In [3]:
import pytimber
import time
In [4]:
db = pytimber.LoggingDB()
Search for parameters¶
Use the wildcard %
In [5]:
db.search("%BEAM_INTENSITY%")
Out[5]:
['LHC.BCTDC.A6R4.B1:BEAM_INTENSITY', 'LHC.BCTDC.A6R4.B1:BEAM_INTENSITY_ADC24BIT', 'LHC.BCTDC.A6R4.B2:BEAM_INTENSITY', 'LHC.BCTDC.A6R4.B2:BEAM_INTENSITY_ADC24BIT', 'LHC.BCTDC.B6R4.B1:BEAM_INTENSITY', 'LHC.BCTDC.B6R4.B1:BEAM_INTENSITY_ADC24BIT', 'LHC.BCTDC.B6R4.B2:BEAM_INTENSITY', 'LHC.BCTDC.B6R4.B2:BEAM_INTENSITY_ADC24BIT', 'LHC.BCTFR.A6R4.B1:BEAM_INTENSITY', 'LHC.BCTFR.A6R4.B1:BEAM_INTENSITY_LOBW', 'LHC.BCTFR.A6R4.B2:BEAM_INTENSITY', 'LHC.BCTFR.A6R4.B2:BEAM_INTENSITY_LOBW', 'LHC.BCTFR.B6R4.B1.DEV:BEAM_INTENSITY_HBW', 'LHC.BCTFR.B6R4.B1.DEV:BEAM_INTENSITY_LBW', 'LHC.BCTFR.B6R4.B1:BEAM_INTENSITY', 'LHC.BCTFR.B6R4.B1:BEAM_INTENSITY_LOBW', 'LHC.BCTFR.B6R4.B2.DEV:BEAM_INTENSITY_HBW', 'LHC.BCTFR.B6R4.B2.DEV:BEAM_INTENSITY_LBW', 'LHC.BCTFR.B6R4.B2:BEAM_INTENSITY', 'LHC.BCTFR.B6R4.B2:BEAM_INTENSITY_LOBW', 'LHC.BWCM.B1:BEAM_INTENSITY', 'LHC.BWCM.B2:BEAM_INTENSITY']
Explore the parameter tree¶
use the ipython autocomplete (TAB) to explorer the parameter tree interactively
In [6]:
db.tree.LHC.Beam_Quality.Beam_1.get_vars()
Out[6]:
['LHC.BQM.B1:ADC_AQUISITION_MAX', 'LHC.BQM.B1:ADC_AQUISITION_MIN', 'LHC.BQM.B1:ADC_WAVEFORM', 'LHC.BQM.B1:ATTENUATION', 'LHC.BQM.B1:BUNCH_INTENSITIES', 'LHC.BQM.B1:BUNCH_INTENSITY_MAX', 'LHC.BQM.B1:BUNCH_INTENSITY_MEAN', 'LHC.BQM.B1:BUNCH_INTENSITY_MIN', 'LHC.BQM.B1:BUNCH_INTENSITY_STDDEV', 'LHC.BQM.B1:BUNCH_LENGTHS', 'LHC.BQM.B1:BUNCH_LENGTHS_CUT1', 'LHC.BQM.B1:BUNCH_LENGTHS_CUT2', 'LHC.BQM.B1:BUNCH_LENGTHS_CUT3', 'LHC.BQM.B1:BUNCH_LENGTH_CUT1_MAX', 'LHC.BQM.B1:BUNCH_LENGTH_CUT1_MEAN', 'LHC.BQM.B1:BUNCH_LENGTH_CUT1_MIN', 'LHC.BQM.B1:BUNCH_LENGTH_CUT1_STDDEV', 'LHC.BQM.B1:BUNCH_LENGTH_CUT2_MAX', 'LHC.BQM.B1:BUNCH_LENGTH_CUT2_MEAN', 'LHC.BQM.B1:BUNCH_LENGTH_CUT2_MIN', 'LHC.BQM.B1:BUNCH_LENGTH_CUT2_STDDEV', 'LHC.BQM.B1:BUNCH_LENGTH_CUT3_MAX', 'LHC.BQM.B1:BUNCH_LENGTH_CUT3_MEAN', 'LHC.BQM.B1:BUNCH_LENGTH_CUT3_MIN', 'LHC.BQM.B1:BUNCH_LENGTH_CUT3_STDDEV', 'LHC.BQM.B1:BUNCH_LENGTH_MAX', 'LHC.BQM.B1:BUNCH_LENGTH_MEAN', 'LHC.BQM.B1:BUNCH_LENGTH_MIN', 'LHC.BQM.B1:BUNCH_LENGTH_STDDEV', 'LHC.BQM.B1:BUNCH_MEANS', 'LHC.BQM.B1:BUNCH_PEAKS', 'LHC.BQM.B1:BUNCH_PEAK_MAX', 'LHC.BQM.B1:BUNCH_PEAK_MEAN', 'LHC.BQM.B1:BUNCH_PEAK_MIN', 'LHC.BQM.B1:BUNCH_PEAK_STDDEV', 'LHC.BQM.B1:FILLED_BUCKETS', 'LHC.BQM.B1:NO_BUNCHES']
Get Data¶
Specify a variable name and a timestamp or an interval. Data is always returned as a dictionary of timestamp, values arrays.
In [7]:
db.get("HX:FILLN",time.time())
Out[7]:
{'HX:FILLN': (array([ 1.49807956e+09]), array([ 5856.]))}
Scaling algorithms¶
getScaled can be used to exploit the time scaling functionality of Timber. Specify
- the scale Interval (an integer)
- the scaleSize (one of ['SECOND', 'MINUTE', 'HOUR', 'DAY', 'WEEK', 'MONTH', 'YEAR'])
- the scaling algorithm (one of ['MAX','MIN','AVG','COUNT','SUM','REPEAT','INTERPOLATE'])
In [8]:
db.getScaled('MSC01.ZT8.107:COUNTS','2016-08-03 16:30:00.000','2016-08-03 18:30:00.000',
timescaleAlgorithm='SUM', scaleSize='MINUTE', timescaleInterval='1')
Out[8]:
{'MSC01.ZT8.107:COUNTS': (array([ 1.47023460e+09, 1.47023466e+09, 1.47023472e+09,
1.47023478e+09, 1.47023484e+09, 1.47023490e+09,
1.47023496e+09, 1.47023502e+09, 1.47023508e+09,
1.47023514e+09, 1.47023520e+09, 1.47023526e+09,
1.47023532e+09, 1.47023538e+09, 1.47023544e+09,
1.47023550e+09, 1.47023556e+09, 1.47023562e+09,
1.47023568e+09, 1.47023574e+09, 1.47023580e+09,
1.47023586e+09, 1.47023592e+09, 1.47023598e+09,
1.47023604e+09, 1.47023610e+09, 1.47023616e+09,
1.47023622e+09, 1.47023628e+09, 1.47023634e+09,
1.47023640e+09, 1.47023646e+09, 1.47023652e+09,
1.47023658e+09, 1.47023664e+09, 1.47023670e+09,
1.47023676e+09, 1.47023682e+09, 1.47023688e+09,
1.47023694e+09, 1.47023700e+09, 1.47023706e+09,
1.47023712e+09, 1.47023718e+09, 1.47023724e+09,
1.47023730e+09, 1.47023736e+09, 1.47023742e+09,
1.47023748e+09, 1.47023754e+09, 1.47023760e+09,
1.47023766e+09, 1.47023772e+09, 1.47023778e+09,
1.47023784e+09, 1.47023790e+09, 1.47023796e+09,
1.47023802e+09, 1.47023808e+09, 1.47023814e+09,
1.47023820e+09, 1.47023826e+09, 1.47023832e+09,
1.47023838e+09, 1.47023844e+09, 1.47023850e+09,
1.47023856e+09, 1.47023862e+09, 1.47023868e+09,
1.47023874e+09, 1.47023880e+09, 1.47023886e+09,
1.47023892e+09, 1.47023898e+09, 1.47023904e+09,
1.47023910e+09, 1.47023916e+09, 1.47023922e+09,
1.47023928e+09, 1.47023934e+09, 1.47023940e+09,
1.47023946e+09, 1.47023952e+09, 1.47023958e+09,
1.47023964e+09, 1.47023970e+09, 1.47023976e+09,
1.47023982e+09, 1.47023988e+09, 1.47023994e+09,
1.47024000e+09, 1.47024006e+09, 1.47024012e+09,
1.47024018e+09, 1.47024024e+09, 1.47024030e+09,
1.47024036e+09, 1.47024042e+09, 1.47024048e+09,
1.47024054e+09, 1.47024060e+09, 1.47024066e+09,
1.47024072e+09, 1.47024078e+09, 1.47024084e+09,
1.47024090e+09, 1.47024096e+09, 1.47024102e+09,
1.47024108e+09, 1.47024114e+09, 1.47024120e+09,
1.47024126e+09, 1.47024132e+09, 1.47024138e+09,
1.47024144e+09, 1.47024150e+09, 1.47024156e+09,
1.47024162e+09, 1.47024168e+09, 1.47024174e+09]),
array([ 48716., 83349., 68454., 65721., 62828., 66109.,
69350., 65951., 64980., 66038., 66906., 67626.,
65394., 86594., 53405., 82254., 51565., 86870.,
54157., 84856., 50992., 87217., 50903., 85103.,
67041., 67771., 89024., 69782., 72953., 70206.,
71077., 71592., 75637., 74618., 71098., 91168.,
55943., 91858., 55929., 92111., 55824., 90917.,
57133., 90806., 58038., 93778., 75370., 76122.,
76543., 73851., 49015., 63228., 50158., 48936.,
51472., 49533., 52097., 65964., 51398., 50641.,
51607., 43196., 53561., 66049., 52192., 49752.,
51868., 50800., 53446., 66838., 51320., 50184.,
50715., 50679., 51528., 60718., 51232., 50734.,
50903., 51679., 50376., 111994., 117708., 95456.,
112002., 109072., 110102., 108036., 111607., 89616.,
125673., 71285., 146653., 71363., 151098., 73567.,
129369., 89825., 113856., 113342., 113937., 112483.,
113210., 113724., 111253., 113254., 95043., 134197.,
73561., 152340., 75128., 152441., 74029., 137886.,
92577., 117675., 113715., 115766., 0., nan]))}
In [9]:
now=time.time()
"The unixtime `%.3f` correspond to `%s` local time."%(now,pytimber.dumpdate(now))
Out[9]:
'The unixtime `1498117494.614` correspond to `2017-06-22 09:44:54.613` local time.'
In [10]:
db.get("HX:FILLN",time.time())
Out[10]:
{'HX:FILLN': (array([ 1.49807956e+09]), array([ 5856.]))}
In [11]:
db.get("HX:FILLN",'2016-08-03 16:30:00.000')
Out[11]:
{'HX:FILLN': (array([ 1.47022719e+09]), array([ 5160.]))}
In [12]:
db.get("HX:FILLN",'2016-08-03 16:30:00.000',unixtime=False)
Out[12]:
{'HX:FILLN': (array([datetime.datetime(2016, 8, 3, 14, 26, 27, 234000)], dtype=object),
array([ 5160.]))}
In [13]:
db.get("HX:FILLN",'2016-08-02 16:30:00.000','next')
Out[13]:
{'HX:FILLN': (array([ 1.47015166e+09]), array([ 5156.]))}
In [14]:
db.get("HX:FILLN",'2016-08-02 16:30:00.000','2016-08-03 16:30:00.000')
Out[14]:
{'HX:FILLN': (array([ 1.47015166e+09, 1.47019695e+09, 1.47019821e+09,
1.47020552e+09, 1.47022719e+09]),
array([ 5156., 5157., 5158., 5159., 5160.]))}
Variables¶
Variables can be given as a string, as a pattern, as a list of strings.
In [15]:
db.get("LHC.BCTDC.A6R4.B1:BEAM_INTENSITY",now)
Out[15]:
{'LHC.BCTDC.A6R4.B1:BEAM_INTENSITY': (array([ 1.49811749e+09]),
array([ 1.97829365e+14]))}
In [16]:
db.get("LHC.BCTDC.A6R4.B%:BEAM_INTENSITY",now)
Out[16]:
{'LHC.BCTDC.A6R4.B1:BEAM_INTENSITY': (array([ 1.49811749e+09]),
array([ 1.97829365e+14])),
'LHC.BCTDC.A6R4.B2:BEAM_INTENSITY': (array([ 1.49811749e+09]),
array([ 2.09327495e+14]))}
In [17]:
db.get(["LHC.BCTDC.A6R4.B1:BEAM_INTENSITY","LHC.BCTDC.A6R4.B2:BEAM_INTENSITY"],now)
Out[17]:
{'LHC.BCTDC.A6R4.B1:BEAM_INTENSITY': (array([ 1.49811749e+09]),
array([ 1.97829365e+14])),
'LHC.BCTDC.A6R4.B2:BEAM_INTENSITY': (array([ 1.49811749e+09]),
array([ 2.09327495e+14]))}
Values¶
Values can be scalar (floating point values or string) or vectors. If in a query the length of the vectors is the same, as 2D array is returned, else a list of arrays is returned instead.
In [18]:
# prepare for plotting
%matplotlib notebook
import matplotlib.pyplot as pl
In [19]:
ts=pytimber.parsedate("2016-07-01 03:10:15.000")
ib1="LHC.BCTFR.A6R4.B1:BUNCH_INTENSITY"
ib2="LHC.BCTFR.A6R4.B2:BUNCH_INTENSITY"
data=db.get([ib1,ib2],ts,'next')
timestamps,valuesb1=data[ib1]
timestamps,valuesb2=data[ib2]
pl.figure()
pl.plot(valuesb1[0])
pl.plot(valuesb2[0])
pl.xlabel("slot number")
pl.ylabel("protons per bunch")
Out[19]:
<matplotlib.text.Text at 0x7f41be549940>
In [20]:
ts=pytimber.parsedate("2016-07-01 03:10:15.000")
ib1="LHC.BCTFR.A6R4.B1:BUNCH_INTENSITY"
ib2="LHC.BCTFR.A6R4.B2:BUNCH_INTENSITY"
data=db.get([ib1,ib2],ts,ts+60)
timestamps,valuesb1=data[ib1]
timestamps,valuesb2=data[ib2]
In [21]:
pl.figure()
pl.imshow(valuesb1,aspect='auto',origin='bottom')
pl.ylabel('seconds'); pl.xlabel("slot number")
pl.colorbar(); pl.clim(9e10,12e10)
In [22]:
pl.figure()
pl.imshow(valuesb2,aspect='auto',origin='bottom')
pl.ylabel('seconds'); pl.xlabel("slot number")
pl.colorbar(); pl.clim(9e10,12e10)
