It compiles!

__tests__/Distributions__Test.re

@@ -24,7 +24,7 @@ let makeTestCloseEquality = (~only=false, str, item1, item2, ~digits) =>
 describe("Shape", () => {
   describe("Continuous", () => {
     open Distributions.Continuous;
-    let continuous = make(`Linear, shape);
+    let continuous = make(`Linear, shape, None);
     makeTest("minX", T.minX(continuous), 1.0);
     makeTest("maxX", T.maxX(continuous), 8.0);
     makeTest(
@@ -57,7 +57,7 @@ describe("Shape", () => {
         );
       });
       describe("when Stepwise", () => {
-        let continuous = make(`Stepwise, shape);
+        let continuous = make(`Stepwise, shape, None);
         makeTest(
           "at 4.0",
           T.xToY(4., continuous),
@@ -89,7 +89,7 @@ describe("Shape", () => {
       "toLinear",
       {
         let continuous =
-          make(`Stepwise, {xs: [|1., 4., 8.|], ys: [|0.1, 5., 1.0|]});
+          make(`Stepwise, {xs: [|1., 4., 8.|], ys: [|0.1, 5., 1.0|]}, None);
         continuous |> toLinear |> E.O.fmap(getShape);
       },
       Some({
@@ -100,7 +100,7 @@ describe("Shape", () => {
     makeTest(
       "toLinear",
       {
-        let continuous = make(`Stepwise, {xs: [|0.0|], ys: [|0.3|]});
+        let continuous = make(`Stepwise, {xs: [|0.0|], ys: [|0.3|]}, None);
         continuous |> toLinear |> E.O.fmap(getShape);
       },
       Some({xs: [|0.0|], ys: [|0.3|]}),
@@ -123,7 +123,7 @@ describe("Shape", () => {
     makeTest(
       "integralEndY",
       continuous
-      |> T.scaleToIntegralSum(~intendedSum=1.0)
+      |> T.normalize //scaleToIntegralSum(~intendedSum=1.0)
       |> T.Integral.sum(~cache=None),
       1.0,
     );
@@ -135,12 +135,12 @@ describe("Shape", () => {
       xs: [|1., 4., 8.|],
       ys: [|0.3, 0.5, 0.2|],
     };
-    let discrete = shape;
+    let discrete = make(shape, None);
     makeTest("minX", T.minX(discrete), 1.0);
     makeTest("maxX", T.maxX(discrete), 8.0);
     makeTest(
       "mapY",
-      T.mapY(r => r *. 2.0, discrete) |> (r => r.ys),
+      T.mapY(r => r *. 2.0, discrete) |> (r => getShape(r).ys),
       [|0.6, 1.0, 0.4|],
     );
     makeTest(
@@ -160,19 +160,22 @@ describe("Shape", () => {
     );
     makeTest(
       "scaleBy",
-      T.scaleBy(~scale=4.0, discrete),
+      scaleBy(~scale=4.0, discrete),
-      {xs: [|1., 4., 8.|], ys: [|1.2, 2.0, 0.8|]},
+      make({xs: [|1., 4., 8.|], ys: [|1.2, 2.0, 0.8|]}, None),
     );
     makeTest(
-      "scaleToIntegralSum",
+      "normalize, then scale by 4.0",
-      T.scaleToIntegralSum(~intendedSum=4.0, discrete),
+      discrete
-      {xs: [|1., 4., 8.|], ys: [|1.2, 2.0, 0.8|]},
+      |> T.normalize
+      |> scaleBy(~scale=4.0),
+      make({xs: [|1., 4., 8.|], ys: [|1.2, 2.0, 0.8|]}, None),
     );
     makeTest(
       "scaleToIntegralSum: back and forth",
       discrete
-      |> T.scaleToIntegralSum(~intendedSum=4.0)
+      |> T.normalize
-      |> T.scaleToIntegralSum(~intendedSum=1.0),
+      |> scaleBy(~scale=4.0)
+      |> T.normalize,
       discrete,
     );
     makeTest(
@@ -181,12 +184,13 @@ describe("Shape", () => {
       Distributions.Continuous.make(
         `Stepwise,
         {xs: [|1., 4., 8.|], ys: [|0.3, 0.8, 1.0|]},
+        None
       ),
     );
     makeTest(
       "integral with 1 element",
-      T.Integral.get(~cache=None, {xs: [|0.0|], ys: [|1.0|]}),
+      T.Integral.get(~cache=None, Distributions.Discrete.make({xs: [|0.0|], ys: [|1.0|]}, None)),
-      Distributions.Continuous.make(`Stepwise, {xs: [|0.0|], ys: [|1.0|]}),
+      Distributions.Continuous.make(`Stepwise, {xs: [|0.0|], ys: [|1.0|]}, None),
     );
     makeTest(
       "integralXToY",
@@ -205,27 +209,22 @@ describe("Shape", () => {
 
   describe("Mixed", () => {
     open Distributions.Mixed;
-    let discrete: DistTypes.xyShape = {
+    let discreteShape: DistTypes.xyShape = {
       xs: [|1., 4., 8.|],
       ys: [|0.3, 0.5, 0.2|],
     };
+    let discrete = Distributions.Discrete.make(discreteShape, None);
     let continuous =
       Distributions.Continuous.make(
         `Linear,
         {xs: [|3., 7., 14.|], ys: [|0.058, 0.082, 0.124|]},
+        None
       )
-      |> Distributions.Continuous.T.scaleToIntegralSum(~intendedSum=1.0);
+      |> Distributions.Continuous.T.normalize; //scaleToIntegralSum(~intendedSum=1.0);
-    let mixed =
+    let mixed = Distributions.Mixed.make(
-      MixedShapeBuilder.build(
         ~continuous,
         ~discrete,
-        ~assumptions={
+      );
-          continuous: ADDS_TO_CORRECT_PROBABILITY,
-          discrete: ADDS_TO_CORRECT_PROBABILITY,
-          discreteProbabilityMass: Some(0.5),
-        },
-      )
-      |> E.O.toExn("");
     makeTest("minX", T.minX(mixed), 1.0);
     makeTest("maxX", T.maxX(mixed), 14.0);
     makeTest(
@@ -243,9 +242,9 @@ describe("Shape", () => {
                 0.24775224775224775,
               |],
             },
+            None
           ),
-        ~discrete={xs: [|1., 4., 8.|], ys: [|0.6, 1.0, 0.4|]},
+        ~discrete=Distributions.Discrete.make({xs: [|1., 4., 8.|], ys: [|0.6, 1.0, 0.4|]}, None)
-        ~discreteProbabilityMassFraction=0.5,
       ),
     );
     makeTest(
@@ -266,7 +265,7 @@ describe("Shape", () => {
     makeTest("integralEndY", T.Integral.sum(~cache=None, mixed), 1.0);
     makeTest(
       "scaleBy",
-      T.scaleBy(~scale=2.0, mixed),
+      Distributions.Mixed.scaleBy(~scale=2.0, mixed),
       Distributions.Mixed.make(
         ~continuous=
           Distributions.Continuous.make(
@@ -279,9 +278,9 @@ describe("Shape", () => {
                 0.24775224775224775,
               |],
             },
+            None
           ),
-        ~discrete={xs: [|1., 4., 8.|], ys: [|0.6, 1.0, 0.4|]},
+        ~discrete=Distributions.Discrete.make({xs: [|1., 4., 8.|], ys: [|0.6, 1.0, 0.4|]}, None),
-        ~discreteProbabilityMassFraction=0.5,
       ),
     );
     makeTest(
@@ -303,33 +302,30 @@ describe("Shape", () => {
             1.0,
           |],
       },
+      None,
       ),
     );
   });
 
   describe("Distplus", () => {
     open Distributions.DistPlus;
-    let discrete: DistTypes.xyShape = {
+    let discreteShape: DistTypes.xyShape = {
       xs: [|1., 4., 8.|],
       ys: [|0.3, 0.5, 0.2|],
     };
+    let discrete = Distributions.Discrete.make(discreteShape, None);
     let continuous =
       Distributions.Continuous.make(
         `Linear,
         {xs: [|3., 7., 14.|], ys: [|0.058, 0.082, 0.124|]},
+        None
       )
-      |> Distributions.Continuous.T.scaleToIntegralSum(~intendedSum=1.0);
+      |> Distributions.Continuous.T.normalize; //scaleToIntegralSum(~intendedSum=1.0);
     let mixed =
-      MixedShapeBuilder.build(
+      Distributions.Mixed.make(
         ~continuous,
         ~discrete,
-        ~assumptions={
+      );
-          continuous: ADDS_TO_CORRECT_PROBABILITY,
-          discrete: ADDS_TO_CORRECT_PROBABILITY,
-          discreteProbabilityMass: Some(0.5),
-        },
-      )
-      |> E.O.toExn("");
     let distPlus =
       Distributions.DistPlus.make(
         ~shape=Mixed(mixed),
@@ -374,6 +370,7 @@ describe("Shape", () => {
               1.0,
             |],
           },
+          None,
         ),
       ),
     );
@@ -386,9 +383,9 @@ describe("Shape", () => {
     let numSamples = 10000;
     open Distributions.Shape;
     let normal: SymbolicDist.dist = `Normal({mean, stdev});
-    let normalShape = TreeNode.toShape(numSamples, normal);
+    let normalShape = TreeNode.toShape(numSamples, `DistData(`Symbolic(normal)));
     let lognormal = SymbolicDist.Lognormal.fromMeanAndStdev(mean, stdev);
-    let lognormalShape = TreeNode.toShape(numSamples, lognormal);
+    let lognormalShape = TreeNode.toShape(numSamples, `DistData(`Symbolic(lognormal)));
 
     makeTestCloseEquality(
       "Mean of a normal",

src/components/DistBuilder.re

@@ -17,7 +17,7 @@ module FormConfig = [%lenses
     //
     sampleCount: string,
     outputXYPoints: string,
-    truncateTo: string,
+    downsampleTo: string,
     kernelWidth: string,
   }
 ];
@@ -25,7 +25,7 @@ module FormConfig = [%lenses
 type options = {
   sampleCount: int,
   outputXYPoints: int,
-  truncateTo: option(int),
+  downsampleTo: option(int),
   kernelWidth: option(float),
 };
 
@@ -115,7 +115,7 @@ type inputs = {
   samplingInputs: RenderTypes.ShapeRenderer.Sampling.inputs,
   guesstimatorString: string,
   length: int,
-  shouldTruncateSampledDistribution: int,
+  shouldDownsampleSampledDistribution: int,
 };
 
 module DemoDist = {
@@ -141,8 +141,8 @@ module DemoDist = {
                  kernelWidth: options.kernelWidth,
                },
                ~distPlusIngredients,
-               ~shouldTruncate=options.truncateTo |> E.O.isSome,
+               ~shouldDownsample=options.downsampleTo |> E.O.isSome,
-               ~recommendedLength=options.truncateTo |> E.O.default(10000),
+               ~recommendedLength=options.downsampleTo |> E.O.default(10000),
                (),
              );
            let response = DistPlusRenderer.run(inputs);
@@ -182,7 +182,7 @@ let make = () => {
         unit: "days",
         sampleCount: "30000",
         outputXYPoints: "10000",
-        truncateTo: "1000",
+        downsampleTo: "1000",
         kernelWidth: "5",
       },
       (),
@@ -210,7 +210,7 @@ let make = () => {
   let sampleCount = reform.state.values.sampleCount |> Js.Float.fromString;
   let outputXYPoints =
     reform.state.values.outputXYPoints |> Js.Float.fromString;
-  let truncateTo = reform.state.values.truncateTo |> Js.Float.fromString;
+  let downsampleTo = reform.state.values.downsampleTo |> Js.Float.fromString;
   let kernelWidth = reform.state.values.kernelWidth |> Js.Float.fromString;
 
   let domain =
@@ -252,20 +252,20 @@ let make = () => {
     };
 
   let options =
-    switch (sampleCount, outputXYPoints, truncateTo) {
+    switch (sampleCount, outputXYPoints, downsampleTo) {
     | (_, _, _)
         when
           !Js.Float.isNaN(sampleCount)
           && !Js.Float.isNaN(outputXYPoints)
-          && !Js.Float.isNaN(truncateTo)
+          && !Js.Float.isNaN(downsampleTo)
           && sampleCount > 10.
           && outputXYPoints > 10. =>
       Some({
         sampleCount: sampleCount |> int_of_float,
         outputXYPoints: outputXYPoints |> int_of_float,
-        truncateTo:
+        downsampleTo:
-          int_of_float(truncateTo) > 0
+          int_of_float(downsampleTo) > 0
-            ? Some(int_of_float(truncateTo)) : None,
+            ? Some(int_of_float(downsampleTo)) : None,
         kernelWidth: kernelWidth == 0.0 ? None : Some(kernelWidth),
       })
     | _ => None
@@ -287,7 +287,7 @@ let make = () => {
         reform.state.values.unit,
         reform.state.values.sampleCount,
         reform.state.values.outputXYPoints,
-        reform.state.values.truncateTo,
+        reform.state.values.downsampleTo,
         reform.state.values.kernelWidth,
         reloader |> string_of_int,
       |],
@@ -481,7 +481,7 @@ let make = () => {
               />
             </Col>
             <Col span=4>
-              <FieldFloat field=FormConfig.TruncateTo label="Truncate To" />
+              <FieldFloat field=FormConfig.DownsampleTo label="Downsample To" />
             </Col>
             <Col span=4>
               <FieldFloat field=FormConfig.KernelWidth label="Kernel Width" />

src/components/DistBuilder3.re

@@ -43,7 +43,7 @@ module DemoDist = {
 
     let str =
       switch (parsed1) {
-      | Ok(r) => SymbolicDist.toString(r)
+      | Ok(r) => TreeNode.toString(r)
       | Error(e) => e
       };
 
@@ -58,7 +58,7 @@ module DemoDist = {
                ~guesstimatorString=None,
                (),
              )
-             |> Distributions.DistPlus.T.scaleToIntegralSum(~intendedSum=1.0);
+             |> Distributions.DistPlus.T.normalize;
            <DistPlusPlot distPlus />;
          })
       |> E.O.default(ReasonReact.null);

src/distPlus/distribution/Distributions.re

@@ -3,7 +3,8 @@ module type dist = {
   type integral;
   let minX: t => float;
   let maxX: t => float;
-  let mapY: (~knownIntegralSumFn: float => option(float)=?, float => float, t) => t;
+  let mapY:
+    (~knownIntegralSumFn: float => option(float)=?, float => float, t) => t;
   let xToY: (float, t) => DistTypes.mixedPoint;
   let toShape: t => DistTypes.shape;
   let toContinuous: t => option(DistTypes.continuousShape);
@@ -13,6 +14,7 @@ module type dist = {
   let normalizedToDiscrete: t => option(DistTypes.discreteShape);
   let toDiscreteProbabilityMassFraction: t => float;
   let downsample: (~cache: option(integral)=?, int, t) => t;
+  let truncate: (option(float), option(float), t) => t;
 
   let integral: (~cache: option(integral), t) => integral;
   let integralEndY: (~cache: option(integral), t) => float;
@@ -38,6 +40,7 @@ module Dist = (T: dist) => {
   let toContinuous = T.toContinuous;
   let toDiscrete = T.toDiscrete;
   let normalize = T.normalize;
+  let truncate = T.truncate;
   let normalizedToContinuous = T.normalizedToContinuous;
   let normalizedToDiscrete = T.normalizedToDiscrete;
   let mean = T.mean;
@@ -52,7 +55,22 @@ module Dist = (T: dist) => {
   };
 };
 
-module Continuous {
+module Common = {
+  let combineIntegralSums =
+      (
+        combineFn: (float, float) => option(float),
+        t1KnownIntegralSum: option(float),
+        t2KnownIntegralSum: option(float),
+      ) => {
+    switch (t1KnownIntegralSum, t2KnownIntegralSum) {
+    | (None, _)
+    | (_, None) => None
+    | (Some(s1), Some(s2)) => combineFn(s1, s2)
+    };
+  };
+};
+
+module Continuous = {
   type t = DistTypes.continuousShape;
   let getShape = (t: t) => t.xyShape;
   let interpolation = (t: t) => t.interpolation;
@@ -78,17 +96,21 @@ module Continuous {
     knownIntegralSum: Some(0.0),
   };
   let combine =
-      (fn, t1: DistTypes.continuousShape, t2: DistTypes.continuousShape)
+      (
+        ~knownIntegralSumsFn,
+        fn,
+        t1: DistTypes.continuousShape,
+        t2: DistTypes.continuousShape,
+      )
       : DistTypes.continuousShape => {
-
     // If we're adding the distributions, and we know the total of each, then we
     // can just sum them up. Otherwise, all bets are off.
     let combinedIntegralSum =
-      switch (fn, t1.knownIntegralSum, t2.knownIntegralSum) {
+      Common.combineIntegralSums(
-      | (_, None, _)
+        knownIntegralSumsFn,
-      | (_, _, None) => None
+        t1.knownIntegralSum,
-      | ((+.), Some(s1), Some(s2)) => Some(s1 +. s2)
+        t2.knownIntegralSum,
-      };
+      );
 
     make(
       `Linear,
@@ -102,7 +124,6 @@ module Continuous {
       combinedIntegralSum,
     );
   };
-  let reduce = (fn, items) => items |> E.A.fold_left(combine(fn), empty);
 
   let toLinear = (t: t): option(t) => {
     switch (t) {
@@ -114,7 +135,19 @@ module Continuous {
     };
   };
   let shapeFn = (fn, t: t) => t |> getShape |> fn;
-  let updateKnownIntegralSum = (knownIntegralSum, t: t): t => ({...t, knownIntegralSum});
+  let updateKnownIntegralSum = (knownIntegralSum, t: t): t => {
+    ...t,
+    knownIntegralSum,
+  };
+
+  let reduce =
+      (
+        ~knownIntegralSumsFn: (float, float) => option(float)=(_, _) => None,
+        fn,
+        continuousShapes,
+      ) =>
+    continuousShapes
+    |> E.A.fold_left(combine(~knownIntegralSumsFn, fn), empty);
 
   // Contracts every point in the continuous xyShape into a single dirac-Delta-like point,
   // using the centerpoints between adjacent xs and the area under each trapezoid.
@@ -128,11 +161,18 @@ module Continuous {
         Belt.Array.set(
           pointMassesY,
           x,
-          (xs[x + 1] -. xs[x]) *. ((ys[x] +. ys[x + 1]) /. 2.)); // = dx * (1/2) * (avgY)
+          (xs[x + 1] -. xs[x]) *. ((ys[x] +. ys[x + 1]) /. 2.),
+        ); // = dx * (1/2) * (avgY)
       ();
     };
 
-    {xyShape: {xs: xs, ys: pointMassesY}, knownIntegralSum: t.knownIntegralSum};
+    {
+      xyShape: {
+        xs,
+        ys: pointMassesY,
+      },
+      knownIntegralSum: t.knownIntegralSum,
+    };
   };
 
   /* Performs a discrete convolution between two continuous distributions A and B.
@@ -153,18 +193,25 @@ module Continuous {
     let t1n = t1s |> XYShape.T.length;
     let t2n = t2s |> XYShape.T.length;
 
-    let outXYShapes: array(array((float, float))) = Belt.Array.makeUninitializedUnsafe(t1n);
+    let outXYShapes: array(array((float, float))) =
+      Belt.Array.makeUninitializedUnsafe(t1n);
 
     for (i in 0 to t1n - 1) {
       // create a new distribution
-      let dxyShape: array((float, float)) = Belt.Array.makeUninitializedUnsafe(t2n);
+      let dxyShape: array((float, float)) =
+        Belt.Array.makeUninitializedUnsafe(t2n);
       for (j in 0 to t2n - 1) {
-        let _ = Belt.Array.set(dxyShape, j, (fn(t1s.xs[i], t2s.xs[j]), t1s.ys[i] *. t2s.ys[j]));
+        let _ =
+          Belt.Array.set(
+            dxyShape,
+            j,
+            (fn(t1s.xs[i], t2s.xs[j]), t1s.ys[i] *. t2s.ys[j]),
+          );
         ();
-      }
+      };
       let _ = Belt.Array.set(outXYShapes, i, dxyShape);
       ();
-    }
+    };
 
     let combinedIntegralSum =
       switch (t1.knownIntegralSum, t2.knownIntegralSum) {
@@ -185,35 +232,22 @@ module Continuous {
   let convolve = (fn, t1: t, t2: t) =>
     convolveWithDiscrete(fn, t1, toDiscretePointMasses(t2));
 
-  let mapY = (~knownIntegralSumFn=(previousKnownIntegralSum => None), fn, t: t) => {
+  let mapY = (~knownIntegralSumFn=previousKnownIntegralSum => None, fn, t: t) => {
     let u = E.O.bind(_, knownIntegralSumFn);
     let yMapFn = shapeMap(XYShape.T.mapY(fn));
 
     t |> yMapFn |> updateKnownIntegralSum(u(t.knownIntegralSum));
   };
 
-  let scaleBy = (~scale=1.0, ~knownIntegralSum=None, t: t): t =>
+  let scaleBy = (~scale=1.0, t: t): t => {
-    t |> mapY((r: float) => r *. scale) |> updateKnownIntegralSum(knownIntegralSum);
-
-  let truncate = (leftCutoff: option(float), rightCutoff: option(float), t: t) => {
-    let truncatedZippedPairs =
     t
-      |> getShape
+    |> mapY((r: float) => r *. scale)
-      |> XYShape.T.zip
+    |> updateKnownIntegralSum(
-      |> XYShape.Zipped.filterByX(x => x >= E.O.default(neg_infinity, leftCutoff) || x <= E.O.default(infinity, rightCutoff));
+         E.O.bind(t.knownIntegralSum, v => Some(scale *. v)),
-
+       );
-    let eps = (t |> getShape |> XYShape.T.xTotalRange) *. 0.0001;
-
-    let leftNewPoint = leftCutoff |> E.O.dimap(lc => [| (lc -. eps, 0.) |], _ => [||]);
-    let rightNewPoint = rightCutoff |> E.O.dimap(rc => [| (rc +. eps, 0.) |], _ => [||]);
-
-    let truncatedZippedPairsWithNewPoints =
-      E.A.concatMany([| leftNewPoint, truncatedZippedPairs, rightNewPoint |]);
-    let truncatedShape = XYShape.T.fromZippedArray(truncatedZippedPairsWithNewPoints);
-   
-    make(`Linear, truncatedShape, None);
   };
 
+
   module T =
     Dist({
       type t = DistTypes.continuousShape;
@@ -236,12 +270,31 @@ module Continuous {
         |> DistTypes.MixedPoint.makeContinuous;
       };
 
-      // let combineWithFn = (t1: t, t2: t, fn: (float, float) => float) => {
+      let truncate =
-      //   switch(t1, t2){
+          (leftCutoff: option(float), rightCutoff: option(float), t: t) => {
-      //     | ({interpolation: `Stepwise}, {interpolation: `Stepwise}) => 3.0
+        let truncatedZippedPairs =
-      //     | ({interpolation: `Linear}, {interpolation: `Linear}) => 3.0
+          t
-      //   }
+          |> getShape
-      // };
+          |> XYShape.T.zip
+          |> XYShape.Zipped.filterByX(x =>
+              x >= E.O.default(neg_infinity, leftCutoff)
+              || x <= E.O.default(infinity, rightCutoff)
+            );
+
+        let eps = (t |> getShape |> XYShape.T.xTotalRange) *. 0.0001;
+
+        let leftNewPoint =
+          leftCutoff |> E.O.dimap(lc => [|(lc -. eps, 0.)|], _ => [||]);
+        let rightNewPoint =
+          rightCutoff |> E.O.dimap(rc => [|(rc +. eps, 0.)|], _ => [||]);
+
+        let truncatedZippedPairsWithNewPoints =
+          E.A.concatMany([|leftNewPoint, truncatedZippedPairs, rightNewPoint|]);
+        let truncatedShape =
+          XYShape.T.fromZippedArray(truncatedZippedPairsWithNewPoints);
+
+        make(`Linear, truncatedShape, None);
+      };
 
       // TODO: This should work with stepwise plots.
       let integral = (~cache, t) =>
@@ -272,9 +325,9 @@ module Continuous {
       let toDiscrete = _ => None;
 
       let normalize = (t: t): t => {
-        let continuousIntegralSum = integralEndY(~cache=None, t);
+        t
-
+        |> scaleBy(~scale=1. /. integralEndY(~cache=None, t))
-        scaleBy(~scale=(1. /. continuousIntegralSum), ~knownIntegralSum=Some(1.0), t);
+        |> updateKnownIntegralSum(Some(1.0));
       };
 
       let normalizedToContinuous = t => Some(t); // TODO: this should be normalized
@@ -316,40 +369,41 @@ module Discrete = {
 
   let lastY = (t: t) => t |> getShape |> XYShape.T.lastY;
 
-  let combineIntegralSums = (combineFn: ((float, float) => option(float)), t1KnownIntegralSum: option(float), t2KnownIntegralSum: option(float)) => {
+  let combine =
-    switch (t1KnownIntegralSum, t2KnownIntegralSum) {
+      (
-    | (None, _)
+        ~knownIntegralSumsFn,
-    | (_, None) => None
+        fn,
-    | (Some(s1), Some(s2)) => combineFn(s1, s2)
+        t1: DistTypes.discreteShape,
-    };
+        t2: DistTypes.discreteShape,
-  };
+      )
-
-  let combine = (combineIntegralSumsFn, fn, t1: DistTypes.discreteShape, t2: DistTypes.discreteShape)
       : DistTypes.discreteShape => {
-
+    let combinedIntegralSum =
-    let combinedIntegralSum = combineIntegralSums(combineIntegralSumsFn, t1.knownIntegralSum, t2.knownIntegralSum);
+      Common.combineIntegralSums(
+        knownIntegralSumsFn,
+        t1.knownIntegralSum,
+        t2.knownIntegralSum,
+      );
 
     make(
       XYShape.Combine.combine(
         ~xsSelection=ALL_XS,
         ~xToYSelection=XYShape.XtoY.stepwiseIfAtX,
-        ~fn,  // stepwiseIfAtX returns option(float), so this fn needs to handle None, which is what the _default0 wrapper is for
+        ~fn=((a, b) => fn(E.O.default(0.0, a), E.O.default(0.0, b))), // stepwiseIfAtX returns option(float), so this fn needs to handle None
         t1.xyShape,
         t2.xyShape,
       ),
       combinedIntegralSum,
     );
   };
-  let _default0 = (fn, a, b) =>
-    fn(E.O.default(0.0, a), E.O.default(0.0, b));
-  let reduce = (fn, items) =>
-    items |> E.A.fold_left(combine((_, _) => None, _default0(fn)), empty);
-  // a special version of reduce that adds the results (which should be the most common case by far),
-  // and conveniently also adds the knownIntegralSums.
-  let reduceAdd = (fn, items) =>
-    items |> E.A.fold_left(combine((s1, s2) => Some(s1 +. s2), _default0((+.))), empty);
 
-  let updateKnownIntegralSum = (knownIntegralSum, t: t): t => ({...t, knownIntegralSum});
+  let reduce = (~knownIntegralSumsFn=(_, _) => None, fn, discreteShapes): DistTypes.discreteShape =>
+    discreteShapes
+    |> E.A.fold_left(combine(~knownIntegralSumsFn, fn), empty);
+
+  let updateKnownIntegralSum = (knownIntegralSum, t: t): t => {
+    ...t,
+    knownIntegralSum,
+  };
 
   let convolve = (fn, t1: t, t2: t) => {
     let t1s = t1 |> getShape;
@@ -357,7 +411,12 @@ module Discrete = {
     let t1n = t1s |> XYShape.T.length;
     let t2n = t2s |> XYShape.T.length;
 
-    let combinedIntegralSum = combineIntegralSums((s1, s2) => Some(s1 *. s2), t1.knownIntegralSum, t2.knownIntegralSum);
+    let combinedIntegralSum =
+      Common.combineIntegralSums(
+        (s1, s2) => Some(s1 *. s2),
+        t1.knownIntegralSum,
+        t2.knownIntegralSum,
+      );
 
     let xToYMap = E.FloatFloatMap.empty();
 
@@ -368,8 +427,8 @@ module Discrete = {
         let my = t1s.ys[i] *. t2s.ys[j];
         let _ = Belt.MutableMap.set(xToYMap, x, cv +. my);
         ();
-      }
+      };
-    }
+    };
 
     let rxys = xToYMap |> E.FloatFloatMap.toArray |> XYShape.Zipped.sortByX;
 
@@ -378,25 +437,19 @@ module Discrete = {
     make(convolvedShape, combinedIntegralSum);
   };
 
-  let mapY = (~knownIntegralSumFn=(previousKnownIntegralSum => None), fn, t: t) => {
+  let mapY = (~knownIntegralSumFn=previousKnownIntegralSum => None, fn, t: t) => {
     let u = E.O.bind(_, knownIntegralSumFn);
     let yMapFn = shapeMap(XYShape.T.mapY(fn));
 
     t |> yMapFn |> updateKnownIntegralSum(u(t.knownIntegralSum));
   };
 
-  let scaleBy = (~scale=1.0, ~knownIntegralSum=None, t: t): t =>
+  let scaleBy = (~scale=1.0, t: t): t => {
-    t |> mapY((r: float) => r *. scale) |> updateKnownIntegralSum(knownIntegralSum);
-
-  let truncate = (leftCutoff: option(float), rightCutoff: option(float), t: t) => {
-    let truncatedShape =
     t
-      |> getShape
+    |> mapY((r: float) => r *. scale)
-      |> XYShape.T.zip
+    |> updateKnownIntegralSum(
-      |> XYShape.Zipped.filterByX(x => x >= E.O.default(neg_infinity, leftCutoff) || x <= E.O.default(infinity, rightCutoff))
+         E.O.bind(t.knownIntegralSum, v => Some(scale *. v)),
-      |> XYShape.T.fromZippedArray;
+       );
-
-    make(truncatedShape, None);
   };
 
   module T =
@@ -414,7 +467,8 @@ module Discrete = {
           )
         };
       let integralEndY = (~cache, t: t) =>
-        t.knownIntegralSum |> E.O.default(t |> integral(~cache) |> Continuous.lastY);
+        t.knownIntegralSum
+        |> E.O.default(t |> integral(~cache) |> Continuous.lastY);
       let minX = shapeFn(XYShape.T.minX);
       let maxX = shapeFn(XYShape.T.maxX);
       let toDiscreteProbabilityMassFraction = _ => 1.0;
@@ -424,9 +478,9 @@ module Discrete = {
       let toDiscrete = t => Some(t);
 
       let normalize = (t: t): t => {
-        let discreteIntegralSum = integralEndY(~cache=None, t);
+        t
-
+        |> scaleBy(~scale=1. /. integralEndY(~cache=None, t))
-        scaleBy(~scale=(1. /. discreteIntegralSum), ~knownIntegralSum=Some(1.0), t);
+        |> updateKnownIntegralSum(Some(1.0));
       };
 
       let normalizedToContinuous = _ => None;
@@ -448,6 +502,21 @@ module Discrete = {
         make(clippedShape, None); // if someone needs the sum, they'll have to recompute it
       };
 
+      let truncate =
+          (leftCutoff: option(float), rightCutoff: option(float), t: t): t => {
+        let truncatedShape =
+          t
+          |> getShape
+          |> XYShape.T.zip
+          |> XYShape.Zipped.filterByX(x =>
+              x >= E.O.default(neg_infinity, leftCutoff)
+              || x <= E.O.default(infinity, rightCutoff)
+            )
+          |> XYShape.T.fromZippedArray;
+
+        make(truncatedShape, None);
+      };
+
       let xToY = (f, t) =>
         t
         |> getShape
@@ -477,53 +546,43 @@ module Discrete = {
         XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares);
       };
     });
-
 };
 
-// TODO: I think this shouldn't assume continuous/discrete are normalized to 1.0, and thus should not need the discreteProbabilityMassFraction being separate.
 module Mixed = {
   type t = DistTypes.mixedShape;
-  let make = (~continuous, ~discrete): t => {
+  let make = (~continuous, ~discrete): t => {continuous, discrete};
-    continuous,
-    discrete,
-  };
 
   let totalLength = (t: t): int => {
-    let continuousLength = t.continuous |> Continuous.getShape |> XYShape.T.length;
+    let continuousLength =
+      t.continuous |> Continuous.getShape |> XYShape.T.length;
     let discreteLength = t.discrete |> Discrete.getShape |> XYShape.T.length;
 
     continuousLength + discreteLength;
   };
 
-  // TODO: Put into scaling module
+  let scaleBy = (~scale=1.0, {discrete, continuous}: t): t => {
-  //let normalizeMixedPoint = (t, f) => f *. discreteProbabilityMassFraction;*/
+    let scaledDiscrete = Discrete.scaleBy(~scale, discrete);
+    let scaledContinuous = Continuous.scaleBy(~scale, continuous);
+    make(~discrete=scaledDiscrete, ~continuous=scaledContinuous);
+  };
 
-  //TODO: Warning: This currently computes the integral, which is expensive.
+  let toContinuous = ({continuous}: t) => Some(continuous);
-  /*let scaleContinuousFn =
+  let toDiscrete = ({discrete}: t) => Some(discrete);
-      ({discreteProbabilityMassFraction}: DistTypes.mixedShape, f) =>
-    f *. (1.0 -. discreteProbabilityMassFraction); */
 
-  //TODO: Warning: This currently computes the integral, which is expensive.
+  let combine = (~knownIntegralSumsFn, fn, t1: t, t2: t) => {
+    let reducedDiscrete =
+      [|t1, t2|]
+      |> E.A.fmap(toDiscrete)
+      |> E.A.O.concatSomes
+      |> Discrete.reduce(~knownIntegralSumsFn, fn);
 
-  // Normalizes to 1.0.
+    let reducedContinuous =
-  /*let scaleContinuous = ({discreteProbabilityMassFraction}: t, continuous) =>
+      [|t1, t2|]
-     // get only the continuous, and scale it to the respective
+      |> E.A.fmap(toContinuous)
-      continuous
+      |> E.A.O.concatSomes
-      |> Continuous.T.scaleToIntegralSum(
+      |> Continuous.reduce(~knownIntegralSumsFn, fn);
-           ~intendedSum=1.0 -. discreteProbabilityMassFraction,
-         );
 
-    let scaleDiscrete = ({discreteProbabilityMassFraction}: t, disrete) =>
+    make(~discrete=reducedDiscrete, ~continuous=reducedContinuous);
-      disrete
-      |> Discrete.T.scaleToIntegralSum(
-           ~intendedSum=discreteProbabilityMassFraction,
-         );*/
-
-  let truncate = (leftCutoff: option(float), rightCutoff: option(float), {discrete, continuous}: t) => {
-    let truncatedDiscrete = Discrete.truncate(leftCutoff, rightCutoff, discrete);
-    let truncatedContinuous = Continuous.truncate(leftCutoff, rightCutoff, continuous);
-
-    make(~discrete=truncatedDiscrete, ~continuous=truncatedContinuous);
   };
 
   module T =
@@ -536,19 +595,40 @@ module Mixed = {
       let maxX = ({continuous, discrete}: t) =>
         max(Continuous.T.maxX(continuous), Discrete.T.maxX(discrete));
       let toShape = (t: t): DistTypes.shape => Mixed(t);
-      let toContinuous = ({continuous}: t) => Some(continuous);
+
-      let toDiscrete = ({discrete}: t) => Some(discrete);
+      let toContinuous = toContinuous;
+      let toDiscrete = toDiscrete;
+
+      let truncate =
+          (
+            leftCutoff: option(float),
+            rightCutoff: option(float),
+            {discrete, continuous}: t,
+          ) => {
+        let truncatedContinuous = Continuous.T.truncate(leftCutoff, rightCutoff, continuous);
+        let truncatedDiscrete = Discrete.T.truncate(leftCutoff, rightCutoff, discrete);
+
+        make(~discrete=truncatedDiscrete, ~continuous=truncatedContinuous);
+      };
 
       let normalize = (t: t): t => {
-        let continuousIntegralSum = Continuous.T.Integral.sum(~cache=None, t.continuous);
+        let continuousIntegralSum =
-        let discreteIntegralSum = Discrete.T.Integral.sum(~cache=None, t.discrete);
+          Continuous.T.Integral.sum(~cache=None, t.continuous);
+        let discreteIntegralSum =
+          Discrete.T.Integral.sum(~cache=None, t.discrete);
         let totalIntegralSum = continuousIntegralSum +. discreteIntegralSum;
 
         let newContinuousSum = continuousIntegralSum /. totalIntegralSum;
         let newDiscreteSum = discreteIntegralSum /. totalIntegralSum;
 
-        let normalizedContinuous = Continuous.scaleBy(~scale=(1. /. newContinuousSum), ~knownIntegralSum=Some(newContinuousSum), t.continuous);
+        let normalizedContinuous =
-        let normalizedDiscrete = Discrete.scaleBy(~scale=(1. /. newDiscreteSum), ~knownIntegralSum=Some(newDiscreteSum), t.discrete);
+          t.continuous
+          |> Continuous.scaleBy(~scale=1. /. newContinuousSum)
+          |> Continuous.updateKnownIntegralSum(Some(newContinuousSum));
+        let normalizedDiscrete =
+          t.discrete
+          |> Discrete.scaleBy(~scale=1. /. newDiscreteSum)
+          |> Discrete.updateKnownIntegralSum(Some(newDiscreteSum));
 
         make(~continuous=normalizedContinuous, ~discrete=normalizedDiscrete);
       };
@@ -563,8 +643,10 @@ module Mixed = {
       };
 
       let toDiscreteProbabilityMassFraction = ({discrete, continuous}: t) => {
-        let discreteIntegralSum = Discrete.T.Integral.sum(~cache=None, discrete);
+        let discreteIntegralSum =
-        let continuousIntegralSum = Continuous.T.Integral.sum(~cache=None, continuous);
+          Discrete.T.Integral.sum(~cache=None, discrete);
+        let continuousIntegralSum =
+          Continuous.T.Integral.sum(~cache=None, continuous);
         let totalIntegralSum = discreteIntegralSum +. continuousIntegralSum;
 
         discreteIntegralSum /. totalIntegralSum;
@@ -575,20 +657,25 @@ module Mixed = {
         // The easiest way to do this is to simply go by the previous probability masses.
 
         // The cache really isn't helpful here, because we would need two separate caches
-        let discreteIntegralSum = Discrete.T.Integral.sum(~cache=None, discrete);
+        let discreteIntegralSum =
-        let continuousIntegralSum = Continuous.T.Integral.sum(~cache=None, continuous);
+          Discrete.T.Integral.sum(~cache=None, discrete);
+        let continuousIntegralSum =
+          Continuous.T.Integral.sum(~cache=None, continuous);
         let totalIntegralSum = discreteIntegralSum +. continuousIntegralSum;
 
         let downsampledDiscrete =
           Discrete.T.downsample(
-            int_of_float(float_of_int(count) *. (discreteIntegralSum /. totalIntegralSum)),
+            int_of_float(
+              float_of_int(count) *. (discreteIntegralSum /. totalIntegralSum),
+            ),
             discrete,
           );
 
         let downsampledContinuous =
           Continuous.T.downsample(
             int_of_float(
-              float_of_int(count) *. (continuousIntegralSum /. totalIntegralSum),
+              float_of_int(count)
+              *. (continuousIntegralSum /. totalIntegralSum),
             ),
             continuous,
           );
@@ -596,23 +683,20 @@ module Mixed = {
         {discrete: downsampledDiscrete, continuous: downsampledContinuous};
       };
 
-    let normalizedToContinuous = (t: t) =>
+      let normalizedToContinuous = (t: t) => Some(normalize(t).continuous);
-      Some(normalize(t).continuous);
 
       let normalizedToDiscrete = ({discrete} as t: t) =>
         Some(normalize(t).discrete);
 
-      let integral =
+      let integral = (~cache, {continuous, discrete}: t) => {
-          (
-            ~cache,
-            {continuous, discrete}: t,
-          ) => {
         switch (cache) {
         | Some(cache) => cache
-        | None => {
+        | None =>
           // note: if the underlying shapes aren't normalized, then these integrals won't be either!
-          let continuousIntegral = Continuous.T.Integral.get(~cache=None, continuous);
+          let continuousIntegral =
-          let discreteIntegral = Discrete.T.Integral.get(~cache=None, discrete);
+            Continuous.T.Integral.get(~cache=None, continuous);
+          let discreteIntegral =
+            Discrete.T.Integral.get(~cache=None, discrete);
 
           Continuous.make(
             `Linear,
@@ -623,7 +707,6 @@ module Mixed = {
             ),
             None,
           );
-        }
         };
       };
 
@@ -648,14 +731,26 @@ module Mixed = {
       // This pipes all ys (continuous and discrete) through fn.
       // If mapY is a linear operation, we might be able to update the knownIntegralSums as well;
       // if not, they'll be set to None.
-      let mapY = (~knownIntegralSumFn=(previousIntegralSum => None), fn, {discrete, continuous}: t): t => {
+      let mapY =
+          (
+            ~knownIntegralSumFn=previousIntegralSum => None,
+            fn,
+            {discrete, continuous}: t,
+          )
+          : t => {
         let u = E.O.bind(_, knownIntegralSumFn);
 
         let yMappedDiscrete =
-          discrete |> Discrete.T.mapY(fn) |> Discrete.updateKnownIntegralSum(u(discrete.knownIntegralSum));
+          discrete
+          |> Discrete.T.mapY(fn)
+          |> Discrete.updateKnownIntegralSum(u(discrete.knownIntegralSum));
 
         let yMappedContinuous =
-          continuous |> Continuous.T.mapY(fn) |> Continuous.updateKnownIntegralSum(u(continuous.knownIntegralSum));
+          continuous
+          |> Continuous.T.mapY(fn)
+          |> Continuous.updateKnownIntegralSum(
+               u(continuous.knownIntegralSum),
+             );
 
         {
           discrete: yMappedDiscrete,
@@ -668,34 +763,55 @@ module Mixed = {
         let continuousMean = Continuous.T.mean(continuous);
 
         // the combined mean is the weighted sum of the two:
-        let discreteIntegralSum = Discrete.T.Integral.sum(~cache=None, discrete);
+        let discreteIntegralSum =
-        let continuousIntegralSum = Continuous.T.Integral.sum(~cache=None, continuous);
+          Discrete.T.Integral.sum(~cache=None, discrete);
+        let continuousIntegralSum =
+          Continuous.T.Integral.sum(~cache=None, continuous);
         let totalIntegralSum = discreteIntegralSum +. continuousIntegralSum;
 
-        (discreteMean *. discreteIntegralSum +. continuousMean *. continuousIntegralSum) /. totalIntegralSum;
+        (
+          discreteMean
+          *. discreteIntegralSum
+          +. continuousMean
+          *. continuousIntegralSum
+        )
+        /. totalIntegralSum;
       };
 
       let variance = ({discrete, continuous} as t: t): float => {
         // the combined mean is the weighted sum of the two:
-        let discreteIntegralSum = Discrete.T.Integral.sum(~cache=None, discrete);
+        let discreteIntegralSum =
-        let continuousIntegralSum = Continuous.T.Integral.sum(~cache=None, continuous);
+          Discrete.T.Integral.sum(~cache=None, discrete);
+        let continuousIntegralSum =
+          Continuous.T.Integral.sum(~cache=None, continuous);
         let totalIntegralSum = discreteIntegralSum +. continuousIntegralSum;
 
         let getMeanOfSquares = ({discrete, continuous} as t: t) => {
-          let discreteMean = discrete |> Discrete.shapeMap(XYShape.Analysis.squareXYShape) |> Discrete.T.mean;
+          let discreteMean =
-          let continuousMean = continuous |> XYShape.Analysis.getMeanOfSquaresContinuousShape;
+            discrete
-          (discreteMean *. discreteIntegralSum +. continuousMean *. continuousIntegralSum) /. totalIntegralSum
+            |> Discrete.shapeMap(XYShape.Analysis.squareXYShape)
+            |> Discrete.T.mean;
+          let continuousMean =
+            continuous |> XYShape.Analysis.getMeanOfSquaresContinuousShape;
+          (
+            discreteMean
+            *. discreteIntegralSum
+            +. continuousMean
+            *. continuousIntegralSum
+          )
+          /. totalIntegralSum;
         };
 
         switch (discreteIntegralSum /. totalIntegralSum) {
         | 1.0 => Discrete.T.variance(discrete)
         | 0.0 => Continuous.T.variance(continuous)
-        | _ => XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares)
+        | _ =>
+          XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares)
         };
       };
     });
 
-  let convolve = (fn: ((float, float) => float), t1: t, t2: t): t => {
+  let convolve = (fn: (float, float) => float, t1: t, t2: t): t => {
     // Discrete convolution can cause a huge increase in the number of samples,
     // so we'll first downsample.
 
@@ -713,16 +829,21 @@ module Mixed = {
 
     // 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.convolve(fn, t1d.continuous, t2d.continuous);
+    let ccConvResult =
-    let dcConvResult = Continuous.convolveWithDiscrete(fn, t2d.continuous, t1d.discrete);
+      Continuous.convolve(fn, t1d.continuous, t2d.continuous);
-    let cdConvResult = Continuous.convolveWithDiscrete(fn, t1d.continuous, t2d.discrete);
+    let dcConvResult =
-    let continuousConvResult = Continuous.reduce((+.), [|ccConvResult, dcConvResult, cdConvResult|]);
+      Continuous.convolveWithDiscrete(fn, t2d.continuous, t1d.discrete);
+    let cdConvResult =
+      Continuous.convolveWithDiscrete(fn, t1d.continuous, t2d.discrete);
+    let continuousConvResult =
+      Continuous.reduce((+.), [|ccConvResult, dcConvResult, cdConvResult|]);
 
     // ... finally, discrete (*) discrete => discrete, obviously:
-    let discreteConvResult = Discrete.convolve(fn, t1d.discrete, t2d.discrete);
+    let discreteConvResult =
+      Discrete.convolve(fn, t1d.discrete, t2d.discrete);
 
     {discrete: discreteConvResult, continuous: continuousConvResult};
-  }
+  };
 };
 
 module Shape = {
@@ -741,7 +862,8 @@ module Shape = {
     | Continuous(m) => Continuous(fn3(m))
     };
 
-  let toMixed = mapToAll((
+  let toMixed =
+    mapToAll((
       m => m,
       d => Mixed.make(~discrete=d, ~continuous=Continuous.empty),
       c => Mixed.make(~discrete=Discrete.empty, ~continuous=c),
@@ -751,33 +873,20 @@ module Shape = {
     Mixed(Mixed.convolve(fn, toMixed(t1), toMixed(t2)));
   };
 
-  let downsample = (~cache=None, i, t) =>
+  let combine = (~knownIntegralSumsFn=(_, _) => None, fn, t1: t, t2: t) =>
-    fmap((
+    switch ((t1, t2)) {
-      Mixed.T.downsample(i),
+    | (Continuous(m1), Continuous(m2)) => DistTypes.Continuous(Continuous.combine(~knownIntegralSumsFn, fn, m1, m2))
-      Discrete.T.downsample(i),
+    | (Discrete(m1), Discrete(m2)) => DistTypes.Discrete(Discrete.combine(~knownIntegralSumsFn, fn, m1, m2))
-      Continuous.T.downsample(i),
+    | (m1, m2) => {
-    ), t);
+        DistTypes.Mixed(Mixed.combine(~knownIntegralSumsFn, fn, toMixed(m1), toMixed(m2)))
-
+      }
-  let normalize =
+    };
-    fmap((
-      Mixed.T.normalize,
-      Discrete.T.normalize,
-      Continuous.T.normalize,
-    ));
-
-  let truncate (leftCutoff, rightCutoff, t): t =
-    fmap((
-      Mixed.truncate(leftCutoff, rightCutoff),
-      Discrete.truncate(leftCutoff, rightCutoff),
-      Continuous.truncate(leftCutoff, rightCutoff),
-    ), t);
 
   module T =
     Dist({
       type t = DistTypes.shape;
       type integral = DistTypes.continuousShape;
 
-
       let xToY = (f: float) =>
         mapToAll((
           Mixed.T.xToY(f),
@@ -789,9 +898,31 @@ module Shape = {
 
       let toContinuous = t => None;
       let toDiscrete = t => None;
-    let downsample = (~cache=None, i, t) => t;
+
+
+      let downsample = (~cache=None, i, t) =>
+        fmap(
+          (
+            Mixed.T.downsample(i),
+            Discrete.T.downsample(i),
+            Continuous.T.downsample(i),
+          ),
+          t,
+        );
+
+      let truncate = (leftCutoff, rightCutoff, t): t =>
+        fmap(
+          (
+            Mixed.T.truncate(leftCutoff, rightCutoff),
+            Discrete.T.truncate(leftCutoff, rightCutoff),
+            Continuous.T.truncate(leftCutoff, rightCutoff),
+          ),
+          t,
+        );
+
       let toDiscreteProbabilityMassFraction = t => 0.0;
-    let normalize = t => t;
+      let normalize =
+        fmap((Mixed.T.normalize, Discrete.T.normalize, Continuous.T.normalize));
       let toContinuous =
         mapToAll((
           Mixed.T.toContinuous,
@@ -853,7 +984,7 @@ module Shape = {
         ));
       };
       let maxX = mapToAll((Mixed.T.maxX, Discrete.T.maxX, Continuous.T.maxX));
-      let mapY = (~knownIntegralSumFn=(previousIntegralSum => None), fn) =>
+      let mapY = (~knownIntegralSumFn=previousIntegralSum => None, fn) =>
         fmap((
           Mixed.T.mapY(~knownIntegralSumFn, fn),
           Discrete.T.mapY(~knownIntegralSumFn, fn),
@@ -935,14 +1066,18 @@ module DistPlus = {
       let toDiscrete = shapeFn(Shape.T.toDiscrete);
 
       let normalize = (t: t): t => {
-        let normalizedShape =
+        let normalizedShape = t |> toShape |> Shape.T.normalize;
-          t |> toShape |> Shape.T.normalize;
 
         t |> updateShape(normalizedShape);
-
         // TODO: also adjust for domainIncludedProbabilityMass here.
       };
 
+      let truncate = (leftCutoff, rightCutoff, t: t): t => {
+        let truncatedShape = t |> toShape |> Shape.T.truncate(leftCutoff, rightCutoff);
+
+        t |> updateShape(truncatedShape);
+      };
+
       // TODO: replace this with
       let normalizedToContinuous = (t: t) => {
         t
@@ -980,7 +1115,13 @@ module DistPlus = {
       let downsample = (~cache=None, i, t): t =>
         updateShape(t |> toShape |> Shape.T.downsample(i), t);
       // todo: adjust for limit, maybe?
-      let mapY = (~knownIntegralSumFn=(previousIntegralSum => None), fn, {shape, _} as t: t): t =>
+      let mapY =
+          (
+            ~knownIntegralSumFn=previousIntegralSum => None,
+            fn,
+            {shape, _} as t: t,
+          )
+          : t =>
         Shape.T.mapY(~knownIntegralSumFn, fn, shape) |> updateShape(_, t);
 
       let integralEndY = (~cache as _, t: t) =>

src/distPlus/renderers/DistPlusRenderer.re

@@ -1,13 +1,13 @@
-let truncateIfShould =
+let downsampleIfShould =
     (
-      {recommendedLength, shouldTruncate}: RenderTypes.DistPlusRenderer.inputs,
+      {recommendedLength, shouldDownsample}: RenderTypes.DistPlusRenderer.inputs,
       outputs: RenderTypes.ShapeRenderer.Combined.outputs,
       dist,
     ) => {
-      let willTruncate = 
+      let willDownsample =
-  shouldTruncate
+  shouldDownsample
   && RenderTypes.ShapeRenderer.Combined.methodUsed(outputs) == `Sampling;
-    willTruncate ? dist |> Distributions.DistPlus.T.truncate(recommendedLength) : dist;
+    willDownsample ? dist |> Distributions.DistPlus.T.downsample(recommendedLength) : dist;
 };
 
 let run =
@@ -21,7 +21,7 @@ let run =
       ~guesstimatorString=Some(inputs.distPlusIngredients.guesstimatorString),
       (),
     )
-    |> Distributions.DistPlus.T.scaleToIntegralSum(~intendedSum=1.0);
+    |> Distributions.DistPlus.T.normalize;
   let outputs =
     ShapeRenderer.run({
       samplingInputs: inputs.samplingInputs,
@@ -32,6 +32,6 @@ let run =
     });
   let shape = outputs |> RenderTypes.ShapeRenderer.Combined.getShape;
   let dist =
-    shape |> E.O.fmap(toDist) |> E.O.fmap(truncateIfShould(inputs, outputs));
+    shape |> E.O.fmap(toDist) |> E.O.fmap(downsampleIfShould(inputs, outputs));
   RenderTypes.DistPlusRenderer.Outputs.make(outputs, dist);
 };

src/distPlus/renderers/RenderTypes.re

@@ -75,7 +75,7 @@ module ShapeRenderer = {
 
 module DistPlusRenderer = {
   let defaultRecommendedLength = 10000;
-  let defaultShouldTruncate = true;
+  let defaultShouldDownsample = true;
   type ingredients = {
     guesstimatorString: string,
     domain: DistTypes.domain,
@@ -85,7 +85,7 @@ module DistPlusRenderer = {
     distPlusIngredients: ingredients,
     samplingInputs: ShapeRenderer.Sampling.inputs,
     recommendedLength: int,
-    shouldTruncate: bool,
+    shouldDownsample: bool,
   };
   module Ingredients = {
     let make =
@@ -105,7 +105,7 @@ module DistPlusRenderer = {
       (
         ~samplingInputs=ShapeRenderer.Sampling.Inputs.empty,
         ~recommendedLength=defaultRecommendedLength,
-        ~shouldTruncate=defaultShouldTruncate,
+        ~shouldDownsample=defaultShouldDownsample,
         ~distPlusIngredients,
         (),
       )
@@ -113,7 +113,7 @@ module DistPlusRenderer = {
     distPlusIngredients,
     samplingInputs,
     recommendedLength,
-    shouldTruncate,
+    shouldDownsample,
   };
   type outputs = {
     shapeRenderOutputs: ShapeRenderer.Combined.outputs,

src/distPlus/symbolic/MathJsParser.re

@@ -154,16 +154,17 @@ module MathAdtToDistDst = {
         weights: option(array(float)),
       ) => {
     let weights = weights |> E.O.default([||]);
-    let dists =
+
+    /*let dists:  =
       args
       |> E.A.fmap(
            fun
           | Ok(a) => a
           | Error(e) => Error(e)
-          );
+          );*/
 
-    let firstWithError = dists |> Belt.Array.getBy(_, Belt.Result.isError);
+    let firstWithError = args |> Belt.Array.getBy(_, Belt.Result.isError);
-    let withoutErrors = dists |> E.A.fmap(E.R.toOption) |> E.A.O.concatSomes;
+    let withoutErrors = args |> E.A.fmap(E.R.toOption) |> E.A.O.concatSomes;
 
     switch (firstWithError) {
       | Some(Error(e)) => Error(e)
@@ -174,16 +175,16 @@ module MathAdtToDistDst = {
         |> E.A.fmapi((index, t) => {
             let w = weights |> E.A.get(_, index) |> E.O.default(1.0);
 
-            `Operation(`ScaleBy(`Multiply, t, `DistData(`Symbolic(`Float(w)))))
+            `Operation(`ScaleOperation(`Multiply, t, `DistData(`Symbolic(`Float(w)))))
           });
 
         let pointwiseSum = components
         |> Js.Array.sliceFrom(1)
         |> E.A.fold_left((acc, x) => {
-          `PointwiseSum(acc, x)
+          `Operation(`PointwiseOperation(`Add, acc, x))
           }, E.A.unsafe_get(components, 0))
 
-        Ok(`Normalize(pointwiseSum))
+        Ok(`Operation(`Normalize(pointwiseSum)))
       }
     };
   };
@@ -254,21 +255,21 @@ module MathAdtToDistDst = {
           args
           |> E.A.fmap(functionParser)
           |> (fun
-              | [|Ok(l), Ok(r)|] => Ok(`Combination(l, r, `AddOperation))
+              | [|Ok(l), Ok(r)|] => Ok(`Operation(`StandardOperation(`Add, l, r)))
               | _ => Error("Addition needs two operands"))
       }
       | Fn({name: "subtract", args}) => {
           args
           |> E.A.fmap(functionParser)
           |> (fun
-              | [|Ok(l), Ok(r)|] => Ok(`Combination(l, r, `SubtractOperation))
+              | [|Ok(l), Ok(r)|] => Ok(`Operation(`StandardOperation(`Subtract, l, r)))
               | _ => Error("Subtraction needs two operands"))
       }
       | Fn({name: "multiply", args}) => {
           args
           |> E.A.fmap(functionParser)
           |> (fun
-              | [|Ok(l), Ok(r)|] => Ok(`Combination(l, r, `MultiplyOperation))
+              | [|Ok(l), Ok(r)|] => Ok(`Operation(`StandardOperation(`Multiply, l, r)))
               | _ => Error("Multiplication needs two operands"))
       }
       | Fn({name: "divide", args}) => {
@@ -276,28 +277,37 @@ module MathAdtToDistDst = {
           |> E.A.fmap(functionParser)
           |> (fun
               | [|Ok(l), Ok(`DistData(`Symbolic(`Float(0.0))))|] => Error("Division by zero")
-              | [|Ok(l), Ok(r)|] => Ok(`Combination(l, r, `DivideOperation))
+              | [|Ok(l), Ok(r)|] => Ok(`Operation(`StandardOperation(`Divide, l, r)))
               | _ => Error("Division needs two operands"))
       }
       | Fn({name: "pow", args}) => {
           args
           |> E.A.fmap(functionParser)
           |> (fun
-              | [|Ok(l), Ok(r)|] => Ok(`Combination(l, r, `ExponentiateOperation))
+              | [|Ok(l), Ok(r)|] => Ok(`Operation(`StandardOperation(`Exponentiate, l, r)))
+              | _ => Error("Division needs two operands")
               | _ => Error("Exponentiations needs two operands"))
       }
       | Fn({name: "leftTruncate", args}) => {
           args
           |> E.A.fmap(functionParser)
           |> (fun
-              | [|Ok(l), Ok(`DistData(`Symbolic(`Float(r))))|] => Ok(`LeftTruncate(l, r))
+              | [|Ok(d), Ok(`DistData(`Symbolic(`Float(lc))))|] => Ok(`Operation(`Truncate(Some(lc), None, d)))
               | _ => Error("leftTruncate needs two arguments: the expression and the cutoff"))
       }
       | Fn({name: "rightTruncate", args}) => {
           args
           |> E.A.fmap(functionParser)
           |> (fun
-              | [|Ok(l), Ok(`DistData(`Symbolic(`Float(r))))|] => Ok(`RightTruncate(l, r))
+              | [|Ok(d), Ok(`DistData(`Symbolic(`Float(rc))))|] => Ok(`Operation(`Truncate(None, Some(rc), d)))
+              | _ => Error("rightTruncate needs two arguments: the expression and the cutoff"))
+      }
+      | Fn({name: "truncate", args}) => {
+          args
+          |> E.A.fmap(functionParser)
+          |> (fun
+              | [|Ok(d), Ok(`DistData(`Symbolic(`Float(lc)))), Ok(`DistData(`Symbolic(`Float(rc))))|] => Ok(`Operation(`Truncate(Some(lc), Some(rc), d)))
+              // TODO: allow on-the-fly evaluations of FloatFromDists to be used as cutoff arguments here.
               | _ => Error("rightTruncate needs two arguments: the expression and the cutoff"))
       }
       | Fn({name}) => Error(name ++ ": function not supported")

src/distPlus/symbolic/TreeNode.re

@@ -1,69 +1,60 @@
 /* This module represents a tree node. */
 
-/* TreeNodes are either Data (i.e. symbolic or rendered distributions) or Operations. */
+type distData = [
-type treeNode = [
-    | `DistData(distData)
-    | `Operation(operation)
-] and distData = [
   | `Symbolic(SymbolicDist.dist)
   | `RenderedShape(DistTypes.shape)
-] and operation = [
+];
-  // binary operations
+
-  | `StandardOperation(standardOperation, treeNode, treeNode)
+type standardOperation = [
-  | `PointwiseOperation(pointwiseOperation, treeNode, treeNode)
-  | `ScaleOperation(scaleOperation, treeNode, scaleBy)
-  // unary operations
-  | `Render(treeNode) // always evaluates to `DistData(`RenderedShape(...))
-  | `Truncate(leftCutoff, rightCutoff, treeNode)
-  | `Normalize(treeNode)
-  // direct evaluations of dists (e.g. cdf, sample)
-  | `FloatFromDist(distToFloatOperation, treeNode)
-] and standardOperation = [
   | `Add
   | `Multiply
   | `Subtract
   | `Divide
   | `Exponentiate
-] and pointwiseOperation = [
+];
-  | `Add
+type pointwiseOperation = [ | `Add | `Multiply];
-  | `Multiply
+type scaleOperation = [ | `Multiply | `Exponentiate | `Log];
-] and scaleOperation = [
+type distToFloatOperation = [ | `Pdf(float) | `Inv(float) | `Mean | `Sample];
-  | `Multiply
+
-  | `Log
+/* TreeNodes are either Data (i.e. symbolic or rendered distributions) or Operations. */
+type treeNode = [
+  | `DistData(distData) // a leaf node that describes a distribution
+  | `Operation(operation) // an operation on two child nodes
 ]
-and scaleBy = treeNode and leftCutoff = option(float) and rightCutoff = option(float)
+and operation = [
-and distToFloatOperation = [
+  | // binary operations
-  | `Pdf(float)
+    `StandardOperation(
-  | `Cdf(float)
+      standardOperation,
-  | `Inv(float)
+      treeNode,
-  | `Sample
+      treeNode,
+    )
+    // unary operations
+  | `PointwiseOperation(pointwiseOperation, treeNode, treeNode) // always evaluates to `DistData(`RenderedShape(...))
+  | `ScaleOperation(scaleOperation, treeNode, treeNode) // always evaluates to `DistData(`RenderedShape(...))
+  | `Render(treeNode) // always evaluates to `DistData(`RenderedShape(...))
+  | `Truncate // always evaluates to `DistData(`RenderedShape(...))
+(
+      option(float),
+      option(float),
+      treeNode,
+    ) // leftCutoff and rightCutoff
+  | `Normalize // always evaluates to `DistData(`RenderedShape(...))
+ // leftCutoff and rightCutoff
+(
+      treeNode,
+    )
+  | `FloatFromDist // always evaluates to `DistData(`RenderedShape(...))
+ // leftCutoff and rightCutoff
+(
+      distToFloatOperation,
+      treeNode,
+    )
 ];
 
 module TreeNode = {
   type t = treeNode;
   type simplifier = treeNode => result(treeNode, string);
 
-  type renderParams = {
-    operationToDistData: (int, operation) => result(t, string),
-    sampleCount: int,
-  }
-
-  let rec renderToShape = (renderParams, t: t): result(DistTypes.shape, string) => {
-    switch (t) {
-    | `DistData(`RenderedShape(s)) => Ok(s) // already a rendered shape, we're done here
-    | `DistData(`Symbolic(d)) =>
-      switch (d) {
-      | `Float(v) =>
-        Ok(Discrete(Distributions.Discrete.make({xs: [|v|], ys: [|1.0|]}, Some(1.0))));
-      | _ =>
-        let xs = SymbolicDist.GenericDistFunctions.interpolateXs(~xSelection=`ByWeight, d, renderParams.sampleCount);
-        let ys = xs |> E.A.fmap(x => SymbolicDist.GenericDistFunctions.pdf(x, d));
-        Ok(Continuous(Distributions.Continuous.make(`Linear, {xs, ys}, Some(1.0))));
-      }
-    | `Operation(op) => E.R.bind(renderParams.operationToDistData(renderParams.sampleCount, op), renderToShape(renderParams))
-    };
-  };
-
   /* The following modules encapsulate everything we can do with
    * different kinds of operations. */
 
@@ -154,105 +145,136 @@ module TreeNode = {
       };
     };
 
-    let evaluateNumerically = (standardOp, renderParams, t1, t2) => {
+    let evaluateNumerically = (standardOp, operationToDistData, t1, t2) => {
       let func = funcFromOp(standardOp);
 
-      // TODO: downsample the two shapes
+      // force rendering into shapes
-      let renderedShape1 = t1 |> renderToShape(renderParams);
+      let renderedShape1 = operationToDistData(`Render(t1));
-      let renderedShape2 = t2 |> renderToShape(renderParams);
+      let renderedShape2 = operationToDistData(`Render(t2));
 
-      // This will most likely require a mixed
+      switch (renderedShape1, renderedShape2) {
-
+      | (
-      switch ((renderedShape1, renderedShape2)) {
+          Ok(`DistData(`RenderedShape(s1))),
+          Ok(`DistData(`RenderedShape(s2))),
+        ) =>
+        Ok(
+          `DistData(
+            `RenderedShape(Distributions.Shape.convolve(func, s1, s2)),
+          ),
+        )
       | (Error(e1), _) => Error(e1)
       | (_, Error(e2)) => Error(e2)
-        | (Ok(s1), Ok(s2)) => Ok(`DistData(`RenderedShape(Distributions.Shape.convolve(func, s1, s2))))
+      | _ => Error("Could not render shapes.")
       };
     };
 
     let evaluateToDistData =
-        (standardOp: standardOperation, renderParams, t1: t, t2: t): result(treeNode, string) =>
+        (standardOp: standardOperation, operationToDistData, t1: t, t2: t)
+        : result(treeNode, string) =>
       standardOp
       |> Simplify.attempt(_, t1, t2)
       |> E.R.bind(
            _,
            fun
            | `DistData(d) => Ok(`DistData(d)) // the analytical simplifaction worked, nice!
-           | `Operation(_) => // if not, run the convolution
+           | `Operation(_) =>
-             evaluateNumerically(standardOp, renderParams, t1, t2),
+             // if not, run the convolution
+             evaluateNumerically(standardOp, operationToDistData, t1, t2),
          );
   };
 
   module ScaleOperation = {
-    let rec mean = (renderParams, t: t): result(float, string) => {
-      switch (t) {
-      | `DistData(`RenderedShape(s)) => Ok(Distributions.Shape.T.mean(s))
-      | `DistData(`Symbolic(s)) => SymbolicDist.GenericDistFunctions.mean(s)
-          // evaluating the operation returns result(treeNode(distData)). We then want to make sure
-      | `Operation(op) => E.R.bind(renderParams.operationToDistData(renderParams.sampleCount, op), mean(renderParams))
-      }
-    };
-
     let fnFromOp =
       fun
       | `Multiply => ( *. )
-      | `Log => ((a, b) => ( log(a) /. log(b) ));
+      | `Exponentiate => ( ** )
+      | `Log => ((a, b) => log(a) /. log(b));
 
     let knownIntegralSumFnFromOp =
       fun
-      | `Multiply => (a, b) => Some(a *. b)
+      | `Multiply => ((a, b) => Some(a *. b))
+      | `Exponentiate => ((_, _) => None)
       | `Log => ((_, _) => None);
 
-    let evaluateToDistData = (scaleOp, renderParams, t, scaleBy) => {
+    let evaluateToDistData = (scaleOp, operationToDistData, t, scaleBy) => {
+      // scaleBy has to be a single float, otherwise we'll return an error.
       let fn = fnFromOp(scaleOp);
       let knownIntegralSumFn = knownIntegralSumFnFromOp(scaleOp);
-      let renderedShape = t |> renderToShape(renderParams);
-      let scaleByMeanValue = mean(renderParams, scaleBy);
 
-      switch ((renderedShape, scaleByMeanValue)) {
+      let renderedShape = operationToDistData(`Render(t));
+
+      switch (renderedShape, scaleBy) {
       | (Error(e1), _) => Error(e1)
-      | (_, Error(e2)) => Error(e2)
+      | (
-      | (Ok(rs), Ok(sm)) =>
+          Ok(`DistData(`RenderedShape(rs))),
-            Ok(`DistData(`RenderedShape(Distributions.Shape.T.mapY(~knownIntegralSumFn=knownIntegralSumFn(sm), fn(sm), rs))))
+          `DistData(`Symbolic(`Float(sm))),
-      }
+        ) =>
+        Ok(
+          `DistData(
+            `RenderedShape(
+              Distributions.Shape.T.mapY(
+                ~knownIntegralSumFn=knownIntegralSumFn(sm),
+                fn(sm),
+                rs,
+              ),
+            ),
+          ),
+        )
+      | (_, _) => Error("Can only scale by float values.")
+      };
     };
   };
 
   module PointwiseOperation = {
-    let funcFromOp: (pointwiseOperation => ((float, float) => float)) =
+    let pointwiseAdd = (operationToDistData, t1, t2) => {
-      fun
+        let renderedShape1 = operationToDistData(`Render(t1));
-      | `Add => (+.)
+        let renderedShape2 = operationToDistData(`Render(t2));
-      | `Multiply => ( *. );
 
-    let evaluateToDistData = (pointwiseOp, renderParams, t1, t2) => {
+      switch ((renderedShape1, renderedShape2)) {
-      let func = funcFromOp(pointwiseOp);
+      | (Error(e1), _) => Error(e1)
-      let renderedShape1 = t1 |> renderToShape(renderParams);
+      | (_, Error(e2)) => Error(e2)
-      let renderedShape2 = t2 |> renderToShape(renderParams);
+      | (Ok(`DistData(`RenderedShape(rs1))), Ok(`DistData(`RenderedShape(rs2)))) => Ok(`DistData(`RenderedShape(Distributions.Shape.combine(~knownIntegralSumsFn=(a, b) => Some(a +. b), (+.), rs1, rs2))))
+      | _ => Error("Could not perform pointwise addition.")
+      };
+    };
 
-      // TODO: figure out integral, diff between pointwiseAdd and pointwiseProduct and other stuff
+    let pointwiseMultiply = (operationToDistData, t1, t2) => {
-      // Distributions.Shape.reduce(func, renderedShape1, renderedShape2);
+      // TODO: construct a function that we can easily sample from, to construct
+      // a RenderedShape. Use the xMin and xMax of the rendered shapes to tell the sampling function where to look.
+      Error("Pointwise multiplication not yet supported.");
+    };
 
-      Error("Pointwise operations currently not supported.")
+    let evaluateToDistData = (pointwiseOp, operationToDistData, t1, t2) => {
+      switch (pointwiseOp) {
+      | `Add => pointwiseAdd(operationToDistData, t1, t2)
+      | `Multiply => pointwiseMultiply(operationToDistData, t1, t2)
+      }
     };
   };
 
   module Truncate = {
     module Simplify = {
-      let tryTruncatingNothing: simplifier = fun
+      let tryTruncatingNothing: simplifier =
-      | `Operation(`Truncate(None, None, `DistData(d))) => Ok(`DistData(d))
+        fun
+        | `Operation(`Truncate(None, None, `DistData(d))) =>
+          Ok(`DistData(d))
         | t => Ok(t);
 
-      let tryTruncatingUniform: simplifier = fun
+      let tryTruncatingUniform: simplifier =
+        fun
         | `Operation(`Truncate(lc, rc, `DistData(`Symbolic(`Uniform(u))))) => {
             // just create a new Uniform distribution
             let newLow = max(E.O.default(neg_infinity, lc), u.low);
             let newHigh = min(E.O.default(infinity, rc), u.high);
-          Ok(`DistData(`Symbolic(`Uniform({low: newLow, high: newHigh}))));
+            Ok(
+              `DistData(`Symbolic(`Uniform({low: newLow, high: newHigh}))),
+            );
           }
         | t => Ok(t);
 
       let attempt = (leftCutoff, rightCutoff, t): result(treeNode, string) => {
-          let originalTreeNode = `Operation(`Truncate(leftCutoff, rightCutoff, t));
+        let originalTreeNode =
+          `Operation(`Truncate((leftCutoff, rightCutoff, t)));
 
         originalTreeNode
         |> tryTruncatingNothing
@@ -260,101 +282,191 @@ module TreeNode = {
       };
     };
 
-    let evaluateNumerically = (leftCutoff, rightCutoff, renderParams, t) => {
+    let evaluateNumerically =
+        (leftCutoff, rightCutoff, operationToDistData, t) => {
       // TODO: use named args in renderToShape; if we're lucky we can at least get the tail
       // of a distribution we otherwise wouldn't get at all
-      let renderedShape = t |> renderToShape(renderParams);
+      let renderedShape = operationToDistData(`Render(t));
 
-      E.R.bind(renderedShape, rs => {
+      switch (renderedShape) {
-        let truncatedShape = rs |> Distributions.Shape.truncate(leftCutoff, rightCutoff);
+      | Ok(`DistData(`RenderedShape(rs))) =>
+        let truncatedShape =
+          rs |> Distributions.Shape.T.truncate(leftCutoff, rightCutoff);
         Ok(`DistData(`RenderedShape(rs)));
-      });
+      | Error(e1) => Error(e1)
+      | _ => Error("Could not truncate distribution.")
+      };
     };
 
-    let evaluateToDistData = (leftCutoff: option(float), rightCutoff: option(float), renderParams, t: treeNode): result(treeNode, string) => {
+    let evaluateToDistData =
+        (
+          leftCutoff: option(float),
+          rightCutoff: option(float),
+          operationToDistData,
+          t: treeNode,
+        )
+        : result(treeNode, string) => {
       t
       |> Simplify.attempt(leftCutoff, rightCutoff)
       |> E.R.bind(
            _,
            fun
            | `DistData(d) => Ok(`DistData(d)) // the analytical simplifaction worked, nice!
-           | `Operation(_) => evaluateNumerically(leftCutoff, rightCutoff, renderParams, t),
+           | `Operation(_) =>
+             evaluateNumerically(
+               leftCutoff,
+               rightCutoff,
+               operationToDistData,
+               t,
+             ),
          ); // if not, run the convolution
     };
   };
 
   module Normalize = {
-    let rec evaluateToDistData = (renderParams, t: treeNode): result(treeNode, string) => {
+    let rec evaluateToDistData =
+            (operationToDistData, t: treeNode): result(treeNode, string) => {
       switch (t) {
       | `DistData(`Symbolic(_)) => Ok(t)
-      | `DistData(`RenderedShape(s)) => {
+      | `DistData(`RenderedShape(s)) =>
-          let normalized = Distributions.Shape.normalize(s);
+        let normalized = Distributions.Shape.T.normalize(s);
         Ok(`DistData(`RenderedShape(normalized)));
-      }
+      | `Operation(op) =>
-      | `Operation(op) => E.R.bind(renderParams.operationToDistData(renderParams.sampleCount, op), evaluateToDistData(renderParams))
+        E.R.bind(
-      }
+          operationToDistData(op),
-    }
+          evaluateToDistData(operationToDistData),
+        )
+      };
+    };
   };
 
   module FloatFromDist = {
     let evaluateFromSymbolic = (distToFloatOp: distToFloatOperation, s) => {
-      let value = switch (distToFloatOp) {
+      let value =
-        | `Pdf(f) => SymbolicDist.GenericDistFunctions.pdf(f, s)
+        switch (distToFloatOp) {
-        | `Cdf(f) => 0.0
+        | `Pdf(f) => Ok(SymbolicDist.GenericDistFunctions.pdf(f, s))
-        | `Inv(f) => SymbolicDist.GenericDistFunctions.inv(f, s)
+        | `Inv(f) => Ok(SymbolicDist.GenericDistFunctions.inv(f, s))
-        | `Sample => SymbolicDist.GenericDistFunctions.sample(s)
+        | `Sample => Ok(SymbolicDist.GenericDistFunctions.sample(s))
-      }
+        | `Mean => SymbolicDist.GenericDistFunctions.mean(s)
-      Ok(`DistData(`Symbolic(`Float(value))));
         };
-    let evaluateFromRenderedShape = (distToFloatOp: distToFloatOperation, rs: DistTypes.shape): result(treeNode, string) => {
+      E.R.bind(value, v => Ok(`DistData(`Symbolic(`Float(v)))));
-      // evaluate the pdf, cdf, get sample, etc. from the renderedShape rs
-      // Should be a float like Ok(`DistData(`Symbolic(Float(0.0))));
-      Error("Float from dist is not yet implemented.");
     };
-    let rec evaluateToDistData = (distToFloatOp: distToFloatOperation, renderParams, t: treeNode): result(treeNode, string) => {
+    let evaluateFromRenderedShape =
+        (distToFloatOp: distToFloatOperation, rs: DistTypes.shape)
+        : result(treeNode, string) => {
+      Ok(`DistData(`Symbolic(`Float(Distributions.Shape.T.mean(rs)))));
+    };
+    let rec evaluateToDistData =
+            (
+              distToFloatOp: distToFloatOperation,
+              operationToDistData,
+              t: treeNode,
+            )
+            : result(treeNode, string) => {
       switch (t) {
       | `DistData(`Symbolic(s)) => evaluateFromSymbolic(distToFloatOp, s) // we want to evaluate the distToFloatOp on the symbolic dist
-      | `DistData(`RenderedShape(rs)) => evaluateFromRenderedShape(distToFloatOp, rs)
+      | `DistData(`RenderedShape(rs)) =>
-      | `Operation(op) => E.R.bind(renderParams.operationToDistData(renderParams.sampleCount, op), evaluateToDistData(distToFloatOp, renderParams))
+        evaluateFromRenderedShape(distToFloatOp, rs)
-      }
+      | `Operation(op) =>
-    }
+        E.R.bind(
+          operationToDistData(op),
+          evaluateToDistData(distToFloatOp, operationToDistData),
+        )
+      };
+    };
   };
 
   module Render = {
-    let evaluateToRenderedShape = (renderParams, t: treeNode): result(t, string) => {
+    let rec evaluateToRenderedShape =
-      E.R.bind(renderToShape(renderParams, t), rs => Ok(`DistData(`RenderedShape(rs))));
+        (operationToDistData: operation => result(t, string), sampleCount: int, t: treeNode)
+        : result(t, string) => {
+      switch (t) {
+      | `DistData(`RenderedShape(s)) => Ok(`DistData(`RenderedShape(s))) // already a rendered shape, we're done here
+      | `DistData(`Symbolic(d)) =>
+        switch (d) {
+        | `Float(v) =>
+          Ok(
+            `DistData(
+              `RenderedShape(
+                Discrete(
+                  Distributions.Discrete.make(
+                    {xs: [|v|], ys: [|1.0|]},
+                    Some(1.0),
+                  ),
+                ),
+              ),
+            ),
+          )
+        | _ =>
+          let xs =
+            SymbolicDist.GenericDistFunctions.interpolateXs(
+              ~xSelection=`ByWeight,
+              d,
+              sampleCount,
+            );
+          let ys =
+            xs |> E.A.fmap(x => SymbolicDist.GenericDistFunctions.pdf(x, d));
+          Ok(
+            `DistData(
+              `RenderedShape(
+                Continuous(
+                  Distributions.Continuous.make(
+                    `Linear,
+                    {xs, ys},
+                    Some(1.0),
+                  ),
+                ),
+              ),
+            ),
+          );
         }
+      | `Operation(op) =>
+        E.R.bind(
+          operationToDistData(op),
+          evaluateToRenderedShape(operationToDistData, sampleCount),
+        )
+      };
+    };
   };
 
   let rec operationToDistData =
           (sampleCount: int, op: operation): result(t, string) => {
-
     // the functions that convert the Operation nodes to DistData nodes need to
     // have a way to call this function on their children, if their children are themselves Operation nodes.
-
-    let renderParams: renderParams = {
-      operationToDistData: operationToDistData,
-      sampleCount: sampleCount,
-    };
-
     switch (op) {
     | `StandardOperation(standardOp, t1, t2) =>
       StandardOperation.evaluateToDistData(
-        standardOp, renderParams, t1, t2 // we want to give it the option to render or simply leave it as is
+        standardOp,
+        operationToDistData(sampleCount),
+        t1,
+        t2 // we want to give it the option to render or simply leave it as is
       )
     | `PointwiseOperation(pointwiseOp, t1, t2) =>
       PointwiseOperation.evaluateToDistData(
         pointwiseOp,
-        renderParams,
+        operationToDistData(sampleCount),
         t1,
         t2,
       )
     | `ScaleOperation(scaleOp, t, scaleBy) =>
-      ScaleOperation.evaluateToDistData(scaleOp, renderParams, t, scaleBy)
+      ScaleOperation.evaluateToDistData(
-    | `Truncate(leftCutoff, rightCutoff, t) => Truncate.evaluateToDistData(leftCutoff, rightCutoff, renderParams, t)
+        scaleOp,
-    | `FloatFromDist(distToFloatOp, t) => FloatFromDist.evaluateToDistData(distToFloatOp, renderParams, t)
+        operationToDistData(sampleCount),
-    | `Normalize(t) => Normalize.evaluateToDistData(renderParams, t)
+        t,
-    | `Render(t) => Render.evaluateToRenderedShape(renderParams, t)
+        scaleBy,
+      )
+    | `Truncate(leftCutoff, rightCutoff, t) =>
+      Truncate.evaluateToDistData(
+        leftCutoff,
+        rightCutoff,
+        operationToDistData(sampleCount),
+        t,
+      )
+    | `FloatFromDist(distToFloatOp, t) =>
+      FloatFromDist.evaluateToDistData(distToFloatOp, operationToDistData(sampleCount), t)
+    | `Normalize(t) => Normalize.evaluateToDistData(operationToDistData(sampleCount), t)
+    | `Render(t) =>
+      Render.evaluateToRenderedShape(operationToDistData(sampleCount), sampleCount, t)
     };
   };
 
@@ -372,7 +484,8 @@ module TreeNode = {
   };
 
   let rec toString = (t: t): string => {
-    let stringFromStandardOperation = fun
+    let stringFromStandardOperation =
+      fun
       | `Add => " + "
       | `Subtract => " - "
       | `Multiply => " * "
@@ -384,31 +497,53 @@ module TreeNode = {
       | `Add => " .+ "
       | `Multiply => " .* ";
 
+    let stringFromFloatFromDistOperation =
+        fun
+        | `Pdf(f) => "pdf(x=$f, "
+        | `Inv(f) => "inv(c=$f, "
+        | `Sample => "sample("
+        | `Mean => "mean(";
+
+
     switch (t) {
-    | `DistData(`Symbolic(d)) => SymbolicDist.GenericDistFunctions.toString(d)
+    | `DistData(`Symbolic(d)) =>
+      SymbolicDist.GenericDistFunctions.toString(d)
     | `DistData(`RenderedShape(s)) => "[shape]"
-    | `Operation(`StandardOperation(op, t1, t2)) => toString(t1) ++ stringFromStandardOperation(op) ++ toString(t2)
+    | `Operation(`StandardOperation(op, t1, t2)) =>
-    | `Operation(`PointwiseOperation(op, t1, t2)) => toString(t1) ++ stringFromPointwiseOperation(op) ++ toString(t2)
+      toString(t1) ++ stringFromStandardOperation(op) ++ toString(t2)
-    | `Operation(`ScaleOperation(_scaleOp, t, scaleBy)) => toString(t) ++ " @ " ++ toString(scaleBy)
+    | `Operation(`PointwiseOperation(op, t1, t2)) =>
+      toString(t1) ++ stringFromPointwiseOperation(op) ++ toString(t2)
+    | `Operation(`ScaleOperation(_scaleOp, t, scaleBy)) =>
+      toString(t) ++ " @ " ++ toString(scaleBy)
     | `Operation(`Normalize(t)) => "normalize(" ++ toString(t) ++ ")"
-    | `Operation(`Truncate(lc, rc, t)) => "truncate(" ++ toString(t) ++ ", " ++ E.O.dimap(string_of_float, () => "-inf", lc) ++ ", " ++ E.O.dimap(string_of_float, () => "inf", rc) ++ ")"
+    | `Operation(`FloatFromDist(floatFromDistOp, t)) => stringFromFloatFromDistOperation(floatFromDistOp) ++ toString(t) ++ ")"
+    | `Operation(`Truncate(lc, rc, t)) =>
+      "truncate("
+      ++ toString(t)
+      ++ ", "
+      ++ E.O.dimap(Js.Float.toString, () => "-inf", lc)
+      ++ ", "
+      ++ E.O.dimap(Js.Float.toString, () => "inf", rc)
+      ++ ")"
     | `Operation(`Render(t)) => toString(t)
-    }
+    };
   };
 };
 
 let toShape = (sampleCount: int, treeNode: treeNode) => {
-  let renderResult = TreeNode.toDistData(`Operation(`Render(treeNode)), sampleCount);
+  let renderResult =
-
+    TreeNode.toDistData(`Operation(`Render(treeNode)), sampleCount);
 
   switch (renderResult) {
-  | Ok(`DistData(`RenderedShape(rs))) => {
+  | Ok(`DistData(`RenderedShape(rs))) =>
     let continuous = Distributions.Shape.T.toContinuous(rs);
     let discrete = Distributions.Shape.T.toDiscrete(rs);
     let shape = MixedShapeBuilder.buildSimple(~continuous, ~discrete);
     shape |> E.O.toExt("");
-    }
   | Ok(_) => E.O.toExn("Rendering failed.", None)
-  | Error(message) => E.O.toExn("No shape found!", None)
+  | Error(message) => E.O.toExn("No shape found, error: " ++ message, None)
-  }
   };
+};
+
+let toString = (treeNode: treeNode) =>
+  TreeNode.toString(treeNode);

src/interface/FormBuilder.re

@@ -22,7 +22,7 @@ let propValue = (t: Prop.Value.t) => {
       RenderTypes.DistPlusRenderer.make(
         ~distPlusIngredients=r,
         ~recommendedLength=10000,
-        ~shouldTruncate=true,
+        ~shouldDownsample=true,
         (),
       )
       |> DistPlusRenderer.run