def create_quantile_floor_0d_e(floor_dict):
mc = get_mc(floor_dict)
e_pdf = EnergyPDF.create(floor_dict["e_pdf_dict"])
default, bounds, name = e_pdf.return_energy_parameters()
if len(name) != 1:
raise Exception(
"Trying to scan just one energy parameter, "
"but selected energy pdf gave the following parameters:"
" {} {} {}".format(name, default, bounds))
x_range = np.linspace(bounds[0][0], bounds[0][1], n_step)
y_range = []
for x in x_range:
weights = e_pdf.weight_mc(mc, x)
quantile_floor = weighted_quantile(mc["raw_sigma"],
floor_dict["floor_quantile"],
weights)
y_range.append(quantile_floor)
y_range = np.array(y_range)
save_path = floor_pickle(floor_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 = floor_pickle(floor_dict)[:-3] + "pdf"
plt.figure()
plt.plot(x_range, np.degrees(y_range))
plt.savefig(plot_path)
plt.close()
def create_quantile_floor_1d(floor_dict):
mc = get_mc(floor_dict)
e_pdf = EnergyPDF.create(floor_dict["e_pdf_dict"])
weights = e_pdf.weight_mc(mc)
bins = np.linspace(2., 6., 30)
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
)
quantile_floor = weighted_quantile(
mc["raw_sigma"][mask], floor_dict["floor_quantile"], weights[mask])
y_range.append(quantile_floor)
x_range = np.array([0.] + list(x_range) + [10.])
y_range = np.array([y_range[0]] + list(y_range) + [y_range[-1]])
save_path = floor_pickle(floor_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 = floor_pickle(floor_dict)[:-3] + "pdf"
plt.figure()
plt.plot(x_range, np.degrees(y_range))
plt.savefig(plot_path)
plt.close()
def create_quantile_floor_0d(floor_dict):
mc = get_mc(floor_dict)
e_pdf = EnergyPDF.create(floor_dict["e_pdf_dict"])
weights = e_pdf.weight_mc(mc)
quantile_floor = weighted_quantile(mc["raw_sigma"],
floor_dict["floor_quantile"], weights)
save_path = floor_pickle(floor_dict)
with open(save_path, "wb") as f:
Pickle.dump(quantile_floor, f)
print("Saved to", save_path)
def __init__(self, floor_dict):
self.floor_dict = floor_dict
self.season = floor_dict["season"]
self.pickle_name = floor_pickle(floor_dict)
def create_quantile_floor_1d_e(floor_dict):
mc = get_mc(floor_dict)
e_pdf = EnergyPDF.create(floor_dict["e_pdf_dict"])
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.linspace(bounds[0][0], bounds[0][1], n_step)
bins = np.linspace(2.0, 6.0, 20)
z_range = []
for j, lower in enumerate(bins[:-1]):
upper = bins[j + 1]
mask = np.logical_and(mc["logE"] >= lower, mc["logE"] < upper)
cut_mc = mc[mask]
vals = []
for e in e_range:
weights = e_pdf.weight_mc(cut_mc, e)
quantile_floor = weighted_quantile(cut_mc["raw_sigma"],
floor_dict["floor_quantile"],
weights)
vals.append(quantile_floor)
z_range.append(vals)
x_range = 0.5 * (bins[1:] + bins[:-1])
x_range = np.array([0.0] + list(x_range) + [10.0])
z_range = np.array([z_range[0]] + z_range + [z_range[-1]])
save_path = floor_pickle(floor_dict)
res = [x_range, e_range, np.log(z_range)]
with open(save_path, "wb") as f:
Pickle.dump(res, f)
print("Saved to", save_path)
from scipy.interpolate import RectBivariateSpline
spline = RectBivariateSpline(x_range,
e_range,
np.log(np.degrees(z_range)),
kx=1,
ky=1,
s=0)
Z = []
for x in x_range:
Z.append(spline(x, e_range)[0])
Z = np.array(Z)
plot_path = floor_pickle(floor_dict)[:-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,
Z.T,
cmap="viridis",
)
plt.colorbar(cbar, label="Log(Angular Error Floor/deg)")
plt.ylabel(name[0])
plt.xlabel("Log(Energy proxy)")
plt.savefig(plot_path)
plt.close()
