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')
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')
