def __init__(self):
cwd = os.getcwd()
sys.path.append(cwd)
tspath = getTopOfSuitesDir()
assert (os.path.exists(tspath))
ls = os.listdir(tspath)
ls = [os.path.join(tspath, f) for f in ls]
dirs = [(os.path.join(cwd, d), []) for d in ls
if os.path.isdir(d) and d.find('.svn') == -1]
toFind = ['TestCase.py', 'RTTSummaryReference.xml']
for fn in toFind:
print
for (dir, missing) in dirs:
if not os.path.exists(os.path.join(dir, fn)):
missing.append(fn)
fn = 'RTTSummary.xml'
for (dir, missing) in dirs:
if len(listFiles(dir, fn)) != 1:
missing.append(fn)
self.okdirs = [d for (d, missing) in dirs if len(missing) == 0]
self.baddirs = [(d, missing) for (d, missing) in dirs
if len(missing) != 0]
def FCSLoadG(fnameRoot, folderName="", printFileNames=True):
G = correlations()
files = listFiles(folderName, "csv", fnameRoot)
for file in files:
setattr(G, stripGfname(file, fnameRoot, printFileNames), csv2array(file, ','))
G.dwellTime = 1e6 * csv2array(file, ',')[1, 0] # in µs
print('--------------------------')
print(str(len(files)) + ' files found.')
print('--------------------------')
return G
def setUp(self):
if 'dom' in self.__dict__.keys(): return
infileName = listFiles('.', 'RTTSummary.xml')[0]
infile = open(infileName, 'r')
refFile = open('RTTSummaryReference.xml')
self.dom = xml.dom.minidom.parseString(infile.read())
self.refDom = xml.dom.minidom.parseString(refFile.read())
self.logfile = open('TestCase.log', 'w')
def allBin2Pickle(
directory='C:\\Users\\SPAD-FCS\\OneDrive - Fondazione Istituto Italiano Tecnologia'
):
"""
Convert all FCS bin files in the given directory to picle files.
Files that already already have a bin file are skipped
return parameter: data from the last file
"""
binfiles = listFiles(directory, 'bin')
picklefiles = listFiles(directory, 'pickle')
data = []
for file in binfiles:
picklefile = file[0:-4] + '_data.pickle'
if picklefile not in picklefiles:
print(file)
data = binFile2Data(file, storePickle=True)
print('Done.')
return (data)
def FCSBinToCSVAll(folderName=[],
Glist=['central', 'sum3', 'sum5', 'chessboard', 'ullr'],
split=10):
# PARSE INPUT
if folderName == []:
folderName = getcwd()
folderName = folderName.replace("\\", "/")
folderName = Path(folderName)
# CHECK BIN FILES
allFiles = listFiles(folderName, 'bin')
# GO THROUGH EACH FILE
for file in allFiles:
fileName = ntpath.basename(file)
print("File found: " + fileName)
[G, data] = FCSLoadAndCorrSplit(file, Glist, 50, split)
corr2csv(G, file[0:-4], [0, 0], 0)
def logFileChecker(root, globpar, strings):
res = 'looking for files below %s matching %s\n' % (root, globpar)
logfiles = listFiles(root, globpar)
res += 'Found %d log files\n' % len(logfiles)
checker = Checker(strings)
[checker.checkForStrings(f) for f in logfiles]
res += '\nerror strings:\n'
dict = checker.dict
res += 'Occurences text\n'
for s in dict:
if dict[s] > 0: res += '%10d %s\n' % (dict[s], s)
res += '\n Strings searched for:\n'
for s in dict.keys():
res += s + '\n'
for s in checker.fdict.keys():
if len(checker.fdict[s]):
res += '\n\n"%s" was found in %d files:\n' % (
s, len(checker.fdict[s]))
for f in checker.fdict[s]:
res += f + '\n'
else:
res += '\n"%s" was found in no files\n\n' % s
res += '\n\n%d files with no strings:\n\n' % (
checker.sdict.values()).count([])
for f in checker.sdict.keys():
if not checker.sdict[f]: res += f + '\n'
return res
def plotPyCorrAll(folderName=[]):
if folderName == []:
folderName = getcwd()
folderName = folderName.replace("\\", "/")
folderName = Path(folderName)
fileList = listFiles(folderName, 'csv')
data = np.empty((0, 3), float)
# go through each file
for file in fileList:
if "chunk" not in file and "Gn" not in file and "sum3_average_fit_results" in file and "SPAD" in ntpath.basename(
file):
# file found
print(ntpath.basename(file) + " found.")
if "fit_results" in file:
# file with fit found
result = plotPyCorrFit(file, savefig=0)
data = np.append(
data, [[result.Gfitstart, result.tauD, result.chi2]],
axis=0)
else:
# file with experimental G found
plotGcsv(file, savefig=0)
return data
def doit():
cwd = os.getcwd()
sdir = os.path.join(cwd, getSuiteDir())
if not os.path.exists(sdir):
print 'Non -existant test suite directory ', sdir
sys.exit(0)
rfile = os.path.join(sdir, 'RTTSummaryReference.xml')
if not os.path.exists(os.path.join(sdir,rfile)):
print 'No reference file ', rfile
sys.exit(0)
parser = xml.sax.make_parser()
handler = KFHandler()
parser.setContentHandler(handler)
parser.parse(rfile)
rkfiles = handler.kfiles
lfiles = listFiles(sdir, 'RTTSummary.xml')
if len(lfiles) != 1:
print 'error looking for RTTSummaryFile', lfiles
sys.exit(0)
handler = KFHandler()
parser.setContentHandler(handler)
parser.parse(lfiles[0])
skfiles = handler.kfiles
diff = [f for f in rkfiles if f not in skfiles]
pp = pprint.PrettyPrinter()
print
print ' in Reference and not in Summary'
pp.pprint(diff)
diff = [f for f in skfiles if f not in rkfiles]
print
print ' in Summary and not in Reference'
pp.pprint(diff)
m += '\n string: %s\n' % s
for l in self.dict[s]:
m += ' %s' % l
return m
def dump(self):
print self
config = open(sys.argv[1], 'r')
lines = config.readlines()
config.close()
toRemove = [l for l in lines if l.startswith('#')]
[lines.remove(l) for l in toRemove]
lines = [stripstr(l) for l in lines]
toRemove = [s for s in lines if len(s)==0]
[lines.remove(l) for l in toRemove]
root = lines[0]
globpar = lines[1]
strings = lines[2:]
print 'looking for files below', root,'matching',globpar
logfiles = listFiles(root, globpar)
print 'Found %d files' % len(logfiles)
checkers = [Checker(f, strings).dump() for f in logfiles]
def FCS2docx(folderName=[], Mtype='25ch'):
"""
Analyze FCS data and store results in image files and a .docx file
===========================================================================
Input Meaning
---------------------------------------------------------------------------
folderName Folder name with FCS data
Leave blank for current folder
===========================================================================
===========================================================================
Output
---------------------------------------------------------------------------
images of time traces, autocorrelations and an overview .docx file
===========================================================================
"""
# PARSE INPUT
if folderName == []:
folderName = getcwd()
folderName = folderName.replace("\\", "/")
folderName = Path(folderName)
# OPEN WORD FILE
document = Document()
section = document.sections[0]
section.page_height = Mm(297)
section.page_width = Mm(210)
section.bottom_margin = Mm(25)
section.top_margin = Mm(25)
section.left_margin = Mm(25)
section.right_margin = Mm(25)
# TITLE
title = date.today().strftime("%Y-%m-%d")
title = title + ' FCS analysis'
document.add_heading(title, 0)
# CHECK PICKLE FILES
outp = listFiles(folderName, 'bin')
# GO THROUGH EACH FILE
for file in outp:
if 'arrival_times' not in file:
# file with photon arrival times found
fileName = ntpath.basename(file)
print('==========================================================')
print('File found: ' + fileName)
print('==========================================================')
# ================================================================
document.add_heading('File ' + fileName, level=1)
# ================================================================
# get pixel dwell time from text file (TO DO)
FCSinfo = getFCSinfo(file[0:-4] + '_info.txt')
#pxdwell = 1e-3 / ReadSpeed # s
pxdwell = FCSinfo.dwellTime
# # open file
# # data = arrivalTimes2Data(file)
data = file_to_count(file)
data = data[0]
data = addSumColumn(data)
#
# # bin files for plotting time trace
print('Binning data for plotting time trace.')
binSize = 2000000
binDuration = pxdwell * binSize
Nrows = 1000000
# # -----
dataB = binData(data[0:Nrows, :], binSize)
plt.figure()
plt.plot(np.arange(0, binDuration * len(dataB), binDuration),
dataB[:, -1])
plt.xlabel('Time [s]')
plt.ylabel('Total # of photons per ' + str(1000 * binDuration) +
' ms')
plt.xlim([0, binDuration * len(dataB)])
plt.title(fileName[0:30] + '...')
plt.tight_layout()
picName = fileName[0:-4] + '_timetrace.png'
# plt.savefig(picName)
# # ================================================================
# document.add_heading('Time trace', level=2)
# document.add_picture(picName, width=Inches(4))
# # ================================================================
#
# # plot Airy pattern
# print('Calculating Airy pattern.')
# airy = plotAiry(data)
# picName = fileName[0:-4] + '_airy.png'
# plt.savefig(picName)
# # ================================================================
# document.add_heading('Airy pattern', level=2)
# document.add_paragraph('Number of photons per channel over the entire measurement')
# document.add_picture(picName, width=Inches(4))
# # ================================================================
#
# # calculate autocorrelations
# # if single element is used, only calculate autocorr of this element
# # else do complete calculation
# print('Calculating correlations.')
# =================================================================
document.add_heading('Correlations', level=2)
# =================================================================
zoom = int(1e-5 / pxdwell)
# if np.max(airy[0:25]) / airy[25] > 0.99:
# # single element used
# det = int(np.argmax(airy[0:25]))
# G = FCS2Corr(data, 1e6*pxdwell, [det])
# Gsingle = getattr(G, 'det'+ str(det))
# plotFCScorrelations(G, plotList='all')
# plt.xlim(left=2*pxdwell)
# plt.ylim(top=np.max(Gsingle[2:,1]))
# picName = fileName[0:-4] + '_G.png'
# plt.savefig(picName)
# # =============================================================
# document.add_heading('Autocorrelation single detector', level=3)
# document.add_picture(picName, width=Inches(4))
# # =============================================================
# plt.xlim(left=zoom*pxdwell)
# plt.ylim(top=np.max(Gsingle[zoom:,1]))
# picName = fileName[0:-4] + '_Gzoom.png'
# plt.savefig(picName)
# # =============================================================
# document.add_picture(picName, width=Inches(4))
# =============================================================
if Mtype == '2MPD':
G = FCS2CorrSplit(data, 1e6 * pxdwell, ['2MPD'], 50, 6)
corr2csv(G, fileName[0:-4])
plotFCScorrelations(G, [
'auto1_average', 'auto2_average', 'cross12_average',
'cross21_average', 'cross_average'
])
plt.xlim(left=2 * pxdwell)
picName = fileName[0:-4] + '_crosscorr.png'
plt.savefig(picName)
# =============================================================
document.add_heading('Correlations', level=3)
document.add_picture(picName, width=Inches(4))
# =============================================================
else:
plotList = ['central', 'sum3', 'sum5', 'chessboard', 'ullr']
maxG1 = np.zeros([len(plotList), 1])
maxG2 = np.zeros([len(plotList), 1])
# G = FCS2CorrSplit(data, 1e6*pxdwell, plotList, 50, 5)
G = FCSLoadAndCorrSplit(file, plotList, 16, 10)
corr2csv(G, fileName[0:-4])
plotList = [
'central_average', 'sum3_average', 'sum5_average',
'chessboard_average', 'ullr_average'
]
for i in range(len(plotList)):
Gsingle = getattr(G, plotList[i])
maxG1[i] = np.max(Gsingle[2:, 1])
maxG2[i] = np.max(Gsingle[zoom:, 1])
plotFCScorrelations(G,
plotList=[
'central_average', 'sum3_average',
'sum5_average'
])
plt.xlim(left=2 * pxdwell)
plt.ylim(top=np.max(maxG1[0:2]))
picName = fileName[0:-4] + '_G135.png'
plt.savefig(picName)
# =============================================================
document.add_heading('Autocorrelations', level=3)
document.add_picture(picName, width=Inches(4))
# =============================================================
plt.xlim(left=zoom * pxdwell)
plt.ylim(top=np.max(maxG2[0:2]))
picName = fileName[0:-4] + '_G135_zoom.png'
plt.savefig(picName)
# =============================================================
document.add_picture(picName, width=Inches(4))
# =============================================================
plotFCScorrelations(G, plotList=['sum5_average'])
plt.xlim(left=zoom * pxdwell)
plt.ylim(top=np.max(maxG2[2]))
picName = fileName[0:-4] + '_sum5_zoom.png'
plt.savefig(picName)
# =============================================================
document.add_picture(picName, width=Inches(4))
# =============================================================
#plotFCScorrelations(G, plotList=['sum5', 'det-15'])
#plt.xlim(left=zoom*pxdwell)
#plt.ylim(top=np.max(maxG2[2:4]))
#picName = fileName[0:-4] + '_sum5MinusHotPixel_zoom.png'
#plt.savefig(picName)
# =============================================================
#document.add_paragraph('det-15 means the sum of all detector elements except for pixel 15 (hot pixel).')
#document.add_picture(picName, width=Inches(4))
# =============================================================
plotFCScorrelations(
G, plotList=['chessboard_average', 'ullr_average'])
plt.xlim(left=pxdwell)
plt.ylim(top=np.max(maxG2[4:]))
picName = fileName[0:-4] + '_crosscorr_zoom.png'
plt.savefig(picName)
# =============================================================
document.add_heading('Cross correlations', level=3)
document.add_paragraph(
'Chessboard means the cross-correlation between the even number pixels and the odd numbered pixels. ULLR means the cross-correlation between the top-left and the bottom-right triangle (UpperLeft-LowerRight).'
)
document.add_picture(picName, width=Inches(4))
# =============================================================
document.save('Overview_results.docx')
def __init__(self):
self.dict = {}
self.sumsize = 0
self.ncalls = 0
def checkSize(self, f):
self.ncalls += 1
size = int((os.stat(f)).st_size)
self.dict[os.path.basename(f)] = size
self.sumsize += size
root = '/afs/cern.ch/atlas/project/RTT/Work/rel_4/dev/build/i686-slc4-gcc34-opt'
print 'looking for files in %s' % root
logfiles = listFiles(root, '*_log')
print 'Found %d log files' % len(logfiles)
checker = Checker()
[checker.checkSize(f) for f in logfiles]
print 'Root directory = ', root
print '\nlog file sizes:'
for s in checker.dict.items():
print '%10s %d' % s
print 'log files below:', root
print 'No of files:', len(checker.dict.keys())
print 'Total size (kbytes):', checker.sumsize / 1024.