AuroraMaster

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= Introduction =
 
= Introduction =
  
The AuroraMaster package contains python classes providing high level interfaces to the Aurora algorithms and tools. To begin working with AuroraMaster you need to instantiate the AuroraMaster class:
+
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_SCT_parametric_simulation|Simple parametric simulation]]
 +
* Full simulation with DD4Hep and Geant4
 +
* [[Use_Analysis_package|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
 
  from AuroraMaster.auroramaster import AuroraMaster
  am = AuroraMaster(purpose='parsim', olvl='info')
+
  am = AuroraMaster(olvl='info')
  
The first argument specifies purpose of the job option. Possible values are: <code>'parsim'</code>, <code>'fullsim'</code>, <code>'evtgen'</code>, and <code>'analysis'</code>. AuroraMaster initializes
+
The <code>olvl</code> argument specifies the default output level: 'debug' or 'info', where the latter is used as the default.
Aurora services corresponding to the job option purpose. The second argument specifies general output level (<code>'debug'</code> or <code>'info'</code>).
+
  
A job option is formed by a stack of predefined components. Each component has corresponding method in the AuroraMaster class. Let's begin with an example. The following code snippet is a complete job option for event generation and saving them to ROOT file:
+
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
 
  from AuroraMaster.auroramaster import AuroraMaster, AuroraConfig
 
  # Instantiate AuroraMaster
 
  # Instantiate AuroraMaster
 
  am = AuroraMaster('evtgen', 'info')
 
  am = AuroraMaster('evtgen', 'info')
  # Plug EvtGen generator with specified root particle and user's decay file
+
  # Plug in component for EvtGen
  evtgenCfg = {
+
  evtgenCfg = AuroraConfig({
 
     'root' : 'psi(3770)',
 
     'root' : 'psi(3770)',
 
     'dec': './dkpi.dec'
 
     'dec': './dkpi.dec'
  }
+
  })
  am.add_evtgen(cfg=evtgenCfg)
+
  am.add_signal_provider('evtgen', evtgenCfg)
  # Plug  
+
  # Plug in component for SCT EDM output
  edmoutputCfg = {
+
  edmoutputCfg = AuroraConfig{
 
     'filename': 'parsim.root',
 
     'filename': 'parsim.root',
 
     'commands': ['keep *'],
 
     'commands': ['keep *'],
  }
+
  })
 
  am.add_edmo(cfg=edmoutputCfg)
 
  am.add_edmo(cfg=edmoutputCfg)
 
  am.run(evtmax=10**4)
 
  am.run(evtmax=10**4)
 +
 +
The <code>run</code> method must be invoked at the end.
 +
 +
= 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.
 +
 +
= AuroraMaster 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.
 +
 +
== add_edmi() ==
 +
 +
The AuroraMaster.add_edmi() method initialized ScTauDataSvc, instantiates a PodioInput class and has the following default configuration:
 +
 +
edminputCfg = AuroraConfig({
 +
    'name' : 'EDMReader',
 +
    'olevel' : 'info',
 +
    'filename': 'input.root',
 +
    'collections': ['Particles'],
 +
})
 +
 +
== add_edmo() ==
 +
 +
The AuroraMaster.add_edmo() method initialized ScTauDataSvc, instantiates a PodioOutput class and has the following default configuration:
 +
 +
edmoutputCfg = AuroraConfig({
 +
    'filename': 'output.root',
 +
    'commands': ['keep *'],
 +
    'olevel' : 'info',
 +
})
 +
 +
== add_parsim() ==
 +
 +
The AuroraMaster.add_parsim() method initializes tools for parametric simulation and instantiates the required algorithm. Selection of specific implementation of the parametric simulation is done by the parameter `which`. The code snipped below shows how to plug in simple parametric simulation:
 +
 +
simplepartimCfg = AuroraConfig({
 +
    'olevel': 'info',
 +
    '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
 +
        }
 +
    }
 +
})
 +
am.add_parsim(which='simple', cfg=parsimCfg)
 +
 +
See [[Simple SCT parametric simulation|the detailed description]] of the simple SCT parametric simulation.
 +
 +
== add_fullsim() ==
 +
 +
fullsimCfg = AuroraConfig({
 +
    'detector': 'SimG4DD4hepDetector',
 +
    'subsystems': ['ALL'],
 +
    'physicslist': 'SimG4FtfpBert',
 +
    'energyCut': 0.1 * units.GeV,
 +
    'field' : {
 +
        'Bz' : 1.0 * units.tesla,
 +
        'rmax' : 100.0 * units.m,
 +
        'zmax' : 100.0 * units.m
 +
    },
 +
    'olevel': 'info',
 +
})
 +
 +
== add_signal_provider() ==
 +
 +
eventgenCfg = AuroraConfig({
 +
    'root' : 'vpho',
 +
    'dec' : '',
 +
    'ecms' : 0.,
 +
    'olevel': 'info',
 +
})
 +
am.add_signal_provider('evtgen', evtgenCfg)
 +
 +
or
 +
 +
particlegunCfg = AuroraConfig({
 +
    'momentum' : np.array([0.1, 1.5]) * units.GeV,
 +
    'phi': [0., 2*np.pi],
 +
    'theta': np.array([10, 170]) * units.rad,
 +
    'particles': [11, 13, 211],
 +
    'olevel': 'info',
 +
})
 +
am.add_signal_provider('gun', gunCfg)

Latest revision as of 18:25, 4 August 2021

Contents

[edit] 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:

[edit] 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 olvl 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 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 run method must be invoked at the end.

[edit] 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 to_json and from_json. It is recommended to create json files with configuration using this interface, and not create json manualy.

[edit] AuroraMaster components

All AuroraMaster class methods of the add_{component} format has two parameters:

  • cfg - an object of the AuroraConfig class. Default value it None
  • json - string path to a json file with configuration. Default value it None

A component set up is dome with three steps:

  1. Default configuration
  2. Configuration with passed json file. It overwrites any subset of default parameters. Parameters not specified in json keep the default values
  3. Configuration with AuroraConfig object. It overrides values of the specified parameters leaving other parameters unchanged

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

[edit] add_edmi()

The AuroraMaster.add_edmi() method initialized ScTauDataSvc, instantiates a PodioInput class and has the following default configuration:

edminputCfg = AuroraConfig({
   'name' : 'EDMReader',
   'olevel' : 'info',
   'filename': 'input.root',
   'collections': ['Particles'],
})

[edit] add_edmo()

The AuroraMaster.add_edmo() method initialized ScTauDataSvc, instantiates a PodioOutput class and has the following default configuration:

edmoutputCfg = AuroraConfig({
   'filename': 'output.root',
   'commands': ['keep *'],
   'olevel' : 'info',
})

[edit] add_parsim()

The AuroraMaster.add_parsim() method initializes tools for parametric simulation and instantiates the required algorithm. Selection of specific implementation of the parametric simulation is done by the parameter `which`. The code snipped below shows how to plug in simple parametric simulation:

simplepartimCfg = AuroraConfig({
   'olevel': 'info',
   '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
       }
   }
})
am.add_parsim(which='simple', cfg=parsimCfg)

See the detailed description of the simple SCT parametric simulation.

[edit] add_fullsim()

fullsimCfg = AuroraConfig({
   'detector': 'SimG4DD4hepDetector',
   'subsystems': ['ALL'],
   'physicslist': 'SimG4FtfpBert',
   'energyCut': 0.1 * units.GeV,
   'field' : {
       'Bz' : 1.0 * units.tesla,
       'rmax' : 100.0 * units.m,
       'zmax' : 100.0 * units.m
   },
   'olevel': 'info',
})

[edit] add_signal_provider()

eventgenCfg = AuroraConfig({
   'root' : 'vpho',
   'dec' : ,
   'ecms' : 0.,
   'olevel': 'info',
})
am.add_signal_provider('evtgen', evtgenCfg)

or

particlegunCfg = AuroraConfig({
   'momentum' : np.array([0.1, 1.5]) * units.GeV,
   'phi': [0., 2*np.pi],
   'theta': np.array([10, 170]) * units.rad,
   'particles': [11, 13, 211],
   'olevel': 'info',
}) 
am.add_signal_provider('gun', gunCfg)
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