def process_frame(frame, gcdfile='/cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2013.56429_V0.i3.gz', charge_scale=1.0, time_scale=1e-3):
""" Processes a frame to create an event graph and metadata out of it.
Parameters:
-----------
frame : I3Frame
The data frame to extract an event graph with features from.
gcd_file : str
Path to the gcd file.
charge_scale : float
The normalization constant for charge.
time_scale : float
The normalization constant for time.
"""
### Meta data of the event for analysis of the classifier and creation of ground truth
primary = dataclasses.get_most_energetic_neutrino(frame['I3MCTree'])
if primary is None:
get_weighted_primary(frame, MCPrimary='MCPrimary')
primary = frame['MCPrimary']
# Obtain the PDG Encoding for ground truth
#frame['PDGEncoding'] = dataclasses.I3Double(primary.pdg_encoding)
#frame['InteractionType'] = dataclasses.I3Double(frame['I3MCWeightDict']['InteractionType'])
frame['RunID'] = icetray.I3Int(frame['I3EventHeader'].run_id)
frame['EventID'] = icetray.I3Int(frame['I3EventHeader'].event_id)
frame['PrimaryEnergy'] = dataclasses.I3Double(primary.energy)
### Create features for each event
features, coordinates, _ = get_events_from_frame(frame, charge_scale=charge_scale, time_scale=time_scale)
for feature_name in vertex_features:
frame[feature_name] = dataclasses.I3VectorFloat(features[feature_name])
### Create offset lookups for the flattened feature arrays per event
frame['NumberVertices'] = icetray.I3Int(len(features[features.keys()[0]]))
### Create coordinates for each vertex
C = np.vstack(coordinates.values()).T
#C, C_mean, C_std = normalize_coordinates(C, weights=None, copy=True)
#C_cog, C_mean_cog, C_std_cog = normalize_coordinates(C, weights=features['TotalCharge'], copy=True)
frame['VertexX'] = dataclasses.I3VectorFloat(C[:, 0])
frame['VertexY'] = dataclasses.I3VectorFloat(C[:, 1])
frame['VertexZ'] = dataclasses.I3VectorFloat(C[:, 2])
### Output centering and true debug information
frame['PrimaryX'] = dataclasses.I3Double(primary.pos.x)
frame['PrimaryY'] = dataclasses.I3Double(primary.pos.y)
frame['PrimaryZ'] = dataclasses.I3Double(primary.pos.z)
frame['PrimaryAzimuth'] = dataclasses.I3Double(primary.dir.azimuth)
frame['PrimaryZenith'] = dataclasses.I3Double(primary.dir.zenith)
### Apply labeling
classify_wrapper(frame, None, gcdfile=gcdfile)
return True
def add_weighted_primary(phy_frame):
if reco_q.is_data(phy_frame):
return True
if not 'MCPrimary1' in phy_frame.keys():
get_weighted_primary(phy_frame, MCPrimary='MCPrimary1')
return
def find_primary(frame):
get_weighted_primary(frame)
def add_weighted_primary(phy_frame):
if not 'MCPrimary' in phy_frame.keys():
get_weighted_primary(phy_frame, MCPrimary='MCPrimary')
return
def getweights(frame, generator, flux):
get_weighted_primary(frame)
energy = frame['MCPrimary'].energy
ptype = frame['MCPrimary'].type
weight = (flux(energy, ptype) / generator(energy, ptype))
frame['Weight'] = dataclasses.I3Double(weight)
