| 1 | # Programm zur Bestimmung der Genauigkeit des konvergierten Algorithmus
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| 2 | #
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| 3 | # Remo Dietlicher
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| 4 | #
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| 5 | #
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| 6 |
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| 7 | from optparse import OptionParser
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| 8 | import pyfact
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| 9 | from myhisto import *
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| 10 | from hist import *
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| 11 | import numpy as np
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| 12 | import numpy.random as rnd
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| 13 | from ROOT import *
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| 14 | from time import time
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| 15 |
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| 16 | def periods2(Data, CellTime, h):
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| 17 |
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| 18 | NROI = 1024 # length of the DRS pipeline
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| 19 | fsampling = 2. # sampling frequency
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| 20 | freq = 250. # testfrequency
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| 21 | P_nom = 1000./freq # nominal Period due to testfrequency
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| 22 |
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| 23 |
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| 24 | nomCellTime = np.linspace(0., 1024., 1025)/fsampling
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| 25 |
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| 26 |
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| 27 | # FUNCTION TO DETERMINE CROSSINGS
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| 28 |
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| 29 | def Crossing(Mean, rCellTime):
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| 30 | TimeXing = np.zeros(NROI)
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| 31 | MeanXing = np.zeros(NROI)
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| 32 | NumXing = 0
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| 33 |
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| 34 | for i in range(NROI-1):
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| 35 | if ((Data[i] > Mean) & (Data[i+1] < Mean)):
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| 36 | MeanXing[NumXing] = i
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| 37 |
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| 38 | FirstCell = rCellTime[i]
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| 39 | SecondCell = rCellTime[i+1]
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| 40 |
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| 41 | TimeXing[NumXing] = FirstCell+(SecondCell-FirstCell)/(1.-Data[i+1]/(Data[i]))*(1.-Mean/(Data[i]))
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| 42 | NumXing += 1
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| 43 |
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| 44 | return MeanXing, TimeXing
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| 45 |
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| 46 | def CalculatePeriods(rCellTime, name):
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| 47 |
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| 48 | Period = np.zeros(126)
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| 49 |
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| 50 | MeanXing, TimeXing = Crossing(np.average(Data), rCellTime)
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| 51 |
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| 52 | for i in range(int(126)):
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| 53 | Period[i] = TimeXing[i+1] - TimeXing[i]
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| 54 |
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| 55 | for val in Period:
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| 56 | h.dict[str(name)].Fill(val)
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| 57 |
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| 58 | return
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| 59 |
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| 60 | CalculatePeriods(CellTime, "avperiods")
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| 61 | CalculatePeriods(nomCellTime, "avperiods0")
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| 62 |
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| 63 | return
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| 64 |
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| 65 |
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| 66 |
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| 67 |
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