visualization3d-dataset00000200.ipynb

{
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  "name": "visualization3d-dataset00000200"
 },
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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import yt",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from numpy import *",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "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": 7
    },
    {
     "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/data00000200.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 11:49:02,190 openPMD: found 1 iterations in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:49:02,190 openPMD: only choose to load first iteration in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:02,240 Projection completed\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:02,241 xlim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:02,241 ylim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:02,245 xlim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:02,245 ylim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:02,247 Making a fixed resolution buffer of (('openPMD', 'E_x')) 800 by 800\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:49:02,295 Plot image for field ('openPMD', 'E_x') has both positive and negative values. Min = -728445152469.264526, Max = 728445152469.296875.\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:49:02,296 Switching to symlog colorbar scaling unless linear scaling is specified later\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "1: General Informations:\nlevel\t# grids\t       # cells\t     # cells^3\n----------------------------------------------\n  0\t     1\t        135876\t            52\n----------------------------------------------\n   \t     1\t        135876\n\n\nt = 6.56942429e-14 = 6.56942429e-14 s = 2.08172494e-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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tTddvfrqqyVJ06dPV1VVlaP8nXfe0erVqzV27Fj16tUrJmb33w1R3z53M8Zo4sSJOuqoo1Jq393GWWedpUsvvVS77babJOnWW29V165dtXDhQt12222NbhsAAK8i6QEAaHZ1Q1CMMXr11VcbtM57770nqfYEP94NKjt37qwnn3xSrVq10vnnn6+PP/44UjZlyhStXbtW//rXvxp0w9G8vDw99NBDKigo0OrVq3XooYeqS5cu6tatm/baay+Fw2Fdc801kfr33XefunTpoq1bt2rMmDFq1aqVOnTooN69e+vggw/W8OHDI3X79Omjdu3aqbq6Wuecc46mTp2q6667TlLtCXPdsAR3IqC8vFx/+ctfVFVVpV//+teqqKiQVJuAOO+88zRgwAD94x//cKxTd3POTZs2OZ7/73//K6l2/9d3Tw23xrxmkvT6669Lkj799FNHAqF///566KGH5PP59Jvf/CaSyAmFQrrwwgvVqlUrPfXUU46ZeuquwHnyySd18803a+zYsfUOZ6pz1llnafz48VqzZo3OPvvsSKJp06ZNmjhxosrKynT77bc71lmyZElkWxcuXNjgbZWkdevWRdatb0jN559/rtGjR+s///mPjj766JTaj3bDDTdo1apVOu+88yLP7bLLLpHE3hVXXKH58+c3un0AADzJAADQTP7617+aPffc01iWZWzbNrZtG7/fb/bff3/zyiuvxF2npqbGDBgwwLFOSUmJOfjgg+PWX716tTnzzDNN9+7dzaGHHmqGDh1qzjvvPPPVV1/FrX/11Vcby7LM2LFjY8oWL15sRo4cadq1a2cKCwvNoEGDzO23325CoVBM3f/973/mjDPOMJ07dzaFhYXmoIMOMk888UTcPh999FGz6667ms6dO5tLL73UBAIB87e//c306NEjso2WZZk999zTvPHGG451X3nlFXPMMceY3Xff3Rx11FFm6NCh5vrrrzdVVVWROsFg0FxyySWmuLjY2LZtCgoKzIUXXmjWrl1rXnnlFTN48OBIP4MHDzbPPPNM3Djr+hs0aFCkfl1se++9t7nlllvqXW/hwoVmr732crxuXbt2NZMnT3bUe++998yoUaNM9+7dzdChQ80RRxxhLr74YvP999/HtBkIBMxvfvMb07p1a7PvvvuaF154od7+3Z544glzxBFHmF69epmjjz7alJWVmb/97W8mGAxG6lRUVJgDDjjAFBQURGLOy8sz++23n3nyyScTtv/WW2+Zv/zlL6ZNmzaRdYuKiswBBxxghg4daoYOHWoOOuggs9tuuxnLsoxlWaZTp04Njt+tvLzcWJZlfD6f+f3vfx95/vnnnzcdOnSIxODz+czll1/e6H4AAPAayxgGgAIAdh6TJk3Stddeq3PPPVd33313tsMBAABAM2J4CwBgp7TLLrtkOwQAAAA0s7zkVQAA8I66+0zss88+WY4EO6sLL7ww6b1Jou2333669dZbmzEiAAC8i6QHAMDTXnvtNVVVVWnEiBGSpO+//175+flZmbIUkEQCAwCADGJ4CwDA084880yNHDlSM2bMUCAQ0MKFC3XZZZepU6dO2Q4NAAAAzYwbmQIAPG3WrFm6+uqrtWbNGvXv319jxozRxIkTsx0WAAAAMoCkh4dt2LBB//73v7XHHnuoqKgo2+EAAAAA8LBNmzbp7bff1iGHHKJ27dplvP9+/fqpuLg44/0it3FPDw/797//rdNPPz3bYQAAAABAs3v33Xd1wAEHZDsM5BiSHh62xx57SJJO/r98de5Ze/uWjiWWo06e5V4rsx6+abtOv7ggLW0F03zNUjAkPXbzdp3yp8bFF2jERVShUGr1n7qtRr/6Q76k1Lc/mGJfjdm/L06r0YjxP8YXbtoLFAw3afW45t0ZUPnv/T+2n3sXvc2fEVDZef5maTvPbvqbP3r/NVVeM9xh6j/TAxp2ftPjS8e+iif6/ZGLiK9piK9pvBJftv5vSdfnX3MhvqbJ5fg+WxTSqw8Edaakrhnsd52kv2ewP7QsJD08rG5IS+eetrr1rT2j2LW188zCn+Vb2Ra3stSrvy8tbQXSfFIcCEnFrS313Kdx8dU04otOqomIolaWevSrjS+QYn+BFPtqzP4tLLG02972j+s37YtfqvE2REErqWuf9MTXHApKLHXp0zxvUn8aTuSj919T+dPzMeBQ0Erade+mx5eOfRVP9PsjFxFf0xBf03glvmz935Kuz7/mQnxNk8vxbfhf7RfGrpJ2z24oQERuvluw0zhkBHm3pjhgWG7vv4HlzXAmm0b9y3I7vlyX6/uv9Kjcji/X3x+5Ltf3H/F5W67vv1z//Mv1+HId+w9ITW6fMcHzhvw8Ny/NaykG53jSY9+jczu+/kfldny5Ltf3X2l5bseX6++PXJfr+4/4vC3X91+uf/7leny5riXsv0JLKsrgMPpCIyn3LtpFjsj9dwwAAAAAAC4LjfSOkaqyHQhyGsNbgAR+Mpy8ILKnX44PH4G35frl+/A2jj9kE8NHWo6fWtJEWxqd5ckZkNs4owMSOPgYht8ge/Yh6YEsyvXL9+FtHH/IppYwfCTX5ftqh7hkrD8jKZi5/tCycKUHAAAAAADwJJIeAAAAAADAk7h2CwAAAACQNvm2VGhnbnxLfpipW1A/rvQAAAAAAACeRNIDAAAAAAB4EsNbAAAAAABpU2hLRRn8eb0wc12hBeJKDwAAAAAA4EkkPQAAAAAAgCcxvAUAAAAAkDb5tlToy2B/mesKLRBXegAAAAAAAE8i6QEAAAAAADyJ4S0AAAAAgLQpsGtncMlYfyZzfaHl4UoPAAAAAADgSSQ9AAAAAACAJzG8BQAAAACQNvlWZoe35Icz1xdaHq70AAAAAAAAnsSVHgAAAACAFufFGqOXaqRKrvRAAiQ9AAAAAABpU+CzVOSzmr2fE4osnVAkLQ8ajfqBKVwQH8NbAAAAAACAJ5H0AAAAAAAAnkTSAwAAAAAAeBL39AAAAAAApE2BLRX6MtgfNzJFAlzpAQAAAAAAPImkBwAAAAAA8CSGtwAAAAAA0ibflgoz+PN6Pj/lIwEODwAAAAAA4EkkPQAAAAAAgCcxvAUAAAAAkDYFtlSUydlbQpnrCy0PV3oAAAAAAABPIukBAAAAAAA8ieEtAAAAAIC0Kcjw7C0F/JSPBDg8AAAAAACAJ3Glx05gzt9qVNTK0r5H+7Trr/KzHQ4AAAAAD1k2L6hlr4S0Zb3JdihADJIeO4FjJ+SrW18u6gEAAACQfqXleSotz9PSuUE9PTkgv20p32dlrH+/LUkkXBAfZ8IAAAAAAMCTSHoAAAAAAABPYngLAAAAACB9bGX253V+ykcCHB4AAAAAAMCTSHoAAAAAAABPYngLAAAAACB9bEm+5u9m9uawZm8xqgg3f19oubjSAwAAAADQ4oxuY+vZbj7d2onTWtSPowMAAAAAAHgSw1sAAAAAAOnD7C3IIRweAAAAAADAk0h6AAAAAAAATyLpAQAAAAAAPIl7egAAAAAA0sdSZn9etzLYF1ocrvQAAAAAAACeRNIDAAAAAAB4EsNbAAAAAADp47NqHxnrL3NdoeXhSg8AAAAAAOBJJD0AAAAAAIAnMbxlJ5Dnk/xxLvny25I/k5edNVEgZBKW+xuRwguEE7TXgMvkAqH6y/LtxPu2Jhy7PXkJ+gwm6EuS/K7+AnHad9R39ZVoW2rbj30u0f6LXT+1+GLWTzHeVDU1vlzn3r5sash7q1n7z6F9AcDbvP5/C1AvW5kdcsJP+UiApMdOIM/a8Z9usd/5n29jEgWZFH1Sne4ETSBkUt5+90l+Kidv7pP0VJMiiRIiUmxSJN6JXaIvW/G2JdVECEmQ3JHtE/tMJzayvb0AAADITTl+ygsAAAAAANA4XOkBAAAAAEgfW5n9eZ2f8pEAhwcAAAAAAPAkkh4AAAAAAMCTGN4CAAAAAEgfZm9BDuHwAAAAAAAAnkTSIwe88847ysvL04IFC7IdCgAAAAAAnkHSI8tqamo0duxYGWPilhtjdM899+jggw/W4Ycfrn333Venn366vvjii8wGCgAAAAANYWfhAdSDwyPLrr32Wn322Wf1Jj3Gjh2riy66SHfeeadef/11LVmyRKFQSAceeKCWLVuW4WgBAAAAAGg5SHpk0QcffKBFixbpkEMOiVt+yy23aObMmbr88ss1ePBgSVJ+fr7uvvtuhcNhnXjiiQoEApkMGQAAAACAFoOkR5YEg0Gdf/75mjFjRtzyqqoq3XDDDbJtW2PGjHGUtWnTRqNGjdKqVat0//33ZyBaAAAAAGggS5kd2mJlZrPQMpH0yJKpU6fq+OOP11577RW3/KmnntLGjRvVp08f7brrrjHlQ4cOlSQ99NBDzRkmAAAAAAAtVl62A9gZrVixQi+++KLeeOONeuu89dZbkqT+/fvHLS8tLZUkLV68WJs3b1abNm3SHygAAAAAAC0YSY8MC4VCOu+88zRjxgxZVv3XYS1fvlyS1K1bt7jl0Vd/LFu2TEOGDElvoAAAAADQGD6r9tHMZn8X1uzvwqoINntXaMEY3pJht956q4YOHaqBAwcmrLdx40ZJUuvWreOWR1/Z8d1336UvQAAAAABoAUZ3svVs/zzduqcv26Egh3GlRwZ9+umnmj17thYuXJi0blVVlaTa2VriKSgokCQZY7R169b0BQkAAAAAgEdwpUeGGGM0btw4TZs2TX6/P2n9oqIiSap3Stq65y3LUnFxcfoCBQAAAADAI0h6ZMj06dM1cOBAHXLIIfXWMcZE/u7ataskqbKyMm7d6Oe7dOmSpigBAAAAoIlsSb4MPjirRQIMb8mQJ598UgsWLNC0adPqrVNWViapdjraQYMGad68eVq7dm3cunXPW5ZV7wwvdZ66rUZFrWpvJFTw4yt+6M/9Omxk8itOAAAAAMBt2byglr0Scjy3Zb2ppzaQPSQ9MqRnz57q169f3LIvv/xS27Zt0+67767i4mL17NlTZWVluuOO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\"><br>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<yt.visualization.plot_window.ProjectionPlot at 0x7f2ff70048d0>"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:03,262 Saving plot data00000200.h5_Projection_y_E_x.png\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:49:03,607 Field ('openPMD', 'absE_x') already exists. To override use force_override=True.\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:03,620 xlim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:03,621 ylim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:03,624 xlim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:03,625 ylim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:03,627 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-8-5e6c18de60da>\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": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "p.save()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:49:07,142 Saving plot data00000200.h5_Projection_y_E_x.png\n"
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": "['data00000200.h5_Projection_y_E_x.png']"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}