Coverage for src/km3dq_common/grl_library.py: 85%

1#! /usr/bin/env python 

2######################################################################################## 

3 

4from .config_library import configure_dataquality_tag 

5from .config_library import configure_var_name 

6from .config_library import configure_var_bit 

7from .config_library import configure_var_thresholds 

8from .common_library import create_ttree_from_qaqc 

9from .lw_db_defect_library import decode_defect_diag_byte 

10import numpy as np 

11 

12 

13######################################################################################## 

14def get_good_run_list(det, dq_tag_name, add_info=[], qaqc_input="qaqc_sftp"): 

15 """ 

16 Production of good run lists 

17 

18 === Arguments === 

19 - det : detector name - [string] - Ex: "D0ARCA021", "D0ORCA018"... 

20 - dq_tag_name : data-quality tag name - [string] 

21 - add_info : list of additional informations (qaqc variables or defect) 

22 to be retrieved in the same time as the GRL - 

23 [array of strings] - 

24 Ex: ["livetime", "def_operation/partly", "sign_off/run_coordinator"] 

25 - source : QAQC source, a priori "qaqc_sftp" - [string] 

26 

27 === Output === 

28 - GRL dictionnary including the additional info. 

29 

30 """ 

31 

32 dq_tag_0 = configure_dataquality_tag(dq_tag_name) 

33 

34 grl_0 = { 

35 "var_prop": {}, 

36 "good": {}, 

37 "poor": {}, 

38 "bad": {}, 

39 "discard": {}, 

40 "add_info": {}, 

41 } 

42 

43 configure_var_name(grl_0["var_prop"]) 

44 configure_var_bit(grl_0["var_prop"]) 

45 configure_var_thresholds(grl_0["var_prop"], det, dq_tag_0) 

46 

47 # The TTree must contain: 

48 # - the run number and livetime 

49 # - the variables (QAQC + defects) used in the Veto / Qscore computation 

50 # (required by create_ttree_from_qaqc) 

51 # - the variables (QAQC + defects) used in the GRL generation 

52 # - the additional variables to be reported 

53 

54 var_to_store = ["run", "livetime_s"] 

55 var_to_store += list(grl_0["var_prop"]["veto_thresholds"].keys()) 

56 var_to_store += list(grl_0["var_prop"]["qsco_thresholds"].keys()) 

57 

58 for i_q in ("good", "poor", "bad", "discard"): 

59 var_to_store += list(grl_0["var_prop"][f"{i_q}_thresholds"].keys()) 

60 

61 for i_var in add_info: 

62 if i_var.startswith("def_"): 

63 # Extract the defect type to be included in the tree 

64 i_var_def_type = i_var.split("/")[0] 

65 var_to_store.append(i_var_def_type) 

66 grl_0["add_info"][i_var] = [] 

67 else: 

68 grl_0["add_info"][i_var] = {} 

69 

70 var_to_store = set(var_to_store) 

71 var_to_store.discard("veto") 

72 var_to_store.discard("qsco") 

73 

74 (n1, _) = create_ttree_from_qaqc(det, var_to_store, qaqc_input, dq_tag_0, True) 

75 

76 masks = {} 

77 for quality in ("good", "poor", "bad", "discard"): 

78 mask = np.ones(len(n1), dtype=bool) 

79 

80 for variable, (lower, upper) in grl_0["var_prop"][ 

81 f"{quality}_thresholds" 

82 ].items(): 

83 mask = mask & (n1[variable] >= lower) & (n1[variable] <= upper) 

84 

85 if quality == "discard": # Discard is True when excluded 

86 mask = ~mask 

87 

88 grl_0[quality]["run_list"] = n1.run[mask].values 

89 grl_0[quality]["livetime_s"] = np.sum(n1.livetime_s[mask]) 

90 

91 for variable in add_info: 

92 grl_0["add_info"][variable] = dict(zip(n1.run, n1[variable])) 

93 

94 return grl_0