def plot_fit_results(tool):
"""plot the SEDs result of the gammapy / sherpa fit
for comparison with the literature we choose only the Mayer spectrum
Here we plot the butterfly as a result of the multivariate sampling
with the 68% containment in flux
"""
fig, ax = plt.subplots()
model_meyer_ref = CrabSpectrum("meyer").model
model_meyer_ref.plot(
[10 * u.GeV, 100 * u.TeV],
energy_power=2,
flux_unit="erg-1 cm-2 s-1",
ls=":",
lw=2.2,
color="#555555",
label="Meyer et al. (2010)",
)
# where to take the results, configurations for the individual butterflies
instruments = ["fermi", "magic", "veritas", "fact", "hess", "joint"]
labels = ["Fermi-LAT", "MAGIC", "VERITAS", "FACT", "H.E.S.S.", "joint fit"]
lss = ["--", "--", "--", "--", "--", "-"]
colors = COLORS
# with one loop we realize all the butterfly plots
for instrument, label, color, ls in zip(instruments, labels, colors, lss):
path = (
config.repo_path /
f"results/fit/{tool}/{instrument}/fit_results_logparabola.yaml")
if not path.exists():
log.warning(f"Missing: {path} . Skipping.")
continue
results = load_yaml(path)
parameters = results["parameters"]
model_lp = LogParabola.from_log10(
amplitude=parameters[0]["value"] * u.Unit(parameters[0]["unit"]),
reference=parameters[1]["value"] * u.Unit(parameters[1]["unit"]),
alpha=parameters[2]["value"] * u.Unit(parameters[2]["unit"]),
beta=parameters[3]["value"] * u.Unit(parameters[3]["unit"]),
)
# energy range for the plot
dataset = config.get_dataset(instrument)
energy_range = dataset.energy_range
# just in case of the joint fit put a thicker line and a less transparent butterfly
if instrument == "joint":
model_lp.plot(
energy_range,
energy_power=2,
flux_unit="erg-1 cm-2 s-1",
ls=ls,
lw=3,
color=color,
label=label,
)
else:
model_lp.plot(
energy_range,
energy_power=2,
flux_unit="erg-1 cm-2 s-1",
ls=ls,
lw=2.2,
color=color,
label=label,
)
# read the butterfly from the multivariate sampling results
table_path = Path(
f"{config.repo_path}/results/figures/stat_err/{instrument}_flux_errorband.dat"
)
log.info(f"reading butterfly values from {table_path}")
t = Table.read(table_path, format="ascii.ecsv")
energies = t["energies"].data * t["energies"].unit
flux_lo = t["flux_lo"].data * t["flux_lo"].unit
flux_hi = t["flux_hi"].data * t["flux_hi"].unit
if instrument == "joint":
alpha = 0.38
else:
alpha = 0.28
plt.fill_between(
energies.to("TeV"),
(energies**2 * flux_lo).to("erg cm-2 s-1"),
(energies**2 * flux_hi).to("erg cm-2 s-1"),
color=color,
alpha=alpha,
label="",
)
ax.legend(fontsize=FONTSIZE)
ax.set_ylim([1e-12, 2e-10])
ax.set_xlabel(E_UNIT_LABEL, size=FONTSIZE)
ax.set_ylabel(SED_UNIT_LABEL, size=FONTSIZE)
# make axis thicker
for axis in ["top", "bottom", "left", "right"]:
ax.spines[axis].set_linewidth(1.6)
ax.tick_params("both",
length=7,
width=1.6,
which="major",
labelsize=FONTSIZE)
ax.tick_params("both",
length=4,
width=1.6,
which="minor",
labelsize=FONTSIZE)
plt.tight_layout()
filename = f"results/figures/crab_sed_{tool}_fit.png"
filename_pdf = f"results/figures/crab_sed_{tool}_fit.pdf"
log.info(f"Writing {filename}")
fig.savefig(filename)
fig.savefig(filename_pdf)
def counts_histogram(predicted=False):
"""function to plot the excesses per dataset and compare them with the predicted counts
if predicted == True will shwo the predicted counts from the results of the fit (for debug purpose)
"""
log.info("loading the results from the joint fit to predict the counts")
results = load_yaml(
f"{config.repo_path}/results/fit/gammapy/joint/fit_results_logparabola.yaml"
)
parameters = results["parameters"]
model_lp = LogParabola.from_log10(
amplitude=parameters[0]["value"] * u.Unit(parameters[0]["unit"]),
reference=parameters[1]["value"] * u.Unit(parameters[1]["unit"]),
alpha=parameters[2]["value"] * u.Unit(parameters[2]["unit"]),
beta=parameters[3]["value"] * u.Unit(parameters[3]["unit"]),
)
# defining the figure
dict_color = {
"fermi": COLORS[0],
"magic": COLORS[1],
"veritas": COLORS[2],
"fact": COLORS[3],
"hess": COLORS[4],
}
fig, ax = plt.subplots()
for which in config.all_datasets:
log.info(f"predicting counts for {which} dataset")
dataset = config.get_dataset(which)
obs = dataset.get_SpectrumObservationList().stack()
cts_pred = CountsPredictor(model=model_lp,
aeff=obs.aeff,
edisp=obs.edisp,
livetime=obs.livetime)
cts_pred.run()
e_max = dataset.energy_range[1].to("TeV").value
e_min = dataset.energy_range[0].to("TeV").value
kwargs_mdl = dict(ls=":",
range=(e_min, e_max),
lw=2.2,
color=dict_color[which])
kwargs_data = dict(ls="-",
range=(e_min, e_max),
lw=2.2,
color=dict_color[which])
# CountsSpectrum with observed and predicted excesses
ex_pred = cts_pred.npred
ex_obs = obs.excess_vector
# if it is an IACT rebin the counts before plotting
if which != "fermi":
ex_pred = ex_pred.rebin(2)
ex_obs = ex_obs.rebin(2)
if predicted: # if you want to display the predicted counts
ex_pred.plot_hist(ax, **kwargs_mdl)
ex_obs.plot_hist(ax, **kwargs_data)
# custom legend
legend_observed = mlines.Line2D([], [],
color="gray",
marker="",
ls="-",
lw=2,
label="observed")
legend_expected = mlines.Line2D([], [],
color="gray",
marker="",
ls=":",
lw=2,
label="expected")
legend_fermi = mlines.Line2D([], [],
color=COLORS[0],
marker="",
ls="-",
lw=2,
label="Fermi-LAT")
legend_magic = mlines.Line2D([], [],
color=COLORS[1],
marker="",
ls="-",
lw=2,
label="MAGIC")
legend_veritas = mlines.Line2D([], [],
color=COLORS[2],
marker="",
ls="-",
lw=2,
label="VERITAS")
legend_fact = mlines.Line2D([], [],
color=COLORS[3],
marker="",
ls="-",
lw=2,
label="FACT")
legend_hess = mlines.Line2D([], [],
color=COLORS[4],
marker="",
ls="-",
lw=2,
label="H.E.S.S.")
legend_handles = [
legend_fermi,
legend_magic,
legend_veritas,
legend_fact,
legend_hess,
]
if predicted: # if you want to display the predicted counts
legend_handles = [legend_observed, legend_expected] + legend_handles
ax.legend(handles=legend_handles, fontsize=FONTSIZE)
ax.set_xscale("log")
ax.set_ylabel("Excess counts", size=FONTSIZE)
ax.set_xlabel(E_UNIT_LABEL, size=FONTSIZE)
# make axis thicker
for axis in ["top", "bottom", "left", "right"]:
ax.spines[axis].set_linewidth(1.6)
ax.tick_params("both",
length=7,
width=1.6,
which="major",
labelsize=FONTSIZE)
ax.tick_params("both",
length=4,
width=1.6,
which="minor",
labelsize=FONTSIZE)
plt.tight_layout()
filename = f"{config.repo_path}/results/figures/counts_spectra.png"
filename_pdf = f"{config.repo_path}/results/figures/counts_spectra.pdf"
log.info(f"saving figure in {filename}")
fig.savefig(filename)
fig.savefig(filename_pdf)
model=LogParabola(
alpha=2.3 * u.Unit(""),
amplitude=4 / u.cm ** 2 / u.s / u.TeV,
reference=1 * u.TeV,
beta=0.5 * u.Unit(""),
),
val_at_2TeV=u.Quantity(0.6387956571420305, "cm-2 s-1 TeV-1"),
integral_1_10TeV=u.Quantity(2.255689748270628, "cm-2 s-1"),
eflux_1_10TeV=u.Quantity(3.9586515834989267, "TeV cm-2 s-1"),
e_peak=0.74082 * u.TeV,
),
dict(
name="logpar10",
model=LogParabola.from_log10(
alpha=2.3 * u.Unit(""),
amplitude=4 / u.cm ** 2 / u.s / u.TeV,
reference=1 * u.TeV,
beta=1.151292546497023 * u.Unit(""),
),
val_at_2TeV=u.Quantity(0.6387956571420305, "cm-2 s-1 TeV-1"),
integral_1_10TeV=u.Quantity(2.255689748270628, "cm-2 s-1"),
eflux_1_10TeV=u.Quantity(3.9586515834989267, "TeV cm-2 s-1"),
e_peak=0.74082 * u.TeV,
),
dict(
name="constant",
model=ConstantModel(const=4 / u.cm ** 2 / u.s / u.TeV),
val_at_2TeV=u.Quantity(4, "cm-2 s-1 TeV-1"),
integral_1_10TeV=u.Quantity(35.9999999999999, "cm-2 s-1"),
eflux_1_10TeV=u.Quantity(198.00000000000006, "TeV cm-2 s-1"),
),
dict(
