Civ_Mil-A.ipynb

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     "text": [
      "/opt/local/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/importlib/_bootstrap.py:222: QAWarning: pyne.data is not yet QA compliant.\n",
      "  return f(*args, **kwds)\n",
      "/opt/local/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/importlib/_bootstrap.py:222: QAWarning: pyne.material is not yet QA compliant.\n",
      "  return f(*args, **kwds)\n",
      "/opt/local/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/importlib/_bootstrap.py:222: QAWarning: pyne.enrichment is not yet QA compliant.\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "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": 2,
   "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"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>civ_lwr&#45;&gt;civ_str_uox_spent</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M321.0934,-86.9735C321.0934,-75.1918 321.0934,-59.5607 321.0934,-46.1581\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"324.5935,-46.0033 321.0934,-36.0034 317.5935,-46.0034 324.5935,-46.0033\"/>\n",
       "<text text-anchor=\"middle\" x=\"352.0934\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">uox_spent </text>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.dot.Digraph at 0x12427fc18>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = \"civ_mil_A/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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12426cc18>"
      ]
     },
     "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124520518>"
      ]
     },
     "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12426c470>"
      ]
     },
     "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1245a4b00>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trans_unat = tm.transactions(ev_, senders=['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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124629550>"
      ]
     },
     "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": 9,
   "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 0x1249b6da0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1249bb9e8>"
      ]
     },
     "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": "raw",
   "metadata": {},
   "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": []
  },
  {
   "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
}