Civ.ipynb

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 "cells": [
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   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys \n",
    "import os\n",
    "sys.path.append(os.path.abspath(\"/Users/mouginot/work/helpmetric\"))\n",
    "import cymetrichelper as cyh\n",
    "import pandahelper as pdh\n",
    "\n",
    "\n",
    "import cymetric as cym\n",
    "import pandas as pd\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from cymetric import graphs as cgr\n",
    "from cymetric import timeseries as tm\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cymetric.tools import format_nucs, reduce, merge, add_missing_time_step\n",
    "def SWU(evaler, facilities=()):\n",
    "    \"\"\"\n",
    "    Shape the reduced SWU Data Frame. Applying nuclides/facilities selection when required.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    evaler : evaler\n",
    "    facilities :  of the facility\n",
    "    nucs :  of nuclide to select.\n",
    "    \"\"\"\n",
    "\n",
    "    # Get inventory table\n",
    "    df = evaler.eval('TimeSeriesEnrichmentSWU')\n",
    "    agents = evaler.eval('AgentEntry')\n",
    "\n",
    "    rdc_table = []  # because we want to get rid of the nuclide asap\n",
    " \n",
    "    if len(facilities) != 0:\n",
    "        agents = agents[agents['Prototype'].isin(facilities)]\n",
    "        rdc_table.append(['AgentId', agents['AgentId'].tolist()])\n",
    "\n",
    "    df = reduce(df, rdc_table)\n",
    "\n",
    "    base_col = ['SimId', 'AgentId']\n",
    "    added_col = base_col + ['Prototype']\n",
    "    df = merge(df, base_col, agents, added_col)\n",
    "\n",
    "    df = df[['Time', 'Value']].groupby(['Time']).sum()\n",
    "    df.reset_index(inplace=True)\n",
    "    return df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
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   "source": [
    "file = \"civilian/main.sqlite\"\n",
    "label=''\n",
    "db_ = cym.dbopen(file)\n",
    "ev_ = cym.Evaluator(db=db_, write=False)\n",
    "cgr.flow_graph(evaler=ev_, label='com')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123c716a0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SWU_civ = SWU(ev_, facilities=['civ_enrichment'])\n",
    "#SWU_civ[SWU_civ['Value']>1e50] = 0\n",
    "\n",
    "ax = cyh.month2year(SWU_civ, 1, 12, 'Value').plot(subplots=True, x='Time', y='Value', style='*', label='Civilian')\n",
    "plt.ylabel('SWU utilisation [-]')\n",
    "plt.xlabel('Time [month]')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1232285f8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trans_civ = tm.transactions(ev_, senders=['civ_enrichment'])\n",
    "ax = cyh.month2year(trans_civ, 1, 12, 'Mass').plot(subplots=True, x='Time', y='Mass', style='*', label='Civilian')\n",
    "plt.ylabel('LEU Shipment [kg]')\n",
    "plt.xlabel('Time [month]')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MGTKk2mFkrh7qWQ91BNcz78aPH78gIsZ2Va5UgtiG5DqHy9i0u2dtRKzsaWCSxpLcDfaIiJgn6SqSVerOKkwIklZHRMlxiKampli8eHGpIjWhpaWF5ubmaoeRuXqoZz3UEVzPvJNUVoLotIspIlZHxNMR8Ungz8BaYB0wUNJOvYhtObA8Iual27OBdwMvSBqZBj8S6HESMjOz3itnkPpLwCrgfpLB43uAOT09YEQ8DzwrqSnddRRJd9PtwOR032Tgtp4ew8zMeq+cm/V9Ddg3IlZV8LhnAT+TtAVJ6+QzJMlqlqTPAc8AEyt4PDMz66ZyEsRy4OVKHjQi/gAU6/86qpLHMTOznisnQTwN3Cvp18BbCwBHxPTMojIzs6orJ0GsSH+2yjgWMzPLkS4TRER8G0DSoIhY31V5MzOrDeXMYjpM0hPAknT7IElXZx6ZmZlVVTm32pgOfBR4CSAiHgPGZxmUmZlVXzkJoiEinumw780sgjEzs/woZ5D6WUmHASFpAMk1DE9lG5aZmVVbOS2ILwFfBXYFXgAOT/eZmVkNK2cW00rgpD6IxczMcqTLBCFpV2AqsHth+Yg4MbuwzMys2soZg7gd+AlwN7Ax23DMzCwvykkQr0fEP2ceiZmZ5Uo5CeJqSRcAv2HTezE9nllUZmZWdeUkiH2AzwPH8HYXU5CsK21mZjWqnARxMrC778NkZlZfyrkO4klgaNaBmJlZvpTTghgK/J+keWw6BuFprmZmNaycBPHdzKMwM7PcKedK6nskjQAOJRmcfrjC61ObmVkOlbMexGeAR4BTgE8BD0uanHVgZmZWXeV0MZ0HvLu91ZC2Jn4H3JRlYGZmVl3lzGJ6GXilYPuVdJ+ZmdWwcloQi4EHJP2SZAziBGChpLMBImJ6hvGZmVmVlLVgUPozKN3+n/T3iEwiMjOzXChnFtO3+yIQMzPLl04ThKQrI+Jrkm4l6VrahC+UMzOrbaVaEDenv6/pi0DMzCxfOk0QETE//X1P34VjZmZ5Uc6FcodLulPSHyU9JWmJpKd6e2BJAyQ9KunX6fYekuZJelrSzZK26O0xzMys58q5DuJG4F+ADwFHAu9Lf/fWOcCigu3vAz+MiL2A1cDnKnAMMzProXISxJqI+FVE/DUiXmj/6c1BJY0CjgNuSLcFfBCYnRa5ieR6CzMzqxJFbDZBKXlCOjB9eHL6+xYqtOSopNnAZSS3Ev9H4HTgwbT1gKRdgDsjYnSR104BpgCMGDFizKxZs3oaRr/R2trKkCFDqh1G5uqhnvVQR3A98278+PELImJsV+VKzWK6tsP2+woe93jJUUkfBVZGxAJJzd19fURcD1wP0NTUFM3N3X6LfqelpQXXszbUQx3B9awVpWYxVWKcoZgjgOMlHQsMBrYCrgKGSRoYERuAUcBzGR3fzMzK0OkYhKRjJe1asP1NSQsk3SJpt54eMCLOj4hREbE7cBJwb0ScCswFJqTFJgO39fQYZmbWe6UGqS8DXgKQdBzwWeBM4C7gugxiORf4qqSngeHAjAyOYWZmZSo1BhER8Vr6+ETghoiYB8yT9MVKHDwiWoCW9PGfgcMq8b5mZtZ7pVoQDZK2TKegHgXcW/DcoE5eY2ZmNaJUC+Jq4FHgVWBJ+603JB0EPN8HsZmZWRWVmsX0Y0m/AXYgWZO63Ysk4xFmZlbDSq4HERHLgGUd9nn6qZlZHSjnVhtmZlaHnCDMzKyocm73fXqRfdMyicbMzHKjyzWpgZMlrYuImwEkTQe2zjYsMzOrtnISxCeAX0naCBwDrIuIydmGZWZm1VbqXkxbSdqKJIlMBr4J/A04P91vZmY1rFQL4kmS23qr4PfH058Adu38pWZm1t+VulBul74MxMzM8qWcWUxnSBpWsL1NuqqbmZnVsHKugzgjIl5p34iI1cCXsgvJzMzyoJwEMaBwQ1ID0JhNOGZmlhflTHO9W9LPgX9Lt88A5mQXkpmZ5UE5CeLrJCvJfSXdvptsVpQzM7Mc6TJBRMSbJGtDXJ19OGZmlhddJghJS0iue9hEROyTSURmZpYL5XQxva/g8WDgk/heTGZmNa/LWUwR8ULBzzMRcQXJPZnMzKyGldPFdGDBZgMwFhiUWURmZpYL5XQxXVvweAPwF2BSNuGYmVlelEwQkgYAV0XE7D6Kx8zMcqLkGEQ6xfX8PorFzMxypJxbbdwl6cuSRravEeH1IMzMal85YxCfSn9/jU3Xh/B6EGZmNazTBCHp8Ih40OtCmJnVp1JdTP+SxQEl7SJprqQ/SnpS0jnp/m0l3S1pSfp7myyOb2Zm5SlnDKLSNgBfi4j9gMOBf5C0H3AecE9E7A3ck26bmVmVlBqD+DtJt3f2ZEQc35MDRsQKYEX6eK2kRcDOJGtdN6fFbgJagHN7cgwzM+s9RWx2H77kieQmfZ/v7IURcV+vDy7tDvwWGA0si4hh6X4Bq9u3O7xmCjAFYMSIEWNmzZrV2zByr7W1lSFDhlQ7jMzVQz3roY7geubd+PHjF0TE2K7KlUoQj0TEuyse2dvvPwS4D/huRNwi6ZXChCBpdUSUHIdoamqKxYsXZxVibrS0tNDc3FztMDJXD/WshzqC65l3kspKEKXGIJZWLpxNSWoEfgH8LCJuSXe/IGlk+vxIYGVWxzczs651miAi4sQsDph2H80AFkXEPxc8dTswOX08Gbgti+ObmVl5yrlQrtKOAE4DnpD0h3TfN4HLgVmSPgc8A0ysQmxmZpbq8wQREb8juRq7mKP6MhYzM+tcqSupOw5QB/BiRDybbUhmZpYHpVoQVxbZt62kLYCTI+IPRZ43M7Ma0WmCiIjxxfZLGgtMB96fVVBmZlZ93b7VRkQ8DPS/K0PMzKxbup0gJO1AMh5hZmY1rNQg9dVsngi2Bd4LnJNlUGZmVn2lBqkf7rAdwEvAVyPCVzmbmdW4Ugni1ohYU+wJSbtGxLKMYjIzsxwoNQbR0v5A0j0dnvtlJtGYmVlulEoQhVc7b1viOTMzq0GlEkR08rjYtpmZ1ZhSYxDbS/oqSWuh/THp9ojMIzMzs6oqlSB+DAwt8hjghswiMjOzXCh1q41LOntO0pezCcfMzPKi21dSp77adREzM+vPepogPIvJzKzG9TRBeBaTmVmNK3UvprUUTwQC3pFZRGZmlgulBqmHdvacmZnVvp52MZmZWY1zgjAzs6KcIMzMrCgnCDMzK8oJwszMinKCMDOzopwgzMysKCcIMzMrygnCzMyKylWCkPQRSYslPS3pvK7Kr3htIyvXtpX13ivXtDHxugfKKt+dsn3x3t+bt65fxp1VPburu7GYWSI3CULSAOBa4BhgP+BkSfuVes36N2H6nCVlvf/0e5bw0NKXyyrfnbJ98d5LVm/sl3FnVc/u6m4sZpZQRD5uzCrpPcDFEXF0un0+QERc1tlrBo3cO0ZO/lHyeGADi6cds1mZpgvuZP2GjZu/tkj57pT1e/f+vXtSvjuyfO+eamlpobm5uSrH7kuuZ75JWhARY7ssl6MEMQH4SER8Pt0+DRgXEVM7lJsCTAHYYse9xuz2mR/x7h0GcNK7tmDYoM0bRK+0bWTm4td55IU3eX0jbNFAp+W7U9bv3fv37kn57sjyvXuqtbWVIUOGVOXYfcn1zLfx48eXlSBKrUmdSxFxPXA9wOCRe8cbAXvtujMnHH1Ap695aN0TzHt+GYMGNvD6mxtLlu9O2b5678YG6KqeeYw7i3p2V3djyVp//cbZXa5nbchTgngO2KVge1S6r1Mj39nAqeN2Y1UXg48vtq7n1HG7ccphu/Kf85eVLN+dsn313vs0vMBTG3fod3FnUc/u6m4sZva2PHUxDQSeAo4iSQwPAadExJOdvaapqSkWL17cRxFWT61/S2lXD/WshzqC65l35Y5B5KYFEREbJE0FfgMMAP69VHIwM7Ns5SZBAETEHcAd1Y7DzMxydB2EmZnlixOEmZkV5QRhZmZF5WYWU09IWgvU/jQm2A54sdpB9IF6qGc91BFcz7zbLSJGdFUoV4PUPbC4nKla/Z2kh13P2lAPdQTXs1a4i8nMzIpygjAzs6L6e4K4vtoB9BHXs3bUQx3B9awJ/XqQ2szMstPfWxBmZpYRJwgzMysq9wkivctrzZPU5Zzk/k6Sqh2DWXdIyv05Mku5rbykgZKuAK6U9KFqx5MVSQMkXQr8XtJu1Y4nY+9of1CryULSGEn9b4mxbpL0aUkfkLR1up3bc0lPSToLOE/SVtWOpVpy+aGmJ4/pwEhgPnCupH+QNKi6kVWWpCOBJcBQ4MiIeKbKIWVC0lGSfgdcK+lTAFFjsyPSOt4PfB6oqbq1U2KkpLnAZOAU4F8lbRcRG2sl6UsaJ+lB4IPA7RGxptoxVUteu2+GAgcDR0fEWkkvAscCnwR+WtXIKmsNMDQivgIgaQ/glYhYXd2wKkfStsA04ErgJeAcSXtExHckNUTExupG2HPpCbEB+CLwbeArETGz8PlaSYSSBkTEm5KGAs9FxKckDQB+SDLV88TqRth7Ba2gU0jq+Pfp/i0j4m/Vi6x6cpkgImKNpKXA6cDVwP+StCbeK2lORDxfxfAqJiIek3SrpFnAaqAJWC/px8CtEfFmdSPsmfb/aOnJfyfgCdL6SFoOzJN0Q0Ss6K8n0YI6vinpNeDnwNz0uWOBB4C1wIb+WkdIEgPwHWCApDuArYA34a26fxn4q6QPRMR9/THpF9SxEfgF8CvgCEmTgHcBu0p6ALg3Iv7cH+vYU7nsYkrdChwsaWREtJKcZNaTJIpa8nXgQOCvEdEMzASOBA6pZlA9JekzwHKS/3AArcB7SG5qRkQsAX4GXFOVACugoI7fTXfdQZIMbpD0R2AK8G/AxVUJsEIkfQBYAGwDPE3ymb4BjJd0GLz1JeDi9If+duLsUMenSFq6QZIELwV2BP6b5P/jdOh/deyNPCeI35HcJfF0gIhYABxKwUBnLYiIV4HmiLgk3b4R2JvkH2a/kg7Ofhz4PnCMpKaIWAo8AvyooOi3gFGS9u5v36w71PFoSftExCqSVu5S4OSIOIGkW+1jkg7ob3UssBG4MiK+FBE/BhYCewAXAv8Kb7Wkfgms6qeTLDrW8VFgHEkymJzuvwW4ANhS0ugqxtrncpsgImIFcBvJieaTknYH2oAN1YwrC4VdZpL2JOn6W1W9iHombemdHRFXAXfxdiviTOAoSe9Jt18DHiP5PPuVInW8NH1qDvCtiHgs3V5EcrLpzwO3C4BZaRcMJElw14j4D5Iup7PSb9OjgA39dJJFxzr+Htg6HZieV1BuX+A5ks+1buQ2QQBExO+By4BjgP8BfhkR86sbVeWls0OGS/oJcDMwOyLmdfW6PIqIZenDHwG7SzouIl4DLgEuSLtnLiDpVmutUpi90qGOfyfp6PRE+VpBsXNJTpzP9nV8lRIRf4uI9QVjYR/m7S8unwH2lfRrkvGXR6H/TV8uUsf/R9J9SESEpO0lfYukxfRQOu7Sr+rYG/3iXkySGkk+r5prPbRLuy5OBf4jItZXO55KkPRF4FMRcWS6fQwwHtgZOC8i+u3Js11ax1Mi4gPp9nHAN0i+bX49Ip6rZnyVkH67DpK++LMi4mlJe5F0AY8G/tLf69mhjlMj4k9pa/4EYE/gslr499pd/SJBWP/TPtND0mzgeZK+3huAJ/pxn/wmOtRxBUmL6A/Akoh4pLrRVU76jXkLks/vVuCzJFOWz6qVawSK1PHzwDPAhekYU13K5TRX6//SE+eWwPbAB4DvRMTjVQ6rojrUsRm4NCJurm5UlZd2tRxC0sLdA7gxImZUOayKqoc69oQThGXpTJIZTB+ulW6zIuqhjpD0y38L+Ocarmc91LFb3MVkmamHC4rqoY5Wv5wgzMysqFxPczUzs+pxgjAzs6KcIMzMrCgnCDMzK8oJwmpKesuSP6Q/z0t6rmD79xkc73RJqyTdUOn37nCcg9PbiLdvXyzpH4uUe0da19clbZdlTFb7fB2E1ZSIeIlksSkkXQy0RsQVGR/25oiYmvExDgZgklh3AAACDklEQVTGktxavFMRsY7kNvlLM47H6oBbEFY3JLWmv5sl3SfpNkl/lnS5pFMlzZf0RHoPHiSNkPQLSQ+lP0eUcYzTJf1S0t2SlkqaKumrkh6V9KCSFfbaWwQPSnpcyaJR26T7WyR9P43lKUlHStqC5K6xk9LWwaT0cPul5f8s6exM/mhW15wgrF4dBJxBchvn04B9IuIwknvxnJWWuQr4YUQcCvx9+lw5RpMswXkoyaJCf4uIQ0hWmft0WuYnwLkRcSDJYlgXFbx+YBrLl4GLIuJ1kjUYbo6Igwtu5/Eu4GjgMOCi9KaWZhXjLiarVw+la44g6U8kaztAcrIenz7+EMm39PbXbCVpSLomRClzI2ItsFbSqyRLWLa/94GStgaGRcR96f6bgP8qeP0t6e8FwO4ljvPf6S0h1ktaCexAeqtqs0pwgrB6VXivnY0F2xt5+/9FA3B4RHR3YaNy3ruc17/ZRfnC43RV1qzb3MVk1rm7eLu7CUkHV+JN02VmV0s6Mt11GnBfiZdAsub10Eoc36xcThBmnTsbGJsOJP+RZMyiUiYD/yTpcZIZSpd2UX4uSXdX4SC1WaZ8sz6zXpB0OjC2D6a5dks6zXVsRLxY7Vis/3ILwqx31gHHZH2hXLnaL5QDGknGPMx6zC0IMzMryi0IMzMrygnCzMyKcoIwM7OinCDMzKwoJwgzMyvq/wMb5wC14sYSGQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123e37438>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trans_civ_truck = tm.transactions(ev_, senders=['civ_enrichment'])\n",
    "trans_civ_truck['Mass'] *= 1/5000\n",
    "ax = cyh.month2year(trans_civ_truck, 1, 12, 'Mass').plot(subplots=True, x='Time', y='Mass', style='*', label='Civilian')\n",
    "plt.ylabel('LEU Truck Shipment [5t/truck]')\n",
    "plt.xlabel('Time [month]')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12332cef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trans_unat = tm.transactions(ev_, receivers=['civ_enrichment'])\n",
    "ax = cyh.month2year(trans_unat, 1, 12, 'Mass').plot(subplots=True, x='Time', y='Mass', style='*', label='Civilian')\n",
    "plt.ylabel('Enrichment feed [kg]')\n",
    "plt.xlabel('Time [month]')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1233dd208>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trans_uore = tm.transactions(ev_, senders=['mine'])\n",
    "ax = cyh.month2year(trans_uore, 1, 12, 'Mass').plot(subplots=True, x='Time', y='Mass', style='*', label='Civilian')\n",
    "plt.ylabel('Uranium Ore shipment [kg]')\n",
    "plt.xlabel('Time [year]')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mouginot/.local/lib/python3.5/site-packages/cymetric/timeseries.py:205: UserWarning: no faciity provided\n",
      "  warnings.warn(wng_msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1234b7b38>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1234c1ef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tm.get_power(ev_).plot(x='Time', y='Value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}