def create(self, frame, group):
mcTree = frame[list(self.keys())[1]]
referenceTrack = dataclasses.get_most_energetic_muon(mcTree)
if referenceTrack is None:
referenceTrack = dataclasses.get_most_energetic_cascade(mcTree)
elif mcTree.depth(referenceTrack) > 1:
referenceTrack = dataclasses.get_most_energetic_cascade(mcTree)
else:
# change the shape for tracks so that
# phys_services.I3Calculator.time_residual
# does not become confused
referenceTrack.shape = dataclasses.I3Particle.InfiniteTrack
photonSeriesMap = frame[list(self.keys())[0]]
for key, photonSeries in photonSeriesMap:
for photon in photonSeries:
self.add_one_photon(photon, referenceTrack, group)
def handle_I3MCTree(mctree):
# Interaction
beam_particle = get_most_energetic_primary2(mctree)
if beam_particle:
if beam_particle.is_neutrino:
ptypes = [p.type for p in mctree.primaries]
if len(ptypes) == 2:
interaction = "{} + {}".format(*ptypes)
elif len(ptypes) == 1:
interaction = "{}".format(ptypes[0])
else:
interaction = "{} primaries, wtf!?".format(len(ptypes))
daughters = mctree.get_daughters(beam_particle)
if daughters:
interaction += " -> "
dtypes = [str(d.type) for d in daughters]
interaction += " + ".join(dtypes)
elif beam_particle.type == I3Particle.Gamma:
interaction = "Gamma event"
else:
interaction = "Cosmic ray event"
out = 'Interaction:\n {0:s}\n'.format(interaction)
else:
out = 'No beam particle\n'
# Primaries
for p in mctree.primaries:
out += ('Primary\n'+\
' Type : {0!s}\n'+\
' Energy: {1:.2e}GeV\n').format(p.type, p.energy)
# Muons
p = get_most_energetic_muon(mctree)
if p:
out += ('Muon\n'+\
' Type : {0!s}\n'+\
' Energy: {1:.2e}GeV\n').format(p.type, p.energy)
# Cascades
p = get_most_energetic_cascade(mctree)
if p:
out += ('Cascade\n'+\
' Type : {0!s}\n'+\
' Energy: {1:.2e}GeV\n').format(p.type, p.energy)
return out
def DAQ(self, frame):
if self.frame_key not in frame:
return
mctree = frame[self.frame_key]
try:
mem = mctree.most_energetic_muon
except AttributeError:
from icecube.dataclasses import get_most_energetic_muon
mem = get_most_energetic_muon(mctree)
if mem and mem.energy > 0:
self.histograms["LogMostEnergeticMuon"].fill(
log10(mem.energy / I3Units.GeV))
for p in mctree:
if p.is_cascade and p.energy > 0:
self.histograms["CascadeEnergy"].fill(log10(p.energy))
if (p.type == dataclasses.I3Particle.MuMinus
or p.type == dataclasses.I3Particle.MuPlus):
self.histograms["NMu"].fill(1)
self.histograms["MuonLength"].fill(p.length / I3Units.m)
if (p.type == dataclasses.I3Particle.TauMinus
or p.type == dataclasses.I3Particle.TauPlus):
self.histograms["TauLength"].fill(p.length / I3Units.m)
if mctree.depth(p) > 0:
self.histograms["SecondaryZenith"].fill(p.dir.zenith /
I3Units.degree)
self.histograms["SecondaryCosZenith"].fill(cos(p.dir.zenith))
if p.energy > 0:
self.histograms["SecondaryEnergy"].fill(
log10(p.energy / I3Units.MeV))
int_type = type_to_int_dict[p.type] \
if p.type in type_to_int_dict else 29
self.histograms["SecondaryType"].fill(int_type)
def Primary(frame, FitName, GenieOrCorsika="Genie"):
if frame.Has(FitName):
fit = copy.deepcopy(frame[FitName])
if not fit.fit_status == dataclasses.I3Particle.OK:
print "Bad Primary Seed"
return False
if frame.Has("I3MCTree"):
if GenieOrCorsika == "Genie":
prim = dataclasses.get_most_energetic_neutrino(
frame['I3MCTree'])
elif GenieOrCorsika == "Corsika":
prim = dataclasses.get_most_energetic_muon(frame['I3MCTree'])
else:
print "Specify if neutrino or muons"
return False
fit.pos = prim.pos
fit.dir = prim.dir
fit.time = prim.time
frame['Primary'] = fit
return True
print "No Primary"
return False
from icecube.dataclasses import get_most_energetic_nucleus primary = dc.I3Particle() primary.energy = 10 * I3Units.TeV tree = dc.I3MCTree() tree.add_primary(primary) mep = get_most_energetic_primary(tree) I3Test.ENSURE(mep.id == primary.id, "got the wrong particle.") I3Test.ENSURE(not get_most_energetic_cascade(tree), "got a particle, but shouldn't.") I3Test.ENSURE(not get_most_energetic_inice(tree), "got a particle, but shouldn't.") I3Test.ENSURE(not get_most_energetic_track(tree), "got a particle, but shouldn't.") I3Test.ENSURE(not get_most_energetic_neutrino(tree), "got a particle, but shouldn't.") I3Test.ENSURE(not get_most_energetic_muon(tree), "got a particle, but shouldn't.") I3Test.ENSURE(not get_most_energetic_nucleus(tree), "got a particle, but shouldn't.") primary2 = dc.I3Particle() primary2.energy = 9 * I3Units.TeV cascade = dc.I3Particle() cascade.type = dc.I3Particle.EMinus inice = dc.I3Particle() inice.location_type = dc.I3Particle.InIce track = dc.I3Particle() track.energy = 10 * I3Units.TeV track.type = dc.I3Particle.TauMinus
def do_things(frame):
"""Retrieve and calculate frames from an i3 file.
Args:
frame (i3 frame): i3 frame from i3 file.
Returns:
i3 object: i3 object with requested frames.
"""
cond1 = frame['I3EventHeader'].sub_event_stream != 'InIceSplit'
cond2 = 'LineFit' not in frame or 'ToI' not in frame
cond3 = 'SRTInIcePulses' not in frame
cond4 = not frame['L5_oscNext_bool']
final_cond = cond1 or cond2 or cond3 or cond4
if final_cond:
return False
else:
linefit_result = frame['LineFit']
linefit_fit_status = linefit_result.fit_status
toi_result = frame['ToI']
toi_fit_status = toi_result.fit_status
try:
retro_crs_prefit_result = frame[
'retro_crs_prefit__median__neutrino'
]
retro_crs_prefit_status = frame[
'retro_crs_prefit__fit_status'
][0]
except Exception:
return False
if linefit_fit_status != 0:
return False
elif toi_fit_status != 0:
return False
elif retro_crs_prefit_status != 0:
return False
hit_stats = frame['HitStatisticsValues']
hit_mult = frame['HitMultiplicityValues']
data['dom_n_hit_multiple_doms'].append(
hit_mult.n_hit_doms - hit_mult.n_hit_doms_one_pulse
)
time_char = frame['TimeCharacteristicsValues']
data['dom_timelength_fwhm'].append(time_char.timelength_fwhm)
# TODO Could we maybe do this in a smarter, programmatic way?
# Get the true muon particle
# Note that the muon is really produced in the atmosphere, but for the
# simulation it is generated at the surface of a cylinder
# surrounding IceCube
# <icecube.dataclasses.I3Particle>
true_primary = dataclasses.get_most_energetic_primary(
frame['I3MCTree']
)
true_muon = dataclasses.get_most_energetic_muon(frame['I3MCTree'])
if true_muon == None:
data['secondary_track_length'].append(np.nan)
else:
data['secondary_track_length'].append(true_muon.length)
# Add the muon energy (defined at the generation cylinder) to the
# data dicitonary
data['true_primary_energy'].append(np.log10(true_primary.energy))
# Direction of the muon
# <icecube.dataclasses.I3Particle>
true_primary_direction = true_primary.dir
# Add the muon direction to the data dictionary
data['true_primary_direction_x'].append(true_primary_direction.x)
data['true_primary_direction_y'].append(true_primary_direction.y)
data['true_primary_direction_z'].append(true_primary_direction.z)
data['true_primary_time'].append(true_primary.time)
data['true_primary_speed'].append(true_primary.speed)
# Point on the generation cylinder at which the muon is produced
# <icecube.dataclasses.I3Particle>
true_primary_entry_position = true_primary.pos
# Add the entry position to the data dictionary
data['true_primary_position_x'].append(
true_primary_entry_position.x
)
data['true_primary_position_y'].append(
true_primary_entry_position.y
)
data['true_primary_position_z'].append(
true_primary_entry_position.z
)
# Get the uncleaned pulses
# <icecube.dataclasses.I3RecoPulseSeriesMap>
uncleaned_pulses = frame['SplitInIcePulses'].apply(frame)
cleaned_pulses = frame['SRTInIcePulses'].apply(frame)
dom_geom = frame['I3Geometry'].omgeo
# Create empty lists for holding the pulse information
temp = np.empty((0, 11))
dom_key_temp = []
# dom_x_temp = np.empty(0)
# dom_y_temp = np.empty(0)
# dom_z_temp = np.empty(0)
# dom_time_temp = np.empty(0)
# dom_charge_temp = np.empty(0)
# dom_lc_temp = np.empty(0)
# dom_atwd_temp = np.empty(0)
# dom_fadc_temp = np.empty(0)
# dom_pulse_width_temp = np.empty(0)
# Go through all pulses, get OM key and pair with time and charge info
for entry in uncleaned_pulses.items():
cleaned_time_list = []
this_om_key = entry[0]
if this_om_key in cleaned_pulses.keys():
cleaned_temp = cleaned_pulses[this_om_key]
for cleaned_entry in cleaned_temp:
cleaned_time_list.append(cleaned_entry.time)
# This grabs you an object containing the geometry for this
# particular OM
this_om_geom = dom_geom[this_om_key]
# This has x,y,z members
this_om_position = this_om_geom.position
for i, pulse in enumerate(entry[1]):
if pulse.time in cleaned_time_list:
cleaned_mask = 1
else:
cleaned_mask = 0
# dom_x_temp = np.append(dom_x_temp, this_om_position.x)
# dom_y_temp = np.append(dom_y_temp, this_om_position.y)
# dom_z_temp = np.append(dom_z_temp, this_om_position.z)
# dom_time_temp = np.append(dom_time_temp, pulse.time)
# dom_charge_temp = np.append(dom_charge_temp, pulse.charge)
# dom_lc_temp = np.append(dom_lc_temp, (pulse.flags & 0x1) >> 0)
# dom_atwd_temp = np.append(
# dom_atwd_temp,
# (pulse.flags & 0x2) >> 1
# )
# dom_fadc_temp = np.append(
# dom_fadc_temp,
# (pulse.flags & 0x4) >> 2
# )
# dom_pulse_width_temp = np.append(
# dom_pulse_width_temp,
# pulse.width
# )
dom_key = str(this_om_key[0]) + '_' + str(this_om_key[1]) + '_' + str(this_om_key[2])
pulses = np.array(
[
this_om_position.x,
this_om_position.y,
this_om_position.z,
pulse.time,
pulse.charge,
(pulse.flags & 0x1) >> 0,
(pulse.flags & 0x2) >> 1,
(pulse.flags & 0x4) >> 2,
pulse.width,
1,
cleaned_mask
],
ndmin=2
)
temp = np.append(
temp,
pulses,
axis=0
)
dom_key_temp.append(dom_key)
# Add Numpy arrays to data dictionary
# data['dom_x'].append(dom_x_temp)
# data['dom_y'].append(dom_y_temp)
# data['dom_z'].append(dom_z_temp)
# data['dom_time'].append(dom_time_temp)
# data['dom_charge'].append(dom_charge_temp)
# data['dom_lc'].append(dom_lc_temp)
# data['dom_atwd'].append(dom_atwd_temp)
# data['dom_fadc'].append(dom_fadc_temp)
# data['dom_pulse_width'].append(dom_pulse_width_temp)
# data['dom_key'].append(np.array(dom_key_temp))
dom_key_temp = np.array(dom_key_temp)
dom_key_temp = dom_key_temp[temp[:, 3].argsort()]
data['dom_key'].append(dom_key_temp)
temp = temp[temp[:, 3].argsort()]
data['dom_x'].append(temp[:, 0])
data['dom_y'].append(temp[:, 1])
data['dom_z'].append(temp[:, 2])
data['dom_time'].append(temp[:, 3])
data['dom_charge'].append(temp[:, 4])
data['dom_lc'].append(temp[:, 5])
data['dom_atwd'].append(temp[:, 6])
data['dom_fadc'].append(temp[:, 7])
data['dom_pulse_width'].append(temp[:, 8])
flatten = lambda l: [item for sublist in l for item in sublist]
split_indices = np.argwhere(temp[:, 9])
split_indices = np.array(flatten(split_indices))
srt_indices = np.array(np.argwhere(temp[:, 10]))
srt_indices = np.array(flatten(srt_indices))
masks['SplitInIcePulses'].append(split_indices)
masks['SRTInIcePulses'].append(srt_indices)
# Get the cleaned pulses (if available)
# <icecube.dataclasses.I3RecoPulseSeriesMap>
# cleaned_pulses = frame['SRTInIcePulses'].apply(frame)
# Get the LineFit reconstruction
# The direction of the straight line
# <icecube.dataclasses.I3Particle>
linefit_direction = linefit_result.dir
# Add the direction of the linefit to the data dictionary
data['linefit_direction_x'].append(linefit_direction.x)
data['linefit_direction_y'].append(linefit_direction.y)
data['linefit_direction_z'].append(linefit_direction.z)
# An arbitrary point along the line
# <icecube.dataclasses.I3Particle>
linefit_point_on_line = linefit_result.pos
# Add the arbitrary point along the line to the data dictionary
data['linefit_point_on_line_x'].append(linefit_point_on_line.x)
data['linefit_point_on_line_y'].append(linefit_point_on_line.y)
data['linefit_point_on_line_z'].append(linefit_point_on_line.z)
# Some additional params
# <icecube.recclasses.I3LineFitParams>
# linefit_params = frame['LineFitParams']
# Get the tensor of inertia
# The direction of the ToI
# <icecube.dataclasses.I3Particle>
toi_direction = toi_result.dir
# Add the direction to the data dictionary
data['toi_direction_x'].append(toi_direction.x)
data['toi_direction_y'].append(toi_direction.y)
data['toi_direction_z'].append(toi_direction.z)
# An arbitrary point along the line
# <icecube.dataclasses.I3Particle>
toi_point_on_line = toi_result.pos
# Add arbitrary point along the line to data dictionary
data['toi_point_on_line_x'].append(toi_point_on_line.x)
data['toi_point_on_line_y'].append(toi_point_on_line.y)
data['toi_point_on_line_z'].append(toi_point_on_line.z)
# Some additional params
# <icecube.recclasses.I3TensorOfInertiaFitParams>
toi_params = frame['ToIParams']
# This is the ratio of the smallest component of the ToI to the sum of
# them all. A value close to 0. means a track-like event.
# Addd it to the data dictionary
data['toi_evalratio'].append(toi_params.evalratio)
retro_crs_prefit_position = retro_crs_prefit_result.pos
retro_crs_prefit_direction = retro_crs_prefit_result.dir
data['retro_crs_prefit_x'].append(retro_crs_prefit_position.x)
data['retro_crs_prefit_y'].append(retro_crs_prefit_position.y)
data['retro_crs_prefit_z'].append(retro_crs_prefit_position.z)
data['retro_crs_prefit_azimuth'].append(retro_crs_prefit_direction.azimuth)
data['retro_crs_prefit_zenith'].append(retro_crs_prefit_direction.zenith)
data['retro_crs_prefit_energy'].append(retro_crs_prefit_result.energy)
data['retro_crs_prefit_time'].append(retro_crs_prefit_result.time)
def Make_Image(frame):
global data
global geometry
global st_info
#Log id info
id = np.zeros(1,dtype = id_dtype)
H = frame["I3EventHeader"]
id[["run_id","sub_run_id","event_id","sub_event_id"]] = (H.run_id,H.sub_run_id,H.event_id,H.sub_event_id)
#Log Weight info
weight = np.zeros(1,dtype = weight_dtype)
if data_type in ['genie', 'corsika']:
w = dict(frame[WEIGHT_KEY])
weight[list(w.keys())] = tuple(w.values())
#Log MCTree info
primary = np.zeros(1,dtype = particle_dtype)
prim_daughter = np.zeros(1,dtype = particle_dtype)
if not data_type == 'data':
#find primary particle
mctree = frame[MCTREE_KEY]
daughter = None
if data_type == 'genie':
prim = dataclasses.get_most_energetic_neutrino(mctree)
max_energy = 0
for part in mctree.children(prim.id):
if part.energy > max_energy:
max_enegy = part.energy
daughter = part
else:
prim = dataclasses.get_most_energetic_primary(mctree)
daughter = dataclasses.get_most_energetic_muon(mctree)
#if no children, then daughter is a duplicate of primary
if daughter is None:
print("MCTree has no primary children")
primary[["tree_id","pdg","energy","position","direction","time","length"]] =\
([prim.id.majorID, prim.id.minorID], prim.pdg_encoding, prim.energy,\
[prim.pos.x,prim.pos.y,prim.pos.z],\
[prim.dir.zenith,prim.dir.azimuth],prim.time, prim.length)
prim_daughter[["tree_id","pdg","energy","position","direction","time","length"]] =\
([prim.id.majorID, prim.id.minorID], prim.pdg_encoding, prim.energy,\
[prim.pos.x,prim.pos.y,prim.pos.z],\
[prim.dir.zenith,prim.dir.azimuth], prim.time, prim.length)
#if there are children, daughter is the child with highest energy
else:
primary[["tree_id","pdg","energy","position","direction","time","length"]] =\
([prim.id.majorID, prim.id.minorID], prim.pdg_encoding, prim.energy,\
[prim.pos.x,prim.pos.y,prim.pos.z],\
[prim.dir.zenith,prim.dir.azimuth],prim.time, prim.length)
prim_daughter[["tree_id","pdg","energy","position","direction","time","length"]] =\
([daughter.id.majorID, daughter.id.minorID], daughter.pdg_encoding,daughter.energy,\
[daughter.pos.x,daughter.pos.y,daughter.pos.z],\
[daughter.dir.zenith,daughter.dir.azimuth],daughter.time,daughter.length)
#Log HESE veto perameters
hese = np.zeros(1,dtype = hese_dtype)
hese_vheselfveto = True
hese_pos =[-9999,-9999,-9999]
hese_time = -999
if frame.Has("HESE3_VHESelfVeto"):
hese_vheselfveto = frame["HESE3_VHESelfVeto"].value
hese_pos = [frame["HESE3_VHESelfVetoVertexPos"].x,frame["HESE3_VHESelfVetoVertexPos"].y,frame["HESE3_VHESelfVetoVertexPos"].z]
hese_time = frame["HESE3_VHESelfVetoVertexTime"].value
hese[["vheselfveto","vheselfvetovertexpos","vheselfvetovertextime"]][0] =\
(hese_vheselfveto,hese_pos,hese_time)
#Log logan's veto parameters
veto = np.zeros(1,dtype = veto_dtype)
veto_cas_rlogl = -999
veto_spe_rlogl = 999
veto_cas_rlogl_ndc = -999
veto_spe_rlogl_ndc = 999
veto_fh_z = -999
veto_svv_z = -999
veto_ldp = -999
if frame.Has('HESE3_VHESelfVetoVertexPos') and frame.Has('SPEFit32_DPFitParams') and frame.Has('CascadeLlhVertexFit_DPParams')\
and frame.Has('SPEFit32_noDC_DPFitParams') and frame.Has('CascadeLlhVertexFit_noDC_DPParams') and frame.Has('depthFirstHit')\
and frame.Has("LeastDistanceToPolygon_Veto"):
veto_cas_rlogl = frame['CascadeLlhVertexFit_DPParams'].ReducedLlh
veto_spe_rlogl = frame['SPEFit32_DPFitParams'].rlogl
veto_cas_rlogl_ndc = frame['CascadeLlhVertexFit_noDC_DPParams'].ReducedLlh
veto_spe_rlogl_ndc = frame['SPEFit32_noDC_DPFitParams'].rlogl
veto_fh_z = frame['depthFirstHit'].value
veto_svv_z = frame['HESE3_VHESelfVetoVertexPos'].z
veto_ldp = frame["LeastDistanceToPolygon_Veto"].value
trck = frame['CascadeLlhVertexFit_DP']
cscd = frame['SPEFit32_DP']
veto[["SPE_rlogl","Cascade_rlogl","SPE_rlogl_noDC", "Cascade_rlogl_noDC","FirstHitZ","VHESelfVetoVertexPosZ","LeastDistanceToPolygon_Veto"]] =\
(veto_spe_rlogl,veto_cas_rlogl,veto_spe_rlogl_ndc,veto_cas_rlogl_ndc,veto_fh_z,veto_svv_z,veto_ldp)
pulses= dataclasses.I3RecoPulseSeriesMap.from_frame(frame, PULSES_KEY)
wf_map = frame["CalibratedWaveformsHLCATWD"]
#make image from raw waveforms
wfms = []
wf_times = [] #storage for waveforms starting times
wf_widths = [] #storage for waveform bin widths
for img_ch, (q, stnum, dist) in enumerate(st_info):
for omkey in wf_map.keys():
if (omkey.string == stnum):
for wf in wf_map.get(omkey, []):
if wf.status == 0 and wf.source_index == 0:
wf_times.append(wf.time)
wf_widths.append(wf.bin_width)
wfms.append({
'wfm': wf.waveform,
'time': wf.time,
'width': wf.bin_width,
'dom_idx': omkey.om - 1,
'img_ch': img_ch,
'om_pos': [geometry[omkey].position.x,geometry[omkey].position.y,geometry[omkey].position.z]
})
im = np.zeros(shape=(N_X_BINS, N_Y_BINS, N_CHANNELS))
wf_times_arr = np.zeros(shape=(N_Y_BINS, N_CHANNELS))
wf_pos_arr = np.zeros(shape=(3,N_Y_BINS, N_CHANNELS))
if len(wfms) <= 4:
print("FAILED only %d WF" %len(wfms) )
return False
#we neeed to prevent early noise hits from shifting the actual
#interaction from the image time frame
#first work out when the first waveforn starts
wf_times = np.array(wf_times)
wf_times = wf_times[wf_times.argsort()]
#find the biggest differnece between starting times
diff_times = np.diff(wf_times[:N_NOISE_HITS])
max_diff_pos = np.argmax(diff_times)
#check if the images needs to be shifted and work out the shift
if diff_times[max_diff_pos] > MAX_TIME_SHIFT:
min_time = wf_times[max_diff_pos+1]
else:
min_time = wf_times[0]
#make images
for wfm in wfms:
wf_shift = 0
start_ind = min(N_X_BINS, int((wfm['time'] - min_time) / wfm['width']))
if start_ind >= 0:
end_ind = min(N_X_BINS, start_ind + len(wfm['wfm']))
wfm_vals = wfm['wfm'][0:end_ind-start_ind]
else:
wf_shift = abs(start_ind)
start_ind = 0
end_ind = min(N_X_BINS, len(wfm['wfm'])-wf_shift)
if end_ind <0:
end_ind = 0
wfm_vals = wfm['wfm'][wf_shift:len(wfm['wfm'])]
im[start_ind:end_ind, wfm['dom_idx'], wfm['img_ch']] = wfm_vals
wf_times_arr[wfm['dom_idx'], wfm['img_ch']] = wfm['time']
wf_pos_arr[0:3,wfm['dom_idx'], wfm['img_ch']] = wfm['om_pos']
if wf_shift > 0:
print("the images were shifted by {0:.3f}".format(wf_shift))
im = np.true_divide(im, 10**(-8))
im = im.astype(np.float32)
if np.sum(im[:,:,0])==0:
print("FAILED no image 0")
return False
if np.sum(im[:,:,1])==0:
print("FAILED no image 1")
return False
if np.sum(im[:,:,2])==0:
print("FAILED no image 2")
return False
#Log all the event info
event = np.zeros(1,dtype = info_dtype)
event[["id","image","qtot","qst","primary","prim_daughter","logan_veto","hese","weight_dict"]]=\
(id[0], im, qtot, st_info, primary[0], prim_daughter[0], veto[0],hese[0], weight[0])
data.append(event)
if not frame["L4BDT"]["BDTScore"] > 0.04: continue
# Lets just weight to H3a for now
w = frame["GaisserH3aWeight"].value
if "9036" in filename: w /= 100000.0
elif "9255" in filename: w /= 95000.0
elif "10282" in filename w /= 11200.0
elif "10309" in filename w /= 12200.0
elif "10369" in filename w /= 400.0
elif "11905" in filename w /= 90000.0
elif "12268" in filename w /= 96000.0
elif "12332" in filename w /= 99000.0
# Now, we want the primary. Let's get it
primary = dataclasses.get_most_energetic_primary(frame["I3MCTree"])
muon = dataclasses.get_most_energetic_muon(frame["I3MCTree"])
# Propagate the muons muons to the MuonGun generating surface
outSurface = Cylinder(1600*I3Units.m, 800*I3Units.m)
partVec = MuonGun.muons_at_surface(frame, outSurface)
muongun_energy = 1e-9
for p in partVec:
if p.energy > muongun_energy:
muongun_energy = p.energy
weights.append(w)
energy_of_primary.append(primary.energy)
energy_at_depth.append(muongun_energy)
coszens.append(numpy.cos(muon.dir.zenith))
def DAQ(self, frame):
if frame.Has("I3MCTree"):
MCprimary = dataclasses.get_most_energetic_primary(
frame["I3MCTree"])
MCmuon = dataclasses.get_most_energetic_muon(frame["I3MCTree"])
histo_MCPrimary_x.Fill(MCprimary.pos.x)
histo_MCPrimary_y.Fill(MCprimary.pos.y)
histo_MCPrimary_z.Fill(MCprimary.pos.z)
histo_MCPrimary_xy.Fill(MCprimary.pos.x, MCprimary.pos.y)
histo_MCPrimary_xz.Fill(MCprimary.pos.x, MCprimary.pos.z)
histo_MCPrimary_yz.Fill(MCprimary.pos.y, MCprimary.pos.z)
histo_MC_angle.Fill(
phys_services.I3Calculator.angle(MCprimary, MCmuon))
histo_MC_energy_angle.Fill(
numpy.log10(MCprimary.energy),
phys_services.I3Calculator.angle(MCprimary, MCmuon))
if frame.Has("WIMP_params"):
wimp_params = frame["WIMP_params"]
histo_wimp_params_nu_weight.Fill(wimp_params.nu_weight)
histo_wimp_params_lep_weight.Fill(
numpy.log10(wimp_params.lep_weight))
histo_wimp_params_had_weight.Fill(
numpy.log10(wimp_params.had_weight))
histo_wimp_params_vgen.Fill(wimp_params.vgen)
histo_wimp_params_time.Fill(
float(wimp_params.time.mod_julian_day_double))
if frame.Has("WIMP_params") and frame.Has("I3MCTree"):
histo_wimp_params_time_nuweight.Fill(
float(wimp_params.time.mod_julian_day_double),
wimp_params.nu_weight)
histo_MCPrimary_zenith_nuweight.Fill(MCprimary.dir.zenith,
wimp_params.nu_weight)
histo_MCPrimary_azimuth_nuweight.Fill(MCprimary.dir.azimuth,
wimp_params.nu_weight)
histo_MCPrimary_energy_nuweight.Fill(
numpy.log10(MCprimary.energy), wimp_params.nu_weight)
histo_MCMuon_zenith_nuweight.Fill(MCmuon.dir.zenith,
wimp_params.nu_weight)
histo_MCMuon_azimuth_nuweight.Fill(MCmuon.dir.azimuth,
wimp_params.nu_weight)
histo_MCMuon_energy_nuweight.Fill(numpy.log10(MCmuon.energy),
wimp_params.nu_weight)
histo_MC_angle_lepweight.Fill(
phys_services.I3Calculator.angle(MCprimary, MCmuon),
wimp_params.lep_weight)
histo_MC_nuenergy_angle_lepweight.Fill(
numpy.log10(MCprimary.energy),
phys_services.I3Calculator.angle(MCprimary, MCmuon),
wimp_params.lep_weight)
histo_MC_muenergy_angle_lepweight.Fill(
numpy.log10(MCmuon.energy),
phys_services.I3Calculator.angle(MCprimary, MCmuon),
wimp_params.lep_weight)
histo_nuenergy_lepweight.Fill(
numpy.log10(MCprimary.energy),
numpy.log10(wimp_params.lep_weight))
histo_nuenergy_lepweight_lepweight.Fill(
numpy.log10(MCprimary.energy),
numpy.log10(wimp_params.lep_weight),
wimp_params.lep_weight)
self.PushFrame(frame)
return
primary.energy = 10 * I3Units.TeV
tree = dc.I3MCTree()
tree.add_primary(primary)
mep = get_most_energetic_primary(tree)
I3Test.ENSURE(mep.id == primary.id, "got the wrong particle.")
I3Test.ENSURE(not get_most_energetic_cascade(tree),
"got a particle, but shouldn't.")
I3Test.ENSURE(not get_most_energetic_inice(tree),
"got a particle, but shouldn't.")
I3Test.ENSURE(not get_most_energetic_track(tree),
"got a particle, but shouldn't.")
I3Test.ENSURE(not get_most_energetic_neutrino(tree),
"got a particle, but shouldn't.")
I3Test.ENSURE(not get_most_energetic_muon(tree),
"got a particle, but shouldn't.")
I3Test.ENSURE(not get_most_energetic_nucleus(tree),
"got a particle, but shouldn't.")
primary2 = dc.I3Particle()
primary2.energy = 9 * I3Units.TeV
cascade = dc.I3Particle()
cascade.type = dc.I3Particle.EMinus
inice = dc.I3Particle()
inice.location_type = dc.I3Particle.InIce
track = dc.I3Particle()
track.energy = 10 * I3Units.TeV