mice-analysis-scripts/data_stats.py at master · e-overton/mice-analysis-scripts · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
#/bin/env python

print "Run info generator..."

from cdb import Beamline, CoolingChannel       #  get beamline information for some run
from datetime import datetime
import time
import pprint
import sys

import ROOT

# Start/end dates to collate data over:
start_date = datetime.strptime("2016-12-04", "%Y-%m-%d")
end_date = datetime.strptime("2016-12-07", "%Y-%m-%d")

# Function to help collate infomation later:
def add_key(dict, key, value):
    if not key in dict:
        dict[key] = value
    else:
        dict[key] += value

###############################################################################
# Load CDB elements
###############################################################################
print " Loading Beamline...",
try:
    beamline = Beamline()
except:
    print "  ERROR"
    sys.exit()
else:
    print "  OK"

print " Loading Cooling Channel...",
try:
    channel = CoolingChannel()
except:
    print "  ERROR"
    sys.exit()
else:
    print "  OK"

###############################################################################
# Setup processing
###############################################################################

# CDB spill counter
date0_unix = time.mktime(start_date.timetuple())
date1_unix = time.mktime(end_date.timetuple())
th1d_spills_hr = ROOT.TH1D("th1d_spills_hr", "Spills/hr",
                           int((date1_unix-date0_unix)/900), date0_unix - 450.0,
                           date1_unix + 450.0)

# Tag tally infomation
tag_tally = {}

print "****************************************************"
runs = beamline.get_beamlines_for_dates(start_date, end_date)
for run_number in runs.keys():
    print "Found run: %i"%run_number
    # Generate Dips/hr infomation:
    this_beamline = runs[run_number]
    run_start_time = this_beamline['start_time']
    run_end_time = this_beamline['end_time']
    start_unix = time.mktime(run_start_time.timetuple())
    end_unix = time.mktime(run_end_time.timetuple())

    run_time = float((run_end_time - run_start_time).seconds)
    target_dips = this_beamline['end_pulse']-this_beamline['start_pulse']
    dip_rate = target_dips/run_time
    # Find overlapping runs and bins, then fill the bins with dips
    for xbin in range(1, th1d_spills_hr.GetNbinsX()+1):
        bin_min = th1d_spills_hr.GetBinLowEdge(xbin)
        bin_max = th1d_spills_hr.GetBinLowEdge(xbin+1)

        overlap = min(bin_max, end_unix) - max(bin_min, start_unix)
        if overlap > 0:
            th1d_spills_hr.SetBinContent(xbin, th1d_spills_hr.GetBinContent(xbin) + overlap*dip_rate*4)

    # Collate run infomation if run time is long enough:
    if run_time > 900:
        # Append beamline tag infomation
        tag_id = this_beamline["optics"]
        print "Tag: %s"%tag_id
        if tag_id not in tag_tally:
            tag_tally[tag_id] = {}

        # Sum infomation together
        for k in ['Particle Triggers', 'Requested Triggers', 'ToF0 Triggers',
                  'ToF1 Triggers', 'ToF2 Triggers']:
            add_key(tag_tally[tag_id], k, this_beamline['scalars'][k])
        add_key(tag_tally[tag_id], "target_dips", target_dips)
        add_key(tag_tally[tag_id], "run_time", run_end_time - run_start_time)

        # Log each run assosoiated with a particular tag:
        if "runs" not in tag_tally[tag_id]:
            tag_tally[tag_id]["runs"] = []
        tag_tally[tag_id]["runs"].append(run_number)

    print "****************************************************"

print ""
print "###  Tag summary ########################################"
print "%20s %10s %10s %10s %10s  %s" % ("Tag", "Time(H:M)", "Dips",
                                "P.Trig(k)", "TOF2(k)", "  Runs")

for tag in tag_tally:
    tag_info = tag_tally[tag]
    txt_time = "%3i:%2i" % (tag_info["run_time"].seconds/3600,
                            (tag_info["run_time"].seconds/60) % 60)
    txt_runs = ','.join(map(str, tag_info["runs"]))

    print "%20s %10s %10i %10i %10i %s" % (tag, txt_time, tag_info["target_dips"],
                                    tag_info["Particle Triggers"]/1000,
                                    tag_info["ToF2 Triggers"]/1000,
                                    txt_runs)


#pprint.pprint(tag_tally)
th1d_spills_hr.Draw()
th1d_spills_hr.GetXaxis().SetTimeDisplay(1)
th1d_spills_hr.SetFillColor(ROOT.kBlue)
th1d_spills_hr.SetLineColor(ROOT.kBlue)

raw_input("Done")