def deleteSimilarBeads(mm, maxDist, cc): #merges Beads that are too close cleanmm = [] for i in range(len(mm)): dist=1000 if len(cleanmm) == 0: cleanmm.append(mm[i]) else: for j in range(len(cleanmm)): currdist = (cleanmm[j][0]-mm[i][0])**2+(cleanmm[j][1]-mm[i][1])**2 if currdist < dist: dist = currdist index = j if dist < 10: x = (cleanmm[index][0] + mm[i][0])/2 y = (cleanmm[index][1] + mm[i][1])/2 roi = [x-2*maxDist, x+2*maxDist, y-2*maxDist, y+2*maxDist] candidate = coord2im.cropROI(cc, roi)[:,:4] mm2,stddev,intensitiy = beadVariance(candidate) cleanmm[index][0] = mm2[0] cleanmm[index][1] = mm2[1] else: cleanmm.append(mm[i]) return np.array(cleanmm)
def getCircledBead(
self, layer
): # returns the bead (layer=0 for red, layer=1 for green) in the rectangular selected by single mouseclick
listItem = self.ui.fileWidget.item(layer)
filename = listItem.data(QtCore.Qt.DisplayRole).toString()
dims, cc = coords.readfile(filename)
rect = CursorGraphicsScene.getCursorPosition(self.scene)
maxDist = CursorGraphicsScene.cursorRadius(self.scene)
roi = [rect[0] - maxDist, rect[0] + maxDist, rect[1] - maxDist, rect[1] + maxDist]
candidate = coords.cropROI(cc, roi)[:, :4]
if len(candidate) < self.cutoff * dims[2]:
print "Number of points below percentage of occurrency 2"
bead = "false"
else:
mm, stddev, intensitiy = beadLocalisation.beadVariance(candidate)
color = QtGui.QColor.fromRgbF(*self.m_colors[layer])
bead = BeadCircle(mm[0], mm[1], color, layer)
if len(candidate) > dims[2]:
print " WARNING: Number of points below percentage of occurrency because len(candidate) =%d > dims[2] = %d" % (
len(candidate),
dims[2],
)
# bead='false'
return bead
def detectBeads(dims, cc, cutoff,maxDist=2, maxStdDev=1.3):
numFrames = cc[-1,2]+1
beads = []
singleConsidered=0
skipped = 0 #counter
scatterplotData = []
meanData = []
# sort beads in 'bins'
intensities = cc[:,3]
#~ allCandidates = cc[intensities > 2*np.mean(intensities)] # only high intensities
print(dims[2])
initialCandidates = cc[cc[:,2]<50]#getCandidates(dims,cc, np.min([50, dims[2]]))
#initialCandidates = allCandidates[allCandidates[:,2]<100] # from frame 0 to 49
counter=0
for x, y, frame, intensity in initialCandidates[:,0:4]:
counter=counter+1
#print counter
nearestDist, nearestIdx = nearestNeighbor((x,y,intensity), beads, maxDist)
# print 'nearestDist: %1.2f, Bead: (%3.2f, %3.2f)' %(nearestDist, x,y)
if nearestDist > 2*maxDist:
beads.append([x,y,intensity])
else:
#print "merging beads too close together: ", (x,y,intensity), beads[nearestIdx]
beads.append([(x+beads[nearestIdx][0])/2., (y+beads[nearestIdx][1])/2.,(intensity+beads[nearestIdx][2])/2.])
beads.remove(beads[nearestIdx])
#print 'smaller', time.time()-start2
for beadCandidate in beads: # over all candidates
x,y,intensity = beadCandidate
roi = [x-maxDist, x+maxDist, y-maxDist, y+maxDist]
candidate = coord2im.cropROI(cc, roi)[:,:4]
singleConsidered+=len(candidate)
#if len(candidate) > numFrames or len(candidate) < cutoff*numFrames: # allowing few percent not detected
if len(candidate) < cutoff*numFrames: # allowing few percent not detected
skipped += 1
continue
mm,stddev,intensitiy = beadVariance(candidate)
if stddev < maxStdDev:
print("mean: ", mm, "variance of dist: ", stddev, "@intensity: ", intensity, "#", len(candidate))
scatterplotData.append( (intensity, stddev))
meanData.append(mm)
else:
print("IGNORED (variance too large): mean: ", mm, "variance of dist: ", stddev, "@intensity: ", intensity, "#", len(candidate))
print(singleConsidered, "single detections considered.")
print(skipped, "initial candidates skipped.")
meanData = deleteSimilarBeads(meanData, maxDist, cc)
return meanData, scatterplotData
def countDetections(cc,x,y,radius): roi = x-radius, x+radius, y-radius, y+radius cc = coords.cropROI(cc, roi) number = 0 intensity = 0. center = np.array([x,y]) cc[:,4] = np.sqrt(np.sum((cc[:,0:2]-center)**2,axis=1)) idxs = cc[:,4]<radius if(idxs!=[]): cc = cc[idxs] return len(cc), np.sum(cc[:,3]) else: # empty set return 0, 0.
def doTransformationButton_clicked(self):
self.heatmatrix = self.transformcontroller.calculateTransform([self.m_npimages[0].shape[0]/self.m_factor, self.m_npimages[0].shape[1]/self.m_factor])
self.emit(QtCore.SIGNAL("hideBeads()"))
for i in range(1,len(self.m_npimages)): #Transforms the coordinates of the green according to the transformation
points = self.m_coords[i]
p_transformed = self.transformcontroller.doTransform(points[:,0:2], i)
points[:,0:2] = p_transformed
points = coords.cropROI(points, (0, self.m_dims[0][0]-1, 0, self.m_dims[0][1]-1))
img = coords.coords2Image(self.m_dims[i], points, self.m_factor)
colorimg = np.zeros((img.shape[0],img.shape[1],4),dtype=np.uint8)
mx = np.max(img)
colorimg[:,:,1] = 1*img*(255./mx) # green
self.m_npimages[i] = colorimg
self.recalculateResult()
if __name__ == "__main__":
path = r'./'
files = [ # list of files to compare
'20a_MT_Atto520-coords-HCI.txt']
roi = [77,91,72,82]
#~ roi = [91,105,14,33 ]
# Overview plot (whole image)
overviewfile=files[0]
dims, coords = cr.readfile(path+overviewfile)
plt_overview(dims, coords, roi)
for f in files:
print "processing file " + f
dims, coords = cr.readfile(path+f)
coords = cr.cropROI(coords, roi)
linewidth, inliers, fitparams = lw.robust_measure(coords)
print np.sqrt(linewidth)
#Plot data
plt_fit(coords, inliers, roi, fitparams)
plt.title("Data: " + f)
plt.axis(roi)
#plot residuals histogram
model = lw.AffineLinearModel()
resids = model.get_error(inliers, fitparams)
plt.figure()
plt.title("Residuals: " + f)
plt.hist(resids,bins=50,range=[0,2])
