def find_offpeak(ft1, name, rad, peaks, pwncat1phase, TSdc):
"""plot light curve using pointlike script in whixh is added a line to plot the edges of the offpulse region
a=minimum of the edge
b=maximum of the edge"""
plc = lc_plotting_func.PulsarLightCurve(ft1, psrname=name, radius=rad, emin=50, emax=300000)
plc.fill_phaseogram()
phases = plc.get_phases()
primitives = []
for peak in peaks:
if isinstance(peak, numbers.Real):
primitives.append(lp.LCLorentzian2(p=[0.4, 0.05, 0.05, peak]))
else:
primitives.append(eval(peak))
lct = lf.LCTemplate(primitives=primitives)
init_template = copy.deepcopy(lct)
print "WARNING: HERE I SHOULD BE USING A WEIGHTED LIGHT CURVE"
lcf = lf.LCFitter(lct, phases)
print lcf
lcf.fit(quick_fit_first=True)
print lcf
lcf.plot()
dom = np.linspace(0, 1, 200)
P.plot(dom, init_template(dom), color="green", lw=1)
# if np.any([isinstance(p, LCLorentzian) or isinstance(p, LCLorentzian2) for p in primitives]):
# TScontamination = 0.1
# else:
# TScontamination = 1
# contamination = 1/(np.sqrt(TSdc)*10)
# contamination = .01*(100/TSdc)**(1./2)
# contamination = .01*(100/TSdc)**(1./3)
# contamination = .01*(100/TSdc)**(1./4)
contamination = 0.01
op = OffPeak(lcf, contamination=contamination)
op.off_peak.axvspan(label="lande", alpha=0.25, color="green")
pwncat1phase.axvspan(label="pwncat1", alpha=0.25, color="blue")
P.legend()
P.title(name)
P.savefig("results_%s.pdf" % name)
# P.savefig('phaseogram_%s.pdf' % name)
global results
results = tolist(
dict(
lande_phase=op.off_peak.tolist(), pwncat1phase=pwncat1phase.tolist(), TSdc=TSdc, contamitation=contamination
)
)
yaml.dump(results, open("results_%s.yaml" % name, "w"))
results["lcf"] = lcf
results["init_template"] = init_template
results["final_template"] = lct
pickle.dump(results, open("results_%s.pickle" % name, "w"))
spec,lis,lisname=get_spec_par(like,liste_source[i])
if liste_source[i]!=name:
for j in range(len(lisname)):
param=like.par_index(liste_source[i],lisname[j])
like.freeze(param)
ul,xx=gtlike_ul(like,args.emin,args.emax,name,sigma=2)
r['gtlike_ul']=ul
# calculate TScutoff ...
#roi.save('roi_%s.dat' % name)
open('results_%s.yaml' % name,'w').write(
yaml.dump(
tolist(results)
)
)
# save stuff out
roi.plot_tsmap(filename='residual_tsmap_0.1_%s.pdf' % name,pixelsize=0.10,size=8)
roi.plot_tsmap(filename='residual_tsmap_0.25_%s.pdf' % name,pixelsize=0.25,size=8)
roi.toRegion('Region_file_%s.reg'%name)
roi.plot_sources(filename='sources_%s.pdf' % name, size=8, label_psf=False)
roi.toXML(filename="srcmodel_res_%s"%name,convert_extended=True)
roi.plot_sed(which=name,filename="sed_%s.png"%name)
def save_results():
open('results_%s.yaml' % name,'w').write(
yaml.dump(tolist(results)))
def find_offpeak(ft1,name,rad,peaks,pwncat1phase):
"""plot light curve using pointlike script in whixh is added a line to plot the edges of the offpulse region
a=minimum of the edge
b=maximum of the edge"""
plc = lc_plotting_func.PulsarLightCurve(ft1,
psrname=name, radius=rad,
emin=50, emax=300000)
plc.fill_phaseogram()
phases = plc.get_phases()
primitives = []
for peak in peaks:
if isinstance(peak,numbers.Real):
primitives.append(lp.LCLorentzian2(p=[0.4,0.05,0.05,peak]))
else:
primitives.append(eval(peak))
lct = lf.LCTemplate(primitives=primitives)
init_template = copy.deepcopy(lct)
lcf = lf.LCFitter(lct,phases)
print lcf
lcf.fit(quick_fit_first=True)
print lcf
lcf.plot()
dom = np.linspace(0,1,200)
P.plot(dom,init_template(dom),color='green',lw=1)
op = OffPeak(lcf)
for a,b in op.off_peak.tolist(dense=False):
P.axvspan(a, b, label='lande', alpha=0.25, color='green')
for a,b in pwncat1phase.tolist(dense=False):
P.axvspan(a, b, label='pwncat1', alpha=0.25, color='blue')
P.legend()
P.title(name)
P.savefig('results_%s.pdf' % name)
#P.savefig('phaseogram_%s.pdf' % name)
global results
results=tolist(
dict(
lande_phase = op.off_peak.tolist(),
pwncat1phase = pwncat1phase,
)
)
yaml.dump(results,
open('results_%s.yaml' % name,'w')
)
results['lcf'] = lcf
results['init_template'] = init_template
results['final_template'] = lct
pickle.dump(
results,
open('results_%s.pickle' % name,'w')
)
def save_results():
open("results_%s.yaml" % name, "w").write(yaml.dump(tolist(results)))
fit()
try:
roi.fit_extension(which=name_mc, bandfits=False, estimate_errors=False)
ts_ext = roi.TS_ext(which=name_mc, bandfits=False)
except Exception, err:
print "\n\n\n\nERROR FITTING EXTENSION: %s\n\n\n" % (str(err))
ts_ext = -99
fit()
ts = roi.TS(which=name_mc, bandfits=False)
source = roi.get_source(which=name_mc)
sigma = source.spatial_model["sigma"]
results_dict[-1]["ts_%s" % fit_irf] = ts
results_dict[-1]["ts_ext_%s" % fit_irf] = ts_ext
results_dict[-1]["sigma_%s" % fit_irf] = sigma
del roi
results_dict = toolbag.tolist(results_dict)
temp = open(results, "w")
temp.write(yaml.dump(results_dict))
temp.close()
shutil.rmtree(tempdir)