1 | #!/usr/bin/python -tt
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2 |
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3 | ##############################################
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4 | # based on plot_trigger_rate.py by QW and TPK
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5 | ##############################################
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6 |
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7 | from array import array
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8 | import os
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9 | import re
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10 | import sys
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11 | import numpy as np
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12 | import time
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13 |
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14 | from pyfact import SlowData
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15 |
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16 | #from ROOT import TCanvas, TGraph, TGraphErrors, TH2F
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17 | #from ROOT import gStyle
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18 |
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19 | import matplotlib.pyplot as plt
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20 | import matplotlib.dates
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21 |
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22 | filelist = []
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23 | if len(sys.argv) > 1:
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24 | base_path = sys.argv[1]
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25 | else:
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26 | print 'Usage:', sys.argv[0], '/your/search/path'
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27 |
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28 | for base,subdirs,files in os.walk(base_path):
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29 | for filename in files:
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30 | #include only run files
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31 | regex = re.search(r'\d\d\d\d\d\d\d\d\.FSC_CONTROL_TEMPERATURE.fits',filename)
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32 | #include run files and also the nightly file
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33 | #regex = re.search(r'FTM_CONTROL_TRIGGER_RATES',filename)
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34 | if regex:
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35 | filelist.append(os.path.join(base,filename))
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36 |
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37 |
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38 | plotlist = ['T_aux', 'T_back', 'T_crate', 'T_eth', 'T_ps', 'T_sens']
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39 | plotfmts = ['.:g', '.:k', '.:r', '.:y', '.:b', '.:m']
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40 | per_name = []
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41 |
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42 | for filename in filelist:
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43 |
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44 | print filename
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45 |
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46 | f = SlowData(filename)
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47 | # 'columns': {'QoS': (1L, 4L, 'J', ''),
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48 | # 'T_aux': (4L, 4L, 'E', 'deg'),
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49 | # 'T_back': (4L, 4L, 'E', 'deg'),
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50 | # 'T_crate': (8L, 4L, 'E', 'deg'),
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51 | # 'T_eth': (4L, 4L, 'E', 'deg'),
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52 | # 'T_ps': (8L, 4L, 'E', 'deg'),
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53 | # 'T_sens': (31L, 4L, 'E', 'deg'),
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54 | # 'Time': (1L, 8L, 'D', 'MJD'),
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55 | # 't': (1L, 4L, 'E', 's')},
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56 |
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57 | f.register("all")
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58 |
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59 | f.stack()
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60 | for row in f:
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61 | pass
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62 |
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63 |
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64 | for name in plotlist:
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65 | per_name.append(f.columns[name][0])
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66 |
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67 | fig = plt.figure()
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68 | ax = fig.add_subplot(111)
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69 | plt.hold(True)
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70 | for i,name in enumerate(plotlist):
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71 | for number in range(per_name[i]):
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72 | print name, number
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73 | Time = f.stacked_cols['Time']
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74 | data = f.stacked_cols[name][:,number]
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75 | med = np.median(Time)
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76 | # get rid of Times of the previous day.
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77 | Time_today = Time[np.where( Time>med-0.25 )[0]]
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78 | data_today = data[np.where( Time>med-0.25)[0]]
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79 |
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80 | ax.plot_date( Time_today, data_today, fmt=plotfmts[i])
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81 | today_str = time.strftime('%d.%m.' ,time.gmtime(Time_today[0]*24*3600))
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82 | print today_str
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83 | plt.title('T_ps:blue T_eth:yellow T_aux:green T_crate:red T_back:black T_sens:magenta \n'+today_str)
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84 | ax.xaxis.set_major_locator(
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85 | matplotlib.dates.HourLocator(byhour=range(24), interval=1)
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86 | )
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87 | ax.xaxis.set_major_formatter(
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88 | matplotlib.dates.DateFormatter('%Hh')
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89 | )
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90 |
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91 | plt.ylim(12,41)
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92 |
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93 | if len(sys.argv) > 2:
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94 | plt.savefig(sys.argv[2])
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95 | print 'plot saved to', sys.argv[2]
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96 | else:
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97 | print "WARNING:"
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98 | print "plot was not saved..."
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99 | print "please type: plt.savefig('<filename.png>') or so ... to save it"
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