def get_toas(evtfile, flags, tcoords=None, minweight=0, minMJD=0, maxMJD=100000): if evtfile[:-3] == 'tim': usepickle = False if 'usepickle' in flags: usepickle = flags['usepickle'] ts = toa.get_TOAs(evtfile, usepickle=False) #Prune out of range MJDs mask = np.logical_or(ts.get_mjds() < minMJD * u.day, ts.get_mjds() > maxMJD * u.day) ts.table.remove_rows(mask) ts.table = ts.table.group_by('obs') else: if 'usepickle' in flags and flags['usepickle']: try: picklefile = toa._check_pickle(evtfile) if not picklefile: picklefile = evtfile ts = toa.TOAs(picklefile) return ts except: pass weightcol = flags['weightcol'] if 'weightcol' in flags else None target = tcoords if weightcol == 'CALC' else None tl = fermi.load_Fermi_TOAs(evtfile, weightcolumn=weightcol, targetcoord=target, minweight=minweight) tl = filter(lambda t: (t.mjd.value > minMJD) and (t.mjd.value < maxMJD), tl) ts = toa.TOAs(toalist=tl) ts.filename = evtfile ts.compute_TDBs() ts.compute_posvels(ephem="DE421", planets=False) ts.pickle() log.info("There are %d events we will use" % len(ts.table)) return ts
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): if len(argv)==3: eventfile, parfile, weightcol = sys.argv[1:] elif len(argv)==2: eventfile, parfile = sys.argv[1:] weightcol=None else: print("usage: htest_optimize eventfile parfile [weightcol]") sys.exit() # Read in initial model modelin = pint.models.get_model(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 (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 where we have IERS corrections for tl = [tl[ii] for ii in range(len(tl)) if (tl[ii].mjd.value < maxMJD and (weightcol is None or tl[ii].flags['weight'] > minWeight))] print("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']]) print("Original weights have min / max weights %.3f / %.3f" % \ (weights.min(), weights.max())) 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']]) print("There are %d events, with min / max weights %.3f / %.3f" % \ (len(weights), weights.min(), weights.max())) else: weights = None print("There are %d events, no weights are being used." % (len(weights))) # Now define the requirements for emcee ftr = emcee_fitter(ts, modelin, weights) # Use this if you want to see the effect of setting minWeight if minWeight == 0.0: print("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() like_start = -1.0*sf_hm(hmw(phss,weights=ftr.weights),logprob=True) print("Starting pulse likelihood:", like_start) ftr.phaseogram(file=ftr.model.PSR.value+"_pre.png") plt.close() ftr.phaseogram() # 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'] print("Optimization likelihood:", 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: # Keep the starting deviations small... pos = [ftr.fitvals + ftr.fiterrs/errfact * np.random.randn(ndim) for ii in range(nwalkers)] # Set starting params with uniform priors to uniform in the prior for param in ["GLPH_1", "GLEP_1", "SINI", "M2", "E", "ECC", "PX", "A1"]: if param in ftr.fitkeys: idx = ftr.fitkeys.index(param) if param=="GLPH_1": svals = np.random.uniform(-0.5, 0.5, nwalkers) elif param=="GLEP_1": svals = np.random.uniform(minMJD+100, maxMJD-100, nwalkers) #svals = 55422.0 + np.random.randn(nwalkers) elif param=="SINI": svals = np.random.uniform(0.0, 1.0, nwalkers) elif param=="M2": svals = np.random.uniform(0.1, 0.6, nwalkers) elif param in ["E", "ECC", "PX", "A1"]: # Ensure all positive svals = np.fabs(ftr.fitvals[idx] + ftr.fiterrs[idx] * np.random.randn(nwalkers)) if param in ["E", "ECC"]: svals[svals>1.0] = 1.0 - (svals[svals>1.0] - 1.0) for ii in range(nwalkers): pos[ii][idx] = svals[ii] else: pos = [newfitvals + ftr.fiterrs/errfact*np.random.randn(ndim) for i in range(nwalkers)] # Set the 0th walker to have the initial pre-fit solution # This way, one walker should always be in a good position pos[0] = ftr.fitvals import emcee #sampler = emcee.EnsembleSampler(nwalkers, ndim, ftr.lnposterior, threads=10) sampler = emcee.EnsembleSampler(nwalkers, ndim, ftr.lnposterior) # The number is the number of points in the chain sampler.run_mcmc(pos, nsteps) def chains_to_dict(names, sampler): chains = [sampler.chain[:,:,ii].T for ii in range(len(names))] return dict(zip(names,chains)) def plot_chains(chain_dict, file=False): np = len(chain_dict) fig, axes = plt.subplots(np, 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[np-1].set_xlabel("Step Number") fig.tight_layout() if file: fig.savefig(file) plt.close() else: plt.show() plt.close() chains = chains_to_dict(ftr.fitkeys, sampler) plot_chains(chains, file=ftr.model.PSR.value+"_chains.png") # Make the triangle plot. try: import corner samples = sampler.chain[:, burnin:, :].reshape((-1, ndim)) fig = corner.corner(samples, labels=ftr.fitkeys, bins=50) fig.savefig(ftr.model.PSR.value+"_triangle.png") plt.close() except: 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, ftr.maxpost_fitvals))) ftr.phaseogram(file=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))) print("Post-MCMC values (50th percentile +/- (16th/84th percentile):") for name, vals in zip(ftr.fitkeys, ranges): print("%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): if len(argv) == 3: eventfile, parfile, weightcol = sys.argv[1:] elif len(argv) == 2: eventfile, parfile = sys.argv[1:] weightcol = None else: print("usage: htest_optimize eventfile parfile [weightcol]") sys.exit() # Read in initial model modelin = pint.models.get_model(parfile) # Remove the dispersion delay as it is unnecessary modelin.delay_funcs.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 ( 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 where we have IERS corrections for tl = [ tl[ii] for ii in range(len(tl)) if ( tl[ii].mjd.value < maxMJD and (weightcol is None or tl[ii].flags["weight"] > minWeight) ) ] print("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"]]) print( "Original weights have min / max weights %.3f / %.3f" % (weights.min(), weights.max()) ) 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"]]) print( "There are %d events, with min / max weights %.3f / %.3f" % (len(weights), weights.min(), weights.max()) ) else: weights = None print("There are %d events, no weights are being used." % (len(weights))) # Now define the requirements for emcee ftr = emcee_fitter(ts, modelin, weights) # Use this if you want to see the effect of setting minWeight if minWeight == 0.0: print("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() like_start = -1.0 * sf_hm(hmw(phss, weights=ftr.weights), logprob=True) print("Starting pulse likelihood:", like_start) ftr.phaseogram(file=ftr.model.PSR.value + "_pre.png") plt.close() ftr.phaseogram() # 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"] print("Optimization likelihood:", 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: # Keep the starting deviations small... pos = [ ftr.fitvals + ftr.fiterrs / errfact * np.random.randn(ndim) for ii in range(nwalkers) ] # Set starting params with uniform priors to uniform in the prior for param in ["GLPH_1", "GLEP_1", "SINI", "M2", "E", "ECC", "PX", "A1"]: if param in ftr.fitkeys: idx = ftr.fitkeys.index(param) if param == "GLPH_1": svals = np.random.uniform(-0.5, 0.5, nwalkers) elif param == "GLEP_1": svals = np.random.uniform(minMJD + 100, maxMJD - 100, nwalkers) # svals = 55422.0 + np.random.randn(nwalkers) elif param == "SINI": svals = np.random.uniform(0.0, 1.0, nwalkers) elif param == "M2": svals = np.random.uniform(0.1, 0.6, nwalkers) elif param in ["E", "ECC", "PX", "A1"]: # Ensure all positive svals = np.fabs( ftr.fitvals[idx] + ftr.fiterrs[idx] * np.random.randn(nwalkers) ) if param in ["E", "ECC"]: svals[svals > 1.0] = 1.0 - (svals[svals > 1.0] - 1.0) for ii in range(nwalkers): pos[ii][idx] = svals[ii] else: pos = [ newfitvals + ftr.fiterrs / errfact * np.random.randn(ndim) for i in range(nwalkers) ] # Set the 0th walker to have the initial pre-fit solution # This way, one walker should always be in a good position pos[0] = ftr.fitvals import emcee # sampler = emcee.EnsembleSampler(nwalkers, ndim, ftr.lnposterior, threads=10) sampler = emcee.EnsembleSampler(nwalkers, ndim, ftr.lnposterior) # The number is the number of points in the chain sampler.run_mcmc(pos, nsteps) def chains_to_dict(names, sampler): chains = [sampler.chain[:, :, ii].T for ii in range(len(names))] return dict(zip(names, chains)) def plot_chains(chain_dict, file=False): np = len(chain_dict) fig, axes = plt.subplots(np, 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[np - 1].set_xlabel("Step Number") fig.tight_layout() if file: fig.savefig(file) plt.close() else: plt.show() plt.close() chains = chains_to_dict(ftr.fitkeys, sampler) plot_chains(chains, file=ftr.model.PSR.value + "_chains.png") # Make the triangle plot. try: import corner samples = sampler.chain[:, burnin:, :].reshape((-1, ndim)) fig = corner.corner(samples, labels=ftr.fitkeys, bins=50) 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, ftr.maxpost_fitvals))) ftr.phaseogram(file=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)), ) print("Post-MCMC values (50th percentile +/- (16th/84th percentile):") for name, vals in zip(ftr.fitkeys, ranges): print("%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"))
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))] print "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']]) print "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))
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 where we have IERS corrections for tl = [tl[ii] for ii in range(len(tl)) if (tl[ii].mjd.value < maxMJD and (weightcol is None or tl[ii].flags['weight'] > minWeight))] print "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']]) print "Original weights have min / max weights %.3f / %.3f" % \ (weights.min(), weights.max()) 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]