def imrefspec(fpmlist, idetlist, auxil,
cxbp={'pl': 1.29, 'ecut': 41.13, 'norm': 0.002353},
texp=1.0e9):
queue = []
# Check for RMF files
for idet in idetlist:
idet = int(idet)
if idet not in (0, 1, 2, 3):
continue
for ab in fpmlist:
if ab not in ('A', 'B'):
continue
rmffile = 'det%d%s.rmf' % (idet, ab)
if os.path.exists(rmffile):
queue.append((idet, ab))
print('Queued FPM%s det%d: %s' %
(ab, idet, rmffile))
else:
print('Skipped FPM%s det%d: %s not found.' %
(ab, idet, rmffile))
if len(queue) == 0:
print('Nothing to be done.')
return True
m = xspec.Model('cutoffpl')
m.cutoffpl.PhoIndex = cxbp['pl']
m.cutoffpl.HighECut = cxbp['ecut']
m.cutoffpl.norm = cxbp['norm']
for idet, ab in queue:
# If there is existing spectrum loaded, RMF and ARF in FakeSettings
# will be ignored (the 'from existing spectra' flow). We need to do
# the 'from scratch' flow.
xspec.AllData.clear()
fakesettings_ap = xspec.FakeitSettings(
response='det%d%s.rmf' % (idet, ab),
arf='%s/be.arf' % auxil,
exposure=texp,
correction=0.0,
fileName='aperbgd%s_det%d.pha' % (ab, idet)
)
fakesettings_fcxb = xspec.FakeitSettings(
response='det%d%s.rmf' % (idet, ab),
arf='%s/fcxb%s.arf' % (auxil, ab),
exposure=texp,
correction=0.0,
fileName='fcxbbgd%s_det%d.pha' % (ab, idet)
)
xspec.AllData.fakeit(
nSpectra=2,
settings=[fakesettings_ap, fakesettings_fcxb],
applyStats=False,
filePrefix='')
return True
def fake_data(**kwargs):
bkg = kwargs['bkg'] + "{1}"
rsp = kwargs['rsp']
xs.AllData.clear()
xs.AllModels.clear()
m = xs.Model('ALP')
for i in range(len(N)):
m.ALP.norm.values = N[i]
m.ALP.norm.frozen = True
xs.AllData.clear()
fs = xs.FakeitSettings(background=bkg,
response=rsp,
fileName='fake_spec_%s.fak' % i)
sl = 2000 * [fs] # making 2000 simulations
xs.AllData.fakeit(2000, sl)
if kwargs['show']:
import matplotlib.pyplot as plt
xs.AllData.clear()
for i in range(1, 2001):
xs.AllData += "fake_spec_20.fak{%i}" % i
xs.AllModels.clear()
m = xs.Model("ALP")
xs.Fit.statMethod = "pgstat"
xs.Plot.device = "/xs"
xs.Plot.xAxis = "MeV"
xs.Plot.add = True
xs.Plot.background = True
xs.Plot.xLog = True
xs.Plot.yLog = True
xs.Plot.show()
xs.Plot("ufspec")
xerr_uf = np.zeros((2001, len(E_LLE)))
yerr_uf = np.zeros((2001, len(E_LLE)))
spec_uf = np.zeros((2001, len(E_LLE)))
for i in range(1, 2001):
xerr_uf[i, :] = np.asarray(xs.Plot.xErr(i))
yerr_uf[i, :] = np.asarray(xs.Plot.yErr(i))
spec_uf[i, :] = np.asarray(xs.Plot.y(i))
for i in range(1, 2001):
plt.errorbar(E_LLE,
spec_uf[i, :],
xerr=xerr_uf[i, :],
yerr=yerr_uf[i, :])
plt.plot(E_LLE,
SED * N[20],
'r',
zorder=2001,
label='Theoretical spectrum')
plt.xlabel('Energy, [MeV]')
plt.ylabel('[photons cm$^{-2}$ s$^{-1}$ MeV$^{-1}$] ')
plt.xscale('log')
plt.grid()
plt.legend()
plt.show()
def xspec_flux(x):
Temperature, z_, ind_file, El_, Eh_, Z_ = x
L_factor, norm, abundance = 1e44, 1.0, Z_
E_norm = [El_, Eh_]
xspec.Model("apec")
m1 = xspec.AllModels(1)
m1.setPars(Temperature, abundance, z_, norm)
filename = mydirectory+"/parameter_files/xspec/" + str(ind_file) + ".fak"
fs1 = xspec.FakeitSettings(response, ancillary, fileName = filename, \
exposure = 100000.0)
xspec.AllData.fakeit(1,fs1,applyStats=False, noWrite = True)
spec1 = xspec.AllData(1)
xspec.AllModels.setEnergies(".01 100. 1000 log")
xspec.AllModels.calcLumin("%f %f %f" %(E_norm[0],E_norm[1],z_))
L = spec1.lumin[0]
xspec.AllModels.calcFlux("%f %f" %(E_norm[0],E_norm[1]))
F = spec1.flux[0]
new_norm = L_factor / (L * 1e44)
m1.setPars({4:new_norm})
xspec.AllModels.calcLumin("%f %f %f" %(E_norm[0],E_norm[1],z_))
L_new = spec1.lumin[0]
xspec.AllModels.calcFlux("%f %f" %(E_norm[0],E_norm[1]))
F_new = spec1.flux[0]
spec1.ignore("**-%f %f-**" %(E_norm[0],E_norm[1]))
rate = spec1.rate[3]
xspec.AllData.clear()
return [F_new, rate]
def test_OGIP_response_against_xspec():
# Test for various photon indexes
for index in [-0.5, 0.0, 0.5, 1.5, 2.0, 3.0, 4.0]:
print("Processing index %s" % index)
# First reset xspec
xspec.AllData.clear()
# Create a model in XSpec
mo = xspec.Model("po")
# Change the default value for the photon index
# (remember that in XSpec the definition of the powerlaw is norm * E^(-PhoIndex),
# so PhoIndex is positive normally. This is the opposite of astromodels.
mo.powerlaw.PhoIndex = index
mo.powerlaw.norm = 12.2
# Now repeat the same in 3ML
# Generate the astromodels function and set it to the same values as the XSpec power law
# (the pivot in XSpec is set to 1). Remember also that the definition in xspec has the
# sign of the photon index opposite
powerlaw = Powerlaw()
powerlaw.piv = 1.0
powerlaw.index = -mo.powerlaw.PhoIndex.values[0]
powerlaw.K = mo.powerlaw.norm.values[0]
# Exploit the fact that the power law integral is analytic
powerlaw_integral = Powerlaw()
# Remove transformation
powerlaw_integral.K._transformation = None
powerlaw_integral.K.bounds = (None, None)
powerlaw_integral.index = powerlaw.index.value + 1
powerlaw_integral.K = old_div(powerlaw.K.value, (powerlaw.index.value + 1))
powerlaw_integral.display()
integral_function = lambda e1, e2: powerlaw_integral(e2) - powerlaw_integral(e1)
# Now check that the two convoluted model give the same number of counts in each channel
# Fake a spectrum so we can actually compute the convoluted model
# Get path of response file
rsp_file = str(get_path_of_data_file("ogip_test_gbm_n6.rsp"))
fs1 = xspec.FakeitSettings(
rsp_file, exposure=1.0, fileName="_fake_spectrum.pha"
)
xspec.AllData.fakeit(noWrite=True, applyStats=False, settings=fs1)
# Get the expected counts
xspec_counts = mo.folded(1)
# Now get the convolution from 3ML
rsp = OGIPResponse(rsp_file)
rsp.set_function(integral_function)
threeML_counts = rsp.convolve()
# Compare them
assert np.allclose(xspec_counts, threeML_counts)
# Now do the same with a matrix with a ARF
# First reset xspec
xspec.AllData.clear()
# Then load rsp and arf in XSpec
rsp_file = str(get_path_of_data_file("ogip_test_xmm_pn.rmf"))
arf_file = str(get_path_of_data_file("ogip_test_xmm_pn.arf"))
fs1 = xspec.FakeitSettings(
rsp_file, arf_file, exposure=1.0, fileName="_fake_spectrum.pha"
)
xspec.AllData.fakeit(noWrite=True, applyStats=False, settings=fs1)
# Get the expected counts
xspec_counts = mo.folded(1)
# Now get the convolution from 3ML
rsp = OGIPResponse(rsp_file, arf_file=arf_file)
rsp.set_function(integral_function)
threeML_counts = rsp.convolve()
# Compare them
assert np.allclose(xspec_counts, threeML_counts)
def test_response_against_xspec():
# Make a response and write to a FITS OGIP file
matrix, mc_energies, ebounds = get_matrix_elements()
rsp = InstrumentResponse(matrix, ebounds, mc_energies)
temp_file = "__test.rsp"
rsp.to_fits(temp_file, "TEST", "TEST", overwrite=True)
# Test for various photon indexes
for index in np.linspace(-2.0, 2.0, 10):
if index == 1.0:
# This would make the integral of the power law different, so let's just
# skip it
continue
# First reset xspec
xspec.AllData.clear()
# Create a model in XSpec
mo = xspec.Model("po")
# Change the default value for the photon index
# (remember that in XSpec the definition of the powerlaw is norm * E^(-PhoIndex),
# so PhoIndex is positive normally. This is the opposite of astromodels.
mo.powerlaw.PhoIndex = index
mo.powerlaw.norm = 12.2
# Now repeat the same in 3ML
# Generate the astromodels function and set it to the same values as the XSpec power law
# (the pivot in XSpec is set to 1). Remember also that the definition in xspec has the
# sign of the photon index opposite
powerlaw = Powerlaw()
powerlaw.piv = 1.0
powerlaw.index = -mo.powerlaw.PhoIndex.values[0]
powerlaw.K = mo.powerlaw.norm.values[0]
# Exploit the fact that the power law integral is analytic
powerlaw_integral = Powerlaw()
powerlaw_integral.K._transformation = None
powerlaw_integral.K.bounds = (None, None)
powerlaw_integral.index = powerlaw.index.value + 1
powerlaw_integral.K = old_div(powerlaw.K.value, (powerlaw.index.value + 1))
integral_function = lambda e1, e2: powerlaw_integral(e2) - powerlaw_integral(e1)
# Now check that the two convoluted model give the same number of counts in each channel
# Fake a spectrum so we can actually compute the convoluted model
# Get path of response file
fs1 = xspec.FakeitSettings(
temp_file, exposure=1.0, fileName="_fake_spectrum.pha"
)
xspec.AllData.fakeit(noWrite=True, applyStats=False, settings=fs1)
# Get the expected counts
xspec_counts = mo.folded(1)
# Now get the convolution from 3ML
rsp.set_function(integral_function)
threeML_counts = rsp.convolve()
# Compare them
assert np.allclose(xspec_counts, threeML_counts)
os.remove(temp_file)
assert model.flux[0] > 0
# clean up
xspec.AllData.clear()
xspec.AllModels.clear()
try:
# this will fail as expected: model does not exist anymore
model.flux
assert False
except Exception:
pass
# second version:
# try again with fakeit to include response
model = xspec.Model('powerlaw')
ero_settings = xspec.FakeitSettings(
arf='arf01_100nmAl_200nmPI_sdtq.fits',
response='rmf01_sdtq.fits',
exposure=10000,
background='bkg1000000.fak',
fileName='test.fak',
)
xspec.AllData.fakeit(1, settings=[ero_settings])
xspec.AllModels.calcFlux("2.3 6.0")
print(model.flux)
# we expect a model count rate > 0 as before, but this fails
assert model.flux[0] > 0