#801 rip out bower, move to unmanaged externals

1 files changed, 0 insertions, 389 deletions

diff --git a/www/external/origjs/NeuQuant.js b/www/external/origjs/NeuQuant.js
deleted file mode 100644
index f0c124ba..00000000
--- a/www/external/origjs/NeuQuant.js
+++ /dev/null
@@ -1,389 +0,0 @@
-NeuQuant = function () {
-  function NeuQuant() {
-    var netsize = 256;
-    var prime1 = 499;
-    var prime2 = 491;
-    var prime3 = 487;
-    var prime4 = 503;
-    var minpicturebytes = 3 * prime4;
-    var maxnetpos = netsize - 1;
-    var netbiasshift = 4;
-    var ncycles = 100;
-    var intbiasshift = 16;
-    var intbias = 1 << intbiasshift;
-    var gammashift = 10;
-    var gamma = 1 << gammashift;
-    var betashift = 10;
-    var beta = intbias >> betashift;
-    var betagamma = intbias << gammashift - betashift;
-    var initrad = netsize >> 3;
-    var radiusbiasshift = 6;
-    var radiusbias = 1 << radiusbiasshift;
-    var initradius = initrad * radiusbias;
-    var radiusdec = 30;
-    var alphabiasshift = 10;
-    var initalpha = 1 << alphabiasshift;
-    var alphadec;
-    var radbiasshift = 8;
-    var radbias = 1 << radbiasshift;
-    var alpharadbshift = alphabiasshift + radbiasshift;
-    var alpharadbias = 1 << alpharadbshift;
-    var thepicture;
-    var lengthcount;
-    var samplefac;
-    var network;
-    var netindex = [];
-    var bias = [];
-    var freq = [];
-    var radpower = [];
-    function NeuQuantConstructor(thepic, len, sample) {
-      var i;
-      var p;
-      thepicture = thepic;
-      lengthcount = len;
-      samplefac = sample;
-      network = new Array(netsize);
-      for (i = 0; i < netsize; i++) {
-        network[i] = new Array(4);
-        p = network[i];
-        p[0] = p[1] = p[2] = (i << netbiasshift + 8) / netsize | 0;
-        freq[i] = intbias / netsize | 0;
-        bias[i] = 0;
-      }
-    }
-    function colorMap() {
-      var map = [];
-      var index = new Array(netsize);
-      for (var i = 0; i < netsize; i++)
-        index[network[i][3]] = i;
-      var k = 0;
-      for (var l = 0; l < netsize; l++) {
-        var j = index[l];
-        map[k++] = network[j][0];
-        map[k++] = network[j][1];
-        map[k++] = network[j][2];
-      }
-      return map;
-    }
-    function inxbuild() {
-      var i;
-      var j;
-      var smallpos;
-      var smallval;
-      var p;
-      var q;
-      var previouscol;
-      var startpos;
-      previouscol = 0;
-      startpos = 0;
-      for (i = 0; i < netsize; i++) {
-        p = network[i];
-        smallpos = i;
-        smallval = p[1];
-        for (j = i + 1; j < netsize; j++) {
-          q = network[j];
-          if (q[1] < smallval) {
-            smallpos = j;
-            smallval = q[1];
-          }
-        }
-        q = network[smallpos];
-        if (i != smallpos) {
-          j = q[0];
-          q[0] = p[0];
-          p[0] = j;
-          j = q[1];
-          q[1] = p[1];
-          p[1] = j;
-          j = q[2];
-          q[2] = p[2];
-          p[2] = j;
-          j = q[3];
-          q[3] = p[3];
-          p[3] = j;
-        }
-        if (smallval != previouscol) {
-          netindex[previouscol] = startpos + i >> 1;
-          for (j = previouscol + 1; j < smallval; j++) {
-            netindex[j] = i;
-          }
-          previouscol = smallval;
-          startpos = i;
-        }
-      }
-      netindex[previouscol] = startpos + maxnetpos >> 1;
-      for (j = previouscol + 1; j < 256; j++) {
-        netindex[j] = maxnetpos;
-      }
-    }
-    function learn() {
-      var i;
-      var j;
-      var b;
-      var g;
-      var r;
-      var radius;
-      var rad;
-      var alpha;
-      var step;
-      var delta;
-      var samplepixels;
-      var p;
-      var pix;
-      var lim;
-      if (lengthcount < minpicturebytes) {
-        samplefac = 1;
-      }
-      alphadec = 30 + (samplefac - 1) / 3;
-      p = thepicture;
-      pix = 0;
-      lim = lengthcount;
-      samplepixels = lengthcount / (3 * samplefac);
-      delta = samplepixels / ncycles | 0;
-      alpha = initalpha;
-      radius = initradius;
-      rad = radius >> radiusbiasshift;
-      if (rad <= 1) {
-        rad = 0;
-      }
-      for (i = 0; i < rad; i++) {
-        radpower[i] = alpha * ((rad * rad - i * i) * radbias / (rad * rad));
-      }
-      if (lengthcount < minpicturebytes) {
-        step = 3;
-      } else if (lengthcount % prime1 !== 0) {
-        step = 3 * prime1;
-      } else {
-        if (lengthcount % prime2 !== 0) {
-          step = 3 * prime2;
-        } else {
-          if (lengthcount % prime3 !== 0) {
-            step = 3 * prime3;
-          } else {
-            step = 3 * prime4;
-          }
-        }
-      }
-      i = 0;
-      while (i < samplepixels) {
-        b = (p[pix + 0] & 255) << netbiasshift;
-        g = (p[pix + 1] & 255) << netbiasshift;
-        r = (p[pix + 2] & 255) << netbiasshift;
-        j = contest(b, g, r);
-        altersingle(alpha, j, b, g, r);
-        if (rad !== 0) {
-          alterneigh(rad, j, b, g, r);
-        }
-        pix += step;
-        if (pix >= lim) {
-          pix -= lengthcount;
-        }
-        i++;
-        if (delta === 0) {
-          delta = 1;
-        }
-        if (i % delta === 0) {
-          alpha -= alpha / alphadec;
-          radius -= radius / radiusdec;
-          rad = radius >> radiusbiasshift;
-          if (rad <= 1) {
-            rad = 0;
-          }
-          for (j = 0; j < rad; j++) {
-            radpower[j] = alpha * ((rad * rad - j * j) * radbias / (rad * rad));
-          }
-        }
-      }
-    }
-    function map(b, g, r) {
-      var i;
-      var j;
-      var dist;
-      var a;
-      var bestd;
-      var p;
-      var best;
-      bestd = 1000;
-      best = -1;
-      i = netindex[g];
-      j = i - 1;
-      while (i < netsize || j >= 0) {
-        if (i < netsize) {
-          p = network[i];
-          dist = p[1] - g;
-          if (dist >= bestd) {
-            i = netsize;
-          } else {
-            i++;
-            if (dist < 0) {
-              dist = -dist;
-            }
-            a = p[0] - b;
-            if (a < 0) {
-              a = -a;
-            }
-            dist += a;
-            if (dist < bestd) {
-              a = p[2] - r;
-              if (a < 0) {
-                a = -a;
-              }
-              dist += a;
-              if (dist < bestd) {
-                bestd = dist;
-                best = p[3];
-              }
-            }
-          }
-        }
-        if (j >= 0) {
-          p = network[j];
-          dist = g - p[1];
-          if (dist >= bestd) {
-            j = -1;
-          } else {
-            j--;
-            if (dist < 0) {
-              dist = -dist;
-            }
-            a = p[0] - b;
-            if (a < 0) {
-              a = -a;
-            }
-            dist += a;
-            if (dist < bestd) {
-              a = p[2] - r;
-              if (a < 0) {
-                a = -a;
-              }
-              dist += a;
-              if (dist < bestd) {
-                bestd = dist;
-                best = p[3];
-              }
-            }
-          }
-        }
-      }
-      return best;
-    }
-    function process() {
-      learn();
-      unbiasnet();
-      inxbuild();
-      return colorMap();
-    }
-    function unbiasnet() {
-      var i;
-      var j;
-      for (i = 0; i < netsize; i++) {
-        network[i][0] >>= netbiasshift;
-        network[i][1] >>= netbiasshift;
-        network[i][2] >>= netbiasshift;
-        network[i][3] = i;
-      }
-    }
-    function alterneigh(rad, i, b, g, r) {
-      var j;
-      var k;
-      var lo;
-      var hi;
-      var a;
-      var m;
-      var p;
-      lo = i - rad;
-      if (lo < -1) {
-        lo = -1;
-      }
-      hi = i + rad;
-      if (hi > netsize) {
-        hi = netsize;
-      }
-      j = i + 1;
-      k = i - 1;
-      m = 1;
-      while (j < hi || k > lo) {
-        a = radpower[m++];
-        if (j < hi) {
-          p = network[j++];
-          try {
-            p[0] -= a * (p[0] - b) / alpharadbias | 0;
-            p[1] -= a * (p[1] - g) / alpharadbias | 0;
-            p[2] -= a * (p[2] - r) / alpharadbias | 0;
-          } catch (e) {
-          }
-        }
-        if (k > lo) {
-          p = network[k--];
-          try {
-            p[0] -= a * (p[0] - b) / alpharadbias | 0;
-            p[1] -= a * (p[1] - g) / alpharadbias | 0;
-            p[2] -= a * (p[2] - r) / alpharadbias | 0;
-          } catch (e) {
-          }
-        }
-      }
-    }
-    function altersingle(alpha, i, b, g, r) {
-      var n = network[i];
-      var alphaMult = alpha / initalpha;
-      n[0] -= alphaMult * (n[0] - b) | 0;
-      n[1] -= alphaMult * (n[1] - g) | 0;
-      n[2] -= alphaMult * (n[2] - r) | 0;
-    }
-    function contest(b, g, r) {
-      var i;
-      var dist;
-      var a;
-      var biasdist;
-      var betafreq;
-      var bestpos;
-      var bestbiaspos;
-      var bestd;
-      var bestbiasd;
-      var n;
-      bestd = ~(1 << 31);
-      bestbiasd = bestd;
-      bestpos = -1;
-      bestbiaspos = bestpos;
-      for (i = 0; i < netsize; i++) {
-        n = network[i];
-        dist = n[0] - b;
-        if (dist < 0) {
-          dist = -dist;
-        }
-        a = n[1] - g;
-        if (a < 0) {
-          a = -a;
-        }
-        dist += a;
-        a = n[2] - r;
-        if (a < 0) {
-          a = -a;
-        }
-        dist += a;
-        if (dist < bestd) {
-          bestd = dist;
-          bestpos = i;
-        }
-        biasdist = dist - (bias[i] >> intbiasshift - netbiasshift);
-        if (biasdist < bestbiasd) {
-          bestbiasd = biasdist;
-          bestbiaspos = i;
-        }
-        betafreq = freq[i] >> betashift;
-        freq[i] -= betafreq;
-        bias[i] += betafreq << gammashift;
-      }
-      freq[bestpos] += beta;
-      bias[bestpos] -= betagamma;
-      return bestbiaspos;
-    }
-    NeuQuantConstructor.apply(this, arguments);
-    var exports = {};
-    exports.map = map;
-    exports.process = process;
-    return exports;
-  }
-  return NeuQuant;
-}();
\ No newline at end of file