Civ_Mil-B.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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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   "source": [
    "file = \"civ_mil_B/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"
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  {
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   "execution_count": 4,
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125c88828>"
      ]
     },
     "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 0x126237cc0>"
      ]
     },
     "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 0x12624a0f0>"
      ]
     },
     "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 0x126342b70>"
      ]
     },
     "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 0x12641eba8>"
      ]
     },
     "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 0x1264040f0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1263fa320>"
      ]
     },
     "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": []
  },
  {
   "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
}