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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

The GUI as described above prepares a configuration file for you to run pyana. You can either run it from the GUI or you can run it from the command line. The configfile sets input parameters for the pyana modules in this package. Feel free to use one or more of these modules as a starting point for your more elaborate pyana analysis!

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More information about pyana can be found on confluence.

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

Saving (and loading) a numpy array (e.g. image) to (from) a file

If you want to save one array (max 2 dimensions), you can use binary numpy file or ascii file:

Code Block

import numpy as np

# binary file .npy format
np.save("filename.npy", array)
array = np.load("filename.npy")

# txt file
np.savetxt("filename.dat", array)
array = loadtxt("filename.dat")

If you need to save multiple events/shots in the same file you will need to do some tricks (e.g. flatten the array and stack 1d arrays into 2d arrays where axis2 represent event number). Or you could save as an HDF5 file.

You can save an array or several into an HDF5 file (example from pyana):

Code Block

import h5py

def beginjob(self,evt,env):
    self.ofile = h5py.File("outputfile.hdf5", 'w') # open for writing (overwrites existing file)
    self.shot_counter = 0

def event(self,evt,env)
    # example: store several arrays from one shot in a group labeled with shot (event) number
    self.shot_counter += 1
    group = self.ofile.create_group("Shot%d" % self.shot_counter)

    image1_source = "CxiSc1-0|TM6740-1"
    image2_source = "CxiSc1-0|TM6740-2"

    frame = evt.getFrameValue(image1_source)
    image1 = frame.data()
    frame = evt.getFrameValue(image2_source)
    image2 = frame.data()

    dataset1 = group.create_dataset("%s"%image1_source,data=image1)
    dataset2 = group.create_dataset("%s"%image2_source,data=image2)

def endjob(self,env)
    self.ofile.close()

Or you can group your datasets any other way you find useful, of course.

A comparison with MatLab.

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MatLab

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MatPlotLib

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Comments

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Loglog plot of one array vs. another

Code Block
%
%
%
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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]]></ac:plain-text-body></ac:structured-macro>

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test

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test

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Test

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array of limits from graphical input

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array of limits from graphical input

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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
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)

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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 declared a 4x2 array of zeros to start with, then fill it with ginput().

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filter

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filter

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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') 

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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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