def plot_background(bg_rate, rad_max, ax=None, label=None):
# pyirf data
reco_bins = np.append(bg_rate["ENERG_LO"], bg_rate["ENERG_HI"][-1])
# first fov bin, [0, 1] deg
fov_bin = 0
rate_bin = bg_rate["BKG"].T[:, fov_bin]
# interpolate theta cut for given e reco bin
e_center_bg = 0.5 * (bg_rate["ENERG_LO"] + bg_rate["ENERG_HI"])
e_center_theta = 0.5 * (rad_max["ENERG_LO"] + rad_max["ENERG_HI"])
theta_cut = np.interp(e_center_bg, e_center_theta, rad_max["RAD_MAX"].T[:,
0])
# undo normalization
rate_bin *= cone_solid_angle(theta_cut)
rate_bin *= np.diff(reco_bins)
ax.errorbar(
0.5 *
(bg_rate["ENERG_LO"] + bg_rate["ENERG_HI"]).to_value(u.TeV)[1:-1],
rate_bin.to_value(1 / u.s)[1:-1],
xerr=np.diff(reco_bins).to_value(u.TeV)[1:-1] / 2,
ls="",
label=label,
)
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_xlabel("True energy / TeV")
ax.set_ylabel("Background rate [1/s]")
return 0
def test_background():
from pyirf.irf import background_2d
from pyirf.utils import cone_solid_angle
np.random.seed(0)
N1 = 1000
N2 = 100
N = N1 + N2
# toy event data set with just two energies
# and a psf per energy bin, point-like
events = QTable(
{
"reco_energy": np.append(np.full(N1, 1), np.full(N2, 2)) * u.TeV,
"source_fov_offset": np.zeros(N) * u.deg,
"weight": np.ones(N),
}
)
energy_bins = [0, 1.5, 3] * u.TeV
fov_bins = [0, 1] * u.deg
# We return a table with one row as needed for gadf
bg = background_2d(events, energy_bins, fov_bins, t_obs=1 * u.s)
# 2 energy bins, 1 fov bin, 200 source distance bins
assert bg.shape == (2, 1)
assert bg.unit == u.Unit("TeV-1 s-1 sr-1")
# check that psf is normalized
bin_solid_angle = np.diff(cone_solid_angle(fov_bins))
e_width = np.diff(energy_bins)
assert np.allclose(np.sum((bg.T * e_width).T * bin_solid_angle, axis=1), [1000, 100] / u.s)
def test_psf():
from pyirf.irf import psf_table
from pyirf.utils import cone_solid_angle
np.random.seed(0)
N = 1000
TRUE_SIGMA_1 = 0.2
TRUE_SIGMA_2 = 0.1
TRUE_SIGMA = np.append(np.full(N, TRUE_SIGMA_1), np.full(N, TRUE_SIGMA_2))
# toy event data set with just two energies
# and a psf per energy bin, point-like
events = QTable({
"true_energy":
np.append(np.full(N, 1), np.full(N, 2)) * u.TeV,
"source_fov_offset":
np.zeros(2 * N) * u.deg,
"theta":
np.random.normal(0, TRUE_SIGMA) * u.deg,
})
energy_bins = [0, 1.5, 3] * u.TeV
fov_bins = [0, 1] * u.deg
source_bins = np.linspace(0, 1, 201) * u.deg
# We return a table with one row as needed for gadf
psf = psf_table(events, energy_bins, source_bins, fov_bins)
# 2 energy bins, 1 fov bin, 200 source distance bins
assert psf.shape == (2, 1, 200)
assert psf.unit == u.Unit("sr-1")
# check that psf is normalized
bin_solid_angle = np.diff(cone_solid_angle(source_bins))
assert np.allclose(np.sum(psf * bin_solid_angle, axis=2), 1.0)
cumulated = np.cumsum(psf * bin_solid_angle, axis=2)
# first energy and only fov bin
bin_centers = 0.5 * (source_bins[1:] + source_bins[:-1])
assert u.isclose(
bin_centers[np.where(cumulated[0, 0, :] >= 0.68)[0][0]],
TRUE_SIGMA_1 * u.deg,
rtol=0.1,
)
# second energy and only fov bin
assert u.isclose(
bin_centers[np.where(cumulated[1, 0, :] >= 0.68)[0][0]],
TRUE_SIGMA_2 * u.deg,
rtol=0.1,
)
def psf_hdu():
from pyirf.io import create_psf_table_hdu
from pyirf.utils import cone_solid_angle
psf = np.zeros((len(e_bins) - 1, len(source_bins) - 1, len(fov_bins) - 1))
psf[:, 0, :] = 1
psf = psf / cone_solid_angle(source_bins[1])
hdu = create_psf_table_hdu(psf,
e_bins,
source_bins,
fov_bins,
point_like=False)
return psf, hdu
def plot_background_rate_from_file(filename, ax=None, **kwargs):
ax = plt.gca() if ax is None else ax
rad_max = QTable.read(filename, hdu="RAD_MAX")[0]
bg_rate = QTable.read(filename, hdu="BACKGROUND")[0]
reco_bins = np.append(bg_rate["ENERG_LO"], bg_rate["ENERG_HI"][-1])
# first fov bin, [0, 1] deg
fov_bin = 0
rate_bin = bg_rate["BKG"].T[:, fov_bin]
# interpolate theta cut for given e reco bin
e_center_bg = 0.5 * (bg_rate["ENERG_LO"] + bg_rate["ENERG_HI"])
e_center_theta = 0.5 * (rad_max["ENERG_LO"] + rad_max["ENERG_HI"])
theta_cut = np.interp(e_center_bg, e_center_theta, rad_max["RAD_MAX"].T[:, 0])
# undo normalization
rate_bin *= cone_solid_angle(theta_cut)
rate_bin *= np.diff(reco_bins)
if "ls" not in kwargs and "linestyle" not in kwargs:
kwargs["ls"] = ""
ax.errorbar(
e_center_bg.to_value(u.TeV)[1:-1],
rate_bin.to_value(1 / u.s)[1:-1],
xerr=np.diff(reco_bins).to_value(u.TeV)[1:-1] / 2,
**kwargs,
)
# Style settings
ax.set_xscale("log")
ax.set_xlabel(r"$E_\mathrm{Reco} [\mathrm{TeV}]$")
ax.set_ylabel("Background rate [Hz]")
ax.grid(True, which="both")
ax.legend()
ax.set_yscale("log")
return ax