package main

import "fmt"
import "math"
import rand "math/rand/v2"

var r = rand.New(rand.NewPCG(1, 2))

func sample_unit_uniform() float64 {
	return r.Float64()
}

func sample_unit_normal() float64 {
	return r.NormFloat64()
}

func sample_uniform(start float64, end float64) float64 {
	return sample_unit_uniform()*(end-start) + start
}

func sample_normal(mean float64, sigma float64) float64 {
	return mean + sample_unit_normal()*sigma
}

func sample_lognormal(logmean float64, logstd float64) float64 {
	return (math.Exp(sample_normal(logmean, logstd)))
}

func sample_normal_from_90_ci(low float64, high float64) float64 {
	var normal90 float64 = 1.6448536269514727
	var mean float64 = (high + low) / 2.0
	var std float64 = (high - low) / (2.0 * normal90)
	return sample_normal(mean, std)

}

func sample_to(low float64, high float64) float64 {
	// Given a (positive) 90% confidence interval,
	// returns a sample from a lognorma with a matching 90% c.i.
	// Key idea: If we want a lognormal with 90% confidence interval [a, b]
	// we need but get a normal with 90% confidence interval [log(a), log(b)].
	// Then see code for sample_normal_from_90_ci
	var loglow float64 = math.Log(low)
	var loghigh float64 = math.Log(high)
	return math.Exp(sample_normal_from_90_ci(loglow, loghigh))
}

func main() {
	fmt.Println("Hello world!")
	fmt.Printf("%v\n", r.Float64())
	fmt.Printf("%v\n", r.NormFloat64())
}