diff --git a/orcasong/mc_info_extr.py b/orcasong/mc_info_extr.py
index f36c0f2ce2fb31e5bc9487d8b8d277b7b3cf844d..beb98638a4832d83470ce7ed5f174bd3703a5997 100644
--- a/orcasong/mc_info_extr.py
+++ b/orcasong/mc_info_extr.py
@@ -13,442 +13,488 @@ import numpy as np
from km3pipe.io.hdf5 import HDF5Header
from h5py import File
-__author__ = 'Daniel Guderian'
+__author__ = "Daniel Guderian"
def get_std_reco(blob):
- """
- Function to extract std reco info. The implemented strategy is the following:
- First, look for whether a rec stag has been reached and only then extract the reconstructed
- paramater from it. If not, set it to a dummy value (for now 0). This means that for an analysis the events with
- exactly zero have to be filtered out!
-
- The 'best track' is the first (highest lik) while a certain rec stage has to be reached. This might
- have to be adjusted for other recos than JMuonGandalf chain.
-
-
- Members of the Tracks:
- dtype([('E', '<f8'), ('JCOPY_Z_M', '<f4'), ('JENERGY_CHI2', '<f4'), ('JENERGY_ENERGY', '<f4'),
- ('JENERGY_MUON_RANGE_METRES', '<f4'), ('JENERGY_NDF', '<f4'), ('JENERGY_NOISE_LIKELIHOOD', '<f4'),
- ('JENERGY_NUMBER_OF_HITS', '<f4'), ('JGANDALF_BETA0_RAD', '<f4'), ('JGANDALF_BETA1_RAD', '<f4'),
- ('JGANDALF_CHI2', '<f4'), ('JGANDALF_LAMBDA', '<f4'), ('JGANDALF_NUMBER_OF_HITS', '<f4'),
- ('JGANDALF_NUMBER_OF_ITERATIONS', '<f4'), ('JSHOWERFIT_ENERGY', '<f4'), ('JSTART_LENGTH_METRES', '<f4'),
- ('JSTART_NPE_MIP', '<f4'), ('JSTART_NPE_MIP_TOTAL', '<f4'), ('JVETO_NPE', '<f4'), ('JVETO_NUMBER_OF_HITS', '<f4'),
- ('dir_x', '<f8'), ('dir_y', '<f8'), ('dir_z', '<f8'), ('id', '<i4'), ('idx', '<i8'), ('length', '<f8'),
- ('likelihood', '<f8'), ('pos_x', '<f8'), ('pos_y', '<f8'), ('pos_z', '<f8'), ('rec_type', '<i4'),
- ('t', '<f8'), ('group_id', '<i8')])
-
-
- members of rec stages:
- .idx (corresponding to the track id),
- .rec_stage (rec stage identifier, for JMuonGandalf for example: 1=prefit, 2=simplex, 3=gandalf,
- 4=engery, 5=start),
- .group_id (event id in file)
-
-
- Parameters
- ----------
- blob : blob containing the reco info
-
- Returns
- -------
- std_reco_info : dict
- Dict with the most common std reco params. Can be expanded.
-
- """
-
- #use this later to identify not reconstructed events
- dummy_value = 0
-
- #if there was no std reco at all, this will not exist
- #these are events that stopped at/before prefit
- try:
- rec_stages = blob["RecStages"]
- #get first track only
- rec_stages_best_track = rec_stages.rec_stage[rec_stages.idx==0]
-
- # often enough: best track is the first
- best_track = blob["Tracks"][0]
-
- except KeyError:
- rec_stages_best_track = []
- print("An event didnt have any reco. Setting everything to" + str(dummy_value) + ".")
-
- #take the direction only if JGanalf was executed
- if 3 in rec_stages_best_track:
-
- std_dir_x = best_track["dir_x"]
- std_dir_y = best_track["dir_y"]
- std_dir_z = best_track["dir_z"]
-
- std_beta0 = best_track["JGANDALF_BETA0_RAD"]
- std_lik = best_track["likelihood"]
- std_n_hits_gandalf = best_track["JGANDALF_NUMBER_OF_HITS"]
-
- else:
-
- std_dir_x = dummy_value
- std_dir_y = dummy_value
- std_dir_z = dummy_value
-
- std_beta0 = dummy_value
- std_lik = dummy_value
- std_n_hits_gandalf = dummy_value
-
- #energy fit from JEnergy
- if 4 in rec_stages_best_track:
-
- std_energy = best_track["E"]
- lik_energy = best_track["JENERGY_CHI2"]
-
- else:
- std_energy = dummy_value
- lik_energy = dummy_value
-
- #vertex and length from JStart
- if 5 in rec_stages_best_track:
-
- std_pos_x = best_track["pos_x"]
- std_pos_y = best_track["pos_y"]
- std_pos_z = best_track["pos_z"]
-
- std_length = best_track["JSTART_LENGTH_METRES"]
-
- else:
-
- std_pos_x = dummy_value
- std_pos_y = dummy_value
- std_pos_z = dummy_value
-
- std_length = dummy_value
-
- std_reco_info = {
- 'std_dir_x':std_dir_x,'std_dir_y':std_dir_y,'std_dir_z':std_dir_z,
- 'std_beta0':std_beta0,'std_lik':std_lik,'std_n_hits_gandalf':std_n_hits_gandalf,
-
- 'std_pos_x':std_pos_x,'std_pos_y':std_pos_y,'std_pos_z':std_pos_z,
- 'std_energy':std_energy,'std_lik_energy':lik_energy,'std_length':std_length,
- }
-
- return std_reco_info
-
-
+ """
+ Function to extract std reco info. The implemented strategy is the following:
+ First, look for whether a rec stag has been reached and only then extract the reconstructed
+ paramater from it. If not, set it to a dummy value (for now 0). This means that for an analysis the events with
+ exactly zero have to be filtered out!
+
+ The 'best track' is the first (highest lik) while a certain rec stage has to be reached. This might
+ have to be adjusted for other recos than JMuonGandalf chain.
+
+
+ Members of the Tracks:
+ dtype([('E', '<f8'), ('JCOPY_Z_M', '<f4'), ('JENERGY_CHI2', '<f4'), ('JENERGY_ENERGY', '<f4'),
+ ('JENERGY_MUON_RANGE_METRES', '<f4'), ('JENERGY_NDF', '<f4'), ('JENERGY_NOISE_LIKELIHOOD', '<f4'),
+ ('JENERGY_NUMBER_OF_HITS', '<f4'), ('JGANDALF_BETA0_RAD', '<f4'), ('JGANDALF_BETA1_RAD', '<f4'),
+ ('JGANDALF_CHI2', '<f4'), ('JGANDALF_LAMBDA', '<f4'), ('JGANDALF_NUMBER_OF_HITS', '<f4'),
+ ('JGANDALF_NUMBER_OF_ITERATIONS', '<f4'), ('JSHOWERFIT_ENERGY', '<f4'), ('JSTART_LENGTH_METRES', '<f4'),
+ ('JSTART_NPE_MIP', '<f4'), ('JSTART_NPE_MIP_TOTAL', '<f4'), ('JVETO_NPE', '<f4'), ('JVETO_NUMBER_OF_HITS', '<f4'),
+ ('dir_x', '<f8'), ('dir_y', '<f8'), ('dir_z', '<f8'), ('id', '<i4'), ('idx', '<i8'), ('length', '<f8'),
+ ('likelihood', '<f8'), ('pos_x', '<f8'), ('pos_y', '<f8'), ('pos_z', '<f8'), ('rec_type', '<i4'),
+ ('t', '<f8'), ('group_id', '<i8')])
+
+
+ members of rec stages:
+ .idx (corresponding to the track id),
+ .rec_stage (rec stage identifier, for JMuonGandalf for example: 1=prefit, 2=simplex, 3=gandalf,
+ 4=engery, 5=start),
+ .group_id (event id in file)
+
+
+ Parameters
+ ----------
+ blob : blob containing the reco info
+
+ Returns
+ -------
+ std_reco_info : dict
+ Dict with the most common std reco params. Can be expanded.
+
+ """
+
+ # use this later to identify not reconstructed events
+ dummy_value = 0
+
+ # if there was no std reco at all, this will not exist
+ # these are events that stopped at/before prefit
+ try:
+ rec_stages = blob["RecStages"]
+ # get first track only
+ rec_stages_best_track = rec_stages.rec_stage[rec_stages.idx == 0]
+
+ # often enough: best track is the first
+ best_track = blob["Tracks"][0]
+
+ except KeyError:
+ rec_stages_best_track = []
+ print(
+ "An event didnt have any reco. Setting everything to"
+ + str(dummy_value)
+ + "."
+ )
+
+ # take the direction only if JGanalf was executed
+ if 3 in rec_stages_best_track:
+
+ std_dir_x = best_track["dir_x"]
+ std_dir_y = best_track["dir_y"]
+ std_dir_z = best_track["dir_z"]
+
+ std_beta0 = best_track["JGANDALF_BETA0_RAD"]
+ std_lik = best_track["likelihood"]
+ std_n_hits_gandalf = best_track["JGANDALF_NUMBER_OF_HITS"]
+
+ else:
+
+ std_dir_x = dummy_value
+ std_dir_y = dummy_value
+ std_dir_z = dummy_value
+
+ std_beta0 = dummy_value
+ std_lik = dummy_value
+ std_n_hits_gandalf = dummy_value
+
+ # energy fit from JEnergy
+ if 4 in rec_stages_best_track:
+
+ std_energy = best_track["E"]
+ lik_energy = best_track["JENERGY_CHI2"]
+
+ else:
+ std_energy = dummy_value
+ lik_energy = dummy_value
+
+ # vertex and length from JStart
+ if 5 in rec_stages_best_track:
+
+ std_pos_x = best_track["pos_x"]
+ std_pos_y = best_track["pos_y"]
+ std_pos_z = best_track["pos_z"]
+
+ std_length = best_track["JSTART_LENGTH_METRES"]
+
+ else:
+
+ std_pos_x = dummy_value
+ std_pos_y = dummy_value
+ std_pos_z = dummy_value
+
+ std_length = dummy_value
+
+ std_reco_info = {
+ "std_dir_x": std_dir_x,
+ "std_dir_y": std_dir_y,
+ "std_dir_z": std_dir_z,
+ "std_beta0": std_beta0,
+ "std_lik": std_lik,
+ "std_n_hits_gandalf": std_n_hits_gandalf,
+ "std_pos_x": std_pos_x,
+ "std_pos_y": std_pos_y,
+ "std_pos_z": std_pos_z,
+ "std_energy": std_energy,
+ "std_lik_energy": lik_energy,
+ "std_length": std_length,
+ }
+
+ return std_reco_info
+
+
def get_real_data_info_extr(input_file):
-
- """
- Wrapper function that includes the actual mc_info_extr
- for real data. There are no n_gen like in the neutrino case.
-
- Parameters
- ----------
- input_file : km3net data file
- Can be online or offline format.
-
- Returns
- -------
- mc_info_extr : function
- The actual mc_info_extr function that holds the extractions.
-
- """
-
- #check if std reco is present
- f = File(input_file,"r")
- has_std_reco = "reco" in f.keys()
-
- def mc_info_extr(blob):
-
- """
- Processes a blob and creates the y with mc_info and, if existing, std reco.
-
- For this real data case it is only general event info, like the id.
-
- Parameters
- ----------
- blob : dict
- The blob from the pipeline.
-
- Returns
- -------
- track : dict
- Containing all the specified info the y should have.
-
- """
-
- event_info = blob['EventInfo'][0]
-
- #add n_hits info for the cut
- n_hits = len(blob['Hits'])
-
- track = {
- 'event_id': event_info.event_id,
- 'run_id': event_info.run_id,
- 'trigger_mask': event_info.trigger_mask,
- 'n_hits': n_hits,
- }
-
- #get all the std reco info
- if has_std_reco:
-
- std_reco_info = get_std_reco(blob)
-
- track.update(std_reco_info)
-
- return track
-
- return mc_info_extr
+
+ """
+ Wrapper function that includes the actual mc_info_extr
+ for real data. There are no n_gen like in the neutrino case.
+
+ Parameters
+ ----------
+ input_file : km3net data file
+ Can be online or offline format.
+
+ Returns
+ -------
+ mc_info_extr : function
+ The actual mc_info_extr function that holds the extractions.
+
+ """
+
+ # check if std reco is present
+ f = File(input_file, "r")
+ has_std_reco = "reco" in f.keys()
+
+ def mc_info_extr(blob):
+
+ """
+ Processes a blob and creates the y with mc_info and, if existing, std reco.
+
+ For this real data case it is only general event info, like the id.
+
+ Parameters
+ ----------
+ blob : dict
+ The blob from the pipeline.
+
+ Returns
+ -------
+ track : dict
+ Containing all the specified info the y should have.
+
+ """
+
+ event_info = blob["EventInfo"][0]
+
+ # add n_hits info for the cut
+ n_hits = len(blob["Hits"])
+
+ track = {
+ "event_id": event_info.event_id,
+ "run_id": event_info.run_id,
+ "trigger_mask": event_info.trigger_mask,
+ "n_hits": n_hits,
+ }
+
+ # get all the std reco info
+ if has_std_reco:
+
+ std_reco_info = get_std_reco(blob)
+
+ track.update(std_reco_info)
+
+ return track
+
+ return mc_info_extr
+
def get_random_noise_mc_info_extr(input_file):
-
- """
- Wrapper function that includes the actual mc_info_extr
- for random noise simulations. There are no n_gen like in the neutrino case.
-
- Parameters
- ----------
- input_file : km3net data file
- Can be online or offline format.
-
- Returns
- -------
- mc_info_extr : function
- The actual mc_info_extr function that holds the extractions.
-
- """
-
- #check if std reco is present
- f = File(input_file,"r")
- has_std_reco = "reco" in f.keys()
-
- def mc_info_extr(blob):
-
- """
- Processes a blob and creates the y with mc_info and, if existing, std reco.
-
- For this random noise case it is only general event info, like the id.
-
- Parameters
- ----------
- blob : dict
- The blob from the pipeline.
-
- Returns
- -------
- track : dict
- Containing all the specified info the y should have.
-
- """
- event_info = blob['EventInfo']
-
- track = {
- 'event_id': event_info.event_id[0],
- 'run_id': event_info.run_id[0],
- 'particle_type': 0,
- }
-
- #get all the std reco info
- if has_std_reco:
-
- std_reco_info = get_std_reco(blob)
-
- track.update(std_reco_info)
-
- return track
-
- return mc_info_extr
-
+
+ """
+ Wrapper function that includes the actual mc_info_extr
+ for random noise simulations. There are no n_gen like in the neutrino case.
+
+ Parameters
+ ----------
+ input_file : km3net data file
+ Can be online or offline format.
+
+ Returns
+ -------
+ mc_info_extr : function
+ The actual mc_info_extr function that holds the extractions.
+
+ """
+
+ # check if std reco is present
+ f = File(input_file, "r")
+ has_std_reco = "reco" in f.keys()
+
+ def mc_info_extr(blob):
+
+ """
+ Processes a blob and creates the y with mc_info and, if existing, std reco.
+
+ For this random noise case it is only general event info, like the id.
+
+ Parameters
+ ----------
+ blob : dict
+ The blob from the pipeline.
+
+ Returns
+ -------
+ track : dict
+ Containing all the specified info the y should have.
+
+ """
+ event_info = blob["EventInfo"]
+
+ track = {
+ "event_id": event_info.event_id[0],
+ "run_id": event_info.run_id[0],
+ "particle_type": 0,
+ }
+
+ # get all the std reco info
+ if has_std_reco:
+
+ std_reco_info = get_std_reco(blob)
+
+ track.update(std_reco_info)
+
+ return track
+
+ return mc_info_extr
+
def get_neutrino_mc_info_extr(input_file):
-
- """
- Wrapper function that includes the actual mc_info_extr
- for neutrino simulations. The n_gen parameter, needed for neutrino weighting
- is extracted from the header of the file.
-
- Parameters
- ----------
- input_file : km3net data file
- Can be online or offline format.
-
- Returns
- -------
- mc_info_extr : function
- The actual mc_info_extr function that holds the extractions.
-
- """
-
- #check if std reco is present
- f = File(input_file,"r")
- has_std_reco = "reco" in f.keys()
-
- #get the n_gen
- header = HDF5Header.from_hdf5(input_file)
- n_gen = header.genvol.numberOfEvents
-
- def mc_info_extr(blob):
-
-
- """
- Processes a blob and creates the y with mc_info and, if existing, std reco.
-
- For this neutrino case it is the full mc info for the primary neutrino; there are the several "McTracks":
- check the simulation which index "p" the neutrino has.
-
- Parameters
- ----------
- blob : dict
- The blob from the pipeline.
-
- Returns
- -------
- track : dict
- Containing all the specified info the y should have.
-
- """
-
- #get general info about the event
- event_id = blob['EventInfo'].event_id[0]
- run_id = blob["EventInfo"].run_id[0]
- #weights for neutrino analysis
- weight_w1 = blob["EventInfo"].weight_w1[0]
- weight_w2 = blob["EventInfo"].weight_w2[0]
- weight_w3 = blob["EventInfo"].weight_w3[0]
-
- # first, look for the particular neutrino index of the production
- p = 0 #for ORCA4 (and probably subsequent productions)
-
- mc_track = blob['McTracks'][p]
-
- #some track mc truth info
- particle_type = mc_track.type
- energy = mc_track.energy
- is_cc = mc_track.cc
- bjorkeny = mc_track.by
- dir_x, dir_y, dir_z = mc_track.dir_x, mc_track.dir_y, mc_track.dir_z
- time_interaction = mc_track.time # actually always 0 for primary neutrino, measured in MC time
- vertex_pos_x, vertex_pos_y, vertex_pos_z = mc_track.pos_x, mc_track.pos_y, mc_track.pos_z
-
- #add also the nhits info
- n_hits = len(blob['Hits'])
-
- track = {'event_id': event_id, 'particle_type': particle_type, 'energy': energy, 'is_cc': is_cc,
- 'bjorkeny': bjorkeny, 'dir_x': dir_x, 'dir_y': dir_y, 'dir_z': dir_z,
- 'time_interaction': time_interaction, 'run_id': run_id, 'vertex_pos_x': vertex_pos_x,
- 'vertex_pos_y': vertex_pos_y, 'vertex_pos_z': vertex_pos_z,
- 'n_hits': n_hits,
- 'weight_w1': weight_w1,'weight_w2': weight_w2,'weight_w3': weight_w3,
- 'n_gen':n_gen,
- }
- print(is_cc)
- #get all the std reco info
- if has_std_reco:
-
- std_reco_info = get_std_reco(blob)
-
- track.update(std_reco_info)
-
- return track
-
- return mc_info_extr
-
-
+
+ """
+ Wrapper function that includes the actual mc_info_extr
+ for neutrino simulations. The n_gen parameter, needed for neutrino weighting
+ is extracted from the header of the file.
+
+ Parameters
+ ----------
+ input_file : km3net data file
+ Can be online or offline format.
+
+ Returns
+ -------
+ mc_info_extr : function
+ The actual mc_info_extr function that holds the extractions.
+
+ """
+
+ # check if std reco is present
+ f = File(input_file, "r")
+ has_std_reco = "reco" in f.keys()
+
+ # get the n_gen
+ header = HDF5Header.from_hdf5(input_file)
+ n_gen = header.genvol.numberOfEvents
+
+ def mc_info_extr(blob):
+
+ """
+ Processes a blob and creates the y with mc_info and, if existing, std reco.
+
+ For this neutrino case it is the full mc info for the primary neutrino; there are the several "McTracks":
+ check the simulation which index "p" the neutrino has.
+
+ Parameters
+ ----------
+ blob : dict
+ The blob from the pipeline.
+
+ Returns
+ -------
+ track : dict
+ Containing all the specified info the y should have.
+
+ """
+
+ # get general info about the event
+ event_id = blob["EventInfo"].event_id[0]
+ run_id = blob["EventInfo"].run_id[0]
+ # weights for neutrino analysis
+ weight_w1 = blob["EventInfo"].weight_w1[0]
+ weight_w2 = blob["EventInfo"].weight_w2[0]
+ weight_w3 = blob["EventInfo"].weight_w3[0]
+
+ # first, look for the particular neutrino index of the production
+ p = 0 # for ORCA4 (and probably subsequent productions)
+
+ mc_track = blob["McTracks"][p]
+
+ # some track mc truth info
+ particle_type = mc_track.type
+ energy = mc_track.energy
+ is_cc = mc_track.cc
+ bjorkeny = mc_track.by
+ dir_x, dir_y, dir_z = mc_track.dir_x, mc_track.dir_y, mc_track.dir_z
+ time_interaction = (
+ mc_track.time
+ ) # actually always 0 for primary neutrino, measured in MC time
+ vertex_pos_x, vertex_pos_y, vertex_pos_z = (
+ mc_track.pos_x,
+ mc_track.pos_y,
+ mc_track.pos_z,
+ )
+
+ # add also the nhits info
+ n_hits = len(blob["Hits"])
+
+ track = {
+ "event_id": event_id,
+ "particle_type": particle_type,
+ "energy": energy,
+ "is_cc": is_cc,
+ "bjorkeny": bjorkeny,
+ "dir_x": dir_x,
+ "dir_y": dir_y,
+ "dir_z": dir_z,
+ "time_interaction": time_interaction,
+ "run_id": run_id,
+ "vertex_pos_x": vertex_pos_x,
+ "vertex_pos_y": vertex_pos_y,
+ "vertex_pos_z": vertex_pos_z,
+ "n_hits": n_hits,
+ "weight_w1": weight_w1,
+ "weight_w2": weight_w2,
+ "weight_w3": weight_w3,
+ "n_gen": n_gen,
+ }
+ print(is_cc)
+ # get all the std reco info
+ if has_std_reco:
+
+ std_reco_info = get_std_reco(blob)
+
+ track.update(std_reco_info)
+
+ return track
+
+ return mc_info_extr
+
+
def get_muon_mc_info_extr(input_file):
-
- """
- Wrapper function that includes the actual mc_info_extr
- for atm. muon simulations. There are no n_gen like in the neutrino case.
-
- Parameters
- ----------
- input_file : km3net data file
- Can be online or offline format.
-
- Returns
- -------
- mc_info_extr : function
- The actual mc_info_extr function that holds the extractions.
-
- """
-
- #check if std reco is present
- f = File(input_file,"r")
- has_std_reco = "reco" in f.keys()
-
- #no n_gen here, but needed for concatenation
- n_gen = 1
-
-
- def mc_info_extr(blob):
-
- """
- Processes a blob and creates the y with mc_info and, if existing, std reco.
-
- For this atm. muon case it is the full mc info for the primary; there are the several "McTracks":
- check the simulation to understand what "p" you want. Muons come in bundles that have the same direction.
- For energy: sum of all muons in a bundle,
- for vertex: weighted (energy) mean of the individual vertices .
-
- Parameters
- ----------
- blob : dict
- The blob from the pipeline.
-
- Returns
- -------
- track : dict
- Containing all the specified info the y should have.
-
- """
-
- event_id = blob['EventInfo'].event_id[0]
- run_id = blob["EventInfo"].run_id[0]
-
- p = 0 #for ORCA4 (and probably subsequent productions)
-
- mc_track = blob['McTracks'][p]
-
- particle_type = mc_track.type # assumed that this is the same for all muons in a bundle
- is_cc = mc_track.cc # always 0 actually
- bjorkeny = mc_track.by
- time_interaction = mc_track.time # same for all muons in a bundle
-
- # sum up the energy of all muons
- energy = np.sum(blob['McTracks'].energy)
-
- # all muons in a bundle are parallel, so just take dir of first muon
- dir_x, dir_y, dir_z = mc_track.dir_x, mc_track.dir_y, mc_track.dir_z
-
- # vertex is the weighted (energy) mean of the individual vertices
- vertex_pos_x = np.average(blob['McTracks'][p:].pos_x, weights=blob['McTracks'][p:].energy)
- vertex_pos_y = np.average(blob['McTracks'][p:].pos_y, weights=blob['McTracks'][p:].energy)
- vertex_pos_z = np.average(blob['McTracks'][p:].pos_z, weights=blob['McTracks'][p:].energy)
-
- #add also the nhits info
- n_hits = len(blob['Hits'])
-
- #this is only relevant for neutrinos, though dummy info is needed for the concatenation
- weight_w1 = 10
- weight_w2 = 10
- weight_w3 = 10
-
- track = {'event_id': event_id, 'particle_type': particle_type, 'energy': energy, 'is_cc': is_cc,
- 'bjorkeny': bjorkeny, 'dir_x': dir_x, 'dir_y': dir_y, 'dir_z': dir_z,
- 'time_interaction': time_interaction, 'run_id': run_id, 'vertex_pos_x': vertex_pos_x,
- 'vertex_pos_y': vertex_pos_y, 'vertex_pos_z': vertex_pos_z,
- 'n_hits': n_hits,
-
- 'weight_w1': weight_w1,'weight_w2': weight_w2,'weight_w3': weight_w3,
- 'n_gen':n_gen,
- }
-
- #get all the std reco info
- if has_std_reco:
-
- std_reco_info = get_std_reco(blob)
-
- track.update(std_reco_info)
-
- return track
-
- return mc_info_extr
-
+
+ """
+ Wrapper function that includes the actual mc_info_extr
+ for atm. muon simulations. There are no n_gen like in the neutrino case.
+
+ Parameters
+ ----------
+ input_file : km3net data file
+ Can be online or offline format.
+
+ Returns
+ -------
+ mc_info_extr : function
+ The actual mc_info_extr function that holds the extractions.
+
+ """
+
+ # check if std reco is present
+ f = File(input_file, "r")
+ has_std_reco = "reco" in f.keys()
+
+ # no n_gen here, but needed for concatenation
+ n_gen = 1
+
+ def mc_info_extr(blob):
+
+ """
+ Processes a blob and creates the y with mc_info and, if existing, std reco.
+
+ For this atm. muon case it is the full mc info for the primary; there are the several "McTracks":
+ check the simulation to understand what "p" you want. Muons come in bundles that have the same direction.
+ For energy: sum of all muons in a bundle,
+ for vertex: weighted (energy) mean of the individual vertices .
+
+ Parameters
+ ----------
+ blob : dict
+ The blob from the pipeline.
+
+ Returns
+ -------
+ track : dict
+ Containing all the specified info the y should have.
+
+ """
+
+ event_id = blob["EventInfo"].event_id[0]
+ run_id = blob["EventInfo"].run_id[0]
+
+ p = 0 # for ORCA4 (and probably subsequent productions)
+
+ mc_track = blob["McTracks"][p]
+
+ particle_type = (
+ mc_track.type
+ ) # assumed that this is the same for all muons in a bundle
+ is_cc = mc_track.cc # always 0 actually
+ bjorkeny = mc_track.by
+ time_interaction = mc_track.time # same for all muons in a bundle
+
+ # sum up the energy of all muons
+ energy = np.sum(blob["McTracks"].energy)
+
+ # all muons in a bundle are parallel, so just take dir of first muon
+ dir_x, dir_y, dir_z = mc_track.dir_x, mc_track.dir_y, mc_track.dir_z
+
+ # vertex is the weighted (energy) mean of the individual vertices
+ vertex_pos_x = np.average(
+ blob["McTracks"][p:].pos_x, weights=blob["McTracks"][p:].energy
+ )
+ vertex_pos_y = np.average(
+ blob["McTracks"][p:].pos_y, weights=blob["McTracks"][p:].energy
+ )
+ vertex_pos_z = np.average(
+ blob["McTracks"][p:].pos_z, weights=blob["McTracks"][p:].energy
+ )
+
+ # add also the nhits info
+ n_hits = len(blob["Hits"])
+
+ # this is only relevant for neutrinos, though dummy info is needed for the concatenation
+ weight_w1 = 10
+ weight_w2 = 10
+ weight_w3 = 10
+
+ track = {
+ "event_id": event_id,
+ "particle_type": particle_type,
+ "energy": energy,
+ "is_cc": is_cc,
+ "bjorkeny": bjorkeny,
+ "dir_x": dir_x,
+ "dir_y": dir_y,
+ "dir_z": dir_z,
+ "time_interaction": time_interaction,
+ "run_id": run_id,
+ "vertex_pos_x": vertex_pos_x,
+ "vertex_pos_y": vertex_pos_y,
+ "vertex_pos_z": vertex_pos_z,
+ "n_hits": n_hits,
+ "weight_w1": weight_w1,
+ "weight_w2": weight_w2,
+ "weight_w3": weight_w3,
+ "n_gen": n_gen,
+ }
+
+ # get all the std reco info
+ if has_std_reco:
+
+ std_reco_info = get_std_reco(blob)
+
+ track.update(std_reco_info)
+
+ return track
+
+ return mc_info_extr