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More information on how to run pyana by itself (see 'pyana -h' for more help, or the pyana section of confluence).
The pyana modules
Producing plots via pyana
This is probably where most changes will happen soon. Currently each type of detector is processed in different pyana modules, which makes correlation plots cumbersome. But this is on the list of features to be included. At the moment, only certain plots are displayed, via matplotlib, and the only way to really get the plots that you want is to edit the pyana source code!!! You can find it in XtcEventBrowser/src/ directory, all the pyana modules are called pyana_whatever.py. Feel free to treat these files as examples and edit them to fit your needs. The implementation will soon be more generic and customizable.
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Here are some example plots made. 
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Image Removed Example plots: one event from CsPad with background subtraction and filter. Some beamline data plots:
- image display of the CSPad detector, background subtracted. This is currently the only display that has interactive features. A filter allows you to select events within requested intensity range.
- Beam energy and position
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- Gas detector energy measurements
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A few things to note about the different detectors / pyana modules:
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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 |
|---|
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="8ee86b51a027e5ce-2414eb8a-4a0047db-bfd5b7cf-8ff97938564b7f7a43b29163"><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);
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| | Code Block |
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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') |
| | Code Block |
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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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