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[user@psana0XXX myrelease] addpkg XtcEventBrowser V00-00-11
[user@psana0XXX myrelease] scons
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Note! You can omit the "tag" (VXX-XX-XX) to get the latest version in the svn repository, but this may look different from described here.
Run the program with the command 'xtcbrowser' and optionally give the input xtc files that you want to read as arguments. You can also browse to find files after launching the browser.
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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 |
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pyana -m XtcEventBrowser/src/pyana_cspad.py <xtc files>
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Options must be specified in a configuration file, or the default values will be used, e.g.: | Code Block |
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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
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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="9451a5a9767b8c22-860ecfdf-44744c4b-8a1bbbfe-cad189cec8e49fe3d37e4af1"><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 | |
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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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| Comment |
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