Reading a Pack Response

Learn about the data returned by the Paccurate API

The API Response

The Paccurate API responds to valid requests with a JSON body that contains detailed information for the pack, which can be broken out into Statistics, a list of Boxes, a list of Images, and a list of Leftovers.

Statistics

Paccurate returns several high-level statistics regarding the performance of the pack request.

"totalCost": 486,
"boxTypeChoiceGoalUsed": "lowest-cost"
"lenBoxes": 1,
"lenItems": 7,
"lenLeftovers": 2,
"packTime": 0.005188877,
"renderTime": 0.024311464,
"totalTime": 0.029500341,
  • totalCost is the estimated costs, in cents, of all the boxes used to cartonize the request. This value considers any provided rate tables or costs affiliated with a specific boxType.
  • boxTypeChoiceGoalUsed is how the API prioritized the packing and selection of boxes. It defaults to "lowest-cost" unless specified in the request. The other option for this is “most-items” (fit as many items per box as possible regardless of cost)
  • lenBoxes is the quantity of cartons that were used in completing the pack
  • lenItems is the total number of items across all types that were successfully packed
  • lenLeftovers is the number of items that were impossible to pack with the given containers and constraints. This is often due to item dimensions or weights that exceed the maximums of the containers provided in the boxTypes object in the request body
  • packTime is the amount of time, in seconds, the API took to place the items in their respective containers
  • renderTime is the amount of time the API took to generate the images (SVG or PNG) to be bundled into the response.
  • totalTime is the sum of renderTime and packTime

Boxes

The Boxes list contains all of the packed cartons that the API used to pack the Items provided in the request. Within each box, there is a list of the items along with their placement coordinates and a generated unique ID for the item. Along with the list of items, each box in the list contains data for its price, volume and weight utilization as well as more detailed box properties. Here is an example of the Boxes object from a successful pack.

{
	"boxes": [
    {
      "box": {
        "boxType": {
          "centerOfMass": {
            "x": 0,
            "y": 0,
            "z": 0
          },
          "dimensions": {
            "x": 12,
            "y": 6,
            "z": 6.75
          },
          "itemsInlineOverhang": {
            "x": -1,
            "y": -1,
            "z": -1
          },
          "name": "Small",
          "price": 0,
          "rateTable": {
            "basePrice": 0,
            "carrier": "",
            "dimFactor": 0,
            "priceIncreaseRates": 0,
            "rates": null,
            "service": "",
            "weights": null,
            "zone": ""
          },
          "refId": 0,
          "reservedSpace": null,
          "weightMax": 65,
          "weightTare": 0
        },
        "centerOfMass": {
          "x": 4.75,
          "y": 1.7980769230769236,
          "z": 3.0000000000000004
        },
        "centerOfMassString": "[4.75 1.7980769230769236 3.0000000000000004]",
        "depthOrder": [
          1,
          2,
          3,
          4,
          5
        ],
        "depthOrderString": "[1 2 3 4 5]",
        "dimensionalWeight": 0,
        "dimensionalWeightUsed": false,
        "dimensions": {
          "x": 12,
          "y": 6,
          "z": 6.75
        },
        "id": 0,
        "items": [
          {
            "item": {
              "centerOfMass": {
                "x": 4.75,
                "y": 1.625,
                "z": 2.25
              },
              "color": "green",
              "deltaCost": 486,
              "dimensions": {
                "x": 9.5,
                "y": 3.25,
                "z": 4.5
              },
              "index": 1,
              "message": "packed #2 in 2.497168ms at 4.934929ms in Box(name:Small dimensions:[12,6,6.75] weight:65 index:0)",
              "name": "Dispenser",
              "origin": {
                "x": 0,
                "y": 0,
                "z": 0
              },
              "refId": 0,
              "sequence": "",
              "uniqueId": "0-0",
              "virtual": false,
              "weight": 4
            }
          },
          {
            "item": {
              "centerOfMass": {
                "x": 2.375,
                "y": 2.375,
                "z": 5
              },
              "color": "blue",
              "deltaCost": 0,
              "dimensions": {
                "x": 4.75,
                "y": 4.75,
                "z": 1
              },
              "index": 2,
              "message": "packed #3 in 22.311µs at 4.99247ms in Box(name:Small dimensions:[12,6,6.75] weight:65 index:0)",
              "name": "Tape",
              "origin": {
                "x": 0,
                "y": 0,
                "z": 4.5
              },
              "refId": 1,
              "sequence": "",
              "uniqueId": "1-0",
              "virtual": false,
              "weight": 0.3
            }
          }
        ],
        "name": "Small",
        "origin": {
          "x": 0,
          "y": 0,
          "z": 0
        },
        "price": 486,
        "refId": 0,
        "subspace": null,
        "volumeMax": 486,
        "volumeNet": 229.1875,
        "volumeRemaining": 256.8125,
        "volumeReserved": 0,
        "volumeUsed": 229.1875,
        "volumeUtilization": 0.4715792181069959,
        "weightMax": 65,
        "weightNet": 5.199999999999989,
        "weightRemaining": 59.80000000000001,
        "weightTare": 0,
        "weightUsed": 5.199999999999989,
        "weightUtilization": 0.07999999999999982
      }
    }
  ],
	"lenBoxes":1
}

Note that each instance of an item is listed in the items array — this allows Paccurate to return placement, cost, and center of mass information for each specific item.

Each Box also returns its boxType attributes that had been defined in the request, along with its dimensional weight and whether or not dimensional weight was used when assessing the cost of the box. Paccurate also returns any rateTable information that applies to the box.

The boxes array is very helpful if you want to render out any sort of list of items per box or provide cost and volume reporting on the boxes or items within them.

Images

The images returned from a pack request can be used to help packers place items into containers, as well as provide a representation of how Paccurate sees the item and carton data. By default, Paccurate returns an svgs list. You can change this format to png by changing the imageFormat in your request, and if you can disable images from being returned by setting includeImages to false in your request. We encourage our users to leverage the returned svgs whenever possible.

Each element in the SVGs list is the HTML markup to render the SVG (Scalable Vector Graphics) data for a container and all of the elements within it. These elements combined with the boxes list in the response are essential in building any interactive UI with the pack results. Here is an example of an svg list from a pack response. 

{
"svgs": [
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data-max-depth='8.294228634925972' data-min-depth='3.0980762122193415' class='volume-line' points='77.53300858899107,55.118621784789724 130.56601717798213,24.5 24.5,-36.73724356957945 -28.53300858899106,-6.118621784789724' style='fill:green;' />\n<polygon vector-effect='non-scaling-stroke' data-item-ref-id='1' data-volume-index='1' data-side='[6 6 12] [0 1 0]' data-direction='1 [6 6 12] [0 1 0] [] [] [0,1,0]' data-max-depth='8.294228634925972' data-min-depth='3.0980762122193415' class='volume-line' points='77.53300858899107,116.35586535436916 77.53300858899107,55.118621784789724 -28.53300858899106,-6.118621784789724 -28.53300858899106,55.118621784789724' style='fill:green;' />\n<polygon vector-effect='non-scaling-stroke' data-item-ref-id='1' data-volume-index='1' data-side='[6 6 12] [0 0 1]' data-direction='2 [6 6 12] [0 0 1] [] [] [0,0,1]' data-max-depth='8.294228634925972' data-min-depth='4.830127019788218' class='volume-line' points='130.56601717798213,85.73724356957945 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    "<figure class='box-figure' data-box-index=1><svg viewBox='-28.53300858899106,-179.62414523193146,106.06601717798213,265.3613888015109'>[<line data-volume-index='5' class='volume-line' x1='24.5' y1='24.5' x2='24.5' y2='14.293792738403425'/>\n <line data-volume-index='5' class='volume-line' x1='24.5' y1='24.5' x2='15.661165235168156' y2='29.603103630798287'/>\n <line data-volume-index='5' class='volume-line' x1='24.5' y1='24.5' x2='33.338834764831844' y2='29.603103630798287'/>\n <line data-volume-index='5' class='volume-line' x1='24.5' y1='24.5' x2='77.53300858899107' y2='55.118621784789724'/>\n <line data-volume-index='5' class='volume-line' x1='24.5' y1='24.5' x2='-28.53300858899106' y2='55.118621784789724'/>\n <line data-volume-index='5' class='volume-line' x1='24.5' y1='24.5' x2='24.5' y2='-179.62414523193146'/>\n] <polygon vector-effect='non-scaling-stroke' data-item-ref-id='0' data-volume-index='6' data-side='[11 3 3] [1 0 0]' data-direction='0 [11 3 3] [1 0 0] [] [] [1,0,0]' 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If imageFormat is set to “PNG”, the response contains an images array of objects for each box, each containing png data. PNG rendering does increase response time more than SVG.

{
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0tLSyM27pKa+QFZ8DTKEQ6U9fGnumUQg6S5P5q0XhZzMMjZeuk19gE7ETHHLJNzMusExJScnKygocgsjnl6v3kJcvXxpr7RzDyHBSZw0PF/Ccps7OzqjLaf6XC3hnjYsMFAYvpn9gIPR0/vnEVjUrcUXtleAcT4Okd2kDhoaG+/btA4cgMvlF6z3k0qVLzjbHVgrOsNOH3+VqaGhwCbh7NaVmy29TbI7DN8wNjZcyjRvtfBE+XlpV0uRrmszYOdvR0VFICP4oVUQOv3S9h9jb258N91bdLXRoD7wPlZWVyha3ahv6j6jNVtw9A5yT+qDc1LVskejUwERlcEh6fGGEU4boog2BgYGjR48G56CRDuv9h66uLh0dnbzsRAO1lZKis2AhgwMD8feybIKymMcxuNvxz53DAl7PxYtvvc7UbqJuvPSiz9O4oDJlZWULCwtwCBrRsN7/JzMz00xXfjzbWFs9qamcwHK2t7cHRt0Pjy0RXDI+0AO+m7W9rUfJNL+5rlvbSXrrQfhlhYdeYtFjirGx8d698L00aITCen8qKirK3dFESnSWlc4acEjdxzprn6SkzDr5nVxG2nzgnNevP2rZFE/gZPa/fWDSNOBfnIqiRl/TZKaeOU5OTkuXLgUvBo04WO/Ps7KyunI+6JD8soM74I8Wfp337qhdUnsXYaw7Z8M6+G7WpKRSC4/y5dIzvWPht8TT4t6dcX60aslmf39/BgYGcA4aQbDe/1NTU9OxY8dK8h6YqK9eKQi8W9bX1xcTm+EW+WrqlLFBXkvZxwPnSQiCOBuVH3CpfrfWMl3H9eCQc55P4kPKVFVVzc3NwSFopMB6/4u0tDRzA6VpnCwOBmvZ2cbCQlpbW91D7l5MKJcUm+RmB9/NWl/fccC0oKeTqvFSgiDcdBPeP6UzMTHZs2cPOATRPqz3VwkJCfF2PbFJaq7ZYTFwSO2HD8fdEh+/aVZRmqlxEHh/niCI59k1enYl02aPD0s9xDgGeDJcaUGDv3ky68B8Jycnfn74XxxEy7De38DU1PT2zUhNBWH5TfBHCz989NLEM42Ons7abKGoMPxg87hbxTa+1I6XPrj59opLuZCQUFBQEDgE0Sys97epqqrSVpFpae020xQTWgR8klFPd0/4xQf+lwv45rCc8Yc/zHxovDTiZrOSkaiaOfxBi9Eej+NDytTV1c3MzMAhiAZhvSGSkpKsTdVmc09wNpIZOxZ4F7qpqcnBL/HGg5ptG6damSwAL6aiokXN/N0oOjrryO1CYjzgHFedO2VZ9GZmZrt2wZ+dhmgK1hvOz8/P19N6l+xCQzXgY8aGdrMeMIutbx7QPjx7z3b4btbHjysNHUp5+SeFpcKfSfg+r87P/D47sdDZ2XnRIvhp0IhGYL2pZWBgcP/OBS0lkR3rge/AFArl96Sn1oGZrGyMXg78s3jg56hdu1boFFS9XnGBdSh8vPT+9YKrruUiIiL+/v7gEEQLsN7fQUlJid6hDT29/RZHJBbNBT7JqLOz0zfiXkRsibAgu787fG9Z/8DA6ZD8C0nUjpdGuWbcCavQ1NQ0NjYGh6DhhfX+buLi4uwsNBfycbqayIAPYG5oaLD0vHMvq15hF5f+Ufhu1sLCBk2rIiaW0W4x8ryLJ4NznI/ernwx+vjx4zt27ACHoOGC9f7OPD09A3wc9m0T0D0AP5D81ZvCo7aJ3X2EybG566Xg5UxJKTNzK19M3Xhp8es6f4v7HPSLnZ2dFyyA3wJEPx/W+4fQ0dF5lHJN54DoZing6egDAwPnrz50P/tq+jSm0z6CLCzwse2LF996RdZuUeE3PQUfL713Je+ae8WKFSv8/PzAIegnw3r/KHl5eYZHttLR0VlqSfDxAA9ja29rdwlMvJhYvlaS08UavpemtbVnv2leS32PnqvMJiX4B/szzhlJZyo1NTWNjIzAIeinwXr/WFevXnU8qbNs8XQno7XgkLqPdUbO8Vn5LYcOzFJVgj/Y8PXrj1rWxROnjPOL3w8eLx3sH3TRSah5Ncbc3Hzr1q3gxaCfAOv9Mzg7O4cFuSnvFNRUhJ+4+DAjx8gzjYGB3sFqoaAA8PhHgiASE0pPeJWLyPB43VAEh7x7WRtgkTJ5jICbmxsfH/wWIPqhsN4/SX9/v7a29ovHvx9TFl0nBjyAua+vLyQ6OSDm7fy5LOFU7GYlCCIyKj8opn73UarGS+/G5F3zKF+1apWvry81i0E/CNb7p3rx4oWJ9m6msQy2elJcU4GPFm5tabH2uRObWrNj87QTRvPBi/n4sV35+NvergET/42Sm+E5EU7p96Kqjx49qq+vDw5BPwLWexhcuHDB2dZAQpjbWk8KHFJZUWXolFBQ2aGrOWfXlungnGfPao7Zl0yfPf5M2iH60cDx0v6+QRetOx/zmCwsLDZv3gxeDPq+sN7DxsbG5mKUn5qckMpu+ImLN24/tg3OYmdnPOWyZMZ0JnDO0HipxDY+5wvwAx4KXnw4f6KKmZnZzc2Nl5cXnIO+F6z3cGpra9NQWFle02qktkp8OfCWeHd3t3fo3YjYYhFhdj9X+JdeFAolLDzvzM3m/SYrVM3gx0gmXHh9w6tCQkLC29sbHIK+C6z38Hv06JG5viLHBGa7Y9KcE5lhIU1NTcdd41Oy6/fJc+tqwN85Kypa1I6/HUU/yvoMVeOlYfZpKec/aGlp6enpgUMQlbDetCIiIsLDyWydGO+JoxLgkJe5747YJfUOEBaG86TEgcMtf/zFyag0ciqdIzAp9AF8vLS3p99VO6HhLYu5ufmmTZvAOQgM601bLCwsbsSEauxdprRNAJZAoVCiYlI9zr6cwcV0JmAZIyPwbtmfe3LeOQfXUDlempdVfeFkNSsrq7u7Ow8P/HIAAWC9aU5dXd2RA2vqGjpN1FeJLgUe8NDZ2enod+diQrmsNKfjSfhu1r6+/tDQgot329StxOS14OOlt8/l3vAql5KS8vLyAoegb4X1plEpKSmWxgdnTGFzMJRmYwEewNxQ36BvH/fsXcth5VkHFeC7WQvfNRy2KmRmZfS4pjhrPvxh5qG2D1MvfdTS0tLV1QWHoK+H9aZpwcHB3m6WW6TnmWqsBoekZLw0cX/IOIbexXrx4kXAvTR/jZe6lvOvnBqQAB8v7erodddLbCpks7S0lJWVBeegr4H1HgFMTEwSYqM09wnv2QC8zB7oHwiIuhcQk794AWuoL1W7WS9cLPCO/LhNbYmxN/xu2ZvMqvNWVezs7B4eHtzc3NSsB30B1ntkqKio0FFd39beY6YpJrgQ+Liy9rZ2S8/f4x7W7No6/bjBPPBiWlt7lEzyWht79FyoGi+9HZ173ats3bp17u7u4BD0BVjvkSQhIcHmuPqcmRNcjGUYGYEHMJdVVBk7Jbyt7DDQ4tu2EXhUO0EQua8+atsWc0wd539n/8TJ8IeZB1unZlyp19HR0dLSAoegz8J6jzy+vr6+njZ7Ni7SV4Hfyr4W/8QuKHMiB2OA+5Ipk+G7WRMS3lt6VVA5XtrZ1uumm9BWOsHS0nLdunXgHPQJrPdIdXivcOarKu39y7fJAM8/6+vtdQ9OjIgtWi060dsZ/pxjgkJEns0PimnYo71M2x5ezpePKi/ZVHFwcHh4eHBxccHXg/4/rPcIVlRUdEx9Y2//gMURiUV8wD1qLS0txo5xqS/qDyhwax2C72b9WPvneGn3gHngJrGN8A/2cZEvb/qUy8rKurm5gUPQEKz3iBcbG2tveXTxXE43U/jBDDmv3ln5PKhp6bU2W7BaFP7N9tB4Kdcc9ohUNfB4KUEQQVYPntxo0tHROXLkCDgEYb1JwsPDI8DH8eDOJVpKIuCQiEspHpGvZs1kigxaRk8PL+etW8W2vpWSO/gcz8HHS1ubuiP1P5aXl588eVJaWhqc8yvDepOK8q6lbwrr9A6K/iYBPP+su7vb1if+UmL5BpnJDpbwx4xRKJTQsDeRt1r2G1M1XvoyvfyCTdXkyZO9vb2nTJkCzvk1Yb3JJjc31/DodkYGeivtNbO5gc8Pb2pq0rWJfVHYoqk2W0kOvu2kvKzlkMVbegZ626jtS1bCd8XGRuTc9CnbtGmTi4sLOOQXhPUmp5iYGGebY6JLuOwN4Je1d1NzzLweMjExuDvwz+djBeekZ1QaO5XyUTdeShBEoGVKZmyLnp6ehoYGNTm/Dqw3mTk4OESGeqnuFlSXB25EpVAovuFJgTH5AovZQnyEqFnM1avvXEJqZBUXWp2GP66spaHzjP7Hqqoqa2trSUlJatbzK8B6k1xPT4/anuXF5U36KiukV86GhXR2dJq5xt7O+CC3dbqpPvxLr56e/rDw/Et32zVsJPZowp/B9iKt/KJN5fTp0728vDg54adWkB7W+5eQlZVlpivPyjLGRk9y+mTgZXZZaZWhS0JxdaeJHt+GdcB973+Nl1oWsrCNcbumQM146Y3Q57f8KrZs2eLk5AQOITes9y8kOjrazd5ojQjPSV34Ze3VuCe2wU8nTxob7C04cSIjOOf+/bLjbuUCq6f53z4IDiEIwt/8fnZ8m56enrq6OjU5pIT1/uVYW1tfig5Q3yN4cCfwAOaB/gHngNtnbhWJr5jo5UTFblaCOH++wOds3TY1AWrGS5vqOs/of6itrbW2tpaQgJ9URz5Y719RS0vLYcXVlbWtRmqrxISB31e1tbUZ2Mem5dQrK87UVAV+qv9zMV37TQvaGnuOua3boAj/Y/EspTTGvmrGjBmnTp2aMAH4jSDJYL1/Xenp6RYGSpwczPb60hzswAOYs3PeWfs+qG3ps7NYKCoML1Xuq4/aNsUc08YFJSmP54BPsF0PeR7rV7Zjxw4HBwdwCGlgvX91YWFhns7msuK8FlQcwBxx4YH72RxennFnQ5ZTs5iEhPeWnhWisjye1+DjpQRBXDned+fOHQMDAxUVFWpyRjqsN/qDnvKKexklh/cuU9wKPIC5t7f3pGdcTGL5ZtnJthbU7GYloqLygi437tURPmonA86pr2mPNPxQX19va2u7ejX8pLoRDeuN/lJbW3v0oFRDU6eJhpiIAPCZhC0tLUetbrwqatVSn713F/AU56Hx0oNmBf29g8cDqBovzUx+H2NfycPD4+vrO378eHDOCIX1Rv8lOTnZykRl5jQ2RyMZFmbg9153H+S4hT3pHhj0dBaYMwt+TlNWVo2+Q8kMPvbwh2rUTLBdC86O9SuTk5OztbUFh4xEWG/0GYGBgafcrbbJzDdWh1/Wep9ODLycLyTAFuRN1W7WmzeL7P2rqBwvJQjikkn33bt3DQwMlJXhBzmPLFhv9D9p7Vuell2hpbR8l+xCWEJ3d7ex482ERzV7tk831oNfY/81XhrXctBkpbIJ/BZgXVVbpNGHpqYmW1vblSvhJ9WNFFhv9CVlZWU6quu7uvvNj4jxzwOOW5eUVps43yn50GlmMG+91GTwYkpLm9VPvGNgoLc7t1NABP7B/klS8WXHqjlz5pw6dYqVFT4JR/uw3ujfxcfH25ofXsDL4Wq6btSoUbCQy3GP7QKfTp3CdNpfcDwLfDfrn+Ol7+cunXz6vio45I/1BGTFBZTLy8vb2NhQk0PLgP+vQr+UzZs3Z76qEpJUFVc44xv1FBYiv3VVbvyxNYLT1m17ZGyZC16MuNiMJ/ES24ToJFidHY7EgnPktUWi83a3t7cvW7YsOjoanEPL8N0bfZtDe5Y9f1Otc0B0izTws3RnR6euzfWMVw0q+7g1lOG7WXt6+hWM3tRXdVI5XlpT1hJtWtvS0mJvby8iAj+pjgZhvdE3KygoMNDcQqFQThxdM282cKIzM/udrf+D+vY+B8tFy5aygxfz7m2DplUhy/ixHjcUZvJNBOc8TiiOcayYN2+ev78/ExN8VyxNwXojoOvXrztYaQsunOpsDN9bFnb+gUdUzjw+ljOBwtQsJjm5zNy9TGD1dCrHS2P8suICyxQUFKytranJoRH42RsB7dq16/mbmvkiCqv2hAddfAYLUVeSen1bZz7XhOXSD2yd88CLkZHhyUxYs3ZO/xp2V0+j2+Ccvboi5/LlWlpahIWFL1y4AM6hEfjujag1ODioslsov6hOT3mFrPgcWEhL85+7WUta9TR5d2+DP4GoubnrgEl+W3Ovged6WXng/nmCIKpLm6OMazo6OhwcHISFqbqyGEZYb/R9vHz50lhr5xhGhpM6a3i4gJ+lE1Jy3MOe9BGDPq5LeWYAZ1SHxku1rIs4p7MEUjdemnG76LJTxcKFC/39/ceMGQPOGS5Yb/Q9Xbp0ydnm2ErBGXb68AOYPU8nBF0uWL6ULcCTqt2sd26/t/KuWPEbj8dVqsZL03w4goOD9+/fb2lpSU3Oz4efvdH3pKCg8LKglp1trLhCRPiVF7AQo8Mb8m5rcTAxr1z3wMu/ELyYjZtmZyWtWc7RIT3RLehkMjhHQr/hfMGe+vp6ERGRS5cugXN+Pnz3Rj9EV1fXIXnR9xVNBmorJUVnwULeFVeauSaWfew8YTRfSgJ+4PGHD20qx98O9FGOB21aLTsXnFNR1Bht9qG7u9vBwUFIiKori58D641+oMzMTDNd+fFsY231pKZyAidDY2If2wU/5ZrGFBEgzMQEHwvNzKzWd3jPPZc9MoOqh5ykxb274lLJz88fEBDAwMBATdSPRk/iDbdo2HFxcalo6PdQWI0sfcurWiRFeQAh/Au4tfevLC6u1rfOKSpuW78WONnCxcV6SHHm6K5W5e2337+tXbsTeKQMz3yOjYdmdxRxq6qqtrW10fLZrPjZG/1wysrKr999ZGSkl9wXefbmK1iIjeG2vNtHBrtGiW94GBH9HryYHTv4su+umc/QKDPZPdorA5yzxqDx4ru9Hz58EBERuXLlCjjnh8KLc/TzNDU1Hd4nVlPXbqK+eqUgcKLzcfZbe7/Uxo4+F9vFAovg5yuVljarW7wbzUhvG03VeGnZu4Zos5r+/n4nJycBAfg37T8C1hv9bGlpaeYGStM4WRwM1rKzjYWFnD73wPNszoK5LBEBVO05SU+rMHYpnSc4OSSZqvHS1Ni3V1wqli5dGhgYCJ6Z/e7wszf62Xh4eNQOGzR1MBwz9/3Y0CEOeo6C8JJZOkorsnPeG9q8qv3QJSkOvK8+k2e8uhJPX0ODulxidXnDmi0LYDmzFkzapM7b9pZLVVW1s7NTXFwclvN94bs3Gk46+0UfZJZqKgjLb1oMS2hsbNY6efNNaav+kTk7tgAPeP3z3Kh+ReM3DdWdh+0kd6tTdVr7GZ2mzMxMU1NTOTk5anKoh/VGw6yqqkpbRaaltdtMU0xo0TRYyO3kHI/wxwOjCH+PpdOnwnehFuTXH7EuZB3P5B2ryMULf+hKaUFD9PFqCoXi6OjIz88PzqES1hvRhKSkJGtTtdncE5yNZMaOBX6Z7BGSGHw5T0SI3d8D+HTEIcnJZcfdypaKc/nFH6Am5/71gmtuFcuWLQsMDKQmBwzrjWiIk5HMmWsvdskuNFRbBUvo7+/Xs756N/ODws7p+lrwDWoEQZw7V3DqXN1OjSUG7hupydFeeffp06fDUjSsN6I5mgrCT3IqtZREdqwH3ugqKKw87pZQUddlabJgjdgk8Eqam7v2G+d3tPbpe6wDjJdeP/08zC5t5ryJTeUMRUVF4GWAYb0RLSopKdE7tKGnt9/iiMSiucC74hdvPgo4/4JlPMPZEGFqHnLyMqdWx7Z4Mhdr0L2DrOxf9cG+oqjRXOF6Z2vvybObBFfO2st3EeuN0H+Ji4uzs9BcyMfpaiID/jL5pMet6PgiGYlJrnZU7Tm5fbvkpHfl14yXmu6+kp1aLq+zXNNGcugnw1VvWvn+HaF/2rp1a1ZuNe/MCWJ7z/hFZ8JC7Iy3vU3Q6e+gE9/wMOpCGXgxmzbx/jVeyuEWbHv/s68JsUmVmeRJEERyvdHf3R5G+O6NRgY1OaGc/A86B0Q3SwFvmGVkvbX3T23u6nO35180nw28kprqNlXztwP9FIvTW1fK8A798HFSibtOAjMbo8dNuakzPj2sBi/OEfoXeXl5hke20tHRWWpJ8PEAD2AOjk7xOvty8TzWsIBl1CwmM7Na3/79zPkTzqSrH1kbXf6uUd9HWlZuyWdfjPVG6KtcvXrV8aTOssXTnYzWgkOM7K/eSKnYvnGKpQnw6YhDVI4+zcrp2Llz54nrX9p1h5+9EfoqcnJyL/I+TJ00brV8eMjFbFiIp5Xc4xiV6tJe6S1pt+5UAxKuxFau3ZZORz+ahYVFzmX0q1ev2js6YIv5cfDdG41U/f39anLLCkoajimLrhMDHsAcf++FR/gTgoEI8hbinPRVZ6GWVXYaW77u6BxwspwvuGTiStmslNZjrW1tJSUl48aNm8PL+8+b/HhxjhDEixcvTLR3M41lsNWT4poKvGHmFngn5GrByuXsvm7/spv1mHnusxfNOqrcinv/OkBuqN5D/7v248eS4mLumTNncP3XUe14cY4QhJCQ0L1HJYrqNspmsba+D2AhplobC+8dY2UYu2p9qn/I589mDQgrEduQRhBERoLE393+xJTJk1etWtXf35/17FljYyNsMd8R1huRwb59+3Lf1o5jZlyjeCbyWg4gYdSoUcFOirH+ctlZrb/tzHj0pP7vf3qU2bBJ/vGD9PprkctPOf/73phZPDxLliypra19/eZNV3c3YDHfC16cI1Jpa2vTUFhZXtNqpLZKfDnkoAiCIM5ffxx44TnbBIZzoaKqOs/LyjuP682RXff5YdX/vDj/RHNLS3FxMTs7u4XsU/zsjdD38ejRI3N9RY4JzHbHpDknAh9mZOl+K+x64aG90wz1v/Tl2RfqPcTvRMJZ5+zBwUHYMqiBF+eIhFavXp2aWbZm+Ux5vSuOQWmwEAeTbSwsLAc3Che++tjV3gtISLiQe0AknKVlxbB0G+uNyEzfMTGvqI6OIKSUos7fygUk0NHRzZ8/f/Gs1fVFYyoKGwcHv/ZSN/dJle6GC8nXCmIvpPr7+wN+9XdB089YQIh6p6Ke1tXVHTmw5ubdAhP1VaJLv/nMY1Y2VkFBwdra2qKnJezcfZNnsH7hxdXvm4OtU6vfNztZhGzcSNU5ENTDeiPy4+TkvJaQn5KSYml8cMYUNgdDaTaWbz6AecqfSktL87LezOCdwPaP5wo313dGuz/OTi07csBC+6z291s+HF6co1+FtLR0RnbFcoFpWw5fcgt9BAuZNWuW6JJ13R8nFL+u6+nqH/phX+/AJb9M411XphASj+4WaGvTRLfxzjn6RWkriTzMKtPcJ7xnw5dGQebKni5MOvzZf2pubi4pLqFnb96uns+/asqkaSzeVlcXLACeHvWD4Ls3+hUFnM+6nZx9N/29ov61nPwPgAR2dvZOYpKNe013d7erZcSd869prdv42Rv9uri5uWPvFSQkJFgfV58zc4KLsQwj49fWISf/g3fkk6aWbhNLv2F/VsEX4Ls3+qVt2LDhSU7lIj5O6QNnfSKf/Ovri8oaj5yMt/C8v0Vq3tOcSlruNtYboT+c8LpfVNbY0NT1m+q5W8kFn31NbX27vmPiUev41cu4s19X69je/unL/GZ4cY7QX07HZBcVFR1T33j5Tp7FEYlFfH8dwNzR2esQmPYkp1Jxq8DFOMj2mOGC9Ubo//Dx8cU/KIyNjTWxPLr4z/PVbf1T76aXyG9alFdUN9yr+2Z4cY7Qp7Zv3/4st1pg9YG2trZJvBsLSxscA4Eb14cXfu+NEGnhuzdCpIX1Roi0sN4IkRbWGyHSwnojRFpYb4RIC+uNEGlhvREiLaw3QqSF9UaItLDeCJEW1hsh0sJ6I0RaWG+ESAvrjRBpYb0RIi2sN0KkhfVGiLSw3giRFtYbIdLCeiNEWlhvhEgL640QaWG9ESItrDdCpIX1Roi0sN4IkRbWGyHSwnojRFpYb4RIC+uNEGlhvREiLaw3QqSF9UaItLDeCJEW1hsh0sJ6I0RaWG+ESAvrjRBpYb0RIi2sN0KkhfVGiLSw3giRFtYbIdLCeiNEWlhvhEgL640QaWG9ESItrDdCpIX1Roi0sN4IkRbWGyHSwnojRFpYb4RIC+uNEGlhvREiLaw3QqSF9UaItLDeCJEW1hsh0sJ6I0RaWG+ESAvrjRBpYb0RIi2sN0KkhfVGiLSw3giRFtYbIdLCeiNEWlhvhEgL640QaWG9ESItrDdCpIX1Roi0sN4IkRbWGyHSwnojRFpYb4RIC+uNEGlhvREiLaw3QqSF9UaItLDeCJEW1hsh0sJ6I0RaWG+ESAvrjRBpYb0RIi2sN0KkhfVGiLSw3giRFtYbIdLCeiNEWlhvhEgL640QaWG9ESItrDdCpIX1Roi0sN4IkRbWGyHSwnojRFpYb4RIC+uNEGlhvREiLaw3QqSF9UaItLDeCJEW1hsh0sJ6I0RaWG+ESAvrjRBpYb0RIi2sN0KkhfVGiLSw3giRFtYbIdLCeiNEWlhvhEgL640QaWG9ESItrDdCpIX1Roi0sN4IkRbWGyHSwnojRFpYb4RIC+uNEGlhvREiLaw3QqSF9UaItLDeCJHW/wsAAP//4Zc2NTHOfz4AAAAASUVORK5CYII="
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"
    }
	]
}

This output png data can be used in any context that renders png image (within the src attribute of an image tag on a webpage for example).

Leftovers

The Leftovers list contains any Items **that could not be packed in the containers provided in the request. In most cases this list is empty. For responses that do have leftovers, it could be due to the item’s dimensions or weight exceeding those of the provided cartons, or because of any additional rules or options that were applied to the request, further restricting where the item can be placed. Here is an example of a leftover item as part of the response.

"leftovers": [
    {
      "item": {
        "centerOfMass": {
          "x": 0,
          "y": 0,
          "z": 0
        },
        "color": "red",
        "deltaCost": 0,
        "dimensions": {
          "x": 28,
          "y": 4.38,
          "z": 23.38
        },
        "index": 0,
        "message": "couldn't fit Item(id:2 dimensions:[28,4.38,23.38] origin:[0,0,0]) into any box",
        "name": "Big Thing",
        "origin": {
          "x": 0,
          "y": 0,
          "z": 0
        },
        "refId": 2,
        "sequence": "",
        "uniqueId": "2-0",
        "virtual": false,
        "weight": 17
      }
    }
  ],

In addition to the item attributes that were provided in the request, each item in the leftover has a uniqueId and a message, all of which can help determine the conditions of why the item was not packed.

Updated about 1 year ago