def test_track_finding(self): # Test 1: track_analysis.find_tracks(input_tracklets_file=analysis_utils.get_data('fixtures/track_analysis/Tracklets_small.h5', output=os.path.join(testing_path, 'fixtures/track_analysis/Tracklets_small.h5')), input_alignment_file=analysis_utils.get_data('fixtures/track_analysis/Alignment_result.h5', output=os.path.join(testing_path, 'fixtures/track_analysis/Alignment_result.h5')), output_track_candidates_file=os.path.join(self.output_folder, 'TrackCandidates.h5')) data_equal, error_msg = test_tools.compare_h5_files(analysis_utils.get_data('fixtures/track_analysis/TrackCandidates_result.h5', output=os.path.join(testing_path, 'fixtures/track_analysis/TrackCandidates_result.h5')), os.path.join(self.output_folder, 'TrackCandidates.h5')) self.assertTrue(data_equal, msg=error_msg) # Test 2: chunked track_analysis.find_tracks(input_tracklets_file=analysis_utils.get_data('fixtures/track_analysis/Tracklets_small.h5', output=os.path.join(testing_path, 'fixtures/track_analysis/Tracklets_small.h5')), input_alignment_file=analysis_utils.get_data('fixtures/track_analysis/Alignment_result.h5', output=os.path.join(testing_path, 'fixtures/track_analysis/Alignment_result.h5')), output_track_candidates_file=os.path.join(self.output_folder, 'TrackCandidates_2.h5'), chunk_size=293) data_equal, error_msg = test_tools.compare_h5_files(analysis_utils.get_data('fixtures/track_analysis/TrackCandidates_result.h5', output=os.path.join(testing_path, 'fixtures/track_analysis/TrackCandidates_result.h5')), os.path.join(self.output_folder, 'TrackCandidates_2.h5')) self.assertTrue(data_equal, msg=error_msg)
def test_noisy_pixel_masking(self): # Test 1: output_mask_file = hit_analysis.generate_pixel_mask( input_hits_file=self.noisy_data_file, output_mask_file=os.path.join( self.output_folder, 'TestBeamData_Mimosa26_DUT0_small_noisy_pixel_mask.h5'), pixel_mask_name="NoisyPixelMask", threshold=10.0, n_pixel=(1152, 576), pixel_size=(18.4, 18.4), plot=True) output_cluster_file = hit_analysis.cluster_hits( input_hits_file=self.noisy_data_file, input_noisy_pixel_mask_file=output_mask_file, min_hit_charge=1, max_hit_charge=1, column_cluster_distance=2, row_cluster_distance=2, frame_cluster_distance=1) data_equal, error_msg = test_tools.compare_h5_files( analysis_utils.get_data( 'fixtures/hit_analysis/Mimosa26_noisy_pixels_cluster_result.h5', output=os.path.join( testing_path, 'fixtures/hit_analysis/Mimosa26_noisy_pixels_cluster_result.h5' )), output_cluster_file, exact=False) self.assertTrue(data_equal, msg=error_msg) # Test 2: smaller chunks output_mask_file = hit_analysis.generate_pixel_mask( input_hits_file=self.noisy_data_file, output_mask_file=os.path.join( self.output_folder, 'TestBeamData_Mimosa26_DUT0_small_noisy_pixel_mask.h5'), pixel_mask_name="NoisyPixelMask", threshold=10.0, n_pixel=(1152, 576), pixel_size=(18.4, 18.4), plot=True) output_cluster_file = hit_analysis.cluster_hits( input_hits_file=self.noisy_data_file, input_noisy_pixel_mask_file=output_mask_file, min_hit_charge=1, max_hit_charge=1, column_cluster_distance=2, row_cluster_distance=2, frame_cluster_distance=1, chunk_size=4999) data_equal, error_msg = test_tools.compare_h5_files( analysis_utils.get_data( 'fixtures/hit_analysis/Mimosa26_noisy_pixels_cluster_result.h5', output=os.path.join( testing_path, 'fixtures/hit_analysis/Mimosa26_noisy_pixels_cluster_result.h5' )), output_cluster_file, exact=False) self.assertTrue(data_equal, msg=error_msg)
def test_cluster_correlation( self): # Check the cluster correlation function dut_alignment.correlate_cluster(input_cluster_files=self.data_files, output_correlation_file=os.path.join( self.output_folder, 'Correlation.h5'), n_pixels=self.n_pixels, pixel_size=self.pixel_size) data_equal, error_msg = test_tools.compare_h5_files( analysis_utils.get_data( 'fixtures/dut_alignment/Correlation_result.h5', output=os.path.join( testing_path, 'fixtures/dut_alignment/Correlation_result.h5')), os.path.join(self.output_folder, 'Correlation.h5'), exact=True) self.assertTrue(data_equal, msg=error_msg) # Retest with tiny chunk size to force chunked correlation dut_alignment.correlate_cluster(input_cluster_files=self.data_files, output_correlation_file=os.path.join( self.output_folder, 'Correlation_2.h5'), n_pixels=self.n_pixels, pixel_size=self.pixel_size, chunk_size=293) data_equal, error_msg = test_tools.compare_h5_files( analysis_utils.get_data( 'fixtures/dut_alignment/Correlation_result.h5', output=os.path.join( testing_path, 'fixtures/dut_alignment/Correlation_result.h5')), os.path.join(self.output_folder, 'Correlation_2.h5'), exact=True) self.assertTrue(data_equal, msg=error_msg)
def test_prealignment(self): # Check the hit alignment function dut_alignment.prealignment( input_correlation_file=analysis_utils.get_data( 'fixtures/dut_alignment/Correlation_result.h5', output=os.path.join( testing_path, 'fixtures/dut_alignment/Correlation_result.h5')), output_alignment_file=os.path.join(self.output_folder, 'Alignment.h5'), z_positions=self.z_positions, pixel_size=self.pixel_size, non_interactive=True, fit_background=False, iterations=3 ) # Due to too little test data the alignment result is only rather stable for more iterations # FIXME: residuals should be checked not prealingment data data_equal, error_msg = test_tools.compare_h5_files( analysis_utils.get_data( 'fixtures/dut_alignment/Prealignment_result.h5', output=os.path.join( testing_path, 'fixtures/dut_alignment/Prealignment_result.h5')), os.path.join(self.output_folder, 'Alignment.h5'), exact=False, rtol=0.05, # 5 % error allowed atol=5) # 5 um absolute tolerance allowed self.assertTrue(data_equal, msg=error_msg) dut_alignment.prealignment( input_correlation_file=analysis_utils.get_data( 'fixtures/dut_alignment/Correlation_difficult.h5', output=os.path.join( testing_path, 'fixtures/dut_alignment/Correlation_difficult.h5')), output_alignment_file=os.path.join(self.output_folder, 'Alignment_difficult.h5'), z_positions=self.z_positions, pixel_size=self.pixel_size, non_interactive=True, fit_background=True, iterations=2 ) # Due to too little test data the alignment result is only rather stable for more iterations data_equal, error_msg = test_tools.compare_h5_files( analysis_utils.get_data( r'fixtures/dut_alignment/Alignment_difficult_result.h5', output=os.path.join( testing_path, 'fixtures/dut_alignment/Alignment_difficult_result.h5')), os.path.join(self.output_folder, 'Alignment_difficult.h5'), exact=False, rtol=0.05, # 5 % error allowed atol=5) # 5 um absolute tolerance allowed self.assertTrue(data_equal, msg=error_msg)
def test_alignment(self): dut_alignment.alignment( input_track_candidates_file=analysis_utils.get_data( 'fixtures/dut_alignment/TrackCandidates_prealigned.h5', output=os.path.join( testing_path, 'fixtures/dut_alignment/TrackCandidates_prealigned.h5')), input_alignment_file=analysis_utils.get_data( 'fixtures/dut_alignment/Alignment.h5', output=os.path.join(testing_path, 'fixtures/dut_alignment/Alignment.h5')), n_pixels=[(1152, 576)] * 6, pixel_size=[(18.4, 18.4)] * 6) # FIXME: test should check residuals not alignment resulds # FIXME: translation error can be in the order of um, angle error not data_equal, error_msg = test_tools.compare_h5_files( analysis_utils.get_data( 'fixtures/dut_alignment/Alignment.h5', output=os.path.join(testing_path, 'fixtures/dut_alignment/Alignment.h5')), analysis_utils.get_data( 'fixtures/dut_alignment/Alignment_result.h5', output=os.path.join( testing_path, 'fixtures/dut_alignment/Alignment_result.h5')), exact=False, rtol=0.01, # 1 % error allowed atol=5) # 0.0001 absolute tolerance allowed self.assertTrue(data_equal, msg=error_msg)
def test_apply_alignment(self): dut_alignment.apply_alignment( input_hit_file=analysis_utils.get_data( 'fixtures/dut_alignment/Merged_result.h5', output=os.path.join( testing_path, 'fixtures/dut_alignment/Merged_result.h5')), input_alignment_file=analysis_utils.get_data( 'fixtures/dut_alignment/Prealignment_result.h5', output=os.path.join( testing_path, 'fixtures/dut_alignment/Prealignment_result.h5')), output_hit_file=os.path.join(self.output_folder, 'Tracklets.h5'), use_prealignment=True) data_equal, error_msg = test_tools.compare_h5_files( analysis_utils.get_data( 'fixtures/dut_alignment/Tracklets_result.h5', output=os.path.join( testing_path, 'fixtures/dut_alignment/Tracklets_result.h5')), os.path.join(self.output_folder, 'Tracklets.h5')) self.assertTrue(data_equal, msg=error_msg) # Retest with tiny chunk size to force chunked alignment apply dut_alignment.apply_alignment( input_hit_file=analysis_utils.get_data( 'fixtures/dut_alignment/Merged_result.h5', output=os.path.join( testing_path, 'fixtures/dut_alignment/Merged_result.h5')), input_alignment_file=analysis_utils.get_data( 'fixtures/dut_alignment/Prealignment_result.h5', output=os.path.join( testing_path, 'fixtures/dut_alignment/Prealignment_result.h5')), output_hit_file=os.path.join(self.output_folder, 'Tracklets_2.h5'), use_prealignment=True, chunk_size=293) data_equal, error_msg = test_tools.compare_h5_files( analysis_utils.get_data( 'fixtures/dut_alignment/Tracklets_result.h5', output=os.path.join( testing_path, 'fixtures/dut_alignment/Tracklets_result.h5')), os.path.join(self.output_folder, 'Tracklets_2.h5')) self.assertTrue(data_equal, msg=error_msg)
def test_hit_clustering(self): # Test 1: output_cluster_file = hit_analysis.cluster_hits( input_hits_file=self.data_file, min_hit_charge=0, max_hit_charge=13, output_cluster_file=os.path.join( self.output_folder, 'TestBeamData_FEI4_DUT0_small_clustered.h5'), column_cluster_distance=1, row_cluster_distance=2, frame_cluster_distance=2) data_equal, error_msg = test_tools.compare_h5_files( analysis_utils.get_data( 'fixtures/hit_analysis/FEI4_cluster_result.h5', output=os.path.join( testing_path, 'fixtures/hit_analysis/FEI4_cluster_result.h5')), output_cluster_file, exact=False) self.assertTrue(data_equal, msg=error_msg) # Test 2: smaller chunks output_cluster_file = hit_analysis.cluster_hits( input_hits_file=self.data_file, min_hit_charge=0, max_hit_charge=13, output_cluster_file=os.path.join( self.output_folder, 'TestBeamData_FEI4_DUT0_small_clustered.h5'), column_cluster_distance=1, row_cluster_distance=2, frame_cluster_distance=2, chunk_size=4999) data_equal, error_msg = test_tools.compare_h5_files( analysis_utils.get_data( 'fixtures/hit_analysis/FEI4_cluster_result.h5', output=os.path.join( testing_path, 'fixtures/hit_analysis/FEI4_cluster_result.h5')), output_cluster_file, exact=False) self.assertTrue(data_equal, msg=error_msg)
def test_cluster_merging(self): cluster_files = [ analysis_utils.get_data( 'fixtures/dut_alignment/Cluster_DUT%d_cluster.h5' % i, output=os.path.join( testing_path, 'fixtures/dut_alignment/Cluster_DUT%d_cluster.h5' % i)) for i in range(4) ] dut_alignment.merge_cluster_data(cluster_files, output_merged_file=os.path.join( self.output_folder, 'Merged.h5'), n_pixels=self.n_pixels, pixel_size=self.pixel_size) data_equal, error_msg = test_tools.compare_h5_files( analysis_utils.get_data( 'fixtures/dut_alignment/Merged_result.h5', output=os.path.join( testing_path, 'fixtures/dut_alignment/Merged_result.h5')), os.path.join(self.output_folder, 'Merged.h5')) self.assertTrue(data_equal, msg=error_msg) # Retest with tiny chunk size to force chunked merging dut_alignment.merge_cluster_data(cluster_files, output_merged_file=os.path.join( self.output_folder, 'Merged_2.h5'), pixel_size=self.pixel_size, n_pixels=self.n_pixels, chunk_size=293) data_equal, error_msg = test_tools.compare_h5_files( analysis_utils.get_data( 'fixtures/dut_alignment/Merged_result.h5', output=os.path.join( testing_path, 'fixtures/dut_alignment/Merged_result.h5')), os.path.join(self.output_folder, 'Merged_2.h5')) self.assertTrue(data_equal, msg=error_msg)
def test_simulation(self): ''' Check the full simulation ''' self.simulate_data.reset() self.simulate_data.set_std_settings() self.assertEqual(self.simulate_data.n_duts, 6) self.simulate_data.create_data_and_store('simulated_data', n_events=10000) for dut_index in range(self.simulate_data.n_duts): data_equal, error_msg = test_tools.compare_h5_files( 'simulated_data_DUT%d.h5' % dut_index, analysis_utils.get_data( 'fixtures/simulation/simulated_data_DUT%d.h5' % dut_index, output=os.path.join( testing_path, 'fixtures/simulation/simulated_data_DUT%d.h5' % dut_index)), exact=False) self.assertTrue(data_equal, msg=error_msg)
def test_generate_pixel_mask(self): output_mask_file = hit_analysis.generate_pixel_mask( input_hits_file=self.big_noisy_data_file, output_mask_file=os.path.join( self.output_folder, 'TestBeamData_Mimosa26_DUT0_noisy_pixel_mask.h5'), pixel_mask_name="NoisyPixelMask", threshold=0.5, n_pixel=(1152, 576), pixel_size=(18.4, 18.4), plot=True) data_equal, error_msg = test_tools.compare_h5_files( analysis_utils.get_data( 'fixtures/hit_analysis/TestBeamData_Mimosa26_DUT0_noisy_pixel_mask.h5', output=os.path.join( testing_path, 'fixtures/hit_analysis/TestBeamData_Mimosa26_DUT0_noisy_pixel_mask.h5' )), output_mask_file) self.assertTrue(data_equal, msg=error_msg)
def test_track_fitting(self): # Test 1: Fit DUTs and always exclude one DUT (normal mode for unbiased residuals and efficiency determination) track_analysis.fit_tracks(input_track_candidates_file=analysis_utils.get_data('fixtures/track_analysis/TrackCandidates_result.h5', output=os.path.join(testing_path, 'fixtures/track_analysis/TrackCandidates_result.h5')), input_alignment_file=analysis_utils.get_data('fixtures/track_analysis/Alignment_result.h5', output=os.path.join(testing_path, 'fixtures/track_analysis/Alignment_result.h5')), output_tracks_file=os.path.join(self.output_folder, 'Tracks.h5'), selection_track_quality=1, use_prealignment=True) data_equal, error_msg = test_tools.compare_h5_files(analysis_utils.get_data('fixtures/track_analysis/Tracks_result.h5', output=os.path.join(testing_path, 'fixtures/track_analysis/Tracks_result.h5')), os.path.join(self.output_folder, 'Tracks.h5'), exact=False) self.assertTrue(data_equal, msg=error_msg) # Test 2: As test 1 but chunked data analysis, should result in the same tracks track_analysis.fit_tracks(input_track_candidates_file=analysis_utils.get_data('fixtures/track_analysis/TrackCandidates_result.h5', output=os.path.join(testing_path, 'fixtures/track_analysis/TrackCandidates_result.h5')), input_alignment_file=analysis_utils.get_data('fixtures/track_analysis/Alignment_result.h5', output=os.path.join(testing_path, 'fixtures/track_analysis/Alignment_result.h5')), output_tracks_file=os.path.join(self.output_folder, 'Tracks_2.h5'), selection_track_quality=1, use_prealignment=True, chunk_size=4999) data_equal, error_msg = test_tools.compare_h5_files(analysis_utils.get_data('fixtures/track_analysis/Tracks_result.h5', output=os.path.join(testing_path, 'fixtures/track_analysis/Tracks_result.h5')), os.path.join(self.output_folder, 'Tracks_2.h5'), exact=False) self.assertTrue(data_equal, msg=error_msg) # Test 3: Fit all DUTs at once (special mode for constrained residuals) track_analysis.fit_tracks(input_track_candidates_file=analysis_utils.get_data('fixtures/track_analysis/TrackCandidates_result.h5', output=os.path.join(testing_path, 'fixtures/track_analysis/TrackCandidates_result.h5')), input_alignment_file=analysis_utils.get_data('fixtures/track_analysis/Alignment_result.h5', output=os.path.join(testing_path, 'fixtures/track_analysis/Alignment_result.h5')), output_tracks_file=os.path.join(self.output_folder, 'Tracks_All.h5'), exclude_dut_hit=False, selection_track_quality=1, use_prealignment=True) # Fit DUTs consecutevly, but use always the same DUTs. Should result in the same data as above track_analysis.fit_tracks(input_track_candidates_file=analysis_utils.get_data('fixtures/track_analysis/TrackCandidates_result.h5', output=os.path.join(testing_path, 'fixtures/track_analysis/TrackCandidates_result.h5')), input_alignment_file=analysis_utils.get_data('fixtures/track_analysis/Alignment_result.h5', output=os.path.join(testing_path, 'fixtures/track_analysis/Alignment_result.h5')), output_tracks_file=os.path.join(self.output_folder, 'Tracks_All_Iter.h5'), select_hit_duts=range(4), exclude_dut_hit=False, selection_track_quality=1, use_prealignment=True) data_equal, error_msg = test_tools.compare_h5_files(os.path.join(self.output_folder, 'Tracks_All.h5'), os.path.join(self.output_folder, 'Tracks_All_Iter.h5'), exact=False) self.assertTrue(data_equal, msg=error_msg) # Fit DUTs consecutevly, but use always the same DUTs defined for each DUT separately. Should result in the same data as above track_analysis.fit_tracks(input_track_candidates_file=analysis_utils.get_data('fixtures/track_analysis/TrackCandidates_result.h5', output=os.path.join(testing_path, 'fixtures/track_analysis/TrackCandidates_result.h5')), input_alignment_file=analysis_utils.get_data('fixtures/track_analysis/Alignment_result.h5', output=os.path.join(testing_path, 'fixtures/track_analysis/Alignment_result.h5')), output_tracks_file=os.path.join(self.output_folder, 'Tracks_All_Iter_2.h5'), select_hit_duts=[range(4), range(4), range(4), range(4)], exclude_dut_hit=False, selection_track_quality=1, use_prealignment=True) data_equal, error_msg = test_tools.compare_h5_files(os.path.join(self.output_folder, 'Tracks_All.h5'), os.path.join(self.output_folder, 'Tracks_All_Iter_2.h5'), exact=False) self.assertTrue(data_equal, msg=error_msg) # Fit tracks and eliminate merged tracks track_analysis.fit_tracks(input_track_candidates_file=analysis_utils.get_data('fixtures/track_analysis/TrackCandidates_result.h5', output=os.path.join(testing_path, 'fixtures/track_analysis/TrackCandidates_result.h5')), input_alignment_file=analysis_utils.get_data('fixtures/track_analysis/Alignment_result.h5', output=os.path.join(testing_path, 'fixtures/track_analysis/Alignment_result.h5')), output_tracks_file=os.path.join(self.output_folder, 'Tracks_merged.h5'), selection_track_quality=1, min_track_distance=True, # Activate track merge cut, use_prealignment=True) data_equal, error_msg = test_tools.compare_h5_files(analysis_utils.get_data('fixtures/track_analysis/Tracks_merged_result.h5', output=os.path.join(testing_path, 'fixtures/track_analysis/Tracks_merged_result.h5')), os.path.join(self.output_folder, 'Tracks_merged.h5'), exact=False) self.assertTrue(data_equal, msg=error_msg)