def test_correlation_flag(self): # check behavior of the correlation flag
event_numbers, ref_column, column, ref_row, row, ref_charge, charge, corr = get_random_data(500)
column[5:16] = 0
row[5:16] = 0
charge[5:16] = 0
column[16:20] = ref_column[6:10]
row[16:20] = ref_row[6:10]
charge[16:20] = ref_charge[6:10]
corr[16:18] = 0 # create not correlated event
# Check with correlation hole
n_fixes = analysis_functions.fix_event_alignment(event_numbers, ref_column, column, ref_row, row, ref_charge, charge, corr, error=0.1, n_bad_events=3, n_good_events=3, correlation_search_range=100, good_events_search_range=100)
self.assertEqual(n_fixes, 0) # no fixes are expected
# Correlation flag check
self.assertTrue(np.all(corr[0:6] == 1))
self.assertTrue(np.all(corr[6:19] == 0))
self.assertTrue(np.all(corr[20:] == 1))
event_numbers, ref_column, column, ref_row, row, ref_charge, charge, corr = get_random_data(50)
column[5:16] = 0
row[5:16] = 0
charge[5:16] = 0
column[16:20] = ref_column[6:10]
row[16:20] = ref_row[6:10]
charge[16:20] = ref_charge[6:10]
corr[16:18] = 0 # create not correlated event
# check with event copying
n_fixes = analysis_functions.fix_event_alignment(event_numbers, ref_column, column, ref_row, row, ref_charge, charge, corr, error=0.1, n_bad_events=3, n_good_events=1, correlation_search_range=100, good_events_search_range=4)
self.assertEqual(n_fixes, 1) # 1 fixe are expected
# Correlation flag check
self.assertTrue(np.all(corr[0:6] == 1))
self.assertTrue(np.all(corr[6:8] == 0))
self.assertTrue(np.all(corr[8:10] == 1))
self.assertTrue(np.all(corr[10:20] == 0))
self.assertTrue(np.all(corr[20:] == 1))
# Data check
self.assertTrue(np.all(ref_row[:5] == row[:5]))
self.assertTrue(np.all(ref_column[:5] == column[:5]))
self.assertTrue(np.all(ref_charge[:5] == charge[:5]))
self.assertTrue(np.all(ref_row[6:10] == row[6:10]))
self.assertTrue(np.all(ref_column[6:10] == column[6:10]))
self.assertTrue(np.all(ref_charge[6:10] == charge[6:10]))
self.assertTrue(np.all(ref_row[20:] == row[20:]))
self.assertTrue(np.all(ref_column[20:] == column[20:]))
self.assertTrue(np.all(ref_charge[20:] == charge[20:]))
def test_no_correction(self): # check behavior if no correction is needed
event_numbers, ref_column, column, ref_row, row, ref_charge, charge, corr = get_random_data(5000)
# Check with correlation hole
n_fixes = analysis_functions.fix_event_alignment(event_numbers, ref_column, column, ref_row, row, ref_charge, charge, corr, error=0.1, n_bad_events=3, n_good_events=3, correlation_search_range=100, good_events_search_range=100)
self.assertEqual(n_fixes, 0) # no fixes are expected
self.assertTrue(np.all(corr == 1)) # Correlation flag check
self.assertTrue(np.all(ref_column == column)) # Similarity check
self.assertTrue(np.all(ref_row == row)) # Similarity check
self.assertTrue(np.all(ref_charge == charge)) # Similarity check
def fix_event_alignment(event_numbers, ref_column, column, ref_row, row, ref_charge, charge, error=3., n_bad_events=5, n_good_events=3, correlation_search_range=2000, good_events_search_range=10):
correlated = np.ascontiguousarray(np.ones(shape=event_numbers.shape, dtype=np.uint8)) # array to signal correlation to be ables to omit not correlated events in the analysis
event_numbers = np.ascontiguousarray(event_numbers)
ref_column = np.ascontiguousarray(ref_column)
column = np.ascontiguousarray(column)
ref_row = np.ascontiguousarray(ref_row)
row = np.ascontiguousarray(row)
ref_charge = np.ascontiguousarray(ref_charge, dtype=np.uint16)
charge = np.ascontiguousarray(charge, dtype=np.uint16)
n_fixes = analysis_functions.fix_event_alignment(event_numbers, ref_column, column, ref_row, row, ref_charge, charge, correlated, error, n_bad_events, correlation_search_range, n_good_events, good_events_search_range)
return correlated, n_fixes
corr = np.ascontiguousarray(np.ones(shape=event_numbers.shape, dtype=np.uint8)) # array to signal correlation to be ables to omit not correlated events in the analysis
corr[17] = 0
for index, (event, hit_1, hit_2, c) in enumerate(np.column_stack((event_numbers, ref_row, row, corr))):
print index, int(event), hit_1, hit_2, c
print '___________________'
# n_good_events number of correlated events after the first correlated hit candidate within the good_events_search_range
event_numbers = np.ascontiguousarray(event_numbers)
ref_column = np.ascontiguousarray(ref_column)
column = np.ascontiguousarray(column)
ref_row = np.ascontiguousarray(ref_row)
row = np.ascontiguousarray(row)
n_fixes = analysis_functions.fix_event_alignment(event_numbers, ref_column, column, ref_row, row, corr, error=0.1, n_bad_events=3, n_good_events=3, correlation_search_range=100, good_events_search_range=10)
print n_fixes
print '___________________'
for index, (event, hit_1, hit_2, c) in enumerate(np.column_stack((event_numbers, ref_row, row, corr))):
print index, int(event), hit_1, hit_2, c
print '___________________'
print (ref_column[np.logical_and(corr == 1, column !=0)] == column[np.logical_and(corr == 1, column !=0)])
print 'DONE'
def test_missing_events(self): # test behaviour if events are missing
event_numbers, ref_column, column, ref_row, row, ref_charge, charge, corr = get_random_data(20, hits_per_event=1)
# Event offset = 3 and two consecutive events missing
column[:3] = 0
row[:3] = 0
charge[:3] = 0
column[3:] = ref_column[:-3]
row[3:] = ref_row[:-3]
charge[3:] = ref_charge[:-3]
event_numbers = np.delete(event_numbers, [9, 10], axis=0)
ref_column = np.delete(ref_column, [9, 10], axis=0)
column = np.delete(column, [9, 10], axis=0)
ref_row = np.delete(ref_row, [9, 10], axis=0)
row = np.delete(row, [9, 10], axis=0)
ref_charge = np.delete(ref_charge, [9, 10], axis=0)
charge = np.delete(charge, [9, 10], axis=0)
corr = np.delete(corr, [9, 10], axis=0)
n_fixes = analysis_functions.fix_event_alignment(event_numbers, ref_column, column, ref_row, row, ref_charge, charge, corr, error=0.1, n_bad_events=3, n_good_events=2, correlation_search_range=100, good_events_search_range=100)
# one fix are expected
self.assertEqual(n_fixes, 1)
# Correlation flag check
self.assertTrue(np.all(corr[:-3] == 1))
self.assertTrue(np.all(corr[-3:] == 0))
# Similarity check
self.assertTrue(np.all(ref_column[column != 0] == column[column != 0]))
self.assertTrue(np.all(ref_row[column != 0] == row[column != 0]))
self.assertTrue(np.all(ref_charge[column != 0] == charge[column != 0]))
self.assertTrue(np.all(column[6:8] == 0))
self.assertTrue(np.all(row[6:8] == 0))
self.assertTrue(np.all(charge[6:8] == 0))
# Event offset = 1, no events missing, but hits of one event missing
event_numbers, ref_column, column, ref_row, row, ref_charge, charge, corr = get_random_data(20, hits_per_event=3)
column[:3] = 0
row[:3] = 0
charge[:3] = 0
column[3:] = ref_column[:-3]
row[3:] = ref_row[:-3]
charge[3:] = ref_charge[:-3]
event_numbers = np.delete(event_numbers, [9, 10], axis=0)
ref_column = np.delete(ref_column, [9, 10], axis=0)
column = np.delete(column, [9, 10], axis=0)
ref_row = np.delete(ref_row, [9, 10], axis=0)
row = np.delete(row, [9, 10], axis=0)
ref_charge = np.delete(ref_charge, [9, 10], axis=0)
charge = np.delete(charge, [9, 10], axis=0)
corr = np.delete(corr, [9, 10], axis=0)
n_fixes = analysis_functions.fix_event_alignment(event_numbers, ref_column, column, ref_row, row, ref_charge, charge, corr, error=0.1, n_bad_events=3, n_good_events=2, correlation_search_range=100, good_events_search_range=100)
# one fix are expected
self.assertEqual(n_fixes, 1)
# Correlation flag check
self.assertTrue(np.all(corr[:9] == 1))
self.assertEqual(corr[9], 0)
self.assertTrue(np.all(corr[10:14] == 1))
self.assertTrue(np.all(corr[16:] == 0))
# Similarity check
self.assertTrue(np.all(ref_column[0:6] == column[0:6]))
self.assertTrue(np.all(ref_row[0:6] == row[0:6]))
self.assertTrue(np.all(ref_charge[0:6] == charge[0:6]))
self.assertTrue(np.all(ref_column[10:15] == column[10:15]))
self.assertTrue(np.all(ref_row[10:15] == row[10:15]))
self.assertTrue(np.all(ref_charge[10:15] == charge[10:15]))
self.assertTrue(np.all(column[15:] == 0))
self.assertTrue(np.all(row[15:] == 0))
self.assertTrue(np.all(charge[15:] == 0))
# Event offset = 1, 1 hit events, missing hits
event_numbers, ref_column, column, ref_row, row, ref_charge, charge, corr = get_random_data(20, hits_per_event=1)
ref_column[5] = 0
ref_row[5] = 0
ref_charge[5] = 0
ref_column[11:13] = 0
ref_row[11:13] = 0
ref_charge[11:13] = 0
column[:3] = 0
row[:3] = 0
charge[:3] = 0
column[3:] = ref_column[:-3]
row[3:] = ref_row[:-3]
charge[3:] = ref_charge[:-3]
corr = np.ones_like(event_numbers, dtype=np.uint8)
n_fixes = analysis_functions.fix_event_alignment(event_numbers, ref_column, column, ref_row, row, ref_charge, charge, corr, error=0.1, n_bad_events=3, n_good_events=2, correlation_search_range=100, good_events_search_range=100)
# one fix are expected
self.assertEqual(n_fixes, 1)
# Correlation flag check
self.assertTrue(np.all(corr[:17] == 1))
self.assertTrue(np.all(corr[17:] == 0))
# Similarity check
self.assertTrue(np.all(ref_column[corr == 1] == column[corr == 1]))
self.assertTrue(np.all(ref_row[corr == 1] == row[corr == 1]))
self.assertTrue(np.all(ref_charge[corr == 1] == charge[corr == 1]))
def test_virtual_hit_copying(self): # check behavior for virtual hits
# Test with virtual hits in dut
event_numbers, ref_column, column, ref_row, row, ref_charge, charge, corr = get_random_data(20)
event_numbers[:12] = np.array([0, 1, 1, 1, 2, 2, 3, 3, 4, 4, 4, 5])
column[4:] = column[1:-3]
row[4:] = row[1:-3]
charge[4:] = charge[1:-3]
column[1:4] = 0
row[1:4] = 0
charge[1:4] = 0
column[4:-1] = column[5:]
row[4:-1] = row[5:]
charge[4:-1] = charge[5:]
column[9:] = column[8:-1]
row[9:] = row[8:-1]
charge[9:] = charge[8:-1]
column[8] = 0
row[8] = 0
charge[8] = 0
column[13:] = ref_column[11:-2]
row[13:] = ref_row[11:-2]
charge[13:] = ref_charge[11:-2]
column[12] = 0
row[12] = 0
charge[12] = 0
# Check with correlation hole
n_fixes = analysis_functions.fix_event_alignment(event_numbers, ref_column, column, ref_row, row, ref_charge, charge, corr, error=0.1, n_bad_events=2, n_good_events=2, correlation_search_range=100, good_events_search_range=100)
# one fix are expected
self.assertEqual(n_fixes, 1)
# Correlation flag check
self.assertTrue(np.all(corr[:18] == 1))
self.assertTrue(np.all(corr[18:] == 0))
# Similarity check
self.assertEqual(ref_column[0], column[0])
self.assertEqual(ref_row[0], row[0])
self.assertEqual(ref_charge[0], charge[0])
self.assertTrue(np.all(ref_column[4:9] == column[4:9]))
self.assertTrue(np.all(ref_row[4:9] == row[4:9]))
self.assertTrue(np.all(ref_charge[4:9] == charge[4:9]))
self.assertTrue(np.all(ref_column[11:18] == column[11:18]))
self.assertTrue(np.all(ref_row[11:18] == row[11:18]))
self.assertTrue(np.all(ref_charge[11:18] == charge[11:18]))
# Virtual hits check
self.assertEqual(column[3], 0)
self.assertEqual(row[3], 0)
self.assertEqual(charge[3], 0)
self.assertTrue(np.all(column[9:11] == 0))
self.assertTrue(np.all(row[9:11] == 0))
self.assertTrue(np.all(charge[9:11] == 0))
self.assertTrue(np.all(column[18:] == 0))
self.assertTrue(np.all(row[18:] == 0))
self.assertTrue(np.all(charge[18:] == 0))
# Test with virtual hits in reference, positive event number offset (ref to dut)
event_numbers, ref_column, column, ref_row, row, ref_charge, charge, corr = get_random_data(20)
event_numbers[:12] = np.array([0, 1, 1, 1, 2, 2, 3, 3, 4, 4, 4, 5])
column[4:] = column[1:-3]
row[4:] = row[1:-3]
charge[4:] = charge[1:-3]
column[1:4] = 0
row[1:4] = 0
charge[1:4] = 0
ref_column[4] = 0
ref_row[4] = 0
ref_charge[4] = 0
column[4:-1] = column[5:]
row[4:-1] = row[5:]
charge[4:-1] = charge[5:]
column[9:] = column[8:-1]
row[9:] = row[8:-1]
charge[9:] = charge[8:-1]
column[8] = 0
row[8] = 0
charge[8] = 0
column[13:] = ref_column[11:-2]
row[13:] = ref_row[11:-2]
charge[13:] = ref_charge[11:-2]
column[12] = 0
row[12] = 0
charge[12] = 0
ref_column[14:16] = 0
ref_row[14:16] = 0
ref_charge[14:16] = 0
n_fixes = analysis_functions.fix_event_alignment(event_numbers, ref_column, column, ref_row, row, ref_charge, charge, corr, error=5, n_bad_events=2, n_good_events=2, correlation_search_range=100, good_events_search_range=100)
# Correlation flag check
self.assertTrue(np.all(corr[:18] == 1))
self.assertTrue(np.all(corr[18:] == 0))
# Similarity check
self.assertEqual(ref_column[0], column[0])
self.assertEqual(ref_row[0], row[0])
self.assertEqual(ref_charge[0], charge[0])
self.assertTrue(np.all(ref_column[5:9] == column[5:9]))
self.assertTrue(np.all(ref_row[5:9] == row[5:9]))
self.assertTrue(np.all(ref_charge[5:9] == charge[5:9]))
self.assertTrue(np.all(ref_column[11:14] == column[11:14]))
self.assertTrue(np.all(ref_row[11:14] == row[11:14]))
self.assertTrue(np.all(ref_charge[11:14] == charge[11:14]))
self.assertTrue(np.all(ref_column[16:18] == column[16:18]))
self.assertTrue(np.all(ref_row[16:18] == row[16:18]))
self.assertTrue(np.all(ref_charge[16:18] == charge[16:18]))
# Virtual hits check
self.assertEqual(column[3], 0)
self.assertEqual(row[3], 0)
self.assertEqual(charge[3], 0)
self.assertEqual(ref_column[4], 0)
self.assertEqual(ref_row[4], 0)
self.assertEqual(ref_charge[4], 0)
self.assertTrue(np.all(column[9:11] == 0))
self.assertTrue(np.all(row[9:11] == 0))
self.assertTrue(np.all(charge[9:11] == 0))
self.assertTrue(np.all(ref_column[14:16] == 0))
self.assertTrue(np.all(ref_row[14:16] == 0))
self.assertTrue(np.all(ref_charge[14:16] == 0))
self.assertTrue(np.all(column[18:] == 0))
self.assertTrue(np.all(row[18:] == 0))
self.assertTrue(np.all(charge[18:] == 0))
# Test with virtual hits, forward fixing, real data, negative event number offset (ref to dut)
event_numbers, ref_column, column, ref_row, row, ref_charge, charge, corr = get_random_data(11)
event_numbers[:11] = np.array([0, 1, 2, 3, 4, 5, 5, 6, 7, 8, 9])
ref_column[0], column[0], ref_row[0], row[0] = 53.5, 40.55479431152344, 281.5, 105.0828857421875
ref_column[1], column[1], ref_row[1], row[1] = 40.5, 30.663848876953125, 102.5, 266.4919738769531
ref_column[2], column[2], ref_row[2], row[2] = 30.5, 58.33549880981445, 269.5, 171.42666625976562
ref_column[3], column[3], ref_row[3], row[3] = 58.5, 50.44178771972656, 172.5, 64.61354064941406
ref_column[4], column[4], ref_row[4], row[4] = 50.5, 64.23998260498047, 64.375, 189.2677764892578
ref_column[5], column[5], ref_row[5], row[5] = 63.5, 72.08361053466797, 188.5, 116.95103454589844
ref_column[6], column[6], ref_row[6], row[6] = 0.00, 0.0, 0.0, 0.0
ref_column[7], column[7], ref_row[7], row[7] = 72.5, 73.06128692626953, 113.5, 87.29881286621094
ref_column[8], column[8], ref_row[8], row[8] = 72.8, 0.0, 87.5, 0.0
ref_column[9], column[9], ref_row[9], row[9] = 0.0, 8.888647079467773, 0.0, 332.7086486816406
ref_column[10], column[10], ref_row[10], row[10] = 8.5, 4.0904927253723145, 336.5, 307.4698181152344
n_fixes = analysis_functions.fix_event_alignment(event_numbers, ref_column, column, ref_row, row, ref_charge, charge, corr, error=5, n_bad_events=2, n_good_events=2, correlation_search_range=100, good_events_search_range=100)
# one fix is expected
self.assertEqual(n_fixes, 1)
# Correlation flag check
self.assertEqual(corr[0], 0)
self.assertTrue(np.all(corr[1:11] == 1))
# Similarity check
self.assertEqual(column[0], 0)
self.assertEqual(row[0], 0)
self.assertTrue(np.all(np.abs(ref_column[1:5] - column[1:5]) < 5))
self.assertTrue(np.all(np.abs(ref_row[1:5] - row[1:5]) < 5))
self.assertTrue(np.all(np.abs(ref_column[7:11] - column[7:11]) < 5))
self.assertTrue(np.all(np.abs(ref_row[7:11] - row[7:11]) < 5))
# Virtual hits check
self.assertEqual(column[0], 0)
self.assertEqual(row[0], 0)
self.assertEqual(ref_column[6], 0)
self.assertEqual(ref_row[6], 0)
