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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.
- Currently it has the following settings:
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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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xtcscanner
This is a command-line interface to the XtcScanner class that makes a summary of the xtc file.
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MatLab | MatPlotLib | Comments |
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Loglog plot of one array vs. another Code Block |
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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')
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| Loglog plot of one array vs. another Code Block |
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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')
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| channels is a 4xN array of floats, where N is the number of events. Each column corresponds to one out of four Ipimb channels.
Note that the arrays are indexed with 1,2,3,4 in MatLab and 0,1,2,3 in MatPlotLib/NumPy/Python.
<ac:structured-macro ac:name="unmigrated-wiki-markup" ac:schema-version="1" ac:macro-id="6bd2a061bc5e1f46-2991e6d6-4f6443b9-9b8394c5-fd9d5bc8021021eb976c4912"><ac:plain-text-body><![CDATA[Note also the use of paranthesis, array() in MatLab, array[] in MatPlotLib. | ]]></ac:plain-text-body></ac:structured-macro> |
test | test | Test |
array of limits from graphical input | array of limits from graphical input | |
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axes(a1)
hold on
lims(1:2,:) = ginput(2);
axes(a2)
hold on
lims(3:4,:) = ginput(2);
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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] )
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| In MatLab, lims is an expandable array that holds limits as set by input from mouse click on the plot (ginput). NumPy arrays cannot be expanded, so I've chosen to append to a python list first, then fill a NumPy array for the usage to look the same.
The exact usage of the lims array depends on where you place each limit. I think perhaps I've done it differently from the MatLab version. |
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filter | filter | |
Code Block |
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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') |
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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
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