From db1402557b27ac686b9c42d05f6c69cc7250d7c6 Mon Sep 17 00:00:00 2001
From: Andy Buckley <a.g.buckley@gmail.com>
Date: Thu, 14 Apr 2016 21:14:39 +0200
Subject: [PATCH] 'Add an InvSampler, and an example JO for flat field
curvature and beamline IP sampling. Plus some more general code tweaks.'
(ParticleGun-02-01-00)
* Tagging: ParticleGun-02-01-00
* Add an example JO showing how to use the inverse sampler to
sample flat in B-field curvature and how to couple together
momentum and position sampling to give an impact parameter
distribution.
2016-04-05 Andy Buckley <andy.buckley@cern.ch>
* Explicitly force more sampler constructor args to float type.
* Add InverseSampler, for e.g. B-field curvature ~ 1/pT.
---
...Option.ParticleGun_flatcurvature_flatip.py | 41 +++++++++++++++++++
Generators/ParticleGun/python/samplers.py | 25 ++++++++---
2 files changed, 60 insertions(+), 6 deletions(-)
create mode 100644 Generators/ParticleGun/examples/jobOption.ParticleGun_flatcurvature_flatip.py
diff --git a/Generators/ParticleGun/examples/jobOption.ParticleGun_flatcurvature_flatip.py b/Generators/ParticleGun/examples/jobOption.ParticleGun_flatcurvature_flatip.py
new file mode 100644
index 00000000000..38233563de9
--- /dev/null
+++ b/Generators/ParticleGun/examples/jobOption.ParticleGun_flatcurvature_flatip.py
@@ -0,0 +1,41 @@
+#! -*- python -*-
+
+# Copyright (C) 2002-2017 CERN for the benefit of the ATLAS collaboration
+
+include("GeneratorUtils/StdEvgenSetup.py")
+theApp.EvtMax = 100
+
+import ParticleGun as PG
+
+class MyParticleSampler(PG.ParticleSampler):
+ """
+ A special sampler to generate single particles flat in 1/pT and in
+ impact parameter to the beam, with flat z0.
+ """
+
+ def __init__(self):
+ psamp = PG.PtEtaMPhiSampler(pt=PG.InvSampler(4000, 400000), eta=[0.1,0.3], phi=[0.3, 0.5])
+ xsamp = PG.PosSampler(0, 0, [-150,150], 0)
+ PG.ParticleSampler.__init__(self, pid={13,-13}, mom=psamp, pos=xsamp)
+ self.ip = PG.mksampler([-2,2])
+
+ def shoot(self):
+ "Return a vector of sampled particles"
+ ps = PG.ParticleSampler.shoot(self)
+ assert len(ps) == 1
+ p = ps[0]
+ from math import sqrt
+ m = -p.mom.X() / p.mom.Y() #< gradient of azimuthal IP sampling line, perp to mom
+ x = self.ip() / sqrt(1 + m**2) #< just decomposing sampled IP into x component...
+ y = m*x #< ... and y-component
+ p.pos.SetX(x)
+ p.pos.SetY(m*x)
+ return [p]
+
+topSeq += PG.ParticleGun()
+topSeq.ParticleGun.randomSeed = 123456
+topSeq.ParticleGun.sampler = MyParticleSampler()
+
+include("GeneratorUtils/postJO.CopyWeights.py")
+include("GeneratorUtils/postJO.PoolOutput.py")
+include("GeneratorUtils/postJO.DumpMC.py")
diff --git a/Generators/ParticleGun/python/samplers.py b/Generators/ParticleGun/python/samplers.py
index b76af6a8889..423f07ac124 100644
--- a/Generators/ParticleGun/python/samplers.py
+++ b/Generators/ParticleGun/python/samplers.py
@@ -148,8 +148,8 @@ class LogSampler(ContinuousSampler):
"Randomly sample from an exponential distribution (i.e. uniformly on a log scale)."
def __init__(self, low, high):
- self.low = low
- self.high = high
+ self.low = float(low)
+ self.high = float(high)
def shoot(self):
rand = random.random()
@@ -162,13 +162,25 @@ class GaussianSampler(ContinuousSampler):
"Randomly sample from a 1D Gaussian distribution."
def __init__(self, mean, sigma):
- self.mean = mean
- self.sigma = sigma
+ self.mean = float(mean)
+ self.sigma = float(sigma)
def shoot(self):
return random.gauss(self.mean, self.sigma)
+class InvSampler(ContinuousSampler):
+ "Randomly sample from a 1/x distribution."
+
+ def __init__(self, low, high):
+ self.low = float(low)
+ self.high = float(high)
+
+ def shoot(self):
+ invx = random.uniform(1/self.high, 1/self.low) #< limit inversion not actually necessary
+ return 1./invx
+
+
########################################
@@ -242,9 +254,9 @@ def mksampler(x):
- if x can be called, i.e. x() is valid, we just return x;
- a Python list (square brackets) will be converted to a continuous
UniformSampler or DisjointUniformSampler;
- - TODO: a Python tuple (round brackets/parentheses) will be treated
+ - a Python tuple (round brackets/parentheses) will be treated
as a discrete CyclicSeqSampler;
- - TODO: a Python set (curly brackets/braces) will be treated
+ - a Python set (curly brackets/braces) will be treated
as a discrete RandomSeqSampler;
- otherwise a ConstSampler will be created from x, so that x is
returned when the sampler is called.
@@ -889,6 +901,7 @@ class ParticleSampler(Sampler):
m = self.massdict[abs(pid)]
self.mom.mass = m
p.mass = m
+ # TODO: Should the particle generated_mass be set from the sampler by default?
## Sample momentum and vertex positions into the particle
p.mom = self.mom()
p.pos = self.pos()
--
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