'CMakeLists.txt' (ParticleGun-02-00-07)

	* Tagging: ParticleGun-02-00-07

	* Try harder to distinguish between single and disjoint uniform sampling interval shorthand sequences.

	* Fix accidental tuple expansion in DisjointRandomSequence expansion.

	* Adding randomSeed property setting to examples (ParticleGun RNG stream not configured for vanilla Athena).

Generators/ParticleGun/CMakeLists.txt

+################################################################################
+# Package: ParticleGun
+################################################################################
+
+# Declare the package name:
+atlas_subdir( ParticleGun )
+
+# Install files from the package:
+atlas_install_python_modules( python/*.py )
+

Generators/ParticleGun/examples/jobOption.ParticleGun_correlated.py

@@ -26,6 +26,7 @@ class MyParticleSampler(PG.ParticleSampler):
         return [p1, p2]
 
 topSeq += PG.ParticleGun()
+topSeq.ParticleGun.randomSeed = 123456
 topSeq.ParticleGun.sampler = MyParticleSampler()
 
 include("GeneratorUtils/postJO.CopyWeights.py")

Generators/ParticleGun/examples/jobOption.ParticleGun_corrhist.py

@@ -33,6 +33,7 @@ class PtEtaHistParticleSampler(PG.ParticleSampler):
         return particles
 
 topSeq += PG.ParticleGun()
+topSeq.ParticleGun.randomSeed = 123456
 topSeq.ParticleGun.sampler = PtEtaHistParticleSampler(11, "data_histos_el_1470pt.root")
 
 include("GeneratorUtils/postJO.CopyWeights.py")

Generators/ParticleGun/examples/jobOption.ParticleGun_flatpt_2particle.py

@@ -7,6 +7,7 @@ theApp.EvtMax = 100
 
 import ParticleGun as PG
 pg = PG.ParticleGun()
+pg.randomSeed = 123456
 pg.samplers.append(PG.ParticleSampler()) # add a second sampler
 pg.samplers[0].pid = (-13, 13) # cycle mu+-
 pg.samplers[0].mom = PG.PtEtaMPhiSampler(pt=[4000, 100000], eta=[1.0, 3.2]) # flat in pt and +ve eta

Generators/ParticleGun/examples/jobOption.ParticleGun_fwd_sequence.py

@@ -7,6 +7,7 @@ theApp.EvtMax = 100
 
 import ParticleGun as PG
 pg = PG.ParticleGun()
+pg.randomSeed = 123456
 pg.sampler.pid = (2112, 22, 2112, 22)
 pg.sampler.mom = PG.EThetaMPhiSampler(energy=(1360000, 500000, 1360000, 500000),
                                       theta=(0, 0, PG.PI, PG.PI))

Generators/ParticleGun/examples/jobOption.ParticleGun_vtx.py

@@ -7,6 +7,7 @@ theApp.EvtMax = 100
 
 import ParticleGun as PG
 pg = PG.ParticleGun()
+pg.randomSeed = 123456
 pg.sampler.pid = 13
 pg.sampler.pos = PG.PosSampler(x=3140.0, y=[-154.134,154.134], z=[4938.76,5121.29], t=5929.7)
 pg.sampler.mom = PG.EEtaMPhiSampler(energy=100000, eta=1.25, phi=0.0)

Generators/ParticleGun/python/__init__.py

@@ -34,7 +34,7 @@ class ParticleGun(EvgenAlg):
 
     def initialize(self):
         """
-        Pass the AtRndmGenSvc seed to Python's random module (or fall back to a fixed value for reproducibility).
+        Pass the AtRndmGenSvc seed to Python's random module, or use a fixed value set via pg.randomSeed.
         """
         seed = None
         ## Use self.randomSeed directly, or if it's None find a seed string from the ATLAS random number service
@@ -57,6 +57,7 @@ class ParticleGun(EvgenAlg):
             random.seed(seed)
             return StatusCode.Success
         else:
+            self.msg.error("ParticleGun: randomSeed property not set, and no %s random number service found" % self.randomSvcName)
             return StatusCode.Failure
 
 

Generators/ParticleGun/python/samplers.py

@@ -252,13 +252,13 @@ def mksampler(x):
     if hasattr(x, "__call__"):
         return x
     elif type(x) is list:
-        # TODO: allow disjoint ranges as nested lists, e.g. [(1,2), (4,5)]
-        if len(x) == 2:
+        # NB: disjoint ranges can be given as nested lists, e.g. [(1,2), (4,5)]
+        if len(x) == 2 and type(x[0]) in (int,float) and type(x[1]) in (int,float):
             #print "MKSAMPLER: Casting %s to UniformSampler" % str(x)
             return UniformSampler(*x)
-        elif len(x) > 2:
+        elif len(x) > 2 or (len(x) > 0 and type(x[0]) not in (int,float)):
             #print "MKSAMPLER: Casting %s to DisjointUniformSampler" % str(x)
-            return DisjointUniformSampler(*x)
+            return DisjointUniformSampler(x)
         if len(x) < 2:
             raise Exception("Supplied list could not be converted to a continuous sampler")
     elif type(x) is tuple: