Page History

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• 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 can be given to define dark images, and good images. Background subtracted images can also be used, where 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!). An optional parameter also allow you to set
    nicknames to the images (defaults will be 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.

• 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_nicknames = Im1 Im2 Im3 # If none provided, these will be the names image_rotations = 7.1 6.2 5.3 # Angle in degrees image_scales = # 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 # plot for each event? output_file = test_.txt # base name for output (numpy arrays) for each event. Valid extensions are .txt (ascii) or .npy (numpy binary)

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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