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(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)