Untitled17.ipynb

{
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   "execution_count": 1,
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     "text": [
      "yt : [INFO     ] 2017-06-30 15:35:07,580 Parameters: current_time              = 0.0060000200028298\n",
      "yt : [INFO     ] 2017-06-30 15:35:07,581 Parameters: domain_dimensions         = [32 32 32]\n",
      "yt : [INFO     ] 2017-06-30 15:35:07,582 Parameters: domain_left_edge          = [ 0.  0.  0.]\n",
      "yt : [INFO     ] 2017-06-30 15:35:07,583 Parameters: domain_right_edge         = [ 1.  1.  1.]\n",
      "yt : [INFO     ] 2017-06-30 15:35:07,585 Parameters: cosmological_simulation   = 0.0\n"
     ]
    }
   ],
   "source": [
    "import yt\n",
    "import numpy as np\n",
    "\n",
    "from yt.units import Mpc\n",
    "from yt.utilities.amr_kdtree.amr_kdtree import AMRKDTree\n",
    "from yt.visualization.streamlines import Streamlines\n",
    "\n",
    "ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsing Hierarchy : 100%|██████████| 173/173 [00:00<00:00, 19168.77it/s]\n",
      "yt : [INFO     ] 2017-06-30 15:35:07,623 Gathering a field list (this may take a moment.)\n"
     ]
    }
   ],
   "source": [
    "# create AMRKDTRee that uses extrapolation to generate vertex centered data\n",
    "volume = AMRKDTree(ds)\n",
    "volume.set_fields(fields=['velocity_x', 'velocity_y', 'velocity_z'], log_fields=[False, False, False], no_ghost=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scale = ds.domain_width[0]\n",
    "pos_dx = np.random.random((100,3))*scale-scale/2.\n",
    "pos = ds.domain_center+pos_dx\n",
    "\n",
    "streamlines = Streamlines(ds, pos, 'velocity_x', 'velocity_y', 'velocity_z',\n",
    "                          length=0.5*Mpc, get_magnitude=True, volume=volume)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Streamlining: 100%|██████████| 100/100 [00:18<00:00,  5.41it/s]\n"
     ]
    }
   ],
   "source": [
    "streamlines.integrate_through_volume()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.3825317   0.90573005  0.38431177]\n",
      " [ 0.38256523  0.90561744  0.38434483]\n",
      " [ 0.38259877  0.90550483  0.38437789]\n",
      " ..., \n",
      " [ 0.50379712  0.49873317  0.50014693]\n",
      " [ 0.50385362  0.49884102  0.5001553 ]\n",
      " [ 0.50390518  0.49895133  0.50016361]] code_length\n"
     ]
    }
   ],
   "source": [
    "print(streamlines.streamlines[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# try again, this time with an AMRKDTree that samples a subset of the simulation volume\n",
    "# using a data object\n",
    "volume = AMRKDTree(ds, data_source=ds.sphere(ds.domain_center, 1*Mpc))\n",
    "volume.set_fields(fields=['velocity_x', 'velocity_y', 'velocity_z'], log_fields=[False, False, False], no_ghost=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scale = ds.domain_width[0]\n",
    "pos_dx = np.random.random((100,3))*scale-scale/2.\n",
    "pos = ds.domain_center+pos_dx\n",
    "\n",
    "streamlines = Streamlines(ds, pos, 'velocity_x', 'velocity_y', 'velocity_z',\n",
    "                          length=0.5*Mpc, get_magnitude=True, volume=volume)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Streamlining: 100%|██████████| 100/100 [00:18<00:00,  5.33it/s]\n"
     ]
    }
   ],
   "source": [
    "streamlines.integrate_through_volume()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.5102313   0.96801447  0.35891466]\n",
      " [ 0.51023265  0.96789774  0.3589503 ]\n",
      " [ 0.510234    0.96778102  0.35898595]\n",
      " ..., \n",
      " [ 0.46327867  0.52574021  0.48639458]\n",
      " [ 0.46335171  0.52565211  0.48643703]\n",
      " [ 0.46342487  0.52556414  0.48647953]] code_length\n"
     ]
    }
   ],
   "source": [
    "print(streamlines.streamlines[0])"
   ]
  }
 ],
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