exampleFilename = fileNameFormat[threshes["Organelle"]] % threshes
currentImage = Image.open(imageDataPath + exampleFilename)
asArray = fromimage(currentImage).reshape((numImagePoints, 3))
# port to thresholdNDArray
if applyExpertThresholds:
expertThresholds = dict(((c, threshes[c]) for c in colorNames))
thresholdNDArray(asArray, expertThresholds)
# asArray = fromimage(currentImage).reshape((numImagePoints, 3))
stackArray = np.concatenate((stackArray, asArray))
org = simplifyOrgStain(threshes["Organelle"], threshes["Stain"])
# r = threshes["Series"] - 1
t = timeToIdx(threshes["Time"])
print org, t
# convert image stack to counts and add to histograms
for c1, c2 in colorPairs:
probs = toProbs(stackArray[:, c1],
stackArray[:, c2],
removeNonresponders=removeNonresponders,
removeSaturated=removeSaturated,
useLogscaleCounts=useLogscaleCounts)
cnd = (org, c1, c2, t)
storedProbs[cnd] = probs
outputFile = open(outputFile, "wb")
cPickle.dump(storedProbs, outputFile, -1)
qDict = {"R8D": qs[R][0], "R9D": qs[R][1],
"G8D": qs[G][0], "G9D": qs[G][1],
"B8D": qs[B][0], "B9D": qs[B][1]}
predThreshes = dict((c, applyCurrentLM(qDict,
c)) for c in colorNames)
# print ",".join("%5.4f" % t for t in predThresholds.values())
thresholdNDArray(pixels, predThreshes, dropSaturated=True)
expArray = np.concatenate((expArray, pixels))
#########################################
# all to here is necessary to get joined series
#########################################
org = simplifyOrgStain(cnd["Organelle"], cnd["Stain"])
t = timeToIdx(cnd["Time"])
print org, t
# convert image stack to counts and add to histograms
for c1, c2 in colorPairs:
probs = toProbs(expArray[:,c1], expArray[:,c2],
removeNonresponders = removeNonresponders,
removeSaturated = removeSaturated,
useLogscaleCounts = useLogscaleCounts)
cnd = (org, c1, c2, t)
storedProbs[cnd] = probs
outputFile = open(outputFile, "wb")
cPickle.dump(storedProbs, outputFile, -1)
outputFile.close()