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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Using the yt Cosmology Calculator\n",
      "=================================\n",
      "\n",
      "The cosmology calculator is a super useful tool for helping plan and analyze your cosmological simulations. In this exercise, we're going to load up a dataset, then access things like the comoving radial distance and angular scales for the cosmology used for the simulation."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import yt\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ds = yt.load(\"../../sample_data/enzo_cosmology_plus/DD0046/DD0046\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "After a Dataset is loaded, we can access the cosmology tool with ds.cosmology. From it, we can calculate things like the comoving radial distance from z=0 to z=2 in comoving Mpc/h:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ds.cosmology"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<yt.utilities.cosmology.Cosmology at 0x7fb36114b990>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ds.cosmology.comoving_radial_distance(0.0, 2.0).in_units(\"Mpccm/h\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "3730.68924253 Mpccm/h"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's see how a few cosmological distances vary with starting and ending redshifts."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "zrange = np.linspace(0.0, 10.0, 100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cm_radial = [ds.cosmology.comoving_radial_distance(0.0, z).in_units(\"Mpc/h\") for z in zrange]\n",
      "lookback = [ds.cosmology.lookback_time(0.0, z).in_units(\"Gyr\") for z in zrange]\n",
      "angular = [ds.cosmology.angular_scale(0.0, z).in_units(\"kpc/arcsec\") for z in zrange]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pylab as pl"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl.figure(figsize=[8,8])\n",
      "pl.loglog(zrange, cm_radial)\n",
      "pl.xlabel(\"z\")\n",
      "pl.ylabel(\"co-moving radial distance [Mpc]\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.text.Text at 0x7fb35e7a5f50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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33RZOsJMklaYkYb8K8AnQdanHDfsK9emn0LMnbLUV3HADtFnRYJAkKSp30FOL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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb35e7780d0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl.figure(figsize=[8,8])\n",
      "pl.loglog(zrange, lookback)\n",
      "pl.xlabel(\"z\")\n",
      "pl.ylabel(\"Lookback time [Gyr]\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<matplotlib.text.Text at 0x7fb35e5a4190>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NmwfDhsGUKWFTm/XXj12RJNW3hoYG6ERme81enzJjBuyxR5hil88b9JKUAsNe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\nkiRJkiRJkiRJkiRJkiRJkiRJkqS69P8BtZaU1iq5e2sAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb35e3c6a10>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl.figure(figsize=[8,8])\n",
      "pl.loglog(zrange, angular)\n",
      "pl.xlabel(\"z\")\n",
      "pl.ylabel(\"Angular Scale [kpc/arcsec]\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next let's play a game where we guess what the Planck2018 cosmology will be, and see how things change.  We can initalize a new cosmology using yt.utitlities.cosmology.Cosmology().  Check out the online docs or the docstrings associated with that object for information on initializing your favorite cosmology."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "planck2018 = yt.utilities.cosmology.Cosmology(hubble_constant=0.7,\n",
      "                                              omega_matter=0.75,\n",
      "                                              omega_lambda=0.25,\n",
      "                                              omega_curvature=0.0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cm_radial = [planck2018.comoving_radial_distance(0.0, z).in_units(\"Mpc/h\") for z in zrange]\n",
      "lookback = [planck2018.lookback_time(0.0, z).in_units(\"Gyr\") for z in zrange]\n",
      "angular = [planck2018.angular_scale(0.0, z).in_units(\"kpc/arcsec\") for z in zrange]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl.figure(figsize=[8,8])\n",
      "pl.loglog(zrange, cm_radial)\n",
      "pl.xlabel(\"z\")\n",
      "pl.ylabel(\"co-moving radial distance [Mpc]\")\n",
      "\n",
      "pl.figure(figsize=[8,8])\n",
      "pl.loglog(zrange, lookback)\n",
      "pl.xlabel(\"z\")\n",
      "pl.ylabel(\"Lookback time [Gyr]\")\n",
      "\n",
      "pl.figure(figsize=[8,8])\n",
      "pl.loglog(zrange, angular)\n",
      "pl.xlabel(\"z\")\n",
      "pl.ylabel(\"Angular Scale [kpc/arcsec]\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.text.Text at 0x7fb35e1c84d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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h1roVVwyb2XTqlHZFklQ9SnHN/kRgJjACeBj4V/NDVeKtt2D77WHTTeGf/zTo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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb35e162b90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BLwMHxy5EkiQVxj+AK2MXIUmSCqMrMCh2EZIkqTBaE87Y3ih2IZIk\nqTD6AlNZOknvzrjlSJIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkqSz9H6m4wOj7vga2AAAAAElF\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb35e15ab50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7fb3611043d0>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}