Bump lots of changes

7 files changed, 1001 insertions, 0 deletions

diff --git a/web/nms.gathering.org/speedometer/d3-master/lib/science/LICENSE b/web/nms.gathering.org/speedometer/d3-master/lib/science/LICENSE
new file mode 100644
index 0000000..a59d17e
--- /dev/null
+++ b/web/nms.gathering.org/speedometer/d3-master/lib/science/LICENSE
@@ -0,0 +1,26 @@
+Copyright (c) 2011, Jason Davies
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+  * Redistributions of source code must retain the above copyright notice, this
+    list of conditions and the following disclaimer.
+
+  * Redistributions in binary form must reproduce the above copyright notice,
+    this list of conditions and the following disclaimer in the documentation
+    and/or other materials provided with the distribution.
+
+  * The name Jason Davies may not be used to endorse or promote products
+    derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL JASON DAVIES BE LIABLE FOR ANY DIRECT, INDIRECT,
+INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
+OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
+ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/web/nms.gathering.org/speedometer/d3-master/lib/science/science.js b/web/nms.gathering.org/speedometer/d3-master/lib/science/science.js
new file mode 100644
index 0000000..2f5556a
--- /dev/null
+++ b/web/nms.gathering.org/speedometer/d3-master/lib/science/science.js
@@ -0,0 +1,225 @@
+(function(){science = {version: "1.7.0"}; // semver
+science.ascending = function(a, b) {
+  return a - b;
+};
+// Euler's constant.
+science.EULER = .5772156649015329;
+// Compute exp(x) - 1 accurately for small x.
+science.expm1 = function(x) {
+  return (x < 1e-5 && x > -1e-5) ? x + .5 * x * x : Math.exp(x) - 1;
+};
+science.functor = function(v) {
+  return typeof v === "function" ? v : function() { return v; };
+};
+// Based on:
+// http://www.johndcook.com/blog/2010/06/02/whats-so-hard-about-finding-a-hypotenuse/
+science.hypot = function(x, y) {
+  x = Math.abs(x);
+  y = Math.abs(y);
+  var max,
+      min;
+  if (x > y) { max = x; min = y; }
+  else       { max = y; min = x; }
+  var r = min / max;
+  return max * Math.sqrt(1 + r * r);
+};
+science.quadratic = function() {
+  var complex = false;
+
+  function quadratic(a, b, c) {
+    var d = b * b - 4 * a * c;
+    if (d > 0) {
+      d = Math.sqrt(d) / (2 * a);
+      return complex
+        ? [{r: -b - d, i: 0}, {r: -b + d, i: 0}]
+        : [-b - d, -b + d];
+    } else if (d === 0) {
+      d = -b / (2 * a);
+      return complex ? [{r: d, i: 0}] : [d];
+    } else {
+      if (complex) {
+        d = Math.sqrt(-d) / (2 * a);
+        return [
+          {r: -b, i: -d},
+          {r: -b, i: d}
+        ];
+      }
+      return [];
+    }
+  }
+
+  quadratic.complex = function(x) {
+    if (!arguments.length) return complex;
+    complex = x;
+    return quadratic;
+  };
+
+  return quadratic;
+};
+// Constructs a multi-dimensional array filled with zeroes.
+science.zeroes = function(n) {
+  var i = -1,
+      a = [];
+  if (arguments.length === 1)
+    while (++i < n)
+      a[i] = 0;
+  else
+    while (++i < n)
+      a[i] = science.zeroes.apply(
+        this, Array.prototype.slice.call(arguments, 1));
+  return a;
+};
+science.vector = {};
+science.vector.cross = function(a, b) {
+  // TODO how to handle non-3D vectors?
+  // TODO handle 7D vectors?
+  return [
+    a[1] * b[2] - a[2] * b[1],
+    a[2] * b[0] - a[0] * b[2],
+    a[0] * b[1] - a[1] * b[0]
+  ];
+};
+science.vector.dot = function(a, b) {
+  var s = 0,
+      i = -1,
+      n = Math.min(a.length, b.length);
+  while (++i < n) s += a[i] * b[i];
+  return s;
+};
+science.vector.length = function(p) {
+  return Math.sqrt(science.vector.dot(p, p));
+};
+science.vector.normalize = function(p) {
+  var length = science.vector.length(p);
+  return p.map(function(d) { return d / length; });
+};
+// 4x4 matrix determinant.
+science.vector.determinant = function(matrix) {
+  var m = matrix[0].concat(matrix[1]).concat(matrix[2]).concat(matrix[3]);
+  return (
+    m[12] * m[9]  * m[6]  * m[3]  - m[8] * m[13] * m[6]  * m[3]  -
+    m[12] * m[5]  * m[10] * m[3]  + m[4] * m[13] * m[10] * m[3]  +
+    m[8]  * m[5]  * m[14] * m[3]  - m[4] * m[9]  * m[14] * m[3]  -
+    m[12] * m[9]  * m[2]  * m[7]  + m[8] * m[13] * m[2]  * m[7]  +
+    m[12] * m[1]  * m[10] * m[7]  - m[0] * m[13] * m[10] * m[7]  -
+    m[8]  * m[1]  * m[14] * m[7]  + m[0] * m[9]  * m[14] * m[7]  +
+    m[12] * m[5]  * m[2]  * m[11] - m[4] * m[13] * m[2]  * m[11] -
+    m[12] * m[1]  * m[6]  * m[11] + m[0] * m[13] * m[6]  * m[11] +
+    m[4]  * m[1]  * m[14] * m[11] - m[0] * m[5]  * m[14] * m[11] -
+    m[8]  * m[5]  * m[2]  * m[15] + m[4] * m[9]  * m[2]  * m[15] +
+    m[8]  * m[1]  * m[6]  * m[15] - m[0] * m[9]  * m[6]  * m[15] -
+    m[4]  * m[1]  * m[10] * m[15] + m[0] * m[5]  * m[10] * m[15]);
+};
+// Performs in-place Gauss-Jordan elimination.
+//
+// Based on Jarno Elonen's Python version (public domain):
+// http://elonen.iki.fi/code/misc-notes/python-gaussj/index.html
+science.vector.gaussjordan = function(m, eps) {
+  if (!eps) eps = 1e-10;
+
+  var h = m.length,
+      w = m[0].length,
+      y = -1,
+      y2,
+      x;
+
+  while (++y < h) {
+    var maxrow = y;
+
+    // Find max pivot.
+    y2 = y; while (++y2 < h) {
+      if (Math.abs(m[y2][y]) > Math.abs(m[maxrow][y]))
+        maxrow = y2;
+    }
+
+    // Swap.
+    var tmp = m[y];
+    m[y] = m[maxrow];
+    m[maxrow] = tmp;
+
+    // Singular?
+    if (Math.abs(m[y][y]) <= eps) return false;
+
+    // Eliminate column y.
+    y2 = y; while (++y2 < h) {
+      var c = m[y2][y] / m[y][y];
+      x = y - 1; while (++x < w) {
+        m[y2][x] -= m[y][x] * c;
+      }
+    }
+  }
+
+  // Backsubstitute.
+  y = h; while (--y >= 0) {
+    var c = m[y][y];
+    y2 = -1; while (++y2 < y) {
+      x = w; while (--x >= y) {
+        m[y2][x] -=  m[y][x] * m[y2][y] / c;
+      }
+    }
+    m[y][y] /= c;
+    // Normalize row y.
+    x = h - 1; while (++x < w) {
+      m[y][x] /= c;
+    }
+  }
+  return true;
+};
+// Find matrix inverse using Gauss-Jordan.
+science.vector.inverse = function(m) {
+  var n = m.length
+      i = -1;
+
+  // Check if the matrix is square.
+  if (n !== m[0].length) return;
+
+  // Augment with identity matrix I to get AI.
+  m = m.map(function(row, i) {
+    var identity = new Array(n),
+        j = -1;
+    while (++j < n) identity[j] = i === j ? 1 : 0;
+    return row.concat(identity);
+  });
+
+  // Compute IA^-1.
+  science.vector.gaussjordan(m);
+
+  // Remove identity matrix I to get A^-1.
+  while (++i < n) {
+    m[i] = m[i].slice(n);
+  }
+
+  return m;
+};
+science.vector.multiply = function(a, b) {
+  var m = a.length,
+      n = b[0].length,
+      p = b.length,
+      i = -1,
+      j,
+      k;
+  if (p !== a[0].length) throw {"error": "columns(a) != rows(b); " + a[0].length + " != " + p};
+  var ab = new Array(m);
+  while (++i < m) {
+    ab[i] = new Array(n);
+    j = -1; while(++j < n) {
+      var s = 0;
+      k = -1; while (++k < p) s += a[i][k] * b[k][j];
+      ab[i][j] = s;
+    }
+  }
+  return ab;
+};
+science.vector.transpose = function(a) {
+  var m = a.length,
+      n = a[0].length,
+      i = -1,
+      j,
+      b = new Array(n);
+  while (++i < n) {
+    b[i] = new Array(m);
+    j = -1; while (++j < m) b[i][j] = a[j][i];
+  }
+  return b;
+};
+})()
\ No newline at end of file
diff --git a/web/nms.gathering.org/speedometer/d3-master/lib/science/science.lin.js b/web/nms.gathering.org/speedometer/d3-master/lib/science/science.lin.js
new file mode 100644
index 0000000..cbdc2ac
--- /dev/null
+++ b/web/nms.gathering.org/speedometer/d3-master/lib/science/science.lin.js
@@ -0,0 +1,27 @@
+(function(){science.lin = {};
+/**
+ * Solves tridiagonal systems of linear equations.
+ *
+ * Source: http://en.wikipedia.org/wiki/Tridiagonal_matrix_algorithm
+ *
+ * @param {number[]} a
+ * @param {number[]} b
+ * @param {number[]} c
+ * @param {number[]} d
+ * @param {number[]} x
+ * @param {number} n
+ */
+science.lin.tridag = function(a, b, c, d, x, n) {
+  var i,
+      m;
+  for (i = 1; i < n; i++) {
+    m = a[i] / b[i - 1];
+    b[i] -= m * c[i - 1];
+    d[i] -= m * d[i - 1];
+  }
+  x[n - 1] = d[n - 1] / b[n - 1];
+  for (i = n - 2; i >= 0; i--) {
+    x[i] = (d[i] - c[i] * x[i + 1]) / b[i];
+  }
+};
+})()
\ No newline at end of file
diff --git a/web/nms.gathering.org/speedometer/d3-master/lib/science/science.lin.min.js b/web/nms.gathering.org/speedometer/d3-master/lib/science/science.lin.min.js
new file mode 100644
index 0000000..a470da4
--- /dev/null
+++ b/web/nms.gathering.org/speedometer/d3-master/lib/science/science.lin.min.js
@@ -0,0 +1 @@
+(function(){science.lin={},science.lin.tridag=function(a,b,c,d,e,f){var g,h;for(g=1;g<f;g++)h=a[g]/b[g-1],b[g]-=h*c[g-1],d[g]-=h*d[g-1];e[f-1]=d[f-1]/b[f-1];for(g=f-2;g>=0;g--)e[g]=(d[g]-c[g]*e[g+1])/b[g]}})()
\ No newline at end of file
diff --git a/web/nms.gathering.org/speedometer/d3-master/lib/science/science.min.js b/web/nms.gathering.org/speedometer/d3-master/lib/science/science.min.js
new file mode 100644
index 0000000..41cf0eb
--- /dev/null
+++ b/web/nms.gathering.org/speedometer/d3-master/lib/science/science.min.js
@@ -0,0 +1 @@
+(function(){science={version:"1.7.0"},science.ascending=function(a,b){return a-b},science.EULER=.5772156649015329,science.expm1=function(a){return a<1e-5&&a>-0.00001?a+.5*a*a:Math.exp(a)-1},science.functor=function(a){return typeof a=="function"?a:function(){return a}},science.hypot=function(a,b){a=Math.abs(a),b=Math.abs(b);var c,d;a>b?(c=a,d=b):(c=b,d=a);var e=d/c;return c*Math.sqrt(1+e*e)},science.quadratic=function(){function b(b,c,d){var e=c*c-4*b*d;if(e>0){e=Math.sqrt(e)/(2*b);return a?[{r:-c-e,i:0},{r:-c+e,i:0}]:[-c-e,-c+e]}if(e===0){e=-c/(2*b);return a?[{r:e,i:0}]:[e]}if(a){e=Math.sqrt(-e)/(2*b);return[{r:-c,i:-e},{r:-c,i:e}]}return[]}var a=!1;b.complex=function(c){if(!arguments.length)return a;a=c;return b};return b},science.zeroes=function(a){var b=-1,c=[];if(arguments.length===1)while(++b<a)c[b]=0;else while(++b<a)c[b]=science.zeroes.apply(this,Array.prototype.slice.call(arguments,1));return c},science.vector={},science.vector.cross=function(a,b){return[a[1]*b[2]-a[2]*b[1],a[2]*b[0]-a[0]*b[2],a[0]*b[1]-a[1]*b[0]]},science.vector.dot=function(a,b){var c=0,d=-1,e=Math.min(a.length,b.length);while(++d<e)c+=a[d]*b[d];return c},science.vector.length=function(a){return Math.sqrt(science.vector.dot(a,a))},science.vector.normalize=function(a){var b=science.vector.length(a);return a.map(function(a){return a/b})},science.vector.determinant=function(a){var b=a[0].concat(a[1]).concat(a[2]).concat(a[3]);return b[12]*b[9]*b[6]*b[3]-b[8]*b[13]*b[6]*b[3]-b[12]*b[5]*b[10]*b[3]+b[4]*b[13]*b[10]*b[3]+b[8]*b[5]*b[14]*b[3]-b[4]*b[9]*b[14]*b[3]-b[12]*b[9]*b[2]*b[7]+b[8]*b[13]*b[2]*b[7]+b[12]*b[1]*b[10]*b[7]-b[0]*b[13]*b[10]*b[7]-b[8]*b[1]*b[14]*b[7]+b[0]*b[9]*b[14]*b[7]+b[12]*b[5]*b[2]*b[11]-b[4]*b[13]*b[2]*b[11]-b[12]*b[1]*b[6]*b[11]+b[0]*b[13]*b[6]*b[11]+b[4]*b[1]*b[14]*b[11]-b[0]*b[5]*b[14]*b[11]-b[8]*b[5]*b[2]*b[15]+b[4]*b[9]*b[2]*b[15]+b[8]*b[1]*b[6]*b[15]-b[0]*b[9]*b[6]*b[15]-b[4]*b[1]*b[10]*b[15]+b[0]*b[5]*b[10]*b[15]},science.vector.gaussjordan=function(a,b){b||(b=1e-10);var c=a.length,d=a[0].length,e=-1,f,g;while(++e<c){var h=e;f=e;while(++f<c)Math.abs(a[f][e])>Math.abs(a[h][e])&&(h=f);var i=a[e];a[e]=a[h],a[h]=i;if(Math.abs(a[e][e])<=b)return!1;f=e;while(++f<c){var j=a[f][e]/a[e][e];g=e-1;while(++g<d)a[f][g]-=a[e][g]*j}}e=c;while(--e>=0){var j=a[e][e];f=-1;while(++f<e){g=d;while(--g>=e)a[f][g]-=a[e][g]*a[f][e]/j}a[e][e]/=j,g=c-1;while(++g<d)a[e][g]/=j}return!0},science.vector.inverse=function(a){var b=a.length;i=-1;if(b===a[0].length){a=a.map(function(a,c){var d=Array(b),e=-1;while(++e<b)d[e]=c===e?1:0;return a.concat(d)}),science.vector.gaussjordan(a);while(++i<b)a[i]=a[i].slice(b);return a}},science.vector.multiply=function(a,b){var c=a.length,d=b[0].length,e=b.length,f=-1,g,h;if(e!==a[0].length)throw{error:"columns(a) != rows(b); "+a[0].length+" != "+e};var i=Array(c);while(++f<c){i[f]=Array(d),g=-1;while(++g<d){var j=0;h=-1;while(++h<e)j+=a[f][h]*b[h][g];i[f][g]=j}}return i},science.vector.transpose=function(a){var b=a.length,c=a[0].length,d=-1,e,f=Array(c);while(++d<c){f[d]=Array(b),e=-1;while(++e<b)f[d][e]=a[e][d]}return f}})()
\ No newline at end of file
diff --git a/web/nms.gathering.org/speedometer/d3-master/lib/science/science.stats.js b/web/nms.gathering.org/speedometer/d3-master/lib/science/science.stats.js
new file mode 100644
index 0000000..d98df12
--- /dev/null
+++ b/web/nms.gathering.org/speedometer/d3-master/lib/science/science.stats.js
@@ -0,0 +1,720 @@
+(function(){science.stats = {};
+// Bandwidth selectors for Gaussian kernels.
+// Based on R's implementations in `stats.bw`.
+science.stats.bandwidth = {
+
+  // Silverman, B. W. (1986) Density Estimation. London: Chapman and Hall.
+  nrd0: function(x) {
+    var hi = Math.sqrt(science.stats.variance(x));
+    if (!(lo = Math.min(hi, science.stats.iqr(x) / 1.34)))
+      (lo = hi) || (lo = Math.abs(x[1])) || (lo = 1);
+    return .9 * lo * Math.pow(x.length, -.2);
+  },
+
+  // Scott, D. W. (1992) Multivariate Density Estimation: Theory, Practice, and
+  // Visualization. Wiley.
+  nrd: function(x) {
+    var h = science.stats.iqr(x) / 1.34;
+    return 1.06 * Math.min(Math.sqrt(science.stats.variance(x)), h)
+      * Math.pow(x.length, -1/5);
+  }
+};
+science.stats.distance = {
+  euclidean: function(a, b) {
+    var n = a.length,
+        i = -1,
+        s = 0,
+        x;
+    while (++i < n) {
+      x = a[i] - b[i];
+      s += x * x;
+    }
+    return Math.sqrt(s);
+  },
+  manhattan: function(a, b) {
+    var n = a.length,
+        i = -1,
+        s = 0;
+    while (++i < n) s += Math.abs(a[i] - b[i]);
+    return s;
+  },
+  minkowski: function(p) {
+    return function(a, b) {
+      var n = a.length,
+          i = -1,
+          s = 0;
+      while (++i < n) s += Math.pow(Math.abs(a[i] - b[i]), p);
+      return Math.pow(s, 1 / p);
+    };
+  },
+  chebyshev: function(a, b) {
+    var n = a.length,
+        i = -1,
+        max = 0,
+        x;
+    while (++i < n) {
+      x = Math.abs(a[i] - b[i]);
+      if (x > max) max = x;
+    }
+    return max;
+  },
+  hamming: function(a, b) {
+    var n = a.length,
+        i = -1,
+        d = 0;
+    while (++i < n) if (a[i] !== b[i]) d++;
+    return d;
+  },
+  jaccard: function(a, b) {
+    var n = a.length,
+        i = -1,
+        s = 0;
+    while (++i < n) if (a[i] === b[i]) s++;
+    return s / n;
+  },
+  braycurtis: function(a, b) {
+    var n = a.length,
+        i = -1,
+        s0 = 0,
+        s1 = 0,
+        ai,
+        bi;
+    while (++i < n) {
+      ai = a[i];
+      bi = b[i];
+      s0 += Math.abs(ai - bi);
+      s1 += Math.abs(ai + bi);
+    }
+    return s0 / s1;
+  }
+};
+// Based on implementation in http://picomath.org/.
+science.stats.erf = function(x) {
+  var a1 =  0.254829592,
+      a2 = -0.284496736,
+      a3 =  1.421413741,
+      a4 = -1.453152027,
+      a5 =  1.061405429,
+      p  =  0.3275911;
+
+  // Save the sign of x
+  var sign = x < 0 ? -1 : 1;
+  if (x < 0) {
+    sign = -1;
+    x = -x;
+  }
+
+  // A&S formula 7.1.26
+  var t = 1 / (1 + p * x);
+  return sign * (
+    1 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1)
+    * t * Math.exp(-x * x));
+};
+science.stats.phi = function(x) {
+  return .5 * (1 + science.stats.erf(x / Math.SQRT2));
+};
+// See <http://en.wikipedia.org/wiki/Kernel_(statistics)>.
+science.stats.kernel = {
+  uniform: function(u) {
+    if (u <= 1 && u >= -1) return .5;
+    return 0;
+  },
+  triangular: function(u) {
+    if (u <= 1 && u >= -1) return 1 - Math.abs(u);
+    return 0;
+  },
+  epanechnikov: function(u) {
+    if (u <= 1 && u >= -1) return .75 * (1 - u * u);
+    return 0;
+  },
+  quartic: function(u) {
+    if (u <= 1 && u >= -1) {
+      var tmp = 1 - u * u;
+      return (15 / 16) * tmp * tmp;
+    }
+    return 0;
+  },
+  triweight: function(u) {
+    if (u <= 1 && u >= -1) {
+      var tmp = 1 - u * u;
+      return (35 / 32) * tmp * tmp * tmp;
+    }
+    return 0;
+  },
+  gaussian: function(u) {
+    return 1 / Math.sqrt(2 * Math.PI) * Math.exp(-.5 * u * u);
+  },
+  cosine: function(u) {
+    if (u <= 1 && u >= -1) return Math.PI / 4 * Math.cos(Math.PI / 2 * u);
+    return 0;
+  }
+};
+// http://exploringdata.net/den_trac.htm
+science.stats.kde = function() {
+  var kernel = science.stats.kernel.gaussian,
+      sample = [],
+      bandwidth = science.stats.bandwidth.nrd;
+
+  function kde(points, i) {
+    var bw = bandwidth.call(this, sample);
+    return points.map(function(x) {
+      var i = -1,
+          y = 0,
+          n = sample.length;
+      while (++i < n) {
+        y += kernel((x - sample[i]) / bw);
+      }
+      return [x, y / bw / n];
+    });
+  }
+
+  kde.kernel = function(x) {
+    if (!arguments.length) return kernel;
+    kernel = x;
+    return kde;
+  };
+
+  kde.sample = function(x) {
+    if (!arguments.length) return sample;
+    sample = x;
+    return kde;
+  };
+
+  kde.bandwidth = function(x) {
+    if (!arguments.length) return bandwidth;
+    bandwidth = science.functor(x);
+    return kde;
+  };
+
+  return kde;
+};
+// Based on figue implementation by Jean-Yves Delort.
+// http://code.google.com/p/figue/
+science.stats.kmeans = function() {
+  var distance = science.stats.distance.euclidean,
+      maxIterations = 1000,
+      k = 1;
+
+  function kmeans(vectors) {
+    var n = vectors.length,
+        assignments = [],
+        clusterSizes = [],
+        repeat = 1,
+        iterations = 0,
+        centroids = science_stats_kmeansRandom(k, vectors),
+        newCentroids,
+        i,
+        j,
+        x,
+        d,
+        min,
+        best;
+
+    while (repeat && iterations < maxIterations) {
+      // Assignment step.
+      j = -1; while (++j < k) {
+        clusterSizes[j] = 0;
+      }
+
+      i = -1; while (++i < n) {
+        x = vectors[i];
+        min = Infinity;
+        j = -1; while (++j < k) {
+          d = distance.call(this, centroids[j], x);
+          if (d < min) {
+            min = d;
+            best = j;
+          }
+        }
+        clusterSizes[assignments[i] = best]++;
+      }
+
+      // Update centroids step.
+      newCentroids = [];
+      i = -1; while (++i < n) {
+        x = assignments[i];
+        d = newCentroids[x];
+        if (d == null) newCentroids[x] = vectors[i].slice();
+        else {
+          j = -1; while (++j < d.length) {
+            d[j] += vectors[i][j];
+          }
+        }
+      }
+      j = -1; while (++j < k) {
+        x = newCentroids[j];
+        d = 1 / clusterSizes[j];
+        i = -1; while (++i < x.length) x[i] *= d;
+      }
+
+      // Check convergence.
+      repeat = 0;
+      j = -1; while (++j < k) {
+        if (!science_stats_kmeansCompare(newCentroids[j], centroids[j])) {
+          repeat = 1;
+          break;
+        }
+      }
+      centroids = newCentroids;
+      iterations++;
+    }
+    return {assignments: assignments, centroids: centroids};
+  }
+
+  kmeans.k = function(x) {
+    if (!arguments.length) return k;
+    k = x;
+    return kmeans;
+  };
+
+  kmeans.distance = function(x) {
+    if (!arguments.length) return distance;
+    distance = x;
+    return kmeans;
+  };
+
+  return kmeans;
+};
+
+function science_stats_kmeansCompare(a, b) {
+  if (!a || !b || a.length !== b.length) return false;
+  var n = a.length,
+      i = -1;
+  while (++i < n) if (a[i] !== b[i]) return false;
+  return true;
+}
+
+// Returns an array of k distinct vectors randomly selected from the input
+// array of vectors. Returns null if k > n or if there are less than k distinct
+// objects in vectors.
+function science_stats_kmeansRandom(k, vectors) {
+  var n = vectors.length;
+  if (k > n) return null;
+  
+  var selected_vectors = [];
+  var selected_indices = [];
+  var tested_indices = {};
+  var tested = 0;
+  var selected = 0;
+  var i,
+      vector,
+      select;
+
+  while (selected < k) {
+    if (tested === n) return null;
+    
+    var random_index = Math.floor(Math.random() * n);
+    if (random_index in tested_indices) continue;
+    
+    tested_indices[random_index] = 1;
+    tested++;
+    vector = vectors[random_index];
+    select = true;
+    for (i = 0; i < selected; i++) {
+      if (science_stats_kmeansCompare(vector, selected_vectors[i])) {
+        select = false;
+        break;
+      }
+    }
+    if (select) {
+      selected_vectors[selected] = vector;
+      selected_indices[selected] = random_index;
+      selected++;
+    }
+  }
+  return selected_vectors;
+}
+science.stats.hcluster = function() {
+  var distance = science.stats.distance.euclidean,
+      linkage = "simple"; // simple, complete or average
+
+  function hcluster(vectors) {
+    var n = vectors.length,
+        dMin = [],
+        cSize = [],
+        distMatrix = [],
+        clusters = [],
+        c1,
+        c2,
+        c1Cluster,
+        c2Cluster,
+        p,
+        root,
+        i,
+        j;
+
+    // Initialise distance matrix and vector of closest clusters.
+    i = -1; while (++i < n) {
+      dMin[i] = 0;
+      distMatrix[i] = [];
+      j = -1; while (++j < n) {
+        distMatrix[i][j] = i === j ? Infinity : distance(vectors[i] , vectors[j]);
+        if (distMatrix[i][dMin[i]] > distMatrix[i][j]) dMin[i] = j;
+      }
+    }
+
+    // create leaves of the tree
+    i = -1; while (++i < n) {
+      clusters[i] = [];
+      clusters[i][0] = {
+        left: null,
+        right: null,
+        dist: 0,
+        centroid: vectors[i],
+        size: 1,
+        depth: 0
+      };
+      cSize[i] = 1;
+    }
+
+    // Main loop
+    for (p = 0; p < n-1; p++) {
+      // find the closest pair of clusters
+      c1 = 0;
+      for (i = 0; i < n; i++) {
+        if (distMatrix[i][dMin[i]] < distMatrix[c1][dMin[c1]]) c1 = i;
+      }
+      c2 = dMin[c1];
+
+      // create node to store cluster info 
+      c1Cluster = clusters[c1][0];
+      c2Cluster = clusters[c2][0];
+
+      newCluster = {
+        left: c1Cluster,
+        right: c2Cluster,
+        dist: distMatrix[c1][c2],
+        centroid: calculateCentroid(c1Cluster.size, c1Cluster.centroid,
+          c2Cluster.size, c2Cluster.centroid),
+        size: c1Cluster.size + c2Cluster.size,
+        depth: 1 + Math.max(c1Cluster.depth, c2Cluster.depth)
+      };
+      clusters[c1].splice(0, 0, newCluster);
+      cSize[c1] += cSize[c2];
+
+      // overwrite row c1 with respect to the linkage type
+      for (j = 0; j < n; j++) {
+        switch (linkage) {
+          case "single":
+            if (distMatrix[c1][j] > distMatrix[c2][j])
+              distMatrix[j][c1] = distMatrix[c1][j] = distMatrix[c2][j];
+            break;
+          case "complete":
+            if (distMatrix[c1][j] < distMatrix[c2][j])
+              distMatrix[j][c1] = distMatrix[c1][j] = distMatrix[c2][j];
+            break;
+          case "average":
+            distMatrix[j][c1] = distMatrix[c1][j] = (cSize[c1] * distMatrix[c1][j] + cSize[c2] * distMatrix[c2][j]) / (cSize[c1] + cSize[j]);
+            break;
+        }
+      }
+      distMatrix[c1][c1] = Infinity;
+
+      // infinity ­out old row c2 and column c2
+      for (i = 0; i < n; i++)
+        distMatrix[i][c2] = distMatrix[c2][i] = Infinity;
+
+      // update dmin and replace ones that previous pointed to c2 to point to c1
+      for (j = 0; j < n; j++) {
+        if (dMin[j] == c2) dMin[j] = c1;
+        if (distMatrix[c1][j] < distMatrix[c1][dMin[c1]]) dMin[c1] = j;
+      }
+
+      // keep track of the last added cluster
+      root = newCluster;
+    }
+
+    return root;
+  }
+
+  hcluster.distance = function(x) {
+    if (!arguments.length) return distance;
+    distance = x;
+    return hcluster;
+  };
+
+  return hcluster;
+};
+
+function calculateCentroid(c1Size, c1Centroid, c2Size, c2Centroid) {
+  var newCentroid = [],
+      newSize = c1Size + c2Size,
+      n = c1Centroid.length,
+      i = -1;
+  while (++i < n) {
+    newCentroid[i] = (c1Size * c1Centroid[i] + c2Size * c2Centroid[i]) / newSize;
+  }
+  return newCentroid;
+}
+science.stats.iqr = function(x) {
+  var quartiles = science.stats.quantiles(x, [.25, .75]);
+  return quartiles[1] - quartiles[0];
+};
+// Based on org.apache.commons.math.analysis.interpolation.LoessInterpolator
+// from http://commons.apache.org/math/
+science.stats.loess = function() {    
+  var bandwidth = .3,
+      robustnessIters = 2,
+      accuracy = 1e-12;
+
+  function smooth(xval, yval, weights) {
+    var n = xval.length,
+        i;
+
+    if (n !== yval.length) throw {error: "Mismatched array lengths"};
+    if (n == 0) throw {error: "At least one point required."};
+
+    if (arguments.length < 3) {
+      weights = [];
+      i = -1; while (++i < n) weights[i] = 1;
+    }
+
+    science_stats_loessFiniteReal(xval);
+    science_stats_loessFiniteReal(yval);
+    science_stats_loessFiniteReal(weights);
+    science_stats_loessStrictlyIncreasing(xval);
+
+    if (n == 1) return [yval[0]];
+    if (n == 2) return [yval[0], yval[1]];
+
+    var bandwidthInPoints = Math.floor(bandwidth * n);
+
+    if (bandwidthInPoints < 2) throw {error: "Bandwidth too small."};
+
+    var res = [],
+        residuals = [],
+        robustnessWeights = [];
+
+    // Do an initial fit and 'robustnessIters' robustness iterations.
+    // This is equivalent to doing 'robustnessIters+1' robustness iterations
+    // starting with all robustness weights set to 1.
+    i = -1; while (++i < n) {
+      res[i] = 0;
+      residuals[i] = 0;
+      robustnessWeights[i] = 1;
+    }
+
+    var iter = -1;
+    while (++iter <= robustnessIters) {
+      var bandwidthInterval = [0, bandwidthInPoints - 1];
+      // At each x, compute a local weighted linear regression
+      var x;
+      i = -1; while (++i < n) {
+        x = xval[i];
+
+        // Find out the interval of source points on which
+        // a regression is to be made.
+        if (i > 0) {
+          science_stats_loessUpdateBandwidthInterval(xval, weights, i, bandwidthInterval);
+        }
+
+        var ileft = bandwidthInterval[0],
+            iright = bandwidthInterval[1];
+
+        // Compute the point of the bandwidth interval that is
+        // farthest from x
+        var edge = (xval[i] - xval[ileft]) > (xval[iright] - xval[i]) ? ileft : iright;
+
+        // Compute a least-squares linear fit weighted by
+        // the product of robustness weights and the tricube
+        // weight function.
+        // See http://en.wikipedia.org/wiki/Linear_regression
+        // (section "Univariate linear case")
+        // and http://en.wikipedia.org/wiki/Weighted_least_squares
+        // (section "Weighted least squares")
+        var sumWeights = 0,
+            sumX = 0,
+            sumXSquared = 0,
+            sumY = 0,
+            sumXY = 0,
+            denom = Math.abs(1 / (xval[edge] - x));
+
+        for (var k = ileft; k <= iright; ++k) {
+          var xk   = xval[k],
+              yk   = yval[k],
+              dist = k < i ? x - xk : xk - x,
+              w    = science_stats_loessTricube(dist * denom) * robustnessWeights[k] * weights[k],
+              xkw  = xk * w;
+          sumWeights += w;
+          sumX += xkw;
+          sumXSquared += xk * xkw;
+          sumY += yk * w;
+          sumXY += yk * xkw;
+        }
+
+        var meanX = sumX / sumWeights,
+            meanY = sumY / sumWeights,
+            meanXY = sumXY / sumWeights,
+            meanXSquared = sumXSquared / sumWeights;
+
+        var beta = (Math.sqrt(Math.abs(meanXSquared - meanX * meanX)) < accuracy)
+            ? 0 : ((meanXY - meanX * meanY) / (meanXSquared - meanX * meanX));
+
+        var alpha = meanY - beta * meanX;
+
+        res[i] = beta * x + alpha;
+        residuals[i] = Math.abs(yval[i] - res[i]);
+      }
+
+      // No need to recompute the robustness weights at the last
+      // iteration, they won't be needed anymore
+      if (iter === robustnessIters) {
+        break;
+      }
+
+      // Recompute the robustness weights.
+
+      // Find the median residual.
+      var sortedResiduals = residuals.slice();
+      sortedResiduals.sort();
+      var medianResidual = sortedResiduals[Math.floor(n / 2)];
+
+      if (Math.abs(medianResidual) < accuracy)
+        break;
+
+      var arg,
+          w;
+      i = -1; while (++i < n) {
+        arg = residuals[i] / (6 * medianResidual);
+        robustnessWeights[i] = (arg >= 1) ? 0 : ((w = 1 - arg * arg) * w);
+      }
+    }
+
+    return res;
+  }
+
+  smooth.bandwidth = function(x) {
+    if (!arguments.length) return x;
+    bandwidth = x;
+    return smooth;
+  };
+
+  smooth.robustnessIterations = function(x) {
+    if (!arguments.length) return x;
+    robustnessIters = x;
+    return smooth;
+  };
+
+  smooth.accuracy = function(x) {
+    if (!arguments.length) return x;
+    accuracy = x;
+    return smooth;
+  };
+
+  return smooth;
+};
+
+function science_stats_loessFiniteReal(values) {
+  var n = values.length,
+      i = -1;
+
+  while (++i < n) if (!isFinite(values[i])) return false;
+
+  return true;
+}
+
+function science_stats_loessStrictlyIncreasing(xval) {
+  var n = xval.length,
+      i = 0;
+
+  while (++i < n) if (xval[i - 1] >= xval[i]) return false;
+
+  return true;
+}
+
+// Compute the tricube weight function.
+// http://en.wikipedia.org/wiki/Local_regression#Weight_function
+function science_stats_loessTricube(x) {
+  return (x = 1 - x * x * x) * x * x;
+}
+
+// Given an index interval into xval that embraces a certain number of
+// points closest to xval[i-1], update the interval so that it embraces
+// the same number of points closest to xval[i], ignoring zero weights.
+function science_stats_loessUpdateBandwidthInterval(
+  xval, weights, i, bandwidthInterval) {
+
+  var left = bandwidthInterval[0],
+      right = bandwidthInterval[1];
+
+  // The right edge should be adjusted if the next point to the right
+  // is closer to xval[i] than the leftmost point of the current interval
+  var nextRight = science_stats_loessNextNonzero(weights, right);
+  if ((nextRight < xval.length) && (xval[nextRight] - xval[i]) < (xval[i] - xval[left])) {
+    var nextLeft = science_stats_loessNextNonzero(weights, left);
+    bandwidthInterval[0] = nextLeft;
+    bandwidthInterval[1] = nextRight;
+  }
+}
+
+function science_stats_loessNextNonzero(weights, i) {
+  var j = i + 1;
+  while (j < weights.length && weights[j] === 0) j++;
+  return j;
+}
+// Welford's algorithm.
+science.stats.mean = function(x) {
+  var n = x.length;
+  if (n === 0) return NaN;
+  var m = 0,
+      i = -1;
+  while (++i < n) m += (x[i] - m) / (i + 1);
+  return m;
+};
+science.stats.median = function(x) {
+  return science.stats.quantiles(x, [.5])[0];
+};
+science.stats.mode = function(x) {
+  x = x.slice().sort(science.ascending);
+  var mode,
+      n = x.length,
+      i = -1,
+      l = i,
+      last = null,
+      max = 0,
+      tmp,
+      v;
+  while (++i < n) {
+    if ((v = x[i]) !== last) {
+      if ((tmp = i - l) > max) {
+        max = tmp;
+        mode = last;
+      }
+      last = v;
+      l = i;
+    }
+  }
+  return mode;
+};
+// Uses R's quantile algorithm type=7.
+science.stats.quantiles = function(d, quantiles) {
+  d = d.slice().sort(science.ascending);
+  var n_1 = d.length - 1;
+  return quantiles.map(function(q) {
+    if (q === 0) return d[0];
+    else if (q === 1) return d[n_1];
+
+    var index = 1 + q * n_1,
+        lo = Math.floor(index),
+        h = index - lo,
+        a = d[lo - 1];
+
+    return h === 0 ? a : a + h * (d[lo] - a);
+  });
+};
+// Unbiased estimate of a sample's variance.
+// Also known as the sample variance, where the denominator is n - 1.
+science.stats.variance = function(x) {
+  var n = x.length;
+  if (n < 1) return NaN;
+  if (n === 1) return 0;
+  var mean = science.stats.mean(x),
+      i = -1,
+      s = 0;
+  while (++i < n) {
+    var v = x[i] - mean;
+    s += v * v;
+  }
+  return s / (n - 1);
+};
+})()
\ No newline at end of file
diff --git a/web/nms.gathering.org/speedometer/d3-master/lib/science/science.stats.min.js b/web/nms.gathering.org/speedometer/d3-master/lib/science/science.stats.min.js
new file mode 100644
index 0000000..fd69f13
--- /dev/null
+++ b/web/nms.gathering.org/speedometer/d3-master/lib/science/science.stats.min.js
@@ -0,0 +1 @@
+(function(){function h(a,b){var c=b+1;while(c<a.length&&a[c]===0)c++;return c}function g(a,b,c,d){var e=d[0],f=d[1],g=h(b,f);if(g<a.length&&a[g]-a[c]<a[c]-a[e]){var i=h(b,e);d[0]=i,d[1]=g}}function f(a){return(a=1-a*a*a)*a*a}function e(a){var b=a.length,c=0;while(++c<b)if(a[c-1]>=a[c])return!1;return!0}function d(a){var b=a.length,c=-1;while(++c<b)if(!isFinite(a[c]))return!1;return!0}function c(a,b,c,d){var e=[],f=a+c,g=b.length,h=-1;while(++h<g)e[h]=(a*b[h]+c*d[h])/f;return e}function b(b,c){var d=c.length;if(b>d)return null;var e=[],f=[],g={},h=0,i=0,j,k,l;while(i<b){if(h===d)return null;var m=Math.floor(Math.random()*d);if(m in g)continue;g[m]=1,h++,k=c[m],l=!0;for(j=0;j<i;j++)if(a(k,e[j])){l=!1;break}l&&(e[i]=k,f[i]=m,i++)}return e}function a(a,b){if(!a||!b||a.length!==b.length)return!1;var c=a.length,d=-1;while(++d<c)if(a[d]!==b[d])return!1;return!0}science.stats={},science.stats.bandwidth={nrd0:function(a){var b=Math.sqrt(science.stats.variance(a));(lo=Math.min(b,science.stats.iqr(a)/1.34))||(lo=b)||(lo=Math.abs(a[1]))||(lo=1);return.9*lo*Math.pow(a.length,-0.2)},nrd:function(a){var b=science.stats.iqr(a)/1.34;return 1.06*Math.min(Math.sqrt(science.stats.variance(a)),b)*Math.pow(a.length,-0.2)}},science.stats.distance={euclidean:function(a,b){var c=a.length,d=-1,e=0,f;while(++d<c)f=a[d]-b[d],e+=f*f;return Math.sqrt(e)},manhattan:function(a,b){var c=a.length,d=-1,e=0;while(++d<c)e+=Math.abs(a[d]-b[d]);return e},minkowski:function(a){return function(b,c){var d=b.length,e=-1,f=0;while(++e<d)f+=Math.pow(Math.abs(b[e]-c[e]),a);return Math.pow(f,1/a)}},chebyshev:function(a,b){var c=a.length,d=-1,e=0,f;while(++d<c)f=Math.abs(a[d]-b[d]),f>e&&(e=f);return e},hamming:function(a,b){var c=a.length,d=-1,e=0;while(++d<c)a[d]!==b[d]&&e++;return e},jaccard:function(a,b){var c=a.length,d=-1,e=0;while(++d<c)a[d]===b[d]&&e++;return e/c},braycurtis:function(a,b){var c=a.length,d=-1,e=0,f=0,g,h;while(++d<c)g=a[d],h=b[d],e+=Math.abs(g-h),f+=Math.abs(g+h);return e/f}},science.stats.erf=function(a){var b=.254829592,c=-0.284496736,d=1.421413741,e=-1.453152027,f=1.061405429,g=.3275911,h=a<0?-1:1;a<0&&(h=-1,a=-a);var i=1/(1+g*a);return h*(1-((((f*i+e)*i+d)*i+c)*i+b)*i*Math.exp(-a*a))},science.stats.phi=function(a){return.5*(1+science.stats.erf(a/Math.SQRT2))},science.stats.kernel={uniform:function(a){return a<=1&&a>=-1?.5:0},triangular:function(a){return a<=1&&a>=-1?1-Math.abs(a):0},epanechnikov:function(a){return a<=1&&a>=-1?.75*(1-a*a):0},quartic:function(a){if(a<=1&&a>=-1){var b=1-a*a;return.9375*b*b}return 0},triweight:function(a){if(a<=1&&a>=-1){var b=1-a*a;return 35/32*b*b*b}return 0},gaussian:function(a){return 1/Math.sqrt(2*Math.PI)*Math.exp(-0.5*a*a)},cosine:function(a){return a<=1&&a>=-1?Math.PI/4*Math.cos(Math.PI/2*a):0}},science.stats.kde=function(){function d(d,e){var f=c.call(this,b);return d.map(function(c){var d=-1,e=0,g=b.length;while(++d<g)e+=a((c-b[d])/f);return[c,e/f/g]})}var a=science.stats.kernel.gaussian,b=[],c=science.stats.bandwidth.nrd;d.kernel=function(b){if(!arguments.length)return a;a=b;return d},d.sample=function(a){if(!arguments.length)return b;b=a;return d},d.bandwidth=function(a){if(!arguments.length)return c;c=science.functor(a);return d};return d},science.stats.kmeans=function(){function f(f){var g=f.length,h=[],i=[],j=1,l=0,m=b(e,f),n,o,p,q,r,s,t;while(j&&l<d){p=-1;while(++p<e)i[p]=0;o=-1;while(++o<g){q=f[o],s=Infinity,p=-1;while(++p<e)r=c.call(this,m[p],q),r<s&&(s=r,t=p);i[h[o]=t]++}n=[],o=-1;while(++o<g){q=h[o],r=n[q];if(r==null)n[q]=f[o].slice();else{p=-1;while(++p<r.length)r[p]+=f[o][p]}}p=-1;while(++p<e){q=n[p],r=1/i[p],o=-1;while(++o<q.length)q[o]*=r}j=0,p=-1;while(++p<e)if(!a(n[p],m[p])){j=1;break}m=n,l++}return{assignments:h,centroids:m}}var c=science.stats.distance.euclidean,d=1e3,e=1;f.k=function(a){if(!arguments.length)return e;e=a;return f},f.distance=function(a){if(!arguments.length)return c;c=a;return f};return f},science.stats.hcluster=function(){function d(d){var e=d.length,f=[],g=[],h=[],i=[],j,k,l,m,n,o,p,q;p=-1;while(++p<e){f[p]=0,h[p]=[],q=-1;while(++q<e)h[p][q]=p===q?Infinity:a(d[p],d[q]),h[p][f[p]]>h[p][q]&&(f[p]=q)}p=-1;while(++p<e)i[p]=[],i[p][0]={left:null,right:null,dist:0,centroid:d[p],size:1,depth:0},g[p]=1;for(n=0;n<e-1;n++){j=0;for(p=0;p<e;p++)h[p][f[p]]<h[j][f[j]]&&(j=p);k=f[j],l=i[j][0],m=i[k][0],newCluster={left:l,right:m,dist:h[j][k],centroid:c(l.size,l.centroid,m.size,m.centroid),size:l.size+m.size,depth:1+Math.max(l.depth,m.depth)},i[j].splice(0,0,newCluster),g[j]+=g[k];for(q=0;q<e;q++)switch(b){case"single":h[j][q]>h[k][q]&&(h[q][j]=h[j][q]=h[k][q]);break;case"complete":h[j][q]<h[k][q]&&(h[q][j]=h[j][q]=h[k][q]);break;case"average":h[q][j]=h[j][q]=(g[j]*h[j][q]+g[k]*h[k][q])/(g[j]+g[q])}h[j][j]=Infinity;for(p=0;p<e;p++)h[p][k]=h[k][p]=Infinity;for(q=0;q<e;q++)f[q]==k&&(f[q]=j),h[j][q]<h[j][f[j]]&&(f[j]=q);o=newCluster}return o}var a=science.stats.distance.euclidean,b="simple";d.distance=function(b){if(!arguments.length)return a;a=b;return d};return d},science.stats.iqr=function(a){var b=science.stats.quantiles(a,[.25,.75]);return b[1]-b[0]},science.stats.loess=function(){function h(h,i,j){var k=h.length,l;if(k!==i.length)throw{error:"Mismatched array lengths"};if(k==0)throw{error:"At least one point required."};if(arguments.length<3){j=[],l=-1;while(++l<k)j[l]=1}d(h),d(i),d(j),e(h);if(k==1)return[i[0]];if(k==2)return[i[0],i[1]];var m=Math.floor(a*k);if(m<2)throw{error:"Bandwidth too small."};var n=[],o=[],p=[];l=-1;while(++l<k)n[l]=0,o[l]=0,p[l]=1;var q=-1;while(++q<=b){var r=[0,m-1],s;l=-1;while(++l<k){s=h[l],l>0&&g(h,j,l,r);var t=r[0],u=r[1],v=h[l]-h[t]>h[u]-h[l]?t:u,w=0,x=0,y=0,z=0,A=0,B=Math.abs(1/(h[v]-s));for(var C=t;C<=u;++C){var D=h[C],E=i[C],F=C<l?s-D:D-s,G=f(F*B)*p[C]*j[C],H=D*G;w+=G,x+=H,y+=D*H,z+=E*G,A+=E*H}var I=x/w,J=z/w,K=A/w,L=y/w,M=Math.sqrt(Math.abs(L-I*I))<c?0:(K-I*J)/(L-I*I),N=J-M*I;n[l]=M*s+N,o[l]=Math.abs(i[l]-n[l])}if(q===b)break;var O=o.slice();O.sort();var P=O[Math.floor(k/2)];if(Math.abs(P)<c)break;var Q,G;l=-1;while(++l<k)Q=o[l]/(6*P),p[l]=Q>=1?0:(G=1-Q*Q)*G}return n}var a=.3,b=2,c=1e-12;h.bandwidth=function(b){if(!arguments.length)return b;a=b;return h},h.robustnessIterations=function(a){if(!arguments.length)return a;b=a;return h},h.accuracy=function(a){if(!arguments.length)return a;c=a;return h};return h},science.stats.mean=function(a){var b=a.length;if(b===0)return NaN;var c=0,d=-1;while(++d<b)c+=(a[d]-c)/(d+1);return c},science.stats.median=function(a){return science.stats.quantiles(a,[.5])[0]},science.stats.mode=function(a){a=a.slice().sort(science.ascending);var b,c=a.length,d=-1,e=d,f=null,g=0,h,i;while(++d<c)(i=a[d])!==f&&((h=d-e)>g&&(g=h,b=f),f=i,e=d);return b},science.stats.quantiles=function(a,b){a=a.slice().sort(science.ascending);var c=a.length-1;return b.map(function(b){if(b===0)return a[0];if(b===1)return a[c];var d=1+b*c,e=Math.floor(d),f=d-e,g=a[e-1];return f===0?g:g+f*(a[e]-g)})},science.stats.variance=function(a){var b=a.length;if(b<1)return NaN;if(b===1)return 0;var c=science.stats.mean(a),d=-1,e=0;while(++d<b){var f=a[d]-c;e+=f*f}return e/(b-1)}})()
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