Python calculate_predicted_counts Examples

Example #1

File: test_spectrum_model.py Project: OlgaVorokh/gammapy

def test_model(model):
    print(model)
    print(model(energy=Q(10, 'TeV')))
    print(model.integral(emin=Q(1, 'TeV'), emax=Q(2, 'TeV')))

    # plot
    # butterfly
    # npred
    reco_bins = 5
    true_bins = 10 
    e_reco = Q(np.logspace(-1,1,reco_bins+1), 'TeV')
    e_true = Q(np.logspace(-1.5, 1.5, true_bins+1), 'TeV')
    livetime = Q(26, 'min')
    aeff_data = Q(np.ones(true_bins) * 1e5, 'cm2')
    aeff = EffectiveAreaTable(energy=e_true, data=aeff_data)
    edisp_data = make_perfect_resolution(e_true, e_reco)
    edisp = EnergyDispersion(edisp_data, EnergyBounds(e_true),
                             EnergyBounds(e_reco))
    npred = calculate_predicted_counts(model=model,
                                       livetime=livetime,
                                       aeff=aeff,
                                       edisp=edisp)
    print(npred.data)

Example #2

def test_model(model):
    print(model)
    print(model(energy=Q(10, 'TeV')))
    print(model.integral(emin=Q(1, 'TeV'), emax=Q(2, 'TeV')))

    # plot
    # butterfly
    # npred
    reco_bins = 5
    true_bins = 10
    e_reco = Q(np.logspace(-1, 1, reco_bins + 1), 'TeV')
    e_true = Q(np.logspace(-1.5, 1.5, true_bins + 1), 'TeV')
    livetime = Q(26, 'min')
    aeff_data = Q(np.ones(true_bins) * 1e5, 'cm2')
    aeff = EffectiveAreaTable(energy=e_true, data=aeff_data)
    edisp_data = make_perfect_resolution(e_true, e_reco)
    edisp = EnergyDispersion(edisp_data, EnergyBounds(e_true),
                             EnergyBounds(e_reco))
    npred = calculate_predicted_counts(model=model,
                                       livetime=livetime,
                                       aeff=aeff,
                                       edisp=edisp)
    print(npred.data)

Example #3

File: simluate.py Project: OlgaVorokh/gammapy-extra

# EDISP
edisp = EnergyDispersion.from_gauss(e_true=e_true, e_reco=e_true, sigma=0.2)

# AEFF
nodes = np.sqrt(e_true[:-1] * e_true[1:])
data = abramowski_effective_area(energy=nodes)
aeff = EffectiveAreaTable(data=data, energy=e_true)
lo_threshold = aeff.find_energy(0.1 * aeff.max_area)


# MODEL
model = PowerLaw(index=2.3 * u.Unit(""), amplitude=2.5 * 1e-12 * u.Unit("cm-2 s-1 TeV-1"), reference=1 * u.TeV)

# COUNTS
livetime = 2 * u.h
npred = calculate_predicted_counts(model=model, aeff=aeff, edisp=edisp, livetime=livetime)

bkg = 0.2 * npred.data
alpha = 0.1
counts_kwargs = dict(
    energy=npred.energy,
    exposure=livetime,
    obs_id=31415,
    creator="Simulation",
    hi_threshold=50 * u.TeV,
    lo_threshold=lo_threshold,
)

rand = get_random_state(42)

on_counts = rand.poisson(npred.data) + rand.poisson(bkg)

Example #4

File: simluate.py Project: msjacob/gammapy-extra

# AEFF
nodes = np.sqrt(e_true[:-1] * e_true[1:])
data = abramowski_effective_area(energy=nodes)
aeff = EffectiveAreaTable(data=data, energy=e_true)
lo_threshold = aeff.find_energy(0.1 * aeff.max_area)

# MODEL
model = PowerLaw(index=2.3 * u.Unit(''),
                 amplitude=2.5 * 1e-12 * u.Unit('cm-2 s-1 TeV-1'),
                 reference=1 * u.TeV)

# COUNTS
livetime = 2 * u.h
npred = calculate_predicted_counts(model=model,
                                   aeff=aeff,
                                   edisp=edisp,
                                   livetime=livetime)

bkg = 0.2 * npred.data
alpha = 0.1
counts_kwargs = dict(energy=npred.energy,
                     exposure=livetime,
                     obs_id=31415,
                     creator='Simulation',
                     hi_threshold=50 * u.TeV,
                     lo_threshold=lo_threshold)

rand = get_random_state(42)

on_counts = rand.poisson(npred.data) + rand.poisson(bkg)
on_vector = PHACountsSpectrum(data=on_counts, backscal=1, **counts_kwargs)