def __init__(self, data_path):
self.data = tables.openFile(data_path, 'a')
self.station_groups = ['/s%d' % u for u in self.stations]
self.cluster = clusters.ScienceParkCluster(self.stations)
self.trig_threshold = .5
def __init__(self, data_path):
self.data = tables.open_file(data_path, 'a')
self.station_groups = ['/s%d' % u for u in self.stations]
self.cluster = clusters.ScienceParkCluster(self.stations)
self.detector_offsets = []
self.station_offsets = []
def plot_sciencepark_cluster():
stations = range(501, 507)
cluster = clusters.ScienceParkCluster(stations)
figure()
x_list, y_list = [], []
x_stations, y_stations = [], []
for station in cluster.stations:
x_detectors, y_detectors = [], []
for detector in station.detectors:
x, y = detector.get_xy_coordinates()
x_detectors.append(x)
y_detectors.append(y)
scatter(x, y, c='black', s=3)
x_list.extend(x_detectors)
y_list.extend(y_detectors)
x_stations.append(mean(x_detectors))
y_stations.append(mean(y_detectors))
axis('equal')
cluster = clusters.ScienceParkCluster([501, 503, 506])
pos = []
for station in cluster.stations:
x, y, alpha = station.get_xyalpha_coordinates()
pos.append((x, y))
for (x0, y0), (x1, y1) in itertools.combinations(pos, 2):
plot([x0, x1], [y0, y1], 'gray')
utils.savedata([x_list, y_list])
utils.saveplot()
artist.utils.save_data([x_list, y_list],
suffix='detectors',
dirname='plots')
artist.utils.save_data([stations, x_stations, y_stations],
suffix='stations',
dirname='plots')
def calc_theta_error_for_station_cluster(theta, station, cluster):
phis = linspace(-pi, pi, 50)
rec = DirectionReconstruction
sciencepark = clusters.ScienceParkCluster(range(501, 507))
r1, phi1 = sciencepark.stations[station].calc_r_and_phi_for_detectors(1, 3)
r2, phi2 = sciencepark.stations[station].calc_r_and_phi_for_detectors(1, 4)
err_single = rec.rel_theta1_errorsq(theta, phis, phi1, phi2, r1, r2)
r1, phi1 = sciencepark.calc_r_and_phi_for_stations(cluster[0], cluster[1])
r2, phi2 = sciencepark.calc_r_and_phi_for_stations(cluster[0], cluster[2])
err_cluster = rec.rel_theta1_errorsq(theta, phis, phi1, phi2, r1, r2)
# errors are already squared!!
err_total = sqrt(STATION_TIMING_ERR**2 * err_single +
CLUSTER_TIMING_ERR**2 * err_cluster)
return mean(err_total)
def calc_phi_error_for_station_station(theta, station1, station2):
phis = linspace(-pi, pi, 50)
rec = DirectionReconstruction
sciencepark = clusters.ScienceParkCluster(range(501, 507))
r1, phi1 = sciencepark.stations[station1].calc_r_and_phi_for_detectors(
1, 3)
r2, phi2 = sciencepark.stations[station1].calc_r_and_phi_for_detectors(
1, 4)
err_single1 = rec.rel_phi_errorsq(theta, phis, phi1, phi2, r1, r2)
r1, phi1 = sciencepark.stations[station2].calc_r_and_phi_for_detectors(
1, 3)
r2, phi2 = sciencepark.stations[station2].calc_r_and_phi_for_detectors(
1, 4)
err_single2 = rec.rel_phi_errorsq(theta, phis, phi1, phi2, r1, r2)
# errors are already squared!!
err_total = sqrt(STATION_TIMING_ERR**2 * err_single1 +
STATION_TIMING_ERR**2 * err_single2)
return mean(err_total)
def do_energies_simulations(self):
cluster = clusters.ScienceParkCluster()
for energy in ['100TeV', '10PeV']:
shower_group = '/showers/E_%s/zenith_22_5' % energy
for shower in self.get_showers_in_group(shower_group):
self.perform_simulation(cluster, shower)
def do_cluster_simulations(self):
cluster = clusters.ScienceParkCluster()
for angle in self.get_shower_angles_from_shower_data():
for shower in self.get_showers_in_group(angle):
self.perform_simulation(cluster, shower)
def plot_N_vs_R(data):
stations = range(501, 507)
station_ids = range(6)
cluster = clusters.ScienceParkCluster(stations)
c_index = data.root.coincidences.c_index
observables = data.root.coincidences.observables
figure()
#clf()
global c_x, c_y
if 'c_x' in globals():
scatter(c_x, c_y)
else:
stations_in_coincidence = []
for coincidence_events in c_index:
stations = [
observables[u]['station_id'] for u in coincidence_events
]
stations_in_coincidence.append(stations)
c_x = []
c_y = []
for station1, station2 in itertools.combinations(station_ids, 2):
condition = [
station1 in u and station2 in u
for u in stations_in_coincidence
]
N = sum(condition)
R, phi = cluster.calc_r_and_phi_for_stations(station1, station2)
scatter(R, N)
c_x.append(R)
c_y.append(N)
print R, N, station1, station2
ldf = KascadeLdf()
R = linspace(100, 500)
E = linspace(1e14, 1e19, 100)
F = E**-2.7
N = []
for r in R:
x = []
for f, e in zip(F, E):
Ne = e / 1e15 * 10**4.8
density = ldf.get_ldf_value_for_size(r, Ne)
prob = 1 - exp(-.5 * density)
x.append(f * prob)
N.append(mean(x))
N = array(N)
f = lambda x, S: S * interp(x, R, N)
c_x = array(c_x)
c_y = array(c_y)
# WTF wrong with point at slightly less than 100 m? 501 / 502??
sc_x = c_x.compress(c_x >= 100)
sc_y = c_y.compress(c_x >= 100)
popt, pcov = curve_fit(f, sc_x, sc_y, p0=(1e45))
plot(R, f(R, popt[0]))
#ylim(0, 150000)
ylim(0, 500000)
xlim(0, 500)
xlabel("Distance [m]")
ylabel("Number of coincidences")
utils.saveplot()
utils.savedata([sc_x, sc_y], suffix='data')
utils.savedata([R, f(R, popt[0])], suffix='fit')
