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#!/usr/bin/env python3
import sys
import math
from ROOT import gROOT, TClass, TFile
from copy import deepcopy
from scipy import signal
gROOT.SetBatch() # don't pop up canvases
class InefficientDeadROCs:
############################################################################
def __TraverseDirTree(self, dir):
for obj in dir.GetListOfKeys():
if not obj.IsFolder():
if obj.ReadObjectAny(TClass.GetClass("TH2")):
th1 = deepcopy(obj.ReadObj())
name = th1.GetName()
if name.startswith(self.lookForStr): #take only module lvl plots
# print(''.join([dir.GetPath(), '/', name]))
newName = name.split(self.lookForStr)[1]
th1.SetName(newName)
# used to sort outputs by disk/layer
layer = 0
# print(newName)
if newName.startswith("B"):
layer = "B" + ((newName.split("_LYR"))[1])[0]
else:
layer = ((newName.split("_D"))[1])[0]
if newName.startswith("FPix_Bm"):
layer = "-" + layer
layer = "F" + layer
if layer in self.dicOfModuleHistograms:
self.dicOfModuleHistograms[layer].append(th1)
else:
self.dicOfModuleHistograms.update({layer : [th1]})
else:
self.__TraverseDirTree(obj.ReadObj())
def __init__(self, inputDQMName, outputFileName, noiseOutputFileName, dirs):
self.inputFileName = inputDQMName
self.outputFileName = outputFileName
self.noiseOutputFileName = noiseOutputFileName
self.dirs = dirs
self.lookForStr = "digi_occupancy_per_col_per_row_"
self.rocMaxCol = 52
self.rocMaxRow = 80
self.rocsInRow = 8
self.rocsInCol = 2
self.inputFile = TFile(self.inputFileName)
self.dicOfModuleHistograms = {}
### THRESHOLDS SECTION
self.pixelNoisynessTh = 6
self.rocOccupancyTh = 200
self.barrelNoisyColumnTh = 1.35
self.barrelNoisyColumnTh2 = 4.5
self.endcapNoisyColumnTh = 1.5
self.barrelInefficientDColTh = 8#2.5
self.endcapInefficientDColTh = 30#8
### ###################
if self.inputFile.IsOpen():
print("%s opened successfully!" % (self.inputFileName))
#Get all neeeded histograms
for dir in self.dirs:
self.__TraverseDirTree(self.inputFile.Get(dir))
print("Histograms to read: %d" % (len(self.dicOfModuleHistograms)))
self.detDict = {}
else:
print("Unable to open file %s" % (self.inputFileName))
def __lmsExp(self, data, xMin, xMax):
meanOfX = (xMax + xMin) * 0.5
meanOfY = sum( [math.log(data[i]) for i in range(len(data))] ) / len(data)
D = 0
for i in range(xMin, xMax + 1):
D = D + (i - meanOfX)**2
# print(D)
a = 0
for i in range(len(data)):
a = a + math.log(data[i]) * (xMin + i - meanOfX)
a = a/D
lnb = meanOfY - a * meanOfX
return a, math.exp(lnb)
def __lmsLin(self, data, xMin, xMax):
meanOfX = (xMax + xMin) * 0.5
meanOfY = sum(data) / len(data)
D = 0
for i in range(xMin, xMax + 1):
D = D + (i - meanOfX)**2
a = 0
for i in range(len(data)):
a = a + data[i] * (xMin + i - meanOfX)
a = a/D
b = meanOfY - a * meanOfX
return a, b, D
def __customMedianFilter(self, array, radius = 2):
# contrary to scipy implementation it provides adaptive kernel size instead of copying data on boundaries
filtered = [0 for i in range(len(array))]
currArray = []
for i in range(len(array)):
if i - radius < 0:
currArray = array[0 : i + radius + 1]
elif i + radius + 1 >= len(array):
currArray = array[i - radius : ]
currArray.sort()
filtered[i] = currArray[len(currArray) // 2]
return filtered
def __getROCData(self, hist, startPixel, endPixel, row, repeatFilter = 3, filterKernelSize = 5):
pixelArr = []
columnsWithSuspiciouslyNoisyPixels = []
for x in range(startPixel, endPixel):
columnPixels = [hist.GetBinContent(x, y + 1) for y in range(row * self.rocMaxRow, (row + 1) * self.rocMaxRow)]
columnSum = sum(columnPixels)
columnMean = columnSum / len(columnPixels)
for i in range(len(columnPixels)):
if columnPixels[i] > self.pixelNoisynessTh * columnMean:
# col = (startPixel % self.rocMaxCol) + 1
columnsWithSuspiciouslyNoisyPixels.append(x)
# print("WARNING:\t %s : %dx%d:%d may contain NOISY PIXELS instead of NOISY COLUMNS" % (hist.GetName(), col, row + 1, startPixel + i))
break
pixelArr.append(columnSum)
if len(pixelArr) == 0:
return None, None, None # ROC down
medFiltRes, sciPyMedFiltRes = deepcopy(pixelArr), deepcopy(pixelArr)
for i in range(repeatFilter):
sciPyMedFiltRes = signal.medfilt(sciPyMedFiltRes, filterKernelSize) # 5 is obligatory to filter doublets!!!
medFiltRes = self.__customMedianFilter(medFiltRes, filterKernelSize // 2)
return pixelArr, medFiltRes, columnsWithSuspiciouslyNoisyPixels, sciPyMedFiltRes
def __getPixelArrWithRemovedDrops(self, pixelArr, medFiltRes):
return [ (pixelArr[i] if pixelArr[i] > medFiltRes[i] else medFiltRes[i]) if 0 < i < len(pixelArr) - 1 else min(medFiltRes) for i in range(len(pixelArr))]
def __normalizeArray(self, pixelArr):
c_min, c_max = min(pixelArr), max(pixelArr)
if c_min != c_max:
c_diff_inv = 1.0 / (c_max - c_min)
return [ (pixelArr[i] - c_min) * c_diff_inv for i in range(len(pixelArr))]
return [0 for i in range(len(pixelArr))]
def __setNormalizedArrayZeroInThePoint(self, pixelArr, pt):
c_diff_inv = 1.0 / (1.0 - pt)
return [ (pixelArr[i] - pt) * c_diff_inv for i in range(len(pixelArr))]
def __determineBarrelNoise(self, noiseFile, columnsWithSuspiciouslyNoisyPixels, histName, meanOfPixels, maxMed, val, pos, rocCol, rocRow):
noisyROC = False;
if meanOfPixels < self.rocOccupancyTh:
#print("Very low mean occupancy: %f in %s in (col, row) (%d, %d)...\tSkipping noisy ROC calculation" % (meanOfPixels, histName, rocCol, rocRow) )
noisyROC = True
else:
th = self.barrelNoisyColumnTh * maxMed
if val > th:
if pos not in columnsWithSuspiciouslyNoisyPixels:
rocNum, xCoordInROC = self.__convertCoordinatesFromHistToROCSpace(histName, pos, rocRow)
noiseFile.write("%s\t(x, row)->[rocNum, xRoc]\t(%d, %d)->[%d, %d];\t{VAL, TH}\t{%f, %f}\n" % (histName, pos, rocRow+1, rocNum, xCoordInROC, val, th))
return 1, noisyROC
# else:
# print("WARNING: rejecting %s (x, row) (%d, %d) as being affected by a few noisy pixel(s)" % (histName, pos, rocRow+1))
return 0, noisyROC
def __determineBarrelNoise2(self, noiseFile, columnsWithSuspiciouslyNoisyPixels, histName, meanOfPixels, normMeanOfPixels, normVal, pos, rocCol, rocRow):
noisyROC = False;
if meanOfPixels < self.rocOccupancyTh:
#print("Very low mean occupancy: %f in %s in (col, row) (%d, %d)...\tSkipping noisy ROC calculation" % (meanOfPixels, histName, rocCol, rocRow) )
noisyROC = True
else:
th = self.barrelNoisyColumnTh2 * normMeanOfPixels
if normVal > th:
if pos not in columnsWithSuspiciouslyNoisyPixels:
rocNum, xCoordInROC = self.__convertCoordinatesFromHistToROCSpace(histName, pos, rocRow)
noiseFile.write("%s\t(x, row)->[rocNum, xRoc]\t(%d, %d)->[%d, %d];\t{NORMVAL, TH}\t{%f, %f}\n" % (histName, pos, rocRow+1, rocNum, xCoordInROC, normVal, th))
return 1, noisyROC
# else:
# print("WARNING: rejecting %s (x, row) (%d, %d) as being affected by a few noisy pixel(s)" % (histName, pos, rocRow+1))
return 0, noisyROC
def __determineEndcapNoise(self, noiseFile, columnsWithSuspiciouslyNoisyPixels, histName, meanOfPixels, linVal, val, pos, rocCol, rocRow):
noisyROC = False;
if meanOfPixels < self.rocOccupancyTh:
# print("Very low mean occupancy: %f in %s in (col, row) (%d, %d)...\tSkipping noisy ROC calculation" % (meanOfPixels, histName, rocCol, rocRow) )
noisyROC = True
else:
th = self.endcapNoisyColumnTh * linVal
if val > th:
if pos not in columnsWithSuspiciouslyNoisyPixels:
rocNum, xCoordInROC = self.__convertCoordinatesFromHistToROCSpace(histName, pos, rocRow)
noiseFile.write("%s\t(x, row)->[rocNum, xRoc]\t(%d, %d)->[%d, %d];\t{VAL, TH}\t{%f, %f}\n" % (histName, pos, rocRow+1, rocNum, xCoordInROC, val, th))
return 1, noisyROC
# else:
# print("WARNING: rejecting %s (x, row) (%d, %d) as being affected by a few noisy pixel(s)" % (histName, pos, rocRow+1))
return 0, noisyROC
def __convertCoordinatesFromHistToROCSpace(self, histName, histXpos, histRow):
tempXROC = (histXpos / self.rocMaxCol) # 0,...,7
tempYROC = histRow
tempXCoordInROC = histXpos % self.rocMaxCol
realXROC, realYROC = tempXROC, tempYROC
xCoordInROC = tempXCoordInROC
rocNum = 0
if histName.find("BPix_Bp") != -1: #zero ROC is in top left corner
realYROC = 1 - tempYROC
if realYROC == 1:
rocNum = 15 - realXROC
xCoordInROC = self.rocMaxCol - 1 - xCoordInROC
else:
rocNum = realXROC
else: # zero ROC is in bottom right corner
realXROC = 7 - tempXROC
if realYROC == 1:
rocNum = 15 - realXROC
else:
rocNum = realXROC
xCoordInROC = self.rocMaxCol - 1 - xCoordInROC
return rocNum, xCoordInROC
def __determineBarrelDColInefficiencyAndNoise(self, medFiltRes, histName, pixelArr, pixelArrWithoutDrops, startPixel, rocCol, rocRow, outputFile, columnsWithSuspiciouslyNoisyPixels, noiseFile):
meanOfPixels = sum(medFiltRes) / len(medFiltRes)
maxMed = max(medFiltRes)
minMed = min(medFiltRes)
normMeanOfPixels = sum(pixelArrWithoutDrops) / len(pixelArrWithoutDrops)
# print( meanOfPixels, maxMed, minMed )
doubleDeadCols = 0
noisyColsNum = 0
noisyROC = 0
# for x in range(startPixel, endPixel, 1):
for i in range(1, len(pixelArr) - 2):
# print(i , i + 1)
bin1valDiff = minMed - pixelArr[i + 0]#hist.GetBinContent(x+0)
bin2valDiff = minMed - pixelArr[i + 1]
# WE ONLY WANT A SET OF TWO COLUMNS SO ADJACENT COLUMNS HAVE TO BE NORMAL
bin0valDiff = minMed - pixelArr[i - 1]
bin3valDiff = minMed - pixelArr[i + 2]
# currentDoubleBinThreshold = minMed / math.sqrt(meanOfPixels) * self.barrelInefficientDColTh # error in bin entry grows as sqrt(N)
currentDoubleBinThreshold = math.sqrt(meanOfPixels) * self.barrelInefficientDColTh # error in bin entry grows as sqrt(N)
if bin1valDiff > currentDoubleBinThreshold and bin2valDiff > currentDoubleBinThreshold and not bin3valDiff > currentDoubleBinThreshold and not bin0valDiff > currentDoubleBinThreshold:
doubleColInRoc = ((i + startPixel) % (self.rocMaxCol)) // 2 + 1
doubleDeadCols = doubleDeadCols + 1
# outputFile.write("%s,\tX: %d-%d\tROC COLUMN: %d\tROC ROW: %d\tDOUBLE COL IN ROC: %d\tTH: %f\tMIN IN ROC: %f\tBINVAL: %f\n" % (histName, startPixel + (i + 0), startPixel + (i + 1), rocCol, rocRow, doubleColInRoc, currentDoubleBinThreshold, minMed, pixelArr[i]))
rocNum, xCoordInROC = self.__convertCoordinatesFromHistToROCSpace(histName, startPixel + i, rocRow)
outputFile.write("%s\t(x, row)->[rocNum, doubleXPixelColInROC]\t(%d, %d)->[%d, %d];\t{MIN - VAL, TH}\t{%f, %f}\n" % (histName, startPixel + i, rocRow + 1, rocNum, xCoordInROC / 2, bin1valDiff, currentDoubleBinThreshold))
# HANDLE NOISY PIXELS
if noisyROC == True: #don't go inside if noisyness was determined already
continue
# res = self.__determineBarrelNoise(noiseFile, columnsWithSuspiciouslyNoisyPixels, histName, meanOfPixels, maxMed, pixelArr[i], startPixel + i, rocCol, rocRow)
# noisyColsNum, noisyROC = noisyColsNum + res[0], res[1]
# if i == len(pixelArr) - 3: # CHECK NOISYNESS IN THE RIGHTMOST INNER COL
# res = self.__determineBarrelNoise(noiseFile, columnsWithSuspiciouslyNoisyPixels, histName, meanOfPixels, maxMed, pixelArr[i + 1], startPixel + i + 1, rocCol, rocRow)
# noisyColsNum, noisyROC = noisyColsNum + res[0], res[1]
# NORMALIZED MEAN NOISE DETERMINATION METHOD
res = self.__determineBarrelNoise2(noiseFile, columnsWithSuspiciouslyNoisyPixels, histName, meanOfPixels, normMeanOfPixels, pixelArrWithoutDrops[i], startPixel + i, rocCol, rocRow)
noisyColsNum, noisyROC = noisyColsNum + res[0], res[1]
if i == len(pixelArr) - 3: # CHECK NOISYNESS IN THE RIGHTMOST INNER COL
res = self.__determineBarrelNoise2(noiseFile, columnsWithSuspiciouslyNoisyPixels, histName, meanOfPixels, normMeanOfPixels, pixelArrWithoutDrops[i + 1], startPixel + i + 1, rocCol, rocRow)
noisyColsNum, noisyROC = noisyColsNum + res[0], res[1]
return doubleDeadCols, noisyColsNum
def __determineEndcapDColInefficiencyAndNoise(self, medFiltRes, histName, pixelArr, startPixel, rocCol, rocRow, outputFile, columnsWithSuspiciouslyNoisyPixels, noiseFile):
doubleDeadCols = 0
noisyColsNum = 0
noisyROC = 0
useLin = True
# <D> might be used for high noise ROC recognition
a, b, D = self.__lmsLin(medFiltRes, startPixel, len(medFiltRes) + startPixel)
meanOfPixels = sum(medFiltRes) / len(medFiltRes)
# for x in range(startPixel, endPixel, 1):
for i in range(1, len(pixelArr) - 2):
if useLin == True:
linVal1 = a * (i + startPixel + 0) + b
linVal2 = a * (i + startPixel + 1) + b
linVal0 = a * (i + startPixel - 1) + b
linVal3 = a * (i + startPixel + 2) + b
else:
linVal1 = b * math.exp(a * (i + startPixel + 0))
linVal2 = b * math.exp(a * (i + startPixel + 1))
linVal0 = b * math.exp(a * (i + startPixel - 1))
linVal3 = b * math.exp(a * (i + startPixel + 2))
bin1valDiff = linVal1 - pixelArr[i + 0]
bin2valDiff = linVal2 - pixelArr[i + 1]
# WE ONLY WANT A SET OF TWO COLUMNS SO ADJACENT COLUMNS HAVE TO BE NORMAL
bin0valDiff = linVal0 - pixelArr[i - 1]
bin3valDiff = linVal3 - pixelArr[i + 2]
try:
currentDoubleBinThreshold = math.sqrt((linVal1 + linVal2) * 0.5) * self.endcapInefficientDColTh
except:
# print(a, b, startPixel, i, linVal1, linVal2)
continue
if bin1valDiff > currentDoubleBinThreshold and bin2valDiff > currentDoubleBinThreshold and not bin3valDiff > currentDoubleBinThreshold and not bin0valDiff > currentDoubleBinThreshold:
doubleColInRoc = ((i + startPixel) % (self.rocMaxCol)) // 2 + 1
doubleDeadCols = doubleDeadCols + 1
# outputFile.write("%s,\tX: %d-%d\tROC COLUMN: %d\tROC ROW: %d\tDOUBLE COL IN ROC: %d\tTH: %f\tLINVAL: %f\tBINVAL: %f\n" % (histName, startPixel + (i + 0), startPixel + (i + 1), rocCol, rocRow, doubleColInRoc, currentDoubleBinThreshold, linVal1, pixelArr[i]))
rocNum, xCoordInROC = self.__convertCoordinatesFromHistToROCSpace(histName, startPixel + i, rocRow)
outputFile.write("%s\t(x, row)->[rocNum, doubleXPixelColInROC]\t(%d, %d)->[%d, %d];\t{LIN(x) - VAL, TH}\t{%f, %f}\n" % (histName, startPixel + i, rocRow + 1, rocNum, xCoordInROC / 2, bin1valDiff, currentDoubleBinThreshold))
# HANDLE NOISY PIXELS
if noisyROC == True: #don't go inside if noisyness was determined already
continue
res = self.__determineEndcapNoise(noiseFile, columnsWithSuspiciouslyNoisyPixels, histName, meanOfPixels, linVal1, pixelArr[i], i + startPixel, rocCol, rocRow)
noisyColsNum, noisyROC = noisyColsNum + res[0], res[1]
if i == len(pixelArr) - 3: # CHECK NOISYNESS IN THE RIGHTMOST INNER COL
res = self.__determineEndcapNoise(noiseFile, columnsWithSuspiciouslyNoisyPixels, histName, meanOfPixels, linVal2, pixelArr[i + 1], i + 1 + startPixel, rocCol, rocRow)
noisyColsNum, noisyROC = noisyColsNum + res[0], res[1]
return doubleDeadCols, noisyColsNum
def ReadHistograms(self):
doubleDeadCols, noisyColsNum = 0, 0
with open(self.noiseOutputFileName, "w") as noiseFile:
with open(self.outputFileName, "w") as outputFile:
for layer in self.dicOfModuleHistograms:
doubleDeadColsInLayer, noisyColsNumInLayer = 0, 0
outputFile.write("-> " + layer + "\n\n")
noiseFile.write("-> " + layer + "\n\n")
for hist in self.dicOfModuleHistograms[layer]:
for row in range(2):
for rocNum in range(self.rocsInRow):
startPixel = rocNum * self.rocMaxCol + 1
endPixel = (rocNum + 1) * self.rocMaxCol + 1 # - 1 ???
rocCol = rocNum + 1
pixelArr, medFiltRes, columnsWithSuspiciouslyNoisyPixels, sciPyMedFiltRes = self.__getROCData(hist, startPixel, endPixel, row, 3, 5)
if pixelArr == None:
continue
# meanOfPixels = sum(pixelArr) / len(pixelArr)
# pixelArrSorted = deepcopy(pixelArr)
# pixelArrSorted.sort()
# outputFile.write("%s: <x> <med_min> VS. <med_max> | x_min:\t%f %f %f | %f, %f, %f, %f\n" % (hist.GetName(), meanOfPixels, min(medFiltRes), max(medFiltRes), pixelArrSorted[0], pixelArrSorted[1], pixelArrSorted[2], pixelArrSorted[3]))
if "F" not in layer:
# pixelArrWithoutDrops = self.__getPixelArrWithRemovedDrops(pixelArr, medFiltRes)
pixelArrWithoutDrops = self.__getPixelArrWithRemovedDrops(pixelArr, sciPyMedFiltRes)
pixelArrWithoutDropsNormalized = self.__normalizeArray(pixelArrWithoutDrops)
# tmp_mean = sum(pixelArrWithoutDropsNormalized) / len(pixelArrWithoutDropsNormalized)
# pixelArrWithoutDropsNormalized = self.__setNormalizedArrayZeroInThePoint(pixelArrWithoutDropsNormalized, tmp_mean)
# print(min(pixelArrWithoutDropsNormalized), max(pixelArrWithoutDropsNormalized))
result = self.__determineBarrelDColInefficiencyAndNoise(medFiltRes, hist.GetName(), pixelArr, pixelArrWithoutDropsNormalized, startPixel, rocCol, row, outputFile, columnsWithSuspiciouslyNoisyPixels, noiseFile)
else:
result = self.__determineEndcapDColInefficiencyAndNoise(medFiltRes, hist.GetName(), pixelArr, startPixel, rocCol, row, outputFile, columnsWithSuspiciouslyNoisyPixels, noiseFile)
doubleDeadCols, doubleDeadColsInLayer = doubleDeadCols + result[0], doubleDeadColsInLayer + result[0]
noisyColsNum, noisyColsNumInLayer = noisyColsNum + result[1], noisyColsNumInLayer + result[1]
outputFile.write("\n\tTOTAL IN LAYER/DISK: %d\n\n" % (doubleDeadColsInLayer))
noiseFile.write("\n\tTOTAL IN LAYER/DISK: %d\n\n" % (noisyColsNumInLayer))
print("Number of inefficient double columns: %d"%(doubleDeadCols))
print("Number of noisy cols: %d"%(noisyColsNum))
#--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--
for i in range(1, len(sys.argv), 1):
if i == 1:
inputFileName = sys.argv[i]
runNum = ((inputFileName.split("/")[-1].split("."))[0].split("_R000"))[1]
print("Run number: %s"%(runNum))
baseRootDir = ["DQMData/Run " + runNum + "/PixelPhase1/Run summary/Phase1_MechanicalView"]
print(baseRootDir[0])
outputFileName = "inefficientDPixelColumns.txt"
noiseOutputFileName = "noisyPixelColumns.txt"
readerObj = InefficientDeadROCs(inputFileName, outputFileName, noiseOutputFileName, baseRootDir)
readerObj.ReadHistograms()
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