def sample_coord(self, map_coord, random_state=0):
"""Apply the energy dispersion corrections on the coordinates of a set of simulated events.
Parameters
----------
map_coord : `~gammapy.maps.MapCoord` object.
Sequence of coordinates and energies of sampled events.
random_state : {int, 'random-seed', 'global-rng', `~numpy.random.RandomState`}
Defines random number generator initialisation.
Passed to `~gammapy.utils.random.get_random_state`.
Returns
-------
`~gammapy.maps.MapCoord`.
Sequence of Edisp-corrected coordinates of the input map_coord map.
"""
random_state = get_random_state(random_state)
migra_axis = self.edisp_map.geom.get_axis_by_name("migra")
coord = {
"skycoord": map_coord.skycoord.reshape(-1, 1),
"energy_true": map_coord["energy_true"].reshape(-1, 1),
"migra": migra_axis.center,
}
pdf_edisp = self.edisp_map.interp_by_coord(coord)
sample_edisp = InverseCDFSampler(pdf_edisp, axis=1, random_state=random_state)
pix_edisp = sample_edisp.sample_axis()
migra = migra_axis.pix_to_coord(pix_edisp)
energy_reco = map_coord["energy_true"] * migra
return MapCoord.create({"skycoord": map_coord.skycoord, "energy": energy_reco})
def sample_coord(self, n_events, random_state=0):
"""Sample position and energy of events.
Parameters
----------
n_events : int
Number of events to sample.
random_state : {int, 'random-seed', 'global-rng', `~numpy.random.RandomState`}
Defines random number generator initialisation.
Passed to `~gammapy.utils.random.get_random_state`.
Returns
-------
coords : `~gammapy.maps.MapCoord` object.
Sequence of coordinates and energies of the sampled events.
"""
random_state = get_random_state(random_state)
sampler = InverseCDFSampler(pdf=self.data, random_state=random_state)
coords_pix = sampler.sample(n_events)
coords = self.geom.pix_to_coord(coords_pix[::-1])
# TODO: pix_to_coord should return a MapCoord object
axes_names = ["lon", "lat"] + self.geom.axes.names
cdict = OrderedDict(zip(axes_names, coords))
return MapCoord.create(cdict, frame=self.geom.frame)
def test_norm_dist_sampling():
n_sampled = 1000
x = np.linspace(-2, 2, n_sampled)
mu, sigma = 0, 0.1
pdf = gauss_dist(x=x, mu=mu, sigma=sigma)
sampler = InverseCDFSampler(pdf=pdf, random_state=0)
idx = sampler.sample(int(1e5))
x_sampled = np.interp(idx, np.arange(n_sampled), x)
assert_allclose(np.mean(x_sampled), mu, atol=0.01)
assert_allclose(np.std(x_sampled), sigma, atol=0.005)
def sample_time(self,
n_events,
t_min,
t_max,
t_delta="1 s",
random_state=0):
"""Sample arrival times of events.
Parameters
----------
n_events : int
Number of events to sample.
t_min : `~astropy.time.Time`
Start time of the sampling.
t_max : `~astropy.time.Time`
Stop time of the sampling.
t_delta : `~astropy.units.Quantity`
Time step used for sampling of the temporal model.
random_state : {int, 'random-seed', 'global-rng', `~numpy.random.RandomState`}
Defines random number generator initialisation.
Passed to `~gammapy.utils.random.get_random_state`.
Returns
-------
time : `~astropy.units.Quantity`
Array with times of the sampled events.
"""
time_unit = getattr(u, self.table.meta["TIMEUNIT"])
t_min = Time(t_min)
t_max = Time(t_max)
t_delta = u.Quantity(t_delta)
random_state = get_random_state(random_state)
ontime = u.Quantity((t_max - t_min).sec, "s")
t_stop = ontime.to_value(time_unit)
# TODO: the separate time unit handling is unfortunate, but the quantity support for np.arange and np.interp
# is still incomplete, refactor once we change to recent numpy and astropy versions
t_step = t_delta.to_value(time_unit)
t = np.arange(0, t_stop, t_step)
pdf = self.evaluate(t)
sampler = InverseCDFSampler(pdf=pdf, random_state=random_state)
time_pix = sampler.sample(n_events)[0]
time = np.interp(time_pix, np.arange(len(t)), t) * time_unit
return t_min + time
def test_uniform_dist_sampling():
n_sampled = 1000
x = np.linspace(-2, 2, n_sampled)
a, b = -1, 1
pdf = uniform_dist(x, a=a, b=b)
sampler = InverseCDFSampler(pdf=pdf, random_state=0)
idx = sampler.sample(int(1e4))
x_sampled = np.interp(idx, np.arange(n_sampled), x)
assert_allclose(np.mean(x_sampled), 0.5 * (a + b), atol=0.01)
assert_allclose(np.std(x_sampled),
np.sqrt(1 / 3 * (a**2 + a * b + b**2)),
rtol=0.01)
def test_axis_sampling():
n_sampled = 1000
x = np.linspace(-2, 2, n_sampled)
a, b = -1, 1
pdf_uniform = uniform_dist(x, a=a, b=b)
mu, sigma = 0, 0.1
pdf_gauss = gauss_dist(x=x, mu=mu, sigma=sigma)
pdf = np.vstack([pdf_gauss, pdf_uniform])
sampler = InverseCDFSampler(pdf, random_state=0, axis=1)
idx = sampler.sample_axis()
x_sampled = np.interp(idx, np.arange(n_sampled), x)
assert_allclose(x_sampled, [0.01042147, 0.43061014], rtol=1e-5)
def sample_coord(self, map_coord, random_state=0):
"""Apply PSF corrections on the coordinates of a set of simulated events.
Parameters
----------
map_coord : `~gammapy.maps.MapCoord` object.
Sequence of coordinates and energies of sampled events.
random_state : {int, 'random-seed', 'global-rng', `~numpy.random.RandomState`}
Defines random number generator initialisation.
Passed to `~gammapy.utils.random.get_random_state`.
Returns
-------
corr_coord : `~gammapy.maps.MapCoord` object.
Sequence of PSF-corrected coordinates of the input map_coord map.
"""
random_state = get_random_state(random_state)
rad_axis = self.psf_map.geom.axes["rad"]
coord = {
"skycoord": map_coord.skycoord.reshape(-1, 1),
"energy_true": map_coord["energy_true"].reshape(-1, 1),
"rad": rad_axis.center,
}
pdf = (
self.psf_map.interp_by_coord(coord)
* rad_axis.center.value
* rad_axis.bin_width.value
)
sample_pdf = InverseCDFSampler(pdf, axis=1, random_state=random_state)
pix_coord = sample_pdf.sample_axis()
separation = rad_axis.pix_to_coord(pix_coord)
position_angle = random_state.uniform(360, size=len(map_coord.lon)) * u.deg
event_positions = map_coord.skycoord.directional_offset_by(
position_angle=position_angle, separation=separation
)
return MapCoord.create(
{"skycoord": event_positions, "energy_true": map_coord["energy_true"]}
)
