pyn-stars-ec.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib.pylab import *\n",
    "rcParams['figure.figsize'] = (12,8)\n",
    "rcParams['font.size'] = 18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pynbody\n",
    "import pynbody.plot.sph as sph\n",
    "pynbody.ramses.multiprocess_num = 256\n",
    "pynbody.config['number_of_threads'] = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:pynbody:Loading using backend <class 'pynbody.snapshot.ramses.RamsesSnap'>\n",
      "INFO:pynbody:Loading using backend <class 'pynbody.snapshot.ramses.RamsesSnap'>\n",
      "INFO:pynbody:Loading using backend <class 'pynbody.snapshot.ramses.RamsesSnap'>\n"
     ]
    }
   ],
   "source": [
    "s0 = pynbody.load('/Users/earnric/Research/RAMSES-Data/newECFixedTau/output_00010')\n",
    "s1 = pynbody.load('/Users/earnric/Research/RAMSES-Data/newECFixedTau/output_00011')\n",
    "s2 = pynbody.load('/Users/earnric/Research/RAMSES-Data/newECFixedTau/output_00012')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look at the # of number of star, gas (cells) and DM particles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note that in pynbody everything is considered a 'particle' -- i.e. - cells become particles in this model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 4, 4)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s0.stars),len(s1.stars),len(s2.stars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(SimArray([ 7891.04984151,  7891.04984151,  7891.04984151,  7891.04984151], 'Msol'),\n",
       " SimArray([ 7891.04984151,  7891.04984151,  7891.04984151,  7891.04984151], 'Msol'),\n",
       " SimArray([ 7891.04984151,  7891.04984151,  7891.04984151,  7891.04984151], 'Msol'))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s0.s['mass'].in_units('Msol'),s1.s['mass'].in_units('Msol'),s2.s['mass'].in_units('Msol'),"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "M6upfwFCDJ8MC/lA8wAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x130ecc810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pynbody.plot.stars.sfh(s0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "1EoGK5IkSZJayWBFkiRJUisZrEiSJElqpf8HarMZsy9URcQAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ed50590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pynbody.plot.stars.sfh(s1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "DAgAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10063e310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pynbody.plot.stars.sfh(s2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print z for this output step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10.999961007123227, 9.995896551329034, 9.73409401460295)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#redshift\n",
    "1/s0.properties['a']-1,1/s1.properties['a']-1,1/s2.properties['a']-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "z=1/s.properties['a']-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.993704457079804"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35.14656587261756"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s0.properties['boxsize'].in_units('kpc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "boxsizestring = \"%.2f\" % s.properties['boxsize'].in_units('kpc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "boxsizestring += \" kpc\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'42.20 kpc'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boxsizestring"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### So comoving box size is ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "299.4477682040461"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.properties['boxsize'].in_units('kpc') * s.properties['h'] / s.properties['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "562.1631277116882"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.properties['time'].in_units('Myr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NORMALIZE data coords -- center the values on (0,0,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RAMSES uses particle locations between 0 and 1... Shift this to -0.5 to 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do this BEFORE transforming to physical units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s['pos'] -= 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:pynbody.snapshot:Converting pos units from 1.30e+23 cm to kpc\n",
      "INFO:pynbody.snapshot:Converting vel units from 3.00e+07 cm s**-1 to km s**-1\n",
      "INFO:pynbody.snapshot:Converting tv units from 3.00e+07 cm s**-1 to km s**-1\n",
      "INFO:pynbody.snapshot:Converting smooth units from 1.30e+23 cm to kpc\n",
      "INFO:pynbody.snapshot:Converting p units from 2.27e-12 g cm**-1 s**-2 to km**2 Msol s**-2 kpc**-3\n",
      "INFO:pynbody.snapshot:Converting rho units from 2.53e-27 g cm**-3 to Msol kpc**-3\n"
     ]
    }
   ],
   "source": [
    "s.physical_units()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zero-out metals lower than 10^-8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25016242"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s.gas['metal'][s.gas['metal']< 1.0e-8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1709024"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s.gas['metal'][s.gas['metal']>=1e-8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s.gas['metal'][s.gas['metal']<1e-8]=1.0e-8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s.gas['metal'][s.gas['metal']< 1.0e-8])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the metals in a slice at the center of the simulation box"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remember - you get a slice at box-center unless you ask for something else... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'42.20 kpc'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boxsizestring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:pynbody.sph:Rendering image on 16 threads...\n"
     ]
    },
    {
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       "mpT0BkkTkpYCu5IdScw+/1jNZIcAF3eZ9VURcTnpaONKSZN1X196/ypSV/RvSUcV15EuaCm2d7Gk\n",
       "3bt8t5nZ2HOSaGazdSxwJfB84N3AWrKLVCS9hXThSdnbgR+3mV+e+OX7p+OB7UnnF5Yd1ppI2g44\n",
       "DjgpItZExMOki2Y+ImnLwjQrsjaamVkHioim22Bm85iklwAfA/4E3AFsCRwArAL+EhEXlOrvAHwm\n",
       "Ig6vmde7gROAPYGbgbOAx4CfAYuBa4BjSInkfaSEbxvSEcM9gR9GxCWleS4D3ka6GnodcG1E/GoA\n",
       "i25mtklzkmhmQyXpJOD3EfGLWcxjR1KSuGNErB5Q08zMrMDdzWY2bAcCV81yHuUuaTMzGzDvYM1s\n",
       "aCS9HLgrIjZ6fOasS/oiIICLJB06qPaZmdk0dzeb2dBIOhu4MCJubbotZmbWmY8kmtkwbecE0cxs\n",
       "fvCRRDMzMzOr8JFEMzMzM6twkmhmZmZmFU4SzczMzKzCSaKZmZmZVThJNDMzM7MKJ4lmZmZmVuEk\n",
       "0czMzMwqnCSamZmZWcX/AWdUIVo3Qwh8AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x102478f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with pynbody.transformation.translate(s,[0,0,2]):\n",
    "    titleStr = \"Z @ z = %2d\" % z + \"\\nSlice @ zAxis +2kpc\"\n",
    "    sph.image(s.g,qty=\"metal\",width=boxsizestring,cmap=\"YlOrRd\", av_z=False, denoise=False ,approximate_fast=False,title=titleStr);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}