def test_proj_bubble(self):
self.run_thread('proj_bubble')
# Fit -- Results from reminimize
self.assertEqualWithinTol(self.locals['mek1'].kt.val, 17.8849, 1e-2)
self.assertEqualWithinTol(self.locals['mek1'].norm.val, 4.15418e-06,
1e-2)
# Covar
self.assertEqualWithinTol(ui.get_covar_results().parmins[0],
-0.328832, 1e-2)
self.assertEqualWithinTol(ui.get_covar_results().parmins[1],
-8.847916e-07, 1e-2)
self.assertEqualWithinTol(ui.get_covar_results().parmaxes[0],
0.328832, 1e-2)
self.assertEqualWithinTol(ui.get_covar_results().parmaxes[1],
8.847916e-07, 1e-2)
# Proj -- Upper bound of kT can't be found
self.assertEqualWithinTol(ui.get_proj_results().parmins[0],
-12.048069, 1e-2)
self.assertEqualWithinTol(ui.get_proj_results().parmins[1],
-9.510913e-07, 1e-2)
self.assertEqual(ui.get_proj_results().parmaxes[0], None)
self.assertEqualWithinTol(ui.get_proj_results().parmaxes[1],
2.403640e-06, 1e-2)
def test_proj_bubble(self):
self.run_thread('proj_bubble')
# Fit -- Results from reminimize
self.assertEqualWithinTol(self.locals['mek1'].kt.val, 17.8849, 1e-2)
self.assertEqualWithinTol(self.locals['mek1'].norm.val, 4.15418e-06,
1e-2)
# Covar
self.assertEqualWithinTol(ui.get_covar_results().parmins[0], -0.328832,
1e-2)
self.assertEqualWithinTol(ui.get_covar_results().parmins[1],
-8.847916e-07, 1e-2)
self.assertEqualWithinTol(ui.get_covar_results().parmaxes[0], 0.328832,
1e-2)
self.assertEqualWithinTol(ui.get_covar_results().parmaxes[1],
8.847916e-07, 1e-2)
# Proj -- Upper bound of kT can't be found
self.assertEqualWithinTol(ui.get_proj_results().parmins[0], -12.048069,
1e-2)
self.assertEqualWithinTol(ui.get_proj_results().parmins[1],
-9.510913e-07, 1e-2)
self.assertEqual(ui.get_proj_results().parmaxes[0], None)
self.assertEqualWithinTol(ui.get_proj_results().parmaxes[1],
2.403640e-06, 1e-2)
def test_bug_276(make_data_path):
ui.load_pha(make_data_path('3c273.pi'))
ui.set_model('polynom1d.p1')
ui.fit()
ui.covar()
scal = ui.get_covar_results().parmaxes
ui.sample_flux(ui.get_model_component('p1'), 0.5, 1, num=5, correlated=False, scales=scal)
def test_background():
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
kT_sim = 1.0
Z_sim = 0.0
norm_sim = 4.0e-2
nH_sim = 0.04
redshift = 0.01
exp_time = (200., "ks")
area = (1000., "cm**2")
wcs = create_dummy_wcs()
abs_model = WabsModel(nH_sim)
events = EventList.create_empty_list(exp_time, area, wcs)
spec_model = TableApecModel(0.05, 12.0, 5000, thermal_broad=False)
spec = spec_model.return_spectrum(kT_sim, Z_sim, redshift, norm_sim)
new_events = events.add_background(spec_model.ebins, spec, prng=prng,
absorb_model=abs_model)
new_events = ACIS_I(new_events, rebin=False, convolve_psf=False, prng=prng)
new_events.write_spectrum("background_evt.pi", clobber=True)
os.system("cp %s ." % new_events.parameters["ARF"])
os.system("cp %s ." % new_events.parameters["RMF"])
load_user_model(mymodel, "wapec")
add_user_pars("wapec", ["nH", "kT", "metallicity", "redshift", "norm"],
[0.01, 4.0, 0.2, redshift, norm_sim*0.8],
parmins=[0.0, 0.1, 0.0, -20.0, 0.0],
parmaxs=[10.0, 20.0, 10.0, 20.0, 1.0e9],
parfrozen=[False, False, False, True, False])
load_pha("background_evt.pi")
set_stat("cstat")
set_method("simplex")
ignore(":0.5, 8.0:")
set_model("wapec")
fit()
set_covar_opt("sigma", 1.6)
covar()
res = get_covar_results()
assert np.abs(res.parvals[0]-nH_sim) < res.parmaxes[0]
assert np.abs(res.parvals[1]-kT_sim) < res.parmaxes[1]
assert np.abs(res.parvals[2]-Z_sim) < res.parmaxes[2]
assert np.abs(res.parvals[3]-norm_sim) < res.parmaxes[3]
os.chdir(curdir)
shutil.rmtree(tmpdir)
def test_proj_bubble(parallel, clean_astro_ui, fix_xspec, run_thread):
# How sensitive are the results to the change from bcmc to vern
# made in XSPEC 12.10.1? It looks like the mekal best-fit
# temperature can jump from ~17.9 to 18.6, so require bcmc
# in this test (handled by fix_xspec).
#
# Note that the error on kT is large, so we can expect that
# changes to the system could change these results. In particular,
# the covariance errors on kt are < 1 but from other error
# analysis they are > 10 or even unbound, so it is likely that the
# covariance error results can change significantly.
#
# The fit results change in XSPEC 12.10.0 since the mekal model
# was changed (FORTRAN to C++). A 1% difference is used for the
# parameter ranges from covar and proj (matches the tolerance for
# the fit results). Note that this tolerance has been relaced to
# 10% for the kT errors, as there is a significant change seen
# with different XSPEC versions for the covariance results.
#
# fit_results is unused
def cmp_thread(fit_results, mek1, covarerr):
assert covarerr[0] == approx(0, rel=1e-4)
assert covarerr[1] == approx(8.74608e-07, rel=1e-3)
assert mek1.kt.val == approx(17.8849, rel=1e-2)
assert mek1.norm.val == approx(4.15418e-06, rel=1e-2)
check_thread(run_thread, 'proj_bubble', parallel, cmp_thread, ['mek1'])
# Covar
#
# TODO: should this check that parmaxes is -1 * parmins instead?
covar = ui.get_covar_results()
assert covar.parmins[0] == approx(-0.653884, rel=0.1)
assert covar.parmins[1] == approx(-8.94436e-07, rel=0.01)
assert covar.parmaxes[0] == approx(0.653884, rel=0.1)
assert covar.parmaxes[1] == approx(8.94436e-07, rel=0.01)
# Proj -- Upper bound of kT can't be found
#
proj = ui.get_proj_results()
assert proj.parmins[0] == approx(-12.048069, rel=0.01)
assert proj.parmins[1] == approx(-9.510913e-07, rel=0.01)
assert proj.parmaxes[0] is None
assert proj.parmaxes[1] == approx(2.403640e-06, rel=0.01)
# Conf
#
conf = ui.get_conf_results()
assert conf.parmins[0] == approx(-12.1073, rel=0.01)
assert conf.parmins[1] == approx(-9.5568e-07, rel=0.01)
assert conf.parmaxes[0] is None
assert conf.parmaxes[1] == approx(2.39937e-06, rel=0.01)
def test_bug_276(make_data_path):
ui.load_pha(make_data_path('3c273.pi'))
ui.set_model('polynom1d.p1')
ui.fit()
ui.covar()
scal = ui.get_covar_results().parmaxes
ui.sample_flux(ui.get_model_component('p1'),
0.5,
1,
num=5,
correlated=False,
scales=scal)
def run_hspec_fit(self, model, thres_low, thres_high):
"""Run the gammapy.hspec fit
Parameters
----------
model : str
Sherpa model
thres_high : `~gammapy.spectrum.Energy`
Upper threshold of the spectral fit
thres_low : `~gammapy.spectrum.Energy`
Lower threshold of the spectral fit
"""
log.info("Starting HSPEC")
import sherpa.astro.ui as sau
from ..hspec import wstat
from sherpa.models import PowLaw1D
if model == 'PL':
p1 = PowLaw1D('p1')
p1.gamma = 2.2
p1.ref = 1e9
p1.ampl = 6e-19
else:
raise ValueError('Desired Model is not defined')
thres = thres_low.to('keV').value
emax = thres_high.to('keV').value
sau.freeze(p1.ref)
sau.set_conf_opt("max_rstat", 100)
list_data = []
for obs in self.observations:
datid = obs.phafile.parts[-1][7:12]
sau.load_data(datid, str(obs.phafile))
sau.notice_id(datid, thres, emax)
sau.set_source(datid, p1)
list_data.append(datid)
wstat.wfit(list_data)
sau.covar()
fit_val = sau.get_covar_results()
fit_attrs = ('parnames', 'parvals', 'parmins', 'parmaxes')
fit = dict((attr, getattr(fit_val, attr)) for attr in fit_attrs)
fit = self.apply_containment(fit)
sau.clean()
self.fit = fit
def test_proj_bubble(run_thread):
# How sensitive are the results to the change from bcmc to vern
# made in XSPEC 12.10.1? It looks like the mekal best-fit
# temperature can jump from ~17.9 to 18.6, so require bcmc
# in this test.
#
xspec.set_xsxsect('bcmc')
models = run_thread('proj_bubble')
fit_results = ui.get_fit_results()
covarerr = sqrt(fit_results.extra_output['covar'].diagonal())
assert covarerr[0] == approx(0, rel=1e-4)
assert covarerr[1] == approx(8.74608e-07, rel=1e-2)
# Fit -- Results from reminimize
assert models['mek1'].kt.val == approx(17.8849, rel=1e-2)
assert models['mek1'].norm.val == approx(4.15418e-06, rel=1e-2)
# Fit -- Results from reminimize
# The fit results change in XSPEC 12.10.0 since the mekal model
# was changed (FORTRAN to C++). A 1% difference is used for the
# parameter ranges from covar and proj (matches the tolerance for
# the fit results). Note that this tolerance has been relaced to
# 10% for the kT errors, as there is a significant change seen
# with different XSPEC versions for the covariance results.
#
# Covar
#
# TODO: should this check that parmaxes is -1 * parmins instead?
covar = ui.get_covar_results()
assert covar.parmins[0] == approx(-0.328832, rel=0.1)
assert covar.parmins[1] == approx(-8.847916e-7, rel=0.01)
assert covar.parmaxes[0] == approx(0.328832, rel=0.1)
assert covar.parmaxes[1] == approx(8.847916e-7, rel=0.01)
# Proj -- Upper bound of kT can't be found
#
proj = ui.get_proj_results()
assert proj.parmins[0] == approx(-12.048069, rel=0.01)
assert proj.parmins[1] == approx(-9.510913e-07, rel=0.01)
assert proj.parmaxes[1] == approx(2.403640e-06, rel=0.01)
assert proj.parmaxes[0] is None
def get_fluxes(ids, z, fluxes, fitstats, nsims=10):
shp.covar()
dataScale = shp.get_covar_results().parmaxes
if fitstats.rstat <= 3.0:
if all(d is None for d in dataScale):
fx = _calc_fluxes(ids, z=z, nsims=nsims)
fluxes["fx"], fluxes["fx_obs"], fluxes["fx_int"] = fx[:, 0]
else:
fx = _calc_fluxes(ids, z=z, dataScale=dataScale, nsims=nsims)
fluxes["fx"], fluxes["fx_ErrMin"], fluxes["fx_ErrMax"] = fx[0, :]
fluxes["fx_obs"], fluxes["fx_obs_ErrMin"], fluxes[
"fx_obs_ErrMax"] = fx[1, :]
fluxes["fx_int"], fluxes["fx_int_ErrMin"], fluxes[
"fx_int_ErrMax"] = fx[2, :]
else:
fx = _calc_fluxes(ids, z=z, nsims=nsims)
fluxes["fx"], fluxes["fx_obs"], fluxes["fx_int"] = fx[:, 0]
return fluxes
def do_beta_model(source, v_field, em_field):
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
ds = source.ds
A = 3000.
exp_time = 1.0e5
redshift = 0.05
nH_sim = 0.02
apec_model = TableApecModel(0.1, 11.5, 20000, thermal_broad=False)
abs_model = TBabsModel(nH_sim)
sphere = ds.sphere("c", (0.5, "Mpc"))
kT_sim = source.kT
Z_sim = source.Z
thermal_model = ThermalSourceModel(apec_model,
Zmet=Z_sim,
prng=source.prng)
photons = PhotonList.from_data_source(sphere, redshift, A, exp_time,
thermal_model)
D_A = photons.parameters["FiducialAngularDiameterDistance"]
norm_sim = sphere.quantities.total_quantity(em_field)
norm_sim *= 1.0e-14 / (4 * np.pi * D_A * D_A * (1. + redshift) *
(1. + redshift))
norm_sim = float(norm_sim.in_cgs())
v1, v2 = sphere.quantities.weighted_variance(v_field, em_field)
sigma_sim = float(v1.in_units("km/s"))
mu_sim = -float(v2.in_units("km/s"))
events = photons.project_photons("z",
absorb_model=abs_model,
prng=source.prng)
events = ACIS_I(events, rebin=False, convolve_psf=False, prng=source.prng)
events.write_spectrum("beta_model_evt.pi", clobber=True)
os.system("cp %s ." % events.parameters["ARF"])
os.system("cp %s ." % events.parameters["RMF"])
load_user_model(mymodel, "tbapec")
add_user_pars("tbapec", ["nH", "kT", "metallicity", "redshift", "norm"],
[0.01, 4.0, 0.2, redshift, norm_sim * 0.8],
parmins=[0.0, 0.1, 0.0, -20.0, 0.0],
parmaxs=[10.0, 20.0, 10.0, 20.0, 1.0e9],
parfrozen=[False, False, False, True, False])
load_pha("beta_model_evt.pi")
set_stat("cstat")
set_method("simplex")
ignore(":0.5, 8.0:")
set_model("tbapec")
fit()
set_covar_opt("sigma", 1.645)
covar()
res = get_covar_results()
assert np.abs(res.parvals[0] - nH_sim) < res.parmaxes[0]
assert np.abs(res.parvals[1] - kT_sim) < res.parmaxes[1]
assert np.abs(res.parvals[2] - Z_sim) < res.parmaxes[2]
assert np.abs(res.parvals[3] - norm_sim) < res.parmaxes[3]
os.chdir(curdir)
shutil.rmtree(tmpdir)
# Set start parameters close to simulation values to make the fit converge
sau.set_par('source.xpos', 101)
sau.set_par('source.ypos', 101)
sau.set_par('source.ampl', 1.1e3)
sau.set_par('source.fwhm', 10)
sau.set_par('background.c0', 1.1)
# Run fit and covariance estimation
# Results are automatically printed to the screen
sau.fit()
sau.covar()
# Sherpa uses fwhm instead of sigma as extension parameter ... need to convert
# http://cxc.harvard.edu/sherpa/ahelp/gauss2d.html
fwhm_to_sigma = 1. / np.sqrt(8 * np.log(2))
cov = sau.get_covar_results()
sigma = fwhm_to_sigma * cov.parvals[0]
sigma_err = fwhm_to_sigma * cov.parmaxes[0]
print('sigma: {0} +- {1}'.format(sigma, sigma_err))
# Compute correlation coefficient for sigma and norm
c = cov.extra_output
c_norm = c[3, 3]
c_sigma = fwhm_to_sigma ** 2 * c[0, 0]
c_norm_sigma = fwhm_to_sigma * c[0, 3]
corr_norm_sigma = c_norm_sigma / np.sqrt(c_norm * c_sigma)
print('corr_norm_sigma: {0}'.format(corr_norm_sigma))
# Save model excess image
sau.save_model('model_sherpa.fits.gz', clobber=True)
# Set start parameters close to simulation values to make the fit converge
sau.set_par('source.xpos', 101)
sau.set_par('source.ypos', 101)
sau.set_par('source.ampl', 1.1e3)
sau.set_par('source.fwhm', 10)
sau.set_par('background.c0', 1.1)
# Run fit and covariance estimation
# Results are automatically printed to the screen
sau.fit()
sau.covar()
# Sherpa uses fwhm instead of sigma as extension parameter ... need to convert
# http://cxc.harvard.edu/sherpa/ahelp/gauss2d.html
fwhm_to_sigma = 1. / np.sqrt(8 * np.log(2))
cov = sau.get_covar_results()
sigma = fwhm_to_sigma * cov.parvals[0]
sigma_err = fwhm_to_sigma * cov.parmaxes[0]
print('sigma: {0} +- {1}'.format(sigma, sigma_err))
# Compute correlation coefficient for sigma and norm
c = cov.extra_output
c_norm = c[3, 3]
c_sigma = fwhm_to_sigma**2 * c[0, 0]
c_norm_sigma = fwhm_to_sigma * c[0, 3]
corr_norm_sigma = c_norm_sigma / np.sqrt(c_norm * c_sigma)
print('corr_norm_sigma: {0}'.format(corr_norm_sigma))
# Save model excess image
sau.save_model('model_sherpa.fits.gz', clobber=True)
def plaw_fit(alpha_sim):
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
bms = BetaModelSource()
ds = bms.ds
def _hard_emission(field, data):
return YTQuantity(1.0e-18, "s**-1*keV**-1")*data["density"]*data["cell_volume"]/mp
ds.add_field(("gas", "hard_emission"), function=_hard_emission, units="keV**-1*s**-1")
nH_sim = 0.02
abs_model = WabsModel(nH_sim)
A = YTQuantity(2000., "cm**2")
exp_time = YTQuantity(2.0e5, "s")
redshift = 0.01
sphere = ds.sphere("c", (100.,"kpc"))
plaw_model = PowerLawSourceModel(1.0, 0.01, 11.0, "hard_emission",
alpha_sim, prng=prng)
photons = PhotonList.from_data_source(sphere, redshift, A, exp_time,
plaw_model)
D_A = photons.parameters["FiducialAngularDiameterDistance"]
dist_fac = 1.0/(4.*np.pi*D_A*D_A*(1.+redshift)**3).in_cgs()
norm_sim = float((sphere["hard_emission"]).sum()*dist_fac.in_cgs())*(1.+redshift)
events = photons.project_photons("z", absorb_model=abs_model,
prng=bms.prng,
no_shifting=True)
events = ACIS_I(events, rebin=False, convolve_psf=False, prng=bms.prng)
events.write_spectrum("plaw_model_evt.pi", clobber=True)
os.system("cp %s ." % events.parameters["ARF"])
os.system("cp %s ." % events.parameters["RMF"])
load_user_model(mymodel, "wplaw")
add_user_pars("wplaw", ["nH", "norm", "redshift", "alpha"],
[0.01, norm_sim*1.1, redshift, 0.9],
parmins=[0.0, 0.0, 0.0, 0.1],
parmaxs=[10.0, 1.0e9, 10.0, 10.0],
parfrozen=[False, False, True, False])
load_pha("plaw_model_evt.pi")
set_stat("cstat")
set_method("simplex")
ignore(":0.6, 7.0:")
set_model("wplaw")
fit()
set_covar_opt("sigma", 1.645)
covar()
res = get_covar_results()
assert np.abs(res.parvals[0]-nH_sim) < res.parmaxes[0]
assert np.abs(res.parvals[1]-norm_sim) < res.parmaxes[1]
assert np.abs(res.parvals[2]-alpha_sim) < res.parmaxes[2]
os.chdir(curdir)
shutil.rmtree(tmpdir)
def fit_sherpa(obsid_list,
redshift,
nH_Gal,
energy,
min_counts=25,
kT_guess=3,
Ab_guess=1,
fix_nH_Gal=True,
fix_abund=False,
find_errors=True):
spectra = []
for obs in obsid_list:
temp = glob.glob('xaf_*_' + obs + '.pi') # get spectra of regions
spectra.append(
temp
) # spectra will be made of lists which have xaf_*_obs.pi filenames - access with spectra[i][j]
spectra.sort()
num_obs = len(obsid_list)
num_reg = len(temp)
filename = 'spectra_wabs_mekal.dat'
results_file = open(filename, "w")
results_file.write(
'# Fit results for wabs*mekal (zeros indicate that no fitting was performed)\n'
)
results_file.write(
'# Reg_no. kT kT_loerr kT_hierr Z Z_loerr Z_hierr norm norm_loerr norm_hierr nH_Gal nH_loerr nH_hierr red_chisq total_counts num_bins\n'
)
for i in range(num_reg):
sherpa.clean() # clean everything
cnts = numpy.zeros(
num_obs
) # make array of zeros with index length same as num_obs to store counts
max_rate = numpy.zeros(num_obs) # max count rate [counts/s/keV]
data_set = 0 # data set number
good_src_ids = numpy.zeros(num_obs, dtype=int) - 1
for j in range(num_obs):
sherpa.load_pha(
data_set, spectra[j]
[i]) # load xaf_#_obs_####.pi and .arf and .rmf files.
sherpa.ignore_id(data_set, 0.0, energy[0])
sherpa.ignore_id(data_set, energy[1], None)
cnts[j] = sherpa.calc_data_sum(energy[0], energy[1], data_set)
cnt_rate = sherpa.get_rate(data_set, filter=True)
if len(cnt_rate) == 0:
max_rate[
j] = 0.0 # when few counts (<50), get_rate can return zero-length array
else:
max_rate[j] = numpy.max(cnt_rate)
sherpa.subtract(data_set) # subtract background
sherpa.set_source(
data_set, sherpa.xswabs.abs1 *
sherpa.xsmekal.plsm1) # 1 temperature mekal model fit
good_src_ids[j] = data_set
data_set += 1 # same run for region but different obs
# Filter out ignored obs
good_src_ids_indx = numpy.where(good_src_ids >= 0)
good_src_ids = good_src_ids[good_src_ids_indx]
max_rate = max_rate[good_src_ids_indx]
cnts = cnts[good_src_ids_indx]
totcnts = numpy.sum(cnts)
if totcnts >= min_counts:
print('Fitting spectra in region: ' + str(i))
abs1.nH = nH_Gal
abs1.cache = 0
if fix_nH_Gal:
sherpa.freeze(abs1.nH)
else:
sherpa.thaw(abs1.nH)
plsm1.kt = kT_guess
sherpa.thaw(plsm1.kt)
plsm1.Abundanc = Ab_guess
if fix_abund:
sherpa.freeze(plsm1.Abundanc)
else:
sherpa.thaw(plsm1.Abundanc)
plsm1.redshift = redshift
sherpa.freeze(plsm1.redshift)
plsm1.cache = 0
sherpa.fit()
fit_result = sherpa.get_fit_results()
red_chi2 = fit_result.rstat
num_bins = fit_result.numpoints
if fix_nH_Gal:
nH = nH_Gal
kT = fit_result.parvals[0]
if fix_abund:
Z = Ab_guess
norm = fit_result.parvals[1]
else:
Z = fit_result.parvals[1]
norm = fit_result.parvals[2]
else:
nH = fit_result.parvals[0]
kT = fit_result.parvals[1]
if fix_abund:
Z = Ab_guess
norm = fit_result.parvals[2]
else:
Z = fit_result.parvals[2]
norm = fit_result.parvals[3]
del fit_result
if find_errors:
sherpa.covar()
covar_result = sherpa.get_covar_results()
if fix_nH_Gal:
nH_loerr = 0.0
nH_hierr = 0.0
kT_loerr = covar_result.parmins[0]
kT_hierr = covar_result.parmaxes[0]
if fix_abund:
Z_loerr = 0.0
Z_hierr = 0.0
norm_loerr = covar_result.parmins[1]
norm_hierr = covar_result.parmaxes[1]
else:
Z_loerr = covar_result.parmins[1]
Z_hierr = covar_result.parmaxes[1]
norm_loerr = covar_result.parmins[2]
norm_hierr = covar_result.parmaxes[2]
else:
nH_loerr = covar_result.parmins[0]
nH_hierr = covar_result.parmaxes[0]
kT_loerr = covar_result.parmins[1]
kT_hierr = covar_result.parmaxes[1]
if fix_abund:
Z_loerr = 0.0
Z_hierr = 0.0
norm_loerr = covar_result.parmins[2]
norm_hierr = covar_result.parmaxes[2]
else:
Z_loerr = covar_result.parmins[2]
Z_hierr = covar_result.parmaxes[2]
norm_loerr = covar_result.parmins[3]
norm_hierr = covar_result.parmaxes[3]
del covar_result
# Check for failed errors (= None) and set them to +/- best-fit value
if not fix_nH_Gal:
if nH_loerr is None: nH_loerr = -nH # is was ==
if nH_hierr is None: nH_hierr = nH
if kT_loerr is None: kT_loerr = -kT
if kT_hierr is None: kT_hierr = kT
if not fix_abund:
if Z_loerr is None: Z_loerr = -Z
if Z_hierr is None: Z_hierr = Z
if norm_loerr is None: norm_loerr = -norm
if norm_hierr is None: norm_hierr = norm
else:
kT_loerr = 0.0
Z_loerr = 0.0
nH_loerr = 0.0
norm_loerr = 0.0
kT_hierr = 0.0
Z_hierr = 0.0
nH_hierr = 0.0
norm_hierr = 0.0
else: # if total counts < min_counts, just write zeros
print('\n Warning: no fit performed for for region: ' + str(i))
print(
'\n Spectra have insufficient counts after filtering or do not exist.'
)
print('\n --> All parameters for this region set to 0.0.')
kT = 0.0
Z = 0.0
nH = 0.0
norm = 0.0
kT_loerr = 0.0
Z_loerr = 0.0
nH_loerr = 0.0
norm_loerr = 0.0
kT_hierr = 0.0
Z_hierr = 0.0
nH_hierr = 0.0
norm_hierr = 0.0
red_chi2 = 0.0
num_bins = 0
reg_id = spectra[0][i].split(
'_'
) # Splits string after every underscore so that region number can be accessed. reg_id[1] is accessed because that is the region number after 'xaf'
results_file.write(
'%7r %7.4f %7.4f %7.4f %7.4f %7.4f %7.4f %6.4e %6.4e %6.4e %7.4f %7.4f %7.4f %7.4f %8.1f %8r\n'
% (int(reg_id[1]), kT, kT_loerr, kT_hierr, Z, Z_loerr, Z_hierr,
norm, norm_loerr, norm_hierr, nH, nH_loerr, nH_hierr, red_chi2,
totcnts, num_bins)) # Write all data to a file
results_file.close()
b1, b2 = pars
return BoxBOD.model(x, b1, b2)
ui.load_user_model(boxbod_func, "boxbod")
ui.add_user_pars("boxbod", ["b1", "b2"])
bb = boxbod
ui.set_model(bb)
bb.b1, bb.b2 = BoxBOD.p0[0], BoxBOD.p0[1]
# Perform fit
ui.set_stat('chi2datavar')
#ui.set_method('levmar') # ['levmar', 'moncar', 'neldermead', 'simplex']
ui.set_method_opt('xtol', 1e-10)
ui.fit() # Compute best-fit parameters
ui.set_covar_opt('eps', 1e-5) # @todo: Why does this parameter have no effect
ui.covariance() # Compute covariance matrix (i.e. errors)
#ui.conf() # Compute profile errors
#ui.show_all() # Print a very nice summary of your session to less
# Report results
fr = ui.get_fit_results()
cr = ui.get_covar_results()
# Report results (we have to apply the s factor ourselves)
popt = np.array(fr.parvals)
chi2 = fr.statval
s_factor = np.sqrt(chi2 / fr.dof)
perr = s_factor * np.array(cr.parmaxes)
report_results('sherpa', popt, perr, chi2)
def test_annulus():
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
r_in = 10.0
r_out = 30.0
e = spec.generate_energies(exp_time, area, prng=prng)
ann_src = AnnulusModel(ra0, dec0, r_in, r_out, e.size, prng=prng)
write_photon_list("ann",
"ann",
e.flux,
ann_src.ra,
ann_src.dec,
e,
overwrite=True)
instrument_simulator("ann_simput.fits",
"ann_evt.fits",
exp_time,
"hdxi", [ra0, dec0],
ptsrc_bkgnd=False,
instr_bkgnd=False,
foreground=False,
prng=prng)
inst = get_instrument_from_registry("hdxi")
arf = AuxiliaryResponseFile(inst["arf"])
cspec = ConvolvedSpectrum(spec, arf)
ph_flux = cspec.get_flux_in_band(0.5, 7.0)[0].value
S0 = ph_flux / (np.pi * (r_out**2 - r_in**2))
write_radial_profile("ann_evt.fits",
"ann_evt_profile.fits", [ra0, dec0],
1.1 * r_in,
0.9 * r_out,
100,
ctr_type="celestial",
emin=0.5,
emax=7.0,
overwrite=True)
load_data(1, "ann_evt_profile.fits", 3, ["RMID", "SUR_BRI", "SUR_BRI_ERR"])
set_stat("chi2")
set_method("levmar")
set_source("const1d.src")
src.c0 = 0.8 * S0
fit()
set_covar_opt("sigma", 1.645)
covar()
res = get_covar_results()
assert np.abs(res.parvals[0] - S0) < res.parmaxes[0]
os.chdir(curdir)
shutil.rmtree(tmpdir)
filename = 'skymap_ex.fits'
nomposstr = '05h34m31.94s 22d00m52.2s'
header = fits.getheader(filename)
proj = wcs.Projection(header)
xc, yc = float(header['NAXIS1']) / 2., float(header['NAXIS2']) / 2.
ui.load_image(filename)
ui.notice2d('circle({0}, {1}, {2})'.format(xc, yc, float(header['NAXIS2']) / 4.))
ui.set_source(ui.gauss2d.g1 + ui.gauss2d.g2)
g1.xpos = xc
g1.ypos = yc
g2.fwhm = g1.fwhm = 3.
ui.link(g2.xpos, g1.xpos)
ui.link(g2.ypos, g1.ypos)
g2.ampl = 50.
g1.ampl = 50.
ui.guess()
ui.fit()
ui.image_fit()
ui.covar()
conf = ui.get_covar_results()
conf_dict = dict([(n,(v, l, h)) for n,v,l,h in
zip(conf.parnames, conf.parvals, conf.parmins, conf.parmaxes)])
x, y = proj.toworld((conf_dict['g1.xpos'][0], conf_dict['g1.ypos'][0]))
xmin, ymin = proj.toworld((conf_dict['g1.xpos'][0] + conf_dict['g1.xpos'][1],
conf_dict['g1.ypos'][0] + conf_dict['g1.ypos'][1]))
xmax, ymax = proj.toworld((conf_dict['g1.xpos'][0] + conf_dict['g1.xpos'][2],
conf_dict['g1.ypos'][0] + conf_dict['g1.ypos'][2]))
nompos = positions.str2pos(nomposstr, proj)
print('{0} ({1}-{2}) vs {3}'.format(x, xmin, xmax, nompos[0][0][0]))
print('{0} ({1}-{2}) vs {3}'.format(y, ymin, ymax, nompos[0][0][1]))
def test_beta_model_flux():
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
r_c = 20.0
beta = 1.0
prng = 34
e = spec.generate_energies(exp_time, area, prng=prng)
beta_src = BetaModel(ra0, dec0, r_c, beta, e.size, prng=prng)
write_photon_list("beta",
"beta",
e.flux,
beta_src.ra,
beta_src.dec,
e,
overwrite=True)
instrument_simulator("beta_simput.fits",
"beta_evt.fits",
exp_time,
"acisi_cy0", [ra0, dec0],
ptsrc_bkgnd=False,
instr_bkgnd=False,
foreground=False,
roll_angle=37.0,
prng=prng)
ph_flux = spec.get_flux_in_band(0.5, 7.0)[0].value
S0 = 3.0 * ph_flux / (2.0 * np.pi * r_c * r_c)
wspec = spec.new_spec_from_band(0.5, 7.0)
make_exposure_map("beta_evt.fits",
"beta_expmap.fits",
wspec.emid.value,
weights=wspec.flux.value,
overwrite=True)
write_radial_profile("beta_evt.fits",
"beta_evt_profile.fits", [ra0, dec0],
0.0,
100.0,
200,
ctr_type="celestial",
emin=0.5,
emax=7.0,
expmap_file="beta_expmap.fits",
overwrite=True)
load_data(1, "beta_evt_profile.fits", 3,
["RMID", "SUR_FLUX", "SUR_FLUX_ERR"])
set_stat("chi2")
set_method("levmar")
set_source("beta1d.src")
src.beta = 1.0
src.r0 = 10.0
src.ampl = 0.8 * S0
freeze(src.xpos)
fit()
set_covar_opt("sigma", 1.645)
covar()
res = get_covar_results()
assert np.abs(res.parvals[0] - r_c) < res.parmaxes[0]
assert np.abs(res.parvals[1] - beta) < res.parmaxes[1]
assert np.abs(res.parvals[2] - S0) < res.parmaxes[2]
os.chdir(curdir)
shutil.rmtree(tmpdir)
def test_beta_model():
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
r_c = 20.0
beta = 1.0
exp_time = Quantity(500.0, "ks")
e = spec.generate_energies(exp_time, area, prng=prng)
beta_src = BetaModel(ra0, dec0, r_c, beta, e.size, prng=prng)
write_photon_list("beta",
"beta",
e.flux,
beta_src.ra,
beta_src.dec,
e,
overwrite=True)
instrument_simulator("beta_simput.fits",
"beta_evt.fits",
exp_time,
"hdxi", [ra0, dec0],
ptsrc_bkgnd=False,
instr_bkgnd=False,
foreground=False,
prng=prng)
inst = get_instrument_from_registry("hdxi")
arf = AuxiliaryResponseFile(inst["arf"])
cspec = ConvolvedSpectrum(spec, arf)
ph_flux = cspec.get_flux_in_band(0.5, 7.0)[0].value
S0 = 3.0 * ph_flux / (2.0 * np.pi * r_c * r_c)
write_radial_profile("beta_evt.fits",
"beta_evt_profile.fits", [ra0, dec0],
0.0,
100.0,
200,
ctr_type="celestial",
emin=0.5,
emax=7.0,
overwrite=True)
load_data(1, "beta_evt_profile.fits", 3,
["RMID", "SUR_BRI", "SUR_BRI_ERR"])
set_stat("chi2")
set_method("levmar")
set_source("beta1d.src")
src.beta = 1.0
src.r0 = 10.0
src.ampl = 0.8 * S0
freeze(src.xpos)
fit()
set_covar_opt("sigma", 1.645)
covar()
res = get_covar_results()
assert np.abs(res.parvals[0] - r_c) < res.parmaxes[0]
assert np.abs(res.parvals[1] - beta) < res.parmaxes[1]
assert np.abs(res.parvals[2] - S0) < res.parmaxes[2]
os.chdir(curdir)
shutil.rmtree(tmpdir)
def simulate_null_images(img_file: str,
psf_file: str,
n_null_sims: int,
no_core: bool = False,
mcmciter: int = 5000,
**kwargs) -> None:
"""
Simulates a specified number of baseline images for a given input observation and a psf file
:param img_file: Path to the input image file
:param psf_file: Path to the psf image file
:param n_null_sims: Number of baseline replicates to be simulated
:param no_core: Setting this to True will only generate baseline replicates with a flat background while the default value includes a point source at the location of the core
:param mcmciter: The number of MCMC samples to draw for simulating the baselines
"""
print("Creating the null file")
clean()
set_stat("cstat")
set_method("simplex")
load_image(img_file)
load_psf("mypsf", psf_file)
set_psf(mypsf)
if no_core:
set_model(const2d.c0)
set_par(c0.c0, min=0)
else:
set_model(gauss2d.q1 + const2d.c0)
set_par(c0.c0, min=0)
# set_par(q1.fwhm,max=0.5)
guess(q1)
fit()
results = get_fit_results()
save("core_source_fit.save", clobber=True)
save_source("null_q1_c1.fits", clobber=True)
covar()
if no_core:
for i in range(n_null_sims):
fake()
save_image("sim_null_{}.fits".format(i), clobber=True)
clean()
return
normgauss1d.g1
g1.pos = q1.fwhm
g1.fwhm = get_covar_results().parmaxes[0]
# check if there is a valid upper bound.
print(get_covar_results())
if (get_covar_results().parmaxes[0] is None
or get_covar_results().parmins[1] is None
or get_covar_results().parmins[0] is None):
for i in range(n_null_sims):
fake()
save_image("sim_null_{}.fits".format(i), clobber=True)
clean()
return
# if not go for the regular
set_prior(q1.fwhm, g1)
set_sampler_opt("defaultprior", False)
set_sampler_opt("priorshape", [True, False, False, False, False])
set_sampler_opt("originalscale", [True, True, True, True, True])
if mcmciter < n_null_sims * 100:
mcmciter = n_null_sims * 100
# the following code throws an error sometimes #bug
try:
stats, accept, params = get_draws(1, niter=mcmciter)
except:
params = [np.repeat(q1.fwhm.val, mcmciter)]
# print('Simulating the null files')
for i in range(n_null_sims):
set_par(q1.fwhm, params[0][(i + 1) * 100 - 1])
fake()
save_image("sim_null_{}.fits".format(i), clobber=True)
save_all(outfile="lira_input_baseline_sim.log", clobber=True)
clean()
def write_all(filename='results.json'):
"""Dump source, fit results and conf results to a JSON file.
http://www.astropython.org/snippet/2010/7/Save-sherpa-fit-and-conf-results-to-a-JSON-file
"""
import sherpa.astro.ui as sau
out = dict()
if 0:
src = sau.get_source()
src_par_attrs = ('name', 'frozen', 'modelname', 'units', 'val',
'fullname')
out['src'] = dict(name=src.name,
pars=[
dict((attr, getattr(par, attr))
for attr in src_par_attrs)
for par in src.pars
])
try:
fit_attrs = ('methodname', 'statname', 'succeeded', 'statval',
'numpoints', 'dof', 'rstat', 'qval', 'nfev', 'message',
'parnames', 'parvals')
fit = sau.get_fit_results()
out['fit'] = dict((attr, getattr(fit, attr)) for attr in fit_attrs)
except Exception as err:
print(err)
try:
conf_attrs = ('datasets', 'methodname', 'fitname', 'statname', 'sigma',
'percent', 'parnames', 'parvals', 'parmins', 'parmaxes',
'nfits')
conf = sau.get_conf_results()
out['conf'] = dict((attr, getattr(conf, attr)) for attr in conf_attrs)
except Exception as err:
print(err)
try:
covar_attrs = ('datasets', 'methodname', 'fitname', 'statname',
'sigma', 'percent', 'parnames', 'parvals', 'parmins',
'parmaxes', 'nfits')
covar = sau.get_covar_results()
out['covar'] = dict(
(attr, getattr(covar, attr)) for attr in covar_attrs)
except Exception as err:
print(err)
if 0:
out['pars'] = []
for par in src.pars:
fullname = par.fullname
if any(fullname == x['name'] for x in out['pars']):
continue # Parameter was already processed
outpar = dict(name=fullname, kind=par.name)
# None implies no calculated confidence interval for Measurement
parmin = None
parmax = None
try:
if fullname in conf.parnames: # Confidence limits available from conf
i = conf.parnames.index(fullname)
parval = conf.parvals[i]
parmin = conf.parmins[i]
parmax = conf.parmaxes[i]
if parmin is None:
parmin = -float(
'inf'
) # None from conf means infinity, so set accordingly
if parmax is None:
parmax = float('inf')
elif fullname in fit.parnames: # Conf failed or par is uninteresting and wasn't sent to conf
i = fit.parnames.index(fullname)
parval = fit.parvals[i]
else: # No fit or conf value (maybe frozen)
parval = par.val
except Exception as err:
print(err)
out['pars'].append(outpar)
if filename is None:
return out
else:
json.dump(out, open(filename, 'w'), sort_keys=True, indent=4)
def write_all(filename='results.json'):
"""Dump source, fit results and conf results to a JSON file.
http://www.astropython.org/snippet/2010/7/Save-sherpa-fit-and-conf-results-to-a-JSON-file
"""
import sherpa.astro.ui as sau
out = dict()
if 0:
src = sau.get_source()
src_par_attrs = ('name', 'frozen', 'modelname', 'units', 'val', 'fullname')
out['src'] = dict(name=src.name,
pars=[dict((attr, getattr(par, attr)) for attr in src_par_attrs)
for par in src.pars])
try:
fit_attrs = ('methodname', 'statname', 'succeeded', 'statval', 'numpoints', 'dof',
'rstat', 'qval', 'nfev', 'message', 'parnames', 'parvals')
fit = sau.get_fit_results()
out['fit'] = dict((attr, getattr(fit, attr)) for attr in fit_attrs)
except Exception as err:
print(err)
try:
conf_attrs = ('datasets', 'methodname', 'fitname', 'statname', 'sigma', 'percent',
'parnames', 'parvals', 'parmins', 'parmaxes', 'nfits')
conf = sau.get_conf_results()
out['conf'] = dict((attr, getattr(conf, attr)) for attr in conf_attrs)
except Exception as err:
print(err)
try:
covar_attrs = ('datasets', 'methodname', 'fitname', 'statname', 'sigma', 'percent',
'parnames', 'parvals', 'parmins', 'parmaxes', 'nfits')
covar = sau.get_covar_results()
out['covar'] = dict((attr, getattr(covar, attr)) for attr in covar_attrs)
except Exception as err:
print(err)
if 0:
out['pars'] = []
for par in src.pars:
fullname = par.fullname
if any(fullname == x['name'] for x in out['pars']):
continue # Parameter was already processed
outpar = dict(name=fullname, kind=par.name)
# None implies no calculated confidence interval for Measurement
parmin = None
parmax = None
try:
if fullname in conf.parnames: # Confidence limits available from conf
i = conf.parnames.index(fullname)
parval = conf.parvals[i]
parmin = conf.parmins[i]
parmax = conf.parmaxes[i]
if parmin is None:
parmin = -float('inf') # None from conf means infinity, so set accordingly
if parmax is None:
parmax = float('inf')
elif fullname in fit.parnames: # Conf failed or par is uninteresting and wasn't sent to conf
i = fit.parnames.index(fullname)
parval = fit.parvals[i]
else: # No fit or conf value (maybe frozen)
parval = par.val
except Exception as err:
print(err)
out['pars'].append(outpar)
if filename is None:
return out
else:
json.dump(out, open(filename, 'w'), sort_keys=True, indent=4)
nomposstr = '05h34m31.94s 22d00m52.2s'
header = fits.getheader(filename)
proj = wcs.Projection(header)
xc, yc = float(header['NAXIS1']) / 2., float(header['NAXIS2']) / 2.
ui.load_image(filename)
ui.notice2d('circle({0}, {1}, {2})'.format(xc, yc,
float(header['NAXIS2']) / 4.))
ui.set_source(ui.gauss2d.g1 + ui.gauss2d.g2)
g1.xpos = xc
g1.ypos = yc
g2.fwhm = g1.fwhm = 3.
ui.link(g2.xpos, g1.xpos)
ui.link(g2.ypos, g1.ypos)
g2.ampl = 50.
g1.ampl = 50.
ui.guess()
ui.fit()
ui.image_fit()
ui.covar()
conf = ui.get_covar_results()
conf_dict = dict([(n, (v, l, h)) for n, v, l, h in zip(
conf.parnames, conf.parvals, conf.parmins, conf.parmaxes)])
x, y = proj.toworld((conf_dict['g1.xpos'][0], conf_dict['g1.ypos'][0]))
xmin, ymin = proj.toworld((conf_dict['g1.xpos'][0] + conf_dict['g1.xpos'][1],
conf_dict['g1.ypos'][0] + conf_dict['g1.ypos'][1]))
xmax, ymax = proj.toworld((conf_dict['g1.xpos'][0] + conf_dict['g1.xpos'][2],
conf_dict['g1.ypos'][0] + conf_dict['g1.ypos'][2]))
nompos = positions.str2pos(nomposstr, proj)
print('{0} ({1}-{2}) vs {3}'.format(x, xmin, xmax, nompos[0][0][0]))
print('{0} ({1}-{2}) vs {3}'.format(y, ymin, ymax, nompos[0][0][1]))
def get_parameter_info(id=None):
"""Returns the parameter information needed for calling mht.
This routine will call covariance() if needed, but not
fit().
For now only works with a single dataset, and requires
the Cash statistic.
"""
if id == None:
idval = ui.get_default_id()
else:
idval = id
# Employ a lot of safety checks
#
fr = ui.get_fit_results()
if fr == None:
raise RuntimeError, "No fit results available!"
if len(fr.datasets) != 1:
raise RuntimeError, "Fit is for multiple datasets (%s) which we do not support!" % fr.datasets
if fr.datasets[0] != idval:
raise RuntimeError, "Fit results are for dataset %s, not %s" % (
fr.datasets[0], idval)
if fr.statname != "cash":
raise RuntimeError, "Fit was run using statistic=%s rather than cash!" % fr.statname
if not fr.succeeded:
# Should use standard sherpa logging
print "Warning: fit to dataset %s did not complete successfully:\n%s" % (
idval, fr.message)
cr = ui.get_covar_results()
if cr == None or len(cr.datasets) != 1 or cr.datasets[0] != idval:
# Should use standard sherpa logging
print "Running covariance for dataset %s" % idval
ui.covariance(idval)
cr = ui.get_covar_results()
if cr.statname != "cash":
raise RuntimeError, "Covariance was run using statistic=%s rather than cash!" % cr.statname
if len(fr.parnames) != len(cr.parnames):
raise RuntimeError, "Number of parameters used in fit and covariance analysis do not agree!\n fit=%s\n covar=%s\n" % (
fr.parnames, cr.parnames)
for (p1, p2) in zip(fr.parnames, cr.parnames):
if p1 != p2:
raise RuntimeError, "Order of fit/covariance parameters does not match: %s vs %s" % (
p1, p2)
for (pname, v1, v2) in zip(fr.parnames, fr.parvals, cr.parvals):
if v1 != v2:
raise RuntimeError, "Value of fit/covariance parameters does not match for parameter %s: %g vs %g" % (
pname, v1, v2)
if not hasattr(cr, "extra_output") or cr.extra_output == None:
raise RuntimeError, "get_covar_results has no .extra_output or it is None; is this CIAOX?"
# Store the information, we explicitly copy all items to avoid
# problems if fit/covariance are run again. This is done by converting
# all tuples to numpy arrays, even for strings, and is actually
# not needed.
#
out = {
"dataset": idval,
"npars": len(fr.parnames),
"parnames": np.asarray(fr.parnames),
"parvals": np.asarray(fr.parvals),
"parmins": np.asarray(cr.parmins),
"parmaxes": np.asarray(cr.parmaxes),
"sigma": cr.sigma,
"covar": cr.extra_output.copy(),
"statval": fr.statval
}
return out
def plaw_fit(alpha_sim):
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
nH_sim = 0.02
norm_sim = 1.0e-4
redshift = 0.01
exp_time = 5.0e4
area = 40000.0
inst_name = "hdxi"
spec = Spectrum.from_powerlaw(alpha_sim, redshift, norm_sim)
spec.apply_foreground_absorption(nH_sim)
e = spec.generate_energies(exp_time, area)
pt_src = PointSourceModel(30.0, 45.0, e.size)
write_photon_list("plaw_model",
"plaw_model",
e.flux,
pt_src.ra,
pt_src.dec,
e,
clobber=True)
instrument_simulator("plaw_model_simput.fits",
"plaw_model_evt.fits",
exp_time,
inst_name, [30.0, 45.0],
astro_bkgnd=None,
instr_bkgnd_scale=0.0)
inst = get_instrument_from_registry(inst_name)
arf = AuxiliaryResponseFile(inst["arf"])
rmf = RedistributionMatrixFile(inst["rmf"])
os.system("cp %s ." % arf.filename)
os.system("cp %s ." % rmf.filename)
write_spectrum("plaw_model_evt.fits", "plaw_model_evt.pha", clobber=True)
load_user_model(mymodel, "wplaw")
add_user_pars("wplaw", ["nH", "norm", "redshift", "alpha"],
[0.01, norm_sim * 0.8, redshift, 0.9],
parmins=[0.0, 0.0, 0.0, 0.1],
parmaxs=[10.0, 1.0e9, 10.0, 10.0],
parfrozen=[False, False, True, False])
load_pha("plaw_model_evt.pha")
set_stat("cstat")
set_method("simplex")
ignore(":0.5, 9.0:")
set_model("wplaw")
fit()
set_covar_opt("sigma", 1.645)
covar()
res = get_covar_results()
assert np.abs(res.parvals[0] - nH_sim) < res.parmaxes[0]
assert np.abs(res.parvals[1] - norm_sim) < res.parmaxes[1]
assert np.abs(res.parvals[2] - alpha_sim) < res.parmaxes[2]
os.chdir(curdir)
shutil.rmtree(tmpdir)
def get_parameter_info(id=None):
"""Returns the parameter information needed for calling mht.
This routine will call covariance() if needed, but not
fit().
For now only works with a single dataset, and requires
the Cash statistic.
"""
if id == None:
idval = ui.get_default_id()
else:
idval = id
# Employ a lot of safety checks
#
fr = ui.get_fit_results()
if fr == None:
raise RuntimeError, "No fit results available!"
if len(fr.datasets) != 1:
raise RuntimeError, "Fit is for multiple datasets (%s) which we do not support!" % fr.datasets
if fr.datasets[0] != idval:
raise RuntimeError, "Fit results are for dataset %s, not %s" % (fr.datasets[0], idval)
if fr.statname != "cash":
raise RuntimeError, "Fit was run using statistic=%s rather than cash!" % fr.statname
if not fr.succeeded:
# Should use standard sherpa logging
print "Warning: fit to dataset %s did not complete successfully:\n%s" % (idval, fr.message)
cr = ui.get_covar_results()
if cr == None or len(cr.datasets) != 1 or cr.datasets[0] != idval:
# Should use standard sherpa logging
print "Running covariance for dataset %s" % idval
ui.covariance(idval)
cr = ui.get_covar_results()
if cr.statname != "cash":
raise RuntimeError, "Covariance was run using statistic=%s rather than cash!" % cr.statname
if len(fr.parnames) != len(cr.parnames):
raise RuntimeError, "Number of parameters used in fit and covariance analysis do not agree!\n fit=%s\n covar=%s\n" % (
fr.parnames,
cr.parnames,
)
for (p1, p2) in zip(fr.parnames, cr.parnames):
if p1 != p2:
raise RuntimeError, "Order of fit/covariance parameters does not match: %s vs %s" % (p1, p2)
for (pname, v1, v2) in zip(fr.parnames, fr.parvals, cr.parvals):
if v1 != v2:
raise RuntimeError, "Value of fit/covariance parameters does not match for parameter %s: %g vs %g" % (
pname,
v1,
v2,
)
if not hasattr(cr, "extra_output") or cr.extra_output == None:
raise RuntimeError, "get_covar_results has no .extra_output or it is None; is this CIAOX?"
# Store the information, we explicitly copy all items to avoid
# problems if fit/covariance are run again. This is done by converting
# all tuples to numpy arrays, even for strings, and is actually
# not needed.
#
out = {
"dataset": idval,
"npars": len(fr.parnames),
"parnames": np.asarray(fr.parnames),
"parvals": np.asarray(fr.parvals),
"parmins": np.asarray(cr.parmins),
"parmaxes": np.asarray(cr.parmaxes),
"sigma": cr.sigma,
"covar": cr.extra_output.copy(),
"statval": fr.statval,
}
return out
def test_point_source():
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
nH_sim = 0.02
norm_sim = 1.0e-4
alpha_sim = 0.95
redshift = 0.02
exp_time = (100., "ks")
area = (3000., "cm**2")
wcs = create_dummy_wcs()
ebins = np.linspace(0.1, 11.5, 2001)
emid = 0.5*(ebins[1:]+ebins[:-1])
spec = norm_sim*(emid*(1.0+redshift))**(-alpha_sim)
de = np.diff(ebins)[0]
abs_model = TBabsModel(nH_sim)
events = EventList.create_empty_list(exp_time, area, wcs)
positions = [(30.01, 45.0)]
new_events = events.add_point_sources(positions, ebins, spec, prng=prng,
absorb_model=abs_model)
new_events = ACIS_S(new_events, prng=prng)
scalex = float(np.std(new_events['xpix'])*sigma_to_fwhm*new_events.parameters["dtheta"])
scaley = float(np.std(new_events['ypix'])*sigma_to_fwhm*new_events.parameters["dtheta"])
psf_scale = ACIS_S.psf_scale
assert (scalex - psf_scale)/psf_scale < 0.01
assert (scaley - psf_scale)/psf_scale < 0.01
new_events.write_spectrum("point_source_evt.pi", clobber=True)
os.system("cp %s ." % new_events.parameters["ARF"])
os.system("cp %s ." % new_events.parameters["RMF"])
load_user_model(mymodel, "tplaw")
add_user_pars("tplaw", ["nH", "norm", "redshift", "alpha"],
[0.01, norm_sim*0.8, redshift, 0.9],
parmins=[0.0, 0.0, 0.0, 0.1],
parmaxs=[10.0, 1.0e9, 10.0, 10.0],
parfrozen=[False, False, True, False])
load_pha("point_source_evt.pi")
set_stat("cstat")
set_method("simplex")
ignore(":0.5, 9.0:")
set_model("tplaw")
fit()
set_covar_opt("sigma", 1.6)
covar()
res = get_covar_results()
assert np.abs(res.parvals[0]-nH_sim) < res.parmaxes[0]
assert np.abs(res.parvals[1]-norm_sim/de) < res.parmaxes[1]
assert np.abs(res.parvals[2]-alpha_sim) < res.parmaxes[2]
os.chdir(curdir)
shutil.rmtree(tmpdir)
def fit_sherpa(obsid_list, redshift, nH_Gal, energy, min_counts=25, kT_guess=3, Ab_guess=1, fix_nH_Gal=True, fix_abund=False, find_errors=True):
spectra = []
for obs in obsid_list:
temp = glob.glob('xaf_*_' + obs + '.pi') # get spectra of regions
spectra.append(temp) # spectra will be made of lists which have xaf_*_obs.pi filenames - access with spectra[i][j]
spectra.sort()
num_obs = len(obsid_list)
num_reg = len(temp)
filename = 'spectra_wabs_mekal.dat'
results_file = open(filename, "w")
results_file.write('# Fit results for wabs*mekal (zeros indicate that no fitting was performed)\n')
results_file.write('# Reg_no. kT kT_loerr kT_hierr Z Z_loerr Z_hierr norm norm_loerr norm_hierr nH_Gal nH_loerr nH_hierr red_chisq total_counts num_bins\n')
for i in range(num_reg):
sherpa.clean() # clean everything
cnts = numpy.zeros(num_obs) # make array of zeros with index length same as num_obs to store counts
max_rate = numpy.zeros(num_obs) # max count rate [counts/s/keV]
data_set = 0 # data set number
good_src_ids = numpy.zeros(num_obs, dtype=int) - 1
for j in range(num_obs):
sherpa.load_pha(data_set, spectra[j][i]) # load xaf_#_obs_####.pi and .arf and .rmf files.
sherpa.ignore_id(data_set, 0.0, energy[0])
sherpa.ignore_id(data_set, energy[1], None)
cnts[j] = sherpa.calc_data_sum(energy[0], energy[1], data_set)
cnt_rate = sherpa.get_rate(data_set, filter=True)
if len(cnt_rate) == 0:
max_rate[j] = 0.0 # when few counts (<50), get_rate can return zero-length array
else:
max_rate[j] = numpy.max(cnt_rate)
sherpa.subtract(data_set) # subtract background
sherpa.set_source(data_set, sherpa.xswabs.abs1 * sherpa.xsmekal.plsm1) # 1 temperature mekal model fit
good_src_ids[j] = data_set
data_set += 1 # same run for region but different obs
# Filter out ignored obs
good_src_ids_indx = numpy.where(good_src_ids >= 0)
good_src_ids = good_src_ids[good_src_ids_indx]
max_rate = max_rate[good_src_ids_indx]
cnts = cnts[good_src_ids_indx]
totcnts = numpy.sum(cnts)
if totcnts >= min_counts:
print('Fitting spectra in region: ' + str(i))
abs1.nH = nH_Gal
abs1.cache = 0
if fix_nH_Gal:
sherpa.freeze(abs1.nH)
else:
sherpa.thaw(abs1.nH)
plsm1.kt = kT_guess
sherpa.thaw(plsm1.kt)
plsm1.Abundanc = Ab_guess
if fix_abund:
sherpa.freeze(plsm1.Abundanc)
else:
sherpa.thaw(plsm1.Abundanc)
plsm1.redshift = redshift
sherpa.freeze(plsm1.redshift)
plsm1.cache = 0
sherpa.fit()
fit_result = sherpa.get_fit_results()
red_chi2 = fit_result.rstat
num_bins = fit_result.numpoints
if fix_nH_Gal:
nH = nH_Gal
kT = fit_result.parvals[0]
if fix_abund:
Z = Ab_guess
norm = fit_result.parvals[1]
else:
Z = fit_result.parvals[1]
norm = fit_result.parvals[2]
else:
nH = fit_result.parvals[0]
kT = fit_result.parvals[1]
if fix_abund:
Z = Ab_guess
norm = fit_result.parvals[2]
else:
Z = fit_result.parvals[2]
norm = fit_result.parvals[3]
del fit_result
if find_errors:
sherpa.covar()
covar_result = sherpa.get_covar_results()
if fix_nH_Gal:
nH_loerr = 0.0
nH_hierr = 0.0
kT_loerr = covar_result.parmins[0]
kT_hierr = covar_result.parmaxes[0]
if fix_abund:
Z_loerr = 0.0
Z_hierr = 0.0
norm_loerr = covar_result.parmins[1]
norm_hierr = covar_result.parmaxes[1]
else:
Z_loerr = covar_result.parmins[1]
Z_hierr = covar_result.parmaxes[1]
norm_loerr = covar_result.parmins[2]
norm_hierr = covar_result.parmaxes[2]
else:
nH_loerr = covar_result.parmins[0]
nH_hierr = covar_result.parmaxes[0]
kT_loerr = covar_result.parmins[1]
kT_hierr = covar_result.parmaxes[1]
if fix_abund:
Z_loerr = 0.0
Z_hierr = 0.0
norm_loerr = covar_result.parmins[2]
norm_hierr = covar_result.parmaxes[2]
else:
Z_loerr = covar_result.parmins[2]
Z_hierr = covar_result.parmaxes[2]
norm_loerr = covar_result.parmins[3]
norm_hierr = covar_result.parmaxes[3]
del covar_result
# Check for failed errors (= None) and set them to +/- best-fit value
if not fix_nH_Gal:
if nH_loerr is None: nH_loerr = -nH # is was ==
if nH_hierr is None: nH_hierr = nH
if kT_loerr is None: kT_loerr = -kT
if kT_hierr is None: kT_hierr = kT
if not fix_abund:
if Z_loerr is None: Z_loerr = -Z
if Z_hierr is None: Z_hierr = Z
if norm_loerr is None: norm_loerr = -norm
if norm_hierr is None: norm_hierr = norm
else:
kT_loerr = 0.0
Z_loerr = 0.0
nH_loerr = 0.0
norm_loerr = 0.0
kT_hierr = 0.0
Z_hierr = 0.0
nH_hierr = 0.0
norm_hierr = 0.0
else: # if total counts < min_counts, just write zeros
print('\n Warning: no fit performed for for region: ' + str(i))
print('\n Spectra have insufficient counts after filtering or do not exist.')
print('\n --> All parameters for this region set to 0.0.')
kT = 0.0
Z = 0.0
nH = 0.0
norm = 0.0
kT_loerr = 0.0
Z_loerr = 0.0
nH_loerr = 0.0
norm_loerr = 0.0
kT_hierr = 0.0
Z_hierr = 0.0
nH_hierr = 0.0
norm_hierr = 0.0
red_chi2 = 0.0
num_bins = 0
reg_id = spectra[0][i].split('_') # Splits string after every underscore so that region number can be accessed. reg_id[1] is accessed because that is the region number after 'xaf'
results_file.write('%7r %7.4f %7.4f %7.4f %7.4f %7.4f %7.4f %6.4e %6.4e %6.4e %7.4f %7.4f %7.4f %7.4f %8.1f %8r\n' % (int(reg_id[1]), kT, kT_loerr, kT_hierr, Z, Z_loerr, Z_hierr, norm, norm_loerr, norm_hierr, nH, nH_loerr, nH_hierr, red_chi2, totcnts, num_bins)) # Write all data to a file
results_file.close()