View source for AuroraMaster

= Introduction =

The AuroraMaster package contains python classes providing high level interfaces to the Aurora algorithms and tools. The following tools are implemented in AuroraMaster at the moment:

* Read/write SCT EDM data
* Primary event generators
** Particle gun
** [https://evtgen.hepforge.org/ EvtGen]
* Parametric simulation
** Main SCT parametric simulation
** Simple parametric simulation
* Full simulation with DD4Hep and Geant4
* Event analysis and selection with the Analysis package
** Access to reconstructed final-state-particles
** Reconstruction of particle decay trees
** Saving flat n-tuples for further physics analysis

= The AuroraMaster class =

Each job option employing AuroraMaster must contain one instance of the AuroraMaster class:

 from AuroraMaster.auroramaster import AuroraMaster
 am = AuroraMaster(olvl='info')

The <code>olvl</code> argument specifies the default output level: 'debug' or 'info', where the latter is used as the default.

The job option logic is formed by invoking methods of the AuroraMaster instance. Each method has the 'cfg' parameter that takes an AuroraConfig object.

The following example shows a ready-to-use job option for event generation with [https://evtgen.hepforge.org/ EvtGen] and saving them to file in SCT EDM format:

 from AuroraMaster.auroramaster import AuroraMaster, AuroraConfig
 # Instantiate AuroraMaster
 am = AuroraMaster('evtgen', 'info')
 # Plug in component for EvtGen
 evtgenCfg = AuroraConfig({
    'root' : 'psi(3770)',
    'dec': './dkpi.dec'
 })
 am.add_signal_provider('evtgen', evtgenCfg)
 # Plug in component for SCT EDM output
 edmoutputCfg = AuroraConfig{
    'filename': 'parsim.root',
    'commands': ['keep *'],
 })
 am.add_edmo(cfg=edmoutputCfg)
 am.run(evtmax=10**4)

The <code>run</code> method must be invoked at the end.

= Components =

All AuroraMaster class methods of the <code>add_{component}</code> format has two parameters:

* <code>cfg</code> - an object of the <code>AuroraConfig</code> class. Default value it None
* <code>json</code> - string path to a json file with configuration. Default value it None

A component set up is dome with three steps:

# Default configuration
# Configuration with passed json file. It overwrites any subset of default parameters. Parameters not specified in json keep the default values
# Configuration with <code>AuroraConfig</code> object. It overrides values of the specified parameters leaving other parameters unchanged

If some parameter is specified in both json file and <code>AuroraConfig</code> object, the final value is taken from the <code>AuroraConfig</code> object.

= AuroraConfig =

The AuroraConfig is a data structure very similar to python dict. It can contain nested lists, dicts and other AuroraConfig objects. An example below shows configuration of simple parametric simulation:

 parsimCfg = AuroraConfig({
    'Tracker' : {
        'deteff': 0.99,
        'ptcut': 50e-3,
        'bfield': 1.5,
        'maxCosth': np.cos(10./180. * np.pi),
        'momentumSampler' : {
            'mean' : np.zeros(3),
            'covar': np.diag(np.ones(3)) * 1.e-3**2
        },
        'vertexSampler' : {
            'mean' : np.zeros(3),
            'covar': np.diag(np.ones(3)) * 1.e-3**2
        }
    },
    'PID' : {
        'eff' : 0.95,
        'sigmaKpi' : 6.,
        'sigmaMupi' : 4.,
        'sigmaKp' : 3.,
        'sigmaE' : 3.,
    },
    'Calorimeter' : {
        'deteff': 1.0,
        'energyThreshold' : 15e-3,
        'maxCosth' : np.cos(10./180. * np.pi),
        'sampler' : {
            'mean' : np.zeros(3),
            'covar': (np.diag(np.ones(3)) * 1.e-2**2).ravel()
        }
    }
 })

An AuroraConfig object can be serialized to and serialized from json with methods <code>to_json</code> and <code>from_json</code>. It is recommended to create json files with configuration using this interface, and not create json manualy.

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