tools/analysis.ipynb

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   8   {
   9    "cells": [
  10     {
  11      "cell_type": "code",
  12      "collapsed": false,
  13      "input": [
  14       "%pylab inline"
  15      ],
  16      "language": "python",
  17      "metadata": {},
  18      "outputs": [
  19       {
  20        "output_type": "stream",
  21        "stream": "stdout",
  22        "text": [
  23         "Populating the interactive namespace from numpy and matplotlib\n"
  24        ]
  25       }
  26      ],
  27      "prompt_number": 283
  28     },
  29     {
  30      "cell_type": "code",
  31      "collapsed": false,
  32      "input": [
  33       "import sys\n",
  34       "import os\n",
  35       "import time\n",
  36       "import subprocess\n",
  37       "from progressbar import * # From ipython github site\n",
  38       "from IPython.core.display import Image\n",
  39       "from IPython.display import display, clear_output"
  40      ],
  41      "language": "python",
  42      "metadata": {},
  43      "outputs": [],
  44      "prompt_number": 284
  45     },
  46     {
  47      "cell_type": "code",
  48      "collapsed": false,
  49      "input": [
  50       "options = {\n",
  51       "    \"real_names\" : [\"single\", \"double\", \"long\", \"virtual\", \"Rational_GMPint\", \"Rational_Arbint\", \"mpfrc++\", \"iRRAM\", \"ParanoidNumber\"],\n",
  52       "    \"ipdf_src\" : \"../src/\",\n",
  53       "    \"ipdf_bin\" : \"../bin/ipdf\",\n",
  54       "    \"local_bin\" : \"./\",\n",
  55       "    \"ignore\" : [\"virtual\", \"Rational_Arbint\", \"iRRAM\", \"ParanoidNumber\"],\n",
  56       "    \"test\" : [\"single\", \"double\", \"mpfrc++\"],\n",
  57       "    \"built\" : []\n",
  58       "}"
  59      ],
  60      "language": "python",
  61      "metadata": {},
  62      "outputs": [],
  63      "prompt_number": 414
  64     },
  65     {
  66      "cell_type": "markdown",
  67      "metadata": {},
  68      "source": [
  69       "# Compile programs\n",
  70       "\n",
  71       "Build for each type of real, then save a local copy of the executable."
  72      ]
  73     },
  74     {
  75      "cell_type": "code",
  76      "collapsed": false,
  77      "input": [
  78       "def build(real_type, quadtree=False, controlpanel=False):\n",
  79       "    global options\n",
  80       "    real_name = \"\"\n",
  81       "    if (type(real_type) == str):\n",
  82       "        quadtree = \"enabled\" if (real_type.split(\"-\")[-1] == \"qtree\") else quadtree\n",
  83       "        real_type = real_type.split(\"-\")[0]\n",
  84       "        real_name = real_type\n",
  85       "        real_type = options[\"real_names\"].index(real_type)\n",
  86       "    else:\n",
  87       "        real_name = options[\"real_names\"][real_type]\n",
  88       "        \n",
  89       "    quadtree = \"enabled\" if quadtree else \"disabled\"\n",
  90       "    controlpanel = \"enabled\" if controlpanel else \"disabled\"\n",
  91       "    if (os.system(\"make -C %s clean\" % options[\"ipdf_src\"]) != 0):\n",
  92       "        raise Exception(\"Make clean failed.\")\n",
  93       "    if (os.system(\"make -C %s REALTYPE=%d QUADTREE=%s CONTROLPANEL=%s\" % (options[\"ipdf_src\"], real_type, quadtree, controlpanel)) != 0):\n",
  94       "        raise Exception(\"Make failed.\")\n",
  95       "        \n",
  96       "    q = \"-qtree\" if quadtree == \"enabled\" else \"\"\n",
  97       "    os.rename(options[\"ipdf_bin\"], options[\"local_bin\"]+real_name+q)"
  98      ],
  99      "language": "python",
 100      "metadata": {},
 101      "outputs": [],
 102      "prompt_number": 407
 103     },
 104     {
 105      "cell_type": "code",
 106      "collapsed": false,
 107      "input": [
 108       "p = ProgressBar(len(options[\"test\"]))\n",
 109       "p.animate(0)\n",
 110       "for (i,b) in enumerate(options[\"test\"]): #options[\"real_names\"]:\n",
 111       "    if b in options[\"ignore\"]:\n",
 112       "        continue\n",
 113       "    try:\n",
 114       "        build(b, False, False)\n",
 115       "        options[\"built\"] += [b]\n",
 116       "        #display(\"Built %s\" % b)\n",
 117       "    except:\n",
 118       "        display(\"Failed to build %s\" % b)\n",
 119       "    p.animate(i+1)\n"
 120      ],
 121      "language": "python",
 122      "metadata": {},
 123      "outputs": [
 124       {
 125        "output_type": "stream",
 126        "stream": "stdout",
 127        "text": [
 128         " \r",
 129         "[                  0%                  ]"
 130        ]
 131       },
 132       {
 133        "output_type": "stream",
 134        "stream": "stdout",
 135        "text": [
 136         " \r",
 137         "[*************    33%                  ]  1 of 3 complete"
 138        ]
 139       },
 140       {
 141        "output_type": "stream",
 142        "stream": "stdout",
 143        "text": [
 144         " \r",
 145         "[*****************67%*****             ]  2 of 3 complete"
 146        ]
 147       },
 148       {
 149        "output_type": "stream",
 150        "stream": "stdout",
 151        "text": [
 152         " \r",
 153         "[****************100%******************]  3 of 3 complete"
 154        ]
 155       },
 156       {
 157        "output_type": "stream",
 158        "stream": "stdout",
 159        "text": [
 160         "\n"
 161        ]
 162       }
 163      ],
 164      "prompt_number": 418
 165     },
 166     {
 167      "cell_type": "code",
 168      "collapsed": false,
 169      "input": [
 170       "options[\"built\"]"
 171      ],
 172      "language": "python",
 173      "metadata": {},
 174      "outputs": [
 175       {
 176        "metadata": {},
 177        "output_type": "pyout",
 178        "prompt_number": 419,
 179        "text": [
 180         "['single', 'double', 'mpfrc++']"
 181        ]
 182       }
 183      ],
 184      "prompt_number": 419
 185     },
 186     {
 187      "cell_type": "markdown",
 188      "metadata": {},
 189      "source": [
 190       "## Time to render frames\n",
 191       "\n",
 192       "Compare CPU and GPU frame rates for test image and fixed bounds"
 193      ]
 194     },
 195     {
 196      "cell_type": "code",
 197      "collapsed": false,
 198      "input": [
 199       "def time_vs_frames(exec_name, bounds = [0.,0.,1.,1.], min_frames=0, \\\n",
 200       "                    max_frames=100, step=10,test_image=\"svg-tests/rabbit_simple.svg\"):\n",
 201       "    binname = options[\"local_bin\"]+exec_name\n",
 202       "    data = []\n",
 203       "    p = ProgressBar(sum(xrange(min_frames, max_frames, step)))\n",
 204       "    display(\"Time VS Frames for %s\" % exec_name)\n",
 205       "    p.animate(0)\n",
 206       "    i = 0\n",
 207       "    for frames in xrange(min_frames, max_frames+step, step):\n",
 208       "        pt = [frames]\n",
 209       "        # -l means to turn off lazy rendering\n",
 210       "        # -Q means don't show the window\n",
 211       "        cmd = binname + \" -l -Q -b %s %s %s %s -f %d %s\" % tuple(map(str, bounds) + [frames, test_image])\n",
 212       "        \n",
 213       "        # Everything on GPU\n",
 214       "        start = time.time()\n",
 215       "        os.system(cmd + \" -r gpu -T gpu\")\n",
 216       "        end = time.time()\n",
 217       "        pt += [(end - start)]\n",
 218       "                \n",
 219       "        # Everything on CPU\n",
 220       "        start = time.time()\n",
 221       "        os.system(cmd + \" -r cpu -T cpu\")\n",
 222       "        end = time.time()\n",
 223       "        pt += [(end - start)]\n",
 224       "        \n",
 225       "        data += [pt]\n",
 226       "        i += frames\n",
 227       "        p.animate(i)\n",
 228       "        \n",
 229       "    return asarray(data)"
 230      ],
 231      "language": "python",
 232      "metadata": {},
 233      "outputs": [],
 234      "prompt_number": 420
 235     },
 236     {
 237      "cell_type": "code",
 238      "collapsed": false,
 239      "input": [
 240       "def plot_time_vs_frames(tf, new_figure=True):\n",
 241       "    if new_figure:\n",
 242       "        figure(figsize=(9,7))\n",
 243       "        yscale('linear')\n",
 244       "        xscale('linear')\n",
 245       "        legend([\"GPU\", \"CPU\"])\n",
 246       "        xlabel(\"Frames\")\n",
 247       "        ylabel(\"Time To Render\")\n",
 248       "        title(\"Quick, to the TARDIS of Infinite Precision!\")    \n",
 249       "        \n",
 250       "    plot(tf[:,0], tf[:,1], 'o-')\n",
 251       "    plot(tf[:,0], tf[:,2], 'x-')\n",
 252       "    "
 253      ],
 254      "language": "python",
 255      "metadata": {},
 256      "outputs": [],
 257      "prompt_number": 421
 258     },
 259     {
 260      "cell_type": "code",
 261      "collapsed": false,
 262      "input": [
 263       "for p in options[\"built\"]:\n",
 264       "    plot_time_vs_frames(time_vs_frames(p, bounds=[0.5,0.5,1e-14,1e-14]), False)"
 265      ],
 266      "language": "python",
 267      "metadata": {},
 268      "outputs": [
 269       {
 270        "metadata": {},
 271        "output_type": "display_data",
 272        "text": [
 273         "'Time VS Frames for single'"
 274        ]
 275       },
 276       {
 277        "output_type": "stream",
 278        "stream": "stdout",
 279        "text": [
 280         "\r",
 281         "[                  0%                  ]"
 282        ]
 283       },
 284       {
 285        "output_type": "stream",
 286        "stream": "stdout",
 287        "text": [
 288         " \r",
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 290        ]
 291       },
 292       {
 293        "output_type": "stream",
 294        "stream": "stdout",
 295        "text": [
 296         " \r",
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 299       },
 300       {
 301        "output_type": "stream",
 302        "stream": "stdout",
 303        "text": [
 304         " \r",
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 307       },
 308       {
 309        "output_type": "stream",
 310        "stream": "stdout",
 311        "text": [
 312         " \r",
 313         "[***               7%                  ]  31 of 450 complete"
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 315       },
 316       {
 317        "output_type": "stream",
 318        "stream": "stdout",
 319        "text": [
 320         " \r",
 321         "[*****            14%                  ]  61 of 450 complete"
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 323       },
 324       {
 325        "output_type": "stream",
 326        "stream": "stdout",
 327        "text": [
 328         " \r",
 329         "[********         22%                  ]  101 of 450 complete"
 330        ]
 331       },
 332       {
 333        "output_type": "stream",
 334        "stream": "stdout",
 335        "text": [
 336         " \r",
 337         "[*************    34%                  ]  151 of 450 complete"
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 339       },
 340       {
 341        "output_type": "stream",
 342        "stream": "stdout",
 343        "text": [
 344         " \r",
 345         "[*****************47%                  ]  211 of 450 complete"
 346        ]
 347       },
 348       {
 349        "output_type": "stream",
 350        "stream": "stdout",
 351        "text": [
 352         " \r",
 353         "[*****************62%****              ]  281 of 450 complete"
 354        ]
 355       },
 356       {
 357        "output_type": "stream",
 358        "stream": "stdout",
 359        "text": [
 360         " \r",
 361         "[*****************80%**********        ]  361 of 450 complete"
 362        ]
 363       },
 364       {
 365        "output_type": "stream",
 366        "stream": "stdout",
 367        "text": [
 368         " \r",
 369         "[****************100%******************]  451 of 450 complete"
 370        ]
 371       },
 372       {
 373        "metadata": {},
 374        "output_type": "display_data",
 375        "text": [
 376         "'Time VS Frames for double'"
 377        ]
 378       },
 379       {
 380        "output_type": "stream",
 381        "stream": "stdout",
 382        "text": [
 383         " \r",
 384         "[                  0%                  ]"
 385        ]
 386       },
 387       {
 388        "output_type": "stream",
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 390        "text": [
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 399         " \r",
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 404        "output_type": "stream",
 405        "stream": "stdout",
 406        "text": [
 407         " \r",
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 411       {
 412        "output_type": "stream",
 413        "stream": "stdout",
 414        "text": [
 415         " \r",
 416         "[***               7%                  ]  31 of 450 complete"
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 418       },
 419       {
 420        "output_type": "stream",
 421        "stream": "stdout",
 422        "text": [
 423         " \r",
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 426       },
 427       {
 428        "output_type": "stream",
 429        "stream": "stdout",
 430        "text": [
 431         " \r",
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 433        ]
 434       },
 435       {
 436        "output_type": "stream",
 437        "stream": "stdout",
 438        "text": [
 439         " \r",
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 442       },
 443       {
 444        "output_type": "stream",
 445        "stream": "stdout",
 446        "text": [
 447         " \r",
 448         "[*****************47%                  ]  211 of 450 complete"
 449        ]
 450       },
 451       {
 452        "output_type": "stream",
 453        "stream": "stdout",
 454        "text": [
 455         " \r",
 456         "[*****************62%****              ]  281 of 450 complete"
 457        ]
 458       },
 459       {
 460        "output_type": "stream",
 461        "stream": "stdout",
 462        "text": [
 463         " \r",
 464         "[*****************80%**********        ]  361 of 450 complete"
 465        ]
 466       },
 467       {
 468        "output_type": "stream",
 469        "stream": "stdout",
 470        "text": [
 471         " \r",
 472         "[****************100%******************]  451 of 450 complete"
 473        ]
 474       },
 475       {
 476        "metadata": {},
 477        "output_type": "display_data",
 478        "text": [
 479         "'Time VS Frames for mpfrc++'"
 480        ]
 481       },
 482       {
 483        "output_type": "stream",
 484        "stream": "stdout",
 485        "text": [
 486         " \r",
 487         "[                  0%                  ]"
 488        ]
 489       },
 490       {
 491        "output_type": "stream",
 492        "stream": "stdout",
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 494         " \r",
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 509        "text": [
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 516        "stream": "stdout",
 517        "text": [
 518         " \r",
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 521       },
 522       {
 523        "output_type": "stream",
 524        "stream": "stdout",
 525        "text": [
 526         " \r",
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 528        ]
 529       },
 530       {
 531        "output_type": "stream",
 532        "stream": "stdout",
 533        "text": [
 534         " \r",
 535         "[********         22%                  ]  101 of 450 complete"
 536        ]
 537       },
 538       {
 539        "output_type": "stream",
 540        "stream": "stdout",
 541        "text": [
 542         " \r",
 543         "[*************    34%                  ]  151 of 450 complete"
 544        ]
 545       },
 546       {
 547        "output_type": "stream",
 548        "stream": "stdout",
 549        "text": [
 550         " \r",
 551         "[*****************47%                  ]  211 of 450 complete"
 552        ]
 553       },
 554       {
 555        "output_type": "stream",
 556        "stream": "stdout",
 557        "text": [
 558         " \r",
 559         "[*****************62%****              ]  281 of 450 complete"
 560        ]
 561       },
 562       {
 563        "output_type": "stream",
 564        "stream": "stdout",
 565        "text": [
 566         " \r",
 567         "[*****************80%**********        ]  361 of 450 complete"
 568        ]
 569       },
 570       {
 571        "output_type": "stream",
 572        "stream": "stdout",
 573        "text": [
 574         " \r",
 575         "[****************100%******************]  451 of 450 complete"
 576        ]
 577       },
 578       {
 579        "output_type": "stream",
 580        "stream": "stdout",
 581        "text": [
 582         "\n"
 583        ]
 584       },
 585       {
 586        "metadata": {},
 587        "output_type": "display_data",
 588 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1rp+5qauEina22lBwQQq9W6+tcQ54gEvOjVFMVBWdmb5Oc/SlDQDPT6p/D07B3LYXxmNFJZuonH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\nX5yPZfrtfn5MpqVD3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 589        "text": [
 590         "<matplotlib.figure.Figure at 0x7f94bad311d0>"
 591        ]
 592       }
 593      ],
 594      "prompt_number": 422
 595     },
 596     {
 597      "cell_type": "markdown",
 598      "metadata": {},
 599      "source": [
 600       "## Precision of rendering\n",
 601       "\n",
 602       "Compare images"
 603      ]
 604     },
 605     {
 606      "cell_type": "code",
 607      "collapsed": false,
 608      "input": [
 609       "def rgb2gray(rgb):\n",
 610       "    return np.dot(rgb[...,:3], [0.299/256., 0.587/256., 0.144/256.])"
 611      ],
 612      "language": "python",
 613      "metadata": {},
 614      "outputs": [
 615       {
 616        "output_type": "stream",
 617        "stream": "stdout",
 618        "text": [
 619         "\n"
 620        ]
 621       }
 622      ],
 623      "prompt_number": 424
 624     },
 625     {
 626      "cell_type": "code",
 627      "collapsed": false,
 628      "input": [
 629       "def bmpdiff(img1, img2):\n",
 630       "    p = subprocess.Popen(\"./bmpdiff %s %s\" % (img1, img2), bufsize=0, stdin=subprocess.PIPE, \\\n",
 631       "                         stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)\n",
 632       "    out = p.stdout.readlines()\n",
 633       "    if len(out) != 1:\n",
 634       "        raise Exception(\"bmpdiff %s %s failed: %s \" % (img1, img2, str(map(lambda e : e.strip(\"\\n\"),p.stderr.readlines()))))\n",
 635       "    result = map(float, out[0].strip(\" \\r\\n\").split(\"\\t\"))\n",
 636       "    for i in xrange(len(result)):\n",
 637       "        if isnan(result[i]):\n",
 638       "            result[i] = 0\n",
 639       "    return result"
 640      ],
 641      "language": "python",
 642      "metadata": {},
 643      "outputs": [
 644       {
 645        "output_type": "stream",
 646        "stream": "stdout",
 647        "text": [
 648         "\n"
 649        ]
 650       }
 651      ],
 652      "prompt_number": 428
 653     },
 654     {
 655      "cell_type": "code",
 656      "collapsed": false,
 657      "input": [
 658       "def precision_vs_zoom(exec_name, xy, start_dims, end_dims, steps, test_image=\"svg-tests/rabbit_simple.svg\", show_outputs=False):\n",
 659       "    binname = options[\"local_bin\"]+exec_name\n",
 660       "    data = []\n",
 661       "    bounds = [xy[0], xy[1], start_dims[0], start_dims[1]]\n",
 662       "    dw = float(end_dims[0] - start_dims[0])/float(steps)\n",
 663       "    dh = float(end_dims[1] - start_dims[1])/float(steps)\n",
 664       "    p = ProgressBar(steps)\n",
 665       "    display(\"Precision vs Zoom of \\\"%s\\\" using %s\" % (test_image, exec_name))\n",
 666       "    p.animate(0)\n",
 667       "    im = None\n",
 668       "    figure(figsize=(10,10))\n",
 669       "    for i in xrange(steps+1):\n",
 670       "       \n",
 671       "        cmd = binname + \" -l -b %s %s %s %s %s\" % tuple(map(str, bounds) + [test_image])\n",
 672       "        \n",
 673       "        # Don't show the windows\n",
 674       "        os.system(cmd + \" -Q -r gpu -T gpu -o gpu%d.bmp\" % i)\n",
 675       "        os.system(cmd + \" -Q -r cpu -T cpu -o cpu%d.bmp\" % i)\n",
 676       "        \n",
 677       "        pt = [i]\n",
 678       "        pt += bounds\n",
 679       "        pt += [bmpdiff(\"gpu\"+str(i)+\".bmp\", \"gpu\"+str(max(i-1,0))+\".bmp\")[3]]\n",
 680       "        pt += [bmpdiff(\"gpu\"+str(i)+\".bmp\", \"gpu0.bmp\")[3]]\n",
 681       "        pt += [bmpdiff(\"cpu\"+str(i)+\".bmp\", \"cpu\"+str(max(i-1,0))+\".bmp\")[3]]\n",
 682       "        pt += [bmpdiff(\"cpu\"+str(i)+\".bmp\", \"cpu0.bmp\")[3]]\n",
 683       "        pt += [bmpdiff(\"cpu\"+str(i)+\".bmp\", \"gpu\"+str(i)+\".bmp\")[3]]\n",
 684       "\n",
 685       "        if show_outputs != False:\n",
 686       "            clear_output()\n",
 687       "            if i == 0:\n",
 688       "                im = imshow(imread(\"cpu0.bmp\"), cmap=get_cmap(\"gray\"))\n",
 689       "            else:\n",
 690       "                im.set_data(imread(\"cpu\"+str(i)+\".bmp\"))\n",
 691       "                draw()\n",
 692       "            title(\"Bounds: %s\" % str(bounds))\n",
 693       "            \n",
 694       "        display(\"Precision vs Zoom of \\\"%s\\\" using %s\" % (test_image, exec_name))\n",
 695       "        p.animate(i)\n",
 696       "        \n",
 697       "        if show_outputs != False:\n",
 698       "            display(gcf())        \n",
 699       "            \n",
 700       "        \n",
 701       "        # [step, x, y, w, h, gpu_delta, gpu_total_delta, cpu_delta, cpu_total_delta, gpu_vs_cpu\n",
 702       "        data += [pt]\n",
 703       "        bounds[2] += dw\n",
 704       "        bounds[3] += dh\n",
 705       "\n",
 706       "        \n",
 707       "    close(gcf())\n",
 708       "    return asarray(data)"
 709      ],
 710      "language": "python",
 711      "metadata": {},
 712      "outputs": [],
 713      "prompt_number": 429
 714     },
 715     {
 716      "cell_type": "code",
 717      "collapsed": false,
 718      "input": [
 719       "pz = precision_vs_zoom(\"single\", [0.5,0.5],[1e-5,1e-5],[1e-6,1e-6], 100, \"svg-tests/fox-vector.svg\", show_outputs=True)"
 720      ],
 721      "language": "python",
 722      "metadata": {},
 723      "outputs": [
 724       {
 725        "metadata": {},
 726        "output_type": "display_data",
 727        "text": [
 728         "'Precision vs Zoom of \"svg-tests/fox-vector.svg\" using single'"
 729        ]
 730       },
 731       {
 732        "output_type": "stream",
 733        "stream": "stdout",
 734        "text": [
 735         " \r",
 736         "[****************100%******************]  100 of 100 complete"
 737        ]
 738       },
 739       {
 740        "metadata": {},
 741        "output_type": "display_data",
 742        "png": "iVBORw0KGgoAAAANSUhEUgAAAlsAAAHNCAYAAAApEr6yAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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EQoLi5OERER8vX11fz586s8xQgAAFCfVRu2BgwYUOmtG9auXVvh8oceekgPPfTQ+VUGAABQD3AH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\nxGGLiIiIyEVfAQcK06wQj5M7AAAAAElFTkSuQmCC\n",
 743        "text": [
 744         "<matplotlib.figure.Figure at 0x7f94bab242d0>"
 745        ]
 746       },
 747       {
 748        "output_type": "stream",
 749        "stream": "stdout",
 750        "text": [
 751         "\n"
 752        ]
 753       }
 754      ],
 755      "prompt_number": 430
 756     },
 757     {
 758      "cell_type": "code",
 759      "collapsed": false,
 760      "input": [
 761       "pzd = precision_vs_zoom(\"double\", [0.5,0.5],[1e-13,1e-13],[1e-15,1e-15], 100, \"svg-tests/fox-vector.svg\", show_outputs=True)"
 762      ],
 763      "language": "python",
 764      "metadata": {},
 765      "outputs": [
 766       {
 767        "metadata": {},
 768        "output_type": "display_data",
 769        "text": [
 770         "'Precision vs Zoom of \"svg-tests/fox-vector.svg\" using double'"
 771        ]
 772       },
 773       {
 774        "output_type": "stream",
 775        "stream": "stdout",
 776        "text": [
 777         " \r",
 778         "[****************100%******************]  100 of 100 complete"
 779        ]
 780       },
 781       {
 782        "metadata": {},
 783        "output_type": "display_data",
 784 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mtaICIiQhEREZKk4OBgdezYUfn5+VqwYIHee+89SVJaWpoSEhKUkZGh+fPnKzU1VY0aNVJ0dLTa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\nYII=\n",
 785        "text": [
 786         "<matplotlib.figure.Figure at 0x7f94bacc6310>"
 787        ]
 788       },
 789       {
 790        "output_type": "stream",
 791        "stream": "stdout",
 792        "text": [
 793         "\n"
 794        ]
 795       }
 796      ],
 797      "prompt_number": 437
 798     },
 799     {
 800      "cell_type": "code",
 801      "collapsed": false,
 802      "input": [
 803       "pzmp = precision_vs_zoom(\"mpfrc++\", [0.5,0.5],[1e-13,1e-13],[1e-15,1e-15], 1, \"svg-tests/fox-vector.svg\", show_outputs=True)\n",
 804       "# Need to adapt mpfrc++ for required precision. Probably not worth it, just go back to mpfr?"
 805      ],
 806      "language": "python",
 807      "metadata": {},
 808      "outputs": [
 809       {
 810        "metadata": {},
 811        "output_type": "display_data",
 812        "text": [
 813         "'Precision vs Zoom of \"svg-tests/fox-vector.svg\" using mpfrc++'"
 814        ]
 815       },
 816       {
 817        "output_type": "stream",
 818        "stream": "stdout",
 819        "text": [
 820         " \r",
 821         "[****************100%******************]  1 of 1 complete"
 822        ]
 823       },
 824       {
 825        "metadata": {},
 826        "output_type": "display_data",
 827 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QAA4BBhCwAAwCHCFgAAgEOELQAAAIcIWwAAAA55zMzO+k49HgVgt0CDu+CCC07aVllZqdatW5/F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\nQ4QtAAAAh/4XJX37efghybUAAAAASUVORK5CYII=\n",
 828        "text": [
 829         "<matplotlib.figure.Figure at 0x7f94bafb4390>"
 830        ]
 831       },
 832       {
 833        "output_type": "stream",
 834        "stream": "stdout",
 835        "text": [
 836         "\n"
 837        ]
 838       }
 839      ],
 840      "prompt_number": 439
 841     },
 842     {
 843      "cell_type": "code",
 844      "collapsed": false,
 845      "input": [
 846       "def plot_precision_vs_zoom(pz, gpu=True):\n",
 847       "    figure(figsize=(9,7))\n",
 848       "    if gpu:\n",
 849       "        plot(pz[:,3], pz[:,6], 'o-')\n",
 850       "        legend([\"GPU\", \"CPU\"])\n",
 851       "        \n",
 852       "    yscale('log')\n",
 853       "    xscale('log')\n",
 854       "    plot(pz[:,3], pz[:,8], 'o-')\n",
 855       "    xlabel(\"Width\")\n",
 856       "    ylabel(\"Average distance to closest pixel in original\")\n",
 857       "    title(\"Arr Captain We Be Sinking Into The Sea of Precision\")"
 858      ],
 859      "language": "python",
 860      "metadata": {},
 861      "outputs": [],
 862      "prompt_number": 454
 863     },
 864     {
 865      "cell_type": "code",
 866      "collapsed": false,
 867      "input": [
 868       "plot_precision_vs_zoom(pz)"
 869      ],
 870      "language": "python",
 871      "metadata": {},
 872      "outputs": [
 873       {
 874        "metadata": {},
 875        "output_type": "display_data",
 876 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Cl4+vTpmDlzJszMzJCUlISpU6fi8ccf10dsRERERFrROOQ0aNAgHD16FDKZDKmpqSplhsIhJyIi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\n",
 877        "text": [
 878         "<matplotlib.figure.Figure at 0x7f94baa28290>"
 879        ]
 880       }
 881      ],
 882      "prompt_number": 384
 883     },
 884     {
 885      "cell_type": "code",
 886      "collapsed": false,
 887      "input": [
 888       "plot_precision_vs_zoom(pzd, False)"
 889      ],
 890      "language": "python",
 891      "metadata": {},
 892      "outputs": [
 893       {
 894        "metadata": {},
 895        "output_type": "display_data",
 896 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tWr8PHxMVmHpiRHkgMAhYXSbsk//CCdfRXC71uT2rQJGDcOWL4ccHBIQmzsTuTnO0KlUuHEiR44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\n0ev1bTZ+IvKcr67t+fn5lqVLl1oWLFhgefXVV92+bru0dXBl4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 897        "text": [
 898         "<matplotlib.figure.Figure at 0x7f94bae39bd0>"
 899        ]
 900       }
 901      ],
 902      "prompt_number": 455
 903     },
 904     {
 905      "cell_type": "code",
 906      "collapsed": false,
 907      "input": [],
 908      "language": "python",
 909      "metadata": {},
 910      "outputs": []
 911     }
 912    ],
 913    "metadata": {}
 914   }
 915  ]
 916 }