pyn-rickFeb14-Z-P_analysis.ipynb

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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "rick-Feb11 - Comparing 08 - 09 - 19 - rbubble=150pc"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "#%config InlineBackend.figure_format = 'retina'\n",
      "from matplotlib.pylab import *\n",
      "rcParams['figure.figsize'] = (12,8)\n",
      "rcParams['font.size'] = 18\n",
      "#import logging"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pynbody\n",
      "import pynbody.plot.sph as sph\n",
      "import mmap\n",
      "pynbody.ramses.multiprocess_num = 16\n",
      "pynbody.config['number_of_threads'] = 64"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = pynbody.load('output_00004')\n",
      "s['pos']\n",
      "s['pos'] -= 0.5\n",
      "s.physical_units();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody:Loading using backend <class 'pynbody.snapshot.ramses.RamsesSnap'>\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot.ramses:Loading hydro files\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot.ramses:Done\n",
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      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
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       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting vel units from 7.80e+07 cm s**-1 to km s**-1\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting tv units from 7.80e+07 cm s**-1 to km s**-1\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting smooth units from 5.01e+22 cm to kpc\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting p units from 2.70e-10 g cm**-1 s**-2 to km**2 Msol s**-2 kpc**-3\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting rho units from 4.44e-26 g cm**-3 to Msol kpc**-3\n"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = 1/s.properties['a']-1\n",
      "print \"Redshift =\",z\n",
      "boxsizestring = \"%.2f\" % s.properties['boxsize'].in_units('kpc')\n",
      "boxsizestring += \" kpc\"\n",
      "print boxsizestring"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Redshift = 24.9885292951\n",
        "16.23 kpc\n"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.g['metal'][s.g['metal']<1e-6] = 0.0\n",
      "s.g['pzf'][s.g['pzf']<1e-6] = 0.0\n",
      "s.s['ppf'][s.s['ppf']>1.0] = 1.0\n",
      "linex=(1e-7,1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.loglog(s.g['metal'],1.0-s.g['pgf']+1e-6,'.',linex,linex)\n",
      "plt.title(\"Z vs (1-Pf) z=%.1f\"%z)\n",
      "plt.grid(True, which=\"both\")\n",
      "axes().set_xlabel(\"Z\")\n",
      "axes().set_ylabel(\"1-P\")\n",
      "plt.ylim(1e-7, 1)\n",
      "plt.xlim(1e-7, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 102,
       "text": [
        "(1e-07, 1)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Wv85lPGslXZjfjpD0+I722kV9pyRdIOlYSR9V9jHsH5H0mlEdbFVmZmaSi1d2jFE/x6Ji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\nDwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7c2ad10>"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "s = pynbody.load('output_00005')\n",
      "s['pos']\n",
      "s['pos'] -= 0.5\n",
      "s.physical_units();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody:Loading using backend <class 'pynbody.snapshot.ramses.RamsesSnap'>\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot.ramses:Loading hydro files\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot.ramses:Done\n",
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       "output_type": "stream",
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      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting vel units from 7.79e+07 cm s**-1 to km s**-1\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting tv units from 7.79e+07 cm s**-1 to km s**-1\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting smooth units from 5.01e+22 cm to kpc\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting p units from 2.69e-10 g cm**-1 s**-2 to km**2 Msol s**-2 kpc**-3\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting rho units from 4.43e-26 g cm**-3 to Msol kpc**-3\n"
       ]
      }
     ],
     "prompt_number": 113
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.properties"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 114,
       "text": [
        "{'a': 0.038507301295694,\n",
        " 'boxsize': Unit(\"1.62e+01 kpc\"),\n",
        " 'h': 0.71,\n",
        " 'omegaL0': 0.732999980449677,\n",
        " 'omegaM0': 0.266999989748001,\n",
        " 'time': Unit(\"1.37e-01 kpc s km**-1\")}"
       ]
      }
     ],
     "prompt_number": 114
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = 1/s.properties['a']-1\n",
      "print \"Redshift =\",z\n",
      "boxsizestring = \"%.2f\" % s.properties['boxsize'].in_units('kpc')\n",
      "boxsizestring += \" kpc\"\n",
      "print boxsizestring"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Redshift = 24.9691010887\n",
        "16.24 kpc\n"
       ]
      }
     ],
     "prompt_number": 115
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "min(s.g['metal'][s.g['metal']>0.0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 116,
       "text": [
        "1.1441792087365815e-229"
       ]
      }
     ],
     "prompt_number": 116
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.g['metal'][s.g['metal']<1e-6] = 0.0\n",
      "s.g['pzf'][s.g['pzf']<1e-6] = 0.0\n",
      "s.s['ppf'][s.s['ppf']>1.0] = 1.0\n",
      "linex=(1e-7,1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 117
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "min(s.g['metal'][s.g['metal']>0.0]),min(s.g['pgf'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 118,
       "text": [
        "(1.0000243432518367e-06, 0.99957116673575974)"
       ]
      }
     ],
     "prompt_number": 118
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.loglog(s.g['metal'],1.0-s.g['pgf']+1e-6,'.',linex,linex)\n",
      "plt.title(\"Z vs (1-Pf) z=%.1f\"%z)\n",
      "plt.grid(True, which=\"both\")\n",
      "axes().set_xlabel(\"Z\")\n",
      "axes().set_ylabel(\"1-P\")\n",
      "plt.ylim(1e-7, 1)\n",
      "plt.xlim(1e-7, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 119,
       "text": [
        "(1e-07, 1)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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tdL5hI5V4Zcco2j/FXMSIGSL/qeYiRkzyHy8e1564Met+7hfdN5/8Dz5mFVL8\neQt17QmFyT4AABgbrOYD+6Nmvw9q9gEASAe1+UgZNfsVMLNDzWy9me00s/8ws++a2btiHxcAABgc\nq/lAbxM12Zd0sKTtkl7h7kdI+i+SLjGzZtSj6kDt2uDjDivVWkXqNuPGJP/x4nHtiRuzLud+0dr8\nKmMOa5zyP6wUf96o2a8xd3/E3d/v7v+Wt7dIulXSKXGPDAAAlMFqPlDMRNfsm9nBkv5Z0kfc/f/u\n8jg1+wAA1Ay1+RhH1OyH8RFJD0v6ZOwDAQAA/bGaD5RXm8m+ma0ys5vMbIeZzZnZXX36LjKzt5vZ\nNjP7sZl9z8zWmNnijj5nm9nD+e2hLmOskfRySa9098fDPKvyqF0bfNxhpVqrSN1m3JjkP148rj1x\nY4763B+2Nn+Y46paivkPJcWfN2r2B3eFpIakb0vaLalf/cyHJX1I0lZJF0u6SdLbJN1qZiZJ7n6D\nux+Z347q/GYza0n6NUm/6u4PVP1EAABANVjNB4ZTm5p9M1vq7jvzf2+VtNjdn9Ol30mS7pR0s7uv\n7Lj/YkkflXS2u3+2T5yPSjpd0unuft8Cx0TNPgAAkVCbj0ky9jX77Yl+AW/Mv7bm3X+tpEckndPr\nG83sOGV/CXiupLs6ynz+uuThAgCAQFjNB6pTm8l+CS+VtFfS5s473f1RSVvyx7ty9++6+yJ3X9xR\n4nOku7867CEXR+3a4OMOK9VaReo248Yk//Hice2JGzPUuR+qNn+Y46pjTK498eJRsx/eMyTd5+57\nujx2t6Rj8i01AQBAIuZ8Trd88xZW84GKpTgpXizp0R6P/aSjzwE78Ayi2Wxq6dKlkqQlS5Zo2bJl\najQakp74Ta3qdluo8WPHK9pu3zeu8eqc/0ajMfavN/nfv92+L7V4nWNV1X/U+U/59e4ca5j+G27d\noKs2XaXFz1usTedv0q6tu7Tx9o3kP8L5P+p2ij9v7fuG7d/+986dOxVSbd6g22mBN+jeKekYdz+2\ny2M3SnqdpMOq2E6TN+gCABDOnM9p3R3rtHpmtVYtX6WpU6aCluwAdTb2b9At4R5lpTqHdHnsmcpK\nfGqzb35Z83/7TiFe2TGK9k8xFzFihsh/qrmIEZP8x4vHtSduzGHz3602f+PtG4c+rrImNf9VjFmF\nFH/eQl17Qklxsr9Z0kGSXtZ5p5kdLmmZpK/HOCgAALAwdtoBRivFMp4XKtt15xZ3f33H/ZdI+oik\nc9x9Q0UqJan/AAAgAElEQVTHQRkPAAAVYd98oLdQZTy1eYOumZ0r6bi8+VRJh5jZu/P2Tne/XpLc\nfauZfVzSxWZ2s6QvSTpR0iWSZqqa6AMAgGpQmw/EU6cynvMlXZbfjpH05I72+fP6Tkl6p6STJH1M\n0huUfXrua0Z1sKFQuzb4uMNKtVaRus24Mcl/vHhce+LGLDpGuzb/Ezd9YsF981PNRYyYXHvixUut\nZr82K/vufnqJvnOS1ua3oJrNpprN5si2hpqdnR3p1ldVxGuruv/s7Gzlz7dO8eqe/1G3yX/cdoo/\nb2Vfv1FfX+vcHkX+53xO3zjiG1o9s1orF6/Umceeua9sJ7WfjxTzP0j/ttj5qXu77Ou3UP9Wq7Wv\nTwi1rNmvC2r2AQAoj9p8oDy23gQAALXGTjtA/TDZr5n5f0pLIV7ZMYr2TzEXMWKGyH+quYgRk/zH\ni8e1J27M+WN02ze/szafc7/amFx74sULde0Jhck+AAAYGKv5QL1Rs98HNfsAAPRGbT5QHWr2AQBA\nLbCaD6SDyX7NULs2+LjDSrVWkbrNuDHJf7x4XHvixCyzb37ZmKnlImZMrj3x4lGzP2aazea+F2lm\nZma/FyxEu3Of1XGMV6Y9Ozs71vHqnv9xf73Jf/o/b2VfP17vJ9qD5P+26dv2reaf+KMTdf6x5++3\nbz75L97m/E+7Xfb1W6h/q9VSs9lUKNTs90HNPgAA1OYDo0DNPgAAGClq84H0Mdmvmc4/66QSr+wY\nRfunmIsYMUPkP9VcxIhJ/uPF49oTNmYV++aXjVnXXNQxJvmPFy/UtScUJvsAAGAfVvOB8ULNfh/U\n7AMAJgm1+UA81OwDAIAgWM0HxheT/Zqhdm3wcYeVaq0idZtxY5L/ePG49lQTc6Ha/CJjlI1Z1ZhV\nSDUm+Y8Xj5p9AABQe6zmA5OBmv0+qNkHAIwjavOB+qFmHwAADIXVfGDyHBz7AOqu2Wyq2Wyq0Wjs\nq7lqNBqSFKQ9OzurqampYOOHiNe+r+r+rVZLy5YtC/r8Y8arc/7n9w31/Dvb5H/mgMdCP//Odoo/\nb2Vfv6L9R53/UcW75+F7dM391+ixvY/pgscu0Esee8m+2vy65H9+TsYp/53tup7/83MyjvmvIl7Z\n12+h/q1WS7OzswrG3bn1uGXpGa3p6enk4pUdo2j/FHMRI2aI/KeaixgxyX+8eFx7itk7t9ev/trV\nfvSVR/uaTWv88b2P1zb/k3DuVxWT/MeLF+rak887K5/PUrPfBzX7AICUUZsPpIOafQAAUAi1+QDa\nmOzXTGctWSrxyo5RtH+KuYgRM0T+U81FjJjkP148rj3dFdk3v675n4Rzv6qY5D9evFDXnlCY7AMA\nMAZYzQfQDTX7fVCzDwBIAbX5QPqo2QcAAPthNR/AQpjs1wy1a4OPO6xUaxWp24wbk/zHizfp154i\ntflVxxxmDM79amOS/3jxqNkHAADBsJoPoAxq9vugZh8AUCfU5gPji5p9AAAmFKv5AAbFZL9mqF0b\nfNxhpVqrSN1m3JjkP168Sbn2DFObP2jMEGNw7lcbk/zHi0fNPgAAGBqr+QCqQM1+H9TsAwBioDYf\nmDzU7AMAMOZYzQdQNSb7NUPt2uDjDivVWkXqNuPGJP/x4o3btSdEbf5CMUc5Bud+tTHJf7x41OwD\nAIDCWM0HEBI1+31Qsw8ACInafABt1OxH0mw29/35ZWZmZr8/xdCmTZs2bdqDtG+bvk2XfuLSfav5\nlx9/uXZt3VWb46NNm/bo2q1WS81mU8G4O7cetyw9ozU9PZ1cvLJjFO2fYi5ixAyR/1RzESMm+Y8X\nL9Vrz/YHtvuK9Sv81OtO9W33bkv29ebcjxuT/MeLF+rak887K5/PsrIPAMAIUJsPIAZq9vugZh8A\nUAVq8wEshJp9AAASw2o+gNiY7NdM5xs2UolXdoyi/VPMRYyYIfKfai5ixCT/8eLV/dpTdN/8VF9v\nzv24Mcl/vHihrj2hMNkHAKBCrOYDqBNq9vugZh8AUAa1+QAGRc1+RczsT8zs383sYTO738w+Z2Y/\nE/u4AADpYjUfQF1N3GRf0lpJz3X3IyU9X9JPS3p/3EN6ArVrg487rFRrFanbjBuT/MeLV5drT9Ha\n/KriVaGu+U81FzFikv948ajZrzl3/1d3fzRvLspvd0U8JABAgljNB5CCiazZN7N3SfoDSU+SdJOk\n3+pWnE/NPgCgG2rzAVSNmv0Kufsfu/tRkp6b3y6PfEgAgASwmg8gNbWZ7JvZKjO7ycx2mNmcmfUs\nrTGzRWb2djPbZmY/NrPvmdkaM1vc0efs/E24D5vZQ93Gcfe7lNXrv7n6ZzQYatcGH3dYqdYqUrcZ\nNyb5jxdv1NeeYWvzhz2uKtU1/6nmIkZM8h8vHjX7g7tCUkPStyXtltSvfubDkj4kaauki5WV4rxN\n0q1mZpLk7je4+5H57ag+Yx0kac/whw8AGEes5gNIWW1q9s1sqbvvzP+9VdJid39Ol34nSbpT0s3u\nvrLj/oslfVTS2e7+2R4xjpD0Wkmfd/dHzOwFkv5M0t+4+6ou/anZB4AJRm0+gFEZ+5r99kS/gDfm\nX1vz7r9W0iOSzukXRtLvSPp3M3tY0t9I+itJf1j8SAEA447VfADjojaT/RJeKmmvpM2dd+bbaW7J\nH+/K3R9x9zPc/Sl5ec/x7v4+d58Le8jFUbs2+LjDSrVWkbrNuDHJf7x4oa49G27dEKQ2f9jjqltM\nzv24Mcl/vHjU7If3DEn3uXu3Ovu7JR1jZgeP+JgAAIlrr+Zf9IWLWM0HMDZSnBQvlvRoj8d+0tGn\n6w48ZTWbTS1dulSStGTJEi1btkyNRkPSE7+pVd1uCzV+7HhF2+37xjVenfPfaDTG/vUm//u32/el\nFq9zrGH6b7h1g67adJUWP2+xNl+xWbu27tLG2zfyeo8o//y8DTZe51gh+o9j/quI175v2P7tf+/c\nuVMh1eYNup0WeIPunZKOcfdjuzx2o6TXSTrM3R+v4Dh4gy4AjLE5n9O6O9Zp9cxqrVq+SlOnTAUt\n2QGAXsb+Dbol3KOsVOeQLo89U1mJz9AT/Vjm//adQryyYxTtn2IuYsQMkf9UcxEjJvmPF2/Y3Pfa\nNz/FXMSIybkfNyb5jxcv1LwnlBQn+5uV7Y3/ss47zexwScskfT3GQQEA0sBOOwAmSYplPC9UtuvO\nLe7++o77L5H0EUnnuPuGio6DMh4AGCPsmw+grkKV8dTmDbpmdq6k4/LmUyUdYmbvzts73f16SXL3\nrWb2cUkXm9nNkr4k6URJl0iaqWqiDwAYH9TmA5hUtZnsSzpf0or83+3l9MvyrzOSru/oOyVpp6QL\nJb1a0r3KPj33vVUfVLPZVLPZHNm7xWdnZzU1NRVs/BDx2vdV3b/Vao1k96NY8eqc//l9Qz3/zjb5\nnzngsdDPv7Od4s9b0ddvx+4dOuvKs/TDu36oTR/fpBcc84Ja5T/V17vsz0+R/vNzQv57t8l/vHhl\nX7+F+rdaLc3OzioYd+fW45alZ7Smp6eTi1d2jKL9U8xFjJgh8p9qLmLEJP/x4i00xt65vX711672\no6882tdsWuNf/rsvj+zYykj19ebcjxuT/MeLF2rek887K5/P1rJmvy6o2QeANFGbDyA1bL0JAMAC\n2GkHAPbHZL9mOmvJUolXdoyi/VPMRYyYIfKfai5ixCT/8eLNH6PXvvllY6aYixgxOffjxiT/8eKF\nmveEwmQfAJA0VvMBoDdq9vugZh8A6o3afADjgpp9AAByrOYDQDF12me/lthnP84+41Ka+36Xbdc1\n//P7hnr+nW3yP3PAY6Gff2c7pZ+3svvmt9tFX+9R5z/V15t93sl/ivmvIh777I/RTeyzH2QM9tmv\nNiZ7LceNSf5HF2/QffPLxkwhF3WIybkfNyb5jxePffbHCDX7AFAP1OYDGHfU7AMAJg61+QAwHCb7\nNdNZS5ZKvLJjFO2fYi5ixAyR/1RzESMm+Q8Xr6p988v2r2Mu6hiTcz9uTPIfL16oa08ohSb7ZrbI\nzJ5mZoeFPiAAwGRjNR8AqrNgzb6ZrZL0+5KOlPS4pD+XdIG7PxL+8OKiZh8ARovafACTKkrNvpmd\nK+kKSQdJ+pqk+yS9UdLHqj4QAMDkYjUfAMJYqIznQkl3SXquu58m6ThJN0l6k5kdEfrgJhG1a4OP\nO6xUaxWp24wbk/wPH2+h2vwiY5SNWeW4w0r19ebcjxuT/MeLN241+78g6Rp3/4Ekufvjkj4g6VBJ\nJwQ+NgCJuvBC6eCDJbPstny59MMfxj4q1A2r+QAQXt+afTPbK+k8d7+h475jJP1A0hnuPhP8CCOi\nZh8o58ILpU99Stq798DHVq6Ubrxx9MeEeqI2HwD2F2uffZPUa7bLtp0A9rnwQunaa7tP9E86Sbrm\nmtEfE+qH1XwAGK2DC/R5qZn9pKN9VP71FWa2ZH5nd/+LSo6sJprNpprNphqNxr6aq0ajIUlB2rOz\ns5qamgo2foh47fuq7t9qtbRs2bKgzz9mvDrnf37ffuNt2NDQtddKUvt7GvnXrP2iFzV0223S7Gz/\n4yP/Mwc8Fvr5d7ZHkf97Hr5H19x/jR7b+5gueOwCveSxl+yrzR/F61e0/6jzn+rrHSL/83NC/nu3\nyX+8eGVfv4X6t1otzc7OKhh373mTNFfytrffeKndsvSM1vT0dHLxyo5RtH+KuYgRM0T+i44p9b/d\neGO1x1W11PNfpZAx987t9au/drUffeXRvmbTGn987+O1zf0g4w4r1debcz9uTPIfL16oa08+76x8\nPrtQzX6z/O8O/pmS31Nb1OwD3dkCFYXPe560ebO05IC//WHSUJsPAMWEqtlf8EO1JhmTfWB/7br8\nft7+dmnt2tEcD+przue07o51Wj2zWquWr9LUKVOFttMEgEkV6w26GLHOWrJU4pUdo2j/FHMRI2aI\n/HfrY9Z/or9okbRly+ATffI/+JhVqDJmkX3z65r7QcYdVqqvN+d+3JjkP168UNeeUAae7JvZ081s\nr5mdUeUBAaiXCy9cuGxn1apsF56TTx7NMaGe2GkHAOpn4DIeM3u6pHsk/Sd3v63So6oJyngw6Raa\n5EvSzp3ScccFPxTUHLX5ADAcyngAjEyR1fyXvzzbc4eJ/mRjNR8A6o3Jfs1Quzb4uMNKtVax6vxn\ntfn9+2zcKH31q6XCLoj8Dz5mFQaJWaQ2v8p4w47BtafamJN87tchJvmPF29iavYlPSbpdkk/rOhY\nAERUZDX/2GOz1fzly0dzTKgnVvMBIB1svdkHNfuYFEVq8z/4Qemd7wx/LKg3avMBIIxQNfsHVz0g\ngHQsWSI9+ODC/fidF+ybDwBpqqxm38zOMbOx3JVnlKhdG3zcYaVaqzho/s36TfSzPmefPbqJ/qTl\nv8oxq9Av5jC1+YPECzUG155qY07CuV/nmOQ/XrxJqtmfb6mkRoXjAQjgNa8pVrbjLl1/ffjjQX1R\nmw8A6ausZt/M3i3pMncfmx1+qNnHuCkyyV+6VLrrruCHgpqjNh8ARitKzb6Z3SWp6Gx3SYm+AEao\nyCRfojYf1OYDwLhZaBX+OElPlvRIgduecIc5OahdG3zcYaVaq7jQGAdO9A/sf/TR+0/0U81FjJjj\nVDcboja/X7xRj8G1p9qY43TupxiT/MeLl1rN/kK78eyU9G13//WFBmqX8VRxUACGd+ih0p4Cv4Jv\n2SKdfHL440F9zfmcbvnmLbph8w2s5gPAmOlbs29mN0lquPtTFxyImn2gNoqU7Tz3udJ3vhP+WFBv\n1OYDQD2EqtlfaGL+j5KONrOlBcb6rqSvDHtAddNsNvf9+WVmZma/P8XQpl239mteM9Mx0Z/R/iU7\nT7Q3bpSuuy7+8dKO175t+jZd+olL9+20c/nxl2vX1l21OT7atGnTnpR2q9VSs9lUMO7OrcctS89o\nTU9PJxev7BhF+6eYixgx22NkVff9b4cd5n7rrQvHTDUXMWKGOP9D52L7A9t9xfoVfup1p/q2e7eN\nJOZ8dc39IOMOi3N/8DGrkGpM8h8vXqhrTz7vrHw+OzYlN8CkKrpv/pYt0k9+Ij3pSeGPCfXEvvkA\nMHkq22d/HFGzj7orMsk/8kjpoYfCHwvqjdp8AKi3WDX7AGpoyZJiE/0bb2SiP+lYzQeAycZkv2Y6\n37CRSryyYxTtn2IuRhHTTHrwwf1GOaDPk56UVemvXDlYzFRyUYeYIc7/qnJRZt/8FH/euPbEjVnn\nc7+MVGOS/3jxQl17QmGyDySkyGr+F78oPfxw+GNBfbGaDwBoo2a/D2r2URdFJvlPfrL0wx+GPxbU\nG7X5AJAmavYrZGY/ZWbfMbPdsY8F6GfRomIT/csuY6I/6VjNBwB0M5GTfUmXSfo3SbVbtqd2bfBx\nh1W3WkWzrO5+IdddN6P3vKeamGX6VC3VmHWomy1Tm19VzGHVNfeDjDsszv3Bx6xCqjHJf7x41OzX\nnJm9WNIrJf2RpMr/VAIM64QTiq3mX3RR9svAc58b/phQT6zmAwAWMlE1+2Z2sKT/Lel3lf2ic4u7\n/3Sf/tTsY6SKTPIlaffubPtNTC5q8wFgvFCzX413SNri7rfHPhCgk1m51Xwm+pOL1XwAQBm1mOyb\n2Sozu8nMdpjZnJnd1afvIjN7u5ltM7Mfm9n3zGyNmS3u6HO2mT2c3x7K73uupLdI+r3wz2hw1K4N\nPu6wYtUqllnNX7eu+xhlY1bRp2qpxhxl/quozR/muKpU19wPMu6wOPcHH7MKqcYk//HiUbM/mCsk\nNSR9W9Ju9X/j7IclfUjSVkkXS7pJ0tsk3WqWTZvc/QZ3PzK/HZV/3yskPVXSv5jZLkk3SzrKzHaZ\n2SkBnhPQ16GHSqefvnC/lStZzZ90rOYDAAZVi5p9M1vq7jvzf2+VtNjdn9Ol30mS7pR0s7uv7Lj/\nYkkflXS2u3+2R4zDJR3VcdfLJa2X9HxJu919T5fvoWYfQRRdzd+yRTr55LDHgnqjNh8AJsNY1+y3\nJ/oFvDH/2pp3/7WSHpF0Tp8YP3H3H7Rvyv6CMJe3D5joAyEsWVJsot9oZKv5TPQnF6v5AIAq1GKy\nX8JLJe2VtLnzTnd/VNKW/PFC3H3G3Z9S7eENj9q1wccdVuh4ZtKDDx4Q9YB+W7ZI09PFx6VuM27M\nEPnfcOuGYLX5wxxX3eJx7Ykbk2tP3JjkP148avbDeoak+3qsxN8t6Zh8e02gNorutMNqPtqr+Rd9\n4SJW8wEAlUhtYrxY0qM9HvtJR5+HqgrYbDa1dOlSSdKSJUu0bNkyNRoNSU/8plZ1uy3U+LHjFW23\n70s5XvYG3Pb4M/nX+e1sp53Z2RnNzNQj/41GY+xf77qd/xtu3aCrNl2lxc9brM1XbNaurbu08faN\nY5v/quJ1jlVV/1Gf/yn/vHWOFaI/+e/f7hwrRP9xzH8V8dr3Ddu//e+dO3cqpFq8QbfTAm/QvVPS\nMe5+bJfHbpT0OkmHufvjFR0Lb9DFQA49VNpT4J0gK1dKN94Y/nhQX3M+p3V3rNPqmdVatXyVpk6Z\nCl6yAwCon7F+g24J9ygr1Tmky2PPVFbiU8lEP5b5v32nEK/sGEX7p5gLKSvZKTLR37JFestbho8Z\nIv+jzn3KMYfJf69981PNxajjce2JG5NrT9yY5D9evFDXnlBSm+xvlnSQpJd13plvq7lM0tdjHBQg\nSSecUKw2/1WvojZ/0rHTDgBgVFIr43mhsl13bnH313fcf4mkj0g6x903VHgslPGgkKL75m/cKC1f\nHvZYUG/smw8A6CZUGU8t3qBrZudKOi5vPlXSIWb27ry9092vlyR332pmH5d0sZndLOlLkk6UdImk\nmSon+kARJ5wgfetbC/d7/vOL9cP4ojYfABBDLSb7ks6XtCL/d3sp/bL864yk6zv6TknaKelCSa+W\ndK+yT899b4gDazabajabI3u3+OzsrKampoKNHyJe+76q+7darZHsfjRIvAsvlK69tv18GvnX7u0v\nfrGhV74yrfzP7zvM8YXIf1XtUeV/x+4dOuvKs7Rn7x6tXb5W55123sTnv4p4ZV+/ov1Hnf9UX+8Q\n+Z+fE/Lfu03+48Ur+/ot1L/Vaml2dlbBuDu3HrcsPaM1PT2dXLyyYxTtX9dcZBX3C99WrqwuZpVj\nFOk/6tynHLPfGHvn9vrVX7vaj77yaF+zaY0/vvdx8l9hvEm79tQtJteeuDHJf7x4oa49+byz8vls\n7Wr264SafXTKVvOL9eW0AbX5AIAy2HoTiGjJkmIT/de+lon+pGOnHQBAnTDZr5nOWrJU4pUdo2j/\nuuTCTHrwwYW/f+NG6fOfryZmyDGK9B917lOO2TlGr33zy8ZMNRejjjfu1566x+TaEzcm+Y8XL9S1\nJxQm+0APS5YU21LzsMOknTvZUnOSsZoPAKgravb7oGZ/chXdN/+LX5Re+cqwx4J6ozYfAFAFavaB\nETj00GIT/SOPzFbzmehPLlbzAQApqMs++7XFPvvV7DM+SP9R7/tt1pK0TFIjP8r28e7f3r27oSVL\nsu+/667xzP/8vsMcX6h9i2Oe/2X3ze9st++b9PzXdZ/xGPlP9fVmn3fyn2L+q4jHPvtjdBP77AcZ\no257XT+xN/50333zL7qo+th1zf8k7LU8SMxB980vGzOFXNQhXurXnljxqorJuR83JvmPF4999scI\nNfvjr2ht/pYt0sknhz0W1Bu1+QCAkKjZByq0aFGxif5FF2Xr+kz0Jxe1+QCAlDHZr5nOWrJU4pUd\no2j/ELm48MJskt/9Dzb7x9u4UVq3rvJD2D9iTfM/6vOwrjGr2je/bP865qKO8VK69tQpXlUxOffj\nxiT/8eKFuvaEwht0MTGKluwsXSr90z9l++xjMs35nNbdsU6rZ1Zr1fJVmjpl6oBJPgAAKaBmvw9q\n9sdH0Yn+jTdKK1eGPRbUG7X5AIAYqNkHBmBWbKJ/xBHZvvlM9CcXtfkAgHHEZL9mqF0bfNxOS5YU\nX83fuFH60Y+ku+4aPN6g6pr/Sajb7IxZpDZ/oTHKxqxyzCpw7Rl83GGl+npz7seNSf7jxUutZp/J\nPsaOmfTggwv3W78+e6Pu8uXhjwn1xGo+AGDcUbPfBzX76Sm6mr97N2/AnXTU5gMA6oSa/Uiazea+\nP7/MzMzs96cY2vVpZ7X5M9p/+8wD28ceO7Nvol+n46c9unZ7Nf9F73qRTvzRiftW8+tyfLRp06ZN\ne7LarVZLzWZTwYT4WN5xuWXpGS0+Nrpcvxe8wD0rxln49qpXue/ePfxxVamu+U81FwvZ/sB2X7F+\nhZ963am+7d5t5D9izLrmfpBxh5Xq6825Hzcm+Y8XL9S1J593Vj6fZWUfyTKTvvWthftddlk23f/r\nv6Z0Z1JRmw8AmFTU7PdBzX49XXihdO21C/c79FDp+99ngj/pqM0HAKSAmn1A2Wp+kYn+298uPfoo\nE/1Jxmo+AABM9mun8w0bqcQrO0bR/p39Tjih+E47O3dKa9eWOqQD4o1KXfOfai7aiu6bT/7jxaxr\n7gcZd1ipvt6c+3Fjkv948UJde0Jhso9au/DC4rX5J56Ybal53HHhjwv1xGo+AAD7o2a/D2r24zr0\nUGnPnmJ9t2yRTj457PGg3qjNBwCkjJp9TBSzYhP9VauynXaY6E8uVvMBAOiNyX7NULu2X8+ejzzp\nSVnJzgc+UCp0/2iJ1ipOct1m0dr8KmMOO8Y45T92PGr248bk3I8bk/zHi0fNPhDY+vXSww+z084k\nYzUfAIBiqNnvg5r9eLrtvHPssdI3vsEkf9JRmw8AGEfU7GOi3XijdM89TPQnGav5AACUx2S/Zqhd\ny6xfL0kzOuqobN/8lSsHOLCSUq1VnIS6zWFr8weJGWqMFPNf13jU7MeNybkfNyb5jxePmn2gAs2m\nND0tPfgg++ZPMlbzAQAYDjX7fVCzD8RDbT4AYJJQsw9gIrCaDwBAdZjs1wy1a4OPO6xUaxXHqW4z\nVG1+v5ijHqPO+U8tHteeuDE59+PGJP/x4lGzDwAlzfmcbvnmLazmAwBQMWr2+6BmHwiP2nwAAKjZ\nj6bZbO7788vMzMx+f4qhTZv24O3bpm/TpZ+4dN9q/uXHX65dW3fV5vho06ZNmzbtUbRbrZaazaaC\ncXduPW5ZekZreno6uXhlxyjaP8VcxIgZIv+hc7H9ge2+Yv0KP/W6U33bvdtGErObSc1/HWLWNfeD\njDusVF9vzv24Mcl/vHihrj35vLPy+Swr+wBGhp12AAAYLWr2+6BmH6gOtfkAAPRGzT6AJLGaDwBA\nPEz2a6bzDRupxCs7RtH+KeYiRswQ+a8qF2X2zSf/g49ZhRR/3rj2xI3JuR83JvmPFy/UtScUJvsA\nKsdqPgAA9TBxNftm9mlJb5T0WMfdDXf/hy59qdkHSqI2HwCA8qjZr45L+ri7H9lxO2CiD6AcVvMB\nAKifSZzsW36rJWrXBh93WKnWKtahbrNMbX5VMaswLvmvQoo/b1x74sbk3I8bk/zHi0fNfv25pDeb\n2QNmtt3M3m1mtZ38A3XGaj4AAPU2iTX7J0v6rrs/aGYvlnSTpE+6+we69KVmH+iB2nwAAKoz9jX7\nZrbKzG4ysx1mNmdmd/Xpu8jM3m5m28zsx2b2PTNbY2aLO/qcbWYP57eH2ve7+z+7+4P5v/9R0gck\nvSHkcwPGCav5AACkozaTfUlXSGpI+rak3crKbXr5sKQPSdoq6WJlq/Nvk3RruyTH3W/oeAPuUQvE\nrk0ZD7Vrg487rFRrFUdZt1lFbf4wx1W11PIfUoo/b1x74sbk3I8bk/zHi0fN/uCe4+5Pdfdfl7Sr\nVyczO0nSJZJudvfXu/sn3f0dkn5X0umSfqtfEDP7zfZfAPKSnndJ+vOqngQwjljNBwAgTbWs2Tez\nrZIWu/tzujz2fkl/IOkV7r6p4/7DJN0v6Svu/uo+Y2+U9EJJB0u6T9J6SZd3K86nZh+gNh8AgFEI\nVcPK1GMAAByxSURBVLN/cNUDjsBLJe2VtLnzTnd/1My25I/35O6vCHhswNiY8zmtu2OdVs+s1qrl\nqzR1ylRlJTsAAGA06lTGU9QzJN3n7nu6PHa3pGPMLMVfYiRRuzbMuMNKtVYxRP433LohWG3+MMdV\nx5jUzcaLx7UnbkzO/bgxyX+8eNTsh7dY0qM9HvtJRx8AJbVr8y/6wkXU5gMAMAZSXAF/RNIxPR47\nXNkuPo9UFazZbGrp0qWSpCVLlmjZsmVqNBqSnvhNrep2W6jxY8cr2m7fN67x6pb/Dbdu0FWbrtLi\n5y3W5is2a9fWXdp4+0byH6HdaDT4eQv0+hXpP+r8p/x6d44Voj/579/uHCtE/3HMfxXx2vcN27/9\n7507dyqkFN+g+7eSzsgf3zPvsU2Sfs7dn1bRcfAGXYw9avMBAIhv7D9Uq4TNkg6S9LLOO83scEnL\nJH09xkFVZf5v3ynEKztG0f4p5iJGzGHy32vf/FRzESNmiPM/1VyMOh7XnrgxOffjxiT/8eKFuvaE\nkuJk/3PKSnWm5t1/gaSfknTDyI8ISAz75gMAMBlqU8ZjZudKOi5vXiLpEElr8/ZOd7++o+9HlX1y\n7i2SviTpxPx7vuruZ1R4TJTxYOywbz4AAPUTqoynTpP9aUkr8mb7oNpPeKZzEm9mi5St7F8oaamk\ne5Wt+L/X3St7cy6TfYwTavMBAKivsf9QLXc/vUTfOWWr/msX6jusZrOpZrM5sneLz87OampqKtj4\nIeK176u6f6vVGsnuR7HijTL/O3bv0FlXnqU9e/do7fK1Ou+08/r2nz/2KPIxzvkv238S8l9FvLKv\nX9H+o85/qq93iPzPzwn5790m//HilX39FurfarU0OzurYNydW49blp7Rmp6eTi5e2TGK9k8xFzFi\n9htj79xev/prV/vRVx7tazat8cf3Pl4oZqq5iBEzxPmfai5GHY9rT9yYnPtxY5L/ePFCXXvyeWfl\n89nalPHUEWU8SBm1+QAApIOtNwEUwk47AACgjcl+zXTWkqUSr+wYRfunmIsYMTvH6LVvftmYqeYi\nRswQ53+quRh1PK49cWNy7seNSf7jxQt17QmFyT4wBljNBwAA3VCz3wc1+0gBtfkAAKSPmn0A+2E1\nHwAALKQ2++zXFfvsx9lnXEpz3++y7UHzX3bf/M52+75+/ef3HUU+Usp/Z7t9H/kffTz22R+//M/P\nCfnv3Sb/8eKxz/4Y3cQ++0HGYK/rwWMOum9+2Zgp5KIuMcl/vHhce+LG5NyPG5P8x4vHPvtjhJp9\n1Am1+QAAjC9q9oEJRW0+AAAYFJP9mumsJUslXtkxivZPMRdVx6xq3/yy/euYi7rGJP/x4nHtiRuT\ncz9uTPIfL16oa08oTPaBGmI1HwAAVIGa/T6o2UcM1OYDADB5qNkHxhyr+QAAoGpM9muG2rXBxx1W\nzFrFIrX5C41RNmaVY1Yh1ZjkP148rj1xY3Lux41J/uPFo2YfQGGs5gMAgJCo2e+Dmn2ERG0+AABo\no2Y/kmazue/PLzMzM/v9KYY27UHa7dX8F73rRTrxRyfuW82vy/HRpk2bNm3atEfXbrVaajabCibE\nx/KOyy1Lz2jxsdGDjzusUcTb/sB2X7F+hZ963am+7d5ttc3/JHxkelUxyX+8eFx74sbk3I8bk/zH\nixfq2pPPOyufz7KyD4wAtfkAACAGavb7oGYfVaA2HwAALISafSAxrOYDAIDYmOzXTOcbNlKJV3aM\nov1TzEVb0X3z65r/Uec+5ZjkP148rj1xY3Lux41J/uPFC3XtCYXJPlAhVvMBAECdULPfBzX7KIPa\nfAAAMChq9oGaYjUfAADUFZP9mqF2bfBxhzVIvKK1+VXGHHYM6jarjUn+48Wb5GtPHWJy7seNSf7j\nxaNmH5gArOYDAIAUULPfBzX76IbafAAAUDVq9oHIWM0HAACpYbJfM9SuDT7usPrFG7Y2f5CYocag\nbrPamOQ/XrxJuPbUOSbnftyY5D9ePGr2gTHCaj4AAEgZNft9ULM/2ajNBwAAo0LNPjAirOYDAIBx\nwWS/ZqhdG3zcYc3MzASrze8Xc9RjULdZbUzyHy/eOF17Rq2u+U81FzFikv948ajZBxI053O65Zu3\nsJoPAADGysGxD6Dums2mms2mGo3Gvt/MGo2GJAVrt41rvKLt9n2h4z37F5+t8//yfN27416tXb5W\n55123kifb+dzHUW8Iu1Rnu+jfr3Jf/d2+77U4nWOVVX/Uec/5de7c6wQ/cl//3bnWCH6j2P+q4jX\nvq+q/q1WS7OzswqFN+j2wRt0x9ucz2ndHeu0ema1Vi1fpalTpoKW7AAAAPTCG3QnxPzfvlOIV3aM\nov1D5qJbbf7G2zcGi9dLXfM/6vMw5ZjkP168FK89dYhXVUzO/bgxyX+8eKGuPaEw2cdEYacdAAAw\nSSjj6YMynvHCvvkAAKCuKOMBBsRqPgAAmFRM9muG2rXBx+2mzL75qdYqUrcZNyb5jxevzteeMlJ9\nvTn348Yk//HiUbMP1ACr+QAAABNYs29mZ0r6n5J+TtKPJX3I3a/q0Zea/QRRmw8AAFJDzX4FzOw3\nJH1c0lvc/ShJx0v6q7hHhaqwmg8AALC/iZrsS7pc0nvc/X9Lkrs/4u7bIh/TfqhdG2zcMrX5VcSr\nSl3zn2ouYsQk//Hi1eHaU4VUX2/O/bgxyX+8eNTs15SZHSHpxZKeZmb/Ymb3m9mXzOz42MeGwbGa\nDwAA0NvE1Oyb2c9K+p6kf5L0Gkn3S/qgpNPd/eQe30PNfo1Rmw8AAMbFWNfsm9kqM7vJzHaY2ZyZ\n3dWn7yIze7uZbTOzH5vZ98xsjZkt7uhztpk9nN8eyu9+OP/6EXff5e6PSXqvpBea2dJQzw3VYzUf\nAACgmFpM9iVdIakh6duSdkvqt5z+YUkfkrRV0sWSbpL0Nkm3mplJkrvf4O5H5rej8vselPTdYM+g\nItSu9e9XRW3+sMdVpbrmP9VcxIhJ/uPFo2Y/bkzO/bgxyX+8eNTsD+Y57v5Ud/91Sbt6dTKzkyRd\nIulmd3+9u3/S3d8h6XclnS7ptxaI8yeS3mZmP2Nmh0haLelOd99ZxZNAOKzmAwAAlFe7mn0z2ypp\nsbs/p8tj75f0B5Je4e6bOu4/TFkN/lfc/dV9xjZJfyTptyUdLun/k3RRr8k+Nfv1QG0+AAAYd2Nd\ns1/CSyXtlbS58053f1TSlvzxnjzzLnd/mrs/2d1fyap+fbGaDwAAMJzUJvvPkHSfu+/p8tjdko4x\ns4NHfEyVonYts2P3Dr141YuD1OYPc1x1jEndZtyY5D9ePGr248bk3I8bk/zHi5dazX5qE+PFkh7t\n8dhPOvo81KNPac1mU0uXLpUkLVmyRMuWLVOj0ZD0xItXZXt2djbo+CHitVXRf87n9I0jvqHVM6t1\n2o9P06XHX7pvNT90PmZnZ4OOn0L+Y7bJf9z2qPNfRbyyr9+or691btc1/22x80P+wz7/1NtlX79e\n/dv/3rlzp0JKrWb/TknHuPuxXR67UdLrJB3m7o9XdCzU7I8QtfkAAGBSUbOfuUdZqc4hXR57prIS\nn0om+hgdavMBAADCSG2yv1nSQZJe1nmnmR0uaZmkr8c4qCrN/1NaCvHKjtHZv9+++SnmIkbMYfJf\n1ZhVSDUm+Y8XL0TuBxl3WKm+3pz7cWOS/3jxQl17Qkltsv85ZR+4NTXv/gsk/ZSkG0Z+RBgIq/kA\nAADh1aJm38zOlXRc3rxE0iGS1ubtne5+fUffjyr75NxbJH1J0on593zV3c+o+Lio2Q+A2nwAAID9\nharZr8tkf1rSirzZPqD2k53pnMSb2SJlK/sXSloq6V5lK/7vdfdHKj4uJvsVmvM5rbtjnVbPrNaq\n5as0dcpU8O00AQAAUhBqsl+LrTfd/fQSfeeUrfqvXahvFZrNpprNphqNxki2dpqdndXU1FSw8UPE\na9/Xr/+O3Tt01pVnac/ePVq7fK3OO+28BcdvtVrBtzqNGW+U+S/bf37fUM+/s03+Zw54LPTz72yn\n+PNW9vUr2n/U+U/19Q6R//k5If+92+Q/Xryyr99C/Vut1r7tOYNwd249btr3obujMz09nVy8fmPs\nndvrV3/taj/6yqN9zaY1/vjexwvHTDEXMWKWHaNI/1RzESMm+Y8XL0TuBxl3WKm+3pz7cWOS/3jx\nQl178nln5fPZWpTx1BVlPMOhNh8AAKAY9tlHMthpBwAAoB6Y7NdMZy1ZKvE6x+i3b37ZmCnmIkbM\nsmMU6Z9qLmLEJP/x4oXI/SDjDivV15tzP25M8h8vXqhrTyhM9lEJVvMBAADqh5r9PqjZL4bafAAA\ngOFQs4/aYTUfAACg3mqxz36dsc9+9/Yg++bP39u26n1sh22zz/vMAY+Ffv6dbfI/c8BjoZ9/ZzvF\nnzf22R+//M/PCfnv3Sb/8eKxz/4Y3cQ++wcYZt/8sjHrnou6xGSv5bgxyX+8eFx74sbk3I8bk/zH\ni8c++2OEmv39UZsPAAAQBjX7iIbafAAAgDQx2a+ZzlqyOsSrct/8sv3rlou6xgyR/1RzESMm+Y8X\nj2tP3Jic+3Fjkv948UJde0Jhso+uWM0HAABIHzX7fUxqzT61+QAAAKNFzT6CYzUfAABgvDDZr5lY\ntWtFavMXGqNszKrHHVaqtYrUbcaNSf7jxePaEzcm537cmOQ/Xjxq9pEUVvMBAADGFzX7fYx7zT61\n+QAAAPVAzX4kzWZz359fZmZm9vtTTKrt9mr+i971Ip34oxP3rebX5fho06ZNmzZt2rQnpd1qtdRs\nNhVMiI/lHZdblp7RCv2x0dsf2O4r1q/wU6871bfdu63WHxud4kdox4jJR6bHjUn+48Xj2hM3Jud+\n3JjkP168UNeefN5Z+XyWlf0JQW0+AADA5KFmv49xqdmnNh8AAKDeqNlHaazmAwAATDYm+zXT+YaN\nYRTdN7+KeGXHKNq/qlwUNep4VcUMkf9UcxEjJvmPF49rT9yYnPtxY5L/ePFCXXtCYbI/ZljNBwAA\nQBs1+32kVrNPbT4AAECaqNlHT6zmAwAAoBsm+zVTtq6raG1+VfGqGIO62WpjUrcZNyb5jxePa0/c\nmJz7cWOS/3jxqNnHSLCaDwAAgIVQs99HXWv2qc0HAAAYL9Tsg9V8AAAAlMJkv2Z61XUNW5tfNl7I\nMaibrTYmdZtxY5L/ePG49sSNybkfNyb5jxePmn1UitV8AAAADIqa/T5i1+xTmw8AADAZqNmfIKzm\nAwAAoApM9mtmw60bgtTm91Ln2rUU6/hixKRuM25M8h8vHteeuDE59+PGJP/x4lGzj4G0V/Mv+sJF\nrOYDAACgEgfHPoC6azabajabajQa+34zazQaklRZ+9n/p727j5Wsru84/v4uDy6LCJaHRg2yukmR\n2hZj66ptkUKMbbWNfQJrBWxBTWOxLKwWlgK5thasWWHRYoyUdrVSS6ih1gCa1rIajc1q7K6uhkJh\nF5EHC0jL8iAi99s/zrnd8eY+zL13zvnNnPN+JSfcM3Pu+X3nM3OH3575zjnHP58zP3UmD3zzAT74\n2g9yxs+fMdL9L7Y+o63xhl2fua2r441z/k2+3s1/8fU+5D+q8Qb3Nart285/kp/vwX01sb35L7w+\nuK8mtu9i/qMYb+a2UW2/ZcsWduzYQVP8gu4Cmv6C7nRO86GvfIipbVNs+sVNbHjFhkZbdiRJkjSe\n/IJux8x33vzZ//pu2ijGW+o+ht1+ErMoMWYT+U9qFiXGNP9y4/neU3ZMX/tlxzT/cuM19d7TFCf7\nLfNMO5IkSWqLbTwLGHUbj+fNlyRJ0lxs45lgHs2XJElSCU72GzZfb/587F1b/n5XalJ7Fe3bLDum\n+Zcbz/eesmP62i87pvmXG8+efQEezZckSVJ5vevZj4hHgcEHvRr4VmYeP8e2y+rZtzdfkiRJS2HP\n/ohk5jMz85CZBfgW8IlR7Nuj+ZIkSRonvZvsD4qI9cBPAltXuq+l9ubPx9615e93pSa1V9G+zbJj\nmn+58XzvKTumr/2yY5p/ufHs2Z8sZwE3Zeb9y92BR/MlSZI0rnrXsz8jIg4G7gFOz8xPz7PNgj37\n9uZLkiRpFDrfsx8RmyLi+oi4MyKmI2L3AtuuiohzI+LWiHgiIr4dEZsjYs3ANm+KiL318sgcuzkF\neAy4cam1ejRfkiRJk2BsJvvAXwC/BNwOPMyPnjFntiuA9wO7gLOB64E/Bj4dEQGQmdcOfBH3WXPs\n4y3ARzNzeilFjqo3fz72ri1/vys1qb2K9m2WHdP8y43ne0/ZMX3tlx3T/MuNZ8/+8r0wM4/MzF8G\n7ptvo4h4MfAO4JOZ+TuZeU1mbgTOA04CfnexgSLiWOCVwDXDFufR/Pbt2LGjdAm9ZfZlmX9Z5l+O\n2Zdl/t00lj37EbELWJOZL5zjvvcAFwInZOaXBm5/BvAQ8PnMfN0i+/9LYH1mnrTIdpmZ9uYXMjU1\nxdTUVOkyesnsyzL/ssy/HLMvy/zL6nzP/hK8DHga2D54Y2Y+Ceys719QZp6/2ER/RttH8/04q5xJ\n/fjS/MuOaf7lxjP7smOaf9kxzb/ceJOW/SRO9p8LPJiZT81x3z3AERGx/6gGa6o3fz5bt25tfIxR\nj7fUfQy7/Z49e5Zcy0q0nf2oxmwi/7azB/Mf1If8xzV78L2nqX342h/tmOZfbrym3nuaMoltPHcA\n+2Xm2jnu+xhwGnBYZs51Bp6l1jF+4UiSJKmTmmjjGdkR8BY9Dhwxz32rqc7i8/goBmoicEmSJKkt\nk9jGcy9Vq84Bc9z3PKoWnx+2XJMkSZI0diZxsr8d2A94+eCNEbEaeAnw1RJFSZIkSeNmEif711G1\n6myYdftbgYOAa1uvCIiIRweu2Ls3Ip6KiJ0laumjiPj1iPhaRDwSEd+NiD8pXVNfRMTWiHhy1uv/\nZ0vX1TcRcVBE/FdEPFy6lr6IiA9HxHfq1/xDEXFdRBxVuq4+iIgDI+JvI2JPRDwWEXdFxAWl6+qT\niPijiNgeEU9ExC2l6+m6iNg/Iq6MiAcj4nsRcXVEHDjM745Nz35EnA4cU68eCRwQERfV63sy8+MA\nmbkrIq4Czo6ITwI3A8dRXWhrW2b+fculU9f1zMH1eqL/iRK19E1E/ApwFXBqZv57RKwBnl+4rD5J\n4KrMPK90IT33Z8DdwI+VLqRHLgfOycwnI+Jwqvf89wBvK1tWL+wP3AFckpl3R8TxwE0RcX9mbi1b\nWm/cC1wKrKe6UKmadSFVV8tPAD8E/hm4DNi42C+Ozdl46n8VnlivzhQ18wXZbZl58sC2q6iO7L8N\nWAs8QHXE/5LMHMmXc1ciItYDXwKOzsz7S9fTdRHxFeCvMvOjpWvpo4jYCjycmeeWrqWvIuKlwMeo\nriR+XWY+u3BJvRMRR1JN9j+XmZeVrqePIuLDAJn5h6Vr6ZOI2AC8ftjrF2l5IuIu4O2ZeWO9fiJw\nQ2YueoBnbNp4MvOkzFxVL/vVy8z6ybO2nc7MyzPzRZm5OjOPzsx3jsNEv3YWcJMT/eZFxMHAS4Ef\nj4hv1h+l3xwRLyhdW48k8Ob6Y8U7IuKiiPBMVi2pryvyEeDtwA8Kl9M7EXFBRDwCfJfqKu7vLVxS\nL9V/B68C/qN0LdKoRcRhwNHAjoGbdwCHRcTRi/3+2Ez2u6KefL4B+OvStfTEs6k+AXoD8GrgOcBt\nwKdKFtUzVwAvqI8unAL8AbCpbEm9shHYmZlfKF1IH2XmezPzWcC6evnzwiX11ZXAXuCa0oVIDTik\n/u9jA7ftnXXfvDo/2Y+ITRFxfUTcGRHTEbF7gW1XRcS5EXFr/YWTb0fE5roHfGabNw18CXGuC3ed\nQvVk3NjAw5koLWU/82K/MjPvy8wfAJcAPxURa5t6bJOgrdd+Zn49M/+3/vlrVD2cpzb52CZBG/lH\nxDqqI/rvav4RTY4C7/tk5m6qfv03j/4RTZa284+IzcAvAL/qqbfLvP71o0b9HLBvrnPwwG2HzLpv\nfpnZ6QWYpurp/yzVR6x3LrDtlfX2/0jVivN+qo/FP0f9/YYhxvsicGnpxz0OS1vZA7uBMwbWD633\ntbZ0Bn3If459nUV1pLl4Bl3PH/h9qosI3lcvDwFP1z+/onQGXc5+nn399kJj9WVpM39gC7ATOLz0\n4x6XpcC85xzgltKPe5yWJp4D4C7gdQPrJwLfG6qe0oG0EPjagZ93zRc48OI67Otn3X52ffsbhxjr\n2Pp/tOtKP+5xWNrKHjif6voKRwEHULWVONlsL//fANbUP/8McDtwcenHX3ppI3+qq4YfNbD8JvA/\nM38LpTPoePYHA7838No/lqpf/LLSj7/00uJ7zweAbwBHlH7M47S0mP9+9XvQO4HPA88ADiz9+Mdh\naeI5AC4Gvkx1xrVDgVuAzcPU0/k2nszcM+Smb6z/u2XW7VdTHTk7bYh9nAl8ITPvGHLMTmsx+/cB\n/0r1pv8g8CLg9UOO3Vkt5r8RuCci9lJ9V+LvqNoZeq2N/DPz+5n53zML8DAwXa8/tdSau6Kl134C\nbwG+U7/2P0N1Krw/Hb7Sbmoj/4g4hmpCtA7YPdBm0vsW2hbf+y+ut3sfcALwBNXfQe819BxcSnVh\n2duoOhpupzod56LG5jz7Y+BlVEfltw/emNX5k3fW9y8oM89vqLauW1H2Wf2T94J60dKtNP8TGqyt\nD1b83jPwO9vwPPtLsezsszr728nz3a+hrCT/u+jB9w4bttL3/ilgqqniemLo5yAzn6ZqmTpnqYP4\nh7LPc4EH5zkadg9wRFSn9tLomX1Z5l+W+Zdj9mWZf1nmX14rz4GT/X3WAE/Oc9/3B7bR6Jl9WeZf\nlvmXY/ZlmX9Z5l9eK8+Bk/19Hqf6cslcVlP1Z47LRbu6xuzLMv+yzL8csy/L/Msy//JaeQ6c7O9z\nL9XHJQfMcd/zqD5m6f35exti9mWZf1nmX47Zl2X+ZZl/ea08B07299lOdRqplw/eGBGrgZdQndpR\nzTD7ssy/LPMvx+zLMv+yzL+8Vp4DJ/v7XEf1ccmGWbe/FTgIuLb1ivrD7Msy/7LMvxyzL8v8yzL/\n8lp5DqI+UX9nRcTpwDH16juoLrp0eb2+JzM/PrDtB6jO23sDcDNwXP07X8xMT7G2RGZflvmXZf7l\nmH1Z5l+W+Zc3ds9B6auMNb1QXWFsul6erpeZ9X+bte0q4DzgVqpvQd8NbKa+QqKL2U/SYv7m39fF\n7M2/z4v5l1/G7Tno/JF9SZIkqa/s2ZckSZI6ysm+JEmS1FFO9iVJkqSOcrIvSZIkdZSTfUmSJKmj\nnOxLkiRJHeVkX5IkSeooJ/uSJElSRznZlyRJkjrKyb4kSZLUUU72JUnLFhHTQy6vKl2rJPXR/qUL\nkCRNtNMWuG8d8G7gAeA/2ylHkjQoMrN0DZKkjomINcCXgeOA12TmtrIVSVI/eWRfktSEvwF+Gtjo\nRF+SyrFnX5I0UhHxLuBU4B8y84rS9UhSn9nGI0kamYh4NfAZYBfwysx8onBJktRrTvYlSSMREWuB\nrwIB/Fxm7i5akCTJnn1J0srVX8i9ATgU+DUn+pI0HpzsS5JG4WrgeOCizPxs6WIkSRXbeCRJKxIR\n5wGbgX/KzN8qXY8kaR8n+5KkZYuIk4B/AW4D1mfmo4VLkiQNcLIvSVqWiHgO8HXgcOBS4NYFNt+Z\nmd9opTBJ0v+zZ1+StFzHUk30E7hwge0SeDfgZF+SWuaRfUmSJKmjvIKuJEmS1FFO9iVJkqSOcrIv\nSZIkdZSTfUmSJKmjnOxLkiRJHeVkX5IkSeooJ/uSJElSRznZlyRJkjrKyb4kSZLUUU72JUmSpI76\nPyGPCzqKACmHAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xfb9fe90>"
       ]
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = pynbody.load('output_00007')\n",
      "s['pos']\n",
      "s['pos'] -= 0.5\n",
      "s.physical_units();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody:Loading using backend <class 'pynbody.snapshot.ramses.RamsesSnap'>\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot.ramses:Loading hydro files\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot.ramses:Done\n",
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       "output_type": "stream",
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      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting vel units from 7.26e+07 cm s**-1 to km s**-1\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting tv units from 7.26e+07 cm s**-1 to km s**-1\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting smooth units from 5.37e+22 cm to kpc\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting p units from 1.90e-10 g cm**-1 s**-2 to km**2 Msol s**-2 kpc**-3\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting rho units from 3.59e-26 g cm**-3 to Msol kpc**-3\n"
       ]
      }
     ],
     "prompt_number": 109
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = 1/s.properties['a']-1\n",
      "print \"Redshift =\",z\n",
      "boxsizestring = \"%.2f\" % s.properties['boxsize'].in_units('kpc')\n",
      "boxsizestring += \" kpc\"\n",
      "print boxsizestring"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Redshift = 23.2161553577\n",
        "17.42 kpc\n"
       ]
      }
     ],
     "prompt_number": 110
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.g['metal'][s.g['metal']<1e-6] = 0.0\n",
      "s.g['pzf'][s.g['pzf']<1e-6] = 0.0\n",
      "s.s['ppf'][s.s['ppf']>1.0] = 1.0\n",
      "linex=(1e-7,1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 111
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.loglog(s.g['metal'],1-s.g['pgf']+1e-6,'.',linex,linex)\n",
      "plt.title(\"Z vs (1-Pf) z=%.1f\"%z)\n",
      "plt.grid(True, which=\"both\")\n",
      "axes().set_xlabel(\"Z\")\n",
      "axes().set_ylabel(\"1-P\")\n",
      "plt.ylim(1e-7, 1)\n",
      "plt.xlim(1e-7, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 112,
       "text": [
        "(1e-07, 1)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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I1fw04Wo++YY1+3WwZp+IqL3lcqb05oUXGut/yCHAr3/NLTXTirX5ZBNr9omI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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x57b0c10>"
       ]
      }
     ],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = pynbody.load('output_00009')\n",
      "s['pos']\n",
      "s['pos'] -= 0.5\n",
      "s.physical_units();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody:Loading using backend <class 'pynbody.snapshot.ramses.RamsesSnap'>\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot.ramses:Loading hydro files\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot.ramses:Done\n",
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       "output_type": "stream",
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      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting vel units from 7.09e+07 cm s**-1 to km s**-1\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting tv units from 7.09e+07 cm s**-1 to km s**-1\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting smooth units from 5.51e+22 cm to kpc\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting p units from 1.68e-10 g cm**-1 s**-2 to km**2 Msol s**-2 kpc**-3\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting rho units from 3.34e-26 g cm**-3 to Msol kpc**-3\n"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = 1/s.properties['a']-1\n",
      "print \"Redshift =\",z\n",
      "boxsizestring = \"%.2f\" % s.properties['boxsize'].in_units('kpc')\n",
      "boxsizestring += \" kpc\"\n",
      "print boxsizestring"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Redshift = 22.6277843309\n",
        "17.85 kpc\n"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.g['metal'][s.g['metal']<1e-6] = 0.0\n",
      "s.g['pzf'][s.g['pzf']<1e-6] = 0.0\n",
      "s.s['ppf'][s.s['ppf']>1.0] = 1.0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.loglog(s.g['metal'],1-s.g['pgf']+1e-6,'.',linex,linex)\n",
      "plt.grid(True, which=\"both\")\n",
      "plt.title(\"Z vs (1-Pf) z=%.1f\"%z)\n",
      "axes().set_xlabel(\"Z\")\n",
      "axes().set_ylabel(\"1-P\")\n",
      "plt.ylim(1e-7, 1)\n",
      "plt.xlim(1e-7, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "(1e-07, 1)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nrP37+voYGBggNs453WrcfHpaq7+/P3PxwrYRdP8s5iKJmHHkP6u5SCKm8p9c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\nZqU194202yx99htvMwpZjan8JxdPNfspZ2YnAGc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J+8/fkHStpLenfG1AnbCbDwBAdZRm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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xd8fd490>"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = pynbody.load('output_00011')\n",
      "s['pos']\n",
      "s['pos'] -= 0.5\n",
      "s.physical_units();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody:Loading using backend <class 'pynbody.snapshot.ramses.RamsesSnap'>\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
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      },
      {
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       "text": [
        "INFO:pynbody.snapshot:Converting vel units from 6.72e+07 cm s**-1 to km s**-1\n"
       ]
      },
      {
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        "INFO:pynbody.snapshot:Converting tv units from 6.72e+07 cm s**-1 to km s**-1\n"
       ]
      },
      {
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       "text": [
        "INFO:pynbody.snapshot:Converting smooth units from 5.81e+22 cm to kpc\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting p units from 1.28e-10 g cm**-1 s**-2 to km**2 Msol s**-2 kpc**-3\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting rho units from 2.84e-26 g cm**-3 to Msol kpc**-3\n"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = 1/s.properties['a']-1\n",
      "print \"Redshift =\",z\n",
      "boxsizestring = \"%.2f\" % s.properties['boxsize'].in_units('kpc')\n",
      "boxsizestring += \" kpc\"\n",
      "print boxsizestring\n",
      "s.g['metal'][s.g['metal']<1e-6] = 0.0\n",
      "s.g['pzf'][s.g['pzf']<1e-6] = 0.0\n",
      "s.s['ppf'][s.s['ppf']>1.0] = 1.0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Redshift = 21.4019608479\n",
        "18.83 kpc\n"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.loglog(s.g['metal'],1-s.g['pgf']+1e-6,'.',linex,linex)\n",
      "plt.grid(True, which=\"both\")\n",
      "plt.title(\"Z vs (1-Pf) z=%.1f\"%z)\n",
      "axes().set_xlabel(\"Z\")\n",
      "axes().set_ylabel(\"1-P\")\n",
      "plt.ylim(1e-7, 1)\n",
      "plt.xlim(1e-7, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 82,
       "text": [
        "(1e-07, 1)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mPwbOuX2ccyudc9ucc887537rnPtU6P0SEWllc+bAd79b+f1p0zTQl8ZoNl+k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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3471710>"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = pynbody.load('output_00013')\n",
      "s['pos']\n",
      "s['pos'] -= 0.5\n",
      "s.physical_units();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody:Loading using backend <class 'pynbody.snapshot.ramses.RamsesSnap'>\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot.ramses:Loading hydro files\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot.ramses:Done\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 1\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 5\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 9\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 13\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 17\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 21\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 25\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 29\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 33\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 37\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 41\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 45\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 49\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 53\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 57\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 61\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 2\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 6\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 10\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 14\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 18\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 22\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 26\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 30\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 34\n",
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      },
      {
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       "text": [
        "INFO:pynbody.snapshot:Converting vel units from 6.48e+07 cm s**-1 to km s**-1\n"
       ]
      },
      {
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        "INFO:pynbody.snapshot:Converting tv units from 6.48e+07 cm s**-1 to km s**-1\n"
       ]
      },
      {
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       "text": [
        "INFO:pynbody.snapshot:Converting smooth units from 6.02e+22 cm to kpc\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting p units from 1.07e-10 g cm**-1 s**-2 to km**2 Msol s**-2 kpc**-3\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting rho units from 2.55e-26 g cm**-3 to Msol kpc**-3\n"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = 1/s.properties['a']-1\n",
      "print \"Redshift =\",z\n",
      "boxsizestring = \"%.2f\" % s.properties['boxsize'].in_units('kpc')\n",
      "boxsizestring += \" kpc\"\n",
      "print boxsizestring\n",
      "s.g['metal'][s.g['metal']<1e-6] = 0.0\n",
      "s.g['pzf'][s.g['pzf']<1e-6] = 0.0\n",
      "s.s['ppf'][s.s['ppf']>1.0] = 1.0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Redshift = 20.6029612834\n",
        "19.52 kpc\n"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.loglog(s.g['metal'],1-s.g['pgf']+1e-6,'.',linex,linex)\n",
      "plt.grid(True, which=\"both\")\n",
      "plt.title(\"Z vs (1-Pf) z=%.1f\"%z)\n",
      "axes().set_xlabel(\"Z\")\n",
      "axes().set_ylabel(\"1-P\")\n",
      "plt.ylim(1e-7, 1)\n",
      "plt.xlim(1e-7, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "(1e-07, 1)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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f8NkTPPWhWieEEEIijJ1O8733gMOHg8u2M3u20f1zNdzagZl2SKWgZr8A1OwT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\nB6xe7V+AT4KDmXYIISR6lCzjEZGTAewC8GlVXe1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3L7v7xZI+LOksSW9rFcTM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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7c0fe50>"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = pynbody.load('output_00014')\n",
      "s['pos']\n",
      "s['pos'] -= 0.5\n",
      "s.physical_units();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody:Loading using backend <class 'pynbody.snapshot.ramses.RamsesSnap'>\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot.ramses:Loading hydro files\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot.ramses:Done\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 1\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 5\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 9\n",
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      },
      {
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       "text": [
        "INFO:pynbody.snapshot:Converting vel units from 6.30e+07 cm s**-1 to km s**-1\n"
       ]
      },
      {
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        "INFO:pynbody.snapshot:Converting tv units from 6.30e+07 cm s**-1 to km s**-1\n"
       ]
      },
      {
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       "text": [
        "INFO:pynbody.snapshot:Converting smooth units from 6.20e+22 cm to kpc\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting p units from 9.30e-11 g cm**-1 s**-2 to km**2 Msol s**-2 kpc**-3\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting rho units from 2.34e-26 g cm**-3 to Msol kpc**-3\n"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = 1/s.properties['a']-1\n",
      "print \"Redshift =\",z\n",
      "boxsizestring = \"%.2f\" % s.properties['boxsize'].in_units('kpc')\n",
      "boxsizestring += \" kpc\"\n",
      "print boxsizestring\n",
      "s.g['metal'][s.g['metal']<1e-6] = 0.0\n",
      "s.g['pzf'][s.g['pzf']<1e-6] = 0.0\n",
      "s.s['ppf'][s.s['ppf']>1.0] = 1.0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Redshift = 19.9991061237\n",
        "20.08 kpc\n"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.loglog(s.g['metal'],1-s.g['pgf']+1e-6,'.',linex,linex)\n",
      "plt.grid(True, which=\"both\")\n",
      "plt.title(\"Z vs (1-Pf) z=%.1f\"%z)\n",
      "axes().set_xlabel(\"Z\")\n",
      "axes().set_ylabel(\"1-P\")\n",
      "plt.ylim(1e-7, 1)\n",
      "plt.xlim(1e-7, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 94,
       "text": [
        "(1e-07, 1)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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0DB4M7N+vI/npRvAJsaA2n6SDmn1CCCEkACZPBvbtU+lOukA/FgNmzgTWrMks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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1096b5d0>"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = pynbody.load('output_00015')\n",
      "s['pos']\n",
      "s['pos'] -= 0.5\n",
      "s.physical_units();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody:Loading using backend <class 'pynbody.snapshot.ramses.RamsesSnap'>\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot.ramses:Loading hydro files\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot.ramses:Done\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 1\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 5\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 9\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 13\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 17\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 21\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 25\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 29\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 33\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 37\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 41\n",
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      },
      {
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       "text": [
        "INFO:pynbody.snapshot:Converting vel units from 6.29e+07 cm s**-1 to km s**-1\n"
       ]
      },
      {
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        "INFO:pynbody.snapshot:Converting tv units from 6.29e+07 cm s**-1 to km s**-1\n"
       ]
      },
      {
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       "text": [
        "INFO:pynbody.snapshot:Converting smooth units from 6.21e+22 cm to kpc\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting p units from 9.20e-11 g cm**-1 s**-2 to km**2 Msol s**-2 kpc**-3\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting rho units from 2.33e-26 g cm**-3 to Msol kpc**-3\n"
       ]
      }
     ],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = 1/s.properties['a']-1\n",
      "print \"Redshift =\",z\n",
      "boxsizestring = \"%.2f\" % s.properties['boxsize'].in_units('kpc')\n",
      "boxsizestring += \" kpc\"\n",
      "print boxsizestring\n",
      "s.g['metal'][s.g['metal']<1e-6] = 0.0\n",
      "s.g['pzf'][s.g['pzf']<1e-6] = 0.0\n",
      "s.s['ppf'][s.s['ppf']>1.0] = 1.0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Redshift = 19.9524015095\n",
        "20.13 kpc\n"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.loglog(s.g['metal'],1-s.g['pgf']+1e-6,'.',linex,linex)\n",
      "plt.grid(True, which=\"both\")\n",
      "plt.title(\"Z vs (1-Pf) z=%.1f\"%z)\n",
      "axes().set_xlabel(\"Z\")\n",
      "axes().set_ylabel(\"1-P\")\n",
      "plt.ylim(1e-7, 1)\n",
      "plt.xlim(1e-7, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 97,
       "text": [
        "(1e-07, 1)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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s/+y6fp2/s0z/pwbt8/v8neUo/r2V+v25rW99vuoq4NVXdT+QyrznLn/1qymc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\n9LOfdf788+THPw45Q75xFVwAKL7UavbN7DJJf+fuPVMaRM1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p5+73SPqMpDeZ2VfN7C/M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\nAADoUf8/Fy9cx0gK4ugAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x587a150>"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = pynbody.load('output_00016')\n",
      "s['pos']\n",
      "s['pos'] -= 0.5\n",
      "s.physical_units();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody:Loading using backend <class 'pynbody.snapshot.ramses.RamsesSnap'>\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot.ramses:Loading hydro files\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot.ramses:Done\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 1\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 5\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 9\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 13\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 17\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 21\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 25\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 29\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 33\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 37\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 41\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 45\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 49\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 53\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 57\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 61\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 2\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 6\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 10\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 14\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 18\n",
        "INFO:pynbody.snapshot.ramses:Loading data from CPU 22\n",
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      },
      {
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       "text": [
        "INFO:pynbody.snapshot:Converting vel units from 6.00e+07 cm s**-1 to km s**-1\n"
       ]
      },
      {
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        "INFO:pynbody.snapshot:Converting tv units from 6.00e+07 cm s**-1 to km s**-1\n"
       ]
      },
      {
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       "text": [
        "INFO:pynbody.snapshot:Converting smooth units from 6.51e+22 cm to kpc\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting p units from 7.29e-11 g cm**-1 s**-2 to km**2 Msol s**-2 kpc**-3\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting rho units from 2.02e-26 g cm**-3 to Msol kpc**-3\n"
       ]
      }
     ],
     "prompt_number": 120
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = 1/s.properties['a']-1\n",
      "print \"Redshift =\",z\n",
      "boxsizestring = \"%.2f\" % s.properties['boxsize'].in_units('kpc')\n",
      "boxsizestring += \" kpc\"\n",
      "print boxsizestring\n",
      "s.g['metal'][s.g['metal']<1e-6] = 0.0\n",
      "s.g['pzf'][s.g['pzf']<1e-6] = 0.0\n",
      "s.s['ppf'][s.s['ppf']>1.0] = 1.0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Redshift = 18.9999264388\n",
        "21.09 kpc\n"
       ]
      }
     ],
     "prompt_number": 121
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.loglog(s.g['metal'],1-s.g['pgf']+1e-6,'.',linex,linex)\n",
      "plt.grid(True, which=\"both\")\n",
      "plt.title(\"Z vs (1-Pf) z=%.1f\"%z)\n",
      "axes().set_xlabel(\"Z\")\n",
      "axes().set_ylabel(\"1-P\")\n",
      "plt.ylim(1e-7, 1)\n",
      "plt.xlim(1e-7, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 122,
       "text": [
        "(1e-07, 1)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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g3J0dx2bNGk2puWABcOKJwM6dqfXu9JnZMMkf+Kurge5uzuYTzuaTgaFmPwfU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       "text": [
        "<matplotlib.figure.Figure at 0x7c109d0>"
       ]
      }
     ],
     "prompt_number": 122
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}