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analyze_emcee.py
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analyze_emcee.py
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## December 11, 2014 : Took out reliance on AEFF, which I initially took from PIMMS
## June 26, 2013 : Support code for emcee analysis
import numpy as np
import matplotlib.pyplot as plt
import radprofile as rp
import constants as c
import model_halo as MH
import dust
import sigma_scat as ss
import cPickle
from scipy.interpolate import interp1d
##-------- Supporting constants, Cyg X-3 obsid 6601 -------##
ALPHA = np.arange( 1.0, 200.0, 1.0 ) # 0.5 arcsec resolution
AMIN = 0.005
##-------- Supporting structure, from emcee_fit -------##
## Parse text files containing walker positions
def string_to_walker( string ):
pos_string = string.strip().strip('[').strip(']')
walker = np.array( [] )
for param in pos_string.split():
walker = np.append( walker, np.float(param) )
return np.array( [walker] )
def read_pos( filename, acor=1 ):
f = open( filename )
first_line = f.readline()
result = string_to_walker( first_line )
end_of_file = False
counter = 1
while not( end_of_file ):
try:
next_line = f.readline()
if (counter % acor) == 0:
walker = string_to_walker( next_line )
result = np.concatenate( (result,walker) )
else: pass
counter += 1
except:
end_of_file = True
f.close()
return result
def read_prob( filename ):
prob_data = open( filename, 'r' )
prob = []
end_of_file = False
while not end_of_file:
try:
newprob = prob_data.readline().strip()
prob.append( np.float(newprob) )
except:
end_of_file = True
prob = np.array( prob )
prob_data.close()
return prob
## Unpickle stuff
def eat_pickle( filename ):
pickle_data = open( filename, 'rb' )
data = cPickle.load( pickle_data )
pickle_data.close()
return data
## Simulate the halos
def uniform_halo( filename, params, alpha=ALPHA, **kwargs ):
logNH, amax, p = params
return MH.simulate_uniform( filename, \
NH=np.power(10.0,logNH), a0=AMIN, a1=amax, p=p, alpha=alpha, **kwargs )
def screen_halo( filename, params, alpha=ALPHA, **kwargs ):
xg, logNH, amax, p = params
return MH.simulate_screen( filename, \
xg=xg, NH=np.power(10.0,logNH), a0=AMIN, a1=amax, p=p, alpha=alpha, **kwargs)
def sum_interp( sb1, sb2 ):
## Takes interp objects and sums them to create another interp object
## Assumes same x values for both
if sb1.x.all() != sb2.x.all():
print 'Error: Interp objects must have same x-axis values'
return
else:
return interp1d( sb1.x, sb1.y + sb2.y )
def multiscreen_halo( specfile, params, amin=AMIN, alpha=ALPHA, **kwargs ):
x1, x2, logNH1, logNH2, amax, p = params
s1 = MH.simulate_screen( specfile, xg=x1, NH=np.power(10.0,logNH1), \
a0=AMIN, a1=amax, p=p, alpha=alpha, **kwargs )
s2 = MH.simulate_screen( specfile, xg=x2, NH=np.power(10.0,logNH2), \
a0=AMIN, a1=amax, p=p, alpha=alpha, **kwargs )
return sum_interp( s1, s2 )
def uniscreen( specfile, params, alpha=ALPHA, **kwargs ):
logNHu, logNHs, a_u, a_s, p_u, p_s, x_s = params
nhu = np.power( 10.0, logNHu )
UU = MH.simulate_uniform( specfile, NH=nhu, \
a0=AMIN, a1=a_u, p=p_u, alpha=alpha, **kwargs )
nhs = np.power( 10.0, logNHs )
SS = MH.simulate_screen( specfile, xg=x_s, NH=nhs, \
a0=AMIN, a1=a_s, p=p_s, alpha=alpha, **kwargs )
return sum_interp( UU, SS )
def red_chisq( xdata, ydata, sigma, model, nparams ):
chi = ( ydata - model(xdata) ) / sigma
return np.sum(chi**2) / ( len(xdata) - nparams )
def chisq( xdata, ydata, sigma, model ):
chi = ( ydata - model(xdata) ) / sigma
return np.sum(chi**2)
##-------- Some basic plotting stuff -------##
def plot_chains( chainfile, title=None, unit=None, opt_values=None, **kwargs ):
chain = eat_pickle( chainfile )
nwalkers, nsteps, ndim = chain.shape
for d in range(ndim):
plt.figure()
for i in range(nwalkers):
plt.plot( range(nsteps), chain[i,:,d], **kwargs )
if title != None : plt.title( title[d] )
if unit != None : plt.ylabel( unit[d] )
if opt_values != None :
plt.axhline( opt_values[d], lw=3, ls='--', color='r' )
return
def plot_whist( walkers, nbins, title=None, unit=None, opt_values=None, \
histtype='step', **kwargs ):
ndim = len( walkers[0] )
for d in range(ndim):
plt.figure()
plt.hist( walkers[:,d], nbins, histtype=histtype, **kwargs )
if title != None : plt.title( title[d] )
if unit != None : plt.xlabel( unit[d] )
if opt_values != None :
plt.axvline( opt_values[d], lw=3, ls='--', color='r' )
return
def compare_walkers( w1, w2, nbins, wlabels=None, \
title=None, unit=None, opt_values=None, histtype='stepfilled' ):
ndim = len( w1[0] )
if wlabels == None : wlabels = ['','']
for d in range(ndim):
plt.figure()
plt.hist( w1[:,d], nbins, histtype='stepfilled', \
color='k', alpha=0.3, label=wlabels[0] )
plt.hist( w2[:,d], nbins, histtype='stepfilled', \
color='b', alpha=0.3, label=wlabels[1] )
if wlabels != None : plt.legend( loc='upper right', frameon=False )
if title != None : plt.title( title[d] )
if unit != None : plt.xlabel( unit[d] )
if opt_values != None :
plt.axvline( opt_values[d], lw=3, ls='--', color='k' )
return
##--------- Grab items from sample ---------##
def sample_halos( sample, isample, mscreen=False, **kwargs ):
result = []
if mscreen:
for i in isample:
x1, x2, logNH1, logNH2, amax, p = sample[i]
NH1, NH2 = np.power(10.0,logNH1), np.power(10.0,logNH2)
print 'x =', x1, x2, '\tNH =', NH1, NH2, '\tamax =', amax, '\tp =', p
result.append( multiscreen_halo( x1, x2, NH1, NH2, amax=amax, p=p, **kwargs ) )
else:
for i in isample:
logNH, amax, p = sample[i]
NH = np.power(10.0,logNH)
print 'NH =', NH, '\tamax =', amax, '\tp =', p
result.append( uniform_halo( NH=NH, amax=amax, p=p, **kwargs ) )
return result
def multiscreen_tau( sample, d2g=0.009, scatm=ss.makeScatmodel('RG','Drude') ):
result = []
for walker in sample:
logNHu, logNHs, a_u, a_s, p_u, p_s, x_s = walker
MDu, MDs = np.power(10.0,logNHu) * c.mp() * d2g, np.power(10.0,logNHs) * c.mp() * d2g
da_u, da_s = (a_u-AMIN)/10.0, (a_s-AMIN)/10.0
Udust = dust.Dustdist( rad=np.arange(AMIN,a_u+da_u,da_u), p=p_u )
Sdust = dust.Dustdist( rad=np.arange(AMIN,a_s+da_s,da_s), p=p_s )
Ukappa = ss.Kappascat( E=1.0, dist=dust.Dustspectrum( rad=Udust, md=MDu ), scatm=scatm ).kappa[0]
Skappa = ss.Kappascat( E=1.0, dist=dust.Dustspectrum( rad=Sdust, md=MDs ), scatm=scatm ).kappa[0]
result.append( Ukappa*MDu + Skappa*MDs )
return np.array( result )
def sample_tau( sample, d2g=0.009, mscreen=False ):
result = []
for walker in sample:
if mscreen:
x1, x2, logNH1, logNH2, amax, p = walker
nhtot = np.power(10.0,logNH1) + np.power(10.0,logNH2)
md = nhtot * c.mp() * d2g
else:
logNH, amax, p = walker
md = np.power(10.0,logNH) * c.mp() * d2g
da = (amax-AMIN)/100.0
DD = dust.Dustdist( rad=np.arange(AMIN,amax+da,da), p=p )
DS = dust.Dustspectrum( rad=DD, md=md )
KK = ss.Kappascat( E=1.0, dist=DS ).kappa[0]
result.append( KK * md )
return np.array(result)
def sample_logMD( sample, d2g=0.009, replace=False, mscreen=False ):
if mscreen:
nhtot = np.power(10.0,sample[:,2]) + np.power(10.0,sample[:,3])
logmd = np.log10( nhtot * c.mp() * d2g )
if replace:
result = np.copy( sample )
result[:,2] = sample[:,2] + np.log10( c.mp()*d2g )
result[:,3] = sample[:,3] + np.log10( c.mp()*d2g )
return result
else:
logmd = sample[:,0] + np.log10( c.mp()*d2g )
if replace :
result = np.copy( sample )
result[:,0] = logmd
return result
return logmd
def sample_extinction( sample, lam, isample, \
NA=20, d2g=0.009, scatm=ss.makeScatmodel('RG','Drude') ):
energy = c.kev2lam() / lam # lam must be in cm to get keV
logMD = sample_logMD( sample )
MD = np.power( 10.0, logMD )
result = []
for i in isample:
logNH, amax, p = sample[i]
print 'logNH =', logNH, '\tamax =', amax, '\tp =', p
da = (amax-AMIN)/np.float(NA)
dist = dust.Dustdist( rad=np.arange(AMIN,amax+da,da), p=p )
spec = dust.Dustspectrum( rad=dist, md=MD[i] )
kappa = ss.Kappascat( E=energy, dist=spec, scatm=scatm ).kappa
result.append( 1.086 * MD[i] * kappa )
return result
def extinction_curves( ext_list, lam, V=0.5470 ):
# lam in micron this time
A_V = []
curve = []
for ext in ext_list:
A_lam = interp1d( lam, ext )
A_V.append( A_lam(V) )
curve.append( A_lam.y / A_lam(V) )
return np.array(A_V), curve