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[user@psana0XXX myrelease] xtcbrowser /reg/d/psdm/cxi/cxi80410/xtc/e55-r0581*
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Description of the GUIs
LCLS Xtc Event Browser
The procedure above will open a GUI, the main browser. It allows you to browse for files, and to run a scan to see what's in the file. Maybe "scan" is not a good choice of word... it parses the xtc file and investigate what kind of data is there.
- File section: Shows a list of currently selected file(s). As you may have guessed, "File Browser" opens a file browser and "Clear File List" clears the current list of files. This section also allows you to add a file name by hand (or paste).
- Scan section: The two buttons to the left allows you to scan the xtc file to get a summary of what datagrams are stored in it. Note, for most purposes, a "Quick Scan" is sufficient. If you need to scan the whole file, e.g. if you want to know the total number of events, number of calibration cycles, etc, you can enable the "Scan File(s)" button. If the files are big, this will take a lot of time...
Pyana Control Center
After scanning, a new GUI will pop up showing you a list of detectors/devices found in the file. A little more information is written to the terminal window too.
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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="b8a8806d48f1bc15-b7a9359b-4d6c4cfd-8e9da6ec-c8316189d7b28c8801ac2e24"><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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