gistfile1.txt

{
 "metadata": {
  "kernelspec": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "display_name": "IPython (Python 2)",
   "language": "python",
   "name": "python2"
  },
  "name": "",
  "signature": "sha256:70802c366c42d2fa4265dfd0540996c201da166e153628d5920594dd6ab82b1a"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import yt\n",
      "\n",
      "import numpy as np    \n",
      "\n",
      "from IPython.display import display"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def create_fake_dataset(px=0.2):\n",
      "\n",
      "    grid_data = [\n",
      "        dict(left_edge=[0.0, 0.0, 0.0],\n",
      "             right_edge=[1.0,1.0,1.0],\n",
      "             level=0,\n",
      "             dimensions = [8, 8, 8],\n",
      "             number_of_particles=0),\n",
      "        dict(left_edge=[0.25, 0.25, 0.25],\n",
      "             right_edge=[0.75,0.75,0.75],\n",
      "             level=1,\n",
      "             dimensions = [8, 8, 8],\n",
      "             number_of_particles=0),\n",
      "    ]\n",
      "\n",
      "    particle_position = [px, 0.5, 0.5]\n",
      "\n",
      "    if particle_position[0] < 0.25 or particle_position[0] > 0.75:\n",
      "        update_grid = 0\n",
      "    else:\n",
      "        update_grid = 1\n",
      "\n",
      "    field_data = {\n",
      "        'particle_position_x': np.array([particle_position[0]]),\n",
      "        'particle_position_y': np.array([particle_position[1]]),\n",
      "        'particle_position_z': np.array([particle_position[2]]),\n",
      "        'particle_mass': np.array([1.0,]),\n",
      "        'number_of_particles': 1\n",
      "    }\n",
      "\n",
      "    grid_data[update_grid].update(field_data)\n",
      "\n",
      "    bbox = np.array([[0,1],[0,1],[0,1]])\n",
      "\n",
      "    ds = yt.load_amr_grids(grid_data, [8,8,8], bbox=bbox)\n",
      "    \n",
      "    return yt.SlicePlot(ds, 2, ('deposit', 'io_cic')).annotate_grids()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for px in [0.24, 0.26]:\n",
      "    display(create_fake_dataset(px))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/atmyers/yt-x86_64/lib/python2.7/site-packages/matplotlib-1.4.0-py2.7-macosx-10.4-x86_64.egg/matplotlib/colorbar.py:837: RuntimeWarning: divide by zero encountered in true_divide\n",
        "  z = np.take(y, i0) + (xn - np.take(b, i0)) * dy / db\n"
       ]
      },
      {
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\"><br>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<yt.visualization.plot_window.AxisAlignedSlicePlot at 0x10ba727d0>"
       ]
      },
      {
       "ename": "YTFieldNotFound",
       "evalue": "Could not find field '('deposit', 'io_cic')' in AMRGridData.",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mYTFieldNotFound\u001b[0m                           Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-3-c336063963cb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.24\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.26\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcreate_fake_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m<ipython-input-2-21df8fa90f84>\u001b[0m in \u001b[0;36mcreate_fake_dataset\u001b[0;34m(px)\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0myt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_amr_grids\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0myt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSlicePlot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'deposit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'io_cic'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mannotate_grids\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/atmyers/yt-x86_64/src/yt-hg/yt/visualization/plot_window.pyc\u001b[0m in \u001b[0;36mSlicePlot\u001b[0;34m(ds, normal, fields, axis, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1840\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'north_vector'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1841\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1842\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mAxisAlignedSlicePlot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/atmyers/yt-x86_64/src/yt-hg/yt/visualization/plot_window.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, ds, axis, fields, center, width, axes_unit, origin, fontsize, field_parameters, window_size, aspect)\u001b[0m\n\u001b[1;32m   1012\u001b[0m         slc = ds.slice(axis, center[axis],\n\u001b[1;32m   1013\u001b[0m             field_parameters = field_parameters, center=center)\n\u001b[0;32m-> 1014\u001b[0;31m         \u001b[0mslc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1015\u001b[0m         PWViewerMPL.__init__(self, slc, bounds, origin=origin,\n\u001b[1;32m   1016\u001b[0m                              \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfontsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/atmyers/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.pyc\u001b[0m in \u001b[0;36mget_data\u001b[0;34m(self, fields)\u001b[0m\n\u001b[1;32m    604\u001b[0m         \u001b[0mnfields\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    605\u001b[0m         \u001b[0mapply_fields\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 606\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mfield\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_determine_fields\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    607\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mfield\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfiltered_particle_types\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    608\u001b[0m                 \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mknown_filters\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfield\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/atmyers/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.pyc\u001b[0m in \u001b[0;36m_determine_fields\u001b[0;34m(self, fields)\u001b[0m\n\u001b[1;32m    484\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mYTFieldNotParseable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfield\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    485\u001b[0m                 \u001b[0mftype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfield\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 486\u001b[0;31m                 \u001b[0mfinfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_field_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mftype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    487\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    488\u001b[0m                 \u001b[0mfname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfield\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/atmyers/yt-x86_64/src/yt-hg/yt/data_objects/static_output.pyc\u001b[0m in \u001b[0;36m_get_field_info\u001b[0;34m(self, ftype, fname)\u001b[0m\n\u001b[1;32m    480\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_last_finfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfield_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mftype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_last_finfo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mYTFieldNotFound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mftype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setup_classes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mYTFieldNotFound\u001b[0m: Could not find field '('deposit', 'io_cic')' in AMRGridData."
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}