def run(): """ Run the macro """ # This input generates empty spills, to be filled by the beam maker later on my_input = MAUS.InputPySpillGenerator() # Create an empty array of mappers, then populate it # with the functionality you want to use. my_map = MAUS.MapPyGroup() my_map.append(MAUS.MapPyBeamMaker()) # beam construction my_map.append(MAUS.MapCppSimulation()) # geant4 simulation my_map.append(MAUS.MapCppTOFMCDigitizer()) # TOF MC Digitizer #my_map.append(MAUS.MapCppTOFSlabHits()) # TOF SlabHit Reco #my_map.append(MAUS.MapCppTOFSpacePoints()) # TOF SpacePoint Reco my_map.append(MAUS.MapCppKLMCDigitizer()) # KL MC Digitizer my_map.append(MAUS.MapCppKLCellHits()) # KL CellHit Reco # can specify datacards here or by using appropriate command line calls datacards = io.StringIO(u"") reducer = MAUS.ReducePyDoNothing() # Then construct a MAUS output component - filename comes from datacards my_output = MAUS.OutputPyJSON() # The Go() drives all the components you pass in, then check the file # (default simulation.out) for output MAUS.Go(my_input, my_map, reducer, my_output, datacards)
def run(): """ Analyze data from the MICE experiment This reads in and processes data taken from the MICE experiment. """ # Set up data cards. data_cards_list = [] data_cards_list.append("output_file_name='scalers'\n") # Convert data_cards to string. data_cards = io.StringIO(unicode("".join(data_cards_list))) # Set up the input that reads from DAQ my_input = MAUS.InputCppDAQOfflineData() # Create an empty array of mappers, then populate it # with the functionality you want to use. my_map = MAUS.MapPyGroup() my_map.append(MAUS.MapCppTOFDigits()) my_map.append(MAUS.MapCppTOFSlabHits()) my_map.append(MAUS.MapCppTOFSpacePoints()) # Histogram reducer. reducer = MAUS.ReducePyScalersTable() # Save images as EPS and meta-data as JSON. output_worker = MAUS.OutputPyFile() # Run the workflow. MAUS.Go(my_input, my_map, reducer, output_worker, data_cards)
def run(): """ Run the macro """ # This input generates empty spills, to be filled by the beam maker later on my_input = MAUS.InputPySpillGenerator() # my_input = MAUS.InputCppRoot() # Create an empty array of mappers, then populate it # with the functionality you want to use. my_map = MAUS.MapPyGroup() my_map.append(MAUS.MapPyBeamMaker()) # beam construction my_map.append(MAUS.MapCppSimulation()) # geant4 simulation # my_map.append(MAUS.MapCppTrackerMCNoise()) # SciFi noise my_map.append(MAUS.MapCppTrackerMCDigitization()) # SciFi electronics my_map.append(MAUS.MapCppTrackerRecon()) # SciFi recon # can specify datacards here or by using appropriate command line calls datacards = io.StringIO(u"") # reducer = MAUS.ReduceCppPatternRecognition() # Turn on event display reducer = MAUS.ReducePyDoNothing() # Then construct a MAUS output component - filename comes from datacards # my_output = MAUS.OutputPyJSON() my_output = MAUS.OutputCppRoot() # The Go() drives all the components you pass in, then check the file # (default simulation.out) for output MAUS.Go(my_input, my_map, reducer, my_output, datacards)
def run(data_path, run_num): """Analyze data from the MICE experiment This will read in and process data taken from the MICE experiment. It will eventually include things like cabling information, calibrations, and fits. """ # Here you specify the path to the data and also the file you want to # analyze. my_input = MAUS.InputCppDAQOfflineData(data_path, data_file) # Create an empty array of mappers, then populate it # with the functionality you want to use. my_map = MAUS.MapPyGroup() my_map.append(MAUS.MapCppTrackerDigits()) my_map.append(MAUS.MapCppTrackerRecon()) reducer = MAUS.ReduceCppTracker() #reducer = MAUS.ReducePyDoNothing() # reducer = MAUS.ReduceCppTrackerErrorLog() output_file = open("unpacked_1901", 'w') # Uncompressed my_output = MAUS.OutputPyJSON(output_file) # The Go() drives all the components you pass in then put all the output # into a file called 'mausput' MAUS.Go(my_input, my_map, reducer, my_output)
def run(): """ Run the macro """ # This input generates empty spills, to be filled by the beam maker later on my_input = MAUS.InputPySpillGenerator() # Create an empty array of mappers, then populate it # with the functionality you want to use. my_map = MAUS.MapPyGroup() # GEANT4 my_map.append(MAUS.MapPyBeamMaker()) # beam construction my_map.append(MAUS.MapCppSimulation()) # geant4 simulation # Pre detector set up my_map.append(MAUS.MapCppMCReconSetup()) # geant4 simulation # SciFi my_map.append(MAUS.MapCppTrackerMCDigitization()) # SciFi electronics model my_map.append(MAUS.MapCppTrackerClusterRecon()) # SciFi channel clustering my_map.append(MAUS.MapCppTrackerSpacePointRecon()) # SciFi spacepoint recon my_map.append(MAUS.MapCppTrackerPatternRecognition()) # SciFi track finding my_map.append(MAUS.MapCppTrackerTrackFit()) # SciFi track fit # Then construct a MAUS output component - filename comes from datacards my_output = MAUS.OutputCppRoot() # can specify datacards here or by using appropriate command line calls datacards = io.StringIO(u"") # The Go() drives all the components you pass in, then check the file # (default simulation.out) for output MAUS.Go(my_input, my_map, MAUS.ReducePyDoNothing(), my_output, datacards)
def run(): """Analyze data from the MICE experiment This will read in and process data taken from the MICE experiment. It will eventually include things like cabling information, calibrations, and fits. """ # Here you specify the path to the data and also the file you want to # analyze. my_input = MAUS.InputCppDAQOnlineData() # Create an empty array of mappers, then populate it # with the functionality you want to use. my_map = MAUS.MapPyGroup() my_map.append(MAUS.MapPyScalersDump()) #my_map.append(MAUS.MapCppTOFDigits()) #my_map.append(MAUS.MapCppTOFSlabHits()) #my_map.append(MAUS.MapCppTOFSpacePoints()) #my_map.append(MAUS.MapPyTOFPlot()) # The Go() drives all the components you pass in then put all the output # into a file called 'mausput' MAUS.Go(my_input, my_map, MAUS.ReducePyDoNothing(), MAUS.OutputPyJSON())
def run(): """ Run the macro """ my_input = MAUS.InputPySpillGenerator() my_map = MAUS.MapPyGroup() my_map.append(MAUS.MapPyBeamMaker()) # beam construction my_map.append(MAUS.MapCppSimulation()) # geant4 simulation my_map.append( MAUS.MapCppTrackerMCDigitization()) # SciFi electronics model my_map.append(MAUS.MapCppTrackerClusterRecon()) # SciFi channel clustering my_map.append( MAUS.MapCppTrackerSpacePointRecon()) # SciFi spacepoint recon my_map.append( MAUS.MapCppTrackerPatternRecognition()) # SciFi track finding my_map.append(MAUS.MapCppTrackerTrackFit()) # SciFi track fit datacards = io.StringIO(u"") # reducer = MAUS.ReduceCppPatternRecognition() reducer = MAUS.ReducePyDoNothing() # my_output = MAUS.OutputPyJSON() my_output = MAUS.OutputCppRoot() MAUS.Go(my_input, my_map, reducer, my_output, datacards)
def run(): """ Run the macro """ # This input generates empty spills, to be filled by the beam maker later on my_input = MAUS.InputCppRootData() # Create an empty array of mappers, then populate it # with the functionality you want to use. my_map = MAUS.MapPyGroup() # Global my_map.append(MAUS.MapCppGlobalReconImport()) my_map.append(MAUS.MapCppGlobalTrackMatching()) my_reduce = MAUS.ReducePyDoNothing() # Then construct a MAUS output component - filename comes from datacards my_output = MAUS.OutputCppRoot() # can specify datacards here or by using appropriate command line calls datacards = io.StringIO(u"") # The Go() drives all the components you pass in, then check the file # (default simulation.out) for output MAUS.Go(my_input, my_map, my_reduce, my_output, datacards)
def run(): """ Run the macro """ # This input generates empty spills, to be filled by the beam maker later on input_file = open('maus_output.json', 'r') # my_input = MAUS.InputPyJSON(input_file) #my_input = MAUS.InputPySpillGenerator() # Create an empty array of mappers, then populate it # with the functionality you want to use. my_map = MAUS.MapPyGroup() #my_map.append(MAUS.MapPyBeamMaker()) # beam construction #my_map.append(MAUS.MapCppSimulation()) # geant4 simulation my_map.append( MAUS.MapCppTrackerMCDigitization()) # SciFi electronics model my_map.append(MAUS.MapCppTrackerRecon()) # SciFi recon # can specify datacards here or by using appropriate command line calls datacards = io.StringIO(u"") #reducer = MAUS.ReduceCppTracker() reducer = MAUS.ReducePyDoNothing() output_file = open("recon_mc", 'w') # Uncompressed # Then construct a MAUS output component my_output = MAUS.OutputPyJSON(output_file) # The Go() drives all the components you pass in, then check the file # (default simulation.out) for output MAUS.Go(my_input, my_map, reducer, my_output, datacards)
def run(): """ Run the macro """ # Use the G4BL JSON chunks as an input to the simulation my_input = MAUS.InputPyJSON() # Create an empty array of mappers, then populate it # with the functionality you want to use. my_map = MAUS.MapPyGroup() # No need for the beam maker, as we use G4BL chunks # my_map.append(MAUS.MapPyBeamMaker()) # beam construction # Run the GEANT4 simulation my_map.append(MAUS.MapCppSimulation()) # geant4 simulation # Pre detector set up my_map.append(MAUS.MapCppMCReconSetup()) # geant4 simulation # TOF my_map.append(MAUS.MapCppTOFMCDigitizer()) # TOF MC Digitizer my_map.append(MAUS.MapCppTOFSlabHits()) # TOF MC Slab Hits my_map.append(MAUS.MapCppTOFSpacePoints()) # TOF Space Points # KL my_map.append(MAUS.MapCppKLMCDigitizer()) # KL MC Digitizer my_map.append(MAUS.MapCppKLCellHits()) # KL CellHit Reco # SciFi my_map.append( MAUS.MapCppTrackerMCDigitization()) # SciFi electronics model my_map.append(MAUS.MapCppTrackerClusterRecon()) # SciFi channel clustering my_map.append( MAUS.MapCppTrackerSpacePointRecon()) # SciFi spacepoint recon my_map.append( MAUS.MapCppTrackerPatternRecognition()) # SciFi track finding my_map.append(MAUS.MapCppTrackerPRSeed()) # Set the Seed from PR my_map.append(MAUS.MapCppTrackerTrackFit()) # SciFi track fit # EMR my_map.append(MAUS.MapCppEMRMCDigitization()) # EMR MC Digitization my_map.append(MAUS.MapCppEMRSpacePoints()) # EMR MC Digitization my_map.append(MAUS.MapCppEMRRecon()) # EMR Recon # Ckov my_map.append(MAUS.MapCppCkovMCDigitizer()) # Global Digits - post detector digitisation # Then construct a MAUS output component - filename comes from datacards my_output = MAUS.OutputCppRoot() # can specify datacards here or by using appropriate command line calls datacards = io.StringIO(u"") # The Go() drives all the components you pass in, then check the file # (default simulation.out) for output MAUS.Go(my_input, my_map, MAUS.ReducePyDoNothing(), my_output, datacards)
def run(input_beam, configuration, out_file): """run""" document_file = io.StringIO(unicode(input_beam)) my_input = MAUS.InputPyJSON(document_file) my_map = MAUS.MapCppSimulation() my_output = MAUS.OutputPyJSON(open(out_file, 'w')) MAUS.Go(my_input, my_map, MAUS.ReducePyDoNothing(), my_output, io.StringIO(unicode(configuration)))
def run(): """ Analyze data from the MICE experiment This reads in and processes data taken from the MICE experiment. """ # Set up data cards. data_cards_list = [] # batch mode = runs ROOT in batch mode so that canvases are not displayed # 1 = True, Batch Mode # 0 = False, Interactive Mode # setting it to false/0 will cause canvases to pop up on screen and # will get refreshed every N spills set by the refresh_rate data # card. data_cards_list.append("root_batch_mode='%d'\n" % 1) # refresh_rate = once in how many spills should canvases be updated data_cards_list.append("refresh_rate='%d'\n" % 1) # Add auto-numbering to the image tags. If False then each # histogram output for successive spills will have the same tag # so there are no spill-specific histograms. This is the # recommended use for online reconstruction. data_cards_list.append("histogram_auto_number=%s\n" % False) # Default image type is eps. For online use, use PNG. data_cards_list.append("histogram_image_type=\"png\"\n") # Directory for images. Default: $MAUS_WEB_MEDIA_RAW if set # else the current directory is used. # Uncomment and change the following if you want to hard # code a different default path. # data_cards_list.append("image_directory='%s'\n" % os.getcwd()) # Convert data_cards to string. data_cards = io.StringIO(unicode("".join(data_cards_list))) # Set up the input that reads from DAQ # my_input = MAUS.InputCppDAQData() # my_input = MAUS.InputCppDAQOnlineData() my_input = MAUS.InputCppDAQOnlineData() # pylint: disable = E1101 # Create an empty array of mappers, then populate it # with the functionality you want to use. my_map = MAUS.MapPyGroup() # add ReconSetup map -- analyze_data_offline seems to have it already my_map.append(MAUS.MapCppReconSetup()) my_map.append(MAUS.MapCppTOFDigits()) my_map.append(MAUS.MapCppTOFSlabHits()) my_map.append(MAUS.MapCppTOFSpacePoints()) my_map.append(MAUS.MapCppCkovDigits()) # Histogram reducer. reducer = MAUS.ReducePyDoNothing() #reducer = MAUS.ReducePyDoNothing() # Save images as EPS and meta-data as JSON. #output_worker = MAUS.OutputPyDoNothing() output_worker = MAUS.OutputPyJSON() # Run the workflow. MAUS.Go(my_input, my_map, reducer, output_worker, data_cards)
def run(): """ Run the macro """ # This input generates empty spills, to be filled by the beam maker later on my_input = MAUS.InputPySpillGenerator() # Create an empty array of mappers, then populate it # with the functionality you want to use. my_map = MAUS.MapPyGroup() # GEANT4 my_map.append(MAUS.MapPyBeamMaker()) # beam construction my_map.append(MAUS.MapCppSimulation()) # geant4 simulation # Pre detector set up my_map.append(MAUS.MapPyMCReconSetup()) # geant4 simulation # TOF my_map.append(MAUS.MapCppTOFMCDigitizer()) # TOF MC Digitizer my_map.append(MAUS.MapCppTOFSlabHits()) # TOF MC Slab Hits my_map.append(MAUS.MapCppTOFSpacePoints()) # TOF Space Points # SciFi my_map.append(MAUS.MapCppTrackerMCDigitization()) # SciFi electronics model my_map.append(MAUS.MapCppTrackerRecon()) # SciFi Recon # KL my_map.append(MAUS.MapCppKLMCDigitizer()) # KL MC Digitizer my_map.append(MAUS.MapCppKLCellHits()) # KL CellHit Reco # EMR my_map.append(MAUS.MapCppEMRMCDigitization()) # EMR MC Digitization my_map.append(MAUS.MapCppEMRSpacePoints()) my_map.append(MAUS.MapCppEMRRecon()) # EMR Recon # Ckov my_map.append(MAUS.MapCppCkovMCDigitizer()) # Global my_map.append(MAUS.MapCppGlobalReconImport()) my_map.append(MAUS.MapCppGlobalTrackMatching()) my_reduce = MAUS.ReducePyDoNothing() # Then construct a MAUS output component - filename comes from datacards #~ my_output = MAUS.OutputCppRoot() my_output = MAUS.OutputPyDoNothing() # can specify datacards here or by using appropriate command line calls datacards = io.StringIO(u"") # The Go() drives all the components you pass in, then check the file # (default simulation.out) for output MAUS.Go(my_input, my_map, my_reduce, my_output, datacards)
def run(): """Run the macro""" # This generates events (usually spills) from a root binary file my_input = MAUS.InputCppRoot() # This outputs events (usually spills) to a json ascii file my_output = MAUS.OutputPyJSON() # Execute inputter and outputter # Mapper and Reducer does nothing MAUS.Go(my_input, MAUS.MapPyDoNothing(), MAUS.ReducePyDoNothing(), \ my_output, io.StringIO(u""))
def run(): """ Run the macro """ # Take as input a maus root file. my_input = MAUS.InputCppRootData() my_output = MAUS.OutputPyDoNothing() # datacard specifies the hypothesis for which PDFs are to be made datacards = io.StringIO(u"") # The Go() drives all the components you pass in MAUS.Go(my_input, MAUS.MapPyDoNothing(), MAUS.ReduceCppGlobalPID(), my_output, datacards)
def run(number_of_spills): """ Run the macro """ # Here we create a pseudo-file with an event in it. If you were to copy # and paste this to a file, then you could also do: # # input_file = open('myFileName.txt', 'r') # # where the file format has a JSON document per line. I just toss the file # in here for simplicity. input_file = io.StringIO( number_of_spills * u"""{"mc": [{"primary":{"position": { "x": 0.0, "y": -0.0, "z": -5000.0 },"particle_id" : 13,"energy" : 210.0, "random_seed" : 10, "momentum" : { "x":0.0, "y":0.0, "z":1.0 }, "time" : 0.0}}]}\n""" ) # pylint: disable=C0301 my_input = MAUS.InputPyJSON(input_file) # Create an empty array of mappers, then populate it # with the functionality you want to use. my_map = MAUS.MapPyGroup() my_map.append(MAUS.MapCppSimulation()) # geant4 simulation my_map.append(MAUS.MapCppTOFMCDigitizer()) # TOF electronics model my_map.append(MAUS.MapCppTrackerMCDigitization()) # SCiFi electronics datacards = io.StringIO(u"keep_steps = True") # You may specify datacards if you wish. To do so you create a file object # which can either be a StringIO object or a native python file. If you # want to store your datacards in a file 'datacards.dat' then uncomment: # datacards = open('datacards.dat', 'r') # Choose from either a compressed or uncompressed output file # output_file = open(os.environ["MAUS_ROOT_DIR"] + "/tmp/simulation.out", 'w') # Uncompressed #output_file = gzip.GzipFile("mausput.gz", 'wb') # Compressed # # Then construct a MAUS output component my_output = MAUS.OutputPyJSON(output_file) # The Go() drives all the components you pass in, then check the file # 'mausput' for the output MAUS.Go(my_input, my_map, MAUS.ReducePyDoNothing(), my_output, datacards)
def run(): """ Analyze data from the MICE experiment """ # Set up the input that reads from DAQ my_input = MAUS.InputCppDAQOfflineData() # Create an empty array of mappers, then populate it # with the functionality you want to use. my_map = MAUS.MapPyGroup() # Trigger my_map.append(MAUS.MapCppReconSetup()) # Detectors my_map.append(MAUS.MapCppTOFDigits()) my_map.append(MAUS.MapCppTOFSlabHits()) my_map.append(MAUS.MapCppTOFSpacePoints()) my_map.append(MAUS.MapCppCkovDigits()) my_map.append(MAUS.MapCppKLDigits()) my_map.append(MAUS.MapCppKLCellHits()) my_map.append(MAUS.MapCppTrackerDigits()) # SciFi real data digitization my_map.append(MAUS.MapCppTrackerClusterRecon()) # SciFi channel clustering my_map.append(MAUS.MapCppTrackerSpacePointRecon()) # SciFi spacepoint recon my_map.append(MAUS.MapCppTrackerPatternRecognition()) # SciFi track finding my_map.append(MAUS.MapCppTrackerPRSeed()) # Set the Seed from PR my_map.append(MAUS.MapCppTrackerTrackFit()) # SciFi track fit my_map.append(MAUS.MapCppEMRPlaneHits()) my_map.append(MAUS.MapCppEMRSpacePoints()) my_map.append(MAUS.MapCppEMRRecon()) my_reduce = MAUS.ReducePyDoNothing() # The Go() drives all the components you pass in then put all the output # into a file called 'mausput' MAUS.Go(my_input, my_map, my_reduce, MAUS.OutputCppRoot())
def run(): """ Create a JSON document and create a histogram. """ if not os.environ.get("MAUS_ROOT_DIR"): raise Exception('InitializeFail', 'MAUS_ROOT_DIR unset!') config = Configuration().getConfigJSON() config_json = json.loads(config) datapath = '%s/src/input/InputCppDAQData' % \ os.environ.get("MAUS_ROOT_DIR") # Set up a run configuration datafile = '05466' if os.environ['MAUS_UNPACKER_VERSION'] == "StepIV": datafile = '06008' config_json["daq_data_path"] = datapath config_json["daq_data_file"] = datafile # Set up data cards. data_cards_list = [] data_cards_list.append("output_file_name='%s'\n" % "scalers") data_cards_list.append("output_file_auto_number=%s\n" % True) data_cards_list.append("daq_data_path='%s'\n" % datapath) data_cards_list.append("daq_data_file='%s'\n" % datafile) data_cards_list.append("DAQ_cabling_by='%s'\n" % "date") data_cards = io.StringIO(unicode("".join(data_cards_list))) print data_cards # Create workers. # inputter = InputCppDAQOfflineData(datapath, datafile) # inputter.birth(json.dumps(config_json)) inputter = InputCppDAQOfflineData() mappers = MAUS.MapPyGroup() mappers.append(MAUS.MapPyDoNothing()) reducer = MAUS.ReducePyScalersTable() outputter = MAUS.OutputPyFile() # Execute the workers. MAUS.Go(inputter, mappers, reducer, outputter, data_cards)
def run(): """ Run the macro """ my_input = MAUS.InputCppDAQOfflineData() # my_input = MAUS.InputPyJSON() my_map = MAUS.MapPyGroup() my_map.append(MAUS.MapCppTrackerDigits()) my_map.append(MAUS.MapCppTrackerRecon()) # SciFi recon datacards = io.StringIO(u"") # my_output = MAUS.OutputPyJSON() my_output = MAUS.OutputCppRoot() # my_reduce = MAUS.ReducePyDoNothing() my_reduce = MAUS.ReduceCppPatternRecognition() MAUS.Go(my_input, my_map, my_reduce, my_output, datacards)
def run(number_of_spills): #pylint: disable =W0621 """Simulate the MICE experiment This will simulate 'number_of_spills' MICE events through the entirity of MICE using Geant4. At present, TOF and Tracker hits will be digitized. """ # Here we create a pseudo-file with an event in it. If you were to copy # and paste this to a file, then you could also do: # # documentFile = open('myFileName.txt', 'r') # # where the file format has a JSON document per line. I just toss the file # in here for simplicity. document_file = io.StringIO( number_of_spills * u"""{"mc": [{"position": { "x": 0.0, "y": -0.0, "z": -5000 },"particle_id" : 13,"energy" : 210, "random_seed" : 10, "unit_momentum" : { "x":0, "y":0, "z":1 }}]}\n""" ) #pylint: disable =C0301 my_input = MAUS.InputPyJSON(document_file) # Create an empty array of mappers, then populate it # with the functionality you want to use. my_map = MAUS.MapPyGroup() my_map.append(MAUS.MapCppSimulation()) # geant4 simulation my_map.append(MAUS.MapCppTOFMCDigitizer()) # TOF detector digitization my_map.append(MAUS.MapCppTrackerMCDigitization()) # tracker digitization datacards = io.StringIO(u"keep_tracks = False\n"\ "simulation_geometry_filename = \"Stage5.dat\"") # You may specify datacards if you wish. To do so you create a file object # which can either be a StringIO object or a native python file. If you # want to store your datacards in a file 'datacards.dat' then uncomment: # datacards = open('datacards.dat', 'r') # The Go() drives all the components you pass in, then check the file # 'mausput' for the output MAUS.Go(my_input, my_map, MAUS.ReducePyDoNothing(), MAUS.OutputPyJSON(), datacards)
def run(): """Analyze data from the MICE experiment This will read in and process data taken from the MICE experiment. It will eventually include things like cabling information, calibrations, and fits. """ # Set up the input that reads from DAQ #my_input = MAUS.InputCppDAQData() my_input = MAUS.InputCppDAQOfflineData() # Create an empty array of mappers, then populate it # with the functionality you want to use. my_map = MAUS.MapPyGroup() my_map.append(MAUS.MapCppTOFDigits()) my_map.append(MAUS.MapCppTOFSlabHits()) my_map.append(MAUS.MapCppTOFSpacePoints()) my_map.append(MAUS.MapCppCkovDigits()) reducer = MAUS.ReducePyCkov() # The Go() drives all the components you pass in then put all the output # into a file called 'mausput' MAUS.Go(my_input, my_map, reducer, MAUS.OutputPyImage())
def run(): """ Analyze data from the MICE experiment """ # Set up the input that reads from DAQ my_input = MAUS.InputCppDAQOnlineData() # Create an empty array of mappers, then populate it # with the functionality you want to use. my_map = MAUS.MapPyGroup() # Trigger my_map.append(MAUS.MapCppReconSetup()) # Detectors my_map.append(MAUS.MapCppTOFDigits()) my_map.append(MAUS.MapCppTOFSlabHits()) my_map.append(MAUS.MapCppTOFSpacePoints()) my_map.append(MAUS.MapCppCkovDigits()) my_map.append(MAUS.MapCppKLDigits()) my_map.append(MAUS.MapCppKLCellHits()) my_map.append(MAUS.MapCppTrackerDigits()) my_map.append(MAUS.MapCppTrackerRecon()) my_map.append(MAUS.MapCppEMRPlaneHits()) my_map.append(MAUS.MapCppEMRRecon()) my_reduce = MAUS.ReducePyDoNothing() # The Go() drives all the components you pass in then put all the output # into a file called 'mausput' MAUS.Go(my_input, my_map, my_reduce, MAUS.OutputCppRoot())
def run(): """ Run the macro """ # This input generates empty spills, to be filled by the beam maker later on my_input = MAUS.InputPySpillGenerator() # Create an empty array of mappers, then populate it # with the functionality you want to use. my_map = MAUS.MapPyGroup() # G4beamline my_map.append(MAUS.MapPyBeamlineSimulation()) # GEANT4 # my_map.append(MAUS.MapPyBeamMaker()) # beam construction my_map.append(MAUS.MapCppSimulation()) # geant4 simulation # Pre detector set up my_map.append(MAUS.MapPyMCReconSetup()) # geant4 simulation # TOF my_map.append(MAUS.MapCppTOFMCDigitizer()) # TOF MC Digitizer my_map.append(MAUS.MapCppTOFSlabHits()) # TOF MC Slab Hits my_map.append(MAUS.MapCppTOFSpacePoints()) # TOF Space Points # KL my_map.append(MAUS.MapCppKLMCDigitizer()) # KL MC Digitizer my_map.append(MAUS.MapCppKLCellHits()) # KL CellHit Reco # SciFi # MAUS 2.5.0 #my_map.append(MAUS.MapCppTrackerMCDigitization()) # SciFi electronics model #my_map.append(MAUS.MapCppTrackerRecon()) # SciFi Recon my_map.append( MAUS.MapCppTrackerMCDigitization()) # SciFi electronics model my_map.append(MAUS.MapCppTrackerClusterRecon()) # SciFi channel clustering my_map.append( MAUS.MapCppTrackerSpacePointRecon()) # SciFi spacepoint recon my_map.append( MAUS.MapCppTrackerPatternRecognition()) # SciFi track finding my_map.append(MAUS.MapCppTrackerPRSeed()) # Set the Seed from PR my_map.append(MAUS.MapCppTrackerTrackFit()) # SciFi track fit my_map.append( MAUS.MapCppTrackerTOFReFit()) # SciFi track refit based on TOF # EMR my_map.append(MAUS.MapCppEMRMCDigitization()) # EMR MC Digitizer my_map.append(MAUS.MapCppEMRSpacePoints()) # EMR Space Points my_map.append(MAUS.MapCppEMRRecon()) # EMR Recon # Global my_map.append(MAUS.MapCppGlobalReconImport()) my_map.append(MAUS.MapCppGlobalTrackMatching()) # Then construct a MAUS output component - filename comes from datacards my_output = MAUS.OutputCppRoot() # can specify datacards here or by using appropriate command line calls datacards = io.StringIO(u"") # The Go() drives all the components you pass in, then check the file # (default simulation.out) for output MAUS.Go(my_input, my_map, MAUS.ReducePyDoNothing(), my_output, datacards)