Merge pull request #239 from quantified-uncertainty/format-pass

`yarn format`
2022-04-12 20:29:01 -04:00 · 2022-04-12 20:29:01 -04:00 · 71f8a8bb06
commit 71f8a8bb06
parent 550acb552a 0ac6357ae6
27 changed files with 284 additions and 282 deletions
--- a/package.json
+++ b/package.json
@ -2,7 +2,8 @@
  "private": true,
  "name": "squiggle",
  "scripts": {
-    "nodeclean": "rm -r node_modules && rm -r packages/*/node_modules"
+    "nodeclean": "rm -r node_modules && rm -r packages/*/node_modules",
+    "format:all": "prettier --write . && cd packages/squiggle-lang && yarn format"
  },
  "devDependencies": {
    "prettier": "^2.6.2"
--- a/packages/squiggle-lang/tests/Bandwidth_test.res
+++ b/packages/squiggle-lang/tests/Bandwidth_test.res
@ -4,10 +4,10 @@ open Expect
 describe("Bandwidth", () => {
  test("nrd0()", () => {
    let data = [1., 4., 3., 2.]
-    expect(SampleSetDist_Bandwidth.nrd0(data)) -> toEqual(0.7625801874014622)
+    expect(SampleSetDist_Bandwidth.nrd0(data))->toEqual(0.7625801874014622)
  })
  test("nrd()", () => {
    let data = [1., 4., 3., 2.]
-    expect(SampleSetDist_Bandwidth.nrd(data)) -> toEqual(0.8981499984950554)
+    expect(SampleSetDist_Bandwidth.nrd(data))->toEqual(0.8981499984950554)
  })
 })
--- a/packages/squiggle-lang/tests/Distributions/GenericDist_Fixtures.res
+++ b/packages/squiggle-lang/tests/Distributions/GenericDist_Fixtures.res
@ -6,6 +6,8 @@ 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 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}))
--- a/packages/squiggle-lang/tests/Distributions/Mixture_test.res
+++ b/packages/squiggle-lang/tests/Distributions/Mixture_test.res
@ -1,70 +1,73 @@
 open Jest
-open Expect 
-open TestHelpers 
+open Expect
+open TestHelpers

-// TODO: use Normal.make (etc.), but preferably after the new validation dispatch is in. 
+// 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 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
+    "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 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)
+    "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)
+    },
  )
 })
-
--- a/packages/squiggle-lang/tests/Distributions/SampleSetDist_test.res
+++ b/packages/squiggle-lang/tests/Distributions/SampleSetDist_test.res
@ -38,4 +38,3 @@ describe("Continuous and discrete splits", () => {
  let toArr2 = discrete2 |> E.FloatFloatMap.toArray
  makeTest("splitMedium at count=500", toArr2 |> Belt.Array.length, 500)
 })
-
--- a/packages/squiggle-lang/tests/Distributions/Symbolic_test.res
+++ b/packages/squiggle-lang/tests/Distributions/Symbolic_test.res
@ -9,125 +9,109 @@ 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)
+    normalizedValue->unpackDist->expect->toEqual(normalValue)
  })
 })

 describe("(Symbolic) mean", () => {
  testAll("of normal distributions", list{-1e8, -16.0, -1e-2, 0.0, 1e-4, 32.0, 1e16}, mean => {
-    run(FromDist(ToFloat(#Mean), mkNormal(mean, 4.0))) 
-    -> unpackFloat 
-    -> expect 
-    -> toBeCloseTo(mean)
+    run(FromDist(ToFloat(#Mean), mkNormal(mean, 4.0)))->unpackFloat->expect->toBeCloseTo(mean)
  })

  Skip.test("of normal(0, -1) (it NaNs out)", () => {
-    run(FromDist(ToFloat(#Mean), mkNormal(1e1, -1e0)))
-    -> unpackFloat
-    -> expect
-    -> ExpectJs.toBeFalsy
+    run(FromDist(ToFloat(#Mean), mkNormal(1e1, -1e0)))->unpackFloat->expect->ExpectJs.toBeFalsy
  })

  test("of normal(0, 1e-8) (it doesn't freak out at tiny stdev)", () => {
-    run(FromDist(ToFloat(#Mean), mkNormal(0.0, 1e-8)))
-    -> unpackFloat
-    -> expect
-    -> toBeCloseTo(0.0)
+    run(FromDist(ToFloat(#Mean), mkNormal(0.0, 1e-8)))->unpackFloat->expect->toBeCloseTo(0.0)
  })

  testAll("of exponential distributions", list{1e-7, 2.0, 10.0, 100.0}, rate => {
-    let meanValue = run(FromDist(ToFloat(#Mean), 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
+    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)
+    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/
-  })
+  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: 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
+    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 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
-  })
+  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)
+    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
+    | #Normal(params) => params
    }
    x => SymbolicDist.Normal.pdf(x, normalDistAtSumMean)
  }
@ -138,24 +122,25 @@ describe("Normal distribution with sparklines", () => {

  test("mean=5 pdf", () => {
    let pdfNormalDistAtMean5 = x => SymbolicDist.Normal.pdf(x, normalDistAtMean5)
-    let sparklineMean5 = fnImage(pdfNormalDistAtMean5, range20Float) 
-    Sparklines.create(sparklineMean5, ()) 
-    -> expect 
-    -> toEqual(`▁▂▃▆██▇▅▂▁▁▁▁▁▁▁▁▁▁▁`)
+    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)
+
+  test("parameter-wise addition of two normal distributions", () => {
+    let sparklineMean15 =
+      normalDistAtMean5->parameterWiseAdditionPdf(normalDistAtMean10)->fnImage(range20Float)
    Sparklines.create(sparklineMean15, ())
-    -> expect
-    -> toEqual(`▁▁▁▁▁▁▁▁▁▂▃▄▆███▇▅▄▂`)
+    ->expect
+    ->toEqual(`▁▁▁▁▁▁▁▁▁▂▃▄▆███▇▅▄▂`)
  })

  test("mean=10 cdf", () => {
    let cdfNormalDistAtMean10 = x => SymbolicDist.Normal.cdf(x, normalDistAtMean10)
    let sparklineMean10 = fnImage(cdfNormalDistAtMean10, range20Float)
    Sparklines.create(sparklineMean10, ())
-    -> expect
-    -> toEqual(`▁▁▁▁▁▁▁▁▂▄▅▇████████`)
+    ->expect
+    ->toEqual(`▁▁▁▁▁▁▁▁▂▄▅▇████████`)
  })
 })
--- a/packages/squiggle-lang/tests/Lodash_test.res
+++ b/packages/squiggle-lang/tests/Lodash_test.res
@ -3,8 +3,8 @@ open Expect

 let makeTest = (~only=false, str, item1, item2) =>
  only
-    ? Only.test(str, () => expect(item1) -> toEqual(item2))
-    : test(str, () => expect(item1) -> toEqual(item2))
+    ? Only.test(str, () => expect(item1)->toEqual(item2))
+    : test(str, () => expect(item1)->toEqual(item2))

 describe("Lodash", () =>
  describe("Lodash", () => {
--- a/packages/squiggle-lang/tests/Reducer/Reducer_Dispatch/Reducer_Dispatch_BuiltIn_test.res
+++ b/packages/squiggle-lang/tests/Reducer/Reducer_Dispatch/Reducer_Dispatch_BuiltIn_test.res
@ -6,8 +6,7 @@ open Expect
 let expectEvalToBe = (expr: string, answer: string) =>
  Reducer.evaluate(expr)->ExpressionValue.toStringResult->expect->toBe(answer)

-let testEval = (expr, answer) =>
-  test(expr, () => expectEvalToBe(expr, answer))
+let testEval = (expr, answer) => test(expr, () => expectEvalToBe(expr, answer))

 describe("builtin", () => {
  // All MathJs operators and functions are available for string, number and boolean
--- a/packages/squiggle-lang/tests/Reducer/Reducer_MathJs/Reducer_MathJsParse_test.res
+++ b/packages/squiggle-lang/tests/Reducer/Reducer_MathJs/Reducer_MathJsParse_test.res
@ -14,7 +14,8 @@ let testDescriptionParse = (desc, expr, answer) => test(desc, () => expectParseT
 module MySkip = {
  let testParse = (expr, answer) => Skip.test(expr, () => expectParseToBe(expr, answer))

-  let testDescriptionParse = (desc, expr, answer) => Skip.test(desc, () => expectParseToBe(expr, answer))
+  let testDescriptionParse = (desc, expr, answer) =>
+    Skip.test(desc, () => expectParseToBe(expr, answer))
 }

 describe("MathJs parse", () => {
@ -60,7 +61,8 @@ describe("MathJs parse", () => {
    MySkip.testDescriptionParse("define", "# This is a comment", "???")
  })

-  describe("if statement", () => { // TODO Tertiary operator instead
+  describe("if statement", () => {
+    // TODO Tertiary operator instead
    MySkip.testDescriptionParse("define", "if (true) { 1 } else { 0 }", "???")
  })
 })
--- a/packages/squiggle-lang/tests/Reducer/Reducer_test.res
+++ b/packages/squiggle-lang/tests/Reducer/Reducer_test.res
@ -3,7 +3,8 @@ open Reducer_TestHelpers

 let testParseToBe = (expr, answer) => test(expr, () => expectParseToBe(expr, answer))

-let testDescriptionParseToBe = (desc, expr, answer) => test(desc, () => expectParseToBe(expr, answer))
+let testDescriptionParseToBe = (desc, expr, answer) =>
+  test(desc, () => expectParseToBe(expr, answer))

 let testEvalToBe = (expr, answer) => test(expr, () => expectEvalToBe(expr, answer))

@ -44,13 +45,21 @@ describe("reducer using mathjs parse", () => {
  })
  describe("multi-line", () => {
    testParseToBe("1; 2", "Ok((:$$bindExpression (:$$bindStatement (:$$bindings) 1) 2))")
-    testParseToBe("1+1; 2+1", "Ok((:$$bindExpression (:$$bindStatement (:$$bindings) (:add 1 1)) (:add 2 1)))")
+    testParseToBe(
+      "1+1; 2+1",
+      "Ok((:$$bindExpression (:$$bindStatement (:$$bindings) (:add 1 1)) (:add 2 1)))",
+    )
  })
  describe("assignment", () => {
-    testParseToBe("x=1; x", "Ok((:$$bindExpression (:$$bindStatement (:$$bindings) (:$let :x 1)) :x))")
-    testParseToBe("x=1+1; x+1", "Ok((:$$bindExpression (:$$bindStatement (:$$bindings) (:$let :x (:add 1 1))) (:add :x 1)))")
+    testParseToBe(
+      "x=1; x",
+      "Ok((:$$bindExpression (:$$bindStatement (:$$bindings) (:$let :x 1)) :x))",
+    )
+    testParseToBe(
+      "x=1+1; x+1",
+      "Ok((:$$bindExpression (:$$bindStatement (:$$bindings) (:$let :x (:add 1 1))) (:add :x 1)))",
+    )
  })
-
 })

 describe("eval", () => {
@ -101,5 +110,9 @@ describe("test exceptions", () => {
    "javascriptraise('div by 0')",
    "Error(JS Exception: Error: 'div by 0')",
  )
-  testDescriptionEvalToBe("rescript exception", "rescriptraise()", "Error(TODO: unhandled rescript exception)")
+  testDescriptionEvalToBe(
+    "rescript exception",
+    "rescriptraise()",
+    "Error(TODO: unhandled rescript exception)",
+  )
 })
--- a/packages/squiggle-lang/tests/ReducerInterface/ReducerInterface_Distribution_test.res
+++ b/packages/squiggle-lang/tests/ReducerInterface/ReducerInterface_Distribution_test.res
@ -111,7 +111,11 @@ describe("parse on distribution functions", () => {
  })
  describe("pointwise arithmetic expressions", () => {
    testParse(~skip=true, "normal(5,2) .+ normal(5,1)", "Ok((:dotAdd (:normal 5 2) (:normal 5 1)))")
-    testParse(~skip=true, "normal(5,2) .- normal(5,1)", "Ok((:dotSubtract (:normal 5 2) (:normal 5 1)))")
+    testParse(
+      ~skip=true,
+      "normal(5,2) .- normal(5,1)",
+      "Ok((:dotSubtract (:normal 5 2) (:normal 5 1)))",
+    )
    testParse("normal(5,2) .* normal(5,1)", "Ok((:dotMultiply (:normal 5 2) (:normal 5 1)))")
    testParse("normal(5,2) ./ normal(5,1)", "Ok((:dotDivide (:normal 5 2) (:normal 5 1)))")
    testParse("normal(5,2) .^ normal(5,1)", "Ok((:dotPow (:normal 5 2) (:normal 5 1)))")
--- a/packages/squiggle-lang/tests/TestHelpers.res
+++ b/packages/squiggle-lang/tests/TestHelpers.res
@ -3,9 +3,8 @@ open Expect

 let makeTest = (~only=false, str, item1, item2) =>
  only
-    ? Only.test(str, () => expect(item1) -> toEqual(item2))
-    : test(str, () => expect(item1) -> toEqual(item2))
-
+    ? Only.test(str, () => expect(item1)->toEqual(item2))
+    : test(str, () => expect(item1)->toEqual(item2))

 let {toFloat, toDist, toString, toError, fmap} = module(DistributionOperation.Output)

@ -20,7 +19,9 @@ 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 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
+let unpackFloat = x => x->toFloat->toExtFloat
+let unpackDist = y => y->toDist->toExtDist
--- a/packages/squiggle-lang/tests/XYShape_test.res
+++ b/packages/squiggle-lang/tests/XYShape_test.res
@ -3,8 +3,8 @@ open Expect

 let makeTest = (~only=false, str, item1, item2) =>
  only
-    ? Only.test(str, () => expect(item1) -> toEqual(item2))
-    : test(str, () => expect(item1) -> toEqual(item2))
+    ? Only.test(str, () => expect(item1)->toEqual(item2))
+    : test(str, () => expect(item1)->toEqual(item2))

 let pointSetDist1: PointSetTypes.xyShape = {xs: [1., 4., 8.], ys: [0.2, 0.4, 0.8]}

@ -21,7 +21,11 @@ let pointSetDist3: PointSetTypes.xyShape = {
 describe("XYShapes", () => {
  describe("logScorePoint", () => {
    makeTest("When identical", XYShape.logScorePoint(30, pointSetDist1, pointSetDist1), Some(0.0))
-    makeTest("When similar", XYShape.logScorePoint(30, pointSetDist1, pointSetDist2), Some(1.658971191043856))
+    makeTest(
+      "When similar",
+      XYShape.logScorePoint(30, pointSetDist1, pointSetDist2),
+      Some(1.658971191043856),
+    )
    makeTest(
      "When very different",
      XYShape.logScorePoint(30, pointSetDist1, pointSetDist3),
--- a/packages/squiggle-lang/src/rescript/Distributions/DistributionOperation/DistributionOperation.resi
+++ b/packages/squiggle-lang/src/rescript/Distributions/DistributionOperation/DistributionOperation.resi
@ -39,57 +39,52 @@ module Output: {
 }

 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>
+  @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>
 }
--- a/packages/squiggle-lang/src/rescript/Distributions/DistributionTypes.res
+++ b/packages/squiggle-lang/src/rescript/Distributions/DistributionTypes.res
@ -55,7 +55,11 @@ module DistributionOperation = {
  type fromDist =
    | ToFloat(Operation.toFloat)
    | ToDist(toDist)
-    | ToDistCombination(Operation.direction, Operation.arithmeticOperation, [#Dist(genericDist) | #Float(float)])
+    | ToDistCombination(
+        Operation.direction,
+        Operation.arithmeticOperation,
+        [#Dist(genericDist) | #Float(float)],
+      )
    | ToString

  type singleParamaterFunction =
--- a/packages/squiggle-lang/src/rescript/Distributions/GenericDist/GenericDist_Types.res
+++ b/packages/squiggle-lang/src/rescript/Distributions/GenericDist/GenericDist_Types.res
@ -15,7 +15,7 @@ module Error = {
  type t = error

  let fromString = (s: string): t => Other(s)
-  
+
  @genType
  let toString = (x: t) => {
    switch x {
--- a/packages/squiggle-lang/src/rescript/Distributions/PointSetDist/AlgebraicShapeCombination.res
+++ b/packages/squiggle-lang/src/rescript/Distributions/PointSetDist/AlgebraicShapeCombination.res
@ -100,7 +100,6 @@ let combineShapesContinuousContinuous = (
  s1: PointSetTypes.xyShape,
  s2: PointSetTypes.xyShape,
 ): PointSetTypes.xyShape => {
-
  // if we add the two distributions, we should probably use normal filters.
  // if we multiply the two distributions, we should probably use lognormal filters.
  let t1m = toDiscretePointMassesFromTriangulars(s1)
--- a/packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Continuous.res
+++ b/packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Continuous.res
@ -235,18 +235,10 @@ module T = Dist({
    let indefiniteIntegralStepwise = (p, h1) => h1 *. p ** 2.0 /. 2.0
    let indefiniteIntegralLinear = (p, a, b) => a *. p ** 2.0 /. 2.0 +. b *. p ** 3.0 /. 3.0

-    Analysis.integrate(
-      ~indefiniteIntegralStepwise,
-      ~indefiniteIntegralLinear,
-      t,
-    )
+    Analysis.integrate(~indefiniteIntegralStepwise, ~indefiniteIntegralLinear, t)
  }
  let variance = (t: t): float =>
-    XYShape.Analysis.getVarianceDangerously(
-      t,
-      mean,
-      Analysis.getMeanOfSquares,
-    )
+    XYShape.Analysis.getVarianceDangerously(t, mean, Analysis.getMeanOfSquares)
 })

 let downsampleEquallyOverX = (length, t): t =>
--- a/packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Mixed.res
+++ b/packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Mixed.res
@ -212,8 +212,7 @@ module T = Dist({
    let totalIntegralSum = discreteIntegralSum +. continuousIntegralSum

    let getMeanOfSquares = ({discrete, continuous}: t) => {
-      let discreteMean =
-        discrete |> Discrete.shapeMap(XYShape.T.square) |> Discrete.T.mean
+      let discreteMean = discrete |> Discrete.shapeMap(XYShape.T.square) |> Discrete.T.mean
      let continuousMean = continuous |> Continuous.Analysis.getMeanOfSquares
      (discreteMean *. discreteIntegralSum +. continuousMean *. continuousIntegralSum) /.
        totalIntegralSum
--- a/packages/squiggle-lang/src/rescript/Distributions/PointSetDist/PointSetDist.res
+++ b/packages/squiggle-lang/src/rescript/Distributions/PointSetDist/PointSetDist.res
@ -207,4 +207,4 @@ 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())
+  ->E.R2.fmap(r => Continuous.getShape(r).ys->Sparklines.create())
--- a/packages/squiggle-lang/src/rescript/Distributions/PointSetDist/PointSetTypes.res
+++ b/packages/squiggle-lang/src/rescript/Distributions/PointSetDist/PointSetTypes.res
@ -14,10 +14,10 @@ type distributionType = [
  | #CDF
 ]

-type xyShape = XYShape.xyShape;
-type interpolationStrategy = XYShape.interpolationStrategy;
-type extrapolationStrategy = XYShape.extrapolationStrategy;
-type interpolator = XYShape.extrapolationStrategy;
+type xyShape = XYShape.xyShape
+type interpolationStrategy = XYShape.interpolationStrategy
+type extrapolationStrategy = XYShape.extrapolationStrategy
+type interpolator = XYShape.extrapolationStrategy

@genType
 type rec continuousShape = {
--- a/packages/squiggle-lang/src/rescript/Distributions/SymbolicDist/SymbolicDist.res
+++ b/packages/squiggle-lang/src/rescript/Distributions/SymbolicDist/SymbolicDist.res
@ -81,7 +81,7 @@ module Triangular = {
    low < medium && medium < high
      ? Ok(#Triangular({low: low, medium: medium, high: high}))
      : Error("Triangular values must be increasing order.")
-  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 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)
@ -346,7 +346,11 @@ module T = {
    | _ => #NoSolution
    }

-  let toPointSetDist = (~xSelection=#ByWeight, 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]}))
    | _ =>
--- a/packages/squiggle-lang/src/rescript/OldInterpreter/AST.res
+++ b/packages/squiggle-lang/src/rescript/OldInterpreter/AST.res
@ -21,4 +21,4 @@ let toPointSetDist = (samplingInputs, environment, node: node) =>
 let runFunction = (samplingInputs, environment, inputs, fn: ASTTypes.Function.t) => {
  let params = envs(samplingInputs, environment)
  ASTTypes.Function.run(params, inputs, fn)
-}
+}
--- a/packages/squiggle-lang/src/rescript/OldInterpreter/typeSystem/HardcodedFunctions.res
+++ b/packages/squiggle-lang/src/rescript/OldInterpreter/typeSystem/HardcodedFunctions.res
@ -22,7 +22,7 @@ let makeSymbolicFromTwoFloats = (name, fn) =>
    ~inputTypes=[#Float, #Float],
    ~run=x =>
      switch x {
-      | [#Float(a), #Float(b)] => fn(a, b) |> E.R.fmap(r => (#SymbolicDist(r)))
+      | [#Float(a), #Float(b)] => fn(a, b) |> E.R.fmap(r => #SymbolicDist(r))
      | e => wrongInputsError(e)
      },
    (),
@ -90,7 +90,8 @@ let floatFromDist = (
  switch t {
  | #SymbolicDist(s) =>
    SymbolicDist.T.operate(distToFloatOp, s) |> E.R.bind(_, v => Ok(#SymbolicDist(#Float(v))))
-  | #RenderedDist(rs) => PointSetDist.operate(distToFloatOp, rs) |> (v => Ok(#SymbolicDist(#Float(v))))
+  | #RenderedDist(rs) =>
+    PointSetDist.operate(distToFloatOp, rs) |> (v => Ok(#SymbolicDist(#Float(v))))
  }

 let verticalScaling = (scaleOp, rs, scaleBy) => {
@ -125,10 +126,15 @@ module Multimodal = {
        ->E.R.bind(TypeSystem.TypedValue.toArray)
        ->E.R.bind(r => r |> E.A.fmap(TypeSystem.TypedValue.toFloat) |> E.A.R.firstErrorOrOpen)

-        E.R.merge(dists, weights) -> E.R.bind(((a, b)) =>
-          E.A.length(b) > E.A.length(a) ?
-            Error("Too many weights provided") :
-            Ok(E.A.zipMaxLength(a, b) |> E.A.fmap(((a, b)) => (a |> E.O.toExn(""), b |> E.O.default(1.0))))
+      E.R.merge(dists, weights)->E.R.bind(((a, b)) =>
+        E.A.length(b) > E.A.length(a)
+          ? Error("Too many weights provided")
+          : Ok(
+              E.A.zipMaxLength(a, b) |> E.A.fmap(((a, b)) => (
+                a |> E.O.toExn(""),
+                b |> E.O.default(1.0),
+              )),
+            )
      )
    | _ => Error("Needs items")
    }
--- a/packages/squiggle-lang/src/rescript/OldInterpreter/typeSystem/TypeSystem.res
+++ b/packages/squiggle-lang/src/rescript/OldInterpreter/typeSystem/TypeSystem.res
@ -86,11 +86,7 @@ module TypedValue = {
      |> E.R.fmap(r => #Array(r))
    | (#Hash(named), #Hash(r)) =>
      let keyValues =
-        named |> E.A.fmap(((name, intendedType)) => (
-          name,
-          intendedType,
-          Hash.getByName(r, name),
-        ))
+        named |> E.A.fmap(((name, intendedType)) => (name, intendedType, Hash.getByName(r, name)))
      let typedHash =
        keyValues
        |> E.A.fmap(((name, intendedType, optionNode)) =>
@ -180,11 +176,7 @@ module Function = {
        _coerceInputNodes(evaluationParams, t.inputTypes, t.shouldCoerceTypes),
      )

-    let run = (
-      evaluationParams: ASTTypes.evaluationParams,
-      inputNodes: inputNodes,
-      t: t,
-    ) =>
+    let run = (evaluationParams: ASTTypes.evaluationParams, inputNodes: inputNodes, t: t) =>
      inputsToTypedValues(evaluationParams, inputNodes, t)->E.R.bind(t.run)
        |> (
          x =>
--- a/packages/squiggle-lang/src/rescript/Utility/E.res
+++ b/packages/squiggle-lang/src/rescript/Utility/E.res
@ -179,14 +179,15 @@ module R = {
 }

 module R2 = {
-  let fmap = (a,b) => R.fmap(b,a)
-  let bind = (a, b) => R.bind(b, a) 
+  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){
+  let errMap = (a, map) =>
+    switch a {
    | Ok(r) => Ok(r)
    | Error(e) => map(e)
-  }
+    }
 }

 let safe_fn_of_string = (fn, s: string): option<'a> =>
@ -300,7 +301,6 @@ module A = {
      |> Rationale.Result.return
    }

-
  // This zips while taking the longest elements of each array.
  let zipMaxLength = (array1, array2) => {
    let maxLength = Int.max(length(array1), length(array2))
@ -456,7 +456,6 @@ module A = {
    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 {
@ -474,7 +473,7 @@ module A = {
 }

 module A2 = {
-  let fmap = (a,b) => A.fmap(b,a)
+  let fmap = (a, b) => A.fmap(b, a)
  let joinWith = (a, b) => A.joinWith(b, a)
 }

--- a/packages/squiggle-lang/src/rescript/Utility/Jstat.res
+++ b/packages/squiggle-lang/src/rescript/Utility/Jstat.res
@ -36,8 +36,8 @@ module Exponential = {
  @module("jstat") @scope("exponential") external pdf: (float, float) => float = "pdf"
  @module("jstat") @scope("exponential") external cdf: (float, float) => float = "cdf"
  @module("jstat") @scope("exponential") external inv: (float, float) => float = "inv"
-  @module("jstat") @scope("exponential") external sample: (float) => float = "sample"
-  @module("jstat") @scope("exponential") external mean: (float) => float = "mean"
+  @module("jstat") @scope("exponential") external sample: float => float = "sample"
+  @module("jstat") @scope("exponential") external mean: float => float = "mean"
 }

 module Cauchy = {
@ -56,7 +56,6 @@ module Triangular = {
  @module("jstat") @scope("triangular") external mean: (float, float, float) => float = "mean"
 }

-
 module Pareto = {
  @module("jstat") @scope("pareto") external pdf: (float, float, float) => float = "pdf"
  @module("jstat") @scope("pareto") external cdf: (float, float, float) => float = "cdf"
@ -66,20 +65,20 @@ module Pareto = {
 module Poisson = {
  @module("jstat") @scope("poisson") external pdf: (float, float) => float = "pdf"
  @module("jstat") @scope("poisson") external cdf: (float, float) => float = "cdf"
-  @module("jstat") @scope("poisson") external sample: (float) => float = "sample"
-  @module("jstat") @scope("poisson") external mean: (float) => float = "mean"
+  @module("jstat") @scope("poisson") external sample: float => float = "sample"
+  @module("jstat") @scope("poisson") external mean: float => float = "mean"
 }

 module Weibull = {
  @module("jstat") @scope("weibull") external pdf: (float, float, float) => float = "pdf"
-  @module("jstat") @scope("weibull") external cdf: (float, float,float ) => float = "cdf"
-  @module("jstat") @scope("weibull") external sample: (float,float) => float = "sample"
-  @module("jstat") @scope("weibull") external mean: (float,float) => float = "mean"
+  @module("jstat") @scope("weibull") external cdf: (float, float, float) => float = "cdf"
+  @module("jstat") @scope("weibull") external sample: (float, float) => float = "sample"
+  @module("jstat") @scope("weibull") external mean: (float, float) => float = "mean"
 }

 module Binomial = {
  @module("jstat") @scope("binomial") external pdf: (float, float, float) => float = "pdf"
-  @module("jstat") @scope("binomial") external cdf: (float, float,float ) => float = "cdf"
+  @module("jstat") @scope("binomial") external cdf: (float, float, float) => float = "cdf"
 }

@module("jstat") external sum: array<float> => float = "sum"