diff --git a/inc/LauSplineDecayTimeEfficiency.hh b/inc/LauSplineDecayTimeEfficiency.hh
index 12e0dcb..e78091c 100644
--- a/inc/LauSplineDecayTimeEfficiency.hh
+++ b/inc/LauSplineDecayTimeEfficiency.hh
@@ -1,441 +1,449 @@
 
 /*
 Copyright 2021 University of Warwick
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
 
     http://www.apache.org/licenses/LICENSE-2.0
 
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 */
 
 /*
 Laura++ package authors:
 John Back
 Paul Harrison
 Thomas Latham
 */
 
 /*! \file LauSplineDecayTimeEfficiency.hh
     \brief File containing declaration of LauSplineDecayTimeEfficiency class.
 */
 
 /*! \class LauSplineDecayTimeEfficiency
     \brief Class for defining a spline-interpolated model for decay time efficiency
 */
 
 #ifndef LAU_SPLINE_DECAYTIME_EFFICIENCY
 #define LAU_SPLINE_DECAYTIME_EFFICIENCY
 
 #include <array>
 #include <memory>
 #include <vector>
 
 #include "Rtypes.h"
 #include "TFitResult.h"
 #include "TMath.h"
 #include "TSystem.h"
 
 #include "Lau1DCubicSpline.hh"
 #include "LauAbsDecayTimeEfficiency.hh"
 #include "LauParameter.hh"
 
 class TH1;
 
 //! Defines the allowed orders of spline polynomials
 enum class LauSplineOrder : std::size_t {
 	Cubic = 3, //!< 3rd order
 	Sixth = 6  //!< 6th order
 };
 
 template <LauSplineOrder Order>
 class LauSplineDecayTimeEfficiency : public LauAbsDecayTimeEfficiency {
 	public:
 		//! Constructor for a Cubic spline
 		/*!
 		    \param [in] prefix the prefix to use when forming the names of the knot parameters
 		    \param [in] effSpline the cubic spline to be used to represent the efficiency variation
 		*/
 		template <LauSplineOrder SpOrder = Order, typename std::enable_if_t<SpOrder == LauSplineOrder::Cubic,bool> = true>
 		LauSplineDecayTimeEfficiency( const TString& prefix, std::unique_ptr<Lau1DCubicSpline> effSpline ) :
 			prefix_{prefix},
 			effSpline_{std::move(effSpline)}
 		{
 			this->initialSetup();
 		}
 
 		//! Constructor for a Sixth order spline
 		/*!
 		    \param [in] prefix the prefix to use when forming the names of the knot parameters
 		    \param [in] effSpline the cubic spline being used to represent the efficiency variation in another channel
 		    \param [in] correctionSpline the cubic spline to be used to represent the correction to effSpline to obtain the efficiency variation for this channel
 		*/
 		template <LauSplineOrder SpOrder = Order, typename std::enable_if_t<SpOrder == LauSplineOrder::Sixth,bool> = true>
 		LauSplineDecayTimeEfficiency( const TString& prefix, const LauSplineDecayTimeEfficiency<LauSplineOrder::Cubic>& effSpline, std::unique_ptr<Lau1DCubicSpline> correctionSpline ) :
 			prefix_{prefix},
 			effSpline_{std::move(correctionSpline)},
 			otherSpline_{std::make_unique<LauSplineDecayTimeEfficiency<LauSplineOrder::Cubic>>(effSpline)}
 		{
 			this->initialSetup();
 		}
 
 		//! Destructor
 		~LauSplineDecayTimeEfficiency() = default;
 		//! Move constructor (default)
 		LauSplineDecayTimeEfficiency(LauSplineDecayTimeEfficiency<Order>&& other) = default;
 		//! Move assignment operator (default)
 		LauSplineDecayTimeEfficiency& operator=(LauSplineDecayTimeEfficiency<Order>&& other) = default;
 		//! Copy constructor (custom)
 		LauSplineDecayTimeEfficiency(const LauSplineDecayTimeEfficiency<Order>& other);
 		//! Copy assignment operator (deleted)
 		LauSplineDecayTimeEfficiency& operator=(const LauSplineDecayTimeEfficiency<Order>& other) = delete;
 
 
 		//! The number of coefficients of each spline segment
 		static constexpr std::size_t nCoeffs { static_cast<std::underlying_type_t<LauSplineOrder>>(Order) + 1 };
 
 		//! Retrieve the efficiency for a given value of the decay time
 		/*!
 		    \param abscissa the value of the decay time
 		    \return the efficiency
 		*/
 		Double_t getEfficiency( const Double_t abscissa ) const override;
 
 		//! Retrieve the parameters of the efficiency model so that they can be loaded into a fit
 		/*!
 		    \return the parameters of the efficiency model
 		*/
 		std::vector<LauAbsRValue*> getParameters() override { return params_; }
 
 		//! Initialise the parameter cache
 		/*!
 			Must be called prior to starting fitting or generation
 		*/
 		void initialise() override;
 
 		//! Propagate any updates to parameters and recalculate information as neeeded
 		/*!
 			Should be called at each fit iteration
 		*/
 		void propagateParUpdates() override;
 
 		//! Retrieve the number of segments in the spline
 		std::size_t nSegments() const { return effSpline_->getnKnots() - 1; }
 
 		//! Retrieve the positions of the spline knots
 		const std::vector<Double_t>& knotPositions() const { return effSpline_->getXValues(); }
 
 		//! Fix all knots
 		void fixKnots();
 
 		//! Float all knots
 		void floatKnots();
 
 		//! Fix or float a specific knot
 		/*!
 		    \param knotIndex the index of the knot to fix/float
 		    \param fixed true if knot to be fixed, false if knot to be floated
 		*/
 		void fixKnot( const std::size_t knotIndex, const bool fixed );
 
 		//! Retrieve the polynomial coefficients for the given spline segment
 		/*!
 		    \param segmentIndex the index of the spline segment
 		    \return the polynomial coefficients
 		*/
 		std::array<Double_t,nCoeffs> getCoefficients( const std::size_t segmentIndex ) const;
 
 		//! Retrieve whether any of the parameters are floating in the fit
 		bool anythingFloating() const { return anyKnotFloating_; }
 
 		//! Retrieve whether any of the parameters have changed in the latest fit iteration
 		const bool& anythingChanged() const override { return anyKnotChanged_; }
 
 		//! Fit our spline to a TH1
 		/*!
 			Useful to obtain reasonable starting values and uncertainties for the knot parameters
 
 			\param [in] hist the histogram to be fit to
 			\param [in] printLevel the level of printout desired from fit
 			\param [in] doWL If true do a weighted likelihood fit, else chi2
 		*/
 		void fitToTH1( TH1* hist, const LauOutputLevel printLevel = LauOutputLevel::Quiet, const bool doWL = false );
 
 		//! Find the maximum of the spline
 		/*!
 			Works analytically for cubic splines and numerically for 6th order splines
 
 			\return the maximum of the spline
 		*/
 		Double_t findMaximum() const;
 
 	private:
 		//! Perform the initial setup - shared by the various constructors
 		void initialSetup( const LauSplineDecayTimeEfficiency<Order>* other = nullptr );
 
 		//! Update the cached parameter values
 		void updateParameterCache();
 
 		//! The prefix for the parameter names
 		const TString prefix_;
 
 		//! The spline
 		std::unique_ptr<Lau1DCubicSpline> effSpline_;
 
 		//! The other spline
 		std::unique_ptr<LauSplineDecayTimeEfficiency<LauSplineOrder::Cubic>> otherSpline_;
 
 		//! The spline parameters
 		std::vector<std::unique_ptr<LauParameter>> ownedParams_;
 
 		//! The spline parameters (for giving access)
 		std::vector<LauAbsRValue*> params_;
 
 		//! The spline values
 		std::vector<Double_t> values_;
 
 		//! Are any of our knot parameters floating in the fit?
 		bool ourKnotsFloating_{false};
 
 		//! Are any of the other spline's knot parameters floating in the fit?
 		bool otherKnotsFloating_{false};
 
 		//! Are any of the knot parameters floating in the fit?
 		bool anyKnotFloating_{false};
 
 		//! Have any of our knot parameters changed in the latest fit iteration?
 		bool ourKnotsChanged_{false};
 
 		//! Have any of the other spline's knot parameters changed in the latest fit iteration?
 		bool otherKnotsChanged_{false};
 
 		//! Have any of the knot parameters changed in the lastest fit iteration?
 		bool anyKnotChanged_{false};
 
 		ClassDefOverride(LauSplineDecayTimeEfficiency, 0)
 };
 
 templateClassImp(LauSplineDecayTimeEfficiency);
 
 template <LauSplineOrder Order>
 void LauSplineDecayTimeEfficiency<Order>::initialSetup( const LauSplineDecayTimeEfficiency<Order>* other )
 {
 	if ( not effSpline_ ) {
 		std::cerr<<"ERROR in LauSplineDecayTimeEfficiency::initialSetup : supplied efficiency spline pointer is null"<<std::endl;
 		gSystem->Exit(EXIT_FAILURE);
 	}
 
 	if constexpr ( Order == LauSplineOrder::Sixth ) {
 		std::vector<Double_t> ourKnots { effSpline_->getXValues() };
 		std::vector<Double_t> otherKnots { otherSpline_->knotPositions() };
 
 		if ( ourKnots != otherKnots ) {
 			std::cerr<<"ERROR in LauSplineDecayTimeEfficiency<LauSplineOrder::Sixth>::initialSetup : mismatch in knot positions"<<std::endl;
 			gSystem->Exit(EXIT_FAILURE);
 		}
 
 		// TODO - any other checks we need to do?
 	}
 
 	values_ = effSpline_->getYValues();
 
 	const std::size_t nPars { values_.size() };
 	ownedParams_.reserve( nPars );
 
 	if ( other ) {
 		for ( auto& ptr : other->ownedParams_ ) {
 			// TODO - maybe need a way to give these a unique prefix?
 			ownedParams_.emplace_back( ptr->createClone() );
 		}
 	} else {
 		for( std::size_t index{0}; index < nPars; ++index ) {
 			constexpr Double_t maxVal { (Order == LauSplineOrder::Cubic) ? 1.0 : 2.0 };
 			ownedParams_.emplace_back( std::make_unique<LauParameter>( Form( "%s_knot_%lu", prefix_.Data(), index ), values_[index], 0.0, maxVal, kTRUE ) );
 		}
 	}
 
 	if constexpr ( Order == LauSplineOrder::Sixth ) {
 		params_ = otherSpline_->getParameters();
 		params_.reserve( 2*nPars );
 	} else {
 		params_.reserve( nPars );
 	}
 
 	for ( auto& ptr : ownedParams_ ) {
 		params_.push_back( ptr.get() );
 	}
 }
 
 template <LauSplineOrder Order>
 LauSplineDecayTimeEfficiency<Order>::LauSplineDecayTimeEfficiency(const LauSplineDecayTimeEfficiency<Order>& other) :
 	prefix_{ other.prefix_ },
 	effSpline_{ std::make_unique<Lau1DCubicSpline>( * other.effSpline_ ) },
 	otherSpline_{ other.otherSpline_ ? std::make_unique<LauSplineDecayTimeEfficiency<LauSplineOrder::Cubic>>( *other.otherSpline_ ) : nullptr }
 {
 	this->initialSetup( &other );
 }
 
 template <LauSplineOrder Order>
 void LauSplineDecayTimeEfficiency<Order>::fixKnots()
 {
 	for ( auto& ptr : ownedParams_ ) {
 		ptr->fixed(true);
 	}
 }
 
 template <LauSplineOrder Order>
 void LauSplineDecayTimeEfficiency<Order>::floatKnots()
 {
 	for ( auto& ptr : ownedParams_ ) {
 		ptr->fixed(false);
 	}
 }
 
 template <LauSplineOrder Order>
 void LauSplineDecayTimeEfficiency<Order>::fixKnot( const std::size_t knotIndex, const bool fixed )
 {
 	if ( knotIndex >= ownedParams_.size() ) {
 		std::cerr<<"ERROR in LauSplineDecayTimeEfficiency::fixKnot : supplied knot index is out of range"<<std::endl;
 		return;
 	}
 	ownedParams_[knotIndex]->fixed(fixed);
 }
 
 template <LauSplineOrder Order>
 Double_t LauSplineDecayTimeEfficiency<Order>::getEfficiency( const Double_t abscissa ) const
 {
 	Double_t eff { effSpline_->evaluate( abscissa ) };
 	if constexpr ( Order == LauSplineOrder::Sixth ) {
 		eff *= otherSpline_->getEfficiency( abscissa );
 	}
 	return eff;
 }
 
 template <LauSplineOrder Order>
 std::array<Double_t,LauSplineDecayTimeEfficiency<Order>::nCoeffs> LauSplineDecayTimeEfficiency<Order>::getCoefficients( const std::size_t segmentIndex ) const
 {
 	if constexpr ( Order == LauSplineOrder::Cubic ) {
 		return effSpline_->getCoefficients(segmentIndex);
 	} else {
 		std::array<Double_t,4> ourCoeffs { effSpline_->getCoefficients(segmentIndex) };
 		std::array<Double_t,4> otherCoeffs { otherSpline_->getCoefficients(segmentIndex) };
 		std::array<Double_t,nCoeffs> coeffs;
 		coeffs[0] = ourCoeffs[0] * otherCoeffs[0];
 		coeffs[1] = ourCoeffs[0] * otherCoeffs[1] + ourCoeffs[1] * otherCoeffs[0];
 		coeffs[2] = ourCoeffs[0] * otherCoeffs[2] + ourCoeffs[1] * otherCoeffs[1] + ourCoeffs[2] * otherCoeffs[0];
 		coeffs[3] = ourCoeffs[0] * otherCoeffs[3] + ourCoeffs[1] * otherCoeffs[2] + ourCoeffs[2] * otherCoeffs[1] + ourCoeffs[3] * otherCoeffs[0];
 		coeffs[4] = ourCoeffs[1] * otherCoeffs[3] + ourCoeffs[2] * otherCoeffs[2] + ourCoeffs[3] * otherCoeffs[1];
 		coeffs[5] = ourCoeffs[2] * otherCoeffs[3] + ourCoeffs[3] * otherCoeffs[2];
 		coeffs[6] = ourCoeffs[3] * otherCoeffs[3];
 		return coeffs;
 	}
 }
 
 template <LauSplineOrder Order>
 void LauSplineDecayTimeEfficiency<Order>::initialise()
 {
 	static auto isFixed = []( const std::unique_ptr<LauParameter>& par ){ return par->fixed(); };
 
 	anyKnotFloating_ = ourKnotsFloating_ = not std::all_of( ownedParams_.begin(), ownedParams_.end(), isFixed );
 
 	if constexpr ( Order == LauSplineOrder::Sixth ) {
 		otherSpline_->initialise();
 		otherKnotsFloating_ = otherSpline_->anythingFloating();
 		anyKnotFloating_ |= otherKnotsFloating_;
 	}
 
 	this->updateParameterCache();
 }
 
 template <LauSplineOrder Order>
 void LauSplineDecayTimeEfficiency<Order>::updateParameterCache()
 {
 	static auto assignValue = []( const std::unique_ptr<LauParameter>&  par ){ return par->unblindValue(); };
 
 	// Update our local cache
 	std::transform( ownedParams_.begin(), ownedParams_.end(), values_.begin(), assignValue );
 
 	// Update the spline itself
 	effSpline_->updateYValues( values_ );
 }
 
 template <LauSplineOrder Order>
 void LauSplineDecayTimeEfficiency<Order>::propagateParUpdates()
 {
 	// If none of the parameters are floating there's nothing to do
 	if ( not anyKnotFloating_ ) {
 		return;
 	}
 
 	// Otherwise, determine which of the floating parameters have changed (if any) and act accordingly
 
 	if ( ourKnotsFloating_ ) {
 		static auto checkEquality = []( const std::unique_ptr<LauParameter>& par, const Double_t cacheVal ){ return par->unblindValue() == cacheVal; };
 
 		anyKnotChanged_ = ourKnotsChanged_ = not std::equal( ownedParams_.begin(), ownedParams_.end(), values_.begin(), checkEquality);
 
 		// Update the acceptance spline if any of the knot values have changed
 		if ( ourKnotsChanged_ ) {
 			this->updateParameterCache();
 		}
 	}
 
 	if constexpr ( Order == LauSplineOrder::Sixth ) {
 		if ( otherKnotsFloating_ ) {
 			otherSpline_->propagateParUpdates();
 			otherKnotsChanged_ = otherSpline_->anythingChanged();
 			anyKnotChanged_ |= otherKnotsChanged_;
 		}
 	}
 }
 
 template <LauSplineOrder Order>
 void LauSplineDecayTimeEfficiency<Order>::fitToTH1( TH1* hist, const LauOutputLevel printLevel, const bool doWL )
 {
 	std::cout << "INFO in LauSplineDecayTimeEfficiency::fitToTH1 : fitting " << prefix_ << " spline to supplied histogram \"" << hist->GetName() << "\"\n";
 	std::cout << "                                               : to obtain initial estimates of parameter values and errors" << std::endl;
 
 	TFitResultPtr fitResult { effSpline_->fitToTH1( hist, printLevel, doWL ) };
 	if ( fitResult->Status() != 0 || fitResult->CovMatrixStatus() < 2 ) {
 		std::cerr << "WARNING in LauSplineDecayTimeEfficiency::fitToTH1 : fit failed with status " << fitResult->Status() << " and covariance matrix status " << fitResult->CovMatrixStatus() << "\n";
 		std::cerr << "                                                  : will not update spline parameters based on this fit result" << std::endl;
 		return;
 	}
 	if ( fitResult->CovMatrixStatus() == 2 )
 	{
 		std::cerr << "WARNING in LauSplineDecayTimeEfficiency::fitToTH1 : final covariance matrix not positive definite\n";
 		std::cerr << "                                                  : continuing, but double check your fit!" << std::endl;
 	}
 
 	const std::size_t nKnots { ownedParams_.size() };
 	for ( std::size_t knot{0}; knot < nKnots; ++knot ) {
 		const Double_t knotVal { fitResult->Value(knot) };
 		const Double_t knotErr { fitResult->Error(knot) };
 
 		std::cout << "                                               : Setting parameter " << ownedParams_[knot]->name() << " to " << knotVal << " +/- " << knotErr << std::endl;
 
 		ownedParams_[knot]->value( knotVal );
 		ownedParams_[knot]->genValue( knotVal );
 		ownedParams_[knot]->initValue( knotVal );
 		ownedParams_[knot]->error( knotErr );
 	}
 
 	this->updateParameterCache();
 }
 
 template <LauSplineOrder Order>
 Double_t LauSplineDecayTimeEfficiency<Order>::findMaximum() const
 {
 	if(Order == LauSplineOrder::Cubic)
 	{return effSpline_->findMaximum();}
-	//TODO do numerically for 6th order spline
+	if(Order == LauSplineOrder::Sixth)
+	{
+		//Build a lambda to construct a TF1
+		auto sixthOrder = [this](Double_t x[], Double_t /*p[]*/)
+		{return effSpline_->evaluate(x[0])*otherSpline_->effSpline_->evaluate(x[0]);};
+		TF1 func("function", sixthOrder, effSpline_->getXValues().front(), otherSpline_->effSpline_->getXValues().back(), 0);
+		return func.GetMaximum(func.GetXmin(), func.GetXmax());
+	}
+	//If all else fails return 0
 	return 0.0;
 }
 
 #endif
diff --git a/src/Lau1DCubicSpline.cc b/src/Lau1DCubicSpline.cc
index 50624d3..dc5008b 100644
--- a/src/Lau1DCubicSpline.cc
+++ b/src/Lau1DCubicSpline.cc
@@ -1,685 +1,687 @@
 
 /*
 Copyright 2015 University of Warwick
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
 
     http://www.apache.org/licenses/LICENSE-2.0
 
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 */
 
 /*
 Laura++ package authors:
 John Back
 Paul Harrison
 Thomas Latham
 */
 
 /*! \file Lau1DCubicSpline.cc
   \brief File containing implementation of Lau1DCubicSpline class.
  */
 
 #include <cmath>
 #include <cstdlib>
 #include <fstream>
 #include <iostream>
 #include <algorithm>
 
 #include <nlohmann/json.hpp>
 
 #include <TH2.h>
 #include <TSystem.h>
 
 #include "Lau1DCubicSpline.hh"
 #include "LauAbsRValue.hh"
 #include "LauParameter.hh"
 
 ClassImp(Lau1DCubicSpline)
 
 
 Lau1DCubicSpline::Lau1DCubicSpline(const std::vector<Double_t>& xs, const std::vector<Double_t>& ys,
 				   const SplineType type,
 				   const BoundaryType leftBound,
 				   const BoundaryType rightBound,
 				   const Double_t dydx0, const Double_t dydxn) :
 	nKnots_{xs.size()},
 	x_{xs},
 	y_{ys},
 	type_{type},
 	leftBound_{leftBound},
 	rightBound_{rightBound},
 	dydx0_{dydx0},
 	dydxn_{dydxn}
 {
 	init();
 }
 
 std::unique_ptr<Lau1DCubicSpline> Lau1DCubicSpline::readFromJson(const TString& filename, const TString& splineName)
 {
 	using nlohmann::json;
 
 	std::ifstream in(filename, std::ios_base::in);
 	if ( not in ) {
 		std::cerr << "ERROR in Lau1DCubicSpline::readFromJson : couldn't open file \"" << filename << "\"" << std::endl;
 		return std::unique_ptr<Lau1DCubicSpline>{};
 	}
 
 	json j;
 	in >> j;
 
 	if ( splineName != "" ) {
 		j = j[splineName.Data()];
 	}
 
 	// NB can't use std::make_unique here because the default constructor is private
 	std::unique_ptr<Lau1DCubicSpline> spline{ new Lau1DCubicSpline };
 	j.get_to(*spline);
 	spline->init();
 
 	return spline;
 }
 
 void Lau1DCubicSpline::writeToJson(const TString& filename, const TString& splineName, const bool append) const
 {
 	using nlohmann::json;
 
 	// if we're appending, we need to first load in the exising JSON from the file
 	json j;
 	if ( append ) {
 		// if appending we must have a name, so check that first
 		if ( splineName == "" ) {
 			std::cerr << "ERROR in Lau1DCubicSpline::writeToJson : when appending to a file it is mandatory to give a name to the spline" << std::endl;
 			return;
 		}
 
 		std::ifstream in{filename, std::ios_base::in};
 		if ( not in ) {
 			std::cerr << "ERROR in Lau1DCubicSpline::writeToJson : couldn't open file \"" << filename << "\" for initial reading" << std::endl;
 			return;
 		}
 
 		in >> j;
 	}
 
 	if(splineName == "") {
 		j = *this;
 	} else {
 		j[splineName.Data()] = *this;
 	}
 
 	// now we can overwrite the file
 	std::ofstream out{filename, std::ios_base::out};
 	if(not out) {
 		std::cerr << "ERROR in Lau1DCubicSpline::writeToJson : couldn't open file \"" << filename << "\" for writing. No file will be written!" << std::endl;
 		return;
 	}
 
 	out << j.dump(4);
 	out << std::endl;
 	out.close();
 
 	return;
 }
 
 Double_t Lau1DCubicSpline::evaluate(const Double_t x) const
 {
 	// do not attempt to extrapolate the spline
 	if( x<x_[0] || x>x_[nKnots_-1] ) {
 		std::cerr << "WARNING in Lau1DCubicSpline::evaluate : function is only defined between " << x_[0] << " and " << x_[nKnots_-1] << std::endl;
 		std::cerr << "                                        value at " << x << " returned as 0" << std::endl;
 		return 0.;
 	}
 
 	// first determine which 'cell' of the spline x is in
 	// cell i runs from knot i to knot i+1
 	std::size_t cell{0};
 
 	while( x > x_[cell+1] ) {
 		++cell;
 	}
 
 	// obtain x- and y-values of the neighbouring knots
 	const Double_t xLow  { x_[cell] };
 	const Double_t xHigh { x_[cell+1] };
 	const Double_t yLow  { y_[cell] };
 	const Double_t yHigh { y_[cell+1] };
 
 	if(type_ == SplineType::LinearInterpolation) {
 		return yHigh*(x-xLow)/(xHigh-xLow) + yLow*(xHigh-x)/(xHigh-xLow);
 	}
 
 	// obtain t, the normalised x-coordinate within the cell,
 	// and the coefficients a and b, which are defined in cell i as:
 	//
 	//      a_i =  k_i  *(x_i+1 - x_i) - (y_i+1 - y_i),
 	//      b_i = -k_i+1*(x_i+1 - x_i) + (y_i+1 - y_i)
 	//
 	// where k_i is (by construction) the first derivative at knot i
 	const Double_t t { (x - xLow) / (xHigh - xLow) };
 	const Double_t a {     dydx_[cell]   * (xHigh - xLow) - (yHigh - yLow) };
 	const Double_t b { -1.*dydx_[cell+1] * (xHigh - xLow) + (yHigh - yLow) };
 
 	const Double_t retVal { (1 - t) * yLow + t * yHigh + t * (1 - t) * ( a * (1 - t) + b * t ) };
 
 	return retVal;
 }
 
 void Lau1DCubicSpline::updateYValues(const std::vector<Double_t>& ys)
 {
 	y_ = ys;
 	this->calcDerivatives();
 }
 
 void Lau1DCubicSpline::updateYValues(const std::vector<LauParameter*>& ys)
 {
 	for (std::size_t i{0}; i<nKnots_; ++i){
 		y_[i] = ys[i]->unblindValue();
 	}
 	this->calcDerivatives();
 }
 
 void Lau1DCubicSpline::updateYValues(const std::vector<LauAbsRValue*>& ys)
 {
 	for (std::size_t i{0}; i<nKnots_; ++i){
 		y_[i] = ys[i]->unblindValue();
 	}
 	this->calcDerivatives();
 }
 
 void Lau1DCubicSpline::updateType(const SplineType type)
 {
 	if(type_ != type) {
 		type_ = type;
 		this->calcDerivatives();
 	}
 }
 
 void Lau1DCubicSpline::updateBoundaryConditions(const BoundaryType leftBound,
 		const BoundaryType rightBound,
 		const Double_t dydx0, const Double_t dydxn)
 {
 	bool updateDerivatives{false};
 
 	if(leftBound_ != leftBound || rightBound_ != rightBound) {
 		leftBound_ = leftBound;
 		rightBound_ = rightBound;
 		updateDerivatives = true;
 	}
 
 	if(dydx0_ != dydx0) {
 		dydx0_ = dydx0;
 		if(leftBound_ == BoundaryType::Clamped) updateDerivatives = true;
 	}
 
 	if(dydxn_ != dydxn) {
 		dydxn_ = dydxn;
 		if(rightBound_ == BoundaryType::Clamped) updateDerivatives = true;
 	}
 
 	if(updateDerivatives) {
 		this->calcDerivatives();
 	}
 }
 
 std::array<Double_t,4> Lau1DCubicSpline::getCoefficients(const std::size_t segIndex, const bool normalise) const
 {
 	std::array<Double_t,4> result = {0.,0.,0.,0.};
 
 	if(segIndex >= nKnots_-1)
 	{
 		std::cerr << "ERROR in Lau1DCubicSpline::getCoefficients requested for too high a knot value" << std::endl;
 		return result;
 	}
 
 	Double_t xL = x_[segIndex]   , xH = x_[segIndex+1];
 	Double_t yL = y_[segIndex]   , yH = y_[segIndex+1];
 	Double_t h = xH-xL; //This number comes up a lot
 
 	switch(type_)
 	{
 		case SplineType::StandardSpline:
 		case SplineType::AkimaSpline:
 			{
 				Double_t kL = dydx_[segIndex], kH = dydx_[segIndex+1];
 
 				//a and b based on definitions from https://en.wikipedia.org/wiki/Spline_interpolation#Algorithm_to_find_the_interpolating_cubic_spline
 				Double_t a = kL*h-(yH-yL);
 				Double_t b =-kH*h+(yH-yL);
 
 				Double_t denom = -h*h*h;//The terms have a common demoninator
 
 				result[0] = -b*xL*xL*xH + a*xL*xH*xH + h*h*(xL*yH - xH*yL);
 				result[1] = -a*xH*(2*xL+xH) + b*xL*(xL+2*xH) + h*h*(yL-yH);
 				result[2] = -b*(2*xL+xH) + a*(xL+2*xH);
 				result[3] = -a+b;
 
 				for(auto& res : result){res /= denom;}
 				break;
 			}
 /*		case SplineType::AkimaSpline: // Double check the Akima description of splines (in evaluate) right now they're the same except for the first derivatives
 			{
 				//using fomulae from https://asmquantmacro.com/2015/09/01/akima-spline-interpolation-in-excel/
 				std::function<Double_t(Int_t)> m = [&](Int_t j) //formula to get the straight line gradient
 				{
 					if(j < 0){return 2*m(j+1)-m(j+2);}
 					if(j >= nKnots_){return 2*m(j-1)-m(j-2);}
 					return (y_[j+1]-y_[j]) / (x_[j+1]-x_[j]);
 				};
 
 				auto t = [&](Int_t j)
 				{
 					Double_t res = 0.; //originally res was called 't' but that produced a shadow warning
 					Double_t denom = TMath::Abs(m(j+1)-m(j)) + TMath::Abs(m(j-1)-m(j-2));
 					if(denom == 0){res = (m(j)-m(j-1))/2;} //Special case for when denom = 0
 					else
 					{
 						res = TMath::Abs(m(j+1)-m(j))*m(j-1) + TMath::Abs(m(j-1)-m(j-2))*m(j);
 						res /= denom;
 					}
 					return res;
 				};
 
 				//These are the p's to get the spline in the form p_k(x-xL)^k
 				Double_t pDenom = x_[segIndex+1]/x_[segIndex]; //a denominator used for p[2] and p[3]
 				std::array<Double_t,4> p = {y_[segIndex],t(segIndex),0.,0.}; //we'll do the last 2 below
 
 				p[2] = 3*m(segIndex)-2*t(segIndex)-t(segIndex+1);
 				p[2]/= pDenom;
 
 				p[3] = t(segIndex)+t(segIndex+1)-2*m(segIndex);
 				p[3]/= pDenom*pDenom;
 
 				//Now finally rearranging the p's into the desired results
 				result[0] = p[0]-p[1]*xL+p[2]*xL*xL-p[3]*xL*xL*xL;
 				result[1] = p[1]-2*p[2]*xL+3*p[3]*xL*xL;
 				result[2] = p[2]-3*p[3]*xL;
 				result[3] = p[3];
 
 				break;
 			}*/
 		case SplineType::LinearInterpolation:
 			{
 				result[0] = xH*yL-xL*yH;
 				result[1] = yH-yL;
 
 				for(auto& res : result){res /= h;}
 				break;
 			}
 	}
 
 	if (normalise) {
 		const Double_t integral { this->integral() };
 		for(auto& res : result){res /= integral;}
 	}
 
 	return result;
 }
 
 Double_t Lau1DCubicSpline::integral() const
 {
 	Double_t integral{0.0};
 	for(std::size_t iKnot{0}; iKnot < nKnots_ -1; ++iKnot)
 	{
 		const Double_t minAbs { x_[iKnot] };
 		const Double_t maxAbs { x_[iKnot+1] };
 
 		const std::array<Double_t,4> coeffs { this->getCoefficients(iKnot, false) };
 
 		auto integralFunc = [&coeffs](Double_t x){return coeffs[0]*x + coeffs[1]*x*x/2.0 + coeffs[2]*x*x*x/3.0 + coeffs[3]*x*x*x*x/4.0;};
 
 		integral += integralFunc(maxAbs);
 		integral -= integralFunc(minAbs);
 	}
 	return integral;
 }
 
 TF1* Lau1DCubicSpline::makeTF1(const bool normalise) const
 {
 	auto fitf = [this](Double_t* x, Double_t* par) {//there is only 1 x (the abscissa) and 1 par (a scaling of the entire thing for normalisation)
 		return this->evaluate( x[0] ) * par[0];
 	};
 
 	//Make the function
 	TF1* func = new TF1("FittedFunc",fitf,x_.front(),x_.back(),1);
 
 	func -> SetParNames("Normalisation");
 	if(normalise)
 	{
 		func->SetParameter(0, 1./func->Integral(x_.front(),x_.back()));
 	}
 	else{func->SetParameter(0,1.);}
 
 	return func;
 }
 
 TF1* Lau1DCubicSpline::normaliseToTH1(TH1* hist, const LauOutputLevel printLevel) const
 {
 	//first define the fit function
 	auto fitf = [this](Double_t* x, Double_t* par) {//there is only 1 x (the abscissa) and 1 par (a scaling of the entire thing)
 		return this->evaluate( x[0] ) * par[0];
 	};
 	//Make the function
 	TF1* FittedFunc = new TF1("FittedFunc",fitf,x_.front(),x_.back(),1);
 	//Set the parameter name
 	FittedFunc -> SetParNames("Constant");
 	//Set the parameter limits and default value
 	FittedFunc -> SetParLimits(0,0.,10.);
 	FittedFunc -> SetParameter(0,1.);
 
 	// Options to:
 	// - N = do not store the graphics function
 	// - 0 = do not plot the fit result
 	TString fitOptions {"N 0"};
 
 	switch ( printLevel ) {
 		case LauOutputLevel::Verbose :
 			fitOptions += " V";
 			break;
 		case LauOutputLevel::Quiet :
 			fitOptions += " Q";
 			break;
 		case LauOutputLevel::Standard :
 			break;
 	}
 
 	hist->Fit( "FittedFunc", fitOptions );
 
 	return FittedFunc;
 }
 
 TFitResultPtr Lau1DCubicSpline::fitToTH1(TH1* hist, const LauOutputLevel printLevel, const bool doWL)
 {
 	auto fitf = [this](Double_t* x, Double_t* par) {
 		this -> updateYValues( std::vector<Double_t>(par, par + nKnots_) );
 		return this -> evaluate( x[0] );
 	};
 
 	TF1 FittedFunc("FittedFunc",fitf,x_.front(),x_.back(),nKnots_);
 
 	const Double_t knotMax { hist->GetMaximum() * 1.5 };
 
 	for(std::size_t knot{0}; knot <= nKnots_ ; ++knot)
 	{
 		FittedFunc.SetParName(knot, Form("Knot%lu",knot));
 		FittedFunc.SetParLimits(knot, 0., knotMax);
 		FittedFunc.SetParameter(knot, y_[knot]);
 	}
 
 	// Options to:
 	// - N = do not store the graphics function
 	// - 0 = do not plot the fit result
 	// - S = save the result of the fit
 	// - WL= do a weighted likelihood fit (not chi2)
 	TString fitOptions {"N 0 S"};
 	if(doWL){fitOptions += " WL";}
 
 	switch ( printLevel ) {
 		case LauOutputLevel::Verbose :
 			fitOptions += " V";
 			break;
 		case LauOutputLevel::Quiet :
 			fitOptions += " Q";
 			break;
 		case LauOutputLevel::Standard :
 			break;
 	}
 
 	return hist->Fit( "FittedFunc", fitOptions );
 }
 
 void Lau1DCubicSpline::init()
 {
 	if( y_.size() != x_.size()) {
 		std::cerr << "ERROR in Lau1DCubicSpline::init : The number of y-values given does not match the number of x-values" << std::endl;
 		std::cerr << "                                  Found " <<  y_.size() << ", expected " << x_.size() << std::endl;
 		gSystem->Exit(EXIT_FAILURE);
 	}
 
 	if( nKnots_ < 3 ) {
 		std::cerr << "ERROR in Lau1DCubicSpline::init : The number of knots is too small" << std::endl;
 		std::cerr << "                                  Found " <<  nKnots_ << ", expected at least 3 (to have at least 1 internal knot)" << std::endl;
 		gSystem->Exit(EXIT_FAILURE);
 	}
 
 	dydx_.assign(nKnots_,0.0);
 
 	a_.assign(nKnots_,0.0);
 	b_.assign(nKnots_,0.0);
 	c_.assign(nKnots_,0.0);
 	d_.assign(nKnots_,0.0);
 
 	this->calcDerivatives();
 }
 
 void Lau1DCubicSpline::calcDerivatives()
 {
 	switch ( type_ ) {
 		case SplineType::StandardSpline :
 			this->calcDerivativesStandard();
 			break;
 		case SplineType::AkimaSpline :
 			this->calcDerivativesAkima();
 			break;
 		case SplineType::LinearInterpolation :
 			//derivatives not needed for linear interpolation
 			break;
 	}
 }
 
 void Lau1DCubicSpline::calcDerivativesStandard()
 {
 	// derivatives are determined such that the second derivative is continuous at internal knots
 
 	// derivatives, k_i, are the solutions to a set of linear equations of the form:
 	// a_i * k_i-1  +  b_i * k+i  +  c_i * k_i+1  =  d_i with a_0 = 0, c_n-1 = 0
 
 	// this is solved using the tridiagonal matrix algorithm as on en.wikipedia.org/wiki/Tridiagonal_matrix_algorithm
 
 	// first and last equations give boundary conditions
 	// - for natural boundary, require f''(x) = 0 at end knot
 	// - for 'not a knot' boundary, require f'''(x) continuous at second knot
 	// - for clamped boundary, require predefined value of f'(x) at end knot
 
 	// non-zero values of a_0 and c_n-1 would give cyclic boundary conditions
 	a_[0] = 0.;
 	c_[nKnots_-1] = 0.;
 
 	// set left boundary condition
 	switch ( leftBound_ ) {
 		case BoundaryType::Natural :
 			{
 				b_[0] = 2./(x_[1]-x_[0]);
 				c_[0] = 1./(x_[1]-x_[0]);
 				d_[0] = 3.*(y_[1]-y_[0])/((x_[1]-x_[0])*(x_[1]-x_[0]));
 				break;
 			}
 
 		case BoundaryType::NotAKnot :
 			{
 				// define the width, h, and the 'slope', delta, of the first cell
 				const Double_t h1{x_[1]-x_[0]};
 				const Double_t h2{x_[2]-x_[0]};
 				const Double_t delta1{(y_[1]-y_[0])/h1};
 				const Double_t delta2{(y_[2]-y_[1])/h2};
 				// these coefficients can be determined by requiring f'''_0(x_1) = f'''_1(x_1)
 				// the requirement f''_0(x_1) = f''_1(x_1) has been used to remove the dependence on k_2
 				b_[0] = h2;
 				c_[0] = h1+h2;
 				d_[0] = delta1*(2.*h2*h2 + 3.*h1*h2)/(h1+h2) + delta2*5.*h1*h1/(h1+h2);
 				break;
 			}
 
 		case BoundaryType::Clamped :
 			{
 				b_[0] = 1.;
 				c_[0] = 0.;
 				d_[0] = dydx0_;
 				break;
 			}
 	}
 
 	// set right boundary condition
 	switch ( rightBound_ ) {
 		case BoundaryType::Natural :
 			{
 				a_[nKnots_-1] = 1./(x_[nKnots_-1]-x_[nKnots_-2]);
 				b_[nKnots_-1] = 2./(x_[nKnots_-1]-x_[nKnots_-2]);
 				d_[nKnots_-1] = 3.*(y_[nKnots_-1]-y_[nKnots_-2])/((x_[nKnots_-1]-x_[nKnots_-2])*(x_[nKnots_-1]-x_[nKnots_-2]));
 				break;
 			}
 		case BoundaryType::NotAKnot :
 			{
 				// define the width, h, and the 'slope', delta, of the last cell
 				const Double_t hnm1{x_[nKnots_-1]-x_[nKnots_-2]};
 				const Double_t hnm2(x_[nKnots_-2]-x_[nKnots_-3]);
 				const Double_t deltanm1{(y_[nKnots_-1]-y_[nKnots_-2])/hnm1};
 				const Double_t deltanm2{(y_[nKnots_-2]-y_[nKnots_-3])/hnm2};
 				// these coefficients can be determined by requiring f'''_n-3(x_n-2) = f'''_n-2(x_n-2)
 				// the requirement f''_n-3(x_n-2) = f''_n-2(x_n-2) has been used to remove
 				// the dependence on k_n-3
 				a_[nKnots_-1] = hnm2 + hnm1;
 				b_[nKnots_-1] = hnm1;
 				d_[nKnots_-1] = deltanm2*hnm1*hnm1/(hnm2+hnm1) + deltanm1*(2.*hnm2*hnm2 + 3.*hnm2*hnm1)/(hnm2+hnm1);
 				break;
 			}
 
 		case BoundaryType::Clamped :
 			{
 				a_[nKnots_-1] = 0.;
 				b_[nKnots_-1] = 1.;
 				d_[nKnots_-1] = dydxn_;
 				break;
 			}
 	}
 
 	// the remaining equations ensure that f_i-1''(x_i) = f''_i(x_i) for all internal knots
 	for(std::size_t i{1}; i<nKnots_-1; ++i) {
 		a_[i] = 1./(x_[i]-x_[i-1]);
 		b_[i] = 2./(x_[i]-x_[i-1]) + 2./(x_[i+1]-x_[i]);
 		c_[i] =                      1./(x_[i+1]-x_[i]);
 		d_[i] = 3.*(y_[i]-y_[i-1])/((x_[i]-x_[i-1])*(x_[i]-x_[i-1])) + 3.*(y_[i+1]-y_[i])/((x_[i+1]-x_[i])*(x_[i+1]-x_[i]));
 	}
 
 	// forward sweep - replace c_i and d_i with
 	//
 	// c'_i =  c_i /  b_i                 for i = 0
 	//         c_i / (b_i - a_i * c_i-1)  otherwise
 	//
 	// and
 	//
 	// d'_i =   d_i                /  b_i                 for i = 0
 	//         (d_i - a_i * d_i-1) / (b_i - a_i * c_i-1)  otherwise
 
 	c_[0] /= b_[0];
 	d_[0] /= b_[0];
 
 	for(std::size_t i{1}; i<nKnots_-1; ++i) {
 		c_[i] =  c_[i]                  / (b_[i] - a_[i]*c_[i-1]);
 		d_[i] = (d_[i] - a_[i]*d_[i-1]) / (b_[i] - a_[i]*c_[i-1]);
 	}
 
 	d_[nKnots_-1] = (d_[nKnots_-1] - a_[nKnots_-1]*d_[nKnots_-2]) / (b_[nKnots_-1] - a_[nKnots_-1]*c_[nKnots_-2]);
 
 	// back substitution - calculate k_i = dy/dx at each knot
 	//
 	// k_i = d'_i                   for i = n-1
 	//       d'_i - c'_i * k_i+1    otherwise
 
 	dydx_[nKnots_-1] = d_[nKnots_-1];
 
 	for(int i {static_cast<int>(nKnots_-2)}; i>=0; --i) {
 		dydx_[i] = d_[i] - c_[i]*dydx_[i+1];
 	}
 }
 
 void Lau1DCubicSpline::calcDerivativesAkima() {
 	//derivatives are calculated according to the Akima method
 	// J.ACM vol. 17 no. 4 pp 589-602
 
 	Double_t am1{0.0}, an{0.0}, anp1{0.0};
 
 	// a[i] is the slope of the segment from point i-1 to point i
 	//
 	// n.b. segment 0 is before point 0 and segment n is after point n-1
 	//      internal segments are numbered 1 - n-1
 	for(std::size_t i{1}; i<nKnots_; ++i) {
 		a_[i] = (y_[i]-y_[i-1])/(x_[i]-x_[i-1]);
 	}
 
 	// calculate slopes between additional points on each end according to the Akima method
 	// method assumes that the additional points follow a quadratic defined by the last three points
 	// this leads to the relations a[2] - a[1] = a[1] - a[0] = a[0] - a[-1] and a[n-1] - a[n-2] = a[n] - a[n-1] = a[n+1] - a[n]
 	a_[0]         = 2*a_[1]         - a_[2];
 	am1           = 2*a_[0]         - a_[1];
 
 	an            = 2*a_[nKnots_-1] - a_[nKnots_-2];
 	anp1          = 2*an            - a_[nKnots_-1];
 
 	// b[i] is the weight of a[i]   towards dydx[i]
 	// c[i] is the weight of a[i+1] towards dydx[i]
 	// See Appendix A of J.ACM vol. 17 no. 4 pp 589-602 for a justification of these weights
 	b_[0] = TMath::Abs(a_[1] - a_[0]);
 	b_[1] = TMath::Abs(a_[2] - a_[1]);
 
 	c_[0] = TMath::Abs(a_[0] - am1  );
 	c_[1] = TMath::Abs(a_[1] - a_[0]);
 
 	for(std::size_t i{2}; i<nKnots_-2; ++i) {
 		b_[i] = TMath::Abs(a_[i+2] - a_[i+1]);
 		c_[i] = TMath::Abs(a_[i]   - a_[i-1]);
 	}
 
 	b_[nKnots_-2] = TMath::Abs(an            - a_[nKnots_-1]);
 	b_[nKnots_-1] = TMath::Abs(anp1          - an           );
 
 	c_[nKnots_-2] = TMath::Abs(a_[nKnots_-2] - a_[nKnots_-3]);
 	c_[nKnots_-1] = TMath::Abs(a_[nKnots_-1] - a_[nKnots_-2]);
 
 	// dy/dx calculated as the weighted average of a[i] and a[i+1]:
 	// dy/dx_i = ( | a_i+2 - a_i+1 | a_i  +  | a_i - a_i-1 | a_i+1 ) / ( | a_i+2 - a_i+1 |  +  | a_i - a_i-1 | )
 	// in the special case a_i-1 == a_i != a_i+1 == a_i+2 this function is undefined so dy/dx is then defined as (a_i + a_i+1) / 2
 	for(std::size_t i{0}; i<nKnots_-2; ++i) {
 		if(b_[i]==0 && c_[i]==0) {
 			dydx_[i] = ( a_[i] + a_[i+1] ) / 2.;
 		} else {
 			dydx_[i] = ( b_[i] * a_[i] + c_[i] * a_[i+1] ) / ( b_[i] + c_[i] );
 		}
 	}
 
 	if(b_[nKnots_-1]==0 && c_[nKnots_-1]==0) {
 		dydx_[nKnots_-1] = ( a_[nKnots_-1] + an ) / 2.;
 	} else {
 		dydx_[nKnots_-1] = ( b_[nKnots_-1] * a_[nKnots_-1] + c_[nKnots_-1] * an ) / ( b_[nKnots_-1] + c_[nKnots_-1] );
 	}
 }
 
 Double_t Lau1DCubicSpline::findMaximum() const
 {
 	//as a starting point, find the highest knot
 	Double_t max = *std::max_element(y_.begin(), y_.end());
 	//if linear interpolation is used, the maximum is the highest knot
 	if(type_ == SplineType::LinearInterpolation){return max;}
 
 	//Look for any local maxima greater than max
 	for(size_t nKnot = 0; nKnot < nKnots_ - 1; ++nKnot)
 	{
 		std::array<Double_t,4> coeffs = this->getCoefficients(nKnot);
 		//first derivative is a parabola
 		Double_t a = coeffs[3]*3, b = coeffs[2]*2, c = coeffs[1];
 		Double_t descriminant = b*b - 4*a*c;
 		if(descriminant < 0){continue;}
 		Double_t x1,x2;
 		x1 = (-b + TMath::Sqrt(descriminant))/(2*a);
-		if(this->evaluate(x1) > max){max = this->evaluate(x1);}
+		if(x1 > x_[nKnot] and x1 < x_[nKnot+1] and this->evaluate(x1) > max)
+		{max = this->evaluate(x1);}
 		x2 = (-b - TMath::Sqrt(descriminant))/(2*a);
-		if(this->evaluate(x2) > max){max = this->evaluate(x2);}
+		if(x2 > x_[nKnot] and x2 < x_[nKnot+1] and this->evaluate(x2) > max)
+		{max = this->evaluate(x2);}
 	}
 	return max;
 }