def test_lightcurve_temporal_model_integral():
time = np.arange(0, 10, 0.06) * u.hour
table = Table()
table["TIME"] = time
table["NORM"] = np.ones(len(time))
table.meta = dict(MJDREFI=55197.0, MJDREFF=0, TIMEUNIT="hour")
temporal_model = LightCurveTemplateTemporalModel(table)
start = [1, 3, 5] * u.hour
stop = [2, 3.5, 6] * u.hour
gti = GTI.create(start, stop, reference_time=Time("2010-01-01T00:00:00"))
val = temporal_model.integral(gti.time_start, gti.time_stop)
assert len(val) == 3
assert_allclose(np.sum(val), 1.0, rtol=1e-5)
def models():
spatial_model = GaussianSpatialModel(lon_0="0 deg",
lat_0="0 deg",
sigma="0.2 deg",
frame="galactic")
spectral_model = PowerLawSpectralModel(amplitude="1e-11 cm-2 s-1 TeV-1")
t_max = 1000 * u.s
time = np.arange(t_max.value) * u.s
tau = u.Quantity("2e2 s")
norm = np.exp(-time / tau)
table = Table()
table["TIME"] = time
table["NORM"] = norm / norm.max()
t_ref = Time("2000-01-01")
table.meta = dict(MJDREFI=t_ref.mjd, MJDREFF=0, TIMEUNIT="s")
temporal_model = LightCurveTemplateTemporalModel(table)
model = SkyModel(
spatial_model=spatial_model,
spectral_model=spectral_model,
temporal_model=temporal_model,
name="test-source",
)
bkg_model = FoVBackgroundModel(dataset_name="test")
return [model, bkg_model]
def dataset():
position = SkyCoord(0.0, 0.0, frame="galactic", unit="deg")
energy_axis = MapAxis.from_bounds(1,
10,
nbin=3,
unit="TeV",
name="energy",
interp="log")
spatial_model = GaussianSpatialModel(lon_0="0 deg",
lat_0="0 deg",
sigma="0.2 deg",
frame="galactic")
spectral_model = PowerLawSpectralModel(amplitude="1e-11 cm-2 s-1 TeV-1")
t_min = 0 * u.s
t_max = 1000 * u.s
time = np.arange(t_max.value) * u.s
tau = u.Quantity("2e2 s")
norm = np.exp(-time / tau)
table = Table()
table["TIME"] = time
table["NORM"] = norm / norm.max()
t_ref = Time("2000-01-01")
table.meta = dict(MJDREFI=t_ref.mjd, MJDREFF=0, TIMEUNIT="s")
temporal_model = LightCurveTemplateTemporalModel(table)
skymodel = SkyModel(
spatial_model=spatial_model,
spectral_model=spectral_model,
temporal_model=temporal_model,
)
geom = WcsGeom.create(skydir=position,
binsz=1,
width="5 deg",
frame="galactic",
axes=[energy_axis])
gti = GTI.create(start=t_min, stop=t_max, reference_time=t_ref)
geom_true = geom.copy()
geom_true.axes[0].name = "energy_true"
dataset = get_map_dataset(sky_model=skymodel,
geom=geom,
geom_etrue=geom_true,
edisp="edispmap")
dataset.gti = gti
return dataset
def test_time_sampling(tmp_path):
time = np.arange(0, 10, 0.06) * u.hour
table = Table()
table["TIME"] = time
table["NORM"] = rate(time)
table.meta = dict(MJDREFI=55197.0, MJDREFF=0, TIMEUNIT="hour")
temporal_model = LightCurveTemplateTemporalModel(table)
filename = str(make_path(tmp_path / "tmp.fits"))
temporal_model.write(path=filename)
model_read = temporal_model.read(filename)
assert temporal_model.filename == filename
assert model_read.filename == filename
assert_allclose(model_read.table["TIME"].quantity.value, time.value)
t_ref = "2010-01-01T00:00:00"
t_min = "2010-01-01T00:00:00"
t_max = "2010-01-01T08:00:00"
sampler = temporal_model.sample_time(n_events=2,
t_min=t_min,
t_max=t_max,
random_state=0,
t_delta="10 min")
sampler = u.Quantity((sampler - Time(t_ref)).sec, "s")
assert len(sampler) == 2
assert_allclose(sampler.value, [12661.65802564, 7826.92991], rtol=1e-5)
table = Table()
table["TIME"] = time
table["NORM"] = np.ones(len(time))
table.meta = dict(MJDREFI=55197.0, MJDREFF=0, TIMEUNIT="hour")
temporal_model_uniform = LightCurveTemplateTemporalModel(table)
sampler_uniform = temporal_model_uniform.sample_time(n_events=2,
t_min=t_min,
t_max=t_max,
random_state=0,
t_delta="10 min")
sampler_uniform = u.Quantity((sampler_uniform - Time(t_ref)).sec, "s")
assert len(sampler_uniform) == 2
assert_allclose(sampler_uniform.value, [1261.65802564, 6026.9299098],
rtol=1e-5)
def test_time_sampling():
time = np.arange(0, 10, 0.06) * u.hour
table = Table()
table["TIME"] = time
table["NORM"] = rate(time)
temporal_model = LightCurveTemplateTemporalModel(table)
t_ref = "2010-01-01T00:00:00"
t_min = "2010-01-01T00:00:00"
t_max = "2010-01-01T08:00:00"
sampler = temporal_model.sample_time(n_events=2,
t_min=t_min,
t_max=t_max,
random_state=0,
t_delta="10 min")
sampler = u.Quantity((sampler - Time(t_ref)).sec, "s")
table = Table()
table["TIME"] = time
table["NORM"] = np.ones(len(time))
temporal_model_uniform = LightCurveTemplateTemporalModel(table)
sampler_uniform = temporal_model_uniform.sample_time(n_events=2,
t_min=t_min,
t_max=t_max,
random_state=0,
t_delta="10 min")
sampler_uniform = u.Quantity((sampler_uniform - Time(t_ref)).sec, "s")
assert len(sampler) == 2
assert len(sampler_uniform) == 2
assert_allclose(sampler.value, [12661.65802564, 26.9299098], rtol=1e-5)
assert_allclose(sampler_uniform.value, [1261.65802564, 6026.9299098],
rtol=1e-5)
def light_curve():
path = "$GAMMAPY_DATA/tests/models/light_curve/lightcrv_PKSB1222+216.fits"
return LightCurveTemplateTemporalModel.read(path)
This model parametrises a lightCurve time model.
"""
from astropy.time import Time
from gammapy.modeling.models import (
LightCurveTemplateTemporalModel,
Models,
PowerLawSpectralModel,
SkyModel,
)
time_range = [Time("59100", format="mjd"), Time("59365", format="mjd")]
path = "$GAMMAPY_DATA/tests/models/light_curve/lightcrv_PKSB1222+216.fits"
light_curve_model = LightCurveTemplateTemporalModel.read(path)
light_curve_model.plot(time_range)
# %%
# YAML representation
# -------------------
# Here is an example YAML file using the model:
model = SkyModel(
spectral_model=PowerLawSpectralModel(),
temporal_model=light_curve_model,
name="light_curve_model",
)
models = Models([model])
print(models.to_yaml())
def test_time_sampling(tmp_path):
time = np.arange(0, 10, 0.06) * u.hour
table = Table()
table["TIME"] = time
table["NORM"] = rate(time)
table.meta = dict(MJDREFI=55197.0, MJDREFF=0, TIMEUNIT="hour")
temporal_model = LightCurveTemplateTemporalModel(table)
filename = str(make_path(tmp_path / "tmp.fits"))
temporal_model.write(path=filename)
model_read = temporal_model.read(filename)
assert temporal_model.filename == filename
assert model_read.filename == filename
assert_allclose(model_read.table["TIME"].quantity.value, time.value)
t_ref = "2010-01-01T00:00:00"
t_min = "2010-01-01T00:00:00"
t_max = "2010-01-01T08:00:00"
sampler = temporal_model.sample_time(n_events=2,
t_min=t_min,
t_max=t_max,
random_state=0,
t_delta="10 min")
sampler = u.Quantity((sampler - Time(t_ref)).sec, "s")
assert len(sampler) == 2
assert_allclose(sampler.value, [12661.65802564, 7826.92991], rtol=1e-5)
table = Table()
table["TIME"] = time
table["NORM"] = np.ones(len(time))
table.meta = dict(MJDREFI=55197.0, MJDREFF=0, TIMEUNIT="hour")
temporal_model_uniform = LightCurveTemplateTemporalModel(table)
sampler_uniform = temporal_model_uniform.sample_time(n_events=2,
t_min=t_min,
t_max=t_max,
random_state=0,
t_delta="10 min")
sampler_uniform = u.Quantity((sampler_uniform - Time(t_ref)).sec, "s")
assert len(sampler_uniform) == 2
assert_allclose(sampler_uniform.value, [1261.65802564, 6026.9299098],
rtol=1e-5)
temporal_model = ConstantTemporalModel()
sampler_costant = temporal_model.sample_time(n_events=2,
t_min=t_min,
t_max=t_max,
random_state=0)
sampler_costant = u.Quantity((sampler_costant - Time(t_ref)).sec, "s")
assert len(sampler_costant) == 2
assert_allclose(sampler_costant.value, [4330.10377559, 3334.04566256],
rtol=1e-5)
temporal_model = ExpDecayTemporalModel(t_ref=Time(t_ref).mjd * u.d)
sampler_expo = temporal_model.sample_time(n_events=2,
t_min=t_min,
t_max=t_max,
random_state=0)
sampler_expo = u.Quantity((sampler_expo.mjd - Time(t_ref).mjd), "d")
assert sampler_expo.unit == u.d
assert_allclose(sampler_expo.to("s").value, [11824.1055276, 7273.04658336],
rtol=1e-8)