#!/usr/bin/env python3
import unittest
import awkward1 as ak
import numpy as np
from pathlib import Path
from km3net_testdata import data_path
from km3io import OfflineReader
from km3io.tools import (to_num, cached_property, unfold_indices, unique,
uniquecount, fitinf, fitparams, count_nested, _find,
mask, best_track, rec_types, get_w2list_param,
get_multiplicity)
OFFLINE_FILE = OfflineReader(data_path("offline/km3net_offline.root"))
# class TestGetw2listParam(unittest.TestCase):
# def test_get_w2list_param(self):
# xsec_mean = get_w2list_param(OFFLINE_FILE.events, "gseagen", "W2LIST_GSEAGEN_XSEC_MEAN")
# print(xsec_mean)
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.events = OFFLINE_FILE.events
self.one_event = OFFLINE_FILE.events[0]
def test_best_tracks(self):
# test selection from multiple events
events = self.events[self.events.n_tracks > 0]
first_tracks = best_track(events.tracks, strategy="first")
default_best = best_track(events.tracks,
strategy="default",
rec_type="JPP_RECONSTRUCTION_TYPE")
assert first_tracks.dir_z[0] == events.tracks.dir_z[0][0]
assert first_tracks.dir_x[1] == events.tracks.dir_x[1][0]
assert default_best.lik[0] == ak.max(events.tracks.lik[0])
assert default_best.lik[1] == ak.max(events.tracks.lik[1])
assert default_best.rec_type[0] == 4000
# test selection from one event
first_track = best_track(self.one_event.tracks, strategy="first")
best = best_track(self.one_event.tracks,
strategy="default",
rec_type="JPP_RECONSTRUCTION_TYPE")
assert first_track.dir_z == self.one_event.tracks.dir_z[0]
assert first_track.lik == self.one_event.tracks.lik[0]
assert best.lik == ak.max(self.one_event.tracks.lik)
assert best.rec_type == 4000
# test raising ValueError
with self.assertRaises(ValueError):
best_track(events.tracks, strategy="Zineb")
class TestGetMultiplicity(unittest.TestCase):
def test_get_multiplicity(self):
rec_stages_tracks = get_multiplicity(OFFLINE_FILE.events.tracks,
[1, 3, 5, 4])
assert rec_stages_tracks.rec_stages[0] == [1, 3, 5, 4]
assert rec_stages_tracks.rec_stages[1] == [1, 3, 5, 4]
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):
def run_random_test_with_dtype(self, dtype):
max_range = 100
for i in range(23):
low = np.random.randint(0, max_range)
high = np.random.randint(low + 1,
low + 2 + np.random.randint(max_range))
n = np.random.randint(max_range)
arr = np.random.randint(low, high, n).astype(dtype)
np_reference = np.sort(np.unique(arr))
result = np.sort(unique(arr, dtype=dtype))
try:
np.allclose(np_reference, result, atol=1e-1)
except ValueError:
print("low:", low)
print("high:", high)
print("n:", n)
print("arr =", list(arr))
print("np.unique(arr) =", np_reference)
print("unique(arr) =", result)
assert False
def test_unique_with_dtype_int8(self):
self.run_random_test_with_dtype(np.int8)
def test_unique_with_dtype_int16(self):
self.run_random_test_with_dtype(np.int16)
def test_unique_with_dtype_int32(self):
self.run_random_test_with_dtype(np.int32)
def test_unique_with_dtype_int64(self):
self.run_random_test_with_dtype(np.int64)
def test_unique_with_dtype_uint8(self):
self.run_random_test_with_dtype(np.uint8)
def test_unique_with_dtype_uint16(self):
self.run_random_test_with_dtype(np.uint16)
def test_unique_with_dtype_uint32(self):
self.run_random_test_with_dtype(np.uint32)
def test_unique_with_dtype_uint64(self):
self.run_random_test_with_dtype(np.uint64)
class TestUniqueCount(unittest.TestCase):
def test_uniquecount(self):
arr = ak.Array([[1, 2, 3], [2, 2, 2], [3, 4, 5, 6, 6], [4, 4, 3, 1]])
assert np.allclose([3, 1, 4, 3], uniquecount(arr))
def test_uniquecount_with_empty_subarrays(self):
arr = ak.Array([[1, 2, 3], [2, 2, 2], [], [4, 4, 3, 1]])
assert np.allclose([3, 1, 0, 3], uniquecount(arr))
class TestToNum(unittest.TestCase):
def test_to_num(self):
self.assertEqual(10, to_num("10"))
self.assertEqual(10.5, to_num("10.5"))
self.assertEqual("test", to_num("test"))
self.assertIsNone(to_num(None))
class TestCachedProperty(unittest.TestCase):
def test_cached_property(self):
class Test:
@cached_property
def prop(self):
pass
self.assertTrue(isinstance(Test.prop, cached_property))
class TestUnfoldIndices(unittest.TestCase):
def test_unfold_indices(self):
data = range(10)
indices = [slice(2, 5), 0]
assert data[indices[0]][indices[1]] == unfold_indices(data, indices)
indices = [slice(1, 9, 2), slice(1, 4), 2]
assert data[indices[0]][indices[1]][indices[2]] == unfold_indices(
data, indices)
def test_unfold_indices_raises_index_error(self):
data = range(10)
indices = [slice(2, 5), 99]
with self.assertRaises(IndexError):
unfold_indices(data, indices)