def test_binned_fit():
with within_directory(datasets_dir):
start, stop = 0, 50
poly = [1]
arrival_times = np.loadtxt('test_event_data.txt')
evt_list = EventListWithDeadTime(
arrival_times=arrival_times,
measurement=np.zeros_like(arrival_times),
n_channels=1,
start_time=arrival_times[0],
stop_time=arrival_times[-1],
dead_time=np.zeros_like(arrival_times))
evt_list.set_polynomial_fit_interval("%f-%f" % (start + 1, stop - 1),
unbinned=False)
evt_list.set_active_time_intervals("0-1")
results = evt_list.get_poly_info()['coefficients']
assert evt_list.time_intervals == TimeIntervalSet.from_list_of_edges(
[0, 1])
assert evt_list._poly_counts.sum() > 0
evt_list.__repr__()
def test_pha_write_no_bkg():
with within_directory(__example_dir):
# custom remove background
f = fits.open("test.pha")
f["SPECTRUM"].data["BACKFILE"] = "NONE"
f.writeto("test_pha_nobkg.pha", overwrite=True)
ogip = OGIPLike("test_ogip", observation="test_pha_nobkg.pha{1}")
ogip.write_pha("test_write_nobkg", overwrite=True)
written_ogip = OGIPLike("write_ogip", observation="test_write_nobkg.pha{1}")
pha_info = written_ogip.get_pha_files()
for key in ["pha"]:
assert isinstance(pha_info[key], PHASpectrum)
f = fits.open("test_write_nobkg.pha")
assert f["SPECTRUM"].data["BACKFILE"][0] == "NONE"
assert pha_info["pha"].background_file is None
assert pha_info["pha"].ancillary_file is None
assert pha_info["pha"].instrument == "GBM_NAI_03"
assert pha_info["pha"].mission == "GLAST"
assert pha_info["pha"].is_poisson == True
assert pha_info["pha"].n_channels == len(pha_info["pha"].rates)
def test_binned_fit():
with within_directory(datasets_dir):
start, stop = 0, 50
poly = [1]
arrival_times = np.loadtxt('test_event_data.txt')
evt_list = EventListWithDeadTime(arrival_times=arrival_times,
measurement=np.zeros_like(arrival_times),
n_channels=1,
start_time=arrival_times[0],
stop_time=arrival_times[-1],
dead_time=np.zeros_like(arrival_times)
)
evt_list.set_polynomial_fit_interval("%f-%f" % (start + 1, stop - 1), unbinned=False)
evt_list.set_active_time_intervals("0-1")
results = evt_list.get_poly_info()['coefficients']
assert evt_list.time_intervals == TimeIntervalSet.from_list_of_edges([0,1])
assert evt_list._poly_counts.sum() > 0
evt_list.__repr__()
def test_read_gbm_cspec():
with within_directory(datasets_directory):
data_dir = os.path.join('gbm', 'bn080916009')
nai3 = TimeSeriesBuilder.from_gbm_cspec_or_ctime('NAI3',
os.path.join(data_dir, "glg_cspec_n3_bn080916009_v01.pha"),
rsp_file=os.path.join(data_dir,
"glg_cspec_n3_bn080916009_v00.rsp2"),
poly_order=-1)
nai3.set_active_time_interval('0-1')
assert not nai3.time_series.poly_fit_exists
assert nai3.time_series.binned_spectrum_set.n_channels>0
nai3.set_background_interval('-20--10', '100-200')
assert nai3.time_series.poly_fit_exists
speclike = nai3.to_spectrumlike()
assert isinstance(speclike, DispersionSpectrumLike)
assert not speclike.background_spectrum.is_poisson
speclike = nai3.to_spectrumlike(extract_measured_background=True)
assert isinstance(speclike, DispersionSpectrumLike)
assert speclike.background_spectrum.is_poisson
nai3.write_pha_from_binner('test_from_nai3', start=0, stop=2, overwrite=True)
def test_read_gbm_cspec():
with within_directory(datasets_directory):
data_dir = os.path.join('gbm', 'bn080916009')
nai3 = TimeSeriesBuilder.from_gbm_cspec_or_ctime('NAI3',
os.path.join(data_dir, "glg_cspec_n3_bn080916009_v01.pha"),
rsp_file=os.path.join(data_dir,
"glg_cspec_n3_bn080916009_v00.rsp2"),
poly_order=-1)
nai3.set_active_time_interval('0-1')
assert not nai3.time_series.poly_fit_exists
assert nai3.time_series.binned_spectrum_set.n_channels>0
nai3.set_background_interval('-20--10', '100-200')
assert nai3.time_series.poly_fit_exists
speclike = nai3.to_spectrumlike()
assert isinstance(speclike, DispersionSpectrumLike)
assert not speclike.background_spectrum.is_poisson
speclike = nai3.to_spectrumlike(extract_measured_background=True)
assert isinstance(speclike, DispersionSpectrumLike)
assert speclike.background_spectrum.is_poisson
nai3.write_pha_from_binner('test_from_nai3', start=0, stop=2, overwrite=True)
def test_various_effective_area():
with within_directory(__example_dir):
ogip = OGIPLike("test_ogip", observation="test.pha{1}")
ogip.use_effective_area_correction()
ogip.fix_effective_area_correction()
def test_loading_xrt():
with within_directory(datasets_dir):
xrt_dir = 'xrt'
xrt = SwiftXRTLike("XRT", observation=os.path.join(xrt_dir, "xrt_src.pha"),
background=os.path.join(xrt_dir, "xrt_bkg.pha"),
response=os.path.join(xrt_dir, "xrt.rmf"),
arf_file=os.path.join(xrt_dir, "xrt.arf"))
def test_simulating_data_sets():
with within_directory(__example_dir):
ogip = OGIPLike('test_ogip', observation='test.pha{1}')
with pytest.raises(AssertionError):
_ = ogip.simulated_parameters
n_data_points = 128
ogip.set_active_measurements("all")
assert ogip._n_synthetic_datasets == 0
ab = AnalysisBuilder(ogip)
_ = ab.get_jl('normal')
new_ogip = ogip.get_simulated_dataset('sim')
assert new_ogip.name == 'sim'
assert ogip._n_synthetic_datasets == 1
assert new_ogip.n_data_points == n_data_points
assert new_ogip.n_data_points == sum(new_ogip._mask)
assert sum(new_ogip._mask) == new_ogip.n_data_points
assert new_ogip.tstart == 0.
assert 'cons_sim' in new_ogip.nuisance_parameters
assert new_ogip.nuisance_parameters['cons_sim'].fix == True
assert new_ogip.nuisance_parameters['cons_sim'].free == False
pha_info = new_ogip.get_pha_files()
assert 'pha' in pha_info
assert 'bak' in pha_info
assert 'rsp' in pha_info
del ogip
del new_ogip
ogip = OGIPLike('test_ogip', observation='test.pha{1}')
ab = AnalysisBuilder(ogip)
_ = ab.get_jl('normal')
# Now check that generationing a lot of data sets works
sim_data_sets = [
ogip.get_simulated_dataset('sim%d' % i) for i in range(100)
]
assert len(sim_data_sets) == ogip._n_synthetic_datasets
for i, ds in enumerate(sim_data_sets):
assert ds.name == "sim%d" % i
assert sum(ds._mask) == sum(ogip._mask)
assert ds._rebinner is None
def test_loading_xrt():
with within_directory(datasets_dir):
xrt_dir = 'xrt'
xrt = SwiftXRTLike("XRT",
observation=os.path.join(xrt_dir, "xrt_src.pha"),
background=os.path.join(xrt_dir, "xrt_bkg.pha"),
response=os.path.join(xrt_dir, "xrt.rmf"),
arf_file=os.path.join(xrt_dir, "xrt.arf"))
def test_loading_bad_keywords_file():
with within_directory(__example_dir):
pha_fn = "example_integral_spi.pha"
rsp_fn = "example_integral_spi.rsp"
pha_spectrum = PHASpectrum(pha_fn, rsp_file=rsp_fn)
assert type(pha_spectrum.is_poisson) == bool
ogip = OGIPLike("test_ogip", observation=pha_fn, response=rsp_fn)
ogip.__repr__()
def test_swift_gbm():
with within_directory(__example_dir):
gbm_dir = "gbm"
bat_dir = "bat"
bat = OGIPLike(
"BAT",
observation=os.path.join(bat_dir, "gbm_bat_joint_BAT.pha"),
response=os.path.join(bat_dir, "gbm_bat_joint_BAT.rsp"),
)
bat.set_active_measurements("15-150")
bat.view_count_spectrum()
nai6 = OGIPLike(
"n6",
os.path.join(gbm_dir, "gbm_bat_joint_NAI_06.pha"),
os.path.join(gbm_dir, "gbm_bat_joint_NAI_06.bak"),
os.path.join(gbm_dir, "gbm_bat_joint_NAI_06.rsp"),
spectrum_number=1,
)
nai6.set_active_measurements("8-900")
nai6.view_count_spectrum()
bgo0 = OGIPLike(
"b0",
os.path.join(gbm_dir, "gbm_bat_joint_BGO_00.pha"),
os.path.join(gbm_dir, "gbm_bat_joint_BGO_00.bak"),
os.path.join(gbm_dir, "gbm_bat_joint_BGO_00.rsp"),
spectrum_number=1,
)
bgo0.set_active_measurements("250-10000")
bgo0.view_count_spectrum()
bat.use_effective_area_correction(0.2, 1.5)
bat.fix_effective_area_correction(0.6)
bat.use_effective_area_correction(0.2, 1.5)
band = Band()
model = Model(PointSource("joint_fit", 0, 0, spectral_shape=band))
band.K = 0.04
band.xp = 300.0
data_list = DataList(bat, nai6, bgo0)
jl = JointLikelihood(model, data_list)
_ = jl.fit()
_ = display_spectrum_model_counts(jl, step=False)
def test_reading_of_written_pha():
with within_directory(datasets_directory):
# check the number of items written
with fits.open('test_from_nai3.rsp') as f:
# 2 ext + 5 rsp ext
assert len(f) == 7
# make sure we can read spectrum number
_ = OGIPLike('test', observation='test_from_nai3.pha', spectrum_number=1)
_ = OGIPLike('test', observation='test_from_nai3.pha', spectrum_number=2)
os.remove('test_from_nai3.pha')
def test_reading_of_written_pha():
with within_directory(datasets_directory):
# check the number of items written
with fits.open('test_from_nai3.rsp') as f:
# 2 ext + 5 rsp ext
assert len(f) == 7
# make sure we can read spectrum number
_ = OGIPLike('test', observation='test_from_nai3.pha', spectrum_number=1)
_ = OGIPLike('test', observation='test_from_nai3.pha', spectrum_number=2)
os.remove('test_from_nai3.pha')
def test_ogip_energy_selection():
with within_directory(__example_dir):
ogip = OGIPLike("test_ogip", observation="test.pha{1}")
assert sum(ogip._mask) == sum(ogip.quality.good)
# Test that selecting a subset reduces the number of data points
ogip.set_active_measurements("10-30")
assert sum(ogip._mask) == ogip.n_data_points
assert sum(ogip._mask) < 128
# Test selecting all channels
ogip.set_active_measurements("all")
assert sum(ogip._mask) == ogip.n_data_points
assert sum(ogip._mask) == 128
# Test channel setting
ogip.set_active_measurements(exclude=["c0-c1"])
assert sum(ogip._mask) == ogip.n_data_points
assert sum(ogip._mask) == 126
# Test mixed ene/chan setting
ogip.set_active_measurements(exclude=["0-c1"], verbose=True)
assert sum(ogip._mask) == ogip.n_data_points
assert sum(ogip._mask) == 126
# Test that energies cannot be input backwards
with pytest.raises(AssertionError):
ogip.set_active_measurements("50-30")
with pytest.raises(AssertionError):
ogip.set_active_measurements("c20-c10")
with pytest.raises(AssertionError):
ogip.set_active_measurements("c100-0")
with pytest.raises(AssertionError):
ogip.set_active_measurements("c1-c200")
with pytest.raises(AssertionError):
ogip.set_active_measurements("10-c200")
ogip.set_active_measurements("reset")
assert sum(ogip._mask) == sum(ogip.quality.good)
def test_swift_gbm():
with within_directory(__example_dir):
gbm_dir = "gbm"
bat_dir = "bat"
bat = OGIPLike('BAT',
observation=os.path.join(bat_dir,
'gbm_bat_joint_BAT.pha'),
response=os.path.join(bat_dir, 'gbm_bat_joint_BAT.rsp'))
bat.set_active_measurements('15-150')
bat.view_count_spectrum()
nai6 = OGIPLike('n6',
os.path.join(gbm_dir, 'gbm_bat_joint_NAI_06.pha'),
os.path.join(gbm_dir, 'gbm_bat_joint_NAI_06.bak'),
os.path.join(gbm_dir, 'gbm_bat_joint_NAI_06.rsp'),
spectrum_number=1)
nai6.set_active_measurements('8-900')
nai6.view_count_spectrum()
bgo0 = OGIPLike('b0',
os.path.join(gbm_dir, 'gbm_bat_joint_BGO_00.pha'),
os.path.join(gbm_dir, 'gbm_bat_joint_BGO_00.bak'),
os.path.join(gbm_dir, 'gbm_bat_joint_BGO_00.rsp'),
spectrum_number=1)
bgo0.set_active_measurements('250-10000')
bgo0.view_count_spectrum()
bat.use_effective_area_correction(.2, 1.5)
bat.fix_effective_area_correction(.6)
bat.use_effective_area_correction(.2, 1.5)
band = Band()
model = Model(PointSource('joint_fit', 0, 0, spectral_shape=band))
band.K = .04
band.xp = 300.
data_list = DataList(bat, nai6, bgo0)
jl = JointLikelihood(model, data_list)
_ = jl.fit()
_ = display_spectrum_model_counts(jl, step=False)
def test_read_lle():
with within_directory(datasets_directory):
data_dir = "lat"
lle = TimeSeriesBuilder.from_lat_lle(
"lle",
os.path.join(data_dir, "gll_lle_bn080916009_v10.fit"),
os.path.join(data_dir, "gll_pt_bn080916009_v10.fit"),
rsp_file=os.path.join(data_dir, "gll_cspec_bn080916009_v10.rsp"),
poly_order=-1,
)
lle.view_lightcurve()
lle.set_active_time_interval("0-10")
lle.set_background_interval("-150-0", "100-250", unbinned=False)
speclike = lle.to_spectrumlike()
assert isinstance(speclike, DispersionSpectrumLike)
# will test background with lle data
old_coefficients, old_errors = lle.get_background_parameters()
old_tmin_list = lle._time_series.poly_intervals
lle.save_background("temp_lle", overwrite=True)
lle = TimeSeriesBuilder.from_lat_lle(
"lle",
os.path.join(data_dir, "gll_lle_bn080916009_v10.fit"),
os.path.join(data_dir, "gll_pt_bn080916009_v10.fit"),
rsp_file=os.path.join(data_dir, "gll_cspec_bn080916009_v10.rsp"),
restore_background="temp_lle.h5",
)
new_coefficients, new_errors = lle.get_background_parameters()
new_tmin_list = lle._time_series.poly_intervals
assert new_coefficients == old_coefficients
assert new_errors == old_errors
assert old_tmin_list == new_tmin_list
def test_read_gbm_cspec():
with within_directory(datasets_directory):
data_dir = os.path.join('gbm', 'bn080916009')
nai3 = TimeSeriesBuilder.from_gbm_cspec_or_ctime('NAI3',
os.path.join(data_dir, "glg_cspec_n3_bn080916009_v01.pha"),
rsp_file=os.path.join(data_dir,
"glg_cspec_n3_bn080916009_v00.rsp2"),
poly_order=-1)
nai3.set_active_time_interval('0-1')
nai3.set_background_interval('-200--10', '100-200')
speclike = nai3.to_spectrumlike()
assert isinstance(speclike, DispersionSpectrumLike)
nai3.write_pha_from_binner('test_from_nai3', start=0, stop=2, overwrite=True)
def test_likelihood_ratio_test():
with within_directory(__example_dir):
ogip = OGIPLike('test_ogip', observation='test.pha{1}')
ogip.set_active_measurements("all")
ab = AnalysisBuilder(ogip)
jl1 = ab.get_jl('normal')
res1, _ = jl1.fit(compute_covariance=True)
jl2 = ab.get_jl('cpl')
res2, _ = jl2.fit(compute_covariance=True)
lrt = LikelihoodRatioTest(jl1, jl2)
null_hyp_prob, TS, data_frame, like_data_frame = lrt.by_mc(
n_iterations=50, continue_on_failure=True)
def test_read_lle():
with within_directory(datasets_directory):
data_dir = 'lat'
lle = TimeSeriesBuilder.from_lat_lle('lle', os.path.join(data_dir, "gll_lle_bn080916009_v10.fit"),
os.path.join(data_dir, "gll_pt_bn080916009_v10.fit"),
rsp_file=os.path.join(data_dir, "gll_cspec_bn080916009_v10.rsp"),
poly_order=-1)
lle.view_lightcurve()
lle.set_active_time_interval("0-10")
lle.set_background_interval("-150-0", "100-250")
speclike = lle.to_spectrumlike()
assert isinstance(speclike, DispersionSpectrumLike)
# will test background with lle data
old_coefficients, old_errors = lle.get_background_parameters()
old_tmin_list = lle._time_series.poly_intervals
lle.save_background('temp_lle', overwrite=True)
lle = TimeSeriesBuilder.from_lat_lle('lle', os.path.join(data_dir, "gll_lle_bn080916009_v10.fit"),
os.path.join(data_dir, "gll_pt_bn080916009_v10.fit"),
rsp_file=os.path.join(data_dir, "gll_cspec_bn080916009_v10.rsp"),
restore_background='temp_lle.h5')
new_coefficients, new_errors = lle.get_background_parameters()
new_tmin_list = lle._time_series.poly_intervals
assert new_coefficients == old_coefficients
assert new_errors == old_errors
assert old_tmin_list == new_tmin_list
def test_xrt():
with within_directory(__example_dir):
trigger = "GRB110731A"
dec = -28.546
ra = 280.52
xrt_dir = 'xrt'
xrt = SwiftXRTLike("XRT",
observation=os.path.join(xrt_dir, "xrt_src.pha"),
background=os.path.join(xrt_dir, "xrt_bkg.pha"),
response=os.path.join(xrt_dir, "xrt.rmf"),
arf_file=os.path.join(xrt_dir, "xrt.arf"))
spectral_model = Powerlaw()
ptsrc = PointSource(trigger, ra, dec, spectral_shape=spectral_model)
model = Model(ptsrc)
data = DataList(xrt)
jl = JointLikelihood(model, data, verbose=False)
def test_loading_a_generic_pha_file():
with within_directory(__example_dir):
ogip = OGIPLike('test_ogip', observation='test.pha{1}')
pha_info = ogip.get_pha_files()
assert ogip.name == 'test_ogip'
assert ogip.n_data_points == sum(ogip._mask)
assert sum(ogip._mask) == ogip.n_data_points
assert ogip.tstart == 0.
assert ogip.tstop == 9.95012
assert 'cons_test_ogip' in ogip.nuisance_parameters
assert ogip.nuisance_parameters['cons_test_ogip'].fix == True
assert ogip.nuisance_parameters['cons_test_ogip'].free == False
assert 'pha' in pha_info
assert 'bak' in pha_info
assert 'rsp' in pha_info
ogip.__repr__()
def test_pha_write():
with within_directory(__example_dir):
ogip = OGIPLike('test_ogip', observation='test.pha{1}')
ogip.write_pha('test_write', overwrite=True)
written_ogip = OGIPLike('write_ogip', observation='test_write.pha{1}')
pha_info = written_ogip.get_pha_files()
for key in ['pha', 'bak']:
assert isinstance(pha_info[key], PHASpectrum)
assert pha_info['pha'].background_file == 'test_bak.pha{1}'
assert pha_info['pha'].ancillary_file is None
assert pha_info['pha'].instrument == 'GBM_NAI_03'
assert pha_info['pha'].mission == 'GLAST'
assert pha_info['pha'].is_poisson == True
assert pha_info['pha'].n_channels == len(pha_info['pha'].rates)
def test_loading_a_loose_ogip_pha_file():
with within_directory(__example_dir):
ogip = OGIPLike("test_ogip", observation="example_integral.pha")
pha_info = ogip.get_pha_files()
assert ogip.name == "test_ogip"
assert ogip.n_data_points == sum(ogip._mask)
assert sum(ogip._mask) == ogip.n_data_points
# assert ogip.tstart is None
# assert ogip.tstop is None
assert "cons_test_ogip" in ogip.nuisance_parameters
assert ogip.nuisance_parameters["cons_test_ogip"].fix == True
assert ogip.nuisance_parameters["cons_test_ogip"].free == False
assert "pha" in pha_info
# assert 'bak' in pha_info
assert "rsp" in pha_info
ogip.__repr__()
def test_pha_write():
with within_directory(__example_dir):
ogip = OGIPLike("test_ogip", observation="test.pha{1}")
ogip.write_pha("test_write", overwrite=True)
written_ogip = OGIPLike("write_ogip", observation="test_write.pha{1}")
pha_info = written_ogip.get_pha_files()
for key in ["pha", "bak"]:
assert isinstance(pha_info[key], PHASpectrum)
assert pha_info["pha"].background_file == "test_bak.pha{1}"
assert pha_info["pha"].ancillary_file is None
assert pha_info["pha"].instrument == "GBM_NAI_03"
assert pha_info["pha"].mission == "GLAST"
assert pha_info["pha"].is_poisson == True
assert pha_info["pha"].n_channels == len(pha_info["pha"].rates)
def test_loading_a_generic_pha_file():
with within_directory(__example_dir):
ogip = OGIPLike("test_ogip", observation="test.pha{1}")
pha_info = ogip.get_pha_files()
assert ogip.name == "test_ogip"
assert ogip.n_data_points == sum(ogip._mask)
assert sum(ogip._mask) == ogip.n_data_points
assert ogip.tstart == 0.0
assert ogip.tstop == 9.95012
assert "cons_test_ogip" in ogip.nuisance_parameters
assert ogip.nuisance_parameters["cons_test_ogip"].fix == True
assert ogip.nuisance_parameters["cons_test_ogip"].free == False
assert "pha" in pha_info
assert "bak" in pha_info
assert "rsp" in pha_info
ogip.__repr__()
def test_ogip_rebinner():
with within_directory(__example_dir):
ogip = OGIPLike("test_ogip", observation="test.pha{1}")
n_data_points = 128
ogip.set_active_measurements("all")
assert ogip.n_data_points == n_data_points
ogip.rebin_on_background(min_number_of_counts=100)
assert ogip.n_data_points < 128
with pytest.raises(AssertionError):
ogip.set_active_measurements("all")
ogip.remove_rebinning()
assert ogip._rebinner is None
assert ogip.n_data_points == n_data_points
ogip.view_count_spectrum()
def test_hist_constructor():
with within_directory(__this_dir__):
bins=[-3,-2,-1,0,1,2,3]
bounds = IntervalSet.from_list_of_edges(bins)
contents = np.ones(len(bins) - 1)
hh1 = Histogram(bounds, contents, is_poisson=True)
assert hh1.is_poisson == True
assert len(hh1) == len(bins)-1
hh1.display()
# rnum = np.loadtxt('test_hist_data.txt')
#
#
# #rnum = np.random.randn(1000)
# hrnum = np.histogram(rnum, bins=bins, normed=False)
# hh2 = Histogram.from_numpy_histogram(hrnum, is_poisson=True)
# hh3 = Histogram.from_entries(bounds,rnum)
#
# assert hh2==hh3
hh4 = Histogram(bounds, contents, errors=contents)
assert hh4.is_poisson == False
with pytest.raises(AssertionError):
hh4 = Histogram(bounds, contents, errors=contents,is_poisson=True)
def test_hist_constructor():
with within_directory(__this_dir__):
bins = [-3, -2, -1, 0, 1, 2, 3]
bounds = IntervalSet.from_list_of_edges(bins)
contents = np.ones(len(bins) - 1)
hh1 = Histogram(bounds, contents, is_poisson=True)
assert hh1.is_poisson == True
assert len(hh1) == len(bins) - 1
hh1.display()
# rnum = np.loadtxt('test_hist_data.txt')
#
#
# #rnum = np.random.randn(1000)
# hrnum = np.histogram(rnum, bins=bins, normed=False)
# hh2 = Histogram.from_numpy_histogram(hrnum, is_poisson=True)
# hh3 = Histogram.from_entries(bounds,rnum)
#
# assert hh2==hh3
hh4 = Histogram(bounds, contents, errors=contents)
assert hh4.is_poisson == False
with pytest.raises(AssertionError):
hh4 = Histogram(bounds, contents, errors=contents, is_poisson=True)
def test_spectrum_like_with_background_model():
energies = np.logspace(1, 3, 51)
low_edge = energies[:-1]
high_edge = energies[1:]
sim_K = 1e-1
sim_kT = 20.0
# get a blackbody source function
source_function = Blackbody(K=sim_K, kT=sim_kT)
# power law background function
background_function = Powerlaw(K=5, index=-1.5, piv=100.0)
spectrum_generator = SpectrumLike.from_function(
"fake",
source_function=source_function,
background_function=background_function,
energy_min=low_edge,
energy_max=high_edge,
)
background_plugin = SpectrumLike.from_background("background",
spectrum_generator)
bb = Blackbody()
pl = Powerlaw()
pl.piv = 100
bkg_ps = PointSource("bkg", 0, 0, spectral_shape=pl)
bkg_model = Model(bkg_ps)
jl_bkg = JointLikelihood(bkg_model, DataList(background_plugin))
_ = jl_bkg.fit()
plugin_bkg_model = SpectrumLike("full",
spectrum_generator.observed_spectrum,
background=background_plugin)
pts = PointSource("mysource", 0, 0, spectral_shape=bb)
model = Model(pts)
# MLE fitting
jl = JointLikelihood(model, DataList(plugin_bkg_model))
result = jl.fit()
K_variates = jl.results.get_variates("mysource.spectrum.main.Blackbody.K")
kT_variates = jl.results.get_variates(
"mysource.spectrum.main.Blackbody.kT")
assert np.all(
np.isclose([K_variates.average, kT_variates.average], [sim_K, sim_kT],
rtol=0.5))
## test with ogiplike
with within_directory(__example_dir):
ogip = OGIPLike("test_ogip",
observation="test.pha{1}",
background=background_plugin)
def test_read_gbm_tte():
with within_directory(datasets_directory):
data_dir = os.path.join('gbm', 'bn080916009')
nai3 = TimeSeriesBuilder.from_gbm_tte('NAI3',
os.path.join(data_dir, "glg_tte_n3_bn080916009_v01.fit.gz"),
rsp_file=os.path.join(data_dir, "glg_cspec_n3_bn080916009_v00.rsp2"),
poly_order=-1)
nai3.set_active_time_interval('0-1')
nai3.set_background_interval('-20--10', '100-200')
speclike = nai3.to_spectrumlike()
assert isinstance(speclike, DispersionSpectrumLike)
assert not speclike.background_spectrum.is_poisson
speclike = nai3.to_spectrumlike(extract_measured_background=True)
assert isinstance(speclike, DispersionSpectrumLike)
assert speclike.background_spectrum.is_poisson
# test binning
# should not have bins yet
with pytest.raises(RuntimeError):
nai3.bins
# First catch the errors
# This is without specifying the correct options name
with pytest.raises(RuntimeError):
nai3.create_time_bins(start=0, stop=10, method='constant')
with pytest.raises(RuntimeError):
nai3.create_time_bins(start=0, stop=10, method='significance')
with pytest.raises(RuntimeError):
nai3.create_time_bins(start=0, stop=10, method='constant', p0=.1)
with pytest.raises(RuntimeError):
nai3.create_time_bins(start=0, stop=10, method='significance', dt=1)
# now incorrect options
with pytest.raises(RuntimeError):
nai3.create_time_bins(start=0, stop=10, method='not_a_method')
# Now test values
nai3.create_time_bins(start=0, stop=10, method='constant', dt=1)
assert len(nai3.bins) == 10
assert nai3.bins.argsort() == range(len(nai3.bins))
nai3.create_time_bins(start=0, stop=10, method='bayesblocks', p0=.1)
assert nai3.bins.argsort() == range(len(nai3.bins))
assert len(nai3.bins) == 5
nai3.create_time_bins(start=0, stop=10, method='significance', sigma=40)
assert nai3.bins.argsort() == range(len(nai3.bins))
assert len(nai3.bins) == 5
nai3.view_lightcurve(use_binner=True)
nai3.write_pha_from_binner('test_from_nai3', overwrite=True)
def test_read_gbm_tte():
with within_directory(datasets_directory):
data_dir = os.path.join('gbm', 'bn080916009')
nai3 = TimeSeriesBuilder.from_gbm_tte(
'NAI3',
os.path.join(data_dir, "glg_tte_n3_bn080916009_v01.fit.gz"),
rsp_file=os.path.join(data_dir,
"glg_cspec_n3_bn080916009_v00.rsp2"),
poly_order=-1)
nai3.set_active_time_interval('0-1')
nai3.set_background_interval('-20--10', '100-200')
speclike = nai3.to_spectrumlike()
assert isinstance(speclike, DispersionSpectrumLike)
assert not speclike.background_spectrum.is_poisson
speclike = nai3.to_spectrumlike(extract_measured_background=True)
assert isinstance(speclike, DispersionSpectrumLike)
assert speclike.background_spectrum.is_poisson
# test binning
# should not have bins yet
with pytest.raises(RuntimeError):
nai3.bins
# First catch the errors
# This is without specifying the correct options name
with pytest.raises(RuntimeError):
nai3.create_time_bins(start=0, stop=10, method='constant')
with pytest.raises(RuntimeError):
nai3.create_time_bins(start=0, stop=10, method='significance')
with pytest.raises(RuntimeError):
nai3.create_time_bins(start=0, stop=10, method='constant', p0=.1)
with pytest.raises(RuntimeError):
nai3.create_time_bins(start=0,
stop=10,
method='significance',
dt=1)
# now incorrect options
with pytest.raises(RuntimeError):
nai3.create_time_bins(start=0, stop=10, method='not_a_method')
# Now test values
nai3.create_time_bins(start=0, stop=10, method='constant', dt=1)
assert len(nai3.bins) == 10
assert nai3.bins.argsort() == range(len(nai3.bins))
nai3.create_time_bins(start=0, stop=10, method='bayesblocks', p0=.1)
assert nai3.bins.argsort() == range(len(nai3.bins))
assert len(nai3.bins) == 5
nai3.create_time_bins(start=0,
stop=10,
method='significance',
sigma=40)
assert nai3.bins.argsort() == range(len(nai3.bins))
assert len(nai3.bins) == 5
nai3.view_lightcurve(use_binner=True)
nai3.write_pha_from_binner('test_from_nai3', overwrite=True)
def test_read_gbm_tte():
with within_directory(datasets_directory):
data_dir = os.path.join("gbm", "bn080916009")
nai3 = TimeSeriesBuilder.from_gbm_tte(
"NAI3",
os.path.join(data_dir, "glg_tte_n3_bn080916009_v01.fit.gz"),
rsp_file=os.path.join(data_dir,
"glg_cspec_n3_bn080916009_v00.rsp2"),
poly_order=-1,
)
nai3.set_active_time_interval("0-1")
nai3.set_background_interval("-20--10", "100-200", unbinned=False)
speclike = nai3.to_spectrumlike()
assert isinstance(speclike, DispersionSpectrumLike)
assert not speclike.background_spectrum.is_poisson
speclike = nai3.to_spectrumlike(extract_measured_background=True)
assert isinstance(speclike, DispersionSpectrumLike)
assert speclike.background_spectrum.is_poisson
# test binning
# should not have bins yet
with pytest.raises(RuntimeError):
nai3.bins
# First catch the errors
# This is without specifying the correct options name
with pytest.raises(RuntimeError):
nai3.create_time_bins(start=0, stop=10, method="constant")
with pytest.raises(RuntimeError):
nai3.create_time_bins(start=0, stop=10, method="significance")
with pytest.raises(RuntimeError):
nai3.create_time_bins(start=0, stop=10, method="constant", p0=0.1)
with pytest.raises(RuntimeError):
nai3.create_time_bins(start=0,
stop=10,
method="significance",
dt=1)
# now incorrect options
with pytest.raises(RuntimeError):
nai3.create_time_bins(start=0, stop=10, method="not_a_method")
# Now test values
nai3.create_time_bins(start=0, stop=10, method="constant", dt=1)
assert len(nai3.bins) == 10
assert nai3.bins.argsort() == list(range(len(nai3.bins)))
nai3.create_time_bins(start=0, stop=10, method="bayesblocks", p0=0.1)
assert nai3.bins.argsort() == list(range(len(nai3.bins)))
assert len(nai3.bins) == 5
nai3.create_time_bins(start=0,
stop=10,
method="significance",
sigma=40)
assert nai3.bins.argsort() == list(range(len(nai3.bins)))
assert len(nai3.bins) == 5
nai3.view_lightcurve(use_binner=True)
nai3.write_pha_from_binner("test_from_nai3",
overwrite=True,
force_rsp_write=True)
def test_loading_ogip():
with within_directory(datasets_dir):
_ = OGIPLike('test_ogip', observation='test.pha{1}')
def test_loading_ogip():
with within_directory(datasets_dir):
_ = OGIPLike("test_ogip", observation="test.pha{1}")
def test_pha_files_in_generic_ogip_constructor_spec_number_in_file_name():
with within_directory(__example_dir):
ogip = OGIPLike('test_ogip', observation='test.pha{1}')
ogip.set_active_measurements('all')
pha_info = ogip.get_pha_files()
for key in ['pha', 'bak']:
assert isinstance(pha_info[key], PHASpectrum)
assert pha_info['pha'].background_file == 'test_bak.pha{1}'
assert pha_info['pha'].ancillary_file is None
assert pha_info['pha'].instrument == 'GBM_NAI_03'
assert pha_info['pha'].mission == 'GLAST'
assert pha_info['pha'].is_poisson == True
assert pha_info['pha'].n_channels == ogip.n_data_points
assert pha_info['pha'].n_channels == len(pha_info['pha'].rates)
# Test that Poisson rates cannot call rate error
assert pha_info['pha'].rate_errors is None
assert sum(pha_info['pha'].sys_errors == np.zeros_like(
pha_info['pha'].rates)) == pha_info['bak'].n_channels
assert pha_info['pha'].response_file.split(
'/')[-1] == 'glg_cspec_n3_bn080916009_v07.rsp'
assert pha_info['pha'].scale_factor == 1.0
assert pha_info['bak'].background_file is None
# Test that we cannot get a bak file
#
#
# with pytest.raises(KeyError):
#
# _ = pha_info['bak'].background_file
# Test that we cannot get a anc file
# with pytest.raises(KeyError):
#
# _ = pha_info['bak'].ancillary_file
# Test that we cannot get a RSP file
assert pha_info['bak'].response_file is None
assert pha_info['bak'].ancillary_file is None
# with pytest.raises(AttributeError):
# _ = pha_info['bak'].response_file
assert pha_info['bak'].instrument == 'GBM_NAI_03'
assert pha_info['bak'].mission == 'GLAST'
assert pha_info['bak'].is_poisson == False
assert pha_info['bak'].n_channels == ogip.n_data_points
assert pha_info['bak'].n_channels == len(pha_info['pha'].rates)
assert len(pha_info['bak'].rate_errors) == pha_info['bak'].n_channels
assert sum(pha_info['bak'].sys_errors == np.zeros_like(
pha_info['pha'].rates)) == pha_info['bak'].n_channels
assert pha_info['bak'].scale_factor == 1.0
assert isinstance(pha_info['rsp'], OGIPResponse)
def test_pha_files_in_generic_ogip_constructor_spec_number_in_arguments():
with within_directory(__example_dir):
ogip = OGIPLike("test_ogip", observation="test.pha", spectrum_number=1)
ogip.set_active_measurements("all")
pha_info = ogip.get_pha_files()
for key in ["pha", "bak"]:
assert isinstance(pha_info[key], PHASpectrum)
assert pha_info["pha"].background_file == "test_bak.pha{1}"
assert pha_info["pha"].ancillary_file is None
assert pha_info["pha"].instrument == "GBM_NAI_03"
assert pha_info["pha"].mission == "GLAST"
assert pha_info["pha"].is_poisson == True
assert pha_info["pha"].n_channels == ogip.n_data_points
assert pha_info["pha"].n_channels == len(pha_info["pha"].rates)
# Test that Poisson rates cannot call rate error
assert pha_info["pha"].rate_errors is None
assert (
sum(pha_info["pha"].sys_errors == np.zeros_like(pha_info["pha"].rates))
== pha_info["bak"].n_channels
)
assert (
pha_info["pha"].response_file.split("/")[-1]
== "glg_cspec_n3_bn080916009_v07.rsp"
)
assert pha_info["pha"].scale_factor == 1.0
assert pha_info["bak"].background_file is None
# Test that we cannot get a bak file
#
# with pytest.raises(KeyError):
#
# _ = pha_info['bak'].background_file
#
# Test that we cannot get a anc file
# with pytest.raises(KeyError):
#
# _ = pha_info['bak'].ancillary_file
assert pha_info["bak"].response_file is None
assert pha_info["bak"].ancillary_file is None
# # Test that we cannot get a RSP file
# with pytest.raises(AttributeError):
# _ = pha_info['bak'].response_file
assert pha_info["bak"].instrument == "GBM_NAI_03"
assert pha_info["bak"].mission == "GLAST"
assert pha_info["bak"].is_poisson == False
assert pha_info["bak"].n_channels == ogip.n_data_points
assert pha_info["bak"].n_channels == len(pha_info["pha"].rates)
assert len(pha_info["bak"].rate_errors) == pha_info["bak"].n_channels
assert (
sum(pha_info["bak"].sys_errors == np.zeros_like(pha_info["pha"].rates))
== pha_info["bak"].n_channels
)
assert pha_info["bak"].scale_factor == 1.0
assert isinstance(pha_info["rsp"], OGIPResponse)
