+ "\r",
+ "[ 0% ]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " \r",
+ "[*** 7% ] 1 of 15 complete"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " \r",
+ "[*** 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"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " \r",
+ "[********** 27% ] 4 of 15 complete"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " \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": 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NIZCeeoABG5qe2mEI00O60eBQVynlln640/7RFNX62FlsJHvS6qlU09QbHOLj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\nyOQgSZIkNSCTgyRJktTA/wOgbRbeVj3ezAAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7f3051f4b3d0>"
+ ]
+ }
+ ],
+ "prompt_number": 195
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "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": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 196,
+ "text": [
+ "<matplotlib.text.Text at 0x7f3051dd37d0>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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LWL0auHFDt6QQE4NmuxofB2bOBJ56imDG1grwf86BjwcNWwb7YIqDQ6OdyBq5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\n4EBRFEVJ+H8hbiqovByBAQAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7f30521fc8d0>"
+ ]
+ }
+ ],
+ "prompt_number": 196
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Time to render frames\n",
+ "\n",
+ "Compare CPU and GPU frame rates for test image and fixed bounds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "def time_vs_frames(exec_name, bounds = [0.,0.,1.,1.], min_frames=0, \\\n",
+ " max_frames=100, step=10,test_image=\"svg-tests/rabbit_simple.svg\"):\n",
+ " binname = options[\"local_bin\"]+exec_name\n",
+ " data = []\n",
+ " p = ProgressBar(sum(xrange(min_frames, max_frames, step)))\n",
+ " display(\"Time VS Frames for %s\" % exec_name)\n",
+ " p.animate(0)\n",
+ " i = 0\n",
+ " for frames in xrange(min_frames, max_frames+step, step):\n",
+ " pt = [frames]\n",
+ " # -l means to turn off lazy rendering\n",
+ " # -Q means don't show the window\n",
+ " cmd = binname + \" -l -Q -b %s %s %s %s -f %d %s\" % tuple(map(str, bounds) + [frames, test_image])\n",
+ " \n",
+ " # Everything on GPU\n",
+ " start = time.time()\n",
+ " os.system(cmd + \" -r gpu -T gpu\")\n",
+ " end = time.time()\n",
+ " pt += [(end - start)]\n",
+ " \n",
+ " # Everything on CPU\n",
+ " start = time.time()\n",
+ " os.system(cmd + \" -r cpu -T cpu\")\n",
+ " end = time.time()\n",
+ " pt += [(end - start)]\n",
+ " \n",
+ " data += [pt]\n",
+ " i += frames\n",
+ " p.animate(i)\n",
+ " \n",
+ " return asarray(data)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "def plot_time_vs_frames(tf, new_figure=True):\n",
+ " if new_figure:\n",
+ " figure(figsize=(9,7))\n",
+ " yscale('linear')\n",
+ " xscale('linear')\n",
+ " legend([\"GPU\", \"CPU\"])\n",
+ " xlabel(\"Frames\")\n",
+ " ylabel(\"Time To Render\")\n",
+ " title(\"Quick, to the TARDIS of Infinite Precision!\") \n",
+ " \n",
+ " plot(tf[:,0], tf[:,1], 'o-')\n",
+ " plot(tf[:,0], tf[:,2], 'x-')\n",
+ " "
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "for p in options[\"built\"]:\n",
+ " plot_time_vs_frames(time_vs_frames(p, bounds=[0.5,0.5,1e-14,1e-14]), False)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "text": [
+ "'Time VS Frames for single'"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "\r",
+ "[ 0% ]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " \r",
+ "[ 0% ] 1 of 450 complete"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " \r",
+ "[ 0% ] 1 of 450 complete"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " \r",
+ "[* 2% ] 11 of 450 complete"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " \r",
+ "[*** 7% ] 31 of 450 complete"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " \r",
+ "[***** 14% ] 61 of 450 complete"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " \r",
+ "[******** 22% ] 101 of 450 complete"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " \r",
+ "[************* 34% ] 151 of 450 complete"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " \r",
+ "[*****************47% ] 211 of 450 complete"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " \r",
+ "[*****************62%**** ] 281 of 450 complete"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " \r",
+ "[*****************80%********** ] 361 of 450 complete"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " \r",
+ "[****************100%******************] 451 of 450 complete"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "text": [
+ "'Time VS Frames for double'"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " \r",