visualization3d_dataset00000500.ipynb

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     "cell_type": "code",
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     "input": "import yt",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from numpy import *",
     "language": "python",
     "metadata": {},
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     "prompt_number": 6
    },
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     "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/data00000500.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",
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     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:26:17,886 openPMD: found 1 iterations in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:26:17,886 openPMD: only choose to load first iteration in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:17,925 Parameters: current_time              = 1.6423560726e-13\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:17,925 Parameters: domain_dimensions         = [ 26  26 201]\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:17,926 Parameters: domain_left_edge          = [ 0.  0.  0.]\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:17,927 Parameters: domain_right_edge         = [ 1.  1.  1.]\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:17,927 Parameters: cosmological_simulation   = 0\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:26:17,929 openPMD: found 1 iterations in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:26:17,930 openPMD: only choose to load first iteration in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:26:17,932 openPMD: found 1 iterations in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:26:17,932 openPMD: only choose to load first iteration in file\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:17,941 Loading field plugins.\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:17,941 Loaded angular_momentum (8 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:17,942 Loaded astro (15 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:17,942 Loaded cosmology (22 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:17,943 Loaded fluid (64 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:17,945 Loaded fluid_vector (96 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:17,945 Loaded geometric (112 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:17,946 Loaded local (112 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:17,946 Loaded magnetic_field (120 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:17,947 Loaded my_plugins (120 new fields)\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:17,947 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 11:26:18,291 Projection completed\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:18,291 xlim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:18,292 ylim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:18,295 xlim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:18,296 ylim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:18,297 Making a fixed resolution buffer of (('openPMD', 'E_x')) 800 by 800\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:26:18,349 Plot image for field ('openPMD', 'E_x') has both positive and negative values. Min = -891015870557.733521, Max = 891015870557.773071.\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [WARNING  ] 2016-01-29 11:26:18,349 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 = 1.64235607e-13 = 1.64235607e-13 s = 5.20431235e-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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Hnn99dcViUS0evXqQocCoJuQ9AA64Vvf+pYaGxtlGEbGOk8++aTGjx+vcePGacyYMZo2bZrWr1/vY5QIiiVLluiiiy5SZWWlYrGYLrzwQi1ZsiTrOvQ/eGHbtm2aMWOGzjnnHI0bN07nnnuuHnzwwUKHhQBJJBL6zW9+ozFjxqiiokJ9+vTRxIkTtWzZsrT1bdvWAw88oAsvvFDjx4/XqFGjdO2112r79u3+Bo5Aamlp0cyZM5XpuQ70PyAgbABZ/e53v7PLyspswzDsRx55JG2dn/70p3Y0GrWXLl1q27ZtW5Zlz5kzx66oqLBfeOEFP8NFibvppptswzDsSCRiR6NROxQK2YZh2IZh2HPmzEm7Dv0PXli3bp3dp08fe8aMGXY8Hrdt27Zffvllu7Ky0r7++usLHB2CwLIs+8tf/rJtGIZdVlZmh8NhxzHuv/7rv1LWqaurs6uqquy1a9fatm3bR48eta+++mq7f//+9oYNG/zeBQTMLbfc0n6O97e//S2lnP4HBANJDyCLjz76yL7gggvsq6++OmPS48knn7QNw0i5KIjH4/bpp59uDxgwwN63b59fIaOE/fWvf7UHDx5sL1261DZN0zZN0/773/9un3POOe0XBW+88YZjHfofvHDw4EH7xBNPtAcMGGA3NDQ4ym677TbbMAx70aJFBYoOQXH//ffbZ511lr169Wo7kUjYR44csZctW2afeuqptmEYdnl5ub1jx472+gsWLLANw7Dnz5/v2M6hQ4fsvn372sOGDbNbWlr83g0ExLp16+xLL73UnjBhQtqkB/0PCA6mtwBZ3HDDDVqwYIF69eqVtty2bd16660yDEOzZs1ylIXDYdXV1Wnfvn26++67/QgXJe5Xv/qVnnrqKV1xxRWKRCKKRCIaN26cli9frpqaGknSn//85/b69D94ZeHChfroo4/0la98Rb1793aUtfWtW2+9VfF4vBDhISAWLlyo5cuXa/z48QqFQiovL9fUqVP1zDPPKBqN6ujRo1q+fLkkqampSfPnz1coFNI3vvENx3b69OmjL3/5y9q6daseeuihAuwJSl08Htd3v/tdLVy4MG05/Q8IFpIeQAaPPvqoTjjhBH3uc5/LWGfNmjXasmWLKioqdMEFF6SUT5gwQZK0ePHijPNFAUlqbGzUwIEDdf7556eUDRw4UFOnTpUk7d+/v/11+h+80nbfjosvvjil7IQTTtDQoUO1Y8cOvfDCCz5HhqDYunWrpkyZopNOOiml7KyzztJ5550nqeMY99RTT2n//v06/fTTdfzxx6es03Z8W7RoUfcFjcD6j//4D02bNk2nnnpq2nL6HxAsJD2ANHbv3q0FCxbk/Av5mjVrJEnDhg1Le5PTkSNHtm9vw4YN3geKwIjFYrr33nszlp944omSpCFDhrS/Rv+DF/bu3att27bJMAydddZZaeu09aUVK1b4GRoC5JRTTtFPf/rTjOXuY1zb8S1Xn3zjjTd0+PBhL0NFwG3evFnLly/XD3/4w4x16H9AsJD0ANKYPXu25s+fnzLM223Tpk2SpMGDB6ctr6mpUXl5uWzb1saNGz2PE8FhGIYqKyszlu/atUuSdNVVV7W/Rv+DF9r6kZS5L33mM59JqQvkIxKJqKKiImP5rl27VF5erssuu0xS7uNb8l/fOb6hsxKJhL7zne9o4cKFWZ/IR/8DgoWkB+DyxBNPtM8zzqVtGG5VVVXGOm1le/fu9SZA9EivvvqqJk+erDPPPLP9NfofvJA8ZSpTX+rTp48kac+ePb7EhJ6lublZ//u//6sZM2aob9++knIf39r6pMTxDZ3361//WhMmTNDZZ5+dtR79DwiWSKEDAIrJ/v37dfvtt2vVqlWdqt/U1CRJKisry1in7SaojY2NXY4PPdMrr7yiDz74QEuXLnW8Tv+DF9r6kSRFo9G0dehH6E5PPfWUysvLdeedd7a/luv41tYnbdumX6JT/vGPf+ixxx7Tq6++mrMu/Q8IFkZ6AEm+//3v65ZbblG/fv06Vb9tqK5pmhnrtJXFYrGuB4gex7Is3XjjjXrggQd02mmnOcrof/BC8pSDTE9noR+huzQ2NuqnP/2pHnvssfZRHlLu41vb64Zh0C+Rk23buv7663XPPfdkTO4mo/8BwULSA4GycOFChUKhvH9uvvlmPffcc6qvr9dXv/rVjNt3PwFj0KBBkqSGhoaM67SV1dbWerCHKGZd6X+Z3H777ZoyZYq+/vWvp5TR/+CF5LnpmfoS/QjdZfbs2frRj36kiRMnOl7PdXxLfp1+iVx++9vf6uyzz9bYsWMz1kk+x6P/AcHC9BYESr9+/TI+ySKb2tpa/fGPf9Szzz6rUChzLnDmzJmaOXOmhgwZovfff7/9rt4ff/xx2voNDQ1qbGyUYRgaMWJEXjGh9HSl/6Xz6KOPavfu3brvvvvSlrf1KfofuiL5PjE7d+5UTU1NSp22PkY/gpd+8YtfaNiwYfrmN7+ZUjZixAitXLky4/Gt7fVsTx0C2jzxxBNavXq17rnnnox12hJvEyZM0OjRo+l/QICQ9ECgTJ8+XdOnTz+mdefOnavhw4enLdu5c6cOHz6s448/XtXV1e138540aZIk6d133027Xtvdv/v3769Ro0YdU1woHV3pf27Lly/XihUrtHjx4ox12k7Q6H/oir59+2r06NFav369Nm/enPYEvq0vuf8aDxyrBx98UPX19brrrrvSlk+cOFH/+Z//qc2bN6ctb+uTo0ePVnV1dbfFiWAYMmRIxnO8Dz74QEeOHNFJJ52kWCymIUOG0P+AgGF6C/Cp+fPna/PmzWl/pk2bJkm68847tXnzZj3//POSWn/ZDR8+XIcPH9a6detStrl69WpJ8uxCGD3DSy+9pIceekgPP/xw2vK2G5rS/+CVq6++WlJr33PbvXu3tm7dqv79+2vy5Ml+h4YAevzxx7V27dqMCY+lS5dqypQpqqmp0ZYtW/TJJ5+k1Gk7vl1zzTXdGiuC4ZFHHsl4jnf++edLkhYvXqzNmzdr0aJFmjJlivr27Uv/AwKCpAfQRXPnzpWklAtUy7K0aNEixWIxzZkzpxChoQS99dZbuuuuu7RkyRKFw2FHWUtLix566CGtXbu2/TX6H7xw/fXX67jjjtMf/vAHHTlyxFH28MMPy7ZtzZkzp/2JBcCxWr58uZ555hnde++9KWVNTU2aN2+e9uzZo/Lycv3gBz9QIpFIGfHW0NCgxx9/XLW1tfrWt77lV+joQcrLy3XTTTfR/4CgsAHkVFdXZxuGYT/yyCNpy6+77jq7rKzMXr58uW3btm1Zlj137ly7rKzMfvTRR/0MFSXsnXfesQcMGGCHw+G0P4Zh2KFQyH7//fcd69H/4IW//vWvdiwWs2fNmmWbpmnbtm2//vrrdnV1tX3llVfalmUVOEKUutWrV9sVFRV2JBLJeIyrqqqyGxsbbdu2bdM07S984Qt2TU2N/eabb9q2bdstLS32jBkz7MrKSnvlypWF3B0ExIQJE2zDMOy//e1vjtfpf0BwcE8PoBMMw8h6c8oHH3xQ48eP12233ab58+erublZtbW1WrVqVdY7hQNt9u3bp8mTJ2v//v0Z6xiGoUmTJunkk092vE7/gxcmT56sV199VXfccYcuuugixWIxNTY2at68eZo9e3beN+gFkm3dulVXXXWVjh49mrGOYRj66le/2v4I0EgkoqVLl+qee+7R9ddfr969e6uhoUHDhg3Ta6+9xg0k4YlM53j0PyA4DNt2PYMTAAAAAAAgALinBwAAAAAACCSSHgAAAAAAIJBIegAAAAAAgEAi6QEAAAAAAAKJpAcAAAAAAAgkkh4AAAAAACCQSHoAAAAAAIBAIukBAAAAAAACiaQHAAAAAAAIJJIeAAAAAAAgkEh6AAAAAACAQCLpAQAAAAAAAomkBwAAAAAACCSSHgAAAAAAIJBIegAA0EPt379fX/nKV3Tw4MFCh9Lu8ccf11133VXoMAAAQECQ9AAAoAdqbGzUtGnT9POf/1w1NTWFDqfd9OnTdejQId1xxx2FDgUAAAQASQ8AAHqg6667Tl/72tc0cuTIQoeSYt68eVq2bJlWrlxZ6FAAAECJI+kBAEAP88wzz2jr1q369re/XehQ0gqHw/rNb36juro6NTY2FjocAABQwkh6AADQg1iWpR/96Ef6yU9+UuhQsjr//PN19tln67e//W2hQwEAACWMpAcAAD3Is88+q/379+uLX/xioUPJ6frrr9eCBQtkmmahQwEAACWKpAcAAD3IkiVLdOWVVyocDhc6lJwmT56sw4cPc28PAABwzEh6AADQQ5imqb/85S+6+OKLCx1Kp/Tu3VvnnXeenn766UKHAgAAShRJDwAAisTJJ5+sUCikPn366Pzzz9fFF1+siRMn6qyzzlIoFNKwYcO6tP1169apqalJo0ePzll3/fr1uuaaa3TBBRdo0qRJuuyyy/T9739f//rXvyRJ7733niZOnKgRI0aotrZWv/zlL3Xw4EHdeOONuvLKKzVixAhNmjRJr7/+uiRp586duv7663X55ZfrjDPO0Oc//3n9/e9/zxnHOeeco1deeaVL+w0AAHoukh4AABQJwzD0ve99Tzt37tQbb7yhVatW6fnnn1dlZaVCoZDuu+++Lm3/7bffltSaXMlmyZIluvDCCzV69Gi9/vrrevHFF/Xcc89p/fr1+vznP69EIqFBgwZp7ty5OuOMM7R37141NDRo9uzZ+sEPfqClS5dq/fr1amho0JQpU/TOO+/opptu0u23365ly5Zp06ZNOnLkiKZOnaqdO3dmjWXo0KHasmWLEolEl/YdAAD0TCQ9AAAoEtXV1VqwYIEqKyvbX5s7d67Wrl2rf//3f9ekSZO6tP3t27fLMAz16dMnY51169bpm9/8pi699FL9+Mc/bn9906ZNeumll/Svf/1L9fX1qqys1GWXXabZs2dLku6//37ddNNNGjJkiCQpEonoS1/6kg4dOqTLL79cd999twYNGiRJikaj+tKXvqTGxkY999xzWWMePHiwTNPURx991KV9BwAAPVOk0AEAAIBWw4YNUyTS8at5xYoV+uUvf6nzzz9fv/jFL7q8/QMHDqimpiZrndtvv12maWrmzJmO1z/72c/qd7/7nfr16+fYRtsNUU888USde+65jnWOO+44SdJ5553XngxpM3DgQEnSnj17ssbTlgA6dOhQ1noAAADpkPQAAKBI/PznP2///+7duzVjxgxVVVXpscce8+RpK83NzY5RJG6WZWnZsmUyDENjxoxJKb/uuusyrnvGGWekvNYWc7p7kbSV5Zq20qtXL0lSQ0ND1noAAADpkPQAAKBInHnmmZIk27ZVV1enPXv2aPHixTrllFM82X55eblaWloylu/du1emacowDPXt2zevbZeVlR1TWS5Hjx6V1Bo7AABAvrinBwAARWbBggVasWKFvva1r+naa69tf/3b3/52l7bbr1+/rCMm+vfvr1Co9dTgwIEDXWrLK23x5puEAQAAkEh6AABQVNauXau5c+fqlFNOSXlay5YtW7q07aFDh6qxsVH19fVpyyORiC699FLZtq3XXnstbZ0dO3Zo27ZtXYojHzt27FAoFNKJJ57oW5sAACA4SHoAAFAk6uvrdc0118gwDP3P//yP4/4bO3bsUHNzc5e2P2rUKEnSP//5z4x1fvaznykcDuvee+/NWO6nbdu26cwzz3Tc4BUAAKCzSHoAAFAkf7NLXwAAAwpJREFUbrjhBm3btk233XabLrjggvbXm5qaNHv27PYnnhyrMWPGqHfv3nrzzTcz1hk7dqzuu+8+vf766/rud7/bnmixLEu//vWvVVtbq1NPPbW9/uHDhx3/Jmt7Ld2TV7KVJVu3bp3GjRuXY88AAADSM2zbtgsdBAAAPd3LL7+s8ePHKxQKacqUKQqFQrIsS4cPH9bbb7+thoYGfeMb39BDDz3UpXauvvpqRaNRLVmyJGu9tWvX6u6779amTZtUW1urWCym6dOna8aMGZKkrVu3asaMGdq4caOOHDki27Y1YsQI/du//ZtGjRql7373u3r77bfbb4x69tln6+abb9agQYM0Z84cbdiwIaVs+vTpjhgaGho0YMAAPf300/rCF77Qpf0GAAA9E0kPAAB6kOeee051dXXatWtX0U8ZeeKJJ3TjjTfqww8/9OSRvQAAoOdhegsAAD3IFVdcodraWv3xj38sdCg5PfDAA/re975HwgMAABwzRnoAANDDPPvss5o7d67Wr1/f/ojaYvPaa69p+vTpeueddxSLxQodDgAAKFHFeaYDAAC6zVVXXaURI0bonnvuKXQoaSUSCd10001auHAhCQ8AANAljPQAAKAHamxs1KWXXqp77rlH5557bqHDcbjllltUUVGhW2+9tdChAACAEsdIDwAAeqDevXtr2bJlmjdvng4ePFjocNo9+eSTJDwAAIBnGOkBAAAAAAACiZEeAAAAAAAgkEh6AAAAAACAQCLpAQAAAAAAAomkBwAAAAAACCSSHgAAAAAAIJBIegAAAAAAgEAi6QEAAAAAAAKJpAcAAAAAAAgkkh4AAAAAACCQSHoAAAAAAIBAIukBAAAAAAACiaQHAAAAAAAIJJIeAAAAAAAgkEh6AAAAAACAQCLpAQAAAAAAAomkBwAAAAAACCSSHgAAAAAAIJBIegAAAAAAgEAi6QEAAAAAAAKJpAcAAAAAAAgkkh4AAAAAACCQSHoAAAAAAIBAIukBAAAAAAACiaQHAAAAAAAIJJIeAAAAAAAgkP5/r2qABVo72gUAAAAASUVORK5CYII=\"><br>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<yt.visualization.plot_window.ProjectionPlot at 0x7f87b7ea3990>"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:19,301 Saving plot data00000500.h5_Projection_y_E_x.png\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:19,675 xlim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:19,675 ylim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:19,679 xlim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:19,680 ylim = 0.000000 1.000000\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "yt : [INFO     ] 2016-01-29 11:26:19,681 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-8-41ef97e8c8cf>\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 11:30:34,501 Saving plot data00000500.h5_Projection_y_E_x.png\n"
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": "['data00000500.h5_Projection_y_E_x.png']"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}