Commits

Anthony Scopatz  committed 7d64152

Added ray tester

  • Participants
  • Parent commits 16698bb

Comments (0)

Files changed (2)

File cylindrical_rays1.ipynb

       "import matplotlib.pyplot as plt\n",
       "import numpy as np\n",
       "\n",
-      "pf = load('cylindrical_data/nif2013_hdf5_plt_cnt_0006')\\\n",
+      "pf = load('cylindrical_data/nif2013_hdf5_plt_cnt_0006')\n",
       "\n",
       "tocart = lambda x: np.array((x[...,0]*np.cos(x[...,2]), x[...,0]*np.sin(x[...,2]), x[...,1])).T\n",
       "\n",
        "output_type": "stream",
        "stream": "stderr",
        "text": [
-        "yt : [WARNING  ] 2012-08-30 18:46:01,717 integer runtime parameter checkpointfilenumber overwrites a simulation scalar of the same name\n"
+        "yt : [WARNING  ] 2012-09-05 14:27:54,074 integer runtime parameter checkpointfilenumber overwrites a simulation scalar of the same name\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stderr",
        "text": [
-        "yt : [WARNING  ] 2012-08-30 18:46:01,718 integer runtime parameter forcedplotfilenumber overwrites a simulation scalar of the same name\n"
+        "yt : [WARNING  ] 2012-09-05 14:27:54,074 integer runtime parameter forcedplotfilenumber overwrites a simulation scalar of the same name\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stderr",
        "text": [
-        "yt : [WARNING  ] 2012-08-30 18:46:01,719 integer runtime parameter nbegin overwrites a simulation scalar of the same name\n"
+        "yt : [WARNING  ] 2012-09-05 14:27:54,075 integer runtime parameter nbegin overwrites a simulation scalar of the same name\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stderr",
        "text": [
-        "yt : [WARNING  ] 2012-08-30 18:46:01,720 integer runtime parameter plotfilenumber overwrites a simulation scalar of the same name\n"
+        "yt : [WARNING  ] 2012-09-05 14:27:54,075 integer runtime parameter plotfilenumber overwrites a simulation scalar of the same name\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stderr",
        "text": [
-        "yt : [INFO     ] 2012-08-30 18:46:01,733 Parameters: current_time              = 8.00057343882e-10\n"
+        "yt : [INFO     ] 2012-09-05 14:27:54,080 Parameters: current_time              = 8.00057343882e-10\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stderr",
        "text": [
-        "yt : [INFO     ] 2012-08-30 18:46:01,734 Parameters: domain_dimensions         = [48 96  1]\n"
+        "yt : [INFO     ] 2012-09-05 14:27:54,081 Parameters: domain_dimensions         = [48 96  1]\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stderr",
        "text": [
-        "yt : [INFO     ] 2012-08-30 18:46:01,736 Parameters: domain_left_edge          = [ 0.     -1.2288  0.    ]\n"
+        "yt : [INFO     ] 2012-09-05 14:27:54,082 Parameters: domain_left_edge          = [ 0.     -1.2288  0.    ]\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stderr",
        "text": [
-        "yt : [INFO     ] 2012-08-30 18:46:01,738 Parameters: domain_right_edge         = [ 1.2288      1.2288      6.28318531]\n"
+        "yt : [INFO     ] 2012-09-05 14:27:54,083 Parameters: domain_right_edge         = [ 1.2288      1.2288      6.28318531]\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stderr",
        "text": [
-        "yt : [INFO     ] 2012-08-30 18:46:01,741 Parameters: cosmological_simulation   = 0.0\n"
+        "yt : [INFO     ] 2012-09-05 14:27:54,084 Parameters: cosmological_simulation   = 0.0\n"
        ]
       }
      ],
-     "prompt_number": 341
+     "prompt_number": 12
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "# Ray tracer\n",
+      "\n",
+      "# Everything different\n",
       "#E = np.array([0.5, -1.0, 0.0])\n",
-      "E = np.array([0.5, 0.0, 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",
-      "F = np.array([0.5, 0.0, 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])\n",
       "\n",
       "D = F - E\n",
       "\n",
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 355
+     "prompt_number": 13
     },
     {
      "cell_type": "code",
       "tpmright = np.logical_and(~np.isnan(tpright), rright <= F[0])\n",
       "\n",
       "tmmleft = np.logical_and(~np.isnan(tmleft), rleft <= E[0])\n",
-      "tpmleft = np.logical_and(~np.isnan(tpleft), rleft <= F[0])\n",
-      "\n",
-      "tleft = np.concatenate([tmleft[tmmleft][::-1], tpleft[tpmleft]])\n",
-      "tright = np.concatenate([tmright[tmmright][::-1], tpright[tpmright]])"
+      "tpmleft = np.logical_and(~np.isnan(tpleft), rleft <= F[0])"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 356
+     "prompt_number": 47
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "ind = np.unique(np.concatenate([np.argwhere(tmmleft).flat, np.argwhere(tpmleft).flat, np.argwhere(tmmright).flat, np.argwhere(tpmright).flat,]))\n",
+      "\n",
+      "thetaleft = np.arctan2((Ecart[1] + tmleft[ind]*Dcart[1]), (Ecart[0] + tmleft[ind]*Dcart[0]))\n",
+      "nans = np.isnan(thetaleft)\n",
+      "thetaleft[nans] = np.arctan2((Ecart[1] + tpleft[ind[nans]]*Dcart[1]), (Ecart[0] + tpleft[ind[nans]]*Dcart[0]))\n",
+      "\n",
+      "thetaright = np.arctan2((Ecart[1] + tmright[ind]*Dcart[1]), (Ecart[0] + tmright[ind]*Dcart[0]))\n",
+      "nans = np.isnan(thetaleft)\n",
+      "thetaright[nans] = np.arctan2((Ecart[1] + tpright[ind[nans]]*Dcart[1]), (Ecart[0] + tpright[ind[nans]]*Dcart[0]))\n",
+      "\n",
+      "#thetaleft += np.pi*3/2\n",
+      "#thetaright += np.pi*3/2\n",
+      "\n",
       "if 0 == len(ind):\n",
       "    print \"Ind len zero\"\n",
-      "    ind = np.arange(len(rleft))\n",
+      "    I = len(rleft)\n",
+      "    ind = np.arange(I)\n",
+      "    thetaleft = np.empty(I)\n",
+      "    thetaleft.fill(E[2])\n",
+      "    thetaright = np.empty(I)\n",
+      "    thetaleft.fill(F[2])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 48
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#_ = np.concatenate([rleft[ind], zright[ind], thetaleft, rleft[ind], zleft[ind], thetaleft])\n",
+      "_ = np.concatenate([rleft[ind],  rleft[ind], zright[ind],zleft[ind], thetaleft, thetaleft])\n",
       "\n",
-      "thetaleft = np.arctan((Ecart[1] + tmleft[ind]*Dcart[1])/(Ecart[0] + tmleft[ind]*Dcart[0]))\n",
-      "nans = np.isnan(thetaleft)\n",
-      "thetaleft[nans] = np.arctan((Ecart[1] + tpleft[ind[nans]]*Dcart[1])/(Ecart[0] + tpleft[ind[nans]]*Dcart[0]))\n",
-      "\n",
-      "thetaright = np.arctan((Ecart[1] + tmright[ind]*Dcart[1])/(Ecart[0] + tmright[ind]*Dcart[0]))\n",
-      "nans = np.isnan(thetaleft)\n",
-      "thetaright[nans] = np.arctan((Ecart[1] + tpright[ind[nans]]*Dcart[1])/(Ecart[0] + tpright[ind[nans]]*Dcart[0]))\n",
-      "\n",
+      "_.shape = (3, 2*len(thetaleft))\n",
+      "print _\n",
+      "print\n",
+      "print [rleft[ind][0], zright[ind][0], thetaleft[0]]\n",
+      "print\n",
+      "print [rleft[ind][-1], zleft[ind][-1], thetaleft[-1]]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[[ 0.          0.          0.         ...,  0.2048      0.4096      0.4096    ]\n",
+        " [-1.024      -1.1264     -1.1776     ...,  1.024       0.8192      1.024     ]\n",
+        " [ 0.          0.          0.         ..., -1.57079633 -1.57079633\n",
+        "  -1.57079633]]\n",
+        "\n",
+        "[0.0, -1.024, 0.0]\n",
+        "\n",
+        "[0.40959999999999996, 1.024, -1.5707963267948968]\n"
+       ]
+      }
+     ],
+     "prompt_number": 50
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
       "a = E\n",
       "b = F\n",
       "\n",
       "ind = np.append(ind, origind)\n",
       "\n",
       "tsec, intsec = intersect(a, b, c, d)\n",
+      "print tsec\n",
+      "print np.isnan(tsec).all(), len(tsec)\n",
       "tmask = np.logical_and(0.0<=tsec, tsec<=1.0)\n",
-      "print np.isnan(tsec).all(), len(tsec)\n",
       "tsec, utmask = np.unique(tsec[tmask], return_index=True)\n",
+      "print tsec.max(), tsec.ptp(), len(utmask)\n",
       "ind = ind[tmask][utmask]\n",
       "xyz = intsec[tmask][utmask]\n",
       "s = np.sqrt(((xyz - Ecart)**2).sum(axis=1))\n",
       "t = t[si]\n",
       "ind = ind[si]\n",
       "xyz = xyz[si]\n",
-      "rztheta = np.array([np.sqrt(xyz[:,0]**2 + xyz[:,1]**2), xyz[:,2], np.arctan(xyz[:,1]/xyz[:,0])]).T\n",
-      "theta = rztheta[:,2]\n",
-      "rztheta[theta < 0,2] = theta[theta < 0] + np.pi"
+      "rztheta = np.array([np.sqrt(xyz[:,0]**2 + xyz[:,1]**2), xyz[:,2], np.arctan2(xyz[:,1], xyz[:,0])]).T\n",
+      "rztheta[:,2] = 0.0 + (rztheta[:,2] - np.pi*3/2)%(2*np.pi)"
      ],
      "language": "python",
      "metadata": {},
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "(201616,) (201616, 3)\n",
-        "False"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        " 201616\n"
+        "[  5.94409496e+15   1.63312394e+15   9.46599833e+14 ...,              nan\n",
+        "              nan              nan]\n",
+        "False 201616\n",
+        "0.9992 0.9992 957\n"
        ]
       }
      ],
-     "prompt_number": 357
+     "prompt_number": 7
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "len(ind)\n",
       "t"
      ],
      "language": "python",
      "outputs": [
       {
        "output_type": "pyout",
-       "prompt_number": 358,
+       "prompt_number": 10,
        "text": [
-        "array([ 0.    ,  0.012 ,  0.1144,  0.1144,  0.3192])"
+        "array([  0.00000000e+00,   5.55111512e-17,   1.11022302e-16,\n",
+        "         3.20000000e-03,   4.80000000e-03,   6.40000000e-03,\n",
+        "         6.40000000e-03,   6.40000000e-03,   8.00000000e-03,\n",
+        "         9.60000000e-03,   1.28000000e-02,   1.28000000e-02,\n",
+        "         1.28000000e-02,   1.60000000e-02,   1.92000000e-02,\n",
+        "         2.56000000e-02,   2.56000000e-02,   2.56000000e-02,\n",
+        "         3.20000000e-02,   3.84000000e-02,   5.12000000e-02,\n",
+        "         5.12000000e-02,   5.12000000e-02,   6.40000000e-02,\n",
+        "         7.68000000e-02,   9.04000000e-02,   9.04000000e-02,\n",
+        "         1.02400000e-01,   1.02400000e-01,   1.02400000e-01,\n",
+        "         1.28000000e-01,   1.41600000e-01,   1.53600000e-01,\n",
+        "         1.53600000e-01,   1.79200000e-01,   1.92800000e-01,\n",
+        "         1.92800000e-01,   2.04800000e-01,   2.04800000e-01,\n",
+        "         2.04800000e-01,   2.17600000e-01,   2.18400000e-01,\n",
+        "         2.30400000e-01,   2.30400000e-01,   2.36800000e-01,\n",
+        "         2.40000000e-01,   2.43200000e-01,   2.43200000e-01,\n",
+        "         2.43200000e-01,   2.44000000e-01,   2.44000000e-01,\n",
+        "         2.46400000e-01,   2.49600000e-01,   2.49600000e-01,\n",
+        "         2.52800000e-01,   2.54400000e-01,   2.56000000e-01,\n",
+        "         2.56000000e-01,   2.56000000e-01,   2.56000000e-01,\n",
+        "         2.56000000e-01,   2.56000000e-01,   2.57600000e-01,\n",
+        "         2.59200000e-01,   2.59200000e-01,   2.60800000e-01,\n",
+        "         2.62400000e-01,   2.62400000e-01,   2.62400000e-01,\n",
+        "         2.64000000e-01,   2.65600000e-01,   2.65600000e-01,\n",
+        "         2.67200000e-01,   2.68000000e-01,   2.68800000e-01,\n",
+        "         2.68800000e-01,   2.68800000e-01,   2.68800000e-01,\n",
+        "         2.68800000e-01,   2.69600000e-01,   2.69600000e-01,\n",
+        "         2.70400000e-01,   2.70400000e-01,   2.71200000e-01,\n",
+        "         2.72000000e-01,   2.72000000e-01,   2.72000000e-01,\n",
+        "         2.72800000e-01,   2.73600000e-01,   2.73600000e-01,\n",
+        "         2.74400000e-01,   2.75200000e-01,   2.75200000e-01,\n",
+        "         2.75200000e-01,   2.75200000e-01,   2.76000000e-01,\n",
+        "         2.76800000e-01,   2.76800000e-01,   2.77600000e-01,\n",
+        "         2.78400000e-01,   2.78400000e-01,   2.80000000e-01,\n",
+        "         2.81600000e-01,   2.81600000e-01,   2.81600000e-01,\n",
+        "         2.81600000e-01,   2.82400000e-01,   2.84800000e-01,\n",
+        "         2.88000000e-01,   2.94400000e-01,   2.94400000e-01,\n",
+        "         2.95200000e-01,   2.95200000e-01,   3.00800000e-01,\n",
+        "         3.07200000e-01,   3.07200000e-01,   3.07200000e-01,\n",
+        "         3.07200000e-01,   3.08000000e-01,   3.08000000e-01,\n",
+        "         3.14400000e-01,   3.20000000e-01,   3.20800000e-01,\n",
+        "         3.20800000e-01,   3.20800000e-01,   3.27200000e-01,\n",
+        "         3.30400000e-01,   3.32800000e-01,   3.33600000e-01,\n",
+        "         3.33600000e-01,   3.35200000e-01,   3.36800000e-01,\n",
+        "         3.36800000e-01,   3.38400000e-01,   3.38400000e-01,\n",
+        "         3.39200000e-01,   3.40000000e-01,   3.40000000e-01,\n",
+        "         3.40000000e-01,   3.40800000e-01,   3.41600000e-01,\n",
+        "         3.42400000e-01,   3.43200000e-01,   3.43200000e-01,\n",
+        "         3.44000000e-01,   3.44800000e-01,   3.44800000e-01,\n",
+        "         3.46400000e-01,   3.46400000e-01,   3.46400000e-01,\n",
+        "         3.49600000e-01,   3.49600000e-01,   3.52800000e-01,\n",
+        "         3.52800000e-01,   3.56000000e-01,   3.57600000e-01,\n",
+        "         3.58400000e-01,   3.58400000e-01,   3.59200000e-01,\n",
+        "         3.59200000e-01,   3.60800000e-01,   3.61600000e-01,\n",
+        "         3.62400000e-01,   3.62400000e-01,   3.63200000e-01,\n",
+        "         3.64000000e-01,   3.64800000e-01,   3.65600000e-01,\n",
+        "         3.66400000e-01,   3.67200000e-01,   3.67200000e-01,\n",
+        "         3.68000000e-01,   3.68000000e-01,   3.68800000e-01,\n",
+        "         3.68800000e-01,   3.68800000e-01,   3.69600000e-01,\n",
+        "         3.70400000e-01,   3.70400000e-01,   3.71200000e-01,\n",
+        "         3.72000000e-01,   3.72000000e-01,   3.72000000e-01,\n",
+        "         3.72800000e-01,   3.73600000e-01,   3.73600000e-01,\n",
+        "         3.74400000e-01,   3.75200000e-01,   3.75200000e-01,\n",
+        "         3.76000000e-01,   3.76000000e-01,   3.76800000e-01,\n",
+        "         3.77600000e-01,   3.77600000e-01,   3.78400000e-01,\n",
+        "         3.78400000e-01,   3.79200000e-01,   3.80000000e-01,\n",
+        "         3.80800000e-01,   3.81600000e-01,   3.81600000e-01,\n",
+        "         3.83200000e-01,   3.84000000e-01,   3.84800000e-01,\n",
+        "         3.84800000e-01,   3.86400000e-01,   3.86400000e-01,\n",
+        "         3.88000000e-01,   3.88000000e-01,   3.89600000e-01,\n",
+        "         3.89600000e-01,   3.91200000e-01,   3.91200000e-01,\n",
+        "         3.92800000e-01,   3.92800000e-01,   3.94400000e-01,\n",
+        "         3.94400000e-01,   3.96000000e-01,   3.96000000e-01,\n",
+        "         3.97600000e-01,   3.97600000e-01,   3.97600000e-01,\n",
+        "         3.99200000e-01,   3.99200000e-01,   4.00800000e-01,\n",
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+        "         4.04000000e-01,   4.04000000e-01,   4.05600000e-01,\n",
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+        "         9.67200000e-01,   9.67200000e-01,   9.70400000e-01,\n",
+        "         9.70400000e-01,   9.72800000e-01,   9.72800000e-01,\n",
+        "         9.73600000e-01,   9.73600000e-01,   9.73600000e-01,\n",
+        "         9.73600000e-01,   9.76800000e-01,   9.76800000e-01,\n",
+        "         9.80000000e-01,   9.80000000e-01,   9.80000000e-01,\n",
+        "         9.83200000e-01,   9.83200000e-01,   9.86400000e-01,\n",
+        "         9.86400000e-01,   9.86400000e-01,   9.89600000e-01,\n",
+        "         9.89600000e-01,   9.92800000e-01,   9.92800000e-01,\n",
+        "         9.96000000e-01,   9.96000000e-01,   9.98400000e-01,\n",
+        "         9.99200000e-01,   9.99200000e-01,   9.99200000e-01])"
        ]
       }
      ],
-     "prompt_number": 358
+     "prompt_number": 10
     },
     {
      "cell_type": "code",
      "outputs": [
       {
        "output_type": "pyout",
-       "prompt_number": 359,
+       "prompt_number": 11,
        "text": [
-        "<matplotlib.legend.Legend at 0x7fa1598f8510>"
+        "<matplotlib.legend.Legend at 0x91d9a90>"
        ]
       },
       {
        "output_type": "display_data",
-       "png": 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DB01Yezyme9nkyYQ1gO5BSAMdcOuWtG+ftHy5WS5z3Tpp/HjbVQEIdXQcAzogMtKsrFVT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\nYII=\n"
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xwXcEGzdulNPp1PDhw2NSGAAA6SjoMebq6mq1traqq6tLo0aNUl1dnfr7+yVJNTU1Wr9+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\nEMwAABiEYAYAwCAEMwAABvk/zTUPKPr4LMQAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x3ec72d0>"
+       ]
       }
      ],
-     "prompt_number": 359
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pf.h.ray("
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 11
     },
     {
      "cell_type": "code",

File 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": 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G8+3bZ5Yw7dhRSkkJzqWaAC/98EPpCWWFhSaof/Yz6fXXg/966ktBO9xZ0tPT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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",
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+        "        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": {}
+  }
+ ]
+}