visualization3d-dataset00000300.ipynb

{
 "metadata": {
  "name": "visualization3d-dataset00000300"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "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)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "['', '/home/fwktg1/.local/lib/python2.7/site-packages/pyzmq-15.2.0-py2.7-linux-x86_64.egg', '/home/fwktg1/.local/lib/python2.7/site-packages/tornado-4.3-py2.7-linux-x86_64.egg', '/home/fwktg1/.local/lib/python2.7/site-packages/backports_abc-0.4-py2.7.egg', '/home/fwktg1/.local/lib/python2.7/site-packages/certifi-2015.11.20.1-py2.7.egg', '/home/fwktg1/.local/lib/python2.7/site-packages/singledispatch-3.4.0.3-py2.7.egg', '/home/fwktg1/.local/lib/python2.7/site-packages/backports.ssl_match_hostname-3.5.0.1-py2.7.egg', '/home/fwktg1/.local/lib/python2.7/site-packages/Cython-0.23.4-py2.7-linux-x86_64.egg', '/bigdata/hplsim/external/fwktg1/yt/openPMD-yt', '/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages/distribute-0.6.28-py2.7.egg', '/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages/matplotlib-1.4.2-py2.7-linux-x86_64.egg', '/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages/mock-1.0.1-py2.7.egg', '/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages/nose-1.3.4-py2.7.egg', '/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages/pyparsing-2.0.3-py2.7.egg', '/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages/pytz-2014.10-py2.7.egg', '/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages/python_dateutil-2.2-py2.7.egg', '/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages/Cython-0.21.1-py2.7-linux-x86_64.egg', '/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages/Jinja2-2.7.2-py2.7.egg', '/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages/MarkupSafe-0.23-py2.7-linux-x86_64.egg', '/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages/numexpr-2.2.2-py2.7-linux-x86_64.egg', '/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages/simplegeneric-0.8.1-py2.7.egg', '/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages/pydicom-0.9.8-py2.7.egg', '/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages/netCDF4-1.1.1-py2.7-linux-x86_64.egg', '/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages/h5py-2.3.1-py2.7-linux-x86_64.egg', '/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages/joblib-0.8.4-py2.7.egg', '/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages/tofu-0.9-py2.7.egg', '/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages/pcircle-0.15rc1_1.g705f198-py2.7.egg', '/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages/pandas-0.17.1-py2.7-linux-x86_64.egg', '/opt/pkg/devel/python/2.7.8/bin', '/home/fwktg1/.local/lib/python2.7/site-packages', '/opt/pkg/devel/python/2.7.8/lib/python27.zip', '/opt/pkg/devel/python/2.7.8/lib/python2.7', '/opt/pkg/devel/python/2.7.8/lib/python2.7/plat-linux2', '/opt/pkg/devel/python/2.7.8/lib/python2.7/lib-tk', '/opt/pkg/devel/python/2.7.8/lib/python2.7/lib-old', '/opt/pkg/devel/python/2.7.8/lib/python2.7/lib-dynload', '/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages', '/opt/pkg/devel/python/2.7.8/lib/python2.7/site-packages/IPython/extensions']\n"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "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/data00000300.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 11:45:13,775 openPMD: found 1 iterations in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:45:13,776 openPMD: only choose to load first iteration in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:13,840 Parameters: current_time              = 9.85413643563e-14\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:13,840 Parameters: domain_dimensions         = [ 26  26 201]\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:13,841 Parameters: domain_left_edge          = [ 0.  0.  0.]\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:13,842 Parameters: domain_right_edge         = [ 1.  1.  1.]\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:13,843 Parameters: cosmological_simulation   = 0\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:45:13,845 openPMD: found 1 iterations in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:45:13,845 openPMD: only choose to load first iteration in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:45:13,847 openPMD: found 1 iterations in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:45:13,848 openPMD: only choose to load first iteration in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:13,857 Loading field plugins.\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:13,858 Loaded angular_momentum (8 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:13,859 Loaded astro (15 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:13,859 Loaded cosmology (22 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:13,860 Loaded fluid (64 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:13,861 Loaded fluid_vector (96 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:13,862 Loaded geometric (112 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:13,862 Loaded local (112 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:13,863 Loaded magnetic_field (120 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:13,864 Loaded my_plugins (120 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:13,864 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 11:45:14,189 Projection completed\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:14,189 xlim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:14,190 ylim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:14,193 xlim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:14,194 ylim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:14,196 Making a fixed resolution buffer of (('openPMD', 'E_x')) 800 by 800\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:45:14,256 Plot image for field ('openPMD', 'E_x') has both positive and negative values. Min = -812377122000.037720, Max = 812377122000.073730.\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:45:14,257 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 = 9.85413644e-14 = 9.85413644e-14 s = 3.12258741e-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"
      },
      {
       "html": "<img style=\"max-width:100%%;max-height:100%%;\" src=\"data:image/png;base64,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\"><br>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<yt.visualization.plot_window.ProjectionPlot at 0x7fc973944190>"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:15,381 Saving plot data00000300.h5_Projection_y_E_x.png\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:15,742 xlim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:15,743 ylim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:15,746 xlim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:15,747 ylim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:45:15,748 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-4-79a3852ebdce>\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 11:45:19,849 Saving plot data00000300.h5_Projection_y_E_x.png\n"
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": "['data00000300.h5_Projection_y_E_x.png']"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}