def run(self, dataset, observation):
"""Run all steps.
Parameters
----------
dataset : `SpectrumDataset`
Input dataset.
observation : `Observation`
Data store observation.
Returns
-------
dataset_on_off : `SpectrumDatasetOnOff`
On off dataset.
"""
counts_off = self.make_counts_off(dataset, observation)
counts = self.make_counts(dataset, observation)
acceptance = np.sum([_[1] - _[0] for _ in self.on_phase])
acceptance_off = np.sum([_[1] - _[0] for _ in self.off_phase])
dataset_on_off = SpectrumDatasetOnOff.from_spectrum_dataset(
dataset=dataset,
counts_off=counts_off,
acceptance=acceptance,
acceptance_off=acceptance_off,
)
dataset_on_off.counts = counts
return dataset_on_off
def test_cta_sensitivity_estimator(spectrum_dataset):
dataset_on_off = SpectrumDatasetOnOff.from_spectrum_dataset(
dataset=spectrum_dataset, acceptance=1, acceptance_off=5
)
sens = SensitivityEstimator(gamma_min=20)
table = sens.run(dataset_on_off)
assert len(table) == 4
assert table.colnames == ["energy", "e2dnde", "excess", "background", "criterion"]
assert table["energy"].unit == "TeV"
assert table["e2dnde"].unit == "erg / (cm2 s)"
row = table[0]
assert_allclose(row["energy"], 1.33352, rtol=1e-3)
assert_allclose(row["e2dnde"], 3.40101e-11, rtol=1e-3)
assert_allclose(row["excess"], 334.454, rtol=1e-3)
assert_allclose(row["background"], 3600, rtol=1e-3)
assert row["criterion"] == "significance"
row = table[3]
assert_allclose(row["energy"], 7.49894, rtol=1e-3)
assert_allclose(row["e2dnde"], 1.14367e-11, rtol=1e-3)
assert_allclose(row["excess"], 20, rtol=1e-3)
assert_allclose(row["background"], 3.6, rtol=1e-3)
assert row["criterion"] == "gamma"
def run(self, dataset, observation):
"""Run reflected regions background maker
Parameters
----------
dataset : `SpectrumDataset`
Spectrum dataset.
observation : `DatastoreObservation`
Data store observation.
Returns
-------
dataset_on_off : `SpectrumDatasetOnOff`
On off dataset.
"""
counts_off, acceptance_off = self.make_counts_off(dataset, observation)
acceptance = RegionNDMap.from_geom(geom=dataset.counts.geom, data=1)
dataset_onoff = SpectrumDatasetOnOff.from_spectrum_dataset(
dataset=dataset,
acceptance=acceptance,
acceptance_off=acceptance_off,
counts_off=counts_off,
name=dataset.name,
)
if dataset_onoff.counts_off is None:
dataset_onoff.mask_safe.data[...] = False
log.warning(
f"ReflectedRegionsBackgroundMaker failed. Setting {dataset_onoff.name} mask to False."
)
return dataset_onoff
def test_cta_sensitivity_estimator(spectrum_dataset):
dataset_on_off = SpectrumDatasetOnOff.from_spectrum_dataset(
dataset=spectrum_dataset, acceptance=1, acceptance_off=5)
sens = SensitivityEstimator(gamma_min=25, bkg_syst_fraction=0.075)
table = sens.run(dataset_on_off)
assert len(table) == 4
assert table.colnames == [
"energy", "e2dnde", "excess", "background", "criterion"
]
assert table["energy"].unit == "TeV"
assert table["e2dnde"].unit == "erg / (cm2 s)"
row = table[0]
assert_allclose(row["energy"], 1.33352, rtol=1e-3)
assert_allclose(row["e2dnde"], 2.74559e-08, rtol=1e-3)
assert_allclose(row["excess"], 270000, rtol=1e-3)
assert_allclose(row["background"], 3.6e+06, rtol=1e-3)
assert row["criterion"] == "bkg"
row = table[1]
assert_allclose(row["energy"], 2.37137, rtol=1e-3)
assert_allclose(row["e2dnde"], 6.04795e-11, rtol=1e-3)
assert_allclose(row["excess"], 334.454, rtol=1e-3)
assert_allclose(row["background"], 3600, rtol=1e-3)
assert row["criterion"] == "significance"
row = table[3]
assert_allclose(row["energy"], 7.49894, rtol=1e-3)
assert_allclose(row["e2dnde"], 1.42959e-11, rtol=1e-3)
assert_allclose(row["excess"], 25, rtol=1e-3)
assert_allclose(row["background"], 3.6, rtol=1e-3)
assert row["criterion"] == "gamma"
def run(self, dataset, observation):
"""Run reflected regions background maker
Parameters
----------
dataset : `SpectrumDataset`
Spectrum dataset.
observation : `DatastoreObservation`
Data store observation.
Returns
-------
dataset_on_off : `SpectrumDatasetOnOff`
On off dataset.
"""
counts_off, acceptance_off = self.make_counts_off(dataset, observation)
return SpectrumDatasetOnOff.from_spectrum_dataset(
dataset=dataset,
acceptance=1,
acceptance_off=acceptance_off,
counts_off=counts_off,
)
def createonoff_from_simulation(mc, random_state='rendom-seed', debug=False):
"""
Transform the stored SpectrumDataset list in the MonteCarlo class of
SoHAPPy into a list of Datasets of type SpectrumDatasetOnOff.
Parameters
----------
mc : MonteCarlo instantiation
A SoHAPPy MonteCarlo class instantiation
random_state : randome seed
The default is 'randome-seed'
debug : boolean, optional
If True, verbosy. The default is False.
Returns
-------
dlist_onoff : Datasets
A list of on-off datasets from the originals.
"""
import sys
sys.path.append("../SoHAPPy")
import mcsim_res as mcres
import mcsim_config as mcf
from utilities import Log
if (mc.slot == None):
print(" Simulation was not runable - no dataset available")
return None
if debug:
# story(mc, ref="VIS",saveplots="False",outfile="")
# story(mc, ref="notVIS",saveplots="False",outfile="")
# stat(mc,saveplots="False",outfile="")
log = Log(name="out.log", talk=True)
mcres.result(mc, mc.slot.grb, log=log)
print(mc.slot)
dset_list = mc.dset_list
# Create on-off datasets fom the original list
# which is a list of list to account for multiple sites
dlist_onoff = Datasets()
for ds_site in dset_list:
for ds in ds_site:
# Remove NAN background (corrected in 0.19)
ds.background.data = np.nan_to_num(ds.background.data)
# It seems that above 0.17, dataset need to be simulated in order
# to go to on-off
ds.fake(random_state=random_state)
ds_onoff = SpectrumDatasetOnOff.from_spectrum_dataset(
dataset=ds, acceptance=1, acceptance_off=1 / mcf.alpha)
ds_onoff.fake(npred_background=ds_onoff.npred_background())
if (debug): print(ds_onoff)
dlist_onoff.append(ds_onoff)
return dlist_onoff
def generate_dataset(Eflux,
flux,
Erange=None,
tstart=Time('2000-01-01 02:00:00', scale='utc'),
tobs=100 * u.s,
irf_file=None,
alpha=1 / 5,
name=None,
fake=True,
onoff=True,
seed='random-seed',
debug=False):
"""
Generate a dataset from a list of energies and flux points either as
a SpectrumDataset or a SpectrumDatasetOnOff
Note :
- in SpectrumDataset, the backgound counts are assumed precisely know and
are not fluctuated.
- in SpectrumDatasetOnOff, the background counts (off counts) are
fluctuated from the IRF known values.
Parameters
----------
Eflux : Quantity
Energies at which the flux is given.
flux : Quantity
Flux corresponding to the given energies.
Erange : List, optional
The energy boundaries within which the flux is defined, if not over all
energies. The default is None.
tstart : Time object, optional
Start date of the dataset.
The default is Time('2000-01-01 02:00:00',scale='utc').
tobs : Quantity, optional
Duration of the observation. The default is 100*u.s.
irf_file : String, optional
The IRf file name. The default is None.
alpha : Float, optional
The on over off surface ratio for the On-Off analysis.
The default is 1/5.
name : String, optional
The dataset name, also used to name tthe spectrum. The default is None.
fake : Boolean, optional
If True, the dataset counts are fluctuated. The default is True.
onoff : Boolean, optional
If True, use SpectrumDatasetOnOff, otherwise SpectrumDataSet.
The default is True.
seed : String, optional
The seed for the randome generator; If an integer will generate the
same random series at each run. The default is 'random-seed'.
debug: Boolean
If True, let's talk a bit. The default is False.
Returns
-------
ds : Dataset object
The dataset.
"""
random_state = get_random_state(seed)
### Define on region
on_pointing = SkyCoord(ra=0 * u.deg, dec=0 * u.deg,
frame="icrs") # Observing region
on_region = CircleSkyRegion(center=on_pointing, radius=0.5 * u.deg)
# Define energy axis (see spectrum analysis notebook)
# edges for SpectrumDataset - all dataset should have the same axes
# Note that linear spacing is clearly problematic for powerlaw fluxes
# Axes can also be defined using MapAxis
unit = u.GeV
E1v = min(Eflux).to(unit).value
E2v = max(Eflux).to(unit).value
# ereco = np.logspace(np.log10(1.1*E1v), np.log10(0.9*E2v), 20) * unit
# ereco_axis = MapAxis.from_edges(ereco.to("TeV").value,
# unit="TeV",
# name="energy",
# interp="log")
ereco_axis = MapAxis.from_energy_bounds(1.1 * E1v * unit,
0.9 * E2v * unit,
nbin=4,
per_decade=True,
name="energy")
# etrue = np.logspace(np.log10( E1v), np.log10( E2v), 50) * unit
# etrue_axis = MapAxis.from_edges(etrue.to("TeV").value,
# unit="TeV",
# name="energy_true",
# interp="log")
etrue_axis = MapAxis.from_energy_bounds(E1v * unit,
E2v * unit,
nbin=4,
per_decade=True,
name="energy_true")
if (debug):
print("Dataset ", name)
print("Etrue : ", etrue_axis.edges)
print("Ereco : ", ereco_axis.edges)
# Load IRF
irf = load_cta_irfs(irf_file)
spec = TemplateSpectralModel(energy=Eflux,
values=flux,
interp_kwargs={"values_scale": "log"})
model = SkyModel(spectral_model=spec, name="Spec" + str(name))
obs = Observation.create(obs_id=1,
pointing=on_pointing,
livetime=tobs,
irfs=irf,
deadtime_fraction=0,
reference_time=tstart)
ds_empty = SpectrumDataset.create(
e_reco=ereco_axis, # Ereco.edges,
e_true=etrue_axis, #Etrue.edges,
region=on_region,
name=name)
maker = SpectrumDatasetMaker(containment_correction=False,
selection=["exposure", "background", "edisp"])
ds = maker.run(ds_empty, obs)
ds.models = model
mask = ds.mask_safe.geom.energy_mask(energy_min=Erange[0],
energy_max=Erange[1])
mask = mask & ds.mask_safe.data
ds.mask_safe = RegionNDMap(ds.mask_safe.geom, data=mask)
ds.fake(random_state=random_state) # Fake is mandatory ?
# Transform SpectrumDataset into SpectrumDatasetOnOff if needed
if (onoff):
ds = SpectrumDatasetOnOff.from_spectrum_dataset(dataset=ds,
acceptance=1,
acceptance_off=1 /
alpha)
print("Transformed in ONOFF")
if fake:
print(" Fluctuations : seed = ", seed)
if (onoff):
ds.fake(npred_background=ds.npred_background())
else:
ds.fake(random_state=random_state)
print("ds.energy_range = ", ds.energy_range)
return ds
region=on_region,
name="obs-0")
maker = SpectrumDatasetMaker(selection=["exposure", "edisp", "background"])
# Set the number of observations we want to create
n_obs = 50
print("Creating the", n_obs, "On/Off simulations")
datasets = Datasets()
for idx in range(n_obs):
dataset = maker.run(dataset_empty, obs)
# Set the model on the dataset, and fake the counts
dataset.models = model
dataset.fake(random_state=idx)
# Set off regions
dataset_on_off = SpectrumDatasetOnOff.from_spectrum_dataset(
dataset=dataset, acceptance=1, acceptance_off=3)
dataset_on_off.fake(random_state=idx,
npred_background=dataset.npred_background())
dataset_fake = dataset_on_off.copy(name=f"obs-{idx}")
dataset_fake.meta_table["OBS_ID"] = [idx]
datasets.append(dataset_fake)
# Save the data from the simulations
table = datasets.info_table()
table.write('observations.txt', format='ascii')
# Check counts in one selected realization
fig_2 = plt.figure(1)
datasets[0].npred().plot_hist(label='Predicted S+B')
datasets[0].npred_signal().plot_hist(label='Predicted S')
datasets[0].npred_background().plot_hist(label='Predicted B')
