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Anthony Scopatz committed c8656b1

r,z,theta ray trace now working for test points.

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  • Parent commits 0fef5e2

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cylindrical_rays1.ipynb

        "output_type": "stream",
        "stream": "stderr",
        "text": [
-        "yt : [WARNING  ] 2012-08-29 13:22:20,156 integer runtime parameter checkpointfilenumber overwrites a simulation scalar of the same name\n"
+        "yt : [WARNING  ] 2012-08-30 14:39:51,212 integer runtime parameter checkpointfilenumber overwrites a simulation scalar of the same name\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stderr",
        "text": [
-        "yt : [WARNING  ] 2012-08-29 13:22:20,156 integer runtime parameter forcedplotfilenumber overwrites a simulation scalar of the same name\n"
+        "yt : [WARNING  ] 2012-08-30 14:39:51,212 integer runtime parameter forcedplotfilenumber overwrites a simulation scalar of the same name\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stderr",
        "text": [
-        "yt : [WARNING  ] 2012-08-29 13:22:20,157 integer runtime parameter nbegin overwrites a simulation scalar of the same name\n"
+        "yt : [WARNING  ] 2012-08-30 14:39:51,213 integer runtime parameter nbegin overwrites a simulation scalar of the same name\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stderr",
        "text": [
-        "yt : [WARNING  ] 2012-08-29 13:22:20,157 integer runtime parameter plotfilenumber overwrites a simulation scalar of the same name\n"
+        "yt : [WARNING  ] 2012-08-30 14:39:51,213 integer runtime parameter plotfilenumber overwrites a simulation scalar of the same name\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stderr",
        "text": [
-        "yt : [INFO     ] 2012-08-29 13:22:20,166 Parameters: current_time              = 8.00057343882e-10\n"
+        "yt : [INFO     ] 2012-08-30 14:39:51,221 Parameters: current_time              = 8.00057343882e-10\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stderr",
        "text": [
-        "yt : [INFO     ] 2012-08-29 13:22:20,166 Parameters: domain_dimensions         = [48 96  1]\n"
+        "yt : [INFO     ] 2012-08-30 14:39:51,222 Parameters: domain_dimensions         = [48 96  1]\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stderr",
        "text": [
-        "yt : [INFO     ] 2012-08-29 13:22:20,167 Parameters: domain_left_edge          = [ 0.     -1.2288  0.    ]\n"
+        "yt : [INFO     ] 2012-08-30 14:39:51,223 Parameters: domain_left_edge          = [ 0.     -1.2288  0.    ]\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stderr",
        "text": [
-        "yt : [INFO     ] 2012-08-29 13:22:20,169 Parameters: domain_right_edge         = [ 1.2288      1.2288      6.28318531]\n"
+        "yt : [INFO     ] 2012-08-30 14:39:51,225 Parameters: domain_right_edge         = [ 1.2288      1.2288      6.28318531]\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stderr",
        "text": [
-        "yt : [INFO     ] 2012-08-29 13:22:20,170 Parameters: cosmological_simulation   = 0.0\n"
+        "yt : [INFO     ] 2012-08-30 14:39:51,227 Parameters: cosmological_simulation   = 0.0\n"
        ]
       }
      ],
-     "prompt_number": 128
+     "prompt_number": 1
     },
     {
      "cell_type": "code",
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 394
+     "prompt_number": 29
     },
     {
      "cell_type": "code",
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 415
+     "prompt_number": 30
     },
     {
      "cell_type": "code",
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 416
+     "prompt_number": 31
     },
     {
      "cell_type": "code",
       "\n",
       "tsec, intsec = intersect(a, b, c, d)\n",
       "tmask = np.logical_and(0.0<=tsec, tsec<=1.0)\n",
-      "rztheta = intsec[tmask]\n",
-      "s = np.sqrt(((tocart(rztheta) - Ecart)**2).sum(axis=1))\n",
+      "xyz = intsec[tmask]\n",
+      "s = np.sqrt(((xyz - Ecart)**2).sum(axis=1))\n",
       "t = s/np.sqrt((Dcart**2).sum())\n",
       "si = s.argsort()\n",
       "s = s[si]\n",
       "t = t[si]\n",
-      "rztheta = rztheta[si]"
+      "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"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 417
+     "prompt_number": 32
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "fig = plt.figure(figsize=(8,6))\n",
-      "plt.plot(t, rztheta[:,0], figure=fig)\n",
+      "plt.plot(t, rztheta, figure=fig)\n",
       "#plt.plot(t, s, figure=fig)\n",
       "#plt.plot(thetaleft, figure=fig)\n",
       "#plt.plot(t, dr_dt, 'g-', figure=fig)\n",
      "outputs": [
       {
        "output_type": "pyout",
-       "prompt_number": 414,
+       "prompt_number": 33,
        "text": [
-        "[<matplotlib.lines.Line2D at 0x11581d90>]"
+        "[<matplotlib.lines.Line2D at 0x7fa176fdc6d0>]"
        ]
       },
       {
        "output_type": "display_data",
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AysTAgYWDvG5duytyPkIcAFBm7r3XDK23aSN9/rlUp47dFTkbIQ4AKFP33Wd6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\nm/ZJgfP8Trnjjju0e/duHTt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/+QsQHg5MnCj3aJottawMz166hJBTp9CzbVtcDgnBUm9vtGVPcSK6Qya9xx0b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\n"
       }
      ],
-     "prompt_number": 414
+     "prompt_number": 33
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "np.arctan(np.array([-1.]) / np.array([0.0]))"
+      "s"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
-       "prompt_number": 406,
+       "prompt_number": 23,
        "text": [
-        "array([-1.57079633])"
+        "array([ 0.05974362,  0.1274708 ,  0.13626709,  0.21279055,  0.21735933,\n",
+        "        0.22692477,  0.2364902 ,  0.24605563,  0.2474015 ,  0.24778964,\n",
+        "        0.25218422,  0.25562107,  0.25696693,  0.26174965,  0.26490341,\n",
+        "        0.2651865 ,  0.26653237,  0.2669205 ,  0.2669205 ,  0.26729477,\n",
+        "        0.26968613,  0.27131508,  0.27207749,  0.27648594,  0.27648594,\n",
+        "        0.28051773,  0.28126865,  0.28126865,  0.28366001,  0.28605137,\n",
+        "        0.28605137,  0.28605137,  0.28605137,  0.28844273,  0.28931402,\n",
+        "        0.29083409,  0.29506001,  0.2956168 ,  0.2956168 ,  0.29625569,\n",
+        "        0.29745137,  0.29864705,  0.30039952,  0.30518224,  0.30518224,\n",
+        "        0.31474767,  0.31511239,  0.31750375,  0.31750375,  0.31869943,\n",
+        "        0.31989511,  0.31989511,  0.32109079,  0.32228647,  0.3243131 ,\n",
+        "        0.32467782,  0.32706918,  0.34344397,  0.35961179,  0.36583748,\n",
+        "        0.43356466,  0.44236095,  0.51265871,  0.51888441,  0.57373251,\n",
+        "        0.58661159,  0.59540787,  0.66570564,  0.67193134,  0.72677944,\n",
+        "        0.73965852,  0.7484548 ,  0.81875257,  0.82497827,  0.82497827,\n",
+        "        0.86324   ,  0.87982637,  0.89270545,  0.89270545,  0.90150173,\n",
+        "        0.90150173,  0.9026062 ,  0.93976346,  0.94086794,  0.96922891,\n",
+        "        0.9717995 ,  0.9780252 ,  0.9780252 ,  0.97912967,  1.01278301,\n",
+        "        1.01628693,  1.0173914 ,  1.03287329,  1.04575237,  1.04575237,\n",
+        "        1.05454866,  1.05565313,  1.08930647,  1.09391486,  1.12227584,\n",
+        "        1.12484643,  1.13107212,  1.16582994,  1.18592022,  1.1987993 ,\n",
+        "        1.1987993 ,  1.2423534 ,  1.27532277,  1.27789336,  1.31887687,\n",
+        "        1.33896715,  1.35184623,  1.39540033,  1.43094029,  1.49201408,\n",
+        "        1.50489316,  1.58398721,  1.64506101,  1.65794009,  1.73703414,\n",
+        "        1.79810794,  1.81098702,  1.89008107,  1.95115487,  2.043128  ,\n",
+        "        2.10420179,  2.25724872])"
        ]
       }
      ],
-     "prompt_number": 406
+     "prompt_number": 23
     },
     {
      "cell_type": "code",