def __init__(self, params,datadir=None,**kwargs):
'''
Constructor
'''
self.datadir = datadir
self.spectrum = FileFunction(normalization=1, file=os.path.join(datadir,params['spectrum']))
self.center = SkyDir(194.9531, 27.9807,SkyDir.EQUATORIAL)
self.spatialModel = TabulatedProfile(os.path.join(datadir,params['profile']),center=self.center)
self.convolvedTemplate = None
if 'name' in params:
self.name = params['name']
self.__dict__.update(kwargs)
class CRModel(object):
'''
Note a CR model is defined by a spectrum, a spatial model and a unique name.
'''
def __init__(self, params,datadir=None,**kwargs):
'''
Constructor
'''
self.datadir = datadir
self.spectrum = FileFunction(normalization=1, file=os.path.join(datadir,params['spectrum']))
self.center = SkyDir(194.9531, 27.9807,SkyDir.EQUATORIAL)
self.spatialModel = TabulatedProfile(os.path.join(datadir,params['profile']),center=self.center)
self.convolvedTemplate = None
if 'name' in params:
self.name = params['name']
self.__dict__.update(kwargs)
def __repr__(self):
'''
the string representation of the object
'''
return self.name
def quickConvolution(self,parfile,verbose=True,cleanup=True):
'''
performs a quick gtsrcmap call on the model
'''
if not self.convolvedTemplate is None:
print 'Warning, convolved template attribute already set, will overwrite model definition.'
templateName = "%s_spatial.fits"%self.name
self.spatialModel.save_template(templateName,npix=500)
os.environ['SRC_TEMPLATE']=os.path.abspath(templateName)
cmd,parDict = par2cmd(parfile,returnDict=True)
# replace the datadir
while "$(DATADIR)" in cmd:
cmd = cmd.replace("$(DATADIR)",os.path.abspath(os.getenv("DATADIR")))
print cmd
pts = subprocess.Popen(cmd,shell=True)
pts.communicate()
if cleanup:
os.remove(templateName)
# add more cleanup files.
# need to cleanup stuff...
ofile = parDict['outfile']
while '"' in ofile:
ofile = ofile.replace('"',"")
self.convolvedTemplate = os.path.abspath(ofile)
return self.convolvedTemplate
def findRadius(self,scaled_srcmap,algorithm='delta_average'):
'''
depending on the algorithm, return the disk radius that matches best
'''
print 'INFO: Algorithm: %s'%algorithm
def val2str(v): return ("%1.1f"%v).replace(".","d")
def str2val(s): return float(s.replace("d","."))
def average(a,ebounds=None):
return np.average(a,axis=0)
def weighted_energy(a,ebounds,index=-2.0):
def scaled_slice(slice,e,e0):
return slice*np.power(e/e0,index)
e_min, e_max = ebounds['E_MIN'],ebounds['E_MAX']
energies = np.power(10,np.log10(e_min)+(np.log10(e_max)-np.log10(e_min))/2.)
e0 = e_min[0]
Z_a = np.sum(np.array([scaled_slice(a[i],e,e0) for i,e in enumerate(e_min)]),axis=0)
return Z_a
# implement other algorithms as we need.
def weighted_energy_spectra(a,ebounds):
def scaled_slice(a,e):
return a*self.spectrum(e)#add integral here.
e_min, e_max = ebounds['E_MIN'],ebounds['E_MAX']
energies = np.power(10,np.log10(e_min)+(np.log10(e_max)-np.log10(e_min))/2.)
e0 = e_min[0]
Z_a = np.sum(np.array([scaled_slice(a[i],e) for i,e in enumerate(e_min)]),axis=0)
return Z_a
def weighted_energy_spectra_integral(a,ebounds):
def scaled_slice(a,e,e0):
return a*self.spectrum.i_flux(e0,e,e_weight=0,cgs=False)#add integral here.
e_min, e_max = ebounds['E_MIN'],ebounds['E_MAX']
energies = np.power(10,np.log10(e_min)+(np.log10(e_max)-np.log10(e_min))/2.)
e0 = e_min[0]
Z_a = np.sum(np.array([scaled_slice(a[i],e,e0) for i,e in enumerate(e_min)]),axis=0)
return Z_a
if self.convolvedTemplate is None:
raise Exception("can't find convolved template. Have you run the convolution yet?")
print 'loading convolved template %s'%self.convolvedTemplate
target, tHeader = pyfits.getdata(self.convolvedTemplate,"target",header=True)
ebounds = pyfits.getdata(self.convolvedTemplate,"ebounds")
target2D = eval(algorithm)(np.array(target,dtype=float),ebounds=ebounds)
target_profile = array2DtoProfile(target2D, pixelsize=np.abs(tHeader['CDELT1']), interpolate=True)
target_profile_interp = target_profile[1]
matching = None
disk_radii = np.arange(0.1,1.1,0.1)
foms = np.nan*np.ones( len(disk_radii) )
for i in range(0,10):
val = (.1+float(i)/10.)
reference, rHeader = pyfits.getdata(scaled_srcmap,val2str(val),header=True)
ref2D = eval(algorithm)(np.array(reference,dtype=float),ebounds=ebounds)
ref_profile = array2DtoProfile(ref2D, pixelsize=np.abs(rHeader['CDELT1']), interpolate=True)
x = target_profile[0]
ref_profile_interp = ref_profile[1]
fractional_difference = np.abs(ref_profile_interp(x) - target_profile_interp(x))/ref_profile_interp(x)
fom = np.sum(fractional_difference)
foms[i] = fom
print '*DEBUG* val, rel difference (sum): ',val, fom
row = np.array([val,fom])
if matching is None:
matching = row
else:
matching = np.vstack((matching,row))
# best key
# minimize the fractional difference
return val2str(matching.T[0][np.argmin(matching.T[1])]),(disk_radii,foms)
