Esempio n. 1
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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)
Esempio n. 2
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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)
Esempio n. 3
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#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']
Esempio n. 4
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#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]),
Esempio n. 5
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    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)
Esempio n. 6
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    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)
Esempio n. 7
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    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)
Esempio n. 8
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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)
Esempio n. 9
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    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
Esempio n. 10
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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)
Esempio n. 11
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    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)
Esempio n. 12
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    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)