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_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 boxbod_func(pars, x):
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 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)
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Compute results with Sherpa"""
from __future__ import print_function, division
# __doctest_skip__
__doctest_skip__ = ['*']
import numpy as np
import sherpa.astro.ui as sau
sau.load_data('counts.fits.gz')
sau.set_source('normgauss2d.source + const2d.background')
sau.set_stat('cstat')
# Ask for high-precision results
sau.set_method_opt('ftol', 1e-20)
sau.set_covar_opt('eps', 1e-20)
# 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()
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 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)
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 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)
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Compute results with Sherpa"""
import numpy as np
import sherpa.astro.ui as sau
sau.load_data('counts.fits.gz')
sau.set_source('normgauss2d.source + const2d.background')
sau.set_stat('cstat')
# Ask for high-precision results
sau.set_method_opt('ftol', 1e-20)
sau.set_covar_opt('eps', 1e-20)
# 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]
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Compute results with Sherpa"""
from __future__ import print_function, division
import numpy as np
import sherpa.astro.ui as sau
sau.load_data("counts.fits.gz")
sau.set_source("normgauss2d.source + const2d.background")
sau.set_stat("cstat")
# Ask for high-precision results
sau.set_method_opt("ftol", 1e-20)
sau.set_covar_opt("eps", 1e-20)
# 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 = 1.0 / 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]
