Merge branch 'develop' into reducer-dev

.github/workflows/ci.yml

@@ -68,6 +68,8 @@ jobs:
         working-directory: packages/squiggle-lang
     steps:
       - uses: actions/checkout@v2
+        with:
+          fetch-depth: 2
       - name: Install dependencies from monorepo level
         run: cd ../../ && yarn
       - name: Build rescript codebase

.github/workflows/codeql-analysis.yml

@@ -12,12 +12,6 @@
 name: "CodeQL"
 
 on:
-  push:
-    branches:
-      - master
-      - production
-      - staging
-      - develop
   schedule:
     - cron: "42 19 * * 0"
 

README.md

@@ -40,8 +40,6 @@ the packages can be found in `packages`.
 - `packages/website` is the main descriptive website for squiggle,
   it is hosted at `squiggle-language.com`.
 
-The playground depends on the components library which then depends on the language. This means that if you wish to work on the components library, you will need to build (no need to bundle) the language, and as of this writing playground doesn't really work.
-
 # Develop
 
 For any project in the repo, begin by running `yarn` in the top level

packages/components/README.md

@@ -5,6 +5,8 @@
 
 This package contains the react components for squiggle. These can be used either as a library or hosted as a [storybook](https://storybook.js.org/).
 
+The `@quri/squiggle-components` package offers several components and utilities for people who want to embed Squiggle components into websites.
+
 # Usage in a `react` project
 
 For example, in a fresh `create-react-app` project

packages/components/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@quri/squiggle-components",
-  "version": "0.2.19",
+  "version": "0.2.20",
   "license": "MIT",
   "dependencies": {
     "@quri/squiggle-lang": "^0.2.8",
@@ -10,14 +10,14 @@
     "react-ace": "^10.1.0",
     "react-dom": "^18.1.0",
     "react-use": "^17.3.2",
-    "react-vega": "^7.5.0",
+    "react-vega": "^7.5.1",
     "styled-components": "^5.3.5",
     "vega": "^5.22.1",
     "vega-embed": "^6.20.6",
     "vega-lite": "^5.2.0"
   },
   "devDependencies": {
-    "@babel/plugin-proposal-private-property-in-object": "^7.16.7",
+    "@babel/plugin-proposal-private-property-in-object": "^7.17.12",
     "@storybook/addon-actions": "^6.4.22",
     "@storybook/addon-essentials": "^6.4.22",
     "@storybook/addon-links": "^6.4.22",
@@ -28,12 +28,12 @@
     "@storybook/react": "^6.4.22",
     "@testing-library/jest-dom": "^5.16.4",
     "@testing-library/react": "^13.2.0",
-    "@testing-library/user-event": "^14.1.1",
+    "@testing-library/user-event": "^14.2.0",
     "@types/jest": "^27.5.0",
     "@types/lodash": "^4.14.182",
-    "@types/node": "^17.0.31",
+    "@types/node": "^17.0.34",
-    "@types/react": "^18.0.3",
+    "@types/react": "^18.0.9",
-    "@types/react-dom": "^18.0.2",
+    "@types/react-dom": "^18.0.4",
     "@types/styled-components": "^5.1.24",
     "@types/webpack": "^5.28.0",
     "cross-env": "^7.0.3",
@@ -43,9 +43,9 @@
     "tsconfig-paths-webpack-plugin": "^3.5.2",
     "typescript": "^4.6.3",
     "web-vitals": "^2.1.4",
-    "webpack": "^5.72.0",
+    "webpack": "^5.72.1",
     "webpack-cli": "^4.9.2",
-    "webpack-dev-server": "^4.8.1"
+    "webpack-dev-server": "^4.9.0"
   },
   "scripts": {
     "start": "cross-env REACT_APP_FAST_REFRESH=false && start-storybook -p 6006 -s public",

packages/components/src/components/DistributionChart.tsx

@@ -1,7 +1,11 @@
 import * as React from "react";
 import _ from "lodash";
-import type { Distribution } from "@quri/squiggle-lang";
+import {
-import { distributionErrorToString } from "@quri/squiggle-lang";
+  Distribution,
+  result,
+  distributionError,
+  distributionErrorToString,
+} from "@quri/squiggle-lang";
 import { Vega, VisualizationSpec } from "react-vega";
 import * as chartSpecification from "../vega-specs/spec-distributions.json";
 import { ErrorBox } from "./ErrorBox";
@@ -13,11 +17,14 @@ import {
   expYScale,
 } from "./DistributionVegaScales";
 import styled from "styled-components";
+import { NumberShower } from "./NumberShower";
 
 type DistributionChartProps = {
   distribution: Distribution;
   width?: number;
   height: number;
+  /** Whether to show a summary of means, stdev, percentiles etc */
+  showSummary: boolean;
   /** Whether to show the user graph controls (scale etc) */
   showControls?: boolean;
 };
@@ -25,6 +32,7 @@ type DistributionChartProps = {
 export const DistributionChart: React.FC<DistributionChartProps> = ({
   distribution,
   height,
+  showSummary,
   width,
   showControls = false,
 }: DistributionChartProps) => {
@@ -37,7 +45,7 @@ export const DistributionChart: React.FC<DistributionChartProps> = ({
         shape.value.continuous.some((x) => x.x <= 0) ||
         shape.value.discrete.some((x) => x.x <= 0);
       let spec = buildVegaSpec(isLogX, isExpY);
-      let widthProp = width ? width - 20 : size.width - 10;
+      let widthProp = width ? width : size.width;
 
       // Check whether we should disable the checkbox
       var logCheckbox = (
@@ -58,21 +66,22 @@ export const DistributionChart: React.FC<DistributionChartProps> = ({
       }
 
       var result = (
-        <div>
+        <ChartContainer width={widthProp + "px"}>
           <Vega
             spec={spec}
             data={{ con: shape.value.continuous, dis: shape.value.discrete }}
-            width={widthProp}
+            width={widthProp - 10}
             height={height}
             actions={false}
           />
+          {showSummary && <SummaryTable distribution={distribution} />}
           {showControls && (
             <div>
               {logCheckbox}
               <CheckBox label="Exp Y scale" value={isExpY} onChange={setExpY} />
             </div>
           )}
-        </div>
+        </ChartContainer>
       );
     } else {
       var result = (
@@ -87,6 +96,12 @@ export const DistributionChart: React.FC<DistributionChartProps> = ({
   return sized;
 };
 
+type ChartContainerProps = { width: string };
+
+let ChartContainer = styled.div<ChartContainerProps>`
+  width: ${(props) => props.width};
+`;
+
 function buildVegaSpec(isLogX: boolean, isExpY: boolean): VisualizationSpec {
   return {
     ...chartSpecification,
@@ -128,3 +143,90 @@ export const CheckBox = ({
     </span>
   );
 };
+
+type SummaryTableProps = {
+  distribution: Distribution;
+};
+
+const Table = styled.table`
+  margin-left: auto;
+  margin-right: auto;
+  border-collapse: collapse;
+  text-align: center;
+  border-style: hidden;
+`;
+
+const TableHead = styled.thead`
+  border-bottom: 1px solid rgb(141 149 167);
+`;
+
+const TableHeadCell = styled.th`
+  border-right: 1px solid rgb(141 149 167);
+  border-left: 1px solid rgb(141 149 167);
+  padding: 0.3em;
+`;
+
+const TableBody = styled.tbody``;
+
+const Row = styled.tr``;
+
+const Cell = styled.td`
+  padding: 0.3em;
+  border-right: 1px solid rgb(141 149 167);
+  border-left: 1px solid rgb(141 149 167);
+`;
+
+const SummaryTable: React.FC<SummaryTableProps> = ({
+  distribution,
+}: SummaryTableProps) => {
+  let mean = distribution.mean();
+  let p5 = distribution.inv(0.05);
+  let p10 = distribution.inv(0.1);
+  let p25 = distribution.inv(0.25);
+  let p50 = distribution.inv(0.5);
+  let p75 = distribution.inv(0.75);
+  let p90 = distribution.inv(0.9);
+  let p95 = distribution.inv(0.95);
+  let unwrapResult = (
+    x: result<number, distributionError>
+  ): React.ReactNode => {
+    if (x.tag === "Ok") {
+      return <NumberShower number={x.value} />;
+    } else {
+      return (
+        <ErrorBox heading="Distribution Error">
+          {distributionErrorToString(x.value)}
+        </ErrorBox>
+      );
+    }
+  };
+
+  return (
+    <Table>
+      <TableHead>
+        <Row>
+          <TableHeadCell>{"Mean"}</TableHeadCell>
+          <TableHeadCell>{"5%"}</TableHeadCell>
+          <TableHeadCell>{"10%"}</TableHeadCell>
+          <TableHeadCell>{"25%"}</TableHeadCell>
+          <TableHeadCell>{"50%"}</TableHeadCell>
+          <TableHeadCell>{"75%"}</TableHeadCell>
+          <TableHeadCell>{"90%"}</TableHeadCell>
+          <TableHeadCell>{"95%"}</TableHeadCell>
+        </Row>
+      </TableHead>
+      <TableBody>
+        <Row>
+          <Cell>{unwrapResult(mean)}</Cell>
+          <Cell>{unwrapResult(p5)}</Cell>
+          <Cell>{unwrapResult(p10)}</Cell>
+          <Cell>{unwrapResult(p25)}</Cell>
+          <Cell>{unwrapResult(p50)}</Cell>
+          <Cell>{unwrapResult(p75)}</Cell>
+          <Cell>{unwrapResult(p90)}</Cell>
+          <Cell>{unwrapResult(p95)}</Cell>
+        </Row>
+      </TableBody>
+    </Table>
+  );
+};

packages/components/src/components/FunctionChart.tsx

@@ -1,18 +1,26 @@
 import * as React from "react";
 import _ from "lodash";
 import type { Spec } from "vega";
-import type { Distribution, errorValue, result } from "@quri/squiggle-lang";
+import {
+  Distribution,
+  result,
+  lambdaValue,
+  environment,
+  runForeign,
+  squiggleExpression,
+  errorValue,
+  errorValueToString,
+} from "@quri/squiggle-lang";
 import { createClassFromSpec } from "react-vega";
 import * as percentilesSpec from "../vega-specs/spec-percentiles.json";
 import { DistributionChart } from "./DistributionChart";
+import { NumberShower } from "./NumberShower";
 import { ErrorBox } from "./ErrorBox";
 
 let SquigglePercentilesChart = createClassFromSpec({
   spec: percentilesSpec as Spec,
 });
 
-type distPlusFn = (a: number) => result<Distribution, errorValue>;
-
 const _rangeByCount = (start: number, stop: number, count: number) => {
   const step = (stop - start) / (count - 1);
   const items = _.range(start, stop, step);
@@ -27,89 +35,177 @@ function unwrap<a, b>(x: result<a, b>): a {
     throw Error("FAILURE TO UNWRAP");
   }
 }
+export type FunctionChartSettings = {
+  start: number;
+  stop: number;
+  count: number;
+};
 
-function mapFilter<a, b>(xs: a[], f: (x: a) => b | undefined): b[] {
+interface FunctionChartProps {
-  let initial: b[] = [];
+  fn: lambdaValue;
-  return xs.reduce((previous, current) => {
+  chartSettings: FunctionChartSettings;
-    let value: b | undefined = f(current);
+  environment: environment;
-    if (value !== undefined) {
-      return previous.concat([value]);
-    } else {
-      return previous;
-    }
-  }, initial);
 }
 
-export const FunctionChart: React.FC<{
+type percentiles = {
-  distPlusFn: distPlusFn;
+  x: number;
-  diagramStart: number;
+  p1: number;
-  diagramStop: number;
+  p5: number;
-  diagramCount: number;
+  p10: number;
-}> = ({ distPlusFn, diagramStart, diagramStop, diagramCount }) => {
+  p20: number;
+  p30: number;
+  p40: number;
+  p50: number;
+  p60: number;
+  p70: number;
+  p80: number;
+  p90: number;
+  p95: number;
+  p99: number;
+}[];
+
+type errors = _.Dictionary<
+  {
+    x: number;
+    value: string;
+  }[]
+>;
+
+type point = { x: number; value: result<Distribution, string> };
+
+let getPercentiles = ({ chartSettings, fn, environment }) => {
+  let chartPointsToRender = _rangeByCount(
+    chartSettings.start,
+    chartSettings.stop,
+    chartSettings.count
+  );
+
+  let chartPointsData: point[] = chartPointsToRender.map((x) => {
+    let result = runForeign(fn, [x], environment);
+    if (result.tag === "Ok") {
+      if (result.value.tag == "distribution") {
+        return { x, value: { tag: "Ok", value: result.value.value } };
+      } else {
+        return {
+          x,
+          value: {
+            tag: "Error",
+            value:
+              "Cannot currently render functions that don't return distributions",
+          },
+        };
+      }
+    } else {
+      return {
+        x,
+        value: { tag: "Error", value: errorValueToString(result.value) },
+      };
+    }
+  });
+
+  let initialPartition: [
+    { x: number; value: Distribution }[],
+    { x: number; value: string }[]
+  ] = [[], []];
+
+  let [functionImage, errors] = chartPointsData.reduce((acc, current) => {
+    if (current.value.tag === "Ok") {
+      acc[0].push({ x: current.x, value: current.value.value });
+    } else {
+      acc[1].push({ x: current.x, value: current.value.value });
+    }
+    return acc;
+  }, initialPartition);
+
+  let groupedErrors: errors = _.groupBy(errors, (x) => x.value);
+
+  let percentiles: percentiles = functionImage.map(({ x, value }) => {
+    // We convert it to to a pointSet distribution first, so that in case its a sample set
+    // distribution, it doesn't internally convert it to a pointSet distribution for every
+    // single inv() call.
+    let toPointSet: Distribution = unwrap(value.toPointSet());
+    return {
+      x: x,
+      p1: unwrap(toPointSet.inv(0.01)),
+      p5: unwrap(toPointSet.inv(0.05)),
+      p10: unwrap(toPointSet.inv(0.1)),
+      p20: unwrap(toPointSet.inv(0.2)),
+      p30: unwrap(toPointSet.inv(0.3)),
+      p40: unwrap(toPointSet.inv(0.4)),
+      p50: unwrap(toPointSet.inv(0.5)),
+      p60: unwrap(toPointSet.inv(0.6)),
+      p70: unwrap(toPointSet.inv(0.7)),
+      p80: unwrap(toPointSet.inv(0.8)),
+      p90: unwrap(toPointSet.inv(0.9)),
+      p95: unwrap(toPointSet.inv(0.95)),
+      p99: unwrap(toPointSet.inv(0.99)),
+    };
+  });
+
+  return { percentiles, errors: groupedErrors };
+};
+
+export const FunctionChart: React.FC<FunctionChartProps> = ({
+  fn,
+  chartSettings,
+  environment,
+}: FunctionChartProps) => {
   let [mouseOverlay, setMouseOverlay] = React.useState(0);
-  function handleHover(...args) {
+  function handleHover(_name: string, value: unknown) {
-    setMouseOverlay(args[1]);
+    setMouseOverlay(value as number);
   }
   function handleOut() {
     setMouseOverlay(NaN);
   }
   const signalListeners = { mousemove: handleHover, mouseout: handleOut };
-  let mouseItem = distPlusFn(mouseOverlay);
+  let mouseItem: result<squiggleExpression, errorValue> = !!mouseOverlay
+    ? runForeign(fn, [mouseOverlay], environment)
+    : {
+        tag: "Error",
+        value: {
+          tag: "REExpectedType",
+          value: "Hover x-coordinate returned NaN. Expected a number.",
+        },
+      };
   let showChart =
-    mouseItem.tag === "Ok" ? (
+    mouseItem.tag === "Ok" && mouseItem.value.tag == "distribution" ? (
       <DistributionChart
-        distribution={mouseItem.value}
+        distribution={mouseItem.value.value}
         width={400}
         height={140}
+        showSummary={false}
       />
     ) : (
       <></>
     );
-  let data1 = _rangeByCount(diagramStart, diagramStop, diagramCount);
-  let valueData = mapFilter(data1, (x) => {
-    let result = distPlusFn(x);
-    if (result.tag === "Ok") {
-      return { x: x, value: result.value };
-    }
-  }).map(({ x, value }) => {
-    return {
-      x: x,
-      p1: unwrap(value.inv(0.01)),
-      p5: unwrap(value.inv(0.05)),
-      p10: unwrap(value.inv(0.12)),
-      p20: unwrap(value.inv(0.2)),
-      p30: unwrap(value.inv(0.3)),
-      p40: unwrap(value.inv(0.4)),
-      p50: unwrap(value.inv(0.5)),
-      p60: unwrap(value.inv(0.6)),
-      p70: unwrap(value.inv(0.7)),
-      p80: unwrap(value.inv(0.8)),
-      p90: unwrap(value.inv(0.9)),
-      p95: unwrap(value.inv(0.95)),
-      p99: unwrap(value.inv(0.99)),
-    };
-  });
 
-  let errorData = mapFilter(data1, (x) => {
+  let getPercentilesMemoized = React.useMemo(
-    let result = distPlusFn(x);
+    () => getPercentiles({ chartSettings, fn, environment }),
-    if (result.tag === "Error") {
+    [environment, fn]
-      return { x: x, error: result.value };
+  );
-    }
+
-  });
-  let error2 = _.groupBy(errorData, (x) => x.error);
   return (
     <>
       <SquigglePercentilesChart
-        data={{ facet: valueData }}
+        data={{ facet: getPercentilesMemoized.percentiles }}
         actions={false}
         signalListeners={signalListeners}
       />
       {showChart}
-      {_.keysIn(error2).map((k) => (
+      {_.entries(getPercentilesMemoized.errors).map(
-        <ErrorBox heading={k}>
+        ([errorName, errorPoints]) => (
-          {`Values: [${error2[k].map((r) => r.x.toFixed(2)).join(",")}]`}
+          <ErrorBox key={errorName} heading={errorName}>
-        </ErrorBox>
+            Values:{" "}
-      ))}
+            {errorPoints
+              .map((r, i) => <NumberShower key={i} number={r.x} />)
+              .reduce((a, b) => (
+                <>
+                  {a}, {b}
+                </>
+              ))}
+          </ErrorBox>
+        )
+      )}
     </>
   );
 };

packages/components/src/components/SquiggleChart.tsx

@@ -6,14 +6,16 @@ import {
   errorValueToString,
   squiggleExpression,
   bindings,
-  samplingParams,
+  environment,
   jsImports,
   defaultImports,
   defaultBindings,
+  defaultEnvironment,
 } from "@quri/squiggle-lang";
 import { NumberShower } from "./NumberShower";
 import { DistributionChart } from "./DistributionChart";
 import { ErrorBox } from "./ErrorBox";
+import { FunctionChart, FunctionChartSettings } from "./FunctionChart";
 
 const variableBox = {
   Component: styled.div`
@@ -36,7 +38,7 @@ const variableBox = {
 interface VariableBoxProps {
   heading: string;
   children: React.ReactNode;
-  showTypes?: boolean;
+  showTypes: boolean;
 }
 
 export const VariableBox: React.FC<VariableBoxProps> = ({
@@ -54,7 +56,7 @@ export const VariableBox: React.FC<VariableBoxProps> = ({
       </variableBox.Component>
     );
   } else {
-    return <>{children}</>;
+    return <div>{children}</div>;
   }
 };
 
@@ -65,18 +67,27 @@ export interface SquiggleItemProps {
   expression: squiggleExpression;
   width?: number;
   height: number;
+  /** Whether to show a summary of statistics for distributions */
+  showSummary: boolean;
   /** Whether to show type information */
-  showTypes?: boolean;
+  showTypes: boolean;
   /** Whether to show users graph controls (scale etc) */
-  showControls?: boolean;
+  showControls: boolean;
+  /** Settings for displaying functions */
+  chartSettings: FunctionChartSettings;
+  /** Environment for further function executions */
+  environment: environment;
 }
 
 const SquiggleItem: React.FC<SquiggleItemProps> = ({
   expression,
   width,
   height,
+  showSummary,
   showTypes = false,
   showControls = false,
+  chartSettings,
+  environment,
 }: SquiggleItemProps) => {
   switch (expression.tag) {
     case "number":
@@ -103,6 +114,7 @@ const SquiggleItem: React.FC<SquiggleItemProps> = ({
             distribution={expression.value}
             height={height}
             width={width}
+            showSummary={showSummary}
             showControls={showControls}
           />
         </VariableBox>
@@ -136,13 +148,17 @@ const SquiggleItem: React.FC<SquiggleItemProps> = ({
     case "array":
       return (
         <VariableBox heading="Array" showTypes={showTypes}>
-          {expression.value.map((r) => (
+          {expression.value.map((r, i) => (
             <SquiggleItem
+              key={i}
               expression={r}
               width={width !== undefined ? width - 20 : width}
               height={50}
               showTypes={showTypes}
               showControls={showControls}
+              chartSettings={chartSettings}
+              environment={environment}
+              showSummary={showSummary}
             />
           ))}
         </VariableBox>
@@ -151,30 +167,38 @@ const SquiggleItem: React.FC<SquiggleItemProps> = ({
       return (
         <VariableBox heading="Record" showTypes={showTypes}>
           {Object.entries(expression.value).map(([key, r]) => (
-            <>
+            <div key={key}>
               <RecordKeyHeader>{key}</RecordKeyHeader>
               <SquiggleItem
                 expression={r}
                 width={width !== undefined ? width - 20 : width}
                 height={50}
                 showTypes={showTypes}
+                showSummary={showSummary}
                 showControls={showControls}
+                chartSettings={chartSettings}
+                environment={environment}
               />
-            </>
+            </div>
           ))}
         </VariableBox>
       );
     case "arraystring":
       return (
         <VariableBox heading="Array String" showTypes={showTypes}>
-          {expression.value.map((r) => `"${r}"`)}
+          {expression.value.map((r) => `"${r}"`).join(", ")}
         </VariableBox>
       );
     case "lambda":
       return (
-        <ErrorBox heading="No Viewer">
+        <FunctionChart
-          There is no viewer currently available for function types.
+          fn={expression.value}
-        </ErrorBox>
+          chartSettings={chartSettings}
+          environment={{
+            sampleCount: environment.sampleCount / 10,
+            xyPointLength: environment.xyPointLength / 10,
+          }}
+        />
       );
   }
 };
@@ -185,15 +209,9 @@ export interface SquiggleChartProps {
   /** If the output requires monte carlo sampling, the amount of samples */
   sampleCount?: number;
   /** The amount of points returned to draw the distribution */
-  outputXYPoints?: number;
+  environment?: environment;
-  kernelWidth?: number;
+  /** If the result is a function, where the function starts, ends and the amount of stops */
-  pointDistLength?: number;
+  chartSettings?: FunctionChartSettings;
-  /** If the result is a function, where the function starts */
-  diagramStart?: number;
-  /** If the result is a function, where the function ends */
-  diagramStop?: number;
-  /** If the result is a function, how many points along the function it samples */
-  diagramCount?: number;
   /** When the environment changes */
   onChange?(expr: squiggleExpression): void;
   /** CSS width of the element */
@@ -203,6 +221,8 @@ export interface SquiggleChartProps {
   bindings?: bindings;
   /** JS imported parameters */
   jsImports?: jsImports;
+  /** Whether to show a summary of the distirbution */
+  showSummary?: boolean;
   /** Whether to show type information about returns, default false */
   showTypes?: boolean;
   /** Whether to show graph controls (scale etc)*/
@@ -215,28 +235,23 @@ const ChartWrapper = styled.div`
     "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";
 `;
 
+let defaultChartSettings = { start: 0, stop: 10, count: 20 };
+
 export const SquiggleChart: React.FC<SquiggleChartProps> = ({
   squiggleString = "",
-  sampleCount = 1000,
+  environment,
-  outputXYPoints = 1000,
   onChange = () => {},
   height = 60,
   bindings = defaultBindings,
   jsImports = defaultImports,
+  showSummary = false,
   width,
   showTypes = false,
   showControls = false,
+  chartSettings = defaultChartSettings,
 }: SquiggleChartProps) => {
-  let samplingInputs: samplingParams = {
+  let expressionResult = run(squiggleString, bindings, environment, jsImports);
-    sampleCount: sampleCount,
+  let e = environment ? environment : defaultEnvironment;
-    xyPointLength: outputXYPoints,
-  };
-  let expressionResult = run(
-    squiggleString,
-    bindings,
-    samplingInputs,
-    jsImports
-  );
   let internal: JSX.Element;
   if (expressionResult.tag === "Ok") {
     let expression = expressionResult.value;
@@ -246,8 +261,11 @@ export const SquiggleChart: React.FC<SquiggleChartProps> = ({
         expression={expression}
         width={width}
         height={height}
+        showSummary={showSummary}
         showTypes={showTypes}
         showControls={showControls}
+        chartSettings={chartSettings}
+        environment={e}
       />
     );
   } else {

packages/components/src/components/SquiggleEditor.tsx

@@ -5,7 +5,7 @@ import { CodeEditor } from "./CodeEditor";
 import styled from "styled-components";
 import type {
   squiggleExpression,
-  samplingParams,
+  environment,
   bindings,
   jsImports,
 } from "@quri/squiggle-lang";
@@ -21,11 +21,7 @@ export interface SquiggleEditorProps {
   /** The input string for squiggle */
   initialSquiggleString?: string;
   /** If the output requires monte carlo sampling, the amount of samples */
-  sampleCount?: number;
+  environment?: environment;
-  /** The amount of points returned to draw the distribution */
-  outputXYPoints?: number;
-  kernelWidth?: number;
-  pointDistLength?: number;
   /** If the result is a function, where the function starts */
   diagramStart?: number;
   /** If the result is a function, where the function ends */
@@ -43,7 +39,9 @@ export interface SquiggleEditorProps {
   /** Whether to show detail about types of the returns, default false */
   showTypes?: boolean;
   /** Whether to give users access to graph controls */
-  showControls: boolean;
+  showControls?: boolean;
+  /** Whether to show a summary table */
+  showSummary?: boolean;
 }
 
 const Input = styled.div`
@@ -55,20 +53,23 @@ const Input = styled.div`
 export let SquiggleEditor: React.FC<SquiggleEditorProps> = ({
   initialSquiggleString = "",
   width,
-  sampleCount,
+  environment,
-  outputXYPoints,
+  diagramStart = 0,
-  kernelWidth,
+  diagramStop = 10,
-  pointDistLength,
+  diagramCount = 20,
-  diagramStart,
-  diagramStop,
-  diagramCount,
   onChange,
   bindings = defaultBindings,
   jsImports = defaultImports,
   showTypes = false,
   showControls = false,
+  showSummary = false,
 }: SquiggleEditorProps) => {
   let [expression, setExpression] = React.useState(initialSquiggleString);
+  let chartSettings = {
+    start: diagramStart,
+    stop: diagramStop,
+    count: diagramCount,
+  };
   return (
     <div>
       <Input>
@@ -82,19 +83,15 @@ export let SquiggleEditor: React.FC<SquiggleEditorProps> = ({
       </Input>
       <SquiggleChart
         width={width}
+        environment={environment}
         squiggleString={expression}
-        sampleCount={sampleCount}
+        chartSettings={chartSettings}
-        outputXYPoints={outputXYPoints}
-        kernelWidth={kernelWidth}
-        pointDistLength={pointDistLength}
-        diagramStart={diagramStart}
-        diagramStop={diagramStop}
-        diagramCount={diagramCount}
         onChange={onChange}
         bindings={bindings}
         jsImports={jsImports}
         showTypes={showTypes}
         showControls={showControls}
+        showSummary={showSummary}
       />
     </div>
   );
@@ -136,11 +133,7 @@ export interface SquigglePartialProps {
   /** The input string for squiggle */
   initialSquiggleString?: string;
   /** If the output requires monte carlo sampling, the amount of samples */
-  sampleCount?: number;
+  environment?: environment;
-  /** The amount of points returned to draw the distribution */
-  outputXYPoints?: number;
-  kernelWidth?: number;
-  pointDistLength?: number;
   /** If the result is a function, where the function starts */
   diagramStart?: number;
   /** If the result is a function, where the function ends */
@@ -161,14 +154,9 @@ export let SquigglePartial: React.FC<SquigglePartialProps> = ({
   initialSquiggleString = "",
   onChange,
   bindings = defaultBindings,
-  sampleCount = 1000,
+  environment,
-  outputXYPoints = 1000,
   jsImports = defaultImports,
 }: SquigglePartialProps) => {
-  let samplingInputs: samplingParams = {
-    sampleCount: sampleCount,
-    xyPointLength: outputXYPoints,
-  };
   let [expression, setExpression] = React.useState(initialSquiggleString);
   let [error, setError] = React.useState<string | null>(null);
 
@@ -176,7 +164,7 @@ export let SquigglePartial: React.FC<SquigglePartialProps> = ({
     let squiggleResult = runPartial(
       expression,
       bindings,
-      samplingInputs,
+      environment,
       jsImports
     );
     if (squiggleResult.tag == "Ok") {

packages/components/src/components/SquigglePlayground.tsx

@@ -4,6 +4,11 @@ import ReactDOM from "react-dom";
 import { SquiggleChart } from "./SquiggleChart";
 import CodeEditor from "./CodeEditor";
 import styled from "styled-components";
+import {
+  defaultBindings,
+  environment,
+  defaultImports,
+} from "@quri/squiggle-lang";
 
 interface FieldFloatProps {
   label: string;
@@ -40,18 +45,11 @@ function FieldFloat(Props: FieldFloatProps) {
   );
 }
 
-interface Props {
+interface ShowBoxProps {
-  initialSquiggleString?: string;
-  height?: number;
-  showTypes?: boolean;
-  showControls?: boolean;
-}
-
-interface Props2 {
   height: number;
 }
 
-const ShowBox = styled.div<Props2>`
+const ShowBox = styled.div<ShowBoxProps>`
   border: 1px solid #eee;
   border-radius: 2px;
   height: ${(props) => props.height};
@@ -76,12 +74,26 @@ const Row = styled.div`
 `;
 const Col = styled.div``;
 
-let SquigglePlayground: FC<Props> = ({
+interface PlaygroundProps {
+  /** The initial squiggle string to put in the playground */
+  initialSquiggleString?: string;
+  /** How many pixels high is the playground */
+  height?: number;
+  /** Whether to show the types of outputs in the playground */
+  showTypes?: boolean;
+  /** Whether to show the log scale controls in the playground */
+  showControls?: boolean;
+  /** Whether to show the summary table in the playground */
+  showSummary?: boolean;
+}
+
+let SquigglePlayground: FC<PlaygroundProps> = ({
   initialSquiggleString = "",
   height = 300,
   showTypes = false,
   showControls = false,
-}: Props) => {
+  showSummary = false,
+}: PlaygroundProps) => {
   let [squiggleString, setSquiggleString] = useState(initialSquiggleString);
   let [sampleCount, setSampleCount] = useState(1000);
   let [outputXYPoints, setOutputXYPoints] = useState(1000);
@@ -89,6 +101,15 @@ let SquigglePlayground: FC<Props> = ({
   let [diagramStart, setDiagramStart] = useState(0);
   let [diagramStop, setDiagramStop] = useState(10);
   let [diagramCount, setDiagramCount] = useState(20);
+  let chartSettings = {
+    start: diagramStart,
+    stop: diagramStop,
+    count: diagramCount,
+  };
+  let env: environment = {
+    sampleCount: sampleCount,
+    xyPointLength: outputXYPoints,
+  };
   return (
     <ShowBox height={height}>
       <Row>
@@ -105,15 +126,14 @@ let SquigglePlayground: FC<Props> = ({
           <Display maxHeight={height - 3}>
             <SquiggleChart
               squiggleString={squiggleString}
-              sampleCount={sampleCount}
+              environment={env}
-              outputXYPoints={outputXYPoints}
+              chartSettings={chartSettings}
-              diagramStart={diagramStart}
-              diagramStop={diagramStop}
-              diagramCount={diagramCount}
-              pointDistLength={pointDistLength}
               height={150}
               showTypes={showTypes}
               showControls={showControls}
+              bindings={defaultBindings}
+              jsImports={defaultImports}
+              showSummary={showSummary}
             />
           </Display>
         </Col>
@@ -122,7 +142,7 @@ let SquigglePlayground: FC<Props> = ({
   );
 };
 export default SquigglePlayground;
-export function renderSquigglePlaygroundToDom(props: Props) {
+export function renderSquigglePlaygroundToDom(props: PlaygroundProps) {
   let parent = document.createElement("div");
   ReactDOM.render(<SquigglePlayground {...props} />, parent);
   return parent;

packages/components/src/index.ts

@@ -9,3 +9,5 @@ import SquigglePlayground, {
   renderSquigglePlaygroundToDom,
 } from "./components/SquigglePlayground";
 export { SquigglePlayground, renderSquigglePlaygroundToDom };
+
+export { mergeBindings } from "@quri/squiggle-lang";

packages/components/src/stories/SquiggleChart.stories.mdx

@@ -153,6 +153,20 @@ to allow large and small numbers being printed cleanly.
   </Story>
 </Canvas>
 
+## Functions
+
+<Canvas>
+  <Story
+    name="Function"
+    args={{
+      squiggleString: "foo(t) = normal(t,2)*normal(5,3); foo",
+      width,
+    }}
+  >
+    {Template.bind({})}
+  </Story>
+</Canvas>
+
 ## Records
 
 <Canvas>

packages/components/src/vega-specs/spec-distributions.json

@@ -48,7 +48,7 @@
             "value": 0
           },
           "fill": {
-            "signal": "{gradient: 'linear', x1: 1, y1: 1, x2: 0, y2: 1, stops: [ {offset: 0.0, color: '#4C78A8'}] }"
+            "value": "#4C78A8"
           },
           "interpolate": {
             "value": "monotone"

packages/squiggle-lang/README.md

@@ -13,6 +13,10 @@ For instance, in a javascript project, you can
 yarn add @quri/squiggle-lang
 ```
 
+The `@quri/squiggle-lang` package exports a single function, `run`, which given
+a string of Squiggle code, will execute the code and return any exports and the
+environment created from the squiggle code.
+
 ```js
 import { run } from "@quri/squiggle-lang";
 run(
@@ -22,6 +26,16 @@ run(
 
 **However, for most use cases you'll prefer to use our [library of react components](https://www.npmjs.com/package/@quri/squiggle-components)**, and let your app transitively depend on `@quri/squiggle-lang`.
 
+`run` has two optional arguments. The first optional argument allows you to set
+sampling settings for Squiggle when representing distributions. The second optional
+argument allows you to pass an environment previously created by another `run`
+call. Passing this environment will mean that all previously declared variables
+in the previous environment will be made available.
+
+The return type of `run` is a bit complicated, and comes from auto generated `js`
+code that comes from rescript. We highly recommend using typescript when using
+this library to help navigate the return type.
+
 # Build for development
 
 We assume that you ran `yarn` at the monorepo level.

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

@@ -0,0 +1,57 @@
+open Jest
+open Expect
+open TestHelpers
+open FastCheck
+open Arbitrary
+open Property.Sync
+
+describe("dotSubtract", () => {
+  test("mean of normal minus exponential (unit)", () => {
+    let mean = 0.0
+    let rate = 10.0
+    exception MeanFailed
+    let dotDifference = DistributionOperation.Constructors.pointwiseSubtract(
+      ~env,
+      mkNormal(mean, 1.0),
+      mkExponential(rate),
+    )
+    let meanResult = E.R2.bind(DistributionOperation.Constructors.mean(~env), dotDifference)
+    let meanAnalytical =
+      mean -.
+      SymbolicDist.Exponential.mean({rate: rate})->E.R2.toExn(
+        "On trusted input this should never happen",
+      )
+    switch meanResult {
+    | Ok(meanValue) => meanValue->expect->toBeCloseTo(meanAnalytical)
+    | Error(_) => raise(MeanFailed)
+    }
+  })
+  /*
+    It seems like this test should work, and it's plausible that
+    there's some bug in `pointwiseSubtract`
+ */
+  Skip.test("mean of normal minus exponential (property)", () => {
+    assert_(
+      property2(float_(), floatRange(1e-5, 1e5), (mean, rate) => {
+        // We limit ourselves to stdev=1 so that the integral is trivial
+        let dotDifference = DistributionOperation.Constructors.pointwiseSubtract(
+          ~env,
+          mkNormal(mean, 1.0),
+          mkExponential(rate),
+        )
+        let meanResult = E.R2.bind(DistributionOperation.Constructors.mean(~env), dotDifference)
+        // according to algebra or random variables,
+        let meanAnalytical =
+          mean -.
+          SymbolicDist.Exponential.mean({rate: rate})->E.R2.toExn(
+            "On trusted input this should never happen",
+          )
+        switch meanResult {
+        | Ok(meanValue) => abs_float(meanValue -. meanAnalytical) /. abs_float(meanValue) < 1e-2 // 1% relative error
+        | Error(err) => err === DistributionTypes.OperationError(DivisionByZeroError)
+        }
+      }),
+    )
+    pass
+  })
+})

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

@@ -11,4 +11,15 @@ let triangularDist: DistributionTypes.genericDist = Symbolic(
 )
 let exponentialDist: DistributionTypes.genericDist = Symbolic(#Exponential({rate: 2.0}))
 let uniformDist: DistributionTypes.genericDist = Symbolic(#Uniform({low: 9.0, high: 10.0}))
+let uniformDist2: DistributionTypes.genericDist = Symbolic(#Uniform({low: 8.0, high: 11.0}))
 let floatDist: DistributionTypes.genericDist = Symbolic(#Float(1e1))
+
+exception KlFailed
+exception MixtureFailed
+let float1 = 1.0
+let float2 = 2.0
+let float3 = 3.0
+let {mkDelta} = module(TestHelpers)
+let point1 = mkDelta(float1)
+let point2 = mkDelta(float2)
+let point3 = mkDelta(float3)

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

@@ -0,0 +1,228 @@
+open Jest
+open Expect
+open TestHelpers
+open GenericDist_Fixtures
+
+// integral from low to high of 1 / (high - low) log(normal(mean, stdev)(x) / (1 / (high - low))) dx
+let klNormalUniform = (mean, stdev, low, high): float =>
+  -.Js.Math.log((high -. low) /. Js.Math.sqrt(2.0 *. MagicNumbers.Math.pi *. stdev ** 2.0)) +.
+  1.0 /.
+  stdev ** 2.0 *.
+  (mean ** 2.0 -. (high +. low) *. mean +. (low ** 2.0 +. high *. low +. high ** 2.0) /. 3.0)
+
+describe("klDivergence: continuous -> continuous -> float", () => {
+  let klDivergence = DistributionOperation.Constructors.klDivergence(~env)
+
+  let testUniform = (lowAnswer, highAnswer, lowPrediction, highPrediction) => {
+    test("of two uniforms is equal to the analytic expression", () => {
+      let answer =
+        uniformMakeR(lowAnswer, highAnswer)->E.R2.errMap(s => DistributionTypes.ArgumentError(s))
+      let prediction =
+        uniformMakeR(
+          lowPrediction,
+          highPrediction,
+        )->E.R2.errMap(s => DistributionTypes.ArgumentError(s))
+      // integral along the support of the answer of answer.pdf(x) times log of prediction.pdf(x) divided by answer.pdf(x) dx
+      let analyticalKl = Js.Math.log((highPrediction -. lowPrediction) /. (highAnswer -. lowAnswer))
+      let kl = E.R.liftJoin2(klDivergence, prediction, answer)
+      switch kl {
+      | Ok(kl') => kl'->expect->toBeSoCloseTo(analyticalKl, ~digits=7)
+      | Error(err) => {
+          Js.Console.log(DistributionTypes.Error.toString(err))
+          raise(KlFailed)
+        }
+      }
+    })
+  }
+  // The pair on the right (the answer) can be wider than the pair on the left (the prediction), but not the other way around.
+  testUniform(0.0, 1.0, -1.0, 2.0)
+  testUniform(0.0, 1.0, 0.0, 2.0) // equal left endpoints
+  testUniform(0.0, 1.0, -1.0, 1.0) // equal rightendpoints
+  testUniform(0.0, 1e1, 0.0, 1e1) // equal (klDivergence = 0)
+  // testUniform(-1.0, 1.0, 0.0, 2.0)
+
+  test("of two normals is equal to the formula", () => {
+    // This test case comes via Nuño https://github.com/quantified-uncertainty/squiggle/issues/433
+    let mean1 = 4.0
+    let mean2 = 1.0
+    let stdev1 = 4.0
+    let stdev2 = 1.0
+
+    let prediction =
+      normalMakeR(mean1, stdev1)->E.R2.errMap(s => DistributionTypes.ArgumentError(s))
+    let answer = normalMakeR(mean2, stdev2)->E.R2.errMap(s => DistributionTypes.ArgumentError(s))
+    // https://stats.stackexchange.com/questions/7440/kl-divergence-between-two-univariate-gaussians
+    let analyticalKl =
+      Js.Math.log(stdev1 /. stdev2) +.
+      (stdev2 ** 2.0 +. (mean2 -. mean1) ** 2.0) /. (2.0 *. stdev1 ** 2.0) -. 0.5
+    let kl = E.R.liftJoin2(klDivergence, prediction, answer)
+
+    switch kl {
+    | Ok(kl') => kl'->expect->toBeSoCloseTo(analyticalKl, ~digits=3)
+    | Error(err) => {
+        Js.Console.log(DistributionTypes.Error.toString(err))
+        raise(KlFailed)
+      }
+    }
+  })
+
+  test("of a normal and a uniform is equal to the formula", () => {
+    let prediction = normalDist10
+    let answer = uniformDist
+    let kl = klDivergence(prediction, answer)
+    let analyticalKl = klNormalUniform(10.0, 2.0, 9.0, 10.0)
+    switch kl {
+    | Ok(kl') => kl'->expect->toBeSoCloseTo(analyticalKl, ~digits=1)
+    | Error(err) => {
+        Js.Console.log(DistributionTypes.Error.toString(err))
+        raise(KlFailed)
+      }
+    }
+  })
+})
+
+describe("klDivergence: discrete -> discrete -> float", () => {
+  let klDivergence = DistributionOperation.Constructors.klDivergence(~env)
+  let mixture = a => DistributionTypes.DistributionOperation.Mixture(a)
+  let a' = [(point1, 1e0), (point2, 1e0)]->mixture->run
+  let b' = [(point1, 1e0), (point2, 1e0), (point3, 1e0)]->mixture->run
+  let (a, b) = switch (a', b') {
+  | (Dist(a''), Dist(b'')) => (a'', b'')
+  | _ => raise(MixtureFailed)
+  }
+  test("agrees with analytical answer when finite", () => {
+    let prediction = b
+    let answer = a
+    let kl = klDivergence(prediction, answer)
+    // Sigma_{i \in 1..2} 0.5 * log(0.5 / 0.33333)
+    let analyticalKl = Js.Math.log(3.0 /. 2.0)
+    switch kl {
+    | Ok(kl') => kl'->expect->toBeSoCloseTo(analyticalKl, ~digits=7)
+    | Error(err) =>
+      Js.Console.log(DistributionTypes.Error.toString(err))
+      raise(KlFailed)
+    }
+  })
+  test("returns infinity when infinite", () => {
+    let prediction = a
+    let answer = b
+    let kl = klDivergence(prediction, answer)
+    switch kl {
+    | Ok(kl') => kl'->expect->toEqual(infinity)
+    | Error(err) =>
+      Js.Console.log(DistributionTypes.Error.toString(err))
+      raise(KlFailed)
+    }
+  })
+})
+
+describe("klDivergence: mixed -> mixed -> float", () => {
+  let klDivergence = DistributionOperation.Constructors.klDivergence(~env)
+  let mixture' = a => DistributionTypes.DistributionOperation.Mixture(a)
+  let mixture = a => {
+    let dist' = a->mixture'->run
+    switch dist' {
+    | Dist(dist) => dist
+    | _ => raise(MixtureFailed)
+    }
+  }
+  let a = [(point1, 1.0), (uniformDist, 1.0)]->mixture
+  let b = [(point1, 1.0), (floatDist, 1.0), (normalDist10, 1.0)]->mixture
+  let c = [(point1, 1.0), (point2, 1.0), (point3, 1.0), (uniformDist, 1.0)]->mixture
+  let d =
+    [(point1, 1.0), (point2, 1.0), (point3, 1.0), (floatDist, 1.0), (uniformDist2, 1.0)]->mixture
+
+  test("finite klDivergence produces correct answer", () => {
+    let prediction = b
+    let answer = a
+    let kl = klDivergence(prediction, answer)
+    // high = 10; low = 9; mean = 10; stdev = 2
+    let analyticalKlContinuousPart = klNormalUniform(10.0, 2.0, 9.0, 10.0) /. 2.0
+    let analyticalKlDiscretePart = 1.0 /. 2.0 *. Js.Math.log(2.0 /. 1.0)
+    switch kl {
+    | Ok(kl') =>
+      kl'->expect->toBeSoCloseTo(analyticalKlContinuousPart +. analyticalKlDiscretePart, ~digits=1)
+    | Error(err) =>
+      Js.Console.log(DistributionTypes.Error.toString(err))
+      raise(KlFailed)
+    }
+  })
+  test("returns infinity when infinite", () => {
+    let prediction = a
+    let answer = b
+    let kl = klDivergence(prediction, answer)
+    switch kl {
+    | Ok(kl') => kl'->expect->toEqual(infinity)
+    | Error(err) =>
+      Js.Console.log(DistributionTypes.Error.toString(err))
+      raise(KlFailed)
+    }
+  })
+  test("finite klDivergence produces correct answer", () => {
+    let prediction = d
+    let answer = c
+    let kl = klDivergence(prediction, answer)
+    let analyticalKlContinuousPart = Js.Math.log((11.0 -. 8.0) /. (10.0 -. 9.0)) /. 4.0 // 4 = length of c' array
+    let analyticalKlDiscretePart = 3.0 /. 4.0 *. Js.Math.log(4.0 /. 3.0)
+    switch kl {
+    | Ok(kl') =>
+      kl'->expect->toBeSoCloseTo(analyticalKlContinuousPart +. analyticalKlDiscretePart, ~digits=1)
+    | Error(err) =>
+      Js.Console.log(DistributionTypes.Error.toString(err))
+      raise(KlFailed)
+    }
+  })
+})
+
+describe("combineAlongSupportOfSecondArgument0", () => {
+  // This tests the version of the function that we're NOT using. Haven't deleted the test in case we use the code later.
+  test("test on two uniforms", _ => {
+    let combineAlongSupportOfSecondArgument = XYShape.PointwiseCombination.combineAlongSupportOfSecondArgument0
+    let lowAnswer = 0.0
+    let highAnswer = 1.0
+    let lowPrediction = 0.0
+    let highPrediction = 2.0
+
+    let answer =
+      uniformMakeR(lowAnswer, highAnswer)->E.R2.errMap(s => DistributionTypes.ArgumentError(s))
+    let prediction =
+      uniformMakeR(lowPrediction, highPrediction)->E.R2.errMap(s => DistributionTypes.ArgumentError(
+        s,
+      ))
+    let answerWrapped = E.R.fmap(a => run(FromDist(ToDist(ToPointSet), a)), answer)
+    let predictionWrapped = E.R.fmap(a => run(FromDist(ToDist(ToPointSet), a)), prediction)
+
+    let interpolator = XYShape.XtoY.continuousInterpolator(#Stepwise, #UseZero)
+    let integrand = PointSetDist_Scoring.KLDivergence.integrand
+
+    let result = switch (answerWrapped, predictionWrapped) {
+    | (Ok(Dist(PointSet(Continuous(a)))), Ok(Dist(PointSet(Continuous(b))))) =>
+      Some(combineAlongSupportOfSecondArgument(integrand, interpolator, a.xyShape, b.xyShape))
+    | _ => None
+    }
+    result
+    ->expect
+    ->toEqual(
+      Some(
+        Ok({
+          xs: [
+            0.0,
+            MagicNumbers.Epsilon.ten,
+            2.0 *. MagicNumbers.Epsilon.ten,
+            1.0 -. MagicNumbers.Epsilon.ten,
+            1.0,
+            1.0 +. MagicNumbers.Epsilon.ten,
+          ],
+          ys: [
+            -0.34657359027997264,
+            -0.34657359027997264,
+            -0.34657359027997264,
+            -0.34657359027997264,
+            -0.34657359027997264,
+            infinity,
+          ],
+        }),
+      ),
+    )
+  })
+})

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

@@ -0,0 +1,38 @@
+open Jest
+open Expect
+open TestHelpers
+
+describe("Scale logarithm", () => {
+  /* These tests may not be important, because scalelog isn't normalized
+  The first one may be failing for a number of reasons.
+ */
+  Skip.test("mean of the base e scalar logarithm of an exponential(10)", () => {
+    let rate = 10.0
+    let scalelog = DistributionOperation.Constructors.scaleLogarithm(
+      ~env,
+      mkExponential(rate),
+      MagicNumbers.Math.e,
+    )
+
+    let meanResult = E.R2.bind(DistributionOperation.Constructors.mean(~env), scalelog)
+    // expected value of log of exponential distribution.
+    let meanAnalytical = Js.Math.log(rate) +. 1.0
+    switch meanResult {
+    | Ok(meanValue) => meanValue->expect->toBeCloseTo(meanAnalytical)
+    | Error(err) => err->expect->toBe(DistributionTypes.OperationError(DivisionByZeroError))
+    }
+  })
+  let low = 10.0
+  let high = 100.0
+  let scalelog = DistributionOperation.Constructors.scaleLogarithm(~env, mkUniform(low, high), 2.0)
+
+  test("mean of the base 2 scalar logarithm of a uniform(10, 100)", () => {
+    //For uniform pdf `_ => 1 / (b - a)`, the expected value of log of uniform is `integral from a to b of x * log(1 / (b -a)) dx`
+    let meanResult = E.R2.bind(DistributionOperation.Constructors.mean(~env), scalelog)
+    let meanAnalytical = -.Js.Math.log2(high -. low) /. 2.0 *. (high ** 2.0 -. low ** 2.0) // -. Js.Math.log2(high -. low)
+    switch meanResult {
+    | Ok(meanValue) => meanValue->expect->toBeCloseTo(meanAnalytical)
+    | Error(err) => err->expect->toEqual(DistributionTypes.OperationError(NegativeInfinityError))
+    }
+  })
+})

packages/squiggle-lang/__tests__/Reducer/Reducer_Dispatch/Reducer_Dispatch_BuiltIn_test.res

@@ -17,6 +17,10 @@ describe("builtin", () => {
   testEval("1-1", "Ok(0)")
   testEval("2>1", "Ok(true)")
   testEval("concat('a','b')", "Ok('ab')")
+  testEval(
+    "addOne(t)=t+1; toInternalSampleArray(mapSamples(fromSamples([1,2,3,4,5,6]), addOne))",
+    "Ok([2,3,4,5,6,7])",
+  )
 })
 
 describe("builtin exception", () => {

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

@@ -31,6 +31,9 @@ describe("eval on distribution functions", () => {
     testEval("mean(normal(5,2))", "Ok(5)")
     testEval("mean(lognormal(1,2))", "Ok(20.085536923187668)")
     testEval("mean(gamma(5,5))", "Ok(25)")
+    testEval("mean(bernoulli(0.2))", "Ok(0.2)")
+    testEval("mean(bernoulli(0.8))", "Ok(0.8)")
+    testEval("mean(logistic(5,1))", "Ok(5)")
   })
   describe("toString", () => {
     testEval("toString(normal(5,2))", "Ok('Normal(5,2)')")

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

@@ -1,4 +1,9 @@
-import { Distribution, resultMap, defaultBindings } from "../../src/js/index";
+import {
+  Distribution,
+  resultMap,
+  defaultBindings,
+  mergeBindings,
+} from "../../src/js/index";
 import { testRun, testRunPartial } from "./TestHelpers";
 
 function Ok<b>(x: b) {
@@ -66,6 +71,17 @@ describe("Partials", () => {
       value: 10,
     });
   });
+  test("Can merge bindings from three partials", () => {
+    let bindings1 = testRunPartial(`x = 1`);
+    let bindings2 = testRunPartial(`y = 2`);
+    let bindings3 = testRunPartial(`z = 3`);
+    expect(
+      testRun(`x + y + z`, mergeBindings([bindings1, bindings2, bindings3]))
+    ).toEqual({
+      tag: "number",
+      value: 6,
+    });
+  });
 });
 
 describe("JS Imports", () => {

packages/squiggle-lang/__tests__/TestHelpers.res

@@ -51,6 +51,7 @@ let mkExponential = rate => DistributionTypes.Symbolic(#Exponential({rate: rate}
 let mkUniform = (low, high) => DistributionTypes.Symbolic(#Uniform({low: low, high: high}))
 let mkCauchy = (local, scale) => DistributionTypes.Symbolic(#Cauchy({local: local, scale: scale}))
 let mkLognormal = (mu, sigma) => DistributionTypes.Symbolic(#Lognormal({mu: mu, sigma: sigma}))
+let mkDelta = x => DistributionTypes.Symbolic(#Float(x))
 
 let normalMake = SymbolicDist.Normal.make
 let betaMake = SymbolicDist.Beta.make
@@ -60,3 +61,13 @@ let cauchyMake = SymbolicDist.Cauchy.make
 let lognormalMake = SymbolicDist.Lognormal.make
 let triangularMake = SymbolicDist.Triangular.make
 let floatMake = SymbolicDist.Float.make
+
+let fmapGenDist = symbdistres => E.R.fmap(s => DistributionTypes.Symbolic(s), symbdistres)
+let normalMakeR = (mean, stdev) => fmapGenDist(SymbolicDist.Normal.make(mean, stdev))
+let betaMakeR = (alpha, beta) => fmapGenDist(SymbolicDist.Beta.make(alpha, beta))
+let exponentialMakeR = rate => fmapGenDist(SymbolicDist.Exponential.make(rate))
+let uniformMakeR = (low, high) => fmapGenDist(SymbolicDist.Uniform.make(low, high))
+let cauchyMakeR = (local, rate) => fmapGenDist(SymbolicDist.Cauchy.make(local, rate))
+let lognormalMakeR = (mu, sigma) => fmapGenDist(SymbolicDist.Lognormal.make(mu, sigma))
+let triangularMakeR = (low, mode, high) =>
+  fmapGenDist(SymbolicDist.Triangular.make(low, mode, high))

packages/squiggle-lang/__tests__/XYShape_test.res

@@ -38,19 +38,6 @@ 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 very different",
-      XYShape.logScorePoint(30, pointSetDist1, pointSetDist3),
-      Some(210.3721280423322),
-    )
-  })
   describe("integrateWithTriangles", () =>
     makeTest(
       "integrates correctly",

packages/squiggle-lang/bsconfig.json

@@ -20,7 +20,8 @@
   ],
   "suffix": ".bs.js",
   "namespace": true,
-  "bs-dependencies": ["@glennsl/rescript-jest", "bisect_ppx"],
+  "bs-dependencies": ["bisect_ppx"],
+  "bs-dev-dependencies": ["@glennsl/rescript-jest", "rescript-fast-check"],
   "gentypeconfig": {
     "language": "typescript",
     "module": "commonjs",

packages/squiggle-lang/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@quri/squiggle-lang",
-  "version": "0.2.8",
+  "version": "0.2.9",
   "homepage": "https://squiggle-language.com",
   "license": "MIT",
   "scripts": {
@@ -10,16 +10,16 @@
     "build:typescript": "tsc",
     "bundle": "webpack",
     "start": "rescript build -w -with-deps",
-    "clean": "rescript clean && rm -r dist",
+    "clean": "rescript clean && rm -rf dist",
     "test:reducer": "jest __tests__/Reducer*/",
     "benchmark": "ts-node benchmark/conversion_tests.ts",
     "test": "jest",
     "test:ts": "jest __tests__/TS/",
     "test:rescript": "jest --modulePathIgnorePatterns=__tests__/TS/*",
     "test:watch": "jest --watchAll",
-    "coverage:rescript": "rm -f *.coverage; yarn clean; BISECT_ENABLE=yes yarn build; yarn test:rescript; bisect-ppx-report html",
+    "coverage:rescript": "rm -f *.coverage && yarn clean && BISECT_ENABLE=yes yarn build && yarn test:rescript && bisect-ppx-report html",
-    "coverage:ts": "yarn clean; yarn build; nyc --reporter=lcov yarn test:ts",
+    "coverage:ts": "yarn clean && yarn build && nyc --reporter=lcov yarn test:ts",
-    "coverage:rescript:ci": "yarn clean; BISECT_ENABLE=yes yarn build; yarn test:rescript; bisect-ppx-report send-to Codecov",
+    "coverage:rescript:ci": "yarn clean && BISECT_ENABLE=yes yarn build:rescript && yarn test:rescript && bisect-ppx-report send-to Codecov",
     "coverage:ts:ci": "yarn coverage:ts && codecov",
     "lint:rescript": "./lint.sh",
     "lint:prettier": "prettier --check .",
@@ -34,34 +34,34 @@
     "Rescript"
   ],
   "author": "Quantified Uncertainty Research Institute",
-  "license": "MIT",
   "dependencies": {
-    "rescript": "^9.1.4",
+    "@stdlib/stats": "^0.0.13",
     "jstat": "^1.9.5",
+    "mathjs": "^10.5.2",
     "pdfast": "^0.2.0",
-    "mathjs": "^10.5.0"
+    "rescript": "^9.1.4"
   },
   "devDependencies": {
-    "bisect_ppx": "^2.7.1",
-    "lodash": "^4.17.21",
-    "rescript-fast-check": "^1.1.1",
     "@glennsl/rescript-jest": "^0.9.0",
     "@istanbuljs/nyc-config-typescript": "^1.0.2",
     "@types/jest": "^27.5.0",
     "babel-plugin-transform-es2015-modules-commonjs": "^6.26.2",
+    "bisect_ppx": "^2.7.1",
     "chalk": "^5.0.1",
     "codecov": "^3.8.3",
     "fast-check": "^2.25.0",
     "gentype": "^4.3.0",
     "jest": "^27.5.1",
+    "lodash": "^4.17.21",
     "moduleserve": "^0.9.1",
     "nyc": "^15.1.0",
     "reanalyze": "^2.19.0",
+    "rescript-fast-check": "^1.1.1",
     "ts-jest": "^27.1.4",
     "ts-loader": "^9.3.0",
     "ts-node": "^10.7.0",
     "typescript": "^4.6.3",
-    "webpack": "^5.72.0",
+    "webpack": "^5.72.1",
     "webpack-cli": "^4.9.2"
   },
   "source": "./src/js/index.ts",

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

@@ -35,7 +35,7 @@ import {
   Constructors_pointwiseSubtract,
   Constructors_pointwiseLogarithm,
   Constructors_pointwisePower,
-} from "../rescript/Distributions/DistributionOperation/DistributionOperation.gen";
+} from "../rescript/Distributions/DistributionOperation.gen";
 
 export type point = { x: number; y: number };
 

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

@@ -1,6 +1,5 @@
 import * as _ from "lodash";
 import {
-  samplingParams,
   environment,
   defaultEnvironment,
   evaluatePartialUsingExternalBindings,
@@ -8,6 +7,7 @@ import {
   externalBindings,
   expressionValue,
   errorValue,
+  foreignFunctionInterface,
 } from "../rescript/TypescriptInterface.gen";
 export {
   makeSampleSetDist,
@@ -15,25 +15,31 @@ export {
   distributionErrorToString,
   distributionError,
 } from "../rescript/TypescriptInterface.gen";
-export type {
+export type { errorValue, externalBindings as bindings, jsImports };
-  samplingParams,
-  errorValue,
-  externalBindings as bindings,
-  jsImports,
-};
 import {
   jsValueToBinding,
+  jsValueToExpressionValue,
   jsValue,
   rescriptExport,
   squiggleExpression,
   convertRawToTypescript,
+  lambdaValue,
 } from "./rescript_interop";
 import { result, resultMap, tag, tagged } from "./types";
 import { Distribution, shape } from "./distribution";
 
-export { Distribution, squiggleExpression, result, resultMap, shape };
+export {
+  Distribution,
+  squiggleExpression,
+  result,
+  resultMap,
+  shape,
+  lambdaValue,
+  environment,
+  defaultEnvironment,
+};
 
-export let defaultSamplingInputs: samplingParams = {
+export let defaultSamplingInputs: environment = {
   sampleCount: 10000,
   xyPointLength: 10000,
 };
@@ -48,7 +54,7 @@ export function run(
   let i = imports ? imports : defaultImports;
   let e = environment ? environment : defaultEnvironment;
   let res: result<expressionValue, errorValue> = evaluateUsingOptions(
-    { externalBindings: mergeImports(b, i), environment: e },
+    { externalBindings: mergeImportsWithBindings(b, i), environment: e },
     squiggleString
   );
   return resultMap(res, (x) => createTsExport(x, e));
@@ -67,12 +73,26 @@ export function runPartial(
 
   return evaluatePartialUsingExternalBindings(
     squiggleString,
-    mergeImports(b, i),
+    mergeImportsWithBindings(b, i),
     e
   );
 }
 
-function mergeImports(
+export function runForeign(
+  fn: lambdaValue,
+  args: jsValue[],
+  environment?: environment
+): result<squiggleExpression, errorValue> {
+  let e = environment ? environment : defaultEnvironment;
+  let res: result<expressionValue, errorValue> = foreignFunctionInterface(
+    fn,
+    args.map(jsValueToExpressionValue),
+    e
+  );
+  return resultMap(res, (x) => createTsExport(x, e));
+}
+
+function mergeImportsWithBindings(
   bindings: externalBindings,
   imports: jsImports
 ): externalBindings {
@@ -90,6 +110,12 @@ type jsImports = { [key: string]: jsValue };
 export let defaultImports: jsImports = {};
 export let defaultBindings: externalBindings = {};
 
+export function mergeBindings(
+  allBindings: externalBindings[]
+): externalBindings {
+  return allBindings.reduce((acc, x) => ({ ...acc, ...x }));
+}
+
 function createTsExport(
   x: expressionValue,
   environment: environment

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

@@ -1,5 +1,6 @@
 import * as _ from "lodash";
 import {
+  expressionValue,
   mixedShape,
   sampleSetDist,
   genericDist,
@@ -87,6 +88,8 @@ export type squiggleExpression =
   | tagged<"number", number>
   | tagged<"record", { [key: string]: squiggleExpression }>;
 
+export { lambdaValue };
+
 export function convertRawToTypescript(
   result: rescriptExport,
   environment: environment
@@ -168,3 +171,21 @@ export function jsValueToBinding(value: jsValue): rescriptExport {
     return { TAG: 7, _0: _.mapValues(value, jsValueToBinding) };
   }
 }
+
+export function jsValueToExpressionValue(value: jsValue): expressionValue {
+  if (typeof value === "boolean") {
+    return { tag: "EvBool", value: value as boolean };
+  } else if (typeof value === "string") {
+    return { tag: "EvString", value: value as string };
+  } else if (typeof value === "number") {
+    return { tag: "EvNumber", value: value as number };
+  } else if (Array.isArray(value)) {
+    return { tag: "EvArray", value: value.map(jsValueToExpressionValue) };
+  } else {
+    // Record
+    return {
+      tag: "EvRecord",
+      value: _.mapValues(value, jsValueToExpressionValue),
+    };
+  }
+}

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

@@ -10,14 +10,15 @@ type env = {
 }
 
 let defaultEnv = {
-  sampleCount: 10000,
+  sampleCount: MagicNumbers.Environment.defaultSampleCount,
-  xyPointLength: 10000,
+  xyPointLength: MagicNumbers.Environment.defaultXYPointLength,
 }
 
 type outputType =
   | Dist(genericDist)
   | Float(float)
   | String(string)
+  | FloatArray(array<float>)
   | Bool(bool)
   | GenDistError(error)
 
@@ -128,7 +129,7 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
   let fromDistFn = (
     subFnName: DistributionTypes.DistributionOperation.fromDist,
     dist: genericDist,
-  ) => {
+  ): outputType => {
     let response = switch subFnName {
     | ToFloat(distToFloatOperation) =>
       GenericDist.toFloatOperation(dist, ~toPointSetFn, ~distToFloatOperation)
@@ -144,6 +145,19 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
         Dist(dist)
       }
     | ToDist(Normalize) => dist->GenericDist.normalize->Dist
+    | ToScore(KLDivergence(t2)) =>
+      GenericDist.Score.klDivergence(dist, t2, ~toPointSetFn)
+      ->E.R2.fmap(r => Float(r))
+      ->OutputLocal.fromResult
+    | ToScore(LogScore(answer, prior)) =>
+      GenericDist.Score.logScoreWithPointResolution(
+        ~prediction=dist,
+        ~answer,
+        ~prior,
+        ~toPointSetFn,
+      )
+      ->E.R2.fmap(r => Float(r))
+      ->OutputLocal.fromResult
     | ToBool(IsNormalized) => dist->GenericDist.isNormalized->Bool
     | ToDist(Truncate(leftCutoff, rightCutoff)) =>
       GenericDist.truncate(~toPointSetFn, ~leftCutoff, ~rightCutoff, dist, ())
@@ -159,6 +173,15 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
       ->GenericDist.toPointSet(~xyPointLength, ~sampleCount, ())
       ->E.R2.fmap(r => Dist(PointSet(r)))
       ->OutputLocal.fromResult
+    | ToDist(Scale(#LogarithmWithThreshold(eps), f)) =>
+      dist
+      ->GenericDist.pointwiseCombinationFloat(
+        ~toPointSetFn,
+        ~algebraicCombination=#LogarithmWithThreshold(eps),
+        ~f,
+      )
+      ->E.R2.fmap(r => Dist(r))
+      ->OutputLocal.fromResult
     | ToDist(Scale(#Logarithm, f)) =>
       dist
       ->GenericDist.pointwiseCombinationFloat(~toPointSetFn, ~algebraicCombination=#Logarithm, ~f)
@@ -248,6 +271,13 @@ module Constructors = {
   let pdf = (~env, dist, f) => C.pdf(dist, f)->run(~env)->toFloatR
   let normalize = (~env, dist) => C.normalize(dist)->run(~env)->toDistR
   let isNormalized = (~env, dist) => C.isNormalized(dist)->run(~env)->toBoolR
+  let klDivergence = (~env, dist1, dist2) => C.klDivergence(dist1, dist2)->run(~env)->toFloatR
+  let logScoreWithPointResolution = (
+    ~env,
+    ~prediction: DistributionTypes.genericDist,
+    ~answer: float,
+    ~prior: option<DistributionTypes.genericDist>,
+  ) => C.logScoreWithPointResolution(~prediction, ~answer, ~prior)->run(~env)->toFloatR
   let toPointSet = (~env, dist) => C.toPointSet(dist)->run(~env)->toDistR
   let toSampleSet = (~env, dist, n) => C.toSampleSet(dist, n)->run(~env)->toDistR
   let fromSamples = (~env, xs) => C.fromSamples(xs)->run(~env)->toDistR
@@ -266,6 +296,8 @@ module Constructors = {
   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 scalePower = (~env, dist, n) => C.scalePower(dist, n)->run(~env)->toDistR
+  let scaleLogarithm = (~env, dist, n) => C.scaleLogarithm(dist, n)->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

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

@@ -14,6 +14,7 @@ type outputType =
   | Dist(genericDist)
   | Float(float)
   | String(string)
+  | FloatArray(array<float>)
   | Bool(bool)
   | GenDistError(error)
 
@@ -60,6 +61,15 @@ module Constructors: {
   @genType
   let isNormalized: (~env: env, genericDist) => result<bool, error>
   @genType
+  let klDivergence: (~env: env, genericDist, genericDist) => result<float, error>
+  @genType
+  let logScoreWithPointResolution: (
+    ~env: env,
+    ~prediction: genericDist,
+    ~answer: float,
+    ~prior: option<genericDist>,
+  ) => result<float, error>
+  @genType
   let toPointSet: (~env: env, genericDist) => result<genericDist, error>
   @genType
   let toSampleSet: (~env: env, genericDist, int) => result<genericDist, error>
@@ -86,6 +96,10 @@ module Constructors: {
   @genType
   let algebraicPower: (~env: env, genericDist, genericDist) => result<genericDist, error>
   @genType
+  let scaleLogarithm: (~env: env, genericDist, float) => result<genericDist, error>
+  @genType
+  let scalePower: (~env: env, genericDist, float) => result<genericDist, error>
+  @genType
   let pointwiseAdd: (~env: env, genericDist, genericDist) => result<genericDist, error>
   @genType
   let pointwiseMultiply: (~env: env, genericDist, genericDist) => result<genericDist, error>

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

@@ -37,6 +37,7 @@ module Error = {
     | LogarithmOfDistributionError(s) => `Logarithm of input error: ${s}`
     | SampleSetError(TooFewSamples) => "Too Few Samples"
     | SampleSetError(NonNumericInput(err)) => `Found a non-number in input: ${err}`
+    | SampleSetError(OperationError(err)) => Operation.Error.toString(err)
     | OperationError(err) => Operation.Error.toString(err)
     | PointSetConversionError(err) => SampleSetDist.pointsetConversionErrorToString(err)
     | SparklineError(err) => PointSetTypes.sparklineErrorToString(err)
@@ -72,6 +73,7 @@ module DistributionOperation = {
   type toScaleFn = [
     | #Power
     | #Logarithm
+    | #LogarithmWithThreshold(float)
   ]
 
   type toDist =
@@ -90,9 +92,12 @@ module DistributionOperation = {
     | ToString
     | ToSparkline(int)
 
+  type toScore = KLDivergence(genericDist) | LogScore(float, option<genericDist>)
+
   type fromDist =
     | ToFloat(toFloat)
     | ToDist(toDist)
+    | ToScore(toScore)
     | ToDistCombination(direction, Operation.Algebraic.t, [#Dist(genericDist) | #Float(float)])
     | ToString(toString)
     | ToBool(toBool)
@@ -115,6 +120,8 @@ module DistributionOperation = {
     | ToFloat(#Pdf(r)) => `pdf(${E.Float.toFixed(r)})`
     | ToFloat(#Sample) => `sample`
     | ToFloat(#IntegralSum) => `integralSum`
+    | ToScore(KLDivergence(_)) => `klDivergence`
+    | ToScore(LogScore(x, _)) => `logScore against ${E.Float.toFixed(x)}`
     | ToDist(Normalize) => `normalize`
     | ToDist(ToPointSet) => `toPointSet`
     | ToDist(ToSampleSet(r)) => `toSampleSet(${E.I.toString(r)})`
@@ -122,6 +129,8 @@ module DistributionOperation = {
     | ToDist(Inspect) => `inspect`
     | ToDist(Scale(#Power, r)) => `scalePower(${E.Float.toFixed(r)})`
     | ToDist(Scale(#Logarithm, r)) => `scaleLog(${E.Float.toFixed(r)})`
+    | ToDist(Scale(#LogarithmWithThreshold(eps), r)) =>
+      `scaleLogWithThreshold(${E.Float.toFixed(r)}, epsilon=${E.Float.toFixed(eps)})`
     | ToString(ToString) => `toString`
     | ToString(ToSparkline(n)) => `toSparkline(${E.I.toString(n)})`
     | ToBool(IsNormalized) => `isNormalized`
@@ -153,8 +162,17 @@ module Constructors = {
     let fromSamples = (xs): t => FromSamples(xs)
     let truncate = (dist, left, right): t => FromDist(ToDist(Truncate(left, right)), dist)
     let inspect = (dist): t => FromDist(ToDist(Inspect), dist)
+    let klDivergence = (dist1, dist2): t => FromDist(ToScore(KLDivergence(dist2)), dist1)
+    let logScoreWithPointResolution = (~prediction, ~answer, ~prior): t => FromDist(
+      ToScore(LogScore(answer, prior)),
+      prediction,
+    )
     let scalePower = (dist, n): t => FromDist(ToDist(Scale(#Power, n)), dist)
     let scaleLogarithm = (dist, n): t => FromDist(ToDist(Scale(#Logarithm, n)), dist)
+    let scaleLogarithmWithThreshold = (dist, n, eps): t => FromDist(
+      ToDist(Scale(#LogarithmWithThreshold(eps), n)),
+      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(

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

@@ -59,6 +59,46 @@ let integralEndY = (t: t): float =>
 
 let isNormalized = (t: t): bool => Js.Math.abs_float(integralEndY(t) -. 1.0) < 1e-7
 
+module Score = {
+  let klDivergence = (prediction, answer, ~toPointSetFn: toPointSetFn): result<float, error> => {
+    let pointSets = E.R.merge(toPointSetFn(prediction), toPointSetFn(answer))
+    pointSets |> E.R2.bind(((predi, ans)) =>
+      PointSetDist.T.klDivergence(predi, ans)->E.R2.errMap(x => DistributionTypes.OperationError(x))
+    )
+  }
+
+  let logScoreWithPointResolution = (
+    ~prediction: DistributionTypes.genericDist,
+    ~answer: float,
+    ~prior: option<DistributionTypes.genericDist>,
+    ~toPointSetFn: toPointSetFn,
+  ): result<float, error> => {
+    switch prior {
+    | Some(prior') =>
+      E.R.merge(toPointSetFn(prior'), toPointSetFn(prediction))->E.R.bind(((
+        prior'',
+        prediction'',
+      )) =>
+        PointSetDist.T.logScoreWithPointResolution(
+          ~prediction=prediction'',
+          ~answer,
+          ~prior=prior''->Some,
+        )->E.R2.errMap(x => DistributionTypes.OperationError(x))
+      )
+    | None =>
+      prediction
+      ->toPointSetFn
+      ->E.R.bind(x =>
+        PointSetDist.T.logScoreWithPointResolution(
+          ~prediction=x,
+          ~answer,
+          ~prior=None,
+        )->E.R2.errMap(x => DistributionTypes.OperationError(x))
+      )
+    }
+  }
+}
+
 let toFloatOperation = (
   t,
   ~toPointSetFn: toPointSetFn,
@@ -384,14 +424,12 @@ let pointwiseCombinationFloat = (
   ~algebraicCombination: Operation.algebraicOperation,
   ~f: float,
 ): result<t, error> => {
-  let m = switch algebraicCombination {
+  let executeCombination = arithOp =>
-  | #Add | #Subtract => Error(DistributionTypes.DistributionVerticalShiftIsInvalid)
-  | (#Multiply | #Divide | #Power | #Logarithm) as arithmeticOperation =>
     toPointSetFn(t)->E.R.bind(t => {
       //TODO: Move to PointSet codebase
-      let fn = (secondary, main) => Operation.Scale.toFn(arithmeticOperation, main, secondary)
+      let fn = (secondary, main) => Operation.Scale.toFn(arithOp, main, secondary)
-      let integralSumCacheFn = Operation.Scale.toIntegralSumCacheFn(arithmeticOperation)
+      let integralSumCacheFn = Operation.Scale.toIntegralSumCacheFn(arithOp)
-      let integralCacheFn = Operation.Scale.toIntegralCacheFn(arithmeticOperation)
+      let integralCacheFn = Operation.Scale.toIntegralCacheFn(arithOp)
       PointSetDist.T.mapYResult(
         ~integralSumCacheFn=integralSumCacheFn(f),
         ~integralCacheFn=integralCacheFn(f),
@@ -399,6 +437,11 @@ let pointwiseCombinationFloat = (
         t,
       )->E.R2.errMap(x => DistributionTypes.OperationError(x))
     })
+  let m = switch algebraicCombination {
+  | #Add | #Subtract => Error(DistributionTypes.DistributionVerticalShiftIsInvalid)
+  | (#Multiply | #Divide | #Power | #Logarithm) as arithmeticOperation =>
+    executeCombination(arithmeticOperation)
+  | #LogarithmWithThreshold(eps) => executeCombination(#LogarithmWithThreshold(eps))
   }
   m->E.R2.fmap(r => DistributionTypes.PointSet(r))
 }

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

@@ -23,6 +23,16 @@ let toFloatOperation: (
   ~distToFloatOperation: DistributionTypes.DistributionOperation.toFloat,
 ) => result<float, error>
 
+module Score: {
+  let klDivergence: (t, t, ~toPointSetFn: toPointSetFn) => result<float, error>
+  let logScoreWithPointResolution: (
+    ~prediction: t,
+    ~answer: float,
+    ~prior: option<t>,
+    ~toPointSetFn: toPointSetFn,
+  ) => result<float, error>
+}
+
 @genType
 let toPointSet: (
   t,

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

@@ -86,6 +86,7 @@ let stepwiseToLinear = (t: t): t =>
 
 // Note: This results in a distribution with as many points as the sum of those in t1 and t2.
 let combinePointwise = (
+  ~combiner=XYShape.PointwiseCombination.combine,
   ~integralSumCachesFn=(_, _) => None,
   ~distributionType: PointSetTypes.distributionType=#PDF,
   fn: (float, float) => result<float, Operation.Error.t>,
@@ -119,7 +120,7 @@ let combinePointwise = (
 
   let interpolator = XYShape.XtoY.continuousInterpolator(t1.interpolation, extrapolation)
 
-  XYShape.PointwiseCombination.combine(fn, interpolator, t1.xyShape, t2.xyShape)->E.R2.fmap(x =>
+  combiner(fn, interpolator, t1.xyShape, t2.xyShape)->E.R2.fmap(x =>
     make(~integralSumCache=combinedIntegralSum, x)
   )
 }
@@ -269,11 +270,26 @@ module T = Dist({
   }
   let variance = (t: t): float =>
     XYShape.Analysis.getVarianceDangerously(t, mean, Analysis.getMeanOfSquares)
+
+  let klDivergence = (prediction: t, answer: t) => {
+    let newShape = XYShape.PointwiseCombination.combineAlongSupportOfSecondArgument(
+      PointSetDist_Scoring.KLDivergence.integrand,
+      prediction.xyShape,
+      answer.xyShape,
+    )
+    newShape->E.R2.fmap(x => x->make->integralEndY)
+  }
+  let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
+    let priorPdf = prior->E.O2.fmap((shape, x) => XYShape.XtoY.linear(x, shape.xyShape))
+    let predictionPdf = x => XYShape.XtoY.linear(x, prediction.xyShape)
+    PointSetDist_Scoring.LogScoreWithPointResolution.score(~priorPdf, ~predictionPdf, ~answer)
+  }
 })
 
 let isNormalized = (t: t): bool => {
   let areaUnderIntegral = t |> updateIntegralCache(Some(T.integral(t))) |> T.integralEndY
-  areaUnderIntegral < 1. +. 1e-7 && areaUnderIntegral > 1. -. 1e-7
+  areaUnderIntegral < 1. +. MagicNumbers.Epsilon.seven &&
+    areaUnderIntegral > 1. -. MagicNumbers.Epsilon.seven
 }
 
 let downsampleEquallyOverX = (length, t): t =>

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

@@ -33,29 +33,22 @@ let shapeFn = (fn, t: t) => t |> getShape |> fn
 let lastY = (t: t) => t |> getShape |> XYShape.T.lastY
 
 let combinePointwise = (
+  ~combiner=XYShape.PointwiseCombination.combine,
   ~integralSumCachesFn=(_, _) => None,
-  fn,
+  ~fn=(a, b) => Ok(a +. b),
   t1: PointSetTypes.discreteShape,
   t2: PointSetTypes.discreteShape,
 ): result<PointSetTypes.discreteShape, 'e> => {
-  let combinedIntegralSum = Common.combineIntegralSums(
+  //  let combinedIntegralSum = Common.combineIntegralSums(
-    integralSumCachesFn,
+  //    integralSumCachesFn,
-    t1.integralSumCache,
+  //    t1.integralSumCache,
-    t2.integralSumCache,
+  //    t2.integralSumCache,
-  )
+  //  )
 
   // TODO: does it ever make sense to pointwise combine the integrals here?
   // It could be done for pointwise additions, but is that ever needed?
 
-  make(
+  combiner(fn, XYShape.XtoY.discreteInterpolator, t1.xyShape, t2.xyShape)->E.R2.fmap(make)
-    ~integralSumCache=combinedIntegralSum,
-    XYShape.PointwiseCombination.combine(
-      fn,
-      XYShape.XtoY.discreteInterpolator,
-      t1.xyShape,
-      t2.xyShape,
-    )->E.R.toExn("Addition operation should never fail", _),
-  )->Ok
 }
 
 let reduce = (
@@ -63,7 +56,7 @@ let reduce = (
   fn: (float, float) => result<float, 'e>,
   discreteShapes: array<PointSetTypes.discreteShape>,
 ): result<t, 'e> => {
-  let merge = combinePointwise(~integralSumCachesFn, fn)
+  let merge = combinePointwise(~integralSumCachesFn, ~fn)
   discreteShapes |> E.A.R.foldM(merge, empty)
 }
 
@@ -228,4 +221,15 @@ module T = Dist({
     let getMeanOfSquares = t => t |> shapeMap(XYShape.T.square) |> mean
     XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares)
   }
+
+  let klDivergence = (prediction: t, answer: t) => {
+    combinePointwise(
+      ~fn=PointSetDist_Scoring.KLDivergence.integrand,
+      prediction,
+      answer,
+    )->E.R2.fmap(integralEndY)
+  }
+  let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
+    Error(Operation.NotYetImplemented)
+  }
 })

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

@@ -33,6 +33,12 @@ module type dist = {
 
   let mean: t => float
   let variance: t => float
+  let klDivergence: (t, t) => result<float, Operation.Error.t>
+  let logScoreWithPointResolution: (
+    ~prediction: t,
+    ~answer: float,
+    ~prior: option<t>,
+  ) => result<float, Operation.Error.t>
 }
 
 module Dist = (T: dist) => {
@@ -55,6 +61,8 @@ module Dist = (T: dist) => {
   let mean = T.mean
   let variance = T.variance
   let integralEndY = T.integralEndY
+  let klDivergence = T.klDivergence
+  let logScoreWithPointResolution = T.logScoreWithPointResolution
 
   let updateIntegralCache = T.updateIntegralCache
 

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

@@ -36,6 +36,47 @@ let updateIntegralCache = (integralCache, t: t): t => {
   integralCache: integralCache,
 }
 
+let combinePointwise = (
+  ~integralSumCachesFn=(_, _) => None,
+  ~integralCachesFn=(_, _) => None,
+  fn: (float, float) => result<float, 'e>,
+  t1: t,
+  t2: t,
+): result<t, 'e> => {
+  let reducedDiscrete =
+    [t1, t2]
+    |> E.A.fmap(toDiscrete)
+    |> E.A.O.concatSomes
+    |> Discrete.reduce(~integralSumCachesFn, fn)
+    |> E.R.toExn("Theoretically unreachable state")
+
+  let reducedContinuous =
+    [t1, t2]
+    |> E.A.fmap(toContinuous)
+    |> E.A.O.concatSomes
+    |> Continuous.reduce(~integralSumCachesFn, fn)
+
+  let combinedIntegralSum = Common.combineIntegralSums(
+    integralSumCachesFn,
+    t1.integralSumCache,
+    t2.integralSumCache,
+  )
+
+  let combinedIntegral = Common.combineIntegrals(
+    integralCachesFn,
+    t1.integralCache,
+    t2.integralCache,
+  )
+  reducedContinuous->E.R2.fmap(continuous =>
+    make(
+      ~integralSumCache=combinedIntegralSum,
+      ~integralCache=combinedIntegral,
+      ~discrete=reducedDiscrete,
+      ~continuous,
+    )
+  )
+}
+
 module T = Dist({
   type t = PointSetTypes.mixedShape
   type integral = PointSetTypes.continuousShape
@@ -259,6 +300,15 @@ module T = Dist({
     | _ => XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares)
     }
   }
+
+  let klDivergence = (prediction: t, answer: t) => {
+    let klDiscretePart = Discrete.T.klDivergence(prediction.discrete, answer.discrete)
+    let klContinuousPart = Continuous.T.klDivergence(prediction.continuous, answer.continuous)
+    E.R.merge(klDiscretePart, klContinuousPart)->E.R2.fmap(t => fst(t) +. snd(t))
+  }
+  let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
+    Error(Operation.NotYetImplemented)
+  }
 })
 
 let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t): t => {

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

@@ -86,7 +86,7 @@ let combinePointwise = (
   | (Discrete(m1), Discrete(m2)) =>
     Discrete.combinePointwise(
       ~integralSumCachesFn,
-      fn,
+      ~fn,
       m1,
       m2,
     )->E.R2.fmap(x => PointSetTypes.Discrete(x))
@@ -195,6 +195,23 @@ module T = Dist({
     | Discrete(m) => Discrete.T.variance(m)
     | Continuous(m) => Continuous.T.variance(m)
     }
+
+  let klDivergence = (prediction: t, answer: t) =>
+    switch (prediction, answer) {
+    | (Continuous(t1), Continuous(t2)) => Continuous.T.klDivergence(t1, t2)
+    | (Discrete(t1), Discrete(t2)) => Discrete.T.klDivergence(t1, t2)
+    | (m1, m2) => Mixed.T.klDivergence(m1->toMixed, m2->toMixed)
+    }
+
+  let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
+    switch (prior, prediction) {
+    | (Some(Continuous(t1)), Continuous(t2)) =>
+      Continuous.T.logScoreWithPointResolution(~prediction=t2, ~answer, ~prior=t1->Some)
+    | (None, Continuous(t2)) =>
+      Continuous.T.logScoreWithPointResolution(~prediction=t2, ~answer, ~prior=None)
+    | _ => Error(Operation.NotYetImplemented)
+    }
+  }
 })
 
 let pdf = (f: float, t: t) => {

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

@@ -0,0 +1,46 @@
+module KLDivergence = {
+  let logFn = Js.Math.log // base e
+  let integrand = (predictionElement: float, answerElement: float): result<
+    float,
+    Operation.Error.t,
+  > =>
+    // We decided that negative infinity, not an error at answerElement = 0.0, is a desirable value.
+    if answerElement == 0.0 {
+      Ok(0.0)
+    } else if predictionElement == 0.0 {
+      Ok(infinity)
+    } else {
+      let quot = predictionElement /. answerElement
+      quot < 0.0 ? Error(Operation.ComplexNumberError) : Ok(-.answerElement *. logFn(quot))
+    }
+}
+
+module LogScoreWithPointResolution = {
+  let logFn = Js.Math.log
+  let score = (
+    ~priorPdf: option<float => float>,
+    ~predictionPdf: float => float,
+    ~answer: float,
+  ): result<float, Operation.Error.t> => {
+    let numerator = answer->predictionPdf
+    if numerator < 0.0 {
+      Operation.PdfInvalidError->Error
+    } else if numerator == 0.0 {
+      infinity->Ok
+    } else {
+      -.(
+        switch priorPdf {
+        | None => numerator->logFn
+        | Some(f) => {
+            let priorDensityOfAnswer = f(answer)
+            if priorDensityOfAnswer == 0.0 {
+              neg_infinity
+            } else {
+              (numerator /. priorDensityOfAnswer)->logFn
+            }
+          }
+        }
+      )->Ok
+    }
+  }
+}

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

@@ -1,12 +1,14 @@
 @genType
 module Error = {
   @genType
-  type sampleSetError = TooFewSamples | NonNumericInput(string)
+  type sampleSetError =
+    TooFewSamples | NonNumericInput(string) | OperationError(Operation.operationError)
 
   let sampleSetErrorToString = (err: sampleSetError): string =>
     switch err {
     | TooFewSamples => "Too few samples when constructing sample set"
     | NonNumericInput(err) => `Found a non-number in input: ${err}`
+    | OperationError(err) => Operation.Error.toString(err)
     }
 
   @genType
@@ -16,6 +18,8 @@ module Error = {
     switch err {
     | TooFewSamplesForConversionToPointSet => "Too Few Samples to convert to point set"
     }
+
+  let fromOperationError = e => OperationError(e)
 }
 
 include Error
@@ -83,6 +87,14 @@ let sampleN = (t: t, n) => {
   }
 }
 
+let samplesMap = (~fn: float => result<float, Operation.Error.t>, t: t): result<
+  t,
+  sampleSetError,
+> => {
+  let samples = T.get(t)->E.A2.fmap(fn)
+  E.A.R.firstErrorOrOpen(samples)->E.R2.errMap(Error.fromOperationError) |> E.R2.bind(make)
+}
+
 //TODO: Figure out what to do if distributions are different lengths. ``zip`` is kind of inelegant for this.
 let map2 = (~fn: (float, float) => result<float, Operation.Error.t>, ~t1: t, ~t2: t): result<
   t,
@@ -96,7 +108,7 @@ let map2 = (~fn: (float, float) => result<float, Operation.Error.t>, ~t1: t, ~t2
   // I could prove this to the type system (say, creating a {first: float, second: float, ..., fifth: float, rest: array<float>}
   // But doing so would take too much time, so I'll leave it as an assertion
   E.A.R.firstErrorOrOpen(samples)->E.R2.fmap(x =>
-    E.R.toExn("Input of samples should be larger than 5", make(x))
+    E.R.toExnFnString(Error.sampleSetErrorToString, make(x))
   )
 }
 

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

@@ -216,6 +216,50 @@ module Uniform = {
   }
 }
 
+module Logistic = {
+  type t = logistic
+  let make = (location, scale) =>
+    scale > 0.0
+      ? Ok(#Logistic({location: location, scale: scale}))
+      : Error("Scale must be positive")
+
+  let pdf = (x, t: t) => Stdlib.Logistic.pdf(x, t.location, t.scale)
+  let cdf = (x, t: t) => Stdlib.Logistic.cdf(x, t.location, t.scale)
+  let inv = (p, t: t) => Stdlib.Logistic.quantile(p, t.location, t.scale)
+  let sample = (t: t) => {
+    let s = Uniform.sample({low: 0.0, high: 1.0})
+    inv(s, t)
+  }
+  let mean = (t: t) => Ok(Stdlib.Logistic.mean(t.location, t.scale))
+  let toString = ({location, scale}: t) => j`Logistic($location,$scale)`
+}
+
+module Bernoulli = {
+  type t = bernoulli
+  let make = p =>
+    p >= 0.0 && p <= 1.0
+      ? Ok(#Bernoulli({p: p}))
+      : Error("Bernoulli parameter must be between 0 and 1")
+  let pmf = (x, t: t) => Stdlib.Bernoulli.pmf(x, t.p)
+
+  //Bernoulli is a discrete distribution, so it doesn't really have a pdf().
+  //We fake this for now with the pmf function, but this should be fixed at some point.
+  let pdf = (x, t: t) => Stdlib.Bernoulli.pmf(x, t.p)
+  let cdf = (x, t: t) => Stdlib.Bernoulli.cdf(x, t.p)
+  let inv = (p, t: t) => Stdlib.Bernoulli.quantile(p, t.p)
+  let mean = (t: t) => Ok(Stdlib.Bernoulli.mean(t.p))
+  let min = (t: t) => t.p == 1.0 ? 1.0 : 0.0
+  let max = (t: t) => t.p == 0.0 ? 0.0 : 1.0
+  let sample = (t: t) => {
+    let s = Uniform.sample({low: 0.0, high: 1.0})
+    inv(s, t)
+  }
+  let toString = ({p}: t) => j`Bernoulli($p)`
+  let toPointSetDist = ({p}: t): PointSetTypes.pointSetDist => Discrete(
+    Discrete.make(~integralSumCache=Some(1.0), {xs: [0.0, 1.0], ys: [1.0 -. p, p]}),
+  )
+}
+
 module Gamma = {
   type t = gamma
   let make = (shape: float, scale: float) => {
@@ -252,6 +296,9 @@ module Float = {
   let mean = (t: t) => Ok(t)
   let sample = (t: t) => t
   let toString = (t: t) => j`Delta($t)`
+  let toPointSetDist = (t: t): PointSetTypes.pointSetDist => Discrete(
+    Discrete.make(~integralSumCache=Some(1.0), {xs: [t], ys: [1.0]}),
+  )
 }
 
 module From90thPercentile = {
@@ -275,9 +322,11 @@ module T = {
     | #Cauchy(n) => Cauchy.pdf(x, n)
     | #Gamma(n) => Gamma.pdf(x, n)
     | #Lognormal(n) => Lognormal.pdf(x, n)
+    | #Logistic(n) => Logistic.pdf(x, n)
     | #Uniform(n) => Uniform.pdf(x, n)
     | #Beta(n) => Beta.pdf(x, n)
     | #Float(n) => Float.pdf(x, n)
+    | #Bernoulli(n) => Bernoulli.pdf(x, n)
     }
 
   let cdf = (x, dist) =>
@@ -287,10 +336,12 @@ module T = {
     | #Exponential(n) => Exponential.cdf(x, n)
     | #Cauchy(n) => Cauchy.cdf(x, n)
     | #Gamma(n) => Gamma.cdf(x, n)
+    | #Logistic(n) => Logistic.cdf(x, n)
     | #Lognormal(n) => Lognormal.cdf(x, n)
     | #Uniform(n) => Uniform.cdf(x, n)
     | #Beta(n) => Beta.cdf(x, n)
     | #Float(n) => Float.cdf(x, n)
+    | #Bernoulli(n) => Bernoulli.cdf(x, n)
     }
 
   let inv = (x, dist) =>
@@ -300,10 +351,12 @@ module T = {
     | #Exponential(n) => Exponential.inv(x, n)
     | #Cauchy(n) => Cauchy.inv(x, n)
     | #Gamma(n) => Gamma.inv(x, n)
+    | #Logistic(n) => Logistic.inv(x, n)
     | #Lognormal(n) => Lognormal.inv(x, n)
     | #Uniform(n) => Uniform.inv(x, n)
     | #Beta(n) => Beta.inv(x, n)
     | #Float(n) => Float.inv(x, n)
+    | #Bernoulli(n) => Bernoulli.inv(x, n)
     }
 
   let sample: symbolicDist => float = x =>
@@ -313,10 +366,12 @@ module T = {
     | #Exponential(n) => Exponential.sample(n)
     | #Cauchy(n) => Cauchy.sample(n)
     | #Gamma(n) => Gamma.sample(n)
+    | #Logistic(n) => Logistic.sample(n)
     | #Lognormal(n) => Lognormal.sample(n)
     | #Uniform(n) => Uniform.sample(n)
     | #Beta(n) => Beta.sample(n)
     | #Float(n) => Float.sample(n)
+    | #Bernoulli(n) => Bernoulli.sample(n)
     }
 
   let doN = (n, fn) => {
@@ -336,10 +391,12 @@ module T = {
     | #Cauchy(n) => Cauchy.toString(n)
     | #Normal(n) => Normal.toString(n)
     | #Gamma(n) => Gamma.toString(n)
+    | #Logistic(n) => Logistic.toString(n)
     | #Lognormal(n) => Lognormal.toString(n)
     | #Uniform(n) => Uniform.toString(n)
     | #Beta(n) => Beta.toString(n)
     | #Float(n) => Float.toString(n)
+    | #Bernoulli(n) => Bernoulli.toString(n)
     }
 
   let min: symbolicDist => float = x =>
@@ -349,8 +406,10 @@ module T = {
     | #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)
     | #Uniform({low}) => low
+    | #Bernoulli(n) => Bernoulli.min(n)
     | #Beta(n) => Beta.inv(minCdfValue, n)
     | #Float(n) => n
     }
@@ -363,7 +422,9 @@ module T = {
     | #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)
+    | #Bernoulli(n) => Bernoulli.max(n)
     | #Uniform({high}) => high
     | #Float(n) => n
     }
@@ -376,8 +437,10 @@ module T = {
     | #Normal(n) => Normal.mean(n)
     | #Lognormal(n) => Lognormal.mean(n)
     | #Beta(n) => Beta.mean(n)
+    | #Logistic(n) => Logistic.mean(n)
     | #Uniform(n) => Uniform.mean(n)
     | #Gamma(n) => Gamma.mean(n)
+    | #Bernoulli(n) => Bernoulli.mean(n)
     | #Float(n) => Float.mean(n)
     }
 
@@ -396,8 +459,9 @@ module T = {
     | (#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).
-      let dx = 0.00001 *. (n.high -. n.low)
+      let distance = n.high -. n.low
-      [n.low -. dx, n.low +. dx, n.high -. dx, n.high +. dx]
+      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)
       ys |> E.A.fmap(y => inv(y, dist))
@@ -452,7 +516,8 @@ module T = {
     d: symbolicDist,
   ): PointSetTypes.pointSetDist =>
     switch d {
-    | #Float(v) => Discrete(Discrete.make(~integralSumCache=Some(1.0), {xs: [v], ys: [1.0]}))
+    | #Float(v) => Float.toPointSetDist(v)
+    | #Bernoulli(v) => Bernoulli.toPointSetDist(v)
     | _ =>
       let xs = interpolateXs(~xSelection, d, sampleCount)
       let ys = xs |> E.A.fmap(x => pdf(x, d))

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

@@ -36,6 +36,13 @@ type gamma = {
   scale: float,
 }
 
+type logistic = {
+  location: float,
+  scale: float,
+}
+
+type bernoulli = {p: float}
+
 @genType
 type symbolicDist = [
   | #Normal(normal)
@@ -47,6 +54,8 @@ type symbolicDist = [
   | #Triangular(triangular)
   | #Gamma(gamma)
   | #Float(float)
+  | #Bernoulli(bernoulli)
+  | #Logistic(logistic)
 ]
 
 type analyticalSimplificationResult = [

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

@@ -6,11 +6,13 @@ module Math = {
 module Epsilon = {
   let ten = 1e-10
   let seven = 1e-7
+  let five = 1e-5
 }
 
 module Environment = {
   let defaultXYPointLength = 1000
   let defaultSampleCount = 10000
+  let sparklineLength = 20
 }
 
 module OpCost = {

packages/squiggle-lang/src/rescript/Reducer/Reducer_Dispatch/Reducer_Dispatch_BuiltIn.res

@@ -101,6 +101,18 @@ let callInternal = (call: functionCall, environment, reducer: ExpressionT.reduce
     rMappedList->Result.map(mappedList => mappedList->Belt.List.toArray->EvArray)
   }
 
+  let doMapSampleSetDist = (sampleSetDist: SampleSetDist.t, aLambdaValue) => {
+    let fn = r =>
+      switch Lambda.doLambdaCall(aLambdaValue, list{EvNumber(r)}, environment, reducer) {
+      | Ok(EvNumber(f)) => Ok(f)
+      | _ => Error(Operation.SampleMapNeedsNtoNFunction)
+      }
+    switch SampleSetDist.samplesMap(~fn, sampleSetDist) {
+    | Ok(r) => Ok(EvDistribution(SampleSet(r)))
+    | Error(r) => Error(REDistributionError(SampleSetError(r)))
+    }
+  }
+
   let doReduceArray = (aValueArray, initialValue, aLambdaValue) => {
     aValueArray->Belt.Array.reduce(Ok(initialValue), (rAcc, elem) =>
       rAcc->Result.flatMap(acc =>
@@ -130,6 +142,8 @@ let callInternal = (call: functionCall, environment, reducer: ExpressionT.reduce
   | ("keep", [EvArray(aValueArray), EvLambda(aLambdaValue)]) =>
     doKeepArray(aValueArray, aLambdaValue)
   | ("map", [EvArray(aValueArray), EvLambda(aLambdaValue)]) => doMapArray(aValueArray, aLambdaValue)
+  | ("mapSamples", [EvDistribution(SampleSet(dist)), EvLambda(aLambdaValue)]) =>
+    doMapSampleSetDist(dist, aLambdaValue)
   | ("reduce", [EvArray(aValueArray), initialValue, EvLambda(aLambdaValue)]) =>
     doReduceArray(aValueArray, initialValue, aLambdaValue)
   | ("reduceReverse", [EvArray(aValueArray), initialValue, EvLambda(aLambdaValue)]) =>

packages/squiggle-lang/src/rescript/Reducer/Reducer_ErrorValue.res

@@ -4,6 +4,7 @@ type errorValue =
   | REArrayIndexNotFound(string, int)
   | REAssignmentExpected
   | REDistributionError(DistributionTypes.error)
+  | REOperationError(Operation.operationError)
   | REExpressionExpected
   | REFunctionExpected(string)
   | REJavaScriptExn(option<string>, option<string>) // Javascript Exception
@@ -29,6 +30,7 @@ let errorToString = err =>
   | REExpressionExpected => "Expression expected"
   | REFunctionExpected(msg) => `Function expected: ${msg}`
   | REDistributionError(err) => `Distribution Math Error: ${DistributionTypes.Error.toString(err)}`
+  | REOperationError(err) => `Math Error: ${Operation.Error.toString(err)}`
   | REJavaScriptExn(omsg, oname) => {
       let answer = "JS Exception:"
       let answer = switch oname {

packages/squiggle-lang/src/rescript/Reducer/Reducer_Expression/Reducer_Expression_ExpressionBuilder.res

@@ -14,7 +14,7 @@ let eArray = anArray => anArray->BExpressionValue.EvArray->BExpressionT.EValue
 let eArrayString = anArray => anArray->BExpressionValue.EvArrayString->BExpressionT.EValue
 
 let eBindings = (anArray: array<(string, BExpressionValue.expressionValue)>) =>
-  anArray->Js.Dict.fromArray->EvRecord->BExpressionT.EValue
+  anArray->Js.Dict.fromArray->BExpressionValue.EvRecord->BExpressionT.EValue
 
 let eBool = aBool => aBool->BExpressionValue.EvBool->BExpressionT.EValue
 

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

@@ -1,12 +1,5 @@
 module ExpressionValue = ReducerInterface_ExpressionValue
-type expressionValue = ReducerInterface_ExpressionValue.expressionValue
+type expressionValue = ExpressionValue.expressionValue
-
-let defaultEnv: DistributionOperation.env = {
-  sampleCount: MagicNumbers.Environment.defaultSampleCount,
-  xyPointLength: MagicNumbers.Environment.defaultXYPointLength,
-}
-
-let runGenericOperation = DistributionOperation.run(~env=defaultEnv)
 
 module Helpers = {
   let arithmeticMap = r =>
@@ -39,37 +32,44 @@ module Helpers = {
   let toFloatFn = (
     fnCall: DistributionTypes.DistributionOperation.toFloat,
     dist: DistributionTypes.genericDist,
+    ~env: DistributionOperation.env,
   ) => {
     FromDist(DistributionTypes.DistributionOperation.ToFloat(fnCall), dist)
-    ->runGenericOperation
+    ->DistributionOperation.run(~env)
     ->Some
   }
 
   let toStringFn = (
     fnCall: DistributionTypes.DistributionOperation.toString,
     dist: DistributionTypes.genericDist,
+    ~env: DistributionOperation.env,
   ) => {
     FromDist(DistributionTypes.DistributionOperation.ToString(fnCall), dist)
-    ->runGenericOperation
+    ->DistributionOperation.run(~env)
     ->Some
   }
 
   let toBoolFn = (
     fnCall: DistributionTypes.DistributionOperation.toBool,
     dist: DistributionTypes.genericDist,
+    ~env: DistributionOperation.env,
   ) => {
     FromDist(DistributionTypes.DistributionOperation.ToBool(fnCall), dist)
-    ->runGenericOperation
+    ->DistributionOperation.run(~env)
     ->Some
   }
 
-  let toDistFn = (fnCall: DistributionTypes.DistributionOperation.toDist, dist) => {
+  let toDistFn = (
+    fnCall: DistributionTypes.DistributionOperation.toDist,
+    dist,
+    ~env: DistributionOperation.env,
+  ) => {
     FromDist(DistributionTypes.DistributionOperation.ToDist(fnCall), dist)
-    ->runGenericOperation
+    ->DistributionOperation.run(~env)
     ->Some
   }
 
-  let twoDiststoDistFn = (direction, arithmetic, dist1, dist2) => {
+  let twoDiststoDistFn = (direction, arithmetic, dist1, dist2, ~env: DistributionOperation.env) => {
     FromDist(
       DistributionTypes.DistributionOperation.ToDistCombination(
         direction,
@@ -77,8 +77,9 @@ module Helpers = {
         #Dist(dist2),
       ),
       dist1,
-    )->runGenericOperation
+    )->DistributionOperation.run(~env)
   }
+
   let parseNumber = (args: expressionValue): Belt.Result.t<float, string> =>
     switch args {
     | EvNumber(x) => Ok(x)
@@ -103,47 +104,85 @@ module Helpers = {
   let mixtureWithGivenWeights = (
     distributions: array<DistributionTypes.genericDist>,
     weights: array<float>,
+    ~env: DistributionOperation.env,
   ): DistributionOperation.outputType =>
     E.A.length(distributions) == E.A.length(weights)
-      ? Mixture(Belt.Array.zip(distributions, weights))->runGenericOperation
+      ? Mixture(Belt.Array.zip(distributions, weights))->DistributionOperation.run(~env)
       : GenDistError(
           ArgumentError("Error, mixture call has different number of distributions and weights"),
         )
 
   let mixtureWithDefaultWeights = (
     distributions: array<DistributionTypes.genericDist>,
+    ~env: DistributionOperation.env,
   ): DistributionOperation.outputType => {
     let length = E.A.length(distributions)
     let weights = Belt.Array.make(length, 1.0 /. Belt.Int.toFloat(length))
-    mixtureWithGivenWeights(distributions, weights)
+    mixtureWithGivenWeights(distributions, weights, ~env)
   }
 
-  let mixture = (args: array<expressionValue>): DistributionOperation.outputType =>
+  let mixture = (
-    switch E.A.last(args) {
+    args: array<expressionValue>,
-    | Some(EvArray(b)) => {
+    ~env: DistributionOperation.env,
-        let weights = parseNumberArray(b)
+  ): DistributionOperation.outputType => {
-        let distributions = parseDistributionArray(
+    let error = (err: string): DistributionOperation.outputType =>
-          Belt.Array.slice(args, ~offset=0, ~len=E.A.length(args) - 1),
+      err->DistributionTypes.ArgumentError->GenDistError
-        )
+    switch args {
-        switch E.R.merge(distributions, weights) {
+    | [EvArray(distributions)] =>
-        | Ok(d, w) => mixtureWithGivenWeights(d, w)
+      switch parseDistributionArray(distributions) {
-        | Error(err) => GenDistError(ArgumentError(err))
+      | Ok(distrs) => mixtureWithDefaultWeights(distrs, ~env)
+      | Error(err) => error(err)
+      }
+    | [EvArray(distributions), EvArray(weights)] =>
+      switch (parseDistributionArray(distributions), parseNumberArray(weights)) {
+      | (Ok(distrs), Ok(wghts)) => mixtureWithGivenWeights(distrs, wghts, ~env)
+      | (Error(err), Ok(_)) => error(err)
+      | (Ok(_), Error(err)) => error(err)
+      | (Error(err1), Error(err2)) => error(`${err1}|${err2}`)
+      }
+    | _ =>
+      switch E.A.last(args) {
+      | Some(EvArray(b)) => {
+          let weights = parseNumberArray(b)
+          let distributions = parseDistributionArray(
+            Belt.Array.slice(args, ~offset=0, ~len=E.A.length(args) - 1),
+          )
+          switch E.R.merge(distributions, weights) {
+          | Ok(d, w) => mixtureWithGivenWeights(d, w, ~env)
+          | Error(err) => error(err)
+          }
         }
+      | Some(EvNumber(_))
+      | Some(EvDistribution(_)) =>
+        switch parseDistributionArray(args) {
+        | Ok(distributions) => mixtureWithDefaultWeights(distributions, ~env)
+        | Error(err) => error(err)
+        }
+      | _ => error("Last argument of mx must be array or distribution")
       }
-    | Some(EvNumber(_))
-    | Some(EvDistribution(_)) =>
-      switch parseDistributionArray(args) {
-      | Ok(distributions) => mixtureWithDefaultWeights(distributions)
-      | Error(err) => GenDistError(ArgumentError(err))
-      }
-    | _ => GenDistError(ArgumentError("Last argument of mx must be array or distribution"))
     }
+  }
+
+  let klDivergenceWithPrior = (
+    prediction: DistributionTypes.genericDist,
+    answer: DistributionTypes.genericDist,
+    prior: DistributionTypes.genericDist,
+    env: DistributionOperation.env,
+  ) => {
+    let term1 = DistributionOperation.Constructors.klDivergence(~env, prediction, answer)
+    let term2 = DistributionOperation.Constructors.klDivergence(~env, prior, answer)
+    switch E.R.merge(term1, term2)->E.R2.fmap(((a, b)) => a -. b) {
+    | Ok(x) => x->DistributionOperation.Float->Some
+    | Error(_) => None
+    }
+  }
 }
 
 module SymbolicConstructors = {
   let oneFloat = name =>
     switch name {
     | "exponential" => Ok(SymbolicDist.Exponential.make)
+    | "bernoulli" => Ok(SymbolicDist.Bernoulli.make)
     | _ => Error("Unreachable state")
     }
 
@@ -153,6 +192,7 @@ module SymbolicConstructors = {
     | "uniform" => Ok(SymbolicDist.Uniform.make)
     | "beta" => Ok(SymbolicDist.Beta.make)
     | "lognormal" => Ok(SymbolicDist.Lognormal.make)
+    | "logistic" => Ok(SymbolicDist.Logistic.make)
     | "cauchy" => Ok(SymbolicDist.Cauchy.make)
     | "gamma" => Ok(SymbolicDist.Gamma.make)
     | "to" => Ok(SymbolicDist.From90thPercentile.make)
@@ -174,19 +214,27 @@ module SymbolicConstructors = {
     }
 }
 
-let dispatchToGenericOutput = (call: ExpressionValue.functionCall, _environment): option<
+let dispatchToGenericOutput = (
-  DistributionOperation.outputType,
+  call: ExpressionValue.functionCall,
-> => {
+  env: DistributionOperation.env,
+): option<DistributionOperation.outputType> => {
   let (fnName, args) = call
   switch (fnName, args) {
-  | ("exponential" as fnName, [EvNumber(f)]) =>
+  | (("exponential" | "bernoulli") as fnName, [EvNumber(f)]) =>
     SymbolicConstructors.oneFloat(fnName)
     ->E.R.bind(r => r(f))
     ->SymbolicConstructors.symbolicResultToOutput
   | ("delta", [EvNumber(f)]) =>
     SymbolicDist.Float.makeSafe(f)->SymbolicConstructors.symbolicResultToOutput
   | (
-      ("normal" | "uniform" | "beta" | "lognormal" | "cauchy" | "gamma" | "to") as fnName,
+      ("normal"
+      | "uniform"
+      | "beta"
+      | "lognormal"
+      | "cauchy"
+      | "gamma"
+      | "to"
+      | "logistic") as fnName,
       [EvNumber(f1), EvNumber(f2)],
     ) =>
     SymbolicConstructors.twoFloat(fnName)
@@ -196,13 +244,16 @@ let dispatchToGenericOutput = (call: ExpressionValue.functionCall, _environment)
     SymbolicConstructors.threeFloat(fnName)
     ->E.R.bind(r => r(f1, f2, f3))
     ->SymbolicConstructors.symbolicResultToOutput
-  | ("sample", [EvDistribution(dist)]) => Helpers.toFloatFn(#Sample, dist)
+  | ("sample", [EvDistribution(dist)]) => Helpers.toFloatFn(#Sample, dist, ~env)
-  | ("mean", [EvDistribution(dist)]) => Helpers.toFloatFn(#Mean, dist)
+  | ("sampleN", [EvDistribution(dist), EvNumber(n)]) =>
-  | ("integralSum", [EvDistribution(dist)]) => Helpers.toFloatFn(#IntegralSum, dist)
+    Some(FloatArray(GenericDist.sampleN(dist, Belt.Int.fromFloat(n))))
-  | ("toString", [EvDistribution(dist)]) => Helpers.toStringFn(ToString, dist)
+  | ("mean", [EvDistribution(dist)]) => Helpers.toFloatFn(#Mean, dist, ~env)
-  | ("toSparkline", [EvDistribution(dist)]) => Helpers.toStringFn(ToSparkline(20), dist)
+  | ("integralSum", [EvDistribution(dist)]) => Helpers.toFloatFn(#IntegralSum, dist, ~env)
+  | ("toString", [EvDistribution(dist)]) => Helpers.toStringFn(ToString, dist, ~env)
+  | ("toSparkline", [EvDistribution(dist)]) =>
+    Helpers.toStringFn(ToSparkline(MagicNumbers.Environment.sparklineLength), dist, ~env)
   | ("toSparkline", [EvDistribution(dist), EvNumber(n)]) =>
-    Helpers.toStringFn(ToSparkline(Belt.Float.toInt(n)), dist)
+    Helpers.toStringFn(ToSparkline(Belt.Float.toInt(n)), dist, ~env)
   | ("exp", [EvDistribution(a)]) =>
     // https://mathjs.org/docs/reference/functions/exp.html
     Helpers.twoDiststoDistFn(
@@ -210,56 +261,96 @@ let dispatchToGenericOutput = (call: ExpressionValue.functionCall, _environment)
       "pow",
       GenericDist.fromFloat(MagicNumbers.Math.e),
       a,
+      ~env,
     )->Some
-  | ("normalize", [EvDistribution(dist)]) => Helpers.toDistFn(Normalize, dist)
+  | ("normalize", [EvDistribution(dist)]) => Helpers.toDistFn(Normalize, dist, ~env)
-  | ("isNormalized", [EvDistribution(dist)]) => Helpers.toBoolFn(IsNormalized, dist)
+  | ("klDivergence", [EvDistribution(prediction), EvDistribution(answer)]) =>
-  | ("toPointSet", [EvDistribution(dist)]) => Helpers.toDistFn(ToPointSet, dist)
+    Some(DistributionOperation.run(FromDist(ToScore(KLDivergence(answer)), prediction), ~env))
+  | ("klDivergence", [EvDistribution(prediction), EvDistribution(answer), EvDistribution(prior)]) =>
+    Helpers.klDivergenceWithPrior(prediction, answer, prior, env)
+  | (
+    "logScoreWithPointAnswer",
+    [EvDistribution(prediction), EvNumber(answer), EvDistribution(prior)],
+  )
+  | (
+    "logScoreWithPointAnswer",
+    [EvDistribution(prediction), EvDistribution(Symbolic(#Float(answer))), EvDistribution(prior)],
+  ) =>
+    DistributionOperation.run(
+      FromDist(ToScore(LogScore(answer, prior->Some)), prediction),
+      ~env,
+    )->Some
+  | ("logScoreWithPointAnswer", [EvDistribution(prediction), EvNumber(answer)])
+  | (
+    "logScoreWithPointAnswer",
+    [EvDistribution(prediction), EvDistribution(Symbolic(#Float(answer)))],
+  ) =>
+    DistributionOperation.run(FromDist(ToScore(LogScore(answer, None)), prediction), ~env)->Some
+  | ("isNormalized", [EvDistribution(dist)]) => Helpers.toBoolFn(IsNormalized, dist, ~env)
+  | ("toPointSet", [EvDistribution(dist)]) => Helpers.toDistFn(ToPointSet, dist, ~env)
   | ("scaleLog", [EvDistribution(dist)]) =>
-    Helpers.toDistFn(Scale(#Logarithm, MagicNumbers.Math.e), dist)
+    Helpers.toDistFn(Scale(#Logarithm, MagicNumbers.Math.e), dist, ~env)
-  | ("scaleLog10", [EvDistribution(dist)]) => Helpers.toDistFn(Scale(#Logarithm, 10.0), dist)
+  | ("scaleLog10", [EvDistribution(dist)]) => Helpers.toDistFn(Scale(#Logarithm, 10.0), dist, ~env)
   | ("scaleLog", [EvDistribution(dist), EvNumber(float)]) =>
-    Helpers.toDistFn(Scale(#Logarithm, float), dist)
+    Helpers.toDistFn(Scale(#Logarithm, float), dist, ~env)
+  | ("scaleLogWithThreshold", [EvDistribution(dist), EvNumber(base), EvNumber(eps)]) =>
+    Helpers.toDistFn(Scale(#LogarithmWithThreshold(eps), base), dist, ~env)
   | ("scalePow", [EvDistribution(dist), EvNumber(float)]) =>
-    Helpers.toDistFn(Scale(#Power, float), dist)
+    Helpers.toDistFn(Scale(#Power, float), dist, ~env)
   | ("scaleExp", [EvDistribution(dist)]) =>
-    Helpers.toDistFn(Scale(#Power, MagicNumbers.Math.e), dist)
+    Helpers.toDistFn(Scale(#Power, MagicNumbers.Math.e), dist, ~env)
-  | ("cdf", [EvDistribution(dist), EvNumber(float)]) => Helpers.toFloatFn(#Cdf(float), dist)
+  | ("cdf", [EvDistribution(dist), EvNumber(float)]) => Helpers.toFloatFn(#Cdf(float), dist, ~env)
-  | ("pdf", [EvDistribution(dist), EvNumber(float)]) => Helpers.toFloatFn(#Pdf(float), dist)
+  | ("pdf", [EvDistribution(dist), EvNumber(float)]) => Helpers.toFloatFn(#Pdf(float), dist, ~env)
-  | ("inv", [EvDistribution(dist), EvNumber(float)]) => Helpers.toFloatFn(#Inv(float), dist)
+  | ("inv", [EvDistribution(dist), EvNumber(float)]) => Helpers.toFloatFn(#Inv(float), dist, ~env)
   | ("toSampleSet", [EvDistribution(dist), EvNumber(float)]) =>
-    Helpers.toDistFn(ToSampleSet(Belt.Int.fromFloat(float)), dist)
+    Helpers.toDistFn(ToSampleSet(Belt.Int.fromFloat(float)), dist, ~env)
   | ("toSampleSet", [EvDistribution(dist)]) =>
-    Helpers.toDistFn(ToSampleSet(MagicNumbers.Environment.defaultSampleCount), dist)
+    Helpers.toDistFn(ToSampleSet(env.sampleCount), dist, ~env)
+  | ("toInternalSampleArray", [EvDistribution(SampleSet(dist))]) =>
+    Some(FloatArray(SampleSetDist.T.get(dist)))
   | ("fromSamples", [EvArray(inputArray)]) => {
       let _wrapInputErrors = x => SampleSetDist.NonNumericInput(x)
       let parsedArray = Helpers.parseNumberArray(inputArray)->E.R2.errMap(_wrapInputErrors)
       switch parsedArray {
-      | Ok(array) => runGenericOperation(FromSamples(array))
+      | Ok(array) => DistributionOperation.run(FromSamples(array), ~env)
       | Error(e) => GenDistError(SampleSetError(e))
       }->Some
     }
-  | ("inspect", [EvDistribution(dist)]) => Helpers.toDistFn(Inspect, dist)
+  | ("inspect", [EvDistribution(dist)]) => Helpers.toDistFn(Inspect, dist, ~env)
   | ("truncateLeft", [EvDistribution(dist), EvNumber(float)]) =>
-    Helpers.toDistFn(Truncate(Some(float), None), dist)
+    Helpers.toDistFn(Truncate(Some(float), None), dist, ~env)
   | ("truncateRight", [EvDistribution(dist), EvNumber(float)]) =>
-    Helpers.toDistFn(Truncate(None, Some(float)), dist)
+    Helpers.toDistFn(Truncate(None, Some(float)), dist, ~env)
   | ("truncate", [EvDistribution(dist), EvNumber(float1), EvNumber(float2)]) =>
-    Helpers.toDistFn(Truncate(Some(float1), Some(float2)), dist)
+    Helpers.toDistFn(Truncate(Some(float1), Some(float2)), dist, ~env)
-  | ("mx" | "mixture", args) => Helpers.mixture(args)->Some
+  | ("mx" | "mixture", args) => Helpers.mixture(args, ~env)->Some
   | ("log", [EvDistribution(a)]) =>
     Helpers.twoDiststoDistFn(
       Algebraic(AsDefault),
       "log",
       a,
       GenericDist.fromFloat(MagicNumbers.Math.e),
+      ~env,
     )->Some
   | ("log10", [EvDistribution(a)]) =>
-    Helpers.twoDiststoDistFn(Algebraic(AsDefault), "log", a, GenericDist.fromFloat(10.0))->Some
+    Helpers.twoDiststoDistFn(
+      Algebraic(AsDefault),
+      "log",
+      a,
+      GenericDist.fromFloat(10.0),
+      ~env,
+    )->Some
   | ("unaryMinus", [EvDistribution(a)]) =>
-    Helpers.twoDiststoDistFn(Algebraic(AsDefault), "multiply", a, GenericDist.fromFloat(-1.0))->Some
+    Helpers.twoDiststoDistFn(
+      Algebraic(AsDefault),
+      "multiply",
+      a,
+      GenericDist.fromFloat(-1.0),
+      ~env,
+    )->Some
   | (("add" | "multiply" | "subtract" | "divide" | "pow" | "log") as arithmetic, [_, _] as args) =>
     Helpers.catchAndConvertTwoArgsToDists(args)->E.O2.fmap(((fst, snd)) =>
-      Helpers.twoDiststoDistFn(Algebraic(AsDefault), arithmetic, fst, snd)
+      Helpers.twoDiststoDistFn(Algebraic(AsDefault), arithmetic, fst, snd, ~env)
     )
   | (
       ("dotAdd"
@@ -270,7 +361,7 @@ let dispatchToGenericOutput = (call: ExpressionValue.functionCall, _environment)
       [_, _] as args,
     ) =>
     Helpers.catchAndConvertTwoArgsToDists(args)->E.O2.fmap(((fst, snd)) =>
-      Helpers.twoDiststoDistFn(Pointwise, arithmetic, fst, snd)
+      Helpers.twoDiststoDistFn(Pointwise, arithmetic, fst, snd, ~env)
     )
   | ("dotExp", [EvDistribution(a)]) =>
     Helpers.twoDiststoDistFn(
@@ -278,6 +369,7 @@ let dispatchToGenericOutput = (call: ExpressionValue.functionCall, _environment)
       "dotPow",
       GenericDist.fromFloat(MagicNumbers.Math.e),
       a,
+      ~env,
     )->Some
   | _ => None
   }
@@ -292,6 +384,7 @@ let genericOutputToReducerValue = (o: DistributionOperation.outputType): result<
   | Float(d) => Ok(EvNumber(d))
   | String(d) => Ok(EvString(d))
   | Bool(d) => Ok(EvBool(d))
+  | FloatArray(d) => Ok(EvArray(d |> E.A.fmap(r => ReducerInterface_ExpressionValue.EvNumber(r))))
   | GenDistError(err) => Error(REDistributionError(err))
   }
 

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

@@ -1,4 +1,3 @@
-let defaultEnv: DistributionOperation.env
 let dispatch: (
   ReducerInterface_ExpressionValue.functionCall,
   ReducerInterface_ExpressionValue.environment,

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

@@ -77,10 +77,13 @@ let distributionErrorToString = DistributionTypes.Error.toString
 type lambdaValue = ReducerInterface_ExpressionValue.lambdaValue
 
 @genType
-let defaultSamplingEnv = ReducerInterface_GenericDistribution.defaultEnv
+let defaultSamplingEnv = DistributionOperation.defaultEnv
 
 @genType
 type environment = ReducerInterface_ExpressionValue.environment
 
 @genType
 let defaultEnvironment = ReducerInterface_ExpressionValue.defaultEnvironment
+
+@genType
+let foreignFunctionInterface = Reducer.foreignFunctionInterface

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

@@ -235,13 +235,16 @@ module R = {
     | Ok(a) => f(a)
     | Error(err) => Error(err)
     }
-
   let toExn = (msg: string, x: result<'a, 'b>): 'a =>
     switch x {
     | Ok(r) => r
     | Error(_) => raise(Assertion(msg))
     }
-
+  let toExnFnString = (errorToStringFn, o) =>
+    switch o {
+    | Ok(r) => r
+    | Error(r) => raise(Assertion(errorToStringFn(r)))
+    }
   let default = (default, res: Belt.Result.t<'a, 'b>) =>
     switch res {
     | Ok(r) => r
@@ -607,6 +610,9 @@ module A = {
   let filter = Js.Array.filter
   let joinWith = Js.Array.joinWith
 
+  let all = (p: 'a => bool, xs: array<'a>): bool => length(filter(p, xs)) == length(xs)
+  let any = (p: 'a => bool, xs: array<'a>): bool => length(filter(p, xs)) > 0
+
   module O = {
     let concatSomes = (optionals: array<option<'a>>): array<'a> =>
       optionals
@@ -617,6 +623,19 @@ module A = {
       | Some(o) => o
       | None => []
       }
+    // REturns `None` there are no non-`None` elements
+    let rec arrSomeToSomeArr = (optionals: array<option<'a>>): option<array<'a>> => {
+      let optionals' = optionals->Belt.List.fromArray
+      switch optionals' {
+      | list{} => []->Some
+      | list{x, ...xs} =>
+        switch x {
+        | Some(_) => xs->Belt.List.toArray->arrSomeToSomeArr
+        | None => None
+        }
+      }
+    }
+    let firstSome = x => Belt.Array.getBy(x, O.isSome)
   }
 
   module R = {

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

@@ -8,6 +8,7 @@ type algebraicOperation = [
   | #Divide
   | #Power
   | #Logarithm
+  | #LogarithmWithThreshold(float)
 ]
 
 type convolutionOperation = [
@@ -18,7 +19,7 @@ type convolutionOperation = [
 
 @genType
 type pointwiseOperation = [#Add | #Multiply | #Power]
-type scaleOperation = [#Multiply | #Power | #Logarithm | #Divide]
+type scaleOperation = [#Multiply | #Power | #Logarithm | #LogarithmWithThreshold(float) | #Divide]
 type distToFloatOperation = [
   | #Pdf(float)
   | #Cdf(float)
@@ -35,7 +36,7 @@ module Convolution = {
     | #Add => Some(#Add)
     | #Subtract => Some(#Subtract)
     | #Multiply => Some(#Multiply)
-    | #Divide | #Power | #Logarithm => None
+    | #Divide | #Power | #Logarithm | #LogarithmWithThreshold(_) => None
     }
 
   let canDoAlgebraicOperation = (op: algebraicOperation): bool =>
@@ -52,6 +53,11 @@ module Convolution = {
 type operationError =
   | DivisionByZeroError
   | ComplexNumberError
+  | InfinityError
+  | NegativeInfinityError
+  | SampleMapNeedsNtoNFunction
+  | PdfInvalidError
+  | NotYetImplemented // should be removed when `klDivergence` for mixed and discrete is implemented.
 
 @genType
 module Error = {
@@ -62,6 +68,11 @@ module Error = {
     switch err {
     | DivisionByZeroError => "Cannot divide by zero"
     | ComplexNumberError => "Operation returned complex result"
+    | InfinityError => "Operation returned positive infinity"
+    | NegativeInfinityError => "Operation returned negative infinity"
+    | SampleMapNeedsNtoNFunction => "SampleMap needs a function that converts a number to a number"
+    | PdfInvalidError => "This Pdf is invalid"
+    | NotYetImplemented => "This pathway is not yet implemented"
     }
 }
 
@@ -86,6 +97,8 @@ let logarithm = (a: float, b: float): result<float, Error.t> =>
     Ok(0.)
   } else if a > 0.0 && b > 0.0 {
     Ok(log(a) /. log(b))
+  } else if a == 0.0 {
+    Error(NegativeInfinityError)
   } else {
     Error(ComplexNumberError)
   }
@@ -102,6 +115,12 @@ module Algebraic = {
     | #Power => power(a, b)
     | #Divide => divide(a, b)
     | #Logarithm => logarithm(a, b)
+    | #LogarithmWithThreshold(eps) =>
+      if a < eps {
+        Ok(0.0)
+      } else {
+        logarithm(a, b)
+      }
     }
 
   let toString = x =>
@@ -112,6 +131,7 @@ module Algebraic = {
     | #Power => "**"
     | #Divide => "/"
     | #Logarithm => "log"
+    | #LogarithmWithThreshold(_) => "log"
     }
 
   let format = (a, b, c) => b ++ (" " ++ (toString(a) ++ (" " ++ c)))
@@ -151,6 +171,12 @@ module Scale = {
     | #Divide => divide(a, b)
     | #Power => power(a, b)
     | #Logarithm => logarithm(a, b)
+    | #LogarithmWithThreshold(eps) =>
+      if a < eps {
+        Ok(0.0)
+      } else {
+        logarithm(a, b)
+      }
     }
 
   let format = (operation: t, value, scaleBy) =>
@@ -159,14 +185,14 @@ module Scale = {
     | #Divide => j`verticalDivide($value, $scaleBy) `
     | #Power => j`verticalPower($value, $scaleBy) `
     | #Logarithm => j`verticalLog($value, $scaleBy) `
+    | #LogarithmWithThreshold(eps) => j`verticalLog($value, $scaleBy, epsilon=$eps) `
     }
 
   let toIntegralSumCacheFn = x =>
     switch x {
     | #Multiply => (a, b) => Some(a *. b)
     | #Divide => (a, b) => Some(a /. b)
-    | #Power => (_, _) => None
+    | #Power | #Logarithm | #LogarithmWithThreshold(_) => (_, _) => None
-    | #Logarithm => (_, _) => None
     }
 
   let toIntegralCacheFn = x =>
@@ -175,6 +201,7 @@ module Scale = {
     | #Divide => (_, _) => None
     | #Power => (_, _) => None
     | #Logarithm => (_, _) => None
+    | #LogarithmWithThreshold(_) => (_, _) => None
     }
 }
 

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

@@ -0,0 +1,40 @@
+module Bernoulli = {
+  @module external cdf: (float, float) => float = "@stdlib/stats/base/dists/bernoulli/cdf"
+  let cdf = cdf
+
+  @module external pmf: (float, float) => float = "@stdlib/stats/base/dists/bernoulli/pmf"
+  let pmf = pmf
+
+  @module external quantile: (float, float) => float = "@stdlib/stats/base/dists/bernoulli/quantile"
+  let quantile = quantile
+
+  @module external mean: float => float = "@stdlib/stats/base/dists/bernoulli/mean"
+  let mean = mean
+
+  @module external stdev: float => float = "@stdlib/stats/base/dists/bernoulli/stdev"
+  let stdev = stdev
+
+  @module external variance: float => float = "@stdlib/stats/base/dists/bernoulli/variance"
+  let variance = variance
+}
+
+module Logistic = {
+  @module external cdf: (float, float, float) => float = "@stdlib/stats/base/dists/logistic/cdf"
+  let cdf = cdf
+
+  @module external pdf: (float, float, float) => float = "@stdlib/stats/base/dists/logistic/pdf"
+  let pdf = pdf
+
+  @module
+  external quantile: (float, float, float) => float = "@stdlib/stats/base/dists/logistic/quantile"
+  let quantile = quantile
+
+  @module external mean: (float, float) => float = "@stdlib/stats/base/dists/logistic/mean"
+  let mean = mean
+
+  @module external stdev: (float, float) => float = "@stdlib/stats/base/dists/logistic/stdev"
+  let stdev = stdev
+
+  @module external variance: (float, float) => float = "@stdlib/stats/base/dists/logistic/variance"
+  let variance = variance
+}

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

@@ -96,7 +96,21 @@ module T = {
   let fromZippedArray = (pairs: array<(float, float)>): t => pairs |> Belt.Array.unzip |> fromArray
   let equallyDividedXs = (t: t, newLength) => E.A.Floats.range(minX(t), maxX(t), newLength)
   let toJs = (t: t) => {"xs": t.xs, "ys": t.ys}
-
+  let filterYValues = (fn, t: t): t => t |> zip |> E.A.filter(((_, y)) => fn(y)) |> fromZippedArray
+  let filterOkYs = (xs: array<float>, ys: array<result<float, 'b>>): t => {
+    let n = E.A.length(xs) // Assume length(xs) == length(ys)
+    let newXs = []
+    let newYs = []
+    for i in 0 to n - 1 {
+      switch ys[i] {
+      | Ok(y) =>
+        let _ = Js.Array.push(xs[i], newXs)
+        let _ = Js.Array.push(y, newYs)
+      | Error(_) => ()
+      }
+    }
+    {xs: newXs, ys: newYs}
+  }
   module Validator = {
     let fnName = "XYShape validate"
     let notSortedError = (p: string): error => NotSorted(p)
@@ -376,6 +390,128 @@ module PointwiseCombination = {
       }
     `)
 
+  /*
+    This is from an approach to kl divergence that was ultimately rejected. Leaving it in for now because it may help us factor `combine` out of raw javascript soon.
+ */
+  let combineAlongSupportOfSecondArgument0: (
+    (float, float) => result<float, Operation.Error.t>,
+    interpolator,
+    T.t,
+    T.t,
+  ) => result<T.t, Operation.Error.t> = (fn, interpolator, t1, t2) => {
+    let newYs = []
+    let newXs = []
+    let (l1, l2) = (E.A.length(t1.xs), E.A.length(t2.xs))
+    let (i, j) = (ref(0), ref(0))
+    let minX = t2.xs[0]
+    let maxX = t2.xs[l2 - 1]
+    while j.contents < l2 - 1 && i.contents < l1 - 1 {
+      let someTuple = {
+        let x1 = t1.xs[i.contents + 1]
+        let x2 = t2.xs[j.contents + 1]
+        if (
+          /* if t1 has to catch up to t2 */
+          i.contents < l1 - 1 && j.contents < l2 && x1 < x2 && minX <= x1 && x2 <= maxX
+        ) {
+          i := i.contents + 1
+          let x = x1
+          let y1 = t1.ys[i.contents]
+          let y2 = interpolator(t2, j.contents, x)
+          Some((x, y1, y2))
+        } else if (
+          /* if t2 has to catch up to t1 */
+          i.contents < l1 && j.contents < l2 - 1 && x1 > x2 && x2 >= minX && maxX >= x1
+        ) {
+          j := j.contents + 1
+          let x = x2
+          let y1 = interpolator(t1, i.contents, x)
+          let y2 = t2.ys[j.contents]
+          Some((x, y1, y2))
+        } else if (
+          /* move both ahead if they are equal */
+          i.contents < l1 - 1 && j.contents < l2 - 1 && x1 == x2 && x1 >= minX && maxX >= x2
+        ) {
+          i := i.contents + 1
+          j := j.contents + 1
+          let x = x1
+          let y1 = t1.ys[i.contents]
+          let y2 = t2.ys[j.contents]
+          Some((x, y1, y2))
+        } else {
+          i := i.contents + 1
+          None
+        }
+      }
+      switch someTuple {
+      | Some((x, y1, y2)) => {
+          let _ = Js.Array.push(fn(y1, y2), newYs)
+          let _ = Js.Array.push(x, newXs)
+        }
+      | None => ()
+      }
+    }
+    T.filterOkYs(newXs, newYs)->Ok
+  }
+
+  /* *Dead code*: Nuño wrote this function to try to increase precision, but it didn't work.
+     If another traveler comes through with a similar idea, we hope this implementation will help them.
+     By "enrich" we mean to increase granularity.
+ */
+  let enrichXyShape = (t: T.t): T.t => {
+    let defaultEnrichmentFactor = 10
+    let length = E.A.length(t.xs)
+    let points =
+      length < MagicNumbers.Environment.defaultXYPointLength
+        ? defaultEnrichmentFactor * MagicNumbers.Environment.defaultXYPointLength / length
+        : defaultEnrichmentFactor
+
+    let getInBetween = (x1: float, x2: float): array<float> => {
+      if abs_float(x1 -. x2) < 2.0 *. MagicNumbers.Epsilon.seven {
+        [x1]
+      } else {
+        let newPointsArray = Belt.Array.makeBy(points - 1, i => i)
+        // don't repeat the x2 point, it will be gotten in the next iteration.
+        let result = Js.Array.mapi((pos, i) =>
+          if i == 0 {
+            x1
+          } else {
+            let points' = Belt.Float.fromInt(points)
+            let pos' = Belt.Float.fromInt(pos)
+            x1 *. (points' -. pos') /. points' +. x2 *. pos' /. points'
+          }
+        , newPointsArray)
+        result
+      }
+    }
+    let newXsUnflattened = Js.Array.mapi(
+      (x, i) => i < length - 2 ? getInBetween(x, t.xs[i + 1]) : [x],
+      t.xs,
+    )
+    let newXs = Belt.Array.concatMany(newXsUnflattened)
+    let newYs = E.A.fmap(x => XtoY.linear(x, t), newXs)
+    {xs: newXs, ys: newYs}
+  }
+  // This function is used for klDivergence
+  let combineAlongSupportOfSecondArgument: (
+    (float, float) => result<float, Operation.Error.t>,
+    T.t,
+    T.t,
+  ) => result<T.t, Operation.Error.t> = (fn, prediction, answer) => {
+    let combineWithFn = (answerX: float, i: int) => {
+      let answerY = answer.ys[i]
+      let predictionY = XtoY.linear(answerX, prediction)
+      fn(predictionY, answerY)
+    }
+    let newYsWithError = Js.Array.mapi((x, i) => combineWithFn(x, i), answer.xs)
+    let newYsOrError = E.A.R.firstErrorOrOpen(newYsWithError)
+    let result = switch newYsOrError {
+    | Ok(a) => Ok({xs: answer.xs, ys: a})
+    | Error(b) => Error(b)
+    }
+
+    result
+  }
+
   let addCombine = (interpolator: interpolator, t1: T.t, t2: T.t): T.t =>
     combine((a, b) => Ok(a +. b), interpolator, t1, t2)->E.R.toExn(
       "Add operation should never fail",
@@ -467,7 +603,7 @@ module Range = {
   // TODO: I think this isn't needed by any functions anymore.
   let stepsToContinuous = t => {
     // TODO: It would be nicer if this the diff didn't change the first element, and also maybe if there were a more elegant way of doing this.
-    let diff = T.xTotalRange(t) |> (r => r *. 0.00001)
+    let diff = T.xTotalRange(t) |> (r => r *. MagicNumbers.Epsilon.five)
     let items = switch E.A.toRanges(Belt.Array.zip(t.xs, t.ys)) {
     | Ok(items) =>
       Some(
@@ -489,25 +625,6 @@ module Range = {
   }
 }
 
-let pointLogScore = (prediction, answer) =>
-  switch answer {
-  | 0. => 0.0
-  | answer => answer *. Js.Math.log2(Js.Math.abs_float(prediction /. answer))
-  }
-
-let logScorePoint = (sampleCount, t1, t2) =>
-  PointwiseCombination.combineEvenXs(
-    ~fn=pointLogScore,
-    ~xToYSelection=XtoY.linear,
-    sampleCount,
-    t1,
-    t2,
-  )
-  |> Range.integrateWithTriangles
-  |> E.O.fmap(T.accumulateYs(\"+."))
-  |> E.O.fmap(Pairs.last)
-  |> E.O.fmap(Pairs.y)
-
 module Analysis = {
   let getVarianceDangerously = (t: 't, mean: 't => float, getMeanOfSquares: 't => float): float => {
     let meanSquared = mean(t) ** 2.0

packages/website/docs/Discussions/Bugs.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Known Bugs"
-sidebar_position: 6
+sidebar_position: 1
 ---
 
 import { SquiggleEditor } from "../../src/components/SquiggleEditor";

packages/website/docs/Discussions/Future-Features.md

@@ -1,9 +1,8 @@
 ---
-sidebar_position: 4
+title: Future Features
+sidebar_position: 3
 ---
 
-# Future Features
-
 Squiggle is still very early. The main first goal is to become stable. This means having a clean codebase, having decent test coverage, and having a syntax we are reasonably confident in. Later on, there are many other features that will be interesting to explore.
 
 ## Programming Language Features

packages/website/docs/Discussions/Gallery.md

@@ -1,7 +1,8 @@
 ---
-sidebar_position: 6
+sidebar_position: 2
 title: Gallery
 ---
 
 - [Adjusting probabilities for the passage of time](https://www.lesswrong.com/s/rDe8QE5NvXcZYzgZ3/p/j8o6sgRerE3tqNWdj) by Nuño Sempere
 - [GiveWell's GiveDirectly cost effectiveness analysis](https://observablehq.com/@hazelfire/givewells-givedirectly-cost-effectiveness-analysis) by Sam Nolan
+- [Astronomical Waste](https://observablehq.com/@quinn-dougherty/waste)

packages/website/docs/Discussions/Three-Formats-Of-Distributions.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 5
+sidebar_position: 4
 title: Three Formats of Distributions
 author: Ozzie Gooen
 date: 02-19-2022

packages/website/docs/Features/Distributions.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Distribution Creation"
-sidebar_position: 8
+sidebar_position: 2
 ---
 
 import TOCInline from "@theme/TOCInline";
@@ -70,8 +70,10 @@ If both values are above zero, a `lognormal` distribution is used. If not, a `no
 
 ## Mixture
 
-`mixture(...distributions: Distribution[], weights?: number[])`  
+`mixture(...distributions: Distribution[], weights?: number[])`
 `mx(...distributions: Distribution[], weights?: number[])`
+`mixture(distributions: Distributions[], weights?: number[])`
+`mx(distributions: Distributions[], weights?: number[])`
 
 The `mixture` mixes combines multiple distributions to create a mixture. You can optionally pass in a list of proportional weights.
 
@@ -85,6 +87,9 @@ The `mixture` mixes combines multiple distributions to create a mixture. You can
   <TabItem value="ex3" label="With Continuous and Discrete Inputs">
     <SquiggleEditor initialSquiggleString="mixture(1 to 5, 8 to 10, 1, 3, 20)" />
   </TabItem>
+  <TabItem value="ex4" label="Array of Distributions Input">
+    <SquiggleEditor initialSquiggleString="mx([1 to 2, exponential(1)], [1,1])" />
+  </TabItem>
 </Tabs>
 
 ### Arguments

packages/website/docs/Features/Functions.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Functions Reference"
-sidebar_position: 7
+sidebar_position: 3
 ---
 
 import { SquiggleEditor } from "../../src/components/SquiggleEditor";

packages/website/docs/Features/Language.mdx

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 2
+sidebar_position: 1
 title: Language Basics
 ---
 
@@ -49,5 +49,6 @@ ozzie_estimate(1) * nuno_estimate(1, 1)`}
 
 ## See more
 
-- [Functions reference](https://squiggle-language.com/docs/Features/Functions)
+- [Distribution creation](./Distributions)
-- [Gallery](https://squiggle-language.com/docs/Discussions/Gallery)
+- [Functions reference](./Functions)
+- [Gallery](../Discussions/Gallery)

packages/website/docs/Features/Node-Packages.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 3
+sidebar_position: 4
 title: Node Packages
 ---
 
@@ -12,25 +12,13 @@ Types are available for both packages.
 
 ## Squiggle Language
 
-The `@quri/squiggle-lang` package exports a single function, `run`, which given
+[_See `README.md` in Github_](https://github.com/quantified-uncertainty/squiggle/tree/develop/packages/squiggle-lang#use-the-npm-package)
-a string of Squiggle code, will execute the code and return any exports and the
-environment created from the squiggle code.
-
-`run` has two optional arguments. The first optional argument allows you to set
-sampling settings for Squiggle when representing distributions. The second optional
-argument allows you to pass an environment previously created by another `run`
-call. Passing this environment will mean that all previously declared variables
-in the previous environment will be made available.
-
-The return type of `run` is a bit complicated, and comes from auto generated `js`
-code that comes from rescript. We highly recommend using typescript when using
-this library to help navigate the return type.
 
 ## Squiggle Components
 
-The `@quri/squiggle-components` package offers several components and utilities
+[_See `README.md` in Github_](https://github.com/quantified-uncertainty/squiggle/tree/develop/packages/components#usage-in-a-react-project)
-for people who want to embed Squiggle components into websites. This documentation
+
-uses `@quri/squiggle-components` frequently.
+This documentation uses `@quri/squiggle-components` frequently.
 
 We host [a storybook](https://squiggle-components.netlify.app/) with details
 and usage of each of the components made available.

packages/website/docs/Introduction.md

@@ -7,10 +7,10 @@ Squiggle is an _estimation language_, and a syntax for _calculating and expressi
 
 ## Get started
 
-- [Gallery](https://www.squiggle-language.com/docs/Discussions/Gallery)
+- [Gallery](./Discussions/Gallery)
-- [Squiggle playground](https://squiggle-language.com/playground)
+- [Squiggle playground](/playground)
-- [Language basics](https://www.squiggle-language.com/docs/Features/Language)
+- [Language basics](./Features/Language)
-- [Squiggle functions source of truth](https://www.squiggle-language.com/docs/Features/Functions)
+- [Squiggle functions source of truth](./docs/Features/Functions)
-- [Known bugs](https://www.squiggle-language.com/docs/Discussions/Bugs)
+- [Known bugs](./Discussions/Bugs)
 - [Original lesswrong sequence](https://www.lesswrong.com/s/rDe8QE5NvXcZYzgZ3)
 - [Author your squiggle models as Observable notebooks](https://observablehq.com/@hazelfire/squiggle)

packages/website/docusaurus.config.js

@@ -9,8 +9,8 @@ const path = require("path");
 
 /** @type {import('@docusaurus/types').Config} */
 const config = {
-  title: "Squiggle (alpha)",
+  title: "Squiggle",
-  tagline: "Estimation language for forecasters",
+  tagline: "An estimation language for forecasters",
   url: "https://squiggle-language.com",
   baseUrl: "/",
   onBrokenLinks: "throw",

packages/website/package.json

@@ -12,11 +12,11 @@
     "format": "prettier --write ."
   },
   "dependencies": {
-    "@docusaurus/core": "2.0.0-beta.18",
+    "@docusaurus/core": "2.0.0-beta.20",
-    "@docusaurus/preset-classic": "2.0.0-beta.18",
+    "@docusaurus/preset-classic": "2.0.0-beta.20",
-    "@quri/squiggle-components": "0.2.9",
+    "@quri/squiggle-components": "^0.2.20",
     "clsx": "^1.1.1",
-    "prism-react-renderer": "^1.2.1",
+    "prism-react-renderer": "^1.3.3",
     "react": "^18.1.0",
     "react-dom": "^18.1.0",
     "remark-math": "^3",

packages/website/src/pages/index.js

@@ -12,6 +12,9 @@ function HomepageHeader() {
     <header className={clsx("hero hero--primary", styles.heroBanner)}>
       <div className="container">
         <h1 className="hero__title">{siteConfig.title}</h1>
+        <p className="hero__subtitle">
+          <i>Early access</i>
+        </p>
         <p className="hero__subtitle">{siteConfig.tagline}</p>
         <div className={styles.buttons}></div>
       </div>

packages/website/src/pages/markdown-page.md

@@ -1,7 +0,0 @@
----
-title: Markdown page example
----
-
-# Markdown page example
-
-You don't need React to write simple standalone pages.

packages/website/src/pages/playground.js

@@ -10,7 +10,11 @@ export default function PlaygroundPage() {
           maxWidth: 2000,
         }}
       >
-        <SquigglePlayground initialSquiggleString="normal(0,1)" height={700} />
+        <SquigglePlayground
+          initialSquiggleString="normal(0,1)"
+          height={700}
+          showTypes={true}
+        />
       </div>
     </Layout>
   );