{"url":"https://api.github.com/gists/3711652","forks_url":"https://api.github.com/gists/3711652/forks","commits_url":"https://api.github.com/gists/3711652/commits","id":"3711652","node_id":"MDQ6R2lzdDM3MTE2NTI=","git_pull_url":"https://gist.github.com/3711652.git","git_push_url":"https://gist.github.com/3711652.git","html_url":"https://gist.github.com/mbostock/3711652","files":{".block":{"filename":".block","type":"text/plain","language":null,"raw_url":"https://gist.githubusercontent.com/mbostock/3711652/raw/a8d68c34d8f91069be475c86899c4cac3238ce01/.block","size":79,"truncated":false,"content":"license: gpl-3.0\nredirect: https://observablehq.com/@d3/projection-transitions\n","encoding":"utf-8"},"README.md":{"filename":"README.md","type":"text/markdown","language":"Markdown","raw_url":"https://gist.githubusercontent.com/mbostock/3711652/raw/05a0df41b42a54249bd36c46df2979c2e36d29f3/README.md","size":124,"truncated":false,"content":"These projections are available in the [geo.projection plugin](https://github.com/d3/d3-plugins/tree/master/geo/projection).","encoding":"utf-8"},"index.html":{"filename":"index.html","type":"text/html","language":"HTML","raw_url":"https://gist.githubusercontent.com/mbostock/3711652/raw/4a9d6a4f5bb3ee77dd62d127b706263e2d27b0fd/index.html","size":5783,"truncated":false,"content":"<!DOCTYPE html>\n<meta charset=\"utf-8\">\n<style>\n\nbody {\n  background: #fcfcfa;\n  height: 500px;\n  position: relative;\n  width: 960px;\n}\n\n#projection-menu {\n  position: absolute;\n  right: 10px;\n  top: 10px;\n}\n\n.stroke {\n  fill: none;\n  stroke: #000;\n  stroke-width: 3px;\n}\n\n.fill {\n  fill: #fff;\n}\n\n.graticule {\n  fill: none;\n  stroke: #777;\n  stroke-width: .5px;\n  stroke-opacity: .5;\n}\n\n.land {\n  fill: #222;\n}\n\n.boundary {\n  fill: none;\n  stroke: #fff;\n  stroke-width: .5px;\n}\n\n</style>\n<select id=\"projection-menu\"></select>\n<script src=\"//d3js.org/d3.v3.min.js\"></script>\n<script src=\"//d3js.org/d3.geo.projection.v0.min.js\"></script>\n<script src=\"//d3js.org/topojson.v1.min.js\"></script>\n<script>\n\nvar width = 960,\n    height = 500;\n\nvar options = [\n  {name: \"Aitoff\", projection: d3.geo.aitoff()},\n  {name: \"Albers\", projection: d3.geo.albers().scale(145).parallels([20, 50])},\n  {name: \"August\", projection: d3.geo.august().scale(60)},\n  {name: \"Baker\", projection: d3.geo.baker().scale(100)},\n  {name: \"Boggs\", projection: d3.geo.boggs()},\n  {name: \"Bonne\", projection: d3.geo.bonne().scale(120)},\n  {name: \"Bromley\", projection: d3.geo.bromley()},\n  {name: \"Collignon\", projection: d3.geo.collignon().scale(93)},\n  {name: \"Craster Parabolic\", projection: d3.geo.craster()},\n  {name: \"Eckert I\", projection: d3.geo.eckert1().scale(165)},\n  {name: \"Eckert II\", projection: d3.geo.eckert2().scale(165)},\n  {name: \"Eckert III\", projection: d3.geo.eckert3().scale(180)},\n  {name: \"Eckert IV\", projection: d3.geo.eckert4().scale(180)},\n  {name: \"Eckert V\", projection: d3.geo.eckert5().scale(170)},\n  {name: \"Eckert VI\", projection: d3.geo.eckert6().scale(170)},\n  {name: \"Eisenlohr\", projection: d3.geo.eisenlohr().scale(60)},\n  {name: \"Equirectangular (Plate Carrée)\", projection: d3.geo.equirectangular()},\n  {name: \"Hammer\", projection: d3.geo.hammer().scale(165)},\n  {name: \"Hill\", projection: d3.geo.hill()},\n  {name: \"Goode Homolosine\", projection: d3.geo.homolosine()},\n  {name: \"Kavrayskiy VII\", projection: d3.geo.kavrayskiy7()},\n  {name: \"Lambert cylindrical equal-area\", projection: d3.geo.cylindricalEqualArea()},\n  {name: \"Lagrange\", projection: d3.geo.lagrange().scale(120)},\n  {name: \"Larrivée\", projection: d3.geo.larrivee().scale(95)},\n  {name: \"Laskowski\", projection: d3.geo.laskowski().scale(120)},\n  {name: \"Loximuthal\", projection: d3.geo.loximuthal()},\n  // {name: \"Mercator\", projection: d3.geo.mercator().scale(490 / 2 / Math.PI)},\n  {name: \"Miller\", projection: d3.geo.miller().scale(100)},\n  {name: \"McBryde–Thomas Flat-Polar Parabolic\", projection: d3.geo.mtFlatPolarParabolic()},\n  {name: \"McBryde–Thomas Flat-Polar Quartic\", projection: d3.geo.mtFlatPolarQuartic()},\n  {name: \"McBryde–Thomas Flat-Polar Sinusoidal\", projection: d3.geo.mtFlatPolarSinusoidal()},\n  {name: \"Mollweide\", projection: d3.geo.mollweide().scale(165)},\n  {name: \"Natural Earth\", projection: d3.geo.naturalEarth()},\n  {name: \"Nell–Hammer\", projection: d3.geo.nellHammer()},\n  {name: \"Polyconic\", projection: d3.geo.polyconic().scale(100)},\n  {name: \"Robinson\", projection: d3.geo.robinson()},\n  {name: \"Sinusoidal\", projection: d3.geo.sinusoidal()},\n  {name: \"Sinu-Mollweide\", projection: d3.geo.sinuMollweide()},\n  {name: \"van der Grinten\", projection: d3.geo.vanDerGrinten().scale(75)},\n  {name: \"van der Grinten IV\", projection: d3.geo.vanDerGrinten4().scale(120)},\n  {name: \"Wagner IV\", projection: d3.geo.wagner4()},\n  {name: \"Wagner VI\", projection: d3.geo.wagner6()},\n  {name: \"Wagner VII\", projection: d3.geo.wagner7()},\n  {name: \"Winkel Tripel\", projection: d3.geo.winkel3()}\n];\n\noptions.forEach(function(o) {\n  o.projection.rotate([0, 0]).center([0, 0]);\n});\n\nvar interval = setInterval(loop, 1500),\n    i = 0,\n    n = options.length - 1;\n\nvar projection = options[i].projection;\n\nvar path = d3.geo.path()\n    .projection(projection);\n\nvar graticule = d3.geo.graticule();\n\nvar svg = d3.select(\"body\").append(\"svg\")\n    .attr(\"width\", width)\n    .attr(\"height\", height);\n\nsvg.append(\"defs\").append(\"path\")\n    .datum({type: \"Sphere\"})\n    .attr(\"id\", \"sphere\")\n    .attr(\"d\", path);\n\nsvg.append(\"use\")\n    .attr(\"class\", \"stroke\")\n    .attr(\"xlink:href\", \"#sphere\");\n\nsvg.append(\"use\")\n    .attr(\"class\", \"fill\")\n    .attr(\"xlink:href\", \"#sphere\");\n\nsvg.append(\"path\")\n    .datum(graticule)\n    .attr(\"class\", \"graticule\")\n    .attr(\"d\", path);\n\nd3.json(\"/mbostock/raw/4090846/world-110m.json\", function(error, world) {\n  if (error) throw error;\n\n  svg.insert(\"path\", \".graticule\")\n      .datum(topojson.feature(world, world.objects.land))\n      .attr(\"class\", \"land\")\n      .attr(\"d\", path);\n});\n\nvar menu = d3.select(\"#projection-menu\")\n    .on(\"change\", change);\n\nmenu.selectAll(\"option\")\n    .data(options)\n  .enter().append(\"option\")\n    .text(function(d) { return d.name; });\n\nfunction loop() {\n  var j = Math.floor(Math.random() * n);\n  menu.property(\"selectedIndex\", i = j + (j >= i));\n  update(options[i]);\n}\n\nfunction change() {\n  clearInterval(interval);\n  update(options[this.selectedIndex]);\n}\n\nfunction update(option) {\n  svg.selectAll(\"path\").transition()\n      .duration(750)\n      .attrTween(\"d\", projectionTween(projection, projection = option.projection));\n}\n\nfunction projectionTween(projection0, projection1) {\n  return function(d) {\n    var t = 0;\n\n    var projection = d3.geo.projection(project)\n        .scale(1)\n        .translate([width / 2, height / 2]);\n\n    var path = d3.geo.path()\n        .projection(projection);\n\n    function project(λ, φ) {\n      λ *= 180 / Math.PI, φ *= 180 / Math.PI;\n      var p0 = projection0([λ, φ]), p1 = projection1([λ, φ]);\n      return [(1 - t) * p0[0] + t * p1[0], (1 - t) * -p0[1] + t * -p1[1]];\n    }\n\n    return function(_) {\n      t = _;\n      return path(d);\n    };\n  };\n}\n\n</script>\n","encoding":"utf-8"},"thumbnail.png":{"filename":"thumbnail.png","type":"image/png","language":null,"raw_url":"https://gist.githubusercontent.com/mbostock/3711652/raw/e7ba4d67fbd586fbaf935872fed7a03d8fbc012c/thumbnail.png","size":28098,"truncated":false,"content":"iVBORw0KGgoAAAANSUhEUgAAAOYAAAB4CAYAAADmBo6IAAAKg2lDQ1BpY20A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E526tqQzZcNpvNmDRpEioq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\nJqAuGSuNnDkXoLm5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