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from ctools import *
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from gammalib import *
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from math import *
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from cscripts import obsutils
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import os
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import glob
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import sys
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import numpy
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def single_obs(pntdir, tstart=0.0, duration=1800.0, deadc=0.95, \
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emin=0.1, emax=100.0, rad=5.0, \
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irf="South_50h", caldb="prod2", id="000000", instrument="CTA"):
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"""
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Returns a single CTA observation.
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Parameters:
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pntdir - Pointing direction [GSkyDir]
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Keywords:
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tstart - Start time [seconds] (default: 0.0)
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duration - Duration of observation [seconds] (default: 1800.0)
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deadc - Deadtime correction factor (default: 0.95)
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emin - Minimum event energy [TeV] (default: 0.1)
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emax - Maximum event energy [TeV] (default: 100.0)
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rad - ROI radius used for analysis [deg] (default: 5.0)
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irf - Instrument response function (default: cta_dummy_irf)
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caldb - Calibration database path (default: "dummy")
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id - Run identifier (default: "000000")
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instrument - Intrument (default: "CTA")
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"""
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obs_cta = GCTAObservation()
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db = GCaldb()
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if (os.path.isdir(caldb)):
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db.rootdir(caldb)
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else:
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db.open("cta", caldb)
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pnt = GCTAPointing()
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pnt.dir(pntdir)
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obs_cta.pointing(pnt)
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roi = GCTARoi()
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instdir = GCTAInstDir()
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instdir.dir(pntdir)
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roi.centre(instdir)
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roi.radius(rad)
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gti = GGti()
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gti.append(GTime(tstart), GTime(tstart+duration))
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ebounds = GEbounds(GEnergy(emin, "TeV"), \
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GEnergy(emax, "TeV"))
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events = GCTAEventList()
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events.roi(roi)
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events.gti(gti)
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events.ebounds(ebounds)
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obs_cta.events(events)
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obs_cta.response(irf, db)
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obs_cta.ontime(duration)
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obs_cta.livetime(duration*deadc)
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obs_cta.deadc(deadc)
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obs_cta.id(id)
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return obs_cta
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def add_background_model(models,name="Background",instru="CTA",id="",bgd_file="",bgd_sigma=0.0):
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"""
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Add CTA background model to a model container.
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Parameters:
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models - a model container
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bgd_file - file function for background spectral dependence
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bgd_sigma - sigma parameter for the gaussian offset angle dependence of the background rate
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"""
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if bgd_sigma > 0.0:
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bgd_radial = GCTAModelRadialGauss(bgd_sigma)
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if len(bgd_file) > 0:
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bgd_spectrum = GModelSpectralFunc(bgd_file,1.0)
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else:
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pivot_nrj=GEnergy(1.0e6, "MeV")
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bgd_spectrum = GModelSpectralPlaw(1.0e-6, -2.0, pivot_nrj)
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bgd_spectrum["Prefactor"].value(61.8e-6)
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bgd_spectrum["Prefactor"].scale(1.0e-6)
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bgd_spectrum["PivotEnergy"].value(1.0)
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bgd_spectrum["PivotEnergy"].scale(1.0e6)
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bgd_spectrum["Index"].value(-1.85)
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bgd_spectrum["Index"].scale(1.0)
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bgd_model = GCTAModelRadialAcceptance(bgd_radial, bgd_spectrum)
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else:
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pivot_nrj=GEnergy(1.0e6, "MeV")
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bgd_spectrum = GModelSpectralPlaw(1.0e-6, -2.0, pivot_nrj)
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bgd_spectrum["Prefactor"].value(1.0)
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bgd_spectrum["Prefactor"].scale(1.0)
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bgd_spectrum["PivotEnergy"].value(1.0)
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bgd_spectrum["PivotEnergy"].scale(1.0e6)
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bgd_spectrum["Index"].value(0.0)
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bgd_spectrum["Index"].scale(1.0)
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bgd_model = GCTAModelIrfBackground(bgd_spectrum)
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if (len(name) > 0):
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bgd_model.name(name)
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else:
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bgd_model.name("Background")
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if (len(instru) > 0):
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bgd_model.instruments(instru)
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else:
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bgd_model.instruments("CTA")
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if (len(id) > 0):
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bgd_model.ids(id)
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if (len(bgd_file) > 0):
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bgd_model['Normalization'].free()
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else:
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bgd_model['Prefactor'].free()
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bgd_model['Index'].free()
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models.append(bgd_model)
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return models
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def add_source_model(models, name, ra, dec, coord='CEL', \
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skymap='', specfile='', \
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flux=5.7e-16, index=-2.5, pivot=3.0e5, \
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type="point", sigma=1.0, radius=1.0, width=0.1, \
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nnode=0, emin=1.0, emax=10.0, \
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pivot_scale=1e6, flux_scale=1.0e-16):
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"""
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Adds a single point-source with power-law spectrum to a model container.
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The default is crab-like.
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Parameters:
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models - a model container
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name - Unique source name
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ra - RA of source location [deg]
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dec - Declination of source location [deg]
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Keywords:
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flux - Source flux density in 1.0e-16 ph/cm2/s/MeV
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index - Source spectral index
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pivot - Pivot energy in TeV
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type - Source spatial model
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sigma/radius/width - Spatial model parameters
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nnode - Number of nodes (if > 0, spectral model is a node function)
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"""
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if (nnode > 0) and (emin != emax):
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spectrum = GModelSpectralNodes()
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dloge=log10(emax/emin)/nnode
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logemin=math.log10(emin)
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spectrum.reserve(nnode)
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for i in range(nnode):
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enode = GEnergy()
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enode.TeV(10.0**(logemin+(i+0.5)*dloge))
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inode=flux*(enode.MeV()/pivot)**(index)
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spectrum.append(enode, inode)
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spectrum[i*2].scale(pivot_scale)
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spectrum[i*2].fix()
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spectrum[i*2+1].scale(flux_scale)
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spectrum[i*2+1].free()
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elif (len(specfile) > 0):
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spectrum = GModelSpectralFunc(specfile)
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spectrum["Normalization"].value(1.0)
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spectrum["Normalization"].free()
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else:
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pivot_nrj=GEnergy()
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pivot_nrj.TeV(pivot*pivot_scale/1e6)
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spectrum = GModelSpectralPlaw(flux, index,pivot_nrj)
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spectrum["Prefactor"].scale(flux_scale)
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spectrum["Prefactor"].value(flux)
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spectrum["Prefactor"].free()
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spectrum["PivotEnergy"].scale(pivot_scale)
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spectrum["PivotEnergy"].value(pivot)
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spectrum["Index"].value(index)
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spectrum["Index"].scale(1.0)
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spectrum["Index"].free()
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location = GSkyDir()
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if (coord == 'CEL'):
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location.radec_deg(ra, dec)
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else:
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location.lb_deg(ra, dec)
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if (len(skymap) > 0):
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spatial = GModelSpatialDiffuseMap(skymap,1.0)
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source = GModelSky(spatial, spectrum)
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spatial[0].free()
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elif type == "point":
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spatial = GModelSpatialPointSource(location)
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spatial[0].free()
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spatial[1].free()
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source = GModelSky(spatial, spectrum)
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elif type == "gauss":
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radial = GModelSpatialRadialGauss(location, sigma)
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radial[0].free()
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radial[1].free()
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radial[2].free()
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source = GModelSky(radial, spectrum)
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elif type == "disk":
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radial = GModelSpatialRadialDisk(location, radius)
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radial[0].free()
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radial[1].free()
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radial[2].free()
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source = GModelSky(radial, spectrum)
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elif type == "shell":
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radial = GModelSpatialRadialShell(location, radius, width)
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radial[0].free()
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radial[1].free()
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radial[2].free()
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radial[3].free()
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source = GModelSky(radial, spectrum)
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else:
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print "ERROR: Unknown source type '"+type+"'."
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return None
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source.name(name)
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models.append(source)
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return models
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def set_fit_param(model, parname=[], parfit=[], parval=[]):
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"""
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Set the fit parameters for a given model
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Arguments
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- model: a model container
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- parname: array of parameters names
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- parfit: array of fit/fix flags (True=fit,False=fix)
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- parval: array of initial/fix parameters values
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Notes
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- Arrays should have same numbers of elements
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- A way to not set a value but just the fit/fix flag is to pass/leave parval empty
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- If among several parameters some values must be set and others not, separate these and call the function two times
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"""
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if (len(parname) != len(parfit)):
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print "Incorrect input. Fit parameters not set or modified."
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return 0
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else:
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num_toset=len(parname)
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for i in range(model.size()):
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name=model[i].name()
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if (name in parname):
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i_par=parname.index(name)
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if (parfit[i_par]):
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model[name].free()
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else:
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model[name].fix()
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if (len(parval) > 0):
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model[name].value(parval[i_par])
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return model
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def sim(obs, log=False, debug=False, chatter=2, edisp=False, seed=0, nbins=0,
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binsz=0.05, npix=200, proj="TAN", coord="GAL", outfile=""):
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"""
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Simulate events for all observations in the container.
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Parameters:
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obs - Observation container
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Keywords:
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log - Create log file(s)
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debug - Create console dump?
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edisp - Apply energy dispersion?
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seed - Seed value for simulations (default: 0)
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nbins - Number of energy bins (default: 0=unbinned)
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binsz - Pixel size for binned simulation (deg/pixel)
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npix - Number of pixels in X and Y for binned simulation
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"""
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sim = ctobssim(obs)
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sim["seed"] = seed
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sim["edisp"] = edisp
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sim["outevents"] = outfile
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if log:
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sim.logFileOpen()
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if debug:
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sim["debug"] = True
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sim["chatter"] = chatter
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sim.run()
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if nbins > 0:
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emin = None
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emax = None
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for run in sim.obs():
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run_emin = run.events().ebounds().emin().TeV()
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run_emax = run.events().ebounds().emax().TeV()
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if emin == None:
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emin = run_emin
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elif run_emin > emin:
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emin = run_emin
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if emax == None:
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emax = run_emax
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elif run_emax > emax:
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emax = run_emax
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bin = ctbin(sim.obs())
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bin["ebinalg"] = "LOG"
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bin["emin"] = emin
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bin["emax"] = emax
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bin["enumbins"] = nbins
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bin["usepnt"] = True
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bin["nxpix"] = npix
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bin["nypix"] = npix
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bin["binsz"] = binsz
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bin["coordsys"] = coord
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bin["proj"] = proj
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if log:
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bin.logFileOpen()
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if debug:
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bin["debug"] = True
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bin["chatter"] = chatter
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bin.run()
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obs = bin.obs().copy()
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else:
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obs = sim.obs().copy()
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if (len(outfile) > 0):
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sim.save()
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del sim
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return obs
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def make_simple_model(models,name,ra,dec,flux,idx):
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models = add_background_model(models,bgd_sigma=0.0,instru='CTA',id="0001",name="Background left")
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set_fit_param(models["Background left"],parname=['Sigma','Normalization','Prefactor','Index'],parfit=[False,True,True,False])
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models = add_background_model(models,bgd_sigma=0.0,instru='CTA',id="0002",name="Background right")
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set_fit_param(models["Background right"],parname=['Sigma','Normalization','Prefactor','Index'],parfit=[False,True,True,False])
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models = add_source_model(models, name, ra, dec, flux=flux, index=idx, pivot=1.0e6, type="point", pivot_scale=1e6, flux_scale=1.0e-16)
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set_fit_param(models["Test source"],parname=['Prefactor','Index','RA','DEC'],parfit=[True,True,False,False])
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return models
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453
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def make_simple_model_onoff_fit(models,name,ra,dec,flux,idx):
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455
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models = add_background_model(models,bgd_sigma=0.0,instru='CTAOnOff',id="0001",name="Background left")
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set_fit_param(models["Background left"],parname=['Sigma','Normalization','Prefactor','Index'],parfit=[False,True,True,False])
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models = add_background_model(models,bgd_sigma=0.0,instru='CTAOnOff',id="0002",name="Background right")
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set_fit_param(models["Background right"],parname=['Sigma','Normalization','Prefactor','Index'],parfit=[False,True,True,False])
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462
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models = add_source_model(models, name, ra, dec, flux=flux, index=idx, pivot=1.0e6, type="point", pivot_scale=1e6, flux_scale=1.0e-16)
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set_fit_param(models["Test source"],parname=['Prefactor','Index','RA','DEC'],parfit=[True,True,False,False])
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return models
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469
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470
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471
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472
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|
473
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def reset_simple_model(models,ra,dec,flux,idx):
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474
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|
475
|
|
476
|
set_fit_param(models["Background left"],parname=['Sigma','Normalization','Prefactor','Index'],parfit=[False,True,True,False],parval=[4.0,1.0,1.0,0.0])
|
477
|
set_fit_param(models["Background right"],parname=['Sigma','Normalization','Prefactor','Index'],parfit=[False,True,True,False],parval=[4.0,1.0,1.0,0.0])
|
478
|
set_fit_param(models["Test source"],parname=['Prefactor','Index','RA','DEC'],parfit=[True,True,False,False],parval=[flux,idx,ra,dec])
|
479
|
|
480
|
|
481
|
return models
|
482
|
|
483
|
|
484
|
|
485
|
|
486
|
|
487
|
def print_fit_results(obslist):
|
488
|
|
489
|
|
490
|
print "Background left prefactor: %.3e +/- %.3e " % (obslist.models()["Background left"]['Prefactor'].value(),obslist.models()["Background left"]['Prefactor'].error())
|
491
|
print "Background left index: %.3e +/- %.3e " % (obslist.models()["Background left"]['Index'].value(),obslist.models()["Background left"]['Index'].error())
|
492
|
print "Background right prefactor: %.3e +/- %.3e " % (obslist.models()["Background right"]['Prefactor'].value(),obslist.models()["Background right"]['Prefactor'].error())
|
493
|
print "Background right index: %.3e +/- %.3e " % (obslist.models()["Background right"]['Index'].value(),obslist.models()["Background right"]['Index'].error())
|
494
|
print "Source prefactor: %.3e +/- %.3e " % (obslist.models()["Test source"]['Prefactor'].value(),obslist.models()["Test source"]['Prefactor'].error())
|
495
|
print "Source index: %.3e +/- %.3e " % (obslist.models()["Test source"]['Index'].value(),obslist.models()["Test source"]['Index'].error())
|
496
|
print "Fit ended with value %.3e and status %d after %d iterations." % (opt.value(),opt.status(),opt.iter())
|
497
|
|
498
|
|
499
|
return
|
500
|
|
501
|
|
502
|
|
503
|
|
504
|
|
505
|
if __name__ == '__main__':
|
506
|
|
507
|
"""
|
508
|
Aims:
|
509
|
This script tests the ON-OFF analysis functionality of the gammalib
|
510
|
|
511
|
Arguments:
|
512
|
- None
|
513
|
|
514
|
Notes:
|
515
|
- Hard-coded source, observations, and regions (more flexible script later...)
|
516
|
- Approximate method to set OFF regions (works only on equator)
|
517
|
- Have to set up path to data base (db and irf)
|
518
|
- Source flux set as flux density at 1TeV
|
519
|
"""
|
520
|
|
521
|
|
522
|
|
523
|
db="prod2"
|
524
|
irf="South_50h"
|
525
|
bgd=""
|
526
|
rsp_sigma=4.0
|
527
|
bgd_sigma=4.0
|
528
|
duration=1800.0
|
529
|
rad=5.0
|
530
|
|
531
|
emin=0.1
|
532
|
emax=100.0
|
533
|
nbins=9
|
534
|
nnodes=5
|
535
|
|
536
|
name="Test source"
|
537
|
ra=0.0
|
538
|
dec=0.0
|
539
|
flux=5.0e-17
|
540
|
idx=-2.5
|
541
|
dflux=1.5
|
542
|
didx=0.3
|
543
|
|
544
|
onshift=1.0
|
545
|
onsize=0.3
|
546
|
noff=3
|
547
|
|
548
|
chatter=4
|
549
|
seed=10
|
550
|
Check=True
|
551
|
|
552
|
print 'Got params...'
|
553
|
|
554
|
|
555
|
obslist = GObservations()
|
556
|
print 'Create observation container...'
|
557
|
|
558
|
|
559
|
leftdir = GSkyDir()
|
560
|
leftdir.radec_deg(ra+onshift, dec)
|
561
|
obs_left=single_obs(leftdir, tstart=0.0, duration=duration, deadc=0.95, \
|
562
|
emin=emin, emax=emax, rad=rad, \
|
563
|
irf=irf, caldb=db, id="000000", instrument="CTA")
|
564
|
obs_left.name("Left")
|
565
|
obs_left.id("0001")
|
566
|
obslist.append(obs_left)
|
567
|
rightdir = GSkyDir()
|
568
|
rightdir.radec_deg(ra-onshift, dec)
|
569
|
obs_right=single_obs(rightdir, tstart=0.0, duration=duration, deadc=0.95, \
|
570
|
emin=emin, emax=emax, rad=rad, \
|
571
|
irf=irf, caldb=db, id="000000", instrument="CTA")
|
572
|
obs_right.name("Right")
|
573
|
obs_right.id("0002")
|
574
|
obslist.append(obs_right)
|
575
|
print 'Add two offset observations...'
|
576
|
|
577
|
|
578
|
models = GModels()
|
579
|
models_onoff_fit = GModels()
|
580
|
|
581
|
|
582
|
models = make_simple_model(models,name,ra,dec,flux,idx)
|
583
|
models_onoff_fit = make_simple_model_onoff_fit(models_onoff_fit,name,ra,dec,flux*dflux,idx-didx)
|
584
|
|
585
|
obslist.models(models)
|
586
|
obslist.models().save("sim_model.xml")
|
587
|
print 'Added model to the container...'
|
588
|
|
589
|
|
590
|
obslist = sim(obslist, log=False, debug=False, chatter=chatter, edisp=False, seed=seed, nbins=0, outfile='onoff_events.xml')
|
591
|
print 'Made event list...'
|
592
|
|
593
|
|
594
|
ondir = GSkyDir()
|
595
|
ondir.radec_deg(ra,dec)
|
596
|
on = GSkyRegions()
|
597
|
onreg=GSkyRegionCircle(ondir,onsize)
|
598
|
on.append(onreg)
|
599
|
on.save("on_regions.reg")
|
600
|
print 'On regions created...'
|
601
|
|
602
|
|
603
|
offleft = GSkyRegions()
|
604
|
offright = GSkyRegions()
|
605
|
for i in range(noff):
|
606
|
phi=(i+1)*360./(noff+1.0)
|
607
|
|
608
|
offdir = GSkyDir(leftdir)
|
609
|
offdir.rotate_deg(phi-90., onshift)
|
610
|
offreg=GSkyRegionCircle(offdir, onsize)
|
611
|
offleft.append(offreg)
|
612
|
|
613
|
offdir = GSkyDir(rightdir)
|
614
|
offdir.rotate_deg(phi+90.0, onshift)
|
615
|
offreg=GSkyRegionCircle(offdir, onsize)
|
616
|
offright.append(offreg)
|
617
|
|
618
|
offleft.save("off_left_regions.reg")
|
619
|
offright.save("off_right_regions.reg")
|
620
|
print 'Off regions created...'
|
621
|
|
622
|
|
623
|
onofflist = GObservations()
|
624
|
|
625
|
|
626
|
etrue = GEbounds(nbins, GEnergy(emin, "TeV"), GEnergy(emax, "TeV"))
|
627
|
ereco = GEbounds(nbins, GEnergy(emin, "TeV"), GEnergy(emax, "TeV"))
|
628
|
|
629
|
|
630
|
obs_left=obslist[0]
|
631
|
obs_right=obslist[1]
|
632
|
obsl=GCTAOnOffObservation(obs_left, etrue, ereco, on, offleft)
|
633
|
obsl.name("Left")
|
634
|
obsl.id("0001")
|
635
|
onofflist.append(obsl)
|
636
|
obsr=GCTAOnOffObservation(obs_right, etrue, ereco, on, offright)
|
637
|
obsr.name("Right")
|
638
|
obsr.id("0002")
|
639
|
onofflist.append(obsr)
|
640
|
|
641
|
|
642
|
onofflist.save("onoffobservations.xml")
|
643
|
|
644
|
|
645
|
if Check:
|
646
|
print "Observation %s with %.1fs ON time" % (onofflist[0].name(),onofflist[0].ontime())
|
647
|
print "Observation %s with %.1fs ON time" % (onofflist[1].name(),onofflist[1].ontime())
|
648
|
|
649
|
print '\nLeft'
|
650
|
for i in range(nbins):
|
651
|
print "ON counts %d - OFF counts %d" % (onofflist[0].on_spec()[i],onofflist[0].off_spec()[i])
|
652
|
print '\nRight'
|
653
|
for i in range(nbins):
|
654
|
print "ON counts %d - OFF counts %d" % (onofflist[1].on_spec()[i],onofflist[1].off_spec()[i])
|
655
|
print '\n'
|
656
|
|
657
|
|
658
|
models_onoff_fit=reset_simple_model(models_onoff_fit,ra,dec,dflux*flux,idx-didx)
|
659
|
onofflist.models(models_onoff_fit)
|
660
|
|
661
|
|
662
|
log=GLog('fit_onoff.log',True)
|
663
|
log.chatter(chatter)
|
664
|
opt=GOptimizerLM(log)
|
665
|
|
666
|
|
667
|
print '\nON-OFF fitting...'
|
668
|
onofflist.optimize(opt)
|
669
|
onofflist.errors(opt)
|
670
|
print_fit_results(onofflist)
|
671
|
|
672
|
|
673
|
del opt
|
674
|
del log
|
675
|
|
676
|
|
677
|
models=reset_simple_model(models,ra,dec,dflux*flux,idx-didx)
|
678
|
obslist.models(models)
|
679
|
|
680
|
|
681
|
log=GLog('fit_unbinned.log',True)
|
682
|
log.chatter(chatter)
|
683
|
opt=GOptimizerLM(log)
|
684
|
|
685
|
|
686
|
print '\nUnbinned fitting...'
|
687
|
obslist.optimize(opt)
|
688
|
obslist.errors(opt)
|
689
|
print_fit_results(obslist)
|
690
|
|
691
|
|
692
|
del opt
|
693
|
del log
|
694
|
|
695
|
|