def update_figure(self):
global ImageDisplayed
self.ax0.cla()
self.axx.cla()
self.axy.cla()
self.ax0.imshow(ImageDisplayed,
aspect='auto',
cmap=beam_map,
origin='lower')
projx, projy = ImageDisplayed.sum(axis=0), ImageDisplayed.sum(axis=1)
x = np.arange(len(projx))
y = np.arange(len(projy))
if FLAG_fit > 0:
p2X = imgtl.FitProfile(projx, x)
p2Y = imgtl.FitProfile(projy, y)
# # top plot
self.axx.plot(projx)
if FLAG_fit > 0:
self.axx.plot(x, imgtl.dg(x, p2X), 'r--', linewidth=3)
self.axx.set_xlim(self.ax0.get_xlim())
# # Right plot
self.axy.plot(projy, range(len(projy)))
if FLAG_fit > 0:
self.axy.plot(imgtl.dg(y, p2Y), y, 'r--', linewidth=3)
self.axy.set_ylim(self.ax0.get_ylim())
# Remove tick labels
nullfmt = ticker.NullFormatter()
self.axx.xaxis.set_major_formatter(nullfmt)
self.axx.yaxis.set_major_formatter(nullfmt)
self.axy.xaxis.set_major_formatter(nullfmt)
self.axy.yaxis.set_major_formatter(nullfmt)
self.draw()
print("fitX: ", p2X)
print("fitY: ", p2Y)
plt.figure()
disp=filenameIm.split('/')
print(disp)
print(len(disp))
plt.subplot (2,2,1)
plt.title (disp[len(disp)-1])
plt.imshow(ndimage.gaussian_filter(IMGf, 10), extent=[min(x), max(x), min(y), max(y)], \
aspect='auto', cmap='coolwarm')
plt.subplot (2,2,3)
plt.plot (x, histx)
plt.plot (x, imgtl.dg(x,p2X),'--',linewidth=3)
plt.xlim(min(x), max(x))
plt.subplot (2,2,2)
plt.plot (histy,y)
plt.plot (imgtl.dg(y,p2Y),y,'--',linewidth=3)
plt.ylim(min(y), max(y))
plt.subplot (2,2,4)
plt.ylim(0,10)
plt.xlim(0,10)
text1='rms_x = '+str('{0:.2f}'.format((np.mean(stdx0[len(stdx0)-2:len(stdx0)+2])))) \
+'+/-'+str('{0:.2f}'.format(np.std(stdx0[len(stdx0)-2:len(stdx0)+2])))
text2='rms_y = '+str('{0:.2f}'.format((np.mean(stdy0[len(stdy0)-2:len(stdy0)+2])))) \
+'+/-'+str('{0:.2f}'.format(np.std(stdy0[len(stdy0)-2:len(stdy0)+2])))
text3='<xy>/(rms_x*rms_y) = '+str('{0:.2f}'.format((np.mean(correl0[len(correl0)-2:len(correl0)+2])))) \
+'+/-'+str('{0:.2f}'.format(np.std(correl0[len(correl0)-2:len(correl0)+2])))
axisX=np.arange(len(histX))
axisY=np.arange(len(histY))
# center the histogram and select a region of interest (ROI) around the peak
window=1000.
histXc, axisXc = imgtl.center_and_ROI(histX, axisX, window)
histYc, axisYc = imgtl.center_and_ROI(histY, axisY, window)
# remove background (there are many way of doing this)
window = 10
histXc, axisXc = imgtl.removebackground(histXc, axisXc, window)
histYc, axisYc = imgtl.removebackground(histYc, axisYc, window)
p2X= imgtl.FitProfile(histXc, axisXc)
p2Y= imgtl.FitProfile(histYc, axisYc)
print "fitX: ", p2X
print "fitY: ", p2Y
plt.figure()
plt.plot(axisXc, histXc,'ob',alpha=0.1)
plt.plot(axisXc, imgtl.dg(axisXc,p2X),'--b',linewidth=3)
plt.plot(axisYc, histYc,'or',alpha=0.1)
plt.plot(axisYc, imgtl.dg(axisYc,p2Y),'--r',linewidth=3)
plt.title("point number: "+str(i))
plt.show()
histY = np.loadtxt(filenameY)
# create the horizontal coordinate
axisX = np.arange(len(histX))
axisY = np.arange(len(histY))
# center the histogram and select a region of interest (ROI) around the peak
window = 1000.
histXc, axisXc = imgtl.center_and_ROI(histX, axisX, window)
histYc, axisYc = imgtl.center_and_ROI(histY, axisY, window)
# remove background (there are many way of doing this)
window = 10
histXc, axisXc = imgtl.removebackground(histXc, axisXc, window)
histYc, axisYc = imgtl.removebackground(histYc, axisYc, window)
p2X = imgtl.FitProfile(histXc, axisXc)
p2Y = imgtl.FitProfile(histYc, axisYc)
print("fitX: ", p2X)
print("fitY: ", p2Y)
plt.figure()
plt.plot(axisXc, histXc, 'ob', alpha=0.1)
plt.plot(axisXc, imgtl.dg(axisXc, p2X), '--b', linewidth=3)
plt.plot(axisYc, histYc, 'or', alpha=0.1)
plt.plot(axisYc, imgtl.dg(axisYc, p2Y), '--r', linewidth=3)
plt.title("point number: " + str(i))
plt.show()
y=y-y[np.argmax(histy)]
p2X= imgtl.FitProfile(histx, x)
p2Y= imgtl.FitProfile(histy, y)
print 'RMS_x=',np.mean(stdx0[len(stdx0)-2:len(stdx0)+2])
print 'RMS_y=',np.mean(stdy0[len(stdy0)-2:len(stdy0)+2])
print "fitX: ", p2X
print "fitY: ", p2Y
plt.figure()
plt.subplot (2,2,1)
plt.imshow(ndimage.gaussian_filter(IMGf, 10), extent=[min(x), max(x), min(y), max(y)], \
aspect='auto', origin='lower', cmap='spectral')
plt.subplot (2,2,3)
plt.plot (x, histx)
plt.plot (x, imgtl.dg(x,p2X),'--',linewidth=3)
plt.xlim(min(x), max(x))
plt.subplot (2,2,2)
plt.plot (histy,y)
plt.plot (imgtl.dg(y,p2Y),y,'--',linewidth=3)
plt.ylim(min(y), max(y))
plt.subplot (2,2,4)
plt.ylim(0,10)
plt.xlim(0,10)
text1='rms_x = '+str('{0:.2f}'.format((np.mean(stdx0[len(stdx0)-2:len(stdx0)+2])))) \
+'+/-'+str('{0:.2f}'.format(np.std(stdx0[len(stdx0)-2:len(stdx0)+2])))
text2='rms_y = '+str('{0:.2f}'.format((np.mean(stdy0[len(stdy0)-2:len(stdy0)+2])))) \
+'+/-'+str('{0:.2f}'.format(np.std(stdy0[len(stdy0)-2:len(stdy0)+2])))
text3='<xy>/(rms_x*rms_y) = '+str('{0:.2f}'.format((np.mean(correl0[len(correl0)-2:len(correl0)+2])))) \
+'+/-'+str('{0:.2f}'.format(np.std(correl0[len(correl0)-2:len(correl0)+2])))
