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BinSampler.h
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// -*- C++ -*-
//
// BinSampler.h is a part of Herwig - A multi-purpose Monte Carlo event generator
// Copyright (C) 2002-2012 The Herwig Collaboration
//
// Herwig is licenced under version 2 of the GPL, see COPYING for details.
// Please respect the MCnet academic guidelines, see GUIDELINES for details.
//
#ifndef Herwig_BinSampler_H
#define Herwig_BinSampler_H
//
// This is the declaration of the BinSampler class.
//
#include "ThePEG/Handlers/StandardEventHandler.h"
#include "ThePEG/Utilities/Exception.h"
#include "ThePEG/Repository/UseRandom.h"
#include "MultiIterationStatistics.h"
#include "Remapper.h"
namespace Herwig {
using namespace ThePEG;
class GeneralSampler;
/**
* \ingroup Matchbox
* \author Simon Platzer
*
* \brief BinSampler samples XCombs bins. This default implementation
* performs flat MC integration.
*
* @see \ref BinSamplerInterfaces "The interfaces"
* defined for BinSampler.
*/
class BinSampler: public Herwig::MultiIterationStatistics {
public:
/** @name Standard constructors and destructors. */
//@{
/**
* The default constructor.
*/
BinSampler();
/**
* The destructor.
*/
virtual ~BinSampler();
//@}
public:
/**
* Clone this object.
*/
Ptr<BinSampler>::ptr cloneMe() const {
return dynamic_ptr_cast<Ptr<BinSampler>::ptr>(clone());
}
public:
/**
* Evaluate the cross section
*/
double evaluate(vector<double> p,
bool remap = true);
/**
* Return the bias with which this sampler is selected. The sampler
* needs to divide out this bias in its weight calculation.
*/
double bias() const { return theBias; }
/**
* Set the bias with which this sampler is selected.
*/
void bias(double b) { theBias = b; }
/**
* Set the event handler
*/
void eventHandler(tStdEHPtr eh) { theEventHandler = eh; }
/**
* Return the event handler
*/
tStdEHPtr eventHandler() const { return theEventHandler; }
/**
* Set the containing sampler
*/
void sampler(Ptr<GeneralSampler>::tptr);
/**
* Get the containing sampler
*/
Ptr<GeneralSampler>::tptr sampler() const;
/**
* Return the bin
*/
int bin() const { return theBin; }
/**
* Set the bin
*/
void bin(int b) { theBin = b; }
/**
* Return a string describing the process handled by this sampler.
*/
string process() const;
/**
* Return a short string describing the process handled by this sampler.
*/
string shortprocess() const;
/**
* Return a string identifying the process handled by this sampler.
*/
string id() const;
/**
* Return the last generated point.
*/
const vector<double>& lastPoint() const { return theLastPoint; }
/**
* Access the last generated point.
*/
vector<double>& lastPoint() { return theLastPoint; }
/**
* Return the reference weight to be used
*/
double referenceWeight() const { return theReferenceWeight; }
/**
* Set the reference weight to be used
*/
void referenceWeight(double w) { theReferenceWeight = w; }
/**
* Return true, if this sampler can provide unweighted events; if
* the proposal density is not an overestimate, weights larger than
* one can be generated, the handling of these points being subject
* to the GeneralSampler class.
*/
virtual bool canUnweight() const { return true; }
/**
* Return true, if this sampler adapts on the fly while generating
* events. Cross sections in the GeneralSampler class are calculated
* from adding up the cross sections quoted by individual samplers.
*/
virtual bool adaptsOnTheFly() const { return false; }
/**
* If this sampler features a compensation algorithm, return true if
* more events need to be generated to finish the compensation.
*/
virtual bool compensating() const { return false; }
/**
* Return true, if weighted events should be generated
*/
bool weighted() const { return theWeighted; }
/**
* Indicate that weighted events should be generated
*/
void doWeighted(bool yes = true) { theWeighted = yes; }
/**
* Exception to be thrown if cross section information should be updated.
*/
struct NextIteration {};
/**
* Generate the next point and return its weight; store the point in
* lastPoint().
*/
virtual double generate();
/**
* Fill and finalize the remappers present
*/
void fillRemappers(bool progress);
/**
* Write remappers to grid file
*/
void saveRemappers() const;
/**
* Write integration data to grid files
*/
void saveIntegrationData() const;
/**
* Save grid data
*/
virtual void saveGrid() const {}
/**
* Read integration data from grid files
*/
void readIntegrationData();
/**
* Read remappers from grid file
*/
void setupRemappers(bool progress);
/**
* Run a single iteration of n points, optionally printing a
* progress bar to cout. Calls generate n times.
*/
void runIteration(unsigned long n, bool progress);
/**
* Adapt this sampler after an iteration has been run
*/
virtual void adapt() {}
/**
* Initialize this bin sampler. This default version calls runIteration.
*/
virtual void initialize(bool progress);
/**
* Return true, if this sampler has already been initialized.
*/
bool initialized() const { return theInitialized; }
/**
* Indicate that this sampler has already been initialized.
*/
void isInitialized() { theInitialized = true; }
/**
* Return true, if integration has already been performed
*/
bool integrated() const { return theIntegrated; }
/**
* Return true, if remappers have been set up
*/
bool remappersFilled() const { return theRemappersFilled; }
/**
* Return true, if this sampler has already read grid data.
*/
bool hasGrids() const { return theHasGrids; }
/**
* Indicate that this sampler has already read grid data.
*/
void didReadGrids() { theHasGrids = true; }
/**
* Finalize this sampler.
*/
virtual void finalize(bool);
/**
* Return the total integrated cross section determined from the
* Monte Carlo sampling so far.
*/
virtual CrossSection integratedXSec() const {
return averageWeight()*nanobarn;
}
/**
* Return the error on the total integrated cross section determined
* from the Monte Carlo sampling so far.
*/
virtual CrossSection integratedXSecErr() const {
return sqrt(abs(averageWeightVariance()))*nanobarn;
}
/**
* Define the key for the collinear subtraction data.
*/
struct RandomNumberHistogram {
/**
* The lower bound
*/
double lower;
/**
* The bins, indexed by upper bound.
*/
map<double,double > bins;
map<double,double > binsw1;
/**
* Constructor
*/
RandomNumberHistogram(double low = 0.0,
double up = 1.,
unsigned int nbins = 20);
/**
* Book an event.
*/
void book(double inv, double weight) {
map<double,double>::iterator b = bins.upper_bound(inv);
if ( b == bins.end() ) return;
b->second = b->second+weight;
map<double,double>::iterator b2 = binsw1.upper_bound(inv);
if ( b2 == binsw1.end() ) return;
b2->second = b2->second+1.;
}
/**
* Write to file given name and invariant.
*/
void dump(const std::string& folder,const std::string& prefix, const std::string& process,const int NR)const;
};
typedef pair<string,size_t > RandomNumberIndex;
map<RandomNumberIndex,pair<RandomNumberHistogram,double> > RandomNumberHistograms;
public:
/**
* Return the dimension.
*/
int dimension() const { return theEventHandler->nDim(bin()); }
/**
* Return the number of points to be used for initial integration.
*/
unsigned long initialPoints() const { return theInitialPoints; }
/**
* Set the number of points to be used for initial integration.
*/
void initialPoints(unsigned long n) { theInitialPoints = n; }
/**
* Return the number of iterations to be considered for initialization.
*/
size_t nIterations() const { return theNIterations; }
/**
* Set the number of iterations to be considered for initialization.
*/
void nIterations(size_t n) { theNIterations = n; }
/**
* Set the factor to enhance the number of points for the next
* iteration.
*/
void enhancementFactor(double f) { theEnhancementFactor = f; }
/**
* Return the factor to enhance the number of points for the next
* iteration.
*/
double enhancementFactor() const { return theEnhancementFactor; }
/**
* Return the folder for the random number plots.
*/
string randomNumberString() const {return theRandomNumbers;}
public:
/** @name Functions used by the persistent I/O system. */
//@{
/**
* Function used to write out object persistently.
* @param os the persistent output stream written to.
*/
void persistentOutput(PersistentOStream & os) const;
/**
* Function used to read in object persistently.
* @param is the persistent input stream read from.
* @param version the version number of the object when written.
*/
void persistentInput(PersistentIStream & is, int version);
//@}
/**
* The standard Init function used to initialize the interfaces.
* Called exactly once for each class by the class description system
* before the main function starts or
* when this class is dynamically loaded.
*/
static void Init();
protected:
/** @name Clone Methods. */
//@{
/**
* Make a simple clone of this object.
* @return a pointer to the new object.
*/
virtual IBPtr clone() const;
/** Make a clone of this object, possibly modifying the cloned object
* to make it sane.
* @return a pointer to the new object.
*/
virtual IBPtr fullclone() const;
//@}
// If needed, insert declarations of virtual function defined in the
// InterfacedBase class here (using ThePEG-interfaced-decl in Emacs).
private:
/**
* The bias with which this sampler is selected.
*/
double theBias;
/**
* True, if weighted events should be generated
*/
bool theWeighted;
/**
* The number of points to use for initial integration.
*/
unsigned long theInitialPoints;
/**
* The number of iterations to be considered for initialization.
*/
size_t theNIterations;
/**
* Factor to enhance the number of points for the next iteration.
*/
double theEnhancementFactor;
/**
* Switch to count only non zero weights in presampling.
*/
bool theNonZeroInPresampling;
/**
* Switch to require that we get half of the points
* in each iteration below the maximum weight of the iteration.
*/
bool theHalfPoints;
/**
* The maximum number of allowed new maxima,
* in combination with HalfPoints, in order to prevent unstable
* processes.
*/
int theMaxNewMax;
/**
* The reference weight to be used
*/
double theReferenceWeight;
/**
* The bin to be sampled.
*/
int theBin;
/**
* Wether or not this sampler has already been initialized.
*/
bool theInitialized;
/**
* The last generated point.
*/
vector<double> theLastPoint;
/**
* The event handler to be used.
*/
tStdEHPtr theEventHandler;
/**
* The containing sampler
*/
Ptr<GeneralSampler>::tptr theSampler;
/**
* Folder for the random number plots.
*/
string theRandomNumbers;
/**
* Remapper objects indexed by dimension
*/
map<size_t,Remapper> remappers;
/**
* The number of points to be used for initial filling of the remappers
*/
unsigned long theRemapperPoints;
/**
* True if channels should get a remapper
*/
bool theRemapChannelDimension;
/**
* The number of bins to be used for luminosity dimensions
*/
unsigned long theLuminosityMapperBins;
/**
* The number of bins to be used for any other dimension
*/
unsigned long theGeneralMapperBins;
/**
* The minimum selection probability for remapper bins
*/
double theRemapperMinSelection;
/**
* True, if integration has already be performed
*/
bool theIntegrated;
/**
* True, if remappers have been set up
*/
bool theRemappersFilled;
/**
* True, if this sampler has already read grid data.
*/
bool theHasGrids;
private:
/**
* The assignment operator is private and must never be called.
* In fact, it should not even be implemented.
*/
BinSampler & operator=(const BinSampler &);
};
}
#endif /* Herwig_BinSampler_H */

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