def findMaxWithGFit(img, sigma=0.5, win=11): ''' find sub-pixel peaks from input img using n-dimensional Guassian fitting sigma: scaler or [simgaZ,sigmaY..] window: a window where the Guassian fit is performed on return [v, zyx, sigma] ''' imgFit.fitFailedClear() vzyx = U.findMax(img) ndim = img.ndim try: ret, check = imgFit.fitGaussianND(img, vzyx[-ndim:], sigma, win) except IndexError: # too close to the edge imgFit.fitFailedAppend("at %s" % str(vzyx[-ndim:])) sigma = imgFit._scalerToSeq(sigma, ndim) return list(vzyx)[0:1] + [vzyx[-ndim:]] + [list(sigma)] if check == 5: imgFit.fitFailedAppend("at %s, %s, check=%i" % (str(vzyx[-ndim:]), str(ret), check)) sigma = imgFit._scalerToSeq(sigma, ndim) return list(vzyx)[0:1] + [vzyx[-ndim:]] + [sigma] else: v = ret[1] zyx = ret[2:2 + ndim] sigma = ret[2 + ndim:2 + ndim * 2] return [v, zyx, sigma]
def findMaxWithGFit(img, sigma=0.5, win=11): ''' find sub-pixel peaks from input img using n-dimensional Guassian fitting sigma: scaler or [simgaZ,sigmaY..] window: a window where the Guassian fit is performed on return [v, zyx, sigma] ''' vzyx = N.array(U.findMax(img)) ndim = img.ndim try: ret, check = imgFit.fitGaussianND(img, vzyx[-ndim:], sigma, win) except IndexError: # too close to the edge imgFit.fitFailedAppend("at %s" % str(vzyx[-ndim:])) sigma = imgFit._scalerToSeq(sigma, ndim) return vzyx[0:1], vzyx[-ndim:], list(sigma) if check == 5 or N.any(ret[2:2 + ndim] > (vzyx[-ndim:] + win)) or N.any( ret[2:2 + ndim] < (vzyx[-ndim:] - win)): imgFit.fitFailedAppend("at %s, %s, check=%i" % (str(vzyx[-ndim:]), str(ret), check)) sigma = imgFit._scalerToSeq(sigma, ndim) return [vzyx[0:1], vzyx[-ndim:], sigma] #x= (vzyx[0:1] + [vzyx[-ndim:]] + [sigma]) # print x # return x else: v = ret[1] zyx = ret[2:2 + ndim] sigma = ret[2 + ndim:2 + ndim * 2] return [v, zyx, sigma]
def findMaxWithGFit(img, sigma=0.5, win=11): ''' find sub-pixel peaks from input img using n-dimensional Guassian fitting sigma: scaler or [simgaZ,sigmaY..] window: a window where the Guassian fit is performed on return [v, zyx, sigma] ''' imgFit.fitFailedClear() vzyx = U.findMax(img) ndim = img.ndim try: ret, check = imgFit.fitGaussianND(img, vzyx[-ndim:], sigma, win) except IndexError: # too close to the edge imgFit.fitFailedAppend("at %s" % str(vzyx[-ndim:])) sigma = imgFit._scalerToSeq(sigma, ndim) return list(vzyx)[0:1] + [vzyx[-ndim:]] + [list(sigma)] if check == 5: imgFit.fitFailedAppend("at %s, %s, check=%i" % (str(vzyx[-ndim:]), str(ret), check)) sigma = imgFit._scalerToSeq(sigma, ndim) return list(vzyx)[0:1] + [vzyx[-ndim:]] + [sigma] else: v = ret[1] zyx = ret[2:2+ndim] sigma = ret[2+ndim:2+ndim*2] return [v,zyx,sigma]
def findMaxWithGFit(img, sigma=0.5, win=11): ''' find sub-pixel peaks from input img using n-dimensional Guassian fitting sigma: scaler or [simgaZ,sigmaY..] window: a window where the Guassian fit is performed on return [v, zyx, sigma] ''' vzyx = N.array(U.findMax(img)) ndim = img.ndim try: ret, check = imgFit.fitGaussianND(img, vzyx[-ndim:], sigma, win) except IndexError: # too close to the edge imgFit.fitFailedAppend("at %s" % str(vzyx[-ndim:])) sigma = imgFit._scalerToSeq(sigma, ndim) return vzyx[0:1], vzyx[-ndim:], list(sigma) if check == 5 or N.any(ret[2:2+ndim] > (vzyx[-ndim:] + win)) or N.any(ret[2:2+ndim] < (vzyx[-ndim:] - win)): imgFit.fitFailedAppend("at %s, %s, check=%i" % (str(vzyx[-ndim:]), str(ret), check)) sigma = imgFit._scalerToSeq(sigma, ndim) return [vzyx[0:1], vzyx[-ndim:], sigma] #x= (vzyx[0:1] + [vzyx[-ndim:]] + [sigma]) # print x # return x else: v = ret[1] zyx = ret[2:2+ndim] sigma = ret[2+ndim:2+ndim*2] return [v,zyx,sigma]
def findMaxWithGFitAll(img, thre=0, sigma_peak=0.5, win=11, mask_npxls=3): """ find peaks until either 1. any pxls becomes below thre 2. the same peak was found as the maximum mask_npxls: number of pixels to mask when Gaussian fitting failed return poslist """ img = img.copy() imgFit.fitFailedClear() ndim = img.ndim sigma_peak = imgFit._scalerToSeq(sigma_peak, ndim) poses = [] vzyx = U.findMax(img) while vzyx[0] > thre: prev = vzyx try: ret, check = imgFit.fitGaussianND(img, vzyx[-ndim:], sigma_peak, win) except IndexError: # too close to the edge imgFit.fitFailedAppend("at %s" % str(vzyx[-ndim:])) mask_value(img, vzyx[-ndim:], r=mask_npxls, value=img.min()) poses.append(list(vzyx)[0:1] + [vzyx[-ndim:]] + [list(sigma_peak)]) if check == 5: imgFit.fitFailedAppend("at %s, %s, check=%i" % (str(vzyx[-ndim:]), str(ret), check)) mask_value(img, vzyx[-ndim:], r=mask_npxls, value=img.min()) poses.append(list(vzyx)[0:1] + [vzyx[-ndim:]] + [sigma_peak]) else: v = ret[1] zyx = ret[2:2+ndim] if N.any(N.abs(zyx - vzyx[1:]) > win/2.):#zyx < 0 or zyx > img.shape or ): mask_value(img, vzyx[-ndim:], r=mask_npxls, value=img.min()) poses.append(list(vzyx)[0:1] + [vzyx[-ndim:]] + [sigma_peak]) else: sigma = ret[2+ndim:2+ndim*2] mask_gaussianND(img, zyx, v, sigma) poses.append([v,zyx,sigma]) vzyx = U.findMax(img) if N.all(vzyx == prev): break return poses#, img
def findMaxWithGFitAll(img, thre=0, sigma_peak=0.5, win=11, mask_npxls=3): """ find peaks until either 1. any pxls becomes below thre 2. the same peak was found as the maximum mask_npxls: number of pixels to mask when Gaussian fitting failed return poslist """ img = img.copy() imgFit.fitFailedClear() ndim = img.ndim sigma_peak = imgFit._scalerToSeq(sigma_peak, ndim) poses = [] vzyx = U.findMax(img) while vzyx[0] > thre: prev = vzyx try: ret, check = imgFit.fitGaussianND(img, vzyx[-ndim:], sigma_peak, win) except IndexError: # too close to the edge imgFit.fitFailedAppend("at %s" % str(vzyx[-ndim:])) mask_value(img, vzyx[-ndim:], r=mask_npxls, value=img.min()) poses.append(list(vzyx)[0:1] + [vzyx[-ndim:]] + [list(sigma_peak)]) if check == 5: imgFit.fitFailedAppend("at %s, %s, check=%i" % (str(vzyx[-ndim:]), str(ret), check)) mask_value(img, vzyx[-ndim:], r=mask_npxls, value=img.min()) poses.append(list(vzyx)[0:1] + [vzyx[-ndim:]] + [sigma_peak]) else: v = ret[1] zyx = ret[2:2 + ndim] sigma = ret[2 + ndim:2 + ndim * 2] mask_gaussianND(img, zyx, v, sigma) poses.append([v, zyx, sigma]) vzyx = U.findMax(img) if N.all(vzyx == prev): break return poses #, img
def fitSkew1D(img, ts=None, sigma=0.5, exp=0): """ img: 1D array containg one skewd gaussian peak """ mi, ma, me, sd = U.mmms(img) ma, _1, _2, t = U.findMax(img) if ts is not None: t = ts[t] img = list(zip(ts, img)) return U.fitAny(ySkew, (mi, ma - mi, t, sigma, exp), img, warning=None)
def fitSkew1D(img, ts=None, sigma=0.5, exp=0): """ img: 1D array containg one skewd gaussian peak """ mi, ma, me, sd = U.mmms(img) ma, _1, _2, t = U.findMax(img) if ts is not None: t = ts[t] img = list(zip(ts, img)) return U.fitAny(ySkew, (mi, ma-mi, t, sigma, exp), img, warning=None)
def _findMaxXcor(c, win, gFit=True, niter=2): if gFit: for i in range(niter): v, zyx, s = findMaxWithGFit(c, win=win + (i * 2)) if v: if N.any(zyx > N.array(c.shape)) or N.any(zyx < 0): vzyx = N.array(U.findMax(c)) #continue v = vzyx[0] zyx = vzyx[-c.ndim:] + 0.5 # pixel center s = 2.5 else: break if not v: v = U.findMax(c)[0] else: vzyx = U.findMax(c) v = vzyx[0] zyx = N.array(vzyx[-c.ndim:]) + 0.5 # pixel center s = 2.5 return v, zyx, s
def _findMaxXcor(c, win, gFit=True, niter=2): if gFit: for i in range(niter): v, zyx, s = findMaxWithGFit(c, win=win+(i*2)) if v: if N.any(zyx > N.array(c.shape)) or N.any(zyx < 0): vzyx = N.array(U.findMax(c))#continue v = vzyx[0] zyx = vzyx[-c.ndim:] + 0.5 # pixel center s = 2.5 else: break if not v: v = U.findMax(c)[0] else: vzyx = U.findMax(c) v = vzyx[0] zyx = N.array(vzyx[-c.ndim:]) + 0.5 # pixel center s = 2.5 return v, zyx, s
def chopYX(shapeYX, ncpu=8): """ return axis, YXmmlist """ n, _1, _2, axis = U.findMax(shapeYX) n0 = n // ncpu mms = [] pixel = 0 for cpu in range(ncpu): mms += [[pixel, pixel + n0]] pixel += n0 mms[-1][-1] = n return axis, mms
def testCorrelation(arr, win=64): from Priithon.all import U half = win / 2. arr = arr.copy() for i in range(10): v, z, y, x = U.findMax(arr) if y - half >= 0 and y + half < arr.shape[ 0] and x - half >= 0 and x + half < arr.shape[1]: a = arr[y - half:y + half, x - half:x + half] break else: arr[y, x] = 0 b = a.copy() yx, c = xcorr.Xcorr(a, b, phaseContrast=True) cme = c[:c.shape[0] // 4].mean() return c.max() - cme
def Xcorr(a, b, highpassSigma=2.5, wiener=0.2, cutoffFreq=3, forceSecondPeak=None, acceptOrigin=True, maskSigmaFact=1., removeY=None, removeX=None, ret=None, normalize=True, gFit=True, lap=None, win=11): """ returns (y,x), image if ret is True, returns [v, yx, image] to get yx cordinate of the image, yx += N.divide(picture.shape, 2) a, b: 2D array highpassSigma: sigma value used for highpass pre-filter wiener: wiener value used for highpass pre-filter cutoffFreq: kill lowest frequency component from 0 to this level forceSecondPeak: If input is n>0 (True is 1), pick up n-th peak acceptOrigin: If None, result at origin is rejected, look for the next peak maskSigmaFact: Modifier to remove previous peak to look for another peak removeYX: Rremove given number of pixel high intensity lines of the Xcorr Y: Vertical, X: Horizontal normalize: intensity normalized gFit: peak is fitted to 2D gaussian array, if None use center of mass win: window for gFit if b is a + (y,x) then, answer is (-y,-x) """ shapeA = N.asarray(a.shape) shapeB = N.asarray(b.shape) shapeM = N.max([shapeA, shapeB], axis=0) shapeM = N.where(shapeM % 2, shapeM+1, shapeM) center = shapeM / 2. arrs = [a,b] arrsS = ['a','b'] arrsF = [] for i, arr in enumerate(arrs): if arr.dtype not in [N.float32, N.float64]: arr = N.asarray(arr, N.float32) # this convolution has to be done beforehand to remove 2 pixels at the edge if lap == 'nothing': pass elif lap: arr = arr_Laplace(arr, mask=2) else: arr = arr_sorbel(arr, mask=1) if N.sometrue(shapeA < shapeM): arr = paddingMed(arr, shapeM) if normalize: mi, ma, me, sd = U.mmms(arr) arr = (arr - me) / sd if i ==1: arr = F.shift(arr) af = F.rfft(arr) af = highPassF(af, highpassSigma, wiener, cutoffFreq) arrsF.append(af) # start cross correlation af, bf = arrsF bf = bf.conjugate() cf = af * bf # go back to space domain c = F.irfft(cf) # c = _changeOrigin(cr) # removing lines if removeX: yi, xi = N.indices((removeX, shapeM[-1]))#sx)) yi += center[-2] - removeX/2.#sy/2 - removeX/2 c[yi, xi] = 0 if removeY: yi, xi = N.indices((shapeM[-2], removeY))#sy, removeY)) xi += center[-1] - removeY/2.#sx/2 - removeY/2 c[yi, xi] = 0 # find the first peak if gFit: v, yx, s = findMaxWithGFit(c, win=win)#, window=win, gFit=gFit) if v == 0: v, yx, s = findMaxWithGFit(c, win=win+2)#, window=win+2, gFit=gFit) if v == 0: v = U.findMax(c)[0] yx = N.add(yx, 0.5) #yx += 0.5 else: vzyx = U.findMax(c) v = vzyx[0] yx = vzyx[-2:] s = 2.5 yx -= center if N.alltrue(N.abs(yx) < 1.0) and not acceptOrigin: forceSecondPeak = True # forceSecondPeak: if not forceSecondPeak: forceSecondPeak = 0 for i in range(int(forceSecondPeak)): print('%i peak was removed' % (i+1)) #, sigma: %.2f' % (i+1, s) yx += center g = gaussianArr2D(c.shape, sigma=s/maskSigmaFact, peakVal=v, orig=yx) c = c - g #c = mask_gaussian(c, yx[0], yx[1], v, s) if gFit: v, yx, s = findMaxWithGFit(c, win=win)#, window=win, gFit=gFit) if v == 0: v, yx, s = findMaxWithGFit(c, win=win+2)#, window=win+2, gFit=gFit) if v == 0: v = U.findMax(c)[0] yx -= (center - 0.5) else: vzyx = U.findMax(c) v = vzyx[0] if not gFit: yx = centerOfMass(c, vzyx[-2:]) - center if lap is not 'nothing': c = paddingValue(c, shapeM+2) if ret == 2: return yx, af, bf.conjugate() elif ret: return v, yx, c else: return yx, c
def Xcorr(a, b, highpassSigma=2.5, wiener=0.2, cutoffFreq=3, forceSecondPeak=None, acceptOrigin=True, maskSigmaFact=1., removeY=None, removeX=None, ret=None, normalize=True, gFit=True, lap=None, win=11): """ returns (y,x), image if ret is True, returns [v, yx, image] to get yx cordinate of the image, yx += N.divide(picture.shape, 2) a, b: 2D array highpassSigma: sigma value used for highpass pre-filter wiener: wiener value used for highpass pre-filter cutoffFreq: kill lowest frequency component from 0 to this level forceSecondPeak: If input is n>0 (True is 1), pick up n-th peak acceptOrigin: If None, result at origin is rejected, look for the next peak maskSigmaFact: Modifier to remove previous peak to look for another peak removeYX: Rremove given number of pixel high intensity lines of the Xcorr Y: Vertical, X: Horizontal normalize: intensity normalized gFit: peak is fitted to 2D gaussian array, if None use center of mass win: window for gFit if b is a + (y,x) then, answer is (-y,-x) """ shapeA = N.asarray(a.shape) shapeB = N.asarray(b.shape) shapeM = N.max([shapeA, shapeB], axis=0) shapeM = N.where(shapeM % 2, shapeM + 1, shapeM) center = shapeM / 2. arrs = [a, b] arrsS = ['a', 'b'] arrsF = [] for i, arr in enumerate(arrs): if arr.dtype not in [N.float32, N.float64]: arr = N.asarray(arr, N.float32) # this convolution has to be done beforehand to remove 2 pixels at the edge if lap == 'nothing': pass elif lap: arr = arr_Laplace(arr, mask=2) else: arr = arr_sorbel(arr, mask=1) if N.sometrue(shapeA < shapeM): arr = paddingMed(arr, shapeM) if normalize: mi, ma, me, sd = U.mmms(arr) arr = (arr - me) / sd if i == 1: arr = F.shift(arr) af = F.rfft(arr) af = highPassF(af, highpassSigma, wiener, cutoffFreq) arrsF.append(af) # start cross correlation af, bf = arrsF bf = bf.conjugate() cf = af * bf # go back to space domain c = F.irfft(cf) # c = _changeOrigin(cr) # removing lines if removeX: yi, xi = N.indices((removeX, shapeM[-1])) #sx)) yi += center[-2] - removeX / 2. #sy/2 - removeX/2 c[yi, xi] = 0 if removeY: yi, xi = N.indices((shapeM[-2], removeY)) #sy, removeY)) xi += center[-1] - removeY / 2. #sx/2 - removeY/2 c[yi, xi] = 0 # find the first peak if gFit: v, yx, s = findMaxWithGFit(c, win=win) #, window=win, gFit=gFit) if v == 0: v, yx, s = findMaxWithGFit(c, win=win + 2) #, window=win+2, gFit=gFit) if v == 0: v = U.findMax(c)[0] yx = N.add(yx, 0.5) #yx += 0.5 else: vzyx = U.findMax(c) v = vzyx[0] yx = vzyx[-2:] s = 2.5 yx -= center if N.alltrue(N.abs(yx) < 1.0) and not acceptOrigin: forceSecondPeak = True # forceSecondPeak: if not forceSecondPeak: forceSecondPeak = 0 for i in range(int(forceSecondPeak)): print('%i peak was removed' % (i + 1)) #, sigma: %.2f' % (i+1, s) yx += center g = gaussianArr2D(c.shape, sigma=s / maskSigmaFact, peakVal=v, orig=yx) c = c - g #c = mask_gaussian(c, yx[0], yx[1], v, s) if gFit: v, yx, s = findMaxWithGFit(c, win=win) #, window=win, gFit=gFit) if v == 0: v, yx, s = findMaxWithGFit(c, win=win + 2) #, window=win+2, gFit=gFit) if v == 0: v = U.findMax(c)[0] yx -= (center - 0.5) else: vzyx = U.findMax(c) v = vzyx[0] if not gFit: yx = centerOfMass(c, vzyx[-2:]) - center if lap is not 'nothing': c = paddingValue(c, shapeM + 2) if ret == 2: return yx, af, bf.conjugate() elif ret: return v, yx, c else: return yx, c
def beads_analyzeBeads(fn, thre_sigma=1., refwave=2, nbeads=60, win=5, maxdist=600, maxrefdist=5): #0.6, maxrefdist=0.005): """ maxdist: nm maxrefdist: nm return dic """ from PriCommon import mrcIO, imgFilters, imgFit, imgGeo, microscope from Priithon.all import Y h = mrcIO.MrcReader(fn) if refwave >= h.nw: raise ValueError, 'reference wave does not exists' shape = h.hdr.Num[::-1].copy() shape[0] /= (h.hdr.NumTimes * h.hdr.NumWaves) pzyx = h.hdr.d[::-1] NA = 1.35 difflimitYX = microscope.resolution(NA, h.hdr.wave[refwave]) / 1000. difflimit = N.array([difflimitYX * 2, difflimitYX, difflimitYX]) / pzyx print 'diffraction limit (px) is:', difflimit simres = difflimit / 1.5 #2. arr = h.get3DArr(w=refwave).copy() ndim = arr.ndim me = arr.mean() sd = arr.std() thre = me + sd * thre_sigma sigma = 1.5 zyxs = [] #found = [] sigmas = [] i = 0 pmax = 0 failed = 0 while i < nbeads: Y.refresh() amax, z, y, x = U.findMax(arr) zyx = N.array((z, y, x), N.float32) #found.append(zyx) zyx0 = N.where(zyx > simres, zyx - simres, 0).astype(N.int) zyx1 = N.where(zyx + simres < shape, zyx + simres, shape).astype(N.int) rmslice = [slice(*zyx) for zyx in zip(zyx0, zyx1)] if failed > 10: raise RuntimeError break elif amax == pmax: arr[rmslice] = me elif amax > thre: # Gaussian fitting try: ret, check = imgFit.fitGaussianND(arr, [z, y, x][-ndim:], sigma, win) except IndexError: # too close to the edge arr[rmslice] = me print 'Index Error, too close to the edge', z, y, x failed += 1 continue if check == 5: arr[rmslice] = me print 'fit failed', z, y, x failed += 1 continue # retreive results v = ret[1] zyx = ret[2:2 + ndim] sigma = ret[2 + ndim:2 + ndim * 2] if any(sigma) < 1: sigma = N.where(sigma < 1, 1, sigma) # check if the result is inside the image if N.any(zyx < 0) or N.any(zyx > (shape)): print 'fitting results outside the image', zyx, z, y, x, rmslice arr[rmslice] = me failed += 1 continue # too close to the edge # remove the point imgFilters.mask_gaussianND(arr, zyx, v, sigma) # check if the bead is too close to the exisisting ones skipped = """ if len(found):#zyxs): close = imgGeo.closeEnough(zyx, found, difflimit*2)#zyxs, difflimit*2) if N.any(close): if len(zyxs): close = imgGeo.closeEnough(zyx, zyxs, difflimit*2) if N.any(close): idx = close.argmax() already = zyxs.pop(idx) #print 'too close', zyx, already print 'found in the previous list' continue""" # remove beads too close to the edge if N.any(zyx < 2) or N.any(zyx > (shape - 2)): print 'too close to the edge', zyx if N.any(arr[rmslice] > thre): arr[rmslice] = me failed += 1 continue # too close to the edge # remove beads with bad shape elif N.any(sigma < 0.5) or N.any(sigma > 3): print 'sigma too different from expected', sigma, zyx if N.any(arr[rmslice] > thre): arr[rmslice] = me sigma = 1.5 failed += 1 continue old = """ # remove beads too elliptic elif sigma[1] / sigma[2] > 1.5 or sigma[2] / sigma[1] > 1.5: print 'too elliptic', sigma[2] / sigma[1] if N.any(arr[rmslice] > thre): arr[rmslice] = me #if (sigma[2] / sigma[1]) > 20: #raise ValueError sigma = 1.5 failed += 1 continue elif N.any(sigma[-2:] > 3.0): print 'sigma too large', sigma, zyx if N.any(arr[rmslice] > thre): arr[rmslice] = me sigma = 1.5 failed += 1 continue elif sigma[0] < 0.5 or sigma[0] > 3: print 'sigma z too different from expected', sigma, zyx if N.any(arr[rmslice] > thre): arr[rmslice] = me sigma = 1.5 failed += 1 continue #elif imgGeo.closeEnough(zyx, N.array((17,719,810), N.float32), 4): # raise RuntimeError""" # add results zyxs.append(zyx) sigmas.append(sigma) i += 1 pmax = amax failed = 0 #print i # maximum is below threshold else: break del arr sigmas = N.array(sigmas) sigmaYX = N.mean(sigmas[:, 1:], axis=1) sigmaZ = sigmas[:, 0] idxyx = sigmaYX.argmax() idxz = sigmaZ.argmax() print 'sigmaYX', round(sigmaYX.mean(), 3), round(sigmaYX.min(), 3), round(sigmaYX.std(), 3), round(sigmaYX.max(), 3), zyxs[idxyx] print 'sigmaZ', round(sigmaZ.mean(), 3), round(sigmaZ.min(), 3), round(sigmaZ.std(), 3), round(sigmaZ.max(), 3), zyxs[idxz] # remove overlaps zyxs2 = [] zyxs = N.array(zyxs) for i, zyx in enumerate(zyxs): close = imgGeo.closeEnough(zyx, zyxs, difflimit * 4) if not N.any(close[:i]) and not N.any(close[i + 1:]): #idx = close.argmax() #already = zyxs.pop(idx) zyxs2.append(zyx) zyxs = zyxs2 waves = range(h.nw) #waves.remove(refwave) difdic = {} removes = [] checks = 0 zeros = 0 toofars = 0 pzyx *= 1000 for w in waves: if w == refwave: continue arr = h.get3DArr(w=w) #me = arr.mean() #sd = arr.std() #thre = me + sd * thre_sigma for zyx in zyxs: key = tuple(zyx * pzyx) if key in removes: continue #win0 = win #while win0 >= 3: try: ret, check = imgFit.fitGaussianND(arr, zyx, sigma=sigma, window=win) #0) #break except (IndexError, ValueError): ret = zyx check = 5 #win0 -= 2 #ret, check = imgFit.fitGaussianND(arr, zyx, sigma=sigma, window=3) dif = (zyx - ret[2:5]) * pzyx if check == 5 or ( w != refwave and N.any(N.abs(dif) > maxdist)) or ( w == refwave and N.any(N.abs(dif) > maxrefdist) ): #(N.all(dif == 0) or N.any(N.abs(dif) > maxdist))): if check == 5: checks += 1 #elif N.all(dif == 0): #zeros += 1 elif w == refwave and N.any(N.abs(dif) > maxrefdist): zeros += 1 elif N.any(N.abs(dif) > maxdist): toofars += 1 #if N.any(N.abs(dif) > maxdist): # raise ValueError #raise RuntimeError removes.append(key) if difdic.has_key(key): del difdic[key] #elif not N.sum(dif) or N.any(dif > 10): #raise RuntimeError, 'something is wrong' else: difdic.setdefault(key, []) difdic[key].append(dif) sigma = ret[5:8] #del arr h.close() del arr del h print 'check:', checks, 'zeros:', zeros, 'toofars', toofars # subtracting the center newdic = {} keys = difdic.keys() newkeys = N.array(keys) zmin = N.min(newkeys[:, 0]) center = (shape * pzyx) / 2. #yx0 = (N.max(newkeys[:,1:], axis=0) - N.min(newkeys[:,1:], axis=0)) / 2. newkeys[:, 0] -= zmin newkeys[:, 1:] -= center[1:] #yx0 newkeys = [tuple(key) for key in newkeys] items = [difdic[key] for key in keys] newdic = dict(zip(newkeys, items)) difdic = newdic # plot P.figure(0, figsize=(8, 8)) keys = N.array(difdic.keys()) P.hold(0) P.scatter(keys[:, 2], keys[:, 1], alpha=0.5) P.xlabel('X (nm)') P.ylabel('Y (nm)') P.savefig(fn + '_fig0.png') return difdic #, shape