def applyPredThresh(pixels):
# zero removal for quantile computation
nonzp = pixels[np.any(pixels, 1)]
counts = {c:np.bincount(nonzp[:,c], minlength=256) for c in colors}
qs = {c :
countQuantiles(counts[c], iles) for c in colors}
# clunky:
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]}
predictedThreshes = \
dict((c, applyCurrentLM(qDict, c)) for c in colorNames)
# print ",".join("%5.4f" % t for t in predictedThresholds.values())
thresholdNDArray(pixels, predictedThreshes, dropSaturated=True)
def applyPredThresh(pixels):
# zero removal for quantile computation
nonzp = pixels[np.any(pixels, 1)]
counts = {c: np.bincount(nonzp[:, c], minlength=256) for c in colors}
qs = {c: countQuantiles(counts[c], iles) for c in colors}
# clunky:
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]
}
predictedThreshes = \
dict((c, applyCurrentLM(qDict, c)) for c in colorNames)
# print ",".join("%5.4f" % t for t in predictedThresholds.values())
thresholdNDArray(pixels, predictedThreshes, dropSaturated=True)
{"15m60xendser301.TIF",
"15m60xendser301.TIF",
"15m60xendser301.TIF",
"120m60xac17ser24.TIF",
"120m60xac17ser27.TIF"}:
print ".....skipping:"
continue
currentImage = Image.open(imageDataPath+exampleFilename)
pixels = fromimage(currentImage).reshape((numImagePoints,3))
# zero removal
if REMOVE_ZEROS:
pixels = pixels[np.any(pixels, 1)]
counts = {c:np.bincount(pixels[:,c], minlength=256) for c in colors}
qs = {c :
countQuantiles(counts[c], iles) for c in colors}
# import pdb; pdb.set_trace()
# clunky:
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]}
predictedThresholds = \
dict((c, applyCurrentLM(qDict, c)) for c in colorNames)
print ",".join("%5.4f" % t for t in predictedThresholds.values())
thresholdNDArray(pixels, predictedThresholds)
stackArray = np.concatenate((stackArray, pixels))
"120m60xac17ser24.TIF",
"120m60xac17ser27.TIF"}:
print ".....skipping:"
continue
currentImage = Image.open(imageDataPath + exampleFilename)
pixels = fromimage(currentImage).reshape((numImagePoints, 3))
# zero removal
if REMOVE_ZEROS:
pixels = pixels[np.any(pixels, 1)]
counts = {c: np.bincount(pixels[:, c], minlength=256) for c in colors}
qs = {c: countQuantiles(counts[c], iles) for c in colors}
# import pdb; pdb.set_trace()
# clunky:
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]
}
predictedThresholds = \
dict((c, applyCurrentLM(qDict, c)) for c in colorNames)
print ",".join("%5.4f" % t for t in predictedThresholds.values())
thresholdNDArray(pixels, predictedThresholds)
stackArray = np.concatenate((stackArray, pixels))
currentImage = Image.open(imageDataPath+exampleFilename)
pixels = fromimage(currentImage).reshape((numImagePoints,3))
if applyPredThresholds:
# zero removal for quantile computation
pixels = pixels[np.any(pixels, 1)]
counts = {c:np.bincount(pixels[:,c],
minlength=256) for c in colors}
qs = {c:countQuantiles(counts[c], iles) for c in colors}
# clunky:
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: