visualization3d-dataset00000400.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import yt",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages/matplotlib-1.4.2-py2.7-linux-x86_64.egg/mpl_toolkits/__init__.py:2: UserWarning: Module argparse was already imported from /opt/pkg/devel/python/2.7.8/lib/python2.7/argparse.pyc, but /opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages is being added to sys.path\n  __import__('pkg_resources').declare_namespace(__name__)\n"
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from numpy import *",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import sys\nprint(sys.path)",
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     "input": "\"\"\"\n\nProgramm: YT_Example_Plots\nAuthor: Daniel Grassinger\nLicense: Modified BSD License (also known as New or Revised BSD)\nDate: 25.09.2015\nPython: 2.7.6\nYT Version: 3.2\n\nDescription: This file show some examples how to plot data in yt\n\nmore examples and docomentation on:\nhttp://yt-project.org/doc/visualizing/\n\nRequired: an example openPMD called \"data00000800.h5\" file must\n          exitst in the same folder\n\n\"\"\"\n\nimport yt\nimport numpy as np\nimport h5py\nimport math\n\n\n# load an example dataset\nds = yt.load(\"/home/fwktg1/openPMD-example-datasets/example-3d/hdf5/data00000400.h5\")\n\n#First some general informations on the dataset\n\nprint \"1: General Informations:\"\nds.print_stats()\n\nprint \"   This Fields does yt know or could generate them\"\nds.field_list\n\nprint \"   Size of Simulation Box: \"\nprint ds.domain_width\n\n# This returns the data of this field as a YTArray with units\nprint \"   The Data and Units of the Field E_x\"\nad = ds.all_data()\nprint ad[\"E_x\"] \n\n#First simple plots\nprint \"2: Simple Plots\"\n\nprint \"   Projection Plot of E_x\"\np = yt.ProjectionPlot(ds, \"y\", \"E_x\")\n\n# This adds a Title to the Plot\np.annotate_title('Projection Plot of E_x')\n\n# Shows the Plot on Screen\np.show()\n# Saves the Plot as .png\np.save()\n\n# This makes a SlicePlot of rho and overplot the contur of E\n\n# This Function discribes how E is calculated out of E_x, E_y, E_z\ndef _absE_x(field, data):\n    return np.sqrt(data[\"E_x\"]*data[\"E_x\"]+data[\"E_y\"]*data[\"E_y\"]+data[\"E_z\"]*data[\"E_z\"])\n\n# Here E is defined as a new Field called \"absE_x\"\nds.add_field((\"openPMD\", \"absE_x\"),function=_absE_x,units=\"V/m\")\n\n# This makes a SlicePlot of rho\np = yt.SlicePlot(ds, \"y\", ['rho'])\n\n# This overplots the contur of E\np.annotate_contour((\"openPMD\",\"absE_x\"),ncont=5, clim=(1e12,1e11))\np.show()\n\n# There are a lot of another annotations and additional parameters for the visualisation on the yt website\n\n#Plotting time series\nprint \"3: Plotting time series\"\n\n# Only load with yt.load but use ? or * instead of characters creats a time series\ntimeseries = yt.load(\"/home/fwktg1/openPMD-example-datasets/example-3d/hdf5/data0000??00.h5\")\n\n# Get every dataset from timeseries and plot it\nfor ds in timeseries:\n    p = yt.SlicePlot(ds, \"y\",\"E_x\") \n    p.show()\n\n# Volume Rendering of 3D Data\n# As OpenPMD 3D Datasets are not tested yet we use other sample Data\n#Sample Data from http://yt-project.org/data/\nds = yt.load(\"/bigdata/hplsim/external/fwktg1/yt/IsolatedGalaxy/galaxy0030/galaxy0030\")\n\n#This creates and shows a 3D Model of the sample Data\nprint \"4: Volume Rendering of Example Galaxy\"\ntf = yt.ColorTransferFunction((-28, -25))\ntf.add_layers(4, w=0.03)\ncam = ds.camera([0.5, 0.5, 0.5], [1.0, 1.0, 1.0], (20.0, 'kpc'), 512, tf, no_ghost=False)\ncam.show(clip_ratio=4.0)\n\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:49:53,051 openPMD: found 1 iterations in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:49:53,052 openPMD: only choose to load first iteration in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,111 Parameters: current_time              = 1.31388485808e-13\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,111 Parameters: domain_dimensions         = [ 26  26 201]\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,112 Parameters: domain_left_edge          = [ 0.  0.  0.]\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,113 Parameters: domain_right_edge         = [ 1.  1.  1.]\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,113 Parameters: cosmological_simulation   = 0\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:49:53,116 openPMD: found 1 iterations in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:49:53,116 openPMD: only choose to load first iteration in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:49:53,118 openPMD: found 1 iterations in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:49:53,119 openPMD: only choose to load first iteration in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,129 Loading field plugins.\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,129 Loaded angular_momentum (8 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,130 Loaded astro (15 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,130 Loaded cosmology (22 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,131 Loaded fluid (64 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,133 Loaded fluid_vector (96 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,133 Loaded geometric (112 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,134 Loaded local (112 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,134 Loaded magnetic_field (120 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,135 Loaded my_plugins (120 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,135 Loaded species (122 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "1: General Informations:\nlevel\t# grids\t       # cells\t     # cells^3"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,465 Projection completed\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,465 xlim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,466 ylim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,470 xlim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,470 ylim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:53,471 Making a fixed resolution buffer of (('openPMD', 'E_x')) 800 by 800\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:49:53,534 Plot image for field ('openPMD', 'E_x') has both positive and negative values. Min = -880485761061.579224, Max = 880485761061.618286.\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:49:53,534 Switching to symlog colorbar scaling unless linear scaling is specified later\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n----------------------------------------------\n  0\t     1\t        135876\t            52\n----------------------------------------------\n   \t     1\t        135876\n\n\nt = 1.31388486e-13 = 1.31388486e-13 s = 4.16344988e-21 years\n\nSmallest Cell:\n\tWidth: 1.246e-24 Mpc\n\tWidth: 1.246e-18 pc\n\tWidth: 2.571e-13 AU\n\tWidth: 3.846e+00 cm\n   This Fields does yt know or could generate them\n   Size of Simulation Box: \n[ 1.  1.  1.] code_length\n   The Data and Units of the Field E_x\n[ 0.  0.  0. ...,  0.  0.  0.] V/m\n2: Simple Plots\n   Projection Plot of E_x\n"
      },
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       "metadata": {},
       "output_type": "display_data",
       "text": "<yt.visualization.plot_window.ProjectionPlot at 0x7f28a14a9c10>"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:54,670 Saving plot data00000400.h5_Projection_y_E_x.png\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:55,039 xlim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:55,040 ylim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:55,043 xlim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:55,043 ylim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:55,045 Making a fixed resolution buffer of (('openPMD', 'rho')) 800 by 800\n"
      },
      {
       "ename": "error",
       "evalue": "data is of incorrect type (wanted 1D float)",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31merror\u001b[0m                                     Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-4-de2978e26776>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     67\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     68\u001b[0m \u001b[1;31m# This makes a SlicePlot of rho\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 69\u001b[1;33m \u001b[0mp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0myt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSlicePlot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"y\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'rho'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     70\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     71\u001b[0m \u001b[1;31m# This overplots the contur of E\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/bigdata/hplsim/external/fwktg1/yt/openPMD-yt/yt/visualization/plot_window.pyc\u001b[0m in \u001b[0;36mSlicePlot\u001b[1;34m(ds, normal, fields, axis, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2131\u001b[0m             \u001b[1;32mdel\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'north_vector'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2133\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mAxisAlignedSlicePlot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnormal\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32m/bigdata/hplsim/external/fwktg1/yt/openPMD-yt/yt/visualization/plot_window.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, ds, axis, fields, center, width, axes_unit, origin, fontsize, field_parameters, window_size, aspect, data_source)\u001b[0m\n\u001b[0;32m   1272\u001b[0m         PWViewerMPL.__init__(self, slc, bounds, origin=origin,\n\u001b[0;32m   1273\u001b[0m                              \u001b[0mfontsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfontsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1274\u001b[1;33m                              window_size=window_size, aspect=aspect)\n\u001b[0m\u001b[0;32m   1275\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0maxes_unit\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1276\u001b[0m             \u001b[0maxes_unit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_axes_unit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/bigdata/hplsim/external/fwktg1/yt/openPMD-yt/yt/visualization/plot_window.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    667\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_type\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"plot_type\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    668\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_splat_color\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"splat_color\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 669\u001b[1;33m         \u001b[0mPlotWindow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    670\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    671\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_setup_origin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/bigdata/hplsim/external/fwktg1/yt/openPMD-yt/yt/visualization/plot_window.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data_source, bounds, buff_size, antialias, periodic, origin, oblique, window_size, fields, fontsize, aspect, setup)\u001b[0m\n\u001b[0;32m    247\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_field_transform\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfield\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlinear_transform\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    248\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetup_callbacks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 249\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setup_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    250\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    251\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_initialize_dataset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/bigdata/hplsim/external/fwktg1/yt/openPMD-yt/yt/visualization/plot_window.pyc\u001b[0m in \u001b[0;36m_setup_plots\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    735\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    736\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data_valid\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 737\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_recreate_frb\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    738\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data_valid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    739\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_colorbar_valid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/bigdata/hplsim/external/fwktg1/yt/openPMD-yt/yt/visualization/plot_window.pyc\u001b[0m in \u001b[0;36m_recreate_frb\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    307\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data_valid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    308\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mold_fields\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 309\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_frb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_data_source_fields\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    310\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    311\u001b[0m             \u001b[1;31m# Restore the old fields\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/bigdata/hplsim/external/fwktg1/yt/openPMD-yt/yt/visualization/fixed_resolution.pyc\u001b[0m in \u001b[0;36m_get_data_source_fields\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    156\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfields\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    157\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mexclude\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata_source\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparticle_types\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 158\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    159\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    160\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_is_ion\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfname\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/bigdata/hplsim/external/fwktg1/yt/openPMD-yt/yt/visualization/fixed_resolution.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m    135\u001b[0m         buff = self.ds.coordinates.pixelize(self.data_source.axis,\n\u001b[0;32m    136\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata_source\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbounds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuff_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 137\u001b[1;33m             int(self.antialias))\n\u001b[0m\u001b[0;32m    138\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    139\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filters\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/bigdata/hplsim/external/fwktg1/yt/openPMD-yt/yt/geometry/coordinates/cartesian_coordinates.pyc\u001b[0m in \u001b[0;36mpixelize\u001b[1;34m(self, dimension, data_source, field, bounds, size, antialias, periodic)\u001b[0m\n\u001b[0;32m     55\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdimension\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     56\u001b[0m             return self._ortho_pixelize(data_source, field, bounds, size,\n\u001b[1;32m---> 57\u001b[1;33m                                         antialias, dimension, periodic)\n\u001b[0m\u001b[0;32m     58\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     59\u001b[0m             return self._oblique_pixelize(data_source, field, bounds, size,\n",
        "\u001b[1;32m/bigdata/hplsim/external/fwktg1/yt/openPMD-yt/yt/geometry/coordinates/cartesian_coordinates.pyc\u001b[0m in \u001b[0;36m_ortho_pixelize\u001b[1;34m(self, data_source, field, bounds, size, antialias, dim, periodic)\u001b[0m\n\u001b[0;32m     72\u001b[0m                              \u001b[0mdata_source\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfield\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     73\u001b[0m                              \u001b[0mbounds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mantialias\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m                              period, int(periodic)).transpose()\n\u001b[0m\u001b[0;32m     75\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mbuff\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     76\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31merror\u001b[0m: data is of incorrect type (wanted 1D float)"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "p.save()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:58,401 Saving plot data00000400.h5_Projection_y_E_x.png\n"
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": "['data00000400.h5_Projection_y_E_x.png']"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}