def test_beta_model_flux(answer_store, answer_dir):
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
r_c = 20.0
beta = 1.0
prng = 34
beta_src_pos = BetaModel(ra0, dec0, r_c, beta)
sim_cat = SimputCatalog.from_models("beta", "beta", spec, beta_src_pos,
exp_time, area, prng=prng)
sim_cat.write_catalog(overwrite=True)
instrument_simulator("beta_simput.fits", "beta_flux_evt.fits", exp_time,
"acisi_cy0", [ra0, dec0], ptsrc_bkgnd=False,
instr_bkgnd=False, foreground=False,
roll_angle=37.0, prng=prng)
wspec = spec.new_spec_from_band(0.5, 7.0)
make_exposure_map("beta_flux_evt.fits", "beta_expmap.fits", wspec.emid.value,
weights=wspec.flux.value, overwrite=True)
write_radial_profile("beta_flux_evt.fits", "beta_flux_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)
file_answer_testing("EVENTS", "beta_flux_evt.fits", answer_store, answer_dir)
file_answer_testing("PROFILE", "beta_flux_evt_profile.fits", answer_store, answer_dir)
os.chdir(curdir)
shutil.rmtree(tmpdir)
def test_annulus(answer_store, answer_dir):
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
r_in = 10.0
r_out = 30.0
ann_pos = AnnulusModel(ra0, dec0, r_in, r_out)
ann_src = SimputPhotonList.from_models("ann", spec, ann_pos,
exp_time, area, prng=prng)
sim_cat = SimputCatalog.from_source("ann_simput.fits", ann_src,
overwrite=True)
instrument_simulator("ann_simput.fits", "ann_evt.fits", exp_time,
"lynx_hdxi", [ra0, dec0], ptsrc_bkgnd=False,
instr_bkgnd=False, foreground=False, prng=prng)
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)
file_answer_testing("EVENTS", "ann_evt.fits", answer_store, answer_dir)
file_answer_testing("PROFILE", "ann_evt_profile.fits", answer_store,
answer_dir)
os.chdir(curdir)
shutil.rmtree(tmpdir)
def test_thermal(answer_store, answer_dir):
prng = RandomState(71)
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
spectrum_answer_testing(spec, "thermal_spec.h5", answer_store, answer_dir)
pt_src_pos = PointSourceModel(30.0, 45.0)
sim_cat = SimputCatalog.from_models("thermal_model", "thermal_model", spec,
pt_src_pos, exp_time, area, prng=prng)
sim_cat.write_catalog(overwrite=True)
instrument_simulator("thermal_model_simput.fits", "thermal_model_evt.fits", exp_time,
inst_name, [30.0, 45.0], ptsrc_bkgnd=False, foreground=False,
instr_bkgnd=False, prng=prng)
write_spectrum("thermal_model_evt.fits", "thermal_model_evt.pha", overwrite=True)
file_answer_testing("EVENTS", "thermal_model_evt.fits", answer_store, answer_dir)
file_answer_testing("SPECTRUM", "thermal_model_evt.pha", answer_store, answer_dir)
os.chdir(curdir)
shutil.rmtree(tmpdir)
def test_annulus(answer_store, answer_dir):
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
r_in = 10.0
r_out = 30.0
ann_pos = AnnulusModel(ra0, dec0, r_in, r_out)
sim_cat = SimputCatalog.from_models("ann", "ann", spec, ann_pos,
exp_time, area, prng=prng)
sim_cat.write_catalog(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)
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)
file_answer_testing("EVENTS", "ann_evt.fits", answer_store, answer_dir)
file_answer_testing("PROFILE", "ann_evt_profile.fits", answer_store, answer_dir)
os.chdir(curdir)
shutil.rmtree(tmpdir)
def test_add_background():
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
prng1 = RandomState(29)
prng2 = RandomState(29)
ra0 = 30.0
dec0 = 45.0
ra1 = 22.0
dec1 = 22.0
exp_time = 50000.0
instrument_simulator(None, "evt1.fits", exp_time, "lynx_hdxi",
[ra0, dec0], prng=prng1, overwrite=True)
make_background_file("bkg_evt.fits", exp_time, "lynx_hdxi", [ra0, dec0],
prng=prng2, overwrite=True)
instrument_simulator(None, "evt2.fits", exp_time, "lynx_hdxi",
[ra1, dec1], bkgnd_file="bkg_evt.fits",
prng=prng2, overwrite=True)
f1 = pyfits.open("evt1.fits")
f2 = pyfits.open("evt2.fits")
for key in ["X", "Y", "ENERGY", "PHA"]:
assert_allclose(f1["EVENTS"].data[key], f2["EVENTS"].data[key], rtol=1.0e-6)
f1.close()
f2.close()
os.chdir(curdir)
shutil.rmtree(tmpdir)
def test_beta_model(answer_store, answer_dir):
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
prng = 32
r_c = 20.0
beta = 1.0
exp_time = Quantity(500.0, "ks")
beta_src_pos = BetaModel(ra0, dec0, r_c, beta)
beta_src = SimputPhotonList.from_models("beta", spec, beta_src_pos,
exp_time, area, prng=prng)
sim_cat = SimputCatalog.from_source("beta_simput.fits", beta_src,
overwrite=True)
instrument_simulator("beta_simput.fits", "beta_evt.fits", exp_time,
"chandra_acisi_cy0", [ra0, dec0], ptsrc_bkgnd=False,
instr_bkgnd=False, foreground=False, prng=prng)
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)
file_answer_testing("EVENTS", "beta_evt.fits", answer_store,
answer_dir)
file_answer_testing("PROFILE", "beta_evt_profile.fits", answer_store,
answer_dir)
os.chdir(curdir)
shutil.rmtree(tmpdir)
def test_beta_model(answer_store, answer_dir):
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
prng = 32
r_c = 20.0
beta = 1.0
exp_time = Quantity(500.0, "ks")
beta_src_pos = BetaModel(ra0, dec0, r_c, beta)
sim_cat = SimputCatalog.from_models("beta", "beta", spec, beta_src_pos,
exp_time, area, prng=prng)
sim_cat.write_catalog(overwrite=True)
instrument_simulator("beta_simput.fits", "beta_evt.fits", exp_time,
"acisi_cy0", [ra0, dec0], ptsrc_bkgnd=False,
instr_bkgnd=False, foreground=False, prng=prng)
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)
file_answer_testing("EVENTS", "beta_evt.fits", answer_store, answer_dir)
file_answer_testing("PROFILE", "beta_evt_profile.fits", answer_store, answer_dir)
os.chdir(curdir)
shutil.rmtree(tmpdir)
def test_point_source():
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
pt_src = SimputSpectrum.from_spectrum("pt_src", spec, ra0, dec0)
cat = SimputCatalog.from_source("pt_src_simput.fits", pt_src,
overwrite=True)
inst = get_instrument_from_registry("lynx_hdxi")
inst["name"] = "hdxi_big_psf"
inst["psf"] = ["gaussian", 5.0]
add_instrument_to_registry(inst)
instrument_simulator("pt_src_simput.fits", "pt_src_evt.fits", exp_time,
"hdxi_big_psf", [ra0, dec0], ptsrc_bkgnd=False,
instr_bkgnd=False, foreground=False, prng=prng)
psf_scale = inst["psf"][1]
dtheta = inst["fov"]*60.0/inst["num_pixels"]
f = pyfits.open("pt_src_evt.fits")
x = f["EVENTS"].data["X"]
y = f["EVENTS"].data["Y"]
f.close()
scalex = np.std(x)*sigma_to_fwhm*dtheta
scaley = np.std(y)*sigma_to_fwhm*dtheta
assert (scalex - psf_scale)/psf_scale < 0.03
assert (scaley - psf_scale)/psf_scale < 0.03
os.chdir(curdir)
shutil.rmtree(tmpdir)
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")
fov = (10.0, "arcmin")
prng = 24
agen = ApecGenerator(0.05, 12.0, 5000, broadening=False)
spec = agen.get_spectrum(kT_sim, Z_sim, redshift, norm_sim)
spec.apply_foreground_absorption(norm_sim)
events = make_background(area, exp_time, fov, (30.0, 45.0), spec, prng=prng)
events.write_simput_file("bkgnd", overwrite=True)
instrument_simulator("bkgnd_simput.fits", "bkgnd_evt.fits",
exp_time, "sq_acisi_cy19", [30.0, 45.0],
overwrite=True, foreground=False, ptsrc_bkgnd=False,
instr_bkgnd=False,
prng=prng)
write_spectrum("bkgnd_evt.fits", "background_evt.pi", overwrite=True)
os.system("cp %s %s ." % (arf.filename, rmf.filename))
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()
res = get_fit_results()
assert np.abs(res.parvals[0]-nH_sim)/nH_sim < 0.1
assert np.abs(res.parvals[1]-kT_sim)/kT_sim < 0.05
assert np.abs(res.parvals[2]-Z_sim) < 0.05
assert np.abs(res.parvals[3]-norm_sim)/norm_sim < 0.05
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")
spec = Spectrum.from_powerlaw(alpha_sim, redshift, norm_sim,
emin=0.1, emax=11.5, nbins=2000)
spec.apply_foreground_absorption(nH_sim, model="tbabs")
positions = [(30.01, 45.0)]
events = make_point_sources(area, exp_time, positions, (30.0, 45.0),
spec, prng=prng)
events.write_simput_file("ptsrc", overwrite=True)
instrument_simulator("ptsrc_simput.fits", "ptsrc_evt.fits",
exp_time, "sq_aciss_cy19", [30.0, 45.0],
overwrite=True, foreground=False, ptsrc_bkgnd=False,
instr_bkgnd=False,
prng=prng)
write_spectrum("ptsrc_evt.fits", "point_source_evt.pi", overwrite=True)
os.system("cp %s %s ." % (arf.filename, rmf.filename))
load_user_model(mymodel, "tplaw")
add_user_pars("tplaw", ["nH", "norm", "redshift", "alpha"],
[0.02, 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=[True, False, True, False])
load_pha("point_source_evt.pi")
set_stat("cstat")
set_method("simplex")
ignore(":0.4, 9.0:")
set_model("tplaw")
fit()
res = get_fit_results()
assert np.abs(res.parvals[0]-norm_sim)/norm_sim < 0.05
assert np.abs(res.parvals[1]-alpha_sim)/alpha_sim < 0.05
os.chdir(curdir)
shutil.rmtree(tmpdir)
def test_beta_model_flux(answer_store, answer_dir):
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
r_c = 20.0
beta = 1.0
prng = 34
beta_src_pos = BetaModel(ra0, dec0, r_c, beta)
sim_cat = SimputCatalog.from_models("beta",
"beta",
spec,
beta_src_pos,
exp_time,
area,
prng=prng)
sim_cat.write_catalog(overwrite=True)
instrument_simulator("beta_simput.fits",
"beta_flux_evt.fits",
exp_time,
"acisi_cy0", [ra0, dec0],
ptsrc_bkgnd=False,
instr_bkgnd=False,
foreground=False,
roll_angle=37.0,
prng=prng)
wspec = spec.new_spec_from_band(0.5, 7.0)
make_exposure_map("beta_flux_evt.fits",
"beta_expmap.fits",
wspec.emid.value,
weights=wspec.flux.value,
overwrite=True)
write_radial_profile("beta_flux_evt.fits",
"beta_flux_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)
file_answer_testing("EVENTS", "beta_flux_evt.fits", answer_store,
answer_dir)
file_answer_testing("PROFILE", "beta_flux_evt_profile.fits", answer_store,
answer_dir)
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")
spec = Spectrum.from_powerlaw(alpha_sim, redshift, norm_sim,
emin=0.1, emax=11.5, nbins=2000)
spec.apply_foreground_absorption(nH_sim, model="tbabs")
positions = [(30.01, 45.0)]
events = make_point_sources(area, exp_time, positions, (30.0, 45.0),
spec, prng=prng)
events.write_simput_file("ptsrc", overwrite=True)
instrument_simulator("ptsrc_simput.fits", "ptsrc_evt.fits",
exp_time, "sq_aciss_cy20", [30.0, 45.0],
overwrite=True, foreground=False, ptsrc_bkgnd=False,
instr_bkgnd=False,
prng=prng)
write_spectrum("ptsrc_evt.fits", "point_source_evt.pi", overwrite=True)
os.system("cp %s %s ." % (arf.filename, rmf.filename))
load_user_model(mymodel, "tplaw")
add_user_pars("tplaw", ["nH", "norm", "redshift", "alpha"],
[0.02, 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=[True, False, True, False])
load_pha("point_source_evt.pi")
set_stat("cstat")
set_method("simplex")
ignore(":0.4, 9.0:")
set_model("tplaw")
fit()
res = get_fit_results()
assert np.abs(res.parvals[0]-norm_sim)/norm_sim < 0.05
assert np.abs(res.parvals[1]-alpha_sim)/alpha_sim < 0.05
os.chdir(curdir)
shutil.rmtree(tmpdir)
def plaw_fit(alpha_sim, answer_store, answer_dir):
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
nH_sim = 0.02
norm_sim = 1.0e-4
redshift = 0.01
exp_time = (50.0, "ks")
area = 40000.0
inst_name = "new_hdxi"
spec = Spectrum.from_powerlaw(alpha_sim, redshift, norm_sim, 0.1, 10.0,
20000)
spec.apply_foreground_absorption(nH_sim, model="tbabs")
spectrum_answer_testing(spec, f"power_law_{alpha_sim}.h5", answer_store,
answer_dir)
pt_src_pos = PointSourceModel(30.0, 45.0)
pt_src = SimputPhotonList.from_models("plaw_model",
spec,
pt_src_pos,
exp_time,
area,
prng=prng)
cat = SimputCatalog.from_source("plaw_model_simput.fits",
pt_src,
overwrite=True)
instrument_simulator("plaw_model_simput.fits",
f"plaw_model_{alpha_sim}_evt.fits",
exp_time,
inst_name, [30.0, 45.0],
instr_bkgnd=False,
ptsrc_bkgnd=False,
foreground=False,
prng=prng)
write_spectrum(f"plaw_model_{alpha_sim}_evt.fits",
f"plaw_model_{alpha_sim}_evt.pha",
overwrite=True)
file_answer_testing("EVENTS", f"plaw_model_{alpha_sim}_evt.fits",
answer_store, answer_dir)
file_answer_testing("SPECTRUM", f"plaw_model_{alpha_sim}_evt.pha",
answer_store, answer_dir)
os.chdir(curdir)
shutil.rmtree(tmpdir)
def test_emission_line(answer_store, answer_dir):
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
const_flux = 1.0e-4
line_pos = 5.0
line_width = 0.02
line_amp = 1.0e-5
exp_time = (100.0, "ks")
area = 30000.0
inst_name = "lynx_lxm"
spec = Spectrum.from_constant(const_flux, 1.0, 10.0, 20000)
spec.add_emission_line(line_pos, line_width, line_amp)
spectrum_answer_testing(spec, "emission_line_test.h5", answer_store,
answer_dir)
pt_src_pos = PointSourceModel(30.0, 45.0)
sim_cat = SimputCatalog.from_models("emission_line",
"emission_line",
spec,
pt_src_pos,
exp_time,
area,
prng=prng)
sim_cat.write_catalog(overwrite=True)
instrument_simulator("emission_line_simput.fits",
"emission_line_evt.fits",
exp_time,
inst_name, [30.0, 45.0],
instr_bkgnd=False,
ptsrc_bkgnd=False,
foreground=False,
prng=prng)
write_spectrum("emission_line_evt.fits",
"emission_line_evt.pha",
overwrite=True)
file_answer_testing("EVENTS", "emission_line_evt.fits", answer_store,
answer_dir)
file_answer_testing("SPECTRUM", "emission_line_evt.pha", answer_store,
answer_dir)
os.chdir(curdir)
shutil.rmtree(tmpdir)
def test_point_source():
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
e = spec.generate_energies(exp_time, area, prng=prng)
pt_src = PointSourceModel(ra0, dec0, e.size)
write_photon_list("pt_src",
"pt_src",
e.flux,
pt_src.ra,
pt_src.dec,
e,
overwrite=True)
inst = get_instrument_from_registry("hdxi")
inst["name"] = "hdxi_big_psf"
inst["psf"] = ["gaussian", 5.0]
add_instrument_to_registry(inst)
instrument_simulator("pt_src_simput.fits",
"pt_src_evt.fits",
exp_time,
"hdxi_big_psf", [ra0, dec0],
ptsrc_bkgnd=False,
instr_bkgnd=False,
foreground=False,
prng=prng)
psf_scale = inst["psf"][1]
dtheta = inst["fov"] * 60.0 / inst["num_pixels"]
f = pyfits.open("pt_src_evt.fits")
x = f["EVENTS"].data["X"]
y = f["EVENTS"].data["Y"]
f.close()
scalex = np.std(x) * sigma_to_fwhm * dtheta
scaley = np.std(y) * sigma_to_fwhm * dtheta
assert (scalex - psf_scale) / psf_scale < 0.01
assert (scaley - psf_scale) / psf_scale < 0.01
os.chdir(curdir)
shutil.rmtree(tmpdir)
def test_add_background():
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
prng1 = RandomState(29)
prng2 = RandomState(29)
ra0 = 30.0
dec0 = 45.0
ra1 = 22.0
dec1 = 22.0
exp_time = 50000.0
ra = np.array([])
dec = np.array([])
e = np.array([])
empty_cat = {"ra": [ra], "dec": [dec], "energy": [e],
"flux": [0.0], "emin": [0.1], "emax": [10.0],
"sources": ["empty"]}
instrument_simulator(empty_cat, "evt1.fits", exp_time, "hdxi",
[ra0, dec0], prng=prng1, overwrite=True)
make_background_file("bkg_evt.fits", exp_time, "hdxi", [ra0, dec0],
prng=prng2, overwrite=True)
instrument_simulator(empty_cat, "evt2.fits", exp_time, "hdxi",
[ra1, dec1], bkgnd_file="bkg_evt.fits",
prng=prng2, overwrite=True)
f1 = pyfits.open("evt1.fits")
f2 = pyfits.open("evt2.fits")
for key in ["X", "Y", "ENERGY", "PHA"]:
assert_allclose(f1["EVENTS"].data[key], f2["EVENTS"].data[key],
)
f1.close()
f2.close()
os.chdir(curdir)
shutil.rmtree(tmpdir)
def test_thermal_nei(answer_store, answer_dir):
prng = RandomState(71)
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
spectrum_answer_testing(spec_nei, "thermal_spec_nei.h5", answer_store,
answer_dir)
pt_src_pos = PointSourceModel(30.0, 45.0)
pt_src = SimputPhotonList.from_models("thermal_model_nei",
spec_nei,
pt_src_pos,
exp_time,
area,
prng=prng)
sim_cat = SimputCatalog.from_source("thermal_model_nei_simput.fits",
pt_src,
overwrite=True)
instrument_simulator("thermal_model_nei_simput.fits",
"thermal_model_nei_evt.fits",
exp_time,
inst_name, [30.0, 45.0],
ptsrc_bkgnd=False,
foreground=False,
instr_bkgnd=False,
prng=prng)
write_spectrum("thermal_model_nei_evt.fits",
"thermal_model_nei_evt.pha",
overwrite=True)
file_answer_testing("EVENTS", "thermal_model_nei_evt.fits", answer_store,
answer_dir)
file_answer_testing("SPECTRUM", "thermal_model_nei_evt.pha", answer_store,
answer_dir)
os.chdir(curdir)
shutil.rmtree(tmpdir)
def plaw_fit(alpha_sim, answer_store, answer_dir):
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
nH_sim = 0.02
norm_sim = 1.0e-4
redshift = 0.01
exp_time = (50.0, "ks")
area = 40000.0
inst_name = "new_hdxi"
spec = Spectrum.from_powerlaw(alpha_sim, redshift, norm_sim, 0.1, 10.0, 20000)
spec.apply_foreground_absorption(nH_sim, model="tbabs")
spectrum_answer_testing(spec, "power_law_%s.h5" % alpha_sim, answer_store,
answer_dir)
pt_src_pos = PointSourceModel(30.0, 45.0)
sim_cat = SimputCatalog.from_models("plaw_model", "plaw_model", spec, pt_src_pos,
exp_time, area, prng=prng)
sim_cat.write_catalog(overwrite=True)
instrument_simulator("plaw_model_simput.fits", "plaw_model_%s_evt.fits" % alpha_sim,
exp_time, inst_name, [30.0, 45.0], instr_bkgnd=False,
ptsrc_bkgnd=False, foreground=False, prng=prng)
write_spectrum("plaw_model_%s_evt.fits" % alpha_sim,
"plaw_model_%s_evt.pha" % alpha_sim,
overwrite=True)
file_answer_testing("EVENTS", "plaw_model_%s_evt.fits" % alpha_sim,
answer_store, answer_dir)
file_answer_testing("SPECTRUM", "plaw_model_%s_evt.pha" % alpha_sim,
answer_store, answer_dir)
os.chdir(curdir)
shutil.rmtree(tmpdir)
def test_point_source():
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
pt_src_pos = PointSourceModel(ra0, dec0)
sim_cat = SimputCatalog.from_models("pt_src", "pt_src", spec, pt_src_pos,
exp_time, area, prng=prng)
sim_cat.write_catalog(overwrite=True)
inst = get_instrument_from_registry("hdxi")
inst["name"] = "hdxi_big_psf"
inst["psf"] = ["gaussian", 5.0]
add_instrument_to_registry(inst)
instrument_simulator("pt_src_simput.fits", "pt_src_evt.fits", exp_time,
"hdxi_big_psf", [ra0, dec0], ptsrc_bkgnd=False,
instr_bkgnd=False, foreground=False, prng=prng)
psf_scale = inst["psf"][1]
dtheta = inst["fov"]*60.0/inst["num_pixels"]
f = pyfits.open("pt_src_evt.fits")
x = f["EVENTS"].data["X"]
y = f["EVENTS"].data["Y"]
f.close()
scalex = np.std(x)*sigma_to_fwhm*dtheta
scaley = np.std(y)*sigma_to_fwhm*dtheta
assert (scalex - psf_scale)/psf_scale < 0.03
assert (scaley - psf_scale)/psf_scale < 0.03
os.chdir(curdir)
shutil.rmtree(tmpdir)
def do_beta_model(source, v_field, em_field, axis="z",
prng=None):
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
if prng is None:
prng = source.prng
ds = source.ds
A = 30000.
exp_time = 1.0e4
redshift = 0.05
nH_sim = 0.02
sphere = ds.sphere("c", (0.5, "Mpc"))
kT_sim = source.kT
Z_sim = source.Z
thermal_model = ThermalSourceModel("apec", 0.1, 11.5, 20000,
Zmet=Z_sim, prng=prng)
photons = PhotonList.from_data_source(sphere, redshift, A, exp_time,
thermal_model)
D_A = photons.parameters["fid_d_a"]
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)
if isinstance(axis, string_types):
if axis == "z":
fac = 1.0
else:
fac = 0.0
else:
axis /= np.sqrt(np.dot(axis, axis))
fac = np.dot(axis, [0.0, 0.0, 1.0])
sigma_sim = fac*float(v1.in_units("km/s"))
mu_sim = -fac*float(v2.in_units("km/s"))
events = photons.project_photons(axis, [30.0, 45.0], absorb_model="tbabs",
nH=nH_sim, prng=prng)
events.write_simput_file("beta_model", overwrite=True)
instrument_simulator("beta_model_simput.fits", "beta_model_evt.fits",
exp_time, "mucal", [30.0, 45.0],
overwrite=True, foreground=False, ptsrc_bkgnd=False,
instr_bkgnd=False,
prng=prng)
write_spectrum("beta_model_evt.fits", "beta_model_evt.pi", overwrite=True)
os.system("cp %s %s ." % (arf.filename, rmf.filename))
load_user_model(mymodel, "tbapec")
add_user_pars("tbapec", ["nH", "kT", "metallicity", "redshift", "norm", "velocity"],
[0.02, 4.0, 0.2, 0.04, norm_sim*0.8, 300.0],
parmins=[0.0, 0.1, 0.0, -200.0, 0.0, 0.0],
parmaxs=[10.0, 20.0, 10.0, 200.0, 1.0e9, 20000.0],
parfrozen=[True, False, False, False, False, False])
load_pha("beta_model_evt.pi")
set_stat("cstat")
set_method("levmar")
ignore(":0.6, 8.0:")
set_model("tbapec")
fit()
res = get_fit_results()
redshift_sim = (1.0+mu_sim/ckms)*(1.0+redshift) - 1.0
assert np.abs(res.parvals[0]-kT_sim)/kT_sim < 0.05
assert np.abs(res.parvals[1]-Z_sim)/Z_sim < 0.05
assert np.abs(res.parvals[2]-redshift_sim)/redshift_sim < 0.05
assert np.abs(res.parvals[3]-norm_sim) < 0.05
assert np.abs(res.parvals[4]-sigma_sim) < 30.0
os.chdir(curdir)
shutil.rmtree(tmpdir)
def make_mosaic_events(pointing_list,
input_source,
out_prefix,
exp_time,
instrument,
overwrite=False,
instr_bkgnd=True,
foreground=True,
ptsrc_bkgnd=True,
bkgnd_file=None,
no_dither=False,
dither_params=None,
subpixel_res=False,
aimpt_shift=None,
prng=None):
"""
Observe a source from many different pointings.
Parameters
----------
pointing_list : list of tuples or str
Either a list of tuples or a two-column ASCII table, containing
RA and Dec pointings for each mock observation.
input_source : string
The path to the SIMPUT catalog file which contains the input
source(s).
out_prefix : string
The prefix for the event files which will be generated.
exp_time : float, (value, unit) tuple, or :class:`~astropy.units.Quantity`
The exposure time in seconds.
instrument : string
The name of the instrument to use, which picks an instrument
specification from the instrument registry.
overwrite : boolean, optional
Whether or not to overwrite an existing file with the same name.
Default: False
instr_bkgnd : boolean, optional
Whether or not to include the instrumental/particle background.
Default: True
foreground : boolean, optional
Whether or not to include the local foreground.
Default: True
ptsrc_bkgnd : boolean, optional
Whether or not to include the point-source background.
Default: True
bkgnd_file : string, optional
If set, backgrounds will be loaded from this file and not generated
on the fly. Default: None
no_dither : boolean, optional
If True, turn off dithering entirely. Default: False
dither_params : array-like of floats, optional
The parameters to use to control the size and period of the dither
pattern. The first two numbers are the dither amplitude in x and y
detector coordinates in arcseconds, and the second two numbers are
the dither period in x and y detector coordinates in seconds.
Default: [8.0, 8.0, 1000.0, 707.0].
subpixel_res: boolean, optional
If True, event positions are not randomized within the pixels
within which they are detected. Default: False
aimpt_shift : array-like, optional
A two-float array-like object which shifts the aimpoint on the
detector from the nominal position. Units are in arcseconds.
Default: None, which results in no shift from the nominal aimpoint.
prng : :class:`~numpy.random.RandomState` object, integer, or None
A pseudo-random number generator. Typically will only
be specified if you have a reason to generate the same
set of random numbers, such as for a test. Default is None,
which sets the seed based on the system time.
"""
if isinstance(pointing_list, str):
t = ascii.read(pointing_list,
format='commented_header',
guess=False,
header_start=0,
delimiter="\t")
elif not isinstance(pointing_list, Table):
t = Table(np.array(pointing_list), names=["ra", "dec"])
out_list = []
for i, row in enumerate(t):
out_file = f"{out_prefix}_{i}_evt.fits"
out_list.append(out_file)
instrument_simulator(input_source,
out_file,
exp_time,
instrument, (row["ra"], row["dec"]),
overwrite=overwrite,
instr_bkgnd=instr_bkgnd,
foreground=foreground,
ptsrc_bkgnd=ptsrc_bkgnd,
bkgnd_file=bkgnd_file,
no_dither=no_dither,
dither_params=dither_params,
subpixel_res=subpixel_res,
aimpt_shift=aimpt_shift,
prng=prng)
t["evtfile"] = out_list
outfile = f"{out_prefix}_event_mosaic.dat"
mylog.info(f"Writing mosaic information to {outfile}.")
t.write(outfile,
overwrite=overwrite,
delimiter="\t",
format='ascii.commented_header')
return outfile
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 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_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 plaw_fit(alpha_sim, prng=None):
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
bms = BetaModelSource()
ds = bms.ds
if prng is None:
prng = bms.prng
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
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["fid_d_a"]
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", [30., 45.],
absorb_model="wabs",
nH=nH_sim,
prng=bms.prng,
no_shifting=True)
events.write_simput_file("plaw", overwrite=True)
instrument_simulator("plaw_simput.fits",
"plaw_evt.fits",
exp_time,
"sq_acisi_cy19", [30.0, 45.0],
overwrite=True,
foreground=False,
ptsrc_bkgnd=False,
instr_bkgnd=False,
prng=prng)
write_spectrum("plaw_evt.fits", "plaw_model_evt.pi", overwrite=True)
os.system("cp %s %s ." % (arf.filename, rmf.filename))
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()
res = get_fit_results()
assert np.abs(res.parvals[0] - nH_sim) / nH_sim < 0.1
assert np.abs(res.parvals[1] - norm_sim) / norm_sim < 0.05
assert np.abs(res.parvals[2] - alpha_sim) / alpha_sim < 0.05
os.chdir(curdir)
shutil.rmtree(tmpdir)
def test_add_background():
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
prng1 = RandomState(29)
prng2 = RandomState(29)
ra0 = 30.0
dec0 = 45.0
ra1 = 22.0
dec1 = 22.0
exp_time = 50000.0
ra = np.array([])
dec = np.array([])
e = np.array([])
empty_cat = {
"ra": [ra],
"dec": [dec],
"energy": [e],
"flux": [0.0],
"emin": [0.1],
"emax": [10.0],
"sources": ["empty"]
}
instrument_simulator(empty_cat,
"evt1.fits",
exp_time,
"hdxi", [ra0, dec0],
prng=prng1,
overwrite=True)
make_background_file("bkg_evt.fits",
exp_time,
"hdxi", [ra0, dec0],
prng=prng2,
overwrite=True)
instrument_simulator(empty_cat,
"evt2.fits",
exp_time,
"hdxi", [ra1, dec1],
bkgnd_file="bkg_evt.fits",
prng=prng2,
overwrite=True)
f1 = pyfits.open("evt1.fits")
f2 = pyfits.open("evt2.fits")
for key in ["X", "Y", "ENERGY", "PHA"]:
assert_allclose(
f1["EVENTS"].data[key],
f2["EVENTS"].data[key],
)
f1.close()
f2.close()
os.chdir(curdir)
shutil.rmtree(tmpdir)
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_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")
fov = (10.0, "arcmin")
prng = 24
agen = ApecGenerator(0.05, 12.0, 5000, broadening=False)
spec = agen.get_spectrum(kT_sim, Z_sim, redshift, norm_sim)
spec.apply_foreground_absorption(norm_sim)
events = make_background(area,
exp_time,
fov, (30.0, 45.0),
spec,
prng=prng)
events.write_simput_file("bkgnd", overwrite=True)
instrument_simulator("bkgnd_simput.fits",
"bkgnd_evt.fits",
exp_time,
"sq_acisi_cy19", [30.0, 45.0],
overwrite=True,
foreground=False,
ptsrc_bkgnd=False,
instr_bkgnd=False,
prng=prng)
write_spectrum("bkgnd_evt.fits", "background_evt.pi", overwrite=True)
os.system("cp %s %s ." % (arf.filename, rmf.filename))
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()
res = get_fit_results()
assert np.abs(res.parvals[0] - nH_sim) / nH_sim < 0.1
assert np.abs(res.parvals[1] - kT_sim) / kT_sim < 0.05
assert np.abs(res.parvals[2] - Z_sim) < 0.05
assert np.abs(res.parvals[3] - norm_sim) / norm_sim < 0.05
os.chdir(curdir)
shutil.rmtree(tmpdir)
