def run_gammapy_fit(obs_list, fit_range, eval_contours=None):
"""Run fit with Gammapy, using iminiuit"""
model_lp = Log10Parabola(
amplitude=3.80 * 1e-11 * u.Unit("cm-2 s-1 TeV-1"),
reference=1 * u.Unit("TeV"),
alpha=2.47 * u.Unit(""),
beta=0.24 * u.Unit(""),
)
# note this step is very important to iminuit, to initialize the parameter error!
fit = SpectrumFit(obs_list=obs_list, model=model_lp, fit_range=fit_range)
log.info("Starting fit ...")
t = time()
fit.optimize(backend="minuit", pedantic=True)
fit.run()
t = time() - t
log.info(f"Finished fit in {t} seconds.")
results = extract_spectrum_results_gammapy(fit)
print(fit.result[0])
if eval_contours is not None:
log.info("storing likelihood contours ...")
# points along the contour
like_points = 80
sigma = 1.
log.info(f"computing amplitude vs alpha {sigma} sigma contour")
cont = fit.minuit.mncontour("par_000_amplitude",
"par_002_alpha",
numpoints=like_points,
sigma=sigma)
# the third element of mncontour's returned object is a list of tuples with the contour coordinates
# (x_1,y_1), ..., (x_n, y_n)]
cont = np.asarray(cont[2])
amplitude = cont.T[0] # transpose and take the first row
alpha = cont.T[1] # transpose and take the
# trick to make a close circle when plotting: just repeat the first coordinate
amplitude = np.append(amplitude, amplitude[0])
alpha = np.append(alpha, alpha[0])
contour_amplitude_alpha = {"amplitude": amplitude, "alpha": alpha}
log.info(f"computing amplitude vs beta {sigma} sigma contour")
cont = fit.minuit.mncontour("par_000_amplitude",
"par_003_beta",
numpoints=like_points,
sigma=sigma)
cont = np.asarray(cont[2])
amplitude = cont.T[0] # transpose and take the first row
beta = cont.T[1] # transpose and take the
# trick to make a close circle when plotting: just repeat the first coordinate
amplitude = np.append(amplitude, amplitude[0])
beta = np.append(beta, beta[0])
contour_amplitude_beta = {"amplitude": amplitude, "beta": beta}
log.info(f"computing alpha vs beta {sigma} sigma contour")
cont = fit.minuit.mncontour("par_002_alpha",
"par_003_beta",
numpoints=like_points,
sigma=sigma)
cont = np.asarray(cont[2])
alpha = cont.T[0] # transpose and take the first row
beta = cont.T[1] # transpose and take the
# trick to make a close circle when plotting: just repeat the first coordinate
alpha = np.append(alpha, alpha[0])
beta = np.append(beta, beta[0])
contour_alpha_beta = {"alpha": alpha, "beta": beta}
# define the general dictionary and dump it in a .npy object
contours = {
"contour_amplitude_alpha": contour_amplitude_alpha,
"contour_amplitude_beta": contour_amplitude_beta,
"contour_alpha_beta": contour_alpha_beta,
}
logging.info(f"storing .yaml with contours in {eval_contours}")
path = Path(eval_contours)
path.mkdir(parents=True, exist_ok=True)
np.save(f"{eval_contours}/fit_{sigma}_sigma_contours_logparabola.npy",
contours)
return results
