def analyseFile(filename):
print(filename)
seriesName = os.path.splitext(os.path.split(filename)[-1])[0]
PL.ExtendContext({'seriesName': seriesName})
try:
pipe = Pipeline(filename)
except RuntimeError:
print(('Error opening %s' % filename))
PL.PopContext()
return
#only look at first 7k frames
#pipe.filterKeys['t'] = (0, 7000)
pipe.Rebuild()
autoDrift.correctDrift(pipe)
imb = visHelpers.ImageBounds.estimateFromSource(pipe)
jitVals = 1.0 * pipe['error_x']
im = visHelpers.rendJitTriang(pipe['x'], pipe['y'], 20, jitVals, 1, imb, 5)
pipe.CloseFiles()
PL.PopContext()
def colfcnwrap(pipeline):
colourFilter = pipeline.colourFilter
metadata = pipeline.mdh
chans = colourFilter.getColourChans()
if USE_GUI:
figure(os.path.split(pipeline.filename)[-1] + ' - ' + fcn.__name__)
if len(chans) == 0:
fcn(colourFilter, metadata)
else:
curChan = colourFilter.currentColour
if 'rng' in args:
nPh = getPhotonNums(colourFilter, metadata)
rng = 6 * nPh.mean()
chanNames = chans[:]
if 'Sample.Labelling' in metadata.getEntryNames():
lab = metadata.getEntry('Sample.Labelling')
for i in range(len(lab)):
if lab[i][0] in chanNames:
chanNames[chanNames.index(lab[i][0])] = lab[i][1]
for ch, i in zip(chans, range(len(chans))):
colourFilter.setColour(ch)
PL.ExtendContext({'chan': chanNames[i]})
if 'rng' in args:
fcn(colourFilter, metadata, chanNames[i], i, rng)
else:
fcn(colourFilter, metadata, chanNames[i], i)
PL.PopContext()
colourFilter.setColour(curChan)
def analyseFile(filename):
print(filename)
seriesName = os.path.splitext(os.path.split(filename)[-1])[0]
PL.ExtendContext({'seriesName':seriesName})
try:
pipe = Pipeline(filename)
except RuntimeError:
print(('Error opening %s' % filename))
PL.PopContext()
return
#only look at first 7k frames
pipe.filterKeys['t'] = (0, 7000)
pipe.Rebuild()
trackUtils.findTracks(pipe, 'error_x', 2, 20)
pipe.Rebuild()
extraParams = {}
extraParams['cycleTime'] = pipe.mdh['Camera.CycleTime']
nPhot = kinModels.getPhotonNums(pipe.colourFilter, pipe.mdh)
extraParams['MedianPhotons'] = np.median(nPhot)
extraParams['MeanPhotons'] = np.mean(nPhot)
extraParams['NEvents'] = len(nPhot)
extraParams['MeanBackground'] = pipe['fitResults_background'].mean() - pipe.mdh['Camera.ADOffset']
extraParams['MedianBackground'] = np.median(pipe['fitResults_background']) - pipe.mdh['Camera.ADOffset']
extraParams['MeanClumpSize'] = pipe['clumpSize'].mean()
extraParams['MeanClumpPhotons'] = (pipe['clumpSize']*nPhot).mean()
PL.AddRecord('/Photophysics/ExtraParams', dictToRecarray(extraParams))
kinModels.fitDecay(pipe)
kinModels.fitFluorBrightness(pipe)
#kinModels.fitFluorBrightnessT(pipe)
#max_off_ts = [3,5,10,20,40]
#max_off_ts = [20]
#for ot in max_off_ts:
#PL.ExtendContext({'otMax':ot})
#find molecules appearing across multiple frames
kinModels.fitOnTimes(pipe)
#PL.PopContext()
pipe.CloseFiles()
PL.PopContext()