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, 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)
#pf = PARfile('./1713.ext.PBDOT.par')
#pf = PARfile('./1713.noguppi.par.t2.Jul30')
#pf = PARfile('./1713.noguppi.par.mcmc')
#pf = PARfile('./1713.ext.FD.par.t2')
#pf = PARfile('./1713.DMX.final.par.t2')
#pf = PARfile('./1713.DMX.noOMDOT.par.t2')
#pf = PARfile('./1909.Paul.par')
#pf = PARfile('./1909-3744.par')
#pf = PARfile('./1713.Oct.mcmc.par')
#pf = PARfile('./1713.Nov.test.par')
#pf = PARfile('./mcmc.par')
#pf = PARfile('./mcmcresult.par')
#pf = PARfile('./J1713+0747.par')
#pf = PARfile('./1713.final.par')
#pf = PARfile('./Oct.T2.par')
pf = PARfile('./Feb.T2.RN.par')
def Pbdot_exc(psr, GdotOG, KD):
GdotOG = GdotOG/secperday/365.24218967
M1 = psr['M1']
M2 = psr['M2']
q = M1/M2
return -2.* GdotOG * (1 - (1 + M2/2./(M1+M2))*psr['Sp']) - 4. * PI**2 *Tsun*M2 * KD * psr['Sp']**2 * q/(q+1)/psr['Pb']**2
J1713 = {'M1':M1(pf), 'M2':float(pf.M2[0]), 'Sp':Sp(pf), 'Pb':float(pf.PB[0])*secperday}
#print J1713
#print Pbdot_exc(J1713, 6.e-13, 2.e-4)
#print '1:', Pbdot_exc(J1713, 1.52789743984e-14, 0.00163158650413)
J1012 = {'M2':0.16, 'M1':0.16*10.5, 'Pb':0.60467271355*secperday}
J1012['Sp'] = 0.1*J1012['M1']
#pf = PARfile('./1713.ext.PBDOT.par')
#pf = PARfile('./1713.noguppi.par.t2.Jul30')
#pf = PARfile('./1713.noguppi.par.mcmc')
#pf = PARfile('./1713.ext.FD.par.t2')
#pf = PARfile('./1713.DMX.final.par.t2')
#pf = PARfile('./1713.DMX.noOMDOT.par.t2')
#pf = PARfile('./1909.Paul.par')
#pf = PARfile('./1909-3744.par')
#pf = PARfile('./1713.Oct.mcmc.par')
#pf = PARfile('./1713.Nov.test.par')
#pf = PARfile('./mcmc.par')
#pf = PARfile('./mcmcresult.par')
#pf = PARfile('./J1713+0747.par')
#pf = PARfile('./1713.final.par')
#pf = PARfile('./Oct.T2.par')
pf = PARfile('./Feb.T2.RN.par')
def Pbdot_exc(psr, GdotOG, KD):
GdotOG = GdotOG / secperday / 365.24218967
M1 = psr['M1']
M2 = psr['M2']
q = M1 / M2
return -2. * GdotOG * (
1 - (1 + M2 / 2. / (M1 + M2)) * psr['Sp']
) - 4. * PI**2 * Tsun * M2 * KD * psr['Sp']**2 * q / (q + 1) / psr['Pb']**2
J1713 = {
'M1': M1(pf),
'M2': float(pf.M2[0]),
parser.add_option("-m", '--mixing', type='int', nargs=1, dest='mixing', help="number of mixing steps", default=1000)
parser.add_option("-p", '--parallel', type='int', nargs=1, dest='paral', help="number of parallel processes")
parser.add_option("-s", '--seed', type='int', nargs=1, dest='seed', default=int(os.getpid()), help="random number seed")
parser.add_option("-z", '--stepsize', type='float', nargs=1, dest='stepsize', default=1., help="step size")
(options, args) = parser.parse_args(args=sys.argv[1:])
print options
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)
err1 = A_x * fac1
err2 = A_z * sqrt(fac1**2 + fac2**2 + fac3**2)
err = sqrt(err1**2 + err2**2) * float(Pb)
return val, err
if __name__ == '__main__':
from datatools.tempo import PARfile
#pf = PARfile('1909-3744.par')
#pf = PARfile('./J1909.zww.par')
#pf = PARfile('./J1713+0747.par')
#pf = PARfile('./1713.Sep.par')
#pf = PARfile('./mcmcresult.par')
#pf = PARfile('./1713.Apr.par')
pf = PARfile('./1713.all.par')
if pf.PBDOT[0] > Decimal('1.e-10'):#tempo 1 units
pf.PBDOT[0] *= Decimal('1.e-12')
pf.PBDOT[1] *= Decimal('1.e-12')
from round import shortform as SF
Shl = Shlkovskii(pf)
Gal = Pbdot_Gal(pf)
GW = Pbdot_GW(pf)
if pf.__dict__.has_key('PSR'):
print pf.PSR
else:
print pf.PSRJ
print 'PBdot(shlkovskii):', SF(Shl)
print 'PBdot(Galaxtic):' , SF(Gal)
print 'PBdot(GW):', SF(GW)
err1 = A_x * fac1
err2 = A_z * sqrt(fac1**2 + fac2**2 + fac3**2)
err = sqrt(err1**2 + err2**2) * float(Pb)
return val, err
if __name__ == '__main__':
from datatools.tempo import PARfile
#pf = PARfile('1909-3744.par')
#pf = PARfile('./J1909.zww.par')
#pf = PARfile('./J1713+0747.par')
#pf = PARfile('./1713.Sep.par')
#pf = PARfile('./mcmcresult.par')
#pf = PARfile('./1713.Apr.par')
pf = PARfile('./Oct.T2.par')
if pf.PBDOT[0] > Decimal('1.e-10'):#tempo 1 units
pf.PBDOT[0] *= Decimal('1.e-12')
pf.PBDOT[1] *= Decimal('1.e-12')
from round import shortform as SF
Shl = Shlkovskii(pf)
Gal = Pbdot_Gal(pf)
GW = Pbdot_GW(pf)
if pf.__dict__.has_key('PSR'):
print pf.PSR
else:
print pf.PSRJ
print 'PBdot(shlkovskii):', SF(Shl)
print 'PBdot(Galaxtic):' , SF(Gal)
print 'PBdot(GW):', SF(GW)
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)
parser.add_option("-m", '--mixing', type='int', nargs=1, dest='mixing', help="number of mixing steps", default=1000)
parser.add_option("-p", '--parallel', type='int', nargs=1, dest='paral', help="number of parallel processes")
parser.add_option("-s", '--seed', type='int', nargs=1, dest='seed', default=int(os.getpid()), help="random number seed")
parser.add_option("-z", '--stepsize', type='float', nargs=1, dest='stepsize', default=1., help="step size")
(options, args) = parser.parse_args(args=sys.argv[1:])
print options
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)
pf.freezeall()
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
def run(s):
seed(s) # assigning different initial seed for the random number generator in different threads.
#steps = 50000
with MChain() as Chain:
#print steps, tmpdir
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)
dec = Dec(pf.DECJ[0])
pos = coord.SkyCoord(str(ra) + ' ' + str(dec))
l = pos.galactic.l.rad
b = pos.galactic.b.rad
L = pos.galactic.l.deg
B = pos.galactic.b.deg
Pb = float(pf.PB[0] * secperday)
d = float(1 / pf.PX[0])
pf.DIST = d
print 'L, B, D:', L, B, d
#output = getoutput('./GalPotMcMillan2016/calcGalPdot.exe %s %s %s' % (L, B, d))
output = getoutput('./GalPotMcMillan2016/calcGalAcc.exe %s %s %s' %
(L, B, d))
print output, np.sqrt(sum([float(f)**2 for f in output.split(' ')]))
px = np.random.normal(float(pf.PX[0]), float(pf.PX[1]), 10000)
res = np.array(spamit(McMillanPot, [(L, B, 1 / x) for x in px]))
val = res.mean() * Pb
fac2 = 8 / 240 #Omega_0
fac3 = 0.16 / 8.34 #R0 Reid et al 2014
fac1 = res.std() / res.mean()
err = abs(sqrt(fac1**2 + fac2**2 + fac3**2) * val)
return val, err
if __name__ == '__main__':
from datatools.tempo import tempofit, tempo2fit, touchparfile, uniquename, PARfile
pf = PARfile('1713.cut.par')
print Pbdot_Gal(pf)
err1 = A_x * fac1
err2 = A_z * sqrt(fac1**2 + fac2**2 + fac3**2)
err = sqrt(err1**2 + err2**2) * float(Pb)
return val, err
if __name__ == '__main__':
from datatools.tempo import PARfile
#pf = PARfile('1909-3744.par')
#pf = PARfile('./J1909.zww.par')
#pf = PARfile('./J1713+0747.par')
#pf = PARfile('./1713.Sep.par')
#pf = PARfile('./mcmcresult.par')
#pf = PARfile('./1713.Apr.par')
#pf = PARfile('./Oct.T2.par')
pf = PARfile('./Feb.T2.RN.par')
if pf.PBDOT[0] > Decimal('1.e-10'): #tempo 1 units
pf.PBDOT[0] *= Decimal('1.e-12')
pf.PBDOT[1] *= Decimal('1.e-12')
from round import shortform as SF
Shl = Shlkovskii(pf)
Gal = Pbdot_Gal(pf)
GW = Pbdot_GW(pf)
if pf.__dict__.has_key('PSR'):
print pf.PSR
else:
print pf.PSRJ
print 'PBdot(shlkovskii):', SF(Shl)
print 'PBdot(Galaxtic):', SF(Gal)
print 'PBdot(GW):', SF(GW)
