def create_pull_2d(pull_dict):
mc = get_mc(pull_dict)
pulls = get_pulls(mc)
e_pdf = EnergyPDF.create(pull_dict["e_pdf_dict"])
weights = e_pdf.weight_mc(mc)
x_bins = np.linspace(2., 6., n_ebins)
ymax = pull_dict["season"]["sinDec bins"][-1]
ymin = pull_dict["season"]["sinDec bins"][0]
# y_bins = np.linspace(ymin, ymax, 41)
y_bins = pull_dict["season"]["sinDec bins"]
x_range = 0.5 * (x_bins[1:] + x_bins[:-1])
y_range = 0.5 * (y_bins[1:] + y_bins[:-1])
z_range = np.ones([len(x_range), len(y_range)])
for j, lower in enumerate(x_bins[:-1]):
upper = x_bins[j + 1]
mask = np.logical_and(
mc["logE"] >= lower,
mc["logE"] < upper
)
cut_mc = mc[mask]
for k, lower_dec in enumerate(y_bins[:-1]):
upper_dec = y_bins[k + 1]
bin_mask = np.logical_and(
cut_mc["sinDec"] >= lower_dec,
cut_mc["sinDec"] < upper_dec
)
median_pull = weighted_quantile(
pulls[mask][bin_mask], 0.5, weights[mask][bin_mask])
z_range[j][k] = np.log(median_pull)
# x_range = np.array([0.] + list(x_range) + [10.])
# y_range = np.array([y_range[0]] + list(y_range) + [y_range[-1]])
save_path = pull_pickle(pull_dict)
res = [x_range, y_range, z_range]
with open(save_path, "wb") as f:
Pickle.dump(res, f)
print("Saved to", save_path)
plot_path = save_path[:-3] + "pdf"
ax = plt.subplot(111)
X, Y = np.meshgrid(x_bins, y_bins)
cbar = ax.pcolor(X, Y, z_range.T, cmap="seismic",
vmax=1.0, vmin=-1.0)
plt.colorbar(cbar, label="Log(Median Pull)")
plt.ylabel(r"$\sin(\delta)$")
plt.xlabel("Log(Energy proxy)")
plt.savefig(plot_path)
plt.close()
def __init__(self, pull_dict):
self.season = pull_dict["season"]
self.floor = BaseFloorClass.create(pull_dict)
self.pull_dict = pull_dict
self.pull_name = pull_pickle(pull_dict)
# precision in gamma
self.precision = pull_dict.get("gamma_precision", "flarestack")
def create_pull_0d_e(pull_dict):
mc = get_mc(pull_dict)
pulls = get_pulls(mc)
e_pdf = EnergyPDF.create(pull_dict["e_pdf_dict"])
# gamma_precision = pull_dict.get('gamma_precision', 'flarestack')
gamma_precision = pull_dict["gamma_precision"]
default, bounds, name = e_pdf.return_energy_parameters()
if name != ["gamma"]:
raise Exception(
"Trying to scan gamma parameter, "
"but selected energy pdf gave the following parameters:"
" {} {} {}".format(name, default, bounds))
res_dict = dict()
x_range = np.array(
sorted(list(get_gamma_support_points(precision=gamma_precision))))
y_range = []
for x in x_range:
weights = e_pdf.weight_mc(mc, x)
median_pull = weighted_quantile(pulls, 0.5, weights)
y_range.append(median_pull)
res_dict[x] = np.log(median_pull)
y_range = np.array(y_range)
save_path = pull_pickle(pull_dict)
plot_path = save_path[:-3] + "pdf"
# print x_range, y_range
plt.figure()
plt.plot(x_range, y_range)
plt.axhline(1.0, linestyle="--")
plt.ylabel("Median Pull")
plt.xlabel(name[0])
plt.savefig(plot_path)
plt.close()
with open(save_path, "wb") as f:
Pickle.dump(res_dict, f)
print("Saved to", save_path)
def create_pull_1d(pull_dict):
mc = get_mc(pull_dict)
pulls = get_pulls(mc)
e_pdf = EnergyPDF.create(pull_dict["e_pdf_dict"])
weights = e_pdf.weight_mc(mc)
bins = np.linspace(2., 6., n_ebins)
x_range = 0.5 * (bins[1:] + bins[:-1])
y_range = []
for j, lower in enumerate(bins[:-1]):
upper = bins[j + 1]
mask = np.logical_and(
mc["logE"] >= lower,
mc["logE"] < upper
)
median_pull = weighted_quantile(
pulls[mask], 0.5, weights[mask])
y_range.append(median_pull)
x_range = np.array([0.] + list(x_range) + [10.])
y_range = np.array([y_range[0]] + list(y_range) + [y_range[-1]])
save_path = pull_pickle(pull_dict)
res = [x_range, np.log(y_range)]
with open(save_path, "wb") as f:
Pickle.dump(res, f)
print("Saved to", save_path)
plot_path = save_path[:-3] + "pdf"
plt.figure()
plt.plot(x_range, y_range)
plt.axhline(1.0, linestyle="--")
plt.ylabel("Median Pull")
plt.xlabel("Log(Energy Proxy/GeV)")
plt.savefig(plot_path)
plt.close()
def create_pull_2d_e(pull_dict):
save_path = pull_pickle(pull_dict)
base_dir = save_path[:-3] + "/"
try:
os.makedirs(base_dir)
except OSError:
pass
mc = get_mc(pull_dict)
pulls = get_pulls(mc)
e_pdf = EnergyPDF.create(pull_dict["e_pdf_dict"])
gamma_precision = pull_dict.get("gamma_precision", "flarestack")
x_bins = np.linspace(2.0, 6.0, n_ebins)
ymax = pull_dict["season"]["sinDec bins"][-1]
ymin = pull_dict["season"]["sinDec bins"][0]
y_bins = np.linspace(ymin, ymax, 11)
x_range = 0.5 * (x_bins[1:] + x_bins[:-1])
y_range = 0.5 * (y_bins[1:] + y_bins[:-1])
res_dict = dict()
e_range = np.array(
sorted(list(get_gamma_support_points(precision=gamma_precision))))
for e in e_range:
z_range = np.ones([len(x_range), len(y_range)])
weights = e_pdf.weight_mc(mc, e)
for j, lower in enumerate(x_bins[:-1]):
upper = x_bins[j + 1]
mask = np.logical_and(mc["logE"] >= lower, mc["logE"] < upper)
cut_mc = mc[mask]
for k, lower_dec in enumerate(y_bins[:-1]):
upper_dec = y_bins[k + 1]
bin_mask = np.logical_and(cut_mc["sinDec"] >= lower_dec,
cut_mc["sinDec"] < upper_dec)
median_pull = weighted_quantile(pulls[mask][bin_mask], 0.5,
weights[mask][bin_mask])
z_range[j][k] = np.log(median_pull)
res_dict[e] = [x_range, y_range, z_range]
plot_path = base_dir + str(e) + ".pdf"
ax = plt.subplot(111)
X, Y = np.meshgrid(x_bins, y_bins)
cbar = ax.pcolor(X, Y, z_range.T, cmap="seismic", vmax=1.0, vmin=-1.0)
plt.colorbar(cbar, label="Log(Median Pull)")
plt.ylabel(r"$\sin(\delta)$")
plt.xlabel("Log(Energy proxy)")
plt.savefig(plot_path)
plt.close()
save_path = pull_pickle(pull_dict)
with open(save_path, "wb") as f:
Pickle.dump(res_dict, f)
print("Saved to", save_path)
def create_pull_1d_e(floor_dict):
mc = get_mc(floor_dict)
pulls = get_pulls(mc)
e_pdf = EnergyPDF.create(floor_dict["e_pdf_dict"])
gamma_precision = floor_dict.get("gamma_precision", "flarestack")
default, bounds, name = e_pdf.return_energy_parameters()
if name != ["gamma"]:
raise Exception(
"Trying to scan gamma parameter, "
"but selected energy pdf gave the following parameters:"
" {} {} {}".format(name, default, bounds))
e_range = np.array(
sorted(list(get_gamma_support_points(precision=gamma_precision))))
bins = np.linspace(2.0, 6.0, 5)
x_range = 0.5 * (bins[1:] + bins[:-1])
x_range = np.array([0.0] + list(x_range) + [10.0])
res_dict = dict()
z_range = []
for e in sorted(e_range):
weights = e_pdf.weight_mc(mc, e)
vals = []
for j, lower in enumerate(bins[:-1]):
upper = bins[j + 1]
mask = np.logical_and(mc["logE"] >= lower, mc["logE"] < upper)
median_pull = weighted_quantile(pulls[mask], 0.5, weights[mask])
vals.append(median_pull)
vals = [vals[0]] + vals + [vals[-1]]
z_range.append(vals)
res_dict[e] = [x_range, np.log(vals)]
z_range = np.array(z_range)
# z_range = np.vstack((z_range.T[0], z_range.T)).T
#
# z_range = np.vstack((z_range.T, z_range.T[-1])).T
save_path = pull_pickle(floor_dict)
# res = [x_range, e_range, z_range]
with open(save_path, "wb") as f:
Pickle.dump(res_dict, f)
print("Saved to", save_path)
plot_path = save_path[:-3] + "pdf"
ax = plt.subplot(111)
X, Y = np.meshgrid(x_range, e_range)
# cbar = ax.pcolor(X, Y, np.log(np.degrees(z_range.T)), cmap="viridis", )
cbar = ax.pcolor(X,
Y,
np.log(z_range),
cmap="seismic",
vmax=1.0,
vmin=-1.0)
plt.colorbar(cbar, label="Log(Median Pull)")
plt.ylabel(name[0])
plt.xlabel("Log(Energy proxy)")
plt.savefig(plot_path)
plt.close()