def probcal(Omega, cosi, m2, pf):
pf.write()
if m2 <= 0 or Omega > 360 or Omega < -360:
return 0
sini = sqrt(1 - cosi**2)
xdot = -1.* fac * x * mu * (cosi/sini) * sin((thetamu-Omega)*twopi/360.)
touchparfile(parfile, NITS=1, PAASCNODE=Omega, SINI = sini, M2 = m2, XDOT = xdot)
chisq, dof = tempofit(parfile, toafile = toafile)
#print chisq, dof
if chisq >= 99999.:return 0
#return (dof/chisq)
#return chisqprob(chisq, dof)
smallest = 308.28
#f = 1./sqrt(6.283185307179586)
#return f*exp(smallest - chisq)*sqrt(chisq - smallest)
return exp((smallest - chisq)/2.) #Ingrid/Paul?
def probcal(Omega, cosi, m2, pf):
pf.write()
if m2 <= 0 or Omega > 360 or Omega < -360:
return 0
sini = sqrt(1 - cosi**2)
xdot = -1. * fac * x * mu * (cosi / sini) * sin(
(thetamu - Omega) * twopi / 360.)
touchparfile(parfile, NITS=1, PAASCNODE=Omega, SINI=sini, M2=m2, XDOT=xdot)
chisq, dof = tempofit(parfile, toafile=toafile)
#print chisq, dof
if chisq >= 99999.: return 0
#return (dof/chisq)
#return chisqprob(chisq, dof)
smallest = 308.28
#f = 1./sqrt(6.283185307179586)
#return f*exp(smallest - chisq)*sqrt(chisq - smallest)
return exp((smallest - chisq) / 2.) #Ingrid/Paul?
def probcal(pf):
pf.write()
#if m2 <= 0 or Omega > 360 or Omega < -360:
#return 0
#sini = sqrt(1 - cosi**2)
#xdot = -1.* fac * x * mu * (cosi/sini) * sin((thetamu-Omega)*twopi/360.)
#touchparfile(parfile, NITS=1, PAASCNODE=Omega, SINI = sini, M2 = m2, XDOT = xdot)
chisq, dof = tempofit(parfile, toafile = toafile, pulsefile = pulsefile)
pf.chisq = chisq
#print dof, chisq
#print parfile, toafile
#print chisq, smallestchisq
#if chisq >= 999999999.:return 0
#smallestchisq = 308.28
try:
return exp((smallestchisq - chisq)/2.) #Ingrid/Paul?
except OverflowError:
print chisq, smallestchisq
print pf.parfile
raise OverflowError
def probcal(pf):
global smallestchisq
pf.write()
m2 = float(str(pf.M2[0]))
Omega = float(str(pf.PAASCNODE))
sini = float(str(pf.SINI[0]))
if m2 <= 0 or Omega > 360 or Omega < -360 or sini > 1.:
return 0
#sini = sqrt(1 - cosi**2)
#xdot = -1.* fac * x * mu * (cosi/sini) * sin((thetamu-Omega)*twopi/360.)
#touchparfile(parfile, NITS=1, PAASCNODE=Omega, SINI = sini, M2 = m2, XDOT = xdot)
chisq, dof = tempofit(parfile, toafile = toafile, pulsefile = pulsefile)
#print dof, chisq, smallestchisq
pf.chisq = chisq
if chisq < smallestchisq: smallestchisq = chisq
try:
#return exp((smallestchisq - chisq)/2.) #Ingrid/Paul?
return (smallestchisq - chisq)/2. #Ingrid/Paul?
except OverflowError:
print chisq, smallestchisq
print pf.parfile
raise OverflowError
parfile = options.parfile
toafile = options.toafile
pulsefile = options.pulsefile
steps = options.steps
mixing = options.mixing
rseed = options.seed
stepsize = options.stepsize
px = options.paral
pf = PARfile(parfile)
#pf = model(parfile)
#touchparfile(parfile, NITS=1)
#pf.thawall('JUMP_')
pf.write('mcmc.par')
touchparfile('mcmc.par', NITS=1)
chisq, dof = tempofit('mcmc.par', toafile = toafile, pulsefile = pulsefile)
#pf.tempofit(TOAfile(toafile), pulsefile = pulsefile)
smallestchisq = chisq
#print 'smallestchisq', smallestchisq, dof
cwd=os.getcwd()
plist = [x for x in pf.manifest if x in pf.parameters.keys() if not x.startswith('DMX') and not x.startswith('JUMP')]
bestpar = {'BEST':[pf.__dict__[p][0] for p in plist] + [pf.PAASCNODE, chisq], 'parfile':pf.parfile, 'parameters':plist + ['PAASCNODE', 'chisq']}
pickle.dump(bestpar, open('%s/bestpar.p' % cwd, 'w', 0), protocol=2)
def run(argv):
s, MarkovChain = argv
def mcmc(Chain, runtime, mixingtime=1000, stepsize=1):
#mixingtime = 1000
#runtime = 50000
pb = progressBar(maxValue = runtime + mixingtime)
cwd=os.getcwd()
tmpdir = cwd+'/.'+uniquename()
if not tmpdir == None:
if os.path.exists(tmpdir):
os.chdir(tmpdir)
else:
os.mkdir(tmpdir)
os.chdir(tmpdir)
os.system('cp %s/%s %s/%s' % (cwd, parfile, tmpdir, parfile))
os.system('cp %s/%s %s/%s' % (cwd, pulsefile, tmpdir, pulsefile))
motifile(toafile, cwd, tmpdir)
MarkovChain = Chain
pf = PARfile(parfile)
#pf = model(parfile)
#pf.thawall()
pf.freezeall('DMX_0')
#pf.parameters['SINI'] = '0'
#pf.parameters['M2'] = '0'
#pf.parameters['XDOT'] = '0'
pf.write()
chisq, dof = tempofit(parfile, toafile = toafile, pulsefile = pulsefile)
#pf.tempofit(toafile, pulsefile = pulsefile)
chisq, dof = chisq, dof
pf.matrix(toafile)
#pf.freezeall()
#pf.thawall('JUMP_')
pf.write()
#if 'PAASCNODE'in pf.__dict__:
#plist = [x for x in pf.manifest if x in pf.parameters.keys()] + ['PAASCNODE']
#dict = {'BEST':[pf.__dict__[p][0] for p in plist[:-1]] + [pf.__dict__[p] for p in plist[-1:]], 'parfile':pf.parfile, 'parameters':plist + ['chisq']}
#else:
plist = [x for x in pf.manifest if x in pf.parameters.keys()]
dict = {'BEST':[pf.__dict__[p][0] for p in plist] + [pf.PAASCNODE, chisq], 'parfile':pf.parfile, 'parameters':plist + ['PAASCNODE', 'chisq']}
pickle.dump(dict, open('%s/bestpar.p' % cwd, 'w'), protocol=2)
p0 = probcal(pf)
p = p0
#print 'P0', p0
#try:
#MChain = pickle.load(open('MChain.p', 'r'))
#except:
#MChain = {'Chain':[]}
#MChain['parameters'] = plist
#pickle.dump(MChain, open('MChain.p', 'w'))
n = count()
m = count()
while n.next() <= mixingtime + runtime:
npf = pf.randomnew(stepsize=stepsize)
randomnew(npf, stepsize)
p1 = probcal(npf)
c = m.next()
if c % 30 == 0:pb(c)
if p1 > p0:
if c > mixingtime:
MarkovChain.append([npf.__dict__[p][0] for p in plist] + [npf.PAASCNODE, npf.chisq])
pf = npf
p0 = p1
if p1 > p:
p = p1
#if 'PAASCNODE' in plist:
#dict['BEST'] = [pf.__dict__[p][0] for p in plist[:-1]] + [pf.__dict__[p] for p in plist[-1:]] + [npf.chisq]
#else:
dict['BEST'] = [npf.__dict__[p][0] for p in plist] + [npf.PAASCNODE, npf.chisq]
pickle.dump(dict, open('%s/bestpar.p' % cwd, 'w'), protocol=2)
else:
t = uniform(0,1,1)[0]
if t < p1/p0:
if c > mixingtime:
MarkovChain.append([npf.__dict__[p][0] for p in plist] + [npf.PAASCNODE, npf.chisq])
pf = npf
p0 = p1
else:
if c > mixingtime:
MarkovChain.append([pf.__dict__[p][0] for p in plist] + [npf.PAASCNODE, npf.chisq])
#print MarkovChain
#print best
print '%d points added to the Chain.' % len(MarkovChain)
#OldChain = pickle.load(open('MChain.p','r'))['Chain']
#dict['Chain'] = OldChain + MarkovChain
#dict['Chain'] = MarkovChain
os.chdir(cwd)
