vega-statistics - npm Package Compare versions

Comparing version 1.7.7 to 1.7.8

build/vega-statistics.js

@@ -42,9 +42,7 @@ (function (global, factory) {
   function estimateBandwidth(array, f) {
-    let n = array.length,
-        v = d3Array.deviation(array, f),
-        q = quartiles(array, f),
-        h = (q[2] - q[0]) / 1.34;
-
-    v = Math.min(v, h) || v || Math.abs(q[0]) || 1;
-
+    const n = array.length,
+          d = d3Array.deviation(array, f),
+          q = quartiles(array, f),
+          h = (q[2] - q[0]) / 1.34,
+          v = Math.min(d, h) || d || Math.abs(q[0]) || 1;
     return 1.06 * v * Math.pow(n, -0.2);
@@ -55,11 +53,13 @@ }
     // determine range
-    let maxb = _.maxbins || 20,
-        base = _.base || 10,
-        logb = Math.log(base),
-        div  = _.divide || [5, 2],
-        min  = _.extent[0],
+    const maxb = _.maxbins || 20,
+          base = _.base || 10,
+          logb = Math.log(base),
+          div  = _.divide || [5, 2];
+
+    let min  = _.extent[0],
         max  = _.extent[1],
-        span = _.span || (max - min) || Math.abs(min) || 1,
-        step, level, minstep, precision, v, i, n, eps;
+        step, level, minstep, v, i, n;
 
+    const span = _.span || (max - min) || Math.abs(min) || 1;
+
     if (_.step) {
@@ -94,4 +94,4 @@ // if step size is explicitly given, use that
     v = Math.log(step);
-    precision = v >= 0 ? 0 : ~~(-v / logb) + 1;
-    eps = Math.pow(base, -precision - 1);
+    const precision = v >= 0 ? 0 : ~~(-v / logb) + 1,
+          eps = Math.pow(base, -precision - 1);
     if (_.nice || _.nice === undefined) {
@@ -119,6 +119,6 @@ v = Math.floor(min / step + eps) * step;
 
-    let values = Float64Array.from(numbers(array, f)),
-        n = values.length,
-        m = samples,
-        a, i, j, mu;
+    const values = Float64Array.from(numbers(array, f)),
+          n = values.length,
+          m = samples;
+    let a, i, j, mu;
 
@@ -146,5 +146,6 @@ for (j=0, mu=Array(m); j<m; ++j) {
 
+    const n = array.length,
+          v = new Float64Array(n);
+
     let i = 0, j = 1,
-        n = array.length,
-        v = new Float64Array(n),
         a = f(array[0]),
@@ -176,4 +177,4 @@ b = a,
   function smoothing(v, thresh) {
-    let n = v.length,
-        a = 0,
+    const n = v.length;
+    let a = 0,
         b = 1,
@@ -298,5 +299,5 @@ c, d;
 
-    let cd,
-        z = (value - mean) / stdev,
-        Z = Math.abs(z);
+    const z = (value - mean) / stdev,
+          Z = Math.abs(z);
+    let cd;
 
@@ -306,3 +307,4 @@ if (Z > 37) {
     } else {
-      let sum, exp = Math.exp(-Z * Z / 2);
+      const exp = Math.exp(-Z * Z / 2);
+      let sum;
       if (Z < 7.07106781186547) {
@@ -558,5 +560,6 @@ sum = 3.52624965998911e-02 * Z + 0.700383064443688;
     function normalize(x) {
-      let w = [], sum = 0, i;
-      for (i=0; i<m; ++i) { sum += (w[i] = (x[i]==null ? 1 : +x[i])); }
-      for (i=0; i<m; ++i) { w[i] /= sum; }
+      const w = [];
+      let sum = 0, i;
+      for (i = 0; i < m; ++i) { sum += (w[i] = (x[i]==null ? 1 : +x[i])); }
+      for (i = 0; i < m; ++i) { w[i] /= sum; }
       return w;
@@ -589,4 +592,4 @@ }
       sample() {
-        let r = exports.random(),
-            d = dists[m-1],
+        const r = exports.random();
+        let d = dists[m-1],
             v = w[0],
@@ -737,3 +740,3 @@ i = 0;
 
-    for (let d of data) {
+    for (const d of data) {
       u = x(d);
@@ -1041,4 +1044,4 @@ v = y(d);
 
-        let W = 0, X = 0, Y = 0, XY = 0, X2 = 0,
-            denom = 1 / Math.abs(xv[edge] - dx || 1); // avoid singularity!
+        let W = 0, X = 0, Y = 0, XY = 0, X2 = 0;
+        const denom = 1 / Math.abs(xv[edge] - dx || 1); // avoid singularity!
 
@@ -1091,4 +1094,4 @@ for (let k = i0; k <= i1; ++k) {
   function updateInterval(xv, i, interval) {
-    let val = xv[i],
-        left = interval[0],
+    const val = xv[i];
+    let left = interval[0],
         right = interval[1] + 1;
@@ -1095,0 +1098,0 @@

build/vega-statistics.min.js

@@ -1,1 +0,1 @@
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u,f,i,s=0,c=0,h=0,d=0,p=0;v(t,n,e,(t,n)=>{u=r[p++],f=Math.log(n),i=u*n,s+=(n*f-s)/p,c+=(i-c)/p,h+=(i*f-h)/p,d+=(u*i-d)/p});const[m,M]=N(c/a,s/a,h/a,d/a),g=t=>Math.exp(m+M*(t-l));return{coef:[Math.exp(m-M*l),M],predict:g,rSquared:A(t,n,e,a,g)}},t.regressionLinear=F,t.regressionLoess=function(t,e,r,o){const[l,a,u,f]=q(t,e,r,!0),i=l.length,s=Math.max(2,~~(o*i)),c=new Float64Array(i),h=new Float64Array(i),d=new Float64Array(i).fill(1);for(let t=-1;++t<=2;){const e=[0,s-1];for(let t=0;t<i;++t){const n=l[t],r=e[0],o=e[1],u=n-l[r]>l[o]-n?r:o;let f=0,i=0,s=0,p=0,m=0;const M=1/Math.abs(l[u]-n||1);for(let t=r;t<=o;++t){const e=l[t],r=a[t],o=E(Math.abs(n-e)*M)*d[t],u=e*o;f+=o,i+=u,s+=r*o,p+=r*u,m+=e*u}const[g,b]=N(i/f,s/f,p/f,m/f);c[t]=g+b*n,h[t]=Math.abs(a[t]-c[t]),P(l,t+1,e)}if(2===t)break;const r=n.median(h);if(Math.abs(r)<1e-12)break;for(let t,n,e=0;e<i;++e)t=h[e]/(6*r),d[e]=t>=1?1e-12:(n=1-t*t)*n}return function(t,n,e,r){const o=t.length,l=[];let a,u=0,f=0,i=[];for(;u<o;++u)a=t[u]+e,i[0]===a?i[1]+=(n[u]-i[1])/++f:(f=0,i[1]+=r,i=[a,n[u]],l.push(i));return i[1]+=r,l}(l,c,u,f)},t.regressionLog=function(t,n,e){let r=0,o=0,l=0,a=0,u=0;v(t,n,e,(t,n)=>{++u,t=Math.log(t),r+=(t-r)/u,o+=(n-o)/u,l+=(t*n-l)/u,a+=(t*t-a)/u});const f=N(r,o,l,a),i=t=>f[0]+f[1]*Math.log(t);return{coef:f,predict:i,rSquared:A(t,n,e,o,i)}},t.regressionPoly=function(t,n,e,r){if(1===r)return F(t,n,e);if(2===r)return L(t,n,e);const[o,l,a,u]=q(t,n,e),f=o.length,i=[],s=[],c=r+1;let h,d,p,m,M;for(h=0;h<c;++h){for(p=0,m=0;p<f;++p)m+=Math.pow(o[p],h)*l[p];for(i.push(m),M=new Float64Array(c),d=0;d<c;++d){for(p=0,m=0;p<f;++p)m+=Math.pow(o[p],h+d);M[d]=m}s.push(M)}s.push(i);const g=function(t){const n=t.length-1,e=[];let r,o,l,a,u;for(r=0;r<n;++r){for(a=r,o=r+1;o<n;++o)Math.abs(t[r][o])>Math.abs(t[r][a])&&(a=o);for(l=r;l<n+1;++l)u=t[l][r],t[l][r]=t[l][a],t[l][a]=u;for(o=r+1;o<n;++o)for(l=n;l>=r;l--)t[l][o]-=t[l][r]*t[r][o]/t[r][r]}for(o=n-1;o>=0;--o){for(u=0,l=o+1;l<n;++l)u+=t[l][o]*e[l];e[o]=(t[n][o]-u)/t[o][o]}return e}(s),b=t=>{t-=a;let n=u+g[0]+g[1]*t+g[2]*t*t;for(h=3;h<c;++h)n+=g[h]*Math.pow(t,h);return n};return{coef:S(c,g,-a,u),predict:b,rSquared:A(t,n,e,u,b)}},t.regressionPow=function(t,n,e){let r=0,o=0,l=0,a=0,u=0,f=0;v(t,n,e,(t,n)=>{const e=Math.log(t),i=Math.log(n);++f,r+=(e-r)/f,o+=(i-o)/f,l+=(e*i-l)/f,a+=(e*e-a)/f,u+=(n-u)/f});const i=N(r,o,l,a),s=t=>i[0]*Math.pow(t,i[1]);return i[0]=Math.exp(i[0]),{coef:i,predict:s,rSquared:A(t,n,e,u,s)}},t.regressionQuad=L,t.sampleCurve=function(t,n,e,r){e=e||25,r=Math.max(e,r||200);const o=n=>[n,t(n)],l=n[0],a=n[1],u=a-l,f=u/r,i=[o(l)],s=[];if(e===r){for(let t=1;t<r;++t)i.push(o(l+t/e*u));return i.push(o(a)),i}s.push(o(a));for(let t=e;--t>0;)s.push(o(l+t/e*u));let c=i[0],h=s[s.length-1];for(;h;){const t=o((c[0]+h[0])/2);t[0]-c[0]>=f&&I(c,t,h)>U?s.push(t):(c=h,i.push(h),s.pop()),h=s[s.length-1]}return i},t.sampleLogNormal=p,t.sampleNormal=i,t.sampleUniform=b,t.setRandom=function(n){t.random=n},Object.defineProperty(t,"__esModule",{value:!0})}));

package.json

 {
   "name": "vega-statistics",
-  "version": "1.7.7",
+  "version": "1.7.8",
   "description": "Statistical routines and probability distributions.",
@@ -27,5 +27,5 @@ "keywords": [
   "dependencies": {
-    "d3-array": "^2.5.1"
+    "d3-array": "^2.7.0"
   },
-  "gitHead": "28db83352e43e321dfe55fc5cb6489b211e45662"
+  "gitHead": "8d6793f4ca7eaaf2d22186764e9ce2dae687cf52"
 }

src/bandwidth.js

@@ -7,10 +7,8 @@ import quartiles from './quartiles';
 export default function(array, f) {
-  let n = array.length,
-      v = deviation(array, f),
-      q = quartiles(array, f),
-      h = (q[2] - q[0]) / 1.34;
-
-  v = Math.min(v, h) || v || Math.abs(q[0]) || 1;
-
+  const n = array.length,
+        d = deviation(array, f),
+        q = quartiles(array, f),
+        h = (q[2] - q[0]) / 1.34,
+        v = Math.min(d, h) || d || Math.abs(q[0]) || 1;
   return 1.06 * v * Math.pow(n, -0.2);
 }

src/bin.js

 export default function(_) {
   // determine range
-  let maxb = _.maxbins || 20,
-      base = _.base || 10,
-      logb = Math.log(base),
-      div  = _.divide || [5, 2],
-      min  = _.extent[0],
+  const maxb = _.maxbins || 20,
+        base = _.base || 10,
+        logb = Math.log(base),
+        div  = _.divide || [5, 2];
+
+  let min  = _.extent[0],
       max  = _.extent[1],
-      span = _.span || (max - min) || Math.abs(min) || 1,
-      step, level, minstep, precision, v, i, n, eps;
+      step, level, minstep, v, i, n;
 
+  const span = _.span || (max - min) || Math.abs(min) || 1;
+
   if (_.step) {
@@ -41,4 +43,4 @@ // if step size is explicitly given, use that
   v = Math.log(step);
-  precision = v >= 0 ? 0 : ~~(-v / logb) + 1;
-  eps = Math.pow(base, -precision - 1);
+  const precision = v >= 0 ? 0 : ~~(-v / logb) + 1,
+        eps = Math.pow(base, -precision - 1);
   if (_.nice || _.nice === undefined) {
@@ -45,0 +47,0 @@ v = Math.floor(min / step + eps) * step;

src/bootstrapCI.js

@@ -8,6 +8,6 @@ import numbers from './numbers';
 
-  let values = Float64Array.from(numbers(array, f)),
-      n = values.length,
-      m = samples,
-      a, i, j, mu;
+  const values = Float64Array.from(numbers(array, f)),
+        n = values.length,
+        m = samples;
+  let a, i, j, mu;
 
@@ -14,0 +14,0 @@ for (j=0, mu=Array(m); j<m; ++j) {

src/dotbin.js

@@ -7,5 +7,6 @@ // Dot density binning for dot plot construction.
 
+  const n = array.length,
+        v = new Float64Array(n);
+
   let i = 0, j = 1,
-      n = array.length,
-      v = new Float64Array(n),
       a = f(array[0]),
@@ -37,4 +38,4 @@ b = a,
 function smoothing(v, thresh) {
-  let n = v.length,
-      a = 0,
+  const n = v.length;
+  let a = 0,
       b = 1,
@@ -41,0 +42,0 @@ c, d;

src/mixture.js

@@ -7,5 +7,6 @@ import {random} from './random';
   function normalize(x) {
-    let w = [], sum = 0, i;
-    for (i=0; i<m; ++i) { sum += (w[i] = (x[i]==null ? 1 : +x[i])); }
-    for (i=0; i<m; ++i) { w[i] /= sum; }
+    const w = [];
+    let sum = 0, i;
+    for (i = 0; i < m; ++i) { sum += (w[i] = (x[i]==null ? 1 : +x[i])); }
+    for (i = 0; i < m; ++i) { w[i] /= sum; }
     return w;
@@ -38,4 +39,4 @@ }
     sample() {
-      let r = random(),
-          d = dists[m-1],
+      const r = random();
+      let d = dists[m-1],
           v = w[0],
@@ -42,0 +43,0 @@ i = 0;

src/normal.js

@@ -39,5 +39,5 @@ import {SQRT2, SQRT2PI} from './constants';
 
-  let cd,
-      z = (value - mean) / stdev,
-      Z = Math.abs(z);
+  const z = (value - mean) / stdev,
+        Z = Math.abs(z);
+  let cd;
 
@@ -47,3 +47,4 @@ if (Z > 37) {
   } else {
-    let sum, exp = Math.exp(-Z * Z / 2);
+    const exp = Math.exp(-Z * Z / 2);
+    let sum;
     if (Z < 7.07106781186547) {
@@ -50,0 +51,0 @@ sum = 3.52624965998911e-02 * Z + 0.700383064443688;

src/regression/loess.js

@@ -28,4 +28,4 @@ import ols from './ols';
 
-      let W = 0, X = 0, Y = 0, XY = 0, X2 = 0,
-          denom = 1 / Math.abs(xv[edge] - dx || 1); // avoid singularity!
+      let W = 0, X = 0, Y = 0, XY = 0, X2 = 0;
+      const denom = 1 / Math.abs(xv[edge] - dx || 1); // avoid singularity!
 
@@ -78,4 +78,4 @@ for (let k = i0; k <= i1; ++k) {
 function updateInterval(xv, i, interval) {
-  let val = xv[i],
-      left = interval[0],
+  const val = xv[i];
+  let left = interval[0],
       right = interval[1] + 1;
@@ -82,0 +82,0 @@

src/regression/points.js

@@ -37,3 +37,3 @@ export function points(data, x, y, sort) {
 
-  for (let d of data) {
+  for (const d of data) {
     u = x(d);
@@ -40,0 +40,0 @@ v = y(d);

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107288

increased by0.2%

Lines of code

2132

increased by0.19%

Dependency changes

• Updatedd3-array@^2.7.0