def main(input_files, output, site, complementary, label):
bins, bin_center, bin_widths = make_energy_bins(e_min=0.003 * u.TeV,
e_max=330 * u.TeV,
bins=10)
for input, color, l in zip(input_files, color_cycle, label):
df = fact.io.read_data(
input,
key='array_events',
columns=['mc_energy', 'h_max_prediction', 'mc_x_max']).dropna()
thickness, altitude = get_atmosphere_profile_functions(site)
mc_h_max = altitude(df.mc_x_max.values * u.Unit('g/cm^2')).value
y = mc_h_max - df.h_max_prediction
x = df.mc_energy.values
b_50, bin_edges, binnumber = binned_statistic(x,
y,
statistic='median',
bins=bins)
bin_centers = np.sqrt(bin_edges[1:] * bin_edges[:-1])
plt.step(bin_centers, b_50, lw=2, color=color, label=l, where='mid')
plt.xscale('log')
# plt.yscale('log')
plt.ylabel('Distance to true H max / meter')
plt.xlabel(r'True Energy / TeV ')
plt.legend()
plt.tight_layout()
if output:
plt.savefig(output)
else:
plt.show()
def main(input_files, output, threshold, multiplicity, reference,
complementary):
columns = [
'mc_alt', 'mc_az', 'mc_energy', 'az_prediction', 'alt_prediction',
'num_triggered_telescopes'
]
if threshold > 0:
columns.append('gamma_prediction_mean')
bins, bin_center, bin_widths = make_energy_bins(e_min=0.003 * u.TeV,
e_max=330 * u.TeV,
bins=15)
for input_file in input_files:
df = fact.io.read_data(input_file, key='array_events',
columns=columns).dropna()
if threshold > 0:
df = df.query(f'gamma_prediction_mean > {threshold}').copy()
if multiplicity > 2:
df = df.query(f'num_triggered_telescopes >= {multiplicity}').copy()
distance = calculate_distance_to_true_source_position(df)
b_68, bin_edges, binnumber = binned_statistic(
df.mc_energy.values,
distance,
statistic=lambda y: np.percentile(y, 68),
bins=bins)
plt.step(bin_center,
b_68,
lw=2,
label=os.path.basename(input_file),
where='mid')
if reference:
df = load_angular_resolution_requirement()
plt.plot(df.energy,
df.resolution,
'--',
color='#5b5b5b',
label='Prod3B Reference')
plt.xscale('log')
plt.yscale('log')
plt.ylabel('Distance to True Position / degree')
plt.xlabel(r'True Energy / TeV ')
plt.ylim([0.001, 100.8])
plt.legend()
plt.tight_layout()
if output:
plt.savefig(output)
else:
plt.show()
def main(gammas, protons, output):
t_obs = 50 * u.h
gammas = fact.io.read_data(gammas, key='array_events')
gammas = gammas.dropna()
gamma_runs = fact.io.read_data(gammas, key='runs')
mc_production_gamma = MCSpectrum.from_cta_runs(gamma_runs)
protons = fact.io.read_data(protons, key='array_events')
protons = protons.dropna()
# print(f'Plotting {len(protons)} protons and {len(gammas)} gammas.')
proton_runs = fact.io.read_data(protons, key='runs')
mc_production_proton = MCSpectrum.from_cta_runs(proton_runs)
crab = CrabSpectrum()
cosmic = CosmicRaySpectrum()
gammas['weight'] = mc_production_gamma.reweigh_to_other_spectrum(
crab, gammas.mc_energy.values * u.TeV, t_assumed_obs=t_obs)
protons['weight'] = mc_production_proton.reweigh_to_other_spectrum(
cosmic, protons.mc_energy.values * u.TeV, t_assumed_obs=t_obs)
# gammas_gammalike = gammas.query(f'gamma_prediction_mean > {cut}')
# protons_gammalike = protons.query(f'gamma_prediction_mean > {cut}')
bin_edges, _, _ = make_energy_bins(gammas.mc_energy.values * u.TeV,
bins=20)
on, off, alpha = coordinates.split_on_off(gammas,
protons,
on_region_radius=0.4 * u.deg)
print(f'alpha:{alpha}')
on['energy_bin'] = pd.cut(on.mc_energy, bin_edges)
off['energy_bin'] = pd.cut(off.mc_energy, bin_edges)
for ((_, g_on), (_, g_off)) in zip(on.groupby('energy_bin'),
off.groupby('energy_bin')):
n_on = g_on.weight.sum()
n_off = g_off.weight.sum()
print('----' * 20)
print(n_on, n_off)
print(g_on.size, g_off.size)
print(li_ma_significance(n_on, n_off, alpha=1))
if output:
plt.savefig(output)
else:
plt.show()
def main(
gamma_input, proton_input, output,
n_bins,
n_jobs,
optimize,
iterations,
):
'''
Calculates a sensitivity curve vs real energy. For each energy bin it performs a gridsearch
to find the theta and gamma_prediction_mean cuts that produce the highest sensitivity.
'''
t_obs = 50 * u.h
e_min, e_max = 0.003 * u.TeV, 330 * u.TeV
bin_edges, _, _ = make_energy_bins(e_min=e_min, e_max=e_max, bins=n_bins)
columns = ['gamma_prediction_mean', 'az_prediction', 'alt_prediction', 'mc_alt', 'mc_az', 'mc_energy']
gammas = fact.io.read_data(gamma_input, key='array_events', columns=columns)
gammas = gammas.dropna()
gamma_runs = fact.io.read_data(gamma_input, key='runs')
mc_production_gamma = MCSpectrum.from_cta_runs(gamma_runs)
protons = fact.io.read_data(proton_input, key='array_events', columns=columns)
protons = protons.dropna()
proton_runs = fact.io.read_data(proton_input, key='runs')
mc_production_proton = MCSpectrum.from_cta_runs(proton_runs)
crab = CrabSpectrum()
cosmic = CosmicRaySpectrum()
gammas['weight'] = mc_production_gamma.reweigh_to_other_spectrum(crab, gammas.mc_energy.values * u.TeV, t_assumed_obs=t_obs)
protons['weight'] = mc_production_proton.reweigh_to_other_spectrum(cosmic, protons.mc_energy.values * u.TeV, t_assumed_obs=t_obs)
if optimize:
result_table = optimize_differential_sensitivity(gammas, protons, bin_edges=bin_edges, num_threads=n_jobs)
else:
result_table = calculate_differential_sensitivity(gammas, protons, bin_edges=bin_edges)
result_table.write(output, overwrite=True)
def main(input_files, output, threshold, complementary, label):
bins, bin_center, bin_widths = make_energy_bins(e_min=0.003 * u.TeV,
e_max=330 * u.TeV,
bins=10)
columns = [
'mc_core_x', 'mc_core_y', 'core_x_prediction', 'core_y_prediction',
'mc_energy'
]
for input_file, color, l in zip(input_files, color_cycle, label):
df = fact.io.read_data(input_file, key='array_events',
columns=columns).dropna()
distance = np.sqrt((df.mc_core_x - df.core_x_prediction)**2 +
(df.mc_core_y - df.core_y_prediction)**2)
x = df.mc_energy.values
y = distance
b_50, bin_edges, binnumber = binned_statistic(x,
y,
statistic='median',
bins=bins)
bin_centers = np.sqrt(bin_edges[1:] * bin_edges[:-1])
plt.step(bin_centers, b_50, lw=2, color=color, label=l, where='mid')
plt.xscale('log')
plt.yscale('log')
plt.ylabel('Distance to True Impact Position / meter')
plt.xlabel(r'True Energy / TeV ')
plt.legend()
plt.tight_layout()
if output:
plt.savefig(output)
else:
plt.show()
def main(gammas_dl3, protons_dl3, output_pdf, bins, threshold):
cols = ['az_prediction', 'alt_prediction', 'mc_energy']
gammas = fact.io.read_data(gammas_dl3, key='array_events')
print(f'Reading {len(gammas)} gammas')
# import IPython; IPython.embed()
gammas = gammas.dropna(subset=cols)
protons = fact.io.read_data(protons_dl3, key='array_events')
print(f'Reading {len(protons)} protons')
protons = protons.dropna(subset=cols)
gamma_runs = fact.io.read_data(gammas_dl3, key='runs')
mc_production_gamma = MCSpectrum.from_cta_runs(gamma_runs)
proton_runs = fact.io.read_data(protons_dl3, key='runs')
mc_production_proton = MCSpectrum.from_cta_runs(proton_runs)
crab = CrabSpectrum()
cosmic = CosmicRaySpectrum()
t_obs = 60 * u.s
gammas['weight'] = mc_production_gamma.reweigh_to_other_spectrum(
crab, gammas.mc_energy.values * u.TeV, t_assumed_obs=t_obs)
protons['weight'] = mc_production_proton.reweigh_to_other_spectrum(
cosmic, protons.mc_energy.values * u.TeV, t_assumed_obs=t_obs)
with PdfPages(output_pdf) as pdf:
plt.figure()
bin_edges, _, _ = make_energy_bins(gammas.mc_energy.values * u.TeV,
bins=20)
plt.hist(gammas.mc_energy,
bins=bin_edges,
histtype='step',
lw=2,
label='gammas true energy')
plt.hist(protons.mc_energy,
bins=bin_edges,
histtype='step',
lw=2,
label='proton true energy')
plt.legend()
plt.xscale('log')
plt.xlabel(r'$Energy / \mathrm{TeV}$')
plt.ylabel('Counts')
pdf.savefig()
plt.close()
plt.figure()
gammas['theta'] = calculate_distance(gammas)
protons['theta'] = calculate_distance(protons)
bins = np.linspace(0, 25.2, 40)
kwargs = {'histtype': 'step', 'lw': 2.0}
plt.hist(gammas['theta'],
bins=bins,
label='gamma',
density=True,
**kwargs)
plt.hist(protons['theta'],
bins=bins,
label='proton',
density=True,
**kwargs)
plt.legend()
plt.xlabel(r'$Distance / Degree$')
plt.ylabel('Normalized Counts')
pdf.savefig()
plt.close()
plt.figure()
plt.hist(gammas['theta']**2,
bins=bins,
label='gamma',
density=True,
**kwargs)
plt.hist(protons['theta']**2,
bins=bins,
label='proton',
density=True,
**kwargs)
plt.legend()
plt.xlabel(r'$Distance Squared / Degree^2$')
plt.ylabel('Normalized Counts')
pdf.savefig()
plt.close()
plt.figure()
bins = np.linspace(0, 0.30, 40)
plt.hist(gammas['theta']**2,
bins=bins,
label='gamma',
density=True,
**kwargs)
plt.hist(protons['theta']**2,
bins=bins,
label='proton',
density=True,
**kwargs)
plt.legend()
plt.xlabel(r'$Distance Squared / Degree^2$')
plt.ylabel('Normalized Counts')
pdf.savefig()
plt.close()
plt.figure()
bins = np.linspace(0, 0.30, 40)
on = gammas.append(protons)
plt.hist(gammas['theta']**2,
bins=bins,
label='gammas',
weights=gammas.weight,
**kwargs)
plt.hist(protons['theta']**2,
bins=bins,
label='proton',
weights=protons.weight,
**kwargs)
plt.legend()
plt.xlabel(r'$Distance Squared / Degree^2$')
plt.ylabel('weighted Counts')
pdf.savefig()
plt.close()
plt.figure()
bins = np.linspace(0, 0.30, 40)
on = gammas.append(protons)
plt.hist(on['theta']**2,
bins=bins,
label='on (gammas + protons)',
weights=on.weight,
**kwargs)
plt.hist(protons['theta']**2,
bins=bins,
label='proton',
weights=protons.weight,
**kwargs)
plt.legend()
plt.xlabel(r'$Distance Squared / Degree^2$')
plt.ylabel('weighted Counts')
pdf.savefig()
plt.close()
fig, ax = plt.subplots()
c = coordinates.skyccords_from_dl3_table(gammas).icrs
ra = c.ra.deg
dec = c.dec.deg
ra_bins = np.linspace(11, 17, 50)
dec_bins = np.linspace(41, 47, 50)
_, _, _, im = ax.hist2d(ra,
dec,
bins=[ra_bins, dec_bins],
cmap='magma')
ax.set_aspect('equal', )
plt.colorbar(im, ax=ax)
plt.title('gammas')
plt.xlabel(r'$Right Ascension / Degree$')
plt.ylabel(r'$Declination / Degree$')
pdf.savefig()
plt.close()
fig, ax = plt.subplots()
c = coordinates.skyccords_from_dl3_table(protons).icrs
ra = c.ra.deg
dec = c.dec.deg
_, _, _, im = ax.hist2d(ra,
dec,
bins=[ra_bins, dec_bins],
cmap='magma')
ax.set_aspect('equal')
plt.title('protons')
plt.colorbar(im, ax=ax)
plt.xlabel(r'$Right Ascension / Degree$')
plt.ylabel(r'$Declination / Degree$')
pdf.savefig()
plt.close()
fig, ax = plt.subplots()
on, off, _ = coordinates.split_on_off(gammas, protons)
c = coordinates.skyccords_from_dl3_table(on).icrs
_, _, _, im = ax.hist2d(c.ra.deg,
c.dec.deg,
bins=[ra_bins, dec_bins],
cmap='magma')
ax.set_aspect('equal')
plt.title('on region')
plt.colorbar(im, ax=ax)
pdf.savefig()
plt.close()
fig, ax = plt.subplots()
c = coordinates.skyccords_from_dl3_table(off).icrs
_, _, _, im = ax.hist2d(c.ra.deg,
c.dec.deg,
bins=[ra_bins, dec_bins],
cmap='magma')
ax.set_aspect('equal')
plt.colorbar(im, ax=ax)
plt.title('off region')
pdf.savefig()
plt.close()
plt.figure()
plt.hist(on.mc_energy,
bins=bin_edges,
histtype='step',
lw=2,
label='on region true energy',
density=True)
plt.hist(off.mc_energy,
bins=bin_edges,
histtype='step',
lw=2,
label='off region true energy',
density=True)
plt.hist(gammas.mc_energy,
bins=bin_edges,
histtype='step',
lw=2,
label='gammas true energy',
color='gray',
density=True)
plt.hist(protons.mc_energy,
bins=bin_edges,
histtype='step',
lw=2,
label='proton true energy',
color='lightgray',
density=True)
plt.legend()
plt.xscale('log')
plt.xlabel(r'$Energy / \mathrm{TeV}$')
plt.ylabel(' Normalized Counts')
pdf.savefig()
plt.close()
gammas = gammas.query(f'gamma_prediction_mean > {threshold}')
protons = protons.query(f'gamma_prediction_mean > {threshold}')
plt.figure()
bins = np.linspace(0, 0.22, 40)
plt.hist(gammas['theta']**2,
bins=bins,
label='gamma',
density=True,
**kwargs)
plt.hist(protons['theta']**2,
bins=bins,
label='proton',
density=True,
**kwargs)
plt.legend()
plt.xlabel(r'$Distance Squared / Degree^2$')
plt.ylabel(f'Normalized Counts t > {threshold}')
pdf.savefig()
plt.close()
fig, ax = plt.subplots()
on, off, _ = coordinates.split_on_off(gammas, protons)
c = coordinates.skyccords_from_dl3_table(on).icrs
_, _, _, im = ax.hist2d(c.ra.deg,
c.dec.deg,
bins=[ra_bins, dec_bins],
cmap='magma')
ax.set_aspect('equal')
plt.colorbar(im, ax=ax)
plt.title(
f'{len(on)} gammalike events in on region threshold={threshold}')
pdf.savefig()
plt.close()
fig, ax = plt.subplots()
c = coordinates.skyccords_from_dl3_table(off).icrs
_, _, _, im = ax.hist2d(c.ra.deg,
c.dec.deg,
bins=[ra_bins, dec_bins],
cmap='magma')
ax.set_aspect('equal')
plt.colorbar(im, ax=ax)
plt.title(
f'{len(off)} gammalike events in off region threshold={threshold}')
pdf.savefig()
plt.close()
plt.figure()
plt.title('gammalike events')
plt.hist(on.mc_energy,
bins=bin_edges,
histtype='step',
lw=2,
label='on region true energy',
density=True)
plt.hist(off.mc_energy,
bins=bin_edges,
histtype='step',
lw=2,
label='off region true energy',
density=True)
plt.hist(gammas.mc_energy,
bins=bin_edges,
histtype='step',
lw=2,
label='gammas true energy',
color='gray',
density=True)
plt.hist(protons.mc_energy,
bins=bin_edges,
histtype='step',
lw=2,
label='proton true energy',
color='lightgray',
density=True)
plt.legend()
plt.xscale('log')
plt.xlabel(r'$Energy / \mathrm{TeV}$')
plt.ylabel('Normalized Counts')
pdf.savefig()
plt.close()
plt.figure()
plt.title('gammalike events')
plt.hist(
on.mc_energy,
bins=bin_edges,
histtype='step',
lw=2,
label='on region true energy',
)
plt.hist(
off.mc_energy,
bins=bin_edges,
histtype='step',
lw=2,
label='off region true energy',
)
plt.hist(
gammas.mc_energy,
bins=bin_edges,
histtype='step',
lw=2,
label='gammas true energy',
color='gray',
)
plt.hist(
protons.mc_energy,
bins=bin_edges,
histtype='step',
lw=2,
label='proton true energy',
color='lightgray',
)
plt.legend()
plt.xscale('log')
plt.yscale('log')
plt.xlabel(r'$Energy / \mathrm{TeV}$')
plt.ylabel('Counts')
pdf.savefig()
plt.close()