add gaussian pdf and cdf functions

lib/gaussian.js

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+import { erf } from 'mathjs';
+
+/**
+ * This file contains functions to produce
+ * Gaussian distribution and density functions
+ */
+
+
+/**
+ *
+ * @param sd - standard deviation
+ * @param m - mean
+ * @return {function(*): *} Gaussian probability density function
+ */
+export const createGaussianPDF = ({sd,m}) => {
+  return (x) => {
+    const exp =  (-0.5)*(x-m/sd)*(x-m/sd);
+    return (1/(sd*Math.sqrt(2*Math.PI)))*Math.exp(exp);
+  }
+}
+
+/**
+ *
+ * @param sd - standard deviation
+ * @param m - mean
+ * @return {function(*): *} Gaussian probability density function
+ */
+export const createGaussianCDF = ({sd,m}) => {
+  return (x) => {
+    return (1 - erf((m - x ) / (Math.sqrt(2) * sd))) / 2;
+  }
+}

lib/time-series.js

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+/**
+ * This function generates  a random time series for
+ * a given time interval with given precession,standard deviation, etc....
+ */
+import { generateRandomNumbers } from './random-sd';
+import { getUnixTime,addMilliseconds,parseISO,getTime } from 'date-fns';
+
+export const generateTimeSeries = ({
+    from,
+    to,
+    precession, // in milliseconds,
+    numberOfSamples,
+    sd
+  }) => {
+
+  const milliSecondsFrom = getTime(parseISO(from));
+  const milliSecondsTo = getTime(parseISO(to));
+  const duration = milliSecondsTo - milliSecondsFrom;
+  const __numberOfSamples = numberOfSamples
+    || duration/precession
+    || 1000;
+
+  const timeArray = new Array(Math.floor(__numberOfSamples))
+    .fill(1)
+    .map((i,index) =>{
+      return addMilliseconds(parseISO(from),index*precession);
+  });
+
+  const randomNumbers = generateRandomNumbers({
+    sd,
+    length: timeArray.length
+  });
+
+  return timeArray.map((time,index)=>{
+    return [time,randomNumbers[index]];
+  });
+};