polymorphic variants to neatly constrain types

packages/squiggle-lang/__tests__/Distributions/DistributionOperation_test.res

@@ -34,7 +34,7 @@ describe("sparkline", () => {
     expected: DistributionOperation.outputType,
   ) => {
     test(name, () => {
-      let result = DistributionOperation.run(~env, FromDist(ToString(ToSparkline(20)), dist))
+      let result = DistributionOperation.run(~env, FromDist(#ToString(ToSparkline(20)), dist))
       expect(result)->toEqual(expected)
     })
   }
@@ -81,8 +81,8 @@ describe("sparkline", () => {
 describe("toPointSet", () => {
   test("on symbolic normal distribution", () => {
     let result =
-      run(FromDist(ToDist(ToPointSet), normalDist5))
+      run(FromDist(#ToDist(ToPointSet), normalDist5))
-      ->outputMap(FromDist(ToFloat(#Mean)))
+      ->outputMap(FromDist(#ToFloat(#Mean)))
       ->toFloat
       ->toExt
     expect(result)->toBeSoCloseTo(5.0, ~digits=0)
@@ -90,10 +90,10 @@ describe("toPointSet", () => {
 
   test("on sample set", () => {
     let result =
-      run(FromDist(ToDist(ToPointSet), normalDist5))
+      run(FromDist(#ToDist(ToPointSet), normalDist5))
-      ->outputMap(FromDist(ToDist(ToSampleSet(1000))))
+      ->outputMap(FromDist(#ToDist(ToSampleSet(1000))))
-      ->outputMap(FromDist(ToDist(ToPointSet)))
+      ->outputMap(FromDist(#ToDist(ToPointSet)))
-      ->outputMap(FromDist(ToFloat(#Mean)))
+      ->outputMap(FromDist(#ToFloat(#Mean)))
       ->toFloat
       ->toExt
     expect(result)->toBeSoCloseTo(5.0, ~digits=-1)

packages/squiggle-lang/__tests__/Distributions/Mixture_test.res

@@ -11,7 +11,7 @@ describe("mixture", () => {
       let (mean1, mean2) = tup
       let meanValue = {
         run(Mixture([(mkNormal(mean1, 9e-1), 0.5), (mkNormal(mean2, 9e-1), 0.5)]))->outputMap(
-          FromDist(ToFloat(#Mean)),
+          FromDist(#ToFloat(#Mean)),
         )
       }
       meanValue->unpackFloat->expect->toBeSoCloseTo((mean1 +. mean2) /. 2.0, ~digits=-1)
@@ -28,7 +28,7 @@ describe("mixture", () => {
       let meanValue = {
         run(
           Mixture([(mkBeta(alpha, beta), betaWeight), (mkExponential(rate), exponentialWeight)]),
-        )->outputMap(FromDist(ToFloat(#Mean)))
+        )->outputMap(FromDist(#ToFloat(#Mean)))
       }
       let betaMean = 1.0 /. (1.0 +. beta /. alpha)
       let exponentialMean = 1.0 /. rate
@@ -52,7 +52,7 @@ describe("mixture", () => {
             (mkUniform(low, high), uniformWeight),
             (mkLognormal(mu, sigma), lognormalWeight),
           ]),
-        )->outputMap(FromDist(ToFloat(#Mean)))
+        )->outputMap(FromDist(#ToFloat(#Mean)))
       }
       let uniformMean = (low +. high) /. 2.0
       let lognormalMean = mu +. sigma ** 2.0 /. 2.0

packages/squiggle-lang/__tests__/Distributions/Scoring/KlDivergence_test.res

@@ -186,8 +186,8 @@ describe("combineAlongSupportOfSecondArgument0", () => {
       uniformMakeR(lowPrediction, highPrediction)->E.R2.errMap(s => DistributionTypes.ArgumentError(
         s,
       ))
-    let answerWrapped = E.R.fmap(a => run(FromDist(ToDist(ToPointSet), a)), answer)
+    let answerWrapped = E.R.fmap(a => run(FromDist(#ToDist(ToPointSet), a)), answer)
-    let predictionWrapped = E.R.fmap(a => run(FromDist(ToDist(ToPointSet), a)), prediction)
+    let predictionWrapped = E.R.fmap(a => run(FromDist(#ToDist(ToPointSet), a)), prediction)
 
     let interpolator = XYShape.XtoY.continuousInterpolator(#Stepwise, #UseZero)
     let integrand = PointSetDist_Scoring.WithDistAnswer.integrand

packages/squiggle-lang/__tests__/Distributions/Symbolic_test.res

@@ -8,34 +8,34 @@ let mkNormal = (mean, stdev) => DistributionTypes.Symbolic(#Normal({mean: mean,
 describe("(Symbolic) normalize", () => {
   testAll("has no impact on normal distributions", list{-1e8, -1e-2, 0.0, 1e-4, 1e16}, mean => {
     let normalValue = mkNormal(mean, 2.0)
-    let normalizedValue = run(FromDist(ToDist(Normalize), normalValue))
+    let normalizedValue = run(FromDist(#ToDist(Normalize), normalValue))
     normalizedValue->unpackDist->expect->toEqual(normalValue)
   })
 })
 
 describe("(Symbolic) mean", () => {
   testAll("of normal distributions", list{-1e8, -16.0, -1e-2, 0.0, 1e-4, 32.0, 1e16}, mean => {
-    run(FromDist(ToFloat(#Mean), mkNormal(mean, 4.0)))->unpackFloat->expect->toBeCloseTo(mean)
+    run(FromDist(#ToFloat(#Mean), mkNormal(mean, 4.0)))->unpackFloat->expect->toBeCloseTo(mean)
   })
 
   Skip.test("of normal(0, -1) (it NaNs out)", () => {
-    run(FromDist(ToFloat(#Mean), mkNormal(1e1, -1e0)))->unpackFloat->expect->ExpectJs.toBeFalsy
+    run(FromDist(#ToFloat(#Mean), mkNormal(1e1, -1e0)))->unpackFloat->expect->ExpectJs.toBeFalsy
   })
 
   test("of normal(0, 1e-8) (it doesn't freak out at tiny stdev)", () => {
-    run(FromDist(ToFloat(#Mean), mkNormal(0.0, 1e-8)))->unpackFloat->expect->toBeCloseTo(0.0)
+    run(FromDist(#ToFloat(#Mean), mkNormal(0.0, 1e-8)))->unpackFloat->expect->toBeCloseTo(0.0)
   })
 
   testAll("of exponential distributions", list{1e-7, 2.0, 10.0, 100.0}, rate => {
     let meanValue = run(
-      FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Exponential({rate: rate}))),
+      FromDist(#ToFloat(#Mean), DistributionTypes.Symbolic(#Exponential({rate: rate}))),
     )
     meanValue->unpackFloat->expect->toBeCloseTo(1.0 /. rate) // https://en.wikipedia.org/wiki/Exponential_distribution#Mean,_variance,_moments,_and_median
   })
 
   test("of a cauchy distribution", () => {
     let meanValue = run(
-      FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Cauchy({local: 1.0, scale: 1.0}))),
+      FromDist(#ToFloat(#Mean), DistributionTypes.Symbolic(#Cauchy({local: 1.0, scale: 1.0}))),
     )
     meanValue->unpackFloat->expect->toBeSoCloseTo(1.0098094001641797, ~digits=5)
     //-> toBe(GenDistError(Other("Cauchy distributions may have no mean value.")))
@@ -48,7 +48,7 @@ describe("(Symbolic) mean", () => {
       let (low, medium, high) = tup
       let meanValue = run(
         FromDist(
-          ToFloat(#Mean),
+          #ToFloat(#Mean),
           DistributionTypes.Symbolic(#Triangular({low: low, medium: medium, high: high})),
         ),
       )
@@ -63,7 +63,7 @@ describe("(Symbolic) mean", () => {
     tup => {
       let (alpha, beta) = tup
       let meanValue = run(
-        FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Beta({alpha: alpha, beta: beta}))),
+        FromDist(#ToFloat(#Mean), DistributionTypes.Symbolic(#Beta({alpha: alpha, beta: beta}))),
       )
       meanValue->unpackFloat->expect->toBeCloseTo(1.0 /. (1.0 +. beta /. alpha)) // https://en.wikipedia.org/wiki/Beta_distribution#Mean
     },
@@ -72,7 +72,7 @@ describe("(Symbolic) mean", () => {
   // TODO: When we have our theory of validators we won't want this to be NaN but to be an error.
   test("of beta(0, 0)", () => {
     let meanValue = run(
-      FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Beta({alpha: 0.0, beta: 0.0}))),
+      FromDist(#ToFloat(#Mean), DistributionTypes.Symbolic(#Beta({alpha: 0.0, beta: 0.0}))),
     )
     meanValue->unpackFloat->expect->ExpectJs.toBeFalsy
   })
@@ -83,7 +83,7 @@ describe("(Symbolic) mean", () => {
     tup => {
       let (mu, sigma) = tup
       let meanValue = run(
-        FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Lognormal({mu: mu, sigma: sigma}))),
+        FromDist(#ToFloat(#Mean), DistributionTypes.Symbolic(#Lognormal({mu: mu, sigma: sigma}))),
       )
       meanValue->unpackFloat->expect->toBeCloseTo(Js.Math.exp(mu +. sigma ** 2.0 /. 2.0)) // https://brilliant.org/wiki/log-normal-distribution/
     },
@@ -95,14 +95,14 @@ describe("(Symbolic) mean", () => {
     tup => {
       let (low, high) = tup
       let meanValue = run(
-        FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Uniform({low: low, high: high}))),
+        FromDist(#ToFloat(#Mean), DistributionTypes.Symbolic(#Uniform({low: low, high: high}))),
       )
       meanValue->unpackFloat->expect->toBeCloseTo((low +. high) /. 2.0) // https://en.wikipedia.org/wiki/Continuous_uniform_distribution#Moments
     },
   )
 
   test("of a float", () => {
-    let meanValue = run(FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Float(7.7))))
+    let meanValue = run(FromDist(#ToFloat(#Mean), DistributionTypes.Symbolic(#Float(7.7))))
     meanValue->unpackFloat->expect->toBeCloseTo(7.7)
   })
 })

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

@@ -101,14 +101,14 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
   }
 
   let toPointSetFn = r => {
-    switch reCall(~functionCallInfo=FromDist(ToDist(ToPointSet), r), ()) {
+    switch reCall(~functionCallInfo=FromDist(#ToDist(ToPointSet), r), ()) {
     | Dist(PointSet(p)) => Ok(p)
     | e => Error(OutputLocal.toErrorOrUnreachable(e))
     }
   }
 
   let toSampleSetFn = r => {
-    switch reCall(~functionCallInfo=FromDist(ToDist(ToSampleSet(sampleCount)), r), ()) {
+    switch reCall(~functionCallInfo=FromDist(#ToDist(ToSampleSet(sampleCount)), r), ()) {
     | Dist(SampleSet(p)) => Ok(p)
     | e => Error(OutputLocal.toErrorOrUnreachable(e))
     }
@@ -116,13 +116,13 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
 
   let scaleMultiply = (r, weight) =>
     reCall(
-      ~functionCallInfo=FromDist(ToDistCombination(Pointwise, #Multiply, #Float(weight)), r),
+      ~functionCallInfo=FromDist(#ToDistCombination(Pointwise, #Multiply, #Float(weight)), r),
       (),
     )->OutputLocal.toDistR
 
   let pointwiseAdd = (r1, r2) =>
     reCall(
-      ~functionCallInfo=FromDist(ToDistCombination(Pointwise, #Add, #Dist(r2)), r1),
+      ~functionCallInfo=FromDist(#ToDistCombination(Pointwise, #Add, #Dist(r2)), r1),
       (),
     )->OutputLocal.toDistR
 
@@ -131,40 +131,40 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
     dist: genericDist,
   ): outputType => {
     let response = switch subFnName {
-    | ToFloat(distToFloatOperation) =>
+    | #ToFloat(distToFloatOperation) =>
       GenericDist.toFloatOperation(dist, ~toPointSetFn, ~distToFloatOperation)
       ->E.R2.fmap(r => Float(r))
       ->OutputLocal.fromResult
-    | ToString(ToString) => dist->GenericDist.toString->String
+    | #ToString(ToString) => dist->GenericDist.toString->String
-    | ToString(ToSparkline(bucketCount)) =>
+    | #ToString(ToSparkline(bucketCount)) =>
       GenericDist.toSparkline(dist, ~sampleCount, ~bucketCount, ())
       ->E.R2.fmap(r => String(r))
       ->OutputLocal.fromResult
-    | ToDist(Inspect) => {
+    | #ToDist(Inspect) => {
         Js.log2("Console log requested: ", dist)
         Dist(dist)
       }
-    | ToDist(Normalize) => dist->GenericDist.normalize->Dist
+    | #ToDist(Normalize) => dist->GenericDist.normalize->Dist
-    | ToScore(LogScore(answer, prior)) =>
+    | #ToScore(LogScore(answer, prior)) =>
       GenericDist.Score.logScore(~estimate=Score_Dist(dist), ~answer, ~prior)
       ->E.R2.fmap(s => Float(s))
       ->OutputLocal.fromResult
-    | ToBool(IsNormalized) => dist->GenericDist.isNormalized->Bool
+    | #ToBool(IsNormalized) => dist->GenericDist.isNormalized->Bool
-    | ToDist(Truncate(leftCutoff, rightCutoff)) =>
+    | #ToDist(Truncate(leftCutoff, rightCutoff)) =>
       GenericDist.truncate(~toPointSetFn, ~leftCutoff, ~rightCutoff, dist, ())
       ->E.R2.fmap(r => Dist(r))
       ->OutputLocal.fromResult
-    | ToDist(ToSampleSet(n)) =>
+    | #ToDist(ToSampleSet(n)) =>
       dist
       ->GenericDist.toSampleSetDist(n)
       ->E.R2.fmap(r => Dist(SampleSet(r)))
       ->OutputLocal.fromResult
-    | ToDist(ToPointSet) =>
+    | #ToDist(ToPointSet) =>
       dist
       ->GenericDist.toPointSet(~xyPointLength, ~sampleCount, ())
       ->E.R2.fmap(r => Dist(PointSet(r)))
       ->OutputLocal.fromResult
-    | ToDist(Scale(#LogarithmWithThreshold(eps), f)) =>
+    | #ToDist(Scale(#LogarithmWithThreshold(eps), f)) =>
       dist
       ->GenericDist.pointwiseCombinationFloat(
         ~toPointSetFn,
@@ -173,23 +173,23 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
       )
       ->E.R2.fmap(r => Dist(r))
       ->OutputLocal.fromResult
-    | ToDist(Scale(#Multiply, f)) =>
+    | #ToDist(Scale(#Multiply, f)) =>
       dist
       ->GenericDist.pointwiseCombinationFloat(~toPointSetFn, ~algebraicCombination=#Multiply, ~f)
       ->E.R2.fmap(r => Dist(r))
       ->OutputLocal.fromResult
-    | ToDist(Scale(#Logarithm, f)) =>
+    | #ToDist(Scale(#Logarithm, f)) =>
       dist
       ->GenericDist.pointwiseCombinationFloat(~toPointSetFn, ~algebraicCombination=#Logarithm, ~f)
       ->E.R2.fmap(r => Dist(r))
       ->OutputLocal.fromResult
-    | ToDist(Scale(#Power, f)) =>
+    | #ToDist(Scale(#Power, f)) =>
       dist
       ->GenericDist.pointwiseCombinationFloat(~toPointSetFn, ~algebraicCombination=#Power, ~f)
       ->E.R2.fmap(r => Dist(r))
       ->OutputLocal.fromResult
-    | ToDistCombination(Algebraic(_), _, #Float(_)) => GenDistError(NotYetImplemented)
+    | #ToDistCombination(Algebraic(_), _, #Float(_)) => GenDistError(NotYetImplemented)
-    | ToDistCombination(Algebraic(strategy), arithmeticOperation, #Dist(t2)) =>
+    | #ToDistCombination(Algebraic(strategy), arithmeticOperation, #Dist(t2)) =>
       dist
       ->GenericDist.algebraicCombination(
         ~strategy,
@@ -200,12 +200,12 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
       )
       ->E.R2.fmap(r => Dist(r))
       ->OutputLocal.fromResult
-    | ToDistCombination(Pointwise, algebraicCombination, #Dist(t2)) =>
+    | #ToDistCombination(Pointwise, algebraicCombination, #Dist(t2)) =>
       dist
       ->GenericDist.pointwiseCombination(~toPointSetFn, ~algebraicCombination, ~t2)
       ->E.R2.fmap(r => Dist(r))
       ->OutputLocal.fromResult
-    | ToDistCombination(Pointwise, algebraicCombination, #Float(f)) =>
+    | #ToDistCombination(Pointwise, algebraicCombination, #Float(f)) =>
       dist
       ->GenericDist.pointwiseCombinationFloat(~toPointSetFn, ~algebraicCombination, ~f)
       ->E.R2.fmap(r => Dist(r))
@@ -216,8 +216,7 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
 
   switch functionCallInfo {
   | FromDist(subFnName, dist) => fromDistFn(subFnName, dist)
-  | FromFloat(subFnName, float) =>
+  | FromFloat(subFnName, x) => reCall(~functionCallInfo=FromFloat(subFnName, x), ())
-    reCall(~functionCallInfo=FromDist(subFnName, GenericDist.fromFloat(float)), ())
   | Mixture(dists) =>
     dists
     ->GenericDist.mixture(~scaleMultiplyFn=scaleMultiply, ~pointwiseAddFn=pointwiseAdd)

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

@@ -27,7 +27,7 @@ let runFromDist: (
 ) => outputType
 let runFromFloat: (
   ~env: env,
-  ~functionCallInfo: DistributionTypes.DistributionOperation.fromDist,
+  ~functionCallInfo: DistributionTypes.DistributionOperation.fromFloat,
   float,
 ) => outputType
 

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

@@ -102,58 +102,82 @@ module DistributionOperation = {
 
   type toScore = LogScore(genericDistOrScalar, option<genericDistOrScalar>)
 
-  type fromDist =
+  type fromFloat = [
-    | ToFloat(toFloat)
+    | #ToFloat(toFloat)
-    | ToDist(toDist)
+    | #ToDist(toDist)
-    | ToScore(toScore)
+    | #ToDistCombination(direction, Operation.Algebraic.t, [#Dist(genericDist) | #Float(float)])
-    | ToDistCombination(direction, Operation.Algebraic.t, [#Dist(genericDist) | #Float(float)])
+    | #ToString(toString)
-    | ToString(toString)
+    | #ToBool(toBool)
-    | ToBool(toBool)
+  ]
+
+  type fromDist = [
+    | fromFloat
+    | #ToScore(toScore)
+  ]
 
   type singleParamaterFunction =
     | FromDist(fromDist)
-    | FromFloat(fromDist)
+    | FromFloat(fromFloat)
 
   type genericFunctionCallInfo =
     | FromDist(fromDist, genericDist)
-    | FromFloat(fromDist, float)
+    | FromFloat(fromFloat, float)
     | FromSamples(array<float>)
     | Mixture(array<(genericDist, float)>)
 
-  let distCallToString = (distFunction: fromDist): string =>
+  let floatCallToString = (floatFunction: fromFloat): string =>
-    switch distFunction {
+    switch floatFunction {
-    | ToFloat(#Cdf(r)) => `cdf(${E.Float.toFixed(r)})`
+    | #ToFloat(#Cdf(r)) => `cdf(${E.Float.toFixed(r)})`
-    | ToFloat(#Inv(r)) => `inv(${E.Float.toFixed(r)})`
+    | #ToFloat(#Inv(r)) => `inv(${E.Float.toFixed(r)})`
-    | ToFloat(#Mean) => `mean`
+    | #ToFloat(#Mean) => `mean`
-    | ToFloat(#Min) => `min`
+    | #ToFloat(#Min) => `min`
-    | ToFloat(#Max) => `max`
+    | #ToFloat(#Max) => `max`
-    | ToFloat(#Stdev) => `stdev`
+    | #ToFloat(#Stdev) => `stdev`
-    | ToFloat(#Variance) => `variance`
+    | #ToFloat(#Variance) => `variance`
-    | ToFloat(#Mode) => `mode`
+    | #ToFloat(#Mode) => `mode`
-    | ToFloat(#Pdf(r)) => `pdf(${E.Float.toFixed(r)})`
+    | #ToFloat(#Pdf(r)) => `pdf(${E.Float.toFixed(r)})`
-    | ToFloat(#Sample) => `sample`
+    | #ToFloat(#Sample) => `sample`
-    | ToFloat(#IntegralSum) => `integralSum`
+    | #ToFloat(#IntegralSum) => `integralSum`
-    | ToScore(_) => `logScore`
+    | #ToDist(Normalize) => `normalize`
-    | ToDist(Normalize) => `normalize`
+    | #ToDist(ToPointSet) => `toPointSet`
-    | ToDist(ToPointSet) => `toPointSet`
+    | #ToDist(ToSampleSet(r)) => `toSampleSet(${E.I.toString(r)})`
-    | ToDist(ToSampleSet(r)) => `toSampleSet(${E.I.toString(r)})`
+    | #ToDist(Truncate(_, _)) => `truncate`
-    | ToDist(Truncate(_, _)) => `truncate`
+    | #ToDist(Inspect) => `inspect`
-    | ToDist(Inspect) => `inspect`
+    | #ToDist(Scale(#Power, r)) => `scalePower(${E.Float.toFixed(r)})`
-    | ToDist(Scale(#Power, r)) => `scalePower(${E.Float.toFixed(r)})`
+    | #ToDist(Scale(#Multiply, r)) => `scaleMultiply(${E.Float.toFixed(r)})`
-    | ToDist(Scale(#Multiply, r)) => `scaleMultiply(${E.Float.toFixed(r)})`
+    | #ToDist(Scale(#Logarithm, r)) => `scaleLog(${E.Float.toFixed(r)})`
-    | ToDist(Scale(#Logarithm, r)) => `scaleLog(${E.Float.toFixed(r)})`
+    | #ToDist(Scale(#LogarithmWithThreshold(eps), r)) =>
-    | ToDist(Scale(#LogarithmWithThreshold(eps), r)) =>
       `scaleLogWithThreshold(${E.Float.toFixed(r)}, epsilon=${E.Float.toFixed(eps)})`
-    | ToString(ToString) => `toString`
+    | #ToString(ToString) => `toString`
-    | ToString(ToSparkline(n)) => `sparkline(${E.I.toString(n)})`
+    | #ToString(ToSparkline(n)) => `sparkline(${E.I.toString(n)})`
-    | ToBool(IsNormalized) => `isNormalized`
+    | #ToBool(IsNormalized) => `isNormalized`
-    | ToDistCombination(Algebraic(_), _, _) => `algebraic`
+    | #ToDistCombination(Algebraic(_), _, _) => `algebraic`
-    | ToDistCombination(Pointwise, _, _) => `pointwise`
+    | #ToDistCombination(Pointwise, _, _) => `pointwise`
+    }
+
+  let distCallToString = (
+    distFunction: [
+      | #ToFloat(toFloat)
+      | #ToDist(toDist)
+      | #ToDistCombination(direction, Operation.Algebraic.t, [#Dist(genericDist) | #Float(float)])
+      | #ToString(toString)
+      | #ToBool(toBool)
+      | #ToScore(toScore)
+    ],
+  ): string =>
+    switch distFunction {
+    | #ToScore(_) => `logScore`
+    | #ToFloat(x) => floatCallToString(#ToFloat(x))
+    | #ToDist(x) => floatCallToString(#ToDist(x))
+    | #ToString(x) => floatCallToString(#ToString(x))
+    | #ToBool(x) => floatCallToString(#ToBool(x))
+    | #ToDistCombination(x, y, z) => floatCallToString(#ToDistCombination(x, y, z))
     }
 
   let toString = (d: genericFunctionCallInfo): string =>
     switch d {
-    | FromDist(f, _) | FromFloat(f, _) => distCallToString(f)
+    | FromDist(f, _) => distCallToString(f)
+    | FromFloat(f, _) => floatCallToString(f)
     | Mixture(_) => `mixture`
     | FromSamples(_) => `fromSamples`
     }
@@ -163,93 +187,93 @@ module Constructors = {
 
   module UsingDists = {
     @genType
-    let mean = (dist): t => FromDist(ToFloat(#Mean), dist)
+    let mean = (dist): t => FromDist(#ToFloat(#Mean), dist)
-    let stdev = (dist): t => FromDist(ToFloat(#Stdev), dist)
+    let stdev = (dist): t => FromDist(#ToFloat(#Stdev), dist)
-    let variance = (dist): t => FromDist(ToFloat(#Variance), dist)
+    let variance = (dist): t => FromDist(#ToFloat(#Variance), dist)
-    let sample = (dist): t => FromDist(ToFloat(#Sample), dist)
+    let sample = (dist): t => FromDist(#ToFloat(#Sample), dist)
-    let cdf = (dist, x): t => FromDist(ToFloat(#Cdf(x)), dist)
+    let cdf = (dist, x): t => FromDist(#ToFloat(#Cdf(x)), dist)
-    let inv = (dist, x): t => FromDist(ToFloat(#Inv(x)), dist)
+    let inv = (dist, x): t => FromDist(#ToFloat(#Inv(x)), dist)
-    let pdf = (dist, x): t => FromDist(ToFloat(#Pdf(x)), dist)
+    let pdf = (dist, x): t => FromDist(#ToFloat(#Pdf(x)), dist)
-    let normalize = (dist): t => FromDist(ToDist(Normalize), dist)
+    let normalize = (dist): t => FromDist(#ToDist(Normalize), dist)
-    let isNormalized = (dist): t => FromDist(ToBool(IsNormalized), dist)
+    let isNormalized = (dist): t => FromDist(#ToBool(IsNormalized), dist)
-    let toPointSet = (dist): t => FromDist(ToDist(ToPointSet), dist)
+    let toPointSet = (dist): t => FromDist(#ToDist(ToPointSet), dist)
-    let toSampleSet = (dist, r): t => FromDist(ToDist(ToSampleSet(r)), dist)
+    let toSampleSet = (dist, r): t => FromDist(#ToDist(ToSampleSet(r)), dist)
     let fromSamples = (xs): t => FromSamples(xs)
-    let truncate = (dist, left, right): t => FromDist(ToDist(Truncate(left, right)), dist)
+    let truncate = (dist, left, right): t => FromDist(#ToDist(Truncate(left, right)), dist)
-    let inspect = (dist): t => FromDist(ToDist(Inspect), dist)
+    let inspect = (dist): t => FromDist(#ToDist(Inspect), dist)
     module LogScore = {
       let distEstimateDistAnswer = (estimate, answer): t => FromDist(
-        ToScore(LogScore(Score_Dist(answer), None)),
+        #ToScore(LogScore(Score_Dist(answer), None)),
         estimate,
       )
       let distEstimateDistAnswerWithPrior = (estimate, answer, prior): t => FromDist(
-        ToScore(LogScore(Score_Dist(answer), Some(prior))),
+        #ToScore(LogScore(Score_Dist(answer), Some(prior))),
         estimate,
       )
       let distEstimateScalarAnswer = (estimate, answer): t => FromDist(
-        ToScore(LogScore(Score_Scalar(answer), None)),
+        #ToScore(LogScore(Score_Scalar(answer), None)),
         estimate,
       )
       let distEstimateScalarAnswerWithPrior = (estimate, answer, prior): t => FromDist(
-        ToScore(LogScore(Score_Scalar(answer), Some(prior))),
+        #ToScore(LogScore(Score_Scalar(answer), Some(prior))),
         estimate,
       )
     }
-    let scaleMultiply = (dist, n): t => FromDist(ToDist(Scale(#Multiply, n)), dist)
+    let scaleMultiply = (dist, n): t => FromDist(#ToDist(Scale(#Multiply, n)), dist)
-    let scalePower = (dist, n): t => FromDist(ToDist(Scale(#Power, n)), dist)
+    let scalePower = (dist, n): t => FromDist(#ToDist(Scale(#Power, n)), dist)
-    let scaleLogarithm = (dist, n): t => FromDist(ToDist(Scale(#Logarithm, n)), dist)
+    let scaleLogarithm = (dist, n): t => FromDist(#ToDist(Scale(#Logarithm, n)), dist)
     let scaleLogarithmWithThreshold = (dist, n, eps): t => FromDist(
-      ToDist(Scale(#LogarithmWithThreshold(eps), n)),
+      #ToDist(Scale(#LogarithmWithThreshold(eps), n)),
       dist,
     )
-    let toString = (dist): t => FromDist(ToString(ToString), dist)
+    let toString = (dist): t => FromDist(#ToString(ToString), dist)
-    let toSparkline = (dist, n): t => FromDist(ToString(ToSparkline(n)), dist)
+    let toSparkline = (dist, n): t => FromDist(#ToString(ToSparkline(n)), dist)
     let algebraicAdd = (dist1, dist2: genericDist): t => FromDist(
-      ToDistCombination(Algebraic(AsDefault), #Add, #Dist(dist2)),
+      #ToDistCombination(Algebraic(AsDefault), #Add, #Dist(dist2)),
       dist1,
     )
     let algebraicMultiply = (dist1, dist2): t => FromDist(
-      ToDistCombination(Algebraic(AsDefault), #Multiply, #Dist(dist2)),
+      #ToDistCombination(Algebraic(AsDefault), #Multiply, #Dist(dist2)),
       dist1,
     )
     let algebraicDivide = (dist1, dist2): t => FromDist(
-      ToDistCombination(Algebraic(AsDefault), #Divide, #Dist(dist2)),
+      #ToDistCombination(Algebraic(AsDefault), #Divide, #Dist(dist2)),
       dist1,
     )
     let algebraicSubtract = (dist1, dist2): t => FromDist(
-      ToDistCombination(Algebraic(AsDefault), #Subtract, #Dist(dist2)),
+      #ToDistCombination(Algebraic(AsDefault), #Subtract, #Dist(dist2)),
       dist1,
     )
     let algebraicLogarithm = (dist1, dist2): t => FromDist(
-      ToDistCombination(Algebraic(AsDefault), #Logarithm, #Dist(dist2)),
+      #ToDistCombination(Algebraic(AsDefault), #Logarithm, #Dist(dist2)),
       dist1,
     )
     let algebraicPower = (dist1, dist2): t => FromDist(
-      ToDistCombination(Algebraic(AsDefault), #Power, #Dist(dist2)),
+      #ToDistCombination(Algebraic(AsDefault), #Power, #Dist(dist2)),
       dist1,
     )
     let pointwiseAdd = (dist1, dist2): t => FromDist(
-      ToDistCombination(Pointwise, #Add, #Dist(dist2)),
+      #ToDistCombination(Pointwise, #Add, #Dist(dist2)),
       dist1,
     )
     let pointwiseMultiply = (dist1, dist2): t => FromDist(
-      ToDistCombination(Pointwise, #Multiply, #Dist(dist2)),
+      #ToDistCombination(Pointwise, #Multiply, #Dist(dist2)),
       dist1,
     )
     let pointwiseDivide = (dist1, dist2): t => FromDist(
-      ToDistCombination(Pointwise, #Divide, #Dist(dist2)),
+      #ToDistCombination(Pointwise, #Divide, #Dist(dist2)),
       dist1,
     )
     let pointwiseSubtract = (dist1, dist2): t => FromDist(
-      ToDistCombination(Pointwise, #Subtract, #Dist(dist2)),
+      #ToDistCombination(Pointwise, #Subtract, #Dist(dist2)),
       dist1,
     )
     let pointwiseLogarithm = (dist1, dist2): t => FromDist(
-      ToDistCombination(Pointwise, #Logarithm, #Dist(dist2)),
+      #ToDistCombination(Pointwise, #Logarithm, #Dist(dist2)),
       dist1,
     )
     let pointwisePower = (dist1, dist2): t => FromDist(
-      ToDistCombination(Pointwise, #Power, #Dist(dist2)),
+      #ToDistCombination(Pointwise, #Power, #Dist(dist2)),
       dist1,
     )
   }

packages/squiggle-lang/src/rescript/ReducerInterface/ReducerInterface_GenericDistribution.res

@@ -34,9 +34,7 @@ module Helpers = {
     dist: DistributionTypes.genericDist,
     ~env: DistributionOperation.env,
   ) => {
-    FromDist(DistributionTypes.DistributionOperation.ToFloat(fnCall), dist)
+    FromDist(#ToFloat(fnCall), dist)->DistributionOperation.run(~env)->Some
-    ->DistributionOperation.run(~env)
-    ->Some
   }
 
   let toStringFn = (
@@ -44,9 +42,7 @@ module Helpers = {
     dist: DistributionTypes.genericDist,
     ~env: DistributionOperation.env,
   ) => {
-    FromDist(DistributionTypes.DistributionOperation.ToString(fnCall), dist)
+    FromDist(#ToString(fnCall), dist)->DistributionOperation.run(~env)->Some
-    ->DistributionOperation.run(~env)
-    ->Some
   }
 
   let toBoolFn = (
@@ -54,9 +50,7 @@ module Helpers = {
     dist: DistributionTypes.genericDist,
     ~env: DistributionOperation.env,
   ) => {
-    FromDist(DistributionTypes.DistributionOperation.ToBool(fnCall), dist)
+    FromDist(#ToBool(fnCall), dist)->DistributionOperation.run(~env)->Some
-    ->DistributionOperation.run(~env)
-    ->Some
   }
 
   let toDistFn = (
@@ -64,18 +58,12 @@ module Helpers = {
     dist,
     ~env: DistributionOperation.env,
   ) => {
-    FromDist(DistributionTypes.DistributionOperation.ToDist(fnCall), dist)
+    FromDist(#ToDist(fnCall), dist)->DistributionOperation.run(~env)->Some
-    ->DistributionOperation.run(~env)
-    ->Some
   }
 
   let twoDiststoDistFn = (direction, arithmetic, dist1, dist2, ~env: DistributionOperation.env) => {
     FromDist(
-      DistributionTypes.DistributionOperation.ToDistCombination(
+      #ToDistCombination(direction, arithmeticMap(arithmetic), #Dist(dist2)),
-        direction,
-        arithmeticMap(arithmetic),
-        #Dist(dist2),
-      ),
       dist1,
     )->DistributionOperation.run(~env)
   }
@@ -229,7 +217,7 @@ let dispatchToGenericOutput = (call: IEV.functionCall, env: DistributionOperatio
     Some(
       DistributionOperation.run(
         FromDist(
-          ToScore(LogScore(DistributionTypes.DistributionOperation.Score_Dist(answer), None)),
+          #ToScore(LogScore(DistributionTypes.DistributionOperation.Score_Dist(answer), None)),
           prediction,
         ),
         ~env,
@@ -242,7 +230,7 @@ let dispatchToGenericOutput = (call: IEV.functionCall, env: DistributionOperatio
     Some(
       DistributionOperation.run(
         FromDist(
-          ToScore(
+          #ToScore(
             LogScore(
               DistributionTypes.DistributionOperation.Score_Dist(answer),
               Some(DistributionTypes.DistributionOperation.Score_Dist(prior)),
@@ -267,7 +255,7 @@ let dispatchToGenericOutput = (call: IEV.functionCall, env: DistributionOperatio
   ) =>
     DistributionOperation.run(
       FromDist(
-        ToScore(
+        #ToScore(
           LogScore(
             DistributionTypes.DistributionOperation.Score_Scalar(answer),
             DistributionTypes.DistributionOperation.Score_Dist(prior)->Some,
@@ -284,7 +272,7 @@ let dispatchToGenericOutput = (call: IEV.functionCall, env: DistributionOperatio
   ) =>
     DistributionOperation.run(
       FromDist(
-        ToScore(LogScore(DistributionTypes.DistributionOperation.Score_Scalar(answer), None)),
+        #ToScore(LogScore(DistributionTypes.DistributionOperation.Score_Scalar(answer), None)),
         prediction,
       ),
       ~env,