Statistics with channels and first-class functions

main.go

package main

import (
	"log"
)

type fchan chan float64
type ichan chan int

type datafun func(Data, ichan, fchan)

type Data []float64

func (d Data) Analyze(fn datafun) float64 {

	// Length of data used to track progress
	dl := len(d)

	// Make the int chan which is used for
	// showing progress, the buffer is the
	// amount of items in the data
	ci := make(ichan, dl)

	// This float64 chan will get the final
	// result so it only needs a buffer of 1
	cf := make(fchan, 1)

	fn(d, ci, cf)

	// Since we know the amount of items in the
	// data we know the amount of progress reports
	// sent on the ci channel so watch for them all
	for i := 0; i < dl; i++ {

		// When we get a report send it and the
		// total to our progress helper func
		progress(dl, <-ci)
	}

	// Only return when we get the final result
	return <-cf
}

func mean(d Data, ci ichan, cf fchan) {
	var sum float64
	for i, n := range d {
		sum += n

		// Show progress by sending to ichan
		ci <- i
	}

	// When we're done send the final result
	cf <- sum / float64(len(d))

	// And close down the channels, although
	// I think they would be garbage collected
	close(ci)
	close(cf)
}

func progress(t int, c int) {
	log.Println(c+1, "/", t)
}

func makeFloatSlice(c int) []float64 {
	fslice := make([]float64, 0, c)
	for i := 0; i < c; i++ {
		f := float64(i)
		fslice = append(fslice, f)
	}
	return fslice
}

func main() {
	var d Data = makeFloatSlice(10000)
	log.Println(d.Analyze(mean))
}