Source

yt.milestones / cylindrical_rays2.ipynb

{
 "metadata": {
  "name": "cylindrical_rays2"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from yt.mods import *\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "from yt.utilities.lib.alt_ray_tracers import _cart_intersect, _cyl2cart, clyindrical_ray_trace\n",
      "\n",
      "pf = load('cylindrical_data/nif2013_hdf5_plt_cnt_0006')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [WARNING  ] 2012-09-05 19:09:33,074 integer runtime parameter checkpointfilenumber overwrites a simulation scalar of the same name\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [WARNING  ] 2012-09-05 19:09:33,075 integer runtime parameter forcedplotfilenumber overwrites a simulation scalar of the same name\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [WARNING  ] 2012-09-05 19:09:33,075 integer runtime parameter nbegin overwrites a simulation scalar of the same name\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [WARNING  ] 2012-09-05 19:09:33,076 integer runtime parameter plotfilenumber overwrites a simulation scalar of the same name\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2012-09-05 19:09:33,107 Parameters: current_time              = 8.00057343882e-10\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2012-09-05 19:09:33,107 Parameters: domain_dimensions         = [48 96  1]\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2012-09-05 19:09:33,108 Parameters: domain_left_edge          = [ 0.     -1.2288  0.    ]\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2012-09-05 19:09:33,109 Parameters: domain_right_edge         = [ 1.2288      1.2288      6.28318531]\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2012-09-05 19:09:33,110 Parameters: cosmological_simulation   = 0.0\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Everything different\n",
      "E = np.array([0.5, -1.0, 0.0])\n",
      "F = np.array([1.0, 1.0, 0.75*np.pi])\n",
      "\n",
      "# r same\n",
      "#E = np.array([0.5, -1.0, 0.0])\n",
      "#F = np.array([0.5, 1.0, 0.75*np.pi])\n",
      "\n",
      "# diagonal through z-axis\n",
      "#E = np.array([0.5, -1.0, 0.0])\n",
      "#F = np.array([0.5, 1.0, np.pi])\n",
      "\n",
      "# straight through z-axis\n",
      "#E = np.array([0.5, 0.0, 0.0])\n",
      "#F = np.array([0.5, 0.0, np.pi])\n",
      "#E = np.array([0.5, 0.0, np.pi*3/2 + 0.0])\n",
      "#F = np.array([0.5, 0.0, np.pi*3/2 + np.pi])\n",
      "#E = np.array([0.5, 0.0, np.pi/2 + 0.0])\n",
      "#F = np.array([0.5, 0.0, np.pi/2 + np.pi])\n",
      "#E = np.array([0.5, 0.0, np.pi + 0.0])\n",
      "#F = np.array([0.5, 0.0, np.pi + np.pi])\n",
      "\n",
      "# const z, not through z-axis\n",
      "#E = np.array([0.5, 0.1, 0.0])\n",
      "#F = np.array([0.5, 0.1, 0.75*np.pi])\n",
      "#E = np.array([0.5, 0.1, np.pi + 0.0])\n",
      "#F = np.array([0.5, 0.1, np.pi + 0.75*np.pi])\n",
      "#E = np.array([0.5, 0.1, np.pi*3/2 + 0.0])\n",
      "#F = np.array([0.5, 0.1, np.pi*3/2 + 0.75*np.pi])\n",
      "#E = np.array([0.5, 0.1, np.pi/2 + 0.0])\n",
      "#F = np.array([0.5, 0.1, np.pi/2 + 0.75*np.pi])\n",
      "#E = np.array([0.5, 0.1, 2*np.pi + 0.0])\n",
      "#F = np.array([0.5, 0.1, 2*np.pi + 0.75*np.pi])\n",
      "#E = np.array([0.5, 0.1, np.pi/4 + 0.0])\n",
      "#F = np.array([0.5, 0.1, np.pi/4 + 0.75*np.pi])\n",
      "#E = np.array([0.5, 0.1, np.pi*3/8 + 0.0])\n",
      "#F = np.array([0.5, 0.1, np.pi*3/8 + 0.75*np.pi])\n",
      "\n",
      "# r,z different - theta same\n",
      "#E = np.array([0.5, -1.0, 0.75*np.pi])\n",
      "#F = np.array([1.0, 1.0, 0.75*np.pi])\n",
      "\n",
      "# z-axis parallel\n",
      "#E = np.array([0.5, -1.0, 0.75*np.pi])\n",
      "#F = np.array([0.5, 1.0, 0.75*np.pi])\n",
      "\n",
      "# z-axis itself\n",
      "#E = np.array([0.0, -1.0, 0.0])\n",
      "#F = np.array([0.0, 1.0, 0.0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "left = pf.h.grid_left_edge\n",
      "right = pf.h.grid_right_edge\n",
      "t, s, rztheta, inds = clyindrical_ray_trace(E, F, left, right)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(8,6))\n",
      "plt.plot(t, rztheta, figure=fig)\n",
      "plt.legend(['r', 'z', '$\\\\theta$'], loc=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "<matplotlib.legend.Legend at 0x7f978593ac90>"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAe4AAAFtCAYAAADBM4kgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAMBBCG4AAByE4AYAwEH+P/zxRrqIjH7NAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f9785be6290>"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print len(inds)\n",
      "t"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "412\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 75,
       "text": [
        "array([ 0.0055586 ,  0.00997515,  0.01306679,  0.01307315,  0.02138298,\n",
        "        0.02275872,  0.03332344,  0.0392    ,  0.04249134,  0.0500059 ,\n",
        "        0.05530442,  0.06615628,  0.06720359,  0.07797843,  0.07797843,\n",
        "        0.07942409,  0.08579146,  0.08641293,  0.08693864,  0.08716721,\n",
        "        0.0904    ,  0.11253286,  0.11635683,  0.11924577,  0.12387138,\n",
        "        0.12443203,  0.12673689,  0.13950242,  0.1416    ,  0.15019995,\n",
        "        0.15157569,  0.15328957,  0.16080412,  0.1697613 ,  0.17233526,\n",
        "        0.18166047,  0.18354963,  0.18354963,  0.18354963,  0.19022231,\n",
        "        0.19259192,  0.1928    ,  0.1928    ,  0.19773687,  0.20216917,\n",
        "        0.21460843,  0.21598418,  0.21960179,  0.22542475,  0.22698974,\n",
        "        0.22715506,  0.23292845,  0.23466961,  0.2349121 ,  0.2388889 ,\n",
        "        0.24294068,  0.244     ,  0.24568141,  0.25321681,  0.25792677,\n",
        "        0.26368774,  0.2640878 ,  0.26567139,  0.26723543,  0.27160235,\n",
        "        0.27768456,  0.27851424,  0.27901691,  0.27921249,  0.27921249,\n",
        "        0.28039266,  0.28421818,  0.28435383,  0.28627597,  0.28755569,\n",
        "        0.29269192,  0.29444703,  0.2952    ,  0.29611734,  0.29643068,\n",
        "        0.2987709 ,  0.29926849,  0.30102054,  0.30398135,  0.3085351 ,\n",
        "        0.31427585,  0.31741299,  0.31933513,  0.3208    ,  0.32085243,\n",
        "        0.32520632,  0.32718997,  0.33160373,  0.33434098,  0.33585979,\n",
        "        0.33795329,  0.34144661,  0.34243636,  0.3434254 ,  0.34480114,\n",
        "        0.34546784,  0.3464    ,  0.34939092,  0.35047215,  0.35186039,\n",
        "        0.35203919,  0.35239429,  0.35334578,  0.3559656 ,  0.35744372,\n",
        "        0.35794925,  0.36402029,  0.36515337,  0.36708914,  0.36973741,\n",
        "        0.372     ,  0.37488603,  0.37902765,  0.37990885,  0.38240058,\n",
        "        0.38353131,  0.38410451,  0.38469323,  0.38478736,  0.38545345,\n",
        "        0.38560423,  0.38672489,  0.38743563,  0.38870854,  0.3976    ,\n",
        "        0.3976    ,  0.3976    ,  0.3976    ,  0.39867505,  0.39885998,\n",
        "        0.40061159,  0.40248558,  0.40305565,  0.40494988,  0.40513385,\n",
        "        0.40718816,  0.40783388,  0.40920963,  0.41057422,  0.41181877,\n",
        "        0.41435391,  0.41659047,  0.41781112,  0.41851261,  0.41933333,\n",
        "        0.41946783,  0.4201838 ,  0.42179688,  0.42200678,  0.42219552,\n",
        "        0.42283207,  0.4232    ,  0.42451135,  0.4287721 ,  0.43195432,\n",
        "        0.43466878,  0.43676226,  0.43788202,  0.44053029,  0.44095792,\n",
        "        0.44109264,  0.44211176,  0.44339917,  0.44377946,  0.44482622,\n",
        "        0.44721931,  0.44875152,  0.44875161,  0.4488    ,  0.4488    ,\n",
        "        0.4488    ,  0.44888498,  0.44964962,  0.45035603,  0.45157177,\n",
        "        0.45221941,  0.4522692 ,  0.45257175,  0.45410405,  0.45498366,\n",
        "        0.45558023,  0.45571339,  0.45590349,  0.45626607,  0.45792419,\n",
        "        0.45803937,  0.45945649,  0.45990906,  0.46001176,  0.46021103,\n",
        "        0.46069567,  0.46242663,  0.46266291,  0.46327663,  0.46334619,\n",
        "        0.4634756 ,  0.46421881,  0.46480893,  0.4651411 ,  0.46536339,\n",
        "        0.46544284,  0.46590387,  0.46625554,  0.46780266,  0.46822278,\n",
        "        0.46862907,  0.46903547,  0.46991166,  0.47016136,  0.47091981,\n",
        "        0.47097097,  0.47100271,  0.47113854,  0.47159672,  0.4718154 ,\n",
        "        0.47192834,  0.47193996,  0.47224236,  0.47234646,  0.47258407,\n",
        "        0.47301479,  0.47335499,  0.47361811,  0.47377311,  0.47378264,\n",
        "        0.47386371,  0.47398151,  0.4744    ,  0.4744    ,  0.4744    ,\n",
        "        0.47447297,  0.47459533,  0.47466453,  0.47478164,  0.47519976,\n",
        "        0.47529854,  0.4755138 ,  0.4756045 ,  0.47590753,  0.47620829,\n",
        "        0.47656257,  0.47662642,  0.47675645,  0.47695936,  0.47728956,\n",
        "        0.47737526,  0.47763494,  0.4779945 ,  0.47842107,  0.47880027,\n",
        "        0.47886019,  0.47933395,  0.47934251,  0.47945622,  0.47964919,\n",
        "        0.47988279,  0.48091794,  0.48129735,  0.48134451,  0.48169301,\n",
        "        0.48226531,  0.48237965,  0.48254193,  0.48270878,  0.48274151,\n",
        "        0.48280622,  0.4829824 ,  0.48384137,  0.48439441,  0.48458575,\n",
        "        0.48492752,  0.48514329,  0.48543467,  0.48559975,  0.4856764 ,\n",
        "        0.48568426,  0.48589217,  0.48642528,  0.48664105,  0.48694732,\n",
        "        0.48699019,  0.48717417,  0.48719418,  0.4872    ,  0.4872    ,\n",
        "        0.4872    ,  0.4872    ,  0.48738994,  0.48747849,  0.48792305,\n",
        "        0.48828765,  0.48832741,  0.48867525,  0.48872354,  0.48981701,\n",
        "        0.49025291,  0.49037123,  0.49126863,  0.49134638,  0.49178227,\n",
        "        0.49182419,  0.49204157,  0.49259713,  0.49268303,  0.49281451,\n",
        "        0.49287574,  0.49319881,  0.49331164,  0.49337007,  0.49339209,\n",
        "        0.4935239 ,  0.49358745,  0.4936    ,  0.4936    ,  0.4936    ,\n",
        "        0.4936    ,  0.49361567,  0.49414301,  0.49436039,  0.49436809,\n",
        "        0.49440511,  0.494841  ,  0.49491596,  0.49593447,  0.49637037,\n",
        "        0.49672652,  0.4968    ,  0.4968    ,  0.4968    ,  0.4968    ,\n",
        "        0.49746384,  0.49837588,  0.4984    ,  0.4984    ,  0.4984    ,\n",
        "        0.4984    ,  0.4992    ,  0.4992    ,  0.5       ,  0.5       ,\n",
        "        0.5       ,  0.5       ,  0.5       ,  0.5       ,  0.5008    ,\n",
        "        0.5008    ,  0.5016    ,  0.5016    ,  0.5016    ,  0.5016    ,\n",
        "        0.5016    ,  0.5032    ,  0.5032    ,  0.5032    ,  0.50451887,\n",
        "        0.5048    ,  0.5064    ,  0.5064    ,  0.5064    ,  0.5064    ,\n",
        "        0.5078533 ,  0.50853126,  0.5096    ,  0.5096    ,  0.5128    ,\n",
        "        0.5128    ,  0.5128    ,  0.51313193,  0.51510783,  0.51564564,\n",
        "        0.5192    ,  0.5192    ,  0.522617  ,  0.5250311 ,  0.5256    ,\n",
        "        0.5256    ,  0.5256    ,  0.5256    ,  0.53011519,  0.53013155,\n",
        "        0.53665085,  0.53669176,  0.53802659,  0.5384    ,  0.5512    ,\n",
        "        0.5512    ,  0.55169912,  0.55954974,  0.5768    ,  0.5768    ,\n",
        "        0.5768    ,  0.58225954,  0.60105933,  0.6024    ,  0.6024    ,\n",
        "        0.6024    ,  0.60243508,  0.6536    ,  0.66546781,  0.66684356,\n",
        "        0.7048    ,  0.7048    ,  0.7298763 ,  0.8072    ,  0.8072    ,\n",
        "        0.9096    ,  0.9096    ])"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}