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Short	Long	Config File	Option type	Default	Description
-v	--verbose	verbose	integer	0	Command line options do not need any values but can be repeated multiple times, configuration file option accepts single integer number.
-c file	--config=file		path	pyana.cfg	Name of the configuration file.
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-l file	--file-list=file	file-list	path		The list of input data files will be read form a given file which must contain one file name per line.
-n number	--num-events=number	num-events	integer	0	Maximum number of events to process, this counter will include damaged events too.
-j name	--job-name=name	job-name	string		Sets job name which is accessible to user code via environment method. Default name is based on the input file names.
-m name	--module=name	modules	string		User analysis module(s). Command line options can be repeated several times, configuration file option accepts space-separated list of names.
-p number	--num-cpu=number	num-cpu	integer	1	Number of processes to run, if greater than 1 then multi-processing mode will be used.

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Code Block

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# user analysis class
class myana(object):
    def beginjob(self, event, env):
        # get histogram manager
        hmgr = env.hmgr()

        # book histograms, store referemcesreferences
        self.hist1 = hmgr.h1d('energy', 'Energy Distribution', 100, 0., 1.)
        self.hist2 = hmgr.h2d('energy vs something', 'Histo2 title', 100, 0., 1., 100, -20., 20.)

    def event(self, event, env):
        # fill histograms
        energy = ...
        something = ...
        self.hist1.Fill(energy)
        self.hist2.Fill(energy, something)

Anchor

	SciPyAlgorithms
	SciPyAlgorithms

SciPy Algorithms

Few data classes such as camera.FrameV1 and acqiris.DataDescV1 present their data as NumPy arrays. There are several packages out there that implement efficient algorithms working with NumPy arrays. Probably one of the most widely used packages is SciPy which is a collection of various types of algorithms including optimization, integration, FFT, image processing, statistics, special functions, and few more. The rich interface and close integration with NumPy makes it a good candidate for use in user analysis modules.

SciPy is installed as a part of the standard LCLS analysis releases. SciPy interactive features and Matlab-like API make it attractive for interactive use, but it can also be efficiently used in batch analysis jobs. There is extensive on-line documentation of both NumPy and SciPy.

Here is vary brief example of using SciPy for integration of experimental data:

Code Block

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from scipy import integrate

class myana(object):

    def event(self, event, env):
        # Get Acqiris waveform object
        ddesc = evt.getAcqValue( "AmoITof", channel, env )
        wf = ddesc.waveform()
        ts = ddesc.timestamps()

        # integrate it using Simpson's rule
        integral = integrate.simps (wf, ts)

Anchor

	Multi-processing
	Multi-processing

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SciPy Algorithms