diff --git a/f.go b/f.go
index c8e946f..6bceac8 100644
--- a/f.go
+++ b/f.go
@@ -4,6 +4,7 @@ import (
 	"bufio"
 	"errors"
 	"fmt"
+	"git.nunosempere.com/NunoSempere/fermi/sample"
 	"math"
 	"os"
 	"strconv"
@@ -26,14 +27,16 @@ type Lognormal struct {
 }
 
 func (l Lognormal) Samples() []float64 {
-
+	sampler := func(r sample.Src) float64 { return sample.Sample_to(l.low, l.high, r) }
+	return sample.Sample_parallel(sampler, 1_000_000)
 }
 
 // Actually, I should look up how do do a) enums in go, b) union types
-type Lognormal struct {
+/*type Lognormal struct {
 	low  float64
 	high float64
 }
+*/
 
 type Dist struct {
 	Type      string
@@ -176,6 +179,11 @@ func prettyPrintDist(dist Dist) {
 
 /* Main event loop */
 func main() {
+
+	sample_0 := func(r sample.Src) float64 { return 0 }
+	x := sample.Sample_parallel(sample_0, 10)
+	fmt.Printf("%v\n", x)
+
 	reader := bufio.NewReader(os.Stdin)
 	init_dist := Dist{Type: "Lognormal", Lognormal: Lognormal{low: 1, high: 1}, Samples: nil} // Could also just be a scalar
 	old_dist := init_dist
diff --git a/go.mod b/go.mod
index 9232354..4ef7fee 100644
--- a/go.mod
+++ b/go.mod
@@ -1,3 +1,3 @@
-module git.nunosempere.com/NunoSempere/fermi.git
+module git.nunosempere.com/NunoSempere/fermi
 
 go 1.22.1
diff --git a/sample/sample.go b/sample/sample.go
index 4ceb225..b82ca64 100644
--- a/sample/sample.go
+++ b/sample/sample.go
@@ -1,55 +1,53 @@
-package squiggle
+package sample
 
-import "fmt"
 import "math"
 import "sync"
 import rand "math/rand/v2"
 
 // https://pkg.go.dev/math/rand/v2
 
-type src = *rand.Rand
-type func64 = func(src) float64
+type Src = *rand.Rand
+type func64 = func(Src) float64
 
-func Sample_unit_uniform(r src) float64 {
+func Sample_unit_uniform(r Src) float64 {
 	return r.Float64()
 }
 
-func Sample_unit_normal(r src) float64 {
+func Sample_unit_normal(r Src) float64 {
 	return r.NormFloat64()
 }
 
-func Sample_uniform(start float64, end float64, r src) float64 {
-	return sample_unit_uniform(r)*(end-start) + start
+func Sample_uniform(start float64, end float64, r Src) float64 {
+	return Sample_unit_uniform(r)*(end-start) + start
 }
 
-func Sample_normal(mean float64, sigma float64, r src) float64 {
-	return mean + sample_unit_normal(r)*sigma
+func Sample_normal(mean float64, sigma float64, r Src) float64 {
+	return mean + Sample_unit_normal(r)*sigma
 }
 
-func Sample_lognormal(logmean float64, logstd float64, r src) float64 {
-	return (math.Exp(sample_normal(logmean, logstd, r)))
+func Sample_lognormal(logmean float64, logstd float64, r Src) float64 {
+	return (math.Exp(Sample_normal(logmean, logstd, r)))
 }
 
-func Sample_normal_from_90_ci(low float64, high float64, r src) float64 {
+func Sample_normal_from_90_ci(low float64, high float64, r Src) 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, r)
-
+	return Sample_normal(mean, std, r)
 }
 
-func Sample_to(low float64, high float64, r src) float64 {
+func Sample_to(low float64, high float64, r Src) 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
+	// 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, r))
+	return math.Exp(Sample_normal_from_90_ci(loglow, loghigh, r))
 }
 
-func Sample_mixture(fs []func64, weights []float64, r src) float64 {
+func Sample_mixture(fs []func64, weights []float64, r Src) float64 {
 
 	// fmt.Println("weights initially: ", weights)
 	var sum_weights float64 = 0
@@ -104,6 +102,7 @@ func Sample_parallel(f func64, n_samples int) []float64 {
 	return xs
 
 }
+
 /*
 func main() {
 
@@ -112,15 +111,15 @@ func main() {
 	var p_c float64 = p_a * p_b
 	ws := [4](float64){1 - p_c, p_c / 2, p_c / 4, p_c / 4}
 
-	sample_0 := func(r src) float64 { return 0 }
-	sample_1 := func(r src) float64 { return 1 }
-	sample_few := func(r src) float64 { return sample_to(1, 3, r) }
-	sample_many := func(r src) float64 { return sample_to(2, 10, r) }
-	fs := [4](func64){sample_0, sample_1, sample_few, sample_many}
+	Sample_0 := func(r Src) float64 { return 0 }
+	Sample_1 := func(r Src) float64 { return 1 }
+	Sample_few := func(r Src) float64 { return Sample_to(1, 3, r) }
+	Sample_many := func(r Src) float64 { return Sample_to(2, 10, r) }
+	fs := [4](func64){Sample_0, Sample_1, Sample_few, Sample_many}
 
-	model := func(r src) float64 { return sample_mixture(fs[0:], ws[0:], r) }
+	model := func(r Src) float64 { return Sample_mixture(fs[0:], ws[0:], r) }
 	n_samples := 1_000_000
-	xs := sample_parallel(model, n_samples)
+	xs := Sample_parallel(model, n_samples)
 	var avg float64 = 0
 	for _, x := range xs {
 		avg += x
@@ -133,10 +132,9 @@ func main() {
 			var r = rand.New(rand.NewPCG(uint64(1), uint64(2)))
 			var avg float64 = 0
 			for i := 0; i < n_samples; i++ {
-				avg += sample_mixture(fs[0:], ws[0:], r)
+				avg += Sample_mixture(fs[0:], ws[0:], r)
 			}
 			avg = avg / float64(n_samples)
 			fmt.Printf("Average: %v\n", avg)
-	*/
 }
 */