def mcmc(Chain, runtime, mixingtime=1000):
#mixingtime = 1000
#runtime = 50000
pb = progressBar(maxValue=runtime)
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))
motifile(toafile, cwd, tmpdir)
dict = pickle.load(open('%s/bestpar.p' % cwd, 'r'))
Omega, cosi, m2 = dict['BEST']
MarkovChain = Chain
pf = PARfile(parfile)
pf.thawall()
pf.write()
pf.matrix(toafile)
pf.freezeall()
pf.write()
p0 = probcal(Omega, cosi, m2, pf)
p = p0
best = (Omega, cosi, m2)
#dict = {'BEST':best}
#pickle.dump(dict, open('bestpar.p', 'w'))
n = count()
m = count()
while n.next() < mixingtime + runtime:
npf = pf.randomnew()
Ncosi = cosi + uniform(-0.05, 0.05)
#Nm2 = m2 + normal(0, 0.02)
NOmega = (Omega + normal(0, 5))
#Nsini = float(str(npf.SINI[0]))
#Ncosi = -1. * sqrt(1 - Nsini**2)
Nm2 = float(str(npf.M2[0]))
#print Ncosi, cosi, Ncosi - cosi, Nm2 - m2
p1 = probcal(NOmega, Ncosi, Nm2, npf)
c = m.next()
if p1 > p0:
if c > mixingtime:
MarkovChain.append((NOmega, Ncosi, Nm2))
pb(c - mixingtime)
pf = npf
p0 = p1
cosi, m2, Omega = Ncosi, Nm2, NOmega
if p1 > p:
best = (NOmega, Ncosi, Nm2)
p = p1
dict['BEST'] = best
pickle.dump(dict, open('%s/bestpar.p' % cwd, 'w'))
else:
t = uniform(0, 1, 1)[0]
if t < p1 / p0:
if c > mixingtime:
MarkovChain.append((NOmega, Ncosi, Nm2))
pb(c - mixingtime)
pf = npf
p0 = p1
cosi, m2, Omega = Ncosi, Nm2, NOmega
else:
if c > mixingtime:
MarkovChain.append((Omega, cosi, m2))
#print MarkovChain
print best
print '%d new points generated.' % len(MarkovChain)
#OldChain = pickle.load(open('MChain.p','r'))['Chain']
#dict['Chain'] = OldChain + MarkovChain
#dict['Chain'] = MarkovChain
#os.rmdir(tmpdir)
os.chdir(cwd)
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)
def mcmc(Chain, runtime, MarkovChain, mixingtime=1000, stepsize=1, seed=0 ):
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)
touchparfile(parfile, NITS=1)
pf = PARfile(parfile)
#chisq, dof = tempofit(parfile, toafile = toafile, pulsefile = pulsefile)
pf.matrix(toafile)
pf.freezeall()
pf.thawall('JUMP')
p0 = probcal(pf)
pmax = p0
ThisChain = []
c = 0
randomlist = uniform(0,1,stepsize)
while c <= mixingtime + runtime:
c+=1
npf = pf.randomnew(stepsize=stepsize)
#randomnew(npf, stepsize) #only use this for 1713
p1 = probcal(npf)
if c % 30 == 0:pb(c)
if c > mixingtime:
t = randomlist[c-mixingtime-1]
if t < exp(p1-p0):
Chain.Chain.append([npf.__dict__[p][0] for p in plist] + [ npf.chisq])
pf = npf
p0 = p1
if p1 > pmax:
pmax = p1
bestpar['BEST'] = [npf.__dict__[p][0] for p in plist] + [ npf.chisq]
pickle.dump(bestpar, open('%s/bestpar.p' % cwd, 'wb', 0), protocol=2)
else:
Chain.Chain.append([pf.__dict__[p][0] for p in plist] + [ npf.chisq])
if c % (100+(seed%100)) == 0:
MarkovChain.extend(Chain.Chain)
ThisChain.extend(Chain.Chain)
Chain.Chain = [] #empty the list
#try:
TC = np.array(ThisChain)
dit = {'Chain':TC}
pid = str(os.getpid())
try:
os.remove(cwd+'/MChain.p'+pid)
except:pass
f = open(cwd+'/MChain.p'+pid, 'wb', 0)
pickle.dump(dit, f, protocol=2)
f.flush()
f.close()
del dit
del TC
MarkovChain.extend(Chain.Chain)
os.chdir(cwd)
def mcmc(Chain, runtime, mixingtime=1000):
#mixingtime = 1000
#runtime = 50000
pb = progressBar(maxValue = runtime)
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))
motifile(toafile, cwd, tmpdir)
dict = pickle.load(open('%s/bestpar.p' % cwd, 'r'))
Omega, cosi, m2 = dict['BEST']
MarkovChain = Chain
pf = PARfile(parfile)
pf.thawall()
pf.write()
pf.matrix(toafile)
pf.freezeall()
pf.write()
p0 = probcal(Omega, cosi, m2, pf)
p = p0
best = (Omega, cosi, m2 )
#dict = {'BEST':best}
#pickle.dump(dict, open('bestpar.p', 'w'))
n = count()
m = count()
while n.next() < mixingtime + runtime:
npf = pf.randomnew()
Ncosi = cosi + uniform(-0.05, 0.05)
#Nm2 = m2 + normal(0, 0.02)
NOmega = (Omega + normal(0, 5))
#Nsini = float(str(npf.SINI[0]))
#Ncosi = -1. * sqrt(1 - Nsini**2)
Nm2 = float(str(npf.M2[0]))
#print Ncosi, cosi, Ncosi - cosi, Nm2 - m2
p1 = probcal(NOmega, Ncosi, Nm2, npf)
c = m.next()
if p1 > p0:
if c > mixingtime:
MarkovChain.append((NOmega, Ncosi, Nm2 ))
pb(c - mixingtime)
pf = npf
p0 = p1
cosi, m2, Omega = Ncosi, Nm2, NOmega
if p1 > p:
best = (NOmega, Ncosi, Nm2)
p = p1
dict['BEST'] = best
pickle.dump(dict, open('%s/bestpar.p' % cwd, 'w'))
else:
t = uniform(0,1,1)[0]
if t < p1/p0:
if c > mixingtime:
MarkovChain.append((NOmega, Ncosi, Nm2 ))
pb(c - mixingtime)
pf = npf
p0 = p1
cosi, m2, Omega = Ncosi, Nm2, NOmega
else:
if c > mixingtime:
MarkovChain.append((Omega, cosi, m2))
#print MarkovChain
print best
print '%d new points generated.' %len(MarkovChain)
#OldChain = pickle.load(open('MChain.p','r'))['Chain']
#dict['Chain'] = OldChain + MarkovChain
#dict['Chain'] = MarkovChain
#os.rmdir(tmpdir)
os.chdir(cwd)
