def detectBeadsFromFile(filename, cutoff, maxDist=2,maxStdDev=1.1): dims, cc = coord2im.readfile(filename) import time start = time.time() beadMeans, beadScatter = detectBeads(dims, cc, cutoff,maxDist, maxStdDev) print "calculated in %f seconds" % (time.time()-start) return beadMeans
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 loadImage(filename, color=(1.,1.,0.), factor=4): print "reading file %s" % filename dims, cc = coords.readfile(filename) img = coords.coords2Image(dims, cc, factor=factor) colorimg = np.zeros((img.shape[0],img.shape[1],4),dtype=np.uint8) mx = np.max(img) colorimg[:,:,0] = color[2]*img*(255./mx) # blue = LSB colorimg[:,:,1] = color[1]*img*(255./mx) # green colorimg[:,:,2] = color[0]*img*(255./mx) # red return colorimg, cc, dims
def exportTransformedCoordinates_triggered(self):
'''export coordinate file list of tranformed points for all layers'''
for i in xrange(1,self.ui.fileWidget.count()):
listItem = self.ui.fileWidget.item(i)
filename = listItem.data(QtCore.Qt.DisplayRole).toString()
name = QtCore.QFileInfo(filename).fileName() # without path
newfilename = QtGui.QFileDialog.getSaveFileName(self.main_window, "Save Coordinate List %s" % name, filter="Coordinate List (*.txt)")
if not newfilename.contains('.'):
newfilename += ".txt" # add default extension
dimensions, points = coords.readfile(filename)
p_transformed = self.transformcontroller.doTransform(points[:,0:2], i)
points[:,0:2] = p_transformed
coords.writefile(newfilename, dimensions[0:2], points)
plt.figure()
plt.plot(coords[:,0], coords[:,1], 'b,')
plt.plot(inliers[:,0], inliers[:,2], 'rx')
plotLine(fit, roi[0], roi[1])
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
return np.array(cleanmm)
def detectBeadsFromFile(filename, cutoff, maxDist=2,maxStdDev=1.1):
dims, cc = coord2im.readfile(filename)
import time
start = time.time()
beadMeans, beadScatter = detectBeads(dims, cc, cutoff,maxDist, maxStdDev)
print "calculated in %f seconds" % (time.time()-start)
return beadMeans
if __name__ == "__main__":
if len(sys.argv != 2):
print "Usage: %s coordsfile.txt" % sys.argv[0]
sys.exit(1)
filename = sys.argv[1]
dims, cc = coord2im.readfile(filename)
beadMeans, beadScatter = detectBeads(dims, cc)
print "number of beads: ", len(beadMeans)
#~ print singleConsidered, "single detections considered."
#~ print skipped, "beads skipped."
print "mean variance: %f" % (np.mean(beadScatter, axis=0)[1])
#plot:
import matplotlib.pyplot as plt
xx,yy=np.hsplit(np.array(beadScatter),np.array([1]))
plt.plot(xx,yy,'rx')
plt.xlabel('Beat Intensity')
plt.ylabel('stddev of detected position')
plt.title('STORM: localisation precision at different beat intensities')
plt.ylabel('stddev of detected position')
parametersX = myoutputX.res_var
parametersY = myoutputY.res_var
return [parametersX, parametersY]
# if run standalone
if __name__ == "__main__":
import matplotlib.pyplot as plt
import coords
import scipy
file1 = 'data/HeLa5_10000z_3_polyL_actinmEOS2-steve.txt'
file1_landmarks = 'data/HeLa5_10000z_3_polyL_actinmEOS2-steve.txt_beads.txt'
file2 = 'data/HeLa5_10000z_3_polyL_ER647.txt'
file2_landmarks = 'data/HeLa5_10000z_3_polyL_ER647.txt_beads.txt'
thrash, landmarks1 = coords.readfile(file1_landmarks)
thrash, landmarks2 = coords.readfile(file2_landmarks)
landmarks1 = landmarks1[:,:2]
landmarks2 = landmarks2[:,:2]
trafo = affineMatrix2DFromCorrespondingPoints(landmarks2, landmarks1)
print("Transformation: \n", trafo)
dims, cc1 = coords.readfile(file1)
dims, cc2 = coords.readfile(file2)
cc2_ones = np.hstack([cc2[:,:2], np.ones((len(cc2),1))]) # add column with ones for affine trafo
cc2_transformed = np.dot(cc2_ones, trafo.T)[:,:2]
cc2_transformed = np.hstack([cc2_transformed,cc2[:,2:]]) # add intensity information again
im1 = coords.coords2Image(dims, cc1)
im2 = coords.coords2Image(dims, cc2_transformed)
ws[ws < threshold] = 0
legalIndices = np.nonzero(ws)
legalPairs = np.array(d)[legalIndices]
return legalPairs
def robust_measure(coords):
xs = coords[:,0]
ys = coords[:,1]
ll = len(coords)
fit = robreg.lmrob(xs,ys)
all_data = np.hstack( [xs.reshape([ll,1]),np.ones((ll,1)),ys.reshape([ll,1])] )
inliers = remove_outliers(all_data, fit)
bestfit = np.array([[fit['a']],[fit['b']]])
model = AffineLinearModel()
resids = model.get_error(inliers, bestfit)
estimLWPercentile = scipy.stats.scoreatpercentile(resids, 95)
return estimLWPercentile, inliers, bestfit
if __name__ == "__main__":
if not len(sys.argv) == 6:
print ( "Usage: " + sys.argv[0] + "coordsfile.txt xmin xmax ymin ymax")
sys.exit(1)
filename = sys.argv[1]
roi = [int(sys.argv[j]) for j in range(2,6)]
dimensions, coords = cr.readfile(filename)
coords = cr.cropROI(coords, roi)
lw, inl, fit = robust_measure(coords)
print filename, np.sqrt(lw)
def robust_measure(coords):
xs = coords[:, 0]
ys = coords[:, 1]
ll = len(coords)
fit = robreg.lmrob(xs, ys)
all_data = np.hstack(
[xs.reshape([ll, 1]),
np.ones((ll, 1)),
ys.reshape([ll, 1])])
inliers = remove_outliers(all_data, fit)
bestfit = np.array([[fit['a']], [fit['b']]])
model = AffineLinearModel()
resids = model.get_error(inliers, bestfit)
estimLWPercentile = scipy.stats.scoreatpercentile(resids, 95)
return estimLWPercentile, inliers, bestfit
if __name__ == "__main__":
if not len(sys.argv) == 6:
print("Usage: " + sys.argv[0] + "coordsfile.txt xmin xmax ymin ymax")
sys.exit(1)
filename = sys.argv[1]
roi = [int(sys.argv[j]) for j in range(2, 6)]
dimensions, coords = cr.readfile(filename)
coords = cr.cropROI(coords, roi)
lw, inl, fit = robust_measure(coords)
print filename, np.sqrt(lw)