pynSizesMass-MBP.ipynb

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     "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"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pynbody\n",
      "import pynbody.plot.sph as sph\n",
      "pynbody.ramses.multiprocess_num = 4\n",
      "pynbody.config['number_of_threads'] = 16"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ImportError",
       "evalue": "dlopen(/Users/earnric/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pynbody/_util.so, 2): Library not loaded: /Applications/mesasdk/lib/libgomp.1.dylib\n  Referenced from: /Users/earnric/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pynbody/_util.so\n  Reason: image not found",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-2-5337f041beb6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpynbody\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpynbody\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msph\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mpynbody\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mramses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmultiprocess_num\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mpynbody\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'number_of_threads'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m16\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/earnric/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pynbody/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m \u001b[0;31m# Import subpackages\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mutil\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msnapshot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0msnapshot\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtipsy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgadget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgadgethdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mramses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrafic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnchilada\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mascii\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0manalysis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhalo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mderived\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbridge\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgravity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransformation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/earnric/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pynbody/util.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0mlogger\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlogging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetLogger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pynbody.util'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_util\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mImportError\u001b[0m: dlopen(/Users/earnric/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pynbody/_util.so, 2): Library not loaded: /Applications/mesasdk/lib/libgomp.1.dylib\n  Referenced from: /Users/earnric/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pynbody/_util.so\n  Reason: image not found"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = pynbody.load('./output_00013')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody:Loading using backend <class 'pynbody.snapshot.ramses.RamsesSnap'>\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Look at the # of number of star, gas (cells) and DM particles"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Note that in pynbody everything is considered a 'particle' -- i.e. - cells become particles in this model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.properties"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "{'a': 0.0833487751478619,\n",
        " 'boxsize': Unit(\"1.08e+24 cm\"),\n",
        " 'h': 0.71,\n",
        " 'omegaL0': 0.732999980449677,\n",
        " 'omegaM0': 0.266999989748001,\n",
        " 'time': Unit(\"4.27e-01 Gyr\")}"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.properties['boxsize'].in_units('kpc')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "351.5296337523177"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.properties['boxsize'].in_units('kpc')*0.71/s.properties['a']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "2994.4775975576895"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(s.stars),len(s.gas),len(s.dm)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "(4, 134217728, 134217728)"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Actually load the data. This is done when you ask for the data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s['pos'];"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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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       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.properties"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "{'a': 0.0833487751478619,\n",
        " 'boxsize': Unit(\"1.08e+24 cm\"),\n",
        " 'h': 0.71,\n",
        " 'omegaL0': 0.732999980449677,\n",
        " 'omegaM0': 0.266999989748001,\n",
        " 'time': Unit(\"4.27e-01 Gyr\")}"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Print z for this output step"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.g['mass']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Deriving array mass\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "SimArray([  5.42744735e-10,   1.15312782e-09,   7.55430681e-10, ...,\n",
        "         4.23695693e-10,   3.94274873e-10,   3.85806420e-10], '5.58e+45 g')"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.sum(s.g['mass']).in_units('g')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "SimArray(9.399218415556063e+44, 'g')"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.sum(s.g['mass']).in_units('Msol')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "SimArray(472579008484.8091, 'Msol')"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.s['mass']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/earnric/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pynbody/snapshot/__init__.py:1378: RuntimeWarning: Conjoining derived and non-derived arrays. Assuming result is non-derived, so no further updates will be made.\n",
        "  \"Conjoining derived and non-derived arrays. Assuming result is non-derived, so no further updates will be made.\", RuntimeWarning)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "SimArray([  5.62801631e-06,   2.81400816e-06,   2.81400816e-06,\n",
        "         2.81400816e-06], '5.58e+45 g')"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.s['mass'].in_units('Msol')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "SimArray([ 15782098.34723941,   7891049.17361971,   7891049.1736197 ,\n",
        "         7891049.17361971], 'Msol')"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.sum(s.s['mass']).in_units('g')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "SimArray(7.847332761197849e+40, 'g')"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.sum(s.s['mass']).in_units('Msol')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "SimArray(39455245.86809852, 'Msol')"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Check comoving box size is ... "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.properties['boxsize'].in_units('Mpc') * s.properties['h'] / s.properties['a']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "2.994477597557689"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.properties['time'].in_units('Myr')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "427.44952454608733"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "NORMALIZE data coords -- center the values on (0,0,0)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "RAMSES uses particle locations between 0 and 1... Shift this to -0.5 to 0.5"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Do this BEFORE transforming to physical units"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s['pos'] -= 0.5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.physical_units();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting pos units from 9.30e+22 cm to kpc\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting vel units from 4.20e+07 cm s**-1 to km s**-1\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting tv units from 4.20e+07 cm s**-1 to km s**-1\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting smooth units from 9.30e+22 cm to kpc\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.snapshot:Converting p units from 1.22e-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 6.94e-27 g cm**-3 to Msol kpc**-3\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Zero-out metals lower than 10^-8"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(s.gas['metal'][s.gas['metal']< 1.0e-8])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "16278413"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(s.gas['metal'][s.gas['metal']>=1e-8])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "498803"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.gas['metal'][s.gas['metal']<1e-8]=1.0e-8"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(s.gas['metal'][s.gas['metal']< 1.0e-8])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "max(s.gas['metal'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "0.087296607645947696"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "min(s.gas['pgf']),len(s.gas['pgf'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "(0.0, 16777216)"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plot the metals in a slice at the center of the simulation box"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Remember - you get a slice at box-center unless you ask for something else... "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "boxsizestring"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "'30.13 kpc'"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "titleStr = \"Z @ z = %.1fd\" % z + \"\\nVol weighted Ave\"\n",
      "sph.image(s.g,qty=\"metal\",width=boxsizestring,cmap=\"YlOrRd\",  av_z=True, denoise=False ,\n",
      "          approximate_fast=False,title=titleStr);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.sph:Rendering image on 16 threads...\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO:pynbody.sph:Rendering image on 16 threads...\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QOEGcceVmwEN3achCCCGE2AKmFjgLY0+IROCJwCXpff5penZ6PQI0ItHdP2Fm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lAAAg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\ngxNe4xzgze4+OFHDZk3dzSIiI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rs+TehqfviEx/QZtDX6P22PUEicIjMb+6sqjlP7ZcKH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\no5AoIiIiImP+P9xZTIqI2xwaAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1a2931610>"
       ]
      }
     ],
     "prompt_number": 58
    }
   ],
   "metadata": {}
  }
 ]
}