+ "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": 26
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "#scaling_data = {}\n",
+ "p = ProgressBar(len(options[\"built\"]))\n",
+ "p.animate(0)\n",
+ "for i,b in enumerate(options[\"built\"]):\n",
+ " if b in scaling_data.keys():\n",
+ " #print \"Skip %s\" % b\n",
+ " continue\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",
+ "png": 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ajVcQSmER3aEA0iTRILoiSmEJRFejEx2hHkgTRT0QRZTCEoZ6IERXaBpvE6XV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\nWEDXgVAKSwgaAyG6VNkD0f8/KL0PILQWFl0HIgi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+ "text": [
+ "<matplotlib.figure.Figure at 0x7f1de96875d0>"
+ ]
+ }
+ ],
+ "prompt_number": 10