About to break everything with a merge
[ipdf/code.git] / tools / analysis.ipynb
index a91b2f4..f7e4e02 100644 (file)
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "from common import *"
+      "!pkill -9 ipdf"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 2
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from common import *\n",
+      "import build\n",
+      "import scaling\n",
+      "import saveload"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# If things are changed run this instead of restarting kernel\n",
+      "scaling = reload(scaling)\n",
+      "build = reload(build)\n",
+      "saveload = reload(saveload)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 24
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "def build(real_type, quadtree=False, controlpanel=False):\n",
-      "    global options\n",
-      "    real_name = \"\"\n",
-      "    if (type(real_type) == str):\n",
-      "        quadtree = \"enabled\" if (real_type.split(\"-\")[-1] == \"qtree\") else quadtree\n",
-      "        real_type = real_type.split(\"-\")[0]\n",
-      "        real_name = real_type\n",
-      "        real_type = options[\"real_names\"].index(real_type)\n",
-      "    else:\n",
-      "        real_name = options[\"real_names\"][real_type]\n",
-      "        \n",
-      "    quadtree = \"enabled\" if quadtree else \"disabled\"\n",
-      "    controlpanel = \"enabled\" if controlpanel else \"disabled\"\n",
-      "    if (os.system(\"make -C %s clean\" % options[\"ipdf_src\"]) != 0):\n",
-      "        raise Exception(\"Make clean failed.\")\n",
-      "    if (os.system(\"make -C %s REALTYPE=%d QUADTREE=%s CONTROLPANEL=%s\" % (options[\"ipdf_src\"], real_type, quadtree, controlpanel)) != 0):\n",
-      "        raise Exception(\"Make failed.\")\n",
-      "        \n",
-      "    q = \"-qtree\" if quadtree == \"enabled\" else \"\"\n",
-      "    os.rename(options[\"ipdf_bin\"], options[\"local_bin\"]+real_name+q)"
+      "try:\n",
+      "    options[\"built\"] = saveload.load_obj(\"built\")\n",
+      "except:\n",
+      "    options[\"built\"] = []\n",
+      "options[\"tobuild\"] = [\"float\", \"double\", \"mpfr-16\",\"mpfr-32\", \"mpfr-64\", \"mpfr-256\", \"mpfr-512\", \"mpfr-1024\", \"Gmprat\", \"cumul-float\", \"path-float\", \"path-Gmprat\", \"path-mpfr-1024\"]\n",
+      "options[\"tobuild\"] = [b for b in options[\"tobuild\"] if b not in options[\"built\"]]\n",
+      "options[\"tobuild\"] = [\"path-Gmprat\",\"path-mpfr-1024\"]\n",
+      "    "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "build.BuildAll()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Building: ['path-Gmprat', 'path-mpfr-1024']\n",
+        "\r",
+        "[                  0%                  ]"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " \r",
+        "[*****************50%                  ]  1 of 2 complete"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " \r",
+        "[****************100%******************]  2 of 2 complete"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "saveload.save_obj(options[\"built\"], \"built\")\n",
+      "options[\"built\"]\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 16,
+       "text": [
+        "['float',\n",
+        " 'double',\n",
+        " 'mpfr-16',\n",
+        " 'mpfr-32',\n",
+        " 'mpfr-64',\n",
+        " 'mpfr-256',\n",
+        " 'mpfr-512',\n",
+        " 'mpfr-1024',\n",
+        " 'Gmprat',\n",
+        " 'cumul-float',\n",
+        " 'path-float',\n",
+        " 'path-Gmprat',\n",
+        " 'path-mpfr-1024',\n",
+        " 'path-Gmprat',\n",
+        " 'path-mpfr-1024']"
+       ]
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Accuracy of Rendering VS Scaling"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "scaling_data = saveload.load_dict(\"scaling_data\")\n",
+      "scaling_data.keys()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 20,
+       "text": [
+        "['mpfr-32',\n",
+        " 'mpfr-256',\n",
+        " 'double',\n",
+        " 'float',\n",
+        " 'mpfr-512',\n",
+        " 'path-mpfr-1024',\n",
+        " 'mpfr-1024',\n",
+        " 'cumul-float',\n",
+        " 'mpfr-64',\n",
+        " 'path-float',\n",
+        " 'path-Gmprat',\n",
+        " 'mpfr-16',\n",
+        " 'Gmprat']"
+       ]
+      }
+     ],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "del scaling_data[\"path-Gmprat\"]\n",
+      "del scaling_data[\"path-mpfr-1024\"]"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 26
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "p = ProgressBar(len(options[\"tobuild\"]))\n",
+      "#scaling_data = {}\n",
+      "p = ProgressBar(len(options[\"built\"]))\n",
       "p.animate(0)\n",
-      "for (i,b) in enumerate(options[\"tobuild\"]): #options[\"real_names\"]:\n",
-      "    if b in options[\"ignore\"]:\n",
+      "for i,b in enumerate(options[\"built\"]):\n",
+      "    if b in scaling_data.keys():\n",
+      "        #print \"Skip %s\" % b\n",
       "        continue\n",
-      "    try:\n",
-      "        build(b, False, False)\n",
-      "        options[\"built\"] += [b]\n",
-      "        #display(\"Built %s\" % b)\n",
-      "    except:\n",
-      "        display(\"Failed to build %s\" % b)\n",
-      "    p.animate(i+1)\n"
+      "    print b\n",
+      "    p.animate(i)\n",
+      "    scaling_data[b] = scaling.FixedScales(\"./\"+b, steps=2000, fps=10, xz=0.5, yz=0.5)\n",
+      "saveload.save_obj(scaling_data, \"scaling_data\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\r",
+        "[                  0%                  ]"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " path-Gmprat\n",
+        "\r",
+        "[***               7%                  ]  1 of 15 complete"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " ./path-Gmprat - Quit early after 149 steps - Exception [Errno 32] Broken pipe\n",
+        "./path-Gmprat - Couldn't exit - [Errno 32] Broken pipe\n",
+        "path-mpfr-1024\n",
+        "\r",
+        "[*****************80%**********        ]  12 of 15 complete"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " ./path-mpfr-1024 - Quit early after 149 steps - Exception [Errno 32] Broken pipe\n",
+        "./path-mpfr-1024 - Couldn't exit - [Errno 32] Broken pipe\n"
+       ]
+      }
+     ],
+     "prompt_number": 27
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<table>\n",
+      "    <tr><td><b>Original</b></td><td><b>Scaled</b></td></tr>\n",
+      "    <tr><td><img src=\"original.bmp\" width=\"50%\"/></td><td><img src=\"final.bmp\" width=\"50%\"/></td></tr>\n",
+      "</table>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "fig = figure(figsize=(6,4))\n",
+      "l = []\n",
+      "for b in [\"mpfr-16\", \"mpfr-32\", \"mpfr-64\", \"mpfr-256\", \"mpfr-512\", \"mpfr-1024\",\"path-Gmprat\"]: \n",
+      "    plot(-1.0*scaling_data[b][\"accuracy\"][:,4], scaling_data[b][\"accuracy\"][:,-1])\n",
+      "    l += [b]\n",
+      "#xscale(\"log\")\n",
+      "legend(l, loc=\"best\")\n",
+      "title(\"Loss of Precision for a 1x1 pixel grid\\nView Top Left: (0.5,0.5)\")\n",
+      "xlabel(\"Log10(Magnification)\")\n",
+      "ylabel(\"Representable Lines\")\n",
+      "\n",
+      "fig.savefig('../../sam/figures/loss_of_precision_grid_0.5.pdf', format='PDF')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
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EV2hFFYTRSksRSJajVcQSmER3aEA0iTRILoiSmEJRFejEx2hHkgTRT0QRZTCEoZ6IERXaBpvE6XV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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f1de96875d0>"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "fig = figure(figsize=(6,4))\n",
+      "l = []\n",
+      "for b in [\"mpfr-1024\", \"Gmprat\"]:\n",
+      "    plot(-1.0*scaling_data[b][\"accuracy\"][:,4], scaling_data[b][\"performance\"][:,3])\n",
+      "    l += [b]\n",
+      "#xscale(\"log\")\n",
+      "\n",
+      "legend(l, loc=\"best\")\n",
+      "xlabel(\"Magnification\")\n",
+      "ylabel(\"Average FPS (100 frames)\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 42,
+       "text": [
+        "<matplotlib.text.Text at 0x7f37e035ca10>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
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KWHVqlVEREQYpImIiGD58uUAJCQk4OPjQ9u2bc3mzc6uqCzWrl1L165dHXULEifDmGLRCI0Dr+ew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\nrkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f37e04233d0>"
+       ]
+      }
+     ],
+     "prompt_number": 42
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Invariance after scaling"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "invariance_data = saveload.load_dict(\"invariance_data\")\n",
+      "invariance_data.keys()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "invariance_data = {}\n",
+      "p = ProgressBar(len(options[\"built\"]))\n",
+      "p.animate(0)\n",
+      "for i,b in enumerate(options[\"built\"]):\n",
+      "    if b in invariance_data.keys():\n",
+      "        continue\n",
+      "    invariance_data[b] = scaling.TestInvariance(\"./\"+b, fps=10, x0 = 0.5, y0 = 0.5, w0 = 1e-6, h0 = 1e-6, xz=400, yz=300)\n",
+      "    p.animate(i)\n",
+      "   \n",
+      "#saveload.save_obj(invariance_data, \"invariance_data\")"
      ],
      "language": "python",
      "metadata": {},
        "stream": "stdout",
        "text": [
         " \r",
-        "[*************    33%                  ]  1 of 3 complete"
+        "[***               7%                  ]  1 of 15 complete"
        ]
       },
       {
        "stream": "stdout",
        "text": [
         " \r",
-        "[*****************67%*****             ]  2 of 3 complete"
+        "[***               7%                  ]  1 of 15 complete"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " ./mpfr-16 - Quit early after 1 steps - No precision left\n",
+        "\r",
+        "[*****            13%                  ]  2 of 15 complete"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " ./mpfr-32 - Quit early after 801 steps - Took too long to render frames\n",
+        "\r",
+        "[********         20%                  ]  3 of 15 complete"
        ]
       },
       {
        "stream": "stdout",
        "text": [
         " \r",
-        "[****************100%******************]  3 of 3 complete"
+        "[**********       27%                  ]  4 of 15 complete"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "\n"
+        " \r",
+        "[*************    33%                  ]  5 of 15 complete"
+       ]
+      },
+      {
+       "ename": "KeyboardInterrupt",
+       "evalue": "",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+        "\u001b[1;32m<ipython-input-28-471152fea0f5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mb\u001b[0m \u001b[1;32min\u001b[0m \u001b[0minvariance_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m         \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[0minvariance_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscaling\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTestInvariance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"./\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1e-6\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1e-6\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m400\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m     \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0manimate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;32m/home/sam/Documents/University/2014/ipdf/code/tools/scaling.py\u001b[0m in \u001b[0;36mTestInvariance\u001b[1;34m(binname, x0, y0, w0, h0, s, steps, xz, yz, testsvg, renderer, fps)\u001b[0m\n\u001b[0;32m    102\u001b[0m                         \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"loop %d pxzoom %s %s %s\\n\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    103\u001b[0m                         \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"querygpubounds step%d.dat\\n\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 104\u001b[1;33m                         \u001b[1;32mwhile\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misfile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"step%d.dat\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    105\u001b[0m                                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    106\u001b[0m                         \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"loop %d printspf\\n\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mfps\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Print an FPS count to signal it is safe to read the file\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;32m/usr/lib/python2.7/genericpath.pyc\u001b[0m in \u001b[0;36misfile\u001b[1;34m(path)\u001b[0m\n\u001b[0;32m     27\u001b[0m     \u001b[1;34m\"\"\"Test whether a path is a regular file\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 29\u001b[1;33m         \u001b[0mst\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     30\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merror\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     31\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+       ]
+      }
+     ],
+     "prompt_number": 28
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "fig = figure(figsize=(6,4))\n",
+      "for b in invariance_data.keys():\n",
+      "    plot(invariance_data[b][\"accuracy\"][:,4], invariance_data[b][\"accuracy\"][:,-2])\n",
+      "#xscale(\"log\")\n",
+      "yscale(\"log\")\n",
+      "legend(scaling_data.keys(), loc=\"best\")\n",
+      "xlabel(\"Total scaling factor\")\n",
+      "ylabel(\"Accumulated Error\")\n",
+      "#fig.savefig(\"../../sam/figures/cumulative_error_grid.pdf\", format=\"PDF\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEYCAYAAAC3LjroAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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h7qI4z9fR+whTVHNIZCeeoABG5qe2mEI00O60eBQVynlln640/7RFNX62FlsJHvS6qlU09QbHOLj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\nyOQgSZIkNSCTgyRJktTA/wOgbRbeVj3ezAAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f3051f4b3d0>"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 195
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "options[\"built\"]"
+      "for b in invariance_data:\n",
+      "    plot(1.0/invariance_data[b][\"accuracy\"][:,2], invariance_data[b][\"performance\"][:,3])\n",
+      "xscale(\"log\")\n",
+      "legend(invariance_data.keys())\n",
+      "xlabel(\"Magnification\")\n",
+      "ylabel(\"Average FPS (100 frames)\")"
      ],
      "language": "python",
      "metadata": {},
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 6,
+       "prompt_number": 196,
        "text": [
-        "['single', 'double', 'GMPrat']"
+        "<matplotlib.text.Text at 0x7f3051dd37d0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
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4DDVAWzXju0Su3ULWL0auHFDt6QQE4NmuxofB2bOBJ56imDG1grwf86BjwcNWwb7YIqDQ6OdyBq5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\n1dXJ7Ayj5dPyu0L5HaEe6v4NaPnqKb9DB4fg4GDSvXt3oqOjQ+zt7cnB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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f30521fc8d0>"
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 196
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "outputs": [
       {
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "'Precision vs Zoom of \"svg-tests/fox-vector.svg\" using single'"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        " \r",
-        "[****************100%******************]  100 of 100 complete"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
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REQoLi5OERER8vX11fz586s8xQgAAFCfVRu2BgwYUOmtG9auXVvh8oceekgPPfTQ+VUGAABQD3AH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\nxGGLiIiIyEVfAQcK06wQj5M7AAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f25259bf290>"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
+       "ename": "NameError",
+       "evalue": "name 'precision_vs_zoom' is not defined",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+        "\u001b[1;32m<ipython-input-1-177c6c5e9c41>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprecision_vs_zoom\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"single\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1e-5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1e-5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1e-6\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1e-6\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"svg-tests/fox-vector.svg\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshow_outputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[1;31mNameError\u001b[0m: name 'precision_vs_zoom' is not defined"
        ]
       }
      ],
-     "prompt_number": 13
+     "prompt_number": 1
     },
     {
      "cell_type": "code",

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