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[user@psana0XXX myrelease] addpkg XtcEventBrowser V00-00-1416 [user@psana0XXX myrelease] scons

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Currently it has the following settings:

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image_addresses  =  CxiSc1-0|TM6740-1 CxiSc1-0|TM6740-2 CxiSc1-0|TM6740-3 # Address of Detector-Id|Device-Id
dark_range  =  50--250                # low and high limit for what we define as dark image
good_range  =  250--1050              # low and high limit for what we define as a good image (with signal)
image_rotations  =  7.1 6.2 5.3       # Angle in degrees
image_shifts  =  (0,0) (0,0) (0,0)    # Shift (number of pixels (x,y)) to be applied
image_scales  =                       # Scale factor to be applied to zoom in or out
image_nicknames = Im1 Im2 Im3         # If none provided, these will be the names
image_manipulations =                 # String containing keywords: "Diff" for difference plots, FFT for FFT of difference arrays
draw_each_event  =  Yes               # plot for each event?
output_file = myarrays.hdf5           # base name for output file. Valid extensions are .hdf5, .txt (ascii) or .npy (numpy binary)
                                      # numpy arrays can only be written one per file. 
Nn_hdf5                                # if HDF5 output, this parameter allows you to split the output with N events in each

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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

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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="fc7c52954275fc3c-d561ace9-43664cb7-be629d1a-5699a133e72a0a2018e3258f"><ac:plain-text-body><![CDATA[Note also the use of paranthesis, array() in MatLab, array[] in MatPlotLib.

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test

Test

array of limits from graphical input

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axes(a1) hold on lims(1:2,:) = ginput(2); axes(a2) hold on lims(3:4,:) = ginput(2);

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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] )

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

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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

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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