def updateRoi():
selected = roi.getArrayRegion(self.ImageArray.T, self.img)
p2.plot(selected.sum(axis=1), clear=True)
datahor = selected.sum(axis=1)
FittedParamsHor = MatTools.FitGaussian(datahor)[0]
xhor = np.arange(datahor.size)
p2.plot(MatTools.gaussian(xhor,*FittedParamsHor), pen=(0,255,0))
p3.plot(selected.sum(axis=0), clear=True).rotate(-90)
datavert = selected.sum(axis=0)
FittedParamsVert = MatTools.FitGaussian(datavert)[0]
xvert = np.arange(datavert.size)
p3.plot(MatTools.gaussian(xvert,*FittedParamsVert), pen=(0,255,0)).rotate(-90)
hLine.setPos(FittedParamsVert[2]+roi.pos()[1])
vLine.setPos(FittedParamsHor[2]+roi.pos()[0])
pos = np.array([[(FittedParamsHor[2]+roi.pos()[0]),(FittedParamsVert[2]+roi.pos()[1])]])
peak.setData(pos=pos,symbol=symbol,size=25, symbolPen='g', symbolBrush='g')
def updateRoi():
'''
In this method the Roi data is updated. It is called when the Roi position and/or shape changes
and when the image is updated. The ROI data is summed up in vertical and horizontal directions
and plotted in the according diagrams. The total sum of the ROI is seen as Amplitude and displayed
in the plot. A gaussian is fitted to the sums in horizontal and vertical direction. The fit results
are used to plot the peak position and contour. The beam properties are stored in a buffer and can
be plotted in the time evolution plot.
'''
global ImageArray, img, databuffer
# Get ROI data
selected = roi.getArrayRegion(ImageArray.T, img)
# Calculate amplitude
amplitude = selected.sum()
# Shift buffer one step forward and store amplitude and time stamp
databuffer[1:,:-1] = databuffer[1:,1:]
actualtime = time.time()
databuffer[1,-1] = actualtime - starttime
databuffer[2,-1] = amplitude
# Plot sum in horizontal direction and fit gaussian
datahor = selected.sum(axis=1)
p2.plot(datahor, clear=True)
FittedParamsHor = MatTools.FitGaussian(datahor)[0]
xhor = np.arange(datahor.size)
if ui.fitCheck.isChecked():
p2.plot(MatTools.gaussian(xhor,*FittedParamsHor), pen=(0,255,0))
# Plot amplitude
xamp = np.array([1.,2.])
yamp = np.array([amplitude])
amphist.plot(xamp, yamp, stepMode=True, clear=True, fillLevel=0, brush=(0,0,255,150))
# Plot sum in vertical direction and fit gaussian, save fit results in buffer and show in text box
datavert = selected.sum(axis=0)
p3.plot(datavert, clear=True).rotate(90)
FittedParamsVert = MatTools.FitGaussian(datavert)[0]
xvert = np.arange(datavert.size)
if ui.fitCheck.isChecked():
p3.plot(MatTools.gaussian(xvert,*FittedParamsVert), pen=(0,255,0)).rotate(90)
poshor = FittedParamsHor[2]+roi.pos()[0]
posvert = FittedParamsVert[2]+roi.pos()[1]
waistx = FittedParamsHor[1]
waisty = FittedParamsVert[1]
databuffer[3,-1] = poshor
databuffer[4,-1] = posvert
databuffer[5,-1] = waistx
databuffer[6,-1] = waisty
updatetext(amplitude,poshor,posvert,waistx,waisty)
if ui.trackCheck.isChecked():
# Adjust cross hair
hLine.setPos(FittedParamsVert[2]+roi.pos()[1])
vLine.setPos(FittedParamsHor[2]+roi.pos()[0])
vLine.show()
hLine.show()
# Plot peak
pos = np.array([[(FittedParamsHor[2]+roi.pos()[0]),(FittedParamsVert[2]+roi.pos()[1])]])
peak.setData(pos,clear=True)
# Plot contour
x = np.linspace(-(FittedParamsHor[1]),(FittedParamsHor[1]),1000)
sigmax = FittedParamsHor[1]
sigmay = FittedParamsVert[1]
y = ellipse(x,sigmax,sigmay)
x = np.append(x,-x)
y = np.append(y,-y)
x += FittedParamsHor[2]+roi.pos()[0]
y += FittedParamsVert[2]+roi.pos()[1]
# X,Y = np.meshgrid(x,y)
# contour.clear()
contour.setData(x,y,clear=True)
else:
# view.removeItem(hLine)
# view.removeItem(vLine)
# Hide cross hair, peak and contour if 'Track beam' is not checked
vLine.hide()
hLine.hide()
contour.clear()
peak.clear()
#When checked, the reference beam peak and contour is plotted
if ui.refCheck.isChecked():
peakposition = np.array([[ui.x0Spin.value(),ui.y0Spin.value()]])
peakpos.setData(peakposition,clear=True)
sigmax = ui.sigmaxSpin.value()
sigmay = ui.sigmaySpin.value()
x = np.linspace(-(sigmax),(sigmax),1000)
y = ellipse(x,sigmax,sigmay)
x = np.append(x,-x)
y = np.append(y,-y)
x += ui.x0Spin.value()
y += ui.y0Spin.value()
# X,Y = np.meshgrid(x,y)
# contour.clear()
refcontour.setData(x,y,clear=True)
else:
peakpos.clear()
refcontour.clear()
# Update the time evolution plot
updatetimescrolling()
def beamProfilerAnalysis():
'''
In this method the Roi data is updated. It is called when the Roi position and/or shape changes
and when the image is updated. The centroid of the roi is determined
two cuts (x and y through the roi are fit by gaussians and both the data and
the fit is shown. Peak amplitude is shown and the localtion of the centroid
'''
global ImageArray, img, databuffer
# Get ROI data
selected = roi.getArrayRegion(ImageArray.T, img)
# Calculate amplitude
amplitude = np.nansum(selected)
# Shift buffer one step forward and store amplitude and time stamp
databuffer[1:,:-1] = databuffer[1:,1:]
actualtime = time.time()
databuffer[1,-1] = actualtime - starttime
databuffer[2,-1] = amplitude
#find centroid
# xs,ys = selected.shape
# X,Y=np.meshgrid(np.arange(ys),np.arange(xs))
# ss = selected-np.nanmin(selected)
# s = np.nansum(ss)
# my = int(np.nansum(X*ss)/s)
# mx = int(np.nansum(Y*ss)/s)
#find max
mx,my = np.where(selected==np.nanmax(selected))
mx, my = mx[0],my[0]
# Plot sum in horizontal direction and fit gaussian
datahor = selected[:,my]
p2.plot(datahor, clear=True)
FittedParamsHor = MatTools.FitGaussian(datahor)[0]
xhor = np.arange(datahor.size)
if ui.fitCheck.isChecked():
p2.plot(MatTools.gaussian(xhor,*FittedParamsHor), pen=(0,255,0))
# Plot amplitude
xamp = np.array([1.,2.])
yamp = np.array([amplitude])
amphist.plot(xamp, yamp, stepMode=True, clear=True, fillLevel=0, brush=(0,0,255,150))
# Plot sum in vertical direction and fit gaussian, save fit results in buffer and show in text box
datavert = selected[mx]
p3.plot(datavert, clear=True).rotate(90)
FittedParamsVert = MatTools.FitGaussian(datavert)[0]
xvert = np.arange(datavert.size)
if ui.fitCheck.isChecked():
p3.plot(MatTools.gaussian(xvert,*FittedParamsVert), pen=(0,255,0)).rotate(90)
poshor = FittedParamsHor[2]+roi.pos()[0]
posvert = FittedParamsVert[2]+roi.pos()[1]
waistx = FittedParamsHor[1]
waisty = FittedParamsVert[1]
databuffer[3,-1] = poshor
databuffer[4,-1] = posvert
databuffer[5,-1] = waistx
databuffer[6,-1] = waisty
updatetext(amplitude,poshor,posvert,waistx,waisty)
if ui.trackCheck.isChecked():
# Adjust cross hair
# hLine.setPos(FittedParamsVert[2]+roi.pos()[1])
# vLine.setPos(FittedParamsHor[2]+roi.pos()[0])
hLine.setPos(my+roi.pos()[1])
vLine.setPos(mx+roi.pos()[0])
vLine.show()
hLine.show()
# Plot peak
pos = np.array([[(FittedParamsHor[2]+roi.pos()[0]),(FittedParamsVert[2]+roi.pos()[1])]])
peak.setData(pos,clear=True)
# Plot contour
x = np.linspace(-(FittedParamsHor[1]),(FittedParamsHor[1]),1000)
sigmax = FittedParamsHor[1]
sigmay = FittedParamsVert[1]
y = ellipse(x,sigmax,sigmay)
x = np.append(x,-x)
y = np.append(y,-y)
x += FittedParamsHor[2]+roi.pos()[0]
y += FittedParamsVert[2]+roi.pos()[1]
# X,Y = np.meshgrid(x,y)
# contour.clear()
contour.setData(x,y,clear=True)
else:
# view.removeItem(hLine)
# view.removeItem(vLine)
# Hide cross hair, peak and contour if 'Track beam' is not checked
vLine.hide()
hLine.hide()
contour.clear()
peak.clear()
# Update the time evolution plot
updatetimescrolling()
