Move selection logic to chooseCalculationMethod

Note: Not for use yet

packages/components/src/components/SquiggleChart.tsx

@@ -6,11 +6,12 @@ import {
   errorValueToString,
   squiggleExpression,
 } from "@quri/squiggle-lang";
-import type { samplingParams, exportEnv } from "@quri/squiggle-lang";
+import type { samplingParams } from "@quri/squiggle-lang";
 import { NumberShower } from "./NumberShower";
 import { DistributionChart } from "./DistributionChart";
 import { ErrorBox } from "./ErrorBox";
 import useSize from "@react-hook/size";
+type exportEnv = unknown;
 
 const variableBox = {
   Component: styled.div`

packages/components/src/components/SquiggleEditor.tsx

@@ -2,9 +2,10 @@ import * as React from "react";
 import * as ReactDOM from "react-dom";
 import { SquiggleChart } from "./SquiggleChart";
 import { CodeEditor } from "./CodeEditor";
-import type { exportEnv } from "@quri/squiggle-lang";
 import styled from "styled-components";
 
+type exportEnv = unknown;
+
 export interface SquiggleEditorProps {
   /** The input string for squiggle */
   initialSquiggleString?: string;

packages/squiggle-lang/__tests__/ReducerInterface/ReducerInterface_Distribution_test.res

@@ -67,7 +67,7 @@ describe("eval on distribution functions", () => {
     testEval("lognormal(10,2) / lognormal(5,2)", "Ok(Lognormal(5,2.8284271247461903))")
     testEval("lognormal(5, 2) / 2", "Ok(Lognormal(4.306852819440055,2))")
     testEval("2 / lognormal(5, 2)", "Ok(Lognormal(-4.306852819440055,2))")
-    testEval("2 / normal(10, 2)", "Ok(Point Set Distribution)")
+    testEval("2 / normal(10, 2)", "Ok(Sample Set Distribution)")
     testEval("normal(10, 2) / 2", "Ok(Normal(5,1))")
   })
   describe("truncate", () => {
@@ -77,21 +77,21 @@ describe("eval on distribution functions", () => {
   })
 
   describe("exp", () => {
-    testEval("exp(normal(5,2))", "Ok(Point Set Distribution)")
+    testEval("exp(normal(5,2))", "Ok(Sample Set Distribution)")
   })
 
   describe("pow", () => {
-    testEval("pow(3, uniform(5,8))", "Ok(Point Set Distribution)")
-    testEval("pow(uniform(5,8), 3)", "Ok(Point Set Distribution)")
+    testEval("pow(3, uniform(5,8))", "Ok(Sample Set Distribution)")
+    testEval("pow(uniform(5,8), 3)", "Ok(Sample Set Distribution)")
     testEval("pow(uniform(5,8), uniform(9, 10))", "Ok(Sample Set Distribution)")
   })
 
   describe("log", () => {
-    testEval("log(2, uniform(5,8))", "Ok(Point Set Distribution)")
-    testEval("log(normal(5,2), 3)", "Ok(Point Set Distribution)")
+    testEval("log(2, uniform(5,8))", "Ok(Sample Set Distribution)")
+    testEval("log(normal(5,2), 3)", "Ok(Sample Set Distribution)")
     testEval("log(normal(5,2), normal(10,1))", "Ok(Sample Set Distribution)")
-    testEval("log(uniform(5,8))", "Ok(Point Set Distribution)")
-    testEval("log10(uniform(5,8))", "Ok(Point Set Distribution)")
+    testEval("log(uniform(5,8))", "Ok(Sample Set Distribution)")
+    testEval("log10(uniform(5,8))", "Ok(Sample Set Distribution)")
   })
 
   describe("dotLog", () => {

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

@@ -1,9 +1,4 @@
 import * as _ from "lodash";
-import type {
-  exportEnv,
-  exportDistribution,
-} from "../rescript/ProgramEvaluator.gen";
-export type { exportEnv, exportDistribution };
 import {
   genericDist,
   samplingParams,
@@ -48,7 +43,6 @@ import {
   Constructors_pointwiseLogarithm,
   Constructors_pointwisePower,
 } from "../rescript/Distributions/DistributionOperation/DistributionOperation.gen";
-import { pointSetDistFn } from "../rescript/OldInterpreter/DistPlus.bs";
 export type { samplingParams, errorValue };
 
 export let defaultSamplingInputs: samplingParams = {
@@ -99,7 +93,7 @@ export type squiggleExpression =
 export function run(
   squiggleString: string,
   samplingInputs?: samplingParams,
-  _environment?: exportEnv
+  _environment?: unknown
 ): result<squiggleExpression, errorValue> {
   let si: samplingParams = samplingInputs
     ? samplingInputs

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

@@ -158,7 +158,7 @@ module AlgebraicCombination = {
 
   let runConvolution = (
     toPointSet: toPointSetFn,
-    arithmeticOperation: GenericDist_Types.Operation.arithmeticOperation,
+    arithmeticOperation: Operation.convolutionOperation,
     t1: t,
     t2: t,
   ) =>
@@ -191,10 +191,23 @@ module AlgebraicCombination = {
     | _ => 1000
     }
 
-  let chooseConvolutionOrMonteCarlo = (t2: t, t1: t) =>
+  type calculationMethod = MonteCarlo | Convolution(Operation.convolutionOperation)
+
+  let chooseConvolutionOrMonteCarlo = (
+    op: Operation.algebraicOperation,
+    t2: t,
+    t1: t,
+  ): calculationMethod =>
+    switch op {
+    | #Divide
+    | #Power
+    | #Logarithm =>
+      MonteCarlo
+    | (#Add | #Subtract | #Multiply) as convOp =>
       expectedConvolutionCost(t1) * expectedConvolutionCost(t2) > 10000
-      ? #CalculateWithMonteCarlo
-      : #CalculateWithConvolution
+        ? MonteCarlo
+        : Convolution(convOp)
+    }
 
   let run = (
     t1: t,
@@ -207,15 +220,10 @@ module AlgebraicCombination = {
     | Some(Ok(symbolicDist)) => Ok(Symbolic(symbolicDist))
     | Some(Error(e)) => Error(Other(e))
     | None =>
-      switch chooseConvolutionOrMonteCarlo(t1, t2) {
-      | #CalculateWithMonteCarlo => runMonteCarlo(toSampleSetFn, arithmeticOperation, t1, t2)
-      | #CalculateWithConvolution =>
-        runConvolution(
-          toPointSetFn,
-          arithmeticOperation,
-          t1,
-          t2,
-        )->E.R2.fmap(r => DistributionTypes.PointSet(r))
+      switch chooseConvolutionOrMonteCarlo(arithmeticOperation, t1, t2) {
+      | MonteCarlo => runMonteCarlo(toSampleSetFn, arithmeticOperation, t1, t2)
+      | Convolution(convOp) =>
+        runConvolution(toPointSetFn, convOp, t1, t2)->E.R2.fmap(r => DistributionTypes.PointSet(r))
       }
     }
   }

packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Continuous.res

@@ -247,9 +247,10 @@ let downsampleEquallyOverX = (length, t): t =>
 /* This simply creates multiple copies of the continuous distribution, scaled and shifted according to
  each discrete data point, and then adds them all together. */
 let combineAlgebraicallyWithDiscrete = (
-  op: Operation.algebraicOperation,
+  op: Operation.convolutionOperation,
   t1: t,
   t2: PointSetTypes.discreteShape,
+  discreteFirst: bool,
 ) => {
   let t1s = t1 |> getShape
   let t2s = t2.xyShape // TODO would like to use Discrete.getShape here, but current file structure doesn't allow for that
@@ -262,15 +263,15 @@ let combineAlgebraicallyWithDiscrete = (
     | #Stepwise => stepwiseToLinear(t1)
     }
 
-    let combinedShape = AlgebraicShapeCombination.combineShapesContinuousDiscrete(
+    let combinedShape = NumericShapeCombination.combineShapesContinuousDiscrete(
       op,
       continuousAsLinear |> getShape,
       t2s,
+      discreteFirst,
     )
 
     let combinedIntegralSum = switch op {
-    | #Multiply
-    | #Divide =>
+    | #Multiply =>
       Common.combineIntegralSums((a, b) => Some(a *. b), t1.integralSumCache, t2.integralSumCache)
     | _ => None
     }
@@ -280,7 +281,7 @@ let combineAlgebraicallyWithDiscrete = (
   }
 }
 
-let combineAlgebraically = (op: Operation.algebraicOperation, t1: t, t2: t) => {
+let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t) => {
   let s1 = t1 |> getShape
   let s2 = t2 |> getShape
   let t1n = s1 |> XYShape.T.length
@@ -288,7 +289,7 @@ let combineAlgebraically = (op: Operation.algebraicOperation, t1: t, t2: t) => {
   if t1n == 0 || t2n == 0 {
     empty
   } else {
-    let combinedShape = AlgebraicShapeCombination.combineShapesContinuousContinuous(op, s1, s2)
+    let combinedShape = NumericShapeCombination.combineShapesContinuousContinuous(op, s1, s2)
     let combinedIntegralSum = Common.combineIntegralSums(
       (a, b) => Some(a *. b),
       t1.integralSumCache,

packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Discrete.res

@@ -85,7 +85,7 @@ let updateIntegralCache = (integralCache, t: t): t => {
 
 /* This multiples all of the data points together and creates a new discrete distribution from the results.
  Data points at the same xs get added together. It may be a good idea to downsample t1 and t2 before and/or the result after. */
-let combineAlgebraically = (op: Operation.algebraicOperation, t1: t, t2: t): t => {
+let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t): t => {
   let t1s = t1 |> getShape
   let t2s = t2 |> getShape
   let t1n = t1s |> XYShape.T.length
@@ -97,7 +97,7 @@ let combineAlgebraically = (op: Operation.algebraicOperation, t1: t, t2: t): t =
     t2.integralSumCache,
   )
 
-  let fn = Operation.Algebraic.toFn(op)
+  let fn = Operation.Convolution.toFn(op)
   let xToYMap = E.FloatFloatMap.empty()
 
   for i in 0 to t1n - 1 {

packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Mixed.res

@@ -226,7 +226,7 @@ module T = Dist({
   }
 })
 
-let combineAlgebraically = (op: Operation.algebraicOperation, t1: t, t2: t): t => {
+let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t): t => {
   // Discrete convolution can cause a huge increase in the number of samples,
   // so we'll first downsample.
 
@@ -242,8 +242,18 @@ let combineAlgebraically = (op: Operation.algebraicOperation, t1: t, t2: t): t =
   // continuous (*) continuous => continuous, but also
   // discrete (*) continuous => continuous (and vice versa). We have to take care of all combos and then combine them:
   let ccConvResult = Continuous.combineAlgebraically(op, t1.continuous, t2.continuous)
-  let dcConvResult = Continuous.combineAlgebraicallyWithDiscrete(op, t2.continuous, t1.discrete)
-  let cdConvResult = Continuous.combineAlgebraicallyWithDiscrete(op, t1.continuous, t2.discrete)
+  let dcConvResult = Continuous.combineAlgebraicallyWithDiscrete(
+    op,
+    t2.continuous,
+    t1.discrete,
+    true,
+  )
+  let cdConvResult = Continuous.combineAlgebraicallyWithDiscrete(
+    op,
+    t1.continuous,
+    t2.discrete,
+    false,
+  )
   let continuousConvResult = Continuous.reduce(\"+.", [ccConvResult, dcConvResult, cdConvResult])
 
   // ... finally, discrete (*) discrete => discrete, obviously:

packages/squiggle-lang/src/rescript/Distributions/PointSetDist/NumericShapeCombination.res

@@ -96,7 +96,7 @@ let toDiscretePointMassesFromTriangulars = (
 }
 
 let combineShapesContinuousContinuous = (
-  op: Operation.algebraicOperation,
+  op: Operation.convolutionOperation,
   s1: PointSetTypes.xyShape,
   s2: PointSetTypes.xyShape,
 ): PointSetTypes.xyShape => {
@@ -104,7 +104,6 @@ let combineShapesContinuousContinuous = (
   // if we multiply the two distributions, we should probably use lognormal filters.
   let t1m = toDiscretePointMassesFromTriangulars(s1)
   let t2m = switch op {
-  | #Divide => toDiscretePointMassesFromTriangulars(~inverse=true, s2)
   | _ => toDiscretePointMassesFromTriangulars(~inverse=false, s2)
   }
 
@@ -112,9 +111,6 @@ let combineShapesContinuousContinuous = (
   | #Add => (m1, m2) => m1 +. m2
   | #Subtract => (m1, m2) => m1 -. m2
   | #Multiply => (m1, m2) => m1 *. m2
-  | #Divide => (m1, mInv2) => m1 *. mInv2
-  | #Power => (m1, mInv2) => m1 ** mInv2
-  | #Logarithm => (m1, m2) => log(m1) /. log(m2)
   } // note: here, mInv2 = mean(1 / t2) ~= 1 / mean(t2)
 
   // TODO: Variances are for exponentatiation or logarithms are almost totally made up and very likely very wrong.
@@ -123,9 +119,6 @@ let combineShapesContinuousContinuous = (
   | #Add => (v1, v2, _, _) => v1 +. v2
   | #Subtract => (v1, v2, _, _) => v1 +. v2
   | #Multiply => (v1, v2, m1, m2) => v1 *. v2 +. v1 *. m2 ** 2. +. v2 *. m1 ** 2.
-  | #Power => (v1, v2, m1, m2) => v1 *. v2 +. v1 *. m2 ** 2. +. v2 *. m1 ** 2.
-  | #Logarithm => (v1, v2, m1, m2) => v1 *. v2 +. v1 *. m2 ** 2. +. v2 *. m1 ** 2.
-  | #Divide => (v1, vInv2, m1, mInv2) => v1 *. vInv2 +. v1 *. mInv2 ** 2. +. vInv2 *. m1 ** 2.
   }
 
   // TODO: If operating on two positive-domain distributions, we should take that into account
@@ -199,59 +192,28 @@ let toDiscretePointMassesFromDiscrete = (s: PointSetTypes.xyShape): pointMassesW
 }
 
 let combineShapesContinuousDiscrete = (
-  op: Operation.algebraicOperation,
+  op: Operation.convolutionOperation,
   continuousShape: PointSetTypes.xyShape,
   discreteShape: PointSetTypes.xyShape,
+  discreteFirst: bool,
 ): PointSetTypes.xyShape => {
-  let t1n = continuousShape |> XYShape.T.length
-  let t2n = discreteShape |> XYShape.T.length
-
   // each x pair is added/subtracted
-  let fn = Operation.Algebraic.toFn(op)
+  let opFunc = Operation.Convolution.toFn(op)
+  let fn = discreteFirst ? (a, b) => opFunc(b, a) : opFunc
 
-  let outXYShapes: array<array<(float, float)>> = Belt.Array.makeUninitializedUnsafe(t2n)
+  let discretePoints = Belt.Array.zip(discreteShape.xs, discreteShape.ys)
+  let continuousPoints = Belt.Array.zip(continuousShape.xs, continuousShape.ys)
 
-  switch op {
+  let outXYShapes = switch op {
   | #Add
   | #Subtract =>
-    for j in 0 to t2n - 1 {
-      // creates a new continuous shape for each one of the discrete points, and collects them in outXYShapes.
-      let dxyShape: array<(float, float)> = Belt.Array.makeUninitializedUnsafe(t1n)
-      for i in 0 to t1n - 1 {
-        Belt.Array.set(
-          dxyShape,
-          i,
-          (
-            fn(continuousShape.xs[i], discreteShape.xs[j]),
-            continuousShape.ys[i] *. discreteShape.ys[j],
-          ),
-        ) |> ignore
-        ()
-      }
-      Belt.Array.set(outXYShapes, j, dxyShape) |> ignore
-      ()
-    }
-  | #Multiply
-  | #Power
-  | #Logarithm
-  | #Divide =>
-    for j in 0 to t2n - 1 {
-      // creates a new continuous shape for each one of the discrete points, and collects them in outXYShapes.
-      let dxyShape: array<(float, float)> = Belt.Array.makeUninitializedUnsafe(t1n)
-      for i in 0 to t1n - 1 {
-        Belt.Array.set(
-          dxyShape,
-          i,
-          (
-            fn(continuousShape.xs[i], discreteShape.xs[j]),
-            continuousShape.ys[i] *. discreteShape.ys[j] /. discreteShape.xs[j],
-          ),
-        ) |> ignore
-        ()
-      }
-      Belt.Array.set(outXYShapes, j, dxyShape) |> ignore
-      ()
-    }
+    discretePoints->E.A2.fmap(((dx, dy)) =>
+      continuousPoints->E.A2.fmap(((cx, cy)) => (fn(cx, dx), cy *. dy))
+    )
+  | #Multiply =>
+    discretePoints->E.A2.fmap(((dx, dy)) =>
+      continuousPoints->E.A2.fmap(((cx, cy)) => (fn(cx, dx), cy *. dy /. dx))
+    )
   }
 
   outXYShapes

packages/squiggle-lang/src/rescript/Distributions/PointSetDist/NumericShapeCombination2.res

@@ -0,0 +1,187 @@
+// Types
+
+let emptyXYShape: PointSetTypes.xyShape = {xs: [], ys: []}
+exception LogicallyInconsistent(string)
+
+// Helpers
+
+let getArithmeticComplementOfDistributionForSubstraction = (
+  dist: PointSetTypes.xyShape,
+): PointSetTypes.xyShape => {
+  let newXs = Belt.Array.map(dist.xs, x => -.x)
+  {xs: newXs, ys: dist.ys}
+}
+
+let getApproximatePdfOfContinuousDistributionAtPoint = (
+  dist: PointSetTypes.xyShape,
+  point: float,
+): float => {
+  let closestFromBelowIndex = E.A.reducei(dist.xs, None, (accumulator, item, index) =>
+    item < point ? Some(index) : accumulator
+  ) // This could be improved by taking advantage of the fact that these are ordered
+  let closestFromAboveIndexOption = Belt.Array.getIndexBy(dist.xs, item => item > point)
+
+  let weightedMean = (
+    point: float,
+    closestFromBelow: float,
+    closestFromAbove: float,
+    valueclosestFromBelow,
+    valueclosestFromAbove,
+  ): float => {
+    let distance = closestFromAbove -. closestFromBelow
+    let w1 = (point -. closestFromBelow) /. distance
+    let w2 = (closestFromAbove -. point) /. distance
+    let result = w1 *. valueclosestFromAbove +. w2 *. valueclosestFromBelow
+    result
+  }
+
+  let result = switch (closestFromBelowIndex, closestFromAboveIndexOption) {
+  | (None, None) =>
+    raise(
+      LogicallyInconsistent(
+        "Logically inconsistent option in NumericShapeCombination2.res. Possibly caused by empty distribution",
+      ), // to do: give an error type
+    ) // all are smaller, and all are larger
+  | (None, Some(i)) => 0.0 // none are smaller, all are larger
+  | (Some(i), None) => 0.0 // all are smaller, none are larger
+  | (Some(i), Some(j)) => weightedMean(point, dist.xs[i], dist.xs[j], dist.ys[i], dist.ys[j]) // there is a lowerBound and an upperBound.
+  }
+
+  result
+}
+
+// Inner functions
+
+let addContinuousContinuous = (
+  s1: PointSetTypes.xyShape,
+  s2: PointSetTypes.xyShape,
+): PointSetTypes.xyShape => {
+  // Assumption: xyShapes are ordered on the x coordinate.
+
+  // Get some needed variables
+  let len1 = XYShape.T.length(s1)
+  let mins1xs = s1.xs[0] // Belt.Array.reduce(s1.xs, s1.xs[0], (a, b) => a < b ? a : b)
+  let maxs1xs = s1.xs[len1 - 1] // Belt.Array.reduce(s1.xs, s1.xs[0], (a, b) => a > b ? a : b)
+
+  let len2 = XYShape.T.length(s2)
+  let mins2xs = s2.xs[0] // Belt.Array.reduce(s1.xs, s1.xs[0], (a, b) => a < b ? a : b)
+  let maxs2xs = s2.xs[len1 - 1] // Belt.Array.reduce(s1.xs, s1.xs[0], (a, b) => a > b ? a : b)
+
+  let lowerBound = mins1xs +. mins2xs
+  let upperBound = maxs1xs +. maxs2xs
+  let numIntervals = 2 * Js.Math.max_int(len1, len2) // 5000
+  let epsilon = (upperBound -. lowerBound) /. Belt.Int.toFloat(numIntervals) // Js.Math.pow_float(~base=2.0, ~exp=-16.0)
+
+  let newXs: array<float> = Belt.Array.makeUninitializedUnsafe(numIntervals)
+  let newYs: array<float> = Belt.Array.makeUninitializedUnsafe(numIntervals)
+
+  let getApproximatePdfOfS1AtPoint = x => getApproximatePdfOfContinuousDistributionAtPoint(s1, x)
+  let getApproximatePdfOfS2AtPoint = x => getApproximatePdfOfContinuousDistributionAtPoint(s2, x)
+  let float = x => Belt.Int.toFloat(x)
+  // Compute the integral numerically.
+  // I wouldn't worry too much about the O(n^3). At 5000 samples, this takes on the order of 25 million operations
+  // The AMD Ryzen 7 processor in my computer can do around 300K million operations per second.
+  // src: https://wikiless.org/wiki/Instructions_per_second?lang=en#Thousand_instructions_per_second_(TIPS/kIPS)
+  for i in 0 to numIntervals - 1 {
+    // where are the x points in the resulting distribution
+    let z = lowerBound +. float(i) *. epsilon
+    newXs[i] = z
+    newYs[i] = 0.0
+    for j in 0 to numIntervals - 1 {
+      // how fine-grained do we want our approximation of the integral to be.
+      let x = lowerBound +. float(j) *. epsilon
+      let deltaYi = getApproximatePdfOfS1AtPoint(x) *. getApproximatePdfOfS2AtPoint(z -. x)
+      newYs[i] = newYs[i] +. deltaYi
+    }
+  }
+  // This could be improved by, for instance, choosing the location of the xs strategically
+  // for example, such that each of them is "equidistant" in a cdf, that is, such that the
+  // cdf increases by constant amounts from one point to another.
+  {xs: newXs, ys: newYs}
+}
+
+let multiplyContinuousContinuous = (
+  s1: PointSetTypes.xyShape,
+  s2: PointSetTypes.xyShape,
+): PointSetTypes.xyShape => {
+  // Assumption: xyShapes are ordered on the x coordinate.
+
+  // Get some needed variables
+  let len1 = XYShape.T.length(s1)
+  let mins1xs = s1.xs[0] // Belt.Array.reduce(s1.xs, s1.xs[0], (a, b) => a < b ? a : b)
+  let maxs1xs = s1.xs[len1 - 1] // Belt.Array.reduce(s1.xs, s1.xs[0], (a, b) => a > b ? a : b)
+
+  let len2 = XYShape.T.length(s2)
+  let mins2xs = s2.xs[0] // Belt.Array.reduce(s1.xs, s1.xs[0], (a, b) => a < b ? a : b)
+  let maxs2xs = s2.xs[len1 - 1] // Belt.Array.reduce(s1.xs, s1.xs[0], (a, b) => a > b ? a : b)
+
+  let lowerBound = mins1xs *. mins2xs
+  let upperBound = maxs1xs *. maxs2xs
+  let numIntervals = 2 * Js.Math.max_int(len1, len2) // 5000
+  let epsilon = (upperBound -. lowerBound) /. Belt.Int.toFloat(numIntervals) // Js.Math.pow_float(~base=2.0, ~exp=-16.0)
+  let epsilonForIgnoreInIntegral = Js.Math.pow_float(~base=2.0, ~exp=-16.0)
+
+  let newXs: array<float> = Belt.Array.makeUninitializedUnsafe(numIntervals)
+  let newYs: array<float> = Belt.Array.makeUninitializedUnsafe(numIntervals)
+
+  let getApproximatePdfOfS1AtPoint = x => getApproximatePdfOfContinuousDistributionAtPoint(s1, x)
+  let getApproximatePdfOfS2AtPoint = x => getApproximatePdfOfContinuousDistributionAtPoint(s2, x)
+  let float = x => Belt.Int.toFloat(x)
+  // Compute the integral numerically.
+  // I wouldn't worry too much about the O(n^3). At 5000 samples, this takes on the order of 25 million operations
+  // The AMD Ryzen 7 processor in my computer can do around 300K million operations per second.
+  // src: https://wikiless.org/wiki/Instructions_per_second?lang=en#Thousand_instructions_per_second_(TIPS/kIPS)
+  for i in 0 to numIntervals - 1 {
+    // where are the x points in the resulting distribution
+    let z = lowerBound +. float(i) *. epsilon
+    newXs[i] = z
+    newYs[i] = 0.0
+    for j in 0 to numIntervals - 1 {
+      // how fine-grained do we want our approximation of the integral to be.
+      let x = lowerBound +. float(j) *. epsilon
+      let absX = Js.Math.abs_float(x)
+      if absX > epsilonForIgnoreInIntegral {
+        let deltaYi =
+          getApproximatePdfOfS1AtPoint(x) *. getApproximatePdfOfS2AtPoint(z /. x) *. (1.0 /. x)
+        newYs[i] = newYs[i] +. deltaYi
+      }
+    }
+  }
+  // This could be improved by, for instance, choosing the location of the xs strategically
+  // for example, such that each of them is "equidistant" in a cdf, that is, such that the
+  // cdf increases by constant amounts from one point to another.
+  {xs: newXs, ys: newYs}
+}
+
+// Main function
+
+let combineShapesContinuousContinuous = (
+  op: Operation.algebraicOperation,
+  s1: PointSetTypes.xyShape,
+  s2: PointSetTypes.xyShape,
+): PointSetTypes.xyShape => {
+  let result = switch op {
+  | #Add => addContinuousContinuous(s1, s2)
+  | #Subtract =>
+    addContinuousContinuous(s1, getArithmeticComplementOfDistributionForSubstraction(s2))
+  | #Multiply => emptyXYShape
+  | #Divide => emptyXYShape
+  | #Power => emptyXYShape
+  | #Logarithm => emptyXYShape
+  }
+  result
+}
+
+// Not sure I understand how to combine continuous and discrete distribution
+
+let combineShapesContinuousDiscrete = (
+  op: Operation.algebraicOperation,
+  continuousShape: PointSetTypes.xyShape,
+  discreteShape: PointSetTypes.xyShape,
+): PointSetTypes.xyShape => emptyXYShape
+
+let combineShapesDiscreteContinuous = (
+  op: Operation.algebraicOperation,
+  discreteShape: PointSetTypes.xyShape,
+  continuousShape: PointSetTypes.xyShape,
+): PointSetTypes.xyShape => emptyXYShape

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

@@ -34,14 +34,14 @@ let toMixed = mapToAll((
     ),
 ))
 
-//TODO WARNING: The combineAlgebraicallyWithDiscrete will break for subtraction and division, like, discrete - continous
-let combineAlgebraically = (op: Operation.algebraicOperation, t1: t, t2: t): t =>
+let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t): t =>
   switch (t1, t2) {
   | (Continuous(m1), Continuous(m2)) =>
     Continuous.combineAlgebraically(op, m1, m2) |> Continuous.T.toPointSetDist
-  | (Continuous(m1), Discrete(m2))
-  | (Discrete(m2), Continuous(m1)) =>
-    Continuous.combineAlgebraicallyWithDiscrete(op, m1, m2) |> Continuous.T.toPointSetDist
+  | (Discrete(m1), Continuous(m2)) =>
+    Continuous.combineAlgebraicallyWithDiscrete(op, m2, m1, true) |> Continuous.T.toPointSetDist
+  | (Continuous(m1), Discrete(m2)) =>
+    Continuous.combineAlgebraicallyWithDiscrete(op, m1, m2, false) |> Continuous.T.toPointSetDist
   | (Discrete(m1), Discrete(m2)) =>
     Discrete.combineAlgebraically(op, m1, m2) |> Discrete.T.toPointSetDist
   | (m1, m2) => Mixed.combineAlgebraically(op, toMixed(m1), toMixed(m2)) |> Mixed.T.toPointSetDist

packages/squiggle-lang/src/rescript/OldInterpreter/AST.res

@@ -1,24 +0,0 @@
-open ASTTypes
-
-let toString = ASTTypes.Node.toString
-
-let envs = (samplingInputs, environment) => {
-  samplingInputs: samplingInputs,
-  environment: environment,
-  evaluateNode: ASTEvaluator.toLeaf,
-}
-
-let toLeaf = (samplingInputs, environment, node: node) =>
-  ASTEvaluator.toLeaf(envs(samplingInputs, environment), node)
-
-let toPointSetDist = (samplingInputs, environment, node: node) =>
-  switch toLeaf(samplingInputs, environment, node) {
-  | Ok(#RenderedDist(pointSetDist)) => Ok(pointSetDist)
-  | Ok(_) => Error("Rendering failed.")
-  | Error(e) => Error(e)
-  }
-
-let runFunction = (samplingInputs, environment, inputs, fn: ASTTypes.Function.t) => {
-  let params = envs(samplingInputs, environment)
-  ASTTypes.Function.run(params, inputs, fn)
-}

packages/squiggle-lang/src/rescript/OldInterpreter/ASTEvaluator.res

@@ -1,257 +0,0 @@
-open ASTTypes
-
-type tResult = node => result<node, string>
-
-/* Given two random variables A and B, this returns the distribution
-   of a new variable that is the result of the operation on A and B.
-   For instance, normal(0, 1) + normal(1, 1) -> normal(1, 2).
-   In general, this is implemented via convolution. */
-module AlgebraicCombination = {
-  let tryAnalyticalSimplification = (operation, t1: node, t2: node) =>
-    switch (operation, t1, t2) {
-    | (operation, #SymbolicDist(d1), #SymbolicDist(d2)) =>
-      switch SymbolicDist.T.tryAnalyticalSimplification(d1, d2, operation) {
-      | #AnalyticalSolution(symbolicDist) => Ok(#SymbolicDist(symbolicDist))
-      | #Error(er) => Error(er)
-      | #NoSolution => Ok(#AlgebraicCombination(operation, t1, t2))
-      }
-    | _ => Ok(#AlgebraicCombination(operation, t1, t2))
-    }
-
-  let combinationByRendering = (evaluationParams, algebraicOp, t1: node, t2: node): result<
-    node,
-    string,
-  > =>
-    E.R.merge(
-      Node.ensureIsRenderedAndGetShape(evaluationParams, t1),
-      Node.ensureIsRenderedAndGetShape(evaluationParams, t2),
-    ) |> E.R.fmap(((a, b)) => #RenderedDist(PointSetDist.combineAlgebraically(algebraicOp, a, b)))
-
-  let nodeScore: node => int = x =>
-    switch x {
-    | #SymbolicDist(#Float(_)) => 1
-    | #SymbolicDist(_) => 1000
-    | #RenderedDist(Discrete(m)) => m.xyShape |> XYShape.T.length
-    | #RenderedDist(Mixed(_)) => 1000
-    | #RenderedDist(Continuous(_)) => 1000
-    | _ => 1000
-    }
-
-  let choose = (t1: node, t2: node) =>
-    nodeScore(t1) * nodeScore(t2) > 10000 ? #Sampling : #Analytical
-
-  let combine = (evaluationParams, algebraicOp, t1: node, t2: node): result<node, string> =>
-    E.R.merge(
-      ASTTypes.SamplingDistribution.renderIfIsNotSamplingDistribution(evaluationParams, t1),
-      ASTTypes.SamplingDistribution.renderIfIsNotSamplingDistribution(evaluationParams, t2),
-    ) |> E.R.bind(_, ((a, b)) =>
-      switch choose(a, b) {
-      | #Sampling =>
-        ASTTypes.SamplingDistribution.combineShapesUsingSampling(
-          evaluationParams,
-          algebraicOp,
-          a,
-          b,
-        )
-      | #Analytical => combinationByRendering(evaluationParams, algebraicOp, a, b)
-      }
-    )
-
-  let operationToLeaf = (
-    evaluationParams: evaluationParams,
-    algebraicOp: Operation.algebraicOperation,
-    t1: node,
-    t2: node,
-  ): result<node, string> =>
-    algebraicOp
-    |> tryAnalyticalSimplification(_, t1, t2)
-    |> E.R.bind(_, x =>
-      switch x {
-      | #SymbolicDist(_) as t => Ok(t)
-      | _ => combine(evaluationParams, algebraicOp, t1, t2)
-      }
-    )
-}
-
-module PointwiseCombination = {
-  //TODO: This is crude and slow. It forces everything to be pointSetDist, even though much
-  //of the process could happen on symbolic distributions without a conversion to be a pointSetDist.
-  let pointwiseAdd = (evaluationParams: evaluationParams, t1: node, t2: node) =>
-    switch (Node.render(evaluationParams, t1), Node.render(evaluationParams, t2)) {
-    | (Ok(#RenderedDist(rs1)), Ok(#RenderedDist(rs2))) =>
-      Ok(
-        #RenderedDist(
-          PointSetDist.combinePointwise(
-            ~integralSumCachesFn=(a, b) => Some(a +. b),
-            ~integralCachesFn=(a, b) => Some(
-              Continuous.combinePointwise(~distributionType=#CDF, \"+.", a, b),
-            ),
-            \"+.",
-            rs1,
-            rs2,
-          ),
-        ),
-      )
-    | (Error(e1), _) => Error(e1)
-    | (_, Error(e2)) => Error(e2)
-    | _ => Error("Pointwise combination: rendering failed.")
-    }
-
-  let pointwiseCombine = (fn, evaluationParams: evaluationParams, t1: node, t2: node) =>
-    switch // TODO: construct a function that we can easily sample from, to construct
-    // a RenderedDist. Use the xMin and xMax of the rendered pointSetDists to tell the sampling function where to look.
-    // TODO: This should work for symbolic distributions too!
-    (Node.render(evaluationParams, t1), Node.render(evaluationParams, t2)) {
-    | (Ok(#RenderedDist(rs1)), Ok(#RenderedDist(rs2))) =>
-      Ok(#RenderedDist(PointSetDist.combinePointwise(fn, rs1, rs2)))
-    | (Error(e1), _) => Error(e1)
-    | (_, Error(e2)) => Error(e2)
-    | _ => Error("Pointwise combination: rendering failed.")
-    }
-
-  let operationToLeaf = (
-    evaluationParams: evaluationParams,
-    pointwiseOp: Operation.pointwiseOperation,
-    t1: node,
-    t2: node,
-  ) =>
-    switch pointwiseOp {
-    | #Add => pointwiseAdd(evaluationParams, t1, t2)
-    | #Multiply => pointwiseCombine(\"*.", evaluationParams, t1, t2)
-    | #Power => pointwiseCombine(\"**", evaluationParams, t1, t2)
-    }
-}
-
-module Truncate = {
-  type simplificationResult = [
-    | #Solution(ASTTypes.node)
-    | #Error(string)
-    | #NoSolution
-  ]
-
-  let trySimplification = (leftCutoff, rightCutoff, t): simplificationResult =>
-    switch (leftCutoff, rightCutoff, t) {
-    | (None, None, t) => #Solution(t)
-    | (Some(lc), Some(rc), _) if lc > rc =>
-      #Error("Left truncation bound must be smaller than right truncation bound.")
-    | (lc, rc, #SymbolicDist(#Uniform(u))) =>
-      #Solution(#SymbolicDist(#Uniform(SymbolicDist.Uniform.truncate(lc, rc, u))))
-    | _ => #NoSolution
-    }
-
-  let truncateAsShape = (evaluationParams: evaluationParams, leftCutoff, rightCutoff, t) =>
-    switch // TODO: use named args for xMin/xMax in renderToShape; if we're lucky we can at least get the tail
-    // of a distribution we otherwise wouldn't get at all
-    Node.ensureIsRendered(evaluationParams, t) {
-    | Ok(#RenderedDist(rs)) =>
-      Ok(#RenderedDist(PointSetDist.T.truncate(leftCutoff, rightCutoff, rs)))
-    | Error(e) => Error(e)
-    | _ => Error("Could not truncate distribution.")
-    }
-
-  let operationToLeaf = (
-    evaluationParams,
-    leftCutoff: option<float>,
-    rightCutoff: option<float>,
-    t: node,
-  ): result<node, string> =>
-    t
-    |> trySimplification(leftCutoff, rightCutoff)
-    |> (
-      x =>
-        switch x {
-        | #Solution(t) => Ok(t)
-        | #Error(e) => Error(e)
-        | #NoSolution => truncateAsShape(evaluationParams, leftCutoff, rightCutoff, t)
-        }
-    )
-}
-
-module Normalize = {
-  let rec operationToLeaf = (evaluationParams, t: node): result<node, string> =>
-    switch t {
-    | #RenderedDist(s) => Ok(#RenderedDist(PointSetDist.T.normalize(s)))
-    | #SymbolicDist(_) => Ok(t)
-    | _ => ASTTypes.Node.evaluateAndRetry(evaluationParams, operationToLeaf, t)
-    }
-}
-
-module FunctionCall = {
-  let _runHardcodedFunction = (name, evaluationParams, args) =>
-    TypeSystem.Function.Ts.findByNameAndRun(HardcodedFunctions.all, name, evaluationParams, args)
-
-  let _runLocalFunction = (name, evaluationParams: evaluationParams, args) =>
-    Environment.getFunction(evaluationParams.environment, name) |> E.R.bind(_, ((argNames, fn)) =>
-      ASTTypes.Function.run(evaluationParams, args, (argNames, fn))
-    )
-
-  let _runWithEvaluatedInputs = (
-    evaluationParams: ASTTypes.evaluationParams,
-    name,
-    args: array<ASTTypes.node>,
-  ) =>
-    _runHardcodedFunction(name, evaluationParams, args) |> E.O.default(
-      _runLocalFunction(name, evaluationParams, args),
-    )
-
-  // TODO: This forces things to be floats
-  let run = (evaluationParams, name, args) =>
-    args
-    |> E.A.fmap(a => evaluationParams.evaluateNode(evaluationParams, a))
-    |> E.A.R.firstErrorOrOpen
-    |> E.R.bind(_, _runWithEvaluatedInputs(evaluationParams, name))
-}
-
-module Render = {
-  let rec operationToLeaf = (evaluationParams: evaluationParams, t: node): result<node, string> =>
-    switch t {
-    | #Function(_) => Error("Cannot render a function")
-    | #SymbolicDist(d) =>
-      Ok(
-        #RenderedDist(
-          SymbolicDist.T.toPointSetDist(evaluationParams.samplingInputs.pointSetDistLength, d),
-        ),
-      )
-    | #RenderedDist(_) as t => Ok(t) // already a rendered pointSetDist, we're done here
-    | _ => ASTTypes.Node.evaluateAndRetry(evaluationParams, operationToLeaf, t)
-    }
-}
-
-/* This function recursively goes through the nodes of the parse tree,
-   replacing each Operation node and its subtree with a Data node.
-   Whenever possible, the replacement produces a new Symbolic Data node,
-   but most often it will produce a RenderedDist.
-   This function is used mainly to turn a parse tree into a single RenderedDist
-   that can then be displayed to the user. */
-let rec toLeaf = (evaluationParams: ASTTypes.evaluationParams, node: node): result<node, string> =>
-  switch node {
-  // Leaf nodes just stay leaf nodes
-  | #SymbolicDist(_)
-  | #Function(_)
-  | #RenderedDist(_) =>
-    Ok(node)
-  | #Array(args) =>
-    args |> E.A.fmap(toLeaf(evaluationParams)) |> E.A.R.firstErrorOrOpen |> E.R.fmap(r => #Array(r))
-  // Operations nevaluationParamsd to be turned into leaves
-  | #AlgebraicCombination(algebraicOp, t1, t2) =>
-    AlgebraicCombination.operationToLeaf(evaluationParams, algebraicOp, t1, t2)
-  | #PointwiseCombination(pointwiseOp, t1, t2) =>
-    PointwiseCombination.operationToLeaf(evaluationParams, pointwiseOp, t1, t2)
-  | #Truncate(leftCutoff, rightCutoff, t) =>
-    Truncate.operationToLeaf(evaluationParams, leftCutoff, rightCutoff, t)
-  | #Normalize(t) => Normalize.operationToLeaf(evaluationParams, t)
-  | #Render(t) => Render.operationToLeaf(evaluationParams, t)
-  | #Hash(t) =>
-    t
-    |> E.A.fmap(((name: string, node: node)) =>
-      toLeaf(evaluationParams, node) |> E.R.fmap(r => (name, r))
-    )
-    |> E.A.R.firstErrorOrOpen
-    |> E.R.fmap(r => #Hash(r))
-  | #Symbol(r) =>
-    ASTTypes.Environment.get(evaluationParams.environment, r)
-    |> E.O.toResult("Undeclared variable " ++ r)
-    |> E.R.bind(_, toLeaf(evaluationParams))
-  | #FunctionCall(name, args) =>
-    FunctionCall.run(evaluationParams, name, args) |> E.R.bind(_, toLeaf(evaluationParams))
-  }

packages/squiggle-lang/src/rescript/OldInterpreter/ASTTypes.res

@@ -1,233 +0,0 @@
-@genType
-type rec hash = array<(string, node)>
-and node = [
-  | #SymbolicDist(SymbolicDistTypes.symbolicDist)
-  | #RenderedDist(PointSetTypes.pointSetDist)
-  | #Symbol(string)
-  | #Hash(hash)
-  | #Array(array<node>)
-  | #Function(array<string>, node)
-  | #AlgebraicCombination(Operation.algebraicOperation, node, node)
-  | #PointwiseCombination(Operation.pointwiseOperation, node, node)
-  | #Normalize(node)
-  | #Render(node)
-  | #Truncate(option<float>, option<float>, node)
-  | #FunctionCall(string, array<node>)
-]
-
-type statement = [
-  | #Assignment(string, node)
-  | #Expression(node)
-]
-type program = array<statement>
-
-type environment = Belt.Map.String.t<node>
-
-type rec evaluationParams = {
-  samplingInputs: SamplingInputs.samplingInputs,
-  environment: environment,
-  evaluateNode: (evaluationParams, node) => Belt.Result.t<node, string>,
-}
-
-module Environment = {
-  type t = environment
-  module MS = Belt.Map.String
-  let fromArray = MS.fromArray
-  let empty: t = []->fromArray
-  let mergeKeepSecond = (a: t, b: t) =>
-    MS.merge(a, b, (_, a, b) =>
-      switch (a, b) {
-      | (_, Some(b)) => Some(b)
-      | (Some(a), _) => Some(a)
-      | _ => None
-      }
-    )
-  let update = (t, str, fn) => MS.update(t, str, fn)
-  let get = (t: t, str) => MS.get(t, str)
-  let getFunction = (t: t, str) =>
-    switch get(t, str) {
-    | Some(#Function(argNames, fn)) => Ok((argNames, fn))
-    | _ => Error("Function " ++ (str ++ " not found"))
-    }
-}
-
-module Node = {
-  let getFloat = (node: node) =>
-    node |> (
-      x =>
-        switch x {
-        | #RenderedDist(Discrete({xyShape: {xs: [x], ys: [1.0]}})) => Some(x)
-        | #SymbolicDist(#Float(x)) => Some(x)
-        | _ => None
-        }
-    )
-
-  let evaluate = (evaluationParams: evaluationParams) =>
-    evaluationParams.evaluateNode(evaluationParams)
-
-  let evaluateAndRetry = (evaluationParams, fn, node) =>
-    node |> evaluationParams.evaluateNode(evaluationParams) |> E.R.bind(_, fn(evaluationParams))
-
-  let rec toString: node => string = x =>
-    switch x {
-    | #SymbolicDist(d) => SymbolicDist.T.toString(d)
-    | #RenderedDist(_) => "[renderedShape]"
-    | #AlgebraicCombination(op, t1, t2) =>
-      Operation.Algebraic.format(op, toString(t1), toString(t2))
-    | #PointwiseCombination(op, t1, t2) =>
-      Operation.Pointwise.format(op, toString(t1), toString(t2))
-    | #Normalize(t) => "normalize(k" ++ (toString(t) ++ ")")
-    | #Truncate(lc, rc, t) => Operation.Truncate.toString(lc, rc, toString(t))
-    | #Render(t) => toString(t)
-    | #Symbol(t) => "Symbol: " ++ t
-    | #FunctionCall(name, args) =>
-      "[Function call: (" ++
-      (name ++
-      ((args |> E.A.fmap(toString) |> Js.String.concatMany(_, ",")) ++ ")]"))
-    | #Function(args, internal) =>
-      "[Function: (" ++ ((args |> Js.String.concatMany(_, ",")) ++ (toString(internal) ++ ")]"))
-    | #Array(a) => "[" ++ ((a |> E.A.fmap(toString) |> Js.String.concatMany(_, ",")) ++ "]")
-    | #Hash(h) =>
-      "{" ++
-      ((h
-      |> E.A.fmap(((name, value)) => name ++ (":" ++ toString(value)))
-      |> Js.String.concatMany(_, ",")) ++
-      "}")
-    }
-
-  let render = (evaluationParams: evaluationParams, r) => #Render(r) |> evaluate(evaluationParams)
-
-  let ensureIsRendered = (params, t) =>
-    switch t {
-    | #RenderedDist(_) => Ok(t)
-    | _ =>
-      switch render(params, t) {
-      | Ok(#RenderedDist(r)) => Ok(#RenderedDist(r))
-      | Ok(_) => Error("Did not render as requested")
-      | Error(e) => Error(e)
-      }
-    }
-
-  let ensureIsRenderedAndGetShape = (params, t) =>
-    switch ensureIsRendered(params, t) {
-    | Ok(#RenderedDist(r)) => Ok(r)
-    | Ok(_) => Error("Did not render as requested")
-    | Error(e) => Error(e)
-    }
-
-  let toPointSetDist = (item: node) =>
-    switch item {
-    | #RenderedDist(r) => Some(r)
-    | _ => None
-    }
-
-  let _toFloat = (t: PointSetTypes.pointSetDist) =>
-    switch t {
-    | Discrete({xyShape: {xs: [x], ys: [1.0]}}) => Some(#SymbolicDist(#Float(x)))
-    | _ => None
-    }
-
-  let toFloat = (item: node): result<node, string> =>
-    item |> toPointSetDist |> E.O.bind(_, _toFloat) |> E.O.toResult("Not valid shape")
-}
-
-module Function = {
-  type t = (array<string>, node)
-  let fromNode: node => option<t> = node =>
-    switch node {
-    | #Function(r) => Some(r)
-    | _ => None
-    }
-  let argumentNames = ((a, _): t) => a
-  let internals = ((_, b): t) => b
-  let run = (evaluationParams: evaluationParams, args: array<node>, t: t) =>
-    if E.A.length(args) == E.A.length(argumentNames(t)) {
-      let newEnvironment = Belt.Array.zip(argumentNames(t), args) |> Environment.fromArray
-      let newEvaluationParams: evaluationParams = {
-        samplingInputs: evaluationParams.samplingInputs,
-        environment: Environment.mergeKeepSecond(evaluationParams.environment, newEnvironment),
-        evaluateNode: evaluationParams.evaluateNode,
-      }
-      evaluationParams.evaluateNode(newEvaluationParams, internals(t))
-    } else {
-      Error("Wrong number of variables")
-    }
-}
-
-module SamplingDistribution = {
-  type t = [
-    | #SymbolicDist(SymbolicDistTypes.symbolicDist)
-    | #RenderedDist(PointSetTypes.pointSetDist)
-  ]
-
-  let isSamplingDistribution: node => bool = x =>
-    switch x {
-    | #SymbolicDist(_) => true
-    | #RenderedDist(_) => true
-    | _ => false
-    }
-
-  let fromNode: node => result<t, string> = x =>
-    switch x {
-    | #SymbolicDist(n) => Ok(#SymbolicDist(n))
-    | #RenderedDist(n) => Ok(#RenderedDist(n))
-    | _ => Error("Not valid type")
-    }
-
-  let renderIfIsNotSamplingDistribution = (params, t): result<node, string> =>
-    !isSamplingDistribution(t)
-      ? switch Node.render(params, t) {
-        | Ok(r) => Ok(r)
-        | Error(e) => Error(e)
-        }
-      : Ok(t)
-
-  let map = (~renderedDistFn, ~symbolicDistFn, node: node) =>
-    node |> (
-      x =>
-        switch x {
-        | #RenderedDist(r) => Some(renderedDistFn(r))
-        | #SymbolicDist(s) => Some(symbolicDistFn(s))
-        | _ => None
-        }
-    )
-
-  let sampleN = n =>
-    map(~renderedDistFn=PointSetDist.sampleNRendered(n), ~symbolicDistFn=SymbolicDist.T.sampleN(n))
-
-  let getCombinationSamples = (n, algebraicOp, t1: node, t2: node) =>
-    switch (sampleN(n, t1), sampleN(n, t2)) {
-    | (Some(a), Some(b)) =>
-      Some(
-        Belt.Array.zip(a, b) |> E.A.fmap(((a, b)) => Operation.Algebraic.toFn(algebraicOp, a, b)),
-      )
-    | _ => None
-    }
-
-  let combineShapesUsingSampling = (
-    evaluationParams: evaluationParams,
-    algebraicOp,
-    t1: node,
-    t2: node,
-  ) => {
-    let i1 = renderIfIsNotSamplingDistribution(evaluationParams, t1)
-    let i2 = renderIfIsNotSamplingDistribution(evaluationParams, t2)
-    E.R.merge(i1, i2) |> E.R.bind(_, ((a, b)) => {
-      let samples =
-        getCombinationSamples(
-          evaluationParams.samplingInputs.sampleCount,
-          algebraicOp,
-          a,
-          b,
-        ) |> E.O.toResult("Could not get samples")
-
-      let sampleSetDist = samples->E.R.bind(SampleSetDist.make)
-
-      let pointSetDist =
-        sampleSetDist->E.R.bind(r =>
-          SampleSetDist.toPointSetDist(~samplingInputs=evaluationParams.samplingInputs, ~samples=r)
-        )
-      pointSetDist |> E.R.fmap(r => #Normalize(#RenderedDist(r)))
-    })
-  }
-}

packages/squiggle-lang/src/rescript/OldInterpreter/DistPlus.res

@@ -1,87 +0,0 @@
-open PointSetTypes
-
-@genType
-type t = PointSetTypes.distPlus
-
-let pointSetDistIntegral = pointSetDist => PointSetDist.T.Integral.get(pointSetDist)
-let make = (~pointSetDist, ~squiggleString, ()): t => {
-  let integral = pointSetDistIntegral(pointSetDist)
-  {pointSetDist: pointSetDist, integralCache: integral, squiggleString: squiggleString}
-}
-
-let update = (~pointSetDist=?, ~integralCache=?, ~squiggleString=?, t: t) => {
-  pointSetDist: E.O.default(t.pointSetDist, pointSetDist),
-  integralCache: E.O.default(t.integralCache, integralCache),
-  squiggleString: E.O.default(t.squiggleString, squiggleString),
-}
-
-let updateShape = (pointSetDist, t) => {
-  let integralCache = pointSetDistIntegral(pointSetDist)
-  update(~pointSetDist, ~integralCache, t)
-}
-
-let toPointSetDist = ({pointSetDist, _}: t) => pointSetDist
-
-let pointSetDistFn = (fn, {pointSetDist}: t) => fn(pointSetDist)
-
-module T = Distributions.Dist({
-  type t = PointSetTypes.distPlus
-  type integral = PointSetTypes.distPlus
-  let toPointSetDist = toPointSetDist
-  let toContinuous = pointSetDistFn(PointSetDist.T.toContinuous)
-  let toDiscrete = pointSetDistFn(PointSetDist.T.toDiscrete)
-
-  let normalize = (t: t): t => {
-    let normalizedShape = t |> toPointSetDist |> PointSetDist.T.normalize
-    t |> updateShape(normalizedShape)
-  }
-
-  let truncate = (leftCutoff, rightCutoff, t: t): t => {
-    let truncatedShape = t |> toPointSetDist |> PointSetDist.T.truncate(leftCutoff, rightCutoff)
-
-    t |> updateShape(truncatedShape)
-  }
-
-  let xToY = (f, t: t) => t |> toPointSetDist |> PointSetDist.T.xToY(f)
-
-  let minX = pointSetDistFn(PointSetDist.T.minX)
-  let maxX = pointSetDistFn(PointSetDist.T.maxX)
-  let toDiscreteProbabilityMassFraction = pointSetDistFn(
-    PointSetDist.T.toDiscreteProbabilityMassFraction,
-  )
-
-  // This bit is kind of awkward, could probably use rethinking.
-  let integral = (t: t) => updateShape(Continuous(t.integralCache), t)
-
-  let updateIntegralCache = (integralCache: option<PointSetTypes.continuousShape>, t) =>
-    update(~integralCache=E.O.default(t.integralCache, integralCache), t)
-
-  let downsample = (i, t): t => updateShape(t |> toPointSetDist |> PointSetDist.T.downsample(i), t)
-  // todo: adjust for limit, maybe?
-  let mapY = (
-    ~integralSumCacheFn=previousIntegralSum => None,
-    ~integralCacheFn=previousIntegralCache => None,
-    ~fn,
-    {pointSetDist, _} as t: t,
-  ): t => PointSetDist.T.mapY(~integralSumCacheFn, ~fn, pointSetDist) |> updateShape(_, t)
-
-  // get the total of everything
-  let integralEndY = (t: t) => {
-    PointSetDist.T.Integral.sum(toPointSetDist(t))
-  }
-
-  //   TODO: Fix this below, obviously. Adjust for limits
-  let integralXtoY = (f, t: t) => {
-    PointSetDist.T.Integral.xToY(f, toPointSetDist(t))
-  }
-
-  // TODO: This part is broken when there is a limit, if this is supposed to be taken into account.
-  let integralYtoX = (f, t: t) => {
-    PointSetDist.T.Integral.yToX(f, toPointSetDist(t))
-  }
-
-  let mean = (t: t) => {
-    PointSetDist.T.mean(t.pointSetDist)
-  }
-  let variance = (t: t) => PointSetDist.T.variance(t.pointSetDist)
-})

packages/squiggle-lang/src/rescript/OldInterpreter/typeSystem/HardcodedFunctions.res

@@ -1,240 +0,0 @@
-open TypeSystem
-
-let wrongInputsError = (r: array<typedValue>) => {
-  let inputs = r |> E.A.fmap(TypedValue.toString) |> Js.String.concatMany(_, ",")
-  Js.log3("Inputs were", inputs, r)
-  Error("Wrong inputs. The inputs were:" ++ inputs)
-}
-
-let to_: (float, float) => result<node, string> = (low, high) =>
-  switch (low, high) {
-  | (low, high) if low <= 0.0 && low < high =>
-    Ok(#SymbolicDist(SymbolicDist.Normal.from90PercentCI(low, high)))
-  | (low, high) if low < high =>
-    Ok(#SymbolicDist(SymbolicDist.Lognormal.from90PercentCI(low, high)))
-  | (_, _) => Error("Low value must be less than high value.")
-  }
-
-let makeSymbolicFromTwoFloats = (name, fn) =>
-  Function.T.make(
-    ~name,
-    ~outputType=#SamplingDistribution,
-    ~inputTypes=[#Float, #Float],
-    ~run=x =>
-      switch x {
-      | [#Float(a), #Float(b)] => fn(a, b) |> E.R.fmap(r => #SymbolicDist(r))
-      | e => wrongInputsError(e)
-      },
-    (),
-  )
-
-let makeSymbolicFromOneFloat = (name, fn) =>
-  Function.T.make(
-    ~name,
-    ~outputType=#SamplingDistribution,
-    ~inputTypes=[#Float],
-    ~run=x =>
-      switch x {
-      | [#Float(a)] => fn(a) |> E.R.fmap(r => #SymbolicDist(r))
-      | e => wrongInputsError(e)
-      },
-    (),
-  )
-
-let makeDistFloat = (name, fn) =>
-  Function.T.make(
-    ~name,
-    ~outputType=#SamplingDistribution,
-    ~inputTypes=[#SamplingDistribution, #Float],
-    ~run=x =>
-      switch x {
-      | [#SamplingDist(a), #Float(b)] => fn(a, b)
-      | [#RenderedDist(a), #Float(b)] => fn(#RenderedDist(a), b)
-      | e => wrongInputsError(e)
-      },
-    (),
-  )
-
-let makeRenderedDistFloat = (name, fn) =>
-  Function.T.make(
-    ~name,
-    ~outputType=#RenderedDistribution,
-    ~inputTypes=[#RenderedDistribution, #Float],
-    ~shouldCoerceTypes=true,
-    ~run=x =>
-      switch x {
-      | [#RenderedDist(a), #Float(b)] => fn(a, b)
-      | e => wrongInputsError(e)
-      },
-    (),
-  )
-
-let makeDist = (name, fn) =>
-  Function.T.make(
-    ~name,
-    ~outputType=#SamplingDistribution,
-    ~inputTypes=[#SamplingDistribution],
-    ~run=x =>
-      switch x {
-      | [#SamplingDist(a)] => fn(a)
-      | [#RenderedDist(a)] => fn(#RenderedDist(a))
-      | e => wrongInputsError(e)
-      },
-    (),
-  )
-
-let floatFromDist = (
-  distToFloatOp: Operation.distToFloatOperation,
-  t: TypeSystem.samplingDist,
-): result<node, string> =>
-  switch t {
-  | #SymbolicDist(s) =>
-    SymbolicDist.T.operate(distToFloatOp, s) |> E.R.bind(_, v => Ok(#SymbolicDist(#Float(v))))
-  | #RenderedDist(rs) =>
-    PointSetDist.operate(distToFloatOp, rs) |> (v => Ok(#SymbolicDist(#Float(v))))
-  }
-
-let verticalScaling = (scaleOp, rs, scaleBy) => {
-  // scaleBy has to be a single float, otherwise we'll return an error.
-  let fn = (secondary, main) => Operation.Scale.toFn(scaleOp, main, secondary)
-  let integralSumCacheFn = Operation.Scale.toIntegralSumCacheFn(scaleOp)
-  let integralCacheFn = Operation.Scale.toIntegralCacheFn(scaleOp)
-  Ok(
-    #RenderedDist(
-      PointSetDist.T.mapY(
-        ~integralSumCacheFn=integralSumCacheFn(scaleBy),
-        ~integralCacheFn=integralCacheFn(scaleBy),
-        ~fn=fn(scaleBy),
-        rs,
-      ),
-    ),
-  )
-}
-
-module Multimodal = {
-  let getByNameResult = Hash.getByNameResult
-
-  let _paramsToDistsAndWeights = (r: array<typedValue>) =>
-    switch r {
-    | [#Hash(r)] =>
-      let dists =
-        getByNameResult(r, "dists")
-        ->E.R.bind(TypeSystem.TypedValue.toArray)
-        ->E.R.bind(r => r |> E.A.fmap(TypeSystem.TypedValue.toDist) |> E.A.R.firstErrorOrOpen)
-      let weights =
-        getByNameResult(r, "weights")
-        ->E.R.bind(TypeSystem.TypedValue.toArray)
-        ->E.R.bind(r => r |> E.A.fmap(TypeSystem.TypedValue.toFloat) |> E.A.R.firstErrorOrOpen)
-
-      E.R.merge(dists, weights)->E.R.bind(((a, b)) =>
-        E.A.length(b) > E.A.length(a)
-          ? Error("Too many weights provided")
-          : Ok(
-              E.A.zipMaxLength(a, b) |> E.A.fmap(((a, b)) => (
-                a |> E.O.toExn(""),
-                b |> E.O.default(1.0),
-              )),
-            )
-      )
-    | _ => Error("Needs items")
-    }
-  let _runner: array<typedValue> => result<node, string> = r => {
-    let paramsToDistsAndWeights =
-      _paramsToDistsAndWeights(r) |> E.R.fmap(
-        E.A.fmap(((dist, weight)) =>
-          #FunctionCall("scaleMultiply", [dist, #SymbolicDist(#Float(weight))])
-        ),
-      )
-    let pointwiseSum: result<node, string> =
-      paramsToDistsAndWeights->E.R.bind(E.R.errorIfCondition(E.A.isEmpty, "Needs one input"))
-        |> E.R.fmap(r =>
-          r
-          |> Js.Array.sliceFrom(1)
-          |> E.A.fold_left((acc, x) => #PointwiseCombination(#Add, acc, x), E.A.unsafe_get(r, 0))
-        )
-    pointwiseSum
-  }
-
-  let _function = Function.T.make(
-    ~name="multimodal",
-    ~outputType=#SamplingDistribution,
-    ~inputTypes=[#Hash([("dists", #Array(#SamplingDistribution)), ("weights", #Array(#Float))])],
-    ~run=_runner,
-    (),
-  )
-}
-
-let all = [
-  makeSymbolicFromTwoFloats("normal", SymbolicDist.Normal.make),
-  makeSymbolicFromTwoFloats("uniform", SymbolicDist.Uniform.make),
-  makeSymbolicFromTwoFloats("beta", SymbolicDist.Beta.make),
-  makeSymbolicFromTwoFloats("lognormal", SymbolicDist.Lognormal.make),
-  makeSymbolicFromTwoFloats("lognormalFromMeanAndStdDev", SymbolicDist.Lognormal.fromMeanAndStdev),
-  makeSymbolicFromOneFloat("exponential", SymbolicDist.Exponential.make),
-  Function.T.make(
-    ~name="to",
-    ~outputType=#SamplingDistribution,
-    ~inputTypes=[#Float, #Float],
-    ~run=x =>
-      switch x {
-      | [#Float(a), #Float(b)] => to_(a, b)
-      | e => wrongInputsError(e)
-      },
-    (),
-  ),
-  Function.T.make(
-    ~name="triangular",
-    ~outputType=#SamplingDistribution,
-    ~inputTypes=[#Float, #Float, #Float],
-    ~run=x =>
-      switch x {
-      | [#Float(a), #Float(b), #Float(c)] =>
-        SymbolicDist.Triangular.make(a, b, c) |> E.R.fmap(r => #SymbolicDist(r))
-      | e => wrongInputsError(e)
-      },
-    (),
-  ),
-  Function.T.make(
-    ~name="log",
-    ~outputType=#Float,
-    ~inputTypes=[#Float],
-    ~run=x =>
-      switch x {
-      | [#Float(a)] => Ok(#SymbolicDist(#Float(Js.Math.log(a))))
-      | e => wrongInputsError(e)
-      },
-    (),
-  ),
-  makeDistFloat("pdf", (dist, float) => floatFromDist(#Pdf(float), dist)),
-  makeDistFloat("inv", (dist, float) => floatFromDist(#Inv(float), dist)),
-  makeDistFloat("cdf", (dist, float) => floatFromDist(#Cdf(float), dist)),
-  makeDist("mean", dist => floatFromDist(#Mean, dist)),
-  makeDist("sample", dist => floatFromDist(#Sample, dist)),
-  Function.T.make(
-    ~name="render",
-    ~outputType=#RenderedDistribution,
-    ~inputTypes=[#RenderedDistribution],
-    ~run=x =>
-      switch x {
-      | [#RenderedDist(c)] => Ok(#RenderedDist(c))
-      | e => wrongInputsError(e)
-      },
-    (),
-  ),
-  Function.T.make(
-    ~name="normalize",
-    ~outputType=#SamplingDistribution,
-    ~inputTypes=[#SamplingDistribution],
-    ~run=x =>
-      switch x {
-      | [#SamplingDist(#SymbolicDist(c))] => Ok(#SymbolicDist(c))
-      | [#SamplingDist(#RenderedDist(c))] => Ok(#RenderedDist(PointSetDist.T.normalize(c)))
-      | e => wrongInputsError(e)
-      },
-    (),
-  ),
-  makeRenderedDistFloat("scaleExp", (dist, float) => verticalScaling(#Power, dist, float)),
-  makeRenderedDistFloat("scaleMultiply", (dist, float) => verticalScaling(#Multiply, dist, float)),
-  makeRenderedDistFloat("scaleLog", (dist, float) => verticalScaling(#Logarithm, dist, float)),
-  Multimodal._function,
-]

packages/squiggle-lang/src/rescript/OldInterpreter/typeSystem/TypeSystem.res

@@ -1,196 +0,0 @@
-type node = ASTTypes.node
-let getFloat = ASTTypes.Node.getFloat
-
-type samplingDist = [
-  | #SymbolicDist(SymbolicDistTypes.symbolicDist)
-  | #RenderedDist(PointSetTypes.pointSetDist)
-]
-
-type rec hashType = array<(string, _type)>
-and _type = [
-  | #Float
-  | #SamplingDistribution
-  | #RenderedDistribution
-  | #Array(_type)
-  | #Hash(hashType)
-]
-
-type rec hashTypedValue = array<(string, typedValue)>
-and typedValue = [
-  | #Float(float)
-  | #RenderedDist(PointSetTypes.pointSetDist)
-  | #SamplingDist(samplingDist)
-  | #Array(array<typedValue>)
-  | #Hash(hashTypedValue)
-]
-
-type _function = {
-  name: string,
-  inputTypes: array<_type>,
-  outputType: _type,
-  run: array<typedValue> => result<node, string>,
-  shouldCoerceTypes: bool,
-}
-
-type functions = array<_function>
-type inputNodes = array<node>
-
-module TypedValue = {
-  let rec toString: typedValue => string = x =>
-    switch x {
-    | #SamplingDist(_) => "[sampling dist]"
-    | #RenderedDist(_) => "[rendered PointSetDist]"
-    | #Float(f) => "Float: " ++ Js.Float.toString(f)
-    | #Array(a) => "[" ++ ((a |> E.A.fmap(toString) |> Js.String.concatMany(_, ",")) ++ "]")
-    | #Hash(v) =>
-      "{" ++
-      ((v
-      |> E.A.fmap(((name, value)) => name ++ (":" ++ toString(value)))
-      |> Js.String.concatMany(_, ",")) ++
-      "}")
-    }
-
-  let rec fromNode = (node: node): result<typedValue, string> =>
-    switch node {
-    | #SymbolicDist(#Float(r)) => Ok(#Float(r))
-    | #SymbolicDist(s) => Ok(#SamplingDist(#SymbolicDist(s)))
-    | #RenderedDist(s) => Ok(#RenderedDist(s))
-    | #Array(r) => r |> E.A.fmap(fromNode) |> E.A.R.firstErrorOrOpen |> E.R.fmap(r => #Array(r))
-    | #Hash(hash) =>
-      hash
-      |> E.A.fmap(((name, t)) => fromNode(t) |> E.R.fmap(r => (name, r)))
-      |> E.A.R.firstErrorOrOpen
-      |> E.R.fmap(r => #Hash(r))
-    | e => Error("Wrong type: " ++ ASTTypes.Node.toString(e))
-    }
-
-  // todo: Arrays and hashes
-  let rec fromNodeWithTypeCoercion = (evaluationParams, _type: _type, node) =>
-    switch (_type, node) {
-    | (#Float, _) =>
-      switch getFloat(node) {
-      | Some(a) => Ok(#Float(a))
-      | _ => Error("Type Error: Expected float.")
-      }
-    | (#SamplingDistribution, _) =>
-      ASTTypes.SamplingDistribution.renderIfIsNotSamplingDistribution(
-        evaluationParams,
-        node,
-      ) |> E.R.bind(_, fromNode)
-    | (#RenderedDistribution, _) =>
-      ASTTypes.Node.render(evaluationParams, node) |> E.R.bind(_, fromNode)
-    | (#Array(_type), #Array(b)) =>
-      b
-      |> E.A.fmap(fromNodeWithTypeCoercion(evaluationParams, _type))
-      |> E.A.R.firstErrorOrOpen
-      |> E.R.fmap(r => #Array(r))
-    | (#Hash(named), #Hash(r)) =>
-      let keyValues =
-        named |> E.A.fmap(((name, intendedType)) => (name, intendedType, Hash.getByName(r, name)))
-      let typedHash =
-        keyValues
-        |> E.A.fmap(((name, intendedType, optionNode)) =>
-          switch optionNode {
-          | Some(node) =>
-            fromNodeWithTypeCoercion(evaluationParams, intendedType, node) |> E.R.fmap(node => (
-              name,
-              node,
-            ))
-          | None => Error("Hash parameter not present in hash.")
-          }
-        )
-        |> E.A.R.firstErrorOrOpen
-        |> E.R.fmap(r => #Hash(r))
-      typedHash
-    | _ => Error("fromNodeWithTypeCoercion error, sorry.")
-    }
-
-  let toFloat: typedValue => result<float, string> = x =>
-    switch x {
-    | #Float(x) => Ok(x)
-    | _ => Error("Not a float")
-    }
-
-  let toArray: typedValue => result<array<'a>, string> = x =>
-    switch x {
-    | #Array(x) => Ok(x)
-    | _ => Error("Not an array")
-    }
-
-  let toNamed: typedValue => result<hashTypedValue, string> = x =>
-    switch x {
-    | #Hash(x) => Ok(x)
-    | _ => Error("Not a named item")
-    }
-
-  let toDist: typedValue => result<node, string> = x =>
-    switch x {
-    | #SamplingDist(#SymbolicDist(c)) => Ok(#SymbolicDist(c))
-    | #SamplingDist(#RenderedDist(c)) => Ok(#RenderedDist(c))
-    | #RenderedDist(c) => Ok(#RenderedDist(c))
-    | #Float(x) => Ok(#SymbolicDist(#Float(x)))
-    | x => Error("Cannot be converted into a distribution: " ++ toString(x))
-    }
-}
-
-module Function = {
-  type t = _function
-  type ts = functions
-
-  module T = {
-    let make = (~name, ~inputTypes, ~outputType, ~run, ~shouldCoerceTypes=true, _): t => {
-      name: name,
-      inputTypes: inputTypes,
-      outputType: outputType,
-      run: run,
-      shouldCoerceTypes: shouldCoerceTypes,
-    }
-
-    let _inputLengthCheck = (inputNodes: inputNodes, t: t) => {
-      let expectedLength = E.A.length(t.inputTypes)
-      let actualLength = E.A.length(inputNodes)
-      expectedLength == actualLength
-        ? Ok(inputNodes)
-        : Error(
-            "Wrong number of inputs. Expected" ++
-            ((expectedLength |> E.I.toString) ++
-            (". Got:" ++ (actualLength |> E.I.toString))),
-          )
-    }
-
-    let _coerceInputNodes = (evaluationParams, inputTypes, shouldCoerce, inputNodes) =>
-      Belt.Array.zip(inputTypes, inputNodes)
-      |> E.A.fmap(((def, input)) =>
-        shouldCoerce
-          ? TypedValue.fromNodeWithTypeCoercion(evaluationParams, def, input)
-          : TypedValue.fromNode(input)
-      )
-      |> E.A.R.firstErrorOrOpen
-
-    let inputsToTypedValues = (
-      evaluationParams: ASTTypes.evaluationParams,
-      inputNodes: inputNodes,
-      t: t,
-    ) =>
-      _inputLengthCheck(inputNodes, t)->E.R.bind(
-        _coerceInputNodes(evaluationParams, t.inputTypes, t.shouldCoerceTypes),
-      )
-
-    let run = (evaluationParams: ASTTypes.evaluationParams, inputNodes: inputNodes, t: t) =>
-      inputsToTypedValues(evaluationParams, inputNodes, t)->E.R.bind(t.run)
-        |> (
-          x =>
-            switch x {
-            | Ok(i) => Ok(i)
-            | Error(r) => Error("Function " ++ (t.name ++ (" error: " ++ r)))
-            }
-        )
-  }
-
-  module Ts = {
-    let findByName = (ts: ts, n: string) => ts |> Belt.Array.getBy(_, ({name}) => name == n)
-
-    let findByNameAndRun = (ts: ts, n: string, evaluationParams, inputTypes) =>
-      findByName(ts, n) |> E.O.fmap(T.run(evaluationParams, inputTypes))
-  }
-}

packages/squiggle-lang/src/rescript/OldParser/Parser.res

@@ -1,290 +0,0 @@
-module MathJsonToMathJsAdt = {
-  type rec arg =
-    | Symbol(string)
-    | Value(float)
-    | Fn(fn)
-    | Array(array<arg>)
-    | Blocks(array<arg>)
-    | Object(Js.Dict.t<arg>)
-    | Assignment(arg, arg)
-    | FunctionAssignment(fnAssignment)
-  and fn = {
-    name: string,
-    args: array<arg>,
-  }
-  and fnAssignment = {
-    name: string,
-    args: array<string>,
-    expression: arg,
-  }
-
-  let rec run = (j: Js.Json.t) => {
-    open Json.Decode
-    switch field("mathjs", string, j) {
-    | "FunctionNode" =>
-      let args = j |> field("args", array(run))
-      let name = j |> optional(field("fn", field("name", string)))
-      name |> E.O.fmap(name => Fn({name: name, args: args |> E.A.O.concatSomes}))
-    | "OperatorNode" =>
-      let args = j |> field("args", array(run))
-      Some(
-        Fn({
-          name: j |> field("fn", string),
-          args: args |> E.A.O.concatSomes,
-        }),
-      )
-    | "ConstantNode" => optional(field("value", Json.Decode.float), j) |> E.O.fmap(r => Value(r))
-    | "ParenthesisNode" => j |> field("content", run)
-    | "ObjectNode" =>
-      let properties = j |> field("properties", dict(run))
-      Js.Dict.entries(properties)
-      |> E.A.fmap(((key, value)) => value |> E.O.fmap(v => (key, v)))
-      |> E.A.O.concatSomes
-      |> Js.Dict.fromArray
-      |> (r => Some(Object(r)))
-    | "ArrayNode" =>
-      let items = field("items", array(run), j)
-      Some(Array(items |> E.A.O.concatSomes))
-    | "SymbolNode" => Some(Symbol(field("name", string, j)))
-    | "AssignmentNode" =>
-      let object_ = j |> field("object", run)
-      let value_ = j |> field("value", run)
-      switch (object_, value_) {
-      | (Some(o), Some(v)) => Some(Assignment(o, v))
-      | _ => None
-      }
-    | "BlockNode" =>
-      let block = r => r |> field("node", run)
-      let args = j |> field("blocks", array(block)) |> E.A.O.concatSomes
-      Some(Blocks(args))
-    | "FunctionAssignmentNode" =>
-      let name = j |> field("name", string)
-      let args = j |> field("params", array(field("name", string)))
-      let expression = j |> field("expr", run)
-      expression |> E.O.fmap(expression => FunctionAssignment({
-        name: name,
-        args: args,
-        expression: expression,
-      }))
-    | n =>
-      Js.log3("Couldn't parse mathjs node", j, n)
-      None
-    }
-  }
-}
-
-module MathAdtToDistDst = {
-  open MathJsonToMathJsAdt
-
-  let handleSymbol = sym => Ok(#Symbol(sym))
-
-  // TODO: This only works on the top level, which needs to be refactored. Also, I think functions don't need to be done like this anymore.
-  module MathAdtCleaner = {
-    let transformWithSymbol = (f: float, s: string) =>
-      switch s {
-      | "K" => Some(f *. 1000.)
-      | "M" => Some(f *. 1000000.)
-      | "B" => Some(f *. 1000000000.)
-      | "T" => Some(f *. 1000000000000.)
-      | _ => None
-      }
-    let rec run = x =>
-      switch x {
-      | Fn({name: "multiply", args: [Value(f), Symbol(s)]}) as doNothing =>
-        transformWithSymbol(f, s) |> E.O.fmap(r => Value(r)) |> E.O.default(doNothing)
-      | Fn({name: "unaryMinus", args: [Value(f)]}) => Value(-1.0 *. f)
-      | Fn({name, args}) => Fn({name: name, args: args |> E.A.fmap(run)})
-      | Array(args) => Array(args |> E.A.fmap(run))
-      | Symbol(s) => Symbol(s)
-      | Value(v) => Value(v)
-      | Blocks(args) => Blocks(args |> E.A.fmap(run))
-      | Assignment(a, b) => Assignment(a, run(b))
-      | FunctionAssignment(a) => FunctionAssignment(a)
-      | Object(v) =>
-        Object(
-          v
-          |> Js.Dict.entries
-          |> E.A.fmap(((key, value)) => (key, run(value)))
-          |> Js.Dict.fromArray,
-        )
-      }
-  }
-
-  let lognormal = (args, parseArgs, nodeParser) =>
-    switch args {
-    | [Object(o)] =>
-      let g = s =>
-        Js.Dict.get(o, s) |> E.O.toResult("Variable was empty") |> E.R.bind(_, nodeParser)
-      switch (g("mean"), g("stdev"), g("mu"), g("sigma")) {
-      | (Ok(mean), Ok(stdev), _, _) =>
-        Ok(#FunctionCall("lognormalFromMeanAndStdDev", [mean, stdev]))
-      | (_, _, Ok(mu), Ok(sigma)) => Ok(#FunctionCall("lognormal", [mu, sigma]))
-      | _ => Error("Lognormal distribution needs either mean and stdev or mu and sigma")
-      }
-    | _ => parseArgs() |> E.R.fmap((args: array<ASTTypes.node>) => #FunctionCall("lognormal", args))
-    }
-
-  //  Error("Dotwise exponentiation needs two operands")
-  let operationParser = (name: string, args: result<array<ASTTypes.node>, string>): result<
-    ASTTypes.node,
-    string,
-  > => {
-    let toOkAlgebraic = r => Ok(#AlgebraicCombination(r))
-    let toOkPointwise = r => Ok(#PointwiseCombination(r))
-    let toOkTruncate = r => Ok(#Truncate(r))
-    args |> E.R.bind(_, args =>
-      switch (name, args) {
-      | ("add", [l, r]) => toOkAlgebraic((#Add, l, r))
-      | ("add", _) => Error("Addition needs two operands")
-      | ("unaryMinus", [l]) => toOkAlgebraic((#Multiply, #SymbolicDist(#Float(-1.0)), l))
-      | ("subtract", [l, r]) => toOkAlgebraic((#Subtract, l, r))
-      | ("subtract", _) => Error("Subtraction needs two operands")
-      | ("multiply", [l, r]) => toOkAlgebraic((#Multiply, l, r))
-      | ("multiply", _) => Error("Multiplication needs two operands")
-      | ("pow", [l, r]) => toOkAlgebraic((#Power, l, r))
-      | ("pow", _) => Error("Exponentiation needs two operands")
-      | ("dotMultiply", [l, r]) => toOkPointwise((#Multiply, l, r))
-      | ("dotMultiply", _) => Error("Dotwise multiplication needs two operands")
-      | ("dotPow", [l, r]) => toOkPointwise((#Power, l, r))
-      | ("dotPow", _) => Error("Dotwise exponentiation needs two operands")
-      | ("rightLogShift", [l, r]) => toOkPointwise((#Add, l, r))
-      | ("rightLogShift", _) => Error("Dotwise addition needs two operands")
-      | ("divide", [l, r]) => toOkAlgebraic((#Divide, l, r))
-      | ("divide", _) => Error("Division needs two operands")
-      | ("leftTruncate", [d, #SymbolicDist(#Float(lc))]) => toOkTruncate((Some(lc), None, d))
-      | ("leftTruncate", _) =>
-        Error("leftTruncate needs two arguments: the expression and the cutoff")
-      | ("rightTruncate", [d, #SymbolicDist(#Float(rc))]) => toOkTruncate((None, Some(rc), d))
-      | ("rightTruncate", _) =>
-        Error("rightTruncate needs two arguments: the expression and the cutoff")
-      | ("truncate", [d, #SymbolicDist(#Float(lc)), #SymbolicDist(#Float(rc))]) =>
-        toOkTruncate((Some(lc), Some(rc), d))
-      | ("truncate", _) => Error("truncate needs three arguments: the expression and both cutoffs")
-      | _ => Error("This type not currently supported")
-      }
-    )
-  }
-
-  let functionParser = (
-    nodeParser: MathJsonToMathJsAdt.arg => Belt.Result.t<ASTTypes.node, string>,
-    name: string,
-    args: array<MathJsonToMathJsAdt.arg>,
-  ): result<ASTTypes.node, string> => {
-    let parseArray = ags => ags |> E.A.fmap(nodeParser) |> E.A.R.firstErrorOrOpen
-    let parseArgs = () => parseArray(args)
-    switch name {
-    | "lognormal" => lognormal(args, parseArgs, nodeParser)
-    | "multimodal"
-    | "add"
-    | "subtract"
-    | "multiply"
-    | "unaryMinus"
-    | "dotMultiply"
-    | "dotPow"
-    | "rightLogShift"
-    | "divide"
-    | "pow"
-    | "leftTruncate"
-    | "rightTruncate"
-    | "truncate" =>
-      operationParser(name, parseArgs())
-    | "mm" =>
-      let weights =
-        args
-        |> E.A.last
-        |> E.O.bind(_, x =>
-          switch x {
-          | Array(values) => Some(parseArray(values))
-          | _ => None
-          }
-        )
-      let possibleDists = E.O.isSome(weights)
-        ? Belt.Array.slice(args, ~offset=0, ~len=E.A.length(args) - 1)
-        : args
-      let dists = parseArray(possibleDists)
-      switch (weights, dists) {
-      | (Some(Error(r)), _) => Error(r)
-      | (_, Error(r)) => Error(r)
-      | (None, Ok(dists)) =>
-        let hash: ASTTypes.node = #FunctionCall(
-          "multimodal",
-          [#Hash([("dists", #Array(dists)), ("weights", #Array([]))])],
-        )
-        Ok(hash)
-      | (Some(Ok(weights)), Ok(dists)) =>
-        let hash: ASTTypes.node = #FunctionCall(
-          "multimodal",
-          [#Hash([("dists", #Array(dists)), ("weights", #Array(weights))])],
-        )
-        Ok(hash)
-      }
-    | name => parseArgs() |> E.R.fmap((args: array<ASTTypes.node>) => #FunctionCall(name, args))
-    }
-  }
-
-  let rec nodeParser: MathJsonToMathJsAdt.arg => result<ASTTypes.node, string> = x =>
-    switch x {
-    | Value(f) => Ok(#SymbolicDist(#Float(f)))
-    | Symbol(sym) => Ok(#Symbol(sym))
-    | Fn({name, args}) => functionParser(nodeParser, name, args)
-    | _ => Error("This type not currently supported")
-    }
-
-  // | FunctionAssignment({name, args, expression}) => {
-  //   let evaluatedExpression = run(expression);
-  //   `Function(_ => Ok(evaluatedExpression));
-  // }
-  let rec topLevel = (r): result<ASTTypes.program, string> =>
-    switch r {
-    | FunctionAssignment({name, args, expression}) =>
-      switch nodeParser(expression) {
-      | Ok(r) => Ok([#Assignment(name, #Function(args, r))])
-      | Error(r) => Error(r)
-      }
-    | Value(_) as r => nodeParser(r) |> E.R.fmap(r => [#Expression(r)])
-    | Fn(_) as r => nodeParser(r) |> E.R.fmap(r => [#Expression(r)])
-    | Array(_) => Error("Array not valid as top level")
-    | Symbol(s) => handleSymbol(s) |> E.R.fmap(r => [#Expression(r)])
-    | Object(_) => Error("Object not valid as top level")
-    | Assignment(name, value) =>
-      switch name {
-      | Symbol(symbol) => nodeParser(value) |> E.R.fmap(r => [#Assignment(symbol, r)])
-      | _ => Error("Symbol not a string")
-      }
-    | Blocks(blocks) =>
-      blocks |> E.A.fmap(b => topLevel(b)) |> E.A.R.firstErrorOrOpen |> E.R.fmap(E.A.concatMany)
-    }
-
-  let run = (r): result<ASTTypes.program, string> => r |> MathAdtCleaner.run |> topLevel
-}
-
-/* The MathJs parser doesn't support '.+' syntax, but we want it because it
-   would make sense with '.*'. Our workaround is to change this to >>>, which is
-   logShift in mathJS. We don't expect to use logShift anytime soon, so this tradeoff
-   seems fine.
- */
-let pointwiseToRightLogShift = Js.String.replaceByRe(%re("/\.\+/g"), ">>>")
-
-let fromString2 = str => {
-  /* We feed the user-typed string into Mathjs.parseMath,
-       which returns a JSON with (hopefully) a single-element array.
-       This array element is the top-level node of a nested-object tree
-       representing the functions/arguments/values/etc. in the string.
-
-       The function MathJsonToMathJsAdt then recursively unpacks this JSON into a typed data structure we can use.
-       Inside of this function, MathAdtToDistDst is called whenever a distribution function is encountered.
- */
-  let mathJsToJson = str |> pointwiseToRightLogShift |> Mathjs.parseMath
-
-  let mathJsParse = E.R.bind(mathJsToJson, r =>
-    switch MathJsonToMathJsAdt.run(r) {
-    | Some(r) => Ok(r)
-    | None => Error("MathJsParse Error")
-    }
-  )
-
-  let value = E.R.bind(mathJsParse, MathAdtToDistDst.run)
-  value
-}
-
-let fromString = str => fromString2(str)

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

@@ -1,185 +0,0 @@
-// TODO: This setup is more confusing than it should be, there's more work to do in cleanup here.
-module Inputs = {
-  module SamplingInputs = {
-    type t = {
-      sampleCount: option<int>,
-      outputXYPoints: option<int>,
-      kernelWidth: option<float>,
-      pointDistLength: option<int>,
-    }
-  }
-  let defaultRecommendedLength = 100
-  let defaultShouldDownsample = true
-
-  type inputs = {
-    squiggleString: string,
-    samplingInputs: SamplingInputs.t,
-    environment: ASTTypes.environment,
-  }
-
-  let empty: SamplingInputs.t = {
-    sampleCount: None,
-    outputXYPoints: None,
-    kernelWidth: None,
-    pointDistLength: None,
-  }
-
-  let make = (
-    ~samplingInputs=empty,
-    ~squiggleString,
-    ~environment=ASTTypes.Environment.empty,
-    (),
-  ): inputs => {
-    samplingInputs: samplingInputs,
-    squiggleString: squiggleString,
-    environment: environment,
-  }
-}
-
-type exportDistribution = [
-  | #DistPlus(DistPlus.t)
-  | #Float(float)
-  | #Function(float => Belt.Result.t<DistPlus.t, string>)
-]
-
-type exportEnv = array<(string, ASTTypes.node)>
-
-type exportType = {
-  environment: exportEnv,
-  exports: array<exportDistribution>,
-}
-
-module Internals = {
-  let addVariable = (
-    {samplingInputs, squiggleString, environment}: Inputs.inputs,
-    str,
-    node,
-  ): Inputs.inputs => {
-    samplingInputs: samplingInputs,
-    squiggleString: squiggleString,
-    environment: ASTTypes.Environment.update(environment, str, _ => Some(node)),
-  }
-
-  type outputs = {
-    graph: ASTTypes.node,
-    pointSetDist: PointSetTypes.pointSetDist,
-  }
-  let makeOutputs = (graph, shape): outputs => {graph: graph, pointSetDist: shape}
-
-  let makeInputs = (inputs: Inputs.inputs): SamplingInputs.samplingInputs => {
-    sampleCount: inputs.samplingInputs.sampleCount |> E.O.default(10000),
-    outputXYPoints: inputs.samplingInputs.outputXYPoints |> E.O.default(10000),
-    kernelWidth: inputs.samplingInputs.kernelWidth,
-    pointSetDistLength: inputs.samplingInputs.pointDistLength |> E.O.default(10000),
-  }
-
-  let runNode = (inputs, node) => AST.toLeaf(makeInputs(inputs), inputs.environment, node)
-
-  let renderIfNeeded = (inputs: Inputs.inputs, node: ASTTypes.node): result<
-    ASTTypes.node,
-    string,
-  > =>
-    node |> (
-      x =>
-        switch x {
-        | #Normalize(_) as n
-        | #SymbolicDist(_) as n =>
-          #Render(n)
-          |> runNode(inputs)
-          |> (
-            x =>
-              switch x {
-              | Ok(#RenderedDist(_)) as r => r
-              | Error(r) => Error(r)
-              | _ => Error("Didn't render, but intended to")
-              }
-          )
-
-        | n => Ok(n)
-        }
-    )
-
-  let outputToDistPlus = (inputs: Inputs.inputs, pointSetDist: PointSetTypes.pointSetDist) =>
-    DistPlus.make(~pointSetDist, ~squiggleString=Some(inputs.squiggleString), ())
-
-  let rec returnDist = (
-    functionInfo: (array<string>, ASTTypes.node),
-    inputs: Inputs.inputs,
-    env: ASTTypes.environment,
-  ) => {
-    (input: float) => {
-      let foo: Inputs.inputs = {...inputs, environment: env}
-      evaluateFunction(foo, functionInfo, [#SymbolicDist(#Float(input))]) |> E.R.bind(_, a =>
-        switch a {
-        | #DistPlus(d) => Ok(DistPlus.T.normalize(d))
-        | n =>
-          Js.log2("Error here", n)
-          Error("wrong type")
-        }
-      )
-    }
-  }
-  // TODO: Consider using ExpressionTypes.ExpressionTree.getFloat or similar in this function
-  and coersionToExportedTypes = (inputs, env: ASTTypes.environment, ex: ASTTypes.node): result<
-    exportDistribution,
-    string,
-  > =>
-    ex
-    |> renderIfNeeded(inputs)
-    |> E.R.bind(_, x =>
-      switch x {
-      | #RenderedDist(Discrete({xyShape: {xs: [x], ys: [1.0]}})) => Ok(#Float(x))
-      | #SymbolicDist(#Float(x)) => Ok(#Float(x))
-      | #RenderedDist(n) => Ok(#DistPlus(outputToDistPlus(inputs, n)))
-      | #Function(n) => Ok(#Function(returnDist(n, inputs, env)))
-      | n => Error("Didn't output a rendered distribution. Format:" ++ AST.toString(n))
-      }
-    )
-
-  and evaluateFunction = (inputs: Inputs.inputs, fn: (array<string>, ASTTypes.node), fnInputs) => {
-    let output = AST.runFunction(makeInputs(inputs), inputs.environment, fnInputs, fn)
-    output |> E.R.bind(_, coersionToExportedTypes(inputs, inputs.environment))
-  }
-
-  let runProgram = (inputs: Inputs.inputs, p: ASTTypes.program) => {
-    let ins = ref(inputs)
-    p
-    |> E.A.fmap(x =>
-      switch x {
-      | #Assignment(name, node) =>
-        ins := addVariable(ins.contents, name, node)
-        None
-      | #Expression(node) => Some(runNode(ins.contents, node))
-      }
-    )
-    |> E.A.O.concatSomes
-    |> E.A.R.firstErrorOrOpen
-    |> E.R.bind(_, d =>
-      d
-      |> E.A.fmap(x => coersionToExportedTypes(inputs, ins.contents.environment, x))
-      |> E.A.R.firstErrorOrOpen
-    )
-    |> E.R.fmap(ex => {
-      environment: Belt.Map.String.toArray(ins.contents.environment),
-      exports: ex,
-    })
-  }
-
-  let inputsToLeaf = (inputs: Inputs.inputs) =>
-    Parser.fromString(inputs.squiggleString) |> E.R.bind(_, g => runProgram(inputs, g))
-}
-
-@genType
-let runAll: (string, Inputs.SamplingInputs.t, exportEnv) => result<exportType, string> = (
-  squiggleString,
-  samplingInputs,
-  environment,
-) => {
-  let inputs = Inputs.make(
-    ~samplingInputs,
-    ~squiggleString,
-    ~environment=Belt.Map.String.fromArray(environment),
-    (),
-  )
-  Internals.inputsToLeaf(inputs)
-}

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

@@ -9,6 +9,13 @@ type algebraicOperation = [
   | #Power
   | #Logarithm
 ]
+
+type convolutionOperation = [
+  | #Add
+  | #Multiply
+  | #Subtract
+]
+
 @genType
 type pointwiseOperation = [#Add | #Multiply | #Power]
 type scaleOperation = [#Multiply | #Power | #Logarithm | #Divide]
@@ -20,6 +27,16 @@ type distToFloatOperation = [
   | #Sample
 ]
 
+module Convolution = {
+  type t = convolutionOperation
+  let toFn: (t, float, float) => float = x =>
+    switch x {
+    | #Add => \"+."
+    | #Subtract => \"-."
+    | #Multiply => \"*."
+    }
+}
+
 module Algebraic = {
   type t = algebraicOperation
   let toFn: (t, float, float) => float = x =>