diff --git a/src/utility/CdfLibrary.js b/src/utility/CdfLibrary.js
index ffe63322..2e23da7e 100644
--- a/src/utility/CdfLibrary.js
+++ b/src/utility/CdfLibrary.js
@@ -1,174 +1,170 @@
 const {
-    Cdf,
-    Pdf,
-    ContinuousDistribution,
-    ContinuousDistributionCombination,
-    scoringFunctions,
-  } = require("@foretold/cdf/lib");
-  const _ = require("lodash");
-  
-  /**
-   *
-   * @param xs
-   * @param ys
-   * @returns {{ys: *, xs: *}}
-   */
-  function cdfToPdf({ xs, ys }) {
-    let cdf = new Cdf(xs, ys);
-    let pdf = cdf.toPdf();
-    return { xs: pdf.xs, ys: pdf.ys };
-  }
+  Cdf,
+  Pdf,
+  ContinuousDistribution,
+  ContinuousDistributionCombination,
+  scoringFunctions,
+} = require("@foretold/cdf/lib");
+const _ = require("lodash");
 
-  /**
-   *
-   * @param xs
-   * @param ys
-   * @returns {{ys: *, xs: *}}
-   */
-  function pdfToCdf({ xs, ys }) {
-    let cdf = new Pdf(xs, ys);
-    let pdf = cdf.toCdf();
-    return { xs: pdf.xs, ys: pdf.ys };
-  }
-  
-  /**
-   *
-   * @param sampleCount
-   * @param vars
-   * @returns {{ys: *, xs: *}}
-   */
-  function mean(sampleCount, vars) {
-    let cdfs = vars.map(r => new Cdf(r.xs, r.ys));
-    let comb = new ContinuousDistributionCombination(cdfs);
-    let newCdf = comb.combineYsWithMean(sampleCount);
-  
-    return { xs: newCdf.xs, ys: newCdf.ys };
-  }
-  
-  /**
-   *
-   * @param sampleCount
-   * @param predictionCdf
-   * @param resolutionCdf
-   */
-  function scoreNonMarketCdfCdf(sampleCount, predictionCdf, resolutionCdf, resolutionUniformAdditionWeight=0) {
-    let toCdf = (r) => (new Cdf(r.xs, r.ys));
-    let prediction = toCdf(predictionCdf);
-    if (_.isFinite(resolutionUniformAdditionWeight)){
-      prediction = prediction.combineWithUniformOfCdf(
-        {
-          cdf: toCdf(resolutionCdf),
-          uniformWeight: resolutionUniformAdditionWeight,
-          sampleCount
-        }
-      );
-    }
-  
-    return scoringFunctions.distributionInputDistributionOutputMarketless({
-      predictionCdf: prediction,
-      resultCdf: toCdf(resolutionCdf),
-      sampleCount,
-    });
-  }
-  
-  /**
-   *
-   * @param sampleCount
-   * @param cdf
-   */
-  function differentialEntropy(sampleCount, cdf) {
-    let toCdf = (r) => (new Cdf(r.xs, r.ys));
-  
-    return scoringFunctions.differentialEntropy({
-      cdf: toCdf(cdf),
-      sampleCount: sampleCount
-    });
-  }
-  
-  /**
-   *
-   * @param x
-   * @param xs
-   * @param ys
-   * @returns {number}
-   */
-  function findY(x, { xs, ys }) {
-    let cdf = new Cdf(xs, ys);
-    return cdf.findY(x);
-  }
+/**
+ *
+ * @param xs
+ * @param ys
+ * @returns {{ys: *, xs: *}}
+ */
+function cdfToPdf({ xs, ys }) {
+  let cdf = new Cdf(xs, ys);
+  let pdf = cdf.toPdf();
+  return { xs: pdf.xs, ys: pdf.ys };
+}
 
-  /**
-   *
-   * @param x
-   * @param xs
-   * @param ys
-   * @returns {number[]}
-   */
-  function convertToNewLength(n, { xs, ys }) {
-    let dist = new ContinuousDistribution(xs, ys);
-    return dist.convertToNewLength(n);
-  }
-  
-  /**
-   *
-   * @param y
-   * @param xs
-   * @param ys
-   * @returns {number}
-   */
-  function findX(y, { xs, ys }) {
-    let cdf = new Cdf(xs, ys);
-    return cdf.findX(y);
-  }
-  
-  /**
-   *
-   * @param xs
-   * @param ys
-   * @returns {number[]}
-   */
-  function integral({ xs, ys }) {
-    if (_.includes(ys, NaN)){
-      return NaN;
-    }
-    else if (_.includes(ys, Infinity) && _.includes(ys, -Infinity)){
-      return NaN;
-    }
-    else if (_.includes(ys, Infinity)){
-      return Infinity;
-    }
-    else if (_.includes(ys, -Infinity)){
-      return -Infinity;
-    }
-  
-    let integral = 0;
-    for (let i = 1; i < ys.length; i++) {
-      let thisY = ys[i];
-      let lastY = ys[i - 1];
-      let thisX = xs[i];
-      let lastX = xs[i - 1];
-  
-      if (
-        _.isFinite(thisY) && _.isFinite(lastY) &&
-        _.isFinite(thisX) && _.isFinite(lastX)
-      ) {
-        let sectionInterval = ((thisY + lastY) / 2) * (thisX - lastX);
-        integral = integral + sectionInterval;
+/**
+ *
+ * @param xs
+ * @param ys
+ * @returns {{ys: *, xs: *}}
+ */
+function pdfToCdf({ xs, ys }) {
+  let cdf = new Pdf(xs, ys);
+  let pdf = cdf.toCdf();
+  return { xs: pdf.xs, ys: pdf.ys };
+}
+
+/**
+ *
+ * @param sampleCount
+ * @param vars
+ * @returns {{ys: *, xs: *}}
+ */
+function mean(sampleCount, vars) {
+  let cdfs = vars.map(r => new Cdf(r.xs, r.ys));
+  let comb = new ContinuousDistributionCombination(cdfs);
+  let newCdf = comb.combineYsWithMean(sampleCount);
+
+  return { xs: newCdf.xs, ys: newCdf.ys };
+}
+
+/**
+ *
+ * @param sampleCount
+ * @param predictionCdf
+ * @param resolutionCdf
+ */
+function scoreNonMarketCdfCdf(sampleCount, predictionCdf, resolutionCdf, resolutionUniformAdditionWeight = 0) {
+  let toCdf = (r) => (new Cdf(r.xs, r.ys));
+  let prediction = toCdf(predictionCdf);
+  if (_.isFinite(resolutionUniformAdditionWeight)) {
+    prediction = prediction.combineWithUniformOfCdf(
+      {
+        cdf: toCdf(resolutionCdf),
+        uniformWeight: resolutionUniformAdditionWeight,
+        sampleCount
       }
-  
-    }
-    return integral;
+    );
   }
-  
-  module.exports = {
-    cdfToPdf,
-    pdfToCdf,
-    findY,
-    findX,
-    convertToNewLength,
-    mean,
-    scoreNonMarketCdfCdf,
-    differentialEntropy,
-    integral,
-  };
-  
\ No newline at end of file
+
+  return scoringFunctions.distributionInputDistributionOutputMarketless({
+    predictionCdf: prediction,
+    resultCdf: toCdf(resolutionCdf),
+    sampleCount,
+  });
+}
+
+/**
+ *
+ * @param sampleCount
+ * @param cdf
+ */
+function differentialEntropy(sampleCount, cdf) {
+  let toCdf = (r) => (new Cdf(r.xs, r.ys));
+
+  return scoringFunctions.differentialEntropy({
+    cdf: toCdf(cdf),
+    sampleCount: sampleCount
+  });
+}
+
+/**
+ *
+ * @param x
+ * @param xs
+ * @param ys
+ * @returns {number}
+ */
+function findY(x, { xs, ys }) {
+  let cdf = new Cdf(xs, ys);
+  return cdf.findY(x);
+}
+
+/**
+ *
+ * @param x
+ * @param xs
+ * @param ys
+ * @returns {number[]}
+ */
+function convertToNewLength(n, { xs, ys }) {
+  let dist = new ContinuousDistribution(xs, ys);
+  return dist.convertToNewLength(n);
+}
+
+/**
+ *
+ * @param y
+ * @param xs
+ * @param ys
+ * @returns {number}
+ */
+function findX(y, { xs, ys }) {
+  let cdf = new Cdf(xs, ys);
+  return cdf.findX(y);
+}
+
+/**
+ *
+ * @param xs
+ * @param ys
+ * @returns {number[]}
+ */
+function integral({ xs, ys }) {
+  if (_.includes(ys, NaN)) {
+    return NaN;
+  } else if (_.includes(ys, Infinity) && _.includes(ys, -Infinity)) {
+    return NaN;
+  } else if (_.includes(ys, Infinity)) {
+    return Infinity;
+  } else if (_.includes(ys, -Infinity)) {
+    return -Infinity;
+  }
+
+  let integral = 0;
+  for (let i = 1; i < ys.length; i++) {
+    let thisY = ys[i];
+    let lastY = ys[i - 1];
+    let thisX = xs[i];
+    let lastX = xs[i - 1];
+
+    if (
+      _.isFinite(thisY) && _.isFinite(lastY) &&
+      _.isFinite(thisX) && _.isFinite(lastX)
+    ) {
+      let sectionInterval = ((thisY + lastY) / 2) * (thisX - lastX);
+      integral = integral + sectionInterval;
+    }
+
+  }
+  return integral;
+}
+
+module.exports = {
+  cdfToPdf,
+  pdfToCdf,
+  findY,
+  findX,
+  convertToNewLength,
+  mean,
+  scoreNonMarketCdfCdf,
+  differentialEntropy,
+  integral,
+};