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(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():
"""
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" % 5)
# 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.InputCppDAQOfflineData()
# 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()
my_map.append(MAUS.MapCppReconSetup())
my_map.append(MAUS.MapCppEMRPlaneHits())
my_map.append(MAUS.MapCppEMRSpacePoints())
my_map.append(MAUS.MapCppEMRRecon())
# Histogram reducer.
#reducer = MAUS.ReducePyDoNothing()
reducer = MAUS.ReduceCppEMRPlot()
# Save images as eps and meta-data as JSON.
#output_worker = MAUS.OutputPyDoNothing()
output_worker = MAUS.OutputPyRootImage()
# Run the workflow.
MAUS.Go(my_input, my_map, reducer, output_worker, data_cards)
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 test_something(self):
""" Check against different issues"""
inputter = MAUS.InputCppDAQOfflineData(self._datapath, self._datafile)
conf_json = json.loads(self._c.getConfigJSON())
conf_json["DAQ_cabling_by"] = "date"
inputter.birth(json.dumps(conf_json))
success = self.mapper.birth("{}")
self.assertFalse(success)
success = self.mapper.add("")
self.assertFalse(success)
success = self.mapper.add("{}")
self.assertFalse(success)
success = self.mapper.dump()
self.assertTrue(success)
result = self.mapper.process("{}")
self.assertEqual(len(result), 2)
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():
"""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())
