AuroraMaster - Revision history

K.A.Chilikin: Replaced content with " The AuroraMaster analysis configuration system used in Aurora version 2.x has been removed in the development version (branch [https://git.inp.nsk.su/sctau/aurora/-/tree/..."

2025-05-30T09:52:50Z

Replaced content with " The AuroraMaster analysis configuration system used in Aurora version 2.x has been removed in the development version (branch [https://git.inp.nsk.su/sctau/aurora/-/tree/..."

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V.S.Vorobev at 11:25, 4 August 2021

2021-08-04T11:25:57Z

V.S.Vorobev: /* Introduction */

2021-04-16T08:59:47Z

‎Introduction

V.S.Vorobev at 07:29, 11 February 2021

2021-02-11T07:29:58Z

V.S.Vorobev at 07:18, 11 February 2021

2021-02-11T07:18:44Z

V.S.Vorobev at 07:18, 11 February 2021

2021-02-11T07:18:14Z

V.S.Vorobev at 02:48, 10 February 2021

2021-02-10T02:48:59Z

V.S.Vorobev at 12:50, 3 February 2021

2021-02-03T12:50:35Z

V.S.Vorobev at 08:58, 3 February 2021

2021-02-03T08:58:13Z

V.S.Vorobev at 08:37, 3 February 2021

2021-02-03T08:37:40Z

← Older revision		Revision as of 11:25, 4 August 2021
Line 199:		Line 199:
	})		})
	am.add_signal_provider('gun', gunCfg)		am.add_signal_provider('gun', gunCfg)
−
−	~~[[Category:Not_public]]~~

@@ Line 9: / Line 9: @@
 * Parametric simulation
 ** Main SCT parametric simulation
-** Simple parametric simulation
+** [[Simple_SCT_parametric_simulation|Simple parametric simulation]]
 * Full simulation with DD4Hep and Geant4
-* Event analysis and selection with the Analysis package
+* [[Use_Analysis_package|Event analysis and selection with the Analysis package]]
 ** Access to reconstructed final-state-particles
 ** Reconstruction of particle decay trees

@@ Line 48: / Line 48: @@
 The <code>run</code> method must be invoked at the end.
-= Components =
+= AuroraConfig =
 All AuroraMaster class methods of the <code>add_{component}</code> format has two parameters:
@@ Line 63: / Line 102: @@
 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 =
+== add_edmi() ==
-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:
+The AuroraMaster.add_edmi() method initialized ScTauDataSvc, instantiates a PodioInput class and has the following default configuration:
-  parsimCfg = AuroraConfig({
+  edminputCfg = AuroraConfig({
      'Tracker' : {
          'deteff': 0.99,
@@ Line 95: / Line 156: @@
          'sampler' : {
              'mean' : np.zeros(3),
-             'covar': (np.diag(np.ones(3)) * 1.e-2**2).ravel()
+             'covar': np.diag(np.ones(3)) * 1.e-2**2
+         }
+     }
   })
-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.
+See [[Simple SCT parametric simulation|the detailed description]] of the simple SCT parametric simulation.
 [[Category:Not_public]]

← Older revision		Revision as of 07:18, 11 February 2021
Line 101:		Line 101:

	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.		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.
		+
		+

	[[Category:Not_public]]		[[Category:Not_public]]

@@ Line 1: / Line 1: @@
 = Introduction =
-The AuroraMaster package contains python classes providing high level interfaces to the Aurora algorithms and tools. First thing you need to do is instantiate an AuroraMaster object:
+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:
- from AuroraMaster.auroramaster import AuroraMaster
+* Read/write SCT EDM data
- am = AuroraMaster(purpose='parsim', olvl='info')
+* Primary event generators
-The first argument specifies purpose of the job option. Possible values are:
+= The AuroraMaster class =
-* <code>'parsim'</code>
+Each job option employing AuroraMaster must contain one instance of the AuroraMaster class:
-* <code>'fullsim'</code>
-* <code>'evtgen'</code>
-* <code>'analysis'</code>
-An AuroraMaster object initializes Aurora services corresponding to the job option purpose. The second argument specifies general output level:
+ from AuroraMaster.auroramaster import AuroraMaster
-* <code>'debug'</code>
+The <code>olvl</code> argument specifies the default output level: 'debug' or 'info', where the latter is used as the default.
-* <code>'info'</code>
-A job option must contain only one AuroraMaster object. A job option logic is formed by stacking the predefined components. Each component has corresponding method in the AuroraMaster class. 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:
+The job option logic is formed by invoking methods of the AuroraMaster instance. Each method has the 'cfg' parameter that takes an AuroraConfig object.
   from AuroraMaster.auroramaster import AuroraMaster, AuroraConfig
@@ Line 28: / Line 37: @@
      'dec': './dkpi.dec'
   })
-  am.add_evtgen(cfg=evtgenCfg)
+  am.add_signal_provider('evtgen', evtgenCfg)
   # Plug in component for SCT EDM output
   edmoutputCfg = AuroraConfig{
@@ Line 37: / Line 46: @@
   am.run(evtmax=10**4)
+The <code>run</code> method must be invoked at the end.
-The <code>run</code> method should be invoked at the end.
 = Components =
-An AuroraMaster class method <code>add_{component}</code> receives two parameters:
+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

← Older revision		Revision as of 02:48, 10 February 2021
Line 93:		Line 93:

	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.		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.
		+
		+	[[Category:Not_public]]

@@ Line 1: / Line 1: @@
 = 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. First thing you need to do is instantiate an AuroraMaster object:
   from AuroraMaster.auroramaster import AuroraMaster
   am = AuroraMaster(purpose='parsim', 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 first argument specifies purpose of the job option. Possible values are:
-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:
+* <code>'parsim'</code>
   from AuroraMaster.auroramaster import AuroraMaster, AuroraConfig
   # Instantiate AuroraMaster
   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)',
      'dec': './dkpi.dec'
+  })
   am.add_evtgen(cfg=evtgenCfg)
-  # Plug
+  # Plug in component for SCT EDM output
-  edmoutputCfg = {
+  edmoutputCfg = AuroraConfig{
      'filename': 'parsim.root',
      'commands': ['keep *'],
+  })
   am.add_edmo(cfg=edmoutputCfg)
   am.run(evtmax=10**4)

← Older revision		Revision as of 08:37, 3 February 2021
Line 1:		Line 1:
	= Introduction =		= Introduction =

−	The AuroraMaster package contains ~~a set of~~ python classes providing high level interfaces to Aurora algorithms and tools.	+	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:
		+
		+	from AuroraMaster.auroramaster import AuroraMaster
		+	am = AuroraMaster(purpose='parsim', 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
		+	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:
		+
		+	from AuroraMaster.auroramaster import AuroraMaster, AuroraConfig
		+	# Instantiate AuroraMaster
		+	am = AuroraMaster('evtgen', 'info')
		+	# Plug EvtGen generator with specified root particle and user
		+	evtgenCfg = {
		+	'root' : 'psi(3770)',
		+	'dec': './dkpi.dec'
		+	}
		+	am.add_evtgen(cfg=evtgenCfg)
		+	edmoutputCfg = {
		+	'filename': 'parsim.root',
		+	'commands': ['keep *'],
		+	}
		+	am.add_edmo(cfg=edmoutputCfg)
		+	am.run(evtmax=10**4)