def all(epos):
if hasattr(epos, 'chain'):
print '\nPlotting chain...'
chain(epos)
try:
corners(epos)
except NameError:
print ' (skipping corner plot)'
if epos.Parametric:
parametric.oneD(epos, MCMC=True)
parametric.twoD(epos, MCMC=True)
if epos.Multi:
parametric.oneD_x(epos, MCMC=True, Log=False)
# plot sample from posterior
if hasattr(epos, 'plotsample'):
periodradius.panels(epos, MCMC=True)
if epos.Multi:
multi.multiplicity(epos, MCMC=True)
multi.periodratio(epos, MCMC=True)
multi.periodinner(epos, MCMC=True)
else:
print '\nNo chain to plot, did you run EPOS.run.mcmc()? \n'
def prep_obs(epos):
# occurrence pdf on sma from plot_input_diag?
# cdf of obs, zoomed only
z = epos.obs_zoom = {}
ix = (epos.xzoom[0] <= epos.obs_xvar) & (epos.obs_xvar <= epos.xzoom[1])
iy = (epos.yzoom[0] <= epos.obs_yvar) & (epos.obs_yvar <= epos.yzoom[1])
if np.sum(ix & iy) == 0:
raise ValueError('No planets in the box!')
x = epos.obs_xvar[ix & iy]
y = epos.obs_yvar[ix & iy]
for key, var in zip(['x', 'y'], [x, y]):
z[key] = np.sort(var) # add (x0,0) and (x1,1)?
z[key + ' cum'] = np.arange(z[key].size, dtype=float)
z[key + ' cdf'] = z[key + ' cum'] / z[key + ' cum'][-1]
# multis
z['multi'] = {}
z['multi']['bin'], z['multi']['count'] = multi.frequency(
epos.obs_starID[ix & iy])
z['multi']['pl cnt'] = z['multi']['bin'] * z['multi']['count']
z['multi']['Pratio'], z['multi']['Pinner']= \
multi.periodratio(epos.obs_starID[ix&iy], epos.obs_xvar[ix&iy])
z['multi']['cdf'] = multi.cdf(epos.obs_starID[ix & iy])
def all(epos, color='C1'):
if hasattr(epos, 'synthetic_survey'):
print '\nPlotting output...'
if epos.MonteCarlo:
periodradius.periodradius(epos, SNR=False)
periodradius.periodradius(epos, SNR=True)
if not epos.Parametric:
periodradius.periodradius(epos,
Model=True,
SNR=False,
color=color)
else:
# plot period-radius for non-MC
pass
periodradius.cdf(epos, color=color)
periodradius.panels(epos, color=color)
if epos.Multi:
multi.periodradius(epos)
multi.periodradius(epos, Nth=True)
multi.multiplicity(epos, MC=True, color=color)
multi.multiplicity(epos, MC=True, Planets=True, color=color)
multi.multiplicity_cdf(epos, MC=True)
multi.periodratio(epos, MC=True, color=color)
if epos.Parametric and epos.spacing is not None:
multi.periodratio(epos, MC=True, Input=True, color=color)
multi.periodratio_cdf(epos, MC=True, color=color)
multi.periodinner(epos, MC=True, color=color)
if epos.Parametric and epos.spacing is not None:
multi.periodinner(epos, MC=True, Input=True, color=color)
multi.periodinner_cdf(epos, MC=True, color=color)
# pdf per subgroup
#periodradius.pdf(epos)
#periodradius.pdf_3d(epos)
if epos.MassRadius:
massradius.massradius(epos, MC=True, color=color)
massradius.massradius(epos, MC=True, Log=True, color=color)
else:
if epos.Parametric and epos.Multi and not epos.RV:
multi.periodratio(epos, MC=True, N=True)
if epos.spacing is not None:
multi.periodratio(epos, MC=True, N=True, Input=True)
multi.periodinner(epos, MC=True, N=True)
else:
print '\nNo output to plot, did you run EPOS.run.once()? \n'
def MC(epos,
fpara,
Store=False,
Sample=False,
StorePopulation=False,
Extra=None,
Verbose=True):
'''
Do the Monte Carlo Simulations
Note:
variable x/X is P
variable y/Y is R/M
TODO: split into multiple functions
'''
if Verbose: tstart = time.time()
#if not Store: logging.debug(' '.join(['{:.3g}'.format(fpar) for fpar in fpara]))
''' Seed the random number generator '''
if epos.seed is not None: np.random.seed(epos.seed)
''' construct 1D arrays allP, allR or allM
dimension equal to sample size * planets_per_star
also keeping track of:
ID: star identifier
I: inc
N: Nth planet in system
dP: period ratio
'''
if epos.Parametric:
''' parameters within bounds? '''
try:
epos.pdfpars.checkbounds(fpara)
except ValueError as message:
if Store: raise
else:
logging.debug(message)
return -np.inf
''' Draw (inner) planet from distribution '''
pps = epos.fitpars.getpps_fromlist(fpara)
fpar2d = epos.fitpars.get2d_fromlist(fpara)
npl = epos.fitpars.getmc('npl', fpara) if epos.RandomPairing else 1
try:
sysX, sysY = draw_from_2D_distribution(epos, pps, fpar2d, npl=npl)
except ValueError:
if Store: raise
else: return -np.inf
''' Multi-planet systems '''
if not epos.Multi:
allX = sysX
allY = sysY
elif epos.RandomPairing:
# set ID, nth planet in system
isys = np.arange(sysX.size / npl)
allID = np.repeat(isys, npl)
order = np.lexsort((sysX, allID)) # sort by ID, then P
assert np.all(allID[order] == allID)
allX = sysX[order]
allY = sysY #[order] # random order anyway
allN = np.tile(np.arange(npl), isys.size) # ignores xzoom
# isotropic or inc?
dInc = epos.fitpars.getmc('inc', fpara)
if dInc is not None:
f_iso = epos.fitpars.getmc('f_iso', fpara)
allI = np.random.rayleigh(dInc, allID.size)
f_cor = epos.fitpars.getmc('f_cor', fpara)
f_dP, f_inc = 1.0, 1.0 # no need to fudge these
else:
''' retrieve fit parameters for multi-planets'''
npl = epos.fitpars.getmc('npl', fpara)
dR = epos.fitpars.getmc('dR', fpara)
dInc = epos.fitpars.getmc('inc', fpara)
f_iso = epos.fitpars.getmc('f_iso', fpara)
f_cor = epos.fitpars.getmc('f_cor', fpara)
f_dP, f_inc = 1.0, 1.0 # no need to fudge these
''' Parameter bounds '''
if npl < 1:
logging.debug('npl = {:.3g} < 1'.format(npl))
if Store: raise ValueError('at least one planet per system')
return -np.inf
if (dInc <= 0) or (dR <= 0) or not (0 <= f_iso <= 1):
if Store: raise ValueError('parameters out of bounds')
return -np.inf
''' Draw multiplanet distributions '''
allX, allY, allI, allN, allID= \
draw_multi(epos, sysX, sysY, npl, dInc, dR, fpara, Store)
''' convert to observable parameters '''
allP = allX
if epos.RV or epos.MassRadius: allM = allY
else: allR = allY
else:
pfm = epos.pfm
''' parameters within bounds? '''
# move out of loop?
try:
epos.fitpars.checkbounds(fpara)
except ValueError as message:
if Store: raise
else:
logging.debug(message)
return -np.inf
''' Fit parameters'''
pps = epos.fitpars.getpps_fromlist(fpara)
f_cor = epos.fitpars.getmc('f_cor', fpara)
f_iso = epos.fitpars.getmc('f_iso', fpara)
f_inc = epos.fitpars.getmc('f_inc', fpara)
f_dP = epos.fitpars.getmc('f_dP', fpara)
# need this here?
if not (0 <= f_iso <= 1) or not (0 < pps) or not (0 <= f_cor <= 1):
#\or not (0<=f_dP<=10) or not (0 <= f_inc < 10):
if Store: raise ValueError('parameters out of bounds')
return -np.inf
'''
Draw from all
'''
if not 'draw prob' in pfm:
ndraw = int(round(1. * epos.nstars * pps / pfm['ns']))
if Verbose:
print ' {} planets in {} simulations'.format(
pfm['np'], pfm['ns'])
print ' {} stars in survey, {} draws, eta={:.2g}'.format(
epos.nstars, ndraw, pps)
allP = np.tile(pfm['P'], ndraw)
allM = np.tile(pfm['M'], ndraw)
if 'R' in pfm:
allR = np.tile(pfm['R'], ndraw)
if epos.Multi:
allI = np.tile(pfm['inc'], ndraw)
allN = np.tile(pfm['kth'], ndraw)
# ID or system index?
allID= np.tile(pfm['ID'], ndraw) \
+ np.repeat(np.arange(ndraw)*pfm['ns'], pfm['np'])
else:
'''
Draw from some distributions according to 'tag' parameter
TODO: functions to calculate draw probability from tag
'''
#draw planetary systems from simulations
ndraw = int(round(1. * epos.nstars * pps))
if Verbose: print '\nDraw {} systems'.format(ndraw)
system_index = np.random.choice(pfm['system index'],
size=ndraw,
p=pfm['draw prob'])
#create a list of planets
indexlist = [
pfm['planet index'][pfm['ID'] == i] for i in system_index
]
starIDlist = [
np.full((pfm['ID'] == i).sum(), ID)
for ID, i in enumerate(system_index)
]
planets = np.concatenate(indexlist)
allID = np.concatenate(starIDlist)
allP = pfm['P'][planets]
allM = pfm['M'][planets]
allR = pfm['R'][planets]
allI = pfm['inc'][planets]
allN = pfm['kth'][planets]
#allID= pfm['ID'][planets]
if Verbose: print ' {} planets'.format(allP.size)
allY = allM
dInc = False # Isotropic inclinations not implemented
'''
Identify transiting planets (itrans is a T/F array)
'''
if epos.RV:
# RV keep all
itrans = np.full(allP.size, True, np.bool)
elif (not epos.Multi) or (epos.Multi and dInc == None):
# geometric transit probability
p_trans = epos.fgeo_prefac * allP**epos.Pindex
itrans = p_trans >= np.random.uniform(0, 1, allP.size)
else:
#multi-transit probability
itrans = istransit(epos,
allID,
allI,
allP,
f_iso,
f_inc,
Verbose=Verbose)
# Print multi statistics
if Verbose and epos.Multi and not epos.RV:
print '\n {} planets, {} transit their star'.format(
itrans.size, itrans.sum())
multi.frequency(allID[itrans], Verbose=True)
'''
remove planets according to transit probability
'''
MC_P = allP[itrans]
if epos.MassRadius or epos.RV: MC_M = allM[itrans]
else: MC_R = allR[itrans]
if epos.Multi:
MC_ID = allID[itrans]
if epos.Parametric:
MC_N = allN[itrans] # also for PFM?
else:
#MC_P*= (1.+0.1*np.random.normal(size=MC_ID.size) )
pass
'''
Set the observable MC_Y (R or Msin i)
'''
if epos.RV:
''' M sin i.'''
# Note different conventions for i in Msini (i=0 is pole-on)
# sin(arccos(chi)) == cos(arcsin(chi)) == sqrt(1-chi^2)
MC_Y = MC_Msini = MC_M * np.sqrt(
1. - np.random.uniform(0, 1, MC_P.size)**2.)
else:
''' Convert Mass to Radius '''
if epos.MassRadius:
mean, dispersion = epos.MR(MC_M)
MC_R = mean + dispersion * np.random.normal(size=MC_M.size)
''' uncertainty in stellar radius? '''
MC_Y = MC_R * (1. +
epos.radiusError * np.random.normal(size=MC_R.size))
'''
Store (transiting) planet sample for verification plot
'''
if Store:
tr = epos.transit = {}
tr['P'] = MC_P
tr['Y'] = MC_Y
'''
Identify detectable planets based on SNR (idet is a T/F array)
'''
f_snr = interpolate.RectBivariateSpline(epos.MC_xvar, epos.MC_yvar,
epos.MC_eff)
p_snr = f_snr(MC_P, MC_Y, grid=False)
assert p_snr.ndim == 1
idet = p_snr >= np.random.uniform(0, 1, MC_P.size)
# draw same random number for S/N calc, 1=correlated noise
if epos.Multi and f_cor > 0:
IDsys, toplanet = np.unique(MC_ID, return_inverse=True)
idet_cor = p_snr >= np.random.uniform(0, 1, IDsys.size)[toplanet]
cor_sys = (np.random.uniform(0, 1, IDsys.size) < f_cor)
cor_pl = cor_sys[toplanet]
idet = np.where(cor_pl, idet_cor, idet)
'''
Remove undetectable planets
'''
# arrays with detected planets
if epos.Multi:
det_ID = MC_ID[idet]
if not epos.RV and epos.Parametric: det_N = MC_N[idet]
det_P = MC_P[idet]
det_Y = MC_Y[idet]
#if len(alldP)>0:
# if not epos.Parametric and 'all_Pratio' in sg:
# det_dP= MC_dP[idet]
if Verbose and epos.Multi:
print ' {} transiting planets, {} detectable'.format(
idet.size, idet.sum())
multi.frequency(det_ID, Verbose=True)
'''
Probability that simulated data matches observables
TODO:
- store ln prob, D
- switches for in/excluding parameters
- likelyhood from D instead of prob?
'''
# bugfix
#np.seterr(divide='raise')
tstart = time.time()
prob = {}
lnp = {}
# make sure that x=P, y=R (where?)
ix = (epos.xzoom[0] <= det_P) & (det_P <= epos.xzoom[1])
iy = (epos.yzoom[0] <= det_Y) & (det_Y <= epos.yzoom[1])
if (ix & iy).sum() < 1:
if Store: raise ValueError('no planets detectable')
return -np.inf
if epos.goftype == 'KS':
prob_2samp = _prob_ks
elif epos.goftype == 'AD':
prob_2samp = _prob_ad
else:
raise ValueError('{} not a goodness-of-fit type (KS, AD)'.format(
epos.goftype))
if 'xvar' in epos.summarystatistic:
prob['xvar'], lnp['xvar'] = prob_2samp(epos.obs_zoom['x'],
det_P[ix & iy])
if 'yvar' in epos.summarystatistic:
prob['yvar'], lnp['yvar'] = prob_2samp(epos.obs_zoom['y'],
det_Y[ix & iy])
if 'N' in epos.summarystatistic:
# chi^2: (np-nobs)/nobs**0.5 -> p: e^-0.5 x^2
chi2 = (epos.obs_zoom['x'].size -
np.sum(ix & iy))**2. / epos.obs_zoom['x'].size
lnp['N'] = -0.5 * chi2
prob['N'] = np.exp(-0.5 * chi2)
if epos.Multi:
''' Multi-planet frequency, pearson chi_squared '''
k, Nk = multi.frequency(det_ID[ix & iy])
Nk_obs = epos.obs_zoom['multi']['count']
ncont = max(len(Nk), len(Nk_obs))
# pad with zeros
obs = np.zeros((2, ncont), dtype=int)
obs[0, :len(Nk)] = Nk
obs[1, :len(Nk_obs)] = Nk_obs
# remove double zero frequencies
obs = obs[:, ~((obs[0, :] == 0) & (obs[1, :] == 0))]
try:
_, prob['Nk'], _, _ = chi2_contingency(obs)
except ValueError:
#print Nk
#print Nk_obs
#print obs
raise
with np.errstate(divide='ignore'):
lnp['Nk'] = np.log(prob['Nk'])
''' Period ratio, innermost planet '''
sim_dP, sim_Pinner = multi.periodratio(det_ID[ix & iy], det_P[ix & iy])
if (len(sim_dP) > 0) & (len(sim_Pinner) > 0):
prob['dP'], lnp['dP'] = prob_2samp(
epos.obs_zoom['multi']['Pratio'], f_dP * sim_dP)
prob['Pin'], lnp['Pin'] = prob_2samp(
epos.obs_zoom['multi']['Pinner'], sim_Pinner)
else:
logging.debug('no multi-planet statistics, {}'.format(len(sim_dP)))
prob['dP'], prob['Pin'] = 0, 0
lnp['dP'], lnp['Pin'] = -np.inf, -np.inf
# combine with Fischer's rule:
lnprob = np.sum([lnp[key] for key in epos.summarystatistic])
#dof= len(prob_keys)
chi_fischer = -2. * lnprob
if Verbose:
print '\nGoodness-of-fit'
print ' logp= {:.1f}'.format(lnprob)
print ' - p(n={})={:.2g}'.format(np.sum(ix & iy), prob['N'])
if 'xvar' in prob: print ' - p(x)={:.2g}'.format(prob['xvar'])
if 'yvar' in prob: print ' - p(y)={:.2g}'.format(prob['yvar'])
if 'Nk' in prob: print ' - p(N_k)={:.2g}'.format(prob['Nk'])
if 'dP' in prob: print ' - p(P ratio)={:.2g}'.format(prob['dP'])
if 'Pin' in prob: print ' - p(P inner)={:.2g}'.format(prob['Pin'])
tgof = time.time()
if Verbose:
print ' observation comparison in {:.3f} sec'.format(tgof - tstart)
''' Store _systems_ with at least one detected planet '''
# StorePopulation
if Store and epos.Multi and (not epos.RV):
# include/exclude singles w/ iso_pl?
itransdet = np.copy(itrans)
itransdet[itrans] = idet
isysdet, isingle, imulti, order = storepopulation(
allID, allP, det_ID, itransdet)
pop = epos.population = {}
pop['order'] = order
pop['P'] = allP
pop['k'] = allN
for key, subset in zip(['system', 'single', 'multi'],
[isysdet, isingle, imulti]):
pop[key] = {}
pop[key]['Y'] = allY[subset] # R? M?
pop[key]['P'] = allP[subset]
pop[key]['order'] = order[subset]
''' Store detectable planet population '''
if Store:
ss = {}
ss['P'] = det_P # MC_P[idet]
ss['Y'] = det_Y
if epos.MassRadius:
# or if has mass and radius
ss['M'] = MC_M[idet]
ss['R'] = MC_R[idet]
#if len(alldP)>0
if epos.Multi:
ss['ID'] = det_ID
ss['multi'] = {}
ss['multi']['bin'], ss['multi']['count'] = multi.frequency(
det_ID[ix & iy])
ss['multi']['pl cnt'] = ss['multi']['bin'] * ss['multi']['count']
ss['multi']['Pratio'], ss['multi']['Pinner']= \
multi.periodratio(det_ID[ix&iy], det_P[ix&iy]) # *f_dP
ss['multi']['cdf'] = multi.cdf(det_ID[ix & iy])
if not epos.RV and epos.Parametric:
ss['multi']['PN'], ss['multi']['dPN'] = multi.periodratio(
det_ID[ix & iy], det_P[ix & iy], N=det_N[ix & iy])
epos.prob = prob
epos.lnprob = lnprob
ss['P zoom'] = det_P[ix & iy]
ss['Y zoom'] = det_Y[ix & iy]
ss['nobs'] = ss['P zoom'].size
# Store as an extra model
if Sample:
return ss
elif Extra is not None:
ss['name'] = Extra
if not hasattr(epos, 'ss_extra'):
epos.ss_extra = []
epos.ss_extra.append(ss)
#print 'saving extra {}'.format(Extra)
else:
epos.synthetic_survey = ss
else:
# return probability
if np.isnan(lnprob):
return -np.inf
return lnprob
def all(epos, color=None, imin=1e-2):
print '\nPlotting input...'
#add decorators for zoom T/F?
# color into epos?
if epos.Parametric:
parametric.oneD(epos)
parametric.twoD(epos)
parametric.panels(epos)
# plot the input distribution converted from M->Msini or M->R
if not epos.MonteCarlo and epos.Msini:
parametric.oneD_y(epos, Convert=True) # no conversion for x (yet)
parametric.twoD(epos, Convert=True)
parametric.panels(epos, Convert=True)
else:
if 'R' in epos.pfm:
model.panels_radius(epos, color=color)
if 'M' in epos.pfm:
model.panels_mass(epos, color=color)
model.period(epos, color=color)
model.multiplicity(epos, color=color)
if hasattr(epos, 'func'):
if epos.MassRadius:
model.panels_mass(epos, Population=True, color=color)
else:
model.panels_radius(epos,
Population=True,
Shade=False,
color=color) #Zoom?
if hasattr(epos, 'occurrence'):
# model counts (debiases data)
if 'planet' in epos.occurrence:
model.panels_radius(epos, Occurrence=True, color=color)
if 'R' in epos.pfm or epos.MassRadius:
model.period(epos, Occurrence=True, color=color)
if epos.Zoom:
model.panels_radius(epos,
Occurrence=True,
color=color,
Zoom=True)
# planet counts (biases model)
if 'model' in epos.occurrence:
model.panels_radius(epos, Observation=True, color=color)
if epos.Zoom:
model.panels_radius(epos,
Observation=True,
color=color,
Zoom=True)
if 'tag' in epos.pfm:
model.panels_radius(epos, Tag=True) # Zoom?
model.period(epos, Tag=True)
if 'inc' in epos.pfm:
model.inclination(epos, Simple=True, color=color, imin=imin)
if 'dP' in epos.pfm:
model.periodratio(epos, color=color, Simple=True)
if 'R' in epos.pfm:
model.periodratio_size(epos, color=color)
if 'inc' in epos.pfm:
model.periodratio_inc(epos, color=color, imin=imin)
if epos.Multi:
if epos.Parametric:
multi.periodradius(epos, MC=False)
multi.periodradius(epos, MC=False, Nth=True)
multi.multiplicity(epos, MC=False)
multi.periodratio(epos, MC=False)
multi.periodratio_cdf(epos, MC=False)
multi.periodinner(epos, MC=False)
multi.periodinner_cdf(epos, MC=False)
else:
pass
if epos.MassRadius:
massradius.massradius(epos, MC=False)
massradius.massradius(epos, MC=False, Log=True)