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• CsPad: CsPad data is reconstructed in pyana_cspad.py. The image plot value limits are adjusted automatically, but if
  you want to change them, click on the color bar (left-click for low limit, right-click for high limit).
  The successive events will be plotted with the new limits. Revert to the original by middle-clicking.
  To run this module by itself with pyana:

  Code Block
  pyana -m XtcEventBrowser/src/pyana_cspad.py <xtc files>

  Options must be specified in a configuration file, or the default values will be used, e.g.:

  Code Block

  image_source = CxiDs1-0|Cspad-0 # string, Address of Detector-Id|Device-ID draw_each_event = # bool, Draw plot for each event? (Default=False). dark_img_file = # filename, Dark image file to be loaded, if any plot_vrange = # range=vmin-vmax (intensity) to be plotted, default is full range threshold = # lower threshold for image intensity in threshold area of the plot thr_area = # range=xmin,xmax,ymin,ymax defining threshold area output_file = # filename for saving numpy array with average of images

• Pulnix TM6740 images are processed with pyana_image.py processes generic camera frames, e.g from Pulnix TM6740 device. It allows any number of images, given as a space-separated list of addresses in the
  configuration file.
  • You can set ranges to define good images and dark images. If both are set, you have the option to display good images background subtracted, where background subtraction is based on the average of background images so far collected.
  • Each image can be separately rotated, shifted and scaled (zoomed in/out).
  • Nicknames can be given to the input images. Defaults are Im1, Im2... etc. These names will be used if you plot differences, or other manipulations of the original images.
  • The images are subtracted and differences displayed as well as fourier transform of differences. Examples of what may be displayed. To display other things, at this stage you have to edit pyana_image.py to change this behaviour.

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a1 = subplot(121);
loglog(channels(:,1),channels(:,2),'o')
xlabel('CH0')
ylabel('CH1')
a2 = subplot(122);
loglog(channels(:,3),channels(:,4),'o')
xlabel('CH2')
ylabel('CH3')

import matplotlib.pyplot as plt
import numpy as np

a1 = plt.subplot(221)
plt.loglog(channels[:,0],channels[:,1], 'o' )
plt.xlabel('CH0')
plt.ylabel('CH1')
a2 = plt.subplot(222)
plt.loglog(channels[:,2],channels[:,3], 'o' )
plt.xlabel('CH2')
plt.ylabel('CH3')

axes(a1)
hold on
lims(1:2,:) = ginput(2);

axes(a2)
hold on
lims(3:4,:) = ginput(2);

plt.axes(a1)
plt.hold(True)
limslista = plt.ginput(2)

plt.axes(a2)
plt.hold(True)

limslistb = plt.ginput(2)
limsa = np.array(limslista)
limsb = np.array(limslistb)

lims = np.hstack( [limsa, limsb] )

fbool1 = (channels(:,1)>min(lims(1:2,1)))&(channels(:,1)<max(lims(1:2,1)))
fbool2 = (channels(:,2)>min(lims(1:2,2)))&(channels(:,2)<max(lims(1:2,2)));
fbool = fbool1&fbool2
loglog(channels(fbool,1),channels(fbool,2),'or')

fbool3 = (channels(:,3)>min(lims(3:4,3)))&(channels(:,3)<max(lims(3:4,3)))
fbool4 = (channels(:,4)>min(lims(3:4,4)))&(channels(:,4)<max(lims(3:4,4)));
fbool = fbool3&fbool4
loglog(channels(fbool,3),channels(fbool,4),'or') 

fbools0 = (channels[:,0]>lims[:,0].min())&(channels[:,0]<lims[:,0].max())
fbools1 = (channels[:,1]>lims[:,1].min())&(channels[:,1]<lims[:,1].max())
fbools = fbools0 & fbools1

fbools2 = (channels[:,2]>lims[:,2].min())&(channels[:,2]<lims[:,2].max())
fbools3 = (channels[:,3]>lims[:,3].min())&(channels[:,3]<lims[:,3].max())
fbools = fbools2&fbools3