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_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_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_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 _create_timeseries(self):
"""
create all the time series for each detector
:return: None
"""
self._time_series = collections.OrderedDict()
for det_num in range(14):
# detectors are arranged [time,det,channel]
# for now just keep the normal exposure
# we will create binned spectra for each time slice
drm_gen = DRMGenTrig(
self._qauts,
self._sc_pos,
det_num, # det number
tstart=self._tstart,
tstop=self._tstop,
mat_type=2,
time=0,
occult=True
)
# we will use a single response for each detector
tmp_drm = BALROG_DRM(drm_gen, 0, 0)
# extract the counts
counts = self._rates[:, det_num, :] * self._time_intervals.widths.reshape(
(len(self._time_intervals), 1)
)
# now create a binned spectrum for each interval
binned_spectrum_list = []
for c, start, stop in zip(counts, self._tstart, self._tstop):
binned_spectrum_list.append(
BinnedSpectrumWithDispersion(
counts=c,
exposure=stop - start,
response=tmp_drm,
tstart=start,
tstop=stop,
)
)
# make a binned spectrum set
bss = BinnedSpectrumSet(
binned_spectrum_list,
reference_time=0.0,
time_intervals=self._time_intervals,
)
# convert that set to a series
bss2 = BinnedSpectrumSeries(bss, first_channel=0)
# now we need to get the name of the detector
name = lu[det_num]
if self._restore_poly_fit is not None:
bkg_fit_file = self._restore_poly_fit.get(name, None)
else:
bkg_fit_file = None
# create a time series builder which can produce plugins
tsb = TimeSeriesBuilder(
name,
bss2,
response=tmp_drm,
verbose=self._verbose,
poly_order=self._poly_order,
restore_poly_fit=bkg_fit_file,
)
# attach that to the full list
self._time_series[name] = tsb
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 _set_plugins(self):
"""
Set the plugins using the saved background hdf5 files
:return:
"""
det_ts = []
det_rsp = []
for det in self._use_dets:
# set up responses
tte_file = f"{base_dir}/{self._grb_name}/tte/data/glg_tte_{det}_bn{self._grb_name[3:]}_{self._version}.fit"
cspec_file = f"{base_dir}/{self._grb_name}/tte/data/glg_cspec_{det}_bn{self._grb_name[3:]}_{self._version}.pha"
rsp = drm.DRMGenTTE(tte_file=tte_file,
trigdat=self._trigdat_file,
mat_type=2,
cspecfile=cspec_file,
occult=True)
det_rsp.append(rsp)
# Time Series
gbm_tte_file = GBMTTEFile(tte_file)
event_list = EventListWithDeadTime(
arrival_times=gbm_tte_file.arrival_times -
gbm_tte_file.trigger_time,
measurement=gbm_tte_file.energies,
n_channels=gbm_tte_file.n_channels,
start_time=gbm_tte_file.tstart - gbm_tte_file.trigger_time,
stop_time=gbm_tte_file.tstop - gbm_tte_file.trigger_time,
dead_time=gbm_tte_file.deadtime,
first_channel=0,
instrument=gbm_tte_file.det_name,
mission=gbm_tte_file.mission,
verbose=True,
)
success_restore = False
i = 0
while not success_restore:
try:
ts = TimeSeriesBuilder(
det,
event_list,
response=BALROG_DRM(rsp, 0.0, 0.0),
unbinned=False,
verbose=True,
container_type=BinnedSpectrumWithDispersion,
restore_poly_fit=self._bkg_fit_files[det],
)
success_restore = True
i = 0
except:
time.sleep(1)
i += 1
if i == 50:
raise AssertionError("Can not restore background fit...")
ts.set_active_time_interval(
f"{self._active_time_start}-{self._active_time_stop}")
det_ts.append(ts)
# Mean of active time
rsp_time = (float(self._active_time_start) +
float(self._active_time_stop)) / 2
# Spectrum Like
det_sl = []
# set up energy range
for series in det_ts:
if series._name != "b0" and series._name != "b1":
sl = series.to_spectrumlike()
sl.set_active_measurements("8.1-700")
det_sl.append(sl)
else:
sl = series.to_spectrumlike()
sl.set_active_measurements("350-25000")
det_sl.append(sl)
# Make Balrog Like
det_bl = []
for i, det in enumerate(self._use_dets):
det_bl.append(
drm.BALROGLike.from_spectrumlike(det_sl[i],
rsp_time,
det_rsp[i],
free_position=True))
self._data_list = DataList(*det_bl)
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 _build_bkg_plugins(self):
"""
Build the background plugins for all dets
:return:
"""
# Time selection from yaml file
with open(self._time_selection_file_path, "r") as f:
data = yaml.safe_load(f)
active_time = (
f"{data['active_time']['start']}-{data['active_time']['stop']}"
)
background_time_neg = f"{data['background_time']['before']['start']}-{data['background_time']['before']['stop']}"
background_time_pos = f"{data['background_time']['after']['start']}-{data['background_time']['after']['stop']}"
poly_order = data["poly_order"]
det_ts = []
for det in _gbm_detectors:
# Response Setup
rsp = BALROG_DRM(
drm.DRMGenTTE(
tte_file=self._tte_files[det],
trigdat=self._trigdat_file,
mat_type=2,
cspecfile=self._cspec_files[det],
),
0.0,
0.0,
)
# Time Series
gbm_tte_file = GBMTTEFile(self._tte_files[det])
event_list = EventListWithDeadTime(
arrival_times=gbm_tte_file.arrival_times -
gbm_tte_file.trigger_time,
measurement=gbm_tte_file.energies,
n_channels=gbm_tte_file.n_channels,
start_time=gbm_tte_file.tstart - gbm_tte_file.trigger_time,
stop_time=gbm_tte_file.tstop - gbm_tte_file.trigger_time,
dead_time=gbm_tte_file.deadtime,
first_channel=0,
instrument=gbm_tte_file.det_name,
mission=gbm_tte_file.mission,
verbose=True,
)
ts = TimeSeriesBuilder(
det,
event_list,
response=rsp,
poly_order=poly_order,
unbinned=False,
verbose=True,
container_type=BinnedSpectrumWithDispersion,
)
ts.set_background_interval(background_time_neg,
background_time_pos)
ts.set_active_time_interval(active_time)
det_ts.append(ts)
self._ts = det_ts