def diffOfGaussParam(imgarray, params):
apix = params["apix"]
bin = params["bin"]
diam = params["diam"]
k = params["kfactor"]
numslices = params["numslices"]
sizerange = params["sizerange"]
if diam == 0:
apDisplay.printError("difference of Gaussian; radius = 0")
pixrad = float(diam / apix / float(bin) / 2.0)
if numslices is None or numslices < 2:
dogarray = diffOfGauss(imgarray, pixrad, k=k)
dogarray = apImage.normStdev(dogarray) / 4.0
return [dogarray]
else:
pixrange = float(sizerange / apix / float(bin) / 2.0)
dogarrays, pixradlist = diffOfGaussLevels(imgarray, pixrad, numslices, pixrange)
diamarray = numpy.asarray(pixradlist, dtype=numpy.float32) * apix * float(bin) * 2.0
apDisplay.printColor("diameter list= " + str(numpy.around(diamarray, 3)), "cyan")
params["diamarray"] = diamarray
return dogarrays
def diffOfGaussParam(imgarray, params):
apix = params['apix']
bin = params['bin']
diam = params['diam']
k = params['kfactor']
numslices = params['numslices']
sizerange = params['sizerange']
if diam == 0:
apDisplay.printError("difference of Gaussian; radius = 0")
pixrad = float(diam/apix/float(bin)/2.0)
if numslices is None or numslices < 2:
dogarray = diffOfGauss(imgarray, pixrad, k=k)
dogarray = apImage.normStdev(dogarray)/4.0
return [dogarray]
else:
pixrange = float(sizerange/apix/float(bin)/2.0)
dogarrays, pixradlist = diffOfGaussLevels(imgarray, pixrad, numslices, pixrange)
diamarray = numpy.asarray(pixradlist, dtype=numpy.float32) * apix * float(bin) * 2.0
apDisplay.printColor("diameter list= "+str(numpy.around(diamarray,3)), "cyan")
params['diamarray'] = diamarray
return dogarrays
def diffOfGaussLevels(imgarray, r0, N, dr, writeImg=False, apix=1):
if writeImg is True:
apImage.arrayToJpeg(imgarray, "binned-image.jpg")
if dr >= 1.95 * r0:
apDisplay.printError("size range has be less than twice the diameter")
# initial params
# print "r0=", r0*apix
# print "dr=", dr*apix
# print "N=", N
# find k based on info
k = estimateKfactorIncrement(r0, dr, N)
# print "k=", k
# find xi (E) function of k
Ek = math.sqrt((k ** 2 - 1.0) / (2.0 * k ** 2 * math.log(k)))
##Ek = 1.0 / Ek
# print "E(k)=", Ek
# convert r0 to sigma1
sigma1 = Ek * r0
# print "sigma1=", sigma1*apix
# find sigmaprime
sigmaprime = sigma1 * math.sqrt(k ** 2 - 1.0)
# print "sigma'=", sigmaprime*apix
# sigma0 = sigma1 * k ^ (1-N)/2
power = float(1 - N) / 2.0
# print "power=", power
sigma0 = sigma1 * k ** (float(1 - N) / 2.0)
# print "sigma0=", sigma0*apix
# calculate first image blur
sigma = sigma0
gaussmap = ndimage.gaussian_filter(imgarray, sigma=sigma0)
sigmavals = [sigma0]
sigprimes = []
gaussmaps = [gaussmap]
for i in range(N):
sigmaprime = sigma * math.sqrt(k ** 2 - 1.0)
sigprimes.append(sigmaprime)
# calculate new sigma
sigma = math.sqrt(sigma ** 2 + sigmaprime ** 2)
sigmavals.append(sigma)
# all subsequent blurs are by sigmaprime
lastmap = gaussmaps[-1]
gaussmap = ndimage.gaussian_filter(lastmap, sigma=sigmaprime)
gaussmaps.append(gaussmap)
# print "sigma' values= ", numpy.array(sigprimes)*apix
sizevals = numpy.array(sigmavals) / Ek / math.sqrt(k) * apix
# print "map sigma sizes= ", numpy.array(sigmavals)*apix
sizevals = numpy.array(sigmavals) / Ek / math.sqrt(k) * apix
# print "map central sizes=", sizevals
sizevals = numpy.array(sigmavals) / Ek * apix
# print "map pixel sizes= ", sizevals[:-1]
if writeImg is True:
for i, gaussmap in enumerate(gaussmaps):
apImage.arrayToJpeg(gaussmap, "gaussmap" + str(i) + ".jpg")
dogarrays = []
pixradlist = []
for i in range(N):
pixrad = r0 * k ** (float(i) - float(N - 1) / 2.0)
pixradlist.append(pixrad)
# subtract blurs to get dog maps
dogarray = gaussmaps[i] - gaussmaps[i + 1]
dogarray = apImage.normStdev(dogarray) / 4.0
dogarrays.append(dogarray)
if writeImg is True:
apImage.arrayToJpeg(dogarray, "dogmap" + str(i) + ".jpg")
sizevals = numpy.array(pixradlist)
print "particle pixel sizes=", sizevals * apix
# sys.exit(1)
return dogarrays, sizevals
def diffOfGaussLevels(imgarray, r0, N, dr, writeImg=False, apix=1):
if writeImg is True:
apImage.arrayToJpeg(imgarray, "binned-image.jpg")
if dr >= 1.95 * r0:
apDisplay.printError("size range has be less than twice the diameter")
# initial params
#print "r0=", r0*apix
#print "dr=", dr*apix
#print "N=", N
# find k based on info
k = estimateKfactorIncrement(r0, dr, N)
#print "k=", k
# find xi (E) function of k
Ek = math.sqrt((k**2 - 1.0) / (2.0 * k**2 * math.log(k)))
##Ek = 1.0 / Ek
#print "E(k)=", Ek
# convert r0 to sigma1
sigma1 = Ek * r0
#print "sigma1=", sigma1*apix
# find sigmaprime
sigmaprime = sigma1 * math.sqrt(k**2 - 1.0)
#print "sigma'=", sigmaprime*apix
#sigma0 = sigma1 * k ^ (1-N)/2
power = (float(1 - N) / 2.0)
#print "power=", power
sigma0 = sigma1 * k**(float(1 - N) / 2.0)
#print "sigma0=", sigma0*apix
# calculate first image blur
sigma = sigma0
gaussmap = ndimage.gaussian_filter(imgarray, sigma=sigma0)
sigmavals = [
sigma0,
]
sigprimes = []
gaussmaps = [
gaussmap,
]
for i in range(N):
sigmaprime = sigma * math.sqrt(k**2 - 1.0)
sigprimes.append(sigmaprime)
#calculate new sigma
sigma = math.sqrt(sigma**2 + sigmaprime**2)
sigmavals.append(sigma)
# all subsequent blurs are by sigmaprime
lastmap = gaussmaps[-1]
gaussmap = ndimage.gaussian_filter(lastmap, sigma=sigmaprime)
gaussmaps.append(gaussmap)
#print "sigma' values= ", numpy.array(sigprimes)*apix
sizevals = numpy.array(sigmavals) / Ek / math.sqrt(k) * apix
#print "map sigma sizes= ", numpy.array(sigmavals)*apix
sizevals = numpy.array(sigmavals) / Ek / math.sqrt(k) * apix
#print "map central sizes=", sizevals
sizevals = numpy.array(sigmavals) / Ek * apix
#print "map pixel sizes= ", sizevals[:-1]
if writeImg is True:
for i, gaussmap in enumerate(gaussmaps):
apImage.arrayToJpeg(gaussmap, "gaussmap" + str(i) + ".jpg")
dogarrays = []
pixradlist = []
for i in range(N):
pixrad = r0 * k**(float(i) - float(N - 1) / 2.0)
pixradlist.append(pixrad)
# subtract blurs to get dog maps
dogarray = gaussmaps[i] - gaussmaps[i + 1]
dogarray = apImage.normStdev(dogarray) / 4.0
dogarrays.append(dogarray)
if writeImg is True:
apImage.arrayToJpeg(dogarray, "dogmap" + str(i) + ".jpg")
sizevals = numpy.array(pixradlist)
print "particle pixel sizes=", sizevals * apix
#sys.exit(1)
return dogarrays, sizevals