diff --git a/docs/orcasong.rst b/docs/orcasong.rst
index e52cbd43ebee5a9b7cd96db32cb2a0f495aa015c..7ad90195e6c844adbfb0fbbf8ffd1c7684761a2f 100644
--- a/docs/orcasong.rst
+++ b/docs/orcasong.rst
@@ -130,7 +130,7 @@ calibrate the data on the fly:
Adding mc_info
^^^^^^^^^^^^^^
-Define a function ``my_mcinfo_extractor``, which takes as an input a km3pipe blob,
+Define a function ``my_extractor``, which takes as an input a km3pipe blob,
and outputs a dict mapping str to float.
It should contain everything you need later down the pipeline, e.g. labels,
event identifiers, ...
@@ -140,5 +140,5 @@ the str being the dtype names. Set up like follows:
.. code-block:: python
- fb = FileBinner(bin_edges_list, mc_info_extr=my_mcinfo_extractor)
+ fb = FileBinner(bin_edges_list, extractor=my_extractor)
diff --git a/orcasong/core.py b/orcasong/core.py
index cf0c6895b6241d36a89a395f3d244e0785fc3213..73c2780b83c8f6c2a72d39de83316788121e6ac9 100644
--- a/orcasong/core.py
+++ b/orcasong/core.py
@@ -21,12 +21,12 @@ class BaseProcessor:
Parameters
----------
- mc_info_extr : function, optional
- Function that extracts desired mc_info from a blob, which is then
+ extractor : function, optional
+ Function that extracts desired info from a blob, which is then
stored as the "y" datafield in the .h5 file.
The function takes the km3pipe blob as an input, and returns
a dict mapping str to floats.
- Some examples can be found in orcasong.mc_info_extr.
+ Examples can be found in orcasong.extractors.
det_file : str, optional
Path to a .detx detector geometry file, which can be used to
calibrate the hits.
@@ -82,7 +82,7 @@ class BaseProcessor:
each pipeline.
"""
- def __init__(self, mc_info_extr=None,
+ def __init__(self, extractor=None,
det_file=None,
center_time=True,
add_t0=False,
@@ -93,7 +93,7 @@ class BaseProcessor:
keep_mc_tracks=False,
overwrite=True,
mc_info_to_float64=True):
- self.mc_info_extr = mc_info_extr
+ self.extractor = extractor
self.det_file = det_file
self.center_time = center_time
self.add_t0 = add_t0
@@ -198,9 +198,9 @@ class BaseProcessor:
def get_cmpts_post(self, outfile):
""" Modules that postproc and save the events. """
cmpts = []
- if self.mc_info_extr is not None:
+ if self.extractor is not None:
cmpts.append((modules.McInfoMaker, {
- "mc_info_extr": self.mc_info_extr,
+ "extractor": self.extractor,
"to_float64": self.mc_info_to_float64,
"store_as": "mc_info"}))
diff --git a/orcasong/extractors.py b/orcasong/extractors.py
new file mode 100644
index 0000000000000000000000000000000000000000..beb98638a4832d83470ce7ed5f174bd3703a5997
--- /dev/null
+++ b/orcasong/extractors.py
@@ -0,0 +1,500 @@
+"""
+Functions that extract info from a blob for the mc_info / y datafield
+in the h5 files.
+
+These are made for the specific given runs. They might not be
+applicable to other data, and could cause errors or produce unexpected
+results when used on data other then the specified.
+
+"""
+
+import warnings
+import numpy as np
+from km3pipe.io.hdf5 import HDF5Header
+from h5py import File
+
+__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
+
+
+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
+
+
+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
+
+
+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
+
+
+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
diff --git a/orcasong/mc_info_extr.py b/orcasong/mc_info_extr.py
deleted file mode 100644
index 81456541d99e54bce65bf69be8e7db1b50c10985..0000000000000000000000000000000000000000
--- a/orcasong/mc_info_extr.py
+++ /dev/null
@@ -1,44 +0,0 @@
-"""
-Functions that extract info from a blob for the mc_info / y datafield
-in the h5 files. Very much WIP.
-
-These are made for the specific given runs. They might not be
-applicable to other data, and could cause errors or produce unexpected
-results when used on data other then the specified.
-
-"""
-
-import warnings
-import numpy as np
-
-__author__ = 'Stefan Reck'
-
-
-def get_real_data(blob):
- """
- Get info present in real data.
- Designed for the 2017 one line runs.
-
- """
- event_info = blob['EventInfo'][0]
- track = {
- 'event_id': event_info.event_id,
- 'run_id': event_info.run_id,
- 'trigger_mask': event_info.trigger_mask,
- }
- return track
-
-
-def get_pure_noise(blob):
- """
- For simulated pure noise events, which have particle_type 0.
-
- """
- event_info = blob['EventInfo']
-
- track = {
- 'event_id': event_info.event_id[0],
- 'run_id': event_info.run_id,
- 'particle_type': 0
- }
- return track
diff --git a/orcasong/modules.py b/orcasong/modules.py
index d08696b226d2361ec89e35a3d98afecd1a9e5729..88e5a2348c4a7b729c3294109eef1bd49122dc4c 100644
--- a/orcasong/modules.py
+++ b/orcasong/modules.py
@@ -12,11 +12,11 @@ __author__ = 'Stefan Reck'
class McInfoMaker(kp.Module):
"""
- Store mc info as float64 in the blob.
+ Stores info as float64 in the blob.
Attributes
----------
- mc_info_extr : function
+ extractor : function
Function to extract the info. Takes the blob as input, outputs
a dict with the desired mc_infos.
store_as : str
@@ -25,12 +25,12 @@ class McInfoMaker(kp.Module):
"""
def configure(self):
- self.mc_info_extr = self.require('mc_info_extr')
+ self.extractor = self.require('extractor')
self.store_as = self.require('store_as')
self.to_float64 = self.get("to_float64", default=True)
def process(self, blob):
- track = self.mc_info_extr(blob)
+ track = self.extractor(blob)
if self.to_float64:
dtypes = []
for key, v in track.items():
diff --git a/tests/data/neutrino_detector_file.detx b/tests/data/neutrino_detector_file.detx
new file mode 100644
index 0000000000000000000000000000000000000000..b580cf233beb6c4b2f3597d12a60b2cbd2511034
--- /dev/null
+++ b/tests/data/neutrino_detector_file.detx
@@ -0,0 +1,2998 @@
+# GIT 12.0.0-D
+# ROOT 5.34/38
+# application JFitK40
+# command /pbs/throng/km3net/src/Jpp/v12.0.0/out//Linux/bin//JFitK40 -d 2 -w -A -E 0.206 -@ \"Rate_Hz=18.460546; p1=3.0767; p2=-1.2078; p3=0.9905; p4=0.9379\" -y \"-15 +15\" -a KM3NeT_00000044_00005900.v5.4_T0.K40.detx -f KM3NeT_00000044_00005900.v5.4_T0.jcalk40.jmergek40.root -o KM3NeT_00000044_00005900.v5.4_T0.jcalk40.jmergek40.jfitk40.root -P KM3NeT_00000044_00005900.v5.4_T0.ToT.QE.PMTeff.txt --!
+# namespace KM3NET
+# system Linux ccwsge0133 3.10.0-957.12.2.el7.x86_64 #1 SMP Tue May 14 21:24:32 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux
+# GIT 12.0.0-D
+# ROOT 5.34/38
+# application JFitK40
+# command /pbs/throng/km3net/src/Jpp/v12.0.0/out//Linux/bin//JFitK40 -d 2 -w -A -E 0.206 -@ \"Rate_Hz=18.460546; p1=3.0767; p2=-1.2078; p3=0.9905; p4=0.9379\" -y \"-15 +15\" -a KM3NeT_00000044_00005900.v5.4_PMTeff.K40.detx -f KM3NeT_00000044_00005900.v5.4_PMTeff.jcalk40.jmergek40.root -o KM3NeT_00000044_00005900.v5.4_PMTeff.jcalk40.jmergek40.jfitk40.root -P KM3NeT_00000044_00005900.v5.4_PMTeff.ToT.QE.PMTeff.txt --!
+# namespace KM3NET
+# system Linux ccwsge0645 3.10.0-1062.1.1.el7.x86_64 #1 SMP Fri Sep 13 22:55:44 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux
+# GIT 12.1.0-33-g7c63133
+# ROOT 5.34/38
+# application JEditDetector
+# command /pbs/throng/km3net/src/Jpp/master/out//Linux/bin//JEditDetector -a K40files/KM3NeT_00000044_00005900.v5.4_PMTeff.K40.detx -o ./KM3NeT_00000044_00005900.v5.4_PMTeff.K40_muonCalibration.detx -S \"-1 mul 0.02\" -S \"2 add 0.0 -0.3 0.0\" -S \"2 add -0.7\" -S \"2 rot -0.20\" -S \"3 add -0.2 0.0 0.0\" -S \"3 add -0.8\" -S \"3 rot 0.03\" -S \"4 add 0.3 0.2 0.0\" -S \"4 add 1.3\" -S \"4 rot -0.50\" -S \"5 add -0.3 -0.3 0.0\" -S \"5 add -0.5\" -S \"5 rot -0.21\"
+# namespace KM3NET
+# system Linux cca004 3.10.0-1062.4.3.el7.x86_64 #1 SMP Wed Nov 13 23:58:53 UTC 2019 x86_64
+# GIT 12.1.0-33-g7c63133
+# ROOT 5.34/38
+# application JEditDetector
+# command /pbs/throng/km3net/src/Jpp/master/out//Linux/bin//JEditDetector -a ./KM3NeT_00000044_00005900.v5.4_PMTeff.K40_muonCalibration.detx -o ./KM3NeT_00000044_00005900.v5.4_PMTeff.K40_muonCalibration_shift00.detx -S \"-1 add -444.5 -577.2 3.0\"
+# namespace KM3NET
+# system Linux cca004 3.10.0-1062.4.3.el7.x86_64 #1 SMP Wed Nov 13 23:58:53 UTC 2019 x86_64
+44 v2
+1556228627.7 9999999999.0
+UTM WGS84 32N 256500.0 4743000.0 -2440.0
+90
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+808964883 5 15 31
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+808982066 5 17 31
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+ 9340 19.650 -12.292 192.600 0.247 -0.465 -0.850 208400.784
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+
diff --git a/tests/data/neutrino_file.h5 b/tests/data/neutrino_file.h5
new file mode 100644
index 0000000000000000000000000000000000000000..5413d7037ee4691849c84bfd6f77dcd59207460e
Binary files /dev/null and b/tests/data/neutrino_file.h5 differ
diff --git a/tests/test_core.py b/tests/test_core.py
index 70156ec03df774eaf82195a2d0803e54e3b0c461..e21d48d5ace8d37c4bbda7eb8ad4ec4ea3c716e4 100644
--- a/tests/test_core.py
+++ b/tests/test_core.py
@@ -4,7 +4,7 @@ import tempfile
import numpy as np
import h5py
import orcasong.core
-import orcasong.mc_info_extr
+import orcasong.extractors as extractors
from orcasong.plotting.plot_binstats import read_hists_from_h5file
@@ -26,7 +26,7 @@ class TestFileBinner(TestCase):
["time", np.linspace(0, 600, 3)],
["channel_id", np.linspace(-0.5, 30.5, 3)],
],
- mc_info_extr=orcasong.mc_info_extr.get_real_data,
+ extractor=extractors.get_real_data_info_extr(MUPAGE_FILE),
det_file=DET_FILE,
add_t0=True,
)
@@ -86,7 +86,7 @@ class TestFileGraph(TestCase):
max_n_hits=3,
time_window=[0, 50],
hit_infos=["pos_z", "time", "channel_id"],
- mc_info_extr=orcasong.mc_info_extr.get_real_data,
+ extractor=extractors.get_real_data_info_extr(MUPAGE_FILE),
det_file=DET_FILE,
add_t0=True,
)
diff --git a/tests/test_extractor.py b/tests/test_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..70e0bef4744b5e634eaa41fe786f527a4af32378
--- /dev/null
+++ b/tests/test_extractor.py
@@ -0,0 +1,193 @@
+import os
+from unittest import TestCase
+import tempfile
+import numpy as np
+import h5py
+import orcasong.core
+import orcasong.extractors as extractors
+
+
+__author__ = "Daniel Guderian"
+
+
+test_dir = os.path.dirname(os.path.realpath(__file__))
+NEUTRINO_FILE = os.path.join(test_dir, "data", "neutrino_file.h5")
+DET_FILE_NEUTRINO = os.path.join(test_dir, "data", "neutrino_detector_file.detx")
+
+
+class TestStdRecoExtractor(TestCase):
+ """ Assert that the neutrino info is extracted correctly File has 18 events. """
+
+ @classmethod
+ def setUpClass(cls):
+ cls.proc = orcasong.core.FileGraph(
+ max_n_hits=3,
+ time_window=[0, 50],
+ hit_infos=["pos_z", "time", "channel_id"],
+ extractor=extractors.get_neutrino_mc_info_extr(NEUTRINO_FILE),
+ det_file=DET_FILE_NEUTRINO,
+ add_t0=True,
+ )
+ cls.tmpdir = tempfile.TemporaryDirectory()
+ cls.outfile = os.path.join(cls.tmpdir.name, "binned.h5")
+ cls.proc.run(infile=NEUTRINO_FILE, outfile=cls.outfile)
+ cls.f = h5py.File(cls.outfile, "r")
+
+ @classmethod
+ def tearDownClass(cls):
+ cls.f.close()
+ cls.tmpdir.cleanup()
+
+ def test_keys(self):
+ self.assertSetEqual(
+ set(self.f.keys()),
+ {
+ "_i_event_info",
+ "_i_group_info",
+ "_i_y",
+ "event_info",
+ "group_info",
+ "x",
+ "x_indices",
+ "y",
+ },
+ )
+
+ def test_y(self):
+ y = self.f["y"][()]
+ target = {
+ "weight_w2": np.array(
+ [
+ 29650.0,
+ 297100.0,
+ 41450.0,
+ 371400.0,
+ 1101000000.0,
+ 2757000.0,
+ 15280000.0,
+ 262800000.0,
+ 22590.0,
+ 24240.0,
+ 80030.0,
+ 3018000.0,
+ 120600.0,
+ 872200.0,
+ 50440000.0,
+ 21540.0,
+ 42170.0,
+ 25230.0,
+ ]
+ ),
+ "n_gen": np.array(
+ [
+ 60000.0,
+ 60000.0,
+ 60000.0,
+ 60000.0,
+ 60000.0,
+ 60000.0,
+ 60000.0,
+ 60000.0,
+ 60000.0,
+ 60000.0,
+ 60000.0,
+ 60000.0,
+ 60000.0,
+ 60000.0,
+ 60000.0,
+ 60000.0,
+ 60000.0,
+ 60000.0,
+ ]
+ ),
+ "dir_z": np.array(
+ [
+ -0.896549,
+ -0.835252,
+ 0.300461,
+ 0.108997,
+ 0.128445,
+ -0.543621,
+ -0.23205,
+ -0.297228,
+ 0.694932,
+ 0.73835,
+ -0.007682,
+ 0.437847,
+ -0.126804,
+ 0.153432,
+ -0.263229,
+ 0.820217,
+ 0.452473,
+ 0.294217,
+ ]
+ ),
+ "is_cc": np.array(
+ [
+ 2.0,
+ 2.0,
+ 2.0,
+ 2.0,
+ 2.0,
+ 2.0,
+ 2.0,
+ 2.0,
+ 2.0,
+ 2.0,
+ 2.0,
+ 2.0,
+ 2.0,
+ 2.0,
+ 2.0,
+ 2.0,
+ 2.0,
+ 2.0,
+ ]
+ ),
+ "std_dir_z": np.array(
+ [
+ -0.923199825369434,
+ -0.6422689266782661,
+ 0.38853917922036363,
+ -0.16690804339142448,
+ -0.01584853496341109,
+ -0.10151549881670698,
+ -0.0409694104272829,
+ -0.32964369874021787,
+ -0.3294926806601529,
+ 0.6524241250799204,
+ -0.3899574246450216,
+ 0.27872277417339086,
+ 0.0019490791409933206,
+ 0.20341370281708737,
+ -0.15739475718286297,
+ 0.8040250543935723,
+ 0.08772622550043882,
+ -0.7766722433951796,
+ ]
+ ),
+ "std_energy": np.array(
+ [
+ 4.7187625606210775,
+ 4.169818842606011,
+ 1.0056373761749966,
+ 5.908597073055873,
+ 12.409377607517195,
+ 7.566695371401163,
+ 1.3546775620239864,
+ 2.659528737837978,
+ 1.0056373761749966,
+ 2.1968321463948755,
+ 1.4821714294894754,
+ 10.135831333340658,
+ 2.6003934443336765,
+ 1.4492149732348223,
+ 71.69167874147956,
+ 8.094744120333358,
+ 3.148088080484504,
+ 1.0056373761749966,
+ ]
+ ),
+ }
+ for k, v in target.items():
+ np.testing.assert_equal(y[k], v)
diff --git a/tests/test_modules.py b/tests/test_modules.py
index 242f26ef86e5688a68bdb8a65bf19f3feb273e27..5c4623166fc0763682c29fab70c6b07714186e89 100644
--- a/tests/test_modules.py
+++ b/tests/test_modules.py
@@ -10,7 +10,7 @@ __author__ = 'Stefan Reck'
class TestModules(TestCase):
def test_mc_info_maker(self):
""" Test the mcinfo maker on some dummy data. """
- def mc_info_extr(blob):
+ def extractor(blob):
hits = blob["Hits"]
return {"dom_id_0": hits.dom_id[0],
"time_2": hits.time[2]}
@@ -23,7 +23,7 @@ class TestModules(TestCase):
})
}
module = modules.McInfoMaker(
- mc_info_extr=mc_info_extr, store_as="test")
+ extractor=extractor, store_as="test")
out_blob = module.process(in_blob)
self.assertSequenceEqual(list(out_blob.keys()), ["Hits", "test"])
@@ -36,7 +36,7 @@ class TestModules(TestCase):
def test_mc_info_maker_dtype(self):
""" Test the mcinfo maker on some dummy data. """
- def mc_info_extr(blob):
+ def extractor(blob):
hits = blob["Hits"]
return {"dom_id_0": hits.dom_id[0],
"time_2": hits.time[2]}
@@ -48,7 +48,7 @@ class TestModules(TestCase):
})
}
module = modules.McInfoMaker(
- mc_info_extr=mc_info_extr, store_as="test", to_float64=False)
+ extractor=extractor, store_as="test", to_float64=False)
out_blob = module.process(in_blob)
np.testing.assert_array_equal(