{
 "metadata": {
  "name": "exPlotImage2D"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "sys.path.append('../')\n",
      "\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "from SimPEG import TensorMesh"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Test 1D Plots\n",
      "\n",
      "For 1D nodal or cell-centered plots are supported.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x0 = np.zeros(1)\n",
      "h = np.random.rand(32)\n",
      "mesh = TensorMesh([h],x0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sin = lambda x: np.sin(x)\n",
      "xc   = mesh.gridCC\n",
      "xn   = mesh.gridN\n",
      "\n",
      "fig = plt.figure(1)\n",
      "fig.clf()\n",
      "ax1 = subplot(121)\n",
      "ax2 = subplot(122)\n",
      "ph1 = mesh.plotImage(sin(xc),ax=ax1)\n",
      "ph2 = mesh.plotImage(sin(xn),ax=ax2,imageType='N')\n",
      "ax1.set_title('sin(x) on CC grid')\n",
      "ax2.set_title('sin(x) on N grid')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/larsruthotto/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/figure.py:362: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
        "  \"matplotlib is currently using a non-GUI backend, \"\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<matplotlib.text.Text at 0x10c3ba050>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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AmpvJf0ajVdxjY0lITKStNYBGcecxAQBoF0QavhYh5Y6lz91oWPrcTcDvf/97\nLFu2DPPmzcPPf/5zREdHY8uWLdiyZQsA4J133kFycjKmTZuGd955B7/73e98tqc1DRIQZyhofc67\nHpRtNFrFnW6tEeG7uKLZLUMnQEtLC3JycjonAAAsW7YM77zzDjZv3oxevXphypQpPU4AANoFsW9f\ncnotCx+xVlj53EUQRFZuGRFTCzyQlpaGki4VRpctW9b584oVK7BixQrF7Um3jDu0BMFNN7Hpkxqu\nXCEWt5r9kq7QXaojRrDpFws0izvrCYC2NjJ6Y2O1dYymQxop7moO+fZEdLQYZYxNFFAVERZumago\nsgIwunhYdTUwbpy2NkSw3OkhHVrvpYh+d/F2qFZXA5GRQC+Nzx0Rct3VHPLtCVF87gHmlmENC7dM\n795kYWr0PjAWz3kRxF2rS4YioriLV/KX1d0WIdedhb8dEGMt3t5OCrJFRmprJ4CP2qupASZN0t4O\nHQ5G7gNjEVsXYZeqVrlZtMiG8nKS4JefD7zzDnk9IcFZ5tkoxBN3Fj5qQAzLndV3ESHP/dIlcsy9\n1lWIa30ZEYqS6wirfWh0OGjZeKMVFj73uDjgwAE2/VGLVnEvLwd277Z1/tt5Fo2t+8U6I55bhpW1\na7C42/PzkffEE7AdPoy8zEzY8/PVNyaC5c5Kmfr0MVV9GZaw8LkDYgwHlgFVI2HlKBAR61ruBrpl\n7Pn5KMjNxfrSUvJCYSFWd/ys6gzUiAigsZEU9NAai1ALy8wj6poRvL4Ma1j43AHjxd3hYPNdRPG5\nqzgB0xRIy50DhRs2OIW9g/WlpdjpoWqmIlyLhxkFy9o29BDNAIPVLTRa3BsanAswLYgi7la13MUT\nd5Y+d4Ms915eduCGXLumvlGj/e4sImiUAMyYaW0liy+1Z7a4YrS4s3DJACRDuLLS2L2GVhZ38dwy\nrCz3yEhyAtKVK2RDk460ejFp2vr0Ud+o0TOapeUegBkzdXWkiiKLQ5Sjo4GyMu3tqIWVuPfpQw71\nqqtjE4tQg1ZxT0gAaPD0k0+AuXPJQpu8biziiTsryz0oyOma0brbwk8ycnKwurTUzTXzVGIislau\nVN+o0bnuLH3uAeiWYflsFOE5z2oo0KCqEeLucJCVg5Y9hq7pjiNHAm++KYawAyKKOyvLHXAGVXUW\ndxo0XbNgAUKmT0dbeDiyVq5UF0ylGO2WqakBJk5k01ZMDHDuHJu2TIKVxJ2V5Q44/e4s8v/9pb6e\nrBy0LKiBplKaAAAgAElEQVRdiYoiqxAp7h7ImzcPvZqb0frDHyIjJ0ebGAKGBlVTs7ORGhwM7NxJ\n8sO1IsKMZumWMUl9GVbU1rKzTq00FIwMqrL2t9PSEKIglLiv+9e/yA9aUwcpRua6NzeTXZ2s/P3R\n0WQNaRQyW0YTVrPcWYmilcQ9MlIscRcvW6YDTamDFCNLEFBTjdUuTKNnNOuAqhR31URGkg3D7e1s\n2vMXlj53I0sQWN1yF1bcAY2pg4CxljvLdThg/MhhvYkpAMWd1XDo1QsYOJAIvBGw9LkbuUu1okJ7\nwVZXjJ6iXRHKLdMVTamDAOzl5Sj86CP0Sk9Ha1gYGz++UljOZsBYy72lhaSUstpRSsU9gOrL1NYC\nkyeza48OB6113NQgfe6eiYoCzpxh155WhBV3ramD9vx8FGzciPWXLwO7dwMAGz++Ulhb7kaKO31Q\nsRJi1/oyAVKCgKVbBnAOB50TwQDwyZbRE1rJ8dgxsqnsww/J61orOUZFGV8IzRWhxH3NqFEICQlB\n29ixmlMHCzdswPouj9H1paVYs3GjFHd/Ya1MgNN6l+KuCqOHg5nF3bWSY2UlcPw4/Y1NU7uiBVSF\nEvdn0tKA9HRg8WLNbXEpAeAPrKpEUYwsHsZL3KuqgDFj2LYrKFZ51jc3kyHIIrsXIPekoQG4cYMc\nRGJmRPO5ixVQZTgDuJQA8AfWszk4mOxfr6tj16ZSeJxFG2DpkFax3Km/nZWHLjiYDAUjs3xZIcXd\nFwwFMSMnB6u7nGbwVGIi7tRSAsAfWIs7YNyM5umWCQBaWtgVDaMYKe6sn/Mi1HVnAd2hKgpCuWVY\nCmJnCYDFixESG4u24cO1lwDwB9bZMoDx5hpLAqh4GMuiYZToaBIQ1BseizgRSv+yICKC5Ai0tZHi\nYUZjWXEHOkoAfO97pFTb0qXM2lUED8vdqHUfjzPdYmMDpr4M6/ALYC3LXW9xT0gArl2z4eBBYM4c\n99e1EBICDBpEataw/nurQSxxb2ggJg5LjBJEq7llUlLYthkTI1beGEd4eLWstIjTe5fq1q02HDgA\nLFkCFBWxbZvKjQjiLpbPfeBA9pkgRoq7VWY0j7V4ALllrCbuZrfcAT62FyBWUFUscefxuDPibrM8\ndscVK5lrAZQtw8tDZ5XnvBEB1bo6Prt7RQqqiiXuPB6lRhxyUV9PhJ1lBA0w1ucus2VUw+P20eBd\nayvbdntCWu6+EWkjk1g+d6usk3hkygDGWO4OB19xD4D6MjU17M6foYSEOLc9xMaybdsXrBdxixbZ\ncOwYcOgQ2b9I0VoKoCd4Wu5S3D1hFXHnZRYYIe5XrxLx7dePabP2f/0LhW1t6JWaitZ+/fQt6qYz\nNTVsi4ZR6HDQW9xZWu7l5cDevTYAnSWgOrCx+xAP1NYCI0awb1eKuzd4ibvegmglcefgZLXn56Mg\nNxfr29qATz8FoHNRN53hlT1hkeFgCLW1wNSp7NuNigIOH2bfrhqs73OPjCQ+cIeDfdve4DWbjTAL\nOARTCzdscDs8HGB0OIug8PBqAfqLe2srn2xlI6ir42dLyoCqJ3jc7d69gb59yajUC16WOy0e1tLC\nvm1vcFAmw4u66YxVxL22lgi7CLsvtVJby8fnLlJA1friDuifMcNL3IODyejR0zTgoEyGF3XTGat4\n6XhkyhgFT8tdFHG3vs+dtltby34LvTdqaoDRo/m0TWMILM8H8wUHJ2tGTg5Wl5a6uWa0Hs4iMjy2\nPABE3PU8RZKHv51s+behpITYLXRYay0F0BO8LHcp7t7gLe56wctUA/RfhXCw3DuLuq1ahZDr19E2\nYYK+Rd10JjKS/ZYHgPxZDh1i3643eFjuNN0xN5cI+qpVbNv3RHs7OX9Wirue8BR3vZ2TPMVd77X4\n9OnMm03NzkbquXNAcTGwZQvz9kXCSkOBV80UPW2WhgZy2AiPM2/69yeB5+ZmEuozksDwuVvNctdz\nRvOKBgJimTkcsYIgAnx97noOa14bmACyJUSUjBmxxJ3xRplOjBB3noIoxd1U8BR3vYcCL3HXcyjw\ntL0AcYa1WOLOCz3vtsPBLxQPGONz5zWjRcob44hVxF1a7sqQ4q4negpiYyMQFsbvtF8rOVpFWb9y\nhtdzftAg4tu9cYNP+13hPRSsZLmLMKwDQ9z1HDm8ioZR9BT39na+Zo4oJg5neAki9e/qdQul5a4M\nURakYmXL8MJqZoFes+DSJXKASmgon/ZpjOXqVX7xFgHgeSoPFUXWVSc9oYfPXY8ioWb3uS9aZEN5\nec/XBY646yWIvEeOni4mnsFUCjVzLCzuVljI8ar8TOnTh9gQTU3EnuBJXR3f/YxRUXwPGSsvB3bv\ntnX862mv10m3DGt4H6Co5/pVjxKAojgoOWKF4dDQQATYS+UIJug1Tc1uuSslMCz3/v2BtjZ9dhbw\nHjnh4cCVK6R4GC93CUhZ3sK8PPQ6dw6tmZn86q2LMhM48uijNvTty+cACr3EXY+6MvS78C49oIfP\nXQR7JTDE3TXyNHw438/iLe7BwaQ0X20tOZ+MA5311mntl8JCfvXWA0Dc9+2zdfxk83GVOvQSd70W\ncdJyZ4dQbpm8zEzY8/P5NK6Xr5p3tgzA/bvoWm9dlNQCk6JXOElPy503Ms/dANYVFqIgN5ePwFvF\nLAC4zwJd660L7HO32+2YMGECxo4di41eHmxPPvkkRo8ejZkzZ+LYsWM+2zt2rJx5H/V0y/COrVtl\nivL+HgkJQFqaDUlJNp/XaRZ31hOAm4VolZEDcJ/RutZbF8XM8UBubi62bNmCXbt2YdOmTajpcs/3\n7duHTz75BF9++SUef/xxPP744z7b4/FstKLPnSdtbWSfYXg4v8+gPndeh79t3WpDUZENP/uZzed1\nmsWd9QQAOFqIeswC3tkyAPfvkpGTg9VdcsWeSkzEnTzqrQsq7g0dJ3elpqZi1KhRyMjIwN69e92u\n2bt3L374wx8iMjIS999/P0pKSnTvp5V87np8l/p6Iuw8T5Oih79dvszvM4Cep42mgKrrBADQOQGy\nXYJuXSdAXl5ej+2a2kLUy3Ln+F06663/5CcIGT0abbGx/OqtC+pz379/P5KSkjr/PXHiRHzxxRdu\nY3vfvn144IEHOv8dExOD0tJSJHZLok4HALS2VqCoqAjp6enM+qmn5T55Mt/P0GOK8va3U+h3Yb1C\nKCoqQlFREQCgoMD3tZrEne0EILkE/xo8GGOiophPAkRFAefPs2vPG3qJO+cjeFKzs5EaHQ38z/8A\nY8fy+yBOPnfXScALh8MBR5e1d5DH7ZWkHzffbGM7pmEtn7se30WP6Qk4xZ31gWzp6emdY6ikBPjy\nS++bmLinQiqfAEBbZibW87IQo6L4H1tz7Rqp4jRgAN/P0eO7APrsUOVkrrlOAgB4+mnvk8ATs2bN\nwhNPPNH57yNHjiArK8vtmpSUFBw9ehSZmZkAgOrqaozmdbyiF+gWDt4VHPTwuethufM6Xq8ren0X\nX2gSd9YT4JkPP9TSHd/okQpJzQLexTH0MHFaWshmKZ6RJ0BYn3t4x/e22+0YOXIkdu7cibVr17pd\nk5KSgsceewwPPvggCgoKMGHCBI9tpaXZAPDZnBMU5BzaPMXdKj53ntW4AWfdl5IS4LHHgOeeI6/z\n2MDW04JXk7iznADc0etRqseaT68HFa/DP10ZPJhEudrb+X+Wn/z+97/HsmXL0NLSgpycHERHR2NL\nx5GAy5Ytw+zZs/Fv//ZvuPnmmxEZGYlt27Z5bKeoyMa1n1QUR4zg9xl6Wu48i4fxttxd675UVRGR\nJ9iYfxZXyx1gNwG4o5e483ZjAPqYOHq4ZABSQqF/f5JaEBHB//P8IC0trVsGzLJly9z+/dxzz+E5\nap4ZBO/h0NxMzgXl7W3s14+I+tWrZEjwgLflrifcxd0sE0CXVEg9ozVWEXfA+eAVTNzNAm9xp8FU\n3t5GwDkUeIl7bS0wcSKftvXk+nXyny/EWgfzJCKC7F5obeX3GXqJOy0exvMIHiPEXaIK3uKuh7+d\nwvu7WMVyV/I9AqNwGEB2LYSHE/8ur5GqR10ZgPimaQohp+Jhuoq7KGX0TAqvEAwNDtbXA+fOATT5\niEdwkML7Oa+X/cUbmq9fWen9msARd8A5cniJe20tMHQon7a7Qk0cK4i7tNw1ER0N9FDVQxXuh0IA\nu3fTn2zdL2aEHpY7z4AqyYiyobEROHkSmDHD9XV2KHlIBZa4884yqa0FkpP5te8Kb7+7HoW1KVLc\nNaH3mek8MbvlTlc0J08CWVkAr310Sh5SgeNzB/QZOXpZu3o4WqXlbgqsJO5mt9wpvIudKnlISXFn\niZ4OPSuJO0efuz0/H3kdG+isipXEnecUvXGDpHUOGsSnfVfCw/nmb0hx7wpvV4be4s7zQWUBy52e\nKLWusJB52yJhJXHn+V3q68meOT1SOmn+xqVLfNpXsgIJLJ+7lSx33oXQ9KgUhY6zWl98Eb3Kypif\n1erpRCkrQm0W1js7aXDw4EHyM92GwDMUw9Nm0TtThhY85TGNlBQlCzxxLyvj03ZbGzkifvBgPu13\nJToaOHiQX/s6WO68z2r1dqKU1ejbF+jVi2x9YLmLlAYHJ00Ctm3jX/IX4Lu41svfTuHpd5cB1a7w\nNAvq64kzj+cpAK7wXL9evUoeVry2CXbA+6xWbydKWRGew0FPD52VLHeejgKZCtkVo+82S3jPAh32\nm/M+qzUjJwerS0st7ZqhG41qa4EFC4CBA8nrrDYatbfru6vTSpY7z3NopM+9K7zFXS/zBuA7C3Qy\n1Xif1ZqanQ04HFhz991M2hMR141GBw64/sbGpP2GBrKACw1l0lyP0Pr0zc3E3cQSIyx3Xm4ZmS3T\nFatZ7iYXdz3Oak1NTcUzvMsZWhg968oAZLHIa5paxXJ3OKRbpjs8C0brLe7h4cS8uXGDnMjLEp3E\nvfOs1o0bEfLpp2ibOhVZTz3F9iQuWoi8qYldmwGEnv52CrVbhg9n225tLTByJNs2fREVBRw5wr7d\n5mYiXz2tbAJL3MPCiBA2NrLfyaBX0TBKUJDTNIiPZ9u2jjM6NTubiPl3vgM8+SQwbx7bD6DfhVeW\nlMXRKSPWDdaWO41LHD0KfPop8Ne/ktd5FkAD+FnuSu3IwBJ3wDlyWIu7EeXmqIljYnHvJCaGKAlr\njPguFsJIy50VrnEJ9yFmY/chHuDlc1fqXgo8cadZJjfdxLbd2lpg1Ci2bfYEL797TQ2g93GIPL+L\nhcWdbjSqqSHlXydNcn1dO0bcPiuUGlq0yIavvwZOnHCWSgbYrBak5e4NXiNH72wZgK8g6hlFA/hZ\n7kb4FXSECsXu3UBeHvsqhFaw3I2gvBw4cMAGwLVUMsBitaDUcg+sbBmAr7jr7Zbh9V2sNKONeFAZ\nQEyMdRY+VrDceaJUagJT3HnMAp3F3Z6fj7xdu2B7/nnkZWbCnp/PrnEjrF3pc9eElW6fFSx3nki3\njDd4Wrs6iXtnTRaaAVJezrQmi6VmdICIe2QkqUDY1sa2AoYVLPeEBODKFRuOHgVmzXJ/3YzU1QFD\nhvR8XWCK+4kTbNtUuquAEd5qsqzZuFG7uDsc+qd1AtYyPQ0gJIRUbayrY+uFssJzfutWGwoLgRde\nAHbtYteuUdTWAhMn9nxdYIo7a8u9qYnsz2a0bb4nuNZkaWwk+wF0+i6d8AyoBoDPHXCKohXEnfUU\nrahgnzHsC5rF9M035HOprcRitSBTIb3BY+TonCnDtSaLUZYuTQpubweCGYaCAsRyB5zPR1ZZrK2t\nwOXLzjruesEjLFZRwe8seU/QLKaf/QyYORNYtoxd2zKg6g0elrvOwVSuNVmMEsPQUFKMnOXRNa2t\n+tbYN5joaLaLn7o6cuv0qmJNGTgQuH6d/MeKixf1FXdKXBz5bJZIy90bFhB3t5oshYVomzcPWbm5\n5g2mUqhfgVV1p/p6YnbqrU4GwTod0qih4Fo8bOhQNm1WVLgHU/UiPh44dEh7O7SEAgCcOgU8/HDP\nJaUCU9xZr/kMCEB21mQZMQL405/MvSWRQv0K48axaS+A/O0Ae8vd6Oc8a3E3ynIvKNDejmsJBQD4\n/HP609Ne3xN4bpmBA0klRZZrPiM2MFGGDmW77jN6RlvB9DQI1pa7kc9G1jaYkW6Zigr9PxcIRHF3\nrabICiPFPT4euHCBXXsiWO6sCEBxt8rtY533oHe2DIWHz10pgSfuAHu/uxF1ZSjx8day3K2iTgZg\npYUPS8u9uZn8p3fWD+C03B0O/T87MMWdtVlgtOVuFXHnEREMIJ+71Sx3VkOB+ts5Hwnskb59yX8s\nk8CUEngBVYCP5W6kz/2zz9i1Z6QgxsQAhw+za6+6mgScAwQelvv06eza84eoKODcOTZtGeWSoVDr\nXUtGLt0UdeEC2TNJcw7cK066I8WdBUZs16ew9rkbWSLXSupkANRyZ3WKpNGWe3Exm7aMypShUL+7\nls1ldFPUr38NtLQAzzxDXg8Kktky7jBy6Nnz85GXmQnbkSPI+8Uv2FZmVIrV3DJW8SsYQL9+RNSv\nXGHTntE+d1b2l1GZMpT4eHYZM5WVQGyssmsD13LXeLc7KzPSAl6ffILVHRY00wOee4JlKmRbG9n4\no+cR8a7wsNwDyOcOOMMWAwZob8toy52lz10EtwwLKiuBtDRl1wau5a7RLPBWmXHnxo2a2vWbmBgi\nyDduaG/r0iVytmwvg575rC13i5/C5AmWCUdWsdxFcMuwFHcl5X6BQBV3BtkyXCsz+kNICBHFykrt\nbRntxhgwgDgUm5vZtGf09zEAVglH166RfX4DB2pvSw0sLXej3TIsc92luPcEA7OAa2VGf2HlmjFa\nDIOC2KlTczN5ULDwT5gIVosfunXDiPRBAAgPB65eJX9CrVjNLSPF3RcMxJ1rZUZ/YRVUNVrcAXZ+\nhdpaonRGqZNBsLJ4jR4KLDeSG+2WYRVQvXbNv81YARlQtR84gMLTp9ErPR2tYWHIyMnxOwjaWZnx\nl79ESH092qZMQdbKlfoGUyms0iGNntEAO8tdhO9iAKwsdxHCFdR7qkWY29v9s3Z5wMpyr6oimTJK\n7ZWAE3d7fj4K/uM/sL61tXMHgNrzR1Ozs5G6fz9JLH7ae74pd6zilgGspU4GEB0NnD6tvR0RhgKL\njOW6OhI38OJF1YWoKJKrcONGz2V6feHvQyrg3DLMs1wuXDDWoQdYzy0jLXfVsFz4GJ1FyqJKiNEu\nGcCZ81BVpa0dKe49wDzL5eJFdkWn1WI1twwLy10EdTIAViELEYYCC8vd6EwZCgu/uxT3HmCe5XLh\nghjiLi13d0T4LgZgpZAFK8vd6IU1wMbvLsW9B5hnuYjglmHlcxfh5CLpc9eEtNzdEcEtA7DJdfdX\n3AMuoNqZ5bJ8OULCwtCWmKg+y6W1lZgWRobiAfL5NTWkP1p2l4owo6XlronISKCx0TpD4ZtvtLUh\ngtcUYGO5V1UBs2crvz7gxB3oyHL52c9I0uj69eobqqois8mo7fqUXr1IP6qqtI1kEWa0AD73xsZG\nLFy4EMXFxZgxYwa2bduGAR42QyUkJGDQoEEICQlBaGgo9u3bp7XXmgkOJqVltdocIgwFVpb7zJls\n+qOF+HigpERbG7q5ZRobG3HPPfdg5MiRuPfee9HU1OTxuoSEBEyZMgXTp0/HbH8eO7xh8SgVwd9O\n0eqaaWkh5QTDw9n1SQ0sxV2lOm3evBkjR47EyZMnMXz4cLzyyiserwsKCkJRURGKi4uFEHYKi8WP\nCOJulWwZwGQ+d7NPACZ3W5Q1H6A9qFpbS6z/YIPDMJGRpBBaW5u2djSo0759+7BkyRKEhYXhoYce\nwt69e71e6zDi/LQe0Pp8dDiMPaKAYqVsGSN87qpnstknADPL3ehgKkVrOqQIphpAXEzh4UTg1aJR\nnfbv34+kpCQAQFJSklejJCgoCHfccQfuvfde7NixQ3V3WaM1qHrlCsnN7tePXZ/UwGIFYpVsmZYW\noKHBvyGt2lns7wS46aab8NBDD2H+/Ple27TZbJ0/p6enIz09XW33esZqlrtWt4wo4g44Z7Xa/ly+\njKLQUBQ9+6zXS+68805UePj7r1+/XrExsmfPHsTHx6OkpAR33303Zs+ejTgPZqKu4xra0yFFGQoR\nEdqCw9eukeJjWo63Y4XrQdlqyh1VVxNh/+STIhQVFSl6j89bpucEANwnAXdiY8kda29X74q4cEGM\naA1AzBMt54+KMqMBp1+hw3jwm5oapMfFId1lPD3dpTzEzp07vb799ddfR0lJCaZPn46SkhLMmjXL\n43XxHSbhhAkTMH/+fLz//vt4+OGHu12n67iGdstdlKFAg8N1dcpPH3KlooK4MUSoHTdgAFkNNTaS\nIxP8hdaV6WocdB3XrvhUtZ07d+Lrr7/u9t/8+fMxa9YslHSEf/2dAELQuze5y1oiNtItwwet63GN\n3yUlJQWvvfYampub8dprr2HOnDndrrl69SoaGxsBANXV1SgoKEBWVpbqz2SJVstdpC0CWoKqorhk\nKFr87mqKn6n2uZt9AgDQ7pqRbhk+aI0IalSn5cuX4+zZsxg/fjy+/fZbPPLIIwCACxcuILtjP0RF\nRQVuu+02TJs2Dffddx9+8YtfYMSIEer7zBCrWO6AtqCqKJkyFC1yo0bcVfvcly9fjoULF2L8+PGY\nMWMGnn/+eQBkAjz88MPIz89HRUUFfvCDHwAAoqKihJoAAJx3OzlZ3ftFs9y1ivuoUez6owUW6qRh\np+3AgQPx3nvvdXt96NChyO84BH306NE4ePCg6s/giVV87oA2y12UTBmKlvoyuoq72ScAAG2P0tZW\nMguM3p1KiYsjjjm1MYSaGnHiBzExwLlz6t8vkjoZgLTcCSK6ZfQU94CrLePGkCHq73ZVFZkBRu9O\npdAYgtpZLdKM1uCWsefnI2/TJtjefRd5mZmwdxgagYRWr5ZIBTW1+txFstz19rkLokwGoeVRKpJL\nhkL97mpWE6JF0VSYa/b8fBTk5mJ9eTl5oays8yCWQILePrVpdyI956Oi1D+oLl4E7rqLbX+0EBcH\nnDih7r2Vlf5nDAW25R4XR+6aGkQKplK0+N1FMtdUmp7MD2IxKX37AqGhJO1ODSKJu5WyZbT43Kuq\npFvGP7Ra7iKKu9p0SNFmtArLnflBLCZGS1BVpKEgs2UI0ufuL1Z1y/jL1auklkv//uz7pAaVljvz\ng1hMjJagqkjirtZydziMPxi7K2p97u3t5G/ir1tG+tzVivvFi+Jkl1Di44Fjx/x6iz0/H4UvvIBe\nDgdas7KQkZOjrrY9Q+wff4zCa9fQKzUVrX37Ku5TRk4OVpeWurlmnuqo17+uoIBnl4XDX8t90SIb\naKiiuhr40Y+Ivz4hAdi61cahh8pQa7nX1RFbRaTnekwM6Ze/5RRqa0muRGiof58X2OIeFQVcvqzu\nWPILF4DvfY9Pv9QSHw98/LHiyzsDkFQMCws7A5BGCbw9Px8Fjz6K9Q4H8MknAKC4T6nZ2UB1NdY8\n8ghC5sxBW58+6g9iMTn+Wu7l5cDu3bbOf9vt9Cdb94t1YtEiG06eBE6fBlzL8Sh54IjmkgGcxy5U\nV/u36Fe7AglscQ8Odh5LPny4f+8V0ec+dKhfPndvAcg1GzcaJoha+5Q6bhxSp08HFBZXsiqszlI1\nkvJy4LPPbACA3btdf2Pr8b2ibWCi0KCqv+KuprZOYIs74HTN+CvuFsiWETEAqblP58/7/7e0ENS9\ncvYsWf5/8AF53Wj3it6IlilDoX736dOVv0dNpgwgxV2d353uTlXzOOUJNQsUJjiLGIDU3KcAF/eu\n7hXnRl9b94stjIhuGUCd3Kh1ywR2tgyg7m6LtjuV0qcPiSIpTC/IyMnB6sREt9eeSkzEnStX8uid\nIjT3KcDFXUIQ1S2jp7gLpk4GoOZui+hvp1DXjIJcNurDXrNwIUISEtA2ZIjhAcjOPj37LEKKi9F2\n223+9en8eSAlhWMPrUlCAgDY8PXXZGjTE3/I6+ajosI/14dexMcD/m6arqwEutg7ipDiPmSI/3uC\nRcxxp1BxV1jpMjU7G6l9+wI7dgCCVOxMzc5G6rx55Cie994DvLhqPCItd1VQf/yYMcD27erPSWEJ\nfeBcugSUlTnFWskDR2S3zKef+vceabmrJS7ONe9LGSIGUyn+liBobibJt6J9n7AwMotPnPCvJLMU\nd9Vcu0ZunxorkQf0gVNfT6pRf/SR8oKnVnPLyGwZNah1y4hqufuZDonycmDkSHIGmGhMmgR8841y\ncW9rEzdNQieotQsAX3wBTJtGQjFKrN1Tp8h1/m6W4c3gwWQRV1am/MEj6jCQPnc9UVM87MIF4Oab\n+fRHK/HxZNeHUsrKgJtu4tcfLUyeDBw5ovz6ykriLPZ3Q5qFcE13nDsXeP554LbblL332DEx3DGe\nmDoVOHRImbhfvw40NYlxMHZX/C0e5nCoT4WU2TJqHqVWcsucPg2MHs2vP1qYNMk/cZcuGTdGjPDv\nzBMziLsS6MHYas+958nAgWSB2dSk7PqGBuKh7NvX/88S8OvrjL93G7CWW0Zky12KuyYCWdxFnZ5B\nQf7Zk1qKn0lxp3fbH9eMlSz3sjJxLfexY4k6yd2pqghkcRcxmEqR4q4n/tzt1laySUi03akUKu4O\nh7LrT58W13IPDSVOVqWVLqW4u+GPuDsc5DaPH8+3T2pJTCQFtxoaer5W1EwZij9+d7WZMoAUd4K/\nj9KoKPF2p1L69ycBRSWzwOEQ2y0D+OeakeLuhj/i/u23wIABYgYhAZLMNXkycPhwz9eK7JYB/Kvr\nLi13rfgj7iK7ZChKT2SqqyNuKVFnNCDFXQP+iPvx4+K6ZChKXTNWcsuozZQBpLjDnp+PvMJC2H7z\nG+RlZsKen+/7DSIHUylK/e7UaldzirJe0Fx3JUhxdyM2lhxX0Nzc87Ui+9spSsVddLeMXj53QX0L\n+loFG+8AAAyxSURBVNDtsIozZ3o+GELkujIUpcftiRxMpSjNdW9vJ76FYcP498kkBAeT23H+PIlN\n+8Is4r51a8/Xie6W8dfnLsVdBaoOhrCSW0bkYColMZHc86tXgX79vF9XU0POIhPpXDUBoK4ZJeIu\n+oFVycnkOd/W5ntDtRncMr5sL9cjD4uLgTNngA0b/K/JH9DirupgCJF3p1Li44m51hNlZcCUKfz7\no4VevYgylZT4PrNWumQ8otTvbgbLfdAgYsWePOm9rw6HOcTdl+XetSb/wYP0J1v3i30Q0D53VQdD\nmMVyV+KWMYPlDigLqkpx94gScW9sJNm9I0fq0yct9OR3r68nCzyRF3CxsSSts62N7+cEtLirOhjC\naj53Ke6WRom4nzgBjBsn5nb9rvQk7qJb7QDJVI6IUHymjmoC2i3TeTDExo3kYIjYWGQ995zvgyHM\nki3Tk8+9rY0ctGmG0xgmTQL++799XyPF3SMjRjjPUfWGGVwylKlTfQ8F0TNlKNQ1w3MvZECLO9Bx\nMER2NvDss+RR6kvYW1tJbriou1MpStwy335LTmsSef1KUWq5f+c7+vTHRCix3M0m7r42MomeKUOh\nQVWeIa+AF/dOJk8G/vhH39eIvjuVMnAg+X9jo/PnrpghDZIyejS5901NZBulJ6Tl7hGl4v6DH+jT\nH60kJJDN13V1QGRk99+bwS0D+A6q0pr8Z86QDF/qOfV3kS24SumIEuvQDP52gGxKoq4Zb8VCzBJM\nBUje2/jxwNGjwOzZnq+R4u6RyEjgxg3fz3kzWe7BwcTaPXQIuP327r83m1vGEzTdceFCYN48YNEi\ndZ8hxZ2SkEDcMg0NQHi452vMkClDoa4Zb+JulmAqhW5m6iLu9vx8FG7YgF6lpWhdsQIZq1YZesC3\nSLjmS3/nO85tAq750m1t5ASmceOM6KE6aFDVk7hXVJDfi058fM8rqpMngeXL1X+GFHdKcDAwcSKx\nDm+5xfM1ZgimUnrKmDl9GrjzTv36oxUPK6tuO4z/9S+s7lAzKfDu+dL797v+xtb505kzJITUv7+O\nHdPI1KnA5597/p2Z3DL79vm+5uTJnjef+cIEyU864sM1Y8/PR95vfgNbYaGyGjRG01PGjNksdw9/\nG287jHdu3Khnz0yNmVwyFF/pkFZwywDEidDWBsTEqP8Mabm74qVIVaeFSM8mVVKDxmh6ypgxU0AV\n8CjuqnYYS9wwo7hPnkz63dLS/TBvM2XL+BJ3arVrqeknxd2VyZOBwsJuL6uqQWM0Q4d6N2+am0m6\ngVniBwBxFNfVkTKHgwYBULnDWOLGsWPA9OlG98I/+vcnWUDHj5MpS7l+nQSOPWXRiMSiRTacOkVi\nHenpztddYyFaXTKAFHd3vLhlzGgh2s+cQeE//4le6eloDQtDRk6O80FUXg6MGmWOLYmU4GBiYh49\nCsyZA6Bjh/GpU84VFcgO4yxfO4wlbhw7Btx/v9G98B/qmpk82Rk4vn6dWLp33EGu8bfQll6UlwN7\n9tgAALt3u/7G1vmTFHfWjBhBcqm7JNGazUK05+ejYMsWrK+v7xw9bm4kM6VBukLdZh3inpqdDezd\nizWbNiEkORltffoga+VKcVdTOkPzpQESfmloACZMcM+XNqNbBnCK+09+0r3QllMwbd3fKDDHjh1E\neroNAKmTFxkJ2O3qH1JS3F0JCnJa77fd1vlyRkoKVn/8Mda3tHS+JrKFWLhhA9Z3ybNycyOZLZhK\n8bCySr14Eal5ecCqVQZ1SlxcBaG6mliC+fnOzJjaWmLtmiEA2ZWpUwGrxc0vX45we0hVVdHjg21e\n3uEbKe5d6SrubW1I/dvfgKeewpovvkDItWvCW4g9upHMFkylTJ4M7Nrl/Hd7Oymc8v/+n3F9Mgkx\nMWTBk58P/J//Q16jR+uJfBCXN5SeymQmXGxHJkhx70pX6/CNN4CoKKSuXYtUk8yCHt1Ip097z+UX\nma5/my+/JGvXLpU9JZ657z7gr391irtZXTIA2Yx84wapSmEV+vdXdq69UqS4d2XyZGDHDvJzczPw\nH/8BvP22qcybjJwcrC4tdcvweSoxEcPnzEFeZiZ67dmD1ooKZPTtK+zqwyMjR5JsmUuXSM3UHTuA\n+fON7pVpuPdeIDfXuQnbzOIeFGRe6901FuLKsWNS3Pniah3+4Q/ArFmms3LdShmfPIm2q1cxfOFC\nfLttm1Pwv/gCq3Nz3a4XnqAgsov4yBHg1luJuG/ZYnSvTENEBNmy/957wIMPEjFZvNjoXqmHintC\nAnDpkg1lZe5pnaJWs/YWHE1PtzFdiUhx74L9wAEU1tejV0oKWouLkbFhA1KN7pQKOksZNzUBiYnI\n27XLfLn6nqAP36FDyZrcWyExiUfuu494Gqm4m9VyB4i4/+tfwLZtNqxbR6ze3/zG6F6px5tFr/Yh\nJcXdBXt+PgoefRTrW1s7Cz+s/u1vgREjzCWArgwYAKxahV4vveTx1yLn6nuEivu1a8D3vuf7pGRJ\nN+6+G3jkEbJ5+exZc4cr3n3Xho8/JhuBDh8Ghg0jNXREzW/vCdZ9luLugq+dqO39+yPddTuZgBQV\nFXnu44oVqM7L8/gePXP1vfbPHyZNAv7xDyLwgqaiisyKFTb07k0WPMHBwOzZ5YiISBBaEL2Nm4YG\n4MoVW2dee309/Y1Np54RmIxrDphoiyJ/fKUQFhUV6dsZFXjro91ux7W+fbG6y+ur4uJ8nxfLGBb3\n0F5RgTy7HbaiIuRt3Ch+ATfBKC8HqqttOH/ehuZmGw4dSsDu3c7SwCLiadwsWmTD4cPluvfFE6Jq\ng7TcXfCVQmjmxX/hhg14takJdgBrAIQAaAPQNHSoqdxN9vx8FKxbR9xmgCzxG8CUlwMNDQlGd0No\npOXuQkZODlZ3cUI+lZioq3XLA7oiSQXwDMii9RkAw7wdzSMossSvRKKcIIfD4TC6EwAQZKI8cok5\nMWKoy3Et4Y23cS2MuEskEomEHdItI5FIJBZEirtEIpFYECnuCkhISMCUKVMwffp0zBZgR+RDDz2E\nIUOGIDk5ufO1xsZG3HPPPRg5ciTuvfdeNDU1GdhDz3202WwYPnw4pk+fjunTp+PDDz80sIf8sdvt\nmDBhAsaOHYuNAgZ95bj2HzONaynuCggKCkJRURGKi4uxr6cjy3Vg8eLF3QbQ5s2bMXLkSJw8eRLD\nhw/HK6+8YlDvCJ76GBQUhMceewzFxcUoLi5GVlaWQb3Th9zcXGzZsgW7du3Cpk2bUFNTY3SX3JDj\n2n/MNK6luCtEpLjzbbfdhsGDB7u9tm/fPixZsgRhYWF46KGHsHfvXoN6R/DUR0Cs+8iTho7yfqmp\nqRg1ahQyMjIM/5t4QqS/hxzXbJHiroCgoCDccccduPfee7GDlgMWjP379yOpowpUUlKSEJaYJzZu\n3Ig5c+bg+eefR2Njo9Hd4Ybr3wMAJk6ciC+++MLAHnVHjmt2iDiupbgrYM+ePTh06BCeffZZPPbY\nY6ioqDC6S90Q0XLoyvLly1FWVoaCggKUlpZiiyzXayhyXLNB1HEtxV0B8fHxAIAJEyZg/vz5eP/9\n9w3uUXdmzZqFkpISAEBJSQlmzZplcI+6Exsbi6CgIISHh2PFihX4+9//bnSXuDFr1iwcIwdgAgCO\nHDmCOR0He4uCHNdsEHVcS3HvgatXr3Yus6qrq1FQUCBMwMSVlJQUvPbaa2hubsZrr70mnJAAwMWL\nFwEAra2teOutt/Dd737X4B7xIzw8HADJmCkvL8fOnTuRkpJicK+cyHHNDmHHtUPik9OnTzumTp3q\nmDp1quOOO+5wvPrqq0Z3yXHfffc54uPjHb1793YMHz7c8dprrzkuX77smD9/vmPEiBGOe+65x9HY\n2ChEH0NDQx3Dhw93vPrqq44HHnjAkZyc7Jg5c6Zj1apVjtraWkP7yJuioiJHUlKSIzEx0fHyyy8b\n3R035LjW1kczjGtZfkAikUgsiHTLSCQSiQWR4i6RSCQWRIq7RCKRWBAp7hKJRGJBpLibiP3792Pq\n1Km4fv06rly5gsmTJ+Po0aNGd0si0Ywc2+yR2TImY82aNbh27Rqam5sxYsQI/OpXvzK6SxIJE+TY\nZosUd5PR0tKCm2++GX379sXnn38uj3GTWAY5ttki3TImo6amBleuXEFTUxOam5uN7o5Ewgw5ttki\nLXeTMX/+fPz4xz/G6dOncfHiRSEPgZBI1CDHNlt6Gd0BiXLeeOMNhIWF4b777kN7ezvmzp2LoqIi\npKenG901iUQTcmyzR1ruEolEYkGkz10ikUgsiBR3iUQisSBS3CUSicSCSHGXSCQSCyLFXSKRSCyI\nFHeJRCKxIP8f8Dc5GSDGi2gAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c3803d0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Test 2D Plots\n",
      "\n",
      "Plot x and y coordinates of cell-centred points"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x0 = np.zeros(2)\n",
      "h1 = np.linspace(.1,.5,3)\n",
      "h2 = np.linspace(.1,.5,5)\n",
      "mesh = TensorMesh([h1,h2],x0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(1)\n",
      "fig.clf()\n",
      "ax1 = subplot(121)\n",
      "ax2 = subplot(122)\n",
      "mesh.plotImage(mesh.gridCC[:,0],ax = ax1)\n",
      "ax1.set_title('mesh.gridCC[:,0]') \n",
      "mesh.plotImage(mesh.gridFx[:,1],ax = ax2,imageType='Fx')\n",
      "ax2.set_title('mesh.gridFx[:,1]')  \n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.text.Text at 0x10c81eb90>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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MQc+ePZGRkYGDBw9q0QaRqjjXpHfurnjQsrIyjB8/3nbb19cXR48exd13393E0qYbrgfK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\nGGPJ5QAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bf3a1d0>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Test 3D Plots\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x0 = np.zeros(3)\n",
      "h1 = np.linspace(.1,.5,3)\n",
      "h1 = np.r_[1,2,1]\n",
      "h2 = np.r_[1,3]\n",
      "h3 = np.linspace(.1,.5,5)\n",
      "\n",
      "mesh = TensorMesh([h1,h2,h3],x0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax1 = ax=subplot(131)\n",
      "ax2 = ax=subplot(132)\n",
      "ax3 = ax=subplot(133)\n",
      "a1 = mesh.plotImage(mesh.gridCC[:,0],ax=ax1)\n",
      "a2 = mesh.plotImage(mesh.gridFx[:,1],ax=ax2,imageType='Fx')\n",
      "a3 = mesh.plotImage(mesh.gridEz[:,2],ax=ax3,imageType='Ez')\n",
      "\n",
      "ax1.set_title('mesh.gridCC[:,0]')\n",
      "ax2.set_title('mesh.gridFx[:,1]')\n",
      "ax3.set_title('mesh.gridEz[:,2]')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.text.Text at 0x10cb23490>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10c846d90>"
       ]
      }
     ],
     "prompt_number": 7
    }
   ],
   "metadata": {}
  }
 ]
}