def test_line_emission():
bms = BetaModelSource()
ds = bms.ds
def _dm_emission(field, data):
return cross_section * (data["dark_matter_density"] /
m_chi)**2 * data["cell_volume"]
ds.add_field(("gas", "dm_emission"), function=_dm_emission, units="s**-1")
location = YTQuantity(3.5, "keV")
sigma = YTQuantity(1000., "km/s")
sigma_E = (location * sigma / clight).in_units("keV")
A = YTQuantity(1000., "cm**2")
exp_time = YTQuantity(2.0e5, "s")
redshift = 0.01
sphere = ds.sphere("c", (100., "kpc"))
line_model = LineSourceModel(location,
"dm_emission",
sigma="dark_matter_dispersion",
prng=32)
photons = PhotonList.from_data_source(sphere, redshift, A, exp_time,
line_model)
D_A = photons.parameters["fid_d_a"]
dist_fac = 1.0 / (4. * np.pi * D_A * D_A * (1. + redshift)**3)
dm_E = (sphere["dm_emission"]).sum()
E = uconcatenate(photons["energy"])
n_E = len(E)
n_E_pred = (exp_time * A * dm_E * dist_fac).in_units("dimensionless")
loc = location / (1. + redshift)
sig = sigma_E / (1. + redshift)
assert np.abs(loc - E.mean()) < 1.645 * sig / np.sqrt(n_E)
assert np.abs(E.std()**2 -
sig * sig) < 1.645 * np.sqrt(2 * (n_E - 1)) * sig**2 / n_E
assert np.abs(n_E - n_E_pred) < 1.645 * np.sqrt(n_E)
def test_line_emission():
bms = BetaModelSource()
ds = bms.ds
def _dm_emission(field, data):
return cross_section*(data["dark_matter_density"]/m_chi)**2*data["cell_volume"]
ds.add_field(("gas","dm_emission"), function=_dm_emission, units="s**-1")
location = YTQuantity(3.5, "keV")
sigma = YTQuantity(1000., "km/s")
sigma_E = (location*sigma/clight).in_units("keV")
A = YTQuantity(1000., "cm**2")
exp_time = YTQuantity(2.0e5, "s")
redshift = 0.01
sphere = ds.sphere("c", (100.,"kpc"))
line_model = LineSourceModel(location, "dm_emission",
sigma="dark_matter_dispersion", prng=32)
photons = PhotonList.from_data_source(sphere, redshift, A, exp_time,
line_model)
D_A = photons.parameters["fid_d_a"]
dist_fac = 1.0/(4.*np.pi*D_A*D_A*(1.+redshift)**3)
dm_E = (sphere["dm_emission"]).sum()
E = uconcatenate(photons["energy"])
n_E = len(E)
n_E_pred = (exp_time*A*dm_E*dist_fac).in_units("dimensionless")
loc = location/(1.+redshift)
sig = sigma_E/(1.+redshift)
assert np.abs(loc-E.mean()) < 1.645*sig/np.sqrt(n_E)
assert np.abs(E.std()**2-sig*sig) < 1.645*np.sqrt(2*(n_E-1))*sig**2/n_E
assert np.abs(n_E-n_E_pred) < 1.645*np.sqrt(n_E)
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)
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_vapec_beta_model():
bms = BetaModelSource()
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
prng = 45
ds = bms.ds
A = 30000.
exp_time = 1.0e4
redshift = 0.05
nH_sim = 0.02
sphere = ds.sphere("c", (0.5, "Mpc"))
kT_sim = bms.kT
Z_sim = bms.Z
O_sim = bms.O
Ca_sim = bms.Ca
var_elem = {"O": ("stream", "oxygen"),
"Ca": ("stream", "calcium")}
thermal_model = ThermalSourceModel("apec", 0.1, 11.5, 20000,
var_elem=var_elem,
Zmet=("gas","metallicity"),
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("emission_measure")
norm_sim *= 1.0e-14/(4*np.pi*D_A*D_A*(1.+redshift)*(1.+redshift))
norm_sim = float(norm_sim.in_cgs())
events = photons.project_photons("z", [30.0, 45.0], absorb_model="tbabs",
nH=nH_sim, prng=prng, no_shifting=True)
new_events = Lynx_Calorimeter(events, prng=prng)
os.system("cp %s %s ." % (arf.filename, rmf.filename))
new_events.write_channel_spectrum("var_abund_beta_model_evt.pha", overwrite=True)
load_user_model(mymodel_var, "tbapec")
add_user_pars("tbapec", ["nH", "kT", "abund", "redshift", "norm", "O", "Ca"],
[nH_sim, 4.0, Z_sim, redshift, norm_sim*0.8, 0.3, 0.5],
parmins=[0.0, 0.1, 0.0, -20.0, 0.0, 0.0, 0.0],
parmaxs=[10.0, 20.0, 10.0, 20.0, 1.0e9, 10.0, 10.0],
parfrozen=[True, False, True, True, False, False, False])
load_pha("var_abund_beta_model_evt.pha")
set_stat("cstat")
set_method("levmar")
ignore(":0.6, 8.0:")
set_model("tbapec")
fit()
res = get_fit_results()
assert np.abs(res.parvals[0]-kT_sim)/kT_sim < 0.05
assert np.abs(res.parvals[1]-norm_sim)/norm_sim < 0.05
assert np.abs(res.parvals[2]-O_sim)/O_sim < 0.05
assert np.abs(res.parvals[3]-Ca_sim)/Ca_sim < 0.15
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 test_sloshing():
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
prng = RandomState(0x4d3d3d3)
ds = data_dir_load(gslr)
A = 2000.
exp_time = 1.0e4
redshift = 0.1
sphere = ds.sphere("c", (0.1, "Mpc"))
sphere.set_field_parameter("X_H", 0.75)
thermal_model = ThermalSourceModel("apec", 0.1, 11.0, 10000, Zmet=0.3,
thermal_broad=False, prng=prng)
photons1 = PhotonList.from_data_source(sphere, redshift, A, exp_time,
thermal_model)
return_photons = return_data(photons1.photons)
nphots = 0
for i in range(4):
phots = PhotonList.from_data_source(sphere, redshift, A, 0.25*exp_time,
thermal_model)
phots.write_h5_file("split_photons_%d.h5" % i)
nphots += len(phots.photons["energy"])
merge_files(["split_photons_%d.h5" % i for i in range(4)],
"merged_photons.h5", add_exposure_times=True,
overwrite=True)
merged_photons = PhotonList.from_file("merged_photons.h5")
assert len(merged_photons.photons["energy"]) == nphots
assert merged_photons.parameters["fid_exp_time"] == exp_time
events1 = photons1.project_photons([1.0,-0.5,0.2], [30., 45.],
absorb_model="tbabs", nH=0.1, prng=prng)
return_events = return_data(events1.events)
events1.write_spectrum("test_events_spec.fits", 0.2, 10.0, 2000)
f = pyfits.open("test_events_spec.fits")
return_spec = return_data(f["SPECTRUM"].data["COUNTS"])
f.close()
events1.write_fits_image("test_events_img.fits", (20.0, "arcmin"),
1024)
f = pyfits.open("test_events_img.fits")
return_img = return_data(f[0].data)
f.close()
tests = [GenericArrayTest(ds, return_photons, args=["photons"]),
GenericArrayTest(ds, return_events, args=["events"]),
GenericArrayTest(ds, return_spec, args=["spec"]),
GenericArrayTest(ds, return_img, args=["img"])]
for test in tests:
test_sloshing.__name__ = test.description
yield test
photons1.write_h5_file("test_photons.h5")
events1.write_h5_file("test_events.h5")
events1.write_fits_file("test_events.fits", 20.0, 1024)
photons2 = PhotonList.from_file("test_photons.h5")
events2 = EventList.from_h5_file("test_events.h5")
events3 = EventList.from_fits_file("test_events.fits")
for k in photons1.keys():
if k == "energy":
arr1 = uconcatenate(photons1[k])
arr2 = uconcatenate(photons2[k])
else:
arr1 = photons1[k]
arr2 = photons2[k]
assert_array_equal(arr1, arr2)
for k in events2.keys():
assert_array_equal(events1[k], events2[k])
assert_allclose(events2[k], events3[k], rtol=1.0e-6)
nevents = 0
for i in range(4):
events = photons1.project_photons([1.0, -0.5, 0.2], [30., 45.],
absorb_model="tbabs", nH=0.1,
prng=prng)
events.write_h5_file("split_events_%d.h5" % i)
nevents += len(events["xsky"])
merge_files(["split_events_%d.h5" % i for i in range(4)],
"merged_events.h5", add_exposure_times=True,
overwrite=True)
merged_events = EventList.from_h5_file("merged_events.h5")
assert len(merged_events["xsky"]) == nevents
assert merged_events.parameters["exp_time"] == 4.0*exp_time
os.chdir(curdir)
shutil.rmtree(tmpdir)
def test_sloshing():
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
prng = RandomState(0x4d3d3d3)
ds = data_dir_load(gslr)
A = 2000.
exp_time = 1.0e4
redshift = 0.1
apec_model = TableApecModel(0.1, 11.0, 10000)
tbabs_model = TBabsModel(0.1)
sphere = ds.sphere("c", (0.1, "Mpc"))
sphere.set_field_parameter("X_H", 0.75)
thermal_model = ThermalSourceModel(apec_model, Zmet=0.3, prng=prng)
photons1 = PhotonList.from_data_source(sphere, redshift, A, exp_time,
thermal_model)
return_photons = return_data(photons1.photons)
tests = [GenericArrayTest(ds, return_photons, args=["photons"])]
for a, r, i in zip(arfs, rmfs, instruments):
arf = AuxiliaryResponseFile(a, rmffile=r)
events1 = photons1.project_photons([1.0, -0.5, 0.2],
area_new=arf.max_area,
absorb_model=tbabs_model,
prng=prng)
events1["xsky"]
events1 = i(events1, rebin=False, convolve_psf=False, prng=prng)
return_events = return_data(events1.events)
tests.append(GenericArrayTest(ds, return_events, args=[a]))
for test in tests:
test_sloshing.__name__ = test.description
yield test
photons1.write_h5_file("test_photons.h5")
events1.write_h5_file("test_events.h5")
photons2 = PhotonList.from_file("test_photons.h5")
events2 = EventList.from_h5_file("test_events.h5")
for k in photons1.keys():
if k == "Energy":
arr1 = uconcatenate(photons1[k])
arr2 = uconcatenate(photons2[k])
else:
arr1 = photons1[k]
arr2 = photons2[k]
yield assert_array_equal, arr1, arr2
for k in events1.keys():
yield assert_array_equal, events1[k], events2[k]
nevents = 0
for i in range(4):
events = photons1.project_photons([1.0, -0.5, 0.2],
exp_time_new=0.25 * exp_time,
absorb_model=tbabs_model,
prng=prng)
events.write_h5_file("split_events_%d.h5" % i)
nevents += len(events["xsky"])
merge_files(["split_events_%d.h5" % i for i in range(4)],
"merged_events.h5",
add_exposure_times=True,
clobber=True)
merged_events = EventList.from_h5_file("merged_events.h5")
assert len(merged_events["xsky"]) == nevents
assert merged_events.parameters["ExposureTime"] == exp_time
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)