...
To run, you need to set up an offline release in your directory:
| Code Block |
|---|
[user@psana0Xuser@psana0XXX ~] newrel ana-current myrelease
[user@psana0Xuser@psana0XXX ~] cd myrelease
|
Add the xtc browser package to your analysis release and "compile":
| Code Block |
|---|
[user@psana0Xuser@psana0XXX myrelease] addpkg XtcEventBrowser V00-00-06
[user@psana0Xuser@psana0XXX myrelease] scons
|
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.
| Code Block |
|---|
[user@psana0XXX myrelease] xtcbrowser /reg/d/psdm/cxi/cxi80410/xtc/e55-r0581*
|
This will open a Gui. After opening file(s), click the "Quick Scan" button to scan the first 1000 events in the file. After scanning, a new window will pop up, allowing to select detectors/devices to make plots from. The plots are made via pyana. Configuration file for pyana will be generated automatically. To customize your analysis, you can edit the pyana config file and pyana files in XtcEventBrowser package to fit your need, then run pyana by itself (see the pyana section of confluence).
Xtc file reader: xtcsummary.py
...
MatLab | MatPlotLib | Comments |
|---|
Loglog plot of one array vs. another | Code Block |
|---|
%
%
%
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')
|
| Loglog plot of one array vs. another | Code Block |
|---|
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')
|
| 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="dccbc8bbe25a7472-68ad9cfc-4c8047a3-b892a520-b5db20c94d8d28b99e42702e"><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 | |
| Code Block |
|---|
axes(a1)
hold on
lims(1:2,:) = ginput(2);
axes(a2)
hold on
lims(3:4,:) = ginput(2);
|
| | Code Block |
|---|
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] )
|
| 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. |
| | |
filter | filter | |
| Code Block |
|---|
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') |
| | Code Block |
|---|
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
|
| Comment |
| | |
