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The most pain-free way to access LCLS XTC data files from python is through LCLS's python framework, pyana. It is a non-interactive framework, but to some extent you can work interactively with the data it produces
All about pyana.
C++ framework: psana
The idea is the same as for pyana. Non-interactive. No interactive support as of yet.
All about psana - Original Documentation.
If you like GUIs:
The XTC Explorer - Old gives you an "interactive" way to configure your analysis.
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Python/IPython can be used to analyze data after you've saved them, or they can be embedded into a pyana module to give you interactive access to the data at regular intervals throughout your analysis.
'IPython' (http://ipython.org/
Image Removed) is an enhanced python shell for interactive use. Many of the examples here would work equally well with a 'regular' python shell.
Plotting is done with 'matplotlib' (http://matplotlib.sourceforge.net/Image Removed)
If you're looking for an IDE to work with, consider 'Spyder' (http://code.google.com/p/spyderlib/Image Removed).
Interactively exploring the XTC file.
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With interactive python embedded, see: https://confluence.slac.stanford.edu/display/PCDS/XTC+Explorer#XTCExplorer-InteractiveplottingwithIPythonImage Removed
IPython used "like" MATLAB
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Table of comparison (MATLAB vs MatPlotLib)
See also http://www.scipy.org/NumPy_for_Matlab_UsersImage Removed
MatLab | MatPlotLib | Comments |
|---|
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 |
|---|
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.
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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 |
|---|
lims = np.zeros((4,2),dtype="float")
plt.axes(a1)
plt.hold(True)
lims[0:2,:] = plt.ginput(2)
plt.axes(a2)
plt.hold(True)
lims[2:4,:] = plt.ginput(2)
|
| 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 declared a 4x2 array of zeros to start with, then fill it with ginput().
|
| | |
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
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| Comment |
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