visualization3d-dataset00000100.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/data00000100.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 11:42:48,886 openPMD: found 1 iterations in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:42:48,886 openPMD: only choose to load first iteration in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:48,944 Parameters: current_time              = 3.28471214521e-14\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:48,945 Parameters: domain_dimensions         = [ 26  26 201]\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:48,945 Parameters: domain_left_edge          = [ 0.  0.  0.]\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:48,946 Parameters: domain_right_edge         = [ 1.  1.  1.]\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:48,947 Parameters: cosmological_simulation   = 0\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:42:48,949 openPMD: found 1 iterations in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:42:48,949 openPMD: only choose to load first iteration in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:42:48,951 openPMD: found 1 iterations in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:42:48,952 openPMD: only choose to load first iteration in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:48,961 Loading field plugins.\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:48,962 Loaded angular_momentum (8 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:48,962 Loaded astro (15 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:48,963 Loaded cosmology (22 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:48,964 Loaded fluid (64 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:48,965 Loaded fluid_vector (96 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:48,966 Loaded geometric (112 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:48,966 Loaded local (112 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:48,967 Loaded magnetic_field (120 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:48,967 Loaded my_plugins (120 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:48,968 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 11:42:49,293 Projection completed\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:49,293 xlim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:49,294 ylim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:49,298 xlim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:49,298 ylim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:49,299 Making a fixed resolution buffer of (('openPMD', 'E_x')) 800 by 800\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:42:49,361 Plot image for field ('openPMD', 'E_x') has both positive and negative values. Min = -160430521630.776642, Max = 160430521630.783783.\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:42:49,362 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 = 3.28471215e-14 = 3.28471215e-14 s = 1.04086247e-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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\"><br>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<yt.visualization.plot_window.ProjectionPlot at 0x7f1294889f50>"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:50,524 Saving plot data00000100.h5_Projection_y_E_x.png\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:50,895 xlim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:50,896 ylim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:50,899 xlim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:50,899 ylim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:42:50,901 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-e43233d939a5>\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 11:43:23,395 Saving plot data00000100.h5_Projection_y_E_x.png\n"
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": "['data00000100.h5_Projection_y_E_x.png']"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}