 * R_jup #Radius of planet
T = 1200 #Temperature of isothermal part of the atmosphere
#create a TransitDepthCalculator object and compute wavelength dependent transit depths
depth_calculator = TransitDepthCalculator()
wavelengths, transit_depths = depth_calculator.compute_depths(
Rs, Mp, Rp, T, CO_ratio=0.2, cloudtop_pressure=1e4)
# Uncomment the code below to print
#print("#Wavelength(m) Depth")
#for i in range(len(wavelengths)):
# print(wavelengths[i], transit_depths[i])
# Uncomment the code below to plot
#plt.plot(1e6*wavelengths, transit_depths)
#plt.xlabel("Wavelength (um)")
#plt.ylabel("Transit depth")
#plt.show()
import numpy as np
import matplotlib.pyplot as plt
from platon.transit_depth_calculator import TransitDepthCalculator
from platon.constants import M_jup, R_sun, R_jup
# All quantities in SI
Rs = 1.16 * R_sun #Radius of star
Mp = 0.73 * M_jup #Mass of planet
Rp = 1.40 * R_jup #Radius of planet
T = 1200 #Temperature of isothermal part of the atmosphere
#create a TransitDepthCalculator object and compute wavelength dependent transit depths
depth_calculator = TransitDepthCalculator(
method="ktables") #put "xsec" for opacity sampling
wavelengths, transit_depths = depth_calculator.compute_depths(
Rs, Mp, Rp, T, CO_ratio=0.2, cloudtop_pressure=1e4)
# Uncomment the code below to print
#print("#Wavelength(m) Depth")
#for i in range(len(wavelengths)):
# print(wavelengths[i], transit_depths[i])
# Uncomment the code below to plot
#plt.semilogx(1e6*wavelengths, transit_depths)
#plt.xlabel("Wavelength (um)")
#plt.ylabel("Transit depth")
#plt.show()
time_stamp = datetime.utcnow().strftime("%Y%m%d%H%M%S")
result_dict = {'samples':result.samples, 'weights':result.weights, 'logl':result.logl}
joblib.dump(result_dict, 'multinest_results_{}.joblib.save'.format(time_stamp))
# Establish the Range in Wavelength to plot high resolution figures
wave_min = wave_bins.min()
wave_max = wave_bins.max()
n_theory_pts = 500
wavelengths_theory = np.linspace(wave_min, wave_max, n_theory_pts)
half_diff_lam = 0.5*np.median(np.diff(wavelengths_theory))
# Setup calculator to use the theoretical wavelengths
calculator = TransitDepthCalculator(include_condensation=True)
calculator.change_wavelength_bins(np.transpose([wavelengths_theory-half_diff_lam, wavelengths_theory+half_diff_lam]))
retriever._validate_params(fit_info, calculator)
# Allocate the best-fit parameters from the `result` class
best_params_arr = result.samples[np.argmax(result.logl)]
best_params_dict = {key:val for key,val in zip(fit_info.fit_param_names, best_params_arr)}
# Set the static parameteres to the default values
for key in fit_info.all_params.keys():
if key not in best_params_dict.keys():
best_params_dict[key] = fit_info.all_params[key].best_guess
# Assign the best fit model parameters to necessary variables
Rs = best_params_dict['Rs']
class SpectrumGenerator:
def __init__(self):
'''
The SpectrumGenerator generates spectra at different resolutions and
with noises
'''
self.calculator = TransitDepthCalculator()
def generate_spectrum(self,
Rs,
Mp,
Rp,
T_planet,
T_star,
Ms,
logZ=0,
CO_ratio=0.53,
scattering_factor=1,
cloudtop_pressure=np.inf,
resolution=None,
noise_level=None,
max_wavelength=3e-5,
min_wavelength=3e-7,
albedo=0.8):
'''
Generates a spectrum using the PLATON TransitDepthCalculator.
Parameters
----------
Rs : float
Stellar radius in solar radii
Mp : float
Planet mass in Jupiter masses
Rp : float
Planet radius in Jupiter radii
T_planet : float
Temperature of the isothermal atmosphere in Kelvin
T_star : float
Blackbody temperature of the star
Ms : float
Mass of the star in solar masses
logZ : float, optional
base-10 logarithm of the metallicity in solar units. Default is 0.
CO_ratio : float, optional
C/O atomic ratio in the atmosphere. The default value is 0.53
(solar) value.
scattering_factor : float, optional
Makes rayleigh scattering this many times as strong. Default is 1
cloudtop_pressure : float, optional
Pressure level (in PA) below which light cannot penetrate. Use
np.inf for cloudless. Default is np.inf
resolution : int or None, optional
If provided, will produce spectra of the given spectral resolution.
Default is None
noise_level : float or None, optional
The noise on the data in ppm. If provided, Gaussian noise will be
added to the spectrum. Default is None.
max_wavelength : float, optional
The maximum wavelength to consider in m. Default is 3e-5
min_wavelength : float, optional
The minimum wavelength to consider in m. Default is 3e-7
Returns
-------
spectrum : TransitCurveGen.Spectrum
A Spectrum object containing all the information on the spectrum
'''
# Generate the basic spectrum
wavelengths, depths = self.calculator.compute_depths(
Rs * R_sun,
Mp * M_jup,
Rp * R_jup,
T_planet,
logZ=logZ,
CO_ratio=CO_ratio,
scattering_factor=scattering_factor,
cloudtop_pressure=cloudtop_pressure)
# Remove wavelengths not of interest
# First cut off the large wavelengths from depths and wavelengths.
# MUST be done in this order else depths can't reference wavelengths
depths = depths[wavelengths <= max_wavelength]
wavelengths = wavelengths[wavelengths <= max_wavelength]
depths = depths[wavelengths >= min_wavelength]
wavelengths = wavelengths[wavelengths >= min_wavelength]
# Convolve to give a particular resolution
if resolution is not None:
wavelengths, depths = self._bin_spectrum(wavelengths, depths,
resolution)
# Add in noise if required
if noise_level is not None:
wavelengths, depths = self._add_noise(wavelengths, depths,
noise_level)
info_dict = {
'Rs': Rs,
'Mp': Mp,
'Rp': Rp,
'T_planet': T_planet,
'logZ': logZ,
'CO_ratio': CO_ratio,
'scattering_factor': scattering_factor,
'cloudtop_pressure': cloudtop_pressure,
'resolution': resolution,
'noise': noise_level,
'T_star': T_star,
'Ms': Ms,
'albedo': albedo
}
return Spectrum(wavelengths, depths, info_dict)
def _bin_spectrum(self, wavelengths, depths, R):
'''
Bins a given spectrum to a spectral resolution R
Parameters
----------
wavelengths : array_like, shape (n_wavelengths,)
The wavelengths of each data point. We assume that these are
logarithmically spaced.
depths : array_like, shape (n_wavelengths,)
The transit depth of each data point.
R : int
Spectral resolution to bin to.
Returns
-------
wavelengths : np.array
The centre wavelength of each new wavelength bin
depths : np.array
The transit depth in each of the new wavelength bins
Notes
-----
The binning is done using convolution with a top-hat.
'''
# Take log base-10 of the wavelengths to work on resolution
log_wl = np.log10(wavelengths)
# Find the wavelength spacing in log space
delta = round(log_wl[1] - log_wl[0], 7)
# Find the original resolution of the spectrum
R_original = 1 // delta
if R > R_original:
raise ValueError(
'Spectrum has resolution {}: cannot increase resolution to {}'.
format(R_original, R))
# Work out how many current wl bins we need for the given resolution
nbins = int(1 // (R * delta))
# Do the binning
wavelengths = wavelengths[:(wavelengths.size // nbins) *
nbins].reshape(-1, nbins).mean(axis=1)
depths = depths[:(depths.size // nbins) * nbins].reshape(
-1, nbins).mean(axis=1)
return wavelengths, depths
def _add_noise(self, wavelengths, depths, noise_ppm):
'''
Adds Gaussian noise to a given spectrum
Parameters
----------
wavelengths : array_like, shape (n_wavelengths,)
The wavelengths of each data point. We assume that these are
logarithmically spaced.
depths : array_like, shape (n_wavelengths,)
The transit depth of each data point.
noise_ppm : int
The noise in parts per million
Returns
-------
wavelengths : np.array
The wavelengths
depths : np.array
The depths with Gaussian noise added.
'''
disp_arr = np.zeros(len(depths))
for i, d in enumerate(depths):
disp_arr[i] = np.random.normal(0, noise_ppm * 1e-6)
depths += disp_arr
return wavelengths, depths
