From 1c6883ab65458714114d0f10623c0f60c1013542 Mon Sep 17 00:00:00 2001
From: zineb aly <aly.zineb.az@gmail.com>
Date: Fri, 5 Jun 2020 10:35:05 +0200
Subject: [PATCH] reorganise tools
---
km3io/offline.py | 2 +
km3io/tools.py | 155 ++++++++++++++++++++++++++++++++++++++++++
tests/test_offline.py | 93 +------------------------
tests/test_tools.py | 99 ++++++++++++++++++++++++++-
4 files changed, 256 insertions(+), 93 deletions(-)
diff --git a/km3io/offline.py b/km3io/offline.py
index e51f319..9026e68 100644
--- a/km3io/offline.py
+++ b/km3io/offline.py
@@ -164,8 +164,10 @@ def best_track(tracks, strategy="first", rec_type=None, rec_stages=None):
"""
if strategy == "first":
return tracks[:, 0]
+
if strategy == "rec_stages" and rec_stages is not None:
return tracks[mask(tracks.rec_stages, rec_stages)]
+
if strategy == "default" and rec_type is not None:
return tracks[tracks.rec_type == reconstruction[rec_type]][
tracks.lik == ak1.max(tracks.lik, axis=1)][:, 0]
diff --git a/km3io/tools.py b/km3io/tools.py
index f664db0..acbb258 100644
--- a/km3io/tools.py
+++ b/km3io/tools.py
@@ -1,8 +1,11 @@
#!/usr/bin/env python3
import numba as nb
import numpy as np
+import awkward1 as ak1
import uproot
+from .definitions import fitparameters, reconstruction
+
# 110 MB based on the size of the largest basket found so far in km3net
BASKET_CACHE_SIZE = 110 * 1024**2
BASKET_CACHE = uproot.cache.ThreadSafeArrayCache(BASKET_CACHE_SIZE)
@@ -85,3 +88,155 @@ def uniquecount(array, dtype=np.int64):
else:
out[i] = len(unique(sub_array))
return out
+
+
+def rec_types():
+ """name of the reconstruction type as defined in the official
+ KM3NeT-Dataformat.
+
+ Returns
+ -------
+ dict_keys
+ reconstruction types.
+ """
+ return reconstruction.keys()
+
+
+def fitinf(fitparam, tracks):
+ """Access fit parameters in tracks.fitinf.
+
+ Parameters
+ ----------
+ fitparam : str
+ the fit parameter name according to fitparameters defined in
+ KM3NeT-Dataformat.
+ tracks : class km3io.offline.OfflineBranch
+ the tracks class. both full tracks branch or a slice of the
+ tracks branch (example tracks[:, 0]) work.
+
+ Returns
+ -------
+ awkward array
+ awkward array of the values of the fit parameter requested.
+ """
+ fit = tracks.fitinf
+ index = fitparameters[fitparam]
+ try:
+ params = fit[count_nested(fit, axis=2) > index]
+ return ak1.Array([i[:, index] for i in params])
+ except ValueError:
+ # This is the case for tracks[:, 0] or any other selection.
+ params = fit[count_nested(fit, axis=1) > index]
+ return params[:, index]
+
+
+def fitparams():
+ """name of the fit parameters as defined in the official
+ KM3NeT-Dataformat.
+
+ Returns
+ -------
+ dict_keys
+ fit parameters keys.
+ """
+ return fitparameters.keys()
+
+
+def count_nested(Array, axis=0):
+ """count elements in a nested awkward Array.
+
+ Parameters
+ ----------
+ Array : Awkward1 Array
+ Array of data. Example tracks.fitinf or tracks.rec_stages.
+ axis : int, optional
+ axis = 0: to count elements in the outmost level of nesting.
+ axis = 1: to count elements in the first level of nesting.
+ axis = 2: to count elements in the second level of nesting.
+
+ Returns
+ -------
+ awkward1 Array or int
+ counts of elements found in a nested awkward1 Array.
+ """
+ if axis == 0:
+ return ak1.num(Array, axis=0)
+ if axis == 1:
+ return ak1.num(Array, axis=1)
+ if axis == 2:
+ return ak1.count(Array, axis=2)
+
+
+@nb.jit(nopython=True)
+def _find(rec_stages, stages, builder):
+ """construct an awkward1 array with the same structure as tracks.rec_stages.
+ When stages are found, the Array is filled with value 1, otherwise it is filled
+ with value 0.
+
+ Parameters
+ ----------
+ rec_stages : awkward1 Array
+ tracks.rec_stages .
+ stages : awkward1 Array
+ reconstruction stages of interest.
+ builder : awkward1.highlevel.ArrayBuilder
+ awkward1 Array builder.
+ """
+ for s in rec_stages:
+ builder.begin_list()
+ for i in s:
+ num_stages = len(i)
+ if num_stages == len(stages):
+ found = 0
+ for j in range(num_stages):
+ if i[j] == stages[j]:
+ found += 1
+ if found == num_stages:
+ builder.append(1)
+ else:
+ builder.append(0)
+ else:
+ builder.append(0)
+ builder.end_list()
+
+
+def mask(rec_stages, stages):
+ """create a mask on tracks.rec_stages .
+
+ Parameters
+ ----------
+ rec_stages : awkward1 Array
+ tracks.rec_stages .
+ stages : list
+ reconstruction stages of interest.
+
+ Returns
+ -------
+ awkward1 Array
+ an awkward1 Array mask where True corresponds to the positions
+ where stages were found. False otherwise.
+ """
+ builder = ak1.ArrayBuilder()
+ _find(rec_stages, ak1.Array(stages), builder)
+ return builder.snapshot() == 1
+
+
+def best_track(tracks, strategy="first", rec_type=None, rec_stages=None):
+ """best track selection based on different strategies
+
+ Parameters
+ ----------
+ tracks : class km3io.offline.OfflineBranch
+ the tracks branch.
+ strategy : str
+ the trategy desired to select the best tracks.
+ """
+ if strategy == "first":
+ return tracks[:, 0]
+
+ if strategy == "rec_stages" and rec_stages is not None:
+ return tracks[mask(tracks.rec_stages, rec_stages)]
+
+ if strategy == "default" and rec_type is not None:
+ return tracks[tracks.rec_type == reconstruction[rec_type]][
+ tracks.lik == ak1.max(tracks.lik, axis=1)][:, 0]
diff --git a/tests/test_offline.py b/tests/test_offline.py
index 0f4f42b..4fb616b 100644
--- a/tests/test_offline.py
+++ b/tests/test_offline.py
@@ -1,10 +1,9 @@
import unittest
import numpy as np
-import awkward1 as ak1
from pathlib import Path
from km3io import OfflineReader
-from km3io.offline import _nested_mapper, Header, fitinf, fitparams, count_nested, _find, mask, best_track, rec_types
+from km3io.offline import _nested_mapper, Header
SAMPLES_DIR = Path(__file__).parent / 'samples'
OFFLINE_FILE = OfflineReader(SAMPLES_DIR / 'km3net_offline.root')
@@ -16,96 +15,6 @@ OFFLINE_MC_TRACK_USR = OfflineReader(
OFFLINE_NUMUCC = OfflineReader(SAMPLES_DIR / "numucc.root") # with mc data
-class TestFitinf(unittest.TestCase):
- def setUp(self):
- self.tracks = OFFLINE_FILE.events.tracks
- self.fit = self.tracks.fitinf
- self.best = self.tracks[:, 0]
- self.best_fit = self.best.fitinf
-
- def test_fitinf(self):
- beta = fitinf('JGANDALF_BETA0_RAD', self.tracks)
- best_beta = fitinf('JGANDALF_BETA0_RAD', self.best)
-
- assert beta[0][0] == self.fit[0][0][0]
- assert beta[0][1] == self.fit[0][1][0]
- assert beta[0][2] == self.fit[0][2][0]
-
- assert best_beta[0] == self.best_fit[0][0]
- assert best_beta[1] == self.best_fit[1][0]
- assert best_beta[2] == self.best_fit[2][0]
-
- def test_fitparams(self):
- keys = set(fitparams())
-
- assert "JGANDALF_BETA0_RAD" in keys
-
-
-class TestRecoTypes(unittest.TestCase):
- def test_reco_types(self):
- keys = set(rec_types())
-
- assert "JPP_RECONSTRUCTION_TYPE" in keys
-
-
-class TestBestTrack(unittest.TestCase):
- def setUp(self):
- self.tracks = OFFLINE_FILE.events.tracks
-
- def test_best_tracks(self):
- first_tracks = best_track(self.tracks, strategy="first")
- rec_stages_tracks = best_track(self.tracks,
- strategy="rec_stages",
- rec_stages=[1, 3, 5, 4])
- default_best = best_track(self.tracks,
- strategy="default",
- rec_type="JPP_RECONSTRUCTION_TYPE")
-
- assert first_tracks.dir_z[0] == self.tracks.dir_z[0][0]
- assert first_tracks.dir_x[1] == self.tracks.dir_x[1][0]
-
- assert rec_stages_tracks.rec_stages[0] == [1, 3, 5, 4]
- assert rec_stages_tracks.rec_stages[1] == [1, 3, 5, 4]
-
- assert default_best.lik[0] == ak1.max(self.tracks.lik[0])
- assert default_best.lik[1] == ak1.max(self.tracks.lik[1])
- assert default_best.rec_type[0] == 4000
-
-
-class TestCountNested(unittest.TestCase):
- def test_count_nested(self):
- fit = OFFLINE_FILE.events.tracks.fitinf
-
- assert count_nested(fit, axis=0) == 10
- assert count_nested(fit, axis=1)[0:4] == ak1.Array([56, 55, 56, 56])
- assert count_nested(fit, axis=2)[0][0:4] == ak1.Array([17, 11, 8, 8])
-
-
-class TestRecStagesMasks(unittest.TestCase):
- def setUp(self):
- self.nested = ak1.Array([[[1, 2, 3], [1, 2, 3], [1]], [[0], [1, 2, 3]],
- [[0], [0, 1, 3], [0], [1, 2, 3], [1, 2, 3]]])
-
- def test_find(self):
- builder = ak1.ArrayBuilder()
- _find(self.nested, ak1.Array([1, 2, 3]), builder)
- labels = builder.snapshot()
-
- assert labels[0][0] == 1
- assert labels[0][1] == 1
- assert labels[0][2] == 0
- assert labels[1][0] == 0
-
- def test_mask(self):
- rec_stages = OFFLINE_FILE.events.tracks.rec_stages
- stages = [1, 3, 5, 4]
- masks = mask(rec_stages, stages)
-
- assert masks[0][0] == all(rec_stages[0][0] == ak1.Array(stages))
- assert masks[1][0] == all(rec_stages[1][0] == ak1.Array(stages))
- assert masks[0][1] == False
-
-
class TestOfflineReader(unittest.TestCase):
def setUp(self):
self.r = OFFLINE_FILE
diff --git a/tests/test_tools.py b/tests/test_tools.py
index 5306f42..325e681 100644
--- a/tests/test_tools.py
+++ b/tests/test_tools.py
@@ -3,8 +3,105 @@
import unittest
import awkward1 as ak
import numpy as np
+
+from pathlib import Path
+from km3io import OfflineReader
from km3io.tools import (to_num, cached_property, unfold_indices, unique,
- uniquecount)
+ uniquecount, fitinf, fitparams, count_nested, _find,
+ mask, best_track, rec_types)
+
+SAMPLES_DIR = Path(__file__).parent / 'samples'
+OFFLINE_FILE = OfflineReader(SAMPLES_DIR / 'km3net_offline.root')
+
+
+class TestFitinf(unittest.TestCase):
+ def setUp(self):
+ self.tracks = OFFLINE_FILE.events.tracks
+ self.fit = self.tracks.fitinf
+ self.best = self.tracks[:, 0]
+ self.best_fit = self.best.fitinf
+
+ def test_fitinf(self):
+ beta = fitinf('JGANDALF_BETA0_RAD', self.tracks)
+ best_beta = fitinf('JGANDALF_BETA0_RAD', self.best)
+
+ assert beta[0][0] == self.fit[0][0][0]
+ assert beta[0][1] == self.fit[0][1][0]
+ assert beta[0][2] == self.fit[0][2][0]
+
+ assert best_beta[0] == self.best_fit[0][0]
+ assert best_beta[1] == self.best_fit[1][0]
+ assert best_beta[2] == self.best_fit[2][0]
+
+ def test_fitparams(self):
+ keys = set(fitparams())
+
+ assert "JGANDALF_BETA0_RAD" in keys
+
+
+class TestRecoTypes(unittest.TestCase):
+ def test_reco_types(self):
+ keys = set(rec_types())
+
+ assert "JPP_RECONSTRUCTION_TYPE" in keys
+
+
+class TestBestTrack(unittest.TestCase):
+ def setUp(self):
+ self.tracks = OFFLINE_FILE.events.tracks
+
+ def test_best_tracks(self):
+ first_tracks = best_track(self.tracks, strategy="first")
+ rec_stages_tracks = best_track(self.tracks,
+ strategy="rec_stages",
+ rec_stages=[1, 3, 5, 4])
+ default_best = best_track(self.tracks,
+ strategy="default",
+ rec_type="JPP_RECONSTRUCTION_TYPE")
+
+ assert first_tracks.dir_z[0] == self.tracks.dir_z[0][0]
+ assert first_tracks.dir_x[1] == self.tracks.dir_x[1][0]
+
+ assert rec_stages_tracks.rec_stages[0] == [1, 3, 5, 4]
+ assert rec_stages_tracks.rec_stages[1] == [1, 3, 5, 4]
+
+ assert default_best.lik[0] == ak.max(self.tracks.lik[0])
+ assert default_best.lik[1] == ak.max(self.tracks.lik[1])
+ assert default_best.rec_type[0] == 4000
+
+
+class TestCountNested(unittest.TestCase):
+ def test_count_nested(self):
+ fit = OFFLINE_FILE.events.tracks.fitinf
+
+ assert count_nested(fit, axis=0) == 10
+ assert count_nested(fit, axis=1)[0:4] == ak.Array([56, 55, 56, 56])
+ assert count_nested(fit, axis=2)[0][0:4] == ak.Array([17, 11, 8, 8])
+
+
+class TestRecStagesMasks(unittest.TestCase):
+ def setUp(self):
+ self.nested = ak.Array([[[1, 2, 3], [1, 2, 3], [1]], [[0], [1, 2, 3]],
+ [[0], [0, 1, 3], [0], [1, 2, 3], [1, 2, 3]]])
+
+ def test_find(self):
+ builder = ak.ArrayBuilder()
+ _find(self.nested, ak.Array([1, 2, 3]), builder)
+ labels = builder.snapshot()
+
+ assert labels[0][0] == 1
+ assert labels[0][1] == 1
+ assert labels[0][2] == 0
+ assert labels[1][0] == 0
+
+ def test_mask(self):
+ rec_stages = OFFLINE_FILE.events.tracks.rec_stages
+ stages = [1, 3, 5, 4]
+ masks = mask(rec_stages, stages)
+
+ assert masks[0][0] == all(rec_stages[0][0] == ak.Array(stages))
+ assert masks[1][0] == all(rec_stages[1][0] == ak.Array(stages))
+ assert masks[0][1] == False
class TestUnique(unittest.TestCase):
--
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