Fix failing tests due to environment change

packages/components/src/components/SquiggleItem.tsx

@@ -219,6 +219,7 @@ export const SquiggleItem: React.FC<SquiggleItemProps> = ({
             distributionPlotSettings={distributionPlotSettings}
             height={height}
             environment={{
+              ...environment,
               sampleCount: environment.sampleCount / 10,
               xyPointLength: environment.xyPointLength / 10,
             }}
@@ -234,6 +235,7 @@ export const SquiggleItem: React.FC<SquiggleItemProps> = ({
             distributionPlotSettings={distributionPlotSettings}
             height={height}
             environment={{
+              ...environment,
               sampleCount: environment.sampleCount / 10,
               xyPointLength: environment.xyPointLength / 10,
             }}
@@ -246,7 +248,7 @@ export const SquiggleItem: React.FC<SquiggleItemProps> = ({
         <VariableBox heading="Module" showTypes={showTypes}>
           <div className="space-y-3">
             {Object.entries(expression.value)
-              .filter(([key, r]) => key !== "Math")
+              .filter(([key, _]) => key !== "Math")
               .map(([key, r]) => (
                 <div key={key} className="flex space-x-2">
                   <div className="flex-none">

packages/components/src/components/SquigglePlayground.tsx

@@ -55,6 +55,7 @@ const schema = yup.object({}).shape({
     .default(1000)
     .min(10)
     .max(10000),
+  percentile: yup.number().required().positive().default(0.9998).min(0).max(1),
   chartHeight: yup.number().required().positive().integer().default(350),
   leftSizePercent: yup
     .number()
@@ -155,6 +156,20 @@ const SamplingSettings: React.FC<{ register: UseFormRegister<FormFields> }> = ({
         </Text>
       </div>
     </div>
+    <div>
+      <InputItem
+        name="percentile"
+        type="number"
+        label="Symbolic Distribution Percentile"
+        register={register}
+      />
+      <div className="mt-2">
+        <Text>
+          When converting symbolic distributions to PointSet distributions, what
+          percentile to sample the points within.
+        </Text>
+      </div>
+    </div>
   </div>
 );
 
@@ -436,6 +451,7 @@ export const SquigglePlayground: FC<PlaygroundProps> = ({
   const { register, control } = useForm({
     resolver: yupResolver(schema),
     defaultValues: {
+      percentile: 0.9998,
       sampleCount: 1000,
       xyPointLength: 1000,
       chartHeight: 150,
@@ -468,6 +484,7 @@ export const SquigglePlayground: FC<PlaygroundProps> = ({
 
   const env: environment = useMemo(
     () => ({
+      percentile: Number(vars.percentile),
       sampleCount: Number(vars.sampleCount),
       xyPointLength: Number(vars.xyPointLength),
     }),

packages/components/src/lib/distributionSpecBuilder.ts

@@ -42,7 +42,7 @@ export let linearYScale: LinearScale = {
   name: "yscale",
   type: "linear",
   range: "height",
-  zero: false,
+  zero: true,
   domain: {
     fields: [
       {
@@ -84,7 +84,7 @@ export let expYScale: PowScale = {
   type: "pow",
   exponent: 0.1,
   range: "height",
-  zero: false,
+  zero: true,
   nice: false,
   domain: {
     fields: [

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

@@ -1,10 +1,6 @@
 open Jest
 open Expect
-
-let env: DistributionOperation.env = {
-  sampleCount: 100,
-  xyPointLength: 100,
-}
+open TestHelpers
 
 let {
   normalDist5,

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

@@ -58,7 +58,7 @@ describe("eval on distribution functions", () => {
   describe("subtract", () => {
     testEval("10 - normal(5, 1)", "Ok(Normal(5,1))")
     testEval("normal(5, 1) - 10", "Ok(Normal(-5,1))")
-    testEval("mean(1 - toPointSet(normal(5, 2)))", "Ok(-4.002309896304692)")
+    testEval("mean(1 - toPointSet(normal(5, 2)))", "Ok(-4.002309896304693)")
   })
   describe("multiply", () => {
     testEval("normal(10, 2) * 2", "Ok(Normal(20,4))")

packages/squiggle-lang/__tests__/TS/JS_test.ts

@@ -3,6 +3,7 @@ import {
   resultMap,
   defaultBindings,
   mergeBindings,
+  defaultEnvironment,
 } from "../../src/js/index";
 import { testRun, testRunPartial } from "./TestHelpers";
 
@@ -109,7 +110,7 @@ describe("JS Imports", () => {
 describe("Distribution", () => {
   //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 env = { ...defaultEnvironment, sampleCount: 8, xyPointLength: 100 };
   let dist1Samples = [3, 4, 5, 6, 6, 7, 10, 15, 30];
   let dist1SampleCount = dist1Samples.length;
   let dist = new Distribution(

packages/squiggle-lang/__tests__/TS/SampleSet_test.ts

@@ -1,4 +1,4 @@
-import { Distribution } from "../../src/js/index";
+import { Distribution, defaultEnvironment } from "../../src/js/index";
 import { expectErrorToBeBounded, failDefault, testRun } from "./TestHelpers";
 import * as fc from "fast-check";
 
@@ -16,6 +16,7 @@ let arrayGen = () =>
     );
 describe("cumulative density function", () => {
   let n = 10000;
+  let env = { ...defaultEnvironment, sampleCount: n, xyPointLength: 100 };
 
   // We should fix this.
   test.skip("'s codomain is bounded above", () => {
@@ -23,10 +24,7 @@ describe("cumulative density function", () => {
       fc.property(arrayGen(), fc.float(), (xs_, x) => {
         let xs = Array.from(xs_);
         // Should compute with squiggle strings once interpreter has `sample`
-        let dist = new Distribution(
-          { tag: "SampleSet", value: xs },
-          { sampleCount: n, xyPointLength: 100 }
-        );
+        let dist = new Distribution({ tag: "SampleSet", value: xs }, env);
         let cdfValue = dist.cdf(x).value;
         let epsilon = 5e-7;
         expect(cdfValue).toBeLessThanOrEqual(1 + epsilon);
@@ -39,10 +37,7 @@ describe("cumulative density function", () => {
       fc.property(arrayGen(), fc.float(), (xs_, x) => {
         let xs = Array.from(xs_);
         // Should compute with squiggle strings once interpreter has `sample`
-        let dist = new Distribution(
-          { tag: "SampleSet", value: xs },
-          { sampleCount: n, xyPointLength: 100 }
-        );
+        let dist = new Distribution({ tag: "SampleSet", value: xs }, env);
         let cdfValue = dist.cdf(x).value;
         expect(cdfValue).toBeGreaterThanOrEqual(0);
       })
@@ -57,10 +52,7 @@ describe("cumulative density function", () => {
         let xs = Array.from(xs_);
         let max = Math.max(...xs);
         // Should compute with squiggle strings once interpreter has `sample`
-        let dist = new Distribution(
-          { tag: "SampleSet", value: xs },
-          { sampleCount: n, xyPointLength: 100 }
-        );
+        let dist = new Distribution({ tag: "SampleSet", value: xs }, env);
         let cdfValue = dist.cdf(max).value;
         expect(cdfValue).toBeCloseTo(1.0, 2);
       })
@@ -74,10 +66,7 @@ describe("cumulative density function", () => {
         let xs = Array.from(xs_);
         let min = Math.min(...xs);
         // Should compute with squiggle strings once interpreter has `sample`
-        let dist = new Distribution(
-          { tag: "SampleSet", value: xs },
-          { sampleCount: n, xyPointLength: 100 }
-        );
+        let dist = new Distribution({ tag: "SampleSet", value: xs }, env);
         let cdfValue = dist.cdf(min).value;
         let max = Math.max(...xs);
         let epsilon = 5e-3;
@@ -95,10 +84,7 @@ describe("cumulative density function", () => {
     fc.assert(
       fc.property(arrayGen(), fc.float(), (xs_, x) => {
         let xs = Array.from(xs_);
-        let dist = new Distribution(
-          { tag: "SampleSet", value: xs },
-          { sampleCount: n, xyPointLength: 100 }
-        );
+        let dist = new Distribution({ tag: "SampleSet", value: xs }, env);
         let cdfValue = dist.cdf(x).value;
         let max = Math.max(...xs);
         if (x > max) {
@@ -117,10 +103,7 @@ describe("cumulative density function", () => {
     fc.assert(
       fc.property(arrayGen(), fc.float(), (xs_, x) => {
         let xs = Array.from(xs_);
-        let dist = new Distribution(
-          { tag: "SampleSet", value: xs },
-          { sampleCount: n, xyPointLength: 100 }
-        );
+        let dist = new Distribution({ tag: "SampleSet", value: xs }, env);
         let cdfValue = dist.cdf(x).value;
         expect(cdfValue).toBeGreaterThanOrEqual(0);
       })
@@ -131,6 +114,7 @@ describe("cumulative density function", () => {
 // I no longer believe this is true.
 describe("probability density function", () => {
   let n = 1000;
+  let env = { ...defaultEnvironment, sampleCount: n, xyPointLength: 100 };
 
   test.skip("assigns to the max at most the weight of the mean", () => {
     fc.assert(
@@ -139,10 +123,7 @@ describe("probability density function", () => {
         let max = Math.max(...xs);
         let mean = xs.reduce((a, b) => a + b, 0.0) / xs.length;
         // Should be from squiggleString once interpreter exposes sampleset
-        let dist = new Distribution(
-          { tag: "SampleSet", value: xs },
-          { sampleCount: n, xyPointLength: 100 }
-        );
+        let dist = new Distribution({ tag: "SampleSet", value: xs }, env);
         let pdfValueMean = dist.pdf(mean).value;
         let pdfValueMax = dist.pdf(max).value;
         if (typeof pdfValueMean == "number" && typeof pdfValueMax == "number") {
@@ -166,7 +147,7 @@ describe("mean is mean", () => {
           let n = xs.length;
           let dist = new Distribution(
             { tag: "SampleSet", value: xs },
-            { sampleCount: 2 * n, xyPointLength: 4 * n }
+            { ...defaultEnvironment, sampleCount: 2 * n, xyPointLength: 4 * n }
           );
           let mean = dist.mean();
           if (typeof mean.value == "number") {
@@ -193,7 +174,11 @@ describe("mean is mean", () => {
           let n = xs.length;
           let dist = new Distribution(
             { tag: "SampleSet", value: xs },
-            { sampleCount: Math.floor(n / 2), xyPointLength: 4 * n }
+            {
+              ...defaultEnvironment,
+              sampleCount: Math.floor(n / 2),
+              xyPointLength: 4 * n,
+            }
           );
           let mean = dist.mean();
           if (typeof mean.value == "number") {

packages/squiggle-lang/__tests__/TestHelpers.res

@@ -30,6 +30,7 @@ let {toFloat, toDist, toString, toError, fmap} = module(DistributionOperation.Ou
 let fnImage = (theFn, inps) => Js.Array.map(theFn, inps)
 
 let env: DistributionOperation.env = {
+  percentile: 0.9998,
   sampleCount: MagicNumbers.Environment.defaultSampleCount,
   xyPointLength: MagicNumbers.Environment.defaultXYPointLength,
 }

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

@@ -105,14 +105,7 @@ export class Distribution {
   }
 
   pointSet(): result<shape, distributionError> {
-    let pointSet = toPointSet(
-      this.t,
-      {
-        xyPointLength: this.env.xyPointLength,
-        sampleCount: this.env.sampleCount,
-      },
-      undefined
-    );
+    let pointSet = toPointSet(this.t, this.env, undefined);
     if (pointSet.tag === "Ok") {
       let distribution = pointSet.value;
       if (distribution.tag === "Continuous") {

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

@@ -39,20 +39,17 @@ export type { result, shape, environment, lambdaValue, squiggleExpression };
 
 export { parse } from "./parse";
 
-export let defaultSamplingInputs: environment = {
-  sampleCount: 10000,
-  xyPointLength: 10000,
-};
-
 export function run(
   squiggleString: string,
   bindings?: externalBindings,
-  environment?: environment,
+  environment?: Partial<environment>,
   imports?: jsImports
 ): result<squiggleExpression, errorValue> {
   let b = bindings ? bindings : defaultBindings;
   let i = imports ? imports : defaultImports;
-  let e = environment ? environment : defaultEnvironment;
+  let e = environment
+    ? _.merge(defaultEnvironment, environment)
+    : defaultEnvironment;
   let res: result<expressionValue, errorValue> = evaluateUsingOptions(
     { externalBindings: mergeImportsWithBindings(b, i), environment: e },
     squiggleString
@@ -64,12 +61,14 @@ export function run(
 export function runPartial(
   squiggleString: string,
   bindings?: externalBindings,
-  environment?: environment,
+  environment?: Partial<environment>,
   imports?: jsImports
 ): result<externalBindings, errorValue> {
   let b = bindings ? bindings : defaultBindings;
   let i = imports ? imports : defaultImports;
-  let e = environment ? environment : defaultEnvironment;
+  let e = environment
+    ? _.merge(defaultEnvironment, environment)
+    : defaultEnvironment;
 
   return evaluatePartialUsingExternalBindings(
     squiggleString,

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

@@ -5,11 +5,13 @@ type error = DistributionTypes.error
 // TODO: It could be great to use a cache for some calculations (basically, do memoization). Also, better analytics/tracking could go a long way.
 
 type env = {
+  percentile: float,
   sampleCount: int,
   xyPointLength: int,
 }
 
 let defaultEnv = {
+  percentile: 0.9998,
   sampleCount: MagicNumbers.Environment.defaultSampleCount,
   xyPointLength: MagicNumbers.Environment.defaultXYPointLength,
 }
@@ -137,7 +139,7 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
       ->OutputLocal.fromResult
     | ToString(ToString) => dist->GenericDist.toString->String
     | ToString(ToSparkline(bucketCount)) =>
-      GenericDist.toSparkline(dist, ~sampleCount, ~bucketCount, ())
+      GenericDist.toSparkline(dist, ~percentile=env.percentile, ~sampleCount, ~bucketCount, ())
       ->E.R2.fmap(r => String(r))
       ->OutputLocal.fromResult
     | ToDist(Inspect) => {
@@ -170,7 +172,7 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
       ->OutputLocal.fromResult
     | ToDist(ToPointSet) =>
       dist
-      ->GenericDist.toPointSet(~xyPointLength, ~sampleCount, ())
+      ->GenericDist.toPointSet(~percentile=env.percentile, ~xyPointLength, ~sampleCount, ())
       ->E.R2.fmap(r => Dist(PointSet(r)))
       ->OutputLocal.fromResult
     | ToDist(Scale(#LogarithmWithThreshold(eps), f)) =>

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

@@ -1,5 +1,6 @@
 @genType
 type env = {
+  percentile: float,
   sampleCount: int,
   xyPointLength: int,
 }

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

@@ -70,8 +70,8 @@ module DistributionOperation = {
     | #IntegralSum
     | #Mode
     | #Stdev
-    | #Min
-    | #Max
+    | #Min(float)
+    | #Max(float)
     | #Variance
   ]
 
@@ -123,8 +123,8 @@ module DistributionOperation = {
     | ToFloat(#Cdf(r)) => `cdf(${E.Float.toFixed(r)})`
     | ToFloat(#Inv(r)) => `inv(${E.Float.toFixed(r)})`
     | ToFloat(#Mean) => `mean`
-    | ToFloat(#Min) => `min`
-    | ToFloat(#Max) => `max`
+    | ToFloat(#Min(_)) => `min`
+    | ToFloat(#Max(_)) => `max`
     | ToFloat(#Stdev) => `stdev`
     | ToFloat(#Variance) => `variance`
     | ToFloat(#Mode) => `mode`

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

@@ -108,7 +108,7 @@ let toFloatOperation = (
 ) => {
   switch distToFloatOperation {
   | #IntegralSum => Ok(integralEndY(t))
-  | (#Pdf(_) | #Cdf(_) | #Inv(_) | #Mean | #Sample | #Min | #Max) as op => {
+  | (#Pdf(_) | #Cdf(_) | #Inv(_) | #Mean | #Sample | #Min(_) | #Max(_)) as op => {
       let trySymbolicSolution = switch (t: t) {
       | Symbolic(r) => SymbolicDist.T.operate(op, r)->E.R.toOption
       | _ => None
@@ -118,8 +118,8 @@ let toFloatOperation = (
       | (SampleSet(sampleSet), #Mean) => SampleSetDist.mean(sampleSet)->Some
       | (SampleSet(sampleSet), #Sample) => SampleSetDist.sample(sampleSet)->Some
       | (SampleSet(sampleSet), #Inv(r)) => SampleSetDist.percentile(sampleSet, r)->Some
-      | (SampleSet(sampleSet), #Min) => SampleSetDist.min(sampleSet)->Some
-      | (SampleSet(sampleSet), #Max) => SampleSetDist.max(sampleSet)->Some
+      | (SampleSet(sampleSet), #Min(_)) => SampleSetDist.min(sampleSet)->Some
+      | (SampleSet(sampleSet), #Max(_)) => SampleSetDist.max(sampleSet)->Some
       | _ => None
       }
 
@@ -150,6 +150,7 @@ let toFloatOperation = (
 // Also, change the outputXYPoints/pointSetDistLength details
 let toPointSet = (
   t,
+  ~percentile: float,
   ~xyPointLength,
   ~sampleCount,
   ~xSelection: DistributionTypes.DistributionOperation.pointsetXSelection=#ByWeight,
@@ -157,7 +158,7 @@ let toPointSet = (
 ): result<PointSetTypes.pointSetDist, error> => {
   switch (t: t) {
   | PointSet(pointSet) => Ok(pointSet)
-  | Symbolic(r) => Ok(SymbolicDist.T.toPointSetDist(~xSelection, xyPointLength, r))
+  | Symbolic(r) => Ok(SymbolicDist.T.toPointSetDist(~percentile, ~xSelection, xyPointLength, r))
   | SampleSet(r) =>
     SampleSetDist.toPointSetDist(
       ~samples=r,
@@ -177,9 +178,12 @@ let toPointSet = (
   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, ()): result<string, error> =>
+let toSparkline = (t: t, ~percentile: float, ~sampleCount: int, ~bucketCount: int=20, ()): result<
+  string,
+  error,
+> =>
   t
-  ->toPointSet(~xSelection=#Linear, ~xyPointLength=bucketCount * 3, ~sampleCount, ())
+  ->toPointSet(~percentile, ~xSelection=#Linear, ~xyPointLength=bucketCount * 3, ~sampleCount, ())
   ->E.R.bind(r =>
     r->PointSetDist.toSparkline(bucketCount)->E.R2.errMap(x => DistributionTypes.SparklineError(x))
   )

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

@@ -37,12 +37,19 @@ module Score: {
 @genType
 let toPointSet: (
   t,
+  ~percentile: float,
   ~xyPointLength: int,
   ~sampleCount: int,
   ~xSelection: DistributionTypes.DistributionOperation.pointsetXSelection=?,
   unit,
 ) => result<PointSetTypes.pointSetDist, error>
-let toSparkline: (t, ~sampleCount: int, ~bucketCount: int=?, unit) => result<string, error>
+let toSparkline: (
+  t,
+  ~percentile: float,
+  ~sampleCount: int,
+  ~bucketCount: int=?,
+  unit,
+) => result<string, error>
 
 let truncate: (
   t,

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

@@ -254,8 +254,8 @@ let operate = (distToFloatOp: Operation.distToFloatOperation, s): float =>
   | #Inv(f) => inv(f, s)
   | #Sample => sample(s)
   | #Mean => T.mean(s)
-  | #Min => T.minX(s)
-  | #Max => T.maxX(s)
+  | #Min(_) => T.minX(s)
+  | #Max(_) => T.maxX(s)
   }
 
 let toSparkline = (t: t, bucketCount): result<string, PointSetTypes.sparklineError> =>

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

@@ -331,9 +331,6 @@ module From90thPercentile = {
 }
 
 module T = {
-  let minCdfValue = 0.0001
-  let maxCdfValue = 0.9999
-
   let pdf = (x, dist) =>
     switch dist {
     | #Normal(n) => Normal.pdf(x, n)
@@ -419,35 +416,39 @@ module T = {
     | #Bernoulli(n) => Bernoulli.toString(n)
     }
 
-  let min: symbolicDist => float = x =>
+  let min = (~percentile: float, x: symbolicDist): float => {
+    let minCdf = (1. -. percentile) /. 2.
     switch x {
     | #Triangular({low}) => low
-    | #Exponential(n) => Exponential.inv(minCdfValue, n)
-    | #Cauchy(n) => Cauchy.inv(minCdfValue, n)
-    | #Normal(n) => Normal.inv(minCdfValue, n)
-    | #Lognormal(n) => Lognormal.inv(minCdfValue, n)
-    | #Logistic(n) => Logistic.inv(minCdfValue, n)
-    | #Gamma(n) => Gamma.inv(minCdfValue, n)
+    | #Exponential(n) => Exponential.inv(minCdf, n)
+    | #Cauchy(n) => Cauchy.inv(minCdf, n)
+    | #Normal(n) => Normal.inv(minCdf, n)
+    | #Lognormal(n) => Lognormal.inv(minCdf, n)
+    | #Logistic(n) => Logistic.inv(minCdf, n)
+    | #Gamma(n) => Gamma.inv(minCdf, n)
     | #Uniform({low}) => low
     | #Bernoulli(n) => Bernoulli.min(n)
-    | #Beta(n) => Beta.inv(minCdfValue, n)
+    | #Beta(n) => Beta.inv(minCdf, n)
     | #Float(n) => n
     }
+  }
 
-  let max: symbolicDist => float = x =>
+  let max = (~percentile: float, x: symbolicDist): float => {
+    let maxCdf = 1. -. (1. -. percentile) /. 2.
     switch x {
     | #Triangular(n) => n.high
-    | #Exponential(n) => Exponential.inv(maxCdfValue, n)
-    | #Cauchy(n) => Cauchy.inv(maxCdfValue, n)
-    | #Normal(n) => Normal.inv(maxCdfValue, n)
-    | #Gamma(n) => Gamma.inv(maxCdfValue, n)
-    | #Lognormal(n) => Lognormal.inv(maxCdfValue, n)
-    | #Logistic(n) => Logistic.inv(maxCdfValue, n)
-    | #Beta(n) => Beta.inv(maxCdfValue, n)
+    | #Exponential(n) => Exponential.inv(maxCdf, n)
+    | #Cauchy(n) => Cauchy.inv(maxCdf, n)
+    | #Normal(n) => Normal.inv(maxCdf, n)
+    | #Gamma(n) => Gamma.inv(maxCdf, n)
+    | #Lognormal(n) => Lognormal.inv(maxCdf, n)
+    | #Logistic(n) => Logistic.inv(maxCdf, n)
+    | #Beta(n) => Beta.inv(maxCdf, n)
     | #Bernoulli(n) => Bernoulli.max(n)
     | #Uniform({high}) => high
     | #Float(n) => n
     }
+  }
 
   let mean: symbolicDist => result<float, string> = x =>
     switch x {
@@ -469,15 +470,20 @@ module T = {
     | #Cdf(f) => Ok(cdf(f, s))
     | #Pdf(f) => Ok(pdf(f, s))
     | #Inv(f) => Ok(inv(f, s))
-    | #Min => Ok(min(s))
-    | #Max => Ok(max(s))
+    | #Min(percentile) => Ok(min(~percentile, s))
+    | #Max(percentile) => Ok(min(~percentile, s))
     | #Sample => Ok(sample(s))
     | #Mean => mean(s)
     }
 
-  let interpolateXs = (~xSelection: [#Linear | #ByWeight]=#Linear, dist: symbolicDist, n) =>
+  let interpolateXs = (
+    ~percentile: float,
+    ~xSelection: [#Linear | #ByWeight]=#Linear,
+    dist: symbolicDist,
+    n,
+  ) => {
     switch (xSelection, dist) {
-    | (#Linear, _) => E.A.Floats.range(min(dist), max(dist), n)
+    | (#Linear, _) => E.A.Floats.range(min(~percentile, dist), max(~percentile, dist), n)
     | (#ByWeight, #Uniform(n)) =>
       // In `ByWeight mode, uniform distributions get special treatment because we need two x's
       // on either side for proper rendering (just left and right of the discontinuities).
@@ -485,9 +491,12 @@ module T = {
       let dx = MagicNumbers.Epsilon.ten *. distance
       [n.low -. dx, n.low, n.low +. dx, n.high -. dx, n.high, n.high +. dx]
     | (#ByWeight, _) =>
-      let ys = E.A.Floats.range(minCdfValue, maxCdfValue, n)
+      let minCdf = (1. -. percentile) /. 2.
+      let maxCdf = 1. -. minCdf
+      let ys = E.A.Floats.range(minCdf, maxCdf, n)
       ys |> E.A.fmap(y => inv(y, dist))
     }
+  }
 
   /* Calling e.g. "Normal.operate" returns an optional that wraps a result.
      If the optional is None, there is no valid analytic solution. If it Some, it
@@ -533,6 +542,7 @@ module T = {
     }
 
   let toPointSetDist = (
+    ~percentile: float,
     ~xSelection=#ByWeight,
     sampleCount,
     d: symbolicDist,
@@ -541,7 +551,7 @@ module T = {
     | #Float(v) => Float.toPointSetDist(v)
     | #Bernoulli(v) => Bernoulli.toPointSetDist(v)
     | _ =>
-      let xs = interpolateXs(~xSelection, d, sampleCount)
+      let xs = interpolateXs(~percentile, ~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/ReducerInterface/ReducerInterface_GenericDistribution.res

@@ -216,8 +216,8 @@ let dispatchToGenericOutput = (call: IEV.functionCall, env: DistributionOperatio
       | "mean" => #Mean
       | "stdev" => #Stdev
       | "variance" => #Variance
-      | "min" => #Min
-      | "max" => #Max
+      | "min" => #Min(env.percentile)
+      | "max" => #Max(env.percentile)
       | "mode" => #Mode
       | _ => #Mean
       }

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

@@ -26,8 +26,8 @@ type distToFloatOperation = [
   | #Inv(float)
   | #Mean
   | #Sample
-  | #Min
-  | #Max
+  | #Min(float)
+  | #Max(float)
 ]
 
 module Convolution = {