def get_number_of_events_for_flux(energies,
flux,
Veff,
livetime,
nuCrsScn='ctw'):
"""
calculates the number of expected neutrinos for a certain flux assumption
Parameters
-----------
energies: array of floats
energies (the bin centers), the binning in log10(E) must be equidistant!
flux: array of floats
the flux at energy logE
Veff: array of floats
the effective volume per energy logE
livetime: float
the livetime of the detector (including signal efficiency)
Returns
-------
array of floats: number of events per energy bin
"""
energies = np.array(energies)
Veff = np.array(Veff)
logE = np.log10(energies)
dlogE = logE[1] - logE[0]
return np.log(
10
) * livetime * flux * energies * Veff / cross_sections.get_interaction_length(
energies, cross_section_type=nuCrsScn) * dlogE
def get_exposure(energy, Veff, field_of_view=2 * np.pi):
"""
calculate exposure from effective volume
Parameters
----------
energy: float
neutrino energy (needed to calculate interaction length)
Veff: float
effective volume
field_of_view: float
the field of view of the detector
Returns
float: exposure
"""
return Veff / field_of_view / cross_sections.get_interaction_length(energy)
def get_limit_flux(energy,
veff_sr,
livetime,
signalEff=1.00,
energyBinsPerDecade=1.000,
upperLimOnEvents=2.44,
nuCrsScn='ctw'):
"""
Limit from effective volume
Parameters:
--------------
energy: array of floats
neutrino energy
veff_sr: array of floats
effective volume x solid angle
livetime: float
time used
signalEff: float
efficiency of signal reconstruction
energyBinsPerDecade: float
1 for decade bins, 2 for half-decade bins, etc.
upperLimOnEvents: float
2.3 for Neyman UL w/ 0 background,
2.44 for F-C UL w/ 0 background, etc
nuCrsScn: str
type of neutrino cross-section
"""
evtsPerFluxPerEnergy = veff_sr * signalEff
evtsPerFluxPerEnergy *= livetime
evtsPerFluxPerEnergy /= cross_sections.get_interaction_length(
energy, cross_section_type=nuCrsScn)
ul = upperLimOnEvents / evtsPerFluxPerEnergy
ul *= energyBinsPerDecade / np.log(10)
ul /= energy
return ul
# but, we also need the veff per zenith band at at given energy
aeff_vzen = np.zeros((9,20))
energies = np.zeros(9)
zen_bins = None
for iF, flavor in enumerate(flavors):
filename = f'results/{det}_{flavor}.pkl'
data = pickle.load(open(filename, 'br'))
for i, key in enumerate(data.keys()): # assume all have the same number of energies
energies[i] = np.power(10.,float(key))
the_veff = data[key]['total_veff']/n_to_divide
# the_aeff = the_veff / cross_sections.get_interaction_length(np.power(10., float(key)))
# deep_veff[i] += the_veff * 4 * np.pi /units.km**3
# deep_aeff[i] += the_aeff * 4 * np.pi /units.km**2'
if zen_bins is None:
zen_bins = data[key]['czmins']
for iz in range(20):
the_veff_zbin = data[key]['total_veff_zen_bins'][iz]/n_to_divide
the_aeff_zbin = the_veff_zbin / cross_sections.get_interaction_length(np.power(10., float(key)))
aeff_vzen[i][iz] += the_aeff_zbin/units.km**2
aeff_vzen/=3
filename = f'{det}_skycoverage.npz'
np.savez(filename, energies=energies, zen_bins=zen_bins, aeff_vzen = aeff_vzen)
def generate_eventlist_cylinder(filename, n_events, Emin, Emax,
full_rmin=None, full_rmax=None, full_zmin=None, full_zmax=None,
thetamin=0.*units.rad, thetamax=np.pi * units.rad,
phimin=0.*units.rad, phimax=2 * np.pi * units.rad,
start_event_id=1,
flavor=[12, -12, 14, -14, 16, -16],
n_events_per_file=None,
spectrum='log_uniform',
start_file_id=0):
"""
Event generator
Generates neutrino interactions, i.e., vertex positions, neutrino directions,
neutrino flavor, charged currend/neutral current and inelastiviy distributions.
All events are saved in an hdf5 file.
Parameters
----------
filename: string
the output filename of the hdf5 file
n_events: int
number of events to generate
Emin: float
the minimum neutrino energy (energies are randomly chosen assuming a
uniform distribution in the logarithm of the energy)
Emax: float
the maximum neutrino energy (energies are randomly chosen assuming a
uniform distribution in the logarithm of the energy)
full_rmin: float (default None)
lower r coordinate of simulated volume (if None it is set to 1/3 of the fiducial volume, if second vertices are not activated it is set to the fiducial volume)
full_rmax: float (default None)
upper r coordinate of simulated volume (if None it is set to 5x the fiducial volume, if second vertices are not activated it is set to the fiducial volume)
full_zmin: float (default None)
lower z coordinate of simulated volume (if None it is set to 1/3 of the fiducial volume, if second vertices are not activated it is set to the fiducial volume)
full_zmax: float (default None)
upper z coordinate of simulated volume (if None it is set to 5x the fiducial volume, if second vertices are not activated it is set to the fiducial volume)
thetamin: float
lower zenith angle for neutrino arrival direction
thetamax: float
upper zenith angle for neutrino arrival direction
phimin: float
lower azimuth angle for neutrino arrival direction
phimax: float
upper azimuth angle for neutrino arrival direction
start_event: int
default: 1
event number of first event
flavor: array of ints
default: [12, -12, 14, -14, 16, -16]
specify which neutrino flavors to generate. A uniform distribution of
all specified flavors is assumed.
The neutrino flavor (integer) encoded as using PDF numbering scheme,
particles have positive sign, anti-particles have negative sign,
relevant for us are:
* 12: electron neutrino
* 14: muon neutrino
* 16: tau neutrino
n_events_per_file: int or None
the maximum number of events per output files. Default is None, which
means that all events are saved in one file. If 'n_events_per_file' is
smaller than 'n_events' the event list is split up into multiple files.
This is useful to split up the computing on multiple cores.
spectrum: string
defines the probability distribution for which the neutrino energies are generated
* 'log_uniform': uniformly distributed in the logarithm of energy
start_file_id: int (default 0)
in case the data set is distributed over several files, this number specifies the id of the first file
(useful if an existing data set is extended)
if True, generate deposited energies instead of primary neutrino energies
"""
attributes = {}
n_events = int(n_events)
# save current NuRadioMC version as attribute
# save NuRadioMC and NuRadioReco versions
attributes['NuRadioMC_EvtGen_version'] = NuRadioMC.__version__
attributes['NuRadioMC_EvtGen_version_hash'] = version.get_NuRadioMC_commit_hash()
attributes['start_event_id'] = start_event_id
attributes['fiducial_rmin'] = full_rmin
attributes['fiducial_rmax'] = full_rmax
attributes['fiducial_zmin'] = full_zmin
attributes['fiducial_zmax'] = full_zmax
attributes['rmin'] = full_rmin
attributes['rmax'] = full_rmax
attributes['zmin'] = full_zmin
attributes['zmax'] = full_zmax
attributes['flavors'] = flavor
attributes['Emin'] = Emin
attributes['Emax'] = Emax
attributes['thetamin'] = thetamin
attributes['thetamax'] = thetamax
attributes['phimin'] = phimin
attributes['phimax'] = phimax
# define cylinder by two points and the radius
h_cylinder = full_zmax - full_zmin
r_cylinder = full_rmax
pt1 = np.array([0, 0, R_earth + full_zmax])
pt2 = np.array([0, 0, R_earth + full_zmin])
data_sets = {}
# calculate maximum width of projected area
theta_max = np.arctan(h_cylinder / 2 / r_cylinder)
d = 2 * r_cylinder * np.cos(theta_max) + h_cylinder * np.sin(theta_max) # width of area
print(f"cylinder r = {r_cylinder/units.km:.1f}km, h = {h_cylinder/units.km:.1f}km -> dmax = {d/units.km:.1f}km")
def perp(a) :
b = np.empty_like(a)
b[0] = -a[1]
b[1] = a[0]
return b
# line segment a given by endpoints a1, a2
# line segment b given by endpoints b1, b2
# return
def seg_intersect(a1, a2, b1, b2) :
da = a2 - a1
db = b2 - b1
dp = a1 - b1
dap = perp(da)
denom = np.dot(dap, db)
num = np.dot(dap, dp)
return (num / denom) * db + b1
def get_R(t, v, X):
""""
calculate distance to center of Earth as a function of travel distance
Parameters
-----------
t: 3dim array
travel distance
v: 3dim array
direction
X: 3dim array
start point
"""
return np.linalg.norm(v * t + X)
def get_density(t, v, X):
"""
calculates density as a function of travel distance
Parameters
-----------
t: 3dim array
travel distance
v: 3dim array
direction
X: 3dim array
start point
"""
return earth.density(get_R(t, v, X))
def slant_depth(t, v, X):
"""
calculates slant depth (grammage) as a function of travel distance
Parameters
-----------
t: 3dim array
travel distance
v: 3dim array
direction
X: 3dim array
start point
"""
res = quad(get_density, 0, t, args=(v, X), limit=200)
return res[0]
def slant_depth_num(t, v, X, step=50 * units.m):
"""
calculates slant depth (grammage) as a function of travel distance
Parameters
-----------
t: 3dim array
travel distance
v: 3dim array
direction
X: 3dim array
start point
"""
tt = np.linspace(0, t, t / step)
rr = np.linalg.norm(X + np.outer(tt, v), axis=1)
res = np.trapz(earth.density(rr), tt)
return res
def obj_dist_to_surface(t, v, X):
return get_R(t, v, X) - R_earth
def obj(t, v, X, Lint):
"""
objective function to determine at which travel distance we reached the interaction point
"""
return slant_depth(t, v, X) - Lint
def points_in_cylinder(pt1, pt2, r, q):
"""
determines if point lies within a cylinder
Parameters
-----------
pt1: 3dim array
lowest point on cylinder axis
pt2: 3dim array
highest point on cylinder axis
r: float
radius of cylinder
q: 3dim array
point under test
Returns True/False
"""
vec = pt2 - pt1
const = r * np.linalg.norm(vec)
return len(np.where(np.dot(q - pt1, vec) >= 0 and np.dot(q - pt2, vec) <= 0 and np.linalg.norm(np.cross(q - pt1, vec)) <= const)[0]) > 0
# precalculate the maximum slant depth to the detector
if(not os.path.exists("buffer_Llimit.pkl")):
zens = np.arange(0, 180.1 * units.deg, 2 * units.deg)
Lint_max = np.zeros_like(zens)
Lint_min = np.zeros_like(zens)
Xs = np.array([[0, -0.5 * r_cylinder, -h_cylinder + R_earth],
[0, -0.5 * r_cylinder, R_earth],
[0, 0.5 * r_cylinder, -h_cylinder + R_earth],
[0, 0.5 * r_cylinder, R_earth]]
)
v_tmps = -hp.spherical_to_cartesian(zens, np.zeros_like(zens)) # neutrino direction
for i in range(len(v_tmps)):
v = v_tmps[i]
sdepth_tmp = np.zeros(4)
for j, X in enumerate(Xs):
if((X[2] == R_earth) and (zens[i] <= 90 * units.deg)):
t = 0
sdepth_tmp[j] = 0
else:
t = brentq(obj_dist_to_surface, 100, 2 * R_earth, args=(-v, X))
sdepth_tmp[j] = slant_depth_num(t, -v, X)
# print(i, zens[i] / units.deg, X, sdepth_tmp[j])
# enter_point = X + (-v * t)
Lint_max[i] = np.max(sdepth_tmp)
Lint_min[i] = np.min(sdepth_tmp)
pickle.dump([zens, Lint_max, Lint_min], open("buffer_Llimit.pkl", "wb"), protocol=4)
else:
zens, Lint_max, Lint_min = pickle.load(open("buffer_Llimit.pkl", "rb"))
get_Lmax = interp1d(zens, Lint_max, kind='next')
get_Lmin = interp1d(zens, Lint_min, kind='previous')
if 0:
fig, a = plt.subplots(1, 1)
ztmp = np.linspace(0, 180 * units.deg, 10000)
a.plot(ztmp / units.deg, get_Lmax(ztmp) / units.g * units.cm ** 2, 'C0-', label="max possible Lint")
# a.plot(zens / units.deg, Lint_max / units.g * units.cm ** 2, 'oC0')
a.plot(ztmp / units.deg, get_Lmin(ztmp) / units.g * units.cm ** 2, 'C1-', label="min possible Lint")
# a.plot(zens / units.deg, Lint_min / units.g * units.cm ** 2, 'dC1')
a.hlines(cs.get_interaction_length(.1 * units.EeV, 1, 12, "total") / units.g * units.cm ** 2, 0, 180, label="0.1 EeV", colors='C2')
a.hlines(cs.get_interaction_length(1 * units.EeV, 1, 12, "total") / units.g * units.cm ** 2, 0, 180 , label="1 EeV", colors='C3')
a.hlines(cs.get_interaction_length(10 * units.EeV, 1, 12, "total") / units.g * units.cm ** 2, 0, 180 , label="10 EeV", colors='C4')
a.set_xlabel("zenith angle [deg]")
a.set_ylabel("slant depth [g/cm^2]")
a.semilogy(True)
a.set_xticks(np.arange(0, 181, 10))
a.set_ylim(5e5)
a.legend()
fig.tight_layout()
fig.savefig("Lvszen.png")
plt.show()
failed = 0
if(spectrum == 'log_uniform'):
Enu = 10 ** np.random.uniform(np.log10(Emin), np.log10(Emax), n_events)
flavors = np.array([flavor[i] for i in np.random.randint(0, high=len(flavor), size=n_events)])
az = np.random.uniform(phimin, phimax, n_events)
zen = np.arccos(np.random.uniform(np.cos(thetamax), np.cos(thetamin), n_events))
# generate random positions on an area perpendicular do neutrino direction
ax, ay = np.random.uniform(-0.5 * d, 0.5 * d, (2, n_events))
# az = np.ones(n_events) * (R_earth - .5 * h_cylinder) # move plane to the center of the cylinder
# calculate grammage (g/cm^2) after which neutrino interacted
Lint = np.random.exponential(cs.get_interaction_length(Enu, 1, flavors, "total"), n_events)
mask = (Lint < get_Lmax(zen)) & (Lint > get_Lmin(zen))
print(f"{np.sum(mask)}/{n_events} = {np.sum(mask)/n_events:.2g} can potentially interact in simulation volume")
# calculate position where neutrino interacts
data_sets = {'xx': [],
'yy': [],
'zz': [],
'azimuths': [],
'zeniths': [],
'flavors': [],
'energies': []}
# calculate rotation matrix to transform position on area to 3D
mask_int = np.zeros_like(mask, dtype=np.bool)
t0 = time.perf_counter()
n_cylinder = 0
for j, i in enumerate(np.arange(n_events, dtype=np.int)[mask]):
if(j % 1000 == 0 and i > 0):
eta = (time.perf_counter() - t0) * (n_events - i) / i
logger.info(f"{i}/{n_events} interacting = {np.sum(mask_int)}, failed = {failed}, n_cylinder = {n_cylinder}, eta = {pretty_time_delta(eta)}")
# print(f"calculating interaction point of event {i}"),
c, s = np.cos(az[i]), np.sin(az[i])
Raz = np.array(((c, -s, 0), (s, c, 0), (0, 0, 1)))
c, s = np.cos(zen[i]), np.sin(zen[i])
# Rzen = np.array(((c, 1, -s), (0, 1, 0), (s, 0, c)))
Rzen = hp.get_rotation(hp.spherical_to_cartesian(0, az[i]), hp.spherical_to_cartesian(zen[i], az[i]))
# R = hp.get_rotation(np.array([0, 0, 1]), hp.spherical_to_cartesian(zen[i], az[i]))
R = np.matmul(Rzen, Raz)
v = -hp.spherical_to_cartesian(zen[i], az[i]) # neutrino direction
X = np.matmul(R, np.array([ax[i], ay[i], 0])) + np.array([0, 0, -0.5 * h_cylinder])
if 0:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
fig = plt.figure()
a = fig.add_subplot(111, projection='3d')
# Cylinder
x = np.linspace(-r_cylinder, r_cylinder, 100)
z = np.linspace(0, -h_cylinder, 100)
Xc, Zc = np.meshgrid(x, z)
Yc = np.sqrt(r_cylinder ** 2 - Xc ** 2)
# Draw parameters
rstride = 20
cstride = 10
a.plot_surface(Xc, Yc, Zc, alpha=0.2, rstride=rstride, cstride=cstride)
a.plot_surface(Xc, -Yc, Zc, alpha=0.2, rstride=rstride, cstride=cstride)
a.set_title(f"zenith = {zen[i]/units.deg:.0f}")
xx = []
yy = []
zz = []
for vert in np.array([[-0.5 * d, -0.5 * d, 0], [0.5 * d, -0.5 * d, 0], [0.5 * d, 0.5 * d, 0], [-0.5 * d, 0.5 * d, 0]]):
t = np.matmul(Raz, vert) + np.array([0, 0, -0.5 * h_cylinder])
xx.append(t[0])
yy.append(t[1])
zz.append(t[2])
verts = [list(zip(xx, yy, zz))]
a.add_collection3d(Poly3DCollection(verts, alpha=0.5))
xx = []
yy = []
zz = []
for vert in np.array([[-0.5 * d, -0.5 * d, 0], [0.5 * d, -0.5 * d, 0], [0.5 * d, 0.5 * d, 0], [-0.5 * d, 0.5 * d, 0]]):
t = np.matmul(Rzen, np.matmul(Raz, vert)) + np.array([0, 0, -0.5 * h_cylinder])
xx.append(t[0])
yy.append(t[1])
zz.append(t[2])
verts = [list(zip(xx, yy, zz))]
a.add_collection3d(Poly3DCollection(verts, alpha=0.5))
s = np.array([0, 0, -0.5 * h_cylinder])
t = v * 10 * units.km + s
a.plot([s[0], t[0]], [s[1], t[1]], [s[2], t[2]], '-d')
s = np.array([0, 0, -0.5 * h_cylinder])
t = -hp.spherical_to_cartesian(90 * units.deg, az[i]) * 10 * units.km + s
a.plot([s[0], t[0]], [s[1], t[1]], [s[2], t[2]], '--d')
# check if neutrino axis is perpendicular
t = np.array(verts[0][0]) - np.array(verts[0][1])
t /= np.linalg.norm(t)
print(np.dot(t, v))
a.set_xlabel("x")
a.set_zlabel("z")
a.set_ylabel("y")
plt.show()
# check if trajectory passes through cylinder
# if(not points_in_cylinder(pt1, pt2, r_cylinder, X)):
# we rotate everything in the plane defined by z and the propagration direction (such that v_y = 0)
Xaz = np.matmul(Raz.T, X)
rmin = Xaz[1] # the closest distance to the z axis (center of cyllinder)
if(abs(rmin) >= r_cylinder):
continue
# define the projected square of the cylinder
# the two endpoints of the two horizontal lines are
xtmp = (r_cylinder ** 2 - rmin ** 2) ** 0.5
Lh1 = np.array([[-xtmp, 0], [xtmp, 0]])
Lh2 = np.array([[-xtmp, -h_cylinder], [xtmp, -h_cylinder]])
# the two endpoints of the two vertical lines are
Lv1 = np.array([[-xtmp, 0], [-xtmp, -h_cylinder]])
Lv2 = np.array([[xtmp, 0], [xtmp, -h_cylinder]])
# define line of neutrino propagation by two points
vaz = np.matmul(Raz.T, v)
if(abs(vaz[1]) > 1e-10):
a = 1 / 0
v2d = np.array([vaz[0], vaz[2]])
X2d = np.array([Xaz[0], Xaz[2]])
t = 2 * d
Paz = np.array([X2d + -t * v2d, X2d + t * v2d])
# calculate points that intersect any of the 4 area (projected cylinder) boundaries
intersects = []
for k, (a1, a2) in enumerate([Lh1, Lh2]):
tmp = seg_intersect(a1, a2, Paz[0], Paz[1])
if((tmp[0] >= a1[0]) and (tmp[0] <= a2[0])):
intersects.append(tmp)
for k, (a1, a2) in enumerate([Lv1, Lv2]):
tmp = seg_intersect(a1, a2, Paz[0], Paz[1])
if((tmp[1] <= a1[1]) and (tmp[1] >= a2[1])):
intersects.append(tmp)
intersects = np.array(intersects)
if(len(intersects) != 2):
if 0:
print(len(intersects))
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
a = fig.add_subplot(111, projection='3d')
# Cylinder
x = np.linspace(-r_cylinder, r_cylinder, 100)
z = np.linspace(0, -h_cylinder, 100)
Xc, Zc = np.meshgrid(x, z)
Yc = np.sqrt(r_cylinder ** 2 - Xc ** 2)
# Draw parameters
rstride = 20
cstride = 10
a.plot_surface(Xc, Yc, Zc, alpha=0.2, rstride=rstride, cstride=cstride)
a.plot_surface(Xc, -Yc, Zc, alpha=0.2, rstride=rstride, cstride=cstride)
X_enter = X + 10 * units.km * v
X_leave = X - 10 * units.km * v
a.plot([X_enter[0], X_leave[0]], [X_enter[1], X_leave[1]], [X_enter[2], X_leave[2]], '-o')
a.set_xlabel("x")
a.set_zlabel("z")
a.set_ylabel("y")
a.legend()
a.set_title("no intersection")
fig = plt.figure()
a = fig.add_subplot(111, projection='3d')
for (a1, a2) in [Lh1, Lh2, Lv1, Lv2]:
a.plot([a1[0], a2[0]], [rmin, rmin], [a1[1], a2[1]])
a.plot([Paz[0][0], Paz[1][0]], [rmin, rmin], [Paz[0][1], Paz[1][1]])
a.set_xlabel("x")
a.set_zlabel("z")
a.set_ylabel("y")
a.legend()
plt.show()
continue # neutrino is not passing through cylinder
n_cylinder += 1
ss = []
for tmp in intersects:
ss.append(np.dot(tmp - X2d, v2d.T))
argsort = np.argsort(np.array(ss)) # check which intersection happens first along neutrino path
if 0:
if(len(intersects)):
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
a = fig.add_subplot(111, projection='3d')
for (a1, a2) in [Lh1, Lh2, Lv1, Lv2]:
a.plot([a1[0], a2[0]], [rmin, rmin], [a1[1], a2[1]])
a.plot([Paz[0][0], Paz[1][0]], [rmin, rmin], [Paz[0][1], Paz[1][1]])
for k, tmp in enumerate(intersects):
a.plot([tmp[0]], [rmin], [tmp[1]], 'o', label=f"s = {ss[k]:.0f}")
a.set_xlabel("x")
a.set_zlabel("z")
a.set_ylabel("y")
a.legend()
# plt.ion()
plt.show()
# calculate the 3D points where the neutrino enters/leaves the cylinder and transform to outside Earth
X_enter = np.matmul(Raz, np.array([intersects[argsort][0][0], rmin, intersects[argsort][0][1]])) + np.array([0, 0, R_earth])
X_leave = np.matmul(Raz, np.array([intersects[argsort][1][0], rmin, intersects[argsort][1][1]])) + np.array([0, 0, R_earth])
X += np.array([0, 0, R_earth])
# calculate point where neutrino enters Earth
if(np.linalg.norm(X_enter) > R_earth): # if enter point is outside of Earth (can happen because cylinder does not account for Earth curvature)
if(np.linalg.norm(X_leave) > R_earth): # check if leave point is also outside of Earth (can also happen because cylinder does not account for Earth curvature)
continue
t = brentq(obj_dist_to_surface, 0, 5 * d, args=(-v, X_leave))
enter_point = X_leave + (-v * t)
X_enter = enter_point # define point where neutrino enters the cylinder as the point where it enters the Earth
else:
t = brentq(obj_dist_to_surface, 0, 2 * R_earth, args=(-v, X_enter))
enter_point = X_enter + (-v * t)
# logger.debug(f"zen = {zen[i]/units.deg:.0f}deg, trajectory enters Earth at {enter_point[0]:.1f}, {enter_point[0]:.1f}, {enter_point[0]:.1f}. Dist to core = {np.linalg.norm(enter_point)/R_earth:.5f}, dist to (0,0,R) = {np.linalg.norm(enter_point - np.array([0,0,R_earth]))/R_earth:.4f}")
# check if event interacts at all
# calcualte slant depth to point of entering cylinder
t = np.linalg.norm(enter_point - X_enter)
slant_depth_min = slant_depth(t, v, enter_point)
if(t == 0):
slant_depth_min = 0
# calculate slant depth through the cylinder
s = np.linalg.norm(X_leave - X_enter)
slant_depth_max = slant_depth(s, v, X_enter) + slant_depth_min # full slant depth from outside Earth to point when it leaves the cylinder
if 0:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
a = fig.add_subplot(111, projection='3d')
# Cylinder
x = np.linspace(-r_cylinder, r_cylinder, 100)
z = np.linspace(0, -h_cylinder, 100)
Xc, Zc = np.meshgrid(x, z)
Yc = np.sqrt(r_cylinder ** 2 - Xc ** 2)
# Draw parameters
rstride = 20
cstride = 10
a.plot_surface(Xc, Yc, Zc, alpha=0.2, rstride=rstride, cstride=cstride)
a.plot_surface(Xc, -Yc, Zc, alpha=0.2, rstride=rstride, cstride=cstride)
a.plot([X_enter[0], X_leave[0]], [X_enter[1], X_leave[1]], [X_enter[2] - R_earth, X_leave[2] - R_earth], '-o')
a.set_xlabel("x")
a.set_zlabel("z")
a.set_ylabel("y")
print(f"Lmin = {slant_depth_min:.2g}, Lmax = {slant_depth_max:.2g}, Lnu = {Lint[i]:.2g}")
a.legend()
plt.show()
# a = 1 / 0
if((Lint[i] <= slant_depth_min) or (Lint[i] >= slant_depth_max)):
logger.debug("neutrino does not interact in cylinder, skipping to next event")
continue
try:
# calculate interaction point by inegrating the density of Earth along the neutrino path until we wind the interaction length
t = brentq(obj, 0, s, args=(v, X_enter, Lint[i] - slant_depth_min), maxiter=500)
except:
logger.warning("failed to converge, skipping event")
failed += 1
continue
Xint = X_enter + v * t # calculate interaction point
is_in_cylinder = points_in_cylinder(pt1, pt2, r_cylinder, Xint)
mask_int[i] = is_in_cylinder
if(is_in_cylinder):
logger.debug(f"event {i}, interaction point ({Xint[0]:.1f}, {Xint[1]:.1f}, {Xint[2]-R_earth:.1f}), in cylinder {is_in_cylinder}")
data_sets['xx'].append(Xint[0])
data_sets['yy'].append(Xint[1])
data_sets['zz'].append(Xint[2] - R_earth)
data_sets['zeniths'].append(zen[i])
data_sets['azimuths'].append(az[i])
data_sets['flavors'].append(flavors[i])
data_sets['energies'].append(Enu[i])
else:
logger.error("interaction is not in cylinder but it should be")
a = 1 / 0
data_sets['event_ids'] = range(np.sum(mask_int))
data_sets['flavors'] = np.ones(np.sum(mask_int))
data_sets["event_ids"] = np.arange(np.sum(mask_int)) + start_event_id
data_sets["n_interaction"] = np.ones(np.sum(mask_int), dtype=np.int)
data_sets["vertex_times"] = np.zeros(np.sum(mask_int), dtype=np.float)
data_sets["interaction_type"] = inelasticities.get_ccnc(np.sum(mask_int))
data_sets["inelasticity"] = inelasticities.get_neutrino_inelasticity(np.sum(mask_int))
attributes['n_events'] = n_events
logger.info(f"{np.sum(mask_int)} event interacted in simulation volume")
write_events_to_hdf5(filename, data_sets, attributes,
n_events_per_file=n_events_per_file, start_file_id=start_file_id)
def get_weight(theta_nu,
pnu,
flavors,
mode='simple',
cross_section_type='ctw',
vertex_position=None,
phi_nu=None):
"""
calculates neutrino weight due to Earth absorption for different models
Parameters
----------
theta_nu: float or array of floats
the zenith angle of the neutrino direction (where it came from, i.e., opposite to the direction of propagation)
pnu: float or array of floats
the momentum of the neutrino
mode: string
* 'simple': assuming interaction happens at the surface and approximating the Earth with constant density
* 'core_mantle_crust_simple': assuming interaction happens at the surface and approximating the Earth with 3 layers of constant density
* 'core_mantle_crust': approximating the Earth with 3 layers of constant density, path through Earth to interaction vertex is considered
* 'PREM': density of Earth is parameterized as a fuction of radius, path through Earth to interaction vertex is considered
cross_section_type: string
'ghandi', 'ctw' or 'csms' (see description in `cross_sections.py`)
vertex_position: 3-dim array or None (default)
the position of the neutrino interaction
phi_nu: float
the azimuth angle of the neutrino direction
"""
if (mode == 'simple'):
return get_simple_weight(theta_nu,
pnu,
cross_section_type=cross_section_type)
elif (mode == "core_mantle_crust_simple"):
return get_core_mantle_crust_weight(
theta_nu, pnu, flavors, cross_section_type=cross_section_type)
elif (mode == "core_mantle_crust"):
earth = CoreMantleCrustModel()
direction = hp.spherical_to_cartesian(theta_nu, phi_nu)
slant_depth = earth.slant_depth(vertex_position, direction)
# by requesting the interaction length for a density of 1, we get it in units of length**2/weight
L_int = cross_sections.get_interaction_length(
pnu,
density=1.,
flavor=flavors,
inttype='total',
cross_section_type=cross_section_type)
return np.exp(-slant_depth / L_int)
elif (mode == "PREM"):
earth = PREM()
direction = hp.spherical_to_cartesian(theta_nu, phi_nu)
slant_depth = earth.slant_depth(vertex_position, direction)
# by requesting the interaction length for a density of 1, we get it in units of length**2/weight
L_int = cross_sections.get_interaction_length(
pnu,
density=1.,
flavor=flavors,
inttype='total',
cross_section_type=cross_section_type)
return np.exp(-slant_depth / L_int)
elif (mode == "None"):
return 1.
else:
logger.error('mode {} not supported'.format(mode))
raise NotImplementedError
result[flavor] = {}
result[flavor]['E'] = []
for l in the_list:
result[flavor][l + '_veff'] = []
result[flavor][l + '_aeff'] = []
pkl_file_name = os.path.join('results/overlap_' + detector + "_deeptrig_" +
deep_trigger + "_shallowtrig_" +
shallow_trigger + "_mode_" + mode + '_' +
flavor + ".pkl")
with open(pkl_file_name, "rb") as fin:
coincidences = pickle.load(fin)
for lgE, value in coincidences.items():
result[flavor]['E'].append(lgE)
lint = cross_sections.get_interaction_length(np.power(10., float(lgE)))
for l in the_list:
v_temp = value[l] # the veff water equivalent
a_temp = v_temp / lint # get the aeff water equivalent
v_temp = v_temp * 4 * np.pi / units.km**3 # convert to km^3 steradians
a_temp = a_temp * 4 * np.pi / units.km**2 # convert to km^2 steradians
result[flavor][l + '_veff'].append(v_temp)
result[flavor][l + '_aeff'].append(a_temp)
energies = result['e']['E']
result_average = {}
for l in the_list:
result_average[l + '_veff'] = np.zeros(9)
result_average[l + '_aeff'] = np.zeros(9)
if len(energies) != 11:
print(
"I expected 11 energy bins...You may not get the zenith bins you expected..."
)
which_e_bin = 6 # should be 1 EeV, counting from 0 = 1E15
start_bin = which_e_bin * 20
stop_bin = start_bin + 20
# first the deep station
det = 'deep'
trigger = r[det]['trigger']
for veff, energy in zip(r[det]['Veff'][trigger]['Veff'], energies):
this_veff = get_Veff_water_equivalent(veff / r[det]['n_stations'] * 4 *
np.pi)
this_aeff = this_veff / cross_sections.get_interaction_length(energy)
deep_veff.append(this_veff / units.km**3)
deep_aeff.append(this_aeff / units.km**2)
# and now for aeff for a selection of the zenith bins
deep_veff_vs_zen = get_Veff_water_equivalent(
r[det]['Veff'][trigger]['Veff_zen'][start_bin:stop_bin] /
r[det]['n_stations'])
deep_aeff_vs_zen = deep_veff_vs_zen / cross_sections.get_interaction_length(
energies[which_e_bin]) / units.km**2
# print("deep veff vs zen {}".format(deep_veff_vs_zen))
det = 'shallow'
trigger = r[det]['trigger']
for veff, energy in zip(r[det]['Veff'][trigger]['Veff'], energies):
this_veff = get_Veff_water_equivalent(veff / r[det]['n_stations'] * 4 *
calculates the fluence limit for a integrated exposure
"""
return 2.39 / int_exp
if __name__ == "__main__": # this part of the code gets only executed it the script is directly called
energy = 10**18 * units.eV
veff = 2150 * units.km**3 * units.sr
livetime = 5 * units.year
print("Cross section: {} cm^2".format(
cross_sections.get_nu_cross_section(energy, cross_section_type='ctw')))
print("interaction length: {} km".format(
cross_sections.get_interaction_length(energy, cross_section_type='ctw')
/ units.km))
print("calculating flux limit for {time} years and Veff of {veff} km^3 sr".
format(time=livetime / units.year,
veff=veff / (units.km**3 * units.sr)))
print("Flux limit: {} GeV/(cm^2 s sr)".format(
get_limit_e2_flux(energy, veff, livetime) /
(units.GeV * units.cm**-2 * units.second**-1 * units.sr**-1)))
aeff = np.array([0.0032, 0.033, 0.43, 3.1, 21, 68, 167
]) * units.km**2 * units.sr
energies = 10**np.array([18, 18.5, 19, 19.5, 20, 20.5, 21]) * units.eV
print("Flux limit: {} GeV/(cm^2 s sr)".format(
energies**2 * get_limit_from_aeff(energies, aeff, livetime) /
veff_sr=data_review['veff'] *
units.km**3 * units.sr,
livetime=livetime * uptime,
upperLimOnEvents=1)
ax.plot(energies / limits.plotUnitsEnergy,
limit_review / limits.plotUnitsFlux,
'C4-',
linewidth=3,
label='{}, {} yrs, {}% uptime'.format(det, livetime / units.year,
uptime * 100))
ax_veff[0].plot(energies, data_review['veff'], 'C4-', label='{}'.format(det))
review_aeff = []
for e, v in zip(energies * units.eV,
data_review['veff'] * units.km**3 * units.sr):
lint = cross_sections.get_interaction_length(float(e))
aeff_temp = v / lint
review_aeff.append(aeff_temp)
ax_aeff.plot(energies,
review_aeff,
linewidth=2,
label='Feb 2021 Radio Review Array, Trigger Level')
########################
########################
# now plot the new arrays
########################
########################
deep_trigger = 'PA_4channel_100Hz'
shallow_trigger = 'LPDA_2of4_100Hz'