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For some more useful analysis examples, in the following we'll stick to writing customized pyana modules and running pyana from the command line.
But before getting to the pyana modules, I'll briefly touch on a few items general to python that may be useful: saving files, matplotlib for plotting, and IPython for interactive work.

Saving data arrays

Here are a few examples of how you can save data arrays in python.

saving numpy arrays to numpy file

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For more examples, see How to access HDF5 data from Python and http://code.google.com/p/h5py/

Plotting with MatPlotLib

One of the most commonly used tools for plotting in python: matplotlib. Other alternatives exist too.

Matplotlib:

• http://matplotlib.sourceforge.net/users/pyplot_tutorial.html

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• Or you can load arrays from a file and interactively plot them in iPython. The same ('recommended') syntax as above can be used, or if you use 'import *' you don't need to prepend the commands with the package name, which is handy when plotting interactively:

  Code Block
  from matplotlib.pyplot import * ion() plot(array) draw()

Related useful tools and links

• http://www.scipy.org/NumPy_for_Matlab_Users^{Image Removed}
• http://www.scipy.org/^{Image Removed}
• http://www.sagemath.org/^{Image Removed}
• http://code.google.com/p/spyderlib/^{Image Removed}

Interactive analysis with IPython

Interactive analysis with IPython

The LCLS offline analysis The LCLS offline analysis group does have plans for a real interactive pyana, but currently this is not available.
2011-11-04 iPsana Interactive Analysis Framework.pdf

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[ofte@psana0106 xpptutorial]$ ipython
Python 2.4.3 (#1, Nov  3 2010, 12:52:40)
Type "copyright", "credits" or "license" for more information.

IPython 0.9.1 -- An enhanced Interactive Python.
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object'. ?object also works, ?? prints more.

In [1]: from numpy import *

In [2]: from matplotlib.pyplot import *

In [3]: ipm3 = load('point_scan_delay.npy')

In [4]: ipm3.shape
Out[4]: (200, 3)

In [5]: ion()

In [6]: delay = ipm3[:,0]

In [7]: ipmraw = ipm3[:,1]

In [8]: ipmnorm = ipm3[:,2]

n [9]: plot(delay,ipmnorm,'ro')
Out[9]: [<matplotlib.lines.Line2D object at 0x59c4c10>]

In [10]: draw()

In [11]:

Links

• http://www.scipy.org/NumPy_for_Matlab_Users^{Image Added}
• http://www.scipy.org/^{Image Added}
• http://www.sagemath.org/^{Image Added}
• http://code.google.com/p/spyderlib/^{Image Added}

Extracting the data with pyana, some examples

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Image peak finding

Here are a collection of useful algorithms for image analysis: http://docs.scipy.org/doc/scipy/reference/ndimage.html

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