Event browser for Xtc files
Xtc is the online data format. For a faster look at the data, we need a quick tool to make simple analysis plots from the xtc data.
Xtc file reader: xtcsummary.py
Text output listing the contents of an xtc file.
Analysis with pyana
Pyana is already a complete tool for analyzing xtc files. The user needs to write some code in python to load the data of interest. (We should provide more examples). Matplotlib is suitable for plotting in the pyana framework.
Plotting with MatPlotLib. A comparison with MatLab.
MatLab |
MatPlotLib |
Comments |
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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') |
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. |
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test |
test |
Test |
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axes(a1) hold on lims(1:2,:) = ginput(2); 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') axes(a2) hold on lims(3:4,:) = ginput(2); 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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axes(a1) hold on lims(1:2,:) = ginput(2); 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') axes(a2) hold on lims(3:4,:) = ginput(2); 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') |
plt.axes(a1) plt.hold(True) limslista = plt.ginput(2) # list: (x0,y0),(x1,y1) plt.axes(a2) plt.hold(True) limslistb = plt.ginput(2) # list: (x2,y2),(x3,y3) limsa = np.array(limslista) #[ x0 y0 # x1 y1 ] limsb = np.array(limslistb) #[ x2 y2 # x3 y3 ] lims = np.hstack( [limsa, limsb] ) # [ x0 y0 x2 y2 = [ ch0 ch1 ch2 ch3 ] # x1 y1 x3 y3 ] # now each column corresponds to one channel. 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
| 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. |