Merge branch 'staging' into Components-improvement-april-2

* staging: (41 commits)
  Fixed tests
  Samples_test -> SampleSetDist_test
  Bandwidth -> SampleSetDist_Bandwidth
  Added genType to SampleSetDist to make pass tests, other minor fixes
  Power should be ** to be consistent
  Cleaned up resultStringToResultError
  Cleanup from previous refactor
  Start of refactor for toPointSetDist
  Gave SampleSetDist a private type
  Namechange: Exponential -> Power
  Cleanup and commenting for PR
  Added more tests to JS__Test.ts, and added SampleN functionality to SampleSetDist
  Added tests for index.js and fixed some corresponding functionality
  Minor refactor of DistributionOperation Constructors
  Fix from CR
  Added to index.ts
  Added a bunch of manual functions for DistributionOperation
  Update README.md
  Cleanup from merge
  Added sparkline and toString to ReducerInterface
  ...

README.md

@@ -1,4 +1,5 @@
 # Squiggle
+![Packages check](https://github.com/QURIresearch/squiggle/actions/workflows/ci.yaml/badge.svg)
 
 This is an experiment DSL/language for making probabilistic estimates. The full story can be found [here](https://www.lesswrong.com/s/rDe8QE5NvXcZYzgZ3). 
 

packages/squiggle-lang/README.md

@@ -13,6 +13,9 @@ Other:
 yarn start  # listens to files and recompiles at every mutation
 yarn test
 yarn test:watch  # keeps an active session and runs all tests at every mutation
+
+# where o := open in osx and o := xdg-open in linux,  
+yarn coverage; o _coverage/index.html  # produces coverage report and opens it in browser
 ```
 
 # TODO: clean up this README.md 

packages/squiggle-lang/__tests__/Bandwidth_test.res

@@ -4,10 +4,10 @@ open Expect
 describe("Bandwidth", () => {
   test("nrd0()", () => {
     let data = [1., 4., 3., 2.]
-    expect(Bandwidth.nrd0(data)) -> toEqual(0.7625801874014622)
+    expect(SampleSetDist_Bandwidth.nrd0(data)) -> toEqual(0.7625801874014622)
   })
   test("nrd()", () => {
     let data = [1., 4., 3., 2.]
-    expect(Bandwidth.nrd(data)) -> toEqual(0.8981499984950554)
+    expect(SampleSetDist_Bandwidth.nrd(data)) -> toEqual(0.8981499984950554)
   })
 })

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

@@ -0,0 +1,103 @@
+open Jest
+open Expect
+
+let env: DistributionOperation.env = {
+  sampleCount: 100,
+  xyPointLength: 100,
+}
+
+let {
+  normalDist5,
+  normalDist10,
+  normalDist20,
+  normalDist,
+  uniformDist,
+  betaDist,
+  lognormalDist,
+  cauchyDist,
+  triangularDist,
+  exponentialDist,
+} = module(GenericDist_Fixtures)
+let mkNormal = (mean, stdev) => GenericDist_Types.Symbolic(#Normal({mean: mean, stdev: stdev}))
+
+let {toFloat, toDist, toString, toError} = module(DistributionOperation.Output)
+let {run} = module(DistributionOperation)
+let {fmap} = module(DistributionOperation.Output)
+let run = run(~env)
+let outputMap = fmap(~env)
+let toExt: option<'a> => 'a = E.O.toExt(
+  "Should be impossible to reach (This error is in test file)",
+)
+
+describe("sparkline", () => {
+  let runTest = (
+    name: string,
+    dist: GenericDist_Types.genericDist,
+    expected: DistributionOperation.outputType,
+  ) => {
+    test(name, () => {
+      let result = DistributionOperation.run(~env, FromDist(ToString(ToSparkline(20)), dist))
+      expect(result)->toEqual(expected)
+    })
+  }
+
+  runTest(
+    "normal",
+    normalDist,
+    String(`▁▁▁▁▁▂▄▆▇██▇▆▄▂▁▁▁▁▁`),
+  )
+
+  runTest(
+    "uniform",
+    uniformDist,
+    String(`████████████████████`),
+  )
+
+  runTest("beta", betaDist, String(`▁▄▇████▇▆▅▄▃▃▂▁▁▁▁▁▁`))
+
+  runTest(
+    "lognormal",
+    lognormalDist,
+    String(`▁█▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁`),
+  )
+
+  runTest(
+    "cauchy",
+    cauchyDist,
+    String(`▁▁▁▁▁▁▁▁▁██▁▁▁▁▁▁▁▁▁`),
+  )
+
+  runTest(
+    "triangular",
+    triangularDist,
+    String(`▁▁▂▃▄▅▆▇████▇▆▅▄▃▂▁▁`),
+  )
+
+  runTest(
+    "exponential",
+    exponentialDist,
+    String(`█▅▄▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁`),
+  )
+})
+
+describe("toPointSet", () => {
+  test("on symbolic normal distribution", () => {
+    let result =
+      run(FromDist(ToDist(ToPointSet), normalDist5))
+      ->outputMap(FromDist(ToFloat(#Mean)))
+      ->toFloat
+      ->toExt
+    expect(result)->toBeSoCloseTo(5.0, ~digits=0)
+  })
+
+  test("on sample set", () => {
+    let result =
+      run(FromDist(ToDist(ToPointSet), normalDist5))
+      ->outputMap(FromDist(ToDist(ToSampleSet(1000))))
+      ->outputMap(FromDist(ToDist(ToPointSet)))
+      ->outputMap(FromDist(ToFloat(#Mean)))
+      ->toFloat
+      ->toExt
+    expect(result)->toBeSoCloseTo(5.0, ~digits=-1)
+  })
+})

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

@@ -0,0 +1,11 @@
+let normalDist5: GenericDist_Types.genericDist = Symbolic(#Normal({mean: 5.0, stdev: 2.0}))
+let normalDist10: GenericDist_Types.genericDist = Symbolic(#Normal({mean: 10.0, stdev: 2.0}))
+let normalDist20: GenericDist_Types.genericDist = Symbolic(#Normal({mean: 20.0, stdev: 2.0}))
+let normalDist: GenericDist_Types.genericDist = normalDist5
+
+let betaDist: GenericDist_Types.genericDist = Symbolic(#Beta({alpha: 2.0, beta: 5.0}))
+let lognormalDist: GenericDist_Types.genericDist = Symbolic(#Lognormal({mu: 0.0, sigma: 1.0}))
+let cauchyDist: GenericDist_Types.genericDist = Symbolic(#Cauchy({local: 1.0, scale: 1.0}))
+let triangularDist: GenericDist_Types.genericDist = Symbolic(#Triangular({low: 1.0, medium: 2.0, high: 3.0}))
+let exponentialDist: GenericDist_Types.genericDist = Symbolic(#Exponential({rate: 2.0}))
+let uniformDist: GenericDist_Types.genericDist = Symbolic(#Uniform({low: 9.0, high: 10.0}))

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

@@ -0,0 +1,70 @@
+open Jest
+open Expect 
+open TestHelpers 
+
+// TODO: use Normal.make (etc.), but preferably after the new validation dispatch is in. 
+let mkNormal = (mean, stdev) => GenericDist_Types.Symbolic(#Normal({mean: mean, stdev: stdev}))
+let mkBeta = (alpha, beta) => GenericDist_Types.Symbolic(#Beta({alpha: alpha, beta: beta}))
+let mkExponential = rate => GenericDist_Types.Symbolic(#Exponential({rate: rate}))
+let mkUniform = (low, high) => GenericDist_Types.Symbolic(#Uniform({low: low, high: high})) 
+let mkCauchy = (local, scale) => GenericDist_Types.Symbolic(#Cauchy({local: local, scale: scale}))
+let mkLognormal = (mu, sigma) => GenericDist_Types.Symbolic(#Lognormal({mu: mu, sigma: sigma}))
+
+describe("mixture", () => {
+  testAll("fair mean of two normal distributions", list{(0.0, 1e2), (-1e1, -1e-4), (-1e1, 1e2), (-1e1, 1e1)}, tup => {  // should be property
+    let (mean1, mean2) = tup
+    let meanValue = {
+        run(Mixture([(mkNormal(mean1, 9e-1), 0.5), (mkNormal(mean2, 9e-1), 0.5)])) 
+        -> outputMap(FromDist(ToFloat(#Mean)))
+    }
+    meanValue -> unpackFloat -> expect -> toBeSoCloseTo((mean1 +. mean2) /. 2.0, ~digits=-1) 
+  })
+  testAll(
+      "weighted mean of a beta and an exponential",
+      // This would not survive property testing, it was easy for me to find cases that NaN'd out. 
+      list{((128.0, 1.0), 2.0), ((2e-1, 64.0), 16.0), ((1e0, 1e0), 64.0)}, 
+      tup => {
+        let ((alpha, beta), rate) = tup
+        let betaWeight = 0.25
+        let exponentialWeight = 0.75
+        let meanValue = {
+          run(Mixture(
+              [
+                (mkBeta(alpha, beta), betaWeight), 
+                (mkExponential(rate), exponentialWeight)
+              ]
+          )) -> outputMap(FromDist(ToFloat(#Mean)))
+        }
+        let betaMean = 1.0 /. (1.0 +. beta /. alpha)
+        let exponentialMean = 1.0 /. rate
+        meanValue 
+        -> unpackFloat 
+        -> expect 
+        -> toBeSoCloseTo(
+            betaWeight *. betaMean +. exponentialWeight *. exponentialMean, 
+            ~digits=-1
+        )
+      }
+  )
+  testAll(
+      "weighted mean of lognormal and uniform", 
+      // Would not survive property tests: very easy to find cases that NaN out. 
+      list{((-1e2,1e1), (2e0,1e0)), ((-1e-16,1e-16), (1e-8,1e0)), ((0.0,1e0), (1e0,1e-2))}, 
+      tup => {
+          let ((low, high), (mu, sigma)) = tup
+          let uniformWeight = 0.6
+          let lognormalWeight = 0.4
+          let meanValue = {
+              run(Mixture([(mkUniform(low, high), uniformWeight), (mkLognormal(mu, sigma), lognormalWeight)]))
+              -> outputMap(FromDist(ToFloat(#Mean)))
+          }
+          let uniformMean = (low +. high) /. 2.0
+          let lognormalMean = mu +. sigma ** 2.0 /. 2.0
+          meanValue
+          -> unpackFloat
+          -> expect
+          -> toBeSoCloseTo(uniformWeight *. uniformMean +. lognormalWeight *. lognormalMean, ~digits=-1)
+      }
+  )
+})
+

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

@@ -0,0 +1,41 @@
+open Jest
+open TestHelpers
+
+describe("Continuous and discrete splits", () => {
+  makeTest(
+    "splits (1)",
+    SampleSetDist_ToPointSet.Internals.T.splitContinuousAndDiscrete([1.432, 1.33455, 2.0]),
+    ([1.432, 1.33455, 2.0], E.FloatFloatMap.empty()),
+  )
+  makeTest(
+    "splits (2)",
+    SampleSetDist_ToPointSet.Internals.T.splitContinuousAndDiscrete([
+      1.432,
+      1.33455,
+      2.0,
+      2.0,
+      2.0,
+      2.0,
+    ]) |> (((c, disc)) => (c, disc |> E.FloatFloatMap.toArray)),
+    ([1.432, 1.33455], [(2.0, 4.0)]),
+  )
+
+  let makeDuplicatedArray = count => {
+    let arr = Belt.Array.range(1, count) |> E.A.fmap(float_of_int)
+    let sorted = arr |> Belt.SortArray.stableSortBy(_, compare)
+    E.A.concatMany([sorted, sorted, sorted, sorted]) |> Belt.SortArray.stableSortBy(_, compare)
+  }
+
+  let (_, discrete1) = SampleSetDist_ToPointSet.Internals.T.splitContinuousAndDiscrete(
+    makeDuplicatedArray(10),
+  )
+  let toArr1 = discrete1 |> E.FloatFloatMap.toArray
+  makeTest("splitMedium at count=10", toArr1 |> Belt.Array.length, 10)
+
+  let (_c, discrete2) = SampleSetDist_ToPointSet.Internals.T.splitContinuousAndDiscrete(
+    makeDuplicatedArray(500),
+  )
+  let toArr2 = discrete2 |> E.FloatFloatMap.toArray
+  makeTest("splitMedium at count=500", toArr2 |> Belt.Array.length, 500)
+})
+

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

@@ -0,0 +1,161 @@
+open Jest
+open Expect
+open TestHelpers
+
+// TODO: use Normal.make (but preferably after teh new validation dispatch is in)
+let mkNormal = (mean, stdev) => GenericDist_Types.Symbolic(#Normal({mean: mean, stdev: stdev}))
+
+describe("(Symbolic) normalize", () => {
+  testAll("has no impact on normal distributions", list{-1e8, -1e-2, 0.0, 1e-4, 1e16}, mean => {
+    let normalValue = mkNormal(mean, 2.0)
+    let normalizedValue = run(FromDist(ToDist(Normalize), normalValue))
+    normalizedValue 
+    -> unpackDist
+    -> expect
+    -> toEqual(normalValue)
+  })
+})
+
+describe("(Symbolic) mean", () => {
+  testAll("of normal distributions", list{-1e8, -16.0, -1e-2, 0.0, 1e-4, 32.0, 1e16}, mean => {
+    run(FromDist(ToFloat(#Mean), mkNormal(mean, 4.0))) 
+    -> unpackFloat 
+    -> expect 
+    -> toBeCloseTo(mean)
+  })
+
+  Skip.test("of normal(0, -1) (it NaNs out)", () => {
+    run(FromDist(ToFloat(#Mean), mkNormal(1e1, -1e0)))
+    -> unpackFloat
+    -> expect
+    -> ExpectJs.toBeFalsy
+  })
+
+  test("of normal(0, 1e-8) (it doesn't freak out at tiny stdev)", () => {
+    run(FromDist(ToFloat(#Mean), mkNormal(0.0, 1e-8)))
+    -> unpackFloat
+    -> expect
+    -> toBeCloseTo(0.0)
+  })
+
+  testAll("of exponential distributions", list{1e-7, 2.0, 10.0, 100.0}, rate => {
+    let meanValue = run(FromDist(ToFloat(#Mean), GenericDist_Types.Symbolic(#Exponential({rate: rate}))))
+    meanValue -> unpackFloat -> expect -> toBeCloseTo(1.0 /. rate)  // https://en.wikipedia.org/wiki/Exponential_distribution#Mean,_variance,_moments,_and_median
+  })
+
+  test("of a cauchy distribution", () => {
+    let meanValue = run(FromDist(ToFloat(#Mean), GenericDist_Types.Symbolic(#Cauchy({local: 1.0, scale: 1.0}))))
+    meanValue
+    -> unpackFloat
+    -> expect
+    -> toBeCloseTo(2.01868297874546)
+    //-> toBe(GenDistError(Other("Cauchy distributions may have no mean value.")))
+  })
+
+  testAll("of triangular distributions", list{(1.0,2.0,3.0), (-1e7,-1e-7,1e-7), (-1e-7,1e0,1e7), (-1e-16,0.0,1e-16)}, tup => {
+    let (low, medium, high) = tup
+    let meanValue = run(FromDist(
+      ToFloat(#Mean), 
+      GenericDist_Types.Symbolic(#Triangular({low: low, medium: medium, high: high}))
+    ))
+    meanValue 
+    -> unpackFloat 
+    -> expect 
+    -> toBeCloseTo((low +. medium +. high) /. 3.0)  // https://www.statology.org/triangular-distribution/
+  })
+
+  // TODO: nonpositive inputs are SUPPOSED to crash. 
+  testAll("of beta distributions", list{(1e-4, 6.4e1), (1.28e2, 1e0), (1e-16, 1e-16), (1e16, 1e16), (-1e4, 1e1), (1e1, -1e4)}, tup => {
+    let (alpha, beta) = tup
+    let meanValue = run(FromDist(
+      ToFloat(#Mean), 
+      GenericDist_Types.Symbolic(#Beta({alpha: alpha, beta: beta}))
+    ))
+    meanValue 
+    -> unpackFloat 
+    -> expect 
+    -> toBeCloseTo(1.0 /. (1.0 +. (beta /. alpha)))  // https://en.wikipedia.org/wiki/Beta_distribution#Mean
+  })
+
+  // TODO: When we have our theory of validators we won't want this to be NaN but to be an error.
+  test("of beta(0, 0)", () => {
+    let meanValue = run(FromDist(
+      ToFloat(#Mean), 
+      GenericDist_Types.Symbolic(#Beta({alpha: 0.0, beta: 0.0}))
+    ))
+    meanValue
+    -> unpackFloat
+    -> expect
+    -> ExpectJs.toBeFalsy
+  })
+
+  testAll("of lognormal distributions", list{(2.0, 4.0), (1e-7, 1e-2), (-1e6, 10.0), (1e3, -1e2), (-1e8, -1e4), (1e2, 1e-5)}, tup => { 
+    let (mu, sigma) = tup
+    let meanValue = run(FromDist(
+      ToFloat(#Mean), 
+      GenericDist_Types.Symbolic(#Lognormal({mu: mu, sigma: sigma}))
+    ))
+    meanValue 
+    -> unpackFloat 
+    -> expect 
+    -> toBeCloseTo(Js.Math.exp(mu +. sigma ** 2.0 /. 2.0 ))  // https://brilliant.org/wiki/log-normal-distribution/
+  })
+
+  testAll("of uniform distributions", list{(1e-5, 12.345), (-1e4, 1e4), (-1e16, -1e2), (5.3e3, 9e9)}, tup => {
+    let (low, high) = tup
+    let meanValue = run(FromDist(
+      ToFloat(#Mean), 
+      GenericDist_Types.Symbolic(#Uniform({low: low, high: high}))
+    ))
+    meanValue 
+    -> unpackFloat 
+    -> expect 
+    -> toBeCloseTo((low +. high) /. 2.0)  // https://en.wikipedia.org/wiki/Continuous_uniform_distribution#Moments
+  })
+
+  test("of a float", () => {
+    let meanValue = run(FromDist(
+      ToFloat(#Mean), 
+      GenericDist_Types.Symbolic(#Float(7.7))
+    ))
+    meanValue -> unpackFloat -> expect -> toBeCloseTo(7.7)
+  })
+})
+
+describe("Normal distribution with sparklines", () => {
+
+  let parameterWiseAdditionPdf = (n1: SymbolicDistTypes.normal, n2: SymbolicDistTypes.normal) => {
+    let normalDistAtSumMeanConstr = SymbolicDist.Normal.add(n1, n2)
+    let normalDistAtSumMean: SymbolicDistTypes.normal = switch normalDistAtSumMeanConstr {
+      | #Normal(params) => params
+    }
+    x => SymbolicDist.Normal.pdf(x, normalDistAtSumMean)
+  }
+
+  let normalDistAtMean5: SymbolicDistTypes.normal = {mean: 5.0, stdev: 2.0}
+  let normalDistAtMean10: SymbolicDistTypes.normal = {mean: 10.0, stdev: 2.0}
+  let range20Float = E.A.Floats.range(0.0, 20.0, 20) // [0.0,1.0,2.0,3.0,4.0,...19.0,]
+
+  test("mean=5 pdf", () => {
+    let pdfNormalDistAtMean5 = x => SymbolicDist.Normal.pdf(x, normalDistAtMean5)
+    let sparklineMean5 = fnImage(pdfNormalDistAtMean5, range20Float) 
+    Sparklines.create(sparklineMean5, ()) 
+    -> expect 
+    -> toEqual(`▁▂▃▆██▇▅▂▁▁▁▁▁▁▁▁▁▁▁`)
+  })
+  
+  test("parameter-wise addition of two normal distributions", () => {  
+    let sparklineMean15 = normalDistAtMean5 -> parameterWiseAdditionPdf(normalDistAtMean10) -> fnImage(range20Float)
+    Sparklines.create(sparklineMean15, ())
+    -> expect
+    -> toEqual(`▁▁▁▁▁▁▁▁▁▂▃▄▆███▇▅▄▂`)
+  })
+
+  test("mean=10 cdf", () => {
+    let cdfNormalDistAtMean10 = x => SymbolicDist.Normal.cdf(x, normalDistAtMean10)
+    let sparklineMean10 = fnImage(cdfNormalDistAtMean10, range20Float)
+    Sparklines.create(sparklineMean10, ())
+    -> expect
+    -> toEqual(`▁▁▁▁▁▁▁▁▂▄▅▇████████`)
+  })
+})

packages/squiggle-lang/__tests__/GenericDist/GenericOperation__Test.res

@@ -1,76 +0,0 @@
-open Jest
-open Expect
-
-let env: DistributionOperation.env = {
-  sampleCount: 100,
-  xyPointLength: 100,
-}
-
-let normalDist5: GenericDist_Types.genericDist = Symbolic(#Normal({mean: 5.0, stdev: 2.0}))
-let normalDist10: GenericDist_Types.genericDist = Symbolic(#Normal({mean: 10.0, stdev: 2.0}))
-let normalDist20: GenericDist_Types.genericDist = Symbolic(#Normal({mean: 20.0, stdev: 2.0}))
-let uniformDist: GenericDist_Types.genericDist = Symbolic(#Uniform({low: 9.0, high: 10.0}))
-
-let {toFloat, toDist, toString, toError} = module(DistributionOperation.Output)
-let {run} = module(DistributionOperation)
-let {fmap} = module(DistributionOperation.Output)
-let run = run(~env)
-let outputMap = fmap(~env)
-let toExt: option<'a> => 'a = E.O.toExt(
-  "Should be impossible to reach (This error is in test file)",
-)
-
-describe("normalize", () => {
-  test("has no impact on normal dist", () => {
-    let result = run(FromDist(ToDist(Normalize), normalDist5))
-    expect(result)->toEqual(Dist(normalDist5))
-  })
-})
-
-describe("mean", () => {
-  test("for a normal distribution", () => {
-    let result = DistributionOperation.run(~env, FromDist(ToFloat(#Mean), normalDist5))
-    expect(result)->toEqual(Float(5.0))
-  })
-})
-
-describe("mixture", () => {
-  test("on two normal distributions", () => {
-    let result =
-      run(Mixture([(normalDist10, 0.5), (normalDist20, 0.5)]))
-      ->outputMap(FromDist(ToFloat(#Mean)))
-      ->toFloat
-      ->toExt
-    expect(result)->toBeCloseTo(15.28)
-  })
-})
-
-describe("toPointSet", () => {
-  test("on symbolic normal distribution", () => {
-    let result =
-      run(FromDist(ToDist(ToPointSet), normalDist5))
-      ->outputMap(FromDist(ToFloat(#Mean)))
-      ->toFloat
-      ->toExt
-    expect(result)->toBeCloseTo(5.09)
-  })
-
-  test("on sample set distribution with under 4 points", () => {
-    let result =
-      run(FromDist(ToDist(ToPointSet), SampleSet([0.0, 1.0, 2.0, 3.0])))->outputMap(
-        FromDist(ToFloat(#Mean)),
-      )
-    expect(result)->toEqual(GenDistError(Other("Converting sampleSet to pointSet failed")))
-  })
-
-  Skip.test("on sample set", () => {
-    let result =
-      run(FromDist(ToDist(ToPointSet), normalDist5))
-      ->outputMap(FromDist(ToDist(ToSampleSet(1000))))
-      ->outputMap(FromDist(ToDist(ToPointSet)))
-      ->outputMap(FromDist(ToFloat(#Mean)))
-      ->toFloat
-      ->toExt
-    expect(result)->toBeCloseTo(5.09)
-  })
-})

packages/squiggle-lang/__tests__/JS__Test.ts

@@ -1,34 +1,87 @@
-import { run } from '../src/js/index';
+import { run, GenericDist, resultMap, makeSampleSetDist } from "../src/js/index";
 
 let testRun = (x: string) => {
-  let result = run(x)
+  let result = run(x);
-  if(result.tag == 'Ok'){
+  if (result.tag == "Ok") {
-    return { tag: 'Ok', value: result.value.exports }
+    return { tag: "Ok", value: result.value.exports };
+  } else {
+    return result;
   }
-  else {
+};
-    return result
-  }
-}
 
 describe("Simple calculations and results", () => {
-    test("mean(normal(5,2))", () => {
+  test("mean(normal(5,2))", () => {
-        expect(testRun("mean(normal(5,2))")).toEqual({ tag: 'Ok', value: [ { NAME: 'Float', VAL: 5  } ] })
+    expect(testRun("mean(normal(5,2))")).toEqual({
-    })
+      tag: "Ok",
-    test("10+10", () => {
+      value: [{ NAME: "Float", VAL: 5 }],
-        let foo = testRun("10 + 10")
+    });
-        expect(foo).toEqual({ tag: 'Ok', value: [ { NAME: 'Float', VAL: 20  } ] })
+  });
-    })
+  test("10+10", () => {
-})
+    let foo = testRun("10 + 10");
+    expect(foo).toEqual({ tag: "Ok", value: [{ NAME: "Float", VAL: 20 }] });
+  });
+});
 describe("Log function", () => {
-    test("log(1) = 0", () => {
+  test("log(1) = 0", () => {
-      let foo = testRun("log(1)")
+    let foo = testRun("log(1)");
-      expect(foo).toEqual({ tag: 'Ok', value: [ { NAME: 'Float', VAL: 0} ]})
+    expect(foo).toEqual({ tag: "Ok", value: [{ NAME: "Float", VAL: 0 }] });
-    })
+  });
-})
+});
 
 describe("Multimodal too many weights error", () => {
-    test("mm(0,0,[0,0,0])", () => {
+  test("mm(0,0,[0,0,0])", () => {
-      let foo = testRun("mm(0,0,[0,0,0])")
+    let foo = testRun("mm(0,0,[0,0,0])");
-      expect(foo).toEqual({ "tag": "Error", "value": "Function multimodal error: Too many weights provided" })
+    expect(foo).toEqual({
-    })
+      tag: "Error",
+      value: "Function multimodal error: Too many weights provided",
+    });
+  });
+});
+
+describe("GenericDist", () => {
+
+  //It's important that sampleCount is less than 9. If it's more, than that will create randomness
+  //Also, note, the value should be created using makeSampleSetDist() later on.
+  let env = { sampleCount: 8, xyPointLength: 100 };
+  let dist = new GenericDist(
+    { tag: "SampleSet", value: [3, 4, 5, 6, 6, 7, 10, 15, 30] },
+    env
+  );
+  let dist2 = new GenericDist(
+    { tag: "SampleSet", value: [20, 22, 24, 29, 30, 35, 38, 44, 52] },
+    env
+  );
+
+  test("mean", () => {
+    expect(dist.mean().value).toBeCloseTo(3.737);
+  });
+  test("pdf", () => {
+    expect(dist.pdf(5.0).value).toBeCloseTo(0.0431);
+  });
+  test("cdf", () => {
+    expect(dist.cdf(5.0).value).toBeCloseTo(0.155);
+  });
+  test("inv", () => {
+    expect(dist.inv(0.5).value).toBeCloseTo(9.458);
+  });
+  test("toPointSet", () => {
+    expect(
+      resultMap(dist.toPointSet(), (r: GenericDist) => r.toString()).value.value
+    ).toBe("Point Set Distribution");
+  });
+  test("toSparkline", () => {
+    expect(dist.toSparkline(20).value).toBe("▁▁▃▅███▆▄▃▂▁▁▂▂▃▂▁▁▁");
+  });
+  test("algebraicAdd", () => {
+    expect(
+      resultMap(dist.algebraicAdd(dist2), (r: GenericDist) => r.toSparkline(20))
+        .value.value
+    ).toBe("▁▁▂▄▆████▇▆▄▄▃▃▃▂▁▁▁");
+  });
+  test("pointwiseAdd", () => {
+    expect(
+      resultMap(dist.pointwiseAdd(dist2), (r: GenericDist) => r.toSparkline(20))
+        .value.value
+    ).toBe("▁▂▅██▅▅▅▆▇█▆▅▃▃▂▂▁▁▁");
+  });
 });

packages/squiggle-lang/__tests__/Reducer/Reducer_TestHelpers.res

@@ -9,6 +9,3 @@ let expectParseToBe = (expr: string, answer: string) =>
 
 let expectEvalToBe = (expr: string, answer: string) =>
   Reducer.eval(expr)->ExpressionValue.toStringResult->expect->toBe(answer)
-
-// Current configuration does not ignore this file so we have to have a test
-test("test helpers", () => expect(1)->toBe(1))

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

@@ -30,6 +30,9 @@ describe("eval on distribution functions", () => {
     testEval("mean(normal(5,2))", "Ok(5)")
     testEval("mean(lognormal(1,2))", "Ok(20.085536923187668)")
   })
+  describe("toString", () => {
+    testEval("toString(normal(5,2))", "Ok('Normal(5,2)')")
+  })
   describe("normalize", () => {
     testEval("normalize(normal(5,2))", "Ok(Normal(5,2))")
   })

packages/squiggle-lang/__tests__/Samples__test.res

@@ -1,47 +0,0 @@
-open Jest
-open Expect
-
-let makeTest = (~only=false, str, item1, item2) =>
-  only
-    ? Only.test(str, () => expect(item1) -> toEqual(item2))
-    : test(str, () => expect(item1) -> toEqual(item2))
-
-describe("Lodash", () =>
-  describe("Lodash", () => {
-    makeTest(
-      "split",
-      SampleSet.Internals.T.splitContinuousAndDiscrete([1.432, 1.33455, 2.0]),
-      ([1.432, 1.33455, 2.0], E.FloatFloatMap.empty()),
-    )
-    makeTest(
-      "split",
-      SampleSet.Internals.T.splitContinuousAndDiscrete([
-        1.432,
-        1.33455,
-        2.0,
-        2.0,
-        2.0,
-        2.0,
-      ]) |> (((c, disc)) => (c, disc |> E.FloatFloatMap.toArray)),
-      ([1.432, 1.33455], [(2.0, 4.0)]),
-    )
-
-    let makeDuplicatedArray = count => {
-      let arr = Belt.Array.range(1, count) |> E.A.fmap(float_of_int)
-      let sorted = arr |> Belt.SortArray.stableSortBy(_, compare)
-      E.A.concatMany([sorted, sorted, sorted, sorted]) |> Belt.SortArray.stableSortBy(_, compare)
-    }
-
-    let (_, discrete) = SampleSet.Internals.T.splitContinuousAndDiscrete(
-      makeDuplicatedArray(10),
-    )
-    let toArr = discrete |> E.FloatFloatMap.toArray
-    makeTest("splitMedium", toArr |> Belt.Array.length, 10)
-
-    let (_c, discrete) = SampleSet.Internals.T.splitContinuousAndDiscrete(
-      makeDuplicatedArray(500),
-    )
-    let toArr = discrete |> E.FloatFloatMap.toArray
-    makeTest("splitMedium", toArr |> Belt.Array.length, 500)
-  })
-)

packages/squiggle-lang/__tests__/Symbolic_test.res

@@ -1,33 +0,0 @@
-open Jest
-open Expect
-open Js.Array
-open SymbolicDist
-
-let makeTest = (~only=false, str, item1, item2) =>
-  only
-    ? Only.test(str, () => expect(item1) -> toEqual(item2))
-    : test(str, () => expect(item1) -> toEqual(item2))
-
-let pdfImage = (thePdf, inps) => map(thePdf, inps)
-
-let parameterWiseAdditionHelper = (n1: SymbolicDistTypes.normal, n2: SymbolicDistTypes.normal) => {
-  let normalDistAtSumMeanConstr = Normal.add(n1, n2)
-  let normalDistAtSumMean: SymbolicDistTypes.normal = switch normalDistAtSumMeanConstr {
-    | #Normal(params) => params
-  }
-  x => Normal.pdf(x, normalDistAtSumMean)
-}
-
-describe("Normal distribution with sparklines", () => {
-
-  let normalDistAtMean5: SymbolicDistTypes.normal = {mean: 5.0, stdev: 2.0}
-  let normalDistAtMean10: SymbolicDistTypes.normal = {mean: 10.0, stdev: 2.0}
-  let range20Float = E.A.rangeFloat(0, 20) // [0.0,1.0,2.0,3.0,4.0,...19.0,]
-
-  let pdfNormalDistAtMean5 = x => Normal.pdf(x, normalDistAtMean5)
-  let sparklineMean5 = pdfImage(pdfNormalDistAtMean5, range20Float)
-  makeTest("mean=5", Sparklines.create(sparklineMean5, ()), `▁▂▃▅███▅▃▂▁▁▁▁▁▁▁▁▁▁▁`)
-
-  let sparklineMean15 = normalDistAtMean5 -> parameterWiseAdditionHelper(normalDistAtMean10) -> pdfImage(range20Float)
-  makeTest("parameter-wise addition of two normal distributions", Sparklines.create(sparklineMean15, ()), `▁▁▁▁▁▁▁▁▁▁▂▃▅▇███▇▅▃▂`)
-})

packages/squiggle-lang/__tests__/TestHelpers.res

@@ -0,0 +1,26 @@
+open Jest
+open Expect
+
+let makeTest = (~only=false, str, item1, item2) =>
+  only
+    ? Only.test(str, () => expect(item1) -> toEqual(item2))
+    : test(str, () => expect(item1) -> toEqual(item2))
+
+
+let {toFloat, toDist, toString, toError, fmap} = module(DistributionOperation.Output)
+
+let fnImage = (theFn, inps) => Js.Array.map(theFn, inps)
+
+let env: DistributionOperation.env = {
+  sampleCount: 100,
+  xyPointLength: 100,
+}
+
+let run = DistributionOperation.run(~env)
+let outputMap = fmap(~env)
+let unreachableInTestFileMessage = "Should be impossible to reach (This error is in test file)"
+let toExtFloat: option<float> => float = E.O.toExt(unreachableInTestFileMessage)
+let toExtDist: option<GenericDist_Types.genericDist> => GenericDist_Types.genericDist = E.O.toExt(unreachableInTestFileMessage)
+// let toExt: option<'a> => 'a = E.O.toExt(unreachableInTestFileMessage)
+let unpackFloat = x => x -> toFloat -> toExtFloat
+let unpackDist = y => y -> toDist -> toExtDist

packages/squiggle-lang/bsconfig.json

@@ -33,7 +33,7 @@
   "gentypeconfig": {
     "language": "typescript",
     "module": "commonjs",
-    "shims": {},
+    "shims": {"Js": "Js"},
     "debug": {
       "all": false,
       "basic": false
@@ -47,7 +47,7 @@
     [
       "../../node_modules/bisect_ppx/ppx", 
       "--exclude-files", 
-      ".*_Test\\.res$$"
+      ".*_test\\.res$$"
     ]
   ]
 }

packages/squiggle-lang/jest.config.js

@@ -1,8 +1,13 @@
 /** @type {import('ts-jest/dist/types').InitialOptionsTsJest} */
 module.exports = {
-  preset: 'ts-jest',
+  preset: "ts-jest",
-  testEnvironment: 'node',
+  testEnvironment: "node",
   setupFilesAfterEnv: [
-    "<rootdir>/../../node_modules/bisect_ppx/src/runtime/js/jest.bs.js"
+    "<rootdir>/../../node_modules/bisect_ppx/src/runtime/js/jest.bs.js",
+  ],
+  testPathIgnorePatterns: [
+    ".*Fixtures.bs.js",
+    "/node_modules/",
+    ".*Helpers.bs.js",
   ],
 };

packages/squiggle-lang/package.json

@@ -10,7 +10,7 @@
     "test:reducer": "jest --testPathPattern '.*__tests__/Reducer.*'",
     "test": "jest",
     "test:watch": "jest --watchAll",
-    "coverage": "rm -f *.coverage; yarn clean; BISECT_ENABLE=yes yarn build; yarn test; ./node_modules/.bin/bisect-ppx-report html", 
+    "coverage": "rm -f *.coverage; yarn clean; BISECT_ENABLE=yes yarn build; yarn test; bisect-ppx-report html", 
     "all": "yarn build && yarn bundle && yarn test"
   },
   "keywords": [

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

@@ -1,17 +1,228 @@
-import {runAll} from '../rescript/ProgramEvaluator.gen';
+import { runAll } from "../rescript/ProgramEvaluator.gen";
-import type { Inputs_SamplingInputs_t as SamplingInputs, exportEnv, exportType, exportDistribution} from '../rescript/ProgramEvaluator.gen';
+import type {
-export type { SamplingInputs, exportEnv, exportDistribution }
+  Inputs_SamplingInputs_t as SamplingInputs,
-export type {t as DistPlus} from '../rescript/OldInterpreter/DistPlus.gen';
+  exportEnv,
+  exportType,
+  exportDistribution,
+} from "../rescript/ProgramEvaluator.gen";
+export type { SamplingInputs, exportEnv, exportDistribution };
+export type { t as DistPlus } from "../rescript/OldInterpreter/DistPlus.gen";
+import {
+  genericDist,
+  env,
+  resultDist,
+  resultFloat,
+  resultString,
+} from "../rescript/TypescriptInterface.gen";
+export {makeSampleSetDist} from "../rescript/TypescriptInterface.gen";
+import {
+  Constructors_mean,
+  Constructors_sample,
+  Constructors_pdf,
+  Constructors_cdf,
+  Constructors_inv,
+  Constructors_normalize,
+  Constructors_toPointSet,
+  Constructors_toSampleSet,
+  Constructors_truncate,
+  Constructors_inspect,
+  Constructors_toString,
+  Constructors_toSparkline,
+  Constructors_algebraicAdd,
+  Constructors_algebraicMultiply,
+  Constructors_algebraicDivide,
+  Constructors_algebraicSubtract,
+  Constructors_algebraicLogarithm,
+  Constructors_algebraicPower,
+  Constructors_pointwiseAdd,
+  Constructors_pointwiseMultiply,
+  Constructors_pointwiseDivide,
+  Constructors_pointwiseSubtract,
+  Constructors_pointwiseLogarithm,
+  Constructors_pointwisePower,
+} from "../rescript/Distributions/DistributionOperation/DistributionOperation.gen";
 
-export let defaultSamplingInputs : SamplingInputs = {
+export let defaultSamplingInputs: SamplingInputs = {
-  sampleCount : 10000,
+  sampleCount: 10000,
-  outputXYPoints : 10000,
+  outputXYPoints: 10000,
-  pointDistLength : 1000
+  pointDistLength: 1000,
+};
+
+export function run(
+  squiggleString: string,
+  samplingInputs?: SamplingInputs,
+  environment?: exportEnv
+): { tag: "Ok"; value: exportType } | { tag: "Error"; value: string } {
+  let si: SamplingInputs = samplingInputs
+    ? samplingInputs
+    : defaultSamplingInputs;
+  let env: exportEnv = environment ? environment : [];
+  return runAll(squiggleString, si, env);
 }
 
-export function run(squiggleString : string, samplingInputs? : SamplingInputs, environment?: exportEnv) : { tag: "Ok"; value: exportType }
+//This is clearly not fully typed. I think later we should use a functional library to
-  | { tag: "Error"; value: string } {
+// provide a better Either type and corresponding functions.
-  let si : SamplingInputs = samplingInputs ? samplingInputs : defaultSamplingInputs
+type result =
-  let env : exportEnv = environment ? environment : []
+  | {
-  return runAll(squiggleString, si, env)
+      tag: "Ok";
+      value: any;
+    }
+  | {
+      tag: "Error";
+      value: any;
+    };
+
+export function resultMap(r: result, mapFn: any): result {
+  if (r.tag === "Ok") {
+    return { tag: "Ok", value: mapFn(r.value) };
+  } else {
+    return r;
+  }
+}
+
+export function resultExn(r: result): any {
+  r.value
+}
+
+export class GenericDist {
+  t: genericDist;
+  env: env;
+
+  constructor(t: genericDist, env: env) {
+    this.t = t;
+    this.env = env;
+    return this;
+  }
+
+  mapResultDist(r: resultDist) {
+    return resultMap(r, (v: genericDist) => new GenericDist(v, this.env));
+  }
+
+  mean() {
+    return Constructors_mean({ env: this.env }, this.t);
+  }
+
+  sample(): resultFloat {
+    return Constructors_sample({ env: this.env }, this.t);
+  }
+
+  pdf(n: number): resultFloat {
+    return Constructors_pdf({ env: this.env }, this.t, n);
+  }
+
+  cdf(n: number): resultFloat {
+    return Constructors_cdf({ env: this.env }, this.t, n);
+  }
+
+  inv(n: number): resultFloat {
+    return Constructors_inv({ env: this.env }, this.t, n);
+  }
+
+  normalize() {
+    return this.mapResultDist(
+      Constructors_normalize({ env: this.env }, this.t)
+    );
+  }
+
+  toPointSet() {
+    return this.mapResultDist(
+      Constructors_toPointSet({ env: this.env }, this.t)
+    );
+  }
+
+  toSampleSet(n: number) {
+    return this.mapResultDist(
+      Constructors_toSampleSet({ env: this.env }, this.t, n)
+    );
+  }
+
+  truncate(left: number, right: number) {
+    return this.mapResultDist(
+      Constructors_truncate({ env: this.env }, this.t, left, right)
+    );
+  }
+
+  inspect() {
+    return this.mapResultDist(Constructors_inspect({ env: this.env }, this.t));
+  }
+
+  toString(): resultString {
+    return Constructors_toString({ env: this.env }, this.t);
+  }
+
+  toSparkline(n: number): resultString {
+    return Constructors_toSparkline({ env: this.env }, this.t, n);
+  }
+
+  algebraicAdd(d2: GenericDist) {
+    return this.mapResultDist(
+      Constructors_algebraicAdd({ env: this.env }, this.t, d2.t)
+    );
+  }
+
+  algebraicMultiply(d2: GenericDist) {
+    return this.mapResultDist(
+      Constructors_algebraicMultiply({ env: this.env }, this.t, d2.t)
+    );
+  }
+
+  algebraicDivide(d2: GenericDist) {
+    return this.mapResultDist(
+      Constructors_algebraicDivide({ env: this.env }, this.t, d2.t)
+    );
+  }
+
+  algebraicSubtract(d2: GenericDist) {
+    return this.mapResultDist(
+      Constructors_algebraicSubtract({ env: this.env }, this.t, d2.t)
+    );
+  }
+
+  algebraicLogarithm(d2: GenericDist) {
+    return this.mapResultDist(
+      Constructors_algebraicLogarithm({ env: this.env }, this.t, d2.t)
+    );
+  }
+
+  algebraicPower(d2: GenericDist) {
+    return this.mapResultDist(
+      Constructors_algebraicPower({ env: this.env }, this.t, d2.t)
+    );
+  }
+
+  pointwiseAdd(d2: GenericDist) {
+    return this.mapResultDist(
+      Constructors_pointwiseAdd({ env: this.env }, this.t, d2.t)
+    );
+  }
+
+  pointwiseMultiply(d2: GenericDist) {
+    return this.mapResultDist(
+      Constructors_pointwiseMultiply({ env: this.env }, this.t, d2.t)
+    );
+  }
+
+  pointwiseDivide(d2: GenericDist) {
+    return this.mapResultDist(
+      Constructors_pointwiseDivide({ env: this.env }, this.t, d2.t)
+    );
+  }
+
+  pointwiseSubtract(d2: GenericDist) {
+    return this.mapResultDist(
+      Constructors_pointwiseSubtract({ env: this.env }, this.t, d2.t)
+    );
+  }
+
+  pointwiseLogarithm(d2: GenericDist) {
+    return this.mapResultDist(
+      Constructors_pointwiseLogarithm({ env: this.env }, this.t, d2.t)
+    );
+  }
+
+  pointwisePower(d2: GenericDist) {
+    return this.mapResultDist(
+      Constructors_pointwisePower({ env: this.env }, this.t, d2.t)
+    );
+  }
 }

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

@@ -10,10 +10,10 @@ type env = {
 }
 
 type outputType =
-  | Dist(GenericDist_Types.genericDist)
+  | Dist(genericDist)
   | Float(float)
   | String(string)
-  | GenDistError(GenericDist_Types.error)
+  | GenDistError(error)
 
 /*
 We're going to add another function to this module later, so first define a
@@ -113,7 +113,11 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
       GenericDist.toFloatOperation(dist, ~toPointSetFn, ~distToFloatOperation)
       ->E.R2.fmap(r => Float(r))
       ->OutputLocal.fromResult
-    | ToString => dist->GenericDist.toString->String
+    | ToString(ToString) => dist->GenericDist.toString->String
+    | ToString(ToSparkline(bucketCount)) =>
+      GenericDist.toSparkline(dist, ~sampleCount, ~bucketCount, ())
+      ->E.R2.fmap(r => String(r))
+      ->OutputLocal.fromResult
     | ToDist(Inspect) => {
         Js.log2("Console log requested: ", dist)
         Dist(dist)
@@ -124,10 +128,13 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
       ->E.R2.fmap(r => Dist(r))
       ->OutputLocal.fromResult
     | ToDist(ToSampleSet(n)) =>
-      dist->GenericDist.sampleN(n)->E.R2.fmap(r => Dist(SampleSet(r)))->OutputLocal.fromResult
+      dist
+      ->GenericDist.toSampleSetDist(n)
+      ->E.R2.fmap(r => Dist(SampleSet(r)))
+      ->OutputLocal.fromResult
     | ToDist(ToPointSet) =>
       dist
-      ->GenericDist.toPointSet(~xyPointLength, ~sampleCount)
+      ->GenericDist.toPointSet(~xyPointLength, ~sampleCount, ())
       ->E.R2.fmap(r => Dist(PointSet(r)))
       ->OutputLocal.fromResult
     | ToDistCombination(Algebraic, _, #Float(_)) => GenDistError(NotYetImplemented)
@@ -181,3 +188,43 @@ module Output = {
     newFnCall->E.R2.fmap(run(~env))->OutputLocal.fromResult
   }
 }
+
+// See comment above GenericDist_Types.Constructors to explain the purpose of this module.
+// I tried having another internal module called UsingDists, similar to how its done in
+// GenericDist_Types.Constructors. However, this broke GenType for me, so beware.
+module Constructors = {
+  module C = GenericDist_Types.Constructors.UsingDists
+  open OutputLocal
+  let mean = (~env, dist) => C.mean(dist)->run(~env)->toFloatR
+  let sample = (~env, dist) => C.sample(dist)->run(~env)->toFloatR
+  let cdf = (~env, dist, f) => C.cdf(dist, f)->run(~env)->toFloatR
+  let inv = (~env, dist, f) => C.inv(dist, f)->run(~env)->toFloatR
+  let pdf = (~env, dist, f) => C.pdf(dist, f)->run(~env)->toFloatR
+  let normalize = (~env, dist) => C.normalize(dist)->run(~env)->toDistR
+  let toPointSet = (~env, dist) => C.toPointSet(dist)->run(~env)->toDistR
+  let toSampleSet = (~env, dist, n) => C.toSampleSet(dist, n)->run(~env)->toDistR
+  let truncate = (~env, dist, leftCutoff, rightCutoff) =>
+    C.truncate(dist, leftCutoff, rightCutoff)->run(~env)->toDistR
+  let inspect = (~env, dist) => C.inspect(dist)->run(~env)->toDistR
+  let toString = (~env, dist) => C.toString(dist)->run(~env)->toStringR
+  let toSparkline = (~env, dist, bucketCount) =>
+    C.toSparkline(dist, bucketCount)->run(~env)->toStringR
+  let algebraicAdd = (~env, dist1, dist2) => C.algebraicAdd(dist1, dist2)->run(~env)->toDistR
+  let algebraicMultiply = (~env, dist1, dist2) =>
+    C.algebraicMultiply(dist1, dist2)->run(~env)->toDistR
+  let algebraicDivide = (~env, dist1, dist2) => C.algebraicDivide(dist1, dist2)->run(~env)->toDistR
+  let algebraicSubtract = (~env, dist1, dist2) =>
+    C.algebraicSubtract(dist1, dist2)->run(~env)->toDistR
+  let algebraicLogarithm = (~env, dist1, dist2) =>
+    C.algebraicLogarithm(dist1, dist2)->run(~env)->toDistR
+  let algebraicPower = (~env, dist1, dist2) => C.algebraicPower(dist1, dist2)->run(~env)->toDistR
+  let pointwiseAdd = (~env, dist1, dist2) => C.pointwiseAdd(dist1, dist2)->run(~env)->toDistR
+  let pointwiseMultiply = (~env, dist1, dist2) =>
+    C.pointwiseMultiply(dist1, dist2)->run(~env)->toDistR
+  let pointwiseDivide = (~env, dist1, dist2) => C.pointwiseDivide(dist1, dist2)->run(~env)->toDistR
+  let pointwiseSubtract = (~env, dist1, dist2) =>
+    C.pointwiseSubtract(dist1, dist2)->run(~env)->toDistR
+  let pointwiseLogarithm = (~env, dist1, dist2) =>
+    C.pointwiseLogarithm(dist1, dist2)->run(~env)->toDistR
+  let pointwisePower = (~env, dist1, dist2) => C.pointwisePower(dist1, dist2)->run(~env)->toDistR
+}

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

@@ -1,19 +1,24 @@
+@genType
 type env = {
   sampleCount: int,
   xyPointLength: int,
 }
 
+open GenericDist_Types
+
+@genType
 type outputType =
-  | Dist(GenericDist_Types.genericDist)
+  | Dist(genericDist)
   | Float(float)
   | String(string)
-  | GenDistError(GenericDist_Types.error)
+  | GenDistError(error)
 
+@genType
 let run: (~env: env, GenericDist_Types.Operation.genericFunctionCallInfo) => outputType
 let runFromDist: (
   ~env: env,
   ~functionCallInfo: GenericDist_Types.Operation.fromDist,
-  GenericDist_Types.genericDist,
+  genericDist,
 ) => outputType
 let runFromFloat: (
   ~env: env,
@@ -23,12 +28,68 @@ let runFromFloat: (
 
 module Output: {
   type t = outputType
-  let toDist: t => option<GenericDist_Types.genericDist>
+  let toDist: t => option<genericDist>
-  let toDistR: t => result<GenericDist_Types.genericDist, GenericDist_Types.error>
+  let toDistR: t => result<genericDist, error>
   let toFloat: t => option<float>
-  let toFloatR: t => result<float, GenericDist_Types.error>
+  let toFloatR: t => result<float, error>
   let toString: t => option<string>
-  let toStringR: t => result<string, GenericDist_Types.error>
+  let toStringR: t => result<string, error>
-  let toError: t => option<GenericDist_Types.error>
+  let toError: t => option<error>
   let fmap: (~env: env, t, GenericDist_Types.Operation.singleParamaterFunction) => t
 }
+
+module Constructors: {
+    @genType
+    let mean: (~env: env, genericDist) => result<float, error>
+    @genType
+    let sample: (~env: env, genericDist) => result<float, error>
+    @genType
+    let cdf: (~env: env, genericDist, float) => result<float, error>
+    @genType
+    let inv: (~env: env, genericDist, float) => result<float, error>
+    @genType
+    let pdf: (~env: env, genericDist, float) => result<float, error>
+    @genType
+    let normalize: (~env: env, genericDist) => result<genericDist, error>
+    @genType
+    let toPointSet: (~env: env, genericDist) => result<genericDist, error>
+    @genType
+    let toSampleSet: (~env: env, genericDist, int) => result<genericDist, error>
+    @genType
+    let truncate: (
+      ~env: env,
+      genericDist,
+      option<float>,
+      option<float>,
+    ) => result<genericDist, error>
+    @genType
+    let inspect: (~env: env, genericDist) => result<genericDist, error>
+    @genType
+    let toString: (~env: env, genericDist) => result<string, error>
+    @genType
+    let toSparkline: (~env: env, genericDist, int) => result<string, error>
+    @genType
+    let algebraicAdd: (~env: env, genericDist, genericDist) => result<genericDist, error>
+    @genType
+    let algebraicMultiply: (~env: env, genericDist, genericDist) => result<genericDist, error>
+    @genType
+    let algebraicDivide: (~env: env, genericDist, genericDist) => result<genericDist, error>
+    @genType
+    let algebraicSubtract: (~env: env, genericDist, genericDist) => result<genericDist, error>
+    @genType
+    let algebraicLogarithm: (~env: env, genericDist, genericDist) => result<genericDist, error>
+    @genType
+    let algebraicPower: (~env: env, genericDist, genericDist) => result<genericDist, error>
+    @genType
+    let pointwiseAdd: (~env: env, genericDist, genericDist) => result<genericDist, error>
+    @genType
+    let pointwiseMultiply: (~env: env, genericDist, genericDist) => result<genericDist, error>
+    @genType
+    let pointwiseDivide: (~env: env, genericDist, genericDist) => result<genericDist, error>
+    @genType
+    let pointwiseSubtract: (~env: env, genericDist, genericDist) => result<genericDist, error>
+    @genType
+    let pointwiseLogarithm: (~env: env, genericDist, genericDist) => result<genericDist, error>
+    @genType
+    let pointwisePower: (~env: env, genericDist, genericDist) => result<genericDist, error>
+}

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

@@ -19,7 +19,7 @@ module Operation = {
     | #Multiply
     | #Subtract
     | #Divide
-    | #Exponentiate
+    | #Power
     | #Logarithm
   ]
 
@@ -28,7 +28,7 @@ module Operation = {
     | #Add => \"+."
     | #Multiply => \"*."
     | #Subtract => \"-."
-    | #Exponentiate => \"**"
+    | #Power => \"**"
     | #Divide => \"/."
     | #Logarithm => (a, b) => log(a) /. log(b)
     }

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

@@ -2,17 +2,20 @@
 type t = GenericDist_Types.genericDist
 type error = GenericDist_Types.error
 type toPointSetFn = t => result<PointSetTypes.pointSetDist, error>
-type toSampleSetFn = t => result<array<float>, error>
+type toSampleSetFn = t => result<SampleSetDist.t, error>
 type scaleMultiplyFn = (t, float) => result<t, error>
 type pointwiseAddFn = (t, t) => result<t, error>
 
 let sampleN = (t: t, n) =>
   switch t {
-  | PointSet(r) => Ok(PointSetDist.sampleNRendered(n, r))
+  | PointSet(r) => PointSetDist.sampleNRendered(n, r)
-  | Symbolic(r) => Ok(SymbolicDist.T.sampleN(n, r))
+  | Symbolic(r) => SymbolicDist.T.sampleN(n, r)
-  | SampleSet(_) => Error(GenericDist_Types.NotYetImplemented)
+  | SampleSet(r) => SampleSetDist.sampleN(r, n)
   }
 
+let toSampleSetDist = (t: t, n) =>
+  SampleSetDist.make(sampleN(t, n))->GenericDist_Types.Error.resultStringToResultError
+
 let fromFloat = (f: float): t => Symbolic(SymbolicDist.Float.make(f))
 
 let toString = (t: t) =>
@@ -49,32 +52,45 @@ let toFloatOperation = (
   }
 }
 
-//Todo: If it's a pointSet, but the xyPointLenght is different from what it has, it should change.
+//Todo: If it's a pointSet, but the xyPointLength is different from what it has, it should change.
 // This is tricky because the case of discrete distributions.
 // Also, change the outputXYPoints/pointSetDistLength details
-let toPointSet = (~xyPointLength, ~sampleCount, t): result<PointSetTypes.pointSetDist, error> => {
+let toPointSet = (
+  t,
+  ~xyPointLength,
+  ~sampleCount,
+  ~xSelection: GenericDist_Types.Operation.pointsetXSelection=#ByWeight,
+  unit,
+): result<PointSetTypes.pointSetDist, error> => {
   switch (t: t) {
   | PointSet(pointSet) => Ok(pointSet)
-  | Symbolic(r) => Ok(SymbolicDist.T.toPointSetDist(xyPointLength, r))
+  | Symbolic(r) => Ok(SymbolicDist.T.toPointSetDist(~xSelection, xyPointLength, r))
-  | SampleSet(r) => {
+  | SampleSet(r) =>
-      let response = SampleSet.toPointSetDist(
+    SampleSetDist.toPointSetDist(
-        ~samples=r,
+      ~samples=r,
-        ~samplingInputs={
+      ~samplingInputs={
-          sampleCount: sampleCount,
+        sampleCount: sampleCount,
-          outputXYPoints: xyPointLength,
+        outputXYPoints: xyPointLength,
-          pointSetDistLength: xyPointLength,
+        pointSetDistLength: xyPointLength,
-          kernelWidth: None,
+        kernelWidth: None,
-        },
+      },
-        (),
+    )->GenericDist_Types.Error.resultStringToResultError
-      ).pointSetDist
-      switch response {
-      | Some(r) => Ok(r)
-      | None => Error(Other("Converting sampleSet to pointSet failed"))
-      }
-    }
   }
 }
 
+/*
+  PointSetDist.toSparkline calls "downsampleEquallyOverX", which downsamples it to n=bucketCount.
+  It first needs a pointSetDist, so we convert to a pointSetDist. In this process we want the 
+  xyPointLength to be a bit longer than the eventual toSparkline downsampling. I chose 3 
+  fairly arbitrarily.
+ */
+let toSparkline = (t: t, ~sampleCount: int, ~bucketCount: int=20, unit): result<string, error> =>
+  t
+  ->toPointSet(~xSelection=#Linear, ~xyPointLength=bucketCount * 3, ~sampleCount, ())
+  ->E.R.bind(r =>
+    r->PointSetDist.toSparkline(bucketCount)->GenericDist_Types.Error.resultStringToResultError
+  )
+
 module Truncate = {
   let trySymbolicSimplification = (leftCutoff, rightCutoff, t: t): option<t> =>
     switch (leftCutoff, rightCutoff, t) {
@@ -147,10 +163,12 @@ module AlgebraicCombination = {
     t1: t,
     t2: t,
   ) => {
-    let arithmeticOperation = Operation.Algebraic.toFn(arithmeticOperation)
+    let fn = Operation.Algebraic.toFn(arithmeticOperation)
-    E.R.merge(toSampleSet(t1), toSampleSet(t2))->E.R2.fmap(((a, b)) => {
+    E.R.merge(toSampleSet(t1), toSampleSet(t2))
-      Belt.Array.zip(a, b)->E.A2.fmap(((a, b)) => arithmeticOperation(a, b))
+    ->E.R.bind(((t1, t2)) => {
+      SampleSetDist.map2(~fn, ~t1, ~t2)->GenericDist_Types.Error.resultStringToResultError
     })
+    ->E.R2.fmap(r => GenericDist_Types.SampleSet(r))
   }
 
   //I'm (Ozzie) really just guessing here, very little idea what's best
@@ -181,13 +199,7 @@ module AlgebraicCombination = {
     | Some(Error(e)) => Error(Other(e))
     | None =>
       switch chooseConvolutionOrMonteCarlo(t1, t2) {
-      | #CalculateWithMonteCarlo =>
+      | #CalculateWithMonteCarlo => runMonteCarlo(toSampleSetFn, arithmeticOperation, t1, t2)
-        runMonteCarlo(
-          toSampleSetFn,
-          arithmeticOperation,
-          t1,
-          t2,
-        )->E.R2.fmap(r => GenericDist_Types.SampleSet(r))
       | #CalculateWithConvolution =>
         runConvolution(
           toPointSetFn,
@@ -228,7 +240,7 @@ let pointwiseCombinationFloat = (
 ): result<t, error> => {
   let m = switch arithmeticOperation {
   | #Add | #Subtract => Error(GenericDist_Types.DistributionVerticalShiftIsInvalid)
-  | (#Multiply | #Divide | #Exponentiate | #Logarithm) as arithmeticOperation =>
+  | (#Multiply | #Divide | #Power | #Logarithm) as arithmeticOperation =>
     toPointSetFn(t)->E.R2.fmap(t => {
       //TODO: Move to PointSet codebase
       let fn = (secondary, main) => Operation.Scale.toFn(arithmeticOperation, main, secondary)
@@ -253,7 +265,7 @@ let mixture = (
   ~pointwiseAddFn: pointwiseAddFn,
 ) => {
   if E.A.length(values) == 0 {
-    Error(GenericDist_Types.Other("mixture must have at least 1 element"))
+    Error(GenericDist_Types.Other("Mixture error: mixture must have at least 1 element"))
   } else {
     let totalWeight = values->E.A2.fmap(E.Tuple2.second)->E.A.Floats.sum
     let properlyWeightedValues =

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

@@ -1,11 +1,13 @@
 type t = GenericDist_Types.genericDist
 type error = GenericDist_Types.error
 type toPointSetFn = t => result<PointSetTypes.pointSetDist, error>
-type toSampleSetFn = t => result<array<float>, error>
+type toSampleSetFn = t => result<SampleSetDist.t, error>
 type scaleMultiplyFn = (t, float) => result<t, error>
 type pointwiseAddFn = (t, t) => result<t, error>
 
-let sampleN: (t, int) => result<array<float>, error>
+let sampleN: (t, int) => array<float>
+
+let toSampleSetDist: (t, int) => Belt.Result.t<QuriSquiggleLang.SampleSetDist.t, error>
 
 let fromFloat: float => t
 
@@ -20,17 +22,20 @@ let toFloatOperation: (
 ) => result<float, error>
 
 let toPointSet: (
+  t,
   ~xyPointLength: int,
   ~sampleCount: int,
-  t,
+  ~xSelection: GenericDist_Types.Operation.pointsetXSelection=?,
+  unit,
 ) => result<PointSetTypes.pointSetDist, error>
+let toSparkline: (t, ~sampleCount: int, ~bucketCount: int=?, unit) => result<string, error>
 
 let truncate: (
   t,
   ~toPointSetFn: toPointSetFn,
   ~leftCutoff: option<float>=?,
   ~rightCutoff: option<float>=?,
-  unit
+  unit,
 ) => result<t, error>
 
 let algebraicCombination: (

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

@@ -1,14 +1,24 @@
 type genericDist =
   | PointSet(PointSetTypes.pointSetDist)
-  | SampleSet(array<float>)
+  | SampleSet(SampleSetDist.t)
   | Symbolic(SymbolicDistTypes.symbolicDist)
 
+@genType
 type error =
   | NotYetImplemented
   | Unreachable
   | DistributionVerticalShiftIsInvalid
   | Other(string)
 
+module Error = {
+  type t = error
+
+  let fromString = (s: string): t => Other(s)
+
+  let resultStringToResultError: result<'a, string> => result<'a, error> = n =>
+    n->E.R2.errMap(r => r->fromString->Error)
+}
+
 module Operation = {
   type direction =
     | Algebraic
@@ -19,7 +29,7 @@ module Operation = {
     | #Multiply
     | #Subtract
     | #Divide
-    | #Exponentiate
+    | #Power
     | #Logarithm
   ]
 
@@ -28,7 +38,7 @@ module Operation = {
     | #Add => \"+."
     | #Multiply => \"*."
     | #Subtract => \"-."
-    | #Exponentiate => \"**"
+    | #Power => \"**"
     | #Divide => \"/."
     | #Logarithm => (a, b) => log(a) /. log(b)
     }
@@ -41,6 +51,8 @@ module Operation = {
     | #Sample
   ]
 
+  type pointsetXSelection = [#Linear | #ByWeight]
+
   type toDist =
     | Normalize
     | ToPointSet
@@ -50,16 +62,21 @@ module Operation = {
 
   type toFloatArray = Sample(int)
 
+  type toString =
+    | ToString
+    | ToSparkline(int)
+
   type fromDist =
     | ToFloat(toFloat)
     | ToDist(toDist)
     | ToDistCombination(direction, arithmeticOperation, [#Dist(genericDist) | #Float(float)])
-    | ToString
+    | ToString(toString)
 
   type singleParamaterFunction =
     | FromDist(fromDist)
     | FromFloat(fromDist)
 
+  @genType
   type genericFunctionCallInfo =
     | FromDist(fromDist, genericDist)
     | FromFloat(fromDist, float)
@@ -77,7 +94,8 @@ module Operation = {
     | ToDist(ToSampleSet(r)) => `toSampleSet(${E.I.toString(r)})`
     | ToDist(Truncate(_, _)) => `truncate`
     | ToDist(Inspect) => `inspect`
-    | ToString => `toString`
+    | ToString(ToString) => `toString`
+    | ToString(ToSparkline(n)) => `toSparkline(${E.I.toString(n)})`
     | ToDistCombination(Algebraic, _, _) => `algebraic`
     | ToDistCombination(Pointwise, _, _) => `pointwise`
     }
@@ -88,3 +106,79 @@ module Operation = {
     | Mixture(_) => `mixture`
     }
 }
+
+/*
+It can be a pain to write out the genericFunctionCallInfo. The constructors help with this. 
+This code only covers some of genericFunctionCallInfo: many arguments could be called with either a
+float or a distribution. The "UsingDists" module assumes that everything is a distribution.
+This is a tradeoff of some generality in order to get a bit more simplicity.
+I could see having a longer interface in the future, but it could be messy.
+Like, algebraicAddDistFloat vs. algebraicAddDistDist
+*/
+module Constructors = {
+  type t = Operation.genericFunctionCallInfo
+
+  module UsingDists = {
+    @genType
+    let mean = (dist): t => FromDist(ToFloat(#Mean), dist)
+    let sample = (dist): t => FromDist(ToFloat(#Sample), dist)
+    let cdf = (dist, x): t => FromDist(ToFloat(#Cdf(x)), dist)
+    let inv = (dist, x): t => FromDist(ToFloat(#Inv(x)), dist)
+    let pdf = (dist, x): t => FromDist(ToFloat(#Pdf(x)), dist)
+    let normalize = (dist): t => FromDist(ToDist(Normalize), dist)
+    let toPointSet = (dist): t => FromDist(ToDist(ToPointSet), dist)
+    let toSampleSet = (dist, r): t => FromDist(ToDist(ToSampleSet(r)), dist)
+    let truncate = (dist, left, right): t => FromDist(ToDist(Truncate(left, right)), dist)
+    let inspect = (dist): t => FromDist(ToDist(Inspect), dist)
+    let toString = (dist): t => FromDist(ToString(ToString), dist)
+    let toSparkline = (dist, n): t => FromDist(ToString(ToSparkline(n)), dist)
+    let algebraicAdd = (dist1, dist2: genericDist): t => FromDist(
+      ToDistCombination(Algebraic, #Add, #Dist(dist2)),
+      dist1,
+    )
+    let algebraicMultiply = (dist1, dist2): t => FromDist(
+      ToDistCombination(Algebraic, #Multiply, #Dist(dist2)),
+      dist1,
+    )
+    let algebraicDivide = (dist1, dist2): t => FromDist(
+      ToDistCombination(Algebraic, #Divide, #Dist(dist2)),
+      dist1,
+    )
+    let algebraicSubtract = (dist1, dist2): t => FromDist(
+      ToDistCombination(Algebraic, #Subtract, #Dist(dist2)),
+      dist1,
+    )
+    let algebraicLogarithm = (dist1, dist2): t => FromDist(
+      ToDistCombination(Algebraic, #Logarithm, #Dist(dist2)),
+      dist1,
+    )
+    let algebraicPower = (dist1, dist2): t => FromDist(
+      ToDistCombination(Algebraic, #Power, #Dist(dist2)),
+      dist1,
+    )
+    let pointwiseAdd = (dist1, dist2): t => FromDist(
+      ToDistCombination(Pointwise, #Add, #Dist(dist2)),
+      dist1,
+    )
+    let pointwiseMultiply = (dist1, dist2): t => FromDist(
+      ToDistCombination(Pointwise, #Multiply, #Dist(dist2)),
+      dist1,
+    )
+    let pointwiseDivide = (dist1, dist2): t => FromDist(
+      ToDistCombination(Pointwise, #Divide, #Dist(dist2)),
+      dist1,
+    )
+    let pointwiseSubtract = (dist1, dist2): t => FromDist(
+      ToDistCombination(Pointwise, #Subtract, #Dist(dist2)),
+      dist1,
+    )
+    let pointwiseLogarithm = (dist1, dist2): t => FromDist(
+      ToDistCombination(Pointwise, #Logarithm, #Dist(dist2)),
+      dist1,
+    )
+    let pointwisePower = (dist1, dist2): t => FromDist(
+      ToDistCombination(Pointwise, #Power, #Dist(dist2)),
+      dist1,
+    )
+  }
+}

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

@@ -114,7 +114,7 @@ let combineShapesContinuousContinuous = (
   | #Subtract => (m1, m2) => m1 -. m2
   | #Multiply => (m1, m2) => m1 *. m2
   | #Divide => (m1, mInv2) => m1 *. mInv2
-  | #Exponentiate => (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)
 
@@ -124,7 +124,7 @@ 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.
-  | #Exponentiate => (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.
   }
@@ -233,7 +233,7 @@ let combineShapesContinuousDiscrete = (
       ()
     }
   | #Multiply
-  | #Exponentiate
+  | #Power
   | #Logarithm
   | #Divide =>
     for j in 0 to t2n - 1 {

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

@@ -249,6 +249,9 @@ module T = Dist({
     )
 })
 
+let downsampleEquallyOverX = (length, t): t =>
+  t |> shapeMap(XYShape.XsConversion.proportionEquallyOverX(length))
+
 /* 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 = (

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

@@ -191,7 +191,6 @@ let isFloat = (t: t) =>
 let sampleNRendered = (n, dist) => {
   let integralCache = T.Integral.get(dist)
   let distWithUpdatedIntegralCache = T.updateIntegralCache(Some(integralCache), dist)
-
   doN(n, () => sample(distWithUpdatedIntegralCache))
 }
 
@@ -203,3 +202,9 @@ let operate = (distToFloatOp: Operation.distToFloatOperation, s): float =>
   | #Sample => sample(s)
   | #Mean => T.mean(s)
   }
+
+let toSparkline = (t: t, bucketCount) =>
+  T.toContinuous(t)
+  ->E.O2.fmap(Continuous.downsampleEquallyOverX(bucketCount))
+  ->E.O2.toResult("toContinous Error: Could not convert into continuous distribution")
+  ->E.R2.fmap(r => Continuous.getShape(r).ys->Sparklines.create())

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

@@ -0,0 +1,68 @@
+/*
+This is used as a smart constructor. The only way to create a SampleSetDist.t is to call
+this constructor.
+https://stackoverflow.com/questions/66909578/how-to-make-a-type-constructor-private-in-rescript-except-in-current-module
+*/
+module T: {
+  //This really should be hidden (remove the array<float>). The reason it isn't is to act as an escape hatch in JS__Test.ts.
+  //When we get a good functional library in TS, we could refactor that out.
+  @genType
+  type t = array<float>
+  let make: array<float> => result<t, string>
+  let get: t => array<float>
+} = {
+  type t = array<float>
+  let make = (a: array<float>) =>
+    if E.A.length(a) > 5 {
+      Ok(a)
+    } else {
+      Error("too small")
+    }
+  let get = (a: t) => a
+}
+
+include T
+
+let length = (t: t) => get(t)->E.A.length
+
+/*
+TODO: Refactor to get a more precise estimate. Also, this code is just fairly messy, could use 
+some refactoring.
+*/
+let toPointSetDist = (~samples: t, ~samplingInputs: SamplingInputs.samplingInputs): result<
+  PointSetTypes.pointSetDist,
+  string,
+> =>
+  SampleSetDist_ToPointSet.toPointSetDist(
+    ~samples=get(samples),
+    ~samplingInputs,
+    (),
+  ).pointSetDist->E.O2.toResult("Failed to convert to PointSetDist")
+
+//Randomly get one sample from the distribution
+let sample = (t: t): float => {
+  let i = E.Int.random(~min=0, ~max=E.A.length(get(t)) - 1)
+  E.A.unsafe_get(get(t), i)
+}
+
+/*
+If asked for a length of samples shorter or equal the length of the distribution,
+return this first n samples of this distribution.
+Else, return n random samples of the distribution.
+The former helps in cases where multiple distributions are correlated.
+However, if n > length(t), then there's no clear right answer, so we just randomly
+sample everything.
+*/
+let sampleN = (t: t, n) => {
+  if n <= E.A.length(get(t)) {
+    E.A.slice(get(t), ~offset=0, ~len=n)
+  } else {
+    Belt.Array.makeBy(n, _ => sample(t))
+  }
+}
+
+//TODO: Figure out what to do if distributions are different lengths. ``zip`` is kind of inelegant for this.
+let map2 = (~fn: (float, float) => float, ~t1: t, ~t2: t) => {
+  let samples = Belt.Array.zip(get(t1), get(t2))->E.A2.fmap(((a, b)) => fn(a, b))
+  make(samples)
+}

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

@@ -1,4 +1,4 @@
-//The math here was taken from https://github.com/jasondavies/science.js/blob/master/src/stats/bandwidth.js
+//The math here was taken from https://github.com/jasondavies/science.js/blob/master/src/stats/SampleSetDist_Bandwidth.js
 
 let len = x => E.A.length(x) |> float_of_int
 

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

@@ -1,5 +1,3 @@
-// TODO: Refactor to raise correct error when not enough samples
-
 module Internals = {
   module Types = {
     type samplingStats = {
@@ -57,6 +55,7 @@ module Internals = {
           : {
               let _ = Js.Array.push(element, continuous)
             }
+
         ()
       })
       (continuous, discrete)
@@ -71,7 +70,7 @@ module Internals = {
     let formatUnitWidth = w => Jstat.max([w, 1.0]) |> int_of_float
 
     let suggestedUnitWidth = (samples, outputXYPoints) => {
-      let suggestedXWidth = Bandwidth.nrd0(samples)
+      let suggestedXWidth = SampleSetDist_Bandwidth.nrd0(samples)
       xWidthToUnitWidth(samples, outputXYPoints, suggestedXWidth)
     }
 
@@ -98,7 +97,7 @@ let toPointSetDist = (
   let pdf =
     continuousPart |> E.A.length > 5
       ? {
-          let _suggestedXWidth = Bandwidth.nrd0(continuousPart)
+          let _suggestedXWidth = SampleSetDist_Bandwidth.nrd0(continuousPart)
           // todo: This does some recalculating from the last step.
           let _suggestedUnitWidth = Internals.T.suggestedUnitWidth(
             continuousPart,

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

@@ -55,7 +55,7 @@ module Exponential = {
             rate: rate,
           }),
         )
-      : Error("Exponential distributions mean must be larger than 0")
+      : Error("Exponential distributions rate must be larger than 0.")
   let pdf = (x, t: t) => Jstat.Exponential.pdf(x, t.rate)
   let cdf = (x, t: t) => Jstat.Exponential.cdf(x, t.rate)
   let inv = (p, t: t) => Jstat.Exponential.inv(p, t.rate)
@@ -71,7 +71,7 @@ module Cauchy = {
   let cdf = (x, t: t) => Jstat.Cauchy.cdf(x, t.local, t.scale)
   let inv = (p, t: t) => Jstat.Cauchy.inv(p, t.local, t.scale)
   let sample = (t: t) => Jstat.Cauchy.sample(t.local, t.scale)
-  let mean = (_: t) => Error("Cauchy distributions have no mean value.")
+  let mean = (_: t) => Error("Cauchy distributions may have no mean value.")
   let toString = ({local, scale}: t) => j`Cauchy($local, $scale)`
 }
 
@@ -80,8 +80,8 @@ module Triangular = {
   let make = (low, medium, high): result<symbolicDist, string> =>
     low < medium && medium < high
       ? Ok(#Triangular({low: low, medium: medium, high: high}))
-      : Error("Triangular values must be increasing order")
+      : Error("Triangular values must be increasing order.")
-  let pdf = (x, t: t) => Jstat.Triangular.pdf(x, t.low, t.high, t.medium)
+  let pdf = (x, t: t) => Jstat.Triangular.pdf(x, t.low, t.high, t.medium) // not obvious in jstat docs that high comes before medium? 
   let cdf = (x, t: t) => Jstat.Triangular.cdf(x, t.low, t.high, t.medium)
   let inv = (p, t: t) => Jstat.Triangular.inv(p, t.low, t.high, t.medium)
   let sample = (t: t) => Jstat.Triangular.sample(t.low, t.high, t.medium)
@@ -346,11 +346,11 @@ module T = {
     | _ => #NoSolution
     }
 
-  let toPointSetDist = (sampleCount, d: symbolicDist): PointSetTypes.pointSetDist =>
+  let toPointSetDist = (~xSelection=#ByWeight, sampleCount, d: symbolicDist): PointSetTypes.pointSetDist =>
     switch d {
     | #Float(v) => Discrete(Discrete.make(~integralSumCache=Some(1.0), {xs: [v], ys: [1.0]}))
     | _ =>
-      let xs = interpolateXs(~xSelection=#ByWeight, d, sampleCount)
+      let xs = interpolateXs(~xSelection, d, sampleCount)
       let ys = xs |> E.A.fmap(x => pdf(x, d))
       Continuous(Continuous.make(~integralSumCache=Some(1.0), {xs: xs, ys: ys}))
     }

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

@@ -118,7 +118,7 @@ module PointwiseCombination = {
     switch pointwiseOp {
     | #Add => pointwiseAdd(evaluationParams, t1, t2)
     | #Multiply => pointwiseCombine(\"*.", evaluationParams, t1, t2)
-    | #Exponentiate => pointwiseCombine(\"**", evaluationParams, t1, t2)
+    | #Power => pointwiseCombine(\"**", evaluationParams, t1, t2)
     }
 }
 

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

@@ -218,15 +218,14 @@ module SamplingDistribution = {
         algebraicOp,
         a,
         b,
-      )
+      ) |> E.O.toResult("Could not get samples")
+
+      let sampleSetDist = samples -> E.R.bind(SampleSetDist.make)
 
       let pointSetDist = 
-        samples
+        sampleSetDist
-        |> E.O.fmap(r =>
+        -> E.R.bind(r =>
-          SampleSet.toPointSetDist(~samplingInputs=evaluationParams.samplingInputs, ~samples=r, ())
+          SampleSetDist.toPointSetDist(~samplingInputs=evaluationParams.samplingInputs, ~samples=r));
-        )
-        |> E.O.bind(_, r => r.pointSetDist)
-        |> E.O.toResult("No response")
       pointSetDist |> E.R.fmap(r => #Normalize(#RenderedDist(r)))
     })
   }

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

@@ -227,7 +227,7 @@ let all = [
       },
     (),
   ),
-  makeRenderedDistFloat("scaleExp", (dist, float) => verticalScaling(#Exponentiate, dist, float)),
+  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/OldParser/Parser.res

@@ -144,11 +144,11 @@ module MathAdtToDistDst = {
       | ("subtract", _) => Error("Subtraction needs two operands")
       | ("multiply", [l, r]) => toOkAlgebraic((#Multiply, l, r))
       | ("multiply", _) => Error("Multiplication needs two operands")
-      | ("pow", [l, r]) => toOkAlgebraic((#Exponentiate, l, r))
+      | ("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((#Exponentiate, l, r))
+      | ("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")

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

@@ -18,8 +18,8 @@ module Helpers = {
     | "divide" => #Divide
     | "log" => #Logarithm
     | "dotDivide" => #Divide
-    | "pow" => #Exponentiate
+    | "pow" => #Power
-    | "dotPow" => #Exponentiate
+    | "dotPow" => #Power
     | "multiply" => #Multiply
     | "dotMultiply" => #Multiply
     | "dotLog" => #Logarithm
@@ -45,6 +45,13 @@ module Helpers = {
     FromDist(GenericDist_Types.Operation.ToFloat(fnCall), dist)->runGenericOperation->Some
   }
 
+  let toStringFn = (
+    fnCall: GenericDist_Types.Operation.toString,
+    dist: GenericDist_Types.genericDist,
+  ) => {
+    FromDist(GenericDist_Types.Operation.ToString(fnCall), dist)->runGenericOperation->Some
+  }
+
   let toDistFn = (fnCall: GenericDist_Types.Operation.toDist, dist) => {
     FromDist(GenericDist_Types.Operation.ToDist(fnCall), dist)->runGenericOperation->Some
   }
@@ -119,6 +126,9 @@ let dispatchToGenericOutput = (call: ExpressionValue.functionCall): option<
     ->SymbolicConstructors.symbolicResultToOutput
   | ("sample", [EvDistribution(dist)]) => Helpers.toFloatFn(#Sample, dist)
   | ("mean", [EvDistribution(dist)]) => Helpers.toFloatFn(#Mean, dist)
+  | ("toString", [EvDistribution(dist)]) => Helpers.toStringFn(ToString, dist)
+  | ("toSparkline", [EvDistribution(dist)]) => Helpers.toStringFn(ToSparkline(20), dist)
+  | ("toSparkline", [EvDistribution(dist), EvNumber(n)]) => Helpers.toStringFn(ToSparkline(Belt.Float.toInt(n)), dist)
   | ("exp", [EvDistribution(a)]) =>
     // https://mathjs.org/docs/reference/functions/exp.html
     Helpers.twoDiststoDistFn(Algebraic, "pow", GenericDist.fromFloat(Math.e), a)->Some

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

@@ -0,0 +1,27 @@
+/*
+This is meant as a file to contain @genType declarations as needed for Typescript.
+I would ultimately want to have all @genType declarations here, vs. other files, but
+@genType doesn't play as nicely with renaming Modules and functions as
+would be preferable. 
+
+The below few seem to work fine. In the future there's definitely more work to do here.
+*/
+
+@genType
+type env = DistributionOperation.env
+
+@genType
+type genericDist = GenericDist_Types.genericDist
+
+@genType
+type error = GenericDist_Types.error
+
+@genType
+type resultDist = result<genericDist, error>
+@genType
+type resultFloat = result<float, error>
+@genType
+type resultString = result<string, error>
+
+@genType
+let makeSampleSetDist = SampleSetDist.make

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

@@ -24,6 +24,7 @@ module FloatFloatMap = {
 
 module Int = {
   let max = (i1: int, i2: int) => i1 > i2 ? i1 : i2
+  let random = (~min, ~max) => Js.Math.random_int(min, max)
 }
 /* Utils */
 module U = {
@@ -101,6 +102,7 @@ module O2 = {
   let default = (a, b) => O.default(b, a)
   let toExn = (a, b) => O.toExn(b, a)
   let fmap = (a, b) => O.fmap(b, a)
+  let toResult = (a, b) => O.toResult(b, a)
 }
 
 /* Functions */
@@ -178,6 +180,13 @@ module R = {
 
 module R2 = {
   let fmap = (a,b) => R.fmap(b,a)
+  let bind = (a, b) => R.bind(b, a) 
+
+  //Converts result type to change error type only
+  let errMap = (a, map) => switch(a){
+    | Ok(r) => Ok(r)
+    | Error(e) => map(e)
+  }
 }
 
 let safe_fn_of_string = (fn, s: string): option<'a> =>
@@ -269,6 +278,7 @@ module A = {
   let fold_right = Array.fold_right
   let concatMany = Belt.Array.concatMany
   let keepMap = Belt.Array.keepMap
+  let slice = Belt.Array.slice
   let init = Array.init
   let reduce = Belt.Array.reduce
   let reducei = Belt.Array.reduceWithIndex
@@ -289,8 +299,7 @@ module A = {
       ))
       |> Rationale.Result.return
     }
-  let rangeFloat = (~step=1, start, stop) =>
+
-    Belt.Array.rangeBy(start, stop, ~step) |> fmap(Belt.Int.toFloat)
 
   // This zips while taking the longest elements of each array.
   let zipMaxLength = (array1, array2) => {
@@ -442,6 +451,12 @@ module A = {
     let mean = a => sum(a) /. (Array.length(a) |> float_of_int)
     let random = Js.Math.random_int
 
+    // Gives an array with all the differences between values
+    // diff([1,5,3,7]) = [4,-2,4]
+    let diff = (arr: array<float>): array<float> =>
+      Belt.Array.zipBy(arr, Belt.Array.sliceToEnd(arr, 1), (left, right) => right -. left)
+
+
     exception RangeError(string)
     let range = (min: float, max: float, n: int): array<float> =>
       switch n {

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

@@ -6,12 +6,12 @@ type algebraicOperation = [
   | #Multiply
   | #Subtract
   | #Divide
-  | #Exponentiate
+  | #Power
   | #Logarithm
 ]
 @genType
-type pointwiseOperation = [#Add | #Multiply | #Exponentiate]
+type pointwiseOperation = [#Add | #Multiply | #Power]
-type scaleOperation = [#Multiply | #Exponentiate | #Logarithm | #Divide]
+type scaleOperation = [#Multiply | #Power | #Logarithm | #Divide]
 type distToFloatOperation = [
   | #Pdf(float)
   | #Cdf(float)
@@ -27,7 +27,7 @@ module Algebraic = {
     | #Add => \"+."
     | #Subtract => \"-."
     | #Multiply => \"*."
-    | #Exponentiate => \"**"
+    | #Power => \"**"
     | #Divide => \"/."
     | #Logarithm => (a, b) => log(a) /. log(b)
     }
@@ -43,7 +43,7 @@ module Algebraic = {
     | #Add => "+"
     | #Subtract => "-"
     | #Multiply => "*"
-    | #Exponentiate => "**"
+    | #Power => "**"
     | #Divide => "/"
     | #Logarithm => "log"
     }
@@ -56,7 +56,7 @@ module Pointwise = {
   let toString = x =>
     switch x {
     | #Add => "+"
-    | #Exponentiate => "^"
+    | #Power => "**"
     | #Multiply => "*"
     }
 
@@ -83,7 +83,7 @@ module Scale = {
     switch x {
     | #Multiply => \"*."
     | #Divide => \"/."
-    | #Exponentiate => \"**"
+    | #Power => \"**"
     | #Logarithm => (a, b) => log(a) /. log(b)
     }
 
@@ -91,7 +91,7 @@ module Scale = {
     switch operation {
     | #Multiply => j`verticalMultiply($value, $scaleBy) `
     | #Divide => j`verticalDivide($value, $scaleBy) `
-    | #Exponentiate => j`verticalExponentiate($value, $scaleBy) `
+    | #Power => j`verticalPower($value, $scaleBy) `
     | #Logarithm => j`verticalLog($value, $scaleBy) `
     }
 
@@ -99,7 +99,7 @@ module Scale = {
     switch x {
     | #Multiply => (a, b) => Some(a *. b)
     | #Divide => (a, b) => Some(a /. b)
-    | #Exponentiate => (_, _) => None
+    | #Power => (_, _) => None
     | #Logarithm => (_, _) => None
     }
 
@@ -107,7 +107,7 @@ module Scale = {
     switch x {
     | #Multiply => (_, _) => None // TODO: this could probably just be multiplied out (using Continuous.scaleBy)
     | #Divide => (_, _) => None
-    | #Exponentiate => (_, _) => None
+    | #Power => (_, _) => None
     | #Logarithm => (_, _) => None
     }
 }