def __draw_2d_correlation_map(self, event, correlation_map):
fig4 = plt.figure(figsize=(4, 12))
ax4_1 = fig4.add_subplot(311)
d_0, z_0 = np.meshgrid(self.__distances_2d, self.__z_coordinates_2d)
ax4_1.pcolor(d_0, z_0, np.max(correlation_map, axis=2).T)
ax4_1.grid()
theta_0, d_0 = np.meshgrid(self.__azimuths_2d, self.__distances_2d)
ax4_2 = fig4.add_subplot(312, projection='polar')
ax4_2.pcolor(theta_0, d_0, np.max(correlation_map, axis=1))
ax4_2.grid()
ax4_3 = fig4.add_subplot(313)
theta_0, z_0 = np.meshgrid(self.__azimuths_2d, self.__z_coordinates_2d)
ax4_3.pcolor(theta_0 / units.deg, z_0, np.max(correlation_map, axis=0))
ax4_3.grid()
sim_vertex = None
for sim_shower in event.get_sim_showers():
sim_vertex = sim_shower.get_parameter(shp.vertex)
break
if sim_vertex is not None:
ax4_1.scatter([np.sqrt(sim_vertex[0]**2 + sim_vertex[1]**2)],
[sim_vertex[2]],
c='r',
alpha=.5,
marker='+')
ax4_2.scatter([
hp.cartesian_to_spherical(sim_vertex[0], sim_vertex[1],
sim_vertex[2])[1]
], [np.sqrt(sim_vertex[0]**2 + sim_vertex[1]**2)],
c='r',
alpha=.5,
marker='+')
ax4_3.scatter([
hp.get_normalized_angle(
hp.cartesian_to_spherical(sim_vertex[0], sim_vertex[1],
sim_vertex[2])[1]) / units.deg
], [(sim_vertex[2])],
c='r',
alpha=.5,
marker='+')
ax4_1.set_xlabel('d [m]')
ax4_1.set_ylabel('z [m]')
ax4_3.set_xlabel(r'$\phi [^\circ]$')
ax4_3.set_ylabel('z [m]')
fig4.tight_layout()
fig4.savefig('{}/{}_{}_2D_correlation_maps.png'.format(
self.__debug_folder, event.get_run_number(), event.get_id()))
def calculate_polarization_vector(launch_vector, shower_axis):
""" calculates the polarization vector in spherical coordinates (eR, eTheta, ePhi)
"""
polarization_direction = np.cross(launch_vector,
np.cross(shower_axis, launch_vector))
polarization_direction /= np.linalg.norm(polarization_direction)
cs = cstrans.cstrafo(*hp.cartesian_to_spherical(*launch_vector))
return cs.transform_from_ground_to_onsky(polarization_direction)
def find_receiving_zenith(self, station, ray_type, channel_id):
solution_types = {1: 'direct',
2: 'refracted',
3: 'reflected'}
nu_vertex_2D = station.get_parameter(stnp.vertex_2D_fit)
nu_vertex = [nu_vertex_2D[0], 0, nu_vertex_2D[1]]
ray_tracer = NuRadioMC.SignalProp.analyticraytracing.ray_tracing(
nu_vertex,
self.__detector.get_relative_position(station.get_id(), channel_id) + self.__detector.get_absolute_position(station.get_id()),
NuRadioMC.utilities.medium.get_ice_model('greenland_simple')
)
ray_tracer.find_solutions()
for i_solution, solution in enumerate(ray_tracer.get_results()):
if solution_types[ray_tracer.get_solution_type(i_solution)] == ray_type:
receive_vector = ray_tracer.get_receive_vector(i_solution)
receive_zenith = hp.cartesian_to_spherical(receive_vector[0], receive_vector[1], receive_vector[2])[0]
travel_time = ray_tracer.get_travel_time(i_solution)
return receive_zenith, travel_time
return None, None
def dump(filename):
print(f"!!!!!!!!!!!! dumping relevant file content of {filename}:!!!!!!!!!!!!!!!")
fin = h5py.File(filename, 'r')
stations = []
for key in fin.keys():
if(key.startswith("station_")):
stations.append(key)
event_group_ids = np.array(fin['event_group_ids'])
for iE, evt_gid in enumerate(event_group_ids):
t = "index, "
for key in keys_event:
t += f"{key}, "
print(t)
t = f"{iE} "
for key in keys_event:
t += f"{fin[key][iE]} "
print(t)
t = "stationid, channelid, rayid, "
for key in station_keys:
t += f"{key}, "
for key in station_keys_3dim:
t += f"{key}, "
t += f" zen, az"
print(t)
for station in stations:
if not 'ray_tracing_C0' in station:
print(f'{station} has not entries')
continue
nCh, nR = np.array(fin[station]['ray_tracing_C0'][iE]).shape
for iCh in range(nCh):
for iR in range(nR):
t = f"\t{station} {iCh} {iR}: "
for key in station_keys:
t += f"{fin[station][key][iE][iCh][iR]:.9g} "
for key in station_keys_3dim:
t += "("
for iD in range(3):
t += f"{fin[station][key][iE][iCh][iR][iD]:.5g},"
t += ") "
zen, az = hp.cartesian_to_spherical(*np.array(fin[station]["receive_vectors"][iE][iCh][iR]))
t += f" {zen/units.deg:.2f} {az/units.deg:.2f}"
print(t)
def run_nu_kernel(self, frame):
# we'll need an array of frequencies at which we'll calculate attens etc
# declare it here so we don't wast time declaring it over and over again
ff = np.fft.rfftfreq(self._n_samples, self._dt)
# get the list of particles from the *thinned* mctree
if 'I3MCTreeThin' not in frame:
icetray.logging.log_fatal('Frame does not contain the thinned tree I3MCTreeThin \nPlease tray.AddModule(TreeThinner) first!')
mctree = frame.Get('I3MCTreeThin')
primary = mctree.primaries[0]
num_particles = len(mctree)
if num_particles == 1:
icetray.logging.log_info("There are no children of the primary")
# load the list of antennas
antgeomap = self.antgeomap
# for every particle, we create a map between the particle ID and the I3RadioMCSummary
particle_radio_mc_map = icetradio.I3ParticleRadioMCSummaryMap()
# now, we loop over every particle, and every antenna, and do ray tracing
for particle in mctree:
# skip the primary
if particle == primary:
continue
# time of the vertex in nanoseconds
vertex_time = particle.time
# get the source (vertex) position, and move it into surface oriented coordinates
source = particle.pos
source = util_geo.convert_i3_to_global(source)
# for this particle, we need a map between the particle id and the signal
iceantenna_radio_mc_map = icetradio.I3IceAntennaRadioMCSummaryMap()
# where it's GOING
# in I3Position format (not simple numpy array)
# so that we are uniform between shower_axis and launch_vector
shower_axis = dataclasses.I3Position(particle.dir.x, particle.dir.y, particle.dir.z)
deposited_energy = particle.energy / icetray.I3Units.eV # in eV!
shower_type = particle.type
em_or_had = util_phys.pick_em_or_had(shower_type)
if em_or_had is 'UDEF':
icetray.logging.log_warn("A particle ({}) has an undefined type. Skipping it.".format(shower_type))
continue
n_index = self.ice.get_index_of_refraction(source)
for iceantkey, g in antgeomap:
# need a radio mc summary object
radio_mc_summary = icetradio.I3RadioMCSummary()
# get the target (antenna) position, and move it into surface oriented coordinates
target = g.position
target = util_geo.convert_i3_to_global(target)
# do ray tracing
record, attens = signal_prop.do_ray_tracing(
propagator=self.propagator,
ice_model=self.ice,
attenuation_model=self._att_model,
source=source,
target=target,
ff=ff,
sampling_rate=self._sampling_rate
)
# store the ray trace record
radio_mc_summary.ray_trace_record = record
# now, do signals
for iS in range(record.numSolutions):
# the time this field arrives at the antenna is the sum
# of the time of the vertex and the propagation time
# this is a value in ns, so we have to convert to seconds
# when we pass it to the signal generator
arrival_time = vertex_time + record.solutions[iS].travelTime
signal = signal_gen.generate_signal(
deposited_energy=deposited_energy,
shower_axis=shower_axis,
em_or_had=em_or_had,
launch_vector=record.solutions[iS].launchVector,
distance=record.solutions[iS].pathLength,
arrival_time=arrival_time / icetray.I3Units.s,
n_index=n_index,
attenuation_values=attens[iS],
dt=self._dt,
n_samples=self._n_samples,
model=self._askaryan_model,
seed=self._seed,
)
# convert the arrival information into a theta and phi for the signal
local_recieve_vector = util_dataclasses.i3pos_to_np(record.solutions[iS].receiveVector)
theta, phi = hp.cartesian_to_spherical(*local_recieve_vector)
signal.arrival_theta = theta * icetray.I3Units.rad
signal.arrival_phi = phi * icetray.I3Units.rad
# finish filling out the signal container
signal.sol_num = record.solutions[iS].solutionNumber
signal.sol_type = record.solutions[iS].solutionType
# append the signal
radio_mc_summary.signals.append(signal)
# now, put this radio mc summary into the map for this vertex
iceantenna_radio_mc_map[iceantkey] = radio_mc_summary
# now, for this particle, we store the map of radio truths
particle_radio_mc_map[particle.id] = iceantenna_radio_mc_map
# now, this gets stored in the frame
if(len(particle_radio_mc_map)>0):
# only store the frame if one of the daughter particles actually contributed
frame.Put("ParticleRadioMCSummaryMap",particle_radio_mc_map)
for i in range(flav.size):
if (flav[i]):
receive_vectors = np.append(receive_vectors, [rvtemp[i]], axis=0)
weightstemp = np.append(weightstemp, weights[i])
# print("--------------------------")
# print(rvtemp[0])
# print("----------")
# print(receive_vectors[0])
# print("--------------------------")
receive_vectors = np.delete(receive_vectors, 0, axis=0)
# receive_vectors = np.array(station['receive_vectors'])[triggered]
# for all events, antennas and ray tracing solutions
zeniths, azimuths = hp.cartesian_to_spherical(receive_vectors[:, :, :, 0].flatten(),
receive_vectors[:, :, :, 1].flatten(),
receive_vectors[:, :, :, 2].flatten())
for i in range(len(azimuths)):
azimuths[i] = hp.get_normalized_angle(azimuths[i])
weights_matrix = np.outer(weightstemp, np.ones(np.prod(receive_vectors.shape[1:-1]))).flatten()
mask = ~np.isnan(azimuths) # exclude antennas with not ray tracing solution (or with just one ray tracing solution)
fig, axs = php.get_histograms([zeniths[mask] / units.deg, azimuths[mask] / units.deg],
bins=[np.arange(0, 181, 5), np.arange(0, 361, 45)],
xlabels=['zenith [deg]', 'azimuth [deg]'],
weights=weights_matrix[mask], stats=False)
# axs[0].xaxis.set_ticks(np.arange(0, 181, 45))
majorLocator = MultipleLocator(45)
majorFormatter = FormatStrFormatter('%d')
minorLocator = MultipleLocator(5)
ax.plot(x1[0], x1[2], 'ko')
for i, x in enumerate([x2, x3, x4, x5]):
print('finding solutions for ', x)
r = ray.ray_tracing(x1,x,ice)
r.find_solutions()
if(r.has_solution()):
for iS in range(r.get_number_of_solutions()):
ray_tracing_C0[i, iS] = r.get_results()[iS]['C0']
ray_tracing_solution_type[i, iS] = r.get_solution_type(iS)
print(" Solution %d, Type %d: "%(iS,ray_tracing_solution_type[i, iS]))
R = r.get_path_length(iS) # calculate path length
T = r.get_travel_time(iS) # calculate travel time
print(" Ray Distance %.3f and Travel Time %.3f"%(R/units.m,T/units.ns))
receive_vector = r.get_receive_vector(iS)
receive_vectors[i, iS] = receive_vector
zenith, azimuth = hp.cartesian_to_spherical(*receive_vector)
print(" Receiving Zenith %.3f and Azimuth %.3f "%(zenith/units.deg, azimuth/units.deg))
#to readout the actual trace, we have to flatten to 2D
dX = x - x1
dPhi = -np.arctan2(dX[1],dX[0])
c,s = np.cos(dPhi), np.sin(dPhi)
R = np.array(((c,-s,0),(s,c,0),(0,0,1)))
X1r = x1
X2r = np.dot(R, x-x1) + x1
x1_2d = np.array([X1r[0],X1r[2]])
x2_2d = np.array([X2r[0],X2r[2]])
r_2d = ray.ray_tracing_2D(ice)
yy,zz = r_2d.get_path(x1_2d,x2_2d,ray_tracing_C0[i, iS])
ax.plot(yy, zz, '{}'.format(php.get_color_linestyle(i)), label='{} C0 = {:.4f}'.format(ray_tracing_solution_type[i,iS], ray_tracing_C0[i,iS]))
ax.plot(x2_2d[0], x2_2d[1], '{}{}-'.format('d',php.get_color(i)))
continue
# if(d > -800):
# continue
# calcualte expected angles
r = ray.ray_tracing(pos_spice + np.array([0, 0, d]),
pos_SP1,
medium.southpole_simple(),
log_level=logging.WARNING)
r.find_solutions()
if (not r.has_solution()):
continue
results['depth'].append(d)
rvec = r.get_receive_vector(0)
zen, az = hp.cartesian_to_spherical(*rvec)
az = hp.get_normalized_angle(az)
results['exp'].append((zen, az))
print("{} depth = {:.1f}m -> {:.2f} {:.2f} (solution type {})".format(
t, d, zen / units.deg, az / units.deg, r.get_solution_type(0)))
channelResampler.run(evt, station, det, 50 * units.GHz)
channelBandPassFilter.run(evt,
station,
det,
passband=[120 * units.MHz, 300 * units.MHz],
filter_type='butterabs',
order=10)
channelBandPassFilter.run(evt,
station,
det,
def __draw_search_zones(self, event, slope, offset, line_fit_d, line_fit_z,
min_z_offset, max_z_offset, i_max_d, i_max_z,
corr_mask_d, corr_mask_z, z_fit, i_max_theta,
theta_corr_mask, median_theta):
fig5 = plt.figure(figsize=(8, 8))
ax5_1_1 = fig5.add_subplot(221)
ax5_1_1.grid()
ax5_1_2 = fig5.add_subplot(222)
ax5_1_2.grid()
ax5_1_1.fill_between(
self.__distances_2d,
self.__distances_2d * slope + offset + 1.1 * max_z_offset,
self.__distances_2d * slope + offset - 1.1 * min_z_offset,
color='k',
alpha=.2)
ax5_1_1.scatter(self.__distances_2d[corr_mask_d],
self.__z_coordinates_2d[i_max_d][corr_mask_d])
ax5_1_1.scatter(self.__distances_2d[~corr_mask_d],
self.__z_coordinates_2d[i_max_d][~corr_mask_d],
c='k',
alpha=.5)
ax5_1_1.plot(self.__distances_2d,
self.__distances_2d * slope + offset,
color='k',
linestyle=':')
ax5_1_1.fill_between(
self.__distances_2d,
self.__distances_2d * slope + offset + 1.1 * max_z_offset,
self.__distances_2d * slope + offset - 1.1 * min_z_offset,
color='k',
alpha=.2)
ax5_1_1.plot(self.__distances_2d,
self.__distances_2d * line_fit_d[0] + line_fit_d[1],
color='r',
linestyle=':')
ax5_1_1.fill_between(self.__distances_2d,
self.__distances_2d * line_fit_d[0] +
line_fit_d[1] + 1.1 * max_z_offset,
self.__distances_2d * line_fit_d[0] +
line_fit_d[1] - 1.1 * min_z_offset,
color='r',
alpha=.2)
ax5_1_2.scatter(self.__distances_2d[i_max_z][corr_mask_z],
self.__z_coordinates_2d[corr_mask_z])
ax5_1_2.scatter(self.__distances_2d[i_max_z][~corr_mask_z],
self.__z_coordinates_2d[~corr_mask_z],
c='k',
alpha=.5)
ax5_1_2.plot(self.__distances_2d,
self.__distances_2d * slope + offset,
color='k',
linestyle=':')
ax5_1_2.plot(self.__distances_2d,
self.__distances_2d * line_fit_z[0] + line_fit_z[1],
color='r',
linestyle=':')
ax5_1_2.fill_between(self.__distances_2d,
self.__distances_2d * line_fit_z[0] +
line_fit_z[1] + 1.1 * max_z_offset,
self.__distances_2d * line_fit_z[0] +
line_fit_z[1] - 1.1 * min_z_offset,
color='r',
alpha=.2)
ax5_1_2.plot(self.__distances_2d,
self.__distances_2d * slope + offset,
color='k',
linestyle=':')
ax5_1_2.fill_between(
self.__distances_2d,
self.__distances_2d * slope + offset + 1.1 * max_z_offset,
self.__distances_2d * slope + offset - 1.1 * min_z_offset,
color='k',
alpha=.2)
if z_fit:
ax5_2 = fig5.add_subplot(2, 2, (3, 4))
ax5_2.grid()
else:
ax5_2 = fig5.add_subplot(2, 2, (3, 4), projection='polar')
if z_fit:
ax5_2.scatter(self.__azimuths_2d[i_max_theta][theta_corr_mask],
np.abs(self.__z_coordinates_2d)[theta_corr_mask])
ax5_2.scatter(self.__azimuths_2d[i_max_theta][~theta_corr_mask],
np.abs(self.__z_coordinates_2d)[~theta_corr_mask],
c='k',
alpha=.5)
ax5_2.plot([median_theta, median_theta], [
np.abs(self.__z_coordinates_2d[0]),
np.abs(self.__z_coordinates_2d[-1])
],
color='k',
linestyle=':')
else:
ax5_2.scatter(self.__azimuths_2d[i_max_theta][theta_corr_mask],
self.__distances_2d[theta_corr_mask])
ax5_2.scatter(self.__azimuths_2d[i_max_theta][~theta_corr_mask],
self.__distances_2d[~theta_corr_mask],
c='k',
alpha=.5)
ax5_2.plot([median_theta, median_theta],
[self.__distances_2d[0], self.__distances_2d[-1]],
color='k',
linestyle=':')
sim_vertex = None
for sim_shower in event.get_sim_showers():
sim_vertex = sim_shower.get_parameter(shp.vertex)
break
if sim_vertex is not None:
ax5_1_1.axhline(sim_vertex[2], color='r', linestyle='--', alpha=.5)
ax5_1_1.axvline(np.sqrt(sim_vertex[0]**2 + sim_vertex[1]**2),
color='r',
linestyle='--',
alpha=.5)
ax5_1_2.axhline(sim_vertex[2], color='r', linestyle='--', alpha=.5)
ax5_1_2.axvline(np.sqrt(sim_vertex[0]**2 + sim_vertex[1]**2),
color='r',
linestyle='--',
alpha=.5)
ax5_2.scatter([
hp.cartesian_to_spherical(sim_vertex[0], sim_vertex[1],
sim_vertex[2])[1]
], [np.sqrt(sim_vertex[0]**2 + sim_vertex[1]**2)],
c='r',
alpha=.5,
marker='+')
ax5_1_1.set_xlabel('d [m]')
ax5_1_2.set_xlabel('d [m]')
ax5_1_1.set_ylabel('z [m]')
ax5_1_2.set_ylabel('z [m]')
if z_fit:
ax5_2.set_xlabel(r'$\phi [^\circ]$')
ax5_2.set_ylabel('|z| [m]')
fig5.tight_layout()
fig5.savefig('{}/{}_{}_search_zones.png'.format(
self.__debug_folder, event.get_run_number(), event.get_id()))
