In [1]:

    

from __future__ import print_function, division
from IPython import parallel

rc = parallel.Client()
view = rc.load_balanced_view()
dview = rc[:]


    

In [2]:

    

%load boostrap_samp.py


    

In [2]:

    

import numpy as np
from numpy.random import choice
from ktransit import FitTransit
import h5py


def fitt_bootstrap(time, flux, ferr, resid, fitp, fits):
    T0, period, impact = fitp['pnum0']['T0'], fitp['pnum0']['period'], fitp['pnum0']['impact']
    rprs, ecosw, esinw = fitp['pnum0']['rprs'], fitp['pnum0']['ecosw'], fitp['pnum0']['esinw']
    zpt, rho, ld1, ld2 = fits['zpt'], fits['rho'], fits['ld1'], fits['ld2']
    fitT = FitTransit()
    fitT.add_guess_star(rho=rho,zpt=zpt,ld1=ld1,ld2=ld2)
    fitT.add_guess_planet(
        period=period, impact=impact,
        T0=T0, rprs=rprs,ecosw=ecosw,esinw=esinw)
    flux_resid = flux + choice(resid,size=len(flux))

    fitT.add_data(time=time, flux=flux_resid, ferr=ferr)
    vary_star = ['rho', 'zpt','ld1','ld2']      # free stellar parameters
    vary_planet = (['period',       # free planetary parameters
        'T0', 'impact',
        'rprs','ecosw','esinw'])
    fitT.free_parameters(vary_star, vary_planet)
    fitT.do_fit()                   # run the fitting
    return fitT.fitresult


    

In [3]:

    

%%px
import numpy as np
from numpy.random import choice
from ktransit import FitTransit
import h5py

def fitt_bootstrap(time, flux, ferr, resid, fitp, fits):
    T0, period, impact = fitp['pnum0']['T0'], fitp['pnum0']['period'], fitp['pnum0']['impact']
    rprs, ecosw, esinw = fitp['pnum0']['rprs'], fitp['pnum0']['ecosw'], fitp['pnum0']['esinw']
    zpt, rho, ld1, ld2 = fits['zpt'], fits['rho'], fits['ld1'], fits['ld2']
    fitT = FitTransit()
    fitT.add_guess_star(rho=rho,zpt=zpt,ld1=ld1,ld2=ld2)
    fitT.add_guess_planet(
        period=period, impact=impact,
        T0=T0, rprs=rprs,ecosw=ecosw,esinw=esinw)
    flux_resid = flux + choice(resid,size=len(flux))

    fitT.add_data(time=time, flux=flux_resid, ferr=ferr)
    vary_star = ['rho', 'zpt','ld1','ld2']      # free stellar parameters
    vary_planet = (['period',       # free planetary parameters
        'T0', 'impact',
        'rprs','ecosw','esinw'])
    fitT.free_parameters(vary_star, vary_planet)
    fitT.do_fit()                   # run the fitting
    return fitT.fitresult


    

In [4]:

    

fn = '/Users/tom/gitcode/GP_model_Kepler_data/data/koi2133_np1_priorTrue_dil0.0GP.hdf5'
f = h5py.File(fn)

time = f['time'][:61000]
flux = f['flux'][:61000]
ferr = f['err'][:61000]


    

In [5]:

    

fitT = FitTransit()
fitT.add_guess_star(rho=43.47/1000.,zpt=-5.68308831e-05,ld1=4.32157219e-01,ld2=2.69514828e-01)
fitT.add_guess_planet(
        period=6.2467332, impact=0.09,
        T0=1.36383739e+02, rprs=0.019429,ecosw=0.0,esinw=0.0)
fitT.add_data(time=time[:61000], flux=flux[:61000], ferr=ferr[:61000])
vary_star = ['rho', 'zpt','ld1','ld2']      # free stellar parameters
vary_planet = (['period',       # free planetary parameters
        'T0', 'impact',
        'rprs','ecosw','esinw'])
fitT.free_parameters(vary_star, vary_planet)
fitT.do_fit()                   # run the fitting

fitT.print_results()
initial_fitp = fitT.fitresultplanets
initial_fits = fitT.fitresultstellar
initial_resid = flux - fitT.transitmodel


    

    




Best-fitting stellar parameters
ld2: 0.269514828
ld1: 0.432157219
zpt: -4.39110753749e-05
rho: 0.0440230258012

Best-fitting planet parameters for planet 0
impact: 0.0579965811567
ecosw: 0.103674446616
period: 6.24670798237
T0: 136.38312855
rprs: 0.0193178600262
esinw: -0.00365621799929

In [6]:

    

dview.push(dict(time=time, flux=flux, ferr=ferr,
                initial_resid=initial_resid, 
                initial_fitp=initial_fitp,
                initial_fits=initial_fits))

def mapfunc(a):
    outarr = fitt_bootstrap(time[:61000], flux[:61000], ferr[:61000],
        initial_resid, initial_fitp, initial_fits)
    return outarr


    

In [6]:

    




    

In [57]:

    

%%time
npars = 10
#running 6*30 takes about an hour
nsamps = 6*30*12
outmap = np.zeros([nsamps,npars])

results = view.map(mapfunc, np.arange(nsamps),ordered=False)

for i,res in enumerate(results):
    outmap[i] = res


    

    




CPU times: user 4h 12min 45s, sys: 4min 9s, total: 4h 16min 55s
Wall time: 9h 18min 46s

In [53]:

    




    

In [58]:

    

[print('{}, {}, {}, {}, {}, {}, {}, {}, {}, {}'.format(*x)) for x in outmap]


    

    




0.0444891219707, -4.25014227162e-05, 0.432157219, 0.269514828, 6.24668985715, 136.384406724, 0.0115179779541, 0.0197710211981, 0.105987261636, 0.0130489059424
0.045387960265, -4.43649146022e-05, 0.432157219, 0.269514828, 6.24671016912, 136.38399696, 0.0167299846981, 0.0192238423587, -0.0403018717754, 0.00586597298782
0.0434575945879, -4.36944703672e-05, 0.432157219, 0.269514828, 6.24670327353, 136.385887144, 0.108954522554, 0.0193322075728, 0.000150235977635, -0.0045858898535
0.0388646530843, -4.42330270301e-05, 0.432157219, 0.269514828, 6.24670140226, 136.382406523, 0.0209838030798, 0.019257617636, 0.236932079703, -0.00117119381038
0.0448416350372, -4.56662793197e-05, 0.432157219, 0.269514828, 6.24672582861, 136.381990927, 0.0382692183354, 0.0192741187157, 0.0214672853644, 0.000587199030771
0.043211493463, -4.44852781708e-05, 0.432157219, 0.269514828, 6.2466846683, 136.385252584, 0.0165285878603, 0.0191503764823, 0.0578238761694, -0.00973492918641
0.0445103614592, -4.23325754958e-05, 0.432157219, 0.269514828, 6.24667294137, 136.389534104, 0.0258152443602, 0.0194092043878, 0.0964098560324, -0.00321929070178
0.0453696578742, -4.17160064527e-05, 0.432157219, 0.269514828, 6.2467434662, 136.374194406, 0.114457233008, 0.0194686632916, -0.0787190884571, -0.00507002532362
0.0450839931416, -4.3353056642e-05, 0.432157219, 0.269514828, 6.24677010058, 136.378762953, -0.0213699567303, 0.0192812388448, 0.180272578947, -0.0141421233064
0.0440541978025, -4.58277603822e-05, 0.432157219, 0.269514828, 6.24670975523, 136.383367047, 0.0717083805079, 0.0192686629303, 0.0879787299337, -0.0029551917175
0.0423454015156, -4.75775436287e-05, 0.432157219, 0.269514828, 6.24670429513, 136.384043623, 0.0169162795291, 0.0192653411484, 0.172284256306, -0.0129969688098
0.0446600933334, -4.25569456072e-05, 0.432157219, 0.269514828, 6.2467016946, 136.389419666, -0.0638975461595, 0.0192997111407, 0.0492643212098, -0.00958173628221
0.0429289382663, -4.61384855003e-05, 0.432157219, 0.269514828, 6.24672592817, 136.380103043, 0.132524407372, 0.0193725340579, -0.0392359268091, 0.0125379630648
0.0443600124546, -4.51464863235e-05, 0.432157219, 0.269514828, 6.24669433372, 136.384258845, 0.0612276575786, 0.0191211226216, 0.110780848495, -0.00209596669599
0.0392135298668, -4.56834518161e-05, 0.432157219, 0.269514828, 6.24670488367, 136.381213227, -0.025896857428, 0.0194054104553, 0.125072660232, 0.0364712310437
0.0445242188183, -4.12486353888e-05, 0.432157219, 0.269514828, 6.24668754584, 136.386853409, 0.0109888080007, 0.0194984123372, 0.069486992165, 0.00273259801473
0.0273309259022, -4.440261769e-05, 0.432157219, 0.269514828, 6.24674226098, 136.380320073, 0.449740690432, 0.0197363780019, 0.514601979458, -0.0821103428955
0.0436216490312, -4.6442838447e-05, 0.432157219, 0.269514828, 6.24671449903, 136.381134014, 0.0113282700897, 0.0193505576689, 0.0956065065088, -0.00637644797582
0.0443349337572, -4.27712123603e-05, 0.432157219, 0.269514828, 6.2466976669, 136.383475025, 0.129466424599, 0.0193882516873, 0.0313680457338, -0.00465200889661
0.0432354600835, -4.43562049719e-05, 0.432157219, 0.269514828, 6.24668788152, 136.384273911, -0.0217937495947, 0.0193973259806, 0.0837544526028, -0.00953290211881
0.0433316276372, -4.40851643475e-05, 0.432157219, 0.269514828, 6.24672108635, 136.377844072, 0.211619155312, 0.0193583145334, 0.0309122085806, -0.000227934759547
0.04386845973, -4.48610890574e-05, 0.432157219, 0.269514828, 6.24672315897, 136.379276979, -0.0286190146745, 0.0195242660624, 0.0574295445445, -0.00330531819486
0.0430658734913, -4.61126585419e-05, 0.432157219, 0.269514828, 6.24666444801, 136.386008786, 0.00275351733515, 0.0192065802712, 0.118840952286, -0.00449996205469
0.0446171373729, -4.53669104701e-05, 0.432157219, 0.269514828, 6.24673553114, 136.38252207, 1.02251436668e-05, 0.0193754795893, -0.0300922029687, 0.00255434958521
0.0424353662522, -4.10068550061e-05, 0.432157219, 0.269514828, 6.24665084813, 136.389275596, 0.00776035579688, 0.0196168398526, -0.310229090924, -0.0343122609607
0.044225227355, -4.39093283717e-05, 0.432157219, 0.269514828, 6.24669724397, 136.38331149, 0.0301306316606, 0.0192832960342, 0.123574138475, -0.00574782462904
0.0431947956906, -4.37001253819e-05, 0.432157219, 0.269514828, 6.246725478, 136.381191028, 0.0244398243865, 0.0191918841877, 0.0632005347287, -0.0105728904778
0.0450240120206, -4.21347842884e-05, 0.432157219, 0.269514828, 6.24672103366, 136.382526536, 0.0134849281265, 0.0191854484052, 0.147107202615, -0.00115049809659
0.0426448021756, -4.38537845824e-05, 0.432157219, 0.269514828, 6.24666026827, 136.388145679, 0.122271173443, 0.0195212421197, 0.138214474956, 0.00748879497794
0.0449702416111, -4.59910847724e-05, 0.432157219, 0.269514828, 6.24674989475, 136.37987233, 0.0320162544925, 0.0195155289209, 0.124044223024, -0.00204614004742
0.0411139387488, -4.71228994346e-05, 0.432157219, 0.269514828, 6.24667291385, 136.384805181, 0.302148960635, 0.0194534877981, 0.0217526094207, -0.0204013097546
0.0428460986318, -4.31119176119e-05, 0.432157219, 0.269514828, 6.24669820565, 136.384868397, -0.0188124046901, 0.0195241563071, 0.164032888246, -0.00836359747526
0.0433979349201, -4.35208259611e-05, 0.432157219, 0.269514828, 6.24667226187, 136.388607495, 0.0188563604396, 0.0190933935298, 0.0665781159331, -0.0087317334633
0.044517860121, -4.4753945769e-05, 0.432157219, 0.269514828, 6.24669760272, 136.381917839, 0.0141741209837, 0.0192333566615, 0.101928260232, -0.00422871031919
0.0396974361905, -4.69719418716e-05, 0.432157219, 0.269514828, 6.2467251728, 136.378300958, 0.296221810353, 0.0196481720418, 0.0929140450771, -0.0194144394727
0.0421887800728, -4.18848683939e-05, 0.432157219, 0.269514828, 6.24665315009, 136.387416898, 0.0445280080473, 0.0193106913754, 0.0353746542791, 0.013273974136
0.043165383526, -4.32877872731e-05, 0.432157219, 0.269514828, 6.24670191678, 136.380136138, 0.0291583540277, 0.0192686427341, 0.0914839869129, -0.00866374243847
0.0427650925493, -4.61714356729e-05, 0.432157219, 0.269514828, 6.24675252493, 136.378254659, 0.150275394622, 0.0195206155372, 0.136760381114, -0.0150619062983
0.0444814667011, -4.80263823289e-05, 0.432157219, 0.269514828, 6.24669755892, 136.383887309, 0.0183481851669, 0.0191481915757, 0.112083672689, 0.00608943885077
0.0420843712036, -4.32921481058e-05, 0.432157219, 0.269514828, 6.24670653815, 136.384702924, 0.157813433482, 0.0193847116988, 0.189012361448, -0.0141004221668
0.0438227301672, -4.87755298452e-05, 0.432157219, 0.269514828, 6.24671059662, 136.381953849, 0.0489873078234, 0.019317104898, 0.128618509901, 0.00817101909539
0.0434292397658, -4.26292933314e-05, 0.432157219, 0.269514828, 6.24668237389, 136.387873489, 0.0209784105883, 0.0195378344645, -0.00259682451211, -0.00342677418047
0.00333733075499, -4.14373361879e-05, 0.432157219, 0.269514828, 6.24661505461, 136.391104564, 0.960618600485, 0.0264568185771, -0.359344342853, -0.385623257886
0.0454674137655, -4.53272571329e-05, 0.432157219, 0.269514828, 6.24664639695, 136.391786378, 0.00653439415252, 0.0194127723289, 0.0720855962732, -0.000892945487937
0.0433287108277, -4.6500319309e-05, 0.432157219, 0.269514828, 6.24671910928, 136.383025782, 0.231594804349, 0.0193953110409, 0.177492785794, -0.024660111819
0.0418018579038, -4.34862691787e-05, 0.432157219, 0.269514828, 6.24670282139, 136.379525684, -0.0103664999281, 0.0192370312373, 0.22711208737, -0.0197411270602
0.0427058757392, -4.51458547338e-05, 0.432157219, 0.269514828, 6.24677987517, 136.378174434, 0.0148569777807, 0.0191593458245, 0.257657096636, -0.0211472106289
0.0434423312232, -4.24325007453e-05, 0.432157219, 0.269514828, 6.2467040292, 136.380394128, 0.0401213573369, 0.0195216055977, 0.144132029236, -0.00713768666311
0.0460003703936, -4.46743157748e-05, 0.432157219, 0.269514828, 6.24672688088, 136.384178951, 0.0113862612245, 0.0192879569439, 0.172151542334, -0.00855492380282
0.0439789934917, -4.17242538269e-05, 0.432157219, 0.269514828, 6.24670370281, 136.383096296, -0.0106326623646, 0.0194654337579, 0.162053739736, -0.0210316006448
0.0452110439107, -4.5968897857e-05, 0.432157219, 0.269514828, 6.2467023487, 136.38383742, 0.0146350259763, 0.0191688909236, 0.117826346015, -0.00502313916743
0.0447222576251, -4.45130641146e-05, 0.432157219, 0.269514828, 6.24672631053, 136.381666678, 0.02107105873, 0.0194753196371, 0.0738731954533, 0.00570394029129
0.0435910119971, -4.44336103085e-05, 0.432157219, 0.269514828, 6.24671560561, 136.382037465, -0.00420740786284, 0.0192532528421, 0.118311117964, -0.00323504442357
0.00600261129876, -4.26950380249e-05, 0.432157219, 0.269514828, 6.24669462374, 136.384975366, 0.931925686853, 0.0247198697094, -0.462527943295, -0.383034436598
0.0438505634391, -4.28661895353e-05, 0.432157219, 0.269514828, 6.24674374503, 136.38154282, 0.0484541800982, 0.0192576079191, 0.0943503046429, -0.00526067080951
0.0461720567549, -4.4750701868e-05, 0.432157219, 0.269514828, 6.24670618561, 136.384413899, 0.0505023009952, 0.0195045658996, 0.178812863868, -0.0185104601959
0.0194400155084, -4.30469182841e-05, 0.432157219, 0.269514828, 6.24671939887, 136.408763706, 0.639193056525, 0.021115477885, 0.698598658297, -0.129069193968
0.0434219821597, -4.5781658411e-05, 0.432157219, 0.269514828, 6.24667127692, 136.387996545, 0.0978176343228, 0.0191860657102, 0.0379318427559, 0.014439415745
0.0438117974226, -4.22113357856e-05, 0.432157219, 0.269514828, 6.24667040547, 136.384972962, -0.0221114846229, 0.0191877098629, 0.109378989839, -0.00524235248626
0.0448692170056, -4.44966089073e-05, 0.432157219, 0.269514828, 6.24673519986, 136.381810541, 0.0141219629414, 0.0194301627309, -0.042185010801, 0.0110593788036
0.045117822115, -4.57484241157e-05, 0.432157219, 0.269514828, 6.24675267083, 136.380879995, 0.155606051384, 0.0190952075246, 0.117042788868, -0.0177965543462
0.0439486460845, -4.33771594963e-05, 0.432157219, 0.269514828, 6.24671555985, 136.382148357, -0.0132798431921, 0.019159283924, 0.0585219220804, -0.000122145230702
0.043508288859, -4.42284401442e-05, 0.432157219, 0.269514828, 6.24670719352, 136.38331357, 0.00476619569195, 0.0191511210399, 0.0776892778737, -0.00861173031717
0.0440556496737, -4.58494654018e-05, 0.432157219, 0.269514828, 6.24669764302, 136.38590298, 0.0534666555196, 0.0192366724489, 0.106856087402, -0.00830584996834
0.0430208179501, -4.4216864537e-05, 0.432157219, 0.269514828, 6.24674165609, 136.382173653, 0.0105800737291, 0.0190332473532, 0.0591265679046, -0.0124902658424
0.0452503295579, -4.58707317237e-05, 0.432157219, 0.269514828, 6.24676448392, 136.376208448, -0.00879438967271, 0.0194806138982, 0.0864601290212, -0.00342528784957
0.0443874121253, -4.33632819549e-05, 0.432157219, 0.269514828, 6.24674957286, 136.379388031, -0.00853898291123, 0.0193263296007, 0.0652780876883, -0.00526292054105
0.0438867720663, -4.58609133299e-05, 0.432157219, 0.269514828, 6.24672039121, 136.384564415, 0.0237767217633, 0.0191162244483, 0.10101771465, 0.000925733563592
0.0432950692925, -4.3407383355e-05, 0.432157219, 0.269514828, 6.24666273996, 136.386172944, 0.0307232308355, 0.0192551345562, 0.135128806734, -0.00712456258886
0.0454798431007, -4.26870598594e-05, 0.432157219, 0.269514828, 6.24672224851, 136.380374266, 0.126205945072, 0.0191147849384, 0.176549145784, -0.0199151782913
0.0117627471246, -4.46320401368e-05, 0.432157219, 0.269514828, 6.24668470009, 136.396708186, 0.848778854497, 0.0229889710907, 0.58605957985, -0.305865026097
0.0372105794796, -4.36586096457e-05, 0.432157219, 0.269514828, 6.24673275077, 136.380502382, 0.351769741869, 0.0196222377209, 0.09204988817, -0.0114332018953
0.0437503115499, -4.30646142891e-05, 0.432157219, 0.269514828, 6.24671973652, 136.381374914, 0.183345607065, 0.0195263675474, 0.15607901661, -0.012267533319
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Out[58]:





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 ...]

In [59]:

    

t=  genfromtxt('../data/samples_bootstrap.txt',unpack=True, delimiter=',')
fn = '/Users/tom/gitcode/GP_model_Kepler_data/data/koi2133_np1_priorTrue_dil0.0GP.hdf5'
f = h5py.File(fn)
g = f['mcmc']['chain']
lnprob = f['mcmc']['lnprob']
lnproblimy = 407039
lnproblimx = 4000
#funky business to only get good chains
#m = lnprob[:,lnproblimx] >= lnproblimy
#g = g[m,lnproblimx:,:]
#gshape = np.shape(g)
#g1 = g.reshape(gshape[0]*gshape[1],gshape[2])

#playing with jefferys prior
m = lnprob[:,lnproblimx] >= lnproblimy
g = g[m,lnproblimx:,:]
gshape = np.shape(g)
g1 = g.reshape(gshape[0]*gshape[1],gshape[2])


    

In [96]:

    

params = {#'backend': 'png',
                        'axes.linewidth': 1.5,
                        'axes.labelsize': 24,
                        'font.family': 'sans-serif',
                        #'axes.fontweight' : 'bold',
                        'font.size': 22,
                        'legend.fontsize': 14,
                        'xtick.labelsize': 16,
                        'ytick.labelsize': 16,
                        'text.usetex': False,
                        #'font.family': 'Palatino'
                        }
plt.rcParams.update(params)

fig, [ax1,ax2] = plt.subplots(2, 1, figsize=[8,12])
ax1.hist(t[0],bins=arange(0.03,0.05,0.001),normed=True,histtype='stepfilled',color='blue',alpha=0.3,log=True,
         label='Assuming white noise')
ax1.hist(g1[:,0],bins=20,normed=True,histtype='stepfilled',color='grey',alpha=0.3,log=True,
         label='GP noise model and stellar density prior')
ax1.minorticks_on()
ax1.set_xlabel(r'$\rho$ (g cm$^{-3}$)')
ax1.set_ylabel(r'Posterior density')
ax1.legend(fontsize=14,loc=1)
ax1.set_ylim([2,8000])
#ax1.set_ylim([50,2700])
ax1.set_xscale('log')
ax1.set_xlim([0.004,0.1])

ax2.hist(t[7],bins=arange(0.019,0.029,0.00003),normed=True,histtype='stepfilled',color='blue',alpha=0.3,
         label='Assuming white noise')
ax2.hist(g1[:,10],bins=arange(0.019,0.029,0.00003),normed=True,histtype='stepfilled',color='grey',alpha=0.3,
         label='GP noise model and stellar density prior')
ax2.minorticks_on()
ax2.set_xlabel(r'$R_p/R_\star$')
ax2.set_ylabel(r'Posterior density')
ax2.legend(fontsize=14,loc=1)
ax2.set_xlim([0.0185,0.025])
ax2.set_ylim([50,2700])

fig.tight_layout()
fig.savefig('/Users/tom/gitcode/GP_model_Kepler_data/latex/comp_noGP.png',dpi=200)


    

In [39]:

    

x = hist(normal(0.0073,0.0001,size=10000),bins=100,normed=True,histtype='stepfilled',color='grey',alpha=0.5,log=True)
hist(t[0],bins=100,normed=True,histtype='stepfilled',color='blue',alpha=0.5,log=True)
ylim([10,10000])


    

    
Out[39]:





(10, 10000)






    

In [44]:

    

percentile(t[7],[15.9,50,84.1])


    

    
Out[44]:





array([ 0.01918416,  0.01934809,  0.01955161])

In [43]:

    

print(0.04468306 -0.04372588 )
print(0.04372588 -0.04056347 )


    

    




0.00095718
0.00316241

In [45]:

    

0.01955161 -0.01934809


    

    
Out[45]:





0.00020352000000000217

In [46]:

    

0.01934809 -0.01918416


    

    
Out[46]:





0.00016392999999999963

In [ ]: