store SampleSet as typed array

packages/squiggle-lang/__tests__/Bandwidth_test.res

@@ -3,11 +3,11 @@ open Expect
 
 describe("Bandwidth", () => {
   test("nrd0()", () => {
-    let data = [1., 4., 3., 2.]
+    let data = [1., 4., 3., 2.]->E.FloatArray.make
     expect(SampleSetDist_Bandwidth.nrd0(data))->toEqual(0.7625801874014622)
   })
   test("nrd()", () => {
-    let data = [1., 4., 3., 2.]
+    let data = [1., 4., 3., 2.]->E.FloatArray.make
     expect(SampleSetDist_Bandwidth.nrd(data))->toEqual(0.8981499984950554)
   })
 })

packages/squiggle-lang/__tests__/E/splitContinuousAndDiscrete_test.res

@@ -2,9 +2,10 @@ open Jest
 open TestHelpers
 
 let prepareInputs = (ar, minWeight) =>
-  E.A.Floats.Sorted.splitContinuousAndDiscreteForMinWeight(ar, ~minDiscreteWeight=minWeight) |> (
-    ((c, disc)) => (c, disc |> E.FloatFloatMap.toArray)
-  )
+  E.FloatArray.Sorted.splitContinuousAndDiscreteForMinWeight(
+    ar->E.FloatArray.make,
+    ~minDiscreteWeight=minWeight,
+  ) |> (((c, disc)) => (c->E.FloatArray.toArray, disc->E.FloatFloatMap.toArray))
 
 describe("Continuous and discrete splits", () => {
   makeTest(
@@ -37,18 +38,10 @@ describe("Continuous and discrete splits", () => {
     E.A.concatMany([sorted, sorted, sorted, sorted]) |> Belt.SortArray.stableSortBy(_, compare)
   }
 
-  let (_, discrete1) = E.A.Floats.Sorted.splitContinuousAndDiscreteForMinWeight(
-    makeDuplicatedArray(10),
-    ~minDiscreteWeight=2,
-  )
-  let toArr1 = discrete1 |> E.FloatFloatMap.toArray
-  makeTest("splitMedium at count=10", toArr1 |> Belt.Array.length, 10)
+  let (_, toArr1) = prepareInputs(makeDuplicatedArray(10), 2)
+  makeTest("splitMedium at count=10", toArr1->Belt.Array.length, 10)
 
-  let (_c, discrete2) = E.A.Floats.Sorted.splitContinuousAndDiscreteForMinWeight(
-    makeDuplicatedArray(500),
-    ~minDiscreteWeight=2,
-  )
-  let toArr2 = discrete2 |> E.FloatFloatMap.toArray
-  makeTest("splitMedium at count=500", toArr2 |> Belt.Array.length, 500)
+  let (_, toArr2) = prepareInputs(makeDuplicatedArray(500), 2)
+  makeTest("splitMedium at count=500", toArr2->Belt.Array.length, 500)
   // makeTest("foo", [] |> Belt.Array.length, 500)
 })

packages/squiggle-lang/src/js/SqDistribution.ts

@@ -90,9 +90,9 @@ export class SqPointSetDistribution extends SqAbstractDistribution {
 export class SqSampleSetDistribution extends SqAbstractDistribution {
   tag = Tag.SampleSet as const;
 
-  value(): number[] {
-    return this.valueMethod(RSDistribution.getSampleSet);
-  }
+  // value(): number[] {
+  //   return this.valueMethod(RSDistribution.getSampleSet);
+  // }
 }
 
 export class SqSymbolicDistribution extends SqAbstractDistribution {

packages/squiggle-lang/src/rescript/Distributions/DistributionOperation.res

@@ -221,7 +221,7 @@ let rec run = (~env: env, functionCallInfo: functionCallInfo): outputType => {
     ->OutputLocal.fromResult
   | FromSamples(xs) =>
     xs
-    ->SampleSetDist.make
+    ->SampleSetDist.makeFromJsArray
     ->E.R2.errMap(x => DistributionTypes.SampleSetError(x))
     ->E.R2.fmap(x => x->DistributionTypes.SampleSet->Dist)
     ->OutputLocal.fromResult

packages/squiggle-lang/src/rescript/Distributions/DistributionOperation.resi

@@ -98,7 +98,7 @@ module Constructors: {
   @genType
   let toSampleSet: (~env: GenericDist.env, genericDist, int) => result<genericDist, error>
   @genType
-  let fromSamples: (~env: GenericDist.env, SampleSetDist.t) => result<genericDist, error>
+  let fromSamples: (~env: GenericDist.env, array<float>) => result<genericDist, error>
   @genType
   let truncate: (
     ~env: GenericDist.env,

packages/squiggle-lang/src/rescript/Distributions/DistributionTypes.res

@@ -38,6 +38,7 @@ module Error = {
     | SampleSetError(TooFewSamples) => "Too Few Samples"
     | SampleSetError(NonNumericInput(err)) => `Found a non-number in input: ${err}`
     | SampleSetError(OperationError(err)) => Operation.Error.toString(err)
+    | SampleSetError(UnequalSizes) => "Expected sample sets of equal size"
     | OperationError(err) => Operation.Error.toString(err)
     | PointSetConversionError(err) => SampleSetDist.pointsetConversionErrorToString(err)
     | SparklineError(err) => PointSetTypes.sparklineErrorToString(err)

packages/squiggle-lang/src/rescript/Distributions/GenericDist.res

@@ -40,7 +40,9 @@ let sampleN = (t: t, n) =>
 let sample = (t: t) => sampleN(t, 1)->E.A.first |> E.O.toExn("Should not have happened")
 
 let toSampleSetDist = (t: t, n) =>
-  SampleSetDist.make(sampleN(t, n))->E.R2.errMap(DistributionTypes.Error.sampleErrorToDistErr)
+  SampleSetDist.makeFromJsArray(sampleN(t, n))->E.R2.errMap(
+    DistributionTypes.Error.sampleErrorToDistErr,
+  )
 
 let fromFloat = (f: float): t => Symbolic(SymbolicDist.Float.make(f))
 

packages/squiggle-lang/src/rescript/Distributions/SampleSetDist/SampleSetDist.res

@@ -2,14 +2,10 @@
 module Error = {
   @genType
   type sampleSetError =
-    TooFewSamples | NonNumericInput(string) | OperationError(Operation.operationError)
-
-  let sampleSetErrorToString = (err: sampleSetError): string =>
-    switch err {
-    | TooFewSamples => "Too few samples when constructing sample set"
-    | NonNumericInput(err) => `Found a non-number in input: ${err}`
-    | OperationError(err) => Operation.Error.toString(err)
-    }
+    | TooFewSamples
+    | NonNumericInput(string)
+    | OperationError(Operation.operationError)
+    | UnequalSizes
 
   @genType
   type pointsetConversionError = TooFewSamplesForConversionToPointSet
@@ -26,6 +22,7 @@ module Error = {
     | TooFewSamples => "Too few samples when constructing sample set"
     | NonNumericInput(err) => `Found a non-number in input: ${err}`
     | OperationError(err) => Operation.Error.toString(err)
+    | UnequalSizes => "Expected sample sets of equal size"
     }
   }
 }
@@ -38,26 +35,29 @@ this constructor.
 https://stackoverflow.com/questions/66909578/how-to-make-a-type-constructor-private-in-rescript-except-in-current-module
 */
 module T: {
-  //This really should be hidden (remove the array<float>). The reason it isn't is to act as an escape hatch in JS__Test.ts.
-  //When we get a good functional library in TS, we could refactor that out.
-  @genType
-  type t = array<float>
-  let make: array<float> => result<t, sampleSetError>
-  let get: t => array<float>
+  @genType.opaque
+  type t
+  let makeFromTypedArray: E.FloatArray.t => result<t, sampleSetError>
+  let makeFromJsArray: array<float> => result<t, sampleSetError>
+  let toJsArray: t => array<float>
+  let get: t => E.FloatArray.t
 } = {
-  type t = array<float>
-  let make = (a: array<float>) =>
-    if E.A.length(a) > 5 {
+  type t = E.FloatArray.t
+  let makeFromTypedArray = (a: E.FloatArray.t): result<t, sampleSetError> =>
+    if E.FloatArray.length(a) > 5 {
       Ok(a)
     } else {
       Error(TooFewSamples)
     }
-  let get = (a: t) => a
+  let makeFromJsArray = (a: array<float>): result<t, sampleSetError> =>
+    E.FloatArray.make(a)->makeFromTypedArray
+  let toJsArray = (t: t) => t->E.FloatArray.toArray
+  let get = (t: t) => t
 }
 
 include T
 
-let length = (t: t) => get(t)->E.A.length
+let length = (t: T.t) => T.get(t)->E.FloatArray.length
 
 /*
 TODO: Refactor to get a more precise estimate. Also, this code is just fairly messy, could use 
@@ -68,15 +68,15 @@ let toPointSetDist = (~samples: t, ~samplingInputs: SamplingInputs.samplingInput
   pointsetConversionError,
 > =>
   SampleSetDist_ToPointSet.toPointSetDist(
-    ~samples=get(samples),
+    ~samples=T.get(samples),
     ~samplingInputs,
     (),
   ).pointSetDist->E.O2.toResult(TooFewSamplesForConversionToPointSet)
 
 //Randomly get one sample from the distribution
 let sample = (t: t): float => {
-  let i = E.Int.random(~min=0, ~max=E.A.length(get(t)) - 1)
-  E.A.unsafe_get(get(t), i)
+  let i = E.Int.random(~min=0, ~max=E.FloatArray.length(get(t)) - 1)
+  E.FloatArray.unsafe_get(get(t), i)
 }
 
 /*
@@ -87,52 +87,144 @@ The former helps in cases where multiple distributions are correlated.
 However, if n > length(t), then there's no clear right answer, so we just randomly
 sample everything.
 */
-let sampleN = (t: t, n) => {
-  if n <= E.A.length(get(t)) {
-    E.A.slice(get(t), ~offset=0, ~len=n)
+let sampleN = (t: t, n): array<float> => {
+  if n <= length(t) {
+    E.FloatArray.slice(~start=0, ~end_=n, get(t))->E.FloatArray.toArray
   } else {
     Belt.Array.makeBy(n, _ => sample(t))
   }
 }
 
-let _fromSampleResultArray = (samples: array<result<float, QuriSquiggleLang.Operation.Error.t>>) =>
-  E.A.R.firstErrorOrOpen(samples)->E.R2.errMap(Error.fromOperationError) |> E.R2.bind(make)
-
 let samplesMap = (~fn: float => result<float, Operation.Error.t>, t: t): result<
   t,
   sampleSetError,
-> => T.get(t)->E.A2.fmap(fn)->_fromSampleResultArray
+> => {
+  try {
+    T.get(t)
+    ->E.FloatArray.map((. v) => {
+      switch fn(v) {
+      | Ok(res) => res
+      | Error(err) => err->Operation.Error.OperationException->raise
+      }
+    })
+    ->T.makeFromTypedArray
+  } catch {
+  | Operation.Error.OperationException(err) => Error.fromOperationError(err)->Error
+  }
+}
 
-//TODO: Figure out what to do if distributions are different lengths. ``zip`` is kind of inelegant for this.
 let map2 = (~fn: (float, float) => result<float, Operation.Error.t>, ~t1: t, ~t2: t): result<
   t,
   sampleSetError,
-> => E.A.zip(get(t1), get(t2))->E.A2.fmap(E.Tuple2.toFnCall(fn))->_fromSampleResultArray
+> => {
+  let length1 = t1->length
+  let length2 = t2->length
+  if length1 == length2 {
+    try {
+      let res = E.FloatArray.fromLength(length1)
+      for i in 0 to length1 - 1 {
+        let v = switch fn(
+          get(t1)->E.FloatArray.unsafe_get(i),
+          get(t2)->E.FloatArray.unsafe_get(i),
+        ) {
+        | Ok(fnResult) => fnResult
+        | Error(err) => err->Operation.Error.OperationException->raise
+        }
+        res->E.FloatArray.set(i, v)
+      }
+      res->T.makeFromTypedArray
+    } catch {
+    | Operation.Error.OperationException(err) => Error.fromOperationError(err)->Error
+    }
+  } else {
+    Error.UnequalSizes->Error
+  }
+}
 
 let map3 = (
   ~fn: (float, float, float) => result<float, Operation.Error.t>,
   ~t1: t,
   ~t2: t,
   ~t3: t,
-): result<t, sampleSetError> =>
-  E.A.zip3(get(t1), get(t2), get(t3))->E.A2.fmap(E.Tuple3.toFnCall(fn))->_fromSampleResultArray
+): result<t, sampleSetError> => {
+  let length1 = t1->length
+  let length2 = t2->length
+  let length3 = t3->length
+  if length1 == length2 && length2 == length3 {
+    try {
+      let res = E.FloatArray.fromLength(length1)
+      for i in 0 to length1 - 1 {
+        let v = switch fn(
+          get(t1)->E.FloatArray.unsafe_get(i),
+          get(t2)->E.FloatArray.unsafe_get(i),
+          get(t3)->E.FloatArray.unsafe_get(i),
+        ) {
+        | Ok(fnResult) => fnResult
+        | Error(err) => err->Operation.Error.OperationException->raise
+        }
+        res->E.FloatArray.set(i, v)
+      }
+      res->T.makeFromTypedArray
+    } catch {
+    | Operation.Error.OperationException(err) => Error.fromOperationError(err)->Error
+    }
+  } else {
+    Error.UnequalSizes->Error
+  }
+}
 
 let mapN = (~fn: array<float> => result<float, Operation.Error.t>, ~t1: array<t>): result<
   t,
   sampleSetError,
-> => E.A.transpose(E.A.fmap(get, t1))->E.A2.fmap(fn)->_fromSampleResultArray
+> => {
+  let lengths = t1->E.A2.fmap(t => t->length)
+  let l0 = lengths[0]
+  if lengths->E.A.all(l => l == l0, _) {
+    try {
+      let res = E.FloatArray.fromLength(l0)
+      for i in 0 to l0 - 1 {
+        let v = switch fn(t1->E.A2.fmap(t => get(t)->E.FloatArray.unsafe_get(i))) {
+        | Ok(fnResult) => fnResult
+        | Error(err) => err->Operation.Error.OperationException->raise
+        }
+        res->E.FloatArray.set(i, v)
+      }
+      res->T.makeFromTypedArray
+    } catch {
+    | Operation.Error.OperationException(err) => Error.fromOperationError(err)->Error
+    }
+  } else {
+    Error.UnequalSizes->Error
+  }
+}
 
-let mean = t => T.get(t)->E.A.Floats.mean
-let geomean = t => T.get(t)->E.A.Floats.geomean
-let mode = t => T.get(t)->E.A.Floats.mode
-let sum = t => T.get(t)->E.A.Floats.sum
-let min = t => T.get(t)->E.A.Floats.min
-let max = t => T.get(t)->E.A.Floats.max
-let stdev = t => T.get(t)->E.A.Floats.stdev
-let variance = t => T.get(t)->E.A.Floats.variance
-let percentile = (t, f) => T.get(t)->E.A.Floats.percentile(f)
+let makeBy = (n: int, fn: int => result<float, Operation.Error.t>): result<t, sampleSetError> => {
+  let res = E.FloatArray.fromLength(n)
+  try {
+    for i in 0 to n - 1 {
+      let fnResult = fn(i)
+      switch fnResult {
+      | Ok(v) => res->E.FloatArray.set(i, v)
+      | Error(err) => err->Operation.Error.OperationException->raise
+      }
+    }
+    res->T.makeFromTypedArray
+  } catch {
+  | Operation.Error.OperationException(err) => Error.fromOperationError(err)->Error
+  }
+}
+
+let mean = t => T.get(t)->E.FloatArray.mean
+let geomean = t => T.get(t)->E.FloatArray.geomean
+let mode = t => T.get(t)->E.FloatArray.mode
+let sum = t => T.get(t)->E.FloatArray.sum
+let min = t => T.get(t)->E.FloatArray.min
+let max = t => T.get(t)->E.FloatArray.max
+let stdev = t => T.get(t)->E.FloatArray.stdev
+let variance = t => T.get(t)->E.FloatArray.variance
+let percentile = (t, f) => T.get(t)->E.FloatArray.percentile(f)
 let cdf = (t: t, f: float) => {
-  let countBelowF = t->E.A.reduce(0, (acc, x) => acc + (x <= f ? 1 : 0))
+  let countBelowF = T.get(t)->E.FloatArray.reduce((. acc, x) => acc + (x <= f ? 1 : 0), 0)
   countBelowF->Js.Int.toFloat /. t->length->Js.Int.toFloat
 }
 
@@ -149,14 +241,14 @@ let mixture = (values: array<(t, float)>, intendedLength: int) => {
     discreteSamples
     ->Belt.Array.mapWithIndex((index, distIndexToChoose) => {
       let chosenDist = E.A.get(dists, E.Float.toInt(distIndexToChoose))
-      chosenDist->E.O.bind(E.A.get(_, index))
+      chosenDist->E.O.bind(E.FloatArray.get(_, index))
     })
     ->E.A.O.openIfAllSome
-  samples->E.O2.toExn("Mixture unreachable error")->T.make
+  samples->E.O2.toExn("Mixture unreachable error")->T.makeFromJsArray
 }
 
-let truncateLeft = (t, f) => T.get(t)->E.A2.filter(x => x >= f)->T.make
-let truncateRight = (t, f) => T.get(t)->E.A2.filter(x => x <= f)->T.make
+let truncateLeft = (t, f) => T.get(t)->E.FloatArray.filter((. x) => x >= f)->T.makeFromTypedArray
+let truncateRight = (t, f) => T.get(t)->E.FloatArray.filter((. x) => x <= f)->T.makeFromTypedArray
 
 let truncate = (t, ~leftCutoff: option<float>, ~rightCutoff: option<float>) => {
   let withTruncatedLeft = t => leftCutoff |> E.O.dimap(left => truncateLeft(t, left), _ => Ok(t))

packages/squiggle-lang/src/rescript/Distributions/SampleSetDist/SampleSetDist_Bandwidth.res

@@ -2,16 +2,19 @@
 let {iqr_percentile, nrd0_lo_denominator, one, nrd0_coef, nrd_coef, nrd_fractionalPower} = module(
   MagicNumbers.SampleSetBandwidth
 )
-let len = x => E.A.length(x) |> float_of_int
+let len = x => E.FloatArray.length(x)->float_of_int
 
-let iqr = x =>
-  Jstat.percentile(x, iqr_percentile, true) -. Jstat.percentile(x, 1.0 -. iqr_percentile, true)
+let iqr = x => {
+  let xArr = x->E.FloatArray.toArray
+  Jstat.percentile(xArr, iqr_percentile, true) -.
+  Jstat.percentile(xArr, 1.0 -. iqr_percentile, true)
+}
 
 // Silverman, B. W. (1986) Density Estimation. London: Chapman and Hall.
 let nrd0 = x => {
-  let hi = Js_math.sqrt(Jstat.variance(x))
+  let hi = Js_math.sqrt(Stdlib.Base.variance(x))
   let lo = Js_math.minMany_float([hi, iqr(x) /. nrd0_lo_denominator])
-  let e = Js_math.abs_float(x[1])
+  let e = Js_math.abs_float(x->E.FloatArray.unsafe_get(1))
   let lo' = switch (lo, hi, e) {
   | (lo, _, _) if !Js.Float.isNaN(lo) => lo
   | (_, hi, _) if !Js.Float.isNaN(hi) => hi
@@ -25,6 +28,6 @@ let nrd0 = x => {
 let nrd = x => {
   let h = iqr(x) /. nrd0_lo_denominator
   nrd_coef *.
-  Js.Math.min_float(Js.Math.sqrt(Jstat.variance(x)), h) *.
+  Js.Math.min_float(Js.Math.sqrt(Stdlib.Base.variance(x)), h) *.
   Js.Math.pow_float(~base=len(x), ~exp=nrd_fractionalPower)
 }

packages/squiggle-lang/src/rescript/Distributions/SampleSetDist/SampleSetDist_ToPointSet.res

@@ -37,40 +37,40 @@ module Internals = {
   }
 
   module T = {
-    type t = array<float>
+    type t = E.FloatArray.t
 
-    let xWidthToUnitWidth = (samples, outputXYPoints, xWidth) => {
-      let xyPointRange = E.A.Sorted.range(samples)->E.O2.default(0.0)
+    let xWidthToUnitWidth = (samples: t, outputXYPoints, xWidth) => {
+      let xyPointRange = E.FloatArray.Sorted.range(samples)->E.O2.default(0.0)
       let xyPointWidth = xyPointRange /. float_of_int(outputXYPoints)
       xWidth /. xyPointWidth
     }
 
     let formatUnitWidth = w => Jstat.max([w, 1.0])->int_of_float
 
-    let suggestedUnitWidth = (samples, outputXYPoints) => {
+    let suggestedUnitWidth = (samples: t, outputXYPoints) => {
       let suggestedXWidth = SampleSetDist_Bandwidth.nrd0(samples)
       xWidthToUnitWidth(samples, outputXYPoints, suggestedXWidth)
     }
 
-    let kde = (~samples, ~outputXYPoints, width) =>
-      KDE.normalSampling(samples, outputXYPoints, width)
+    let kde = (~samples: t, ~outputXYPoints, width) =>
+      KDE.normalSampling(samples->E.FloatArray.toArray, outputXYPoints, width)
   }
 }
 
 let toPointSetDist = (
-  ~samples: Internals.T.t,
+  ~samples: E.FloatArray.t,
   ~samplingInputs: SamplingInputs.samplingInputs,
   (),
 ): Internals.Types.outputs => {
-  let samples = samples->E.A.Floats.sort
+  let samples = samples->E.FloatArray.sort
 
   let minDiscreteToKeep = MagicNumbers.ToPointSet.minDiscreteToKeep(samples)
-  let (continuousPart, discretePart) = E.A.Floats.Sorted.splitContinuousAndDiscreteForMinWeight(
+  let (continuousPart, discretePart) = E.FloatArray.Sorted.splitContinuousAndDiscreteForMinWeight(
     samples,
     ~minDiscreteWeight=minDiscreteToKeep,
   )
 
-  let length = samples->E.A.length->float_of_int
+  let length = samples->E.FloatArray.length->float_of_int
   let discrete: PointSetTypes.discreteShape =
     discretePart
     ->E.FloatFloatMap.fmap(r => r /. length, _)
@@ -79,7 +79,7 @@ let toPointSetDist = (
     ->Discrete.make
 
   let pdf =
-    continuousPart->E.A.length > 5
+    continuousPart->E.FloatArray.length > 5
       ? {
           let _suggestedXWidth = SampleSetDist_Bandwidth.nrd0(continuousPart)
           // todo: This does some recalculating from the last step.

packages/squiggle-lang/src/rescript/FR/FR_Sampleset.res

@@ -28,7 +28,7 @@ module Internal = {
   let fromFn = (aLambdaValue, environment: Reducer_T.environment, reducer: Reducer_T.reducerFn) => {
     let sampleCount = environment.sampleCount
     let fn = r => doLambdaCall(aLambdaValue, [IEvNumber(r)], environment, reducer)
-    Belt_Array.makeBy(sampleCount, r => fn(r->Js.Int.toFloat))->E.A.R.firstErrorOrOpen
+    SampleSetDist.makeBy(sampleCount, r => fn(r->Js.Int.toFloat))
   }
 
   let map1 = (sampleSetDist: t, aLambdaValue, environment: Reducer_T.environment, reducer) => {
@@ -116,7 +116,9 @@ let libaryBase = [
         ~run=(inputs, _, _) => {
           let sampleSet =
             inputs->Prepare.ToTypedArray.numbers
-              |> E.R2.bind(r => SampleSetDist.make(r)->E.R2.errMap(_ => "AM I HERE? WHYERE AMI??"))
+              |> E.R2.bind(r =>
+                SampleSetDist.makeFromJsArray(r)->E.R2.errMap(_ => "AM I HERE? WHYERE AMI??")
+              )
           sampleSet
           ->E.R2.fmap(Wrappers.sampleSet)
           ->E.R2.fmap(Wrappers.evDistribution)
@@ -140,7 +142,7 @@ let libaryBase = [
         ~run=(inputs, _, _) =>
           switch inputs {
           | [IEvDistribution(SampleSet(dist))] =>
-            dist->E.A2.fmap(Wrappers.evNumber)->Wrappers.evArray->Ok
+            dist->SampleSetDist.toJsArray->E.A2.fmap(Wrappers.evNumber)->Wrappers.evArray->Ok
           | _ => Error(impossibleError)
           },
         (),
@@ -163,7 +165,7 @@ let libaryBase = [
           | [IEvLambda(lambda)] =>
             switch Internal.fromFn(lambda, environment, reducer) {
             | Ok(r) => Ok(r->Wrappers.sampleSet->Wrappers.evDistribution)
-            | Error(e) => e->Reducer_ErrorValue.REOperationError->Error
+            | Error(e) => e->SampleSetError->Reducer_ErrorValue.REDistributionError->Error
             }
           | _ => Error(impossibleError)
           },

packages/squiggle-lang/src/rescript/MagicNumbers.res

@@ -35,7 +35,7 @@ module ToPointSet = {
   it would strike a reasonable trade-off, but I’m really unsure what’s 
   best right now.
  */
-  let minDiscreteToKeep = samples => max(20, E.A.length(samples) / 50)
+  let minDiscreteToKeep = samples => max(20, E.FloatArray.length(samples) / 50)
 }
 
 module SampleSetBandwidth = {

packages/squiggle-lang/src/rescript/Utility/E.res

@@ -11,6 +11,7 @@ module B = E_B
 module Dict = E_Dict
 module F = E_F
 module Float = E_Float
+module FloatArray = E_FloatArray
 module FloatFloatMap = E_FloatFloatMap
 module I = E_I
 module Int = E_Int

packages/squiggle-lang/src/rescript/Utility/E/E_A.res

@@ -303,55 +303,6 @@ module Floats = {
 
     let makeIncrementalDown = (a, b) =>
       Array.make(a - b + 1, a) |> Array.mapi((i, c) => c - i) |> Belt.Array.map(_, float_of_int)
-
-    /*
-      This function goes through a sorted array and divides it into two different clusters:
-      continuous samples and discrete samples. The discrete samples are stored in a mutable map.
-      Samples are thought to be discrete if they have at least `minDiscreteWight` duplicates.
-
-      If the min discrete weight is 4, that would mean that at least four elements needed from a specific
-      value for that to be kept as discrete. This is important because in some cases, we can expect that
-      some common elements will be generated by regular operations. The final continuous array will be sorted.
-
-      This function is performance-critical, don't change it significantly without benchmarking
-      SampleSet->PointSet conversion performance.
- */
-    let splitContinuousAndDiscreteForMinWeight = (
-      sortedArray: array<float>,
-      ~minDiscreteWeight: int,
-    ) => {
-      let continuous: array<float> = []
-      let discrete = FloatFloatMap.empty()
-
-      let addData = (count: int, value: float): unit => {
-        if count >= minDiscreteWeight {
-          FloatFloatMap.add(value, count->Belt.Int.toFloat, discrete)
-        } else {
-          for _ in 1 to count {
-            continuous->Js.Array2.push(value)->ignore
-          }
-        }
-      }
-
-      let (finalCount, finalValue) = sortedArray->Belt.Array.reduce(
-        // initial prev value doesn't matter; if it collides with the first element of the array, flush won't do anything
-        (0, 0.),
-        ((count, prev), element) => {
-          if element == prev {
-            (count + 1, prev)
-          } else {
-            // new value, process previous ones
-            addData(count, prev)
-            (1, element)
-          }
-        },
-      )
-
-      // flush final values
-      addData(finalCount, finalValue)
-
-      (continuous, discrete)
-    }
   }
 }
 module Sorted = Floats.Sorted

packages/squiggle-lang/src/rescript/Utility/E/E_FloatArray.res

@@ -0,0 +1,89 @@
+module FloatArray = Js.TypedArray2.Float64Array
+
+type t = FloatArray.t
+let make = FloatArray.make
+let fromLength = FloatArray.fromLength
+let length = FloatArray.length
+let reduce = FloatArray.reduce
+let filter = FloatArray.filter
+let slice = FloatArray.slice
+let sort = (t: t) => t->FloatArray.copy->FloatArray.sortInPlace
+let toArray: t => array<float> = %raw(`a => Array.from(a)`)
+let map = FloatArray.map
+
+let unsafe_get = (t: t, i: int) => t->FloatArray.unsafe_get(i)
+let get = (t: t, i: int) => i >= t->length ? None : Some(t->FloatArray.unsafe_get(i))
+let set = (t: t, i: int) => t->FloatArray.unsafe_set(i)
+
+let mean = t => t->toArray->Jstat.mean
+let geomean = t => t->toArray->Jstat.geomean
+let mode = t => t->toArray->Jstat.mode
+let variance = t => t->toArray->Jstat.variance
+let stdev = t => t->toArray->Jstat.stdev
+let sum = t => t->toArray->Jstat.sum
+let product = Jstat.product
+let random = Js.Math.random_int
+let min = t => t->toArray->Js.Math.minMany_float
+let max = t => t->toArray->Js.Math.maxMany_float
+let percentile = (t, b) => Jstat.percentile(t->toArray, b, false)
+
+module Sorted = {
+  let min = (t: t) => t->length > 0 ? Some(FloatArray.unsafe_get(t, 0)) : None
+  let max = (t: t) => t->length > 0 ? Some(FloatArray.unsafe_get(t, t->length - 1)) : None
+  let range = (~min=min, ~max=max, a) =>
+    switch (min(a), max(a)) {
+    | (Some(min), Some(max)) => Some(max -. min)
+    | _ => None
+    }
+
+  /*
+      This function goes through a sorted array and divides it into two different clusters:
+      continuous samples and discrete samples. The discrete samples are stored in a mutable map.
+      Samples are thought to be discrete if they have at least `minDiscreteWight` duplicates.
+
+      If the min discrete weight is 4, that would mean that at least four elements needed from a specific
+      value for that to be kept as discrete. This is important because in some cases, we can expect that
+      some common elements will be generated by regular operations. The final continuous array will be sorted.
+
+      This function is performance-critical, don't change it significantly without benchmarking
+      SampleSet->PointSet conversion performance.
+ */
+  let splitContinuousAndDiscreteForMinWeight = (sortedArray: t, ~minDiscreteWeight: int): (
+    t,
+    E_FloatFloatMap.t,
+  ) => {
+    // TODO - preallocate as typed array, then trim
+    let continuous: array<float> = []
+    let discrete = E_FloatFloatMap.empty()
+
+    let addData = (count: int, value: float): unit => {
+      if count >= minDiscreteWeight {
+        E_FloatFloatMap.add(value, count->Belt.Int.toFloat, discrete)
+      } else {
+        for _ in 1 to count {
+          continuous->Js.Array2.push(value)->ignore
+        }
+      }
+    }
+
+    let (finalCount, finalValue) = reduce(
+      sortedArray,
+      // initial prev value doesn't matter; if it collides with the first element of the array, flush won't do anything
+      (. (count, prev), element) => {
+        if element == prev {
+          (count + 1, prev)
+        } else {
+          // new value, process previous ones
+          addData(count, prev)
+          (1, element)
+        }
+      },
+      (0, 0.),
+    )
+
+    // flush final values
+    addData(finalCount, finalValue)
+
+    (continuous->make, discrete)
+  }
+}

packages/squiggle-lang/src/rescript/Utility/Operation.res

@@ -78,6 +78,8 @@ module Error = {
     | NotYetImplemented => "This pathway is not yet implemented"
     | Other(t) => t
     }
+
+  exception OperationException(t)
 }
 
 let power = (a: float, b: float): result<float, Error.t> =>

packages/squiggle-lang/src/rescript/Utility/Stdlib.res

@@ -51,3 +51,11 @@ module Random = {
 module Math = {
   @module external factorial: float => float = "@stdlib/math/base/special/factorial"
 }
+
+module Base = {
+  @module
+  external variance: (int, int, Js.TypedArray2.Float64Array.t, int) => float =
+    "@stdlib/stats/base/variance"
+  let variance = (t: Js.TypedArray2.Float64Array.t) =>
+    variance(t->Js.TypedArray2.Float64Array.length, 0, t, 1)
+}