Images: Fixes before merge!

src/components/images/attributes.js

@@ -37,7 +37,7 @@ module.exports = {
         ].join(' ')
     },
 
-    sizey: {
+    sizex: {
         valType: 'number',
         role: 'info',
         dflt: 0,
@@ -49,7 +49,7 @@ module.exports = {
         ].join(' ')
     },
 
-    sizex: {
+    sizey: {
         valType: 'number',
         role: 'info',
         dflt: 0,

src/components/images/draw.js

@@ -91,7 +91,6 @@ module.exports = function draw(gd) {
         gd._promises.push(imagePromise);
     }
 
-
     function applyAttributes(d) {
 
         var thisImage = d3.select(this);
@@ -111,8 +110,8 @@ module.exports = function draw(gd) {
         var sizing = anchors.x[d.xanchor].sizing + anchors.y[d.yanchor].sizing;
 
         // Final positions
-        var xPos = (xref ? xref.l2p(d.x) : d.x * width) + xOffset,
-            yPos = (yref ? yref.l2p(d.y) : -d.y * width) + yOffset;
+        var xPos = (xref ? xref.l2p(d.x) : d.x * size.w) + xOffset,
+            yPos = (yref ? yref.l2p(d.y) : size.h - d.y * size.h) + yOffset;
 
 
         // Construct the proper aspectRatio attribute

src/lib/index.js

@@ -495,15 +495,15 @@ lib.isIE = function() {
  * filled array of the given size.
  *
  * @example
- * lib.toObject('nested.test[2].path', 'value');
+ * lib.objectFromPath('nested.test[2].path', 'value');
  * // returns { nested: { test: [null, null, { path: 'value' }]}
  *
  * @param   {string}    path to nested value
  * @param   {*}         any value to be set
  *
  * @return {Object} the constructed object with a full nested path
  */
-lib.toObject = function(path, value) {
+lib.objectFromPath = function(path, value) {
     var keys = path.split('.'),
         tmpObj,
         obj = tmpObj = {};

src/plot_api/plot_api.js

@@ -2300,7 +2300,7 @@ Plotly.relayout = function relayout(gd, astr, val) {
             objModule.draw(gd, objNum, p.parts.slice(2).join('.'), aobj[ai]);
             delete aobj[ai];
         } else if(p.parts[0] === 'images') {
-            var update = Lib.toObject(astr, vi);
+            var update = Lib.objectFromPath(astr, vi);
             Lib.extendDeepAll(gd.layout, update);
 
             Images.supplyLayoutDefaults(gd.layout, gd._fullLayout);
@@ -2652,10 +2652,10 @@ function makePlotFramework(gd) {
     // (only for shapes to be drawn below the whole plot)
     var layerBelow = fullLayout._paper.append('g')
         .classed('layer-below', true);
-    fullLayout._shapeLowerLayer = layerBelow.append('g')
-        .classed('shapelayer', true);
     fullLayout._imageLowerLayer = layerBelow.append('g')
         .classed('imagelayer', true);
+    fullLayout._shapeLowerLayer = layerBelow.append('g')
+        .classed('shapelayer', true);
 
     var subplots = Plotly.Axes.getSubplots(gd);
     if(subplots.join('') !== Object.keys(gd._fullLayout._plots || {}).join('')) {
@@ -2669,17 +2669,17 @@ function makePlotFramework(gd) {
 
     // shape layers in subplots
     var layerSubplot = fullLayout._paper.selectAll('.layer-subplot');
-    fullLayout._shapeSubplotLayer = layerSubplot.selectAll('.shapelayer');
     fullLayout._imageSubplotLayer = layerSubplot.selectAll('.imagelayer');
+    fullLayout._shapeSubplotLayer = layerSubplot.selectAll('.shapelayer');
 
     // upper shape layer
     // (only for shapes to be drawn above the whole plot, including subplots)
     var layerAbove = fullLayout._paper.append('g')
         .classed('layer-above', true);
-    fullLayout._shapeUpperLayer = layerAbove.append('g')
-        .classed('shapelayer', true);
     fullLayout._imageUpperLayer = layerAbove.append('g')
         .classed('imagelayer', true);
+    fullLayout._shapeUpperLayer = layerAbove.append('g')
+        .classed('shapelayer', true);
 
     // single pie layer for the whole plot
     fullLayout._pielayer = fullLayout._paper.append('g').classed('pielayer', true);

test/image/mocks/layout_image.json

@@ -12,23 +12,22 @@
         "xref": "paper",
         "yref": "paper",
         "x": 0,
-        "y": 0,
+        "y": 1,
         "sizex": 0.2,
         "sizey": 0.2,
         "xanchor": "right",
-      "yanchor": "bottom"
+        "yanchor": "bottom"
       },
       {
         "source": "https://images.plot.ly/language-icons/api-home/js-logo.png",
         "xref": "x",
         "yref": "y",
-        "x": 2.5,
+        "x": 1.5,
         "y": 2,
         "sizex": 1,
         "sizey": 1,
-        "xanchor": "center",
-        "yanchor": "middle",
-        "layer": "below"
+        "xanchor": "right",
+        "yanchor": "bottom"
       },
       {
         "source": "https://images.plot.ly/language-icons/api-home/r-logo.png",
@@ -39,19 +38,32 @@
         "sizex": 2,
         "sizey": 2,
         "sizing": "stretch",
-        "opacity": 0.4
+        "opacity": 0.4,
+        "layer": "below"
       },
       {
         "source": "https://images.plot.ly/language-icons/api-home/matlab-logo.png",
         "xref": "x",
         "yref": "paper",
-        "x": 2.75,
+        "x": 3,
         "y": 0,
         "sizex": 0.5,
         "sizey": 1,
         "opacity": 1,
-        "xanchor": "left",
-        "yanchor": "top"
+        "xanchor": "right",
+        "yanchor": "middle"
+      },
+      {
+        "source": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAANwAAAD1CAMAAAAMJ2tNAAAC9FBMVEUAAABEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEettEets5iYiFAAAA+3RSTlMAABVISkcS8/nwPvY/AUZraWpLAwSrtgZSmJZ7DIzRFIvQCAkXqcHAwlce4XQCI7G/L+z+X+sfBx0K5OWEMNTeN/H8VPuXy8jMdiW5ycc6wzJtyqQPho8FGLBbK73FQgsODVxWWRnZLUAqG876pfeFvDbupyYRXn+mvodoO1qauqNiNRD9tSETet9QVbPpb2dyGvXiIuO4M/jTYNZ1YdLmJy7nUbKOrbehU5mD2JFsT0lOMeCBfqLccZXdiETyOO/Pu9tzgCQ5zeqgqImc1xzGKUxF9EOTeRZ3rCB96NpNbp+SXZR8m7SqY6+NncQoLGWugpDtWD3VNDxwZtDaXE4AAAAJcEhZcwAALiMAAC4jAXilP3YAAAu4SURBVHja7Z15fEzXHsDzIyLBtIkZhmna1ExIJQZPyEYsIYKHR2jHTlQsEbEm9n2ndqlYaq21qK3UmvcoLX1FUBTP0r7XV6V9S9u3dP55c3/nZrZ77ph7eTXvvt/vD5+P+zv3nPO995zf+Z3f79xMUBAJCQkJCQkJCQkJCQkJCQkJyS8lUKZssFTKhYAm4MqWD5VKWAVtwAWH2qVSsRLBERzBERzBERzBERzBERzBEdz/KxzoXngxXCIRlfWgBThDFc5N9qpGgiM4giM4giM4giM4giM4giM4ggsAOKhW3SSR6i9FgibgXuYRvBKlDbhXeQQ1CI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7g/qfgQGe2cMQAmoCLrlkrRiKv1dYGXGwcT1WH4AiO4AiO4AiO4AiO4AiO4Ajuv0Zg5e4gha8wNQBXtx5nB1m/nDbgftWA02B8Q43ANSI4giM4giM4giM4giM4giM47cOBLjohViIJwp++DQw4SEyK5UiyP9xgrpkSJ5HGTQIHLrWptH9xzYL9grPE8Ko1BQ5c8xYcTVpLDcO1IjiCIziCIziCIziCIziCCxy4Z5fCeuX57AoS01tnSKRNW6Hadr9uL1V1EHZS0PE3Uk37Wp0EVWdOfZldujoI9K9zVK3fsDlU3bpncu7qIdTXsxenF737+AUHfftxxCxUq8/iqWyCysDT9BPOJIOZp4nUOQggmadKBofKym3K6LsXJCQkJCQkJCQkJIEpENk/Ijw84k3zM/ZaIXmAUG//yOfpDYvbLmHn+WzrzR4o1BsXS3AER3AER3BYgW7Q4MFDdKBNuJyhobnD9BqFK5dnt1cxaBIOYLhdu3CRIzQMN3KUhuE62zULB4bRzxcOQG+2mA0AauBAZ7RYEq0gpx4T9wvDQX5CbGwS26FB8qCCseNGxIyvMmHipHxpH33AAWT1nNx2StWYmKnTxk6fMVNys6NAn9GzhNtnz3EeLMxh+Qx3OIAQ4YxkQrQOuM9+rg+ttPi8lLi4+QuEarsufGvRrNIMWIuMxbHePZSFA13CkqmL0pw5vFG9l46xgkeBZQVTyzPtcufBwhWtV3LglhQ6VIVvc4cHmIcJJyhTmvgJh8nC4QCwqnuRR4Jv1uo1FvALDvJfXDvLK0fZbN1c17MB/Tvrczk5xA0bJXBBkNpY+E9eZS7cps2o3OLfAGZw79pgwXxJ23lbZ8KT4QDabWvFSX5uH+Skk8nf8uEMw1C3Q88b/EtRl5GvBO615J2NxUeeG5ob7+zgrq7wJDiA5pmutx0amuZKhFcqpVMCFwS1MSc9vx0HLovV8x4ogdvdshlOtKl79r6/b/9e01BxELVq4p7C5MEBHFgvgh0cdqhHcPCADw4fEQF3dxQ7AbYPq5tMR7vjCz7m/G2b9OPRPLgofFizJnIs2omTgqrRKT/NKoMr2uD4p/jopEisECB6zW9Z/8IWuDXBhSvzO1by9JkxbCCBdWXEK+xaRoLHO65U0Z+lAOAjvLlXpNTist/FecusCA6f6E5XqwArq7DBmdHNJxzoj2O5+O2pbvYDzk5js3BKIiiGC4IDOEWKz0nKRX2MbU0EpXCfvO9xC2RXZdPOrSYe3IwN7MDFMk8j0/U8W1Aqu794f+FsF/DmTyVLUTWcjUfq+rvYO+HG6rxs4KbTeL13vg84MPweC33W06sjcPESg+6rAg4m45Rfv8yrTl11rHOXVSncam/fB+AMDriiar7gLhcKV3IjJA8ZSnDun7yiHM5pUt7xrBWSPhMuX73mt5dWCie1rpDDzMLnruXKGw7gOhZpc0Nq2CzdUVXdNSD8h4ObeO8XRs81Zzp6Ch3ylcJdrQD8fbNjXGbJw1nYxDzOmeHQH8fW6TnK4YKgZ4pwYfMqDzhbTWzsOiiFa9qN073BywXVok3ycLcOotmowGkOEnDpLCqnBs6GmyP7bXcOuIw+1J1U//cOItwIztIBObtx6PeXh7uG+H9YxoNLrIc1z1MBFwR383DQuLl/APeULXIuuPscNxss41HXWR5uHWsvkQcHD5htc01ZBXBRbfAc4F03OPNs9Jm+BMVwY3nds3ZB3QSniyiB+woLpHPbg8WM3KgGDv4oMUczFnnNYb/hJnK71wR103QycADpWOAN/r59P1qUoSEq4Er3NgPnuAzzPGzrT1bFcPEFIBvQsX9tlIOzst3JS3y4jrg7nZ+kCs74Z09PC+bi2rf8BSWhCAaXtpALtwaX8foWOTjbu56T0vPuVDxp23iMGrggWFDkYT7gBD6qzBAVcG9y4QoQLkYWzsiWuXA+HDsjLe7ZFMPdQCe5waRSl+xTcZAoh5MZlt+wvYdZ9s09xAKLfb65durgYKxdDICwIk3Ru++kKEAmGpQlXLgzqKtnk4PTnZc1tQ7tJBxIxy6qgnM8mzvCxbU5DG4/LnwXbGrgrnNXqm9R9whkraVJ9D65NW/BPV2bfJVwxm3ok6M1ACu2lPalssimCGfieYfiqBsrv4izm6fw1zm2wj+0qYQTd2+H8cT4rWP4GhNUwfH+yDdEPkZdhDxcQ7Q4vZK57917hVcGFwRlOghXCwVrC7VxFGwFVXAdsjhw36Fv3qqaPNwmNIjNeHHw0h3DTTW+pduy7djVOfbE55Uvci64O99z4Lagi3GwrjxcVA10AXkBVFiJhfOuqIULggNoUraZAc5iXTH56uA4H8eAfgdbCSw+Nqu72NDjud1/wQDf2iRvuL/6DWeYhgvlZYD+WNc6UAdnHydtMhVDCPa/ye/EHVuTq7jnWSl9NJFv491HwRtuhMXfLA8sFExK/HSwHcZF7oDSLFAp3IYZ3jXr2EIQNthXDCWkg9cDcNk6xG7hFvCACgi3NtpvuOjWwvUu+lvzVSxy7tGvHWav6XzlE7w+O9kXHOxFM1Y4yTv6Fcti7F+7R78GYY0NVvkNB7fRXuW0FCZ/WoHi9J0TruI9vYf3szGDBVdKfMcto3pjscwhnlHPEJwu9uJ97nHLmazKjySvWS75CKuEx5F39+8YnL+lHs5eXCfLNbtgUC129Yu+viPOUPIDlvu4gmujBbqNVVi64HOPVAOwuGPKYO/XLAvHTMqe1fgvqITLE3pYNLtkrpgryN57hLHFdXpSrkB/iEXONx8fJOZJrbc+ZPFc++OLnkP9R1Z0d2Uba8ZsA99pY1goDMhGgl8Z9pPypDKDK6yDe/i8jPR1LffdjTDVEBNuLRoCPCmFlTWFvaX4DVWGF1QO/vH2jtNilucfI72iqtlismvUhXsl+2rfPPrwn0+Cy25TOrBi+qmFO/mvgkWln4O65eda/NvwxPxcEORvzePm59p39B5+0OOq3ZkGTBM/BPUJx0yKIG1BLZyjlZ0DJbnBwm8MfmRWHY7WXmlW1p53fqU0iJ34XpHkK1efpxlgSIrYl+9UHHUQ51wfgO8f/eDRu6sXRur8zIlD6iOvL5NDH/fnetOWtpuVwYnhWRWLnBMud7/DOzX+9GBg6bApijscLPUkci6tEA4aSF08sJ06vj6sdDgvb9ZlYYjMURbrufuFYto2vjizJ4O7Ub8wLm7FpRze46iGYz50spozKgyOuZagX3Ztybw96V99EF4yhxNoBUNOQmxsQraVG+Qsc2r60ldNppfb/nwiycevJIGxbst7r5tMExavGRnFhoajXaHeHN6PDEHzURjuz35auAAUgOP4lh+AFuGicVNVXEFV/wIdbgCa1/pZGoQDY00xVaRFuBkY+0y5rPIQZCDDARzCFzfapkW4s5j+LF+i9mhuAMOBgZ0Ymp2lPTiHnx2GfuB+1d8xBCwc6Huwk4TjkjUDB/pEq/ArkImdTOh42VesUt23wIM7tf3+oTrrvp0qbh/ywkFDcJ5/3yh3wlN8/hvgcCebPM0nyAENl1aj9lP9xGzgwZ3bzUIR8YvGd74I8HR1RYSHhy+pG0DWMqHsz44uhTdM7Qv0N6JISEhISEhISEhISEhISEieg/wHXMU13xThNeUAAABhdEVYdGNvbW1lbnQARmlsZSBzb3VyY2U6IGh0dHA6Ly9jb21tb25zLndpa2ltZWRpYS5vcmcvd2lraS9GaWxlOlBsb3RseV9sb2dvX2Zvcl9kaWdpdGFsX2ZpbmFsXyg2KS5wbmetqmbcAAAAJXRFWHRkYXRlOmNyZWF0ZQAyMDE0LTA5LTIxVDAzOjM1OjExKzAwOjAwKhOa5QAAACV0RVh0ZGF0ZTptb2RpZnkAMjAxNC0wOS0yMVQwMzozNToxMSswMDowMFtOIlkAAABGdEVYdHNvZnR3YXJlAEltYWdlTWFnaWNrIDYuNi45LTcgMjAxNC0wMy0wNiBRMTYgaHR0cDovL3d3dy5pbWFnZW1hZ2ljay5vcmeB07PDAAAAGHRFWHRUaHVtYjo6RG9jdW1lbnQ6OlBhZ2VzADGn/7svAAAAGXRFWHRUaHVtYjo6SW1hZ2U6OmhlaWdodAAxNzA0VsMjbQAAABh0RVh0VGh1bWI6OkltYWdlOjpXaWR0aAAxNTMxndUDgQAAABl0RVh0VGh1bWI6Ok1pbWV0eXBlAGltYWdlL3BuZz+yVk4AAAAXdEVYdFRodW1iOjpNVGltZQAxNDExMjcwNTEx0Rvi6AAAABN0RVh0VGh1bWI6OlNpemUAMjkuOUtCQvmCzmYAAAAzdEVYdFRodW1iOjpVUkkAZmlsZTovLy90bXAvbG9jYWxjb3B5X2UwNDYwYmM4MDVlYS0xLnBuZ1YfjUoAAAAASUVORK5CYII=",
+        "xref": "paper",
+        "yref": "paper",
+        "x": 0.5,
+        "y": 1,
+        "sizex": 0.2,
+        "sizey": 0.2,
+        "opacity": 1,
+        "xanchor": "middle",
+        "yanchor": "bottom"
       }
     ],
     "width": 800,

test/jasmine/tests/lib_test.js

@@ -441,6 +441,40 @@ describe('Test lib.js:', function() {
         });
     });
 
+    describe('objectFromPath', function() {
+
+        it('should return an object', function() {
+            var obj = Lib.objectFromPath('test', 'object');
+
+            expect(obj).toEqual({ test: 'object' });
+        });
+
+        it('should work for deep objects', function() {
+            var obj = Lib.objectFromPath('deep.nested.test', 'object');
+
+            expect(obj).toEqual({ deep: { nested: { test: 'object' }}});
+        });
+
+        it('should work for arrays', function() {
+            var obj = Lib.objectFromPath('nested[2].array', 'object');
+
+            expect(Object.keys(obj)).toEqual(['nested']);
+            expect(Array.isArray(obj.nested)).toBe(true);
+            expect(obj.nested[0]).toBe(undefined);
+            expect(obj.nested[2]).toEqual({ array: 'object' });
+        });
+
+        it('should work for any given value', function() {
+            var obj = Lib.objectFromPath('test.type', { an: 'object' });
+
+            expect(obj).toEqual({ test: { type: { an: 'object' }}});
+
+            obj = Lib.objectFromPath('test.type', [42]);
+
+            expect(obj).toEqual({ test: { type: [42] }});
+        });
+    });
+
     describe('coerce', function() {
         var coerce = Lib.coerce,
             out;
@@ -811,7 +845,6 @@ describe('Test lib.js:', function() {
             el.attr('transform', 'rotate(20)');
             expect(Lib.getTranslate(el)).toEqual({ x: 0, y: 0 });
         });
-
     });
 
     describe('setTranslate', function() {

test/jasmine/tests/plotschema_test.js

@@ -115,7 +115,7 @@ describe('plot schema', function() {
 
     it('should convert _isLinkedToArray attributes to items object', function() {
         var astrs = [
-            'annotations', 'shapes',
+            'annotations', 'shapes', 'images',
             'xaxis.rangeselector.buttons', 'yaxis.rangeselector.buttons'
         ];