import numpy as np
import pylab
from scipy.stats.distributions import gamma, norm as norm_dist
import Infer
from MyFuncs import Transit_aRs
#light curve parameters
lc_pars = [.0, 2.5, 11., .1, 0.6, 0.2, 0.3, 1., 0.]
gp = [.0003, 2.5, 10., .104, 0.6, 0.2, 0.3, 1., 0.]
hp = [0.0004, 0.1, 0.0001]
#create the data set (ie training data)
t = np.linspace(-0.1, 0.1, 300)
t_pred = np.linspace(-0.12, 0.12, 1000)
flux = Transit_aRs(lc_pars,
t) + 0.0005 * np.sin(2 * np.pi * 40 * t) + np.random.normal(
0, hp[-1], t.size)
#guess parameter values and guess uncertainties
guess_pars = hp + gp
err_pars = np.array([0.0004, 0.1, 0.0001] +
[0.00001, 0, 0.2, 0.0003, 0.02, 0.0, 0.0, 0.001, 0.])
#construct the GP
MyGP = Infer.GP(flux,
np.matrix([
t,
]).T,
p=guess_pars,
mf=Transit_aRs,
mf_args=t,
import numpy as np import pylab import os import Infer from MyFuncs import Transit_aRs from MyFuncs import LogLikelihood_iid_mf #light curve parameters lc_pars = [.0, 2.5, 11., .1, 0.6, 0.2, 0.3, 1., 0.] wn = 0.0003 #create the data set (ie training data) time = np.arange(-0.1, 0.1, 0.001) flux = Transit_aRs(lc_pars, time) + np.random.normal(0, wn, time.size) #guess parameter values and guess uncertainties guess_pars = lc_pars + [wn] err_pars = [0.00001, 0, 0.2, 0.0003, 0.02, 0.0, 0.0, 0.001, 0.0001, 0.0001] #plot the light curve + guess function pylab.figure(1) pylab.errorbar(time, flux, yerr=wn, fmt='.') pylab.plot(time, Transit_aRs(guess_pars[:-1], time), 'r--') #define MCMC parameters chain_len = 20000 conv = 10000 thin = 10 no_ch = 2
import sys import time import pylab import Infer from MyFuncs import Transit_aRs #light curve parameters lc_pars = [.0,2.5,11.,.1,0.6,0.2,0.3,1.,0.] hp = [0.0003,0.01,0.0003] #create the data set (ie training data) t = np.linspace(-0.1,0.1,300) t_pred = np.linspace(-0.12,0.12,1000) flux = Transit_aRs(lc_pars,t) + np.random.normal(0,hp[-1],t.size) #guess parameter values and guess uncertainties guess_pars = hp + lc_pars err_pars = np.array([0.00001,1,0.0001] + [0.00001,0,0.2,0.0003,0.02,0.0,0.0,0.001,0.0001]) X = np.matrix([t,]).T X_pred = np.matrix([t_pred,]).T MyGP = Infer.GP(flux,X,p=guess_pars,mf=Transit_aRs,mf_args=t,n_hp=3) #make plot of the function pylab.figure(1) pylab.plot(MyGP.mf_args,MyGP.t,'k.') pylab.plot(MyGP.mf_args,MyGP.mf(lc_pars,MyGP.mf_args),'g-')