Belle II Software  release-08-01-10
caf_boostvector.py
1 
8 
9 """
10 Airflow script to perform BoostVector calibration.
11 """
12 
13 from prompt import CalibrationSettings, INPUT_DATA_FILTERS
14 from prompt.calibrations.caf_beamspot import settings as beamspot
15 
16 
17 settings = CalibrationSettings(
18  name="BoostVector Calibrations",
19  expert_username="zlebcr",
20  description=__doc__,
21  input_data_formats=["cdst"],
22  input_data_names=["mumu_tight_or_highm_calib"],
23  input_data_filters={
24  "mumu_tight_or_highm_calib": [
25  INPUT_DATA_FILTERS["Data Tag"]["mumu_tight_or_highm_calib"],
26  INPUT_DATA_FILTERS["Run Type"]["physics"],
27  INPUT_DATA_FILTERS["Data Quality Tag"]["Good Or Recoverable"],
28  INPUT_DATA_FILTERS["Magnet"]["On"]]},
29  expert_config={
30  "outerLoss": "pow(rawTime - 8.0, 2) + 10 * pow(maxGap, 2)",
31  "innerLoss": "pow(rawTime - 8.0, 2) + 10 * pow(maxGap, 2)"},
32  depends_on=[beamspot])
33 
34 
35 
36 
37 def get_calibrations(input_data, **kwargs):
38  """
39  Parameters:
40  input_data (dict): Should contain every name from the 'input_data_names' variable as a key.
41  Each value is a dictionary with {"/path/to/file_e1_r5.root": IoV(1,5,1,5), ...}. Useful for
42  assigning to calibration.files_to_iov
43 
44  **kwargs: Configuration options to be sent in. Since this may change we use kwargs as a way to help prevent
45  backwards compatibility problems. But you could use the correct arguments in b2caf-prompt-run for this
46  release explicitly if you want to.
47 
48  Currently only kwargs["output_iov"] is used. This is the output IoV range that your payloads should
49  correspond to. Generally your highest ExpRun payload should be open ended e.g. IoV(3,4,-1,-1)
50 
51  Returns:
52  list(caf.framework.Calibration): All of the calibration objects we want to assign to the CAF process
53  """
54  import basf2
55  # Set up config options
56 
57  # In this script we want to use one sources of input data.
58  # Get the input files from the input_data variable
59  file_to_iov_physics = input_data["mumu_tight_or_highm_calib"]
60 
61  # We might have requested an enormous amount of data across a run range.
62  # There's a LOT more files than runs!
63  # Lets set some limits because this calibration doesn't need that much to run.
64  max_files_per_run = 1000000
65 
66  # We filter out any more than 100 files per run. The input data files are sorted alphabetically by b2caf-prompt-run
67  # already. This procedure respects that ordering
68  from prompt.utils import filter_by_max_files_per_run
69 
70  reduced_file_to_iov_physics = filter_by_max_files_per_run(file_to_iov_physics, max_files_per_run)
71  input_files_physics = list(reduced_file_to_iov_physics.keys())
72  basf2.B2INFO(f"Total number of files actually used as input = {len(input_files_physics)}")
73 
74  # Get the overall IoV we our process should cover. Includes the end values that we may want to ignore since our output
75  # IoV should be open ended. We could also use this as part of the input data selection in some way.
76  requested_iov = kwargs.get("requested_iov", None)
77 
78  from caf.utils import IoV
79  # The actual value our output IoV payload should have. Notice that we've set it open ended.
80  output_iov = IoV(requested_iov.exp_low, requested_iov.run_low, -1, -1)
81 
82 
84 
85  from ROOT.Belle2 import BoostVectorAlgorithm
86  from basf2 import create_path, register_module
87  import modularAnalysis as ana
88  import vertex
89 
90 
92 
93  from caf.framework import Calibration
94  from caf.strategies import SingleIOV
95  from reconstruction import prepare_cdst_analysis
96 
97  # module to be run prior the collector
98  rec_path_1 = create_path()
99  prepare_cdst_analysis(path=rec_path_1, components=['CDC', 'ECL', 'KLM'])
100 
101  muSelection = '[p>1.0]'
102  muSelection += ' and abs(dz)<2.0 and abs(dr)<0.5'
103  muSelection += ' and nPXDHits >=1 and nSVDHits >= 8 and nCDCHits >= 20'
104  ana.fillParticleList('mu+:BV', muSelection, path=rec_path_1)
105  ana.reconstructDecay('Upsilon(4S):BV -> mu+:BV mu-:BV', '9.5<M<11.5', path=rec_path_1)
106  vertex.treeFit('Upsilon(4S):BV', updateAllDaughters=True, ipConstraint=True, path=rec_path_1)
107 
108  collector_bv = register_module('BoostVectorCollector', Y4SPListName='Upsilon(4S):BV')
109  algorithm_bv = BoostVectorAlgorithm()
110  algorithm_bv.setOuterLoss(kwargs['expert_config']['outerLoss'])
111  algorithm_bv.setInnerLoss(kwargs['expert_config']['innerLoss'])
112 
113  calibration_bv = Calibration('BoostVector',
114  collector=collector_bv,
115  algorithms=algorithm_bv,
116  input_files=input_files_physics,
117  pre_collector_path=rec_path_1)
118 
119  calibration_bv.strategies = SingleIOV
120 
121  # Do this for the default AlgorithmStrategy to force the output payload IoV
122  # It may be different if you are using another strategy like SequentialRunByRun
123  for algorithm in calibration_bv.algorithms:
124  algorithm.params = {"iov_coverage": output_iov}
125 
126  # Most other options like database chain and backend args will be overwritten by b2caf-prompt-run.
127  # So we don't bother setting them.
128 
129  # You must return all calibrations you want to run in the prompt process, even if it's only one
130  return [calibration_bv]
131 
132 
def treeFit(list_name, conf_level=0.001, massConstraint=[], ipConstraint=False, updateAllDaughters=False, customOriginConstraint=False, customOriginVertex=[0.001, 0, 0.0116], customOriginCovariance=[0.0048, 0, 0, 0, 0.003567, 0, 0, 0, 0.0400], originDimension=3, treatAsInvisible='', ignoreFromVertexFit='', path=None)
Definition: vertex.py:239