def profile_rows(sdata,rows):
li = []
for col in range (0,12):
li.append(0)
for row in range (0, len(rows)):
wellname = rows[row] + str(col+1)
welldata = clas.findRowByWellName(wellname, sdata)
if welldata:
value = clas.getBBValue(welldata)
li[col] += value
li[col] = round (li[col]/len(rows),3)
return li
def profile_ToF(sdata):
li = []
for col in range (1,25):
Hwell = 'H' + str(col)
Iwell = 'I' + str(col)
Hdata = clas.findRowByWellName(Hwell, sdata)
Idata = clas.findRowByWellName(Iwell, sdata)
H_val = clas.getBBToF (Hdata)
I_val = clas.getBBToF (Idata)
if H_val and I_val:
avgval = round((H_val + I_val)/2,3)
li.append(avgval)
return li
def findKernel2(flist, Ptype, Resin):
AvgList = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
]
numfiles = 0
if not (Ptype == 'BBV' or Ptype == 'Thk' or Ptype == 'BBV_Comp'):
# print ('Profile type not recognized. Type is \'BBV\' or \'Thk' or \'BBV_Comp')
return None
for f in flist:
if len(f) < 215:
sdata = clas.ReadEchoPlateSurveyLisa(f, checkfilename=False)
if Ptype == 'BBV':
lis = plate.profile(sdata)
elif Ptype == 'Thk':
lis = plate.profile_Thk(sdata)
elif Ptype == 'BBV_Comp':
if Resin == 'PPL':
lis = plate.profile_corrBBVpp(sdata, PPL_fit, PPL_xlimits)
if Resin == 'PPG':
lis = plate.profile_corrBBVpp(sdata, PPG_fit, PPG_xlimits)
if (len(lis) == 24):
numfiles += 1
for l in range(0, len(lis)):
AvgList[l] += lis[l]
for l in range(0, len(AvgList)):
AvgList[l] = AvgList[l] / numfiles
AvgList[l] = (round(AvgList[l], 4))
return (AvgList)
def findKernel(flist, Ptype, Ncol):
AvgList = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
]
AvgList = AvgList[0:Ncol]
numfiles = 0
Ptypes = ('BBV', 'Thk', 'BBV_Comp')
if not Ptype in Ptypes:
print("""Profile type not regognized. Types are {0}, {1}, {2} """.
format(Ptypes[0], Ptypes[1], Ptypes[2]))
return None
for f in flist:
if len(f) < 215:
sdata = clas.ReadEchoPlateSurveyLisa(f, checkfilename=False)
if Ptype == 'BBV':
lis = plate.profile(sdata)
elif Ptype == 'Thk':
lis = plate.profile_Thk(sdata)
elif Ptype == 'BBV_Comp':
lis = plate.profile_corrBBVpp(sdata, PPL_fit, PPL_xlimits)
if (len(lis) == Ncol):
numfiles += 1
for l in range(0, len(lis)):
AvgList[l] += lis[l]
# divide by length of List:
print(len(AvgList), 'AvgList length', numfiles, ' Files included')
for l in range(0, len(AvgList)):
AvgList[l] = AvgList[l] / numfiles
AvgList[l] = (round(AvgList[l], 4))
return (AvgList)
def findKernelN(flist, Ptype):
AvgList = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
]
numfiles = 0
if not (Ptype == 'BBV' or Ptype == 'Thk' or Ptype == 'BBV_Comp'):
# print ('Profile type not recognized. Type is \'BBV\' or \'Thk' or \'BBV_Comp')
return None
for f in flist:
if len(f) < 215:
sdata = clas.ReadEchoPlateSurveyLisa(f, checkfilename=False)
if Ptype == 'BBV':
lis = plate.profile(sdata)
elif Ptype == 'Thk':
lis = plate.profile_Thk(sdata)
elif Ptype == 'BBV_Comp':
lis = plate.profile_corrBBVpp(sdata, PPL_fit, PPL_xlimits)
if (len(lis) == 24):
numfiles += 1
for l in range(0, len(lis)):
AvgList[l] += lis[l]
# divide by length of List:
for l in range(0, len(AvgList)):
AvgList[l] = AvgList[l] / numfiles
# return (AvgList)
#normalize list to mean = 1:
listAve = np.mean(AvgList)
for l in range(0, len(AvgList)):
AvgList[l] = AvgList[l] / listAve
AvgList[l] = (round(AvgList[l], 4))
return (AvgList)
def profile_TBV(sdata):
li = []
for col in range (1,25):
Hwell = 'H' + str(col)
Iwell = 'I' + str(col)
Hdata = clas.findRowByWellName(Hwell, sdata)
Idata = clas.findRowByWellName(Iwell, sdata)
H_Vpp = clas.getTBValue (Hdata)
I_Vpp = clas.getTBValue (Idata)
# print (H_Vpp, I_Vpp) Test because not all platesurvey files contain all wells
if H_Vpp and I_Vpp:
avgVpp = round((H_Vpp + I_Vpp)/2,4)
li.append(avgVpp)
return li
def profile_ToF_12(sdata):
li = []
for col in range (1,13):
Hwell = 'H' + str(col)
Iwell = 'I' + str(col)
Hdata = clas.findRowByWellName(Hwell, sdata)
Idata = clas.findRowByWellName(Iwell, sdata)
H_val = clas.getBBToF (Hdata)
I_val = clas.getBBToF (Idata)
# print (H_Vpp, I_Vpp) Test because not all platesurvey files contain all wells
if H_val and I_val:
avgval = round((H_val + I_val)/2,3)
li.append(avgval)
if H_val and not I_val:
li.append(H_val)
return li
def profile_Thk(sdata):
li = []
for col in range (1,25):
Hwell = 'H' + str(col)
Iwell = 'I' + str(col)
Hdata = clas.findRowByWellName(Hwell, sdata)
Idata = clas.findRowByWellName(Iwell, sdata)
H_val = clas.getMembraneThickness (Hdata)
I_val = clas.getMembraneThickness (Idata)
# print (H_Vpp, I_Vpp) Test because not all platesurvey files contain all wells
avgval = 0
if H_val and I_val:
avgval = round((H_val + I_val)/2,3)
elif H_val:
avgval = H_val
elif I_val:
avgval = I_val
li.append(avgval)
return li
def profile_corrBBVpp (sdata, fitFunction, xLimits):
li = []
scaleFactor = fitFunction(xLimits[1])
for col in range (1,25):
Hwell = 'H' + str(col)
Iwell = 'I' + str(col)
Hdata = clas.findRowByWellName(Hwell, sdata)
Idata = clas.findRowByWellName(Iwell, sdata)
if Hdata and Idata:
H_BBToF = (clas.getBBToF (Hdata))
I_BBToF = (clas.getBBToF (Hdata))
HFactor = fitFunction(H_BBToF)
IFactor = fitFunction(I_BBToF)
Hraw = clas.getBBValue (Hdata)
Iraw = clas.getBBValue (Idata)
Hcorr = Hraw * scaleFactor/HFactor
Icorr = Iraw * scaleFactor/IFactor
# print (Hwell, Hraw, H_BBToF, scaleFactor, HFactor, Hcorr)
avg_corr = round((Hcorr + Icorr)/2,4)
li.append(avg_corr)
return li
def profile(sdata):
li = []; pctDeltaList = []
for col in range (1,25):
Hwell = 'H' + str(col)
Iwell = 'I' + str(col)
Hdata = clas.findRowByWellName(Hwell, sdata)
Idata = clas.findRowByWellName(Iwell, sdata)
H_Vpp = clas.getBBValue (Hdata)
I_Vpp = clas.getBBValue (Idata)
# print (H_Vpp, I_Vpp) Test because not all platesurvey files contain all wells
if H_Vpp and I_Vpp:
avgVpp = round((H_Vpp + I_Vpp)/2,4)
pctDelta = abs((H_Vpp - I_Vpp)/avgVpp); pctDeltaList.append (pctDelta)
li.append(avgVpp)
if H_Vpp and not I_Vpp:
avgVpp = H_Vpp
li.append(avgVpp)
# maxPctDelta = max(pctDeltaList) #; print ("%.4f"%maxPctDelta, f)
return li #, maxPctDelta
def profile_exclude_outliers(sdata,rows):
li = []
for col in range (0,12):
li.append ([])
for row in range (0, len(rows)):
wellname = rows[row] + str(col+1)
welldata = clas.findRowByWellName(wellname, sdata)
if welldata:
value = clas.getBBValue(welldata)
li[col].append (value)
wellMean = numpy.mean(li[col])
fivesig = 5* numpy.std(li[col])
if fivesig > wellMean:
print (wellMean, f)
li[col].remove(min(li[col]))
for v in li[col]:
oneError = abs((v - wellMean)/wellMean)
if oneError > (0.10):
# print (col, v)
li[col].remove(v)
li[col] = numpy.mean(li[col])
# li[col] = round (li[col]/len(rows),3)
return li
def profile_imped(sdata):
li = []
for col in range (1,25):
Hwell = 'H' + str(col)
Iwell = 'I' + str(col)
Hdata = clas.findRowByWellName(Hwell, sdata)
Idata = clas.findRowByWellName(Iwell, sdata)
H_num = clas.getTBValue (Hdata)
I_num = clas.getTBValue (Idata)
H_denom = clas.getBBValue(Hdata)
I_denom = clas.getBBValue(Idata)
if (H_num and I_num and H_denom and I_denom):
H_ratio = H_num/H_denom
I_ratio = I_num/I_denom
avgval = round((H_ratio + I_ratio)/2,3)
li.append(avgval)
return li