def main(argv=None): parser = argparse.ArgumentParser( description= "PINT tool for MCMC optimization of timing models using event data from multiple sources." ) parser.add_argument("eventfiles", help="Specify a file listing all event files") parser.add_argument("parfile", help="par file to read model from") parser.add_argument("--ft2", help="Path to FT2 file.", default=None) parser.add_argument("--nwalkers", help="Number of MCMC walkers (def 200)", type=int, default=200) parser.add_argument( "--burnin", help="Number of MCMC steps for burn in (def 100)", type=int, default=100, ) parser.add_argument( "--nsteps", help="Number of MCMC steps to compute (def 1000)", type=int, default=1000, ) parser.add_argument("--minMJD", help="Earliest MJD to use (def 54680)", type=float, default=54680.0) parser.add_argument("--maxMJD", help="Latest MJD to use (def 57250)", type=float, default=57250.0) parser.add_argument("--phs", help="Starting phase offset [0-1] (def is to measure)", type=float) parser.add_argument("--phserr", help="Error on starting phase", type=float, default=0.03) parser.add_argument( "--minWeight", help="Minimum weight to include (def 0.05)", type=float, default=0.05, ) parser.add_argument( "--wgtexp", help= "Raise computed weights to this power (or 0.0 to disable any rescaling of weights)", type=float, default=0.0, ) parser.add_argument( "--testWeights", help="Make plots to evalute weight cuts?", default=False, action="store_true", ) parser.add_argument( "--initerrfact", help= "Multiply par file errors by this factor when initializing walker starting values", type=float, default=0.1, ) parser.add_argument( "--priorerrfact", help= "Multiple par file errors by this factor when setting gaussian prior widths", type=float, default=10.0, ) parser.add_argument( "--samples", help="Pickle file containing samples from a previous run", default=None, ) global nwalkers, nsteps, ftr args = parser.parse_args(argv) parfile = args.parfile if args.ft2 is not None: # Instantiate FermiObs once so it gets added to the observatory registry FermiObs(name="Fermi", ft2name=args.ft2) nwalkers = args.nwalkers burnin = args.burnin nsteps = args.nsteps if burnin >= nsteps: log.error("burnin must be < nsteps") sys.exit(1) nbins = 256 # For likelihood calculation based on gaussians file outprof_nbins = 256 # in the text file, for pygaussfit.py, for instance minMJD = args.minMJD maxMJD = args.maxMJD # Usually set by coverage of IERS file minWeight = args.minWeight wgtexp = args.wgtexp # Read in initial model modelin = pint.models.get_model(parfile) # Set the target coords for automatic weighting if necessary if "ELONG" in modelin.params: tc = SkyCoord( modelin.ELONG.quantity, modelin.ELAT.quantity, frame="barycentrictrueecliptic", ) else: tc = SkyCoord(modelin.RAJ.quantity, modelin.DECJ.quantity, frame="icrs") eventinfo = load_eventfiles(args.eventfiles, tcoords=tc, minweight=minWeight, minMJD=minMJD, maxMJD=maxMJD) nsets = len(eventinfo["toas"]) log.info("Total number of events:\t%d" % np.array([len(t.table) for t in eventinfo["toas"]]).sum()) log.info("Total number of datasets:\t%d" % nsets) funcs = {"prob": lnlikelihood_prob, "resid": lnlikelihood_resid} lnlike_funcs = [None] * nsets wlist = [None] * nsets gtemplates = [None] * nsets # Loop over all TOA sets for i in range(nsets): # Determine lnlikelihood function for this set try: lnlike_funcs[i] = funcs[eventinfo["lnlikes"][i]] except: raise ValueError("%s is not a recognized function" % eventinfo["lnlikes"][i]) # Load in weights ts = eventinfo["toas"][i] if eventinfo["weightcol"][i] is not None: if eventinfo["weightcol"][i] == "CALC": weights = np.asarray([x["weight"] for x in ts.table["flags"]]) log.info( "Original weights have min / max weights %.3f / %.3f" % (weights.min(), weights.max())) # Rescale the weights, if requested (by having wgtexp != 0.0) if wgtexp != 0.0: weights **= wgtexp wmx, wmn = weights.max(), weights.min() # make the highest weight = 1, but keep min weight the same weights = wmn + ((weights - wmn) * (1.0 - wmn) / (wmx - wmn)) for ii, x in enumerate(ts.table["flags"]): x["weight"] = weights[ii] weights = np.asarray([x["weight"] for x in ts.table["flags"]]) log.info( "There are %d events, with min / max weights %.3f / %.3f" % (len(weights), weights.min(), weights.max())) else: weights = None log.info("There are %d events, no weights are being used." % ts.ntoas) wlist[i] = weights # Load in templates tname = eventinfo["templates"][i] if tname == "none": continue if tname[-6:] == "pickle" or tname == "analytic": # Analytic template try: gtemplate = cPickle.load(file(tname)) except: phases = (modelin.phase(ts)[1]).astype(np.float64) phases[phases < 0] += 1 * u.dimensionless_unscaled gtemplate = lctemplate.get_gauss2() lcf = lcfitters.LCFitter(gtemplate, phases, weights=wlist[i]) lcf.fit(unbinned=False) cPickle.dump( gtemplate, file("%s_template%d.pickle" % (jname, i), "wb"), protocol=2, ) phases = (modelin.phase(ts)[1]).astype(np.float64) phases[phases < 0] += 1 * u.dimensionless_unscaled lcf = lcfitters.LCFitter(gtemplate, phases.value, weights=wlist[i], binned_bins=200) lcf.fit_position(unbinned=False) lcf.fit(overall_position_first=True, estimate_errors=False, unbinned=False) for prim in lcf.template: prim.free[:] = False lcf.template.norms.free[:] = False else: # Binned template gtemplate = read_gaussfitfile(tname, nbins) gtemplate /= gtemplate.mean() gtemplates[i] = gtemplate # Set the priors on the parameters in the model, before # instantiating the emcee_fitter # Currently, this adds a gaussian prior on each parameter # with width equal to the par file uncertainty * priorerrfact, # and then puts in some special cases. # *** This should be replaced/supplemented with a way to specify # more general priors on parameters that need certain bounds phs = 0.0 if args.phs is None else args.phs sampler = EmceeSampler(nwalkers) ftr = CompositeMCMCFitter( eventinfo["toas"], modelin, sampler, lnlike_funcs, templates=gtemplates, weights=wlist, phs=phs, phserr=args.phserr, minMJD=minMJD, maxMJD=maxMJD, ) fitkeys, fitvals, fiterrs = ftr.get_fit_keyvals() # Use this if you want to see the effect of setting minWeight if args.testWeights: log.info("Checking H-test vs weights") ftr.prof_vs_weights(use_weights=True) ftr.prof_vs_weights(use_weights=False) sys.exit() ftr.phaseogram(plotfile=ftr.model.PSR.value + "_pre.png") like_start = ftr.lnlikelihood(ftr, ftr.get_parameters()) log.info("Starting Pulse Likelihood:\t%f" % like_start) # Set up the initial conditions for the emcee walkers ndim = ftr.n_fit_params if args.samples is None: pos = None else: chains = cPickle.load(file(args.samples)) chains = np.reshape(chains, [nwalkers, -1, ndim]) pos = chains[:, -1, :] ftr.fit_toas(nsteps, pos=pos, priorerrfact=args.priorerrfact, errfact=args.initerrfact) def plot_chains(chain_dict, file=False): npts = len(chain_dict) fig, axes = plt.subplots(npts, 1, sharex=True, figsize=(8, 9)) for ii, name in enumerate(chain_dict.keys()): axes[ii].plot(chain_dict[name], color="k", alpha=0.3) axes[ii].set_ylabel(name) axes[npts - 1].set_xlabel("Step Number") fig.tight_layout() if file: fig.savefig(file) plt.close() else: plt.show() plt.close() chains = sampler.chains_to_dict(ftr.fitkeys) plot_chains(chains, file=ftr.model.PSR.value + "_chains.png") # Make the triangle plot. samples = sampler.sampler.chain[:, burnin:, :].reshape( (-1, ftr.n_fit_params)) try: import corner fig = corner.corner( samples, labels=ftr.fitkeys, bins=50, truths=ftr.maxpost_fitvals, plot_contours=True, ) fig.savefig(ftr.model.PSR.value + "_triangle.png") plt.close() except ImportError: pass # Make a phaseogram with the 50th percentile values # ftr.set_params(dict(zip(ftr.fitkeys, np.percentile(samples, 50, axis=0)))) # Make a phaseogram with the best MCMC result ftr.set_parameters(ftr.maxpost_fitvals) ftr.phaseogram(plotfile=ftr.model.PSR.value + "_post.png") plt.close() # Write out the par file for the best MCMC parameter est f = open(ftr.model.PSR.value + "_post.par", "w") f.write(ftr.model.as_parfile()) f.close() # Print the best MCMC values and ranges ranges = map( lambda v: (v[1], v[2] - v[1], v[1] - v[0]), zip(*np.percentile(samples, [16, 50, 84], axis=0)), ) log.info("Post-MCMC values (50th percentile +/- (16th/84th percentile):") for name, vals in zip(ftr.fitkeys, ranges): log.info("%8s:" % name + "%25.15g (+ %12.5g / - %12.5g)" % vals) # Put the same stuff in a file f = open(ftr.model.PSR.value + "_results.txt", "w") f.write("Post-MCMC values (50th percentile +/- (16th/84th percentile):\n") for name, vals in zip(ftr.fitkeys, ranges): f.write("%8s:" % name + " %25.15g (+ %12.5g / - %12.5g)\n" % vals) f.write("\nMaximum likelihood par file:\n") f.write(ftr.model.as_parfile()) f.close() import cPickle cPickle.dump(samples, open(ftr.model.PSR.value + "_samples.pickle", "wb"))
def test_sampler(): r = [] for i in range(2): random.seed(0) numpy.random.seed(0) s = numpy.random.mtrand.RandomState(0) parfile = join(datadir, "PSRJ0030+0451_psrcat.par") eventfile = join( datadir, "J0030+0451_P8_15.0deg_239557517_458611204_ft1weights_GEO_wt.gt.0.4.fits", ) gaussianfile = join(datadir, "templateJ0030.3gauss") weightcol = "PSRJ0030+0451" minWeight = 0.9 nwalkers = 10 nsteps = 1 nbins = 256 phs = 0.0 model = pint.models.get_model(parfile) tl = fermi.load_Fermi_TOAs(eventfile, weightcolumn=weightcol, minweight=minWeight) ts = toa.TOAs(toalist=tl) # Introduce a small error so that residuals can be calculated ts.table["error"] = 1.0 ts.filename = eventfile ts.compute_TDBs() ts.compute_posvels(ephem="DE421", planets=False) weights, _ = ts.get_flag_value("weight", as_type=float) weights = np.array(weights) template = read_gaussfitfile(gaussianfile, nbins) template /= template.mean() sampler = EmceeSampler(nwalkers) fitter = MCMCFitterBinnedTemplate(ts, model, sampler, template=template, weights=weights, phs=phs) fitter.sampler.random_state = s # phases = fitter.get_event_phases() # maxbin, like_start = marginalize_over_phase(phases, template, # weights=fitter.weights, # minimize=True, # showplot=True) # fitter.fitvals[-1] = 1.0 - maxbin[0] / float(len(template)) # fitter.set_priors(fitter, 10) pos = fitter.sampler.get_initial_pos( fitter.fitkeys, fitter.fitvals, fitter.fiterrs, 0.1, minMJD=fitter.minMJD, maxMJD=fitter.maxMJD, ) # pos = fitter.clip_template_params(pos) fitter.sampler.initialize_sampler(fitter.lnposterior, fitter.n_fit_params) fitter.sampler.run_mcmc(pos, nsteps) # fitter.fit_toas(maxiter=nsteps, pos=None) # fitter.set_parameters(fitter.maxpost_fitvals) # fitter.phaseogram() # samples = sampler.sampler.chain[:, 10:, :].reshape((-1, fitter.n_fit_params)) # r.append(np.random.randn()) r.append(sampler.sampler.chain[0]) assert_array_equal(r[0], r[1])
def main(argv=None): parser = argparse.ArgumentParser(description="PINT tool for MCMC optimization of timing models using event data.") parser.add_argument("eventfile",help="event file to use") parser.add_argument("parfile",help="par file to read model from") parser.add_argument("gaussianfile",help="gaussian file that defines template") parser.add_argument("--ft2",help="Path to FT2 file.",default=None) parser.add_argument("--weightcol",help="name of weight column (or 'CALC' to have them computed",default=None) parser.add_argument("--nwalkers",help="Number of MCMC walkers (def 200)",type=int, default=200) parser.add_argument("--burnin",help="Number of MCMC steps for burn in (def 100)", type=int, default=100) parser.add_argument("--nsteps",help="Number of MCMC steps to compute (def 1000)", type=int, default=1000) parser.add_argument("--minMJD",help="Earliest MJD to use (def 54680)",type=float, default=54680.0) parser.add_argument("--maxMJD",help="Latest MJD to use (def 57250)",type=float, default=57250.0) parser.add_argument("--phs",help="Starting phase offset [0-1] (def is to measure)",type=float) parser.add_argument("--phserr",help="Error on starting phase",type=float, default=0.03) parser.add_argument("--minWeight",help="Minimum weight to include (def 0.05)", type=float,default=0.05) parser.add_argument("--wgtexp", help="Raise computed weights to this power (or 0.0 to disable any rescaling of weights)", type=float, default=0.0) parser.add_argument("--testWeights",help="Make plots to evalute weight cuts?", default=False,action="store_true") parser.add_argument("--doOpt",help="Run initial scipy opt before MCMC?", default=False,action="store_true") parser.add_argument("--initerrfact",help="Multiply par file errors by this factor when initializing walker starting values",type=float,default=0.1) parser.add_argument("--priorerrfact",help="Multiple par file errors by this factor when setting gaussian prior widths",type=float,default=10.0) parser.add_argument("--usepickle",help="Read events from pickle file, if available?", default=False,action="store_true") global nwalkers, nsteps, ftr args = parser.parse_args(argv) eventfile = args.eventfile parfile = args.parfile gaussianfile = args.gaussianfile weightcol = args.weightcol if args.ft2 is not None: # Instantiate FermiObs once so it gets added to the observatory registry FermiObs(name='Fermi',ft2name=args.ft2) nwalkers = args.nwalkers burnin = args.burnin nsteps = args.nsteps if burnin >= nsteps: log.error('burnin must be < nsteps') sys.exit(1) nbins = 256 # For likelihood calculation based on gaussians file outprof_nbins = 256 # in the text file, for pygaussfit.py, for instance minMJD = args.minMJD maxMJD = args.maxMJD # Usually set by coverage of IERS file minWeight = args.minWeight do_opt_first = args.doOpt wgtexp = args.wgtexp # Read in initial model modelin = pint.models.get_model(parfile) # The custom_timing version below is to manually construct the TimingModel # class, which allows it to be pickled. This is needed for parallelizing # the emcee call over a number of threads. So far, it isn't quite working # so it is disabled. The code above constructs the TimingModel class # dynamically, as usual. #modelin = custom_timing(parfile) # Remove the dispersion delay as it is unnecessary #modelin.delay_funcs['L1'].remove(modelin.dispersion_delay) # Set the target coords for automatic weighting if necessary if 'ELONG' in modelin.params: tc = SkyCoord(modelin.ELONG.quantity,modelin.ELAT.quantity, frame='barycentrictrueecliptic') else: tc = SkyCoord(modelin.RAJ.quantity,modelin.DECJ.quantity,frame='icrs') target = tc if weightcol=='CALC' else None # TODO: make this properly handle long double if not args.usepickle or (not (os.path.isfile(eventfile+".pickle") or os.path.isfile(eventfile+".pickle.gz"))): # Read event file and return list of TOA objects tl = fermi.load_Fermi_TOAs(eventfile, weightcolumn=weightcol, targetcoord=target, minweight=minWeight) # Limit the TOAs to ones in selected MJD range and above minWeight tl = [tl[ii] for ii in range(len(tl)) if (tl[ii].mjd.value > minMJD and tl[ii].mjd.value < maxMJD and (weightcol is None or tl[ii].flags['weight'] > minWeight))] log.info("There are %d events we will use" % len(tl)) # Now convert to TOAs object and compute TDBs and posvels ts = toa.TOAs(toalist=tl) ts.filename = eventfile ts.compute_TDBs() ts.compute_posvels(ephem="DE421", planets=False) ts.pickle() else: # read the events in as a pickle file picklefile = toa._check_pickle(eventfile) if not picklefile: picklefile = eventfile ts = toa.TOAs(picklefile) if weightcol is not None: if weightcol=='CALC': weights = np.asarray([x['weight'] for x in ts.table['flags']]) log.info("Original weights have min / max weights %.3f / %.3f" % \ (weights.min(), weights.max())) # Rescale the weights, if requested (by having wgtexp != 0.0) if wgtexp != 0.0: weights **= wgtexp wmx, wmn = weights.max(), weights.min() # make the highest weight = 1, but keep min weight the same weights = wmn + ((weights - wmn) * (1.0 - wmn) / (wmx - wmn)) for ii, x in enumerate(ts.table['flags']): x['weight'] = weights[ii] weights = np.asarray([x['weight'] for x in ts.table['flags']]) log.info("There are %d events, with min / max weights %.3f / %.3f" % \ (len(weights), weights.min(), weights.max())) else: weights = None log.info("There are %d events, no weights are being used." % ts.ntoas) # Now load in the gaussian template and normalize it gtemplate = read_gaussfitfile(gaussianfile, nbins) gtemplate /= gtemplate.mean() # Set the priors on the parameters in the model, before # instantiating the emcee_fitter # Currently, this adds a gaussian prior on each parameter # with width equal to the par file uncertainty * priorerrfact, # and then puts in some special cases. # *** This should be replaced/supplemented with a way to specify # more general priors on parameters that need certain bounds phs = 0.0 if args.phs is None else args.phs sampler = EmceeSampler(nwalkers) ftr = MCMCFitterBinnedTemplate(ts, modelin, sampler, template=gtemplate, \ weights=weights, phs=phs, phserr=args.phserr, minMJD=minMJD, maxMJD=maxMJD) fitkeys, fitvals, fiterrs = ftr.get_fit_keyvals() # Use this if you want to see the effect of setting minWeight if args.testWeights: log.info("Checking H-test vs weights") ftr.prof_vs_weights(use_weights=True) ftr.prof_vs_weights(use_weights=False) sys.exit() # Now compute the photon phases and see if we see a pulse phss = ftr.get_event_phases() maxbin, like_start = marginalize_over_phase(phss, gtemplate, weights=ftr.weights, minimize=True, showplot=False) log.info("Starting pulse likelihood: %f" % like_start) if args.phs is None: fitvals[-1] = 1.0 - maxbin[0] / float(len(gtemplate)) if fitvals[-1] > 1.0: fitvals[-1] -= 1.0 if fitvals[-1] < 0.0: fitvals[-1] += 1.0 log.info("Starting pulse phase: %f" % fitvals[-1]) else: log.info("Measured starting pulse phase is %f, but using %f" % \ (1.0 - maxbin / float(len(gtemplate)), args.phs)) fitvals[-1] = args.phs ftr.fitvals[-1] = fitvals[-1] ftr.phaseogram(plotfile=ftr.model.PSR.value+"_pre.png") plt.close() # Write out the starting pulse profile vs, xs = np.histogram(ftr.get_event_phases(), outprof_nbins, \ range=[0,1], weights=ftr.weights) f = open(ftr.model.PSR.value+"_prof_pre.txt", 'w') for x, v in zip(xs, vs): f.write("%.5f %12.5f\n" % (x, v)) f.close() # Try normal optimization first to see how it goes if do_opt_first: result = op.minimize(ftr.minimize_func, np.zeros_like(ftr.fitvals)) newfitvals = np.asarray(result['x']) * ftr.fiterrs + ftr.fitvals like_optmin = -result['fun'] log.info("Optimization likelihood: %f" % like_optmin) ftr.set_params(dict(zip(ftr.fitkeys, newfitvals))) ftr.phaseogram() else: like_optmin = -np.inf # Set up the initial conditions for the emcee walkers. Use the # scipy.optimize newfitvals instead if they are better ndim = ftr.n_fit_params if like_start > like_optmin: pos = None else: pos = [newfitvals + ftr.fiterrs*args.initerrfact*np.random.randn(ndim) for i in range(nwalkers)] pos[0] = ftr.fitvals ftr.fit_toas(maxiter=nsteps, pos=pos, priorerrfact=args.priorerrfact, errfact=args.initerrfact) def plot_chains(chain_dict, file=False): npts = len(chain_dict) fig, axes = plt.subplots(npts, 1, sharex=True, figsize=(8, 9)) for ii, name in enumerate(chain_dict.keys()): axes[ii].plot(chain_dict[name], color="k", alpha=0.3) axes[ii].set_ylabel(name) axes[npts-1].set_xlabel("Step Number") fig.tight_layout() if file: fig.savefig(file) plt.close() else: plt.show() plt.close() chains = sampler.chains_to_dict(ftr.fitkeys) plot_chains(chains, file=ftr.model.PSR.value+"_chains.png") # Make the triangle plot. samples = sampler.sampler.chain[:, burnin:, :].reshape((-1, ftr.n_fit_params)) try: import corner fig = corner.corner(samples, labels=ftr.fitkeys, bins=50, truths=ftr.maxpost_fitvals, plot_contours=True) fig.savefig(ftr.model.PSR.value+"_triangle.png") plt.close() except ImportError: pass # Make a phaseogram with the 50th percentile values #ftr.set_params(dict(zip(ftr.fitkeys, np.percentile(samples, 50, axis=0)))) # Make a phaseogram with the best MCMC result ftr.set_params(dict(zip(ftr.fitkeys[:-1], ftr.maxpost_fitvals[:-1]))) ftr.phaseogram(plotfile=ftr.model.PSR.value+"_post.png") plt.close() # Write out the output pulse profile vs, xs = np.histogram(ftr.get_event_phases(), outprof_nbins, \ range=[0,1], weights=ftr.weights) f = open(ftr.model.PSR.value+"_prof_post.txt", 'w') for x, v in zip(xs, vs): f.write("%.5f %12.5f\n" % (x, v)) f.close() # Write out the par file for the best MCMC parameter est f = open(ftr.model.PSR.value+"_post.par", 'w') f.write(ftr.model.as_parfile()) f.close() # Print the best MCMC values and ranges ranges = map(lambda v: (v[1], v[2]-v[1], v[1]-v[0]), zip(*np.percentile(samples, [16, 50, 84], axis=0))) log.info("Post-MCMC values (50th percentile +/- (16th/84th percentile):") for name, vals in zip(ftr.fitkeys, ranges): log.info("%8s:"%name + "%25.15g (+ %12.5g / - %12.5g)"%vals) # Put the same stuff in a file f = open(ftr.model.PSR.value+"_results.txt", 'w') f.write("Post-MCMC values (50th percentile +/- (16th/84th percentile):\n") for name, vals in zip(ftr.fitkeys, ranges): f.write("%8s:"%name + " %25.15g (+ %12.5g / - %12.5g)\n"%vals) f.write("\nMaximum likelihood par file:\n") f.write(ftr.model.as_parfile()) f.close() import cPickle cPickle.dump(samples, open(ftr.model.PSR.value+"_samples.pickle", "wb"))
def main(argv=None): parser = argparse.ArgumentParser(description="PINT tool for MCMC optimization of timing models using event data from multiple sources.") parser.add_argument("eventfiles",help="Specify a file listing all event files") parser.add_argument("parfile",help="par file to read model from") parser.add_argument("--ft2",help="Path to FT2 file.",default=None) parser.add_argument("--nwalkers",help="Number of MCMC walkers (def 200)",type=int, default=200) parser.add_argument("--burnin",help="Number of MCMC steps for burn in (def 100)", type=int, default=100) parser.add_argument("--nsteps",help="Number of MCMC steps to compute (def 1000)", type=int, default=1000) parser.add_argument("--minMJD",help="Earliest MJD to use (def 54680)",type=float, default=54680.0) parser.add_argument("--maxMJD",help="Latest MJD to use (def 57250)",type=float, default=57250.0) parser.add_argument("--phs",help="Starting phase offset [0-1] (def is to measure)",type=float) parser.add_argument("--phserr",help="Error on starting phase",type=float, default=0.03) parser.add_argument("--minWeight",help="Minimum weight to include (def 0.05)", type=float,default=0.05) parser.add_argument("--wgtexp", help="Raise computed weights to this power (or 0.0 to disable any rescaling of weights)", type=float, default=0.0) parser.add_argument("--testWeights",help="Make plots to evalute weight cuts?", default=False,action="store_true") parser.add_argument("--initerrfact",help="Multiply par file errors by this factor when initializing walker starting values",type=float,default=0.1) parser.add_argument("--priorerrfact",help="Multiple par file errors by this factor when setting gaussian prior widths",type=float,default=10.0) parser.add_argument("--samples",help="Pickle file containing samples from a previous run", default=None) global nwalkers, nsteps, ftr args = parser.parse_args(argv) parfile = args.parfile if args.ft2 is not None: # Instantiate FermiObs once so it gets added to the observatory registry FermiObs(name='Fermi',ft2name=args.ft2) nwalkers = args.nwalkers burnin = args.burnin nsteps = args.nsteps if burnin >= nsteps: log.error('burnin must be < nsteps') sys.exit(1) nbins = 256 # For likelihood calculation based on gaussians file outprof_nbins = 256 # in the text file, for pygaussfit.py, for instance minMJD = args.minMJD maxMJD = args.maxMJD # Usually set by coverage of IERS file minWeight = args.minWeight wgtexp = args.wgtexp # Read in initial model modelin = pint.models.get_model(parfile) # Set the target coords for automatic weighting if necessary if 'ELONG' in modelin.params: tc = SkyCoord(modelin.ELONG.quantity,modelin.ELAT.quantity, frame='barycentrictrueecliptic') else: tc = SkyCoord(modelin.RAJ.quantity,modelin.DECJ.quantity,frame='icrs') eventinfo = load_eventfiles(args.eventfiles, tcoords=tc, minweight=minWeight, minMJD=minMJD, maxMJD=maxMJD) nsets = len(eventinfo['toas']) log.info('Total number of events:\t%d' % np.array([len(t.table) for t in eventinfo['toas']]).sum()) log.info('Total number of datasets:\t%d' % nsets) funcs = {'prob' : lnlikelihood_prob, 'resid' : lnlikelihood_resid} lnlike_funcs = [None] * nsets wlist = [None] * nsets gtemplates = [None] * nsets #Loop over all TOA sets for i in range(nsets): #Determine lnlikelihood function for this set try: lnlike_funcs[i] = funcs[eventinfo['lnlikes'][i]] except: raise ValueError('%s is not a recognized function' % eventinfo['lnlikes'][i]) #Load in weights ts = eventinfo['toas'][i] if eventinfo['weightcol'][i] is not None: if eventinfo['weightcol'][i] == 'CALC': weights = np.asarray([x['weight'] for x in ts.table['flags']]) log.info("Original weights have min / max weights %.3f / %.3f" % \ (weights.min(), weights.max())) # Rescale the weights, if requested (by having wgtexp != 0.0) if wgtexp != 0.0: weights **= wgtexp wmx, wmn = weights.max(), weights.min() # make the highest weight = 1, but keep min weight the same weights = wmn + ((weights - wmn) * (1.0 - wmn) / (wmx - wmn)) for ii, x in enumerate(ts.table['flags']): x['weight'] = weights[ii] weights = np.asarray([x['weight'] for x in ts.table['flags']]) log.info("There are %d events, with min / max weights %.3f / %.3f" % \ (len(weights), weights.min(), weights.max())) else: weights = None log.info("There are %d events, no weights are being used." % ts.ntoas) wlist[i] = weights #Load in templates tname = eventinfo['templates'][i] if tname == 'none': continue if tname[-6:] == 'pickle' or tname == 'analytic': #Analytic template try: gtemplate = cPickle.load(file(tname)) except: phases = (modelin.phase(ts)[1]).astype(np.float64) phases[phases < 0] += 1 * u.cycle gtemplate = lctemplate.get_gauss2() lcf = lcfitters.LCFitter(gtemplate, phases, weights=wlist[i]) lcf.fit(unbinned=False) cPickle.dump(gtemplate, file('%s_template%d.pickle'%(jname, i), 'wb'),protocol=2) phases = (modelin.phase(ts)[1]).astype(np.float64) phases[phases <0] += 1*u.cycle lcf = lcfitters.LCFitter( gtemplate,phases.value,weights=wlist[i],binned_bins=200) lcf.fit_position(unbinned=False) lcf.fit(overall_position_first=True, estimate_errors=False,unbinned=False) for prim in lcf.template: prim.free[:] = False lcf.template.norms.free[:] = False else: #Binned template gtemplate = read_gaussfitfile(tname, nbins) gtemplate /= gtemplate.mean() gtemplates[i] = gtemplate # Set the priors on the parameters in the model, before # instantiating the emcee_fitter # Currently, this adds a gaussian prior on each parameter # with width equal to the par file uncertainty * priorerrfact, # and then puts in some special cases. # *** This should be replaced/supplemented with a way to specify # more general priors on parameters that need certain bounds phs = 0.0 if args.phs is None else args.phs sampler = EmceeSampler(nwalkers) ftr = CompositeMCMCFitter(eventinfo['toas'], modelin, sampler, lnlike_funcs, templates=gtemplates, weights=wlist, phs=phs, phserr=args.phserr, minMJD=minMJD, maxMJD=maxMJD) fitkeys, fitvals, fiterrs = ftr.get_fit_keyvals() # Use this if you want to see the effect of setting minWeight if args.testWeights: log.info("Checking H-test vs weights") ftr.prof_vs_weights(use_weights=True) ftr.prof_vs_weights(use_weights=False) sys.exit() ftr.phaseogram(plotfile=ftr.model.PSR.value+"_pre.png") like_start = ftr.lnlikelihood(ftr, ftr.get_parameters()) log.info('Starting Pulse Likelihood:\t%f' % like_start) # Set up the initial conditions for the emcee walkers ndim = ftr.n_fit_params if args.samples is None: pos = None else: chains = cPickle.load(file(args.samples)) chains = np.reshape(chains, [nwalkers, -1, ndim]) pos = chains[:, -1, :] ftr.fit_toas(nsteps, pos=pos, priorerrfact=args.priorerrfact, errfact=args.initerrfact) def plot_chains(chain_dict, file=False): npts = len(chain_dict) fig, axes = plt.subplots(npts, 1, sharex=True, figsize=(8, 9)) for ii, name in enumerate(chain_dict.keys()): axes[ii].plot(chain_dict[name], color="k", alpha=0.3) axes[ii].set_ylabel(name) axes[npts-1].set_xlabel("Step Number") fig.tight_layout() if file: fig.savefig(file) plt.close() else: plt.show() plt.close() chains = sampler.chains_to_dict(ftr.fitkeys) plot_chains(chains, file=ftr.model.PSR.value+"_chains.png") # Make the triangle plot. samples = sampler.sampler.chain[:, burnin:, :].reshape((-1, ftr.n_fit_params)) try: import corner fig = corner.corner(samples, labels=ftr.fitkeys, bins=50, truths=ftr.maxpost_fitvals, plot_contours=True) fig.savefig(ftr.model.PSR.value+"_triangle.png") plt.close() except ImportError: pass # Make a phaseogram with the 50th percentile values #ftr.set_params(dict(zip(ftr.fitkeys, np.percentile(samples, 50, axis=0)))) # Make a phaseogram with the best MCMC result ftr.set_parameters(ftr.maxpost_fitvals) ftr.phaseogram(plotfile=ftr.model.PSR.value+"_post.png") plt.close() # Write out the par file for the best MCMC parameter est f = open(ftr.model.PSR.value+"_post.par", 'w') f.write(ftr.model.as_parfile()) f.close() # Print the best MCMC values and ranges ranges = map(lambda v: (v[1], v[2]-v[1], v[1]-v[0]), zip(*np.percentile(samples, [16, 50, 84], axis=0))) log.info("Post-MCMC values (50th percentile +/- (16th/84th percentile):") for name, vals in zip(ftr.fitkeys, ranges): log.info("%8s:"%name + "%25.15g (+ %12.5g / - %12.5g)"%vals) # Put the same stuff in a file f = open(ftr.model.PSR.value+"_results.txt", 'w') f.write("Post-MCMC values (50th percentile +/- (16th/84th percentile):\n") for name, vals in zip(ftr.fitkeys, ranges): f.write("%8s:"%name + " %25.15g (+ %12.5g / - %12.5g)\n"%vals) f.write("\nMaximum likelihood par file:\n") f.write(ftr.model.as_parfile()) f.close() import cPickle cPickle.dump(samples, open(ftr.model.PSR.value+"_samples.pickle", "wb"))