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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 output_file = # filename for saving numpy array with average of images 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

Image Removed

• output_file = # filename for saving numpy array with average of images

Image Added

• Pulnix Pulnix TM6740 images are processed with pyana_image.py. It allows any number of images, given as a space-separated list of addresses in the
  configuration file.
  • Ranges You can be given set ranges to define dark images, and good images. Background subtracted images can also be used, where 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 is subtracted from the good images before plotting.
  • Each image can be separately rotated (Done), shifted (TODO!) and scaled (TODO!zoomed in/out).
  • Nicknames can be given An optional parameter also allow you to set
    nicknames to the images (defaults will be input images. Defaults are Im1, Im2... etc), these . 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.

• Currently it has the following settings:

  Code Block

  image_addresses = CxiSc1-0|TM6740-1 CxiSc1-0|TM6740-2 CxiSc1-0|TM6740-3 # Address of Detector-Id|Device-Id dark_range = 50--250 # low and high limit for what we define as dark image good_range = 250--1050 # low and high limit for what we define as a good image (with signal) image_rotations = 7.1 6.2 5.3 # Angle in degrees image_shifts = (0,0) (0,0) (0,0) # Shift (number of pixels (x,y)) to be applied image_scales = # Scale factor to be applied to zoom in or out image_nicknames = Im1 Im2 Im3 # lowIf andnone highprovided, limitthese forwill whatbe we define as dark image good_range = 250--1050the names image_manipulations = # lowString andcontaining high limitkeywords: "Diff" for whatdifference we define as a good image (with signal) image_nicknames = Im1 Im2 Im3plots, FFT for FFT of difference arrays draw_each_event = Yes # If none provided, these# willplot befor theeach namesevent? imageoutput_rotationsfile = 7.1 6.2 5.3myarrays.hdf5 # Angle in degrees image_scales# base =name for output file. Valid extensions are .hdf5, .txt (ascii) or .npy (numpy binary) # Scale factor to be applied to zoom in or out image_shifts = (0,0) (0,0) (0,0) # Shift (number of pixels (x,y)) to be applied draw_each_event = Yes # numpy arrays can only be written one per file. N_hdf5 # plot for each event? output_file = test_.txt # baseif nameHDF5 for output, (numpythis arrays)parameter forallows eachyou event.to Validsplit extensionsthe areoutput .txt (ascii) or .npy (numpy binary)with N events in each

xtcscanner

This is a command-line interface to the XtcScanner class that makes a summary of the xtc file.

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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