{
 "metadata": {
  "name": "",
  "signature": "sha256:75ae1af23b9776e6b39810605179bb56818b0034b20faba7a21e7cbcc9b80d1a"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "from scipy.integrate import odeint\n",
      "from IPython.html.widgets import interact, interactive, fixed\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import Toomre as Toomre"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Toomre.Generate_Data()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = interact(Toomre.Make_Plots,results = fixed(np.load('Toomre_A.npy')), t = (0.1,20.1), dt = fixed(0.0075))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dyH1VxWj2tm3V+2ZUfl51BVWUn1/r65I0e9s25S5cyFWWh9HDAnzs6aeljAzp\n/PPdTuIdM4NBPVRUVG9/bjCoWYWFkqS8zEzlrV1b7zV5w4Ypz6dXPYmCHhaQoH7+c7cTeE/K0aNh\n9yeXllZ/XtaqVdjXlKemxiUTYoMelkOMOZtFbtvOnTF9u4gS4XxHU4xGTJmiGYFAra9PDwSUNXly\nXPJFkgjn2ySusAAP++c/pdxcae1a6d13pdNOczuR942YMkUztm2r1aOaHghoZI1iVNWnyl24UMml\npSpPTdXIyZPpX3kcPSzAg/bssWf7vfiidOedUk4Oz6hyorigQKtrFKMsipFv8DwswEcKC6XrrpNu\nvtm+r+r0091OBJjD87BiiDFns5pj7iFDpH/8Q3rkEfPFyuvnO9L9VV7PHQm5naGHBXhMu3b2B2pr\n6P4qnXyyS6lgEkOCgAu+/VZ64gl7ZYpLLnE7jT9Ec38V/I8hQcAjDh2yh/oCAfsZVV27up3IP6K5\nvwqJjYLlEGPOZiVK7tJS6dFH7UL19tvSa69JS5dKPXq4ky8SL5/vhu6v8nLuhpDbGQoWYIBlSR9+\naD/td9kyqU8ftxP5j1du9oV76GEB8A3ur0p83IcFGHLwoPTppw0/hh5AZEy6iCHGnM3yS+4vvpCm\nT7d7VH/5i39y10Vus8jtDAULaII9e6SpU6XzzpO++sqeUPHAA26nal6KCwq06O67Iz6sEYmDIUGg\nCX70I/teqrvuks4+2+00zU/Ym4kDAQUXLFBGdraKCwpUlJ+vlKNHVdaqlUZMmULPy+N4HhYQJ8XF\nUgvGKVzT0JODJZ3wycPwF/5Tc4gxZ7O8kvurr8Lvj1SsvJLbKb/lrrqZOFRnf3JpacRitrqymHmB\n3853FXpYgMdYln0Fddll0o9/bG/DWxq6mZiVMRJPXApWXl6eunTpovT0dKWnp6swgdb5yszMdDtC\no5A7ehUV0ksvSRdfbD/iY8wY6f/+T0pKiv49ON9mVN1MnFljX9XNxNE8edhtfjvfVdzKHZceVlJS\nkqZOnaqpU6fG4+2BuLr5ZmnrVunee+1ilZzsdiJEkpGdrXc3btQ1Tzyh1mVlOpKSomHXX1/dozrR\nk4fhL3EbEkzUGYCMOZvlRu7HHpM2bpR++tPGFyvOtxnFBQXa/fzzunX/fi3++mst279fu59/XsUF\nBcrIzlZwwQLlBoPKGzZMucGgRlbOHvQKv53vKgn3PKyFCxfqueee08CBAzV//ny14wE/8JjSUinc\n6FD79uZzHD+6AAATXklEQVSzoHGqJlaEauyrmiWYkZ1d/YHE0OiClZWVpZKSknr7Z8+erVtvvVW/\n/OUvJUm5ubm66667tGjRonqvHT9+vNLS0iRJ7dq1U//+/avHRqsqONux2a7a55U8bm7v2CHdeWdI\nb7wh7dqVqdRUzrdft2tOrAhJ1b2sXSUlnP84blfti8X7hUIhLV68WJKq60Ekcb9xeOfOnRo9erS2\nbt1a+8DcOAzD3nlHevhhqahIuuUWKSdH6tTJ7VRoCh7qmHiMryW4d+/e6s9XrFihvn37xuMwrqj6\ny8BvmnvuefOkUaOk9HRp+3Zp7tz4Fqvmfr5NqZolGKqxz0+PHPHb+a7iVu649LCmTZumLVu2KCkp\nSd26ddMzzzwTj8MAUbvpJumOO8L3rOBfVf2p3z3wgEInnaTy1FSN5JEjCYu1BJFQIk2kAOAPPF4E\nCW/XLum//ks65xzpyy/dTgMgHihYDjHmbNaJcm/aJF13nf3ARMuS3npLOuMMM9kakqjn26vIbVZC\n9bAAExYulB55xJ7t9+ST0qmnup2o+eIxHjCBHhZ86+uvpZNOklq2dDtJ83aiZ1IBTtDDgq99/XX4\n/aeeSrHyAj88xgOJgYLlEGPO5mzbJo0ZE1JamrRzp9tpnPHj+ZYal9sLj/EIl7u4oEAzg0HlZWZq\nZjCo4oICY3mi1Zx+T2KBHhY8xbKkN96Qfv1rae1aaeRI6f33pc6d3U6GSLz4GI+ww5Q8bdj36GHB\nU5591l4+KSdHGj9eatPG7UQ4kXDFYXog4OrK6CzZ5F8N1QausOAp118vTZgQ+dHz8J6qopS7cKGS\nS0s9sdqEF4YpEXv8b8Ehxpxj4+OP7Sf71pWaWrtYeS13tJpb7ozsbM0qLFReKKRZhYXGi1Xd3F4c\npgynuf2eNBUFC8ZYlr1S+mWXSUOH2pMqgHioWhS3Jj8tiovw6GEh7o4ckf77v6XHH5eSkqQ775TG\njWPNP8RXcUGBVtcYpsxiUVxfaKg2ULAQd3/8o7Rsmb1a+o9/bBctAAiHG4djiDFn5264QXr5ZenS\nS50XK863WeQ2i9zOULAQE+Xl0sqV9uM9ACAeGBJEk3zzjfT730v5+dLpp0svvCCde67bqQD4Ffdh\nIeZ27bInUSxebPel/vhH6Yc/pD/lVaymjkTAkKBDjDnbPvzQLk6bNkl//rN08cXxKVac76arWoni\noaIi5a1dq4eKirQqJyfs2npeyu0Euc2ihwVfycqSHn1U+v733U6CE2E1dSQKeliIaNcu6emnpalT\n7f4U/CkvM1N5a9fW3z9smPJ8+hc+EhfT2hE1y5Jef126+mr7sfPffmvPAIR/+WWZIuBEKFgOJfKY\n87p10sCB9uKzQ4faz6BasEDq0CHu8SJK5PNtipNliryU2wlym8XzsOC6006TZs2yn0HFaumJw4ur\nqQONQQ8LAOAZ9LAgSTp6VHr+eWnIEGn7drfTIBp+eMw7YAoFyyE/jjmXlEjjx4eUliYtWSLNmOGf\n6eh+PN9SbHI7uX8qVprz+XYDuZ2hYCW4P/1J6t1bOnBA+tvfpNWrpdGjpeRkt5PhRLx4/xRXfHAT\nPawE9+WX9gSK005zOwmc8tr9U1VXfDWL6IxAQMEFC5jAgZihh9UMHDgQfv8ZZ1CsvCbaqxSv3T/l\nxSs+NC8ULIe8Nua8aZN0001SICB9+mnk13ktd7QSLbeTvpQbj3lv6HynHD0adn+yB54pk2i/J17H\nfViI2rFj0osvSk88IX32mXTbbdI//8nySX4Q6Sold+HCesNqXrt/ymtXfGh+6GH50OOPS3/9qzR5\nsnT55VIKf3Z4Ws1He+x45x0tOXiw3muc9qXceFxIuB7W9EBAI+lhIYZ4HlaCycmR7rjD7RSIRt3/\nyc+M8Lovvvmm0e8pSTMqP49n4fDaFR+aIcslLh66SdasWWPkOIcOWdZzz1lWWVls3s9U7ljze+4Z\nI0ZYlr2msGVJ1lrJml5j25Ks+yRrQqdO1tqXX47qveu+Z9XHzGAwZrn9htxmxTN3Q7WBSRces327\n9F//JZ1zjrR8ufTVV24nQlPUnaiQISkoaaykPEm5kkZKWlRSEvVsOy9PfgDiiYLlUGZmZlze9803\n7Rt6Bw2yn9y7caP0v/9rT0uPhXjljje/5w43USFDUnfZBWtW5bYUfcGJ5+QHv59vvyG3MxQsj9i/\nX7rySulf/5J+9SupWze3EyEWwk1Nn9S6tbLCvDbaguPGdHfAC5gl6FAoFPLlX0XkNqtm7uKCAq2u\nMVGh85Ah2v38802abVf3PbNiNPkhEc63n5C7PmYJekBZmT3E99xz0tKlUuvWbieCKRnZ2fWKSfFF\nFzVptl249wQSHVdYcfbFF9Kzz0pPPWVPpPjFL6T/+A+pZUu3kwGA97CWoEsef1zq2dNeheKll6TX\nX5euvZZiBQCNQcFyyMkaWpdfbherRYukAQPilykarFlmFrnNIrdZrCXoY//+t3TKKfX3d+9uPgsA\nJCp6WI1kWdLatfYCtP/3f/YNv6wBCgBNQw8rhg4dkp55RrrwQunWW6Xhw6UPP6RYAUC8UbAcGjcu\npMJCe0LF++9Lt98efjjQaxgrN4vcZpHbLHpYPpGTI11yidspAKD5oYcFAPAMelgAAN+jYDnEmLNZ\n5DaL3GaR2xkKFgDAF+hhAQA8gx4WAMD3KFgOMeZsFrnNIrdZ5Ham0QVr+fLl6tOnj5KTk7Vp06Za\nX5s7d6569OihXr16qaioqMkhAQBodA/rww8/VIsWLTRp0iTNnz9fAyqXI3///fc1btw4bdy4Ubt3\n79all16qjz/+WC1a1K6N9LAAAHXFpYfVq1cv9ezZs97+lStXauzYsWrZsqXS0tLUvXt3bdiwobGH\nAQBAUhx6WHv27FGXLl2qt7t06aLdu3fH+jCuYczZLHKbRW6zyO1Mg2sJZmVlqaSkpN7+OXPmaPTo\n0VEfJCkpKez+8ePHKy0tTZLUrl079e/fX5mZmZKOnxCvbVfxSp5ot7ds2eKpPJxvb25X8Uoezrc3\nt2N5vkOhkBYvXixJ1fUgkibfhzV8+PBaPax58+ZJku69915J0siRI/XAAw9o8ODBtQ9MDwsAUEfc\n78Oq+eZXXHGFXnjhBR07dkw7duzQJ598okGDBsXiMACAZqzRBWvFihXq2rWr1q9fr+zsbI0aNUqS\ndP755+vqq6/W+eefr1GjRunJJ5+MOCToR3Uv5f2C3GaR2yxym+VW7kY/D2vMmDEaM2ZM2K9Nnz5d\n06dPb3QoAADqYi1BAIBnsJYgAMD3KFgOMeZsFrnNIrdZ5HaGggUA8AV6WAAAz6CHBQDwPQqWQ4w5\nm0Vus8htFrmdoWABAHyBHhYAwDPoYQEAfI+C5RBjzmaR2yxym0VuZyhYAABfoIcFAPAMelgAAN+j\nYDnEmLNZ5DaL3GaR2xkKFgDAF+hhAQA8gx4WAMD3KFgOMeZsFrnNIrdZ5HaGggUA8AV6WAAAz6CH\nBQDwPQqWQ4w5m0Vus8htFrmdoWABAHyBHhYAwDPoYQEAfI+C5RBjzmaR2yxym0VuZyhYAABfoIcF\nAPAMelgAAN+jYDnEmLNZ5DaL3GaR2xkKFgDAF+hhAQA8gx4WAMD3KFgOMeZsFrnNIrdZ5HaGggUA\n8AV6WAAAz6CHBQDwPQqWQ4w5m0Vus8htFrmdoWABAHyBHhYAwDPoYQEAfI+C5RBjzmaR2yxym0Vu\nZyhYAABfoIcFAPAMelgAAN+jYDnEmLNZ5DaL3GaR2xkKFgDAF+hhAQA8gx4WAMD3KFgOMeZsFrnN\nIrdZ5HaGggUA8AV6WAAAz6CHBQDwvUYXrOXLl6tPnz5KTk7Wpk2bqvfv3LlTrVu3Vnp6utLT03Xb\nbbfFJKhXMOZsFrnNIrdZ5HYmpbHf2LdvX61YsUKTJk2q97Xu3btr8+bNTQoGAEBNTe5hDR8+XPPn\nz9eAAQMk2VdYo0eP1tatWxs+MD0sAEAdxntYO3bsUHp6ujIzM/X666/H4xAAgGamwSHBrKwslZSU\n1Ns/Z84cjR49Ouz3nHXWWdq1a5fat2+vTZs26corr9R7772ntm3b1nvt+PHjlZaWJklq166d+vfv\nr8zMTEnHx0i9tl21zyt5ot1+/PHHfXF+Od/ublft80oezrc3t2N5vkOhkBYvXixJ1fUgIquJMjMz\nrbffftvx12NwaFesWbPG7QiNQm6zyG0Wuc2KZ+6GakNMeliPPvqofvCDH0iSvvzyS7Vv317Jycna\nvn27MjIy9O6776pdu3a1vo8eFgCgrrj0sFasWKGuXbtq/fr1ys7O1qhRoyRJa9euVb9+/ZSenq6r\nrrpKzzzzTL1iBQCAU6x04VAoFKoeh/UTcptFbrPIbVY8c7PSBQDA97jCAgB4BldYAADfo2A5VPP+\nCT8ht1nkNovcZrmVm4Ll0JYtW9yO0CjkNovcZpHbLLdyU7AcOnjwoNsRGoXcZpHbLHKb5VZuChYA\nwBcoWA7t3LnT7QiNQm6zyG0Wuc1yK7dr09r79++vd955x41DAwA8atiwYREndbhWsAAAcIIhQQCA\nL1CwAAC+QMECAPiC8YK1fPly9enTR8nJydq0aVOtr82dO1c9evRQr169VFRUZDpa1DZs2KBBgwYp\nPT1dF110kTZu3Oh2pKgtXLhQvXv31gUXXKBp06a5HceR+fPnq0WLFjpw4IDbUaJy9913q3fv3urX\nr59+8pOf6Ouvv3Y7UoMKCwvVq1cv9ejRQw8//LDbcaKya9cuDR8+XH369NEFF1yg/Px8tyNFrby8\nXOnp6RGf3u5VBw8e1E9/+lP17t1b559/vtavX2/u4HF7bGQEH3zwgfXRRx/VexLxe++9Z/Xr1886\nduyYtWPHDisQCFjl5eWm40Vl2LBhVmFhoWVZlvXKK69YmZmZLieKzmuvvWZdeuml1rFjxyzLsqzP\nP//c5UTR+9e//mUFg0ErLS3N2r9/v9txolJUVFT9Ozxt2jRr2rRpLieKrKyszAoEAtaOHTusY8eO\nWf369bPef/99t2Od0N69e63NmzdblmVZ33zzjdWzZ09f5LYsy5o/f741btw4a/To0W5HceTGG2+0\nFi1aZFmWZX333XfWwYMHjR3b+BVWr1691LNnz3r7V65cqbFjx6ply5ZKS0tT9+7dtWHDBtPxotK5\nc+fqv5YPHjyos88+2+VE0Xnqqad03333qWXLlpKkM8880+VE0Zs6daoeeeQRt2M4kpWVpRYt7P/E\nBg8erM8++8zlRJFt2LBB3bt3V1pamlq2bKlrr71WK1eudDvWCXXq1En9+/eXJLVp00a9e/fWnj17\nXE51Yp999pleeeUVTZw40VdPrfj666+1bt06TZgwQZKUkpKiU0891djxPdPD2rNnj7p06VK93aVL\nF+3evdvFRJHNmzdPd911l8455xzdfffdmjt3rtuRovLJJ5+ouLhYQ4YMUWZmpt566y23I0Vl5cqV\n6tKliy688EK3ozTa73//e1122WVux4ho9+7d6tq1a/W2l//7i2Tnzp3avHmzBg8e7HaUE7rzzjv1\nq1/9qvoPGr/YsWOHzjzzTN10000aMGCAbrnlFh0+fNjY8VPi8aZZWVkqKSmpt3/OnDmOxmuTkpJi\nGcuRSD/D7NmzlZ+fr/z8fI0ZM0bLly/XhAkTtHr1ahdS1tdQ7rKyMn311Vdav369Nm7cqKuvvlrb\nt293IWV9DeWeO3durZ6ml/4ijeZ3ffbs2fre976ncePGmY4XNTf/W4uFb7/9Vj/96U+1YMECtWnT\nxu04DXr55ZfVoUMHpaen+2619rKyMm3atElPPPGELrroIt1xxx2aN2+eHnzwQTMBjA0+1lG3hzV3\n7lxr7ty51dvBYNBav369G9FOqG3bttWfV1RUWKeccoqLaaI3cuRIKxQKVW8HAgHryy+/dDHRiW3d\nutXq0KGDlZaWZqWlpVkpKSnW97//fWvfvn1uR4vKH/7wB+viiy+2jhw54naUBr355ptWMBis3p4z\nZ441b948FxNF79ixY9aIESOsX//6125Hicp9991ndenSxUpLS7M6depknXTSSdYNN9zgdqyo7N27\n10pLS6veXrdunZWdnW3s+K4WrLfeeqt6u2rSxdGjR63t27db5557rlVRUeFWvAalp6dX/4//b3/7\nmzVw4ECXE0Xn6aeftn75y19almVZH330kdW1a1eXEznnp0kXr776qnX++edbX3zxhdtRTui7776z\nzj33XGvHjh3W0aNHfTPpoqKiwrrhhhusO+64w+0ojRIKhazLL7/c7RiODB061Proo48sy7Ks+++/\n37rnnnuMHdt4wfrLX/5idenSxUpNTbU6duxojRw5svprs2fPtgKBgHXeeedVz8Lzoo0bN1qDBg2y\n+vXrZw0ZMsTatGmT25GicuzYMev666+3LrjgAmvAgAHWmjVr3I7kWLdu3XxTsLp3726dc845Vv/+\n/a3+/ftbt956q9uRGvTKK69YPXv2tAKBgDVnzhy340Rl3bp1VlJSktWvX7/q8/zqq6+6HStqoVDI\nd7MEt2zZYg0cONC68MILrTFjxhidJchaggAAX/DXFBUAQLNFwQIA+AIFCwDgCxQsAIAvULAAAL5A\nwQIA+AIFCwDgC/8fmHkWJIKgkR8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa642b38>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = interact(Toomre.Make_Plots,results = fixed(np.load('Toomre_B.npy')),t = (0.1,20.1), dt = fixed(0.0075))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LX31lL6D48Y/dTgLAFGZYEUTPOfL27z/9+y1aSJs3h4xmiRQvH+/qkNsscjtD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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3c1fe48>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}