go-hep.org/x/hep@v0.38.1/hbook/ops.go

// Copyright ©2017 The go-hep Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

package hbook

import (
	"fmt"
	"math"
)

// DivideH1D divides 2 1D-histograms and returns a 2D scatter.
// DivideH1D returns an error if the binning of the 1D histograms are not compatible.
// If no DivOptions is passed, NaN raised during division are kept.
func DivideH1D(num, den *H1D, opts ...DivOptions) (*S2D, error) {

	cfg := newDivConfig()
	for _, opt := range opts {
		opt(cfg)
	}

	var s2d S2D

	bins1 := num.Binning.Bins
	bins2 := den.Binning.Bins

	for i := range bins1 {
		b1 := bins1[i]
		b2 := bins2[i]

		if !fuzzyEq(b1.XMin(), b2.XMin()) || !fuzzyEq(b1.XMax(), b2.XMax()) {
			return nil, fmt.Errorf("hbook: x binnings are not equivalent in %v / %v", num.Name(), den.Name())
		}

		// assemble the x value and error
		// use the midpoint of the "bin" for the new central value
		x := b1.XMid()
		exm := x - b1.XMin()
		exp := b1.XMax() - x

		// assemble the y value and error
		var y, ey float64
		b2h := b2.SumW() / b2.XWidth() // height of the bin
		b1h := b1.SumW() / b1.XWidth() // ditto
		b2herr := math.Sqrt(b2.SumW2()) / b2.XWidth()
		b1herr := math.Sqrt(b1.SumW2()) / b1.XWidth()

		switch {
		case b2h == 0 || (b1h == 0 && b1herr != 0): // TODO(sbinet): is it OK?
			if cfg.ignoreNaN {
				continue
			} else {
				y = cfg.replaceNaN
				ey = 0.0 // TODO(rmadar): I guess this is the most sensitive case
				// but another field could be added to divConfig
			}
		default:
			y = b1h / b2h
			// TODO(sbinet): is this the exact error treatment for all (uncorrelated) cases?
			// What should be the behaviour around 0? +1 and -1 fills?
			relerr1 := 0.0
			if b1herr != 0 {
				relerr1 = math.Sqrt(b1.SumW2()) / b1.SumW() // TODO(sbinet) refactor as bin1d.RelErr() ?
			}
			relerr2 := 0.0
			if b2herr != 0 {
				relerr2 = math.Sqrt(b2.SumW2()) / b2.SumW()
			}
			ey = y * math.Sqrt(relerr1*relerr1+relerr2*relerr2)
		}

		// deal with +/- errors separately, inverted for the denominator contributions:
		// TODO(sbinet): check correctness with different signed numerator and denominator.

		s2d.Fill(Point2D{X: x, Y: y, ErrX: Range{Min: exm, Max: exp}, ErrY: Range{Min: ey, Max: ey}})
	}
	return &s2d, nil
}

// DivOptions allows to customize the behaviour of DivideH1D
type DivOptions func(c *divConfig)

// divConfig type specifies the possible configurations
// passed as DivOptions.
type divConfig struct {
	ignoreNaN  bool
	replaceNaN float64
}

// newDivConfig function builds the default configuration
// for DivideH1D() option.
func newDivConfig() *divConfig {
	return &divConfig{replaceNaN: math.NaN()}
}

// DivIgnoreNaNs function configures DivideH1D to
// ignore data points with NaNs.
func DivIgnoreNaNs() DivOptions {
	return func(c *divConfig) {
		c.ignoreNaN = true
	}
}

// DivReplaceNaNs function configures DivideH1D to replace
// NaN raised during divisions with the provided value.
func DivReplaceNaNs(v float64) DivOptions {
	return func(c *divConfig) {
		c.replaceNaN = v
	}
}

// fuzzyEq returns true if a and b are equal with a degree of fuzziness
func fuzzyEq(a, b float64) bool {
	const tol = 1e-5
	aa := math.Abs(a)
	bb := math.Abs(b)
	absavg := 0.5 * (aa + bb)
	absdiff := math.Abs(a - b)
	return (aa < 1e-8 && bb < 1e-8) || absdiff < tol*absavg
}

// AddScaledH1D returns the histogram with the bin-by-bin h1+alpha*h2
// operation, assuming statistical uncertainties are uncorrelated.
func AddScaledH1D(h1 *H1D, alpha float64, h2 *H1D) *H1D {
	if h1.Len() != h2.Len() {
		panic(fmt.Errorf("hbook: h1 and h2 have different number of bins"))
	}

	if h1.XMin() != h2.XMin() || h1.XMax() != h2.XMax() {
		panic(fmt.Errorf("hbook: h1 and h2 have different range"))
	}

	var (
		o  = h1.Clone()
		a2 = alpha * alpha
	)

	for i := range o.Binning.Bins {
		o := &o.Binning.Bins[i]
		o.addScaled(alpha, a2, h2.Binning.Bins[i])
	}

	o.Binning.Dist.addScaled(alpha, a2, h2.Binning.Dist)
	o.Binning.Outflows[0].addScaled(alpha, a2, h2.Binning.Outflows[0])
	o.Binning.Outflows[1].addScaled(alpha, a2, h2.Binning.Outflows[1])
	return o
}

// AddH1D returns the bin-by-bin summed histogram of h1 and h2
// assuming their statistical uncertainties are uncorrelated.
func AddH1D(h1, h2 *H1D) *H1D {
	return AddScaledH1D(h1, 1, h2)
}

// SubH1D returns the bin-by-bin subtracted histogram of h1 and h2
// assuming their statistical uncertainties are uncorrelated.
func SubH1D(h1, h2 *H1D) *H1D {
	return AddScaledH1D(h1, -1, h2)
}