• Created by Unknown User (ofte), last modified on Jan 21, 2011

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Event browser for Xtc files

This page summarizes initial attempts at an event browser or simple plotting tool for Xtc files that should be easy to use.

Xtc file reader: xtcsummary.py

Text output listing the contents of an xtc file. Similar to pyxtcreader, but instead of listing all datagrams, it loops through and prints only a summary.

Usage:

xtcsummary.py <filename(s)>

XtcExplorer

LclsXtcEventBrowser.py

Further analysis with pyana

Any serious data analysis will need more customized tools than we can provide in a GUI interface. This will require the user / analyst to program his/her own tools. Pyana is a complete framework for programming a user analysis in python. The Gui Event Browser can provide simple analysis code that can be expanded by the user. "Blank" analysis code can also be generated with the codegen script (try codegen -h and codegen -p for options).

More information about pyana can be found on confluence.

Data visualization with NumPy (arrays) and MatPlotLib (plots).

A comparison with MatLab.

MatLab

MatPlotLib

Comments

Loglog plot of one array vs. another

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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')

Loglog plot of one array vs. another

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.

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test

test

Test

array of limits from graphical input

array of limits from graphical input

 

 
axes(a1)
hold on
lims(1:2,:) = ginput(2);

axes(a2)
hold on
lims(3:4,:) = ginput(2);

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

 

 
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') 
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

 

 

 
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