ryneches/Cura Paper

## Cura Paper
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   "cells": [
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     "cell_type": "markdown",
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     "source": [
      "# Run times for Cura, Slic3r, Skeinforge and Makerware\n",
      "\n",
      "## Run times for tool path generation for Yoda's head\n",
      "\n",
      "### Cura 14.01\n",
      "\n",
      "Run command:\n",
      "\n",
      "    #!/bin/bash\n",
      "    for run in {1..10}; do\n",
      "        time -p python/python -m Cura.cura -i /c/Models/Cura.ini      \\\n",
      "                                           -s /c/Models/Yoda-Lite.stl \\\n",
      "                                           -o /c/Models/Yoda-Lite.gcode > /dev/null;\n",
      "    done\n",
      "\n",
      "Times: 9.65 9.56 9.60 9.64 9.51 9.59 9.57 9.63 9.72 9.68\n",
      "\n",
      "Average running time: 9.61 seconds\n",
      "\n",
      "### Slic3r 0.9.10b\n",
      "\n",
      "Run command:\n",
      "    \n",
      "    #!/bin/bash\n",
      "    for run in {1..10}; do\n",
      "        time -p slic3r.exe --load /c/Models/Slic3r.ini -o /c/Models/Yoda-Lite.gcode \\\n",
      "                                                          /c/Models/Yoda-Lite.stl > /dev/null;\n",
      "    done\n",
      "\n",
      "Times: 74.32 75.38 73.88 73.11 73.17 73.01 72.90 73.24 73.00 73.67\n",
      "\n",
      "### Skeinforge 50\n",
      "\n",
      "Run command:\n",
      "    \n",
      "    #!/bin/bash\n",
      "    for run in {1..10}; do \n",
      "        time -p /c/Python27/python skeinforge.py /c/Models/Yoda-Lite.stl > /dev/null;\n",
      "    done\n",
      "\n",
      "Times: 838.71 834.55 859.63 839.39 793.91 782.47 787.52 847.69 829.26 820.32\n",
      "\n",
      "### Makerware 2.4.1.24\n",
      "\n",
      "Run command:\n",
      "    \n",
      "    #!/bin/bash\n",
      "    for run in {1..10}; do \n",
      "        time -p miracle_grue.exe\n",
      "             -h 0.1\n",
      "             -w 0.4\n",
      "             -p 20\n",
      "             -n 2\n",
      "             -b 6\n",
      "             -t 6\n",
      "             /c/Models/Yoda-Lite.stl > /dev/null;\n",
      "    done\n",
      "\n",
      "Times: 67.29 67.06 67.75 67.22 67.85 67.13 67.40 66.81 67.78 67.43\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cura = [ 9.65, 9.56, 9.60, 9.64, 9.51, 9.59, 9.57, 9.63, 9.72, 9.68 ]\n",
      "slicer = [ 74.32, 75.38, 73.88, 73.11, 73.17, 73.01, 72.90, 73.24, 73.00, 73.67 ]\n",
      "skeinforge = [ 838.71, 834.55, 859.63, 839.39, 793.91, 782.47, 787.52, 847.69, 829.26, 820.32 ]\n",
      "makerware = [ 67.29, 67.06, 67.75, 67.22, 67.85, 67.13, 67.40, 66.81, 67.78, 67.43 ]\n",
      "\n",
      "print \"Cura\", mean(cura), numpy.std(cura)\n",
      "print \"Slic3r\", mean(slicer), numpy.std(slicer)\n",
      "print \"Skeinforge\", mean(skeinforge), numpy.std(skeinforge)\n",
      "print \"Makerware\", mean(makerware), numpy.std(makerware)\n",
      "\n",
      "boxplot([cura, slicer, makerware, skeinforge])\n",
      "xticks( [1,2,3,4], [\"Cura\", \"Slic3r\", \"Makerware\", \"Skeinforge\"])\n",
      "#semilogy()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cura 9.615 0.0585234995536\n",
        "Slic3r 73.568 0.742466160845\n",
        "Skeinforge 823.345 25.284601737\n",
        "Makerware 67.372 0.323041792962\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 108,
       "text": [
        "([<matplotlib.axis.XTick at 0x67d9e90>,\n",
        "  <matplotlib.axis.XTick at 0x6f093d0>,\n",
        "  <matplotlib.axis.XTick at 0x699bb50>,\n",
        "  <matplotlib.axis.XTick at 0x699e1d0>],\n",
        " <a list of 4 Text xticklabel objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NTU0UFRUl4ZBERCSRhFfmJzsxZLJq1Sr8fj+bNm3C5XJRVVUFgNvtxu/343a7SU1NZePG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\nWMD/AzGWr3O0PlBoAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x65bf310>"
       ]
      }
     ],
     "prompt_number": 108
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Run times for 150mm box\n",
      "\n",
      "### Cura 14.01\n",
      "\t\n",
      "Run command:\n",
      "    \n",
      "    #!/bin/bash\n",
      "    for run in {1..10}; do\n",
      "        time -p python/python -m Cura.cura -i /c/Models/Cura.ini    \\\n",
      "                                           -s /c/Models/Box_150.stl \\\n",
      "                                           -o /c/Models/Box_150.gcode > /dev/null; \n",
      "    done\n",
      "\t\n",
      "Times: 2.46 2.28 2.31 2.27 2.26 2.21 2.22 2.23 2.23 2.26\n",
      "\n",
      "### Slic3r 0.9.10b\n",
      "\n",
      "Run command:\n",
      "    \n",
      "    #!/bin/bash\n",
      "    for run in {1..10}; do\n",
      "        time -p slic3r.exe --load /c/Models/Slic3r.ini   \\\n",
      "                           -o /c/Models/Box_150.gcode    \\\n",
      "                           /c/Models/Box_150.stl > /dev/null;\n",
      "    done\n",
      "\t\n",
      "Times: 21.20 20.95 20.94 22.06 22.06 22.24 21.32 20.77 20.89 20.62\n",
      "\n",
      "### Skeinforge 50\n",
      "\t\n",
      "Run command:\n",
      "\n",
      "    #!/bin/bash\n",
      "    for run in {1..10}; do\n",
      "        time -p /c/Python27/python skeinforge.py /c/Models/Box_150.stl > /dev/null;\n",
      "    done\n",
      "\t\n",
      "Times: 8156.47 7585.18 7718.89 7853.33 8093.76\n",
      " \n",
      "### Makerware 2.4.1.24\n",
      "\t\n",
      "Run command:\n",
      "\n",
      "    #!/bin/bash\n",
      "    for run in {1..10}; do\n",
      "        time -p miracle_grue.exe \\\n",
      "             -h 0.1              \\\n",
      "             -w 0.4              \\\n",
      "             -p 20               \\\n",
      "             -n 2                \\\n",
      "             -b 6                \\\n",
      "             -t 6                \\\n",
      "             /c/Models/Box_150.stl > /dev/null;\n",
      "    done\n",
      "\t\n",
      "Times: 59.39 59.51 58.32 57.53 57.16 58.63 56.92 60.08 63.94 60.37"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cura = [ 2.46, 2.28, 2.31, 2.27, 2.26, 2.21, 2.22, 2.23, 2.23, 2.26 ]\n",
      "slicer = [ 21.20, 20.95, 20.94, 22.06, 22.06, 22.24, 21.32, 20.77, 20.89, 20.62 ]\n",
      "skeinforge = [ 8156.47, 7585.18, 7718.89, 7853.33, 8093.76 ]\n",
      "makerware = [ 59.39, 59.51, 58.32, 57.53, 57.16, 58.63, 56.92, 60.08, 63.94, 60.37 ]\n",
      "\n",
      "print \"Cura\", mean(cura), numpy.std(cura)\n",
      "print \"Slic3r\", mean(slicer), numpy.std(slicer)\n",
      "print \"Skeinforge\", mean(skeinforge), numpy.std(skeinforge)\n",
      "print \"Makerware\", mean(makerware), numpy.std(makerware)\n",
      "\n",
      "boxplot([cura, slicer, makerware, skeinforge])\n",
      "xticks( [1,2,3,4], [\"Cura\", \"Slic3r\", \"Makerware\", \"Skeinforge\"])\n",
      "semilogy()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cura 2.273 0.0687095335452\n",
        "Slic3r 21.305 0.56678479161\n",
        "Skeinforge 7881.526 217.119279899\n",
        "Makerware 59.185 1.94907798715\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 109,
       "text": [
        "[]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEDCAYAAADHmORTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLZ7wb8u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       "text": [
        "<matplotlib.figure.Figure at 0x72e2ad0>"
       ]
      }
     ],
     "prompt_number": 109
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Run times for Ultimaker robot\n",
      "\n",
      "### Cura 14.01\n",
      "\t\n",
      "Run command:\n",
      "\n",
      "    #!/bin/bash\n",
      "    for run in {1..10}; do \n",
      "        time -p python/python -m Cura.cura                              \\\n",
      "                              -i /c/Models/Cura.ini                     \\\n",
      "                              -s /c/Models/UltimakerRobot_support.stl   \\\n",
      "                              -o /c/Models/UltimakerRobot_support.gcode > /dev/null; \n",
      "    done\n",
      "\n",
      "Times: 5.34 5.39 5.38 5.34 5.35 5.31 5.29 5.28 5.26 5.31\n",
      "\n",
      "### Slic3r 0.9.10b\n",
      "\n",
      "Run command:\n",
      "\n",
      "    #!/bin/bash\n",
      "    for run in {1..10}; do\n",
      "        time -p slic3r.exe --load /c/Models/Slic3r.ini                \\\n",
      "                           -o /c/Models/UltimakerRobot_support.gcode  \\\n",
      "                           /c/Models/UltimakerRobot_support.stl > /dev/null;\n",
      "    done\n",
      "\n",
      "Times: 64.00 66.97 64.25 63.30 64.09 72.46 69.24 63.98 67.13 68.31\n",
      "\n",
      "### Skeinforge 50\n",
      "\n",
      "Run command:\n",
      "\n",
      "    #!/bin/bash\n",
      "    for run in {1..10}; do \n",
      "        time -p /c/Python27/python skeinforge.py     \\\n",
      "                /c/Models/UltimakerRobot_support.stl > /dev/null;\n",
      "    done\n",
      "\n",
      "Times: 270.90 271.11 249.83 250.58 250.38 249.52 249.60 249.38 250.05 249.59\n",
      " \n",
      "### Makerware 2.4.1.24\n",
      "\t\n",
      "Run command:\n",
      "\n",
      "    #!/bin/bash \n",
      "    for run in {1..10}; do\n",
      "        time -p miracle_grue.exe -h 0.1  \\\n",
      "                                 -w 0.4  \\\n",
      "                                 -p 20   \\\n",
      "                                 -n 2    \\\n",
      "                                 -b 6    \\\n",
      "                                 -t 6    \\\n",
      "                                 /c/Models/UltimakerRobot_support.stl > /dev/null;\n",
      "    done\n",
      "\n",
      "Times: 10.44 10.39 10.30 10.29 10.23 10.26 10.30 10.37 10.27 10.22"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cura = [ 5.34, 5.39, 5.38, 5.34, 5.35, 5.31, 5.29, 5.28, 5.26, 5.31 ]\n",
      "slicer = [ 64.00, 66.97, 64.25, 63.30, 64.09, 72.46, 69.24, 63.98, 67.13, 68.31 ]\n",
      "skeinforge = [270.90, 271.11, 249.83, 250.58, 250.38, 249.52, 249.60, 249.38, 250.05, 249.59]\n",
      "makerware = [ 10.44, 10.39, 10.30, 10.29, 10.23, 10.26, 10.30, 10.37, 10.27, 10.22]\n",
      "\n",
      "print \"Cura\", mean(cura), numpy.std(cura)\n",
      "print \"Slic3r\", mean(slicer), numpy.std(slicer)\n",
      "print \"Skeinforge\", mean(skeinforge), numpy.std(skeinforge)\n",
      "print \"Makerware\", mean(makerware), numpy.std(makerware)\n",
      "\n",
      "boxplot([cura, slicer, makerware, skeinforge])\n",
      "xticks( [1,2,3,4], [\"Cura\", \"Slic3r\", \"Makerware\", \"Skeinforge\"])\n",
      "#semilogy()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cura 5.325 0.0403112887415\n",
        "Slic3r 66.373 2.83677299057\n",
        "Skeinforge 254.094 8.46338490204\n",
        "Makerware 10.307 0.0678306715284\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 110,
       "text": [
        "([<matplotlib.axis.XTick at 0x747da90>,\n",
        "  <matplotlib.axis.XTick at 0x72f7150>,\n",
        "  <matplotlib.axis.XTick at 0x77270d0>,\n",
        "  <matplotlib.axis.XTick at 0x7727710>],\n",
        " <a list of 4 Text xticklabel objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x76ea5d0>"
       ]
      }
     ],
     "prompt_number": 110
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Run time as a function of volume\n",
      "\n",
      "\n",
      "    #!/bin/bash\n",
      "    \n",
      "    for i in `seq 10 210`; do\n",
      "        echo \":: $i\"\n",
      "        echo \"n = $i; cube([$i,$i,$i]);\" > cube_$i.scad\n",
      "        openscad -o cube_$i.stl cube_$i.scad &> /dev/null\n",
      "        time -p python -m Cura.cura                                     \\\n",
      "            -i Cura.ini                                                 \\\n",
      "            -s /home/russell/Projects/CuraPaper/scaled_cube/cube_$i.stl \\\n",
      "            -o /home/russell/Projects/CuraPaper/scaled_cube/cube_$i.gcode > /dev/null\n",
      "    done\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "T,N = [],[]\n",
      "for n,line in enumerate(open(\"Projects/CuraPaper/scaled_cube/scaled.log\").read().split('::')) :\n",
      "    if n == 0 : continue\n",
      "    ll = line.split()\n",
      "    N.append( int(ll[0]) )\n",
      "    T.append( float(ll[2]) )\n",
      "subplot(2,2,1)\n",
      "plot(N,T)\n",
      "xlabel(\"cube length (mm)\")\n",
      "ylabel(\"time (seconds)\")\n",
      "yticks([0.5, 1.0, 1.5])\n",
      "\n",
      "subplot(2,2,3)\n",
      "plot(N,T)\n",
      "xlabel(\"cube length (mm)\")\n",
      "ylabel(\"time (seconds)\")\n",
      "semilogy()\n",
      "\n",
      "subplot(2,2,2)\n",
      "plot(array(N)**3/float(100**3),T)\n",
      "xlabel(r'$\\mathbf{dL}^3$')\n",
      "ylabel('time (seconsd)')\n",
      "yticks([0.5, 1.0, 1.5])\n",
      "\n",
      "subplot(2,2,4)\n",
      "plot(array(N)**3/float(100**3),T)\n",
      "xlabel(r'$\\mathbf{dL}^3$')\n",
      "ylabel('time (seconsd)')\n",
      "semilogy()\n",
      "\n",
      "tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VCn379tW7rPI+T3SVnZ2NQYMGoXXr1vDw8MCRI0d0Luv8+fOaPFWpVLCzs9Pr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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7463650>"
       ]
      }
     ],
     "prompt_number": 111
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Run time as a function of facets\n",
      "\n",
      "    #!/bin/bash\n",
      "    \n",
      "    for i in `seq 3 203`; do\n",
      "        echo \":: $i\"\n",
      "        echo \"\\$fn = $i; cylinder( r=50, h=100 );\" > cylinder_$i.scad\n",
      "        openscad -o cylinder_$i.stl cylinder_$i.scad &> /dev/null\n",
      "        time -p python -m Cura.cura                                                    \\\n",
      "                       -i Cura.ini                                                     \\\n",
      "                       -s /home/russell/Projects/CuraPaper/scaled_cube/cylinder_$i.stl \\\n",
      "                       -o /home/russell/Projects/CuraPaper/scaled_cube/cylinder_$i.gcode > /dev/null\n",
      "    done"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "T,N = [],[]\n",
      "for n,line in enumerate(open(\"Projects/CuraPaper/scaled_cube/nfaces.log\").read().split('::')) :\n",
      "    if n == 0 : continue\n",
      "    ll = line.split()\n",
      "    N.append( int(ll[0]) )\n",
      "    T.append( float(ll[2]) )\n",
      "subplot(1,2,1)\n",
      "plot(N,T)\n",
      "xlabel(\"facets\")\n",
      "xlim([0,210])\n",
      "xticks([50,100,150,200])\n",
      "ylabel(\"time (seconds)\")\n",
      "yticks([1,2,3])\n",
      "\n",
      "subplot(1,2,2)\n",
      "plot(N,T)\n",
      "xlabel(\"facets\")\n",
      "xlim([0,210])\n",
      "xticks([50,100,150,200])\n",
      "ylabel(\"time (seconds)\")\n",
      "semilogy()\n",
      "\n",
      "tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ci4+3/MzMmXJUQYsWMr/PChN1pzb3pi36Sm3Zu69NnQp88omsmWZkACtWyFEa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\n6o6j3psVMdrrIeOwdm9aXZO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       "text": [
        "<matplotlib.figure.Figure at 0x66038d0>"
       ]
      }
     ],
     "prompt_number": 95
    }
   ],
   "metadata": {}
  }
 ]
}