コード例 #1
0
void l1menu::TriggerRatePlot::addSample( const l1menu::ISample& sample, std::vector<TriggerRatePlot>& ratePlots )
{
	float weightPerEvent=sample.eventRate()/sample.sumOfWeights();

	// Create cached triggers for each of the rate plots, which depending on the concrete type
	// of the ISample may or may not significantly increase the speed at which this next loop happens.
	std::vector< std::unique_ptr<l1menu::ICachedTrigger> > cachedTriggers;
	for( const auto& ratePlot : ratePlots ) cachedTriggers.push_back( sample.createCachedTrigger( *ratePlot.pTrigger_ ) );

	// Now instead of calling addSample() for each TriggerRatePlot individually, get each IEvent from the sample
	// and pass that to each rate plot. This is because (depending on the ISample concrete type) getting the
	// IEvent can be computationally expensive.
	std::vector< std::unique_ptr<l1menu::ICachedTrigger> >::const_iterator iTrigger;
	std::vector<TriggerRatePlot>::iterator iRatePlot;
	for( size_t eventNumber=0; eventNumber<sample.numberOfEvents(); ++eventNumber )
	{
		const l1menu::IEvent& event=sample.getEvent(eventNumber);

		for( iTrigger=cachedTriggers.begin(), iRatePlot=ratePlots.begin();
			iTrigger!=cachedTriggers.end() && iRatePlot!=ratePlots.end();
			++iTrigger, ++iRatePlot )
		{
			iRatePlot->addEvent( event, *iTrigger, weightPerEvent );
		}
	} // end of loop over events

}
コード例 #2
0
void l1menu::TriggerRatePlot::addSample( const l1menu::ISample& sample )
{
	float weightPerEvent=sample.eventRate()/sample.sumOfWeights();

	// Create a cached trigger, which depending on the concrete type of the ISample
	// may or may not significantly increase the speed at which this next loop happens.
	std::unique_ptr<l1menu::ICachedTrigger> pCachedTrigger=sample.createCachedTrigger( *pTrigger_ );

	for( size_t eventNumber=0; eventNumber<sample.numberOfEvents(); ++eventNumber )
	{
		addEvent( sample.getEvent(eventNumber), pCachedTrigger, weightPerEvent );
	} // end of loop over events

}
コード例 #3
0
l1menu::implementation::MenuRateImplementation::MenuRateImplementation( const l1menu::TriggerMenu& menu, const l1menu::ISample& sample )
{

	// The sum of event weights that pass each trigger
	std::vector<float> weightOfEventsPassed( menu.numberOfTriggers() );
	// The sume of weights squared that pass each trigger. Used to calculate the error.
	std::vector<float> weightSquaredOfEventsPassed( menu.numberOfTriggers() );

	// The number of events that only pass the given trigger
	std::vector<float> weightOfEventsPure( menu.numberOfTriggers() );
	std::vector<float> weightSquaredOfEventsPure( menu.numberOfTriggers() );

	float weightOfEventsPassingAnyTrigger=0;
	float weightSquaredOfEventsPassingAnyTrigger=0;
	float weightOfAllEvents=0;

	// Using cached triggers significantly increases speed for ReducedSample
	// because it cuts out expensive string comparisons when querying the trigger
	// parameters.
	std::vector< std::unique_ptr<l1menu::ICachedTrigger> > cachedTriggers;
	for( size_t triggerNumber=0; triggerNumber<menu.numberOfTriggers(); ++triggerNumber )
	{
		cachedTriggers.push_back( sample.createCachedTrigger( menu.getTrigger( triggerNumber ) ) );
	}

	size_t numberOfLastPassedTrigger=0; // This is just so I can work out the pure rate

	for( size_t eventNumber=0; eventNumber<sample.numberOfEvents(); ++eventNumber )
	{
		const l1menu::IEvent& event=sample.getEvent(eventNumber);
		float weight=event.weight();
		weightOfAllEvents+=weight;

		size_t numberOfTriggersPassed=0;

		for( size_t triggerNumber=0; triggerNumber<cachedTriggers.size(); ++triggerNumber )
		{
			if( cachedTriggers[triggerNumber]->apply(event) )
			{
				// If the event passes the trigger, increment the counters
				++numberOfTriggersPassed;
				weightOfEventsPassed[triggerNumber]+=weight;
				weightSquaredOfEventsPassed[triggerNumber]+=(weight*weight);
				numberOfLastPassedTrigger=triggerNumber; // If only one event passes, this is used to increment the pure counter
			}
		}

		// See if I should increment any of the pure or total counters
		if( numberOfTriggersPassed==1 )
		{
			weightOfEventsPure[numberOfLastPassedTrigger]+=weight;
			weightSquaredOfEventsPure[numberOfLastPassedTrigger]+=(weight*weight);
		}
		if( numberOfTriggersPassed>0 )
		{
			weightOfEventsPassingAnyTrigger+=weight;
			weightSquaredOfEventsPassingAnyTrigger+=(weight*weight);
		}
	}

	float scaling=sample.eventRate();

	for( size_t triggerNumber=0; triggerNumber<cachedTriggers.size(); ++triggerNumber )
	{
		float fraction=weightOfEventsPassed[triggerNumber]/weightOfAllEvents;
		float fractionError=std::sqrt(weightSquaredOfEventsPassed[triggerNumber])/weightOfAllEvents;
		float pureFraction=weightOfEventsPure[triggerNumber]/weightOfAllEvents;
		float pureFractionError=std::sqrt(weightSquaredOfEventsPure[triggerNumber])/weightOfAllEvents;
		triggerRates_.push_back( std::move(TriggerRateImplementation(menu.getTrigger(triggerNumber),fraction,fractionError,fraction*scaling,fractionError*scaling,pureFraction,pureFractionError,pureFraction*scaling,pureFractionError*scaling) ) );
		//triggerRates_.push_back( std::move(TriggerRateImplementation(menu.getTrigger(triggerNumber),weightOfEventsPassed[triggerNumber],weightSquaredOfEventsPassed[triggerNumber],weightOfEventsPure[triggerNumber],weightSquaredOfEventsPure[triggerNumber],*this)) );
	}

	//
	// Now I have everything I need to calculate all of the values required by the interface
	//
	totalFraction_=weightOfEventsPassingAnyTrigger/weightOfAllEvents;
	totalFractionError_=std::sqrt(weightSquaredOfEventsPassingAnyTrigger)/weightOfAllEvents;
	totalRate_=totalFraction_*scaling;
	totalRateError_=totalFractionError_*scaling;
}